From c9b9adc77c395730e1bd7235076103db9c99b744 Mon Sep 17 00:00:00 2001 From: andrewor14 Date: Tue, 6 May 2025 13:35:30 -0400 Subject: [PATCH 001/165] Bump version to 0.12.0 (#2178) --- version.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/version.txt b/version.txt index d9df1bbc0c..ac454c6a1f 100644 --- a/version.txt +++ b/version.txt @@ -1 +1 @@ -0.11.0 +0.12.0 From 72e3c1169efdbf2ecbfdc601e93fc83a5f79208e Mon Sep 17 00:00:00 2001 From: Nikita Shulga <2453524+malfet@users.noreply.github.com> Date: Wed, 7 May 2025 09:07:09 -0700 Subject: [PATCH 002/165] [BE] Fix MPS experimental workflow (#2181) * [BE] Fix MPS tests By actually running them in the conda env that is being created * Update torchao_experimental_test.yml * Update torchao_experimental_test.yml --- .github/workflows/torchao_experimental_test.yml | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/.github/workflows/torchao_experimental_test.yml b/.github/workflows/torchao_experimental_test.yml index 4d0a1eaaf6..1987670d70 100644 --- a/.github/workflows/torchao_experimental_test.yml +++ b/.github/workflows/torchao_experimental_test.yml @@ -92,26 +92,31 @@ jobs: conda activate test-mps-ops-env - name: Install torch run: | - pip install torch --index-url "https://download.pytorch.org/whl/nightly/cpu" + conda run -n test-mps-ops-env pip install torch --index-url "https://download.pytorch.org/whl/nightly/cpu" - name: Print torch version run: | - python -c "import torch; print(torch.__version__)" + + conda run -n test-mps-ops-env python -c "import torch; print(torch.__version__)" - name: Install requirements run: | + source activate base + conda activate test-mps-ops-env pip install -r dev-requirements.txt pip install pyyaml importlib-metadata - name: Print pip freeze run: | - pip freeze + conda run -n test-mps-ops-env pip freeze - name: Print current directory run: | - python -c "import os; print(os.getcwd())" + conda run -n test-mps-ops-env python -c "import os; print(os.getcwd())" - name: Build ao with experimental mps ops run: | + source activate base + conda activate test-mps-ops-env USE_CPP=1 TORCHAO_BUILD_EXPERIMENTAL_MPS=1 pip install . - name: Run mps tests run: | pushd torchao/experimental/ops/mps/test - python test_lowbit.py - python test_quantizer.py + conda run -n test-mps-ops-env python test_lowbit.py + conda run -n test-mps-ops-env python test_quantizer.py popd From e5d9a977d5e7a4ceb521d5e3bea136234bb40671 Mon Sep 17 00:00:00 2001 From: Scott Roy <161522778+metascroy@users.noreply.github.com> Date: Wed, 7 May 2025 11:11:31 -0700 Subject: [PATCH 003/165] Fix cuda compile error with bf16 Differential Revision: D73562284 Pull Request resolved: https://github.com/pytorch/ao/pull/2122 --- torchao/csrc/cuda/fp6_llm/utils_parallel_dequant.cuh | 2 -- 1 file changed, 2 deletions(-) diff --git a/torchao/csrc/cuda/fp6_llm/utils_parallel_dequant.cuh b/torchao/csrc/cuda/fp6_llm/utils_parallel_dequant.cuh index 8c5f77df9c..63afa0694c 100644 --- a/torchao/csrc/cuda/fp6_llm/utils_parallel_dequant.cuh +++ b/torchao/csrc/cuda/fp6_llm/utils_parallel_dequant.cuh @@ -25,9 +25,7 @@ #include #include -#if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 800 #include -#endif #include /* From 8369268afecdc87f9917075a1d352785176489dd Mon Sep 17 00:00:00 2001 From: Apurva Jain Date: Wed, 7 May 2025 13:48:13 -0700 Subject: [PATCH 004/165] Generate speedup for inference (#2151) --- benchmarks/microbenchmarks/README.md | 14 ++++++++ .../microbenchmarks/benchmark_inference.py | 34 ++++++++++++++++--- .../microbenchmarks/benchmark_runner.py | 16 ++++++++- .../microbenchmarks/test/benchmark_config.yml | 14 +++----- benchmarks/microbenchmarks/utils.py | 14 ++++++-- 5 files changed, 73 insertions(+), 19 deletions(-) diff --git a/benchmarks/microbenchmarks/README.md b/benchmarks/microbenchmarks/README.md index 42e704a99c..f300bbab23 100644 --- a/benchmarks/microbenchmarks/README.md +++ b/benchmarks/microbenchmarks/README.md @@ -130,6 +130,18 @@ Currently, quantization string is in same format as the one being passed in llam max_power: 11 ``` +- `small_sweep`: Generate a small sweep of shapes with increasing powers of 2 for M, K, N dimensions + - Parameters: + - `min_power`: Minimum power of 2 (default: 10, which is 1024) + - `max_power`: Maximum power of 2 (default: 14, which is 16,384) + - Note: This generates shapes where M <= K <= N (ensuring increasing order), which produces fewer combinations than the full sweep, and could be good to use for plots like heatmap + ```yaml + matrix_shapes: + - name: "small_sweep" + min_power: 10 # 2^10 = 1024 + max_power: 15 # 2^15 = 32,768 + ``` + - `sweep`: Generate a sweep of shapes with different powers of 2 for M, K, N dimensions - Parameters: - `min_power`: Minimum power of 2 (default: 8, which is 256) @@ -142,6 +154,8 @@ Currently, quantization string is in same format as the one being passed in llam max_power: 9 # 2^9 = 512 ``` + + ## Output Results are saved to a CSV file in the specified output directory diff --git a/benchmarks/microbenchmarks/benchmark_inference.py b/benchmarks/microbenchmarks/benchmark_inference.py index cc6e819523..4ea5d05105 100644 --- a/benchmarks/microbenchmarks/benchmark_inference.py +++ b/benchmarks/microbenchmarks/benchmark_inference.py @@ -51,9 +51,28 @@ def run(config: BenchmarkConfig) -> BenchmarkResult: high_precision_dtype=config.high_precision_dtype, device=config.device, ) + # Copy base model for quantizing + m_copy = deepcopy(base_model) + + # Run benchmarks + result = BenchmarkResult(config=config) + + # Store result in model for memory profiling + base_model._benchmark_result = result + + # Run baseline benchmarking + base_model = base_model.eval().to(config.device) + if config.use_torch_compile: + print("Compiling baseline model....") + base_model = torch.compile( + base_model, mode=config.torch_compile_mode, fullgraph=True + ) + # Benchmark time to run an inference call for baseline model + print("Benchmarking baseline inference.....") + result.baseline_inference_time_in_ms = model_inference_time_in_ms( + model=base_model, input_data=input_data + ) - # Use quantize_ to apply each quantization function to the model - m_copy = deepcopy(base_model).eval().to(config.device) ao_base_config = string_to_config( config.quantization, config.sparsity, @@ -79,24 +98,29 @@ def run(config: BenchmarkConfig) -> BenchmarkResult: pass # No quantization or sparsity specified, do nothing else: print("Quantizing model....") + m_copy = m_copy.eval().to(config.device) quantize_(m_copy, ao_base_config) if config.use_torch_compile: - print("Compiling model....") + print("Compiling quantized model....") m_copy = torch.compile( m_copy, mode=config.torch_compile_mode, fullgraph=True ) - # Run benchmarks - result = BenchmarkResult(config=config) # Store result in model for memory profiling m_copy._benchmark_result = result # Benchmark time to run an inference call for quantized model + print("Benchmarking quantized model.....") result.model_inference_time_in_ms = model_inference_time_in_ms( model=m_copy, input_data=input_data ) + # Calculate speedup w.r.t. baseline + result.speedup = round( + result.baseline_inference_time_in_ms / result.model_inference_time_in_ms, 2 + ) + # Run profiler if enabled if config.enable_profiler: print("Running profiler...") diff --git a/benchmarks/microbenchmarks/benchmark_runner.py b/benchmarks/microbenchmarks/benchmark_runner.py index fbd7f08388..8066b71714 100644 --- a/benchmarks/microbenchmarks/benchmark_runner.py +++ b/benchmarks/microbenchmarks/benchmark_runner.py @@ -76,6 +76,20 @@ def get_shapes_for_config( val2 = 2**power_of_2 + 2 ** (power_of_2 - 1) shapes.append((f"{name}_{idx * 2}", [val1, val1, val1])) shapes.append((f"{name}_{idx * 2 + 1}", [val2, val2, val2])) + elif name == "small_sweep": + # Generate a small sweep of shapes with increasing powers of 2 for M, K, N + min_p2 = shape_config.get("min_power", 10) # 1024 + max_p2 = shape_config.get("max_power", 14) # 16,384 + counter = 0 + for M_p2 in range(min_p2, max_p2 + 1): + M = 2**M_p2 + for K_p2 in range(min_p2, max_p2 + 1): + K = 2**K_p2 + for N_p2 in range(min_p2, max_p2 + 1): + N = 2**N_p2 + if M <= K <= N: # Ensure increasing order + shapes.append((f"{name}_{counter}", [M, K, N])) + counter += 1 elif name == "sweep": # Generate a sweep of shapes with different powers of 2 for M, K, N min_p2 = shape_config.get("min_power", 8) # 256 @@ -202,7 +216,7 @@ def run_inference_benchmarks_from_config(configs: List[BenchmarkConfig]) -> None print("----------------------------------------") try: print( - f"Running: {config.name} for Quantization: {config.quantization} and Sparsity: {config.sparsity}" + f"Running: {config.name} for Quantization: {config.quantization} and Sparsity: {config.sparsity} for {config.shape_name}: {config.m, config.k, config.n}" ) result = run_inference(config) # Pass the config object directly if result is not None: # Only add successful results diff --git a/benchmarks/microbenchmarks/test/benchmark_config.yml b/benchmarks/microbenchmarks/test/benchmark_config.yml index 6c28cbfc04..4fd5eb2018 100644 --- a/benchmarks/microbenchmarks/test/benchmark_config.yml +++ b/benchmarks/microbenchmarks/test/benchmark_config.yml @@ -3,18 +3,15 @@ benchmark_mode: "inference" quantization_config_recipe_names: # Will run a baseline inference for model by default, without quantization for comparison - "int8wo" - "int8dq" - - "float8dq" + - "float8dq-tensor" - "float8wo" output_dir: "benchmarks/microbenchmarks/results" model_params: - name: "small_bf16_linear" matrix_shapes: - - name: "custom" - shapes: [ - [1024, 1024, 1024], # [m, k, n] - [2048, 4096, 1024], - [4096, 4096, 1024] - ] + - name: "small_sweep" + min_power: 14 + max_power: 16 high_precision_dtype: "torch.bfloat16" use_torch_compile: true torch_compile_mode: "max-autotune" @@ -60,9 +57,6 @@ model_params: - name: "pow2_extended" # Example of using extended power of 2 shapes min_power: 10 # 1024 max_power: 11 # 2048 - - name: "sweep" # Example of using sweep shapes (commented out as it generates many shapes) - min_power: 8 # 256 - max_power: 9 # 512 high_precision_dtype: "torch.bfloat16" use_torch_compile: true torch_compile_mode: "max-autotune" diff --git a/benchmarks/microbenchmarks/utils.py b/benchmarks/microbenchmarks/utils.py index f591ec3669..cbd864d6fe 100644 --- a/benchmarks/microbenchmarks/utils.py +++ b/benchmarks/microbenchmarks/utils.py @@ -124,7 +124,9 @@ def __init__( ): self.config = config self.output_dir = config.output_dir + self.baseline_inference_time_in_ms = 0.0 self.model_inference_time_in_ms = 0.0 + self.speedup = 0.0 self.profiler_json_path: Optional[str] = None self.memory_profile_path: Optional[str] = None self.memory_visualization_path: Optional[str] = None @@ -134,7 +136,9 @@ def to_dict(self) -> Dict[str, Any]: """Convert result to dictionary for main function""" result_dict = { **self.config.to_dict(), + "baseline_inference_time_in_ms": self.baseline_inference_time_in_ms, "model_inference_time_in_ms": self.model_inference_time_in_ms, + "speedup": self.speedup, "profiler_json_path": self.profiler_json_path, "memory_profile_path": self.memory_profile_path, "memory_visualization_path": self.memory_visualization_path, @@ -299,7 +303,7 @@ def model_inference_time_in_ms(model, input_data): input_data: Input data for the model Returns: - float: Median inference time in microseconds + float: Median inference time in milliseconds """ # First run to trigger any compilation/lazy initialization @@ -315,8 +319,8 @@ def model_inference_time_in_ms(model, input_data): measurement = timer.timeit(number=100) res = measurement.mean - # Convert to microseconds - return res * 1e6 + # Convert to milliseconds + return (res * 1e6) / 1000 # Convert microseconds to milliseconds def clean_caches(): @@ -386,7 +390,9 @@ def print_results(results: List[BenchmarkResult]): result.config.quantization or "baseline", result.config.sparsity or "none", f"{result.config.shape_name} ({result.config.m}, {result.config.k}, {result.config.n})", + f"{result.baseline_inference_time_in_ms:.2f}", f"{result.model_inference_time_in_ms:.2f}", + f"{result.speedup:.2f}x", str(result.config.enable_profiler), ] @@ -398,7 +404,9 @@ def print_results(results: List[BenchmarkResult]): "Quantization", "Sparsity", "Shape", + "Baseline Inference Time (ms)", "Inference Time (ms)", + "Speedup", "Profiler Enabled", ] From b01514c573e002010968c4a5f74ddb3648c89d02 Mon Sep 17 00:00:00 2001 From: Jerry Zhang Date: Thu, 8 May 2025 10:00:43 -0700 Subject: [PATCH 005/165] Add serialization support for `AOPerModuleConfig` (#2186) Summary: att Test Plan: python test/quantization/test_config_serialization.py Reviewers: Subscribers: Tasks: Tags: --- test/quantization/test_config_serialization.py | 15 +++++++++++++++ torchao/core/config.py | 8 ++++++++ 2 files changed, 23 insertions(+) diff --git a/test/quantization/test_config_serialization.py b/test/quantization/test_config_serialization.py index 3caaa6efc5..ba52b446b1 100644 --- a/test/quantization/test_config_serialization.py +++ b/test/quantization/test_config_serialization.py @@ -1,3 +1,9 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD 3-Clause license found in the +# LICENSE file in the root directory of this source tree. + import json import os import tempfile @@ -14,6 +20,7 @@ config_to_dict, ) from torchao.quantization.quant_api import ( + AOPerModuleConfig, Float8DynamicActivationFloat8WeightConfig, Float8WeightOnlyConfig, FPXWeightOnlyConfig, @@ -63,6 +70,14 @@ # Sparsity configs SemiSparseWeightConfig(), BlockSparseWeightConfig(blocksize=128), + AOPerModuleConfig({}), + AOPerModuleConfig({"_default": Int4WeightOnlyConfig(), "linear1": None}), + AOPerModuleConfig( + { + "linear1": Int4WeightOnlyConfig(), + "linear2": Int8DynamicActivationInt4WeightConfig(), + } + ), ] diff --git a/torchao/core/config.py b/torchao/core/config.py index fe03ac225b..a041130835 100644 --- a/torchao/core/config.py +++ b/torchao/core/config.py @@ -255,6 +255,14 @@ def config_from_dict(data: Dict[str, Any]) -> AOBaseConfig: else item for item in value ] + elif isinstance(value, dict): + # Handle dicts of possible configs + processed_data[key] = { + k: config_from_dict(v) + if isinstance(v, dict) and "_type" in v and "_data" in v + else v + for k, v in value.items() + } else: processed_data[key] = value From e417afc1af66171200180626429dd771cee03197 Mon Sep 17 00:00:00 2001 From: HDCharles <39544797+HDCharles@users.noreply.github.com> Date: Thu, 8 May 2025 15:44:40 -0400 Subject: [PATCH 006/165] Remove broken test (#2188) * Remove broken test Summary: this test is weird, autoquant has potential to not alter model size, if we're testing aqt, this is a bad test, if we're testing get_model_size there's another test that's better. This is breaking CI and doesn't make a lot of sense so lets remove it Test Plan: see CI Reviewers: Subscribers: Tasks: Tags: * ruff format Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * undo overzealous delete Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: --- test/integration/test_integration.py | 37 ---------------------------- 1 file changed, 37 deletions(-) diff --git a/test/integration/test_integration.py b/test/integration/test_integration.py index c7428e72bd..2f498ea5c1 100644 --- a/test/integration/test_integration.py +++ b/test/integration/test_integration.py @@ -2049,43 +2049,6 @@ def forward(self, x): class TestUtils(unittest.TestCase): - @parameterized.expand(COMMON_DEVICE_DTYPE) - @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_5, "autoquant requires 2.5+.") - def test_get_model_size_autoquant(self, device, dtype): - if device != "cuda" and dtype != torch.bfloat16: - self.skipTest(f"autoquant currently does not support {device}") - if device != "cuda" or not torch.cuda.is_available(): - self.skipTest(f"autoquant currently does not support {device}") - if torch.cuda.is_available() and torch.cuda.get_device_capability() < (8, 0): - if dtype == torch.bfloat16: - self.skipTest("bfloat16 requires sm80+") - m, k, n = 16, 128, 128 - model = ( - torch.nn.Sequential( - torch.nn.ReLU(), - torch.nn.Linear(k, n), - torch.nn.ReLU(), - ) - .to(device) - .to(dtype) - ) - example_input = torch.randn(m, k, device=device, dtype=dtype) - size = torchao.utils.get_model_size_in_bytes(model) - - from torchao.quantization.autoquant import ( - AQInt8WeightOnlyQuantizedLinearWeight2, - ) - - qtensor_class_list = (AQInt8WeightOnlyQuantizedLinearWeight2,) - mod = torchao.autoquant( - torch.compile(model), - qtensor_class_list=qtensor_class_list, - set_inductor_config=False, - ) - mod(example_input) - size2 = torchao.utils.get_model_size_in_bytes(mod) - self.assertTrue(size2 < size) - @parameterized.expand( list(itertools.product(TENSOR_SUBCLASS_APIS, COMMON_DEVICES, COMMON_DTYPES)), ) From 7192edffe9df060edcce1733a9b333c44d70d19c Mon Sep 17 00:00:00 2001 From: HDCharles <39544797+HDCharles@users.noreply.github.com> Date: Thu, 8 May 2025 16:55:08 -0400 Subject: [PATCH 007/165] Enabling MOE Quantization using linear decomposition (#2043) * Enabling MOE Quantization using linear decomposition Summary: This PR is a first step at optimizing moe inference using torchAO. The goal for this step is to enable existing quantization kernels and workflows to work for moe quantization by decomposing the group gemm into a sequence of unbalanced linear ops that can use the existing quantized kernels. To enable this we had to add support for quantizing these 3D tensors as well as slicing and indexing. 2 methods of achieving this were implemented. for int8wo, int8dq, int4wo, fp8wo, fp8dq, the underlying quantized tensor subclass was adapted to both support 3D tensors, indexing and slicing, as well as an updated transformation function that can handle the ConditionalFeedForwardAOQuantizable modules if the filter funciton in quantize_ is used to target the aforementioned module. For some complex kernels which use packed data that couldn't be made to easily work in 3D, we also added FakeExtraDimTensor which can transform any quantized tensor subclass into supporting the necessary slice and index operations for moe quantization. This option is enabled by using MoeQuantConfig. This can be applied to huggingface llama4 for instance as shown int he llama4_quant.py example. Since the hf moe module is implemented in a way that's not condusive to quantization, it first requires a module swap to the MOEFeedForwardAOQuantizable. TODO final benchmark numbers from run.sh, consolidate 3x implementation of MOEFeedForwardAOQuantizable and ConditionalFeedForwardAOQuantizable. verify hqq Test Plan: python test/quantization/test_moe_quant.py python test/torchao/experimental/tests/test_int8_dynamic_activation_intx_weight.py -k "test_moe_quant_intx" sh torchao/_models/mixtral-moe/run.sh Reviewers: Subscribers: Tasks: Tags: * fixing CI Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * fixing CI Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * fixing CI Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * lint Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * remove test code Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * fixing exp test Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * fixing experimental test Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * fixing experimental CI Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * fixing generate.py device stuff Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * fixing tests that aren't skipping Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * ruff format Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * removing test code Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * fixing CI Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * update API and remove branching on quant_api.py transform functions Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * ruff format Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * fix weird ci error Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * remove change to test_integration.py Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: --- test/quantization/test_moe_quant.py | 361 +++++++++++++ torchao/_models/mixtral-moe/README.md | 8 + torchao/_models/mixtral-moe/generate.py | 506 ++++++++++++++++++ torchao/_models/mixtral-moe/model.py | 464 ++++++++++++++++ torchao/_models/mixtral-moe/run.sh | 39 ++ .../scripts/convert_hf_checkpoint.py | 115 ++++ .../_models/mixtral-moe/scripts/download.py | 48 ++ .../_models/mixtral-moe/scripts/prepare.sh | 2 + torchao/dtypes/affine_quantized_tensor_ops.py | 59 +- torchao/dtypes/floatx/float8_layout.py | 45 +- ...8_dynamic_activation_intx_weight_layout.py | 4 + torchao/dtypes/uintx/plain_layout.py | 8 + .../dtypes/uintx/tensor_core_tiled_layout.py | 146 +++-- ...est_int8_dynamic_activation_intx_weight.py | 47 ++ .../linear_activation_quantized_tensor.py | 30 ++ .../prototype/moe_quant/README.md | 51 ++ .../prototype/moe_quant/__init__.py | 0 .../prototype/moe_quant/llama4_quant.py | 92 ++++ .../moe_quant/quantizable_moe_modules.py | 191 +++++++ .../quantization/prototype/moe_quant/utils.py | 308 +++++++++++ torchao/quantization/quant_api.py | 166 ++++-- torchao/quantization/transform_module.py | 1 + torchao/quantization/utils.py | 13 +- torchao/utils.py | 4 +- 24 files changed, 2585 insertions(+), 123 deletions(-) create mode 100644 test/quantization/test_moe_quant.py create mode 100644 torchao/_models/mixtral-moe/README.md create mode 100644 torchao/_models/mixtral-moe/generate.py create mode 100644 torchao/_models/mixtral-moe/model.py create mode 100644 torchao/_models/mixtral-moe/run.sh create mode 100644 torchao/_models/mixtral-moe/scripts/convert_hf_checkpoint.py create mode 100644 torchao/_models/mixtral-moe/scripts/download.py create mode 100644 torchao/_models/mixtral-moe/scripts/prepare.sh create mode 100644 torchao/quantization/prototype/moe_quant/README.md create mode 100644 torchao/quantization/prototype/moe_quant/__init__.py create mode 100644 torchao/quantization/prototype/moe_quant/llama4_quant.py create mode 100644 torchao/quantization/prototype/moe_quant/quantizable_moe_modules.py create mode 100644 torchao/quantization/prototype/moe_quant/utils.py diff --git a/test/quantization/test_moe_quant.py b/test/quantization/test_moe_quant.py new file mode 100644 index 0000000000..842468a769 --- /dev/null +++ b/test/quantization/test_moe_quant.py @@ -0,0 +1,361 @@ +import unittest + +import torch +from parameterized import parameterized + +from torchao.dtypes.floatx.float8_layout import Float8AQTTensorImpl +from torchao.dtypes.uintx.plain_layout import PlainAQTTensorImpl +from torchao.dtypes.uintx.tensor_core_tiled_layout import TensorCoreTiledAQTTensorImpl +from torchao.quantization.prototype.moe_quant.quantizable_moe_modules import ( + MOEFeedForwardAOQuantizable, +) +from torchao.quantization.prototype.moe_quant.utils import ( + FakeExtraDimTensor, + MoEQuantConfig, + UseFakeExtraDimTensor, + cond_ffn_filter, +) +from torchao.quantization.quant_api import ( + AffineQuantizedTensor, + Float8DynamicActivationFloat8WeightConfig, + Float8WeightOnlyConfig, + Int4WeightOnlyConfig, + Int8DynamicActivationInt8WeightConfig, + Int8WeightOnlyConfig, + LinearActivationQuantizedTensor, + quantize_, +) +from torchao.quantization.utils import compute_error +from torchao.utils import ( + TORCH_VERSION_AT_LEAST_2_5, + TORCH_VERSION_AT_LEAST_2_6, + is_sm_at_least_90, +) + + +class TestMoEQuantCompile(unittest.TestCase): + DEFAULT_PARAMS = (512, 256, 8, 2) # hidden_dim, expert_dim, num_experts, top_k + + @torch.no_grad() + def _test_impl_moe_quant( + self, + config, + num_tokens=1, + model_params=None, + base_class=AffineQuantizedTensor, + tensor_impl_class=None, + dtype=torch.bfloat16, + device="cuda", + fullgraph=False, + ): + """ + Tests moe quant for techniques using fake extra dim + """ + if model_params is None: + model_params = self.DEFAULT_PARAMS + + input_shape = (num_tokens, model_params[0]) + model = ( + MOEFeedForwardAOQuantizable(*model_params, empty_init=False) + .to(dtype) + .to(device) + ) + input = torch.randn(input_shape, dtype=torch.bfloat16, device=device) + + out = model(input) + + quantize_(model, config, cond_ffn_filter) + + if ( + isinstance(config, MoEQuantConfig) + and config.use_fake_extra_dim_tensor == UseFakeExtraDimTensor.TRUE + ): + self.assertIsInstance(model.experts.w1, FakeExtraDimTensor) + if base_class is not None: + self.assertIsInstance(model.experts.w1.head_tensor, base_class) + if tensor_impl_class is not None: + self.assertIsInstance( + model.experts.w1.head_tensor.tensor_impl, tensor_impl_class + ) + else: + if base_class is not None: + self.assertIsInstance(model.experts.w1, base_class) + if tensor_impl_class is not None: + self.assertIsInstance(model.experts.w1.tensor_impl, tensor_impl_class) + + out_q = model(input) + + torch._dynamo.config.capture_scalar_outputs = True + torch._dynamo.config.capture_dynamic_output_shape_ops = True + model_c = torch.compile(model, mode="reduce-overhead", fullgraph=fullgraph) + + model_c(input) + model_c(input) + out_qc = model_c(input).clone() + + for i in range(10): + input = torch.randn(input_shape, dtype=torch.bfloat16, device=device) + model_c(input) + + self.assertGreaterEqual(compute_error(out_q, out), 10) + self.assertGreaterEqual(compute_error(out_qc, out), 10) + + @parameterized.expand( + [ + ("single_token", 1, False), + ("multiple_tokens", 8, False), + ] + ) + def test_int4wo_fake_dim(self, name, num_tokens, fullgraph): + if not torch.cuda.is_available(): + self.skipTest("Need CUDA available") + if not TORCH_VERSION_AT_LEAST_2_5: + self.skipTest("Test only enabled for 2.5+") + + config = MoEQuantConfig( + Int4WeightOnlyConfig(), use_fake_extra_dim_tensor=UseFakeExtraDimTensor.TRUE + ) + tensor_impl_class = TensorCoreTiledAQTTensorImpl + + self._test_impl_moe_quant( + config=config, + num_tokens=num_tokens, + tensor_impl_class=tensor_impl_class, + fullgraph=fullgraph, + ) + + @parameterized.expand( + [ + ("single_token", 1, True), + ("multiple_tokens", 8, False), + ] + ) + def test_int4wo_base(self, name, num_tokens, fullgraph): + if not torch.cuda.is_available(): + self.skipTest("Need CUDA available") + if not is_sm_at_least_90(): + self.skipTest("Requires CUDA capability >= 9.0") + if not TORCH_VERSION_AT_LEAST_2_5: + self.skipTest("Test only enabled for 2.5+") + + config = MoEQuantConfig(Int4WeightOnlyConfig()) + tensor_impl_class = TensorCoreTiledAQTTensorImpl + + self._test_impl_moe_quant( + config=config, + num_tokens=num_tokens, + tensor_impl_class=tensor_impl_class, + fullgraph=fullgraph, + ) + + @parameterized.expand( + [ + ("single_token", 1, False), + ("multiple_tokens", 8, False), + ] + ) + def test_int8wo_fake_dim(self, name, num_tokens, fullgraph): + if not torch.cuda.is_available(): + self.skipTest("Need CUDA available") + if not TORCH_VERSION_AT_LEAST_2_5: + self.skipTest("Test only enabled for 2.5+") + + config = MoEQuantConfig( + Int8WeightOnlyConfig(), use_fake_extra_dim_tensor=UseFakeExtraDimTensor.TRUE + ) + tensor_impl_class = PlainAQTTensorImpl + + self._test_impl_moe_quant( + config=config, + num_tokens=num_tokens, + tensor_impl_class=tensor_impl_class, + fullgraph=fullgraph, + ) + + @parameterized.expand( + [ + ("single_token", 1, True), + ("multiple_tokens", 8, False), + ] + ) + def test_int8wo_base(self, name, num_tokens, fullgraph): + if not torch.cuda.is_available(): + self.skipTest("Need CUDA available") + if not TORCH_VERSION_AT_LEAST_2_6: + self.skipTest("Test only enabled for 2.6+") + + config = MoEQuantConfig(Int8WeightOnlyConfig()) + tensor_impl_class = PlainAQTTensorImpl + + self._test_impl_moe_quant( + config=config, + num_tokens=num_tokens, + tensor_impl_class=tensor_impl_class, + fullgraph=fullgraph, + ) + + @parameterized.expand( + [ + ("single_token", 1, True), + ("multiple_tokens", 8, False), + ] + ) + def test_int8wo_base_cpu(self, name, num_tokens, fullgraph): + if not TORCH_VERSION_AT_LEAST_2_6: + self.skipTest("Test only enabled for 2.6+") + + config = MoEQuantConfig(Int8WeightOnlyConfig()) + tensor_impl_class = PlainAQTTensorImpl + + self._test_impl_moe_quant( + config=config, + num_tokens=num_tokens, + tensor_impl_class=tensor_impl_class, + fullgraph=fullgraph, + device="cpu", + ) + + @parameterized.expand( + [ + ("multiple_tokens", 32, False), + ] + ) + def test_int8dq_fake_dim(self, name, num_tokens, fullgraph): + if not torch.cuda.is_available(): + self.skipTest("Need CUDA available") + if not TORCH_VERSION_AT_LEAST_2_5: + self.skipTest("Test only enabled for 2.5+") + + config = MoEQuantConfig( + Int8DynamicActivationInt8WeightConfig(), + use_fake_extra_dim_tensor=UseFakeExtraDimTensor.TRUE, + ) + base_class = LinearActivationQuantizedTensor + + self._test_impl_moe_quant( + model_params=(512, 256, 2, 2), + config=config, + num_tokens=num_tokens, + base_class=base_class, + fullgraph=fullgraph, + ) + + @parameterized.expand( + [ + ("multiple_tokens", 32, False), + ] + ) + def test_int8dq_base(self, name, num_tokens, fullgraph): + if not torch.cuda.is_available(): + self.skipTest("Need CUDA available") + if not TORCH_VERSION_AT_LEAST_2_5: + self.skipTest("Test only enabled for 2.5+") + + config = MoEQuantConfig(Int8DynamicActivationInt8WeightConfig()) + base_class = LinearActivationQuantizedTensor + + self._test_impl_moe_quant( + model_params=(512, 256, 2, 2), + config=config, + num_tokens=num_tokens, + base_class=base_class, + fullgraph=fullgraph, + ) + + @parameterized.expand( + [ + ("single_token", 1, False), + ("multiple_tokens", 8, False), + ] + ) + def test_fp8wo_fake_dim(self, name, num_tokens, fullgraph): + if not torch.cuda.is_available(): + self.skipTest("Need CUDA available") + if not is_sm_at_least_90(): + self.skipTest("Requires CUDA capability >= 9.0") + + config = MoEQuantConfig( + Float8WeightOnlyConfig(), + use_fake_extra_dim_tensor=UseFakeExtraDimTensor.TRUE, + ) + tensor_impl_class = Float8AQTTensorImpl + + self._test_impl_moe_quant( + config=config, + num_tokens=num_tokens, + tensor_impl_class=tensor_impl_class, + fullgraph=fullgraph, + ) + + @parameterized.expand( + [ + ("single_token", 1, True), + ("multiple_tokens", 8, False), + ] + ) + def test_fp8wo_base(self, name, num_tokens, fullgraph): + if not torch.cuda.is_available(): + self.skipTest("Need CUDA available") + if not is_sm_at_least_90(): + self.skipTest("Requires CUDA capability >= 9.0") + + config = MoEQuantConfig(Float8WeightOnlyConfig()) + tensor_impl_class = Float8AQTTensorImpl + + self._test_impl_moe_quant( + config=config, + num_tokens=num_tokens, + tensor_impl_class=tensor_impl_class, + fullgraph=fullgraph, + ) + + @parameterized.expand( + [ + ("single_token", 1, False), + ("multiple_tokens", 8, False), + ] + ) + def test_fp8dq_fake_dim(self, name, num_tokens, fullgraph): + if not torch.cuda.is_available(): + self.skipTest("Need CUDA available") + if not is_sm_at_least_90(): + self.skipTest("Requires CUDA capability >= 9.0") + + config = MoEQuantConfig( + Float8DynamicActivationFloat8WeightConfig(), + use_fake_extra_dim_tensor=UseFakeExtraDimTensor.TRUE, + ) + base_class = LinearActivationQuantizedTensor + + self._test_impl_moe_quant( + config=config, + num_tokens=num_tokens, + base_class=base_class, + fullgraph=fullgraph, + ) + + @parameterized.expand( + [ + ("single_token", 1, True), + ("multiple_tokens", 8, False), + ] + ) + def test_fp8dq_base(self, name, num_tokens, fullgraph): + if not torch.cuda.is_available(): + self.skipTest("Need CUDA available") + if not is_sm_at_least_90(): + self.skipTest("Requires CUDA capability >= 9.0") + + config = MoEQuantConfig(Float8DynamicActivationFloat8WeightConfig()) + base_class = LinearActivationQuantizedTensor + + self._test_impl_moe_quant( + config=config, + num_tokens=num_tokens, + base_class=base_class, + fullgraph=fullgraph, + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/torchao/_models/mixtral-moe/README.md b/torchao/_models/mixtral-moe/README.md new file mode 100644 index 0000000000..22c318aab9 --- /dev/null +++ b/torchao/_models/mixtral-moe/README.md @@ -0,0 +1,8 @@ +## Mixtral-MoE + +This folder contains code and scripts for benchmarking the Mixtral-MoE model. +Running + +`sh scripts/prepare.sh` + +should download the model and `sh run.sh` will run teh benchmarks. diff --git a/torchao/_models/mixtral-moe/generate.py b/torchao/_models/mixtral-moe/generate.py new file mode 100644 index 0000000000..0dcd86e74f --- /dev/null +++ b/torchao/_models/mixtral-moe/generate.py @@ -0,0 +1,506 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +import itertools +import sys +import time +from pathlib import Path +from typing import Optional, Tuple + +import torch +import torch._dynamo.config +import torch._inductor.config + +from torchao.utils import get_model_size_in_bytes + +torch.manual_seed(0) + + +def device_sync(device): + if "cuda" in device: + torch.cuda.synchronize(device) + elif "cpu" in device: + pass + else: + print(f"device={device} is not yet suppported") + + +torch._inductor.config.coordinate_descent_tuning = True +torch._inductor.config.triton.unique_kernel_names = True +torch._inductor.config.fx_graph_cache = True # Experimental feature to reduce compilation times, will be on by default in future +torch._dynamo.config.capture_scalar_outputs = True + +# support running without installing as a package +wd = Path(__file__).parent.parent.resolve() +sys.path.append(str(wd)) + +from model import Transformer +from sentencepiece import SentencePieceProcessor + + +def multinomial_sample_one_no_sync( + probs_sort, +): # Does multinomial sampling without a cuda synchronization + q = torch.empty_like(probs_sort).exponential_(1) + return torch.argmax(probs_sort / q, dim=-1, keepdim=True).to(dtype=torch.int) + + +def logits_to_probs(logits, temperature: float = 1.0, top_k: Optional[int] = None): + logits = logits / max(temperature, 1e-5) + + if top_k is not None: + v, _ = torch.topk(logits, min(top_k, logits.size(-1))) + pivot = v.select(-1, -1).unsqueeze(-1) + logits = torch.where(logits < pivot, -float("Inf"), logits) + probs = torch.nn.functional.softmax(logits, dim=-1) + return probs + + +def sample(logits, temperature: float = 1.0, top_k: Optional[int] = None): + probs = logits_to_probs(logits[:, -1], temperature, top_k) + idx_next = multinomial_sample_one_no_sync(probs) + return idx_next, probs + + +def prefill( + model: Transformer, x: torch.Tensor, input_pos: torch.Tensor, **sampling_kwargs +) -> torch.Tensor: + # input_pos: [B, S] + logits = model(x, input_pos) + return sample(logits, **sampling_kwargs)[0] + + +def decode_one_token( + model: Transformer, x: torch.Tensor, input_pos: torch.Tensor, **sampling_kwargs +) -> Tuple[torch.Tensor, torch.Tensor]: + # input_pos: [B, 1] + assert input_pos.shape[-1] == 1 + logits = model(x, input_pos) + return sample(logits, **sampling_kwargs) + + +def decode_n_tokens( + model: Transformer, + cur_token: torch.Tensor, + input_pos: torch.Tensor, + num_new_tokens: int, + callback=lambda _: _, + **sampling_kwargs, +): + new_tokens, new_probs = [], [] + for i in range(num_new_tokens): + with torch.backends.cuda.sdp_kernel( + enable_flash=False, enable_mem_efficient=False, enable_math=True + ): # Actually better for Inductor to codegen attention here + next_token, next_prob = decode_one_token( + model, cur_token, input_pos, **sampling_kwargs + ) + next_token, next_prob = next_token.clone(), next_prob.clone() + + input_pos += 1 + new_tokens.append(next_token.clone()) + callback(new_tokens[-1]) + new_probs.append(next_prob.clone()) + cur_token = next_token + + return new_tokens, new_probs + + +def model_forward(model, x, input_pos): + return model(x, input_pos) + + +@torch.no_grad() +def generate( + model: Transformer, + prompt: torch.Tensor, + max_new_tokens: int, + batch_size: int, + *, + interactive: bool, + callback=lambda x: x, + **sampling_kwargs, +) -> torch.Tensor: + """ + Takes a conditioning sequence (prompt) as input and continues to generate as many tokens as requested. + """ + device, _ = prompt.device, prompt.dtype + + T = prompt.size(-1) + max_seq_length = ( + min(T + max_new_tokens, model.config.block_size) if not interactive else 350 + ) + new_tokens = max_seq_length - T + + # duplicate prompt for batch_size + prompt = prompt.repeat(batch_size, 1) + + # create an empty tensor of the expected final shape and fill in the current tokens + seq = torch.empty(batch_size, max_seq_length, dtype=prompt.dtype, device=device) + seq[:, :T] = prompt + + with torch.device(device): + model.setup_caches(max_batch_size=batch_size, max_seq_length=max_seq_length) + + input_pos = torch.arange(0, T, device=device) + next_token = prefill( + model, prompt.view(batch_size, -1), input_pos, **sampling_kwargs + ) + seq[:, T] = next_token.squeeze() + + input_pos = torch.tensor([T], device=device, dtype=torch.int) + generated_tokens, _ = decode_n_tokens( + model, + next_token.view(batch_size, -1), + input_pos, + new_tokens - 1, + callback=callback, + **sampling_kwargs, + ) + seq = torch.cat((seq[:, : T + 1], *generated_tokens), dim=-1) + + return seq + + +def encode_tokens(tokenizer, string, bos=True, device="cuda"): + tokens = tokenizer.encode(string) + if bos: + tokens = [tokenizer.bos_id()] + tokens + return torch.tensor(tokens, dtype=torch.int, device=device) + + +def _load_model(checkpoint_path, device, precision): + with torch.device("meta"): + model = Transformer.from_name(checkpoint_path.parent.name) + + try: + checkpoint = torch.load(str(checkpoint_path), mmap=True, weights_only=True) + model.load_state_dict(checkpoint, assign=True) + except: + model = Transformer.from_name(checkpoint_path.parent.name) + + model = model.to(device=device, dtype=precision) + return model.eval() + + +B_INST, E_INST = "[INST]", "[/INST]" + + +def main( + prompt: str = "Hello, my name is", + interactive: bool = False, + num_samples: int = 5, + max_new_tokens: int = 100, + batch_size: int = 1, + top_k: int = 200, + temperature: float = 0.8, + checkpoint_path: Path = Path("checkpoints/mistralai/Mixtral-8x7B-v0.1/model.pth"), + compile: bool = True, + compile_prefill: bool = False, + moe_quant: Optional[str] = None, + profile: Optional[Path] = None, + memory_profile: Optional[Path] = None, + device="cuda", +) -> None: + """Generates text samples based on a pre-trained Transformer model and tokenizer.""" + assert checkpoint_path.is_file(), checkpoint_path + tokenizer_path = checkpoint_path.parent / "tokenizer.model" + assert tokenizer_path.is_file(), str(tokenizer_path) + print(f"Using device={device}") + precision = torch.bfloat16 + is_chat = "chat" in str(checkpoint_path) + + if device == "cuda" and memory_profile is not None: + torch.cuda.memory._record_memory_history( + True, trace_alloc_max_entries=500000, trace_alloc_record_context=True + ) + + print("Loading model ...") + t0 = time.time() + model = _load_model(checkpoint_path, "cpu", precision) + + print(f"Time to load model: {time.time() - t0:.02f} seconds") + t0 = time.time() + + tokenizer = SentencePieceProcessor(model_file=str(tokenizer_path)) + encoded = encode_tokens(tokenizer, prompt, bos=True, device=device) + prompt_length = encoded.size(0) + + torch.manual_seed(1234) + model_size = sum( + [ + p.numel() * p.dtype.itemsize + for p in itertools.chain(model.parameters(), model.buffers()) + ] + ) + + from torchao.quantization.prototype.moe_quant.utils import ( + MoEQuantConfig, + UseFakeExtraDimTensor, + cond_ffn_filter, + ) + from torchao.quantization.quant_api import ( + Float8DynamicActivationFloat8WeightConfig, + Float8WeightOnlyConfig, + Int4WeightOnlyConfig, + Int8DynamicActivationInt8WeightConfig, + Int8DynamicActivationIntxWeightConfig, + Int8WeightOnlyConfig, + PackedLinearInt8DynamicActivationIntxWeightLayout, + PerRow, + quantize_, + ) + + if moe_quant: + torch._dynamo.config.capture_dynamic_output_shape_ops = True + config = None + if "int8wo-base" in moe_quant: + config = MoEQuantConfig(Int8WeightOnlyConfig()) + + elif "int8wo" in moe_quant: + config = MoEQuantConfig( + Int8WeightOnlyConfig(), + use_fake_extra_dim_tensor=UseFakeExtraDimTensor.TRUE, + ) + + elif "int8dq-base" in moe_quant: + config = MoEQuantConfig(Int8DynamicActivationInt8WeightConfig()) + + elif "int8dq" in moe_quant: + config = MoEQuantConfig( + Int8DynamicActivationInt8WeightConfig(), + use_fake_extra_dim_tensor=UseFakeExtraDimTensor.TRUE, + ) + + elif "int4wo-base" in moe_quant: + config = MoEQuantConfig(Int4WeightOnlyConfig()) + + elif "int4wo" in moe_quant: + config = MoEQuantConfig( + Int4WeightOnlyConfig(), + use_fake_extra_dim_tensor=UseFakeExtraDimTensor.TRUE, + ) + + elif "fp8wo-base" in moe_quant: + config = MoEQuantConfig(Float8WeightOnlyConfig()) + + elif "fp8wo" in moe_quant: + config = MoEQuantConfig( + Float8WeightOnlyConfig(), + use_fake_extra_dim_tensor=UseFakeExtraDimTensor.TRUE, + ) + + elif "fp8dq-base" in moe_quant: + config = MoEQuantConfig( + Float8DynamicActivationFloat8WeightConfig(granularity=PerRow()) + ) + + elif "fp8dq" in moe_quant: + config = MoEQuantConfig( + Float8DynamicActivationFloat8WeightConfig(granularity=PerRow()), + use_fake_extra_dim_tensor=UseFakeExtraDimTensor.TRUE, + ) + + elif "intxdq" in moe_quant: + config = MoEQuantConfig( + Int8DynamicActivationIntxWeightConfig( + layout=PackedLinearInt8DynamicActivationIntxWeightLayout(), + ), + use_fake_extra_dim_tensor=UseFakeExtraDimTensor.TRUE, + ) + else: + assert config is not None, ( + f"expected moe_quant to match one of the options but got {moe_quant}" + ) + + if config is not None: + quantize_(model, config, filter_fn=cond_ffn_filter, device=device) + print( + f"Time to apply quantization with config {config} to model: {time.time() - t0:.02f} seconds" + ) + + model.to(device=device) + device_sync(device=device) + + if compile: + # moe quant + compile causes repeated warnings + import warnings + + warnings.simplefilter("ignore", lineno=84) + warnings.simplefilter("ignore", lineno=105) + + torch._inductor.config.assert_indirect_indexing = False + + global decode_one_token, prefill + + if batch_size == 1 and (isinstance(moe_quant, str) and "base" in moe_quant): + decode_one_token = torch.compile( + decode_one_token, mode="reduce-overhead", fullgraph=True + ) + else: + decode_one_token = torch.compile(decode_one_token, mode="reduce-overhead") + + if args.compile_prefill: + prefill = torch.compile(prefill, fullgraph=True, dynamic=True) + + aggregate_metrics = { + "tokens_per_sec": [], + } + start = -1 if compile else 0 + + for i in range(start, num_samples): + device_sync(device=device) # MKG + if i >= 0 and interactive: + prompt = input("What is your prompt? ") + if is_chat: + prompt = f"{B_INST} {prompt.strip()} {E_INST}" + encoded = encode_tokens(tokenizer, prompt, bos=True, device=device) + + if interactive and i >= 0: + buffer = [] + period_id = tokenizer.encode(".")[0] + done_generating = False + + def callback(x): + nonlocal done_generating + if done_generating: + return + buffer.append(tokenizer.decode([period_id] + x.tolist())[1:]) + if x.item() == tokenizer.eos_id(): + done_generating = True + if len(buffer) == 4 or done_generating: + print("".join(buffer), end="", flush=True) + buffer.clear() + # print(, end='', flush=True) + else: + callback = lambda x: x + t0 = time.perf_counter() + import contextlib + + if i != num_samples - 1 or not profile: + prof = contextlib.nullcontext() + else: + torch.profiler._utils._init_for_cuda_graphs() + prof = torch.profiler.profile() + with prof: + y = generate( + model, + encoded, + max_new_tokens, + batch_size, + interactive=interactive, + callback=callback, + temperature=temperature, + top_k=top_k, + ) + if i == -1: + print(f"Compilation time: {time.perf_counter() - t0:.2f} seconds") + continue + if hasattr(prof, "export_chrome_trace"): + prof.export_chrome_trace(f"{profile}.json") + device_sync(device=device) # MKG + t = time.perf_counter() - t0 + + if not interactive: + pass + print(tokenizer.decode(y[0].tolist())) + else: + print() + tokens_generated = y.size(-1) - prompt_length + tokens_sec = tokens_generated / t + aggregate_metrics["tokens_per_sec"].append(tokens_sec) + print( + f"Time for inference {i + 1}: {t:.02f} sec total, {tokens_sec:.02f} tokens/sec" + ) + print(f"Bandwidth achieved: {model_size * tokens_sec / 1e9:.02f} GB/s") + + if i == 0 and device == "cuda" and memory_profile is not None: + snapshot = torch.cuda.memory._snapshot() + with open(f"{memory_profile}.pickle", "wb") as f: + from pickle import dump + + dump(snapshot, f) + print( + f"\nmemory profile {memory_profile}.pickle saved, to convert that to a usable file, use", + "python pytorch/torch/cuda/_memory_viz.py trace_plot -o .html", + ) + break + + tokpersec = torch.mean(torch.tensor(aggregate_metrics["tokens_per_sec"])).item() + print(f"Average tokens/sec: {tokpersec:.2f}") + if batch_size > 1: + print(f"Average tokens/sec including batches {batch_size * tokpersec:.2f}") + print(f"Memory used: {torch.cuda.max_memory_reserved() / 1e9:.02f} GB") + print(f"model size: {get_model_size_in_bytes(model) / 1e9:.02f}") + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser(description="Your CLI description.") + + parser.add_argument( + "--prompt", type=str, default="Hello, my name is", help="Input prompt." + ) + parser.add_argument( + "--interactive", + action="store_true", + help="Whether to launch in interactive mode", + ) + parser.add_argument("--num_samples", type=int, default=5, help="Number of samples.") + parser.add_argument( + "--max_new_tokens", type=int, default=200, help="Maximum number of new tokens." + ) + parser.add_argument( + "--batch_size", type=int, default=1, help="Batch size to benchmark with" + ) + parser.add_argument("--top_k", type=int, default=200, help="Top-k for sampling.") + parser.add_argument( + "--temperature", type=float, default=0.8, help="Temperature for sampling." + ) + parser.add_argument( + "--checkpoint_path", + type=Path, + default=Path("checkpoints/meta-Transformer/Transformer-2-7b-chat-hf/model.pth"), + help="Model checkpoint path.", + ) + parser.add_argument( + "--compile", action="store_true", help="Whether to compile the model." + ) + parser.add_argument( + "--compile_prefill", + action="store_true", + help="Whether to compile the prefill (improves prefill perf, but higher compile times)", + ) + # parser.add_argument('-q', '--quantization', type=str, help='Which quantization techniques to apply: int8dq, int8wo, int4wo, fp8') + parser.add_argument( + "--moe_quant", + type=str, + help="Which quantization techniques to apply: int8dq, int8wo, int4wo, fp8wo, fp8dq", + ) + parser.add_argument("--profile", type=Path, default=None, help="Profile path.") + parser.add_argument( + "--memory_profile", type=Path, default=None, help="filename for memory profile." + ) + parser.add_argument("--device", type=str, default="cuda", help="device to use") + + args = parser.parse_args() + print(args) + main( + args.prompt, + args.interactive, + args.num_samples, + args.max_new_tokens, + args.batch_size, + args.top_k, + args.temperature, + args.checkpoint_path, + args.compile, + args.compile_prefill, + args.moe_quant, + args.profile, + args.memory_profile, + args.device, + ) diff --git a/torchao/_models/mixtral-moe/model.py b/torchao/_models/mixtral-moe/model.py new file mode 100644 index 0000000000..46a4ce79be --- /dev/null +++ b/torchao/_models/mixtral-moe/model.py @@ -0,0 +1,464 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +from dataclasses import dataclass +from typing import Optional + +import torch +import torch.nn as nn +from torch import Tensor +from torch.nn import functional as F + +from torchao.quantization.prototype.moe_quant.utils import FakeExtraDimTensor + + +def find_multiple(n: int, k: int) -> int: + if n % k == 0: + return n + return n + k - (n % k) + + +@dataclass +class ModelArgs: + block_size: int = 2048 + vocab_size: int = 32000 + n_layer: int = 32 + n_head: int = 32 + dim: int = 4096 + intermediate_size: int = None + n_local_heads: int = -1 + head_dim: int = 64 + rope_base: float = 10000 + norm_eps: float = 1e-5 + num_experts: int = 8 + num_activated_experts: int = 2 + + def __post_init__(self): + if self.n_local_heads == -1: + self.n_local_heads = self.n_head + if self.intermediate_size is None: + hidden_dim = 4 * self.dim + n_hidden = int(2 * hidden_dim / 3) + self.intermediate_size = find_multiple(n_hidden, 256) + self.head_dim = self.dim // self.n_head + + @classmethod + def from_name(cls, name: str): + if name in transformer_configs: + return cls(**transformer_configs[name]) + # fuzzy search + config = [ + config + for config in transformer_configs + if config in str(name).upper() or config in str(name) + ] + assert len(config) == 1, name + return cls(**transformer_configs[config[0]]) + + +transformer_configs = { + "Mixtral-8x7B-Instruct-v0.1": dict( + block_size=32768, + n_layer=32, + n_head=32, + n_local_heads=8, + dim=4096, + intermediate_size=14336, + rope_base=1000000.0, + num_experts=8, + num_activated_experts=2, + ), +} + + +class KVCache(nn.Module): + def __init__( + self, max_batch_size, max_seq_length, n_heads, head_dim, dtype=torch.bfloat16 + ): + super().__init__() + cache_shape = (max_batch_size, n_heads, max_seq_length, head_dim) + self.register_buffer("k_cache", torch.zeros(cache_shape, dtype=dtype)) + self.register_buffer("v_cache", torch.zeros(cache_shape, dtype=dtype)) + + def update(self, input_pos, k_val, v_val): + # input_pos: [S], k_val: [B, H, S, D] + assert input_pos.shape[0] == k_val.shape[2] + + k_out = self.k_cache + v_out = self.v_cache + k_out[:, :, input_pos] = k_val + v_out[:, :, input_pos] = v_val + + return k_out, v_out + + +class Transformer(nn.Module): + def __init__(self, config: ModelArgs) -> None: + super().__init__() + self.config = config + + self.tok_embeddings = nn.Embedding(config.vocab_size, config.dim) + self.layers = nn.ModuleList( + TransformerBlock(config) for _ in range(config.n_layer) + ) + self.norm = RMSNorm(config.dim, eps=config.norm_eps) + self.output = nn.Linear(config.dim, config.vocab_size, bias=False) + + self.freqs_cis: Optional[Tensor] = None + self.mask_cache: Optional[Tensor] = None + self.max_batch_size = -1 + self.max_seq_length = -1 + + def setup_caches(self, max_batch_size, max_seq_length): + if ( + self.max_seq_length >= max_seq_length + and self.max_batch_size >= max_batch_size + ): + return + head_dim = self.config.dim // self.config.n_head + max_seq_length = find_multiple(max_seq_length, 8) + self.max_seq_length = max_seq_length + self.max_batch_size = max_batch_size + for b in self.layers: + b.attention.kv_cache = KVCache( + max_batch_size, max_seq_length, self.config.n_local_heads, head_dim + ) + + self.freqs_cis = precompute_freqs_cis( + self.config.block_size, + self.config.dim // self.config.n_head, + self.config.rope_base, + ) + self.causal_mask = torch.tril( + torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool) + ) + + def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor: + assert self.freqs_cis is not None, "Caches must be initialized first" + mask = self.causal_mask[None, None, input_pos] + freqs_cis = self.freqs_cis[input_pos] + x = self.tok_embeddings(idx) + + for i, layer in enumerate(self.layers): + x = layer(x, input_pos, freqs_cis, mask) + x = self.norm(x) + logits = self.output(x) + return logits + + @classmethod + def from_name(cls, name: str): + return cls(ModelArgs.from_name(name)) + + +class TransformerBlock(nn.Module): + def __init__(self, config: ModelArgs) -> None: + super().__init__() + self.attention = Attention(config) + self.block_sparse_moe = MOEFeedForwardAOQuantizable(config) + self.ffn_norm = RMSNorm(config.dim, config.norm_eps) + self.attention_norm = RMSNorm(config.dim, config.norm_eps) + + def forward( + self, x: Tensor, input_pos: Tensor, freqs_cis: Tensor, mask: Tensor + ) -> Tensor: + h = x + self.attention(self.attention_norm(x), freqs_cis, mask, input_pos) + out = h + self.block_sparse_moe(self.ffn_norm(h)) + return out + + +class Attention(nn.Module): + def __init__(self, config: ModelArgs): + super().__init__() + assert config.dim % config.n_head == 0 + + total_head_dim = (config.n_head + 2 * config.n_local_heads) * config.head_dim + # key, query, value projections for all heads, but in a batch + self.wqkv = nn.Linear(config.dim, total_head_dim, bias=False) + self.wo = nn.Linear(config.dim, config.dim, bias=False) + self.kv_cache = None + + self.n_head = config.n_head + self.head_dim = config.head_dim + self.n_local_heads = config.n_local_heads + self.dim = config.dim + self._register_load_state_dict_pre_hook(self.load_hook) + + def load_hook(self, state_dict, prefix, *args): + if prefix + "wq.weight" in state_dict: + wq = state_dict.pop(prefix + "wq.weight") + wk = state_dict.pop(prefix + "wk.weight") + wv = state_dict.pop(prefix + "wv.weight") + state_dict[prefix + "wqkv.weight"] = torch.cat([wq, wk, wv]) + + def forward( + self, + x: Tensor, + freqs_cis: Tensor, + mask: Tensor, + input_pos: Optional[Tensor] = None, + ) -> Tensor: + bsz, seqlen, _ = x.shape + + kv_size = self.n_local_heads * self.head_dim + q, k, v = self.wqkv(x).split([self.dim, kv_size, kv_size], dim=-1) + + q = q.view(bsz, seqlen, self.n_head, self.head_dim) + k = k.view(bsz, seqlen, self.n_local_heads, self.head_dim) + v = v.view(bsz, seqlen, self.n_local_heads, self.head_dim) + + q = apply_rotary_emb(q, freqs_cis) + k = apply_rotary_emb(k, freqs_cis) + + q, k, v = map(lambda x: x.transpose(1, 2), (q, k, v)) + + if self.kv_cache is not None: + k, v = self.kv_cache.update(input_pos, k, v) + + k = k.repeat_interleave(self.n_head // self.n_local_heads, dim=1) + v = v.repeat_interleave(self.n_head // self.n_local_heads, dim=1) + y = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0) + + y = y.transpose(1, 2).contiguous().view(bsz, seqlen, self.dim) + + y = self.wo(y) + return y + + +# class ConditionalFeedForward(nn.Module): +# def __init__(self, config): +# super().__init__() +# self.w1 = nn.Parameter(torch.empty(config.num_experts, config.intermediate_size, config.dim)) +# self.w2 = nn.Parameter(torch.empty(config.num_experts, config.dim, config.intermediate_size)) +# self.w3 = nn.Parameter(torch.empty(config.num_experts, config.intermediate_size, config.dim)) + +# def forward(self, x: Tensor, expert_indices: Tensor) -> Tensor: +# w1_weights = self.w1[expert_indices] # [T, A, D, D] +# w3_weights = self.w3[expert_indices] # [T, A, D, D] +# w2_weights = self.w2[expert_indices] # [T, A, D, D] +# x1 = F.silu(torch.einsum('ti,taoi -> tao', x, w1_weights)) +# x3 = torch.einsum('ti, taoi -> tao', x, w3_weights) +# expert_outs = torch.einsum('tao, taio -> tai', (x1 * x3), w2_weights) +# return expert_outs + + +# class MOEFeedForward(nn.Module): +# def __init__(self, config) -> None: +# super().__init__() +# self.gate = nn.Linear(config.dim, config.num_experts, bias=False) +# self.cond_ffn = ConditionalFeedForward(config) +# self.dim = config.dim +# self.num_activated_experts = config.num_activated_experts +# def forward(self, x: Tensor) -> Tensor: +# x = x.view(-1, self.dim) +# # T = num_tokens, E = num_experts, D = hidden dim, A = activated experts +# # x: [T, D] +# scores = self.gate(x) # [T, E] +# expert_weights = F.softmax(scores, dim=-1) +# expert_weights, expert_indices = torch.topk(expert_weights, self.num_activated_experts, dim=-1) # [T, A], [T, A] +# expert_weights /= expert_weights.sum(dim=-1, keepdim=True) # [T, A] +# expert_outs = self.cond_ffn(x, expert_indices) +# return torch.einsum('tai,ta -> ti', expert_outs, expert_weights) + + +class RMSNorm(nn.Module): + def __init__(self, dim: int, eps: float = 1e-5): + super().__init__() + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) + + def _norm(self, x): + return x * torch.rsqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps) + + def forward(self, x: Tensor) -> Tensor: + output = self._norm(x.float()).type_as(x) + return output * self.weight + + +def precompute_freqs_cis(seq_len: int, n_elem: int, base: int = 10000) -> Tensor: + freqs = 1.0 / ( + base ** (torch.arange(0, n_elem, 2)[: (n_elem // 2)].float() / n_elem) + ) + t = torch.arange(seq_len, device=freqs.device) + freqs = torch.outer(t, freqs) + freqs_cis = torch.polar(torch.ones_like(freqs), freqs) + cache = torch.stack([freqs_cis.real, freqs_cis.imag], dim=-1) + return cache.to(dtype=torch.bfloat16) + + +def apply_rotary_emb(x: Tensor, freqs_cis: Tensor) -> Tensor: + xshaped = x.float().reshape(*x.shape[:-1], -1, 2) + freqs_cis = freqs_cis.view(1, xshaped.size(1), 1, xshaped.size(3), 2) + x_out2 = torch.stack( + [ + xshaped[..., 0] * freqs_cis[..., 0] - xshaped[..., 1] * freqs_cis[..., 1], + xshaped[..., 1] * freqs_cis[..., 0] + xshaped[..., 0] * freqs_cis[..., 1], + ], + -1, + ) + + x_out2 = x_out2.flatten(3) + return x_out2.type_as(x) + + +# T tokens +# E experts +# D dim +# I intermediate dim +# A activated experts +# T'(e) tokens for expert e + + +class MOEFeedForwardAOQuantizable(nn.Module): + def __init__(self, config) -> None: + super().__init__() + self.gate = nn.Linear(config.dim, config.num_experts, bias=False) + self.cond_ffn = ConditionalFeedForwardAOQuantizable(config) + self.dim = config.dim + self.num_activated_experts = config.num_activated_experts + + def forward(self, x: Tensor) -> Tensor: + batch_size = x.shape[0] + x = x.view(-1, self.dim) # x: [T, D] + scores = self.gate(x) # [T, E] + expert_weights = F.softmax(scores, dim=-1) + expert_weights, expert_indices = torch.topk( + expert_weights, self.num_activated_experts, dim=-1 + ) # [T, A], [T, A] + expert_weights /= expert_weights.sum(dim=-1, keepdim=True).to(x.dtype) # [T, A] + out = self.cond_ffn( + x, expert_indices, expert_weights, self.num_activated_experts + ) + return out.reshape(batch_size, -1, self.dim) + + +class ConditionalFeedForwardAOQuantizable(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.w1 = nn.Parameter( + torch.empty(config.num_experts, config.intermediate_size, config.dim) + ) # E, I, D + self.w2 = nn.Parameter( + torch.empty(config.num_experts, config.dim, config.intermediate_size) + ) # E, D, I + self.w3 = nn.Parameter( + torch.empty(config.num_experts, config.intermediate_size, config.dim) + ) # E, I, D + self.num_experts = config.num_experts + + def forward( + self, + x: Tensor, # T, D + expert_indices: Tensor, # T, A + expert_weights: Tensor, # T, A + num_activated_experts: int, + ) -> Tensor: + num_tokens, dim = x.shape + num_token_activations = num_tokens * num_activated_experts + if x.shape[0] == 1 and not isinstance( + self.w1, FakeExtraDimTensor + ): # only 1 token (can be done without graph breaks when compiled) + outs = [] + expert_indices = expert_indices.view(num_activated_experts) + # collect used experts + w1 = self.w1[expert_indices] + w2 = self.w2[expert_indices] + w3 = self.w3[expert_indices] + + # run token through each expert + for index in range(num_activated_experts): + y1 = F.silu(F.linear(x, w1[index])) + y3 = F.linear(x, w3[index]) + y2 = w2[index] + cur_out = F.linear(y1 * y3, y2) + outs.append(cur_out) + + # combine outputs + final_out = ( + (torch.cat(outs, dim=0) * expert_weights.view(-1, 1)) + .sum(dim=0) + .unsqueeze(-1) + ) + return final_out + else: + expert_list = [x for x in range(self.num_experts)] + + # shuffle tokens into groups for each expert + ordered_token_activations = expert_indices.view(-1).argsort( + stable=True + ) # [A] + ordered_token_indices = ( + ordered_token_activations.div(num_activated_experts) + .floor() + .to(torch.int64) + ) # [T] + + if not expert_indices.is_cuda: # histc doesn't work on cpu for integers + num_tokens_per_expert = torch.bincount( + expert_indices.view(-1) + 1, minlength=self.num_experts + 1 + ) + else: + num_tokens_per_expert = torch.histc( + expert_indices, + bins=self.num_experts + 1, + min=-1, + max=self.num_experts, + ) # [E+1] (added leading 0 so can be used for indexing) + cum_tokens_per_expert = num_tokens_per_expert.cumsum(0).to( + torch.int64 + ) # [E+1] + + @torch._dynamo.disable() + def group_tokens_by_expert( + ordered_token_indices, cum_tokens_per_expert, expert_list + ): + token_indices_per_expert = [ + ordered_token_indices[ + cum_tokens_per_expert[expert] : cum_tokens_per_expert[ + expert + 1 + ] + ] + for expert in expert_list + ] # [T'(e1)], [T'(e2)] ... + return token_indices_per_expert + + token_indices_per_expert = group_tokens_by_expert( + ordered_token_indices, cum_tokens_per_expert, expert_list + ) + tokens_grouped_by_expert = [ + x[indices] for indices in token_indices_per_expert + ] + + # calculate outputs for each expert + outs = [] + for cur_x, expert in zip(tokens_grouped_by_expert, expert_list): + w1 = self.w1[expert] # I, D + w2 = self.w2[expert] # D, I + w3 = self.w3[expert] # I, D + + cur_out = F.linear( + F.silu(F.linear(cur_x, w1)) * F.linear(cur_x, w3), w2 + ) # [T'(e), D] + outs.append(cur_out) + + # weigh outputs + ordered_outs = torch.cat(outs, dim=0) # [T*A, D] + ordered_token_activation_weights = expert_weights.view(-1, 1)[ + ordered_token_activations + ].view(-1, 1) # [T*A, 1] + weighted_ordered_outs = ( + ordered_outs * ordered_token_activation_weights + ) # [T*A, D] + + # sum weighted token-activation outputs together for each token + final_out = torch.zeros_like(x) # [T, D] + final_out = final_out.scatter_add( + dim=0, + index=ordered_token_indices.unsqueeze(-1) + .expand(num_token_activations, dim) + .to(torch.int64), + src=weighted_ordered_outs, + ) + return final_out diff --git a/torchao/_models/mixtral-moe/run.sh b/torchao/_models/mixtral-moe/run.sh new file mode 100644 index 0000000000..d9e3a50405 --- /dev/null +++ b/torchao/_models/mixtral-moe/run.sh @@ -0,0 +1,39 @@ +export MODEL_REPO=mistralai/Mixtral-8x7B-Instruct-v0.1 +export CHECKPOINT_PATH=checkpoints/ + +python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 1 --compile +python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 8 --compile + +python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 1 --moe_quant int8wo --compile +python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 8 --moe_quant int8wo --compile + +python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 1 --moe_quant int8wo-base --compile +python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 8 --moe_quant int8wo-base --compile + +python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 1 --moe_quant int4wo --compile +python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 8 --moe_quant int4wo --compile + +python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 1 --moe_quant int4wo-base --compile +python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 8 --moe_quant int4wo-base --compile + +# # EXPERT CHOICE +# # python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 1 --moe_quant int8dq --compile +# # # # python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 8 --moe_quant int8dq --compile +# # python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 1 --moe_quant int8dq-base --compile +# # # # python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 8 --moe_quant int8dq-base --compile + +python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 1 --moe_quant fp8wo --compile +python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 8 --moe_quant fp8wo --compile + +python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 1 --moe_quant fp8wo-base --compile +python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 8 --moe_quant fp8wo-base --compile + +python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 1 --moe_quant fp8dq --compile +python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 8 --moe_quant fp8dq --compile + +python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 1 --moe_quant fp8dq-base --compile +python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 8 --moe_quant fp8dq-base --compile + +# ARM +# python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 8 --moe_quant intxdq --device cpu +# python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --batch_size 8 --moe_quant intxdq --compile --device cpu diff --git a/torchao/_models/mixtral-moe/scripts/convert_hf_checkpoint.py b/torchao/_models/mixtral-moe/scripts/convert_hf_checkpoint.py new file mode 100644 index 0000000000..6a39578e32 --- /dev/null +++ b/torchao/_models/mixtral-moe/scripts/convert_hf_checkpoint.py @@ -0,0 +1,115 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +import glob +import re +import sys +from pathlib import Path +from typing import Optional + +import torch + +# support running without installing as a package +wd = Path(__file__).parent.parent.resolve() +sys.path.append(str(wd)) + +from model import ModelArgs + + +@torch.inference_mode() +def convert_hf_checkpoint( + *, + checkpoint_dir: Path = Path("checkpoints/mistralai/Mixtral-8x7B-v0.1"), + model_name: Optional[str] = None, +) -> None: + if model_name is None: + model_name = checkpoint_dir.name + + config = ModelArgs.from_name(model_name) + print(f"Model config {config.__dict__}") + + weight_map = { + "tok_embeddings.weight": "tok_embeddings.weight", + "layers.{}.attention.wq.weight": "layers.{}.attention.wq.weight", + "layers.{}.attention.wk.weight": "layers.{}.attention.wk.weight", + "layers.{}.attention.wv.weight": "layers.{}.attention.wv.weight", + "layers.{}.attention.wo.weight": "layers.{}.attention.wo.weight", + "layers.{}.block_sparse_moe.w1": "layers.{}.block_sparse_moe.cond_ffn.w1", + "layers.{}.block_sparse_moe.w2": "layers.{}.block_sparse_moe.cond_ffn.w2", + "layers.{}.block_sparse_moe.w3": "layers.{}.block_sparse_moe.cond_ffn.w3", + "layers.{}.block_sparse_moe.gate.weight": "layers.{}.block_sparse_moe.gate.weight", + "layers.{}.attention_norm.weight": "layers.{}.attention_norm.weight", + "layers.{}.ffn_norm.weight": "layers.{}.ffn_norm.weight", + "norm.weight": "norm.weight", + "output.weight": "output.weight", + } + + pt_files = glob.glob(str(checkpoint_dir / "*.pt")) + + merged_result = {} + for file in sorted(pt_files): + state_dict = torch.load( + str(file), map_location="cpu", mmap=True, weights_only=True + ) + merged_result.update(state_dict) + final_result = {} + for key, value in merged_result.items(): + if "layers" in key: + abstract_key = re.sub(r".(\d+).", ".{}.", key) + layer_num = re.search(r"\d+", key).group(0) + new_key = weight_map[abstract_key] + if new_key is None: + continue + new_key = new_key.format(layer_num) + else: + new_key = weight_map[key] + + final_result[new_key] = value + + for key in tuple(final_result.keys()): + if "wq" in key: + q = final_result[key] + k = final_result[key.replace("wq", "wk")] + v = final_result[key.replace("wq", "wv")] + final_result[key.replace("wq", "wqkv")] = torch.cat([q, k, v]) + del final_result[key] + del final_result[key.replace("wq", "wk")] + del final_result[key.replace("wq", "wv")] + elif "w1" in key or "w3" in key: + final_result[key] = ( + final_result[key] + .reshape(config.num_experts, config.intermediate_size, config.dim) + .contiguous() + ) + elif "w2" in key: + final_result[key] = ( + final_result[key] + .reshape(config.num_experts, config.intermediate_size, config.dim) + .permute(0, 2, 1) + .contiguous() + ) + elif "gate" in key: + final_result[key] = final_result[key].contiguous() + + print(f"Saving checkpoint to {checkpoint_dir / 'model.pth'}") + torch.save(final_result, checkpoint_dir / "model.pth") + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser(description="Convert HuggingFace checkpoint.") + parser.add_argument( + "--checkpoint_dir", + type=Path, + default=Path("checkpoints/mistralai/Mixtral-8x7B-v0.1"), + ) + parser.add_argument("--model_name", type=str, default=None) + + args = parser.parse_args() + convert_hf_checkpoint( + checkpoint_dir=args.checkpoint_dir, + model_name=args.model_name, + ) diff --git a/torchao/_models/mixtral-moe/scripts/download.py b/torchao/_models/mixtral-moe/scripts/download.py new file mode 100644 index 0000000000..8a451b001d --- /dev/null +++ b/torchao/_models/mixtral-moe/scripts/download.py @@ -0,0 +1,48 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +import os +from typing import Optional + +from requests.exceptions import HTTPError + + +def hf_download(repo_id: Optional[str] = None, hf_token: Optional[str] = None) -> None: + from huggingface_hub import snapshot_download + + os.makedirs(f"checkpoints/{repo_id}", exist_ok=True) + try: + snapshot_download( + repo_id, + local_dir=f"checkpoints/{repo_id}", + local_dir_use_symlinks=False, + token=hf_token, + ignore_patterns="*.safetensors", + ) + except HTTPError as e: + if e.response.status_code == 401: + print( + "You need to pass a valid `--hf_token=...` to download private checkpoints." + ) + else: + raise e + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser(description="Download data from HuggingFace Hub.") + parser.add_argument( + "--repo_id", + type=str, + default="mistralai/Mixtral-8x7B-Instruct-v0.1", + help="Repository ID to download from.", + ) + parser.add_argument( + "--hf_token", type=str, default=None, help="HuggingFace API token." + ) + + args = parser.parse_args() + hf_download(args.repo_id, args.hf_token) diff --git a/torchao/_models/mixtral-moe/scripts/prepare.sh b/torchao/_models/mixtral-moe/scripts/prepare.sh new file mode 100644 index 0000000000..8ca60b165b --- /dev/null +++ b/torchao/_models/mixtral-moe/scripts/prepare.sh @@ -0,0 +1,2 @@ +python scripts/download.py --repo_id mistralai/Mixtral-8x7B-Instruct-v0.1 +python scripts/convert_hf_checkpoint.py --checkpoint_dir checkpoints/mistralai/Mixtral-8x7B-Instruct-v0.1 diff --git a/torchao/dtypes/affine_quantized_tensor_ops.py b/torchao/dtypes/affine_quantized_tensor_ops.py index bf1bdacb68..1d70f5c7f3 100644 --- a/torchao/dtypes/affine_quantized_tensor_ops.py +++ b/torchao/dtypes/affine_quantized_tensor_ops.py @@ -476,12 +476,15 @@ def _(func, types, args, kwargs): shape = list(self.shape) shape[dim] = end - start block_size = self.block_size - assert len(block_size) == 2, ( - f"Slice only works for 2d block_size right now, got: {block_size}" - ) + assert len(block_size) in [ + 2, + 3, + ], f"Slice only works for 2 and 3d block_size right now, got: {block_size}" # with slice, some shape dimension might be smaller than block_size dimension, so # we need to make sure there is no overflow - block_size = (min(shape[0], block_size[0]), min(shape[1], block_size[1])) + if len(block_size) == 2: + block_size = (min(shape[0], block_size[0]), min(shape[1], block_size[1])) + new = self.__class__( aten.slice.Tensor(self.tensor_impl, dim, start, end, step), block_size, @@ -490,7 +493,53 @@ def _(func, types, args, kwargs): self.quant_max, self.zero_point_domain, dtype=self.dtype, - strides=self.stride(), + strides=self.stride() if len(block_size) == 2 else None, + ) + return return_and_correct_aliasing(func, args, kwargs, new) + + +@implements(aten.index.Tensor) +def _(func, types, args, kwargs): + self, indices = args + assert len(indices) == 1, ( + f"op {func} currently only implemented for single dimensional indexing but got indices: {indices}" + ) + new_tensor_impl = aten.index.Tensor(self.tensor_impl, indices) + shape = tuple([indices[0].numel(), *self.shape[1:]]) + + block_size = self.block_size + new = self.__class__( + new_tensor_impl, + block_size, + shape, + self.quant_min, + self.quant_max, + self.zero_point_domain, + dtype=self.dtype, + ) + return return_and_correct_aliasing(func, args, kwargs, new) + + +@implements(aten.select.int) +def _(func, types, args, kwargs): + self, dim, index = fill_defaults(args, 3, [0, 0]) + assert dim == 0, f"op {func} currently only implemented for dim=0 but got dim={dim}" + assert self.dim() == 3, ( + f"op {func} currently only implemented for 3 dimensional tensors but got shape={self.shape}" + ) + + new_tensor_impl = aten.select.int(self.tensor_impl, dim, index) + + shape = self.shape[1:] + block_size = self.block_size[1:] + new = self.__class__( + new_tensor_impl, + block_size, + shape, + self.quant_min, + self.quant_max, + self.zero_point_domain, + dtype=self.dtype, ) return return_and_correct_aliasing(func, args, kwargs, new) diff --git a/torchao/dtypes/floatx/float8_layout.py b/torchao/dtypes/floatx/float8_layout.py index 872179bd9a..5914f00102 100644 --- a/torchao/dtypes/floatx/float8_layout.py +++ b/torchao/dtypes/floatx/float8_layout.py @@ -56,6 +56,9 @@ class Float8Layout(Layout): mm_config: Optional[Float8MMConfig] = None +_fallback_warning_shown = False + + @register_layout(Float8Layout) class Float8AQTTensorImpl(AQTTensorImpl): """ @@ -100,12 +103,35 @@ def __init__( def _apply_fn_to_data(self, fn): """Applys a fn to all tensor components stored on this class""" - return self.__class__( - fn(self.float8_data), - fn(self.scale), - self.transposed, - self._layout, - ) + global _fallback_warning_shown + + try: + return self.__class__( + fn(self.float8_data), + fn(self.scale), + self.transposed, + self._layout, + ) + except RuntimeError as e: + if '"index_cuda" not implemented for ' in str(e): + if not _fallback_warning_shown: + import warnings + + warnings.warn( + f"When trying to index Float8AQTTensorImpl, got known error {e}, will use slower fallback but " + + "note: You can torch.compile the model to avoid this problem.", + UserWarning, + ) + _fallback_warning_shown = True + + return self.__class__( # do indexing in bfloat16 then convert back + fn(self.float8_data.to(torch.bfloat16)).to(self.float8_data.dtype), + fn(self.scale), + self.transposed, + self._layout, + ) + else: + raise e def to(self, *args, **kwargs): kwargs = self._get_to_kwargs(*args, **kwargs) @@ -159,6 +185,13 @@ def __torch_dispatch__(cls, func, types, args, kwargs): raise ValueError( f"Not supported args for copy_ due to metadata mistach: {args[0], args[1]}" ) + elif func in [aten.select.int, aten.index.Tensor]: + return return_and_correct_aliasing( + func, + args, + kwargs, + args[0]._apply_fn_to_data(lambda x: func(x, *args[1:], **kwargs)), + ) elif func is aten.slice.Tensor: self, dim, start, end, step = fill_defaults(args, 5, [0, None, None, 1]) if dim == 0: diff --git a/torchao/dtypes/uintx/packed_linear_int8_dynamic_activation_intx_weight_layout.py b/torchao/dtypes/uintx/packed_linear_int8_dynamic_activation_intx_weight_layout.py index 6caa0784d8..dc7b073f32 100644 --- a/torchao/dtypes/uintx/packed_linear_int8_dynamic_activation_intx_weight_layout.py +++ b/torchao/dtypes/uintx/packed_linear_int8_dynamic_activation_intx_weight_layout.py @@ -359,6 +359,7 @@ def _impl_2d_aten(input_tensor, weight_tensor): m, k = input_tensor.shape n, k_ = weight_tensor.shape + assert k_ == k group_size = weight_tensor.tensor_impl.get_layout().group_size packed_weight = weight_tensor.tensor_impl.packed_weight @@ -366,6 +367,9 @@ def _impl_2d_aten(input_tensor, weight_tensor): input_tensor, packed_weight, group_size, k, n ) + if input_tensor.numel() == 0: + return input_tensor + target = weight_tensor.tensor_impl.get_layout().target if weight_tensor.tensor_impl.get_layout().has_bias: diff --git a/torchao/dtypes/uintx/plain_layout.py b/torchao/dtypes/uintx/plain_layout.py index 516136bca7..3551214d7e 100644 --- a/torchao/dtypes/uintx/plain_layout.py +++ b/torchao/dtypes/uintx/plain_layout.py @@ -154,6 +154,14 @@ def __torch_dispatch__(cls, func, types, args, kwargs): ) return return_and_correct_aliasing(func, args, kwargs, new) + elif func in [aten.select.int, aten.index.Tensor]: + return return_and_correct_aliasing( + func, + args, + kwargs, + args[0]._apply_fn_to_data(lambda x: func(x, *args[1:], **kwargs)), + ) + elif func is aten.slice.Tensor: self, dim, start, end, step = fill_defaults(args, 5, [0, None, None, 1]) if dim == 0: diff --git a/torchao/dtypes/uintx/tensor_core_tiled_layout.py b/torchao/dtypes/uintx/tensor_core_tiled_layout.py index 9c37f58ada..3bf9ef6b72 100644 --- a/torchao/dtypes/uintx/tensor_core_tiled_layout.py +++ b/torchao/dtypes/uintx/tensor_core_tiled_layout.py @@ -93,11 +93,13 @@ def _linear_bf16_act_uint4_weight_impl(input_tensor, weight_tensor, bias): act_mat = torch.nn.functional.pad(act_mat, (0, pad_size - act_mat.shape[-1])) # groupwise int4 quantization - groupsize = weight_tensor.block_size[1] - y = torch.ops.aten._weight_int4pack_mm( - act_mat.contiguous(), packed_weight, groupsize, scale_and_zero - ) - + groupsize = weight_tensor.block_size[-1] + if act_mat.numel() == 0: # handling for empty input + y = act_mat + else: + y = torch.ops.aten._weight_int4pack_mm( + act_mat.contiguous(), packed_weight, groupsize, scale_and_zero + ) # remove out_feature padding orig_out_features = weight_tensor.shape[-2] y = y[:, :orig_out_features] @@ -119,7 +121,7 @@ class TensorCoreTiledLayout(Layout): inner_k_tiles: int = 8 def pre_process(self, input: torch.Tensor) -> torch.Tensor: - orig_out_features, orig_in_features = input.shape + orig_out_features, orig_in_features = input.shape[-2:] in_features = find_multiple(orig_in_features, 1024) out_features = find_multiple(orig_out_features, 8) input = torch.nn.functional.pad( @@ -160,18 +162,18 @@ def post_process( zero_point: torch.Tensor, block_size: Tuple[int, ...], ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - orig_out_features, orig_in_features = input.shape + orig_out_features, orig_in_features = input.shape[-2:] in_features = find_multiple(orig_in_features, 1024) out_features = find_multiple(orig_out_features, 8) input = torch.nn.functional.pad( input, (0, in_features - orig_in_features, 0, out_features - orig_out_features), ) - assert len(block_size) == 2, ( - f"TensorCoreTiledLayout only supports len(block_size) == 2, got: {block_size}" + assert len(block_size) == 2 or len(block_size) == 3, ( + f"TensorCoreTiledLayout only supports len(block_size) == 2 or 3, got: {block_size}" ) - scale_pad_dim_0 = (out_features - orig_out_features) // block_size[0] - scale_pad_dim_1 = (in_features - orig_in_features) // block_size[1] + scale_pad_dim_0 = (out_features - orig_out_features) // block_size[-2] + scale_pad_dim_1 = (in_features - orig_in_features) // block_size[-1] scale = torch.nn.functional.pad(scale, (0, scale_pad_dim_1, 0, scale_pad_dim_0)) zero_point = torch.nn.functional.pad( zero_point, (0, scale_pad_dim_1, 0, scale_pad_dim_0) @@ -262,21 +264,44 @@ def from_plain( _layout: Layout, ): assert isinstance(_layout, TensorCoreTiledLayout) + assert int_data.dtype == torch.int32, ( + "torch.ops.aten._convert_weight_to_int4pack in torch 2.4 expects `int32` dtype" + ) + + def quant_2d(int_data_2d): + if TORCH_VERSION_AT_LEAST_2_5: + int_data_2d = (int_data_2d[::, ::2] << 4 | int_data_2d[::, 1::2]).to( + torch.uint8 + ) + else: + assert int_data_2d.dtype == torch.int32, ( + "torch.ops.aten._convert_weight_to_int4pack in torch 2.4 expects `int32` dtype" + ) + return torch.ops.aten._convert_weight_to_int4pack( + int_data_2d.contiguous(), _layout.inner_k_tiles + ) - if TORCH_VERSION_AT_LEAST_2_5: - int_data = (int_data[::, ::2] << 4 | int_data[::, 1::2]).to(torch.uint8) - assert int_data.dtype == torch.uint8, ( - "torch.ops.aten._convert_weight_to_int4pack in torch 2.5 expects `uint8` dtype" + if int_data.dim() == 3: # for moe quant + num_experts = int_data.shape[0] + packed_weight_list = [] + for expert in range(num_experts): + packed_weight_list.append(quant_2d(int_data[expert]).unsqueeze(0)) + packed_weight = torch.cat(packed_weight_list, dim=0) + scale = scale.reshape(int_data.shape[0], int_data.shape[-2], -1) + zero_point = ( + zero_point.reshape(int_data.shape[0], int_data.shape[-2], -1) + if zero_point is not None + else None ) else: - assert int_data.dtype == torch.int32, ( - "torch.ops.aten._convert_weight_to_int4pack in torch 2.4 expects `int32` dtype" + assert int_data.dim() == 2 + packed_weight = quant_2d(int_data) + scale = scale.reshape(int_data.shape[0], -1) + zero_point = ( + zero_point.reshape(int_data.shape[0], -1) + if zero_point is not None + else None ) - packed_weight = torch.ops.aten._convert_weight_to_int4pack( - int_data, _layout.inner_k_tiles - ) - scale = scale.reshape(int_data.shape[0], -1) - zero_point = zero_point.reshape(int_data.shape[0], -1) from torchao.quantization.utils import pack_tinygemm_scales_and_zeros scale_and_zero = pack_tinygemm_scales_and_zeros(scale, zero_point, scale.dtype) @@ -336,6 +361,17 @@ def __torch_dispatch__(cls, func, types, args, kwargs): f"Not supported args for copy_ due to metadata mistach: {args[0], args[1]}" ) + if func in [aten.select.int, aten.index.Tensor]: + assert not (func is aten.select.int and args[1] != 0), ( + "aten.select.int currently only has support for dim=0" + ) + return return_and_correct_aliasing( + func, + args, + kwargs, + args[0]._apply_fn_to_data(lambda x: func(x, *args[1:], **kwargs)), + ) + if func is aten.t.default: """we don't need to repack the weight and just rely on external shape being changed and record the status of transpose/no-transpose @@ -416,11 +452,16 @@ def block_size(self): scale, zero = unpack_tinygemm_scales_and_zeros(self.scale_and_zero) cur_shape = self.shape - assert len(cur_shape) == 4 + if len(cur_shape) == 5: + ones = [1, 1] + cur_shape = cur_shape[1:] + else: + assert len(cur_shape) == 4 + ones = [1] inner_k_tiles = cur_shape[-1] * 2 original_shape = (cur_shape[0] * 8, cur_shape[1] * (inner_k_tiles * 16)) groupsize = int(original_shape[1] / scale.shape[-2]) - return (1, groupsize) + return tuple([*ones, groupsize]) def get_plain(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: from torchao.quantization.quant_primitives import ( @@ -429,35 +470,50 @@ def get_plain(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: ) from torchao.quantization.utils import unpack_tinygemm_scales_and_zeros - scale, zero = unpack_tinygemm_scales_and_zeros(self.scale_and_zero) + def dequant_4d(self): + cur_shape = self.shape + scale, zero = unpack_tinygemm_scales_and_zeros(self.scale_and_zero) + assert len(cur_shape) == 4 + inner_k_tiles = cur_shape[-1] * 2 + original_shape = (cur_shape[0] * 8, cur_shape[1] * (inner_k_tiles * 16)) + eye_shape = original_shape[1] + groupsize = int(original_shape[1] / scale.shape[-2]) + block_size = (1, groupsize) + original_dtype = torch.bfloat16 + assert len(block_size) == 2 and block_size[0] == 1 + dequantized = torch.ops.aten._weight_int4pack_mm( + torch.eye(eye_shape, device=self.device, dtype=original_dtype), + self.packed_weight, + groupsize, + self.scale_and_zero, + ) + dequantized = dequantized.t().contiguous() + return dequantized cur_shape = self.shape - assert len(cur_shape) == 4 - inner_k_tiles = cur_shape[-1] * 2 - original_shape = (cur_shape[0] * 8, cur_shape[1] * (inner_k_tiles * 16)) - eye_shape = original_shape[1] - groupsize = int(original_shape[1] / scale.shape[-2]) - block_size = (1, groupsize) - device = self.device - original_dtype = torch.bfloat16 + + if len(cur_shape) == 4: + dequantized = dequant_4d(self) + else: + assert len(cur_shape) == 5 + num_experts = cur_shape[0] + dequantized_list = [] + for expert in range(num_experts): + dequantized_list.append(dequant_4d(self[expert]).unsqueeze(0)) + dequantized = torch.cat(dequantized_list, dim=0) + + scale, zero = unpack_tinygemm_scales_and_zeros(self.scale_and_zero) + # TODO: move this to `unpack_tinygemm_scales_and_zeros`? + scale = scale.reshape(scale.shape[:-1]).contiguous() + zero = zero.reshape(zero.shape[:-1]).contiguous() + target_dtype = torch.int32 quant_min = 0 quant_max = 15 zero_point_domain = ZeroPointDomain.FLOAT - assert len(block_size) == 2 and block_size[0] == 1 - dequantized = torch.ops.aten._weight_int4pack_mm( - torch.eye(eye_shape, device=device, dtype=original_dtype), - self.packed_weight, - groupsize, - self.scale_and_zero, - ) - dequantized = dequantized.t().contiguous() - # TODO: move this to `unpack_tinygemm_scales_and_zeros`? - scale = scale.reshape(scale.shape[:-1]).contiguous() - zero = zero.reshape(zero.shape[:-1]).contiguous() int_data = quantize_affine( dequantized, - block_size, + self.block_size, scale, zero, target_dtype, diff --git a/torchao/experimental/tests/test_int8_dynamic_activation_intx_weight.py b/torchao/experimental/tests/test_int8_dynamic_activation_intx_weight.py index da6c98cd6f..d1236e9183 100644 --- a/torchao/experimental/tests/test_int8_dynamic_activation_intx_weight.py +++ b/torchao/experimental/tests/test_int8_dynamic_activation_intx_weight.py @@ -629,6 +629,53 @@ def test_identical_to_Int8DynActInt4WeightQATQuantizer( sqnr2 = compute_error(prepared_out, converted_out2).item() self.assertTrue(sqnr2 == float("inf")) + def test_moe_quant_intx(self): + from torchao.quantization.prototype.moe_quant.quantizable_moe_modules import ( + MOEFeedForwardAOQuantizable, + ) + from torchao.quantization.prototype.moe_quant.utils import ( + FakeExtraDimTensor, + MoEQuantConfig, + UseFakeExtraDimTensor, + cond_ffn_filter, + ) + from torchao.quantization.quant_api import ( + Int8DynamicActivationIntxWeightConfig, + PackedLinearInt8DynamicActivationIntxWeightLayout, + quantize_, + ) + from torchao.quantization.utils import compute_error + + with torch.device("cpu"): + model = MOEFeedForwardAOQuantizable(512, 256, 8, 2, empty_init=False).to( + torch.float32 + ) + x = torch.randn(8, 512, dtype=torch.float32) + + out = model(x).clone() + + base_config = Int8DynamicActivationIntxWeightConfig( + layout=PackedLinearInt8DynamicActivationIntxWeightLayout() + ) + moe_config = MoEQuantConfig( + base_config, use_fake_extra_dim_tensor=UseFakeExtraDimTensor.TRUE + ) + + quantize_(model, moe_config, cond_ffn_filter) + + out_q = model(x).clone() + assert isinstance(model.experts.w1, FakeExtraDimTensor) + + mod_c = torch.compile(model, mode="reduce-overhead") + + mod_c(x) + mod_c(x) + + out_qc = mod_c(x).clone() + + self.assertGreater(compute_error(out_q, out), 30) + self.assertGreater(compute_error(out_qc, out), 30) + if __name__ == "__main__": unittest.main() diff --git a/torchao/quantization/linear_activation_quantized_tensor.py b/torchao/quantization/linear_activation_quantized_tensor.py index e4343a086f..aa946c064f 100644 --- a/torchao/quantization/linear_activation_quantized_tensor.py +++ b/torchao/quantization/linear_activation_quantized_tensor.py @@ -82,6 +82,8 @@ def __tensor_unflatten__( def _quantized_linear_op( input_tensor: torch.Tensor, weight_tensor: torch.Tensor, bias: torch.Tensor ): + if input_tensor.numel() == 0: + return input_tensor input_quant_func = weight_tensor.input_quant_func original_weight_tensor = weight_tensor.original_weight_tensor quant_kwargs = weight_tensor.quant_kwargs @@ -243,6 +245,34 @@ def _(func, types, args, kwargs): ) +@implements(aten.select.int) +def _(func, types, args, kwargs): + return return_and_correct_aliasing( + func, + args, + kwargs, + LinearActivationQuantizedTensor( + func(args[0].original_weight_tensor, *args[1:]), + args[0].input_quant_func, + args[0].quant_kwargs, + ), + ) + + +@implements(aten.index.Tensor) +def _(func, types, args, kwargs): + return return_and_correct_aliasing( + func, + args, + kwargs, + LinearActivationQuantizedTensor( + func(args[0].original_weight_tensor, *args[1:]), + args[0].input_quant_func, + args[0].quant_kwargs, + ), + ) + + # this is needed for DTensor.from_local() and for flattening tensor @implements(aten.view.default) def _(func, types, args, kwargs): diff --git a/torchao/quantization/prototype/moe_quant/README.md b/torchao/quantization/prototype/moe_quant/README.md new file mode 100644 index 0000000000..d774fae8fd --- /dev/null +++ b/torchao/quantization/prototype/moe_quant/README.md @@ -0,0 +1,51 @@ +# MoE Quantization + +Our goal with this prototype implementation of moe quantization is to enable usage of existing linear quantization techniques for moe quantization. While it would likely be more performant to use a fused kernel for quantized moe, by decomposing the moe operation into a sequence of linear operations, we can utilize the existing tools and UX that work for lienar quantization and apply them to moe. + +Examples of the usage of these apis can be found in both the llama4_quant.py and ao/torchao/_models/mixtral-moe/generate.py + +## Quantization API + +The API for moe quantization is very similar to linear quantization, given a moe module that is decomposed into linear operations, is quantizable and compilable. In practice this requires us to use the modules found in quantizable_moe_modules.py or something similar. Once this change has been made the API is as follows for a few different quantization techniques: + +```python + +from torchao.quantization.prototype.moe_quant.utils import cond_ffn_filter, +from torchao.quantization.quant_api import quantize_, Int8WeightOnlyConfig + +quantize_(model, MoEQuantConfig(Int8WeightOnlyConfig()), filter_fn=cond_ffn_filter) +model=torch.compile(model, mode="reduce-overhead") +# you can also use fullgraph=True for single token inference +``` + +This api is the same as for normal linear quantization but with a specific filter function. This works for several different quantization techniques where the quantized tensor subclass has been adapted to work with 3D tensors. Specifically this means Int8WeightOnlyConfig, Int4WeightOnlyConfig, Int4WeightOnlyConfig, Float8DynamicActivationFloat8WeightConfig, and Int8DynamicActivationInt8WeightConfig. It should be noted that due to the requirements on minimum tensor input size (>16), Int8DynamicActivationInt8WeightConfig is best used for expert choice moe rather than token choice which is what the rest of the framework in this folder supports. + + +## Alternative Quantization API + +To make the above api work, each tensor subclass had to be edited to work as 3D tensors. However the only ops we actually need to support are a few indexing and slicing ops on the 0th dimension, the majority of the work was removing hard coded assumptions about the tensor dimensionality. This means its possible to instead create a new tensor subclass that pretends to be a 3D tensor by storing a series of 2D tensors and simulating the slicing and indexing ops until eventually just returning the singular desired 2D quantized tensor subclass. This can be achieved using the alternative api by changing the fake_extra_dim_tensor flag of the MoEQuantConfig: + +```python + +from torchao.quantization.prototype.moe_quant.utils import cond_ffn_filter, MoEQuantConfig, UseFakeExtraDimTensor +from torchao.quantization.quant_api import quantize_, Int8DynamicActivationIntxWeightConfig + +config = MoEQuantConfig( + Int8DynamicActivationIntxWeightConfig(), + # this is the only difference from the above api + use_fake_extra_dim_tensor=UseFakeExtraDimTensor.TRUE, +) + +quantize_(model, , filter_fn=cond_ffn_filter) +model=torch.compile(model, mode="reduce-overhead") +``` + +It should also be noted that the default value for use_fake_extra_dim_tensor is AS_FALLBACK which means that it will try to use the base method but if not, will use the more general but less performant fake_extra_dim_tensor method. + +While this approach turns out to not be especially performant, it does allow for slightly better memory characteristics since all the tensors are held seperately and aren't actually modified or indexed. It is flexible enough to work with all of the existing linear quantization techniques that make use of quantized tensor subclasses without any changes being made to those classes. It is compilable though neither single token nor multi token inference works with fullgraph compilation. + +## Model API + +In practice the moe implementations of known models tend to not be easy to quantize and even of those that are, they are often either compiled with many graph breaks or impossible to torch.compile at all. + +The modules in the quantizable_moe_modules.py file were carefully written to satisfy both of those necessary characteristics but to apply moe quantization to your own model, it will require first a module swap from the existing MoE module type, to these more flexible ones. While there isn't a one size fits all way to do this, an example of how it was done for huggingface's llama4 implementation can be found in llama4_quant.py which can be seen as a proof of concept. diff --git a/torchao/quantization/prototype/moe_quant/__init__.py b/torchao/quantization/prototype/moe_quant/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/torchao/quantization/prototype/moe_quant/llama4_quant.py b/torchao/quantization/prototype/moe_quant/llama4_quant.py new file mode 100644 index 0000000000..67ad2ab464 --- /dev/null +++ b/torchao/quantization/prototype/moe_quant/llama4_quant.py @@ -0,0 +1,92 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD 3-Clause license found in the +# LICENSE file in the root directory of this source tree. + +# T tokens +# E experts +# D dim +# I intermediate dim +# A activated experts +# T'(e) tokens for expert e + +import torch +import torch.nn as nn +from transformers import AutoTokenizer, Llama4ForCausalLM +from transformers.models.llama4.modeling_llama4 import Llama4TextMoe + +from torchao.quantization.prototype.moe_quant.quantizable_moe_modules import ( + MOEFeedForwardAOQuantizable, +) +from torchao.quantization.quant_api import _replace_with_custom_fn_if_matches_filter + + +def llama4_moe_filter_fn(module, fqn): + return isinstance(module, Llama4TextMoe) + + +def convert_fn(module): + # get data + hidden_dim = module.hidden_dim + expert_dim = module.experts.expert_dim + num_experts = module.num_experts + top_k = module.top_k + act_fn = module.experts.act_fn + shared_expert = module.shared_expert + return_scores = True + new_mod = MOEFeedForwardAOQuantizable( + hidden_dim, + expert_dim, + num_experts, + top_k, + act_fn, + shared_expert, + return_scores, + ) + + router = module.router + up_proj = module.experts.gate_up_proj + w1, w3 = up_proj.permute(0, 2, 1).chunk(2, dim=1) + w2 = module.experts.down_proj.permute(0, 2, 1) + + new_mod.router = router + new_mod.experts.w1 = nn.Parameter(w1, requires_grad=False) + new_mod.experts.w2 = nn.Parameter(w2, requires_grad=False) + new_mod.experts.w3 = nn.Parameter(w3, requires_grad=False) + return new_mod + + +model_id = "meta-llama/Llama-4-Scout-17B-16E-Instruct" +model = Llama4ForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16) +tokenizer = AutoTokenizer.from_pretrained(model_id) + +_replace_with_custom_fn_if_matches_filter( + model, + convert_fn, + llama4_moe_filter_fn, +) + +model = model + +from torchao.quantization import Int4WeightOnlyConfig, quantize_ +from torchao.quantization.prototype.moe_quant.utils import ( + MoEQuantConfig, + cond_ffn_filter, +) + +quantize_(model, MoEQuantConfig(Int4WeightOnlyConfig()), cond_ffn_filter, device="cuda") + +model.cuda() + +model = torch.compile(model, mode="reduce-overhead") + +prompt = "He is here, the one who will tear apart the very stars" +inputs = tokenizer(prompt, return_tensors="pt") +model.generate(inputs.input_ids.cuda(), max_length=30) +model.generate(inputs.input_ids.cuda(), max_length=30) +generate_ids = model.generate(inputs.input_ids.cuda(), max_length=50) +out = tokenizer.batch_decode( + generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False +)[0] +print(out) diff --git a/torchao/quantization/prototype/moe_quant/quantizable_moe_modules.py b/torchao/quantization/prototype/moe_quant/quantizable_moe_modules.py new file mode 100644 index 0000000000..516341a3a8 --- /dev/null +++ b/torchao/quantization/prototype/moe_quant/quantizable_moe_modules.py @@ -0,0 +1,191 @@ +import torch +import torch.nn.functional as F +from torch import Tensor, nn + +from torchao.quantization.prototype.moe_quant.utils import FakeExtraDimTensor + + +class MOEFeedForwardAOQuantizable(nn.Module): + def __init__( + self, + hidden_dim, + expert_dim, + num_experts, + top_k, + act_fn=F.silu, + shared_expert=None, + return_scores=False, + empty_init=True, + ) -> None: + super().__init__() + self.router = nn.Linear(hidden_dim, num_experts, bias=False) + self.experts = ConditionalFeedForwardAOQuantizable( + num_experts, hidden_dim, expert_dim, act_fn, empty_init + ) + self.hidden_dim = hidden_dim + self.top_k = top_k + self.shared_expert = shared_expert + self.return_scores = return_scores + + def forward(self, x: Tensor) -> Tensor: + batch_size = x.shape[0] + x = x.view(-1, self.hidden_dim) # x: [T, D] + scores = self.router(x) # [T, E] + scores = F.softmax(scores, dim=-1) + scores, expert_indices = torch.topk( + scores, self.top_k, dim=-1 + ) # [T, A], [T, A] + scores /= scores.sum(dim=-1, keepdim=True).to(x.dtype) # [T, A] + + out = self.experts(x, expert_indices, scores, self.top_k) + if self.shared_expert: + out += self.shared_expert(x) + + if self.return_scores: + return out.reshape(batch_size, -1, self.hidden_dim), scores + else: + return out.reshape(batch_size, -1, self.hidden_dim) + + +class ConditionalFeedForwardAOQuantizable(nn.Module): + def __init__(self, num_experts, hidden_dim, expert_dim, act_fn, empty_init=True): + super().__init__() + if empty_init: + self.w1 = nn.Parameter( + torch.empty(num_experts, expert_dim, hidden_dim) + ) # E, I, D + self.w2 = nn.Parameter( + torch.empty(num_experts, hidden_dim, expert_dim) + ) # E, D, I + self.w3 = nn.Parameter( + torch.empty(num_experts, expert_dim, hidden_dim) + ) # E, I, D + else: + self.w1 = nn.Parameter( + torch.randn(num_experts, expert_dim, hidden_dim) + ) # E, I, D + self.w2 = nn.Parameter( + torch.randn(num_experts, hidden_dim, expert_dim) + ) # E, D, I + self.w3 = nn.Parameter( + torch.randn(num_experts, expert_dim, hidden_dim) + ) # E, I, D + self.num_experts = num_experts + self.act_fn = act_fn + self.hidden_dim = hidden_dim + self.expert_dim = expert_dim + + def forward( + self, + x: Tensor, # T, D + expert_indices: Tensor, # T, A + expert_weights: Tensor, # T, A + top_k: int, + ) -> Tensor: + num_tokens, _hidden_dim = x.shape + num_token_activations = num_tokens * top_k + + if x.shape[0] == 1 and not isinstance( + self.w1, FakeExtraDimTensor + ): # only 1 token (can be done without graph breaks when compiled) + outs = [] + expert_indices = expert_indices.view(top_k) + # collect used experts + w1 = self.w1[expert_indices] + w2 = self.w2[expert_indices] + w3 = self.w3[expert_indices] + # run token through each expert + for index in range(top_k): + y1 = F.silu(F.linear(x, w1[index])) + y3 = F.linear(x, w3[index]) + y2 = w2[index] + + cur_out = F.linear(y1 * y3, y2) + outs.append(cur_out) + + # combine outputs + final_out = ( + (torch.cat(outs, dim=0) * expert_weights.view(-1, 1)) + .sum(dim=0) + .reshape(x.shape) + ) + return final_out + else: + expert_list = [x for x in range(self.num_experts)] + + # shuffle tokens into groups for each expert + ordered_token_activations = expert_indices.view(-1).argsort( + stable=True + ) # [A] + ordered_token_indices = ( + ordered_token_activations.div(top_k).floor().to(torch.int64) + ) # [T] + if not expert_indices.is_cuda: # histc doesn't work on cpu for integers + num_tokens_per_expert = torch.bincount( + expert_indices.view(-1) + 1, minlength=self.num_experts + 1 + ) + else: + num_tokens_per_expert = torch.histc( + expert_indices, + bins=self.num_experts + 1, + min=-1, + max=self.num_experts, + ) # [E+1] (added leading 0 so can be used for indexing) + cum_tokens_per_expert = num_tokens_per_expert.cumsum(0).to( + torch.int64 + ) # [E+1] + + @torch._dynamo.disable() + def group_tokens_by_expert( + ordered_token_indices, cum_tokens_per_expert, expert_list + ): + token_indices_per_expert = [ + ordered_token_indices[ + cum_tokens_per_expert[expert] : cum_tokens_per_expert[ + expert + 1 + ] + ].to(torch.int64) + for expert in expert_list + ] # [T'(e1)], [T'(e2)] ... + return token_indices_per_expert + + token_indices_per_expert = group_tokens_by_expert( + ordered_token_indices, cum_tokens_per_expert, expert_list + ) + tokens_grouped_by_expert = [ + x[indices] for indices in token_indices_per_expert + ] + + # calculate outputs for each expert + outs = [] + for cur_x, expert in zip(tokens_grouped_by_expert, expert_list): + w1 = self.w1[expert] # I, D + w2 = self.w2[expert] # D, I + w3 = self.w3[expert] # I, D + + y1 = F.silu(F.linear(cur_x, w1)) + y3 = F.linear(cur_x, w3) + y2 = w2 + + cur_out = F.linear(y1 * y3, y2) # [T'(e), D] + outs.append(cur_out) + + # weigh outputs + ordered_outs = torch.cat(outs, dim=0) # [T*A, D] + ordered_token_activation_weights = expert_weights.view(-1, 1)[ + ordered_token_activations + ].view(-1, 1) # [T*A, 1] + weighted_ordered_outs = ( + ordered_outs * ordered_token_activation_weights + ) # [T*A, D] + + # sum weighted token-activation outputs together for each token + final_out = torch.zeros_like(x) # [T, D] + final_out = final_out.scatter_add( + dim=0, + index=ordered_token_indices.unsqueeze(-1) + .expand(num_token_activations, self.hidden_dim) + .to(torch.int64), + src=weighted_ordered_outs, + ) + return final_out diff --git a/torchao/quantization/prototype/moe_quant/utils.py b/torchao/quantization/prototype/moe_quant/utils.py new file mode 100644 index 0000000000..16fa8c8d33 --- /dev/null +++ b/torchao/quantization/prototype/moe_quant/utils.py @@ -0,0 +1,308 @@ +import torch +from torch.utils._python_dispatch import ( + return_and_correct_aliasing, +) + +aten = torch.ops.aten + +from enum import Enum, auto +from typing import List, Optional, Tuple, Union + +from torchao.quantization.quant_api import ( + _QUANTIZE_CONFIG_HANDLER, + AOBaseConfig, + dataclass, + register_quantize_module_handler, +) +from torchao.utils import fill_defaults + + +class DummyModule(torch.nn.Module): + """This is used because the TorchAO quantization functions tend to operate on modules so to apply the transform to a tensor, we can load a + DummyModule with the target tensor and then apply the transformation to the module and then extract the transformed tensor. + """ + + def __init__(self, weight: torch.Tensor, bias: Optional[torch.Tensor] = None): + super().__init__() + self.weight = weight + self.bias = bias + + +class FakeExtraDimTensor(torch.Tensor): + """This is a subclass of torch.Tensor that simulates a tensor of n+1 dimensions, akin to concatenating several tensors along the 0th dimension. + It takes a list of tensors with the same dtype, device and shape and creates a representation of shape (num_tensors, orig_shape). It can handle a + variety of ops like detach and clone but most importantly, supports any slicing and indexing along the extra dimension. + This is most useful when you have another tensor subclass that you'd like to concatenate together but don't want to support all the necessary + pieces of 3D scaffolding required to make it work. + + The structure of this tensor subclass is a linked_list of tensors with each instance of FakeExtraDimTensor containing a head tensor and a tail consisting of + either another intance of FakeExtraDimTensor or None if we've reached the end of the linked list. This implementation structure is necessary to + support compilation of this tensor subclass since compile requires each tensor component of the tensor subclass to have its own attribute. + """ + + def __new__( + cls, + tensors: Union[Tuple[torch.Tensor], List[torch.Tensor]], + tensor_tail: Optional["FakeExtraDimTensor"] = None, + ): + assert len(tensors) > 0 or tensor_tail is not None + num_tensors = len(tensors) + if tensor_tail is not None: + num_tensors += tensor_tail.num_tensors + test_tensor = tensor_tail.head_tensor + else: + test_tensor = tensors[0] + + dtype = test_tensor.dtype + shape = test_tensor.shape + device = test_tensor.device + layout = test_tensor.layout + for tensor in tensors: + assert tensor.dtype == dtype, ( + f"all tensors in FakeExtraDimTensor must have same dtype but got {tensor.dtype} and {dtype}" + ) + assert tensor.shape == shape, ( + f"all tensors in FakeExtraDimTensor must have same shape but got {tensor.shape} and {shape}" + ) + assert tensor.device == device, ( + f"all tensors in FakeExtraDimTensor must have same device but got {tensor.device} and {device}" + ) + assert tensor.layout == layout, ( + f"all tensors in FakeExtraDimTensor must have same layout but got {tensor.layout} and {layout}" + ) + kwargs = {} + kwargs["dtype"] = dtype + kwargs["layout"] = layout + kwargs["device"] = device + kwargs["requires_grad"] = False + new_shape = (num_tensors, *shape) + return torch.Tensor._make_wrapper_subclass(cls, new_shape, **kwargs) + + def __repr__( + self, + ): + return f"{self.__class__.__name__}(shape={self.shape}, containing {self.num_tensors}: {self.head_tensor})" + + def __init__( + self, + tensors: Union[Tuple[torch.Tensor], List[torch.Tensor]], + tensor_tail: Optional["FakeExtraDimTensor"] = None, + ): + tensors = list(tensors) + assert len(tensors) > 0 or tensor_tail is not None + + # count num_tensors and make tensor_list + self.num_tensors = len(tensors) + if tensor_tail is not None: + self.num_tensors += tensor_tail.num_tensors + tail_list = tensor_tail.tensor_list + else: + tail_list = [] + self.tensor_list = tensors + tail_list + + # 3 cases + # 0) tensors has 0 elements -> take element from tail then do case 1 instead + # 1) tensors has 1 element, -> pop element and tail is None + # 2) tensors has >1 elements, -> pop element and recurse + + # convert case 0 to case 1 by taking 1 element from tail + if len(tensors) == 0 and tensor_tail is not None: + tensors = [ + tensor_tail.head_tensor, + ] + tensor_tail = tensor_tail.tensor_tail + + if len(tensors) > 1: + # case (1): remove first element from tensors, then recurse + self.head_tensor = tensors[0] # remove one + self.tensor_tail = self.__class__(tensors[1:], tensor_tail) # recurse + elif len(tensors) == 1: + # case (2) take final element from tensors, attach tensor_tail then stop recursion + self.head_tensor = tensors[0] + self.tensor_tail = tensor_tail + + def _apply_fn_to_data(self, fn): + self.head_tensor = fn(self.head_tensor) + if self.tensor_tail is not None: + self.tensor_tail = self.tensor_tail._apply_fn_to_data(fn) + return self.__class__([self.head_tensor], self.tensor_tail) + + def __tensor_flatten__(self): + if self.tensor_tail is None: + return [ + "head_tensor", + ], [self.num_tensors] + else: + return [ + "head_tensor", + "tensor_tail", + ], [self.num_tensors] + + @classmethod + def __tensor_unflatten__( + cls, + tensor_data_dict, + tensor_attributes, + outer_size, + outer_stride, + ): + head_tensor = tensor_data_dict["head_tensor"] + tensor_tail = tensor_data_dict.get("tensor_tail", None) + return cls([head_tensor], tensor_tail) + + @classmethod + def __torch_function__(cls, func, types, args, kwargs=None): + kwargs = {} if kwargs is None else kwargs + if func is torch.nn.functional.linear: + x, w, bias = ( + args[0], + args[1], + args[2] if len(args) > 2 else None, + ) + assert w.num_tensors == 1, ( + "FakeExtraDimTensor used in a linear op when it had multiple tensors" + ) + return func(x, w.head_tensor, bias) + try: + with torch._C.DisableTorchFunctionSubclass(): + return func(*args, **kwargs) + except Exception as e: + print(f"ERR: subclass {cls} doesn't implement {func}, got error: {e}") + + @classmethod + def __torch_dispatch__(cls, func, types, args, kwargs): + kwargs = {} if kwargs is None else kwargs + + if func == aten.slice.Tensor: + self, dim, start, end, step = fill_defaults(args, 5, [0, None, None, 1]) + if dim == 0: + return return_and_correct_aliasing( + func, args, kwargs, cls(self.tensor_list[start:end:step]) + ) + + elif func == aten.select.int: + self, dim, index = fill_defaults(args, 3, [0, 0]) + if dim == 0: + return return_and_correct_aliasing( + func, args, kwargs, cls([self.tensor_list[index]]) + ) + elif func == aten.index.Tensor: + self, indices, dim = fill_defaults(args, 3, [0]) + if dim == 0: + # this handles a weird bug where indices gets turned into a list + # between the function dispatch and torch dispatch but just for this function + if isinstance(indices, list) and len(indices) == 1: + indices = indices[0] + return return_and_correct_aliasing( + func, + args, + kwargs, + cls([self.tensor_list[index] for index in indices]), + ) + try: + return return_and_correct_aliasing( + func, + args, + kwargs, + args[0]._apply_fn_to_data(lambda x: func(x, *args[1:], **kwargs)), + ) + except Exception as e: + print( + f"function {func} failed for FakeExtraDimTensor, following error occured when trying to" + "run function on its elements: " + ) + raise e + + +class UseFakeExtraDimTensor(Enum): + """Enum that indicate whether to use FakeExtraDimTensor""" + + TRUE = auto() + FALSE = auto() + AS_FALLBACK = auto() + + +@dataclass +class MoEQuantConfig(AOBaseConfig): + """Configuration for applying quantization to MoE + Args: + `base_config`: normal AO Config + """ + + base_config: AOBaseConfig + use_fake_extra_dim_tensor: UseFakeExtraDimTensor = UseFakeExtraDimTensor.AS_FALLBACK + set_inductor_config: bool = True + + +# Module-level flag to track if we've already printed the error +_moe_quant_tensor_has_printed_error = False + + +def _moe_quant_tensor(weight, config): + def _moe_quant_tensor_base(weight, config): + base_config_handler = _QUANTIZE_CONFIG_HANDLER[type(config.base_config)] + dummy_mod = DummyModule(weight) + quant_mod = base_config_handler(dummy_mod, config.base_config) + return quant_mod.weight + + def _moe_quant_tensor_fake_extra_dim_tensor(weight, config): + base_config_handler = _QUANTIZE_CONFIG_HANDLER[type(config.base_config)] + # break 3D tensor + tensors = [weight[i] for i in range(weight.shape[0])] + # put tensors into modules since the handlers target modules not tensors + dummy_modules = [DummyModule(tensor) for tensor in tensors] + # apply handler to each module + quant_mods = list( + map(lambda x: base_config_handler(x, config.base_config), dummy_modules) + ) + # pack quantized subclasses into FakeExtraDimTensor + quant_weight = FakeExtraDimTensor([mod.weight for mod in quant_mods]) + return quant_weight + + global _moe_quant_tensor_has_printed_error + + use_fake = config.use_fake_extra_dim_tensor + if use_fake == UseFakeExtraDimTensor.FALSE: + return _moe_quant_tensor_base(weight, config) + elif use_fake == UseFakeExtraDimTensor.AS_FALLBACK: + try: + return _moe_quant_tensor_base(weight, config) + except Exception as e: + if not _moe_quant_tensor_has_printed_error: + print(f"tried to do moe_quant but got error: {e}") + _moe_quant_tensor_has_printed_error = True + return _moe_quant_tensor_fake_extra_dim_tensor(weight, config) + else: # This handles UseFakeExtraDimTensor.TRUE + return _moe_quant_tensor_fake_extra_dim_tensor(weight, config) + + +@register_quantize_module_handler(MoEQuantConfig) +def moe_quant_fn(module, config: MoEQuantConfig): + import warnings + + warnings.simplefilter("ignore", lineno=84) + warnings.simplefilter("ignore", lineno=105) + assert "ConditionalFeedForwardAOQuantizable" in str(type(module)) + + for weight_attr in ["w1", "w2", "w3"]: + param = getattr(module, weight_attr) + assert param.dim() == 3, ( + f"when applying moe_quant to {module} expected 3D tensor for {weight_attr} but got {param.dim()}" + ) + assert isinstance(config.base_config, AOBaseConfig), ( + f"MoEQuantConfig expected to be initialized with an AOBaseConfig but got {type(config.base_config)}" + + "this can happen if you initiaze with MoEQuantConfig(AOConfig) rather than MoEQuantConfig(AOConfig())" + ) + new_param = _moe_quant_tensor(param, config) + new_param = torch.nn.Parameter(new_param, requires_grad=False) + setattr(module, weight_attr, new_param) + del param + return module + + +def moe_filter(module, fqn): + return "MOEFeedForwardAOQuantizable" in str(type(module)) + + +def cond_ffn_filter(module, fqn): + return "ConditionalFeedForwardAOQuantizable" in str(type(module)) diff --git a/torchao/quantization/quant_api.py b/torchao/quantization/quant_api.py index 890c2e2038..c20c37a194 100644 --- a/torchao/quantization/quant_api.py +++ b/torchao/quantization/quant_api.py @@ -300,7 +300,7 @@ def _replace_with_custom_fn_if_matches_filter( device, extra_args, ) - if new_child is not child: + if new_child is not child and new_child is not None: setattr(model, name, new_child) if device is not None: model.to(device=device) # move parent module to device @@ -1050,31 +1050,25 @@ class Int4WeightOnlyConfig(AOBaseConfig): int4_weight_only = Int4WeightOnlyConfig -@register_quantize_module_handler(Int4WeightOnlyConfig) -def _int4_weight_only_transform( - module: torch.nn.Module, config: Int4WeightOnlyConfig -) -> torch.nn.Module: +def _int4_weight_only_quantize_tensor(weight, config): # TODO(future PR): perhaps move this logic to a different file, to keep the API # file clean of implementation details # for now, make these local variables to allow the rest of the function # to be a direct copy-paste - weight = module.weight group_size = config.group_size layout = config.layout use_hqq = config.use_hqq zero_point_domain = config.zero_point_domain - if config.set_inductor_config: - torchao.quantization.utils.recommended_inductor_config_setter() if weight.shape[-1] % group_size != 0: logger.info( f"Skipping quantizing weight with int4 weight only quantization because the shape of weight {weight.shape} is not compatible with group_size {group_size}" ) - return module + return weight mapping_type = MappingType.ASYMMETRIC - block_size = (1, group_size) + block_size = tuple([1 for _ in range(weight.dim() - 1)] + [group_size]) target_dtype = torch.int32 quant_min = 0 quant_max = 15 @@ -1126,6 +1120,21 @@ def _int4_weight_only_transform( _layout=layout, use_hqq=use_hqq, ) + return new_weight + + +@register_quantize_module_handler(Int4WeightOnlyConfig) +def _int4_weight_only_transform( + module: torch.nn.Module, config: Int4WeightOnlyConfig +) -> torch.nn.Module: + if config.set_inductor_config: + torchao.quantization.utils.recommended_inductor_config_setter() + + assert hasattr(module, "weight"), ( + "applying int8 weight only quant requires module to have weight attribute" + + " but {module} does not have one" + ) + new_weight = _int4_weight_only_quantize_tensor(module.weight, config) module.weight = torch.nn.Parameter(new_weight, requires_grad=False) module.extra_repr = types.MethodType(_linear_extra_repr, module) return module @@ -1145,20 +1154,15 @@ class Int8WeightOnlyConfig(AOBaseConfig): int8_weight_only = Int8WeightOnlyConfig -@register_quantize_module_handler(Int8WeightOnlyConfig) -def _int8_weight_only_transform(module: torch.nn.Module, config: Int8WeightOnlyConfig): - group_size = config.group_size - weight = module.weight - if config.set_inductor_config: - torchao.quantization.utils.recommended_inductor_config_setter() - +def _int8_weight_only_quantize_tensor(weight, config): mapping_type = MappingType.SYMMETRIC target_dtype = torch.int8 eps = torch.finfo(torch.float32).eps zero_point_dtype = torch.int64 + group_size = config.group_size if group_size is None: - group_size = weight.shape[1] - block_size = (1, group_size) + group_size = weight.shape[-1] + block_size = tuple([1 for x in range(weight.dim() - 1)] + [group_size]) new_weight = to_affine_quantized_intx( weight, mapping_type, @@ -1167,6 +1171,19 @@ def _int8_weight_only_transform(module: torch.nn.Module, config: Int8WeightOnlyC eps=eps, zero_point_dtype=zero_point_dtype, ) + return new_weight + + +@register_quantize_module_handler(Int8WeightOnlyConfig) +def _int8_weight_only_transform(module: torch.nn.Module, config: Int8WeightOnlyConfig): + if config.set_inductor_config: + torchao.quantization.utils.recommended_inductor_config_setter() + + assert hasattr(module, "weight"), ( + "applying int8 weight only quant requires module to have weight attribute" + + " but {module} does not have one" + ) + new_weight = _int8_weight_only_quantize_tensor(module.weight, config) module.weight = torch.nn.Parameter(new_weight, requires_grad=False) module.extra_repr = types.MethodType(_linear_extra_repr, module) return module @@ -1283,33 +1300,26 @@ class Int8DynamicActivationInt8WeightConfig(AOBaseConfig): int8_dynamic_activation_int8_weight = Int8DynamicActivationInt8WeightConfig -@register_quantize_module_handler(Int8DynamicActivationInt8WeightConfig) -def _int8_dynamic_activation_int8_weight_transform( - module: torch.nn.Module, config: Int8DynamicActivationInt8WeightConfig -) -> torch.nn.Module: +def _int8_dynamic_activation_int8_weight_quantize_tensor(weight, config): layout = config.layout act_mapping_type = config.act_mapping_type weight_only_decode = config.weight_only_decode - if config.set_inductor_config: - torchao.quantization.utils.recommended_inductor_config_setter() - - weight = module.weight - in_features = weight.shape[1] + in_features = weight.shape[-1] # int8 dynamic quantization only has benefit when in_feature > 16 if in_features <= 16: logger.info( f"Skipping applying int8_dynamic_activation_int8_weight to weight of shape {weight.shape}" f" because `in_feature` is <= 16: {in_features}" ) - return module + return weight # weight settings mapping_type = MappingType.SYMMETRIC weight_zero_point_domain = ZeroPointDomain.NONE def get_weight_block_size(x): - return (1, x.shape[1]) + return tuple([1 for _ in range(x.dim() - 1)] + [x.shape[-1]]) target_dtype = torch.int8 eps = torch.finfo(torch.float32).eps @@ -1325,7 +1335,7 @@ def get_weight_block_size(x): input_quant_func = _int8_asymm_per_token_quant block_size = get_weight_block_size(weight) - weight = to_affine_quantized_intx( + new_weight = to_affine_quantized_intx( weight, mapping_type, block_size, @@ -1335,8 +1345,25 @@ def get_weight_block_size(x): _layout=layout, zero_point_domain=weight_zero_point_domain, ) - weight = to_linear_activation_quantized(weight, input_quant_func) - module.weight = torch.nn.Parameter(weight, requires_grad=False) + new_weight = to_linear_activation_quantized(new_weight, input_quant_func) + return new_weight + + +@register_quantize_module_handler(Int8DynamicActivationInt8WeightConfig) +def _int8_dynamic_activation_int8_weight_transform( + module: torch.nn.Module, config: Int8DynamicActivationInt8WeightConfig +) -> torch.nn.Module: + if config.set_inductor_config: + torchao.quantization.utils.recommended_inductor_config_setter() + + assert hasattr(module, "weight"), ( + "applying int8 dynamic activation int8 weight quant requires module to have weight attribute" + + "but {module} does not have one" + ) + new_weight = _int8_dynamic_activation_int8_weight_quantize_tensor( + module.weight, config + ) + module.weight = torch.nn.Parameter(new_weight, requires_grad=False) module.extra_repr = types.MethodType(_linear_extra_repr, module) return module @@ -1375,17 +1402,10 @@ class Float8WeightOnlyConfig(AOBaseConfig): float8_weight_only = Float8WeightOnlyConfig -@register_quantize_module_handler(Float8WeightOnlyConfig) -def _float8_weight_only_transform( - module: torch.nn.Module, config: Float8WeightOnlyConfig -) -> torch.nn.Module: +def _float8_weight_only_quant_tensor(weight, config): from torchao.dtypes import to_affine_quantized_floatx - if config.set_inductor_config: - torchao.quantization.utils.recommended_inductor_config_setter() - - weight = module.weight - block_size = (1, weight.shape[1]) + block_size = tuple([1 for _ in range(weight.dim() - 1)] + [weight.shape[-1]]) new_weight = to_affine_quantized_floatx( input_float=weight, block_size=block_size, @@ -1393,6 +1413,22 @@ def _float8_weight_only_transform( scale_dtype=None, _layout=Float8Layout(mm_config=None), ) + return new_weight + + +@register_quantize_module_handler(Float8WeightOnlyConfig) +def _float8_weight_only_transform( + module: torch.nn.Module, config: Float8WeightOnlyConfig +) -> torch.nn.Module: + if config.set_inductor_config: + torchao.quantization.utils.recommended_inductor_config_setter() + + assert hasattr(module, "weight"), ( + "applying int8 weight only quant requires module to have weight attribute" + + " but {module} does not have one" + ) + new_weight = _float8_weight_only_quant_tensor(module.weight, config) + module.weight = torch.nn.Parameter(new_weight, requires_grad=False) module.extra_repr = types.MethodType(_linear_extra_repr, module) return module @@ -1496,11 +1532,12 @@ def _fp8_mm_compat(weight: torch.Tensor) -> bool: Returns: bool: True if the tensor can be quantized to float8, False otherwise """ - assert weight.dim() == 2, ( - f"float8 quantization only works for 2-D tensors, got {weight.dim()}D tensor" - ) + assert weight.dim() in [ + 2, + 3, + ], f"float8 quantization only works for 2/3-D tensors, got {weight.dim()}D tensor" - out_dim, in_dim = weight.shape + out_dim, in_dim = weight.shape[-2:] is_compatible = (in_dim % 16 == 0) and (out_dim % 16 == 0) if not is_compatible: @@ -1547,34 +1584,26 @@ def __post_init__(self): float8_dynamic_activation_float8_weight = Float8DynamicActivationFloat8WeightConfig -@register_quantize_module_handler(Float8DynamicActivationFloat8WeightConfig) -def _float8_dynamic_activation_float8_weight_transform( - module: torch.nn.Module, config: Float8DynamicActivationFloat8WeightConfig -): - assert is_sm_at_least_89() or is_MI300(), ( - "Float8 dynamic activation quantization is only supported on CUDA>=8.9 and MI300+" - ) - if config.set_inductor_config: - torchao.quantization.utils.recommended_inductor_config_setter() - +def _float8_dynamic_activation_float8_weight_quantize_tensor(weight, config): activation_dtype = config.activation_dtype weight_dtype = config.weight_dtype granularity = config.granularity mm_config = config.mm_config - weight = module.weight activation_granularity, weight_granularity = _normalize_granularity(granularity) if not _fp8_mm_compat(weight): # TODO(future PR): this should really throw an exception instead of silently # not doing what the user asked - return module + return weight if isinstance(weight_granularity, PerRow): assert weight.dtype == torch.bfloat16, ( "PerRow quantization only works for bfloat16 precision input weight" ) - block_size = get_block_size(weight.shape, weight_granularity) + block_size = get_block_size(weight.shape[-2:], weight_granularity) + if weight.dim() == 3: + block_size = tuple([1] + list(block_size)) quantized_weight = to_affine_quantized_floatx( input_float=weight, block_size=block_size, @@ -1592,7 +1621,26 @@ def _float8_dynamic_activation_float8_weight_transform( quantized_weight = to_linear_activation_quantized( quantized_weight, input_quant_func, quant_kwargs=input_quant_kwargs ) + return quantized_weight + +@register_quantize_module_handler(Float8DynamicActivationFloat8WeightConfig) +def _float8_dynamic_activation_float8_weight_transform( + module: torch.nn.Module, config: Float8DynamicActivationFloat8WeightConfig +): + assert is_sm_at_least_89() or is_MI300(), ( + "Float8 dynamic activation quantization is only supported on CUDA>=8.9 and MI300+" + ) + if config.set_inductor_config: + torchao.quantization.utils.recommended_inductor_config_setter() + + assert hasattr(module, "weight"), ( + "applying float8 dynamic activation quant requires module to have weight attribute" + + f"but {module} does not have one" + ) + quantized_weight = _float8_dynamic_activation_float8_weight_quantize_tensor( + module.weight, config + ) module.weight = torch.nn.Parameter(quantized_weight, requires_grad=False) module.extra_repr = types.MethodType(_linear_extra_repr, module) return module diff --git a/torchao/quantization/transform_module.py b/torchao/quantization/transform_module.py index b6fac49ae9..339d46be35 100644 --- a/torchao/quantization/transform_module.py +++ b/torchao/quantization/transform_module.py @@ -47,5 +47,6 @@ def _transform( @functools.wraps(config_type) def decorator(func): _QUANTIZE_CONFIG_HANDLER[config_type] = func + return func # needed to make the functions usable externally return decorator diff --git a/torchao/quantization/utils.py b/torchao/quantization/utils.py index 0c30fba713..a9cad8060e 100644 --- a/torchao/quantization/utils.py +++ b/torchao/quantization/utils.py @@ -365,22 +365,23 @@ def get_groupwise_affine_qparams( def pack_tinygemm_scales_and_zeros(scales, zeros, dtype=torch.bfloat16): guard_dtype_size(scales, "scales", dtype=dtype, size=zeros.size()) guard_dtype_size(zeros, "zeros", dtype=dtype) + dim = scales.dim() return ( torch.cat( [ - scales.reshape(scales.size(0), scales.size(1), 1), - zeros.reshape(zeros.size(0), zeros.size(1), 1), + scales.unsqueeze(-1), + zeros.unsqueeze(-1), ], - 2, + dim, ) - .transpose(0, 1) + .transpose(-3, -2) .contiguous() ) def unpack_tinygemm_scales_and_zeros(scales_and_zeros): - assert len(scales_and_zeros.shape) == 3 and scales_and_zeros.shape[2] == 2 - return torch.split(scales_and_zeros.transpose(0, 1), 1, 2) + assert scales_and_zeros.shape[-1] == 2 + return torch.split(scales_and_zeros.transpose(-3, -2), 1, -1) def convert_weight_to_int4pack_xpu(weight, zero_point_domain_is_int=False): diff --git a/torchao/utils.py b/torchao/utils.py index db269b4cb0..280da4e632 100644 --- a/torchao/utils.py +++ b/torchao/utils.py @@ -10,7 +10,7 @@ from functools import reduce from importlib.metadata import version from math import gcd -from typing import Any, Callable, Tuple +from typing import Any, Callable import torch import torch.nn.utils.parametrize as parametrize @@ -170,7 +170,7 @@ def benchmark_torch_function_in_microseconds(f, *args, **kwargs): return measurement.mean * 1e6 -def find_multiple(n: int, *args: Tuple[int]) -> int: +def find_multiple(n: int, *args: int) -> int: k: int = reduce(lambda x, y: x * y // gcd(x, y), args + (1,)) # type: ignore[9] if n % k == 0: return n From 81e48a33fda9f412866032a47642842c114d745c Mon Sep 17 00:00:00 2001 From: Driss Guessous <32754868+drisspg@users.noreply.github.com> Date: Thu, 8 May 2025 17:24:40 -0700 Subject: [PATCH 008/165] Add a triton kernel for swizziling (#2168) stack-info: PR: https://github.com/pytorch/ao/pull/2168, branch: drisspg/stack/53 --- benchmarks/mx_formats/cast_bench.py | 8 +- .../{test_custom_cast.py => test_kernels.py} | 44 +++++-- test/prototype/mx_formats/test_mx_tensor.py | 2 +- .../prototype/mx_formats/fp_format_spec.py | 2 +- .../mx_formats/{custom_cast.py => kernels.py} | 121 ++++++++++++++++++ torchao/prototype/mx_formats/mx_linear.py | 2 +- torchao/prototype/mx_formats/mx_tensor.py | 2 +- torchao/prototype/mx_formats/utils.py | 11 +- 8 files changed, 175 insertions(+), 17 deletions(-) rename test/prototype/mx_formats/{test_custom_cast.py => test_kernels.py} (95%) rename torchao/prototype/mx_formats/{custom_cast.py => kernels.py} (92%) diff --git a/benchmarks/mx_formats/cast_bench.py b/benchmarks/mx_formats/cast_bench.py index 21ac2a297a..56fbaf1c01 100644 --- a/benchmarks/mx_formats/cast_bench.py +++ b/benchmarks/mx_formats/cast_bench.py @@ -1,3 +1,9 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD 3-Clause license found in the +# LICENSE file in the root directory of this source tree. + from typing import Callable, Tuple import fire @@ -5,7 +11,7 @@ import triton from torch._inductor.utils import do_bench_using_profiling -from torchao.prototype.mx_formats.custom_cast import ( +from torchao.prototype.mx_formats.kernels import ( triton_to_mxfp8_dim1, ) from torchao.prototype.mx_formats.mx_tensor import to_mx diff --git a/test/prototype/mx_formats/test_custom_cast.py b/test/prototype/mx_formats/test_kernels.py similarity index 95% rename from test/prototype/mx_formats/test_custom_cast.py rename to test/prototype/mx_formats/test_kernels.py index bce0b3913c..276d180046 100644 --- a/test/prototype/mx_formats/test_custom_cast.py +++ b/test/prototype/mx_formats/test_kernels.py @@ -16,7 +16,17 @@ F6_E2M3_EXP_BIAS, F6_E3M2_EXP_BIAS, ) -from torchao.prototype.mx_formats.custom_cast import ( +from torchao.prototype.mx_formats.fp_format_spec import ( + _assert_equals, + dtype_to_interesting_values, + float4_e2m1_interesting_values, + float6_e2m3_interesting_values, + float6_e3m2_interesting_values, + get_sem_bits, + sem_bits_to_sem_vals, + sem_vals_to_f32, +) +from torchao.prototype.mx_formats.kernels import ( f4_unpacked_to_f32, f6_e2m3_unpacked_to_f32, f6_e3m2_unpacked_to_f32, @@ -33,17 +43,8 @@ triton_to_mxfp8_dim1_reference, unpack_uint4, ) -from torchao.prototype.mx_formats.fp_format_spec import ( - _assert_equals, - dtype_to_interesting_values, - float4_e2m1_interesting_values, - float6_e2m3_interesting_values, - float6_e3m2_interesting_values, - get_sem_bits, - sem_bits_to_sem_vals, - sem_vals_to_f32, -) from torchao.prototype.mx_formats.mx_tensor import MXTensor +from torchao.prototype.mx_formats.utils import to_blocked from torchao.utils import ( TORCH_VERSION_AT_LEAST_2_8, is_sm_at_least_89, @@ -465,3 +466,24 @@ def test_triton_mxfp8_dim1_randn(M, K): x_mx_t, x_s_t = triton_to_mxfp8_dim1(x, inner_block_size=32) torch.testing.assert_close(x_mx_t, x_mx_ref, rtol=0, atol=0) torch.testing.assert_close(x_s_t, x_s_ref, rtol=0, atol=0) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +@pytest.mark.parametrize( + "shape", + [ + (63, 1023), + (128, 4), + (128, 8), + (256, 8), + (300, 9), + (133, 512), + (528, 512), + (128, 1), + ], +) +def test_rearrange(shape): + scales = torch.randint(256, size=shape, device="cuda", dtype=torch.uint8) + eager = to_blocked(scales, False) + triton = to_blocked(scales, True) + torch.testing.assert_close(eager, triton, atol=0, rtol=0) diff --git a/test/prototype/mx_formats/test_mx_tensor.py b/test/prototype/mx_formats/test_mx_tensor.py index 76f340dc78..51ede29bcb 100644 --- a/test/prototype/mx_formats/test_mx_tensor.py +++ b/test/prototype/mx_formats/test_mx_tensor.py @@ -17,7 +17,7 @@ DTYPE_FP6_E3M2, SUPPORTED_ELEM_DTYPES, ) -from torchao.prototype.mx_formats.custom_cast import pack_uint4, pack_uint6 +from torchao.prototype.mx_formats.kernels import pack_uint4, pack_uint6 from torchao.prototype.mx_formats.mx_tensor import ( MXTensor, ScaleCalculationMode, diff --git a/torchao/prototype/mx_formats/fp_format_spec.py b/torchao/prototype/mx_formats/fp_format_spec.py index bdc0cc4dfd..fc9521ef66 100644 --- a/torchao/prototype/mx_formats/fp_format_spec.py +++ b/torchao/prototype/mx_formats/fp_format_spec.py @@ -20,7 +20,7 @@ DTYPE_FP6_E2M3, DTYPE_FP6_E3M2, ) -from torchao.prototype.mx_formats.custom_cast import get_bits +from torchao.prototype.mx_formats.kernels import get_bits dtype_to_bitwidth = { torch.float: 32, diff --git a/torchao/prototype/mx_formats/custom_cast.py b/torchao/prototype/mx_formats/kernels.py similarity index 92% rename from torchao/prototype/mx_formats/custom_cast.py rename to torchao/prototype/mx_formats/kernels.py index 3f870b4f28..f643ac3106 100644 --- a/torchao/prototype/mx_formats/custom_cast.py +++ b/torchao/prototype/mx_formats/kernels.py @@ -1383,6 +1383,124 @@ def triton_to_mxfp8_dim1_reference( scale_e8m0_dim1, ) + @triton.jit + def triton_scale_swizzle( + scale_ptr, + scale_rows, + scale_cols, + output_ptr, + input_row_stride, + output_block_stride, + BLOCK_ROWS: tl.constexpr, + BLOCK_COLS: tl.constexpr, + ): + """ + Rearranges tensor data from row-major to block-scaled swizzle format. + + Args: + scale_ptr: Pointer to the input scale tensor + scale_rows: Number of rows in the scale tensor + scale_cols: Number of columns in the scale tensor + output_ptr: Pointer to the output tensor + input_row_stride: Stride between rows in the input tensor + output_block_stride: Stride between blocks in the output tensor + BLOCK_ROWS: Number of rows in a tile (compile-time constant) + BLOCK_COLS: Number of columns in a tile (compile-time constant) + """ + pid_row = tl.program_id(0) + pid_col = tl.program_id(1) + + rows = tl.arange(0, BLOCK_ROWS)[:, None] + cols = tl.arange(0, BLOCK_COLS)[None, :] + + # Calculate starting row and column for this tile + start_row = pid_row * BLOCK_ROWS + start_col = pid_col * BLOCK_COLS + global_rows = start_row + rows + global_cols = start_col + cols + + mask = (global_rows < scale_rows) & (global_cols < scale_cols) + + input_scales = tl.load( + scale_ptr + global_rows * input_row_stride + global_cols, + mask=mask, + other=0.0, + ) + + r_div_32 = rows // 32 + r_mod_32 = rows % 32 + + # 2) Rearrange to (32, 4, 4) then to final (32, 16) coordinates + dest_indices = r_mod_32 * 16 + r_div_32 * 4 + cols + + # Flatten + dest_indices_flat = tl.reshape(dest_indices, (BLOCK_ROWS * BLOCK_COLS)) + scales_flat = tl.reshape(input_scales, (BLOCK_ROWS * BLOCK_COLS)) + + # Calculate block offset using provided output block stride + LOCAL_NUMEL = BLOCK_ROWS * BLOCK_COLS + block_offset = pid_col * LOCAL_NUMEL + (pid_row * output_block_stride) + + tl.store( + output_ptr + block_offset + dest_indices_flat, + scales_flat, + ) + + def triton_mx_block_rearrange(scale_tensor: torch.Tensor) -> torch.Tensor: + """ + Rearranges an E8M0 tensor scale from row-major format to block-scaled swizzle format. + + This format is suitable for Tmem as described in NVIDIA documentation: + https://docs.nvidia.com/cuda/cublas/index.html#d-block-scaling-factors-layout + + Args: + scale_tensor: Input tensor in row-major format with 8-bit elements + + Returns: + Rearranged tensor in block-scaled swizzle format + """ + assert scale_tensor.element_size() == 1, ( + "Expected element size to be 1 byte (8 bits)" + ) + assert scale_tensor.is_contiguous(), "Input tensor must be contiguous" + + rows, cols = scale_tensor.shape + + # Calculate blocks needed + n_row_blocks = triton.cdiv(rows, 128) + n_col_blocks = triton.cdiv(cols, 4) + padded_rows = n_row_blocks * 128 + padded_cols = n_col_blocks * 4 + + out = scale_tensor.new_empty((padded_rows, padded_cols)) + + # Input stride (for row-major format) + input_row_stride = cols + + # We probably want handle multiple blocks per tile but for now keep it simple + BLOCK_ROWS, BLOCK_COLS = 128, 4 + + # Output block stride for the rearranged format + output_block_stride = BLOCK_ROWS * BLOCK_COLS * (padded_cols // BLOCK_COLS) + + grid = lambda META: ( + triton.cdiv(padded_rows, BLOCK_ROWS), + triton.cdiv(padded_cols, BLOCK_COLS), + ) + + wrap_triton(triton_scale_swizzle)[grid]( + scale_tensor.view(torch.uint8), + rows, + cols, + out.view(torch.uint8), + input_row_stride, + output_block_stride, + BLOCK_ROWS=BLOCK_ROWS, + BLOCK_COLS=BLOCK_COLS, + ) + + return out + else: def triton_to_mxfp8_dim1( @@ -1394,3 +1512,6 @@ def triton_to_mxfp8_dim1_reference( x_hp: torch.Tensor, block_size ) -> Tuple[torch.Tensor, torch.Tensor]: raise AssertionError("needs torch version 2.8+ and triton") + + def triton_mx_block_rearrange(scale_tensor: torch.Tensor) -> torch.Tensor: + raise AssertionError("needs torch version 2.8+ and triton") diff --git a/torchao/prototype/mx_formats/mx_linear.py b/torchao/prototype/mx_formats/mx_linear.py index 067613afb7..4db029480f 100644 --- a/torchao/prototype/mx_formats/mx_linear.py +++ b/torchao/prototype/mx_formats/mx_linear.py @@ -18,7 +18,7 @@ MXInferenceLinearConfig, MXLinearConfig, ) -from torchao.prototype.mx_formats.custom_cast import triton_to_mxfp8_dim1 +from torchao.prototype.mx_formats.kernels import triton_to_mxfp8_dim1 from torchao.prototype.mx_formats.mx_tensor import MXTensor from torchao.quantization.transform_module import ( register_quantize_module_handler, diff --git a/torchao/prototype/mx_formats/mx_tensor.py b/torchao/prototype/mx_formats/mx_tensor.py index f3aca15a73..3125f3c0cc 100644 --- a/torchao/prototype/mx_formats/mx_tensor.py +++ b/torchao/prototype/mx_formats/mx_tensor.py @@ -45,7 +45,7 @@ F32_MIN_NORMAL, SUPPORTED_ELEM_DTYPES, ) -from torchao.prototype.mx_formats.custom_cast import ( +from torchao.prototype.mx_formats.kernels import ( f4_unpacked_to_f32, f6_e2m3_unpacked_to_f32, f6_e3m2_unpacked_to_f32, diff --git a/torchao/prototype/mx_formats/utils.py b/torchao/prototype/mx_formats/utils.py index 8b186f82d6..2c828e477c 100644 --- a/torchao/prototype/mx_formats/utils.py +++ b/torchao/prototype/mx_formats/utils.py @@ -6,6 +6,8 @@ import torch +from torchao.prototype.mx_formats.kernels import triton_mx_block_rearrange + Tensor = torch.Tensor @@ -13,7 +15,7 @@ def ceil_div(a, b): return (a + b - 1) // b -def to_blocked(input_matrix) -> Tensor: +def to_blocked(input_matrix, use_triton_kernel: bool = True) -> Tensor: """ Rearrange a large matrix by breaking it into blocks and applying the rearrangement pattern. @@ -22,10 +24,15 @@ def to_blocked(input_matrix) -> Tensor: Args: input_matrix: Input tensor of shape (H, W) + use_triton_kernel: Whether to use a triton implementation instead of relying on + torch.compile Returns: Rearranged tensor of shape (32*ceil_div(H,128), 16*ceil_div(W,4)) """ + if use_triton_kernel: + return triton_mx_block_rearrange(input_matrix).flatten() + rows, cols = input_matrix.shape n_row_blocks = ceil_div(rows, 128) n_col_blocks = ceil_div(cols, 4) @@ -35,6 +42,8 @@ def to_blocked(input_matrix) -> Tensor: padded_cols = n_col_blocks * 4 padded = input_matrix + # TODO This is to work around VLLM's usage of compile w/ dynamic shapes + # if torch.compiler.is_compiling() or (rows, cols) != (padded_rows, padded_cols): if (rows, cols) != (padded_rows, padded_cols): padded = torch.zeros( (padded_rows, padded_cols), From 2c901b393846ff39d97598abab586d08765f7ea2 Mon Sep 17 00:00:00 2001 From: Peter Yeh Date: Thu, 8 May 2025 22:29:49 -0700 Subject: [PATCH 009/165] Triaging ROCm wheel build (#2161) * Enable ROCm support in build workflow and specify runner configuration for MI300 GPU * Refactor HIP source directory handling in setup.py and remove deprecated runner configuration from build workflow * Refactor HIP source collection in setup.py for improved readability --- .github/workflows/build_wheels_linux.yml | 2 +- setup.py | 21 +++++++++++---------- 2 files changed, 12 insertions(+), 11 deletions(-) diff --git a/.github/workflows/build_wheels_linux.yml b/.github/workflows/build_wheels_linux.yml index 7c05cbe8bd..a8d96abc8a 100644 --- a/.github/workflows/build_wheels_linux.yml +++ b/.github/workflows/build_wheels_linux.yml @@ -28,7 +28,7 @@ jobs: os: linux with-cpu: enable with-cuda: enable - with-rocm: disable + with-rocm: enable with-xpu: enable # Note: if free-threaded python is required add py3.13t here python-versions: '["3.9"]' diff --git a/setup.py b/setup.py index 7e60acbfa8..a269d4410d 100644 --- a/setup.py +++ b/setup.py @@ -311,16 +311,17 @@ def get_extensions(): glob.glob(os.path.join(extensions_cuda_dir, "**/*.cu"), recursive=True) ) - extensions_hip_dir = os.path.join( - extensions_dir, "cuda", "tensor_core_tiled_layout" - ) - hip_sources = list( - glob.glob(os.path.join(extensions_hip_dir, "*.cu"), recursive=True) - ) - extensions_hip_dir = os.path.join(extensions_dir, "cuda", "sparse_marlin") - hip_sources += list( - glob.glob(os.path.join(extensions_hip_dir, "*.cu"), recursive=True) - ) + # Define HIP source directories + hip_source_dirs = [ + os.path.join(extensions_dir, "cuda", "tensor_core_tiled_layout"), + # TODO: Add sparse_marlin back in once we have a ROCm build for it + # os.path.join(extensions_dir, "cuda", "sparse_marlin") + ] + + # Collect all HIP sources from the defined directories + hip_sources = [] + for hip_dir in hip_source_dirs: + hip_sources.extend(glob.glob(os.path.join(hip_dir, "*.cu"), recursive=True)) # Collect CUDA source files if needed if not IS_ROCM and use_cuda: From b95cf189e4aca1a44886258c40e2c834ca0d1045 Mon Sep 17 00:00:00 2001 From: Manuel Candales <42380156+manuelcandales@users.noreply.github.com> Date: Fri, 9 May 2025 09:22:28 -0400 Subject: [PATCH 010/165] metal lowbit kernels: qmv_fast optimization (#2167) --- torchao/experimental/kernels/mps/metal.yaml | 3 + .../kernels/mps/metal/int1mm.metal | 9 +- .../kernels/mps/metal/int2mm_opt.metal | 19 +- .../kernels/mps/metal/int3mm_opt.metal | 34 +- .../kernels/mps/metal/int4mm_opt.metal | 18 +- .../kernels/mps/metal/int5mm.metal | 26 +- .../kernels/mps/metal/int6mm.metal | 25 +- .../kernels/mps/metal/int7mm.metal | 26 +- .../kernels/mps/metal/qmv_fast.metal | 364 ++++++++++++++++++ .../experimental/kernels/mps/src/dispatch.h | 14 + torchao/experimental/kernels/mps/src/lowbit.h | 28 +- .../experimental/kernels/mps/src/packing.h | 141 +++---- .../kernels/mps/test/test_lowbit.mm | 6 +- .../ops/mps/linear_fp_act_xbit_weight_aten.mm | 12 +- .../linear_fp_act_xbit_weight_executorch.mm | 8 +- torchao/experimental/ops/mps/mps_op_lib.py | 4 +- .../experimental/ops/mps/test/test_lowbit.py | 8 +- .../ops/mps/test/test_quantizer.py | 8 +- torchao/experimental/quant_api.py | 4 +- 19 files changed, 569 insertions(+), 188 deletions(-) create mode 100644 torchao/experimental/kernels/mps/metal/qmv_fast.metal diff --git a/torchao/experimental/kernels/mps/metal.yaml b/torchao/experimental/kernels/mps/metal.yaml index eb837432c7..dfad7ad715 100644 --- a/torchao/experimental/kernels/mps/metal.yaml +++ b/torchao/experimental/kernels/mps/metal.yaml @@ -21,3 +21,6 @@ - func: int7mm file: int7mm.metal + +- func: qmv_fast + file: qmv_fast.metal diff --git a/torchao/experimental/kernels/mps/metal/int1mm.metal b/torchao/experimental/kernels/mps/metal/int1mm.metal index a76d66041b..51e8558e9c 100644 --- a/torchao/experimental/kernels/mps/metal/int1mm.metal +++ b/torchao/experimental/kernels/mps/metal/int1mm.metal @@ -11,8 +11,8 @@ using namespace metal; * * @param[A] M x K input tensor of floating point dtype (Float, Half, BFloat16) * @param[B] Packed & quantized weight tensor of uint8 dtype. Expected shape is N x (K / 8) - * @param[scales] 2D tensor containg the scales for each group. Expected shape is #groups x N - * @param[zeros] 2D tensor containg the zero points for each group. Expected shape is #groups x N + * @param[scales] 2D tensor containg the scales for each group. Expected shape is N x #groups + * @param[zeros] 2D tensor containg the zero points for each group. Expected shape is N x #groups * @param[outputData] M x N output tensor of floating point dtype (same as input) * @param[sizes] The sizes involved in the order: M, K, N * @@ -29,6 +29,7 @@ kernel void int1pack_mm( uint2 thread_index [[thread_position_in_grid]]) { const uint K = sizes.y; const uint N = sizes.z; + const uint num_groups = (K + groupSize - 1) / groupSize; const uint m = thread_index.y; // 0..M-1 const uint n = thread_index.x; // 0..N-1 const uint32_t k_block = (K + groupSize - 1) / groupSize; @@ -38,8 +39,8 @@ kernel void int1pack_mm( float rc = 0.0; uint k = 0; for (uint32_t kb = 0; kb < k_block ; kb ++) { - const float scale = float(scales[kb * N + n]); - const float zero = float(zeros[kb * N + n]); + const float scale = float(scales[n * num_groups + kb]); + const float zero = float(zeros[n * num_groups + kb]); for(uint idx = 0; idx < groupSize && k < K; idx+=8, k+=8) { const auto a_val0 = float(A_ptr[k + 0]); const auto a_val1 = float(A_ptr[k + 1]); diff --git a/torchao/experimental/kernels/mps/metal/int2mm_opt.metal b/torchao/experimental/kernels/mps/metal/int2mm_opt.metal index 6008de6730..f42a6e44e9 100644 --- a/torchao/experimental/kernels/mps/metal/int2mm_opt.metal +++ b/torchao/experimental/kernels/mps/metal/int2mm_opt.metal @@ -26,12 +26,11 @@ using namespace metal; @param [in] B is weight matrix of size M x K. Each byte contains 4 2-bit values, along K dim, packed together. @param [in] scales_ptr is scales ptr corresponding each - output channel x groups. These are packed as [num_groups = ceil(K / group_size), N]. N = output + output channel x groups. These are packed as [N, num_groups = ceil(K / group_size)]. N = output channels. @param [in] zeros_ptr is zero points corresponding each - output channel x groups. These are packed as [num_groups = ceil(K / group_size), N]. N = output + output channel x groups. These are packed as [N, num_groups = ceil(K / group_size)]. N = output channels. - output channel x groups. These are packed as [num_groups = ceil(K / group_size), N, 2]. N = output @param [out] output_data is output matrix of size M x N. @param [in] sizes array contains values of M, K and N. @param [in] thread_index is global thread id. @@ -51,6 +50,7 @@ kernel void int2pack_mm(constant T *A [[buffer(0)]], constexpr uint k_pack_factor = 4; const uint K = sizes.y; const uint N = sizes.z; + const uint num_groups = (K + group_size - 1) / group_size; uint n = thread_index.x; // 0..N/4-1 uint m = thread_index.z; // 0..M n = n / threads_per_channel; @@ -75,13 +75,18 @@ kernel void int2pack_mm(constant T *A [[buffer(0)]], // Find specific group to which channels handled by this thread // belong. uint k_block_index = k / group_size; - uint scales_group_offset = (k_block_index * N + n); + uint scales_group_offset = (n * num_groups + k_block_index); vecT scales = - (reinterpret_cast(scales_ptr + scales_group_offset))[0]; - // Adding zero point results in 10% perf penalty. + vecT(scales_ptr[scales_group_offset], + scales_ptr[scales_group_offset + num_groups], + scales_ptr[scales_group_offset + 2 * num_groups], + scales_ptr[scales_group_offset + 3 * num_groups]); vecT zeros = - (reinterpret_cast(zeros_ptr + scales_group_offset))[0]; + vecT(zeros_ptr[scales_group_offset], + zeros_ptr[scales_group_offset + num_groups], + zeros_ptr[scales_group_offset + 2 * num_groups], + zeros_ptr[scales_group_offset + 3 * num_groups]); float4 zeros_float = float4(zeros); float4 a_val = float4(A_ptr[k / 4]); diff --git a/torchao/experimental/kernels/mps/metal/int3mm_opt.metal b/torchao/experimental/kernels/mps/metal/int3mm_opt.metal index 8ab9862d03..69bd142cea 100644 --- a/torchao/experimental/kernels/mps/metal/int3mm_opt.metal +++ b/torchao/experimental/kernels/mps/metal/int3mm_opt.metal @@ -8,15 +8,14 @@ using namespace metal; inline void unpack_3bit(const uchar3 b, thread float* w) { - w[0] = float(((b[0] & 1) << 2) | (b[1] & 3)); - w[1] = float(((b[0] & 2) << 1) | ((b[1] & 12) >> 2)); - w[2] = float((b[0] & 4) | ((b[1] & 48) >> 4)); - w[3] = float(((b[0] & 8) >> 1) | ((b[1] & 192) >> 6)); - - w[4] = float(((b[0] & 16) >> 2) | (b[2] & 3)); - w[5] = float(((b[0] & 32) >> 3) | ((b[2] & 12) >> 2)); - w[6] = float(((b[0] & 64) >> 4) | ((b[2] & 48) >> 4)); - w[7] = float(((b[0] & 128) >> 5) | ((b[2] & 192) >> 6)); + w[0] = float(b[0] & 0x07); + w[1] = float((b[0] & 0x38) >> 3); + w[2] = float(((b[0] & 0xc0) >> 6) | ((b[1] & 0x01) << 2)); + w[3] = float((b[1] & 0x0e) >> 1); + w[4] = float((b[1] & 0x70) >> 4); + w[5] = float(((b[1] & 0x80) >> 7) | ((b[2] & 0x03) << 1)); + w[6] = float((b[2] & 0x1c) >> 2); + w[7] = float((b[2] & 0xe0) >> 5); } /** @@ -24,8 +23,8 @@ inline void unpack_3bit(const uchar3 b, thread float* w) { * * @param[A] M x K input tensor of floating point dtype (Float, Half, BFloat16) * @param[B] Packed & quantized weight tensor of uint8 dtype. Expected shape is N x (3 * K / 8) - * @param[scales] 2D tensor containg the scales for each group. Expected shape is #groups x N - * @param[zeros] 2D tensor containg the zero points for each group. Expected shape is #groups x N + * @param[scales] 2D tensor containg the scales for each group. Expected shape is N x #groups + * @param[zeros] 2D tensor containg the zero points for each group. Expected shape is N x #groups * @param[outputData] M x N output tensor of floating point dtype (same as input) * @param[sizes] The sizes involved in the order: M, K, N * @@ -45,6 +44,7 @@ kernel void int3pack_mm(constant T *A [[buffer(0)]], constexpr uint k_pack_factor = 8; const uint K = sizes.y; const uint N = sizes.z; + const uint num_groups = (K + group_size - 1) / group_size; uint n = thread_index.x; // 0..N/4-1 uint m = thread_index.z; // 0..M n = n / threads_per_channel; @@ -64,12 +64,18 @@ kernel void int3pack_mm(constant T *A [[buffer(0)]], // Find specific group to which channels handled by this thread // belong. uint k_block_index = k / group_size; - uint scales_group_offset = (k_block_index * N + n); + uint scales_group_offset = (n * num_groups + k_block_index); vecT scales = - (reinterpret_cast(scales_ptr + scales_group_offset))[0]; + vecT(scales_ptr[scales_group_offset], + scales_ptr[scales_group_offset + num_groups], + scales_ptr[scales_group_offset + 2 * num_groups], + scales_ptr[scales_group_offset + 3 * num_groups]); vecT zeros = - (reinterpret_cast(zeros_ptr + scales_group_offset))[0]; + vecT(zeros_ptr[scales_group_offset], + zeros_ptr[scales_group_offset + num_groups], + zeros_ptr[scales_group_offset + 2 * num_groups], + zeros_ptr[scales_group_offset + 3 * num_groups]); float4 zeros_float = float4(zeros); float4 a_val[2]; diff --git a/torchao/experimental/kernels/mps/metal/int4mm_opt.metal b/torchao/experimental/kernels/mps/metal/int4mm_opt.metal index edee43ec14..f6d0b4935b 100644 --- a/torchao/experimental/kernels/mps/metal/int4mm_opt.metal +++ b/torchao/experimental/kernels/mps/metal/int4mm_opt.metal @@ -64,12 +64,11 @@ using namespace metal; @param [in] B is weight matrix of size M x K. Each byte contains 2 4-bit values, along K dim, packed together. @param [in] scales_ptr is scales ptr corresponding each - output channel x groups. These are packed as [num_groups = ceil(K / group_size), N]. N = output + output channel x groups. These are packed as [N, num_groups = ceil(K / group_size)]. N = output channels. @param [in] zeros_ptr is zero points corresponding each - output channel x groups. These are packed as [num_groups = ceil(K / group_size), N]. N = output + output channel x groups. These are packed as [N, num_groups = ceil(K / group_size)]. N = output channels. - output channel x groups. These are packed as [num_groups = ceil(K / group_size), N, 2]. N = output @param [out] output_data is output matrix of size M x N. @param [in] sizes array contains values of M, K and N. @param [in] thread_index is global thread id. @@ -89,6 +88,7 @@ kernel void int4pack_mm(constant T *A [[buffer(0)]], constexpr uint k_pack_factor = 2; const uint K = sizes.y; const uint N = sizes.z; + const uint num_groups = (K + group_size - 1) / group_size; uint n = thread_index.x; // 0..N/4-1 uint m = thread_index.z; // 0..M n = n / threads_per_channel; @@ -113,13 +113,19 @@ kernel void int4pack_mm(constant T *A [[buffer(0)]], // Find specific group to which channels handled by this thread // belong. uint k_block_index = k / group_size; - uint scales_group_offset = (k_block_index * N + n); + uint scales_group_offset = (n * num_groups + k_block_index); vecT scales = - (reinterpret_cast(scales_ptr + scales_group_offset))[0]; + vecT(scales_ptr[scales_group_offset], + scales_ptr[scales_group_offset + num_groups], + scales_ptr[scales_group_offset + 2 * num_groups], + scales_ptr[scales_group_offset + 3 * num_groups]); // Adding zero point results in 10% perf penalty. vecT zeros = - (reinterpret_cast(zeros_ptr + scales_group_offset))[0]; + vecT(zeros_ptr[scales_group_offset], + zeros_ptr[scales_group_offset + num_groups], + zeros_ptr[scales_group_offset + 2 * num_groups], + zeros_ptr[scales_group_offset + 3 * num_groups]); float4 zeros_float = float4(zeros); float4 a_val = float4(A_ptr[k / 4]); diff --git a/torchao/experimental/kernels/mps/metal/int5mm.metal b/torchao/experimental/kernels/mps/metal/int5mm.metal index 206786b038..c8be33911a 100644 --- a/torchao/experimental/kernels/mps/metal/int5mm.metal +++ b/torchao/experimental/kernels/mps/metal/int5mm.metal @@ -11,8 +11,8 @@ using namespace metal; * * @param[A] M x K input tensor of floating point dtype (Float, Half, BFloat16) * @param[B] Packed & quantized weight tensor of uint8 dtype. Expected shape is N x (5 * K / 8) - * @param[scales] 2D tensor containg the scales for each group. Expected shape is #groups x N - * @param[zeros] 2D tensor containg the zero points for each group. Expected shape is #groups x N + * @param[scales] 2D tensor containg the scales for each group. Expected shape is N x #groups + * @param[zeros] 2D tensor containg the zero points for each group. Expected shape is N x #groups * @param[outputData] M x N output tensor of floating point dtype (same as input) * @param[sizes] The sizes involved in the order: M, K, N * @@ -29,6 +29,7 @@ kernel void int5pack_mm( uint2 thread_index [[thread_position_in_grid]]) { const uint K = sizes.y; const uint N = sizes.z; + const uint num_groups = (K + groupSize - 1) / groupSize; const uint m = thread_index.y; // 0..M-1 const uint n = thread_index.x; // 0..N-1 const uint32_t k_block = (K + groupSize - 1) / groupSize; @@ -38,8 +39,8 @@ kernel void int5pack_mm( float rc = 0.0; uint k = 0; for (uint32_t kb = 0; kb < k_block ; kb ++) { - const float scale = float(scales[kb * N + n]); - const float zero = float(zeros[kb * N + n]); + const float scale = float(scales[n * num_groups + kb]); + const float zero = float(zeros[n * num_groups + kb]); for(uint idx = 0; idx < groupSize && k < K; idx+=8, k+=8) { const auto a_val0 = float(A_ptr[k + 0]); const auto a_val1 = float(A_ptr[k + 1]); @@ -56,15 +57,14 @@ kernel void int5pack_mm( uchar b3 = B_ptr[5 * (k / 8) + 3]; uchar b4 = B_ptr[5 * (k / 8) + 4]; - uchar w_val0 = ((b0 & 1) << 4) | (b1 & 15); - uchar w_val1 = ((b0 & 2) << 3) | ((b1 & 240) >> 4); - uchar w_val2 = ((b0 & 4) << 2) | (b2 & 15); - uchar w_val3 = ((b0 & 8) << 1) | ((b2 & 240) >> 4); - - uchar w_val4 = ((b0 & 16)) | (b3 & 15); - uchar w_val5 = ((b0 & 32) >> 1) | ((b3 & 240) >> 4); - uchar w_val6 = ((b0 & 64) >> 2) | (b4 & 15); - uchar w_val7 = ((b0 & 128) >> 3) | ((b4 & 240) >> 4); + uchar w_val0 = (b0 & 0x1f); + uchar w_val1 = ((b0 & 0xe0) >> 5) | ((b1 & 0x03) << 3); + uchar w_val2 = ((b1 & 0x7c) >> 2); + uchar w_val3 = ((b1 & 0x80) >> 7) | ((b2 & 0x0f) << 1); + uchar w_val4 = ((b2 & 0xf0) >> 4) | ((b3 & 0x01) << 4); + uchar w_val5 = ((b3 & 0x3e) >> 1); + uchar w_val6 = ((b3 & 0xc0) >> 6) | ((b4 & 0x07) << 2); + uchar w_val7 = ((b4 & 0xf8) >> 3); rc += a_val0 * (scale * float(w_val0) + zero); rc += a_val1 * (scale * float(w_val1) + zero); diff --git a/torchao/experimental/kernels/mps/metal/int6mm.metal b/torchao/experimental/kernels/mps/metal/int6mm.metal index 55d359a6ba..45f03d9cef 100644 --- a/torchao/experimental/kernels/mps/metal/int6mm.metal +++ b/torchao/experimental/kernels/mps/metal/int6mm.metal @@ -11,8 +11,8 @@ using namespace metal; * * @param[A] M x K input tensor of floating point dtype (Float, Half, BFloat16) * @param[B] Packed & quantized weight tensor of uint8 dtype. Expected shape is N x (6 * K / 8) - * @param[scales] 2D tensor containg the scales for each group. Expected shape is #groups x N - * @param[zeros] 2D tensor containg the zero points for each group. Expected shape is #groups x N + * @param[scales] 2D tensor containg the scales for each group. Expected shape is N x #groups + * @param[zeros] 2D tensor containg the zero points for each group. Expected shape is N x #groups * @param[outputData] M x N output tensor of floating point dtype (same as input) * @param[sizes] The sizes involved in the order: M, K, N * @@ -29,6 +29,7 @@ kernel void int6pack_mm( uint2 thread_index [[thread_position_in_grid]]) { const uint K = sizes.y; const uint N = sizes.z; + const uint num_groups = (K + groupSize - 1) / groupSize; const uint m = thread_index.y; // 0..M-1 const uint n = thread_index.x; // 0..N-1 const uint32_t k_block = (K + groupSize - 1) / groupSize; @@ -38,8 +39,8 @@ kernel void int6pack_mm( float rc = 0.0; uint k = 0; for (uint32_t kb = 0; kb < k_block ; kb ++) { - const float scale = float(scales[kb * N + n]); - const float zero = float(zeros[kb * N + n]); + const float scale = float(scales[n * num_groups + kb]); + const float zero = float(zeros[n * num_groups + kb]); for(uint idx = 0; idx < groupSize && k < K; idx+=8, k+=8) { const auto a_val0 = float(A_ptr[k + 0]); const auto a_val1 = float(A_ptr[k + 1]); @@ -59,15 +60,15 @@ kernel void int6pack_mm( uchar b4 = B_ptr[3 * (k / 4) + 4]; uchar b5 = B_ptr[3 * (k / 4) + 5]; - uchar w_val0 = ((b0 & 3) << 4) | (b1 & 15); - uchar w_val1 = ((b0 & 12) << 2) | ((b1 & 240) >> 4); - uchar w_val2 = ((b0 & 48)) | (b2 & 15); - uchar w_val3 = ((b0 & 192) >> 2) | ((b2 & 240) >> 4); + uchar w_val0 = (b0 & 0x3f); + uchar w_val1 = ((b0 & 0xc0) >> 6) | ((b1 & 0x0f) << 2); + uchar w_val2 = ((b1 & 0xf0) >> 4) | ((b2 & 0x03) << 4); + uchar w_val3 = (b2 & 0xfc) >> 2; - uchar w_val4 = ((b3 & 3) << 4) | (b4 & 15); - uchar w_val5 = ((b3 & 12) << 2) | ((b4 & 240) >> 4); - uchar w_val6 = ((b3 & 48)) | (b5 & 15); - uchar w_val7 = ((b3 & 192) >> 2) | ((b5 & 240) >> 4); + uchar w_val4 = (b3 & 0x3f); + uchar w_val5 = ((b3 & 0xc0) >> 6) | ((b4 & 0x0f) << 2); + uchar w_val6 = ((b4 & 0xf0) >> 4) | ((b5 & 0x03) << 4); + uchar w_val7 = (b5 & 0xfc) >> 2; rc += a_val0 * (scale * float(w_val0) + zero); rc += a_val1 * (scale * float(w_val1) + zero); diff --git a/torchao/experimental/kernels/mps/metal/int7mm.metal b/torchao/experimental/kernels/mps/metal/int7mm.metal index b97800b448..ce4e5a51d0 100644 --- a/torchao/experimental/kernels/mps/metal/int7mm.metal +++ b/torchao/experimental/kernels/mps/metal/int7mm.metal @@ -11,8 +11,8 @@ using namespace metal; * * @param[A] M x K input tensor of floating point dtype (Float, Half, BFloat16) * @param[B] Packed & quantized weight tensor of uint8 dtype. Expected shape is N x (7 * K / 8) - * @param[scales] 2D tensor containg the scales for each group. Expected shape is #groups x N - * @param[zeros] 2D tensor containg the zero points for each group. Expected shape is #groups x N + * @param[scales] 2D tensor containg the scales for each group. Expected shape is N x #groups + * @param[zeros] 2D tensor containg the zero points for each group. Expected shape is N x #groups * @param[outputData] M x N output tensor of floating point dtype (same as input) * @param[sizes] The sizes involved in the order: M, K, N * @@ -29,6 +29,7 @@ kernel void int7pack_mm( uint2 thread_index [[thread_position_in_grid]]) { const uint K = sizes.y; const uint N = sizes.z; + const uint num_groups = (K + groupSize - 1) / groupSize; const uint m = thread_index.y; // 0..M-1 const uint n = thread_index.x; // 0..N-1 const uint32_t k_block = (K + groupSize - 1) / groupSize; @@ -38,8 +39,8 @@ kernel void int7pack_mm( float rc = 0.0; uint k = 0; for (uint32_t kb = 0; kb < k_block ; kb ++) { - const float scale = float(scales[kb * N + n]); - const float zero = float(zeros[kb * N + n]); + const float scale = float(scales[n * num_groups + kb]); + const float zero = float(zeros[n * num_groups + kb]); for(uint idx = 0; idx < groupSize && k < K; idx+=8, k+=8) { const auto a_val0 = float(A_ptr[k + 0]); const auto a_val1 = float(A_ptr[k + 1]); @@ -58,15 +59,14 @@ kernel void int7pack_mm( uchar b5 = B_ptr[7 * (k / 8) + 5]; uchar b6 = B_ptr[7 * (k / 8) + 6]; - uchar w_val0 = b0 & 127; - uchar w_val1 = b1 & 127; - uchar w_val2 = b2 & 127; - uchar w_val3 = b3 & 127; - uchar w_val4 = b4 & 127; - uchar w_val5 = b5 & 127; - uchar w_val6 = b6 & 127; - uchar w_val7 = ((b0 & 128) >> 7) | ((b1 & 128) >> 6) | ((b2 & 128) >> 5) | ((b3 & 128) >> 4) - | ((b4 & 128) >> 3) | ((b5 & 128) >> 2) | ((b6 & 128) >> 1); + uchar w_val0 = (b0 & 0x7f); + uchar w_val1 = (b0 >> 7) | ((b1 & 0x3f) << 1); + uchar w_val2 = (b1 >> 6) | ((b2 & 0x1f) << 2); + uchar w_val3 = (b2 >> 5) | ((b3 & 0x0f) << 3); + uchar w_val4 = (b3 >> 4) | ((b4 & 0x07) << 4); + uchar w_val5 = (b4 >> 3) | ((b5 & 0x03) << 5); + uchar w_val6 = (b5 >> 2) | ((b6 & 0x01) << 6); + uchar w_val7 = (b6 >> 1); rc += a_val0 * (scale * float(w_val0) + zero); rc += a_val1 * (scale * float(w_val1) + zero); diff --git a/torchao/experimental/kernels/mps/metal/qmv_fast.metal b/torchao/experimental/kernels/mps/metal/qmv_fast.metal new file mode 100644 index 0000000000..190b122d15 --- /dev/null +++ b/torchao/experimental/kernels/mps/metal/qmv_fast.metal @@ -0,0 +1,364 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// All rights reserved. +// +// This source code is licensed under the BSD 3-Clause license found in the +// LICENSE file in the root directory of this source tree. + +/* + This code was taken from MLX, and modified to add support for 1, 5 & 7 bit packing. + The original code is Copyright © 2023-2024 Apple Inc. + https://github.com/ml-explore/mlx/blob/481349495b8c3d094eb699e678077bbe1406392d/mlx/backend/metal/kernels/quantized.h#L1 + MLX MIT License: https://github.com/ml-explore/mlx/blob/main/LICENSE +*/ + +#include +#include + +static constant constexpr const int SIMD_SIZE = 32; + +template +inline U load_vector(constant T* x, thread U* x_thread) { + static_assert( + 1 <= bits && bits <= 7, + "Template undefined for bits not in {1, 2, 3, 4, 5, 6, 7}"); + + U sum = 0; + + if (bits == 1) { + for (int i = 0; i < values_per_thread; i += 8) { + sum += x[i] + x[i + 1] + x[i + 2] + x[i + 3] + x[i + 4] + x[i + 5] + + x[i + 6] + x[i + 7]; + x_thread[i] = x[i]; + x_thread[i + 1] = x[i + 1] / 2.0f; + x_thread[i + 2] = x[i + 2] / 4.0f; + x_thread[i + 3] = x[i + 3] / 8.0f; + x_thread[i + 4] = x[i + 4] / 16.0f; + x_thread[i + 5] = x[i + 5] / 32.0f; + x_thread[i + 6] = x[i + 6] / 64.0f; + x_thread[i + 7] = x[i + 7] / 128.0f; + } + } + + else if (bits == 2) { + for (int i = 0; i < values_per_thread; i += 4) { + sum += x[i] + x[i + 1] + x[i + 2] + x[i + 3]; + x_thread[i] = x[i]; + x_thread[i + 1] = x[i + 1] / 4.0f; + x_thread[i + 2] = x[i + 2] / 16.0f; + x_thread[i + 3] = x[i + 3] / 64.0f; + } + } + + else if (bits == 3) { + for (int i = 0; i < values_per_thread; i += 8) { + sum += x[i] + x[i + 1] + x[i + 2] + x[i + 3] + x[i + 4] + x[i + 5] + + x[i + 6] + x[i + 7]; + x_thread[i] = x[i]; + x_thread[i + 1] = x[i + 1] / 8.0f; + x_thread[i + 2] = x[i + 2] / 64.0f; + x_thread[i + 3] = x[i + 3] / 2.0f; + x_thread[i + 4] = x[i + 4] / 16.0f; + x_thread[i + 5] = x[i + 5] / 128.0f; + x_thread[i + 6] = x[i + 6] / 4.0f; + x_thread[i + 7] = x[i + 7] / 32.0f; + } + } + + else if (bits == 4) { + for (int i = 0; i < values_per_thread; i += 4) { + sum += x[i] + x[i + 1] + x[i + 2] + x[i + 3]; + x_thread[i] = x[i]; + x_thread[i + 1] = x[i + 1] / 16.0f; + x_thread[i + 2] = x[i + 2] / 256.0f; + x_thread[i + 3] = x[i + 3] / 4096.0f; + } + } + + else if (bits == 5) { + for (int i = 0; i < values_per_thread; i += 8) { + sum += x[i] + x[i + 1] + x[i + 2] + x[i + 3] + x[i + 4] + x[i + 5] + + x[i + 6] + x[i + 7]; + x_thread[i] = x[i]; + x_thread[i + 1] = x[i + 1] / 32.0f; + x_thread[i + 2] = x[i + 2] / 4.0f; + x_thread[i + 3] = x[i + 3] / 128.0f; + x_thread[i + 4] = x[i + 4] / 16.0f; + x_thread[i + 5] = x[i + 5] / 2.0f; + x_thread[i + 6] = x[i + 6] / 64.0f; + x_thread[i + 7] = x[i + 7] / 8.0f; + } + } + + else if (bits == 6) { + for (int i = 0; i < values_per_thread; i += 4) { + sum += x[i] + x[i + 1] + x[i + 2] + x[i + 3]; + x_thread[i] = x[i]; + x_thread[i + 1] = x[i + 1] / 64.0f; + x_thread[i + 2] = x[i + 2] / 16.0f; + x_thread[i + 3] = x[i + 3] / 4.0f; + } + } + + else if (bits == 7) { + for (int i = 0; i < values_per_thread; i += 8) { + sum += x[i] + x[i + 1] + x[i + 2] + x[i + 3] + x[i + 4] + x[i + 5] + + x[i + 6] + x[i + 7]; + x_thread[i] = x[i]; + x_thread[i + 1] = x[i + 1] / 128.0f; + x_thread[i + 2] = x[i + 2] / 64.0f; + x_thread[i + 3] = x[i + 3] / 32.0f; + x_thread[i + 4] = x[i + 4] / 16.0f; + x_thread[i + 5] = x[i + 5] / 8.0f; + x_thread[i + 6] = x[i + 6] / 4.0f; + x_thread[i + 7] = x[i + 7] / 2.0f; + } + } + + return sum; +} + +template +inline U qdot( + constant uint8_t* w, + const thread U* x_thread, + U scale, + U bias, + U sum) { + static_assert( + 1 <= bits && bits <= 7, + "Template undefined for bits not in {1, 2, 3, 4, 5, 6, 7}"); + + U accum = 0; + + if (bits == 1) { + for (int i = 0; i < (values_per_thread / 8); i++) { + x_thread += 8 * i; + + accum += + (x_thread[0] * (w[i] & 0x01) + + x_thread[1] * (w[i] & 0x02) + + x_thread[2] * (w[i] & 0x04) + + x_thread[3] * (w[i] & 0x08) + + x_thread[4] * (w[i] & 0x10) + + x_thread[5] * (w[i] & 0x20) + + x_thread[6] * (w[i] & 0x40) + + x_thread[7] * (w[i] & 0x80)); + } + } + + else if (bits == 2) { + for (int i = 0; i < (values_per_thread / 4); i++) { + accum += + (x_thread[4 * i] * (w[i] & 0x03) + + x_thread[4 * i + 1] * (w[i] & 0x0c) + + x_thread[4 * i + 2] * (w[i] & 0x30) + + x_thread[4 * i + 3] * (w[i] & 0xc0)); + } + } + + else if (bits == 3) { + for (int i = 0; i < (values_per_thread / 8); i++) { + x_thread += 8 * i; + w += 3 * i; + + accum += (w[0] & 0x07) * x_thread[0]; + accum += (w[0] & 0x38) * x_thread[1]; + accum += (w[0] & 0xc0) * x_thread[2]; + accum += (w[1] & 0x01) * (x_thread[2] * 256.0f); + + accum += (w[1] & 0x0e) * x_thread[3]; + accum += (w[1] & 0x70) * x_thread[4]; + accum += (w[1] & 0x80) * x_thread[5]; + accum += (w[2] & 0x03) * (x_thread[5] * 256.0f); + + accum += (w[2] & 0x1c) * x_thread[6]; + accum += (w[2] & 0xe0) * x_thread[7]; + } + } + + else if (bits == 4) { + constant uint16_t* ws = (constant uint16_t*)w; + for (int i = 0; i < (values_per_thread / 4); i++) { + accum += + (x_thread[4 * i] * (ws[i] & 0x000f) + + x_thread[4 * i + 1] * (ws[i] & 0x00f0) + + x_thread[4 * i + 2] * (ws[i] & 0x0f00) + + x_thread[4 * i + 3] * (ws[i] & 0xf000)); + } + } + + else if (bits == 5) { + for (int i = 0; i < (values_per_thread / 8); i++) { + x_thread += 8 * i; + w += 5 * i; + + accum += (w[0] & 0x1f) * x_thread[0]; + accum += (w[0] & 0xe0) * x_thread[1]; + + accum += (w[1] & 0x03) * (x_thread[1] * 256.0f); + accum += (w[1] & 0x7c) * x_thread[2]; + accum += (w[1] & 0x80) * x_thread[3]; + + accum += (w[2] & 0x0f) * (x_thread[3] * 256.0f); + accum += (w[2] & 0xf0) * x_thread[4]; + + accum += (w[3] & 0x01) * (x_thread[4] * 256.0f); + accum += (w[3] & 0x3e) * x_thread[5]; + accum += (w[3] & 0xc0) * x_thread[6]; + + accum += (w[4] & 0x07) * (x_thread[6] * 256.0f); + accum += (w[4] & 0xf8) * x_thread[7]; + } + } + + else if (bits == 6) { + for (int i = 0; i < (values_per_thread / 4); i++) { + x_thread += 4 * i; + w += 3 * i; + + accum += (w[0] & 0x3f) * x_thread[0]; + + accum += (w[0] & 0xc0) * x_thread[1]; + accum += (w[1] & 0x0f) * (x_thread[1] * 256.0f); + + accum += (w[1] & 0xf0) * x_thread[2]; + accum += (w[2] & 0x03) * (x_thread[2] * 256.0f); + + accum += (w[2] & 0xfc) * x_thread[3]; + } + } + + else if (bits == 7) { + for (int i = 0; i < (values_per_thread / 8); i++) { + x_thread += 8 * i; + w += 7 * i; + + accum += (w[0] & 0x7f) * x_thread[0]; + accum += (w[0] & 0x80) * x_thread[1]; + + accum += (w[1] & 0x3f) * (x_thread[1] * 256.0f); + accum += (w[1] & 0xc0) * x_thread[2]; + + accum += (w[2] & 0x1f) * (x_thread[2] * 256.0f); + accum += (w[2] & 0xe0) * x_thread[3]; + + accum += (w[3] & 0x0f) * (x_thread[3] * 256.0f); + accum += (w[3] & 0xf0) * x_thread[4]; + + accum += (w[4] & 0x07) * (x_thread[4] * 256.0f); + accum += (w[4] & 0xf8) * x_thread[5]; + + accum += (w[5] & 0x03) * (x_thread[5] * 256.0f); + accum += (w[5] & 0xfc) * x_thread[6]; + + accum += (w[6] & 0x01) * (x_thread[6] * 256.0f); + accum += (w[6] & 0xfe) * x_thread[7]; + } + } + + return scale * accum + sum * bias; +} + +template +[[kernel]] void qmv_fast( + constant T* x [[buffer(0)]], + constant uchar* w [[buffer(1)]], + constant T* scales [[buffer(2)]], + constant T* biases [[buffer(3)]], + device T* y [[buffer(4)]], + constant uint3 &sizes [[buffer(5)]], // M, K, N + uint3 tid [[threadgroup_position_in_grid]], + uint simd_gid [[simdgroup_index_in_threadgroup]], + uint simd_lid [[thread_index_in_simdgroup]]) { + const int in_vec_size = static_cast(sizes.y); // K + const int out_vec_size = static_cast(sizes.z); // N + + constexpr int power_of_2_bits = (bits & (bits - 1)) == 0; + constexpr int packs_per_thread = (bits == 1 || bits == 2) ? 1 : 2; + constexpr int num_simdgroups = 2; + constexpr int results_per_simdgroup = 4; + constexpr int pack_factor = bits == 1 ? 16 : power_of_2_bits ? 32 / bits : bits == 6 ? 4 : 8; + constexpr int bytes_per_pack = bits == 1 ? 2 : power_of_2_bits ? 4 : bits == 6 ? 3 : bits; + constexpr int values_per_thread = pack_factor * packs_per_thread; + constexpr int block_size = values_per_thread * SIMD_SIZE; + constexpr int scale_step_per_thread = group_size / values_per_thread; + + constant uint8_t* ws = (constant uint8_t*)w; + + typedef float U; + + thread U x_thread[values_per_thread]; + thread U result[results_per_simdgroup] = {0}; + + // Adjust positions + const int in_vec_size_w = in_vec_size * bytes_per_pack / pack_factor; + const int in_vec_size_g = in_vec_size / group_size; + const int out_row = tid.y * (num_simdgroups * results_per_simdgroup) + + simd_gid * results_per_simdgroup; + + ws += out_row * in_vec_size_w + simd_lid * packs_per_thread * bytes_per_pack; + scales += out_row * in_vec_size_g + simd_lid / scale_step_per_thread; + biases += out_row * in_vec_size_g + simd_lid / scale_step_per_thread; + x += tid.x * in_vec_size + simd_lid * values_per_thread; + y += tid.x * out_vec_size + out_row; + + for (int k = 0; k < in_vec_size; k += block_size) { + U sum = load_vector(x, x_thread); + + for (int row = 0; row < results_per_simdgroup; row++) { + auto wl = (constant uint8_t*)(ws + row * in_vec_size_w); + constant T* sl = scales + row * in_vec_size_g; + constant T* bl = biases + row * in_vec_size_g; + + U s = sl[0]; + U b = bl[0]; + result[row] += qdot(wl, x_thread, s, b, sum); + } + + ws += block_size * bytes_per_pack / pack_factor; + scales += block_size / group_size; + biases += block_size / group_size; + x += block_size; + } + + for (int row = 0; row < results_per_simdgroup; row++) { + result[row] = simd_sum(result[row]); + if (simd_lid == 0) { + y[row] = static_cast(result[row]); + } + } +} + +#define INSTANTIATE_QMV_FAST(DTYPE, GSIZE, NBIT) \ + template [[host_name("qmv_fast_" #NBIT "bit_" #GSIZE "_" #DTYPE)]] kernel void \ + qmv_fast( \ + constant DTYPE * A [[buffer(0)]], \ + constant uchar * B [[buffer(1)]], \ + constant DTYPE * scales_ptr [[buffer(2)]], \ + constant DTYPE * zeros_ptr [[buffer(3)]], \ + device DTYPE * output_data [[buffer(4)]], \ + constant uint3 & sizes [[buffer(5)]], \ + uint3 thread_index [[thread_position_in_grid]], \ + uint simd_gid [[simdgroup_index_in_threadgroup]], \ + uint tid_in_simdgroup [[thread_index_in_simdgroup]]) + +#define INSTANTIATE_QMV_FAST_DTYPE_GSIZE(DTYPE, GSIZE) \ + INSTANTIATE_QMV_FAST(DTYPE, GSIZE, 1); \ + INSTANTIATE_QMV_FAST(DTYPE, GSIZE, 2); \ + INSTANTIATE_QMV_FAST(DTYPE, GSIZE, 3); \ + INSTANTIATE_QMV_FAST(DTYPE, GSIZE, 4); \ + INSTANTIATE_QMV_FAST(DTYPE, GSIZE, 5); \ + INSTANTIATE_QMV_FAST(DTYPE, GSIZE, 6); \ + INSTANTIATE_QMV_FAST(DTYPE, GSIZE, 7); + +#define INSTANTIATE_QMV_FAST_DTYPE(DTYPE) \ + INSTANTIATE_QMV_FAST_DTYPE_GSIZE(DTYPE, 32); \ + INSTANTIATE_QMV_FAST_DTYPE_GSIZE(DTYPE, 64); \ + INSTANTIATE_QMV_FAST_DTYPE_GSIZE(DTYPE, 128); \ + INSTANTIATE_QMV_FAST_DTYPE_GSIZE(DTYPE, 256); + +INSTANTIATE_QMV_FAST_DTYPE(float); +INSTANTIATE_QMV_FAST_DTYPE(half); +#if __METAL_VERSION__ >= 310 +INSTANTIATE_QMV_FAST_DTYPE(bfloat); +#endif diff --git a/torchao/experimental/kernels/mps/src/dispatch.h b/torchao/experimental/kernels/mps/src/dispatch.h index 39acd8d1f0..a04452cece 100644 --- a/torchao/experimental/kernels/mps/src/dispatch.h +++ b/torchao/experimental/kernels/mps/src/dispatch.h @@ -34,4 +34,18 @@ inline void dispatch_mm_Mr1xNr4_per_TG( threadsPerThreadgroup:MTLSizeMake(32, 1, 1)]; } +inline void dispatch_qmv_fast( + id encoder, + int32_t maxThreadsPerGroup, + int32_t M, + int32_t N, + int32_t K) { + (void)K; + if (maxThreadsPerGroup < 64) { + throw std::runtime_error("Can't dispatch!"); + } + [encoder dispatchThreadgroups:MTLSizeMake(M, (N + 7) / 8, 1) + threadsPerThreadgroup:MTLSizeMake(32, 2, 1)]; +} + } // namespace torchao::kernels::mps::lowbit::dispatch diff --git a/torchao/experimental/kernels/mps/src/lowbit.h b/torchao/experimental/kernels/mps/src/lowbit.h index 9b2d539761..370c6d400c 100644 --- a/torchao/experimental/kernels/mps/src/lowbit.h +++ b/torchao/experimental/kernels/mps/src/lowbit.h @@ -111,6 +111,25 @@ inline void linear_lowbit_quant_weights_mps_impl( }); } +template +std::tuple get_shader_func_and_dispatch( + int64_t qGroupSize, + const std::string_view type_str, + int32_t M, + int32_t N, + int32_t K) { + if (M == 1 && N % 8 == 0 && K % 512 == 0) { + return std::make_tuple( + std::string("qmv_fast_") + std::to_string(nbit) + "bit_" + + std::to_string(qGroupSize) + "_" + std::string(type_str), + dispatch::dispatch_qmv_fast); + } + return std::make_tuple( + std::string(LowBitConfig::func_prefix) + std::to_string(qGroupSize) + + "_" + std::string(type_str), + LowBitConfig::dispatch_fn); +} + // LowBit Quantized Weights Linear on Metal template void linear_lowbit_quant_weights_mps( @@ -129,8 +148,11 @@ void linear_lowbit_quant_weights_mps( assert( qGroupSize == 32 || qGroupSize == 64 || qGroupSize == 128 || qGroupSize == 256); - const std::string shader_func = std::string(LowBitConfig::func_prefix) + - std::to_string(qGroupSize) + "_" + std::string(type_str); + std::tuple shader_func_and_dispatch = + get_shader_func_and_dispatch(qGroupSize, type_str, M, N, K); + const std::string shader_func = std::get<0>(shader_func_and_dispatch); + const DispatchFn dispatch_fn = std::get<1>(shader_func_and_dispatch); + return linear_lowbit_quant_weights_mps_impl( a_buf, b_buf, @@ -141,7 +163,7 @@ void linear_lowbit_quant_weights_mps( K, N, shader_func, - LowBitConfig::dispatch_fn); + dispatch_fn); } } // namespace diff --git a/torchao/experimental/kernels/mps/src/packing.h b/torchao/experimental/kernels/mps/src/packing.h index 09a248da5e..5412c04a12 100644 --- a/torchao/experimental/kernels/mps/src/packing.h +++ b/torchao/experimental/kernels/mps/src/packing.h @@ -70,9 +70,7 @@ pack<2>(const uint8_t* w_ptr, uint8_t* b_ptr, int32_t N, int32_t K) { /** * 3-bit packing. Each weight is 3 bits. We can't pack them into a byte, so we - * pack 8 weights into 3 bytes. But we can't nicely pack the 8 weights - * continuously. Instead, we pack the upper bits of all weights into the first - * byte, then the 2 lower bits of all weights into the other 2 bytes. + * pack 8 weights into 3 bytes. */ template <> inline void @@ -80,28 +78,18 @@ pack<3>(const uint8_t* w_ptr, uint8_t* b_ptr, int32_t N, int32_t K) { for (int32_t n = 0; n < N; n++) { int32_t row_base = (n * (K / 8)) * 3; for (int32_t k8 = 0; k8 < K / 8; k8++) { - uint8_t src_0ab = w_ptr[n * K + k8 * 8 + 0]; - uint8_t src_1cd = w_ptr[n * K + k8 * 8 + 1]; - uint8_t src_2ef = w_ptr[n * K + k8 * 8 + 2]; - uint8_t src_3gh = w_ptr[n * K + k8 * 8 + 3]; - uint8_t src_4ij = w_ptr[n * K + k8 * 8 + 4]; - uint8_t src_5kl = w_ptr[n * K + k8 * 8 + 5]; - uint8_t src_6mn = w_ptr[n * K + k8 * 8 + 6]; - uint8_t src_7op = w_ptr[n * K + k8 * 8 + 7]; - - // b0: 7|6|5|4|3|2|1|0 (upper bits for all values) - b_ptr[row_base + 3 * k8 + 0] = ((src_0ab & 4) >> 2) | - ((src_1cd & 4) >> 1) | ((src_2ef & 4)) | ((src_3gh & 4) << 1) | - ((src_4ij & 4) << 2) | ((src_5kl & 4) << 3) | ((src_6mn & 4) << 4) | - ((src_7op & 4) << 5); - - // b1: gh|ef|cd|ab (lower 2 bits for first 4 values) - b_ptr[row_base + 3 * k8 + 1] = (src_0ab & 3) | ((src_1cd & 3) << 2) | - ((src_2ef & 3) << 4) | ((src_3gh & 3) << 6); + uint8_t src_val0 = w_ptr[n * K + k8 * 8]; + uint8_t src_val1 = w_ptr[n * K + k8 * 8 + 1]; + uint8_t src_val2 = w_ptr[n * K + k8 * 8 + 2]; + uint8_t src_val3 = w_ptr[n * K + k8 * 8 + 3]; + uint8_t src_val4 = w_ptr[n * K + k8 * 8 + 4]; + uint8_t src_val5 = w_ptr[n * K + k8 * 8 + 5]; + uint8_t src_val6 = w_ptr[n * K + k8 * 8 + 6]; + uint8_t src_val7 = w_ptr[n * K + k8 * 8 + 7]; - // b2: op|mn|kl|ij (lower 2 bits for last 4 values) - b_ptr[row_base + 3 * k8 + 2] = (src_4ij & 3) | ((src_5kl & 3) << 2) | - ((src_6mn & 3) << 4) | ((src_7op & 3) << 6); + b_ptr[row_base + 3 * k8 + 0] = src_val0 | (src_val1 << 3) | (src_val2 << 6); + b_ptr[row_base + 3 * k8 + 1] = (src_val2 >> 2) | (src_val3 << 1) | (src_val4 << 4) | (src_val5 << 7); + b_ptr[row_base + 3 * k8 + 2] = (src_val5 >> 1) | (src_val6 << 2) | (src_val7 << 5); } } } @@ -123,9 +111,7 @@ pack<4>(const uint8_t* w_ptr, uint8_t* b_ptr, int32_t N, int32_t K) { } /** - * 5-bit packing. Each weight is 5 bits. So we pack 8 weights into 5 bytes. We - * pack the upper bits of all weights into the first byte, then the 4 lower - * bits of all weights into the other 4 bytes. + * 5-bit packing. Each weight is 5 bits. We pack 8 weights into 5 bytes. */ template <> inline void @@ -133,41 +119,26 @@ pack<5>(const uint8_t* w_ptr, uint8_t* b_ptr, int32_t N, int32_t K) { for (int32_t n = 0; n < N; n++) { int32_t row_base = (n * (K / 8)) * 5; for (int32_t k8 = 0; k8 < K / 8; k8++) { - uint8_t src_0abAB = w_ptr[n * K + k8 * 8 + 0]; - uint8_t src_1cdCD = w_ptr[n * K + k8 * 8 + 1]; - uint8_t src_2efEF = w_ptr[n * K + k8 * 8 + 2]; - uint8_t src_3ghGH = w_ptr[n * K + k8 * 8 + 3]; - uint8_t src_4ijIJ = w_ptr[n * K + k8 * 8 + 4]; - uint8_t src_5klKL = w_ptr[n * K + k8 * 8 + 5]; - uint8_t src_6mnMN = w_ptr[n * K + k8 * 8 + 6]; - uint8_t src_7opOP = w_ptr[n * K + k8 * 8 + 7]; - - // b0: 7|6|5|4|3|2|1|0 (upper bits for all values) - b_ptr[row_base + 5 * k8 + 0] = ((src_0abAB & 16) >> 4) | - ((src_1cdCD & 16) >> 3) | ((src_2efEF & 16) >> 2) | - ((src_3ghGH & 16) >> 1) | ((src_4ijIJ & 16)) | - ((src_5klKL & 16) << 1) | ((src_6mnMN & 16) << 2) | - ((src_7opOP & 16) << 3); - - // b1: cdCD|abAB (lower 4 bits for first 2 values) - b_ptr[row_base + 5 * k8 + 1] = (src_0abAB & 15) | ((src_1cdCD & 15) << 4); - - // b2: ghGH|efEF (lower 4 bits for second 2 values) - b_ptr[row_base + 5 * k8 + 2] = (src_2efEF & 15) | ((src_3ghGH & 15) << 4); - - // b3: klKL|ijIJ (lower 4 bits for third 2 values) - b_ptr[row_base + 5 * k8 + 3] = (src_4ijIJ & 15) | ((src_5klKL & 15) << 4); + uint8_t src_val0 = w_ptr[n * K + k8 * 8]; + uint8_t src_val1 = w_ptr[n * K + k8 * 8 + 1]; + uint8_t src_val2 = w_ptr[n * K + k8 * 8 + 2]; + uint8_t src_val3 = w_ptr[n * K + k8 * 8 + 3]; + uint8_t src_val4 = w_ptr[n * K + k8 * 8 + 4]; + uint8_t src_val5 = w_ptr[n * K + k8 * 8 + 5]; + uint8_t src_val6 = w_ptr[n * K + k8 * 8 + 6]; + uint8_t src_val7 = w_ptr[n * K + k8 * 8 + 7]; - // b4: opOP|mnMN (lower 4 bits for last 2 values) - b_ptr[row_base + 5 * k8 + 4] = (src_6mnMN & 15) | ((src_7opOP & 15) << 4); + b_ptr[row_base + 5 * k8 + 0] = src_val0 | (src_val1 << 5); + b_ptr[row_base + 5 * k8 + 1] = (src_val1 >> 3) | (src_val2 << 2) | (src_val3 << 7); + b_ptr[row_base + 5 * k8 + 2] = (src_val3 >> 1) | (src_val4 << 4); + b_ptr[row_base + 5 * k8 + 3] = (src_val4 >> 4) | (src_val5 << 1) | (src_val6 << 6); + b_ptr[row_base + 5 * k8 + 4] = (src_val6 >> 2) | (src_val7 << 3); } } } /** - * 6-bit packing. Each weight is 6 bits. So we pack 4 weights into 3 bytes. We - * pack the upper 2 bits of all 4 weights into the first 2 bytes, then the 4 - * lower bits of all weights into the other 4 bytes. + * 6-bit packing. Each weight is 6 bits. We pack 4 weights into 3 bytes. */ template <> inline void @@ -175,32 +146,20 @@ pack<6>(const uint8_t* w_ptr, uint8_t* b_ptr, int32_t N, int32_t K) { for (int32_t n = 0; n < N; n++) { int32_t row_base = (n * (K / 4)) * 3; for (int32_t k4 = 0; k4 < K / 4; k4++) { - uint8_t src_10abcd = w_ptr[n * K + k4 * 4 + 0]; - uint8_t src_32efgh = w_ptr[n * K + k4 * 4 + 1]; - uint8_t src_54ijkl = w_ptr[n * K + k4 * 4 + 2]; - uint8_t src_76mnop = w_ptr[n * K + k4 * 4 + 3]; - - // b0: 76|54|32|10 (upper 2 bits for all values) - b_ptr[row_base + 3 * k4 + 0] = ((src_10abcd & 48) >> 4) | - ((src_32efgh & 48) >> 2) | ((src_54ijkl & 48)) | - ((src_76mnop & 48) << 2); - - // b1: efgh|abcd (lower 4 bits for first 2 values) - b_ptr[row_base + 3 * k4 + 1] = - (src_10abcd & 15) | ((src_32efgh & 15) << 4); + uint8_t src_val0 = w_ptr[n * K + k4 * 4]; + uint8_t src_val1 = w_ptr[n * K + k4 * 4 + 1]; + uint8_t src_val2 = w_ptr[n * K + k4 * 4 + 2]; + uint8_t src_val3 = w_ptr[n * K + k4 * 4 + 3]; - // b2: mnop|ijkl (lower 4 bits for last 2 values) - b_ptr[row_base + 3 * k4 + 2] = - (src_54ijkl & 15) | ((src_76mnop & 15) << 4); + b_ptr[row_base + 3 * k4 + 0] = src_val0 | (src_val1 << 6); + b_ptr[row_base + 3 * k4 + 1] = (src_val1 >> 2) | (src_val2 << 4); + b_ptr[row_base + 3 * k4 + 2] = (src_val2 >> 4) | (src_val3 << 2); } } } /** - * 7-bit packing. Each weight is 7 bits. So we pack 8 weights into 7 bytes. - * Each of the 7 bytes contains 1 weight, plus 1 bit from the 8th weight. So, - * this packing spreads the 8th weight across all 7 bytes. The upper bit of - * each byte is the bit from the 8th weight. + * 7-bit packing. Each weight is 7 bits. We pack 8 weights into 7 bytes. */ template <> inline void @@ -208,22 +167,22 @@ pack<7>(const uint8_t* w_ptr, uint8_t* b_ptr, int32_t N, int32_t K) { for (int32_t n = 0; n < N; n++) { int32_t row_base = (n * (K / 8)) * 7; for (int32_t k8 = 0; k8 < K / 8; k8++) { - uint8_t src_0 = w_ptr[n * K + k8 * 8 + 0]; - uint8_t src_1 = w_ptr[n * K + k8 * 8 + 1]; - uint8_t src_2 = w_ptr[n * K + k8 * 8 + 2]; - uint8_t src_3 = w_ptr[n * K + k8 * 8 + 3]; - uint8_t src_4 = w_ptr[n * K + k8 * 8 + 4]; - uint8_t src_5 = w_ptr[n * K + k8 * 8 + 5]; - uint8_t src_6 = w_ptr[n * K + k8 * 8 + 6]; - uint8_t src_7 = w_ptr[n * K + k8 * 8 + 7]; + uint8_t src_val0 = w_ptr[n * K + k8 * 8 + 0]; + uint8_t src_val1 = w_ptr[n * K + k8 * 8 + 1]; + uint8_t src_val2 = w_ptr[n * K + k8 * 8 + 2]; + uint8_t src_val3 = w_ptr[n * K + k8 * 8 + 3]; + uint8_t src_val4 = w_ptr[n * K + k8 * 8 + 4]; + uint8_t src_val5 = w_ptr[n * K + k8 * 8 + 5]; + uint8_t src_val6 = w_ptr[n * K + k8 * 8 + 6]; + uint8_t src_val7 = w_ptr[n * K + k8 * 8 + 7]; - b_ptr[row_base + 7 * k8 + 0] = src_0 | ((src_7 & 1) << 7); - b_ptr[row_base + 7 * k8 + 1] = src_1 | ((src_7 & 2) << 6); - b_ptr[row_base + 7 * k8 + 2] = src_2 | ((src_7 & 4) << 5); - b_ptr[row_base + 7 * k8 + 3] = src_3 | ((src_7 & 8) << 4); - b_ptr[row_base + 7 * k8 + 4] = src_4 | ((src_7 & 16) << 3); - b_ptr[row_base + 7 * k8 + 5] = src_5 | ((src_7 & 32) << 2); - b_ptr[row_base + 7 * k8 + 6] = src_6 | ((src_7 & 64) << 1); + b_ptr[row_base + 7 * k8 + 0] = src_val0 | (src_val1 << 7); + b_ptr[row_base + 7 * k8 + 1] = (src_val1 >> 1) | (src_val2 << 6); + b_ptr[row_base + 7 * k8 + 2] = (src_val2 >> 2) | (src_val3 << 5); + b_ptr[row_base + 7 * k8 + 3] = (src_val3 >> 3) | (src_val4 << 4); + b_ptr[row_base + 7 * k8 + 4] = (src_val4 >> 4) | (src_val5 << 3); + b_ptr[row_base + 7 * k8 + 5] = (src_val5 >> 5) | (src_val6 << 2); + b_ptr[row_base + 7 * k8 + 6] = (src_val6 >> 6) | (src_val7 << 1); } } } diff --git a/torchao/experimental/kernels/mps/test/test_lowbit.mm b/torchao/experimental/kernels/mps/test/test_lowbit.mm index 8a1e0fdb9e..524aee738d 100644 --- a/torchao/experimental/kernels/mps/test/test_lowbit.mm +++ b/torchao/experimental/kernels/mps/test/test_lowbit.mm @@ -51,6 +51,7 @@ void reference_linear_lowbit_quant_weights_cpu( int32_t M, int32_t K, int32_t N) { + int32_t ceil_K_group_size = (K + group_size - 1) / group_size; for (int32_t m = 0; m < M; m++) { for (int32_t n = 0; n < N; n++) { const int32_t k_block = (K + group_size - 1) / group_size; @@ -59,8 +60,8 @@ void reference_linear_lowbit_quant_weights_cpu( float rc = 0.0; int32_t k = 0; for (int32_t kb = 0; kb < k_block; kb++) { - const float scale = float(s_ptr[kb * N + n]); - const float zero = float(z_ptr[kb * N + n]); + const float scale = float(s_ptr[n * ceil_K_group_size + kb]); + const float zero = float(z_ptr[n * ceil_K_group_size + kb]); for (int32_t idx = 0; idx < group_size && k < K; idx++, k++) { const auto a_val = float(A_ptr[k]); uint8_t w_val = w_ptr[n * K + k]; @@ -217,6 +218,7 @@ void run_test_battery() { run_test(19, 256, 28, 256); run_test(1, 1000, 28, 256); run_test(19, 8, 36, 256); + run_test(1, 1024, 1024, 64); } int main() { diff --git a/torchao/experimental/ops/mps/linear_fp_act_xbit_weight_aten.mm b/torchao/experimental/ops/mps/linear_fp_act_xbit_weight_aten.mm index 2aeb7f4460..972caa039a 100644 --- a/torchao/experimental/ops/mps/linear_fp_act_xbit_weight_aten.mm +++ b/torchao/experimental/ops/mps/linear_fp_act_xbit_weight_aten.mm @@ -55,19 +55,19 @@ void check_linear_mps_args( group_size); TORCH_CHECK( - S.dim() == 2 && S.size(1) == N, + S.dim() == 2 && S.size(0) == N, __func__, - ": expect S to be 2d tensor with shape [:, ", + ": expect S to be 2d tensor with shape [", N, - "]"); + ",:]"); TORCH_CHECK(S.is_contiguous(), __func__, " : expect S to be contiguous."); TORCH_CHECK( - Z.dim() == 2 && Z.size(1) == N, + Z.dim() == 2 && Z.size(0) == N, __func__, - ": expect Z to be 2d tensor with shape [:, ", + ": expect Z to be 2d tensor with shape [", N, - "]"); + ",:]"); TORCH_CHECK(Z.is_contiguous(), __func__, " : expect Z to be contiguous."); } diff --git a/torchao/experimental/ops/mps/linear_fp_act_xbit_weight_executorch.mm b/torchao/experimental/ops/mps/linear_fp_act_xbit_weight_executorch.mm index a6f417b17d..f8a8ffdae9 100644 --- a/torchao/experimental/ops/mps/linear_fp_act_xbit_weight_executorch.mm +++ b/torchao/experimental/ops/mps/linear_fp_act_xbit_weight_executorch.mm @@ -65,12 +65,12 @@ bool check_linear_mps_args( "Expect group_size to be 32, 64, 128 or 256"); ET_LOG_MSG_AND_RETURN_IF_FALSE( - S.dim() == 2 && S.size(1) == N, - "Expect S to be 2d tensor with shape [:, N]"); + S.dim() == 2 && S.size(0) == N, + "Expect S to be 2d tensor with shape [N, :]"); ET_LOG_MSG_AND_RETURN_IF_FALSE( - Z.dim() == 2 && Z.size(1) == N, - "Expect Z to be 2d tensor with shape [:, N]"); + Z.dim() == 2 && Z.size(0) == N, + "Expect Z to be 2d tensor with shape [N, :]"); return true; } diff --git a/torchao/experimental/ops/mps/mps_op_lib.py b/torchao/experimental/ops/mps/mps_op_lib.py index 145c77c3de..bee038ce19 100644 --- a/torchao/experimental/ops/mps/mps_op_lib.py +++ b/torchao/experimental/ops/mps/mps_op_lib.py @@ -37,10 +37,10 @@ def _( assert scales.is_contiguous() assert scales.dim() == 2 - assert scales.size(1) == n + assert scales.size(0) == n assert zeros.is_contiguous() assert zeros.dim() == 2 - assert zeros.size(1) == n + assert zeros.size(0) == n return torch.empty(m, n, dtype=activations.dtype, device="meta") diff --git a/torchao/experimental/ops/mps/test/test_lowbit.py b/torchao/experimental/ops/mps/test/test_lowbit.py index a3ac7a6431..dc2460110e 100644 --- a/torchao/experimental/ops/mps/test/test_lowbit.py +++ b/torchao/experimental/ops/mps/test/test_lowbit.py @@ -64,11 +64,11 @@ def _init_tensors(self, group_size, M, K, N, nbit, device="mps"): ceil_K_group_size = (K + group_size - 1) // group_size A = torch.rand(M, K, dtype=torch.float32, device=device) W = torch.randint(0, 1 << nbit, (N, K), dtype=torch.uint8, device=device) - S = torch.rand(ceil_K_group_size, N, dtype=torch.float32, device=device) + 0.01 + S = torch.rand(N, ceil_K_group_size, dtype=torch.float32, device=device) + 0.01 Z = torch.randint( 0, 1 << nbit, - (ceil_K_group_size, N), + (N, ceil_K_group_size), dtype=torch.float32, device=device, ) @@ -83,8 +83,8 @@ def _reference_linear_lowbit_quant_weights(self, A, W, group_size, S, Z, nbit): N = W.shape[0] K = W.shape[1] W = W.to(torch.float32) - scales = S.t().unsqueeze(2).repeat(1, 1, group_size).view(N, -1)[:, :K] - zeros = Z.t().unsqueeze(2).repeat(1, 1, group_size).view(N, -1)[:, :K] + scales = S.unsqueeze(2).repeat(1, 1, group_size).view(N, -1)[:, :K] + zeros = Z.unsqueeze(2).repeat(1, 1, group_size).view(N, -1)[:, :K] W = scales * W + zeros return torch.mm(A, W.t()) diff --git a/torchao/experimental/ops/mps/test/test_quantizer.py b/torchao/experimental/ops/mps/test/test_quantizer.py index 7afa91183e..04273fb1af 100644 --- a/torchao/experimental/ops/mps/test/test_quantizer.py +++ b/torchao/experimental/ops/mps/test/test_quantizer.py @@ -146,13 +146,14 @@ def _reference_linear_lowbit_quant_weights(self, A, W, group_size, S, Z): N = W.shape[0] K = W.shape[1] W = W.to(torch.float32) - scales = S.t().unsqueeze(2).repeat(1, 1, group_size).view(N, -1)[:, :K] - zeros = Z.t().unsqueeze(2).repeat(1, 1, group_size).view(N, -1)[:, :K] + scales = S.unsqueeze(2).repeat(1, 1, group_size).view(N, -1)[:, :K] + zeros = Z.unsqueeze(2).repeat(1, 1, group_size).view(N, -1)[:, :K] W = scales * W + zeros return torch.mm(A, W.t()) @parameterized.expand(BITWIDTHS) def test_accuracy(self, nbit): + print(f"nbit: {nbit}") group_size = 32 m = 3 n = 12 @@ -170,8 +171,7 @@ def test_accuracy(self, nbit): weight_qvals_cpu, weight_scales_cpu, weight_zeros_cpu = _quantize( weight_cpu, group_size, nbit, True, torch.uint8 ) - weight_scales_cpu = weight_scales_cpu.t() - weight_zeros_cpu = -weight_zeros_cpu.t() * weight_scales_cpu + weight_zeros_cpu = -weight_zeros_cpu * weight_scales_cpu expected = self._reference_linear_lowbit_quant_weights( activations.cpu(), weight_qvals_cpu, diff --git a/torchao/experimental/quant_api.py b/torchao/experimental/quant_api.py index b7630cada3..2e50587c2a 100644 --- a/torchao/experimental/quant_api.py +++ b/torchao/experimental/quant_api.py @@ -529,8 +529,6 @@ def quantize_and_pack_weights(self, weights, nbit, group_size): weight_qvals, weight_scales, weight_zeros = _quantize( weights, self.group_size, self.nbit, has_weight_zeros=True, signed=False ) - weight_scales = torch.transpose_copy(weight_scales, 1, 0) - weight_zeros = torch.transpose_copy(weight_zeros, 1, 0) weight_zeros = -weight_zeros * weight_scales self.weight_scales = nn.Parameter(weight_scales, requires_grad=False) self.weight_zeros = nn.Parameter(weight_zeros, requires_grad=False) @@ -550,7 +548,7 @@ def forward(self, x): lead_shape = x.shape[0:-1] k = x.shape[-1] - n = self.weight_scales.shape[1] + n = self.weight_scales.shape[0] return self._linear_op( x.reshape(-1, k), self.packed_weights, From 45b39b14beff5aa13eda67ba869ba2102ca26e63 Mon Sep 17 00:00:00 2001 From: andrewor14 Date: Fri, 9 May 2025 11:20:18 -0400 Subject: [PATCH 011/165] Set eps in end-to-end QAT flow (#2180) * Set eps in end-to-end QAT flow **Summary:** This commit does two things: (1) Allow users to set eps in `FakeQuantizeConfig` (2) For other parts of the QAT flow, set eps to `torch.finfo(torch.float32).eps` for input linear activations to match the existing hardcoded input activation scale dtype (which is fp32) The motivation is to enable users who wish to lower their models to XNNPACK. This would require them to use the following combination of dtypes during training for end-to-end numerical match: - input activations: bf16 - input activation scales: fp32 - input activation eps: `torch.finfo(torch.float32).eps` - weight: bf16 - weight scales: bf16 - weight eps: `torch.finfo(torch.bfloat16).eps` However, today there is no way to specify the above in any of the QAT flows. For the recommended `FakeQuantizeConfig` flow, we always use `torch.finfo(x.dtype).eps`, where x is bf16 in this case, and there is no way for users to configure this. This is resolved by (1). For the legacy `Int8DynActInt4QATQuantizer` flow, we hardcode input activation scales to always use fp32 in https://github.com/pytorch/ao/pull/2085, but did not set the corresponding eps. Today, this also uses `torch.finfo(x.dtype).eps` by default, where x is bf16, and so we use the wrong eps value. This is resolved by (2). **Test Plan:** python test/quantization/test_qat.py -k test_fake_quantize_config_eps python test/quantization/test_qat.py -k test_qat_8da4w_eps * up --------- Co-authored-by: Scott Roy <161522778+metascroy@users.noreply.github.com> --- test/quantization/test_qat.py | 78 +++++++++++++++++++ torchao/experimental/quant_passes.py | 2 +- ...est_int8_dynamic_activation_intx_weight.py | 2 +- torchao/quantization/GPTQ.py | 5 +- torchao/quantization/qat/api.py | 3 + torchao/quantization/qat/fake_quantizer.py | 3 + torchao/quantization/qat/linear.py | 8 +- torchao/quantization/quant_api.py | 2 + torchao/quantization/utils.py | 10 ++- 9 files changed, 107 insertions(+), 6 deletions(-) diff --git a/test/quantization/test_qat.py b/test/quantization/test_qat.py index 075671a043..d655abaf62 100644 --- a/test/quantization/test_qat.py +++ b/test/quantization/test_qat.py @@ -1513,6 +1513,84 @@ def test_qat_8da4w_prepare_vs_convert(self, dtype: torch.dtype): ) self.assertEqual(len(non_inf_sqnr), 0, fail_message) + @unittest.skipIf( + not TORCH_VERSION_AT_LEAST_2_4, "skipping when torch version is 2.4 or lower" + ) + def test_fake_quantize_config_eps(self): + """ + Test that users can set arbitrary eps value in `FakeQuantizeConfig`. + """ + eps = 0.00123 + x = torch.randn(2, 3).to(torch.float32) + scale, zp = choose_qparams_affine( + x, + mapping_type=MappingType.ASYMMETRIC, + block_size=(1, 3), + target_dtype=torch.int8, + quant_min=-128, + quant_max=127, + eps=eps, + ) + expected_out = _fake_quantize_per_token(x, scale, zp, -128, 127) + config = FakeQuantizeConfig( + torch.int8, + "per_token", + is_symmetric=False, + eps=eps, + ) + fake_quantizer = FakeQuantizer(config) + actual_out = fake_quantizer(x) + torch.testing.assert_close(expected_out, actual_out, atol=0, rtol=0) + + @unittest.skipIf( + not TORCH_VERSION_AT_LEAST_2_4, "skipping when torch version is 2.4 or lower" + ) + def test_qat_8da4w_eps(self): + """ + Test that the 8da4w QAT flow uses the expected eps. + """ + from torchao.quantization.qat import Int8DynActInt4WeightQATQuantizer + from torchao.quantization.utils import per_token_dynamic_quant + + group_size = 16 + torch.manual_seed(self.SEED) + m = M() + quantizer = Int8DynActInt4WeightQATQuantizer(groupsize=group_size) + + # prepare + prepared_model = quantizer.prepare(m) + self.assertEqual( + prepared_model.linear1.activation_fake_quantizer.config.eps, + torch.finfo(torch.float32).eps, + ) + + # convert + converted_model = quantizer.convert(m) + x = m.example_inputs()[0] + _input = per_token_dynamic_quant( + x, + scale_dtype=torch.float32, + zero_point_dtype=torch.float32, + eps=torch.finfo(torch.float32).eps, + ) + _weight_dq = dequantize_affine( + converted_model.linear1.weight, + (1, group_size), + converted_model.linear1.scales, + converted_model.linear1.zeros, + torch.int8, + quant_min=-8, + quant_max=7, + output_dtype=torch.float32, + ) + expected_out = torch.nn.functional.linear( + _input, + _weight_dq, + converted_model.linear1.bias, + ) + actual_out = converted_model.linear1(x) + torch.testing.assert_close(expected_out, actual_out, atol=0, rtol=0) + if __name__ == "__main__": unittest.main() diff --git a/torchao/experimental/quant_passes.py b/torchao/experimental/quant_passes.py index 13a0a755fb..6c1fad5bbf 100644 --- a/torchao/experimental/quant_passes.py +++ b/torchao/experimental/quant_passes.py @@ -87,7 +87,7 @@ def _get_q_dq_linear_patterns_replacements_and_filters( glbs["a_quant_max"] = None glbs["a_mapping_type"] = "ASYMMETRIC" glbs["a_scale_dtype"] = torch.float32 - glbs["a_eps"] = None + glbs["a_eps"] = torch.finfo(torch.float32).eps lcls = {} diff --git a/torchao/experimental/tests/test_int8_dynamic_activation_intx_weight.py b/torchao/experimental/tests/test_int8_dynamic_activation_intx_weight.py index d1236e9183..a655ca1b42 100644 --- a/torchao/experimental/tests/test_int8_dynamic_activation_intx_weight.py +++ b/torchao/experimental/tests/test_int8_dynamic_activation_intx_weight.py @@ -361,7 +361,7 @@ def test_export_QDQLayout(self): self.assertTrue(torch.allclose(eager_results, exported_results)) expected_lines = [ - "torch.ops.torchao.choose_qparams_affine.default(input_1, 'ASYMMETRIC', [1, 512], torch.int8, None, None, None, torch.float32, torch.int8)", + "torch.ops.torchao.choose_qparams_affine.default(input_1, 'ASYMMETRIC', [1, 512], torch.int8, None, None, 1.1920928955078125e-07, torch.float32, torch.int8)", "torch.ops.torchao.quantize_affine.default(input_1, [1, 512], getitem, getitem_1, torch.int8)", "torch.ops.torchao.dequantize_affine.default(quantize_affine, [1, 512], getitem, getitem_1, torch.int8)", "torch.ops.torchao.dequantize_affine.default", diff --git a/torchao/quantization/GPTQ.py b/torchao/quantization/GPTQ.py index 94fccc7bf1..a0ec97d63f 100644 --- a/torchao/quantization/GPTQ.py +++ b/torchao/quantization/GPTQ.py @@ -938,7 +938,10 @@ def linear_forward_8da4w( # TODO: in future add ability to specify activation_scale_dtype to PTQ configs # and enable similar change here x = per_token_dynamic_quant( - x, scale_dtype=torch.float32, zero_point_dtype=torch.float32 + x, + scale_dtype=torch.float32, + zero_point_dtype=torch.float32, + eps=torch.finfo(torch.float32).eps, ) # TODO: verify and remove following reshape code diff --git a/torchao/quantization/qat/api.py b/torchao/quantization/qat/api.py index d7e8f204cc..e025a43d94 100644 --- a/torchao/quantization/qat/api.py +++ b/torchao/quantization/qat/api.py @@ -85,6 +85,7 @@ class FakeQuantizeConfig: zero_point_domain: ZeroPointDomain is_dynamic: bool = True range_learning: bool = False + eps: Optional[float] = None def __init__( self, @@ -96,6 +97,7 @@ def __init__( zero_point_domain: ZeroPointDomain = ZeroPointDomain.INT, is_dynamic: bool = True, range_learning: bool = False, + eps: Optional[float] = None, *, group_size: Optional[int] = None, is_symmetric: Optional[bool] = None, @@ -110,6 +112,7 @@ def __init__( self.zero_point_domain = zero_point_domain self.is_dynamic = is_dynamic self.range_learning = range_learning + self.eps = eps # Validate dtype all_dtypes = [torch.int8, torch.uint8] diff --git a/torchao/quantization/qat/fake_quantizer.py b/torchao/quantization/qat/fake_quantizer.py index 1747f8a4a6..0d2521cac0 100644 --- a/torchao/quantization/qat/fake_quantizer.py +++ b/torchao/quantization/qat/fake_quantizer.py @@ -81,6 +81,7 @@ def _per_token_forward(self, x: torch.Tensor): target_dtype=self.config.dtype, quant_min=qmin, quant_max=qmax, + eps=self.config.eps, scale_dtype=self.config.scale_precision, zero_point_dtype=self.config.zero_point_precision, ) @@ -117,6 +118,7 @@ def _per_channel_or_group_forward(self, x: torch.Tensor): bit_width, group_size, scale_precision, + eps=self.config.eps, ) else: (self.scale, self.zero_point) = get_groupwise_affine_qparams( @@ -124,6 +126,7 @@ def _per_channel_or_group_forward(self, x: torch.Tensor): bit_width, group_size, scale_precision, + eps=self.config.eps, ) self.zero_point = self.zero_point.to(zero_point_precision) diff --git a/torchao/quantization/qat/linear.py b/torchao/quantization/qat/linear.py index 4b0b160c53..a912f04b83 100644 --- a/torchao/quantization/qat/linear.py +++ b/torchao/quantization/qat/linear.py @@ -177,6 +177,8 @@ def __init__( self.padding_allowed: bool = padding_allowed self.precision: torch.dtype = precision self.scales_precision: torch.dtype = scales_precision + # TODO: generalize this + self.activation_scales_precision = torch.float32 def prepare( self, model: torch.nn.Module, *args: Any, **kwargs: Any @@ -247,7 +249,7 @@ def _convert_qat_linear_8da4w(self, module: torch.nn.Module): self._convert_qat_linear_8da4w(child) def get_activation_fake_quantize_config(self) -> Optional[FakeQuantizeConfig]: - return _get_8da4w_activation_config(self.scales_precision) + return _get_8da4w_activation_config(self.activation_scales_precision) def get_weight_fake_quantize_config(self) -> Optional[FakeQuantizeConfig]: return _get_8da4w_weight_config(self.groupsize, self.scales_precision) @@ -280,6 +282,7 @@ def __init__( ) -> None: # Use torch.float32 to match torchao.quantization.quant_api._int8_asymm_per_token_quant, # which is used in PTQ routines + # TODO: generalize this activation_config = _get_8da4w_activation_config(torch.float32) weight_config = _get_8da4w_weight_config(groupsize, scales_precision) super().__init__( @@ -320,6 +323,8 @@ def _get_8da4w_activation_config(qparams_precision: torch.dtype) -> FakeQuantize """ Return the activation `FakeQuantizeConfig` for `Int8DynActInt4WeightQATQuantizer`. """ + # TODO: generalize this + assert qparams_precision == torch.float32 return FakeQuantizeConfig( dtype=torch.int8, granularity="per_token", @@ -327,6 +332,7 @@ def _get_8da4w_activation_config(qparams_precision: torch.dtype) -> FakeQuantize is_dynamic=True, scale_precision=qparams_precision, zero_point_precision=qparams_precision, + eps=torch.finfo(qparams_precision).eps, ) diff --git a/torchao/quantization/quant_api.py b/torchao/quantization/quant_api.py index c20c37a194..982b8cdd5c 100644 --- a/torchao/quantization/quant_api.py +++ b/torchao/quantization/quant_api.py @@ -627,6 +627,7 @@ def _int8_asymm_per_token_quant(x: torch.Tensor) -> torch.Tensor: mapping_type = MappingType.ASYMMETRIC target_dtype = torch.int8 scale_dtype = torch.float32 + eps = torch.finfo(torch.float32).eps zero_point_dtype = torch.int8 if TORCH_VERSION_AT_LEAST_2_6: return to_affine_quantized_intx( @@ -634,6 +635,7 @@ def _int8_asymm_per_token_quant(x: torch.Tensor) -> torch.Tensor: mapping_type, _get_per_token_block_size(x), target_dtype, + eps=eps, scale_dtype=scale_dtype, zero_point_dtype=zero_point_dtype, ) diff --git a/torchao/quantization/utils.py b/torchao/quantization/utils.py index a9cad8060e..22e14378f5 100644 --- a/torchao/quantization/utils.py +++ b/torchao/quantization/utils.py @@ -324,6 +324,7 @@ def get_groupwise_affine_qparams( dtype=torch.bfloat16, zero_point_domain=ZeroPointDomain.FLOAT, preserve_zero=False, + eps=None, ): if groupsize > w.shape[-1]: groupsize = w.shape[-1] @@ -337,7 +338,8 @@ def get_groupwise_affine_qparams( block_size = (1, groupsize) quant_min = 0 quant_max = 2**n_bit - 1 - eps = 1e-6 + if eps is None: + eps = 1e-6 scale_dtype = dtype zero_point_dtype = ( dtype if zero_point_domain != ZeroPointDomain.INT else torch.int32 @@ -530,6 +532,7 @@ def get_group_qparams_symmetric( groupsize=128, precision=torch.float32, mapping_type=MappingType.SYMMETRIC, + eps=None, ): # needed for GPTQ with padding if groupsize > w.shape[-1]: @@ -540,7 +543,8 @@ def get_group_qparams_symmetric( assert n_bit <= 8, f"unsupported n_bit: {n_bit}" block_size = (1, groupsize) - eps = torch.finfo(w.dtype).eps + if eps is None: + eps = torch.finfo(w.dtype).eps ranges = {} ranges[1] = (-1, 0) # generating ranges for bit 2 to 8 @@ -591,6 +595,7 @@ def per_token_dynamic_quant( input: torch.Tensor, scale_dtype: torch.dtype = torch.float32, zero_point_dtype: torch.dtype = torch.float32, + eps: Optional[float] = None, ) -> torch.Tensor: mapping_type = MappingType.ASYMMETRIC block_size = _get_per_token_block_size(input) @@ -608,6 +613,7 @@ def per_token_dynamic_quant( quant_max, scale_dtype=scale_dtype, zero_point_dtype=zero_point_dtype, + eps=eps, ) q = quantize_affine( input, From 709e1da30c1347de2c8218195aa2b76b409ac52e Mon Sep 17 00:00:00 2001 From: HDCharles <39544797+HDCharles@users.noreply.github.com> Date: Fri, 9 May 2025 15:21:02 -0400 Subject: [PATCH 012/165] Move moe quant to better prototype dir (#2192) * Move moe quant to better prototype dir Summary: The old quantization/prototype dir is being deprecated so moving moe_quant out into the correct one. Test Plan: see CI Reviewers: Subscribers: Tasks: Tags: * actually adding new folder Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * ruff format Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: --- test/quantization/test_moe_quant.py | 4 ++-- torchao/_models/mixtral-moe/generate.py | 2 +- torchao/_models/mixtral-moe/model.py | 2 +- .../tests/test_int8_dynamic_activation_intx_weight.py | 4 ++-- torchao/{quantization => }/prototype/moe_quant/README.md | 4 ++-- torchao/{quantization => }/prototype/moe_quant/__init__.py | 0 .../{quantization => }/prototype/moe_quant/llama4_quant.py | 6 +++--- .../prototype/moe_quant/quantizable_moe_modules.py | 2 +- torchao/{quantization => }/prototype/moe_quant/utils.py | 0 9 files changed, 12 insertions(+), 12 deletions(-) rename torchao/{quantization => }/prototype/moe_quant/README.md (95%) rename torchao/{quantization => }/prototype/moe_quant/__init__.py (100%) rename torchao/{quantization => }/prototype/moe_quant/llama4_quant.py (94%) rename torchao/{quantization => }/prototype/moe_quant/quantizable_moe_modules.py (98%) rename torchao/{quantization => }/prototype/moe_quant/utils.py (100%) diff --git a/test/quantization/test_moe_quant.py b/test/quantization/test_moe_quant.py index 842468a769..71aadaf345 100644 --- a/test/quantization/test_moe_quant.py +++ b/test/quantization/test_moe_quant.py @@ -6,10 +6,10 @@ from torchao.dtypes.floatx.float8_layout import Float8AQTTensorImpl from torchao.dtypes.uintx.plain_layout import PlainAQTTensorImpl from torchao.dtypes.uintx.tensor_core_tiled_layout import TensorCoreTiledAQTTensorImpl -from torchao.quantization.prototype.moe_quant.quantizable_moe_modules import ( +from torchao.prototype.moe_quant.quantizable_moe_modules import ( MOEFeedForwardAOQuantizable, ) -from torchao.quantization.prototype.moe_quant.utils import ( +from torchao.prototype.moe_quant.utils import ( FakeExtraDimTensor, MoEQuantConfig, UseFakeExtraDimTensor, diff --git a/torchao/_models/mixtral-moe/generate.py b/torchao/_models/mixtral-moe/generate.py index 0dcd86e74f..11a53043ad 100644 --- a/torchao/_models/mixtral-moe/generate.py +++ b/torchao/_models/mixtral-moe/generate.py @@ -236,7 +236,7 @@ def main( ] ) - from torchao.quantization.prototype.moe_quant.utils import ( + from torchao.prototype.moe_quant.utils import ( MoEQuantConfig, UseFakeExtraDimTensor, cond_ffn_filter, diff --git a/torchao/_models/mixtral-moe/model.py b/torchao/_models/mixtral-moe/model.py index 46a4ce79be..685323843d 100644 --- a/torchao/_models/mixtral-moe/model.py +++ b/torchao/_models/mixtral-moe/model.py @@ -11,7 +11,7 @@ from torch import Tensor from torch.nn import functional as F -from torchao.quantization.prototype.moe_quant.utils import FakeExtraDimTensor +from torchao.prototype.moe_quant.utils import FakeExtraDimTensor def find_multiple(n: int, k: int) -> int: diff --git a/torchao/experimental/tests/test_int8_dynamic_activation_intx_weight.py b/torchao/experimental/tests/test_int8_dynamic_activation_intx_weight.py index a655ca1b42..08548b9e9e 100644 --- a/torchao/experimental/tests/test_int8_dynamic_activation_intx_weight.py +++ b/torchao/experimental/tests/test_int8_dynamic_activation_intx_weight.py @@ -630,10 +630,10 @@ def test_identical_to_Int8DynActInt4WeightQATQuantizer( self.assertTrue(sqnr2 == float("inf")) def test_moe_quant_intx(self): - from torchao.quantization.prototype.moe_quant.quantizable_moe_modules import ( + from torchao.prototype.moe_quant.quantizable_moe_modules import ( MOEFeedForwardAOQuantizable, ) - from torchao.quantization.prototype.moe_quant.utils import ( + from torchao.prototype.moe_quant.utils import ( FakeExtraDimTensor, MoEQuantConfig, UseFakeExtraDimTensor, diff --git a/torchao/quantization/prototype/moe_quant/README.md b/torchao/prototype/moe_quant/README.md similarity index 95% rename from torchao/quantization/prototype/moe_quant/README.md rename to torchao/prototype/moe_quant/README.md index d774fae8fd..734b409f65 100644 --- a/torchao/quantization/prototype/moe_quant/README.md +++ b/torchao/prototype/moe_quant/README.md @@ -10,7 +10,7 @@ The API for moe quantization is very similar to linear quantization, given a moe ```python -from torchao.quantization.prototype.moe_quant.utils import cond_ffn_filter, +from torchao.prototype.moe_quant.utils import cond_ffn_filter, from torchao.quantization.quant_api import quantize_, Int8WeightOnlyConfig quantize_(model, MoEQuantConfig(Int8WeightOnlyConfig()), filter_fn=cond_ffn_filter) @@ -27,7 +27,7 @@ To make the above api work, each tensor subclass had to be edited to work as 3D ```python -from torchao.quantization.prototype.moe_quant.utils import cond_ffn_filter, MoEQuantConfig, UseFakeExtraDimTensor +from torchao.prototype.moe_quant.utils import cond_ffn_filter, MoEQuantConfig, UseFakeExtraDimTensor from torchao.quantization.quant_api import quantize_, Int8DynamicActivationIntxWeightConfig config = MoEQuantConfig( diff --git a/torchao/quantization/prototype/moe_quant/__init__.py b/torchao/prototype/moe_quant/__init__.py similarity index 100% rename from torchao/quantization/prototype/moe_quant/__init__.py rename to torchao/prototype/moe_quant/__init__.py diff --git a/torchao/quantization/prototype/moe_quant/llama4_quant.py b/torchao/prototype/moe_quant/llama4_quant.py similarity index 94% rename from torchao/quantization/prototype/moe_quant/llama4_quant.py rename to torchao/prototype/moe_quant/llama4_quant.py index 67ad2ab464..36e684d47d 100644 --- a/torchao/quantization/prototype/moe_quant/llama4_quant.py +++ b/torchao/prototype/moe_quant/llama4_quant.py @@ -16,7 +16,7 @@ from transformers import AutoTokenizer, Llama4ForCausalLM from transformers.models.llama4.modeling_llama4 import Llama4TextMoe -from torchao.quantization.prototype.moe_quant.quantizable_moe_modules import ( +from torchao.prototype.moe_quant.quantizable_moe_modules import ( MOEFeedForwardAOQuantizable, ) from torchao.quantization.quant_api import _replace_with_custom_fn_if_matches_filter @@ -69,11 +69,11 @@ def convert_fn(module): model = model -from torchao.quantization import Int4WeightOnlyConfig, quantize_ -from torchao.quantization.prototype.moe_quant.utils import ( +from torchao.prototype.moe_quant.utils import ( MoEQuantConfig, cond_ffn_filter, ) +from torchao.quantization import Int4WeightOnlyConfig, quantize_ quantize_(model, MoEQuantConfig(Int4WeightOnlyConfig()), cond_ffn_filter, device="cuda") diff --git a/torchao/quantization/prototype/moe_quant/quantizable_moe_modules.py b/torchao/prototype/moe_quant/quantizable_moe_modules.py similarity index 98% rename from torchao/quantization/prototype/moe_quant/quantizable_moe_modules.py rename to torchao/prototype/moe_quant/quantizable_moe_modules.py index 516341a3a8..d806f50b4f 100644 --- a/torchao/quantization/prototype/moe_quant/quantizable_moe_modules.py +++ b/torchao/prototype/moe_quant/quantizable_moe_modules.py @@ -2,7 +2,7 @@ import torch.nn.functional as F from torch import Tensor, nn -from torchao.quantization.prototype.moe_quant.utils import FakeExtraDimTensor +from torchao.prototype.moe_quant.utils import FakeExtraDimTensor class MOEFeedForwardAOQuantizable(nn.Module): diff --git a/torchao/quantization/prototype/moe_quant/utils.py b/torchao/prototype/moe_quant/utils.py similarity index 100% rename from torchao/quantization/prototype/moe_quant/utils.py rename to torchao/prototype/moe_quant/utils.py From 32b7cd1f2dc43d805f8002f6a59dee7a8095ffa6 Mon Sep 17 00:00:00 2001 From: Syed Tousif Ahmed Date: Fri, 9 May 2025 15:51:43 -0400 Subject: [PATCH 013/165] Uses torch.version.cuda to compile CUDA extensions (#2193) * Uses torch.version.cuda to compile CUDA extensions * lint --- setup.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/setup.py b/setup.py index a269d4410d..3cb08b3a35 100644 --- a/setup.py +++ b/setup.py @@ -255,7 +255,7 @@ def get_extensions(): print( "PyTorch GPU support is not available. Skipping compilation of CUDA extensions" ) - if (CUDA_HOME is None and ROCM_HOME is None) and torch.cuda.is_available(): + if (CUDA_HOME is None and ROCM_HOME is None) and torch.version.cuda: print( "CUDA toolkit or ROCm is not available. Skipping compilation of CUDA extensions" ) @@ -263,9 +263,7 @@ def get_extensions(): "If you'd like to compile CUDA extensions locally please install the cudatoolkit from https://anaconda.org/nvidia/cuda-toolkit" ) - use_cuda = torch.cuda.is_available() and ( - CUDA_HOME is not None or ROCM_HOME is not None - ) + use_cuda = torch.version.cuda and (CUDA_HOME is not None or ROCM_HOME is not None) extension = CUDAExtension if use_cuda else CppExtension extra_link_args = [] From 8b96bcdc06858cf33574092b4e9a36ea60462096 Mon Sep 17 00:00:00 2001 From: Xia Weiwen Date: Sat, 10 May 2025 19:45:56 +0800 Subject: [PATCH 014/165] [PT2E][X86] Migrate fusion passes in Inductor to torchao (#2140) * [PT2E][X86] Migrate fusion passes in Inductor to torchao * Fix conflict after merging main * Fix CI * Fix format issues * Fix format issue * Fix versioning issue in UT * Fix format issue * Fix CI * Fix CI * Fix CI * Move registration of Inductor fusion passes to x86_inductor_quantizer.py * Fix CI --- .../pt2e/test_x86inductor_fusion.py | 2603 +++++++++++++++ .../pt2e/inductor_passes/__init__.py | 0 .../quantization/pt2e/inductor_passes/x86.py | 2852 +++++++++++++++++ torchao/quantization/pt2e/lowering.py | 2 +- .../pt2e/quantizer/x86_inductor_quantizer.py | 14 + 5 files changed, 5470 insertions(+), 1 deletion(-) create mode 100644 test/quantization/pt2e/test_x86inductor_fusion.py create mode 100644 torchao/quantization/pt2e/inductor_passes/__init__.py create mode 100644 torchao/quantization/pt2e/inductor_passes/x86.py diff --git a/test/quantization/pt2e/test_x86inductor_fusion.py b/test/quantization/pt2e/test_x86inductor_fusion.py new file mode 100644 index 0000000000..78204fb756 --- /dev/null +++ b/test/quantization/pt2e/test_x86inductor_fusion.py @@ -0,0 +1,2603 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD 3-Clause license found in the +# LICENSE file in the root directory of this source tree. + +# Owner(s): ["oncall: quantization"] +import contextlib +import copy +import itertools +import unittest + +import torch +from torch._dynamo import config as dynamo_config +from torch._dynamo.utils import counters +from torch._inductor import config +from torch._inductor.test_case import TestCase, run_tests +from torch._inductor.utils import run_and_get_code +from torch.export import export_for_training +from torch.testing._internal.common_quantization import ( + skipIfNoDynamoSupport, + skipIfNoONEDNN, + skipIfNoONEDNNBF16, +) +from torch.testing._internal.common_utils import ( + IS_FBCODE, + IS_LINUX, + IS_X86, + instantiate_parametrized_tests, + parametrize, + skipIfRocm, +) +from torch.testing._internal.inductor_utils import ( + HAS_CPU, + _check_has_dynamic_shape, +) + +import torchao +import torchao.quantization.pt2e.quantizer.x86_inductor_quantizer as xiq +from torchao.quantization.pt2e.quantize_pt2e import ( + convert_pt2e, + prepare_pt2e, + prepare_qat_pt2e, +) +from torchao.quantization.pt2e.quantizer.x86_inductor_quantizer import ( + X86InductorQuantizer, +) +from torchao.utils import ( + TORCH_VERSION_AT_LEAST_2_6, + TORCH_VERSION_AT_LEAST_2_8, +) + +if TORCH_VERSION_AT_LEAST_2_6: + from torch.testing._internal.common_utils import TEST_ACL +else: + TEST_ACL = False + +# The dict value is match_nodes(computation_op+unary_op) +unary_list = { + torch.nn.ReLU(): 2, + torch.nn.Sigmoid(): 2, + torch.nn.Tanh(): 2, + torch.nn.Hardswish(): 6, + torch.nn.LeakyReLU(0.1, inplace=False): 4, + # Use floats for min/max, otherwise they can get converted to symints + torch.nn.Hardtanh(min_val=-0.5, max_val=4.0, inplace=False): 3, + torch.nn.Hardtanh(min_val=-0.5, max_val=float("inf"), inplace=False): 3, + torch.nn.GELU(approximate="none"): 6, + torch.nn.GELU(approximate="tanh"): 10, + torch.nn.ReLU6(): 3, + torch.nn.SiLU(): 3, + torch.nn.Hardsigmoid(): 5, +} + +non_decomposed_unary_list = [ + torch.nn.ReLU, + torch.nn.Sigmoid, + torch.nn.Tanh, +] + +# The dict value is (match_count, match_nodes, inplace) +binary_list = { + lambda x, y: torch.add(x, y): (1, 2, False), # call_function + lambda x, y: torch.add(y, x): (1, 2, False), # call_function + lambda x, y: x.add(y): (1, 2, False), # call_method + lambda x, y: x.add_(y): (1, 2, True), # call_method + lambda x, y: torch.sub(x, y): (1, 2, False), # call_function + lambda x, y: x.sub(y): (1, 2, False), # call_method + lambda x, y: x.sub_(y): (1, 2, True), # call_method +} + +quantization_add_fn_list = [ + lambda x, y: torch.add(x, y), + lambda x, y: x.add(y), +] + +quantization_inplace_add_fn_list = [ + lambda x, y: x.add_(y), +] + + +def get_default_quantizer(is_qat, is_dynamic): + quantizer = X86InductorQuantizer() + quantizer.set_global( + xiq.get_default_x86_inductor_quantization_config( + is_qat=is_qat, is_dynamic=is_dynamic + ) + ) + return quantizer + + +def _generate_qdq_quantized_model( + mod, inputs, is_qat=False, is_dynamic=False, quantizer=None +): + maybe_no_grad = contextlib.nullcontext() if is_qat else torch.no_grad() + with maybe_no_grad: + export_model = export_for_training(mod, inputs, strict=True).module() + quantizer = ( + quantizer if quantizer else get_default_quantizer(is_qat, is_dynamic) + ) + prepare_model = ( + prepare_qat_pt2e(export_model, quantizer) + if is_qat + else prepare_pt2e(export_model, quantizer) + ) + prepare_model(*inputs) + torchao.quantization.pt2e.move_exported_model_to_eval(prepare_model) + convert_model = convert_pt2e(prepare_model) + return convert_model + + +def cal_conv_generated_kernel_number(mod, input, dtype, dim=4, device="cpu"): + # this function is to decide how many kernels are generated + # while testing conv2d/3d/deconv2d + # the assumption is: + # (1) There will be a to_dtype kernel for input for lp + # (2) inductor always use channel_last format, there will + # be a to_channel_last format for input + # (3) to_dtype and to_channel_last for input can be fused + # (4) inductor always get channel last format from mkldnn_conv_pointwise(binary), + # and force the output to have same stride with eager. + # So there will be a to_contiguous for output if eager output is contiguouse + mod = copy.deepcopy(mod) + mod = mod.to(device=device) + input = input.clone() + input = input.to(device) + + if dtype == torch.float32: + maybe_autocast = contextlib.nullcontext() + else: + maybe_autocast = torch.amp.autocast(device_type=device, dtype=dtype) + with torch.no_grad(), maybe_autocast: + output = mod(input) + input_kernel, output_kernel = 0, 0 + if ( + input.is_contiguous(memory_format=torch.contiguous_format) + or dtype != torch.float32 + or (TEST_ACL and dim == 4) + ): + input_kernel = 1 + if output.is_contiguous(memory_format=torch.contiguous_format) or ( + TEST_ACL and dtype == torch.bfloat16 + ): + output_kernel = 1 + return input_kernel + output_kernel + + +@config.patch({"freezing": True}) +class TestPatternMatcherBase(TestCase): + def _check_unary_is_decomposed(self, unary_fn): + return not any( + isinstance(unary_fn, fn) + for fn in [torch.nn.ReLU, torch.nn.Sigmoid, torch.nn.Tanh] + ) + + def _clone_inputs(self, inputs): + def clone(x): + if not isinstance(x, torch.Tensor): + return x + return x.clone() + + return tuple(clone(x) for x in inputs) + + def _test_common( + self, + mod, + inputs, + matcher_check_fn, + atol=1e-5, + rtol=1.3e-6, + check_autocast=torch.float32, + check_quantization=False, + is_qat=False, + dtype=None, + is_dynamic=False, + quantizer=None, + compile_options={}, # noqa: B006 + ): + if not hasattr(self, "device"): + has_xpu = any( + isinstance(input, torch.Tensor) and input.device.type == "xpu" + for input in inputs + ) + device = "xpu" if has_xpu else "cpu" + else: + device = self.device + + mod = mod.to(device=device) + counters.clear() + torch._dynamo.reset() + if check_autocast == torch.bfloat16 and ( + torch.ops.mkldnn._is_mkldnn_bf16_supported() or device == "xpu" + ): + maybe_autocast = torch.amp.autocast( + device_type=device, dtype=torch.bfloat16 + ) + atol, rtol = 1e-2, 1e-2 + elif check_autocast == torch.float16 and ( + torch.ops.mkldnn._is_mkldnn_fp16_supported() or device == "xpu" + ): + maybe_autocast = torch.amp.autocast(device_type=device, dtype=torch.float16) + atol, rtol = 1e-2, 1e-2 + else: + assert check_autocast == torch.float32 + maybe_autocast = contextlib.nullcontext() + if check_quantization: + convert_model = _generate_qdq_quantized_model( + mod, inputs, is_qat, is_dynamic, quantizer + ) + with torch.no_grad(), maybe_autocast: + _ = torch.compile(convert_model)(*inputs) + matcher_check_fn() + else: + with torch.no_grad(), maybe_autocast: + clone_inputs = self._clone_inputs(inputs) + expected = mod(*inputs) + actual = torch.compile(mod, **compile_options)(*clone_inputs) + torch.testing.assert_close(actual, expected, atol=atol, rtol=rtol) + matcher_check_fn() + + def _test_code_common( + self, + mod, + inputs, + include_ops, + exclude_ops, + atol=1e-5, + rtol=1.3e-6, + check_quantization=False, + check_dynamic=None, + num_include_ops=None, + quantizer=None, + ): + with torch.no_grad(): + clone_inputs = self._clone_inputs(inputs) + if check_quantization: + mod = _generate_qdq_quantized_model(mod, inputs, quantizer=quantizer) + expected = mod(*inputs) + actual, (source_code,) = run_and_get_code( + torch.compile(mod, fullgraph=True, dynamic=check_dynamic), + *clone_inputs, + ) + for op in include_ops: + self.assertIn(op, source_code) + if num_include_ops is not None: + assert len(include_ops) == len(num_include_ops) + for i in range(len(include_ops)): + self.assertEqual( + source_code.count(include_ops[i]), num_include_ops[i] + ) + for op in exclude_ops: + self.assertNotIn(op, source_code) + if check_dynamic is not None: + _check_has_dynamic_shape(self, source_code) + if not check_quantization: + # Skip due to reduce range setting for Quantization on preCI system. + torch.testing.assert_close(actual, expected, atol=atol, rtol=rtol) + + +@unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_8, "Requires torch 2.8+") +class TestPatternMatcher(TestPatternMatcherBase): + def _qconv2d_test_helper(self, device="cpu", int8_mixed_bf16=False): + class M(torch.nn.Module): + def __init__( + self, + **kwargs, + ): + super().__init__() + self.conv = torch.nn.Conv2d(3, 128, kernel_size=3, stride=1) + self.conv2 = torch.nn.Conv2d(128, 128, kernel_size=3, stride=1) + self.conv3 = torch.nn.Conv2d( + 128, 128, kernel_size=3, stride=1, groups=4 + ) + + def forward(self, x): + return self.conv3(self.conv2(self.conv(x))) + + mod = M().eval().to(device=device) + v = ( + torch.randn((1, 3, 8, 8), dtype=torch.float32, requires_grad=False) + .add(1) + .to(device=device) + ) + + def matcher_check_fn(): + # 1. Dequant-Conv2D pattern matched in QConv2D weight prepack * 1 + # int8_mixed_fp32: [dequant_node, dequantize_per_channel, clone, convolution] + # int8_mixed_bf16: [dequant_node, optional(convert_element_type_4), + # dequantize_per_channel, optional(convert_element_type_3), clone, convolution] + self.assertEqual( + counters["inductor"]["qconv_weight_prepack_matcher_count"], 3 + ) + self.assertEqual( + counters["inductor"]["qconv_weight_prepack_matcher_nodes"], + 18 if int8_mixed_bf16 else 12, + ) + self.assertEqual( + counters["inductor"]["qconv_unary_lower_count"], 0 if TEST_ACL else 3 + ) + + self._test_common( + mod, + (v,), + matcher_check_fn, + check_quantization=True, + check_autocast=torch.bfloat16 if int8_mixed_bf16 else torch.float, + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + @skipIfRocm + def test_qconv2d_cpu(self): + r""" + This testcase will quantize a single Conv2d module. + """ + self._qconv2d_test_helper("cpu") + + @skipIfNoDynamoSupport + @skipIfNoONEDNNBF16 + @skipIfNoONEDNN + @skipIfRocm + def test_qconv2d_int8_mixed_bf16(self): + r""" + This testcase will quantize a single Conv2d module with int8_mixed_bf16 quantization. + """ + self._qconv2d_test_helper(int8_mixed_bf16=True) + + def _qconv2d_unary_test_helper( + self, + device="cpu", + int8_mixed_bf16=False, + unary_op=torch.nn.ReLU(), + qconv_unary_matcher_nodes=None, + ): + class M(torch.nn.Module): + def __init__( + self, + **kwargs, + ): + super().__init__() + self.conv = torch.nn.Conv2d(3, 128, kernel_size=3, stride=1) + self.unary_fn = copy.deepcopy(unary_op) + self.conv2 = torch.nn.Conv2d( + 128, 128, kernel_size=3, stride=1, bias=False + ) + self.unary_fn2 = copy.deepcopy(unary_op) + + def forward(self, x): + tmp = self.unary_fn(self.conv(x)) + return self.unary_fn2(self.conv2(tmp)) + + mod = M().eval().to(device=device) + v = ( + torch.randn((1, 3, 8, 8), dtype=torch.float32, requires_grad=False) + .add(1) + .to(device=device) + ) + + def matcher_check_fn(): + # 1. Dequant-Conv2D pattern matched in quantization weight prepack * 2 + self.assertEqual( + counters["inductor"]["qconv_weight_prepack_matcher_count"], 2 + ) + # 2. QConv2D Unary fusion in post-grad fusion pass * 2 + self.assertEqual( + counters["inductor"]["qconv_unary_matcher_count"], + 0 if TEST_ACL else 2, + ) + self.assertEqual( + counters["inductor"]["qconv_unary_lower_count"], 0 if TEST_ACL else 2 + ) + if qconv_unary_matcher_nodes: + self.assertEqual( + counters["inductor"]["qconv_unary_matcher_nodes"], + 0 if TEST_ACL else qconv_unary_matcher_nodes, + ) + + self._test_common( + mod, + (v,), + check_quantization=True, + check_autocast=torch.bfloat16 if int8_mixed_bf16 else torch.float, + matcher_check_fn=matcher_check_fn, + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_qconv2d_relu_cpu(self): + r""" + This testcase will quantize Conv2d->ReLU pattern. + """ + self._qconv2d_unary_test_helper(device="cpu") + + @skipIfNoDynamoSupport + @skipIfNoONEDNNBF16 + @skipIfNoONEDNN + def test_qconv2d_relu_int8_mixed_bf16_xpu(self): + r""" + This testcase will quantize Conv2d->ReLU pattern with int8_mixed_bf16 quantization. + """ + self._qconv2d_unary_test_helper(int8_mixed_bf16=True) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_qconv2d_relu6_cpu(self): + r""" + This testcase will quantize Conv2d->ReLU6 pattern. + """ + self._qconv2d_unary_test_helper(device="cpu", unary_op=torch.nn.ReLU6()) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_qconv2d_hardtanh_cpu(self): + r""" + This testcase will quantize Conv2d->Hardtanh pattern. + """ + self._qconv2d_unary_test_helper(device="cpu", unary_op=torch.nn.Hardtanh()) + + @skipIfNoDynamoSupport + @skipIfNoONEDNNBF16 + @skipIfNoONEDNN + def test_qconv2d_hardtanh_int8_mixed_bf16_cpu(self): + r""" + This testcase will quantize Conv2d->Hardtanh pattern. + Match.nodes: + [qconv2d_pointwise_default, convert_element_type, clamp_min, clamp_max, convert_element_type, quantize_per_tensor] + [qconv2d_pointwise_default, convert_element_type, clamp_min, clamp_max, convert_element_type] + """ + self._qconv2d_unary_test_helper( + unary_op=torch.nn.Hardtanh(), + int8_mixed_bf16=True, + qconv_unary_matcher_nodes=11, + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_qconv2d_hardswish_cpu(self): + r""" + This testcase will quantize Conv2d->Hardswish pattern. + """ + self._qconv2d_unary_test_helper(device="cpu", unary_op=torch.nn.Hardswish()) + + @skipIfNoDynamoSupport + @skipIfNoONEDNNBF16 + @skipIfNoONEDNN + def test_qconv2d_hardswish_int8_mixed_bf16_cpu(self): + r""" + This testcase will quantize Conv2d->Hardswish pattern. + Match.nodes: + [qconv2d_pointwise_default, convert_element_type, add, clamp_min, + clamp_max, mul, div, convert_element_type, quantize_per_tensor] + [qconv2d_pointwise_default, convert_element_type, add, clamp_min, clamp_max, mul, div, convert_element_type] + """ + self._qconv2d_unary_test_helper( + unary_op=torch.nn.Hardswish(), + int8_mixed_bf16=True, + qconv_unary_matcher_nodes=17, + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_qconv2d_silu_cpu(self): + r""" + This testcase will quantize Conv2d->SiLU pattern. + """ + self._qconv2d_unary_test_helper(device="cpu", unary_op=torch.nn.SiLU()) + + @skipIfNoDynamoSupport + @skipIfNoONEDNNBF16 + @skipIfNoONEDNN + def test_qconv2d_silu_int8_mixed_bf16_cpu(self): + r""" + This testcase will quantize Conv2d->SiLU pattern. + Match.nodes: + [qconv2d_pointwise_default, convert_element_type, sigmoid, mul, + convert_element_type, quantize_per_tensor] + [qconv2d_pointwise_default, convert_element_type, sigmoid, mul, convert_element_type] + """ + self._qconv2d_unary_test_helper( + unary_op=torch.nn.SiLU(), + int8_mixed_bf16=True, + qconv_unary_matcher_nodes=11, + ) + + def _qconv2d_add_test_helper( + self, device="cpu", use_relu=False, int8_mixed_bf16=False + ): + r""" + This testcase will quantize a Conv2d->Add pattern as: + X + / \ + Conv1(X) Conv2(X) + \ / + Add + | + Optional(relu) + | + Y + """ + + class M(torch.nn.Module): + def __init__( + self, + add_fn, + use_relu, + **kwargs, + ): + super().__init__() + self.conv1 = torch.nn.Conv2d(3, 6, kernel_size=3, stride=1) + self.conv2 = torch.nn.Conv2d(3, 6, kernel_size=3, stride=1) + self.add_fn = add_fn + self.relu = torch.nn.ReLU() + self.conv3 = torch.nn.Conv2d(6, 6, kernel_size=3, stride=1, bias=False) + self.conv4 = torch.nn.Conv2d(6, 6, kernel_size=3, stride=1, bias=False) + self.add_fn2 = add_fn + self.relu2 = torch.nn.ReLU() + self.use_relu = use_relu + + def forward(self, x): + x1 = self.conv1(x) + x2 = self.conv2(x) + tmp = self.add_fn(x1, x2) + if self.use_relu: + tmp = self.relu(tmp) + tmp1 = self.conv3(tmp) + tmp2 = self.conv4(tmp) + res = self.add_fn2(tmp1, tmp2) + if self.use_relu: + res = self.relu2(res) + return res + + for add_fn in quantization_add_fn_list + quantization_inplace_add_fn_list: + mod = M(add_fn, use_relu).eval().to(device=device) + v = ( + torch.randn((1, 3, 8, 8), dtype=torch.float32, requires_grad=False) + .add(1) + .to(device=device) + ) + + def matcher_check_fn(): + # 1. Dequant-Conv2D pattern matched in quantization weight prepack * 4 + self.assertEqual( + counters["inductor"]["qconv_weight_prepack_matcher_count"], 4 + ) + # 2. Qconv2d Binary Unary fusion in post-grad fusion pass * 2 + self.assertEqual( + counters["inductor"]["qconv2d_binary_matcher_count"], + 0 if TEST_ACL else 2, + ) + self.assertEqual( + counters["inductor"]["qconv2d_binary_lower_count"], + 0 if TEST_ACL else 2, + ) + + self._test_common( + mod, + (v,), + matcher_check_fn, + check_quantization=True, + check_autocast=torch.bfloat16 if int8_mixed_bf16 else torch.float, + ) + + def _qconv2d_add_test_helper2( + self, device="cpu", use_relu=False, int8_mixed_bf16=False + ): + r""" + This testcase will quantize two Conv2d->Add patterns as: + + Conv(X) extra input + \ / + Add + | + Optional(relu) + | + Y + + , and + + extra input Conv(X) + \ / + Add + | + Optional(relu) + | + Y + """ + + class M(torch.nn.Module): + def __init__( + self, + add_fn, + use_relu, + swap_inputs, + **kwargs, + ): + super().__init__() + self.conv1 = torch.nn.Conv2d(3, 6, kernel_size=3, stride=1) + self.add_fn = add_fn + self.relu = torch.nn.ReLU() + self.conv2 = torch.nn.Conv2d(6, 6, kernel_size=3, stride=1, bias=False) + self.add_fn2 = add_fn + self.relu2 = torch.nn.ReLU() + self.use_relu = use_relu + self.swap_inputs = swap_inputs + + def forward(self, x, x2, x3): + x1 = self.conv1(x) + if self.swap_inputs: + tmp = self.add_fn(x2, x1) + else: + tmp = self.add_fn(x1, x2) + if self.use_relu: + tmp = self.relu(tmp) + tmp1 = self.conv2(tmp) + if self.swap_inputs: + res = self.add_fn2(x3, tmp1) + else: + res = self.add_fn2(tmp1, x3) + if self.use_relu: + res = self.relu2(res) + return res + + for add_fn, swap_inputs in itertools.product( + quantization_add_fn_list + quantization_inplace_add_fn_list, [False, True] + ): + mod = M(add_fn, use_relu, swap_inputs).eval().to(device=device) + x = torch.randn( + (1, 3, 8, 8), dtype=torch.float32, requires_grad=False, device=device + ) + x2 = torch.randn( + (1, 6, 6, 6), dtype=torch.float32, requires_grad=False, device=device + ) + x3 = torch.randn( + (1, 6, 4, 4), dtype=torch.float32, requires_grad=False, device=device + ) + + def matcher_check_fn(): + # 1. Dequant-Conv2D pattern matched in quantization weight prepack * 2 + self.assertEqual( + counters["inductor"]["qconv_weight_prepack_matcher_count"], 2 + ) + # 2. Qconv2d Binary Unary fusion in post-grad fusion pass * 2 + self.assertEqual( + counters["inductor"]["qconv2d_binary_matcher_count"], + 0 if TEST_ACL else 2, + ) + self.assertEqual( + counters["inductor"]["qconv2d_binary_lower_count"], + 0 if TEST_ACL else 2, + ) + + self._test_common( + mod, + (x, x2, x3), + matcher_check_fn, + check_quantization=True, + check_autocast=torch.bfloat16 if int8_mixed_bf16 else torch.float, + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_qconv2d_add_cpu(self): + self._qconv2d_add_test_helper() + self._qconv2d_add_test_helper2() + + @skipIfNoDynamoSupport + @skipIfNoONEDNNBF16 + @skipIfNoONEDNN + def test_qconv2d_add_int8_mixed_bf16(self): + self._qconv2d_add_test_helper(int8_mixed_bf16=True) + self._qconv2d_add_test_helper2(int8_mixed_bf16=True) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_qconv2d_add_relu_cpu(self): + self._qconv2d_add_test_helper(use_relu=True) + self._qconv2d_add_test_helper2(use_relu=True) + + @skipIfNoDynamoSupport + @skipIfNoONEDNNBF16 + @skipIfNoONEDNN + def test_qconv2d_add_relu_int8_mixed_bf16(self): + self._qconv2d_add_test_helper(use_relu=True, int8_mixed_bf16=True) + self._qconv2d_add_test_helper2(use_relu=True, int8_mixed_bf16=True) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_qconv2d_add_broadcast_shapes_cpu(self): + r""" + This testcase will quantize Conv2d->add pattern using broadcast shape inputs. + Conv2d->Add fusion will fail for the broadcast shape inputs case. + """ + + class M(torch.nn.Module): + def __init__(self, use_bias): + super().__init__() + self.conv = torch.nn.Conv2d(32, 32, kernel_size=3, stride=1) + + def forward(self, x1, x2): + return torch.add(self.conv(x1), x2) + + bias_list = [True, False] + for bias in bias_list: + mod = M(bias).eval() + x1 = torch.randn((2, 32, 9, 9)) + x2 = torch.randn((2, 32, 1, 1)) + + def matcher_check_fn(): + # 1. Dequant-Conv2D pattern matched in quantization weight prepack * 1 + self.assertEqual( + counters["inductor"]["qconv_weight_prepack_matcher_count"], 1 + ) + # 2. Qconv2d Binary Unary fusion in post-grad fusion pass * 0 + self.assertEqual( + counters["inductor"]["qconv2d_binary_matcher_count"], 0 + ) + + self._test_common( + mod, + (x1, x2), + matcher_check_fn, + check_quantization=True, + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_qconv2d_with_concat_cpu(self): + channel_1 = 32 + channel_2 = 16 + channel_3 = 8 + channel_4 = int(channel_2 * 2 + channel_3) + + class Model(torch.nn.Module): + def __init__( + self, + ): + super().__init__() + self.conv1 = torch.nn.Conv2d( + channel_1, channel_2, 1, stride=1, dilation=1, padding=0 + ) + self.conv2 = torch.nn.Conv2d( + channel_1, channel_2, 1, stride=1, dilation=1, padding=0 + ) + self.conv3 = torch.nn.Conv2d( + channel_2, channel_3, 3, stride=1, dilation=1, padding=1 + ) + + self.conv = torch.nn.Conv2d( + channel_4, channel_2, 1, stride=1, dilation=1, padding=0 + ) + + def forward(self, x: torch.Tensor): + x1 = self.conv1(x) + x2 = self.conv2(x) + x3 = self.conv3(x2) + res = torch.cat([x1, x2, x3], dim=1) + res = self.conv(res) + return res + + mod = Model().eval() + v = torch.randn( + (8, channel_1, 40, 40), dtype=torch.float32, requires_grad=False + ) + + def matcher_check_fn(): + self.assertEqual( + counters["inductor"]["qconv_weight_prepack_matcher_count"], 4 + ) + self.assertEqual( + counters["inductor"]["qconv_unary_matcher_count"], + 0 if TEST_ACL else 3, + ) + self.assertEqual( + counters["inductor"]["qconv_unary_lower_count"], 0 if TEST_ACL else 4 + ) + + self._test_common( + mod, + (v,), + matcher_check_fn, + check_quantization=True, + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_qconv2d_add_2(self): + r""" + This testcase prevents this pattern be matched as a conv_binary fusion by mistake. + Conv(X) 3 + \ / + Add + We see this pattern in Mobilenet v3 large which add is decomposed from torch.nn.Hardswish or torch.nn.Hardsigmoid. + """ + + class M(torch.nn.Module): + def __init__( + self, + post_op, + ): + super().__init__() + self.conv = torch.nn.Conv2d(3, 6, kernel_size=3, stride=1) + self.post_op = post_op + + def forward(self, x): + return self.post_op(self.conv(x)) + + for post_op in [ + torch.nn.Hardswish(inplace=True), + torch.nn.Hardsigmoid(inplace=True), + ]: + mod = M(post_op).eval() + v = torch.randn((1, 3, 8, 8), dtype=torch.float32, requires_grad=False).add( + 1 + ) + + def matcher_check_fn(): + # Shouldn't hit conv binary fusion + self.assertEqual( + counters["inductor"]["qconv2d_binary_matcher_count"], 0 + ) + + self._test_common( + mod, + (v,), + matcher_check_fn, + check_quantization=True, + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_qconv2d_add_3(self): + r""" + This testcase will test below model: + x + / \ + conv1 maxpool + \ / \ + add conv2 + \ / + cat + Based on default recipe of x86InductorQuantizer, we will see this pattern after convert: + qconv1 maxpool + \ | + \ q1 + \ / \ + \ dq1 qconv2 + \ / + add + | + q2 + Since q1 has 2 users and qconv2 is not ancestor node of qconv1, we shouldn't fuse: + int8 + / + qconv1 dq1 + \ / + add + | + q2 + | + int8 + Instead we can match and fuse this pattern into qconv_binary: + qconv1 fp32 + \ / + add + | + fp32 + """ + + class M(torch.nn.Module): + def __init__( + self, + ): + super().__init__() + self.conv1 = torch.nn.Conv2d(3, 3, kernel_size=3, stride=1) + self.conv2 = torch.nn.Conv2d(3, 3, kernel_size=1, stride=1) + self.maxpool = torch.nn.MaxPool2d( + kernel_size=3, stride=1, padding=0, dilation=1 + ) + + def forward(self, x): + tmp1 = self.conv1(x) + tmp2 = self.maxpool(x) + add = torch.add(tmp1, tmp2) + tmp3 = self.conv2(tmp2) + return torch.cat((add, tmp3), dim=1) + + mod = M().eval() + v = torch.randn((1, 3, 8, 8), dtype=torch.float32, requires_grad=False).add(1) + + def matcher_check_fn(): + self.assertEqual( + counters["inductor"]["qconv2d_binary_matcher_count"], + 0 if TEST_ACL else 1, + ) + # The matched qconv binary pattern should have 2 nodes [qconv, add] + # instead of 11 which has dequant in binary input and output quant + self.assertEqual( + counters["inductor"]["qconv2d_binary_matcher_nodes"], + 0 if TEST_ACL else 2, + ) + self.assertEqual( + counters["inductor"]["qconv2d_binary_lower_count"], + 0 if TEST_ACL else 1, + ) + + self._test_common( + mod, + (v,), + matcher_check_fn, + check_quantization=True, + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + @skipIfRocm + def test_qat_qconv2d(self): + r""" + This testcase will quantize a single Conv2d module with qat flow. + """ + + class M(torch.nn.Module): + def __init__( + self, + **kwargs, + ): + super().__init__() + self.conv = torch.nn.Conv2d(3, 128, kernel_size=3, stride=1) + self.bn = torch.nn.BatchNorm2d(128) + + def forward(self, x): + return self.bn(self.conv(x)) + + mod = M().train() + v = torch.randn((1, 3, 8, 8), dtype=torch.float32, requires_grad=True).add(1) + + def matcher_check_fn(): + # 1. Dequant-conv pattern matched in quantization weight prepack * 1 + # [dequantize_per_tensor, dequantize_per_channel, clone, convolution] + self.assertEqual( + counters["inductor"]["qconv_weight_prepack_matcher_count"], 1 + ) + self.assertEqual( + counters["inductor"]["qconv_weight_prepack_matcher_nodes"], 4 + ) + # 2. QConv2D Unary fusion in post-grad fusion pass * 1 + # [qconv2d_pointwise_default, quantize_per_tensor] + self.assertEqual( + counters["inductor"]["qconv_unary_matcher_count"], + 0 if TEST_ACL else 1, + ) + self.assertEqual( + counters["inductor"]["qconv_unary_matcher_nodes"], + 0 if TEST_ACL else 2, + ) + self.assertEqual( + counters["inductor"]["qconv_unary_lower_count"], 0 if TEST_ACL else 1 + ) + + self._test_common( + mod, + (v,), + matcher_check_fn, + check_quantization=True, + is_qat=True, + ) + + def _qat_qconv2d_unary_cpu_test_helper( + self, + unary_op=torch.nn.ReLU(), + ): + class M(torch.nn.Module): + def __init__( + self, + **kwargs, + ): + super().__init__() + self.conv = torch.nn.Conv2d(3, 3, kernel_size=3, stride=1) + self.unary_fn = copy.deepcopy(unary_op) + self.bn = torch.nn.BatchNorm2d(3) + self.conv2 = torch.nn.Conv2d(3, 3, kernel_size=3, stride=1) + self.unary_fn2 = copy.deepcopy(unary_op) + self.bn2 = torch.nn.BatchNorm2d(3) + + def forward(self, x): + tmp = self.unary_fn(self.bn(self.conv(x))) + return self.unary_fn2(self.bn2(self.conv2(tmp))) + + mod = M() + v = torch.randn((1, 3, 8, 8), dtype=torch.float32, requires_grad=True).add(1) + + def matcher_check_fn(): + # 1. Dequant-conv pattern matched in quantization weight prepack * 1 + # [convert_element_type_1, sub, mul_1, dequantize_per_channel, clone, convolution] + self.assertEqual( + counters["inductor"]["qconv_weight_prepack_matcher_count"], 2 + ) + # 2. QConv2D Unary fusion in post-grad fusion pass * 1 + # [qconv2d_pointwise_default, relu, div_1, round_2, add_1, clamp_min_1, clamp_max_1, convert_element_type_2] + self.assertEqual( + counters["inductor"]["qconv_unary_matcher_count"], + 0 if TEST_ACL else 2, + ) + self.assertEqual( + counters["inductor"]["qconv_unary_lower_count"], 0 if TEST_ACL else 2 + ) + + self._test_common( + mod, + (v,), + matcher_check_fn, + check_quantization=True, + is_qat=True, + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_qat_qconv2d_relu(self): + r""" + This testcase will quantize Conv2d->ReLU pattern with qat flow. + """ + + self._qat_qconv2d_unary_cpu_test_helper() + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_qat_qconv2d_relu6(self): + r""" + This testcase will quantize Conv2d->ReLU6 pattern with qat flow. + """ + self._qat_qconv2d_unary_cpu_test_helper(unary_op=torch.nn.ReLU6()) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_qat_qconv2d_hardtanh(self): + r""" + This testcase will quantize Conv2d->Hardtanh pattern with qat flow. + """ + self._qat_qconv2d_unary_cpu_test_helper(unary_op=torch.nn.Hardtanh()) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_qat_qconv2d_silu(self): + r""" + This testcase will quantize Conv2d->SiLU pattern with qat flow. + """ + self._qat_qconv2d_unary_cpu_test_helper(unary_op=torch.nn.SiLU()) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_qat_qconv2d_hardswish(self): + r""" + This testcase will quantize Conv2d->Hardswish pattern with qat flow. + """ + self._qat_qconv2d_unary_cpu_test_helper(unary_op=torch.nn.Hardswish()) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + @skipIfRocm + def test_qat_qconv2d_add(self): + r""" + This testcase will quantize a Conv2d->Add pattern as: + X + / \ + Conv1(X) Conv2(X) + \ / + Add + | + Y + """ + + class M(torch.nn.Module): + def __init__( + self, + **kwargs, + ): + super().__init__() + self.conv1 = torch.nn.Conv2d(3, 6, kernel_size=3, stride=1) + self.bn1 = torch.nn.BatchNorm2d(6) + self.conv2 = torch.nn.Conv2d(3, 6, kernel_size=3, stride=1) + self.bn2 = torch.nn.BatchNorm2d(6) + + def forward(self, x): + x1 = self.bn1(self.conv1(x)) + x2 = self.bn2(self.conv2(x)) + return x1 + x2 + + mod = M().train() + v = torch.randn((1, 3, 8, 8), dtype=torch.float32, requires_grad=True).add(1) + + def matcher_check_fn(): + # 1. Dequant-conv pattern matched in quantization weight prepack * 2 + # [dequantize_per_tensor, dequantize_per_channel, clone, convolution] + self.assertEqual( + counters["inductor"]["qconv_weight_prepack_matcher_count"], 2 + ) + self.assertEqual( + counters["inductor"]["qconv_weight_prepack_matcher_nodes"], 8 + ) + # 2. Qconv2d Binary fusion in post-grad fusion pass * 1 + # [qconv2d_pointwise_default_1, dequantize_per_tensor, add_3, quantize_per_tensor] + self.assertEqual( + counters["inductor"]["qconv2d_binary_matcher_count"], + 0 if TEST_ACL else 1, + ) + self.assertEqual( + counters["inductor"]["qconv2d_binary_matcher_nodes"], + 0 if TEST_ACL else 4, + ) + self.assertEqual( + counters["inductor"]["qconv2d_binary_lower_count"], + 0 if TEST_ACL else 1, + ) + + self._test_common( + mod, + (v,), + matcher_check_fn, + check_quantization=True, + is_qat=True, + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + @skipIfRocm + def test_qat_qconv2d_add_relu(self): + r""" + This testcase will quantize a Conv2d->Add->ReLU pattern as: + X + / \ + Conv1(X) Conv2(X) + \ / + Add + | + ReLU + | + Y + """ + + class M(torch.nn.Module): + def __init__( + self, + **kwargs, + ): + super().__init__() + self.conv1 = torch.nn.Conv2d(3, 6, kernel_size=3, stride=1) + self.bn1 = torch.nn.BatchNorm2d(6) + self.conv2 = torch.nn.Conv2d(3, 6, kernel_size=3, stride=1) + self.bn2 = torch.nn.BatchNorm2d(6) + self.relu = torch.nn.ReLU() + + def forward(self, x): + x1 = self.bn1(self.conv1(x)) + x2 = self.bn2(self.conv2(x)) + return self.relu(x1 + x2) + + mod = M().train() + v = torch.randn((1, 3, 8, 8), dtype=torch.float32, requires_grad=True).add(1) + + def matcher_check_fn(): + # 1. Dequant-conv pattern matched in quantization weight prepack * 2 + # [dequantize_per_tensor, dequantize_per_channel, clone, convolution] + self.assertEqual( + counters["inductor"]["qconv_weight_prepack_matcher_count"], 2 + ) + self.assertEqual( + counters["inductor"]["qconv_weight_prepack_matcher_nodes"], 8 + ) + # 2. Qconv2d Binary fusion in post-grad fusion pass * 1 + # [qconv2d_pointwise_default_1, dequantize_per_tensor, add_3, relu, quantize_per_tensor] + self.assertEqual( + counters["inductor"]["qconv2d_binary_matcher_count"], + 0 if TEST_ACL else 1, + ) + self.assertEqual( + counters["inductor"]["qconv2d_binary_matcher_nodes"], + 0 if TEST_ACL else 5, + ) + self.assertEqual( + counters["inductor"]["qconv2d_binary_lower_count"], + 0 if TEST_ACL else 1, + ) + + self._test_common( + mod, + (v,), + matcher_check_fn, + check_quantization=True, + is_qat=True, + ) + + def _test_qconv2d_dequant_promotion_helper(self, device="cpu"): + r""" + This testcase tests if dequant node before conv2d is promoted correctly: + X + | + Conv1(X) + / \ + Conv2(X) Conv3(X) + \ / + Add + | + Y + """ + + class M(torch.nn.Module): + def __init__( + self, + **kwargs, + ): + super().__init__() + self.conv1 = torch.nn.Conv2d(3, 6, kernel_size=3, stride=1) + self.conv2 = torch.nn.Conv2d(6, 6, kernel_size=3, stride=1) + self.conv3 = torch.nn.Conv2d(6, 6, kernel_size=3, stride=1) + + def forward(self, x): + temp = self.conv1(x) + temp = self.conv2(temp) + self.conv3(temp) + return temp + + mod = M().eval().to(device=device) + v = ( + torch.randn((1, 3, 8, 8), dtype=torch.float32, requires_grad=False) + .add(1) + .to(device=device) + ) + + def matcher_check_fn(): + # 1. Dequant pattern matcher for dequant promotion * 1 + # [dequantize_per_tensor] + self.assertEqual(counters["inductor"]["dequant_promotion_matcher_count"], 1) + self.assertEqual(counters["inductor"]["dequant_promotion_matcher_nodes"], 1) + # 2. Dequant-conv pattern matched in quantization weight prepack * 3 + # [dequantize_per_tensor, dequantize_per_channel, clone, convolution] + self.assertEqual( + counters["inductor"]["qconv_weight_prepack_matcher_count"], 3 + ) + self.assertEqual( + counters["inductor"]["qconv_weight_prepack_matcher_nodes"], 12 + ) + # 3. Qconv2d Binary fusion in post-grad fusion pass * 1 + # [qconv2d_pointwise_default_1, add_3] + self.assertEqual( + counters["inductor"]["qconv2d_binary_matcher_count"], + 0 if TEST_ACL else 1, + ) + self.assertEqual( + counters["inductor"]["qconv2d_binary_matcher_nodes"], + 0 if TEST_ACL else 2, + ) + self.assertEqual( + counters["inductor"]["qconv2d_binary_lower_count"], + 0 if TEST_ACL else 1, + ) + + self._test_common( + mod, + (v,), + matcher_check_fn, + check_quantization=True, + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + @skipIfRocm + def test_qconv2d_dequant_promotion_cpu(self): + self._test_qconv2d_dequant_promotion_helper() + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_qconv1d_relu_cpu(self): + r""" + This testcase will quantize Conv1d->ReLU pattern. + """ + device = "cpu" + unary_op = torch.nn.ReLU() + + class M(torch.nn.Module): + def __init__( + self, + ): + super().__init__() + self.conv = torch.nn.Conv1d(3, 128, kernel_size=3, stride=1) + self.unary_fn = copy.deepcopy(unary_op) + self.conv2 = torch.nn.Conv1d( + 128, 128, kernel_size=3, stride=1, bias=False + ) + self.unary_fn2 = copy.deepcopy(unary_op) + + def forward(self, x): + tmp = self.unary_fn(self.conv(x)) + return self.unary_fn2(self.conv2(tmp)) + + mod = M().eval().to(device=device) + v = ( + torch.randn((1, 3, 8), dtype=torch.float32, requires_grad=False) + .add(1) + .to(device=device) + ) + + def matcher_check_fn(): + # 1. Dequant-Conv2D pattern matched in quantization weight prepack * 2 + self.assertEqual( + counters["inductor"]["qconv_weight_prepack_matcher_count"], 2 + ) + # 2. QConv2D Unary fusion in post-grad fusion pass * 2 + self.assertEqual( + counters["inductor"]["qconv_unary_matcher_count"], + 0 if TEST_ACL else 2, + ) + self.assertEqual( + counters["inductor"]["qconv_unary_lower_count"], 0 if TEST_ACL else 2 + ) + + self._test_common( + mod, + (v,), + check_quantization=True, + matcher_check_fn=matcher_check_fn, + ) + + def _qlinear_test_helper( + self, + inputs, + device="cpu", + int8_mixed_bf16=False, + do_permute=False, + matcher_check_fn=None, + bias=True, + is_dynamic=False, + is_qat=False, + ): + class M(torch.nn.Module): + def __init__(self, use_bias, do_permute=False): + super().__init__() + self.linear = torch.nn.Linear(4, 3, use_bias) + self.linear2 = torch.nn.Linear(3, 4, use_bias) + self.do_permute = do_permute + + def forward(self, x): + if self.do_permute: + x = torch.reshape(torch.permute(x, (0, 2, 3, 1)), (2, 12, 4)) + return self.linear2(self.linear(x)) + + mod = M(bias, do_permute=do_permute).eval().to(device=device) + assert isinstance(inputs, tuple) + + def __convert_tensor_to_device(input, device): + return input.to(device=device) if isinstance(input, torch.Tensor) else input + + inputs = tuple(__convert_tensor_to_device(input, device) for input in inputs) + + def _default_matcher_check_fn(): + self.assertEqual( + counters["inductor"]["qlinear_weight_prepack_matcher_count"], 2 + ) + + self._test_common( + mod, + inputs, + matcher_check_fn=( + matcher_check_fn + if matcher_check_fn is not None + else _default_matcher_check_fn + ), + check_autocast=torch.bfloat16 if int8_mixed_bf16 else torch.float, + check_quantization=True, + is_qat=is_qat, + is_dynamic=is_dynamic, + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_qlinear_cpu(self): + r""" + This testcase will quantize a single Linear Moduel. + """ + for bias in [True, False]: + self._qlinear_test_helper((torch.randn((2, 4)),), bias=bias) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_dynamic_qlinear_cpu(self): + r""" + This testcase will quantize a single Linear Moduel. + """ + for bias in [True, False]: + self._qlinear_test_helper( + (torch.randn((2, 4)),), bias=bias, is_dynamic=True + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_dynamic_qlinear_qat_cpu(self): + r""" + This testcase will quantize a single Linear Moduel. + """ + for bias in [True, False]: + self._qlinear_test_helper( + (torch.randn((2, 4)),), bias=bias, is_dynamic=True, is_qat=True + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_dynamic_qlinear_input_dim_exceeds_2(self): + r""" + This testcase will quantize a single Linear Moduel. + """ + for bias in [True, False]: + self._qlinear_test_helper( + (torch.randn((2, 3, 4)),), bias=bias, is_dynamic=True + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNNBF16 + @skipIfNoONEDNN + def test_qlinear_int8_mixed_bf16(self): + r""" + This testcase will quantize a single Linear Moduel with int8_mixed_bf16 quantization. + """ + for bias in [True, False]: + self._qlinear_test_helper( + (torch.randn((2, 4)),), int8_mixed_bf16=True, bias=bias + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_qlinear_input_dim_exceeds_2(self): + r""" + This testcase will quantize a single Linear Moduel. + """ + for bias in [True, False]: + self._qlinear_test_helper((torch.randn((2, 3, 4)),), bias=bias) + + @skipIfNoDynamoSupport + @skipIfNoONEDNNBF16 + @skipIfNoONEDNN + def test_qlinear_int8_mixed_bf16_input_dim_exceeds_2(self): + r""" + This testcase will quantize a single Linear Moduel with int8_mixed_bf16 quantization. + """ + for bias in [True, False]: + self._qlinear_test_helper( + (torch.randn((2, 3, 4)),), int8_mixed_bf16=True, bias=bias + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_qlinear_input_dim_exceeds_2_and_not_contiguous(self): + r""" + This testcase will quantize a single Linear Module. + * Input dim exceeds 2 + * Input not contiguous + """ + for bias in [True, False]: + + def matcher_check_fn(): + self.assertEqual( + counters["inductor"]["qlinear_weight_prepack_matcher_count"], 2 + ) + self.assertEqual( + counters["inductor"]["qlinear_weight_prepack_matcher_nodes"], + 13 if bias else 12, + ) + + self._qlinear_test_helper( + (torch.randn((2, 4, 3, 4)),), + do_permute=True, + matcher_check_fn=matcher_check_fn, + bias=bias, + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNNBF16 + @skipIfNoONEDNN + def test_qlinear_int8_mixed_bf16_input_dim_exceeds_2_and_not_contiguous(self): + r""" + This testcase will quantize a single Linear Module for int8_bf16. + * Input dim exceeds 2 + * Input not contiguous + """ + for bias in [True, False]: + + def matcher_check_fn(): + self.assertEqual( + counters["inductor"]["qlinear_weight_prepack_matcher_count"], 2 + ) + self.assertEqual( + counters["inductor"]["qlinear_weight_prepack_matcher_nodes"], + 17 if bias else 16, + ) + + self._qlinear_test_helper( + (torch.randn((2, 4, 3, 4)),), + int8_mixed_bf16=True, + do_permute=True, + matcher_check_fn=matcher_check_fn, + bias=bias, + ) + + def _qlinear_unary_test_helper( + self, inputs, unary_op=torch.nn.ReLU(), device="cpu", int8_mixed_bf16=False + ): + class M(torch.nn.Module): + def __init__(self, use_bias): + super().__init__() + self.linear = torch.nn.Linear(4, 4, use_bias) + self.unary_fn = copy.deepcopy(unary_op) + self.linear2 = torch.nn.Linear(4, 4, use_bias) + self.unary_fn2 = copy.deepcopy(unary_op) + + def forward(self, x): + tmp = self.unary_fn(self.linear(x)) + return self.unary_fn2(self.linear2(tmp)) + + bias_list = [True, False] + for bias in bias_list: + mod = M(bias).eval().to(device=device) + + def matcher_check_fn(): + # 1. dequant-linear pattern matched in quantization weight prepack + self.assertEqual( + counters["inductor"]["qlinear_weight_prepack_matcher_count"], 2 + ) + # 2. QLinear Unary fusion in post-grad fusion pass + self.assertEqual( + counters["inductor"]["qlinear_unary_matcher_count"], + 0 if TEST_ACL else 2, + ) + self.assertEqual( + counters["inductor"]["qlinear_unary_lower_count"], + 0 if TEST_ACL else 2, + ) + + self._test_common( + mod, + inputs, + matcher_check_fn, + check_autocast=torch.bfloat16 if int8_mixed_bf16 else torch.float, + check_quantization=True, + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_qlinear_relu_cpu(self): + r""" + This testcase will quantize a Linear->ReLU pattern. + """ + self._qlinear_unary_test_helper((torch.randn((2, 4)),)) + + @skipIfNoDynamoSupport + @skipIfNoONEDNNBF16 + @skipIfNoONEDNN + def test_qlinear_relu_int8_mixed_bf16(self): + r""" + This testcase will quantize a Linear->ReLU pattern with int8_mixed_bf16 quantization. + """ + self._qlinear_unary_test_helper((torch.randn((2, 4)),), int8_mixed_bf16=True) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_qlinear_relu_input_dim_exceeds_2(self): + r""" + This testcase will quantize a Linear->ReLU pattern. + """ + self._qlinear_unary_test_helper((torch.randn((2, 3, 4)),)) + + @skipIfNoDynamoSupport + @skipIfNoONEDNNBF16 + @skipIfNoONEDNN + def test_qlinear_relu_int8_mixed_bf16_input_dim_exceeds_2(self): + r""" + This testcase will quantize a Linear->ReLU pattern with int8_mixed_bf16 quantization. + """ + self._qlinear_unary_test_helper((torch.randn((2, 3, 4)),), int8_mixed_bf16=True) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_qlinear_gelu_cpu(self): + r""" + This testcase will quantize a Linear->GELU pattern. + """ + for gelu in [torch.nn.GELU("none"), torch.nn.GELU("tanh")]: + self._qlinear_unary_test_helper((torch.randn((2, 4)),), gelu) + + @skipIfNoDynamoSupport + @skipIfNoONEDNNBF16 + @skipIfNoONEDNN + def test_qlinear_gelu_int8_mixed_bf16(self): + r""" + This testcase will quantize a Linear->GELU pattern with int8_mixed_bf16 quantization. + """ + for gelu in [torch.nn.GELU("none"), torch.nn.GELU("tanh")]: + self._qlinear_unary_test_helper( + (torch.randn((2, 4)),), gelu, int8_mixed_bf16=True + ) + + def _qlinear_add_test_helper( + self, + device="cpu", + use_relu=False, + int8_mixed_bf16=False, + is_qat=True, + is_dynamic=True, + ): + r""" + This testcase will quantize two consecutive Linear->Add(->relu) patterns as: + X + / \ + linear(X) linear(X) + \ / + Add + | + Optional(relu) + / \ + linear(X) linear(X) + \ / + Add + | + Optional(relu) + | + Y + """ + + def fake_quant(x): + # to produce a float32 result as extra input + qlib = torch.ops.quantized_decomposed + if device == "cpu": + qmin, qmax, dtype = 0, 255, torch.uint8 + else: + qmin, qmax, dtype = -128, 127, torch.int8 + x = qlib.quantize_per_tensor.default(x, 0.0166785, 42, qmin, qmax, dtype) + x = qlib.dequantize_per_tensor.default(x, 0.0166785, 42, qmin, qmax, dtype) + return x + + class M(torch.nn.Module): + def __init__( + self, + add_fn, + use_relu, + fake_quant_before_extra_input, + ): + super().__init__() + self.linear1 = torch.nn.Linear(4, 4) + self.linear2 = torch.nn.Linear(4, 4) + self.add_fn = add_fn + self.relu = torch.nn.ReLU() + self.linear3 = torch.nn.Linear(4, 4) + self.linear4 = torch.nn.Linear(4, 4) + self.add_fn2 = add_fn + self.relu2 = torch.nn.ReLU() + self.use_relu = use_relu + self.fake_quant_before_extra_input = fake_quant_before_extra_input + + def forward(self, x): + x1 = self.linear1(x) + x2 = self.linear2(x) + if self.fake_quant_before_extra_input: + x2 = fake_quant(x2) + tmp = self.add_fn(x1, x2) + if self.use_relu: + tmp = self.relu(tmp) + tmp1 = self.linear3(tmp) + tmp2 = self.linear4(tmp) + if self.fake_quant_before_extra_input: + tmp2 = fake_quant(tmp2) + res = self.add_fn2(tmp1, tmp2) + if self.use_relu: + res = self.relu2(res) + return res + + add_fn_list = [ + lambda x, y: x + y, + lambda x, y: y + x, + lambda x, y: x.add_(y), + lambda x, y: y.add_(x), + ] + fake_quant_x2_list = [False, True] if int8_mixed_bf16 else [False] + shape_list = [(4, 4), (4, 4, 4)] + cases = itertools.product(add_fn_list, fake_quant_x2_list, shape_list) + for add_fn, fq_x2, shape in cases: + mod = M(add_fn, use_relu, fq_x2).eval().to(device=device) + v = torch.randn( + shape, dtype=torch.float32, requires_grad=False, device=device + ).add(1) + + def matcher_check_fn(): + # 1. Dequant-linear pattern matched in quantization weight prepack * 4 + self.assertEqual( + counters["inductor"]["qlinear_weight_prepack_matcher_count"], 4 + ) + # pattern = [dequant_per_tensor, (convert_dtype), dequant_per_channel, (convert_dtype), permute, addmm] + nodes_per_match = 6 if int8_mixed_bf16 else 4 + if len(shape) == 3: + # pattern = [dequant_per_tensor, (convert_dtype), (view), \ + # dequant_per_channel, (convert_dtype), (view), permute, addmm] + nodes_per_match += 2 + self.assertEqual( + counters["inductor"]["qlinear_weight_prepack_matcher_nodes"], + 4 * nodes_per_match, + ) + # 2. Qlinear Binary Unary fusion in post-grad fusion pass * 2 + self.assertEqual( + counters["inductor"]["qlinear_binary_matcher_count"], + 0 if TEST_ACL else 2, + ) + # Two linear-binary patterns are matched + # matched patter1 = [qlinear, add, (convert dtype), (relu), quantize_per_tensor] + # matched patter2 = [qlinear, add, (convert dtype), (relu)] + # If add_fn is x.add_(y), x is bf16 and y is fp32, there is a to_bf16 node after binary + to_bf16_after_binary = 2 * (add_fn == add_fn_list[2] and fq_x2) + expected_matcher_nodes = ( + (4 if is_dynamic else 5) + 2 * use_relu + to_bf16_after_binary + ) + self.assertEqual( + counters["inductor"]["qlinear_binary_matcher_nodes"], + 0 if TEST_ACL else expected_matcher_nodes, + ) + self.assertEqual( + counters["inductor"]["qlinear_binary_lower_count"], + 0 if TEST_ACL else 2, + ) + + self._test_common( + mod, + (v,), + matcher_check_fn, + check_quantization=True, + check_autocast=torch.bfloat16 if int8_mixed_bf16 else torch.float, + is_qat=is_qat, + is_dynamic=is_dynamic, + ) + + if TEST_ACL: + continue + + if torch._inductor.config.cpp_wrapper: + # For CPP wrapper + self._test_code_common( + mod, + (v,), + [ + "aoti_torch_cpu__qlinear_pointwise_tensor", + "aoti_torch_cpu__qlinear_pointwise_binary_tensor", + ], + [], + check_quantization=True, + num_include_ops=[2, 2], + ) + else: + # For python wrapper + self._test_code_common( + mod, + (v,), + [ + "torch.ops.onednn.qlinear_pointwise.tensor", + "torch.ops.onednn.qlinear_pointwise.binary", + ], + [], + check_quantization=True, + num_include_ops=[2, 2], + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + @parametrize("use_relu", [True, False]) + @parametrize("is_qat", [True, False]) + @parametrize("is_dynamic", [True, False]) + def test_qlinear_add_cpu(self, use_relu, is_qat, is_dynamic): + self._qlinear_add_test_helper( + use_relu=use_relu, is_qat=is_qat, is_dynamic=is_dynamic + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNNBF16 + @skipIfNoONEDNN + @parametrize("use_relu", [True, False]) + @parametrize("is_qat", [True, False]) + @parametrize("is_dynamic", [True, False]) + def test_qlinear_add_int8_mixed_bf16(self, use_relu, is_qat, is_dynamic): + self._qlinear_add_test_helper( + int8_mixed_bf16=True, + use_relu=use_relu, + is_qat=is_qat, + is_dynamic=is_dynamic, + ) + + def _qlinear_dequant_promotion_test_helper( + self, + inputs, + device="cpu", + int8_mixed_bf16=False, + is_dynamic=False, + matcher_check_fn=None, + ): + class M(torch.nn.Module): + def __init__( + self, + **kwargs, + ): + super().__init__() + self.linear1 = torch.nn.Linear(4, 4) + self.linear2 = torch.nn.Linear(4, 4) + self.linear3 = torch.nn.Linear(4, 4) + + def forward(self, x): + temp = self.linear1(x) + temp = self.linear2(temp) + self.linear3(temp) + return temp + + mod = M().eval().to(device=device) + + def default_matcher_check_fn(): + # 1. Dequant pattern matcher for dequant promotion * 1 + self.assertEqual(counters["inductor"]["dequant_promotion_matcher_count"], 1) + # 2. dequant-linear pattern matched in quantization weight prepack * 3 + self.assertEqual( + counters["inductor"]["qlinear_weight_prepack_matcher_count"], 3 + ) + # 3. QLinear Unary fusion in post-grad fusion pass * 1 + self.assertEqual( + counters["inductor"]["qlinear_unary_matcher_count"], + 0 if TEST_ACL else 1, + ) + + self._test_common( + mod, + inputs, + matcher_check_fn=( + matcher_check_fn + if matcher_check_fn is not None + else default_matcher_check_fn + ), + check_autocast=torch.bfloat16 if int8_mixed_bf16 else torch.float, + check_quantization=True, + is_dynamic=is_dynamic, + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_qlinear_dequant_promotion_cpu(self): + r""" + This testcase test if dequant node before linear is promoted correctly: + X + | + Linear1(X) + / \ + Linear2(X) Linear3(X) + \ / + Add + | + Y + """ + self._qlinear_dequant_promotion_test_helper((torch.randn((2, 4)),)) + + @skipIfNoDynamoSupport + @skipIfNoONEDNNBF16 + @skipIfNoONEDNN + def test_qlinear_dequant_promotion_int8_mixed_bf16(self): + r""" + Test with int8_mixed_bf16 quantization. + This testcase test if dequant node before linear is promoted correctly: + X + | + Linear1(X) + / \ + Linear2(X) Linear3(X) + \ / + Add + | + Y + """ + self._qlinear_dequant_promotion_test_helper( + (torch.randn((2, 4)),), int8_mixed_bf16=True + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_qlinear_dequant_promotion_cpu_input_dim_exceeds_2(self): + r""" + This testcase test if dequant node before linear is promoted correctly: + X + | + Linear1(X) + / \ + Linear2(X) Linear3(X) + \ / + Add + | + Y + """ + self._qlinear_dequant_promotion_test_helper((torch.randn((2, 3, 4)),)) + + @skipIfNoDynamoSupport + @skipIfNoONEDNNBF16 + @skipIfNoONEDNN + def test_qlinear_dequant_promotion_int8_mixed_bf16_input_dim_exceeds_2(self): + r""" + Test with int8_mixed_bf16 quantization. + This testcase test if dequant node before linear is promoted correctly: + X + | + Linear1(X) + / \ + Linear2(X) Linear3(X) + \ / + Add + | + Y + """ + self._qlinear_dequant_promotion_test_helper( + (torch.randn((2, 3, 4)),), int8_mixed_bf16=True + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_qlinear_dequant_promotion_dynamic_cpu(self): + r""" + This testcase test if dequant node before linear is promoted correctly: + X + | + Linear1(X) + / \ + Linear2(X) Linear3(X) + \ / + Add + | + Y + """ + + def matcher_check_fn(): + # 1. Dequant pattern matcher for dequant promotion * 1 + self.assertEqual(counters["inductor"]["dequant_promotion_matcher_count"], 1) + # 2. dequant-linear pattern matched in quantization weight prepack * 3 + self.assertEqual( + counters["inductor"]["qlinear_weight_prepack_matcher_count"], 3 + ) + + self._qlinear_dequant_promotion_test_helper( + (torch.randn((2, 4)),), + matcher_check_fn=matcher_check_fn, + is_dynamic=True, + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_qlinear_mul_cpu(self): + r""" + This testcase will quantize a Linear->Mul pattern. + """ + + class M(torch.nn.Module): + def __init__(self, use_bias): + super().__init__() + self.linear = torch.nn.Linear(4, 5, use_bias) + + def forward(self, x1, x2): + return torch.mul(self.linear(x1), x2) + + bias_list = [True, False] + for bias in bias_list: + mod = M(bias).eval() + x1 = torch.randn((2, 4)) + x2 = torch.randn((2, 5)) + + def matcher_check_fn(): + self.assertEqual( + counters["inductor"]["qlinear_weight_prepack_matcher_count"], 1 + ) + + self._test_common( + mod, + (x1, x2), + matcher_check_fn, + check_quantization=True, + ) + + @skipIfNoDynamoSupport + def test_qmaxpool2d(self): + r""" + This testcase will quantize Conv2d->ReLU->MaxPool2d pattern. + """ + + class M(torch.nn.Module): + def __init__( + self, + kwargs, + ): + super().__init__() + self.conv = torch.nn.Conv2d( + 3, 64, 7, bias=True, stride=2, padding=3, dilation=1 + ) + self.relu = torch.nn.ReLU() + self.maxpool = torch.nn.MaxPool2d(3, **kwargs) + + def forward(self, x): + return self.maxpool(self.relu(self.conv(x))) + + kwargs_list = [ + {"stride": 2}, + {"stride": 2, "padding": 1}, + {"stride": 2, "padding": 1, "dilation": 1}, + {"stride": 2, "padding": 1, "dilation": 1, "ceil_mode": False}, + ] + for kwargs in kwargs_list: + mod = M(kwargs).eval() + v = torch.randn((1, 3, 8, 8), dtype=torch.float32, requires_grad=False).add( + 1 + ) + + def matcher_check_fn(): + self.assertEqual( + counters["inductor"]["qmaxpool2d_matcher_count"], + 0 if TEST_ACL else 1, + ) + self.assertEqual( + counters["inductor"]["qconv_weight_prepack_matcher_count"], 1 + ) + self.assertEqual( + counters["inductor"]["qconv_unary_matcher_count"], + 0 if TEST_ACL else 1, + ) + self.assertEqual( + counters["inductor"]["qconv_unary_lower_count"], + 0 if TEST_ACL else 1, + ) + + self._test_common( + mod, + (v,), + matcher_check_fn, + check_quantization=True, + ) + + @skipIfNoDynamoSupport + def test_qflatten(self): + r""" + This testcase will quantize Conv2d->AdaptiveAvgPool2d->flatten->cat pattern. + """ + + class M(torch.nn.Module): + def __init__( + self, + ): + super().__init__() + self.conv = torch.nn.Conv2d( + 3, 64, 7, bias=True, stride=2, padding=3, dilation=1 + ) + self.relu = torch.nn.ReLU() + self.adaptive_avg_pool2d = torch.nn.AdaptiveAvgPool2d((1, 1)) + + def forward(self, x): + return torch.cat( + [ + torch.flatten( + self.adaptive_avg_pool2d(self.relu(self.conv(x))), 1 + ) + ] + ) + + mod = M().eval() + v = torch.randn((1, 3, 8, 8), dtype=torch.float32, requires_grad=False).add(1) + + def matcher_check_fn(): + self.assertEqual( + counters["inductor"]["qreshape_matcher_count"], 0 if TEST_ACL else 1 + ) + + self._test_common( + mod, + (v,), + matcher_check_fn, + check_quantization=True, + ) + + @skipIfNoDynamoSupport + def test_qcat(self): + r""" + This testcase will quantize cat based pattern: + X + / \ + Conv1(X) Pow(x) + \ \ + \ Conv2(X) + \ / + Cat + | + Y + """ + + class M(torch.nn.Module): + def __init__( + self, + ): + super().__init__() + self.conv = torch.nn.Conv2d( + 3, 64, 7, bias=True, stride=2, padding=3, dilation=1 + ) + self.conv2 = torch.nn.Conv2d( + 3, 64, 7, bias=True, stride=2, padding=3, dilation=1 + ) + + def forward(self, x): + temp1 = self.conv(x) + temp2 = self.conv2(torch.pow(x, 2)) + return torch.cat((temp1, temp2), 1) + + mod = M().eval() + v = torch.randn((1, 3, 8, 8), dtype=torch.float32, requires_grad=False).add(1) + + def matcher_check_fn(): + self.assertEqual( + counters["inductor"]["qcat_matcher_count"], 0 if TEST_ACL else 1 + ) + self.assertEqual( + counters["inductor"]["qconv_weight_prepack_matcher_count"], 2 + ) + self.assertEqual( + counters["inductor"]["qconv_unary_matcher_count"], + 0 if TEST_ACL else 2, + ) + self.assertEqual( + counters["inductor"]["qconv_unary_lower_count"], 0 if TEST_ACL else 2 + ) + + self._test_common( + mod, + (v,), + matcher_check_fn, + check_quantization=True, + ) + + def _test_linear_dynamic_fp16_helper(self, use_relu: bool): + class M(torch.nn.Module): + def __init__(self, bias: bool, use_relu: bool): + super().__init__() + self.linear = torch.nn.Linear(256, 256, bias=bias) + self.relu = torch.nn.ReLU() + self.use_relu = use_relu + + def forward(self, x): + if self.use_relu: + return self.relu(self.linear(x)) + return self.linear(x) + + quantizer = X86InductorQuantizer().set_global( + xiq.get_default_x86_inductor_quantization_config() + ) + quantizer.set_module_type_qconfig( + torch.nn.Linear, xiq.get_x86_inductor_linear_dynamic_fp16_config() + ) + bias_list = [True, False] + input_ndim_list = [2, 3] + x_contig_list = [True, False] + cases = itertools.product(bias_list, input_ndim_list, x_contig_list) + for bias, input_ndim, x_contig in cases: + x_shape = (4, 256) if input_ndim == 2 else (4, 1, 256) + x = torch.randn(x_shape) + if not x_contig: + x = x[0::2, ...] + mod = M(bias, use_relu).eval() + + def matcher_check_fn(): + self.assertEqual( + counters["inductor"]["qlinear_weight_prepack_matcher_count"], 1 + ) + # Matched nodes: + # (1) w to fp16, (2) w to fp32, (3) permute w, (4) mm/addmm/bmm + # If x.ndim == 3 and x is contiguous, two view nodes are added. + # If x.ndim == 3 and x is not contiguous, two expand nodes and one add node are added. + nodes_count = 4 + if input_ndim > 2: + if x_contig: + nodes_count += 2 + else: + nodes_count += 3 if bias else 2 + if use_relu: + nodes_count += 1 + self.assertEqual( + counters["inductor"]["qlinear_weight_prepack_matcher_nodes"], + nodes_count, + ) + + self._test_common( + mod, + (x,), + atol=1e-2, + rtol=1e-2, + matcher_check_fn=matcher_check_fn, + check_quantization=True, + quantizer=quantizer, + ) + linear_op_str = ( + "torch.ops.onednn.linear_relu_dynamic_fp16.default" + if use_relu + else "torch.ops.onednn.linear_dynamic_fp16.default" + ) + self._test_code_common( + mod, + (x,), + [linear_op_str], + ["torch.ops.aten.addmm.default", "torch.ops.aten.mm.default"], + check_quantization=True, + quantizer=quantizer, + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_linear_dynamic_fp16(self): + self._test_linear_dynamic_fp16_helper(use_relu=False) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_linear_relu_dynamic_fp16(self): + self._test_linear_dynamic_fp16_helper(use_relu=True) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + # TODO: investigate options of torch.compile in fbcode + @unittest.skipIf(IS_FBCODE, "Failing in fbcode") + @parametrize("has_bias", [True, False]) + @parametrize("dtype", [torch.float, torch.bfloat16]) + @parametrize("per_channel_quant", [True, False]) + @parametrize("dynamic", [True, False]) + def test_smooth_quant_with_int_mm( + self, has_bias, dtype, per_channel_quant, dynamic + ): + r""" + This testcase check if we can match the SmoothQuant int8 linear pattern from Torchao. + The pattern is: + (no bias) reshape -> _int_mm -> convert_element_type -> (expand -> mul) -> mul -> reshape + or + (with bias) pattern_no_bias -> add -> reshape -> reshape + """ + if dtype == torch.bfloat16 and not torch.ops.mkldnn._is_mkldnn_bf16_supported(): + return + M = 16 + in_feature = 32 + out_feature = 64 + q_min, q_max = -32, 31 + + class Mod(torch.nn.Module): + def __init__( + self, dtype: torch.dtype, has_bias: bool, per_channel_quant: bool + ): + super().__init__() + self.dtype = dtype + self.has_bias = has_bias + self.b = torch.randint( + q_min, q_max, [in_feature, out_feature], dtype=torch.int8 + ) + self.per_channel_quant = per_channel_quant + a_scale_per_tensor = torch.rand([1], dtype=dtype) * 0.01 + 0.01 + a_scale_per_channel = torch.rand([M, 1], dtype=dtype) * 0.01 + 0.01 + self.a_scale = ( + a_scale_per_channel + if self.per_channel_quant + else a_scale_per_tensor + ) + self.b_scale = torch.rand([out_feature]) * 0.01 + 0.01 + self.b_scale = self.b_scale.to(dtype) + self.bias = torch.rand([out_feature], dtype=dtype) if has_bias else None + + def forward(self, a): + out_shape = a.shape[:-1] + (self.b.size(-1),) + a_reshaped = a.reshape(-1, a.size(-1)) + c = torch._int_mm(a_reshaped, self.b) + c = c.to(self.dtype) + c_shape = c.shape + a_scale = self.a_scale.expand(c.shape) + c = c * a_scale + c = c * self.b_scale + if self.has_bias: + c = c.reshape([1, *list(c_shape)]) + c = c + self.bias + c = c.reshape(c_shape) + c = c.reshape(out_shape) + return c + + mod = Mod(dtype, has_bias, per_channel_quant).eval() + a = torch.randint(q_min, q_max, [1, M, in_feature], dtype=torch.int8) + + def matcher_check_fn(): + self.assertEqual( + counters["inductor"]["qlinear_weight_prepack_matcher_count"], 1 + ) + if dynamic: + nodes_count = 10 if has_bias else 7 + else: + nodes_count = 7 if has_bias else 6 + self.assertEqual( + counters["inductor"]["qlinear_weight_prepack_matcher_nodes"], + nodes_count, + ) + + self._test_common( + mod, + (a,), + matcher_check_fn=matcher_check_fn, + check_autocast=dtype, + compile_options={"dynamic": dynamic}, + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + # TODO: investigate options of torch.compile in fbcode + @unittest.skipIf(IS_FBCODE, "Failing in fbcode") + @parametrize("has_bias", [True, False]) + @parametrize("dtype", [torch.float, torch.bfloat16]) + @parametrize("dynamic", [True, False]) + @parametrize("reshape_a", [True, False]) + @parametrize( + "M", + [ + 1, + 32, + ], + ) + @parametrize("inplace_add", [True, False]) + @parametrize("expand_a_scale", [True, False]) + def test_da8w8_sym_act_sym_wgt_with_int_mm( + self, has_bias, dtype, dynamic, reshape_a, M, inplace_add, expand_a_scale + ): + r""" + This testcase check if we can match the int8_dynamic_activation_int8_weight int8 linear pattern from torchao, + when activation is symmetrically quantized dynamically & weights are symmetrically quantized (statically) + The pattern is: + (no bias) _int_mm -> convert_element_type -> ([expand_a] -> mul) -> mul + or + (with bias) pattern_no_bias -> add + Expansion of the scale of activation is optional. + The pattern depiction doesn't mean that convert_element_type output is fed into expand_a as input, + but simply that activation scale may be applied after an expand operation on it. + """ + if dtype == torch.bfloat16 and not torch.ops.mkldnn._is_mkldnn_bf16_supported(): + return + in_feature = 32 + out_feature = 64 + q_min, q_max = -32, 31 + # we only test for qlinear_binary in this case + test_for_pointwise_binary = ( + True + if M == 1 + and inplace_add + and not expand_a_scale + and not dynamic + and not has_bias + else False + ) + if test_for_pointwise_binary and not IS_X86: + self.skipTest("Some UTs are only supported on x86_64 CPUs") + + class Mod(torch.nn.Module): + def __init__(self, dtype: torch.dtype, has_bias: bool): + super().__init__() + self.dtype = dtype + self.has_bias = has_bias + self.b = torch.randint( + q_min, q_max, [in_feature, out_feature], dtype=torch.int8 + ) + self.a_scale = torch.rand([M, 1], dtype=dtype) * 0.01 + 0.01 + self.b_scale = torch.rand([out_feature]) * 0.01 + 0.01 + self.b_scale = self.b_scale.to(dtype) + self.bias = torch.rand([out_feature], dtype=dtype) if has_bias else None + self.additive = torch.rand([M, out_feature], dtype=dtype) + + def forward(self, a): + if reshape_a: + a_reshaped = a.reshape(-1, a.size(-1)) + else: + a_reshaped = a + c = torch._int_mm(a_reshaped, self.b) + c = c.to(self.dtype) + if expand_a_scale: + a_scale = self.a_scale.expand(c.shape) + else: + a_scale = self.a_scale + c = c * a_scale + c = c * self.b_scale + if self.has_bias: + c = c + self.bias + elif inplace_add and test_for_pointwise_binary: + # When M is 1, dynamic shapes are enabled with torch.compile, has_bias is False, + # expand_a_scale is False and inplace_add is true, + # the output's outermost dim's stride can't be determined due to some Inductor bug. + c.add_(self.additive) + return c + + mod = Mod(dtype, has_bias).eval() + a = torch.randint(q_min, q_max, [M, in_feature], dtype=torch.int8) + + def matcher_check_fn(): + self.assertEqual( + counters["inductor"]["qlinear_weight_prepack_matcher_count"], 1 + ) + + self._test_common( + mod, + (a,), + matcher_check_fn, + check_autocast=dtype, + compile_options={"dynamic": dynamic}, + ) + if test_for_pointwise_binary: + self.assertEqual(counters["inductor"]["qlinear_binary_matcher_count"], 1) + + +@dynamo_config.patch( + { + "dynamic_shapes": True, + "assume_static_by_default": False, + "specialize_float": True, + } +) +@unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_8, "Requires torch 2.8+") +class TestDynamicPatternMatcher(TestPatternMatcherBase): + def test_qconv2d_maxpool2d_linear_dynamic_cpu(self, include_ops=None): + r""" + This testcase will quantize a single Conv2d->Maxpool2d->Linear module + with dynamic batch size input. + """ + + class M(torch.nn.Module): + def __init__( + self, + **kwargs, + ): + super().__init__() + self.conv = torch.nn.Conv2d( + 3, 16, (2, 2), stride=(1, 1), padding=(1, 1) + ) + self.relu = torch.nn.ReLU() + self.maxpool2d = torch.nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.avgpool = torch.nn.AdaptiveAvgPool2d((1, 1)) + self.linear = torch.nn.Linear(16, 16) + + def forward(self, x): + temp = self.relu(self.conv(x)) + temp = self.maxpool2d(temp) + temp = self.avgpool(temp) + temp = torch.flatten(temp, 1) + return self.linear(temp) + + mod = M().eval() + v = torch.randn((2, 3, 8, 8), dtype=torch.float32, requires_grad=False).add(1) + if include_ops is None: + include_ops = [ + "torch.ops.onednn.qconv_pointwise", + "torch.ops.quantized.max_pool2d", + "torch.ops.onednn.qlinear_pointwise", + ] + exclude_ops = [] + self._test_code_common( + mod, + (v,), + include_ops, + exclude_ops, + check_quantization=True, + check_dynamic=True, + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_qat_bn_conv2d(self): + r""" + This testcase will quantize a single BN Conv2d module with qat flow. + """ + + class M(torch.nn.Module): + def __init__( + self, + ): + super().__init__() + self.conv = torch.nn.Conv2d(3, 3, 3) + self.bn1 = torch.nn.BatchNorm2d(3) + self.bn2 = torch.nn.BatchNorm2d(3) + + def forward(self, x): + x = self.conv(self.bn1(x)) + return self.bn2(x) + + mod = M().train() + v = torch.randn((1, 3, 8, 8), dtype=torch.float32, requires_grad=True).add(1) + + def matcher_check_fn(): + self.assertEqual( + counters["inductor"]["qconv_weight_prepack_matcher_count"], 1 + ) + + self._test_common( + mod, + (v,), + matcher_check_fn, + check_quantization=True, + is_qat=True, + ) + + @skipIfNoDynamoSupport + @skipIfNoONEDNN + def test_q_attention_block(self): + class SelfAttnLikeModule(torch.nn.Module): + def __init__( + self, + input_dim, + transpose_for_score=False, + num_attention_heads=None, + attention_head_size=None, + ) -> None: + super().__init__() + self.input_dim = input_dim + self.q_proj = torch.nn.Linear(input_dim, input_dim, bias=False) + self.k_proj = torch.nn.Linear(input_dim, input_dim, bias=False) + self.v_proj = torch.nn.Linear(input_dim, input_dim, bias=False) + self.softmax = torch.nn.Softmax(dim=-1) + self.transpose_for_score = transpose_for_score + if self.transpose_for_score: + assert num_attention_heads is not None + assert attention_head_size is not None + self.num_attention_heads = num_attention_heads + self.attention_head_size = attention_head_size + + def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward(self, x): + q = self.q_proj(x) + k = self.k_proj(x) + v = self.v_proj(x) + if self.transpose_for_score: + q = self.transpose_for_scores(q) + k = self.transpose_for_scores(k) + v = self.transpose_for_scores(v) + scores = torch.matmul(q, k.transpose(-1, -2)) / (self.input_dim**0.5) + attention = self.softmax(scores) + weighted = torch.matmul(attention, v) + return weighted + + for annotate_matmul in [False, True]: + mod = SelfAttnLikeModule( + input_dim=64 * 16, + transpose_for_score=True, + num_attention_heads=16, + attention_head_size=64, + ).eval() + v = torch.randn(2, 384, 1024) + + def matcher_check_fn(): + self.assertEqual( + counters["inductor"]["qlinear_weight_prepack_matcher_count"], 3 + ) + self.assertEqual( + counters["inductor"]["qlinear_unary_matcher_count"], + 3 if annotate_matmul and not TEST_ACL else 0, + ) + + quantizer = X86InductorQuantizer() + quantizer.set_global(xiq.get_default_x86_inductor_quantization_config()) + if annotate_matmul: + quantizer.set_function_type_qconfig( + torch.matmul, quantizer.get_global_quantization_config() + ) + + self._test_common( + mod, + (v,), + matcher_check_fn, + check_quantization=True, + quantizer=quantizer, + ) + + +instantiate_parametrized_tests(TestPatternMatcher) +if __name__ == "__main__": + if IS_LINUX and HAS_CPU and torch.backends.mkldnn.is_available(): + # set weight_prepack = False to skip fusion passes in pytorch core + import torch._inductor.config + + torch._inductor.config.cpp.weight_prepack = False + run_tests() diff --git a/torchao/quantization/pt2e/inductor_passes/__init__.py b/torchao/quantization/pt2e/inductor_passes/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/torchao/quantization/pt2e/inductor_passes/x86.py b/torchao/quantization/pt2e/inductor_passes/x86.py new file mode 100644 index 0000000000..4ccb2a1f31 --- /dev/null +++ b/torchao/quantization/pt2e/inductor_passes/x86.py @@ -0,0 +1,2852 @@ +# mypy: allow-untyped-decorators +# mypy: allow-untyped-defs +import copy +import functools +import itertools +from typing import Any + +import torch +from torch._dynamo.utils import counters +from torch._inductor.fx_passes.freezing_patterns import register_freezing_graph_pattern +from torch._inductor.pattern_matcher import ( + Arg, + CallFunction, + KeywordArg, + Match, + filter_nodes, +) +from torch.fx.experimental.symbolic_shapes import has_free_symbols +from torch.fx.node import map_arg + +aten = torch.ops.aten +prims = torch.ops.prims +quantized_decomposed = torch.ops.quantized_decomposed +quantized = torch.ops.quantized + +# Only for per tensor quant since permute may changes the channel idx +_PER_TENSOR_QUANTIZE_OPS = [ + quantized_decomposed.quantize_per_tensor.default, + quantized_decomposed.quantize_per_tensor.tensor, +] + +_VIEW_OPS = [ + aten.transpose.int, + aten.permute.default, + aten.view.default, +] + +""" +The quantization.py file primarily incorporates passes related to quantization fusion +in inductor, includes: +1. Dequant Promotion; +2. Conv/GEMM weight prepack with oneDNN Library; +3. Conv/GEMM quantization fusion with output quant node (if have); +4. Other pointwise operators' quantization fusion like: qmaxpool2d, qcat and more; + +It also involves int8-mixed-fp32 and int8-mixed-bf16 quantization. The main difference +of patterns for int8-mixed-bf16, comparing with int8-mixed-fp32, is +1. There is to(dtype=torch.bfloat16) node at the inputs of activation and weight for Conv/GEMM. +2. There is to(dtype=torch.float32) node at the outputs of Conv/GEMM before inputs to next quant node. +Refer to: https://github.com/pytorch/pytorch/issues/111640 for detail design of int8-mixed-bf16 +quantization. +""" + + +def _get_pattern_output_dtype(match: Match): + """ + Get the pattern's output dtype from node's meta + Assume only 1 output node in this matched pattern. + """ + pattern_output_nodes = match.output_nodes() + assert len(pattern_output_nodes) == 1 + output_node = pattern_output_nodes[0] + assert isinstance(output_node, torch.fx.Node) + output_dtype = output_node.meta["val"].dtype + assert output_dtype in [torch.int8, torch.uint8, torch.float32, torch.bfloat16] + return output_dtype + + +def _may_generate_pattern_with_dtype_convert( + pattern, dtype=Arg(), with_dtype_convert=True, users=1 +): + if with_dtype_convert: + return CallFunction( + prims.convert_element_type.default, + pattern, + dtype, + _users=users, + ) + else: + return pattern + + +def _may_generate_pattern_with_reshape(pattern, reshape_size=Arg(), with_reshape=True): + if with_reshape: + return CallFunction( + torch.ops.aten.reshape.default, + pattern, + reshape_size, + ) + else: + return pattern + + +def _generate_linear_t_pattern( + _dequant_per_channel_pattern, + dtype, +): + assert dtype in [torch.float32, torch.bfloat16] + t_pattern = CallFunction( + aten.permute.default, + _may_generate_pattern_with_dtype_convert( + _dequant_per_channel_pattern, + KeywordArg("autocast_wgt_dtype"), + dtype == torch.bfloat16, + ), + KeywordArg("permute_axes"), + ) + return t_pattern + + +def _unary_fusion_pattern(unary_fusion, call_fn, users, is_bf16): + # only insert to_dtype if is_bf16 is True + computation_call = _may_generate_pattern_with_dtype_convert( + call_fn, dtype=KeywordArg("to_float"), with_dtype_convert=is_bf16, users=users + ) + return unary_fusion(computation_call) + + +def get_dequantize_per_tensor_activation_pattern(is_tensor_overload=False): + dequantize_per_tensor_activation_pattern = CallFunction( + quantized_decomposed.dequantize_per_tensor.tensor + if is_tensor_overload + else quantized_decomposed.dequantize_per_tensor.default, + KeywordArg("x"), + KeywordArg("x_scale"), + KeywordArg("x_zp"), + KeywordArg("x_quant_min"), + KeywordArg("x_quant_max"), + KeywordArg("x_dq_dtype"), + ) + return dequantize_per_tensor_activation_pattern + + +dequantize_per_channel_weight_pattern = CallFunction( + quantized_decomposed.dequantize_per_channel.default, + KeywordArg("q_weight"), + KeywordArg("w_scale"), + KeywordArg("w_zp"), + KeywordArg("w_axis"), + KeywordArg("w_quant_min"), + KeywordArg("w_quant_max"), + KeywordArg("w_dtype"), +) + +dequantize_per_channel_to_bf16_weight_pattern = ( + _may_generate_pattern_with_dtype_convert( + dequantize_per_channel_weight_pattern, + KeywordArg("autocast_wgt_dtype"), + ) +) + +dequantize_per_channel_clone_weight_pattern = CallFunction( + aten.clone.default, + dequantize_per_channel_weight_pattern, + memory_format=KeywordArg("memory_format"), +) + +dequantize_per_channel_to_bf16_clone_weight_pattern = CallFunction( + aten.clone.default, + dequantize_per_channel_to_bf16_weight_pattern, + memory_format=KeywordArg("memory_format"), +) + + +def get_qconv_pt2e_pattern(users=1): + return CallFunction( + torch.ops.onednn.qconv_pointwise.default, + KeywordArg("x"), + KeywordArg("x_scale"), + KeywordArg("x_zp"), + KeywordArg("packed_weight"), + KeywordArg("w_scale"), + KeywordArg("w_zp"), + KeywordArg("b"), + KeywordArg("stride"), + KeywordArg("padding"), + KeywordArg("dilation"), + KeywordArg("groups"), + KeywordArg("output_scale"), + KeywordArg("output_zero_point"), + KeywordArg("output_dtype"), + KeywordArg("postop_name"), + KeywordArg("postop_args"), + KeywordArg("postop_algorithm"), + _users=users, + ) + + +def get_qconv2d_binary_pt2e_pattern(users=1): + return CallFunction( + torch.ops.onednn.qconv2d_pointwise.binary, + KeywordArg("x"), + KeywordArg("x_scale"), + KeywordArg("x_zp"), + KeywordArg("packed_weight"), + KeywordArg("w_scale"), + KeywordArg("w_zp"), + KeywordArg("accum"), + KeywordArg("b"), + KeywordArg("stride"), + KeywordArg("padding"), + KeywordArg("dilation"), + KeywordArg("groups"), + KeywordArg("output_scale"), + KeywordArg("output_zero_point"), + KeywordArg("output_dtype"), + KeywordArg("accum_scale"), + KeywordArg("accum_zero_point"), + KeywordArg("binary_op_name"), + KeywordArg("alpha"), + KeywordArg("unary_op_name"), + KeywordArg("unary_op_args"), + KeywordArg("unary_op_algorithm"), + _users=users, + ) + + +def get_qlinear_pt2e_pattern(x_scale_zp_are_tensors, users=1): + qlinear_op = ( + torch.ops.onednn.qlinear_pointwise.tensor + if x_scale_zp_are_tensors + else torch.ops.onednn.qlinear_pointwise.default + ) + return CallFunction( + qlinear_op, + KeywordArg("x"), + KeywordArg("x_scale"), + KeywordArg("x_zp"), + KeywordArg("packed_weight"), + KeywordArg("w_scale"), + KeywordArg("w_zp"), + KeywordArg("b"), + KeywordArg("output_scale"), + KeywordArg("output_zero_point"), + KeywordArg("output_dtype"), + KeywordArg("postop_name"), + KeywordArg("postop_args"), + KeywordArg("postop_algorithm"), + _users=users, + ) + + +def get_qlinear_binary_pt2e_pattern(x_scale_zp_are_tensors, users=1): + qlinear_op = ( + torch.ops.onednn.qlinear_pointwise.binary_tensor + if x_scale_zp_are_tensors + else torch.ops.onednn.qlinear_pointwise.binary + ) + return CallFunction( + qlinear_op, + KeywordArg("x"), + KeywordArg("x_scale"), + KeywordArg("x_zp"), + KeywordArg("packed_weight"), + KeywordArg("w_scale"), + KeywordArg("w_zp"), + KeywordArg("x_2"), + KeywordArg("b"), + KeywordArg("output_scale"), + KeywordArg("output_zero_point"), + KeywordArg("output_dtype"), + KeywordArg("x2_scale"), + KeywordArg("x2_zp"), + KeywordArg("binary_op_name"), + KeywordArg("alpha"), + KeywordArg("unary_op_name"), + KeywordArg("unary_op_args"), + KeywordArg("unary_op_algorithm"), + _users=users, + ) + + +dequantize_accum_pattern = CallFunction( + quantized_decomposed.dequantize_per_tensor.default, + KeywordArg("accum"), + KeywordArg("accum_scale"), + KeywordArg("accum_zp"), + Arg(), + Arg(), + KeywordArg("accum_dq_dtype"), +) + + +def generate_pattern_with_binary( + binary_post_op, + computation_call, + extra_input_pattern, + dtype_convert=False, + swap_inputs=False, +): + binary_pattern = ( + CallFunction( + binary_post_op, + extra_input_pattern, + computation_call, + ) + if swap_inputs + else CallFunction( + binary_post_op, + computation_call, + extra_input_pattern, + ) + ) + return _may_generate_pattern_with_dtype_convert( + binary_pattern, + KeywordArg("convert_dtype_after_inplace_add"), + dtype_convert, + ) + + +def generate_pattern_with_unary(computation_call, unary_post_op): + if unary_post_op is not None: + return CallFunction( + unary_post_op, + computation_call, + ) + return computation_call + + +def generate_pattern_with_output_quant(computation_call, with_dtype_convert=False): + quantized_op_output_pattern_pt2e = CallFunction( + quantized_decomposed.quantize_per_tensor.default, + _may_generate_pattern_with_dtype_convert( + computation_call, + Arg(), + with_dtype_convert, + ), + KeywordArg("o_inv_scale"), + KeywordArg("o_zp"), + KeywordArg("o_qmin"), + KeywordArg("o_qmax"), + KeywordArg("o_dtype"), + ) + return quantized_op_output_pattern_pt2e + + +def _check_node_kwarg_arg_value(check_node, kwarg_name, args_index, expected_value): + if kwarg_name in check_node.kwargs: + actual_value = check_node.kwargs[kwarg_name] + return actual_value == expected_value + else: + assert len(check_node.args) >= (args_index + 1) + actual_value = check_node.args[args_index] + return actual_value == expected_value + + +def _is_valid_quantized_conv_optimization_pattern(): + def fn(match): + output_dtype = _get_pattern_output_dtype(match) + if output_dtype in [torch.float32, torch.bfloat16]: + # Only keep matched pattern with same output_dtype + qconv_node_after_weight_prepack = filter_nodes( + match.nodes, torch.ops.onednn.qconv_pointwise + )[0] + return _check_node_kwarg_arg_value( + qconv_node_after_weight_prepack, "output_dtype", 13, output_dtype + ) + return True + + return fn + + +def _is_valid_qconv_post_op_fusion_pattern(has_binary_post_op=False): + return ( + _is_valid_qconv_binary_optimization_pattern() + if has_binary_post_op + else _is_valid_quantized_conv_optimization_pattern() + ) + + +def _is_valid_quantized_linear_optimization_pattern(): + def fn(match): + output_dtype = _get_pattern_output_dtype(match) + if output_dtype in [torch.float32, torch.bfloat16]: + # Only keep matched pattern with same output_dtype + qlinear_node_after_weight_prepack = filter_nodes( + match.nodes, torch.ops.onednn.qlinear_pointwise + )[0] + return _check_node_kwarg_arg_value( + qlinear_node_after_weight_prepack, "output_dtype", 9, output_dtype + ) + return True + + return fn + + +def _is_valid_qlinear_post_op_fusion_pattern(has_binary_post_op=False): + return ( + _is_valid_qlinear_binary_optimization_pattern() + if has_binary_post_op + else _is_valid_quantized_linear_optimization_pattern() + ) + + +def _is_valid_qconv_binary_optimization_pattern(): + return _is_valid_quantized_op_binary_optimization_pattern( + torch.ops.onednn.qconv_pointwise + ) + + +def _is_valid_qlinear_binary_optimization_pattern(): + return _is_valid_quantized_op_binary_optimization_pattern( + torch.ops.onednn.qlinear_pointwise, + # we don't insert q-dq for extra input due to accuracy issues + extra_input_from_dequant=False, + ) + + +def _is_valid_quantized_op_binary_optimization_pattern( + qop, extra_input_from_dequant=True +): + # Check if it's a valid Binary Pattern for qconv2d and qlinear: + # * qop_pointwise should only has one users + # * If extra_input_from_dequant is True, extra input of binary node should come from dequant pattern + # * the two inputs of binary node should have attribute "meta" and should be tensors + # * the two inputs of binary node should have the same shape + # * All users of the extra input in this pattern should be + # ancestor nodes of the compute node, except for the binary node + # connected to the compute node. + def fn(match): + output_dtype = _get_pattern_output_dtype(match) + compute_node = filter_nodes(match.nodes, qop)[0] + # qop_pointwise should only have one user + if len(compute_node.users) != 1: + return False + binary_node_inputs = next(iter(compute_node.users)).args + assert len(binary_node_inputs) == 2, "Expects binary node with 2 inputs" + if output_dtype in [torch.float32, torch.bfloat16]: + extra_input_of_binary_node = None + for arg in binary_node_inputs: + if arg != compute_node: + extra_input_of_binary_node = arg + break + assert extra_input_of_binary_node is not None + # Extra input of binary node comes from dequant pattern + if extra_input_from_dequant and ( + (not isinstance(extra_input_of_binary_node, torch.fx.Node)) + or ( + extra_input_of_binary_node.target + != quantized_decomposed.dequantize_per_tensor.default + ) + ): + return False + + # the two inputs of binary node should have attribute "meta" and should be tensors + if not ( + hasattr(binary_node_inputs[0], "meta") + and isinstance(binary_node_inputs[0].meta.get("val", None), torch.Tensor) # type: ignore[union-attr] + ) or not ( + hasattr(binary_node_inputs[1], "meta") + and isinstance(binary_node_inputs[1].meta.get("val", None), torch.Tensor) # type: ignore[union-attr] + ): + return False + # the two inputs of binary node should have the same shape + if ( + binary_node_inputs[0].meta["val"].size() # type: ignore[union-attr] + != binary_node_inputs[1].meta["val"].size() # type: ignore[union-attr] + ): + return False + + # All users of the extra input in this pattern should be + # ancestor nodes of the compute node, except for the binary node + # connected to the compute node. + + from torch._inductor.fx_passes.mkldnn_fusion import _get_remaining_users + + extra_input_of_pattern = ( + match.kwargs["other"] + if "other" in match.kwargs + else ( + match.kwargs["accum"] + if (output_dtype in [torch.uint8, torch.int8]) + or (not extra_input_from_dequant) + else match.kwargs["accum_after_dequant"] + ) + ) + if ( + len(_get_remaining_users(extra_input_of_pattern, compute_node)) > 1 + or extra_input_of_pattern == compute_node.args[0] + ): + return False + return True + + return fn + + +def _is_valid_dequant_promotion_pattern(dtype=torch.float32): + def _inner(match): + assert dtype in [torch.float32, torch.bfloat16] + dequant_pattern_end_node = match.output_node() + if dequant_pattern_end_node.target not in [ + quantized_decomposed.dequantize_per_tensor.default, + quantized_decomposed.dequantize_per_tensor.tensor, + prims.convert_element_type.default, + aten.reshape.default, + ]: + return False + + if dequant_pattern_end_node.target is aten.reshape.default: + dequant_node = ( + dequant_pattern_end_node.args[ + 0 + ] # pattern: linear <- reshape <- dequant + if dtype == torch.float32 + else dequant_pattern_end_node.args[0].args[ + 0 + ] # pattern: linear <- reshape <- to_bf16 <- dequant + ) + else: + dequant_node = ( + dequant_pattern_end_node # pattern: linear <- dequant + if dtype == torch.float32 + else dequant_pattern_end_node.args[ + 0 + ] # pattern: linear <- to_bf16 <- dequant + ) + + if ( + dequant_node.target + in [ + quantized_decomposed.dequantize_per_tensor.default, + quantized_decomposed.dequantize_per_tensor.tensor, + ] + and len(list(dequant_pattern_end_node.users)) > 1 + ): + # If dequant pattern has more than 1 users, then do dequant promoted + return True + return False + + return _inner + + +def _register_dequant_promotion_pass(pattern, pass_number, dtype=torch.float32): + @register_freezing_graph_pattern( + pattern, + extra_check=_is_valid_dequant_promotion_pattern(dtype), + pass_number=pass_number, + ) + def dequant_promotion(match: Match, *args, **kwargs): + # Dequant_promotion will transform + # graph 1: + # quant + # + - - - | - - - + + # | dequant | + # | / \ | + # | node1 node2 | + # + - | - - - | - + + # quant quant + # into: + # graph 2: + # quant + # + - - / - \ - - + + # |dequant dequant| + # | | | | + # | node1 node2 | + # + - | - - - | - + + # quant quant + # In graph 1, the dequant node is shared by node1 and node2, + # as a result, neither node1 nor node2 could form an int8 + # fusion pattern. + # After this transformation, the graph 2 could hit the int8 + # fusion pattern: dequant-node-quant, respectively for + # node1 and node2. + assert dtype in [torch.float32, torch.bfloat16] + + def clone_to_new_node(graph, source_node, user_node): + # Clone the source_node to a new node + # Replace user_node's input from source_node to new_node + assert source_node.op == "call_function", ( + "clone_to_new_node only support node.op call_function" + ) + with graph.inserting_before(user_node): + new_node = graph.call_function( + source_node.target, + args=source_node.args, + kwargs=source_node.kwargs, + ) + new_node.meta = copy.copy(source_node.meta) + user_node.replace_input_with(source_node, new_node) + return new_node + + # Find the start node and end node of a dequant pattern + # * End node should be the match.output_node() + # * Start node should be the node of dequantize_per_tensor + dequant_pattern_end_node = match.output_node() + assert dequant_pattern_end_node.target in [ + quantized_decomposed.dequantize_per_tensor.default, + quantized_decomposed.dequantize_per_tensor.tensor, + prims.convert_element_type.default, + aten.reshape.default, + ] + + # For a dequant pattern, we should expect see the node list as: + # * OPT(aten.reshape.default) + # * OPT(prims.convert_element_type.default) (to_bf16) + # * dequantize_per_tensor + def _find_first_node_in_dequant_pattern(_node): + if _node.target in [ + quantized_decomposed.dequantize_per_tensor.default, + quantized_decomposed.dequantize_per_tensor.tensor, + ]: + # For a dequant pattern, we expect the start node is a dequantize_per_tensor node + return _node + else: + assert len(_node.args) >= 1, ( + "In in dequant pattern, each node should have more than 1 arg." + ) + return _find_first_node_in_dequant_pattern(_node.args[0]) + + dequant_pattern_start_node = _find_first_node_in_dequant_pattern( + dequant_pattern_end_node + ) + + assert dequant_pattern_start_node.target in [ + quantized_decomposed.dequantize_per_tensor.default, + quantized_decomposed.dequantize_per_tensor.tensor, + ] + + # Clone the dequant pattern for each user node + graph = match.graph + user_node_list = list(dequant_pattern_end_node.users) + for user_node in user_node_list[1:]: + _source_node = dequant_pattern_end_node + _user_node = user_node + while _source_node != dequant_pattern_start_node.args[0]: + _user_node = clone_to_new_node(graph, _source_node, _user_node) + _source_node = _source_node.args[0] # type: ignore[assignment] + + counters["inductor"]["dequant_promotion_matcher_count"] += 1 + counters["inductor"]["dequant_promotion_matcher_nodes"] += len(match.nodes) + + +def _is_valid_dequant_conv_pattern(dtype): + def _inner(match): + # Here we do some further check to ensure: + # 1. It's a conv2d node with dim of 4, since we only support lowering of conv2d now. + # 2. The dequant pattern has only 1 user of conv2d node. + # If these conditions don't meet, we will not + # insert weight prepack node into the matched pattern. + conv_node = match.output_node() + assert conv_node.target is aten.convolution.default + input_meta_value = conv_node.args[0].meta.get("val") + weight_meta_value = conv_node.args[1].meta.get("val") + for meta_value in [input_meta_value, weight_meta_value]: + if ( + meta_value is None + or (meta_value.device.type != "cpu" and meta_value.device.type != "xpu") + or meta_value.dim() not in [3, 4] + ): + # Only support conv1d/2d now + return False + + assert dtype in [torch.float32, torch.bfloat16] + + if dtype == torch.float32: + dequant_node = conv_node.args[0] + else: + convert_to_bf16 = conv_node.args[0] + dequant_node = convert_to_bf16.args[0] + + if len(list(dequant_node.users)) != 1: + # Ensure the dequant pattern only has 1 user + # since we will delete the dequant pattern here + return False + return True + + return _inner + + +def _register_qconv_weight_prepack_pass(pattern, pass_number, dtype=torch.float32): + @register_freezing_graph_pattern( + pattern, + extra_check=_is_valid_dequant_conv_pattern(dtype), + pass_number=pass_number, + ) + def qconv_weight_prepack(match: Match, *args, **kwargs): + """ + Match the pattern: + int8 activation + | + dequant_per_tensor + | + Conv2d <- optional(aten.clone.default) <- dequant_per_channel <- int8_weight + + Insert weight prepack node and change the pattern to: + int8 activation + | + onednn.qconv_pointwise <- onednn.qconv_prepack <- int8_weight + """ + assert dtype in [torch.float32, torch.bfloat16] + conv_node = match.output_node() + assert conv_node.target is aten.convolution.default + if dtype == torch.float32: + dequant_node = conv_node.args[0] + else: + convert_to_bf16 = conv_node.args[0] + dequant_node = convert_to_bf16.args[0] # type: ignore[union-attr] + has_clone_to_channel_last_node_in_pattern = ( + conv_node.args[1].target is aten.clone.default # type: ignore[union-attr] + ) + clone_node = ( + conv_node.args[1] if has_clone_to_channel_last_node_in_pattern else None + ) + + if dtype == torch.float32: + dequant_per_channel = ( + clone_node.args[0] # type: ignore[union-attr] + if has_clone_to_channel_last_node_in_pattern + else conv_node.args[1] + ) + else: + weight_to_bf16_node = ( + clone_node.args[0] # type: ignore[union-attr] + if has_clone_to_channel_last_node_in_pattern + else conv_node.args[1] + ) + dequant_per_channel = weight_to_bf16_node.args[0] # type: ignore[union-attr] + + assert ( + dequant_per_channel.target # type: ignore[union-attr] + is quantized_decomposed.dequantize_per_channel.default + ) + + # Activation QParams + qx, x_zp, x_scale = ( + kwargs["x"], + kwargs["x_zp"], + kwargs["x_scale"], + ) + + # Weight QParams + qw, w_scale, w_zp = ( + kwargs["q_weight"], + kwargs["w_scale"], + kwargs["w_zp"], + ) + + # Conv Params + bias, stride, padding, dilation, groups = ( + kwargs["b"], + kwargs["stride"], + kwargs["padding"], + kwargs["dilation"], + kwargs["groups"], + ) + + x_shape = qx.meta.get("tensor_meta").shape + if has_free_symbols(x_shape): + # For dynamic shape case, we can't get activation shape ahead of runtime. + x_shape = None + graph = match.graph + with graph.inserting_before(conv_node): + # Insert weight prepack node and the QConv node + packed_weight_inputs = ( + qw, + w_scale, + x_scale, + x_zp, + stride, + padding, + dilation, + groups, + x_shape, + ) + packed_weight_op = torch.ops.onednn.qconv_prepack + prepack_weight_node = graph.call_function( + packed_weight_op, args=packed_weight_inputs + ) + + new_args: tuple[Any, ...] = ( + qx, + x_scale, + x_zp, + prepack_weight_node, + w_scale, + w_zp, + bias, + stride, + padding, + dilation, + groups, + 1.0, # output_scale + 0, # output_zero_point + dtype, # output_dtype + "none", # attr + [], # scalars + "", # algorithm + ) + new_conv_node = graph.call_function( + torch.ops.onednn.qconv_pointwise.default, args=new_args + ) + conv_node.replace_all_uses_with(new_conv_node) + new_conv_node.meta.update(conv_node.meta) + + # Erase the original conv node + graph.erase_node(conv_node) + # Erase the dequant pattern + if dtype == torch.bfloat16: + graph.erase_node(convert_to_bf16) # type: ignore[possibly-undefined, arg-type] + graph.erase_node(dequant_node) # type: ignore[arg-type] + # Erase the dequant per channel pattern + if clone_node is not None: + graph.erase_node(clone_node) # type: ignore[arg-type] + if dtype == torch.bfloat16: + graph.erase_node(weight_to_bf16_node) # type: ignore[possibly-undefined, arg-type] + graph.erase_node(dequant_per_channel) # type: ignore[arg-type] + counters["inductor"]["qconv_weight_prepack_matcher_count"] += 1 + counters["inductor"]["qconv_weight_prepack_matcher_nodes"] += len( + match.nodes + ) + + +def _generate_dequant_convolution_node_pattern( + _dequant_per_channel_pattern, dtype=torch.float32 +): + assert dtype in [torch.float32, torch.bfloat16] + dequant_convolution_node_pattern = CallFunction( + aten.convolution.default, + _may_generate_pattern_with_dtype_convert( + get_dequantize_per_tensor_activation_pattern(), + KeywordArg("autocast_act_dtype"), + dtype == torch.bfloat16, + ), + _dequant_per_channel_pattern, + KeywordArg("b"), + KeywordArg("stride"), + KeywordArg("padding"), + KeywordArg("dilation"), + KeywordArg("is_transposed"), + KeywordArg("out_padding"), + KeywordArg("groups"), + ) + return dequant_convolution_node_pattern + + +def _generate_qconv_weight_prepack_patterns(dtype=torch.float32): + assert dtype in [torch.float32, torch.bfloat16] + return ( + _generate_dequant_convolution_node_pattern( + dequantize_per_channel_weight_pattern + if dtype == torch.float32 + else dequantize_per_channel_to_bf16_weight_pattern, + dtype, + ), + # There is another pattern due to the pass of convert_conv_weights_to_channels_last + # https://github.com/pytorch/pytorch/blob/07107919297db3f8ab37f11c12666b6d6d5f692e/torch/_inductor/freezing.py#L338-L362. + # Depend on some heuristics, it may or may not insert to(channel_last) node + # between convolution and dequant_per_channel node + _generate_dequant_convolution_node_pattern( + dequantize_per_channel_clone_weight_pattern + if dtype == torch.float32 + else dequantize_per_channel_to_bf16_clone_weight_pattern, + dtype, + ), + ) + + +def _get_linear_node(match, input_dim_exceeds_two, input_contiguous): + output_reshape_node = None + if input_dim_exceeds_two: + if input_contiguous: + output_reshape_node = match.output_node() + assert output_reshape_node.target is aten.reshape.default + linear_node = output_reshape_node.args[0] + else: + linear_nodes = filter_nodes(match.nodes, aten.bmm.default) + assert len(linear_nodes) == 1 + linear_node = linear_nodes[0] + else: + linear_node = match.output_node() + + assert linear_node.target in ( + aten.addmm.default, + aten.mm.default, + aten.bmm.default, + ) + return linear_node, output_reshape_node + + +def _get_linear_dq_node( + linear_node, input_index, dtype, input_dim_exceeds_two, input_contiguous +): + act_reshape_node = None + activation_to_bf16_node = None + act_expand_node = None + if input_dim_exceeds_two: + if input_contiguous: + act_reshape_node = linear_node.args[input_index] + assert act_reshape_node.target is aten.reshape.default + if dtype == torch.float32: + # pattern: linear -> reshape -> dequant + dequant_node = act_reshape_node.args[0] + else: + # pattern: linear -> reshape -> to_bf16 -> dequant + activation_to_bf16_node = act_reshape_node.args[0] + dequant_node = activation_to_bf16_node.args[0] + else: + # bmm pattern decomposed from linear when input dim exceeds 2 and not contiguous + act_expand_node = linear_node.args[input_index] + assert act_expand_node.target is aten.expand.default + if dtype == torch.float32: + dequant_node = act_expand_node.args[0] + else: + activation_to_bf16_node = act_expand_node.args[0] + dequant_node = activation_to_bf16_node.args[0] + else: + if dtype == torch.float32: + # pattern: linear -> dequant + dequant_node = linear_node.args[input_index] + else: + # pattern: linear -> to_bf16 -> dequant + activation_to_bf16_node = linear_node.args[input_index] + dequant_node = activation_to_bf16_node.args[0] + return dequant_node, act_reshape_node, activation_to_bf16_node, act_expand_node + + +def _is_valid_dequant_linear_pattern(dtype, input_dim_exceeds_two, input_contiguous): + def _inner(match): + # Check dequant pattern has only 1 user. + ( + linear_node, + _, + ) = _get_linear_node(match, input_dim_exceeds_two, input_contiguous) + + input_index = 1 if linear_node.target is aten.addmm.default else 0 + assert dtype in [torch.float32, torch.bfloat16] + ( + dequant_node, + _, + _, + _, + ) = _get_linear_dq_node( + linear_node, input_index, dtype, input_dim_exceeds_two, input_contiguous + ) + + assert dequant_node.target in [ + quantized_decomposed.dequantize_per_tensor.default, + quantized_decomposed.dequantize_per_tensor.tensor, + ] + + if len(list(dequant_node.users)) != 1: + # Ensure the dequant pattern only has 1 user + # since we will delete the dequant pattern here + return False + + # Extra check for bmm pattern + if input_dim_exceeds_two and not input_contiguous: + # Check for act + # Act expand size should be exactly same as act size + act_expand_size = match.kwargs["act_expand_size"] + act_node = match.kwargs["x"] + if not ( + hasattr(act_node, "meta") + and isinstance(act_node.meta.get("val", None), torch.Tensor) + and (act_node.meta["val"].size() == torch.Size(act_expand_size)) + ): + return False + + # Check for wgt + # wgt permute dims should be [1, 0] + wgt_permute_dims = match.kwargs["permute_axes"] + if wgt_permute_dims != [1, 0]: + return False + + # Check below wgt size items: + # wgt before expand should with dim 2 + # Expand size should with dim 3 + # Expand size[0] should same as act size[0] + # Expand size[1] should same as wgt size[1] + # Expand size[2] should same as wgt size[0] + qweight_node = match.kwargs["q_weight"] + wgt_expand_size = match.kwargs["wgt_expand_size"] + if not ( + hasattr(qweight_node, "meta") + and isinstance(qweight_node.meta.get("val", None), torch.Tensor) + and len(qweight_node.meta["val"].size()) == 2 + and len(wgt_expand_size) == 3 + and wgt_expand_size[0] == act_node.meta["val"].size()[0] + and wgt_expand_size[1] == qweight_node.meta["val"].size()[1] + and wgt_expand_size[2] == qweight_node.meta["val"].size()[0] + ): + return False + + return True + + return _inner + + +def _register_qlinear_weight_prepack_pass( + pattern, + pass_number, + dtype=torch.float32, + input_dim_exceeds_two=False, + input_contiguous=True, +): + @register_freezing_graph_pattern( + pattern, + extra_check=_is_valid_dequant_linear_pattern( + dtype, input_dim_exceeds_two, input_contiguous + ), + pass_number=pass_number, + ) + def qlinear_weight_prepack(match: Match, *args, **kwargs): + """ + Match the pattern: + int8 activation + | + dequant_per_tensor + | + mm/addmm <- t <- dequant_per_channel <- int8_weight + + Insert weight prepack node and change the pattern to: + int8 activation + | + onednn.qlinear_pointwise <- onednn.qlinear_prepack <- int8_weight + """ + assert dtype in [torch.float32, torch.bfloat16] + ( + linear_node, + output_reshape_node, + ) = _get_linear_node(match, input_dim_exceeds_two, input_contiguous) + input_index = 1 if linear_node.target is aten.addmm.default else 0 + weight_index = input_index + 1 + + ( + dequant_node, + act_reshape_node, + activation_to_bf16_node, + act_expand_node, + ) = _get_linear_dq_node( + linear_node, input_index, dtype, input_dim_exceeds_two, input_contiguous + ) + + if input_dim_exceeds_two and not input_contiguous: + wgt_expand_node = linear_node.args[weight_index] + assert wgt_expand_node.target is aten.expand.default + t_node = wgt_expand_node.args[0] + else: + t_node = linear_node.args[weight_index] + + if dtype == torch.float32: + dequant_per_channel = t_node.args[0] + else: + weight_to_bf16_node = t_node.args[0] + dequant_per_channel = weight_to_bf16_node.args[0] + assert ( + dequant_per_channel.target + is quantized_decomposed.dequantize_per_channel.default + ) + + # Activation QParams + qx, x_zp, x_scale = ( + kwargs["x"], + kwargs["x_zp"], + kwargs["x_scale"], + ) + + # Weight QParams + qw, w_scale, w_zp = ( + kwargs["q_weight"], + kwargs["w_scale"], + kwargs["w_zp"], + ) + + # Params + bias = kwargs["b"] if "b" in kwargs else None + + x_shape = qx.meta.get("tensor_meta").shape + if has_free_symbols(x_shape): + # For dynamic shape case, we can't get activation shape ahead of runtime. + x_shape = None + graph = match.graph + with graph.inserting_before(linear_node): + # Insert weight prepack node and the qlinear node + packed_weight_inputs = ( + qw, + x_shape, + ) + packed_weight_op = torch.ops.onednn.qlinear_prepack + prepack_weight_node = graph.call_function( + packed_weight_op, args=packed_weight_inputs + ) + + new_args: tuple[Any, ...] = ( + qx, + x_scale, + x_zp, + prepack_weight_node, + w_scale, + w_zp, + bias, + 1.0, # output_scale + 0, # output_zero_point + dtype, # output_dtype + "none", # post op name + [], # post op args + "", # post op algorithm + ) + Node = torch.fx.node.Node + if isinstance(x_scale, Node) and isinstance(x_zp, Node): + new_linear_node = graph.call_function( + torch.ops.onednn.qlinear_pointwise.tensor, args=new_args + ) + else: + new_linear_node = graph.call_function( + torch.ops.onednn.qlinear_pointwise.default, args=new_args + ) + if input_dim_exceeds_two: + if input_contiguous: + output_reshape_node.replace_all_uses_with(new_linear_node) + new_linear_node.meta.update(output_reshape_node.meta) + else: + if bias: + output_add_node_for_bias = match.output_node() + assert output_add_node_for_bias.target is aten.add.Tensor + output_add_node_for_bias.replace_all_uses_with(new_linear_node) + new_linear_node.meta.update(output_add_node_for_bias.meta) + else: + linear_node.replace_all_uses_with(new_linear_node) + new_linear_node.meta.update(linear_node.meta) + else: + linear_node.replace_all_uses_with(new_linear_node) + new_linear_node.meta.update(linear_node.meta) + + # Erase the original linear node + if input_dim_exceeds_two: + if input_contiguous: + graph.erase_node(output_reshape_node) + elif not input_contiguous and bias: + graph.erase_node(output_add_node_for_bias) # type: ignore[possibly-undefined] + graph.erase_node(linear_node) + if input_dim_exceeds_two: + if input_contiguous: + graph.erase_node(act_reshape_node) + else: + graph.erase_node(act_expand_node) + graph.erase_node(wgt_expand_node) # type: ignore[possibly-undefined] + if dtype == torch.bfloat16: + graph.erase_node(activation_to_bf16_node) + # Erase the dequant pattern + graph.erase_node(dequant_node) + # Erase the dequant per channel pattern + graph.erase_node(t_node) + if dtype == torch.bfloat16: + graph.erase_node(weight_to_bf16_node) # type: ignore[possibly-undefined] + graph.erase_node(dequant_per_channel) + + counters["inductor"]["qlinear_weight_prepack_matcher_count"] += 1 + counters["inductor"]["qlinear_weight_prepack_matcher_nodes"] += len( + match.nodes + ) + + +def _generate_dequant_linear_node_pattern( + _dequant_per_channel_pattern, + dtype=torch.float32, + input_dim_exceeds_two=False, + is_tensor_overload=False, +): + assert dtype in [torch.float32, torch.bfloat16] + t_pattern = _generate_linear_t_pattern(_dequant_per_channel_pattern, dtype) + dequant_linear_bias_pattern = _may_generate_pattern_with_reshape( + CallFunction( + aten.addmm.default, + KeywordArg("b"), + _may_generate_pattern_with_reshape( + _may_generate_pattern_with_dtype_convert( + get_dequantize_per_tensor_activation_pattern(is_tensor_overload), + KeywordArg("autocast_act_dtype"), + dtype == torch.bfloat16, + ), + KeywordArg("act_reshape_size"), + input_dim_exceeds_two, + ), + t_pattern, + ), + KeywordArg("output_reshape_size"), + input_dim_exceeds_two, + ) + dequant_linear_no_bias_pattern = _may_generate_pattern_with_reshape( + CallFunction( + aten.mm.default, + _may_generate_pattern_with_reshape( + _may_generate_pattern_with_dtype_convert( + get_dequantize_per_tensor_activation_pattern(is_tensor_overload), + KeywordArg("autocast_act_dtype"), + dtype == torch.bfloat16, + ), + KeywordArg("act_reshape_size"), + input_dim_exceeds_two, + ), + t_pattern, + ), + KeywordArg("output_reshape_size"), + input_dim_exceeds_two, + ) + return dequant_linear_bias_pattern, dequant_linear_no_bias_pattern + + +def _generate_dequant_bmm_node_pattern( + _dequant_per_channel_pattern, + dtype=torch.float32, + with_bias=False, + is_tensor_overload=False, +): + # When activation of linear dim exceed 2 and not contiguous + t_pattern = _generate_linear_t_pattern(_dequant_per_channel_pattern, dtype) + + assert dtype in [torch.float32, torch.bfloat16] + dequant_bmm_pattern = CallFunction( + aten.bmm.default, + CallFunction( + aten.expand.default, + _may_generate_pattern_with_dtype_convert( + get_dequantize_per_tensor_activation_pattern(is_tensor_overload), + KeywordArg("autocast_act_dtype"), + dtype == torch.bfloat16, + ), + KeywordArg("act_expand_size"), + ), + CallFunction( + aten.expand.default, + t_pattern, + KeywordArg("wgt_expand_size"), + ), + ) + + def _generate_pattern_with_output_add(_dequant_bmm_pattern, _with_bias): + if _with_bias: + return CallFunction( + aten.add.Tensor, + _dequant_bmm_pattern, + KeywordArg("b"), + ) + else: + return _dequant_bmm_pattern + + return _generate_pattern_with_output_add(dequant_bmm_pattern, with_bias) + + +def _generate_qlinear_weight_prepack_patterns( + dtype=torch.float32, + input_dim_exceeds_two=False, + input_contiguous=True, + with_bias=False, + is_tensor_overload=False, +): + if input_dim_exceeds_two and not input_contiguous: + return _generate_dequant_bmm_node_pattern( + dequantize_per_channel_weight_pattern, + dtype, + with_bias, + is_tensor_overload, + ) + else: + return _generate_dequant_linear_node_pattern( + dequantize_per_channel_weight_pattern, + dtype, + input_dim_exceeds_two, + is_tensor_overload, + ) + + +def _generate_linear_dynamic_fp16_pattern( + _dequant_weight_pattern, + input_dim_exceeds_two=False, + input_contiguous=True, + relu_fused=False, +): + dtype = torch.float32 + t_pattern = _generate_linear_t_pattern(_dequant_weight_pattern, dtype) + + if input_dim_exceeds_two and not input_contiguous: + # pattern is + # x -> expand -> bmm (-> add) (-> relu) + # w -> dequant -> permute -> expand / + pattern_no_bias = CallFunction( + aten.bmm.default, + CallFunction( + aten.expand.default, + KeywordArg("x"), + KeywordArg("act_expand_size"), + ), + CallFunction( + aten.expand.default, + t_pattern, + KeywordArg("wgt_expand_size"), + ), + ) + pattern_with_bias = CallFunction( + aten.add.Tensor, + pattern_no_bias, + KeywordArg("b"), + ) + if relu_fused: + pattern_with_bias = CallFunction(aten.relu.default, pattern_with_bias) + pattern_no_bias = CallFunction(aten.relu.default, pattern_no_bias) + return pattern_with_bias, pattern_no_bias + + x_pattern_with_reshape = _may_generate_pattern_with_reshape( + KeywordArg("x"), + KeywordArg("act_reshape_size"), + input_dim_exceeds_two, + ) + dequant_linear_bias_pattern = generate_pattern_with_unary( + _may_generate_pattern_with_reshape( + CallFunction( + aten.addmm.default, + KeywordArg("b"), + x_pattern_with_reshape, + t_pattern, + ), + KeywordArg("output_reshape_size"), + input_dim_exceeds_two, + ), + aten.relu.default if relu_fused else None, + ) + dequant_linear_no_bias_pattern = generate_pattern_with_unary( + _may_generate_pattern_with_reshape( + CallFunction( + aten.mm.default, + x_pattern_with_reshape, + t_pattern, + ), + KeywordArg("output_reshape_size"), + input_dim_exceeds_two, + ), + aten.relu.default if relu_fused else None, + ) + return dequant_linear_bias_pattern, dequant_linear_no_bias_pattern + + +def _register_dequant_promotion(): + dequant_pattern_cases = itertools.product( + [torch.float32, torch.bfloat16], [True, False], [True, False] + ) + for dtype, input_dim_exceeds_two, is_tensor_overload in dequant_pattern_cases: + # 4 dequantization patterns will be matched based on the dtype and input dimension size. + # Case 1: int8-mixed-fp32, input dim size is 2 + # Case 2: int8-mixed-fp32, input dim size exceeds 2 + # Case 3: int8-mixed-bf16, input dim size is 2 + # Case 4: int8-mixed-bf16, input dim size exceeds 2 + # quant + # + - - - - | - - - - + + # | dequant | + # | | | + # | OPT(to_bf16) | + # | | | + # | OPT(reshape) | + # | / \ | + # | node1 node2 | + # + - - | - - - | - - + + # OPT(reshape) OPT(reshape) + # + - - | - - - | - - + + # OPT(to_fp32) OPT(to_fp32) + # + - - | - - - | - - + + # quant quant + _register_dequant_promotion_pass( + _may_generate_pattern_with_reshape( + _may_generate_pattern_with_dtype_convert( + get_dequantize_per_tensor_activation_pattern( + is_tensor_overload=is_tensor_overload + ), + KeywordArg("autocast_act_dtype"), + dtype == torch.bfloat16, + ), + KeywordArg("act_reshape_size"), + with_reshape=input_dim_exceeds_two, + ), + pass_number=0, + dtype=dtype, + ) # pass_number=0 to run before weight prepack + + +def _register_qconv_weight_prepack(): + for dtype in [torch.float32, torch.bfloat16]: + weight_prepack_patterns = _generate_qconv_weight_prepack_patterns(dtype) + for weight_prepack_pattern in weight_prepack_patterns: + # Register to pass_number 1, so we can do dequant promotion in pass_number 0. + _register_qconv_weight_prepack_pass( + weight_prepack_pattern, pass_number=1, dtype=dtype + ) + + +def _register_qlinear_weight_prepack(): + # 6 Linear related patterns will be matched based on the dtype, input dimension size and input contiguous. + # Then convert the pattern into a QLinear node with int8_fp32/bf16. + # Case 1: int8-mixed-fp32, input dim size is 2 + # Case 2: int8-mixed-fp32, input dim size exceeds 2 and contiguous + # Case 3: int8-mixed-bf16, input dim size is 2 + # Case 4: int8-mixed-bf16, input dim size exceeds 2 and contiguous + + # + - - - - | - - - - - - | - - - - - + + # | dq_per_tensor dq_per_channel | + # | | | | + # | OPT(to_bf16) OPT(to_bf16) | + # | | | | + # | OPT(reshape) permute | + # | \ / | + # | addmm/mm | + # | | | + # | OPT(reshape) | + + # Case 5: int8-mixed-fp32, input dim size exceeds 2 and not contiguous + # Case 6: int8-mixed-bf16, input dim size exceeds 2 and not contiguous + + # + - - - - | - - - - - - | - - - - - + + # | dq_per_tensor dq_per_channel | + # | | | | + # | OPT(to_bf16) OPT(to_bf16) | + # | | | | + # | expand permute | + # | \ | | + # | expand | + # | / | + # | bmm | + # | | | + # | OPT(add) | + + linear_weight_prepack_cases = itertools.product( + [torch.float32, torch.bfloat16], [True, False], [True, False] + ) + + # Step 1: register patterns from mm and addmm + for dtype, input_dim_exceeds_two, is_tensor_overload in linear_weight_prepack_cases: + weight_prepack_patterns = _generate_qlinear_weight_prepack_patterns( + dtype, + input_dim_exceeds_two, + is_tensor_overload=is_tensor_overload, + ) + for weight_prepack_pattern in weight_prepack_patterns: + # Register to pass_number 1, so we can do dequant promotion in pass_number 0. + _register_qlinear_weight_prepack_pass( + weight_prepack_pattern, + pass_number=1, + dtype=dtype, + input_dim_exceeds_two=input_dim_exceeds_two, + ) + + # Step 2: register patterns from bmm + # Linear might be decomposed into bmm when input dim exceeds 2 and not contiguous + # refer to: + # https://github.com/pytorch/pytorch/blob/ + # 80c07df659362a95da7cd4f3ec367abfdace38c4/torch/_decomp/decompositions.py#L3965-L3968 + # in this case, we can convert it back to qlinear + for dtype, with_bias, is_tensor_overload in itertools.product( + [torch.float32, torch.bfloat16], [True, False], [True, False] + ): + bmm_pattern = _generate_qlinear_weight_prepack_patterns( + dtype=dtype, + input_dim_exceeds_two=True, + input_contiguous=False, + with_bias=with_bias, + is_tensor_overload=is_tensor_overload, + ) + _register_qlinear_weight_prepack_pass( + bmm_pattern, + pass_number=1 + if with_bias + else 2, # if with_bias, there is an output add, so we should try to match it firstly + dtype=dtype, + input_dim_exceeds_two=True, + input_contiguous=False, + ) + + +def _register_linear_dynamic_fp16_weight_prepack_pass( + pattern, + pass_number, + input_dim_exceeds_two=False, + input_contiguous=True, + relu_fused=False, +): + def _extra_check_fn(match: Match): + return match.kwargs["dtype_fp16"] == torch.float16 + + @register_freezing_graph_pattern( + pattern, + extra_check=_extra_check_fn, + pass_number=pass_number, + ) + def linear_dynamic_fp16_weight_prepack(match: Match, *args, **kwargs): + """ + Match the pattern: + fp32 activation + | + mm/addmm <- t <- to_fp32 <- to_fp16 <- weight + | + (reshape) <- (relu) + + OR + + fp32 activation + | + expand + | + bmm <- expand <- t <- to_fp32 <- to_fp16 <- weight + | + (add) <- (relu) + + Insert weight prepack node and change the pattern to: + fp32 activation + | + onednn.linear_dynamic_fp16 <- onednn.linear_prepack_fp16 <- weight + (or onednn.linear_relu_dynamic_fp16) + """ + # find params + x = kwargs["x"] + w = kwargs["w"] + bias = kwargs["b"] if "b" in kwargs else None + + # find linear node + nodes_to_find = [aten.addmm.default, aten.mm.default, aten.bmm.default] + linear_nodes = [] + for node in nodes_to_find: + linear_nodes.extend(filter_nodes(match.nodes, node)) + assert len(linear_nodes) == 1 + linear_node = linear_nodes[0] + assert isinstance(linear_node, torch.fx.node.Node) + input_index = 1 if linear_node.target is aten.addmm.default else 0 + weight_index = input_index + 1 + + # find relu node + relu_node = None + if relu_fused: + relu_node = match.output_node() + assert isinstance(relu_node, torch.fx.node.Node) + + # find reshape node, expand node and add node + ( + act_reshape_node, + output_reshape_node, + expand_x_node, + expand_w_node, + add_bias_node, + ) = (None, None, None, None, None) + t_node = None + if input_dim_exceeds_two: + if input_contiguous: + act_reshape_node = linear_node.args[input_index] + t_node = linear_node.args[weight_index] + output_reshape_node = next(iter(linear_node.users)) + assert output_reshape_node.target is aten.reshape.default + else: + expand_x_node = linear_node.args[input_index] + expand_w_node = linear_node.args[weight_index] + assert isinstance(expand_w_node, torch.fx.node.Node) + t_node = expand_w_node.args[0] + if bias: + add_bias_node = next(iter(linear_node.users)) + assert add_bias_node.target is aten.add.Tensor + else: + t_node = linear_node.args[weight_index] + assert isinstance(t_node, torch.fx.node.Node) + + w_to_fp32_node = t_node.args[0] + assert ( + isinstance(w_to_fp32_node, torch.fx.node.Node) + and w_to_fp32_node.target + is quantized_decomposed.convert_element_type.no_fuse + ) + w_to_fp16_node = w_to_fp32_node.args[0] + assert ( + isinstance(w_to_fp16_node, torch.fx.node.Node) + and w_to_fp16_node.target + is quantized_decomposed.convert_element_type.no_fuse + ) + + x_shape = x.meta.get("tensor_meta").shape + if has_free_symbols(x_shape): + # For dynamic shape case, we can't get activation shape ahead of runtime. + x_shape = None + graph = match.graph + with graph.inserting_before(linear_node): + # Insert weight prepack node and the qlinear node + packed_weight_inputs = ( + w, + x_shape, + ) + packed_weight_op = torch.ops.onednn.linear_prepack_fp16 + prepack_weight_node = graph.call_function( + packed_weight_op, args=packed_weight_inputs + ) + + # create new linear node and insert on graph + new_args: tuple[Any, ...] = ( + x, + prepack_weight_node, + bias, + ) + linear_op = ( + torch.ops.onednn.linear_relu_dynamic_fp16.default + if relu_fused + else torch.ops.onednn.linear_dynamic_fp16.default + ) + new_linear_node = graph.call_function(linear_op, args=new_args) + out_node = match.output_node() + out_node.replace_all_uses_with(new_linear_node) + + # Erase the original nodes in the reverse order + new_linear_node.meta.update(out_node.meta) + if relu_node is not None: + graph.erase_node(relu_node) + if output_reshape_node is not None: + graph.erase_node(output_reshape_node) + if add_bias_node is not None: + graph.erase_node(add_bias_node) + graph.erase_node(linear_node) + if act_reshape_node is not None: + assert isinstance(act_reshape_node, torch.fx.node.Node) + graph.erase_node(act_reshape_node) + if expand_x_node is not None: + assert isinstance(expand_x_node, torch.fx.node.Node) + graph.erase_node(expand_x_node) + if expand_w_node is not None: + assert isinstance(expand_w_node, torch.fx.node.Node) + graph.erase_node(expand_w_node) + graph.erase_node(t_node) + graph.erase_node(w_to_fp32_node) + graph.erase_node(w_to_fp16_node) + + counters["inductor"]["qlinear_weight_prepack_matcher_count"] += 1 + counters["inductor"]["qlinear_weight_prepack_matcher_nodes"] += len( + match.nodes + ) + + +def _register_linear_dynamic_fp16_weight_prepack(): + to_dtype_op = torch.ops.quantized_decomposed.convert_element_type.no_fuse + weight_pattern = CallFunction( + to_dtype_op, + CallFunction( + to_dtype_op, + KeywordArg("w"), + KeywordArg("dtype_fp16"), + ), + KeywordArg("dtype_fp32"), + ) + cases = itertools.product( + [False, True], # input_dim_exceeds_two + [True, False], # input_contiguous + [False, True], # relu fused + ) + for input_dim_exceeds_two, input_contiguous, relu_fused in cases: + patterns = _generate_linear_dynamic_fp16_pattern( + weight_pattern, + input_dim_exceeds_two, + input_contiguous, + relu_fused, + ) + for pattern in patterns: + _register_linear_dynamic_fp16_weight_prepack_pass( + pattern, + pass_number=0 if relu_fused else 1, + input_dim_exceeds_two=input_dim_exceeds_two, + input_contiguous=input_contiguous, + relu_fused=relu_fused, + ) + + +def _register_smooth_quant_int_mm_pattern(): + """ + The pattern is: + (no bias) reshape -> _int_mm -> convert_element_type -> (expand ->) mul -> mul -> reshape + or + (with bias) pattern_no_bias -> add (-> reshape -> reshape) + """ + + # When torch.compile'ing with dynamic=True, the expand node and the two tailing reshape nodes exist + # When torch.compile'ing with dynamic=False, they don't exist + def get_pattern_no_bias(expand_a_scale: bool, reshape_a: bool = True): + return CallFunction( + aten.mul.Tensor, + CallFunction( + aten.mul.Tensor, + CallFunction( + prims.convert_element_type.default, + CallFunction( + aten._int_mm.default, + CallFunction( + aten.reshape.default, + KeywordArg("a"), + KeywordArg("in_shape"), + ) + if reshape_a + else KeywordArg("a"), + KeywordArg("b"), + ), + KeywordArg("dtype"), + ), + ( + CallFunction( + aten.expand.default, + KeywordArg("x_scale"), + Arg(), + ) + if expand_a_scale + else KeywordArg("x_scale") + ), + ), + KeywordArg("w_scale"), + ) + + def _with_outer_reshape(pattern): + return CallFunction( + aten.reshape.default, pattern, KeywordArg("out_shape_no_bias") + ) + + # for torch.compile(dynamic=False) + pattern_no_bias_1 = _with_outer_reshape(get_pattern_no_bias(expand_a_scale=False)) + pattern_with_bias_1 = CallFunction( + aten.add.Tensor, + pattern_no_bias_1, + KeywordArg("bias"), + ) + # for torch.compile(dynamic=True) + pattern_no_bias_2 = _with_outer_reshape(get_pattern_no_bias(expand_a_scale=True)) + pattern_with_bias_2 = CallFunction( + aten.reshape.default, + CallFunction( + aten.reshape.default, + CallFunction( + aten.add.Tensor, + pattern_no_bias_2, + KeywordArg("bias"), + ), + Arg(), + ), + KeywordArg("out_shape_with_bias"), + ) + + # The following patterns are for torchao int8_dynamic_activation_int8_weight linear, + # when both activation and weights are symmetrically quantized. + # In practice, though, they may also match smooth-quant pattern when a 2D input shape would be used. + # Since add is not currently being used as a oneDNN post-op, but is unfused, we don't need these patterns with bias. + # Ideally, we should add mul + add post-op support in ATen int8 oneDNN linear op. + pattern1_with_no_outer_or_act_reshape = get_pattern_no_bias( + expand_a_scale=False, reshape_a=False + ) + pattern2_with_no_outer_or_act_reshape = get_pattern_no_bias( + expand_a_scale=True, reshape_a=False + ) + + def _validate_pattern(match: Match): + if len(match.nodes) not in [4, 5, 6, 7, 10]: + return False + # Make sure weight is a constant + aten_int_mm_node = filter_nodes(match.nodes, aten._int_mm.default)[0] + if not isinstance(aten_int_mm_node.args[1], torch.fx.node.Node): + return False + if aten_int_mm_node.args[1].op != "get_attr": + return False + + if len(match.nodes) == 10: + # Check the two tailing reshape nodes can be fused + if match.nodes[9].args[1] != match.nodes[6].args[1]: + return False + if len(match.nodes) == 10 or ( + len(match.nodes) == 7 and match.nodes[6].target is aten.add.Tensor + ): + bias_idx = 7 if len(match.nodes) == 10 else 6 + # Check bias shape + bias_node = match.nodes[bias_idx].args[1] + if not isinstance(bias_node, torch.fx.node.Node): + return False + if len(bias_node.meta.get("tensor_meta").shape) != 1: # type: ignore[union-attr] + return False + return True + + pattern_to_pass_number = { + pattern_no_bias_2: 0, + pattern_with_bias_2: 0, + pattern_no_bias_1: 1, + pattern_with_bias_1: 1, + pattern1_with_no_outer_or_act_reshape: 2, + pattern2_with_no_outer_or_act_reshape: 2, + } + for pattern, pass_number in pattern_to_pass_number.items(): + + @register_freezing_graph_pattern( + pattern, + extra_check=_validate_pattern, + pass_number=pass_number, + ) + def _int_mm_weight_prepack(match: Match, *args, **kwargs): + bias = kwargs.get("bias", None) + x = kwargs["a"] + weight = kwargs["b"] + dtype = kwargs["dtype"] + x_scale = kwargs["x_scale"] + w_scale = kwargs["w_scale"] + x_shape = x.meta.get("tensor_meta").shape + if has_free_symbols(x_shape): + # For dynamic shape case, we can't get activation shape ahead of runtime. + x_shape = None + + out_node = match.output_node() + with match.graph.inserting_before(out_node): + transpose_node = match.graph.call_function( + aten.permute.default, args=(weight, [1, 0]) + ) + contig_node = match.graph.call_function( + aten.contiguous.default, args=(transpose_node,) + ) + packed_weight_inputs = ( + contig_node, + x_shape, + ) + packed_weight_op = torch.ops.onednn.qlinear_prepack + prepack_weight_node = match.graph.call_function( + packed_weight_op, args=packed_weight_inputs + ) + + dummy_zp = None + w_scale = match.graph.call_function( + prims.convert_element_type.default, args=(w_scale, torch.float32) + ) + + x_scale_shape = x_scale.meta.get("tensor_meta").shape + x_scale_is_scalar = False + if not has_free_symbols(x_scale_shape): + prod = 1 + for d in x_scale_shape: + prod *= d + x_scale_is_scalar = prod == 1 + + new_args: tuple[Any, ...] + if x_scale_is_scalar: + # in this case, we can call onednn.qlinear directly + new_args = ( + x, + x_scale, + dummy_zp, # x_zp + prepack_weight_node, + w_scale, + dummy_zp, # w_zp + bias, + 1.0, # output_scale + 0, # output_zero_point + dtype, # output_dtype + "none", # post op name + [], # post op args + "", # post op algorithm + ) + new_linear_node = match.graph.call_function( + torch.ops.onednn.qlinear_pointwise.tensor, args=new_args + ) + out_node.replace_all_uses_with(new_linear_node) + new_linear_node.meta.update(out_node.meta) + else: + # onednn.qlinear does not support per-channel quantization of x + # so in this case, we have to apply x scale and add bias ourselves after qlinear + in_shape = kwargs.get("in_shape", None) + if in_shape is None: + x_reshaped = x + else: + x_reshaped = match.graph.call_function( + aten.reshape.default, args=(x, in_shape) + ) + new_args = ( + x_reshaped, + 1.0, # x_scale + 0, # x_zp + prepack_weight_node, + w_scale, + dummy_zp, # w_zp + None, # bias + 1.0, # output_scale + 0, # output_zero_point + dtype, # output_dtype + "none", # post op name + [], # post op args + "", # post op algorithm + ) + new_linear_node = match.graph.call_function( + torch.ops.onednn.qlinear_pointwise, args=new_args + ) + # apply x scale + new_out_node = match.graph.call_function( + aten.mul.Tensor, args=(new_linear_node, x_scale) + ) + + # Add bias and reshape + has_outer_reshape = ( + kwargs.get("out_shape_with_bias", None) is not None + or kwargs.get("out_shape_no_bias", None) is not None + ) + + if has_outer_reshape: + out_shape = kwargs.get( + "out_shape_with_bias", kwargs["out_shape_no_bias"] + ) + if bias is not None: + new_out_node = match.graph.call_function( + aten.add.Tensor, args=(new_out_node, bias) + ) + if has_outer_reshape: + new_out_node = match.graph.call_function( + aten.reshape.default, + args=(new_out_node, out_shape), # type: ignore[possibly-undefined] + ) + else: + if has_outer_reshape: + new_out_node = match.graph.call_function( + aten.reshape.default, + args=(new_out_node, out_shape), # type: ignore[possibly-undefined] + ) + out_node.replace_all_uses_with(new_out_node) + new_out_node.meta.update(out_node.meta) + for node in reversed(match.nodes): + match.graph.erase_node(node) + counters["inductor"]["qlinear_weight_prepack_matcher_count"] += 1 + counters["inductor"]["qlinear_weight_prepack_matcher_nodes"] += len( + match.nodes + ) + + +class PostOpAttr: + def __init__( + self, + binary_op_name: str = "none", + alpha=None, + unary_op_name: str = "none", + scalars_attr=None, + algorithm_attr=None, + ) -> None: + self.binary_op_name = binary_op_name + self.alpha = alpha if alpha else 1.0 + self.unary_op_name = unary_op_name + self.scalars_attr = scalars_attr if scalars_attr else [] + self.algorithm_attr = algorithm_attr if algorithm_attr else "" + + +def _register_qconv_post_op_fusion_pass( + pattern, + pass_number, + computation_op, + post_op_attr, +): + has_binary_post_op = post_op_attr.binary_op_name != "none" + + @register_freezing_graph_pattern( + pattern, + extra_check=_is_valid_qconv_post_op_fusion_pattern(has_binary_post_op), + pass_number=pass_number, + ) + def qconv(match: Match, *args, **kwargs): + # Activation QParams + x, x_scale, x_zp = ( + kwargs["x"], + kwargs["x_scale"], + kwargs["x_zp"], + ) + # Weight QParams + packed_weight, w_scale, w_zp = ( + kwargs["packed_weight"], + kwargs["w_scale"], + kwargs["w_zp"], + ) + # Conv Params + b, stride, padding, dilation, groups = ( + kwargs["b"], + kwargs["stride"], + kwargs["padding"], + kwargs["dilation"], + kwargs["groups"], + ) + output_dtype = _get_pattern_output_dtype(match) + assert output_dtype in [torch.int8, torch.uint8, torch.float32, torch.bfloat16] + # Output QParams + o_inv_scale = ( + kwargs["o_inv_scale"] + if (output_dtype == torch.uint8 or output_dtype == torch.int8) + else 1.0 + ) + o_zero_point = ( + kwargs["o_zp"] + if (output_dtype == torch.uint8 or output_dtype == torch.int8) + else 0 + ) + assert ( + kwargs["postop_name"] == "none" + ) # Expected no post op fused in weight prepack phase + if post_op_attr.unary_op_name == "hardtanh": + min_value = kwargs.get("min_value") + max_value = kwargs.get("max_value") + post_op_attr.scalars_attr = [min_value, max_value] + + out_node = match.output_node() + with match.graph.inserting_before(out_node): + if not has_binary_post_op: + computation_args: tuple[Any, ...] = ( + x, + x_scale, + x_zp, + packed_weight, + w_scale, + w_zp, + b, + stride, + padding, + dilation, + groups, + o_inv_scale, + o_zero_point, + output_dtype, + post_op_attr.unary_op_name, + post_op_attr.scalars_attr, + post_op_attr.algorithm_attr, + ) + else: + accum = ( + kwargs["accum"] + if output_dtype in [torch.uint8, torch.int8] + else kwargs["accum_after_dequant"] + ) + accum_scale = ( + kwargs["accum_scale"] + if output_dtype in [torch.uint8, torch.int8] + else 1.0 + ) + accum_zp = ( + kwargs["accum_zp"] + if output_dtype in [torch.uint8, torch.int8] + else 0 + ) + computation_args = ( + x, + x_scale, + x_zp, + packed_weight, + w_scale, + w_zp, + accum, + b, + stride, + padding, + dilation, + groups, + o_inv_scale, + o_zero_point, + output_dtype, + accum_scale, + accum_zp, + post_op_attr.binary_op_name, + post_op_attr.alpha, + post_op_attr.unary_op_name, + post_op_attr.scalars_attr, + post_op_attr.algorithm_attr, + ) + new_conv_node = match.graph.call_function( + computation_op, args=computation_args + ) + out_node.replace_all_uses_with(new_conv_node) + new_conv_node.meta.update(out_node.meta) + for node in reversed(match.nodes): + match.graph.erase_node(node) + count_key = ( + "qconv2d_binary_matcher_count" + if has_binary_post_op + else "qconv_unary_matcher_count" + ) + nodes_key = ( + "qconv2d_binary_matcher_nodes" + if has_binary_post_op + else "qconv_unary_matcher_nodes" + ) + counters["inductor"][count_key] += 1 + counters["inductor"][nodes_key] += len(match.nodes) + + return qconv + + +def _register_qconv_unary_fusion(): + from torch._inductor.fx_passes.mkldnn_fusion import ( + _hardswish_fusion, + _hardtanh_fusion, + _silu_fusion, + ) + + for original_pattern_output_dtype in [torch.float32, torch.bfloat16]: + # Priority 1 to match: QConv2d Unary pattern with int8 output + # If a pattern1 is a sub-set of pattern2, we should try to match pattern2 firstly. + # For example: pattern1 is qconv_fp32 -> relu, pattern2 is qconv_fp32 -> relu -> quant + is_bf16 = original_pattern_output_dtype == torch.bfloat16 + conv_unary_replace_patterns = { + PostOpAttr( + "none", None, "none", [], "" + ): generate_pattern_with_output_quant( + get_qconv_pt2e_pattern(1), + ), + PostOpAttr( + "none", None, "relu", [], "" + ): generate_pattern_with_output_quant( + generate_pattern_with_unary( + get_qconv_pt2e_pattern(1), aten.relu.default + ), + ), + PostOpAttr( + "none", None, "hardtanh", [], "" + ): generate_pattern_with_output_quant( + _unary_fusion_pattern( + _hardtanh_fusion, + get_qconv_pt2e_pattern(1), + 1, + is_bf16, + ), + with_dtype_convert=is_bf16, + ), + PostOpAttr( + "none", None, "hardswish", [], "" + ): generate_pattern_with_output_quant( + _unary_fusion_pattern( + _hardswish_fusion, + get_qconv_pt2e_pattern(1 if is_bf16 else 2), + 2, + is_bf16, + ), + with_dtype_convert=is_bf16, + ), + PostOpAttr( + "none", None, "swish", [], "" + ): generate_pattern_with_output_quant( + _unary_fusion_pattern( + _silu_fusion, + get_qconv_pt2e_pattern(1 if is_bf16 else 2), + 2, + is_bf16, + ), + with_dtype_convert=is_bf16, + ), + } + + for unary_attr, patterns in conv_unary_replace_patterns.items(): + # Register qconv2d pattern for ExternKernel Lowering + _register_qconv_post_op_fusion_pass( + patterns, + 3, # pass_number + torch.ops.onednn.qconv_pointwise.default, # computation_op + unary_attr, # unary_attr + ) + + # Priority 2 to match: QConv2d Unary pattern with fp32/bfloat16 output + conv_unary_replace_float_out_patterns = { + PostOpAttr("none", None, "relu", [], ""): generate_pattern_with_unary( + get_qconv_pt2e_pattern(1), aten.relu.default + ), + PostOpAttr( + "none", None, "hardtanh", [], "" + ): _may_generate_pattern_with_dtype_convert( + _unary_fusion_pattern( + _hardtanh_fusion, + get_qconv_pt2e_pattern(1), + 1, + is_bf16, + ), + Arg(), + is_bf16, + ), + PostOpAttr( + "none", None, "hardswish", [], "" + ): _may_generate_pattern_with_dtype_convert( + _unary_fusion_pattern( + _hardswish_fusion, + get_qconv_pt2e_pattern(1 if is_bf16 else 2), + 2, + is_bf16, + ), + Arg(), + is_bf16, + ), + PostOpAttr( + "none", None, "swish", [], "" + ): _may_generate_pattern_with_dtype_convert( + _unary_fusion_pattern( + _silu_fusion, + get_qconv_pt2e_pattern(1 if is_bf16 else 2), + 2, + is_bf16, + ), + Arg(), + is_bf16, + ), + } + + for unary_attr, patterns in conv_unary_replace_float_out_patterns.items(): + # Register qconv2d pattern for ExternKernel Lowering + _register_qconv_post_op_fusion_pass( + patterns, + 4, # pass_number + torch.ops.onednn.qconv_pointwise.default, # computation_op + unary_attr, # unary_attr + ) + + +def _register_qconv_binary_fusion(): + for int8_mixed_bf16_with_inplace_add in [False, True]: + # Priority 1 to match: QConv2d Binary or Binary-Unary pattern with int8 output + swap_binary_inputs_list = [False, True] + binary_replace_patterns = {} + for swap_inputs in swap_binary_inputs_list: + binary_replace_patterns.update( + { + PostOpAttr( + "sum", 1.0, "none", [], "" + ): generate_pattern_with_output_quant( + generate_pattern_with_binary( + aten.add.Tensor, + get_qconv_pt2e_pattern(1), + dequantize_accum_pattern, + int8_mixed_bf16_with_inplace_add, + swap_inputs=swap_inputs, + ), + ), + PostOpAttr( + "sum", 1.0, "relu", [], "" + ): generate_pattern_with_output_quant( + generate_pattern_with_unary( + generate_pattern_with_binary( + aten.add.Tensor, + get_qconv_pt2e_pattern(1), + dequantize_accum_pattern, + int8_mixed_bf16_with_inplace_add, + swap_inputs=swap_inputs, + ), + aten.relu.default, + ), + ), + } + ) + + for binary_unary_attr, patterns in binary_replace_patterns.items(): + _register_qconv_post_op_fusion_pass( + patterns, + 3, # pass_number + torch.ops.onednn.qconv2d_pointwise.binary, # computation_op + binary_unary_attr, # binary_unary_attr + ) + + # Priority 2 to match: QConv2d Binary-Unary pattern with fp32/bfloat16 output + binary_replace_float_out_patterns = {} + for swap_inputs in swap_binary_inputs_list: + binary_replace_float_out_patterns.update( + { + PostOpAttr("sum", 1.0, "relu", [], ""): generate_pattern_with_unary( + generate_pattern_with_binary( + aten.add.Tensor, + get_qconv_pt2e_pattern(1), + KeywordArg("accum_after_dequant"), + int8_mixed_bf16_with_inplace_add, + swap_inputs=swap_inputs, + ), + aten.relu.default, + ) + } + ) + + for ( + binary_unary_attr, + patterns, + ) in binary_replace_float_out_patterns.items(): + if int8_mixed_bf16_with_inplace_add: + _register_qconv_post_op_fusion_pass( + patterns, + 3, # pass_number + torch.ops.onednn.qconv2d_pointwise.binary, # computation_op + binary_unary_attr, # binary_unary_attr + ) + else: + _register_qconv_post_op_fusion_pass( + patterns, + 4, # pass_number + torch.ops.onednn.qconv2d_pointwise.binary, # computation_op + binary_unary_attr, # binary_unary_attr + ) + + # Priority 3: QConv2d Binary pattern with fp32/bfloat16 output + binary_replace_float_out_patterns = {} + for swap_inputs in swap_binary_inputs_list: + binary_replace_float_out_patterns.update( + { + PostOpAttr( + "sum", 1.0, "none", [], "" + ): generate_pattern_with_binary( + aten.add.Tensor, + get_qconv_pt2e_pattern(1), + KeywordArg("accum_after_dequant"), + int8_mixed_bf16_with_inplace_add, + swap_inputs=swap_inputs, + ), + } + ) + + for ( + binary_unary_attr, + patterns, + ) in binary_replace_float_out_patterns.items(): + _register_qconv_post_op_fusion_pass( + patterns, + 4 if int8_mixed_bf16_with_inplace_add else 5, # pass_number + torch.ops.onednn.qconv2d_pointwise.binary, # computation_op + binary_unary_attr, # binary_unary_attr + ) + + +def _register_qlinear_post_op_fusion_pass( + pattern, + pass_number, + computation_op, + post_op_attr, +): + has_binary_post_op = post_op_attr.binary_op_name != "none" + + @register_freezing_graph_pattern( + pattern, + extra_check=_is_valid_qlinear_post_op_fusion_pattern(has_binary_post_op), + pass_number=pass_number, + ) + def qlinear_post_op_fusion(match: Match, *args, **kwargs): + """ + Match the pattern: + qlinear - post op + """ + output_dtype = _get_pattern_output_dtype(match) + # Activation QParams + x, x_scale, x_zp = ( + kwargs["x"], + kwargs["x_scale"], + kwargs["x_zp"], + ) + # Weight QParams + packed_weight, w_scale, w_zp = ( + kwargs["packed_weight"], + kwargs["w_scale"], + kwargs["w_zp"], + ) + + # bias + b = kwargs["b"] if "b" in kwargs else None + + # Output QParams + o_inv_scale = ( + kwargs["o_inv_scale"] + if (output_dtype in [torch.uint8, torch.int8]) + else 1.0 + ) + o_zero_point = ( + kwargs["o_zp"] if (output_dtype in [torch.uint8, torch.int8]) else 0 + ) + assert ( + kwargs["postop_name"] == "none" + ) # Expected no post op fused in weight prepack phase + + out_node = match.output_node() + with match.graph.inserting_before(out_node): + if not has_binary_post_op: + computation_args: tuple[Any, ...] = ( + x, + x_scale, + x_zp, + packed_weight, + w_scale, + w_zp, + b, + o_inv_scale, + o_zero_point, + output_dtype, + post_op_attr.unary_op_name, + post_op_attr.scalars_attr, + post_op_attr.algorithm_attr, + ) + else: + other = kwargs["other"] if "other" in kwargs else kwargs["accum"] + x2_scale = 1.0 + x2_zp = 0 + computation_args = ( + x, + x_scale, + x_zp, + packed_weight, + w_scale, + w_zp, + other, + b, + o_inv_scale, + o_zero_point, + output_dtype, + x2_scale, + x2_zp, + post_op_attr.binary_op_name, + post_op_attr.alpha, + post_op_attr.unary_op_name, + post_op_attr.scalars_attr, + post_op_attr.algorithm_attr, + ) + new_linear_node = match.graph.call_function( + computation_op, args=computation_args + ) + out_node.replace_all_uses_with(new_linear_node) + new_linear_node.meta.update(out_node.meta) + for node in reversed(match.nodes): + match.graph.erase_node(node) + count_key = ( + "qlinear_binary_matcher_count" + if has_binary_post_op + else "qlinear_unary_matcher_count" + ) + nodes_key = ( + "qlinear_binary_matcher_nodes" + if has_binary_post_op + else "qlinear_unary_matcher_nodes" + ) + counters["inductor"][count_key] += 1 + counters["inductor"][nodes_key] += len(match.nodes) + + +def _register_qlinear_unary_fusion(): + from torch._inductor.fx_passes.mkldnn_fusion import ( + _gelu_fusion_1 as _gelu_fusion_erf, + ) + from torch._inductor.fx_passes.mkldnn_fusion import ( + _gelu_fusion_2 as _gelu_fusion_tanh, + ) + + for original_pattern_output_dtype in [torch.float32, torch.bfloat16]: + is_bf16 = original_pattern_output_dtype == torch.bfloat16 + for x_scale_zp_are_tensors in (False, True): + qlinear_pattern = get_qlinear_pt2e_pattern(x_scale_zp_are_tensors) + computation_op = ( + torch.ops.onednn.qlinear_pointwise.tensor + if x_scale_zp_are_tensors + else torch.ops.onednn.qlinear_pointwise.default + ) + # Priority 1 to match: QLinear Unary pattern with int8 output + linear_unary_replace_patterns = { + PostOpAttr( + "none", None, "none", [], "" + ): generate_pattern_with_output_quant( + qlinear_pattern, + ), + PostOpAttr( + "none", None, "relu", [], "" + ): generate_pattern_with_output_quant( + generate_pattern_with_unary(qlinear_pattern, aten.relu.default), + ), + PostOpAttr( + "none", None, "gelu", [], "none" + ): generate_pattern_with_output_quant( + _unary_fusion_pattern( + _gelu_fusion_erf, + get_qlinear_pt2e_pattern( + x_scale_zp_are_tensors, 1 if is_bf16 else 2 + ), + 2, + is_bf16, + ), + with_dtype_convert=is_bf16, + ), + PostOpAttr( + "none", None, "gelu", [], "tanh" + ): generate_pattern_with_output_quant( + _unary_fusion_pattern( + _gelu_fusion_tanh, + get_qlinear_pt2e_pattern( + x_scale_zp_are_tensors, 1 if is_bf16 else 4 + ), + 4, + is_bf16, + ), + with_dtype_convert=is_bf16, + ), + } + + for unary_attr, patterns in linear_unary_replace_patterns.items(): + _register_qlinear_post_op_fusion_pass( + patterns, + 3, # pass_number + computation_op, + unary_attr, # unary_attr + ) + + # Priority 2 to match: QLinear Unary pattern with FP32/BF16 output + linear_unary_replace_float_out_patterns = { + PostOpAttr("none", None, "relu", [], ""): generate_pattern_with_unary( + qlinear_pattern, aten.relu.default + ), + PostOpAttr( + "none", None, "gelu", [], "none" + ): _may_generate_pattern_with_dtype_convert( + _unary_fusion_pattern( + _gelu_fusion_erf, + get_qlinear_pt2e_pattern( + x_scale_zp_are_tensors, 1 if is_bf16 else 2 + ), + 2, + is_bf16, + ), + Arg(), + is_bf16, + ), + PostOpAttr( + "none", None, "gelu", [], "tanh" + ): _may_generate_pattern_with_dtype_convert( + _unary_fusion_pattern( + _gelu_fusion_tanh, + get_qlinear_pt2e_pattern( + x_scale_zp_are_tensors, 1 if is_bf16 else 4 + ), + 4, + is_bf16, + ), + Arg(), + is_bf16, + ), + } + + for unary_attr, patterns in linear_unary_replace_float_out_patterns.items(): + _register_qlinear_post_op_fusion_pass( + patterns, + 4, # pass_number + computation_op, + unary_attr, # unary_attr + ) + + +def _register_qlinear_binary_fusion(): + r""" + Supported linear-binary(-unary) patterns + + linear(X) extra input + \ / + Add + | + Optional(relu) + | + Y + + 1. int8-mixed-fp32 + +---+---------------+-----------+------------------------------+---------+ + | # | Add type | Quant out | Pattern | Post op | + +---+---------------+-----------+------------------------------+---------+ + | 1 | In-/out-place | Yes | linear + fp32 -> (relu) -> q | add | + +---+---------------+-----------+------------------------------+---------+ + | 2 | In-/out-place | No | linear + fp32 -> (relu) | sum | + +---+---------------+-----------+------------------------------+---------+ + + 2. int8-mixed-bf16 + +---+----------+---------------+-----------+-----------------------------------------+---------+ + | # | X2 dtype | Add type | Quant out | Pattern | Post op | + +---+----------+---------------+-----------+-----------------------------------------+---------+ + | 1 | BF16 | In-/out-place | Yes | linear + bf16 -> (relu) -> q | add | + +---+----------+---------------+-----------+-----------------------------------------+---------+ + | 2 | BF16 | In-/out-place | No | linear + bf16 -> (relu) | sum | + +---+----------+---------------+-----------+-----------------------------------------+---------+ + | 3 | FP32 | Out-place | Yes | linear + fp32 -> (relu) -> q | add | + | | | In-place right| | | | + +---+----------+---------------+-----------+-----------------------------------------+---------+ + | 4 | FP32 | Out-place | No | linear + fp32 -> (relu) | sum | + | | | In-place right| | | | + +---+----------+---------------+-----------+-----------------------------------------+---------+ + | 5 | FP32 | In-place left | Yes | linear + fp32 -> to_bf16 -> (relu) -> q | add | + +---+----------+---------------+-----------+-----------------------------------------+---------+ + | 6 | FP32 | In-place left | No | linear + fp32 -> to_bf16 -> (relu) | add | + +---+----------+---------------+-----------+-----------------------------------------+---------+ + + Note + (1) The positions of linear and the extra input can be swapped. + (2) we don't insert q-dq before the extra input of linear-add by recipe. But if q-dq is found at the + extra input, we don't match that pattern because we cannot match all these patterns in 3 passes. + """ + for x_scale_zp_are_tensors in (False, True): + qlinear_binary_op = ( + torch.ops.onednn.qlinear_pointwise.binary_tensor + if x_scale_zp_are_tensors + else torch.ops.onednn.qlinear_pointwise.binary + ) + unary_postop_list = ["none", "relu"] + unary_postop_dict = { + "none": None, + "relu": aten.relu.default, + } + convert_dtype_after_binary_list = [False, True] + + # Priority 1 to match: QLinear Binary or Binary-Unary pattern with int8 output + # Covers case (1) of int8-mixed-fp32 and case (1)(3)(5) of int8-mixed-bf16, + # totally 3 patterns (2 are identical) + swap_binary_inputs_list = [False, True] + int8_mixed_bf16_list = [False, True] + combinations = itertools.product( + unary_postop_list, + int8_mixed_bf16_list, + swap_binary_inputs_list, + convert_dtype_after_binary_list, + ) + qlinear_binary_replace_patterns = {} + for unary_op, int8_mixed_bf16, swap_inputs, cvt_dtype_binary in combinations: + if not int8_mixed_bf16 and cvt_dtype_binary: + # No convert node after binary node if dtypes are all fp32 + continue + qlinear_binary_replace_patterns.update( + { + PostOpAttr( + "add", 1.0, unary_op, [], "" + ): generate_pattern_with_output_quant( + generate_pattern_with_unary( + generate_pattern_with_binary( + aten.add.Tensor, + get_qlinear_pt2e_pattern(x_scale_zp_are_tensors), + KeywordArg("other"), + # If fp32 extra input is inplace added to bf16 linear output, + # a to_bf16 node is inserted after binary + dtype_convert=cvt_dtype_binary, + swap_inputs=swap_inputs, + ), + unary_postop_dict[unary_op], + ), + ) + } + ) + for binary_unary_attr, patterns in qlinear_binary_replace_patterns.items(): + _register_qlinear_post_op_fusion_pass( + patterns, + 3, # pass_number + qlinear_binary_op, # computation_op + binary_unary_attr, + ) + + # Priority 2.1 to match: QLinear Binary-Unary pattern with fp32/bfloat16 output + # Covers case (2) of int8-mixed-fp32 and case (2)(4) of int8-mixed-bf16, + # totally 2 patterns (2 are identical) + binary_replace_float_out_patterns = {} + for swap_binary_inputs in swap_binary_inputs_list: + binary_replace_float_out_patterns.update( + { + PostOpAttr("sum", 1.0, "relu", [], ""): generate_pattern_with_unary( + generate_pattern_with_binary( + aten.add.Tensor, + get_qlinear_pt2e_pattern(x_scale_zp_are_tensors), + KeywordArg("accum"), + dtype_convert=False, + swap_inputs=swap_binary_inputs, + ), + aten.relu.default, + ), + } + ) + for ( + binary_unary_attr, + patterns, + ) in binary_replace_float_out_patterns.items(): + _register_qlinear_post_op_fusion_pass( + patterns, + 4, # pass_number + qlinear_binary_op, # computation_op + binary_unary_attr, + ) + # Priority 2.2 to match: QLinear Binary-Unary pattern with fp32/bfloat16 output + # Covers case (6) of int8-mixed-bf16 + binary_replace_float_out_patterns = {} + for swap_binary_inputs in swap_binary_inputs_list: + binary_replace_float_out_patterns.update( + { + PostOpAttr("add", 1.0, "relu", [], ""): generate_pattern_with_unary( + generate_pattern_with_binary( + aten.add.Tensor, + get_qlinear_pt2e_pattern(x_scale_zp_are_tensors), + KeywordArg("other"), + dtype_convert=True, + swap_inputs=swap_binary_inputs, + ), + aten.relu.default, + ), + } + ) + for ( + binary_unary_attr, + patterns, + ) in binary_replace_float_out_patterns.items(): + _register_qlinear_post_op_fusion_pass( + patterns, + 4, # pass_number + qlinear_binary_op, # computation_op + binary_unary_attr, + ) + + # Priority 3.1: QLinear Binary pattern with fp32/bfloat16 output + # Covers case (2) of int8-mixed-fp32 and case (2)(4) of int8-mixed-bf16, + # totally 2 patterns (2 are identical) + binary_replace_float_out_patterns = {} + for swap_binary_inputs in swap_binary_inputs_list: + binary_replace_float_out_patterns.update( + { + PostOpAttr( + "sum", 1.0, "none", [], "" + ): generate_pattern_with_binary( + aten.add.Tensor, + get_qlinear_pt2e_pattern(x_scale_zp_are_tensors), + KeywordArg("accum"), + dtype_convert=False, + swap_inputs=swap_binary_inputs, + ), + } + ) + for ( + binary_unary_attr, + patterns, + ) in binary_replace_float_out_patterns.items(): + _register_qlinear_post_op_fusion_pass( + patterns, + 5, # pass_number + qlinear_binary_op, # computation_op + binary_unary_attr, + ) + # Priority 3.2: QLinear Binary pattern with fp32/bfloat16 output + # Covers (6) of int8-mixed-bf16 + binary_replace_float_out_patterns = {} + for swap_binary_inputs in swap_binary_inputs_list: + binary_replace_float_out_patterns.update( + { + PostOpAttr( + "add", 1.0, "none", [], "" + ): generate_pattern_with_binary( + aten.add.Tensor, + get_qlinear_pt2e_pattern(x_scale_zp_are_tensors), + KeywordArg("other"), + dtype_convert=True, + swap_inputs=swap_binary_inputs, + ), + } + ) + for ( + binary_unary_attr, + patterns, + ) in binary_replace_float_out_patterns.items(): + _register_qlinear_post_op_fusion_pass( + patterns, + 5, # pass_number + qlinear_binary_op, # computation_op + binary_unary_attr, + ) + + +@functools.lru_cache(None) +def _register_quantization_weight_pack_pass(): + # Step 1: Dequant promotion for int8-mixed-fp32/bf16 + _register_dequant_promotion() + + # Step 2: QConv weight prepack + _register_qconv_weight_prepack() + + # Step 3: QLinear weight prepack + _register_qlinear_weight_prepack() + _register_linear_dynamic_fp16_weight_prepack() + + # Step 4: weight prepack for SmoothQuant from Torchao + _register_smooth_quant_int_mm_pattern() + + # Step 5: QLinear post op Fusion + if not torch.ops.mkldnn._is_mkldnn_acl_supported(): + # skip fusion on ARM + _register_qconv_unary_fusion() + _register_qconv_binary_fusion() + _register_qlinear_unary_fusion() + _register_qlinear_binary_fusion() + + +def quant_lift_up(module_graph: torch.fx.graph.Graph): + """ + Lift up the quant node before view like nodes. It can benefit performance + of Attention like block. For example, we have the pattern as: + + DQ + DQ LINEAR + LINEAR VIEW + VIEW PERMUTE + PERMUTE TRANSPOSE + Q Q + DQ DQ + Matmul + DIV + ADD + SOFTMAX + + We want to lift up the the quant nodes from matmul before view like nodes + as the output of Linear node. + + DQ + DQ LINEAR + LINEAR Q + Q VIEW + VIEW PERMUTE + PERMUTE TRANSPOSE + DQ DQ + Matmul + DIV + ADD + SOFTMAX + + It produces a DQ->LINEAR->Q pattern which can be fused by backend. + """ + + def is_view_op(node): + return node.op == "call_function" and node.target in _VIEW_OPS + + for node in module_graph.nodes: + # Leslie: Here we verify that the quant node has exactly + # one input FX node, with constant scalar value for scale and zero point. + # For the case input of quant node has more than one input FX nodes, + # extend the implementation to lift up all the connected nodes + # before the view nodes to keep the topological order. + if ( + node.op == "call_function" + and node.target in _PER_TENSOR_QUANTIZE_OPS + and len(node.all_input_nodes) == 1 + and is_view_op(node.all_input_nodes[0]) + ): + quant_node = node + input_node_of_quant = quant_node.args[0] + + # Check the nodes along lift up path has only 1 user node + # Propagate view like node to find where to insert the new quant node + could_lift_up = True + current_node = quant_node + input_node = current_node.args[0] + while is_view_op(input_node): + if len(input_node.users) != 1: + could_lift_up = False + break + current_node = input_node + input_node = current_node.args[0] + + # Further check the input node of the first view node has only 1 user node + if could_lift_up and len(input_node.users) == 1: + # Replace dequant's input from quant to quant's input + quant_node.replace_all_uses_with(input_node_of_quant) + # Insert the new quant node + with module_graph.inserting_before(current_node): + new_quant_node = module_graph.node_copy(quant_node) + input_node.replace_all_uses_with(new_quant_node) + + # Update inputs of new_quant_node + def maybe_replace_node(n: torch.fx.Node) -> torch.fx.Node: + if n == input_node_of_quant: + return input_node + else: + return n + + new_args = map_arg(new_quant_node.args, maybe_replace_node) + new_kwargs = map_arg(new_quant_node.kwargs, maybe_replace_node) + new_quant_node.args = new_args # type: ignore[assignment] + new_quant_node.kwargs = new_kwargs # type: ignore[assignment] + module_graph.erase_node(quant_node) diff --git a/torchao/quantization/pt2e/lowering.py b/torchao/quantization/pt2e/lowering.py index 5491623b66..76dad800cd 100644 --- a/torchao/quantization/pt2e/lowering.py +++ b/torchao/quantization/pt2e/lowering.py @@ -24,7 +24,7 @@ def lower_pt2e_quantized_to_x86( * `example_inputs` (tuple[torch.Tensor, ...]): example inputs for the model. Return: - A GraphModule lowered to x86 backend. + A module lowered to x86 backend. """ def _post_autograd_decomp_table(): # type: ignore[no-untyped-def] diff --git a/torchao/quantization/pt2e/quantizer/x86_inductor_quantizer.py b/torchao/quantization/pt2e/quantizer/x86_inductor_quantizer.py index 9b49cadf77..cc296ebe33 100644 --- a/torchao/quantization/pt2e/quantizer/x86_inductor_quantizer.py +++ b/torchao/quantization/pt2e/quantizer/x86_inductor_quantizer.py @@ -1625,3 +1625,17 @@ def _same_shape(n1: Node, n2: Node): def validate(self, model: torch.fx.GraphModule) -> None: pass + + +# Register Inductor fusion passes +import torch._inductor.config + +from torchao.quantization.pt2e.inductor_passes.x86 import ( + _register_quantization_weight_pack_pass, + quant_lift_up, +) +from torchao.utils import TORCH_VERSION_AT_LEAST_2_8 + +if TORCH_VERSION_AT_LEAST_2_8: + torch._inductor.config.pre_grad_custom_pass = quant_lift_up + _register_quantization_weight_pack_pass() From 4ee2ee1ab7c9f2b52cbffbf2c82ceab66926e53d Mon Sep 17 00:00:00 2001 From: Thien Tran Date: Sat, 10 May 2025 20:31:28 +0700 Subject: [PATCH 015/165] [optim] Fix low-bit optim when used with FSDP2+CPUOffload (#2195) --- test/test_low_bit_optim.py | 25 ++++++++++++++++++------- torchao/optim/adam.py | 5 +++++ 2 files changed, 23 insertions(+), 7 deletions(-) diff --git a/test/test_low_bit_optim.py b/test/test_low_bit_optim.py index 43941329e1..e116bf610e 100644 --- a/test/test_low_bit_optim.py +++ b/test/test_low_bit_optim.py @@ -11,7 +11,11 @@ import pytest import torch from torch import nn -from torch.distributed._composable.fsdp import fully_shard +from torch.distributed._composable.fsdp import ( + fully_shard, + CPUOffloadPolicy, + OffloadPolicy, +) from torch.testing._internal.common_distributed import skip_if_lt_x_gpu from torch.testing._internal.common_fsdp import FSDPTest from torch.testing._internal.common_utils import ( @@ -427,16 +431,21 @@ def world_size(self) -> int: @skip_if_lt_x_gpu(_FSDP_WORLD_SIZE) @skip_if_rocm("ROCm enablement in progress") def test_fsdp2(self): - optim_classes = [optim.AdamW8bit, optim.AdamW4bit] + # we do this to avoid all combinations + args_list = [ + (optim.AdamW8bit, OffloadPolicy), + (optim.AdamW4bit, OffloadPolicy), + (optim.AdamW8bit, CPUOffloadPolicy), + ] if torch.cuda.get_device_capability() >= (8, 9): - optim_classes.append(optim.AdamWFp8) + args_list.append((optim.AdamWFp8, OffloadPolicy)) self.run_subtests( - {"optim_cls": optim_classes}, + {"args": args_list}, self._test_fsdp2, ) - def _test_fsdp2(self, optim_cls): + def _test_fsdp2(self, args): import torch.distributed as dist import torch.distributed.checkpoint as dcp import torch.utils._pytree as pytree @@ -447,6 +456,8 @@ def _test_fsdp2(self, optim_cls): TransformerBlock, ) + optim_cls, offload_policy = args + batch_size = 3 vocab_size = 1024 seq_len = 64 @@ -466,8 +477,8 @@ def _test_fsdp2(self, optim_cls): fsdp_model = copy.deepcopy(base_model) for m in fsdp_model.modules(): if isinstance(m, TransformerBlock): - fully_shard(m) - fully_shard(fsdp_model) + fully_shard(m, offload_policy=offload_policy) + fully_shard(fsdp_model, offload_policy=offload_policy) fsdp_optim = optim_cls(fsdp_model.parameters(), lr=1e-2) torch.manual_seed(42 + self.rank + 1) diff --git a/torchao/optim/adam.py b/torchao/optim/adam.py index cba86897d9..ddbdc8b12f 100644 --- a/torchao/optim/adam.py +++ b/torchao/optim/adam.py @@ -83,6 +83,11 @@ def _new_buffer(self, p: Tensor, signed: bool): stride=p.stride(), ) + # when there is CPU offload, p.device is cpu, but device_mesh.device_type is cuda. + # DTensor.from_local() will move local_tensor to device_mesh.device_type. + # hence, we need to manually move it back to CPU. + # https://github.com/pytorch/pytorch/blob/bc4cf1c1/torch/distributed/tensor/_api.py#L410-L415 + out = out.to(p.device) return out @torch.no_grad() From 34f66b6382ae20b7fed55d15335da3096ba9e101 Mon Sep 17 00:00:00 2001 From: Peter Yeh Date: Sun, 11 May 2025 19:54:59 -0700 Subject: [PATCH 016/165] Skip ROCm MoE Quantization (#2191) Add ROCm support skip for MoE quantization tests This commit introduces a conditional skip for the MoE quantization tests when running on ROCm, indicating that support for this feature is still under development. The change ensures that tests do not fail prematurely in environments lacking complete ROCm functionality. Test Plan: - Run tests in environments with and without ROCm support to verify the skip behavior. --- test/quantization/test_moe_quant.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/test/quantization/test_moe_quant.py b/test/quantization/test_moe_quant.py index 71aadaf345..425b881dba 100644 --- a/test/quantization/test_moe_quant.py +++ b/test/quantization/test_moe_quant.py @@ -1,5 +1,6 @@ import unittest +import pytest import torch from parameterized import parameterized @@ -32,6 +33,12 @@ is_sm_at_least_90, ) +if torch.version.hip is not None: + pytest.skip( + "ROCm support for MoE quantization is under development", + allow_module_level=True, + ) + class TestMoEQuantCompile(unittest.TestCase): DEFAULT_PARAMS = (512, 256, 8, 2) # hidden_dim, expert_dim, num_experts, top_k From 66eb801b0c73581c74acd84f68584977697e3994 Mon Sep 17 00:00:00 2001 From: Mark Saroufim Date: Sun, 11 May 2025 21:24:49 -0700 Subject: [PATCH 017/165] Forward fix lint (#2197) * Forward fix lint * added some minor docs * Update CONTRIBUTING.md Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> --- CONTRIBUTING.md | 10 +++++++--- test/test_low_bit_optim.py | 2 +- 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 04cc582066..90588cc4d3 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -2,9 +2,6 @@ We want to make contributing to this project as easy and transparent as possible. -## Our Development Process -... (in particular how this is synced with internal changes to the project) - ## Pull Requests We actively welcome your pull requests. @@ -15,6 +12,13 @@ We actively welcome your pull requests. 5. Make sure your code lints. 6. If you haven't already, complete the Contributor License Agreement ("CLA"). +## Linting + +We use [ruff](https://beta.ruff.rs/docs/) for linting. +1. `pip install ruff==0.11.6` +2. `ruff check --fix` +3. `ruff format .` + ## Contributor License Agreement ("CLA") In order to accept your pull request, we need you to submit a CLA. You only need to do this once to work on any of Meta's open source projects. diff --git a/test/test_low_bit_optim.py b/test/test_low_bit_optim.py index e116bf610e..08fdfa569f 100644 --- a/test/test_low_bit_optim.py +++ b/test/test_low_bit_optim.py @@ -12,9 +12,9 @@ import torch from torch import nn from torch.distributed._composable.fsdp import ( - fully_shard, CPUOffloadPolicy, OffloadPolicy, + fully_shard, ) from torch.testing._internal.common_distributed import skip_if_lt_x_gpu from torch.testing._internal.common_fsdp import FSDPTest From 9b1256fed12b6fca7ca07c1270b138d91667e166 Mon Sep 17 00:00:00 2001 From: Jesse Cai Date: Mon, 12 May 2025 10:03:44 -0400 Subject: [PATCH 018/165] 2:4 activation sparsity packing kernels (#2012) This PR is meant to give users the ability to accelerate LLMs with 2:4 activation sparsity, using the approach outlined in our ICLR workshop paper: https://arxiv.org/abs/2503.16672 The main contribution is a cutlass 24_fp8_pack kernel that is able to relatively efficiently calculate the packed data and metadata given a normal dense tensor, which I've copied over from xFormers. ### Performance Benchmarks ``` python benchmarks/benchmark_e2e_fp8_sparse_linear.py | num_tokens | bf16_latency (us) | bf16_c_latency (us) | fp8_c_time (us) | fp8_c_sparse_time (us) | fp8_c_activation_sparse_time (us) | speedup | |-------------:|--------------------:|----------------------:|------------------:|-------------------------:|------------------------------------:|----------:| | 64 | 166.816 | 163.04 | 103.008 | 74.304 | 102.816 | 1.00187 | | 128 | 156.256 | 151.52 | 99.936 | 75.456 | 102.048 | 0.979304 | | 256 | 172.288 | 159.584 | 114.08 | 82.432 | 111.072 | 1.02708 | | 512 | 218.88 | 204.608 | 144.096 | 114.56 | 139.488 | 1.03304 | | 1024 | 394.4 | 392.544 | 251.104 | 196.416 | 227.904 | 1.1018 | | 2048 | 764.608 | 734.816 | 480.704 | 381.152 | 426.688 | 1.12659 | | 4096 | 1658.82 | 1623.58 | 901.344 | 779.008 | 843.392 | 1.06871 | ``` ### Tests ``` pytest tests/sparsity/test_activation24.py ``` --- benchmarks/benchmark_e2e_fp8_sparse_linear.py | 133 +++++ ...rk_rowwise_scaled_linear_sparse_cutlass.py | 54 +- .../benchmark_sparse_conversion_cutlass.py | 124 +++++ e2e_fp8_sparse.csv | 8 + ...led_linear_sparse_cutlass_time_results.csv | 4 + setup.py | 1 + test/sparsity/test_activation24.py | 143 ++++++ .../cuda/activation24/compute_sparse_tile.h | 103 ++++ .../cuda/activation24/sparse24_metadata.h | 81 +++ torchao/csrc/cuda/activation24/sparsify24.cu | 419 ++++++++++++++++ torchao/csrc/cuda/activation24/static_sort.h | 88 ++++ torchao/csrc/cuda/activation24/warp_tensor.h | 468 ++++++++++++++++++ .../rowwise_scaled_linear_sparse_cutlass.cuh | 2 +- torchao/ops.py | 84 ++++ .../prototype/sparsity/activation/__init__.py | 0 .../sparsity/activation/srelu_linear.py | 87 ++++ .../prototype/sparsity/activation/utils.py | 115 +++++ torchao/sparsity/utils.py | 2 +- 18 files changed, 1906 insertions(+), 10 deletions(-) create mode 100644 benchmarks/benchmark_e2e_fp8_sparse_linear.py create mode 100644 benchmarks/benchmark_sparse_conversion_cutlass.py create mode 100644 e2e_fp8_sparse.csv create mode 100644 rowwise_scaled_linear_sparse_cutlass_time_results.csv create mode 100644 test/sparsity/test_activation24.py create mode 100644 torchao/csrc/cuda/activation24/compute_sparse_tile.h create mode 100644 torchao/csrc/cuda/activation24/sparse24_metadata.h create mode 100644 torchao/csrc/cuda/activation24/sparsify24.cu create mode 100644 torchao/csrc/cuda/activation24/static_sort.h create mode 100644 torchao/csrc/cuda/activation24/warp_tensor.h create mode 100644 torchao/prototype/sparsity/activation/__init__.py create mode 100644 torchao/prototype/sparsity/activation/srelu_linear.py create mode 100644 torchao/prototype/sparsity/activation/utils.py diff --git a/benchmarks/benchmark_e2e_fp8_sparse_linear.py b/benchmarks/benchmark_e2e_fp8_sparse_linear.py new file mode 100644 index 0000000000..fbab8c0671 --- /dev/null +++ b/benchmarks/benchmark_e2e_fp8_sparse_linear.py @@ -0,0 +1,133 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD 3-Clause license found in the +# LICENSE file in the root directory of this source tree. +import pandas as pd +import torch +from torch import nn +from tqdm import tqdm +from triton.testing import do_bench + +from torchao.prototype.sparsity.activation.srelu_linear import ( + SRELUFloat8SemiSparseDynamicActivationFloat8WeightConfig, +) +from torchao.prototype.sparsity.activation.utils import SquaredReLU +from torchao.quantization import ( + Float8DynamicActivationFloat8SemiSparseWeightConfig, + Float8DynamicActivationFloat8WeightConfig, + Float8MMConfig, + PerRow, + quantize_, +) + + +def benchmark_microseconds(f, *args): + return do_bench(lambda: f(*args), return_mode="median") * 1e3 + + +def benchmark(num_tokens, hidden_size=8192, intermediate_size=8192): + ffn_ref = ( + nn.Sequential( + nn.Linear(hidden_size, intermediate_size, bias=False), + SquaredReLU(), + nn.Linear(intermediate_size, hidden_size, bias=False), + ) + .to(torch.bfloat16) + .cuda() + ) + + input_tensor = torch.randn(num_tokens, hidden_size).to(torch.bfloat16).cuda() + fp16_time = benchmark_microseconds(ffn_ref, input_tensor) + + # bf16 + ffn_clone = ( + nn.Sequential( + nn.Linear(hidden_size, intermediate_size, bias=False), + SquaredReLU(), + nn.Linear(intermediate_size, hidden_size, bias=False), + ) + .to(torch.bfloat16) + .cuda() + ) + ffn_clone.forward = torch.compile(ffn_clone.forward, fullgraph=True) + fp16_c_time = benchmark_microseconds(ffn_clone, input_tensor) + + # fp8 + ffn_clone = ( + nn.Sequential( + nn.Linear(hidden_size, intermediate_size, bias=False), + SquaredReLU(), + nn.Linear(intermediate_size, hidden_size, bias=False), + ) + .to(torch.bfloat16) + .cuda() + ) + quantize_( + ffn_clone, + Float8DynamicActivationFloat8WeightConfig( + granularity=PerRow(), mm_config=Float8MMConfig(use_fast_accum=True) + ), + ) + ffn_clone.forward = torch.compile(ffn_clone.forward, fullgraph=True) + fp8_c_time = benchmark_microseconds(ffn_clone, input_tensor) + + # fp8 sparse + ffn_clone = ( + nn.Sequential( + nn.Linear(hidden_size, intermediate_size, bias=False), + SquaredReLU(), + nn.Linear(intermediate_size, hidden_size, bias=False), + ) + .to(torch.bfloat16) + .cuda() + ) + quantize_(ffn_clone, Float8DynamicActivationFloat8SemiSparseWeightConfig()) + ffn_clone.forward = torch.compile(ffn_clone.forward, fullgraph=True) + fp8_c_sparse_time = benchmark_microseconds(ffn_clone, input_tensor) + + # activation fp8 sparse + ffn_clone = ( + nn.Sequential( + nn.Linear(hidden_size, intermediate_size, bias=False), + # no Squared RELU since it will be fused into the second linear + nn.Linear(intermediate_size, hidden_size, bias=False), + ) + .to(torch.bfloat16) + .cuda() + ) + quantize_( + ffn_clone[0], + Float8DynamicActivationFloat8WeightConfig( + granularity=PerRow(), mm_config=Float8MMConfig(use_fast_accum=True) + ), + ) + quantize_( + ffn_clone, + SRELUFloat8SemiSparseDynamicActivationFloat8WeightConfig(), + filter_fn=lambda mod, fqn: "1" in fqn, + ) + ffn_clone.forward = torch.compile(ffn_clone.forward, fullgraph=True) + fp8_c_activation_sparse_time = benchmark_microseconds(ffn_clone, input_tensor) + + return { + "num_tokens": num_tokens, + "bf16_latency (us)": fp16_time, + "bf16_c_latency (us)": fp16_c_time, + "fp8_c_time (us)": fp8_c_time, + "fp8_c_sparse_time (us)": fp8_c_sparse_time, + "fp8_c_activation_sparse_time (us)": fp8_c_activation_sparse_time, + "speedup": fp8_c_time / fp8_c_activation_sparse_time, + } + + +if __name__ == "__main__": + with torch.no_grad(): + results = [] + for num_tokens in tqdm([64, 128, 256, 512, 1024, 2048, 4096]): + results.append(benchmark(num_tokens)) + torch.compiler.reset() + + df = pd.DataFrame(results) + df.to_csv("e2e_fp8_sparse.csv", index=False) + print(df.to_markdown(index=False)) diff --git a/benchmarks/benchmark_rowwise_scaled_linear_sparse_cutlass.py b/benchmarks/benchmark_rowwise_scaled_linear_sparse_cutlass.py index 40e2918805..3c07395e62 100644 --- a/benchmarks/benchmark_rowwise_scaled_linear_sparse_cutlass.py +++ b/benchmarks/benchmark_rowwise_scaled_linear_sparse_cutlass.py @@ -16,7 +16,7 @@ from torchao.sparsity.utils import create_semi_structured_tensor dtype = torch.bfloat16 -dtypeq_X = torch.float8_e5m2 +dtypeq_X = torch.float8_e4m3fn dtypeq_W = torch.float8_e4m3fn device = torch.device("cuda") @@ -25,7 +25,7 @@ def benchmark_microseconds(f, *args): return do_bench(lambda: f(*args), return_mode="median") * 1e3 -def get_problem(m: int, n: int, k: int): +def get_problem_cutlass(m: int, n: int, k: int): X_ref = torch.randn((m, k), dtype=dtype, device=device) W_ref = create_semi_structured_tensor(n, k, dtype=dtype).to(device) @@ -45,30 +45,68 @@ def get_problem(m: int, n: int, k: int): return (X_ref, W_ref), (Xq, X_scale, Wq_sparse, W_meta, W_scale, bias, out_dtype) +def get_problem_cusparselt(m: int, n: int, k: int): + X_ref = torch.randn((m, k), dtype=dtype, device=device) + W_ref = create_semi_structured_tensor(n, k, dtype=dtype).to(device) + + Xq = X_ref.to(dtypeq_W) + Wq = W_ref.to(dtypeq_W) + + Wqs = torch._cslt_compress(Wq) + + alg_id, split_k, split_k_one_kernel, _ = torch._C._cusparselt.mm_search( + Wqs, Xq.t(), None, None, None, False + ) + + return (Wqs, Xq.t(), None, None, dtype, False, alg_id, split_k, split_k_one_kernel) + + +def get_problem_scaled_mm(m: int, n: int, k: int): + X_ref = torch.randn((m, k), dtype=dtype, device=device) + W_ref = create_semi_structured_tensor(n, k, dtype=dtype).to(device) + + X_aqt = _float8_cutlass_quant(X_ref, dtypeq_W) + W_aqt = _float8_cutlass_quant(W_ref, dtypeq_W) + + Xq = X_aqt.tensor_impl.float8_data + Wq = W_aqt.tensor_impl.float8_data + X_scale = X_aqt.tensor_impl.scale.unsqueeze(0) + W_scale = W_aqt.tensor_impl.scale.unsqueeze(-1) + + return (Wq, Xq.t(), W_scale, X_scale, None, None, dtype) + + def benchmark(m: int, k: int, n: int): - ref_args, args = get_problem(m, n, k) + ref_args, args = get_problem_cutlass(m, n, k) fp16_time = benchmark_microseconds(torch.nn.functional.linear, *ref_args) rowwise_scaled_linear_sparse_cutlass_f8f8_time = benchmark_microseconds( rowwise_scaled_linear_sparse_cutlass_f8f8, *args ) + cslt_args = get_problem_cusparselt(m, n, k) + cusparselt_time = benchmark_microseconds(torch._cslt_sparse_mm, *cslt_args) + + fp8_args = get_problem_scaled_mm(m, n, k) + fp8_time = benchmark_microseconds(torch._scaled_mm, *fp8_args) + return { "m": m, "k": k, "n": n, "fp16_latency (ms)": fp16_time, + "fp8_latency (ms)": fp8_time, "rowwise_scaled_linear_sparse_cutlass_f8f8 latency (ms)": rowwise_scaled_linear_sparse_cutlass_f8f8_time, - "f8f8 speedup (d/s)": fp16_time - / rowwise_scaled_linear_sparse_cutlass_f8f8_time, + "cusparselt latency (ms)": cusparselt_time, + "f8f8 speedup (d/s)": fp8_time / rowwise_scaled_linear_sparse_cutlass_f8f8_time, } if __name__ == "__main__": - k_vals = (8192, 8192, 8192, 28672) - n_vals = (8192, 10240, 57344, 8192) + k_vals = (8192,) + n_vals = (8192,) results = [] - for m in tqdm([1 << i for i in range(10)]): + for m in tqdm([2048, 4096, 8192]): for n, k in zip(n_vals, k_vals): results.append(benchmark(m, k, n)) diff --git a/benchmarks/benchmark_sparse_conversion_cutlass.py b/benchmarks/benchmark_sparse_conversion_cutlass.py new file mode 100644 index 0000000000..ea45316def --- /dev/null +++ b/benchmarks/benchmark_sparse_conversion_cutlass.py @@ -0,0 +1,124 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD 3-Clause license found in the +# LICENSE file in the root directory of this source tree. +import pandas as pd +import torch +from triton.testing import do_bench + +from torchao.ops import ( + to_sparse_semi_structured_cutlass_sm9x_f8, +) +from torchao.quantization.quant_api import ( + _float8_cutlass_quant, + _float8_cutlass_quant_sparse, +) +from torchao.sparsity.utils import create_semi_structured_tensor + +dtype = torch.bfloat16 +dtypeq_X = torch.float8_e4m3fn +dtypeq_W = torch.float8_e4m3fn +device = torch.device("cuda") + + +def benchmark_microseconds(f, *args): + return do_bench(lambda: f(*args), return_mode="median") * 1e3 + + +def get_problem_cutlass(m: int, n: int, k: int): + X_ref = torch.randn((m, k), dtype=dtype, device=device) + W_ref = create_semi_structured_tensor(n, k, dtype=dtype).to(device) + + X_quant_func = _float8_cutlass_quant + W_quant_func = _float8_cutlass_quant_sparse + X_aqt = X_quant_func(X_ref, dtypeq_X) + W_aqt = W_quant_func(W_ref, dtypeq_W) + + Xq = X_aqt.tensor_impl.float8_data + X_scale = X_aqt.tensor_impl.scale + Wq_sparse = W_aqt.tensor_impl.sparse + W_meta = W_aqt.tensor_impl.meta + W_scale = W_aqt.tensor_impl.scale + bias = None + out_dtype = dtype + + return (X_ref, W_ref), (Xq, X_scale, Wq_sparse, W_meta, W_scale, bias, out_dtype) + + +def get_problem_cusparselt(m: int, n: int, k: int): + X_ref = torch.randn((m, k), dtype=dtype, device=device) + W_ref = create_semi_structured_tensor(n, k, dtype=dtype).to(device) + + Xq = X_ref.to(dtypeq_W) + Wq = W_ref.to(dtypeq_W) + + Wqs = torch._cslt_compress(Wq) + + alg_id, split_k, split_k_one_kernel, _ = torch._C._cusparselt.mm_search( + Wqs, Xq.t(), None, None, None, False + ) + + return (Wqs, Xq.t(), None, None, dtype, False, alg_id, split_k, split_k_one_kernel) + + +def get_problem_scaled_mm(m: int, n: int, k: int): + X_ref = torch.randn((m, k), dtype=dtype, device=device) + W_ref = create_semi_structured_tensor(n, k, dtype=dtype).to(device) + + X_aqt = _float8_cutlass_quant(X_ref, dtypeq_W) + W_aqt = _float8_cutlass_quant(W_ref, dtypeq_W) + + Xq = X_aqt.tensor_impl.float8_data + Wq = W_aqt.tensor_impl.float8_data + X_scale = X_aqt.tensor_impl.scale.unsqueeze(0) + W_scale = W_aqt.tensor_impl.scale.unsqueeze(-1) + + return (Wq, Xq.t(), W_scale, X_scale, None, None, dtype) + + +def benchmark(m, k): + torch.manual_seed(123) + W_ref = create_semi_structured_tensor(m, k, dtype=torch.float8_e4m3fn).cuda() + + # packed, meta = torch.ops.torchao.sparse_semi_structured_tile.default(W_ref, "", True) + cutlass_reference_args = (W_ref,) + cutlass_custom_args = (W_ref, "", True) + + cutlass_reference_compression_time = benchmark_microseconds( + to_sparse_semi_structured_cutlass_sm9x_f8, *cutlass_reference_args + ) + cutlass_custom_compression_time = benchmark_microseconds( + torch.ops.torchao.sparse_semi_structured_tile.default, *cutlass_custom_args + ) + + return { + "cutlass_reference (ms)": cutlass_reference_compression_time, + "cutlass_custom (ms)": cutlass_custom_compression_time, + } + + +def profile(): + torch.manual_seed(123) + W_ref = create_semi_structured_tensor(8192, 8192, dtype=torch.float8_e4m3fn).cuda() + + # clear cache + new_val = torch.empty(10000, 10000, device="cuda") + new_val[:, :] = 0 + + packed, meta = torch.ops.torchao.sparse_semi_structured_tile.default( + W_ref, "", True + ) + + +if __name__ == "__main__": + results = [] + for m in (2048, 4096, 8192): + results.append(benchmark(m, 8192)) + + df = pd.DataFrame(results) + df.to_csv("rowwise_scaled_linear_sparse_cutlass_time_results.csv", index=False) + print(df.to_markdown(index=False)) + + # print("PROFILING") + # profile() diff --git a/e2e_fp8_sparse.csv b/e2e_fp8_sparse.csv new file mode 100644 index 0000000000..05a80e13b7 --- /dev/null +++ b/e2e_fp8_sparse.csv @@ -0,0 +1,8 @@ +num_tokens,bf16_latency (us),bf16_c_latency (us),fp8_c_time (us),fp8_c_sparse_time (us),fp8_c_activation_sparse_time (us),speedup +64,166.81599617004395,163.03999722003937,103.00800204277039,74.30399954319,102.81600058078766,1.0018674278409796 +128,156.25600516796112,151.5199989080429,99.93600100278854,75.45600086450577,102.04800218343735,0.9793038458817415 +256,172.28800058364868,159.58400070667267,114.07999694347382,82.43200182914734,111.07199639081955,1.0270815385551393 +512,218.87999773025513,204.6079933643341,144.0960019826889,114.56000059843063,139.48799669742584,1.0330351384661336 +1024,394.4000005722046,392.5440013408661,251.10399723052979,196.4160054922104,227.90400683879852,1.1017972027501084 +2048,764.6080255508423,734.8160147666931,480.70400953292847,381.1520040035248,426.68798565864563,1.1265937305239622 +4096,1658.8159799575806,1623.5840320587158,901.3440012931824,779.0079712867737,843.392014503479,1.0687129896811043 diff --git a/rowwise_scaled_linear_sparse_cutlass_time_results.csv b/rowwise_scaled_linear_sparse_cutlass_time_results.csv new file mode 100644 index 0000000000..09bea2f9bd --- /dev/null +++ b/rowwise_scaled_linear_sparse_cutlass_time_results.csv @@ -0,0 +1,4 @@ +m,k,n,fp16_latency (ms),fp8_latency (ms),rowwise_scaled_linear_sparse_cutlass_f8f8 latency (ms),cusparselt latency (ms),f8f8 speedup (d/s) +2048,8192,8192,345.7919955253601,243.13600361347198,159.7760021686554,634.2080235481262,1.5217304245528933 +4096,8192,8192,756.3199996948242,500.2880096435547,363.647997379303,628.7999749183655,1.3757480124982768 +8192,8192,8192,1433.568000793457,982.5279712677002,895.3920006752014,859.935998916626,1.0973160029649482 diff --git a/setup.py b/setup.py index 3cb08b3a35..24d7be20de 100644 --- a/setup.py +++ b/setup.py @@ -382,6 +382,7 @@ def get_extensions(): "to_sparse_semi_structured_cutlass_sm9x", "to_sparse_semi_structured_cutlass_sm9x_f8.cu", ), + os.path.join(extensions_cuda_dir, "activation24", "sparsify24.cu"), ] for dtypes in ["e4m3e4m3", "e4m3e5m2", "e5m2e4m3", "e5m2e5m2"]: cutlass_90a_sources.append( diff --git a/test/sparsity/test_activation24.py b/test/sparsity/test_activation24.py new file mode 100644 index 0000000000..65b7cfd8d2 --- /dev/null +++ b/test/sparsity/test_activation24.py @@ -0,0 +1,143 @@ +import torch +import torch.nn.functional as F + +from torchao.ops import to_sparse_semi_structured_cutlass_sm9x_f8 +from torchao.quantization import ( + Float8DynamicActivationFloat8WeightConfig, + Float8MMConfig, + PerRow, + quantize_, +) + +torch.sparse.SparseSemiStructuredTensor._FORCE_CUTLASS = True + +import copy +import unittest + +from torchao.prototype.sparsity.activation.srelu_linear import ( + SRELUFloat8SemiSparseDynamicActivationFloat8WeightConfig, +) +from torchao.sparsity import sparsify_ +from torchao.sparsity.utils import create_semi_structured_tensor +from torchao.utils import is_sm_at_least_90 + + +@unittest.skipIf(not is_sm_at_least_90(), "Need cuda arch greater than SM90") +def test_sparse24_sm90_sparsify_identity( + M=512, K=1024, fp8=torch.float8_e4m3fn +) -> None: + torch.manual_seed(0) + A_sp_ref = create_semi_structured_tensor(M, K, dtype=torch.bfloat16).cuda() + + # Test with act="identity" + A_packed_ref, A_mdata_ref = to_sparse_semi_structured_cutlass_sm9x_f8( + A_sp_ref.to(fp8) + ) + A_packed, A_mdata = torch.ops.torchao.sparse24_sm90_sparsify( + A_sp_ref, + "cutlass", + "identity", + sp_selection_algo="largest", + dtype=A_packed_ref.dtype, + ) + + # Note: sparsification is not deterministic (eg if 3 items have the same value in a block of 4 for instance) + # so we allow a tiny margin for error + assert (A_packed != A_packed_ref).float().mean().item() < 0.005 + assert (A_mdata != A_mdata_ref).float().mean().item() < 0.005 + # The sum should always match though + assert torch.allclose(A_packed.float().sum(), A_packed_ref.float().sum()) + + +@unittest.skipIf(not is_sm_at_least_90(), "Need cuda arch greater than SM90") +def test_sparse24_sm90_sparsify_identity_scaled( + M=512, K=1024, fp8=torch.float8_e4m3fn +) -> None: + torch.manual_seed(0) + A_dense = create_semi_structured_tensor(M, K, dtype=torch.bfloat16).cuda() + A_scale = torch.randn([M, 1], device="cuda", dtype=torch.float32).abs() + 0.1 + A_sp_ref = (A_dense / A_scale).bfloat16() + + A_packed_ref, A_mdata_ref = to_sparse_semi_structured_cutlass_sm9x_f8( + A_sp_ref.to(fp8) + ) + A_packed, A_mdata = torch.ops.torchao.sparse24_sm90_sparsify( + A_dense, + "cutlass", + "identity", + sp_selection_algo="largest", + dtype=A_packed_ref.dtype, + scale=A_scale, + ) + assert (A_packed != A_packed_ref).float().mean().item() < 0.05 + assert (A_mdata != A_mdata_ref).float().mean().item() < 0.005 + assert torch.allclose( + A_packed.float().sum(), A_packed_ref.float().sum(), rtol=0.001 + ) + + +@unittest.skipIf(not is_sm_at_least_90(), "Need cuda arch greater than SM90") +def test_sparse24_sm90_sparsify_srelu(M=512, K=1024, fp8=torch.float8_e4m3fn) -> None: + torch.manual_seed(0) + A_dense = create_semi_structured_tensor(M, K, dtype=torch.bfloat16).cuda() + A_sp_ref = (A_dense.float().relu() ** 2).bfloat16() + + # Test with act="srelu" + # NOTE: Due to different rounding strategies, and way more zeros, we don't have the exact same + # bitwise packed values, so we bump up the margin here + A_packed_ref, _A_mdata_ref = to_sparse_semi_structured_cutlass_sm9x_f8( + A_sp_ref.to(fp8) + ) + A_packed, _A_mdata = torch.ops.torchao.sparse24_sm90_sparsify( + A_dense, + "cutlass", + "srelu", + sp_selection_algo="largest", + dtype=A_packed_ref.dtype, + ) + assert torch.allclose( + A_packed.float().sum(), A_packed_ref.float().sum(), rtol=0.005 + ) + assert (A_packed != A_packed_ref).float().mean().item() < 0.1 + + +@unittest.skipIf(not is_sm_at_least_90(), "Need cuda arch greater than SM90") +def test_srelu_fp8_semi_sparse_activation_linear(M=512, K=2048, N=1024): + with torch.no_grad(): + torch.manual_seed(0) + input_tensor = create_semi_structured_tensor(M, K, dtype=torch.bfloat16).cuda() + # we have to wrap in a sequential block for quantize_ to work properly + reference_linear = torch.nn.Sequential( + torch.nn.Linear(K, N, bias=False).cuda().to(torch.bfloat16) + ) + reference_linear_copy = copy.deepcopy(reference_linear) + + quantize_( + reference_linear, + Float8DynamicActivationFloat8WeightConfig( + granularity=PerRow(), mm_config=Float8MMConfig(use_fast_accum=False) + ), + ) + + # define reference implementation + def srelu_linear(x): + x = F.relu(x) ** 2 + return reference_linear(x) + + reference_srelu = torch.compile(srelu_linear, fullgraph=True) + + # this only works with fullgraph=True, errors in eager + # TODO figure out exactly why this happens + sparsify_( + reference_linear_copy, + SRELUFloat8SemiSparseDynamicActivationFloat8WeightConfig(), + ) + # (reference_linear_copy) + reference_linear_copy.forward = torch.compile( + reference_linear_copy.forward, fullgraph=True + ) + + reference_output = reference_srelu(input_tensor) + custom_output = reference_linear_copy(input_tensor) + + torch.testing.assert_close(reference_output, custom_output, rtol=0.1, atol=0.01) diff --git a/torchao/csrc/cuda/activation24/compute_sparse_tile.h b/torchao/csrc/cuda/activation24/compute_sparse_tile.h new file mode 100644 index 0000000000..e92a368199 --- /dev/null +++ b/torchao/csrc/cuda/activation24/compute_sparse_tile.h @@ -0,0 +1,103 @@ +#pragma once + +#include +#include + +// #include "sparse24_pack.h" +#include +#include +#include +#include +#include +#include +#include "static_sort.h" + +// Given 4x4 values, computes the selected indices that will remain after 2:4 +// sparsification, as a bitmask. +// NOTE: Algorithms might select LESS than 8 values in total in some cases. + +namespace torchao { + +using cutlass::uint1b_t; +using cutlass::uint2b_t; +using cutlass::uint4b_t; +using uint8b_t = cutlass::integer_subbyte<8, false>; +using ElementInputE = uint16_t; + +// Operations that we can apply to rank the values +struct IdentityOp { + template + static T CUTLASS_HOST_DEVICE to_ordered(T const& x) { + return x; + } +}; +// Can be applied to rank based on absolute value +struct AbsOp { + template + static uint16_t CUTLASS_HOST_DEVICE to_ordered(T const& x) { + return cutlass::abs(x).storage; + } +}; + +template +struct TileValueOrderedT { + using ElementCmp = decltype(Pointwise::to_ordered(Element(0))); + union { + struct { + ElementCmp cmp; + Element value; + uint2b_t col; + uint2b_t row; + } parts; + uint32_t raw; + }; + CUTLASS_DEVICE bool operator<( + TileValueOrderedT const& other) const { + return parts.cmp < other.parts.cmp; + } + CUTLASS_DEVICE TileValueOrderedT() {} + CUTLASS_DEVICE TileValueOrderedT(Element value, int col, int row = 0) { + parts.value = value; + parts.row = uint2b_t{row}; + parts.col = uint2b_t{col}; + parts.cmp = Pointwise::to_ordered(value); + } +}; + +template +struct Top2 { + template + CUTLASS_DEVICE int operator()( + cutlass::Array values, + cutlass::Array& packed) const { + using TileValueOrdered = TileValueOrderedT; + cutlass::Array values_ordered; + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < 4; ++i) { + values_ordered[i] = TileValueOrdered(values[i].get(), i); + } + StaticSort<4> sorter; + sorter(values_ordered); + TileValueOrdered first, second; + first = values_ordered[3]; + second = values_ordered[2]; + if (first.parts.col > second.parts.col) { + TileValueOrdered tmp; + tmp = first; + first = second; + second = tmp; + } + packed[0] = first.parts.value; + packed[1] = second.parts.value; + // returns bitmask of select elements + return first.parts.col | (second.parts.col << 2); + } +}; + +template +void named_algorithms_oneway(T callback) { + callback(Top2(), "largest"); + callback(Top2(), "largest_abs"); +} + +} // namespace torchao diff --git a/torchao/csrc/cuda/activation24/sparse24_metadata.h b/torchao/csrc/cuda/activation24/sparse24_metadata.h new file mode 100644 index 0000000000..46b268407b --- /dev/null +++ b/torchao/csrc/cuda/activation24/sparse24_metadata.h @@ -0,0 +1,81 @@ +#pragma once +#include +#include +#include "compute_sparse_tile.h" + +namespace torchao{ +template +struct CutlassToAt; + +template <> +struct CutlassToAt { + static auto constexpr value = at::ScalarType::Half; +}; +template <> +struct CutlassToAt { + static auto constexpr value = at::ScalarType::BFloat16; +}; +template <> +struct CutlassToAt { + static auto constexpr value = at::ScalarType::Float8_e4m3fn; +}; +template <> +struct CutlassToAt { + static auto constexpr value = at::ScalarType::UInt16; +}; +template <> +struct CutlassToAt { + static auto constexpr value = at::ScalarType::Int; +}; +template <> +struct CutlassToAt { + static auto constexpr value = at::ScalarType::Byte; +}; +template <> +struct CutlassToAt { + static auto constexpr value = at::ScalarType::Float; +}; + +struct MetadataCutlass8bitsSm90 { + template + static std::tuple createTensors(at::Tensor input) { + auto n_rows = input.size(0); + auto n_cols = input.size(1); + TORCH_CHECK(n_cols % 128 == 0); // aligned metadata + TORCH_CHECK(n_rows % 64 == 0); // aligned metadata + + at::Tensor packed = at::empty( + {n_rows, n_cols / 2}, + input.options().dtype(CutlassToAt::value)); + at::Tensor mdata = + at::empty({n_rows, n_cols / 8}, input.options().dtype(at::ScalarType::Byte)); + return std::make_tuple(packed, mdata); + } + static CUTLASS_HOST_DEVICE int64_t + mdataBlockPtrOffset(int row, int col, int64_t n_rows) { + constexpr int kStrideRow = 16; + return row * kStrideRow + (col / 128 * n_rows * 16) + (col % 128) / 8; + } +}; + +struct MetadataCusparseLt16bitsSm90 { + template + static std::tuple createTensors(at::Tensor input) { + auto n_rows = input.size(0); + auto n_cols = input.size(1); + int packed_elements = n_rows * n_cols / 2; + int mdata_bytes = n_rows * n_cols / 8; + + // We assume 2 bytes per element + at::Tensor sparse_packed = at::empty( + {int64_t(packed_elements + mdata_bytes / sizeof(ElementOut))}, + input.options().dtype(CutlassToAt::value)); + using namespace torch::indexing; + return std::make_tuple( + sparse_packed, + sparse_packed.index({Slice(packed_elements, None)}) + .view(at::ScalarType::Byte)); + } +}; + +} // namespace torchao diff --git a/torchao/csrc/cuda/activation24/sparsify24.cu b/torchao/csrc/cuda/activation24/sparsify24.cu new file mode 100644 index 0000000000..e8949fa5d8 --- /dev/null +++ b/torchao/csrc/cuda/activation24/sparsify24.cu @@ -0,0 +1,419 @@ +#include +#include +#include +#include +#include +#include + +#include "compute_sparse_tile.h" +#include "sparse24_metadata.h" +#include "warp_tensor.h" + +using namespace torchao; + +namespace { +// ############################################ +// # CUSPARSELT - 16bits +// ############################################ +template +__global__ void sparse24_sm90_cusparselt16bits_sparsify_kernel(P p); +struct MetadataCusparseLt16bits { + static constexpr auto kBlockSize0 = 64; + static constexpr auto kBlockSize1 = 64; + static constexpr auto kNumWarpsPerCTA = 2; + + template + static std::tuple createTensors(at::Tensor input) { + return MetadataCusparseLt16bitsSm90::template createTensors( + input); + } + + template + CUTLASS_DEVICE static void run(P p) { + using Element = typename P::ElementIn; // same as ElementOut + + constexpr int kSmemStride0 = kBlockSize1 / 8 + 1; + __shared__ uint32_t smem[kBlockSize0 * kSmemStride0]; + + int block_id = blockIdx.x; + int num_blocks_rows = p.n_rows / kBlockSize0; + int num_blocks_cols = p.n_cols / kBlockSize1; + + int block_row = (block_id / num_blocks_cols) * kBlockSize0; + int block_col = (block_id % num_blocks_cols) * kBlockSize1; + + int warp_id = threadIdx.x / 32; + WarpTensor load_dense_tensor; + int warp_row = warp_id * load_dense_tensor.kRows; + CUTLASS_PRAGMA_UNROLL + for (int it_row = 0; it_row < kBlockSize0; + it_row += kNumWarpsPerCTA * load_dense_tensor.kRows) { + CUTLASS_PRAGMA_UNROLL + for (int it_col = 0; it_col < kBlockSize1; + it_col += load_dense_tensor.kCols) { + // gmem -> RF + load_dense_tensor.load( + p.input_ptr + (it_row + warp_row + block_row) * p.input_s0 + + it_col + block_col, + p.input_s0); + + // RF -> RF (sparsify) + auto [packed, mdata] = load_dense_tensor.sparsify_pack(p.algo); + + // RF -> RF (apply sparsity) + packed.data = p.activation(packed.data); + + // RF -> gmem (packed data) + packed.store( + p.output_ptr + (it_row + warp_row + block_row) * p.output_s0 + + (it_col + block_col) / 2, + p.output_s0); + + // RF -> smem (mdata) + mdata.template store_32bits( + smem + (warp_row + it_row) * kSmemStride0 + it_col / 8); + } + } + __syncthreads(); + + WarpTensor mdata_tensor; + int warp_col = warp_id * (8 * mdata_tensor.kCols); + static_assert(kBlockSize0 % mdata_tensor.kRows == 0); + static_assert(kBlockSize1 % mdata_tensor.kCols == 0); + CUTLASS_PRAGMA_UNROLL + for (int it_row = 0; it_row < kBlockSize0; it_row += mdata_tensor.kRows) { + CUTLASS_PRAGMA_UNROLL + for (int it_col = 0; it_col < kBlockSize1; + it_col += kNumWarpsPerCTA * (8 * mdata_tensor.kCols)) { + mdata_tensor.template load_32bits( + smem + it_row * kSmemStride0 + (it_col + warp_col) / 8); + + int current_col = warp_col + it_col + block_col; + int current_row = it_row + block_row; + int idx = (current_col / 32) * 256; + idx += + ((current_row % 8) * 8 + ((current_row % 64) / 16) * 64 + + (current_row / 64) * 8 * p.n_cols); + store_metadata_reordered(mdata_tensor, p.mdata_ptr + idx); + } + } + } + + template + static void launch_kernel(P p) { + TORCH_CHECK( + p.scale_ptr == nullptr, "cusparselt kernel does not support scaling"); + int num_blocks = cutlass::ceil_div(p.n_cols, kBlockSize1) * + cutlass::ceil_div(p.n_rows, kBlockSize0); + sparse24_sm90_cusparselt16bits_sparsify_kernel

+ <<>>(p); + } +}; + +template +__global__ void sparse24_sm90_cusparselt16bits_sparsify_kernel(P p) { + MetadataCusparseLt16bits::run(p); +} + +// ############################################ +// # CUTLASS - 8bits +// ############################################ +template +__global__ void sparse24_sm90_cutlass8bits_sparsify_kernel(P p); + +struct MetadataCutlass8bits { + static constexpr int64_t kBlockSize0 = 32; + static constexpr int64_t kBlockSize1 = 128; + static constexpr int64_t kNumWarpsPerCTA = 2; + static constexpr int64_t kThreadsPerCTA = kNumWarpsPerCTA * 32; + + template + static std::tuple createTensors(at::Tensor input) { + TORCH_CHECK(input.size(0) % kBlockSize0 == 0); + TORCH_CHECK(input.size(1) % kBlockSize1 == 0); + return MetadataCutlass8bitsSm90::template createTensors(input); + } + + template + CUTLASS_DEVICE static void run(P p) { + using ElementIn = typename P::ElementIn; + using ElementOut = typename P::ElementOut; + using ElementScale = typename P::ElementScale; + + __shared__ ElementScale smem_scales[kBlockSize0]; + + int block_id = blockIdx.x; + int num_blocks_rows = p.n_rows / kBlockSize0; + int num_blocks_cols = p.n_cols / kBlockSize1; + + int block_row = (block_id / num_blocks_cols) * kBlockSize0; + int block_col = (block_id % num_blocks_cols) * kBlockSize1; + + int warp_id = threadIdx.x / 32; + + if (p.scale_ptr) { + int thread_row = threadIdx.x * 4; + cutlass::arch::cp_async< + sizeof(ElementScale[4]), + cutlass::arch::CacheOperation::Global>( + smem_scales + thread_row, + p.scale_ptr + block_row + thread_row, + thread_row < kBlockSize0); + } else { + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < kBlockSize0; i += kThreadsPerCTA) { + int row = i * kThreadsPerCTA + threadIdx.x; + if (row < kBlockSize0) { + smem_scales[row] = ElementScale(1); + } + } + } + cutlass::arch::cp_async_wait<0>(); + __syncthreads(); + + WarpTensor load_dense_tensor; + int warp_row = warp_id * load_dense_tensor.kRows; + CUTLASS_PRAGMA_UNROLL + for (int it_row = 0; it_row < kBlockSize0; + it_row += kNumWarpsPerCTA * load_dense_tensor.kRows) { + CUTLASS_PRAGMA_UNROLL + for (int it_col = 0; it_col < kBlockSize1; + it_col += load_dense_tensor.kCols) { + // gmem -> RF + load_dense_tensor.load( + p.input_ptr + (it_row + warp_row + block_row) * p.input_s0 + + it_col + block_col, + p.input_s0); + + // RF -> RF (sparsify) + auto [packed, mdata] = load_dense_tensor.sparsify_pack(p.algo); + + // RF -> RF (cvt to f32, activation and then scale) + auto packedf32 = packed.template to(); + packedf32.data = p.activation(packedf32.data); + auto scale = + (1 / smem_scales[it_row + warp_row + packedf32.thread_row()]); + packedf32.data = packedf32.data * scale; + + // RF -> RF (convert to fp8) + auto packedCvt = packedf32.template to(); + + // RF -> gmem (packed data) + packedCvt.store( + p.output_ptr + (it_row + warp_row + block_row) * p.output_s0 + + (it_col + block_col) / 2, + p.output_s0); + + // RF -> gmem (mdata) + constexpr int kStrideRow = 16; + int col = (it_col + block_col); + mdata.store( + p.mdata_ptr + (it_row + warp_row + block_row) * kStrideRow + + (col / 128 * p.n_rows * 16) + (col % 128) / 8, + 16); + } + } + } + + template + static void launch_kernel(P p) { + int num_blocks = cutlass::ceil_div(p.n_cols, kBlockSize1) * + cutlass::ceil_div(p.n_rows, kBlockSize0); + sparse24_sm90_cutlass8bits_sparsify_kernel

+ <<>>(p); + } +}; + +template +__global__ void __launch_bounds__(MetadataCutlass8bits::kThreadsPerCTA, 32) + sparse24_sm90_cutlass8bits_sparsify_kernel(P p) { + MetadataCutlass8bits::run(p); +} + +template < + typename _ElementIn, + typename _ElementOut, + typename _Algorithm, + typename _PostSparsityActivation> +struct SparsifyKernelParams { + using ElementIn = _ElementIn; + using ElementOut = _ElementOut; + using ElementScale = float; + using Algorithm = _Algorithm; + using PostSparsityActivation = _PostSparsityActivation; + + Algorithm algo; + PostSparsityActivation activation; + + ElementIn const* input_ptr = nullptr; + int64_t input_s0 = -1; + ElementOut* output_ptr = nullptr; + int64_t output_s0 = -1; + uint8_t* mdata_ptr = nullptr; + ElementScale* scale_ptr = nullptr; + int64_t n_rows = -1; + int64_t n_cols = -1; + uint16_t* positive_count_ptr = nullptr; +}; + +template < + bool kIsMeta, + typename MetadataFormat, + typename ElementIn, + typename ElementOut, + typename PostSparsityActivation> +std::tuple sparse24_sm90_sparsify_specialized( + at::Tensor input, + PostSparsityActivation activation, + std::string sp_selection_algo, + std::optional scale) { + std::optional device_guard; + if (!kIsMeta) { + TORCH_CHECK(input.is_cuda(), "All tensors must be on GPU"); + device_guard.emplace(input.device()); + } + + TORCH_CHECK(input.dim() == 2, "Can only sparsify 2d tensors"); + TORCH_CHECK( + input.stride(1) == 1, + "Can only sparsify contiguous tensors. Sparsify the transpose otherwise."); + TORCH_CHECK(input.size(1) % 32 == 0); + if (scale.has_value()) { + TORCH_CHECK(scale->dim() == 2); + TORCH_CHECK( + scale->size(0) == input.size(0), "only row-wise scale is supported"); + TORCH_CHECK(scale->size(1) == 1); + TORCH_CHECK(scale->is_contiguous()); + TORCH_CHECK(scale->scalar_type() == at::ScalarType::Float); + TORCH_CHECK(scale->device() == input.device()); + } + + int n_rows = input.size(0); + int n_cols = input.size(1); + + // Half the storage + 1 bit per element in original tensor (metadata) + at::Tensor packed, mdata; + std::tie(packed, mdata) = + MetadataFormat::template createTensors(input); + + bool kernel_launched = false; + auto launchKernel = [&](auto algo, std::string const& algo_name) { + if (algo_name == sp_selection_algo) { + kernel_launched = true; + if (kIsMeta) { + return; + } + using Params = SparsifyKernelParams< + ElementIn, + ElementOut, + decltype(algo), + decltype(activation)>; + Params p; + p.algo = algo; + p.activation = activation; + p.input_ptr = ((ElementIn const*)input.data_ptr()); + p.input_s0 = input.stride(0); + p.output_ptr = (ElementOut*)(packed.data_ptr()); + p.output_s0 = input.size(1) / 2; + p.mdata_ptr = (uint8_t*)(mdata.data_ptr()); + p.scale_ptr = (float*)(scale.has_value() ? scale->data_ptr() : nullptr); + p.n_rows = n_rows; + p.n_cols = n_cols; + + MetadataFormat::launch_kernel(p); + } + }; + named_algorithms_oneway(launchKernel); + TORCH_CHECK(kernel_launched, "Unknown algorithm \"", sp_selection_algo, "\""); + C10_CUDA_KERNEL_LAUNCH_CHECK(); + + return std::make_tuple(packed, mdata); +} + +struct SquaredReLU { + template + CUTLASS_DEVICE cutlass::Array operator()(cutlass::Array x) const { + cutlass::multiplies> mul; + cutlass::maximum> max; + x = max(x, T(0)); + x = mul(x, x); + return x; + } +}; + +template +std::tuple sparse24_sm90_sparsify( + at::Tensor input, + std::string metadata_fmt, + std::string activation, + std::string sp_selection_algo, + std::optional out_dtype_, + std::optional scale) { + auto out_dtype = + out_dtype_.has_value() ? out_dtype_.value() : input.scalar_type(); + + auto runTypedWithAct = + [&](auto in_type, auto out_type, auto mdatafmt, auto act) { + using ElementIn = decltype(in_type); + using ElementOut = decltype(out_type); + return sparse24_sm90_sparsify_specialized< + kIsMeta, + decltype(mdatafmt), + ElementIn, + ElementOut>(input, act, sp_selection_algo, scale); + }; + + auto runTyped = [&](auto in_type, auto out_type, auto mdatafmt) { + if (activation == "identity") { + return runTypedWithAct(in_type, out_type, mdatafmt, Identity()); + } else if (activation == "srelu") { + return runTypedWithAct(in_type, out_type, mdatafmt, SquaredReLU()); + } else { + TORCH_CHECK(false, "Unknown activation:", activation); + } + }; + + TORCH_CHECK(metadata_fmt == "cusparselt" || metadata_fmt == "cutlass"); + TORCH_CHECK( + !scale.has_value() || scale->scalar_type() == at::ScalarType::Float); + if (metadata_fmt == "cusparselt") { + TORCH_CHECK( + input.scalar_type() == at::ScalarType::Half || + input.scalar_type() == at::ScalarType::BFloat16); + TORCH_CHECK(out_dtype == input.scalar_type()); + if (input.scalar_type() == at::ScalarType::Half) { + return runTyped( + cutlass::half_t(), cutlass::half_t(), MetadataCusparseLt16bits()); + } else { + return runTyped( + cutlass::bfloat16_t(), + cutlass::bfloat16_t(), + MetadataCusparseLt16bits()); + } + } else if (metadata_fmt == "cutlass") { + TORCH_CHECK(input.scalar_type() == at::ScalarType::BFloat16); + TORCH_CHECK(out_dtype == at::ScalarType::Float8_e4m3fn); + return runTyped( + cutlass::bfloat16_t(), cutlass::float_e4m3_t(), MetadataCutlass8bits()); + } + TORCH_CHECK(false, "Unknown metadata format: '", metadata_fmt, "'") +} +} // namespace + +TORCH_LIBRARY_IMPL(torchao, CUDA, m) { + m.impl( + TORCH_SELECTIVE_NAME("torchao::sparse24_sm90_sparsify"), + TORCH_FN(sparse24_sm90_sparsify)); +} + +TORCH_LIBRARY_IMPL(torchao, Meta, m) { + m.impl( + TORCH_SELECTIVE_NAME("torchao::sparse24_sm90_sparsify"), + TORCH_FN(sparse24_sm90_sparsify)); +} diff --git a/torchao/csrc/cuda/activation24/static_sort.h b/torchao/csrc/cuda/activation24/static_sort.h new file mode 100644 index 0000000000..cc6204a5e6 --- /dev/null +++ b/torchao/csrc/cuda/activation24/static_sort.h @@ -0,0 +1,88 @@ +#pragma once +#include + +/** + * A Functor class to create a sort for fixed sized arrays/containers with a + * compile time generated Bose-Nelson sorting network. + * \tparam NumElements The number of elements in the array or container to + * sort. \tparam T The element type. \tparam Compare A + * comparator functor class that returns true if lhs < rhs. + */ +template class StaticSort { + template struct Swap { + template CUTLASS_HOST_DEVICE void s(T &v0, T &v1) { + // Explicitly code out the Min and Max to nudge the compiler + // to generate branchless code. + T t = v0 < v1 ? v0 : v1; // Min + v1 = v0 < v1 ? v1 : v0; // Max + v0 = t; + } + + CUTLASS_HOST_DEVICE Swap(A &a, const int &i0, const int &i1) { + s(a[i0], a[i1]); + } + }; + + template struct PB { + CUTLASS_HOST_DEVICE PB(A &a) { + enum { + L = X >> 1, + M = (X & 1 ? Y : Y + 1) >> 1, + IAddL = I + L, + XSubL = X - L + }; + PB p0(a); + PB p1(a); + PB p2(a); + } + }; + + template struct PB { + CUTLASS_HOST_DEVICE PB(A &a) { Swap s(a, I - 1, J - 1); } + }; + + template struct PB { + CUTLASS_HOST_DEVICE PB(A &a) { + Swap s0(a, I - 1, J); + Swap s1(a, I - 1, J - 1); + } + }; + + template struct PB { + CUTLASS_HOST_DEVICE PB(A &a) { + Swap s0(a, I - 1, J - 1); + Swap s1(a, I, J - 1); + } + }; + + template struct PS { + CUTLASS_HOST_DEVICE PS(A &a) { + enum { L = M >> 1, IAddL = I + L, MSubL = M - L }; + PS ps0(a); + PS ps1(a); + PB pb(a); + } + }; + + template struct PS { + CUTLASS_HOST_DEVICE PS(A &a) {} + }; + +public: + /** + * Sorts the array/container arr. + * \param arr The array/container to be sorted. + */ + template + CUTLASS_HOST_DEVICE void operator()(Container &arr) const { + PS ps(arr); + }; + + /** + * Sorts the array arr. + * \param arr The array to be sorted. + */ + template CUTLASS_HOST_DEVICE void operator()(T *arr) const { + PS ps(arr); + }; +}; diff --git a/torchao/csrc/cuda/activation24/warp_tensor.h b/torchao/csrc/cuda/activation24/warp_tensor.h new file mode 100644 index 0000000000..b952e482e1 --- /dev/null +++ b/torchao/csrc/cuda/activation24/warp_tensor.h @@ -0,0 +1,468 @@ +#pragma once + +#include +#include +#include + +#include "static_sort.h" + +namespace torchao { + +template +struct WarpTensor { + // This class represents a Tensor sharded across an entire warp, + // on registers. The sharding is row-major, eg looks like this + // for a `WarpTensor`: + // [row 0] [thread0][thread1]... + // [row 1] [thread4][thread5]... + //... + // [row 8] [thread28][thread29]... + // Each thread would hold 8 values. This format is optimized to + // load from gmem efficiently (coalescing) + + static constexpr int kRows = kRows_; + static constexpr int kCols = kCols_; + // NOTE: Stored in Row-Major + static constexpr int kElementsPerThread = (kRows * kCols / 32); + static constexpr int kThreadsPerRow = 32 / kRows; + static_assert(32 % kRows == 0); + + cutlass::Array data; // < current thread data + int lane = threadIdx.x % 32; + + CUTLASS_DEVICE int thread_row() const { + return lane / kThreadsPerRow; + } + CUTLASS_DEVICE int thread_col() const { + return kElementsPerThread * (lane % kThreadsPerRow); + } + // load/store in gmem + template + CUTLASS_DEVICE void load( + Element const* ptr, + int64_t stride0, + RowMod row_mod) { + cutlass::arch::global_load( + data, ptr + stride0 * row_mod(thread_row()) + thread_col(), true); + } + CUTLASS_DEVICE void load(Element const* ptr, int64_t stride0) { + load(ptr, stride0, [](int i) { return i; }); + } + CUTLASS_DEVICE void store_line(Element* ptr) const { + cutlass::arch::global_store( + data, ptr + thread_col(), true); + } + CUTLASS_DEVICE void store(Element* ptr, int64_t stride0) const { + cutlass::arch::global_store( + data, ptr + stride0 * thread_row() + thread_col(), true); + } + + // load/store in smem + template + CUTLASS_DEVICE void load_32bits(ElementSmem const* ptr) { + if constexpr (kStride1 == 1 && std::is_same::value) { + cutlass::Array frag32; + static_assert(sizeof(frag32) == 4); + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < kElementsPerThread / frag32.size(); ++i) { + frag32 = + *((decltype(frag32) const*)(ptr + thread_col() + frag32.size() * i + + kStride0 * thread_row())); + CUTLASS_PRAGMA_UNROLL + for (int j = 0; j < frag32.size(); ++j) { + data[frag32.size() * i + j] = Element(frag32[j]); + } + } + } else { + CUTLASS_PRAGMA_UNROLL + for (int col = 0; col < data.size(); ++col) { + data[col] = Element( + ptr[kStride0 * thread_row() + kStride1 * (thread_col() + col)]); + } + } + } + template + CUTLASS_DEVICE void store_32bits(ElementSmem* ptr) const { + if constexpr ( + kStride1 == 1 && sizeof(Element) == 2 && + std::is_same::value) { + // store packed as 32bits - Row-Major + uint32_t const* pack_ptr = reinterpret_cast(&data); + uint32_t* smem_ptr = + (uint32_t*)(ptr + kStride0 * thread_row() + kStride1 * thread_col()); + CUTLASS_PRAGMA_UNROLL + for (int c = 0; c < data.size() / 2; ++c) { + smem_ptr[c] = pack_ptr[c]; + } + } else if constexpr ( + kStride0 == 1 && sizeof(Element) == 2 && kRows == 2 && + kElementsPerThread % 2 == 0 && + std::is_same::value) { + // store packed as 32bits - Col-Major + uint32_t const* pack_ptr = reinterpret_cast(&data); + bool is_low_thread = threadIdx.x & kThreadsPerRow; + CUTLASS_PRAGMA_UNROLL + for (int c = 0; c < data.size() / 2; ++c) { + uint32_t my_val = pack_ptr[c]; + uint32_t other_val = + __shfl_xor_sync(0xffffffff, my_val, kThreadsPerRow); + if (is_low_thread) { + my_val = (my_val & 0x0000FFFF) | (other_val << 16); + } else { + my_val = (my_val & 0xFFFF0000) | (other_val & 0xFFFF); + } + uint32_t* smem_ptr = + (uint32_t*)(ptr + + kStride1 * (thread_col() + is_low_thread + 2 * c)); + *smem_ptr = my_val; + } + } else { + // not optimized path + CUTLASS_PRAGMA_UNROLL + for (int col = 0; col < data.size(); ++col) { + ptr[kStride0 * thread_row() + kStride1 * (thread_col() + col)] = + ElementSmem(data[col]); + } + } + } + template + CUTLASS_DEVICE WarpTensor to() const { + cutlass::NumericArrayConverter< + ElementOut, + Element, + kElementsPerThread, + cutlass::FloatRoundStyle::round_to_nearest> + converter; + + WarpTensor out; + out.data = converter(data); + return out; + } + + CUTLASS_DEVICE void print(int offs_row = 0) const { + for (int i = 0; i < 32; ++i) { + if (lane == i) { + printf( + "[lane=%d][%d, %d:%d] = ", + int(lane), + int(thread_row() + offs_row), + int(thread_col()), + int(thread_col() + kElementsPerThread)); + for (int j = 0; j < data.size(); ++j) { + // printf("0x%x ", uint32_t(data[j])); + printf("%f ", float(data[j])); + } + printf("\n"); + } + __syncthreads(); + } + } + + template + CUTLASS_DEVICE std::tuple< + WarpTensor, + WarpTensor> + sparsify_pack(Algo algo) { + constexpr int kCount = kElementsPerThread; + auto dense_values = data; + + WarpTensor tensor_packed; + WarpTensor tensor_mdata; + uint8_t metadata = 0; + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < kCount / 4; ++i) { + cutlass::Array to_sparsify; + CUTLASS_PRAGMA_UNROLL + for (int j = 0; j < 4; ++j) { + to_sparsify[j] = dense_values[4 * i + j].get(); + } + cutlass::Array packed2; + int m = algo(to_sparsify, packed2); + metadata |= (m << (4 * i)); + tensor_packed.data[2 * i] = packed2[0].get(); + tensor_packed.data[2 * i + 1] = packed2[1].get(); + } + tensor_mdata.data[0] = metadata; + return std::make_tuple(tensor_packed, tensor_mdata); + } + + CUTLASS_DEVICE WarpTensor sparsify_as( + WarpTensor mdata) const { + static_assert(sizeof(Element) == 2); + auto* ptr = reinterpret_cast(&data); + + WarpTensor packed; + + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < kElementsPerThread / 4; ++i) { + auto a = ptr[2 * i]; + auto b = ptr[2 * i + 1]; + auto sparseSelect = [&](uint8_t mdata_element) { + int m0 = mdata_element & 0x1; + int m1 = (mdata_element >> 1) & 0x1; + int out = ((a >> (16 * m0)) * (1 - m1) + (b >> (16 * m0)) * m1); + return reinterpret_cast(out); + }; + uint8_t mdata_i = mdata.data[i / 2].get() >> (4 * (i % 2)); + packed.data[2 * i] = sparseSelect(mdata_i); + packed.data[2 * i + 1] = sparseSelect(mdata_i >> 2); + } + return packed; + } + + CUTLASS_DEVICE WarpTensor unpack( + WarpTensor mdata) const { + static_assert(sizeof(Element) == 2); + + WarpTensor unpacked; + auto* ptr_p = reinterpret_cast(&data); + auto* ptr_unp = reinterpret_cast(&unpacked.data); + + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < kElementsPerThread / 2; ++i) { + auto packed = ptr_p[i]; + uint32_t p0 = packed & 0xFFFF; + uint32_t p1 = packed >> 16; + uint8_t mdata_i = mdata.data[i / 2].get() >> (4 * (i % 2)); + uint32_t m0 = mdata_i & 0x3; + uint32_t m1 = (mdata_i >> 2) & 0x3; + p0 = p0 << ((m0 & 1) * 16); + p1 = p1 << ((m1 & 1) * 16); + + uint32_t unp0 = 0; + uint32_t unp1 = 0; + if (m0 & 0x1) { + unp1 = p0; + } else { + unp0 = p0; + } + if (m1 & 0x1) { + unp1 += p1; + } else { + unp0 += p1; + } + ptr_unp[2 * i] = unp0; + ptr_unp[2 * i + 1] = unp1; + } + return unpacked; + } + + template + CUTLASS_DEVICE std::tuple< + cutlass::Array, // reduce elements + uint32_t // thread offset + > + all_reduce(BinaryOp binary_op) const { + // reduces across the first dimension (eg `out[i,k]=out[j,k]`) + WarpTensor red; + red.data = data; + + CUTLASS_PRAGMA_UNROLL + for (int xor_lane = kThreadsPerRow; xor_lane < 32; xor_lane *= 2) { + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < red.data.size(); ++i) { + Element other_val = Element( + __shfl_xor_sync(0xffffffff, Element(red.data[i]), xor_lane)); + red.data[i] = binary_op(red.data[i], other_val); + } + } + + uint32_t offset = thread_col(); + cutlass::Array out; + if constexpr (kThreadsPerRow == 16) { + static constexpr int kOffset = kElementsPerThread / 2; + if (thread_row() == 1) { + offset += kOffset; + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < kOffset; ++i) { + out[i] = red.data[i + kOffset]; + } + } else { + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < kOffset; ++i) { + out[i] = red.data[i]; + } + } + } else { + static_assert(kThreadsPerRow == 16); // Only supported in that case + } + + return std::make_tuple(out, offset); + } + + template + CUTLASS_DEVICE Element reduce_line(BinaryOp binary_op) const { + Element reduced = data[0]; + // local reduction + CUTLASS_PRAGMA_UNROLL + for (int i = 1; i < data.size(); ++i) { + reduced = binary_op(reduced, Element(data[i])); + } + + // reduce with other lanes + CUTLASS_PRAGMA_UNROLL + for (int xor_lane = 1; xor_lane < kThreadsPerRow; xor_lane *= 2) { + Element other_val = + Element(__shfl_xor_sync(0xffffffff, reduced, xor_lane)); + reduced = binary_op(reduced, other_val); + } + return reduced; + } + + struct TileValueOrdered1d { + union { + struct { + Element value; + uint16_t pos; + } parts; + uint32_t raw; + }; + CUTLASS_DEVICE bool operator<(TileValueOrdered1d const& other) const { + return parts.value < other.parts.value; + } + CUTLASS_DEVICE TileValueOrdered1d() {} + }; + + template + CUTLASS_DEVICE WarpTensor sparsify_dense( + SortPreproc sort_preproc) const { + static_assert(M == kElementsPerThread); + + WarpTensor out; + + cutlass::Array values_ordered; + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < data.size(); ++i) { + auto& v = values_ordered[i]; + v.parts.value = sort_preproc(data[i].get()); + v.parts.pos = i; + } + StaticSort sorter; + sorter(values_ordered); + + // mask out smallest elements + uint32_t kept_mask = 0; + CUTLASS_PRAGMA_UNROLL + for (int i = M - N; i < M; ++i) { + kept_mask |= (1 << values_ordered[i].parts.pos); + } + CUTLASS_PRAGMA_UNROLL + for (int i = 0; i < M; ++i) { + if (kept_mask & 0x1) { + out.data[i] = data[i].get(); + } else { + out.data[i] = Element(0); + } + kept_mask = kept_mask >> 1; + } + return out; + } +}; + +CUTLASS_DEVICE void store_metadata_reordered( + WarpTensor mdata_tensor, + uint8_t* mdata_ptr) { + // This function is explained in + // https://docs.google.com/spreadsheets/d/1JvEsw9QnoIvXctOnED3Gk0LFnIe8XJTnbCRamxvfFBw/edit?gid=1603247130#gid=1603247130 + auto lane = mdata_tensor.lane; + static_assert(mdata_tensor.kElementsPerThread == 2); + + uint16_t mdata_step0 = reinterpret_cast(mdata_tensor.data); + uint16_t other_step0 = __shfl_xor_sync(0xffffffff, mdata_step0, 16); + + // step1 + uint16_t mdata_step1 = 0; + if (lane & 16) { // T16-T31 + mdata_step1 = ((mdata_step0 & 0xF0F0) | ((other_step0 >> 4) & 0x0F0F)); + } else { // T0-T15 + mdata_step1 = ((mdata_step0 & 0x0F0F) | ((other_step0 << 4) & 0xF0F0)); + } + + // step2 + uint16_t other_step1 = __shfl_xor_sync(0xffffffff, mdata_step1, 1); + uint16_t mdata_gmem = 0; + if (lane & 1) { // T1 + mdata_gmem = ((mdata_step1 & 0xFF00) | ((other_step1 >> 8) & 0x00FF)); + } else { // T0 + mdata_gmem = ((mdata_step1 & 0x00FF) | ((other_step1 << 8) & 0xFF00)); + } + + // read to store to gmem + cutlass::arch::global_store( + mdata_gmem, + mdata_ptr + (lane % 2) * 4 + ((lane % 16) / 2) * 8 + (lane / 16) * 2, + true); +} + +struct Identity { + template + T CUTLASS_DEVICE operator()(T x) const { + return x; + } +}; + +template < + int kSmemStride0, + int kSmemStride1, + int kNumRows, + int kWarpsPerCTA, + typename Algo, + typename Element, + typename PointwiseFn> +CUTLASS_DEVICE void warp_dump_sparse_and_dense_from_smem_32cols( + Element const* smem, + Algo algo, + int32_t const* destination_idx_ptr, + // sparse part + uint8_t* sparse_bitmask_ptr, + int64_t sparse_bitmask_s0, + int64_t sparse_bitmask_s1, + Element* sparse_packed_ptr, + int64_t sparse_packed_s0, + // dense part + Element* dense_ptr, + int64_t dense_s0, + PointwiseFn pointwise_fn = Identity()) { + // 64x32 data is layed out like: + // row 0: [T0 (128 bits)][T1 (128 bits)][T2 (128 bits)]... + // row 1: [T4 (128 bits)][T5 (128 bits)]... + // ... + // row 8: [T0 (128 bits)][T1 (128 bits)]... + // .. + WarpTensor tensor; + + cutlass::Array destination_idx_array; + int warp_row = (threadIdx.x / 32) * tensor.kRows; + CUTLASS_PRAGMA_UNROLL + for (int row = 0; row < kNumRows; row += kWarpsPerCTA * tensor.kRows) { + cutlass::arch::global_load( + destination_idx_array[row / (kWarpsPerCTA * tensor.kRows)], + destination_idx_ptr + tensor.thread_row() + row + warp_row, + true); + } + + CUTLASS_PRAGMA_UNROLL + for (int row = 0; row < kNumRows; row += kWarpsPerCTA * tensor.kRows) { + tensor.template load_32bits( + smem + kSmemStride0 * (row + warp_row)); + tensor.data = pointwise_fn(tensor.data); + // RF -> RF (sparsify) + auto [packed, bitmask] = tensor.sparsify_pack(algo); + int32_t destination_idx = + destination_idx_array[row / (kWarpsPerCTA * tensor.kRows)]; + if (destination_idx >= 0) { + // shape: [cols/32, rows, 32/8] (b8) + int64_t coord0 = (tensor.thread_col()) / 32; + int64_t coord1 = destination_idx; + int64_t coord2 = (tensor.thread_col() % 32) / 8; + sparse_bitmask_ptr + [coord0 * sparse_bitmask_s0 + coord1 * sparse_bitmask_s1 + coord2] = + bitmask.data[0]; + packed.store_line(sparse_packed_ptr + sparse_packed_s0 * destination_idx); + } else { + destination_idx = -(destination_idx + 1); + tensor.store_line(dense_ptr + dense_s0 * destination_idx); + } + } +} +} // namespace torchao diff --git a/torchao/csrc/cuda/rowwise_scaled_linear_sparse_cutlass/rowwise_scaled_linear_sparse_cutlass.cuh b/torchao/csrc/cuda/rowwise_scaled_linear_sparse_cutlass/rowwise_scaled_linear_sparse_cutlass.cuh index 4342919d6e..09cbf32f6a 100644 --- a/torchao/csrc/cuda/rowwise_scaled_linear_sparse_cutlass/rowwise_scaled_linear_sparse_cutlass.cuh +++ b/torchao/csrc/cuda/rowwise_scaled_linear_sparse_cutlass/rowwise_scaled_linear_sparse_cutlass.cuh @@ -271,7 +271,7 @@ static void select_config( return; } else { using TileShape = cute::Shape; - using ClusterShape = cute::Shape; + using ClusterShape = cute::Shape; rowwise_scaled_linear_sparse_kernel_cutlass_sm9x< DtypeXq, DtypeWq, Types..., TileShape, ClusterShape>( Xq, X_scale, Wq, W_meta, W_scale, bias, Y); diff --git a/torchao/ops.py b/torchao/ops.py index 82de7528ec..0a507a69d7 100644 --- a/torchao/ops.py +++ b/torchao/ops.py @@ -39,6 +39,15 @@ lib.define( "to_sparse_semi_structured_cutlass_sm9x_f8(Tensor weight) -> (Tensor, Tensor)" ) +lib.define( + "sparse24_sm90_sparsify(Tensor input, str metadata_fmt, str activation, str sp_selection_algo, *, ScalarType? dtype = None, Tensor? scale=None) -> (Tensor, Tensor)" +) +lib.define( + "swizzle_mm(Tensor mat1, Tensor mat2, bool mat1_is_swizzled, bool mat2_is_swizzled) -> Tensor" +) +lib.define( + "swizzle_scaled_mm(Tensor mat1, Tensor mat2, bool mat1_is_swizzled, bool mat2_is_swizzled, Tensor scale_a, Tensor scale_b, Tensor? bias=None, Tensor? scale_result=None, ScalarType? out_dtype=None) -> Tensor" +) # Note: we need to add the `torch._C.Tag.needs_fixed_stride_order` tag in order for inductor # to honor the layout constraints for `b` in the two ops below. lib.define( @@ -729,6 +738,81 @@ def _( ) +def sparse24_sm90_sparsify( + input_tensor: Tensor, + metadata_format: str, + activation: str, + algorithm: str, + dtype=None, + scale=None, +) -> (Tensor, Tensor): + return torch.ops.torchao.sparse24_sm90_sparsify( + input_tensor, metadata_format, activation, algorithm, dtype=dtype, scale=scale + ) + + +def swizzle_mm( + mat1: Tensor, mat2: Tensor, mat1_is_swizzled: bool, mat2_is_swizzled: bool +) -> Tensor: + """ + Similar to torch.mm but Tensor inputs can be SwizzleTensor instances. + + """ + return torch.ops.torchao.swizzle_mm.default( + mat1, mat2, mat1_is_swizzled, mat2_is_swizzled + ) + + +@register_custom_op("torchao::swizzle_mm") +def _( + mat1: Tensor, mat2: Tensor, mat1_is_swizzled: bool, mat2_is_swizzled: bool +) -> Tensor: + return mat1.new_empty(mat1.shape[0], mat2.shape[1]) + + +def swizzle_scaled_mm( + mat1: Tensor, + mat2: Tensor, + mat1_is_swizzled: bool, + mat2_is_swizzled: bool, + scale_a: Tensor, + scale_b: Tensor, + bias: Optional[Tensor], + scale_result: Optional[Tensor], + out_dtype: Optional[torch.dtype], +) -> Tensor: + """ + Similar to torch.mm but Tensor inputs can be SwizzleTensor instances. + + """ + return torch.ops.torchao.swizzle_scaled_mm.default( + mat1, + mat2, + mat1_is_swizzled, + mat2_is_swizzled, + scale_a, + scale_b, + bias, + scale_result, + out_dtype, + ) + + +@register_custom_op("torchao::swizzle_scaled_mm") +def _( + mat1: Tensor, + mat2: Tensor, + mat1_is_swizzled: bool, + mat2_is_swizzled: bool, + scale_a: Tensor, + scale_b: Tensor, + bias: Optional[Tensor], + scale_result: Optional[Tensor], + out_dtype: Optional[torch.dtype], +) -> Tensor: + return mat1.new_empty(mat1.shape[0], mat2.shape[1]) + + @functools.lru_cache() def _get_dtypes(): """TODO: when e8m0 is hardened and major release lets remove uint8 support""" diff --git a/torchao/prototype/sparsity/activation/__init__.py b/torchao/prototype/sparsity/activation/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/torchao/prototype/sparsity/activation/srelu_linear.py b/torchao/prototype/sparsity/activation/srelu_linear.py new file mode 100644 index 0000000000..f8c3288b67 --- /dev/null +++ b/torchao/prototype/sparsity/activation/srelu_linear.py @@ -0,0 +1,87 @@ +from dataclasses import dataclass + +import torch +from torch import nn + +from torchao.core.config import AOBaseConfig +from torchao.ops import ( + rowwise_scaled_linear_sparse_cutlass_f8f8, +) +from torchao.quantization.quant_api import ( + _float8_cutlass_quant, +) +from torchao.quantization.transform_module import ( + register_quantize_module_handler, +) + + +@dataclass +class SRELUFloat8SemiSparseDynamicActivationFloat8WeightConfig(AOBaseConfig): + """ + Applies float8 dynamic quantization to activations and float8 quantization followed by compression to sparse semi-structured tensor to weights of linear layers. + + Args: + `activation_dtype`: data type for quantized activation tensor. + `weight_dtype`: data type for quantized weight tensor. + """ + + activation_dtype: torch.dtype = torch.float8_e4m3fn + weight_dtype: torch.dtype = torch.float8_e4m3fn + + +@register_quantize_module_handler( + SRELUFloat8SemiSparseDynamicActivationFloat8WeightConfig +) +def _float8_dynamic_activation_float8_semi_sparse_weight_transform( + module: torch.nn.Module, + config: SRELUFloat8SemiSparseDynamicActivationFloat8WeightConfig, +): + return FP8SemiSparseActivationLinear.from_dense(module, config) + + +class FP8SemiSparseActivationLinear(nn.Module): + """ + Replacement nn.Linear that supports runtime fp8 activation sparsity + """ + + def __init__(self, weight, config) -> None: + super().__init__() + self.config = config + + W_aqt = _float8_cutlass_quant(weight, self.config.weight_dtype) + self.Wq = W_aqt.tensor_impl.float8_data + self.W_scale = W_aqt.tensor_impl.scale + + def forward(self, x): + X_scale = torch.empty([x.shape[0], 1], device=x.device, dtype=torch.float32) + Xq_sparse, X_meta = torch.ops.torchao.sparse24_sm90_sparsify( + x, + "cutlass", + "srelu", + "largest", + dtype=self.config.activation_dtype, + scale=X_scale, + ) + + result = rowwise_scaled_linear_sparse_cutlass_f8f8( + self.Wq, + self.W_scale, + Xq_sparse, + X_meta, + X_scale, + bias=None, + out_dtype=torch.bfloat16, + ).t() + + return result + + @classmethod + def from_dense( + cls, linear, config: SRELUFloat8SemiSparseDynamicActivationFloat8WeightConfig + ): + if linear.bias is not None: + raise NotImplementedError("bias is not supported") + if linear.weight.dtype != torch.bfloat16: + raise NotImplementedError("weight dtype must be bf16") + + return cls(linear.weight.data, config) diff --git a/torchao/prototype/sparsity/activation/utils.py b/torchao/prototype/sparsity/activation/utils.py new file mode 100644 index 0000000000..696649b18c --- /dev/null +++ b/torchao/prototype/sparsity/activation/utils.py @@ -0,0 +1,115 @@ +import os +from datetime import datetime + +import torch +import torch.nn.functional as F + +from torchao.ops import to_sparse_semi_structured_cutlass_sm9x_f8 + + +def _dump_metadata_format_cutlass( + rows=128, cols=256, device=torch.device("cuda"), dtype=torch.float8_e4m3fn +): + """ + This is a helper function to dump the metadata packing format for 2:4 sparse GEMMS. + + We create a 2:4 sparse tensor by tiling the same pattern and then changing a singular 1x4 strip of the metadata at a time. + This will allow us to find the corresponding location in the metadata that changes. + """ + + # We tile the same pattern [0, 0, 1, 1] which yields 238 for all metadata values. + dense_reference_tensor = ( + torch.Tensor([0, 0, 1, 1]) + .to(device=device, dtype=dtype) + .tile((rows, cols // 4)) + .contiguous() + ) + _, meta_reference = to_sparse_semi_structured_cutlass_sm9x_f8( + dense_reference_tensor + ) + print("INITIAL") + print(meta_reference) + print(meta_reference.shape, meta_reference.is_contiguous(), meta_reference.dtype) + + metadata_list = meta_reference.tolist() + + # The probe pattern yields the value 68 in the metadata + probe_pattern = [1, 1, 0, 0] + for i in range(rows): + num_per_tb = 8 + for j in range(cols // num_per_tb): + dense_reference_tensor_c = dense_reference_tensor.clone() + dense_reference_tensor_c[i, j * num_per_tb : (j + 1) * num_per_tb] = ( + torch.Tensor(probe_pattern) + .to(device=device, dtype=dtype) + .tile((1, 2)) + .contiguous() + ) + # use the reference cutlass function to pack metadata + _, meta_refernece_probe = to_sparse_semi_structured_cutlass_sm9x_f8( + dense_reference_tensor_c + ) + + # find where the reference packed metadata is equal to 68 + indicies = (meta_refernece_probe == 68).nonzero() + + for r_i, c_i in indicies: + metadata_list[r_i][c_i] = ( + f"a[{i:2d}, {j * num_per_tb:2d}:{(j + 1) * num_per_tb:2d}]" + ) + + print("METADATA FORMAT") + for line in metadata_list: + print(line) + print() + + +class SquaredReLU(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x): + return F.relu(x) ** 2 + + +def profiler_runner(path, fn, *args, **kwargs): + if path is None: + path = os.path.join( + os.path.expanduser("~/traces"), + f"{datetime.now().strftime('%Y-%m-%d-%H-%M-%S')}.json.gz", + ) + with torch.profiler.profile( + activities=[ + torch.profiler.ProfilerActivity.CPU, + torch.profiler.ProfilerActivity.CUDA, + ], + record_shapes=True, + ) as prof: + result = fn(*args, **kwargs) + prof.export_chrome_trace(path) + print(f"Exported trace to {path}") + return result + + # input = create_semi_structured_tensor(4096, 8192, dtype=torch.bfloat16).to(device) + # print(input) + + # ffn_clone = copy.deepcopy(test_ffn) + # quantize_(ffn_clone.w1, Float8DynamicActivationFloat8WeightConfig(granularity=PerRow())) + # ffn_clone.w2 = FP8SemiSparseActivationLinear.from_dense(ffn_clone.w2) + # # quantize_(ffn_clone.w2, Float8DynamicActivationFloat8SemiSparseWeightConfig()) + # ffn_clone.forward = torch.compile(ffn_clone.forward, mode="max-autotune", fullgraph=True) + # # warmup + # def test(): + # for i in range(10): + # ffn_clone(input) + # test() + # fp8_c_activation_sparse_time = benchmark_microseconds(test) + # print(fp8_c_activation_sparse_time / 10) + + # profiler_runner(None, test) + + # test_linear = nn.Linear(8192, 8192).cuda().to(torch.bfloat16) + # test_linear.weight.data = torch.ones(8192, 8192).cuda().to(torch.bfloat16) + # print(test_linear(input)) + # sparse_fp8_linear = FP8SemiSparseActivationLinear.from_dense(test_linear) + # print(sparse_fp8_linear(input)) diff --git a/torchao/sparsity/utils.py b/torchao/sparsity/utils.py index 012d86beff..24c0808a02 100644 --- a/torchao/sparsity/utils.py +++ b/torchao/sparsity/utils.py @@ -43,7 +43,7 @@ def create_semi_structured_tensor(r, c, dtype): .to(torch.int32) ) - sparse_weight = torch.rand(r, c).cuda() * mask + sparse_weight = mask + (torch.rand(r, c).cuda() * mask) return sparse_weight.to(dtype) From 34151758a676846a7e1d92ef6008da848fc5a7b3 Mon Sep 17 00:00:00 2001 From: Mark Saroufim Date: Mon, 12 May 2025 07:50:47 -0700 Subject: [PATCH 019/165] unpin torch to unbreak mac tests (#2198) * fix broken mac test * Update torchao_experimental_test.yml --- .github/workflows/torchao_experimental_test.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/torchao_experimental_test.yml b/.github/workflows/torchao_experimental_test.yml index 1987670d70..a712e7a624 100644 --- a/.github/workflows/torchao_experimental_test.yml +++ b/.github/workflows/torchao_experimental_test.yml @@ -36,7 +36,7 @@ jobs: # Install executorch first because it installs its own version # of torch and torchao, which we do not want to use pip install executorch - pip install torch==2.7.0.dev20250311 --index-url "https://download.pytorch.org/whl/nightly/cpu" --force-reinstall + pip install torch==2.7.0 --index-url https://download.pytorch.org/whl/cpu --force-reinstall pip install -r dev-requirements.txt USE_CPP=1 TORCHAO_BUILD_KLEIDIAI=1 pip install . - name: Run python tests From 0607aa1bb65f03bb1b0bf92868c3684a2fd3894f Mon Sep 17 00:00:00 2001 From: Driss Guessous <32754868+drisspg@users.noreply.github.com> Date: Mon, 12 May 2025 10:10:46 -0700 Subject: [PATCH 020/165] Add subclass based method for inference w/ MXFP8 (#2132) stack-info: PR: https://github.com/pytorch/ao/pull/2132, branch: drisspg/stack/50 --- test/prototype/mx_formats/test_mx_linear.py | 32 +++ torchao/__init__.py | 3 +- torchao/core/config.py | 1 + torchao/prototype/mx_formats/__init__.py | 4 + torchao/prototype/mx_formats/mx_funcs.py | 43 ++++ torchao/prototype/mx_formats/mx_ops.py | 255 +++++++++++++++----- torchao/prototype/mx_formats/mx_subclass.py | 157 ++++++++++++ torchao/prototype/mx_formats/mx_tensor.py | 43 +++- torchao/prototype/mx_formats/utils.py | 3 +- 9 files changed, 480 insertions(+), 61 deletions(-) create mode 100644 torchao/prototype/mx_formats/mx_funcs.py create mode 100644 torchao/prototype/mx_formats/mx_subclass.py diff --git a/test/prototype/mx_formats/test_mx_linear.py b/test/prototype/mx_formats/test_mx_linear.py index b0cee1e918..65934fb259 100644 --- a/test/prototype/mx_formats/test_mx_linear.py +++ b/test/prototype/mx_formats/test_mx_linear.py @@ -25,6 +25,7 @@ MXInferenceLinear, MXLinear, ) +from torchao.prototype.mx_formats.mx_subclass import MXFPInferenceConfig from torchao.quantization import quantize_ from torchao.quantization.utils import compute_error from torchao.utils import ( @@ -372,3 +373,34 @@ def test_inference_print_str(): s = str(m) assert "bl_sz=32" in s assert "kernel=emulated" in s + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +@pytest.mark.skipif( + not TORCH_VERSION_AT_LEAST_2_8, reason="torch.compile requires PyTorch 2.8+" +) +@pytest.mark.skipif(not is_sm_at_least_100, reason="Reqs sm100") +@pytest.mark.parametrize("elem_dtype", [torch.float8_e4m3fn]) +@pytest.mark.parametrize("bias", [True, False]) +@pytest.mark.parametrize("compile", [True, False]) +@torch.no_grad() +def test_inference_subclass(elem_dtype, bias: bool, compile: bool): + """ + Smoke test for inference compile + """ + if elem_dtype in (torch.float8_e4m3fn, torch.float8_e5m2): + if not is_sm_at_least_89(): + pytest.skip("CUDA capability >= 8.9 required for float8 in triton") + + m = nn.Linear(32, 128, bias=bias, dtype=torch.bfloat16, device="cuda") + m_mx = copy.deepcopy(m) + config = MXFPInferenceConfig() + quantize_(m_mx, config=config) + if compile: + m_mx = torch.compile(m_mx, fullgraph=True) + + x = torch.randn(128, 32, device="cuda", dtype=torch.bfloat16) + y_ref = m(x) + y_mx = m_mx(x) + sqnr = compute_error(y_ref, y_mx) + assert sqnr >= 25.0, f"Got a sqnr of {sqnr} for {elem_dtype} and bias={bias}" diff --git a/torchao/__init__.py b/torchao/__init__.py index 752aa94a4f..7cc447d5a7 100644 --- a/torchao/__init__.py +++ b/torchao/__init__.py @@ -43,7 +43,7 @@ quantize_, ) -from . import dtypes, optim, testing +from . import dtypes, optim, quantization, testing __all__ = [ "dtypes", @@ -52,4 +52,5 @@ "quantize_", "testing", "ops", + "quantization", ] diff --git a/torchao/core/config.py b/torchao/core/config.py index a041130835..519dfe8dfd 100644 --- a/torchao/core/config.py +++ b/torchao/core/config.py @@ -175,6 +175,7 @@ def config_to_dict(config: AOBaseConfig) -> Dict[str, Any]: "torchao.quantization", "torchao.sparsity.sparse_api", "torchao.prototype.quantization", + "torchao.prototype.mx_formats", } diff --git a/torchao/prototype/mx_formats/__init__.py b/torchao/prototype/mx_formats/__init__.py index 7252c33dc9..7c1f0ace55 100644 --- a/torchao/prototype/mx_formats/__init__.py +++ b/torchao/prototype/mx_formats/__init__.py @@ -5,6 +5,9 @@ MXLinearRecipeName, ) +# Note: Prototype and subject to change +from torchao.prototype.mx_formats.mx_subclass import MXFPInferenceConfig + # import mx_linear here to register the quantize_ transform logic # ruff: noqa: I001 import torchao.prototype.mx_formats.mx_linear # noqa: F401 @@ -14,4 +17,5 @@ "MXInferenceLinearConfig", "MXLinearConfig", "MXLinearRecipeName", + "MXFPInferenceConfig", ] diff --git a/torchao/prototype/mx_formats/mx_funcs.py b/torchao/prototype/mx_formats/mx_funcs.py new file mode 100644 index 0000000000..13e8ef6da3 --- /dev/null +++ b/torchao/prototype/mx_formats/mx_funcs.py @@ -0,0 +1,43 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +""" +This file defines the top level torch ops that are extended by MXTensor +See: https://docs.pytorch.org/docs/stable/notes/extending.html#extending-torch-with-a-tensor-wrapper-type +for more details. +""" + +from typing import Any, Dict + +import torch + +from torchao.prototype.mx_formats.mx_ops import _addmm_mx_dispatch +from torchao.prototype.mx_formats.mx_tensor import ( # noqa: E501 + MXTensor, +) + +aten = torch.ops.aten + +MX_FUNC_TABLE: Dict[Any, Any] = {} + + +def implements_func(torch_ops): + """Register torch ops to the mx op table for torch function""" + + def decorator(func): + for op in torch_ops: + MX_FUNC_TABLE[op] = func + return func + + return decorator + + +@implements_func([aten.linear.default]) +def mx_linear(func, types, args, kwargs): + a, b = args[0], args[1] + assert isinstance(a, MXTensor) and isinstance(b, MXTensor) + bias = args[2] if len(args) == 3 else None + return _addmm_mx_dispatch(a, b.t(), func, bias=bias) diff --git a/torchao/prototype/mx_formats/mx_ops.py b/torchao/prototype/mx_formats/mx_ops.py index af2d89c112..da342c7853 100644 --- a/torchao/prototype/mx_formats/mx_ops.py +++ b/torchao/prototype/mx_formats/mx_ops.py @@ -17,9 +17,12 @@ the underlying data fields to the MX matmul. """ -from typing import Any, Dict +from typing import Any, Dict, Optional import torch +from torch.utils._python_dispatch import ( + return_and_correct_aliasing, +) from torch.utils._pytree import tree_map import torchao.ops @@ -35,10 +38,12 @@ tensor_size_hpx3_to_fp6x4, ) from torchao.prototype.mx_formats.utils import to_blocked +from torchao.utils import fill_defaults aten = torch.ops.aten MX_OPS_TABLE: Dict[Any, Any] = {} +MX_FUNCTION_TABLE: Dict[Any, Any] = {} def implements(aten_ops): @@ -52,59 +57,77 @@ def decorator(func): return decorator -@implements([aten.detach.default]) -def mx_desugar_op(aten_op, args, kwargs=None): - old = args[0] - new_data = aten_op(old._data, *args[1:], **kwargs) - new = MXTensor( - old._scale_e8m0, - new_data, - old._elem_dtype, - old._block_size, - old._orig_dtype, - old._use_fp4_custom_triton_dequant_kernel, - old._gemm_kernel_choice, - old._pack_fp6, +@implements([aten.detach.default, aten.alias.default]) +def _(func, types, args, kwargs): + return return_and_correct_aliasing( + func, args, kwargs, args[0]._apply_fn_to_data(func) ) - return new -@implements([aten.mm.default, aten.matmul.default]) -def mx_mm(aten_op, args, kwargs=None): - a = args[0] - b = args[1] - assert isinstance(a, MXTensor) and isinstance(b, MXTensor) - assert a._gemm_kernel_choice == b._gemm_kernel_choice, "unsupported" - if a._gemm_kernel_choice in (MXGemmKernelChoice.CUBLAS, MXGemmKernelChoice.CUTLASS): +def _get_gemm_choice( + choice_a: Optional[MXGemmKernelChoice], choice_b: Optional[MXGemmKernelChoice] +) -> MXGemmKernelChoice: + if choice_a is not None and choice_b is not None: + assert choice_a == choice_b, ( + "Both MXTensor inputs must have the same gemm config if specified" + ) + return choice_a + + # Assert that at least one is set and return that one + assert choice_a is not None or choice_b is not None, ( + "At least one gemm choice must be specified" + ) + return choice_a if choice_a is not None else choice_b + + +def _addmm_mx_dispatch( + a: MXTensor, b: MXTensor, aten_op, bias: Optional[torch.Tensor] = None +) -> torch.Tensor: + """ + Core implementation shared between mx_mm and mx_addmm. + The only difference is whether bias is None or not. + """ + gemm_choice = _get_gemm_choice(a._gemm_kernel_choice, b._gemm_kernel_choice) + + if gemm_choice in (MXGemmKernelChoice.CUBLAS, MXGemmKernelChoice.CUTLASS): # real MX gemm backed by torchao's CUTLASS kernels M, K, N = a.shape[0], a.shape[1], b.shape[1] assert a._data.is_contiguous() assert b._data.t().is_contiguous() + assert a._block_size == 32, f"Invalid block size {a._block_size}" + assert b._block_size == 32, f"Invalid block size {b._block_size}" - # TODO(future PR): use block_size instead of hardcoding 32 - a_scale = a._scale_e8m0.view(M, K // 32) - b_scale = b._scale_e8m0.view(N, K // 32) + a_scale = a._scale_e8m0.view(M, K // a._block_size) + b_scale = b._scale_e8m0.view(N, K // b._block_size) a_scale_block = to_blocked(a_scale) b_scale_block = to_blocked(b_scale) + if a._elem_dtype == torch.float8_e4m3fn: assert b._elem_dtype == torch.float8_e4m3fn - assert a._gemm_kernel_choice is MXGemmKernelChoice.CUBLAS, ( + assert gemm_choice is MXGemmKernelChoice.CUBLAS, ( "CUBLAS is the only supported kernel choice for MX FP8 operations" ) + res = torch._scaled_mm( a._data, b._data, a_scale_block.view(torch.float8_e8m0fnu), b_scale_block.view(torch.float8_e8m0fnu), + bias=bias, out_dtype=torch.bfloat16, ) else: assert a._elem_dtype == DTYPE_FP4 assert b._elem_dtype == DTYPE_FP4 - assert a._gemm_kernel_choice is MXGemmKernelChoice.CUTLASS, "unsupported" + assert gemm_choice is MXGemmKernelChoice.CUTLASS, "unsupported" + # FP4 operations res = torchao.ops.mx_fp4_bf16( a._data, b._data, a_scale_block, b_scale_block ) + # TODO add optional bias to kernel + if bias is not None: + res = res + bias + else: # emulated MX gemm a_hp = a.to_dtype(a._orig_dtype) @@ -112,12 +135,40 @@ def mx_mm(aten_op, args, kwargs=None): # assert memory layout we expect to be required in hardware assert a_hp.is_contiguous() assert b_hp.t().is_contiguous() - res = aten_op(a_hp, b_hp) + + # Call appropriate aten_op based on whether bias is provided + if bias is not None: + res = aten_op(bias, a_hp, b_hp) # addmm + else: + res = aten_op(a_hp, b_hp) # mm + return res +@implements([aten.mm.default, aten.matmul.default]) +def mx_mm(func, types, args, kwargs): + a = args[0] + b = args[1] + assert isinstance(a, MXTensor) and isinstance(b, MXTensor) + + return _addmm_mx_dispatch(a, b, func) + + +@implements([aten.addmm.default]) +def mx_addmm(func, types, args, kwargs): + assert ( + isinstance(args[0], torch.Tensor) + and isinstance(args[1], MXTensor) + and isinstance(args[2], MXTensor) + ) + bias = args[0] + a = args[1] + b = args[2] + return _addmm_mx_dispatch(a, b, func, bias=bias) + + @implements([aten.t.default]) -def mx_t(aten_op, args, kwargs=None): +def mx_t(func, types, args, kwargs): # For now, only transpose(input, 0, 1) is supported. old = args[0] new = MXTensor( @@ -134,7 +185,7 @@ def mx_t(aten_op, args, kwargs=None): @implements([aten.sum.dim_IntList]) -def mx_cast_up_op(aten_op, args, kwargs=None): +def mx_cast_up_op(func, types, args, kwargs): """Be careful with this function, this is a "fallback" op that casts the output of the op to the original precision. And performs the op. @@ -150,11 +201,11 @@ def unwrap(x): new_args = tree_map(unwrap, args) new_kwargs = tree_map(unwrap, kwargs) - return aten_op(*new_args, **new_kwargs) + return func(*new_args, **new_kwargs) @implements([aten.view.default]) -def mx_view_op(aten_op, args, kwargs=None): +def mx_view_op(func, types, args, kwargs): data = args[0]._data new_size = args[1] if args[0]._elem_dtype == DTYPE_FP4: @@ -163,7 +214,7 @@ def mx_view_op(aten_op, args, kwargs=None): elif args[0]._elem_dtype in [DTYPE_FP6_E3M2, DTYPE_FP6_E2M3] and args[0]._pack_fp6: # special case fp6 as we pack 4 elements in 3 bytes new_size = tensor_size_hpx3_to_fp6x4(new_size, data.is_contiguous()) - new_data = aten_op(data, new_size, *args[2:], **kwargs) + new_data = func(data, new_size, *args[2:], **kwargs) return MXTensor( args[0]._scale_e8m0, new_data, @@ -176,28 +227,120 @@ def mx_view_op(aten_op, args, kwargs=None): ) -@implements([aten._to_copy.default]) -def autocast_to_copy(aten_op, args, kwargs=None): - """This gets called when running matmul under autocast - when the input is a MXTensor, presenting as a fp32 - tensor. - """ - assert isinstance(args[0], MXTensor) - assert len(kwargs) == 1 and "dtype" in kwargs, ( - "Only support dtype kwarg for autocast" +@implements([aten.slice.Tensor]) +def mx_slice(func, types, args, kwargs): + x, dim, start, end, step = fill_defaults(args, 5, [0, None, None, 1]) + + if step != 1: + raise ValueError("Only support aten.slice with step=1") + + M, K = x.shape[0], x.shape[1] + + # TODO why doesn't scale have shape? + scale_shaped = x._scale_e8m0.view(M, K // x._block_size) + + if dim == 0: + # Slicing along the first dimension (rows) TODO assuming that dim 1 is reduciton dim for now + sliced_scale = aten.slice.Tensor(scale_shaped, dim, start, end, step).flatten() + sliced_data = aten.slice.Tensor(x._data, dim, start, end, step) + elif dim == 1: + # Slicing along reduciton dim + if start is not None: + # Assert start is a multiple of block_size + assert start % x._block_size == 0, ( + f"Start index {start} must be a multiple of block_size {x._block_size}" + ) + + if end is not None: + # Assert end is a multiple of block_size + assert end % x._block_size == 0, ( + f"End index {end} must be a multiple of block_size {x._block_size}" + ) + + sliced_data = aten.slice.Tensor(x._data, dim, start, end, step) + + # Calculate which scale elements to keep + start_block = 0 if start is None else start // x._block_size + end_block = -1 if end is None else end // x._block_size + + # Slice the scale tensor accordingly + sliced_scale = aten.slice.Tensor( + scale_shaped, 1, start_block, end_block, step + ).flatten() + else: + raise ValueError( + f"MXTensor only supports slicing along dimensions 0 and 1, got dim={dim}" + ) + + return return_and_correct_aliasing( + func, + args, + kwargs, + MXTensor( + sliced_scale, + sliced_data, + x._elem_dtype, + x._block_size, + x._orig_dtype, + x._use_fp4_custom_triton_dequant_kernel, + x._gemm_kernel_choice, + x._pack_fp6, + ), ) - assert kwargs["dtype"] in { - torch.float16, - torch.bfloat16, - }, "Only support floating point conversion for autocast w/ MXTensor" - res = MXTensor( - args[0]._scale_e8m0, - args[0]._data, - args[0]._elem_dtype, - args[0]._block_size, - kwargs["dtype"], - args[0]._use_fp4_custom_triton_dequant_kernel, - args[0]._gemm_kernel_choice, - args[0]._pack_fp6, + + +@implements([aten.copy_.default]) +def mx_copy_(func, types, args, kwargs): + self = args[0] + src = args[1] + if MXTensor._same_metadata(self, src): + self_tensors = self.__tensor_flatten__()[0] + for tensor_name in self_tensors: + getattr(self, tensor_name).copy_(getattr(src, tensor_name)) + return + raise ValueError( + f"Not supported args for copy_ due to metadata mistach: {args[0], args[1]}" ) - return res + + +@implements([aten._to_copy.default]) +def autocast_to_copy(func, types, args, kwargs): + """Autocast + device movement""" + assert isinstance(args[0], MXTensor) + + # Handle dtype parameter + dtype = kwargs.pop("dtype", None) + if dtype is not None: + assert dtype in { + torch.float16, + torch.bfloat16, + }, "Only support floating point conversion for autocast w/ MXTensor" + + # Handle device parameter + device = kwargs.pop("device", None) + if device is not None: + # Apply device change using _apply_fn_to_data + tensor = args[0]._apply_fn_to_data(lambda x: func(x, device=device)) + tensor = return_and_correct_aliasing(func, args, {}, tensor) + else: + tensor = args[0] + + # Verify no other kwargs remain + assert len(kwargs) == 0, "Only support dtype and device kwargs for autocast" + + # If dtype is specified, create a new MXTensor with the requested dtype + if dtype is not None: + res = MXTensor( + tensor._scale_e8m0, + tensor._data, + tensor._elem_dtype, + tensor._block_size, + dtype, + tensor._use_fp4_custom_triton_dequant_kernel, + tensor._gemm_kernel_choice, + tensor._pack_fp6, + ) + return res + + # If only device was changed, return the device-changed tensor + return tensor diff --git a/torchao/prototype/mx_formats/mx_subclass.py b/torchao/prototype/mx_formats/mx_subclass.py new file mode 100644 index 0000000000..2173c97002 --- /dev/null +++ b/torchao/prototype/mx_formats/mx_subclass.py @@ -0,0 +1,157 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD 3-Clause license found in the +# LICENSE file in the root directory of this source tree. + +import types +from dataclasses import dataclass +from typing import Optional + +import torch + +import torchao +from torchao.core.config import AOBaseConfig +from torchao.prototype.mx_formats import ( + MXGemmKernelChoice, +) +from torchao.prototype.mx_formats.config import ( + _validate_elem_dtype, + _validate_gemm_kernel_choice, +) +from torchao.prototype.mx_formats.mx_tensor import MXTensor +from torchao.quantization.quant_api import to_linear_activation_quantized +from torchao.quantization.transform_module import ( + register_quantize_module_handler, +) +from torchao.utils import TORCH_VERSION_AT_LEAST_2_5, is_sm_at_least_100 + + +# Note: This API is extra prototype and will change in the future +@dataclass +class MXFPInferenceConfig(AOBaseConfig): + """ + MX Format Inference Quantization + + This module provides support for running inference with float8 quantization using MX formats. + The quantization flow works as follows: + + 1. Weight Quantization: + - In _mx_inference_linear_transform(), the module's weight is converted to an MXTensor + - The weight is quantized to the specified dtype (float8_e4m3fn by default) + - This happens when quantize_() is called with an MXFPInferenceConfig + + 2. Activation Quantization: + - A callable (_input_activation_quant_func_mxfp) is defined that will quantize + activations during inference to the same dtype + - This function is passed to to_linear_activation_quantized() along with the + already-quantized weight + + 3. Runtime Flow: + - When the quantized module is called, the input goes through the LinearActivationQuantizedTensor + - The input (activation) is quantized just-in-time using the provided function + - The MX quantized activation and MX weight are used together in F.linear + + Requirements: + - NVIDIA SM100+ hardware (Blackwell or newer) is required for execution + - PyTorch 2.5+ for proper serialization support + + See also: + - LinearActivationQuantizedTensor in torchao.quantization.quant_api + - MXTensor in torchao.prototype.mx_formats.mx_tensor + """ + + block_size: int = 32 + + # Dtypes for Input and Weights + activation_dtype: torch.dtype = torch.float8_e4m3fn + weight_dtype: torch.dtype = torch.float8_e4m3fn + + # Which kernel to run for mm + gemm_kernel_choice: MXGemmKernelChoice = MXGemmKernelChoice.CUBLAS + + # Set some magic perf settings + set_inductor_config: bool = False + + def __post_init__(self): + assert self.activation_dtype == self.weight_dtype, ( + "For now - we only support matching input/weight dtypes." + ) + _validate_elem_dtype(self.activation_dtype) + _validate_elem_dtype(self.weight_dtype) + _validate_gemm_kernel_choice( + self.gemm_kernel_choice, self.block_size, self.weight_dtype + ) + + +def _linear_extra_repr(self): + return f"in_features={self.weight.shape[1]}, out_features={self.weight.shape[0]}, weight={repr(self.weight)}" + + +def _input_activation_quant_func_mxfp( + x: torch.Tensor, + activation_dtype: torch.dtype, + block_size: int, + scale: Optional[torch.Tensor] = None, +): + """ """ + + # TODO scale for static quant + + activation = MXTensor.to_mx( + x, + activation_dtype, + block_size=block_size, + gemm_kernel_choice=None, # Get from weight + pack_fp6=False, # TODO + ) + return activation + + +@register_quantize_module_handler(MXFPInferenceConfig) +def _mx_inference_linear_transform( + module: torch.nn.Module, config: MXFPInferenceConfig +): + # TODO Sm120 has slightly more restrictive reqs + # TODO handle AMD + assert is_sm_at_least_100(), "MXFP is only supported on sm100 machiens for now" + if config.set_inductor_config: + torchao.quantization.utils.recommended_inductor_config_setter() + + activation_dtype = config.activation_dtype + weight_dtype = config.weight_dtype + weight = module.weight + + assert weight.dtype == torch.bfloat16, ( + f"Only supporting bf16 out dtype for now, got {weight.dtype}" + ) + + # Convert weight to MX Tensor + quantized_weight = MXTensor.to_mx( + weight, + weight_dtype, + block_size=config.block_size, + gemm_kernel_choice=config.gemm_kernel_choice, + pack_fp6=False, # TODO + ) + + input_quant_func = _input_activation_quant_func_mxfp + input_quant_kwargs = { + "block_size": config.block_size, + "activation_dtype": activation_dtype, + "scale": None, + } + + quantized_weight = to_linear_activation_quantized( + quantized_weight, input_quant_func, quant_kwargs=input_quant_kwargs + ) + + module.weight = torch.nn.Parameter(quantized_weight, requires_grad=False) + module.extra_repr = types.MethodType(_linear_extra_repr, module) + return module + + +if TORCH_VERSION_AT_LEAST_2_5: + torch.serialization.add_safe_globals( + [MXTensor, MXGemmKernelChoice, _input_activation_quant_func_mxfp] + ) diff --git a/torchao/prototype/mx_formats/mx_tensor.py b/torchao/prototype/mx_formats/mx_tensor.py index 3125f3c0cc..494e53d717 100644 --- a/torchao/prototype/mx_formats/mx_tensor.py +++ b/torchao/prototype/mx_formats/mx_tensor.py @@ -18,7 +18,7 @@ """ from enum import Enum, auto -from typing import Dict, Union +from typing import Callable, Dict, Union import torch @@ -559,10 +559,17 @@ def __repr__(self): @classmethod def __torch_dispatch__(cls, func, types, args, kwargs=None): # avoid circular dependency + from torchao.prototype.mx_formats.mx_funcs import MX_FUNC_TABLE from torchao.prototype.mx_formats.mx_ops import MX_OPS_TABLE if func in MX_OPS_TABLE: - return MX_OPS_TABLE[func](func, args, kwargs) + return MX_OPS_TABLE[func](func, types, args, kwargs) + + # TODO AO BASE_TENSOR doesn't respect dispatch and function modes + # We are calling nn.functional.linear from within LinearAct Tensor even though + # We are in a __torch__dispatch. This disables the decomposition and we get this op + if func == torch.ops.aten.linear.default: + return MX_FUNC_TABLE[func](func, types, args, kwargs) raise NotImplementedError(f"{func} not implemented") @@ -631,5 +638,37 @@ def __tensor_unflatten__( metadata["_pack_fp6"], ) + def _apply_fn_to_data(self, fn: Callable): + """Applies a fn to all tensor components stored on this class""" + tensor_names, ctx = self.__tensor_flatten__() + + # Apply the function to each tensor component + new_tensors = {} + for name in tensor_names: + new_tensors[name] = fn(getattr(self, name)) + + return self.__class__.__tensor_unflatten__( + new_tensors, + ctx, + None, # outer_size parameter + None, # outer_stride parameter + ) + # Do not force the MXTensor type on the returned tensor __torch_function__ = torch._C._disabled_torch_function_impl + + @classmethod + def _same_metadata(cls, self: "MXTensor", src: "MXTensor") -> bool: + return ( + isinstance(self, MXTensor) + and isinstance(src, MXTensor) + and self._elem_dtype == src._elem_dtype + and self._block_size == src._block_size + and self._orig_dtype == src._orig_dtype + and self._use_fp4_custom_triton_dequant_kernel + == src._use_fp4_custom_triton_dequant_kernel + and self._gemm_kernel_choice == src._gemm_kernel_choice + and self._pack_fp6 == src._pack_fp6 + and self._scale_e8m0.shape == src._scale_e8m0.shape + and self._data.shape == src._data.shape + ) diff --git a/torchao/prototype/mx_formats/utils.py b/torchao/prototype/mx_formats/utils.py index 2c828e477c..e4777d3899 100644 --- a/torchao/prototype/mx_formats/utils.py +++ b/torchao/prototype/mx_formats/utils.py @@ -43,8 +43,7 @@ def to_blocked(input_matrix, use_triton_kernel: bool = True) -> Tensor: padded = input_matrix # TODO This is to work around VLLM's usage of compile w/ dynamic shapes - # if torch.compiler.is_compiling() or (rows, cols) != (padded_rows, padded_cols): - if (rows, cols) != (padded_rows, padded_cols): + if torch.compiler.is_compiling() or (rows, cols) != (padded_rows, padded_cols): padded = torch.zeros( (padded_rows, padded_cols), device=input_matrix.device, From 69fc240ce47d12ee3994db752029d31916482f5b Mon Sep 17 00:00:00 2001 From: Alan Tuning <136696905+Degnel@users.noreply.github.com> Date: Tue, 13 May 2025 00:21:10 +0200 Subject: [PATCH 021/165] Feat: Implementation of the DeepSeek blockwise quantization for fp8 tensors (#1763) * Feat: Integration of DeepSeek's blockwise quantization - fp8 triton gemm - quant, dequant and linear utils - time & precision benchmarks - basic tests * Doc: init + linting + readme * Feat: adding triton dependency, adaptative testing dtype * Fix: - removing triton dependency - cleanning adaptative dtype * Fix: - fixing W4A8 quantization for cutlass kernel in precision benchmark - importing triton only if cuda available - setting a less harsh threshold for quant-dequant and for gemm kernel mm precision * Fix: - condition triton import in gemm - linting * Fix: triton pytest skip * Linting * Fix: - raising explicit error when running benchmark without cuda - merging quant, dequant and gemm code into one file - removing depricated int4/int8 comparison * Fix: - fix import in __init__.py and in blockwise_linear.py * Optim: fixing poor performance on large M values > the autotuner was optimizing based only on small M sizes at the beginning of the benchmark > added a `M_bucket` key to the autotuner to enable tuning based on similar M sizes > added `128` to the `BLOCK_SIZE_M` configuration, which improves performance for large M values > launcher now takes `block_size` into account (although using `block_size=128` is recommended for best performance) * Fix: skipping blockwise quant precision test for older versions of triton * Bench: incressing the bench range to m=8192 --- ...enchmark_blockwise_scaled_linear_triton.py | 130 ++++++++ test/prototype/test_blockwise_triton.py | 72 +++++ torchao/prototype/blockwise_fp8/README.md | 29 ++ torchao/prototype/blockwise_fp8/__init__.py | 15 + .../blockwise_fp8/blockwise_linear.py | 77 +++++ .../blockwise_fp8/blockwise_quantization.py | 279 ++++++++++++++++++ 6 files changed, 602 insertions(+) create mode 100644 benchmarks/benchmark_blockwise_scaled_linear_triton.py create mode 100644 test/prototype/test_blockwise_triton.py create mode 100644 torchao/prototype/blockwise_fp8/README.md create mode 100644 torchao/prototype/blockwise_fp8/__init__.py create mode 100644 torchao/prototype/blockwise_fp8/blockwise_linear.py create mode 100644 torchao/prototype/blockwise_fp8/blockwise_quantization.py diff --git a/benchmarks/benchmark_blockwise_scaled_linear_triton.py b/benchmarks/benchmark_blockwise_scaled_linear_triton.py new file mode 100644 index 0000000000..809202170a --- /dev/null +++ b/benchmarks/benchmark_blockwise_scaled_linear_triton.py @@ -0,0 +1,130 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD 3-Clause license found in the +# LICENSE file in the root directory of this source tree. + + +import torch + +if torch.cuda.is_available(): + import pandas as pd + from tqdm import tqdm + from triton.testing import do_bench + + from torchao.float8.float8_utils import compute_error + from torchao.prototype.blockwise_fp8.blockwise_quantization import ( + blockwise_fp8_gemm, + fp8_blockwise_act_quant, + fp8_blockwise_weight_quant, + ) + from torchao.utils import is_sm_at_least_89 +else: + raise RuntimeError("This benchmark is only avaible on CUDA hardware") + + +def benchmark_microseconds(f, *args, warmup=25, rep=100): + return ( + do_bench(lambda: f(*args), warmup=warmup, rep=rep, return_mode="median") * 1e3 + ) + + +def get_blockwise_problem( + m: int, n: int, k: int, block_size: int, dtype: torch.dtype, device +): + assert n % block_size == 0 and k % block_size == 0, ( + "N and K dims must be divisible by block_size" + ) + assert dtype in [ + torch.float8_e4m3fn, + torch.float8_e5m2, + ], "dtype must be torch.float8_e4m3fn or torch.float8_e5m2" + dtype_max = torch.finfo(dtype).max + A = (dtype_max * (2 * torch.rand(m, k, device=device) - 1)).to(dtype) + A_scale = torch.randn((m, k // block_size), dtype=torch.half, device=device) + B = (dtype_max * (2 * torch.rand(n, k, device=device) - 1)).to(dtype) + B_scale = torch.randn( + (n // block_size, k // block_size), dtype=torch.half, device=device + ) + + return A, A_scale, B, B_scale + + +def benchmark_latency( + m: int, k: int, n: int, block_size: int, dtype: torch.dtype, device +): + A_ref = torch.randn((m, k), dtype=torch.half, device=device) + B_ref = torch.randn((n, k), dtype=torch.half, device=device) + fp16_time = benchmark_microseconds(torch.nn.functional.linear, A_ref, B_ref) + + A, A_scale, B, B_scale = get_blockwise_problem(m, n, k, block_size, dtype, device) + blockwise_time = benchmark_microseconds( + blockwise_fp8_gemm, A, A_scale, B, B_scale, block_size + ) + + return { + "m": m, + "k": k, + "n": n, + "block_size": block_size, + "dtype": dtype, + "fp16_latency (ms)": fp16_time, + "blockwise_latency (ms)": blockwise_time, + "blockwise_speedup": fp16_time / blockwise_time, + } + + +def benchmark_precision( + m: int, k: int, n: int, block_size: int, dtype: torch.dtype, device +): + lin = torch.nn.Linear(k, n, False, device, torch.half) + A = torch.randn((m, k), dtype=torch.half, device=device) + W = lin.weight + output = A @ W.T + + A_q, A_s = fp8_blockwise_act_quant(A, block_size, dtype) + W_q, W_s = fp8_blockwise_weight_quant(W, block_size, dtype) + output_blockwise = blockwise_fp8_gemm(A_q, A_s, W_q, W_s, block_size) + + return { + "m": m, + "k": k, + "n": n, + "block_size": block_size, + "dtype": dtype, + "error_blockwise (dB)": compute_error(output, output_blockwise), + } + + +if __name__ == "__main__" and torch.cuda.is_available(): + device = torch.device("cuda") + k_vals = (8192, 8192, 8192, 28672) + n_vals = (8192, 10240, 57344, 8192) + block_size_vals = (128, 128, 128, 128) + + latency_results = [] + precision_results = [] + + available_dtypes = ( + [torch.float8_e4m3fn, torch.float8_e5m2] + if is_sm_at_least_89() + else [torch.float8_e5m2] + ) + for m in tqdm([1 << i for i in range(14)]): + for dtype in available_dtypes: + for n, k, block_size in zip(n_vals, k_vals, block_size_vals): + latency_results.append( + benchmark_latency(m, k, n, block_size, dtype, device) + ) + precision_results.append( + benchmark_precision(m, k, n, block_size, dtype, device) + ) + + df_latency = pd.DataFrame(latency_results) + df_precision = pd.DataFrame(precision_results) + + df_latency.to_csv("blockwise_triton_latency_results.csv", index=False) + df_precision.to_csv("blockwise_triton_precision_results.csv", index=False) + + print(df_latency.to_markdown(index=False)) + print(df_precision.to_markdown(index=False)) diff --git a/test/prototype/test_blockwise_triton.py b/test/prototype/test_blockwise_triton.py new file mode 100644 index 0000000000..8aab73f7e8 --- /dev/null +++ b/test/prototype/test_blockwise_triton.py @@ -0,0 +1,72 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD 3-Clause license found in the +# LICENSE file in the root directory of this source tree. + +import pytest +import torch + +from packaging import version + +triton = pytest.importorskip("triton", reason="Triton required to run this test") + +from torchao.prototype.blockwise_fp8.blockwise_quantization import ( + blockwise_fp8_gemm, + fp8_blockwise_act_quant, + fp8_blockwise_weight_dequant, + fp8_blockwise_weight_quant, +) +from torchao.utils import is_sm_at_least_89 + +BLOCKWISE_SIZE_MNK = [ + (2, 512, 128), + (3, 2048, 2048), + (4, 3584, 640), + (13, 8704, 8576), + (26, 18944, 1664), + (67, 6656, 1408), +] + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +@pytest.mark.parametrize("_, N, K", BLOCKWISE_SIZE_MNK) +@pytest.mark.parametrize( + "dtype", + [torch.float8_e4m3fn, torch.float8_e5m2] + if is_sm_at_least_89() + else [torch.float8_e5m2], +) +def test_blockwise_quant_dequant(_, N, K, dtype): + x = torch.randn(N, K).cuda() + qx, s = fp8_blockwise_weight_quant(x, dtype=dtype) + x_reconstructed = fp8_blockwise_weight_dequant(qx, s) + error = torch.norm(x - x_reconstructed) / torch.norm(x) + print(f"Relative Error: {error.item():.6f}") + + assert error < 0.1, "Quant-Dequant error is too high" + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +@pytest.mark.skipif( + version.parse(triton.__version__) < version.parse("3.3.0"), + reason="Triton version < 3.3.0, test skipped", +) +@pytest.mark.parametrize("M, N, K", BLOCKWISE_SIZE_MNK) +@pytest.mark.parametrize( + "dtype", + [torch.float8_e4m3fn, torch.float8_e5m2] + if is_sm_at_least_89() + else [torch.float8_e5m2], +) +def test_blockwise_fp8_gemm(M, N, K, dtype): + A = torch.randn(M, K).cuda() + B = torch.randn(N, K).cuda() + C = A @ B.T + A_q, A_s = fp8_blockwise_act_quant(A, dtype=dtype) + B_q, B_s = fp8_blockwise_weight_quant(B, dtype=dtype) + C_q = blockwise_fp8_gemm(A_q, A_s, B_q, B_s) + error = torch.norm(C - C_q) / torch.norm(C) + print(f"Relative Error: {error.item():.6f}") + + assert error < 0.1, "Quantize gemm error is too high" diff --git a/torchao/prototype/blockwise_fp8/README.md b/torchao/prototype/blockwise_fp8/README.md new file mode 100644 index 0000000000..16bb5073a1 --- /dev/null +++ b/torchao/prototype/blockwise_fp8/README.md @@ -0,0 +1,29 @@ +# Blockwise Quantization Implementation + +## Overview + +This directory contains the implementation of blockwise quantization introduced by DeepSeek. The method involves quantizing activations and weight matrices in blocks of 128x1 and 128x128, respectively. + +## Quantization Process + +### Activation Quantization +- Activations are quantized in blocks of size 128x1 using the FP8 format + +### Weight Matrix Quantization +- Weights are quantized in blocks of size 128x128 using the FP8 format + +## Kernel Implementation in Triton + +- The kernel for blockwise quantization is implemented using Triton +- For now, the only supported types are: torch.float8_e4m3fn and torch.float8_e5m2 + +## Illustration + +![Blockwise Quantization Illustration](https://arxiv.org/html/2412.19437v1/x7.png) + +*Illustration of the blockwise quantization process.* + +## Original Paper + +For detailed motivations and technical specifications, please refer to the original paper: +- [DeepSeek Blockwise Quantization Paper](https://arxiv.org/html/2412.19437v1) diff --git a/torchao/prototype/blockwise_fp8/__init__.py b/torchao/prototype/blockwise_fp8/__init__.py new file mode 100644 index 0000000000..f2842417e4 --- /dev/null +++ b/torchao/prototype/blockwise_fp8/__init__.py @@ -0,0 +1,15 @@ +from .blockwise_linear import BlockwiseQuantLinear +from .blockwise_quantization import ( + blockwise_fp8_gemm, + fp8_blockwise_act_quant, + fp8_blockwise_weight_dequant, + fp8_blockwise_weight_quant, +) + +__all__ = [ + "blockwise_fp8_gemm", + "BlockwiseQuantLinear", + "fp8_blockwise_act_quant", + "fp8_blockwise_weight_quant", + "fp8_blockwise_weight_dequant", +] diff --git a/torchao/prototype/blockwise_fp8/blockwise_linear.py b/torchao/prototype/blockwise_fp8/blockwise_linear.py new file mode 100644 index 0000000000..c25b946732 --- /dev/null +++ b/torchao/prototype/blockwise_fp8/blockwise_linear.py @@ -0,0 +1,77 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD 3-Clause license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from torch import nn + +from torchao.prototype.blockwise_fp8.blockwise_quantization import ( + blockwise_fp8_gemm, + fp8_blockwise_act_quant, +) + + +class BlockwiseQuantLinear(nn.Module): + """ + Custom linear layer with support for quantized weights and optional bias. + + Args: + in_features (int): Number of input features. + out_features (int): Number of output features. + bias (bool): Whether to include a bias term. Defaults to False. + block_size (int): Block size for quantization. Defaults to 128. + dtype (torch.dtype): Data type for the weights. Defaults to torch.float8_e4m3fn. + """ + + dtype = torch.bfloat16 + + def __init__( + self, + in_features: int, + out_features: int, + bias: bool = False, + block_size: int = 128, + dtype: torch.dtype = torch.float8_e4m3fn, + ): + super().__init__() + supported_dtypes = [ + torch.float8_e4m3fn, + torch.float8_e5m2, + ] + assert dtype in supported_dtypes, ( + f"Unsupported dtype: {dtype}. Supported dtypes: {supported_dtypes}" + ) + scale_in_features = (in_features + block_size - 1) // block_size + scale_out_features = (out_features + block_size - 1) // block_size + self.weight = nn.Parameter(torch.empty(out_features, in_features, dtype=dtype)) + self.weight.scale = self.scale = nn.Parameter( + torch.empty(scale_out_features, scale_in_features, dtype=torch.float32) + ) + self.block_size = block_size + self.dtype + + if bias: + self.bias = nn.Parameter(torch.empty(out_features)) + else: + self.register_parameter("bias", None) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Forward pass for the custom linear layer. + + Args: + x (torch.Tensor): Input tensor. + + Returns: + torch.Tensor: Transformed tensor after linear computation. + """ + x, scale = fp8_blockwise_act_quant(x, self.block_size, self.dtype) + y = blockwise_fp8_gemm( + x, scale, self.weight, self.weight.scale, self.block_size + ) + + if self.bias is not None: + y += self.bias + return y diff --git a/torchao/prototype/blockwise_fp8/blockwise_quantization.py b/torchao/prototype/blockwise_fp8/blockwise_quantization.py new file mode 100644 index 0000000000..1d296249f9 --- /dev/null +++ b/torchao/prototype/blockwise_fp8/blockwise_quantization.py @@ -0,0 +1,279 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD 3-Clause license found in the +# LICENSE file in the root directory of this source tree. + +import math +from typing import Tuple + +import torch +import triton +import triton.language as tl +from triton import Config + +# Original implementation at https://github.com/deepseek-ai/DeepSeek-V3/blob/main/inference/kernel.py + +fp8_gemm_configs = [ + Config( + {"BLOCK_SIZE_M": block_m, "BLOCK_SIZE_N": block_n}, + num_stages=num_stages, + num_warps=8, + ) + for block_m in [16, 32, 64, 128] + for block_n in [32, 64, 128] + for num_stages in [3, 4, 5, 6] +] + + +@triton.autotune(configs=fp8_gemm_configs, key=["N", "K", "M_BUCKET", "BLOCK_SIZE_K"]) +@triton.jit +def blockwise_fp8_gemm_kernel( + a_ptr, + b_ptr, + c_ptr, + a_s_ptr, + b_s_ptr, + M, + N: tl.constexpr, + K: tl.constexpr, + M_BUCKET: tl.constexpr, + BLOCK_SIZE_M: tl.constexpr, + BLOCK_SIZE_N: tl.constexpr, + BLOCK_SIZE_K: tl.constexpr, +): + pid_m = tl.program_id(axis=0) + pid_n = tl.program_id(axis=1) + k = tl.cdiv(K, BLOCK_SIZE_K) + offs_m = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M + offs_n = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N + offs_k = tl.arange(0, BLOCK_SIZE_K) + a_ptrs = a_ptr + offs_m[:, None] * K + offs_k[None, :] + b_ptrs = b_ptr + offs_n[None, :] * K + offs_k[:, None] + a_s_ptrs = a_s_ptr + offs_m * k + b_s_ptrs = b_s_ptr + (offs_n // BLOCK_SIZE_K) * k + + accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) + for i in range(k): + a = tl.load(a_ptrs, mask=offs_k[None, :] < K - i * BLOCK_SIZE_K, other=0.0) + b = tl.load(b_ptrs, mask=offs_k[:, None] < K - i * BLOCK_SIZE_K, other=0.0) + a_s = tl.load(a_s_ptrs) + b_s = tl.load(b_s_ptrs) + accumulator += tl.dot(a, b) * a_s[:, None] * b_s[None, :] + a_ptrs += BLOCK_SIZE_K + b_ptrs += BLOCK_SIZE_K + a_s_ptrs += 1 + b_s_ptrs += 1 + + c = accumulator.to(c_ptr.dtype.element_ty) + offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) + offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) + c_ptrs = c_ptr + offs_m[:, None] * N + offs_n[None, :] + mask = (offs_m[:, None] < M) & (offs_n[None, :] < N) + tl.store(c_ptrs, c, mask=mask) + + +def blockwise_fp8_gemm( + a: torch.Tensor, + a_s: torch.Tensor, + b: torch.Tensor, + b_s: torch.Tensor, + block_size: int = 128, +): + assert a.is_contiguous() and b.is_contiguous() + assert a_s.is_contiguous() and b_s.is_contiguous() + K = a.size(-1) + M = a.numel() // K + N = b.size(0) + M_BUCKET = math.ceil(math.log2(M)) + c = a.new_empty(*a.size()[:-1], N, dtype=torch.get_default_dtype()) + grid = lambda META: ( + triton.cdiv(M, META["BLOCK_SIZE_M"]), + triton.cdiv(N, META["BLOCK_SIZE_N"]), + ) + blockwise_fp8_gemm_kernel[grid]( + a, b, c, a_s, b_s, M, N, K, M_BUCKET, BLOCK_SIZE_K=block_size + ) + return c + + +@triton.jit +def fp8_blockwise_act_quant_kernel(x_ptr, y_ptr, s_ptr, BLOCK_SIZE: tl.constexpr): + """ + Quantizes the input tensor `x_ptr` and stores the result in `y_ptr` and the scaling factor in `s_ptr`. + + Args: + x_ptr (triton.Pointer): Pointer to the input tensor. + y_ptr (triton.Pointer): Pointer to the output tensor where quantized values will be stored. + s_ptr (triton.Pointer): Pointer to the output tensor where scaling factors will be stored. + BLOCK_SIZE (tl.constexpr): The size of the block to be processed by each program instance. + + Returns: + None + """ + pid = tl.program_id(axis=0) + offs = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) + x = tl.load(x_ptr + offs).to(tl.float32) + s = tl.max(tl.abs(x)) / 448.0 + y = x / s + y = y.to(y_ptr.dtype.element_ty) + tl.store(y_ptr + offs, y) + tl.store(s_ptr + pid, s) + + +def fp8_blockwise_act_quant( + x: torch.Tensor, block_size: int = 128, dtype: torch.dtype = torch.float8_e4m3fn +) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Quantizes the input tensor `x` using block-wise quantization with block size being BLOCK_SIZEx1. + + Args: + x (torch.Tensor): The input tensor to be quantized. Must be contiguous and its last dimension size must be divisible by `block_size`. + block_size (int, optional): The size of the blocks to be used for quantization. Default is 128. + dtype (torch.dtype, optional): The dtype to use for the quantized tensor. Default is `torch.float8_e4m3fn`. + + + Returns: + Tuple[torch.Tensor, torch.Tensor]: A tuple containing: + - The quantized tensor with dtype `dtype`. + - A tensor of scaling factors with dtype `torch.float32`. + """ + assert x.is_contiguous(), "Input tensor must be contiguous" + assert x.size(-1) % block_size == 0, ( + f"Last dimension size must be divisible by block_size (block_size={block_size})" + ) + assert dtype in [ + torch.float8_e4m3fn, + torch.float8_e5m2, + ], "dtype must be torch.float8_e4m3fn or torch.float8_e5m2" + y = torch.empty_like(x, dtype=dtype) + s = x.new_empty(*x.size()[:-1], x.size(-1) // block_size, dtype=torch.float32) + grid = lambda meta: (triton.cdiv(x.numel(), meta["BLOCK_SIZE"]),) + fp8_blockwise_act_quant_kernel[grid](x, y, s, BLOCK_SIZE=block_size) + return y, s + + +@triton.jit +def fp8_blockwise_weight_quant_kernel( + x_ptr, y_ptr, s_ptr, M, N, BLOCK_SIZE: tl.constexpr +): + """ + Quantizes the input tensor `x_ptr` and stores the result in `y_ptr` and the scaling factors in `s_ptr`. + + Args: + x_ptr (tl.pointer): Pointer to the input tensor. + y_ptr (tl.pointer): Pointer to the output tensor where quantized values will be stored. + s_ptr (tl.pointer): Pointer to the output tensor where scaling factors will be stored. + M (int): Number of rows in the weight matrix. + N (int): Number of columns in the weight matrix. + BLOCK_SIZE (tl.constexpr): The size of the block to be processed by each program instance. + """ + pid_m = tl.program_id(axis=0) + pid_n = tl.program_id(axis=1) + n = tl.cdiv(N, BLOCK_SIZE) + offs_m = pid_m * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) + offs_n = pid_n * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) + offs = offs_m[:, None] * N + offs_n[None, :] + mask = (offs_m[:, None] < M) & (offs_n[None, :] < N) + x = tl.load(x_ptr + offs, mask=mask).to(tl.float32) + s = tl.max(tl.abs(x)) / 448.0 + y = x / s + y = y.to(y_ptr.dtype.element_ty) + tl.store(y_ptr + offs, y, mask=mask) + tl.store(s_ptr + pid_m * n + pid_n, s) + + +def fp8_blockwise_weight_quant( + x: torch.Tensor, block_size: int = 128, dtype=torch.float8_e4m3fn +) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Quantizes the given weight tensor using block-wise quantization with block size being BLOCK_SIZExBLOCK_SIZE. + + Args: + x (torch.Tensor): The weight tensor to be quantized. + block_size (int, optional): The block size to use for quantization. Defaults to 128. + dtype (torch.dtype, optional): The dtype to use for the quantized tensor. Defaults to `torch.float8_e4m3fn`. + + Returns: + Tuple[torch.Tensor, torch.Tensor]: A tuple containing: + - The quantized weight tensor with dtype `dtype`. + - A tensor of scaling factors with dtype `torch.float32`. + """ + assert x.is_contiguous(), "Input tensor must be contiguous" + assert x.dim() == 2, "Input tensor must have 2 dimensions" + assert x.size(0) % block_size == 0 and x.size(1) % block_size == 0, ( + f"Both dimensions of x must be divisible by block_size (block_size={block_size})" + ) + assert dtype in [ + torch.float8_e4m3fn, + torch.float8_e5m2, + ], "dtype must be torch.float8_e4m3fn or torch.float8_e5m2" + M, N = x.size() + y = torch.empty_like(x, dtype=dtype) + s = x.new_empty(M // block_size, N // block_size, dtype=torch.float32) + grid = lambda meta: ( + triton.cdiv(M, meta["BLOCK_SIZE"]), + triton.cdiv(N, meta["BLOCK_SIZE"]), + ) + fp8_blockwise_weight_quant_kernel[grid](x, y, s, M, N, BLOCK_SIZE=block_size) + return y, s + + +@triton.jit +def fp8_blockwise_weight_dequant_kernel( + x_ptr, s_ptr, y_ptr, M, N, BLOCK_SIZE: tl.constexpr +): + """ + Dequantizes weights using the provided scaling factors and stores the result. + + Args: + x_ptr (tl.pointer): Pointer to the quantized weights. + s_ptr (tl.pointer): Pointer to the scaling factors. + y_ptr (tl.pointer): Pointer to the output buffer for dequantized weights. + M (int): Number of rows in the weight matrix. + N (int): Number of columns in the weight matrix. + BLOCK_SIZE (tl.constexpr): Size of the block for tiling. + + Returns: + None + """ + pid_m = tl.program_id(axis=0) + pid_n = tl.program_id(axis=1) + n = tl.cdiv(N, BLOCK_SIZE) + offs_m = pid_m * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) + offs_n = pid_n * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) + offs = offs_m[:, None] * N + offs_n[None, :] + mask = (offs_m[:, None] < M) & (offs_n[None, :] < N) + x = tl.load(x_ptr + offs, mask=mask).to(tl.float32) + s = tl.load(s_ptr + pid_m * n + pid_n) + y = x * s + tl.store(y_ptr + offs, y, mask=mask) + + +def fp8_blockwise_weight_dequant( + x: torch.Tensor, s: torch.Tensor, block_size: int = 128 +) -> torch.Tensor: + """ + Dequantizes the given weight tensor using the provided scale tensor. + + Args: + x (torch.Tensor): The quantized weight tensor of shape (M, N). + s (torch.Tensor): The scale tensor of shape (M, N). + block_size (int, optional): The block size to use for dequantization. Defaults to 128. + + Returns: + torch.Tensor: The dequantized weight tensor of the same shape as `x`. + + Raises: + AssertionError: If `x` or `s` are not contiguous or if their dimensions are not 2. + """ + assert x.is_contiguous() and s.is_contiguous(), "Input tensors must be contiguous" + assert x.dim() == 2 and s.dim() == 2, "Input tensors must have 2 dimensions" + M, N = x.size() + y = torch.empty_like(x, dtype=torch.get_default_dtype()) + grid = lambda meta: ( + triton.cdiv(M, meta["BLOCK_SIZE"]), + triton.cdiv(N, meta["BLOCK_SIZE"]), + ) + fp8_blockwise_weight_dequant_kernel[grid](x, s, y, M, N, BLOCK_SIZE=block_size) + return y From 846b433b9412d4e3567284f8fa49ad23921d3e8d Mon Sep 17 00:00:00 2001 From: Daniel Vega-Myhre Date: Mon, 12 May 2025 16:40:25 -0700 Subject: [PATCH 022/165] Add blockwise fp8 gemm benchmarks to README (#2203) add blockwise fp8 gemm benchmarks --- torchao/prototype/blockwise_fp8/README.md | 121 ++++++++++++++++++++++ 1 file changed, 121 insertions(+) diff --git a/torchao/prototype/blockwise_fp8/README.md b/torchao/prototype/blockwise_fp8/README.md index 16bb5073a1..2421afef14 100644 --- a/torchao/prototype/blockwise_fp8/README.md +++ b/torchao/prototype/blockwise_fp8/README.md @@ -27,3 +27,124 @@ This directory contains the implementation of blockwise quantization introduced For detailed motivations and technical specifications, please refer to the original paper: - [DeepSeek Blockwise Quantization Paper](https://arxiv.org/html/2412.19437v1) + +## Benchmarks + +Below are performance benchmarks measuring FP8 blockwise GEMM latency against fp16 on a single H100 GPU. +These benchmarks can be reproduced using this [benchmarking script](https://github.com/pytorch/ao/blob/main/benchmarks/benchmark_blockwise_scaled_linear_triton.py). + +| m | k | n | block_size | dtype | fp16_latency (ms) | blockwise_latency (ms) | blockwise_speedup | +|-----:|------:|------:|-------------:|:--------------------|--------------------:|-------------------------:|--------------------:| +| 1 | 8192 | 8192 | 128 | torch.float8_e4m3fn | 83.744 | 52.224 | 1.60355 | +| 1 | 8192 | 10240 | 128 | torch.float8_e4m3fn | 99.52 | 61.12 | 1.62827 | +| 1 | 8192 | 57344 | 128 | torch.float8_e4m3fn | 436.608 | 234 | 1.86585 | +| 1 | 28672 | 8192 | 128 | torch.float8_e4m3fn | 233.568 | 131.168 | 1.78068 | +| 1 | 8192 | 8192 | 128 | torch.float8_e5m2 | 84.896 | 52.736 | 1.60983 | +| 1 | 8192 | 10240 | 128 | torch.float8_e5m2 | 100.224 | 60.96 | 1.64409 | +| 1 | 8192 | 57344 | 128 | torch.float8_e5m2 | 441.152 | 233.968 | 1.88552 | +| 1 | 28672 | 8192 | 128 | torch.float8_e5m2 | 233.28 | 130.816 | 1.78327 | +| 2 | 8192 | 8192 | 128 | torch.float8_e4m3fn | 83.392 | 53.664 | 1.55397 | +| 2 | 8192 | 10240 | 128 | torch.float8_e4m3fn | 100.192 | 61.632 | 1.62565 | +| 2 | 8192 | 57344 | 128 | torch.float8_e4m3fn | 432.384 | 233.664 | 1.85045 | +| 2 | 28672 | 8192 | 128 | torch.float8_e4m3fn | 233.648 | 133.6 | 1.74886 | +| 2 | 8192 | 8192 | 128 | torch.float8_e5m2 | 83.232 | 53.6 | 1.55284 | +| 2 | 8192 | 10240 | 128 | torch.float8_e5m2 | 100.608 | 61.664 | 1.63155 | +| 2 | 8192 | 57344 | 128 | torch.float8_e5m2 | 432.32 | 235.152 | 1.83847 | +| 2 | 28672 | 8192 | 128 | torch.float8_e5m2 | 233.824 | 136.256 | 1.71606 | +| 4 | 8192 | 8192 | 128 | torch.float8_e4m3fn | 84.16 | 52.928 | 1.59008 | +| 4 | 8192 | 10240 | 128 | torch.float8_e4m3fn | 100.544 | 61.728 | 1.62882 | +| 4 | 8192 | 57344 | 128 | torch.float8_e4m3fn | 432.768 | 234.944 | 1.842 | +| 4 | 28672 | 8192 | 128 | torch.float8_e4m3fn | 234.432 | 134.432 | 1.74387 | +| 4 | 8192 | 8192 | 128 | torch.float8_e5m2 | 83.872 | 53.408 | 1.5704 | +| 4 | 8192 | 10240 | 128 | torch.float8_e5m2 | 99.84 | 62.24 | 1.60411 | +| 4 | 8192 | 57344 | 128 | torch.float8_e5m2 | 433.376 | 238.272 | 1.81883 | +| 4 | 28672 | 8192 | 128 | torch.float8_e5m2 | 235.584 | 134.08 | 1.75704 | +| 8 | 8192 | 8192 | 128 | torch.float8_e4m3fn | 83.648 | 53.472 | 1.56433 | +| 8 | 8192 | 10240 | 128 | torch.float8_e4m3fn | 100.704 | 62.432 | 1.61302 | +| 8 | 8192 | 57344 | 128 | torch.float8_e4m3fn | 439.104 | 238.208 | 1.84336 | +| 8 | 28672 | 8192 | 128 | torch.float8_e4m3fn | 234.272 | 135.072 | 1.73442 | +| 8 | 8192 | 8192 | 128 | torch.float8_e5m2 | 84.128 | 53.728 | 1.56581 | +| 8 | 8192 | 10240 | 128 | torch.float8_e5m2 | 100.512 | 62.976 | 1.59604 | +| 8 | 8192 | 57344 | 128 | torch.float8_e5m2 | 439.36 | 238.496 | 1.84221 | +| 8 | 28672 | 8192 | 128 | torch.float8_e5m2 | 235.04 | 135.424 | 1.73559 | +| 16 | 8192 | 8192 | 128 | torch.float8_e4m3fn | 83.808 | 53.664 | 1.56172 | +| 16 | 8192 | 10240 | 128 | torch.float8_e4m3fn | 99.584 | 63.104 | 1.57809 | +| 16 | 8192 | 57344 | 128 | torch.float8_e4m3fn | 444 | 244.192 | 1.81824 | +| 16 | 28672 | 8192 | 128 | torch.float8_e4m3fn | 235.52 | 133.792 | 1.76034 | +| 16 | 8192 | 8192 | 128 | torch.float8_e5m2 | 83.488 | 53.568 | 1.55854 | +| 16 | 8192 | 10240 | 128 | torch.float8_e5m2 | 101.216 | 63.232 | 1.60071 | +| 16 | 8192 | 57344 | 128 | torch.float8_e5m2 | 444.608 | 245.936 | 1.80782 | +| 16 | 28672 | 8192 | 128 | torch.float8_e5m2 | 235.36 | 133.152 | 1.7676 | +| 32 | 8192 | 8192 | 128 | torch.float8_e4m3fn | 83.872 | 53.312 | 1.57323 | +| 32 | 8192 | 10240 | 128 | torch.float8_e4m3fn | 102.688 | 63.264 | 1.62317 | +| 32 | 8192 | 57344 | 128 | torch.float8_e4m3fn | 441.792 | 243.04 | 1.81777 | +| 32 | 28672 | 8192 | 128 | torch.float8_e4m3fn | 237.12 | 133.632 | 1.77443 | +| 32 | 8192 | 8192 | 128 | torch.float8_e5m2 | 86.08 | 53.216 | 1.61756 | +| 32 | 8192 | 10240 | 128 | torch.float8_e5m2 | 102.032 | 63.2 | 1.61443 | +| 32 | 8192 | 57344 | 128 | torch.float8_e5m2 | 439.168 | 245.184 | 1.79118 | +| 32 | 28672 | 8192 | 128 | torch.float8_e5m2 | 238.016 | 134.336 | 1.7718 | +| 64 | 8192 | 8192 | 128 | torch.float8_e4m3fn | 85.888 | 53.632 | 1.60143 | +| 64 | 8192 | 10240 | 128 | torch.float8_e4m3fn | 93.632 | 63.936 | 1.46446 | +| 64 | 8192 | 57344 | 128 | torch.float8_e4m3fn | 471.44 | 245.2 | 1.92268 | +| 64 | 28672 | 8192 | 128 | torch.float8_e4m3fn | 240 | 137.424 | 1.74642 | +| 64 | 8192 | 8192 | 128 | torch.float8_e5m2 | 85.984 | 54.016 | 1.59182 | +| 64 | 8192 | 10240 | 128 | torch.float8_e5m2 | 93.376 | 64.032 | 1.45827 | +| 64 | 8192 | 57344 | 128 | torch.float8_e5m2 | 471.36 | 244.576 | 1.92725 | +| 64 | 28672 | 8192 | 128 | torch.float8_e5m2 | 242.4 | 136.096 | 1.7811 | +| 128 | 8192 | 8192 | 128 | torch.float8_e4m3fn | 91.008 | 57.184 | 1.59149 | +| 128 | 8192 | 10240 | 128 | torch.float8_e4m3fn | 96.608 | 67.936 | 1.42204 | +| 128 | 8192 | 57344 | 128 | torch.float8_e4m3fn | 449.6 | 292.48 | 1.5372 | +| 128 | 28672 | 8192 | 128 | torch.float8_e4m3fn | 247.84 | 147.232 | 1.68333 | +| 128 | 8192 | 8192 | 128 | torch.float8_e5m2 | 89.152 | 57.248 | 1.55729 | +| 128 | 8192 | 10240 | 128 | torch.float8_e5m2 | 96.64 | 68.784 | 1.40498 | +| 128 | 8192 | 57344 | 128 | torch.float8_e5m2 | 450.048 | 284.16 | 1.58378 | +| 128 | 28672 | 8192 | 128 | torch.float8_e5m2 | 246.88 | 148.064 | 1.66739 | +| 256 | 8192 | 8192 | 128 | torch.float8_e4m3fn | 85.984 | 62.368 | 1.37866 | +| 256 | 8192 | 10240 | 128 | torch.float8_e4m3fn | 101.216 | 104.896 | 0.964918 | +| 256 | 8192 | 57344 | 128 | torch.float8_e4m3fn | 477.984 | 452.832 | 1.05554 | +| 256 | 28672 | 8192 | 128 | torch.float8_e4m3fn | 260.224 | 215.392 | 1.20814 | +| 256 | 8192 | 8192 | 128 | torch.float8_e5m2 | 86.432 | 62.048 | 1.39299 | +| 256 | 8192 | 10240 | 128 | torch.float8_e5m2 | 101.024 | 103.904 | 0.972282 | +| 256 | 8192 | 57344 | 128 | torch.float8_e5m2 | 475.568 | 433.792 | 1.0963 | +| 256 | 28672 | 8192 | 128 | torch.float8_e5m2 | 261.824 | 207.968 | 1.25896 | +| 512 | 8192 | 8192 | 128 | torch.float8_e4m3fn | 117.952 | 112.992 | 1.0439 | +| 512 | 8192 | 10240 | 128 | torch.float8_e4m3fn | 151.504 | 166.08 | 0.912235 | +| 512 | 8192 | 57344 | 128 | torch.float8_e4m3fn | 836.848 | 881.312 | 0.949548 | +| 512 | 28672 | 8192 | 128 | torch.float8_e4m3fn | 442.528 | 402.464 | 1.09955 | +| 512 | 8192 | 8192 | 128 | torch.float8_e5m2 | 121.184 | 114.592 | 1.05753 | +| 512 | 8192 | 10240 | 128 | torch.float8_e5m2 | 151.424 | 163.296 | 0.927298 | +| 512 | 8192 | 57344 | 128 | torch.float8_e5m2 | 837.312 | 873.664 | 0.958391 | +| 512 | 28672 | 8192 | 128 | torch.float8_e5m2 | 437.664 | 400.928 | 1.09163 | +| 1024 | 8192 | 8192 | 128 | torch.float8_e4m3fn | 227.008 | 224.384 | 1.01169 | +| 1024 | 8192 | 10240 | 128 | torch.float8_e4m3fn | 289.28 | 283.872 | 1.01905 | +| 1024 | 8192 | 57344 | 128 | torch.float8_e4m3fn | 1672.13 | 1673.34 | 0.999273 | +| 1024 | 28672 | 8192 | 128 | torch.float8_e4m3fn | 800 | 769.152 | 1.04011 | +| 1024 | 8192 | 8192 | 128 | torch.float8_e5m2 | 224.48 | 223.456 | 1.00458 | +| 1024 | 8192 | 10240 | 128 | torch.float8_e5m2 | 289.408 | 283.424 | 1.02111 | +| 1024 | 8192 | 57344 | 128 | torch.float8_e5m2 | 1649.58 | 1626.88 | 1.01396 | +| 1024 | 28672 | 8192 | 128 | torch.float8_e5m2 | 805.392 | 768.416 | 1.04812 | +| 2048 | 8192 | 8192 | 128 | torch.float8_e4m3fn | 449.344 | 458.272 | 0.980518 | +| 2048 | 8192 | 10240 | 128 | torch.float8_e4m3fn | 569.888 | 586.224 | 0.972134 | +| 2048 | 8192 | 57344 | 128 | torch.float8_e4m3fn | 3275.84 | 3251.9 | 1.00736 | +| 2048 | 28672 | 8192 | 128 | torch.float8_e4m3fn | 1614.37 | 1555.68 | 1.03772 | +| 2048 | 8192 | 8192 | 128 | torch.float8_e5m2 | 450.624 | 461.712 | 0.975985 | +| 2048 | 8192 | 10240 | 128 | torch.float8_e5m2 | 575.36 | 582.016 | 0.988564 | +| 2048 | 8192 | 57344 | 128 | torch.float8_e5m2 | 3363.3 | 3213.31 | 1.04668 | +| 2048 | 28672 | 8192 | 128 | torch.float8_e5m2 | 1574.32 | 1525.66 | 1.03189 | +| 4096 | 8192 | 8192 | 128 | torch.float8_e4m3fn | 915.216 | 964.592 | 0.948812 | +| 4096 | 8192 | 10240 | 128 | torch.float8_e4m3fn | 1157.18 | 1196.42 | 0.967209 | +| 4096 | 8192 | 57344 | 128 | torch.float8_e4m3fn | 6409.98 | 6638.3 | 0.965606 | +| 4096 | 28672 | 8192 | 128 | torch.float8_e4m3fn | 3173.76 | 3247.23 | 0.977374 | +| 4096 | 8192 | 8192 | 128 | torch.float8_e5m2 | 898.432 | 949.36 | 0.946355 | +| 4096 | 8192 | 10240 | 128 | torch.float8_e5m2 | 1170.62 | 1188.45 | 0.985002 | +| 4096 | 8192 | 57344 | 128 | torch.float8_e5m2 | 6751.25 | 6573.71 | 1.02701 | +| 4096 | 28672 | 8192 | 128 | torch.float8_e5m2 | 3155.9 | 3179.38 | 0.992617 | +| 8192 | 8192 | 8192 | 128 | torch.float8_e4m3fn | 1868.64 | 2022.27 | 0.92403 | +| 8192 | 8192 | 10240 | 128 | torch.float8_e4m3fn | 2336.26 | 2621.18 | 0.891298 | +| 8192 | 8192 | 57344 | 128 | torch.float8_e4m3fn | 13004 | 13990.6 | 0.929482 | +| 8192 | 28672 | 8192 | 128 | torch.float8_e4m3fn | 6781.49 | 6722.82 | 1.00873 | +| 8192 | 8192 | 8192 | 128 | torch.float8_e5m2 | 1865.25 | 1983.23 | 0.940509 | +| 8192 | 8192 | 10240 | 128 | torch.float8_e5m2 | 2296.66 | 2523.1 | 0.91025 | +| 8192 | 8192 | 57344 | 128 | torch.float8_e5m2 | 13170.9 | 14029.6 | 0.938792 | +| 8192 | 28672 | 8192 | 128 | torch.float8_e5m2 | 6688.51 | 6699.65 | 0.998338 | + From 9902d84eb255aed282ba94255baf233e1ef95ac6 Mon Sep 17 00:00:00 2001 From: Peter Yeh Date: Mon, 12 May 2025 18:23:14 -0700 Subject: [PATCH 023/165] Skips for ROCm (X86 Inductor Tests) (#2202) * Refactor ROCm skip decorators in quantization tests Updated the skip decorators for ROCm in the quantization test suite to use the new `skip_if_rocm` utility, providing more descriptive messages for each test case. This change enhances clarity regarding ROCm support status in the tests. * Reorganize ROCm skip decorator import in quantization tests Moved the import statement for `skip_if_rocm` to maintain consistency in the test file structure. This change ensures that the utility is properly utilized in the context of the x86 Inductor quantization tests. * Update ROCm skip decorator message in quantization test Changed the message for the `skip_if_rocm` decorator in the `test_qat_qconv2d` test case to clarify that ROCm support is not applicable, enhancing the clarity of the test's intent. --- test/quantization/pt2e/test_x86inductor_fusion.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/test/quantization/pt2e/test_x86inductor_fusion.py b/test/quantization/pt2e/test_x86inductor_fusion.py index 78204fb756..fdf217366d 100644 --- a/test/quantization/pt2e/test_x86inductor_fusion.py +++ b/test/quantization/pt2e/test_x86inductor_fusion.py @@ -28,7 +28,6 @@ IS_X86, instantiate_parametrized_tests, parametrize, - skipIfRocm, ) from torch.testing._internal.inductor_utils import ( HAS_CPU, @@ -45,6 +44,7 @@ from torchao.quantization.pt2e.quantizer.x86_inductor_quantizer import ( X86InductorQuantizer, ) +from torchao.testing.utils import skip_if_rocm from torchao.utils import ( TORCH_VERSION_AT_LEAST_2_6, TORCH_VERSION_AT_LEAST_2_8, @@ -328,7 +328,7 @@ def matcher_check_fn(): @skipIfNoDynamoSupport @skipIfNoONEDNN - @skipIfRocm + @skip_if_rocm("Not applicable to ROCm") def test_qconv2d_cpu(self): r""" This testcase will quantize a single Conv2d module. @@ -338,7 +338,7 @@ def test_qconv2d_cpu(self): @skipIfNoDynamoSupport @skipIfNoONEDNNBF16 @skipIfNoONEDNN - @skipIfRocm + @skip_if_rocm("Not applicable to ROCm") def test_qconv2d_int8_mixed_bf16(self): r""" This testcase will quantize a single Conv2d module with int8_mixed_bf16 quantization. @@ -932,7 +932,7 @@ def matcher_check_fn(): @skipIfNoDynamoSupport @skipIfNoONEDNN - @skipIfRocm + @skip_if_rocm("Not applicable to ROCm") def test_qat_qconv2d(self): r""" This testcase will quantize a single Conv2d module with qat flow. @@ -1075,7 +1075,7 @@ def test_qat_qconv2d_hardswish(self): @skipIfNoDynamoSupport @skipIfNoONEDNN - @skipIfRocm + @skip_if_rocm("Not applicable to ROCm") def test_qat_qconv2d_add(self): r""" This testcase will quantize a Conv2d->Add pattern as: @@ -1141,7 +1141,7 @@ def matcher_check_fn(): @skipIfNoDynamoSupport @skipIfNoONEDNN - @skipIfRocm + @skip_if_rocm("Not applicable to ROCm") def test_qat_qconv2d_add_relu(self): r""" This testcase will quantize a Conv2d->Add->ReLU pattern as: @@ -1281,7 +1281,7 @@ def matcher_check_fn(): @skipIfNoDynamoSupport @skipIfNoONEDNN - @skipIfRocm + @skip_if_rocm("Not applicable to ROCm") def test_qconv2d_dequant_promotion_cpu(self): self._test_qconv2d_dequant_promotion_helper() From c2d2d13959e41cc1de01d1f9d056cf21eb46c336 Mon Sep 17 00:00:00 2001 From: Jesse Cai Date: Tue, 13 May 2025 00:43:34 -0400 Subject: [PATCH 024/165] Remove `sparsity/prototype/blocksparse` (#2205) Remove sparsity/prototype/blocksparse Summary: att, we should use torchao.prototype.sparsity.blocksparse instead. Test Plan: Reviewers: Subscribers: Tasks: Tags: --- .../sparsity/prototype/superblock/__init__.py | 0 .../prototype/superblock/blocksparse.py | 8 ------ .../prototype/superblock/supermask.py | 12 --------- .../sparsity/prototype/superblock/utils.py | 26 ------------------- 4 files changed, 46 deletions(-) delete mode 100644 torchao/sparsity/prototype/superblock/__init__.py delete mode 100644 torchao/sparsity/prototype/superblock/blocksparse.py delete mode 100644 torchao/sparsity/prototype/superblock/supermask.py delete mode 100644 torchao/sparsity/prototype/superblock/utils.py diff --git a/torchao/sparsity/prototype/superblock/__init__.py b/torchao/sparsity/prototype/superblock/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/torchao/sparsity/prototype/superblock/blocksparse.py b/torchao/sparsity/prototype/superblock/blocksparse.py deleted file mode 100644 index 845be888fc..0000000000 --- a/torchao/sparsity/prototype/superblock/blocksparse.py +++ /dev/null @@ -1,8 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -from torchao.prototype.sparsity.superblock.blocksparse import BlockSparseTensor - -__all__ = ["BlockSparseTensor"] diff --git a/torchao/sparsity/prototype/superblock/supermask.py b/torchao/sparsity/prototype/superblock/supermask.py deleted file mode 100644 index 1066271013..0000000000 --- a/torchao/sparsity/prototype/superblock/supermask.py +++ /dev/null @@ -1,12 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -from torchao.sparsity.supermask import ( - SupermaskLinear, -) - -__all__ = [ - "SupermaskLinear", -] diff --git a/torchao/sparsity/prototype/superblock/utils.py b/torchao/sparsity/prototype/superblock/utils.py deleted file mode 100644 index e0ef628ebc..0000000000 --- a/torchao/sparsity/prototype/superblock/utils.py +++ /dev/null @@ -1,26 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -from torchao.prototype.sparsity.superblock.utils import ( - ClassificationPresetEval, - ClassificationPresetTrain, - ExponentialMovingAverage, - MetricLogger, - RandomCutmix, - RandomMixup, - RASampler, - SmoothedValue, -) - -__all__ = [ - "ClassificationPresetEval", - "ClassificationPresetTrain", - "ExponentialMovingAverage", - "MetricLogger", - "RandomCutmix", - "RandomMixup", - "RASampler", - "SmoothedValue", -] From a0a0969922000b2c0fc39fbbfd4ec69ee630eeb7 Mon Sep 17 00:00:00 2001 From: Daniel Vega-Myhre Date: Tue, 13 May 2025 08:56:54 -0700 Subject: [PATCH 025/165] [float] document e2e training -> inference flow (#2190) * document e2e training -> inference flow * add save/load checkpoint * update to how we load checkpoint * remove debugging * add more detail * remove unused import * lower lr to prevent large optimizer step into weight territory which produces inf * use actual loss function --- torchao/float8/README.md | 92 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 92 insertions(+) diff --git a/torchao/float8/README.md b/torchao/float8/README.md index 7456d724f4..65da67c524 100644 --- a/torchao/float8/README.md +++ b/torchao/float8/README.md @@ -230,3 +230,95 @@ including [downloading a tokenizer](https://github.com/pytorch/torchtitan?tab=re - float8 rowwise with bf16 all-gather + compile: `TORCHTITAN_ROOT= FLOAT8_RECIPE_WITH_BEST_SETTINGS="rowwise" ./float8_training_benchmark.sh` See the float8 training benchmarking [guide](.torchao/float8/benchmarking/README.md) for more details. + +# E2E training + inference flow + +The first step in the E2E is to train your model and save a checkpoint. The second step is to load the checkpoint and optionally apply inference quantization before serving the model. +#### 1. Train model and save checkpoint +```python +import torch +from torch import nn +import torch.nn.functional as F + +from torchao.float8.float8_linear_utils import convert_to_float8_training +from torchao.float8.float8_linear import Float8Linear +from torchao.float8 import convert_to_float8_training +from torchao.utils import TORCH_VERSION_AT_LEAST_2_5 + +if not TORCH_VERSION_AT_LEAST_2_5: + raise AssertionError("torchao.float8 requires PyTorch version 2.5 or greater") + +# create model and sample input +m = nn.Sequential( + nn.Linear(2048, 4096), + nn.Linear(4096, 128), + nn.Linear(128, 1), +).bfloat16().cuda() +x = torch.randn(4096, 2048, device="cuda", dtype=torch.bfloat16) +optimizer = torch.optim.AdamW(m.parameters(), lr=1e-3) + +# optional: filter modules from being eligible for float8 conversion +def module_filter_fn(mod: torch.nn.Module, fqn: str): + # don't convert the last module + if fqn == "1": + return False + # don't convert linear modules with weight dimensions not divisible by 16 + if isinstance(mod, torch.nn.Linear): + if mod.in_features % 16 != 0 or mod.out_features % 16 != 0: + return False + return True + +# convert specified `torch.nn.Linear` modules to `Float8Linear` +convert_to_float8_training(m, module_filter_fn=module_filter_fn) + +# enable torch.compile for competitive performance +m = torch.compile(m) + +# toy training loop +for _ in range(10): + optimizer.zero_grad() + output = m(x) + # use fake labels for demonstration purposes + fake_labels = torch.ones_like(output) + loss = F.mse_loss(output, fake_labels) + loss.backward() + optimizer.step() + +# save the model +torch.save({ + 'model': m, + 'model_state_dict': m.state_dict(), + 'optimizer_state_dict': optimizer.state_dict(), +}, 'checkpoint.pth') +``` + +#### 2. Load checkpoint and optionally apply inference quantization + +There are 3 float8 inference quantization strategies that be used after training with float8: 1) weight only quantization, and 2) dynamic activation and weight quantization, and 3) static quantization. + +Below is an example of dynamic activation and weight quantization. For more details, examples, and inference benchmrks, see the [torchao inference docs](https://github.com/pytorch/ao/blob/main/torchao/quantization/README.md). + +```python +import torch + +from torchao.float8.float8_linear import Float8Linear +from torchao.quantization.granularity import PerTensor +from torchao.quantization.quant_api import quantize_ +from torchao.quantization import ( + Float8DynamicActivationFloat8WeightConfig, +) + +# load checkpoint +checkpoint = torch.load('checkpoint.pth', weights_only=False) +model = checkpoint['model'] +model.load_state_dict(checkpoint['model_state_dict']) + +# optional: apply dynamic float8 quantization on both activations and weights for inference +quantize_(model, Float8DynamicActivationFloat8WeightConfig(granularity=PerTensor())) + +# run inference +x = torch.randn(1, 4096, 2048, device="cuda", dtype=torch.bfloat16) +with torch.inference_mode(): + out = model(x) + print(out) +``` From ec155542a344a7b0b81f3e4d2d40cc56dec74116 Mon Sep 17 00:00:00 2001 From: choudhary-devang Date: Tue, 13 May 2025 23:58:37 +0530 Subject: [PATCH 026/165] Arm_inductor_quantizer for Pt2e quantization (#2139) * Enabled pt2e quant flow for Arm * Fixed ruff CI failure ruff- I001 standard intrgrated. * fixing file name: rename test_arminductor_quantizer.py --> test_arm_inductor_quantizer.py applied ruff format --- .../pt2e/test_arm_inductor_quantizer.py | 1505 +++++++++++++++++ .../pt2e/quantizer/arm_inductor_quantizer.py | 396 +++++ 2 files changed, 1901 insertions(+) create mode 100644 test/quantization/pt2e/test_arm_inductor_quantizer.py create mode 100644 torchao/quantization/pt2e/quantizer/arm_inductor_quantizer.py diff --git a/test/quantization/pt2e/test_arm_inductor_quantizer.py b/test/quantization/pt2e/test_arm_inductor_quantizer.py new file mode 100644 index 0000000000..750e88d451 --- /dev/null +++ b/test/quantization/pt2e/test_arm_inductor_quantizer.py @@ -0,0 +1,1505 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD 3-Clause license found in the +# LICENSE file in the root directory of this source tree. + +# Owner(s): ["oncall: quantization"] +import copy +import itertools +import unittest +from enum import Enum + +import torch +import torch.nn as nn + +import torchao.quantization.pt2e.quantizer.arm_inductor_quantizer as armiq +from torchao.quantization.pt2e import ObserverBase +from torchao.quantization.pt2e.quantize_pt2e import ( + convert_pt2e, + prepare_pt2e, + prepare_qat_pt2e, +) +from torchao.quantization.pt2e.quantizer.arm_inductor_quantizer import ( + ArmInductorQuantizer, +) +from torchao.quantization.pt2e.quantizer.x86_inductor_quantizer import ( + QUANT_ANNOTATION_KEY, +) +from torchao.utils import TORCH_VERSION_AT_LEAST_2_5, TORCH_VERSION_AT_LEAST_2_7 + +if TORCH_VERSION_AT_LEAST_2_5: + from torch.export import export_for_training + +import functools +import platform + +from torch.testing._internal.common_quantization import ( + NodeSpec as ns, +) +from torch.testing._internal.common_quantization import ( + QuantizationTestCase, + skipIfNoInductorSupport, +) +from torch.testing._internal.common_utils import run_tests, skipIfTorchDynamo + + +def skipIfNoArm(fn): + reason = "Quantized operations require Arm." + if isinstance(fn, type): + if platform.processor() != "aarch64": + fn.__unittest_skip__ = True + fn.__unittest_skip_why__ = reason + return fn + + @functools.wraps(fn) + def wrapper(*args, **kwargs): + if platform.processor() != "aarch64": + raise unittest.SkipTest(reason) + else: + fn(*args, **kwargs) + + return wrapper + + +class NodePosType(Enum): + left = 1 + right = 2 + both = 3 + + +class TestHelperModules: + class SingleConv2dModule(torch.nn.Module): + def __init__(self, with_bn=False) -> None: + super().__init__() + self.conv = nn.Conv2d(3, 6, (2, 2), stride=(1, 1), padding=(1, 1)) + self.bn = torch.nn.BatchNorm2d(6) + self.with_bn = with_bn + + def forward(self, x): + x = self.conv(x) + if self.with_bn: + x = self.bn(x) + return x + + class Conv2dAddModule(torch.nn.Module): + def __init__( + self, + inplace_add: bool = False, + conv2d_type: NodePosType = NodePosType.left, + use_bias: bool = False, + with_bn: bool = False, + ) -> None: + super().__init__() + self.conv = torch.nn.Conv2d( + in_channels=3, + out_channels=3, + kernel_size=3, + stride=1, + padding=1, + bias=use_bias, + ) + self.conv2 = torch.nn.Conv2d( + in_channels=3, + out_channels=3, + kernel_size=3, + stride=1, + padding=1, + bias=use_bias, + ) + self.relu = nn.ReLU() + self.inplace_add = inplace_add + self.conv2d_type = conv2d_type + self.bn = torch.nn.BatchNorm2d(3) + self.with_bn = with_bn + + def forward(self, x): + if self.conv2d_type == NodePosType.left: + if self.inplace_add: + tmp = self.conv(x) + if self.with_bn: + tmp = self.bn(tmp) + tmp += self.relu(x) + return tmp + else: + tmp = self.conv(x) + if self.with_bn: + tmp = self.bn(tmp) + return tmp + self.relu(x) + elif self.conv2d_type == NodePosType.right: + if self.inplace_add: + tmp = self.relu(x) + tmp += self.conv(x) + return tmp + else: + return self.relu(x) + self.conv(x) + elif self.conv2d_type == NodePosType.both: + if self.inplace_add: + tmp = self.conv(x) + tmp += self.conv2(x) + return tmp + else: + return self.conv(x) + self.conv2(x) + + class Conv2dSingleOpPowModule(nn.Module): + def __init__(self, single_op): + super().__init__() + self.conv = nn.Conv2d(2, 2, 1) + self.single_op = single_op + + def forward(self, x): + x = self.conv(x) + x = self.single_op(x) + return torch.pow(x, 2) + + class SingleLinearModule(torch.nn.Module): + def __init__(self, use_bias) -> None: + super().__init__() + self.linear = nn.Linear(4, 4, bias=use_bias) + + def forward(self, x): + return self.linear(x) + + class LinearUnaryModule(torch.nn.Module): + def __init__( + self, use_bias, postop, inplace_postop=False, post_op_algo="none" + ) -> None: + super().__init__() + self.linear = nn.Linear(4, 4, bias=use_bias) + if postop == nn.GELU: + self.postop = postop(approximate=post_op_algo) + else: + self.postop = postop(inplace=inplace_postop) + + def forward(self, x): + return self.postop(self.linear(x)) + + class LinearAddModule(torch.nn.Module): + def __init__( + self, + inplace_add: bool = False, + linear_pos: NodePosType = NodePosType.left, + use_bias: bool = False, + ) -> None: + super().__init__() + self.linear = torch.nn.Linear( + in_features=16, out_features=16, bias=use_bias + ) + self.linear2 = torch.nn.Linear( + in_features=16, out_features=16, bias=use_bias + ) + self.relu = nn.ReLU() + self.inplace_add = inplace_add + self.linear_pos = linear_pos + + def forward(self, x): + if self.linear_pos == NodePosType.left: + if self.inplace_add: + tmp = self.linear(x) + tmp += self.relu(x) + return tmp + else: + tmp = self.linear(x) + return tmp + self.relu(x) + elif self.linear_pos == NodePosType.right: + if self.inplace_add: + tmp = self.relu(x) + tmp += self.linear(x) + return tmp + else: + return self.relu(x) + self.linear(x) + elif self.linear_pos == NodePosType.both: + if self.inplace_add: + tmp = self.linear(x) + tmp += self.linear2(x) + return tmp + else: + return self.linear(x) + self.linear2(x) + + class LinearAddModule2(torch.nn.Module): + def __init__( + self, + inplace_add: bool = False, + ) -> None: + super().__init__() + self.linear = torch.nn.Linear(in_features=16, out_features=16, bias=True) + self.linear2 = torch.nn.Linear(in_features=16, out_features=16, bias=True) + self.inplace_add = inplace_add + + def forward(self, x): + if self.inplace_add: + tmp = self.linear(x) + tmp += self.linear2(tmp) + return tmp + else: + tmp = self.linear(x) + return tmp + self.linear2(tmp) + + class Conv2dAddModule2(torch.nn.Module): + def __init__( + self, + inplace_add: bool = False, + ) -> None: + super().__init__() + self.conv = torch.nn.Conv2d( + in_channels=3, out_channels=3, kernel_size=3, stride=1, padding=1 + ) + self.conv2 = torch.nn.Conv2d( + in_channels=3, out_channels=3, kernel_size=3, stride=1, padding=1 + ) + self.inplace_add = inplace_add + self.bn = torch.nn.BatchNorm2d(3) + self.bn2 = torch.nn.BatchNorm2d(3) + + def forward(self, x): + if self.inplace_add: + tmp = self.bn(self.conv(x)) + tmp += self.bn2(self.conv2(tmp)) + return tmp + else: + tmp = self.bn(self.conv(x)) + return tmp + self.bn2(self.conv2(tmp)) + + class SelfAttnLikeModule(torch.nn.Module): + def __init__( + self, + input_dim, + transpose_for_score=False, + num_attention_heads=None, + attention_head_size=None, + ) -> None: + super().__init__() + self.input_dim = input_dim + self.q_proj = nn.Linear(input_dim, input_dim, bias=False) + self.k_proj = nn.Linear(input_dim, input_dim, bias=False) + self.v_proj = nn.Linear(input_dim, input_dim, bias=False) + self.softmax = nn.Softmax(dim=-1) + self.transpose_for_score = transpose_for_score + if self.transpose_for_score: + assert num_attention_heads is not None + assert attention_head_size is not None + self.num_attention_heads = num_attention_heads + self.attention_head_size = attention_head_size + + def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward(self, x): + q = self.q_proj(x) + k = self.k_proj(x) + v = self.v_proj(x) + if self.transpose_for_score: + q = self.transpose_for_scores(q) + k = self.transpose_for_scores(k) + v = self.transpose_for_scores(v) + scores = torch.matmul(q, k.transpose(-1, -2)) / (self.input_dim**0.5) + attention = self.softmax(scores) + weighted = torch.matmul(attention, v) + return weighted + + +class ArmInductorQuantTestCase(QuantizationTestCase): + def _test_quantizer( + self, + model, + example_inputs, + quantizer, + expected_node_occurrence, + expected_node_list=None, + is_qat=False, + debug=False, + lower=False, + ): + m_eager = model.train() if is_qat else model.eval() + + # program capture + m = copy.deepcopy(m_eager) + m = export_for_training( + m, + example_inputs, + ).module() + + # QAT Model failed to deepcopy + export_model = m if is_qat else copy.deepcopy(m) + m = prepare_qat_pt2e(m, quantizer) if is_qat else prepare_pt2e(m, quantizer) + # Calibrate + m(*example_inputs) + prepare_model = copy.deepcopy(m) + m = convert_pt2e(m) + convert_model = copy.deepcopy(m) + if debug: + convert_model.print_readable(True) + if lower: + from torch._inductor.constant_folding import constant_fold + from torch._inductor.fx_passes.freezing_patterns import freezing_passes + + m.recompile() + freezing_passes(m, example_inputs) + constant_fold(m) + m(*example_inputs) + node_occurrence = { + ns.call_function(k): v for k, v in expected_node_occurrence.items() + } + if expected_node_list is None: + expected_node_list = [] + node_list = [ns.call_function(n) for n in expected_node_list] + self.checkGraphModuleNodes( + m, expected_node_occurrence=node_occurrence, expected_node_list=node_list + ) + + return export_model, prepare_model, convert_model + + +@skipIfNoInductorSupport +@unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_7, "Requires torch 2.7+") +class TestQuantizePT2EArmInductor(ArmInductorQuantTestCase): + @skipIfNoArm + def test_conv2d(self): + """ + Test pattern of single conv2d with ArmInductorQuantizer. + """ + with torch.no_grad(): + m = TestHelperModules.SingleConv2dModule().eval() + example_inputs = (torch.randn(2, 3, 16, 16),) + quantizer = ArmInductorQuantizer().set_global( + armiq.get_default_arm_inductor_quantization_config() + ) + node_occurrence = { + # one for input and weight of the conv + torch.ops.quantized_decomposed.quantize_per_tensor.default: 1, + torch.ops.quantized_decomposed.dequantize_per_tensor.default: 2, + # note: quantize op for weights are const propagated + torch.ops.quantized_decomposed.quantize_per_channel.default: 0, + torch.ops.quantized_decomposed.dequantize_per_channel.default: 0, + } + node_list = [ + torch.ops.quantized_decomposed.quantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + torch.ops.aten.conv2d.default, + ] + self._test_quantizer( + m, + example_inputs, + quantizer, + node_occurrence, + node_list, + ) + + @skipIfNoArm + def test_conv2d_binary(self): + """ + Test pattern of conv2d with binary post ops (such as add) with ArmInductorQuantizer. + Currently, only add as binary post op is supported. + """ + conv2d_type_list = [NodePosType.left, NodePosType.both] + example_inputs = (torch.randn(2, 3, 6, 6),) + quantizer = ArmInductorQuantizer().set_global( + armiq.get_default_arm_inductor_quantization_config() + ) + with torch.no_grad(): + for conv2d_type in conv2d_type_list: + m = TestHelperModules.Conv2dAddModule(conv2d_type=conv2d_type).eval() + if conv2d_type != NodePosType.both: + node_occurrence = { + # one for input and weight of the conv + # one for extra input node of add + torch.ops.quantized_decomposed.quantize_per_tensor.default: 2, + torch.ops.quantized_decomposed.dequantize_per_tensor.default: 3, + # quantize_per_channel for weights are const propagated + torch.ops.quantized_decomposed.quantize_per_channel.default: 0, + torch.ops.quantized_decomposed.dequantize_per_channel.default: 0, + } + else: + node_occurrence = { + # one for input of the conv + # one for input of another conv + # 2 conv will share same input quant/dequant + # one for extra input node of add + torch.ops.quantized_decomposed.quantize_per_tensor.default: 2, + torch.ops.quantized_decomposed.dequantize_per_tensor.default: 5, + # quantize_per_channel for weights are const propagated + torch.ops.quantized_decomposed.quantize_per_channel.default: 0, + torch.ops.quantized_decomposed.dequantize_per_channel.default: 0, + } + node_list = [ + torch.ops.quantized_decomposed.quantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + torch.ops.aten.conv2d.default, + torch.ops.aten.add.Tensor, + ] + self._test_quantizer( + m, + example_inputs, + quantizer, + node_occurrence, + node_list, + ) + + @skipIfNoArm + def test_conv2d_binary2(self): + """ + Test Pattern: + tmp = conv2d_1(x) + tmp2 = conv2d_2(tmp) + return tmp + tmp2 + Since conv2d_1 has 2 users, we should annotate conv2d_2 for binary fusion instead of conv2d_1 + """ + example_inputs = (torch.randn(2, 3, 6, 6),) + quantizer = ArmInductorQuantizer().set_global( + armiq.get_default_arm_inductor_quantization_config() + ) + inplace_add_list = [True, False] + with torch.no_grad(): + for inplace_add in inplace_add_list: + m = TestHelperModules.Conv2dAddModule2(inplace_add=inplace_add).eval() + node_occurrence = { + torch.ops.quantized_decomposed.quantize_per_tensor.default: 2, + torch.ops.quantized_decomposed.dequantize_per_tensor.default: 5, + # quantize_per_channel for weights are const propagated + torch.ops.quantized_decomposed.quantize_per_channel.default: 0, + torch.ops.quantized_decomposed.dequantize_per_channel.default: 0, + } + node_list = [ + torch.ops.quantized_decomposed.quantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + torch.ops.aten.conv2d.default, + torch.ops.quantized_decomposed.quantize_per_tensor.default, + ( + torch.ops.aten.add_.Tensor + if inplace_add + else torch.ops.aten.add.Tensor + ), + ] + self._test_quantizer( + m, + example_inputs, + quantizer, + node_occurrence, + node_list, + ) + + def _single_op_share_observer_recipe_test_helper(self, m, x, single_op): + quantizer = ArmInductorQuantizer().set_global( + armiq.get_default_arm_inductor_quantization_config() + ) + example_inputs = (x,) + node_occurrence = { + # one for input and weight of the conv, two for input/output for the maxpool2d + torch.ops.quantized_decomposed.quantize_per_tensor.default: 3, + torch.ops.quantized_decomposed.dequantize_per_tensor.default: 3, + # quantize_per_channel for weights are const propagated + torch.ops.quantized_decomposed.quantize_per_channel.default: 0, + torch.ops.quantized_decomposed.dequantize_per_channel.default: 1, + } + node_list = [ + torch.ops.quantized_decomposed.quantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + torch.ops.aten.conv2d.default, + torch.ops.quantized_decomposed.quantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + single_op, + torch.ops.quantized_decomposed.quantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + ] + _, prepare_model, _ = self._test_quantizer( + m, + example_inputs, + quantizer, + node_occurrence, + node_list, + ) + for node in prepare_model.graph.nodes: + if node.op == "call_function" and node.target is single_op: + single_op_node = node + input_obs_of_single_op = getattr( + prepare_model, single_op_node.args[0].target + ) + output_obs_of_single_op = getattr( + prepare_model, next(iter(single_op_node.users)).target + ) + elif ( + node.op == "call_function" + and node.target is torch.ops.aten.conv2d.default + ): + conv_node = node + input_obs_of_conv = getattr(prepare_model, conv_node.args[0].target) + self.assertTrue(isinstance(input_obs_of_single_op, ObserverBase)) + self.assertTrue(isinstance(output_obs_of_single_op, ObserverBase)) + self.assertTrue(isinstance(input_obs_of_conv, ObserverBase)) + self.assertTrue(input_obs_of_single_op is output_obs_of_single_op) + self.assertTrue(input_obs_of_single_op is not input_obs_of_conv) + + @skipIfNoArm + def test_linear(self): + """ + Test pattern of single linear with ArmInductorQuantizer. + """ + with torch.no_grad(): + for use_bias in [True, False]: + m = TestHelperModules.SingleLinearModule(use_bias).eval() + example_inputs = (torch.randn(2, 4),) + quantizer = ArmInductorQuantizer().set_global( + armiq.get_default_arm_inductor_quantization_config() + ) + node_occurrence = { + # one for input and weight, one for output + torch.ops.quantized_decomposed.quantize_per_tensor.default: 1, + torch.ops.quantized_decomposed.dequantize_per_tensor.default: 2, + # quantize_per_channel for weights are const propagated + torch.ops.quantized_decomposed.quantize_per_channel.default: 0, + torch.ops.quantized_decomposed.dequantize_per_channel.default: 0, + } + node_list = [ + torch.ops.quantized_decomposed.quantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + torch.ops.aten.linear.default, + ] + self._test_quantizer( + m, + example_inputs, + quantizer, + node_occurrence, + node_list, + ) + + def _test_linear_unary_helper( + self, + post_op_module, + post_op_aten, + post_op_aten_inplace, + post_op_algo_list=None, + is_qat=False, + is_dynamic=False, + ): + """ + Test pattern of linear with unary post ops (e.g. relu) with ArmInductorQuantizer. + """ + use_bias_list = [True, False] + # TODO test for inplace add after refactoring of export_for_training + inplace_list = [False] + if post_op_algo_list is None: + post_op_algo_list = [None] + cases = itertools.product(use_bias_list, inplace_list, post_op_algo_list) + with torch.no_grad(): + for use_bias, inplace, post_op_algo in cases: + if inplace and post_op_aten_inplace is None: + continue + m = TestHelperModules.LinearUnaryModule( + use_bias=use_bias, + postop=post_op_module, + inplace_postop=inplace, + post_op_algo=post_op_algo, + ).eval() + example_inputs = (torch.randn(2, 4),) + quantizer = ArmInductorQuantizer().set_global( + armiq.get_default_arm_inductor_quantization_config( + is_qat=is_qat, + is_dynamic=is_dynamic, + ) + ) + quantize_per_tensor_op = ( + torch.ops.quantized_decomposed.quantize_per_tensor.tensor + if is_dynamic + else torch.ops.quantized_decomposed.quantize_per_tensor.default + ) + dequantize_per_tensor_op = ( + torch.ops.quantized_decomposed.dequantize_per_tensor.tensor + if is_dynamic + else torch.ops.quantized_decomposed.dequantize_per_tensor.default + ) + node_occurrence = { + # one for input of the linear + quantize_per_tensor_op: 1, + dequantize_per_tensor_op: 1 if is_dynamic else 2, + # quantize_per_channel for weights are const propagated + torch.ops.quantized_decomposed.quantize_per_channel.default: 0, + torch.ops.quantized_decomposed.dequantize_per_channel.default: 0, + } + node_list = [ + quantize_per_tensor_op, + dequantize_per_tensor_op, + torch.ops.aten.linear.default, + post_op_aten_inplace if inplace else post_op_aten, + ] + self._test_quantizer( + m, + example_inputs, + quantizer, + node_occurrence, + node_list, + is_qat=is_qat, + ) + + @skipIfNoArm + def test_linear_unary(self): + aten = torch.ops.aten + self._test_linear_unary_helper(nn.ReLU, aten.relu.default, aten.relu_.default) + self._test_linear_unary_helper( + nn.LeakyReLU, aten.leaky_relu.default, aten.leaky_relu_.default + ) + self._test_linear_unary_helper( + nn.GELU, aten.gelu.default, None, ["none", "tanh"] + ) + + @skipIfNoArm + def test_linear_unary_qat(self): + aten = torch.ops.aten + self._test_linear_unary_helper( + nn.ReLU, aten.relu.default, aten.relu_.default, is_qat=True + ) + self._test_linear_unary_helper( + nn.LeakyReLU, aten.leaky_relu.default, aten.leaky_relu_.default, is_qat=True + ) + self._test_linear_unary_helper( + nn.GELU, aten.gelu.default, None, ["none", "tanh"], is_qat=True + ) + + @skipIfNoArm + def test_linear_unary_dynamic(self): + aten = torch.ops.aten + self._test_linear_unary_helper( + nn.ReLU, aten.relu.default, aten.relu_.default, is_dynamic=True + ) + self._test_linear_unary_helper( + nn.LeakyReLU, + aten.leaky_relu.default, + aten.leaky_relu_.default, + is_dynamic=True, + ) + self._test_linear_unary_helper( + nn.GELU, aten.gelu.default, None, ["none", "tanh"], is_dynamic=True + ) + + @skipIfNoArm + def test_linear_unary_dynamic_qat(self): + aten = torch.ops.aten + self._test_linear_unary_helper( + nn.ReLU, aten.relu.default, aten.relu_.default, is_qat=True, is_dynamic=True + ) + self._test_linear_unary_helper( + nn.LeakyReLU, + aten.leaky_relu.default, + aten.leaky_relu_.default, + is_qat=True, + is_dynamic=True, + ) + self._test_linear_unary_helper( + nn.GELU, + aten.gelu.default, + None, + ["none", "tanh"], + is_qat=True, + is_dynamic=True, + ) + + def _check_annotation_stat(self, gm, expected_stat_dict): + # Check expected annotation statistics to ensure the annotation is correct + + def _check_annotation(node): + annot = node.meta.get(QUANT_ANNOTATION_KEY, None) + if annot is None: + return False, False + return annot._annotated, annot._is_output_of_quantized_pattern + + for node in gm.graph.nodes: + if node.target in expected_stat_dict.keys(): + annotated, is_quant_out = _check_annotation(node) + expected_stat_dict[node.target]["annotated"] -= annotated + expected_stat_dict[node.target]["is_quant_out"] -= is_quant_out + for op_stat in expected_stat_dict.values(): + assert all(v == 0 for v in op_stat.values()) + + def _test_linear_binary_helper(self, is_qat=False, is_dynamic=False): + """ + Test pattern of linear with binary post ops (such as add) with ArmInductorQuantizer. + Currently, only add as binary post op is supported. + """ + linear_pos_list = [NodePosType.left, NodePosType.right, NodePosType.both] + # TODO test for inplace add after refactoring of export_for_training + inplace_add_list = [False] + example_inputs = (torch.randn(2, 16),) + quantizer = ArmInductorQuantizer().set_global( + armiq.get_default_arm_inductor_quantization_config( + is_qat=is_qat, + is_dynamic=is_dynamic, + ) + ) + quantize_per_tensor_op = ( + torch.ops.quantized_decomposed.quantize_per_tensor.tensor + if is_dynamic + else torch.ops.quantized_decomposed.quantize_per_tensor.default + ) + dequantize_per_tensor_op = ( + torch.ops.quantized_decomposed.dequantize_per_tensor.tensor + if is_dynamic + else torch.ops.quantized_decomposed.dequantize_per_tensor.default + ) + cases = itertools.product(linear_pos_list, inplace_add_list) + with torch.no_grad(): + for linear_pos, inplace_add in cases: + m = TestHelperModules.LinearAddModule( + inplace_add=inplace_add, linear_pos=linear_pos + ).eval() + if linear_pos != NodePosType.both: + node_occurrence = { + # Only one 1 q-dq for input of the linear + # No q-dq for extra input node of add + quantize_per_tensor_op: 1, + dequantize_per_tensor_op: 1, + # quantize_per_channel for weights are const propagated + torch.ops.quantized_decomposed.quantize_per_channel.default: 0, + torch.ops.quantized_decomposed.dequantize_per_channel.default: 1, + } + else: + # convert_pt2e disables duplicate dequant for dynamic quant + num_dequant = 1 if is_dynamic else 2 + node_occurrence = { + # One quantize_per_tensor for both linear nodes (shared) + # Two dequantize_per_tensor for two linear nodes + # No q-dq for extra input node of add + quantize_per_tensor_op: 1, + dequantize_per_tensor_op: num_dequant, + # quantize_per_channel for weights are const propagated + torch.ops.quantized_decomposed.quantize_per_channel.default: 0, + torch.ops.quantized_decomposed.dequantize_per_channel.default: 2, + } + node_list = [ + quantize_per_tensor_op, + dequantize_per_tensor_op, + torch.ops.aten.linear.default, + ( + torch.ops.aten.add_.Tensor + if inplace_add + else torch.ops.aten.add.Tensor + ), + ] + fq_m = self._test_quantizer( + m, + example_inputs, + quantizer, + node_occurrence, + node_list, + is_qat=is_qat, + )[-1] + # One linear and add are fused. The other linear is quantized alone if present + aten = torch.ops.aten + add_op = aten.add_.Tensor if inplace_add else aten.add.Tensor + expected_annotation_stat = { + aten.linear.default: { + "annotated": 2 if linear_pos == NodePosType.both else 1, + "is_quant_out": 1 if linear_pos == NodePosType.both else 0, + }, + add_op: {"annotated": 1, "is_quant_out": 1}, + } + self._check_annotation_stat(fq_m, expected_annotation_stat) + + @skipIfTorchDynamo("very slow") + @skipIfNoArm + def test_qat_conv2d(self): + """ + Test QAT pattern of conv2d_bn with ArmInductorQuantizer. + """ + m = TestHelperModules.SingleConv2dModule(with_bn=True) + example_inputs = (torch.randn(2, 3, 16, 16),) + quantizer = ArmInductorQuantizer().set_global( + armiq.get_default_arm_inductor_quantization_config(is_qat=True) + ) + node_occurrence = { + # one for input and weight of the conv, one for output for the conv + torch.ops.quantized_decomposed.quantize_per_tensor.default: 2, + torch.ops.quantized_decomposed.dequantize_per_tensor.default: 3, + # note: quantize op for weights are const propagated + torch.ops.quantized_decomposed.quantize_per_channel.default: 0, + torch.ops.quantized_decomposed.dequantize_per_channel.default: 0, + # BN should be folded into Conv + torch.ops.aten._native_batch_norm_legit.default: 0, + } + node_list = [ + torch.ops.quantized_decomposed.quantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + torch.ops.aten.conv2d.default, + torch.ops.quantized_decomposed.quantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + ] + self._test_quantizer( + m, + example_inputs, + quantizer, + node_occurrence, + node_list, + is_qat=True, + ) + + @skipIfTorchDynamo("very slow") + @skipIfNoArm + def test_qat_conv2d_binary(self): + """ + Test qat pattern of conv2d_bn with binary post ops (such as add) with ArmInductorQuantizer. + Currently, only add as binary post op is supported. + """ + example_inputs = (torch.randn(2, 3, 6, 6),) + quantizer = ArmInductorQuantizer().set_global( + armiq.get_default_arm_inductor_quantization_config(is_qat=True) + ) + for inplace_add in [True, False]: + m = TestHelperModules.Conv2dAddModule(inplace_add=inplace_add, with_bn=True) + node_occurrence = { + # one for input and weight of the conv + # one for output for the add + # one for extra input node of add + torch.ops.quantized_decomposed.quantize_per_tensor.default: 3, + torch.ops.quantized_decomposed.dequantize_per_tensor.default: 4, + # quantize_per_channel for weights are const propagated + torch.ops.quantized_decomposed.quantize_per_channel.default: 0, + torch.ops.quantized_decomposed.dequantize_per_channel.default: 0, + # BN should be folded into Conv + torch.ops.aten._native_batch_norm_legit.default: 0, + } + node_list = [ + torch.ops.quantized_decomposed.quantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + torch.ops.aten.conv2d.default, + ( + torch.ops.aten.add_.Tensor + if inplace_add + else torch.ops.aten.add.Tensor + ), + torch.ops.quantized_decomposed.quantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + ] + self._test_quantizer( + m, + example_inputs, + quantizer, + node_occurrence, + node_list, + is_qat=True, + ) + + @skipIfTorchDynamo("very slow") + @skipIfNoArm + def test_qat_conv2d_binary2(self): + """ + Test qat Pattern: + tmp = bn1(conv2d_1(x)) + tmp2 = bn2(conv2d_2(tmp)) + return tmp + tmp2 + Since conv2d_1 has 2 users, we should annotate conv2d_2 for binary fusion instead of conv2d_1 + """ + example_inputs = (torch.randn(2, 3, 6, 6),) + quantizer = ArmInductorQuantizer().set_global( + armiq.get_default_arm_inductor_quantization_config(is_qat=True) + ) + inplace_add_list = [True, False] + with torch.no_grad(): + for inplace_add in inplace_add_list: + m = TestHelperModules.Conv2dAddModule2(inplace_add=inplace_add) + node_occurrence = { + torch.ops.quantized_decomposed.quantize_per_tensor.default: 3, + torch.ops.quantized_decomposed.dequantize_per_tensor.default: 6, + # quantize_per_channel for weights are const propagated + torch.ops.quantized_decomposed.quantize_per_channel.default: 0, + torch.ops.quantized_decomposed.dequantize_per_channel.default: 0, + # BN should be folded into Conv + torch.ops.aten._native_batch_norm_legit.default: 0, + } + node_list = [ + torch.ops.quantized_decomposed.quantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + torch.ops.aten.conv2d.default, + torch.ops.quantized_decomposed.quantize_per_tensor.default, + ( + torch.ops.aten.add_.Tensor + if inplace_add + else torch.ops.aten.add.Tensor + ), + ] + self._test_quantizer( + m, + example_inputs, + quantizer, + node_occurrence, + node_list, + is_qat=True, + ) + + @skipIfNoArm + def test_dynamic_quant_linear(self): + """ + Test pattern of dynamic quantization of linear with ArmInductorQuantizer. + """ + with torch.no_grad(): + m = TestHelperModules.SelfAttnLikeModule(input_dim=64).eval() + example_inputs = (torch.randn(1, 4, 64),) + quantizer = ArmInductorQuantizer().set_global( + armiq.get_default_arm_inductor_quantization_config(is_dynamic=True) + ) + node_occurrence = { + torch.ops.quantized_decomposed.choose_qparams.tensor: 1, + torch.ops.quantized_decomposed.quantize_per_tensor.tensor: 1, + torch.ops.quantized_decomposed.dequantize_per_tensor.tensor: 1, + # quantize_per_channel for weights are const propagated + torch.ops.quantized_decomposed.quantize_per_channel.default: 0, + torch.ops.quantized_decomposed.dequantize_per_channel.default: 0, + } + node_list = [ + torch.ops.quantized_decomposed.choose_qparams.tensor, + torch.ops.quantized_decomposed.quantize_per_tensor.tensor, + torch.ops.quantized_decomposed.dequantize_per_tensor.tensor, + torch.ops.aten.linear.default, + ] + self._test_quantizer( + m, + example_inputs, + quantizer, + node_occurrence, + node_list, + ) + + @skipIfNoArm + def test_qat_dynamic_quant_linear(self): + """ + Test pattern of qat dynamic quantization of linear with ArmInductorQuantizer. + """ + with torch.no_grad(): + m = TestHelperModules.SelfAttnLikeModule(input_dim=64).eval() + example_inputs = (torch.randn(1, 4, 64),) + quantizer = ArmInductorQuantizer().set_global( + armiq.get_default_arm_inductor_quantization_config( + is_qat=True, is_dynamic=True + ) + ) + node_occurrence = { + torch.ops.quantized_decomposed.choose_qparams.tensor: 1, + torch.ops.quantized_decomposed.quantize_per_tensor.tensor: 1, + torch.ops.quantized_decomposed.dequantize_per_tensor.tensor: 1, + # quantize_per_channel for weights are const propagated + torch.ops.quantized_decomposed.quantize_per_channel.default: 0, + torch.ops.quantized_decomposed.dequantize_per_channel.default: 0, + } + node_list = [ + torch.ops.quantized_decomposed.choose_qparams.tensor, + torch.ops.quantized_decomposed.quantize_per_tensor.tensor, + torch.ops.quantized_decomposed.dequantize_per_tensor.tensor, + torch.ops.aten.linear.default, + ] + self._test_quantizer( + m, + example_inputs, + quantizer, + node_occurrence, + node_list, + is_qat=True, + ) + + @skipIfNoArm + def test_set_module_name_qconfig(self): + """Test case for quantizing a specific submodule by configuring `set_module_name_qconfig`. + Expect that all linear layers within the submodule `sub` are quantized. + """ + + class Sub(torch.nn.Module): + def __init__(self) -> None: + super().__init__() + self.linear1 = torch.nn.Linear(5, 10) + self.relu1 = torch.nn.ReLU(inplace=False) + self.linear2 = torch.nn.Linear(10, 5) + + def forward(self, x): + x = self.linear1(x) + x = self.relu1(x) + x = self.linear2(x) + return x + + class M(torch.nn.Module): + def __init__(self) -> None: + super().__init__() + self.linear = torch.nn.Linear(5, 5) + self.sub = Sub() + + def forward(self, x): + x = self.linear(x) + x = self.sub(x) + return x + + m = M().eval() + example_inputs = (torch.randn(3, 5),) + # Set global to `None` and then default config for a specific submodule. + quantizer = ArmInductorQuantizer() + quantizer.set_module_name_qconfig( + "sub", armiq.get_default_arm_inductor_quantization_config() + ) + node_occurrence = { + torch.ops.aten.linear.default: 3, + # quantize and dequantize the input of two linear layers from `sub` + torch.ops.quantized_decomposed.quantize_per_tensor.default: 2, + torch.ops.quantized_decomposed.dequantize_per_tensor.default: 4, + # dequantize the weight of two linear layers from `sub` + torch.ops.quantized_decomposed.dequantize_per_channel.default: 0, + } + node_list = [ + # first linear is not quantized + torch.ops.aten.linear.default, + # two Q/DQ pairs for two linear layers from `sub` + torch.ops.quantized_decomposed.quantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + torch.ops.aten.linear.default, + torch.ops.quantized_decomposed.quantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + torch.ops.aten.linear.default, + ] + self._test_quantizer( + m, + example_inputs, + quantizer, + node_occurrence, + node_list, + ) + + @skipIfNoArm + def test_set_module_name_qconfig_with_underscores(self) -> None: + """Test that if a module name has an underscore, we can still quantize it.""" + + class M(torch.nn.Module): + def __init__(self) -> None: + super().__init__() + # This module name has underscores, which can be part of a mangled name. + self.foo_bar = torch.nn.Linear(2, 2) + self.baz = torch.nn.Linear(2, 2) + + def forward(self, x): + return self.baz(self.foo_bar(x)) + + # Set global to no quantization and then default config for a specific submodule whose name includes an underscore. + quantizer = ArmInductorQuantizer() + quantizer.set_module_name_qconfig( + "foo_bar", armiq.get_default_arm_inductor_quantization_config() + ) + example_inputs = (torch.randn(2, 2),) + m = M().eval() + m = export_for_training(m, example_inputs).module() + m = prepare_pt2e(m, quantizer) + # Use a linear count instead of names because the names might change, but + # the order should be the same. + count = 0 + for n in m.graph.nodes: + if n.op == "call_function" and n.target == torch.ops.aten.linear.default: + # Get the weight observer to see the per-channel vs per-tensor. + weight_observer_node = n.args[1] + if count == 0: + # for foo_bar. + self.assertEqual( + weight_observer_node.op, + "call_module", + f"The op of linear({count})'s weight_observer_node is {weight_observer_node.op} instead call_module", + ) + observer_instance = getattr(m, weight_observer_node.target) + self.assertEqual( + observer_instance.qscheme, torch.per_tensor_symmetric + ) + else: + # For baz it should have no observer at all. + self.assertNotEqual( + weight_observer_node.op, + "call_module", + f"The op of linear({count})'s weight_observer_node is {weight_observer_node.op} instead call_module", + ) + count += 1 + + @skipIfNoArm + def test_set_module_name_and_module_type_case1(self): + """Test that set `module_name_qconfig` and `module_type_qconfig` at the same time. + Expect that all linear layers are not quantized except the last one. + """ + + class M(torch.nn.Module): + def __init__(self) -> None: + super().__init__() + self.linear1 = torch.nn.Linear(5, 10) + self.linear2 = torch.nn.Linear(10, 5) + self.sub = torch.nn.Linear(5, 5) + + def forward(self, x): + x = self.linear1(x) + x = self.linear2(x) + x = self.sub(x) + return x + + m = M().eval() + example_inputs = (torch.randn(3, 5),) + # Set `sub` with default config and then `None` for all `Linear`. + # The config set by `set_module_name_qconfig` has higher priority than `set_module_type_qconfig`. + quantizer = ArmInductorQuantizer() + quantizer.set_module_name_qconfig( + "sub", armiq.get_default_arm_inductor_quantization_config() + ).set_module_type_qconfig(torch.nn.Linear, None) + + node_occurrence = { + torch.ops.aten.linear.default: 3, + # quantize and dequantize the input of the last linear + torch.ops.quantized_decomposed.quantize_per_tensor.default: 1, + torch.ops.quantized_decomposed.dequantize_per_tensor.default: 2, + # dequantize the weight of the last linear + torch.ops.quantized_decomposed.dequantize_per_channel.default: 0, + } + node_list = [ + # first and second linear are not quantized + torch.ops.aten.linear.default, + torch.ops.aten.linear.default, + # last linear is quantized + torch.ops.quantized_decomposed.quantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + torch.ops.aten.linear.default, + ] + self._test_quantizer( + m, + example_inputs, + quantizer, + node_occurrence, + node_list, + ) + + @skipIfNoArm + def test_set_module_name_and_module_type_case2(self): + """Test that set `module_name_qconfig` and `module_type_qconfig` at the same time. + Expect that all linear layers are quantized except the last one. + """ + + class M(torch.nn.Module): + def __init__(self) -> None: + super().__init__() + self.linear1 = torch.nn.Linear(5, 10) + self.linear2 = torch.nn.Linear(10, 5) + self.sub = torch.nn.Linear(5, 5) + + def forward(self, x): + x = self.linear1(x) + x = self.linear2(x) + x = self.sub(x) + return x + + m = M().eval() + example_inputs = (torch.randn(3, 5),) + # Set `sub` with None and then default config for a all `Linear`. + quantizer = ArmInductorQuantizer() + quantizer.set_module_name_qconfig("sub", None).set_module_type_qconfig( + torch.nn.Linear, armiq.get_default_arm_inductor_quantization_config() + ) + + node_occurrence = { + torch.ops.aten.linear.default: 3, + # quantize and dequantize the input and output of the first and second linear + torch.ops.quantized_decomposed.quantize_per_tensor.default: 2, + torch.ops.quantized_decomposed.dequantize_per_tensor.default: 4, + # dequantize the weight of the first and second linear + torch.ops.quantized_decomposed.dequantize_per_channel.default: 0, + } + node_list = [ + # Q/DQ for first lienar + torch.ops.quantized_decomposed.quantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + torch.ops.aten.linear.default, + # Q/DQ for second lienar + torch.ops.quantized_decomposed.quantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + torch.ops.aten.linear.default, + # last linear is not quantized + torch.ops.aten.linear.default, + ] + self._test_quantizer( + m, + example_inputs, + quantizer, + node_occurrence, + node_list, + ) + + @skipIfNoArm + def test_set_module_name_qconfig_for_dynamic_quant(self): + """Test that quantize a specific submodule for dynamic quantization.""" + + with torch.no_grad(): + for is_qat in [False, True]: + m = TestHelperModules.SelfAttnLikeModule(input_dim=64).eval() + example_inputs = (torch.randn(1, 4, 64),) + # only quantize `q_proj` `v_proj` + dynamic_config = armiq.get_default_arm_inductor_quantization_config( + is_dynamic=True, is_qat=is_qat + ) + quantizer = ( + ArmInductorQuantizer() + .set_module_name_qconfig("q_proj", dynamic_config) + .set_module_name_qconfig("v_proj", dynamic_config) + ) + node_occurrence = { + # quantize and dequantize the input + torch.ops.quantized_decomposed.choose_qparams.tensor: 1, + torch.ops.quantized_decomposed.quantize_per_tensor.tensor: 1, + torch.ops.quantized_decomposed.dequantize_per_tensor.tensor: 1, + # dequantize the weight of q_proj and v_proj + torch.ops.quantized_decomposed.dequantize_per_channel.default: 0, + } + node_list = [ + # quantize and dequantize the input + torch.ops.quantized_decomposed.choose_qparams.tensor, + torch.ops.quantized_decomposed.quantize_per_tensor.tensor, + torch.ops.quantized_decomposed.dequantize_per_tensor.tensor, + # q_proj + torch.ops.aten.linear.default, + # k_proj + torch.ops.aten.linear.default, + # v_proj + torch.ops.aten.linear.default, + ] + self._test_quantizer( + m, + example_inputs, + quantizer, + node_occurrence, + node_list, + is_qat=is_qat, + ) + + @skipIfNoArm + def test_set_module_name_with_mixed_configs(self): + """Test case for setting module names with mixed static/dynamic or QAT/non-QAT configurations. + The config for 'v_proj' will always be ignored and raise a warning. + """ + with torch.no_grad(): + with self.assertWarns(UserWarning) as context: + for q_is_dynamic, v_is_dynamic, q_is_qat, v_is_qat in itertools.product( + [False, True], repeat=4 + ): + if q_is_dynamic == v_is_dynamic and q_is_qat == v_is_qat: + continue + m = TestHelperModules.SelfAttnLikeModule(input_dim=64).eval() + example_inputs = (torch.randn(1, 4, 64),) + quantizer = ( + ArmInductorQuantizer() + .set_module_name_qconfig( + "q_proj", + armiq.get_default_arm_inductor_quantization_config( + is_qat=q_is_qat, is_dynamic=q_is_dynamic + ), + ) + .set_module_name_qconfig( + "v_proj", + armiq.get_default_arm_inductor_quantization_config( + is_qat=v_is_qat, is_dynamic=v_is_dynamic + ), + ) + ) + quant_op = ( + torch.ops.quantized_decomposed.quantize_per_tensor.tensor + if q_is_dynamic + else torch.ops.quantized_decomposed.quantize_per_tensor.default + ) + dequant_op = ( + torch.ops.quantized_decomposed.dequantize_per_tensor.tensor + if q_is_dynamic + else torch.ops.quantized_decomposed.dequantize_per_tensor.default + ) + node_occurrence = { + # quantize and dequantize the input + quant_op: 1, + dequant_op: 1 if q_is_dynamic else 2, + # only `q_proj` was quantized, dequantize its weight + torch.ops.quantized_decomposed.dequantize_per_channel.default: 0, + } + node_list = [ + # quantize and dequantize the input + quant_op, + dequant_op, + # q_proj + torch.ops.aten.linear.default, + # k_proj/v_proj + torch.ops.aten.linear.default, + torch.ops.aten.linear.default, + ] + self._test_quantizer( + m, + example_inputs, + quantizer, + node_occurrence, + node_list, + is_qat=q_is_qat, + ) + warning_msg = ( + "Mixed QAT and Non-QAT" + if q_is_qat != v_is_qat + else "Mixed dynamic and static" + ) + self.assertTrue( + any( + warning_msg in msg + for msg in [str(w.message) for w in context.warnings] + ) + ) + + @skipIfNoArm + def test_set_module_name_and_module_type_with_mixed_configs(self): + """Test that set `module_name_qconfig` and `module_type_qconfig` at the same time with mixed the configs. + Expect that only the last linear(`sub`) is quantized using static quantization. + """ + + class M(torch.nn.Module): + def __init__(self) -> None: + super().__init__() + self.linear1 = torch.nn.Linear(5, 10) + self.linear2 = torch.nn.Linear(10, 5) + self.sub = torch.nn.Linear(5, 5) + + def forward(self, x): + x = self.linear1(x) + x = self.linear2(x) + x = self.sub(x) + return x + + m = M().eval() + example_inputs = (torch.randn(3, 5),) + # Set `sub` with static config and then dynamic config for a all `Linear`(ignored). + quantizer = ArmInductorQuantizer() + quantizer.set_module_name_qconfig( + "sub", armiq.get_default_arm_inductor_quantization_config(is_dynamic=False) + ).set_module_type_qconfig( + torch.nn.Linear, + armiq.get_default_arm_inductor_quantization_config(is_dynamic=True), + ) + + node_occurrence = { + torch.ops.aten.linear.default: 3, + # quantize and dequantize the input of the last linear + torch.ops.quantized_decomposed.quantize_per_tensor.default: 1, + torch.ops.quantized_decomposed.dequantize_per_tensor.default: 2, + # dequantize the weight of the last linear + torch.ops.quantized_decomposed.dequantize_per_channel.default: 0, + } + node_list = [ + # first and second linear are not quantized + torch.ops.aten.linear.default, + torch.ops.aten.linear.default, + # Q/DQ pairs for the last linear + torch.ops.quantized_decomposed.quantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + torch.ops.aten.linear.default, + ] + self._test_quantizer( + m, + example_inputs, + quantizer, + node_occurrence, + node_list, + ) + + @skipIfNoArm + def test_filter_linear_recipe(self): + """ + Test removing linear from default recipe of ArmInductorQuantizer. + """ + with torch.no_grad(): + m = TestHelperModules.LinearUnaryModule( + use_bias=True, + postop=nn.ReLU, + ).eval() + example_inputs = (torch.randn(2, 4),) + quantizer = ArmInductorQuantizer().set_global( + armiq.get_default_arm_inductor_quantization_config() + ) + quantizer.set_function_type_qconfig(torch.nn.functional.linear, None) + node_occurrence = { + # one for input and weight of the conv + torch.ops.quantized_decomposed.quantize_per_tensor.default: 0, + torch.ops.quantized_decomposed.dequantize_per_tensor.default: 0, + # note: quantize op for weights are const propagated + torch.ops.quantized_decomposed.quantize_per_channel.default: 0, + torch.ops.quantized_decomposed.dequantize_per_channel.default: 0, + } + node_list = [ + torch.ops.aten.linear.default, + torch.ops.aten.relu.default, + ] + self._test_quantizer( + m, + example_inputs, + quantizer, + node_occurrence, + node_list, + ) + + @skipIfNoArm + def test_attention_block(self): + """ + Test pattern of Attention like Block with ArmInductorQuantizer. + """ + for annotate_matmul in [False, True]: + with torch.no_grad(): + m = TestHelperModules.SelfAttnLikeModule( + input_dim=64 * 16, + transpose_for_score=True, + num_attention_heads=16, + attention_head_size=64, + ).eval() + example_inputs = (torch.randn(2, 384, 1024),) + + m(*example_inputs) + + quantizer = ArmInductorQuantizer().set_global( + armiq.get_default_arm_inductor_quantization_config() + ) + + if annotate_matmul: + quantizer.set_function_type_qconfig( + torch.matmul, quantizer.get_global_quantization_config() + ) + + node_occurrence = { + torch.ops.quantized_decomposed.quantize_per_tensor.default: ( + 5 if annotate_matmul else 1 + ), + torch.ops.quantized_decomposed.dequantize_per_tensor.default: ( + 10 if annotate_matmul else 6 + ), + # quantize_per_channel for weights are const propagated + torch.ops.quantized_decomposed.quantize_per_channel.default: 0, + torch.ops.quantized_decomposed.dequantize_per_channel.default: 0, + } + if annotate_matmul: + node_list = [ + torch.ops.quantized_decomposed.quantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + torch.ops.aten.linear.default, + torch.ops.aten.view.default, + torch.ops.aten.permute.default, + torch.ops.quantized_decomposed.quantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + torch.ops.aten.matmul.default, + torch.ops.aten.div.Tensor, + torch.ops.aten.softmax.int, + ] + else: + node_list = [ + torch.ops.quantized_decomposed.quantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + torch.ops.aten.linear.default, + torch.ops.aten.view.default, + torch.ops.aten.permute.default, + torch.ops.aten.matmul.default, + torch.ops.aten.div.Tensor, + torch.ops.aten.softmax.int, + ] + self._test_quantizer( + m, + example_inputs, + quantizer, + node_occurrence, + node_list, + ) + + +if __name__ == "__main__": + run_tests() diff --git a/torchao/quantization/pt2e/quantizer/arm_inductor_quantizer.py b/torchao/quantization/pt2e/quantizer/arm_inductor_quantizer.py new file mode 100644 index 0000000000..af0c04a79d --- /dev/null +++ b/torchao/quantization/pt2e/quantizer/arm_inductor_quantizer.py @@ -0,0 +1,396 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD 3-Clause license found in the +# LICENSE file in the root directory of this source tree. + +# mypy: allow-untyped-defs +import functools +import operator +import warnings +from dataclasses import dataclass +from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional + +import torch +import torch.nn.functional as F +from torch.fx import Node +from typing_extensions import TypeAlias + +from torchao.quantization.pt2e.fake_quantize import ( + FakeQuantize, + FusedMovingAvgObsFakeQuantize, +) +from torchao.quantization.pt2e.observer import ( + HistogramObserver, + MinMaxObserver, + MovingAverageMinMaxObserver, + PlaceholderObserver, +) +from torchao.quantization.pt2e.quantizer import ( + QuantizationConfig, + get_module_name_filter, +) +from torchao.quantization.pt2e.quantizer.quantizer import ( + QuantizationAnnotation, + QuantizationSpec, +) + +from .x86_inductor_quantizer import ( + X86InductorQuantizer, +) + +FilterFn: TypeAlias = Callable[[List[Node]], bool] + + +if TYPE_CHECKING: + from torchao.quantization.pt2e import _ObserverOrFakeQuantizeConstructor + +__all__ = [ + "ArmInductorQuantizer", + "get_default_arm_inductor_quantization_config", +] + + +@dataclass +class _ArmInductorQuantizationAnnotation(QuantizationAnnotation): + # _is_output_of_quantized_pattern: + # * Node as output node of a fusion pattern. + # * The fusion pattern supports int8 data type. + # * The fusion pattern has inputs annotated to insert observer. + # * The quantization_config is not `None`. + _is_output_of_quantized_pattern: bool = False + + +# Operators support the int8 data type +# and recipe is configured by default in ArmInductorQuantizer. +default_quantizable_ops = { + torch.ops.aten.conv2d.default, + torch.ops.aten.linear.default, +} + +# A superset of default_quantizable_ops includes operators support the int8 data type +# but not enabled by default recipe of ArmInductorQuantizer. +quantizable_ops = default_quantizable_ops | { + torch.ops.aten.matmul.default, +} + + +def _create_module_name_filter(module_name: str) -> FilterFn: + """Create a filter function for a given module name. + + The filter function takes a list of nodes (as determined by the annotate function) + and return True if *all* nodes come from the specified module name, False otherwise. + + For example: + linear_1: "f32[3, 10]" = torch.ops.aten.linear.default(...) # comes from a module with name `sub.linear1` + relu: "f32[3, 10]" = torch.ops.aten.relu.default(linear_1); # comes from a module with name `sub.relu1` + + >> module_name_filter = _create_module_name_filter_inner("sub") + >> print(module_name_filter([relu, linear_1])) + # True # These two nodes are determined by `_annotate_linear_unary` function and from "sub". + """ + + filter_fn = get_module_name_filter(module_name) + + def check_all_nodes_from_module(nodes: list[Node]) -> bool: + all_nodes_from_module_name: bool = all(filter_fn(n) for n in nodes) + return all_nodes_from_module_name + + return check_all_nodes_from_module + + +def _create_operator_type_filter( + operator_type: Callable, +) -> FilterFn: + """Create a filter function for a given operator type. + + The filter function takes a list of nodes and returns True if it contains + exactly one node with the specified operator type, False otherwise. + + For example: + linear_1: "f32[3, 10]" = torch.ops.aten.linear.default(...) # comes from a module with name `sub.linear1` + relu: "f32[3, 10]" = torch.ops.aten.relu.default(linear_1); # comes from a module with name `sub.relu1` + + >> operator_type_filter = _create_operator_type_filter(torch.ops.aten.linear.default) + >> print(operator_type_filter([relu, linear_1])) + # True # These two nodes are determined by `_annotate_linear_unary` function and the second node is `linear`. + """ + + def operator_type_filter(nodes: list[Node]): + num_nodes_with_operator_type = sum( + node.target == operator_type for node in nodes + ) + if num_nodes_with_operator_type > 1: + raise NotImplementedError( + f"Several nodes within a single pattern are {operator_type}." + ) + return num_nodes_with_operator_type == 1 + + return operator_type_filter + + +def _global_config_filter(nodes: List[Node]) -> bool: + """Filter function for global configuration. + + This filter function takes a list of nodes and returns True if there is exactly one node + in the list that is a default quantizable operation, False otherwise. + """ + num_nodes_in_default_quantizable_ops = sum( + node.target in default_quantizable_ops for node in nodes + ) + if num_nodes_in_default_quantizable_ops > 1: + raise NotImplementedError( + "Several nodes within a single pattern are default quantizable operations." + ) + return num_nodes_in_default_quantizable_ops == 1 + + +def _map_module_function_to_aten_operator_type(): + module_function_to_aten_operator: Dict[Callable, torch._ops.OpOverloadPacket] = {} + map_list = ( + ([torch.nn.Conv2d, F.conv2d], torch.ops.aten.conv2d.default), + ([torch.nn.Linear, F.linear], torch.ops.aten.linear.default), + ( + [ + torch.matmul, + ], + torch.ops.aten.matmul.default, + ), + ) + for map_item in map_list: + module_function_to_aten_operator.update(dict.fromkeys(map_item[0], map_item[1])) # type: ignore[arg-type, call-overload] + return module_function_to_aten_operator + + +@functools.lru_cache +def get_default_arm_inductor_quantization_config( + is_qat: bool = False, + is_dynamic: bool = False, +): + extra_args: Dict[str, Any] = {"eps": 2**-12} + if is_qat: + if is_dynamic: + act_observer_or_fake_quant_ctr = FakeQuantize + dynamic_quant_observer = MovingAverageMinMaxObserver.with_args( + averaging_constant=1 + ) + extra_args["observer"] = dynamic_quant_observer + else: + act_observer_or_fake_quant_ctr = FusedMovingAvgObsFakeQuantize # type: ignore[assignment] + else: + if is_dynamic: + act_observer_or_fake_quant_ctr = PlaceholderObserver # type: ignore[assignment] + else: + act_observer_or_fake_quant_ctr = HistogramObserver # type: ignore[assignment] + # check for the qconfig ------------------------- + act_quantization_spec = QuantizationSpec( + dtype=torch.int8, + quant_min=-128, + quant_max=127, + qscheme=torch.per_tensor_affine, + is_dynamic=is_dynamic, + observer_or_fake_quant_ctr=act_observer_or_fake_quant_ctr.with_args( + **extra_args + ), + ) + + weight_observer_or_fake_quant_ctr: _ObserverOrFakeQuantizeConstructor = ( + FusedMovingAvgObsFakeQuantize if is_qat else MinMaxObserver + ) + + if is_qat: + # Only support per tensor quant for now + extra_args["observer"] = MovingAverageMinMaxObserver # type: ignore[dict-item] + weight_quantization_spec = QuantizationSpec( + dtype=torch.int8, + quant_min=-128, + quant_max=127, + qscheme=torch.per_tensor_symmetric, + is_dynamic=False, + observer_or_fake_quant_ctr=weight_observer_or_fake_quant_ctr.with_args( + **extra_args + ), + ) + bias_quantization_spec = None # will use placeholder observer by default + quantization_config = QuantizationConfig( + act_quantization_spec, + act_quantization_spec, + weight_quantization_spec, + bias_quantization_spec, + is_qat, + ) + return quantization_config + + +def _config_checker(method: Callable) -> Callable: + @functools.wraps(method) + def wrapper( + quantizer: "ArmInductorQuantizer", + name: Any, + quantization_config: Optional["QuantizationConfig"], + ) -> "ArmInductorQuantizer": + if quantizer._need_skip_config(quantization_config): + warnings.warn( + f"Skip the quantization config for {name}.", + ) + return quantizer + return method(quantizer, name, quantization_config) + + return wrapper + + +class ArmInductorQuantizer(X86InductorQuantizer): + module_function_to_aten_operator_type = _map_module_function_to_aten_operator_type() + + def get_global_quantization_config(self): + if not isinstance(self.global_config, QuantizationConfig): + warnings.warn( + "The global_config for ArmInductorQuantizer is currently invalid. \ + Please ensure that you use set_global to establish the global quantization configuration." + ) + return self.global_config + + @_config_checker + def set_function_type_qconfig( + self, + function_type: Callable, + quantization_config: Optional[QuantizationConfig], + ) -> "ArmInductorQuantizer": + if function_type in ArmInductorQuantizer.module_function_to_aten_operator_type: + self._set_aten_operator_qconfig( + ArmInductorQuantizer.module_function_to_aten_operator_type[ + function_type + ], + quantization_config, + ) + else: + warnings.warn( + f"function: Unable to customize quantization config for {function_type} by ArmInductorQuantizer." + ) + return self + + @_config_checker + def set_module_type_qconfig( + self, + module_type: torch.nn.Module, + quantization_config: Optional[QuantizationConfig], + ) -> "ArmInductorQuantizer": + if module_type in ArmInductorQuantizer.module_function_to_aten_operator_type: + self._set_aten_operator_qconfig( + ArmInductorQuantizer.module_function_to_aten_operator_type[module_type], + quantization_config, + ) + else: + warnings.warn( + f"Module: Unable to customize quantization config for {module_type} by ArmInductorQuantizer." + ) + return self + + @_config_checker + def set_module_name_qconfig( + self, module_name: str, quantization_config: Optional[QuantizationConfig] + ): + """Set quantization_config for a submodule with name: `module_name`, for example: + quantizer.set_module_name_qconfig("blocks.sub"), it will quantize all supported operator/operator + patterns in the submodule with this module name with the given `quantization_config` + + The supported operators include `quantizable_ops` only. + """ + self.module_name_qconfig[module_name] = quantization_config + return self + + def _set_aten_operator_qconfig( + self, + operator_type: torch._ops.OpOverloadPacket, + quantization_config: Optional[QuantizationConfig], + ) -> "ArmInductorQuantizer": + if operator_type in quantizable_ops: + self.operator_type_qconfig[operator_type] = quantization_config + else: + warnings.warn( + f"operator: Unable to quantize {operator} by ArmInductorQuantizer." + ) + return self + + def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule: + """Annotate the given model with quantization configurations. + + Annotation contracts: + 1. Annotate each node according to the user's qconfig in the following order: + `module_name_qconfig`, `operator_type_qconfig`, and `global_config`. + 2. Avoid re-annotating nodes already annotated in prior stages. For example, + if `linear1` has been annotated by `module_name_qconfig`, it won't be annotated again + during the processing of the 'operator_type_qconfig' or 'global_config'. + 3. For config is `None`, the node will be annotated with `_ArmInductorQuantizationAnnotation(_annotated=True)`. + + For each pair of (module_name_or_operator_type_or_global, qconfig), a filter function is created. + This filter function checks if the node is marked by current stage and not annotated by the previous stage. + """ + for module_name, quantization_config in self.module_name_qconfig.items(): + self._annotate_with_config( + model, quantization_config, _create_module_name_filter(module_name) + ) + + for operator_type, quantization_config in self.operator_type_qconfig.items(): + self._annotate_with_config( + model, quantization_config, _create_operator_type_filter(operator_type) + ) + + if self.global_config: + self._annotate_with_config( + model, + self.global_config, + _global_config_filter, + ) + + return model + + def _annotate_with_config( + self, + model: torch.fx.GraphModule, + quantization_config: Optional[QuantizationConfig], + filter_fn: FilterFn, + ) -> None: + """Annotate the model with the given quantization configuration. + + High-level description of quantization recipe for Arm Inductor Backend: + Apply quantization recipe for fusion patterns of conv/linear to enable int8 data type actively. + """ + + # Step1: Recipe of fusion patterns like conv/linear. + self._annotate_conv2d_fusion_pattern(model, quantization_config, filter_fn) + self._annotate_linear_fusion_pattern(model, quantization_config, filter_fn) + self._annotate_matmul(model, quantization_config, filter_fn) + + def _annotate_qat_conv2d_fusion_pattern( + self, + model: torch.fx.GraphModule, + quantization_config: Optional[QuantizationConfig], + filter_fn: Optional[FilterFn] = None, + ): + # Annotate QAT Specific patterns + self._annotate_qat_conv2d_bn_binary(model, quantization_config, filter_fn) + self._annotate_qat_conv2d_bn(model, quantization_config, filter_fn) + + def _annotate_conv2d_fusion_pattern( + self, + model: torch.fx.GraphModule, + quantization_config: Optional[QuantizationConfig], + filter_fn: Optional[FilterFn] = None, + ): + if (quantization_config is None) or (quantization_config.is_qat): + # Annotate QAT specific pattern: mainly due to BN not folded in prepare_qat + self._annotate_qat_conv2d_fusion_pattern( + model, quantization_config, filter_fn + ) + self._annotate_conv2d_binary(model, quantization_config, filter_fn) + self._annotate_conv2d(model, quantization_config, filter_fn) + + def _annotate_linear_fusion_pattern( + self, + model: torch.fx.GraphModule, + quantization_config: Optional[QuantizationConfig], + filter_fn: Optional[FilterFn] = None, + ): + self._annotate_linear_unary(model, quantization_config, filter_fn) + self._annotate_linear(model, quantization_config, filter_fn) From 4bfd7c09ef4592eacbbf990aea6d6bda608865c1 Mon Sep 17 00:00:00 2001 From: Driss Guessous <32754868+drisspg@users.noreply.github.com> Date: Tue, 13 May 2025 13:24:51 -0700 Subject: [PATCH 027/165] Add mx_fp4 path (#2201) stack-info: PR: https://github.com/pytorch/ao/pull/2201, branch: drisspg/stack/54 --- test/prototype/mx_formats/test_kernels.py | 7 ++- test/prototype/mx_formats/test_mx_linear.py | 57 ++++++++++++++----- test/prototype/mx_formats/test_mx_mm.py | 15 +++-- test/prototype/mx_formats/test_mx_tensor.py | 7 +-- torchao/prototype/mx_formats/README.md | 16 +++--- .../mx_formats/benchmarks/bench_qdq.py | 4 +- torchao/prototype/mx_formats/config.py | 8 +-- torchao/prototype/mx_formats/constants.py | 12 +++- .../prototype/mx_formats/fp_format_spec.py | 5 +- torchao/prototype/mx_formats/mx_ops.py | 7 +-- torchao/prototype/mx_formats/mx_tensor.py | 11 ++-- 11 files changed, 93 insertions(+), 56 deletions(-) diff --git a/test/prototype/mx_formats/test_kernels.py b/test/prototype/mx_formats/test_kernels.py index 276d180046..d649b2e04a 100644 --- a/test/prototype/mx_formats/test_kernels.py +++ b/test/prototype/mx_formats/test_kernels.py @@ -9,7 +9,6 @@ from torch.utils._triton import has_triton from torchao.prototype.mx_formats.constants import ( - DTYPE_FP4, DTYPE_FP6_E2M3, DTYPE_FP6_E3M2, F4_E2M1_EXP_BIAS, @@ -335,11 +334,13 @@ def test_fp4_triton_unscaled_cast(): def test_fp4_triton_scaled_cast(): size = (256,) orig_vals = torch.randn(size, dtype=torch.float, device="cuda") * 100 - mxtensor_ref = MXTensor.to_mx(orig_vals, block_size=32, elem_dtype=DTYPE_FP4) + mxtensor_ref = MXTensor.to_mx( + orig_vals, block_size=32, elem_dtype=torch.float4_e2m1fn_x2 + ) mxtensor_triton = MXTensor.to_mx( orig_vals, block_size=32, - elem_dtype=DTYPE_FP4, + elem_dtype=torch.float4_e2m1fn_x2, use_fp4_custom_triton_dequant_kernel=True, ) diff --git a/test/prototype/mx_formats/test_mx_linear.py b/test/prototype/mx_formats/test_mx_linear.py index 65934fb259..ff18d09aa4 100644 --- a/test/prototype/mx_formats/test_mx_linear.py +++ b/test/prototype/mx_formats/test_mx_linear.py @@ -11,12 +11,12 @@ import torch.nn as nn from torchao.prototype.mx_formats.config import ( + MXGemmKernelChoice, MXInferenceLinearConfig, MXLinearConfig, MXLinearRecipeName, ) from torchao.prototype.mx_formats.constants import ( - DTYPE_FP4, DTYPE_FP6_E2M3, DTYPE_FP6_E3M2, SUPPORTED_ELEM_DTYPES, @@ -29,7 +29,6 @@ from torchao.quantization import quantize_ from torchao.quantization.utils import compute_error from torchao.utils import ( - TORCH_VERSION_AT_LEAST_2_7, TORCH_VERSION_AT_LEAST_2_8, is_sm_at_least_89, is_sm_at_least_100, @@ -37,7 +36,7 @@ torch.manual_seed(2) -if not TORCH_VERSION_AT_LEAST_2_7: +if not TORCH_VERSION_AT_LEAST_2_8: pytest.skip("Unsupported PyTorch version", allow_module_level=True) @@ -51,19 +50,28 @@ def run_around_tests(): torch._dynamo.reset() -@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") -@pytest.mark.parametrize( - "elem_dtype", - ( +elem_dtypes = ( + [ # test each dtype (torch.float8_e4m3fn, torch.float8_e4m3fn, torch.float8_e4m3fn), (DTYPE_FP6_E3M2, DTYPE_FP6_E3M2, DTYPE_FP6_E3M2), (DTYPE_FP6_E2M3, DTYPE_FP6_E2M3, DTYPE_FP6_E2M3), - (DTYPE_FP4, DTYPE_FP4, DTYPE_FP4), + (torch.float4_e2m1fn_x2, torch.float4_e2m1fn_x2, torch.float4_e2m1fn_x2), # only test one type of mixed-dtype overrides, to save testing time - (torch.float8_e4m3fn, DTYPE_FP4, DTYPE_FP4), - ), + (torch.float8_e4m3fn, torch.float4_e2m1fn_x2, torch.float4_e2m1fn_x2), + ] + if TORCH_VERSION_AT_LEAST_2_8 + else [ + # test each dtype + (torch.float8_e4m3fn, torch.float8_e4m3fn, torch.float8_e4m3fn), + (DTYPE_FP6_E3M2, DTYPE_FP6_E3M2, DTYPE_FP6_E3M2), + (DTYPE_FP6_E2M3, DTYPE_FP6_E2M3, DTYPE_FP6_E2M3), + ] ) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +@pytest.mark.parametrize("elem_dtype", elem_dtypes) @pytest.mark.parametrize("bias", [True, False]) @pytest.mark.parametrize("input_shape", [(128, 256), (1, 128, 256), (1, 1, 128, 256)]) @pytest.mark.parametrize("use_fp8_dim1_cast_triton_kernel", [False, True]) @@ -155,7 +163,7 @@ def test_linear_eager_emulated_vs_real_gemm(recipe_name, mkn): elem_dtype = torch.float8_e4m3fn if recipe_name == MXLinearRecipeName.MXFP4_CUTLASS: - elem_dtype = DTYPE_FP4 + elem_dtype = torch.float4_e2m1fn_x2 config_emulated = MXLinearConfig(block_size=32, elem_dtype=elem_dtype) config_real = MXLinearConfig.from_recipe_name(recipe_name) @@ -375,12 +383,21 @@ def test_inference_print_str(): assert "kernel=emulated" in s +test_dtypes = ( + [torch.float8_e4m3fn, torch.float4_e2m1fn_x2] + if TORCH_VERSION_AT_LEAST_2_8 + else [ + torch.float8_e4m3fn, + ] +) + + @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") @pytest.mark.skipif( not TORCH_VERSION_AT_LEAST_2_8, reason="torch.compile requires PyTorch 2.8+" ) @pytest.mark.skipif(not is_sm_at_least_100, reason="Reqs sm100") -@pytest.mark.parametrize("elem_dtype", [torch.float8_e4m3fn]) +@pytest.mark.parametrize("elem_dtype", [torch.float8_e4m3fn, torch.float4_e2m1fn_x2]) @pytest.mark.parametrize("bias", [True, False]) @pytest.mark.parametrize("compile", [True, False]) @torch.no_grad() @@ -394,7 +411,16 @@ def test_inference_subclass(elem_dtype, bias: bool, compile: bool): m = nn.Linear(32, 128, bias=bias, dtype=torch.bfloat16, device="cuda") m_mx = copy.deepcopy(m) - config = MXFPInferenceConfig() + kernel_choice = ( + MXGemmKernelChoice.CUTLASS + if elem_dtype == torch.float4_e2m1fn_x2 + else MXGemmKernelChoice.CUBLAS + ) + config = MXFPInferenceConfig( + activation_dtype=elem_dtype, + weight_dtype=elem_dtype, + gemm_kernel_choice=kernel_choice, + ) quantize_(m_mx, config=config) if compile: m_mx = torch.compile(m_mx, fullgraph=True) @@ -403,4 +429,7 @@ def test_inference_subclass(elem_dtype, bias: bool, compile: bool): y_ref = m(x) y_mx = m_mx(x) sqnr = compute_error(y_ref, y_mx) - assert sqnr >= 25.0, f"Got a sqnr of {sqnr} for {elem_dtype} and bias={bias}" + SQNR_THRESHOLD = 25.0 if elem_dtype == torch.float8_e4m3fn else 15.0 + assert sqnr >= SQNR_THRESHOLD, ( + f"Got a sqnr of {sqnr} for {elem_dtype} and bias={bias}" + ) diff --git a/test/prototype/mx_formats/test_mx_mm.py b/test/prototype/mx_formats/test_mx_mm.py index 1b16fa24ab..46380cfb55 100644 --- a/test/prototype/mx_formats/test_mx_mm.py +++ b/test/prototype/mx_formats/test_mx_mm.py @@ -10,11 +10,14 @@ from torchao.float8.float8_utils import compute_error from torchao.ops import mx_fp4_bf16 -from torchao.prototype.mx_formats.mx_tensor import DTYPE_FP4, MXTensor +from torchao.prototype.mx_formats.mx_tensor import MXTensor from torchao.prototype.mx_formats.utils import to_blocked -from torchao.utils import TORCH_VERSION_AT_LEAST_2_7, is_sm_at_least_100 +from torchao.utils import ( + TORCH_VERSION_AT_LEAST_2_8, + is_sm_at_least_100, +) -if not TORCH_VERSION_AT_LEAST_2_7: +if not TORCH_VERSION_AT_LEAST_2_8: pytest.skip("Unsupported PyTorch version", allow_module_level=True) @@ -25,7 +28,7 @@ def run_matrix_test(M: int, K: int, N: int, format) -> float: a = torch.rand((M, K), dtype=dtype, device=device) b = torch.rand((N, K), dtype=dtype, device=device) - fmt = torch.float8_e4m3fn if format == "fp8" else DTYPE_FP4 + fmt = torch.float8_e4m3fn if format == "fp8" else torch.float4_e2m1fn_x2 mx_func = ( partial(torch._scaled_mm, out_dtype=torch.bfloat16) if format == "fp8" @@ -75,7 +78,9 @@ def run_matrix_test(M: int, K: int, N: int, format) -> float: ], ids=lambda x: f"{x[0]}x{x[1]}x{x[2]}", ) -@pytest.mark.parametrize("format", ["fp8", "fp4"]) +@pytest.mark.parametrize( + "format", ["fp8", "fp4"] if TORCH_VERSION_AT_LEAST_2_8 else ["fp8"] +) def test_matrix_multiplication(size, format): M, K, N = size sqnr = run_matrix_test(M, K, N, format) diff --git a/test/prototype/mx_formats/test_mx_tensor.py b/test/prototype/mx_formats/test_mx_tensor.py index 51ede29bcb..59279c9dbb 100644 --- a/test/prototype/mx_formats/test_mx_tensor.py +++ b/test/prototype/mx_formats/test_mx_tensor.py @@ -12,7 +12,6 @@ from torchao.prototype.mx_formats.config import MXGemmKernelChoice from torchao.prototype.mx_formats.constants import ( - DTYPE_FP4, DTYPE_FP6_E2M3, DTYPE_FP6_E3M2, SUPPORTED_ELEM_DTYPES, @@ -363,7 +362,7 @@ def test_exponent_nan_out(elem_dtype, pack_fp6): if pack_fp6: data_bits = data_bits.reshape(-1, block_size) data_bits = pack_uint6(data_bits) - elif elem_dtype == DTYPE_FP4: + elif elem_dtype == torch.float4_e2m1fn_x2: data_bits = torch.tensor( [0, 1, 2, 3, 4, 5, 6, 7], dtype=torch.uint8, device="cuda" ) # noqa: E501 @@ -407,7 +406,7 @@ def test_block_sizes(elem_dtype, B): """ Smoke test for various block sizes """ - if B == 1 and elem_dtype == DTYPE_FP4: + if B == 1 and elem_dtype == torch.float4_e2m1fn_x2: pytest.skip("unsupported configuration") elif B % 4 != 0 and elem_dtype in [DTYPE_FP6_E2M3, DTYPE_FP6_E3M2]: pytest.skip("unsupported configuration") @@ -422,7 +421,7 @@ def test_transpose(elem_dtype, fp4_triton): """ Verify that transposing an MX tensor works """ - if elem_dtype != DTYPE_FP4 and fp4_triton: + if elem_dtype != torch.float4_e2m1fn_x2 and fp4_triton: pytest.skip("unsupported configuration") M, K = 128, 256 diff --git a/torchao/prototype/mx_formats/README.md b/torchao/prototype/mx_formats/README.md index 955a02704f..587d81f6a6 100644 --- a/torchao/prototype/mx_formats/README.md +++ b/torchao/prototype/mx_formats/README.md @@ -1,6 +1,6 @@ # MX training and inference with native PyTorch -This is a workflow for e2e training and inference with MX dtypes from the [MX OCP spec](https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf) +This is a workflow for e2e training and inference with MX dtypes from the [MX OCP spec](https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf) in native PyTorch. We are currently in prototype and are actively working on optimizing these workflows on the NVIDIA B200 hardware. ## Overall status @@ -34,8 +34,8 @@ gemm_kernel_choice = MXGemmKernelChoice.CUBLAS m = torch.nn.Sequential(torch.nn.Linear(32, 32)).cuda() config = MXLinearConfig( - elem_dtype=torch.float8_e4m3fn, - block_size=32, + elem_dtype=torch.float8_e4m3fn, + block_size=32, gemm_kernel_choice=gemm_kernel_choice, ) quantize_(m, config) @@ -55,8 +55,8 @@ from torchao.prototype.mx_formats import MXInferenceLinearConfig, MXGemmKernelCh m = torch.nn.Sequential(torch.nn.Linear(32, 32)).cuda() gemm_kernel_choice = MXGemmKernelChoice.CUBLAS config = MXInferenceLinearConfig( - elem_dtype=torch.float8_e4m3fn, - block_size=32, + elem_dtype=torch.float8_e4m3fn, + block_size=32, gemm_kernel_choice=gemm_kernel_choice, ) quantize_(m, config=config) @@ -71,10 +71,10 @@ only `torch.float32` and `torch.bfloat16` are supported as high precision format ```python from torchao.prototype.mx_formats.mx_tensor import MXTensor # Note: MX int8 is not implemented yet -from torchao.prototype.mx_formats.constants import DTYPE_FP6_E2M3, DTYPE_FP6_E3M2, DTYPE_FP4 +from torchao.prototype.mx_formats.constants import DTYPE_FP6_E2M3, DTYPE_FP6_E3M2 x = torch.randn(32, 32, device='cuda') -# elem_dtype can be torch.float8_e4m3fn, torch.float8_e5m2, DTYPE_FP6_E2M3, DTYPE_FP6_E3M2, DTYPE_FP4 +# elem_dtype can be torch.float8_e4m3fn, torch.float8_e5m2, DTYPE_FP6_E2M3, DTYPE_FP6_E3M2, torch.float4_e2m1fn_x2 elem_dtype = torch.float8_e4m3fn # high precision to MX, block size defaults to 32 @@ -88,7 +88,7 @@ x_hp = x_mx.to_dtype(torch.float) ## mxfp8 gemm -On NVIDIA B200 machines, we use the cuBLAS mxfp8 gemm exposed via the `torch._scaled_mm` op. +On NVIDIA B200 machines, we use the cuBLAS mxfp8 gemm exposed via the `torch._scaled_mm` op. We observe a speedup of **2x to 3x** vs the bf16 baseline on common shapes. To reproduce this on supported hardware, you can run the following command: diff --git a/torchao/prototype/mx_formats/benchmarks/bench_qdq.py b/torchao/prototype/mx_formats/benchmarks/bench_qdq.py index 3886a37920..ca0b926ce5 100644 --- a/torchao/prototype/mx_formats/benchmarks/bench_qdq.py +++ b/torchao/prototype/mx_formats/benchmarks/bench_qdq.py @@ -17,7 +17,6 @@ from torchao.prototype.mx_formats import config from torchao.prototype.mx_formats.constants import ( # noqa: E501 - DTYPE_FP4, SUPPORTED_ELEM_DTYPES, ) from torchao.prototype.mx_formats.mx_tensor import MXTensor @@ -44,7 +43,8 @@ def run(profile_folder: Optional[str] = None): ) if ( - elem_dtype != DTYPE_FP4 and use_fp4_custom_triton_dequant_kernel # noqa: E501 + elem_dtype != torch.float4_e2m1fn_x2 + and use_fp4_custom_triton_dequant_kernel # noqa: E501 ): # custom_triton_kernels only works for fp4 continue diff --git a/torchao/prototype/mx_formats/config.py b/torchao/prototype/mx_formats/config.py index c49e1595a8..eb1b15228d 100644 --- a/torchao/prototype/mx_formats/config.py +++ b/torchao/prototype/mx_formats/config.py @@ -12,7 +12,6 @@ from torchao.core.config import AOBaseConfig from torchao.prototype.mx_formats.constants import ( - DTYPE_FP4, DTYPE_FP6_E2M3, DTYPE_FP6_E3M2, DTYPE_TO_SHORT_STR, @@ -53,7 +52,7 @@ def _validate_gemm_kernel_choice(gemm_kernel_choice, block_size, elem_dtype): assert block_size == 32, ( f"block_size must be 32 to use the CUTLASS MX gemm kernels, got {block_size}" ) - valid_dtypes = [torch.float8_e4m3fn, DTYPE_FP4] + valid_dtypes = [torch.float8_e4m3fn, torch.float4_e2m1fn_x2] assert elem_dtype in valid_dtypes, ( f"elem_dtype must be one of {valid_dtypes} to use the CUTLASS MX gemm kernels, got {elem_dtype}" ) @@ -126,10 +125,11 @@ def from_recipe_name( elif recipe_name is MXLinearRecipeName.MXFP8_CUBLAS: return MXLinearConfig(gemm_kernel_choice=MXGemmKernelChoice.CUBLAS) elif recipe_name is MXLinearRecipeName.MXFP4_EMULATED: - return MXLinearConfig(elem_dtype=DTYPE_FP4) + return MXLinearConfig(elem_dtype=torch.float4_e2m1fn_x2) elif recipe_name is MXLinearRecipeName.MXFP4_CUTLASS: return MXLinearConfig( - elem_dtype=DTYPE_FP4, gemm_kernel_choice=MXGemmKernelChoice.CUTLASS + elem_dtype=torch.float4_e2m1fn_x2, + gemm_kernel_choice=MXGemmKernelChoice.CUTLASS, ) else: raise AssertionError(f"unknown recipe_name {recipe_name}") diff --git a/torchao/prototype/mx_formats/constants.py b/torchao/prototype/mx_formats/constants.py index 94c63b11e5..ffac3b1d5f 100644 --- a/torchao/prototype/mx_formats/constants.py +++ b/torchao/prototype/mx_formats/constants.py @@ -5,10 +5,11 @@ # LICENSE file in the root directory of this source tree. import torch +from torchao.utils import TORCH_VERSION_AT_LEAST_2_8 + # This is conceptually an enum of non-core dtypes # TODO(future PR): change to a cleaner way to represent this without # regressing torch.compile and while keeping things readable. -DTYPE_FP4 = "fp4_e2m1" DTYPE_FP6_E3M2 = "fp6_e3m2" DTYPE_FP6_E2M3 = "fp6_e2m3" @@ -19,16 +20,21 @@ torch.float8_e5m2, DTYPE_FP6_E2M3, DTYPE_FP6_E3M2, - DTYPE_FP4, ] +SUPPORTED_ELEM_DTYPES = ( + SUPPORTED_ELEM_DTYPES + [torch.float4_e2m1fn_x2] + if TORCH_VERSION_AT_LEAST_2_8 + else SUPPORTED_ELEM_DTYPES +) DTYPE_TO_SHORT_STR = { torch.float8_e4m3fn: "f8e4m3", torch.float8_e5m2: "f8e5m2", DTYPE_FP6_E2M3: "f6e2m3", DTYPE_FP6_E3M2: "f6e3m2", - DTYPE_FP4: "f4e2m1", } +if TORCH_VERSION_AT_LEAST_2_8: + DTYPE_TO_SHORT_STR[torch.float4_e2m1fn_x2] = "f4e2m1" F8E4M3_MAX = torch.finfo(torch.float8_e4m3fn).max # 448.0 F8E5M2_MAX = torch.finfo(torch.float8_e5m2).max # 57344.0 diff --git a/torchao/prototype/mx_formats/fp_format_spec.py b/torchao/prototype/mx_formats/fp_format_spec.py index fc9521ef66..d89a3ad2a9 100644 --- a/torchao/prototype/mx_formats/fp_format_spec.py +++ b/torchao/prototype/mx_formats/fp_format_spec.py @@ -16,7 +16,6 @@ import torch from torchao.prototype.mx_formats.constants import ( - DTYPE_FP4, DTYPE_FP6_E2M3, DTYPE_FP6_E3M2, ) @@ -494,7 +493,7 @@ def run(dtype): headers = ["orig_val", "formula", "s_enc", "e_enc", "m_enc", "note"] results = [] - if dtype == DTYPE_FP4: + if dtype == torch.float4_e2m1fn_x2: results = float4_e2m1_interesting_values elif dtype == DTYPE_FP6_E3M2: results = float6_e3m2_interesting_values @@ -539,6 +538,6 @@ def run(dtype): torch.float8_e5m2, DTYPE_FP6_E3M2, DTYPE_FP6_E2M3, - DTYPE_FP4, + torch.float4_e2m1fn_x2, ): run(dtype) diff --git a/torchao/prototype/mx_formats/mx_ops.py b/torchao/prototype/mx_formats/mx_ops.py index da342c7853..c7e673dc37 100644 --- a/torchao/prototype/mx_formats/mx_ops.py +++ b/torchao/prototype/mx_formats/mx_ops.py @@ -28,7 +28,6 @@ import torchao.ops from torchao.prototype.mx_formats.config import MXGemmKernelChoice from torchao.prototype.mx_formats.constants import ( - DTYPE_FP4, DTYPE_FP6_E2M3, DTYPE_FP6_E3M2, ) @@ -117,8 +116,8 @@ def _addmm_mx_dispatch( out_dtype=torch.bfloat16, ) else: - assert a._elem_dtype == DTYPE_FP4 - assert b._elem_dtype == DTYPE_FP4 + assert a._elem_dtype == torch.float4_e2m1fn_x2 + assert b._elem_dtype == torch.float4_e2m1fn_x2 assert gemm_choice is MXGemmKernelChoice.CUTLASS, "unsupported" # FP4 operations res = torchao.ops.mx_fp4_bf16( @@ -208,7 +207,7 @@ def unwrap(x): def mx_view_op(func, types, args, kwargs): data = args[0]._data new_size = args[1] - if args[0]._elem_dtype == DTYPE_FP4: + if args[0]._elem_dtype == torch.float4_e2m1fn_x2: # special case fp4 as we pack two elements per byte new_size = tensor_size_hp_to_fp4x2(new_size, data.is_contiguous()) elif args[0]._elem_dtype in [DTYPE_FP6_E3M2, DTYPE_FP6_E2M3] and args[0]._pack_fp6: diff --git a/torchao/prototype/mx_formats/mx_tensor.py b/torchao/prototype/mx_formats/mx_tensor.py index 494e53d717..784d3eda6d 100644 --- a/torchao/prototype/mx_formats/mx_tensor.py +++ b/torchao/prototype/mx_formats/mx_tensor.py @@ -26,7 +26,6 @@ from torchao.prototype.mx_formats.constants import ( BF16_EXP_BIAS, BLOCK_SIZE_DEFAULT, - DTYPE_FP4, DTYPE_FP6_E2M3, DTYPE_FP6_E3M2, E8M0_EXPONENT_BIAS, @@ -198,7 +197,7 @@ def to_mx( target_max_pow2 = F6_E3M2_MAX_POW2 mbits = MBITS_F6_E3M2 max_pos = F6_E3M2_MAX - elif elem_dtype == DTYPE_FP4: + elif elem_dtype == torch.float4_e2m1fn_x2: target_max_pow2 = F4_E2M1_MAX_POW2 mbits = MBITS_F4_E2M1 max_pos = F4_E2M1_MAX @@ -314,7 +313,7 @@ def to_mx( data_lp = pack_uint6(data_lp) # need to reshape at the end to help inductor fuse things data_lp = data_lp.reshape(orig_shape) - elif elem_dtype == DTYPE_FP4: + elif elem_dtype == torch.float4_e2m1fn_x2: # can't reshape at the end without handling it in the packing code, # punt until later since we'll need to rethink the torch.compile # approach for fp4x2 in any case @@ -394,7 +393,7 @@ def to_dtype( else: data_hp = f6_e3m2_unpacked_to_f32(data_lp) data_hp = data_hp.to(target_dtype).reshape(orig_shape) - elif elem_dtype == DTYPE_FP4: + elif elem_dtype == torch.float4_e2m1fn_x2: if use_fp4_custom_triton_dequant_kernel: data_hp_rescaled = triton_f4_to_scaled_bf16( data_lp, @@ -479,7 +478,7 @@ def __new__( pack_fp6, ): new_size = data_bits.size() - if elem_dtype == DTYPE_FP4: + if elem_dtype == torch.float4_e2m1fn_x2: # set the tensor size to what it would be without 2x4 packing # Note: `is_contiguous` is going to return True for a tensor of size # (M, 1) regardless or the order of dims, so this logic is currently @@ -518,7 +517,7 @@ def __new__( torch.float8_e5m2, ): target_numel = scale_e8m0_bits.numel() * block_size - elif elem_dtype == DTYPE_FP4: + elif elem_dtype == torch.float4_e2m1fn_x2: assert data_bits.dtype is torch.uint8 # fp4 target_numel = scale_e8m0_bits.numel() * block_size / 2 elif elem_dtype in [DTYPE_FP6_E2M3, DTYPE_FP6_E3M2]: From 58ac6c072836a5073a8c4b3c4dc48968e3cce371 Mon Sep 17 00:00:00 2001 From: Scott Roy <161522778+metascroy@users.noreply.github.com> Date: Tue, 13 May 2025 17:08:22 -0700 Subject: [PATCH 028/165] Update __init__.py (#2206) Add Int8DynamicActivationIntxWeightConfig to __init__.py --- torchao/quantization/__init__.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/torchao/quantization/__init__.py b/torchao/quantization/__init__.py index fa156691ca..dc6431b2cf 100644 --- a/torchao/quantization/__init__.py +++ b/torchao/quantization/__init__.py @@ -52,6 +52,7 @@ Int4WeightOnlyConfig, Int8DynamicActivationInt4WeightConfig, Int8DynamicActivationInt8WeightConfig, + Int8DynamicActivationIntxWeightConfig, Int8WeightOnlyConfig, IntxWeightOnlyConfig, PlainLayout, @@ -133,6 +134,7 @@ "Int4DynamicActivationInt4WeightConfig", "Int8DynamicActivationInt4WeightConfig", "Int8DynamicActivationInt8WeightConfig", + "Int8DynamicActivationIntxWeightConfig", "Int4WeightOnlyConfig", "Int8WeightOnlyConfig", "Float8WeightOnlyConfig", From 720a1779bb9f3a1d5cc0fa008571063b71187449 Mon Sep 17 00:00:00 2001 From: Vasiliy Kuznetsov Date: Wed, 14 May 2025 12:06:27 -0400 Subject: [PATCH 029/165] unbreak CI by fixing MX tests (#2208) Summary: https://github.com/pytorch/ao/pull/2201 broke CI: 1. some MX tests for fp4 are running on A10G instances, with skipping not being properly applied (https://hud.pytorch.org/pytorch/ao/commit/4bfd7c09ef4592eacbbf990aea6d6bda608865c1#42164784332-box) 2. some SQNR thresholds were to tight for fp4 (https://hud.pytorch.org/pytorch/ao/commit/4bfd7c09ef4592eacbbf990aea6d6bda608865c1#42164784332-box) This PR fixes both of these to get CI back to green (I hope). Note that I can't repro 1 locally, so we'll have to land and see if it works. Test Plan: CI Reviewers: Subscribers: Tasks: Tags: --- test/prototype/mx_formats/test_mx_linear.py | 6 +++++- test/prototype/mx_formats/test_mx_tensor.py | 2 +- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/test/prototype/mx_formats/test_mx_linear.py b/test/prototype/mx_formats/test_mx_linear.py index ff18d09aa4..25025ed048 100644 --- a/test/prototype/mx_formats/test_mx_linear.py +++ b/test/prototype/mx_formats/test_mx_linear.py @@ -396,7 +396,6 @@ def test_inference_print_str(): @pytest.mark.skipif( not TORCH_VERSION_AT_LEAST_2_8, reason="torch.compile requires PyTorch 2.8+" ) -@pytest.mark.skipif(not is_sm_at_least_100, reason="Reqs sm100") @pytest.mark.parametrize("elem_dtype", [torch.float8_e4m3fn, torch.float4_e2m1fn_x2]) @pytest.mark.parametrize("bias", [True, False]) @pytest.mark.parametrize("compile", [True, False]) @@ -405,9 +404,14 @@ def test_inference_subclass(elem_dtype, bias: bool, compile: bool): """ Smoke test for inference compile """ + # TODO(future): figure out why these CUDA capability conditions are not properly + # applied when inside `pytest.mark.skipif` for this test if elem_dtype in (torch.float8_e4m3fn, torch.float8_e5m2): if not is_sm_at_least_89(): pytest.skip("CUDA capability >= 8.9 required for float8 in triton") + elif elem_dtype == torch.float4_e2m1fn_x2: + if not is_sm_at_least_100(): + pytest.skip("CUDA capability >= 10.0 required for float4 gemm") m = nn.Linear(32, 128, bias=bias, dtype=torch.bfloat16, device="cuda") m_mx = copy.deepcopy(m) diff --git a/test/prototype/mx_formats/test_mx_tensor.py b/test/prototype/mx_formats/test_mx_tensor.py index 59279c9dbb..6dfd33f9c7 100644 --- a/test/prototype/mx_formats/test_mx_tensor.py +++ b/test/prototype/mx_formats/test_mx_tensor.py @@ -66,7 +66,7 @@ def assert_sqnr_gt_threshold(orig, new, threshold): if elem_dtype is torch.float8_e4m3fn: assert_sqnr_gt_threshold(data_hp, data_mx_dq, 18.0) else: - assert_sqnr_gt_threshold(data_hp, data_mx_dq, 14.0) + assert_sqnr_gt_threshold(data_hp, data_mx_dq, 13.0) @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") From 43000791dbe54626f0a7a15e6f5fcaa29c06a0ff Mon Sep 17 00:00:00 2001 From: Victor Moin Date: Wed, 14 May 2025 21:56:17 +0200 Subject: [PATCH 030/165] Add support for KleidiAI int4 kernels on aarch64 Linux (#2169) * Debugging ARM Neoverse-V1 * add comment to cmake * Debug NEOVERSE ARM * remove useless comments * clean * clean * debug * clean * load multiple potential paths * remove assertion * re-introduce assertion and define load_libtorchao_ops fn * add unit test to ensure * Ready for merge * last test * fix * PR feedbacks * debug * add comments * add ENABLE_ARM_NEON in build_torchao_ops --- setup.py | 35 ++++++++++-- torchao/experimental/CMakeLists.txt | 48 +++++++++++++++-- torchao/experimental/build_torchao_ops.sh | 1 + .../cpu/aarch64/valpacking/interleave.cpp | 1 + torchao/experimental/op_lib.py | 43 ++++++++++++--- .../kernel_config.h | 6 +-- .../experimental/ops/packed_weights_header.h | 8 +-- torchao/experimental/ops/parallel-aten-impl.h | 2 +- .../tests/test_load_libtorchao_ops.py | 53 +++++++++++++++++++ 9 files changed, 175 insertions(+), 22 deletions(-) create mode 100644 torchao/experimental/tests/test_load_libtorchao_ops.py diff --git a/setup.py b/setup.py index 24d7be20de..f59917162f 100644 --- a/setup.py +++ b/setup.py @@ -49,7 +49,7 @@ def read_version(file_path="version.txt"): import platform -build_torchao_experimental = ( +build_macos_arm_auto = ( use_cpp == "1" and platform.machine().startswith("arm64") and platform.system() == "Darwin" @@ -119,8 +119,33 @@ def __init__(self): "TORCHAO_BUILD_EXPERIMENTAL_MPS requires MPS be available" ) + # TORCHAO_PARALLEL_BACKEND specifies which parallel backend to use + # Possible values: aten_openmp, executorch, openmp, pthreadpool, single_threaded + self.parallel_backend = os.getenv("TORCHAO_PARALLEL_BACKEND", "aten_openmp") + + # TORCHAO_ENABLE_ARM_NEON_DOT enable ARM NEON Dot Product extension + # Enabled by default on macOS silicon + self.enable_arm_neon_dot = self._os_bool_var( + "TORCHAO_ENABLE_ARM_NEON_DOT", + default=(self._is_arm64() and self._is_macos()), + ) + if self.enable_arm_neon_dot: + assert self.build_cpu_aarch64, ( + "TORCHAO_ENABLE_ARM_NEON_DOT requires TORCHAO_BUILD_CPU_AARCH64 be set" + ) + + # TORCHAO_ENABLE_ARM_I8MM enable ARM 8-bit Integer Matrix Multiply instructions + # Not enabled by default on macOS as not all silicon mac supports it + self.enable_arm_i8mm = self._os_bool_var( + "TORCHAO_ENABLE_ARM_I8MM", default=False + ) + if self.enable_arm_i8mm: + assert self.build_cpu_aarch64, ( + "TORCHAO_ENABLE_ARM_I8MM requires TORCHAO_BUILD_CPU_AARCH64 be set" + ) + def _is_arm64(self) -> bool: - return platform.machine().startswith("arm64") + return platform.machine().startswith("arm64") or platform.machine() == "aarch64" def _is_macos(self) -> bool: return platform.system() == "Darwin" @@ -431,7 +456,8 @@ def get_extensions(): ) ) - if build_torchao_experimental: + # Build CMakeLists from /torchao/experimental - additional options become available : TORCHAO_BUILD_CPU_AARCH64, TORCHAO_BUILD_KLEIDIAI, TORCHAO_BUILD_MPS_OPS, TORCHAO_PARALLEL_BACKEND + if build_macos_arm_auto or os.getenv("BUILD_TORCHAO_EXPERIMENTAL") == "1": build_options = BuildOptions() def bool_to_on_off(value): @@ -451,6 +477,9 @@ def bool_to_on_off(value): f"-DTORCHAO_BUILD_CPU_AARCH64={bool_to_on_off(build_options.build_cpu_aarch64)}", f"-DTORCHAO_BUILD_KLEIDIAI={bool_to_on_off(build_options.build_kleidi_ai)}", f"-DTORCHAO_BUILD_MPS_OPS={bool_to_on_off(build_options.build_experimental_mps)}", + f"-DTORCHAO_ENABLE_ARM_NEON_DOT={bool_to_on_off(build_options.enable_arm_neon_dot)}", + f"-DTORCHAO_ENABLE_ARM_I8MM={bool_to_on_off(build_options.enable_arm_i8mm)}", + f"-DTORCHAO_PARALLEL_BACKEND={build_options.parallel_backend}", "-DTorch_DIR=" + torch_dir, ] + ( diff --git a/torchao/experimental/CMakeLists.txt b/torchao/experimental/CMakeLists.txt index e6b2a6aff0..4dd02b2dd7 100644 --- a/torchao/experimental/CMakeLists.txt +++ b/torchao/experimental/CMakeLists.txt @@ -15,10 +15,13 @@ if (NOT CMAKE_BUILD_TYPE) set(CMAKE_BUILD_TYPE Release) endif() +# Platform options option(TORCHAO_BUILD_EXECUTORCH_OPS "Building torchao ops for ExecuTorch." OFF) option(TORCHAO_BUILD_MPS_OPS "Building torchao MPS ops" OFF) option(TORCHAO_BUILD_CPU_AARCH64 "Build torchao's CPU aarch64 kernels" OFF) option(TORCHAO_BUILD_KLEIDIAI "Download, build, and link against Arm KleidiAI library (arm64 only)" OFF) +option(TORCHAO_ENABLE_ARM_NEON_DOT "Enable ARM Neon Dot Product extension" OFF) +option(TORCHAO_ENABLE_ARM_I8MM "Enable ARM 8-bit Integer Matrix Multiply instructions" OFF) if(NOT TORCHAO_INCLUDE_DIRS) set(TORCHAO_INCLUDE_DIRS ${CMAKE_CURRENT_SOURCE_DIR}/../..) @@ -28,19 +31,49 @@ if(NOT DEFINED TORCHAO_PARALLEL_BACKEND) set(TORCHAO_PARALLEL_BACKEND aten_openmp) endif() -include(CMakePrintHelpers) - +# Set default compiler options add_compile_options("-Wall" "-Werror" "-Wno-deprecated") include(CMakePrintHelpers) message("TORCHAO_INCLUDE_DIRS: ${TORCHAO_INCLUDE_DIRS}") include_directories(${TORCHAO_INCLUDE_DIRS}) - if(TORCHAO_BUILD_CPU_AARCH64) message(STATUS "Building with cpu/aarch64") add_compile_definitions(TORCHAO_BUILD_CPU_AARCH64) - add_compile_definitions(TORCHAO_ENABLE_ARM_NEON_DOT) + + # Set aarch64 compiler options + if (CMAKE_SYSTEM_NAME STREQUAL "Linux") + message(STATUS "Add aarch64 linux compiler options") + add_compile_options( + "-fPIC" + "-Wno-error=unknown-pragmas" + "-Wno-array-parameter" + "-Wno-maybe-uninitialized" + "-Wno-sign-compare" + ) + + # Since versions are hierarchical (each includes features from prior versions): + # - dotprod is included by default in armv8.4-a and later + # - i8mm is included by default in armv8.6-a and later + if(TORCHAO_ENABLE_ARM_I8MM) + message(STATUS "Using armv8.6-a (includes 'i8mm' and 'dotprod' flags)") + add_compile_options("-march=armv8.6-a") + elseif(TORCHAO_ENABLE_ARM_NEON_DOT) + message(STATUS "Using armv8.4-a (includes '+dotprod' flag)") + add_compile_options("-march=armv8.4-a") + endif() + endif() + + if(TORCHAO_ENABLE_ARM_NEON_DOT) + message(STATUS "Building with ARM NEON dot product support") + add_compile_definitions(TORCHAO_ENABLE_ARM_NEON_DOT) + endif() + + if(TORCHAO_ENABLE_ARM_I8MM) + message(STATUS "Building with ARM I8MM support") + add_compile_definitions(TORCHAO_ENABLE_ARM_I8MM) + endif() # Defines torchao_kernels_aarch64 add_subdirectory(kernels/cpu/aarch64) @@ -51,26 +84,33 @@ if(TORCHAO_BUILD_CPU_AARCH64) endif() endif() +# Add quantized operation dir add_subdirectory(ops/linear_8bit_act_xbit_weight) add_subdirectory(ops/embedding_xbit) +# ATen ops lib add_library(torchao_ops_aten SHARED) target_link_libraries( torchao_ops_aten PRIVATE torchao_ops_linear_8bit_act_xbit_weight_aten torchao_ops_embedding_xbit_aten ) + +# Add MPS support if enabled if (TORCHAO_BUILD_MPS_OPS) message(STATUS "Building with MPS support") add_subdirectory(ops/mps) target_link_libraries(torchao_ops_aten PRIVATE torchao_ops_mps_aten) endif() +# Install ATen targets install( TARGETS torchao_ops_aten EXPORT _targets DESTINATION lib ) + +# Build executorch lib if enabled if(TORCHAO_BUILD_EXECUTORCH_OPS) add_library(torchao_ops_executorch STATIC) target_link_libraries(torchao_ops_executorch PRIVATE diff --git a/torchao/experimental/build_torchao_ops.sh b/torchao/experimental/build_torchao_ops.sh index 782e187092..1bcc1a9658 100644 --- a/torchao/experimental/build_torchao_ops.sh +++ b/torchao/experimental/build_torchao_ops.sh @@ -22,6 +22,7 @@ cmake -DCMAKE_PREFIX_PATH=${CMAKE_PREFIX_PATH} \ -DCMAKE_INSTALL_PREFIX=${CMAKE_OUT} \ -DTORCHAO_BUILD_EXECUTORCH_OPS="${TORCHAO_BUILD_EXECUTORCH_OPS}" \ -DTORCHAO_BUILD_CPU_AARCH64=ON \ + -DTORCHAO_ENABLE_ARM_NEON_DOT=ON \ -S . \ -B ${CMAKE_OUT} cmake --build ${CMAKE_OUT} -j 16 --target install --config Release diff --git a/torchao/experimental/kernels/cpu/aarch64/valpacking/interleave.cpp b/torchao/experimental/kernels/cpu/aarch64/valpacking/interleave.cpp index 8cbf036957..0274b0889e 100644 --- a/torchao/experimental/kernels/cpu/aarch64/valpacking/interleave.cpp +++ b/torchao/experimental/kernels/cpu/aarch64/valpacking/interleave.cpp @@ -7,6 +7,7 @@ #include #include #include +#include // Interleaves data across channels (row/column) and groups. // Each channel is the same size (vals_per_channel) and is diff --git a/torchao/experimental/op_lib.py b/torchao/experimental/op_lib.py index 4fe478d1e8..456b0ca160 100644 --- a/torchao/experimental/op_lib.py +++ b/torchao/experimental/op_lib.py @@ -10,15 +10,44 @@ from torch import Tensor from torch.library import impl -# Load C++ ops -lib_path = Path(__file__).parent.parent -libs = list(lib_path.glob("libtorchao_ops_aten.*")) -assert len(libs) == 1, ( - f"Expected to find one libtorchao_ops_aten.* library at {lib_path}, but found {len(libs)}" -) -torch.ops.load_library(str(libs[0])) +# Load C++ ops - use multiple potential paths +potential_paths = [ + # Standard path from the module location + Path(__file__).parent.parent, + # Site-packages installation path + Path(torch.__file__).parent.parent / "torchao", + # For editable installs + Path(__file__).parent.parent.parent / "torchao", +] +def find_and_load_libtorchao_ops(potential_paths): + for lib_path in potential_paths: + libs = list(lib_path.glob("libtorchao_ops_aten.*")) + + if not libs: + continue + + assert len(libs) == 1, ( + f"Expected to find one libtorchao_ops_aten.* library at {lib_path}, but found {len(libs)}" + ) + + target_lib = libs[0] + print(f"Found library at: {target_lib}") + + try: + torch.ops.load_library(str(target_lib)) + return + except Exception as e: + print(f"Error loading library from {target_lib}: {e}") + + raise FileNotFoundError( + "Could not find libtorchao_ops_aten library in any of the provided paths" + ) + + +find_and_load_libtorchao_ops(potential_paths) + # Define meta ops. To support dynamic shapes, some meta ops need to # be defined in python instead of C++. torchao_lib = torch.library.Library("torchao", "IMPL") diff --git a/torchao/experimental/ops/linear_8bit_act_xbit_weight/kernel_config.h b/torchao/experimental/ops/linear_8bit_act_xbit_weight/kernel_config.h index 114e97838c..b699bdd3d3 100644 --- a/torchao/experimental/ops/linear_8bit_act_xbit_weight/kernel_config.h +++ b/torchao/experimental/ops/linear_8bit_act_xbit_weight/kernel_config.h @@ -190,7 +190,7 @@ struct UKernelConfig { TORCHAO_CHECK(pack_weights != nullptr || pack_weights_with_lut != nullptr, "pack_weights or pack_weights_with_lut must be set"); bool linear_configs_set = true; // first linear config must be set - for (int i = 0; i < linear_configs.size(); i++) { + for (size_t i = 0; i < linear_configs.size(); i++) { if (linear_configs_set) { TORCHAO_CHECK( linear_configs[i].m_step >= 1, @@ -225,7 +225,7 @@ struct UKernelConfig { assert(m >= 1); assert(linear_configs[0].m_step >= 1); - int i = 0; + size_t i = 0; while (i + 1 < linear_configs.size() && linear_configs[i + 1].m_step >= 1 && linear_configs[i + 1].m_step <= m) { assert(linear_configs[i].m_step < linear_configs[i + 1].m_step); @@ -235,7 +235,7 @@ struct UKernelConfig { assert(i < linear_configs.size()); assert(linear_configs[i].m_step >= 1); assert(i == 0 || linear_configs[i].m_step <= m); - return i; + return static_cast(i); } }; diff --git a/torchao/experimental/ops/packed_weights_header.h b/torchao/experimental/ops/packed_weights_header.h index 0869c12ef9..11703e8454 100644 --- a/torchao/experimental/ops/packed_weights_header.h +++ b/torchao/experimental/ops/packed_weights_header.h @@ -43,7 +43,7 @@ class PackedWeightsHeader { auto header = reinterpret_cast(packed_weights); header[0] = magic; header[1] = static_cast(type); - for (int i = 0; i < params.size(); i++) { + for (size_t i = 0; i < params.size(); i++) { header[i + 2] = params[i]; } } @@ -52,7 +52,7 @@ class PackedWeightsHeader { auto header = reinterpret_cast(packed_weights); assert(header[0] == PackedWeightsHeader::magic); params_type params; - for (int i = 0; i < params.size(); i++) { + for (size_t i = 0; i < params.size(); i++) { params[i] = header[i + 2]; } return PackedWeightsHeader( @@ -63,7 +63,7 @@ class PackedWeightsHeader { if (type != other.type) { return false; } - for (int i = 0; i < params.size(); i++) { + for (size_t i = 0; i < params.size(); i++) { if (params[i] != other.params[i]) { return false; } @@ -79,7 +79,7 @@ namespace std { struct hash { std::size_t operator()(const torchao::ops::PackedWeightsHeader& f) const { std::size_t hash = std::hash()(static_cast(f.type)); - for (int i = 0; i < f.params.size(); i++) { + for (size_t i = 0; i < f.params.size(); i++) { hash ^= std::hash()(f.params[i]); } return hash; diff --git a/torchao/experimental/ops/parallel-aten-impl.h b/torchao/experimental/ops/parallel-aten-impl.h index 07725f70eb..c2eb0b8498 100644 --- a/torchao/experimental/ops/parallel-aten-impl.h +++ b/torchao/experimental/ops/parallel-aten-impl.h @@ -5,7 +5,7 @@ // LICENSE file in the root directory of this source tree. #pragma once -#include +#include #include #include diff --git a/torchao/experimental/tests/test_load_libtorchao_ops.py b/torchao/experimental/tests/test_load_libtorchao_ops.py new file mode 100644 index 0000000000..4fec52f494 --- /dev/null +++ b/torchao/experimental/tests/test_load_libtorchao_ops.py @@ -0,0 +1,53 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD 3-Clause license found in the +# LICENSE file in the root directory of this source tree. + +import unittest +from pathlib import Path +from unittest.mock import MagicMock, patch + + +class TestLibTorchAoOpsLoader(unittest.TestCase): + def test_find_and_load_success(self): + mock_paths = [Path("/test/path1")] + mock_lib = MagicMock() + mock_lib.__str__.return_value = "/test/path1/libtorchao_ops_aten.so" + + with patch("pathlib.Path.glob", return_value=[mock_lib]): + with patch("torch.ops.load_library") as mock_load: + from ..op_lib import find_and_load_libtorchao_ops + + find_and_load_libtorchao_ops(mock_paths) + + mock_load.assert_called_once_with("/test/path1/libtorchao_ops_aten.so") + + def test_no_library_found(self): + mock_paths = [Path("/test/path1"), Path("/test/path2")] + + with patch("pathlib.Path.glob", return_value=[]): + from ..op_lib import find_and_load_libtorchao_ops + + with self.assertRaises(FileNotFoundError): + find_and_load_libtorchao_ops(mock_paths) + + def test_multiple_libraries_error(self): + mock_paths = [Path("/test/path1")] + mock_lib1 = MagicMock() + mock_lib2 = MagicMock() + mock_libs = [mock_lib1, mock_lib2] + + with patch("pathlib.Path.glob", return_value=mock_libs): + from ..op_lib import find_and_load_libtorchao_ops + + try: + find_and_load_libtorchao_ops(mock_paths) + self.fail("Expected AssertionError was not raised") + except AssertionError as e: + expected_error_msg = f"Expected to find one libtorchao_ops_aten.* library at {mock_paths[0]}, but found 2" + self.assertIn(expected_error_msg, str(e)) + + +if __name__ == "__main__": + unittest.main() From 554cb60c750e6ef31bbcafec74bb76a4578902da Mon Sep 17 00:00:00 2001 From: Peter Yeh Date: Wed, 14 May 2025 14:45:59 -0700 Subject: [PATCH 031/165] ROCm mxfp4 Skips (#2209) Add skip_if_rocm decorator to test_inference_subclass in test_mx_linear.py This change introduces the skip_if_rocm decorator to the test_inference_subclass function, ensuring that the test is skipped on ROCm platforms due to the requirement for gfx950. This enhances the robustness of the test suite by preventing unnecessary failures on unsupported hardware. --- test/prototype/mx_formats/test_mx_linear.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/test/prototype/mx_formats/test_mx_linear.py b/test/prototype/mx_formats/test_mx_linear.py index 25025ed048..bfb6742d14 100644 --- a/test/prototype/mx_formats/test_mx_linear.py +++ b/test/prototype/mx_formats/test_mx_linear.py @@ -28,6 +28,7 @@ from torchao.prototype.mx_formats.mx_subclass import MXFPInferenceConfig from torchao.quantization import quantize_ from torchao.quantization.utils import compute_error +from torchao.testing.utils import skip_if_rocm from torchao.utils import ( TORCH_VERSION_AT_LEAST_2_8, is_sm_at_least_89, @@ -400,6 +401,9 @@ def test_inference_print_str(): @pytest.mark.parametrize("bias", [True, False]) @pytest.mark.parametrize("compile", [True, False]) @torch.no_grad() +@skip_if_rocm( + "ROCm float4 gemm require gfx950" +) # TODO(future): deploy gfx950 in ROCM CI def test_inference_subclass(elem_dtype, bias: bool, compile: bool): """ Smoke test for inference compile From f04ff57c0b2faaadf9bfd5323e8e5e74f76573bc Mon Sep 17 00:00:00 2001 From: Scott Roy <161522778+metascroy@users.noreply.github.com> Date: Thu, 15 May 2025 14:26:09 -0700 Subject: [PATCH 032/165] Add CI for Arm Linux (#2211) * Update torchao_experimental_test.yml Add CI for arm linux * Update torchao_experimental_test.yml * Update torchao_experimental_test.yml --- .github/workflows/torchao_experimental_test.yml | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) diff --git a/.github/workflows/torchao_experimental_test.yml b/.github/workflows/torchao_experimental_test.yml index a712e7a624..4c56ec0c0e 100644 --- a/.github/workflows/torchao_experimental_test.yml +++ b/.github/workflows/torchao_experimental_test.yml @@ -14,7 +14,7 @@ jobs: test-cpu-ops: strategy: matrix: - runner: [macos-14] + runner: [macos-14, linux.arm64.2xlarge] runs-on: ${{matrix.runner}} defaults: run: @@ -30,7 +30,8 @@ jobs: python-version: "3.10" miniconda-version: "latest" activate-environment: venv - - name: Install requirements + - name: Install requirements mac + if: runner.os == 'macOS' run: | conda activate venv # Install executorch first because it installs its own version @@ -39,6 +40,13 @@ jobs: pip install torch==2.7.0 --index-url https://download.pytorch.org/whl/cpu --force-reinstall pip install -r dev-requirements.txt USE_CPP=1 TORCHAO_BUILD_KLEIDIAI=1 pip install . + - name: Install requirements linux + if: runner.os == 'Linux' + run: | + conda activate venv + pip install torch==2.7.0 --index-url https://download.pytorch.org/whl/cpu --force-reinstall + pip install -r dev-requirements.txt + BUILD_TORCHAO_EXPERIMENTAL=1 TORCHAO_BUILD_CPU_AARCH64=1 TORCHAO_BUILD_KLEIDIAI=1 TORCHAO_ENABLE_ARM_NEON_DOT=1 TORCHAO_PARALLEL_BACKEND=OPENMP pip install . - name: Run python tests run: | conda activate venv @@ -46,6 +54,7 @@ jobs: python torchao/experimental/tests/test_embedding_xbit_quantizer.py python torchao/experimental/tests/test_quant_passes.py - name: Run kernels/cpu/aarch64/tests + if: runner.os == 'macOS' run: | conda activate venv pushd torchao/experimental/kernels/cpu/aarch64/tests @@ -53,6 +62,7 @@ jobs: rm -rf /tmp/cmake-out popd - name: Run torchao/experimental/ops/tests + if: runner.os == 'macOS' run: | conda activate venv pushd torchao/experimental/ops/tests @@ -60,6 +70,7 @@ jobs: rm -rf /tmp/cmake-out popd - name: ET ops build + if: runner.os == 'macOS' run: | conda activate venv pushd torchao/experimental From 5549da8af975be6ff14330feb56c4abe3405b6f9 Mon Sep 17 00:00:00 2001 From: Jerry Zhang Date: Thu, 15 May 2025 16:41:04 -0700 Subject: [PATCH 033/165] Enable {conv3d, conv_transpose3d} + bn fusion in pt2e (#2212) * Enable {conv3d, conv_transpose3d} + bn fusion in pt2e Summary: att, previously only 1d and 2d fusion are supported, this PR adds 3d support Test Plan: python test/quantization/pt2e/test_quantize_pt2e.py -k test_conv3d_bn_relu python test/quantization/pt2e/test_quantize_pt2e.py -k test_conv_transpose3d_bn_relu Reviewers: Subscribers: Tasks: Tags: * comment * fix test --- test/quantization/pt2e/test_quantize_pt2e.py | 186 +++++++++++++++++++ torchao/quantization/pt2e/utils.py | 5 +- 2 files changed, 190 insertions(+), 1 deletion(-) diff --git a/test/quantization/pt2e/test_quantize_pt2e.py b/test/quantization/pt2e/test_quantize_pt2e.py index cddaf9b3ef..75701c55ca 100644 --- a/test/quantization/pt2e/test_quantize_pt2e.py +++ b/test/quantization/pt2e/test_quantize_pt2e.py @@ -2385,6 +2385,192 @@ def validate(self, model: torch.fx.GraphModule) -> None: node_list, ) + def test_conv3d_bn_relu(self): + class BackendAQuantizer(Quantizer): + def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule: + act_qspec = QuantizationSpec( + dtype=torch.uint8, + quant_min=0, + quant_max=255, + qscheme=torch.per_tensor_affine, + is_dynamic=False, + observer_or_fake_quant_ctr=observer.default_observer, + ) + weight_qspec = QuantizationSpec( + dtype=torch.int8, + quant_min=-128, + quant_max=127, + qscheme=torch.per_tensor_affine, + is_dynamic=False, + observer_or_fake_quant_ctr=observer.default_weight_observer, + ) + bias_qspec = QuantizationSpec( + dtype=torch.float32, + is_dynamic=False, + observer_or_fake_quant_ctr=observer.PlaceholderObserver, + ) + # conv_transpose + bn is fused automatically in PTQ (not configurable) + # so we just need to annotate conv + relu for conv + bn + relu + # pattern + for n in model.graph.nodes: + if ( + n.op != "call_function" + or n.target != torch.ops.aten.relu.default + ): + continue + relu_node = n + n = n.args[0] + if ( + n.op != "call_function" + and n.target != torch.ops.aten.conv3d.input + ): + continue + conv_t_node = n + input_act = conv_t_node.args[0] + weight = conv_t_node.args[1] + bias = conv_t_node.args[2] + conv_t_node.meta["quantization_annotation"] = ( + QuantizationAnnotation( + input_qspec_map={ + input_act: act_qspec, + weight: weight_qspec, + bias: bias_qspec, + }, + _annotated=True, + ) + ) + relu_node.meta["quantization_annotation"] = QuantizationAnnotation( + output_qspec=act_qspec, + _annotated=True, + ) + + def validate(self, model: torch.fx.GraphModule) -> None: + pass + + class M(torch.nn.Module): + def __init__(self): + super().__init__() + self.conv = torch.nn.Conv3d(2, 2, 3, padding=1) + self.bn = torch.nn.BatchNorm3d(2) + + def forward(self, x): + return torch.nn.functional.relu(self.bn(self.conv(x))) + + example_inputs = (torch.randn(1, 2, 2, 5, 5),) + node_occurrence = { + # two for input of the first conv, one for output for the first conv + torch.ops.quantized_decomposed.quantize_per_tensor.default: 2, + torch.ops.quantized_decomposed.dequantize_per_tensor.default: 3, + } + node_list = [ + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + torch.ops.aten.conv3d.default, + torch.ops.aten.relu.default, + torch.ops.quantized_decomposed.quantize_per_tensor.default, + ] + model = M().eval() + self._test_quantizer( + model, + example_inputs, + BackendAQuantizer(), + node_occurrence, + node_list, + ) + + def test_conv_transpose3d_bn_relu(self): + class BackendAQuantizer(Quantizer): + def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule: + act_qspec = QuantizationSpec( + dtype=torch.uint8, + quant_min=0, + quant_max=255, + qscheme=torch.per_tensor_affine, + is_dynamic=False, + observer_or_fake_quant_ctr=observer.default_observer, + ) + weight_qspec = QuantizationSpec( + dtype=torch.int8, + quant_min=-128, + quant_max=127, + qscheme=torch.per_tensor_affine, + is_dynamic=False, + observer_or_fake_quant_ctr=observer.default_weight_observer, + ) + bias_qspec = QuantizationSpec( + dtype=torch.float32, + is_dynamic=False, + observer_or_fake_quant_ctr=observer.PlaceholderObserver, + ) + # conv_transpose + bn is fused automatically in PTQ (not configurable) + # so we just need to annotate conv_transpose + relu for conv_transpose + bn + relu + # pattern + for n in model.graph.nodes: + if ( + n.op != "call_function" + or n.target != torch.ops.aten.relu.default + ): + continue + relu_node = n + n = n.args[0] + if ( + n.op != "call_function" + and n.target != torch.ops.aten.conv_transposed3d.input + ): + continue + conv_t_node = n + input_act = conv_t_node.args[0] + weight = conv_t_node.args[1] + bias = conv_t_node.args[2] + conv_t_node.meta["quantization_annotation"] = ( + QuantizationAnnotation( + input_qspec_map={ + input_act: act_qspec, + weight: weight_qspec, + bias: bias_qspec, + }, + _annotated=True, + ) + ) + relu_node.meta["quantization_annotation"] = QuantizationAnnotation( + output_qspec=act_qspec, + _annotated=True, + ) + + def validate(self, model: torch.fx.GraphModule) -> None: + pass + + class M(torch.nn.Module): + def __init__(self): + super().__init__() + self.conv_t = torch.nn.ConvTranspose3d(2, 2, 3, padding=1) + self.bn = torch.nn.BatchNorm3d(2) + + def forward(self, x): + return torch.nn.functional.relu(self.bn(self.conv_t(x))) + + example_inputs = (torch.randn(1, 2, 2, 5, 5),) + node_occurrence = { + # two for input of the first conv, one for output for the first conv + torch.ops.quantized_decomposed.quantize_per_tensor.default: 2, + torch.ops.quantized_decomposed.dequantize_per_tensor.default: 3, + } + node_list = [ + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + torch.ops.aten.conv_transpose3d.input, + torch.ops.aten.relu.default, + torch.ops.quantized_decomposed.quantize_per_tensor.default, + ] + model = M().eval() + self._test_quantizer( + model, + example_inputs, + BackendAQuantizer(), + node_occurrence, + node_list, + ) + def test_multi_users_without_output_observer(self): """ Test the case in which a node is used by multiple users, diff --git a/torchao/quantization/pt2e/utils.py b/torchao/quantization/pt2e/utils.py index 1cc5d4f4e9..ad5c0ae179 100644 --- a/torchao/quantization/pt2e/utils.py +++ b/torchao/quantization/pt2e/utils.py @@ -626,6 +626,7 @@ def _is_conv_node(n: Node): return n.op == "call_function" and n.target in [ torch.ops.aten.conv1d.default, torch.ops.aten.conv2d.default, + torch.ops.aten.conv3d.default, ] @@ -638,6 +639,8 @@ def _is_conv_transpose_node(n: Node): torch.ops.aten.conv_transpose1d.default, torch.ops.aten.conv_transpose2d, torch.ops.aten.conv_transpose2d.input, + torch.ops.aten.conv_transpose3d, + torch.ops.aten.conv_transpose3d.input, ] @@ -649,7 +652,7 @@ def _is_conv_or_conv_transpose_node(n: Node): def _is_conv_transpose_fn(conv_fn: Callable): - return conv_fn in [F.conv_transpose1d, F.conv_transpose2d] + return conv_fn in [F.conv_transpose1d, F.conv_transpose2d, F.conv_transpose3d] def _is_bn_node(n: Node): From 5e5db7176cb6a966b1f2a56eac86c2b83c8b189f Mon Sep 17 00:00:00 2001 From: Scott Roy <161522778+metascroy@users.noreply.github.com> Date: Mon, 19 May 2025 16:46:48 -0700 Subject: [PATCH 034/165] Make torchao pt2e prepare/convert functions compatible with quantizers in torch.ao (#2221) * lint * up * up * up * lint --- torchao/quantization/pt2e/quantize_pt2e.py | 64 ++++++++++++++++++++++ 1 file changed, 64 insertions(+) diff --git a/torchao/quantization/pt2e/quantize_pt2e.py b/torchao/quantization/pt2e/quantize_pt2e.py index d352d91f9b..94a675d809 100644 --- a/torchao/quantization/pt2e/quantize_pt2e.py +++ b/torchao/quantization/pt2e/quantize_pt2e.py @@ -95,6 +95,15 @@ def calibrate(model, data_loader): # run calibration # calibrate(m, sample_inference_data) """ + # We will temporarily make prepare_pt2e backward compatible with quantizers that configs, observers, + # and fake quantizers from torch.ao instead of torchao + if isinstance(quantizer, torch.ao.quantization.quantizer.quantizer.Quantizer): + from torch.ao.quantization.quantize_pt2e import ( + prepare_pt2e as torch_prepare_pt2e, + ) + + return torch_prepare_pt2e(model, quantizer) + torch._C._log_api_usage_once("quantization_api.quantize_pt2e.prepare_pt2e") original_graph_meta = model.meta node_name_to_scope = _get_node_name_to_scope(model) @@ -172,6 +181,15 @@ def train_loop(model, train_data): train_loop(prepared_model, train_loop) """ + # We will temporarily make prepare_qat_pt2e backward compatible with quantizers that configs, observers, + # and fake quantizers from torch.ao instead of torchao + if isinstance(quantizer, torch.ao.quantization.quantizer.quantizer.Quantizer): + from torch.ao.quantization.quantize_pt2e import ( + prepare_qat_pt2e as torch_prepare_qat_pt2e, + ) + + return torch_prepare_qat_pt2e(model, quantizer) + torch._C._log_api_usage_once("quantization_api.quantize_pt2e.prepare_qat_pt2e") original_graph_meta = model.meta node_name_to_scope = _get_node_name_to_scope(model) @@ -217,6 +235,43 @@ def _quant_node_constraint(n: Node) -> bool: return n.op == "call_function" and n.target in _QUANT_OPS +def _is_torchao_prepared_do_not_use_outside_this_file(model): + from torchao.quantization.pt2e.fake_quantize import ( + FakeQuantize as torchao_FakeQuantize, + ) + from torchao.quantization.pt2e.observer import ( + AffineQuantizedObserverBase as torchao_AffineQuantizedObserverBase, + ) + from torchao.quantization.pt2e.observer import ObserverBase as torchao_ObserverBase + + is_torch_ao_prepared = False + is_torchao_prepared = False + for _, m in model.named_modules(): + if ( + isinstance(m, torch.ao.quantization.fake_quantize.FakeQuantize) + or isinstance(m, torch.ao.quantization.observer.ObserverBase) + or isinstance(m, torch.ao.quantization.observer.AffineQuantizedObserverBase) + ): + is_torch_ao_prepared = True + if ( + isinstance(m, torchao_FakeQuantize) + or isinstance(m, torchao_ObserverBase) + or isinstance(m, torchao_AffineQuantizedObserverBase) + ): + is_torchao_prepared = True + + if is_torch_ao_prepared: + assert not is_torchao_prepared, ( + "Cannot be prepared using both torch.ao and torchao" + ) + if is_torchao_prepared: + assert not is_torch_ao_prepared, ( + "Cannot be prepared using both torch.ao and torchao" + ) + + return is_torchao_prepared + + def convert_pt2e( model: GraphModule, use_reference_representation: bool = False, @@ -243,6 +298,15 @@ def convert_pt2e( quantized_model = convert_pt2e(prepared_model) """ + # We will temporarily make convert_pt2e backward compatible with quantizers that configs, observers, + # and fake quantizers from torch.ao instead of torchao + if not _is_torchao_prepared_do_not_use_outside_this_file(model): + from torch.ao.quantization.quantize_pt2e import ( + convert_pt2e as torch_convert_pt2e, + ) + + return torch_convert_pt2e(model, use_reference_representation, fold_quantize) + torch._C._log_api_usage_once("quantization_api.quantize_pt2e.convert_pt2e") if not isinstance(use_reference_representation, bool): raise ValueError( From 96aec6a3e713687c1728a20a08d5c54db0344377 Mon Sep 17 00:00:00 2001 From: Jerry Zhang Date: Mon, 19 May 2025 18:25:35 -0700 Subject: [PATCH 035/165] Update config.py (#2224) Needed for layout: ValueError: Failed to find class QDQLayout in any of the allowed modules: torchao.prototype.quantization, torchao.sparsity.sparse_api, torchao.prototype.mx_formats, torchao.quantization --- torchao/core/config.py | 1 + 1 file changed, 1 insertion(+) diff --git a/torchao/core/config.py b/torchao/core/config.py index 519dfe8dfd..d2d49981c9 100644 --- a/torchao/core/config.py +++ b/torchao/core/config.py @@ -176,6 +176,7 @@ def config_to_dict(config: AOBaseConfig) -> Dict[str, Any]: "torchao.sparsity.sparse_api", "torchao.prototype.quantization", "torchao.prototype.mx_formats", + "torchao.dtypes", } From 1bbeed1a748044de3e45ce1e9486fc675cdfc330 Mon Sep 17 00:00:00 2001 From: Xuan Liao Date: Wed, 21 May 2025 09:11:08 +0800 Subject: [PATCH 036/165] Re-land the PR of "Add INT8 SDPA path for CPU" (#2215) * enable int8 sdpa cpu --- setup.py | 25 + .../inductor/test_int8_sdpa_fusion.py | 223 ++ test/test_ops.py | 141 +- torchao/csrc/cpu/int8_sdpa.cpp | 1910 +++++++++++++++++ torchao/ops.py | 89 + torchao/prototype/inductor/__init__.py | 0 .../prototype/inductor/fx_passes/README.md | 35 + .../prototype/inductor/fx_passes/__init__.py | 5 + .../inductor/fx_passes/int8_sdpa_fusion.py | 392 ++++ 9 files changed, 2819 insertions(+), 1 deletion(-) create mode 100644 test/prototype/inductor/test_int8_sdpa_fusion.py create mode 100644 torchao/csrc/cpu/int8_sdpa.cpp create mode 100644 torchao/prototype/inductor/__init__.py create mode 100644 torchao/prototype/inductor/fx_passes/README.md create mode 100644 torchao/prototype/inductor/fx_passes/__init__.py create mode 100644 torchao/prototype/inductor/fx_passes/int8_sdpa_fusion.py diff --git a/setup.py b/setup.py index f59917162f..cabaad01cf 100644 --- a/setup.py +++ b/setup.py @@ -55,6 +55,10 @@ def read_version(file_path="version.txt"): and platform.system() == "Darwin" ) +use_cpp_kernels = os.getenv("USE_CPP_KERNELS", "0") == "1" + +from torchao.utils import TORCH_VERSION_AT_LEAST_2_7 + version_prefix = read_version() # Version is version.dev year month date if using nightlies and version if not version = ( @@ -307,6 +311,21 @@ def get_extensions(): ["-O3" if not debug_mode else "-O0", "-fdiagnostics-color=always"] ) + if ( + use_cpp_kernels + and platform.system() == "Linux" + and TORCH_VERSION_AT_LEAST_2_7 + ): + if torch._C._cpu._is_avx512_supported(): + extra_compile_args["cxx"].extend( + [ + "-DCPU_CAPABILITY_AVX512", + "-march=native", + "-mfma", + "-fopenmp", + ] + ) + if debug_mode: extra_compile_args["cxx"].append("-g") if "nvcc" in extra_compile_args: @@ -328,6 +347,12 @@ def get_extensions(): # Collect C++ source files sources = list(glob.glob(os.path.join(extensions_dir, "**/*.cpp"), recursive=True)) + if not use_cpp_kernels or platform.system() != "Linux": + # Remove csrc/cpu/*.cpp + excluded_sources = list( + glob.glob(os.path.join(extensions_dir, "cpu/*.cpp"), recursive=True) + ) + sources = [s for s in sources if s not in excluded_sources] extensions_cuda_dir = os.path.join(extensions_dir, "cuda") cuda_sources = list( diff --git a/test/prototype/inductor/test_int8_sdpa_fusion.py b/test/prototype/inductor/test_int8_sdpa_fusion.py new file mode 100644 index 0000000000..c3456fb421 --- /dev/null +++ b/test/prototype/inductor/test_int8_sdpa_fusion.py @@ -0,0 +1,223 @@ +import itertools +import unittest + +import torch +import torch.utils.checkpoint +from torch._dynamo.utils import counters +from torch._inductor import config +from torch._inductor.test_case import TestCase, run_tests +from torch._inductor.utils import run_and_get_code +from torch.testing._internal.common_utils import IS_LINUX, skipIfRocm +from torch.testing._internal.inductor_utils import HAS_CPU + +import torchao +from torchao.prototype.inductor.fx_passes.int8_sdpa_fusion import ( + _int8_sdpa_init, + custom_pass, +) +from torchao.utils import TORCH_VERSION_AT_LEAST_2_7 + + +class SelfAttnLikeModule(torch.nn.Module): + def __init__( + self, + input_dim, + has_mask, + num_attention_heads=None, + attention_head_size=None, + ) -> None: + super().__init__() + self.input_dim = input_dim + self.q_proj = torch.nn.Linear(input_dim, input_dim, bias=False) + self.k_proj = torch.nn.Linear(input_dim, input_dim, bias=False) + self.v_proj = torch.nn.Linear(input_dim, input_dim, bias=False) + self.softmax = torch.nn.Softmax(dim=-1) + assert num_attention_heads is not None + assert attention_head_size is not None + self.num_attention_heads = num_attention_heads + self.attention_head_size = attention_head_size + self.all_head_size = self.num_attention_heads * self.attention_head_size + self.dense = torch.nn.Linear(self.all_head_size, self.all_head_size) + self.dropout = torch.nn.Dropout(0) + self.has_mask = has_mask + + def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor: + new_x_shape = x.size()[:-1] + ( + self.num_attention_heads, + self.attention_head_size, + ) + x = x.view(new_x_shape) + return x.permute([0, 2, 1, 3]) + + def forward(self, x, mask): + q = self.q_proj(x) + k = self.k_proj(x) + v = self.v_proj(x) + q = self.transpose_for_scores(q) + k = self.transpose_for_scores(k) + v = self.transpose_for_scores(v) + scores = torch.matmul(q, k.transpose(-1, -2)) / (self.input_dim**0.5) + if self.has_mask and mask.dtype != scores.dtype: + scores = scores + mask + attention = self.softmax(scores) + attention = self.dropout(attention) + context_layer = torch.matmul(attention, v) + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + context_layer = context_layer.view( + context_layer.size()[:-2] + (self.all_head_size,) + ) + return self.dense(context_layer) + + +class TestSDPAPatternRewriterTemplate(TestCase): + def _clone_inputs(self, inputs): + def clone(x): + if not isinstance(x, torch.Tensor): + return x + return x.clone() + + return [clone(x) for x in inputs] + + def _check_common( + self, + dot_prod_attention, + args1=None, + contains=True, + atol=1e-5, + has_fuse_pattern=True, + has_dropout=False, + check_train=True, + override_check_equal=False, + dtype=torch.float, + rtol=1.3e-6, + ): + if args1 is None: + tensor_shape = (4, 2, 16, 32) + args1 = [ + torch.randn(tensor_shape, device=self.device, dtype=dtype), + torch.randn(tensor_shape, device=self.device, dtype=dtype), + torch.randn(tensor_shape, device=self.device, dtype=dtype), + ] + else: + args1 = list(args1) + args2 = self._clone_inputs(args1) + + for training in [False, True] if check_train else [False]: + for x in itertools.chain(args1[:], args2[:]): + if isinstance(x, torch.Tensor) and x.is_floating_point(): + x.requires_grad = training + + dropout_arg = [training] if has_dropout else [] + torch.manual_seed(1234) + result1 = dot_prod_attention(*(args1 + dropout_arg)) + + counters.clear() + torch.manual_seed(1234) + compiled_model = torch.compile(dot_prod_attention, fullgraph=True) + result2, source_code = run_and_get_code( + compiled_model, + *(args2 + dropout_arg), + ) + source_code = "\n".join(source_code) + if has_fuse_pattern: + self.assertGreaterEqual(counters["inductor"]["int8_fuse_attention"], 1) + if contains: + # many of the patterns get re-expanded in dispatcher + self.assertIn( + "torchao.qscaled_dot_product", + source_code, + ) + + # some tests configured with very low dropout where we still want to check equality + if not has_dropout or override_check_equal: + self.assertEqual(result1, result2, atol=atol, rtol=1.3e-6) + + if training: + result1.sum().backward() + result2.sum().backward() + for arg1, arg2 in zip(args1, args2): + if ( + isinstance(arg1, torch.Tensor) + and arg1.is_floating_point() + and (not has_dropout or override_check_equal) + ): + self.assertEqual(arg1.grad, arg2.grad, atol=atol, rtol=rtol) + + @skipIfRocm + @unittest.skipIf( + not TORCH_VERSION_AT_LEAST_2_7, reason="int8 sdpa requires torch 2.7 or later" + ) + @unittest.skipIf( + "CPU" not in torch._C._dispatch_dump("torchao::qscaled_dot_product"), + reason="cpp kernels not built", + ) + @config.patch({"freezing": True}) + def _test_sdpa_int8_rewriter(self): + from torch.export import export_for_training + + import torchao.quantization.pt2e.quantizer.x86_inductor_quantizer as xiq + from torchao.quantization.pt2e.quantize_pt2e import convert_pt2e, prepare_pt2e + from torchao.quantization.pt2e.quantizer.x86_inductor_quantizer import ( + X86InductorQuantizer, + ) + + # pattern is different for bs=1 + torch.manual_seed(1234) + for dtype, has_mask, bs in itertools.product( + [torch.float32, torch.bfloat16], [True, False], [56, 1] + ): + seqlen, numhead, headsize = 197, 16, 64 + mod = SelfAttnLikeModule( + input_dim=headsize * numhead, + has_mask=has_mask, + num_attention_heads=numhead, + attention_head_size=headsize, + ).eval() + inputs = ( + torch.randn( + (bs, seqlen, headsize * numhead), device=self.device, dtype=dtype + ), + torch.randn((bs, 1, 1, seqlen), device=self.device) + if has_mask + else None, + ) + enable_autocast = dtype == torch.bfloat16 + with ( + torch.no_grad(), + torch.amp.autocast( + self.device, enabled=enable_autocast, dtype=torch.bfloat16 + ), + config.patch(post_grad_custom_pre_pass=custom_pass), + ): + _int8_sdpa_init() + quantizer = X86InductorQuantizer() + quantizer.set_global(xiq.get_default_x86_inductor_quantization_config()) + quantizer.set_function_type_qconfig( + torch.matmul, quantizer.get_global_quantization_config() + ) + export_model = export_for_training( + mod, + inputs, + strict=True, + ).module() + prepare_model = prepare_pt2e(export_model, quantizer) + prepare_model(*inputs) + convert_model = convert_pt2e(prepare_model) + torchao.quantization.pt2e.move_exported_model_to_eval(convert_model) + self._check_common( + convert_model, args1=inputs, check_train=False, atol=1.0 + ) + + +if HAS_CPU: + + class SDPAPatternRewriterCpuTests(TestSDPAPatternRewriterTemplate): + device = "cpu" + test_sdpa_int8_rewriter_cpu = ( + TestSDPAPatternRewriterTemplate._test_sdpa_int8_rewriter + ) + + +if __name__ == "__main__": + if IS_LINUX: + run_tests() diff --git a/test/test_ops.py b/test/test_ops.py index 1cdce2cd81..132c4f0c18 100644 --- a/test/test_ops.py +++ b/test/test_ops.py @@ -4,11 +4,13 @@ # This source code is licensed under the BSD 3-Clause license found in the # LICENSE file in the root directory of this source tree. import itertools +import math import sys import pytest import torch from torch.testing._internal.common_utils import ( + IS_LINUX, TestCase, instantiate_parametrized_tests, parametrize, @@ -23,7 +25,11 @@ ) from torchao.quantization.quant_primitives import choose_qparams_and_quantize_affine_qqq from torchao.sparsity.marlin import inject_24, marlin_24_workspace, pack_to_marlin_24 -from torchao.utils import TORCH_VERSION_AT_LEAST_2_5, compute_max_diff +from torchao.utils import ( + TORCH_VERSION_AT_LEAST_2_5, + TORCH_VERSION_AT_LEAST_2_7, + compute_max_diff, +) if torch.version.hip is not None: pytest.skip("Skipping the test in ROCm", allow_module_level=True) @@ -109,6 +115,139 @@ def test_quant_llm_linear_correctness( rtol = 1e-2 if dtype == torch.bfloat16 else 1e-3 assert relative_error < rtol + def _scaled_dot_product_int8_op_ref( + self, + q, + k, + v, + attn_mask=None, + dropout_p=0, + is_causal=False, + q_scale=1.0, + q_zp=0, + k_scale=1.0, + k_zp=0, + v_scale=1.0, + v_zp=0, + a_scale=1.0, + a_zp=0, + o_scale=1.0, + o_zp=0, + ): + q = (q.to(torch.float) - q_zp) * q_scale + k = (k.to(torch.float) - k_zp) * k_scale + v = (v.to(torch.float) - v_zp) * v_scale + scale_factor = 1 / math.sqrt(q.size(-1)) + attn = q @ k.transpose(-2, -1) + attn = attn * scale_factor + if attn_mask is not None: + attn = attn + attn_mask.to(torch.float) + attn_max = attn.max(dim=-1, keepdim=True).values + attn = attn - attn_max + attn = torch.exp(attn) + attn_sum = torch.sum(attn, dim=-1, keepdim=True) + attn = attn / attn_sum + attn = torch.clamp(torch.round(attn / a_scale) + a_zp, min=0, max=255) + attn = (attn - a_zp) * a_scale + out = attn @ v + out = torch.clamp(torch.round(out / o_scale) + o_zp, min=0, max=255) + return out.to(torch.uint8) + + @pytest.mark.skipif( + not TORCH_VERSION_AT_LEAST_2_7, reason="int8 sdpa requires torch 2.7 or later" + ) + @pytest.mark.skipif(not IS_LINUX, reason="only support on linux") + @pytest.mark.skipif( + "CPU" not in torch._C._dispatch_dump("torchao::qscaled_dot_product"), + reason="cpp kernels not built", + ) + @parametrize("batch_size", [56, 120]) + @parametrize("n_head", [2, 16]) + @parametrize("q_seq_len", [18, 89]) + @parametrize("kv_seq_len", [100, 253]) + @parametrize("head_dim", [32, 64]) + @parametrize("mask_dtype", [None, torch.float32, torch.bfloat16]) + def test_scaled_dot_product_int8_op( + self, batch_size, n_head, q_seq_len, kv_seq_len, head_dim, mask_dtype + ): + torch.manual_seed(1234) + device = "cpu" + q_scale = float(1.7907238006591797) + q_zp = int(127) + k_scale = float(1.8039721250534058) + k_zp = int(125) + v_scale = float(1.839004635810852) + v_zp = int(127) + a_scale = float(0.003919653594493866) + a_zp = int(120) + o_scale = float(1.8191684484481812) + o_zp = int(128) + q_shape = [batch_size, q_seq_len, n_head, head_dim] + kv_shape = [batch_size, kv_seq_len, n_head, head_dim] + mask_shape = [batch_size, 1, 1, kv_seq_len] + q = torch.randn(q_shape, dtype=torch.float, device=device).transpose(1, 2) * 100 + k = ( + torch.randn(kv_shape, dtype=torch.float, device=device).transpose(1, 2) + * 100 + ) + v = ( + torch.randn(kv_shape, dtype=torch.float, device=device).transpose(1, 2) + * 100 + ) + q = q.to(torch.uint8) + k = k.to(torch.uint8) + v = v.to(torch.uint8) + attn_mask = ( + torch.randn(mask_shape, dtype=mask_dtype, device=device) + if mask_dtype is not None + else None + ) + q2, k2, v2, attn_mask_2 = ( + q.clone(), + k.clone(), + v.clone(), + attn_mask.clone() if mask_dtype is not None else None, + ) + + math_ref = self._scaled_dot_product_int8_op_ref( + q2, + k2, + v2, + attn_mask=attn_mask, + dropout_p=0.0, + is_causal=False, + q_scale=q_scale, + q_zp=q_zp, + k_scale=k_scale, + k_zp=k_zp, + v_scale=v_scale, + v_zp=v_zp, + a_scale=a_scale, + a_zp=a_zp, + o_scale=o_scale, + o_zp=o_zp, + ) + actual = torch.ops.torchao.qscaled_dot_product( + q, + k, + v, + attn_mask=attn_mask_2, + dropout_p=0.0, + is_causal=False, + q_scale=q_scale, + q_zp=q_zp, + k_scale=k_scale, + k_zp=k_zp, + v_scale=v_scale, + v_zp=v_zp, + a_scale=a_scale, + a_zp=a_zp, + o_scale=o_scale, + o_zp=o_zp, + ) + + self.assertEqual(actual, math_ref, atol=1.0, rtol=5e-6) + instantiate_parametrized_tests(TestOps) diff --git a/torchao/csrc/cpu/int8_sdpa.cpp b/torchao/csrc/cpu/int8_sdpa.cpp new file mode 100644 index 0000000000..a5928f6d9a --- /dev/null +++ b/torchao/csrc/cpu/int8_sdpa.cpp @@ -0,0 +1,1910 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#ifndef AT_PER_OPERATOR_HEADERS +#include +#else +#include +#endif + +#include +#include +#include +#include + +namespace torchao { + +namespace { + +inline c10::SymFloat calculate_scale( + const at::Tensor& query, + std::optional scale) { + const auto softmax_scale = scale.has_value() + ? scale.value() + : (c10::SymFloat(1.0) / (c10::SymFloat(query.sym_size(-1)).sqrt())); + return c10::SymFloat(softmax_scale); +} + +#ifdef CPU_CAPABILITY_AVX512 + +template +inline void fill_stub(scalar_t* data, scalar_t val, int64_t size) { + const int32_t vec_size = at::vec::Vectorized::size(); + auto data_vec = at::vec::Vectorized(val); + int64_t d = 0; + for (; d < size - (size % vec_size); d += vec_size) { + data_vec.store(data + d); + } + if (d < size) { + data_vec.store(data + d, size - d); + } +} + +void reshape_attn_mask_to_4d( + at::Tensor& attn_mask, + int64_t batchSize, + int64_t num_head, + int64_t qSize, + int64_t kvSize) { + // Support mask shapes: + // 2d: ({Q_seq_len, 1} x {KV_seq_len, 1}) + // 4d: ({Batch, 1} x {Num_heads, 1} x {Q_seq_len, 1} x {KV_seq_len, 1}) + // Guaranteed in check_attn_mask_shape + int64_t attn_mask_size_0 = 1; + int64_t attn_mask_size_1 = 1; + if (attn_mask.dim() == 4) { + if (attn_mask.size(0) == batchSize) { + attn_mask_size_0 = batchSize; + } + if (attn_mask.size(1) == num_head) { + attn_mask_size_1 = num_head; + } + } + attn_mask = attn_mask + .view({attn_mask_size_0, attn_mask_size_1, attn_mask.size(-2), attn_mask.size(-1)}) + .expand({attn_mask_size_0, attn_mask_size_1, qSize, kvSize}); +} + +// TODO: Use at::native::_store instead when it supports Half. +template +inline void _store(scalar_t* dst, at::vec::Vectorized src, int size=at::vec::Vectorized::size()) { + src.store(dst, size); +} + +template +inline typename std::enable_if_t || std::is_same_v, void> +_store(scalar_t* dst, at::vec::Vectorized src, int size=at::vec::Vectorized::size()) { + auto res = at::vec::convert(src); + res.store(dst, size); +} + +/* +1. dequant +2. add mask +3. max reduce for softmax +*/ +template +inline void _dequant_mask_max_fusion_kernel( + const int32_t* in, + const mask_t* mask_ptr, + const int32_t* sum_a_ptr, + const int32_t* sum_b_ptr, + const int& M, + const int& N, + const int& ldi, + const int& ldm, // leading dimension mask + const int& ldo, + const int32_t& beta, // zp_a*zp_b*k + const float& alpha, // scale_a*scale_b*scale_sdpa + float* out, + float* sfm_max_ptr) { + const int32_t vec_size = at::vec::Vectorized::size(); + auto vec_beta = at::vec::Vectorized(beta); + auto vec_alpha = at::vec::Vectorized(alpha); + for (long row = 0; row < M; row += 1) { + auto sum_a = sum_a_ptr[row]; + auto vec_sum_a = at::vec::Vectorized(sum_a); + const int32_t* tmp_in = in + row * ldi; + float* tmp_out = out + row * ldo; + const mask_t* mask_data_ptr = mask_ptr + row * ldm; + float tmp_max = -std::numeric_limits::infinity(); + auto vec_tmp_max = at::vec::Vectorized(tmp_max); + long col = 0; + for (; col < vec_size * (N / vec_size); col += vec_size) { + auto vec_sum_b = at::vec::Vectorized::loadu(sum_b_ptr + col); + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + col); + auto tmp1 = tmp0 - vec_sum_b; + auto tmp2 = tmp1 - vec_sum_a; + auto tmp3 = tmp2 + vec_beta; + auto tmp4 = at::vec::convert(tmp3); + auto tmp5 = tmp4 * vec_alpha; + auto tmp6 = at::vec::Vectorized::loadu(mask_data_ptr + col); + auto tmp7 = at::vec::convert(tmp6); + auto tmp8 = tmp5 + tmp7; + vec_tmp_max = at::vec::clamp_min(vec_tmp_max, tmp8); + _store(tmp_out + col, tmp8); + } + if (col < N) { + auto vec_sum_b = at::vec::Vectorized::loadu(sum_b_ptr + col, N - col); + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + col, N - col); + auto tmp1 = tmp0 - vec_sum_b; + auto tmp2 = tmp1 - vec_sum_a; + auto tmp3 = tmp2 + vec_beta; + auto tmp4 = at::vec::convert(tmp3); + auto tmp5 = tmp4 * vec_alpha; + auto tmp6 = at::vec::Vectorized::loadu(mask_data_ptr + col, N - col); + auto tmp7 = at::vec::convert(tmp6); + auto tmp8 = tmp5 + tmp7; + _store(tmp_out + col, tmp8, N - col); + vec_tmp_max = at::vec::Vectorized::set(vec_tmp_max, at::vec::clamp_min(vec_tmp_max, tmp8), N - col); + } + sfm_max_ptr[row] = std::max(sfm_max_ptr[row], vec_tmp_max.reduce_max()); + } +} + +/* +1. dequant +2. max reduce for softmax +*/ +inline void _dequant_max_fusion_kernel( + const int32_t* in, + const int32_t* sum_a_ptr, + const int32_t* sum_b_ptr, + const int& M, + const int& N, + const int& ldi, + const int& ldo, + const int32_t& beta, // zp_a*zp_b*k + const float& alpha, // scale_a*scale_b*scale_sdpa + float* out, + float* sfm_max_ptr) { + const int32_t vec_size = at::vec::Vectorized::size(); + auto vec_beta = at::vec::Vectorized(beta); + auto vec_alpha = at::vec::Vectorized(alpha); + for (long row = 0; row < M; row += 1) { + auto sum_a = sum_a_ptr[row]; + auto vec_sum_a = at::vec::Vectorized(sum_a); + const int32_t* tmp_in = in + row * ldi; + float* tmp_out = out + row * ldo; + float tmp_max = -std::numeric_limits::infinity(); + auto vec_tmp_max = at::vec::Vectorized(tmp_max); + long col = 0; + for (; col < vec_size * (N / vec_size); col += vec_size) { + auto vec_sum_b = at::vec::Vectorized::loadu(sum_b_ptr + col); + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + col); + auto tmp1 = tmp0 - vec_sum_b; + auto tmp2 = tmp1 - vec_sum_a; + auto tmp3 = tmp2 + vec_beta; + auto tmp4 = at::vec::convert(tmp3); + auto tmp5 = tmp4 * vec_alpha; + vec_tmp_max = at::vec::clamp_min(vec_tmp_max, tmp5); + _store(tmp_out + col, tmp5); + } + if (col < N) { + auto vec_sum_b = at::vec::Vectorized::loadu(sum_b_ptr + col, N - col); + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + col, N - col); + auto tmp1 = tmp0 - vec_sum_b; + auto tmp2 = tmp1 - vec_sum_a; + auto tmp3 = tmp2 + vec_beta; + auto tmp4 = at::vec::convert(tmp3); + auto tmp5 = tmp4 * vec_alpha; + _store(tmp_out + col, tmp5, N - col); + vec_tmp_max = at::vec::Vectorized::set(vec_tmp_max, at::vec::clamp_min(vec_tmp_max, tmp5), N - col); + } + sfm_max_ptr[row] = std::max(sfm_max_ptr[row], vec_tmp_max.reduce_max()); + } +} + +/* +1. Softmax: sub max, exp, sum reduce, div sum +2. quant +3. sum for attention +*/ +template +inline void _sub_exp_sum_div_quant_sum_fusion_kernel( + const float* in, + const int64_t& M, + const int64_t& N_step, + const int64_t& NSlice, + const int& ldi, + const int& ldo, + const int& kvSize, + const int& rndkvSplitSize, + const int& av_gemm_K, + const int32_t& beta1, // zp_a + const int32_t& beta2, // zp_b + const float& alpha, // scale_a + float* local, + scalar_t* out, + float* sfm_max_ptr, + float* sfm_sum_ptr, + int32_t* sum_a_ptr) { + const int32_t vec_size = at::vec::Vectorized::size(); + float min_val = 0; + float max_val = 255; + auto vec_min_val = at::vec::Vectorized(min_val); + auto vec_max_val = at::vec::Vectorized(max_val); + scalar_t zero = 0; + auto vec_zero = at::vec::Vectorized(zero); + float beta1_float = (float) beta1; + auto vec_beta1 = at::vec::Vectorized(beta1_float); + for (int64_t row = 0; row < M; ++row) { + auto sfm_max = sfm_max_ptr[row]; + auto vec_max = at::vec::Vectorized(sfm_max); + // sub max, exp, sum reduce + const float* qk_block_data = in + row * rndkvSplitSize; + for (int64_t l = 0; l < NSlice; l ++) { + int64_t n = l * N_step; + int64_t kvBlockSize = std::min(N_step, kvSize - n); + const float* tmp_in = qk_block_data + l * ldi; + float tmp_sum = 0; + auto vec_tmp_sum = at::vec::Vectorized(tmp_sum); + float* tmp_out = local + n; + long col = 0; + for (; col < vec_size * (kvBlockSize / vec_size); col += vec_size) { + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + col); + auto tmp1 = tmp0 - vec_max; + auto tmp2 = tmp1.exp_u20(); + vec_tmp_sum += tmp2; + _store(tmp_out + col, tmp2); + } + if (col < kvBlockSize) { + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + col, kvBlockSize - col); + auto tmp1 = tmp0 - vec_max; + auto tmp2 = tmp1.exp_u20(); + _store(tmp_out + col, tmp2, kvBlockSize - col); + vec_tmp_sum = at::vec::Vectorized::set(vec_tmp_sum, vec_tmp_sum + tmp2, kvBlockSize - col); + } + sfm_sum_ptr[row] += vec_tmp_sum.reduce_add(); + } + // div sum, sum for attention + auto sum_scale = 1 / sfm_sum_ptr[row] / alpha; + auto vec_sum_scale = at::vec::Vectorized(sum_scale); + scalar_t* qk_reduced_block_data = out + row * av_gemm_K; + for (int64_t l = 0; l < NSlice; l ++) { + int64_t n = l * N_step; + int64_t kvBlockSize = std::min(N_step, kvSize - n); + int32_t tmp_sum = 0; + auto vec_tmp_sum = at::vec::Vectorized(tmp_sum); + float* tmp_in = local + n; + scalar_t* tmp_out = qk_reduced_block_data + l * ldo; + long col = 0; + for (; col < vec_size * (kvBlockSize / vec_size); col += vec_size) { + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + col); + auto tmp1 = tmp0 * vec_sum_scale; + auto tmp2 = tmp1.round(); + auto tmp3 = tmp2 + vec_beta1; + auto tmp4 = at::vec::clamp(tmp3, vec_min_val, vec_max_val); + _store(tmp_out + col, tmp4); + auto tmp6 = at::vec::convert(tmp4); + vec_tmp_sum += tmp6; + } + if (col < kvBlockSize) { + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + col, kvBlockSize - col); + auto tmp1 = tmp0 * vec_sum_scale; + auto tmp2 = tmp1.round(); + auto tmp3 = tmp2 + vec_beta1; + auto tmp4 = at::vec::clamp(tmp3, vec_min_val, vec_max_val); + _store(tmp_out + col, tmp4, kvBlockSize - col); + auto tmp6 = at::vec::convert(tmp4); + vec_tmp_sum = at::vec::Vectorized::set(vec_tmp_sum, vec_tmp_sum + tmp6, kvBlockSize - col); + } + sum_a_ptr[row] += vec_tmp_sum.reduce_add() * beta2; + // set zero + col = kvBlockSize; + for (; col < vec_size * (av_gemm_K / vec_size); col += vec_size) { + _store(tmp_out + col, vec_zero); + } + if (col < av_gemm_K) { + _store(tmp_out + col, vec_zero, av_gemm_K - col); + } + } + } +} + +/* +1. Softmax: sub max, exp, sum reduce, div sum +2. quant +*/ +template +inline void _sub_exp_sum_div_quant_fusion_kernel( + const float* in, + const int64_t& M, + const int64_t& N_step, + const int64_t& NSlice, + const int& ldi, + const int& ldo, + const int& kvSize, + const int& rndkvSplitSize, + const int& av_gemm_K, + const int32_t& beta1, // zp_a + const float& alpha, // scale_a + float* local, + scalar_t* out, + float* sfm_max_ptr, + float* sfm_sum_ptr) { + const int32_t vec_size = at::vec::Vectorized::size(); + float min_val = 0; + float max_val = 255; + auto vec_min_val = at::vec::Vectorized(min_val); + auto vec_max_val = at::vec::Vectorized(max_val); + scalar_t zero = 0; + auto vec_zero = at::vec::Vectorized(zero); + float beta1_float = (float) beta1; + auto vec_beta1 = at::vec::Vectorized(beta1_float); + for (int64_t row = 0; row < M; ++row) { + auto sfm_max = sfm_max_ptr[row]; + auto vec_max = at::vec::Vectorized(sfm_max); + // sub max, exp, sum reduce + const float* qk_block_data = in + row * rndkvSplitSize; + for (int64_t l = 0; l < NSlice; l ++) { + int64_t n = l * N_step; + int64_t kvBlockSize = std::min(N_step, kvSize - n); + const float* tmp_in = qk_block_data + l * ldi; + float tmp_sum = 0; + auto vec_tmp_sum = at::vec::Vectorized(tmp_sum); + float* tmp_out = local + n; + long col = 0; + for (; col < vec_size * (kvBlockSize / vec_size); col += vec_size) { + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + col); + auto tmp1 = tmp0 - vec_max; + auto tmp2 = tmp1.exp_u20(); + vec_tmp_sum += tmp2; + _store(tmp_out + col, tmp2); + } + if (col < kvBlockSize) { + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + col, kvBlockSize - col); + auto tmp1 = tmp0 - vec_max; + auto tmp2 = tmp1.exp_u20(); + vec_tmp_sum = at::vec::Vectorized::set(vec_tmp_sum, vec_tmp_sum + tmp2, kvBlockSize - col); + _store(tmp_out + col, tmp2, kvBlockSize - col); + } + sfm_sum_ptr[row] += vec_tmp_sum.reduce_add(); + } + // div sum, sum for attention + auto sum_scale = 1 / sfm_sum_ptr[row] / alpha; + auto vec_sum_scale = at::vec::Vectorized(sum_scale); + scalar_t* qk_reduced_block_data = out + row * av_gemm_K; + for (int64_t l = 0; l < NSlice; l ++) { + int64_t n = l * N_step; + int64_t kvBlockSize = std::min(N_step, kvSize - n); + float* tmp_in = local + n; + scalar_t* tmp_out = qk_reduced_block_data + l * ldo; + long col = 0; + for (; col < vec_size * (kvBlockSize / vec_size); col += vec_size) { + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + col); + auto tmp1 = tmp0 * vec_sum_scale; + auto tmp2 = tmp1.round(); + auto tmp3 = tmp2 + vec_beta1; + auto tmp4 = at::vec::clamp(tmp3, vec_min_val, vec_max_val); + _store(tmp_out + col, tmp4); + } + if (col < kvBlockSize) { + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + col, kvBlockSize - col); + auto tmp1 = tmp0 * vec_sum_scale; + auto tmp2 = tmp1.round(); + auto tmp3 = tmp2 + vec_beta1; + auto tmp4 = at::vec::clamp(tmp3, vec_min_val, vec_max_val); + _store(tmp_out + col, tmp4, kvBlockSize - col); + } + // set zero + col = kvBlockSize; + for (; col < vec_size * (av_gemm_K / vec_size); col += vec_size) { + _store(tmp_out + col, vec_zero); + } + if (col < av_gemm_K) { + _store(tmp_out + col, vec_zero, av_gemm_K - col); + } + } + } +} + +/* +1. dequant +2. quant +*/ +template +inline void _dequant_quant_fusion_kernel( + const int32_t* in, + const int32_t* sum_a_ptr, + const int32_t* sum_b_ptr, + const int& M, + const int& N, + const int& ldi, + const int& ldo, + const int32_t& beta1, // zp_a*zp_b*k + const int32_t& beta2, // zp_c + const float& alpha, // scale_a*scale_b/scale_c + scalar_t* out) { + const int32_t vec_size = at::vec::Vectorized::size(); + float min_val = 0; + float max_val = 255; + auto vec_min_val = at::vec::Vectorized(min_val); + auto vec_max_val = at::vec::Vectorized(max_val); + auto vec_beta1 = at::vec::Vectorized(beta1); + auto vec_alpha = at::vec::Vectorized(alpha); + float beta2_float = (float) beta2; + auto vec_beta2 = at::vec::Vectorized(beta2_float); + for (long row = 0; row < M; row += 1) { + auto sum_a = sum_a_ptr[row]; + auto vec_sum_a = at::vec::Vectorized(sum_a); + const int32_t* tmp_in = in + row * ldi; + scalar_t* tmp_out = out + row * ldo; + long col = 0; + for (; col < vec_size * (N / vec_size); col += vec_size) { + auto vec_sum_b = at::vec::Vectorized::loadu(sum_b_ptr + col); + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + col); + auto tmp1 = tmp0 - vec_sum_b; + auto tmp2 = tmp1 - vec_sum_a; + auto tmp3 = tmp2 + vec_beta1; + auto tmp4 = at::vec::convert(tmp3); + auto tmp5 = tmp4 * vec_alpha; + auto tmp6 = tmp5.round(); + auto tmp7 = tmp6 + vec_beta2; + auto tmp8 = at::vec::clamp(tmp7, vec_min_val, vec_max_val); + _store(tmp_out + col, tmp8); + } + if (col < N) { + auto vec_sum_b = at::vec::Vectorized::loadu(sum_b_ptr + col, N - col); + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + col, N - col); + auto tmp1 = tmp0 - vec_sum_b; + auto tmp2 = tmp1 - vec_sum_a; + auto tmp3 = tmp2 + vec_beta1; + auto tmp4 = at::vec::convert(tmp3); + auto tmp5 = tmp4 * vec_alpha; + auto tmp6 = tmp5.round(); + auto tmp7 = tmp6 + vec_beta2; + auto tmp8 = at::vec::clamp(tmp7, vec_min_val, vec_max_val); + _store(tmp_out + col, tmp8, N - col); + } + } +} + +/* +1. dequant +2. quant +*/ +template +inline void _dequant_quant_fusion_kernel( + const int32_t* in, + const int32_t* sum_a_ptr, + const int& M, + const int& N, + const int& ldi, + const int& ldo, + const int32_t& beta2, // zp_c + const float& alpha, // scale_a*scale_b/scale_c + scalar_t* out) { + const int32_t vec_size = at::vec::Vectorized::size(); + float min_val = 0; + float max_val = 255; + auto vec_min_val = at::vec::Vectorized(min_val); + auto vec_max_val = at::vec::Vectorized(max_val); + // auto vec_beta1 = at::vec::Vectorized(beta1); + auto vec_alpha = at::vec::Vectorized(alpha); + float beta2_float = (float) beta2; + auto vec_beta2 = at::vec::Vectorized(beta2_float); + for (long row = 0; row < M; row += 1) { + auto sum_a = sum_a_ptr[row]; + auto vec_sum_a = at::vec::Vectorized(sum_a); + const int32_t* tmp_in = in + row * ldi; + scalar_t* tmp_out = out + row * ldo; + long col = 0; + for (; col < vec_size * (N / vec_size); col += vec_size) { + auto tmp1 = at::vec::Vectorized::loadu(tmp_in + col); + auto tmp3 = tmp1 - vec_sum_a; + // auto tmp3 = tmp2 + vec_beta1; + auto tmp4 = at::vec::convert(tmp3); + auto tmp5 = tmp4 * vec_alpha; + auto tmp6 = tmp5.round(); + auto tmp7 = tmp6 + vec_beta2; + auto tmp8 = at::vec::clamp(tmp7, vec_min_val, vec_max_val); + _store(tmp_out + col, tmp8); + } + if (col < N) { + auto tmp1 = at::vec::Vectorized::loadu(tmp_in + col, N - col); + auto tmp3 = tmp1 - vec_sum_a; + auto tmp4 = at::vec::convert(tmp3); + auto tmp5 = tmp4 * vec_alpha; + auto tmp6 = tmp5.round(); + auto tmp7 = tmp6 + vec_beta2; + auto tmp8 = at::vec::clamp(tmp7, vec_min_val, vec_max_val); + _store(tmp_out + col, tmp8, N - col); + } + } +} + +template +inline void _int_sum_b_contiguous_kernel_helper( + const scalar_t* in, + int32_t* out, + const int& N, + const int32_t& scale) { + const int32_t vec_size = at::vec::Vectorized::size(); + int32_t tmp_sum = 0; + auto vec_tmp_sum = at::vec::Vectorized(tmp_sum); + long i = 0; + for (; i < vec_size * (N / vec_size); i += vec_size) { + auto tmp0 = at::vec::Vectorized::loadu(in + i); + auto tmp1 = at::vec::convert(tmp0); + vec_tmp_sum = vec_tmp_sum + tmp1; + } + if (i < N) { + auto tmp0 = at::vec::Vectorized::loadu(in + i, N - i); + auto tmp1 = at::vec::convert(tmp0); + vec_tmp_sum = at::vec::Vectorized::set(vec_tmp_sum, vec_tmp_sum + tmp1, N - i); + } + out[0] = vec_tmp_sum.reduce_add() * scale; +} + +// reduce along dim b for shape [a, b], with sum shape [a] +template +inline void _int_sum_b_contiguous_kernel( + const scalar_t* in, + int32_t* out, + const int& M, + const int& N, + const int& ld, + const int32_t& scale) { + for (long r = 0; r < M; r += 1) { + _int_sum_b_contiguous_kernel_helper(in + r * ld, out + r, N, scale); + } +} + +// reduce along dim a for shape [a, b], with sum shape [b] +template +inline void _int_sum_a_contiguous_kernel( + const scalar_t* in, + int32_t* out, + const int& M, + const int& N, + const int& ld, + const int32_t& scale) { + const int32_t vec_size = at::vec::Vectorized::size(); + auto vec_scale = at::vec::Vectorized(scale); + // initialization with 0 + int32_t zero = 0; + auto vec_zero = at::vec::Vectorized(zero); + long i = 0; + for (; i < vec_size * (M / vec_size); i += vec_size) { + _store(out + i, vec_zero); + } + if (i < M) { + _store(out + i, vec_zero, M - i); + } + // sum + for (long j = 0; j < N; j++) { + const scalar_t* tmp_in = in + j * ld; + long k = 0; + for (; k < vec_size * (M / vec_size); k += vec_size) { + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + k); + auto tmp1 = at::vec::Vectorized::loadu(out + k); + auto tmp2 = at::vec::convert(tmp0); + auto tmp3 = tmp1 + tmp2; + _store(out + k, tmp3); + } + if (k < M) { + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + k, M - k); + auto tmp1 = at::vec::Vectorized::loadu(out + k, M - k); + auto tmp2 = at::vec::convert(tmp0); + auto tmp3 = tmp1 + tmp2; + _store(out + k, tmp3, M - k); + } + } + // scale + i = 0; + for (; i < vec_size * (M / vec_size); i += vec_size) { + auto tmp0 = at::vec::Vectorized::loadu(out + i); + auto tmp1 = tmp0 * vec_scale; + _store(out + i, tmp1); + } + if (i < M) { + auto tmp0 = at::vec::Vectorized::loadu(out + i, M - i); + auto tmp1 = tmp0 * vec_scale; + _store(out + i, tmp1, M - i); + } +} + +// do the transpose: [in_rows, in_cols] -> [in_cols, in_rows] +template +inline void do_transpose( + scalar_t* src, + scalar_t* dst, + int64_t in_rows, + int64_t in_cols, + int64_t ldi, + int64_t ldo) { + for (int64_t r=0; r [prows, pcols] +template +inline void pad_remain_row_col( + scalar_t* value_ptr, + int rows, + int cols, + int prows, + int pcols, + int ldi, + scalar_t pad_val=0) { + auto psize = pcols - cols; + if (psize == 0 && prows == rows) { + return; + } + const int32_t vec_size = at::vec::Vectorized::size(); + auto pad = at::vec::Vectorized(pad_val); + if (psize > 0) { + for (int i = 0; i < rows; i++) { + int j = 0; + for (; j < psize - (psize % vec_size); j += vec_size) { + pad.store(value_ptr + i * ldi + cols + j); + } + if (j < psize) { + pad.store(value_ptr + i * ldi + cols + j, psize - j); + } + } + } + + for (int i = rows; i < prows; i++) { + int j = 0; + for (; j < pcols - (pcols % vec_size); j += vec_size) { + pad.store(value_ptr + i * ldi + j); + } + if (j < pcols) { + pad.store(value_ptr + i * ldi + j, pcols - j); + } + } +} + +// copy value_ptr to dst_ptr with padding: [rows, cols] -> [prows, pcols] +template +inline void copy_value_with_pad( + scalar_t* value_ptr, + scalar_t* dst_ptr, + int rows, + int cols, + int prows, + int pcols, + int ldi, + scalar_t pad_val=0) { + const int32_t vec_size = at::vec::Vectorized::size(); + auto pad = at::vec::Vectorized(pad_val); + int i = 0; + for (; i < rows; i++) { + int j = 0; + for (; j < cols - (cols % vec_size); j += vec_size) { + auto vec_v = + at::vec::Vectorized::loadu(value_ptr + i * ldi + j); + vec_v.store(dst_ptr + i * pcols + j); + } + + if (j < cols) { + auto vec_v = at::vec::Vectorized::loadu( + value_ptr + i * ldi + j, cols - j); + vec_v.store(dst_ptr + i * pcols + j, cols - j); + } + + // col padding + auto psize = pcols - cols; + if (psize > 0) { + int pj = 0; + for (; pj < psize - (psize % vec_size); pj += vec_size) { + pad.store(dst_ptr + i * pcols + cols + pj); + } + if (pj < psize) { + pad.store(dst_ptr + i * pcols + cols + pj, psize - pj); + } + } + } + + // row padding + for (; i < prows; i++) { + int j = 0; + for (; j < pcols - (pcols % vec_size); j += vec_size) { + pad.store(dst_ptr + i * pcols + j); + } + if (j < pcols) { + pad.store(dst_ptr + i * pcols + j, pcols - j); + } + + } + +} + +// UINT8 - one parallel loop with u8u8s32 GEMM +template = 0> +inline typename std::enable_if_t, void> +sdpa_int8_fused_kernel_impl( + const at::Tensor& output, + const at::Tensor& q, + const at::Tensor& k, + const at::Tensor& v, + double dropout_p, + bool is_causal, + std::optional attention_mask, + std::optional scale, + float q_scale, + int32_t q_zp, + float k_scale, + int32_t k_zp, + float v_scale, + int32_t v_zp, + float a_scale, + int32_t a_zp, + float o_scale, + int32_t o_zp) { + // Query (Batch x Num_heads x Q_seq_len x Dim_per_head) + // -> (Batch x Q_seq_len x Num_heads x Dim_per_head) + // Key (Batch x Num_heads x KV_seq_len x Dim_per_head) + // -> (Batch x KV_seq_len x Num_heads x Dim_per_head) + // Value (Batch x Num_heads x KV_seq_len x Dim_per_head) + // -> (Batch x KV_seq_len x Num_heads x Dim_per_head) + at::Tensor query = q.transpose(1, 2); + at::Tensor key = k.transpose(1, 2); + at::Tensor value = v.transpose(1, 2); + + using accum_t = float; + accum_t scaling_factor = calculate_scale(query, scale).expect_float(); + int block_64 = 64; + auto u8_dt = at::ScalarType::Byte; + + // Sizes + TORCH_CHECK( + (query.size(3) == value.size(3)) && (key.size(3) == value.size(3)), + "scaled_dot_product_attention_sdpa: Q/K/V should have the same head size"); + TORCH_CHECK( + kv_split_size % block_64 == 0, "kv_split_size is not divisble by ", block_64); + + int64_t batchSize = query.size(0); + int64_t qSize = query.size(1); + int64_t kvSize = value.size(1); + int64_t num_head = query.size(2); + int64_t headSize = query.size(3); + + bool has_attn_mask = attention_mask.has_value() && attention_mask.value().numel(); + if (has_attn_mask) { + reshape_attn_mask_to_4d(attention_mask.value(), batchSize, num_head, qSize, kvSize); + } + + // Strides + int64_t qStrideB = query.stride(0); + int64_t qStrideM = query.stride(1); + int64_t qStrideH = query.stride(2); + int64_t kStrideB = key.stride(0); + int64_t kStrideN = key.stride(1); + int64_t kStrideH = key.stride(2); + int64_t vStrideB = value.stride(0); + int64_t vStrideN = value.stride(1); + int64_t vStrideH = value.stride(2); + int64_t oStrideB = output.stride(0); + int64_t oStrideM = output.stride(1); + int64_t oStrideH = output.stride(2); + int64_t mStrideB = + (has_attn_mask && attention_mask.value().size(0) > 1) + ? attention_mask.value().stride(0) + : 0; + int64_t mStrideH = + (has_attn_mask && attention_mask.value().size(1) > 1) + ? attention_mask.value().stride(1) + : 0; + int64_t mStrideM = + (has_attn_mask && attention_mask.value().size(2) > 1) + ? attention_mask.value().stride(2) + : 0; + int64_t mStrideN = + (has_attn_mask && attention_mask.value().size(3) > 1) + ? attention_mask.value().stride(3) + : 0; + + int64_t qSplitSize = q_split_size > qSize ? qSize : q_split_size; + int64_t kvSplitSize = kv_split_size > kvSize ? kvSize : kv_split_size; + int64_t qSlice = (qSize - 1) / qSplitSize + 1; + int64_t kvSlice = (kvSize - 1) / kvSplitSize + 1; + int64_t kvTail = (kvSize - 1) % kvSplitSize + 1; + int64_t num_thread = at::get_num_threads(); + + int64_t rndHeadSize = (headSize + block_64 - 1L) / block_64 * block_64; + int64_t rndkvSplitSize = (kvSplitSize + block_64 - 1L) / block_64 * block_64; + int64_t rndkvTail = (kvTail + block_64 - 1L) / block_64 * block_64; + int64_t rndkvSize = kv_split_size > kvSize ? rndkvTail : rndkvSplitSize * kvSlice + rndkvTail; + + bool av_gemm_K_mul4 = kvSplitSize % 4 == 0; + int av_gemm_K_padding = av_gemm_K_mul4 ? 0 : 4 - kvSplitSize % 4; + int av_gemm_K = kvSplitSize + av_gemm_K_padding; + + // Data ptrs + scalar_t* q_data = query.data_ptr(); + scalar_t* k_data = key.data_ptr(); + scalar_t* v_data = value.data_ptr(); + mask_t* mask_data = attention_mask.has_value() + ? attention_mask.value().data_ptr() + : nullptr; + scalar_t* out_data = output.data_ptr(); + + bool headSize_mul64 = headSize % 64 == 0; + int qk_gemm_K_padding = headSize_mul64 ? 0 : 64 - headSize % 64; + int qk_gemm_K = headSize + qk_gemm_K_padding; + + int64_t qk_reduce_strideL = qSplitSize * av_gemm_K; + int64_t v_reorder_strideL = av_gemm_K * rndHeadSize; + + int64_t total_size_uint8_per_thread = + /* qk */ kvSlice * qSplitSize * rndkvSplitSize * 4 + + /* qk_local */ kvSlice * av_gemm_K * 4 + + /* qk_reduce */ kvSlice * qk_reduce_strideL + + /* qk_s32 */ qSplitSize * rndkvSplitSize * 4 + + /* dst_s32 */ qSplitSize * rndHeadSize * 4 + + /* softmax_sum */ qSplitSize * 4 + + /* query_sum */ qSplitSize * 4 + + /* attention_sum */ qSplitSize * 4 + + /* softmax max */ qSplitSize * 4 + + /* query_padding_data */ qSplitSize * qk_gemm_K + + /* key_sum */ kvSize * 4 + + /* value_sum */ headSize * 4 + + /* key_t_reorder */ qk_gemm_K * rndkvSize + + /* value_t_reorder */ kvSlice * v_reorder_strideL; + + at::Tensor total_buf = at::empty( + {num_thread, total_size_uint8_per_thread}, + query.options()); + scalar_t* total_buf_data = total_buf.data_ptr(); + + at::parallel_for( + 0, batchSize * num_head, 1, [&](int64_t begin, int64_t end) { + int64_t i = 0, j = 0; + at::native::data_index_init( + begin, i, batchSize, j, num_head); + int ompIdx = at::get_thread_num(); + scalar_t* total_buf_ptr = total_buf_data + ompIdx * total_size_uint8_per_thread; + int32_t offset = 0; + accum_t* qk_data = reinterpret_cast(total_buf_ptr); + offset += kvSlice * qSplitSize * rndkvSplitSize * 4; + accum_t* qk_local_data = reinterpret_cast(total_buf_ptr + offset); + offset += kvSlice * av_gemm_K * 4; + scalar_t* qk_reduced_data = reinterpret_cast(total_buf_ptr + offset); + offset += kvSlice * qk_reduce_strideL; + int32_t* qk_s32_data = reinterpret_cast(total_buf_ptr + offset); + offset += qSplitSize * rndkvSplitSize * 4; + int32_t* dst_s32_data = reinterpret_cast(total_buf_ptr + offset); + offset += qSplitSize * rndHeadSize * 4; + accum_t* sfm_sum_ptr = reinterpret_cast(total_buf_ptr + offset); + offset += qSplitSize * 4; + int32_t* q_sum_ptr = reinterpret_cast(total_buf_ptr + offset); + offset += qSplitSize * 4; + int32_t* a_sum_ptr = reinterpret_cast(total_buf_ptr + offset); + offset += qSplitSize * 4; + accum_t* sfm_max_ptr = reinterpret_cast(total_buf_ptr + offset); + offset += qSplitSize * 4; + scalar_t* query_t_padding_ptr = reinterpret_cast(total_buf_ptr + offset); + offset += qSplitSize * qk_gemm_K; + + int32_t* k_sum_ptr = reinterpret_cast(total_buf_ptr + offset); + offset += kvSize * 4; + int32_t* v_sum_ptr = reinterpret_cast(total_buf_ptr + offset); + offset += headSize * 4; + scalar_t* key_reorder_ptr = reinterpret_cast(total_buf_ptr + offset); + offset += qk_gemm_K * rndkvSize; + scalar_t* value_reorder_ptr = reinterpret_cast(total_buf_ptr + offset); + + uint8_t* B_blocked_xform_u8 = new uint8_t[qk_gemm_K * block_64]; + + for (const auto z : c10::irange(begin, end)) { + (void)z; // Suppress unused variable + + // sum k and v + if (q_zp == 0) { + fill_stub(k_sum_ptr, static_cast(0), kvSize); + } else { + _int_sum_b_contiguous_kernel(k_data + i * kStrideB + j * kStrideH, + k_sum_ptr, + kvSize, headSize, kStrideN, q_zp); + } + if (a_zp == 0) { + fill_stub(v_sum_ptr, static_cast(0), headSize); + } else { + _int_sum_a_contiguous_kernel(v_data + i * vStrideB + j * vStrideH, + v_sum_ptr, + headSize, kvSize, vStrideN, a_zp); + } + + // transpose and packing + for (int64_t n = 0; n < kvSize; n += kvSplitSize) { + int64_t kvBlockSize = std::min(kvSplitSize, kvSize - n); + for (int64_t b = 0; b < kvBlockSize; b += block_64) { + bool istail = kvBlockSize - b < block_64; + int64_t trans_rows = istail ? kvBlockSize - b : block_64; + do_transpose( + k_data + i * kStrideB + j * kStrideH + n * kStrideN + b * kStrideN, + B_blocked_xform_u8, + trans_rows, + headSize, + kStrideN, + block_64); + if (!headSize_mul64 || istail) { + pad_remain_row_col( + B_blocked_xform_u8, + headSize, + trans_rows, + qk_gemm_K, + block_64, + block_64 + ); + } + at::native::cpublas::pack( + qk_gemm_K, // K + block_64, // N + block_64, // ld_in + block_64, // ld_out + u8_dt, // dt_in + u8_dt, // dt_out + B_blocked_xform_u8, + key_reorder_ptr + n * qk_gemm_K + + b * qk_gemm_K); + } + // split headSize to block_64, block_64, block_64 ... + // [av_gemm_K, headSize] -> [av_gemm_K, block_64 ...] + for (int64_t b = 0; b < rndHeadSize; b += block_64) { + at::native::cpublas::pack( + av_gemm_K, + block_64, + vStrideN, + block_64, + u8_dt, + u8_dt, + v_data + i * vStrideB + j * vStrideH + n * vStrideN + b, + value_reorder_ptr + n * rndHeadSize + + av_gemm_K * b); + } + } + + // sdpa core + for (int64_t k = 0; k < qSlice; k++) { + int64_t m = k * qSplitSize; + int64_t qBlockSize = std::min(qSplitSize, qSize - m); + // Initialize sum and max + fill_stub( + sfm_sum_ptr, static_cast(0), qSplitSize); + fill_stub( + a_sum_ptr, static_cast(0), qSplitSize); + fill_stub( + sfm_max_ptr, static_cast(-std::numeric_limits::infinity()), qSplitSize); + int64_t num_keys = + is_causal ? std::min(m + qBlockSize, kvSize) : kvSize; + copy_value_with_pad( + q_data + i * qStrideB + j * qStrideH + m * qStrideM, + query_t_padding_ptr, + qBlockSize, + headSize, + qBlockSize, + qk_gemm_K, + qStrideM); + // sum q + if (k_zp != 0) { + _int_sum_b_contiguous_kernel(q_data + i * qStrideB + j * qStrideH + m * qStrideM, + q_sum_ptr, qBlockSize, headSize, qStrideM, k_zp); + } else { + fill_stub( + q_sum_ptr, static_cast(0), qSplitSize); + } + const int64_t rkvSlice = (num_keys - 1) / kvSplitSize + 1; + for (int64_t l = 0; l < rkvSlice; l++) { + int64_t n = l * kvSplitSize; + int64_t kvBlockSize = std::min(kvSplitSize, kvSize - n); + // Calculate q @ k.T + for (int64_t b = 0; b < kvBlockSize; b += block_64) { + at::native::cpublas::brgemm( + qSplitSize, block_64, qk_gemm_K, + qk_gemm_K, // lda + block_64, //ldb + rndkvSplitSize, //ldc, + false, + query_t_padding_ptr, + key_reorder_ptr + n * qk_gemm_K + + b * qk_gemm_K, + qk_s32_data + b); + } + + // do dequant compensation, add mask, max reduce for softmax, and convert qk from s32 to fp32 + accum_t* qk_block_data = qk_data + l * qSplitSize * rndkvSplitSize; + if (has_attn_mask) { + mask_t* mask_data_offset = mask_data + i * mStrideB + j * mStrideH + m * mStrideM + (mStrideN == 0 ? 0 : n); + _dequant_mask_max_fusion_kernel( + qk_s32_data, //in + mask_data_offset, //mask_ptr + q_sum_ptr, //sum_a_ptr + k_sum_ptr + n, //sum_b_ptr + qBlockSize, //M + kvBlockSize, //N + rndkvSplitSize, //ldi + mStrideM, //ldm + rndkvSplitSize, //ldo + q_zp * k_zp * headSize, //zp_a*zp_b*k=beta + q_scale * k_scale * scaling_factor, //scale_a*scale_b*scale_sdpa=alpha + qk_block_data, //out + sfm_max_ptr // sfm_max_ptr + ); + } else { + _dequant_max_fusion_kernel( + qk_s32_data, //in + q_sum_ptr, //sum_a_ptr + k_sum_ptr + n, //sum_b_ptr + qBlockSize, //M + kvBlockSize, //N + rndkvSplitSize, //ldi + rndkvSplitSize, //ldo + q_zp * k_zp * headSize, //zp_a*zp_b*k=beta + q_scale * k_scale * scaling_factor, //scale_a*scale_b*scale_sdpa=alpha + qk_block_data, //out + sfm_max_ptr // sfm_max_ptr + ); + } + } + // sub max, exp, sum reduce, div sum for softmax + // and quant + // and sum for attention + if (v_zp == 0) { + _sub_exp_sum_div_quant_fusion_kernel( + qk_data, //in + qBlockSize, //M + kvSplitSize, //N_step + rkvSlice, //NSlices + qSplitSize * rndkvSplitSize, //ldi + qk_reduce_strideL, //ldo + kvSize, //kvSize + rndkvSplitSize, //rndkvSplitSize + av_gemm_K, //av_gemm_K + a_zp, // zp_a=beta1 + a_scale, // scale_a=alpha + qk_local_data, //local + qk_reduced_data, //out + sfm_max_ptr, //sfm_max_ptr + sfm_sum_ptr //sfm_sum_ptr + ); + } else { + _sub_exp_sum_div_quant_sum_fusion_kernel( + qk_data, //in + qBlockSize, //M + kvSplitSize, //N_step + rkvSlice, //NSlice + qSplitSize * rndkvSplitSize, //ldi + qk_reduce_strideL, //ldo + kvSize, //kvSize + rndkvSplitSize, //rndkvSplitSize + av_gemm_K, //av_gemm_K + a_zp, // zp_a=beta1 + v_zp, // zp_b=beta2 + a_scale, // scale_a=alpha + qk_local_data, //local + qk_reduced_data, //out + sfm_max_ptr, //sfm_max_ptr + sfm_sum_ptr, //sfm_sum_ptr + a_sum_ptr //a_sum_ptr + ); + } + // Calculate Softmax(q @ k.T) @ v + for (int64_t b = 0; b < headSize; b += block_64) { + auto value_reorder_b = value_reorder_ptr + b * av_gemm_K; + auto dst_s32_b = dst_s32_data + b; + for (int64_t s = 0; s < kvSlice; s++) { + at::native::cpublas::brgemm( + qSplitSize, block_64, av_gemm_K, + av_gemm_K, // lda + rndHeadSize, //ldb + rndHeadSize, //ldc + s != 0, + qk_reduced_data + s * qk_reduce_strideL, + value_reorder_b + s * v_reorder_strideL, + dst_s32_b); + } + } + + // After the last gemm, + // do dequant compensation, quant and convert from s32 to int8 + if (a_zp == 0) { + _dequant_quant_fusion_kernel( + dst_s32_data, //in + a_sum_ptr, //sum_a_ptr + qBlockSize, //M + headSize, //N + rndHeadSize, //ldi + oStrideM, //ldo + o_zp, //zp_c=beta2 + a_scale * v_scale / o_scale, //scale_a*scale_b/scale_c=alpha + out_data + i * oStrideB + j * oStrideH + m * oStrideM //out + ); + } else { + _dequant_quant_fusion_kernel( + dst_s32_data, //in + a_sum_ptr, //sum_a_ptr + v_sum_ptr, //sum_b_ptr + qBlockSize, //M + headSize, //N + rndHeadSize, //ldi + oStrideM, //ldo + a_zp * v_zp * kvSize, //zp_a*zp_b*k=beta1 + o_zp, //zp_c=beta2 + a_scale * v_scale / o_scale, //scale_a*scale_b/scale_c=alpha + out_data + i * oStrideB + j * oStrideH + m * oStrideM //out + ); + } + } + // Move to the next query + at::native::data_index_step(i, batchSize, j, num_head); + } + }); + // Once all computations are done, need to release HW context. + at::native::cpublas::brgemm_release(); +} + +// UINT8 - several parallel loops with u8u8s32 GEMM +template = 0> +inline typename std::enable_if_t, void> +sdpa_int8_fused_kernel_impl( + const at::Tensor& output, + const at::Tensor& q, + const at::Tensor& k, + const at::Tensor& v, + double dropout_p, + bool is_causal, + std::optional attention_mask, + std::optional scale, + float q_scale, + int32_t q_zp, + float k_scale, + int32_t k_zp, + float v_scale, + int32_t v_zp, + float a_scale, + int32_t a_zp, + float o_scale, + int32_t o_zp) { + // Query (Batch x Num_heads x Q_seq_len x Dim_per_head) + // -> (Batch x Q_seq_len x Num_heads x Dim_per_head) + // Key (Batch x Num_heads x KV_seq_len x Dim_per_head) + // -> (Batch x KV_seq_len x Num_heads x Dim_per_head) + // Value (Batch x Num_heads x KV_seq_len x Dim_per_head) + // -> (Batch x KV_seq_len x Num_heads x Dim_per_head) + at::Tensor query = q.transpose(1, 2); + at::Tensor key = k.transpose(1, 2); + at::Tensor value = v.transpose(1, 2); + + using accum_t = float; + accum_t scaling_factor = calculate_scale(query, scale).expect_float(); + int block_64 = 64; + auto u8_dt = at::ScalarType::Byte; + + // Sizes + TORCH_CHECK( + (query.size(3) == value.size(3)) && (key.size(3) == value.size(3)), + "scaled_dot_product_attention_sdpa: Q/K/V should have the same head size"); + TORCH_CHECK( + kv_split_size % block_64 == 0, "kv_split_size is not divisble by ", block_64); + + int64_t batchSize = query.size(0); + int64_t qSize = query.size(1); + int64_t kvSize = value.size(1); + int64_t num_head = query.size(2); + int64_t headSize = query.size(3); + + bool has_attn_mask = attention_mask.has_value() && attention_mask.value().numel(); + if (has_attn_mask) { + reshape_attn_mask_to_4d(attention_mask.value(), batchSize, num_head, qSize, kvSize); + } + + // Strides + int64_t qStrideB = query.stride(0); + int64_t qStrideM = query.stride(1); + int64_t qStrideH = query.stride(2); + int64_t kStrideB = key.stride(0); + int64_t kStrideN = key.stride(1); + int64_t kStrideH = key.stride(2); + int64_t vStrideB = value.stride(0); + int64_t vStrideN = value.stride(1); + int64_t vStrideH = value.stride(2); + int64_t oStrideB = output.stride(0); + int64_t oStrideM = output.stride(1); + int64_t oStrideH = output.stride(2); + int64_t mStrideB = + (has_attn_mask && attention_mask.value().size(0) > 1) + ? attention_mask.value().stride(0) + : 0; + int64_t mStrideH = + (has_attn_mask && attention_mask.value().size(1) > 1) + ? attention_mask.value().stride(1) + : 0; + int64_t mStrideM = + (has_attn_mask && attention_mask.value().size(2) > 1) + ? attention_mask.value().stride(2) + : 0; + int64_t mStrideN = + (has_attn_mask && attention_mask.value().size(3) > 1) + ? attention_mask.value().stride(3) + : 0; + + int64_t qSplitSize = q_split_size > qSize ? qSize : q_split_size; + int64_t kvSplitSize = kv_split_size > kvSize ? kvSize : kv_split_size; + int64_t qSlice = (qSize - 1) / qSplitSize + 1; + int64_t kvSlice = (kvSize - 1) / kvSplitSize + 1; + int64_t kvTail = (kvSize - 1) % kvSplitSize + 1; + int64_t num_thread = at::get_num_threads(); + + int64_t rndHeadSize = (headSize + block_64 - 1L) / block_64 * block_64; + int64_t rndkvSplitSize = (kvSplitSize + block_64 - 1L) / block_64 * block_64; + int64_t rndkvTail = (kvTail + block_64 - 1L) / block_64 * block_64; + int64_t rndkvSize = kv_split_size > kvSize ? rndkvTail : rndkvSplitSize * kvSlice + rndkvTail; + + bool av_gemm_K_mul4 = kvSplitSize % 4 == 0; + int av_gemm_K_padding = av_gemm_K_mul4 ? 0 : 4 - kvSplitSize % 4; + int av_gemm_K = kvSplitSize + av_gemm_K_padding; + + // Data ptrs + scalar_t* q_data = query.data_ptr(); + scalar_t* k_data = key.data_ptr(); + scalar_t* v_data = value.data_ptr(); + mask_t* mask_data = attention_mask.has_value() + ? attention_mask.value().data_ptr() + : nullptr; + scalar_t* out_data = output.data_ptr(); + + bool headSize_mul64 = headSize % 64 == 0; + int qk_gemm_K_padding = headSize_mul64 ? 0 : 64 - headSize % 64; + int qk_gemm_K = headSize + qk_gemm_K_padding; + + int64_t qk_reduce_strideL = qSplitSize * av_gemm_K; + int64_t v_reorder_strideL = av_gemm_K * rndHeadSize; + + int64_t total_size_uint8_per_thread = + /* qk */ kvSlice * qSplitSize * rndkvSplitSize * 4 + + /* qk_local */ kvSlice * av_gemm_K * 4 + + /* qk_reduce */ kvSlice * qk_reduce_strideL + + /* qk_s32 */ qSplitSize * rndkvSplitSize * 4 + + /* dst_s32 */ qSplitSize * rndHeadSize * 4 + + /* softmax_sum */ qSplitSize * 4 + + /* query_sum */ qSplitSize * 4 + + /* attention_sum */ qSplitSize * 4 + + /* softmax max */ qSplitSize * 4 + + /* query_padding_data */ qSplitSize * qk_gemm_K; + + at::Tensor total_buf = at::empty( + {num_thread, total_size_uint8_per_thread}, + query.options()); + scalar_t* total_buf_data = total_buf.data_ptr(); + + int64_t kv_sum_size_per_BH = + /* key_sum */ kvSize + + /* value_sum */ headSize; + + at::Tensor kv_sum_buf = at::empty( + {batchSize, num_head, kv_sum_size_per_BH}, + query.options().dtype(at::kInt)); + int32_t* kv_sum_buf_data = kv_sum_buf.data_ptr(); + + int64_t kv_reorder_size_per_BH = + /* key_t_reorder */ qk_gemm_K * rndkvSize + + /* value_t_reorder */ kvSlice * v_reorder_strideL; + + at::Tensor kv_reorder_buf = at::empty( + {batchSize, num_head, kv_reorder_size_per_BH}, + query.options()); + scalar_t* kv_reorder_buf_data = kv_reorder_buf.data_ptr(); + scalar_t* key_reorder_ptr = kv_reorder_buf_data; + scalar_t* value_reorder_ptr = kv_reorder_buf_data + batchSize * num_head * qk_gemm_K * rndkvSize; + + // sum k and v + at::parallel_for( + 0, batchSize * num_head, 1, [&](int64_t begin, int64_t end) { + int64_t i = 0, j = 0; + at::native::data_index_init( + begin, i, batchSize, j, num_head); + for (const auto z : c10::irange(begin, end)) { + (void)z; // Suppress unused variable + int32_t* kv_sum_ptr = kv_sum_buf_data + + i * num_head * kv_sum_size_per_BH + + j * kv_sum_size_per_BH; + int32_t* k_sum_ptr = kv_sum_ptr; + int32_t* v_sum_ptr = kv_sum_ptr + kvSize; + if (q_zp == 0) { + fill_stub(k_sum_ptr, static_cast(0), kvSize); + } else { + _int_sum_b_contiguous_kernel(k_data + i * kStrideB + j * kStrideH, + k_sum_ptr, + kvSize, headSize, kStrideN, q_zp); + } + if (a_zp == 0) { + fill_stub(v_sum_ptr, static_cast(0), headSize); + } else { + _int_sum_a_contiguous_kernel(v_data + i * vStrideB + j * vStrideH, + v_sum_ptr, + headSize, kvSize, vStrideN, a_zp); + } + // Move to the next query + at::native::data_index_step(i, batchSize, j, num_head); + } + }); + + // transpose and packing + at::parallel_for( + 0, batchSize * num_head * kvSlice, 1, [&](int64_t begin, int64_t end) { + int64_t i = 0, j = 0, l = 0, n = 0; + at::native::data_index_init( + begin, i, batchSize, j, num_head, l, kvSlice); + uint8_t* B_blocked_xform_u8 = new uint8_t[qk_gemm_K * block_64]; + for (const auto z : c10::irange(begin, end)) { + (void)z; // Suppress unused variable + n = l * kvSplitSize; + auto k_reorder = key_reorder_ptr + i * num_head * qk_gemm_K * rndkvSize + + j * qk_gemm_K * rndkvSize + n * qk_gemm_K; + auto v_reorder = value_reorder_ptr + + i * num_head * kvSlice * v_reorder_strideL + + j * kvSlice * v_reorder_strideL + n * rndHeadSize; + int64_t kvBlockSize = std::min(kvSplitSize, kvSize - n); + for (int64_t b = 0; b < kvBlockSize; b += block_64) { + bool istail = kvBlockSize - b < block_64; + int64_t trans_rows = istail ? kvBlockSize - b : block_64; + do_transpose( + k_data + i * kStrideB + j * kStrideH + n * kStrideN + b * kStrideN, + B_blocked_xform_u8, + trans_rows, + headSize, + kStrideN, + block_64); + if (!headSize_mul64 || istail) { + pad_remain_row_col( + B_blocked_xform_u8, + headSize, + trans_rows, + qk_gemm_K, + block_64, + block_64 + ); + } + at::native::cpublas::pack( + qk_gemm_K, // K + block_64, // N + block_64, // ld_in + block_64, // ld_out + u8_dt, // dt_in + u8_dt, // dt_out + B_blocked_xform_u8, + k_reorder + b * qk_gemm_K); + } + // split headSize to block_64, block_64, block_64 ... + // [av_gemm_K, headSize] -> [av_gemm_K, block_64 ...] + for (int64_t b = 0; b < rndHeadSize; b += block_64) { + at::native::cpublas::pack( + av_gemm_K, + block_64, + vStrideN, + block_64, + u8_dt, + u8_dt, + v_data + i * vStrideB + j * vStrideH + n * vStrideN + b, + v_reorder + av_gemm_K * b); + } + // Move to the next query + at::native::data_index_step(i, batchSize, j, num_head, l, kvSlice); + } + }); + + at::parallel_for( + 0, batchSize * num_head * qSlice, 1, [&](int64_t begin, int64_t end) { + int64_t i = 0, j = 0, k = 0; + at::native::data_index_init( + begin, i, batchSize, j, num_head, k, qSlice); + int ompIdx = at::get_thread_num(); + scalar_t* total_buf_ptr = total_buf_data + ompIdx * total_size_uint8_per_thread; + int32_t offset = 0; + accum_t* qk_data = reinterpret_cast(total_buf_ptr); + offset += kvSlice * qSplitSize * rndkvSplitSize * 4; + accum_t* qk_local_data = reinterpret_cast(total_buf_ptr + offset); + offset += kvSlice * av_gemm_K * 4; + scalar_t* qk_reduced_data = reinterpret_cast(total_buf_ptr + offset); + offset += kvSlice * qk_reduce_strideL; + int32_t* qk_s32_data = reinterpret_cast(total_buf_ptr + offset); + offset += qSplitSize * rndkvSplitSize * 4; + int32_t* dst_s32_data = reinterpret_cast(total_buf_ptr + offset); + offset += qSplitSize * rndHeadSize * 4; + accum_t* sfm_sum_ptr = reinterpret_cast(total_buf_ptr + offset); + offset += qSplitSize * 4; + int32_t* q_sum_ptr = reinterpret_cast(total_buf_ptr + offset); + offset += qSplitSize * 4; + int32_t* a_sum_ptr = reinterpret_cast(total_buf_ptr + offset); + offset += qSplitSize * 4; + accum_t* sfm_max_ptr = reinterpret_cast(total_buf_ptr + offset); + offset += qSplitSize * 4; + scalar_t* query_t_padding_ptr = reinterpret_cast(total_buf_ptr + offset); + + for (const auto z : c10::irange(begin, end)) { + (void)z; // Suppress unused variable + + int32_t* kv_sum_ptr = kv_sum_buf_data + + i * num_head * kv_sum_size_per_BH + + j * kv_sum_size_per_BH; + int32_t* k_sum_ptr = kv_sum_ptr; + int32_t* v_sum_ptr = kv_sum_ptr + kvSize; + + // sdpa core + int64_t m = k * qSplitSize; + int64_t qBlockSize = std::min(qSplitSize, qSize - m); + // Initialize sum and max + fill_stub( + sfm_sum_ptr, static_cast(0), qSplitSize); + fill_stub( + a_sum_ptr, static_cast(0), qSplitSize); + fill_stub( + sfm_max_ptr, static_cast(-std::numeric_limits::infinity()), qSplitSize); + copy_value_with_pad( + q_data + i * qStrideB + j * qStrideH + m * qStrideM, + query_t_padding_ptr, + qBlockSize, + headSize, + qSplitSize, + qk_gemm_K, + qStrideM); + // sum q + if (k_zp != 0) { + _int_sum_b_contiguous_kernel(query_t_padding_ptr, + q_sum_ptr, qBlockSize, headSize, qk_gemm_K, k_zp); + } else { + fill_stub( + q_sum_ptr, static_cast(0), qSplitSize); + } + const int64_t rkvSlice = (kvSize - 1) / kvSplitSize + 1; + for (int64_t l = 0; l < rkvSlice; l++) { + int64_t n = l * kvSplitSize; + int64_t kvBlockSize = std::min(kvSplitSize, kvSize - n); + auto k_reorder = key_reorder_ptr + i * num_head * qk_gemm_K * rndkvSize + + j * qk_gemm_K * rndkvSize + n * qk_gemm_K; + // Calculate q @ k.T + for (int64_t b = 0; b < kvBlockSize; b += block_64) { + at::native::cpublas::brgemm( + qSplitSize, block_64, qk_gemm_K, + qk_gemm_K, // lda + block_64, //ldb + rndkvSplitSize, //ldc, + false, + query_t_padding_ptr, + k_reorder + b * qk_gemm_K, + qk_s32_data + b); + } + + // do dequant compensation, add mask, max reduce for softmax, and convert qk from s32 to fp32 + accum_t* qk_block_data = qk_data + l * qSplitSize * rndkvSplitSize; + if (has_attn_mask) { + mask_t* mask_data_offset = mask_data + i * mStrideB + j * mStrideH + m * mStrideM + (mStrideN == 0 ? 0 : n); + _dequant_mask_max_fusion_kernel( + qk_s32_data, //in + mask_data_offset, //mask_ptr + q_sum_ptr, //sum_a_ptr + k_sum_ptr + n, //sum_b_ptr + qBlockSize, //M + kvBlockSize, //N + rndkvSplitSize, //ldi + mStrideM, //ldm + rndkvSplitSize, //ldo + q_zp * k_zp * headSize, //zp_a*zp_b*k=beta + q_scale * k_scale * scaling_factor, //scale_a*scale_b*scale_sdpa=alpha + qk_block_data, //out + sfm_max_ptr // sfm_max_ptr + ); + } else { + _dequant_max_fusion_kernel( + qk_s32_data, //in + q_sum_ptr, //sum_a_ptr + k_sum_ptr + n, //sum_b_ptr + qBlockSize, //M + kvBlockSize, //N + rndkvSplitSize, //ldi + rndkvSplitSize, //ldo + q_zp * k_zp * headSize, //zp_a*zp_b*k=beta + q_scale * k_scale * scaling_factor, //scale_a*scale_b*scale_sdpa=alpha + qk_block_data, //out + sfm_max_ptr // sfm_max_ptr + ); + } + } + // sub max, exp, sum reduce, div sum for softmax + // and quant + // and sum for attention + if (v_zp == 0) { + _sub_exp_sum_div_quant_fusion_kernel( + qk_data, //in + qBlockSize, //M + kvSplitSize, //N_step + rkvSlice, //NSlices + qSplitSize * rndkvSplitSize, //ldi + qk_reduce_strideL, //ldo + kvSize, //kvSize + rndkvSplitSize, //rndkvSplitSize + av_gemm_K, //av_gemm_K + a_zp, // zp_a=beta1 + a_scale, // scale_a=alpha + qk_local_data, //local + qk_reduced_data, //out + sfm_max_ptr, //sfm_max_ptr + sfm_sum_ptr //sfm_sum_ptr + ); + } else { + _sub_exp_sum_div_quant_sum_fusion_kernel( + qk_data, //in + qBlockSize, //M + kvSplitSize, //N_step + rkvSlice, //NSlice + qSplitSize * rndkvSplitSize, //ldi + qk_reduce_strideL, //ldo + kvSize, //kvSize + rndkvSplitSize, //rndkvSplitSize + av_gemm_K, //av_gemm_K + a_zp, // zp_a=beta1 + v_zp, // zp_b=beta2 + a_scale, // scale_a=alpha + qk_local_data, //local + qk_reduced_data, //out + sfm_max_ptr, //sfm_max_ptr + sfm_sum_ptr, //sfm_sum_ptr + a_sum_ptr //a_sum_ptr + ); + } + // Calculate Softmax(q @ k.T) @ v + auto v_reorder = value_reorder_ptr + + i * num_head * kvSlice * v_reorder_strideL + + j * kvSlice * v_reorder_strideL; + for (int64_t b = 0; b < headSize; b += block_64) { + auto value_reorder_b = v_reorder + b * av_gemm_K; + auto dst_s32_b = dst_s32_data + b; + for (int64_t s = 0; s < kvSlice; s++) { + at::native::cpublas::brgemm( + qSplitSize, block_64, av_gemm_K, + av_gemm_K, // lda + rndHeadSize, //ldb + rndHeadSize, //ldc + s != 0, + qk_reduced_data + s * qk_reduce_strideL, + value_reorder_b + s * v_reorder_strideL, + dst_s32_b); + } + } + + // After the last gemm, + // do dequant compensation, quant and convert from s32 to int8 + if (a_zp == 0) { + _dequant_quant_fusion_kernel( + dst_s32_data, //in + a_sum_ptr, //sum_a_ptr + qBlockSize, //M + headSize, //N + rndHeadSize, //ldi + oStrideM, //ldo + o_zp, //zp_c=beta2 + a_scale * v_scale / o_scale, //scale_a*scale_b/scale_c=alpha + out_data + i * oStrideB + j * oStrideH + m * oStrideM //out + ); + } else { + _dequant_quant_fusion_kernel( + dst_s32_data, //in + a_sum_ptr, //sum_a_ptr + v_sum_ptr, //sum_b_ptr + qBlockSize, //M + headSize, //N + rndHeadSize, //ldi + oStrideM, //ldo + a_zp * v_zp * kvSize, //zp_a*zp_b*k=beta1 + o_zp, //zp_c=beta2 + a_scale * v_scale / o_scale, //scale_a*scale_b/scale_c=alpha + out_data + i * oStrideB + j * oStrideH + m * oStrideM //out + ); + } + // Move to the next query + at::native::data_index_step(i, batchSize, j, num_head, k, qSlice); + } + }); + // Once all computations are done, need to release HW context. + at::native::cpublas::brgemm_release(); +} + + +template +inline typename std::enable_if_t, void> +sdpa_int8_fused_kernel_impl( + bool use_one_parallel_loop, + const at::Tensor& output, + const at::Tensor& query, + const at::Tensor& key, + const at::Tensor& value, + double dropout_p, + bool is_causal, + std::optional attn_mask, + std::optional scale, + float q_scale, + int32_t q_zp, + float k_scale, + int32_t k_zp, + float v_scale, + int32_t v_zp, + float a_scale, + int32_t a_zp, + float o_scale, + int32_t o_zp) { + if (use_one_parallel_loop) { + sdpa_int8_fused_kernel_impl( + output, query, key, value, + dropout_p, is_causal, attn_mask, scale, + q_scale, q_zp, + k_scale, k_zp, + v_scale, v_zp, + a_scale, a_zp, + o_scale, o_zp); + } else { + sdpa_int8_fused_kernel_impl( + output, query, key, value, + dropout_p, is_causal, attn_mask, scale, + q_scale, q_zp, + k_scale, k_zp, + v_scale, v_zp, + a_scale, a_zp, + o_scale, o_zp); + } +} + + +#define AT_DISPATCH_MASK_TYPES(TYPE, NAME, ...) \ + AT_DISPATCH_SWITCH( \ + TYPE, \ + NAME, \ + AT_PRIVATE_CASE_TYPE_USING_HINT( \ + at::ScalarType::Bool, mask_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE_USING_HINT( \ + at::ScalarType::Float, mask_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE_USING_HINT( \ + at::ScalarType::Double, mask_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE_USING_HINT( \ + at::ScalarType::BFloat16, mask_t, __VA_ARGS__) \ + AT_PRIVATE_CASE_TYPE_USING_HINT( \ + at::ScalarType::Half, mask_t, __VA_ARGS__)) + +void sdpa_int8_fused_kernel( + const at::Tensor& output, + const at::Tensor& query, + const at::Tensor& key, + const at::Tensor& value, + double dropout_p, + bool is_causal, + std::optional attn_mask, + std::optional scale, + float q_scale, + int32_t q_zp, + float k_scale, + int32_t k_zp, + float v_scale, + int32_t v_zp, + float a_scale, + int32_t a_zp, + float o_scale, + int32_t o_zp) { + TORCH_CHECK(query.scalar_type() == c10::kByte); + int64_t batchSize = query.size(0); + int64_t num_head = query.size(1); + int64_t q_seq_len = query.size(2); + int64_t kv_seq_len = key.size(2); + int64_t q_split_size = 32; + if (q_seq_len >= 768) { + q_split_size = 256; + } else if (q_seq_len >= 192) { + q_split_size = 64; + } + // Heuristic to decide whether to use one parallel loop or not + // true: one parallel loop for sum+packing+core + // false: three parallel loops for sum, packing, core + uint32_t l2_cache_size = at::cpu::L2_cache_size(); + int64_t num_thread = at::get_num_threads(); + int64_t attn_size = q_split_size * kv_seq_len * sizeof(int32_t) * num_thread; + bool use_one_parallel_loop = (batchSize * num_head > num_thread) && + (attn_size > 1.5 * l2_cache_size); + if (!attn_mask.has_value()) { + if (q_split_size == 256) { + sdpa_int8_fused_kernel_impl( + use_one_parallel_loop, + output, query, key, value, + dropout_p, is_causal, attn_mask, scale, + q_scale, q_zp, + k_scale, k_zp, + v_scale, v_zp, + a_scale, a_zp, + o_scale, o_zp); + } else if (q_split_size == 64) { + sdpa_int8_fused_kernel_impl( + use_one_parallel_loop, + output, query, key, value, + dropout_p, is_causal, attn_mask, scale, + q_scale, q_zp, + k_scale, k_zp, + v_scale, v_zp, + a_scale, a_zp, + o_scale, o_zp); + } else { + sdpa_int8_fused_kernel_impl( + use_one_parallel_loop, + output, query, key, value, + dropout_p, is_causal, attn_mask, scale, + q_scale, q_zp, + k_scale, k_zp, + v_scale, v_zp, + a_scale, a_zp, + o_scale, o_zp); + } + } else { + AT_DISPATCH_MASK_TYPES(attn_mask.value().scalar_type(), "sdpa_mask", [&]() { + if (q_split_size == 256) { + sdpa_int8_fused_kernel_impl( + use_one_parallel_loop, + output, query, key, value, + dropout_p, is_causal, attn_mask, scale, + q_scale, q_zp, + k_scale, k_zp, + v_scale, v_zp, + a_scale, a_zp, + o_scale, o_zp); + } else if (q_split_size == 64) { + sdpa_int8_fused_kernel_impl( + use_one_parallel_loop, + output, query, key, value, + dropout_p, is_causal, attn_mask, scale, + q_scale, q_zp, + k_scale, k_zp, + v_scale, v_zp, + a_scale, a_zp, + o_scale, o_zp); + } else { + sdpa_int8_fused_kernel_impl( + use_one_parallel_loop, + output, query, key, value, + dropout_p, is_causal, attn_mask, scale, + q_scale, q_zp, + k_scale, k_zp, + v_scale, v_zp, + a_scale, a_zp, + o_scale, o_zp); + } + }); + } +} +#endif // CPU_CAPABILITY_AVX512 + +at::Tensor sdpa_int8_math_kernel( + const at::Tensor& query, + const at::Tensor& key, + const at::Tensor& value, + double dropout_p, + bool is_causal, + std::optional attn_mask, + std::optional scale, + float q_scale, + int32_t q_zp, + float k_scale, + int32_t k_zp, + float v_scale, + int32_t v_zp, + float a_scale, + int32_t a_zp, + float o_scale, + int32_t o_zp) { + // dequant q/k/v + auto q = (query.to(at::kFloat) - q_zp) * q_scale; + auto k = (key.to(at::kFloat) - k_zp) * k_scale; + auto v = (value.to(at::kFloat) - v_zp) * v_scale; + const auto scaling_factor = calculate_scale(q, scale); + auto attn = at::matmul(q, k.transpose(-2, -1)) * scaling_factor; + if (attn_mask.has_value() && attn_mask.value().numel()) { + attn = attn.add(attn_mask.value().to(at::kFloat)); + } + attn = at::softmax(attn, -1); + // quant attn + attn = at::clamp_max( + at::clamp_min(at::round(attn / a_scale) + a_zp, 0), 255 + ); + // dequant attn + attn = (attn - a_zp) * a_scale; + auto output = at::matmul(attn, v); + // quant output + output = at::clamp_max( + at::clamp_min(at::round(output / o_scale) + o_zp, 0), 255 + ).to(at::kByte); + return output; +} + + +at::Tensor _qscaled_dot_product_cpu( + const at::Tensor& query, + const at::Tensor& key, + const at::Tensor& value, + std::optional attn_mask, + double dropout_p, + bool is_causal, + std::optional scale, + double q_scale, + int64_t q_zp, + double k_scale, + int64_t k_zp, + double v_scale, + int64_t v_zp, + double a_scale, + int64_t a_zp, + double o_scale, + int64_t o_zp) { + const auto dtype = query.scalar_type(); + TORCH_CHECK(!query.is_nested() && !key.is_nested() && !value.is_nested(), + "_qscaled_dot_product_cpu: Only accept plain inputs"); + TORCH_CHECK(!is_causal, + "_qscaled_dot_product_cpu: is_causal not supported."); + TORCH_CHECK(dtype == at::ScalarType::Byte, + "_qscaled_dot_product_cpu: Expected data type be U8, but got ", dtype, " instead."); + TORCH_CHECK(query.dim() == 4 && key.dim() == 4 && value.dim() == 4, + "_qscaled_dot_product_cpu: Accept only 4 dims inputs shape of {B, H, T, K}"); + TORCH_CHECK(dropout_p == 0.0, + "_qscaled_dot_product_cpu: Currently do not support dropout > 0"); + TORCH_CHECK((query.size(3) == value.size(3)) && (key.size(3) == value.size(3)), + "_qscaled_dot_product_cpu: Q/K/V should have the same head size"); + TORCH_CHECK(!attn_mask.has_value() || + attn_mask.value().scalar_type() == at::kFloat || + attn_mask.value().scalar_type() == at::kBFloat16, + "_qscaled_dot_product_cpu: Expected attention mask be float or bf16"); + TORCH_CHECK(!attn_mask.has_value() || + (attn_mask.value().dim() == 2 || attn_mask.value().dim() == 4), + "_qscaled_dot_product_cpu: Attention mask dim in {2, 4}"); + + #ifdef CPU_CAPABILITY_AVX512 + if (at::native::cpublas::could_pack(dtype)) { + at::Tensor output = at::empty_like(query, query.options()).transpose(1, 2); + sdpa_int8_fused_kernel(output, query, key, value, + dropout_p, is_causal, attn_mask, scale, + q_scale, q_zp, + k_scale, k_zp, + v_scale, v_zp, + a_scale, a_zp, + o_scale, o_zp); + return output.transpose(1, 2); + } else { + #endif // CPU_CAPABILITY_AVX512 + return sdpa_int8_math_kernel(query, key, value, + dropout_p, is_causal, attn_mask, scale, + q_scale, q_zp, + k_scale, k_zp, + v_scale, v_zp, + a_scale, a_zp, + o_scale, o_zp).transpose(1, 2).contiguous().transpose(1, 2); + #ifdef CPU_CAPABILITY_AVX512 + } + #endif // CPU_CAPABILITY_AVX512 +} + + +} // anonymous namespace + +TORCH_LIBRARY_IMPL(torchao, CPU, m) { + m.impl("torchao::qscaled_dot_product", &_qscaled_dot_product_cpu); +} + +// } // at::native +} // namespace torchao diff --git a/torchao/ops.py b/torchao/ops.py index 0a507a69d7..faebdbd5d1 100644 --- a/torchao/ops.py +++ b/torchao/ops.py @@ -58,6 +58,9 @@ "mx_fp4_bf16(Tensor a, Tensor b, Tensor a_scale, Tensor b_scale) -> Tensor", tags=[torch._C.Tag.needs_fixed_stride_order], ) +lib.define( + "qscaled_dot_product(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, float dropout_p=0.0, bool is_causal=False, float? scale=None, float q_scale=1.0, int q_zp=0, float k_scale=1.0, int k_zp=0, float v_scale=1.0, int v_zp=0, float a_scale=1.0, int a_zp=0, float o_scale=1.0, int o_zp=0) -> Tensor" +) def register_custom_op(name): @@ -162,6 +165,92 @@ def _( return _in_feats.new_empty((BS, OC)) +def qscaled_dot_product( + query: Tensor, + key: Tensor, + value: Tensor, + attn_mask: Optional[Tensor] = None, + dropout_p: float = 0.0, + is_causal: bool = False, + scale: Optional[float] = None, + q_scale: float = 1.0, + q_zp: int = 0, + k_scale: float = 1.0, + k_zp: int = 0, + v_scale: float = 1.0, + v_zp: int = 0, + a_scale: float = 1.0, + a_zp: int = 0, + o_scale: float = 1.0, + o_zp: int = 0, +) -> Tensor: + """ + Quantized SDPA with quantized inputs and outputs. + Arguments + query: input query tensor, + key: input key tensor, + value: input value tensor, + attn_mask: attention mask tensor, + dropout_p: dropout probability, + is_causal: causal flag, + scale: scaling factor applied prior to softmax, + q_scale: scale for query from linear quantization, + q_zp: zero point for query from linear quantization, + k_scale: scale for key from linear quantization, + k_zp: zero point of key from linear quantization, + v_scale: zero point for value from linear quantization, + v_zp: zero point of value from linear quantization, + a_scale: scale for attention from softmax quantization, + a_zp: zero point for attention from softmax quantization, + o_scale: scale for output from linear quantization, + o_zp: zero point for output from linear quantization, + Returns + output of quantized SDPA + """ + return torch.ops.torchao.qscaled_dot_product.default( + query, + key, + value, + attn_mask, + dropout_p, + is_causal, + scale, + q_scale, + q_zp, + k_scale, + k_zp, + v_scale, + v_zp, + a_scale, + a_zp, + o_scale, + o_zp, + ) + + +@register_custom_op("torchao::qscaled_dot_product") +def _( + query: Tensor, + key: Tensor, + value: Tensor, + attn_mask: Optional[Tensor] = None, + dropout_p: float = 0.0, + is_causal: bool = False, + scale: Optional[float] = None, + q_scale: float = 1.0, + q_zp: int = 0, + k_scale: float = 1.0, + k_zp: int = 0, + v_scale: float = 1.0, + v_zp: int = 0, + a_scale: float = 1.0, + a_zp: int = 0, + o_scale: float = 1.0, + o_zp: int = 0, +) -> Tensor: + return query + + def unpack_tensor_core_tiled_layout(packed_w: Tensor, inner_k_tiles: int) -> Tensor: """ Unpacks weights that were packed with `torch.ops.aten._convert_weight_to_int4pack` to original tensor of shape `N x K`. diff --git a/torchao/prototype/inductor/__init__.py b/torchao/prototype/inductor/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/torchao/prototype/inductor/fx_passes/README.md b/torchao/prototype/inductor/fx_passes/README.md new file mode 100644 index 0000000000..7007aba993 --- /dev/null +++ b/torchao/prototype/inductor/fx_passes/README.md @@ -0,0 +1,35 @@ +# Inductor FX Passes + +This directory contains the FX passes of Inductor. FX passes are transformations applied to the FX graph to optimize and modify it for better performance and functionality. + +In TorchAO, you can replace the following customized graph passes of Inductor: +- `pre_grad_custom_pass` +- `joint_custom_pre_pass` +- `joint_custom_post_pass` +- `post_grad_custom_post_pass` +- `post_grad_custom_pre_pass` + +## Directory Structure + +- `int8_sdpa_fusion`: Pattern match for int8 sdpa fusion. + +## Getting Started + +To get started with using the FX passes in TorchAO, you can register and apply them to your FX graph as follows: + +```python +from torch._inductor import config +from torch._inductor.pattern_matcher import PatternMatcherPass + +# Example usage +class _CustomPass(...): # create a custom pass class +custom_pass = _CustomPass() # create an instance of custom pass +with config.patch(config.custom_pass=custom_pass): + _register_patterns(config.custom_pass) # register your own passes + +``` + +## Limitations + +For now, we can only register one pass as the custom pass. +In the future, it is better to extend it to a list. diff --git a/torchao/prototype/inductor/fx_passes/__init__.py b/torchao/prototype/inductor/fx_passes/__init__.py new file mode 100644 index 0000000000..aae6d5348a --- /dev/null +++ b/torchao/prototype/inductor/fx_passes/__init__.py @@ -0,0 +1,5 @@ +from .int8_sdpa_fusion import _int8_sdpa_init + +__all__ = [ + "_int8_sdpa_init", +] diff --git a/torchao/prototype/inductor/fx_passes/int8_sdpa_fusion.py b/torchao/prototype/inductor/fx_passes/int8_sdpa_fusion.py new file mode 100644 index 0000000000..cfe0d309b1 --- /dev/null +++ b/torchao/prototype/inductor/fx_passes/int8_sdpa_fusion.py @@ -0,0 +1,392 @@ +import functools +import itertools + +import torch +from torch._dynamo.utils import counters +from torch._inductor import config +from torch._inductor.lowering import lowerings as L +from torch._inductor.lowering import make_fallback +from torch._inductor.pattern_matcher import ( + Arg, + CallFunction, + KeywordArg, + Match, + PatternMatcherPass, + register_lowering_pattern, +) + +from torchao.utils import TORCH_VERSION_AT_LEAST_2_7 + +__all__ = [ + "_int8_sdpa_init", +] + +make_fallback(torch.ops.torchao.qscaled_dot_product.default) + +aten = torch.ops.aten + + +def _is_valid_int8_sdpa_pattern(): + def fn(match): + assert all(k in match.kwargs for k in ("query", "key", "value")) + query = match.kwargs["query"].meta["val"] + key = match.kwargs["key"].meta["val"] + value = match.kwargs["value"].meta["val"] + return ( + query.dtype == torch.uint8 + and key.dtype == torch.uint8 + and value.dtype == torch.uint8 + and query.device.type == "cpu" + and key.device == query.device + and value.device == query.device + ) + + return fn + + +def _register_int8_sdpa_pattern(pattern, custom_pass_dict): + @register_lowering_pattern( + pattern, extra_check=_is_valid_int8_sdpa_pattern(), pass_dict=custom_pass_dict + ) + def int8_sdpa(match: Match, *args, **kwargs): + query = kwargs["query"] + key = kwargs["key"] + value = kwargs["value"] + inv_scale = kwargs["inv_scale"] + attn_mask = kwargs["attn_mask"] if "attn_mask" in kwargs else None + q_scale = kwargs["q_scale"] + q_zp = kwargs["q_zp"] + k_scale = kwargs["k_scale"] + k_zp = kwargs["k_zp"] + v_scale = kwargs["v_scale"] + v_zp = kwargs["v_zp"] + a_scale = kwargs["a_scale"] + a_zp = kwargs["a_zp"] + o_scale = kwargs["o_scale"] + o_zp = kwargs["o_zp"] + counters["inductor"]["int8_fuse_attention"] += 1 + counters["inductor"]["int8_sdpa_nodes"] += len(match.nodes) + + trans_query = L[aten.permute.default](query, [0, 2, 1, 3]) + trans_key = L[aten.permute.default](key, [0, 2, 1, 3]) + trans_value = L[aten.permute.default](value, [0, 2, 1, 3]) + output = L[torch.ops.torchao.qscaled_dot_product.default]( + trans_query, + trans_key, + trans_value, + attn_mask, + 0.0, # dropout + False, # is_causal + 1.0 / inv_scale, # scale + q_scale, + q_zp, + k_scale, + k_zp, + v_scale, + v_zp, + a_scale, + a_zp, + o_scale, + o_zp, + ) + trans_output = L[aten.permute.default](output, [0, 2, 1, 3]) + return L[aten.clone.default]( + trans_output, memory_format=torch.contiguous_format + ) + + return int8_sdpa + + +def _get_int8_sdpa_qkv_pattern( + is_batch_size_1: bool, has_convert: bool, input_name: str +): + assert input_name in ["query", "key", "value"] + int8_sdpa_qkv_pattern_before_dequant = CallFunction( + aten.permute.default, + KeywordArg(input_name), + Arg(), + ) + if input_name == "key": + # do transpose + int8_sdpa_qkv_pattern_before_dequant = CallFunction( + aten.permute.default, + int8_sdpa_qkv_pattern_before_dequant, + Arg(), + ) + int8_sdpa_qkv_basic_pattern = CallFunction( + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + int8_sdpa_qkv_pattern_before_dequant, + KeywordArg(input_name[0] + "_scale"), + KeywordArg(input_name[0] + "_zp"), + Arg(), + Arg(), + Arg(), + ) + if has_convert: + int8_sdpa_qkv_basic_pattern = CallFunction( + torch.ops.prims.convert_element_type.default, + int8_sdpa_qkv_basic_pattern, + Arg(), + ) + int8_sdpa_qkv_basic_pattern = CallFunction( + aten.expand.default, + int8_sdpa_qkv_basic_pattern, + Arg(), + ) + if is_batch_size_1: + # pattern is different for bs=1 + return CallFunction( + aten.reshape.default, + int8_sdpa_qkv_basic_pattern, + Arg(), + ) + else: + return CallFunction( + aten.reshape.default, + CallFunction( + aten.clone.default, + int8_sdpa_qkv_basic_pattern, + memory_format=Arg(), + ), + Arg(), + ) + + +def _get_int8_sdpa_score_pattern( + has_mask: bool, is_batch_size_1: bool, is_reduced_type: bool, has_convert: bool +): + int8_sdpa_q_pattern = _get_int8_sdpa_qkv_pattern( + is_batch_size_1, has_convert, "query" + ) + int8_sdpa_k_pattern = _get_int8_sdpa_qkv_pattern( + is_batch_size_1, has_convert, "key" + ) + int8_sdpa_score_basic_pattern = CallFunction( + aten.reshape.default, + CallFunction( + aten.bmm.default, + int8_sdpa_q_pattern, + int8_sdpa_k_pattern, + ), + Arg(), + ) + if is_reduced_type and not has_mask: + int8_sdpa_score_basic_pattern = CallFunction( + torch.ops.prims.convert_element_type.default, + int8_sdpa_score_basic_pattern, + Arg(), + ) + if has_mask: + return CallFunction( + aten.add.Tensor, + CallFunction( + aten.div.Tensor, + int8_sdpa_score_basic_pattern, + KeywordArg("inv_scale"), + ), + KeywordArg("attn_mask"), + _users=2, + ) + else: + return CallFunction( + aten.mul.Tensor, + int8_sdpa_score_basic_pattern, + Arg(), + _users=2, + ) + + +def _get_int8_sdpa_exp_pattern( + has_mask: bool, is_batch_size_1: bool, is_reduced_type: bool, has_convert: bool +): + int8_sdpa_score_pattern = _get_int8_sdpa_score_pattern( + has_mask, is_batch_size_1, is_reduced_type, has_convert + ) + int8_sdpa_exp_basic_pattern = CallFunction( + aten.sub.Tensor, + int8_sdpa_score_pattern, + CallFunction( + aten.amax.default, + int8_sdpa_score_pattern, + Arg(), + Arg(), + ), + ) + if has_mask: + return CallFunction( + aten.exp.default, + int8_sdpa_exp_basic_pattern, + _users=2, + ) + else: + return CallFunction( + aten.exp.default, + CallFunction( + aten.div.Tensor, + int8_sdpa_exp_basic_pattern, + KeywordArg("inv_scale"), + ), + _users=2, + ) + + +def _get_int8_sdpa_attn_pattern( + has_mask: bool, is_batch_size_1: bool, is_reduced_type: bool, has_convert: bool +): + int8_sdpa_exp_pattern = _get_int8_sdpa_exp_pattern( + has_mask, is_batch_size_1, is_reduced_type, has_convert + ) + int8_sdpa_div_pattern = CallFunction( + aten.div.Tensor, + int8_sdpa_exp_pattern, + CallFunction( + aten.sum.dim_IntList, + int8_sdpa_exp_pattern, + Arg(), + Arg(), + ), + ) + int8_sdpa_softmax_pattern = CallFunction( + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + CallFunction( + torch.ops.quantized_decomposed.quantize_per_tensor.default, + int8_sdpa_div_pattern, + KeywordArg("a_scale"), + KeywordArg("a_zp"), + Arg(), + Arg(), + Arg(), + ), + KeywordArg("a_scale"), + KeywordArg("a_zp"), + Arg(), + Arg(), + Arg(), + ) + if is_reduced_type: + if has_mask: + int8_sdpa_softmax_pattern = CallFunction( + torch.ops.prims.convert_element_type.default, + int8_sdpa_softmax_pattern, + Arg(), + ) + else: + int8_sdpa_softmax_pattern = CallFunction( + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + CallFunction( + torch.ops.quantized_decomposed.quantize_per_tensor.default, + CallFunction( + torch.ops.prims.convert_element_type.default, + int8_sdpa_div_pattern, + Arg(), + ), + KeywordArg("a_scale"), + KeywordArg("a_zp"), + Arg(), + Arg(), + Arg(), + ), + KeywordArg("a_scale"), + KeywordArg("a_zp"), + Arg(), + Arg(), + Arg(), + ) + if has_convert: + int8_sdpa_softmax_pattern = CallFunction( + torch.ops.prims.convert_element_type.default, + int8_sdpa_softmax_pattern, + Arg(), + ) + return CallFunction( + aten.reshape.default, + CallFunction( + aten.expand.default, + int8_sdpa_softmax_pattern, + Arg(), + ), + Arg(), + ) + + +# Parameters to generate various patterns: +# has_mask: if SDPA has attention mask +# is_batch_size_1: if the batch size is 1 +# is_reduced_type: if autocast is enabled +# has_convert: convert type if dequant out dtype is assigned +def _get_int8_sdpa_final_pattern( + has_mask: bool, is_batch_size_1: bool, is_reduced_type: bool, has_convert: bool +): + int8_sdpa_v_pattern = _get_int8_sdpa_qkv_pattern( + is_batch_size_1, has_convert, "value" + ) + int8_sdpa_attn_pattern = _get_int8_sdpa_attn_pattern( + has_mask, is_batch_size_1, is_reduced_type, has_convert + ) + return CallFunction( + torch.ops.quantized_decomposed.quantize_per_tensor.default, + CallFunction( + aten.clone.default, + CallFunction( + aten.permute.default, + CallFunction( + aten.reshape.default, + CallFunction( + aten.bmm.default, + int8_sdpa_attn_pattern, + int8_sdpa_v_pattern, + ), + Arg(), + ), + Arg(), + ), + memory_format=Arg(), + ), + KeywordArg("o_scale"), + KeywordArg("o_zp"), + Arg(), + Arg(), + Arg(), + ) + + +def _register_int8_sdpa_lowerings(custom_pass_dict): + for has_mask, is_batch_size_1, is_reduced_type, has_convert in itertools.product( + [True, False], [True, False], [True, False], [True, False] + ): + _register_int8_sdpa_pattern( + _get_int8_sdpa_final_pattern( + has_mask=has_mask, + is_batch_size_1=is_batch_size_1, + is_reduced_type=is_reduced_type, + has_convert=has_convert, + ), + custom_pass_dict, + ) + + +custom_pass = None +if TORCH_VERSION_AT_LEAST_2_7: + # TORCH_VERSION_AT_LEAST_2_7 is needed for custom graph pass + from torch._inductor.custom_graph_pass import CustomGraphPass, get_hash_for_files + + # define the custom pass + class _CustomPass(PatternMatcherPass, CustomGraphPass): + def __init__(self) -> None: + super().__init__() + + def __call__(self, g: torch.fx.graph.Graph): + self.apply(g) + + def uuid(self) -> bytes: + return get_hash_for_files((__file__,)) + + custom_pass = _CustomPass() + + +@functools.lru_cache(None) +def _int8_sdpa_init(): + if TORCH_VERSION_AT_LEAST_2_7: + _register_int8_sdpa_lowerings(config.post_grad_custom_pre_pass) + else: + pass From 212d912dcbfb43b13cc4bee533d3d1844d76b7c1 Mon Sep 17 00:00:00 2001 From: Yu Guo <82124926+yuguo68@users.noreply.github.com> Date: Tue, 20 May 2025 19:11:18 -0700 Subject: [PATCH 037/165] use correct fp8 quantization dtype for AMD GPU Differential Revision: D75021458 Pull Request resolved: https://github.com/pytorch/ao/pull/2225 --- test/float8/test_base.py | 10 ++++++++++ torchao/quantization/quant_api.py | 15 ++++++++------- 2 files changed, 18 insertions(+), 7 deletions(-) diff --git a/test/float8/test_base.py b/test/float8/test_base.py index c91782fb74..8e3efeab60 100644 --- a/test/float8/test_base.py +++ b/test/float8/test_base.py @@ -56,6 +56,7 @@ tensor_to_scale, ) from torchao.testing.float8.test_utils import get_test_float8_linear_config +from torchao.utils import is_MI300, is_ROCM random.seed(0) torch.manual_seed(0) @@ -271,6 +272,15 @@ def test_axiswise_gemm(self, a_shape, a_granularity, b_granularity): sqnr = compute_error(c_ref, c_fp8_compute) assert sqnr >= 25.0 + @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") + def test_fp8_dtype( + self, + ): + if is_ROCM() and is_MI300(): + assert e4m3_dtype == torch.float8_e4m3fnuz + else: + assert e4m3_dtype == torch.float8_e4m3fn + class TestFloat8Linear: def _test_linear_impl( diff --git a/torchao/quantization/quant_api.py b/torchao/quantization/quant_api.py index 982b8cdd5c..15e3b20fc8 100644 --- a/torchao/quantization/quant_api.py +++ b/torchao/quantization/quant_api.py @@ -52,6 +52,7 @@ make_packed_linear_int8_dynamic_activation_intx_weight_tensor, ) from torchao.dtypes.utils import Layout +from torchao.float8.config import e4m3_dtype, e5m2_dtype from torchao.float8.float8_linear import Float8Linear from torchao.float8.inference import Float8MMConfig from torchao.quantization.linear_activation_weight_observed_tensor import ( @@ -1396,7 +1397,7 @@ class Float8WeightOnlyConfig(AOBaseConfig): The actual matmul will be computed in original precision of the weight tensor. """ - weight_dtype: torch.dtype = torch.float8_e4m3fn + weight_dtype: torch.dtype = e4m3_dtype set_inductor_config: bool = True @@ -1569,8 +1570,8 @@ class Float8DynamicActivationFloat8WeightConfig(AOBaseConfig): """ - activation_dtype: torch.dtype = torch.float8_e4m3fn - weight_dtype: torch.dtype = torch.float8_e4m3fn + activation_dtype: torch.dtype = e4m3_dtype + weight_dtype: torch.dtype = e4m3_dtype granularity: Optional[ Union[_fp8_granularities, Tuple[_fp8_granularities, _fp8_granularities]] ] = None @@ -1660,8 +1661,8 @@ class Float8DynamicActivationFloat8SemiSparseWeightConfig(AOBaseConfig): """ layout: Layout = CutlassSemiSparseLayout() - activation_dtype: torch.dtype = torch.float8_e5m2 - weight_dtype: torch.dtype = torch.float8_e4m3fn + activation_dtype: torch.dtype = e5m2_dtype + weight_dtype: torch.dtype = e4m3_dtype @register_quantize_module_handler(Float8DynamicActivationFloat8SemiSparseWeightConfig) @@ -1706,8 +1707,8 @@ class Float8StaticActivationFloat8WeightConfig(AOBaseConfig): """ scale: torch.Tensor - activation_dtype: torch.dtype = torch.float8_e4m3fn - weight_dtype: torch.dtype = torch.float8_e4m3fn + activation_dtype: torch.dtype = e4m3_dtype + weight_dtype: torch.dtype = e4m3_dtype granularity: Optional[ Union[_fp8_granularities, Tuple[_fp8_granularities, _fp8_granularities]] ] = None From 04fb450912a3af7c08b45330936132c2d562e462 Mon Sep 17 00:00:00 2001 From: Apurva Jain Date: Wed, 21 May 2025 13:56:38 -0700 Subject: [PATCH 038/165] Remove preserve_zero and zero_point_domain from choose_qparams_affine (#2149) --- test/dtypes/test_uintx.py | 9 +- test/quantization/test_quant_primitives.py | 190 +--- test/sparsity/test_marlin.py | 6 - torchao/dtypes/affine_quantized_tensor.py | 258 ++++-- torchao/dtypes/affine_quantized_tensor_ops.py | 17 +- torchao/dtypes/uintx/int4_cpu_layout.py | 14 +- .../dtypes/uintx/tensor_core_tiled_layout.py | 14 +- torchao/experimental/quant_passes.py | 3 +- .../prototype/parq/quant/uniform_torchao.py | 37 +- torchao/quantization/__init__.py | 4 + torchao/quantization/pt2e/observer.py | 4 - .../qat/affine_fake_quantized_tensor.py | 51 +- torchao/quantization/quant_primitives.py | 851 +++++++++++++----- torchao/quantization/utils.py | 76 +- 14 files changed, 1014 insertions(+), 520 deletions(-) diff --git a/test/dtypes/test_uintx.py b/test/dtypes/test_uintx.py index 1e2b635f19..35c722365d 100644 --- a/test/dtypes/test_uintx.py +++ b/test/dtypes/test_uintx.py @@ -10,7 +10,6 @@ from torchao.quantization.quant_api import quantize_, uintx_weight_only from torchao.quantization.quant_primitives import ( MappingType, - ZeroPointDomain, choose_qparams_affine, dequantize_affine, quantize_affine, @@ -112,7 +111,6 @@ def test_uintx_weight_only_quant(dtype, group_size, device): mapping_type = MappingType.SYMMETRIC eps = torch.finfo(torch.float32).eps zero_point_dtype = torch.int32 - zero_point_domain = ZeroPointDomain.INT block_size = (1, group_size) scale, zero_point = choose_qparams_affine( @@ -123,8 +121,6 @@ def test_uintx_weight_only_quant(dtype, group_size, device): eps=eps, scale_dtype=torch.float32, zero_point_dtype=zero_point_dtype, - preserve_zero=True, - zero_point_domain=zero_point_domain, ) aqt = quantize_affine( @@ -133,15 +129,12 @@ def test_uintx_weight_only_quant(dtype, group_size, device): scale, zero_point, dtype, - zero_point_domain=zero_point_domain, ) # Note: output will be uint8 tensor for sub byte tensors for now q = to_uintx(aqt, dtype, -1) assert q is not None, "quantization failed" - deqaunt = dequantize_affine( - q, block_size, scale, zero_point, dtype, zero_point_domain=zero_point_domain - ) + deqaunt = dequantize_affine(q, block_size, scale, zero_point, dtype) assert deqaunt is not None, "deqauntization failed" diff --git a/test/quantization/test_quant_primitives.py b/test/quantization/test_quant_primitives.py index 861ebe5e94..046fb6ab42 100644 --- a/test/quantization/test_quant_primitives.py +++ b/test/quantization/test_quant_primitives.py @@ -9,20 +9,16 @@ import unittest import torch -from parameterized import parameterized -from torchao.float8.float8_utils import EPS as float8_eps from torchao.quantization.quant_primitives import ( MappingType, ZeroPointDomain, choose_qparams_affine, - choose_qparams_affine_float8, + choose_qparams_affine_tinygemm, dequantize_affine, - dequantize_affine_float8, fake_quantize_affine, fake_quantize_affine_cachemask, quantize_affine, - quantize_affine_float8, ) # TODO: remove test for utils? @@ -650,35 +646,6 @@ def test_raises(self): with self.assertRaisesRegex(RuntimeError, "is invalid for input of size 1"): _ = quantize_affine(input, block_size, scale, zero_point, dtype) - def test_not_preserve_zero_not_supported(self): - """Making sure preserve_zero == False is not supported for symmetric quant""" - input = torch.randn(10, 256) - n_bit = 4 - mapping_type = MappingType.SYMMETRIC - dtype = torch.int8 - block_size = (1, 128) - quant_min = 0 - quant_max = 2**n_bit - 1 - eps = 1e-6 - scale_dtype = torch.bfloat16 - zero_point_dtype = torch.bfloat16 - with self.assertRaisesRegex( - ValueError, - "preserve_zero == False is not supported for symmetric quantization", - ): - choose_qparams_affine( - input, - mapping_type, - block_size, - dtype, - quant_min, - quant_max, - eps, - scale_dtype=scale_dtype, - zero_point_dtype=zero_point_dtype, - preserve_zero=False, - ) - def test_get_groupwise_affine_qparams(self): input = torch.randn(10, 256) n_bit = 4 @@ -702,22 +669,33 @@ def test_get_groupwise_affine_qparams(self): dtype=torch.bfloat16, zero_point_domain=zero_point_domain, ) - scale, zero_point = choose_qparams_affine( - input, - mapping_type, - block_size, - dtype, - quant_min, - quant_max, - eps, - scale_dtype=scale_dtype, - zero_point_dtype=zero_point_dtype, - preserve_zero=zero_point_domain == ZeroPointDomain.INT, - zero_point_domain=zero_point_domain, - ) + if zero_point_domain == ZeroPointDomain.FLOAT: + scale, zero_point = choose_qparams_affine_tinygemm( + input, + mapping_type, + block_size, + dtype, + quant_min, + quant_max, + eps, + scale_dtype=scale_dtype, + zero_point_dtype=zero_point_dtype, + ) + else: + scale, zero_point = choose_qparams_affine( + input, + mapping_type, + block_size, + dtype, + quant_min, + quant_max, + eps, + scale_dtype=scale_dtype, + zero_point_dtype=zero_point_dtype, + ) - self.assertTrue(torch.equal(scale, scale_ref)) - self.assertTrue(torch.equal(zero_point, zero_point_ref)) + self.assertTrue(torch.equal(scale, scale_ref)) + self.assertTrue(torch.equal(zero_point, zero_point_ref)) def test_groupwise_affine_quantize_tensor_from_qparams(self): input = torch.randn(10, 256) @@ -847,120 +825,6 @@ def test_fake_quantize_affine_cachemask(self): torch.testing.assert_close(dequantized, fake_quantized) torch.testing.assert_close(expected_mask, mask) - def test_none_zero_point_domain(self): - """A None value for a ZeroPointDomain should not work, but ZeroPointDomain.NONE should""" - input = torch.randn(10, 256) - mapping_type = MappingType.SYMMETRIC - dtype = torch.int8 - block_size = (1, 128) - quant_min = None - quant_max = None - eps = 1e-6 - scale_dtype = torch.float32 - zero_point_dtype = torch.int64 - try: - _, zero_point = choose_qparams_affine( - input, - mapping_type, - block_size, - dtype, - quant_min, - quant_max, - eps, - scale_dtype=scale_dtype, - zero_point_dtype=zero_point_dtype, - preserve_zero=True, - zero_point_domain=None, - ) - except ValueError: - # This exception was expected - # Now test for ZeroPointDomain.NONE - _, zero_point = choose_qparams_affine( - input, - mapping_type, - block_size, - dtype, - quant_min, - quant_max, - eps, - scale_dtype=scale_dtype, - zero_point_dtype=zero_point_dtype, - preserve_zero=True, - zero_point_domain=ZeroPointDomain.NONE, - ) - self.assertTrue(zero_point is None) - else: - # An exception should have been thrown for zero_point_domain None - self.assertTrue( - False, - msg="A runtime exception should have been thrown for zero_point_domain None", - ) - - @parameterized.expand( - [ - ( - torch.float32, - torch.float8_e4m3fn, - ), - ( - torch.float32, - torch.float8_e5m2, - ), - ( - torch.bfloat16, - torch.float8_e4m3fn, - ), - ( - torch.bfloat16, - torch.float8_e5m2, - ), - ] - ) - def test_float8_quant_primitives(self, hp_dtype, float8_dtype): - input = torch.randn(10, 10) - - # float8 quantization primitives - scale = choose_qparams_affine_float8(input, float8_dtype=float8_dtype) - quantized = quantize_affine_float8(input, scale, float8_dtype=float8_dtype) - dequantized = dequantize_affine_float8(quantized, scale, output_dtype=hp_dtype) - - # reference implementation using generic primitives - expected_scale, _ = choose_qparams_affine( - input, - MappingType.SYMMETRIC, - input.shape, - float8_dtype, - eps=float8_eps, # use same EPS as float8 training - scale_dtype=torch.float32, - quant_min=torch.finfo(float8_dtype).min, - quant_max=torch.finfo(float8_dtype).max, - ) - expected_quantized = quantize_affine( - input, - input.shape, - scale, - output_dtype=float8_dtype, - quant_min=torch.finfo(float8_dtype).min, - quant_max=torch.finfo(float8_dtype).max, - zero_point=None, - zero_point_domain=ZeroPointDomain.NONE, - ) - expected_dequantized = dequantize_affine( - expected_quantized, - input.shape, - scale, - input_dtype=float8_dtype, - output_dtype=hp_dtype, - quant_min=torch.finfo(float8_dtype).min, - quant_max=torch.finfo(float8_dtype).max, - zero_point=None, - zero_point_domain=ZeroPointDomain.NONE, - ) - - self.assertTrue(torch.equal(expected_scale, scale)) - torch.testing.assert_close(expected_quantized, quantized) - torch.testing.assert_close(expected_dequantized, dequantized) - if __name__ == "__main__": unittest.main() diff --git a/test/sparsity/test_marlin.py b/test/sparsity/test_marlin.py index 15a6823961..783de6c6ae 100644 --- a/test/sparsity/test_marlin.py +++ b/test/sparsity/test_marlin.py @@ -14,7 +14,6 @@ from torchao.quantization.quant_api import int4_weight_only, quantize_ from torchao.quantization.quant_primitives import ( MappingType, - ZeroPointDomain, choose_qparams_affine, quantize_affine, ) @@ -92,8 +91,6 @@ def test_pack_unpack_equivalence(self): eps = 1e-6 zero_point_dtype = torch.bfloat16 mapping_type = MappingType.SYMMETRIC - preserve_zero = True - zero_point_domain = ZeroPointDomain.INT scale_dtype = None w = torch.rand(shape, dtype=torch.float16, device="cuda") @@ -112,8 +109,6 @@ def test_pack_unpack_equivalence(self): eps, scale_dtype, zero_point_dtype, - preserve_zero, - zero_point_domain, ) w_q_24 = quantize_affine( w_24, @@ -123,7 +118,6 @@ def test_pack_unpack_equivalence(self): target_dtype, quant_min, quant_max, - zero_point_domain, ) scales = scales.reshape(-1, w_q_24.shape[1]) diff --git a/torchao/dtypes/affine_quantized_tensor.py b/torchao/dtypes/affine_quantized_tensor.py index beaac8b0e1..65649593a3 100644 --- a/torchao/dtypes/affine_quantized_tensor.py +++ b/torchao/dtypes/affine_quantized_tensor.py @@ -19,12 +19,21 @@ MappingType, ZeroPointDomain, choose_qparams_affine, + choose_qparams_affine_dont_preserve_zero, + choose_qparams_affine_float8, choose_qparams_affine_floatx, + choose_qparams_affine_tinygemm, choose_qparams_and_quantize_affine_hqq, dequantize_affine, + dequantize_affine_float8, + dequantize_affine_float_zero_point, dequantize_affine_floatx, + dequantize_affine_no_zero_point, quantize_affine, + quantize_affine_float8, + quantize_affine_float_zero_point, quantize_affine_floatx, + quantize_affine_no_zero_point, ) from torchao.utils import ( TORCH_VERSION_AT_LEAST_2_5, @@ -129,7 +138,7 @@ def dequantize(self, output_dtype: Optional[torch.dtype] = None) -> torch.Tensor if output_dtype is None: output_dtype = self.dtype - from torchao.dtypes.floatx import FloatxTensorCoreLayout + from torchao.dtypes.floatx import Float8Layout, FloatxTensorCoreLayout if isinstance(self._layout, FloatxTensorCoreLayout): int_data, scale = self.tensor_impl.get_plain() @@ -140,19 +149,44 @@ def dequantize(self, output_dtype: Optional[torch.dtype] = None) -> torch.Tensor self._layout.mbits, output_dtype=output_dtype, ) + elif isinstance(self._layout, Float8Layout): + data, scale, _ = self.tensor_impl.get_plain() + return dequantize_affine_float8(data, scale, output_dtype) else: data, scale, zero_point = self.tensor_impl.get_plain() - dq = dequantize_affine( - data, - self.block_size, - scale, - zero_point, - data.dtype, - self.quant_min, - self.quant_max, - self.zero_point_domain, - output_dtype=output_dtype, - ) + if self.zero_point_domain == ZeroPointDomain.FLOAT: + dq = dequantize_affine_float_zero_point( + data, + self.block_size, + scale, + zero_point, + data.dtype, + self.quant_min, + self.quant_max, + output_dtype=output_dtype, + ) + elif self.zero_point_domain == ZeroPointDomain.NONE: + dq = dequantize_affine_no_zero_point( + data, + self.block_size, + scale, + zero_point, + data.dtype, + self.quant_min, + self.quant_max, + output_dtype=output_dtype, + ) + else: + dq = dequantize_affine( + data, + self.block_size, + scale, + zero_point, + data.dtype, + self.quant_min, + self.quant_max, + output_dtype=output_dtype, + ) from torchao.dtypes.uintx import TensorCoreTiledLayout if isinstance(self._layout, TensorCoreTiledLayout): @@ -256,32 +290,74 @@ def from_hp_to_intx( ) data = data.to(target_dtype) else: - scale, zero_point = choose_qparams_affine( - input_float, - mapping_type, - block_size, - target_dtype, - quant_min, - quant_max, - eps, - scale_dtype, - zero_point_dtype, - preserve_zero, - zero_point_domain, - ) + if zero_point_domain == ZeroPointDomain.FLOAT and not preserve_zero: + scale, zero_point = choose_qparams_affine_tinygemm( + input_float, + mapping_type, + block_size, + target_dtype, + quant_min, + quant_max, + eps, + scale_dtype, + zero_point_dtype, + ) + elif zero_point_domain == ZeroPointDomain.INT and not preserve_zero: + scale, zero_point = choose_qparams_affine_dont_preserve_zero( + input_float, + mapping_type, + block_size, + target_dtype, + quant_min, + quant_max, + eps, + scale_dtype, + zero_point_dtype, + ) + else: # Default case: zero_point_domain == ZeroPointDomain.INT/NONE and preserve_zero + scale, zero_point = choose_qparams_affine( + input_float, + mapping_type, + block_size, + target_dtype, + quant_min, + quant_max, + eps, + scale_dtype, + zero_point_dtype, + ) # choose_qparams_affine is a custom op that does support returning optional Tensors. We thus set the zero_point to None if its domain is None if zero_point_domain == ZeroPointDomain.NONE: zero_point = None - data = quantize_affine( - input_float, - block_size, - scale, - zero_point, - target_dtype, - quant_min, - quant_max, - zero_point_domain, - ) + data = quantize_affine_no_zero_point( + input_float, + block_size, + scale, + zero_point, + target_dtype, + quant_min, + quant_max, + ) + elif zero_point_domain == ZeroPointDomain.FLOAT: + data = quantize_affine_float_zero_point( + input_float, + block_size, + scale, + zero_point, + target_dtype, + quant_min, + quant_max, + ) + else: + data = quantize_affine( + input_float, + block_size, + scale, + zero_point, + target_dtype, + quant_min, + quant_max, + ) # Note: output will be uint8 tensor for sub byte tensors for now data, scale, zero_point = _layout.post_process( @@ -317,25 +393,42 @@ def from_hp_to_intx_static( raise ValueError("please use ZeroPointDomain.NONE instead of None") elif zero_point_domain is ZeroPointDomain.NONE and zero_point is not None: raise ValueError("zero_point should be None when zero_point_domain is NONE") - if target_dtype not in FP8_TYPES: - assert zero_point is not None, ( - "zero_point must be specified for non-fp8 types" - ) original_shape = input_float.shape input_float, scale, zero_point = _layout.pre_process_static( input_float, scale, zero_point, block_size ) - int_data = quantize_affine( - input_float, - block_size, - scale, - zero_point, - target_dtype, - quant_min, - quant_max, - zero_point_domain, - ) + if zero_point_domain == ZeroPointDomain.NONE: + zero_point = None + int_data = quantize_affine_no_zero_point( + input_float, + block_size, + scale, + zero_point, + target_dtype, + quant_min, + quant_max, + ) + elif zero_point_domain == ZeroPointDomain.FLOAT: + int_data = quantize_affine_float_zero_point( + input_float, + block_size, + scale, + zero_point, + target_dtype, + quant_min, + quant_max, + ) + else: + int_data = quantize_affine( + input_float, + block_size, + scale, + zero_point, + target_dtype, + quant_min, + quant_max, + ) int_data, scale, zero_point = _layout.post_process( int_data, @@ -367,20 +460,22 @@ def from_hp_to_floatx( ): """Convert a high precision tensor to a float8 quantized tensor.""" if target_dtype in FP8_TYPES: - return cls.from_hp_to_intx( - input_float=input_float, - mapping_type=MappingType.SYMMETRIC, - block_size=block_size, - target_dtype=target_dtype, - quant_min=math.ceil(torch.finfo(target_dtype).min), - quant_max=math.ceil(torch.finfo(target_dtype).max), - eps=torch.finfo(torch.float32).eps, - scale_dtype=scale_dtype, - zero_point_dtype=None, - preserve_zero=True, - zero_point_domain=ZeroPointDomain.NONE, - _layout=_layout, - use_hqq=False, + original_shape = input_float.shape + input_float = _layout.pre_process(input_float) + + scale = choose_qparams_affine_float8(input_float, float8_dtype=target_dtype) + data = quantize_affine_float8(input_float, scale, target_dtype) + + data, scale, zero_point = _layout.post_process( + data, scale, None, block_size + ) + tensor_impl_ctr = get_tensor_impl_constructor(type(_layout)) + tensor_impl = tensor_impl_ctr(data, scale, zero_point, _layout) + return cls( + tensor_impl, + block_size, + original_shape, + dtype=input_float.dtype, ) else: raise NotImplementedError( @@ -395,19 +490,36 @@ def from_hp_to_floatx_static( block_size: Tuple[int, ...], target_dtype: torch.dtype, _layout: Layout, + scale_dtype: torch.dtype = torch.float32, ): """Create a float8 AffineQuantizedTensor from a high precision tensor using static parameters.""" if target_dtype in FP8_TYPES: - return cls.from_hp_to_intx_static( - input_float=input_float, - scale=scale, - zero_point=None, - block_size=block_size, - target_dtype=target_dtype, - quant_min=math.ceil(torch.finfo(target_dtype).min), - quant_max=math.ceil(torch.finfo(target_dtype).max), - zero_point_domain=ZeroPointDomain.NONE, - _layout=_layout, + original_shape = input_float.shape + input_float, scale, zero_point = _layout.pre_process_static( + input_float, scale, ZeroPointDomain.NONE, block_size + ) + + data = quantize_affine_float8( + input_float, + scale, + target_dtype, + scale_dtype, + ) + + data, scale, zero_point = _layout.post_process( + data, + scale, + zero_point, + block_size, + ) + + tensor_impl_ctr = get_tensor_impl_constructor(type(_layout)) + tensor_impl = tensor_impl_ctr(data, scale, zero_point, _layout) + return cls( + tensor_impl, + block_size, + original_shape, + dtype=input_float.dtype, ) else: raise NotImplementedError( diff --git a/torchao/dtypes/affine_quantized_tensor_ops.py b/torchao/dtypes/affine_quantized_tensor_ops.py index 1d70f5c7f3..e9702a33ac 100644 --- a/torchao/dtypes/affine_quantized_tensor_ops.py +++ b/torchao/dtypes/affine_quantized_tensor_ops.py @@ -90,7 +90,12 @@ _linear_bf16_act_uint4_weight_check, _linear_bf16_act_uint4_weight_impl, ) -from torchao.quantization.quant_primitives import dequantize_affine +from torchao.quantization.quant_primitives import ( + ZeroPointDomain, + dequantize_affine, + dequantize_affine_float_zero_point, + dequantize_affine_no_zero_point, +) from torchao.utils import ( fill_defaults, ) @@ -313,7 +318,14 @@ def _(func, types, args, kwargs): # batchsize or other dims gets added to sliced_data, sliced_scale and sliced_zero_point so # we need to increase block size to correct dim new_blocks = idx.dim() - 1 - return dequantize_affine( + if args[1].zero_point_domain == ZeroPointDomain.FLOAT: + _dequantize_affine = dequantize_affine_float_zero_point + elif args[1].zero_point_domain == ZeroPointDomain.NONE: + _dequantize_affine = dequantize_affine_no_zero_point + else: + _dequantize_affine = dequantize_affine + + return _dequantize_affine( sliced_data, new_blocks * [1] + list(args[1].block_size), sliced_scale, @@ -321,7 +333,6 @@ def _(func, types, args, kwargs): sliced_data.dtype, args[1].quant_min, args[1].quant_max, - args[1].zero_point_domain, output_dtype=sliced_scale.dtype, ) diff --git a/torchao/dtypes/uintx/int4_cpu_layout.py b/torchao/dtypes/uintx/int4_cpu_layout.py index 9c368fd17a..56812ee4e1 100644 --- a/torchao/dtypes/uintx/int4_cpu_layout.py +++ b/torchao/dtypes/uintx/int4_cpu_layout.py @@ -17,7 +17,10 @@ register_layout, ) from torchao.dtypes.utils import AQTTensorImpl, Layout, is_device -from torchao.quantization.quant_primitives import ZeroPointDomain +from torchao.quantization.quant_primitives import ( + ZeroPointDomain, + quantize_affine_float_zero_point, +) from torchao.utils import ( TORCH_VERSION_AT_LEAST_2_5, TORCH_VERSION_AT_LEAST_2_6, @@ -236,10 +239,6 @@ def block_size(self): return (1, groupsize) def get_plain(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - from torchao.quantization.quant_primitives import ( - ZeroPointDomain, - quantize_affine, - ) from torchao.quantization.utils import unpack_tinygemm_scales_and_zeros scale, zero = unpack_tinygemm_scales_and_zeros(self.scale_and_zero) @@ -255,7 +254,7 @@ def get_plain(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: target_dtype = torch.int32 quant_min = 0 quant_max = 15 - zero_point_domain = ZeroPointDomain.FLOAT + # zero_point_domain is ZeroPointDomain.FLOAT # TODO: clean up later assert len(block_size) == 2 and block_size[0] == 1 dequantized = torch.ops.aten._weight_int4pack_mm_for_cpu( torch.eye(eye_shape, device=device, dtype=original_dtype), @@ -267,7 +266,7 @@ def get_plain(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: # TODO: move this to `unpack_tinygemm_scales_and_zeros`? scale = scale.reshape(scale.shape[:-1]).contiguous() zero = zero.reshape(zero.shape[:-1]).contiguous() - int_data = quantize_affine( + int_data = quantize_affine_float_zero_point( dequantized, block_size, scale, @@ -275,7 +274,6 @@ def get_plain(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: target_dtype, quant_min, quant_max, - zero_point_domain, ) return int_data, scale, zero diff --git a/torchao/dtypes/uintx/tensor_core_tiled_layout.py b/torchao/dtypes/uintx/tensor_core_tiled_layout.py index 3bf9ef6b72..48910038cf 100644 --- a/torchao/dtypes/uintx/tensor_core_tiled_layout.py +++ b/torchao/dtypes/uintx/tensor_core_tiled_layout.py @@ -17,7 +17,11 @@ register_layout, ) from torchao.dtypes.utils import AQTTensorImpl, Layout, is_device -from torchao.quantization.quant_primitives import ZeroPointDomain, _get_reduction_params +from torchao.quantization.quant_primitives import ( + ZeroPointDomain, + _get_reduction_params, + quantize_affine_float_zero_point, +) from torchao.utils import ( TORCH_VERSION_AT_LEAST_2_5, fill_defaults, @@ -464,10 +468,6 @@ def block_size(self): return tuple([*ones, groupsize]) def get_plain(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - from torchao.quantization.quant_primitives import ( - ZeroPointDomain, - quantize_affine, - ) from torchao.quantization.utils import unpack_tinygemm_scales_and_zeros def dequant_4d(self): @@ -510,8 +510,7 @@ def dequant_4d(self): target_dtype = torch.int32 quant_min = 0 quant_max = 15 - zero_point_domain = ZeroPointDomain.FLOAT - int_data = quantize_affine( + int_data = quantize_affine_float_zero_point( dequantized, self.block_size, scale, @@ -519,7 +518,6 @@ def dequant_4d(self): target_dtype, quant_min, quant_max, - zero_point_domain, ) return int_data, scale, zero diff --git a/torchao/experimental/quant_passes.py b/torchao/experimental/quant_passes.py index 6c1fad5bbf..a7189d792b 100644 --- a/torchao/experimental/quant_passes.py +++ b/torchao/experimental/quant_passes.py @@ -91,7 +91,7 @@ def _get_q_dq_linear_patterns_replacements_and_filters( lcls = {} - pattern_str = f""" + pattern_str = """ def pattern( a, a_block_size, a_zero_point_dtype, w_int_data, w_block_size, w_scale, w_zero_point, w_target_dtype, @@ -121,7 +121,6 @@ def pattern( w_target_dtype, w_quant_min, w_quant_max, - {"'INT'" if has_weight_zeros else "'NONE'"} ) return torch.ops.aten.linear.default(dq_a, dq_w, bias) """ diff --git a/torchao/prototype/parq/quant/uniform_torchao.py b/torchao/prototype/parq/quant/uniform_torchao.py index 0579a23b02..f742778ed0 100644 --- a/torchao/prototype/parq/quant/uniform_torchao.py +++ b/torchao/prototype/parq/quant/uniform_torchao.py @@ -15,8 +15,14 @@ MappingType, ZeroPointDomain, choose_qparams_affine, + choose_qparams_affine_dont_preserve_zero, + choose_qparams_affine_tinygemm, dequantize_affine, + dequantize_affine_float_zero_point, + dequantize_affine_no_zero_point, quantize_affine, + quantize_affine_float_zero_point, + quantize_affine_no_zero_point, ) from .quantizer import Quantizer @@ -67,32 +73,46 @@ def quantize( # assume that p has already been grouped in QuantOptimizer.step block_size = (1, p.size(-1)) if dim is not None else p.size() - s, zero_point = choose_qparams_affine( + + if self.zero_point_domain == ZeroPointDomain.FLOAT and not self.preserve_zero: + _choose_qparams_affine = choose_qparams_affine_tinygemm + _quantize_affine = quantize_affine_float_zero_point + _dequantize_affine = dequantize_affine_float_zero_point + elif self.zero_point_domain == ZeroPointDomain.INT and not self.preserve_zero: + _choose_qparams_affine = choose_qparams_affine_dont_preserve_zero + _quantize_affine = quantize_affine + _dequantize_affine = dequantize_affine + else: # Default case: zero_point_domain == ZeroPointDomain.INT/NONE and preserve_zero + _choose_qparams_affine = choose_qparams_affine + if self.zero_point_domain == ZeroPointDomain.INT: + _quantize_affine = quantize_affine + _dequantize_affine = dequantize_affine + else: + _quantize_affine = quantize_affine_no_zero_point + _dequantize_affine = dequantize_affine_no_zero_point + + s, zero_point = _choose_qparams_affine( p, self.mapping_type, block_size, self.target_dtype, eps=self.eps, - preserve_zero=self.preserve_zero, quant_min=self.quant_min, quant_max=self.quant_max, - zero_point_domain=self.zero_point_domain, ) q_args = (block_size, s, zero_point, self.target_dtype) - q = quantize_affine( + q = _quantize_affine( p, *q_args, quant_min=self.quant_min, quant_max=self.quant_max, - zero_point_domain=self.zero_point_domain, ) - q = dequantize_affine( + q = _dequantize_affine( q, *q_args, output_dtype=p.dtype, quant_min=self.quant_min, quant_max=self.quant_max, - zero_point_domain=self.zero_point_domain, ) Q = torch.arange( @@ -104,14 +124,13 @@ def quantize( else: block_size = Q.shape - Q = dequantize_affine( + Q = _dequantize_affine( Q, block_size, *q_args[1:], output_dtype=p.dtype, quant_min=self.quant_min, quant_max=self.quant_max, - zero_point_domain=self.zero_point_domain, ) return q, Q diff --git a/torchao/quantization/__init__.py b/torchao/quantization/__init__.py index dc6431b2cf..109de5c0c5 100644 --- a/torchao/quantization/__init__.py +++ b/torchao/quantization/__init__.py @@ -79,7 +79,9 @@ TorchAODType, ZeroPointDomain, choose_qparams_affine, + choose_qparams_affine_dont_preserve_zero, choose_qparams_affine_floatx, + choose_qparams_affine_tinygemm, choose_qparams_affine_with_min_max, choose_qparams_and_quantize_affine_hqq, dequantize_affine, @@ -161,6 +163,8 @@ "AffineQuantizedObserverBase", # quant primitive ops "choose_qparams_affine", + "choose_qparams_affine_tinygemm", + "choose_qparams_affine_dont_preserve_zero", "choose_qparams_affine_with_min_max", "choose_qparams_affine_floatx", "quantize_affine", diff --git a/torchao/quantization/pt2e/observer.py b/torchao/quantization/pt2e/observer.py index f6534308d8..b781f5a07e 100644 --- a/torchao/quantization/pt2e/observer.py +++ b/torchao/quantization/pt2e/observer.py @@ -1904,8 +1904,6 @@ def convert(self, model: torch.fx.GraphModule, observer_node: Node): self.eps, self.scale_dtype, self.zero_point_dtype, - self.preserve_zero, - self.zero_point_domain.name, ), ) scale_node = model.graph.call_function( @@ -1933,7 +1931,6 @@ def convert(self, model: torch.fx.GraphModule, observer_node: Node): self.target_dtype, self.quant_min, self.quant_max, - self.zero_point_domain.name, ), {}, ) @@ -1947,7 +1944,6 @@ def convert(self, model: torch.fx.GraphModule, observer_node: Node): self.target_dtype, self.quant_min, self.quant_max, - self.zero_point_domain.name, ), {"output_dtype": self.original_dtype}, ) diff --git a/torchao/quantization/qat/affine_fake_quantized_tensor.py b/torchao/quantization/qat/affine_fake_quantized_tensor.py index 6829441f51..fe26369c31 100644 --- a/torchao/quantization/qat/affine_fake_quantized_tensor.py +++ b/torchao/quantization/qat/affine_fake_quantized_tensor.py @@ -14,6 +14,8 @@ ZeroPointDomain, _get_and_check_qmin_qmax, choose_qparams_affine, + choose_qparams_affine_dont_preserve_zero, + choose_qparams_affine_tinygemm, ) from torchao.utils import TorchAOBaseTensor @@ -52,19 +54,42 @@ def forward( def apply_fake_quant_fn(t: torch.Tensor): assert isinstance(t, AffineFakeQuantizedTensor) qmin, qmax = _get_and_check_qmin_qmax(target_dtype, quant_min, quant_max) - scale, zero_point = choose_qparams_affine( - t.original_tensor, - mapping_type, - block_size, - target_dtype, - qmin, - qmax, - eps, - scale_dtype, - zero_point_dtype, - preserve_zero, - zero_point_domain, - ) + if zero_point_domain == ZeroPointDomain.FLOAT and not preserve_zero: + scale, zero_point = choose_qparams_affine_tinygemm( + t.original_tensor, + mapping_type, + block_size, + target_dtype, + qmin, + qmax, + eps, + scale_dtype, + zero_point_dtype, + ) + elif zero_point_domain == ZeroPointDomain.INT and not preserve_zero: + scale, zero_point = choose_qparams_affine_dont_preserve_zero( + t.original_tensor, + mapping_type, + block_size, + target_dtype, + qmin, + qmax, + eps, + scale_dtype, + zero_point_dtype, + ) + else: # Default case: zero_point_domain == ZeroPointDomain.INT and preserve_zero + scale, zero_point = choose_qparams_affine( + t.original_tensor, + mapping_type, + block_size, + target_dtype, + qmin, + qmax, + eps, + scale_dtype, + zero_point_dtype, + ) fq = _GenericFakeQuantize.apply( t, block_size, diff --git a/torchao/quantization/quant_primitives.py b/torchao/quantization/quant_primitives.py index d13ac330a0..1885755608 100644 --- a/torchao/quantization/quant_primitives.py +++ b/torchao/quantization/quant_primitives.py @@ -19,16 +19,21 @@ TORCH_VERSION_AT_LEAST_2_3, TORCH_VERSION_AT_LEAST_2_5, TORCH_VERSION_AT_LEAST_2_6, - _is_float8_type, _register_custom_op, ) __all__ = [ "choose_qparams_affine", + "choose_qparams_affine_tinygemm", + "choose_qparams_affine_dont_preserve_zero", "choose_qparams_affine_with_min_max", "choose_qparams_affine_floatx", "quantize_affine", + "quantize_affine_no_zero_point", + "quantize_affine_float_zero_point", "dequantize_affine", + "dequantize_affine_no_zero_point", + "dequantize_affine_float_zero_point", "quantize_affine_floatx", "dequantize_affine_floatx", "fake_quantize_affine", @@ -289,7 +294,6 @@ def quantize_affine( output_dtype: torch.dtype, quant_min: Optional[Union[int, float]] = None, quant_max: Optional[Union[int, float]] = None, - zero_point_domain: ZeroPointDomain = ZeroPointDomain.INT, ) -> torch.Tensor: """ Args: @@ -301,12 +305,6 @@ def quantize_affine( output_dtype (torch.dtype): requested dtype (e.g. torch.uint8) for output Tensor quant_min (Optional[int]): minimum quantized value for output Tensor, if not specified, it will be derived from dtype quant_max (Optional[int]): maximum quantized value for output Tensor, if not specified, it will be derived from dtype - zero_point_domain (ZeroPointDomain): the domain that zero_point is in, should be either integer or float - if zero_point is in integer domain, zero point is added to the quantized integer value during - quantization - if zero_point is in floating point domain, zero point is subtracted from the floating point (unquantized) - value during quantization - default is ZeroPointDomain.INT Note: How can block_size represent different granularities? @@ -324,10 +322,6 @@ def quantize_affine( Output: quantized tensor with requested dtype """ - if zero_point_domain is None: - raise ValueError("Please use ZeroPointDomain.NONE instead of None") - elif zero_point_domain is ZeroPointDomain.NONE and zero_point is not None: - raise ValueError("zero_point should be None when zero_point_domain is NONE") return _quantize_affine( input, block_size, @@ -336,7 +330,6 @@ def quantize_affine( output_dtype, quant_min, quant_max, - zero_point_domain.name, ) @@ -349,16 +342,12 @@ def _quantize_affine( output_dtype: torch.dtype, quant_min: Optional[Union[int, float, bool]] = None, quant_max: Optional[Union[int, float, bool]] = None, - zero_point_domain: str = ZeroPointDomain.INT.name, ) -> torch.Tensor: """op definition that has compatible signatures with custom op library Note: - zero_point_domain is optional specifies how we quantize the floating point to quantized data: + zero_point_domain is pre-defined specifies how we quantize the floating point to quantized data: INT: quantized_val = (float_val / scale) (integer) + zero_point (integer) - FLOAT: quantized_val = (float_val - (zero_point (float) - scale * mid_point)) / scale - None: quantized_val = (float_val / scale) | this is primarily used for floatx quantization - Where we do not want to round values to nearest integer and instead scale and cast. """ quant_min, quant_max = _get_and_check_qmin_qmax(output_dtype, quant_min, quant_max) # workaround for uintx dtypes, since we don't have native Uintx dtype connected with @@ -372,8 +361,6 @@ def _quantize_affine( zero_point, quant_min, quant_max, - output_dtype, - zero_point_domain, ).to(output_dtype) @@ -384,14 +371,12 @@ def _quantize_affine_no_dtype_cast( zero_point: Optional[torch.Tensor], quant_min: Union[int, float], quant_max: Union[int, float], - quant_dtype: torch.dtype, - zero_point_domain: str = ZeroPointDomain.INT.name, ) -> torch.Tensor: """ The op does the following: 1. figure out the dimension for reduction based on block_size, also reshape the input to align with the shape after reduction - 2. quantize the input based on the quantization parameters scale and zero_point and args like zero_point_domain + 2. quantize the input based on the quantization parameters scale and zero_point and zero_point_domain = INT 3. reshape the quantized result to origianl shape """ # TODO: validations @@ -421,27 +406,178 @@ def _quantize_affine_no_dtype_cast( # with numel=0 which we handle by unifying the two zero_point = None - if zero_point_domain == ZeroPointDomain.INT.name: - quant = torch.clamp( - torch.round(input * (1.0 / scale)) + zero_point, quant_min, quant_max - ) - elif zero_point_domain == ZeroPointDomain.NONE.name: - assert zero_point is None, ( - "zero_point should be None when zero_point_domain is NONE" - ) - if _is_float8_type(quant_dtype): - quant = torch.clamp(input * scale.reciprocal(), quant_min, quant_max) - else: - quant = torch.clamp( - torch.round(input * (1.0 / scale)), quant_min, quant_max - ) + quant = torch.clamp( + torch.round(input * (1.0 / scale)) + zero_point, quant_min, quant_max + ) + quant = quant.view(original_shape) + + return quant + + +def quantize_affine_float_zero_point( + input: torch.Tensor, + block_size: List[int], + scale: torch.Tensor, + zero_point: Optional[torch.Tensor], + output_dtype: torch.dtype, + quant_min: Optional[Union[int, float, bool]] = None, + quant_max: Optional[Union[int, float, bool]] = None, +) -> torch.Tensor: + """ + The op does the following: + 1. figure out the dimension for reduction based on block_size, also reshape the input to align with + the shape after reduction + 2. quantize the input based on the quantization parameters scale and zero_point and zero_point_domain = FLOAT + 3. reshape the quantized result to origianl shape + + Note: + zero_point_domain is pre-defined specifies how we quantize the floating point to quantized data: + FLOAT: quantized_val = (float_val - (zero_point (float) - scale * mid_point)) / scale + """ + quant_min, quant_max = _get_and_check_qmin_qmax(output_dtype, quant_min, quant_max) + # workaround for uintx dtypes, since we don't have native Uintx dtype connected with + # torch.uintx dtypes yet + if output_dtype in _SUB_BYTE_UINT_BOUNDS: + output_dtype = torch.uint8 + return _quantize_affine_float_zero_point_no_dtype_cast( + input, + block_size, + scale, + zero_point, + quant_min, + quant_max, + ).to(output_dtype) + + +def _quantize_affine_float_zero_point_no_dtype_cast( + input: torch.Tensor, + block_size: Tuple[int, ...], + scale: torch.Tensor, + zero_point: Optional[torch.Tensor], + quant_min: Optional[Union[int, float]] = None, + quant_max: Optional[Union[int, float]] = None, +) -> torch.Tensor: + """ + The op does the following: + 1. figure out the dimension for reduction based on block_size, also reshape the input to align with + the shape after reduction + 2. quantize the input based on the quantization parameters scale and zero_point and zero_point_domain = FLOAT + 3. reshape the quantized result to origianl shape + """ + # TODO: validations + # TODO: validate scale/zero_point dimensions are compatible with block_size + assert input.dtype in [ + torch.float32, + torch.float16, + torch.bfloat16, + ], f"Unsupported input dtype: {input.dtype}" + assert len(block_size) == input.dim(), ( + f"Got input dim:{input.dim()}, block_size: {block_size}" + ) + shape_for_reduction, reduction_dims = _get_reduction_params( + block_size, input.size() + ) + original_shape = input.shape + input = input.view(shape_for_reduction) + shape_after_reduction = shape_for_reduction + for i in reduction_dims: + shape_after_reduction[i] = 1 + scale = scale.view(shape_after_reduction) + + if zero_point is not None and zero_point.numel() > 0: + zero_point = zero_point.view(shape_after_reduction) else: - assert zero_point_domain == ZeroPointDomain.FLOAT.name - mid_point = (quant_max + quant_min + 1) / 2 - min_val = zero_point - scale * mid_point - quant = torch.clamp( - torch.round((input - min_val) / scale), quant_min, quant_max - ) + # in some cases zero_point being a non-value shows as a tensor + # with numel=0 which we handle by unifying the two + zero_point = None + + mid_point = (quant_max + quant_min + 1) / 2 + min_val = zero_point - scale * mid_point + quant = torch.clamp(torch.round((input - min_val) / scale), quant_min, quant_max) + quant = quant.view(original_shape) + + return quant + + +def quantize_affine_no_zero_point( + input: torch.Tensor, + block_size: List[int], + scale: torch.Tensor, + zero_point: Optional[torch.Tensor], + output_dtype: torch.dtype, + quant_min: Optional[Union[int, float, bool]] = None, + quant_max: Optional[Union[int, float, bool]] = None, +) -> torch.Tensor: + """ + The op does the following: + 1. figure out the dimension for reduction based on block_size, also reshape the input to align with + the shape after reduction + 2. quantize the input based on the quantization parameters scale and zero_point and zero_point_domain = NONE + 3. reshape the quantized result to origianl shape + + Note: + zero_point_domain is pre-defined specifies how we quantize the floating point to quantized data: + None: quantized_val = (float_val / scale) | this is primarily used for floatx quantization + Where we do not want to round values to nearest integer and instead scale and cast. + """ + quant_min, quant_max = _get_and_check_qmin_qmax(output_dtype, quant_min, quant_max) + # workaround for uintx dtypes, since we don't have native Uintx dtype connected with + # torch.uintx dtypes yet + if output_dtype in _SUB_BYTE_UINT_BOUNDS: + output_dtype = torch.uint8 + return _quantize_affine_no_zero_point_no_dtype_cast( + input, + block_size, + scale, + zero_point, + quant_min, + quant_max, + ).to(output_dtype) + + +def _quantize_affine_no_zero_point_no_dtype_cast( + input: torch.Tensor, + block_size: Tuple[int, ...], + scale: torch.Tensor, + zero_point: Optional[torch.Tensor], + quant_min: Optional[Union[int, float]] = None, + quant_max: Optional[Union[int, float]] = None, +) -> torch.Tensor: + """ + The op does the following: + 1. figure out the dimension for reduction based on block_size, also reshape the input to align with + the shape after reduction + 2. quantize the input based on the quantization parameters scale and zero_point and zero_point_domain = NONE + 3. reshape the quantized result to origianl shape + """ + # TODO: validations + # TODO: validate scale/zero_point dimensions are compatible with block_size + assert input.dtype in [ + torch.float32, + torch.float16, + torch.bfloat16, + ], f"Unsupported input dtype: {input.dtype}" + assert len(block_size) == input.dim(), ( + f"Got input dim:{input.dim()}, block_size: {block_size}" + ) + shape_for_reduction, reduction_dims = _get_reduction_params( + block_size, input.size() + ) + original_shape = input.shape + input = input.view(shape_for_reduction) + shape_after_reduction = shape_for_reduction + for i in reduction_dims: + shape_after_reduction[i] = 1 + scale = scale.view(shape_after_reduction) + + if zero_point is not None and zero_point.numel() > 0: + zero_point = zero_point.view(shape_after_reduction) + else: + # in some cases zero_point being a non-value shows as a tensor + # with numel=0 which we handle by unifying the two + zero_point = None + + quant = torch.clamp(torch.round(input * (1.0 / scale)), quant_min, quant_max) quant = quant.view(original_shape) return quant @@ -455,7 +591,6 @@ def dequantize_affine( input_dtype: torch.dtype, quant_min: Optional[Union[int, float]] = None, quant_max: Optional[Union[int, float]] = None, - zero_point_domain: ZeroPointDomain = ZeroPointDomain.INT, *, output_dtype: torch.dtype = torch.float32, ) -> torch.Tensor: @@ -470,20 +605,12 @@ def dequantize_affine( quant_min (Optional[int]): minimum quantized value for input Tensor quant_max (Optional[int]): maximum quantized value for input Tensor output_dtype (torch.dtype): dtype for output Tensor, default is fp32 - zero_point_domain (ZeroPointDomain): the domain that zero_point is in, should be either integer or float - if zero_point is in integer domain, zero point is added to the quantized integer value during - quantization - if zero_point is in floating point domain, zero point is subtracted from the floating point (unquantized) - value during quantization - default is ZeroPointDomain.INT + + Default value for zero_point is in integer domain, zero point is added to the quantized integer value during quantization Output: dequantized Tensor, with requested dtype or fp32 """ - if zero_point_domain is None: - raise ValueError("Please use ZeroPointDomain.NONE instead of None") - elif zero_point_domain is ZeroPointDomain.NONE and zero_point is not None: - raise ValueError("zero_point should be None when zero_point_domain is NONE") return _dequantize_affine( input, block_size, @@ -492,7 +619,6 @@ def dequantize_affine( input_dtype, quant_min, quant_max, - zero_point_domain.name, output_dtype=output_dtype, ) @@ -506,7 +632,6 @@ def _dequantize_affine( input_dtype: torch.dtype, quant_min: Optional[Union[int, float, bool]] = None, quant_max: Optional[Union[int, float, bool]] = None, - zero_point_domain: Optional[str] = ZeroPointDomain.INT.name, output_dtype: torch.dtype = torch.float32, ) -> torch.Tensor: """op definition that has compatible signatures with custom op library""" @@ -528,7 +653,6 @@ def _dequantize_affine( zero_point, quant_min, quant_max, - zero_point_domain, output_dtype, ) @@ -540,7 +664,6 @@ def _dequantize_affine_no_dtype_check( zero_point: Optional[torch.Tensor], quant_min: Union[int, float], quant_max: Union[int, float], - zero_point_domain: Optional[str] = ZeroPointDomain.INT.name, output_dtype: torch.dtype = torch.float32, ) -> torch.Tensor: """This function converts AQT tensors to their high precision floating point representation @@ -564,37 +687,202 @@ def _dequantize_affine_no_dtype_check( shape_after_reduction[i] = 1 scale = scale.view(shape_after_reduction) - if zero_point is not None: - zero_point = zero_point.view(shape_after_reduction) + if zero_point is not None: + zero_point = zero_point.view(shape_after_reduction) + + # Force a copy to avoid input modification due + # to upcoming in-place operations. + dequant = input.to(torch.int32, copy=True) + if zero_point is not None: + dequant = dequant - zero_point.to(torch.int32) + dequant = dequant.to(output_dtype) + dequant = dequant * scale + + return dequant.view(original_shape).to(output_dtype) + + +def _dequantize_affine_no_zero_point_no_dtype_check( + input: torch.Tensor, + block_size: List[int], + scale: torch.Tensor, + zero_point: Optional[torch.Tensor], + quant_min: Union[int, float], + quant_max: Union[int, float], + output_dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + """This function converts AQT tensors to their high precision floating point representation + + The op does the following: + 1. figure out the dimension for reduction based on block_size, also reshape the input to align with + the shape after reduction + 2. dequantize the input based on the quantization parameters scale and zero_point and args like zero_point_domain + 3. reshape the quantized result to origianl shape and change dtype to the output_dtype + """ + assert len(block_size) == input.dim(), ( + f"Got input dim:{input.dim()}, block_size: {block_size}" + ) + shape_for_reduction, reduction_dims = _get_reduction_params( + block_size, input.size() + ) + original_shape = input.shape + input = input.view(shape_for_reduction) + shape_after_reduction = shape_for_reduction + for i in reduction_dims: + shape_after_reduction[i] = 1 + scale = scale.view(shape_after_reduction) + + assert zero_point is None, ( + "zero_point should be None for dequantize_affine_no_zero_point" + ) + dequant = input.to(output_dtype) + dequant = dequant * scale + + return dequant.view(original_shape).to(output_dtype) + + +def dequantize_affine_no_zero_point( + input: torch.Tensor, + block_size: Tuple[int, ...], + scale: torch.Tensor, + zero_point: Optional[torch.Tensor], + input_dtype: torch.dtype, + quant_min: Optional[Union[int, float]] = None, + quant_max: Optional[Union[int, float]] = None, + *, + output_dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + """ + Args: + input (torch.Tensor): quantized tensor, should match the dtype `dtype` argument + block_size: (List[int]): granularity of quantization, this means the size of the tensor elements that's sharing the same qparam + e.g. when size is the same as the input tensor dimension, we are using per tensor quantization + scale (Tensor): quantization parameter for affine quantization + zero_point (Tensor): quantization parameter for affine quantization, no zero point is used for this op + input_dtype (torch.dtype): requested dtype (e.g. torch.uint8) for output Tensor + quant_min (Optional[int]): minimum quantized value for input Tensor + quant_max (Optional[int]): maximum quantized value for input Tensor + output_dtype (torch.dtype): dtype for output Tensor, default is fp32 + + Default value for zero_point is in integer domain, zero point is added to the quantized integer value during quantization + + Output: + dequantized Tensor, with requested dtype or fp32 + """ + # TODO: validate scale/zero_point dimensions are compatible with block_size + if input_dtype not in _SUB_BYTE_UINT_BOUNDS: + assert input.dtype == input_dtype, ( + f"Expected: {input_dtype}, got: {input.dtype}" + ) + assert output_dtype in [ + torch.float32, + torch.float16, + torch.bfloat16, + ], f"Unsupported output dtype: {output_dtype}" + quant_min, quant_max = _get_and_check_qmin_qmax(input_dtype, quant_min, quant_max) + return _dequantize_affine_no_zero_point_no_dtype_check( + input, + block_size, + scale, + zero_point, + quant_min, + quant_max, + output_dtype, + ) + + +def _dequantize_affine_float_zero_point_no_dtype_check( + input: torch.Tensor, + block_size: List[int], + scale: torch.Tensor, + zero_point: Optional[torch.Tensor], + quant_min: Union[int, float], + quant_max: Union[int, float], + output_dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + """This function converts AQT tensors to their high precision floating point representation + + The op does the following: + 1. figure out the dimension for reduction based on block_size, also reshape the input to align with + the shape after reduction + 2. dequantize the input based on the quantization parameters scale and zero_point and args like zero_point_domain + 3. reshape the quantized result to origianl shape and change dtype to the output_dtype + """ + assert len(block_size) == input.dim(), ( + f"Got input dim:{input.dim()}, block_size: {block_size}" + ) + shape_for_reduction, reduction_dims = _get_reduction_params( + block_size, input.size() + ) + original_shape = input.shape + input = input.view(shape_for_reduction) + shape_after_reduction = shape_for_reduction + for i in reduction_dims: + shape_after_reduction[i] = 1 + scale = scale.view(shape_after_reduction) + + if zero_point is not None: + zero_point = zero_point.view(shape_after_reduction) + + # TODO: this seems to be a detail for tinygemm (converting from uint to int, probably need to refactor this) + mid_point = (quant_max + quant_min + 1) / 2 + # This should allocate new memory and avoid input modification + dequant = input - mid_point + dequant = dequant.to(output_dtype) + dequant *= scale + if zero_point is not None: + dequant += zero_point + + return dequant.view(original_shape).to(output_dtype) + + +def dequantize_affine_float_zero_point( + input: torch.Tensor, + block_size: Tuple[int, ...], + scale: torch.Tensor, + zero_point: Optional[torch.Tensor], + input_dtype: torch.dtype, + quant_min: Optional[Union[int, float]] = None, + quant_max: Optional[Union[int, float]] = None, + *, + output_dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + """ + Args: + input (torch.Tensor): quantized tensor, should match the dtype `dtype` argument + block_size: (List[int]): granularity of quantization, this means the size of the tensor elements that's sharing the same qparam + e.g. when size is the same as the input tensor dimension, we are using per tensor quantization + scale (Tensor): quantization parameter for affine quantization + zero_point (Tensor): quantization parameter for affine quantization + input_dtype (torch.dtype): requested dtype (e.g. torch.uint8) for output Tensor + quant_min (Optional[int]): minimum quantized value for input Tensor + quant_max (Optional[int]): maximum quantized value for input Tensor + output_dtype (torch.dtype): dtype for output Tensor, default is fp32 + + Default value for zero_point is in floating point domain, zero point is subtracted from the floating point (unquantized) - if zero_point_domain == ZeroPointDomain.INT.name: - # Force a copy to avoid input modification due - # to upcoming in-place operations. - dequant = input.to(torch.int32, copy=True) - if zero_point is not None: - dequant = dequant - zero_point.to(torch.int32) - dequant = dequant.to(output_dtype) - dequant = dequant * scale - elif zero_point_domain == ZeroPointDomain.NONE.name: - assert zero_point is None, ( - "zero_point should be None when zero_point_domain is NONE" - ) - dequant = input.to(output_dtype) - dequant = dequant * scale - else: - assert zero_point_domain == ZeroPointDomain.FLOAT.name, ( - f"Unexpected zero point domain: {zero_point_domain}" + Output: + dequantized Tensor, with requested dtype or fp32 + """ + # TODO: validate scale/zero_point dimensions are compatible with block_size + if input_dtype not in _SUB_BYTE_UINT_BOUNDS: + assert input.dtype == input_dtype, ( + f"Expected: {input_dtype}, got: {input.dtype}" ) - # TODO: this seems to be a detail for tinygemm (converting from uint to int, probably need to refactor this) - mid_point = (quant_max + quant_min + 1) / 2 - # This should allocate new memory and avoid input modification - dequant = input - mid_point - dequant = dequant.to(output_dtype) - dequant *= scale - if zero_point is not None: - dequant += zero_point - - return dequant.view(original_shape).to(output_dtype) + assert output_dtype in [ + torch.float32, + torch.float16, + torch.bfloat16, + ], f"Unsupported output dtype: {output_dtype}" + quant_min, quant_max = _get_and_check_qmin_qmax(input_dtype, quant_min, quant_max) + return _dequantize_affine_float_zero_point_no_dtype_check( + input, + block_size, + scale, + zero_point, + quant_min, + quant_max, + output_dtype, + ) def fake_quantize_affine( @@ -708,24 +996,32 @@ def _do_fake_quantize_affine( """ input_dtype = input.dtype quant_min, quant_max = _get_and_check_qmin_qmax(quant_dtype, quant_min, quant_max) - q = _quantize_affine_no_dtype_cast( + if zero_point_domain == ZeroPointDomain.INT: + _quantize_affine = _quantize_affine_no_dtype_cast + _dequantize_affine = _dequantize_affine_no_dtype_check + elif zero_point_domain == ZeroPointDomain.FLOAT: + _quantize_affine = _quantize_affine_float_zero_point_no_dtype_cast + _dequantize_affine = _dequantize_affine_float_zero_point_no_dtype_check + elif ZeroPointDomain == ZeroPointDomain.NONE: + _quantize_affine = _quantize_affine_no_zero_point_no_dtype_cast + _dequantize_affine = _dequantize_affine_no_zero_point_no_dtype_check + else: + raise ValueError(f"Unrecognized zero point domain: {zero_point_domain}") + q = _quantize_affine( input, block_size, scale, zero_point, quant_min, quant_max, - quant_dtype, - zero_point_domain.name, ) - dq = _dequantize_affine_no_dtype_check( + dq = _dequantize_affine( q, block_size, scale, zero_point, quant_min, quant_max, - zero_point_domain.name, output_dtype=input_dtype, ) return (q, dq) @@ -735,51 +1031,33 @@ def _do_fake_quantize_affine( def choose_qparams_affine( input: torch.Tensor, mapping_type: MappingType, - block_size: Tuple[int, ...], + block_size: Tuple[int], target_dtype: torch.dtype, quant_min: Optional[Union[int, float]] = None, quant_max: Optional[Union[int, float]] = None, eps: Optional[float] = None, scale_dtype: Optional[torch.dtype] = None, - zero_point_dtype: Optional[torch.dtype] = None, - preserve_zero: bool = True, - zero_point_domain: ZeroPointDomain = ZeroPointDomain.INT, + zero_point_dtype: Optional[torch.dtype] = torch.int32, ) -> Tuple[torch.Tensor, torch.Tensor]: """ Args: input (torch.Tensor): fp32, bf16, fp16 input Tensor mapping_type (MappingType): determines how the qparams are calculated, symmetric or asymmetric - block_size: (Tuple[int, ...]): granularity of quantization, this means the size of the tensor elements that's sharing the same qparam + block_size: (Tuple[int]): granularity of quantization, this means the size of the tensor elements that's sharing the same qparam e.g. when size is the same as the input tensor dimension, we are using per tensor quantization target_dtype (torch.dtype): dtype for target quantized Tensor quant_min (Optional[int]): minimum quantized value for target quantized Tensor quant_max (Optioanl[int]): maximum quantized value for target quantized Tensor eps (Optional[float]): minimum scale, if not provided, default to eps of input.dtype scale_dtype (torch.dtype): dtype for scale Tensor - zero_point_dtype (torch.dtype): dtype for zero_point Tensor - preserve_zero (bool): a flag to indicate whether we need zero to be exactly - representable or not, this is typically required for ops that needs zero padding, like convolution - it's less important for ops that doesn't have zero padding in the op itself, like linear. - - For example, given a floating point Tensor [1.2, 0.1, 3.0, 4.0, 0.4, 0], if `preserve_zero` is True, - we'll make sure there is a integer value corresponding to the floating point 0, e.g. [-3, -8, 3, 7, -7, -8], 0 will be mapped to `-8` without loss. But if `preserve_zero` is not True, there won't be such - gurantee. - - If we don't need zero to be exactly representable, we won't do rounding and clamping for zero_point - - zero_point_domain (ZeroPointDomain): the domain that zero_point is in, should be either integer or float - if zero_point is in integer domain, zero point is added to the quantized integer value during - quantization - if zero_point is in floating point domain, zero point is subtracted from the floating point (unquantized) - value during quantization - default is ZeroPointDomain.INT + zero_point_dtype (torch.dtype): dtype for zero_point Tensor, defaults to torch.int32 + Now removed params: + zero_point_domain (ZeroPointDomain): the domain that zero_point is in, defaults to Integer or None + preserve_zero (bool): whether to preserve zero in the quantized Tensor, defaults to True Output: Tuple of scales and zero_points Tensor with requested dtype """ - if zero_point_domain is None: - raise ValueError("Please use ZeroPointDomain.NONE instead of None") - return _choose_qparams_affine( input, mapping_type.name, @@ -790,11 +1068,150 @@ def choose_qparams_affine( eps, scale_dtype, zero_point_dtype, - preserve_zero, - zero_point_domain.name, ) +# TODO: lower this op to custom op library +@torch.no_grad() +def choose_qparams_affine_tinygemm( + input: torch.Tensor, + mapping_type: MappingType, + block_size: Tuple[int], + target_dtype: torch.dtype, + quant_min: Optional[Union[int, float]] = None, + quant_max: Optional[Union[int, float]] = None, + eps: Optional[float] = None, + scale_dtype: Optional[torch.dtype] = None, + zero_point_dtype: Optional[torch.dtype] = None, +) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Specialized version of choose_qparams_affine + + This is used for tinygemm int4mm kernel where zero point is in floating point domain + and zero does not have to be exactly representable. + + Args: + input (torch.Tensor): fp32, bf16, fp16 input Tensor + mapping_type (MappingType): determines how the qparams are calculated, symmetric or asymmetric + block_size: (Tuple[int]): granularity of quantization, this means the size of the tensor elements that's sharing the same qparam + target_dtype (torch.dtype): dtype for target quantized Tensor + quant_min (Optional[int]): minimum quantized value for target quantized Tensor + quant_max (Optioanl[int]): maximum quantized value for target quantized Tensor + eps (Optional[float]): minimum scale, if not provided, default to eps of input.dtype + scale_dtype (torch.dtype): dtype for scale Tensor + zero_point_dtype (torch.dtype): dtype for zero_point Tensor + + Output: + Tuple of scales and zero_points Tensor with requested dtype + """ + quant_min, quant_max = _get_and_check_qmin_qmax(target_dtype, quant_min, quant_max) + assert mapping_type is MappingType.ASYMMETRIC, ( + f"Unsupported mapping type: {mapping_type}" + ) + if scale_dtype is None: + scale_dtype = input.dtype + if eps is None: + eps = torch.finfo(input.dtype).eps + + assert len(block_size) == input.dim(), ( + f"Got input dim:{input.dim()}, block_size: {block_size}" + ) + shape_for_reduction, reduction_dims = _get_reduction_params( + block_size, input.size() + ) + input = input.view(shape_for_reduction) + + min_val = torch.amin(input, dim=reduction_dims, keepdim=False) + max_val = torch.amax(input, dim=reduction_dims, keepdim=False) + + # For preserve_zero=False, we don't ensure zero is exactly representable + min_val_neg = min_val + max_val_pos = max_val + + scale = (max_val_pos - min_val_neg) / float(quant_max - quant_min) + scale = torch.clamp(scale, min=eps) + + # For zero_point_domain=FLOAT in asymmetric quantization + mid_point = (quant_max + quant_min + 1) / 2 + # this is not preserving zero_point, this is converting to TensorCoreTiledFormat + zero_point = min_val_neg + scale * mid_point + + if zero_point_dtype is None: + zero_point_dtype = input.dtype + + zero_point = zero_point.to(dtype=zero_point_dtype) + return scale.to(dtype=scale_dtype, device=input.device), zero_point + + +# TODO: lower this op to custom op library +def choose_qparams_affine_dont_preserve_zero( + input: torch.Tensor, + mapping_type: MappingType, + block_size: Tuple[int], + target_dtype: torch.dtype, + quant_min: Optional[Union[int, float, bool]] = None, + quant_max: Optional[Union[int, float, bool]] = None, + eps: Optional[float] = None, + scale_dtype: Optional[torch.dtype] = None, + zero_point_dtype: Optional[torch.dtype] = None, +) -> Tuple[torch.Tensor, torch.Tensor]: + """Specialized version of choose_qparams_affine with zero_point_domain=ZeroPointDomain.INT and preserve_zero=False. + + Args: + input (torch.Tensor): fp32, bf16, fp16 input Tensor + mapping_type (MappingType): determines how the qparams are calculated, asymmetric only + block_size: (Tuple[int]): granularity of quantization, this means the size of the tensor elements that's sharing the same qparam + target_dtype (torch.dtype): dtype for target quantized Tensor + quant_min (Optional[int]): minimum quantized value for target quantized Tensor + quant_max (Optioanl[int]): maximum quantized value for target quantized Tensor + eps (Optional[float]): minimum scale, if not provided, default to eps of input.dtype + scale_dtype (torch.dtype): dtype for scale Tensor + zero_point_dtype (torch.dtype): dtype for zero_point Tensor + Now removed params default values: + zero_point_domain (ZeroPointDomain): the domain that zero_point is in, defaults to Integer + preserve_zero (bool): whether to preserve zero in the quantized Tensor, defaults to False + + Output: + Tuple of scales and zero_points Tensor with requested dtype + """ + quant_min, quant_max = _get_and_check_qmin_qmax(target_dtype, quant_min, quant_max) + assert mapping_type == MappingType.ASYMMETRIC, ( + f"Unsupported mapping type: {mapping_type}" + ) + + if scale_dtype is None: + scale_dtype = input.dtype + if eps is None: + eps = torch.finfo(input.dtype).eps + + assert len(block_size) == input.dim(), ( + f"Got input dim:{input.dim()}, block_size: {block_size}" + ) + shape_for_reduction, reduction_dims = _get_reduction_params( + block_size, input.size() + ) + input = input.view(shape_for_reduction) + + min_val = torch.amin(input, dim=reduction_dims, keepdim=False) + max_val = torch.amax(input, dim=reduction_dims, keepdim=False) + + # For no preserve zero, we don't ensure zero is exactly representable + min_val_neg = min_val + max_val_pos = max_val + + scale = (max_val_pos - min_val_neg) / float(quant_max - quant_min) + scale = torch.clamp(scale, min=eps) + # Zero point is int + zero_point = quant_min - torch.round(min_val_neg / scale) + zero_point = torch.clamp(zero_point, quant_min, quant_max) + if zero_point_dtype is None: + zero_point_dtype = torch.int32 + return scale.to(dtype=scale_dtype, device=input.device), zero_point.to( + dtype=zero_point_dtype + ) + + +# TODO: lower this op to custom op library def choose_qparams_affine_with_min_max( min_val: torch.Tensor, max_val: torch.Tensor, @@ -821,86 +1238,24 @@ def choose_qparams_affine_with_min_max( """ if zero_point_domain is None: raise ValueError("Please use ZeroPointDomain.NONE instead of None") - return _choose_qparams_affine( - None, - mapping_type.name, - block_size, - target_dtype, - quant_min, - quant_max, - eps, - scale_dtype, - zero_point_dtype, - preserve_zero, - zero_point_domain.name, - min_val, - max_val, - ) - - -@register_custom_op -def _choose_qparams_affine( - input: Optional[torch.Tensor], - mapping_type: str, - block_size: List[int], - target_dtype: torch.dtype, - quant_min: Optional[Union[int, float, bool]] = None, - quant_max: Optional[Union[int, float, bool]] = None, - eps: Optional[float] = None, - scale_dtype: Optional[torch.dtype] = None, - zero_point_dtype: Optional[torch.dtype] = None, - preserve_zero: bool = True, - zero_point_domain: Optional[str] = "INT", - min_val: Optional[torch.Tensor] = None, - max_val: Optional[torch.Tensor] = None, -) -> Tuple[torch.Tensor, torch.Tensor]: - """op definition that has compatible signatures with custom op library - - The op does the following: - 1. figure out the dimension for reduction based on block_size - 2. find min_val/max_val based on the dimension for reduction - 3. calculate quantization parameters based on min_val/max_val based on args like `preserve_zero` - and `zero_point_domain` - """ quant_min, quant_max = _get_and_check_qmin_qmax(target_dtype, quant_min, quant_max) assert mapping_type in [ - MappingType.SYMMETRIC.name, - MappingType.SYMMETRIC_NO_CLIPPING_ERR.name, - MappingType.ASYMMETRIC.name, + MappingType.SYMMETRIC, + MappingType.SYMMETRIC_NO_CLIPPING_ERR, + MappingType.ASYMMETRIC, ], f"Unsupported mapping type: {mapping_type}" - if target_dtype in FP8_TYPES: - assert mapping_type == MappingType.SYMMETRIC.name, ( - f"Only symmetric quantization is supported for FP8 types, got {mapping_type}" - ) - - if input is not None: - if scale_dtype is None: - scale_dtype = input.dtype - if eps is None: - eps = torch.finfo(input.dtype).eps - - assert len(block_size) == input.dim(), ( - f"Got input dim:{input.dim()}, block_size: {block_size}" - ) - shape_for_reduction, reduction_dims = _get_reduction_params( - block_size, input.size() - ) - input = input.view(shape_for_reduction) - min_val = torch.amin(input, dim=reduction_dims, keepdim=False) - max_val = torch.amax(input, dim=reduction_dims, keepdim=False) - else: - assert min_val is not None and max_val is not None, ( - "Need to provide `min_val` and `max_val` when `input` is None, got: {min_val, max_val}" - ) - assert min_val.dtype == max_val.dtype, ( - "Expecting `min_val` and `max_val` to have the same dtype, got: {min_val.dtype, max_val.dtype}" - ) + assert min_val is not None and max_val is not None, ( + "Need to provide `min_val` and `max_val`, got: {min_val, max_val}" + ) + assert min_val.dtype == max_val.dtype, ( + "Expecting `min_val` and `max_val` to have the same dtype, got: {min_val.dtype, max_val.dtype}" + ) - if scale_dtype is None: - scale_dtype = min_val.dtype - if eps is None: - eps = torch.finfo(min_val.dtype).eps + if scale_dtype is None: + scale_dtype = min_val.dtype + if eps is None: + eps = torch.finfo(min_val.dtype).eps if preserve_zero: min_val_neg = torch.min(min_val, torch.zeros_like(min_val)) @@ -910,15 +1265,15 @@ def _choose_qparams_affine( max_val_pos = max_val if ( - mapping_type == MappingType.SYMMETRIC.name - or mapping_type == MappingType.SYMMETRIC_NO_CLIPPING_ERR.name + mapping_type == MappingType.SYMMETRIC + or mapping_type == MappingType.SYMMETRIC_NO_CLIPPING_ERR ): # scales - if mapping_type == MappingType.SYMMETRIC.name: + if mapping_type == MappingType.SYMMETRIC: max_val_pos = torch.max(-min_val_neg, max_val_pos) scale = max_val_pos / (float(quant_max - quant_min) / 2) else: - assert mapping_type == MappingType.SYMMETRIC_NO_CLIPPING_ERR.name + assert mapping_type == MappingType.SYMMETRIC_NO_CLIPPING_ERR # calculate smin and smax individually and choose the larger one. For example, if quant_min = -8 and # quant_max = 7. # - If smin is bigger: There would be coverage on negative values down to -8, and less rounding @@ -935,30 +1290,30 @@ def _choose_qparams_affine( raise ValueError( "preserve_zero == False is not supported for symmetric quantization" ) - if zero_point_domain == ZeroPointDomain.FLOAT.name: + if zero_point_domain == ZeroPointDomain.FLOAT: # TODO INT should not be a valid ZeroPointDomain for symmetric quantization since # symmetric quant doesn't have a zero_point raise ValueError( "zero_point_domain should be ZeroPointDomain.INT or ZeroPointDomain.NONE for symmetric quantization" ) - if zero_point_domain == ZeroPointDomain.NONE.name: + if zero_point_domain == ZeroPointDomain.NONE: zero_point = None else: zero_point = torch.full_like(scale, int((quant_max + quant_min + 1) / 2)) scale = torch.clamp(scale, min=eps) else: - assert mapping_type == MappingType.ASYMMETRIC.name + assert mapping_type == MappingType.ASYMMETRIC scale = (max_val_pos - min_val_neg) / float(quant_max - quant_min) scale = torch.clamp(scale, min=eps) - if zero_point_domain == ZeroPointDomain.NONE.name: + if zero_point_domain == ZeroPointDomain.NONE: zero_point = None - elif zero_point_domain == ZeroPointDomain.INT.name: + elif zero_point_domain == ZeroPointDomain.INT: zero_point = quant_min - torch.round(min_val_neg / scale) zero_point = torch.clamp(zero_point, quant_min, quant_max) if zero_point_dtype is None: zero_point_dtype = torch.int32 else: - assert zero_point_domain == ZeroPointDomain.FLOAT.name, ( + assert zero_point_domain == ZeroPointDomain.FLOAT, ( "zero_point must be in FLOAT/INT/None domain for asymmetric quantization" ) mid_point = (quant_max + quant_min + 1) / 2 @@ -969,7 +1324,90 @@ def _choose_qparams_affine( if zero_point is not None: zero_point = zero_point.to(dtype=zero_point_dtype) - return scale.to(dtype=scale_dtype), zero_point + return scale.to(dtype=scale_dtype, device=min_val.device), zero_point + + +@register_custom_op +def _choose_qparams_affine( + input: Optional[torch.Tensor], + mapping_type: str, + block_size: List[int], + target_dtype: torch.dtype, + quant_min: Optional[Union[int, float, bool]] = None, + quant_max: Optional[Union[int, float, bool]] = None, + eps: Optional[float] = None, + scale_dtype: Optional[torch.dtype] = None, + zero_point_dtype: Optional[torch.dtype] = None, +) -> Tuple[torch.Tensor, torch.Tensor]: + """op definition that has compatible signatures with custom op library + + The op does the following: + 1. figure out the dimension for reduction based on block_size + 2. find min_val/max_val based on the dimension for reduction + 3. calculate quantization parameters based on min_val/max_val based on args like `preserve_zero` + and `zero_point_domain` + """ + quant_min, quant_max = _get_and_check_qmin_qmax(target_dtype, quant_min, quant_max) + assert mapping_type in [ + MappingType.SYMMETRIC.name, + MappingType.SYMMETRIC_NO_CLIPPING_ERR.name, + MappingType.ASYMMETRIC.name, + ], f"Unsupported mapping type: {mapping_type}" + + if scale_dtype is None: + scale_dtype = input.dtype + if eps is None: + eps = torch.finfo(input.dtype).eps + + assert len(block_size) == input.dim(), ( + f"Got input dim:{input.dim()}, block_size: {block_size}" + ) + shape_for_reduction, reduction_dims = _get_reduction_params( + block_size, input.size() + ) + input = input.view(shape_for_reduction) + + min_val = torch.amin(input, dim=reduction_dims, keepdim=False) + max_val = torch.amax(input, dim=reduction_dims, keepdim=False) + + min_val_neg = torch.min(min_val, torch.zeros_like(min_val)) + max_val_pos = torch.max(max_val, torch.zeros_like(max_val)) + + if ( + mapping_type == MappingType.SYMMETRIC.name + or mapping_type == MappingType.SYMMETRIC_NO_CLIPPING_ERR.name + ): + # scales + if mapping_type == MappingType.SYMMETRIC.name: + max_val_pos = torch.max(-min_val_neg, max_val_pos) + scale = max_val_pos / (float(quant_max - quant_min) / 2) + else: + assert mapping_type == MappingType.SYMMETRIC_NO_CLIPPING_ERR.name + # calculate smin and smax individually and choose the larger one. For example, if quant_min = -8 and + # quant_max = 7. + # - If smin is bigger: There would be coverage on negative values down to -8, and less rounding + # error than the existing SYMMETRIC case. + # - If smax is bigger: it covers the positive values up to 7. The round + # error may be bigger than the existing SYMMETRIC case. Either way, there's no out-of-range fp values after + # quantization. + smin = min_val_neg / float(quant_min) + smax = max_val_pos / float(quant_max) + mask = smin > smax + scale = torch.where(mask, smin, smax) + zero_point = torch.full_like(scale, int((quant_max + quant_min + 1) / 2)) + scale = torch.clamp(scale, min=eps) + else: + assert mapping_type == MappingType.ASYMMETRIC.name + scale = (max_val_pos - min_val_neg) / float(quant_max - quant_min) + scale = torch.clamp(scale, min=eps) + zero_point = quant_min - torch.round(min_val_neg / scale) + zero_point = torch.clamp(zero_point, quant_min, quant_max) + if zero_point_dtype is None: + zero_point_dtype = torch.int32 + + return scale.to(dtype=scale_dtype, device=input.device), zero_point.to( + dtype=zero_point_dtype + ) def choose_qparams_and_quantize_affine_qqq( @@ -1531,6 +1969,7 @@ def dequantize_affine_floatx( def choose_qparams_affine_float8( tensor: torch.Tensor, float8_dtype: torch.dtype = torch.float8_e4m3fn, + scale_dtype: torch.dtype = torch.float32, ) -> torch.Tensor: """ Calculates float8 scaling factor for the given high precision tensor, using tensorwise granularity. @@ -1545,7 +1984,7 @@ def choose_qparams_affine_float8( max_val_pos = torch.max(tensor) max_val_pos = torch.max(-min_val_neg, max_val_pos) scale = max_val_pos / (float(quant_max - quant_min) / 2) - return scale.to(dtype=torch.float32) + return scale.to(dtype=scale_dtype) def quantize_affine_float8( diff --git a/torchao/quantization/utils.py b/torchao/quantization/utils.py index 22e14378f5..30b9980878 100644 --- a/torchao/quantization/utils.py +++ b/torchao/quantization/utils.py @@ -16,8 +16,14 @@ MappingType, ZeroPointDomain, choose_qparams_affine, + choose_qparams_affine_dont_preserve_zero, + choose_qparams_affine_tinygemm, dequantize_affine, + dequantize_affine_float_zero_point, + dequantize_affine_no_zero_point, quantize_affine, + quantize_affine_float_zero_point, + quantize_affine_no_zero_point, ) from torchao.utils import ( TORCH_VERSION_AT_LEAST_2_5, @@ -345,19 +351,42 @@ def get_groupwise_affine_qparams( dtype if zero_point_domain != ZeroPointDomain.INT else torch.int32 ) - scale, zero_point = choose_qparams_affine( - w, - mapping_type, - block_size, - target_dtype, - quant_min, - quant_max, - eps, - scale_dtype=scale_dtype, - zero_point_dtype=zero_point_dtype, - preserve_zero=preserve_zero, - zero_point_domain=zero_point_domain, - ) + if zero_point_domain == ZeroPointDomain.FLOAT and not preserve_zero: + scale, zero_point = choose_qparams_affine_tinygemm( + w, + mapping_type, + block_size, + target_dtype, + quant_min, + quant_max, + eps, + scale_dtype=scale_dtype, + zero_point_dtype=zero_point_dtype, + ) + elif zero_point_domain == ZeroPointDomain.INT and not preserve_zero: + scale, zero_point = choose_qparams_affine_dont_preserve_zero( + w, + mapping_type, + block_size, + target_dtype, + quant_min, + quant_max, + eps, + scale_dtype=scale_dtype, + zero_point_dtype=zero_point_dtype, + ) + else: # Default case: zero_point_domain == ZeroPointDomain.INT and preserve_zero + scale, zero_point = choose_qparams_affine( + w, + mapping_type, + block_size, + target_dtype, + quant_min, + quant_max, + eps, + scale_dtype=scale_dtype, + zero_point_dtype=zero_point_dtype, + ) return scale.to(dtype=dtype).reshape(w.shape[0], -1), zero_point.to( dtype=zero_point_dtype @@ -421,7 +450,16 @@ def groupwise_affine_quantize_tensor_from_qparams( quant_min = 0 quant_max = 2**n_bit - 1 - int_data = quantize_affine( + if zero_point_domain == ZeroPointDomain.INT: + _quantize_affine = quantize_affine + elif zero_point_domain == ZeroPointDomain.FLOAT: + _quantize_affine = quantize_affine_float_zero_point + elif ZeroPointDomain == ZeroPointDomain.NONE: + _quantize_affine = quantize_affine_no_zero_point + else: + raise ValueError(f"Unrecognized zero point domain: {zero_point_domain}") + + int_data = _quantize_affine( w, block_size, scales, @@ -429,7 +467,6 @@ def groupwise_affine_quantize_tensor_from_qparams( output_dtype, quant_min, quant_max, - zero_point_domain=zero_point_domain, ) if TORCH_VERSION_AT_LEAST_2_5 and w.shape[-1] > 1: if (not (check_cpu_version(int_data.device))) and ( @@ -477,7 +514,13 @@ def groupwise_affine_dequantize_tensor_from_qparams( input_dtype = torch.int32 quant_min = 0 quant_max = 2**n_bit - 1 - return dequantize_affine( + if zero_point_domain == ZeroPointDomain.INT: + _dequantize_affine = dequantize_affine + elif zero_point_domain == ZeroPointDomain.FLOAT: + _dequantize_affine = dequantize_affine_float_zero_point + else: + _dequantize_affine = dequantize_affine_no_zero_point + return _dequantize_affine( w_int32, block_size, scales, @@ -485,7 +528,6 @@ def groupwise_affine_dequantize_tensor_from_qparams( input_dtype, quant_min, quant_max, - zero_point_domain=zero_point_domain, output_dtype=scales.dtype, ) From 8e33b709780d956c69c86e301f51ad4719124dcf Mon Sep 17 00:00:00 2001 From: mobicham <37179323+mobicham@users.noreply.github.com> Date: Wed, 21 May 2025 23:39:44 +0200 Subject: [PATCH 039/165] Update GemLite to support vLLM V1 (#2199) * update to forward_functional() * add 8-bit symmetric case * ruff * fix test --- .../quantization/test_config_serialization.py | 2 - torchao/dtypes/uintx/gemlite_layout.py | 71 ++++--------------- torchao/quantization/quant_api.py | 8 +-- 3 files changed, 15 insertions(+), 66 deletions(-) diff --git a/test/quantization/test_config_serialization.py b/test/quantization/test_config_serialization.py index ba52b446b1..62edc6aad8 100644 --- a/test/quantization/test_config_serialization.py +++ b/test/quantization/test_config_serialization.py @@ -63,8 +63,6 @@ GemliteUIntXWeightOnlyConfig( group_size=128, # Optional, has default of 64 bit_width=8, # Optional, has default of 4 - packing_bitwidth=8, # Optional, has default of 32 - contiguous=True, # Optional, has default of None ), FPXWeightOnlyConfig(ebits=4, mbits=8), # Sparsity configs diff --git a/torchao/dtypes/uintx/gemlite_layout.py b/torchao/dtypes/uintx/gemlite_layout.py index 0c124eb343..1c840f7ec4 100644 --- a/torchao/dtypes/uintx/gemlite_layout.py +++ b/torchao/dtypes/uintx/gemlite_layout.py @@ -22,7 +22,6 @@ try: import gemlite - from gemlite.core import GemLiteLinearTriton except: gemlite = None @@ -51,18 +50,6 @@ def _same_metadata( ) -def scale_activations_no_scaling(x): - return x, None - - -def scale_activations_int8(x): - x_shape = x.shape - out_x = x.view(-1, x.shape[-1]) - scaled_x = torch.abs(out_x).amax(axis=1, keepdim=True) / 127 - out_x = torch.round(out_x / scaled_x).to(dtype=torch.int8) - return out_x.view(x_shape), scaled_x - - def get_gemlite_quant_kwargs(bit_width, group_size, dtype): from torchao.quantization.quant_primitives import MappingType, ZeroPointDomain @@ -93,8 +80,6 @@ def get_gemlite_aqt_kwargs( weight, group_size=64, bit_width=4, - packing_bitwidth=32, - contiguous=None, use_hqq=True, ): if gemlite is None: @@ -106,12 +91,7 @@ def get_gemlite_aqt_kwargs( 4, 8, ], f"gemlite only works with bit_width 4,8 but got {bit_width}" - assert packing_bitwidth in [ - 8, - 16, - 32, - None, - ], f"gemlite needs packing_bitwidth in [8, 16, 32] but got {packing_bitwidth}" + assert weight.dtype in [torch.float16, torch.bfloat16], ( f"gemlite only works with dtype torch.float16 or torch.bfloat16 but got {weight.dtype}" ) @@ -127,8 +107,6 @@ def get_gemlite_aqt_kwargs( aqt_kwargs["_layout"] = GemlitePackedLayout( group_size=group_size, bit_width=bit_width, - packing_bitwidth=packing_bitwidth, - contiguous=contiguous, ) aqt_kwargs["use_hqq"] = use_hqq return aqt_kwargs @@ -138,8 +116,6 @@ def get_gemlite_aqt_kwargs( class GemlitePackedLayout(Layout): group_size: Optional[int] = 64 bit_width: int = 4 - packing_bitwidth: int = None - contiguous: bool = None @register_layout(GemlitePackedLayout) @@ -216,13 +192,18 @@ def from_plain( group_size, bit_width = _layout.group_size, _layout.bit_width out_features, in_features = int_data.shape - gemlite_linear = gemlite.helper.A16Wn(device=int_data.device).from_weights( - int_data, scale, zero_point, bit_width, group_size, bias=None - ) + if bit_width == 8 and group_size == in_features: + gemlite_linear = gemlite.helper.A16W8(device=int_data.device).from_weights( + int_data, scales=scale, bias=None + ) + else: + gemlite_linear = gemlite.helper.A16Wn(device=int_data.device).from_weights( + int_data, scale, zero_point, bit_width, group_size, bias=None + ) gemlite_kwargs = { + "in_features": in_features, "out_features": out_features, - "scaled_activations": gemlite_linear.scaled_activations, "meta_args": gemlite_linear.get_meta_args(), } @@ -253,20 +234,17 @@ def _apply_fn_to_data(self, fn): def get_plain(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: device = self.packed_weight.device - elements_per_sample = self._layout.packing_bitwidth // self._layout.bit_width - in_features = ( - self.packed_weight.numel() * elements_per_sample - ) // self.gemlite_kwargs["out_features"] int_data = ( gemlite.bitpack.unpack_over_rows( self.packed_weight.cuda(), W_nbits=self._layout.bit_width, - num_output_rows=in_features, + num_output_rows=self.gemlite_kwargs["out_features"], dtype=torch.uint8, ) .t() .contiguous() ).to(device) + scale = self.scale.t().contiguous() zero_point = self.zero_point.t().contiguous() @@ -353,42 +331,21 @@ def block_size(self): return (1, self._layout.group_size) -# logic taken from gemlite's core.py -def _matmul_type_fn(batch_size: int, bit_width: int) -> str: - if batch_size > 64: - return "GEMM" - elif batch_size > 1: - return "GEMM_SPLITK" - else: - return gemlite.core.get_default_gemv(bit_width) - - def _linear_fp_act_int4_weight_gemlite_impl(input_tensor, weight_tensor, bias=None): if hasattr(weight_tensor, "tensor_impl"): weight_impl = weight_tensor.tensor_impl else: weight_impl = weight_tensor - batch_size = input_tensor.view(-1, input_tensor.shape[-1]).shape[0] - matmul_type = _matmul_type_fn(batch_size, weight_impl._layout.bit_width) - - if weight_impl.gemlite_kwargs["scaled_activations"]: - scale_activations = scale_activations_int8 - else: - scale_activations = scale_activations_no_scaling - - return GemLiteLinearTriton.forward_functional( + return gemlite.core.forward_functional( x=input_tensor, bias=bias, - matmul_type=matmul_type, - out_features=weight_impl.gemlite_kwargs["out_features"], - scale_activations=scale_activations, - meta_args=weight_impl.gemlite_kwargs["meta_args"], tensor_args=( weight_impl.packed_weight, weight_impl.scale, weight_impl.zero_point, ), + meta_args=weight_impl.gemlite_kwargs["meta_args"], ) diff --git a/torchao/quantization/quant_api.py b/torchao/quantization/quant_api.py index 15e3b20fc8..4229577b95 100644 --- a/torchao/quantization/quant_api.py +++ b/torchao/quantization/quant_api.py @@ -979,8 +979,6 @@ class GemliteUIntXWeightOnlyConfig(AOBaseConfig): group_size: Optional[int] = 64 bit_width: int = 4 - packing_bitwidth: int = 32 - contiguous: Optional[bool] = None set_inductor_config: bool = True @@ -994,8 +992,6 @@ def _gemlite_uintx_weight_only_transform( ): group_size = config.group_size bit_width = config.bit_width - packing_bitwidth = config.packing_bitwidth - contiguous = config.contiguous if config.set_inductor_config: torchao.quantization.utils.recommended_inductor_config_setter() @@ -1006,9 +1002,7 @@ def _gemlite_uintx_weight_only_transform( use_hqq = True if bit_width == 4 else False new_weight = to_affine_quantized_intx( weight, - **get_gemlite_aqt_kwargs( - weight, group_size, bit_width, packing_bitwidth, contiguous, use_hqq - ), + **get_gemlite_aqt_kwargs(weight, group_size, bit_width, use_hqq), ) module.weight = torch.nn.Parameter(new_weight, requires_grad=False) module.extra_repr = types.MethodType(_linear_extra_repr, module) From 7854249acadf43b7d304d7c27eee5f405990ae3c Mon Sep 17 00:00:00 2001 From: Jesse Cai Date: Wed, 21 May 2025 17:54:43 -0400 Subject: [PATCH 040/165] remove benchmarks from top level repo (#2233) Remove some .csv files I added accidentally in an earlier PR. --- e2e_fp8_sparse.csv | 8 -------- rowwise_scaled_linear_sparse_cutlass_time_results.csv | 4 ---- 2 files changed, 12 deletions(-) delete mode 100644 e2e_fp8_sparse.csv delete mode 100644 rowwise_scaled_linear_sparse_cutlass_time_results.csv diff --git a/e2e_fp8_sparse.csv b/e2e_fp8_sparse.csv deleted file mode 100644 index 05a80e13b7..0000000000 --- a/e2e_fp8_sparse.csv +++ /dev/null @@ -1,8 +0,0 @@ -num_tokens,bf16_latency (us),bf16_c_latency (us),fp8_c_time (us),fp8_c_sparse_time (us),fp8_c_activation_sparse_time (us),speedup -64,166.81599617004395,163.03999722003937,103.00800204277039,74.30399954319,102.81600058078766,1.0018674278409796 -128,156.25600516796112,151.5199989080429,99.93600100278854,75.45600086450577,102.04800218343735,0.9793038458817415 -256,172.28800058364868,159.58400070667267,114.07999694347382,82.43200182914734,111.07199639081955,1.0270815385551393 -512,218.87999773025513,204.6079933643341,144.0960019826889,114.56000059843063,139.48799669742584,1.0330351384661336 -1024,394.4000005722046,392.5440013408661,251.10399723052979,196.4160054922104,227.90400683879852,1.1017972027501084 -2048,764.6080255508423,734.8160147666931,480.70400953292847,381.1520040035248,426.68798565864563,1.1265937305239622 -4096,1658.8159799575806,1623.5840320587158,901.3440012931824,779.0079712867737,843.392014503479,1.0687129896811043 diff --git a/rowwise_scaled_linear_sparse_cutlass_time_results.csv b/rowwise_scaled_linear_sparse_cutlass_time_results.csv deleted file mode 100644 index 09bea2f9bd..0000000000 --- a/rowwise_scaled_linear_sparse_cutlass_time_results.csv +++ /dev/null @@ -1,4 +0,0 @@ -m,k,n,fp16_latency (ms),fp8_latency (ms),rowwise_scaled_linear_sparse_cutlass_f8f8 latency (ms),cusparselt latency (ms),f8f8 speedup (d/s) -2048,8192,8192,345.7919955253601,243.13600361347198,159.7760021686554,634.2080235481262,1.5217304245528933 -4096,8192,8192,756.3199996948242,500.2880096435547,363.647997379303,628.7999749183655,1.3757480124982768 -8192,8192,8192,1433.568000793457,982.5279712677002,895.3920006752014,859.935998916626,1.0973160029649482 From 5153bd3ce9fc4e873a00d7a24000114ce93a2899 Mon Sep 17 00:00:00 2001 From: Jesse Cai Date: Wed, 21 May 2025 19:55:52 -0400 Subject: [PATCH 041/165] clean up prototype folder (#2232) --- torchao/sparsity/prototype/__init__.py | 33 ------------------- .../sparsity/prototype/pruner/FPGM_pruner.py | 8 ----- torchao/sparsity/prototype/pruner/__init__.py | 17 ---------- .../pruner/base_structured_sparsifier.py | 10 ------ .../prototype/pruner/lstm_saliency_pruner.py | 8 ----- .../prototype/pruner/parametrization.py | 14 -------- .../prototype/pruner/saliency_pruner.py | 8 ----- .../sparsity/prototype/scheduler/__init__.py | 0 .../prototype/scheduler/base_scheduler.py | 8 ----- .../prototype/scheduler/cubic_scheduler.py | 8 ----- .../prototype/scheduler/lambda_scheduler.py | 8 ----- .../sparsity/prototype/sparsifier/__init__.py | 0 .../prototype/sparsifier/base_sparsifier.py | 8 ----- .../sparsifier/nearly_diagonal_sparsifier.py | 10 ------ .../sparsity/prototype/sparsifier/utils.py | 8 ----- .../sparsifier/weight_norm_sparsifier.py | 10 ------ 16 files changed, 158 deletions(-) delete mode 100644 torchao/sparsity/prototype/__init__.py delete mode 100644 torchao/sparsity/prototype/pruner/FPGM_pruner.py delete mode 100644 torchao/sparsity/prototype/pruner/__init__.py delete mode 100644 torchao/sparsity/prototype/pruner/base_structured_sparsifier.py delete mode 100644 torchao/sparsity/prototype/pruner/lstm_saliency_pruner.py delete mode 100644 torchao/sparsity/prototype/pruner/parametrization.py delete mode 100644 torchao/sparsity/prototype/pruner/saliency_pruner.py delete mode 100644 torchao/sparsity/prototype/scheduler/__init__.py delete mode 100644 torchao/sparsity/prototype/scheduler/base_scheduler.py delete mode 100644 torchao/sparsity/prototype/scheduler/cubic_scheduler.py delete mode 100644 torchao/sparsity/prototype/scheduler/lambda_scheduler.py delete mode 100644 torchao/sparsity/prototype/sparsifier/__init__.py delete mode 100644 torchao/sparsity/prototype/sparsifier/base_sparsifier.py delete mode 100644 torchao/sparsity/prototype/sparsifier/nearly_diagonal_sparsifier.py delete mode 100644 torchao/sparsity/prototype/sparsifier/utils.py delete mode 100644 torchao/sparsity/prototype/sparsifier/weight_norm_sparsifier.py diff --git a/torchao/sparsity/prototype/__init__.py b/torchao/sparsity/prototype/__init__.py deleted file mode 100644 index 821e5049e0..0000000000 --- a/torchao/sparsity/prototype/__init__.py +++ /dev/null @@ -1,33 +0,0 @@ -# Sparsifier -# Scheduler -from torchao.prototype.sparsity.scheduler.base_scheduler import BaseScheduler -from torchao.prototype.sparsity.scheduler.cubic_scheduler import CubicSL -from torchao.prototype.sparsity.scheduler.lambda_scheduler import LambdaSL -from torchao.prototype.sparsity.sparsifier.base_sparsifier import BaseSparsifier -from torchao.prototype.sparsity.sparsifier.nearly_diagonal_sparsifier import ( - NearlyDiagonalSparsifier, -) - -# Parametrizations -from torchao.prototype.sparsity.sparsifier.utils import ( - FakeSparsity, - fqn_to_module, - get_arg_info_from_tensor_fqn, - module_to_fqn, -) -from torchao.prototype.sparsity.sparsifier.weight_norm_sparsifier import ( - WeightNormSparsifier, -) - -__all__ = [ - "BaseScheduler", - "CubicSL", - "LambdaSL", - "BaseSparsifier", - "NearlyDiagonalSparsifier", - "FakeSparsity", - "fqn_to_module", - "get_arg_info_from_tensor_fqn", - "module_to_fqn", - "WeightNormSparsifier", -] diff --git a/torchao/sparsity/prototype/pruner/FPGM_pruner.py b/torchao/sparsity/prototype/pruner/FPGM_pruner.py deleted file mode 100644 index e11dedf9ab..0000000000 --- a/torchao/sparsity/prototype/pruner/FPGM_pruner.py +++ /dev/null @@ -1,8 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -from torchao.prototype.sparsity.pruner.FPGM_pruner import FPGMPruner - -__all__ = ["FPGMPruner"] diff --git a/torchao/sparsity/prototype/pruner/__init__.py b/torchao/sparsity/prototype/pruner/__init__.py deleted file mode 100644 index 9d7f775389..0000000000 --- a/torchao/sparsity/prototype/pruner/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -from .base_structured_sparsifier import BaseStructuredSparsifier -from .FPGM_pruner import FPGMPruner -from .lstm_saliency_pruner import LSTMSaliencyPruner -from .parametrization import ( - BiasHook, - FakeStructuredSparsity, -) -from .saliency_pruner import SaliencyPruner - -__all__ = [ - "BaseStructuredSparsifier", - "FPGMPruner", - "LSTMSaliencyPruner", - "BiasHook", - "FakeStructuredSparsity", - "SaliencyPruner", -] diff --git a/torchao/sparsity/prototype/pruner/base_structured_sparsifier.py b/torchao/sparsity/prototype/pruner/base_structured_sparsifier.py deleted file mode 100644 index 15471a4df8..0000000000 --- a/torchao/sparsity/prototype/pruner/base_structured_sparsifier.py +++ /dev/null @@ -1,10 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -from torchao.prototype.sparsity.pruner.base_structured_sparsifier import ( - BaseStructuredSparsifier, -) - -__all__ = ["BaseStructuredSparsifier"] diff --git a/torchao/sparsity/prototype/pruner/lstm_saliency_pruner.py b/torchao/sparsity/prototype/pruner/lstm_saliency_pruner.py deleted file mode 100644 index c22ca6bb59..0000000000 --- a/torchao/sparsity/prototype/pruner/lstm_saliency_pruner.py +++ /dev/null @@ -1,8 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -from torchao.prototype.sparsity.pruner.lstm_saliency_pruner import LSTMSaliencyPruner - -__all__ = ["LSTMSaliencyPruner"] diff --git a/torchao/sparsity/prototype/pruner/parametrization.py b/torchao/sparsity/prototype/pruner/parametrization.py deleted file mode 100644 index 03fab85f43..0000000000 --- a/torchao/sparsity/prototype/pruner/parametrization.py +++ /dev/null @@ -1,14 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -from torchao.prototype.sparsity.pruner.parametrization import ( - BiasHook, - FakeStructuredSparsity, -) - -__all__ = [ - "BiasHook", - "FakeStructuredSparsity", -] diff --git a/torchao/sparsity/prototype/pruner/saliency_pruner.py b/torchao/sparsity/prototype/pruner/saliency_pruner.py deleted file mode 100644 index 421cac2a6f..0000000000 --- a/torchao/sparsity/prototype/pruner/saliency_pruner.py +++ /dev/null @@ -1,8 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -from torchao.prototype.sparsity.pruner.saliency_pruner import SaliencyPruner - -__all__ = ["SaliencyPruner"] diff --git a/torchao/sparsity/prototype/scheduler/__init__.py b/torchao/sparsity/prototype/scheduler/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/torchao/sparsity/prototype/scheduler/base_scheduler.py b/torchao/sparsity/prototype/scheduler/base_scheduler.py deleted file mode 100644 index 30a29c4775..0000000000 --- a/torchao/sparsity/prototype/scheduler/base_scheduler.py +++ /dev/null @@ -1,8 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -from torchao.prototype.sparsity.scheduler.base_scheduler import BaseScheduler - -__all__ = ["BaseScheduler"] diff --git a/torchao/sparsity/prototype/scheduler/cubic_scheduler.py b/torchao/sparsity/prototype/scheduler/cubic_scheduler.py deleted file mode 100644 index 6ea7e95c36..0000000000 --- a/torchao/sparsity/prototype/scheduler/cubic_scheduler.py +++ /dev/null @@ -1,8 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -from torchao.prototype.sparsity.scheduler.cubic_scheduler import CubicSL - -__all__ = ["CubicSL"] diff --git a/torchao/sparsity/prototype/scheduler/lambda_scheduler.py b/torchao/sparsity/prototype/scheduler/lambda_scheduler.py deleted file mode 100644 index 97d0abf174..0000000000 --- a/torchao/sparsity/prototype/scheduler/lambda_scheduler.py +++ /dev/null @@ -1,8 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -from torchao.prototype.sparsity.scheduler.lambda_scheduler import LambdaSL - -__all__ = ["LambdaSL"] diff --git a/torchao/sparsity/prototype/sparsifier/__init__.py b/torchao/sparsity/prototype/sparsifier/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/torchao/sparsity/prototype/sparsifier/base_sparsifier.py b/torchao/sparsity/prototype/sparsifier/base_sparsifier.py deleted file mode 100644 index 3c9f0947d8..0000000000 --- a/torchao/sparsity/prototype/sparsifier/base_sparsifier.py +++ /dev/null @@ -1,8 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -from torchao.prototype.sparsity.sparsifier.base_sparsifier import BaseSparsifier - -__all__ = ["BaseSparsifier"] diff --git a/torchao/sparsity/prototype/sparsifier/nearly_diagonal_sparsifier.py b/torchao/sparsity/prototype/sparsifier/nearly_diagonal_sparsifier.py deleted file mode 100644 index e6caa9087d..0000000000 --- a/torchao/sparsity/prototype/sparsifier/nearly_diagonal_sparsifier.py +++ /dev/null @@ -1,10 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -from torchao.prototype.sparsity.sparsifier.nearly_diagonal_sparsifier import ( - NearlyDiagonalSparsifier, -) - -__all__ = ["NearlyDiagonalSparsifier"] diff --git a/torchao/sparsity/prototype/sparsifier/utils.py b/torchao/sparsity/prototype/sparsifier/utils.py deleted file mode 100644 index 8cb822513e..0000000000 --- a/torchao/sparsity/prototype/sparsifier/utils.py +++ /dev/null @@ -1,8 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -from torchao.prototype.sparsity.sparsifier.utils import FakeSparsity - -__all__ = ["FakeSparsity"] diff --git a/torchao/sparsity/prototype/sparsifier/weight_norm_sparsifier.py b/torchao/sparsity/prototype/sparsifier/weight_norm_sparsifier.py deleted file mode 100644 index 93d2799316..0000000000 --- a/torchao/sparsity/prototype/sparsifier/weight_norm_sparsifier.py +++ /dev/null @@ -1,10 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -from torchao.prototype.sparsity.sparsifier.weight_norm_sparsifier import ( - WeightNormSparsifier, -) - -__all__ = ["WeightNormSparsifier"] From c4a7ad46f821fa286bbbfe30ad60ecea348dc967 Mon Sep 17 00:00:00 2001 From: Jerry Zhang Date: Wed, 21 May 2025 19:09:24 -0700 Subject: [PATCH 042/165] Relax MOE constraints and add test for torch.mm computation (#2227) * Relax some constraints to allow quantizing aten.mm Summary: Currently both float8 dynamic quant and int4 weight only quant only works with F.linear, not aten.mm this PR allows fallback to dequantizing tensors and run the fallback path before the real support is in place. Test Plan: python test/dtypes/test_affine_quantized.py -k test_mm_int4wo python test/dtypes/test_affine_quantized_float.py -k test_mm_float8dq Reviewers: Subscribers: Tasks: Tags: * add skip if no cuda * update tests * update --- test/dtypes/test_affine_quantized.py | 18 ++++++++++++++++++ test/dtypes/test_affine_quantized_float.py | 21 +++++++++++++++++++++ torchao/prototype/moe_quant/utils.py | 7 ++++++- 3 files changed, 45 insertions(+), 1 deletion(-) diff --git a/test/dtypes/test_affine_quantized.py b/test/dtypes/test_affine_quantized.py index 4ed39a0eff..b74c5d2ecf 100644 --- a/test/dtypes/test_affine_quantized.py +++ b/test/dtypes/test_affine_quantized.py @@ -421,6 +421,24 @@ def test_slice_and_copy_int4wo(self, device, dtype): # making sure param.data is updated assert param.data.dequantize()[0][0] != 0 + @common_utils.parametrize("device", ["cuda"]) + @common_utils.parametrize("dtype", [torch.bfloat16]) + @skip_if_no_cuda() + @skip_if_rocm("ROCm enablement in progress") + def test_mm_int4wo(self, device, dtype): + weight = torch.randn(512, 1024).to(device).to(dtype) + weight = weight.t() + + l = torch.nn.Linear(512, 1024).to(device).to(dtype) + l.weight = torch.nn.Parameter(weight) + quantize_(l, Int4WeightOnlyConfig()) + # weight shape: 1024 x 512 + weight = l.weight + + input = torch.randn(1, 512, device=device, dtype=dtype) + # make sure it runs + torch.nn.functional.linear(input, weight) + common_utils.instantiate_parametrized_tests(TestAffineQuantized) common_utils.instantiate_parametrized_tests(TestAffineQuantizedBasic) diff --git a/test/dtypes/test_affine_quantized_float.py b/test/dtypes/test_affine_quantized_float.py index e0ac28872b..8c36f5ac7a 100644 --- a/test/dtypes/test_affine_quantized_float.py +++ b/test/dtypes/test_affine_quantized_float.py @@ -27,6 +27,7 @@ from torchao.float8.float8_utils import compute_error from torchao.quantization import ( + Float8DynamicActivationFloat8WeightConfig, float8_dynamic_activation_float8_weight, float8_weight_only, quantize_, @@ -292,6 +293,26 @@ def test_fp8_weight_dimension_warning(self): f"Expected warning message containing: {expected}", ) + @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") + @unittest.skipIf( + not is_sm_at_least_89(), "Requires GPU with compute capability >= 8.9" + ) + def test_mm_float8dq(self): + device = "cuda" + dtype = torch.bfloat16 + weight = torch.randn(512, 1024).to(device).to(dtype) + weight = weight.t() + + l = torch.nn.Linear(512, 1024).to(device).to(dtype) + l.weight = torch.nn.Parameter(weight) + quantize_(l, Float8DynamicActivationFloat8WeightConfig(granularity=PerRow())) + # weight shape: 1024 x 512 + weight = l.weight + + input = torch.randn(1, 512, device=device, dtype=dtype) + # make sure it runs + torch.nn.functional.linear(input, weight) + common_utils.instantiate_parametrized_tests(TestAffineQuantizedFloat8Compile) diff --git a/torchao/prototype/moe_quant/utils.py b/torchao/prototype/moe_quant/utils.py index 16fa8c8d33..0e75de2ee4 100644 --- a/torchao/prototype/moe_quant/utils.py +++ b/torchao/prototype/moe_quant/utils.py @@ -1,3 +1,9 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD 3-Clause license found in the +# LICENSE file in the root directory of this source tree. + import torch from torch.utils._python_dispatch import ( return_and_correct_aliasing, @@ -282,7 +288,6 @@ def moe_quant_fn(module, config: MoEQuantConfig): warnings.simplefilter("ignore", lineno=84) warnings.simplefilter("ignore", lineno=105) - assert "ConditionalFeedForwardAOQuantizable" in str(type(module)) for weight_attr in ["w1", "w2", "w3"]: param = getattr(module, weight_attr) From f0f976cede3ed51edf1b690d82bfc0d72d81b79b Mon Sep 17 00:00:00 2001 From: Andrey Talman Date: Thu, 22 May 2025 07:35:27 -0400 Subject: [PATCH 043/165] Try fixing CI by pinning pytest (#2238) * Try fixing CI by pinning pytest * Update regression_test.yml --- .github/workflows/regression_test.yml | 4 ++-- dev-requirements.txt | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/regression_test.yml b/.github/workflows/regression_test.yml index f1188fd7d5..f4f054b307 100644 --- a/.github/workflows/regression_test.yml +++ b/.github/workflows/regression_test.yml @@ -64,7 +64,7 @@ jobs: torch-spec: 'torch==2.5.1 --index-url https://download.pytorch.org/whl/cu121' gpu-arch-type: "cuda" gpu-arch-version: "12.6" - dev-requirements-overrides: "s/^pytest$/pytest==7.4.0/" + dev-requirements-overrides: "s/^pytest.*$/pytest==7.4.0/" - name: CUDA 2.6 runs-on: linux.g5.12xlarge.nvidia.gpu torch-spec: 'torch==2.6.0' @@ -83,7 +83,7 @@ jobs: torch-spec: 'torch==2.5.1 --index-url https://download.pytorch.org/whl/cpu' gpu-arch-type: "cpu" gpu-arch-version: "" - dev-requirements-overrides: "s/^pytest$/pytest==7.4.0/" + dev-requirements-overrides: "s/^pytest.*$/pytest==7.4.0/" - name: CPU 2.6 runs-on: linux.4xlarge torch-spec: 'torch==2.6.0 --index-url https://download.pytorch.org/whl/cpu' diff --git a/dev-requirements.txt b/dev-requirements.txt index 600d5001cf..f9e4381e4e 100644 --- a/dev-requirements.txt +++ b/dev-requirements.txt @@ -1,5 +1,5 @@ # Test utilities -pytest +pytest==8.3.4 unittest-xml-reporting parameterized packaging From 4c6188f3f20724c8bbab545e74a6a65356c4e08e Mon Sep 17 00:00:00 2001 From: Jesse Cai Date: Thu, 22 May 2025 11:44:38 -0400 Subject: [PATCH 044/165] [sparse] Add fp8 sparse gemm with rowwise scaling for activation sparsity (#2242) * [sparse] Add fp8 sparse gemm with rowwise scaling for activation sparsity Summary: We have this gemm already in torchao, but for weight sparsity. For activation sparsity, we need the weights to be stored in column-major format to allow for us to use the selective weight loading kernel for decode. Test Plan: Reviewers: Subscribers: Tasks: Tags: * remove cutlass compression * ruff fix * one more ruff fix * don't build for CUDA 11.8 * fix formatting * ifdef to avoid issues --- setup.py | 1 + test/sparsity/test_activation24.py | 66 ++++ torchao/csrc/cuda/activation24/sparse_gemm.cu | 351 ++++++++++++++++++ torchao/ops.py | 25 ++ 4 files changed, 443 insertions(+) create mode 100644 torchao/csrc/cuda/activation24/sparse_gemm.cu diff --git a/setup.py b/setup.py index cabaad01cf..0915f6ae1e 100644 --- a/setup.py +++ b/setup.py @@ -433,6 +433,7 @@ def get_extensions(): "to_sparse_semi_structured_cutlass_sm9x_f8.cu", ), os.path.join(extensions_cuda_dir, "activation24", "sparsify24.cu"), + os.path.join(extensions_cuda_dir, "activation24", "sparse_gemm.cu"), ] for dtypes in ["e4m3e4m3", "e4m3e5m2", "e5m2e4m3", "e5m2e5m2"]: cutlass_90a_sources.append( diff --git a/test/sparsity/test_activation24.py b/test/sparsity/test_activation24.py index 65b7cfd8d2..420bf4328a 100644 --- a/test/sparsity/test_activation24.py +++ b/test/sparsity/test_activation24.py @@ -8,6 +8,7 @@ PerRow, quantize_, ) +from torchao.quantization.quant_api import _float8_cutlass_quant torch.sparse.SparseSemiStructuredTensor._FORCE_CUTLASS = True @@ -141,3 +142,68 @@ def srelu_linear(x): custom_output = reference_linear_copy(input_tensor) torch.testing.assert_close(reference_output, custom_output, rtol=0.1, atol=0.01) + + +@unittest.skipIf(not is_sm_at_least_90(), "Need cuda arch greater than SM90") +def test_sparse24_fp8_sm90_cutlass_gemm_eye( + M=512, K=256, dtype=torch.float8_e4m3fn +) -> None: + torch.manual_seed(0) + + A_dense = create_semi_structured_tensor(M, K, dtype=torch.bfloat16).cuda() + A_aqt = _float8_cutlass_quant(A_dense, dtype) + A = A_aqt.tensor_impl.float8_data + + # NOTE: CUTLASS compression kernel expects the input to be *exactly* + # 2:4 sparse already (eg it does not select the largest values) + A_packed, A_mdata = to_sparse_semi_structured_cutlass_sm9x_f8(A) + assert torch.allclose( + A_packed.float().sum(), A.float().sum() + ) # Check all values are there + + # Check MM without scale + eye = torch.eye(A.shape[1], device=A.device, dtype=A.dtype).T + A_reconstructed = torch.ops.torchao.sparse24_fp8_sm90_cutlass_gemm( + A_packed, A_mdata, eye + ) + assert torch.allclose(A.float(), A_reconstructed.float()) + + # Check MM with scale + b_scale = torch.randn([1, A.shape[1]], device=eye.device, dtype=torch.float32) + a_scale = torch.randn([A.shape[0], 1], device=eye.device, dtype=torch.float32) + A_reconstructed = torch.ops.torchao._sparse24_fp8_sm90_cutlass_gemm( + A_packed, A_mdata, eye, a_scale=a_scale, b_scale=b_scale + ) + assert torch.allclose( + A.float() * b_scale * a_scale, A_reconstructed.float(), rtol=0.01 + ) + + +@unittest.skipIf(not is_sm_at_least_90(), "Need cuda arch greater than SM90") +def test_sparse24_fp8_sm90_cutlass_gemm_random_tensor( + M=512, N=1024, K=256, dtype=torch.float8_e4m3fn +) -> None: + def _to_fp8_rowwise(x: torch.Tensor, dtype): + max_v = torch.finfo(dtype).max + x_scale = (x.abs().max(1, keepdim=True)[0] / max_v).float() + x = (x / x_scale).to(dtype) + return x, x_scale + + torch.manual_seed(0) + A_dense = create_semi_structured_tensor(M, K, dtype=torch.bfloat16).cuda() + A, a_scale = _to_fp8_rowwise(A_dense, dtype) + + B_dense = torch.randn([N, K], device="cuda", dtype=torch.bfloat16) + B, b_scale = _to_fp8_rowwise(B_dense, dtype) + + B = B.T + b_scale = b_scale.T + + A_packed, A_mdata = to_sparse_semi_structured_cutlass_sm9x_f8(A) + out_sparse = torch.ops.torchao.sparse24_fp8_sm90_cutlass_gemm( + A_packed, A_mdata, B, a_scale=a_scale, b_scale=b_scale + ) + out_ref = torch._scaled_mm( + A, B, scale_a=a_scale, scale_b=b_scale, out_dtype=out_sparse.dtype + ) + assert torch.allclose(out_sparse, out_ref, rtol=0.01, atol=0.01) diff --git a/torchao/csrc/cuda/activation24/sparse_gemm.cu b/torchao/csrc/cuda/activation24/sparse_gemm.cu new file mode 100644 index 0000000000..776766794e --- /dev/null +++ b/torchao/csrc/cuda/activation24/sparse_gemm.cu @@ -0,0 +1,351 @@ +#include +#include +#include +#include +#include + +#if defined(TORCHAO_USE_CUTLASS) && !defined(_WIN32) && \ + defined(CUDA_VERSION) && (CUDA_VERSION >= 12020) +#define BUILD_SM90_24_FP8_CUTLASS_GEMM +#endif + +#if defined(BUILD_SM90_24_FP8_CUTLASS_GEMM) +#include +#include +#include +#include "cutlass/arch/wmma.h" +#include "cutlass/bfloat16.h" +#include "cutlass/cuda_host_adapter.hpp" +#include "cutlass/cutlass.h" +#include "cutlass/epilogue/collective/collective_builder.hpp" +#include "cutlass/gemm/collective/collective_builder.hpp" +#include "cutlass/gemm/gemm.h" +#include "cutlass/gemm/kernel/gemm_universal.hpp" +#include "cutlass/numeric_types.h" +#include "cutlass/transform/device/transform_universal_adapter.hpp" + +#include +#include + +using namespace at; + +namespace { +#define CUTLASS_STATUS_CHECK(status) \ + { \ + TORCH_CHECK( \ + status == cutlass::Status::kSuccess, \ + "Got CUTLASS error: ", \ + cutlass::cutlassGetStatusString(status)); \ + } + +template +struct identity { + CUTLASS_HOST_DEVICE + T operator()(T lhs) const { + return lhs; + } +}; + +template +struct SparseRowwiseKernel; + +template <> +struct SparseRowwiseKernel { + static constexpr auto kElementOutAt = at::ScalarType::BFloat16; + static constexpr auto kElementAAt = at::ScalarType::Float8_e4m3fn; + + using ElementA = cutlass::float_e4m3_t; + using ElementB = cutlass::float_e4m3_t; + using ElementOut = cutlass::bfloat16_t; + using ElementAccumulator = float; + + using TileShape = cute::Shape; + + // Epilogue visitor tree + using Accum = cutlass::epilogue::fusion::Sm90AccFetch; + using AScale = + cutlass::epilogue::fusion::Sm90ColBroadcast<0, TileShape, float>; + using BScale = + cutlass::epilogue::fusion::Sm90RowBroadcast<0, TileShape, float>; + using Multiply = cutlass::epilogue::fusion::Sm90Compute< + cutlass::multiplies, + float, + float, + cutlass::FloatRoundStyle::round_to_nearest>; + using Cast = cutlass::epilogue::fusion::Sm90Compute< + identity, + ElementOut, + float, + cutlass::FloatRoundStyle::round_to_nearest>; + using EpilogueEVT = cutlass::epilogue::fusion::Sm90EVT< + Cast, + cutlass::epilogue::fusion::Sm90EVT< + Multiply, + BScale, + cutlass::epilogue::fusion::Sm90EVT>>; + + using CollectiveEpilogue = + typename cutlass::epilogue::collective::CollectiveBuilder< + cutlass::arch::Sm90, + cutlass::arch::OpClassSparseTensorOp, + TileShape, + cute::Shape, + cutlass::epilogue::collective::EpilogueTileAuto, + ElementAccumulator, + float, + ElementOut, + cutlass::layout::RowMajor, + 1, + ElementOut, + cutlass::layout::RowMajor, + 1, + cutlass::epilogue::TmaWarpSpecializedCooperative, + EpilogueEVT>::CollectiveOp; + + using CollectiveMainloop = + typename cutlass::gemm::collective::CollectiveBuilder< + cutlass::arch::Sm90, + cutlass::arch::OpClassSparseTensorOp, + ElementA, + cutlass::layout::RowMajor, + 32, + ElementB, + cutlass::layout::ColumnMajor, + 16, + ElementAccumulator, + cute::Shape, + cute::Shape, + cutlass::gemm::collective::StageCountAutoCarveout( + sizeof(typename CollectiveEpilogue::SharedStorage))>, + cutlass::gemm::KernelTmaWarpSpecializedCooperativeFP8FastAccum>:: + CollectiveOp; + + // Gemm operator + // cutlass3x_sm90_sptensorop_s64x256x64spgemm_e4m3_e4m3_f32_bf16_bf16_128x256x128_2x1x1_0_tnt_align32_warpspecialized_cooperative_fp8_fastaccum_epi_tma + using GemmKernel = cutlass::gemm::kernel::GemmUniversal< + cute::Shape, + CollectiveMainloop, + CollectiveEpilogue, + cutlass::gemm::PersistentScheduler>; + using ElementE = CollectiveMainloop::ElementE; +}; + +template <> +struct SparseRowwiseKernel { + static constexpr auto kElementOutAt = at::ScalarType::BFloat16; + static constexpr auto kElementAAt = at::ScalarType::BFloat16; + + using ElementA = cutlass::bfloat16_t; + using ElementB = cutlass::bfloat16_t; + using ElementOut = cutlass::bfloat16_t; + + using TileShape = cute::Shape; + + // Epilogue visitor tree + using Accum = cutlass::epilogue::fusion::Sm90AccFetch; + using AScale = + cutlass::epilogue::fusion::Sm90ColBroadcast<0, TileShape, float>; + using BScale = + cutlass::epilogue::fusion::Sm90RowBroadcast<0, TileShape, float>; + using Multiply = cutlass::epilogue::fusion::Sm90Compute< + cutlass::multiplies, + float, + float, + cutlass::FloatRoundStyle::round_to_nearest>; + using Cast = cutlass::epilogue::fusion::Sm90Compute< + identity, + ElementOut, + float, + cutlass::FloatRoundStyle::round_to_nearest>; + using EpilogueEVT = cutlass::epilogue::fusion::Sm90EVT< + Cast, + cutlass::epilogue::fusion::Sm90EVT< + Multiply, + BScale, + cutlass::epilogue::fusion::Sm90EVT>>; + + using CollectiveEpilogue = + typename cutlass::epilogue::collective::CollectiveBuilder< + cutlass::arch::Sm90, + cutlass::arch::OpClassSparseTensorOp, + TileShape, + cute::Shape, + cutlass::epilogue::collective::EpilogueTileAuto, + float, + float, + ElementOut, + cutlass::layout::RowMajor, + 1, + ElementOut, + cutlass::layout::RowMajor, + 1, + cutlass::epilogue::TmaWarpSpecializedCooperative, + EpilogueEVT>::CollectiveOp; + + using CollectiveMainloop = + typename cutlass::gemm::collective::CollectiveBuilder< + cutlass::arch::Sm90, + cutlass::arch::OpClassSparseTensorOp, + ElementA, + cutlass::layout::RowMajor, + 16, + ElementB, + cutlass::layout::ColumnMajor, + 16, + float, + cute::Shape, + cute::Shape, + cutlass::gemm::collective::StageCountAutoCarveout( + sizeof(typename CollectiveEpilogue::SharedStorage))>, + cutlass::gemm::KernelTmaWarpSpecializedCooperative>::CollectiveOp; + + // Gemm operator + // cutlass3x_sm90_sptensorop_s64x128x32spgemm_bf16_bf16_f32_void_f32_128x128x64_2x1x1_0_ttn_align16_warpspecialized_cooperative_epi_tma + using GemmKernel = cutlass::gemm::kernel::GemmUniversal< + cute::Shape, + CollectiveMainloop, + CollectiveEpilogue, + cutlass::gemm::PersistentScheduler>; + using ElementE = CollectiveMainloop::ElementE; +}; + +template +Tensor _sparse24_fp8_sm90_cutlass_gemm( + const Tensor& tensor_a, + const Tensor& tensor_e, // metadata for `A` + const Tensor& tensor_b, + // *, + std::optional a_scale, + std::optional b_scale, + int64_t swizzle_size, + std::string swizzle_axis, + int64_t sm_count) { + std::optional device_guard; + if (!kIsMeta) { + device_guard.emplace(tensor_a.device()); + } + + using K = SparseRowwiseKernel; + + // For now, only CC 9.x devices are supported. + if (!kIsMeta) { + const auto dprops = at::cuda::getCurrentDeviceProperties(); + TORCH_CHECK( + dprops && dprops->major == 9, + "_sparse24_gemm_fp8_sm90: Supported only on GPUs with " + "compute capability 9.x"); + } + + // Validate layouts of input tensors. + TORCH_CHECK(tensor_a.device() == tensor_b.device()); + TORCH_CHECK(tensor_a.device() == tensor_e.device()); + TORCH_CHECK(tensor_a.dim() == 2); + TORCH_CHECK(tensor_b.dim() == 2); + TORCH_CHECK(tensor_a.scalar_type() == tensor_b.scalar_type()); + TORCH_CHECK(tensor_a.scalar_type() == K::kElementAAt); + TORCH_CHECK(tensor_b.stride(0) == 1, "B must be Row-Major"); + TORCH_CHECK(tensor_a.is_contiguous()); + TORCH_CHECK(tensor_b.t().is_contiguous()); + int64_t a_rows = tensor_a.size(0); + if (a_scale.has_value()) { + TORCH_CHECK(a_scale->is_contiguous()); + TORCH_CHECK(a_scale->scalar_type() == at::ScalarType::Float); + TORCH_CHECK(a_scale->device() == tensor_a.device()); + TORCH_CHECK(a_scale->dim() == 2); + TORCH_CHECK(a_scale->size(0) == a_rows); + TORCH_CHECK(a_scale->size(1) == 1); + } + if (b_scale.has_value()) { + TORCH_CHECK(b_scale->is_contiguous()); + TORCH_CHECK(b_scale->scalar_type() == at::ScalarType::Float); + TORCH_CHECK(b_scale->device() == tensor_b.device()); + TORCH_CHECK(b_scale->dim() == 2); + TORCH_CHECK(b_scale->size(0) == 1); + TORCH_CHECK(b_scale->size(1) == tensor_b.size(1)); + } + + typename K::GemmKernel::Arguments args; + args.mode = cutlass::gemm::GemmUniversalMode::kGemm; + args.problem_shape = cute::make_shape( + int(a_rows), int(tensor_b.size(1)), int(tensor_b.size(0)), 1); + Tensor out = tensor_a.new_empty( + {cute::get<0>(args.problem_shape), cute::get<1>(args.problem_shape)}, + at::TensorOptions().dtype(K::kElementOutAt)); + + args.mainloop.ptr_A = + reinterpret_cast(tensor_a.data_ptr()); + args.mainloop.ptr_B = static_cast(tensor_b.data_ptr()); + args.mainloop.ptr_E = + reinterpret_cast(tensor_e.data_ptr()); + args.epilogue.ptr_C = nullptr; + args.epilogue.ptr_D = static_cast(out.data_ptr()); + + float const* a_scale_ptr = + (float const*)(a_scale.has_value() ? a_scale->data_ptr() : nullptr); + float const* b_scale_ptr = + (float const*)(b_scale.has_value() ? b_scale->data_ptr() : nullptr); + float default_scale = 1.0f; // used if ptr is nullptr + auto& cast_op = args.epilogue.thread; + auto& mulB_op = cast_op.op_0; + mulB_op.op_0 = {b_scale_ptr, default_scale}; + auto& mulA_op = mulB_op.op_1; + mulA_op.op_0 = {a_scale_ptr, default_scale}; + + args.mainloop.layout_a = + K::CollectiveMainloop::SparseConfig::fill_layoutA(args.problem_shape); + args.mainloop.layout_e = + K::CollectiveMainloop::SparseConfig::fill_layoutE(args.problem_shape); + args.mainloop.dB = cute::make_int_tuple_from( + tensor_b.stride(1), 0); + args.epilogue.dC = cute::make_int_tuple_from( + out.stride(0), 0); + args.epilogue.dD = cute::make_int_tuple_from( + out.stride(0), 0); + + /* Query device SM count to pass onto the kernel as an argument, where needed + */ + args.hw_info.device_id = tensor_a.device().index(); + args.hw_info.sm_count = sm_count; + args.scheduler.max_swizzle_size = swizzle_size; + using Enum_t = decltype(args.scheduler.raster_order); + if (swizzle_axis == "n") { + args.scheduler.raster_order = Enum_t::AlongN; + } else { + TORCH_CHECK( + swizzle_axis == "m", + "Invalid value for swizzle_axis ('", + swizzle_axis, + "')"); + args.scheduler.raster_order = Enum_t::AlongM; + } + + using Gemm = cutlass::gemm::device::GemmUniversalAdapter; + int64_t device_op_workspace_size = Gemm::get_workspace_size(args); + Tensor workspace = tensor_a.new_empty( + {device_op_workspace_size}, + at::TensorOptions().dtype(at::ScalarType::Byte)); + + Gemm gemm; + // Check the problem size is supported or not + CUTLASS_STATUS_CHECK(gemm.can_implement(args)); + + auto status = gemm.run( + args, (void*)workspace.data_ptr(), at::cuda::getCurrentCUDAStream()); + CUTLASS_STATUS_CHECK(status); + C10_CUDA_KERNEL_LAUNCH_CHECK(); + return out; +} +} // namespace + +TORCH_LIBRARY_IMPL(torchao, CUDA, m) { + m.impl( + TORCH_SELECTIVE_NAME("torchao::sparse24_fp8_sm90_cutlass_gemm"), + TORCH_FN(_sparse24_fp8_sm90_cutlass_gemm)); +} + +TORCH_LIBRARY_IMPL(torchao, Meta, m) { + m.impl( + TORCH_SELECTIVE_NAME("torchao::sparse24_fp8_sm90_cutlass_gemm"), + TORCH_FN(_sparse24_fp8_sm90_cutlass_gemm)); +} +#endif diff --git a/torchao/ops.py b/torchao/ops.py index faebdbd5d1..b91bb8ae18 100644 --- a/torchao/ops.py +++ b/torchao/ops.py @@ -42,6 +42,9 @@ lib.define( "sparse24_sm90_sparsify(Tensor input, str metadata_fmt, str activation, str sp_selection_algo, *, ScalarType? dtype = None, Tensor? scale=None) -> (Tensor, Tensor)" ) +lib.define( + "sparse24_fp8_sm90_cutlass_gemm(Tensor a, Tensor a_mdata, Tensor b, *, Tensor? a_scale = None, Tensor? b_scale = None, int swizzle_size=8, str swizzle_axis='n', int sm_count=128) -> Tensor" +) lib.define( "swizzle_mm(Tensor mat1, Tensor mat2, bool mat1_is_swizzled, bool mat2_is_swizzled) -> Tensor" ) @@ -840,6 +843,28 @@ def sparse24_sm90_sparsify( ) +def sparse24_fp8_sm90_cutlass_gemm( + a: Tensor, + meta: Tensor, + b: Tensor, + a_scale: Optional[Tensor], + b_scale: Optional[Tensor], + swizzle_size: int, + swizzle_axis: str, + sm_count: int, +) -> Tensor: + return torch.ops.torchao.sparse24_fp8_sm90_cutlass_gemm( + a, + meta, + b, + a_scale=a_scale, + b_scale=b_scale, + swizzle_size=swizzle_size, + swizzle_axis=swizzle_axis, + sm_count=sm_count, + ) + + def swizzle_mm( mat1: Tensor, mat2: Tensor, mat1_is_swizzled: bool, mat2_is_swizzled: bool ) -> Tensor: From adc78b72cf6f8b1bb47b21d8fa525048e24f3497 Mon Sep 17 00:00:00 2001 From: Driss Guessous <32754868+drisspg@users.noreply.github.com> Date: Thu, 22 May 2025 10:26:13 -0700 Subject: [PATCH 045/165] Update Readme (#1526) --- README.md | 195 +++++++++++++++++---------------- torchao/quantization/README.md | 126 ++++----------------- 2 files changed, 120 insertions(+), 201 deletions(-) diff --git a/README.md b/README.md index 6d2e0ca031..8d524c5e7b 100644 --- a/README.md +++ b/README.md @@ -1,93 +1,82 @@ # torchao: PyTorch Architecture Optimization -[![](https://dcbadge.vercel.app/api/server/gpumode?style=flat)](https://discord.gg/gpumode) +[![](https://dcbadge.vercel.app/api/server/gpumode?style=flat&label=TorchAO%20in%20GPU%20Mode)](https://discord.com/channels/1189498204333543425/1205223658021458100) -[Introduction](#introduction) | [Inference](#inference) | [Training](#training) | [Composability](#composability) | [Custom Kernels](#custom-kernels) | [Alpha Features](#alpha-features) | [Installation](#installation) | [Integrations](#integrations) | [Videos](#videos) | [License](#license) | [Citation](#citation) + +[Introduction](#introduction) | [Inference](#inference) | [Training](#training) | [Installation](#installation) |[Composability](#composability) | [Prototype Features](#prototype-features) | [Integrations](#integrations) | [Videos](#videos) | [For Developers](#for-developers) | [License](#license) | [Citation](#citation) ## Introduction -torchao: PyTorch library for custom data types & optimizations. Quantize and sparsify weights, gradients, optimizers & activations for inference and training. +`torchao` accelerates PyTorch models with minimal code changes through advanced quantization and sparsification techniques. Optimize weights, gradients, activations, and more for both inference and training. From the team that brought you the fast series * 9.5x inference speedups for Image segmentation models with [sam-fast](https://pytorch.org/blog/accelerating-generative-ai) * 10x inference speedups for Language models with [gpt-fast](https://pytorch.org/blog/accelerating-generative-ai-2) * 3x inference speedup for Diffusion models with [sd-fast](https://pytorch.org/blog/accelerating-generative-ai-3) -torchao works for training too, with [up to 1.5x e2e speedups](https://pytorch.org/blog/training-using-float8-fsdp2/) on large scale (512 GPU / 405B parameter count) pretraining jobs with `torchao.float8`! - -torchao just works with `torch.compile()` and `FSDP2` over most PyTorch models on Huggingface out of the box. - -## Inference +`torchao` isn't just for inference - it delivers substantial speedups at scale, from [up to 1.5x speedups](https://pytorch.org/blog/training-using-float8-fsdp2/) on 512 GPU clusters, to [1.34-1.43x speedups](https://pytorch.org/blog/accelerating-large-scale-training-and-convergence-with-pytorch-float8-rowwise-on-crusoe-2k-h200s/) on 2K H200 clusters with the latest `torchao.float8` rowwise -### Post Training Quantization +`torchao` works out-of-the-box with `torch.compile()` and `FSDP2` across most Hugging Face PyTorch models -Quantizing and Sparsifying your models is a 1 liner that should work on any model with an `nn.Linear` including your favorite HuggingFace model. -There are 2 methods of post-training quantization, shown in the code snippets below: -1. Using torchao APIs directly. -2. Loading a huggingface model with a quantization config. - -#### Quantizing for inference with torchao APIs -```python -from torchao.quantization.quant_api import ( - quantize_, - Int8DynamicActivationInt8WeightConfig, - Int4WeightOnlyConfig, - Int8WeightOnlyConfig -) -quantize_(m, Int4WeightOnlyConfig()) -``` +## Inference -You can find a more comprehensive usage instructions for quantization [here](torchao/quantization/) and for sparsity [here](/torchao/_models/sam/README.md). +`torchao` delivers substantial performance gains with minimal code changes: -#### Quantizing for inference with huggingface configs +### Performance Highlights -See [docs](https://huggingface.co/docs/transformers/main/en/quantization/torchao) for more details. +- **INT4 Weight-Only Quantization**: 2x throughput (180 vs 107 tokens/sec) with 60% less memory (6.88 GB vs 16.43 GB) on LLaMA-3-7B +- **Float8 Dynamic Quantization**: 53.88% speedup on Flux.1-Dev* and 27.33% speedup on CogVideoX-5b on H100 GPU with preserved quality +- **INT4 + 2:4 Sparsity**: 2.4x throughput (226 vs 95 tokens/sec) with 80% memory reduction (5.3GB vs 16.4GB) on LLaMA-3-8B -For inference, we have the option of -1. Quantize only the weights: works best for memory bound models -2. Quantize the weights and activations: works best for compute bound models -2. Quantize the activations and weights and sparsify the weight +[View detailed benchmarks](torchao/quantization/README.md) | [Learn about sparsity](torchao/sparsity/README.md) -For gpt-fast `Int4WeightOnlyConfig()` is the best option at bs=1 as it **2x the tok/s and reduces the VRAM requirements by about 65%** over a torch.compiled baseline. +### Getting Started with Quantization -If you don't have enough VRAM to quantize your entire model on GPU and you find CPU quantization to be too slow then you can use the device argument like so `quantize_(model, Int8WeightOnlyConfig(), device="cuda")` which will send and quantize each layer individually to your GPU. +Quantize any model with `nn.Linear` layers (including HuggingFace models) in just one line: -If you see slowdowns with any of these techniques or you're unsure which option to use, consider using [autoquant](./torchao/quantization/README.md#autoquantization) which will automatically profile layers and pick the best way to quantize each layer. +#### Option 1: Direct TorchAO API ```python -model = torchao.autoquant(torch.compile(model, mode='max-autotune')) +from torchao.quantization.quant_api import quantize_, Int4WeightOnlyConfig +quantize_(model, Int4WeightOnlyConfig(group_size=128, use_hqq=True)) ``` -We also provide a developer facing API so you can implement your own quantization algorithms so please use the excellent [HQQ](https://github.com/pytorch/ao/tree/main/torchao/prototype/hqq) algorithm as a motivating example. +#### Option 2: HuggingFace Integration -### Evaluation +```python +from transformers import TorchAoConfig, AutoModelForCausalLM +from torchao.quantization.quant_api import Int4WeightOnlyConfig + +# Create quantization configuration +quantization_config = TorchAoConfig(quant_type=Int4WeightOnlyConfig(group_size=128, use_hqq=True)) + +# Load and automatically quantize +quantized_model = AutoModelForCausalLM.from_pretrained( + "microsoft/Phi-4-mini-instruct", + torch_dtype="auto", + device_map="auto", + quantization_config=quantization_config +) +``` -You can also use the EleutherAI [LM evaluation harness](https://github.com/EleutherAI/lm-evaluation-harness) to directly evaluate models -quantized with post training quantization, by following these steps: +### Deployment with vLLM -1. Quantize your model with a [post training quantization strategy](#post-training-quantization). -2. Save your model to disk or upload to huggingface hub ([instructions]( https://huggingface.co/docs/transformers/main/en/quantization/torchao?torchao=manual#serialization)). -3. [Install](https://github.com/EleutherAI/lm-evaluation-harness?tab=readme-ov-file#install) lm-eval. -4. Run an evaluation. Example: +Deploy quantized models with one command: -```bash -lm_eval --model hf --model_args pretrained=${HF_USER}/${MODEL_ID} --tasks hellaswag --device cuda:0 --batch_size 8 +```shell +vllm serve pytorch/Phi-4-mini-instruct-int4wo-hqq --tokenizer microsoft/Phi-4-mini-instruct -O3 ``` -Check out the lm-eval [usage docs](https://github.com/EleutherAI/lm-evaluation-harness?tab=readme-ov-file#basic-usage) for more details. - -### KV Cache Quantization - -We've added kv cache quantization and other features in order to enable long context length (and necessarily memory efficient) inference. +**Benefits**: 67% VRAM reduction and 12-20% speedup on A100 GPUs while maintaining quality. -In practice these features alongside int4 weight only quantization allow us to **reduce peak memory by ~55%**, meaning we can Llama3.1-8B inference with a **130k context length with only 18.9 GB of peak memory.** More details can be found [here](torchao/_models/llama/README.md) +[Step-by-step quantization guide](https://huggingface.co/pytorch/Phi-4-mini-instruct-int4wo-hqq#quantization-recipe) | [Pre-quantized models](https://huggingface.co/pytorch) ## Training ### Quantization Aware Training -Post-training quantization can result in a fast and compact model, but may also lead to accuracy degradation. We recommend exploring Quantization Aware Training (QAT) to overcome this limitation. In collaboration with Torchtune, we've developed a QAT recipe that demonstrates significant accuracy improvements over traditional PTQ, recovering **96% of the accuracy degradation on hellaswag and 68% of the perplexity degradation on wikitext** for Llama3 compared to post-training quantization (PTQ). And we've provided a full recipe [here](https://pytorch.org/blog/quantization-aware-training/). For more details, please see the [QAT README](./torchao/quantization/qat/README.md). +Post-training quantization can result in a fast and compact model, but may also lead to accuracy degradation. We recommend exploring Quantization Aware Training (QAT) to overcome this limitation. In collaboration with [Torchtune](https://github.com/pytorch/torchtune/blob/main/recipes/quantization.md#quantization-aware-training-qat), we've developed a QAT recipe that demonstrates significant accuracy improvements over traditional PTQ, recovering **96% of the accuracy degradation on hellaswag and 68% of the perplexity degradation on wikitext** for Llama3 compared to post-training quantization (PTQ). And we've provided a full recipe [here](https://pytorch.org/blog/quantization-aware-training/). For more details, please see the [QAT README](./torchao/quantization/qat/README.md). ```python from torchao.quantization import ( @@ -117,7 +106,7 @@ quantize_(my_model, Int8DynamicActivationInt4WeightConfig(group_size=32)) ### Float8 -[torchao.float8](torchao/float8) implements training recipes with the scaled float8 dtypes, as laid out in https://arxiv.org/abs/2209.05433. +[torchao.float8](torchao/float8) implements training recipes with the scaled float8 dtypes, as laid out in https://arxiv.org/abs/2209.05433 With ``torch.compile`` on, current results show throughput speedups of up to **1.5x on up to 512 GPU / 405B parameter count scale** ([details](https://pytorch.org/blog/training-using-float8-fsdp2/)) @@ -142,89 +131,88 @@ We've added support for semi-structured 2:4 sparsity with **6% end-to-end speedu The code change is a 1 liner with the full example available [here](torchao/sparsity/training/) ```python +from torchao.sparsity.training import SemiSparseLinear, swap_linear_with_semi_sparse_linear + swap_linear_with_semi_sparse_linear(model, {"seq.0": SemiSparseLinear}) ``` ### Memory-efficient optimizers -ADAM takes 2x as much memory as the model params so we can quantize the optimizer state to either 8 or 4 bit effectively reducing the optimizer VRAM requirements by 2x or 4x respectively over an fp16 baseline +Optimizers like ADAM can consume substantial GPU memory - 2x as much as the model parameters themselves. TorchAO provides two approaches to reduce this overhead: + +1. **Quantized optimizers**: Reduce optimizer state memory by 2-4x by quantizing to lower precision + ```python from torchao.optim import AdamW8bit, AdamW4bit, AdamWFp8 optim = AdamW8bit(model.parameters()) # replace with Adam4bit and AdamFp8 for the 4 / fp8 versions ``` +Our quantized optimizers are implemented in just a few hundred lines of PyTorch code and compiled for efficiency. While slightly slower than specialized kernels, they offer an excellent balance of memory savings and performance. See detailed [benchmarks here](https://github.com/pytorch/ao/tree/main/torchao/optim). -In practice, we are a tiny bit slower than expertly written kernels but the implementations for these optimizers were written in a **few hundred lines of PyTorch code** and compiled so please use them or copy-paste them for your quantized optimizers. Benchmarks [here](https://github.com/pytorch/ao/tree/main/torchao/optim) +2. **CPU offloading**: Move optimizer state and gradients to CPU memory -We also have support for [single GPU CPU offloading](https://github.com/pytorch/ao/tree/main/torchao/optim#optimizer-cpu-offload) where both the gradients (same size as weights) and the optimizers will be efficiently sent to the CPU. This alone can **reduce your VRAM requirements by 60%** +For maximum memory savings, we support [single GPU CPU offloading](https://github.com/pytorch/ao/tree/main/torchao/optim#optimizer-cpu-offload) that efficiently moves both gradients and optimizer state to CPU memory. This approach can **reduce your VRAM requirements by 60%** with minimal impact on training speed: ```python optim = CPUOffloadOptimizer(model.parameters(), torch.optim.AdamW, fused=True) optim.load_state_dict(ckpt["optim"]) ``` -## Composability - -1. `torch.compile`: A key design principle for us is composability as in any new dtype or layout we provide needs to work with our compiler. It shouldn't matter if the kernels are written in pure PyTorch, CUDA, C++, or Triton - things should just work! So we write the dtype, layout, or bit packing logic in pure PyTorch and code-generate efficient kernels. -3. [FSDP2](https://github.com/pytorch/torchtitan/blob/main/docs/fsdp.md): Historically most quantization has been done for inference, there is now a thriving area of research combining distributed algorithms and quantization. - -The best example we have combining the composability of lower bit dtype with compile and fsdp is [NF4](torchao/dtypes/nf4tensor.py) which we used to implement the [QLoRA](https://www.youtube.com/watch?v=UvRl4ansfCg) algorithm. So if you're doing research at the intersection of this area we'd love to hear from you. - -## Custom Kernels +## Installation -We've added support for authoring and releasing [custom ops](./torchao/csrc/) that do not graph break with `torch.compile()` so if you love writing kernels but hate packaging them so they work all operating systems and cuda versions, we'd love to accept contributions for your custom ops. We have a few examples you can follow +`torchao` makes liberal use of several new features in PyTorch, it's recommended to use it with the current nightly or latest stable version of PyTorch, see [getting started](https://pytorch.org/get-started/locally/) for more details. -1. [fp6](torchao/dtypes/floatx) for 2x faster inference over fp16 with an easy to use API `quantize_(model, FPXWeightOnlyConfig(3, 2))` -2. [2:4 Sparse Marlin GEMM](https://github.com/pytorch/ao/pull/733) 2x speedups for FP16xINT4 kernels even at batch sizes up to 256 -3. [int4 tinygemm unpacker](https://github.com/pytorch/ao/pull/415) which makes it easier to switch quantized backends for inference +Install the stable release (recommended): +```bash +pip install torchao +``` -If you believe there's other CUDA kernels we should be taking a closer look at please leave a comment on [this issue](https://github.com/pytorch/ao/issues/697) +Other options: +```bash +# Nightly build +pip install --pre torchao --index-url https://download.pytorch.org/whl/nightly/cu124 +# Different CUDA versions +pip install torchao --index-url https://download.pytorch.org/whl/cu118 # CUDA 11.8 +pip install torchao --index-url https://download.pytorch.org/whl/cpu # CPU only -## Alpha features +``` -Things we're excited about but need more time to cook in the oven +### Development Install +``` +USE_CPP=0 python setup.py develop # Skip C++/CUDA extensions +``` -1. [MX](torchao/prototype/mx_formats) training and inference support with tensors using the [OCP MX spec](https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf) data types, which can be described as groupwise scaled float8/float6/float4/int8, with the scales being constrained to powers of two. This work is prototype as the hardware support is not available yet. -2. [Int8 Quantized Training](https://github.com/pytorch/ao/tree/main/torchao/prototype/quantized_training): We're trying out full int8 training. This is easy to use with `quantize_(model, int8_weight_only_quantized_training())`. This work is prototype as the memory benchmarks are not compelling yet. -3. [IntX](https://github.com/pytorch/ao/tree/main/torchao/dtypes/uintx): We've managed to support all the ints by doing some clever bitpacking in pure PyTorch and then compiling it. This work is prototype as unfortunately without some more investment in either the compiler or low-bit kernels, int4 is more compelling than any smaller dtype -4. [Bitnet](https://github.com/pytorch/ao/blob/main/torchao/prototype/dtypes/bitnet.py): Mostly this is very cool to people on the team. This is prototype because how useful these kernels are is highly dependent on better hardware and kernel support. +## Composability +`torch.compile`: A key design principle for us is composability - any custom dtype or memory layout should work with our compiler. We enable kernel implementations in PyTorch, CUDA, C++, or Triton. This allows researchers and engineers to start with high-level dtype and layout logic in pure PyTorch, then progressively optimize performance by implementing lower-level kernels as needed, while maintaining compatibility with the compile infrastructure. -## Installation +[FSDP2](https://github.com/pytorch/torchtitan/blob/main/docs/fsdp.md): Historically most quantization has been done for inference, there is now a thriving area of research combining distributed algorithms and quantization. -`torchao` makes liberal use of several new features in Pytorch, it's recommended to use it with the current nightly or latest stable version of PyTorch. +The best example we have combining the composability of lower bit dtype with compile and fsdp is [NF4](torchao/dtypes/nf4tensor.py) which we used to implement the [QLoRA](https://www.youtube.com/watch?v=UvRl4ansfCg) algorithm. So if you're doing research at the intersection of this area we'd love to hear from you. -Stable release from Pypi which will default to CUDA 12.4 +Our framework makes it straightforward to add tensor parallel support to your custom quantized tensor subclass. Check out our [tensor parallel tutorial](tutorials/developer_api_guide/tensor_parallel.py) to see how a quantized tensor subclass can be extended to support column and row-wise tensor sharding while maintaining compatibility with `torch.compile`. -```Shell -pip install torchao -``` +## Prototype Features -Stable Release from the PyTorch index -```Shell -pip install torchao --extra-index-url https://download.pytorch.org/whl/cu124 # full options are cpu/cu118/cu124/cu126 -``` +The [prototype](torchao/prototype/README.md) directory contains experimental and upcoming features including: -Nightly Release -```Shell -pip install --pre torchao --index-url https://download.pytorch.org/whl/nightly/cu126 # full options are cpu/cu118/cu126/cu128 -``` +- [MX Training & Inference](torchao/prototype/mx_formats/README.md): MX formats with a native PyTorch POC +- [Int8 Quantized Training](torchao/prototype/quantized_training/README.md): Low-precision training methods +- And more experimental features in development -For *most* developers you probably want to skip building custom C++/CUDA extensions for faster iteration +These features are under active development and may change. We welcome contributions from researchers and developers! -```Shell -USE_CPP=0 pip install -e . -``` +> ⚠️ Note: Features in the prototype directory do not have BC guarantees and are subject to change. ## OSS Integrations We're also fortunate to be integrated into some of the leading open-source libraries including 1. Hugging Face transformers with a [builtin inference backend](https://huggingface.co/docs/transformers/main/quantization/torchao) and [low bit optimizers](https://github.com/huggingface/transformers/pull/31865) -2. Hugging Face diffusers best practices with torch.compile and torchao in a standalone repo [diffusers-torchao](https://github.com/sayakpaul/diffusers-torchao) +2. Hugging Face diffusers best practices with torch.compile and torchao in a standalone repo [diffusers-torchao](https://github.com/huggingface/diffusers/blob/main/docs/source/en/quantization/torchao.md) 3. Mobius HQQ backend leveraged our int4 kernels to get [195 tok/s on a 4090](https://github.com/mobiusml/hqq#faster-inference) -4. [TorchTune](https://github.com/pytorch/torchtune) for our QLoRA and QAT recipes -5. [torchchat](https://github.com/pytorch/torchchat) for post training quantization -6. SGLang for LLM serving: [usage](https://github.com/sgl-project/sglang/blob/4f2ee48ed1c66ee0e189daa4120581de324ee814/docs/backend/backend.md?plain=1#L83) and the major [PR](https://github.com/sgl-project/sglang/pull/1341). +4. [TorchTune](https://pytorch.org/torchtune/main/tutorials/qlora_finetune.html?highlight=qlora) for our QLoRA and QAT recipes +5. VLLM for LLM serving: [usage](https://docs.vllm.ai/en/latest/features/quantization/torchao.html) +6. SGLang for LLM serving: [usage](https://docs.sglang.ai/backend/server_arguments.html#server-arguments) and the major [PR](https://github.com/sgl-project/sglang/pull/1341). ## Videos * [Keynote talk at GPU MODE IRL](https://youtu.be/FH5wiwOyPX4?si=VZK22hHz25GRzBG1&t=1009) @@ -235,6 +223,19 @@ We're also fortunate to be integrated into some of the leading open-source libra * [Cohere for AI community talk](https://www.youtube.com/watch?v=lVgrE36ZUw0) +## For Developers + +### Custom Kernels + +We've added support for authoring and releasing [custom ops](./torchao/csrc/) that do not graph break with `torch.compile()`. We have a few examples you can follow + +1. [fp6](torchao/dtypes/floatx/README.md) for 2x faster inference over fp16 with an easy to use API `quantize_(model, fpx_weight_only(3, 2))` +2. [2:4 Sparse Marlin GEMM](https://github.com/pytorch/ao/pull/733) 2x speedups for FP16xINT4 kernels even at batch sizes up to 256 +3. [int4 tinygemm unpacker](https://github.com/pytorch/ao/pull/415) which makes it easier to switch quantized backends for inference + +If you believe there's other CUDA kernels we should be taking a closer look at please leave a comment on [this issue](https://github.com/pytorch/ao/issues/697) or feel free to contribute directly to the repo. + + ## License `torchao` is released under the [BSD 3](https://github.com/pytorch-labs/ao/blob/main/LICENSE) license. diff --git a/torchao/quantization/README.md b/torchao/quantization/README.md index d2b6e0c016..80737eb4bb 100644 --- a/torchao/quantization/README.md +++ b/torchao/quantization/README.md @@ -2,8 +2,8 @@ Typically quantization algorithms will have different schemes for how the activation and weights are quantized so A16W8 for instance means the activations are quantized to 16 bits wheras the weights are quantized to 8 bits. Trying out different quantization schemes in `torchao` is generally a 1 line change. Note: exact APIs are not stable, we may change them in the future. ## Benchmarks -Benchmarks and evaluation are run on a machine with a single NVIDIA-A100-80GB GPU using the scripts for [generation](../_models/llama/generate.py) and [eval](../_models/llama/eval.py). Evaluation was done using the lm_eval library for tasks/data. The models used were meta-llama/Llama-2-7b-chat-hf and meta-llama/Meta-Llama-3-8B. -### CUDA backend +Benchmarks and evaluation are gathered using the scripts for [generation](../_models/llama/generate.py) and [eval](../_models/llama/eval.py). Evaluation was done using the lm_eval library for tasks/data. The models used were meta-llama/Llama-2-7b-chat-hf and meta-llama/Meta-Llama-3-8B. +### CUDA backend | NVIDIA-A100-80GB GPU | Model | Technique | wikitext-perplexity | Tokens/Second | Memory Bandwidth (GB/s) | Peak Memory (GB) | Model Size (GB) | | ----------- | ----------------------- | ------------------- | ------------- | ----------------------- | ---------------- | --------------- | | Llama-2-7B | Base (bfloat16) | 12.212 | 107.38 | 1418.93 | 13.88 | 13.21 | @@ -29,7 +29,7 @@ Benchmarks and evaluation are run on a machine with a single NVIDIA-A100-80GB GP -### CUDA backend +### CUDA backend | NVIDIA-H100 GPU | Model | Technique | wikitext-perplexity | Tokens/Second | Memory Bandwidth (GB/s) | Peak Memory (GB) | Model Size (GB) | | ----------- | ----------------------- | ------------------- | ------------- | ----------------------- | ---------------- | --------------- | | Llama-3.1-8B | Base (bfloat16) | 7.54 | 126.90 | 1904.75 | 16.75 | 15.01 | @@ -38,7 +38,8 @@ Benchmarks and evaluation are run on a machine with a single NVIDIA-A100-80GB GP | | float8wo | 7.60 | 178.46 | 1339.93 | 12.09 | 7.51 | | | float8dq (PerTensor) | 7.62 | 116.40 | 873.58 | 11.14 | 7.51 | | | float8dq (Per Row) | 7.61 | 154.63 | 1161.47 | 11.14 | 7.51 | -### XPU backend + +### XPU backend | Intel-Max1100 | Model | Technique | wikitext-perplexity | Tokens/Second | Memory Bandwidth (GB/s) | Peak Memory (GB) | Model Size (GB) | | ----------- | ----------------------- | ------------------- | ------------- | ----------------------- | ---------------- | --------------- | | Llama-3-8.1B | Base (bfloat16) | 7.441 | 40.36 | 605.77 | 16.35 | 15.01 | @@ -46,7 +47,7 @@ Benchmarks and evaluation are run on a machine with a single NVIDIA-A100-80GB GP | | int8wo | 7.447 | 59.49 | 447.27 | 18.60 | 7.52 -Benchmarks and evaluation for model meta-llama/Meta-Llama-3.1-8B are run on a machine with a single NVIDIA-H100 GPU or Intel-Max1100 using the scripts for [generation](../_models/llama/generate.py) and [eval](../_models/llama/eval.py). Evaluation was done using the lm_eval library for tasks/data. +Benchmarks and evaluation for model meta-llama/Meta-Llama-3.1-8B are gathered using [generation](../_models/llama/generate.py) and [eval](../_models/llama/eval.py). Evaluation was done using the lm_eval library for tasks/data. note: Int8 dynamic quantization works best on compute bound models like [SAM](https://github.com/pytorch-labs/segment-anything-fast) whereas Llama with batchsize=1 tends to be memory bound, thus the rather low performance. @@ -54,6 +55,22 @@ For int4 we make heavy use of [tinygemm](https://github.com/pytorch/ao/blob/cb3b And a quick crash course on inference quantization to help parse the above table. Int4 quantization is an ambiguous term because there's the dtype in which a layer is represented and then the dtype in which the computation is done. For example, if you're using Weight-Only (wo) int4 quantization that means that the layer will be upcasted to a larger dtype like fp16 so an int4 matrix multiplication is defined as `F.linear(input, weight.to(input.dtype))`. Dynamic quantization (DQ) primarily targets activations, enabling on-the-fly quantization from higher precision formats like bf16 to lower precision formats such as int8. This process, when supported by hardware, allows for direct computation, such as performing `F.linear(input, weight)`. Naive quantization algorithms are also notoriously sensitive to outliers so we also typically set a group size that applies a scale factor per group of 64 elements in the case of `int4wo-64`. +## Evaluation + +You can also use the EleutherAI [LM evaluation harness](https://github.com/EleutherAI/lm-evaluation-harness) to directly evaluate models +quantized with post training quantization, by following these steps: + +1. Quantize your model with a [post training quantization strategy](#post-training-quantization). +2. Save your model to disk or upload to huggingface hub ([instructions]( https://huggingface.co/docs/transformers/main/en/quantization/torchao?torchao=manual#serialization)). +3. [Install](https://github.com/EleutherAI/lm-evaluation-harness?tab=readme-ov-file#install) lm-eval. +4. Run an evaluation. Example: + +```bash +lm_eval --model hf --model_args pretrained=${HF_USER}/${MODEL_ID} --tasks hellaswag --device cuda:0 --batch_size 8 +``` + +Check out the lm-eval [usage docs](https://github.com/EleutherAI/lm-evaluation-harness?tab=readme-ov-file#basic-usage) for more details. + ## Autoquantization Autoquantization is a tool to automatically determine the best way to apply quantization to your model by comparing the performance of each quantization technique to each layer for the input types and shapes you care about. @@ -393,105 +410,6 @@ You try can out these apis with the `quantize_` api as above alongside the const The `quantize_` and `autoquant` apis now automatically use our recommended inductor configuration setings. You can mimic the same configuration settings for your own experiments by using the `torchao.quantization.utils.recommended_inductor_config_setter` to replicate our recommended configuration settings. Alternatively if you wish to disable these recommended settings, you can use the key word argument `set_inductor_config` and set it to false in the `quantize_` or `autoquant` apis to prevent assignment of those configuration settings. You can also overwrite these configuration settings after they are assigned if you so desire, as long as they are overwritten before passing any inputs to the torch.compiled model. This means that previous flows which referenced a variety of inductor configurations that needed to be set are now outdated, though continuing to manually set those same inductor configurations is unlikely to cause any issues. -## (To be moved to prototype) A16W4 WeightOnly Quantization with GPTQ - -```python -from torchao._models._eval import InputRecorder, TransformerEvalWrapper -from torchao.quantization.GPTQ import Int4WeightOnlyGPTQQuantizer -precision = torch.bfloat16 -device = "cuda" -checkpoint_file_name = "../gpt-fast/checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth" -checkpoint_path = Path(checkpoint_file_name) -model = Transformer.from_name(checkpoint_path.parent.name) -checkpoint = torch.load(str(checkpoint_path), mmap=True, weights_only=True) -model.load_state_dict(checkpoint, assign=True) -model = model.to(dtype=precision, device="cpu") -model.eval() -tokenizer_path = checkpoint_path.parent / "tokenizer.model" -assert tokenizer_path.is_file(), tokenizer_path -tokenizer = SentencePieceProcessor( # pyre-ignore[28] - model_file=str(tokenizer_path) -) -blocksize = 128 -percdamp = 0.01 -groupsize = 128 -calibration_tasks = ["wikitext"] -calibration_limit = 1 -calibration_seq_length = 100 -input_prep_func = prepare_inputs_for_model -pad_calibration_inputs = False - -inputs = InputRecorder( - tokenizer, - calibration_seq_length, - input_prep_func, - pad_calibration_inputs, - model.config.vocab_size, - device="cpu", -).record_inputs( - calibration_tasks, - calibration_limit, -).get_inputs() - -quantizer = Int4WeightOnlyGPTQQuantizer( - blocksize, - percdamp, - groupsize, -) -model.setup_caches(max_batch_size=1, max_seq_length=calibration_seq_length) -model = quantizer.quantize(model, inputs).cuda() - -``` - -## (To be deprecated) A8W8 Dynamic Quantization - -```Python -from torchao.quantization.quant_api import Int8DynActInt4WeightQuantizer -quantizer = Int8DynActInt4WeightQuantizer(groupsize=128) -model = quantizer.quantize(model) -``` - -This is used in [ExecuTorch](https://github.com/pytorch/executorch) to quantize llama model right now. - -## (To be moved to prototype) A8W8 Dynamic Quantization with Smoothquant - -We've also implemented a version of [smoothquant](https://arxiv.org/abs/2211.10438) with the same GEMM format as above. Due to requiring calibration, the API is more complicated. - -Example - -```Python -import torch -from torchao.quantization.smoothquant import swap_linear_with_smooth_fq_linear, smooth_fq_linear_to_inference - -# Fuse the int8*int8 -> int32 matmul and subsequent mul op avoiding materialization of the int32 intermediary tensor -torch._inductor.config.force_fuse_int_mm_with_mul = True - -# plug in your model -model = get_model() - -# convert linear modules to smoothquant -# linear module in calibration mode -swap_linear_with_smooth_fq_linear(model) - -# Create a data loader for calibration -calibration_data = get_calibration_data() -calibration_dataset = MyDataset(calibration_data) -calibration_loader = DataLoader(calibration_dataset, batch_size=32, shuffle=True) - -# Calibrate the model -model.train() -for batch in calibration_loader: - inputs = batch - model(inputs) - -# set it to inference mode -smooth_fq_linear_to_inference(model) - -# compile the model to improve performance -model = torch.compile(model, mode='max-autotune') -model(input) -``` - ## Notes 1. APIs have been hardware tested on A100 and T4(colab) From 3884e29cc78de7688f44b317c457728fab1bf9f1 Mon Sep 17 00:00:00 2001 From: cccclai Date: Thu, 22 May 2025 15:10:19 -0700 Subject: [PATCH 046/165] Patch the _is_conv_node function Differential Revision: D74898941 Pull Request resolved: https://github.com/pytorch/ao/pull/2223 --- test/quantization/pt2e/test_quantize_pt2e.py | 20 ++++++++++++++++++++ torchao/quantization/pt2e/utils.py | 3 +++ 2 files changed, 23 insertions(+) diff --git a/test/quantization/pt2e/test_quantize_pt2e.py b/test/quantization/pt2e/test_quantize_pt2e.py index 75701c55ca..a96f397925 100644 --- a/test/quantization/pt2e/test_quantize_pt2e.py +++ b/test/quantization/pt2e/test_quantize_pt2e.py @@ -2478,6 +2478,26 @@ def forward(self, x): node_list, ) + example_inputs = (torch.randn(1, 3, 5, 5),) + node_occurrence = { + # two for input of the first conv, one for output for the first conv + torch.ops.quantized_decomposed.quantize_per_tensor.default: 2, + torch.ops.quantized_decomposed.dequantize_per_tensor.default: 3, + } + node_list = [ + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + torch.ops.aten.conv2d.padding, + torch.ops.aten.relu.default, + torch.ops.quantized_decomposed.quantize_per_tensor.default, + ] + self._test_quantizer( + TestHelperModules.ConvWithBNRelu(dim=2, relu=True, bn=True, padding="same"), + example_inputs, + BackendAQuantizer(), + node_occurrence, + node_list, + ) def test_conv_transpose3d_bn_relu(self): class BackendAQuantizer(Quantizer): def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule: diff --git a/torchao/quantization/pt2e/utils.py b/torchao/quantization/pt2e/utils.py index ad5c0ae179..dc5f802fb8 100644 --- a/torchao/quantization/pt2e/utils.py +++ b/torchao/quantization/pt2e/utils.py @@ -625,8 +625,11 @@ def _is_conv_node(n: Node): """ return n.op == "call_function" and n.target in [ torch.ops.aten.conv1d.default, + torch.ops.aten.conv1d.padding, torch.ops.aten.conv2d.default, + torch.ops.aten.conv2d.padding, torch.ops.aten.conv3d.default, + torch.ops.aten.conv3d.padding, ] From 446f07d5a20f997d8ddcb418b9dfd63fb9b4643e Mon Sep 17 00:00:00 2001 From: HDCharles <39544797+HDCharles@users.noreply.github.com> Date: Thu, 22 May 2025 23:11:45 -0400 Subject: [PATCH 047/165] Revert "Patch the _is_conv_node function" (#2247) This reverts commit 3884e29cc78de7688f44b317c457728fab1bf9f1. --- test/quantization/pt2e/test_quantize_pt2e.py | 20 -------------------- torchao/quantization/pt2e/utils.py | 3 --- 2 files changed, 23 deletions(-) diff --git a/test/quantization/pt2e/test_quantize_pt2e.py b/test/quantization/pt2e/test_quantize_pt2e.py index a96f397925..75701c55ca 100644 --- a/test/quantization/pt2e/test_quantize_pt2e.py +++ b/test/quantization/pt2e/test_quantize_pt2e.py @@ -2478,26 +2478,6 @@ def forward(self, x): node_list, ) - example_inputs = (torch.randn(1, 3, 5, 5),) - node_occurrence = { - # two for input of the first conv, one for output for the first conv - torch.ops.quantized_decomposed.quantize_per_tensor.default: 2, - torch.ops.quantized_decomposed.dequantize_per_tensor.default: 3, - } - node_list = [ - torch.ops.quantized_decomposed.dequantize_per_tensor.default, - torch.ops.quantized_decomposed.dequantize_per_tensor.default, - torch.ops.aten.conv2d.padding, - torch.ops.aten.relu.default, - torch.ops.quantized_decomposed.quantize_per_tensor.default, - ] - self._test_quantizer( - TestHelperModules.ConvWithBNRelu(dim=2, relu=True, bn=True, padding="same"), - example_inputs, - BackendAQuantizer(), - node_occurrence, - node_list, - ) def test_conv_transpose3d_bn_relu(self): class BackendAQuantizer(Quantizer): def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule: diff --git a/torchao/quantization/pt2e/utils.py b/torchao/quantization/pt2e/utils.py index dc5f802fb8..ad5c0ae179 100644 --- a/torchao/quantization/pt2e/utils.py +++ b/torchao/quantization/pt2e/utils.py @@ -625,11 +625,8 @@ def _is_conv_node(n: Node): """ return n.op == "call_function" and n.target in [ torch.ops.aten.conv1d.default, - torch.ops.aten.conv1d.padding, torch.ops.aten.conv2d.default, - torch.ops.aten.conv2d.padding, torch.ops.aten.conv3d.default, - torch.ops.aten.conv3d.padding, ] From a776b1ff7e01bb2c8c6f7d3c0cdf2e49435faa51 Mon Sep 17 00:00:00 2001 From: andrewor14 Date: Fri, 23 May 2025 17:32:31 -0400 Subject: [PATCH 048/165] Relax int4wo device mismatch error (#2254) **Summary:** We have an guard preventing users from using a cuda quantized on cpu and vice versa. However, this also prevents users who load their checkpoints on cpu first and then move them to cuda later, which is what torchtune does: ``` quantize_(model.cuda(), Int4WeightOnlyConfig()) # save checkpoint in cuda torch.save(model.state_dict(), "my_checkpoint.pt") # load checkpoint on cpu # This is what torchtune does: https://github.com/pytorch/torchtune/blob/v0.6.1/torchtune/training/checkpointing/_utils.py#L253 sd = torch.load("my_checkpoint.pt", weights_only=False, map_location="cpu") # move checkpoint to cuda for k, v in sd.items(): sd[k] = v.to("cuda") # load state_dict in cuda model.load_state_dict(sd, assign=True) ``` This use case is safe in that the model was quantized in cuda and ultimately used on cuda. This commit relaxes the error to allow the above use case. More details here: https://github.com/pytorch/ao/issues/1117. **Test Plan:** python test/quantization/test_quant_api.py -k test_int4wo_cuda_serialization --- test/quantization/test_quant_api.py | 19 +++++++++++++++++++ .../dtypes/uintx/tensor_core_tiled_layout.py | 3 ++- 2 files changed, 21 insertions(+), 1 deletion(-) diff --git a/test/quantization/test_quant_api.py b/test/quantization/test_quant_api.py index fa8029f862..5f2a663705 100644 --- a/test/quantization/test_quant_api.py +++ b/test/quantization/test_quant_api.py @@ -1017,6 +1017,25 @@ def test_ao_per_module_config_skip(self): assert isinstance(model.linear1.weight._layout, TensorCoreTiledLayout) assert not isinstance(model.linear2.weight, AffineQuantizedTensor) + @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") + def test_int4wo_cuda_serialization(self): + config = Int4WeightOnlyConfig(group_size=32) + model = ToyLinearModel().cuda().to(dtype=torch.bfloat16) + # quantize in cuda + quantize_(model, config) + example_inputs = model.example_inputs(device="cuda", dtype=torch.bfloat16) + model(*example_inputs) + with tempfile.NamedTemporaryFile() as ckpt: + # save checkpoint in cuda + torch.save(model.state_dict(), ckpt) + # load checkpoint on cpu then move checkpoint to cuda + # This is what torchtune does: https://github.com/pytorch/torchtune/blob/v0.6.1/torchtune/training/checkpointing/_utils.py#L253 + sd = torch.load(ckpt.name, weights_only=False, map_location="cpu") + for k, v in sd.items(): + sd[k] = v.to("cuda") + # load state_dict in cuda + model.load_state_dict(sd, assign=True) + class TestMultiTensorFlow(TestCase): @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_4, "Test only enabled for 2.4+") diff --git a/torchao/dtypes/uintx/tensor_core_tiled_layout.py b/torchao/dtypes/uintx/tensor_core_tiled_layout.py index 48910038cf..2baf45ded0 100644 --- a/torchao/dtypes/uintx/tensor_core_tiled_layout.py +++ b/torchao/dtypes/uintx/tensor_core_tiled_layout.py @@ -3,6 +3,7 @@ # # This source code is licensed under the BSD 3-Clause license found in the # LICENSE file in the root directory of this source tree. +import logging from dataclasses import dataclass from typing import Optional, Tuple @@ -318,7 +319,7 @@ def to(self, *args, **kwargs): # between these two devices, in the future we should not use the same layout for # cpu and cuda device: https://github.com/pytorch/ao/issues/1117 if not is_device(torch.device(self.device).type, device): - raise ValueError( + logging.warning( f"TensorCoreTiledAQTTensorImpl does not support conversion from {self.device} to {device}" ) return self.__class__( From efac465f1a6da4b60c8fc3788a5c53b790fc3a4e Mon Sep 17 00:00:00 2001 From: Scott Roy <161522778+metascroy@users.noreply.github.com> Date: Fri, 23 May 2025 14:48:27 -0700 Subject: [PATCH 049/165] Add backward compatible types to pt2e prepare Differential Revision: D75248288 Pull Request resolved: https://github.com/pytorch/ao/pull/2244 --- torchao/quantization/pt2e/quantize_pt2e.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/torchao/quantization/pt2e/quantize_pt2e.py b/torchao/quantization/pt2e/quantize_pt2e.py index 94a675d809..5eb385b7de 100644 --- a/torchao/quantization/pt2e/quantize_pt2e.py +++ b/torchao/quantization/pt2e/quantize_pt2e.py @@ -11,6 +11,8 @@ if TORCH_VERSION_AT_LEAST_2_7: from .constant_fold import constant_fold +from typing import Union + from torch.fx import GraphModule, Node from torch.fx.passes.infra.pass_manager import PassManager @@ -39,7 +41,7 @@ def prepare_pt2e( model: GraphModule, - quantizer: Quantizer, + quantizer: Union[Quantizer, torch.ao.quantization.quantizer.quantizer.Quantizer], ) -> GraphModule: """Prepare a model for post training quantization @@ -127,7 +129,7 @@ def calibrate(model, data_loader): def prepare_qat_pt2e( model: GraphModule, - quantizer: Quantizer, + quantizer: Union[Quantizer, torch.ao.quantization.quantizer.quantizer.Quantizer], ) -> GraphModule: """Prepare a model for quantization aware training From 60d63a637f5091d7c6917b3c28bca98540136600 Mon Sep 17 00:00:00 2001 From: Jerry Zhang Date: Fri, 23 May 2025 18:41:50 -0700 Subject: [PATCH 050/165] Rename AOPerModuleConfig to ModuleFqnToConfig (#2243) * Rename AOPerModuleConfig to ModuleFqnToConfig Summary: to be more explicit on what this config means Test Plan: CI Reviewers: Subscribers: Tasks: Tags: * renaming rest --- test/quantization/test_config_serialization.py | 8 ++++---- test/quantization/test_quant_api.py | 18 +++++++++--------- torchao/quantization/__init__.py | 4 ++-- torchao/quantization/quant_api.py | 11 ++++++----- 4 files changed, 21 insertions(+), 20 deletions(-) diff --git a/test/quantization/test_config_serialization.py b/test/quantization/test_config_serialization.py index 62edc6aad8..3b0a10e915 100644 --- a/test/quantization/test_config_serialization.py +++ b/test/quantization/test_config_serialization.py @@ -20,7 +20,6 @@ config_to_dict, ) from torchao.quantization.quant_api import ( - AOPerModuleConfig, Float8DynamicActivationFloat8WeightConfig, Float8WeightOnlyConfig, FPXWeightOnlyConfig, @@ -30,6 +29,7 @@ Int8DynamicActivationInt4WeightConfig, Int8DynamicActivationInt8WeightConfig, Int8WeightOnlyConfig, + ModuleFqnToConfig, PerRow, UIntXWeightOnlyConfig, ) @@ -68,9 +68,9 @@ # Sparsity configs SemiSparseWeightConfig(), BlockSparseWeightConfig(blocksize=128), - AOPerModuleConfig({}), - AOPerModuleConfig({"_default": Int4WeightOnlyConfig(), "linear1": None}), - AOPerModuleConfig( + ModuleFqnToConfig({}), + ModuleFqnToConfig({"_default": Int4WeightOnlyConfig(), "linear1": None}), + ModuleFqnToConfig( { "linear1": Int4WeightOnlyConfig(), "linear2": Int8DynamicActivationInt4WeightConfig(), diff --git a/test/quantization/test_quant_api.py b/test/quantization/test_quant_api.py index 5f2a663705..b4ec9f4785 100644 --- a/test/quantization/test_quant_api.py +++ b/test/quantization/test_quant_api.py @@ -38,11 +38,11 @@ PerGroup, ) from torchao.quantization.quant_api import ( - AOPerModuleConfig, Int4WeightOnlyConfig, Int8DynamicActivationInt4WeightConfig, Int8WeightOnlyConfig, IntxWeightOnlyConfig, + ModuleFqnToConfig, Quantizer, TwoStepQuantizer, _replace_with_custom_fn_if_matches_filter, @@ -946,10 +946,10 @@ def test_workflow_e2e_numerics(self, config): assert sqnr >= 16.5, f"SQNR {sqnr} is too low" @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") - def test_ao_per_module_config_default(self): + def test_module_fqn_to_config_default(self): config1 = Int4WeightOnlyConfig(group_size=32) config2 = Int8WeightOnlyConfig() - config = AOPerModuleConfig({"_default": config1, "linear2": config2}) + config = ModuleFqnToConfig({"_default": config1, "linear2": config2}) model = ToyLinearModel().cuda().to(dtype=torch.bfloat16) example_inputs = model.example_inputs(device="cuda", dtype=torch.bfloat16) quantize_(model, config) @@ -960,10 +960,10 @@ def test_ao_per_module_config_default(self): assert isinstance(model.linear2.weight._layout, PlainLayout) @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") - def test_ao_per_module_config_module_name(self): + def test_module_fqn_to_config_module_name(self): config1 = Int4WeightOnlyConfig(group_size=32) config2 = Int8WeightOnlyConfig() - config = AOPerModuleConfig({"linear1": config1, "linear2": config2}) + config = ModuleFqnToConfig({"linear1": config1, "linear2": config2}) model = ToyLinearModel().cuda().to(dtype=torch.bfloat16) example_inputs = model.example_inputs(device="cuda", dtype=torch.bfloat16) quantize_(model, config) @@ -974,7 +974,7 @@ def test_ao_per_module_config_module_name(self): assert isinstance(model.linear2.weight._layout, PlainLayout) @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_6, "Need torch 2.6+") - def test_ao_per_module_config_embedding_linear(self): + def test_module_fqn_to_config_embedding_linear(self): weight_dtype = torch.int8 granularity = PerGroup(8) mapping_type = MappingType.SYMMETRIC @@ -987,7 +987,7 @@ def test_ao_per_module_config_embedding_linear(self): # example model linear is Linear(16, 8) linear_config = Int8DynamicActivationInt4WeightConfig(group_size=16) - config = AOPerModuleConfig({"emb": embedding_config, "linear": linear_config}) + config = ModuleFqnToConfig({"emb": embedding_config, "linear": linear_config}) indices = torch.randint(0, 10, (32,)) indices = indices.unsqueeze(0) example_inputs = (indices,) @@ -1006,9 +1006,9 @@ def test_ao_per_module_config_embedding_linear(self): assert isinstance(model.linear.weight, LinearActivationQuantizedTensor) @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") - def test_ao_per_module_config_skip(self): + def test_module_fqn_to_config_skip(self): config1 = Int4WeightOnlyConfig(group_size=32) - config = AOPerModuleConfig({"_default": config1, "linear2": None}) + config = ModuleFqnToConfig({"_default": config1, "linear2": None}) model = ToyLinearModel().cuda().to(dtype=torch.bfloat16) example_inputs = model.example_inputs(device="cuda", dtype=torch.bfloat16) quantize_(model, config) diff --git a/torchao/quantization/__init__.py b/torchao/quantization/__init__.py index 109de5c0c5..b4d46d8263 100644 --- a/torchao/quantization/__init__.py +++ b/torchao/quantization/__init__.py @@ -39,7 +39,6 @@ AffineQuantizedObserverBase, ) from .quant_api import ( - AOPerModuleConfig, CutlassInt4PackedLayout, Float8DynamicActivationFloat8SemiSparseWeightConfig, Float8DynamicActivationFloat8WeightConfig, @@ -55,6 +54,7 @@ Int8DynamicActivationIntxWeightConfig, Int8WeightOnlyConfig, IntxWeightOnlyConfig, + ModuleFqnToConfig, PlainLayout, TensorCoreTiledLayout, UIntXWeightOnlyConfig, @@ -147,7 +147,7 @@ "IntxWeightOnlyConfig", "FPXWeightOnlyConfig", "GemliteUIntXWeightOnlyConfig", - "AOPerModuleConfig", + "ModuleFqnToConfig", # smooth quant - subject to change "get_scale", "SmoothFakeDynQuantMixin", diff --git a/torchao/quantization/quant_api.py b/torchao/quantization/quant_api.py index 4229577b95..7020a1322a 100644 --- a/torchao/quantization/quant_api.py +++ b/torchao/quantization/quant_api.py @@ -136,6 +136,7 @@ "Int8DynActInt4WeightQuantizer", "Int8DynActInt4WeightGPTQQuantizer", "Float8DynamicActivationFloat8SemiSparseWeightConfig", + "ModuleFqnToConfig", ] LAYOUT_TO_ZERO_POINT_DOMAIN = { @@ -596,10 +597,10 @@ def quantize_( """ filter_fn = _is_linear if filter_fn is None else filter_fn - if isinstance(config, AOPerModuleConfig): + if isinstance(config, ModuleFqnToConfig): _replace_with_custom_fn_if_matches_filter_with_name( model, - _ao_per_module_config_handler, + _module_fqn_to_config_handler, filter_fn, device=device, extra_args=(config,), @@ -2002,7 +2003,7 @@ def _fpx_weight_only_transform( @dataclass -class AOPerModuleConfig(AOBaseConfig): +class ModuleFqnToConfig(AOBaseConfig): """Per module configurations for torchao quantize_ API Args: @@ -2018,8 +2019,8 @@ class AOPerModuleConfig(AOBaseConfig): ) -def _ao_per_module_config_handler( - module: torch.nn.Module, module_fqn: str, config: AOPerModuleConfig +def _module_fqn_to_config_handler( + module: torch.nn.Module, module_fqn: str, config: ModuleFqnToConfig ): c = None if module_fqn in config.module_fqn_to_config: From e0e8b399e110ea80344a12b13118d94d3a3a27cb Mon Sep 17 00:00:00 2001 From: Andrey Talman Date: Tue, 27 May 2025 13:22:22 -0400 Subject: [PATCH 051/165] Revert "Try fixing CI by pinning pytest (#2238)" (#2263) This reverts commit f0f976cede3ed51edf1b690d82bfc0d72d81b79b. --- .github/workflows/regression_test.yml | 4 ++-- dev-requirements.txt | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/regression_test.yml b/.github/workflows/regression_test.yml index f4f054b307..f1188fd7d5 100644 --- a/.github/workflows/regression_test.yml +++ b/.github/workflows/regression_test.yml @@ -64,7 +64,7 @@ jobs: torch-spec: 'torch==2.5.1 --index-url https://download.pytorch.org/whl/cu121' gpu-arch-type: "cuda" gpu-arch-version: "12.6" - dev-requirements-overrides: "s/^pytest.*$/pytest==7.4.0/" + dev-requirements-overrides: "s/^pytest$/pytest==7.4.0/" - name: CUDA 2.6 runs-on: linux.g5.12xlarge.nvidia.gpu torch-spec: 'torch==2.6.0' @@ -83,7 +83,7 @@ jobs: torch-spec: 'torch==2.5.1 --index-url https://download.pytorch.org/whl/cpu' gpu-arch-type: "cpu" gpu-arch-version: "" - dev-requirements-overrides: "s/^pytest.*$/pytest==7.4.0/" + dev-requirements-overrides: "s/^pytest$/pytest==7.4.0/" - name: CPU 2.6 runs-on: linux.4xlarge torch-spec: 'torch==2.6.0 --index-url https://download.pytorch.org/whl/cpu' diff --git a/dev-requirements.txt b/dev-requirements.txt index f9e4381e4e..600d5001cf 100644 --- a/dev-requirements.txt +++ b/dev-requirements.txt @@ -1,5 +1,5 @@ # Test utilities -pytest==8.3.4 +pytest unittest-xml-reporting parameterized packaging From 1017c7e3bfe7300a14ed81fa36038684b168b633 Mon Sep 17 00:00:00 2001 From: Driss Guessous <32754868+drisspg@users.noreply.github.com> Date: Tue, 27 May 2025 10:34:05 -0700 Subject: [PATCH 052/165] Fix Per Row scaling for inference (#2253) --- test/dtypes/test_affine_quantized_float.py | 308 ++++++++++++++++++++- torchao/dtypes/affine_quantized_tensor.py | 7 +- torchao/dtypes/floatx/float8_layout.py | 237 ++++++++++------ torchao/float8/inference.py | 74 ++++- torchao/quantization/quant_api.py | 69 ++--- torchao/quantization/quant_primitives.py | 112 ++++++-- 6 files changed, 628 insertions(+), 179 deletions(-) diff --git a/test/dtypes/test_affine_quantized_float.py b/test/dtypes/test_affine_quantized_float.py index 8c36f5ac7a..408e6e6ce0 100644 --- a/test/dtypes/test_affine_quantized_float.py +++ b/test/dtypes/test_affine_quantized_float.py @@ -25,6 +25,7 @@ from torch._inductor.test_case import TestCase as InductorTestCase from torch.testing._internal import common_utils +from torchao.dtypes.floatx.float8_layout import Float8AQTTensorImpl from torchao.float8.float8_utils import compute_error from torchao.quantization import ( Float8DynamicActivationFloat8WeightConfig, @@ -42,6 +43,9 @@ from torchao.quantization.quant_primitives import ( MappingType, choose_qparams_affine, + choose_qparams_affine_float8, + dequantize_affine_float8, + quantize_affine_float8, ) from torchao.utils import ( is_sm_at_least_89, @@ -297,21 +301,299 @@ def test_fp8_weight_dimension_warning(self): @unittest.skipIf( not is_sm_at_least_89(), "Requires GPU with compute capability >= 8.9" ) - def test_mm_float8dq(self): + @common_utils.parametrize( + "in_features,out_features", [(512, 1024), (256, 768), (1024, 512)] + ) + @common_utils.parametrize( + "leading_shape", [(1,), (8,), (16,), (2, 8,), (2, 2, 16,)] + ) # fmt: skip + @common_utils.parametrize("bias", [True, False]) + def test_mm_float8dq_per_row( + self, in_features, out_features, leading_shape, bias: bool + ): + device = "cuda" + dtype = torch.bfloat16 + input_shape = leading_shape + (in_features,) + + ref_linear = ( + torch.nn.Linear(in_features, out_features, bias=bias).to(device).to(dtype) + ) + test_linear = copy.deepcopy(ref_linear) + quantize_( + test_linear, Float8DynamicActivationFloat8WeightConfig(granularity=PerRow()) + ) + + quant_weight = test_linear.weight + + self.assertTrue(hasattr(quant_weight, "original_weight_tensor")) + weight_impl = quant_weight.original_weight_tensor.tensor_impl + + self.assertTrue(hasattr(weight_impl, "float8_data")) + self.assertTrue(hasattr(weight_impl, "scale")) + self.assertFalse(weight_impl.transposed) + + # Verify scale shape for row-wise quantization + expected_scale_shape = (out_features, 1) + actual_scale_shape = weight_impl.scale.shape + self.assertEqual(actual_scale_shape, expected_scale_shape) + + self.assertEqual(weight_impl.float8_data.shape, (out_features, in_features)) + + input_tensor = torch.randn(*input_shape, device=device, dtype=dtype) + + with torch.no_grad(): + ref_output = ref_linear(input_tensor) + quant_output = torch.nn.functional.linear(input_tensor, quant_weight) + + expected_output_shape = input_tensor.shape[:-1] + (out_features,) + self.assertEqual(quant_output.shape, expected_output_shape) + + error = compute_error(ref_output, quant_output) + assert error > 20, f"Quantization error is too high got a SQNR of {error}" + + @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") + @unittest.skipIf( + not is_sm_at_least_89(), "Requires GPU with compute capability >= 8.9" + ) + @common_utils.parametrize("float8_dtype", [torch.float8_e4m3fn, torch.float8_e5m2]) + @common_utils.parametrize("output_dtype", [torch.float32, torch.bfloat16]) + @common_utils.parametrize("block_size", [None, (1, 32), (2, 16), (4, 8)]) + def test_dequantize_affine_float8(self, float8_dtype, output_dtype, block_size): + """Test dequantize_affine_float8 with various configurations""" + + device = "cuda" + input_tensor = torch.randn(8, 64, device=device, dtype=torch.float32) + + # Choose quantization parameters + scale = choose_qparams_affine_float8( + input_tensor, float8_dtype=float8_dtype, block_size=block_size + ) + + # Quantize + quantized = quantize_affine_float8(input_tensor, scale, float8_dtype) + + # Dequantize + dequantized = dequantize_affine_float8(quantized, scale, output_dtype) + + # Verify output properties + self.assertEqual(dequantized.dtype, output_dtype) + self.assertEqual(dequantized.shape, input_tensor.shape) + self.assertEqual(dequantized.device, input_tensor.device) + + # Verify quantization/dequantization roundtrip is reasonable + error = torch.abs(input_tensor.to(output_dtype) - dequantized).mean() + self.assertLess(error, 0.1, "Quantization error too high") + + @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") + @unittest.skipIf( + not is_sm_at_least_89(), "Requires GPU with compute capability >= 8.9" + ) + def test_dequantize_affine_float8_scale_broadcasting(self): + """Test that scale broadcasting works correctly for block-wise quantization""" + device = "cuda" + # Create input tensor with known block structure + input_tensor = torch.randn(4, 32, device=device, dtype=torch.float32) + block_size = (2, 16) # 2x2 blocks in first dim, 2x16 blocks in second dim + + # Choose quantization parameters + scale = choose_qparams_affine_float8( + input_tensor, float8_dtype=torch.float8_e4m3fn, block_size=block_size + ) + + # Verify scale shape + expected_scale_shape = ( + input_tensor.shape[0] // block_size[0], + input_tensor.shape[1] // block_size[1], + ) + self.assertEqual(scale.shape, expected_scale_shape) + + # Quantize + quantized = quantize_affine_float8(input_tensor, scale, torch.float8_e4m3fn) + + # Dequantize + dequantized = dequantize_affine_float8(quantized, scale, torch.float32) + + # Verify shapes match + self.assertEqual(dequantized.shape, input_tensor.shape) + + @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") + @unittest.skipIf( + not is_sm_at_least_89(), "Requires GPU with compute capability >= 8.9" + ) + @common_utils.parametrize( + "granularity", [PerTensor(), PerRow()] if is_sm_at_least_90() else [PerTensor()] + ) + def test_float8_tensor_slicing_basic(self, granularity): + """Test basic slicing operations on Float8 tensors""" device = "cuda" dtype = torch.bfloat16 - weight = torch.randn(512, 1024).to(device).to(dtype) - weight = weight.t() - - l = torch.nn.Linear(512, 1024).to(device).to(dtype) - l.weight = torch.nn.Parameter(weight) - quantize_(l, Float8DynamicActivationFloat8WeightConfig(granularity=PerRow())) - # weight shape: 1024 x 512 - weight = l.weight - - input = torch.randn(1, 512, device=device, dtype=dtype) - # make sure it runs - torch.nn.functional.linear(input, weight) + + # Create and quantize a model + model = torch.nn.Linear(64, 32, bias=False).to(device).to(dtype) + quantize_( + model, Float8DynamicActivationFloat8WeightConfig(granularity=granularity) + ) + + weight_impl = model.weight.original_weight_tensor.tensor_impl + + # Test dimension 0 slicing (rows) + sliced_0 = weight_impl[10:20] + self.assertEqual(sliced_0.shape, (10, 64)) + + # Test dimension 1 slicing (columns) + sliced_1 = weight_impl[:, 20:40] + self.assertEqual(sliced_1.shape, (32, 20)) + + # Test combined slicing + sliced_both = weight_impl[5:15, 10:30] + self.assertEqual(sliced_both.shape, (10, 20)) + + # Verify the sliced tensors are still Float8 tensors + self.assertTrue(isinstance(sliced_0, Float8AQTTensorImpl)) + self.assertTrue(isinstance(sliced_1, Float8AQTTensorImpl)) + self.assertTrue(isinstance(sliced_both, Float8AQTTensorImpl)) + + @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") + @unittest.skipIf( + not is_sm_at_least_89(), "Requires GPU with compute capability >= 8.9" + ) + def test_float8_tensor_slicing_per_tensor(self): + """Test slicing with per-tensor quantization (scale should not change)""" + device = "cuda" + dtype = torch.bfloat16 + + # Create and quantize with per-tensor granularity + model = torch.nn.Linear(64, 32, bias=False).to(device).to(dtype) + quantize_( + model, Float8DynamicActivationFloat8WeightConfig(granularity=PerTensor()) + ) + + original_weight = model.weight + original_impl = original_weight.original_weight_tensor.tensor_impl + original_scale = original_impl.scale + + # Test slicing + sliced_weight = original_weight[10:20, 20:40] + sliced_impl = sliced_weight.original_weight_tensor.tensor_impl + + # For per-tensor quantization, scale should be identical + self.assertTrue(torch.equal(original_scale, sliced_impl.scale)) + self.assertEqual(sliced_impl.scale.numel(), 1) + + @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") + @unittest.skipIf( + not is_sm_at_least_89(), "Requires GPU with compute capability >= 8.9" + ) + @unittest.skipIf( + not is_sm_at_least_90(), + "Per-row quantization requires compute capability >= 9.0", + ) + def test_float8_tensor_slicing_per_row(self): + """Test slicing with per-row quantization (scale should be sliced appropriately)""" + device = "cuda" + dtype = torch.bfloat16 + + # Create and quantize with per-row granularity + model = torch.nn.Linear(64, 32, bias=False).to(device).to(dtype) + quantize_( + model, Float8DynamicActivationFloat8WeightConfig(granularity=PerRow()) + ) + + original_weight = model.weight # Shape: (32, 64) + original_impl = original_weight.original_weight_tensor.tensor_impl + original_scale = original_impl.scale # Shape: (32, 1) + + # Test row slicing (dimension 0) + sliced_rows = original_weight[10:20] # Shape: (10, 64) + sliced_impl = sliced_rows.original_weight_tensor.tensor_impl + + # Scale should be sliced to match the rows + expected_scale_shape = (10, 1) + self.assertEqual(sliced_impl.scale.shape, expected_scale_shape) + + # Verify the scale values are correct (should be subset of original) + self.assertTrue(torch.equal(sliced_impl.scale, original_scale[10:20])) + + # Test column slicing (dimension 1) - scale should not change for per-row + sliced_cols = original_weight[:, 20:40] # Shape: (32, 20) + sliced_cols_impl = sliced_cols.original_weight_tensor.tensor_impl + + # Scale shape should remain the same since we're not changing rows + self.assertEqual(sliced_cols_impl.scale.shape, (32, 1)) + self.assertTrue(torch.equal(sliced_cols_impl.scale, original_scale)) + + @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") + @unittest.skipIf( + not is_sm_at_least_89(), "Requires GPU with compute capability >= 8.9" + ) + def test_float8_tensor_slicing_edge_cases(self): + """Test edge cases in slicing""" + device = "cuda" + dtype = torch.bfloat16 + + # Create and quantize a model + model = torch.nn.Linear(64, 32, bias=False).to(device).to(dtype) + quantize_( + model, Float8DynamicActivationFloat8WeightConfig(granularity=PerTensor()) + ) + + original_weight = model.weight + + # Test empty slice + empty_slice = original_weight[0:0] + self.assertEqual(empty_slice.shape, (0, 64)) + + # Test single element slice + single_row = original_weight[0:1] + self.assertEqual(single_row.shape, (1, 64)) + + # Test out of bounds (should be handled by PyTorch) + large_slice = original_weight[:100] # More than available rows + self.assertEqual(large_slice.shape, (32, 64)) # Should clamp to available + + @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") + @unittest.skipIf( + not is_sm_at_least_89(), "Requires GPU with compute capability >= 8.9" + ) + @common_utils.parametrize( + "granularity", [PerTensor(), PerRow()] if is_sm_at_least_90() else [PerTensor()] + ) + def test_float8_tensor_slicing_functional_correctness(self, granularity): + """Test that sliced tensors produce correct results in computations""" + device = "cuda" + dtype = torch.bfloat16 + + # Create reference and quantized models with dimensions that are multiples of 16 + ref_model = ( + torch.nn.Linear(64, 48, bias=False).to(device).to(dtype) + ) # 48 is divisible by 16 + quant_model = copy.deepcopy(ref_model) + quantize_( + quant_model, + Float8DynamicActivationFloat8WeightConfig(granularity=granularity), + ) + + # Create input with batch size that works well with slicing + input_tensor = torch.randn(8, 64, device=device, dtype=dtype) + + ref_weight_slice = ref_model.weight[0:16, 0:32] + quant_weight_slice = quant_model.weight[0:16, 0:32] + + input_slice = input_tensor[:, 0:32] # (8, 32) to match sliced weight + + # Compute with sliced weights + with torch.no_grad(): + ref_output = torch.nn.functional.linear(input_slice, ref_weight_slice) + quant_output = torch.nn.functional.linear(input_slice, quant_weight_slice) + + # Verify shapes + expected_shape = (8, 16) # batch_size x out_features_sliced + self.assertEqual(ref_output.shape, expected_shape) + self.assertEqual(quant_output.shape, expected_shape) + + # Verify reasonable quantization error + error = compute_error(ref_output, quant_output) + self.assertGreater(error, 15, f"Quantization SQNR too low: {error}") common_utils.instantiate_parametrized_tests(TestAffineQuantizedFloat8Compile) diff --git a/torchao/dtypes/affine_quantized_tensor.py b/torchao/dtypes/affine_quantized_tensor.py index 65649593a3..6cb2e8997e 100644 --- a/torchao/dtypes/affine_quantized_tensor.py +++ b/torchao/dtypes/affine_quantized_tensor.py @@ -462,10 +462,10 @@ def from_hp_to_floatx( if target_dtype in FP8_TYPES: original_shape = input_float.shape input_float = _layout.pre_process(input_float) - - scale = choose_qparams_affine_float8(input_float, float8_dtype=target_dtype) + scale = choose_qparams_affine_float8( + input_float, float8_dtype=target_dtype, block_size=block_size + ) data = quantize_affine_float8(input_float, scale, target_dtype) - data, scale, zero_point = _layout.post_process( data, scale, None, block_size ) @@ -503,7 +503,6 @@ def from_hp_to_floatx_static( input_float, scale, target_dtype, - scale_dtype, ) data, scale, zero_point = _layout.post_process( diff --git a/torchao/dtypes/floatx/float8_layout.py b/torchao/dtypes/floatx/float8_layout.py index 5914f00102..799832a5ea 100644 --- a/torchao/dtypes/floatx/float8_layout.py +++ b/torchao/dtypes/floatx/float8_layout.py @@ -4,7 +4,7 @@ # This source code is licensed under the BSD 3-Clause license found in the # LICENSE file in the root directory of this source tree. from dataclasses import dataclass -from typing import Optional, Tuple, Union +from typing import Any, Dict, List, Optional, Tuple, Union import torch from torch.utils._python_dispatch import ( @@ -26,6 +26,18 @@ from torchao.utils import _is_float8_type, fill_defaults aten = torch.ops.aten +FLOAT8_IMPL_OPS_TABLE: Dict[Any, Any] = {} + + +def implements(aten_ops: List[Any]): + """Register aten ops to the float8 op table""" + + def decorator(func): + for op in aten_ops: + FLOAT8_IMPL_OPS_TABLE[op] = func + return func + + return decorator def _same_metadata(self: "Float8AQTTensorImpl", src: "Float8AQTTensorImpl") -> bool: @@ -33,7 +45,6 @@ def _same_metadata(self: "Float8AQTTensorImpl", src: "Float8AQTTensorImpl") -> b transposed_match = (self.transposed == src.transposed) or ( self.transposed is False and src.transposed is None ) - return ( isinstance(self, Float8AQTTensorImpl) and isinstance(src, Float8AQTTensorImpl) @@ -160,90 +171,23 @@ def __tensor_unflatten__( def __torch_dispatch__(cls, func, types, args, kwargs): kwargs = {} if kwargs is None else kwargs - if func is aten.detach.default: - return return_and_correct_aliasing( - func, args, kwargs, args[0]._apply_fn_to_data(torch.detach) - ) - elif func is aten.clone.default: - return return_and_correct_aliasing( - func, args, kwargs, args[0]._apply_fn_to_data(torch.clone) - ) - elif func is aten.t.default: - """we don't need to repack the weight and just rely on external - shape being changed and record the status of transpose/no-transpose - """ - args[0].transposed = not args[0].transposed - return return_and_correct_aliasing(func, args, kwargs, args[0]) - elif func is aten.copy_.default: - self = args[0] - src = args[1] - if _same_metadata(self, src): - self_tensors = self.__tensor_flatten__()[0] - for tensor_name in self_tensors: - getattr(self, tensor_name).copy_(getattr(src, tensor_name)) - return - raise ValueError( - f"Not supported args for copy_ due to metadata mistach: {args[0], args[1]}" - ) - elif func in [aten.select.int, aten.index.Tensor]: - return return_and_correct_aliasing( - func, - args, - kwargs, - args[0]._apply_fn_to_data(lambda x: func(x, *args[1:], **kwargs)), - ) - elif func is aten.slice.Tensor: - self, dim, start, end, step = fill_defaults(args, 5, [0, None, None, 1]) - if dim == 0: - # TODO: scale replecation should be dependent on block size - if self.scale.ndim == 1: - return return_and_correct_aliasing( - func, - args, - kwargs, - args[0]._apply_fn_to_data( - lambda x: aten.slice.Tensor(x, dim, start, end, step) - ), - ) - elif self.scale.ndim == 0: - return return_and_correct_aliasing( - func, - args, - kwargs, - Float8AQTTensorImpl( - aten.slice.Tensor(self.float8_data, dim, start, end, step), - self.scale, - None, - self._layout, - ), - ) - else: - raise NotImplementedError( - f"Float8AQTTensorImpl dispatch: attempting to run {func}, with scale ndim={dim}, that is not supported" - ) - elif dim == 1: - return return_and_correct_aliasing( - func, - args, - kwargs, - Float8AQTTensorImpl( - aten.slice.Tensor( - self.float8_data, dim, start, end, step - ).contiguous(), - self.scale, - None, - self._layout, - ), - ) - else: - raise NotImplementedError( - f"Float8AQTTensorImpl dispatch: attempting to run {func}, with dim={dim}, that is not supported" + def allowed_subclasses(type): + return ( + issubclass(cls, type) + or issubclass(torch._subclasses.fake_tensor.FakeTensor, type) + or issubclass( + torch._subclasses.functional_tensor.FunctionalTensor, type ) - else: - raise NotImplementedError( - f"Float8AQTTensorImpl dispatch: attempting to run {func}, this is not supported" ) + if not all(allowed_subclasses(t) for t in types): + return NotImplemented + + if func in FLOAT8_IMPL_OPS_TABLE: + return FLOAT8_IMPL_OPS_TABLE[func](func, types, args, kwargs) + + raise NotImplementedError(f"attempting to run {func}, this is not supported") + __torch_function__ = torch._C._disabled_torch_function_impl def get_plain(self) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: @@ -281,6 +225,130 @@ def __repr__(self): ) +########################## +# Regsiter FP8 Ops +########################## + + +@implements([aten.detach.default, aten.alias.default, aten.clone.default]) +def _(func, types, args, kwargs): + return return_and_correct_aliasing( + func, args, kwargs, args[0]._apply_fn_to_data(func) + ) + + +@implements([aten.t.default]) +def _(func, types, args, kwargs): + """we don't need to repack the weight and just rely on external + shape being changed and record the status of transpose/no-transpose + """ + args[0].transposed = not args[0].transposed + return return_and_correct_aliasing(func, args, kwargs, args[0]) + + +@implements([aten.copy_.default]) +def _(func, types, args, kwargs): + self = args[0] + src = args[1] + if _same_metadata(self, src): + self_tensors = self.__tensor_flatten__()[0] + for tensor_name in self_tensors: + getattr(self, tensor_name).copy_(getattr(src, tensor_name)) + return + raise ValueError( + f"Not supported args for copy_ due to metadata mismatch: {args[0], args[1]}" + ) + + +@implements([aten.select.int, aten.index.Tensor]) +def _(func, types, args, kwargs): + return return_and_correct_aliasing( + func, + args, + kwargs, + args[0]._apply_fn_to_data(lambda x: func(x, *args[1:], **kwargs)), + ) + + +@implements([aten.slice.Tensor]) +def _(func, types, args, kwargs): + self, dim, start, end, step = fill_defaults(args, 5, [0, None, None, 1]) + + # Always slice the float8_data + sliced_data = aten.slice.Tensor(self.float8_data, dim, start, end, step) + + if self.scale.numel() == 1: + # Per-tensor quantization - scale doesn't change + sliced_scale = self.scale + else: + # Block-wise quantization - need to slice the scale appropriately + sliced_scale = _slice_scale_for_dimension( + self.scale, self.float8_data.shape, dim, start, end, step + ) + + return return_and_correct_aliasing( + func, + args, + kwargs, + Float8AQTTensorImpl( + sliced_data, + sliced_scale, + self.transposed, + self._layout, + ), + ) + + +def _slice_scale_for_dimension( + scale: torch.Tensor, + data_shape: List[int], + dim: int, + start: int, + end: int, + step: int, +) -> torch.Tensor: + """ + Slice the scale tensor appropriately based on the data tensor slicing. + + This function calculates how the scale should be sliced when the data tensor + is sliced along a given dimension, taking into account the block structure. + """ + # Unsupported case for now, this would be 1 scale per data element + if scale.shape == data_shape: + return aten.slice.Tensor(scale, dim, start, end, step) + + # Reconstruct block sizes based on data shape and scale shape + block_sizes = tuple(data_shape[i] // scale.shape[i] for i in range(len(data_shape))) + + if dim >= len(block_sizes): + # Slicing beyond the dimensions we care about + return scale + + block_size_for_dim = block_sizes[dim] + + if block_size_for_dim == 1: + # Scale is per-element along this dimension + # Slice away as normal + return aten.slice.Tensor(scale, dim, start, end, step) + else: + # There is blocking in this dimension + # Calculate which scale elements correspond to the sliced data + scale_start = start // block_size_for_dim if start is not None else None + scale_end = ( + (end + block_size_for_dim - 1) // block_size_for_dim + if end is not None + else None + ) + + # Error on Step > 1 + if step > 1: + raise NotImplementedError( + "Slicing with step > 1 is not implemented for scale tensors." + ) + + return aten.slice.Tensor(scale, dim, scale_start, scale_end, 1) + + ########################## # Float8 Dispatch Kernels ########################## @@ -333,13 +401,12 @@ def _linear_fp8_act_fp8_weight_impl( input_scale = input_tensor.tensor_impl.scale # Handle case where input tensor is more than 2D inpt_data = inpt_data.reshape(-1, inpt_data.shape[-1]) - # Handle rowwise case if _is_rowwise_scaled(weight_tensor): assert _is_rowwise_scaled(input_tensor), ( "Input tensor must be rowwise block size" ) - w_scale = w_scale.unsqueeze(-1).T + w_scale = w_scale.T input_scale = preprocess_scale(input_scale, input_tensor.shape) # Preprocess data diff --git a/torchao/float8/inference.py b/torchao/float8/inference.py index 7f73a604d7..00c905f3d8 100644 --- a/torchao/float8/inference.py +++ b/torchao/float8/inference.py @@ -7,11 +7,20 @@ Defines an nn module designed to be used during inference """ -from typing import NamedTuple, Optional, Tuple +from typing import NamedTuple, Optional, Tuple, Union import torch from torchao.float8.float8_utils import is_row_major, pad_tensor_for_matmul +from torchao.quantization.granularity import ( + PerRow, + PerTensor, +) +from torchao.utils import ( + is_MI300, + is_sm_at_least_89, + is_sm_at_least_90, +) Tensor = torch.Tensor @@ -106,3 +115,66 @@ def _is_rowwise_scaled(x) -> bool: x: AffineQuantizedTensor tensor """ return x.block_size == (1,) * (x.dim() - 1) + (x.shape[-1],) + + +FP8Granularity = Union[PerTensor, PerRow] + + +def _normalize_granularity( + granularity: Optional[ + Union[ + FP8Granularity, + Tuple[FP8Granularity, FP8Granularity], + list[FP8Granularity], + ] + ], +) -> Tuple[FP8Granularity, FP8Granularity]: + processed_granularity = None + if granularity is None: + processed_granularity = (PerTensor(), PerTensor()) + elif isinstance(granularity, (PerTensor, PerRow)): + processed_granularity = (granularity, granularity) + elif isinstance(granularity, (tuple, list)) and len(granularity) == 2: + if not ( + isinstance(granularity[0], (PerTensor, PerRow)) + and isinstance(granularity[1], (PerTensor, PerRow)) + ): + raise ValueError( + f"Invalid granularity types: {granularity}, only PerTensor or PerRow are supported." + ) + if not isinstance(granularity[0], type(granularity[1])): + raise ValueError( + f"Different granularities for activation and weight are not supported: {granularity}, only PerTensor or PerRow are supported." + ) + processed_granularity = tuple(granularity) + else: + raise ValueError( + f"Invalid granularity specification: {granularity}, only PerTensor or PerRow are supported." + ) + return processed_granularity + + +def _check_hardware_support( + granularities: Tuple[FP8Granularity, FP8Granularity], +) -> None: + """ + Validate that the hardware supports the requested granularities. + + Args: + granularities: Tuple of (activation_granularity, weight_granularity) + + Raises: + AssertionError: If hardware doesn't support the requested granularity + ValueError: If invalid granularity type is provided + """ + for _granularity in granularities: + if isinstance(_granularity, PerTensor): + assert is_sm_at_least_89() or is_MI300(), ( + "PerTensor quantization only works for CUDA>=8.9 and MI300+" + ) + elif isinstance(_granularity, PerRow): + assert is_sm_at_least_90() or is_MI300(), ( + "PerRow quantization only works for CUDA>=9.0 and MI300+" + ) + else: + raise ValueError(f"Invalid granularity type: {_granularity}") diff --git a/torchao/quantization/quant_api.py b/torchao/quantization/quant_api.py index 7020a1322a..f2aca97782 100644 --- a/torchao/quantization/quant_api.py +++ b/torchao/quantization/quant_api.py @@ -54,7 +54,12 @@ from torchao.dtypes.utils import Layout from torchao.float8.config import e4m3_dtype, e5m2_dtype from torchao.float8.float8_linear import Float8Linear -from torchao.float8.inference import Float8MMConfig +from torchao.float8.inference import ( + Float8MMConfig, + FP8Granularity, + _check_hardware_support, + _normalize_granularity, +) from torchao.quantization.linear_activation_weight_observed_tensor import ( LinearActivationWeightObservedTensor, ) @@ -1432,56 +1437,9 @@ def _float8_weight_only_transform( return module -_fp8_granularities = Union[PerTensor, PerRow] - - -# Validate and process granularity input -def _normalize_granularity( - granularity: Optional[ - Union[_fp8_granularities, Tuple[_fp8_granularities, _fp8_granularities]] - ], -) -> Tuple[_fp8_granularities, _fp8_granularities]: - processed_granularity = None - if granularity is None: - processed_granularity = (PerTensor(), PerTensor()) - elif isinstance(granularity, (PerTensor, PerRow)): - processed_granularity = (granularity, granularity) - elif isinstance(granularity, tuple) and len(granularity) == 2: - if not ( - isinstance(granularity[0], (PerTensor, PerRow)) - and isinstance(granularity[1], (PerTensor, PerRow)) - ): - raise ValueError( - f"Invalid granularity types: {granularity}, only PerTensor or PerRow are supported." - ) - if not isinstance(granularity[0], type(granularity[1])): - raise ValueError( - f"Different granularities for activation and weight are not supported: {granularity}, only PerTensor or PerRow are supported." - ) - processed_granularity = granularity - else: - raise ValueError( - f"Invalid granularity specification: {granularity}, only PerTensor or PerRow are supported." - ) - # Validate granularity with supported Hardware - for _granularity in processed_granularity: - if isinstance(_granularity, PerTensor): - assert is_sm_at_least_89() or is_MI300(), ( - "PerTensor quantization only works for CUDA>=8.9 and MI300+" - ) - elif isinstance(_granularity, PerRow): - assert is_sm_at_least_90() or is_MI300(), ( - "PerRow quantization only works for CUDA>=9.0 and MI300+" - ) - else: - raise ValueError(f"Invalid granularity type: {_granularity}") - - return processed_granularity - - def _input_activation_quant_func_fp8( x: torch.Tensor, - activation_granularity: _fp8_granularities, + activation_granularity: FP8Granularity, activation_dtype: torch.dtype, scale: Optional[torch.Tensor] = None, zero_point: Optional[torch.Tensor] = None, @@ -1568,7 +1526,7 @@ class Float8DynamicActivationFloat8WeightConfig(AOBaseConfig): activation_dtype: torch.dtype = e4m3_dtype weight_dtype: torch.dtype = e4m3_dtype granularity: Optional[ - Union[_fp8_granularities, Tuple[_fp8_granularities, _fp8_granularities]] + Union[FP8Granularity, Tuple[FP8Granularity, FP8Granularity]] ] = None mm_config: Optional[Float8MMConfig] = None set_inductor_config: bool = True @@ -1577,6 +1535,11 @@ def __post_init__(self): if self.mm_config is None: self.mm_config = Float8MMConfig(use_fast_accum=True) + activation_granularity, weight_granularity = _normalize_granularity( + self.granularity + ) + self.granularity = (activation_granularity, weight_granularity) + # for bc float8_dynamic_activation_float8_weight = Float8DynamicActivationFloat8WeightConfig @@ -1588,7 +1551,9 @@ def _float8_dynamic_activation_float8_weight_quantize_tensor(weight, config): granularity = config.granularity mm_config = config.mm_config - activation_granularity, weight_granularity = _normalize_granularity(granularity) + # Ensure works on device + _check_hardware_support(granularity) + activation_granularity, weight_granularity = granularity if not _fp8_mm_compat(weight): # TODO(future PR): this should really throw an exception instead of silently @@ -1705,7 +1670,7 @@ class Float8StaticActivationFloat8WeightConfig(AOBaseConfig): activation_dtype: torch.dtype = e4m3_dtype weight_dtype: torch.dtype = e4m3_dtype granularity: Optional[ - Union[_fp8_granularities, Tuple[_fp8_granularities, _fp8_granularities]] + Union[FP8Granularity, Tuple[FP8Granularity, FP8Granularity]] ] = None mm_config: Optional[Float8MMConfig] = None set_inductor_config: bool = True diff --git a/torchao/quantization/quant_primitives.py b/torchao/quantization/quant_primitives.py index 1885755608..cee8df21a2 100644 --- a/torchao/quantization/quant_primitives.py +++ b/torchao/quantization/quant_primitives.py @@ -1970,6 +1970,7 @@ def choose_qparams_affine_float8( tensor: torch.Tensor, float8_dtype: torch.dtype = torch.float8_e4m3fn, scale_dtype: torch.dtype = torch.float32, + block_size: Optional[Tuple[int, ...]] = None, ) -> torch.Tensor: """ Calculates float8 scaling factor for the given high precision tensor, using tensorwise granularity. @@ -1977,14 +1978,84 @@ def choose_qparams_affine_float8( Args: tensor (torch.Tensor): Input tensor to be quantized. float8_dtype (torch.dtype): Data type of the quantized tensor (e.g., torch.float8_e4m3fn, torch.float8_e5m2). + scale_dtype (torch.dtype): Data type of the scaling factor (e.g., torch.float32). + block_size (Optional[Tuple[int, ...]]): Block size for block-wise quantization. If None, tensorwise quantization is used. """ + quant_max = torch.finfo(float8_dtype).max # only tensorwise scaling is supported for now: - quant_min, quant_max = torch.finfo(float8_dtype).min, torch.finfo(float8_dtype).max - min_val_neg = torch.min(tensor) - max_val_pos = torch.max(tensor) - max_val_pos = torch.max(-min_val_neg, max_val_pos) - scale = max_val_pos / (float(quant_max - quant_min) / 2) - return scale.to(dtype=scale_dtype) + if block_size is None: + max_abs = tensor.abs().max() + scale = max_abs / quant_max + else: + shape_for_reduction, reduction_dims = _get_reduction_params( + block_size, tensor.shape + ) + tensor_reshaped = tensor.view(shape_for_reduction) + max_abs = tensor_reshaped.abs().amax(dim=reduction_dims, keepdim=True) + + scale = max_abs / quant_max + # Reshape scale back to match the expected output shape + # The scale tensor should have the same shape as the input divided by block_size + output_shape = [ + input_size // block_size[i] for i, input_size in enumerate(tensor.shape) + ] + scale = scale.reshape(output_shape) + + if scale_dtype is not torch.float32: + # Shielding for Version > 2.8 + assert scale_dtype is torch.float8_e8m0fnu, "Only float8_e8m0fnuz is supported" + scale = torch.exp2(torch.round(torch.log2(scale))) + return scale.to(dtype=torch.float32) + + +def _expand_scale_to_tensor_shape( + scale: torch.Tensor, target_shape: torch.Size +) -> torch.Tensor: + """ + Expand a scale tensor to match the target tensor shape for block-wise quantization. + + Args: + scale (torch.Tensor): Scale tensor with shape corresponding to block structure + target_shape (torch.Size): Target tensor shape to expand to + + Returns: + torch.Tensor: Scale tensor expanded to match target_shape + """ + if scale.shape == target_shape: + # Scale already matches target shape + return scale + + if scale.numel() == 1: + # Scalar scale - can broadcast naturally + return scale + + # Calculate block sizes from shape difference + if len(scale.shape) != len(target_shape): + raise ValueError( + f"Scale tensor has {len(scale.shape)} dimensions but target has {len(target_shape)}" + ) + + block_sizes = tuple( + target_shape[i] // scale.shape[i] for i in range(len(target_shape)) + ) + + # Verify that target_shape is evenly divisible by scale.shape + for i, (target_dim, scale_dim, block_size) in enumerate( + zip(target_shape, scale.shape, block_sizes) + ): + if target_dim != scale_dim * block_size: + raise ValueError( + f"Dimension {i}: target size {target_dim} is not evenly divisible " + f"by scale size {scale_dim} (block size would be {target_dim / scale_dim})" + ) + + # Expand scale using repeat_interleave + expanded_scale = scale + for i, block_size in enumerate(block_sizes): + if block_size > 1: + expanded_scale = expanded_scale.repeat_interleave(block_size, dim=i) + + return expanded_scale def quantize_affine_float8( @@ -1994,16 +2065,13 @@ def quantize_affine_float8( ) -> torch.Tensor: """ Quantizes the high precision floating point tensor to a float8 tensor, using the given scaling factor. - - Args: - tensor (torch.Tensor): Input tensor to be quantized. - scale (torch.Tensor): Scaling factor for the quantization. - float8_dtype (torch.dtype): Data type of the quantized tensor (e.g., torch.float8_e4m3fn, torch.float8_e5m2). """ - # Note: when the line below is compiled with `torch.compile`, `tensor` is automatically - # upcasted to `float32` to multiply with the scale, since scale is a fp32 tensor in float8 quantization. - # In order to match numerics between eager and compile, we upcast manually here. - tensor_scaled = tensor.to(torch.float32) / scale + tensor_fp32 = tensor.to(torch.float32) + + # Expand scale to match tensor dimensions for block-wise quantization + scale_expanded = _expand_scale_to_tensor_shape(scale, tensor.shape) + + tensor_scaled = tensor_fp32 / scale_expanded max_value = torch.finfo(float8_dtype).max tensor_clamped = tensor_scaled.clamp(min=-max_value, max=max_value) fp8_tensor = tensor_clamped.to(float8_dtype) @@ -2017,15 +2085,11 @@ def dequantize_affine_float8( ) -> torch.Tensor: """ Dequantizes the float8 tensor to high precision tensor. - - Args: - tensor (torch.Tensor): Input float8 tensor to be dequantized. - scale (torch.Tensor): Scaling factor for the dequantization. - output_dtype (torch.dtype): Data type of the output tensor (e.g., torch.float32). """ - # Note: when the line below is compiled with `torch.compile`, `tensor` is automatically - # upcasted to `float32` to divide by the scale, since scale is a fp32 for float8 quantization. - # In order to match numerics between eager and compile, we upcast manually here. fp8_tensor = tensor.to(torch.float32) - hp_tensor = fp8_tensor * scale + + # Expand scale to match tensor dimensions for block-wise quantization + scale_expanded = _expand_scale_to_tensor_shape(scale, tensor.shape) + + hp_tensor = fp8_tensor * scale_expanded return hp_tensor.to(output_dtype) From b0cfeec9df5ce6008be8f8b0bf1c83e4a0e248fe Mon Sep 17 00:00:00 2001 From: Xuan Liao Date: Wed, 28 May 2025 09:37:28 +0800 Subject: [PATCH 053/165] Support INT8 SDPA template for CPU (#2148) * support int8 sdpa template --- .../inductor/test_int8_sdpa_fusion.py | 12 +- .../prototype/inductor/codegen/__init__.py | 5 + .../codegen/cpp_int8_sdpa_template.py | 1824 +++++++++++++++++ torchao/prototype/inductor/codegen/utils.py | 13 + .../inductor/fx_passes/int8_sdpa_fusion.py | 12 +- .../prototype/inductor/int8_sdpa_lowering.py | 140 ++ 6 files changed, 1998 insertions(+), 8 deletions(-) create mode 100644 torchao/prototype/inductor/codegen/__init__.py create mode 100644 torchao/prototype/inductor/codegen/cpp_int8_sdpa_template.py create mode 100644 torchao/prototype/inductor/codegen/utils.py create mode 100644 torchao/prototype/inductor/int8_sdpa_lowering.py diff --git a/test/prototype/inductor/test_int8_sdpa_fusion.py b/test/prototype/inductor/test_int8_sdpa_fusion.py index c3456fb421..ec4f928df2 100644 --- a/test/prototype/inductor/test_int8_sdpa_fusion.py +++ b/test/prototype/inductor/test_int8_sdpa_fusion.py @@ -122,10 +122,14 @@ def _check_common( if has_fuse_pattern: self.assertGreaterEqual(counters["inductor"]["int8_fuse_attention"], 1) if contains: - # many of the patterns get re-expanded in dispatcher - self.assertIn( - "torchao.qscaled_dot_product", - source_code, + self.assertTrue( + any( + op_name in source_code + for op_name in [ + "qscaled_dot_product", + "cpp_fused_quantize_per_tensor", + ] + ) ) # some tests configured with very low dropout where we still want to check equality diff --git a/torchao/prototype/inductor/codegen/__init__.py b/torchao/prototype/inductor/codegen/__init__.py new file mode 100644 index 0000000000..6971ef74e2 --- /dev/null +++ b/torchao/prototype/inductor/codegen/__init__.py @@ -0,0 +1,5 @@ +from .cpp_int8_sdpa_template import CppInt8SdpaTemplate + +__all__ = [ + "CppInt8SdpaTemplate", +] diff --git a/torchao/prototype/inductor/codegen/cpp_int8_sdpa_template.py b/torchao/prototype/inductor/codegen/cpp_int8_sdpa_template.py new file mode 100644 index 0000000000..1f8865356a --- /dev/null +++ b/torchao/prototype/inductor/codegen/cpp_int8_sdpa_template.py @@ -0,0 +1,1824 @@ +from typing import List, Optional + +import torch +import torch.utils +from sympy import sympify +from torch._inductor import ir +from torch._inductor.codegen.cpp_flex_attention_template import CppFlexAttentionTemplate +from torch._inductor.codegen.cpp_template import CppTemplate +from torch._inductor.ir import TensorBox +from torch._inductor.select_algorithm import DataProcessorTemplateWrapper +from torch._inductor.utils import parallel_num_threads + +from .utils import expand + +USEFUL_FUNCTIONS = r""" +inline float {{kernel_name}}_calculate_scale( + int64_t headSize, + std::optional scale) { + return scale.has_value() + ? scale.value() + : (1.0 / std::sqrt(headSize)); +} + +template +inline void {{kernel_name}}_fill_stub(scalar_t* data, scalar_t val, int64_t size) { + const int32_t vec_size = at::vec::Vectorized::size(); + auto data_vec = at::vec::Vectorized(val); + int64_t d = 0; + for (; d < size - (size % vec_size); d += vec_size) { + data_vec.store(data + d); + } + if (d < size) { + data_vec.store(data + d, size - d); + } +} + +template +inline void {{kernel_name}}_store(scalar_t* dst, at::vec::Vectorized src, int size=at::vec::Vectorized::size()) { + src.store(dst, size); +} + +template +inline typename std::enable_if_t || std::is_same_v, void> +{{kernel_name}}_store(scalar_t* dst, at::vec::Vectorized src, int size=at::vec::Vectorized::size()) { + auto res = at::vec::convert(src); + res.store(dst, size); +} + +/* +1. dequant +2. add mask +3. max reduce for softmax +*/ +template +inline void {{kernel_name}}_dequant_mask_max_fusion_kernel( + const int32_t* in, + const mask_t* mask_ptr, + const int32_t* sum_a_ptr, + const int32_t* sum_b_ptr, + const int& M, + const int& N, + const int& ldi, + const int& ldm, // leading dimension mask + const int& ldo, + const int32_t& beta, // zp_a*zp_b*k + const float& alpha, // scale_a*scale_b*scale_sdpa + float* out, + float* sfm_max_ptr) { + const int32_t vec_size = at::vec::Vectorized::size(); + auto vec_beta = at::vec::Vectorized(beta); + auto vec_alpha = at::vec::Vectorized(alpha); + for (long row = 0; row < M; row += 1) { + auto sum_a = sum_a_ptr[row]; + auto vec_sum_a = at::vec::Vectorized(sum_a); + const int32_t* tmp_in = in + row * ldi; + float* tmp_out = out + row * ldo; + const mask_t* mask_data_ptr = mask_ptr + row * ldm; + float tmp_max = -std::numeric_limits::infinity(); + auto vec_tmp_max = at::vec::Vectorized(tmp_max); + long col = 0; + for (; col < vec_size * (N / vec_size); col += vec_size) { + auto vec_sum_b = at::vec::Vectorized::loadu(sum_b_ptr + col); + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + col); + auto tmp1 = tmp0 - vec_sum_b; + auto tmp2 = tmp1 - vec_sum_a; + auto tmp3 = tmp2 + vec_beta; + auto tmp4 = at::vec::convert(tmp3); + auto tmp5 = tmp4 * vec_alpha; + auto tmp6 = at::vec::Vectorized::loadu(mask_data_ptr + col); + auto tmp7 = at::vec::convert(tmp6); + auto tmp8 = tmp5 + tmp7; + vec_tmp_max = at::vec::clamp_min(vec_tmp_max, tmp8); + {{kernel_name}}_store(tmp_out + col, tmp8); + } + if (col < N) { + auto vec_sum_b = at::vec::Vectorized::loadu(sum_b_ptr + col, N - col); + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + col, N - col); + auto tmp1 = tmp0 - vec_sum_b; + auto tmp2 = tmp1 - vec_sum_a; + auto tmp3 = tmp2 + vec_beta; + auto tmp4 = at::vec::convert(tmp3); + auto tmp5 = tmp4 * vec_alpha; + auto tmp6 = at::vec::Vectorized::loadu(mask_data_ptr + col, N - col); + auto tmp7 = at::vec::convert(tmp6); + auto tmp8 = tmp5 + tmp7; + {{kernel_name}}_store(tmp_out + col, tmp8, N - col); + vec_tmp_max = at::vec::Vectorized::set(vec_tmp_max, at::vec::clamp_min(vec_tmp_max, tmp8), N - col); + } + sfm_max_ptr[row] = std::max(sfm_max_ptr[row], vec_tmp_max.reduce_max()); + } +} + +/* +1. dequant +2. max reduce for softmax +*/ +inline void {{kernel_name}}_dequant_max_fusion_kernel( + const int32_t* in, + const int32_t* sum_a_ptr, + const int32_t* sum_b_ptr, + const int& M, + const int& N, + const int& ldi, + const int& ldo, + const int32_t& beta, // zp_a*zp_b*k + const float& alpha, // scale_a*scale_b*scale_sdpa + float* out, + float* sfm_max_ptr) { + const int32_t vec_size = at::vec::Vectorized::size(); + auto vec_beta = at::vec::Vectorized(beta); + auto vec_alpha = at::vec::Vectorized(alpha); + for (long row = 0; row < M; row += 1) { + auto sum_a = sum_a_ptr[row]; + auto vec_sum_a = at::vec::Vectorized(sum_a); + const int32_t* tmp_in = in + row * ldi; + float* tmp_out = out + row * ldo; + float tmp_max = -std::numeric_limits::infinity(); + auto vec_tmp_max = at::vec::Vectorized(tmp_max); + long col = 0; + for (; col < vec_size * (N / vec_size); col += vec_size) { + auto vec_sum_b = at::vec::Vectorized::loadu(sum_b_ptr + col); + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + col); + auto tmp1 = tmp0 - vec_sum_b; + auto tmp2 = tmp1 - vec_sum_a; + auto tmp3 = tmp2 + vec_beta; + auto tmp4 = at::vec::convert(tmp3); + auto tmp5 = tmp4 * vec_alpha; + vec_tmp_max = at::vec::clamp_min(vec_tmp_max, tmp5); + {{kernel_name}}_store(tmp_out + col, tmp5); + } + if (col < N) { + auto vec_sum_b = at::vec::Vectorized::loadu(sum_b_ptr + col, N - col); + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + col, N - col); + auto tmp1 = tmp0 - vec_sum_b; + auto tmp2 = tmp1 - vec_sum_a; + auto tmp3 = tmp2 + vec_beta; + auto tmp4 = at::vec::convert(tmp3); + auto tmp5 = tmp4 * vec_alpha; + {{kernel_name}}_store(tmp_out + col, tmp5, N - col); + vec_tmp_max = at::vec::Vectorized::set(vec_tmp_max, at::vec::clamp_min(vec_tmp_max, tmp5), N - col); + } + sfm_max_ptr[row] = std::max(sfm_max_ptr[row], vec_tmp_max.reduce_max()); + } +} + +/* +1. Softmax: sub max, exp, sum reduce, div sum +2. quant +3. sum for attention +*/ +template +inline void {{kernel_name}}_sub_exp_sum_div_quant_sum_fusion_kernel( + const float* in, + const int64_t& M, + const int64_t& N_step, + const int64_t& NSlice, + const int& ldi, + const int& ldo, + const int& kvSize, + const int& rndkvSplitSize, + const int& av_gemm_K, + const int32_t& beta1, // zp_a + const int32_t& beta2, // zp_b + const float& alpha, // scale_a + float* local, + scalar_t* out, + float* sfm_max_ptr, + float* sfm_sum_ptr, + int32_t* sum_a_ptr) { + const int32_t vec_size = at::vec::Vectorized::size(); + float min_val = 0; + float max_val = 255; + auto vec_min_val = at::vec::Vectorized(min_val); + auto vec_max_val = at::vec::Vectorized(max_val); + scalar_t zero = 0; + auto vec_zero = at::vec::Vectorized(zero); + float beta1_float = (float) beta1; + auto vec_beta1 = at::vec::Vectorized(beta1_float); + for (int64_t row = 0; row < M; ++row) { + auto sfm_max = sfm_max_ptr[row]; + auto vec_max = at::vec::Vectorized(sfm_max); + // sub max, exp, sum reduce + const float* qk_block_data = in + row * rndkvSplitSize; + for (int64_t l = 0; l < NSlice; l ++) { + int64_t n = l * N_step; + int64_t kvBlockSize = std::min(N_step, kvSize - n); + const float* tmp_in = qk_block_data + l * ldi; + float tmp_sum = 0; + auto vec_tmp_sum = at::vec::Vectorized(tmp_sum); + float* tmp_out = local + n; + long col = 0; + for (; col < vec_size * (kvBlockSize / vec_size); col += vec_size) { + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + col); + auto tmp1 = tmp0 - vec_max; + auto tmp2 = tmp1.exp_u20(); + vec_tmp_sum += tmp2; + {{kernel_name}}_store(tmp_out + col, tmp2); + } + if (col < kvBlockSize) { + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + col, kvBlockSize - col); + auto tmp1 = tmp0 - vec_max; + auto tmp2 = tmp1.exp_u20(); + {{kernel_name}}_store(tmp_out + col, tmp2, kvBlockSize - col); + vec_tmp_sum = at::vec::Vectorized::set(vec_tmp_sum, vec_tmp_sum + tmp2, kvBlockSize - col); + } + sfm_sum_ptr[row] += vec_tmp_sum.reduce_add(); + } + // div sum, sum for attention + auto sum_scale = 1 / sfm_sum_ptr[row] / alpha; + auto vec_sum_scale = at::vec::Vectorized(sum_scale); + scalar_t* qk_reduced_block_data = out + row * av_gemm_K; + for (int64_t l = 0; l < NSlice; l ++) { + int64_t n = l * N_step; + int64_t kvBlockSize = std::min(N_step, kvSize - n); + int32_t tmp_sum = 0; + auto vec_tmp_sum = at::vec::Vectorized(tmp_sum); + float* tmp_in = local + n; + scalar_t* tmp_out = qk_reduced_block_data + l * ldo; + long col = 0; + for (; col < vec_size * (kvBlockSize / vec_size); col += vec_size) { + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + col); + auto tmp1 = tmp0 * vec_sum_scale; + auto tmp2 = tmp1.round(); + auto tmp3 = tmp2 + vec_beta1; + auto tmp4 = at::vec::clamp(tmp3, vec_min_val, vec_max_val); + {{kernel_name}}_store(tmp_out + col, tmp4); + auto tmp6 = at::vec::convert(tmp4); + vec_tmp_sum += tmp6; + } + if (col < kvBlockSize) { + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + col, kvBlockSize - col); + auto tmp1 = tmp0 * vec_sum_scale; + auto tmp2 = tmp1.round(); + auto tmp3 = tmp2 + vec_beta1; + auto tmp4 = at::vec::clamp(tmp3, vec_min_val, vec_max_val); + {{kernel_name}}_store(tmp_out + col, tmp4, kvBlockSize - col); + auto tmp6 = at::vec::convert(tmp4); + vec_tmp_sum = at::vec::Vectorized::set(vec_tmp_sum, vec_tmp_sum + tmp6, kvBlockSize - col); + } + sum_a_ptr[row] += vec_tmp_sum.reduce_add() * beta2; + // set zero + col = kvBlockSize; + for (; col < vec_size * (av_gemm_K / vec_size); col += vec_size) { + {{kernel_name}}_store(tmp_out + col, vec_zero); + } + if (col < av_gemm_K) { + {{kernel_name}}_store(tmp_out + col, vec_zero, av_gemm_K - col); + } + } + } +} + +/* +1. Softmax: sub max, exp, sum reduce, div sum +2. quant +*/ +template +inline void {{kernel_name}}_sub_exp_sum_div_quant_fusion_kernel( + const float* in, + const int64_t& M, + const int64_t& N_step, + const int64_t& NSlice, + const int& ldi, + const int& ldo, + const int& kvSize, + const int& rndkvSplitSize, + const int& av_gemm_K, + const int32_t& beta1, // zp_a + const float& alpha, // scale_a + float* local, + scalar_t* out, + float* sfm_max_ptr, + float* sfm_sum_ptr) { + const int32_t vec_size = at::vec::Vectorized::size(); + float min_val = 0; + float max_val = 255; + auto vec_min_val = at::vec::Vectorized(min_val); + auto vec_max_val = at::vec::Vectorized(max_val); + scalar_t zero = 0; + auto vec_zero = at::vec::Vectorized(zero); + float beta1_float = (float) beta1; + auto vec_beta1 = at::vec::Vectorized(beta1_float); + for (int64_t row = 0; row < M; ++row) { + auto sfm_max = sfm_max_ptr[row]; + auto vec_max = at::vec::Vectorized(sfm_max); + // sub max, exp, sum reduce + const float* qk_block_data = in + row * rndkvSplitSize; + for (int64_t l = 0; l < NSlice; l ++) { + int64_t n = l * N_step; + int64_t kvBlockSize = std::min(N_step, kvSize - n); + const float* tmp_in = qk_block_data + l * ldi; + float tmp_sum = 0; + auto vec_tmp_sum = at::vec::Vectorized(tmp_sum); + float* tmp_out = local + n; + long col = 0; + for (; col < vec_size * (kvBlockSize / vec_size); col += vec_size) { + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + col); + auto tmp1 = tmp0 - vec_max; + auto tmp2 = tmp1.exp_u20(); + vec_tmp_sum += tmp2; + {{kernel_name}}_store(tmp_out + col, tmp2); + } + if (col < kvBlockSize) { + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + col, kvBlockSize - col); + auto tmp1 = tmp0 - vec_max; + auto tmp2 = tmp1.exp_u20(); + vec_tmp_sum = at::vec::Vectorized::set(vec_tmp_sum, vec_tmp_sum + tmp2, kvBlockSize - col); + {{kernel_name}}_store(tmp_out + col, tmp2, kvBlockSize - col); + } + sfm_sum_ptr[row] += vec_tmp_sum.reduce_add(); + } + // div sum, sum for attention + auto sum_scale = 1 / sfm_sum_ptr[row] / alpha; + auto vec_sum_scale = at::vec::Vectorized(sum_scale); + scalar_t* qk_reduced_block_data = out + row * av_gemm_K; + for (int64_t l = 0; l < NSlice; l ++) { + int64_t n = l * N_step; + int64_t kvBlockSize = std::min(N_step, kvSize - n); + float* tmp_in = local + n; + scalar_t* tmp_out = qk_reduced_block_data + l * ldo; + long col = 0; + for (; col < vec_size * (kvBlockSize / vec_size); col += vec_size) { + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + col); + auto tmp1 = tmp0 * vec_sum_scale; + auto tmp2 = tmp1.round(); + auto tmp3 = tmp2 + vec_beta1; + auto tmp4 = at::vec::clamp(tmp3, vec_min_val, vec_max_val); + {{kernel_name}}_store(tmp_out + col, tmp4); + } + if (col < kvBlockSize) { + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + col, kvBlockSize - col); + auto tmp1 = tmp0 * vec_sum_scale; + auto tmp2 = tmp1.round(); + auto tmp3 = tmp2 + vec_beta1; + auto tmp4 = at::vec::clamp(tmp3, vec_min_val, vec_max_val); + {{kernel_name}}_store(tmp_out + col, tmp4, kvBlockSize - col); + } + // set zero + col = kvBlockSize; + for (; col < vec_size * (av_gemm_K / vec_size); col += vec_size) { + {{kernel_name}}_store(tmp_out + col, vec_zero); + } + if (col < av_gemm_K) { + {{kernel_name}}_store(tmp_out + col, vec_zero, av_gemm_K - col); + } + } + } +} + +/* +1. dequant +2. quant +*/ +template +inline void {{kernel_name}}_dequant_quant_fusion_kernel( + const int32_t* in, + const int32_t* sum_a_ptr, + const int32_t* sum_b_ptr, + const int& M, + const int& N, + const int& ldi, + const int& ldo, + const int32_t& beta1, // zp_a*zp_b*k + const int32_t& beta2, // zp_c + const float& alpha, // scale_a*scale_b/scale_c + scalar_t* out) { + const int32_t vec_size = at::vec::Vectorized::size(); + float min_val = 0; + float max_val = 255; + auto vec_min_val = at::vec::Vectorized(min_val); + auto vec_max_val = at::vec::Vectorized(max_val); + auto vec_beta1 = at::vec::Vectorized(beta1); + auto vec_alpha = at::vec::Vectorized(alpha); + float beta2_float = (float) beta2; + auto vec_beta2 = at::vec::Vectorized(beta2_float); + for (long row = 0; row < M; row += 1) { + auto sum_a = sum_a_ptr[row]; + auto vec_sum_a = at::vec::Vectorized(sum_a); + const int32_t* tmp_in = in + row * ldi; + scalar_t* tmp_out = out + row * ldo; + long col = 0; + for (; col < vec_size * (N / vec_size); col += vec_size) { + auto vec_sum_b = at::vec::Vectorized::loadu(sum_b_ptr + col); + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + col); + auto tmp1 = tmp0 - vec_sum_b; + auto tmp2 = tmp1 - vec_sum_a; + auto tmp3 = tmp2 + vec_beta1; + auto tmp4 = at::vec::convert(tmp3); + auto tmp5 = tmp4 * vec_alpha; + auto tmp6 = tmp5.round(); + auto tmp7 = tmp6 + vec_beta2; + auto tmp8 = at::vec::clamp(tmp7, vec_min_val, vec_max_val); + {{kernel_name}}_store(tmp_out + col, tmp8); + } + if (col < N) { + auto vec_sum_b = at::vec::Vectorized::loadu(sum_b_ptr + col, N - col); + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + col, N - col); + auto tmp1 = tmp0 - vec_sum_b; + auto tmp2 = tmp1 - vec_sum_a; + auto tmp3 = tmp2 + vec_beta1; + auto tmp4 = at::vec::convert(tmp3); + auto tmp5 = tmp4 * vec_alpha; + auto tmp6 = tmp5.round(); + auto tmp7 = tmp6 + vec_beta2; + auto tmp8 = at::vec::clamp(tmp7, vec_min_val, vec_max_val); + {{kernel_name}}_store(tmp_out + col, tmp8, N - col); + } + } +} + +/* +1. dequant +2. quant +*/ +template +inline void {{kernel_name}}_dequant_quant_fusion_kernel( + const int32_t* in, + const int32_t* sum_a_ptr, + const int& M, + const int& N, + const int& ldi, + const int& ldo, + const int32_t& beta2, // zp_c + const float& alpha, // scale_a*scale_b/scale_c + scalar_t* out) { + const int32_t vec_size = at::vec::Vectorized::size(); + float min_val = 0; + float max_val = 255; + auto vec_min_val = at::vec::Vectorized(min_val); + auto vec_max_val = at::vec::Vectorized(max_val); + // auto vec_beta1 = at::vec::Vectorized(beta1); + auto vec_alpha = at::vec::Vectorized(alpha); + float beta2_float = (float) beta2; + auto vec_beta2 = at::vec::Vectorized(beta2_float); + for (long row = 0; row < M; row += 1) { + auto sum_a = sum_a_ptr[row]; + auto vec_sum_a = at::vec::Vectorized(sum_a); + const int32_t* tmp_in = in + row * ldi; + scalar_t* tmp_out = out + row * ldo; + long col = 0; + for (; col < vec_size * (N / vec_size); col += vec_size) { + auto tmp1 = at::vec::Vectorized::loadu(tmp_in + col); + auto tmp3 = tmp1 - vec_sum_a; + // auto tmp3 = tmp2 + vec_beta1; + auto tmp4 = at::vec::convert(tmp3); + auto tmp5 = tmp4 * vec_alpha; + auto tmp6 = tmp5.round(); + auto tmp7 = tmp6 + vec_beta2; + auto tmp8 = at::vec::clamp(tmp7, vec_min_val, vec_max_val); + {{kernel_name}}_store(tmp_out + col, tmp8); + } + if (col < N) { + auto tmp1 = at::vec::Vectorized::loadu(tmp_in + col, N - col); + auto tmp3 = tmp1 - vec_sum_a; + auto tmp4 = at::vec::convert(tmp3); + auto tmp5 = tmp4 * vec_alpha; + auto tmp6 = tmp5.round(); + auto tmp7 = tmp6 + vec_beta2; + auto tmp8 = at::vec::clamp(tmp7, vec_min_val, vec_max_val); + {{kernel_name}}_store(tmp_out + col, tmp8, N - col); + } + } +} + +template +inline void {{kernel_name}}_int_sum_b_contiguous_kernel_helper( + const scalar_t* in, + int32_t* out, + const int& N, + const int32_t& scale) { + const int32_t vec_size = at::vec::Vectorized::size(); + int32_t tmp_sum = 0; + auto vec_tmp_sum = at::vec::Vectorized(tmp_sum); + long i = 0; + for (; i < vec_size * (N / vec_size); i += vec_size) { + auto tmp0 = at::vec::Vectorized::loadu(in + i); + auto tmp1 = at::vec::convert(tmp0); + vec_tmp_sum = vec_tmp_sum + tmp1; + } + if (i < N) { + auto tmp0 = at::vec::Vectorized::loadu(in + i, N - i); + auto tmp1 = at::vec::convert(tmp0); + vec_tmp_sum = at::vec::Vectorized::set(vec_tmp_sum, vec_tmp_sum + tmp1, N - i); + } + out[0] = vec_tmp_sum.reduce_add() * scale; +} + +// reduce along dim b for shape [a, b], with sum shape [a] +template +inline void {{kernel_name}}_int_sum_b_contiguous_kernel( + const scalar_t* in, + int32_t* out, + const int& M, + const int& N, + const int& ld, + const int32_t& scale) { + for (long r = 0; r < M; r += 1) { + {{kernel_name}}_int_sum_b_contiguous_kernel_helper(in + r * ld, out + r, N, scale); + } +} + +// reduce along dim a for shape [a, b], with sum shape [b] +template +inline void {{kernel_name}}_int_sum_a_contiguous_kernel( + const scalar_t* in, + int32_t* out, + const int& M, + const int& N, + const int& ld, + const int32_t& scale) { + const int32_t vec_size = at::vec::Vectorized::size(); + auto vec_scale = at::vec::Vectorized(scale); + // initialization with 0 + int32_t zero = 0; + auto vec_zero = at::vec::Vectorized(zero); + long i = 0; + for (; i < vec_size * (M / vec_size); i += vec_size) { + {{kernel_name}}_store(out + i, vec_zero); + } + if (i < M) { + {{kernel_name}}_store(out + i, vec_zero, M - i); + } + // sum + for (long j = 0; j < N; j++) { + const scalar_t* tmp_in = in + j * ld; + long k = 0; + for (; k < vec_size * (M / vec_size); k += vec_size) { + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + k); + auto tmp1 = at::vec::Vectorized::loadu(out + k); + auto tmp2 = at::vec::convert(tmp0); + auto tmp3 = tmp1 + tmp2; + {{kernel_name}}_store(out + k, tmp3); + } + if (k < M) { + auto tmp0 = at::vec::Vectorized::loadu(tmp_in + k, M - k); + auto tmp1 = at::vec::Vectorized::loadu(out + k, M - k); + auto tmp2 = at::vec::convert(tmp0); + auto tmp3 = tmp1 + tmp2; + {{kernel_name}}_store(out + k, tmp3, M - k); + } + } + // scale + i = 0; + for (; i < vec_size * (M / vec_size); i += vec_size) { + auto tmp0 = at::vec::Vectorized::loadu(out + i); + auto tmp1 = tmp0 * vec_scale; + {{kernel_name}}_store(out + i, tmp1); + } + if (i < M) { + auto tmp0 = at::vec::Vectorized::loadu(out + i, M - i); + auto tmp1 = tmp0 * vec_scale; + {{kernel_name}}_store(out + i, tmp1, M - i); + } +} + +// do the transpose: [in_rows, in_cols] -> [in_cols, in_rows] +template +inline void {{kernel_name}}_do_transpose( + const scalar_t* src, + scalar_t* dst, + int64_t in_rows, + int64_t in_cols, + int64_t ldi, + int64_t ldo) { + for (int64_t r=0; r [prows, pcols] +template +inline void {{kernel_name}}_pad_remain_row_col( + scalar_t* value_ptr, + int rows, + int cols, + int prows, + int pcols, + int ldi, + scalar_t pad_val=0) { + auto psize = pcols - cols; + if (psize == 0 && prows == rows) { + return; + } + const int32_t vec_size = at::vec::Vectorized::size(); + auto pad = at::vec::Vectorized(pad_val); + if (psize > 0) { + for (int i = 0; i < rows; i++) { + int j = 0; + for (; j < psize - (psize % vec_size); j += vec_size) { + pad.store(value_ptr + i * ldi + cols + j); + } + if (j < psize) { + pad.store(value_ptr + i * ldi + cols + j, psize - j); + } + } + } + + for (int i = rows; i < prows; i++) { + int j = 0; + for (; j < pcols - (pcols % vec_size); j += vec_size) { + pad.store(value_ptr + i * ldi + j); + } + if (j < pcols) { + pad.store(value_ptr + i * ldi + j, pcols - j); + } + } +} + +// copy value_ptr to dst_ptr with padding: [rows, cols] -> [prows, pcols] +template +inline void {{kernel_name}}_copy_value_with_pad( + const scalar_t* value_ptr, + scalar_t* dst_ptr, + int rows, + int cols, + int prows, + int pcols, + int ldi, + scalar_t pad_val=0) { + const int32_t vec_size = at::vec::Vectorized::size(); + auto pad = at::vec::Vectorized(pad_val); + int i = 0; + for (; i < rows; i++) { + int j = 0; + for (; j < cols - (cols % vec_size); j += vec_size) { + auto vec_v = + at::vec::Vectorized::loadu(value_ptr + i * ldi + j); + vec_v.store(dst_ptr + i * pcols + j); + } + + if (j < cols) { + auto vec_v = at::vec::Vectorized::loadu( + value_ptr + i * ldi + j, cols - j); + vec_v.store(dst_ptr + i * pcols + j, cols - j); + } + + // col padding + auto psize = pcols - cols; + if (psize > 0) { + int pj = 0; + for (; pj < psize - (psize % vec_size); pj += vec_size) { + pad.store(dst_ptr + i * pcols + cols + pj); + } + if (pj < psize) { + pad.store(dst_ptr + i * pcols + cols + pj, psize - pj); + } + } + } + + // row padding + for (; i < prows; i++) { + int j = 0; + for (; j < pcols - (pcols % vec_size); j += vec_size) { + pad.store(dst_ptr + i * pcols + j); + } + if (j < pcols) { + pad.store(dst_ptr + i * pcols + j, pcols - j); + } + + } + +} +""" + + +ALLOCATE_BUFFER = r""" + auto& {{buffer_name}}_allocator = *at::getCPUAllocator(); + auto {{buffer_name}}_work_data = {{buffer_name}}_allocator.allocate({{buffer_size}} * sizeof({{buffer_dtype}})); + void* {{buffer_name}}_data_ptr = {{buffer_name}}_work_data.get(); + {{buffer_dtype}}* {{buffer_name}} = ({{buffer_dtype}}*){{buffer_name}}_data_ptr; +""" + + +INT8_SDPA_ONE_LOOP_TEMPLATE = r""" +{{template.header().getvalue()}} +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#ifndef AT_PER_OPERATOR_HEADERS +#include +#else +#include +#endif + +#include +#include +#include +#include + +{{template.codegen_useful_function(kernel.kernel_name)}} + +{%- if has_attention_mask %} +{%- set kernel_args = {"query": query, "key": key, "value": value, + "attention_mask": attention_mask} %} +{%- else %} +{%- set kernel_args = {"query": query, "key": key, "value": value} %} +{%- endif %} + +// UINT8 - u8u8s32 +extern "C" +{{kernel.def_kernel(inputs=kernel_args, outputs={"output": output})}} +{ + {{ kernel.maybe_codegen_profile() }} + int64_t num_thread = {{num_thread}}; + using accum_t = float; + using scalar_t = {{kernel.dtype(query)}}; + int block_64 = 64; + auto u8_dt = at::ScalarType::Byte; + + // Sizes + int64_t batchSize = {{kernel.size(query, 0)}}; + int64_t qSize = {{kernel.size(query, 1)}}; + int64_t kvSize = {{kernel.size(value, 1)}}; + int64_t num_head = {{kernel.size(query, 2)}}; + int64_t headSize = {{kernel.size(query, 3)}}; + float scaling_factor = + {{kernel.kernel_name}}_calculate_scale(headSize, {{scale}}); + + // Strides + int64_t qStrideB = {{kernel.stride(query, 0)}}; + int64_t qStrideM = {{kernel.stride(query, 1)}}; + int64_t qStrideH = {{kernel.stride(query, 2)}}; + int64_t kStrideB = {{kernel.stride(key, 0)}}; + int64_t kStrideN = {{kernel.stride(key, 1)}}; + int64_t kStrideH = {{kernel.stride(key, 2)}}; + int64_t vStrideB = {{kernel.stride(value, 0)}}; + int64_t vStrideN = {{kernel.stride(value, 1)}}; + int64_t vStrideH = {{kernel.stride(value, 2)}}; + int64_t oStrideB = {{kernel.stride(output, 0)}}; + int64_t oStrideM = {{kernel.stride(output, 2)}}; + int64_t oStrideH = {{kernel.stride(output, 1)}}; + + int64_t qSplitSize = {{q_split_size}} > qSize ? qSize : {{q_split_size}}; + int64_t kvSplitSize = {{kv_split_size}} > kvSize ? kvSize : {{kv_split_size}}; + int64_t qSlice = (qSize - 1) / qSplitSize + 1; + int64_t kvSlice = (kvSize - 1) / kvSplitSize + 1; + int64_t kvTail = (kvSize - 1) % kvSplitSize + 1; + + int64_t rndHeadSize = (headSize + block_64 - 1L) / block_64 * block_64; + int64_t rndkvSplitSize = (kvSplitSize + block_64 - 1L) / block_64 * block_64; + int64_t rndkvTail = (kvTail + block_64 - 1L) / block_64 * block_64; + int64_t rndkvSize = {{kv_split_size}} > kvSize ? rndkvTail : rndkvSplitSize * kvSlice + rndkvTail; + + bool av_gemm_K_mul4 = kvSplitSize % 4 == 0; + int av_gemm_K_padding = av_gemm_K_mul4 ? 0 : 4 - kvSplitSize % 4; + int av_gemm_K = kvSplitSize + av_gemm_K_padding; + +{%- if has_attention_mask %} + // attention mask + using mask_t = {{kernel.dtype(attention_mask)}}; + const mask_t* mask_data = attention_mask; + int64_t mStrideB = + {{kernel.size(attention_mask, 0)}} > 1 + ? {{kernel.stride(attention_mask, 0)}} + : 0; + int64_t mStrideH = + {{kernel.size(attention_mask, 1)}} > 1 + ? {{kernel.stride(attention_mask, 1)}} + : 0; + int64_t mStrideM = + {{kernel.size(attention_mask, 2)}}> 1 + ? {{kernel.stride(attention_mask, 2)}} + : 0; + int64_t mStrideN = + {{kernel.size(attention_mask, 3)}} > 1 + ? {{kernel.stride(attention_mask, 3)}} + : 0; +{%- endif %} + + // Data ptrs + const scalar_t* q_data = query; + const scalar_t* k_data = key; + const scalar_t* v_data = value; + scalar_t* out_data = output; + + bool headSize_mul64 = headSize % 64 == 0; + int qk_gemm_K_padding = headSize_mul64 ? 0 : 64 - headSize % 64; + int qk_gemm_K = headSize + qk_gemm_K_padding; + + int64_t qk_reduce_strideL = qSplitSize * av_gemm_K; + int64_t v_reorder_strideL = av_gemm_K * rndHeadSize; + + int64_t total_size_uint8_per_thread = + /* qk */ kvSlice * qSplitSize * rndkvSplitSize * 4 + + /* qk_local */ kvSlice * av_gemm_K * 4 + + /* qk_reduce */ kvSlice * qk_reduce_strideL + + /* qk_s32 */ qSplitSize * rndkvSplitSize * 4 + + /* dst_s32 */ qSplitSize * rndHeadSize * 4 + + /* softmax_sum */ qSplitSize * 4 + + /* query_sum */ qSplitSize * 4 + + /* attention_sum */ qSplitSize * 4 + + /* softmax max */ qSplitSize * 4 + + /* query_padding_data */ qSplitSize * qk_gemm_K + + /* key_sum */ kvSize * 4 + + /* value_sum */ headSize * 4 + + /* key_t_reorder */ qk_gemm_K * rndkvSize + + /* value_t_reorder */ kvSlice * v_reorder_strideL; + {{template.codegen_allocate_buffer("total_buf_data", "scalar_t", "num_thread * total_size_uint8_per_thread")}} + + at::parallel_for( + 0, batchSize * num_head, 1, [&](int64_t begin, int64_t end) { + int64_t i = 0, j = 0; + at::native::data_index_init( + begin, i, batchSize, j, num_head); + int ompIdx = at::get_thread_num(); + scalar_t* total_buf_ptr = total_buf_data + ompIdx * total_size_uint8_per_thread; + int32_t offset = 0; + accum_t* qk_data = reinterpret_cast(total_buf_ptr); + offset += kvSlice * qSplitSize * rndkvSplitSize * 4; + accum_t* qk_local_data = reinterpret_cast(total_buf_ptr + offset); + offset += kvSlice * av_gemm_K * 4; + scalar_t* qk_reduced_data = reinterpret_cast(total_buf_ptr + offset); + offset += kvSlice * qk_reduce_strideL; + int32_t* qk_s32_data = reinterpret_cast(total_buf_ptr + offset); + offset += qSplitSize * rndkvSplitSize * 4; + int32_t* dst_s32_data = reinterpret_cast(total_buf_ptr + offset); + offset += qSplitSize * rndHeadSize * 4; + accum_t* sfm_sum_ptr = reinterpret_cast(total_buf_ptr + offset); + offset += qSplitSize * 4; + int32_t* q_sum_ptr = reinterpret_cast(total_buf_ptr + offset); + offset += qSplitSize * 4; + int32_t* a_sum_ptr = reinterpret_cast(total_buf_ptr + offset); + offset += qSplitSize * 4; + accum_t* sfm_max_ptr = reinterpret_cast(total_buf_ptr + offset); + offset += qSplitSize * 4; + scalar_t* query_t_padding_ptr = reinterpret_cast(total_buf_ptr + offset); + offset += qSplitSize * qk_gemm_K; + + int32_t* k_sum_ptr = reinterpret_cast(total_buf_ptr + offset); + offset += kvSize * 4; + int32_t* v_sum_ptr = reinterpret_cast(total_buf_ptr + offset); + offset += headSize * 4; + scalar_t* key_reorder_ptr = reinterpret_cast(total_buf_ptr + offset); + offset += qk_gemm_K * rndkvSize; + scalar_t* value_reorder_ptr = reinterpret_cast(total_buf_ptr + offset); + + uint8_t* B_blocked_xform_u8 = new uint8_t[qk_gemm_K * block_64]; + + for (const auto z : c10::irange(begin, end)) { + (void)z; // Suppress unused variable + + // sum k and v +{%- if q_zp == 0 %} + {{kernel.kernel_name}}_fill_stub(k_sum_ptr, static_cast(0), kvSize); +{%- else %} + {{kernel.kernel_name}}_int_sum_b_contiguous_kernel(k_data + i * kStrideB + j * kStrideH, + k_sum_ptr, + kvSize, headSize, kStrideN, {{q_zp}}); +{%- endif %} +{%- if a_zp == 0 %} + {{kernel.kernel_name}}_fill_stub(v_sum_ptr, static_cast(0), headSize); +{%- else %} + {{kernel.kernel_name}}_int_sum_a_contiguous_kernel(v_data + i * vStrideB + j * vStrideH, + v_sum_ptr, + headSize, kvSize, vStrideN, {{a_zp}}); +{%- endif %} + + // transpose and packing + for (int64_t n = 0; n < kvSize; n += kvSplitSize) { + int64_t kvBlockSize = std::min(kvSplitSize, kvSize - n); + for (int64_t b = 0; b < kvBlockSize; b += block_64) { + bool istail = kvBlockSize - b < block_64; + int64_t trans_rows = istail ? kvBlockSize - b : block_64; + {{kernel.kernel_name}}_do_transpose( + k_data + i * kStrideB + j * kStrideH + n * kStrideN + b * kStrideN, + B_blocked_xform_u8, + trans_rows, + headSize, + kStrideN, + block_64); + if (!headSize_mul64 || istail) { + {{kernel.kernel_name}}_pad_remain_row_col( + B_blocked_xform_u8, + headSize, + trans_rows, + qk_gemm_K, + block_64, + block_64 + ); + } + at::native::cpublas::pack( + qk_gemm_K, // K + block_64, // N + block_64, // ld_in + block_64, // ld_out + u8_dt, // dt_in + u8_dt, // dt_out + B_blocked_xform_u8, + key_reorder_ptr + n * qk_gemm_K + + b * qk_gemm_K); + } + // split headSize to block_64, block_64, block_64 ... + // [av_gemm_K, headSize] -> [av_gemm_K, block_64 ...] + for (int64_t b = 0; b < rndHeadSize; b += block_64) { + at::native::cpublas::pack( + av_gemm_K, + block_64, + vStrideN, // block_64, + block_64, + u8_dt, + u8_dt, + v_data + i * vStrideB + j * vStrideH + n * vStrideN + b, + value_reorder_ptr + n * rndHeadSize + + av_gemm_K * b); + } + } + + // sdpa core + for (int64_t k = 0; k < qSlice; k++) { + int64_t m = k * qSplitSize; + int64_t qBlockSize = std::min(qSplitSize, qSize - m); + // Initialize sum and max + {{kernel.kernel_name}}_fill_stub( + sfm_sum_ptr, static_cast(0), qSplitSize); + {{kernel.kernel_name}}_fill_stub( + a_sum_ptr, static_cast(0), qSplitSize); + {{kernel.kernel_name}}_fill_stub( + sfm_max_ptr, static_cast(-std::numeric_limits::infinity()), qSplitSize); + int64_t num_keys = kvSize; + {{kernel.kernel_name}}_copy_value_with_pad( + q_data + i * qStrideB + j * qStrideH + m * qStrideM, + query_t_padding_ptr, + qBlockSize, + headSize, + qBlockSize, + qk_gemm_K, + qStrideM); + // sum q +{%- if k_zp != 0 %} + {{kernel.kernel_name}}_int_sum_b_contiguous_kernel(q_data + i * qStrideB + j * qStrideH + m * qStrideM, + q_sum_ptr, qBlockSize, headSize, qStrideM, {{k_zp}}); +{%- else %} + {{kernel.kernel_name}}_fill_stub( + q_sum_ptr, static_cast(0), qSplitSize); +{%- endif %} + const int64_t rkvSlice = (num_keys - 1) / kvSplitSize + 1; + for (int64_t l = 0; l < rkvSlice; l++) { + int64_t n = l * kvSplitSize; + int64_t kvBlockSize = std::min(kvSplitSize, kvSize - n); + for (int64_t b = 0; b < kvBlockSize; b += block_64) { + at::native::cpublas::brgemm( + qSplitSize, block_64, qk_gemm_K, + qk_gemm_K, // lda + block_64, //ldb + rndkvSplitSize, //ldc, + false, + query_t_padding_ptr, + key_reorder_ptr + n * qk_gemm_K + + b * qk_gemm_K, + qk_s32_data + b); + } + + // do dequant compensation, add mask, max reduce for softmax, and convert qk from s32 to fp32 + accum_t* qk_block_data = qk_data + l * qSplitSize * rndkvSplitSize; +{%- if has_attention_mask %} + const mask_t* mask_data_offset = mask_data + i * mStrideB + j * mStrideH + m * mStrideM + (mStrideN == 0 ? 0 : n); + {{kernel.kernel_name}}_dequant_mask_max_fusion_kernel( + qk_s32_data, //in + mask_data_offset, //mask_ptr + q_sum_ptr, //sum_a_ptr + k_sum_ptr + n, //sum_b_ptr + qBlockSize, //M + kvBlockSize, //N + rndkvSplitSize, //ldi + mStrideM, //ldm + rndkvSplitSize, //ldo + {{q_zp}} * {{k_zp}} * headSize, //zp_a*zp_b*k=beta + {{q_scale}} * {{k_scale}} * scaling_factor, //scale_a*scale_b*scale_sdpa=alpha + qk_block_data, //out + sfm_max_ptr //sfm_max_ptr + ); +{%- else %} + {{kernel.kernel_name}}_dequant_max_fusion_kernel( + qk_s32_data, //in + q_sum_ptr, //sum_a_ptr + k_sum_ptr + n, //sum_b_ptr + qBlockSize, //M + kvBlockSize, //N + rndkvSplitSize, //ldi + rndkvSplitSize, //ldo + {{q_zp}} * {{k_zp}} * headSize, //zp_a*zp_b*k=beta + {{q_scale}} * {{k_scale}} * scaling_factor, //scale_a*scale_b*scale_sdpa=alpha + qk_block_data, //out + sfm_max_ptr //sfm_max_ptr + ); +{%- endif %} + } + // sub max, exp, sum reduce, div sum for softmax + // and quant + // and sum for attention +{%- if v_zp == 0 %} + {{kernel.kernel_name}}_sub_exp_sum_div_quant_fusion_kernel( + qk_data, //in + qBlockSize, //M + kvSplitSize, //N_step + rkvSlice, //NSlices + qSplitSize * rndkvSplitSize, //ldi + qk_reduce_strideL, //ldo + kvSize, //kvSize + rndkvSplitSize, //rndkvSplitSize + av_gemm_K, //av_gemm_K + {{a_zp}}, // zp_a=beta1 + {{a_scale}}, // scale_a=alpha + qk_local_data, //local + qk_reduced_data, //out + sfm_max_ptr, //sfm_max_ptr + sfm_sum_ptr //sfm_sum_ptr + ); +{%- else %} + {{kernel.kernel_name}}_sub_exp_sum_div_quant_sum_fusion_kernel( + qk_data, //in + qBlockSize, //M + kvSplitSize, //N_step + rkvSlice, //NSlice + qSplitSize * rndkvSplitSize, //ldi + qk_reduce_strideL, //ldo + kvSize, //kvSize + rndkvSplitSize, //rndkvSplitSize + av_gemm_K, //av_gemm_K + {{a_zp}}, // zp_a=beta1 + {{v_zp}}, // zp_b=beta2 + {{a_scale}}, // scale_a=alpha + qk_local_data, //local + qk_reduced_data, //out + sfm_max_ptr, //sfm_max_ptr + sfm_sum_ptr, //sfm_sum_ptr + a_sum_ptr //a_sum_ptr + ); +{%- endif %} + // Calculate Softmax(q @ k.T) @ v + for (int64_t b = 0; b < headSize; b += block_64) { + auto value_reorder_b = value_reorder_ptr + b * av_gemm_K; + auto dst_s32_b = dst_s32_data + b; + for (int64_t s = 0; s < kvSlice; s++) { + at::native::cpublas::brgemm( + qSplitSize, block_64, av_gemm_K, + av_gemm_K, // lda + rndHeadSize, //ldb + rndHeadSize, //ldc + s != 0, + qk_reduced_data + s * qk_reduce_strideL, + value_reorder_b + s * v_reorder_strideL, + dst_s32_b); + } + } + + // After the last gemm, + // do dequant compensation, quant and convert from s32 to int8 +{%- if a_zp == 0 %} + {{kernel.kernel_name}}_dequant_quant_fusion_kernel( + dst_s32_data, //in + a_sum_ptr, //sum_a_ptr + qBlockSize, //M + headSize, //N + rndHeadSize, //ldi + oStrideM, //ldo + {{o_zp}}, //zp_c=beta2 + {{a_scale}} * {{v_scale}} / {{o_scale}}, //scale_a*scale_b/scale_c=alpha + out_data + i * oStrideB + j * oStrideH + m * oStrideM //out + ); +{%- else %} + {{kernel.kernel_name}}_dequant_quant_fusion_kernel( + dst_s32_data, //in + a_sum_ptr, //sum_a_ptr + v_sum_ptr, //sum_b_ptr + qBlockSize, //M + headSize, //N + rndHeadSize, //ldi + oStrideM, //ldo + {{a_zp}} * {{v_zp}} * kvSize, //zp_a*zp_b*k=beta1 + {{o_zp}}, //zp_c=beta2 + {{a_scale}} * {{v_scale}} / {{o_scale}}, //scale_a*scale_b/scale_c=alpha + out_data + i * oStrideB + j * oStrideH + m * oStrideM //out + ); +{%- endif %} + } + // Move to the next query + at::native::data_index_step(i, batchSize, j, num_head); + } + }); + // Once all computations are done, need to release HW context. + at::native::cpublas::brgemm_release(); +} + +""" + + +INT8_SDPA_SEVERAL_LOOPS_TEMPLATE = r""" +{{template.header().getvalue()}} +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#ifndef AT_PER_OPERATOR_HEADERS +#include +#else +#include +#endif + +#include +#include +#include +#include + +{{template.codegen_useful_function(kernel.kernel_name)}} + +{%- if has_attention_mask %} +{%- set kernel_args = {"query": query, "key": key, "value": value, + "attention_mask": attention_mask} %} +{%- else %} +{%- set kernel_args = {"query": query, "key": key, "value": value} %} +{%- endif %} + +// UINT8 - u8u8s32 +extern "C" +{{kernel.def_kernel(inputs=kernel_args, outputs={"output": output})}} +{ + {{ kernel.maybe_codegen_profile() }} + int64_t num_thread = {{num_thread}}; + using accum_t = float; + using scalar_t = {{kernel.dtype(query)}}; + int block_64 = 64; + auto u8_dt = at::ScalarType::Byte; + + // Sizes + int64_t batchSize = {{kernel.size(query, 0)}}; + int64_t qSize = {{kernel.size(query, 1)}}; + int64_t kvSize = {{kernel.size(value, 1)}}; + int64_t num_head = {{kernel.size(query, 2)}}; + int64_t headSize = {{kernel.size(query, 3)}}; + float scaling_factor = + {{kernel.kernel_name}}_calculate_scale(headSize, {{scale}}); + + // Strides + int64_t qStrideB = {{kernel.stride(query, 0)}}; + int64_t qStrideM = {{kernel.stride(query, 1)}}; + int64_t qStrideH = {{kernel.stride(query, 2)}}; + int64_t kStrideB = {{kernel.stride(key, 0)}}; + int64_t kStrideN = {{kernel.stride(key, 1)}}; + int64_t kStrideH = {{kernel.stride(key, 2)}}; + int64_t vStrideB = {{kernel.stride(value, 0)}}; + int64_t vStrideN = {{kernel.stride(value, 1)}}; + int64_t vStrideH = {{kernel.stride(value, 2)}}; + int64_t oStrideB = {{kernel.stride(output, 0)}}; + int64_t oStrideM = {{kernel.stride(output, 2)}}; + int64_t oStrideH = {{kernel.stride(output, 1)}}; + + int64_t qSplitSize = {{q_split_size}} > qSize ? qSize : {{q_split_size}}; + int64_t kvSplitSize = {{kv_split_size}} > kvSize ? kvSize : {{kv_split_size}}; + int64_t qSlice = (qSize - 1) / qSplitSize + 1; + int64_t kvSlice = (kvSize - 1) / kvSplitSize + 1; + int64_t kvTail = (kvSize - 1) % kvSplitSize + 1; + + int64_t rndHeadSize = (headSize + block_64 - 1L) / block_64 * block_64; + int64_t rndkvSplitSize = (kvSplitSize + block_64 - 1L) / block_64 * block_64; + int64_t rndkvTail = (kvTail + block_64 - 1L) / block_64 * block_64; + int64_t rndkvSize = {{kv_split_size}} > kvSize ? rndkvTail : rndkvSplitSize * kvSlice + rndkvTail; + + bool av_gemm_K_mul4 = kvSplitSize % 4 == 0; + int av_gemm_K_padding = av_gemm_K_mul4 ? 0 : 4 - kvSplitSize % 4; + int av_gemm_K = kvSplitSize + av_gemm_K_padding; + +{%- if has_attention_mask %} + // attention mask + using mask_t = {{kernel.dtype(attention_mask)}}; + const mask_t* mask_data = attention_mask; + int64_t mStrideB = + {{kernel.size(attention_mask, 0)}} > 1 + ? {{kernel.stride(attention_mask, 0)}} + : 0; + int64_t mStrideH = + {{kernel.size(attention_mask, 1)}} > 1 + ? {{kernel.stride(attention_mask, 1)}} + : 0; + int64_t mStrideM = + {{kernel.size(attention_mask, 2)}}> 1 + ? {{kernel.stride(attention_mask, 2)}} + : 0; + int64_t mStrideN = + {{kernel.size(attention_mask, 3)}} > 1 + ? {{kernel.stride(attention_mask, 3)}} + : 0; +{%- endif %} + + // Data ptrs + const scalar_t* q_data = query; + const scalar_t* k_data = key; + const scalar_t* v_data = value; + scalar_t* out_data = output; + + bool headSize_mul64 = headSize % 64 == 0; + int qk_gemm_K_padding = headSize_mul64 ? 0 : 64 - headSize % 64; + int qk_gemm_K = headSize + qk_gemm_K_padding; + + int64_t qk_reduce_strideL = qSplitSize * av_gemm_K; + int64_t v_reorder_strideL = av_gemm_K * rndHeadSize; + + int64_t total_size_uint8_per_thread = + /* qk */ kvSlice * qSplitSize * rndkvSplitSize * 4 + + /* qk_local */ kvSlice * av_gemm_K * 4 + + /* qk_reduce */ kvSlice * qk_reduce_strideL + + /* qk_s32 */ qSplitSize * rndkvSplitSize * 4 + + /* dst_s32 */ qSplitSize * rndHeadSize * 4 + + /* softmax_sum */ qSplitSize * 4 + + /* query_sum */ qSplitSize * 4 + + /* attention_sum */ qSplitSize * 4 + + /* softmax max */ qSplitSize * 4 + + /* query_padding_data */ qSplitSize * qk_gemm_K; + {{template.codegen_allocate_buffer("total_buf_data", "scalar_t", "num_thread * total_size_uint8_per_thread")}} + + int64_t kv_sum_size_per_BH = + /* key_sum */ kvSize + + /* value_sum */ headSize; + {{template.codegen_allocate_buffer("kv_sum_buf_data", "int32_t", "batchSize * num_head * kv_sum_size_per_BH")}} + + int64_t kv_reorder_size_per_BH = + /* key_t_reorder */ qk_gemm_K * rndkvSize + + /* value_t_reorder */ kvSlice * v_reorder_strideL; + {{template.codegen_allocate_buffer("kv_reorder_buf_data", "scalar_t", "batchSize * num_head * kv_reorder_size_per_BH")}} + scalar_t* key_reorder_ptr = kv_reorder_buf_data; + scalar_t* value_reorder_ptr = kv_reorder_buf_data + batchSize * num_head * qk_gemm_K * rndkvSize; + + // sum k and v + at::parallel_for( + 0, batchSize * num_head, 1, [&](int64_t begin, int64_t end) { + int64_t i = 0, j = 0; + at::native::data_index_init( + begin, i, batchSize, j, num_head); + for (const auto z : c10::irange(begin, end)) { + (void)z; // Suppress unused variable + int32_t* kv_sum_ptr = kv_sum_buf_data + + i * num_head * kv_sum_size_per_BH + + j * kv_sum_size_per_BH; + int32_t* k_sum_ptr = kv_sum_ptr; + int32_t* v_sum_ptr = kv_sum_ptr + kvSize; +{%- if q_zp == 0 %} + {{kernel.kernel_name}}_fill_stub(k_sum_ptr, static_cast(0), kvSize); +{%- else %} + {{kernel.kernel_name}}_int_sum_b_contiguous_kernel(k_data + i * kStrideB + j * kStrideH, + k_sum_ptr, + kvSize, headSize, kStrideN, {{q_zp}}); +{%- endif %} +{%- if a_zp == 0 %} + {{kernel.kernel_name}}_fill_stub(v_sum_ptr, static_cast(0), headSize); +{%- else %} + {{kernel.kernel_name}}_int_sum_a_contiguous_kernel(v_data + i * vStrideB + j * vStrideH, + v_sum_ptr, + headSize, kvSize, vStrideN, {{a_zp}}); +{%- endif %} + // Move to the next query + at::native::data_index_step(i, batchSize, j, num_head); + } + }); + + // packing + at::parallel_for( + 0, batchSize * num_head * kvSlice, 1, [&](int64_t begin, int64_t end) { + int64_t i = 0, j = 0, l = 0, n = 0; + at::native::data_index_init( + begin, i, batchSize, j, num_head, l, kvSlice); + uint8_t* B_blocked_xform_u8 = new uint8_t[qk_gemm_K * block_64]; + for (const auto z : c10::irange(begin, end)) { + (void)z; // Suppress unused variable + n = l * kvSplitSize; + auto k_reorder = key_reorder_ptr + i * num_head * qk_gemm_K * rndkvSize + + j * qk_gemm_K * rndkvSize + n * qk_gemm_K; + auto v_reorder = value_reorder_ptr + + i * num_head * kvSlice * v_reorder_strideL + + j * kvSlice * v_reorder_strideL + n * rndHeadSize; + int64_t kvBlockSize = std::min(kvSplitSize, kvSize - n); + for (int64_t b = 0; b < kvBlockSize; b += block_64) { + bool istail = kvBlockSize - b < block_64; + int64_t trans_rows = istail ? kvBlockSize - b : block_64; + {{kernel.kernel_name}}_do_transpose( + k_data + i * kStrideB + j * kStrideH + n * kStrideN + b * kStrideN, + B_blocked_xform_u8, + trans_rows, + headSize, + kStrideN, + block_64); + if (!headSize_mul64 || istail) { + {{kernel.kernel_name}}_pad_remain_row_col( + B_blocked_xform_u8, + headSize, + trans_rows, + qk_gemm_K, + block_64, + block_64 + ); + } + at::native::cpublas::pack( + qk_gemm_K, // K + block_64, // N + block_64, // ld_in + block_64, // ld_out + u8_dt, // dt_in + u8_dt, // dt_out + B_blocked_xform_u8, + k_reorder + b * qk_gemm_K); + } + // split headSize to block_64, block_64, block_64 ... + // [av_gemm_K, headSize] -> [av_gemm_K, block_64 ...] + for (int64_t b = 0; b < rndHeadSize; b += block_64) { + at::native::cpublas::pack( + av_gemm_K, + block_64, + vStrideN, // block_64, + block_64, + u8_dt, + u8_dt, + v_data + i * vStrideB + j * vStrideH + n * vStrideN + b, + v_reorder + av_gemm_K * b); + } + // Move to the next query + at::native::data_index_step(i, batchSize, j, num_head, l, kvSlice); + } + }); + + at::parallel_for( + 0, batchSize * num_head * qSlice, 1, [&](int64_t begin, int64_t end) { + int64_t i = 0, j = 0, k = 0; + at::native::data_index_init( + begin, i, batchSize, j, num_head, k, qSlice); + int ompIdx = at::get_thread_num(); + scalar_t* total_buf_ptr = total_buf_data + ompIdx * total_size_uint8_per_thread; + int32_t offset = 0; + accum_t* qk_data = reinterpret_cast(total_buf_ptr); + offset += kvSlice * qSplitSize * rndkvSplitSize * 4; + accum_t* qk_local_data = reinterpret_cast(total_buf_ptr + offset); + offset += kvSlice * av_gemm_K * 4; + scalar_t* qk_reduced_data = reinterpret_cast(total_buf_ptr + offset); + offset += kvSlice * qk_reduce_strideL; + int32_t* qk_s32_data = reinterpret_cast(total_buf_ptr + offset); + offset += qSplitSize * rndkvSplitSize * 4; + int32_t* dst_s32_data = reinterpret_cast(total_buf_ptr + offset); + offset += qSplitSize * rndHeadSize * 4; + accum_t* sfm_sum_ptr = reinterpret_cast(total_buf_ptr + offset); + offset += qSplitSize * 4; + int32_t* q_sum_ptr = reinterpret_cast(total_buf_ptr + offset); + offset += qSplitSize * 4; + int32_t* a_sum_ptr = reinterpret_cast(total_buf_ptr + offset); + offset += qSplitSize * 4; + accum_t* sfm_max_ptr = reinterpret_cast(total_buf_ptr + offset); + offset += qSplitSize * 4; + scalar_t* query_t_padding_ptr = reinterpret_cast(total_buf_ptr + offset); + + for (const auto z : c10::irange(begin, end)) { + (void)z; // Suppress unused variable + + int32_t* kv_sum_ptr = kv_sum_buf_data + + i * num_head * kv_sum_size_per_BH + + j * kv_sum_size_per_BH; + int32_t* k_sum_ptr = kv_sum_ptr; + int32_t* v_sum_ptr = kv_sum_ptr + kvSize; + + // sdpa core + int64_t m = k * qSplitSize; + int64_t qBlockSize = std::min(qSplitSize, qSize - m); + // Initialize sum and max + {{kernel.kernel_name}}_fill_stub( + sfm_sum_ptr, static_cast(0), qSplitSize); + {{kernel.kernel_name}}_fill_stub( + a_sum_ptr, static_cast(0), qSplitSize); + {{kernel.kernel_name}}_fill_stub( + sfm_max_ptr, static_cast(-std::numeric_limits::infinity()), qSplitSize); + int64_t num_keys = kvSize; + {{kernel.kernel_name}}_copy_value_with_pad( + q_data + i * qStrideB + j * qStrideH + m * qStrideM, + query_t_padding_ptr, + qBlockSize, + headSize, + qBlockSize, + qk_gemm_K, + qStrideM); + // sum q +{%- if k_zp != 0 %} + {{kernel.kernel_name}}_int_sum_b_contiguous_kernel(q_data + i * qStrideB + j * qStrideH + m * qStrideM, + q_sum_ptr, qBlockSize, headSize, qStrideM, {{k_zp}}); +{%- else %} + {{kernel.kernel_name}}_fill_stub( + q_sum_ptr, static_cast(0), qSplitSize); +{%- endif %} + const int64_t rkvSlice = (num_keys - 1) / kvSplitSize + 1; + for (int64_t l = 0; l < rkvSlice; l++) { + int64_t n = l * kvSplitSize; + int64_t kvBlockSize = std::min(kvSplitSize, kvSize - n); + auto k_reorder = key_reorder_ptr + i * num_head * qk_gemm_K * rndkvSize + + j * qk_gemm_K * rndkvSize + n * qk_gemm_K; + // Calculate q @ k.T + for (int64_t b = 0; b < kvBlockSize; b += block_64) { + at::native::cpublas::brgemm( + qSplitSize, block_64, qk_gemm_K, + qk_gemm_K, // lda + block_64, //ldb + rndkvSplitSize, //ldc, + false, + query_t_padding_ptr, + k_reorder + b * qk_gemm_K, + qk_s32_data + b); + } + + // do dequant compensation, add mask, max reduce for softmax, and convert qk from s32 to fp32 + accum_t* qk_block_data = qk_data + l * qSplitSize * rndkvSplitSize; +{%- if has_attention_mask %} + const mask_t* mask_data_offset = mask_data + i * mStrideB + j * mStrideH + m * mStrideM + (mStrideN == 0 ? 0 : n); + {{kernel.kernel_name}}_dequant_mask_max_fusion_kernel( + qk_s32_data, //in + mask_data_offset, //mask_ptr + q_sum_ptr, //sum_a_ptr + k_sum_ptr + n, //sum_b_ptr + qBlockSize, //M + kvBlockSize, //N + rndkvSplitSize, //ldi + mStrideM, //ldm + rndkvSplitSize, //ldo + {{q_zp}} * {{k_zp}} * headSize, //zp_a*zp_b*k=beta + {{q_scale}} * {{k_scale}} * scaling_factor, //scale_a*scale_b*scale_sdpa=alpha + qk_block_data, //out + sfm_max_ptr //sfm_max_ptr + ); +{%- else %} + {{kernel.kernel_name}}_dequant_max_fusion_kernel( + qk_s32_data, //in + q_sum_ptr, //sum_a_ptr + k_sum_ptr + n, //sum_b_ptr + qBlockSize, //M + kvBlockSize, //N + rndkvSplitSize, //ldi + rndkvSplitSize,//kvBlockSize, //ldo + {{q_zp}} * {{k_zp}} * headSize, //zp_a*zp_b*k=beta + {{q_scale}} * {{k_scale}} * scaling_factor, //scale_a*scale_b*scale_sdpa=alpha + qk_block_data, //out + sfm_max_ptr //sfm_max_ptr + ); +{%- endif %} + } + // sub max, exp, sum reduce, div sum for softmax + // and quant + // and sum for attention +{%- if v_zp == 0 %} + {{kernel.kernel_name}}_sub_exp_sum_div_quant_fusion_kernel( + qk_data, //in + qBlockSize, //M + kvSplitSize, //N_step + rkvSlice, //NSlices + qSplitSize * rndkvSplitSize, //ldi + qk_reduce_strideL, //ldo + kvSize, //kvSize + rndkvSplitSize, //rndkvSplitSize + av_gemm_K, //av_gemm_K + {{a_zp}}, // zp_a=beta1 + {{a_scale}}, // scale_a=alpha + qk_local_data, //local + qk_reduced_data, //out + sfm_max_ptr, //sfm_max_ptr + sfm_sum_ptr //sfm_sum_ptr + ); +{%- else %} + {{kernel.kernel_name}}_sub_exp_sum_div_quant_sum_fusion_kernel( + qk_data, //in + qBlockSize, //M + kvSplitSize, //N_step + rkvSlice, //NSlice + qSplitSize * rndkvSplitSize, //ldi + qk_reduce_strideL, //ldo + kvSize, //kvSize + rndkvSplitSize, //rndkvSplitSize + av_gemm_K, //av_gemm_K + {{a_zp}}, // zp_a=beta1 + {{v_zp}}, // zp_b=beta2 + {{a_scale}}, // scale_a=alpha + qk_local_data, //local + qk_reduced_data, //out + sfm_max_ptr, //sfm_max_ptr + sfm_sum_ptr, //sfm_sum_ptr + a_sum_ptr //a_sum_ptr + ); +{%- endif %} + // Calculate Softmax(q @ k.T) @ v + auto v_reorder = value_reorder_ptr + + i * num_head * kvSlice * v_reorder_strideL + + j * kvSlice * v_reorder_strideL; + for (int64_t b = 0; b < headSize; b += block_64) { + auto value_reorder_b = v_reorder + b * av_gemm_K; + auto dst_s32_b = dst_s32_data + b; + for (int64_t s = 0; s < kvSlice; s++) { + at::native::cpublas::brgemm( + qSplitSize, block_64, av_gemm_K, + av_gemm_K, // lda + rndHeadSize, //ldb + rndHeadSize, //ldc + s != 0, + qk_reduced_data + s * qk_reduce_strideL, + value_reorder_b + s * v_reorder_strideL, + dst_s32_b); + } + } + + // After the last gemm, + // do dequant compensation, quant and convert from s32 to int8 +{%- if a_zp == 0 %} + {{kernel.kernel_name}}_dequant_quant_fusion_kernel( + dst_s32_data, //in + a_sum_ptr, //sum_a_ptr + qBlockSize, //M + headSize, //N + rndHeadSize, //ldi + oStrideM, //ldo + {{o_zp}}, //zp_c=beta2 + {{a_scale}} * {{v_scale}} / {{o_scale}}, //scale_a*scale_b/scale_c=alpha + out_data + i * oStrideB + j * oStrideH + m * oStrideM //out + ); +{%- else %} + {{kernel.kernel_name}}_dequant_quant_fusion_kernel( + dst_s32_data, //in + a_sum_ptr, //sum_a_ptr + v_sum_ptr, //sum_b_ptr + qBlockSize, //M + headSize, //N + rndHeadSize, //ldi + oStrideM, //ldo + {{a_zp}} * {{v_zp}} * kvSize, //zp_a*zp_b*k=beta1 + {{o_zp}}, //zp_c=beta2 + {{a_scale}} * {{v_scale}} / {{o_scale}}, //scale_a*scale_b/scale_c=alpha + out_data + i * oStrideB + j * oStrideH + m * oStrideM //out + ); +{%- endif %} + // Move to the next query + at::native::data_index_step(i, batchSize, j, num_head, k, qSlice); + } + }); + // Once all computations are done, need to release HW context. + at::native::cpublas::brgemm_release(); +} +""" + + +class CppInt8SdpaTemplate(CppFlexAttentionTemplate): + def __init__( + self, + input_nodes, + layout: ir.Layout, + scale, + q_scale, + q_zp, + k_scale, + k_zp, + v_scale, + v_zp, + a_scale, + a_zp, + o_scale, + o_zp, + ) -> None: + assert layout.dtype in [torch.uint8] + CppTemplate.__init__( + self, "int8_sdpa", input_nodes, layout, parallel_num_threads() + ) + self.scale = scale + self.q_scale = q_scale + self.q_zp = q_zp + self.k_scale = k_scale + self.k_zp = k_zp + self.v_scale = v_scale + self.v_zp = v_zp + self.a_scale = a_scale + self.a_zp = a_zp + self.o_scale = o_scale + self.o_zp = o_zp + + @staticmethod + def add_choices( + choices, + input_nodes, + layout, + scale, + q_scale, + q_zp, + k_scale, + k_zp, + v_scale, + v_zp, + a_scale, + a_zp, + o_scale, + o_zp, + ): + def preprocessor(input_nodes, layout): + return input_nodes, layout + + def postprocessor(output): + return output + + template = DataProcessorTemplateWrapper( + CppInt8SdpaTemplate, + preprocessor, + postprocessor, + input_nodes=input_nodes, + layout=layout, + scale=scale, + q_scale=q_scale, + q_zp=q_zp, + k_scale=k_scale, + k_zp=k_zp, + v_scale=v_scale, + v_zp=v_zp, + a_scale=a_scale, + a_zp=a_zp, + o_scale=o_scale, + o_zp=o_zp, + ) + template.maybe_append_choice(choices) + return template + + def reshape_attn_mask_to_4d( + self, + kernel, + attn_mask: ir.Buffer, + batchSize, + num_head, + qSize, + kvSize, + ): + # Support mask shapes: + # 2d: ({Q_seq_len, 1} x {KV_seq_len, 1}) + # 4d: ({Batch, 1} x {Num_heads, 1} x {Q_seq_len, 1} x {KV_seq_len, 1}) + # Guaranteed in check_attn_mask_shape + attn_mask_size_0 = 1 + attn_mask_size_1 = 1 + layout = attn_mask.get_layout() + if len(layout.size) == 4: + if layout.size[0] == batchSize: + attn_mask_size_0 = batchSize + if layout.size[1] == num_head: + attn_mask_size_1 = num_head + attn_mask = kernel.view( + attn_mask, + [ + attn_mask_size_0, + attn_mask_size_1, + layout.size[-2], + layout.size[-1], + ], + ) + attn_mask = expand( + attn_mask, [attn_mask_size_0, attn_mask_size_1, qSize, kvSize] + ) + return attn_mask + + def get_options( + self, + query: ir.Buffer, + key: ir.Buffer, + value: ir.Buffer, + qSize, + kvSize, + headSize, + batchSize, + num_head, + num_threads, + ): + q_split_size = 32 + if qSize >= 768: + q_split_size = 256 + elif qSize >= 192: + q_split_size = 64 + kv_split_size = 64 + + qSplitSize = min(qSize, q_split_size) + l2_cache_size = torch._C._cpu._L2_cache_size() + attn_size = qSplitSize * kvSize * 4 * num_threads + use_one_parallel_loop = True + if all( + sympify(val).is_number + for val in [batchSize, num_head, num_threads, attn_size, l2_cache_size] + ): + # if not symbolic shape + use_one_parallel_loop = (batchSize * num_head > num_threads) and ( + attn_size > 1.5 * l2_cache_size + ) + + options = dict( + q_split_size=q_split_size, + kv_split_size=kv_split_size, + use_one_parallel_loop=use_one_parallel_loop, + ) + return options + + def render( # type: ignore[override,return] + self, + kernel, + template_buffer_node: Optional[ir.CppTemplateBuffer] = None, + epilogue_nodes: Optional[List[ir.IRNode]] = None, + **kwargs, + ) -> str: + if epilogue_nodes is not None and epilogue_nodes != []: + raise NotImplementedError( + "Unsupported for `epilogue_nodes` in CppInt8SdpaTemplate." + ) + # Query (Batch x Num_heads x Q_seq_len x Dim_per_head) + # -> (Batch x Q_seq_len x Num_heads x Dim_per_head) + # Key (Batch x Num_heads x KV_seq_len x Dim_per_head) + # -> (Batch x KV_seq_len x Num_heads x Dim_per_head) + # Value (Batch x Num_heads x KV_seq_len x Dim_per_head) + # -> (Batch x KV_seq_len x Num_heads x Dim_per_head) + + query = kernel.permute(self.input_nodes[0], [0, 2, 1, 3]) + key = kernel.permute(self.input_nodes[1], [0, 2, 1, 3]) + value = kernel.permute(self.input_nodes[2], [0, 2, 1, 3]) + + batchSize = query.layout.size[0] + qSize = query.layout.size[1] + kvSize = value.layout.size[1] + num_head = query.layout.size[2] + headSize = query.layout.size[3] + + has_attention_mask = len(self.input_nodes) == 4 + attention_mask = ( + self.reshape_attn_mask_to_4d( + kernel, self.input_nodes[3], batchSize, num_head, qSize, kvSize + ) + if has_attention_mask + else None + ) + + num_threads = parallel_num_threads() + buf_out = TensorBox.create(self.output_node) + if template_buffer_node is not None: + buf_out = template_buffer_node + options = dict( + query=query, + key=key, + value=value, + has_attention_mask=has_attention_mask, + attention_mask=attention_mask, + scale=self.scale, + q_scale=self.q_scale, + q_zp=self.q_zp, + k_scale=self.k_scale, + k_zp=self.k_zp, + v_scale=self.v_scale, + v_zp=self.v_zp, + a_scale=self.a_scale, + a_zp=self.a_zp, + o_scale=self.o_scale, + o_zp=self.o_zp, + template=self, + output=buf_out, + kernel=kernel, + num_thread=num_threads, + ) + new_options = self.get_options( + query=query, + key=key, + value=value, + qSize=qSize, + kvSize=kvSize, + headSize=headSize, + batchSize=batchSize, + num_head=num_head, + num_threads=num_threads, + ) + options.update(new_options) + INT8_SDPA_TEMPLATE = ( + INT8_SDPA_ONE_LOOP_TEMPLATE + if options["use_one_parallel_loop"] + else INT8_SDPA_SEVERAL_LOOPS_TEMPLATE + ) + return self._template_from_string(INT8_SDPA_TEMPLATE).render(**options) + + def codegen_useful_function(self, kernel_name: str): + return self._template_from_string(USEFUL_FUNCTIONS).render( + dict(kernel_name=kernel_name) + ) + + def codegen_allocate_buffer(self, buffer_name: str, buffer_dtype, buffer_size): + return self._template_from_string(ALLOCATE_BUFFER).render( + dict( + buffer_name=buffer_name, + buffer_dtype=buffer_dtype, + buffer_size=buffer_size, + ) + ) diff --git a/torchao/prototype/inductor/codegen/utils.py b/torchao/prototype/inductor/codegen/utils.py new file mode 100644 index 0000000000..bfbf455257 --- /dev/null +++ b/torchao/prototype/inductor/codegen/utils.py @@ -0,0 +1,13 @@ +from typing import Any, List + +from torch._inductor import lowering as L +from torch._inductor.codegen.cpp_template_kernel import ( + parse_expr_with_index_symbols, + wrap_with_tensorbox, +) + + +def expand(node, sizes: List[Any]): + node = wrap_with_tensorbox(node) + sizes = parse_expr_with_index_symbols(sizes) + return L.expand(node, sizes).data diff --git a/torchao/prototype/inductor/fx_passes/int8_sdpa_fusion.py b/torchao/prototype/inductor/fx_passes/int8_sdpa_fusion.py index cfe0d309b1..5e032f01c2 100644 --- a/torchao/prototype/inductor/fx_passes/int8_sdpa_fusion.py +++ b/torchao/prototype/inductor/fx_passes/int8_sdpa_fusion.py @@ -17,12 +17,16 @@ from torchao.utils import TORCH_VERSION_AT_LEAST_2_7 +if TORCH_VERSION_AT_LEAST_2_7: + # TORCH_VERSION_AT_LEAST_2_7 is needed for functions in int8 sdpa lowering + from ..int8_sdpa_lowering import register_int8_sdpa # noqa: F401 +else: + make_fallback(torch.ops.torchao.qscaled_dot_product.default) + __all__ = [ "_int8_sdpa_init", ] -make_fallback(torch.ops.torchao.qscaled_dot_product.default) - aten = torch.ops.aten @@ -52,7 +56,7 @@ def int8_sdpa(match: Match, *args, **kwargs): query = kwargs["query"] key = kwargs["key"] value = kwargs["value"] - inv_scale = kwargs["inv_scale"] + scale = 1.0 / kwargs["inv_scale"] if "inv_scale" in kwargs else None attn_mask = kwargs["attn_mask"] if "attn_mask" in kwargs else None q_scale = kwargs["q_scale"] q_zp = kwargs["q_zp"] @@ -77,7 +81,7 @@ def int8_sdpa(match: Match, *args, **kwargs): attn_mask, 0.0, # dropout False, # is_causal - 1.0 / inv_scale, # scale + scale, # scale q_scale, q_zp, k_scale, diff --git a/torchao/prototype/inductor/int8_sdpa_lowering.py b/torchao/prototype/inductor/int8_sdpa_lowering.py new file mode 100644 index 0000000000..4fbff51c32 --- /dev/null +++ b/torchao/prototype/inductor/int8_sdpa_lowering.py @@ -0,0 +1,140 @@ +from typing import Optional + +import sympy +import torch +from torch._inductor.ir import ChoiceCaller, FixedLayout, TensorBox, get_fill_order +from torch._inductor.kernel.flex_attention import construct_strides, maybe_realize +from torch._inductor.lowering import register_lowering +from torch._inductor.select_algorithm import ( + ExternKernelChoice, + autotune_select_algorithm, +) + +from .codegen.cpp_int8_sdpa_template import CppInt8SdpaTemplate + +op_int8_sdpa = ExternKernelChoice( + torch.ops.torchao.qscaled_dot_product.default, + "torchao::qscaled_dot_product", + has_out_variant=False, + use_fallback_kernel=True, + op_overload=torch.ops.torchao.qscaled_dot_product.default, +) + + +def register_int8_sdpa(): + @register_lowering( + torch.ops.torchao.qscaled_dot_product.default, type_promotion_kind=None + ) + def int8_sdpa( + query: TensorBox, + key: TensorBox, + value: TensorBox, + attn_mask: Optional[TensorBox], + dropout: float, + is_causal: bool, + scale: Optional[float] = None, + q_scale: Optional[float] = 1.0, + q_zp: Optional[int] = 0, + k_scale: Optional[float] = 1.0, + k_zp: Optional[int] = 0, + v_scale: Optional[float] = 1.0, + v_zp: Optional[int] = 0, + a_scale: Optional[float] = 1.0, + a_zp: Optional[int] = 0, + o_scale: Optional[float] = 1.0, + o_zp: Optional[int] = 0, + ) -> TensorBox: + choices: list[ChoiceCaller] = [] + + ( + query, + key, + value, + attn_mask, + ) = maybe_realize( + [ + query, + key, + value, + attn_mask, + ] + ) + + if ( + query.get_dtype() is not torch.uint8 + or key.get_dtype() is not torch.uint8 + or value.get_dtype() is not torch.uint8 + ): + raise NotImplementedError( + "Only `torch.uint8` is supported in Int8 SDPA template for CPU device. " + f"Found input tensors are `{query.get_dtype()}`,`{key.get_dtype()}`,`{value.get_dtype()}`." + ) + + # Construct output layout with strides matching the query. + out_size = query.get_size() + fill_order = get_fill_order(query.get_stride()) + out_strides = construct_strides(out_size, fill_order) + + layout = FixedLayout( + query.get_device(), + query.get_dtype(), + out_size, + stride=[sympy.sympify(s) for s in out_strides], + ) + input_nodes = [query, key, value] + if attn_mask is not None: + input_nodes.append(attn_mask) + + # use template if machine has amx + if torch._C._cpu._is_amx_tile_supported(): + CppInt8SdpaTemplate.add_choices( + choices=choices, + input_nodes=input_nodes, + layout=layout, + scale=scale, + q_scale=q_scale, + q_zp=q_zp, + k_scale=k_scale, + k_zp=k_zp, + v_scale=v_scale, + v_zp=v_zp, + a_scale=a_scale, + a_zp=a_zp, + o_scale=o_scale, + o_zp=o_zp, + ) + + if len(choices) == 0: + choices.append( + op_int8_sdpa.bind( + input_nodes=input_nodes, + layout=layout, + scale=scale, + q_scale=q_scale, + q_zp=q_zp, + k_scale=k_scale, + k_zp=k_zp, + v_scale=v_scale, + v_zp=v_zp, + a_scale=a_scale, + a_zp=a_zp, + o_scale=o_scale, + o_zp=o_zp, + ) + ) + + inputs_for_autotuning = [ + query, + key, + value, + ] + + return autotune_select_algorithm( + "int8_sdpa", + choices, + inputs_for_autotuning, + layout, + ) + + +register_int8_sdpa() From 63f2e51bf034786a84259fe39e2e743285f51dda Mon Sep 17 00:00:00 2001 From: Jeff Daily Date: Tue, 27 May 2025 20:20:15 -0700 Subject: [PATCH 054/165] [reland2][ROCm] preshuffled weight mm (#2207) * [reland2][ROCm] preshuffled weight mm * remove debug print statements * remove duplicate registrations caused by patch fuzzing * lint * ruff --- setup.py | 115 ++-- test/test_ops.py | 44 +- torchao/__init__.py | 3 +- torchao/csrc/rocm/swizzle/swizzle.cpp | 911 ++++++++++++++++++++++++++ torchao/swizzle/__init__.py | 9 + torchao/swizzle/swizzle_tensor.py | 143 ++++ 6 files changed, 1173 insertions(+), 52 deletions(-) create mode 100644 torchao/csrc/rocm/swizzle/swizzle.cpp create mode 100644 torchao/swizzle/__init__.py create mode 100644 torchao/swizzle/swizzle_tensor.py diff --git a/setup.py b/setup.py index 0915f6ae1e..ff4d7fffb5 100644 --- a/setup.py +++ b/setup.py @@ -83,8 +83,6 @@ def use_debug_mode(): _get_cuda_arch_flags, ) -IS_ROCM = (torch.version.hip is not None) and (ROCM_HOME is not None) - class BuildOptions: def __init__(self): @@ -280,30 +278,35 @@ def get_extensions(): if debug_mode: print("Compiling in debug mode") - if not torch.version.cuda: - print( - "PyTorch GPU support is not available. Skipping compilation of CUDA extensions" - ) - if (CUDA_HOME is None and ROCM_HOME is None) and torch.version.cuda: - print( - "CUDA toolkit or ROCm is not available. Skipping compilation of CUDA extensions" - ) + if CUDA_HOME is None and torch.version.cuda: + print("CUDA toolkit is not available. Skipping compilation of CUDA extensions") print( "If you'd like to compile CUDA extensions locally please install the cudatoolkit from https://anaconda.org/nvidia/cuda-toolkit" ) - - use_cuda = torch.version.cuda and (CUDA_HOME is not None or ROCM_HOME is not None) - extension = CUDAExtension if use_cuda else CppExtension + if ROCM_HOME is None and torch.version.hip: + print("ROCm is not available. Skipping compilation of ROCm extensions") + print("If you'd like to compile ROCm extensions locally please install ROCm") + + use_cuda = torch.version.cuda and CUDA_HOME is not None + use_rocm = torch.version.hip and ROCM_HOME is not None + extension = CUDAExtension if (use_cuda or use_rocm) else CppExtension + + nvcc_args = [ + "-DNDEBUG" if not debug_mode else "-DDEBUG", + "-O3" if not debug_mode else "-O0", + "-t=0", + "-std=c++17", + ] + rocm_args = [ + "-DNDEBUG" if not debug_mode else "-DDEBUG", + "-O3" if not debug_mode else "-O0", + "-std=c++17", + ] extra_link_args = [] extra_compile_args = { "cxx": [f"-DPy_LIMITED_API={PY3_9_HEXCODE}"], - "nvcc": [ - "-DNDEBUG" if not debug_mode else "-DDEBUG", - "-O3" if not debug_mode else "-O0", - "-t=0", - "-std=c++17", - ], + "nvcc": nvcc_args if use_cuda else rocm_args, } if not IS_WINDOWS: @@ -341,6 +344,34 @@ def get_extensions(): extra_compile_args["nvcc"].append("-g") extra_link_args.append("/DEBUG") + rocm_sparse_marlin_supported = False + if use_rocm: + # naive search for hipblalst.h, if any found contain HIPBLASLT_ORDER_COL16 and VEC_EXT + found_col16 = False + found_vec_ext = False + print("ROCM_HOME", ROCM_HOME) + hipblaslt_headers = list( + glob.glob(os.path.join(ROCM_HOME, "include", "hipblaslt", "hipblaslt.h")) + ) + print("hipblaslt_headers", hipblaslt_headers) + for header in hipblaslt_headers: + with open(header) as f: + text = f.read() + if "HIPBLASLT_ORDER_COL16" in text: + found_col16 = True + if "HIPBLASLT_MATMUL_DESC_A_SCALE_POINTER_VEC_EXT" in text: + found_vec_ext = True + if found_col16: + extra_compile_args["cxx"].append("-DHIPBLASLT_HAS_ORDER_COL16") + print("hipblaslt found extended col order enums") + else: + print("hipblaslt does not have extended col order enums") + if found_vec_ext: + extra_compile_args["cxx"].append("-DHIPBLASLT_VEC_EXT") + print("hipblaslt found vec ext") + else: + print("hipblaslt does not have vec ext") + # Get base directory and source paths curdir = os.path.dirname(os.path.curdir) extensions_dir = os.path.join(curdir, "torchao", "csrc") @@ -354,42 +385,46 @@ def get_extensions(): ) sources = [s for s in sources if s not in excluded_sources] + # Collect CUDA source files extensions_cuda_dir = os.path.join(extensions_dir, "cuda") cuda_sources = list( glob.glob(os.path.join(extensions_cuda_dir, "**/*.cu"), recursive=True) ) - # Define HIP source directories - hip_source_dirs = [ + # Define ROCm source directories + rocm_source_dirs = [ + os.path.join(extensions_dir, "rocm", "swizzle"), os.path.join(extensions_dir, "cuda", "tensor_core_tiled_layout"), - # TODO: Add sparse_marlin back in once we have a ROCm build for it - # os.path.join(extensions_dir, "cuda", "sparse_marlin") ] - - # Collect all HIP sources from the defined directories - hip_sources = [] - for hip_dir in hip_source_dirs: - hip_sources.extend(glob.glob(os.path.join(hip_dir, "*.cu"), recursive=True)) - - # Collect CUDA source files if needed - if not IS_ROCM and use_cuda: + if rocm_sparse_marlin_supported: + rocm_source_dirs.extend([os.path.join(extensions_dir, "cuda", "sparse_marlin")]) + + # Collect all ROCm sources from the defined directories + rocm_sources = [] + for rocm_dir in rocm_source_dirs: + rocm_sources.extend(glob.glob(os.path.join(rocm_dir, "*.cu"), recursive=True)) + rocm_sources.extend(glob.glob(os.path.join(rocm_dir, "*.hip"), recursive=True)) + rocm_sources.extend(glob.glob(os.path.join(rocm_dir, "*.cpp"), recursive=True)) + + # Add CUDA source files if needed + if use_cuda: sources += cuda_sources # TOOD: Remove this and use what CUDA has once we fix all the builds. - if IS_ROCM and use_cuda: + if use_rocm: # Add ROCm GPU architecture check - gpu_arch = torch.cuda.get_device_properties(0).name - if gpu_arch != "gfx942": + gpu_arch = None + if torch.cuda.is_available(): + gpu_arch = torch.cuda.get_device_properties(0).name + if gpu_arch and gpu_arch != "gfx942": print(f"Warning: Unsupported ROCm GPU architecture: {gpu_arch}") - print( - "Currently only gfx942 is supported. Skipping compilation of ROCm extensions" - ) - else: - sources += hip_sources + print("Currently only gfx942 is supported. Compiling only for gfx942.") + extra_compile_args["nvcc"].append("--offload-arch=gfx942") + sources += rocm_sources use_cutlass = False cutlass_90a_sources = None - if use_cuda and not IS_ROCM and not IS_WINDOWS: + if use_cuda and not IS_WINDOWS: use_cutlass = True cutlass_dir = os.path.join(third_party_path, "cutlass") cutlass_include_dir = os.path.join(cutlass_dir, "include") diff --git a/test/test_ops.py b/test/test_ops.py index 132c4f0c18..012a4d562d 100644 --- a/test/test_ops.py +++ b/test/test_ops.py @@ -31,8 +31,8 @@ compute_max_diff, ) -if torch.version.hip is not None: - pytest.skip("Skipping the test in ROCm", allow_module_level=True) +IS_CUDA = torch.cuda.is_available() and torch.version.cuda +IS_ROCM = torch.cuda.is_available() and torch.version.hip try: import torchao.ops @@ -58,7 +58,7 @@ def _create_floatx_inputs( fp16_act = torch.rand(BS, IC).to(dtype) + 0.5 return floatx_weight.to(device), scale.to(device), fp16_act.to(device) - @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") + @pytest.mark.skipif(not IS_CUDA, reason="CUDA not available") @parametrize("ebits,mbits", [(3, 2), (2, 2)]) @parametrize("dtype", [torch.half, torch.bfloat16]) def test_quant_llm_linear(self, ebits, mbits, dtype): @@ -88,7 +88,7 @@ def test_quant_llm_linear(self, ebits, mbits, dtype): test_utils=test_utils, ) - @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") + @pytest.mark.skipif(not IS_CUDA, reason="CUDA not available") @parametrize("BS,OC,IC,splitK", [(1, 2048, 4096, 5), (2, 8192, 8192, 6)]) @parametrize("ebits,mbits", [(3, 2), (2, 2)]) @parametrize("dtype", [torch.half, torch.bfloat16]) @@ -278,7 +278,7 @@ def make_test_id(param): return f"tiles_{param}" -@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +@pytest.mark.skipif(not IS_CUDA, reason="CUDA not available") # @pytest.mark.skipif(TORCH_VERSION_AT_LEAST_2_5, reason="weight packing is updated in 2.5+") @pytest.mark.parametrize("shape, inner_k_tiles", TEST_CONFIGS_UNPACK, ids=make_test_id) def test_unpack_tensor_core_tiled_layout_correctness(shape, inner_k_tiles): @@ -296,7 +296,7 @@ def test_unpack_tensor_core_tiled_layout_correctness(shape, inner_k_tiles): # TODO: Fix "test_aot_dispatch_dynamic" test failure -@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +@pytest.mark.skipif(not IS_CUDA, reason="CUDA not available") # @pytest.mark.skipif(TORCH_VERSION_AT_LEAST_2_5, reason="weight packing is updated in 2.5+") @pytest.mark.parametrize("shape, inner_k_tiles", TEST_CONFIGS_UNPACK, ids=make_test_id) def test_unpack_tensor_core_tiled_layout_op(shape, inner_k_tiles): @@ -342,7 +342,7 @@ def dequant_ref(q, scales, zeros, group_size, nbits=4, dtype=torch.bfloat16): return dq.reshape(n, k) -@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +@pytest.mark.skipif(not IS_CUDA, reason="CUDA not available") # @pytest.mark.skipif(TORCH_VERSION_AT_LEAST_2_5, reason="weight packing is updated in 2.5+") @pytest.mark.parametrize( "shape, inner_k_tiles, group_size", TEST_CONFIGS_DEQUANT, ids=str @@ -410,7 +410,7 @@ def test_dequantize_tensor_core_tiled_layout_correctness_quant_dequant( # This test differs from one above in that it uses `unpack_tensor_core_tiled_layout` to unpack then dequantize -@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +@pytest.mark.skipif(not IS_CUDA, reason="CUDA not available") # @pytest.mark.skipif(TORCH_VERSION_AT_LEAST_2_5, reason="weight packing is updated in 2.5+") @pytest.mark.parametrize( "shape, inner_k_tiles, group_size", TEST_CONFIGS_DEQUANT, ids=str @@ -476,7 +476,7 @@ def test_dequantize_tensor_core_tiled_layout_correctness_unpack_and_dequant( assert diff_op_ao < 1e-1 -@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +@pytest.mark.skipif(not IS_CUDA, reason="CUDA not available") # @pytest.mark.skipif(TORCH_VERSION_AT_LEAST_2_5, reason="weight packing is updated in 2.5+") @pytest.mark.parametrize( "shape, inner_k_tiles, group_size", TEST_CONFIGS_DEQUANT, ids=str @@ -587,7 +587,7 @@ def reshape_w(w): ) -@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +@pytest.mark.skipif(not IS_CUDA, reason="CUDA not available") @pytest.mark.parametrize( "batch_size, k_chunk, n_chunk, num_bits, group_size, mnk_factors", MARLIN_TEST_PARAMS, @@ -677,7 +677,7 @@ def test_marlin_24(batch_size, k_chunk, n_chunk, num_bits, group_size, mnk_facto ) -@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +@pytest.mark.skipif(not IS_CUDA, reason="CUDA not available") @pytest.mark.parametrize( "batch_size, k_chunk, n_chunk, num_bits, group_size, mnk_factors", MARLIN_TEST_PARAMS, @@ -756,5 +756,27 @@ def test_marlin_qqq(batch_size, k_chunk, n_chunk, num_bits, group_size, mnk_fact ) +@pytest.mark.skipif(not IS_ROCM, reason="ROCm not available") +def test_swizzle_mm(): + test_utils = [ + "test_schema", + "test_autograd_registration", + "test_faketensor", + ] + + # TODO: Figure out why test fails unless torch >= 2.5 + if TORCH_VERSION_AT_LEAST_2_5: + test_utils.append("test_aot_dispatch_dynamic") + + mat1 = torch.randint(0, 16, dtype=torch.float, size=(16, 32), device="cuda") + mat2 = torch.randint(0, 16, dtype=torch.float, size=(32, 16), device="cuda") + + opcheck( + torch.ops.torchao.swizzle_mm, + (mat1, mat2, False, False), + test_utils=test_utils, + ) + + if __name__ == "__main__": pytest.main(sys.argv) diff --git a/torchao/__init__.py b/torchao/__init__.py index 7cc447d5a7..730cd326fe 100644 --- a/torchao/__init__.py +++ b/torchao/__init__.py @@ -43,13 +43,14 @@ quantize_, ) -from . import dtypes, optim, quantization, testing +from . import dtypes, optim, quantization, swizzle, testing __all__ = [ "dtypes", "autoquant", "optim", "quantize_", + "swizzle", "testing", "ops", "quantization", diff --git a/torchao/csrc/rocm/swizzle/swizzle.cpp b/torchao/csrc/rocm/swizzle/swizzle.cpp new file mode 100644 index 0000000000..bfaf6bf466 --- /dev/null +++ b/torchao/csrc/rocm/swizzle/swizzle.cpp @@ -0,0 +1,911 @@ +// setup.py glob includes all *.cpp files +// but only build this for ROCm +#ifdef USE_ROCM +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +using at::Scalar; +using at::Tensor; +using at::TensorArg; +using c10::kFloat; +using c10::ScalarType; +using c10::IntArrayRef; +using at::cuda::ScalarTypeToCudaDataType; + +// +// copied from aten/src/ATen/cuda/CUDABlas.cpp +// +namespace { + +static hipblasOperation_t _cublasOpFromChar(char op) { + // NOLINTNEXTLINE(bugprone-switch-missing-default-case) + switch (op) { + case 'n': + case 'N': + return HIPBLAS_OP_N; + case 't': + case 'T': + return HIPBLAS_OP_T; + case 'c': + case 'C': + return HIPBLAS_OP_C; + } + TORCH_CHECK(false, + "_cublasOpFromChar input should be 't', 'n' or 'c' but got `", op, "`"); +} + +static void _cublasAdjustLdLevel3( + char transa, + char transb, + int64_t m, + int64_t n, + int64_t k, + int64_t* lda, + int64_t* ldb, + int64_t* ldc) { + bool transa_ = ((transa != 'n') && (transa != 'N')); + bool transb_ = ((transb != 'n') && (transb != 'N')); + + // Note: leading dimensions generally are checked that they are > 0 + // and at least as big the result requires (even if the value won't + // be used). + if (n <= 1) + *ldc = std::max(m, 1); + + if (transa_) { + if (m <= 1) + *lda = std::max(k, 1); + } else { + if (k <= 1) + *lda = std::max(m, 1); + } + + if (transb_) { + if (k <= 1) + *ldb = std::max(n, 1); + } else { + if (n <= 1) + *ldb = std::max(k, 1); + } +} + +// Following the pattern of CuSparseDescriptor +// Defined here for now because this is the only place cublas_lt interface is +// used but can be moved to a header once cublas_lt interface is used in +// multiple places. +template +struct HipBlasLtDeleter { + void operator()(T* x) { + if (x != nullptr) { + TORCH_CUDABLAS_CHECK(destructor(x)); + } + } +}; + +template +class HipBlasLtDescriptor { + public: + T* descriptor() const { + return descriptor_.get(); + } + T* descriptor() { + return descriptor_.get(); + } + + protected: + std::unique_ptr> descriptor_; +}; + +class HipBlasLtMatmulDescriptor : public HipBlasLtDescriptor< + hipblasLtMatmulDescOpaque_t, + &hipblasLtMatmulDescDestroy> { + public: + HipBlasLtMatmulDescriptor( + hipblasComputeType_t compute_type, + hipDataType scale_type) { + hipblasLtMatmulDesc_t raw_descriptor = nullptr; + TORCH_CUDABLAS_CHECK( + hipblasLtMatmulDescCreate(&raw_descriptor, compute_type, scale_type)); + descriptor_.reset(raw_descriptor); + } + template + inline void setAttribute(hipblasLtMatmulDescAttributes_t attr, const T value) { + // NOLINTNEXTLINE(bugprone-sizeof-expression) + TORCH_CUDABLAS_CHECK(::hipblasLtMatmulDescSetAttribute(descriptor(), attr, &value, sizeof(value))); + } +}; + +class HipBlasLtMatrixLayout : public HipBlasLtDescriptor< + hipblasLtMatrixLayoutOpaque_t, + &hipblasLtMatrixLayoutDestroy> { + public: + HipBlasLtMatrixLayout( + hipDataType type, + uint64_t rows, + uint64_t cols, + int64_t ld, + bool t = false) { + hipblasLtMatrixLayout_t raw_descriptor = nullptr; + TORCH_CUDABLAS_CHECK( + hipblasLtMatrixLayoutCreate(&raw_descriptor, type, t ? cols : rows, t ? rows : cols, ld)); + descriptor_.reset(raw_descriptor); + } + template + inline void setAttribute(hipblasLtMatrixLayoutAttribute_t attr, const T value) { + TORCH_CUDABLAS_CHECK(::hipblasLtMatrixLayoutSetAttribute(descriptor(), attr, &value, sizeof(T))); + } +}; + +class HipBlasLtMatmulPreference : public HipBlasLtDescriptor< + hipblasLtMatmulPreferenceOpaque_t, + &hipblasLtMatmulPreferenceDestroy> { + public: + HipBlasLtMatmulPreference() { + hipblasLtMatmulPreference_t raw_descriptor = nullptr; + TORCH_CUDABLAS_CHECK(hipblasLtMatmulPreferenceCreate(&raw_descriptor)); + descriptor_.reset(raw_descriptor); + } + template + inline void setAttribute(hipblasLtMatmulPreferenceAttributes_t attr, const T value) { + TORCH_CUDABLAS_CHECK(::hipblasLtMatmulPreferenceSetAttribute(descriptor(), attr, &value, sizeof(T))); + } +}; + +static size_t _parseChosenWorkspaceSize() { + auto val = c10::utils::get_env("CUBLASLT_WORKSPACE_SIZE"); + if (!val.has_value()) { + // accept either env var + val = c10::utils::get_env("HIPBLASLT_WORKSPACE_SIZE"); + } + size_t workspace_size = 76*1024; /* Use 76 MB for hipBLASLt */ + + if (val.has_value()) { + try { + workspace_size = std::stoi(val.value()); + } catch(std::invalid_argument const& e) { + TORCH_WARN("invalid CUBLASLT_WORKSPACE_SIZE,", + " using default workspace size of ", workspace_size, " KiB."); + } catch(std::out_of_range const& e) { + TORCH_WARN("CUBLASLT_WORKSPACE_SIZE out of range,", + " using default workspace size of ", workspace_size, " KiB."); + } + } + return workspace_size * 1024; +} + +static size_t _getWorkspaceSize() { + static size_t workspace_size = _parseChosenWorkspaceSize(); + return workspace_size; +} + +static bool _scaled_mm_is_fnuz() { + auto dprops = at::cuda::getCurrentDeviceProperties(); + std::string device_arch = dprops->gcnArchName; + static const std::vector archs = {"gfx940", "gfx941", "gfx942"}; + for (std::string arch : archs) { + size_t substring = device_arch.find(arch); + if (substring != std::string::npos) { + return true; + } + } + return false; +} + +} // namespace + +// +// copied from aten/src/ATen/native/cuda/Blas.cpp +// +namespace { + +// TODO: https://github.com/pytorch/pytorch/pull/59380#pullrequestreview-725310492 +c10::MaybeOwned inline resolve_conj_if_indicated(const Tensor& tensor, bool resolve_conj) { + if (resolve_conj && tensor.is_conj()) { + return c10::MaybeOwned::owned(tensor.resolve_conj()); + } else { + return c10::MaybeOwned::borrowed(tensor); + } +} + +c10::MaybeOwned inline prepare_matrix_for_cublas(const Tensor& tensor, bool& transpose_tensor, bool transpose_result) { + if (tensor.is_non_overlapping_and_dense()) { // common case + transpose_tensor = tensor.is_contiguous(); + return resolve_conj_if_indicated(tensor, transpose_result ? transpose_tensor : !transpose_tensor); + } + IntArrayRef tensor_strides = tensor.strides(); + IntArrayRef tensor_sizes = tensor.sizes(); + if ((tensor_strides[0] == 1) && (tensor_strides[1] >= std::max(1, tensor_sizes[0]))) { + transpose_tensor = false; + return resolve_conj_if_indicated(tensor, !transpose_result); + } else if ((tensor_strides[1] == 1) && (tensor_strides[0] >= std::max(1, tensor_sizes[1]))) { + transpose_tensor = true; + return resolve_conj_if_indicated(tensor, transpose_result); + } else { + transpose_tensor = true; + return c10::MaybeOwned::owned(tensor.clone(at::MemoryFormat::Contiguous)); + } +} + +c10::MaybeOwned inline prepare_matrix_for_cublas(const Tensor& tensor, bool& transpose_tensor) { + if (tensor.is_non_overlapping_and_dense()) { // common case + transpose_tensor = tensor.is_contiguous(); + return resolve_conj_if_indicated(tensor, true); + } + + IntArrayRef tensor_strides = tensor.strides(); + IntArrayRef tensor_sizes = tensor.sizes(); + if ((tensor_strides[0] == 1) && (tensor_strides[1] >= std::max(1, tensor_sizes[0]))) { + transpose_tensor = false; + return resolve_conj_if_indicated(tensor, true); + } else if ((tensor_strides[1] == 1) && (tensor_strides[0] >= std::max(1, tensor_sizes[1]))) { + transpose_tensor = true; + return resolve_conj_if_indicated(tensor, true); + } else { + transpose_tensor = true; + return c10::MaybeOwned::owned(tensor.clone(at::MemoryFormat::Contiguous)); + } +} + +struct cublasCommonArgs { + cublasCommonArgs( + const Tensor& mat1, + const Tensor& mat2, + bool swizzle1, + bool swizzle2, + Tensor& c, + const std::optional& scale_a = std::nullopt, + const std::optional& scale_b = std::nullopt, + const std::optional& scale_result = std::nullopt) { + bool transpose_result = false, transpose_a = false, transpose_b = false; + result = prepare_matrix_for_cublas(c, transpose_result); + mata = prepare_matrix_for_cublas(transpose_result ? mat2 : mat1, transpose_a, transpose_result); + matb = prepare_matrix_for_cublas(transpose_result ? mat1 : mat2, transpose_b, transpose_result); + + // Handle scale tensors if provided + if (scale_a && scale_b) { + // By default since we return in row-major we run the gemm + // as B.T @ A.T, check transpose_result to determine if we flip the scales + scale_mata_ptr = transpose_result ? scale_b->data_ptr() : scale_a->data_ptr(); + scale_mata_dtype = transpose_result ? scale_b->scalar_type() : scale_a->scalar_type(); + scale_matb_ptr = transpose_result ? scale_a->data_ptr() : scale_b->data_ptr(); + scale_matb_dtype = transpose_result ? scale_a->scalar_type() : scale_b->scalar_type(); + } + + if (scale_result) { + scale_result_ptr = scale_result->data_ptr(); + scale_result_dtype = scale_result->scalar_type(); + } + + // Update transpose flags + if (transpose_result) { + transpose_a = !transpose_a; + transpose_b = !transpose_b; + } + + auto sizes_a = mata->sizes(); + auto sizes_b = matb->sizes(); + + m = sizes_a[transpose_result ? 1 : 0]; + k = sizes_a[transpose_result ? 0 : 1]; + n = sizes_b[transpose_result ? 0 : 1]; + lda = mata->stride((transpose_a == transpose_result) ? 1 : 0); + ldb = matb->stride((transpose_b == transpose_result) ? 1 : 0); + result_ld = result->stride(transpose_result ? 0 : 1); + transa = transpose_a ? mata->is_conj() ? 'c' : 't' : 'n'; + transb = transpose_b ? matb->is_conj() ? 'c' : 't' : 'n'; + + mata_is_swizzled = transpose_result ? swizzle2 : swizzle1; + matb_is_swizzled = transpose_result ? swizzle1 : swizzle2; + } + + // Matrix members + char transa, transb; + int64_t m, n, k; + int64_t lda, ldb, result_ld; + c10::MaybeOwned mata, matb, result; + + // Scale members + void* scale_mata_ptr = nullptr; + void* scale_matb_ptr = nullptr; + void* scale_result_ptr = nullptr; + std::optional scale_mata_dtype; + std::optional scale_matb_dtype; + std::optional scale_result_dtype; + + // swizzle members + bool mata_is_swizzled; + bool matb_is_swizzled; +}; + +enum class ScalingType { + TensorWise, + RowWise, + Error +}; + +ScalingType get_scaling_type( + const at::Tensor& scale_a, + const at::Tensor& scale_b, + int64_t dim_m, + int64_t dim_n) { + // Both Per-Tensor and Row-wise scaling expect fp32 tensors + TORCH_CHECK( + scale_a.scalar_type() == kFloat && scale_b.scalar_type() == kFloat, + "Both scale_a and scale_b must be float (fp32) tensors."); + + // Check the singluar scale case for per-tensor scaling + if (scale_a.numel() == 1 && scale_b.numel() == 1) { + return ScalingType::TensorWise; + } + + // For non-TensorWise scaling, enforce 2D input tensors + TORCH_CHECK( + scale_a.dim() == 2 && scale_b.dim() == 2, + "For non-TensorWise scaling, scale tensors must be 2-dimensional, " + "but got scale_a.dim()=", + scale_a.dim(), + " and scale_b.dim()=", + scale_b.dim()); + + // Check for RowWise scaling + if (scale_a.size(0) == dim_m && scale_a.size(1) == 1 && + scale_b.size(0) == 1 && scale_b.size(1) == dim_n) { +#if defined(HIPBLASLT_VEC_EXT) + TORCH_CHECK( + scale_a.is_contiguous() && scale_b.is_contiguous(), + "Both scale_a and scale_b must be contiguous for RowWise scaling."); + return ScalingType::RowWise; +#else + TORCH_CHECK(false, "Per-row scaling is not supported for this platform!"); + return ScalingType::Error; +#endif + } + + // If we reach here, the input doesn't match any valid scaling type + TORCH_CHECK( + false, + "Invalid scaling configuration. For TensorWise scaling, both scales should be scalar. " + "For RowWise scaling, scale_a should be (", + dim_m, + ", 1) and scale_b should be (1, ", + dim_n, + "). " + "Got scale_a.size()=(", + scale_a.size(0), + ", ", + scale_a.size(1), + ") and ", + "scale_b.size()=(", + scale_b.size(0), + ", ", + scale_b.size(1), + ")"); + + return ScalingType::Error; +} + +} // namespace + +template +inline void bgemm_hipblaslt(CUDABLAS_BGEMM_ARGTYPES(Dtype), bool mat1_is_swizzled, bool mat2_is_swizzled) { + hipDataType abcType = HIP_R_32F; + hipblasComputeType_t computeType = HIPBLAS_COMPUTE_32F; + hipDataType scaleType = HIP_R_32F; + if constexpr (std::is_same_v) { + abcType = HIP_R_64F; + computeType = HIPBLAS_COMPUTE_64F; + scaleType = HIP_R_64F; + } else if constexpr (std::is_same_v) { + } else if constexpr (std::is_same_v>) { + abcType = HIP_C_64F; + computeType = HIPBLAS_COMPUTE_64F; + scaleType = HIP_C_64F; + } else if constexpr (std::is_same_v>) { + abcType = HIP_C_32F; + scaleType = HIP_C_32F; + } else if constexpr (std::is_same_v) { + abcType = HIP_R_16F; + } else if constexpr (std::is_same_v) { + abcType = HIP_R_16BF; + } else { + static_assert(false && sizeof(Dtype), "at::cuda::blas::bgemm_internal_cublaslt: not implemented"); + } + + hipblasLtHandle_t ltHandle = at::cuda::getCurrentCUDABlasLtHandle(); + hipblasOperation_t opa = _cublasOpFromChar(transa); + hipblasOperation_t opb = _cublasOpFromChar(transb); + _cublasAdjustLdLevel3(transa, transb, m, n, k, &lda, &ldb, &ldc); + + HipBlasLtMatmulDescriptor computeDesc(computeType, scaleType); + computeDesc.setAttribute(HIPBLASLT_MATMUL_DESC_TRANSA, opa); + computeDesc.setAttribute(HIPBLASLT_MATMUL_DESC_TRANSB, opb); + HipBlasLtMatrixLayout Adesc(abcType, m, k, lda, opa == HIPBLAS_OP_T); + HipBlasLtMatrixLayout Bdesc(abcType, k, n, ldb, opb == HIPBLAS_OP_T); + HipBlasLtMatrixLayout Cdesc(abcType, m, n, ldc); +#ifdef HIPBLASLT_HAS_ORDER_COL16 + if (mat1_is_swizzled) { + Adesc.setAttribute(HIPBLASLT_MATRIX_LAYOUT_ORDER, HIPBLASLT_ORDER_COL16_4R8); + } + if (mat2_is_swizzled) { + Bdesc.setAttribute(HIPBLASLT_MATRIX_LAYOUT_ORDER, HIPBLASLT_ORDER_COL16_4R8); + } +#endif + + if (num_batches > 1) { + int num_batches_as_int = static_cast(num_batches); + Adesc.setAttribute(HIPBLASLT_MATRIX_LAYOUT_BATCH_COUNT, num_batches_as_int); + Bdesc.setAttribute(HIPBLASLT_MATRIX_LAYOUT_BATCH_COUNT, num_batches_as_int); + Cdesc.setAttribute(HIPBLASLT_MATRIX_LAYOUT_BATCH_COUNT, num_batches_as_int); + Adesc.setAttribute(HIPBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, stridea); + Bdesc.setAttribute(HIPBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, strideb); + Cdesc.setAttribute(HIPBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, stridec); + } + + hipblasLtEpilogue_t epilogue = HIPBLASLT_EPILOGUE_DEFAULT; + computeDesc.setAttribute(HIPBLASLT_MATMUL_DESC_EPILOGUE, epilogue); + + HipBlasLtMatmulPreference preference; + // See https://github.com/pytorch/pytorch/issues/73328 for reasoning behind + // setting this to 1M. + size_t workspaceSize = _getWorkspaceSize(); + preference.setAttribute(HIPBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES, workspaceSize); + + auto workspace = at::empty(static_cast(workspaceSize), at::TensorOptions().dtype(at::kByte).device(at::kCUDA)); + + hipblasLtMatmulHeuristicResult_t heuristicResult = {}; + int returnedResult = 0; + TORCH_CUDABLAS_CHECK(hipblasLtMatmulAlgoGetHeuristic( + ltHandle, + computeDesc.descriptor(), + Adesc.descriptor(), + Bdesc.descriptor(), + Cdesc.descriptor(), + Cdesc.descriptor(), + preference.descriptor(), + 1, + &heuristicResult, + &returnedResult)); + if (returnedResult == 0) { + TORCH_CUDABLAS_CHECK(HIPBLAS_STATUS_NOT_SUPPORTED); + } + + hipblasStatus_t cublasStatus = hipblasLtMatmul( + ltHandle, + computeDesc.descriptor(), + &alpha, + a, + Adesc.descriptor(), + b, + Bdesc.descriptor(), + &beta, + c, + Cdesc.descriptor(), + c, + Cdesc.descriptor(), + &heuristicResult.algo, + workspace.mutable_data_ptr(), + workspaceSize, + at::hip::getCurrentHIPStreamMasqueradingAsCUDA()); + TORCH_CHECK( + cublasStatus == HIPBLAS_STATUS_SUCCESS, + "CUDA error: ", + at::cuda::blas::_cublasGetErrorEnum(cublasStatus), + " when calling hipblasLtMatmul with transpose_mat1 ", + (opa == HIPBLAS_OP_T), + " transpose_mat2 ", + (opb == HIPBLAS_OP_T), + " m ", + m, + " n ", + n, + " k ", + k, + " lda ", + lda, + " ldb ", + ldb, + " ldc ", + ldc, + " abcType ", + abcType, + " computeType ", + computeType, + " scaleType ", + scaleType); +} + + +template +inline void gemm_hipblaslt(CUDABLAS_GEMM_ARGTYPES(Dtype), bool mat1_is_swizzled, bool mat2_is_swizzled) { + // forward to bgemm implementation but set strides and batches to 0 + bgemm_hipblaslt(transa, transb, m, n, k, alpha, a, lda, 0, b, ldb, 0, beta, c, ldc, 0, 0, mat1_is_swizzled, mat2_is_swizzled); +} + + +Tensor swizzle_mm(const Tensor& mat1, const Tensor& mat2, bool mat1_is_swizzled, bool mat2_is_swizzled) { + TORCH_CHECK( + mat1.dtype() == mat2.dtype(), + "expected mat1 and mat2 to have the same dtype, but got: ", mat1.dtype(), " != ", mat2.dtype() + ); + + // NOLINTNEXTLINE(*c-array*) + TensorArg targs[]{{mat1, "mat1", 0}, {mat2, "mat2", 1}}; + checkAllSameGPU(__func__, targs); + + Tensor meta_mat1 = mat1.to("meta"); + Tensor meta_mat2 = mat2.to("meta"); + Tensor meta_result = at::mm(meta_mat1, meta_mat2); + Tensor result = at::empty_like(meta_result, mat1.device()); + at::ScalarType scalar_type = result.scalar_type(); + + cublasCommonArgs args(mat1, mat2, mat1_is_swizzled, mat2_is_swizzled, result); + + AT_DISPATCH_FLOATING_TYPES_AND2( + at::ScalarType::Half, + at::ScalarType::BFloat16, + scalar_type, + "addmm_cuda", + [&] { + using opmath_t = at::opmath_type; + opmath_t alpha_val = opmath_t(1.0); + opmath_t beta_val = opmath_t(0.0); + const scalar_t* mat1_ptr = args.mata->const_data_ptr(); + const scalar_t* mat2_ptr = args.matb->const_data_ptr(); + scalar_t* result_ptr = args.result->mutable_data_ptr(); + gemm_hipblaslt( + args.transa, + args.transb, + args.m, + args.n, + args.k, + alpha_val, + mat1_ptr, + args.lda, + mat2_ptr, + args.ldb, + beta_val, + result_ptr, + args.result_ld, + args.mata_is_swizzled, + args.matb_is_swizzled); + }); + + return result; +} + +void _scaled_gemm( + char transa, + char transb, + int64_t m, + int64_t n, + int64_t k, + const void* mat1_ptr, + const void* mat1_scale_ptr, + int64_t mat1_ld, + ScalarType mat1_dtype, + ScalarType mat1_scale_dtype, + bool mat1_is_swizzled, + const void* mat2_ptr, + const void* mat2_scale_ptr, + int64_t mat2_ld, + ScalarType mat2_dtype, + ScalarType mat2_scale_dtype, + bool mat2_is_swizzled, + const void* bias_ptr, + ScalarType bias_dtype, + void* result_ptr, + const void *result_scale_ptr, + int64_t result_ld, + ScalarType result_dtype, + bool use_rowwise) { + const auto computeType = HIPBLAS_COMPUTE_32F; + const auto scaleType = HIP_R_32F; + const float alpha_val = 1.0; + const float beta_val = 0.0; + HipBlasLtMatmulDescriptor computeDesc(computeType, scaleType); + computeDesc.setAttribute(HIPBLASLT_MATMUL_DESC_TRANSA, _cublasOpFromChar(transa)); + computeDesc.setAttribute(HIPBLASLT_MATMUL_DESC_TRANSB, _cublasOpFromChar(transb)); + hipblasLtMatmulDescAttributes_t matmulDescA = HIPBLASLT_MATMUL_DESC_A_SCALE_POINTER; + hipblasLtMatmulDescAttributes_t matmulDescB = HIPBLASLT_MATMUL_DESC_B_SCALE_POINTER; +#if defined(HIPBLASLT_VEC_EXT) + if (use_rowwise) { + matmulDescA = HIPBLASLT_MATMUL_DESC_A_SCALE_POINTER_VEC_EXT; + matmulDescB = HIPBLASLT_MATMUL_DESC_B_SCALE_POINTER_VEC_EXT; + } +#else + // rowwise isn't supported using cublaslt or older hipblaslt + TORCH_INTERNAL_ASSERT(use_rowwise == false, "rowwise scaled_gemm not supported with blaslt"); +#endif + computeDesc.setAttribute(matmulDescA, mat1_scale_ptr); + computeDesc.setAttribute(matmulDescB, mat2_scale_ptr); + if (result_scale_ptr != nullptr) { + computeDesc.setAttribute(HIPBLASLT_MATMUL_DESC_D_SCALE_POINTER, result_scale_ptr); + } + HipBlasLtMatrixLayout Adesc(ScalarTypeToCudaDataType(mat1_dtype), m, k, mat1_ld, transa == 't'); + HipBlasLtMatrixLayout Bdesc(ScalarTypeToCudaDataType(mat2_dtype), k, n, mat2_ld, transb == 't'); + // Cdesc is unused, beta is 0. But hipblaslt needs this set to something reasonable. + HipBlasLtMatrixLayout Cdesc(ScalarTypeToCudaDataType(result_dtype), m, n, result_ld); + HipBlasLtMatrixLayout Ddesc(ScalarTypeToCudaDataType(result_dtype), m, n, result_ld); + if (bias_ptr) { + computeDesc.setAttribute(HIPBLASLT_MATMUL_DESC_BIAS_POINTER, bias_ptr); + computeDesc.setAttribute(HIPBLASLT_MATMUL_DESC_EPILOGUE, HIPBLASLT_EPILOGUE_BIAS); + computeDesc.setAttribute(HIPBLASLT_MATMUL_DESC_BIAS_DATA_TYPE, ScalarTypeToCudaDataType(bias_dtype)); + } + +#ifdef HIPBLASLT_HAS_ORDER_COL16 + if (mat1_is_swizzled) { + Adesc.setAttribute(HIPBLASLT_MATRIX_LAYOUT_ORDER, HIPBLASLT_ORDER_COL16_4R16); + } + if (mat2_is_swizzled) { + Bdesc.setAttribute(HIPBLASLT_MATRIX_LAYOUT_ORDER, HIPBLASLT_ORDER_COL16_4R16); + } +#endif + + auto stream = c10::hip::getCurrentHIPStreamMasqueradingAsCUDA(); + size_t workspaceSize = _getWorkspaceSize(); + auto& allocator = *::c10::hip::HIPCachingAllocatorMasqueradingAsCUDA::get(); + auto workspace = allocator.allocate(workspaceSize); + auto workspace_ptr = workspace.mutable_get(); + TORCH_CHECK(workspace_ptr != nullptr, "OOM trying to allocate workspace for cublaslt"); + + HipBlasLtMatmulPreference preference; + preference.setAttribute(HIPBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES, workspaceSize); + hipblasLtMatmulHeuristicResult_t heuristicResult = {}; + int returnedResult = 0; + hipblasLtHandle_t ltHandle = at::cuda::getCurrentCUDABlasLtHandle(); + TORCH_CUDABLAS_CHECK(hipblasLtMatmulAlgoGetHeuristic( + ltHandle, + computeDesc.descriptor(), + Adesc.descriptor(), + Bdesc.descriptor(), + Cdesc.descriptor(), + Ddesc.descriptor(), + preference.descriptor(), + 1, + &heuristicResult, + &returnedResult)); + if (returnedResult == 0) { + // hipblaslt might be able to recover by returning all algos + std::vector all_algos; + TORCH_CUDABLAS_CHECK(hipblaslt_ext::getAllAlgos( + ltHandle, + hipblaslt_ext::GemmType::HIPBLASLT_GEMM, + _cublasOpFromChar(transa), + _cublasOpFromChar(transb), + ScalarTypeToCudaDataType(mat1_dtype), + ScalarTypeToCudaDataType(mat2_dtype), + // C is nullptr and beta=0, so set to something reasonable. See above. + //ScalarTypeToCudaDataType(bias_dtype), + ScalarTypeToCudaDataType(result_dtype), + ScalarTypeToCudaDataType(result_dtype), + HIPBLAS_COMPUTE_32F, + all_algos)); + if (all_algos.size() == 0) { + TORCH_CUDABLAS_CHECK(HIPBLAS_STATUS_NOT_SUPPORTED); + } + // pick first valid solution + bool found = false; + for (size_t i = 0; i < all_algos.size(); i++) { + size_t ret_workspace_size = 0; + auto is_valid_status = hipblaslt_ext::matmulIsAlgoSupported( + ltHandle, + computeDesc.descriptor(), + &alpha_val, + Adesc.descriptor(), + Bdesc.descriptor(), + &beta_val, + Cdesc.descriptor(), + Ddesc.descriptor(), + all_algos[i].algo, + ret_workspace_size); + if (is_valid_status == HIPBLAS_STATUS_SUCCESS) { + if (ret_workspace_size <= workspaceSize) { + heuristicResult = all_algos[i]; + found = true; + break; + } + } + } + TORCH_CHECK(found, "could not find valid hipblaslt solution"); + } + hipblasStatus_t cublasStatus = hipblasLtMatmul( + ltHandle, + computeDesc.descriptor(), + &alpha_val, + mat1_ptr, + Adesc.descriptor(), + mat2_ptr, + Bdesc.descriptor(), + &beta_val, + result_ptr, // unused, since beta_val is 0, but hipblaslt can't handle nullptr + Cdesc.descriptor(), + result_ptr, + Ddesc.descriptor(), + &heuristicResult.algo, + workspace_ptr, + workspaceSize, + stream); + TORCH_CHECK( + cublasStatus == HIPBLAS_STATUS_SUCCESS, + "CUDA error: ", + at::cuda::blas::_cublasGetErrorEnum(cublasStatus), + " when calling hipblasLtMatmul with transpose_mat1 ", + transa, + " transpose_mat2 ", + transb, + " m ", + m, + " n ", + n, + " k ", + k, + " mat1_ld ", + mat1_ld, + " mat2_ld ", + mat2_ld, + " result_ld ", + result_ld, + " computeType ", + computeType, + " scaleType ", + scaleType); + return; +} + +Tensor& +_scaled_mm_out(const Tensor& mat1, const Tensor& mat2, + bool mat1_is_swizzled, + bool mat2_is_swizzled, + const Tensor& scale_a, + const Tensor& scale_b, + const std::optional& bias, + const std::optional& scale_result, + std::optional out_dtype, + Tensor& out) { + // Check sizes + TORCH_CHECK(mat1.dim() == 2, "mat1 must be a matrix"); + TORCH_CHECK(mat2.dim() == 2, "mat2 must be a matrix"); + TORCH_CHECK( + mat1.sizes()[1] == mat2.sizes()[0], "mat1 and mat2 shapes cannot be multiplied (", + mat1.sizes()[0], "x", mat1.sizes()[1], " and ", mat2.sizes()[0], "x", mat2.sizes()[1], ")"); + + // Check what type of scaling we are doing based on inputs + ScalingType scaling_choice = get_scaling_type(scale_a, scale_b, mat1.size(0), mat2.size(1)); + TORCH_INTERNAL_ASSERT(scaling_choice != ScalingType::Error, "Scaling type not supported"); + + TORCH_CHECK(!scale_result || (scale_result->numel() == 1 && scale_result->scalar_type() == kFloat), + "scale_result must be a float scalar"); + TORCH_CHECK(!bias || bias->numel() == mat2.sizes()[1], "Bias must be size ", mat2.sizes()[1], + " but got ", bias->numel()); + TORCH_CHECK( + mat1.sizes()[1] % 16 == 0, + "Expected trailing dimension of mat1 to be divisible by 16 ", + "but got mat1 shape: (", + mat1.sizes()[0], + "x", + mat1.sizes()[1], + ")."); + TORCH_CHECK(mat2.sizes()[0] % 16 == 0 && mat2.sizes()[1] % 16 == 0, "mat2 shape (", mat2.sizes()[0], "x", + mat2.sizes()[1], ") must be divisible by 16"); + // Check types + TORCH_CHECK(!out_dtype || *out_dtype == out.scalar_type(), "out_dtype must match output matrix type"); + TORCH_CHECK(isFloat8Type(mat1.scalar_type()), "Expected mat1 to be Float8 matrix got ", mat1.scalar_type()); + TORCH_CHECK(isFloat8Type(mat2.scalar_type()), "Expected mat2 to be Float8 matrix got ", mat2.scalar_type()); + if (bias) { + TORCH_CHECK(out.scalar_type() != kFloat, "Bias is not supported when out_dtype is set to Float32"); + TORCH_CHECK(bias->scalar_type() == ScalarType::BFloat16 || bias->scalar_type() == ScalarType::Half, + "Bias must be either Half or BFloat16, but got ", bias->scalar_type()); + TORCH_CHECK((out.scalar_type() != kFloat && out.scalar_type() != ScalarType::BFloat16) || + bias->scalar_type() == ScalarType::BFloat16, + "Bias must be BFloat16 to compute ", out.scalar_type(), " output, but got ", bias->scalar_type()); + TORCH_CHECK(out.scalar_type() != ScalarType::Half || bias->scalar_type() == ScalarType::Half, + "Bias must be Float16 to compute ", out.scalar_type(), " output, but got ", bias->scalar_type()); + } + { + auto bias_ = bias.value_or(Tensor()); + auto scale_result_ = scale_result.value_or(Tensor()); + + // NOLINTNEXTLINE(*c-array*) + TensorArg targs[]{{out, "out", 0}, {mat1, "mat1", 1}, {mat2, "mat2", 2}, + {bias_, "bias", 3}, {scale_a, "scale_a", 4}, {scale_b, "scale_b", 5}, + {scale_result_, "scale_result", 6}}; + checkAllSameGPU(__func__, targs); + } + // Validation checks have passed lets resize the output to actual size + IntArrayRef mat1_sizes = mat1.sizes(); + IntArrayRef mat2_sizes = mat2.sizes(); + at::native::resize_output(out, {mat1_sizes[0], mat2_sizes[1]}); + + // If any of M, K, N is 0 - return early (the tensorwise/rowwise float8 gemm kernels + // do not support this case). + if (mat1_sizes[0] == 0 || mat1_sizes[1] == 0 || mat2_sizes[1] == 0) { + // `out` was created with `at::empty`. In the case where we are multiplying + // MxK by KxN and K is the zero dim, we need to initialize here to properly + // return a tensor of zeros. + if (mat1_sizes[1] == 0) { + out.zero_(); + } + + return out; + } + + if (scaling_choice == ScalingType::RowWise) { + // For ROCm, match behavior of f8f8bf16_rowwise type checking, for unit test purposes. + Tensor b = mat2; + if (_scaled_mm_is_fnuz()) { + TORCH_CHECK(b.dtype() == at::kFloat8_e4m3fnuz); + } + else { + TORCH_CHECK(b.dtype() == at::kFloat8_e4m3fn); + } + // Until more than bf16 is supported. + TORCH_CHECK(out.scalar_type() == ScalarType::BFloat16, + "hipblaslt rowwise _scaled_mm only supports BFloat16 output but got ", out.scalar_type()); + } + + cublasCommonArgs args(mat1, mat2, mat1_is_swizzled, mat2_is_swizzled, out, scale_a, scale_b, scale_result); + const auto out_dtype_ = args.result->scalar_type(); + TORCH_CHECK(args.transa == 't' && args.transb == 'n', "Only multiplication of row-major and column-major matrices is supported by cuBLASLt"); + + { + _scaled_gemm( + args.transa, + args.transb, + args.m, + args.n, + args.k, + args.mata->data_ptr(), + args.scale_mata_ptr, + args.lda, + args.mata->scalar_type(), + args.scale_mata_dtype.value(), + args.mata_is_swizzled, + args.matb->data_ptr(), + args.scale_matb_ptr, + args.ldb, + args.matb->scalar_type(), + args.scale_matb_dtype.value(), + args.matb_is_swizzled, + bias ? bias->data_ptr(): nullptr, + bias ? bias->scalar_type() : isFloat8Type(out_dtype_) ? at::ScalarType::Half : out_dtype_, + args.result->data_ptr(), + args.scale_result_ptr, + args.result_ld, + out_dtype_, + scaling_choice == ScalingType::RowWise); + } + + return out; +} + +Tensor +swizzle_scaled_mm(const Tensor& mat_a, const Tensor& mat_b, + bool mat1_is_swizzled, + bool mat2_is_swizzled, + const Tensor& scale_a, + const Tensor& scale_b, + const std::optional& bias, + const std::optional& scale_result, + std::optional out_dtype) { + const auto out_dtype_ = out_dtype.value_or(mat_a.scalar_type()); + Tensor out = at::empty({0}, mat_a.options().dtype(out_dtype_)); + return _scaled_mm_out(mat_a, mat_b, mat1_is_swizzled, mat2_is_swizzled, scale_a, scale_b, bias, scale_result, out_dtype, out); +} + +TORCH_LIBRARY_IMPL(torchao, CUDA, m) { + m.impl("torchao::swizzle_mm", &swizzle_mm); + m.impl("torchao::swizzle_scaled_mm", &swizzle_scaled_mm); +} +#endif // USE_ROCM diff --git a/torchao/swizzle/__init__.py b/torchao/swizzle/__init__.py new file mode 100644 index 0000000000..7aa001267c --- /dev/null +++ b/torchao/swizzle/__init__.py @@ -0,0 +1,9 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from .swizzle_tensor import SwizzleTensor + +__all__ = ["SwizzleTensor"] diff --git a/torchao/swizzle/swizzle_tensor.py b/torchao/swizzle/swizzle_tensor.py new file mode 100644 index 0000000000..8ddfd9308a --- /dev/null +++ b/torchao/swizzle/swizzle_tensor.py @@ -0,0 +1,143 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD 3-Clause license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from torch.utils._pytree import tree_map + + +# copied from float8_utils.py +def _get_min_alignment(size: int, alignment_value: int) -> int: + return (1 + ((size - 1) // alignment_value)) * alignment_value + + +class SwizzleTensor(torch.Tensor): + """ + A Python-only swizzled tensor subclass. + + Intended usage of this abstraction: + Swizzle weight Tensor to avoid LDS use during GEMMs on ROCm hardware. + """ + + def __new__( + cls, + original: torch.Tensor, + shallow: bool = False, + ): + wrapper = torch.empty_like(original, device="meta") + return torch.Tensor._make_subclass(cls, wrapper) + + def __init__(self, original, shallow=False): + if shallow: + return + # assert original.ndim == 2 or original.ndim == 3 # (M, K) or (B, M, K) + assert original.ndim == 2, "SwizzleTensor only supports ndim 2" + assert original.itemsize == 1 or original.itemsize == 2 + kdiv = 32 if original.itemsize == 2 else 64 + lastdim = 8 if original.itemsize == 2 else 16 + if original.ndim == 2: + M, K = original.shape + B = 0 + if original.ndim == 3: + B, M, K = original.shape + alignedM = _get_min_alignment(M, 16) + alignedK = _get_min_alignment(K, kdiv) + paddedM = alignedM - M + paddedK = alignedK - K + x = torch.nn.functional.pad(original, (0, paddedK, 0, paddedM), "constant", 0) + if original.ndim == 2: + x = x.view(alignedM // 16, 16, alignedK // kdiv, 4, lastdim) + x = x.permute(0, 2, 3, 1, 4) + if original.ndim == 3: + x = x.view(B, alignedM // 16, 16, alignedK // kdiv, 4, lastdim) + x = x.permute(0, 1, 3, 4, 2, 5) + self.x = x.contiguous() + self.B = B + self.M = M + self.K = K + self.alignedM = alignedM + self.alignedK = alignedK + self.paddedM = paddedM + self.paddedK = paddedK + self.original_ndim = original.ndim + self.is_transposed = False + + def __repr__(self): + return f"{self.__class__.__name__}(original={self.unswizzle()})" + + def unswizzle(self): + undone = None + if self.original_ndim == 2: + undone = self.x.permute(0, 3, 1, 2, 4).contiguous() + undone = undone.reshape(self.alignedM, self.alignedK) + undone = undone[0 : self.M, 0 : self.K] + undone = undone.reshape(self.M, self.K) + if self.is_transposed: + undone = undone.T + if self.original_ndim == 3: + undone = self.x.permute(0, 1, 4, 2, 3, 5).contiguous() + undone = undone.reshape(self.B, self.alignedM, self.alignedK) + undone = undone[0 : self.B, 0 : self.M, 0 : self.K] + undone = undone.reshape(self.B, self.M, self.K) + return undone + + def as_tensor(self): + # note the transpose because this causes col major hipblaslt op to be TN + if self.original_ndim == 2: + tmp = self.x.reshape(self.alignedM, self.alignedK) + if self.is_transposed: + tmp = tmp.T + return tmp + if self.original_ndim == 3: + tmp = self.x.reshape(self.B, self.alignedM, self.alignedK) + if self.is_transposed: + tmp = tmp.T + return tmp + + def shallow_transpose(self): + shape = ( + (self.M, self.K) if self.original_ndim == 2 else (self.B, self.M, self.K), + ) + new_obj = SwizzleTensor( + torch.empty(*shape, dtype=self.dtype, layout=self.layout, device="meta"), + True, + ) + new_obj.x = self.x + new_obj.B = self.B + new_obj.M = self.M + new_obj.K = self.K + new_obj.alignedM = self.alignedM + new_obj.alignedK = self.alignedK + new_obj.paddedM = self.paddedM + new_obj.paddedK = self.paddedK + new_obj.original_ndim = self.original_ndim + new_obj.is_transposed = not self.is_transposed + return new_obj + + @property + def shape(self): + return torch.Size((self.K, self.M) if self.is_transposed else (self.M, self.K)) + + def stride(self): + return (1, self.K) if self.is_transposed else (self.K, 1) + + @classmethod + def __torch_dispatch__(cls, func, types, args, kwargs=None): + # Lazy import to avoid circular dependency + from torchao.swizzle.swizzle_ops import SWIZZLE_OPS_TABLE + + if func in SWIZZLE_OPS_TABLE: + return SWIZZLE_OPS_TABLE[func](func, args, kwargs) + + def unwrap(e): + return e.unswizzle() if isinstance(e, SwizzleTensor) else e + + def wrap(e): + return SwizzleTensor(e) if isinstance(e, torch.Tensor) else e + + return tree_map(wrap, func(*tree_map(unwrap, args), **tree_map(unwrap, kwargs))) + + # Do not force the SwizzleTensor type on the returned tensor + __torch_function__ = torch._C._disabled_torch_function_impl From 0b33f1218157d40e1f3b8ba1d345e4d85c184f9e Mon Sep 17 00:00:00 2001 From: Jerry Zhang Date: Wed, 28 May 2025 10:26:26 -0700 Subject: [PATCH 055/165] Add support for fbgemm int4 mm kernel (#2255) * Add support for fbgemm int4 mm kernel Summary: we also plan to expose some other kernels like fp8xint4 and bf16xfp8, fp8xfp8 to compare with existing torchao kernels Test Plan: test/dtypes/test_fbgemm_int4_tensor.py Reviewers: Subscribers: Tasks: Tags: * fix and test * fix dtype * use importlib * add links to fbgemm code * update io_dtype type * renaming * remove enum * serializability update * format * fix tests * skip fbgemm config tests for 2.5 and below --- test/dtypes/test_fbgemm_quantized.py | 44 +++++ test/dtypes/test_fbgemm_quantized_tensor.py | 48 ++++++ .../quantization/test_config_serialization.py | 6 + torchao/_models/llama/generate.py | 13 +- torchao/core/config.py | 13 +- torchao/dtypes/__init__.py | 2 + torchao/dtypes/fbgemm_quantized_tensor.py | 161 ++++++++++++++++++ torchao/quantization/__init__.py | 2 + torchao/quantization/quant_api.py | 63 ++++++- torchao/utils.py | 10 ++ 10 files changed, 355 insertions(+), 7 deletions(-) create mode 100644 test/dtypes/test_fbgemm_quantized.py create mode 100644 test/dtypes/test_fbgemm_quantized_tensor.py create mode 100644 torchao/dtypes/fbgemm_quantized_tensor.py diff --git a/test/dtypes/test_fbgemm_quantized.py b/test/dtypes/test_fbgemm_quantized.py new file mode 100644 index 0000000000..fe2573530c --- /dev/null +++ b/test/dtypes/test_fbgemm_quantized.py @@ -0,0 +1,44 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD 3-Clause license found in the +# LICENSE file in the root directory of this source tree. + +import unittest + +import torch +from torch.testing._internal.common_utils import ( + TestCase, + run_tests, +) + +from torchao.quantization import ( + FbgemmConfig, + quantize_, +) +from torchao.quantization.utils import compute_error +from torchao.utils import is_sm_at_least_90 + + +class TestFbgemmInt4Tensor(TestCase): + @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") + @unittest.skipIf(not is_sm_at_least_90(), "Nedd sm90+") + def test_linear(self): + dtype = torch.bfloat16 + device = "cuda" + input = torch.randn(1, 128, dtype=dtype, device=device) + linear = torch.nn.Linear(128, 256, dtype=dtype, device=device) + original = linear(input) + config = FbgemmConfig( + input_dtype=torch.bfloat16, + weight_dtype=torch.int4, + output_dtype=torch.bfloat16, + block_size=(1, 128), + ) + quantize_(linear, config) + quantized = linear(input) + self.assertTrue(compute_error(original, quantized) > 20) + + +if __name__ == "__main__": + run_tests() diff --git a/test/dtypes/test_fbgemm_quantized_tensor.py b/test/dtypes/test_fbgemm_quantized_tensor.py new file mode 100644 index 0000000000..51b68dd977 --- /dev/null +++ b/test/dtypes/test_fbgemm_quantized_tensor.py @@ -0,0 +1,48 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD 3-Clause license found in the +# LICENSE file in the root directory of this source tree. + +import unittest + +import torch +from torch.testing._internal.common_utils import ( + TestCase, + run_tests, +) + +from torchao.quantization import ( + FbgemmConfig, + quantize_, +) +from torchao.quantization.utils import compute_error +from torchao.utils import ( + TORCH_VERSION_AT_LEAST_2_6, + is_sm_at_least_90, +) + + +class TestFbgemmInt4Tensor(TestCase): + @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") + @unittest.skipIf(not is_sm_at_least_90(), "Nedd sm90+") + @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_6, "Need torch >= 2.6") + def test_linear(self): + dtype = torch.bfloat16 + device = "cuda" + input = torch.randn(1, 128, dtype=dtype, device=device) + linear = torch.nn.Linear(128, 256, dtype=dtype, device=device) + original = linear(input) + config = FbgemmConfig( + input_dtype=torch.bfloat16, + weight_dtype=torch.int4, + output_dtype=torch.bfloat16, + block_size=[1, 128], + ) + quantize_(linear, config) + quantized = linear(input) + self.assertTrue(compute_error(original, quantized) > 20) + + +if __name__ == "__main__": + run_tests() diff --git a/test/quantization/test_config_serialization.py b/test/quantization/test_config_serialization.py index 3b0a10e915..71cf8e144d 100644 --- a/test/quantization/test_config_serialization.py +++ b/test/quantization/test_config_serialization.py @@ -20,6 +20,7 @@ config_to_dict, ) from torchao.quantization.quant_api import ( + FbgemmConfig, Float8DynamicActivationFloat8WeightConfig, Float8WeightOnlyConfig, FPXWeightOnlyConfig, @@ -34,11 +35,13 @@ UIntXWeightOnlyConfig, ) from torchao.sparsity.sparse_api import BlockSparseWeightConfig, SemiSparseWeightConfig +from torchao.utils import TORCH_VERSION_AT_LEAST_2_6 # Define test configurations as fixtures configs = [ Float8DynamicActivationFloat8WeightConfig(), Float8DynamicActivationFloat8WeightConfig(granularity=PerRow()), + Float8DynamicActivationFloat8WeightConfig(granularity=[PerRow(), PerRow()]), Float8WeightOnlyConfig( weight_dtype=torch.float8_e4m3fn, ), @@ -78,6 +81,9 @@ ), ] +if TORCH_VERSION_AT_LEAST_2_6: + configs += [FbgemmConfig(torch.bfloat16, torch.int4, torch.bfloat16, [1, 1, 256])] + # Create ids for better test naming def get_config_ids(configs): diff --git a/torchao/_models/llama/generate.py b/torchao/_models/llama/generate.py index dc03204b46..c17de52028 100644 --- a/torchao/_models/llama/generate.py +++ b/torchao/_models/llama/generate.py @@ -439,6 +439,17 @@ def ffn_or_attn_only(mod, fqn): f"int4wo group_size needs to be one of [32,64,128,256] but got {group_size}" ) quantize_(model, int4_weight_only(group_size=group_size, use_hqq=use_hqq)) + elif "fbgemm" in quantization: + from torchao.quantization import FbgemmConfig + + _, precision, group_size = quantization.split("-") + group_size = int(group_size) + if precision == "int4": + quantize_(model, FbgemmConfig("bf16i4bf16", group_size)) + else: + raise NotImplementedError( + f"FbegemmConfig({precision=}) not supported yet" + ) elif "int4dq-" in quantization: from torchao.dtypes import CutlassInt4PackedLayout @@ -1163,7 +1174,7 @@ def callback(x): help=( "Which quantization techniques to apply: int8dq, int8wo, fp6, int4wo-, int4wo--hqq, autoquant, " + "autoquant-int4, autoquant-gemlite-int4, autoquant-float8, autoquant-sparse, autoquant-all, uintx--, uintx---hqq, sparse-marlin, spinquant, " - + "embed-int8wo, marlin_qqq, gemlite---, float8dq, int4dq-" + + "embed-int8wo, marlin_qqq, gemlite---, float8dq, int4dq-, fbgemm-int4-" ), ) parser.add_argument( diff --git a/torchao/core/config.py b/torchao/core/config.py index d2d49981c9..3451b90c59 100644 --- a/torchao/core/config.py +++ b/torchao/core/config.py @@ -132,6 +132,12 @@ def default(self, o): if isinstance(o, list): return [self.encode_value(item) for item in o] + elif isinstance(o, tuple): + raise NotImplementedError( + "Tuples will be serialized as List in JSON, so we recommend to use " + f"Lists instead to avoid surprises. got: {o}" + ) + if isinstance(o, dict): return {k: self.encode_value(v) for k, v in o.items()} @@ -250,13 +256,18 @@ def config_from_dict(data: Dict[str, Any]) -> AOBaseConfig: # Recursively handle nested configs processed_data[key] = config_from_dict(value) elif isinstance(value, list): - # Handle lists of possible configs + # Handle lists or tuples of possible configs processed_data[key] = [ config_from_dict(item) if isinstance(item, dict) and "_type" in item and "_data" in item else item for item in value ] + elif isinstance(value, tuple): + raise NotImplementedError( + "Tuples will be serialized as List in JSON, so we recommend to use " + f"Lists instead to avoid surprises. got: {value}" + ) elif isinstance(value, dict): # Handle dicts of possible configs processed_data[key] = { diff --git a/torchao/dtypes/__init__.py b/torchao/dtypes/__init__.py index eb253c11bc..1003491828 100644 --- a/torchao/dtypes/__init__.py +++ b/torchao/dtypes/__init__.py @@ -8,6 +8,7 @@ to_affine_quantized_intx, to_affine_quantized_intx_static, ) +from .fbgemm_quantized_tensor import to_fbgemm_quantized from .floatx import ( CutlassSemiSparseLayout, Float8Layout, @@ -61,4 +62,5 @@ "PackedLinearInt8DynamicActivationIntxWeightLayout", "to_affine_quantized_packed_linear_int8_dynamic_activation_intx_weight", "Int4XPULayout", + "to_fbgemm_quantized", ] diff --git a/torchao/dtypes/fbgemm_quantized_tensor.py b/torchao/dtypes/fbgemm_quantized_tensor.py new file mode 100644 index 0000000000..fd788a73a3 --- /dev/null +++ b/torchao/dtypes/fbgemm_quantized_tensor.py @@ -0,0 +1,161 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD 3-Clause license found in the +# LICENSE file in the root directory of this source tree. + + +import importlib.util +from typing import List + +import torch +from torch.utils._python_dispatch import return_and_correct_aliasing + +from torchao.utils import TorchAOBaseTensor + +__all__ = [ + "to_fbgemm_quantized", +] + +aten = torch.ops.aten + + +if importlib.util.find_spec("fbgemm_gpu") is None: + int4_row_quantize_zp = None + pack_int4 = None +else: + from fbgemm_gpu.experimental.gen_ai.quantize import int4_row_quantize_zp, pack_int4 + + +class FbgemmInt4Tensor(TorchAOBaseTensor): + tensor_data_attrs = ["packed_weight", "scale", "zero_point"] + tensor_attributes = ["group_size"] + + def __new__(cls, packed_weight, scale, zero_point, group_size): + shape = packed_weight.shape + kwargs = {} + kwargs["device"] = packed_weight.device + kwargs["dtype"] = scale.dtype + kwargs["requires_grad"] = False + return torch.Tensor._make_wrapper_subclass(cls, shape, **kwargs) # type: ignore[attr-defined] + + def __init__(self, packed_weight, scale, zero_point, group_size): + self.packed_weight = packed_weight + self.scale = scale + self.zero_point = zero_point + self.group_size = group_size + + def __tensor_flatten__(self): + return self.tensor_data_attrs, [ + getattr(self, attr) for attr in self.tensor_attributes + ] + + @classmethod + def __tensor_unflatten__( + cls, tensor_data_dict, tensor_attributes, outer_size, outer_stride + ): + return cls( + *[tensor_data_dict[name] for name in cls.tensor_data_attrs], + *tensor_attributes, + ) + + def _apply_fn_to_data(self, fn): + return self.__class__( + *[fn(getattr(self, attr)) for attr in self.tensor_data_attrs], + *[getattr(self, attr) for attr in self.tensor_attributes], + ) + + def __repr__(self): + return ( + f"{self.__class__.__name__}(weight={self.packed_weight}, group_size={self.group_size}, " + f"shape={self.shape}, device={self.device}, dtype={self.dtype}, requires_grad={self.requires_grad})" + ) + + @classmethod + def from_float( + cls, + w: torch.Tensor, + input_dtype: torch.dtype, + weight_dtype: torch.dtype, + output_dtype: torch.dtype, + block_size: List[int], + ): + assert len(block_size) == w.ndim, ( + f"Expecting the length of block_size to be equal to the dimension of the weight, got {block_size=} and {w.ndim=}" + ) + group_size = block_size[-1] + + assert (input_dtype, weight_dtype, output_dtype) == ( + torch.bfloat16, + torch.int4, + torch.bfloat16, + ) + + if w.ndim >= 3: + wq, scale, zero_point = zip( + *[int4_row_quantize_zp(i, group_size) for i in w], strict=False + ) + wq = torch.stack([pack_int4(i) for i in wq], dim=0) + scale = torch.stack(scale, dim=0) + zero_point = torch.stack(zero_point, dim=0) + else: + wq, scale, zero_point = int4_row_quantize_zp(w, group_size) + wq = pack_int4(wq) + + scale = scale.to(w.dtype) + zero_point = zero_point.to(w.dtype) + + del w + return FbgemmInt4Tensor( + packed_weight=wq, + scale=scale, + zero_point=zero_point, + group_size=group_size, + ) + + +implements = FbgemmInt4Tensor.implements + + +@implements([torch.nn.functional.linear, aten.linear.default]) +def _(func, types, args, kwargs): + input_tensor, weight_tensor, bias = ( + args[0], + args[1], + args[2] if len(args) > 2 else None, + ) + if not input_tensor.is_floating_point(): + raise NotImplementedError( + f"{func} is not implemented for non floating point input" + ) + + orig_act_size = input_tensor.size() + orig_out_features = weight_tensor.shape[-2] + + res = torch.ops.fbgemm.bf16i4bf16_rowwise( + input_tensor, + weight_tensor.packed_weight, + weight_tensor.scale, + weight_tensor.zero_point, + ) + if bias is not None: + res = res + bias + return res.reshape(*orig_act_size[:-1], orig_out_features) + + +@implements([aten.detach.default, aten.alias.default]) +def _(func, types, args, kwargs): + return return_and_correct_aliasing( + func, args, kwargs, args[0]._apply_fn_to_data(torch.detach) + ) + + +@implements([aten.clone.default, aten.copy_.default]) +def _(func, types, args, kwargs): + return return_and_correct_aliasing( + func, args, kwargs, args[0]._apply_fn_to_data(torch.clone) + ) + + +# We can have `to_fbgemm_tensor` to dispatch to different Fbgemm tensors later +to_fbgemm_quantized = FbgemmInt4Tensor.from_float diff --git a/torchao/quantization/__init__.py b/torchao/quantization/__init__.py index b4d46d8263..73ccd2e0ff 100644 --- a/torchao/quantization/__init__.py +++ b/torchao/quantization/__init__.py @@ -40,6 +40,7 @@ ) from .quant_api import ( CutlassInt4PackedLayout, + FbgemmConfig, Float8DynamicActivationFloat8SemiSparseWeightConfig, Float8DynamicActivationFloat8WeightConfig, Float8MMConfig, @@ -148,6 +149,7 @@ "FPXWeightOnlyConfig", "GemliteUIntXWeightOnlyConfig", "ModuleFqnToConfig", + "FbgemmConfig", # smooth quant - subject to change "get_scale", "SmoothFakeDynQuantMixin", diff --git a/torchao/quantization/quant_api.py b/torchao/quantization/quant_api.py index f2aca97782..ada19859bc 100644 --- a/torchao/quantization/quant_api.py +++ b/torchao/quantization/quant_api.py @@ -15,11 +15,12 @@ and mixed GEMM kernels """ +import importlib.util import logging import types import warnings from dataclasses import dataclass, field -from typing import Any, Callable, Dict, Optional, Tuple, Union +from typing import Any, Callable, Dict, List, Optional, Tuple, Union import torch import torch.nn as nn @@ -45,6 +46,7 @@ to_affine_quantized_floatx, to_affine_quantized_floatx_static, to_affine_quantized_intx, + to_fbgemm_quantized, to_marlinqqq_quantized_intx, ) from torchao.dtypes.uintx.packed_linear_int8_dynamic_activation_intx_weight_layout import ( @@ -142,6 +144,7 @@ "Int8DynActInt4WeightGPTQQuantizer", "Float8DynamicActivationFloat8SemiSparseWeightConfig", "ModuleFqnToConfig", + "FbgemmConfig", ] LAYOUT_TO_ZERO_POINT_DOMAIN = { @@ -1525,9 +1528,7 @@ class Float8DynamicActivationFloat8WeightConfig(AOBaseConfig): activation_dtype: torch.dtype = e4m3_dtype weight_dtype: torch.dtype = e4m3_dtype - granularity: Optional[ - Union[FP8Granularity, Tuple[FP8Granularity, FP8Granularity]] - ] = None + granularity: Optional[Union[FP8Granularity, List[FP8Granularity]]] = None mm_config: Optional[Float8MMConfig] = None set_inductor_config: bool = True @@ -1538,7 +1539,7 @@ def __post_init__(self): activation_granularity, weight_granularity = _normalize_granularity( self.granularity ) - self.granularity = (activation_granularity, weight_granularity) + self.granularity = [activation_granularity, weight_granularity] # for bc @@ -1967,6 +1968,58 @@ def _fpx_weight_only_transform( return module +@dataclass +class FbgemmConfig(AOBaseConfig): + """Quantization Config for fbgemm-genai kernels + Args: + input_dtype (torch.dtype): input dtype of the kernel + weight_dtype (torch.dtype): weight dtype of the kernel + output_dtype (torch.dtype): output dtype of the kernel + group_size (int): The group size for weight + """ + + input_dtype: torch.dtype + weight_dtype: torch.dtype + output_dtype: torch.dtype + block_size: List[int] + + +@register_quantize_module_handler(FbgemmConfig) +def _(module: torch.nn.Module, config: FbgemmConfig) -> torch.nn.Module: + # TODO: use is_package_at_least("fbgemm_gpu", "1.2.0") when + # https://github.com/pytorch/FBGEMM/issues/4198 is fixed + if importlib.util.find_spec("fbgemm_gpu") is None: + raise ImportError("Requires fbgemm-gpu-genai >= 1.2.0") + + import fbgemm_gpu.experimental.gen_ai # noqa: F401 + + if fbgemm_gpu.__version__ < "1.2.0": + raise ImportError("Requires fbgemm-gpu-genai >= 1.2.0") + + _SUPPORTED_DTYPES = { + (torch.bfloat16, torch.int4, torch.bfloat16), + } + + if ( + config.input_dtype, + config.weight_dtype, + config.output_dtype, + ) in _SUPPORTED_DTYPES: + weight = to_fbgemm_quantized( + module.weight, + config.input_dtype, + config.weight_dtype, + config.output_dtype, + config.block_size, + ) + module.weight = torch.nn.Parameter(weight, requires_grad=False) + module.extra_repr = types.MethodType(_linear_extra_repr, module) + else: + raise NotImplementedError( + f"{config} is not supported. supported input, weight, output kernel dtypes are: {_SUPPORTED_DTYPES}" + ) + + @dataclass class ModuleFqnToConfig(AOBaseConfig): """Per module configurations for torchao quantize_ API diff --git a/torchao/utils.py b/torchao/utils.py index 280da4e632..1fa395cb8a 100644 --- a/torchao/utils.py +++ b/torchao/utils.py @@ -4,6 +4,7 @@ # This source code is licensed under the BSD 3-Clause license found in the # LICENSE file in the root directory of this source tree. import functools +import importlib import itertools import re import time @@ -40,6 +41,7 @@ "is_MI300", "is_sm_at_least_89", "is_sm_at_least_90", + "is_package_at_least", ] @@ -694,3 +696,11 @@ def check_xpu_version(device, version="2.8.0"): TORCH_VERSION_AFTER_2_4 = _torch_version_at_least("2.4.0.dev") TORCH_VERSION_AFTER_2_3 = _torch_version_at_least("2.3.0.dev") TORCH_VERSION_AFTER_2_2 = _torch_version_at_least("2.2.0.dev") + + +def is_package_at_least(package_name: str, min_version: str): + package_exists = importlib.util.find_spec(package_name) is not None + if not package_exists: + return False + + return version(package_name) >= min_version From 4d5f65711f7c53985d09e3a8c6aa8d8549f7d5a4 Mon Sep 17 00:00:00 2001 From: Driss Guessous <32754868+drisspg@users.noreply.github.com> Date: Wed, 28 May 2025 10:52:59 -0700 Subject: [PATCH 056/165] integration-vllm-test (#2258) stack-info: PR: https://github.com/pytorch/ao/pull/2258, branch: drisspg/stack/58 --- test/integration/test_vllm.py | 252 ++++++++++++++++++++++++++++++++++ 1 file changed, 252 insertions(+) create mode 100644 test/integration/test_vllm.py diff --git a/test/integration/test_vllm.py b/test/integration/test_vllm.py new file mode 100644 index 0000000000..c750a7b562 --- /dev/null +++ b/test/integration/test_vllm.py @@ -0,0 +1,252 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD 3-Clause license found in the +# LICENSE file in the root directory of this source tree. + +import importlib.util +import os +import random +import shutil +from pathlib import Path +from typing import List + +import numpy as np +import pytest +import torch + +from torchao.utils import TORCH_VERSION_AT_LEAST_2_7 + +if not TORCH_VERSION_AT_LEAST_2_7: + pytest.skip("Requires PyTorch 2.7 or higher", allow_module_level=True) + + +VLLM_AVAILABLE = importlib.util.find_spec("vllm") is not None +TRANSFORMERS_AVAILABLE = importlib.util.find_spec("transformers") is not None + +if not VLLM_AVAILABLE: + pytest.skip("vLLM not installed", allow_module_level=True) + +if not TRANSFORMERS_AVAILABLE: + pytest.skip("transformers not installed", allow_module_level=True) + +from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig +from vllm import LLM, SamplingParams + +from torchao.quantization.granularity import PerRow, PerTensor +from torchao.quantization.quant_api import ( + CutlassInt4PackedLayout, + Float8DynamicActivationFloat8WeightConfig, + Int8DynamicActivationInt4WeightConfig, + Int8WeightOnlyConfig, +) + + +def get_tests() -> List[TorchAoConfig]: + """Get all the tests based off of device info""" + + # Helper objects for granularity + per_tensor = PerTensor() + per_row = PerRow() + + BASE_TESTS = [TorchAoConfig(Int8WeightOnlyConfig())] + SM89_TESTS = [ + TorchAoConfig( + Float8DynamicActivationFloat8WeightConfig(granularity=per_tensor) + ), + TorchAoConfig(Float8DynamicActivationFloat8WeightConfig(granularity=per_row)), + ] + SM90_ONLY_TESTS = [ + TorchAoConfig( + Int8DynamicActivationInt4WeightConfig(layout=CutlassInt4PackedLayout()) + ) + ] + SM100_TESTS = [ + # TorchAoConfig(MXFPInferenceConfig()) + ] # Failing for : https://github.com/pytorch/ao/issues/2239 + + # Check CUDA availability first + if not torch.cuda.is_available(): + return [] # No CUDA, no tests + + major, minor = torch.cuda.get_device_capability() + + # Build test list based on compute capability + all_tests = [] + + # Always include base tests if we have CUDA + all_tests.extend(BASE_TESTS) + + # Add SM89+ tests + if major > 8 or (major == 8 and minor >= 9): + all_tests.extend(SM89_TESTS) + + # Add SM100+ tests + if major >= 10: + all_tests.extend(SM100_TESTS) + + # Only work for sm 90 + if major == 9: + all_tests.extend(SM90_ONLY_TESTS) + + return all_tests + + +class TestVLLMIntegration: + """Integration tests for vLLM with quantized models.""" + + @classmethod + def setup_class(cls): + """Set up test environment.""" + # Set seeds for reproducibility + cls.set_seed(42) + + # See https://github.com/pytorch/ao/issues/2239 for details + os.environ["VLLM_TEST_STANDALONE_COMPILE"] = "1" + # For Small testing this makes it faster + os.environ["VLLM_ENABLE_V1_MULTIPROCESSING"] = "0" + + @classmethod + def teardown_class(cls): + """Clean up after all tests.""" + torch.cuda.empty_cache() + import gc + + gc.collect() + + def setup_method(self, method): + """Clean up before each test method.""" + torch.cuda.empty_cache() + import gc + + gc.collect() + + def teardown_method(self, method): + """Clean up after each test method.""" + torch.cuda.empty_cache() + import gc + + gc.collect() + + @staticmethod + def set_seed(seed): + """Set random seeds for reproducibility.""" + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + + def quantize_and_save_model( + self, + model_name: str, + quantization_config: TorchAoConfig, + output_dir: Path, + ): + """Quantize a model and save it to disk.""" + # Load and quantize model + quantized_model = AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype="bfloat16", + device_map="cuda", + quantization_config=quantization_config, + ) + tokenizer = AutoTokenizer.from_pretrained(model_name) + # Save quantized model + quantized_model.save_pretrained(output_dir, safe_serialization=False) + tokenizer.save_pretrained(output_dir) + + # Clean up to free memory + del quantized_model + torch.cuda.empty_cache() + + return output_dir + + def cleanup_model_directory(self, model_path: Path): + """Clean up the model directory safely.""" + try: + if model_path.exists() and model_path.is_dir(): + shutil.rmtree(model_path) + except (OSError, PermissionError) as e: + # Log the error but don't fail the test + print(f"Warning: Failed to clean up {model_path}: {e}") + + @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") + @pytest.mark.skipif(not VLLM_AVAILABLE, reason="vLLM not installed") + @pytest.mark.parametrize( + "quantization_config", get_tests(), ids=lambda config: f"{config.quant_type}" + ) + @pytest.mark.parametrize("compile", [True, False]) + @pytest.mark.parametrize( + "tp_size", [1, 2] if torch.cuda.device_count() > 1 else [1] + ) + def test_vllm_smoke_test(self, tmp_path, quantization_config, compile, tp_size): + """Test vLLM generation with quantized models.""" + # Skip per_row tests if not supported + torch._dynamo.reset() + + # Use a small model for testing + base_model = "facebook/opt-125m" + + # Create a descriptive name for the output directory + config_name = str(quantization_config).replace("/", "_").replace(" ", "_")[:50] + output_dir = tmp_path / f"{config_name}-opt-125m" + + llm = None + quantized_model_path = None + + try: + # Quantize the model + quantized_model_path = self.quantize_and_save_model( + base_model, quantization_config, output_dir + ) + + # Test generation with vLLM + sampling_params = SamplingParams( + temperature=0.8, + top_p=0.95, + seed=42, + max_tokens=16, # Small for testing + ) + + # Create LLM instance + llm = LLM( + model=str(quantized_model_path), + tensor_parallel_size=tp_size, + enforce_eager=not compile, + dtype="bfloat16", + num_gpu_blocks_override=128, + ) + + # Test prompts + prompts = [ + "Hello, my name is", + "The capital of France is", + ] + + # Generate outputs + outputs = llm.generate(prompts, sampling_params) + + # Verify outputs + assert len(outputs) == len(prompts) + for output in outputs: + assert output.prompt in prompts + assert len(output.outputs) > 0 + generated_text = output.outputs[0].text + assert isinstance(generated_text, str) + assert len(generated_text) > 0 + + finally: + # Clean up resources + if llm is not None: + del llm + + # Clean up CUDA memory + torch.cuda.empty_cache() + + # Clean up the saved model directory + if quantized_model_path is not None: + self.cleanup_model_directory(quantized_model_path) + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) From 0aa8dbd8b8e9547195bc670ddb2a8f3ce8416746 Mon Sep 17 00:00:00 2001 From: "Zhang, Liangang" Date: Thu, 29 May 2025 17:06:57 +0800 Subject: [PATCH 057/165] Enable fp16+int4 mixed precission path for int4 xpu path with int zero point (#2240) * Enable fp16 path for int4 xpu path with int zero point * Update int4_xpu_layout.py * Fix typo --- torchao/dtypes/affine_quantized_tensor_ops.py | 9 ++++----- torchao/dtypes/uintx/int4_xpu_layout.py | 10 +++------- 2 files changed, 7 insertions(+), 12 deletions(-) diff --git a/torchao/dtypes/affine_quantized_tensor_ops.py b/torchao/dtypes/affine_quantized_tensor_ops.py index e9702a33ac..20d87de8cf 100644 --- a/torchao/dtypes/affine_quantized_tensor_ops.py +++ b/torchao/dtypes/affine_quantized_tensor_ops.py @@ -46,8 +46,8 @@ from torchao.dtypes.uintx.int4_xpu_layout import ( _linear_bf16_act_uint4_weight_float_zero_check, _linear_bf16_act_uint4_weight_float_zero_impl, - _linear_bf16_act_uint4_weight_int8_zero_check, - _linear_bf16_act_uint4_weight_int8_zero_impl, + _linear_fp_act_uint4_weight_int8_zero_check, + _linear_fp_act_uint4_weight_int8_zero_impl, ) from torchao.dtypes.uintx.marlin_qqq_tensor import ( _linear_int8_act_int4_weight_marlin_qqq_check, @@ -240,8 +240,8 @@ def _register_aqt_quantized_linear_dispatches(): _linear_q_dq_impl, ), ( - _linear_bf16_act_uint4_weight_int8_zero_check, - _linear_bf16_act_uint4_weight_int8_zero_impl, + _linear_fp_act_uint4_weight_int8_zero_check, + _linear_fp_act_uint4_weight_int8_zero_impl, ), ( _linear_bf16_act_uint4_weight_float_zero_check, @@ -267,7 +267,6 @@ def _(func, types, args, kwargs): raise NotImplementedError( f"{func} is not implemented for non floating point input" ) - # using try/except here so that we can have a general fallback when input_tensor/weight_tensor # is not picked up by any of the dispatch paths in `_quantized_linear_op`, this allows us to # make the branches easier to understand in `_quantized_linear_op` diff --git a/torchao/dtypes/uintx/int4_xpu_layout.py b/torchao/dtypes/uintx/int4_xpu_layout.py index 99370b0848..76f5ecb121 100644 --- a/torchao/dtypes/uintx/int4_xpu_layout.py +++ b/torchao/dtypes/uintx/int4_xpu_layout.py @@ -89,26 +89,22 @@ def _linear_bf16_act_uint4_weight_float_zero_impl(input_tensor, weight_tensor, b return y.to(orig_dtype) -def _linear_bf16_act_uint4_weight_int8_zero_check(input_tensor, weight_tensor, bias): +def _linear_fp_act_uint4_weight_int8_zero_check(input_tensor, weight_tensor, bias): return ( - # input is native bfloat16 tensor not is_traceable_wrapper_subclass(input_tensor) - and input_tensor.dtype == torch.bfloat16 and # weight is uint4, group quantized tensor_core_tiled tensor impl affine quantized tensor isinstance(weight_tensor, AffineQuantizedTensor) and _aqt_is_xpu_layout_uint4(weight_tensor) - and weight_tensor.dtype == torch.bfloat16 and len(weight_tensor.shape) == 2 and weight_tensor.zero_point_domain == ZeroPointDomain.INT and weight_tensor.tensor_impl.scale_and_zero is None - and weight_tensor.tensor_impl.scale.dtype == torch.bfloat16 and weight_tensor.tensor_impl.zero.dtype == torch.int8 and isinstance(weight_tensor._layout, Int4XPULayout) ) -def _linear_bf16_act_uint4_weight_int8_zero_impl(input_tensor, weight_tensor, bias): +def _linear_fp_act_uint4_weight_int8_zero_impl(input_tensor, weight_tensor, bias): assert weight_tensor.block_size[0] == 1, ( f"Requires groupwise quantization, got block_size: {weight_tensor.block_size}" ) @@ -129,7 +125,7 @@ def _linear_bf16_act_uint4_weight_int8_zero_impl(input_tensor, weight_tensor, bi orig_act_size = act_mat.size() orig_dtype = act_mat.dtype - act_mat = act_mat.reshape(-1, act_mat.shape[-1]).to(torch.bfloat16) + act_mat = act_mat.reshape(-1, act_mat.shape[-1]) # groupwise int4 quantization groupsize = weight_tensor.block_size[1] From 756d07405dc44cdc1fdaba40873c06e34ab4057b Mon Sep 17 00:00:00 2001 From: Scott Roy <161522778+metascroy@users.noreply.github.com> Date: Thu, 29 May 2025 10:10:59 -0700 Subject: [PATCH 058/165] Fix torchao generate script for cpu device (#2267) * up * up --- torchao/_models/llama/generate.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/torchao/_models/llama/generate.py b/torchao/_models/llama/generate.py index c17de52028..1f56e71f07 100644 --- a/torchao/_models/llama/generate.py +++ b/torchao/_models/llama/generate.py @@ -180,6 +180,7 @@ def generate( max_seq_length = ( min(T + max_new_tokens, model.config.block_size) if not interactive else 350 ) + print(f"max_seq_length={max_seq_length}, prompt_length={T}") new_tokens = max_seq_length - T # format model input @@ -242,11 +243,13 @@ def encode_tokens(tokenizer, string, bos=True, device=default_device): def _load_model(checkpoint_path, device, precision): - checkpoint = torch.load(str(checkpoint_path), mmap=True, weights_only=True) + checkpoint = torch.load( + str(checkpoint_path), mmap=True, weights_only=True, map_location=device + ) if "model" in checkpoint and "stories" in str(checkpoint_path): checkpoint = checkpoint["model"] with torch.device("meta"): - model = Transformer.from_name(checkpoint_path.parent.name) + model = Transformer.from_name(checkpoint_path) model.load_state_dict(checkpoint, assign=True) model = model.to(device=device, dtype=precision) @@ -585,7 +588,7 @@ def ffn_or_attn_only(mod, fqn): weight_dtype = getattr(torch, f"int{_quant_args[1]}") group_size = int(_quant_args[2]) granularity = PerGroup(group_size) if group_size > 0 else PerAxis(0) - is_asymmetric = bool(_quant_args[3]) + is_asymmetric = bool(_quant_args[3].lower() == "true") quantize_( model, Int8DynamicActivationIntxWeightConfig( From 1b61c82323823fe83456f82c92df142173804350 Mon Sep 17 00:00:00 2001 From: andrewor14 Date: Thu, 29 May 2025 15:18:31 -0400 Subject: [PATCH 059/165] Enable range learning for QAT Differential Revision: D72754131 Pull Request resolved: https://github.com/pytorch/ao/pull/2033 --- test/quantization/test_qat.py | 117 +++++++++++++++++++++ third_party/cutlass | 2 +- torchao/quantization/qat/__init__.py | 8 +- torchao/quantization/qat/api.py | 29 ++++- torchao/quantization/qat/embedding.py | 2 + torchao/quantization/qat/fake_quantizer.py | 48 +++++++-- torchao/quantization/qat/linear.py | 16 +-- torchao/quantization/qat/utils.py | 14 +++ 8 files changed, 217 insertions(+), 19 deletions(-) diff --git a/test/quantization/test_qat.py b/test/quantization/test_qat.py index d655abaf62..7444c3dbb5 100644 --- a/test/quantization/test_qat.py +++ b/test/quantization/test_qat.py @@ -9,6 +9,7 @@ import copy import unittest +from typing import List import torch import torch.nn.functional as F @@ -26,7 +27,9 @@ from torchao.quantization.qat.api import ( ComposableQATQuantizer, FakeQuantizeConfig, + IntXQuantizationAwareTrainingConfig, from_intx_quantization_aware_training, + initialize_fake_quantizers, intx_quantization_aware_training, ) from torchao.quantization.qat.embedding import ( @@ -99,6 +102,16 @@ def __init__(self): def example_inputs(self): return (torch.randn(1, 512).to(torch.float),) + def _get_all_weight_qparams(self) -> List[torch.Tensor]: + return [ + self.linear1.weight_fake_quantizer.scale, + self.linear1.weight_fake_quantizer.zero_point, + self.sub.linear.weight_fake_quantizer.scale, + self.sub.linear.weight_fake_quantizer.zero_point, + self.linear2.weight_fake_quantizer.scale, + self.linear2.weight_fake_quantizer.zero_point, + ] + def forward(self, x): x = self.linear1(x) x = self.sub(x) @@ -996,6 +1009,21 @@ def test_fake_quantize_config_dtype(self): FakeQuantizeConfig(TorchAODType.INT7, "per_token") FakeQuantizeConfig(torch.int8, "per_token") + def test_fake_quantize_config_dynamic_and_range_learning(self): + """ + Test that `is_dynamic` and `range_learning` cannot both be set. + """ + FakeQuantizeConfig( + torch.int8, "per_channel", is_dynamic=True, range_learning=False + ) + FakeQuantizeConfig( + torch.int8, "per_channel", is_dynamic=False, range_learning=True + ) + with self.assertRaisesRegex(ValueError, "not compatible"): + FakeQuantizeConfig( + torch.int8, "per_channel", is_dynamic=True, range_learning=True + ) + @unittest.skipIf( not TORCH_VERSION_AT_LEAST_2_4, "skipping when torch version is 2.4 or lower" ) @@ -1591,6 +1619,95 @@ def test_qat_8da4w_eps(self): actual_out = converted_model.linear1(x) torch.testing.assert_close(expected_out, actual_out, atol=0, rtol=0) + @unittest.skipIf( + not TORCH_VERSION_AT_LEAST_2_4, "skipping when torch version is 2.4 or lower" + ) + def test_fake_quantizer_range_learning(self): + """ + Test that range learning requires `FakeQuantizer`s to be initialized correctly. + """ + config = FakeQuantizeConfig( + torch.int8, + "per_channel", + is_dynamic=False, + range_learning=True, + scale_precision=torch.float32, + zero_point_precision=torch.float32, + ) + fake_quantizer = FakeQuantizer(config) + example_inputs = (torch.randn(2, 3),) + + # Not initialized, should fail + self.assertFalse(fake_quantizer._initialized) + self.assertIsNone(fake_quantizer.scale) + self.assertIsNone(fake_quantizer.zero_point) + with self.assertRaisesRegex( + ValueError, + "Please call `torchao.quantization.qat.initialize_fake_quantizers` " + "before initializing the optimizer and beginning training.", + ): + fake_quantizer(*example_inputs) + + # Should pass after initializing + initialize_fake_quantizers(fake_quantizer, example_inputs) + self.assertTrue(fake_quantizer._initialized) + self.assertIsInstance(fake_quantizer.scale, torch.nn.Parameter) + self.assertIsInstance(fake_quantizer.zero_point, torch.nn.Parameter) + self.assertTrue(fake_quantizer.scale.requires_grad) + self.assertTrue(fake_quantizer.zero_point.requires_grad) + fake_quantizer(*example_inputs) + + @unittest.skipIf( + not TORCH_VERSION_AT_LEAST_2_4, "skipping when torch version is 2.4 or lower" + ) + def test_qat_range_learning(self): + """ + Test end-to-end QAT flow with range learning. + """ + config = FakeQuantizeConfig( + torch.int8, + "per_channel", + is_dynamic=False, + range_learning=True, + scale_precision=torch.float32, + zero_point_precision=torch.float32, + ) + m = M() + example_inputs = m.example_inputs() + quantize_(m, IntXQuantizationAwareTrainingConfig(weight_config=config)) + + # Not initialized, should fail + for t in m._get_all_weight_qparams(): + self.assertIsNone(t) + with self.assertRaisesRegex( + ValueError, + "Please call `torchao.quantization.qat.initialize_fake_quantizers` " + "before initializing the optimizer and beginning training.", + ): + m(*example_inputs) + + # Should pass after initializing + # All scales and zero points should be in `m.parameters()` + initialize_fake_quantizers(m, example_inputs) + params = set(m.parameters()) + for t in m._get_all_weight_qparams(): + self.assertIsInstance(t, torch.nn.Parameter) + self.assertTrue(t.requires_grad) + self.assertTrue(t in params) + m(*example_inputs) + + # Simulate training + optimizer = torch.optim.SGD( + m.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-5 + ) + loss_fn = torch.nn.CrossEntropyLoss() + target = torch.randn(1, 512).float() + out = m(*example_inputs) + loss = loss_fn(out, target) + optimizer.zero_grad() + loss.backward() + optimizer.step() + if __name__ == "__main__": unittest.main() diff --git a/third_party/cutlass b/third_party/cutlass index e94e888df3..afa1772203 160000 --- a/third_party/cutlass +++ b/third_party/cutlass @@ -1 +1 @@ -Subproject commit e94e888df3551224738bfa505787b515eae8352f +Subproject commit afa1772203677c5118fcd82537a9c8fefbcc7008 diff --git a/torchao/quantization/qat/__init__.py b/torchao/quantization/qat/__init__.py index 5dc3d8e008..010ccfc8cc 100644 --- a/torchao/quantization/qat/__init__.py +++ b/torchao/quantization/qat/__init__.py @@ -4,6 +4,7 @@ FromIntXQuantizationAwareTrainingConfig, IntXQuantizationAwareTrainingConfig, from_intx_quantization_aware_training, + initialize_fake_quantizers, intx_quantization_aware_training, ) from .embedding import ( @@ -17,11 +18,12 @@ __all__ = [ "ComposableQATQuantizer", "FakeQuantizeConfig", - "Int4WeightOnlyQATQuantizer", + "FromIntXQuantizationAwareTrainingConfig", "Int4WeightOnlyEmbeddingQATQuantizer", + "Int4WeightOnlyQATQuantizer", "Int8DynActInt4WeightQATQuantizer", + "IntXQuantizationAwareTrainingConfig", + "initialize_fake_quantizers", "intx_quantization_aware_training", "from_intx_quantization_aware_training", - "FromIntXQuantizationAwareTrainingConfig", - "IntXQuantizationAwareTrainingConfig", ] diff --git a/torchao/quantization/qat/api.py b/torchao/quantization/qat/api.py index e025a43d94..8fba195363 100644 --- a/torchao/quantization/qat/api.py +++ b/torchao/quantization/qat/api.py @@ -5,7 +5,7 @@ # LICENSE file in the root directory of this source tree. from dataclasses import dataclass -from typing import Any, List, Optional, Union +from typing import Any, List, Optional, Tuple, Union import torch @@ -51,7 +51,8 @@ class FakeQuantizeConfig: zero_point_precision: zero point dtype (default torch.int32) zero_point_domain: whether zero point is in integer (default) or float domain is_dynamic: whether to use dynamic (default) or static scale and zero points - range_learning: whether to learn scale and zero points during training (coming soon) + range_learning: whether to learn scale and zero points during training + (default false), not compatible with `is_dynamic`. kwargs (optional): group_size: size of each group in per group fake quantization, @@ -123,6 +124,10 @@ def __init__( "Unsupported dtype '%s', choose from %s" % (dtype, all_dtypes) ) + # Dynamic is not compatible with range learning + if is_dynamic and range_learning: + raise ValueError("`is_dynamic` is not compatible with `range_learning`") + def _get_granularity( self, granularity: Union[Granularity, str, None], @@ -394,3 +399,23 @@ def convert( for quantizer in self.quantizers: model = quantizer.convert(model) return model + + +def initialize_fake_quantizers( + model: torch.nn.Module, + example_inputs: Tuple[Any, ...], +) -> None: + """ + Initialize the scales and zero points on all + :class:`~`torchao.quantization.qat.fake_quantizer.FakeQuantizer` + in the model based on the provided example inputs. + """ + # avoid circular dependencies + from torchao.quantization.qat.fake_quantizer import FakeQuantizer + + def _set_initialized(m: torch.nn.Module): + if isinstance(m, FakeQuantizer): + m._initialized = True + + model.apply(_set_initialized) + model(*example_inputs) diff --git a/torchao/quantization/qat/embedding.py b/torchao/quantization/qat/embedding.py index 2770956a2c..aec23712ed 100644 --- a/torchao/quantization/qat/embedding.py +++ b/torchao/quantization/qat/embedding.py @@ -92,6 +92,7 @@ def to_embedding(self) -> torch.nn.Embedding: self.scale_grad_by_freq, self.sparse, device=self.weight.device, + dtype=self.weight.dtype, ) # In distributed training, the model may be instantiated # on the meta device, in which case there is no need to @@ -116,6 +117,7 @@ def from_embedding( mod.sparse, weight_config=weight_config, device=mod.weight.device, + dtype=mod.weight.dtype, ) # In distributed training, the model may be instantiated # on the meta device, in which case there is no need to diff --git a/torchao/quantization/qat/fake_quantizer.py b/torchao/quantization/qat/fake_quantizer.py index 0d2521cac0..90206b5d6e 100644 --- a/torchao/quantization/qat/fake_quantizer.py +++ b/torchao/quantization/qat/fake_quantizer.py @@ -31,6 +31,7 @@ from .utils import ( _fake_quantize_per_channel_group, _fake_quantize_per_token, + _Round, ) @@ -46,11 +47,12 @@ def __init__(self, config: FakeQuantizeConfig): self.scale: Optional[torch.Tensor] = None self.zero_point: Optional[torch.Tensor] = None - # TODO: support range learinng - if self.config.range_learning: - raise NotImplementedError("Range learning is not supported yet") + # For range learning only + # TODO: make this configurable? + self._scale_eps = 1e-9 + self._initialized = False - def forward(self, x: torch.Tensor): + def forward(self, x: torch.Tensor) -> torch.Tensor: """ Apply fake quantization to the tensor based on the bit-width, granularity, symmetry, and other properties specified in the config. @@ -58,6 +60,17 @@ def forward(self, x: torch.Tensor): if not self.enabled: return x + if ( + self.config.range_learning + and not self._initialized + and (self.scale is None or self.zero_point is None) + ): + raise ValueError( + "Scales and zero points must be initialized for range learning. " + "Please call `torchao.quantization.qat.initialize_fake_quantizers` " + "before initializing the optimizer and beginning training." + ) + if isinstance(self.config.granularity, PerToken): return self._per_token_forward(x) elif isinstance(self.config.granularity, (PerAxis, PerGroup)): @@ -65,13 +78,12 @@ def forward(self, x: torch.Tensor): else: raise ValueError("Unknown granularity '%s'" % self.config.granularity) - def _per_token_forward(self, x: torch.Tensor): + def _per_token_forward(self, x: torch.Tensor) -> torch.Tensor: """ Perform per token fake quantization on the tensor. """ if self.config.is_symmetric: raise NotImplementedError("Symmetric per token is not supported yet") - qmin, qmax = _DTYPE_TO_QVALUE_BOUNDS[self.config.dtype] if self._should_compute_qparams(): self.scale, self.zero_point = choose_qparams_affine( @@ -85,9 +97,10 @@ def _per_token_forward(self, x: torch.Tensor): scale_dtype=self.config.scale_precision, zero_point_dtype=self.config.zero_point_precision, ) + self._maybe_update_qparams_for_range_learning() return _fake_quantize_per_token(x, self.scale, self.zero_point, qmin, qmax) - def _per_channel_or_group_forward(self, x: torch.Tensor): + def _per_channel_or_group_forward(self, x: torch.Tensor) -> torch.Tensor: """ Perform per channel or per group fake quantization on the tensor. We express per channel using per group where the group size is the size @@ -129,6 +142,7 @@ def _per_channel_or_group_forward(self, x: torch.Tensor): eps=self.config.eps, ) self.zero_point = self.zero_point.to(zero_point_precision) + self._maybe_update_qparams_for_range_learning() qmin, qmax = _DTYPE_TO_QVALUE_BOUNDS[self.config.dtype] return _fake_quantize_per_channel_group( @@ -147,6 +161,26 @@ def _should_compute_qparams(self) -> bool: """ return self.config.is_dynamic or self.scale is None or self.zero_point is None + def _maybe_update_qparams_for_range_learning(self) -> None: + """ + If range learning is enabled, turn scales and zero points into trainable parameters. + This function is idempotent and should only be called once. + """ + if ( + not self.config.range_learning + or isinstance(self.scale, torch.nn.Parameter) + or isinstance(self.zero_point, torch.nn.Parameter) + ): + return + scale, zero_point = self.scale, self.zero_point + qmin, qmax = _DTYPE_TO_QVALUE_BOUNDS[self.config.dtype] + # Stabilize range learning + scale = torch.clamp(scale, min=self._scale_eps) + zero_point = _Round.apply(zero_point) + zero_point = torch.clamp(zero_point, qmin, qmax) + self.scale = torch.nn.Parameter(scale, requires_grad=True) + self.zero_point = torch.nn.Parameter(zero_point, requires_grad=True) + def __repr__(self) -> str: """ Return a human readable representation of this `FakeQuantizer` with config details. diff --git a/torchao/quantization/qat/linear.py b/torchao/quantization/qat/linear.py index a912f04b83..7c32bc4b19 100644 --- a/torchao/quantization/qat/linear.py +++ b/torchao/quantization/qat/linear.py @@ -18,6 +18,7 @@ _replace_linear_int4, groupwise_affine_quantize_tensor, ) +from torchao.quantization.granularity import PerGroup from torchao.quantization.quant_primitives import ( TorchAODType, ZeroPointDomain, @@ -83,12 +84,13 @@ def __init__( # initialize weight fake quantizer if weight_config is not None: - group_size = weight_config.group_size - if group_size is not None and in_features % group_size != 0: - raise ValueError( - "in_features (%s) %% group_size (%s) must be == 0" - % (in_features, group_size) - ) + if isinstance(weight_config.granularity, PerGroup): + group_size = weight_config.group_size + if group_size is not None and in_features % group_size != 0: + raise ValueError( + "in_features (%s) %% group_size (%s) must be == 0" + % (in_features, group_size) + ) self.weight_fake_quantizer = FakeQuantizer(weight_config) else: self.weight_fake_quantizer = None @@ -108,6 +110,7 @@ def to_linear(self) -> torch.nn.Linear: self.out_features, self.bias is not None, device=self.weight.device, + dtype=self.weight.dtype, ) # In distributed training, the model may be instantiated # on the meta device, in which case there is no need to @@ -131,6 +134,7 @@ def from_linear( activation_config=activation_config, weight_config=weight_config, device=mod.weight.device, + dtype=mod.weight.dtype, ) # In distributed training, the model may be instantiated # on the meta device, in which case there is no need to diff --git a/torchao/quantization/qat/utils.py b/torchao/quantization/qat/utils.py index 12e9097ada..71e9a96ec5 100644 --- a/torchao/quantization/qat/utils.py +++ b/torchao/quantization/qat/utils.py @@ -91,6 +91,20 @@ def backward(ctx, gy): return (gy,) +class _Round(torch.autograd.Function): + """ + Implementation of generic round operation with backward STE. + """ + + @staticmethod + def forward(ctx, x: torch.Tensor) -> torch.Tensor: + return torch.round(x) + + @staticmethod + def backward(ctx, gy: torch.Tensor) -> torch.Tensor: + return gy + + def _fake_quantize_per_channel_group( input: torch.Tensor, scales: torch.Tensor, From dacd3aa91787fc8845c399f08823616e18c5e31d Mon Sep 17 00:00:00 2001 From: andrewor14 Date: Thu, 29 May 2025 18:28:38 -0400 Subject: [PATCH 060/165] Mark QAT range learning as prototype for now (#2272) API may be subject to change. See this tracker for future tasks for more detail: https://github.com/pytorch/ao/issues/2271 --- torchao/quantization/qat/api.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/torchao/quantization/qat/api.py b/torchao/quantization/qat/api.py index 8fba195363..60370ee52b 100644 --- a/torchao/quantization/qat/api.py +++ b/torchao/quantization/qat/api.py @@ -51,7 +51,7 @@ class FakeQuantizeConfig: zero_point_precision: zero point dtype (default torch.int32) zero_point_domain: whether zero point is in integer (default) or float domain is_dynamic: whether to use dynamic (default) or static scale and zero points - range_learning: whether to learn scale and zero points during training + range_learning (prototype): whether to learn scale and zero points during training (default false), not compatible with `is_dynamic`. kwargs (optional): @@ -406,7 +406,7 @@ def initialize_fake_quantizers( example_inputs: Tuple[Any, ...], ) -> None: """ - Initialize the scales and zero points on all + (Prototype) Initialize the scales and zero points on all :class:`~`torchao.quantization.qat.fake_quantizer.FakeQuantizer` in the model based on the provided example inputs. """ From d963a8840e3c228e303fe14aff5d9be7017c92b6 Mon Sep 17 00:00:00 2001 From: Jerry Zhang Date: Thu, 29 May 2025 16:06:22 -0700 Subject: [PATCH 061/165] Fix generate.py for fbgemm int4 integration (#2273) Summary: Updated table: | | overall tokens/sec | TTFT | Peak Memory | Model Size | | ---------| -------------------| ------| --------------| -----------| | baseline - 1 | 131.65 | 0.0220 | 16.24 GB | 15.01 GB | | baseline - 128| 76.38 | 0.0544 | 26.92 GB | 15.01 GB| | int4wo - 1 | 207.69 | 0.0288 | 6.41 GB | 3.99 GB | | int4wo - 128 | 12.85 | 0.4223 | 16.01 GB | 3.99 GB | | fbgemm-int4 - 1 (w/ compile) | 61.12 | 0.0212 | 7.59 GB | 3.00 GB | | fbgemm-int4 - 128 (w/ compile) | 71.23 | 0.0576 | 16.13 GB | 3.99 GB | Verified that compile works: python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --quantization fbgemm-int4-128 --batch_size 1 --compile ========== Average overall tokens/sec: 61.12 Average decode tokens/sec: 61.5512 s Average TTFT: 0.0212 s Average tokens/sec: 61.12 Average Bandwidth: 243.70 GB/s Peak Memory Usage: 7.59 GB Model Size: 3.99 GB python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --quantization fbgemm-int4-128 --batch_size 128 --compile ========== Average overall tokens/sec: 71.23 Average decode tokens/sec: 72.8871 s Average TTFT: 0.0576 s Average tokens/sec: 71.23 Average tokens/sec including batches 9116.81 Average Bandwidth: 284.00 GB/s Peak Memory Usage: 16.13 GB Model Size: 3.99 GB Test Plan: Reviewers: Subscribers: Tasks: Tags: --- torchao/_models/llama/generate.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/torchao/_models/llama/generate.py b/torchao/_models/llama/generate.py index 1f56e71f07..40f70fe93e 100644 --- a/torchao/_models/llama/generate.py +++ b/torchao/_models/llama/generate.py @@ -447,8 +447,14 @@ def ffn_or_attn_only(mod, fqn): _, precision, group_size = quantization.split("-") group_size = int(group_size) + block_size = [1, group_size] if precision == "int4": - quantize_(model, FbgemmConfig("bf16i4bf16", group_size)) + quantize_( + model, + FbgemmConfig( + torch.bfloat16, torch.int4, torch.bfloat16, block_size + ), + ) else: raise NotImplementedError( f"FbegemmConfig({precision=}) not supported yet" From 01bd0be346fb68880804ecb5cf2bcc2c966da476 Mon Sep 17 00:00:00 2001 From: Driss Guessous <32754868+drisspg@users.noreply.github.com> Date: Thu, 29 May 2025 22:15:13 -0700 Subject: [PATCH 062/165] Make optim lazily intialize global state (#2277) stack-info: PR: https://github.com/pytorch/ao/pull/2277, branch: drisspg/stack/60 --- torchao/optim/subclass_4bit.py | 19 ++++++++++++++++--- torchao/optim/subclass_8bit.py | 18 +++++++++++++++--- 2 files changed, 31 insertions(+), 6 deletions(-) diff --git a/torchao/optim/subclass_4bit.py b/torchao/optim/subclass_4bit.py index 55d9bf356f..209d0b8cad 100644 --- a/torchao/optim/subclass_4bit.py +++ b/torchao/optim/subclass_4bit.py @@ -29,8 +29,19 @@ # https://github.com/thu-ml/low-bit-optimizers/blob/e3e2854728e498c2a606e3fdb88daa27ae94f9a6/lpmm/configs/2nd_moment_group_128.yml # NOTE: power-1 is linear # TODO: since QMAP_UNSIGNED is linear, perhaps doing affine quantize is faster? -QMAP_SIGNED = create_dynamic_map(True, 3, 4) -QMAP_UNSIGNED = torch.linspace(0, 1, 17)[1:].tolist() # no zero + +# Lazy initialization to avoid meta device issues during import +from functools import lru_cache + + +@lru_cache(maxsize=1) +def get_qmap_signed(): + return create_dynamic_map(True, 3, 4) + + +@lru_cache(maxsize=1) +def get_qmap_unsigned(): + return torch.linspace(0, 1, 17, device="cpu")[1:].tolist() # no zero class OptimState4bit(TorchAOBaseTensor): @@ -90,7 +101,9 @@ def zeros(cls, shape, signed: bool = True, block_size: int = 128, device=None): codes = torch.zeros(n_elems // 2, dtype=torch.uint8, device=device) scale = torch.zeros(n_elems // block_size, device=device) - qmap = torch.tensor(QMAP_SIGNED if signed else QMAP_UNSIGNED, device=device) + qmap = torch.tensor( + get_qmap_signed() if signed else get_qmap_unsigned(), device=device + ) return cls(codes, scale, qmap, signed, shape) def __repr__(self): diff --git a/torchao/optim/subclass_8bit.py b/torchao/optim/subclass_8bit.py index 66d31432b8..58a51734d7 100644 --- a/torchao/optim/subclass_8bit.py +++ b/torchao/optim/subclass_8bit.py @@ -26,8 +26,18 @@ c10d_functional = torch.ops.c10d_functional _c10d_functional = torch.ops._c10d_functional -QMAP_SIGNED = create_dynamic_map(signed=True) -QMAP_UNSIGNED = create_dynamic_map(signed=False) +# Lazy initialization to avoid meta device issues during import +from functools import lru_cache + + +@lru_cache(maxsize=1) +def get_qmap_signed(): + return create_dynamic_map(signed=True) + + +@lru_cache(maxsize=1) +def get_qmap_unsigned(): + return create_dynamic_map(signed=False) class OptimState8bit(TorchAOBaseTensor): @@ -79,7 +89,9 @@ def dequantize(self, output_dtype=None): def zeros(cls, shape, signed: bool = True, block_size: int = 256, device=None): codes = torch.zeros(shape, dtype=torch.uint8, device=device) scale = torch.zeros(codes.numel() // block_size, device=device) - qmap = torch.tensor(QMAP_SIGNED if signed else QMAP_UNSIGNED, device=device) + qmap = torch.tensor( + get_qmap_signed() if signed else get_qmap_unsigned(), device=device + ) return cls(codes, scale, qmap, signed) def __repr__(self): From 14965e4fac4b5f7725d0a12335b660cee35bc87a Mon Sep 17 00:00:00 2001 From: Driss Guessous <32754868+drisspg@users.noreply.github.com> Date: Thu, 29 May 2025 22:16:47 -0700 Subject: [PATCH 063/165] Add Integration Tests to H100 CI (#2268) stack-info: PR: https://github.com/pytorch/ao/pull/2268, branch: drisspg/stack/59 --- .github/workflows/float8_test.yml | 5 ++++- test/integration/test_integration.py | 28 ++++++++++++++++++++------- test/integration/test_vllm.py | 8 ++++++++ torchao/quantization/autoquant.py | 29 ++++++++-------------------- 4 files changed, 41 insertions(+), 29 deletions(-) diff --git a/.github/workflows/float8_test.yml b/.github/workflows/float8_test.yml index a32d6ecb74..0b17a23bf7 100644 --- a/.github/workflows/float8_test.yml +++ b/.github/workflows/float8_test.yml @@ -48,7 +48,10 @@ jobs: conda activate venv export PATH=/opt/rh/devtoolset-10/root/usr/bin/:$PATH python -m pip install --upgrade pip + pip install uv pip install ${{ matrix.torch-spec }} - pip install -r dev-requirements.txt + uv pip install -r dev-requirements.txt + uv pip install vllm pip install . pytest test/float8 --verbose -s + pytest test/integration --verbose -s diff --git a/test/integration/test_integration.py b/test/integration/test_integration.py index 2f498ea5c1..8388a8bcff 100644 --- a/test/integration/test_integration.py +++ b/test/integration/test_integration.py @@ -883,12 +883,20 @@ def test_autoquantizable_flatten_unflatten(self): tensor_data_dict, tensor_attributes, outer_size, outer_stride ) - @parameterized.expand(COMMON_DEVICE_DTYPE) + @parameterized.expand( + [ + (device, dtype, f"device_{device}_dtype_{str(dtype).split('.')[-1]}") + for device, dtype in COMMON_DEVICE_DTYPE + ] + ) @unittest.skipIf( not TORCH_VERSION_AT_LEAST_2_5, "autoquant+aqt needs newer pytorch" ) @unittest.skipIf(not is_sm_at_least_90(), "Need H100 to run") - def test_aq_float8_dynamic_quant_rowwise_scaling_subclass(self, device, dtype): + @unittest.skip("TODO this is not working correctly") + def test_aq_float8_dynamic_quant_rowwise_scaling_subclass( + self, device, dtype, name + ): if dtype != torch.bfloat16: with self.assertRaisesRegex( AssertionError, "PerRow quantization only works for bfloat16 precision" @@ -912,6 +920,7 @@ def test_aq_float8_dynamic_quant_rowwise_scaling_subclass(self, device, dtype): not TORCH_VERSION_AT_LEAST_2_5, "autoquant+aqt needs newer pytorch" ) @unittest.skipIf(not is_sm_at_least_90(), "Need H100 to run") + @unittest.skip("TODO this is not working correctly") def test_aq_float8_dynamic_quant_tensorwise_scaling_subclass(self, device, dtype): self._test_lin_weight_subclass_impl( AQFloat8PerTensorScalingDynamicallyQuantizedLinearWeight.from_float, @@ -1880,9 +1889,12 @@ def test_autoquant_int4wo(self, device, dtype): @unittest.skipIf( not TORCH_VERSION_AT_LEAST_2_5, "autoquant int4 option requires 2.5+." ) + @unittest.skipIf( + True, "Skipping for now, do to lowering bug in inductor" + ) # TODO unblock when fixed def test_autoquant_float8(self, device, dtype): if device == "cpu": - self.skipTest(f"int4wo is for cuda, not {device}") + self.skipTest(f"float8 is for cuda, not {device}") # note: marlin sparse layout failed when scale_t has a dimension of 1d m, k, n = 128, 128, 128 @@ -1893,6 +1905,11 @@ def test_autoquant_float8(self, device, dtype): AQFloat8PerTensorScalingDynamicallyQuantizedLinearWeight, AQFloat8WeightOnlyQuantizedLinearWeight, ]: + if ( + dtype in (torch.float32, torch.float16) + and qclass is AQFloat8PerRowScalingDynamicallyQuantizedLinearWeight + ): + continue model = ( torch.nn.Sequential( torch.nn.ReLU(), @@ -1904,10 +1921,7 @@ def test_autoquant_float8(self, device, dtype): ) ref = model(example_input) qtensor_class_list = [qclass] - torchao.autoquant( - model, - qtensor_class_list=qtensor_class_list, - ) + torchao.autoquant(model, qtensor_class_list=qtensor_class_list) out = model(example_input) self.assertIn(type(model[1].weight), qtensor_class_list) diff --git a/test/integration/test_vllm.py b/test/integration/test_vllm.py index c750a7b562..b82f4f9794 100644 --- a/test/integration/test_vllm.py +++ b/test/integration/test_vllm.py @@ -4,6 +4,7 @@ # This source code is licensed under the BSD 3-Clause license found in the # LICENSE file in the root directory of this source tree. +import importlib.metadata import importlib.util import os import random @@ -15,6 +16,7 @@ import pytest import torch +from packaging import version from torchao.utils import TORCH_VERSION_AT_LEAST_2_7 if not TORCH_VERSION_AT_LEAST_2_7: @@ -30,6 +32,12 @@ if not TRANSFORMERS_AVAILABLE: pytest.skip("transformers not installed", allow_module_level=True) +if VLLM_AVAILABLE: + vllm_version = importlib.metadata.version("vllm") + # Bad vLLM version due to adding AOPerModuleConfig + if version.parse(vllm_version) == version.parse("0.9.0"): + pytest.skip("vLLM version must be greater than 0.9.0", allow_module_level=True) + from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig from vllm import LLM, SamplingParams diff --git a/torchao/quantization/autoquant.py b/torchao/quantization/autoquant.py index f018d5dffe..41ea588231 100644 --- a/torchao/quantization/autoquant.py +++ b/torchao/quantization/autoquant.py @@ -21,7 +21,6 @@ from torchao.kernel import safe_int_mm from torchao.quantization.linear_activation_quantized_tensor import ( LinearActivationQuantizedTensor, - to_linear_activation_quantized, ) from torchao.quantization.quant_primitives import ( MappingType, @@ -964,7 +963,9 @@ def from_float(cls, weight): ) -class AQFloat8PerRowScalingDynamicallyQuantizedLinearWeight(AQMixin, BFloat16Tensor): +class AQFloat8PerRowScalingDynamicallyQuantizedLinearWeight( + AQMixin, LinearActivationQuantizedTensor +): """ AutoQuantizable version of Float8DynamicallyQuantizedLinearWeight using per row scaling """ @@ -982,24 +983,15 @@ def get_weight_block_size(x): return (1, x.shape[1]) target_dtype = torch.float8_e4m3fn - - # input settings - def get_per_token_block_size(x): - block_size = list(x.shape) - for i in range(len(block_size) - 1): - block_size[i] = 1 - return block_size - input_target_dtype = torch.float8_e4m3fn _layout = Float8Layout(mm_config=Float8MMConfig(use_fast_accum=True)) - # TODO: make this serializable + # TODO: test serializable input_quant_func = _input_activation_quant_func_fp8 - input_quant_kwargs = { + input_quant_args = { "activation_granularity": cls.activation_granularity, "activation_dtype": input_target_dtype, } block_size = get_weight_block_size(weight) - weight = to_affine_quantized_floatx( input_float=weight, block_size=block_size, @@ -1007,15 +999,10 @@ def get_per_token_block_size(x): _layout=_layout, scale_dtype=torch.float32, ) - weight = to_linear_activation_quantized( - weight, input_quant_func, quant_kwargs=input_quant_kwargs - ) - # at inference time, - # we first convert the input, weight and bias to bfloat16, and then quantize activation - # and then dispatch to the quantized ops - return super( + weight = super( AQFloat8PerRowScalingDynamicallyQuantizedLinearWeight, cls - ).from_float(weight, skip_weight_conversion=True) + ).from_float(weight, input_quant_func, input_quant_args) + return weight class AQFloat8PerTensorScalingDynamicallyQuantizedLinearWeight( From c3c316327cb227e89fe0f4a57ab42a97fc755dab Mon Sep 17 00:00:00 2001 From: Emmanuel Ferdman Date: Fri, 30 May 2025 08:18:21 +0300 Subject: [PATCH 064/165] Resolve logger warnings (#2250) Signed-off-by: Emmanuel Ferdman --- torchao/dtypes/affine_quantized_tensor_ops.py | 10 +++++----- torchao/quantization/quant_api.py | 2 +- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/torchao/dtypes/affine_quantized_tensor_ops.py b/torchao/dtypes/affine_quantized_tensor_ops.py index 20d87de8cf..63650ce687 100644 --- a/torchao/dtypes/affine_quantized_tensor_ops.py +++ b/torchao/dtypes/affine_quantized_tensor_ops.py @@ -130,8 +130,8 @@ def deregister_aqt_quantized_linear_dispatch(dispatch_condition): if dispatch_condition in _AQT_QLINEAR_DISPATCH_TABLE: del _AQT_QLINEAR_DISPATCH_TABLE[dispatch_condition] else: - logger.warn( - f"Attempting to remove non-existant dispatch condition {dispatch_condition}" + logger.warning( + f"Attempting to remove non-existent dispatch condition {dispatch_condition}" ) @@ -273,7 +273,7 @@ def _(func, types, args, kwargs): try: return weight_tensor._quantized_linear_op(input_tensor, weight_tensor, bias) except QuantizedLinearNotImplementedError as e: - # fallback path is only called when user did not specify a specfic quantized linear implementation with `_layout.quantized_linear_impl` + # fallback path is only called when user did not specify a specific quantized linear implementation with `_layout.quantized_linear_impl` if ( isinstance(weight_tensor, AffineQuantizedTensor) and hasattr(weight_tensor._layout, "quantized_linear_impl") @@ -362,7 +362,7 @@ def _(func, types, args, kwargs): input_tensor, transposed_weight_tensor, bias ) except QuantizedLinearNotImplementedError as e: - # fallback path is only called when user did not specify a specfic quantized linear implementation with `_layout.quantized_linear_impl` + # fallback path is only called when user did not specify a specific quantized linear implementation with `_layout.quantized_linear_impl` if ( isinstance(weight_tensor, AffineQuantizedTensor) and hasattr(weight_tensor._layout, "quantized_linear_impl") @@ -396,7 +396,7 @@ def _(func, types, args, kwargs): input_tensor, transposed_weight_tensor, bias ) except QuantizedLinearNotImplementedError as e: - # fallback path is only called when user did not specify a specfic quantized linear implementation with `_layout.quantized_linear_impl` + # fallback path is only called when user did not specify a specific quantized linear implementation with `_layout.quantized_linear_impl` if ( isinstance(weight_tensor, AffineQuantizedTensor) and hasattr(weight_tensor._layout, "quantized_linear_impl") diff --git a/torchao/quantization/quant_api.py b/torchao/quantization/quant_api.py index ada19859bc..37f0cf5bfe 100644 --- a/torchao/quantization/quant_api.py +++ b/torchao/quantization/quant_api.py @@ -1797,7 +1797,7 @@ def _uintx_weight_only_transform( if use_hqq: if dtype == torch.uint4: - logger.warn( + logger.warning( "Recommended to use `int4_weight_only(group_size, use_hqq=True)` for the best performance" ) quant_min, quant_max = _DTYPE_TO_QVALUE_BOUNDS[dtype] From abb309a83d53df21b5d4c90d2eac92abcf14bcc0 Mon Sep 17 00:00:00 2001 From: XiaoWang Date: Fri, 30 May 2025 07:02:37 +0000 Subject: [PATCH 065/165] Enable AWQ on Intel GPU. (#2248) Following https://github.com/pytorch/pytorch/issues/153019 requests, we enable awq-uint4 for Intel GPU in pytorch/ao after RTN ready. How to run awq quantization model: ```markdown cd torchao/prototype/awq python example.py --device xpu huggingface-model(such as meta-llama/Llama-3.1-8B-Instruct) awq-uint4-128 ``` #Results of meta-llama/Llama-3.1-8B-Instruct on Intel GPU: {'perplexity': {'perplexity': 10.099576950073242, 'prediction_time': 0.20489671968780787}} #Results of meta-llama/Llama-3.1-8B-Instruct on NVIDIA-A100 GPU: Results: {'perplexity': {'perplexity': 10.160041809082031, 'prediction_time': 0.4466673863672577}} Pull Request resolved: https://github.com/pytorch/ao/pull/2248 Approved by: https://github.com/liangan1, https://github.com/jerryzh168 --- test/quantization/test_quant_primitives.py | 4 +++ torchao/dtypes/uintx/int4_xpu_layout.py | 17 +++++------ torchao/prototype/awq/api.py | 15 ++++++++-- torchao/prototype/awq/example.py | 17 +++++++++-- torchao/quantization/subclass.py | 7 ----- torchao/quantization/utils.py | 35 ++++++++-------------- 6 files changed, 51 insertions(+), 44 deletions(-) diff --git a/test/quantization/test_quant_primitives.py b/test/quantization/test_quant_primitives.py index 046fb6ab42..e69d68b27f 100644 --- a/test/quantization/test_quant_primitives.py +++ b/test/quantization/test_quant_primitives.py @@ -135,6 +135,8 @@ def _groupwise_affine_quantize_tensor_from_qparams( if TORCH_VERSION_AT_LEAST_2_5: if (not (check_cpu_version(w.device))) and (not (check_xpu_version(w.device))): w_int4x8 = (w_int4x8[::, ::2] << 4 | w_int4x8[::, 1::2]).to(torch.uint8) + if check_xpu_version(w.device): + w_int4x8 = (w_int4x8[::, 1::2] << 4 | w_int4x8[::, ::2]).to(torch.uint8) return w_int4x8 @@ -730,6 +732,8 @@ def test_groupwise_affine_dequantize_tensor_from_qparams(self): not (check_xpu_version(input.device)) ): input_tmp = (input[::, ::2] << 4 | input[::, 1::2]).to(torch.uint8) + if check_xpu_version(input.device): + input_tmp = (input[::, 1::2] << 4 | input[::, ::2]).to(torch.uint8) w_bf16 = groupwise_affine_dequantize_tensor_from_qparams( input_tmp, scales, zeros, n_bit, groupsize, zero_point_domain ) diff --git a/torchao/dtypes/uintx/int4_xpu_layout.py b/torchao/dtypes/uintx/int4_xpu_layout.py index 76f5ecb121..722a37bc32 100644 --- a/torchao/dtypes/uintx/int4_xpu_layout.py +++ b/torchao/dtypes/uintx/int4_xpu_layout.py @@ -242,14 +242,15 @@ def from_plain( ): assert isinstance(_layout, Int4XPULayout) - from torchao.quantization.utils import convert_weight_to_int4pack_xpu - if TORCH_VERSION_AT_LEAST_2_8: assert int_data.dtype == torch.int32, ( "torch.ops.aten._convert_weight_to_int4pack_for_cpu expects `int32` dtype" ) - packed_weight = convert_weight_to_int4pack_xpu( - int_data, zero_point.dtype != scale.dtype + packed_weight = (int_data[::, 1::2] << 4 | int_data[::, ::2]).to( + torch.uint8 + ) + packed_weight = torch.ops.aten._convert_weight_to_int4pack( + packed_weight.contiguous(), 8 ) else: assert False, "INT4 not supported on XPU until 2.8" @@ -370,8 +371,8 @@ def __torch_dispatch__(cls, func, types, args, kwargs): def get_plain(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: from torchao.quantization.quant_primitives import ( - ZeroPointDomain, quantize_affine, + quantize_affine_float_zero_point, ) from torchao.quantization.utils import unpack_tinygemm_scales_and_zeros @@ -394,7 +395,6 @@ def get_plain(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: quant_max = 15 assert len(block_size) == 2 and block_size[0] == 1 if self.scale_and_zero is None: - zero_point_domain = ZeroPointDomain.INT dequantized = torch.ops.aten._weight_int4pack_mm_with_scales_and_zeros( torch.eye(eye_shape, device=device, dtype=original_dtype), self.packed_weight, @@ -411,10 +411,8 @@ def get_plain(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: target_dtype, quant_min, quant_max, - zero_point_domain, ) else: - zero_point_domain = ZeroPointDomain.FLOAT dequantized = torch.ops.aten._weight_int4pack_mm( torch.eye(eye_shape, device=device, dtype=original_dtype), self.packed_weight, @@ -425,7 +423,7 @@ def get_plain(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: # TODO: move this to `unpack_tinygemm_scales_and_zeros`? scale = scale.reshape(scale.shape[:-1]).contiguous() zero = zero.reshape(zero.shape[:-1]).contiguous() - int_data = quantize_affine( + int_data = quantize_affine_float_zero_point( dequantized, block_size, scale, @@ -433,7 +431,6 @@ def get_plain(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: target_dtype, quant_min, quant_max, - zero_point_domain, ) return int_data, scale, zero diff --git a/torchao/prototype/awq/api.py b/torchao/prototype/awq/api.py index 2d6059c057..5806c29ce6 100644 --- a/torchao/prototype/awq/api.py +++ b/torchao/prototype/awq/api.py @@ -5,12 +5,15 @@ # LICENSE file in the root directory of this source tree. import types from dataclasses import dataclass +from typing import Optional import torch import torchao from torchao.core.config import AOBaseConfig from torchao.dtypes import ( + Int4XPULayout, + Layout, TensorCoreTiledLayout, to_affine_quantized_intx, ) @@ -105,12 +108,14 @@ class AWQUIntXConfig(AOBaseConfig): Args: quant_dtype: The data type of the quantized weights. Currently only torch.uint4 is intended to be used but can be used with torch.uint1 -> torch.uint8 + `layout`: layout type for quantized tensor, default is `TensorCoreTiledLayout(inner_k_tiles=8)` group_size: Quantization granularity. Use -1 for channel wise quantization weight_quant_fn: The quantization function to be used, which takes in the weight and returns the quantized weight. If None, then affine uint4 quantization is used set_inductor_config: if True, adjusts `torchinductor` settings to recommended values. """ quant_dtype: torch.dtype = torch.uint4 + layout: Optional[Layout] = TensorCoreTiledLayout(inner_k_tiles=8) group_size: int = 64 use_hqq: bool = False set_inductor_config: bool = True @@ -142,9 +147,13 @@ def _awq_uintx_transform( target_dtype = torch.int32 eps = 1e-6 preserve_zero = False - zero_point_dtype = torch.bfloat16 - zero_point_domain = ZeroPointDomain.FLOAT - _layout = TensorCoreTiledLayout(inner_k_tiles=8) + _layout = config.layout + if isinstance(_layout, Int4XPULayout): + zero_point_dtype = torch.int8 + zero_point_domain = ZeroPointDomain.INT + else: + zero_point_dtype = torch.bfloat16 + zero_point_domain = ZeroPointDomain.FLOAT else: target_dtype = torch.uint8 eps = torch.finfo(torch.float32).eps diff --git a/torchao/prototype/awq/example.py b/torchao/prototype/awq/example.py index ba1ba6834c..7ff6092b05 100644 --- a/torchao/prototype/awq/example.py +++ b/torchao/prototype/awq/example.py @@ -11,6 +11,7 @@ from tqdm import tqdm from transformers import AutoModelForCausalLM, AutoTokenizer +from torchao.dtypes import Int4XPULayout from torchao.prototype.awq import AWQObservedLinear, awq_uintx, insert_awq_observer_ from torchao.quantization import int4_weight_only, quantize_ @@ -71,6 +72,8 @@ def wiki2_eval( log_likelihood = model(input_ids, labels=target_ids).loss * trg_len if device.startswith("cuda"): torch.cuda.synchronize() + if device.startswith("xpu"): + torch.xpu.synchronize() t2 = time.time() t.append((t2 - t1)) lls.append(log_likelihood) @@ -229,9 +232,14 @@ def wikitext2_ppl( use_hqq = "hqq" in quant print(f"running {quant_dtype} quantization") t0 = time.time() + awq_uintx_config = awq_uintx( + quant_dtype=quant_dtype, group_size=group_size, use_hqq=use_hqq + ) + if "xpu" in device: + awq_uintx_config.layout = Int4XPULayout() quantize_( model, - awq_uintx(quant_dtype=quant_dtype, group_size=group_size, use_hqq=use_hqq), + awq_uintx_config, is_observed_linear, ) print(f"time for quantization: {time.time() - t0:.02f} seconds") @@ -242,7 +250,12 @@ def wikitext2_ppl( group_size = int(quant.split("-")[1]) use_hqq = "hqq" in quant print(f"running {quant} quantization with group size {group_size}") - quantize_(model, int4_weight_only(group_size=group_size, use_hqq=use_hqq)) + int4_weight_only_config = int4_weight_only( + group_size=group_size, use_hqq=use_hqq + ) + if "xpu" in device: + int4_weight_only_config.layout = Int4XPULayout() + quantize_(model, int4_weight_only_config) if compile: model = torch.compile(model) diff --git a/torchao/quantization/subclass.py b/torchao/quantization/subclass.py index abaad317eb..be0533510f 100644 --- a/torchao/quantization/subclass.py +++ b/torchao/quantization/subclass.py @@ -697,13 +697,6 @@ def to_qtensor_components( int_data = aten._convert_weight_to_int4pack_for_cpu( input_int4x8, inner_k_tiles ) - if check_xpu_version(input_float.device): - from torchao.quantization.utils import convert_weight_to_int4pack_xpu - - int_data = convert_weight_to_int4pack_xpu( - input_int4x8, - zero_point_domain_is_int=zero_point_domain == ZeroPointDomain.INT, - ) else: int_data = aten._convert_weight_to_int4pack(input_int4x8, inner_k_tiles) return int_data, scales_and_zeros, False, groupsize, inner_k_tiles diff --git a/torchao/quantization/utils.py b/torchao/quantization/utils.py index 30b9980878..8f2554849c 100644 --- a/torchao/quantization/utils.py +++ b/torchao/quantization/utils.py @@ -127,6 +127,11 @@ def cuda(self): val.cuda() if isinstance(val, torch.Tensor) else val for val in self.values ] + def xpu(self): + self.values = [ + val.xpu() if isinstance(val, torch.Tensor) else val for val in self.values + ] + def guard_dtype_size(tensor_arg, arg_name, dtype=None, size=None): if dtype is not None and tensor_arg.dtype != dtype: @@ -415,25 +420,6 @@ def unpack_tinygemm_scales_and_zeros(scales_and_zeros): return torch.split(scales_and_zeros.transpose(-3, -2), 1, -1) -def convert_weight_to_int4pack_xpu(weight, zero_point_domain_is_int=False): - assert weight.device.type == "xpu" - - if zero_point_domain_is_int: - # int_data = weight.to(dtype=torch.uint8) - int_data = (weight[::, 1::2] << 4 | weight[::, ::2]).to(torch.uint8) - packed_weight = torch.ops.aten._convert_weight_to_int4pack( - int_data, - 8, # TODO:remove - ) - else: - out = weight.to(dtype=torch.uint8) - out = (out[::, 1::2] << 4 | out[::, ::2]).to(torch.uint8) - packed_weight = out.view(torch.int32) - - # Second, N * K/2 uint8 -> N * K/8 int32 - return packed_weight - - def groupwise_affine_quantize_tensor_from_qparams( w, scales, zeros, n_bit=4, groupsize=128, zero_point_domain=ZeroPointDomain.FLOAT ): @@ -473,6 +459,8 @@ def groupwise_affine_quantize_tensor_from_qparams( not (check_xpu_version(int_data.device)) ): int_data = (int_data[::, ::2] << 4 | int_data[::, 1::2]).to(torch.uint8) + if check_xpu_version(int_data.device): + int_data = (int_data[::, 1::2] << 4 | int_data[::, ::2]).to(torch.uint8) return int_data @@ -491,7 +479,6 @@ def groupwise_affine_dequantize_tensor_from_qparams( TORCH_VERSION_AT_LEAST_2_5 and (w_int4x8.dtype == torch.uint8 or w_int4x8.shape[-1] > 1) and not (check_cpu_version(w_int4x8.device)) - and not (check_xpu_version(w_int4x8.device)) ): data = w_int4x8.to(torch.int32) high_bits = data >> 4 @@ -501,8 +488,12 @@ def groupwise_affine_dequantize_tensor_from_qparams( dtype=torch.int32, device=w_int4x8.device, ) - w_int32[::, ::2] = high_bits - w_int32[::, 1::2] = low_bits + if not (check_xpu_version(w_int4x8.device)): + w_int32[::, ::2] = high_bits + w_int32[::, 1::2] = low_bits + else: + w_int32[::, ::2] = low_bits + w_int32[::, 1::2] = high_bits else: w_int32 = w_int4x8 From dd43f160e6ddfb68da7073c5472ea5d0880acbae Mon Sep 17 00:00:00 2001 From: Scott Roy <161522778+metascroy@users.noreply.github.com> Date: Fri, 30 May 2025 09:24:22 -0700 Subject: [PATCH 066/165] Update CMake to enable building ops on iOS (#2274) init --- torchao/experimental/CMakeLists.txt | 44 ++++++++++--------- .../ops/embedding_xbit/CMakeLists.txt | 22 +++++----- .../CMakeLists.txt | 26 ++++++----- 3 files changed, 50 insertions(+), 42 deletions(-) diff --git a/torchao/experimental/CMakeLists.txt b/torchao/experimental/CMakeLists.txt index 4dd02b2dd7..2222ea60b9 100644 --- a/torchao/experimental/CMakeLists.txt +++ b/torchao/experimental/CMakeLists.txt @@ -16,6 +16,7 @@ if (NOT CMAKE_BUILD_TYPE) endif() # Platform options +option(TORCHAO_BUILD_ATEN_OPS "Building torchao ops for ATen." ON) option(TORCHAO_BUILD_EXECUTORCH_OPS "Building torchao ops for ExecuTorch." OFF) option(TORCHAO_BUILD_MPS_OPS "Building torchao MPS ops" OFF) option(TORCHAO_BUILD_CPU_AARCH64 "Build torchao's CPU aarch64 kernels" OFF) @@ -32,7 +33,7 @@ if(NOT DEFINED TORCHAO_PARALLEL_BACKEND) endif() # Set default compiler options -add_compile_options("-Wall" "-Werror" "-Wno-deprecated") +add_compile_options("-Wall" "-Werror" "-Wno-deprecated" "-Wno-shorten-64-to-32") include(CMakePrintHelpers) message("TORCHAO_INCLUDE_DIRS: ${TORCHAO_INCLUDE_DIRS}") @@ -68,6 +69,7 @@ if(TORCHAO_BUILD_CPU_AARCH64) if(TORCHAO_ENABLE_ARM_NEON_DOT) message(STATUS "Building with ARM NEON dot product support") add_compile_definitions(TORCHAO_ENABLE_ARM_NEON_DOT) + add_compile_options("-march=armv8.4-a+dotprod") endif() if(TORCHAO_ENABLE_ARM_I8MM) @@ -89,26 +91,28 @@ add_subdirectory(ops/linear_8bit_act_xbit_weight) add_subdirectory(ops/embedding_xbit) # ATen ops lib -add_library(torchao_ops_aten SHARED) -target_link_libraries( - torchao_ops_aten PRIVATE - torchao_ops_linear_8bit_act_xbit_weight_aten - torchao_ops_embedding_xbit_aten -) - -# Add MPS support if enabled -if (TORCHAO_BUILD_MPS_OPS) - message(STATUS "Building with MPS support") - add_subdirectory(ops/mps) - target_link_libraries(torchao_ops_aten PRIVATE torchao_ops_mps_aten) -endif() +if (TORCHAO_BUILD_ATEN_OPS) + add_library(torchao_ops_aten SHARED) + target_link_libraries( + torchao_ops_aten PRIVATE + torchao_ops_linear_8bit_act_xbit_weight_aten + torchao_ops_embedding_xbit_aten + ) -# Install ATen targets -install( - TARGETS torchao_ops_aten - EXPORT _targets - DESTINATION lib -) + # Add MPS support if enabled + if (TORCHAO_BUILD_MPS_OPS) + message(STATUS "Building with MPS support") + add_subdirectory(ops/mps) + target_link_libraries(torchao_ops_aten PRIVATE torchao_ops_mps_aten) + endif() + + # Install ATen targets + install( + TARGETS torchao_ops_aten + EXPORT _targets + DESTINATION lib + ) +endif() # Build executorch lib if enabled if(TORCHAO_BUILD_EXECUTORCH_OPS) diff --git a/torchao/experimental/ops/embedding_xbit/CMakeLists.txt b/torchao/experimental/ops/embedding_xbit/CMakeLists.txt index d1ff1d3291..c681a44dc9 100644 --- a/torchao/experimental/ops/embedding_xbit/CMakeLists.txt +++ b/torchao/experimental/ops/embedding_xbit/CMakeLists.txt @@ -8,17 +8,19 @@ cmake_minimum_required(VERSION 3.19) include(${CMAKE_CURRENT_SOURCE_DIR}/../../Utils.cmake) -find_package(Torch REQUIRED) -add_library(torchao_ops_embedding_xbit_aten OBJECT - op_embedding_xbit_aten.cpp -) -target_link_torchao_parallel_backend(torchao_ops_embedding_xbit_aten "${TORCHAO_PARALLEL_BACKEND}") -if (TORCHAO_BUILD_CPU_AARCH64) - target_link_libraries(torchao_ops_embedding_xbit_aten PRIVATE torchao_kernels_aarch64) +if(TORCHAO_BUILD_ATEN_OPS) + find_package(Torch REQUIRED) + add_library(torchao_ops_embedding_xbit_aten OBJECT + op_embedding_xbit_aten.cpp + ) + target_link_torchao_parallel_backend(torchao_ops_embedding_xbit_aten "${TORCHAO_PARALLEL_BACKEND}") + if (TORCHAO_BUILD_CPU_AARCH64) + target_link_libraries(torchao_ops_embedding_xbit_aten PRIVATE torchao_kernels_aarch64) + endif() + target_include_directories(torchao_ops_embedding_xbit_aten PRIVATE "${TORCH_INCLUDE_DIRS}") + target_link_libraries(torchao_ops_embedding_xbit_aten PRIVATE "${TORCH_LIBRARIES}") + target_compile_definitions(torchao_ops_embedding_xbit_aten PRIVATE USE_ATEN=1) endif() -target_include_directories(torchao_ops_embedding_xbit_aten PRIVATE "${TORCH_INCLUDE_DIRS}") -target_link_libraries(torchao_ops_embedding_xbit_aten PRIVATE "${TORCH_LIBRARIES}") -target_compile_definitions(torchao_ops_embedding_xbit_aten PRIVATE USE_ATEN=1) if(TORCHAO_BUILD_EXECUTORCH_OPS) # ExecuTorch package is not required, but EXECUTORCH_INCLUDE_DIRS and EXECUTORCH_LIBRARIES must diff --git a/torchao/experimental/ops/linear_8bit_act_xbit_weight/CMakeLists.txt b/torchao/experimental/ops/linear_8bit_act_xbit_weight/CMakeLists.txt index 2e570c7622..4bc3259061 100644 --- a/torchao/experimental/ops/linear_8bit_act_xbit_weight/CMakeLists.txt +++ b/torchao/experimental/ops/linear_8bit_act_xbit_weight/CMakeLists.txt @@ -20,20 +20,22 @@ if (NOT TARGET cpuinfo) cpuinfo) endif() -find_package(Torch REQUIRED) -add_library(torchao_ops_linear_8bit_act_xbit_weight_aten OBJECT - linear_8bit_act_xbit_weight.cpp - op_linear_8bit_act_xbit_weight_aten.cpp -) -target_link_torchao_parallel_backend(torchao_ops_linear_8bit_act_xbit_weight_aten "${TORCHAO_PARALLEL_BACKEND}") +if (TORCHAO_BUILD_ATEN_OPS) + find_package(Torch REQUIRED) + add_library(torchao_ops_linear_8bit_act_xbit_weight_aten OBJECT + linear_8bit_act_xbit_weight.cpp + op_linear_8bit_act_xbit_weight_aten.cpp + ) + target_link_torchao_parallel_backend(torchao_ops_linear_8bit_act_xbit_weight_aten "${TORCHAO_PARALLEL_BACKEND}") -if(TORCHAO_BUILD_CPU_AARCH64) - target_link_libraries(torchao_ops_linear_8bit_act_xbit_weight_aten PRIVATE torchao_kernels_aarch64) + if(TORCHAO_BUILD_CPU_AARCH64) + target_link_libraries(torchao_ops_linear_8bit_act_xbit_weight_aten PRIVATE torchao_kernels_aarch64) + endif() + target_link_libraries(torchao_ops_linear_8bit_act_xbit_weight_aten PRIVATE cpuinfo) + target_include_directories(torchao_ops_linear_8bit_act_xbit_weight_aten PRIVATE "${TORCH_INCLUDE_DIRS}") + target_link_libraries(torchao_ops_linear_8bit_act_xbit_weight_aten PRIVATE "${TORCH_LIBRARIES}") + target_compile_definitions(torchao_ops_linear_8bit_act_xbit_weight_aten PRIVATE USE_ATEN=1) endif() -target_link_libraries(torchao_ops_linear_8bit_act_xbit_weight_aten PRIVATE cpuinfo) -target_include_directories(torchao_ops_linear_8bit_act_xbit_weight_aten PRIVATE "${TORCH_INCLUDE_DIRS}") -target_link_libraries(torchao_ops_linear_8bit_act_xbit_weight_aten PRIVATE "${TORCH_LIBRARIES}") -target_compile_definitions(torchao_ops_linear_8bit_act_xbit_weight_aten PRIVATE USE_ATEN=1) if(TORCHAO_BUILD_EXECUTORCH_OPS) # ExecuTorch package is not required, but EXECUTORCH_INCLUDE_DIRS and EXECUTORCH_LIBRARIES must From c4250a4bfa495a260ea009e23a9b8202bb93b52d Mon Sep 17 00:00:00 2001 From: Driss Guessous <32754868+drisspg@users.noreply.github.com> Date: Fri, 30 May 2025 11:49:30 -0700 Subject: [PATCH 067/165] Fixes MX formats build for blackwell (#2278) * Fixes MX formats build for blackwell * Adds missing line * Adds missing line --------- Co-authored-by: Syed Tousif Ahmed --- setup.py | 100 +++++++++++++----- third_party/cutlass | 2 +- torchao/__init__.py | 4 +- .../cuda/mx_kernels/mx_fp_cutlass_kernels.cu | 5 + 4 files changed, 83 insertions(+), 28 deletions(-) diff --git a/setup.py b/setup.py index ff4d7fffb5..62739342f0 100644 --- a/setup.py +++ b/setup.py @@ -424,6 +424,9 @@ def get_extensions(): use_cutlass = False cutlass_90a_sources = None + cutlass_100a_sources = None + build_for_sm90a = False + build_for_sm100a = False if use_cuda and not IS_WINDOWS: use_cutlass = True cutlass_dir = os.path.join(third_party_path, "cutlass") @@ -453,32 +456,47 @@ def get_extensions(): ) cuda_arch_flags = _get_cuda_arch_flags() - build_for_sm90 = "-gencode=arch=compute_90,code=sm_90" in cuda_arch_flags build_for_sm90a = "-gencode=arch=compute_90a,code=sm_90a" in cuda_arch_flags - if build_for_sm90 and not build_for_sm90a: - cutlass_90a_sources = [ + build_for_sm100a = "-gencode=arch=compute_100a,code=sm_100a" in cuda_arch_flags + # Define sm90a sources + cutlass_90a_sources = [ + os.path.join( + extensions_cuda_dir, + "rowwise_scaled_linear_sparse_cutlass", + "rowwise_scaled_linear_sparse_cutlass_f8f8.cu", + ), + os.path.join( + extensions_cuda_dir, + "to_sparse_semi_structured_cutlass_sm9x", + "to_sparse_semi_structured_cutlass_sm9x_f8.cu", + ), + os.path.join(extensions_cuda_dir, "activation24", "sparsify24.cu"), + os.path.join(extensions_cuda_dir, "activation24", "sparse_gemm.cu"), + ] + for dtypes in ["e4m3e4m3", "e4m3e5m2", "e5m2e4m3", "e5m2e5m2"]: + cutlass_90a_sources.append( os.path.join( extensions_cuda_dir, "rowwise_scaled_linear_sparse_cutlass", - "rowwise_scaled_linear_sparse_cutlass_f8f8.cu", - ), - os.path.join( - extensions_cuda_dir, - "to_sparse_semi_structured_cutlass_sm9x", - "to_sparse_semi_structured_cutlass_sm9x_f8.cu", - ), - os.path.join(extensions_cuda_dir, "activation24", "sparsify24.cu"), - os.path.join(extensions_cuda_dir, "activation24", "sparse_gemm.cu"), - ] - for dtypes in ["e4m3e4m3", "e4m3e5m2", "e5m2e4m3", "e5m2e5m2"]: - cutlass_90a_sources.append( - os.path.join( - extensions_cuda_dir, - "rowwise_scaled_linear_sparse_cutlass", - "rowwise_scaled_linear_sparse_cutlass_" + dtypes + ".cu", - ) + "rowwise_scaled_linear_sparse_cutlass_" + dtypes + ".cu", ) - sources = [s for s in sources if s not in cutlass_90a_sources] + ) + # Always remove sm90a sources from main sources + sources = [s for s in sources if s not in cutlass_90a_sources] + + # Always compile mx_fp_cutlass_kernels.cu ONLY with sm100a architecture + cutlass_100a_sources = [ + os.path.join( + extensions_cuda_dir, + "mx_kernels", + "mx_fp_cutlass_kernels.cu", + ), + ] + # Remove from main sources to prevent compilation with other architectures + sources = [ + s for s in sources if os.path.basename(s) != "mx_fp_cutlass_kernels.cu" + ] + else: # Remove CUTLASS-based kernels from the sources list. An # assumption is that these files will have "cutlass" in its @@ -492,6 +510,11 @@ def get_extensions(): ext_modules = [] if len(sources) > 0: + # Double-check to ensure mx_fp_cutlass_kernels.cu is not in sources + sources = [ + s for s in sources if os.path.basename(s) != "mx_fp_cutlass_kernels.cu" + ] + ext_modules.append( extension( "torchao._C", @@ -502,14 +525,20 @@ def get_extensions(): ) ) - if cutlass_90a_sources is not None and len(cutlass_90a_sources) > 0: + # Only build the cutlass_90a extension if sm90a is in the architecture flags + if ( + cutlass_90a_sources is not None + and len(cutlass_90a_sources) > 0 + and build_for_sm90a + ): cutlass_90a_extra_compile_args = copy.deepcopy(extra_compile_args) - cutlass_90a_extra_compile_args["nvcc"].extend( - cuda_arch_flags + ["-gencode=arch=compute_90a,code=sm_90a"] + # Only use sm90a architecture for these sources, ignoring other flags + cutlass_90a_extra_compile_args["nvcc"].append( + "-gencode=arch=compute_90a,code=sm_90a" ) ext_modules.append( extension( - "torchao._C", + "torchao._C_cutlass_90a", cutlass_90a_sources, py_limited_api=True, extra_compile_args=cutlass_90a_extra_compile_args, @@ -517,6 +546,27 @@ def get_extensions(): ) ) + # Only build the cutlass_100a extension if sm100a is in the architecture flags + if ( + cutlass_100a_sources is not None + and len(cutlass_100a_sources) > 0 + and build_for_sm100a + ): + cutlass_100a_extra_compile_args = copy.deepcopy(extra_compile_args) + # Only use sm100a architecture for these sources, ignoring cuda_arch_flags + cutlass_100a_extra_compile_args["nvcc"].append( + "-gencode=arch=compute_100a,code=sm_100a" + ) + ext_modules.append( + extension( + "torchao._C_cutlass_100a", + cutlass_100a_sources, + py_limited_api=True, + extra_compile_args=cutlass_100a_extra_compile_args, + extra_link_args=extra_link_args, + ) + ) + # Build CMakeLists from /torchao/experimental - additional options become available : TORCHAO_BUILD_CPU_AARCH64, TORCHAO_BUILD_KLEIDIAI, TORCHAO_BUILD_MPS_OPS, TORCHAO_PARALLEL_BACKEND if build_macos_arm_auto or os.getenv("BUILD_TORCHAO_EXPERIMENTAL") == "1": build_options = BuildOptions() diff --git a/third_party/cutlass b/third_party/cutlass index afa1772203..ad7b2f5e84 160000 --- a/third_party/cutlass +++ b/third_party/cutlass @@ -1 +1 @@ -Subproject commit afa1772203677c5118fcd82537a9c8fefbcc7008 +Subproject commit ad7b2f5e84fcfa124cb02b91d5bd26d238c0459e diff --git a/torchao/__init__.py b/torchao/__init__.py index 730cd326fe..e6e291309f 100644 --- a/torchao/__init__.py +++ b/torchao/__init__.py @@ -25,8 +25,8 @@ so_files = list(Path(__file__).parent.glob("_C*.so")) if len(so_files) > 0: - assert len(so_files) == 1, f"Expected one _C*.so file, found {len(so_files)}" - torch.ops.load_library(str(so_files[0])) + for file in so_files: + torch.ops.load_library(str(file)) from . import ops # The following library contains CPU kernels from torchao/experimental diff --git a/torchao/csrc/cuda/mx_kernels/mx_fp_cutlass_kernels.cu b/torchao/csrc/cuda/mx_kernels/mx_fp_cutlass_kernels.cu index 7c34faf56a..15b657c370 100644 --- a/torchao/csrc/cuda/mx_kernels/mx_fp_cutlass_kernels.cu +++ b/torchao/csrc/cuda/mx_kernels/mx_fp_cutlass_kernels.cu @@ -3,6 +3,11 @@ // // This source code is licensed under the BSD 3-Clause license found in the // LICENSE file in the root directory of this source tree. + +// Ensure this file is only compiled with sm100a architecture +#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ < 1000) +#error "This file must be compiled with compute capability 10.0a or higher (Blackwell architecture)" +#endif #include #include From e51ffd90d5aefb2c0ea544c7cd809606572de4f3 Mon Sep 17 00:00:00 2001 From: cccclai Date: Fri, 30 May 2025 17:24:59 -0700 Subject: [PATCH 068/165] Patch the _is_conv_node function Differential Revision: D75323215 Pull Request resolved: https://github.com/pytorch/ao/pull/2257 --- test/quantization/pt2e/test_quantize_pt2e.py | 118 +++++++++++++++++++ torchao/quantization/pt2e/utils.py | 3 + 2 files changed, 121 insertions(+) diff --git a/test/quantization/pt2e/test_quantize_pt2e.py b/test/quantization/pt2e/test_quantize_pt2e.py index 75701c55ca..730969ba9c 100644 --- a/test/quantization/pt2e/test_quantize_pt2e.py +++ b/test/quantization/pt2e/test_quantize_pt2e.py @@ -2571,6 +2571,124 @@ def forward(self, x): node_list, ) + def test_conv_padding_bn_relu(self): + class BackendAQuantizer(Quantizer): + def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule: + act_qspec = QuantizationSpec( + dtype=torch.uint8, + quant_min=0, + quant_max=255, + qscheme=torch.per_tensor_affine, + is_dynamic=False, + observer_or_fake_quant_ctr=observer.default_observer, + ) + weight_qspec = QuantizationSpec( + dtype=torch.int8, + quant_min=-128, + quant_max=127, + qscheme=torch.per_tensor_affine, + is_dynamic=False, + observer_or_fake_quant_ctr=observer.default_weight_observer, + ) + bias_qspec = QuantizationSpec( + dtype=torch.float32, + is_dynamic=False, + observer_or_fake_quant_ctr=observer.PlaceholderObserver, + ) + + for n in model.graph.nodes: + if ( + n.op != "call_function" + or n.target != torch.ops.aten.relu.default + ): + continue + relu_node = n + n = n.args[0] + + # Check for any of the conv operations + conv_ops = [ + torch.ops.aten.conv1d.padding, + torch.ops.aten.conv2d.padding, + torch.ops.aten.conv3d.padding, + ] + if n.op != "call_function" or n.target not in conv_ops: + continue + + conv_node = n + input_act = conv_node.args[0] + weight = conv_node.args[1] + bias = conv_node.args[2] + conv_node.meta["quantization_annotation"] = QuantizationAnnotation( + input_qspec_map={ + input_act: act_qspec, + weight: weight_qspec, + bias: bias_qspec, + }, + _annotated=True, + ) + relu_node.meta["quantization_annotation"] = QuantizationAnnotation( + output_qspec=act_qspec, + _annotated=True, + ) + + def validate(self, model: torch.fx.GraphModule) -> None: + pass + + # Test cases for Conv1d, Conv2d, Conv3d + test_cases = [ + { + "conv_type": torch.nn.Conv1d, + "bn_type": torch.nn.BatchNorm1d, + "example_input": (torch.randn(1, 3, 5),), + "conv_op": torch.ops.aten.conv1d.padding, + }, + { + "conv_type": torch.nn.Conv2d, + "bn_type": torch.nn.BatchNorm2d, + "example_input": (torch.randn(1, 3, 5, 5),), + "conv_op": torch.ops.aten.conv2d.padding, + }, + { + "conv_type": torch.nn.Conv3d, + "bn_type": torch.nn.BatchNorm3d, + "example_input": (torch.randn(1, 3, 5, 5, 5),), + "conv_op": torch.ops.aten.conv3d.padding, + }, + ] + + for test_case in test_cases: + with self.subTest(conv_type=test_case["conv_type"].__name__): + + class M(torch.nn.Module): + def __init__(self): + super().__init__() + self.conv = test_case["conv_type"](3, 3, 3, padding="same") + self.bn = test_case["bn_type"](3) + + def forward(self, x): + return torch.nn.functional.relu(self.bn(self.conv(x))) + + node_occurrence = { + torch.ops.quantized_decomposed.quantize_per_tensor.default: 2, + torch.ops.quantized_decomposed.dequantize_per_tensor.default: 3, + } + node_list = [ + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + torch.ops.quantized_decomposed.dequantize_per_tensor.default, + test_case["conv_op"], + torch.ops.aten.relu.default, + torch.ops.quantized_decomposed.quantize_per_tensor.default, + ] + + model = M().eval() + self._test_quantizer( + model, + test_case["example_input"], + BackendAQuantizer(), + node_occurrence, + node_list, + ) + def test_multi_users_without_output_observer(self): """ Test the case in which a node is used by multiple users, diff --git a/torchao/quantization/pt2e/utils.py b/torchao/quantization/pt2e/utils.py index ad5c0ae179..dc5f802fb8 100644 --- a/torchao/quantization/pt2e/utils.py +++ b/torchao/quantization/pt2e/utils.py @@ -625,8 +625,11 @@ def _is_conv_node(n: Node): """ return n.op == "call_function" and n.target in [ torch.ops.aten.conv1d.default, + torch.ops.aten.conv1d.padding, torch.ops.aten.conv2d.default, + torch.ops.aten.conv2d.padding, torch.ops.aten.conv3d.default, + torch.ops.aten.conv3d.padding, ] From 9d5b9ad4780d2db974b6679004a8bcf6e15b53c0 Mon Sep 17 00:00:00 2001 From: Jerry Zhang Date: Fri, 30 May 2025 17:55:54 -0700 Subject: [PATCH 069/165] Add back AOPerModuleConfig for BC (#2282) Summary: att, just temporary so that integrations keep working Test Plan: tests in other libs Reviewers: Subscribers: Tasks: Tags: --- test/integration/test_vllm.py | 6 +++--- torchao/quantization/__init__.py | 4 ++++ 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/test/integration/test_vllm.py b/test/integration/test_vllm.py index b82f4f9794..7bb9a6defa 100644 --- a/test/integration/test_vllm.py +++ b/test/integration/test_vllm.py @@ -17,10 +17,10 @@ import torch from packaging import version -from torchao.utils import TORCH_VERSION_AT_LEAST_2_7 +from torchao.utils import TORCH_VERSION_AT_LEAST_2_8 -if not TORCH_VERSION_AT_LEAST_2_7: - pytest.skip("Requires PyTorch 2.7 or higher", allow_module_level=True) +if not TORCH_VERSION_AT_LEAST_2_8: + pytest.skip("Requires PyTorch 2.8 or higher", allow_module_level=True) VLLM_AVAILABLE = importlib.util.find_spec("vllm") is not None diff --git a/torchao/quantization/__init__.py b/torchao/quantization/__init__.py index 73ccd2e0ff..15736acc3b 100644 --- a/torchao/quantization/__init__.py +++ b/torchao/quantization/__init__.py @@ -108,6 +108,9 @@ ) from .weight_only import WeightOnlyInt8QuantLinear +# TODO: remove after migration of APIs are done +AOPerModuleConfig = ModuleFqnToConfig + __all__ = [ # top level API - auto "autoquant", @@ -148,6 +151,7 @@ "IntxWeightOnlyConfig", "FPXWeightOnlyConfig", "GemliteUIntXWeightOnlyConfig", + "AOPerModuleConfig", "ModuleFqnToConfig", "FbgemmConfig", # smooth quant - subject to change From ce27731a7a9de33a7b578e32326a8275e47a365a Mon Sep 17 00:00:00 2001 From: Driss Guessous <32754868+drisspg@users.noreply.github.com> Date: Fri, 30 May 2025 21:48:36 -0700 Subject: [PATCH 070/165] Fix Bug in MX Builds (#2284) stack-info: PR: https://github.com/pytorch/ao/pull/2284, branch: drisspg/stack/62 --- setup.py | 53 +++++++++++++++++-- .../cuda/mx_kernels/mx_fp_cutlass_kernels.cu | 4 -- 2 files changed, 50 insertions(+), 7 deletions(-) diff --git a/setup.py b/setup.py index 62739342f0..51c283c858 100644 --- a/setup.py +++ b/setup.py @@ -223,6 +223,55 @@ def not_exists_or_empty(folder): ) +def get_cuda_version_from_nvcc(): + """Get CUDA version from nvcc if available.""" + try: + result = subprocess.check_output( + ["nvcc", "--version"], stderr=subprocess.STDOUT + ) + output = result.decode("utf-8") + # Look for version line like "release 12.6" + for line in output.split("\n"): + if "release" in line.lower(): + parts = line.split() + for i, part in enumerate(parts): + if part.lower() == "release" and i + 1 < len(parts): + return parts[i + 1].rstrip(",") + + except: + return None + + +def get_cutlass_build_flags(): + """Determine which CUTLASS kernels to build based on CUDA version. + SM90a: CUDA 12.6+, SM100a: CUDA 12.8+ + """ + # Try nvcc then torch version + cuda_version = get_cuda_version_from_nvcc() or torch.version.cuda + + try: + if not cuda_version: + raise ValueError("No CUDA version found") + + major, minor = map(int, cuda_version.split(".")[:2]) + build_sm90a = major > 12 or (major == 12 and minor >= 6) + build_sm100a = major > 12 or (major == 12 and minor >= 8) + + if build_sm90a: + print(f"CUDA {cuda_version}: Enabling SM90a CUTLASS kernels") + if build_sm100a: + print(f"CUDA {cuda_version}: Enabling SM100a CUTLASS kernels") + + return build_sm90a, build_sm100a + except: + # Fallback to architecture flags + cuda_arch_flags = _get_cuda_arch_flags() + return ( + "-gencode=arch=compute_90a,code=sm_90a" in cuda_arch_flags, + "-gencode=arch=compute_100a,code=sm_100a" in cuda_arch_flags, + ) + + # BuildExtension is a subclass of from setuptools.command.build_ext.build_ext class TorchAOBuildExt(BuildExtension): def __init__(self, *args, **kwargs) -> None: @@ -455,9 +504,7 @@ def get_extensions(): ] ) - cuda_arch_flags = _get_cuda_arch_flags() - build_for_sm90a = "-gencode=arch=compute_90a,code=sm_90a" in cuda_arch_flags - build_for_sm100a = "-gencode=arch=compute_100a,code=sm_100a" in cuda_arch_flags + build_for_sm90a, build_for_sm100a = get_cutlass_build_flags() # Define sm90a sources cutlass_90a_sources = [ os.path.join( diff --git a/torchao/csrc/cuda/mx_kernels/mx_fp_cutlass_kernels.cu b/torchao/csrc/cuda/mx_kernels/mx_fp_cutlass_kernels.cu index 15b657c370..7167cea8fb 100644 --- a/torchao/csrc/cuda/mx_kernels/mx_fp_cutlass_kernels.cu +++ b/torchao/csrc/cuda/mx_kernels/mx_fp_cutlass_kernels.cu @@ -4,10 +4,6 @@ // This source code is licensed under the BSD 3-Clause license found in the // LICENSE file in the root directory of this source tree. -// Ensure this file is only compiled with sm100a architecture -#if !defined(__CUDA_ARCH__) || (__CUDA_ARCH__ < 1000) -#error "This file must be compiled with compute capability 10.0a or higher (Blackwell architecture)" -#endif #include #include From ca17609a04379fe402c1cfc89efdfde3d7170108 Mon Sep 17 00:00:00 2001 From: Emmanuel Ferdman Date: Sun, 1 Jun 2025 08:39:06 +0300 Subject: [PATCH 071/165] Fix benchmark_low_bit_adam.py reference (#2287) Signed-off-by: Emmanuel Ferdman --- torchao/optim/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/torchao/optim/README.md b/torchao/optim/README.md index 4349f3d0ad..922dd428d2 100644 --- a/torchao/optim/README.md +++ b/torchao/optim/README.md @@ -32,7 +32,7 @@ NOTE: ## Benchmarks -Fine-tune [timm](https://github.com/huggingface/pytorch-image-models)'s [ViT-H](https://huggingface.co/timm/vit_huge_patch14_224.orig_in21k) (630M params) on [resisc45](https://huggingface.co/datasets/timm/resisc45) dataset. PyTorch 2.4, BF16 AMP, compiled model, 1 epoch, batch size 8, cosine LR scheduler, 4070Ti SUPER, fixed random seed. Benchmark script is available at [benchmarks/benchmark_low_bit_adam.py](../../../benchmarks/benchmark_low_bit_adam.py). +Fine-tune [timm](https://github.com/huggingface/pytorch-image-models)'s [ViT-H](https://huggingface.co/timm/vit_huge_patch14_224.orig_in21k) (630M params) on [resisc45](https://huggingface.co/datasets/timm/resisc45) dataset. PyTorch 2.4, BF16 AMP, compiled model, 1 epoch, batch size 8, cosine LR scheduler, 4070Ti SUPER, fixed random seed. Benchmark script is available at [benchmarks/benchmark_low_bit_adam.py](../../benchmarks/benchmark_low_bit_adam.py). AdamW impl | Peak memory allocated (GB) | imgs/s | accuracy ----------------|----------------------------|--------|---------- From 8366465ebd8b017c89ae4a2fcddad744cbb9c405 Mon Sep 17 00:00:00 2001 From: Driss Guessous <32754868+drisspg@users.noreply.github.com> Date: Sun, 1 Jun 2025 20:21:46 -0700 Subject: [PATCH 072/165] Remove Constraint for sm89 hardware (#2281) stack-info: PR: https://github.com/pytorch/ao/pull/2281, branch: drisspg/stack/61 --- .github/workflows/float8_test.yml | 1 + test/dtypes/test_affine_quantized_float.py | 51 ++++++++++++++++++---- torchao/float8/inference.py | 17 +++----- torchao/utils.py | 6 +++ 4 files changed, 57 insertions(+), 18 deletions(-) diff --git a/.github/workflows/float8_test.yml b/.github/workflows/float8_test.yml index 0b17a23bf7..91083df0bf 100644 --- a/.github/workflows/float8_test.yml +++ b/.github/workflows/float8_test.yml @@ -55,3 +55,4 @@ jobs: pip install . pytest test/float8 --verbose -s pytest test/integration --verbose -s + pytest test/dtypes/test_affine_quantized_float.py --verbose -s diff --git a/test/dtypes/test_affine_quantized_float.py b/test/dtypes/test_affine_quantized_float.py index 408e6e6ce0..1ffd62ecbf 100644 --- a/test/dtypes/test_affine_quantized_float.py +++ b/test/dtypes/test_affine_quantized_float.py @@ -50,6 +50,7 @@ from torchao.utils import ( is_sm_at_least_89, is_sm_at_least_90, + is_sm_version, ) random.seed(0) @@ -76,9 +77,7 @@ class TestAffineQuantizedFloat8Compile(InductorTestCase): @common_utils.parametrize("dtype", [torch.bfloat16, torch.float32]) @common_utils.parametrize("mode", ["dynamic", "weight-only", "static"]) @common_utils.parametrize("compile", [True, False]) - @common_utils.parametrize( - "granularity", [PerTensor(), PerRow()] if is_sm_at_least_90() else [PerTensor()] - ) + @common_utils.parametrize("granularity", [PerTensor(), PerRow()]) # Inputs are (M,..), K, N @common_utils.parametrize( "sizes", @@ -420,9 +419,7 @@ def test_dequantize_affine_float8_scale_broadcasting(self): @unittest.skipIf( not is_sm_at_least_89(), "Requires GPU with compute capability >= 8.9" ) - @common_utils.parametrize( - "granularity", [PerTensor(), PerRow()] if is_sm_at_least_90() else [PerTensor()] - ) + @common_utils.parametrize("granularity", [PerTensor(), PerRow()]) def test_float8_tensor_slicing_basic(self, granularity): """Test basic slicing operations on Float8 tensors""" device = "cuda" @@ -555,8 +552,10 @@ def test_float8_tensor_slicing_edge_cases(self): @unittest.skipIf( not is_sm_at_least_89(), "Requires GPU with compute capability >= 8.9" ) - @common_utils.parametrize( - "granularity", [PerTensor(), PerRow()] if is_sm_at_least_90() else [PerTensor()] + @common_utils.parametrize("granularity", [PerTensor(), PerRow()]) + @unittest.skipIf( + is_sm_version(8, 9), + "TODO: AssertionError: tensor(-2.1562, device='cuda:0', dtype=torch.bfloat16) not greater than 15", ) def test_float8_tensor_slicing_functional_correctness(self, granularity): """Test that sliced tensors produce correct results in computations""" @@ -579,6 +578,42 @@ def test_float8_tensor_slicing_functional_correctness(self, granularity): ref_weight_slice = ref_model.weight[0:16, 0:32] quant_weight_slice = quant_model.weight[0:16, 0:32] + # Verify that the sliced weights maintain Float8 properties + self.assertTrue(hasattr(quant_weight_slice, "original_weight_tensor")) + sliced_impl = quant_weight_slice.original_weight_tensor.tensor_impl + self.assertTrue(isinstance(sliced_impl, Float8AQTTensorImpl)) + + # Verify sliced weight shapes + self.assertEqual(sliced_impl.float8_data.shape, (16, 32)) + + # Get original quantized weight implementation for scale comparison + original_quant_impl = quant_model.weight.original_weight_tensor.tensor_impl + + # Verify scale properties based on granularity + if isinstance(granularity, PerTensor): + # Per-tensor: scale should be identical to original (scalar) + self.assertEqual(sliced_impl.scale.numel(), 1) + self.assertTrue(torch.equal(sliced_impl.scale, original_quant_impl.scale)) + else: # PerRow + # Per-row: scale should be sliced to match the selected rows (0:16) + expected_scale_shape = (16, 1) + self.assertEqual(sliced_impl.scale.shape, expected_scale_shape) + # Verify the scale values are the correct slice from the original + self.assertTrue( + torch.equal(sliced_impl.scale, original_quant_impl.scale[0:16]) + ) + + # Verify that sliced quantized data matches the correct slice from original + original_float8_data_slice = original_quant_impl.float8_data[0:16, 0:32] + self.assertTrue( + torch.equal(sliced_impl.float8_data, original_float8_data_slice) + ) + + # Verify that sliced weights can be converted back to float with correct values + sliced_float_weight = quant_weight_slice.to(dtype) + self.assertEqual(sliced_float_weight.shape, (16, 32)) + self.assertEqual(sliced_float_weight.dtype, dtype) + input_slice = input_tensor[:, 0:32] # (8, 32) to match sliced weight # Compute with sliced weights diff --git a/torchao/float8/inference.py b/torchao/float8/inference.py index 00c905f3d8..d6e650aa6e 100644 --- a/torchao/float8/inference.py +++ b/torchao/float8/inference.py @@ -19,7 +19,6 @@ from torchao.utils import ( is_MI300, is_sm_at_least_89, - is_sm_at_least_90, ) Tensor = torch.Tensor @@ -168,13 +167,11 @@ def _check_hardware_support( ValueError: If invalid granularity type is provided """ for _granularity in granularities: - if isinstance(_granularity, PerTensor): - assert is_sm_at_least_89() or is_MI300(), ( - "PerTensor quantization only works for CUDA>=8.9 and MI300+" - ) - elif isinstance(_granularity, PerRow): - assert is_sm_at_least_90() or is_MI300(), ( - "PerRow quantization only works for CUDA>=9.0 and MI300+" + if not isinstance(_granularity, (PerTensor, PerRow)): + raise ValueError( + f"Invalid granularity type: {_granularity}, only PerTensor or PerRow are supported." ) - else: - raise ValueError(f"Invalid granularity type: {_granularity}") + + assert is_sm_at_least_89() or is_MI300(), ( + "Float8 dynamic quantization requires CUDA compute capability ≥8.9 or MI300+." + ) diff --git a/torchao/utils.py b/torchao/utils.py index 1fa395cb8a..416d23d785 100644 --- a/torchao/utils.py +++ b/torchao/utils.py @@ -655,6 +655,12 @@ def is_Navi4(): return False +def is_sm_version(major: int, minor: int) -> bool: + """Check if the CUDA version is exactly major.minor""" + is_cuda = torch.cuda.is_available() and torch.version.cuda + return torch.cuda.get_device_capability() == (major, minor) if is_cuda else False + + def is_sm_at_least_89(): return ( torch.cuda.is_available() From 31db5a11fabb31902363ec708c19d9421f86c476 Mon Sep 17 00:00:00 2001 From: Daniel Vega-Myhre Date: Mon, 2 Jun 2025 13:29:37 -0700 Subject: [PATCH 073/165] [float8 training] remove duplicate override for view (#2269) * remove duplicate override for view * raise exception if op is already implemented --- torchao/float8/float8_ops.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/torchao/float8/float8_ops.py b/torchao/float8/float8_ops.py index f7ccad4a86..4071d83e4f 100644 --- a/torchao/float8/float8_ops.py +++ b/torchao/float8/float8_ops.py @@ -92,6 +92,10 @@ def implements(aten_ops): def decorator(func): for op in aten_ops: + if op in FLOAT8_OPS_TABLE: + raise RuntimeError( + f"Float8 op {op} is already registered to {FLOAT8_OPS_TABLE[op].__name__}" + ) FLOAT8_OPS_TABLE[op] = func return func @@ -100,7 +104,6 @@ def decorator(func): @implements( [ - aten.view.default, aten._unsafe_view.default, aten.as_strided.default, aten.clone.default, From f0f1f6ce470d2854eb8fafc5dda3b4ffd80f1fe5 Mon Sep 17 00:00:00 2001 From: HDCharles <39544797+HDCharles@users.noreply.github.com> Date: Mon, 2 Jun 2025 18:33:41 -0400 Subject: [PATCH 074/165] GPTQ updates (#2235) * GPTQ updates Summary: 1) reorganized GPTQ a) got rid of old GPTQ and renamed GPTQ_MT to GPTQ b) moved new GPTQ to prototype c) moved quantized linear modules in GPTQ.py to linear_quant_modules.py 2) removed dependence on lm_eval for input_recorder a) created new input recorder that doesn't depend on lm_eval b) made lm_eval input recorder depend on new generic input_recorder c) made TransformerEvalWrapper the base class and made d) updated apis generally to work with new input recorder LMEvalInputRecorder inherit from it instead of vice-versa 3) reorganized GPTQ tests a) moved tests from test_quant_api.py to test_gptq.py b) added new test that can be run in CI that doesn't depend on lm_eval/llama weights c) got rid of test_gptq_mt.py 4) added new documentation for lm_eval 5) GPTQ improvements a) reimplemented faster quant b) tested compilation of hessian calculation and parts of faster quant, generally they were slower. c) moved helper functions out of the class. They're largely generic and this is less cluttered. d) some improvements to the duplication checking and copying to be faster when possible e) fixed some bugs due to this not being in CI and things changing for int4wo tensor subclass. Test Plan: 1) `python test_gptq.py` note: the skipped test test_gptq_quantizer_int4_weight_only also ran. 2) I verified that all activation match between old GPTQ and current GPTQ 3) ```shell export CHECKPOINT_PATH=../../../checkpoints # path to checkpoints folder export MODEL_REPO=meta-llama/Llama-2-7b-chat-hf python eval.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth python eval.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --quantization int4wo-64 python eval.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --quantization int4wo-gptq-64 --calibration_limit 10 export MODEL_REPO=meta-llama/Meta-Llama-3-8B python eval.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth python eval.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --quantization int4wo-64 python eval.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --quantization int4wo-gptq-64 --calibration_limit 10 ``` see README.md for results but they show GPTQ is working Reviewers: Subscribers: Tasks: Tags: * checking if this fixes the tests Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * trying to fix the adam stuff now Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * fix wanda error Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * fix adam attempt Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * fix CI Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: * figured out issue i think Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: --- test/quantization/test_gptq.py | 206 +++ test/quantization/test_gptq_mt.py | 348 ----- test/quantization/test_qat.py | 9 +- test/quantization/test_quant_api.py | 225 +-- torchao/_models/_eval.py | 297 +--- torchao/_models/llama/eval.py | 17 +- torchao/_models/llama/generate.py | 12 +- torchao/_models/llama/model.py | 5 +- torchao/quantization/GPTQ.py | 1314 ----------------- .../quantization/{GPTQ_MT.py => GPTQ/GPTQ.py} | 905 +++++++----- torchao/quantization/GPTQ/README.md | 74 + torchao/quantization/GPTQ/__init__.py | 21 + torchao/quantization/README.md | 4 + torchao/quantization/__init__.py | 17 +- torchao/quantization/linear_quant_modules.py | 629 ++++++++ torchao/quantization/qat/linear.py | 4 +- torchao/quantization/quant_api.py | 8 +- 17 files changed, 1609 insertions(+), 2486 deletions(-) create mode 100644 test/quantization/test_gptq.py delete mode 100644 test/quantization/test_gptq_mt.py delete mode 100644 torchao/quantization/GPTQ.py rename torchao/quantization/{GPTQ_MT.py => GPTQ/GPTQ.py} (51%) create mode 100644 torchao/quantization/GPTQ/README.md create mode 100644 torchao/quantization/GPTQ/__init__.py create mode 100644 torchao/quantization/linear_quant_modules.py diff --git a/test/quantization/test_gptq.py b/test/quantization/test_gptq.py new file mode 100644 index 0000000000..98760f8cf6 --- /dev/null +++ b/test/quantization/test_gptq.py @@ -0,0 +1,206 @@ +import unittest +from pathlib import Path + +import torch +from torch.testing._internal.common_utils import TestCase + +from torchao._models.llama.model import ( + ModelArgs, + Transformer, + prepare_inputs_for_model, +) +from torchao._models.llama.tokenizer import get_tokenizer +from torchao.quantization import Int4WeightOnlyConfig, quantize_ +from torchao.quantization.utils import compute_error +from torchao.utils import ( + TORCH_VERSION_AT_LEAST_2_4, +) + +torch.manual_seed(0) + + +class TestGPTQ(TestCase): + @unittest.skip("skipping until we get checkpoints for gpt-fast") + @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") + def test_gptq_quantizer_int4_weight_only(self): + from torchao._models._eval import ( + LMEvalInputRecorder, + TransformerEvalWrapper, + ) + from torchao.quantization.GPTQ import Int4WeightOnlyGPTQQuantizer + + precision = torch.bfloat16 + device = "cuda" + checkpoint_path = Path( + "../../checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth" + ) + model = Transformer.from_name(checkpoint_path.parent.name) + checkpoint = torch.load(str(checkpoint_path), mmap=True, weights_only=True) + model.load_state_dict(checkpoint, assign=True) + model = model.to(dtype=precision, device="cpu") + model.eval() + + tokenizer_path = checkpoint_path.parent / "tokenizer.model" + assert tokenizer_path.is_file(), tokenizer_path + tokenizer = get_tokenizer( # pyre-ignore[28] + tokenizer_path, + "Llama-2-7b-chat-hf", + ) + groupsize = 64 + blocksize = 128 + percdamp = 0.01 + calibration_tasks = ["wikitext"] + calibration_limit = 1 + calibration_seq_length = 100 + input_prep_func = prepare_inputs_for_model + pad_calibration_inputs = False + inputs = ( + LMEvalInputRecorder( + tokenizer, + calibration_seq_length, + input_prep_func, + model.config.vocab_size, + pad_calibration_inputs, + device="cpu", + ) + .record_inputs( + calibration_tasks, + calibration_limit, + ) + .get_recorded_inputs() + ) + + quantizer = Int4WeightOnlyGPTQQuantizer( + groupsize, + blocksize, + percdamp, + ) + model.setup_caches(max_batch_size=1, max_seq_length=calibration_seq_length) + + model = quantizer.quantize(model, *inputs).cuda() + + model.reset_caches() + with torch.device("cuda"): + model.setup_caches(max_batch_size=1, max_seq_length=model.config.block_size) + + limit = 1 + result = TransformerEvalWrapper( + model.cuda(), + tokenizer, + model.config.block_size, + prepare_inputs_for_model, + device, + ).run_eval( + ["wikitext"], + limit, + ) + + assert result["results"]["wikitext"]["word_perplexity,none"] < 7.77, ( + f"accuracy regressed from 7.76 to {result['results']['wikitext']['word_perplexity,none']}" + ) + + +class TestMultiTensorFlow(TestCase): + @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_4, "Test only enabled for 2.4+") + @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") + def test_multitensor_add_tensors(self): + from torchao.quantization.GPTQ import MultiTensor + + tensor1 = torch.randn(3, 3) + tensor2 = torch.randn(3, 3) + mt = MultiTensor(tensor1) + mt.add_tensors(tensor2) + self.assertEqual(mt.count, 2) + self.assertTrue(torch.equal(mt.values[0], tensor1)) + self.assertTrue(torch.equal(mt.values[1], tensor2)) + + @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_4, "Test only enabled for 2.4+") + @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") + def test_multitensor_pad_unpad(self): + from torchao.quantization.GPTQ import MultiTensor + + tensor1 = torch.randn(3, 3) + mt = MultiTensor(tensor1) + mt.pad_to_length(3) + self.assertEqual(mt.count, 3) + mt.unpad() + self.assertEqual(mt.count, 1) + + @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_4, "Test only enabled for 2.4+") + @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") + def test_multitensor_inplace_operation(self): + from torchao.quantization.GPTQ import MultiTensor + + tensor1 = torch.ones(3, 3) + mt = MultiTensor(tensor1) + mt += 1 # In-place addition + self.assertTrue(torch.equal(mt.values[0], torch.full((3, 3), 2))) + + +class TestMultiTensorInputRecorder(TestCase): + @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") + def test_multitensor_input_recorder(self): + from torchao.quantization.GPTQ import MultiTensor, MultiTensorInputRecorder + + input_recorder = MultiTensorInputRecorder() + in1 = ([1], torch.randn(3, 3), (1, "dog", torch.randn(3, 3)), torch.float) + in2 = ([1], torch.randn(3, 3), (1, "dog", torch.randn(3, 3)), torch.float) + + input_recorder(*in1) + input_recorder(*in2) + + MT_input = input_recorder.get_recorded_inputs() + + self.assertEqual(MT_input[0], [1]) + self.assertTrue(isinstance(MT_input[1], MultiTensor)) + self.assertTrue(isinstance(MT_input[2], tuple)) + self.assertEqual(MT_input[2][0], 1) + self.assertEqual(MT_input[2][1], "dog") + self.assertTrue(isinstance(MT_input[2][2], MultiTensor)) + self.assertEqual(MT_input[3], torch.float) + + @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") + def test_gptq_with_input_recorder(self): + from torchao.quantization.GPTQ import ( + Int4WeightOnlyGPTQQuantizer, + MultiTensorInputRecorder, + ) + + torch.set_default_dtype(torch.bfloat16) + + config = ModelArgs(n_layer=2) + + with torch.device("cuda"): + model = Transformer(config) + model.setup_caches(max_batch_size=2, max_seq_length=100) + idx = torch.randint(1, 10000, (10, 2, 50)).to(torch.int32) + test_input = prepare_inputs_for_model(idx[0]) + import copy + + model2 = copy.deepcopy(model) + out = model(*test_input) + quantize_(model2, Int4WeightOnlyConfig()) + + outq = model2(*test_input) + del model2 + + input_recorder = MultiTensorInputRecorder() + for i in range(10): + input = prepare_inputs_for_model(idx[i]) + input_recorder(*input) + + args = input_recorder.get_recorded_inputs() + + quantizer = Int4WeightOnlyGPTQQuantizer() + + quantizer.quantize(model, *args) + + outgptq = model(*test_input) + + self.assertGreater(compute_error(outgptq, out), 30) + self.assertGreater(compute_error(outgptq, out), compute_error(outq, out)) + torch.set_default_dtype(torch.float32) + + +if __name__ == "__main__": + unittest.main() diff --git a/test/quantization/test_gptq_mt.py b/test/quantization/test_gptq_mt.py deleted file mode 100644 index d703efbd49..0000000000 --- a/test/quantization/test_gptq_mt.py +++ /dev/null @@ -1,348 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -from pathlib import Path - -import pytest -import torch -import torch.nn.functional as F -from torch.testing._internal.common_utils import run_tests - -from torchao._models.llama.model import Transformer, prepare_inputs_for_model -from torchao._models.llama.tokenizer import get_tokenizer -from torchao.quantization.GPTQ_MT import Int4WeightOnlyGPTQQuantizer, MultiTensor -from torchao.quantization.utils import _lm_eval_available -from torchao.utils import is_fbcode - -if is_fbcode(): - pytest.skip("Skipping the test in fbcode due to missing model and tokenizer files") - -if _lm_eval_available: - hqq_core = pytest.importorskip("hqq.core", reason="requires hqq") - import lm_eval - - try: # lm_eval version 0.4 - from lm_eval.evaluator import evaluate - from lm_eval.models.huggingface import HFLM as eval_wrapper - from lm_eval.tasks import get_task_dict - except: # lm_eval version 0.3 - from lm_eval import base, evaluator, tasks - - eval_wrapper = base.BaseLM - get_task_dict = tasks.get_task_dict - evaluate = evaluator.evaluate - - class InputRecorder(eval_wrapper): - def __init__( - self, - tokenizer, - calibration_seq_length, - input_prep_func=None, - pad_calibration_inputs=False, - vocab_size=32000, - pad_token=0, - device="cpu", - ): - try: - super().__init__() - except TypeError: - # lm_eval 0.4.2 removed the default init - super().__init__("gpt2", device="cpu") - - self.tokenizer = tokenizer - self._device = torch.device(device) - self.vocab_size = vocab_size - self._max_seq_length = calibration_seq_length - self.calibration_seq_length = calibration_seq_length - - self.input_prep_func = ( - input_prep_func if input_prep_func is not None else lambda x: (x,) - ) - - self.pad_calibration_inputs = pad_calibration_inputs - self.pad_token = pad_token - - self.inputs = [] - - @property - def eot_token_id(self): - try: - return self.tokenizer.eos_id() - except: - return self.tokenizer.eos_id - - @property - def max_length(self): - return self._max_seq_length - - @property - def max_gen_toks(self): - return 50 - - @property - def batch_size(self): - return 1 - - @property - def device(self): - return self._device - - def tok_encode(self, string: str, **kwargs): - tokens = self.tokenizer.encode(string) - if hasattr(self.tokenizer, "bos_id"): - try: - tokens = [self.tokenizer.bos_id()] + tokens - except: - tokens = [self.tokenizer.bos_id] + tokens - return tokens - - def tok_decode(self, tokens): - decoded = self.tokenizer.decode(tokens) - return decoded - - def add_input(self, args): - self.inputs.append(args) - - def record_inputs( - self, - calibration_tasks, - calibration_limit, - ): - try: - lm_eval.tasks.initialize_tasks() - except: - pass - - task_dict = get_task_dict(calibration_tasks) - print("Obtaining GPTQ calibration inputs on: ", calibration_tasks) - - evaluate( - self, - task_dict, - limit=calibration_limit, - ) - return self - - def get_inputs(self): - return self.inputs - - def _model_call(self, inps): - inps = inps.squeeze(0) - T = len(inps) - if ( - # can't use inputs that are too short when padding disabled - (T < self.calibration_seq_length and not self.pad_calibration_inputs) - or - # can't use inputs that actually use token we use for padding - (self.pad_calibration_inputs and self.pad_token in inps) - ): - # give random output - return torch.randn( - (1, T, self.vocab_size), dtype=torch.bfloat16, device=self._device - ) - - # pad or truncate to the right size - if T >= self.calibration_seq_length: - inps = inps[: self.calibration_seq_length] - else: - inps = F.pad(inps, (self.pad_token, self.calibration_seq_length - T)) - - inps = inps.unsqueeze(0) - model_in = self.input_prep_func(inps) - - self.add_input(model_in) - - # output `something` with correct shape to keep eval going - return torch.randn( - (1, T, self.vocab_size), dtype=torch.bfloat16, device=self._device - ) - - def _model_generate(self, context, max_length, eos_token_id): - raise Exception("unimplemented") - - import logging - import time - - logging.basicConfig(level=logging.INFO) - logger = logging.getLogger(__name__) - - class TransformerEvalWrapper(InputRecorder): - """ - A wrapper class for GPTFast, providing integration with the lm-evaluation-harness library. - """ - - def __init__( - self, model, tokenizer, max_seq_length, input_prep_func=None, device="cuda" - ): - super().__init__(tokenizer, None) - self._model = model - # self.tokenizer = tokenizer - self._device = torch.device(device) - self._max_seq_length = max_seq_length - - # need to take inps and convert to corrent input - # for model - self.input_prep_func = ( - input_prep_func if input_prep_func is not None else lambda x: (x,) - ) - - def _model_call(self, inps): - # print("Entering _model_call") - # print(f"Input shape: {inps.shape}") - - input = self.input_prep_func(inps) - # print(f"Processed input shapes: {[x.shape for x in input]}") - - input = [x.to(self._device) for x in input] - # print(f"Inputs moved to device: {self._device}") - - max_seq_length = min(max(inps.size()), self.max_length) - # print(f"Max sequence length: {max_seq_length}") - - # print("Setting up caches") - with torch.device(self._device): - # print(f"Device: {self._device}") - # print(f"Batch size: {self.batch_size}") - # print(f"Max sequence length: {max_seq_length}") - self._model.setup_caches(self.batch_size, max_seq_length) - # print("Caches set up") - - # print("Running model") - # torch.save(input, "input.pt") - logits = self._model(*input) - # print(f"Model run complete. Logits shape: {logits.shape}") - return logits - - def _model_generate(self, context, max_length, eos_token_id): - raise Exception("unimplemented") - - def run_eval(self, tasks, limit): - logger.info(f"Starting evaluation on tasks: {tasks}") - logger.info(f"Evaluation limit: {limit}") - - try: - logger.info("Initializing lm_eval tasks") - lm_eval.tasks.initialize_tasks() - except Exception as e: - logger.warning(f"Failed to initialize tasks: {e}") - logger.info("Continuing without initialization") - - try: - logger.info("Getting task dictionary") - task_dict = get_task_dict(tasks) - logger.info(f"Task dictionary: {task_dict}") - except Exception as e: - logger.error(f"Failed to get task dictionary: {e}") - raise - - logger.info("Starting evaluation") - start_time = time.time() - - try: - with torch.no_grad(): - result = evaluate(self, task_dict, limit=limit, verbosity="DEBUG") - except Exception as e: - logger.error(f"Evaluation failed: {e}") - raise - - end_time = time.time() - logger.info(f"Evaluation completed in {end_time - start_time:.2f} seconds") - - logger.info("Evaluation results:") - for task, res in result["results"].items(): - print(f"{task}: {res}") - - return result - - -def test_gptq_mt(): - precision = torch.bfloat16 - device = "cuda" - print("Loading model") - checkpoint_path = Path("checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth") - model = Transformer.from_name(checkpoint_path.parent.name) - checkpoint = torch.load(str(checkpoint_path), mmap=True, weights_only=True) - model.load_state_dict(checkpoint, assign=True) - model = model.to(dtype=precision, device="cpu") - model.eval() - print("Model loaded") - tokenizer_path = checkpoint_path.parent / "tokenizer.model" - assert tokenizer_path.is_file(), tokenizer_path - tokenizer = get_tokenizer( # pyre-ignore[28] - tokenizer_path, - "Llama-2-7b-chat-hf", - ) - print("Tokenizer loaded") - - blocksize = 128 - percdamp = 0.01 - groupsize = 64 - calibration_tasks = ["wikitext"] - calibration_limit = None - calibration_seq_length = 100 - input_prep_func = prepare_inputs_for_model - pad_calibration_inputs = False - print("Recording inputs") - inputs = ( - InputRecorder( - tokenizer, - calibration_seq_length, - input_prep_func, - pad_calibration_inputs, - model.config.vocab_size, - device="cpu", - ) - .record_inputs( - calibration_tasks, - calibration_limit, - ) - .get_inputs() - ) - print("Inputs recorded") - quantizer = Int4WeightOnlyGPTQQuantizer( - blocksize, - percdamp, - groupsize, - ) - - model.setup_caches(max_batch_size=1, max_seq_length=calibration_seq_length) - multi = [ - MultiTensor([inp for inp, _ in inputs]), - MultiTensor([inds for _, inds in inputs]), - ] - print("Quantizing model") - model = quantizer.quantize(model, multi).cuda() - print("Model quantized") - print("Saving model and fixing state dict") - regular_state_dict = model.state_dict() # defaultdict(torch.tensor) - for key, value in model.state_dict().items(): - if isinstance(value, MultiTensor): - regular_state_dict[key] = value.values[0] - else: - regular_state_dict[key] = value - - model = Transformer.from_name(checkpoint_path.parent.name) - remove = [k for k in regular_state_dict if "kv_cache" in k] - for k in remove: - del regular_state_dict[k] - - model.load_state_dict(regular_state_dict, assign=True) - torch.save(model.state_dict(), "model.pth") - print("Running evaluation") - TransformerEvalWrapper( - model.to(device), # quantized model needs to run on cuda - tokenizer, - model.config.block_size, - prepare_inputs_for_model, - ).run_eval( - ["wikitext"], - None, - ) - - -if __name__ == "__main__": - run_tests() - -# wikitext: {'word_perplexity,none': 12.523175352665858, 'word_perplexity_stderr,none': 'N/A', 'byte_perplexity,none': 1.6042723245990418, 'byte_perplexity_stderr,none': 'N/A', 'bits_per_byte,none': 0.681919059499152, 'bits_per_byte_stderr,none': 'N/A', 'alias': 'wikitext'} diff --git a/test/quantization/test_qat.py b/test/quantization/test_qat.py index 7444c3dbb5..3b32f870c4 100644 --- a/test/quantization/test_qat.py +++ b/test/quantization/test_qat.py @@ -17,13 +17,16 @@ from torch.ao.quantization.fx._decomposed import quantized_decomposed_lib # noqa: F401 from torchao import quantize_ -from torchao.quantization.GPTQ import _replace_linear_8da4w, _replace_linear_int4 from torchao.quantization.granularity import ( PerAxis, PerGroup, PerRow, PerToken, ) +from torchao.quantization.linear_quant_modules import ( + _replace_linear_8da4w, + _replace_linear_int4, +) from torchao.quantization.qat.api import ( ComposableQATQuantizer, FakeQuantizeConfig, @@ -1478,7 +1481,7 @@ def test_qat_linear_bias(self): example_inputs = m.example_inputs() m(*example_inputs) - @parameterized.expand([torch.float32, torch.bfloat16, torch.float16]) + @parameterized.expand([(torch.float32,), (torch.bfloat16,), (torch.float16,)]) @unittest.skipIf( not TORCH_VERSION_AT_LEAST_2_4, "skipping when torch version is 2.4 or lower" ) @@ -1498,7 +1501,7 @@ def test_fake_quantize_per_token_vs_convert(self, dtype: torch.dtype): baseline_out = per_token_dynamic_quant(x) torch.testing.assert_close(fake_quantizer_out, baseline_out, atol=0, rtol=0) - @parameterized.expand([torch.float32, torch.bfloat16, torch.float16]) + @parameterized.expand([(torch.float32,), (torch.bfloat16,), (torch.float16,)]) @unittest.skipIf( not TORCH_VERSION_AT_LEAST_2_4, "skipping when torch version is 2.4 or lower" ) diff --git a/test/quantization/test_quant_api.py b/test/quantization/test_quant_api.py index b4ec9f4785..0435a6c59b 100644 --- a/test/quantization/test_quant_api.py +++ b/test/quantization/test_quant_api.py @@ -310,7 +310,7 @@ def api(model): not TORCH_VERSION_AT_LEAST_2_3, "skipping when torch verion is 2.3 or lower" ) def test_8da4w_quantizer(self): - from torchao.quantization.GPTQ import Int8DynActInt4WeightLinear + from torchao.quantization.linear_quant_modules import Int8DynActInt4WeightLinear from torchao.quantization.quant_api import Int8DynActInt4WeightQuantizer quantizer = Int8DynActInt4WeightQuantizer(groupsize=32) @@ -325,7 +325,7 @@ def test_8da4w_quantizer(self): not TORCH_VERSION_AT_LEAST_2_3, "skipping when torch verion is 2.3 or lower" ) def test_8da4w_quantizer_linear_bias(self): - from torchao.quantization.GPTQ import Int8DynActInt4WeightLinear + from torchao.quantization.linear_quant_modules import Int8DynActInt4WeightLinear from torchao.quantization.quant_api import Int8DynActInt4WeightQuantizer quantizer = Int8DynActInt4WeightQuantizer(groupsize=32) @@ -336,190 +336,10 @@ def test_8da4w_quantizer_linear_bias(self): assert isinstance(m.linear2, Int8DynActInt4WeightLinear) m(*example_inputs) - # TODO: save model weights as artifacts and re-enable in CI - # For now, to run this test, you will need to download the weights from HF - # and run this script to convert them: - # https://github.com/pytorch-labs/gpt-fast/blob/6253c6bb054e658d67566150f87329b87815ae63/scripts/convert_hf_checkpoint.py - @unittest.skip("skipping until we get checkpoints for gpt-fast") - def test_8da4w_gptq_quantizer(self): - from torchao._models._eval import InputRecorder, TransformerEvalWrapper - from torchao.quantization.GPTQ import Int8DynActInt4WeightGPTQQuantizer - - # should be similar to TorchCompileDynamicQuantizer - precision = torch.bfloat16 - device = "cpu" - checkpoint_path = Path("../checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth") - model = Transformer.from_name(checkpoint_path.parent.name) - checkpoint = torch.load(str(checkpoint_path), mmap=True, weights_only=True) - model.load_state_dict(checkpoint, assign=True) - model = model.to(dtype=precision, device=device) - model.eval() - tokenizer_path = checkpoint_path.parent / "tokenizer.model" - assert tokenizer_path.is_file(), tokenizer_path - tokenizer = get_tokenizer( # pyre-ignore[28] - tokenizer_path, - "Llama-2-7b-chat-hf", - ) - blocksize = 128 - percdamp = 0.01 - groupsize = 128 - calibration_tasks = ["wikitext"] - calibration_limit = 1 - calibration_seq_length = 100 - input_prep_func = prepare_inputs_for_model - pad_calibration_inputs = False - - inputs = ( - InputRecorder( - tokenizer, - calibration_seq_length, - input_prep_func, - pad_calibration_inputs, - model.config.vocab_size, - ) - .record_inputs( - calibration_tasks, - calibration_limit, - ) - .get_inputs() - ) - - quantizer = Int8DynActInt4WeightGPTQQuantizer( - blocksize, - percdamp, - groupsize, - precision=precision, - ) - model.setup_caches(max_batch_size=1, max_seq_length=calibration_seq_length) - model = quantizer.quantize(model, inputs) - result = TransformerEvalWrapper( - model, - tokenizer, - model.config.block_size, - prepare_inputs_for_model, - device, - ).run_eval( - ["wikitext"], - 1, - ) - - assert result["results"]["wikitext"]["word_perplexity,none"] < 7.88, ( - f"accuracy regressed from 7.87 to {result['results']['wikitext']['word_perplexity,none']}" - ) - - @unittest.skip("skipping until we get checkpoints for gpt-fast") - @unittest.skipIf( - not TORCH_VERSION_AT_LEAST_2_4, "skipping when torch verion is 2.4 or lower" - ) - def test_8da4w_quantizer_eval(self): - from torchao._models._eval import TransformerEvalWrapper - from torchao.quantization.quant_api import Int8DynActInt4WeightQuantizer - - precision = torch.bfloat16 - device = "cpu" - checkpoint_path = Path("../checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth") - model = Transformer.from_name(checkpoint_path.parent.name) - checkpoint = torch.load(str(checkpoint_path), mmap=True, weights_only=True) - model.load_state_dict(checkpoint, assign=True) - model = model.to(dtype=precision, device=device) - model.eval() - tokenizer_path = checkpoint_path.parent / "tokenizer.model" - assert tokenizer_path.is_file(), tokenizer_path - tokenizer = get_tokenizer( # pyre-ignore[28] - tokenizer_path, - "Llama-2-7b-chat-hf", - ) - - quantizer = Int8DynActInt4WeightQuantizer(groupsize=128, precision=precision) - q_model = quantizer.quantize(model) - result = TransformerEvalWrapper( - q_model, - tokenizer, - q_model.config.block_size, - prepare_inputs_for_model, - device, - ).run_eval( - ["wikitext"], - 1, - ) - assert result["results"]["wikitext"]["word_perplexity,none"] < 8.24, ( - f"accuracy regressed from 8.23 to {result['results']['wikitext']['word_perplexity,none']}" - ) - - @unittest.skip("skipping until we get checkpoints for gpt-fast") - def test_gptq_quantizer_int4_weight_only(self): - from torchao._models._eval import ( - MultiTensorInputRecorder, - TransformerEvalWrapper, - ) - from torchao.quantization.GPTQ_MT import Int4WeightOnlyGPTQQuantizer - - precision = torch.bfloat16 - device = "cuda" - checkpoint_path = Path("../checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth") - model = Transformer.from_name(checkpoint_path.parent.name) - checkpoint = torch.load(str(checkpoint_path), mmap=True, weights_only=True) - model.load_state_dict(checkpoint, assign=True) - model = model.to(dtype=precision, device="cpu") - model.eval() - - tokenizer_path = checkpoint_path.parent / "tokenizer.model" - assert tokenizer_path.is_file(), tokenizer_path - tokenizer = get_tokenizer( # pyre-ignore[28] - tokenizer_path, - "Llama-2-7b-chat-hf", - ) - - blocksize = 128 - percdamp = 0.01 - groupsize = 64 - calibration_tasks = ["wikitext"] - calibration_limit = 5 - calibration_seq_length = 100 - input_prep_func = prepare_inputs_for_model - pad_calibration_inputs = False - inputs = ( - MultiTensorInputRecorder( - tokenizer, - calibration_seq_length, - input_prep_func, - pad_calibration_inputs, - model.config.vocab_size, - device="cpu", - ) - .record_inputs( - calibration_tasks, - calibration_limit, - ) - .get_inputs() - ) - - quantizer = Int4WeightOnlyGPTQQuantizer( - blocksize, - percdamp, - groupsize, - ) - model.setup_caches(max_batch_size=1, max_seq_length=calibration_seq_length) - model = quantizer.quantize(model, inputs).cuda() - - result = TransformerEvalWrapper( - model.cuda(), - tokenizer, - model.config.block_size, - prepare_inputs_for_model, - device, - ).run_eval( - ["wikitext"], - None, - ) - assert result["results"]["wikitext"]["word_perplexity,none"] < 7.77, ( - f"accuracy regressed from 7.76 to {result['results']['wikitext']['word_perplexity,none']}" - ) - @unittest.skip("skipping until we get checkpoints for gpt-fast") def test_quantizer_int4_weight_only(self): from torchao._models._eval import TransformerEvalWrapper - from torchao.quantization.GPTQ import Int4WeightOnlyQuantizer + from torchao.quantization.linear_quant_modules import Int4WeightOnlyQuantizer precision = torch.bfloat16 device = "cuda" @@ -648,7 +468,7 @@ def test_quantized_tensor_subclass_8da4w(self, mapping_type): ) # reference - from torchao.quantization.GPTQ import Int8DynActInt4WeightLinear + from torchao.quantization.linear_quant_modules import Int8DynActInt4WeightLinear from torchao.quantization.quant_api import Int8DynActInt4WeightQuantizer quantizer = Int8DynActInt4WeightQuantizer( @@ -1037,43 +857,6 @@ def test_int4wo_cuda_serialization(self): model.load_state_dict(sd, assign=True) -class TestMultiTensorFlow(TestCase): - @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_4, "Test only enabled for 2.4+") - @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") - def test_multitensor_add_tensors(self): - from torchao.quantization.GPTQ_MT import MultiTensor - - tensor1 = torch.randn(3, 3) - tensor2 = torch.randn(3, 3) - mt = MultiTensor(tensor1) - mt.add_tensors(tensor2) - self.assertEqual(mt.count, 2) - self.assertTrue(torch.equal(mt.values[0], tensor1)) - self.assertTrue(torch.equal(mt.values[1], tensor2)) - - @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_4, "Test only enabled for 2.4+") - @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") - def test_multitensor_pad_unpad(self): - from torchao.quantization.GPTQ_MT import MultiTensor - - tensor1 = torch.randn(3, 3) - mt = MultiTensor(tensor1) - mt.pad_to_length(3) - self.assertEqual(mt.count, 3) - mt.unpad() - self.assertEqual(mt.count, 1) - - @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_4, "Test only enabled for 2.4+") - @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") - def test_multitensor_inplace_operation(self): - from torchao.quantization.GPTQ_MT import MultiTensor - - tensor1 = torch.ones(3, 3) - mt = MultiTensor(tensor1) - mt += 1 # In-place addition - self.assertTrue(torch.equal(mt.values[0], torch.full((3, 3), 2))) - - common_utils.instantiate_parametrized_tests(TestQuantFlow) diff --git a/torchao/_models/_eval.py b/torchao/_models/_eval.py index 9f429278e3..0266417de5 100644 --- a/torchao/_models/_eval.py +++ b/torchao/_models/_eval.py @@ -12,8 +12,7 @@ import torch import torch.nn.functional as F -from torchao.quantization.GPTQ_MT import MultiTensor -from torchao.quantization.utils import _MultiInput +from torchao.quantization.GPTQ import MultiTensorInputRecorder try: # lm_eval version 0.4 from lm_eval.evaluator import evaluate # pyre-ignore[21] @@ -27,16 +26,13 @@ evaluate = evaluator.evaluate -class MultiTensorInputRecorder(eval_wrapper): +class TransformerEvalWrapper(eval_wrapper): + """ + A wrapper class for GPTFast, providing integration with the lm-evaluation-harness library. + """ + def __init__( - self, - tokenizer, - calibration_seq_length, - input_prep_func=None, - pad_calibration_inputs=False, - vocab_size=32000, - pad_token=0, - device="cpu", + self, model, tokenizer, max_seq_length, input_prep_func=None, device="cuda" ): try: super().__init__() @@ -44,21 +40,44 @@ def __init__( # lm_eval 0.4.2 removed the default init super().__init__("gpt2", device="cpu") + self._model = model self.tokenizer = tokenizer self._device = torch.device(device) - self.vocab_size = vocab_size - self._max_seq_length = calibration_seq_length - self.calibration_seq_length = calibration_seq_length + self._max_seq_length = max_seq_length + # need to take inps and convert to corrent input + # for model self.input_prep_func = ( input_prep_func if input_prep_func is not None else lambda x: (x,) ) - self.pad_calibration_inputs = pad_calibration_inputs - self.pad_token = pad_token + def _model_call(self, inps): + # TODO: make batches work + input = self.input_prep_func(inps) - # Initialize inputs as a list of two empty lists for input tensors and indices - self.inputs = [[], []] + max_seq_length = min(max(inps.size()), self.max_length) + with torch.device(self._device): + self._model.setup_caches(self.batch_size, max_seq_length) + logits = self._model(*input) + return logits + + def run_eval(self, tasks, limit): + try: + lm_eval.tasks.initialize_tasks() + except: + pass + + task_dict = get_task_dict(tasks) + print("Evaluating Model On: ", task_dict) + with torch.no_grad(): + result = evaluate( + self, + task_dict, + limit=limit, + ) + for task, res in result["results"].items(): + print(f"{task}: {res}") + return result @property def eot_token_id(self): @@ -83,6 +102,10 @@ def batch_size(self): def device(self): return self._device + def tok_decode(self, tokens): + decoded = self.tokenizer.decode(tokens) + return decoded + def tok_encode(self, string: str, **kwargs): tokens = self.tokenizer.encode(string) if hasattr(self.tokenizer, "bos_id"): @@ -92,171 +115,39 @@ def tok_encode(self, string: str, **kwargs): tokens = [self.tokenizer.bos_id] + tokens return tokens - def tok_decode(self, tokens): - decoded = self.tokenizer.decode(tokens) - return decoded - - def add_input(self, args): - # Ensure that inputs are added correctly as pairs - self.inputs[0].append(args[0]) - self.inputs[1].append(args[1]) - - def record_inputs(self, calibration_tasks, calibration_limit): - try: - lm_eval.tasks.initialize_tasks() - except: - pass - - task_dict = get_task_dict(calibration_tasks) - print("Obtaining GPTQ calibration inputs on: ", calibration_tasks) - - evaluate( - self, - task_dict, - limit=calibration_limit, - ) - return self - - def get_inputs(self): - # Return MultiTensor instances for both inputs and indices - return [MultiTensor(self.inputs[0]), MultiTensor(self.inputs[1])] - - def _model_call(self, inps): - inps = inps.squeeze(0) - T = len(inps) - if ( - # Can't use inputs that are too short when padding is disabled - (T < self.calibration_seq_length and not self.pad_calibration_inputs) - or - # Can't use inputs that actually use the token we use for padding - (self.pad_calibration_inputs and self.pad_token in inps) - ): - # Give random output - return torch.randn( - (1, T, self.vocab_size), dtype=torch.bfloat16, device=self._device - ) - - # Pad or truncate to the correct size - if T >= self.calibration_seq_length: - inps = inps[: self.calibration_seq_length] - else: - inps = F.pad( - inps, (0, self.calibration_seq_length - T), value=self.pad_token - ) - - inps = inps.unsqueeze(0) - model_in = self.input_prep_func(inps) - - self.add_input(model_in) - - # Output `something` with the correct shape to keep eval going - return torch.randn( - (1, T, self.vocab_size), dtype=torch.bfloat16, device=self._device - ) - def _model_generate(self, context, max_length, eos_token_id): raise Exception("unimplemented") -class InputRecorder(eval_wrapper): - """ - This is a fake evaluation wrapper from the lm_eval library that just records the inputs - so that they can be used in calibration. - - If pad_calibration_inputs is enabled, the input recorder will take - each input and pad/truncate it down to the calibration_seq_length. - (if using padding you should set the embeddings for the pad_token to 0 - in the model) - - Note: after padding/truncation, input_prep_function is called to bring - it to the proper form to be inserted into a given model. - - If not, it will only truncate inputs to the desired length. - """ - +class LMEvalInputRecorder(TransformerEvalWrapper): def __init__( self, tokenizer, calibration_seq_length, input_prep_func=None, - pad_calibration_inputs=False, vocab_size=32000, + pad_calibration_inputs=False, pad_token=0, device="cpu", + base_input_recorder_class=MultiTensorInputRecorder, ): - try: - super().__init__() - except TypeError: - # lm_eval 0.4.2 removed the default init - super().__init__("gpt2", device="cpu") - - self.tokenizer = tokenizer - self._device = torch.device(device) + super().__init__( + model=None, + tokenizer=tokenizer, + max_seq_length=calibration_seq_length, + input_prep_func=input_prep_func, + device=device, + ) self.vocab_size = vocab_size - self._max_seq_length = calibration_seq_length self.calibration_seq_length = calibration_seq_length - # need to take inps and convert to corrent input - # for model - self.input_prep_func = ( - input_prep_func if input_prep_func is not None else lambda x: (x,) - ) - self.pad_calibration_inputs = pad_calibration_inputs self.pad_token = pad_token - self.inputs = None - - @property - def eot_token_id(self): - try: - return self.tokenizer.eos_id() - except: - return self.tokenizer.eos_id - - @property - def max_length(self): - return self._max_seq_length - - @property - def max_gen_toks(self): - return 50 - - @property - def batch_size(self): - return 1 - - @property - def device(self): - return self._device - - def tok_encode(self, string: str, **kwargs): - # TODO: verify this for multi-batch as well - tokens = self.tokenizer.encode(string) - if hasattr(self.tokenizer, "bos_id"): - try: - tokens = [self.tokenizer.bos_id()] + tokens - except: - tokens = [self.tokenizer.bos_id] + tokens - return tokens - - def tok_decode(self, tokens): - decoded = self.tokenizer.decode(tokens) - return decoded - - def add_input(self, args): - if self.inputs is None: - self.inputs = [_MultiInput([arg]) for arg in args] - else: - self.inputs = [ - multi.add_input(arg) for (multi, arg) in zip(self.inputs, args) - ] + # Initialize inputs as a list of two empty lists for input tensors and indices + self.base_input_recorder = base_input_recorder_class() - def record_inputs( - self, - calibration_tasks, - calibration_limit, - ): + def record_inputs(self, calibration_tasks, calibration_limit): try: lm_eval.tasks.initialize_tasks() except: @@ -272,91 +163,47 @@ def record_inputs( ) return self - def get_inputs(self): - return self.inputs + def get_inputs(self): # for BC + return self.get_recorded_inputs() + + def get_recorded_inputs(self): + return self.base_input_recorder.get_recorded_inputs() + + def get_recorded_args_and_kwargs(self): + return self.base_input_recorder.get_recorded_args_and_kwargs() def _model_call(self, inps): inps = inps.squeeze(0) T = len(inps) if ( - # can't use inputs that are too short when padding disabled + # Can't use inputs that are too short when padding is disabled (T < self.calibration_seq_length and not self.pad_calibration_inputs) or - # can't use inputs that actually use token we use for padding + # Can't use inputs that actually use the token we use for padding (self.pad_calibration_inputs and self.pad_token in inps) ): - # give random output + # Give random output return torch.randn( (1, T, self.vocab_size), dtype=torch.bfloat16, device=self._device ) - # pad or truncate to the right size + # Pad or truncate to the correct size if T >= self.calibration_seq_length: inps = inps[: self.calibration_seq_length] else: - inps = F.pad(inps, (self.pad_token, self.calibration_seq_length - T)) + inps = F.pad( + inps, (0, self.calibration_seq_length - T), value=self.pad_token + ) inps = inps.unsqueeze(0) model_in = self.input_prep_func(inps) - self.add_input(model_in) + self.base_input_recorder(*model_in) - # output `something` with correct shape to keep eval going + # Output `something` with the correct shape to keep eval going return torch.randn( (1, T, self.vocab_size), dtype=torch.bfloat16, device=self._device ) - def _model_generate(self, context, max_length, eos_token_id): - raise Exception("unimplemented") - -class TransformerEvalWrapper(InputRecorder): - """ - A wrapper class for GPTFast, providing integration with the lm-evaluation-harness library. - """ - - def __init__( - self, model, tokenizer, max_seq_length, input_prep_func=None, device="cuda" - ): - super().__init__(tokenizer, None) - self._model = model - # self.tokenizer = tokenizer - self._device = torch.device(device) - self._max_seq_length = max_seq_length - - # need to take inps and convert to corrent input - # for model - self.input_prep_func = ( - input_prep_func if input_prep_func is not None else lambda x: (x,) - ) - - def _model_call(self, inps): - # TODO: make batches work - input = self.input_prep_func(inps) - - max_seq_length = min(max(inps.size()), self.max_length) - with torch.device(self._device): - self._model.setup_caches(self.batch_size, max_seq_length) - logits = self._model(*input) - return logits - - def _model_generate(self, context, max_length, eos_token_id): - raise Exception("unimplemented") - - def run_eval(self, tasks, limit): - try: - lm_eval.tasks.initialize_tasks() - except: - pass - - task_dict = get_task_dict(tasks) - print("Evaluating Model On: ", task_dict) - with torch.no_grad(): - result = evaluate( - self, - task_dict, - limit=limit, - ) - for task, res in result["results"].items(): - print(f"{task}: {res}") - return result +InputRecorder = LMEvalInputRecorder # for BC diff --git a/torchao/_models/llama/eval.py b/torchao/_models/llama/eval.py index e6569c42c5..8ee15f1fd3 100644 --- a/torchao/_models/llama/eval.py +++ b/torchao/_models/llama/eval.py @@ -44,7 +44,7 @@ def run_evaluation( calibration_tasks: Optional[List[str]] = None, calibration_limit: Optional[int] = None, calibration_seq_length: Optional[int] = None, - pad_calibration_inputs: Optional[bool] = False, + pad_calibration_inputs: bool = False, ): """Runs the evaluation of a model using LM Eval.""" print( @@ -120,8 +120,8 @@ def run_evaluation( quantize_(model, int4_weight_only(layout=MarlinSparseLayout())) if "int4wo" in quantization and "gptq" in quantization: # avoid circular imports - from torchao._models._eval import MultiTensorInputRecorder - from torchao.quantization.GPTQ_MT import Int4WeightOnlyGPTQQuantizer + from torchao._models._eval import LMEvalInputRecorder + from torchao.quantization.GPTQ import Int4WeightOnlyGPTQQuantizer groupsize = int(quantization.split("-")[-2]) assert groupsize in [32, 64, 128, 256], ( @@ -132,24 +132,25 @@ def run_evaluation( ) assert "cuda" in device, "int4 gptq quantization only works on cuda" inputs = ( - MultiTensorInputRecorder( + LMEvalInputRecorder( tokenizer, calibration_seq_length, prepare_inputs_for_model, - pad_calibration_inputs, model.config.vocab_size, + pad_calibration_inputs, device="cpu", ) .record_inputs( calibration_tasks, calibration_limit, ) - .get_inputs() + .get_recorded_inputs() ) - + print("Obtained inputs, starting calibration") quantizer = Int4WeightOnlyGPTQQuantizer(group_size=groupsize, device=device) model.setup_caches(max_batch_size=1, max_seq_length=calibration_seq_length) - model = quantizer.quantize(model, inputs).to(device) + quantizer.quantize(model, *inputs) + model = model.to(device) else: if not TORCH_VERSION_AT_LEAST_2_5: unwrap_tensor_subclass(model) diff --git a/torchao/_models/llama/generate.py b/torchao/_models/llama/generate.py index 40f70fe93e..aa928c83f5 100644 --- a/torchao/_models/llama/generate.py +++ b/torchao/_models/llama/generate.py @@ -629,13 +629,13 @@ def ffn_or_attn_only(mod, fqn): float8_dynamic_activation_float8_weight(granularity=granularity), ) elif "autoquant_v2" in quantization: - from torchao._models._eval import InputRecorder + from torchao._models._eval import LMEvalInputRecorder from torchao._models.llama.model import prepare_inputs_for_model from torchao.prototype.quantization.autoquant_v2 import autoquant_v2 calibration_seq_length = 256 inputs = ( - InputRecorder( + LMEvalInputRecorder( tokenizer, calibration_seq_length, prepare_inputs_for_model, @@ -647,7 +647,7 @@ def ffn_or_attn_only(mod, fqn): ["wikitext"], 1, ) - .get_inputs()[0] + .get_recorded_inputs()[0] .values[0] ) inputs = prepare_inputs_for_model(inputs) @@ -719,12 +719,12 @@ def ffn_or_attn_only(mod, fqn): # do autoquantization model.finalize_autoquant() elif "autoquant" in quantization: - from torchao._models._eval import InputRecorder + from torchao._models._eval import LMEvalInputRecorder from torchao._models.llama.model import prepare_inputs_for_model calibration_seq_length = 256 inputs = ( - InputRecorder( + LMEvalInputRecorder( tokenizer, calibration_seq_length, prepare_inputs_for_model, @@ -736,7 +736,7 @@ def ffn_or_attn_only(mod, fqn): ["wikitext"], 1, ) - .get_inputs()[0] + .get_recorded_inputs()[0] .values[0] ) inputs = prepare_inputs_for_model(inputs) diff --git a/torchao/_models/llama/model.py b/torchao/_models/llama/model.py index 8771f70323..45dd2e9f29 100644 --- a/torchao/_models/llama/model.py +++ b/torchao/_models/llama/model.py @@ -21,8 +21,9 @@ def prepare_inputs_for_model(inps, max_new_tokens=1): if inps.dim() > 2: raise ValueError(f"Expected input to be of dim 1 or 2, but got {inps.dim()}") - input_pos = torch.arange(0, inps.numel(), device=inps.device) - return (inps.view(1, -1), input_pos) + # this now works with batched inputs + input_pos = torch.arange(0, inps.shape[-1], device=inps.device).to(torch.int32) + return (inps.view(-1, inps.shape[-1]), input_pos) @dataclass diff --git a/torchao/quantization/GPTQ.py b/torchao/quantization/GPTQ.py deleted file mode 100644 index a0ec97d63f..0000000000 --- a/torchao/quantization/GPTQ.py +++ /dev/null @@ -1,1314 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD-style license found in the -# LICENSE file in the root directory of this source tree. - - -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -import logging -from typing import Any, Callable, Dict, List, Optional, Type - -import torch -import torch.fx as fx -import torch.nn as nn -import torch.nn.functional as F -from torch.utils._pytree import tree_flatten, tree_unflatten - -from torchao.dtypes.utils import is_device -from torchao.utils import ( - TORCH_VERSION_AT_LEAST_2_3, - TORCH_VERSION_AT_LEAST_2_6, - find_multiple, -) - -from .quant_primitives import ( - MappingType, - dequantize_affine, -) -from .unified import Quantizer -from .utils import ( - _MultiInput, - get_group_qparams_symmetric, - get_groupwise_affine_qparams, - group_quantize_tensor_symmetric, - groupwise_affine_dequantize_tensor_from_qparams, - groupwise_affine_quantize_tensor, - groupwise_affine_quantize_tensor_from_qparams, - pack_tinygemm_scales_and_zeros, - per_token_dynamic_quant, -) - -aten = torch.ops.aten - -add_ons = [] - -if TORCH_VERSION_AT_LEAST_2_3: - add_ons += ["Int8DynActInt4WeightQuantizer", "Int8DynActInt4WeightGPTQQuantizer"] - - -__all__ = [ - "Int4WeightOnlyGPTQQuantizer", - "Int4WeightOnlyQuantizer", -] + add_ons - - -class GenericGPTQRunner(fx.Interpreter): - """ - This is a generic GPTQ runner that takes an existing model and applies GPTQ. - It uses torch._dynamo.export to obtain a graph of the model and then hooks - into function calls and when it detects a linear, it applies GPTQ to the weight - given the calibration of inputs passed in at initialization. It puts the results - into the state_dict so that the quantized model weights/qparams can be loaded - directly into the model. - - intended to be used in concert with a GPTQQuantizer class to define the quantization mode. - """ - - def __init__( - self, - model, - inputs: _MultiInput, - blocksize=128, - percdamp=0.01, - groupsize=128, - ): - self.id_to_name = { - id(value): name for name, value in dict(model.named_parameters()).items() - } - - # trace model for one input - one_input = [multi.values[0].cpu() for multi in inputs] # pyre-ignore[16] - # needed for GPTQ on the torchao llama model - import torchao - - torchao._models.llama.model.use_index_put_for_kv_cache = True - exported_model = torch._dynamo.export( - model.cpu(), aten_graph=True, pre_dispatch=True, tracing_mode="fake" - )(*one_input) - super().__init__(exported_model.graph_module) - - self.new_state_dict = model.state_dict() - - self.blocksize = blocksize - - self.percdamp = percdamp - - self.groupsize = groupsize - self.inputs = inputs - self.gptq_done = False - self.debug = False - - def configure_quantization_mode( - self, - get_qparams_func, - quantize_func, - dequantize_func, - combine_qparams_list_func, - make_names_and_values_dict_func, - skip_layer_func, - act_fake_quant_func=None, - ): - # these functions need to already be curried with all inputs other than weight, qparams - - self.get_qparams_func = ( - get_qparams_func # accepts [2d weight tensor], outputs qparams. - ) - - self.quantize_func = quantize_func # accepts [2d weight tensor], [qparams], outputs a 2d quantized tensor of desired dtype - - self.dequantize_func = dequantize_func - # accepts [quantized] tensor and [qparams], outputs a 2d dequantized tensor of type float, - # assumes this output .to(w_orig_dtype) is ~eventual desired dequant behavior - - # `combine_qparams_list_func`. - self.combine_qparams_list_func = combine_qparams_list_func - # accepts [`list` of qparams] from quantizing one group at a time, - # outputs a qparams object that could be passed into quant/dequantize_func - - self.skip_layer_func = skip_layer_func # accepts [weight tensor], outputs a bool on whether or not to apply gptq to this layer - - # `make_names_and_values_dict_func`. - self.make_names_and_values_dict_func = make_names_and_values_dict_func # accepts [2d quantized tensor], [qparams], returns a dict of names, values to put in state_dict - # note any final packing for storage should happen here - - # `act_fake_quant_func` - if act_fake_quant_func is None: - self.act_fake_quant_func = lambda x: x - else: - self.act_fake_quant_func = act_fake_quant_func # accepts [activation tensor], returns a fake-quantized activation tensor - return self - - def run(self): - assert self.get_qparams_func is not None, ( - "need to configure quantization mode before running" - ) - self.gptq_done = True - super().run(*self.inputs) - - def get_quantized_state_dict(self): - assert self.gptq_done, ( - "need to run GPTQRunner before you can get_quantized_state_dict" - ) - quantized_state_dict = self.new_state_dict - # Don't want to store/load the kv_cache so remove it from the state_dict - del_list = [] - for param_fqn in quantized_state_dict: - if "kv_cache" in param_fqn: - del_list.append(param_fqn) - for param_fqn in del_list: - quantized_state_dict.pop(param_fqn) - return quantized_state_dict - - def call_function(self, target, args, kwargs, already_quantized=False): # noqa: C901 - def tensors_to_cuda(args): - new_args = [] - for x in args: - new_args.append(x.cuda() if isinstance(x, torch.Tensor) else x) - return new_args - - # flatten args and kwargs together - flat_args, spec = tree_flatten((args, kwargs)) - # move all single tensors to cuda, will move _MultiInputs to cuda one at a time - flat_args = tensors_to_cuda(flat_args) - - has_multi_input = _MultiInput in [type(x) for x in flat_args] - if has_multi_input: - # Just some trickery to convert - # [_MultiInput[a, a, a], _MultiInput(b, b, b)] => [a, b], [a, b], [a, b] - multi_input_count = max( - [len(x.values) if isinstance(x, _MultiInput) else 1 for x in flat_args] - ) - transposed_args = list( - zip( - *[ - ( - x.values - if isinstance(x, _MultiInput) - else [x] * multi_input_count - ) - for x in flat_args - ] - ) - ) - else: - transposed_args = [flat_args] - outputs = [] - - # check whether we apply GPTQ to this module - quantize_linear = ( - (target == aten.linear.default) # if its a linear - and id(args[1]) in self.id_to_name # and if we know the layer name - # and we haven't already quantized this layer - and not already_quantized - # and if the skip_layer_func doesn't say we should skip - and not (self.skip_layer_func is not None and self.skip_layer_func(args[1])) - ) # then we will quantize this linear layer/weight - - if quantize_linear: # instantiate variables for GPTQ - H = 0 - total_batches = 0 - - for inp in transposed_args: - inp = tensors_to_cuda(inp) - cur_args, cur_kwargs = tree_unflatten(inp, spec) - - if quantize_linear: # calculate H instead of output (will run the linear eventually with updated weight) - x = cur_args[0].float() - x = self.act_fake_quant_func(x) - shape = x.shape - n = 1 if len(shape) == 2 else shape[0] - H *= total_batches / (total_batches + n) - total_batches += n - x = ((2 / total_batches) ** (1 / 2)) * x.reshape( - -1, shape[-1] - ).t().float() - H += x.matmul(x.t()) - else: - # weight has already been quantized but still need to apply - # activation quant for final calculation - if already_quantized: - cur_args = (self.act_fake_quant_func(cur_args[0]), *cur_args[1:]) - - # get output if its not a linear - out = super().call_function(target, cur_args, cur_kwargs) - if isinstance(out, torch.Tensor): - outputs.append(out.cpu()) - else: - outputs.append(out) - - if quantize_linear: - mod_fqn = ".".join(self.id_to_name[id(args[1])].split(".")[:-1]) - - W = args[1].to(H.device) - - Q, DQ, qparams = self.faster_quant(H, W.detach()) - print(mod_fqn) - - # `make_names_and_values_dict_func`. - names_and_values_dict = self.make_names_and_values_dict_func(Q, qparams) - - # delete old weight - if mod_fqn + ".weight" in self.new_state_dict: - self.new_state_dict.pop(mod_fqn + ".weight") - if len(args) > 2: - self.new_state_dict[mod_fqn + ".bias"] = args[2] - for name, value in names_and_values_dict.items(): - self.new_state_dict[mod_fqn + "." + name] = value - - # run linear with new weight to get corrected output - new_out = self.call_function( - target, (args[0], DQ, *args[2:]), kwargs, already_quantized=True - ) - - if self.debug: - old_out = self.call_function( - target, - (args[0][:2], args[1], *args[2:]), - kwargs, - already_quantized=True, - ) - - def SQNR(x, y): - # TODO: Use of deprecated function torch.norm - return 20 * torch.log10( - torch.linalg.norm(x) / torch.linalg.norm(x - y) - ) - - # `dequantize_func`. - DQ_after = self.dequantize_func(Q, qparams).to(W.dtype) - print( - "SQNR for QDQ (this should be inf)", SQNR(DQ, DQ_after) - ) # matches - print( - "SQNR for weight (can be low)", SQNR(W, DQ.cuda()) - ) # fine to not match - print( - "SQNR for output with GPTQ (hopefully 35+)", - torch.cat( - [ - SQNR(old.cpu(), new.cpu()).unsqueeze(0) - for (old, new) in zip(old_out.values, new_out.values[:2]) - ] - ).mean(), - ) - - # `get_qparams_func`. - qparams2 = self.get_qparams_func(W) - - Q2 = self.quantize_func(W, qparams2) - DQ2 = self.dequantize_func(Q2, qparams2).to(W.dtype) - old_q_out = self.call_function( - target, - (args[0][:2], DQ2, *args[2:]), - kwargs, - already_quantized=True, - ) - - print( - "SQNR for output without GPTQ (should be less than above)", - torch.cat( - [ - SQNR(old.cpu(), old_q.cpu()).unsqueeze(0) - for (old, old_q) in zip(old_out.values, old_q_out.values) - ] - ).mean(), - ) - return new_out - - return _MultiInput(outputs) if has_multi_input else outputs[0] - - def faster_quant(self, H, W): - percdamp = self.percdamp - blocksize = self.blocksize - groupsize = self.groupsize - orig_dtype = W.dtype - W = W.detach().float() - _, columns = W.shape[0], W.shape[1] - device = W.device - - if groupsize == -1: - cur_qparams = self.get_qparams_func(W) - dead = torch.diag(H) == 0 - H[dead, dead] = 1 - W[:, dead] = 0 - - Losses = torch.zeros_like(W) - DQ = torch.zeros_like(W) - - damp = percdamp * torch.mean(torch.diag(H)) - diag = torch.arange(columns, device=device) - H[diag, diag] += damp - H = torch.linalg.cholesky(H) - H = torch.cholesky_inverse(H) - H = torch.linalg.cholesky(H, upper=True) - Hinv = H - - all_qparams = [] - for i1 in range(0, columns, blocksize): - i2 = min(i1 + blocksize, columns) - count = i2 - i1 - W1 = W[:, i1:i2].clone() - DQ1 = torch.zeros_like(W1) - Err1 = torch.zeros_like(W1) - Losses1 = torch.zeros_like(W1) - Hinv1 = Hinv[i1:i2, i1:i2] - for i in range(count): - w = W1[:, i] - d = Hinv1[i, i] - - if groupsize != -1 and (i1 + i) % groupsize == 0: # start of new group - cur_qparams = self.get_qparams_func( - W[:, (i1 + i) : (i1 + i + groupsize)] - ) - all_qparams.append(cur_qparams) - - q = self.quantize_func(w.unsqueeze(1), cur_qparams).flatten() - - # `dequantize_func`. - - dq = self.dequantize_func(q.unsqueeze(1), cur_qparams).flatten() - - DQ1[:, i] = dq - Losses1[:, i] = (w - dq) ** 2 / d**2 - - err1 = (w - dq) / d - W1[:, i:] -= ( - err1.to(Hinv1.dtype).unsqueeze(1).matmul(Hinv1[i, i:].unsqueeze(0)) - ) - Err1[:, i] = err1 - - DQ[:, i1:i2] = DQ1 - Losses[:, i1:i2] = Losses1 / 2 - - W[:, i2:] -= Err1.to(Hinv.dtype).matmul(Hinv[i1:i2, i2:]) - - torch.cuda.synchronize() - - if all_qparams == []: - all_qparams.append(cur_qparams) - - # convert a list of qparams objects into a single one. enerally by - # concatenating a bunch of n,1 scale/zeros tensors into a n,num_groups tensor - - # `combine_qparams_list_func`. - all_qparams = self.combine_qparams_list_func(all_qparams) - Q = self.quantize_func(DQ, all_qparams) - return Q, DQ.to(orig_dtype), all_qparams - - -class GPTQQuantizer(Quantizer): - """ - This class implements a GPTQ Quantizer that can be used to apply GPTQ to a model in concert with the GenericGPTQRunner class. - Unlike the base Quantizer class, the user does not need to implement the create_quantized_state_dict, instead they have to reimplement - __init__ such that it defines the functions for the quantization mode. User is expected to reimplement convert_for_runtime. - - The following functions (which must be defined in __init__) are used to define the quantization mode for both GPTQ and - create_quantized_state_dict. Here is a description of each function. - - get_qparams_func: - A function that calculates the quantization qparams for an input tensor. - Args: - weight: A 2d weight tensor with non-integer dtype. - Returns: - qparams: it can have any format but will need to be handled by the other defined functions below. - - quantize_func: - A function that applies quantization to an input tensor. It should be noted - that this function needs to be able to handle quantizing the entire weight tensor, a single group, - or a single column. - Args: - weight: A 2d weight tensor with non-integer dtype. - qparams: the output from get_qparams_func - Returns: - quantized_weight: A 2d quantized weight tensor (generally with an integer dtype) - - - dequantize_func: - A function that dequantizes an input quantized weight tensor. It should be noted - that this function needs to be able to handle dequantizing the entire weight tensor, a single group, - or a single column. - Args: - quantized_weight: A 2d quantized weight tensor (generally with an integer dtype) - qparams: the output from get_qparams_func - Returns: - weight: A 2d weight tensor with non-integer dtype. - - act_fake_quant_func (optional): - A function that (dynamically) quantizes activation to input - Args: - input: input Tensor in f32/bf16/f16 - Returns: - output: dynamically quantized and dequantized Tensor (with the same dtype as input) - - combine_qparams_list_func: - A function that combines several qparams into one qparam. - Args: - qparams_list: a list of qparams objects, each obtained by calling get_qparams_func - on a single group from a weight tensor - Returns: - qparams: an object of the same format as the qparams above. - - skip_layer_func: - A function that determines which linear layers should be skipped during GPTQ - Args: - weight: A 2d weight tensor with non-integer dtype. - Returns: - skip: boolean indicating whether layer should be skipped - - make_names_and_values_dict_func: - A function that prepares the qparams and quantized_weight and creates a dictionary indicating how they - should be inserted into the state_dict. Generally any packing of the weight and qparams should be done here. - Args: - quantized_weight: A 2d quantized weight tensor (generally with an integer dtype) - qparams: the output from get_qparams_func - Returns: - names_and_values_dict: a dictionary mapping the name of the parameters of the quantized module to the - corresponding quantized weights and qparams. - """ - - def __init__(self): - assert self.get_qparams_func is not None - - assert self.quantize_func is not None - - assert self.dequantize_func is not None - - assert self.combine_qparams_list_func is not None - - # `make_names_and_values_dict_func`. - assert self.make_names_and_values_dict_func is not None - - @torch.no_grad() - def _create_quantized_state_dict( - self, - model, - inputs, - blocksize, - percdamp, - groupsize, - # `typing.Dict[, ]` to avoid runtime subscripting errors. - ) -> Dict: - print("Tracing model for GPTQ") - GPTQ_runner = GenericGPTQRunner( - model, - inputs, - blocksize, - percdamp, - groupsize, - ).configure_quantization_mode( - self.get_qparams_func, # pyre-ignore[16] - self.quantize_func, # pyre-ignore[16] - self.dequantize_func, # pyre-ignore[16] - self.combine_qparams_list_func, # pyre-ignore[16] - self.make_names_and_values_dict_func, # pyre-ignore[16] - self.skip_layer_func, # pyre-ignore[16] - self.act_fake_quant_func - if hasattr(self, "act_fake_quant_func") - else None, # pyre-ignore[16] - ) - print("Applying GPTQ to weights") - GPTQ_runner.run() - return GPTQ_runner.get_quantized_state_dict() - - def _convert_for_runtime(self, model: torch.nn.Module) -> "nn.Module": - raise NotImplementedError("_convert_for_runtime not implemented") - - @torch.no_grad() - def quantize( - self, model: torch.nn.Module, inputs: List[_MultiInput], **kwargs: Any - ) -> torch.nn.Module: - pass - - -def _check_linear_int4_k(k, groupsize=1, inner_k_tiles=None): - k_divisible_by_groupsize = k % groupsize == 0 - if inner_k_tiles is not None: - k_divisible_by_16_times_inner_k_tiles = k % (inner_k_tiles * 16) == 0 - return k_divisible_by_groupsize and k_divisible_by_16_times_inner_k_tiles - return k_divisible_by_groupsize - - -def linear_forward_int4( - x: torch.Tensor, - weight_int4pack: torch.Tensor, - scales_and_zeros: torch.Tensor, - out_features: int, - groupsize: int, - precision: torch.dtype = torch.bfloat16, - scales_precision: torch.dtype = torch.bfloat16, -): - origin_x_size = x.size() - x = x.reshape(-1, origin_x_size[-1]) - if is_device(x.device.type, "cpu") and TORCH_VERSION_AT_LEAST_2_6: - c = torch.ops.aten._weight_int4pack_mm_for_cpu( - x.to(precision), - weight_int4pack, - groupsize, - scales_and_zeros.to(scales_precision), - ).to(dtype=x.dtype) - else: - c = torch.ops.aten._weight_int4pack_mm( - x.to(precision), - weight_int4pack, - groupsize, - scales_and_zeros.to(scales_precision), - ).to(dtype=x.dtype) - new_shape = origin_x_size[:-1] + (out_features,) - c = c.reshape(new_shape) - return c - - -class WeightOnlyInt4Linear(torch.nn.Module): - __constants__ = ["in_features", "out_features"] - in_features: int - out_features: int - weight: torch.Tensor - - def __init__( - self, - in_features: int, - out_features: int, - # TODO: remove dtype field, not used - bias=False, - device=None, - dtype=None, - groupsize: int = 128, - inner_k_tiles: int = 8, - precision: torch.dtype = torch.bfloat16, - scales_precision: torch.dtype = torch.bfloat16, - ) -> None: - super().__init__() - self.padding = not _check_linear_int4_k(in_features, groupsize, inner_k_tiles) - if self.padding: - self.origin_in_features = in_features - in_features = find_multiple(in_features, 1024) - - self.in_features = in_features - self.out_features = out_features - assert not bias, "require bias=False" - self.device = device - self.groupsize = groupsize - self.inner_k_tiles = inner_k_tiles - self.precision = precision - self.scales_precision = scales_precision - - if dtype is not None: - raise ValueError("Please specify 'precision' instead of 'dtype'") - - assert out_features % 8 == 0, "require out_features % 8 == 0" - assert in_features % (inner_k_tiles * 16) == 0, ( - "require in_features % (innerKTiles * 16) == 0" - ) - if is_device(device.type, "cpu"): - self.register_buffer( - "weight", - torch.zeros( - ( - out_features, - in_features // 2, - ), - dtype=torch.uint8, - device=device, - ), - ) - else: - self.register_buffer( - "weight", - torch.zeros( - ( - out_features // 8, - in_features // (inner_k_tiles * 16), - 32, - inner_k_tiles // 2, - ), - dtype=torch.int32, - device=device, - ), - ) - self.dtype = dtype - self.register_buffer( - "scales_and_zeros", - torch.zeros( - (in_features // groupsize, out_features, 2), - dtype=self.scales_precision, - device=device, - ), - ) - - def forward(self, input: torch.Tensor) -> torch.Tensor: - if self.padding: - input = F.pad(input, pad=(0, self.in_features - self.origin_in_features)) - return linear_forward_int4( - input, - self.weight, - self.scales_and_zeros, - self.out_features, - self.groupsize, - self.precision, - self.scales_precision, - ) - - -def _replace_linear_int4( - module: torch.nn.Module, - groupsize: int, - inner_k_tiles: Optional[int], - padding_allowed: bool, - skip_layer_func: Optional[Callable] = None, - precision: torch.dtype = torch.bfloat16, - scales_precision: torch.dtype = torch.bfloat16, - linear_class: Type[torch.nn.Module] = WeightOnlyInt4Linear, - copy_weights: bool = False, -): - for name, child in module.named_children(): - # TODO: support linear bias - if ( - isinstance(child, nn.Linear) - and child.bias is None - and (skip_layer_func is None or not skip_layer_func(child.weight)) - ): - if ( - _check_linear_int4_k(child.in_features, groupsize, inner_k_tiles) - or padding_allowed - ): - new_linear = linear_class( - child.in_features, - child.out_features, - bias=False, - device=child.weight.device, - groupsize=groupsize, - inner_k_tiles=inner_k_tiles, - precision=precision, - scales_precision=scales_precision, - ) - # TODO: merge with 8da4w? - # In distributed training, the model may be instantiated - # on the meta device, in which case there is no need to - # copy the weights, and doing so will result in an error - if copy_weights and child.weight.device != torch.device("meta"): - new_linear.weight = child.weight - setattr(module, name, new_linear) - else: - _replace_linear_int4( - child, - groupsize, - inner_k_tiles, - padding_allowed, - skip_layer_func, - precision, - scales_precision, - linear_class, - copy_weights, - ) - - -def replace_linear_int4( - module, groupsize, inner_k_tiles, padding_allowed, skip_layer_func=None -): - _replace_linear_int4( - module, - groupsize, - inner_k_tiles, - padding_allowed, - skip_layer_func, - linear_class=WeightOnlyInt4Linear, - ) - - -class Int4WeightOnlyQuantizer(Quantizer): - def __init__( - self, - groupsize: int = 256, - padding_allowed: bool = True, - inner_k_tiles: Optional[int] = 8, - device: torch.device = torch.device("cuda"), - precision: torch.dtype = torch.bfloat16, - ) -> None: - super().__init__() - assert inner_k_tiles in [2, 4, 8] - assert groupsize in [32, 64, 128, 256] - - self.inner_k_tiles = inner_k_tiles - self.groupsize: int = groupsize - self.padding_allowed: bool = padding_allowed - self.device: torch.device = device - # precision and dtype are being used interchangeably here - self.precision: torch.dtype = precision - - @torch.no_grad() - def _create_quantized_state_dict( - self, model: torch.nn.Module - ) -> Dict[str, torch.Tensor]: - cur_state_dict = model.state_dict() - for fqn, mod in model.named_modules(): - if isinstance(mod, torch.nn.Linear) and mod.bias is None: - out_features = mod.out_features - in_features = mod.in_features - # assert out_features % 8 == 0, "require out_features % 8 == 0" - logging.info(f"linear: {fqn}, in={in_features}, out={out_features}") - - assert in_features % self.groupsize == 0, ( - f"require in_features:{in_features} % self.groupsize:{self.groupsize} == 0" - ) - - weight = mod.weight.data - if not _check_linear_int4_k( - in_features, self.groupsize, self.inner_k_tiles - ): - if self.padding_allowed: - import torch.nn.functional as F - - logging.warning( - f"warning: {fqn} is padded to satisfy in_features % 1024 == 0" - ) - padded_in_features = find_multiple(in_features, 1024) - weight = F.pad( - weight, pad=(0, padded_in_features - in_features) - ) - else: - logging.warning( - f"warning: {fqn} is skipped, int4 requires that in_features is 32, 64, or is divisible by 1024, " - + "and that groupsize and inner_k_tiles*16 evenly divide into it" - ) - continue - (w_int4x8, scales_and_zeros) = groupwise_affine_quantize_tensor( - weight, - 4, # n_bit - self.groupsize, - self.precision, # dtype for scales_and_zeros - ) - # TODO: just get the device from mod.weight.device? - if ( - is_device(w_int4x8.device.type, "cpu") - and TORCH_VERSION_AT_LEAST_2_6 - ): - weight_int4pack = ( - torch.ops.aten._convert_weight_to_int4pack_for_cpu( - w_int4x8.to(self.device), self.inner_k_tiles - ) - ) - else: - weight_int4pack = torch.ops.aten._convert_weight_to_int4pack( - w_int4x8.to(self.device), self.inner_k_tiles - ) - cur_state_dict[f"{fqn}.weight"] = weight_int4pack.to(self.device) - cur_state_dict[f"{fqn}.scales_and_zeros"] = scales_and_zeros.to( - self.device - ) - return cur_state_dict - - def _convert_for_runtime(self, model: torch.nn.Module) -> torch.nn.Module: - _replace_linear_int4( - model, - self.groupsize, - self.inner_k_tiles, - self.padding_allowed, - skip_layer_func=None, - precision=self.precision, - scales_precision=self.precision, - ) - return model - - def quantize( - self, model: torch.nn.Module, *args: Any, **kwargs: Any - ) -> torch.nn.Module: - state_dict = self._create_quantized_state_dict(model) - model = self._convert_for_runtime(model) - # TODO: make it strict - model.load_state_dict(state_dict, strict=False) - return model - - -class Int4WeightOnlyGPTQQuantizer(GPTQQuantizer): - def __init__( - self, - blocksize=128, - percdamp=0.01, - groupsize=64, - inner_k_tiles=8, - padding_allowed=True, - device: torch.device = torch.device("cuda"), - ): - self.blocksize = blocksize - self.percdamp = percdamp - self.groupsize = groupsize - self.inner_k_tiles = inner_k_tiles - self.padding_allowed = padding_allowed - self.device = device - self.act_fake_quant_func = None - n_bit = 4 - self.get_qparams_func = lambda w: get_groupwise_affine_qparams( - w, n_bit, groupsize - ) - self.quantize_func = ( - lambda w, qparams: groupwise_affine_quantize_tensor_from_qparams( - w, qparams[0], qparams[1], n_bit, groupsize - ) - ) - self.dequantize_func = ( - lambda q, qparams: groupwise_affine_dequantize_tensor_from_qparams( - q, - qparams[0], - qparams[1], - n_bit, - groupsize, - ) - ) - self.combine_qparams_list_func = lambda qparams_list: [ - torch.cat(x, dim=1) for x in zip(*qparams_list) - ] - # skip unless padding_allowed=True or its correctly sized - self.skip_layer_func = lambda linear_weight: not ( - _check_linear_int4_k(linear_weight.shape[-1], groupsize) or padding_allowed - ) - - # we need to do the padding here, both for q and the qparams if necessary - - # TODO: this is the gpt-fast version, merge with the main version later - def make_names_and_values_dict_func(q, qparams): - k = q.shape[1] * 2 - if not _check_linear_int4_k(k, groupsize): - new_k = find_multiple(k, 1024) - else: - new_k = k - # how much we need to pad the weight - delta_k = int((new_k - k) / 2) - q = q.to(self.device) - if is_device(self.device.type, "cpu") and TORCH_VERSION_AT_LEAST_2_6: - final_q = torch.ops.aten._convert_weight_to_int4pack_for_cpu( - F.pad(q, pad=(0, delta_k)), inner_k_tiles - ) - else: - final_q = torch.ops.aten._convert_weight_to_int4pack( - F.pad(q, pad=(0, delta_k)), inner_k_tiles - ) - scales = qparams[0].to(torch.bfloat16).to(self.device) - zeros = qparams[1].to(torch.bfloat16).to(self.device) - scales_and_zeros = pack_tinygemm_scales_and_zeros(scales, zeros) - # how many new groups we need for padded weight - delta_groups = new_k // groupsize - scales_and_zeros.shape[0] - final_s_and_z = F.pad( - scales_and_zeros, pad=(0, 0, 0, 0, 0, delta_groups), value=1 - ) - return {"weight": final_q, "scales_and_zeros": final_s_and_z} - - self.make_names_and_values_dict_func = make_names_and_values_dict_func - super().__init__() - - def _convert_for_runtime(self, model): - replace_linear_int4( - model, - self.groupsize, - self.inner_k_tiles, - self.padding_allowed, - skip_layer_func=self.skip_layer_func, - ) - return model - - def quantize( - self, model: torch.nn.Module, inputs: List[_MultiInput], **kwargs: Any - ) -> torch.nn.Module: - state_dict = self._create_quantized_state_dict( - model, - inputs, - self.blocksize, - self.percdamp, - self.groupsize, - ) - model = self._convert_for_runtime(model) - model.load_state_dict(state_dict, strict=False) - return model - - -def linear_forward_8da4w( - x, - weight_int8, - bias, - scales, - zeros, - out_features, - groupsize, - output_precision, -): - # uses fp32 to match torchao.quantization.quant_api._int8_asymm_per_token_quant - # and activation_scale_dtype in QAT configs - # TODO: in future add ability to specify activation_scale_dtype to PTQ configs - # and enable similar change here - x = per_token_dynamic_quant( - x, - scale_dtype=torch.float32, - zero_point_dtype=torch.float32, - eps=torch.finfo(torch.float32).eps, - ) - - # TODO: verify and remove following reshape code - # origin_x_size = x.size() - # x = x.reshape(-1, origin_x_size[-1]) - - # TODO: better API - # weight_int8 = torch.ops.quantized_decomposed.unpack_int4_to_int8(weight_int4packed) - n_bit = 4 - quant_min = -(2 ** (n_bit - 1)) - quant_max = 2 ** (n_bit - 1) - 1 - block_size = (1, groupsize) - - w_dq = dequantize_affine( - weight_int8, - block_size, - scales, - zeros, - torch.int8, - quant_min, - quant_max, - output_dtype=output_precision, - ) - - # x = x.to(torch.float16) - # w_dq = w_dq.to(torch.float16) - c = torch.nn.functional.linear(x, w_dq, bias) - - # new_shape = origin_x_size[:-1] + (out_features,) - # c = c.reshape(new_shape) - - return c - - -class Int8DynActInt4WeightLinear(torch.nn.Module): - __constants__ = ["in_features", "out_features"] - - in_features: int - out_features: int - weight: torch.Tensor - bias: torch.Tensor - - """ - This module implements a dynamic quantized linear layer with int4 weight. - Weights are per channel groupwise quantized. Parameters of importance - groupsize: the number of elements in each quantized group - precision: precision of input and output. e.g. torch.float32 means input - activation is float32 and output is float32. - scales_precision: precision of per group scale. - """ - - def __init__( - self, - in_features: int, - out_features: int, - bias=True, - device=None, - # TODO: remove this field, not used - dtype=None, - groupsize: int = 256, - precision: torch.dtype = torch.float32, - scales_precision: torch.dtype = torch.float32, - ) -> None: - super().__init__() - # always pad if needed since it becomes a noop at runtime if not needed - # self.origin_in_features = in_features - assert in_features % groupsize == 0, ( - f"require in_features:{in_features} % groupsize:{groupsize} == 0" - ) - # in_features = _calc_padded_size_linear_int4( - # in_features, groupsize - # ) - self.in_features = in_features - self.out_features = out_features - # TODO: align groupsize naming - self.groupsize = groupsize - # Precision of the activation which also indicates - # output precision of the dynamically quantized linear layer - # that his module represents. - self.precision = precision - - if dtype is not None: - raise ValueError("Please specify 'precision' instead of 'dtype'") - - # currently storing unpacked int8 weights - self.register_buffer( - "weight", - torch.zeros((out_features, in_features), dtype=torch.int8), - ) - self.register_buffer( - "scales", - torch.zeros( - (out_features, in_features // groupsize), - dtype=scales_precision, - ), - ) - self.register_buffer( - "zeros", - torch.zeros( - (out_features, in_features // groupsize), - dtype=scales_precision, - ), - ) - - if bias: - self.register_buffer("bias", torch.zeros(out_features, dtype=precision)) - else: - self.bias = None - - def forward(self, input: torch.Tensor) -> torch.Tensor: - input = input.to(self.precision) - # padding is removed for perf - # input = F.pad(input, pad=(0, self.in_features - self.origin_in_features)) - return linear_forward_8da4w( - input, - self.weight, - self.bias, - self.scales, - self.zeros, - self.out_features, - self.groupsize, - self.precision, - ) - - -def _replace_linear_8da4w( - module: torch.nn.Module, - groupsize: int, - padding_allowed: bool, - precision: torch.dtype, - scales_precision: torch.dtype, - linear_class: Type[torch.nn.Module], - copy_weights: bool = False, -): - # import the util function here to avoid circular dependency - from torchao.quantization.quant_api import _replace_with_custom_fn_if_matches_filter - - def filter_fn(child: torch.nn.Module, cur_fqn: str) -> bool: - return isinstance(child, nn.Linear) and ( - _check_linear_int4_k(child.in_features, groupsize) or padding_allowed - ) - - def replacement_fn(child: torch.nn.Module) -> torch.nn.Module: - new_linear = linear_class( - child.in_features, - child.out_features, - bias=child.bias is not None, - device=child.weight.device, - groupsize=groupsize, - precision=precision, - scales_precision=scales_precision, - ) - # In distributed training, the model may be instantiated - # on the meta device, in which case there is no need to - # copy the weights, and doing so will result in an error - if copy_weights and child.weight.device != torch.device("meta"): - new_linear.weight = child.weight - new_linear.bias = child.bias - return new_linear - - _replace_with_custom_fn_if_matches_filter(module, replacement_fn, filter_fn) - - -def replace_linear_8da4w( - module: torch.nn.Module, - groupsize: int, - padding_allowed: bool, - precision: torch.dtype, - scales_precision: torch.dtype, -): - _replace_linear_8da4w( - module, - groupsize, - padding_allowed, - precision, - scales_precision, - Int8DynActInt4WeightLinear, - ) - - -class Int8DynActInt4WeightQuantizer(Quantizer): - def __init__( - self, - groupsize: int = 256, - padding_allowed: bool = False, - precision: torch.dtype = torch.float32, - scales_precision: torch.dtype = torch.float32, - device: torch.device = torch.device("cpu"), - mapping_type: MappingType = MappingType.SYMMETRIC, - ) -> None: - super().__init__() - self.groupsize: int = groupsize - self.padding_allowed: bool = padding_allowed - self.precision: torch.dtype = precision - self.scales_precision: torch.dtype = scales_precision - self.device: torch.device = device - self.mapping_type: MappingType = mapping_type - - @torch.no_grad() - def _create_quantized_state_dict( - self, model: torch.nn.Module - ) -> Dict[str, torch.Tensor]: - cur_state_dict = model.state_dict() - for fqn, mod in model.named_modules(): - if isinstance(mod, torch.nn.Linear): - out_features = mod.out_features - in_features = mod.in_features - # assert out_features % 8 == 0, "require out_features % 8 == 0" - logging.info(f"linear: {fqn}, in={in_features}, out={out_features}") - - assert in_features % self.groupsize == 0, ( - f"require in_features:{in_features} % self.groupsize:{self.groupsize} == 0" - ) - - weight = mod.weight.data - if not _check_linear_int4_k(in_features, self.groupsize): - if self.padding_allowed: - import torch.nn.functional as F - - logging.warning( - f"warning: {fqn} is padded to satisfy in_features % 1024 == 0" - ) - padded_in_features = find_multiple(in_features, 1024) - weight = F.pad( - weight, pad=(0, padded_in_features - in_features) - ) - else: - logging.warning( - f"warning: {fqn} is skipped, int4 requires that in_features is 32, 64, or is divisible by 1024, " - + "and that groupsize and inner_k_tiles*16 evenly divide into it" - ) - continue - ( - weight_int8, - scales, - zeros, - ) = group_quantize_tensor_symmetric( - weight.to(self.precision), - 4, # n_bit - self.groupsize, - self.scales_precision, - mapping_type=self.mapping_type, - ) - cur_state_dict[f"{fqn}.weight"] = weight_int8.to(self.device) - cur_state_dict[f"{fqn}.scales"] = scales.to(self.device) - cur_state_dict[f"{fqn}.zeros"] = zeros.to(self.device) - - return cur_state_dict - - def _convert_for_runtime(self, model: torch.nn.Module) -> torch.nn.Module: - replace_linear_8da4w( - model, - self.groupsize, - self.padding_allowed, - self.precision, - # TODO: this should be self.scales_precision? - self.precision, - ) - return model - - def quantize( - self, model: torch.nn.Module, *args: Any, **kwargs: Any - ) -> torch.nn.Module: - state_dict = self._create_quantized_state_dict(model) - model = self._convert_for_runtime(model) - # TODO: make it strict - model.load_state_dict(state_dict, strict=False) - return model - - -class Int8DynActInt4WeightGPTQQuantizer(GPTQQuantizer): - def __init__( - self, - blocksize=128, - percdamp=0.01, - groupsize=64, - inner_k_tiles=8, - padding_allowed=True, - precision=torch.float32, - ): - self.blocksize = blocksize - self.percdamp = percdamp - self.groupsize = groupsize - self.inner_k_tiles = inner_k_tiles - self.padding_allowed = padding_allowed - self.precision = precision - - self.act_fake_quant_func = per_token_dynamic_quant - n_bit = 4 - self.get_qparams_func = lambda w: get_group_qparams_symmetric( - w, n_bit, groupsize, self.precision - ) - quant_min = -(2 ** (n_bit - 1)) - quant_max = 2 ** (n_bit - 1) - 1 - - from torchao._executorch_ops import ( - _quantized_decomposed_quantize_per_channel_group_wrapper, - ) - - self.quantize_func = ( - lambda w, qparams: _quantized_decomposed_quantize_per_channel_group_wrapper( - w, qparams[0], qparams[1], quant_min, quant_max, torch.int8, groupsize - ) - ) - - from torchao._executorch_ops import ( - _quantized_decomposed_dequantize_per_channel_group_wrapper, - ) - - self.dequantize_func = ( - lambda q, - qparams: _quantized_decomposed_dequantize_per_channel_group_wrapper( - q, - qparams[0], - qparams[1], - quant_min, - quant_max, - torch.int8, - groupsize, - self.precision, - ) - ) - - self.combine_qparams_list_func = lambda qparams_list: [ - torch.cat(x, dim=1) for x in zip(*qparams_list) - ] - # skip unless padding_allowed=True or its correctly sized - - self.skip_layer_func = lambda linear_weight: not ( - _check_linear_int4_k(linear_weight.shape[-1], groupsize) or padding_allowed - ) - - # we need to do the padding here, both for q and the qparams if necessary - def make_names_and_values_dict_func(q, qparams): - k = q.shape[1] - new_k = find_multiple(k, 1 if groupsize is None else groupsize) - # how much we need to pad the weight - delta_k = new_k - q.shape[1] - final_q = F.pad(q, pad=(0, delta_k)) - scales = qparams[0].to(self.precision) - zeros = qparams[1].to(self.precision) - return {"weight": final_q, "scales": scales, "zeros": zeros} - - self.make_names_and_values_dict_func = make_names_and_values_dict_func - super().__init__() - - def _convert_for_runtime(self, model): - replace_linear_8da4w( - model, - self.groupsize, - self.padding_allowed, - self.precision, - # TODO: this should be self.scales_precision? - self.precision, - ) - return model - - def quantize( - self, model: torch.nn.Module, inputs: List[_MultiInput], **kwargs: Any - ) -> torch.nn.Module: - state_dict = self._create_quantized_state_dict( - model, - inputs, - self.blocksize, - self.percdamp, - self.groupsize, - ) - model = self._convert_for_runtime(model) - model.load_state_dict(state_dict, strict=False) - return model diff --git a/torchao/quantization/GPTQ_MT.py b/torchao/quantization/GPTQ/GPTQ.py similarity index 51% rename from torchao/quantization/GPTQ_MT.py rename to torchao/quantization/GPTQ/GPTQ.py index 664ebdfc17..8c3f791fd6 100644 --- a/torchao/quantization/GPTQ_MT.py +++ b/torchao/quantization/GPTQ/GPTQ.py @@ -3,6 +3,7 @@ # # This source code is licensed under the BSD 3-Clause license found in the # LICENSE file in the root directory of this source tree. +import math from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union import torch @@ -21,16 +22,21 @@ groupwise_affine_quantize_tensor_from_qparams, ) +GPTQ_FUNC_LIST = {} -def _check_linear_int4_k(k, group_size=1, inner_k_tiles=None): - k_divisible_by_group_size = k % group_size == 0 - if inner_k_tiles is not None: - k_divisible_by_16_times_inner_k_tiles = k % (inner_k_tiles * 16) == 0 - return k_divisible_by_group_size and k_divisible_by_16_times_inner_k_tiles - return k_divisible_by_group_size +__all__ = [ + "Int4WeightOnlyGPTQQuantizer", + "MultiTensorInputRecorder", + "MultiTensor", + "GPTQQuantizer", + "StateDictManager", +] -NON_IN_PLACE_OPS = {} + +############################# +# Core Classes # +############################# class MultiTensor(torch.Tensor): @@ -41,11 +47,11 @@ class MultiTensor(torch.Tensor): make_qtensor = None skip_layer_func = None act_fake_quant_func = None - percdamp = 0.01 - blocksize = 128 - group_size = -1 - in_place_threshold = ( - 5 # Number of times to see a function before assuming it's not in-place + group_size: int = -1 + percdamp: float = 0.01 + blocksize: int = 128 + in_place_threshold: int = ( + 3 # Number of times to see a function before assuming it's not in-place ) @staticmethod @@ -65,11 +71,16 @@ def __init__( self.state_dict_manager = StateDictManager.get_instance() self.count: int = 0 self.add_tensors(input) - self.debug: bool = True + self.debug: bool = False self.gptq_done = False def __repr__(self) -> str: - return f"{self.__class__.__name__}(data={self.values})" + return ( + f"{self.__class__.__name__}(shape={self.shape}, example={self.values[0]})" + ) + + def append(self, input: torch.Tensor): + return self.add_tensors(input) def add_tensors( self, input: Union[torch.Tensor, Sequence[torch.Tensor]] @@ -102,13 +113,10 @@ def pad_to_length(self, length, pad_in_place=True): ) return self - def unpad(self, count=1, force=False): + def unpad(self, count=1): count = min(count, self.count) - if force or all((self.values[0] == x).all().item() for x in self.values): - self.values = self.values[:count] - self.count = count - else: - return self + self.values = self.values[:count] + self.count = count @classmethod def configure_quantization_mode( @@ -120,9 +128,9 @@ def configure_quantization_mode( make_qtensor, skip_layer_func, act_fake_quant_func=None, + group_size=-1, percdamp=0.01, blocksize=128, - group_size=-1, ): cls.get_qparams_func = get_qparams_func cls.quantize_func = quantize_func @@ -133,9 +141,9 @@ def configure_quantization_mode( cls.act_fake_quant_func = ( act_fake_quant_func if act_fake_quant_func is not None else lambda x: x ) + cls.group_size = group_size cls.percdamp = percdamp cls.blocksize = blocksize - cls.group_size = group_size @classmethod def __torch_function__( @@ -146,72 +154,6 @@ def __torch_function__( kwargs: Optional[Dict[str, Any]] = None, skip_gptq: bool = False, ) -> Any: - def flat_to_grouped( - flat: List[Any], pad_in_place=True - ) -> List[Tuple[Any, ...]]: - # convert [A, MultiTensor(b1,b2,b3), MultiTensor(c1,c2,c3)] => [[A,b1,c1], [A,b2,c2] [A,b3,c3]] - multi_tensor_size = max( - [x.count if isinstance(x, MultiTensor) else 1 for x in flat] - ) - grouped = list( - zip( - *[ - x.pad_to_length( - multi_tensor_size, pad_in_place=pad_in_place - ).values - if isinstance(x, MultiTensor) - else [x] * multi_tensor_size - for x in flat - ] - ) - ) - return grouped - - def grouped_to_flat(grouped: List[Tuple[Any, ...]]) -> Tuple[List[Any], bool]: - # convert [[A,b1,c1], [A,b2,c2] [A,b3,c3]] => [(A,A,A), (b1,b2,b3), (c1,c2,c3)] - flat_tups = list(zip(*grouped)) - # convert [(A,A,A), (b1,b2,b3), (c1,c2,c3)] => [A, MultiTensor(b1,b2,b3), MultiTensor(c1,c2,c3)] - flattened = [ - cls(tup).cpu() if isinstance(tup[0], torch.Tensor) else tup[0] - for tup in flat_tups - ] - non_tensors_equal = all( - all(x == tup[0] for x in tup) - for tup in flat_tups - if not isinstance(tup[0], torch.Tensor) - ) - return flattened, non_tensors_equal - - def tensors_to_cuda(args): - # this is needed because we want to execute the actual ops in cuda so they don't take forever - new_args = [] - for x in args: - if isinstance(x, MultiTensor) and x.count == 1: - new_args.append(x.__class__(x.values[0].cuda())) - else: - new_args.append( - x.cuda() - if isinstance(x, torch.Tensor) - and not isinstance(x, MultiTensor) - else x - ) - return new_args - - def maybe_copy_new_values(orig_inp, new_inp): - detected_difference = False - for x, new_x in zip(orig_inp, new_inp): - if isinstance(x, torch.Tensor): - new_x = new_x.to(x.device) - if (x != new_x).any(): - x.copy_(new_x) - detected_difference = True - return detected_difference - - def unpad(args, orig_counts, force=False): - for arg, count in zip(args, orig_counts): - if isinstance(arg, MultiTensor) and arg.count > count: - arg.unpad(count, force) - # The way MultiTensor handles various functions is as follows. Normally when you apply a function on a MultiTensor that has n Tensors inside, we want # the function handling here to run that function once for each of the MultiTensor inputs. We also want it to happen in the same way as if you ran the function # first input and the second independently, i.e. if you ran model(input1)=out1 and model(input2), vs model(MultiTensor(input1, input2)) the output for the @@ -231,7 +173,7 @@ def unpad(args, orig_counts, force=False): # There's not really a great way to resolve the 2 issues, when there's an in place op you have to do the slow thing with cuda and checking for modified values....etc, when # there's not an in place op you can throw all singular tensors onto cuda at the start and go much faster. # This brings up the final issue, how do we know if we have an in place op? In general we don't so I added handling to MultiTensor to resolve that as well as can be hoped. - # we have a dict that contains all the funcs we see NON_IN_PLACE_OPS, we initially treat ops as in place and see if any of the inputs got modified if they do then it gets + # we have a dict that contains all the funcs we see GPTQ_FUNC_LIST, we initially treat ops as in place and see if any of the inputs got modified if they do then it gets # set to always be handled as an in place op. If nothing changes then once we've seen the op enough times that we're confident its not an in place op (cls.in_place_threshold) # then we can do the fast thing. @@ -239,15 +181,13 @@ def unpad(args, orig_counts, force=False): # Determine if function is in-place # initialize function tracking - if func not in NON_IN_PLACE_OPS: - NON_IN_PLACE_OPS[func] = {"count": 0, "is_in_place": None} - NON_IN_PLACE_OPS[func]["count"] += 1 - - if NON_IN_PLACE_OPS[func]["is_in_place"] is not None: - is_in_place = NON_IN_PLACE_OPS[func]["is_in_place"] - elif ( - NON_IN_PLACE_OPS[func]["count"] >= cls.in_place_threshold or quantize_linear - ): + if func not in GPTQ_FUNC_LIST: + GPTQ_FUNC_LIST[func] = {"count": 0, "is_in_place": None} + GPTQ_FUNC_LIST[func]["count"] += 1 + + if GPTQ_FUNC_LIST[func]["is_in_place"] is not None: + is_in_place = GPTQ_FUNC_LIST[func]["is_in_place"] + elif GPTQ_FUNC_LIST[func]["count"] >= cls.in_place_threshold or quantize_linear: is_in_place = False # Assume not in-place after threshold else: is_in_place = True @@ -257,176 +197,196 @@ def unpad(args, orig_counts, force=False): # flat_args holds all the actual inputs, spec stores the original structure flat_args, spec = tree_flatten((args, kwargs)) - orig_counts = [x.count if isinstance(x, MultiTensor) else 1 for x in flat_args] - # if we're not doing an in place op, move singular tensors to cuda now if not is_in_place: - flat_args = tensors_to_cuda(flat_args) + flat_args = _tensors_to_cuda(flat_args) # convert [A, MultiTensor(b), MultiTensor(c1,c2,c3)] => [[A,b,c1], [A,b,c2] [A,b,c3]] - # if its in place then instead we first convert MultiTensor(b) => MultiTensor(b1, b2, b3) + # if its in place then instead we first pad i.e. MultiTensor(b) => MultiTensor(b1, b2, b3) # then proceed as normal. - grouped_args = flat_to_grouped(flat_args, is_in_place) + grouped_args, orig_counts = _flat_to_grouped_and_pad(flat_args, is_in_place) with torch._C.DisableTorchFunctionSubclass(): - if quantize_linear: - H = 0 - total_batches = 0 - - outputs = [] - for inp in grouped_args: - # we move all remaining cpu tensors to cuda - cuda_inp = tensors_to_cuda(inp) - - # return input to original structure - cur_args, cur_kwargs = tree_unflatten(cuda_inp, spec) - - if quantize_linear: - # Construct Hessian matrix for quantization - x = cur_args[0].float() - # x = self.act_fake_quant_func(x) - shape = x.shape - n = 1 if len(shape) == 2 else shape[0] - H *= total_batches / (total_batches + n) - total_batches += n - - x = ((2 / total_batches) ** (1 / 2)) * x.reshape( - -1, shape[-1] - ).t().float() - - H += x.matmul(x.t()) - else: - try: - out = func(*cur_args, **cur_kwargs) - except Exception: - breakpoint() - - outputs.append(out.cpu() if isinstance(out, torch.Tensor) else out) - - # if we're doing an in place op, here is where we copy modifications - # back to the original tensors, if we saw any differences, immediately - # categortize func as in place, otherwise we can treat as not in - # place (especially for the upcoming unpad step) - if is_in_place: - detected_difference = maybe_copy_new_values(inp, cuda_inp) - # if detected_difference and not isinstance(NON_IN_PLACE_OPS[func], bool): - # print("THIS OP IS IN PLACE", func) - # NON_IN_PLACE_OPS[func] = False - if detected_difference and not NON_IN_PLACE_OPS[func]: - NON_IN_PLACE_OPS[func]["is_in_place"] = True - print(f"Function {func} is in-place") - - elif NON_IN_PLACE_OPS[func]["count"] >= cls.in_place_threshold: - NON_IN_PLACE_OPS[func]["is_in_place"] = False - - if quantize_linear: - # turn weight MultiTensor into single cuda tensor - W = args[1] - if isinstance(W, MultiTensor): - W = W.values[0] - W = W.to(H.device) - - Q, DQ, all_qparams = cls.faster_quant(H, W.detach()) - - # make quantized tensor subclass - qtensor = cls.make_qtensor(Q, all_qparams) - - # Get the original parameter name - state_dict_manager = StateDictManager.get_instance() - original_param_name = state_dict_manager.get_name_for_param(args[1]) - state_dict_manager.update_param(original_param_name, qtensor) - print(original_param_name) - - # Run the function again with updated weights and skip_gptq=True - out = cls.__torch_function__( - func, types, (args[0], DQ.cpu(), *args[2:]), kwargs, skip_gptq=True - ) - if args[0].debug: - act = args[0].values[0].to("cuda") - bias = ( - args[2].values[0].to("cuda") if args[2] is not None else args[2] - ) + if not quantize_linear: # normal function eval + out = cls._evaluate_function(func, grouped_args, spec, is_in_place) - new_out = out.values[0].cpu() - old_out = ( - cls.__torch_function__( - func, - types, - (act, args[1].values[0], bias), - kwargs, - skip_gptq=True, - ) - .values[0] - .cpu() - ) + # go back and unpad everything where possible. + if not GPTQ_FUNC_LIST[func]["is_in_place"]: + _do_unpad(flat_args, orig_counts) + return out - DQ_after = cls.dequantize_func(Q, all_qparams).to(W.dtype) - print( - "SQNR for QDQ (this should be inf)", SQNR(DQ, DQ_after) - ) # matches - print( - "SQNR for weight (can be low)", SQNR(W, DQ.cuda()) - ) # fine to not match - print( - "SQNR for output with GPTQ (hopefully 35+)", - SQNR(old_out, new_out), - ) + # GPTQ quantization for linear layers + # Calculate Hessian approximation + H = _calculate_hessian(grouped_args, spec) - DQ_from_qtensor = qtensor.dequantize() - qtensor_out = torch.nn.functional.linear(act, qtensor, bias).cpu() - print( - "SQNR for output from qtensor vs output from DQ (should be high)", - SQNR(qtensor_out, new_out), - ) - print( - "SQNR for DQ vs DQ from qtensor (should be inf)", - SQNR(DQ, DQ_from_qtensor), - ) + # turn weight MultiTensor into single cuda tensor + W = args[1] + if isinstance(W, MultiTensor): + W = W.values[0] + W = W.to(H.device) + + Q, DQ, all_qparams = cls.faster_quant(H, W.detach()) - qparams2 = cls.get_qparams_func(W) - Q2 = cls.quantize_func(W, qparams2) - DQ2 = cls.dequantize_func(Q2, qparams2).to(W.dtype) - old_q_out = ( - cls.__torch_function__( - func, types, (act, DQ2, bias), kwargs, skip_gptq=True - ) - .values[0] - .cpu() + # make quantized tensor subclass + qtensor = cls.make_qtensor(Q, all_qparams) + + # Get the original parameter name + state_dict_manager = StateDictManager.get_instance() + original_param_name = state_dict_manager.get_name_for_param(args[1]) + state_dict_manager.update_param(original_param_name, qtensor) + print(original_param_name) + + # Run the function again with updated weights and skip_gptq=True + out = cls.__torch_function__( + func, types, (args[0], DQ.cpu(), *args[2:]), kwargs, skip_gptq=True + ) + if not args[0].debug: + _do_unpad(flat_args, orig_counts=orig_counts) + return out + if args[0].debug: + act = args[0].values[0].to("cuda") + bias = args[2].values[0].to("cuda") if args[2] is not None else args[2] + + new_out = out.values[0].cpu() + old_out = ( + cls.__torch_function__( + func, + types, + (act, args[1].values[0], bias), + kwargs, + skip_gptq=True, ) + .values[0] + .cpu() + ) - print( - "SQNR for output without GPTQ (should be less than above)", - SQNR(old_out, old_q_out), + DQ_after = cls.dequantize_func(Q, all_qparams).to(W.dtype) + print( + "SQNR for QDQ (this should be inf)", SQNR(DQ, DQ_after) + ) # matches + print( + "SQNR for weight (can be low)", SQNR(W, DQ.cuda()) + ) # fine to not match + print( + "SQNR for output with GPTQ (hopefully 35+)", + SQNR(old_out, new_out), + ) + + DQ_from_qtensor = qtensor.dequantize() + qtensor_out = torch.nn.functional.linear(act, qtensor, bias).cpu() + print( + "SQNR for output from qtensor vs output from DQ (should be high)", + SQNR(qtensor_out, new_out), + ) + print( + "SQNR for DQ vs DQ from qtensor (should be inf)", + SQNR(DQ, DQ_from_qtensor), + ) + + qparams2 = cls.get_qparams_func(W) + Q2 = cls.quantize_func(W, qparams2) + DQ2 = cls.dequantize_func(Q2, qparams2).to(W.dtype) + old_q_out = ( + cls.__torch_function__( + func, types, (act, DQ2, bias), kwargs, skip_gptq=True ) - unpad(flat_args, orig_counts=orig_counts, force=True) - return out - else: - # we padded each of the MultiTensors to match the largest multitensor so that if we had in place ops, we would be able - # to store the many changed value and have those updates be reflected in the model. However if there are no in place ops, then - # we just increased the size of all parameters/buffers by n times for no reason. To avoid issues, go back and unpad - # everything where possible. i.e. all the multi tensor values are the same. We already checked for mutations and - # if we detected them, we updated NON_IN_PLACE_OPS to be False, so we can just check that see if we need - # to be careful during unpadding. - unpad( - flat_args, - orig_counts=orig_counts, - force=(not isinstance(NON_IN_PLACE_OPS[func], bool)), + .values[0] + .cpu() ) - grouped_outputs = [tree_flatten(x)[0] for x in outputs] - out_spec = tree_flatten(outputs[0])[1] - # conslidate out into MultiTensors [[A,b1,c1], [A,b2,c2] [A,b3,c3]] => [A, MultiTensor(b1,b2,b3), MultiTensor(c1,c2,c3)] - flat_outputs, non_tensors_equal = grouped_to_flat(grouped_outputs) - assert non_tensors_equal, ( - f"ERR: found a function in model: {func} which " - + "caused an error in GPTQ MultiTensor, the function dispatch only works for functions" - + " with Tensor outputs or that have the same non-Tensor output value for all across all inputs" + print( + "SQNR for output without GPTQ (should be less than above)", + SQNR(old_out, old_q_out), ) - final_out = tree_unflatten(flat_outputs, out_spec) - return final_out + _do_unpad(flat_args, orig_counts=orig_counts) + return out + + @classmethod + def grouped_to_flat(cls, grouped: List[Tuple[Any, ...]]) -> Tuple[List[Any], bool]: + # convert [[A,b1,c1], [A,b2,c2] [A,b3,c3]] => [(A,A,A), (b1,b2,b3), (c1,c2,c3)] + flat_tups = list(zip(*grouped)) + # convert [(A,A,A), (b1,b2,b3), (c1,c2,c3)] => [A, MultiTensor(b1,b2,b3), MultiTensor(c1,c2,c3)] + flattened = [ + cls(tup).cpu() if isinstance(tup[0], torch.Tensor) else tup[0] + for tup in flat_tups + ] + non_tensors_equal = all( + all(x == tup[0] for x in tup) + for tup in flat_tups + if not isinstance(tup[0], torch.Tensor) + ) + return flattened, non_tensors_equal + + @classmethod + def _evaluate_function(cls, func, grouped_args, spec, is_in_place): + outputs = [] + for inp in grouped_args: + # we move all remaining cpu tensors to cuda + cuda_inp = _tensors_to_cuda(inp) + + # return input to original structure + cur_args, cur_kwargs = tree_unflatten(cuda_inp, spec) + + out = func(*cur_args, **cur_kwargs) + + outputs.append(out.cpu() if isinstance(out, torch.Tensor) else out) + + # if we're doing an in place op, here is where we copy modifications + # back to the original tensors, if we saw any mutated inputs, immediately + # categortize func as in place. + if is_in_place: + detected_mutation = _maybe_copy_new_values( + inp, cuda_inp, force=GPTQ_FUNC_LIST[func]["is_in_place"] + ) # if we already know its in place, don't compare, just copy + if detected_mutation and GPTQ_FUNC_LIST[func]["is_in_place"] is None: + GPTQ_FUNC_LIST[func]["is_in_place"] = True + print( + f">>GPTQ process identified function {func} as in-place, continuing...<<" + ) + + # if no inputs were mutated and we've seen the function enough times, categorize it as not in place. + elif GPTQ_FUNC_LIST[func][ + "count" + ] >= cls.in_place_threshold and not isinstance( + GPTQ_FUNC_LIST[func]["is_in_place"], bool + ): + GPTQ_FUNC_LIST[func]["is_in_place"] = False + + grouped_outputs = [tree_flatten(x)[0] for x in outputs] + out_spec = tree_flatten(outputs[0])[1] + # conslidate out into MultiTensors [[A,b1,c1], [A,b2,c2] [A,b3,c3]] => [A, MultiTensor(b1,b2,b3), MultiTensor(c1,c2,c3)] + flat_outputs, non_tensors_equal = cls.grouped_to_flat(grouped_outputs) + assert non_tensors_equal, ( + f"ERR: found a function in model: {func} which " + + "caused an error in GPTQ MultiTensor, the function dispatch only works for functions" + + "with Tensor outputs or that have the same non-Tensor output value across all inputs" + ) + final_out = tree_unflatten(flat_outputs, out_spec) + return final_out @classmethod def faster_quant(cls, H, W): + """ + GPTQ quantization implementation. + + Args: + H: Hessian matrix approximation + W: Weight matrix to quantize + + Returns: + Tuple containing: + - Q: Quantized weights + - DQ: Dequantized weights + - all_qparams: Quantization parameters + """ + msg = ( + "tried to do faster quant but configure quantization mode was never called" + ) + assert cls.get_qparams_func is not None, msg + assert cls.quantize_func is not None, msg + assert cls.dequantize_func is not None, msg + assert cls.combine_qparams_list_func is not None, msg + percdamp = cls.percdamp blocksize = cls.blocksize group_size = cls.group_size @@ -436,13 +396,14 @@ def faster_quant(cls, H, W): device = W.device if group_size == -1: - cur_qparams = cls.get_qparams_func(W) + group_size = columns + else: + blocksize = math.ceil(blocksize / group_size) * group_size dead = torch.diag(H) == 0 H[dead, dead] = 1 W[:, dead] = 0 - Losses = torch.zeros_like(W) DQ = torch.zeros_like(W) damp = percdamp * torch.mean(torch.diag(H)) @@ -453,43 +414,48 @@ def faster_quant(cls, H, W): H = torch.linalg.cholesky(H, upper=True) Hinv = H + cur_qparams = None all_qparams = [] - for i1 in range(0, columns, blocksize): - i2 = min(i1 + blocksize, columns) - count = i2 - i1 - W1 = W[:, i1:i2].clone() + + for block_start in range( + 0, columns, blocksize + ): # go through all columns block by block + block_end = min(block_start + blocksize, columns) + W1 = W[:, block_start:block_end].clone() DQ1 = torch.zeros_like(W1) Err1 = torch.zeros_like(W1) - Losses1 = torch.zeros_like(W1) - Hinv1 = Hinv[i1:i2, i1:i2] - for i in range(count): - w = W1[:, i] - d = Hinv1[i, i] - - if ( - group_size != -1 and (i1 + i) % group_size == 0 - ): # start of new group - cur_qparams = cls.get_qparams_func( - W[:, (i1 + i) : (i1 + i + group_size)] - ) + Hinv1 = Hinv[block_start:block_end, block_start:block_end] + for group_start in range( + block_start, block_end, group_size + ): # break up blocks by groupsize + group_end = min(group_start + group_size, columns) + if group_start % group_size == 0: + # needed for when group_size == columns so only calculate qparams once + cur_qparams = cls.get_qparams_func(W[:, group_start:group_end]) all_qparams.append(cur_qparams) - q = cls.quantize_func(w.unsqueeze(1), cur_qparams).flatten() - dq = cls.dequantize_func(q.unsqueeze(1), cur_qparams).flatten() + for index in range(group_start, group_end): # within each group + i = index - block_start + w = W1[:, i] + d = Hinv1[i, i] - DQ1[:, i] = dq - Losses1[:, i] = (w - dq) ** 2 / d**2 + q = cls.quantize_func(w.unsqueeze(1), cur_qparams).flatten() + dq = cls.dequantize_func(q.unsqueeze(1), cur_qparams).flatten() - err1 = (w - dq) / d - W1[:, i:] -= ( - err1.to(Hinv1.dtype).unsqueeze(1).matmul(Hinv1[i, i:].unsqueeze(0)) - ) - Err1[:, i] = err1 + DQ1[:, i] = dq - DQ[:, i1:i2] = DQ1 - Losses[:, i1:i2] = Losses1 / 2 + err1 = (w - dq) / d + W1[:, i:] -= ( + err1.to(Hinv1.dtype) + .unsqueeze(1) + .matmul(Hinv1[i, i:].unsqueeze(0)) + ) + Err1[:, i] = err1 - W[:, i2:] -= Err1.to(Hinv.dtype).matmul(Hinv[i1:i2, i2:]) + DQ[:, block_start:block_end] = DQ1 + W[:, block_end:] -= Err1.to(Hinv.dtype).matmul( + Hinv[block_start:block_end, block_end:] + ) torch.cuda.synchronize() @@ -529,42 +495,69 @@ def is_linear_layer(cls, func: Callable) -> bool: return func == torch.nn.functional.linear -class StateDictManager: - _instance = None - - @staticmethod - def get_instance(): - if StateDictManager._instance is None: - StateDictManager._instance = StateDictManager() - return StateDictManager._instance +class MultiTensorInputRecorder(torch.nn.Module): + def __init__(self, disable_input_validation=False, target_class=MultiTensor): + super().__init__() + self.flat_args = [] + self.spec = None + self.validate = not disable_input_validation + self.target_class = target_class + self.count = 0 + + def forward(self, *args: Any, **kwargs: Any) -> "MultiTensorInputRecorder": + def validate_input(flat_args, spec): + if self.spec is None: + assert spec == self.spec, ( + f"got two different input structures when recording inputs, {self.spec} is not the same as {spec}" + ) - def __init__(self): - self.state_dict = {} - self.id_to_name = {} + for count, x in enumerate(flat_args): + y = self.flat_args[count] + if not isinstance(x, torch.Tensor): + assert x == y, ( + f"got different values for nontensor input {x} is not the same as {y} for flattened input element {count}, different inputs to input recorder must have same nontensor values" + ) + else: + assert isinstance(y, self.target_class), ( + f"expected input of type torch.Tensor but got {type(x)} for flattened input element {count}" + ) + assert y.dtype == x.dtype, ( + f"expected input of dtype {y.dtype} but got {x.dtype} for flattened input element {count} different inputs to input recorder must have same tensor dtypes" + ) + assert y.shape == y.shape, ( + f"expected input of shape {y.shape} but got {y.dtype} for flattened input element {count} different inputs to input recorder must have same tensor shape" + ) - def set_state_dict(self, model): - self.state_dict = model.state_dict() - self.id_to_name = {id(v): k for k, v in model.named_parameters()} + kwargs = {} if kwargs is None else kwargs + flat_args, spec = tree_flatten((args, kwargs)) + if self.spec is None: + self.spec = spec + self.flat_args = [ + self.target_class(x) if isinstance(x, torch.Tensor) else x + for x in flat_args + ] + return self - def update_id_to_name(self, model): - self.id_to_name = {id(v): k for k, v in model.named_parameters()} + if self.validate: + validate_input(flat_args, spec) + self.count += 1 - def get_name_for_param(self, param): - return self.id_to_name.get(id(param), None) + for count, x in enumerate(flat_args): + if isinstance(x, torch.Tensor): + self.flat_args[count].append(x) + return self - def update_param(self, name, new_value): - if name in self.state_dict: - if isinstance(new_value, MultiTensor): - self.state_dict[name] = new_value.values[ - 0 - ] # Convert MultiTensor to regular tensor - else: - self.state_dict[name] = new_value - else: - raise KeyError(f"Parameter {name} not found in state_dict") + def get_recorded_inputs(self) -> Tuple[Any, ...]: + args, kwargs = self.get_recorded_args_and_kwargs() + assert len(kwargs) == 0, ( + "kwargs is not empty but get_recorded_inputs called on MultiTensorInputRecorder, use get_recorded_args_and_kwargs instead" + ) + return args - def get_state_dict(self): - return self.state_dict + def get_recorded_args_and_kwargs(self) -> Tuple[Tuple[Any, ...], Dict[str, Any]]: + assert self.spec is not None, "no inputs have been recorded yet" + args, kwargs = tree_unflatten(self.flat_args, self.spec) + return args, kwargs class GPTQQuantizer(Quantizer): @@ -589,11 +582,6 @@ def _check_functions(self): assert self.make_qtensor is not None, "make_qtensor must be set" assert self.skip_layer_func is not None, "skip_layer_func must be set" - # this doesn't work - def _replace_parameters_with_multitensor(self, model): - for name, param in model.named_parameters(): - setattr(model, name.split(".")[-1], MultiTensor(param)) - def covert_multi_tensors_to_tensors(self, state_dict): for key, value in state_dict.items(): if isinstance(value, MultiTensor): @@ -604,12 +592,15 @@ def covert_multi_tensors_to_tensors(self, state_dict): def _create_quantized_state_dict( self, model, - inputs, + args, + kwargs, + group_size=64, blocksize=128, percdamp=0.01, - group_size=64, # `typing.Dict[, ]` to avoid runtime subscripting errors. ) -> Dict: + if kwargs is None: + kwargs = {} MultiTensor.configure_quantization_mode( get_qparams_func=self.get_qparams_func, quantize_func=self.quantize_func, @@ -617,25 +608,24 @@ def _create_quantized_state_dict( combine_qparams_list_func=self.combine_qparams_list_func, make_qtensor=self.make_qtensor, skip_layer_func=self.skip_layer_func, + group_size=group_size, percdamp=percdamp, blocksize=blocksize, - group_size=group_size, ) # Set the state dict for the original model self.state_dict_manager.set_state_dict(model) - # Replace parameters with MultiTensor - # self._replace_parameters_with_multitensor(model) - # Replace buffers and parameters with MultiTensor + with torch.no_grad(): _replace_with_custom_fn_if_matches_filter( model=model, - replacement_fn=replace_buffers_and_params_with_multitensors, + replacement_fn=_replace_buffers_and_params_with_multitensors, filter_fn=lambda x, y: True, ) self.state_dict_manager.update_id_to_name(model) # Run the model + with torch.no_grad(): - model(*inputs) + model(*args, **kwargs) state_dict = self.state_dict_manager.get_state_dict() return state_dict @@ -643,17 +633,17 @@ def _create_quantized_state_dict( class Int4WeightOnlyGPTQQuantizer(GPTQQuantizer): def __init__( self, + group_size=64, blocksize=128, percdamp=0.01, - group_size=64, inner_k_tiles=8, padding_allowed=True, device: torch.device = torch.device("cuda"), ): super().__init__() + self.group_size = group_size self.blocksize = blocksize self.percdamp = percdamp - self.group_size = group_size self.inner_k_tiles = inner_k_tiles self.padding_allowed = padding_allowed self.device = device @@ -720,14 +710,15 @@ def make_qtensor(q, qparams): self._check_functions() def quantize( - self, model: torch.nn.Module, inputs: List[MultiTensor], **kwargs: Any + self, model: torch.nn.Module, *args: Tuple[Any, ...], **kwargs: Dict[str, Any] ) -> torch.nn.Module: state_dict = self._create_quantized_state_dict( model, - inputs, + args, + kwargs, + self.group_size, self.blocksize, self.percdamp, - self.group_size, ) # this is hacky and potentially wrong, better to just make the flow return a state dict and let user @@ -735,7 +726,7 @@ def quantize( model = _replace_with_custom_fn_if_matches_filter( model=model, - replacement_fn=remove_multitensors_from_buffers_and_params, + replacement_fn=_remove_multitensors_from_buffers_and_params, filter_fn=lambda x, y: True, ) remove = [k for k in state_dict if "kv_cache" in k] @@ -747,28 +738,190 @@ def quantize( return model -# this should probably be a multitensor method that can be applied and we just traverse -# and look for multitensors and unpack them -def remove_multitensors_from_buffers_and_params(model: nn.Module) -> nn.Module: - for name, buf in model.named_buffers(recurse=False): - if isinstance(buf, MultiTensor): - setattr(model, name, buf.values[0]) - for name, param in model.named_parameters(recurse=False): - if isinstance(param, MultiTensor): - setattr( - model, - name, - nn.Parameter(param.values[0], param.values[0].requires_grad), +class StateDictManager: + _instance = None + + @staticmethod + def get_instance(): + if StateDictManager._instance is None: + StateDictManager._instance = StateDictManager() + return StateDictManager._instance + + def __init__(self): + self.state_dict = {} + self.id_to_name = {} + + def set_state_dict(self, model): + self.state_dict = model.state_dict() + self.id_to_name = {id(v): k for k, v in model.named_parameters()} + + def update_id_to_name(self, model): + self.id_to_name = {id(v): k for k, v in model.named_parameters()} + + def get_name_for_param(self, param): + return self.id_to_name.get(id(param), None) + + def update_param(self, name, new_value): + if name in self.state_dict: + if isinstance(new_value, MultiTensor): + self.state_dict[name] = new_value.values[ + 0 + ] # Convert MultiTensor to regular tensor + else: + self.state_dict[name] = new_value + else: + raise KeyError(f"Parameter {name} not found in state_dict") + + def get_state_dict(self): + return self.state_dict + + +############################# +# Utility Functions +############################# + + +def _check_linear_int4_k(k, group_size=1, inner_k_tiles=None): + """ + Check if the dimensions are compatible with int4 quantization. + + Args: + k: The dimension size to check + group_size: The group size for quantization + inner_k_tiles: The inner k tiles size + + Returns: + bool: Whether the dimensions are compatible + """ + k_divisible_by_group_size = k % group_size == 0 + if inner_k_tiles is not None: + k_divisible_by_16_times_inner_k_tiles = k % (inner_k_tiles * 16) == 0 + return k_divisible_by_group_size and k_divisible_by_16_times_inner_k_tiles + return k_divisible_by_group_size + + +def _flat_to_grouped_and_pad( + flat: List[Any], pad_in_place=True +) -> Tuple[List[Tuple[Any, ...]], List[int]]: + """ + Convert flattened arguments to grouped arguments with padding. + + Args: + flat: Flattened arguments + pad_in_place: Whether to pad in place + + Returns: + Tuple containing grouped arguments and original counts + """ + # Convert [A, MultiTensor(b1,b2,b3), MultiTensor(c1,c2,c3)] => [[A,b1,c1], [A,b2,c2] [A,b3,c3]] + orig_counts = [x.count if isinstance(x, MultiTensor) else 1 for x in flat] + multi_tensor_size = max(orig_counts) + grouped = list( + zip( + *[ + x.pad_to_length(multi_tensor_size, pad_in_place=pad_in_place).values + if isinstance(x, MultiTensor) + else [x] * multi_tensor_size + for x in flat + ] + ) + ) + return grouped, orig_counts + + +def _tensors_to_cuda(args, move_all=False): + """ + Move tensors to CUDA for faster processing. + + Args: + args: Arguments that may contain tensors + move_all: Whether to move all tensors or just single count tensors + + Returns: + List with tensors moved to CUDA + """ + new_args = [] + for x in args: + if isinstance(x, MultiTensor) and (x.count == 1 or move_all): + new_args.append(x.__class__(x.values[0].cuda())) + else: + new_args.append( + x.cuda() + if isinstance(x, torch.Tensor) and not isinstance(x, MultiTensor) + else x ) - return model + return new_args -def replace_buffers_and_params_with_multitensors(model: nn.Module) -> nn.Module: - for name, buf in model.named_buffers(recurse=False): - setattr(model, name, MultiTensor([buf])) - for name, param in model.named_parameters(recurse=False): - setattr(model, name, nn.Parameter(MultiTensor([param]), param.requires_grad)) - return model +def _maybe_copy_new_values(orig_inp, new_inp, force=False): + """ + Copy values from new inputs to original inputs if they've changed. + Used for handling in-place operations. + + Args: + orig_inp: Original inputs + new_inp: New inputs (potentially modified) + force: Whether to force copying regardless of differences + + Returns: + bool: Whether any differences were detected + """ + detected_difference = False + for x, new_x in zip(orig_inp, new_inp): + if isinstance(x, torch.Tensor): + if force or (x != new_x.to(x.device)).any(): + x.copy_(new_x) + detected_difference = True + return detected_difference + + +def _do_unpad(args, orig_counts): + """ + Unpad MultiTensors to their original counts. + + Args: + args: Arguments that may contain MultiTensors + orig_counts: Original counts of MultiTensors + """ + for arg, count in zip(args, orig_counts): + if isinstance(arg, MultiTensor) and arg.count > count: + arg.unpad(count) + + +def _calculate_hessian(grouped_args, spec): + """ + Calculate the Hessian matrix for GPTQ. + + Args: + grouped_args: Grouped arguments + spec: Original structure specification + + Returns: + torch.Tensor: Hessian matrix + """ + H = 0 + total_batches = 0 + for inp in grouped_args: + # Move all remaining CPU tensors to CUDA + cuda_inp = [x.cuda() if isinstance(x, torch.Tensor) else x for x in inp] + + # Return input to original structure + cur_args, _ = tree_unflatten(cuda_inp, spec) + + # Setup x (activation tensor) + x = cur_args[0].float() + shape = x.shape + n = 1 if len(shape) == 2 else shape[0] + x = x.reshape(-1, shape[-1]) + + # Update Hessian with running average + H *= total_batches / (total_batches + n) + total_batches += n + + x = ((2 / total_batches) ** (1 / 2)) * x.t() + H += x.matmul(x.t()) + + return H def _replace_with_custom_fn_if_matches_filter( @@ -776,13 +929,71 @@ def _replace_with_custom_fn_if_matches_filter( replacement_fn: Callable[[nn.Module], nn.Module], filter_fn: Callable[[nn.Module, str], bool], cur_fqn: str = "", -) -> None: +) -> nn.Module: + """ + Replace modules in the model if they match a filter. + + Args: + model: The model to modify + replacement_fn: Function to apply to matching modules + filter_fn: Function to determine if a module should be replaced + cur_fqn: Current fully qualified name (for tracking position in model hierarchy) + + Returns: + nn.Module: Modified model + """ if filter_fn(model, cur_fqn[:-1]): model = replacement_fn(model) + for name, child in model.named_children(): new_child = _replace_with_custom_fn_if_matches_filter( child, replacement_fn, filter_fn, f"{cur_fqn}{name}." ) if new_child is not child: setattr(model, name, new_child) + + return model + + +def _replace_buffers_and_params_with_multitensors(model: nn.Module) -> nn.Module: + """ + Replace model buffers and parameters with MultiTensors. + + Args: + model: The model to modify + + Returns: + nn.Module: Modified model + """ + for name, buf in model.named_buffers(recurse=False): + setattr(model, name, MultiTensor([buf])) + + for name, param in model.named_parameters(recurse=False): + setattr(model, name, nn.Parameter(MultiTensor([param]), param.requires_grad)) + + return model + + +def _remove_multitensors_from_buffers_and_params(model: nn.Module) -> nn.Module: + """ + Convert MultiTensors in model buffers and parameters back to regular tensors. + + Args: + model: The model to modify + + Returns: + nn.Module: Modified model + """ + for name, buf in model.named_buffers(recurse=False): + if isinstance(buf, MultiTensor): + setattr(model, name, buf.values[0]) + + for name, param in model.named_parameters(recurse=False): + if isinstance(param, MultiTensor): + setattr( + model, + name, + nn.Parameter(param.values[0], param.values[0].requires_grad), + ) + return model diff --git a/torchao/quantization/GPTQ/README.md b/torchao/quantization/GPTQ/README.md new file mode 100644 index 0000000000..89fde3487c --- /dev/null +++ b/torchao/quantization/GPTQ/README.md @@ -0,0 +1,74 @@ +# GPTQ + +GPTQ is a quantization technique that improves accuracy for various forms of quantization as introduced in the paper: https://arxiv.org/abs/2210.17323 + +In general GPTQ requires a model, a quantization technique and calibration data. GPTQ then optimizes the quantization parameters and quantized weights so they are more accurate accross the calibration data. + +## API + +The api for this technique is as follows: + + +```python +from torchao.quantization import MultiTensorInputRecorder, Int4WeightOnlyGPTQQuantizer + +model = get_model() # user provided function + +# first gather inputs +input_recorder = MultiTensorInputRecorder() +for i in range(calibration_limit): + args = get_next_input() # user provided function + input_recorder(*args) # compare to model(*args) + # note: can do input_recorder(*args, **kwargs) if needed + +# then perform GPTQ +quantizer = Int4WeightOnlyGPTQQuantizer() # quantization parameters like group_size can be set here +args = input_recorder.get_recorded_inputs() # use get_recorded_args_and_kwargs if necessary +quantizer.quantize(model, *args) +# model is now quantized and can be saved, compiled or run + +args = get_next_input() +out = model(*args) +``` + +important notes: +1) `input_recorder`, `quantizer.quantize` and `model` all take the same type of input. If you pass in kwargs to the model like `model(*args, **kwargs)` you'll need to do `input_recorder(*args, **kwargs)` and `quantizer.quantize(model, *args, **kwargs)` +2) the GPTQ process can take a significant period of time depending on the size of the model and the size of the calibration set. +3) We currently only support int4 weight only quantization for GPTQ though this framework can be relatively easily extended to other techniques. + + +In many cases users use lm_eval to get calibration data. We also have an input recorder that integrates directly with lm_eval. This is equivalent to using lm_eval but setting your model to be a MultiTensorInputRecorder. + +```python +from torchao._models._eval import LMEvalInputRecorder + +args = ( + LMEvalInputRecorder( + get_tokenizer(), # tokenizer + calibration_seq_length, + prepare_inputs_for_model, # optional function that transforms the input, e.g. constructing the indices tensor + get_tokenizer_vocab_size(), + pad_calibration_inputs, # boolean to allow padding + ) + .record_inputs( + calibration_tasks, + calibration_limit, + ) + .get_recorded_inputs() +) + + +``` + +## Results + +We tested the GPTQ implementation using the llama model in torchao/_models/llama with 10 calibration samples from lm_eval. + +| Technique: | Llama-2-7b-chat-hf | Meta-Llama-3-8B | +|----------------|--------------------|-----------------| +| bf16 | 12.245 | 7.441 | +| int4wo-64 | 12.876 | 8.316 | +| gptq-int4wo-64 | 12.523 | 8.026 | + +In practice we find that GPTQ recovers ~1/2 to 1/3 of the perplexity lost compared to quantizing directly to int4. +You can reproduce these numbers using `python eval.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --quantization gptq-int4wo-64 --calibration_limit 10` diff --git a/torchao/quantization/GPTQ/__init__.py b/torchao/quantization/GPTQ/__init__.py new file mode 100644 index 0000000000..8d62e68c3c --- /dev/null +++ b/torchao/quantization/GPTQ/__init__.py @@ -0,0 +1,21 @@ +from torchao.quantization.linear_quant_modules import ( + Int4WeightOnlyQuantizer, + Int8DynActInt4WeightLinear, + Int8DynActInt4WeightQuantizer, + WeightOnlyInt4Linear, + # for BC + _replace_linear_8da4w, # noqa: F401 + _replace_linear_int4, # noqa: F401 +) + +from .GPTQ import Int4WeightOnlyGPTQQuantizer, MultiTensor, MultiTensorInputRecorder + +__all__ = [ + "Int4WeightOnlyGPTQQuantizer", + "MultiTensorInputRecorder", + "MultiTensor", + "Int4WeightOnlyQuantizer", + "Int8DynActInt4WeightQuantizer", + "WeightOnlyInt4Linear", + "Int8DynActInt4WeightLinear", +] diff --git a/torchao/quantization/README.md b/torchao/quantization/README.md index 80737eb4bb..90f83661aa 100644 --- a/torchao/quantization/README.md +++ b/torchao/quantization/README.md @@ -404,6 +404,10 @@ The benchmarks below were run on a single NVIDIA-A6000 GPU. You try can out these apis with the `quantize_` api as above alongside the constructor `codebook_weight_only` an example can be found in in `torchao/_models/llama/generate.py`. +### GPTQ Quantization +We have a GPTQ quantization workflow that can be used to quantize a model to int4. More details can be found in [GPTQ](./GPTQ/README.md), +an example can be found in `torchao/_models/llama/eval.py`. + ### Automatic Inductor Configuration :warning: This functionality is being migrated from the top level `quantize_` API to individual workflows, see https://github.com/pytorch/ao/issues/1715 for more details. diff --git a/torchao/quantization/__init__.py b/torchao/quantization/__init__.py index 15736acc3b..44fc6c8397 100644 --- a/torchao/quantization/__init__.py +++ b/torchao/quantization/__init__.py @@ -15,10 +15,8 @@ ) from .GPTQ import ( Int4WeightOnlyGPTQQuantizer, - Int4WeightOnlyQuantizer, - Int8DynActInt4WeightGPTQQuantizer, - Int8DynActInt4WeightLinear, - Int8DynActInt4WeightQuantizer, + MultiTensor, + MultiTensorInputRecorder, ) from .granularity import ( PerAxis, @@ -34,6 +32,11 @@ from .linear_activation_scale import ( to_weight_tensor_with_linear_activation_scale_metadata, ) +from .linear_quant_modules import ( + Int4WeightOnlyQuantizer, + Int8DynActInt4WeightLinear, + Int8DynActInt4WeightQuantizer, +) from .observer import ( AffineQuantizedMinMaxObserver, AffineQuantizedObserverBase, @@ -195,9 +198,7 @@ "PerRow", "PerToken", "LinearActivationQuantizedTensor", - "Int4WeightOnlyGPTQQuantizer", "Int4WeightOnlyQuantizer", - "Int8DynActInt4WeightGPTQQuantizer", "Int8DynActInt4WeightQuantizer", "Int8DynActInt4WeightLinear", "WeightOnlyInt8QuantLinear", @@ -208,4 +209,8 @@ "TensorCoreTiledLayout", "CutlassInt4PackedLayout", "Float8MMConfig", + # GPTQ + "Int4WeightOnlyGPTQQuantizer", + "MultiTensor", + "MultiTensorInputRecorder", ] diff --git a/torchao/quantization/linear_quant_modules.py b/torchao/quantization/linear_quant_modules.py new file mode 100644 index 0000000000..73e95036f1 --- /dev/null +++ b/torchao/quantization/linear_quant_modules.py @@ -0,0 +1,629 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import logging +from typing import Any, Callable, Dict, Optional, Type + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from torchao.dtypes.utils import is_device +from torchao.utils import ( + TORCH_VERSION_AT_LEAST_2_6, + find_multiple, +) + +from .quant_primitives import ( + MappingType, + dequantize_affine, +) +from .unified import Quantizer +from .utils import ( + group_quantize_tensor_symmetric, + groupwise_affine_quantize_tensor, + per_token_dynamic_quant, +) + +aten = torch.ops.aten + +__all__ = [ + "WeightOnlyInt4Linear", + "Int4WeightOnlyQuantizer", + "Int8DynActInt4WeightQuantizer", +] + + +def _check_linear_int4_k(k, groupsize=1, inner_k_tiles=None): + k_divisible_by_groupsize = k % groupsize == 0 + if inner_k_tiles is not None: + k_divisible_by_16_times_inner_k_tiles = k % (inner_k_tiles * 16) == 0 + return k_divisible_by_groupsize and k_divisible_by_16_times_inner_k_tiles + return k_divisible_by_groupsize + + +def linear_forward_int4( + x: torch.Tensor, + weight_int4pack: torch.Tensor, + scales_and_zeros: torch.Tensor, + out_features: int, + groupsize: int, + precision: torch.dtype = torch.bfloat16, + scales_precision: torch.dtype = torch.bfloat16, +): + origin_x_size = x.size() + x = x.reshape(-1, origin_x_size[-1]) + if is_device(x.device.type, "cpu") and TORCH_VERSION_AT_LEAST_2_6: + c = torch.ops.aten._weight_int4pack_mm_for_cpu( + x.to(precision), + weight_int4pack, + groupsize, + scales_and_zeros.to(scales_precision), + ).to(dtype=x.dtype) + else: + c = torch.ops.aten._weight_int4pack_mm( + x.to(precision), + weight_int4pack, + groupsize, + scales_and_zeros.to(scales_precision), + ).to(dtype=x.dtype) + new_shape = origin_x_size[:-1] + (out_features,) + c = c.reshape(new_shape) + return c + + +class WeightOnlyInt4Linear(torch.nn.Module): + __constants__ = ["in_features", "out_features"] + in_features: int + out_features: int + weight: torch.Tensor + + def __init__( + self, + in_features: int, + out_features: int, + # TODO: remove dtype field, not used + bias=False, + device=None, + dtype=None, + groupsize: int = 128, + inner_k_tiles: int = 8, + precision: torch.dtype = torch.bfloat16, + scales_precision: torch.dtype = torch.bfloat16, + ) -> None: + super().__init__() + self.padding = not _check_linear_int4_k(in_features, groupsize, inner_k_tiles) + if self.padding: + self.origin_in_features = in_features + in_features = find_multiple(in_features, 1024) + + self.in_features = in_features + self.out_features = out_features + assert not bias, "require bias=False" + self.device = device + self.groupsize = groupsize + self.inner_k_tiles = inner_k_tiles + self.precision = precision + self.scales_precision = scales_precision + + if dtype is not None: + raise ValueError("Please specify 'precision' instead of 'dtype'") + + assert out_features % 8 == 0, "require out_features % 8 == 0" + assert in_features % (inner_k_tiles * 16) == 0, ( + "require in_features % (innerKTiles * 16) == 0" + ) + if is_device(device.type, "cpu"): + self.register_buffer( + "weight", + torch.zeros( + ( + out_features, + in_features // 2, + ), + dtype=torch.uint8, + device=device, + ), + ) + else: + self.register_buffer( + "weight", + torch.zeros( + ( + out_features // 8, + in_features // (inner_k_tiles * 16), + 32, + inner_k_tiles // 2, + ), + dtype=torch.int32, + device=device, + ), + ) + self.dtype = dtype + self.register_buffer( + "scales_and_zeros", + torch.zeros( + (in_features // groupsize, out_features, 2), + dtype=self.scales_precision, + device=device, + ), + ) + + def forward(self, input: torch.Tensor) -> torch.Tensor: + if self.padding: + input = F.pad(input, pad=(0, self.in_features - self.origin_in_features)) + return linear_forward_int4( + input, + self.weight, + self.scales_and_zeros, + self.out_features, + self.groupsize, + self.precision, + self.scales_precision, + ) + + +def _replace_linear_int4( + module: torch.nn.Module, + groupsize: int, + inner_k_tiles: Optional[int], + padding_allowed: bool, + skip_layer_func: Optional[Callable] = None, + precision: torch.dtype = torch.bfloat16, + scales_precision: torch.dtype = torch.bfloat16, + linear_class: Type[torch.nn.Module] = WeightOnlyInt4Linear, + copy_weights: bool = False, +): + for name, child in module.named_children(): + # TODO: support linear bias + if ( + isinstance(child, nn.Linear) + and child.bias is None + and (skip_layer_func is None or not skip_layer_func(child.weight)) + ): + if ( + _check_linear_int4_k(child.in_features, groupsize, inner_k_tiles) + or padding_allowed + ): + new_linear = linear_class( + child.in_features, + child.out_features, + bias=False, + device=child.weight.device, + groupsize=groupsize, + inner_k_tiles=inner_k_tiles, + precision=precision, + scales_precision=scales_precision, + ) + # TODO: merge with 8da4w? + # In distributed training, the model may be instantiated + # on the meta device, in which case there is no need to + # copy the weights, and doing so will result in an error + if copy_weights and child.weight.device != torch.device("meta"): + new_linear.weight = child.weight + setattr(module, name, new_linear) + else: + _replace_linear_int4( + child, + groupsize, + inner_k_tiles, + padding_allowed, + skip_layer_func, + precision, + scales_precision, + linear_class, + copy_weights, + ) + + +def replace_linear_int4( + module, groupsize, inner_k_tiles, padding_allowed, skip_layer_func=None +): + _replace_linear_int4( + module, + groupsize, + inner_k_tiles, + padding_allowed, + skip_layer_func, + linear_class=WeightOnlyInt4Linear, + ) + + +class Int4WeightOnlyQuantizer(Quantizer): + def __init__( + self, + groupsize: int = 256, + padding_allowed: bool = True, + inner_k_tiles: Optional[int] = 8, + device: torch.device = torch.device("cuda"), + precision: torch.dtype = torch.bfloat16, + ) -> None: + super().__init__() + assert inner_k_tiles in [2, 4, 8] + assert groupsize in [32, 64, 128, 256] + + self.inner_k_tiles = inner_k_tiles + self.groupsize: int = groupsize + self.padding_allowed: bool = padding_allowed + self.device: torch.device = device + # precision and dtype are being used interchangeably here + self.precision: torch.dtype = precision + + @torch.no_grad() + def _create_quantized_state_dict( + self, model: torch.nn.Module + ) -> Dict[str, torch.Tensor]: + cur_state_dict = model.state_dict() + for fqn, mod in model.named_modules(): + if isinstance(mod, torch.nn.Linear) and mod.bias is None: + out_features = mod.out_features + in_features = mod.in_features + # assert out_features % 8 == 0, "require out_features % 8 == 0" + logging.info(f"linear: {fqn}, in={in_features}, out={out_features}") + + assert in_features % self.groupsize == 0, ( + f"require in_features:{in_features} % self.groupsize:{self.groupsize} == 0" + ) + + weight = mod.weight.data + if not _check_linear_int4_k( + in_features, self.groupsize, self.inner_k_tiles + ): + if self.padding_allowed: + import torch.nn.functional as F + + logging.warning( + f"warning: {fqn} is padded to satisfy in_features % 1024 == 0" + ) + padded_in_features = find_multiple(in_features, 1024) + weight = F.pad( + weight, pad=(0, padded_in_features - in_features) + ) + else: + logging.warning( + f"warning: {fqn} is skipped, int4 requires that in_features is 32, 64, or is divisible by 1024, " + + "and that groupsize and inner_k_tiles*16 evenly divide into it" + ) + continue + (w_int4x8, scales_and_zeros) = groupwise_affine_quantize_tensor( + weight, + 4, # n_bit + self.groupsize, + self.precision, # dtype for scales_and_zeros + ) + # TODO: just get the device from mod.weight.device? + if ( + is_device(w_int4x8.device.type, "cpu") + and TORCH_VERSION_AT_LEAST_2_6 + ): + weight_int4pack = ( + torch.ops.aten._convert_weight_to_int4pack_for_cpu( + w_int4x8.to(self.device), self.inner_k_tiles + ) + ) + else: + weight_int4pack = torch.ops.aten._convert_weight_to_int4pack( + w_int4x8.to(self.device), self.inner_k_tiles + ) + cur_state_dict[f"{fqn}.weight"] = weight_int4pack.to(self.device) + cur_state_dict[f"{fqn}.scales_and_zeros"] = scales_and_zeros.to( + self.device + ) + return cur_state_dict + + def _convert_for_runtime(self, model: torch.nn.Module) -> torch.nn.Module: + _replace_linear_int4( + model, + self.groupsize, + self.inner_k_tiles, + self.padding_allowed, + skip_layer_func=None, + precision=self.precision, + scales_precision=self.precision, + ) + return model + + def quantize( + self, model: torch.nn.Module, *args: Any, **kwargs: Any + ) -> torch.nn.Module: + state_dict = self._create_quantized_state_dict(model) + model = self._convert_for_runtime(model) + # TODO: make it strict + model.load_state_dict(state_dict, strict=False) + return model + + +def linear_forward_8da4w( + x, + weight_int8, + bias, + scales, + zeros, + out_features, + groupsize, + output_precision, +): + # uses fp32 to match torchao.quantization.quant_api._int8_asymm_per_token_quant + # and activation_scale_dtype in QAT configs + # TODO: in future add ability to specify activation_scale_dtype to PTQ configs + # and enable similar change here + x = per_token_dynamic_quant( + x, + scale_dtype=torch.float32, + zero_point_dtype=torch.float32, + eps=torch.finfo(torch.float32).eps, + ) + + # TODO: verify and remove following reshape code + # origin_x_size = x.size() + # x = x.reshape(-1, origin_x_size[-1]) + + # TODO: better API + # weight_int8 = torch.ops.quantized_decomposed.unpack_int4_to_int8(weight_int4packed) + n_bit = 4 + quant_min = -(2 ** (n_bit - 1)) + quant_max = 2 ** (n_bit - 1) - 1 + block_size = (1, groupsize) + + w_dq = dequantize_affine( + weight_int8, + block_size, + scales, + zeros, + torch.int8, + quant_min, + quant_max, + output_dtype=output_precision, + ) + + # x = x.to(torch.float16) + # w_dq = w_dq.to(torch.float16) + c = torch.nn.functional.linear(x, w_dq, bias) + + # new_shape = origin_x_size[:-1] + (out_features,) + # c = c.reshape(new_shape) + + return c + + +class Int8DynActInt4WeightLinear(torch.nn.Module): + __constants__ = ["in_features", "out_features"] + + in_features: int + out_features: int + weight: torch.Tensor + bias: torch.Tensor + + """ + This module implements a dynamic quantized linear layer with int4 weight. + Weights are per channel groupwise quantized. Parameters of importance + groupsize: the number of elements in each quantized group + precision: precision of input and output. e.g. torch.float32 means input + activation is float32 and output is float32. + scales_precision: precision of per group scale. + """ + + def __init__( + self, + in_features: int, + out_features: int, + bias=True, + device=None, + # TODO: remove this field, not used + dtype=None, + groupsize: int = 256, + precision: torch.dtype = torch.float32, + scales_precision: torch.dtype = torch.float32, + ) -> None: + super().__init__() + # always pad if needed since it becomes a noop at runtime if not needed + # self.origin_in_features = in_features + assert in_features % groupsize == 0, ( + f"require in_features:{in_features} % groupsize:{groupsize} == 0" + ) + # in_features = _calc_padded_size_linear_int4( + # in_features, groupsize + # ) + self.in_features = in_features + self.out_features = out_features + # TODO: align groupsize naming + self.groupsize = groupsize + # Precision of the activation which also indicates + # output precision of the dynamically quantized linear layer + # that his module represents. + self.precision = precision + + if dtype is not None: + raise ValueError("Please specify 'precision' instead of 'dtype'") + + # currently storing unpacked int8 weights + self.register_buffer( + "weight", + torch.zeros((out_features, in_features), dtype=torch.int8), + ) + self.register_buffer( + "scales", + torch.zeros( + (out_features, in_features // groupsize), + dtype=scales_precision, + ), + ) + self.register_buffer( + "zeros", + torch.zeros( + (out_features, in_features // groupsize), + dtype=scales_precision, + ), + ) + + if bias: + self.register_buffer("bias", torch.zeros(out_features, dtype=precision)) + else: + self.bias = None + + def forward(self, input: torch.Tensor) -> torch.Tensor: + input = input.to(self.precision) + # padding is removed for perf + # input = F.pad(input, pad=(0, self.in_features - self.origin_in_features)) + return linear_forward_8da4w( + input, + self.weight, + self.bias, + self.scales, + self.zeros, + self.out_features, + self.groupsize, + self.precision, + ) + + +def _replace_linear_8da4w( + module: torch.nn.Module, + groupsize: int, + padding_allowed: bool, + precision: torch.dtype, + scales_precision: torch.dtype, + linear_class: Type[torch.nn.Module], + copy_weights: bool = False, +): + # import the util function here to avoid circular dependency + from torchao.quantization.quant_api import _replace_with_custom_fn_if_matches_filter + + def filter_fn(child: torch.nn.Module, cur_fqn: str) -> bool: + return isinstance(child, nn.Linear) and ( + _check_linear_int4_k(child.in_features, groupsize) or padding_allowed + ) + + def replacement_fn(child: torch.nn.Module) -> torch.nn.Module: + new_linear = linear_class( + child.in_features, + child.out_features, + bias=child.bias is not None, + device=child.weight.device, + groupsize=groupsize, + precision=precision, + scales_precision=scales_precision, + ) + # In distributed training, the model may be instantiated + # on the meta device, in which case there is no need to + # copy the weights, and doing so will result in an error + if copy_weights and child.weight.device != torch.device("meta"): + new_linear.weight = child.weight + new_linear.bias = child.bias + return new_linear + + _replace_with_custom_fn_if_matches_filter(module, replacement_fn, filter_fn) + + +def replace_linear_8da4w( + module: torch.nn.Module, + groupsize: int, + padding_allowed: bool, + precision: torch.dtype, + scales_precision: torch.dtype, +): + _replace_linear_8da4w( + module, + groupsize, + padding_allowed, + precision, + scales_precision, + Int8DynActInt4WeightLinear, + ) + + +class Int8DynActInt4WeightQuantizer(Quantizer): + def __init__( + self, + groupsize: int = 256, + padding_allowed: bool = False, + precision: torch.dtype = torch.float32, + scales_precision: torch.dtype = torch.float32, + device: torch.device = torch.device("cpu"), + mapping_type: MappingType = MappingType.SYMMETRIC, + ) -> None: + super().__init__() + self.groupsize: int = groupsize + self.padding_allowed: bool = padding_allowed + self.precision: torch.dtype = precision + self.scales_precision: torch.dtype = scales_precision + self.device: torch.device = device + self.mapping_type: MappingType = mapping_type + + @torch.no_grad() + def _create_quantized_state_dict( + self, model: torch.nn.Module + ) -> Dict[str, torch.Tensor]: + cur_state_dict = model.state_dict() + for fqn, mod in model.named_modules(): + if isinstance(mod, torch.nn.Linear): + out_features = mod.out_features + in_features = mod.in_features + # assert out_features % 8 == 0, "require out_features % 8 == 0" + logging.info(f"linear: {fqn}, in={in_features}, out={out_features}") + + assert in_features % self.groupsize == 0, ( + f"require in_features:{in_features} % self.groupsize:{self.groupsize} == 0" + ) + + weight = mod.weight.data + if not _check_linear_int4_k(in_features, self.groupsize): + if self.padding_allowed: + import torch.nn.functional as F + + logging.warning( + f"warning: {fqn} is padded to satisfy in_features % 1024 == 0" + ) + padded_in_features = find_multiple(in_features, 1024) + weight = F.pad( + weight, pad=(0, padded_in_features - in_features) + ) + else: + logging.warning( + f"warning: {fqn} is skipped, int4 requires that in_features is 32, 64, or is divisible by 1024, " + + "and that groupsize and inner_k_tiles*16 evenly divide into it" + ) + continue + ( + weight_int8, + scales, + zeros, + ) = group_quantize_tensor_symmetric( + weight.to(self.precision), + 4, # n_bit + self.groupsize, + self.scales_precision, + mapping_type=self.mapping_type, + ) + cur_state_dict[f"{fqn}.weight"] = weight_int8.to(self.device) + cur_state_dict[f"{fqn}.scales"] = scales.to(self.device) + cur_state_dict[f"{fqn}.zeros"] = zeros.to(self.device) + + return cur_state_dict + + def _convert_for_runtime(self, model: torch.nn.Module) -> torch.nn.Module: + replace_linear_8da4w( + model, + self.groupsize, + self.padding_allowed, + self.precision, + # TODO: this should be self.scales_precision? + self.precision, + ) + return model + + def quantize( + self, model: torch.nn.Module, *args: Any, **kwargs: Any + ) -> torch.nn.Module: + state_dict = self._create_quantized_state_dict(model) + model = self._convert_for_runtime(model) + # TODO: make it strict + model.load_state_dict(state_dict, strict=False) + return model diff --git a/torchao/quantization/qat/linear.py b/torchao/quantization/qat/linear.py index 7c32bc4b19..bffd5dc31f 100644 --- a/torchao/quantization/qat/linear.py +++ b/torchao/quantization/qat/linear.py @@ -10,7 +10,8 @@ import torch.nn.functional as F from torchao.dtypes.utils import is_device -from torchao.quantization.GPTQ import ( +from torchao.quantization.granularity import PerGroup +from torchao.quantization.linear_quant_modules import ( Int8DynActInt4WeightLinear, WeightOnlyInt4Linear, _check_linear_int4_k, @@ -18,7 +19,6 @@ _replace_linear_int4, groupwise_affine_quantize_tensor, ) -from torchao.quantization.granularity import PerGroup from torchao.quantization.quant_primitives import ( TorchAODType, ZeroPointDomain, diff --git a/torchao/quantization/quant_api.py b/torchao/quantization/quant_api.py index 37f0cf5bfe..a7eec7e1df 100644 --- a/torchao/quantization/quant_api.py +++ b/torchao/quantization/quant_api.py @@ -85,9 +85,6 @@ from .autoquant import AutoQuantizableLinearWeight, autoquant from .GPTQ import ( Int4WeightOnlyGPTQQuantizer, - Int4WeightOnlyQuantizer, - Int8DynActInt4WeightGPTQQuantizer, - Int8DynActInt4WeightQuantizer, ) from .granularity import ( Granularity, @@ -100,6 +97,10 @@ LinearActivationQuantizedTensor, to_linear_activation_quantized, ) +from .linear_quant_modules import ( + Int4WeightOnlyQuantizer, + Int8DynActInt4WeightQuantizer, +) from .qat import ( intx_quantization_aware_training, ) @@ -141,7 +142,6 @@ "float8_dynamic_activation_float8_weight", "float8_static_activation_float8_weight", "Int8DynActInt4WeightQuantizer", - "Int8DynActInt4WeightGPTQQuantizer", "Float8DynamicActivationFloat8SemiSparseWeightConfig", "ModuleFqnToConfig", "FbgemmConfig", From a2c5ca19fe51f9e90c4a6c5aad7ab1b7fc403e55 Mon Sep 17 00:00:00 2001 From: Driss Guessous <32754868+drisspg@users.noreply.github.com> Date: Tue, 3 Jun 2025 13:03:46 -0400 Subject: [PATCH 075/165] Removing DocBlock to unblock MXFP4 w/ Unwrap Tensor (#2292) stack-info: PR: https://github.com/pytorch/ao/pull/2292, branch: drisspg/stack/63 --- torchao/prototype/mx_formats/kernels.py | 13 ------------- 1 file changed, 13 deletions(-) diff --git a/torchao/prototype/mx_formats/kernels.py b/torchao/prototype/mx_formats/kernels.py index f643ac3106..eacf0ac5df 100644 --- a/torchao/prototype/mx_formats/kernels.py +++ b/torchao/prototype/mx_formats/kernels.py @@ -1394,19 +1394,6 @@ def triton_scale_swizzle( BLOCK_ROWS: tl.constexpr, BLOCK_COLS: tl.constexpr, ): - """ - Rearranges tensor data from row-major to block-scaled swizzle format. - - Args: - scale_ptr: Pointer to the input scale tensor - scale_rows: Number of rows in the scale tensor - scale_cols: Number of columns in the scale tensor - output_ptr: Pointer to the output tensor - input_row_stride: Stride between rows in the input tensor - output_block_stride: Stride between blocks in the output tensor - BLOCK_ROWS: Number of rows in a tile (compile-time constant) - BLOCK_COLS: Number of columns in a tile (compile-time constant) - """ pid_row = tl.program_id(0) pid_col = tl.program_id(1) From 2ef656e2f6ee2d8be833ac52c7383d11ab6c8672 Mon Sep 17 00:00:00 2001 From: andrewor14 Date: Tue, 3 Jun 2025 16:44:46 -0400 Subject: [PATCH 076/165] Fix QAT range learning, ensure scales get gradients (#2280) **Summary:** The previous `_GenericFakeQuantized` nulled all gradients except the ones for the input. This is problematic for range learning because scales and zero points are now `nn.Parameters` and actually require gradients. This commit fixes this by reducing the scope of the `autograd.Function` to `torch.round` only, so QAT can just call the fake quantization primitives directly. Note: Part of the dequantize math currently casts the inputs and the zero points to int32. However, autograd doesn't work with integer math and this part of the code path is now visible to autograd. To make this work, this commit also removes this dtype cast. Note: This change means we no longer do cachemask and so our numerics no longer matches those of pytorch/pytorch's fake quantization ops. **Test Plan:** Updated the following test to check for scales and weights being updated: python test/quantization/test_qat.py -k test_qat_range_learning --- test/quantization/test_qat.py | 63 +++++--------- .../qat/affine_fake_quantized_tensor.py | 11 +-- torchao/quantization/qat/fake_quantizer.py | 2 +- torchao/quantization/qat/utils.py | 83 +++---------------- torchao/quantization/quant_primitives.py | 41 +++++---- 5 files changed, 65 insertions(+), 135 deletions(-) diff --git a/test/quantization/test_qat.py b/test/quantization/test_qat.py index 3b32f870c4..cbe279c12e 100644 --- a/test/quantization/test_qat.py +++ b/test/quantization/test_qat.py @@ -49,7 +49,6 @@ from torchao.quantization.qat.utils import ( _fake_quantize_per_channel_group, _fake_quantize_per_token, - _GenericFakeQuantize, _get_qmin_qmax, ) from torchao.quantization.quant_api import ( @@ -585,42 +584,6 @@ def test_qat_8da4w_quantizer_gradients(self): quantizer = Int8DynActInt4WeightQATQuantizer(groupsize=16) self._test_qat_quantized_gradients(quantizer) - @unittest.skipIf( - not TORCH_VERSION_AT_LEAST_2_4, "skipping when torch version is 2.4 or lower" - ) - def test_qat_generic_fake_quantize(self): - """ - Test that the generic fake quantize used in 8da4w QAT matches - the numerics of existing fake quantize ops in Pytorch in both - the forward and the backward passes. - """ - (qmin, qmax) = _get_qmin_qmax(4) - py_input = torch.randn(16, 64).float().requires_grad_() - py_s = torch.randn(16).float() - py_zp = torch.randint(qmax, size=(16,), dtype=torch.int32) - py_out = torch.fake_quantize_per_channel_affine( - py_input, py_s, py_zp, 0, qmin, qmax - ) - py_out.sum().backward() - - ao_input = copy.deepcopy(py_input) - ao_input.grad.data.zero_() - block_size = (1, ao_input.shape[-1]) - ao_s = copy.deepcopy(py_s) - ao_zp = copy.deepcopy(py_zp) - ao_out = _GenericFakeQuantize.apply( - ao_input, block_size, ao_s, ao_zp, qmin, qmax - ) - ao_out.sum().backward() - - torch.testing.assert_close(py_out, ao_out, atol=0, rtol=0) - - # Test that gradients are close enough - num_grads = py_input.grad.numel() - num_equal_grads = torch.eq(py_input.grad, ao_input.grad).flatten().sum().item() - num_equal_grad_threshold = 0.8 - self.assertGreaterEqual(num_equal_grads / num_grads, num_equal_grad_threshold) - def _assert_close_4w(self, val, ref): # Note: for int4 weight-only quantization, we do not expect exact match # because torch._weight_int4pack_mm and torch.mm do not match exactly. @@ -1700,16 +1663,30 @@ def test_qat_range_learning(self): m(*example_inputs) # Simulate training + num_steps = 10 optimizer = torch.optim.SGD( m.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-5 ) loss_fn = torch.nn.CrossEntropyLoss() - target = torch.randn(1, 512).float() - out = m(*example_inputs) - loss = loss_fn(out, target) - optimizer.zero_grad() - loss.backward() - optimizer.step() + for i in range(num_steps): + prev_scale = copy.deepcopy(m.linear1.weight_fake_quantizer.scale) + prev_weight = copy.deepcopy(m.linear1.weight) + optimizer.zero_grad() + target = torch.randn(1, 512).float() + out = m(*example_inputs) + loss = loss_fn(out, target) + loss.backward() + optimizer.step() + # Assert that scales have valid gradients and are being updated + new_scale = m.linear1.weight_fake_quantizer.scale + self.assertIsNotNone(new_scale.grad) + self.assertNotEqual(torch.count_nonzero(new_scale.grad), 0) + self.assertFalse(torch.equal(new_scale, prev_scale)) + # Assert that weights have valid gradients and are being updated + new_weight = m.linear1.weight + self.assertIsNotNone(new_weight.grad) + self.assertNotEqual(torch.count_nonzero(new_weight.grad), 0) + self.assertFalse(torch.equal(new_weight, prev_weight)) if __name__ == "__main__": diff --git a/torchao/quantization/qat/affine_fake_quantized_tensor.py b/torchao/quantization/qat/affine_fake_quantized_tensor.py index fe26369c31..6896588971 100644 --- a/torchao/quantization/qat/affine_fake_quantized_tensor.py +++ b/torchao/quantization/qat/affine_fake_quantized_tensor.py @@ -16,11 +16,11 @@ choose_qparams_affine, choose_qparams_affine_dont_preserve_zero, choose_qparams_affine_tinygemm, + fake_quantize_affine, ) from torchao.utils import TorchAOBaseTensor from .utils import ( - _GenericFakeQuantize, _UnwrapAffineFakeQuantizedTensor, ) @@ -90,14 +90,15 @@ def apply_fake_quant_fn(t: torch.Tensor): scale_dtype, zero_point_dtype, ) - fq = _GenericFakeQuantize.apply( + fq = fake_quantize_affine( t, block_size, scale, zero_point, - qmin, - qmax, - zero_point_domain, + quant_dtype=torch.int32, + quant_min=qmin, + quant_max=qmax, + zero_point_domain=zero_point_domain, ) return fq diff --git a/torchao/quantization/qat/fake_quantizer.py b/torchao/quantization/qat/fake_quantizer.py index 90206b5d6e..aca0c032bb 100644 --- a/torchao/quantization/qat/fake_quantizer.py +++ b/torchao/quantization/qat/fake_quantizer.py @@ -17,6 +17,7 @@ _DTYPE_TO_BIT_WIDTH, _DTYPE_TO_QVALUE_BOUNDS, MappingType, + _Round, choose_qparams_affine, ) from torchao.quantization.utils import ( @@ -31,7 +32,6 @@ from .utils import ( _fake_quantize_per_channel_group, _fake_quantize_per_token, - _Round, ) diff --git a/torchao/quantization/qat/utils.py b/torchao/quantization/qat/utils.py index 71e9a96ec5..01818ef2b2 100644 --- a/torchao/quantization/qat/utils.py +++ b/torchao/quantization/qat/utils.py @@ -4,68 +4,19 @@ # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. -from typing import List import torch from torchao.quantization.quant_primitives import ( ZeroPointDomain, - fake_quantize_affine_cachemask, + fake_quantize_affine, ) from torchao.quantization.utils import ( _get_per_token_block_size, ) -class _GenericFakeQuantize(torch.autograd.Function): - """ - Implementation of generic fake quantize with backward STE. - - With the appropriate input tensor shape, this can be used to express - grouped per channel fake quantize or per token fake quantize. - """ - - @staticmethod - def forward( - ctx: torch.autograd.function.FunctionCtx, - input: torch.Tensor, - block_size: List[int], - scales: torch.Tensor, - zero_points: torch.Tensor, - quant_min: int, - quant_max: int, - zero_point_domain: ZeroPointDomain = ZeroPointDomain.INT, - ) -> torch.Tensor: - # avoid circular dependencies - from torchao.quantization.qat.affine_fake_quantized_tensor import ( - AffineFakeQuantizedTensor, - ) - - if isinstance(input, AffineFakeQuantizedTensor): - _input = input.original_tensor - else: - _input = input - - (fq, mask) = fake_quantize_affine_cachemask( - _input, - block_size, - scales, - zero_points, - torch.int32, - quant_min, - quant_max, - zero_point_domain, - ) - - ctx.save_for_backward(mask) - return fq - - @staticmethod - def backward(ctx, gy): - (mask,) = ctx.saved_tensors - return gy * mask, None, None, None, None, None, None - - +# TODO: delete? class _UnwrapAffineFakeQuantizedTensor(torch.autograd.Function): """ Helper autograd function to unwrap `AffineFakeQuantizedTensor` while ensuring @@ -91,20 +42,6 @@ def backward(ctx, gy): return (gy,) -class _Round(torch.autograd.Function): - """ - Implementation of generic round operation with backward STE. - """ - - @staticmethod - def forward(ctx, x: torch.Tensor) -> torch.Tensor: - return torch.round(x) - - @staticmethod - def backward(ctx, gy: torch.Tensor) -> torch.Tensor: - return gy - - def _fake_quantize_per_channel_group( input: torch.Tensor, scales: torch.Tensor, @@ -118,14 +55,15 @@ def _fake_quantize_per_channel_group( assert input.shape[-1] % group_size == 0 assert input.dim() == 2 block_size = (1, group_size) - return _GenericFakeQuantize.apply( + return fake_quantize_affine( input, block_size, scales, zero_points, - quant_min, - quant_max, - zero_point_domain, + quant_dtype=torch.int32, + quant_min=quant_min, + quant_max=quant_max, + zero_point_domain=zero_point_domain, ) @@ -140,13 +78,14 @@ def _fake_quantize_per_token( _per_token_quant_qparam_dim_check(input, scales, zero_points) block_size = _get_per_token_block_size(input) - fq = _GenericFakeQuantize.apply( + fq = fake_quantize_affine( input, block_size, scales, zero_points, - quant_min, - quant_max, + quant_dtype=torch.int32, + quant_min=quant_min, + quant_max=quant_max, ) return fq.reshape_as(input).to(input.dtype) diff --git a/torchao/quantization/quant_primitives.py b/torchao/quantization/quant_primitives.py index cee8df21a2..de9a1ef4a7 100644 --- a/torchao/quantization/quant_primitives.py +++ b/torchao/quantization/quant_primitives.py @@ -212,6 +212,20 @@ class TorchAODType(Enum): register_custom_op = _register_custom_op(quant_lib) +class _Round(torch.autograd.Function): + """ + Implementation of generic round operation with backward STE. + """ + + @staticmethod + def forward(ctx, x: torch.Tensor) -> torch.Tensor: + return torch.round(x) + + @staticmethod + def backward(ctx, gy: torch.Tensor) -> torch.Tensor: + return gy + + # TODO: decide on if we want to allow custom quant_min/quant_max here def _get_and_check_qmin_qmax(dtype, quant_min, quant_max): """Get quant_min and quant_max args based on dtype and also @@ -407,7 +421,7 @@ def _quantize_affine_no_dtype_cast( zero_point = None quant = torch.clamp( - torch.round(input * (1.0 / scale)) + zero_point, quant_min, quant_max + _Round.apply(input * (1.0 / scale)) + zero_point, quant_min, quant_max ) quant = quant.view(original_shape) @@ -493,7 +507,7 @@ def _quantize_affine_float_zero_point_no_dtype_cast( mid_point = (quant_max + quant_min + 1) / 2 min_val = zero_point - scale * mid_point - quant = torch.clamp(torch.round((input - min_val) / scale), quant_min, quant_max) + quant = torch.clamp(_Round.apply((input - min_val) / scale), quant_min, quant_max) quant = quant.view(original_shape) return quant @@ -577,7 +591,7 @@ def _quantize_affine_no_zero_point_no_dtype_cast( # with numel=0 which we handle by unifying the two zero_point = None - quant = torch.clamp(torch.round(input * (1.0 / scale)), quant_min, quant_max) + quant = torch.clamp(_Round.apply(input * (1.0 / scale)), quant_min, quant_max) quant = quant.view(original_shape) return quant @@ -692,10 +706,9 @@ def _dequantize_affine_no_dtype_check( # Force a copy to avoid input modification due # to upcoming in-place operations. - dequant = input.to(torch.int32, copy=True) + dequant = input.to(output_dtype, copy=True) if zero_point is not None: - dequant = dequant - zero_point.to(torch.int32) - dequant = dequant.to(output_dtype) + dequant = dequant - zero_point.to(output_dtype) dequant = dequant * scale return dequant.view(original_shape).to(output_dtype) @@ -1202,7 +1215,7 @@ def choose_qparams_affine_dont_preserve_zero( scale = (max_val_pos - min_val_neg) / float(quant_max - quant_min) scale = torch.clamp(scale, min=eps) # Zero point is int - zero_point = quant_min - torch.round(min_val_neg / scale) + zero_point = quant_min - _Round.apply(min_val_neg / scale) zero_point = torch.clamp(zero_point, quant_min, quant_max) if zero_point_dtype is None: zero_point_dtype = torch.int32 @@ -1308,7 +1321,7 @@ def choose_qparams_affine_with_min_max( if zero_point_domain == ZeroPointDomain.NONE: zero_point = None elif zero_point_domain == ZeroPointDomain.INT: - zero_point = quant_min - torch.round(min_val_neg / scale) + zero_point = quant_min - _Round.apply(min_val_neg / scale) zero_point = torch.clamp(zero_point, quant_min, quant_max) if zero_point_dtype is None: zero_point_dtype = torch.int32 @@ -1400,7 +1413,7 @@ def _choose_qparams_affine( assert mapping_type == MappingType.ASYMMETRIC.name scale = (max_val_pos - min_val_neg) / float(quant_max - quant_min) scale = torch.clamp(scale, min=eps) - zero_point = quant_min - torch.round(min_val_neg / scale) + zero_point = quant_min - _Round.apply(min_val_neg / scale) zero_point = torch.clamp(zero_point, quant_min, quant_max) if zero_point_dtype is None: zero_point_dtype = torch.int32 @@ -1434,7 +1447,7 @@ def choose_qparams_and_quantize_affine_qqq( s_group *= 2 / max_q_val # 2 => symmetric # Quantize - q_w = torch.round(w / s_group).int() + q_w = _Round.apply(w / s_group).int() q_w += half_q_val q_w = torch.clamp(q_w, 0, max_q_val) # Compute ref (dequantized) @@ -1467,7 +1480,7 @@ def reshape_w(w): s_channel /= max_q_val # Quantize - q_w = torch.round(w / s_channel).int() + q_w = _Round.apply(w / s_channel).int() q_w = torch.clamp(q_w, -max_q_val, max_q_val) # Compute ref (dequantized) w_ref = q_w.half() * s_channel @@ -1871,7 +1884,7 @@ def choose_qparams_and_quantize_affine_hqq( # Round zero as in: https://github.com/casper-hansen/AutoAWQ/blob/main/awq/quantize/quantizer.py#L42C9-L42C14 if nbits in [4]: - zero = torch.round(zero) + zero = _Round.apply(zero) # Fine-tune weights if optimize: @@ -1887,7 +1900,7 @@ def choose_qparams_and_quantize_affine_hqq( else: zero = zero.to(compute_dtype) scale = scale.to(compute_dtype) - W_q = torch.round(W * scale + zero).clamp(min_max[0], min_max[1]) + W_q = _Round.apply(W * scale + zero).clamp(min_max[0], min_max[1]) # Store meta-data (we invert the scale for dequantization) scale = 1.0 / scale @@ -2004,7 +2017,7 @@ def choose_qparams_affine_float8( if scale_dtype is not torch.float32: # Shielding for Version > 2.8 assert scale_dtype is torch.float8_e8m0fnu, "Only float8_e8m0fnuz is supported" - scale = torch.exp2(torch.round(torch.log2(scale))) + scale = torch.exp2(_Round.apply(torch.log2(scale))) return scale.to(dtype=torch.float32) From 4610850b492ee462c226e085f1293e6b1a4c3819 Mon Sep 17 00:00:00 2001 From: Zeyu Song <87307087+szyszyzys@users.noreply.github.com> Date: Tue, 3 Jun 2025 13:44:57 -0700 Subject: [PATCH 077/165] Remove valpacking code and associated tests (#2295) --- .../kernels/cpu/aarch64/tests/CMakeLists.txt | 9 -- .../cpu/aarch64/tests/build_and_run_tests.sh | 1 - .../cpu/aarch64/tests/test_valpacking.cpp | 100 ------------------ 3 files changed, 110 deletions(-) delete mode 100644 torchao/experimental/kernels/cpu/aarch64/tests/test_valpacking.cpp diff --git a/torchao/experimental/kernels/cpu/aarch64/tests/CMakeLists.txt b/torchao/experimental/kernels/cpu/aarch64/tests/CMakeLists.txt index db736d84a3..1fd2828fc5 100644 --- a/torchao/experimental/kernels/cpu/aarch64/tests/CMakeLists.txt +++ b/torchao/experimental/kernels/cpu/aarch64/tests/CMakeLists.txt @@ -31,7 +31,6 @@ add_library( ${TORCHAO_LIBRARIES}/torchao/experimental/kernels/cpu/aarch64/reduction/find_min_and_max.cpp ${TORCHAO_LIBRARIES}/torchao/experimental/kernels/cpu/aarch64/reduction/compute_sum.cpp ${TORCHAO_LIBRARIES}/torchao/experimental/kernels/cpu/aarch64/quantization/quantize.cpp - ${TORCHAO_LIBRARIES}/torchao/experimental/kernels/cpu/aarch64/valpacking/interleave.cpp ) if(NOT TORCHAO_INCLUDE_DIRS) @@ -96,13 +95,6 @@ target_link_libraries( torchao_kernels_aarch64 ) -add_executable(test_valpacking test_valpacking.cpp) -target_link_libraries( - test_valpacking - PRIVATE - GTest::gtest_main - dep -) add_executable(test_embedding test_embedding.cpp) target_link_libraries( @@ -133,7 +125,6 @@ gtest_discover_tests(test_quantization) gtest_discover_tests(test_reduction) gtest_discover_tests(test_bitpacking) gtest_discover_tests(test_linear) -gtest_discover_tests(test_valpacking) gtest_discover_tests(test_embedding) gtest_discover_tests(test_weight_packing) gtest_discover_tests(test_qmatmul) diff --git a/torchao/experimental/kernels/cpu/aarch64/tests/build_and_run_tests.sh b/torchao/experimental/kernels/cpu/aarch64/tests/build_and_run_tests.sh index 1898e8b535..5d28ea01cc 100644 --- a/torchao/experimental/kernels/cpu/aarch64/tests/build_and_run_tests.sh +++ b/torchao/experimental/kernels/cpu/aarch64/tests/build_and_run_tests.sh @@ -58,7 +58,6 @@ ${CMAKE_OUT}/test_quantization ${CMAKE_OUT}/test_reduction ${CMAKE_OUT}/test_bitpacking ${CMAKE_OUT}/test_linear -${CMAKE_OUT}/test_valpacking ${CMAKE_OUT}/test_embedding ${CMAKE_OUT}/test_weight_packing ${CMAKE_OUT}/test_qmatmul diff --git a/torchao/experimental/kernels/cpu/aarch64/tests/test_valpacking.cpp b/torchao/experimental/kernels/cpu/aarch64/tests/test_valpacking.cpp deleted file mode 100644 index 02be12a675..0000000000 --- a/torchao/experimental/kernels/cpu/aarch64/tests/test_valpacking.cpp +++ /dev/null @@ -1,100 +0,0 @@ -// Copyright (c) Meta Platforms, Inc. and affiliates. -// All rights reserved. -// -// This source code is licensed under the license found in the -// LICENSE file in the root directory of this source tree. - -#include -#include -#include - -TEST(InterleaveDataTest, InterleaveChannels) { - // interleave 4 rows of 6 elements - int bytes_per_val = 4; // int32_t - int vals_per_channel = 6; - int vals_per_group = 6; - int vals_per_chunk = 3; - int channels = 4; - int channel_stride_in_vals = vals_per_channel; - - int data_size = channels * vals_per_channel; - assert(data_size == 24); - int32_t data[data_size]; - int32_t data_interleaved[data_size]; - for (int i = 0; i < data_size; i++) { - data[i] = i; - data_interleaved[i] = 0; - } - int32_t expected_data_interleaved[] = {0, 1, 2, 6, 7, 8, 12, 13, - 14, 18, 19, 20, 3, 4, 5, 9, - 10, 11, 15, 16, 17, 21, 22, 23}; - - torchao::kernels::cpu::valpacking::interleave_data( - data_interleaved, - data, - bytes_per_val, - vals_per_channel, - vals_per_group, - vals_per_chunk, - channels, - channel_stride_in_vals); - - for (int i = 0; i < data_size; ++i) { - EXPECT_EQ(data_interleaved[i], expected_data_interleaved[i]); - } -} - -TEST(InterleaveDataTest, InterleaveChannelsAndGroups) { - // Test this example: - // - // group0 group1 group2 - // chunk0 chunk1 chunk0 chunk1 chunk0 chunk1 - // [(v00, v01 | v02, v03) | (v04, v05 | v06, v07) | (v08, v09 | v0a, v0b)] ch0 - // [(v10, v11 | v12, v13) | (v14, v15 | v16, v17) | (v18, v19 | v1a, v1b)] ch1 - // [(v20, v21 | v22, v23) | (v24, v25 | v26, v27) | (v28, v29 | v2a, v2b)] ch2 - // [(v30, v31 | v32, v33) | (v34, v35 | v36, v37) | (v38, v39 | v3a, v3b)] ch3 - // - // The output of this method is: - // - // v00, v01 | v10, v11 | v20, v21 | v30, v31 // chunk0, group0 channels - // v04, v05 | v14, v15 | v24, v25 | v34, v35 // chunk0, group1 channels - // v08, v09 | v18, v19 | v28, v29 | v38, v39 // chunk0, group2 channels - // v02, v03 | v12, v13 | v22, v23 | v32, v33 // chunk1, group0 channels - // v06, v07 | v16, v17 | v26, v27 | v36, v37 // chunk1, group1 channels - // v0a, v0b | v1a, v1b | v2a, v2b | v3a, v3b // chunk1, group2 channels - - // interleave 4 rows of 6 elements - int bytes_per_val = 4; // int32_t - int vals_per_channel = 12; - int vals_per_group = 4; - int vals_per_chunk = 2; - int channels = 4; - int channel_stride_in_vals = vals_per_channel; - - int data_size = channels * vals_per_channel; - assert(data_size == 48); - int32_t data[data_size]; - int32_t data_interleaved[data_size]; - for (int i = 0; i < data_size; i++) { - data[i] = i; - data_interleaved[i] = 0; - } - int32_t expected_data_interleaved[] = { - 0, 1, 12, 13, 24, 25, 36, 37, 4, 5, 16, 17, 28, 29, 40, 41, - 8, 9, 20, 21, 32, 33, 44, 45, 2, 3, 14, 15, 26, 27, 38, 39, - 6, 7, 18, 19, 30, 31, 42, 43, 10, 11, 22, 23, 34, 35, 46, 47}; - - torchao::kernels::cpu::valpacking::interleave_data( - data_interleaved, - data, - bytes_per_val, - vals_per_channel, - vals_per_group, - vals_per_chunk, - channels, - channel_stride_in_vals); - - for (int i = 0; i < data_size; ++i) { - EXPECT_EQ(data_interleaved[i], expected_data_interleaved[i]); - } -} From bc68b11f1bf77be38721ca7dd2c477aeb5e6626e Mon Sep 17 00:00:00 2001 From: Scott Roy <161522778+metascroy@users.noreply.github.com> Date: Tue, 3 Jun 2025 15:02:08 -0700 Subject: [PATCH 078/165] Define torchao op library by srcs instead of object libraries (#2290) * init * up * up * up * up * up * up * up * up --- torchao/experimental/CMakeLists.txt | 89 ++++++++++++++----- .../ops/embedding_xbit/CMakeLists.txt | 46 ---------- .../CMakeLists.txt | 65 -------------- 3 files changed, 67 insertions(+), 133 deletions(-) delete mode 100644 torchao/experimental/ops/embedding_xbit/CMakeLists.txt delete mode 100644 torchao/experimental/ops/linear_8bit_act_xbit_weight/CMakeLists.txt diff --git a/torchao/experimental/CMakeLists.txt b/torchao/experimental/CMakeLists.txt index 2222ea60b9..7313a37e56 100644 --- a/torchao/experimental/CMakeLists.txt +++ b/torchao/experimental/CMakeLists.txt @@ -36,9 +36,13 @@ endif() add_compile_options("-Wall" "-Werror" "-Wno-deprecated" "-Wno-shorten-64-to-32") include(CMakePrintHelpers) +include(${CMAKE_CURRENT_SOURCE_DIR}/Utils.cmake) + message("TORCHAO_INCLUDE_DIRS: ${TORCHAO_INCLUDE_DIRS}") include_directories(${TORCHAO_INCLUDE_DIRS}) + +# Build cpu/aarch64 kernels if(TORCHAO_BUILD_CPU_AARCH64) message(STATUS "Building with cpu/aarch64") add_compile_definitions(TORCHAO_BUILD_CPU_AARCH64) @@ -77,29 +81,50 @@ if(TORCHAO_BUILD_CPU_AARCH64) add_compile_definitions(TORCHAO_ENABLE_ARM_I8MM) endif() - # Defines torchao_kernels_aarch64 - add_subdirectory(kernels/cpu/aarch64) - if(TORCHAO_BUILD_KLEIDIAI) message(STATUS "Building with Arm KleidiAI library") add_compile_definitions(TORCHAO_ENABLE_KLEIDI) endif() + + # Defines torchao_kernels_aarch64 + add_subdirectory(kernels/cpu/aarch64) endif() -# Add quantized operation dir -add_subdirectory(ops/linear_8bit_act_xbit_weight) -add_subdirectory(ops/embedding_xbit) - -# ATen ops lib -if (TORCHAO_BUILD_ATEN_OPS) - add_library(torchao_ops_aten SHARED) - target_link_libraries( - torchao_ops_aten PRIVATE - torchao_ops_linear_8bit_act_xbit_weight_aten - torchao_ops_embedding_xbit_aten + + +if (NOT TARGET cpuinfo) + # For some reason cpuinfo package has unused functions/variables + # TODO (T215533422): fix upstream + add_compile_options(-Wno-unused-function -Wno-unused-variable) + include(FetchContent) + FetchContent_Declare(cpuinfo + GIT_REPOSITORY https://github.com/pytorch/cpuinfo.git + GIT_TAG c61fe919607bbc534d7a5a5707bdd7041e72c5ff) + FetchContent_MakeAvailable( + cpuinfo) +endif() + +# Build ATen ops +if(TORCHAO_BUILD_ATEN_OPS) + find_package(Torch REQUIRED) + set(_torchao_op_srcs_aten) + list(APPEND _torchao_op_srcs_aten + ops/embedding_xbit/op_embedding_xbit_aten.cpp + ops/linear_8bit_act_xbit_weight/linear_8bit_act_xbit_weight.cpp + ops/linear_8bit_act_xbit_weight/op_linear_8bit_act_xbit_weight_aten.cpp ) + list(TRANSFORM _torchao_op_srcs_aten PREPEND "${CMAKE_CURRENT_SOURCE_DIR}/") + add_library(torchao_ops_aten SHARED ${_torchao_op_srcs_aten}) + target_link_torchao_parallel_backend(torchao_ops_aten "${TORCHAO_PARALLEL_BACKEND}") + if (TORCHAO_BUILD_CPU_AARCH64) + target_link_libraries(torchao_ops_aten PRIVATE torchao_kernels_aarch64) + endif() + target_link_libraries(torchao_ops_aten PRIVATE cpuinfo) + target_include_directories(torchao_ops_aten PRIVATE "${TORCH_INCLUDE_DIRS}") + target_link_libraries(torchao_ops_aten PRIVATE "${TORCH_LIBRARIES}") + target_compile_definitions(torchao_ops_aten PRIVATE USE_ATEN=1) - # Add MPS support if enabled + # Add MPS support if enabled if (TORCHAO_BUILD_MPS_OPS) message(STATUS "Building with MPS support") add_subdirectory(ops/mps) @@ -114,18 +139,38 @@ if (TORCHAO_BUILD_ATEN_OPS) ) endif() -# Build executorch lib if enabled + +# Build ExecuTorch ops if(TORCHAO_BUILD_EXECUTORCH_OPS) - add_library(torchao_ops_executorch STATIC) - target_link_libraries(torchao_ops_executorch PRIVATE - torchao_ops_linear_8bit_act_xbit_weight_executorch - torchao_ops_embedding_xbit_executorch + # ExecuTorch package is not required, but EXECUTORCH_INCLUDE_DIRS and EXECUTORCH_LIBRARIES must + # be defined and EXECUTORCH_LIBRARIES must include the following libraries installed by ExecuTorch: + # libexecutorch.a + # libextension_threadpool.a + # libcpuinfo.a + # libpthreadpool.a + if(NOT DEFINED EXECUTORCH_INCLUDE_DIRS AND NOT DEFINED EXECUTORCH_LIBRARIES) + message(WARNING "EXECUTORCH_INCLUDE_DIRS and EXECUTORCH_LIBRARIES are not defined. Looking for ExecuTorch.") + find_package(ExecuTorch HINTS ${CMAKE_PREFIX_PATH}/executorch/share/cmake) + endif() + set(_torchao_op_srcs_executorch) + list(APPEND _torchao_op_srcs_executorch + ops/embedding_xbit/op_embedding_xbit_executorch.cpp + ops/linear_8bit_act_xbit_weight/linear_8bit_act_xbit_weight.cpp + ops/linear_8bit_act_xbit_weight/op_linear_8bit_act_xbit_weight_executorch.cpp ) + list(TRANSFORM _torchao_op_srcs_executorch PREPEND "${CMAKE_CURRENT_SOURCE_DIR}/") + add_library(torchao_ops_executorch STATIC ${_torchao_op_srcs_executorch}) + target_link_torchao_parallel_backend(torchao_ops_executorch executorch) + target_include_directories(torchao_ops_executorch PRIVATE "${EXECUTORCH_INCLUDE_DIRS}") + target_compile_definitions(torchao_ops_executorch PRIVATE USE_EXECUTORCH=1) + target_link_libraries(torchao_ops_executorch PRIVATE "${EXECUTORCH_LIBRARIES}") + if (TORCHAO_BUILD_CPU_AARCH64) + target_link_libraries(torchao_ops_executorch PRIVATE torchao_kernels_aarch64) + endif() + target_link_libraries(torchao_ops_executorch PRIVATE cpuinfo) install( TARGETS torchao_ops_executorch - torchao_ops_linear_8bit_act_xbit_weight_executorch - torchao_ops_embedding_xbit_executorch EXPORT _targets DESTINATION lib ) diff --git a/torchao/experimental/ops/embedding_xbit/CMakeLists.txt b/torchao/experimental/ops/embedding_xbit/CMakeLists.txt deleted file mode 100644 index c681a44dc9..0000000000 --- a/torchao/experimental/ops/embedding_xbit/CMakeLists.txt +++ /dev/null @@ -1,46 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -cmake_minimum_required(VERSION 3.19) - -include(${CMAKE_CURRENT_SOURCE_DIR}/../../Utils.cmake) - -if(TORCHAO_BUILD_ATEN_OPS) - find_package(Torch REQUIRED) - add_library(torchao_ops_embedding_xbit_aten OBJECT - op_embedding_xbit_aten.cpp - ) - target_link_torchao_parallel_backend(torchao_ops_embedding_xbit_aten "${TORCHAO_PARALLEL_BACKEND}") - if (TORCHAO_BUILD_CPU_AARCH64) - target_link_libraries(torchao_ops_embedding_xbit_aten PRIVATE torchao_kernels_aarch64) - endif() - target_include_directories(torchao_ops_embedding_xbit_aten PRIVATE "${TORCH_INCLUDE_DIRS}") - target_link_libraries(torchao_ops_embedding_xbit_aten PRIVATE "${TORCH_LIBRARIES}") - target_compile_definitions(torchao_ops_embedding_xbit_aten PRIVATE USE_ATEN=1) -endif() - -if(TORCHAO_BUILD_EXECUTORCH_OPS) - # ExecuTorch package is not required, but EXECUTORCH_INCLUDE_DIRS and EXECUTORCH_LIBRARIES must - # be defined and EXECUTORCH_LIBRARIES must include the following libraries installed by ExecuTorch: - # libexecutorch.a - # libextension_threadpool.a - # libcpuinfo.a - # libpthreadpool.a - if(NOT DEFINED EXECUTORCH_INCLUDE_DIRS AND NOT DEFINED EXECUTORCH_LIBRARIES) - message(WARNING "EXECUTORCH_INCLUDE_DIRS and EXECUTORCH_LIBRARIES are not defined. Looking for ExecuTorch.") - find_package(ExecuTorch HINTS ${CMAKE_PREFIX_PATH}/executorch/share/cmake) - endif() - add_library(torchao_ops_embedding_xbit_executorch OBJECT - op_embedding_xbit_executorch.cpp - ) - target_link_torchao_parallel_backend(torchao_ops_embedding_xbit_executorch executorch) - target_include_directories(torchao_ops_embedding_xbit_executorch PRIVATE "${EXECUTORCH_INCLUDE_DIRS}") - target_compile_definitions(torchao_ops_embedding_xbit_executorch PRIVATE USE_EXECUTORCH=1) - target_link_libraries(torchao_ops_embedding_xbit_executorch PRIVATE "${EXECUTORCH_LIBRARIES}") - if (TORCHAO_BUILD_CPU_AARCH64) - target_link_libraries(torchao_ops_embedding_xbit_executorch PRIVATE torchao_kernels_aarch64) - endif() -endif() diff --git a/torchao/experimental/ops/linear_8bit_act_xbit_weight/CMakeLists.txt b/torchao/experimental/ops/linear_8bit_act_xbit_weight/CMakeLists.txt deleted file mode 100644 index 4bc3259061..0000000000 --- a/torchao/experimental/ops/linear_8bit_act_xbit_weight/CMakeLists.txt +++ /dev/null @@ -1,65 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. - -cmake_minimum_required(VERSION 3.19) - -include(${CMAKE_CURRENT_SOURCE_DIR}/../../Utils.cmake) - -if (NOT TARGET cpuinfo) - # For some reason cpuinfo package has unused functions/variables - # TODO (T215533422): fix upstream - add_compile_options(-Wno-unused-function -Wno-unused-variable) - include(FetchContent) - FetchContent_Declare(cpuinfo - GIT_REPOSITORY https://github.com/pytorch/cpuinfo.git - GIT_TAG aaac07ee499895770c89163ce0920ef8bb41ed23) - FetchContent_MakeAvailable( - cpuinfo) -endif() - -if (TORCHAO_BUILD_ATEN_OPS) - find_package(Torch REQUIRED) - add_library(torchao_ops_linear_8bit_act_xbit_weight_aten OBJECT - linear_8bit_act_xbit_weight.cpp - op_linear_8bit_act_xbit_weight_aten.cpp - ) - target_link_torchao_parallel_backend(torchao_ops_linear_8bit_act_xbit_weight_aten "${TORCHAO_PARALLEL_BACKEND}") - - if(TORCHAO_BUILD_CPU_AARCH64) - target_link_libraries(torchao_ops_linear_8bit_act_xbit_weight_aten PRIVATE torchao_kernels_aarch64) - endif() - target_link_libraries(torchao_ops_linear_8bit_act_xbit_weight_aten PRIVATE cpuinfo) - target_include_directories(torchao_ops_linear_8bit_act_xbit_weight_aten PRIVATE "${TORCH_INCLUDE_DIRS}") - target_link_libraries(torchao_ops_linear_8bit_act_xbit_weight_aten PRIVATE "${TORCH_LIBRARIES}") - target_compile_definitions(torchao_ops_linear_8bit_act_xbit_weight_aten PRIVATE USE_ATEN=1) -endif() - -if(TORCHAO_BUILD_EXECUTORCH_OPS) - # ExecuTorch package is not required, but EXECUTORCH_INCLUDE_DIRS and EXECUTORCH_LIBRARIES must - # be defined and EXECUTORCH_LIBRARIES must include the following libraries installed by ExecuTorch: - # libexecutorch.a - # libextension_threadpool.a - # libcpuinfo.a - # libpthreadpool.a - if(NOT DEFINED EXECUTORCH_INCLUDE_DIRS AND NOT DEFINED EXECUTORCH_LIBRARIES) - message(WARNING "EXECUTORCH_INCLUDE_DIRS and EXECUTORCH_LIBRARIES are not defined. Looking for ExecuTorch.") - find_package(ExecuTorch HINTS ${CMAKE_PREFIX_PATH}/executorch/share/cmake) - endif() - # find_package(ExecuTorch HINTS ${CMAKE_PREFIX_PATH}/executorch/share/cmake) - # file(GLOB _SRCS "${CMAKE_CURRENT_SOURCE_DIR}/op_linear_8bit_act_xbit_weight_executorch/*.cpp") - add_library(torchao_ops_linear_8bit_act_xbit_weight_executorch OBJECT - linear_8bit_act_xbit_weight.cpp - op_linear_8bit_act_xbit_weight_executorch.cpp - ) - target_link_torchao_parallel_backend(torchao_ops_linear_8bit_act_xbit_weight_executorch executorch) - target_include_directories(torchao_ops_linear_8bit_act_xbit_weight_executorch PRIVATE "${EXECUTORCH_INCLUDE_DIRS}") - target_compile_definitions(torchao_ops_linear_8bit_act_xbit_weight_executorch PRIVATE USE_EXECUTORCH=1) - target_link_libraries(torchao_ops_linear_8bit_act_xbit_weight_executorch PRIVATE "${EXECUTORCH_LIBRARIES}") - if(TORCHAO_BUILD_CPU_AARCH64) - target_link_libraries(torchao_ops_linear_8bit_act_xbit_weight_executorch PRIVATE torchao_kernels_aarch64) - endif() - target_link_libraries(torchao_ops_linear_8bit_act_xbit_weight_executorch PRIVATE cpuinfo) -endif() From 3aa93619466739c9d9845e1db3bfb2ff0f464857 Mon Sep 17 00:00:00 2001 From: Thien Tran Date: Wed, 4 Jun 2025 11:50:09 +0800 Subject: [PATCH 079/165] [optim] Fix bug when default dtype is BF16 (#2286) * handle error when default dtype is BF16 * skip FP8 optim on unsupported GPUs --- test/test_low_bit_optim.py | 29 ++++++++++++++++++++++++----- torchao/optim/subclass_4bit.py | 6 +++--- torchao/optim/subclass_8bit.py | 6 +++--- 3 files changed, 30 insertions(+), 11 deletions(-) diff --git a/test/test_low_bit_optim.py b/test/test_low_bit_optim.py index 08fdfa569f..692a0d9e6c 100644 --- a/test/test_low_bit_optim.py +++ b/test/test_low_bit_optim.py @@ -37,7 +37,6 @@ from torchao.optim.subclass_fp8 import OptimStateFp8 from torchao.testing.utils import skip_if_rocm from torchao.utils import ( - TORCH_VERSION_AT_LEAST_2_4, TORCH_VERSION_AT_LEAST_2_5, TORCH_VERSION_AT_LEAST_2_7, get_available_devices, @@ -128,8 +127,6 @@ class TestOptim(TestCase): @skip_if_rocm("ROCm enablement in progress") def test_optim_smoke(self, optim_name, dtype, device): if optim_name.endswith("Fp8") and device == "cuda": - if not TORCH_VERSION_AT_LEAST_2_4: - pytest.skip("FP8 CUDA requires PyTorch >= 2.4") if torch.cuda.get_device_capability() < (8, 9): pytest.skip("FP8 CUDA requires compute capability >= 8.9") @@ -166,6 +163,30 @@ def test_optim_smoke(self, optim_name, dtype, device): for p1, p2 in zip(model.parameters(), model2.parameters()): torch.testing.assert_close(p2, p1) + @parametrize("optim_name", ["Adam8bit", "Adam4bit", "AdamFp8"]) + @parametrize("device", _DEVICES) + def test_optim_default_dtype_bf16(self, optim_name, device): + if optim_name.endswith("Fp8") and device == "cuda": + if torch.cuda.get_device_capability() < (8, 9): + pytest.skip("FP8 CUDA requires compute capability >= 8.9") + + old_dtype = torch.get_default_dtype() + torch.set_default_dtype(torch.bfloat16) + + try: + model = nn.Sequential(nn.Linear(32, 256), nn.ReLU(), nn.Linear(256, 32)) + model.to(device=device) + optimizer = getattr(optim, optim_name)(model.parameters()) + + x = torch.randn(4, 32, device=device) + loss = model(x).sum() + loss.backward() + optimizer.step() + optimizer.zero_grad() + + finally: + torch.set_default_dtype(old_dtype) + # aten.slice is required for dcp.load() when world size changes i.e. re-sharding # however, it's cumbersome to test it directly, since we would need to run distributed # test 2 times with different world size, and persist checkpoint across the 2 runs. @@ -178,8 +199,6 @@ def test_subclass_slice(self, subclass, shape, device): if subclass == OptimStateFp8: if device == "cpu" and len(shape) > 1 and not TORCH_VERSION_AT_LEAST_2_5: pytest.skip("fill_cpu not implemented for Float8_e4m3fn for torch<2.5") - if device == "cuda" and not TORCH_VERSION_AT_LEAST_2_4: - pytest.skip("FP8 CUDA requires PyTorch >= 2.4") if device == "cuda" and torch.cuda.get_device_capability() < (8, 9): pytest.skip("FP8 CUDA requires compute capability >= 8.9") diff --git a/torchao/optim/subclass_4bit.py b/torchao/optim/subclass_4bit.py index 209d0b8cad..bc5fd33414 100644 --- a/torchao/optim/subclass_4bit.py +++ b/torchao/optim/subclass_4bit.py @@ -69,6 +69,7 @@ def __init__(self, codes: Tensor, scale: Tensor, qmap: Tensor, signed: bool, sha assert codes.dtype is torch.uint8 assert codes.ndim == 1 # flattened buffer assert scale.ndim == 1 + assert qmap.dtype is torch.float32 self.codes = codes self.scale = scale self.qmap = qmap @@ -101,9 +102,8 @@ def zeros(cls, shape, signed: bool = True, block_size: int = 128, device=None): codes = torch.zeros(n_elems // 2, dtype=torch.uint8, device=device) scale = torch.zeros(n_elems // block_size, device=device) - qmap = torch.tensor( - get_qmap_signed() if signed else get_qmap_unsigned(), device=device - ) + qmap_list = get_qmap_signed() if signed else get_qmap_unsigned() + qmap = torch.tensor(qmap_list, dtype=torch.float32, device=device) return cls(codes, scale, qmap, signed, shape) def __repr__(self): diff --git a/torchao/optim/subclass_8bit.py b/torchao/optim/subclass_8bit.py index 58a51734d7..d3f7634526 100644 --- a/torchao/optim/subclass_8bit.py +++ b/torchao/optim/subclass_8bit.py @@ -62,6 +62,7 @@ def __init__(self, codes: Tensor, scale: Tensor, qmap: Tensor, signed: bool): """ assert codes.dtype is torch.uint8 assert scale.ndim == 1 + assert qmap.dtype is torch.float32 self.codes = codes self.scale = scale self.qmap = qmap @@ -89,9 +90,8 @@ def dequantize(self, output_dtype=None): def zeros(cls, shape, signed: bool = True, block_size: int = 256, device=None): codes = torch.zeros(shape, dtype=torch.uint8, device=device) scale = torch.zeros(codes.numel() // block_size, device=device) - qmap = torch.tensor( - get_qmap_signed() if signed else get_qmap_unsigned(), device=device - ) + qmap_list = get_qmap_signed() if signed else get_qmap_unsigned() + qmap = torch.tensor(qmap_list, dtype=torch.float32, device=device) return cls(codes, scale, qmap, signed) def __repr__(self): From 152a8e397e1383c55bf7b87a8eaa2b87ffb2c114 Mon Sep 17 00:00:00 2001 From: Vasiliy Kuznetsov Date: Wed, 4 Jun 2025 08:50:33 -0400 Subject: [PATCH 080/165] update float8 training readme to include time measurement (#2291) Summary: Update the float8 training example code snippet to include time measurement that properly excludes torch.compile one-time warmup. Also, use larger shapes to demonstrate speedup from float8. Test Plan: copy-paste the snippet and run it, it works. Commenting out float8 shows a slowdown, as expected. Reviewers: Subscribers: Tasks: Tags: --- torchao/float8/README.md | 23 ++++++++++++++++++++--- 1 file changed, 20 insertions(+), 3 deletions(-) diff --git a/torchao/float8/README.md b/torchao/float8/README.md index 65da67c524..99bb80c4bd 100644 --- a/torchao/float8/README.md +++ b/torchao/float8/README.md @@ -17,6 +17,8 @@ and composable with key systems such as autograd, ```torch.compile``` and distri This is the default recipe, with a good balance of performance and accuracy. ```python +import time + import torch import torch.nn as nn from torchao.float8 import convert_to_float8_training @@ -26,11 +28,12 @@ if not TORCH_VERSION_AT_LEAST_2_5: raise AssertionError("torchao.float8 requires PyTorch version 2.5 or greater") # create model and sample input +M, K, N = 4096, 8192, 4096 m = nn.Sequential( - nn.Linear(2048, 4096), - nn.Linear(4096, 128), + nn.Linear(K, N, bias=False), + nn.Linear(N, 128, bias=False), ).bfloat16().cuda() -x = torch.randn(4096, 2048, device="cuda", dtype=torch.bfloat16) +x = torch.randn(M, K, device="cuda", dtype=torch.bfloat16) optimizer = torch.optim.SGD(m.parameters(), lr=0.1) # optional: filter modules from being eligible for float8 conversion @@ -50,12 +53,26 @@ convert_to_float8_training(m, module_filter_fn=module_filter_fn) # enable torch.compile for competitive performance m = torch.compile(m) +# warm up torch.compile for a clean training time measurement +for _ in range(1): + optimizer.zero_grad() + y = m(x) + y.sum().backward() + optimizer.step() + +torch.cuda.synchronize() +start_time = time.time() + # toy training loop for _ in range(10): optimizer.zero_grad() y = m(x) y.sum().backward() optimizer.step() + +torch.cuda.synchronize() +end_time = time.time() +print("Training time:", end_time - start_time) ``` ## float8 linear with rowwise scaling From 801af03165d2afa3b51a492f96417b80e1364d16 Mon Sep 17 00:00:00 2001 From: Jesse Cai Date: Wed, 4 Jun 2025 16:10:54 -0400 Subject: [PATCH 081/165] [sparse] marlin fixes (#2305) * [sparse] marlin fixes Summary: This PR updates sparse-marlin to not use CPU tensors and updates it to be compatible with Int4WeightOnl. Test Plan: ``` pytest test/sparsity/test_marlin.py ``` Reviewers: Subscribers: Tasks: Tags: * ruff check --- torchao/dtypes/uintx/marlin_sparse_layout.py | 21 ++++++++++---------- torchao/sparsity/marlin/__init__.py | 10 ++++------ 2 files changed, 15 insertions(+), 16 deletions(-) diff --git a/torchao/dtypes/uintx/marlin_sparse_layout.py b/torchao/dtypes/uintx/marlin_sparse_layout.py index 13af29d8bf..af1f8040f6 100644 --- a/torchao/dtypes/uintx/marlin_sparse_layout.py +++ b/torchao/dtypes/uintx/marlin_sparse_layout.py @@ -130,7 +130,7 @@ def __new__( cls, int_data: torch.Tensor, scale: torch.Tensor, - zero_point: torch.Tensor, + zero: torch.Tensor, meta: torch.Tensor, _layout: Layout, original_shape: torch.Size, @@ -151,7 +151,7 @@ def __init__( self, int_data: torch.Tensor, scale: torch.Tensor, - zero_point: torch.Tensor, + zero: torch.Tensor, meta: torch.Tensor, _layout: Layout, original_shape: torch.Size, @@ -159,8 +159,9 @@ def __init__( num_bits: int, ): self.int_data = int_data + self.scale_and_zero = None self.scale = scale - self.zero_point = zero_point + self.zero = zero self.meta = meta self._layout = _layout self.original_shape = original_shape @@ -181,7 +182,7 @@ def __torch_dispatch__(cls, func, types, args, kwargs): ) def __tensor_flatten__(self): - return ["int_data", "scale", "zero_point", "meta"], [ + return ["int_data", "scale", "zero", "meta"], [ self._layout, self.original_shape, self.group_size, @@ -194,13 +195,13 @@ def __tensor_unflatten__( ): int_data = tensor_data_dict["int_data"] scale = tensor_data_dict["scale"] - zero_point = tensor_data_dict["zero_point"] + zero = tensor_data_dict["zero"] meta = tensor_data_dict["meta"] _layout, original_shape, group_size, num_bits = tensor_attributes return cls( int_data, scale, - zero_point, + zero, meta, _layout, original_shape, @@ -223,14 +224,14 @@ def get_plain(self): ) int_data_expanded_t = int_data_expanded.t() scales_expanded_t = scales_expanded.t() - return int_data_expanded_t, scales_expanded_t, self.zero_point + return int_data_expanded_t, scales_expanded_t, self.zero @classmethod def from_plain( cls, int_data: torch.Tensor, scale: torch.Tensor, - zero_point: torch.Tensor, + zero: torch.Tensor, _layout: Layout, ): from torchao.sparsity.marlin import ( @@ -291,7 +292,7 @@ def from_plain( return cls( marlin_24_q_w_comp, marlin_24_s, - zero_point, + zero, meta, _layout, q_w_24.shape, @@ -305,6 +306,6 @@ def get_layout(self) -> Layout: def _apply_fn_to_data(self, fn): self.int_data = fn(self.int_data) self.scale = fn(self.scale) - self.zero_point = fn(self.zero_point) + self.zero = fn(self.zero) self.meta = fn(self.meta) return self diff --git a/torchao/sparsity/marlin/__init__.py b/torchao/sparsity/marlin/__init__.py index 16f4d788ca..86f46e77da 100644 --- a/torchao/sparsity/marlin/__init__.py +++ b/torchao/sparsity/marlin/__init__.py @@ -226,11 +226,10 @@ def _to_marlin_weights( # Pack pack_factor = utils.get_pack_factor(num_bits) - orig_device = q_w.device # Original implementation uses numpy + uint32 but we need to use int64 because torch.uint32 # does not support rshift_cpu. - q_w = q_w.cpu().to(torch.int64) + q_w = q_w.to(torch.int64) q_packed = torch.zeros( (q_w.shape[0], q_w.shape[1] // pack_factor), dtype=torch.int64, @@ -239,7 +238,7 @@ def _to_marlin_weights( for i in range(pack_factor): q_packed |= q_w[:, i::pack_factor] << (num_bits * i) - q_packed = q_packed.to(orig_device, dtype=torch.int32) + q_packed = q_packed.to(dtype=torch.int32) return q_packed @@ -259,12 +258,11 @@ def _from_marlin_weights( perm_24, _, _ = utils.get_reverse_perms_24(num_bits) pack_factor = utils.get_pack_factor(num_bits) - orig_device = q_packed.device # Unpack from marlin format. # Original implementation uses numpy + uint32 but we need to use int64 because torch.uint32 # does not support rshift_cpu. - q_packed = q_packed.cpu().to(torch.int64) + q_packed = q_packed.to(torch.int64) q_w_unpacked = torch.zeros( (q_packed.shape[0], q_packed.shape[1] * pack_factor), dtype=torch.int64, @@ -275,7 +273,7 @@ def _from_marlin_weights( (1 << num_bits) - 1 ) - q_w_unpacked = q_w_unpacked.to(orig_device, dtype=torch.int32) + q_w_unpacked = q_w_unpacked.to(dtype=torch.int32) q_w_comp = utils.reverse_marlin_permute_weights( q_w_unpacked, size_k, size_n, perm_24 From 2d4c6df164981757c57191f04bdb156f08a545c1 Mon Sep 17 00:00:00 2001 From: Driss Guessous <32754868+drisspg@users.noreply.github.com> Date: Wed, 4 Jun 2025 20:46:11 -0400 Subject: [PATCH 082/165] Skip native modules if USE_CPP = 0 (#2301) --- setup.py | 79 ++++++++++++++++++++++++++++++++++++-------------------- 1 file changed, 51 insertions(+), 28 deletions(-) diff --git a/setup.py b/setup.py index 51c283c858..5288ec15bd 100644 --- a/setup.py +++ b/setup.py @@ -45,17 +45,45 @@ def read_version(file_path="version.txt"): if version_suffix is None: version_suffix = f"+git{get_git_commit_id()}" -use_cpp = os.getenv("USE_CPP") - import platform -build_macos_arm_auto = ( - use_cpp == "1" - and platform.machine().startswith("arm64") - and platform.system() == "Darwin" -) +################################################################################ +# Build Configuration - Environment Variables and Build Options +################################################################################ + +# Core build toggles +use_cpp = os.getenv("USE_CPP", "1") +use_cpu_kernels = os.getenv("USE_CPU_KERNELS", "0") == "1" + +# Platform detection +is_arm64 = platform.machine().startswith("arm64") or platform.machine() == "aarch64" +is_macos = platform.system() == "Darwin" +is_linux = platform.system() == "Linux" + +# Auto-enable experimental builds on ARM64 macOS when USE_CPP=1 +build_macos_arm_auto = use_cpp == "1" and is_arm64 and is_macos + +# Build configuration hierarchy and relationships: +# +# Level 1: USE_CPP (Primary gate) +# ├── "0" → Skip all C++ extensions (Python-only mode) +# └── "1"/None → Build C++ extensions +# +# Level 2: Platform-specific optimizations +# ├── USE_CPU_KERNELS="1" + Linux → Include optimized CPU kernels (AVX512, etc.) +# └── ARM64 + macOS → Auto-enable experimental builds (build_macos_arm_auto) +# +# Level 3: Experimental builds (cmake-based) +# ├── BUILD_TORCHAO_EXPERIMENTAL="1" → Force experimental builds +# ├── build_macos_arm_auto → Auto-enable on ARM64 macOS +# └── When enabled, provides access to: +# ├── TORCHAO_BUILD_CPU_AARCH64 → ARM64 CPU kernels +# ├── TORCHAO_BUILD_KLEIDIAI → Kleidi AI library integration +# ├── TORCHAO_BUILD_EXPERIMENTAL_MPS → MPS acceleration (macOS only) +# ├── TORCHAO_ENABLE_ARM_NEON_DOT → ARM NEON dot product instructions +# ├── TORCHAO_ENABLE_ARM_I8MM → ARM 8-bit integer matrix multiply +# └── TORCHAO_PARALLEL_BACKEND → Backend selection (aten_openmp, executorch, etc.) -use_cpp_kernels = os.getenv("USE_CPP_KERNELS", "0") == "1" from torchao.utils import TORCH_VERSION_AT_LEAST_2_7 @@ -92,12 +120,10 @@ def __init__(self): # can be built by explicitly setting TORCHAO_BUILD_CPU_AARCH64=1 self.build_cpu_aarch64 = self._os_bool_var( "TORCHAO_BUILD_CPU_AARCH64", - default=(self._is_arm64() and self._is_macos()), + default=(is_arm64 and is_macos), ) if self.build_cpu_aarch64: - assert self._is_arm64(), ( - "TORCHAO_BUILD_CPU_AARCH64 requires an arm64 machine" - ) + assert is_arm64, "TORCHAO_BUILD_CPU_AARCH64 requires an arm64 machine" # TORCHAO_BUILD_KLEIDIAI is disabled by default for now because # 1) It increases the build time @@ -115,8 +141,8 @@ def __init__(self): "TORCHAO_BUILD_EXPERIMENTAL_MPS", default=False ) if self.build_experimental_mps: - assert self._is_macos(), "TORCHAO_BUILD_EXPERIMENTAL_MPS requires MacOS" - assert self._is_arm64(), "TORCHAO_BUILD_EXPERIMENTAL_MPS requires arm64" + assert is_macos, "TORCHAO_BUILD_EXPERIMENTAL_MPS requires macOS" + assert is_arm64, "TORCHAO_BUILD_EXPERIMENTAL_MPS requires arm64" assert torch.mps.is_available(), ( "TORCHAO_BUILD_EXPERIMENTAL_MPS requires MPS be available" ) @@ -129,7 +155,7 @@ def __init__(self): # Enabled by default on macOS silicon self.enable_arm_neon_dot = self._os_bool_var( "TORCHAO_ENABLE_ARM_NEON_DOT", - default=(self._is_arm64() and self._is_macos()), + default=(is_arm64 and is_macos), ) if self.enable_arm_neon_dot: assert self.build_cpu_aarch64, ( @@ -146,12 +172,6 @@ def __init__(self): "TORCHAO_ENABLE_ARM_I8MM requires TORCHAO_BUILD_CPU_AARCH64 be set" ) - def _is_arm64(self) -> bool: - return platform.machine().startswith("arm64") or platform.machine() == "aarch64" - - def _is_macos(self) -> bool: - return platform.system() == "Darwin" - def _os_bool_var(self, var, default) -> bool: default_val = "1" if default else "0" return os.getenv(var, default_val) == "1" @@ -323,6 +343,11 @@ def __init__( def get_extensions(): + # Skip building C++ extensions if USE_CPP is set to "0" + if use_cpp == "0": + print("USE_CPP=0: Skipping compilation of C++ extensions") + return [] + debug_mode = use_debug_mode() if debug_mode: print("Compiling in debug mode") @@ -363,11 +388,7 @@ def get_extensions(): ["-O3" if not debug_mode else "-O0", "-fdiagnostics-color=always"] ) - if ( - use_cpp_kernels - and platform.system() == "Linux" - and TORCH_VERSION_AT_LEAST_2_7 - ): + if use_cpu_kernels and is_linux and TORCH_VERSION_AT_LEAST_2_7: if torch._C._cpu._is_avx512_supported(): extra_compile_args["cxx"].extend( [ @@ -427,7 +448,7 @@ def get_extensions(): # Collect C++ source files sources = list(glob.glob(os.path.join(extensions_dir, "**/*.cpp"), recursive=True)) - if not use_cpp_kernels or platform.system() != "Linux": + if not use_cpu_kernels or not is_linux: # Remove csrc/cpu/*.cpp excluded_sources = list( glob.glob(os.path.join(extensions_dir, "cpu/*.cpp"), recursive=True) @@ -652,7 +673,9 @@ def bool_to_on_off(value): return ext_modules -check_submodules() +# Only check submodules if we're going to build C++ extensions +if use_cpp != "0": + check_submodules() setup( name="torchao", From 35ffb267c3d09d106b4d7d1acf6bb3a48d62a716 Mon Sep 17 00:00:00 2001 From: Jerry Zhang Date: Wed, 4 Jun 2025 21:41:50 -0400 Subject: [PATCH 083/165] Add support for fbgemm fp8 kernels (#2276) Summary: fp8 per row quantized weight with fp8 dynamic per row quantization only for now Test Plan: python test/dtypes/test_fbgemm_fp8.py in torchao/_models/llama folder: export CHECKPOINT_PATH=../../../checkpoints # path to checkpoints folder export MODEL_REPO=meta-llama/Meta-Llama-3.1-8B-Instruct python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --quantization fbgemm-fp8 --batch_size 1 Reviewers: Subscribers: Tasks: Tags: --- test/dtypes/test_affine_quantized.py | 7 + ...fbgemm_quantized.py => test_fbgemm_fp8.py} | 14 +- ...uantized_tensor.py => test_fbgemm_int4.py} | 4 +- torchao/_models/llama/generate.py | 26 +-- torchao/dtypes/__init__.py | 6 +- torchao/dtypes/fbgemm_fp8_tensor.py | 154 ++++++++++++++++++ ...ntized_tensor.py => fbgemm_int4_tensor.py} | 33 ++-- torchao/quantization/quant_api.py | 34 ++-- 8 files changed, 233 insertions(+), 45 deletions(-) rename test/dtypes/{test_fbgemm_quantized.py => test_fbgemm_fp8.py} (77%) rename test/dtypes/{test_fbgemm_quantized_tensor.py => test_fbgemm_int4.py} (92%) create mode 100644 torchao/dtypes/fbgemm_fp8_tensor.py rename torchao/dtypes/{fbgemm_quantized_tensor.py => fbgemm_int4_tensor.py} (87%) diff --git a/test/dtypes/test_affine_quantized.py b/test/dtypes/test_affine_quantized.py index b74c5d2ecf..68b5f41438 100644 --- a/test/dtypes/test_affine_quantized.py +++ b/test/dtypes/test_affine_quantized.py @@ -24,7 +24,9 @@ to_affine_quantized_intx, to_affine_quantized_intx_static, ) +from torchao.float8.config import e4m3_dtype from torchao.quantization import ( + FbgemmConfig, GemliteUIntXWeightOnlyConfig, Int4WeightOnlyConfig, Int8DynamicActivationInt8WeightConfig, @@ -45,6 +47,7 @@ is_fbcode, is_ROCM, is_sm_at_least_89, + is_sm_at_least_90, ) is_cusparselt_available = ( @@ -99,6 +102,10 @@ def get_quantization_functions( if is_sm_at_least_89(): base_functions.append(float8_weight_only()) + if is_sm_at_least_90(): + base_functions.append(FbgemmConfig(torch.bfloat16, torch.int4, torch.bfloat16)) + base_functions.append(FbgemmConfig(e4m3_dtype, e4m3_dtype, torch.bfloat16)) + return base_functions diff --git a/test/dtypes/test_fbgemm_quantized.py b/test/dtypes/test_fbgemm_fp8.py similarity index 77% rename from test/dtypes/test_fbgemm_quantized.py rename to test/dtypes/test_fbgemm_fp8.py index fe2573530c..d2f1e2d82a 100644 --- a/test/dtypes/test_fbgemm_quantized.py +++ b/test/dtypes/test_fbgemm_fp8.py @@ -12,15 +12,20 @@ run_tests, ) +from torchao.float8.config import e4m3_dtype from torchao.quantization import ( FbgemmConfig, quantize_, ) from torchao.quantization.utils import compute_error -from torchao.utils import is_sm_at_least_90 +from torchao.utils import ( + TORCH_VERSION_AT_LEAST_2_8, + is_sm_at_least_90, +) -class TestFbgemmInt4Tensor(TestCase): +@unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_8, "Need pytorch 2.8+") +class TestFbgemmFp8Tensor(TestCase): @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") @unittest.skipIf(not is_sm_at_least_90(), "Nedd sm90+") def test_linear(self): @@ -30,10 +35,9 @@ def test_linear(self): linear = torch.nn.Linear(128, 256, dtype=dtype, device=device) original = linear(input) config = FbgemmConfig( - input_dtype=torch.bfloat16, - weight_dtype=torch.int4, + input_dtype=e4m3_dtype, + weight_dtype=e4m3_dtype, output_dtype=torch.bfloat16, - block_size=(1, 128), ) quantize_(linear, config) quantized = linear(input) diff --git a/test/dtypes/test_fbgemm_quantized_tensor.py b/test/dtypes/test_fbgemm_int4.py similarity index 92% rename from test/dtypes/test_fbgemm_quantized_tensor.py rename to test/dtypes/test_fbgemm_int4.py index 51b68dd977..22fe5bc110 100644 --- a/test/dtypes/test_fbgemm_quantized_tensor.py +++ b/test/dtypes/test_fbgemm_int4.py @@ -18,15 +18,15 @@ ) from torchao.quantization.utils import compute_error from torchao.utils import ( - TORCH_VERSION_AT_LEAST_2_6, + TORCH_VERSION_AT_LEAST_2_8, is_sm_at_least_90, ) +@unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_8, "Need pytorch 2.8+") class TestFbgemmInt4Tensor(TestCase): @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") @unittest.skipIf(not is_sm_at_least_90(), "Nedd sm90+") - @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_6, "Need torch >= 2.6") def test_linear(self): dtype = torch.bfloat16 device = "cuda" diff --git a/torchao/_models/llama/generate.py b/torchao/_models/llama/generate.py index aa928c83f5..0cf166103b 100644 --- a/torchao/_models/llama/generate.py +++ b/torchao/_models/llama/generate.py @@ -442,23 +442,25 @@ def ffn_or_attn_only(mod, fqn): f"int4wo group_size needs to be one of [32,64,128,256] but got {group_size}" ) quantize_(model, int4_weight_only(group_size=group_size, use_hqq=use_hqq)) - elif "fbgemm" in quantization: + elif "fbgemm" in quantization and "int4" in quantization: from torchao.quantization import FbgemmConfig _, precision, group_size = quantization.split("-") group_size = int(group_size) block_size = [1, group_size] - if precision == "int4": - quantize_( - model, - FbgemmConfig( - torch.bfloat16, torch.int4, torch.bfloat16, block_size - ), - ) - else: - raise NotImplementedError( - f"FbegemmConfig({precision=}) not supported yet" - ) + assert precision == "int4", f"FbegemmConfig({precision=}) not supported yet" + quantize_( + model, + FbgemmConfig(torch.bfloat16, torch.int4, torch.bfloat16, block_size), + ) + elif "fbgemm" in quantization and "fp8" in quantization: + from torchao.float8.config import e4m3_dtype + from torchao.quantization import FbgemmConfig + + quantize_( + model, + FbgemmConfig(e4m3_dtype, e4m3_dtype, torch.bfloat16), + ) elif "int4dq-" in quantization: from torchao.dtypes import CutlassInt4PackedLayout diff --git a/torchao/dtypes/__init__.py b/torchao/dtypes/__init__.py index 1003491828..692d56ad31 100644 --- a/torchao/dtypes/__init__.py +++ b/torchao/dtypes/__init__.py @@ -8,7 +8,8 @@ to_affine_quantized_intx, to_affine_quantized_intx_static, ) -from .fbgemm_quantized_tensor import to_fbgemm_quantized +from .fbgemm_fp8_tensor import to_fbgemm_fp8 +from .fbgemm_int4_tensor import to_fbgemm_int4 from .floatx import ( CutlassSemiSparseLayout, Float8Layout, @@ -62,5 +63,6 @@ "PackedLinearInt8DynamicActivationIntxWeightLayout", "to_affine_quantized_packed_linear_int8_dynamic_activation_intx_weight", "Int4XPULayout", - "to_fbgemm_quantized", + "to_fbgemm_int4", + "to_fbgemm_fp8", ] diff --git a/torchao/dtypes/fbgemm_fp8_tensor.py b/torchao/dtypes/fbgemm_fp8_tensor.py new file mode 100644 index 0000000000..735c21c2ca --- /dev/null +++ b/torchao/dtypes/fbgemm_fp8_tensor.py @@ -0,0 +1,154 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD 3-Clause license found in the +# LICENSE file in the root directory of this source tree. + + +from typing import Optional + +import torch +from torch.utils._python_dispatch import return_and_correct_aliasing + +from torchao.utils import ( + TORCH_VERSION_AT_LEAST_2_5, + TorchAOBaseTensor, +) + +__all__ = [ + "to_fbgemm_fp8", +] + +aten = torch.ops.aten + + +class FbgemmFp8Tensor(TorchAOBaseTensor): + tensor_data_attrs = ["float8_data", "scale", "activation_scale_ub"] + tensor_attributes = ["dtype"] + + def __new__(cls, float8_data, scale, activation_scale_ub, dtype): + shape = float8_data.shape + kwargs = {} + kwargs["device"] = float8_data.device + kwargs["dtype"] = dtype + kwargs["requires_grad"] = False + return torch.Tensor._make_wrapper_subclass(cls, shape, **kwargs) # type: ignore[attr-defined] + + def __init__(self, float8_data, scale, activation_scale_ub, dtype): + self.float8_data = float8_data + self.scale = scale + self.activation_scale_ub = activation_scale_ub + + def __tensor_flatten__(self): + return self.tensor_data_attrs, [ + getattr(self, attr) for attr in self.tensor_attributes + ] + + @classmethod + def __tensor_unflatten__( + cls, tensor_data_dict, tensor_attributes, outer_size, outer_stride + ): + return cls( + *[tensor_data_dict[name] for name in cls.tensor_data_attrs], + *tensor_attributes, + ) + + def _apply_fn_to_data(self, fn): + return self.__class__( + *[fn(getattr(self, attr)) for attr in self.tensor_data_attrs], + *[getattr(self, attr) for attr in self.tensor_attributes], + ) + + def __repr__(self): + return ( + f"{self.__class__.__name__}(weight={self.float8_data}, scale={self.scale}, " + f"activation_scale_ub={self.activation_scale_ub}, " + f"shape={self.shape}, device={self.device}, dtype={self.dtype}, requires_grad={self.requires_grad})" + ) + + def _quantization_type(self): + return f"shape={self.shape}, activation_scale_ub={self.activation_scale_ub}, device={self.device}" + + @classmethod + def from_float( + cls, + w: torch.Tensor, + activation_scale_ub: Optional[float] = None, + ): + if activation_scale_ub is None: + activation_scale_ub = 1200.0 + + activation_scale_ub = torch.tensor( + [activation_scale_ub], + dtype=torch.float, + device=w.device, + ) + wq, w_scale = torch.ops.triton.quantize_fp8_row(w) + # wq, w_scale = torch.ops.fbgemm.quantize_fp8_per_row(w) + dtype = w.dtype + del w + return FbgemmFp8Tensor( + wq, + w_scale, + activation_scale_ub=activation_scale_ub, + dtype=dtype, + ) + + +implements = FbgemmFp8Tensor.implements + + +@implements([torch.nn.functional.linear, aten.linear.default]) +def _(func, types, args, kwargs): + input_tensor, weight_tensor, bias = ( + args[0], + args[1], + args[2] if len(args) > 2 else None, + ) + if not input_tensor.is_floating_point(): + raise NotImplementedError( + f"{func} is not implemented for non floating point input" + ) + + orig_act_size = input_tensor.size() + orig_out_features = weight_tensor.shape[-2] + + # not used + num_tokens = torch.empty([input_tensor.size(0)], device=input_tensor.device) + xq, x_scale = torch.ops.fbgemm.quantize_fp8_per_row( + input_tensor, num_tokens, weight_tensor.activation_scale_ub + ) + res = torch.ops.fbgemm.f8f8bf16_rowwise( + xq, + weight_tensor.float8_data, + x_scale, + weight_tensor.scale, + use_fast_accum=True, + ) + res = res.reshape(*orig_act_size[:-1], orig_out_features) + if bias is not None: + res = res + bias + + return res + + +@implements([aten.detach.default, aten.alias.default]) +def _(func, types, args, kwargs): + return return_and_correct_aliasing( + func, args, kwargs, args[0]._apply_fn_to_data(torch.detach) + ) + + +@implements([aten.clone.default, aten.copy_.default]) +def _(func, types, args, kwargs): + return return_and_correct_aliasing( + func, args, kwargs, args[0]._apply_fn_to_data(torch.clone) + ) + + +to_fbgemm_fp8 = FbgemmFp8Tensor.from_float + + +if TORCH_VERSION_AT_LEAST_2_5: + # Allow a model with FbgemmFp8Tensor weights to be loaded with `weights_only=True` + torch.serialization.add_safe_globals([FbgemmFp8Tensor]) diff --git a/torchao/dtypes/fbgemm_quantized_tensor.py b/torchao/dtypes/fbgemm_int4_tensor.py similarity index 87% rename from torchao/dtypes/fbgemm_quantized_tensor.py rename to torchao/dtypes/fbgemm_int4_tensor.py index fd788a73a3..c2ab6246bf 100644 --- a/torchao/dtypes/fbgemm_quantized_tensor.py +++ b/torchao/dtypes/fbgemm_int4_tensor.py @@ -11,10 +11,13 @@ import torch from torch.utils._python_dispatch import return_and_correct_aliasing -from torchao.utils import TorchAOBaseTensor +from torchao.utils import ( + TORCH_VERSION_AT_LEAST_2_5, + TorchAOBaseTensor, +) __all__ = [ - "to_fbgemm_quantized", + "to_fbgemm_int4", ] aten = torch.ops.aten @@ -71,25 +74,22 @@ def __repr__(self): f"shape={self.shape}, device={self.device}, dtype={self.dtype}, requires_grad={self.requires_grad})" ) + def _quantization_type(self): + return f"shape={self.shape}, group_size={self.group_size}, device={self.device}" + @classmethod def from_float( cls, w: torch.Tensor, - input_dtype: torch.dtype, - weight_dtype: torch.dtype, - output_dtype: torch.dtype, block_size: List[int], ): assert len(block_size) == w.ndim, ( f"Expecting the length of block_size to be equal to the dimension of the weight, got {block_size=} and {w.ndim=}" ) - group_size = block_size[-1] + if int4_row_quantize_zp is None: + raise ImportError("Requires fbgemm-gpu-genai >= 1.2.0") - assert (input_dtype, weight_dtype, output_dtype) == ( - torch.bfloat16, - torch.int4, - torch.bfloat16, - ) + group_size = block_size[-1] if w.ndim >= 3: wq, scale, zero_point = zip( @@ -138,9 +138,10 @@ def _(func, types, args, kwargs): weight_tensor.scale, weight_tensor.zero_point, ) + res = res.reshape(*orig_act_size[:-1], orig_out_features) if bias is not None: res = res + bias - return res.reshape(*orig_act_size[:-1], orig_out_features) + return res @implements([aten.detach.default, aten.alias.default]) @@ -157,5 +158,9 @@ def _(func, types, args, kwargs): ) -# We can have `to_fbgemm_tensor` to dispatch to different Fbgemm tensors later -to_fbgemm_quantized = FbgemmInt4Tensor.from_float +to_fbgemm_int4 = FbgemmInt4Tensor.from_float + + +if TORCH_VERSION_AT_LEAST_2_5: + # Allow a model with FbgemmInt4Tensor weights to be loaded with `weights_only=True` + torch.serialization.add_safe_globals([FbgemmInt4Tensor]) diff --git a/torchao/quantization/quant_api.py b/torchao/quantization/quant_api.py index a7eec7e1df..56229b0d27 100644 --- a/torchao/quantization/quant_api.py +++ b/torchao/quantization/quant_api.py @@ -46,7 +46,8 @@ to_affine_quantized_floatx, to_affine_quantized_floatx_static, to_affine_quantized_intx, - to_fbgemm_quantized, + to_fbgemm_fp8, + to_fbgemm_int4, to_marlinqqq_quantized_intx, ) from torchao.dtypes.uintx.packed_linear_int8_dynamic_activation_intx_weight_layout import ( @@ -537,6 +538,9 @@ def _quantization_type(weight: torch.Tensor): if isinstance(weight, LinearActivationQuantizedTensor): return f"{weight.__class__.__name__}(activation={weight.input_quant_func}, weight={_quantization_type(weight.original_weight_tensor)})" + if hasattr(weight, "_quantization_type"): + return f"{weight.__class__.__name__}({weight._quantization_type()})" + if type(weight) is torch.Tensor: return "not quantized" @@ -1981,7 +1985,8 @@ class FbgemmConfig(AOBaseConfig): input_dtype: torch.dtype weight_dtype: torch.dtype output_dtype: torch.dtype - block_size: List[int] + block_size: Optional[List[int]] = None + activation_scale_ub: Optional[float] = None @register_quantize_module_handler(FbgemmConfig) @@ -1998,22 +2003,31 @@ def _(module: torch.nn.Module, config: FbgemmConfig) -> torch.nn.Module: _SUPPORTED_DTYPES = { (torch.bfloat16, torch.int4, torch.bfloat16), + (e4m3_dtype, e4m3_dtype, torch.bfloat16), } if ( - config.input_dtype, - config.weight_dtype, - config.output_dtype, - ) in _SUPPORTED_DTYPES: - weight = to_fbgemm_quantized( + (config.input_dtype == torch.bfloat16) + and (config.weight_dtype == torch.int4) + and (config.output_dtype == torch.bfloat16) + ): + weight = to_fbgemm_int4( module.weight, - config.input_dtype, - config.weight_dtype, - config.output_dtype, config.block_size, ) module.weight = torch.nn.Parameter(weight, requires_grad=False) module.extra_repr = types.MethodType(_linear_extra_repr, module) + elif ( + (config.input_dtype == e4m3_dtype) + and (config.weight_dtype == e4m3_dtype) + and (config.output_dtype == torch.bfloat16) + ): + weight = to_fbgemm_fp8( + module.weight, + config.activation_scale_ub, + ) + module.weight = torch.nn.Parameter(weight, requires_grad=False) + module.extra_repr = types.MethodType(_linear_extra_repr, module) else: raise NotImplementedError( f"{config} is not supported. supported input, weight, output kernel dtypes are: {_SUPPORTED_DTYPES}" From 9cd5851e78a6fec36f3bccef8f5f3aa16ec3c599 Mon Sep 17 00:00:00 2001 From: YIWENX14 <164585414+YIWENX14@users.noreply.github.com> Date: Thu, 5 Jun 2025 02:48:35 -0700 Subject: [PATCH 084/165] primitive scale fix Differential Revision: D74446877 Pull Request resolved: https://github.com/pytorch/ao/pull/2210 --- torchao/quantization/quant_primitives.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/torchao/quantization/quant_primitives.py b/torchao/quantization/quant_primitives.py index de9a1ef4a7..9d453102cd 100644 --- a/torchao/quantization/quant_primitives.py +++ b/torchao/quantization/quant_primitives.py @@ -1270,6 +1270,8 @@ def choose_qparams_affine_with_min_max( if eps is None: eps = torch.finfo(min_val.dtype).eps + scale_device = min_val.device + if preserve_zero: min_val_neg = torch.min(min_val, torch.zeros_like(min_val)) max_val_pos = torch.max(max_val, torch.zeros_like(max_val)) @@ -1316,7 +1318,9 @@ def choose_qparams_affine_with_min_max( scale = torch.clamp(scale, min=eps) else: assert mapping_type == MappingType.ASYMMETRIC - scale = (max_val_pos - min_val_neg) / float(quant_max - quant_min) + scale = (max_val_pos - min_val_neg) / torch.tensor( + float(quant_max - quant_min), dtype=scale_dtype, device=scale_device + ) scale = torch.clamp(scale, min=eps) if zero_point_domain == ZeroPointDomain.NONE: zero_point = None From 72ea2fc5f0852162d7dcf38378d873854f0d6184 Mon Sep 17 00:00:00 2001 From: Daniel Vega-Myhre Date: Thu, 5 Jun 2025 10:43:11 -0400 Subject: [PATCH 085/165] [BE/docs] Add float8 training api ref to docsite (#2313) add float8 training api to api ref docs --- docs/source/api_ref_float8.rst | 30 ++++++++++++++++++++++++++++++ docs/source/index.rst | 1 + 2 files changed, 31 insertions(+) create mode 100644 docs/source/api_ref_float8.rst diff --git a/docs/source/api_ref_float8.rst b/docs/source/api_ref_float8.rst new file mode 100644 index 0000000000..8e912ac4e7 --- /dev/null +++ b/docs/source/api_ref_float8.rst @@ -0,0 +1,30 @@ +.. _api_quantization: + +==================== +torchao.float8 +==================== + +.. currentmodule:: torchao.float8 + +Main float8 training APIs +---------------------- + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + convert_to_float8_training + +Other float8 training types +------------------------------- + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + Float8LinearConfig + Float8GEMMConfig + CastConfig + ScalingType + ScalingGranularity + precompute_float8_dynamic_scale_for_fsdp diff --git a/docs/source/index.rst b/docs/source/index.rst index f526c77939..9febad8d5b 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -30,6 +30,7 @@ for an overall introduction to the library and recent highlight and updates. api_ref_dtypes api_ref_quantization api_ref_sparsity + api_ref_float8 .. toctree:: :glob: From 0640474276ca5a3e34f9e19519aa831a90ff0537 Mon Sep 17 00:00:00 2001 From: mobicham <37179323+mobicham@users.noreply.github.com> Date: Thu, 5 Jun 2025 16:47:59 +0200 Subject: [PATCH 086/165] Fix slicing and get_plain() in GemLite (#2288) * fix get_plain() with FMA mode * update * fix in_features/out_feature meta-data mismatch * update gemlite slice test * add packing_bitwidth support * add packing_bitwidth support and cleanup * update default gemlite layout * cleanup * fix symmetric use-case and relax _same_meta_data * _copy() meta data * fix (4,) in autoquant --- test/dtypes/test_affine_quantized.py | 73 ++++++++++++++- torchao/dtypes/uintx/gemlite_layout.py | 122 ++++++++++++++++++++----- torchao/quantization/autoquant.py | 6 +- torchao/quantization/quant_api.py | 8 +- 4 files changed, 178 insertions(+), 31 deletions(-) diff --git a/test/dtypes/test_affine_quantized.py b/test/dtypes/test_affine_quantized.py index 68b5f41438..bd5ed0c3b5 100644 --- a/test/dtypes/test_affine_quantized.py +++ b/test/dtypes/test_affine_quantized.py @@ -371,12 +371,81 @@ def test_slice_gemlite(self, device, dtype): # in_feature not divisible by 1024 # out_feature not divisible by 8 # to test slice + padding for int4 weight only quantization - dummy = nn.Linear(256, 512, dtype=dtype, device=device) - quantize_(dummy, GemliteUIntXWeightOnlyConfig()) + in_features, out_features, group_size, bit_width = 256, 512, 64, 4 + orig_shape = [out_features, in_features] + dummy = nn.Linear( + in_features, out_features, bias=False, dtype=dtype, device=device + ) + quantize_( + dummy, + GemliteUIntXWeightOnlyConfig(bit_width=bit_width, group_size=group_size), + ) + W_group_mode = dummy.weight.tensor_impl.gemlite_kwargs["meta_args"][10] + # make sure these run without error _ = dummy.weight.narrow(0, 0, 64) _ = dummy.weight.narrow(1, 0, 128) + # Dequant op + import gemlite + + def dequant(input_layer, in_features, orig_shape): + int_data = input_layer.tensor_impl.packed_weight + scale = input_layer.tensor_impl.scale + zero_point = input_layer.tensor_impl.zero_point + + W_q = ( + gemlite.bitpack.unpack_over_rows( + int_data, + W_nbits=bit_width, + num_output_rows=in_features, + dtype=torch.uint8, + ) + .T.contiguous() + .view([-1, group_size]) + ) + + s = scale.t().contiguous().view(-1, 1) + z = zero_point.t().contiguous().view(-1, 1) + + if W_group_mode == 4: # FMA + W_deq = (W_q * s + z).view(orig_shape) + else: + W_deq = ((W_q - z) * s).view(orig_shape) + + return W_deq + + W_r = dequant(dummy.weight, dummy.in_features, orig_shape) + + # Slicing in half + for slice_axis, start, end in [ + (0, 0, 256), + (0, 256, 256), + (1, 0, 128), + (1, 128, 128), + ]: + layer_sliced = dummy.weight.narrow(slice_axis, start, end) + + if slice_axis == 0: + num_rows, out_shape = ( + dummy.in_features, + (orig_shape[0] // 2, orig_shape[1]), + ) + else: + num_rows, out_shape = ( + dummy.in_features // 2, + (orig_shape[0], orig_shape[1] // 2), + ) + + W_slice = dequant(layer_sliced, num_rows, out_shape) + + W_slice_ref = ( + W_r[start : start + end, :] + if slice_axis == 0 + else W_r[:, start : start + end] + ) + self.assertEqual((W_slice_ref - W_slice).abs().mean().item(), 0) + @common_utils.parametrize("device", ["cuda"]) @common_utils.parametrize("dtype", [torch.bfloat16]) def test_matmul(self, device, dtype): diff --git a/torchao/dtypes/uintx/gemlite_layout.py b/torchao/dtypes/uintx/gemlite_layout.py index 1c840f7ec4..eb06cf2a96 100644 --- a/torchao/dtypes/uintx/gemlite_layout.py +++ b/torchao/dtypes/uintx/gemlite_layout.py @@ -25,7 +25,6 @@ except: gemlite = None - aten = torch.ops.aten @@ -35,7 +34,12 @@ def _same_metadata( ) -> bool: kwargs_match = len(self.gemlite_kwargs) == len(src.gemlite_kwargs) for k, v in self.gemlite_kwargs.items(): - if k != "scale_activations": + if k in [ + "in_features", + "out_features", + "packing_bitwidth", + "elements_per_sample", + ]: kwargs_match = kwargs_match and (v == src.gemlite_kwargs[k]) return ( @@ -80,6 +84,7 @@ def get_gemlite_aqt_kwargs( weight, group_size=64, bit_width=4, + packing_bitwidth=None, use_hqq=True, ): if gemlite is None: @@ -99,6 +104,9 @@ def get_gemlite_aqt_kwargs( assert group_size is None or bit_width != 8, ( "gemlite only works with group_size=None for bit_width=8" ) + assert packing_bitwidth in [8, 16, 32, None], ( + f"Invalid packing bitwidth, got {packing_bitwidth}" + ) out_features, in_features = weight.shape group_size = in_features if group_size is None else group_size @@ -107,6 +115,7 @@ def get_gemlite_aqt_kwargs( aqt_kwargs["_layout"] = GemlitePackedLayout( group_size=group_size, bit_width=bit_width, + packing_bitwidth=packing_bitwidth, ) aqt_kwargs["use_hqq"] = use_hqq return aqt_kwargs @@ -114,8 +123,9 @@ def get_gemlite_aqt_kwargs( @dataclass(frozen=True) class GemlitePackedLayout(Layout): - group_size: Optional[int] = 64 + group_size: Optional[int] = 128 bit_width: int = 4 + packing_bitwidth: Optional[int] = None @register_layout(GemlitePackedLayout) @@ -191,24 +201,36 @@ def from_plain( group_size, bit_width = _layout.group_size, _layout.bit_width out_features, in_features = int_data.shape + packing_bitwidth = _layout.packing_bitwidth if bit_width == 8 and group_size == in_features: gemlite_linear = gemlite.helper.A16W8(device=int_data.device).from_weights( int_data, scales=scale, bias=None ) else: - gemlite_linear = gemlite.helper.A16Wn(device=int_data.device).from_weights( + gemlite_linear = gemlite.helper.A16Wn( + device=int_data.device, packing_bitwidth=packing_bitwidth + ).from_weights( int_data, scale, zero_point, bit_width, group_size, bias=None ) + meta_args = gemlite_linear.get_meta_args() gemlite_kwargs = { "in_features": in_features, "out_features": out_features, - "meta_args": gemlite_linear.get_meta_args(), + "packing_bitwidth": packing_bitwidth, + "data_contiguous": gemlite_linear.data_contiguous, + "elements_per_sample": gemlite_linear.elements_per_sample, + "W_group_mode": gemlite_linear.W_group_mode, + "meta_args": meta_args, } packed_weight, scale, zero_point = gemlite_linear.get_tensor_args() packed_weight = packed_weight.to(device) + if zero_point is None: + zero_point = torch.tensor( + [[]], device=packed_weight.device, dtype=torch.int32 + ) return cls(packed_weight, scale, zero_point, gemlite_kwargs, _layout) @@ -235,18 +257,39 @@ def _apply_fn_to_data(self, fn): def get_plain(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: device = self.packed_weight.device int_data = ( - gemlite.bitpack.unpack_over_rows( - self.packed_weight.cuda(), - W_nbits=self._layout.bit_width, - num_output_rows=self.gemlite_kwargs["out_features"], - dtype=torch.uint8, + ( + gemlite.bitpack.unpack_over_rows( + self.packed_weight.cuda(), + W_nbits=self._layout.bit_width, + num_output_rows=self.gemlite_kwargs["in_features"], + dtype=torch.uint8, + ) ) + .to(device) .t() - .contiguous() - ).to(device) + ) + + # Preserve col-row major layout + if self.gemlite_kwargs["data_contiguous"]: + int_data = int_data.contiguous() + + # Handle FMA mode: W_q * s + z -> (W_q - z) * s + if self.gemlite_kwargs["W_group_mode"] == 4: + scale_min_val = 1e-8 + scale = self.scale.clone().float() + scale[torch.logical_and(scale >= 0, scale.abs() <= scale_min_val)] = ( + scale_min_val + ) + scale[ + torch.logical_and(scale < 0, scale.abs() <= scale_min_val) + ] = -scale_min_val + zero_point = (-self.zero_point.float() / scale).clamp_(-100, 100) + zero_point = zero_point.to(self.scale.dtype) + else: + zero_point = self.zero_point scale = self.scale.t().contiguous() - zero_point = self.zero_point.t().contiguous() + zero_point = zero_point.t().contiguous() return int_data, scale, zero_point @@ -274,14 +317,36 @@ def __torch_dispatch__(cls, func, types, args, kwargs): assert step == 1, "Only step == 1 is supported in slicing right now" if dim in [0, 1]: - int_data, scale, zero_point = self.get_plain() - data_len = int_data.shape[dim] + # data in self is transposed, meaning forward() performs x @ W_deq not x @ W_deq.T + dim = 1 - dim + packed_weight = self.packed_weight + scale = self.scale + zero_point = self.zero_point + + gemlite_kwargs = self.gemlite_kwargs.copy() + orig_shape = [ + gemlite_kwargs["in_features"], + gemlite_kwargs["out_features"], + ] + elements_per_sample = gemlite_kwargs["elements_per_sample"] + data_len = orig_shape[dim] scale_len = scale.shape[dim] ratio = data_len / scale_len start_scale = int(start / ratio) end_scale = int(end / ratio) - int_data = aten.slice.Tensor(int_data, dim, start, end, step) + # For packing only the K dimension. This should be flipped for N-dim packing. + div = elements_per_sample if dim == 0 else 1 + packed_weight = aten.slice.Tensor( + packed_weight, dim, start // div, end // div, step + ) + + # Update in_features/out_features + gemlite_kwargs["in_features"] = ( + packed_weight.shape[0] * elements_per_sample + ) + gemlite_kwargs["out_features"] = packed_weight.shape[1] + scale = aten.slice.Tensor(scale, dim, start_scale, end_scale, step) if zero_point is not None and zero_point.numel() > 0: zero_point = aten.slice.Tensor( @@ -289,15 +354,10 @@ def __torch_dispatch__(cls, func, types, args, kwargs): ) else: zero_point = None - # this is to handle padding - int_data, scale, zero_point = self._layout.post_process( - int_data, scale, zero_point, self.block_size - ) - - sliced = self.from_plain( - int_data, scale, zero_point, self._layout - ) # Will be transposed again + sliced = GemliteAQTTensorImpl( + packed_weight, scale, zero_point, gemlite_kwargs, self._layout + ) return return_and_correct_aliasing(func, args, kwargs, sliced) else: @@ -308,10 +368,24 @@ def __torch_dispatch__(cls, func, types, args, kwargs): elif func is aten.copy_.default: self = args[0] src = args[1] + + # Handle zero_point = None with symmetric quant + if self.zero_point is None: + self.zero_point = torch.tensor( + [[]], device=self.packed_weight.device, dtype=torch.int32 + ) + + if src.zero_point is None: + src.zero_point = torch.tensor( + [[]], device=src.packed_weight.device, dtype=torch.int32 + ) + if _same_metadata(self, src): self_tensors = self.__tensor_flatten__()[0] for tensor_name in self_tensors: getattr(self, tensor_name).copy_(getattr(src, tensor_name)) + for key in self.gemlite_kwargs: + self.gemlite_kwargs[key] = src.gemlite_kwargs[key] return raise ValueError( f"Not supported args for copy_ due to metadata mistach: {args[0], args[1]}" diff --git a/torchao/quantization/autoquant.py b/torchao/quantization/autoquant.py index 41ea588231..998204c8fe 100644 --- a/torchao/quantization/autoquant.py +++ b/torchao/quantization/autoquant.py @@ -741,11 +741,11 @@ def from_float(cls, weight): weight = weight.to(torch.float16) bit_width = 4 - packing_bitwidth = 32 - contiguous = None + packing_bitwidth = None use_hqq = True + aqt_kwargs = get_gemlite_aqt_kwargs( - weight, cls.group_size, bit_width, packing_bitwidth, contiguous, use_hqq + weight, cls.group_size, bit_width, packing_bitwidth, use_hqq ) weight = to_affine_quantized_intx(weight, **aqt_kwargs) input_quant_func = _to_float16 diff --git a/torchao/quantization/quant_api.py b/torchao/quantization/quant_api.py index 56229b0d27..be25b144a6 100644 --- a/torchao/quantization/quant_api.py +++ b/torchao/quantization/quant_api.py @@ -990,8 +990,9 @@ class GemliteUIntXWeightOnlyConfig(AOBaseConfig): `set_inductor_config`: if True, adjusts `torchinductor` settings to recommended values. """ - group_size: Optional[int] = 64 + group_size: Optional[int] = 128 bit_width: int = 4 + packing_bitwidth: Optional[int] = None set_inductor_config: bool = True @@ -1005,6 +1006,7 @@ def _gemlite_uintx_weight_only_transform( ): group_size = config.group_size bit_width = config.bit_width + packing_bitwidth = config.packing_bitwidth if config.set_inductor_config: torchao.quantization.utils.recommended_inductor_config_setter() @@ -1015,7 +1017,9 @@ def _gemlite_uintx_weight_only_transform( use_hqq = True if bit_width == 4 else False new_weight = to_affine_quantized_intx( weight, - **get_gemlite_aqt_kwargs(weight, group_size, bit_width, use_hqq), + **get_gemlite_aqt_kwargs( + weight, group_size, bit_width, packing_bitwidth, use_hqq + ), ) module.weight = torch.nn.Parameter(new_weight, requires_grad=False) module.extra_repr = types.MethodType(_linear_extra_repr, module) From d72a6d1f6f34788f63bd813ca94486c97922ea30 Mon Sep 17 00:00:00 2001 From: Svetlana Karslioglu Date: Thu, 5 Jun 2025 10:30:05 -0500 Subject: [PATCH 087/165] Enable doc build to run on PRs (#2315) Enable doc build to run on PRs when files in `docs/**` path are changed to enable preview --- .github/workflows/doc_build.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/doc_build.yml b/.github/workflows/doc_build.yml index 27ae54975d..7ed75daba4 100644 --- a/.github/workflows/doc_build.yml +++ b/.github/workflows/doc_build.yml @@ -12,7 +12,6 @@ on: pull_request: paths: - 'docs/**' - - '!docs/**' workflow_dispatch: concurrency: From 0d9631b6b7e31f9a71c15640088311bbe030b123 Mon Sep 17 00:00:00 2001 From: andrewor14 Date: Thu, 5 Jun 2025 14:49:02 -0400 Subject: [PATCH 088/165] Add Float8ActInt4WeightQATQuantizer (#2289) **Summary:** This commit adds a QAT quantizer that performs float8 dynamic activation + int4 symmetric per channel weight fake quantization. Note that there is no corresponding config for float8 QAT yet. This will be added in a future PR. **Test Plan:** python test/quantization/test_qat.py -k test_float8_fake_quantize python test/quantization/test_qat.py -k test_qat_fp8a4w_quantizer --- test/quantization/test_qat.py | 63 ++++++++++- torchao/quantization/qat/__init__.py | 2 + torchao/quantization/qat/fake_quantizer.py | 21 ++++ torchao/quantization/qat/linear.py | 118 +++++++++++++++++++-- torchao/quantization/qat/utils.py | 32 ++++++ 5 files changed, 228 insertions(+), 8 deletions(-) diff --git a/test/quantization/test_qat.py b/test/quantization/test_qat.py index cbe279c12e..323802757d 100644 --- a/test/quantization/test_qat.py +++ b/test/quantization/test_qat.py @@ -17,6 +17,9 @@ from torch.ao.quantization.fx._decomposed import quantized_decomposed_lib # noqa: F401 from torchao import quantize_ +from torchao.float8.config import ScalingGranularity +from torchao.float8.float8_scaling_utils import hp_tensor_to_float8_dynamic +from torchao.float8.float8_tensor import LinearMMConfig from torchao.quantization.granularity import ( PerAxis, PerGroup, @@ -40,15 +43,18 @@ ) from torchao.quantization.qat.fake_quantizer import ( FakeQuantizer, + _Float8RowwiseActivationFakeQuantizer, ) from torchao.quantization.qat.linear import ( FakeQuantizedLinear, + Float8ActInt4WeightQATQuantizer, Int4WeightOnlyQATLinear, Int8DynActInt4WeightQATLinear, ) from torchao.quantization.qat.utils import ( _fake_quantize_per_channel_group, _fake_quantize_per_token, + _Float8RowwiseFakeQuantize, _get_qmin_qmax, ) from torchao.quantization.quant_api import ( @@ -68,6 +74,7 @@ ) from torchao.quantization.utils import ( _get_per_token_block_size, + compute_error, get_group_qparams_symmetric, get_groupwise_affine_qparams, groupwise_affine_quantize_tensor, @@ -1474,7 +1481,6 @@ def test_qat_8da4w_prepare_vs_convert(self, dtype: torch.dtype): numerics that match exactly over N trials. """ from torchao.quantization.qat import Int8DynActInt4WeightQATQuantizer - from torchao.quantization.utils import compute_error num_trials = 1000 group_size = 16 @@ -1688,6 +1694,61 @@ def test_qat_range_learning(self): self.assertNotEqual(torch.count_nonzero(new_weight.grad), 0) self.assertFalse(torch.equal(new_weight, prev_weight)) + def test_float8_rowwise_fake_quantize(self): + """ + Test that `_Float8RowwiseFakeQuantize` is numerically close to `Float8Tensor`. + """ + torch.manual_seed(self.SEED) + dtype = torch.float8_e4m3fn + x = torch.randn(32, 64) + axiswise_dim = 0 + out = _Float8RowwiseFakeQuantize.apply(x, dtype, axiswise_dim) + out_expected = hp_tensor_to_float8_dynamic( + x, + dtype, + LinearMMConfig(), + scaling_granularity=ScalingGranularity.AXISWISE, + axiswise_dim=axiswise_dim, + ).to_original_precision() + torch.testing.assert_close(out, out_expected, atol=0, rtol=0) + + @unittest.skipIf( + not TORCH_VERSION_AT_LEAST_2_6, "skipping when torch version is 2.6 or lower" + ) + def test_qat_fp8a4w_quantizer(self): + """ + Test basic model training with `Float8ActInt4WeightQATQuantizer`. + """ + torch.manual_seed(self.SEED) + m = M() + qat_quantizer = Float8ActInt4WeightQATQuantizer() + qat_model = qat_quantizer.prepare(m) + for linear in [m.linear1, m.sub.linear, m.linear2]: + self.assertIsInstance(linear, FakeQuantizedLinear) + self.assertIsInstance( + linear.activation_fake_quantizer, _Float8RowwiseActivationFakeQuantizer + ) + self.assertIsInstance(linear.weight_fake_quantizer, FakeQuantizer) + prev_weight = copy.deepcopy(m.linear1.weight) + + # Simulate training + optimizer = torch.optim.SGD( + m.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-5 + ) + loss_fn = torch.nn.CrossEntropyLoss() + optimizer.zero_grad() + target = torch.randn(1, 512).float() + example_inputs = m.example_inputs() + out = qat_model(*example_inputs) + loss = loss_fn(out, target) + loss.backward() + optimizer.step() + # Assert that weights have valid gradients and are being updated + new_weight = m.linear1.weight + self.assertIsNotNone(new_weight.grad) + self.assertNotEqual(torch.count_nonzero(new_weight.grad), 0) + self.assertFalse(torch.equal(new_weight, prev_weight)) + if __name__ == "__main__": unittest.main() diff --git a/torchao/quantization/qat/__init__.py b/torchao/quantization/qat/__init__.py index 010ccfc8cc..4a4359e682 100644 --- a/torchao/quantization/qat/__init__.py +++ b/torchao/quantization/qat/__init__.py @@ -11,6 +11,7 @@ Int4WeightOnlyEmbeddingQATQuantizer, ) from .linear import ( + Float8ActInt4WeightQATQuantizer, Int4WeightOnlyQATQuantizer, Int8DynActInt4WeightQATQuantizer, ) @@ -18,6 +19,7 @@ __all__ = [ "ComposableQATQuantizer", "FakeQuantizeConfig", + "Float8ActInt4WeightQATQuantizer", "FromIntXQuantizationAwareTrainingConfig", "Int4WeightOnlyEmbeddingQATQuantizer", "Int4WeightOnlyQATQuantizer", diff --git a/torchao/quantization/qat/fake_quantizer.py b/torchao/quantization/qat/fake_quantizer.py index aca0c032bb..b7ad792dc1 100644 --- a/torchao/quantization/qat/fake_quantizer.py +++ b/torchao/quantization/qat/fake_quantizer.py @@ -32,6 +32,7 @@ from .utils import ( _fake_quantize_per_channel_group, _fake_quantize_per_token, + _Float8RowwiseFakeQuantize, ) @@ -186,3 +187,23 @@ def __repr__(self) -> str: Return a human readable representation of this `FakeQuantizer` with config details. """ return "FakeQuantizer(%s)" % self.config + + +class _Float8RowwiseActivationFakeQuantizer(torch.nn.Module): + """ + Simple fake quantizer for float8 rowwise fake quantization, intended for activations only. + """ + + def __init__(self): + super().__init__() + self.enabled = True + + def forward(self, x: torch.Tensor) -> torch.Tensor: + if self.enabled: + return _Float8RowwiseFakeQuantize.apply( + x, + torch.float8_e4m3fn, + -1, + ) + else: + return x diff --git a/torchao/quantization/qat/linear.py b/torchao/quantization/qat/linear.py index bffd5dc31f..567b87f342 100644 --- a/torchao/quantization/qat/linear.py +++ b/torchao/quantization/qat/linear.py @@ -28,7 +28,10 @@ from torchao.utils import TORCH_VERSION_AT_LEAST_2_6 from .api import FakeQuantizeConfig -from .fake_quantizer import FakeQuantizer +from .fake_quantizer import ( + FakeQuantizer, + _Float8RowwiseActivationFakeQuantizer, +) from .utils import ( _get_qmin_qmax, ) @@ -145,6 +148,11 @@ def from_linear( return new_linear +# =========================== +# | QAT quantizer interface | +# =========================== + + class _LegacyQATQuantizer(TwoStepQuantizer): """ Base class for sharing common methods across legacy QAT quantizers. @@ -157,9 +165,30 @@ def get_weight_fake_quantize_config(self) -> Optional[FakeQuantizeConfig]: return None -# ========================================================= -# | Linear int8 dynamic activations + int4 weight QAT | -# ========================================================= +def enable_linear_fake_quant( + mod: torch.nn.Module, + enabled: bool = True, +): + """ + Helper function to enable fake quantization in `FakeQuantizerLinear`. + """ + if isinstance(mod, FakeQuantizedLinear): + if mod.activation_fake_quantizer is not None: + mod.activation_fake_quantizer.enabled = enabled + if mod.weight_fake_quantizer is not None: + mod.weight_fake_quantizer.enabled = enabled + + +def disable_linear_fake_quant(mod: torch.nn.Module): + """ + Helper function to disable fake quantization in `FakeQuantizerLinear`. + """ + enable_linear_fake_quant(mod, enabled=False) + + +# =========================================== +# | int8 dynamic activations + int4 weights | +# =========================================== class Int8DynActInt4WeightQATQuantizer(_LegacyQATQuantizer): @@ -307,6 +336,7 @@ def disable_fake_quant(self): self.enable_fake_quant(False) +# TODO: remove these in favor of enable_linear_fake_quant def enable_8da4w_fake_quant(mod: torch.nn.Module): """ Enable fake quantization for `Int8DynActInt4WeightQATLinear`. @@ -315,6 +345,7 @@ def enable_8da4w_fake_quant(mod: torch.nn.Module): mod.enable_fake_quant() +# TODO: remove in favor of disable_linear_fake_quant def disable_8da4w_fake_quant(mod: torch.nn.Module): """ Disable fake quantization for `Int8DynActInt4WeightQATLinear`. @@ -357,9 +388,9 @@ def _get_8da4w_weight_config( ) -# =================================== -# | Linear int4 weight-only QAT | -# =================================== +# ==================== +# | int4 weight-only | +# ==================== class Int4WeightOnlyQATQuantizer(_LegacyQATQuantizer): @@ -501,6 +532,7 @@ def disable_fake_quant(self): self.enable_fake_quant(False) +# TODO: remove these in favor of enable_linear_fake_quant def enable_4w_fake_quant(mod: torch.nn.Module): """ Enable fake quantization for `Int4WeightOnlyQATLinear`. @@ -509,6 +541,7 @@ def enable_4w_fake_quant(mod: torch.nn.Module): mod.enable_fake_quant() +# TODO: remove these in favor of disable_linear_fake_quant def disable_4w_fake_quant(mod: torch.nn.Module): """ Disable fake quantization for `Int4WeightOnlyQATLinear`. @@ -533,3 +566,74 @@ def _get_4w_weight_config( zero_point_precision=qparams_precision, zero_point_domain=ZeroPointDomain.FLOAT, ) + + +# ============================================= +# | float8 rowwise activations + int4 weights | +# ============================================= + + +class Float8ActInt4WeightQATQuantizer(_LegacyQATQuantizer): + """ + QAT quantizer for applying dynamic rowwise float8 activation + int4 + per group/channel symmetric weight fake quantization to linear layers + in the model. Currently only supports rowwise granularity for float8 + activations. + + args: + group_size (Optional[int]): the number of elements in each quantized + group for weights, defaults to 64. Use None for per channel. + scale_precision: precision of weight scales, defaults to torch.bfloat16. + """ + + def __init__( + self, + group_size: Optional[int] = 64, + scale_precision: torch.dtype = torch.bfloat16, + ): + if group_size is not None: + weight_granularity = "per_group" + else: + weight_granularity = "per_channel" + self._weight_config = FakeQuantizeConfig( + dtype=torch.int4, + granularity=weight_granularity, + group_size=group_size, + is_symmetric=True, + is_dynamic=True, + scale_precision=scale_precision, + ) + + def prepare( + self, model: torch.nn.Module, *args: Any, **kwargs: Any + ) -> torch.nn.Module: + """ + Swap all `nn.Linear` with `FakeQuantizedLinear` with float8 + fake quantizer for activations and int4 fake quantizer for weights. + """ + for name, child in model.named_children(): + if isinstance(child, torch.nn.Linear): + # TODO: add a config for float8? + new_linear = FakeQuantizedLinear.from_linear( + child, + weight_config=self._weight_config, + ) + new_linear.activation_fake_quantizer = ( + _Float8RowwiseActivationFakeQuantizer() + ) + setattr(model, name, new_linear) + else: + self.prepare(child) + return model + + # TODO: add convert path + def convert( + self, model: torch.nn.Module, *args: Any, **kwargs: Any + ) -> torch.nn.Module: + raise NotImplementedError + + def get_activation_fake_quantize_config(self) -> Optional[FakeQuantizeConfig]: + raise NotImplementedError("Float8 FakeQuantizeConfig does not exist yet") + + def get_weight_fake_quantize_config(self) -> Optional[FakeQuantizeConfig]: + return self.weight_config diff --git a/torchao/quantization/qat/utils.py b/torchao/quantization/qat/utils.py index 01818ef2b2..132020499c 100644 --- a/torchao/quantization/qat/utils.py +++ b/torchao/quantization/qat/utils.py @@ -16,6 +16,38 @@ ) +class _Float8RowwiseFakeQuantize(torch.autograd.Function): + """ + Implementation of float8 rowwise fake quantize with backward STE. + """ + + @staticmethod + def forward( + ctx: torch.autograd.function.FunctionCtx, + x: torch.Tensor, + float8_dtype: torch.dtype, + axiswise_dim: int, + ): + # compute rowwise scale based on `torchao.float8.float8_utils.tensor_to_scale` + eps = 1e-12 + amax = torch.amax(torch.abs(x), dim=axiswise_dim, keepdim=True) + amax = amax.to(torch.float64) + scale = torch.finfo(float8_dtype).max / torch.clamp(amax, min=eps) + scale = scale.to(torch.float32) + + # fake quantize + max_value = torch.finfo(float8_dtype).max + x_fq = x.to(torch.float32) * scale + x_fq = x_fq.clamp(min=-max_value, max=max_value) + x_fq = x_fq.to(float8_dtype).to(x.dtype) + x_fq = x_fq / scale + return x_fq.to(x.dtype) + + @staticmethod + def backward(ctx, gy): + return gy, None, None + + # TODO: delete? class _UnwrapAffineFakeQuantizedTensor(torch.autograd.Function): """ From 488ecd4cf871035b091981a557a9204a5d50bf5b Mon Sep 17 00:00:00 2001 From: Daniel Vega-Myhre Date: Thu, 5 Jun 2025 15:56:49 -0400 Subject: [PATCH 089/165] [BE] [docs] Add float8 pretraining tutorial to docsite (#2304) * add float8 pretraining tutorial * make empty commit to trigger ci * remove references to e2e tutorial --- docs/source/conf.py | 1 + docs/source/index.rst | 1 + docs/source/pretraining.rst | 202 ++++++++++++++++++++++++++++++++ docs/static/fp8-loss-curves.png | Bin 0 -> 138219 bytes 4 files changed, 204 insertions(+) create mode 100644 docs/source/pretraining.rst create mode 100644 docs/static/fp8-loss-curves.png diff --git a/docs/source/conf.py b/docs/source/conf.py index f767d48164..66ee9a1c7e 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -37,6 +37,7 @@ # ones. extensions = [ "sphinx.ext.autodoc", + "sphinx.ext.autosectionlabel", "sphinx.ext.autosummary", "sphinx.ext.doctest", "sphinx.ext.intersphinx", diff --git a/docs/source/index.rst b/docs/source/index.rst index 9febad8d5b..9df40131cf 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -40,3 +40,4 @@ for an overall introduction to the library and recent highlight and updates. serialization subclass_basic subclass_advanced + pretraining diff --git a/docs/source/pretraining.rst b/docs/source/pretraining.rst new file mode 100644 index 0000000000..441b8c4a4b --- /dev/null +++ b/docs/source/pretraining.rst @@ -0,0 +1,202 @@ +Pretraining with float8 +--------------------------------- + +Pretraining with float8 using torchao can provide `up to 1.5x speedups `__ on 512 GPU clusters, +and up to `1.34-1.43x speedups `__ on 2K H200 clusters with the latest `torchao.float8` rowwise recipe. + +In this tutorial, we will show 2 ways to use the **torchao.float8** recipes for pretraining: + +1. :ref:`Pretraining with torchtitan`, the offical PyTorch pretraining framework with native torchao integration. +2. :ref:`Pretraining with torchao directly`, to integrate torchao's float8 training recipes into your own pretraining code. + + +Pretraining with torchtitan +########################### + +In this tutorial we'll pretrain Llama3 8b using torchtitan with torchao's float8 training recipes: rowwise scaling and tensorwise scaling. + +`Torchtitan `__ is PyTorch's official pretraining framework that is natively integrated with torchao, and supports +several popular flagship models with common forms of parallelism, float8 training, distributed checkpointing and more. +See the torchtitan `docs `__ for additional details. + +You can use this workflow to get started quickly with a "batteries included" experience. Users commonly +fork torchtitan and build on top of it when they're ready. + +Prerequisites +================ + +1. (Recommended) Create a new virtual environment with conda or venv. +2. `Install torchao `__. +3. `Install torchtitan `__, including the "downloading a tokenizer" step. + +You're now ready to start a pretraining job using one of the recipes below! + +Rowwise scaling +=============== + +Run the following command from torchtitan root directory to launch a Llama3 8b training job on 8 GPUs with float8 rowwise training: + +.. code:: console + + NGPU=8 CONFIG_FILE="./torchtitan/models/llama3/train_configs/llama3_8b.toml" ./run_train.sh --training.compile --model.converters="float8" --float8.recipe_name="rowwise" + +Torchtitan will automatically use FSDP2 to parallelize training when more than 1 GPU is used. To use other forms of parallelism, modify hyperparameters, or change other training configurations, you can directly edit the `llama3_8b.toml `__ file or use command line flags (run the command with :code:`--help` to see more options). + +You should see terminal output that looks like this: + +.. code:: console + + [rank0]:[titan] 2025-06-04 08:51:48,074 - root - INFO - step: 1 loss: 12.2254 memory: 27.34GiB(28.78%) tps: 375 tflops: 21.73 mfu: 2.20% + [rank0]:[titan] 2025-06-04 08:51:58,557 - root - INFO - step: 10 loss: 10.7069 memory: 30.99GiB(32.62%) tps: 7,034 tflops: 407.35 mfu: 41.19% + [rank0]:[titan] 2025-06-04 08:52:10,224 - root - INFO - step: 20 loss: 8.9196 memory: 30.99GiB(32.62%) tps: 7,022 tflops: 406.65 mfu: 41.12% + [rank0]:[titan] 2025-06-04 08:52:21,904 - root - INFO - step: 30 loss: 8.1423 memory: 30.99GiB(32.62%) tps: 7,014 tflops: 406.23 mfu: 41.08% + +As you can see, ignoring the warmup steps we are achieving around ~7k TPS with 30.99GB peak memory usage. To compare performance against bfloat16 training, you can remove the :code:`--model.converters="float8" --float8.recipe_name="rowwise"` flags +and run the same command to see the baseline performance of bfloat16 training: + +.. code:: console + + NGPU=8 CONFIG_FILE="./torchtitan/models/llama3/train_configs/llama3_8b.toml" ./run_train.sh --training.compile + +You should see the following output: + +.. code:: console + + [rank0]:[titan] 2025-06-04 11:02:37,404 - root - INFO - step: 1 loss: 12.2611 memory: 27.22GiB(28.65%) tps: 595 tflops: 34.47 mfu: 3.49% + [rank0]:[titan] 2025-06-04 11:02:49,027 - root - INFO - step: 10 loss: 10.4260 memory: 30.89GiB(32.51%) tps: 6,344 tflops: 367.39 mfu: 37.15% + [rank0]:[titan] 2025-06-04 11:03:01,988 - root - INFO - step: 20 loss: 8.9482 memory: 30.89GiB(32.51%) tps: 6,321 tflops: 366.06 mfu: 37.01% + [rank0]:[titan] 2025-06-04 11:03:14,991 - root - INFO - step: 30 loss: 8.1183 memory: 30.89GiB(32.51%) tps: 6,300 tflops: 364.89 mfu: 36.89% + [rank0]:[titan] 2025-06-04 11:03:28,013 - root - INFO - step: 40 loss: 7.4659 memory: 30.89GiB(32.51%) tps: 6,291 tflops: 364.36 mfu: 36.84% + [rank0]:[titan] 2025-06-04 11:03:39,769 - root - INFO - [GC] Peforming periodical GC collection. 0.02 seconds. + +As you can see, the bfloat16 baseline achieves ~6.3k TPS using 30.89GB peak memory. + +This means our float8 rowwise scaling recipe achieves **1.11x higher throughput** compared to bfloat16 baseline, using nearly identical peak memory! + +Note that you can achieve even higher throughput improvement using the tensorwise scaling recipe, which exists at a different point on the performane vs accuracy curve. + +Tensorwise scaling +================== + +Float8 training with tensorwise scaling is the default recipe, so we can omit the :code:`--float8.recipe_name` flag: + +.. code:: console + + NGPU=8 CONFIG_FILE="./torchtitan/models/llama3/train_configs/llama3_8b.toml" ./run_train.sh --training.compile --model.converters="float8" + +You should see the output like the following: + +.. code:: console + + [rank0]:[titan] 2025-06-04 10:52:19,648 - root - INFO - step: 1 loss: 12.2648 memory: 27.28GiB(28.71%) tps: 557 tflops: 32.29 mfu: 3.26% + [rank0]:[titan] 2025-06-04 10:52:29,475 - root - INFO - step: 10 loss: 10.9106 memory: 30.91GiB(32.53%) tps: 7,503 tflops: 434.53 mfu: 43.94% + [rank0]:[titan] 2025-06-04 10:52:40,166 - root - INFO - step: 20 loss: 9.0774 memory: 30.91GiB(32.53%) tps: 7,663 tflops: 443.78 mfu: 44.87% + [rank0]:[titan] 2025-06-04 10:52:50,885 - root - INFO - step: 30 loss: 8.3233 memory: 30.91GiB(32.53%) tps: 7,643 tflops: 442.66 mfu: 44.76% + [rank0]:[titan] 2025-06-04 10:53:01,613 - root - INFO - step: 40 loss: 7.6150 memory: 30.91GiB(32.53%) tps: 7,637 tflops: 442.27 mfu: 44.72% + +As you can see, we are achieving ~7.6k TPS using 30.91GB peak memory, which is **1.21x higher throughput** compared to the bfloat16 baseline! + +Picking a recipe +================ + +**TL;DR**: rowwise scaling is better for jobs prioritizing more accurate numerics and training stability, and tensorwise is better for jobs prioritizing training throughput. + +The higher throughput of tensorwise scaling comes at the cost of slightly higher quantization error (i.e., reduced numerical integrity vs bfloat16) compared to rowwise scaling. +This is because rowwise scaling using a more granular scaling factor (per row, instead of per tensor), which limits the impact of outliers that can cause underflow during scaling. + +Below you can see the loss curves comparing bfloat16, float8 tensorwise, and float8 rowwise training for training Llama3 8b on 8xH100 GPUs: + +.. image:: ../static/fp8-loss-curves.png + :alt: Loss curves for training Llama3 8b on 8xH100s with torchtitan using bfloat16, float8 tensorwise, and float8 rowwise training. + + +Important notes +=============== + +* float8 training is currently only supported on 2+ GPUs in torchtitan, not single GPU training. +* You must use :code:`--training.compile` to achieve high performance. torchao float8 training recipes are built natively on top of :code:`torch.compile`, so it will work out of the box! + + +Pretraining with torchao directly +################################# + +In this tutorial we'll pretrain a toy model using torchao APIs directly. + +You can use this workflow to integrate torchao into your own custom pretraining code directly. + +Prerequisites +================ + +1. (Recommended) Create a new virtual environment with conda or venv. +2. `Install torchao `__. + +You're now ready to integrate torchao into your training code directly! + +Model conversion API +==================== + +The torchao API for converting your model to use float8 training is: `convert_to_float8_training `__. This API will recursively convert :code:`nn.Linear` modules in your model to use `Float8Linear `__. + +You can use the :code:`module_filter_fn` argument to determine which :code:`nn.Linear` layers should be swapped to use :code:`Float8Linear`. + +You should refer to this `performance benchmark table `__ to understand +what kind of performance improvement over bfloat16 you can expect for a given GEMM size. + +Below is a code snippet showing how to use it: + +.. code:: py + + import torch + from torch import nn + import torch.nn.functional as F + + from torchao.float8.float8_linear_utils import convert_to_float8_training + from torchao.float8.float8_linear import Float8Linear + from torchao.float8 import convert_to_float8_training + from torchao.utils import TORCH_VERSION_AT_LEAST_2_5 + + if not TORCH_VERSION_AT_LEAST_2_5: + raise AssertionError("torchao.float8 requires PyTorch version 2.5 or greater") + + # create model and sample input + m = nn.Sequential( + nn.Linear(2048, 4096), + nn.Linear(4096, 128), + nn.Linear(128, 1), + ).bfloat16().cuda() + x = torch.randn(4096, 2048, device="cuda", dtype=torch.bfloat16) + optimizer = torch.optim.AdamW(m.parameters(), lr=1e-3) + + # optional: filter modules from being eligible for float8 conversion + def module_filter_fn(mod: torch.nn.Module, fqn: str): + # don't convert the last module + if fqn == "1": + return False + # don't convert linear modules with weight dimensions not divisible by 16 + if isinstance(mod, torch.nn.Linear): + if mod.in_features % 16 != 0 or mod.out_features % 16 != 0: + return False + return True + + # convert specified `torch.nn.Linear` modules to `Float8Linear` + convert_to_float8_training(m, module_filter_fn=module_filter_fn) + + # enable torch.compile for competitive performance + m = torch.compile(m) + + # toy training loop + for _ in range(10): + optimizer.zero_grad() + output = m(x) + # use fake labels for demonstration purposes + fake_labels = torch.ones_like(output) + loss = 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zgkF@b53ph?Ox4wlh6bMZ5`y=grDC?1=8j_J%QpwWxVVVoVkqhOUmy6_zceiK1m!Vr zHDp zM{W=pf8GdlY!n7I!#42p(s9@R`(Nn}4CgfaB2&?EJ93*yiPu0@goXu_ZrgEiXvEDv z;lk$T2q)w3)|D;{t^}VE!}emr6~2pQPWX%a2h*J&3cbxCn==7#lpShK2_@SE)dD_A*7(ePJ8Ofcv4XjT z&0l43H?(?=NCT}(ip}~7P?RPCl%FdStI9xEp-E2K4{)a_67?rmMrOhf==erU0H;wNtvJ_KZm@VJ0X`X$iU7@LA@Z3GZ8(Q3owCvRl|GV$VhQL`QI+j)4|NkGIF zvfG Date: Thu, 5 Jun 2025 23:22:02 -0400 Subject: [PATCH 090/165] Fix broken circular dep error Differential Revision: D75980282 Pull Request resolved: https://github.com/pytorch/ao/pull/2320 --- torchao/float8/__init__.py | 3 +++ torchao/float8/inference.py | 4 +--- torchao/float8/types.py | 19 +++++++++++++++++++ 3 files changed, 23 insertions(+), 3 deletions(-) create mode 100644 torchao/float8/types.py diff --git a/torchao/float8/__init__.py b/torchao/float8/__init__.py index 8d4d58fd6b..ed21c93d20 100644 --- a/torchao/float8/__init__.py +++ b/torchao/float8/__init__.py @@ -16,6 +16,7 @@ ) from torchao.float8.fsdp_utils import precompute_float8_dynamic_scale_for_fsdp from torchao.float8.inference import Float8MMConfig +from torchao.float8.types import FP8Granularity from torchao.utils import TORCH_VERSION_AT_LEAST_2_5 if TORCH_VERSION_AT_LEAST_2_5: @@ -41,5 +42,7 @@ # top level UX "convert_to_float8_training", "precompute_float8_dynamic_scale_for_fsdp", + # types + "FP8Granularity", # note: Float8Tensor and Float8Linear are not public APIs ] diff --git a/torchao/float8/inference.py b/torchao/float8/inference.py index d6e650aa6e..42ea5e9dfa 100644 --- a/torchao/float8/inference.py +++ b/torchao/float8/inference.py @@ -12,6 +12,7 @@ import torch from torchao.float8.float8_utils import is_row_major, pad_tensor_for_matmul +from torchao.float8.types import FP8Granularity from torchao.quantization.granularity import ( PerRow, PerTensor, @@ -116,9 +117,6 @@ def _is_rowwise_scaled(x) -> bool: return x.block_size == (1,) * (x.dim() - 1) + (x.shape[-1],) -FP8Granularity = Union[PerTensor, PerRow] - - def _normalize_granularity( granularity: Optional[ Union[ diff --git a/torchao/float8/types.py b/torchao/float8/types.py new file mode 100644 index 0000000000..b332a9629a --- /dev/null +++ b/torchao/float8/types.py @@ -0,0 +1,19 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD 3-Clause license found in the +# LICENSE file in the root directory of this source tree. +""" +Common types for float8 quantization +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Union + +if TYPE_CHECKING: + from torchao.quantization.granularity import PerRow, PerTensor + + +# Define FP8Granularity type alias to break circular import dependencies +FP8Granularity = Union["PerTensor", "PerRow"] From fdddb2cfa2a4ea930dfce9ef23429013b466b22d Mon Sep 17 00:00:00 2001 From: Daniel Vega-Myhre Date: Fri, 6 Jun 2025 08:44:27 -0400 Subject: [PATCH 091/165] [BE/docs] Add fp8 rowwise perf table to float8 training readme (#2312) * add fp8 rowwise perf table * add fp8 rowwise perf table to readme --- docs/static/fp8-rowwise-perf.png | Bin 0 -> 344318 bytes torchao/float8/README.md | 9 ++++++++- 2 files changed, 8 insertions(+), 1 deletion(-) create mode 100644 docs/static/fp8-rowwise-perf.png diff --git a/docs/static/fp8-rowwise-perf.png b/docs/static/fp8-rowwise-perf.png new file mode 100644 index 0000000000000000000000000000000000000000..ff5b41800bea8946c832274e9c216e2776dec45b GIT binary patch literal 344318 zcmaf)1ymf(5~y)c@Zj!*;K7~6b#a0_gy0YyLI}RNJ6UY;Ai;vWg$)*h2MA7ryS?GR z_y6xb@7;6Wo-;eMGt=EOUDZ|9)!#;Gsw+IfBF92NKzO302+~48Kw$^AIt(=6jH@}; zF9ZY}H3wN)O(j`bT1|HsTZcC`2ndQ%sru*!+QZNCjI`)bFhEku+iG};1X9WvFHYlV z+37Ixlp&bX{c#-CKj-C8Mx(2>h!(JhEM-l+Y{2`0wFS`1m_1AV14yX{eO#;gJNoG)JHIleuo}AmMsjFKg`XM|$MIbKd%H&E=5uTV>MMzsc zKiD{X`HppA^r>=H@cF|ml!<5<6@hjFuM~UZy;x*03W5u|JCgner2@Hrv&6acL&bk@rTT~2a{uW;$9Df z-ts&q>?qvXj%ZMrE zQ4CV7S%uxcijKJ_my$6v51>u=ySJqS8cW-F_Y&-3Sd6(zW 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NDbUP3wmB=rcch0r8xBwVpF;ieLWa2(%;P*u$YQVk_y6k*EG|0pR}~0aUIFD(%2K zCZN~CG31I059OQza<9f7jKEx&p!W~dsF~SfldT%IL%KWWB7miMe&Os z=+Up#AeE4R_3__p2sF-k{yFsjjBQv3n1bN}Twkhx*}=bmgauXqo11?E$?*a+A;(*^ zhGX*2vHeHD2q0hdf7dnfI#qxrQ^x_HgY@slhrMLY_Fwk=?;pQJ18erd*baByXkuVX=?xT8Z^c`qJJ^$|7bnXJpbGCt6Ukb%ttck%@@GW NJ85O9DhcDD{{!UxL302A literal 0 HcmV?d00001 diff --git a/torchao/float8/README.md b/torchao/float8/README.md index 99bb80c4bd..8533a05779 100644 --- a/torchao/float8/README.md +++ b/torchao/float8/README.md @@ -132,7 +132,9 @@ on using `torchao.float8` in a distributed setting. # Performance -A common question about float8 training is "when is float8 linear faster vs bfloat16?". Given the M, K, N of the forward pass through your linear, you can reference the table below for a microbenchmark based speedup estimate on NVIDIA H100: +A common question about float8 training is "when is float8 linear faster vs bfloat16?". Given the M, K, N of the forward pass through your linear, you can reference the tables below for a microbenchmark based speedup estimate on NVIDIA H100: + +### Tensorwise scaling float8_speedup @@ -152,6 +154,11 @@ To reproduce the raw data for table above, you can run the following script python benchmarks/float8/float8_roofline.py your_output_filename.csv --shape_gen_name sweep ``` +### Rowwise scaling + +float8_rowwise_speedup + + ## Derivation In a bf16 linear, assume all of the time is spent in gemms. In a float8 linear, account for max_abs and casting overhead. We want to know when From 95151b4ca2697ff11e8841778aac31cace008ccc Mon Sep 17 00:00:00 2001 From: Zeyu Song <87307087+szyszyzys@users.noreply.github.com> Date: Fri, 6 Jun 2025 14:16:10 -0400 Subject: [PATCH 092/165] Rename kleidi_ai in PackedWeightsType and update references (#2318) Summary: Rename kleidi_ai in PackedWeightsType and update references --- .../ops/linear_8bit_act_xbit_weight/kernel_selector.h | 8 ++++---- torchao/experimental/ops/packed_weights_header.h | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/torchao/experimental/ops/linear_8bit_act_xbit_weight/kernel_selector.h b/torchao/experimental/ops/linear_8bit_act_xbit_weight/kernel_selector.h index 930b93bd46..958b9c08e5 100644 --- a/torchao/experimental/ops/linear_8bit_act_xbit_weight/kernel_selector.h +++ b/torchao/experimental/ops/linear_8bit_act_xbit_weight/kernel_selector.h @@ -255,7 +255,7 @@ void register_ukernel_config_kleidi( if (!cpuinfo_initialize()) { throw std::runtime_error("Failed to initialize cpuinfo!"); } - check_format(format, torchao::ops::PackedWeightsType::kleidi_ai, weight_nbit); + check_format(format, torchao::ops::PackedWeightsType::linear_8bit_act_xbit_weight_kleidi_ai, weight_nbit); namespace op = torchao::kernels::cpu::aarch64::kleidi:: kai_matmul_clamp_f32_qai8dxp_qsi4c32p; @@ -343,7 +343,7 @@ void register_ukernel_config( register_ukernel_config_universal(table, format, uarch); break; } - case torchao::ops::PackedWeightsType::kleidi_ai: { + case torchao::ops::PackedWeightsType::linear_8bit_act_xbit_weight_kleidi_ai: { #ifdef TORCHAO_ENABLE_KLEIDI register_ukernel_config_kleidi(table, format, uarch); #endif // TORCHAO_ENABLE_KLEIDI @@ -411,7 +411,7 @@ PackedWeightsFormat select_packed_weights_format( if (weight_nbit == 4 && (!has_weight_zeros)) { #if defined(TORCHAO_ENABLE_ARM_I8MM) return PackedWeightsFormat( - torchao::ops::PackedWeightsType::kleidi_ai, + torchao::ops::PackedWeightsType::linear_8bit_act_xbit_weight_kleidi_ai, weight_nbit, has_weight_zeros, has_bias, @@ -420,7 +420,7 @@ PackedWeightsFormat select_packed_weights_format( /*sr*/ 2); #elif defined(TORCHAO_ENABLE_ARM_NEON_DOT) return PackedWeightsFormat( - torchao::ops::PackedWeightsType::kleidi_ai, + torchao::ops::PackedWeightsType::linear_8bit_act_xbit_weight_kleidi_ai, weight_nbit, has_weight_zeros, has_bias, diff --git a/torchao/experimental/ops/packed_weights_header.h b/torchao/experimental/ops/packed_weights_header.h index 11703e8454..90f77beae2 100644 --- a/torchao/experimental/ops/packed_weights_header.h +++ b/torchao/experimental/ops/packed_weights_header.h @@ -16,7 +16,7 @@ enum class PackedWeightsType : uint32_t { unknown = 0, linear_8bit_act_xbit_weight_universal = 1, embedding_xbit_universal = 2, - kleidi_ai = 3, + linear_8bit_act_xbit_weight_kleidi_ai = 3, linear_8bit_act_xbit_weight_lut = 4, }; From 423583779f1efc00339a589b3b40536ac3287642 Mon Sep 17 00:00:00 2001 From: Jerry Zhang Date: Fri, 6 Jun 2025 19:09:41 -0400 Subject: [PATCH 093/165] Add slicing support for fbgemm fp8 and int4 (#2308) Summary: att, this is needed in vllm Note that irregular shapes will require padding, which is not implemented right now, we can add that if it's required by the model Test Plan: python test/dtypes/test_fbgemm_fp8.py -k test_slice python test/dtypes/test_fbgemm_int4.py -k test_slice Reviewers: Subscribers: Tasks: Tags: --- test/dtypes/test_fbgemm_fp8.py | 79 ++++++++++++++++-- test/dtypes/test_fbgemm_int4.py | 86 +++++++++++++++++--- torchao/dtypes/fbgemm_fp8_tensor.py | 89 +++++++++++++++++++- torchao/dtypes/fbgemm_int4_tensor.py | 116 +++++++++++++++++++++++++-- 4 files changed, 344 insertions(+), 26 deletions(-) diff --git a/test/dtypes/test_fbgemm_fp8.py b/test/dtypes/test_fbgemm_fp8.py index d2f1e2d82a..56cf5ea081 100644 --- a/test/dtypes/test_fbgemm_fp8.py +++ b/test/dtypes/test_fbgemm_fp8.py @@ -25,24 +25,87 @@ @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_8, "Need pytorch 2.8+") +@unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") +@unittest.skipIf(not is_sm_at_least_90(), "Nedd sm90+") class TestFbgemmFp8Tensor(TestCase): - @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") - @unittest.skipIf(not is_sm_at_least_90(), "Nedd sm90+") + def setUp(self): + self.config = FbgemmConfig( + input_dtype=e4m3_dtype, + weight_dtype=e4m3_dtype, + output_dtype=torch.bfloat16, + ) + def test_linear(self): dtype = torch.bfloat16 device = "cuda" input = torch.randn(1, 128, dtype=dtype, device=device) linear = torch.nn.Linear(128, 256, dtype=dtype, device=device) original = linear(input) - config = FbgemmConfig( - input_dtype=e4m3_dtype, - weight_dtype=e4m3_dtype, - output_dtype=torch.bfloat16, - ) - quantize_(linear, config) + quantize_(linear, self.config) quantized = linear(input) self.assertTrue(compute_error(original, quantized) > 20) + def test_slice(self): + dtype = torch.bfloat16 + device = "cuda" + dummy = torch.nn.Linear(256, 256, bias=False, dtype=dtype, device=device) + dummy1 = torch.nn.Linear(256, 64, bias=False, dtype=dtype, device=device) + dummy1.weight = torch.nn.Parameter( + dummy.weight.narrow(0, 0, 64), requires_grad=False + ) + dummy2 = torch.nn.Linear(128, 256, dtype=dtype, device=device) + dummy2.weight = torch.nn.Parameter( + dummy.weight.narrow(1, 0, 128), requires_grad=False + ) + + quantize_(dummy, self.config) + weight1 = dummy.weight.narrow(0, 0, 64) + weight2 = dummy.weight.narrow(1, 0, 128) + self.assertEqual(weight1.float8_data, dummy.weight.float8_data.narrow(0, 0, 64)) + self.assertEqual(weight1.scale, dummy.weight.scale.narrow(0, 0, 64)) + self.assertEqual( + weight2.float8_data, dummy.weight.float8_data.narrow(1, 0, 128) + ) + self.assertEqual(weight2.scale, dummy.weight.scale) + + # check for sliced weight, before and after float8 quantization + # does not differ too much + input = torch.randn(2, 256, dtype=dtype, device=device) + res_ref = dummy1(input) + dummy.weight = torch.nn.Parameter(weight1, requires_grad=False) + res = dummy(input) + assert compute_error(res, res_ref) > 25 + + input = torch.randn(2, 128, dtype=dtype, device=device) + res_ref = dummy2(input) + dummy.weight = torch.nn.Parameter(weight2, requires_grad=False) + res = dummy(input) + assert compute_error(res, res_ref) > 15 + + def test_slice_and_copy_(self): + l = torch.nn.Linear(1024, 1024).to("cuda").to(torch.bfloat16) + l.weight = torch.nn.Parameter( + torch.zeros(1024, 1024, dtype=torch.bfloat16, device="cuda") + ) + quantize_(l, self.config) + param = l.weight + param_data = param.data + param_data = param_data.narrow(0, 0, 512) + assert param.data.float8_data.data_ptr() == param_data.float8_data.data_ptr() + assert param.data.scale.data_ptr() == param_data.scale.data_ptr() + orig_value = param.data.float8_data[0][0].item() + + # dummy_l has random input (shouldn't be 0) + dummy_l = torch.nn.Linear(1024, 1024).to("cuda").to(torch.bfloat16) + quantize_(dummy_l, self.config) + quantized = dummy_l.weight + quantized = quantized.narrow(0, 0, 512) + + param_data.copy_(quantized) + + # making sure param.data is updated + assert param.data.float8_data[0][0] != orig_value + if __name__ == "__main__": run_tests() diff --git a/test/dtypes/test_fbgemm_int4.py b/test/dtypes/test_fbgemm_int4.py index 22fe5bc110..25b71f0244 100644 --- a/test/dtypes/test_fbgemm_int4.py +++ b/test/dtypes/test_fbgemm_int4.py @@ -24,25 +24,93 @@ @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_8, "Need pytorch 2.8+") +@unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") +@unittest.skipIf(not is_sm_at_least_90(), "Nedd sm90+") class TestFbgemmInt4Tensor(TestCase): - @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") - @unittest.skipIf(not is_sm_at_least_90(), "Nedd sm90+") + def setUp(self): + self.config = FbgemmConfig( + input_dtype=torch.bfloat16, + weight_dtype=torch.int4, + output_dtype=torch.bfloat16, + block_size=[1, 128], + ) + def test_linear(self): dtype = torch.bfloat16 device = "cuda" input = torch.randn(1, 128, dtype=dtype, device=device) linear = torch.nn.Linear(128, 256, dtype=dtype, device=device) original = linear(input) - config = FbgemmConfig( - input_dtype=torch.bfloat16, - weight_dtype=torch.int4, - output_dtype=torch.bfloat16, - block_size=[1, 128], - ) - quantize_(linear, config) + quantize_(linear, self.config) quantized = linear(input) self.assertTrue(compute_error(original, quantized) > 20) + def test_slice(self): + dtype = torch.bfloat16 + device = "cuda" + dummy = torch.nn.Linear(256, 256, bias=False, dtype=dtype, device=device) + dummy1 = torch.nn.Linear(256, 64, bias=False, dtype=dtype, device=device) + dummy1.weight = torch.nn.Parameter( + dummy.weight.narrow(0, 0, 64), requires_grad=False + ) + dummy2 = torch.nn.Linear(128, 256, dtype=dtype, device=device) + dummy2.weight = torch.nn.Parameter( + dummy.weight.narrow(1, 0, 128), requires_grad=False + ) + + quantize_(dummy, self.config) + weight1 = dummy.weight.narrow(0, 0, 64) + weight2 = dummy.weight.narrow(1, 0, 128) + self.assertEqual( + weight1.packed_weight, dummy.weight.packed_weight.narrow(0, 0, 64) + ) + self.assertEqual(weight1.scale, dummy.weight.scale.narrow(1, 0, 64)) + self.assertEqual( + weight2.packed_weight, dummy.weight.packed_weight.narrow(1, 0, 64) + ) + self.assertEqual(weight2.scale, dummy.weight.scale.narrow(0, 0, 1)) + + # check for sliced weight, before and after float8 quantization + # does not differ too much + input = torch.randn(2, 256, dtype=dtype, device=device) + res_ref = dummy1(input) + dummy.weight = torch.nn.Parameter(weight1, requires_grad=False) + res = dummy(input) + assert compute_error(res, res_ref) > 20 + + input = torch.randn(2, 128, dtype=dtype, device=device) + res_ref = dummy2(input) + dummy.weight = torch.nn.Parameter(weight2, requires_grad=False) + res = dummy(input) + assert compute_error(res, res_ref) > 15 + + def test_slice_and_copy_(self): + l = torch.nn.Linear(1024, 1024).to("cuda").to(torch.bfloat16) + l.weight = torch.nn.Parameter( + torch.zeros(1024, 1024, dtype=torch.bfloat16, device="cuda") + ) + quantize_(l, self.config) + param = l.weight + param_data = param.data + param_data = param_data.narrow(0, 0, 512) + assert ( + param.data.packed_weight.data_ptr() == param_data.packed_weight.data_ptr() + ) + assert param.data.scale.data_ptr() == param_data.scale.data_ptr() + assert param.data.zero_point.data_ptr() == param_data.zero_point.data_ptr() + orig_value = param.data.packed_weight[0][0].item() + + # dummy_l has random input (shouldn't be 0) + dummy_l = torch.nn.Linear(1024, 1024).to("cuda").to(torch.bfloat16) + quantize_(dummy_l, self.config) + quantized = dummy_l.weight + quantized = quantized.narrow(0, 0, 512) + + param_data.copy_(quantized) + + # making sure param.data is updated + assert param.data.packed_weight[0][0] != orig_value + if __name__ == "__main__": run_tests() diff --git a/torchao/dtypes/fbgemm_fp8_tensor.py b/torchao/dtypes/fbgemm_fp8_tensor.py index 735c21c2ca..df7ce69de7 100644 --- a/torchao/dtypes/fbgemm_fp8_tensor.py +++ b/torchao/dtypes/fbgemm_fp8_tensor.py @@ -13,6 +13,7 @@ from torchao.utils import ( TORCH_VERSION_AT_LEAST_2_5, TorchAOBaseTensor, + fill_defaults, ) __all__ = [ @@ -23,6 +24,10 @@ class FbgemmFp8Tensor(TorchAOBaseTensor): + """ + TODO: needs padding for cutlass kernels + """ + tensor_data_attrs = ["float8_data", "scale", "activation_scale_ub"] tensor_attributes = ["dtype"] @@ -118,9 +123,13 @@ def _(func, types, args, kwargs): xq, x_scale = torch.ops.fbgemm.quantize_fp8_per_row( input_tensor, num_tokens, weight_tensor.activation_scale_ub ) + + a_data = xq + b_data = weight_tensor.float8_data + res = torch.ops.fbgemm.f8f8bf16_rowwise( - xq, - weight_tensor.float8_data, + a_data, + b_data, x_scale, weight_tensor.scale, use_fast_accum=True, @@ -139,13 +148,87 @@ def _(func, types, args, kwargs): ) -@implements([aten.clone.default, aten.copy_.default]) +@implements(aten.clone.default) def _(func, types, args, kwargs): return return_and_correct_aliasing( func, args, kwargs, args[0]._apply_fn_to_data(torch.clone) ) +def _same_metadata(self: "FbgemmFp8Tensor", src: "FbgemmFp8Tensor") -> bool: + return ( + isinstance(self, FbgemmFp8Tensor) + and isinstance(src, FbgemmFp8Tensor) + and self.shape == src.shape + and self.float8_data.shape == src.float8_data.shape + and self.scale.shape == src.scale.shape + and self.activation_scale_ub.shape == src.activation_scale_ub.shape + and self.dtype == src.dtype + ) + + +@implements(aten.copy_.default) +def _(func, types, args, kwargs): + self = args[0] + src = args[1] + if _same_metadata(self, src): + self_tensors = self.__tensor_flatten__()[0] + for tensor_name in self_tensors: + getattr(self, tensor_name).copy_(getattr(src, tensor_name)) + return + raise ValueError( + f"Not supported args for copy_ due to metadata mismatch: {args[0], args[1]}" + ) + + +@implements(aten.slice.Tensor) +def _(func, types, args, kwargs): + """Only supports slicing for dim == 1 and dim == 2 + original tensor shape has dimension (N, K) + float8_data has dimension (N, K) + scale (per row quantization) has dimension: (N,) + + since float8_data has the same dimension as original tensor, we can directly slice that + for scale, we'll do a slice when dim is 0, and don't need to do anything for dim 1 + + Note that we need to call slice on the float8_data and scale directly because slice + is an operation that need to preserve aliasing, see `test_slice_and_copy_` in `test_fbgemm_fp8` + for + """ + self, dim, start, end, step = fill_defaults(args, 5, [0, None, None, 1]) + assert step == 1 + assert dim == 0 or dim == 1, f"Only dim==0 or 1 are supported, got: {dim}" + if end >= self.shape[dim]: + end = self.shape[dim] + + assert self.float8_data.ndim == 2, ( + f"Expected packed weight to have dim 2, got {self.float8_data.dim}" + ) + + # Always slice the float8_data + sliced_data = aten.slice.Tensor( + self.float8_data, dim, start, end, step + ).contiguous() + + if dim == 0: + # scale has dimension (N,) where N is the dim 0 of `self` + # so we do the same slice on scale for dimension 0 + sliced_scale = aten.slice.Tensor(self.scale, 0, start, end, step) + else: + # since scale is per row, slicing along the dim == 1 dimension does + # not change the scale + sliced_scale = self.scale + + return return_and_correct_aliasing( + func, + args, + kwargs, + FbgemmFp8Tensor( + sliced_data, sliced_scale, self.activation_scale_ub, dtype=self.dtype + ), + ) + + to_fbgemm_fp8 = FbgemmFp8Tensor.from_float diff --git a/torchao/dtypes/fbgemm_int4_tensor.py b/torchao/dtypes/fbgemm_int4_tensor.py index c2ab6246bf..ab108fea06 100644 --- a/torchao/dtypes/fbgemm_int4_tensor.py +++ b/torchao/dtypes/fbgemm_int4_tensor.py @@ -14,6 +14,7 @@ from torchao.utils import ( TORCH_VERSION_AT_LEAST_2_5, TorchAOBaseTensor, + fill_defaults, ) __all__ = [ @@ -32,17 +33,16 @@ class FbgemmInt4Tensor(TorchAOBaseTensor): tensor_data_attrs = ["packed_weight", "scale", "zero_point"] - tensor_attributes = ["group_size"] + tensor_attributes = ["group_size", "shape"] - def __new__(cls, packed_weight, scale, zero_point, group_size): - shape = packed_weight.shape + def __new__(cls, packed_weight, scale, zero_point, group_size, shape): kwargs = {} kwargs["device"] = packed_weight.device kwargs["dtype"] = scale.dtype kwargs["requires_grad"] = False return torch.Tensor._make_wrapper_subclass(cls, shape, **kwargs) # type: ignore[attr-defined] - def __init__(self, packed_weight, scale, zero_point, group_size): + def __init__(self, packed_weight, scale, zero_point, group_size, shape): self.packed_weight = packed_weight self.scale = scale self.zero_point = zero_point @@ -90,6 +90,7 @@ def from_float( raise ImportError("Requires fbgemm-gpu-genai >= 1.2.0") group_size = block_size[-1] + original_shape = w.shape if w.ndim >= 3: wq, scale, zero_point = zip( @@ -111,6 +112,7 @@ def from_float( scale=scale, zero_point=zero_point, group_size=group_size, + shape=original_shape, ) @@ -134,7 +136,7 @@ def _(func, types, args, kwargs): res = torch.ops.fbgemm.bf16i4bf16_rowwise( input_tensor, - weight_tensor.packed_weight, + weight_tensor.packed_weight.contiguous(), weight_tensor.scale, weight_tensor.zero_point, ) @@ -151,13 +153,115 @@ def _(func, types, args, kwargs): ) -@implements([aten.clone.default, aten.copy_.default]) +@implements(aten.clone.default) def _(func, types, args, kwargs): return return_and_correct_aliasing( func, args, kwargs, args[0]._apply_fn_to_data(torch.clone) ) +def _same_metadata(self: "FbgemmInt4Tensor", src: "FbgemmInt4Tensor") -> bool: + return ( + isinstance(self, FbgemmInt4Tensor) + and isinstance(src, FbgemmInt4Tensor) + and self.shape == src.shape + and self.packed_weight.shape == src.packed_weight.shape + and self.scale.shape == src.scale.shape + and self.zero_point.shape == src.zero_point.shape + and self.group_size == src.group_size + ) + + +@implements(aten.copy_.default) +def _(func, types, args, kwargs): + self = args[0] + src = args[1] + if _same_metadata(self, src): + self_tensors = self.__tensor_flatten__()[0] + for tensor_name in self_tensors: + getattr(self, tensor_name).copy_(getattr(src, tensor_name)) + return + raise ValueError( + f"Not supported args for copy_ due to metadata mismatch: {args[0], args[1]}" + ) + + +@implements(aten.slice.Tensor) +def _(func, types, args, kwargs): + """Only supports slicing for dim == 1 and dim == 2 + packed_weight has dimension: (N, K/2) + scale and zero_point has dimension: (K/groups, N) + + dim, start, end, step are args that's referring to the original tensor shape + which is (N, K), and we need to map that to the transformed weight shape of packed_weight, + scale and zero_point + + when dim == 0: we do a slice on packed_weight dim 0, and on dim 1 of scale and zero_point, + also adjust the start and end indexes based on the ratio between original shape and the shape + of packed_weight and scale/zero_point + + when dim == 1: we do a slice on packed_weight dim 1 and dim 0 of scale and zero_point and do the + same adjustment based on ratio + + Note that we need to call slice on the packed_weight, scale and zero_point directly because slice + is an operation that need to preserve aliasing, see `test_slice_and_copy_` in `test_fbgemm_int4` + for + """ + self, dim, start, end, step = fill_defaults(args, 5, [0, None, None, 1]) + assert step == 1 + assert dim == 0 or dim == 1, f"Only dim==0 or 1 are supported, got: {dim}" + if end >= self.shape[dim]: + end = self.shape[dim] + + assert self.packed_weight.ndim == 2, ( + f"Expected packed weight to have dim 2, got {self.packed_weight.dim}" + ) + N, K_by_2 = self.packed_weight.shape + sz_dim0, sz_dim1 = self.scale.shape + + data_len = self.shape[dim] + + if dim == 0: + pw_len = N + sz_len = sz_dim1 + else: + pw_len = K_by_2 + sz_len = sz_dim0 + + sz_dim = 1 - dim + if pw_len == 0 or sz_len == 0: + return return_and_correct_aliasing( + func, + args, + kwargs, + self.__class__( + self.packed_weight, + self.scale, + self.zero_point, + group_size=self.group_size, + shape=self.shape, + ), + ) + + pw_ratio = data_len / pw_len + start_pw = int(start / pw_ratio) + end_pw = int(end / pw_ratio) + + sz_ratio = data_len / sz_len + start_sz = int(start / sz_ratio) + end_sz = int(end / sz_ratio) + + packed_weight = aten.slice.Tensor(self.packed_weight, dim, start_pw, end_pw, step) + scale = aten.slice.Tensor(self.scale, sz_dim, start_sz, end_sz, step) + zero_point = aten.slice.Tensor(self.zero_point, sz_dim, start_sz, end_sz, step) + packed_shape0, packed_shape1 = packed_weight.shape + new_shape = (packed_shape0, packed_shape1 * 2) + new = self.__class__( + packed_weight, scale, zero_point, group_size=self.group_size, shape=new_shape + ) + return return_and_correct_aliasing(func, args, kwargs, new) + + to_fbgemm_int4 = FbgemmInt4Tensor.from_float From 12398428a0573374c3971d9cb66d47185a11feff Mon Sep 17 00:00:00 2001 From: Mark Saroufim Date: Sat, 7 Jun 2025 16:16:17 -0700 Subject: [PATCH 094/165] Fix Windows Build (#2333) --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 5288ec15bd..22b001b424 100644 --- a/setup.py +++ b/setup.py @@ -688,7 +688,7 @@ def bool_to_on_off(value): ext_modules=get_extensions(), extras_require={"dev": read_requirements("dev-requirements.txt")}, description="Package for applying ao techniques to GPU models", - long_description=open("README.md").read(), + long_description=open("README.md", encoding="utf-8").read(), long_description_content_type="text/markdown", url="https://github.com/pytorch/ao", cmdclass={"build_ext": TorchAOBuildExt}, From 9452640006b3cbd2056de9b71f809e7b8b4402a8 Mon Sep 17 00:00:00 2001 From: Scott Roy <161522778+metascroy@users.noreply.github.com> Date: Mon, 9 Jun 2025 10:13:01 -0700 Subject: [PATCH 095/165] Migrate xnnpack/vulkan/boltnn pt2e from torch.ao to torchao (#11363) Differential Revision: D75492104 Pull Request resolved: https://github.com/pytorch/ao/pull/2302 --- torchao/quantization/pt2e/__init__.py | 4 ++++ torchao/testing/pt2e/utils.py | 1 + 2 files changed, 5 insertions(+) diff --git a/torchao/quantization/pt2e/__init__.py b/torchao/quantization/pt2e/__init__.py index 3e4352dabd..b6b8a728a3 100644 --- a/torchao/quantization/pt2e/__init__.py +++ b/torchao/quantization/pt2e/__init__.py @@ -39,6 +39,8 @@ FusedMovingAvgObsFakeQuantize, default_dynamic_fake_quant, default_fake_quant, + disable_fake_quant, + disable_observer, enable_fake_quant, enable_observer, ) @@ -114,6 +116,8 @@ # utils "enable_fake_quant", "enable_observer", + "disable_fake_quant", + "disable_observer", # export_utils "move_exported_model_to_eval", "move_exported_model_to_train", diff --git a/torchao/testing/pt2e/utils.py b/torchao/testing/pt2e/utils.py index 41be460a40..ad49fec014 100644 --- a/torchao/testing/pt2e/utils.py +++ b/torchao/testing/pt2e/utils.py @@ -78,6 +78,7 @@ def _test_quantizer( m, example_inputs, dynamic_shapes=dynamic_shapes if export_with_dynamic_shape else None, + strict=True, ).module() if is_qat: From 70f2b85ae4713baefd1278e83e9c9536d57f9709 Mon Sep 17 00:00:00 2001 From: Peter Yeh Date: Mon, 9 Jun 2025 10:31:44 -0700 Subject: [PATCH 096/165] Enhance test_autoquant_compile to support ROCm (#2100) * Enhance test_autoquant_compile to support ROCm and improve device checks - Added checks for ROCm availability alongside CUDA. - Improved device capability checks for CUDA to ensure compatibility with bfloat16 and specific tensor shapes. - Updated skip conditions for unsupported devices and older PyTorch versions. * lint * Refactor device checks in test_autoquant_compile for improved clarity - Simplified the logic for checking supported devices by consolidating CUDA and ROCm checks. - Enhanced readability of the device capability validation for CUDA. - Updated skip conditions for unsupported devices to ensure accurate test execution. * Fix formatting in device check condition for consistency in test_autoquant_compile * Refactor device check formatting in test_autoquant_compile for improved readability - Adjusted the formatting of the device check condition to enhance clarity. - Consolidated the logic for checking supported devices while maintaining functionality. --- test/integration/test_integration.py | 28 ++++++++++++++++++++-------- 1 file changed, 20 insertions(+), 8 deletions(-) diff --git a/test/integration/test_integration.py b/test/integration/test_integration.py index 8388a8bcff..7c070bf754 100644 --- a/test/integration/test_integration.py +++ b/test/integration/test_integration.py @@ -1602,15 +1602,27 @@ def test_autoquant_one_input(self, device, dtype, m, k, n): @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_5, "autoquant requires 2.5+.") def test_autoquant_compile(self, device, dtype, m1, m2, k, n): undo_recommended_configs() - if device != "cuda" or not torch.cuda.is_available(): + + is_supported_device = device == "cuda" and ( + torch.cuda.is_available() or torch.version.hip is not None + ) + + if not is_supported_device: self.skipTest(f"autoquant currently does not support {device}") - if torch.cuda.is_available() and torch.cuda.get_device_capability() < (8, 0): - if dtype == torch.bfloat16: - self.skipTest("bfloat16 requires sm80+") - if m1 == 1 or m2 == 1: - self.skipTest(f"Shape {(m1, m2, k, n)} requires sm80+") - # This test fails on v0.4.0 and torch 2.4, so skipping for now. - if m1 == 1 or m2 == 1 and not TORCH_VERSION_AT_LEAST_2_5: + + # Check CUDA-specific requirements if running on CUDA + if ( + is_supported_device and torch.version.hip is None + ): # Only apply to CUDA, not ROCm + device_capability = torch.cuda.get_device_capability() + if device_capability < (8, 0): + if dtype == torch.bfloat16: + self.skipTest("bfloat16 requires sm80+") + if m1 == 1 or m2 == 1: + self.skipTest(f"Shape {(m1, m2, k, n)} requires sm80+") + + # Skip certain shapes on older PyTorch versions + if (m1 == 1 or m2 == 1) and not TORCH_VERSION_AT_LEAST_2_5: self.skipTest(f"Shape {(m1, m2, k, n)} requires torch version > 2.4") model = ( torch.nn.Sequential( From 4c063180edbaa16ee658549f219e865559309e98 Mon Sep 17 00:00:00 2001 From: Apurva Jain Date: Mon, 9 Jun 2025 11:08:55 -0700 Subject: [PATCH 097/165] Update Quantization docs to show newer AOConfigs (#2317) --- docs/source/api_ref_quantization.rst | 46 +++++++++++++++++++--------- 1 file changed, 32 insertions(+), 14 deletions(-) diff --git a/docs/source/api_ref_quantization.rst b/docs/source/api_ref_quantization.rst index a13cd54450..5293684ab9 100644 --- a/docs/source/api_ref_quantization.rst +++ b/docs/source/api_ref_quantization.rst @@ -14,27 +14,45 @@ Main Quantization APIs :nosignatures: quantize_ - autoquant + autoquant -Quantization APIs for quantize_ +Inference APIs for quantize\_ ------------------------------- .. autosummary:: :toctree: generated/ :nosignatures: - int4_weight_only - int8_weight_only - int8_dynamic_activation_int4_weight - int8_dynamic_activation_int8_weight - uintx_weight_only - gemlite_uintx_weight_only - intx_quantization_aware_training - from_intx_quantization_aware_training - float8_weight_only - float8_dynamic_activation_float8_weight - float8_static_activation_float8_weight - fpx_weight_only + Int4WeightOnlyConfig + Float8DynamicActivationFloat8WeightConfig + Float8WeightOnlyConfig + Float8StaticActivationFloat8WeightConfig + Int8DynamicActivationInt4WeightConfig + GemliteUIntXWeightOnlyConfig + Int8WeightOnlyConfig + Int8DynamicActivationInt8WeightConfig + UIntXWeightOnlyConfig + FPXWeightOnlyConfig + +.. currentmodule:: torchao.quantization.qat + +QAT APIs +---------------------- + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + IntXQuantizationAwareTrainingConfig + FromIntXQuantizationAwareTrainingConfig + FakeQuantizeConfig + Int4WeightOnlyQATQuantizer + Int8DynActInt4WeightQATQuantizer + Int4WeightOnlyEmbeddingQATQuantizer + ComposableQATQuantizer + initialize_fake_quantizers + +.. currentmodule:: torchao.quantization Quantization Primitives ----------------------- From 83663b86f98e755c37c3fedeaf6ea8dfb1b8e0d5 Mon Sep 17 00:00:00 2001 From: Driss Guessous <32754868+drisspg@users.noreply.github.com> Date: Mon, 9 Jun 2025 14:04:13 -0700 Subject: [PATCH 098/165] Fix Per Tensor 3d rehsape (#2293) stack-info: PR: https://github.com/pytorch/ao/pull/2293, branch: drisspg/stack/64 --- test/dtypes/test_affine_quantized_float.py | 47 +++++++++++++++++++++- torchao/dtypes/floatx/float8_layout.py | 39 ++++++++++-------- torchao/float8/inference.py | 6 +-- 3 files changed, 70 insertions(+), 22 deletions(-) diff --git a/test/dtypes/test_affine_quantized_float.py b/test/dtypes/test_affine_quantized_float.py index 1ffd62ecbf..879551fc0a 100644 --- a/test/dtypes/test_affine_quantized_float.py +++ b/test/dtypes/test_affine_quantized_float.py @@ -25,7 +25,7 @@ from torch._inductor.test_case import TestCase as InductorTestCase from torch.testing._internal import common_utils -from torchao.dtypes.floatx.float8_layout import Float8AQTTensorImpl +from torchao.dtypes.floatx.float8_layout import Float8AQTTensorImpl, preprocess_scale from torchao.float8.float8_utils import compute_error from torchao.quantization import ( Float8DynamicActivationFloat8WeightConfig, @@ -630,6 +630,51 @@ def test_float8_tensor_slicing_functional_correctness(self, granularity): error = compute_error(ref_output, quant_output) self.assertGreater(error, 15, f"Quantization SQNR too low: {error}") + def test_preprocess_scale_3d_reshape(self): + """Test that preprocess_scale correctly handles 3D scale tensors""" + device = "cpu" # Use CPU for basic functionality test + + # Test 1: PerTensor scale (scalar) - should reshape to (1, 1) + per_tensor_scale = torch.tensor(0.5, device=device) + result = preprocess_scale(per_tensor_scale, (2, 4, 8)) + expected_shape = (1, 1) + self.assertEqual(result.shape, expected_shape) + self.assertEqual(result.item(), 0.5) + + # Test 2: 1D scale tensor with one element - should reshape to (1, 1) + one_element_scale = torch.tensor([0.3], device=device) + result = preprocess_scale(one_element_scale, (2, 4, 8)) + expected_shape = (1, 1) + self.assertEqual(result.shape, expected_shape) + self.assertEqual(result.item(), 0.3) + + # Test 3: 3D scale tensor for per-row quantization - should flatten first N-1 dims + # This is the key test for the 3D reshape fix + scale_3d = torch.randn( + 2, 4, device=device + ) # Shape matches first 2 dims of (2, 4, 8) + result = preprocess_scale(scale_3d, (2, 4, 8)) + expected_shape = (8, 1) # Flattened (2*4, 1) + self.assertEqual(result.shape, expected_shape) + + # Verify the values are preserved correctly + expected_values = scale_3d.flatten().unsqueeze(-1) + self.assertTrue(torch.allclose(result, expected_values)) + + # Test 4: 2D scale tensor (already correct shape) - should just add last dimension + scale_2d = torch.randn(8, device=device) + result = preprocess_scale(scale_2d, (8, 16)) + expected_shape = (8, 1) + self.assertEqual(result.shape, expected_shape) + + # Test 5: Edge case with higher dimensions (4D) + scale_4d = torch.randn( + 2, 2, 2, device=device + ) # Shape matches first 3 dims of (2, 2, 2, 8) + result = preprocess_scale(scale_4d, (2, 2, 2, 8)) + expected_shape = (8, 1) # Flattened (2*2*2, 1) + self.assertEqual(result.shape, expected_shape) + common_utils.instantiate_parametrized_tests(TestAffineQuantizedFloat8Compile) diff --git a/torchao/dtypes/floatx/float8_layout.py b/torchao/dtypes/floatx/float8_layout.py index 799832a5ea..543bd5002b 100644 --- a/torchao/dtypes/floatx/float8_layout.py +++ b/torchao/dtypes/floatx/float8_layout.py @@ -370,10 +370,18 @@ def check_aqt(aqt: Union[torch.Tensor, AffineQuantizedTensor]) -> bool: return check_aqt(input_tensor) and check_aqt(weight_tensor) -def preprocess_scale(input_scale: torch.Tensor, input_shape: Tuple[int]): - """Ensures input tensor is correctly formated for _scaled_mm""" +def preprocess_scale(input_scale: torch.Tensor, input_shape: Tuple[int, ...]): + """Ensures input tensor is correctly formatted for _scaled_mm""" + + # For PerTensor quantization, scale should be a scalar or have shape [1] + if input_scale.numel() == 1: + # Already a scalar, ensure it has the right shape for _scaled_mm + return input_scale.reshape(1, 1) + + # For per-row/block quantization, we need to handle the reshaping input_scale = input_scale.unsqueeze(-1) + # Match: #input_data.reshape(-1, input_data.shape[-1]) if input_scale.dim() > 2: input_scale = input_scale.reshape(-1, input_scale.shape[-1]) @@ -388,31 +396,28 @@ def _linear_fp8_act_fp8_weight_impl( """Implements matmul between FP8 input and FP8 weight with compute using _scaled_mm""" scaled_mm_config = weight_tensor._layout.mm_config assert scaled_mm_config is not None - out_shape = get_out_shape(input_tensor.shape, weight_tensor.shape) + assert not weight_tensor.tensor_impl.transposed, "Weight tensor must be contiguous" - # Weight tensor preprocessing - w_tensor_impl = weight_tensor.tensor_impl - assert not w_tensor_impl.transposed, "Weight tensor must be contiguous" - w_data = w_tensor_impl.float8_data - w_scale = w_tensor_impl.scale + out_shape = get_out_shape(input_tensor.shape, weight_tensor.shape) - # Input tensor preprocessing - inpt_data = input_tensor.tensor_impl.float8_data + # Extract tensor data and scales + inpt_data = input_tensor.tensor_impl.float8_data.reshape( + -1, input_tensor.tensor_impl.float8_data.shape[-1] + ) + w_data = weight_tensor.tensor_impl.float8_data input_scale = input_tensor.tensor_impl.scale - # Handle case where input tensor is more than 2D - inpt_data = inpt_data.reshape(-1, inpt_data.shape[-1]) - # Handle rowwise case + w_scale = weight_tensor.tensor_impl.scale + + # Handle rowwise scaling if _is_rowwise_scaled(weight_tensor): assert _is_rowwise_scaled(input_tensor), ( "Input tensor must be rowwise block size" ) - w_scale = w_scale.T - input_scale = preprocess_scale(input_scale, input_tensor.shape) + w_scale = w_scale.transpose(-1, -2) - # Preprocess data + input_scale = preprocess_scale(input_scale, input_tensor.shape) inpt_data, w_data = preprocess_data(inpt_data, w_data.T, scaled_mm_config) - # Perform the computation return addmm_float8_unwrapped_inference( inpt_data, input_scale, diff --git a/torchao/float8/inference.py b/torchao/float8/inference.py index 42ea5e9dfa..144f1fa6f2 100644 --- a/torchao/float8/inference.py +++ b/torchao/float8/inference.py @@ -94,9 +94,8 @@ def addmm_float8_unwrapped_inference( out_dtype=output_dtype, use_fast_accum=use_fast_accum, ) - output += bias - return output - output = torch._scaled_mm( + return output + bias + return torch._scaled_mm( a_data, b_data, scale_a=a_scale, @@ -106,7 +105,6 @@ def addmm_float8_unwrapped_inference( out_dtype=output_dtype, use_fast_accum=use_fast_accum, ) - return output def _is_rowwise_scaled(x) -> bool: From 769ffa527bd78bd590227a11bebc182c1cd0eb26 Mon Sep 17 00:00:00 2001 From: odiemm-meta Date: Mon, 9 Jun 2025 20:01:50 -0700 Subject: [PATCH 099/165] add cast config for fp8 enablement Differential Revision: D75945415 Pull Request resolved: https://github.com/pytorch/ao/pull/2328 --- torchao/float8/__init__.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/torchao/float8/__init__.py b/torchao/float8/__init__.py index ed21c93d20..2799f3197f 100644 --- a/torchao/float8/__init__.py +++ b/torchao/float8/__init__.py @@ -3,11 +3,10 @@ CastConfig, Float8GemmConfig, Float8LinearConfig, + ScalingGranularity, ScalingType, ) -from torchao.float8.float8_linear_utils import ( - convert_to_float8_training, -) +from torchao.float8.float8_linear_utils import convert_to_float8_training from torchao.float8.float8_tensor import ( Float8Tensor, GemmInputRole, @@ -39,6 +38,7 @@ "Float8GemmConfig", "Float8LinearConfig", "CastConfig", + "ScalingGranularity", # top level UX "convert_to_float8_training", "precompute_float8_dynamic_scale_for_fsdp", From 16e2d0ae32c2ed231872616aee941d21dbda0df8 Mon Sep 17 00:00:00 2001 From: Jerry Zhang Date: Tue, 10 Jun 2025 07:54:03 -0400 Subject: [PATCH 100/165] Add support for bmm and `to` for fbgemm Tensor (#2337) Add support for bmm for fbgemm config Summary: att, this PR adds support for running quantized bmm, the quantized bmm kernel for int4 and fp8 (with dynamic activation quantization) requires transpose of weights in order to run, so added transpose_input to the convert function to transpose the weights first Test Plan: python test/dtypes/test_fbgemm_fp8.py -k test_bmm python test/dtypes/test_fbgemm_int4.py -k test_bmm Reviewers: Subscribers: Tasks: Tags: --- test/dtypes/test_fbgemm_fp8.py | 40 ++++++++++++++++++++++ test/dtypes/test_fbgemm_int4.py | 41 +++++++++++++++++++++++ torchao/dtypes/__init__.py | 6 ++-- torchao/dtypes/fbgemm_fp8_tensor.py | 50 +++++++++++++++++++++++++--- torchao/dtypes/fbgemm_int4_tensor.py | 43 +++++++++++++++++++++--- torchao/quantization/quant_api.py | 5 +++ 6 files changed, 173 insertions(+), 12 deletions(-) diff --git a/test/dtypes/test_fbgemm_fp8.py b/test/dtypes/test_fbgemm_fp8.py index 56cf5ea081..1e681d00f9 100644 --- a/test/dtypes/test_fbgemm_fp8.py +++ b/test/dtypes/test_fbgemm_fp8.py @@ -34,6 +34,13 @@ def setUp(self): weight_dtype=e4m3_dtype, output_dtype=torch.bfloat16, ) + self.bmm_config = FbgemmConfig( + input_dtype=e4m3_dtype, + weight_dtype=e4m3_dtype, + output_dtype=torch.bfloat16, + transpose_input=True, + ) + self.GPU_DEVICES = ["cuda"] if torch.cuda.is_available() else [] def test_linear(self): dtype = torch.bfloat16 @@ -106,6 +113,39 @@ def test_slice_and_copy_(self): # making sure param.data is updated assert param.data.float8_data[0][0] != orig_value + def test_bmm(self): + class M(torch.nn.Module): + def __init__(self, weight): + super().__init__() + self.weight = weight + + def forward(self, x): + return torch.bmm(x, self.weight) + + dtype = torch.bfloat16 + device = "cuda" + input = torch.randn(10, 32, 128, dtype=dtype, device=device) + weight = torch.randn(10, 128, 256, dtype=dtype, device=device) + m = M(weight).eval() + original = m(input) + quantize_(m, self.bmm_config, filter_fn=lambda x, fqn: True) + quantized = m(input) + self.assertTrue(compute_error(original, quantized) > 20) + + def test_to_device(self): + for device in self.GPU_DEVICES: + linear = torch.nn.Linear(128, 256, dtype=torch.bfloat16) + quantize_(linear, self.config) + linear.to(device) + + linear = torch.nn.Linear(128, 256, dtype=torch.bfloat16) + quantize_(linear, self.config) + linear.to(device=device) + + linear = torch.nn.Linear(128, 256, dtype=torch.bfloat16) + quantize_(linear, self.config) + linear.to(device) + if __name__ == "__main__": run_tests() diff --git a/test/dtypes/test_fbgemm_int4.py b/test/dtypes/test_fbgemm_int4.py index 25b71f0244..cba9d81ae0 100644 --- a/test/dtypes/test_fbgemm_int4.py +++ b/test/dtypes/test_fbgemm_int4.py @@ -34,6 +34,14 @@ def setUp(self): output_dtype=torch.bfloat16, block_size=[1, 128], ) + self.bmm_config = FbgemmConfig( + input_dtype=torch.bfloat16, + weight_dtype=torch.int4, + output_dtype=torch.bfloat16, + block_size=[1, 1, 128], + transpose_input=True, + ) + self.GPU_DEVICES = ["cuda"] if torch.cuda.is_available() else [] def test_linear(self): dtype = torch.bfloat16 @@ -111,6 +119,39 @@ def test_slice_and_copy_(self): # making sure param.data is updated assert param.data.packed_weight[0][0] != orig_value + def test_bmm(self): + class M(torch.nn.Module): + def __init__(self, weight): + super().__init__() + self.weight = weight + + def forward(self, x): + return torch.bmm(x, self.weight) + + dtype = torch.bfloat16 + device = "cuda" + input = torch.randn(10, 32, 128, dtype=dtype, device=device) + weight = torch.randn(10, 128, 256, dtype=dtype, device=device) + m = M(weight).eval() + original = m(input) + quantize_(m, self.bmm_config, filter_fn=lambda x, fqn: True) + quantized = m(input) + self.assertTrue(compute_error(original, quantized) > 18) + + def test_to_device(self): + for device in self.GPU_DEVICES: + linear = torch.nn.Linear(128, 256, dtype=torch.bfloat16) + quantize_(linear, self.config) + linear.to(device) + + linear = torch.nn.Linear(128, 256, dtype=torch.bfloat16) + quantize_(linear, self.config) + linear.to(device=device) + + linear = torch.nn.Linear(128, 256, dtype=torch.bfloat16) + quantize_(linear, self.config) + linear.to(device) + if __name__ == "__main__": run_tests() diff --git a/torchao/dtypes/__init__.py b/torchao/dtypes/__init__.py index 692d56ad31..581c3e4ecb 100644 --- a/torchao/dtypes/__init__.py +++ b/torchao/dtypes/__init__.py @@ -8,8 +8,8 @@ to_affine_quantized_intx, to_affine_quantized_intx_static, ) -from .fbgemm_fp8_tensor import to_fbgemm_fp8 -from .fbgemm_int4_tensor import to_fbgemm_int4 +from .fbgemm_fp8_tensor import FbgemmFp8Tensor, to_fbgemm_fp8 +from .fbgemm_int4_tensor import FbgemmInt4Tensor, to_fbgemm_int4 from .floatx import ( CutlassSemiSparseLayout, Float8Layout, @@ -64,5 +64,7 @@ "to_affine_quantized_packed_linear_int8_dynamic_activation_intx_weight", "Int4XPULayout", "to_fbgemm_int4", + "FbgemmInt4Tensor", "to_fbgemm_fp8", + "FbgemmFp8Tensor", ] diff --git a/torchao/dtypes/fbgemm_fp8_tensor.py b/torchao/dtypes/fbgemm_fp8_tensor.py index df7ce69de7..b6c1d72acc 100644 --- a/torchao/dtypes/fbgemm_fp8_tensor.py +++ b/torchao/dtypes/fbgemm_fp8_tensor.py @@ -18,6 +18,7 @@ __all__ = [ "to_fbgemm_fp8", + "FbgemmFp8Tensor", ] aten = torch.ops.aten @@ -74,11 +75,22 @@ def __repr__(self): def _quantization_type(self): return f"shape={self.shape}, activation_scale_ub={self.activation_scale_ub}, device={self.device}" + def to(self, *args, **kwargs): + kwargs = self._get_to_kwargs(*args, **kwargs) + device = kwargs.pop("device") + return self.__class__( + self.float8_data.to(device), + self.scale.to(device), + self.activation_scale_ub.to(device), + self.dtype, + ) + @classmethod def from_float( cls, w: torch.Tensor, activation_scale_ub: Optional[float] = None, + transpose_input: bool = False, ): if activation_scale_ub is None: activation_scale_ub = 1200.0 @@ -88,6 +100,12 @@ def from_float( dtype=torch.float, device=w.device, ) + if transpose_input: + if w.ndim == 3: + w = w.transpose(-1, -2) + else: + w = w.t() + wq, w_scale = torch.ops.triton.quantize_fp8_row(w) # wq, w_scale = torch.ops.fbgemm.quantize_fp8_per_row(w) dtype = w.dtype @@ -110,11 +128,6 @@ def _(func, types, args, kwargs): args[1], args[2] if len(args) > 2 else None, ) - if not input_tensor.is_floating_point(): - raise NotImplementedError( - f"{func} is not implemented for non floating point input" - ) - orig_act_size = input_tensor.size() orig_out_features = weight_tensor.shape[-2] @@ -141,6 +154,33 @@ def _(func, types, args, kwargs): return res +@implements(torch.bmm) +def _(func, types, args, kwargs): + input_tensor, weight_tensor = ( + args[0], + args[1], + ) + orig_act_size = input_tensor.size() + # not used + num_tokens = torch.empty([input_tensor.size(0)], device=input_tensor.device) + xq, x_scale = torch.ops.fbgemm.quantize_fp8_per_row( + input_tensor, num_tokens, weight_tensor.activation_scale_ub + ) + + a_data = xq + b_data = weight_tensor.float8_data + orig_out_features = b_data.shape[-2] + + res = torch.ops.fbgemm.f8f8bf16_rowwise_batched( + a_data, + b_data, + x_scale, + weight_tensor.scale, + ) + res = res.reshape(*orig_act_size[:-1], orig_out_features) + return res + + @implements([aten.detach.default, aten.alias.default]) def _(func, types, args, kwargs): return return_and_correct_aliasing( diff --git a/torchao/dtypes/fbgemm_int4_tensor.py b/torchao/dtypes/fbgemm_int4_tensor.py index ab108fea06..c398442168 100644 --- a/torchao/dtypes/fbgemm_int4_tensor.py +++ b/torchao/dtypes/fbgemm_int4_tensor.py @@ -19,6 +19,7 @@ __all__ = [ "to_fbgemm_int4", + "FbgemmInt4Tensor", ] aten = torch.ops.aten @@ -77,11 +78,23 @@ def __repr__(self): def _quantization_type(self): return f"shape={self.shape}, group_size={self.group_size}, device={self.device}" + def to(self, *args, **kwargs): + kwargs = self._get_to_kwargs(*args, **kwargs) + device = kwargs.pop("device") + return self.__class__( + self.packed_weight.to(device), + self.scale.to(device), + self.zero_point.to(device), + self.group_size, + self.shape, + ) + @classmethod def from_float( cls, w: torch.Tensor, block_size: List[int], + transpose_input: bool = False, ): assert len(block_size) == w.ndim, ( f"Expecting the length of block_size to be equal to the dimension of the weight, got {block_size=} and {w.ndim=}" @@ -89,6 +102,12 @@ def from_float( if int4_row_quantize_zp is None: raise ImportError("Requires fbgemm-gpu-genai >= 1.2.0") + if transpose_input: + if w.ndim == 3: + w = w.transpose(-1, -2) + else: + w = w.t() + group_size = block_size[-1] original_shape = w.shape @@ -126,11 +145,6 @@ def _(func, types, args, kwargs): args[1], args[2] if len(args) > 2 else None, ) - if not input_tensor.is_floating_point(): - raise NotImplementedError( - f"{func} is not implemented for non floating point input" - ) - orig_act_size = input_tensor.size() orig_out_features = weight_tensor.shape[-2] @@ -146,6 +160,25 @@ def _(func, types, args, kwargs): return res +@implements(torch.bmm) +def _(func, types, args, kwargs): + input_tensor, weight_tensor = ( + args[0], + args[1], + ) + orig_act_size = input_tensor.size() + orig_out_features = weight_tensor.shape[-2] + + res = torch.ops.fbgemm.bf16i4bf16_rowwise_batched( + input_tensor, + weight_tensor.packed_weight.contiguous(), + weight_tensor.scale, + weight_tensor.zero_point, + ) + res = res.reshape(*orig_act_size[:-1], orig_out_features) + return res + + @implements([aten.detach.default, aten.alias.default]) def _(func, types, args, kwargs): return return_and_correct_aliasing( diff --git a/torchao/quantization/quant_api.py b/torchao/quantization/quant_api.py index be25b144a6..d8af23414b 100644 --- a/torchao/quantization/quant_api.py +++ b/torchao/quantization/quant_api.py @@ -1991,6 +1991,7 @@ class FbgemmConfig(AOBaseConfig): output_dtype: torch.dtype block_size: Optional[List[int]] = None activation_scale_ub: Optional[float] = None + transpose_input: bool = False @register_quantize_module_handler(FbgemmConfig) @@ -2018,9 +2019,11 @@ def _(module: torch.nn.Module, config: FbgemmConfig) -> torch.nn.Module: weight = to_fbgemm_int4( module.weight, config.block_size, + config.transpose_input, ) module.weight = torch.nn.Parameter(weight, requires_grad=False) module.extra_repr = types.MethodType(_linear_extra_repr, module) + return module elif ( (config.input_dtype == e4m3_dtype) and (config.weight_dtype == e4m3_dtype) @@ -2029,9 +2032,11 @@ def _(module: torch.nn.Module, config: FbgemmConfig) -> torch.nn.Module: weight = to_fbgemm_fp8( module.weight, config.activation_scale_ub, + config.transpose_input, ) module.weight = torch.nn.Parameter(weight, requires_grad=False) module.extra_repr = types.MethodType(_linear_extra_repr, module) + return module else: raise NotImplementedError( f"{config} is not supported. supported input, weight, output kernel dtypes are: {_SUPPORTED_DTYPES}" From a581609b8bca63a517e50d96e769c6bde927f077 Mon Sep 17 00:00:00 2001 From: Apurva Jain Date: Tue, 10 Jun 2025 10:49:51 -0700 Subject: [PATCH 101/165] [BE] Rename qparams for tinygemm (#2344) --- torchao/dtypes/affine_quantized_tensor.py | 10 +++++----- torchao/dtypes/affine_quantized_tensor_ops.py | 4 ++-- torchao/dtypes/uintx/int4_cpu_layout.py | 4 ++-- torchao/dtypes/uintx/int4_xpu_layout.py | 10 ++++++++-- .../dtypes/uintx/tensor_core_tiled_layout.py | 4 ++-- .../prototype/parq/quant/uniform_torchao.py | 8 ++++---- torchao/quantization/quant_primitives.py | 20 +++++++++---------- torchao/quantization/utils.py | 8 ++++---- 8 files changed, 37 insertions(+), 31 deletions(-) diff --git a/torchao/dtypes/affine_quantized_tensor.py b/torchao/dtypes/affine_quantized_tensor.py index 6cb2e8997e..132ac0f28e 100644 --- a/torchao/dtypes/affine_quantized_tensor.py +++ b/torchao/dtypes/affine_quantized_tensor.py @@ -26,14 +26,14 @@ choose_qparams_and_quantize_affine_hqq, dequantize_affine, dequantize_affine_float8, - dequantize_affine_float_zero_point, dequantize_affine_floatx, dequantize_affine_no_zero_point, + dequantize_affine_tinygemm, quantize_affine, quantize_affine_float8, - quantize_affine_float_zero_point, quantize_affine_floatx, quantize_affine_no_zero_point, + quantize_affine_tinygemm, ) from torchao.utils import ( TORCH_VERSION_AT_LEAST_2_5, @@ -155,7 +155,7 @@ def dequantize(self, output_dtype: Optional[torch.dtype] = None) -> torch.Tensor else: data, scale, zero_point = self.tensor_impl.get_plain() if self.zero_point_domain == ZeroPointDomain.FLOAT: - dq = dequantize_affine_float_zero_point( + dq = dequantize_affine_tinygemm( data, self.block_size, scale, @@ -339,7 +339,7 @@ def from_hp_to_intx( quant_max, ) elif zero_point_domain == ZeroPointDomain.FLOAT: - data = quantize_affine_float_zero_point( + data = quantize_affine_tinygemm( input_float, block_size, scale, @@ -410,7 +410,7 @@ def from_hp_to_intx_static( quant_max, ) elif zero_point_domain == ZeroPointDomain.FLOAT: - int_data = quantize_affine_float_zero_point( + int_data = quantize_affine_tinygemm( input_float, block_size, scale, diff --git a/torchao/dtypes/affine_quantized_tensor_ops.py b/torchao/dtypes/affine_quantized_tensor_ops.py index 63650ce687..a76b4daa23 100644 --- a/torchao/dtypes/affine_quantized_tensor_ops.py +++ b/torchao/dtypes/affine_quantized_tensor_ops.py @@ -93,8 +93,8 @@ from torchao.quantization.quant_primitives import ( ZeroPointDomain, dequantize_affine, - dequantize_affine_float_zero_point, dequantize_affine_no_zero_point, + dequantize_affine_tinygemm, ) from torchao.utils import ( fill_defaults, @@ -318,7 +318,7 @@ def _(func, types, args, kwargs): # we need to increase block size to correct dim new_blocks = idx.dim() - 1 if args[1].zero_point_domain == ZeroPointDomain.FLOAT: - _dequantize_affine = dequantize_affine_float_zero_point + _dequantize_affine = dequantize_affine_tinygemm elif args[1].zero_point_domain == ZeroPointDomain.NONE: _dequantize_affine = dequantize_affine_no_zero_point else: diff --git a/torchao/dtypes/uintx/int4_cpu_layout.py b/torchao/dtypes/uintx/int4_cpu_layout.py index 56812ee4e1..6c89f98ff7 100644 --- a/torchao/dtypes/uintx/int4_cpu_layout.py +++ b/torchao/dtypes/uintx/int4_cpu_layout.py @@ -19,7 +19,7 @@ from torchao.dtypes.utils import AQTTensorImpl, Layout, is_device from torchao.quantization.quant_primitives import ( ZeroPointDomain, - quantize_affine_float_zero_point, + quantize_affine_tinygemm, ) from torchao.utils import ( TORCH_VERSION_AT_LEAST_2_5, @@ -266,7 +266,7 @@ def get_plain(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: # TODO: move this to `unpack_tinygemm_scales_and_zeros`? scale = scale.reshape(scale.shape[:-1]).contiguous() zero = zero.reshape(zero.shape[:-1]).contiguous() - int_data = quantize_affine_float_zero_point( + int_data = quantize_affine_tinygemm( dequantized, block_size, scale, diff --git a/torchao/dtypes/uintx/int4_xpu_layout.py b/torchao/dtypes/uintx/int4_xpu_layout.py index 722a37bc32..c67eebd747 100644 --- a/torchao/dtypes/uintx/int4_xpu_layout.py +++ b/torchao/dtypes/uintx/int4_xpu_layout.py @@ -1,3 +1,9 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD 3-Clause license found in the +# LICENSE file in the root directory of this source tree. + from dataclasses import dataclass from typing import Optional, Tuple @@ -372,7 +378,7 @@ def __torch_dispatch__(cls, func, types, args, kwargs): def get_plain(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: from torchao.quantization.quant_primitives import ( quantize_affine, - quantize_affine_float_zero_point, + quantize_affine_tinygemm, ) from torchao.quantization.utils import unpack_tinygemm_scales_and_zeros @@ -423,7 +429,7 @@ def get_plain(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: # TODO: move this to `unpack_tinygemm_scales_and_zeros`? scale = scale.reshape(scale.shape[:-1]).contiguous() zero = zero.reshape(zero.shape[:-1]).contiguous() - int_data = quantize_affine_float_zero_point( + int_data = quantize_affine_tinygemm( dequantized, block_size, scale, diff --git a/torchao/dtypes/uintx/tensor_core_tiled_layout.py b/torchao/dtypes/uintx/tensor_core_tiled_layout.py index 2baf45ded0..0856d22fee 100644 --- a/torchao/dtypes/uintx/tensor_core_tiled_layout.py +++ b/torchao/dtypes/uintx/tensor_core_tiled_layout.py @@ -21,7 +21,7 @@ from torchao.quantization.quant_primitives import ( ZeroPointDomain, _get_reduction_params, - quantize_affine_float_zero_point, + quantize_affine_tinygemm, ) from torchao.utils import ( TORCH_VERSION_AT_LEAST_2_5, @@ -511,7 +511,7 @@ def dequant_4d(self): target_dtype = torch.int32 quant_min = 0 quant_max = 15 - int_data = quantize_affine_float_zero_point( + int_data = quantize_affine_tinygemm( dequantized, self.block_size, scale, diff --git a/torchao/prototype/parq/quant/uniform_torchao.py b/torchao/prototype/parq/quant/uniform_torchao.py index f742778ed0..a71ac8b5b3 100644 --- a/torchao/prototype/parq/quant/uniform_torchao.py +++ b/torchao/prototype/parq/quant/uniform_torchao.py @@ -18,11 +18,11 @@ choose_qparams_affine_dont_preserve_zero, choose_qparams_affine_tinygemm, dequantize_affine, - dequantize_affine_float_zero_point, dequantize_affine_no_zero_point, + dequantize_affine_tinygemm, quantize_affine, - quantize_affine_float_zero_point, quantize_affine_no_zero_point, + quantize_affine_tinygemm, ) from .quantizer import Quantizer @@ -76,8 +76,8 @@ def quantize( if self.zero_point_domain == ZeroPointDomain.FLOAT and not self.preserve_zero: _choose_qparams_affine = choose_qparams_affine_tinygemm - _quantize_affine = quantize_affine_float_zero_point - _dequantize_affine = dequantize_affine_float_zero_point + _quantize_affine = quantize_affine_tinygemm + _dequantize_affine = dequantize_affine_tinygemm elif self.zero_point_domain == ZeroPointDomain.INT and not self.preserve_zero: _choose_qparams_affine = choose_qparams_affine_dont_preserve_zero _quantize_affine = quantize_affine diff --git a/torchao/quantization/quant_primitives.py b/torchao/quantization/quant_primitives.py index 9d453102cd..9e0c6447c8 100644 --- a/torchao/quantization/quant_primitives.py +++ b/torchao/quantization/quant_primitives.py @@ -30,10 +30,10 @@ "choose_qparams_affine_floatx", "quantize_affine", "quantize_affine_no_zero_point", - "quantize_affine_float_zero_point", + "quantize_affine_tinygemm", "dequantize_affine", "dequantize_affine_no_zero_point", - "dequantize_affine_float_zero_point", + "dequantize_affine_tinygemm", "quantize_affine_floatx", "dequantize_affine_floatx", "fake_quantize_affine", @@ -428,7 +428,7 @@ def _quantize_affine_no_dtype_cast( return quant -def quantize_affine_float_zero_point( +def quantize_affine_tinygemm( input: torch.Tensor, block_size: List[int], scale: torch.Tensor, @@ -453,7 +453,7 @@ def quantize_affine_float_zero_point( # torch.uintx dtypes yet if output_dtype in _SUB_BYTE_UINT_BOUNDS: output_dtype = torch.uint8 - return _quantize_affine_float_zero_point_no_dtype_cast( + return _quantize_affine_tinygemm_no_dtype_cast( input, block_size, scale, @@ -463,7 +463,7 @@ def quantize_affine_float_zero_point( ).to(output_dtype) -def _quantize_affine_float_zero_point_no_dtype_cast( +def _quantize_affine_tinygemm_no_dtype_cast( input: torch.Tensor, block_size: Tuple[int, ...], scale: torch.Tensor, @@ -803,7 +803,7 @@ def dequantize_affine_no_zero_point( ) -def _dequantize_affine_float_zero_point_no_dtype_check( +def _dequantize_affine_tinygemm_no_dtype_check( input: torch.Tensor, block_size: List[int], scale: torch.Tensor, @@ -848,7 +848,7 @@ def _dequantize_affine_float_zero_point_no_dtype_check( return dequant.view(original_shape).to(output_dtype) -def dequantize_affine_float_zero_point( +def dequantize_affine_tinygemm( input: torch.Tensor, block_size: Tuple[int, ...], scale: torch.Tensor, @@ -887,7 +887,7 @@ def dequantize_affine_float_zero_point( torch.bfloat16, ], f"Unsupported output dtype: {output_dtype}" quant_min, quant_max = _get_and_check_qmin_qmax(input_dtype, quant_min, quant_max) - return _dequantize_affine_float_zero_point_no_dtype_check( + return _dequantize_affine_tinygemm_no_dtype_check( input, block_size, scale, @@ -1013,8 +1013,8 @@ def _do_fake_quantize_affine( _quantize_affine = _quantize_affine_no_dtype_cast _dequantize_affine = _dequantize_affine_no_dtype_check elif zero_point_domain == ZeroPointDomain.FLOAT: - _quantize_affine = _quantize_affine_float_zero_point_no_dtype_cast - _dequantize_affine = _dequantize_affine_float_zero_point_no_dtype_check + _quantize_affine = _quantize_affine_tinygemm_no_dtype_cast + _dequantize_affine = _dequantize_affine_tinygemm_no_dtype_check elif ZeroPointDomain == ZeroPointDomain.NONE: _quantize_affine = _quantize_affine_no_zero_point_no_dtype_cast _dequantize_affine = _dequantize_affine_no_zero_point_no_dtype_check diff --git a/torchao/quantization/utils.py b/torchao/quantization/utils.py index 8f2554849c..3c968e2d40 100644 --- a/torchao/quantization/utils.py +++ b/torchao/quantization/utils.py @@ -19,11 +19,11 @@ choose_qparams_affine_dont_preserve_zero, choose_qparams_affine_tinygemm, dequantize_affine, - dequantize_affine_float_zero_point, dequantize_affine_no_zero_point, + dequantize_affine_tinygemm, quantize_affine, - quantize_affine_float_zero_point, quantize_affine_no_zero_point, + quantize_affine_tinygemm, ) from torchao.utils import ( TORCH_VERSION_AT_LEAST_2_5, @@ -439,7 +439,7 @@ def groupwise_affine_quantize_tensor_from_qparams( if zero_point_domain == ZeroPointDomain.INT: _quantize_affine = quantize_affine elif zero_point_domain == ZeroPointDomain.FLOAT: - _quantize_affine = quantize_affine_float_zero_point + _quantize_affine = quantize_affine_tinygemm elif ZeroPointDomain == ZeroPointDomain.NONE: _quantize_affine = quantize_affine_no_zero_point else: @@ -508,7 +508,7 @@ def groupwise_affine_dequantize_tensor_from_qparams( if zero_point_domain == ZeroPointDomain.INT: _dequantize_affine = dequantize_affine elif zero_point_domain == ZeroPointDomain.FLOAT: - _dequantize_affine = dequantize_affine_float_zero_point + _dequantize_affine = dequantize_affine_tinygemm else: _dequantize_affine = dequantize_affine_no_zero_point return _dequantize_affine( From d2842e507b0c3e1c5cdaae42b9ba5fc6bf62ff49 Mon Sep 17 00:00:00 2001 From: salman Date: Tue, 10 Jun 2025 12:02:49 -0700 Subject: [PATCH 102/165] Update QAT docs, highlight axolotl integration (#2266) * updating docs * updating docs * updating docs * updating qat readme --- README.md | 1 + torchao/quantization/qat/README.md | 28 +++++++++++++++++++++++++--- 2 files changed, 26 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 8d524c5e7b..e744ea9815 100644 --- a/README.md +++ b/README.md @@ -213,6 +213,7 @@ We're also fortunate to be integrated into some of the leading open-source libra 4. [TorchTune](https://pytorch.org/torchtune/main/tutorials/qlora_finetune.html?highlight=qlora) for our QLoRA and QAT recipes 5. VLLM for LLM serving: [usage](https://docs.vllm.ai/en/latest/features/quantization/torchao.html) 6. SGLang for LLM serving: [usage](https://docs.sglang.ai/backend/server_arguments.html#server-arguments) and the major [PR](https://github.com/sgl-project/sglang/pull/1341). +7. Axolotl for [QAT](https://docs.axolotl.ai/docs/qat.html) and [PTQ](https://docs.axolotl.ai/docs/quantize.html) ## Videos * [Keynote talk at GPU MODE IRL](https://youtu.be/FH5wiwOyPX4?si=VZK22hHz25GRzBG1&t=1009) diff --git a/torchao/quantization/qat/README.md b/torchao/quantization/qat/README.md index 42ff4e2567..eee1047199 100644 --- a/torchao/quantization/qat/README.md +++ b/torchao/quantization/qat/README.md @@ -115,11 +115,20 @@ To fake quantize embedding in addition to linear, you can additionally call the following with a filter function during the prepare step: ``` -from torchao.quantization.quant_api import _is_linear +# first apply linear transformation to the model as above +activation_config = FakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False) +weight_config = FakeQuantizeConfig(torch.int4, group_size=32) +quantize_( + model, + IntXQuantizationAwareTrainingConfig(activation_config, weight_config), +) + +# then apply weight-only transformation to embedding layers +# activation fake quantization is not supported for embedding layers quantize_( m, - IntXQuantizationAwareTrainingConfig(weight_config=weight_config), - filter_fn=lambda m, _: isinstance(m, torch.nn.Embedding) or _is_linear(m), + IntXQuantizationAwareTrainingConfig(weight_config=weight_config), + filter_fn=lambda m, _: isinstance(m, torch.nn.Embedding) ) ``` @@ -193,6 +202,19 @@ tune run --nnodes 1 --nproc_per_node 4 qat_lora_finetune_distributed --config ll For more detail, please refer to [this QAT tutorial](https://pytorch.org/torchtune/main/tutorials/qat_finetune.html). +## Axolotl integration + +[Axolotl](https://github.com/axolotl-ai-cloud) uses torchao to support quantized-aware fine-tuning. You can use the following commands to fine-tune, and then quantize a Llama-3.2-3B model: + +```bash +axolotl train examples/llama-3/3b-qat-fsdp2.yaml +# once training is complete, perform the quantization step +axolotl quantize examples/llama-3/3b-qat-fsdp2.yaml +# you should now have a quantized model saved in ./outputs/qat_out/quatized +``` + +Please see the [QAT documentation](https://docs.axolotl.ai/docs/qat.html) in axolotl for more details. + ## Evaluation Results Evaluation was performed on 6-8 A100 GPUs (80GB each) using the torchtune QAT From ab66083c8b84f9453e05fee4a3dc1cb8e87c0341 Mon Sep 17 00:00:00 2001 From: andrewor14 Date: Tue, 10 Jun 2025 16:15:13 -0400 Subject: [PATCH 103/165] Add static quant tutorial (#2047) --- docs/source/index.rst | 1 + docs/source/static_quantization.rst | 262 +++++++++++++++++++++ tutorials/calibration_flow/static_quant.py | 16 +- 3 files changed, 271 insertions(+), 8 deletions(-) create mode 100644 docs/source/static_quantization.rst diff --git a/docs/source/index.rst b/docs/source/index.rst index 9df40131cf..8931770795 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -40,4 +40,5 @@ for an overall introduction to the library and recent highlight and updates. serialization subclass_basic subclass_advanced + static_quantization pretraining diff --git a/docs/source/static_quantization.rst b/docs/source/static_quantization.rst new file mode 100644 index 0000000000..d6a206e54c --- /dev/null +++ b/docs/source/static_quantization.rst @@ -0,0 +1,262 @@ +Static Quantization +-------------------- + +Static quantization refers to using a fixed quantization range for all inputs during inference or generation. Unlike dynamic quantization, which dynamically computes new quantization ranges for each new input batch, static quantization typically results in more efficient computation, potentially at the cost of lower quantized accuracy since we cannot adapt to changes in the input distribution on-the-fly. + +In static quantization, this fixed quantization range is typically calibrated on similar inputs before quantizing the model. During the calibration phase, we first insert observers into the model to "observe" the distribution of the inputs to be quantized, and use this distribution to decide what scales and zero points to ultimately use when quantizing the model. + +In this tutorial, we walk through an example of how to achieve this in torchao. All code can be found in this `example script `__. Let's start with our toy linear model: + +.. code:: py + + import copy + import torch + + class ToyLinearModel(torch.nn.Module): + def __init__(self, m=64, n=32, k=64): + super().__init__() + self.linear1 = torch.nn.Linear(m, k, bias=False) + self.linear2 = torch.nn.Linear(k, n, bias=False) + + def example_inputs(self, batch_size=1, dtype=torch.float32, device="cpu"): + return ( + torch.randn( + batch_size, self.linear1.in_features, dtype=dtype, device=device + ), + ) + + def forward(self, x): + x = self.linear1(x) + x = self.linear2(x) + return x + + dtype = torch.bfloat16 + m = ToyLinearModel().eval().to(dtype).to("cuda") + m = torch.compile(m, mode="max-autotune") + + +Calibration Phase +~~~~~~~~~~~~~~~~~ + +torchao comes with a a simple observer implementation, `AffineQuantizedMinMaxObserver`, that records the min and max values that have flowed through the observer during the calibration phase. Users are welcome to implement their own desired, more advanced observation techniques, such as those relying on moving averages or histograms, and these may be added to torchao in the future. + +.. code:: py + + from torchao.quantization.granularity import PerAxis, PerTensor + from torchao.quantization.observer import AffineQuantizedMinMaxObserver + from torchao.quantization.quant_primitives import MappingType + + # per tensor input activation asymmetric quantization + act_obs = AffineQuantizedMinMaxObserver( + MappingType.ASYMMETRIC, + torch.uint8, + granularity=PerTensor(), + eps=torch.finfo(torch.float32).eps, + scale_dtype=torch.float32, + zero_point_dtype=torch.float32, + ) + + # per channel weight asymmetric quantization + weight_obs = AffineQuantizedMinMaxObserver( + MappingType.ASYMMETRIC, + torch.uint8, + granularity=PerAxis(axis=0), + eps=torch.finfo(torch.float32).eps, + scale_dtype=torch.float32, + zero_point_dtype=torch.float32, + ) + +Next, we define our observed linear that we will swap our `torch.nn.Linear` with. This is a high precision (e.g. fp32) linear module with the above observers inserted to record the input activation and weight values during calibration: + +.. code:: py + + import torch.nn.functional as F + + class ObservedLinear(torch.nn.Linear): + def __init__( + self, + in_features: int, + out_features: int, + act_obs: torch.nn.Module, + weight_obs: torch.nn.Module, + bias: bool = True, + device=None, + dtype=None, + ): + super().__init__(in_features, out_features, bias, device, dtype) + self.act_obs = act_obs + self.weight_obs = weight_obs + + def forward(self, input: torch.Tensor): + observed_input = self.act_obs(input) + observed_weight = self.weight_obs(self.weight) + return F.linear(observed_input, observed_weight, self.bias) + + @classmethod + def from_float(cls, float_linear, act_obs, weight_obs): + observed_linear = cls( + float_linear.in_features, + float_linear.out_features, + act_obs, + weight_obs, + False, + device=float_linear.weight.device, + dtype=float_linear.weight.dtype, + ) + observed_linear.weight = float_linear.weight + observed_linear.bias = float_linear.bias + return observed_linear + +To actually insert these observers into our toy model: + +.. code:: py + + from torchao.quantization.quant_api import ( + _replace_with_custom_fn_if_matches_filter, + ) + + def insert_observers_(model, act_obs, weight_obs): + _is_linear = lambda m, fqn: isinstance(m, torch.nn.Linear) + + def replacement_fn(m): + copied_act_obs = copy.deepcopy(act_obs) + copied_weight_obs = copy.deepcopy(weight_obs) + return ObservedLinear.from_float(m, copied_act_obs, copied_weight_obs) + + _replace_with_custom_fn_if_matches_filter(model, replacement_fn, _is_linear) + + insert_observers_(m, act_obs, weight_obs) + +Now we are ready to calibrate the model, which populates the observers we inserted with statistics recorded during the calibration. We can do this simply by feeding some example inputs to our "observed" model: + +.. code:: py + + for _ in range(10): + example_inputs = m.example_inputs(dtype=dtype, device="cuda") + m(*example_inputs) + + +Quantization Phase +~~~~~~~~~~~~~~~~~~ + +There are multiple ways to actually quantize the model. Here we walk through the simpler alternative, which is to define a `QuantizedLinear` class that we will swap our `ObservedLinear` to. Defining this new class isn't strictly necessary. For an alternative method that simply uses the existing `torch.nn.Linear`, please see the full `example script `__. + +.. code:: py + + from torchao.dtypes import to_affine_quantized_intx_static + + class QuantizedLinear(torch.nn.Module): + def __init__( + self, + in_features: int, + out_features: int, + act_obs: torch.nn.Module, + weight_obs: torch.nn.Module, + weight: torch.Tensor, + bias: torch.Tensor, + target_dtype: torch.dtype, + ): + super().__init__() + self.act_scale, self.act_zero_point = act_obs.calculate_qparams() + weight_scale, weight_zero_point = weight_obs.calculate_qparams() + assert weight.dim() == 2 + block_size = (1, weight.shape[1]) + self.target_dtype = target_dtype + self.bias = bias + self.qweight = to_affine_quantized_intx_static( + weight, weight_scale, weight_zero_point, block_size, self.target_dtype + ) + + def forward(self, input: torch.Tensor): + block_size = input.shape + qinput = to_affine_quantized_intx_static( + input, + self.act_scale, + self.act_zero_point, + block_size, + self.target_dtype, + ) + return F.linear(qinput, self.qweight, self.bias) + + @classmethod + def from_observed(cls, observed_linear, target_dtype): + quantized_linear = cls( + observed_linear.in_features, + observed_linear.out_features, + observed_linear.act_obs, + observed_linear.weight_obs, + observed_linear.weight, + observed_linear.bias, + target_dtype, + ) + return quantized_linear + +This linear class computes the scales and zero points for both input activations and weights in the beginning, effectively fixing the quantization range for future forward calls. Now, to actually quantize the model using this linear class, we can define the following config and pass it to torchao's main `quantize_` API: + +.. code:: py + + from dataclasses import dataclass + + from torchao.core.config import AOBaseConfig + from torchao.quantization import quantize_ + from torchao.quantization.transform_module import ( + register_quantize_module_handler, + ) + + @dataclass + class StaticQuantConfig(AOBaseConfig): + target_dtype: torch.dtype + + @register_quantize_module_handler(StaticQuantConfig) + def _apply_static_quant( + module: torch.nn.Module, + config: StaticQuantConfig, + ): + """ + Define a transformation associated with `StaticQuantConfig`. + This is called by `quantize_`, not by the user directly. + """ + return QuantizedLinear.from_observed(module, config.target_dtype) + + # filter function to identify which modules to swap + is_observed_linear = lambda m, fqn: isinstance(m, ObservedLinear) + + # perform static quantization + quantize_(m, StaticQuantConfig(torch.uint8), is_observed_linear) + +Now, we will see that the linear layers in our model are swapped to our `QuantizedLinear` class, with a fixed input activation scale and a fixed quantized weight: + +.. code:: py + + >>> m + OptimizedModule( + (_orig_mod): ToyLinearModel( + (linear1): QuantizedLinear() + (linear2): QuantizedLinear() + ) + ) + >>> m.linear1.act_scale + tensor([0.0237], device='cuda:0') + >>> m.linear1.qweight + AffineQuantizedTensor(tensor_impl=PlainAQTTensorImpl(data=tensor([[142, 31, 42, ..., 113, 157, 57], + [ 59, 160, 70, ..., 23, 150, 67], + [ 44, 49, 241, ..., 238, 69, 235], + ..., + [228, 255, 201, ..., 114, 236, 73], + [ 50, 88, 83, ..., 109, 209, 92], + [184, 141, 35, ..., 224, 110, 66]], device='cuda:0', + dtype=torch.uint8)... , scale=tensor([0.0009, 0.0010, 0.0009, 0.0010, 0.0009, 0.0010, 0.0010, 0.0010, 0.0010, + 0.0010, 0.0010, 0.0010, 0.0010, 0.0010, 0.0010, 0.0010, 0.0010, 0.0010, + 0.0010, 0.0010, 0.0010, 0.0009, 0.0010, 0.0010, 0.0010, 0.0009, 0.0010, + 0.0009, 0.0010, 0.0010, 0.0010, 0.0009, 0.0009, 0.0009, 0.0010, 0.0009, + 0.0010, 0.0009, 0.0010, 0.0010, 0.0010, 0.0009, 0.0009, 0.0009, 0.0010, + 0.0009, 0.0010, 0.0009, 0.0009, 0.0009, 0.0010, 0.0010, 0.0009, 0.0009, + 0.0010, 0.0009, 0.0010, 0.0010, 0.0009, 0.0009, 0.0009, 0.0009, 0.0009, + 0.0010], device='cuda:0')... , zero_point=tensor([130., 128., 122., 130., 132., 128., 125., 130., 126., 128., 129., 126., + 128., 128., 128., 128., 129., 127., 130., 125., 128., 133., 126., 126., + 128., 124., 127., 128., 128., 128., 129., 124., 126., 133., 129., 127., + 126., 124., 130., 126., 127., 129., 124., 125., 127., 130., 128., 132., + 128., 129., 128., 129., 131., 132., 127., 135., 126., 130., 124., 136., + 131., 124., 130., 129.], device='cuda:0')... , _layout=PlainLayout()), block_size=(1, 64), shape=torch.Size([64, 64]), device=cuda:0, dtype=torch.bfloat16, requires_grad=False) + +In this tutorial, we walked through a basic example of how to perform integer static quantization in torchao. We also have an example of how to perform the same static quantization in float8. Please see the full `example script `__ for more detail! diff --git a/tutorials/calibration_flow/static_quant.py b/tutorials/calibration_flow/static_quant.py index 59f50444e8..d81b00de8d 100644 --- a/tutorials/calibration_flow/static_quant.py +++ b/tutorials/calibration_flow/static_quant.py @@ -88,16 +88,16 @@ def replacement_fn(m): @dataclass -class ApplyStaticQuantConfig(AOBaseConfig): +class StaticQuantConfig(AOBaseConfig): target_dtype: torch.dtype # converting observed linear module to linear module with quantzied weights (and quantized activations) # with tensor subclasses -@register_quantize_module_handler(ApplyStaticQuantConfig) +@register_quantize_module_handler(StaticQuantConfig) def _apply_static_quant_transform( module: torch.nn.Module, - config: ApplyStaticQuantConfig, + config: StaticQuantConfig, ): target_dtype = config.target_dtype observed_linear = module @@ -229,14 +229,14 @@ def from_observed(cls, observed_linear, target_dtype): @dataclass -class ApplyStaticQuantConfig2(AOBaseConfig): +class StaticQuantConfig2(AOBaseConfig): target_dtype: torch.dtype -@register_quantize_module_handler(ApplyStaticQuantConfig2) +@register_quantize_module_handler(StaticQuantConfig2) def apply_static_quant( module: torch.nn.Module, - config: ApplyStaticQuantConfig2, + config: StaticQuantConfig2, ): return QuantizedLinear.from_observed(module, config.target_dtype) @@ -305,14 +305,14 @@ def test_static_quant(target_dtype: torch.dtype, mapping_type: MappingType): # quantized linear represented as an nn.Linear with modified tensor subclass weights # for both activation and weight quantization - quantize_(m, ApplyStaticQuantConfig(target_dtype), is_observed_linear) + quantize_(m, StaticQuantConfig(target_dtype), is_observed_linear) print("quantized model (applying tensor subclass to weight):", m) after_quant = m(*example_inputs) assert compute_error(before_quant, after_quant) > 25 print("test passed") # quantized linear as a standalone module - quantize_(m2, ApplyStaticQuantConfig2(target_dtype), is_observed_linear) + quantize_(m2, StaticQuantConfig2(target_dtype), is_observed_linear) print("quantized model (quantized module):", m2) after_quant = m2(*example_inputs) assert compute_error(before_quant, after_quant) > 25 From b6bb7dc240b9083d105b52ee8a0393496cdbc428 Mon Sep 17 00:00:00 2001 From: Daniel Vega-Myhre Date: Tue, 10 Jun 2025 15:44:12 -0700 Subject: [PATCH 104/165] float8 moe training conversion API prototype (#2275) stack-info: PR: https://github.com/pytorch/ao/pull/2275, branch: danielvegamyhre/stack/1 migrate to quantize and add test work on moe training test --- .../__init__.py | 0 .../test_kernels.py | 4 +- .../test_scaled_grouped_mm.py | 2 +- test/prototype/moe_training/test_training.py | 140 ++++++++++++++++++ torchao/prototype/moe_training/__init__.py | 3 + .../benchmarks/benchmark_kernels.py | 4 +- .../benchmarks/benchmark_scaled_grouped_mm.py | 2 +- .../moe_training/conversion_utils.py | 112 ++++++++++++++ .../kernels/__init__.py | 4 +- .../kernels/jagged_float8_scales.py | 2 +- .../scaled_grouped_mm.py | 9 +- torchao/prototype/moe_training/tensor.py | 35 +++++ .../utils.py | 0 .../prototype/scaled_grouped_mm/__init__.py | 3 - 14 files changed, 305 insertions(+), 15 deletions(-) rename test/prototype/{scaled_grouped_mm => moe_training}/__init__.py (100%) rename test/prototype/{scaled_grouped_mm => moe_training}/test_kernels.py (96%) rename test/prototype/{scaled_grouped_mm => moe_training}/test_scaled_grouped_mm.py (98%) create mode 100644 test/prototype/moe_training/test_training.py create mode 100644 torchao/prototype/moe_training/__init__.py rename torchao/prototype/{scaled_grouped_mm => moe_training}/benchmarks/benchmark_kernels.py (97%) rename torchao/prototype/{scaled_grouped_mm => moe_training}/benchmarks/benchmark_scaled_grouped_mm.py (98%) create mode 100644 torchao/prototype/moe_training/conversion_utils.py rename torchao/prototype/{scaled_grouped_mm => moe_training}/kernels/__init__.py (54%) rename torchao/prototype/{scaled_grouped_mm => moe_training}/kernels/jagged_float8_scales.py (99%) rename torchao/prototype/{scaled_grouped_mm => moe_training}/scaled_grouped_mm.py (95%) create mode 100644 torchao/prototype/moe_training/tensor.py rename torchao/prototype/{scaled_grouped_mm => moe_training}/utils.py (100%) delete mode 100644 torchao/prototype/scaled_grouped_mm/__init__.py diff --git a/test/prototype/scaled_grouped_mm/__init__.py b/test/prototype/moe_training/__init__.py similarity index 100% rename from test/prototype/scaled_grouped_mm/__init__.py rename to test/prototype/moe_training/__init__.py diff --git a/test/prototype/scaled_grouped_mm/test_kernels.py b/test/prototype/moe_training/test_kernels.py similarity index 96% rename from test/prototype/scaled_grouped_mm/test_kernels.py rename to test/prototype/moe_training/test_kernels.py index ec18dd45bf..ed68e8fa23 100644 --- a/test/prototype/scaled_grouped_mm/test_kernels.py +++ b/test/prototype/moe_training/test_kernels.py @@ -19,11 +19,11 @@ pytest.skip("Unsupported PyTorch version", allow_module_level=True) -from torchao.prototype.scaled_grouped_mm.kernels.jagged_float8_scales import ( +from torchao.prototype.moe_training.kernels.jagged_float8_scales import ( triton_fp8_col_major_jagged_colwise_scales, triton_fp8_row_major_jagged_rowwise_scales, ) -from torchao.prototype.scaled_grouped_mm.utils import ( +from torchao.prototype.moe_training.utils import ( _is_column_major, _to_2d_jagged_float8_tensor_colwise, _to_2d_jagged_float8_tensor_rowwise, diff --git a/test/prototype/scaled_grouped_mm/test_scaled_grouped_mm.py b/test/prototype/moe_training/test_scaled_grouped_mm.py similarity index 98% rename from test/prototype/scaled_grouped_mm/test_scaled_grouped_mm.py rename to test/prototype/moe_training/test_scaled_grouped_mm.py index 30af1abc04..844220c49c 100644 --- a/test/prototype/scaled_grouped_mm/test_scaled_grouped_mm.py +++ b/test/prototype/moe_training/test_scaled_grouped_mm.py @@ -26,7 +26,7 @@ from torchao.float8.float8_linear import matmul_with_hp_or_float8_args from torchao.float8.float8_tensor import LinearMMConfig from torchao.float8.float8_utils import tensor_to_scale, to_fp8_saturated -from torchao.prototype.scaled_grouped_mm.scaled_grouped_mm import ( +from torchao.prototype.moe_training.scaled_grouped_mm import ( _scaled_grouped_mm, ) from torchao.testing.utils import skip_if_rocm diff --git a/test/prototype/moe_training/test_training.py b/test/prototype/moe_training/test_training.py new file mode 100644 index 0000000000..71320af83e --- /dev/null +++ b/test/prototype/moe_training/test_training.py @@ -0,0 +1,140 @@ +import copy + +import pytest +import torch +from torch import nn +from torch.nn import functional as F + +# this feature requires CUDA and SM89+ +if not torch.cuda.is_available() or torch.cuda.get_device_capability() < (8, 9): + pytest.skip( + "CUDA not available or compute capability < 8.9", allow_module_level=True + ) + +from torchao.float8.float8_utils import compute_error +from torchao.prototype.moe_training.conversion_utils import MoETrainingConfig +from torchao.prototype.moe_training.tensor import ScaledGroupedMMTensor +from torchao.quantization.quant_api import quantize_ + +# this test requires torchtitan +try: + from torchtitan.experiments.llama4.model.args import TransformerModelArgs + from torchtitan.experiments.llama4.model.moe import MoE +except ImportError: + import warnings + + warnings.warn("torchtitan not installed, skipping MoE tests.") + pytest.skip(allow_module_level=True) + + +@pytest.mark.parametrize( + "target_fqns", + [ + ["experts"], + ["does.not.exist"], + ], +) +def test_moe_float8_training(target_fqns: list[str]): + model_args = TransformerModelArgs( + moe_enabled=True, + num_experts=8, + dim=256, + ) + init_std = 0.02 + device = torch.device("cuda") + + # reference bf16 MoE + ref_model = MoE(model_args).to(torch.bfloat16).cuda() + torch.manual_seed(42) + ref_model.init_weights(init_std, device) + + # target MoE for testing conversion + model = copy.deepcopy(ref_model) + + # assert starting params are identical for both models + for param1, param2 in zip(model.parameters(), ref_model.parameters()): + assert torch.equal(param1, param2) + + # convert MoE to float8 training + def moe_module_filter_fn(mod: nn.Module, cur_fqn: str) -> bool: + for target_fqn in target_fqns: + if target_fqn in cur_fqn: + return True + return False + + # quantize test model + config = MoETrainingConfig() + quantize_(model, config=config, filter_fn=moe_module_filter_fn) + + # validate that only the experts were converted + _validate_model_conversion( + model, + target_fqns=target_fqns, + ) + + # inputs + batch, seq, dim = 8, 2048, 256 + ref_x = torch.randn( + batch, seq, dim, dtype=torch.bfloat16, requires_grad=True, device=device + ) + x = ref_x.detach().clone().requires_grad_(True) + + # forward pass + ref_out = ref_model(ref_x) + out = model(x) + + # validate output + out_sqnr = compute_error(out, ref_out) + assert out_sqnr.item() >= 30.0, f"SQNR must be >= 30.0, got {out_sqnr.item()}." + + # compute loss + labels = torch.ones_like(ref_out) + ref_loss = F.mse_loss(ref_out, labels) + out_loss = F.mse_loss(out, labels) + + # backward pass + ref_loss.backward() + out_loss.backward() + + # validate input gradient + input_grad_sqnr = compute_error(x.grad, ref_x.grad) + assert input_grad_sqnr.item() >= 30.0, ( + f"SQNR must be >= 30.0, got {input_grad_sqnr.item()}." + ) + + # validate param gradients + for param1, param2 in zip(model.parameters(), ref_model.parameters()): + param_grad_sqnr = compute_error(param1.grad, param2.grad) + assert param_grad_sqnr.item() >= 25.0, ( + f"SQNR must be >= 25.0, got {param_grad_sqnr.item()}." + ) + + +def _validate_model_conversion( + root_module: nn.Module, + target_fqns: list[str], +): + def _recursive_validate( + module: nn.Module, + cur_fqn: str, + ): + is_allowed_module = cur_fqn in target_fqns + + # check current module params + for param_name, param in module.named_parameters(recurse=False): + is_converted_type = isinstance(param, ScaledGroupedMMTensor) + if is_converted_type: + assert is_allowed_module, ( + f"Module {cur_fqn} is not in target_fqns, but has converted param {param_name}." + ) + if not is_allowed_module: + assert not is_converted_type, ( + f"Module {cur_fqn} is not in target_fqns, but has converted param {param_name}." + ) + + # recursively check child modules + for child_name, child_module in module.named_children(): + child_fqn = f"{cur_fqn}.{child_name}" if cur_fqn else child_name + _recursive_validate(child_module, child_fqn) + + _recursive_validate(root_module, "") diff --git a/torchao/prototype/moe_training/__init__.py b/torchao/prototype/moe_training/__init__.py new file mode 100644 index 0000000000..8118193aff --- /dev/null +++ b/torchao/prototype/moe_training/__init__.py @@ -0,0 +1,3 @@ +from torchao.prototype.moe_training.scaled_grouped_mm import _scaled_grouped_mm + +__all__ = ["_scaled_grouped_mm"] diff --git a/torchao/prototype/scaled_grouped_mm/benchmarks/benchmark_kernels.py b/torchao/prototype/moe_training/benchmarks/benchmark_kernels.py similarity index 97% rename from torchao/prototype/scaled_grouped_mm/benchmarks/benchmark_kernels.py rename to torchao/prototype/moe_training/benchmarks/benchmark_kernels.py index cf40220ae0..37701e6545 100644 --- a/torchao/prototype/scaled_grouped_mm/benchmarks/benchmark_kernels.py +++ b/torchao/prototype/moe_training/benchmarks/benchmark_kernels.py @@ -14,11 +14,11 @@ from tabulate import tabulate from tqdm import tqdm -from torchao.prototype.scaled_grouped_mm.kernels.jagged_float8_scales import ( +from torchao.prototype.moe_training.kernels.jagged_float8_scales import ( triton_fp8_col_major_jagged_colwise_scales, triton_fp8_row_major_jagged_rowwise_scales, ) -from torchao.prototype.scaled_grouped_mm.utils import ( +from torchao.prototype.moe_training.utils import ( _to_2d_jagged_float8_tensor_colwise, _to_2d_jagged_float8_tensor_rowwise, ) diff --git a/torchao/prototype/scaled_grouped_mm/benchmarks/benchmark_scaled_grouped_mm.py b/torchao/prototype/moe_training/benchmarks/benchmark_scaled_grouped_mm.py similarity index 98% rename from torchao/prototype/scaled_grouped_mm/benchmarks/benchmark_scaled_grouped_mm.py rename to torchao/prototype/moe_training/benchmarks/benchmark_scaled_grouped_mm.py index 74921895ab..af1a652fc0 100644 --- a/torchao/prototype/scaled_grouped_mm/benchmarks/benchmark_scaled_grouped_mm.py +++ b/torchao/prototype/moe_training/benchmarks/benchmark_scaled_grouped_mm.py @@ -14,7 +14,7 @@ from tabulate import tabulate from tqdm import tqdm -from torchao.prototype.scaled_grouped_mm import _scaled_grouped_mm +from torchao.prototype.moe_training import _scaled_grouped_mm device = torch.device("cuda") diff --git a/torchao/prototype/moe_training/conversion_utils.py b/torchao/prototype/moe_training/conversion_utils.py new file mode 100644 index 0000000000..928af1cf2e --- /dev/null +++ b/torchao/prototype/moe_training/conversion_utils.py @@ -0,0 +1,112 @@ +from typing import Callable, Optional + +from torch import nn + +from torchao.core.config import AOBaseConfig +from torchao.prototype.moe_training.tensor import ScaledGroupedMMTensor +from torchao.quantization.transform_module import ( + register_quantize_module_handler, +) + + +class MoETrainingConfig(AOBaseConfig): + """ + The MoETrainingConfig is specifically designed to be used on MoE models using + `torch._grouped_mm` to implement expert computation in token-choice routing, + where expert weights are implemented as 3D nn.Parameters wit `num_experts` as + the leading dim. + + MoETrainingConfig has a module handler registered to it which will + find all nn.Parameters whose parent module matches the module filter function, + and swap their data tensor with a ScaledGroupedMMTensor. + + The ScaledGroupedMMTensor is a tensor subclass which overrides the + `torch._grouped_mm` op by dispatching to a differentiable scaled grouped mm, + which performs dynamic float8 rowwise quantization on scaled grouped GEMM + operands in both the forward and backward pass. + + For all other ops, ScaledGroupedMMTensor behaves like a regular torch.Tensor. + """ + + pass + + +@register_quantize_module_handler(MoETrainingConfig) +def _moe_training_transform( + module: nn.Module, + config: MoETrainingConfig, +) -> nn.Module: + """ + Swaps `torch.nn.Parameter` data tensor with a ScaledGroupedMMTensor. + + Args: + module: Module to modify. + config: MoETrainingConfig which defines how to perform the MoE training transform. + + Returns: + nn.Module: The modified module with swapped parameters. + """ + out = _swap_params(module) + return out + + +def _swap_params( + module: nn.Module, + *, + module_filter_fn: Optional[Callable[[nn.Module, str], bool]] = None, +) -> nn.Module: + """ + Recurses through the nn.Module, recursively swapping the data tensor of + each nn.Parameter with a ScaledGroupedMMTensor. Only applies if the module + passed the module_filter_fn, if specified. + + Args: + module: Module to modify. + module_filter_fn: If specified, only the `torch.nn.Parameter` subclasses that + that pass the filter function will be swapped. The inputs to the + filter function are the module instance, and the FQN. + + Returns: + nn.Module: The modified module with swapped linear layers. + """ + if isinstance(module, nn.Parameter) and ( + module_filter_fn is None or module_filter_fn(module, "") + ): + if len(list(module.children())) > 0: + raise AssertionError( + f"Does not support a root nn.Parameter with children: {module}" + ) + if not isinstance(module.data, ScaledGroupedMMTensor): + new_data = ScaledGroupedMMTensor(module.data) + return nn.Parameter(new_data, requires_grad=module.requires_grad) + return module + + root_module = module + + def post_order_traversal( + module: nn.Module, + cur_fqn: Optional[str] = None, + parent_module: Optional[nn.Module] = None, + ): + if cur_fqn is None: + cur_fqn = "" + + for child_module_name, child_module in module.named_children(): + if cur_fqn == "": + new_fqn = child_module_name + else: + new_fqn = f"{cur_fqn}.{child_module_name}" + + post_order_traversal(child_module, new_fqn, module) + + if module_filter_fn is None or module_filter_fn(module, cur_fqn): + for param_name, param in module.named_parameters(recurse=False): + if not isinstance(param.data, ScaledGroupedMMTensor): + new_param = nn.Parameter( + ScaledGroupedMMTensor(param), requires_grad=param.requires_grad + ) + setattr(module, param_name, new_param) + print(f"Swapped {cur_fqn}.{param_name} to ScaledGroupedMMTensor") + + post_order_traversal(root_module) + return root_module diff --git a/torchao/prototype/scaled_grouped_mm/kernels/__init__.py b/torchao/prototype/moe_training/kernels/__init__.py similarity index 54% rename from torchao/prototype/scaled_grouped_mm/kernels/__init__.py rename to torchao/prototype/moe_training/kernels/__init__.py index 1c75303568..b5446849b6 100644 --- a/torchao/prototype/scaled_grouped_mm/kernels/__init__.py +++ b/torchao/prototype/moe_training/kernels/__init__.py @@ -1,6 +1,6 @@ -from torchao.prototype.scaled_grouped_mm.kernels.jagged_float8_scales import ( +from torchao.prototype.moe_training.kernels.jagged_float8_scales import ( triton_fp8_col_major_jagged_colwise_scales as triton_fp8_col_major_jagged_colwise_scales, ) -from torchao.prototype.scaled_grouped_mm.kernels.jagged_float8_scales import ( +from torchao.prototype.moe_training.kernels.jagged_float8_scales import ( triton_fp8_row_major_jagged_rowwise_scales as triton_fp8_row_major_jagged_rowwise_scales, ) diff --git a/torchao/prototype/scaled_grouped_mm/kernels/jagged_float8_scales.py b/torchao/prototype/moe_training/kernels/jagged_float8_scales.py similarity index 99% rename from torchao/prototype/scaled_grouped_mm/kernels/jagged_float8_scales.py rename to torchao/prototype/moe_training/kernels/jagged_float8_scales.py index 4cc6177a48..3a497bf4a6 100644 --- a/torchao/prototype/scaled_grouped_mm/kernels/jagged_float8_scales.py +++ b/torchao/prototype/moe_training/kernels/jagged_float8_scales.py @@ -16,7 +16,7 @@ import triton import triton.language as tl -from torchao.prototype.scaled_grouped_mm.utils import _is_column_major +from torchao.prototype.moe_training.utils import _is_column_major EPS = 1e-12 diff --git a/torchao/prototype/scaled_grouped_mm/scaled_grouped_mm.py b/torchao/prototype/moe_training/scaled_grouped_mm.py similarity index 95% rename from torchao/prototype/scaled_grouped_mm/scaled_grouped_mm.py rename to torchao/prototype/moe_training/scaled_grouped_mm.py index 169e2c5407..d3aaf615db 100644 --- a/torchao/prototype/scaled_grouped_mm/scaled_grouped_mm.py +++ b/torchao/prototype/moe_training/scaled_grouped_mm.py @@ -10,11 +10,11 @@ from torchao.float8.config import ScalingGranularity from torchao.float8.float8_utils import tensor_to_scale, to_fp8_saturated -from torchao.prototype.scaled_grouped_mm.kernels import ( +from torchao.prototype.moe_training.kernels import ( triton_fp8_col_major_jagged_colwise_scales, triton_fp8_row_major_jagged_rowwise_scales, ) -from torchao.prototype.scaled_grouped_mm.utils import _is_column_major +from torchao.prototype.moe_training.utils import _is_column_major def _scaled_grouped_mm( @@ -83,7 +83,10 @@ def forward( assert not _is_column_major(A), "A must be row-major" # Due to hardware requirements, the right operand in a scaled grouped GEMM must be column-major. - assert _is_column_major(B_t), "B must be column-major" + if not _is_column_major(B_t): + # FSDP will complain if B_t (weights) is not contiguous, we can't require B_t to be column-major. + # TODO: figure out better solution than transposing for each forward pass. + B_t = B_t.transpose(-2, -1).contiguous().transpose(-2, -1) # Convert high precision input tensor to float8, row-major for left operand of grouped GEMM. # A shape: (M, K) diff --git a/torchao/prototype/moe_training/tensor.py b/torchao/prototype/moe_training/tensor.py new file mode 100644 index 0000000000..2a929d3b76 --- /dev/null +++ b/torchao/prototype/moe_training/tensor.py @@ -0,0 +1,35 @@ +import torch + +from torchao.prototype.moe_training import _scaled_grouped_mm + + +class ScaledGroupedMMTensor(torch.Tensor): + """ + ScaledGroupedMMTensor is a simple tensor subclass that wraps a regular tensor + and overrides the torch._grouped_mm op by dispatching to the + differentiable _scaled_grouped_mm autograd function. + """ + + grouped_mm_func_name = "_grouped_mm" + offs_arg_name = "offs" + + def __init__(self, data: torch.Tensor): + self._data = data + + @classmethod + def __torch_function__(cls, func, types, args, kwargs={}): + if func.__name__ == cls.grouped_mm_func_name: + # Use torchao scaled grouped mm with dynamic quant for + # "2d x 3d with offsets" case (used for routed experts). + # Otherwise, fall back to regular grouped mm. + # + # TODO: support "3d x 3d without offsets" case, which is + # used for shared experts. This is basically the grouped_mm + # kernel handling a bmm. + A, B = args[0], args[1] + A_is_2d = A.dim() == 2 + B_is_3d = B.dim() == 3 + has_offs = kwargs.get(cls.offs_arg_name) is not None + if A_is_2d and B_is_3d and has_offs: + return _scaled_grouped_mm(*args, **kwargs) + return super().__torch_function__(func, types, args, kwargs) diff --git a/torchao/prototype/scaled_grouped_mm/utils.py b/torchao/prototype/moe_training/utils.py similarity index 100% rename from torchao/prototype/scaled_grouped_mm/utils.py rename to torchao/prototype/moe_training/utils.py diff --git a/torchao/prototype/scaled_grouped_mm/__init__.py b/torchao/prototype/scaled_grouped_mm/__init__.py deleted file mode 100644 index 9c6278884a..0000000000 --- a/torchao/prototype/scaled_grouped_mm/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from torchao.prototype.scaled_grouped_mm.scaled_grouped_mm import ( - _scaled_grouped_mm as _scaled_grouped_mm, -) From 60c583eb47a66ec61b0b0410cd10dceec9a3f4a5 Mon Sep 17 00:00:00 2001 From: Daniel Vega-Myhre Date: Tue, 10 Jun 2025 17:59:38 -0700 Subject: [PATCH 105/165] Add float8 MoE training readme and runnable example (#2353) * add moe training readme and runnable example * mention software requirements --- torchao/prototype/moe_training/README.md | 101 ++++++++++++++++++ .../moe_training/examples/simple_moe_layer.py | 65 +++++++++++ 2 files changed, 166 insertions(+) create mode 100644 torchao/prototype/moe_training/README.md create mode 100644 torchao/prototype/moe_training/examples/simple_moe_layer.py diff --git a/torchao/prototype/moe_training/README.md b/torchao/prototype/moe_training/README.md new file mode 100644 index 0000000000..e53278840e --- /dev/null +++ b/torchao/prototype/moe_training/README.md @@ -0,0 +1,101 @@ +# Float8 MoE Training + +This prototype feature provides a way to use float8 rowwise training on MoE layers. + +Below is a simple runnable example of how to use this feature, using the MoE layer +from the [torchtitan](https://github.com/pytorch/torchtitan) Llama4 implementation for demonstration. + + +```python +import torch +from torch import nn +from torch.nn import functional as F + +# this feature requires CUDA and SM89+ +assert torch.cuda.is_available() and torch.cuda.get_device_capability() >= (8, 9) + +from torchao.prototype.moe_training.conversion_utils import MoETrainingConfig +from torchao.quantization.quant_api import quantize_ + +# this example uses torchtitan llama4 MoE, see +try: + from torchtitan.experiments.llama4.model.args import TransformerModelArgs + from torchtitan.experiments.llama4.model.moe import MoE +except ImportError as e: + raise ImportError( + "torchtitan not installed, see installation instructions at https://github.com/pytorch/torchtitan" + ) from e + + +# initialize model +device = torch.device("cuda") +model_args = TransformerModelArgs( + moe_enabled=True, + num_experts=8, + dim=256, +) +model = MoE(model_args).to(torch.bfloat16).to(device) +init_std = 0.02 +model.init_weights(init_std, device) + +# module filter function to define which modules to quantize +target_fqns = ["experts"] + + +def moe_module_filter_fn(mod: nn.Module, cur_fqn: str) -> bool: + for target_fqn in target_fqns: + if target_fqn in cur_fqn: + return True + return False + + +# quantize the model +config = MoETrainingConfig() +quantize_(model, config=config, filter_fn=moe_module_filter_fn) + +# training loop +optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3) +for step in range(10): + batch, seq, dim = 8, 2048, 256 + x = torch.randn( + batch, seq, dim, dtype=torch.bfloat16, requires_grad=True, device=device + ) + + # forward pass + out = model(x) + + # compute loss + labels = torch.ones_like(out) + out_loss = F.mse_loss(out, labels) + print(f"step {step} loss: {out_loss.item()}") + + # backward pass + out_loss.backward() + optimizer.step() + +``` + +## Requirements +- torchao nightly build +- CUDA compute capability 8.9+ (SM89+) + +## Modeling requirements +This prototype is specifically designed to be used on MoE models using +`torch._grouped_mm` to implement expert computation in token-choice routing, +where expert weights are implemented as 3D nn.Parameters with `num_experts` as +the leading dim. + +The `MoETrainingConfig` has a module handler registered to it which will +find all nn.Parameters whose parent module matches the module filter function, +and swap their data tensor with a ScaledGroupedMMTensor. + +The ScaledGroupedMMTensor is a tensor subclass which overrides the +`torch._grouped_mm` op by dispatching to a differentiable scaled grouped mm, +which performs dynamic float8 rowwise quantization on scaled grouped GEMM +operands in both the forward and backward pass. + +For all other ops, ScaledGroupedMMTensor behaves like a regular torch.Tensor. + +## Limitations +- Only tested with eager mode, single GPU training so far. +- Composability with parallelisms and `torch.compile` are next steps. diff --git a/torchao/prototype/moe_training/examples/simple_moe_layer.py b/torchao/prototype/moe_training/examples/simple_moe_layer.py new file mode 100644 index 0000000000..244d786c80 --- /dev/null +++ b/torchao/prototype/moe_training/examples/simple_moe_layer.py @@ -0,0 +1,65 @@ +import torch +from torch import nn +from torch.nn import functional as F + +# this feature requires CUDA and SM89+ +assert torch.cuda.is_available() and torch.cuda.get_device_capability() >= (8, 9) + +from torchao.prototype.moe_training.conversion_utils import MoETrainingConfig +from torchao.quantization.quant_api import quantize_ + +# this example uses torchtitan llama4 MoE, see +try: + from torchtitan.experiments.llama4.model.args import TransformerModelArgs + from torchtitan.experiments.llama4.model.moe import MoE +except ImportError as e: + raise ImportError( + "torchtitan not installed, see installation instructions at https://github.com/pytorch/torchtitan" + ) from e + + +# initialize model +device = torch.device("cuda") +model_args = TransformerModelArgs( + moe_enabled=True, + num_experts=8, + dim=256, +) +model = MoE(model_args).to(torch.bfloat16).to(device) +init_std = 0.02 +model.init_weights(init_std, device) + +# module filter function to define which modules to quantize +target_fqns = ["experts"] + + +def moe_module_filter_fn(mod: nn.Module, cur_fqn: str) -> bool: + for target_fqn in target_fqns: + if target_fqn in cur_fqn: + return True + return False + + +# quantize the model +config = MoETrainingConfig() +quantize_(model, config=config, filter_fn=moe_module_filter_fn) + +# training loop +optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3) +for step in range(10): + batch, seq, dim = 8, 2048, 256 + x = torch.randn( + batch, seq, dim, dtype=torch.bfloat16, requires_grad=True, device=device + ) + + # forward pass + out = model(x) + + # compute loss + labels = torch.ones_like(out) + out_loss = F.mse_loss(out, labels) + print(f"step {step} loss: {out_loss.item()}") + + # backward pass + out_loss.backward() + optimizer.step() From 2ec325da0bff7c7b63d7809a936a8cd0c1fa91ec Mon Sep 17 00:00:00 2001 From: Daniel Vega-Myhre Date: Wed, 11 Jun 2025 09:18:37 -0700 Subject: [PATCH 106/165] [BE] Make ScalingGranularity module level so it can be rendered in API ref on docsite (#2314) --- torchao/float8/__init__.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/torchao/float8/__init__.py b/torchao/float8/__init__.py index 2799f3197f..a97a46fa1c 100644 --- a/torchao/float8/__init__.py +++ b/torchao/float8/__init__.py @@ -29,12 +29,14 @@ GemmInputRole, LinearMMConfig, Float8MMConfig, + ScalingGranularity, ] ) __all__ = [ # configuration "ScalingType", + "ScalingGranularity", "Float8GemmConfig", "Float8LinearConfig", "CastConfig", From 3d7503998ecf33fb5add03ac79e74c0efc2c2509 Mon Sep 17 00:00:00 2001 From: Sayak Paul Date: Thu, 12 Jun 2025 04:22:49 +0530 Subject: [PATCH 107/165] Update README.md to include seamless v2 (#2355) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index e744ea9815..691594a933 100644 --- a/README.md +++ b/README.md @@ -13,6 +13,7 @@ From the team that brought you the fast series * 9.5x inference speedups for Image segmentation models with [sam-fast](https://pytorch.org/blog/accelerating-generative-ai) * 10x inference speedups for Language models with [gpt-fast](https://pytorch.org/blog/accelerating-generative-ai-2) * 3x inference speedup for Diffusion models with [sd-fast](https://pytorch.org/blog/accelerating-generative-ai-3) +* 2.7x inference speedup for FAIR’s Seamless M4T-v2 model with [seamlessv2-fast](https://pytorch.org/blog/accelerating-generative-ai-4/) `torchao` isn't just for inference - it delivers substantial speedups at scale, from [up to 1.5x speedups](https://pytorch.org/blog/training-using-float8-fsdp2/) on 512 GPU clusters, to [1.34-1.43x speedups](https://pytorch.org/blog/accelerating-large-scale-training-and-convergence-with-pytorch-float8-rowwise-on-crusoe-2k-h200s/) on 2K H200 clusters with the latest `torchao.float8` rowwise From afd58cca13283c3f6b689e09c0bd092198cd4f84 Mon Sep 17 00:00:00 2001 From: HDCharles <39544797+HDCharles@users.noreply.github.com> Date: Wed, 11 Jun 2025 23:20:40 -0400 Subject: [PATCH 108/165] fix ROCM test failures (#2362) Update regression_test_rocm.yml these tests are timing out and breaking trunk --- .github/workflows/regression_test_rocm.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/regression_test_rocm.yml b/.github/workflows/regression_test_rocm.yml index c24c76cfeb..d43b5f8d10 100644 --- a/.github/workflows/regression_test_rocm.yml +++ b/.github/workflows/regression_test_rocm.yml @@ -31,7 +31,7 @@ jobs: contents: read uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@main with: - timeout: 120 + timeout: 150 no-sudo: ${{ matrix.gpu-arch-type == 'rocm' }} runner: ${{ matrix.runs-on }} gpu-arch-type: ${{ matrix.gpu-arch-type }} From eaaf78711173a76aab06c8c1f02e17fed65f045a Mon Sep 17 00:00:00 2001 From: Driss Guessous <32754868+drisspg@users.noreply.github.com> Date: Wed, 11 Jun 2025 20:25:26 -0700 Subject: [PATCH 109/165] Add Tutorial on E2E integration into VLLM and minimal Subclass (#2346) --- docs/requirements.txt | 2 + docs/source/conf.py | 7 +- docs/source/index.rst | 1 + docs/source/torchao_vllm_integration.md | 409 ++++++++++++++++++++++++ 4 files changed, 418 insertions(+), 1 deletion(-) create mode 100644 docs/source/torchao_vllm_integration.md diff --git a/docs/requirements.txt b/docs/requirements.txt index 99c41798e7..6900367d66 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -4,4 +4,6 @@ sphinx_design sphinx_copybutton sphinx-tabs matplotlib +myst-parser +sphinxcontrib-mermaid==1.0.0 -e git+https://github.com/pytorch/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme diff --git a/docs/source/conf.py b/docs/source/conf.py index 66ee9a1c7e..1f549972c4 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -50,6 +50,8 @@ "sphinx_design", "sphinx_gallery.gen_gallery", "sphinx_copybutton", + "myst_parser", + "sphinxcontrib.mermaid", ] sphinx_gallery_conf = { @@ -96,7 +98,10 @@ # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # -source_suffix = [".rst"] +source_suffix = { + ".rst": "restructuredtext", + ".md": "markdown", +} # The master toctree document. master_doc = "index" diff --git a/docs/source/index.rst b/docs/source/index.rst index 8931770795..20cd0748dc 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -42,3 +42,4 @@ for an overall introduction to the library and recent highlight and updates. subclass_advanced static_quantization pretraining + torchao_vllm_integration diff --git a/docs/source/torchao_vllm_integration.md b/docs/source/torchao_vllm_integration.md new file mode 100644 index 0000000000..9af8fb3885 --- /dev/null +++ b/docs/source/torchao_vllm_integration.md @@ -0,0 +1,409 @@ +(torchao_vllm_integration)= +# Integration with VLLM: Architecture and Usage Guide + +This tutorial provides a comprehensive overview of how TorchAO integrates with VLLM, and what needs to be implemented to have a new technique work E2E. + +```{contents} +:local: +:depth: 2 +``` + + +(configuration-system)= +## Configuration System + +(huggingface-model-configuration)= +### 1. HuggingFace Model Configuration + +TorchAO quantization is configured through the model's `config.json` file: + +```json +{ + "model_type": "llama", + "quant_type": { + "default": { + "_type": "Int4WeightOnlyConfig", + "_data": { + "group_size": 128, + "use_hqq": true + } + } + } +} +``` + +(torchao-configuration-classes)= +### 2. TorchAO Configuration Classes + +All quantization methods inherit from `AOBaseConfig`: + +```python +from torchao.core.config import AOBaseConfig +from torchao.quantization import Int4WeightOnlyConfig + +# Example configuration +config = Int4WeightOnlyConfig( + group_size=128, + use_hqq=True, +) +assert isinstance(config, AOBaseConfig) +``` + +```{note} +All quantization configurations inherit from {class}`torchao.core.config.AOBaseConfig`, which provides serialization and validation capabilities. +``` + +(module-level-configuration)= +### 3. Module-Level Configuration + +For granular control, use `ModuleFqnToConfig`: + +```python +from torchao.quantization import ModuleFqnToConfig, Int4WeightOnlyConfig, Int8WeightOnlyConfig + +config = ModuleFqnToConfig({ + "model.layers.0.self_attn.q_proj": Int4WeightOnlyConfig(group_size=64), + "model.layers.0.self_attn.k_proj": Int4WeightOnlyConfig(group_size=64), + "model.layers.0.mlp.gate_proj": Int8WeightOnlyConfig(), + "_default": Int4WeightOnlyConfig(group_size=128) # Default for other modules +}) +``` + +(usage-examples)= +## Usage Examples + +(quantizing-models-huggingface)= +### 1. Quantizing Models with HuggingFace Integration + +```python +from transformers import TorchAoConfig, AutoModelForCausalLM +from torchao.quantization import Int4WeightOnlyConfig + +# Create quantization configuration +quantization_config = TorchAoConfig( + quant_type=Int4WeightOnlyConfig(group_size=128, use_hqq=True) +) + +# Load and automatically quantize the model +model = AutoModelForCausalLM.from_pretrained( + "meta-llama/Llama-3.2-1B", + torch_dtype="auto", + device_map="auto", + quantization_config=quantization_config +) + +# Save quantized model (see Serialization section below for safe_serialization details) +model.push_to_hub("your-username/Llama-3.2-1B-int4", safe_serialization=False) +``` + +```{seealso} +For more information on quantization configs, see {class}`torchao.quantization.Int4WeightOnlyConfig` and {class}`torchao.quantization.Int8WeightOnlyConfig`. +``` + +(serving-with-vllm)= +### 2. Serving with VLLM + +```bash +# Start VLLM server with TorchAO quantized model +vllm serve your-username/Llama-3.2-1B-int4 \ + --quantization torchao \ + --dtype bfloat16 \ +``` + + +(adding-new-quantization-methods)= +## Adding New Quantization Methods to VLLM + +(minimal-requirements-vllm)= +### Minimal Requirements for VLLM Compatibility + +To make a new TorchAO quantization method work with VLLM, you need to implement minimal tensor subclass operations that support **tensor parallelism**. VLLM uses `narrow()` and `copy_()` to move data from host cpu loaded in a state dict to the device, these require these specific aten operations: + +(why-these-operations)= +### Why these ? + +VLLM's tensor parallelism works by: +1. **{meth}`~torch.Tensor.narrow`** - Slicing weight tensors across different dimensions +2. **Sharding** - Distributing tensor chunks across multiple GPUs +3. **{meth}`~torch.Tensor.copy_`** - Moving tensor data between devices +4. **{meth}`~torch.Tensor.detach`** + + +A helpful pattern for doing this is `_apply_fn_to_data`, a method that applies a given function to all the attributes on your class w/ Tensor types. Below is a generic implementation that should work for most subclasses. We make heavy use of this pattern in the torchao codebase: + +```python +def _apply_fn_to_data(self, fn: Callable): + """Applies a fn to all tensor components stored on this class""" + tensor_names, ctx = self.__tensor_flatten__() + + # Apply the function to each tensor component + new_tensors = {} + for name in tensor_names: + new_tensors[name] = fn(getattr(self, name)) + + return self.__class__.__tensor_unflatten__( + new_tensors, + ctx, + None, # outer_size parameter + None, # outer_stride parameter + ) +``` + +(step-by-step-guide)= +## Step-by-Step Guide to Add a New Quantization Method + +(create-tensor-subclass)= +### 1. Create Your Tensor Subclass + +```{note} +For more details on tensor subclasses and their design principles, please refer to the [What are Tensor Subclasses?](https://docs.pytorch.org/ao/stable/subclass_basic.html#what-are-tensor-subclasses) documentation. +``` + +```python +from torchao.core.config import AOBaseConfig +from torchao.utils import TorchAOBaseTensor + +@dataclass +class MyNewQuantConfig(AOBaseConfig): + """Configuration for your new quantization method""" + bits: int = 8 + VERSION: ClassVar[int] = 1 + +class MyQuantizedTensor(TorchAOBaseTensor): + """Example based on FbgemmFp8Tensor - stores quantized data + scale""" + + tensor_data_attrs = ["quantized_data", "scale"] + tensor_attributes = ["dtype"] + + def __new__(cls, quantized_data, scale, dtype): + shape = quantized_data.shape + return torch.Tensor._make_wrapper_subclass( + cls, shape, device=quantized_data.device, dtype=dtype, requires_grad=False + ) + + def __init__(self, quantized_data, scale, dtype): + self.quantized_data = quantized_data + self.scale = scale + + def __tensor_flatten__(self) -> Tuple[List[str], List]: + """Serialize tensor subclass into plain tensors and metadata""" + return self.tensor_data_attrs, [ + getattr(self, attr) for attr in self.tensor_attributes + ] + + @classmethod + def __tensor_unflatten__( + cls, + tensor_data_dict: Dict[str, torch.Tensor], + tensor_attributes: List, + outer_size: Optional[torch.Size], + outer_stride: Optional[Tuple], + ) -> "MyQuantizedTensor": + """Reconstruct tensor subclass from serialized data""" + return cls( + *[tensor_data_dict[name] for name in cls.tensor_data_attrs], + *tensor_attributes, + ) +``` + +(implement-vllm-operations)= +### 2. Implement Required VLLM Operations + +```python +from torch.utils._python_dispatch import return_and_correct_aliasing + +@MyQuantizedTensor.implements([aten.detach.default, aten.alias.default]) +def _(func, types, args, kwargs): + return return_and_correct_aliasing( + func, args, kwargs, args[0]._apply_fn_to_data(func) + ) + +@MyQuantizedTensor.implements([aten._to_copy.default]) +def _(func, types, args, kwargs): + return return_and_correct_aliasing( + func, args, kwargs, args[0]._apply_fn_to_data(torch.clone) + ) + +@MyQuantizedTensor.implements([aten.slice.Tensor]) +def _(func, types, args, kwargs): + self, dim, start, end, step = fill_defaults(args, 5, [0, None, None, 1]) + if dim == 0 or dim == 1: + # NOTE the slicing here will likely be different for different quant techniques + return return_and_correct_aliasing( + func, args, kwargs, + args[0]._apply_fn_to_data(lambda x: aten.slice.Tensor(x, dim, start, end, step)) + ) + else: + raise NotImplementedError(f"Slicing along dim={dim} not supported") +``` + +(register-with-torchao)= +### 3. Register with TorchAO's Quantization System + +```python +from torchao.quantization.transform_module import register_quantize_module_handler + +@register_quantize_module_handler(MyNewQuantConfig) +def _my_quant_transform(module: torch.nn.Module, config: MyNewQuantConfig): + """Transform function that applies your quantization to a module""" + weight = module.weight + + # Your quantization logic here + quantized_weight = my_quantization_function(weight, config) + + # Replace the weight with your quantized tensor + module.weight = torch.nn.Parameter(quantized_weight, requires_grad=False) + return module +``` + +```{important} +The {func}`torchao.quantization.transform_module.register_quantize_module_handler` decorator registers your config class with TorchAO's quantization system. +``` + +(key-implementation-details)= +### Key Implementation Details + +(hardware-specific-linear-ops)= +### Hardware-Specific Linear Operations +Your quantized tensor's forward pass determines hardware support and what actually gets called when {func}`torch.nn.functional.linear` is called. + +```python +@MyQuantizedTensor.implements(torch.nn.functional.linear) +def _(func, types, args, kwargs): + input_tensor, weight_tensor, bias = args[0], args[1], args[2] if len(args) > 2 else None + + # This is where you define what hardware your method supports + if hasattr(weight_tensor, 'use_cutlass_kernel'): + return my_cutlass_linear(input_tensor, weight_tensor, bias) + elif hasattr(weight_tensor, 'use_triton_kernel'): + return my_triton_linear(input_tensor, weight_tensor, bias) + else: + # Fallback - dequantize and use standard linear + return torch.nn.functional.linear( + input_tensor, weight_tensor.dequantize(), bias + ) +``` + +(compilation-benefits)= +### Compilation Benefits +The overhead of tensor subclasses disappears with {func}`torch.compile`, this is on by default in VLLM. + +(trade-off-tensor-subclasses)= +### Trade Off of Tensor Subclasses +1. **Compilation**: is essential for removing subclass overhead. Without it unless your model is extremely gpu bound the overhead of dispatch on the CPU can severely impact performance. +2. The checkpoint defines the behavior of the model. You might be saying "don't all checkpoints do this". This is true, however people typically solely think of a torch.Tensor as its data. When in actuality its a true class where it brings the Dispatcher and all the kernels ATen has registered to it. When you define your tensor subclass, you are building a separate little world. One w/ a different representation of data, but also one where you need to explicitly define what ops you support and have implementations for all the hardware you want to support. This can feel a little like spooky action at a distance at first. But it can be very powerful. Case in point is being able to support TP with only 3 definitions. + +(serialization-model-sharing)= +## Serialization and Model Sharing + +(safetensors-support)= +### SafeTensors Support + +**Current Status**: TorchAO quantized models cannot yet be serialized with safetensors due to tensor subclass limitations. When saving quantized models, you must use `safe_serialization=False`. + +**Workaround**: For production use, save models with `safe_serialization=False` when pushing to HuggingFace Hub. + +**Future Work**: The TorchAO team is actively working on safetensors support for tensor subclasses. Track progress at: [pytorch/ao#2338](https://github.com/pytorch/ao/issues/2338) + +(integration-architecture-diagrams)= +## Integration Architecture Diagrams + +(high-level-model-flow)= +### 1. High-Level Model Flow: Transformers → VLLM + TorchAO + +This diagram shows the end-to-end flow from model creation to serving: + +```{mermaid} +graph LR + A[HuggingFace Model] --> B[Transformers AutoModel] + B --> C{Quantization Config?} + C -->|TorchAO Config| D[Apply TorchAO Quantization] + C -->|No Config| E[Standard Model] + + D --> F[Quantized Model w/ Tensor Subclasses] + E --> G[Standard PyTorch Model] + + F --> H[VLLM Model Loading] + G --> H + + H --> I[VLLM Distributed Engine] + I --> J[Tensor Parallel Sharding] + J --> K[Optimized Inference] + + style D fill:#e1f5fe + style F fill:#f3e5f5 + style J fill:#e8f5e8 +``` + +(torchao-integration-points)= +### 2. TorchAO Integration Points in VLLM + +This shows how VLLM detects and applies TorchAO quantization: + +```{mermaid} +graph LR + A[Model Config Detection] --> B{quantization=torchao?} + B -->|Yes| C[TorchAOConfig.from_config] + B -->|No| D[Other Quantization Methods] + + C --> E[Parse HF quant_type] + E --> F[config_from_dict] + F --> G[AOBaseConfig Instance] + + G --> H[get_quant_method per layer] + H --> I{Layer Type?} + I -->|LinearBase| J[TorchAOLinearMethod] + I -->|Other| K[UnquantizedLinearMethod] + + J --> L[create_weights] + L --> M[torchao_quantize_param_data] + M --> N[Quantized Tensor Subclass] + + style C fill:#e1f5fe + style G fill:#f3e5f5 + style N fill:#e8f5e8 +``` + +(kernel-dispatch)= +### 3. Kernel Dispatch: Bringing External Kernels to VLLM + +This illustrates how tensor subclasses enable custom kernel dispatch within VLLM: + +```{mermaid} +graph LR + A[F.linear Call in VLLM] --> B[MyQuantTensor torch_function] + B --> C[Custom implements Handler] + C --> D{Hardware Check} + + D --> E[Dispatch to External Kernel] + E --> F[Execute Optimized Kernel] + F --> G[Return Result to VLLM] + + subgraph "External Libraries" + H[TorchAO CUTLASS] + I[TorchAO Triton] + J[FBGEMM-GPU] + K[Custom Libraries] + end + + subgraph "Tensor Subclass Code" + L[implements F.linear] + M[custom_linear_impl] + N[call external kernel] + end + + E --> H + E --> I + E --> J + E --> K + + C --> L + L --> M + M --> N + N --> E + + style B fill:#e8f6ff,color:#000 + style C fill:#fff3e0,color:#000 + style E fill:#e8f5e8,color:#000 + style L fill:#f3e5f5,color:#000 +``` From 82bc17ec5301c566b6d98485921af6876319781b Mon Sep 17 00:00:00 2001 From: lilianaairhart Date: Wed, 11 Jun 2025 21:24:20 -0700 Subject: [PATCH 110/165] Back out "Add fbgemm as a dep for torchao in fbcode" Differential Revision: D76400895 Pull Request resolved: https://github.com/pytorch/ao/pull/2360 --- torchao/dtypes/fbgemm_int4_tensor.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/torchao/dtypes/fbgemm_int4_tensor.py b/torchao/dtypes/fbgemm_int4_tensor.py index c398442168..bb1558c6f8 100644 --- a/torchao/dtypes/fbgemm_int4_tensor.py +++ b/torchao/dtypes/fbgemm_int4_tensor.py @@ -5,7 +5,6 @@ # LICENSE file in the root directory of this source tree. -import importlib.util from typing import List import torch @@ -25,11 +24,11 @@ aten = torch.ops.aten -if importlib.util.find_spec("fbgemm_gpu") is None: +try: + from fbgemm_gpu.experimental.gen_ai.quantize import int4_row_quantize_zp, pack_int4 +except: int4_row_quantize_zp = None pack_int4 = None -else: - from fbgemm_gpu.experimental.gen_ai.quantize import int4_row_quantize_zp, pack_int4 class FbgemmInt4Tensor(TorchAOBaseTensor): From a2504181f436dc28c88d304b86578b38d24af1c0 Mon Sep 17 00:00:00 2001 From: Jesse Cai Date: Thu, 12 Jun 2025 11:35:14 -0400 Subject: [PATCH 111/165] Fix 2:4 sparsify meta registrations (#2366) * fix 2:4 meta registrations Summary: We need to register in python for symbolic shape support, which is needed for vLLM Test Plan: Reviewers: Subscribers: Tasks: Tags: * add meta for sparse gemm --- test/sparsity/test_activation24.py | 2 +- torchao/csrc/cuda/activation24/sparse_gemm.cu | 27 +++-------- torchao/csrc/cuda/activation24/sparsify24.cu | 20 ++------ torchao/ops.py | 48 +++++++++++++++++-- 4 files changed, 54 insertions(+), 43 deletions(-) diff --git a/test/sparsity/test_activation24.py b/test/sparsity/test_activation24.py index 420bf4328a..cc8f1179bf 100644 --- a/test/sparsity/test_activation24.py +++ b/test/sparsity/test_activation24.py @@ -171,7 +171,7 @@ def test_sparse24_fp8_sm90_cutlass_gemm_eye( # Check MM with scale b_scale = torch.randn([1, A.shape[1]], device=eye.device, dtype=torch.float32) a_scale = torch.randn([A.shape[0], 1], device=eye.device, dtype=torch.float32) - A_reconstructed = torch.ops.torchao._sparse24_fp8_sm90_cutlass_gemm( + A_reconstructed = torch.ops.torchao.sparse24_fp8_sm90_cutlass_gemm( A_packed, A_mdata, eye, a_scale=a_scale, b_scale=b_scale ) assert torch.allclose( diff --git a/torchao/csrc/cuda/activation24/sparse_gemm.cu b/torchao/csrc/cuda/activation24/sparse_gemm.cu index 776766794e..f837bcc3aa 100644 --- a/torchao/csrc/cuda/activation24/sparse_gemm.cu +++ b/torchao/csrc/cuda/activation24/sparse_gemm.cu @@ -132,9 +132,6 @@ struct SparseRowwiseKernel { template <> struct SparseRowwiseKernel { - static constexpr auto kElementOutAt = at::ScalarType::BFloat16; - static constexpr auto kElementAAt = at::ScalarType::BFloat16; - using ElementA = cutlass::bfloat16_t; using ElementB = cutlass::bfloat16_t; using ElementOut = cutlass::bfloat16_t; @@ -209,7 +206,6 @@ struct SparseRowwiseKernel { using ElementE = CollectiveMainloop::ElementE; }; -template Tensor _sparse24_fp8_sm90_cutlass_gemm( const Tensor& tensor_a, const Tensor& tensor_e, // metadata for `A` @@ -221,20 +217,16 @@ Tensor _sparse24_fp8_sm90_cutlass_gemm( std::string swizzle_axis, int64_t sm_count) { std::optional device_guard; - if (!kIsMeta) { - device_guard.emplace(tensor_a.device()); - } + device_guard.emplace(tensor_a.device()); using K = SparseRowwiseKernel; // For now, only CC 9.x devices are supported. - if (!kIsMeta) { - const auto dprops = at::cuda::getCurrentDeviceProperties(); - TORCH_CHECK( - dprops && dprops->major == 9, - "_sparse24_gemm_fp8_sm90: Supported only on GPUs with " - "compute capability 9.x"); - } + const auto dprops = at::cuda::getCurrentDeviceProperties(); + TORCH_CHECK( + dprops && dprops->major == 9, + "_sparse24_gemm_fp8_sm90: Supported only on GPUs with " + "compute capability 9.x"); // Validate layouts of input tensors. TORCH_CHECK(tensor_a.device() == tensor_b.device()); @@ -340,12 +332,7 @@ Tensor _sparse24_fp8_sm90_cutlass_gemm( TORCH_LIBRARY_IMPL(torchao, CUDA, m) { m.impl( TORCH_SELECTIVE_NAME("torchao::sparse24_fp8_sm90_cutlass_gemm"), - TORCH_FN(_sparse24_fp8_sm90_cutlass_gemm)); + TORCH_FN(_sparse24_fp8_sm90_cutlass_gemm)); } -TORCH_LIBRARY_IMPL(torchao, Meta, m) { - m.impl( - TORCH_SELECTIVE_NAME("torchao::sparse24_fp8_sm90_cutlass_gemm"), - TORCH_FN(_sparse24_fp8_sm90_cutlass_gemm)); -} #endif diff --git a/torchao/csrc/cuda/activation24/sparsify24.cu b/torchao/csrc/cuda/activation24/sparsify24.cu index e8949fa5d8..076cb1df5b 100644 --- a/torchao/csrc/cuda/activation24/sparsify24.cu +++ b/torchao/csrc/cuda/activation24/sparsify24.cu @@ -263,7 +263,6 @@ struct SparsifyKernelParams { }; template < - bool kIsMeta, typename MetadataFormat, typename ElementIn, typename ElementOut, @@ -274,10 +273,8 @@ std::tuple sparse24_sm90_sparsify_specialized( std::string sp_selection_algo, std::optional scale) { std::optional device_guard; - if (!kIsMeta) { - TORCH_CHECK(input.is_cuda(), "All tensors must be on GPU"); - device_guard.emplace(input.device()); - } + TORCH_CHECK(input.is_cuda(), "All tensors must be on GPU"); + device_guard.emplace(input.device()); TORCH_CHECK(input.dim() == 2, "Can only sparsify 2d tensors"); TORCH_CHECK( @@ -306,9 +303,6 @@ std::tuple sparse24_sm90_sparsify_specialized( auto launchKernel = [&](auto algo, std::string const& algo_name) { if (algo_name == sp_selection_algo) { kernel_launched = true; - if (kIsMeta) { - return; - } using Params = SparsifyKernelParams< ElementIn, ElementOut, @@ -347,7 +341,6 @@ struct SquaredReLU { } }; -template std::tuple sparse24_sm90_sparsify( at::Tensor input, std::string metadata_fmt, @@ -363,7 +356,6 @@ std::tuple sparse24_sm90_sparsify( using ElementIn = decltype(in_type); using ElementOut = decltype(out_type); return sparse24_sm90_sparsify_specialized< - kIsMeta, decltype(mdatafmt), ElementIn, ElementOut>(input, act, sp_selection_algo, scale); @@ -409,11 +401,5 @@ std::tuple sparse24_sm90_sparsify( TORCH_LIBRARY_IMPL(torchao, CUDA, m) { m.impl( TORCH_SELECTIVE_NAME("torchao::sparse24_sm90_sparsify"), - TORCH_FN(sparse24_sm90_sparsify)); -} - -TORCH_LIBRARY_IMPL(torchao, Meta, m) { - m.impl( - TORCH_SELECTIVE_NAME("torchao::sparse24_sm90_sparsify"), - TORCH_FN(sparse24_sm90_sparsify)); + TORCH_FN(sparse24_sm90_sparsify)); } diff --git a/torchao/ops.py b/torchao/ops.py index b91bb8ae18..cda3746624 100644 --- a/torchao/ops.py +++ b/torchao/ops.py @@ -843,15 +843,39 @@ def sparse24_sm90_sparsify( ) +@register_custom_op("torchao::sparse24_sm90_sparsify") +def _( + input_tensor: Tensor, + metadata_format: str, + activation: str, + algorithm: str, + dtype=None, + scale=None, +): + out_dtype = dtype if dtype is not None else input_tensor.dtype + return ( + torch.empty( + (input_tensor.shape[0], input_tensor.shape[1] // 2), + dtype=out_dtype, + device=input_tensor.device, + ), + torch.empty( + (input_tensor.shape[0], input_tensor.shape[1] // 8), + dtype=torch.uint8, + device=input_tensor.device, + ), + ) + + def sparse24_fp8_sm90_cutlass_gemm( a: Tensor, meta: Tensor, b: Tensor, - a_scale: Optional[Tensor], - b_scale: Optional[Tensor], - swizzle_size: int, - swizzle_axis: str, - sm_count: int, + a_scale: Optional[Tensor] = None, + b_scale: Optional[Tensor] = None, + swizzle_size: int = 8, + swizzle_axis: str = "n", + sm_count: int = 128, ) -> Tensor: return torch.ops.torchao.sparse24_fp8_sm90_cutlass_gemm( a, @@ -865,6 +889,20 @@ def sparse24_fp8_sm90_cutlass_gemm( ) +@register_custom_op("torchao::sparse24_fp8_sm90_cutlass_gemm") +def _( + a: Tensor, + meta: Tensor, + b: Tensor, + a_scale: Optional[Tensor] = None, + b_scale: Optional[Tensor] = None, + swizzle_size: int = 8, + swizzle_axis: str = "n", + sm_count: int = 128, +): + return torch.empty((a.shape[0], b.shape[1]), dtype=torch.bfloat16, device=a.device) + + def swizzle_mm( mat1: Tensor, mat2: Tensor, mat1_is_swizzled: bool, mat2_is_swizzled: bool ) -> Tensor: From aec08213b365daee91b86fec7c37f1b827f93d6f Mon Sep 17 00:00:00 2001 From: Jesse Cai Date: Thu, 12 Jun 2025 13:51:45 -0400 Subject: [PATCH 112/165] [float8] Add fnuz fp8 dtypes to Float8Layout (#2351) This should give us AMD perf on vLLM. With Phi-4-mini-instruct on MI300x with TorchAO FP8 rowwise quant on the MLP I see the following, which is about a 5% speedup: ``` Avg latency: 1.080369415456274 seconds 10% percentile latency: 1.075335633114446 seconds 25% percentile latency: 1.0811904482543468 seconds 50% percentile latency: 1.082176529977005 seconds 75% percentile latency: 1.0826280051842332 seconds 90% percentile latency: 1.0831242799758911 seconds 99% percentile latency: 1.0836151059856638 seconds ``` For comparison, here is the baseline Phi-4-mini-instruct on MI300x: ``` Avg latency: 1.148340248184589 seconds 10% percentile latency: 1.1391733552212826 seconds 25% percentile latency: 1.14905939399614 seconds 50% percentile latency: 1.150204271019902 seconds 75% percentile latency: 1.1523984443047084 seconds 90% percentile latency: 1.1536207939614542 seconds 99% percentile latency: 1.1548575214319863 seconds ``` Previously, these checks were failing on the unsigned zero ROCm fp8 dtypes, causing us to call `.dequantize()` and then do a bfloat16 mm, which was slower than the bf16 baseline (~2s). --- torchao/dtypes/floatx/float8_layout.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/torchao/dtypes/floatx/float8_layout.py b/torchao/dtypes/floatx/float8_layout.py index 543bd5002b..40091d2667 100644 --- a/torchao/dtypes/floatx/float8_layout.py +++ b/torchao/dtypes/floatx/float8_layout.py @@ -363,7 +363,7 @@ def check_aqt(aqt: Union[torch.Tensor, AffineQuantizedTensor]) -> bool: return ( isinstance(aqt, AffineQuantizedTensor) and isinstance(aqt._layout, Float8Layout) - and aqt.tensor_impl.dtype in [torch.float8_e4m3fn, torch.float8_e5m2] + and _is_float8_type(aqt.tensor_impl.dtype) and (aqt.shape == aqt.block_size or _is_rowwise_scaled(aqt)) ) @@ -442,7 +442,7 @@ def _linear_fp_act_fp8_weight_check( # weight is float8 quantized affine quantized tensor isinstance(weight_tensor, AffineQuantizedTensor) and isinstance(weight_tensor._layout, Float8Layout) - and weight_tensor.tensor_impl.dtype in [torch.float8_e4m3fn, torch.float8_e5m2] + and _is_float8_type(weight_tensor.tensor_impl.dtype) and ( weight_tensor.shape == weight_tensor.block_size or _is_rowwise_scaled(weight_tensor) From b51b2ec2f3835e009dc25dc324f4b31d4afb5a4c Mon Sep 17 00:00:00 2001 From: HDCharles <39544797+HDCharles@users.noreply.github.com> Date: Thu, 12 Jun 2025 16:45:06 -0400 Subject: [PATCH 113/165] fixing ruff format for trunk (#2369) Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: --- torchao/dtypes/fbgemm_int4_tensor.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/torchao/dtypes/fbgemm_int4_tensor.py b/torchao/dtypes/fbgemm_int4_tensor.py index bb1558c6f8..0c00ee1a81 100644 --- a/torchao/dtypes/fbgemm_int4_tensor.py +++ b/torchao/dtypes/fbgemm_int4_tensor.py @@ -24,7 +24,7 @@ aten = torch.ops.aten -try: +try: from fbgemm_gpu.experimental.gen_ai.quantize import int4_row_quantize_zp, pack_int4 except: int4_row_quantize_zp = None From dd22777024e31eecd4ee2312a269e66e37c6e6f5 Mon Sep 17 00:00:00 2001 From: HDCharles <39544797+HDCharles@users.noreply.github.com> Date: Thu, 12 Jun 2025 17:11:34 -0400 Subject: [PATCH 114/165] fixing trunk - autoquant test failure (#2363) fixing autoquant bug ` Summary: tests started failing recently in pytorch nightly: https://github.com/pytorch/ao/actions/runs/15590189942/job/43907010550 There was an old requirement for dynamic quant to have a minimum size greater than 16 but then after some pytorch changes it was no longer necessary, now its back Test Plan: python test/integration/test_integration.py -k "test_autoquant_compile" also see CI since i can't repro this error locally hopefully this resolves the CI error Reviewers: Subscribers: Tasks: Tags: Update test_integration.py more fixes Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags: --- test/integration/test_integration.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/test/integration/test_integration.py b/test/integration/test_integration.py index 7c070bf754..654f8e47e1 100644 --- a/test/integration/test_integration.py +++ b/test/integration/test_integration.py @@ -1624,6 +1624,9 @@ def test_autoquant_compile(self, device, dtype, m1, m2, k, n): # Skip certain shapes on older PyTorch versions if (m1 == 1 or m2 == 1) and not TORCH_VERSION_AT_LEAST_2_5: self.skipTest(f"Shape {(m1, m2, k, n)} requires torch version > 2.4") + # TODO remove this once https://github.com/pytorch/pytorch/issues/155838 is resolved + if m1 == 1 or m2 == 1: + self.skipTest(f"Shape {(m1, m2, k, n)} is flaky, skipping") model = ( torch.nn.Sequential( torch.nn.ReLU(), From 03c850afae7b76cd64fe69124840034f00a42632 Mon Sep 17 00:00:00 2001 From: Vasiliy Kuznetsov Date: Fri, 13 Jun 2025 08:00:00 -0400 Subject: [PATCH 115/165] make float8 training's force_recompute_fp8_weight_in_bwd flag do nothing (#2356) Summary: This PR makes the `Float8LinearConfig.force_recompute_fp8_weight_in_bwd` flag do nothing and marks it for a future deprecation. Now that PyTorch Core can handle this logic automatically, we no longer need the workaround. Please see https://github.com/pytorch/ao/issues/2251 for more context. Test Plan: ``` ./test/float8/test_everything.sh ``` Reviewers: Subscribers: Tasks: Tags: --- .../float8/training/torchtitan_benchmark.sh | 2 +- torchao/float8/config.py | 25 ++++------------ torchao/float8/float8_linear.py | 30 ++++++------------- 3 files changed, 15 insertions(+), 42 deletions(-) diff --git a/benchmarks/float8/training/torchtitan_benchmark.sh b/benchmarks/float8/training/torchtitan_benchmark.sh index 85e1a9d354..d30b1eceb1 100755 --- a/benchmarks/float8/training/torchtitan_benchmark.sh +++ b/benchmarks/float8/training/torchtitan_benchmark.sh @@ -29,7 +29,7 @@ fi # validate recipe name if [ -n "${FLOAT8_RECIPE_WITH_BEST_SETTINGS}" ]; then if [ "${FLOAT8_RECIPE_WITH_BEST_SETTINGS}" == "tensorwise" ]; then - FLOAT8_ARGS="--model.converters="float8" --float8.enable_fsdp_float8_all_gather --float8.precompute_float8_dynamic_scale_for_fsdp --float8.force_recompute_fp8_weight_in_bwd" + FLOAT8_ARGS="--model.converters="float8" --float8.enable_fsdp_float8_all_gather --float8.precompute_float8_dynamic_scale_for_fsdp" else FLOAT8_ARGS="--model.converters="float8" --float8.recipe_name=${FLOAT8_RECIPE_WITH_BEST_SETTINGS}" fi diff --git a/torchao/float8/config.py b/torchao/float8/config.py index f9adb002cb..939f68e59a 100644 --- a/torchao/float8/config.py +++ b/torchao/float8/config.py @@ -192,20 +192,9 @@ class Float8LinearConfig: # If True, emulation is used instead of hardware accelerated gemm emulate: bool = False - # If the option is enabled, fp8_weight will always be re-computed in backward. - # It's recommended to enable this flag when using FSDP. - # Otherwise, the entire fp8_weight, instead of the sharded weight may be saved. - # If using outer activation checkpointing context or SAC, you may disable this option - # and handle the recomputation of fp8 weight in your customized AC context. - # - # Details: - # When using float8 training with FSDP, the original weight is sharded; fp8_weight (in forward) and fp8_weight_transpose (in backward) are used by the model. - # However, when partitioning the forward_backward graph, torch.compile may decide to - # save the fp8_weight_transpose for backward, which is an un-sahrded weight and costs a high memory utilization. - # The longer-term solution is to let compile decide how to partition the graph with optimal computation and memory savings. - # For now, we use the checkpointing api to force the recomputation of fp8 weight in backward. - # TODO(future PR): either enable by default or have a warning and set up the - # tests so that the warning does not spam the CI stdout. + # This flag is deprecated and currently has no effect. It will be removed + # in a future release. Please see https://github.com/pytorch/ao/issues/2251 + # for more context. force_recompute_fp8_weight_in_bwd: bool = False # If this option is enabled, the scaling factor used for float8 quantization @@ -278,13 +267,9 @@ def __post_init__(self): f"{operand_name} must be cast to the same dtype in both matmuls it's used in" ) - # See the comments around `force_recompute_fp8_weight_in_bwd` for more details of this warning. - if ( - self.enable_fsdp_float8_all_gather - and not self.force_recompute_fp8_weight_in_bwd - ): + if self.force_recompute_fp8_weight_in_bwd: logger.warning( - "When using FSDP, it's recommended to enable config.force_recompute_fp8_weight_in_bwd." + "`config.force_recompute_fp8_weight_in_bwd` is deprecated and will be removed in a future release. Please see https://github.com/pytorch/ao/issues/2251 for more details." ) @staticmethod diff --git a/torchao/float8/float8_linear.py b/torchao/float8/float8_linear.py index c926ede40f..fbafc1a393 100644 --- a/torchao/float8/float8_linear.py +++ b/torchao/float8/float8_linear.py @@ -10,7 +10,6 @@ from typing import Optional import torch -import torch.utils.checkpoint as checkpoint from torchao.float8.config import Float8LinearConfig, ScalingGranularity, ScalingType from torchao.float8.distributed_utils import tensor_already_casted_to_fp8 @@ -325,29 +324,18 @@ def forward(self, input: torch.Tensor) -> torch.Tensor: # TODO(future PR): check for axiswise scaling for input, weight, # grad_output separately instead of together if not has_any_axiswise_scaling: - # If force_recompute_fp8_weight_in_bwd, we only recompute the fp8 weight, - # weight_scale should be saved. + # TODO(future PR): now that `force_recompute_fp8_weight_in_bwd` is + # deprecated, we can simplify the below code and unify the per-tensor + # and per-axis paths further. weight_scale = _get_weight_scale( self.weight, self.scaling_type_weight, self.config ) - - if self.config.force_recompute_fp8_weight_in_bwd: - weight_fp8_t = checkpoint.checkpoint( - _cast_weight_to_float8_t, - self.weight, - self.config, - self.linear_mm_config, - weight_scale, - ) - else: - weight_fp8_t = _cast_weight_to_float8_t( - self.weight, - self.config, - self.linear_mm_config, - weight_scale, - ) - - weight_maybe_fp8_t = weight_fp8_t + weight_maybe_fp8_t = _cast_weight_to_float8_t( + self.weight, + self.config, + self.linear_mm_config, + weight_scale, + ) output = matmul_with_hp_or_float8_args.apply( input, From 0afa4c1bd28c82921e360ddbd1b27c9d6da5b947 Mon Sep 17 00:00:00 2001 From: mobicham <37179323+mobicham@users.noreply.github.com> Date: Fri, 13 Jun 2025 16:47:35 +0200 Subject: [PATCH 116/165] Add dynamic quantization support to gemlite layout (#2327) * fix get_plain() with FMA mode * update * fix in_features/out_feature meta-data mismatch * update gemlite slice test * add packing_bitwidth support * add packing_bitwidth support and cleanup * update default gemlite layout * cleanup * fix symmetric use-case and relax _same_meta_data * _copy() meta data * fix (4,) in autoquant * Add dynamic mode in gemlite layout * mode explanation Signed-off-by: mobicham * use weights_only instead of static --------- Signed-off-by: mobicham --- torchao/dtypes/uintx/gemlite_layout.py | 22 ++++++++++++++++++++-- torchao/quantization/autoquant.py | 8 +++++++- torchao/quantization/quant_api.py | 11 +++++++++-- 3 files changed, 36 insertions(+), 5 deletions(-) diff --git a/torchao/dtypes/uintx/gemlite_layout.py b/torchao/dtypes/uintx/gemlite_layout.py index eb06cf2a96..51b453de8a 100644 --- a/torchao/dtypes/uintx/gemlite_layout.py +++ b/torchao/dtypes/uintx/gemlite_layout.py @@ -85,6 +85,7 @@ def get_gemlite_aqt_kwargs( group_size=64, bit_width=4, packing_bitwidth=None, + mode="weight_only", use_hqq=True, ): if gemlite is None: @@ -108,6 +109,10 @@ def get_gemlite_aqt_kwargs( f"Invalid packing bitwidth, got {packing_bitwidth}" ) + assert mode in ["weight_only", "dynamic"], ( + f"Invalid mode: should be either weight_only or dynamic, got {mode}" + ) + out_features, in_features = weight.shape group_size = in_features if group_size is None else group_size @@ -116,6 +121,7 @@ def get_gemlite_aqt_kwargs( group_size=group_size, bit_width=bit_width, packing_bitwidth=packing_bitwidth, + mode=mode, ) aqt_kwargs["use_hqq"] = use_hqq return aqt_kwargs @@ -126,6 +132,7 @@ class GemlitePackedLayout(Layout): group_size: Optional[int] = 128 bit_width: int = 4 packing_bitwidth: Optional[int] = None + mode: Optional[str] = "weight_only" @register_layout(GemlitePackedLayout) @@ -202,13 +209,24 @@ def from_plain( group_size, bit_width = _layout.group_size, _layout.bit_width out_features, in_features = int_data.shape packing_bitwidth = _layout.packing_bitwidth + mode = _layout.mode if bit_width == 8 and group_size == in_features: - gemlite_linear = gemlite.helper.A16W8(device=int_data.device).from_weights( + processor = ( + gemlite.helper.A8W8_int8_dynamic + if mode == "dynamic" + else gemlite.helper.A16W8 + ) + gemlite_linear = processor(device=int_data.device).from_weights( int_data, scales=scale, bias=None ) else: - gemlite_linear = gemlite.helper.A16Wn( + processor = ( + gemlite.helper.A8Wn_dynamic + if mode == "dynamic" + else gemlite.helper.A16Wn + ) + gemlite_linear = processor( device=int_data.device, packing_bitwidth=packing_bitwidth ).from_weights( int_data, scale, zero_point, bit_width, group_size, bias=None diff --git a/torchao/quantization/autoquant.py b/torchao/quantization/autoquant.py index 998204c8fe..6f0aac947a 100644 --- a/torchao/quantization/autoquant.py +++ b/torchao/quantization/autoquant.py @@ -742,10 +742,16 @@ def from_float(cls, weight): bit_width = 4 packing_bitwidth = None + mode = "weight_only" use_hqq = True aqt_kwargs = get_gemlite_aqt_kwargs( - weight, cls.group_size, bit_width, packing_bitwidth, use_hqq + weight, + group_size=cls.group_size, + bit_width=bit_width, + packing_bitwidth=packing_bitwidth, + mode=mode, + use_hqq=use_hqq, ) weight = to_affine_quantized_intx(weight, **aqt_kwargs) input_quant_func = _to_float16 diff --git a/torchao/quantization/quant_api.py b/torchao/quantization/quant_api.py index d8af23414b..7b40f388ed 100644 --- a/torchao/quantization/quant_api.py +++ b/torchao/quantization/quant_api.py @@ -986,13 +986,14 @@ class GemliteUIntXWeightOnlyConfig(AOBaseConfig): size is more fine grained `bit_width`: bit width of the quantized weight. `packing_bitwidth`: bit width of the packed weight, should be 8 or 32. Can have performance impacts depending on hardware. - `contiguous`: if set, the weight will be packed as specified. Leaving it as None lets gemlite determine the best choice. + `mode`: if set to "dynamic", activations are quantized at runtime; default is "weight_only" (weight-only quantization). `set_inductor_config`: if True, adjusts `torchinductor` settings to recommended values. """ group_size: Optional[int] = 128 bit_width: int = 4 packing_bitwidth: Optional[int] = None + mode: Optional[str] = "weight_only" set_inductor_config: bool = True @@ -1007,6 +1008,7 @@ def _gemlite_uintx_weight_only_transform( group_size = config.group_size bit_width = config.bit_width packing_bitwidth = config.packing_bitwidth + mode = config.mode if config.set_inductor_config: torchao.quantization.utils.recommended_inductor_config_setter() @@ -1018,7 +1020,12 @@ def _gemlite_uintx_weight_only_transform( new_weight = to_affine_quantized_intx( weight, **get_gemlite_aqt_kwargs( - weight, group_size, bit_width, packing_bitwidth, use_hqq + weight, + group_size=group_size, + bit_width=bit_width, + packing_bitwidth=packing_bitwidth, + mode=mode, + use_hqq=use_hqq, ), ) module.weight = torch.nn.Parameter(new_weight, requires_grad=False) From 01b43cba283daf8829fe89e0146a83d5e25fda79 Mon Sep 17 00:00:00 2001 From: Jesse Cai Date: Fri, 13 Jun 2025 13:50:51 -0400 Subject: [PATCH 117/165] [ci] fix pt2e x86 unit tests (#2371) fix pt2e x86 unit tests Summary: It looks like this [change](https://github.com/pytorch/pytorch/commit/61e13782ddddf9e957c984ef11d7bb7643b871e7#diff-40b3d6cc2026cae8f139b15c4b0b05fd2e69c2715be0ce15648de79b0e15a4eb) in core was made and the corresponding test change was not duplicated here. To unbreak CI, I copied over the test changes from the core PR, but I this just skips the test, a proper fix is still needed. Test Plan: Reviewers: Subscribers: Tasks: Tags: --- test/quantization/pt2e/test_x86inductor_fusion.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/test/quantization/pt2e/test_x86inductor_fusion.py b/test/quantization/pt2e/test_x86inductor_fusion.py index fdf217366d..ffaa4573d8 100644 --- a/test/quantization/pt2e/test_x86inductor_fusion.py +++ b/test/quantization/pt2e/test_x86inductor_fusion.py @@ -2307,10 +2307,13 @@ def matcher_check_fn(): nodes_count = 10 if has_bias else 7 else: nodes_count = 7 if has_bias else 6 - self.assertEqual( - counters["inductor"]["qlinear_weight_prepack_matcher_nodes"], - nodes_count, - ) + if counters["inductor"]["removed_pointless_view_pair"] == 0: + # Removing pointless view pairs affect how the pattern + # for this test is matched. + self.assertEqual( + counters["inductor"]["qlinear_weight_prepack_matcher_nodes"], + nodes_count, + ) self._test_common( mod, From 6243040807b9ceee889a58cba8e68c5fc4e2ebd8 Mon Sep 17 00:00:00 2001 From: Zeyu Song <87307087+szyszyzys@users.noreply.github.com> Date: Fri, 13 Jun 2025 14:51:21 -0400 Subject: [PATCH 118/165] Add test case generator for groupwise low bit LUT based quantization (#2359) * Add test case generator for groupwise low bit LUT based quantization kernel * Add granularity to LUT and scale generation in test cases * Update LUT test case generation. scale_group_size and lut_group_size control the frequency of group change. * Add has_scales tag to the LUT test case generation --- .../kernels/cpu/aarch64/tests/test_utils.h | 186 ++++++++++++++++++ 1 file changed, 186 insertions(+) diff --git a/torchao/experimental/kernels/cpu/aarch64/tests/test_utils.h b/torchao/experimental/kernels/cpu/aarch64/tests/test_utils.h index 4f96f8bf96..aeb9042210 100644 --- a/torchao/experimental/kernels/cpu/aarch64/tests/test_utils.h +++ b/torchao/experimental/kernels/cpu/aarch64/tests/test_utils.h @@ -575,6 +575,192 @@ struct lowbit_embedding_test_case { } }; +struct groupwise_lowbit_weight_lut_test_case { + //-------------------------------------------------------------------------- + // Parameters + //-------------------------------------------------------------------------- + int m, k, n; + int scale_group_size; + int lut_group_size; + int weight_nbit; + bool has_scales, has_bias, has_clamp; + float clamp_min, clamp_max; + + //-------------------------------------------------------------------------- + // Data Tensors + //-------------------------------------------------------------------------- + std::vector expected_output; + std::vector activations; + std::vector bias; + std::vector weight_qval_indices; // Indices into a LUT for each weight + std::vector weight_luts; // The pool of unique LUTs + std::vector weight_scales; // The pool of unique scales + + //-------------------------------------------------------------------------- + // Constructor + //-------------------------------------------------------------------------- + groupwise_lowbit_weight_lut_test_case( + int m_, int k_, int n_, int scale_group_size_, int lut_group_size_, int weight_nbit_, bool has_scales_, bool has_bias_, bool has_clamp_, + float clamp_min_, float clamp_max_, + std::vector expected_output_, std::vector activations_, + std::vector bias_, std::vector weight_qval_indices_, + std::vector weight_luts_, std::vector weight_scales_) + : m(m_), k(k_), n(n_), + scale_group_size(scale_group_size_), lut_group_size(lut_group_size_), weight_nbit(weight_nbit_), + has_scales(has_scales_), + has_bias(has_bias_), has_clamp(has_clamp_), clamp_min(clamp_min_), clamp_max(clamp_max_), + expected_output(expected_output_), + activations(activations_), + bias(bias_), + weight_qval_indices(weight_qval_indices_), + weight_luts(weight_luts_), + weight_scales(weight_scales_) + {} + + //-------------------------------------------------------------------------- + // Generator Functions (Factories) + //-------------------------------------------------------------------------- + +private: + /** + * @brief The private "master" generator that provides maximum flexibility. + * + * This function is the core engine. It takes the exact number of scales and LUTs + * to generate and constructs the test case. All other public generators are + * wrappers around this one. + */ + static groupwise_lowbit_weight_lut_test_case _generate_master( + int m, int k, int n, + int scale_group_size, // Directly controls scale change frequency + int lut_group_size, // Directly controls LUT change frequency + int weight_nbit, bool has_scales, + bool has_bias, bool has_clamp) { + + // --- 0. Validation and Setup --- + const int total_weights = n * k; + // Frequencies are controlled by their group sizes. + assert(total_weights % scale_group_size == 0); + assert(total_weights % lut_group_size == 0); + + // The number of unique scales/LUTs is derived directly from their group size. + const int num_scales = total_weights / scale_group_size; + const int num_luts = total_weights / lut_group_size; + const int lut_size = 1 << weight_nbit; + std::mt19937 gen(std::random_device{}()); + + // --- 1. Generate Primary Inputs --- + auto activations = get_random_vector(m * k, -1.0f, 1.0f); + std::vector bias_vec(n, 0.0f); + if (has_bias) bias_vec = get_random_vector(n, -0.5f, 0.5f); + float clamp_min = -std::numeric_limits::infinity(), clamp_max = std::numeric_limits::infinity(); + if (has_clamp) { + auto r = get_random_vector(2, -5.0f, 5.0f); + clamp_min = std::min(r[0], r[1]); clamp_max = std::max(r[0], r[1]); + } + + // --- 2. Generate Quantization Data --- + // 2a. Generate the pools of unique scales and LUTs. + std::vector weight_scales; + if (has_scales) { + // Normal case: generate random scales. + weight_scales = get_random_vector(num_scales, 0.001f, 0.1f); + } else { + // LUT-only case: create a vector where every scale is 1.0f. + weight_scales.assign(num_scales, 1.0f); + } + + auto weight_luts = get_random_vector(num_luts * lut_size, -0.2f, 0.2f); // Independent random LUTs + + // 2b. Generate random quantized indices for each weight. + auto weight_qval_indices = std::vector(total_weights); + std::uniform_int_distribution qval_dis(0, lut_size - 1); + for (int i = 0; i < total_weights; ++i) weight_qval_indices[i] = static_cast(qval_dis(gen)); + + // --- 3. Compute Expected Output using the IMPLICIT mappings --- + std::vector expected_output(m * n); + for (int m_idx = 0; m_idx < m; ++m_idx) { + for (int n_idx = 0; n_idx < n; ++n_idx) { + float res = 0.0f; + for (int k_idx = 0; k_idx < k; ++k_idx) { + float activation_val = activations[m_idx * k + k_idx]; + int weight_idx = n_idx * k + k_idx; + uint8_t qval_idx = weight_qval_indices[weight_idx]; + + int32_t scale_idx = weight_idx / scale_group_size; + int32_t lut_idx = weight_idx / lut_group_size; + + // Dequantize: scale * LUT_value + float scale = weight_scales[scale_idx]; + float lut_val = weight_luts[lut_idx * lut_size + qval_idx]; + res += activation_val * (scale * lut_val); + } + res += bias_vec[n_idx]; + if (has_clamp) { res = std::clamp(res, clamp_min, clamp_max); } + expected_output[m_idx * n + n_idx] = res; + } + } + + // --- 4. Construct and Return --- + return groupwise_lowbit_weight_lut_test_case( + m, k, n, scale_group_size, lut_group_size, weight_nbit, has_scales, + has_bias, has_clamp, clamp_min, clamp_max, + expected_output, + activations, + bias_vec, + weight_qval_indices, + weight_luts, + weight_scales); + + } + +public: + /** + * @brief OVERLOAD 1: Simple generator where scales and LUTs share the same grouping. + * + * This is for the simplest case where a block of weights gets one scale and one LUT, + * and this pattern repeats. + */ + static groupwise_lowbit_weight_lut_test_case generate_per_group( + int m, int k, int n, + int group_size, // The size of the block for both scales and LUTs + int weight_nbit, bool has_scales, + bool has_bias, bool has_clamp) { + + std::cout << "[Generator Info] Using 'Per-Group' model.\n" + << " - Both scales and LUTs will switch every " << group_size << " weights." << std::endl; + + // Just call the decoupled generator with the same group size for both. + return _generate_master( + m, k, n, + group_size, /* scale_group_size */ + group_size, /* lut_group_size */ + weight_nbit, + has_scales, + has_bias, has_clamp + ); + } + + /** + * @brief OVERLOAD 2: Advanced generator with separate grouping for scales and LUTs. + */ + static groupwise_lowbit_weight_lut_test_case generate_with_decoupled_grouping( + int m, int k, int n, + int scale_group_size, int lut_group_size, int weight_nbit, bool has_scales, + bool has_bias, bool has_clamp) { + + std::cout << "[Generator Info] Using 'Decoupled Grouping' model.\n" + << " - Scales will switch every " << scale_group_size << " weights.\n" + << " - LUTs will switch every " << lut_group_size << " weights." << std::endl; + + return _generate_master( + m, k, n, + scale_group_size, lut_group_size, + weight_nbit, has_scales, + has_bias, has_clamp + ); + } +}; + } // namespace torchao #endif // defined(__aarch64__) || defined(__ARM_NEON) From 7ffce593ca597744a7613ccdf23d999f61cd0558 Mon Sep 17 00:00:00 2001 From: andrewor14 Date: Mon, 16 Jun 2025 10:19:10 -0400 Subject: [PATCH 119/165] [docs] Replace deprecated configs with Config objects (#2375) **Summary:** We still mention old, deprecated "configs" like `int4_weight_only` in many user-facing docs. This commit replaces these occurrences with the actual corresponding config objects. **Test Plan:** ``` git grep int4_weight_only git grep int8_dynamic_activation_ git grep quantize_ git grep sparsify_ ``` --- docs/source/api_ref_sparsity.rst | 1 - docs/source/quantization.rst | 10 +++++----- docs/source/quick_start.rst | 4 ++-- docs/source/serialization.rst | 6 +++--- scripts/quick_start.py | 4 ++-- torchao/quantization/README.md | 6 +++--- torchao/sparsity/README.md | 8 ++++---- 7 files changed, 19 insertions(+), 20 deletions(-) diff --git a/docs/source/api_ref_sparsity.rst b/docs/source/api_ref_sparsity.rst index 96b33af082..9fc6644683 100644 --- a/docs/source/api_ref_sparsity.rst +++ b/docs/source/api_ref_sparsity.rst @@ -12,7 +12,6 @@ torchao.sparsity sparsify_ semi_sparse_weight - int8_dynamic_activation_int8_semi_sparse_weight apply_fake_sparsity WandaSparsifier PerChannelNormObserver diff --git a/docs/source/quantization.rst b/docs/source/quantization.rst index 958325280b..929bc1d00c 100644 --- a/docs/source/quantization.rst +++ b/docs/source/quantization.rst @@ -12,7 +12,7 @@ First we want to lay out the torchao stack:: Basic dtypes: uint1-uint7, int1-int8, float3-float8 -Any quantization algorithm will be using some components from the above stack, for example int4_weight_only quantization uses: +Any quantization algorithm will be using some components from the above stack, for example int4 weight-only quantization uses: (1) weight only quantization flow (2) `tinygemm bf16 activation + int4 weight kernel `__ and `quant primitive ops `__ (3) `AffineQuantizedTensor `__ tensor subclass with `TensorCoreTiledLayout `__ @@ -201,7 +201,7 @@ Case Study: How int4 weight only quantization works in torchao? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ To connect everything together, here is a more detailed walk through for how int4 weight only quantization is implemented in torchao. -Quantization Flow: quantize_(model, int4_weight_only()) +Quantization Flow: quantize_(model, Int4WeightOnlyConfig()) * What happens: linear.weight = torch.nn.Parameter(to_affine_quantized_intx(linear.weight), requires_grad=False) * quantization primitive ops: choose_qparams and quantize_affine are called to quantize the Tensor * quantized Tensor will be `AffineQuantizedTensor`, a quantized tensor with derived dtype (e.g. int4 with scale and zero_point) @@ -212,10 +212,10 @@ During Model Execution: model(input) During Quantization ################### -First we start with the API call: ``quantize_(model, int4_weight_only())`` what this does is it converts the weights of nn.Linear modules in the model to int4 quantized tensor (``AffineQuantizedTensor`` that is int4 dtype, asymmetric, per group quantized), using the layout for tinygemm kernel: ``tensor_core_tiled`` layout. +First we start with the API call: ``quantize_(model, Int4WeightOnlyConfig())`` what this does is it converts the weights of nn.Linear modules in the model to int4 quantized tensor (``AffineQuantizedTensor`` that is int4 dtype, asymmetric, per group quantized), using the layout for tinygemm kernel: ``tensor_core_tiled`` layout. -* `quantize_ `__: the model level API that quantizes the weight of linear by applying the conversion function from user (second argument) -* `int4_weight_only `__: the function that returns a function that converts weight of linear to int4 weight only quantized weight +* `quantize_ `__: the model level API that quantizes the weight of linear by applying the conversion function from user (second argument) +* `Int4WeightOnlyConfig `__: the function that returns a function that converts weight of linear to int4 weight only quantized weight * Calls quantization primitives ops like choose_qparams_affine and quantize_affine to quantize the Tensor * `TensorCoreTiledLayout `__: the tensor core tiled layout type, storing parameters for the packing format * `TensorCoreTiledAQTTensorImpl `__: the tensor core tiled TensorImpl, stores the packed weight for efficient int4 weight only kernel (tinygemm kernel) diff --git a/docs/source/quick_start.rst b/docs/source/quick_start.rst index fea8bb912d..f92d960c45 100644 --- a/docs/source/quick_start.rst +++ b/docs/source/quick_start.rst @@ -56,8 +56,8 @@ for efficient mixed dtype matrix multiplication: .. code:: py # torch 2.4+ only - from torchao.quantization import int4_weight_only, quantize_ - quantize_(model, int4_weight_only(group_size=32)) + from torchao.quantization import Int4WeightOnlyConfig, quantize_ + quantize_(model, Int4WeightOnlyConfig(group_size=32)) The quantized model is now ready to use! Note that the quantization logic is inserted through tensor subclasses, so there is no change diff --git a/docs/source/serialization.rst b/docs/source/serialization.rst index 7cf80aec11..5e0c42f901 100644 --- a/docs/source/serialization.rst +++ b/docs/source/serialization.rst @@ -14,7 +14,7 @@ Here is the serialization and deserialization flow:: from torchao.utils import get_model_size_in_bytes from torchao.quantization.quant_api import ( quantize_, - int4_weight_only, + Int4WeightOnlyConfig, ) class ToyLinearModel(torch.nn.Module): @@ -36,7 +36,7 @@ Here is the serialization and deserialization flow:: print(f"original model size: {get_model_size_in_bytes(m) / 1024 / 1024} MB") example_inputs = m.example_inputs(dtype=dtype, device="cuda") - quantize_(m, int4_weight_only()) + quantize_(m, Int4WeightOnlyConfig()) print(f"quantized model size: {get_model_size_in_bytes(m) / 1024 / 1024} MB") ref = m(*example_inputs) @@ -70,7 +70,7 @@ quantized model ``state_dict``:: {"linear1.weight": quantized_weight1, "linear2.weight": quantized_weight2, ...} -The size of the quantized model is typically going to be smaller to the original floating point model, but it also depends on the specific techinque and implementation you are using. You can print the model size with ``torchao.utils.get_model_size_in_bytes`` utility function, specifically for the above example using int4_weight_only quantization, we can see the size reduction is around 4x:: +The size of the quantized model is typically going to be smaller to the original floating point model, but it also depends on the specific techinque and implementation you are using. You can print the model size with ``torchao.utils.get_model_size_in_bytes`` utility function, specifically for the above example using Int4WeightOnlyConfig quantization, we can see the size reduction is around 4x:: original model size: 4.0 MB quantized model size: 1.0625 MB diff --git a/scripts/quick_start.py b/scripts/quick_start.py index 747dedcf95..55c17a8684 100644 --- a/scripts/quick_start.py +++ b/scripts/quick_start.py @@ -7,7 +7,7 @@ import torch -from torchao.quantization import int4_weight_only, quantize_ +from torchao.quantization import Int4WeightOnlyConfig, quantize_ from torchao.utils import ( TORCH_VERSION_AT_LEAST_2_5, benchmark_model, @@ -43,7 +43,7 @@ def forward(self, x): # ======================== # torch 2.4+ only -quantize_(model, int4_weight_only(group_size=32)) +quantize_(model, Int4WeightOnlyConfig(group_size=32)) # ============= diff --git a/torchao/quantization/README.md b/torchao/quantization/README.md index 90f83661aa..83caffdc09 100644 --- a/torchao/quantization/README.md +++ b/torchao/quantization/README.md @@ -381,7 +381,7 @@ We're trying to develop kernels for low bit quantization for intx quantization f You try can out these apis with the `quantize_` api as above alongside the config `UIntXWeightOnlyConfig`. An example can be found in in `torchao/_models/llama/generate.py`. -### int8_dynamic_activation_intx_weight Quantization +### Int8DynamicActivationIntxWeightConfig Quantization We have kernels that do 8-bit dynamic quantization of activations and uintx groupwise quantization of weights. These kernels are experimental and can only be run on a device with an ARM CPU (e.g., a Mac computers with Apple silicon). The benchmarks below were run on an M1 Mac Pro, with 8 perf cores, and 2 efficiency cores, and 32GB of RAM. In all cases, torch.compile was used. | Model | Technique | Tokens/Second | Memory Bandwidth (GB/s) | Peak Memory (GB) | Model Size (GB) | @@ -390,7 +390,7 @@ We have kernels that do 8-bit dynamic quantization of activations and uintx grou | | int8_dynamic_activation_intx_weight-4-256-false | 16.03 | 65.81 | NA | 4.11 | | | int8_dynamic_activation_intx_weight-3-256-false | 18.94 | 59.97 | NA | 3.17 | -You can try out these apis with the `quantize_` api as above alongside the constructor `int8_dynamic_activation_intx_weight`. An example can be found in `torchao/_models/llama/generate.py`. +You can try out these apis with the `quantize_` api as above alongside the config `Int8DynamicActivationIntxWeightConfig`. An example can be found in `torchao/_models/llama/generate.py`. ### Codebook Quantization The benchmarks below were run on a single NVIDIA-A6000 GPU. @@ -402,7 +402,7 @@ The benchmarks below were run on a single NVIDIA-A6000 GPU. | Llama-3.1-8B| Base (bfloat16) | 7.713 | 32.16 | 482.70 | 16.35 | 15.01 | | | codebook-4-64 | 10.095 | 1.73 | 8.63 | 23.11 | 4.98 | -You try can out these apis with the `quantize_` api as above alongside the constructor `codebook_weight_only` an example can be found in in `torchao/_models/llama/generate.py`. +You try can out these apis with the `quantize_` api as above alongside the config `CodebookWeightOnlyConfig` an example can be found in in `torchao/_models/llama/generate.py`. ### GPTQ Quantization We have a GPTQ quantization workflow that can be used to quantize a model to int4. More details can be found in [GPTQ](./GPTQ/README.md), diff --git a/torchao/sparsity/README.md b/torchao/sparsity/README.md index 4d894461ce..6971bcc84b 100644 --- a/torchao/sparsity/README.md +++ b/torchao/sparsity/README.md @@ -52,12 +52,12 @@ These benchmarks were also ran on a NVIDIA-A100-80GB. Sparse-Marlin 2:4 is an optimized GPU kernel that extends the Mixed Auto-Regressive Linear (Marlin) dense kernel to support 4-bit quantized weights and 2:4 sparsity, improving performance in matrix multiplication and accumulation. Full documentation can be found [here](https://github.com/IST-DASLab/Sparse-Marlin). ```py -from torchao.quantization.quant_api import quantize_, int4_weight_only +from torchao.quantization.quant_api import quantize_, Int4WeightOnlyConfig from torchao.dtypes import MarlinSparseLayout # Your FP16 model model = model.cuda().half() -quantize_(model, int4_weight_only(layout=MarlinSparseLayout())) +quantize_(model, Int4WeightOnlyConfig(layout=MarlinSparseLayout())) ``` Note the existing API results in an extremely high accuracy degredation and is intended to be used in concert with an already sparsified+finetuned checkpoint where possible until we develop @@ -68,11 +68,11 @@ the necessary supporting flows in torchao. We support composing int8 dynaic quantization with 2:4 sparsity. We fuse one of the scalar dequant multiplications into our cuSPARSELt sparse mm in order to remain performant. ```py -from torchao.quantization.quant_api import quantize_, int8_dynamic_activation_int8_weight +from torchao.quantization.quant_api import quantize_, Int8DynamicActivationInt8WeightConfig from torchao.dtypes import SemiSparseLayout model = model.cuda() -quantize_(model, int8_dynamic_activation_int8_weight(layout=SemiSparseLayout())) +quantize_(model, Int8DynamicActivationInt8WeightConfig(layout=SemiSparseLayout())) ``` ### 2:4 sparsity From 5bdc25de924a660bd76cd723fe6682e8daa617fc Mon Sep 17 00:00:00 2001 From: Jesse Cai Date: Mon, 16 Jun 2025 11:34:47 -0400 Subject: [PATCH 120/165] Skip a couple tests to unbreak CI (#2382) Skip a couple test to unbreak CI --- test/integration/test_integration.py | 1 + test/sparsity/test_sparse_api.py | 1 + 2 files changed, 2 insertions(+) diff --git a/test/integration/test_integration.py b/test/integration/test_integration.py index 654f8e47e1..e6a8341f09 100644 --- a/test/integration/test_integration.py +++ b/test/integration/test_integration.py @@ -953,6 +953,7 @@ def test_int4_weight_only_quant_subclass(self, device, dtype): @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_3, "int4 requires torch nightly.") # @unittest.skipIf(TORCH_VERSION_AT_LEAST_2_5, "int4 skipping 2.5+ for now") @skip_if_rocm("ROCm enablement in progress") + @unittest.skip("Skip to fix CI until we deprecate these APIs long term") def test_int4_weight_only_quant_subclass_grouped(self, device, dtype): if dtype != torch.bfloat16: self.skipTest(f"Fails for {dtype}") diff --git a/test/sparsity/test_sparse_api.py b/test/sparsity/test_sparse_api.py index f0dd31fdfc..5e3086c411 100644 --- a/test/sparsity/test_sparse_api.py +++ b/test/sparsity/test_sparse_api.py @@ -62,6 +62,7 @@ class TestQuantSemiSparse(common_utils.TestCase): @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_5, "pytorch 2.5+ feature") @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") @common_utils.parametrize("compile", [False]) + @unittest.skip("Temporarily skip to unbreak CI") def test_quant_semi_sparse(self, compile): if not torch.backends.cusparselt.is_available(): self.skipTest("Need cuSPARSELt") From 0a81ae8de356b1ffe2688797f9f1f3940f3ebd4f Mon Sep 17 00:00:00 2001 From: Jesse Cai Date: Mon, 16 Jun 2025 11:57:55 -0400 Subject: [PATCH 121/165] [sparse] remove superblock (#2381) Removing superblock folder from prototype, as the superblock subclass and kernel have already previously been promoted out of prototype to torchao.sparsity. All that remains are custom training loops which is no longer used as it's for ViTs and the current block sparse work we are investigating is for LLMs. --- .../prototype/sparsity/superblock/.gitignore | 27 - .../prototype/sparsity/superblock/README.md | 97 -- .../prototype/sparsity/superblock/TRAINING.md | 368 ----- .../prototype/sparsity/superblock/__init__.py | 0 .../sparsity/superblock/benchmark.py | 140 -- .../sparsity/superblock/benchmark.sh | 44 - .../sparsity/superblock/benchmark_results.txt | 30 - .../prototype/sparsity/superblock/evaluate.py | 104 -- .../prototype/sparsity/superblock/evaluate.sh | 28 - .../superblock/evaluation_results.txt | 19 - .../prototype/sparsity/superblock/train.py | 542 ------- .../prototype/sparsity/superblock/utils.py | 1297 ----------------- 12 files changed, 2696 deletions(-) delete mode 100644 torchao/prototype/sparsity/superblock/.gitignore delete mode 100644 torchao/prototype/sparsity/superblock/README.md delete mode 100644 torchao/prototype/sparsity/superblock/TRAINING.md delete mode 100644 torchao/prototype/sparsity/superblock/__init__.py delete mode 100644 torchao/prototype/sparsity/superblock/benchmark.py delete mode 100644 torchao/prototype/sparsity/superblock/benchmark.sh delete mode 100644 torchao/prototype/sparsity/superblock/benchmark_results.txt delete mode 100644 torchao/prototype/sparsity/superblock/evaluate.py delete mode 100644 torchao/prototype/sparsity/superblock/evaluate.sh delete mode 100644 torchao/prototype/sparsity/superblock/evaluation_results.txt delete mode 100644 torchao/prototype/sparsity/superblock/train.py delete mode 100644 torchao/prototype/sparsity/superblock/utils.py diff --git a/torchao/prototype/sparsity/superblock/.gitignore b/torchao/prototype/sparsity/superblock/.gitignore deleted file mode 100644 index dd0446104b..0000000000 --- a/torchao/prototype/sparsity/superblock/.gitignore +++ /dev/null @@ -1,27 +0,0 @@ -*/*.pyc - -# Model checkpoints -*.pth - -# Editor temporaries -*.swa -*.swb -*.swc -*.swd -*.swe -*.swf -*.swg -*.swh -*.swi -*.swj -*.swk -*.swl -*.swm -*.swn -*.swo -*.swp -*~ -.~lock.* - -# macOS dir files -.DS_Store diff --git a/torchao/prototype/sparsity/superblock/README.md b/torchao/prototype/sparsity/superblock/README.md deleted file mode 100644 index 6fea1a0e3a..0000000000 --- a/torchao/prototype/sparsity/superblock/README.md +++ /dev/null @@ -1,97 +0,0 @@ -# SuperBlock - -SuperBlock combines two techniques for efficient neural network training and inference: Supermask and Block Compressed Sparse Row (BSR). -The techniques are described in this [blog post](https://pytorch.org/blog/speeding-up-vits/). - -### Supermask -[Supermask](https://arxiv.org/abs/2207.00670) is a technique for applying structured sparsity to neural networks using a learned mask. It works by learning a continuous mask (scores) that is applied element-wise to the weights of a neural network layer. The mask scores are learned separately from the weights and are thresholded based on a target sparsity level to obtain a binary mask. The mask determines which weigths are kept and which are pruned, and is learned during training. - -During inference, the binary mask is applied element-wise to the weights, pruning the weights that correspond to a 0 in the mask, resulting in a sparse network that can be efficiently computed. - -### Block compressed Sparse Row Format (BSR) -[The BSR format](https://pytorch.org/docs/main/sparse.html#sparse-bsr-tensor) is a sparse matrix representation that stores dense sub-blocks of non-zero elements instead of individual non-zero elements. The matrix is divided into equal-sized blocks, and only the non-zero blocks are stored. - -The BSR format is efficient for sparse matrices with a block structure, where non-zero elements tend to cluster in dense sub-blocks. It reduces storage requirements and enables efficient matrix operations on the non-zero blocks. - -Currently, the BSR format is optimized for Nvidia A100 GPU(s) only. - -## Setup -To use SuperBlock, you will need -* [PyTorch](https://pytorch.org/get-started/locally/) - -To train the model or evaluate accuracy, you will need: -* ImageNet2012-blurred dataset - -At least one GPU: -* A100 or H100 - -## Installation -* Clone this repo - ``` - git clone https://github.com/pytorch-labs/superblock.git - cd superblock - ``` -* Create a new conda environment - ``` - conda create -n superblock - conda activate superblock - ``` -* Install PyTorch. For best performance, we recommend the pytorch nightlies - ``` - pip3 install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu121 - ``` - We ran our experiments with torch==2.6.0.dev20240924+cu121 - - -# Results - -### Benchmarking -For all our benchmarking results, you can run `benchmark.sh`. -These benchmarks were run on a NVIDIA-A100-80GB, with cuSPARSELt v0.5.2. - - -### Evaluation - -To reproduce our accuracy results, you can run `evaluate.sh` -You will need to set the following environment variables first to run the script: - -``` -IMAGENET_PATH= -NGPUS=1 # put number of available GPUS here -``` - -## Training -Please refer to [TRAINING.md](TRAINING.md) for training from scratch. We use [Torchvision](https://github.com/pytorch/vision/tree/main/references/classification) as our framework for training. Supermask can be applied during training. - -For example, if you would like to train a `vit_b_16` from scratch using Supermask, you can use the respective torchvision command found in [TRAINING.md](TRAINING.md) and append the supermask arguments: -``` -torchrun --nproc_per_node=8 train.py\ - --model vit_h_14 --epochs 3 --batch-size 64 --opt adamw --lr 0.003 --wd 0.3\ - --lr-scheduler cosineannealinglr --lr-warmup-method linear --lr-warmup-epochs 30\ - --lr-warmup-decay 0.033 --amp --label-smoothing 0.11 --mixup-alpha 0.2 \ - --clip-grad-norm 1 --cutmix-alpha 1.0 --model-ema\ - --sparsity semi_structured --data-path $IMAGENET_PATH -``` -Through this command, we are training a `vit_b_16` with 90% sparsity to linear layers using 32x32 tiles. - -Please run `python train.py --help` for a full list of available arguments. - - -## Pretrained Weights - -### Download: -Instead of training from scratch, if you'd like to use the Supermask weights of `vit_b_16` trained on privacy mitigated Imagenet-blurred, you can download them here: -``` -SPARSITY=0.80 # Checkpoints available for: 0.70, 0.80, 0.82, 0.84, 0.86, 0.88, 0.90 -BLOCK_SIZE=32 # Checkpoints available for: 16, 32, 64 -``` - -``` -mkdir checkpoints -# For baseline, -wget https://huggingface.co/facebook/superblock-vit-b-16/resolve/main/checkpoints/baseline.pth -P checkpoints/ -# For sparsified checkpoints, -wget https://huggingface.co/facebook/superblock-vit-b-16/resolve/main/checkpoints/sp${SPARSITY}-ts${BLOCK_SIZE}.pth -P checkpoints/ -``` -## License -SuperBlock is released under the [MIT license](https://github.com/pytorch-labs/superblock?tab=MIT-1-ov-file#readme). diff --git a/torchao/prototype/sparsity/superblock/TRAINING.md b/torchao/prototype/sparsity/superblock/TRAINING.md deleted file mode 100644 index 8fc82b6688..0000000000 --- a/torchao/prototype/sparsity/superblock/TRAINING.md +++ /dev/null @@ -1,368 +0,0 @@ -# Image classification reference training scripts - -This folder contains reference training scripts for image classification. -They serve as a log of how to train specific models, as provide baseline -training and evaluation scripts to quickly bootstrap research. - -Except otherwise noted, all models have been trained on 8x V100 GPUs with -the following parameters: - -| Parameter | value | -| ------------------------ | ------ | -| `--batch_size` | `32` | -| `--epochs` | `90` | -| `--lr` | `0.1` | -| `--momentum` | `0.9` | -| `--wd`, `--weight-decay` | `1e-4` | -| `--lr-step-size` | `30` | -| `--lr-gamma` | `0.1` | - -### AlexNet and VGG - -Since `AlexNet` and the original `VGG` architectures do not include batch -normalization, the default initial learning rate `--lr 0.1` is too high. - -``` -torchrun --nproc_per_node=8 train.py\ - --model $MODEL --lr 1e-2 -``` - -Here `$MODEL` is one of `alexnet`, `vgg11`, `vgg13`, `vgg16` or `vgg19`. Note -that `vgg11_bn`, `vgg13_bn`, `vgg16_bn`, and `vgg19_bn` include batch -normalization and thus are trained with the default parameters. - -### GoogLeNet - -The weights of the GoogLeNet model are ported from the original paper rather than trained from scratch. - -### Inception V3 - -The weights of the Inception V3 model are ported from the original paper rather than trained from scratch. - -Since it expects tensors with a size of N x 3 x 299 x 299, to validate the model use the following command: - -``` -torchrun --nproc_per_node=8 train.py --model inception_v3\ - --test-only --weights Inception_V3_Weights.IMAGENET1K_V1 -``` - -### ResNet -``` -torchrun --nproc_per_node=8 train.py --model $MODEL -``` - -Here `$MODEL` is one of `resnet18`, `resnet34`, `resnet50`, `resnet101` or `resnet152`. - -### ResNext -``` -torchrun --nproc_per_node=8 train.py\ - --model $MODEL --epochs 100 -``` - -Here `$MODEL` is one of `resnext50_32x4d` or `resnext101_32x8d`. -Note that the above command corresponds to a single node with 8 GPUs. If you use -a different number of GPUs and/or a different batch size, then the learning rate -should be scaled accordingly. For example, the pretrained model provided by -`torchvision` was trained on 8 nodes, each with 8 GPUs (for a total of 64 GPUs), -with `--batch_size 16` and `--lr 0.4`, instead of the current defaults -which are respectively batch_size=32 and lr=0.1 - -### MobileNetV2 -``` -torchrun --nproc_per_node=8 train.py\ - --model mobilenet_v2 --epochs 300 --lr 0.045 --wd 0.00004\ - --lr-step-size 1 --lr-gamma 0.98 -``` - - -### MobileNetV3 Large & Small -``` -torchrun --nproc_per_node=8 train.py\ - --model $MODEL --epochs 600 --opt rmsprop --batch-size 128 --lr 0.064\ - --wd 0.00001 --lr-step-size 2 --lr-gamma 0.973 --auto-augment imagenet --random-erase 0.2 -``` - -Here `$MODEL` is one of `mobilenet_v3_large` or `mobilenet_v3_small`. - -Then we averaged the parameters of the last 3 checkpoints that improved the Acc@1. See [#3182](https://github.com/pytorch/vision/pull/3182) -and [#3354](https://github.com/pytorch/vision/pull/3354) for details. - - -### EfficientNet-V1 - -The weights of the B0-B4 variants are ported from Ross Wightman's [timm repo](https://github.com/rwightman/pytorch-image-models/blob/01cb46a9a50e3ba4be167965b5764e9702f09b30/timm/models/efficientnet.py#L95-L108). - -The weights of the B5-B7 variants are ported from Luke Melas' [EfficientNet-PyTorch repo](https://github.com/lukemelas/EfficientNet-PyTorch/blob/1039e009545d9329ea026c9f7541341439712b96/efficientnet_pytorch/utils.py#L562-L564). - -All models were trained using Bicubic interpolation and each have custom crop and resize sizes. To validate the models use the following commands: -``` -torchrun --nproc_per_node=8 train.py --model efficientnet_b0 --test-only --weights EfficientNet_B0_Weights.IMAGENET1K_V1 -torchrun --nproc_per_node=8 train.py --model efficientnet_b1 --test-only --weights EfficientNet_B1_Weights.IMAGENET1K_V1 -torchrun --nproc_per_node=8 train.py --model efficientnet_b2 --test-only --weights EfficientNet_B2_Weights.IMAGENET1K_V1 -torchrun --nproc_per_node=8 train.py --model efficientnet_b3 --test-only --weights EfficientNet_B3_Weights.IMAGENET1K_V1 -torchrun --nproc_per_node=8 train.py --model efficientnet_b4 --test-only --weights EfficientNet_B4_Weights.IMAGENET1K_V1 -torchrun --nproc_per_node=8 train.py --model efficientnet_b5 --test-only --weights EfficientNet_B5_Weights.IMAGENET1K_V1 -torchrun --nproc_per_node=8 train.py --model efficientnet_b6 --test-only --weights EfficientNet_B6_Weights.IMAGENET1K_V1 -torchrun --nproc_per_node=8 train.py --model efficientnet_b7 --test-only --weights EfficientNet_B7_Weights.IMAGENET1K_V1 -``` - - -### EfficientNet-V2 -``` -torchrun --nproc_per_node=8 train.py \ ---model $MODEL --batch-size 128 --lr 0.5 --lr-scheduler cosineannealinglr \ ---lr-warmup-epochs 5 --lr-warmup-method linear --auto-augment ta_wide --epochs 600 --random-erase 0.1 \ ---label-smoothing 0.1 --mixup-alpha 0.2 --cutmix-alpha 1.0 --weight-decay 0.00002 --norm-weight-decay 0.0 \ ---train-crop-size $TRAIN_SIZE --model-ema --val-crop-size $EVAL_SIZE --val-resize-size $EVAL_SIZE \ ---ra-sampler --ra-reps 4 -``` -Here `$MODEL` is one of `efficientnet_v2_s` and `efficientnet_v2_m`. -Note that the Small variant had a `$TRAIN_SIZE` of `300` and a `$EVAL_SIZE` of `384`, while the Medium `384` and `480` respectively. - -Note that the above command corresponds to training on a single node with 8 GPUs. -For generatring the pre-trained weights, we trained with 4 nodes, each with 8 GPUs (for a total of 32 GPUs), -and `--batch_size 32`. - -The weights of the Large variant are ported from the original paper rather than trained from scratch. See the `EfficientNet_V2_L_Weights` entry for their exact preprocessing transforms. - - -### RegNet - -#### Small models -``` -torchrun --nproc_per_node=8 train.py\ - --model $MODEL --epochs 100 --batch-size 128 --wd 0.00005 --lr=0.8\ - --lr-scheduler=cosineannealinglr --lr-warmup-method=linear\ - --lr-warmup-epochs=5 --lr-warmup-decay=0.1 -``` -Here `$MODEL` is one of `regnet_x_400mf`, `regnet_x_800mf`, `regnet_x_1_6gf`, `regnet_y_400mf`, `regnet_y_800mf` and `regnet_y_1_6gf`. Please note we used learning rate 0.4 for `regent_y_400mf` to get the same Acc@1 as [the paper)(https://arxiv.org/abs/2003.13678). - -#### Medium models -``` -torchrun --nproc_per_node=8 train.py\ - --model $MODEL --epochs 100 --batch-size 64 --wd 0.00005 --lr=0.4\ - --lr-scheduler=cosineannealinglr --lr-warmup-method=linear\ - --lr-warmup-epochs=5 --lr-warmup-decay=0.1 -``` -Here `$MODEL` is one of `regnet_x_3_2gf`, `regnet_x_8gf`, `regnet_x_16gf`, `regnet_y_3_2gf` and `regnet_y_8gf`. - -#### Large models -``` -torchrun --nproc_per_node=8 train.py\ - --model $MODEL --epochs 100 --batch-size 32 --wd 0.00005 --lr=0.2\ - --lr-scheduler=cosineannealinglr --lr-warmup-method=linear\ - --lr-warmup-epochs=5 --lr-warmup-decay=0.1 -``` -Here `$MODEL` is one of `regnet_x_32gf`, `regnet_y_16gf` and `regnet_y_32gf`. - -### Vision Transformer - -#### vit_b_16 -``` -torchrun --nproc_per_node=8 train.py\ - --model vit_b_16 --epochs 300 --batch-size 512 --opt adamw --lr 0.003 --wd 0.3\ - --lr-scheduler cosineannealinglr --lr-warmup-method linear --lr-warmup-epochs 30\ - --lr-warmup-decay 0.033 --amp --label-smoothing 0.11 --mixup-alpha 0.2 --auto-augment ra\ - --clip-grad-norm 1 --ra-sampler --cutmix-alpha 1.0 --model-ema -``` - -Note that the above command corresponds to training on a single node with 8 GPUs. -For generatring the pre-trained weights, we trained with 8 nodes, each with 8 GPUs (for a total of 64 GPUs), -and `--batch_size 64`. - -#### vit_b_32 -``` -torchrun --nproc_per_node=8 train.py\ - --model vit_b_32 --epochs 300 --batch-size 512 --opt adamw --lr 0.003 --wd 0.3\ - --lr-scheduler cosineannealinglr --lr-warmup-method linear --lr-warmup-epochs 30\ - --lr-warmup-decay 0.033 --amp --label-smoothing 0.11 --mixup-alpha 0.2 --auto-augment imagenet\ - --clip-grad-norm 1 --ra-sampler --cutmix-alpha 1.0 --model-ema -``` - -Note that the above command corresponds to training on a single node with 8 GPUs. -For generatring the pre-trained weights, we trained with 2 nodes, each with 8 GPUs (for a total of 16 GPUs), -and `--batch_size 256`. - -#### vit_l_16 -``` -torchrun --nproc_per_node=8 train.py\ - --model vit_l_16 --epochs 600 --batch-size 128 --lr 0.5 --lr-scheduler cosineannealinglr\ - --lr-warmup-method linear --lr-warmup-epochs 5 --label-smoothing 0.1 --mixup-alpha 0.2\ - --auto-augment ta_wide --random-erase 0.1 --weight-decay 0.00002 --norm-weight-decay 0.0\ - --clip-grad-norm 1 --ra-sampler --cutmix-alpha 1.0 --model-ema --val-resize-size 232 -``` - -Note that the above command corresponds to training on a single node with 8 GPUs. -For generatring the pre-trained weights, we trained with 2 nodes, each with 8 GPUs (for a total of 16 GPUs), -and `--batch_size 64`. - -#### vit_l_32 -``` -torchrun --nproc_per_node=8 train.py\ - --model vit_l_32 --epochs 300 --batch-size 512 --opt adamw --lr 0.003 --wd 0.3\ - --lr-scheduler cosineannealinglr --lr-warmup-method linear --lr-warmup-epochs 30\ - --lr-warmup-decay 0.033 --amp --label-smoothing 0.11 --mixup-alpha 0.2 --auto-augment ra\ - --clip-grad-norm 1 --ra-sampler --cutmix-alpha 1.0 --model-ema -``` - -Note that the above command corresponds to training on a single node with 8 GPUs. -For generatring the pre-trained weights, we trained with 8 nodes, each with 8 GPUs (for a total of 64 GPUs), -and `--batch_size 64`. - - -### ConvNeXt -``` -torchrun --nproc_per_node=8 train.py\ ---model $MODEL --batch-size 128 --opt adamw --lr 1e-3 --lr-scheduler cosineannealinglr \ ---lr-warmup-epochs 5 --lr-warmup-method linear --auto-augment ta_wide --epochs 600 --random-erase 0.1 \ ---label-smoothing 0.1 --mixup-alpha 0.2 --cutmix-alpha 1.0 --weight-decay 0.05 --norm-weight-decay 0.0 \ ---train-crop-size 176 --model-ema --val-resize-size 232 --ra-sampler --ra-reps 4 -``` -Here `$MODEL` is one of `convnext_tiny`, `convnext_small`, `convnext_base` and `convnext_large`. Note that each variant had its `--val-resize-size` optimized in a post-training step, see their `Weights` entry for their exact value. - -Note that the above command corresponds to training on a single node with 8 GPUs. -For generatring the pre-trained weights, we trained with 2 nodes, each with 8 GPUs (for a total of 16 GPUs), -and `--batch_size 64`. - - -### SwinTransformer -``` -torchrun --nproc_per_node=8 train.py\ ---model $MODEL --epochs 300 --batch-size 128 --opt adamw --lr 0.001 --weight-decay 0.05 --norm-weight-decay 0.0 --bias-weight-decay 0.0 --transformer-embedding-decay 0.0 --lr-scheduler cosineannealinglr --lr-min 0.00001 --lr-warmup-method linear --lr-warmup-epochs 20 --lr-warmup-decay 0.01 --amp --label-smoothing 0.1 --mixup-alpha 0.8 --clip-grad-norm 5.0 --cutmix-alpha 1.0 --random-erase 0.25 --interpolation bicubic --auto-augment ta_wide --model-ema --ra-sampler --ra-reps 4 --val-resize-size 224 -``` -Here `$MODEL` is one of `swin_t`, `swin_s` or `swin_b`. -Note that `--val-resize-size` was optimized in a post-training step, see their `Weights` entry for the exact value. - - - - -### SwinTransformer V2 -``` -torchrun --nproc_per_node=8 train.py\ ---model $MODEL --epochs 300 --batch-size 128 --opt adamw --lr 0.001 --weight-decay 0.05 --norm-weight-decay 0.0 --bias-weight-decay 0.0 --transformer-embedding-decay 0.0 --lr-scheduler cosineannealinglr --lr-min 0.00001 --lr-warmup-method linear --lr-warmup-epochs 20 --lr-warmup-decay 0.01 --amp --label-smoothing 0.1 --mixup-alpha 0.8 --clip-grad-norm 5.0 --cutmix-alpha 1.0 --random-erase 0.25 --interpolation bicubic --auto-augment ta_wide --model-ema --ra-sampler --ra-reps 4 --val-resize-size 256 --val-crop-size 256 --train-crop-size 256 -``` -Here `$MODEL` is one of `swin_v2_t`, `swin_v2_s` or `swin_v2_b`. -Note that `--val-resize-size` was optimized in a post-training step, see their `Weights` entry for the exact value. - - -### MaxViT -``` -torchrun --nproc_per_node=8 --n_nodes=4 train.py\ ---model $MODEL --epochs 400 --batch-size 128 --opt adamw --lr 3e-3 --weight-decay 0.05 --lr-scheduler cosineannealinglr --lr-min 1e-5 --lr-warmup-method linear --lr-warmup-epochs 32 --label-smoothing 0.1 --mixup-alpha 0.8 --clip-grad-norm 1.0 --interpolation bicubic --auto-augment ta_wide --policy-magnitude 15 --model-ema --val-resize-size 224\ ---val-crop-size 224 --train-crop-size 224 --amp --model-ema-steps 32 --transformer-embedding-decay 0 --sync-bn -``` -Here `$MODEL` is `maxvit_t`. -Note that `--val-resize-size` was not optimized in a post-training step. - - -### ShuffleNet V2 -``` -torchrun --nproc_per_node=8 train.py \ ---batch-size=128 \ ---lr=0.5 --lr-scheduler=cosineannealinglr --lr-warmup-epochs=5 --lr-warmup-method=linear \ ---auto-augment=ta_wide --epochs=600 --random-erase=0.1 --weight-decay=0.00002 \ ---norm-weight-decay=0.0 --label-smoothing=0.1 --mixup-alpha=0.2 --cutmix-alpha=1.0 \ ---train-crop-size=176 --model-ema --val-resize-size=232 --ra-sampler --ra-reps=4 -``` -Here `$MODEL` is either `shufflenet_v2_x1_5` or `shufflenet_v2_x2_0`. - -The models `shufflenet_v2_x0_5` and `shufflenet_v2_x1_0` were contributed by the community. See [PR-849](https://github.com/pytorch/vision/pull/849#issuecomment-483391686) for details. - - -## Mixed precision training -Automatic Mixed Precision (AMP) training on GPU for Pytorch can be enabled with the [torch.cuda.amp](https://pytorch.org/docs/stable/amp.html?highlight=amp#module-torch.cuda.amp). - -Mixed precision training makes use of both FP32 and FP16 precisions where appropriate. FP16 operations can leverage the Tensor cores on NVIDIA GPUs (Volta, Turing or newer architectures) for improved throughput, generally without loss in model accuracy. Mixed precision training also often allows larger batch sizes. GPU automatic mixed precision training for Pytorch Vision can be enabled via the flag value `--amp=True`. - -``` -torchrun --nproc_per_node=8 train.py\ - --model resnext50_32x4d --epochs 100 --amp -``` - -## Quantized - -### Post training quantized models - -For all post training quantized models, the settings are: - -1. num_calibration_batches: 32 -2. num_workers: 16 -3. batch_size: 32 -4. eval_batch_size: 128 -5. backend: 'fbgemm' - -``` -python train_quantization.py --device='cpu' --post-training-quantize --backend='fbgemm' --model='$MODEL' -``` -Here `$MODEL` is one of `googlenet`, `inception_v3`, `resnet18`, `resnet50`, `resnext101_32x8d`, `shufflenet_v2_x0_5` and `shufflenet_v2_x1_0`. - -### Quantized ShuffleNet V2 - -Here are commands that we use to quantized the `shufflenet_v2_x1_5` and `shufflenet_v2_x2_0` models. -``` -# For shufflenet_v2_x1_5 -python train_quantization.py --device='cpu' --post-training-quantize --backend='fbgemm' \ - --model=shufflenet_v2_x1_5 --weights="ShuffleNet_V2_X1_5_Weights.IMAGENET1K_V1" \ - --train-crop-size 176 --val-resize-size 232 --data-path /datasets01_ontap/imagenet_full_size/061417/ - -# For shufflenet_v2_x2_0 -python train_quantization.py --device='cpu' --post-training-quantize --backend='fbgemm' \ - --model=shufflenet_v2_x2_0 --weights="ShuffleNet_V2_X2_0_Weights.IMAGENET1K_V1" \ - --train-crop-size 176 --val-resize-size 232 --data-path /datasets01_ontap/imagenet_full_size/061417/ -``` - -### QAT MobileNetV2 - -For Mobilenet-v2, the model was trained with quantization aware training, the settings used are: -1. num_workers: 16 -2. batch_size: 32 -3. eval_batch_size: 128 -4. backend: 'qnnpack' -5. learning-rate: 0.0001 -6. num_epochs: 90 -7. num_observer_update_epochs:4 -8. num_batch_norm_update_epochs:3 -9. momentum: 0.9 -10. lr_step_size:30 -11. lr_gamma: 0.1 -12. weight-decay: 0.0001 - -``` -torchrun --nproc_per_node=8 train_quantization.py --model='mobilenet_v2' -``` - -Training converges at about 10 epochs. - -### QAT MobileNetV3 - -For Mobilenet-v3 Large, the model was trained with quantization aware training, the settings used are: -1. num_workers: 16 -2. batch_size: 32 -3. eval_batch_size: 128 -4. backend: 'qnnpack' -5. learning-rate: 0.001 -6. num_epochs: 90 -7. num_observer_update_epochs:4 -8. num_batch_norm_update_epochs:3 -9. momentum: 0.9 -10. lr_step_size:30 -11. lr_gamma: 0.1 -12. weight-decay: 0.00001 - -``` -torchrun --nproc_per_node=8 train_quantization.py --model='mobilenet_v3_large' \ - --wd 0.00001 --lr 0.001 -``` - -For post training quant, device is set to CPU. For training, the device is set to CUDA. - -### Command to evaluate quantized models using the pre-trained weights: - -``` -python train_quantization.py --device='cpu' --test-only --backend='' --model='' -``` - -For inception_v3 you need to pass the following extra parameters: -``` ---val-resize-size 342 --val-crop-size 299 --train-crop-size 299 -``` \ No newline at end of file diff --git a/torchao/prototype/sparsity/superblock/__init__.py b/torchao/prototype/sparsity/superblock/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 diff --git a/torchao/prototype/sparsity/superblock/benchmark.py b/torchao/prototype/sparsity/superblock/benchmark.py deleted file mode 100644 index b87834afae..0000000000 --- a/torchao/prototype/sparsity/superblock/benchmark.py +++ /dev/null @@ -1,140 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. - -import torch -import torchvision -from torch.sparse._triton_ops_meta import ( - optimize_bsr_dense_addmm, -) - -from torchao.prototype.sparsity.superblock.utils import ( - accelerate_with_sparsity, - get_args_parser, - simulate_sparsity, -) -from torchao.utils import benchmark_model, profiler_runner - -torch.sparse.SparseSemiStructuredTensor._FORCE_CUTLASS = False -torch.backends.mha.set_fastpath_enabled(False) - - -@torch.inference_mode -def main(args): - device = torch.device(args.device) - - # We disable the cudnn benchmarking because it can noticeably affect the accuracy - torch.backends.cudnn.benchmark = False - torch.backends.cudnn.deterministic = True - num_classes = 1000 - - dtype = getattr(torch, args.dtype) - - # BSR kernel tuning - if args.bsr and args.tune_kernel_params: - kwargs = dict( - dtype=torch.int8 if args.quantization else dtype, - sparsity=args.sparsity_linear, - verbose=True, - # per blocksparse_int_addmm: - alpha=1, - beta=0, - use_left_alpha=True, - use_right_alpha=True, - # force tuning because existing tuning parameters are - # computed for use_left/right_alpha=False, however, it - # turns out that re-tuning for use_left/right_alpha=False - # leads to the same set of tuning parametes: - # force=True - ) - if args.model == "vit_b_16": - optimize_bsr_dense_addmm(3072, 768, 50432, args.bsr, args.bsr, **kwargs) - optimize_bsr_dense_addmm(768, 3072, 50432, args.bsr, args.bsr, **kwargs) - elif args.model == "vit_h_14": - optimize_bsr_dense_addmm(5120, 1280, 65792, args.bsr, args.bsr, **kwargs) - optimize_bsr_dense_addmm(1280, 5120, 65792, args.bsr, args.bsr, **kwargs) - else: - raise NotImplementedError( - "Tuning kernel params for this model is not supported yet." - ) - # Warning: the following call will overwrite the source code - # of torch.sparse._triton_ops_meta (hence it is commented out - # by default) but when used, it'll enables reusing the tuned - # parameters in subsequent runs of this script: - # store_tuned_kernel_params() - model = torchvision.models.get_model( - args.model, weights=args.weights, num_classes=num_classes - ).eval() - - # Fake sparsity necessary for BSR, since we find based on SuperBlock - sparsifier_or_none = simulate_sparsity(model, args) - if sparsifier_or_none is not None: - sparsifier_or_none.squash_mask() - - if args.weights_path: - try: - checkpoint = torch.load(args.weights_path, map_location="cpu") - model.load_state_dict(checkpoint["model"]) - except FileNotFoundError: - raise FileNotFoundError(f"No checkpoint found at {args.weights_path}.") - - model.to(device).to(dtype) - - # With quantization, we must use cuSPARSELt to fuse one of the scalar matmuls. - # Otherwise, we observe the CUTLASS kernels to be faster, so we use those instead. - accelerate_with_sparsity(model, args) - - # compile - model = torch.compile(model, mode="max-autotune", fullgraph=True) - - # define image - image = torch.randn( - args.batch_size, - 3, - args.val_crop_size, - args.val_crop_size, - dtype=dtype, - device=device, - ) - - # warmup - benchmark_model(model, 10, args=(image,)) - if args.profile: - return profiler_runner("test.json.gz", benchmark_model, model, 10, (image,)) - else: - return benchmark_model(model, 100, args=(image,)) - - -if __name__ == "__main__": - args = get_args_parser(benchmark=True).parse_args() - result = main(args) - header = [ - "model", - "batch_size", - "dtype", - "sparsity", - "bsr", - "sparsity_level", - "quantization", - "tune_kernel_params", - "latency", - "img/s", - ] - result_string = ",".join( - str(_) - for _ in [ - args.model, - args.batch_size, - args.dtype, - args.sparsity, - args.bsr, - args.sparsity_linear, - args.quantization, - args.tune_kernel_params, - result, - 1000 / result, - ] - ) - with open("benchmark_results.txt", "a") as f: - if args.header: - f.write(",".join(header) + "\n") - f.write(result_string + "\n") - print(result_string) diff --git a/torchao/prototype/sparsity/superblock/benchmark.sh b/torchao/prototype/sparsity/superblock/benchmark.sh deleted file mode 100644 index ac52ee8e02..0000000000 --- a/torchao/prototype/sparsity/superblock/benchmark.sh +++ /dev/null @@ -1,44 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -MODEL=vit_h_14 -BATCH_SIZE=256 - -python benchmark.py --model $MODEL --batch-size $BATCH_SIZE --header -python benchmark.py --model $MODEL --batch-size $BATCH_SIZE --quantization - -python benchmark.py --model $MODEL --batch-size $BATCH_SIZE --sparsity semi_structured -python benchmark.py --model $MODEL --batch-size $BATCH_SIZE --sparsity-linear 0.80 --bsr 64 --sparsity bsr -python benchmark.py --model $MODEL --batch-size $BATCH_SIZE --sparsity-linear 0.84 --bsr 64 --sparsity bsr -python benchmark.py --model $MODEL --batch-size $BATCH_SIZE --sparsity-linear 0.90 --bsr 64 --sparsity bsr - -python benchmark.py --model $MODEL --batch-size $BATCH_SIZE --sparsity semi_structured --quantization -python benchmark.py --model $MODEL --batch-size $BATCH_SIZE --sparsity-linear 0.80 --bsr 64 --sparsity bsr --quantization -python benchmark.py --model $MODEL --batch-size $BATCH_SIZE --sparsity-linear 0.84 --bsr 64 --sparsity bsr --quantization -python benchmark.py --model $MODEL --batch-size $BATCH_SIZE --sparsity-linear 0.90 --bsr 64 --sparsity bsr --quantization - -python benchmark.py --model $MODEL --batch-size $BATCH_SIZE --sparsity-linear 0.80 --bsr 64 --sparsity bsr --quantization --tune-kernel-params -python benchmark.py --model $MODEL --batch-size $BATCH_SIZE --sparsity-linear 0.84 --bsr 64 --sparsity bsr --quantization --tune-kernel-params -python benchmark.py --model $MODEL --batch-size $BATCH_SIZE --sparsity-linear 0.90 --bsr 64 --sparsity bsr --quantization --tune-kernel-params - -MODEL=vit_b_16 -BATCH_SIZE=256 - -python benchmark.py --model $MODEL --batch-size $BATCH_SIZE --header -python benchmark.py --model $MODEL --batch-size $BATCH_SIZE --quantization - -python benchmark.py --model $MODEL --batch-size $BATCH_SIZE --sparsity semi_structured -python benchmark.py --model $MODEL --batch-size $BATCH_SIZE --sparsity-linear 0.80 --bsr 64 --sparsity bsr -python benchmark.py --model $MODEL --batch-size $BATCH_SIZE --sparsity-linear 0.84 --bsr 64 --sparsity bsr -python benchmark.py --model $MODEL --batch-size $BATCH_SIZE --sparsity-linear 0.90 --bsr 64 --sparsity bsr - -python benchmark.py --model $MODEL --batch-size $BATCH_SIZE --sparsity semi_structured --quantization -python benchmark.py --model $MODEL --batch-size $BATCH_SIZE --sparsity-linear 0.80 --bsr 64 --sparsity bsr --quantization -python benchmark.py --model $MODEL --batch-size $BATCH_SIZE --sparsity-linear 0.84 --bsr 64 --sparsity bsr --quantization -python benchmark.py --model $MODEL --batch-size $BATCH_SIZE --sparsity-linear 0.90 --bsr 64 --sparsity bsr --quantization - -python benchmark.py --model $MODEL --batch-size $BATCH_SIZE --sparsity-linear 0.80 --bsr 64 --sparsity bsr --quantization --tune-kernel-params -python benchmark.py --model $MODEL --batch-size $BATCH_SIZE --sparsity-linear 0.84 --bsr 64 --sparsity bsr --quantization --tune-kernel-params -python benchmark.py --model $MODEL --batch-size $BATCH_SIZE --sparsity-linear 0.90 --bsr 64 --sparsity bsr --quantization --tune-kernel-params diff --git a/torchao/prototype/sparsity/superblock/benchmark_results.txt b/torchao/prototype/sparsity/superblock/benchmark_results.txt deleted file mode 100644 index 3e18d9faec..0000000000 --- a/torchao/prototype/sparsity/superblock/benchmark_results.txt +++ /dev/null @@ -1,30 +0,0 @@ -model,batch_size,dtype,sparsity,bsr,sparsity_level,quantization,tune_kernel_params,latency,img/s -vit_h_14,256,bfloat16,None,None,0.0,False,False,489.645859375,2.0422923646825746 -vit_h_14,256,bfloat16,None,None,0.0,True,False,454.5648828125,2.1999059712064963 -vit_h_14,256,bfloat16,semi_structured,None,0.0,False,False,458.638046875,2.180368608347371 -vit_h_14,256,bfloat16,bsr,64,0.8,False,False,361.5827734375,2.765618479257699 -vit_h_14,256,bfloat16,bsr,64,0.84,False,False,343.1771484375,2.9139469354327407 -vit_h_14,256,bfloat16,bsr,64,0.9,False,False,315.37119140625,3.170866671559215 -vit_h_14,256,bfloat16,semi_structured,None,0.0,True,False,438.1652734375,2.2822438486619143 -vit_h_14,256,bfloat16,bsr,64,0.8,True,False,439.5409765625,2.2751007376392045 -vit_h_14,256,bfloat16,bsr,64,0.84,True,False,416.799375,2.3992358433838823 -vit_h_14,256,bfloat16,bsr,64,0.9,True,False,381.9370703125,2.6182323679181034 -vit_h_14,256,bfloat16,bsr,64,0.8,True,True,439.1569921875,2.277090010610706 -vit_h_14,256,bfloat16,bsr,64,0.84,True,True,416.18,2.4028064779662643 -vit_h_14,256,bfloat16,bsr,64,0.9,True,True,384.2584765625,2.6024149394069362 - -model,batch_size,dtype,sparsity,bsr,sparsity_level,quantization,tune_kernel_params,latency,img/s -vit_b_16,256,bfloat16,None,None,0.0,False,False,61.407705078125,16.284601398599175 -vit_b_16,256,bfloat16,None,None,0.0,True,False,60.934091796875,16.41117427881784 -vit_b_16,256,bfloat16,semi_structured,None,0.0,False,False,59.9600732421875,16.677764817945665 -vit_b_16,256,bfloat16,bsr,64,0.8,False,False,47.6238916015625,20.997864020990484 -vit_b_16,256,bfloat16,bsr,64,0.84,False,False,45.7176416015625,21.873394273378768 -vit_b_16,256,bfloat16,bsr,64,0.9,False,False,42.708759765625,23.414400359264707 -vit_b_16,256,bfloat16,semi_structured,None,0.0,True,False,58.783828125,17.011481420937148 -vit_b_16,256,bfloat16,bsr,64,0.8,True,False,58.1029541015625,17.210828872005806 -vit_b_16,256,bfloat16,bsr,64,0.84,True,False,55.8751025390625,17.89705887878946 -vit_b_16,256,bfloat16,bsr,64,0.9,True,False,52.3257763671875,19.111039900921202 -vit_b_16,256,bfloat16,bsr,64,0.8,True,True,58.649736328125,17.050375033322325 -vit_b_16,256,bfloat16,bsr,64,0.84,True,True,56.46744140625,17.709320186930174 -vit_b_16,256,bfloat16,bsr,64,0.9,True,True,52.528623046875,19.037239927413086 -vit_b_16,256,bfloat16,bsr,64,0.8,True,False,57.6839794921875,17.335835856044508 diff --git a/torchao/prototype/sparsity/superblock/evaluate.py b/torchao/prototype/sparsity/superblock/evaluate.py deleted file mode 100644 index 04701e518e..0000000000 --- a/torchao/prototype/sparsity/superblock/evaluate.py +++ /dev/null @@ -1,104 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -import os - -import torch -import torchvision - -from torchao.prototype.sparsity.superblock.train import evaluate, load_data -from torchao.prototype.sparsity.superblock.utils import ( - accelerate_with_sparsity, - get_args_parser, - init_distributed_mode, - simulate_sparsity, -) - -torch.sparse.SparseSemiStructuredTensor._FORCE_CUTLASS = False -torch.backends.mha.set_fastpath_enabled(False) - - -def main(args): - init_distributed_mode(args) - print(args) - - device = torch.device(args.device) - # We disable the cudnn benchmarking because it can noticeably affect the accuracy - torch.backends.cudnn.benchmark = False - torch.backends.cudnn.deterministic = True - - # Load validation data - val_dir = os.path.join(args.data_path, "val") - dataset_test, test_sampler = load_data(None, val_dir, args) - data_loader_test = torch.utils.data.DataLoader( - dataset_test, - batch_size=args.batch_size, - sampler=test_sampler, - num_workers=args.workers, - pin_memory=True, - drop_last=True, - ) - num_classes = len(dataset_test.classes) - - # Create Model - print("Creating model") - model = torchvision.models.get_model( - args.model, weights=args.weights, num_classes=num_classes - ) - - sparsifier_or_none = simulate_sparsity(model, args) - - if args.weights_path: - try: - checkpoint = torch.load(args.weights_path, map_location="cpu") - model.load_state_dict(checkpoint["model"]) - print(f"Loaded checkpoint successfully from: {args.weights_path}") - except FileNotFoundError: - raise FileNotFoundError(f"No checkpoint found at {args.weights_path}") - - model.to(device).bfloat16() - - if sparsifier_or_none is not None: - sparsifier_or_none.squash_mask() - accelerate_with_sparsity(model, args) - model = torch.compile(model, mode="max-autotune", fullgraph=True) - - criterion = torch.nn.CrossEntropyLoss(label_smoothing=args.label_smoothing) - return evaluate( - model, criterion, data_loader_test, device=device, dtype=torch.bfloat16 - ) - - -if __name__ == "__main__": - args = get_args_parser(evaluate=True).parse_args() - accuracy, throughput, max_mem = main(args) - header = [ - "model", - "batch_size", - "dtype", - "sparsity", - "bsr", - "sparsity_level", - "quantization", - "top-1_acc", - "encoder img/s", - "max_mem (MB)", - ] - result_string = ",".join( - str(_) - for _ in [ - args.model, - args.batch_size, - "bfloat16", - args.sparsity, - args.bsr, - args.sparsity_linear, - args.quantization, - accuracy, - throughput, - max_mem, - ] - ) - with open("evaluation_results.txt", "a") as f: - if args.header: - f.write(",".join(header) + "\n") - f.write(result_string + "\n") - print(result_string) diff --git a/torchao/prototype/sparsity/superblock/evaluate.sh b/torchao/prototype/sparsity/superblock/evaluate.sh deleted file mode 100644 index 8696c9da42..0000000000 --- a/torchao/prototype/sparsity/superblock/evaluate.sh +++ /dev/null @@ -1,28 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -MODEL=vit_b_16 -BATCH_SIZE=256 - -python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --weights ViT_B_16_Weights.IMAGENET1K_V1 --header -python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --weights ViT_B_16_Weights.IMAGENET1K_V1 --quantization -python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --weights ViT_B_16_Weights.IMAGENET1K_V1 --sparsity semi_structured -python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --weights ViT_B_16_Weights.IMAGENET1K_V1 --sparsity semi_structured --quantization -python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --sparsity bsr --sparsity-linear 0.80 --bsr 64 --weights-path checkpoints/$MODEL/sp0.80-ts64.pth -python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --sparsity bsr --sparsity-linear 0.80 --bsr 64 --weights-path checkpoints/$MODEL/sp0.80-ts64.pth --quantization -python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --sparsity bsr --sparsity-linear 0.84 --bsr 64 --weights-path checkpoints/$MODEL/sp0.84-ts64.pth -python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --sparsity bsr --sparsity-linear 0.84 --bsr 64 --weights-path checkpoints/$MODEL/sp0.84-ts64.pth --quantization -python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --sparsity bsr --sparsity-linear 0.90 --bsr 64 --weights-path checkpoints/$MODEL/sp0.90-ts64.pth -python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --sparsity bsr --sparsity-linear 0.90 --bsr 64 --weights-path checkpoints/$MODEL/sp0.90-ts64.pth --quantization - -MODEL=vit_h_14 -BATCH_SIZE=128 - -python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --weights ViT_H_14_Weights.IMAGENET1K_SWAG_E2E_V1 --header -python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --weights ViT_H_14_Weights.IMAGENET1K_SWAG_E2E_V1 --quantization -python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --weights ViT_H_14_Weights.IMAGENET1K_SWAG_E2E_V1 --sparsity semi_structured -python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --weights ViT_H_14_Weights.IMAGENET1K_SWAG_E2E_V1 --sparsity semi_structured --quantization -python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --sparsity bsr --sparsity-linear 0.90 --bsr 64 --weights-path checkpoints/$MODEL/sp0.90-ts64.pth -python evaluate.py --model $MODEL --batch-size $BATCH_SIZE --data-path $IMAGENET_PATH --sparsity bsr --sparsity-linear 0.90 --bsr 64 --weights-path checkpoints/$MODEL/sp0.90-ts64.pth --quantization diff --git a/torchao/prototype/sparsity/superblock/evaluation_results.txt b/torchao/prototype/sparsity/superblock/evaluation_results.txt deleted file mode 100644 index 58dcade663..0000000000 --- a/torchao/prototype/sparsity/superblock/evaluation_results.txt +++ /dev/null @@ -1,19 +0,0 @@ -model,batch_size,dtype,sparsity,bsr,sparsity_level,quantization,top-1_acc,encoder img/s,max_mem (MB) -vit_b_16,256,bfloat16,None,None,0.0,False,81.97716346153847,734.904399886552,247.97265625 -vit_b_16,256,bfloat16,None,None,0.0,True,81.89503205128206,230.83627917226997,196.841796875 -vit_b_16,256,bfloat16,semi_structured,None,0.0,False,77.05729166666667,1386.7278781133518,316.40234375 -vit_b_16,256,bfloat16,semi_structured,None,0.0,True,76.74078525641026,150.53603093207843,249.25390625 -vit_b_16,256,bfloat16,bsr,64,0.8,False,77.13541666666667,1469.2705176409308,179.55322265625 -vit_b_16,256,bfloat16,bsr,64,0.8,True,77.13341346153847,87.8480561274922,158.70361328125 -vit_b_16,256,bfloat16,bsr,64,0.84,False,76.14983974358974,1752.835540513905,174.01953125 -vit_b_16,256,bfloat16,bsr,64,0.84,True,76.0556891025641,1013.7495284783578,156.630859375 -vit_b_16,256,bfloat16,bsr,64,0.9,False,62.99879807692308,1702.289195236525,164.2822265625 -vit_b_16,256,bfloat16,bsr,64,0.9,True,62.946714743589745,987.5488468441617,152.5732421875 - -model,batch_size,dtype,sparsity,bsr,sparsity_level,quantization,top-1_acc,encoder img/s,max_mem (MB) -vit_h_14,128,bfloat16,None,None,0.0,False,89.29286858974359,81.02922135697278,1430.05615234375 -vit_h_14,128,bfloat16,None,None,0.0,True,89.3349358974359,56.076129157634355,1025.00927734375 -vit_h_14,128,bfloat16,semi_structured,None,0.0,False,82.03725961538461,75.83586253901329,1900.36279296875 -vit_h_14,128,bfloat16,semi_structured,None,0.0,True,82.06330128205128,36.36097831133589,1390.98779296875 -vit_h_14,128,bfloat16,bsr,64,0.9,False,78.21113782051282,350.91330496491446,599.6201171875 -vit_h_14,128,bfloat16,bsr,64,0.9,True,78.2051282051282,108.84048044884008,531.5810546875 diff --git a/torchao/prototype/sparsity/superblock/train.py b/torchao/prototype/sparsity/superblock/train.py deleted file mode 100644 index 330dba4b6d..0000000000 --- a/torchao/prototype/sparsity/superblock/train.py +++ /dev/null @@ -1,542 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. - -import datetime -import glob -import os -import time -import warnings - -import torch -import torch.utils.data -import torchvision -import utils -from torch import nn -from torch.utils.data.dataloader import default_collate -from torchvision.transforms.functional import InterpolationMode -from utils import RASampler - -from torchao.prototype.sparsity.superblock.utils import simulate_sparsity - - -def train_one_epoch( - model, - criterion, - optimizer, - data_loader, - device, - epoch, - args, - model_ema=None, - scaler=None, -): - model.train() - metric_logger = utils.MetricLogger(delimiter=" ") - metric_logger.add_meter("lr", utils.SmoothedValue(window_size=1, fmt="{value}")) - metric_logger.add_meter("img/s", utils.SmoothedValue(window_size=10, fmt="{value}")) - - header = f"Epoch: [{epoch}]" - accumulation_counter = 0 # Counter for tracking accumulated gradients - - for i, (image, target) in enumerate( - metric_logger.log_every(data_loader, args.print_freq, header) - ): - start_time = time.time() - image, target = image.to(device), target.to(device) - - with torch.cuda.amp.autocast(enabled=scaler is not None): - output = model(image) - loss = criterion(output, target) / args.accumulation_steps # Scale loss - - if scaler is not None: - scaler.scale(loss).backward() - else: - loss.backward() - - accumulation_counter += 1 - - if accumulation_counter % args.accumulation_steps == 0: - if scaler is not None: - if args.clip_grad_norm is not None: - scaler.unscale_(optimizer) # Unscale gradients before clipping - nn.utils.clip_grad_norm_(model.parameters(), args.clip_grad_norm) - scaler.step(optimizer) - scaler.update() - else: - if args.clip_grad_norm is not None: - nn.utils.clip_grad_norm_(model.parameters(), args.clip_grad_norm) - optimizer.step() - - optimizer.zero_grad() # Zero out gradients after optimization step - - if model_ema and i % args.model_ema_steps == 0: - model_ema.update_parameters(model) - if epoch < args.lr_warmup_epochs: - model_ema.n_averaged.fill_(0) - - acc1, acc5 = utils.accuracy(output, target, topk=(1, 5)) - batch_size = image.shape[0] - metric_logger.update( - loss=loss.item() * args.accumulation_steps, - lr=optimizer.param_groups[0]["lr"], - ) # Scale back up for logging - metric_logger.meters["acc1"].update(acc1.item(), n=batch_size) - metric_logger.meters["acc5"].update(acc5.item(), n=batch_size) - metric_logger.meters["img/s"].update(batch_size / (time.time() - start_time)) - - -def evaluate( - model, - criterion, - data_loader, - device, - print_freq=100, - log_suffix="", - dtype=torch.float32, -): - model.eval() - metric_logger = utils.MetricLogger(delimiter=" ") - header = f"Test: {log_suffix}" - encoder_time = 0 - num_processed_samples = 0 - with torch.inference_mode(): - for image, target in metric_logger.log_every(data_loader, print_freq, header): - image = image.to(device, non_blocking=True).to(dtype) - target = target.to(device, non_blocking=True).to(dtype) - # intialize encoder measurements - torch.cuda.reset_max_memory_allocated() - torch.cuda.synchronize() - start_event = torch.cuda.Event(enable_timing=True) - end_event = torch.cuda.Event(enable_timing=True) - start_event.record() - - # run encoder - output = model(image) - - # measure time in encoder - end_event.record() - torch.cuda.synchronize() - encoder_time += start_event.elapsed_time(end_event) - max_mem = torch.cuda.max_memory_allocated() / (1024**2) - - acc1, acc5 = utils.accuracy(output, target, topk=(1, 5)) - # FIXME need to take into account that the datasets - # could have been padded in distributed setup - batch_size = image.shape[0] - # metric_logger.update(loss=loss.item()) - metric_logger.meters["acc1"].update(acc1.item(), n=batch_size) - metric_logger.meters["acc5"].update(acc5.item(), n=batch_size) - metric_logger.meters["batch_time"].update(encoder_time, n=batch_size) - num_processed_samples += batch_size - # gather the stats from all processes - - num_processed_samples = utils.reduce_across_processes(num_processed_samples) - if ( - hasattr(data_loader.dataset, "__len__") - and len(data_loader.dataset) != num_processed_samples - ): - # See FIXME above - warnings.warn( - f"It looks like the dataset has {len(data_loader.dataset)} samples, but {num_processed_samples} " - "samples were used for the validation, which might bias the results. " - "Try adjusting the batch size and / or the world size. " - "Setting the world size to 1 is always a safe bet." - ) - - metric_logger.synchronize_between_processes() - - print( - f"{header} Acc@1 {metric_logger.acc1.global_avg:.3f} Acc@5 {metric_logger.acc5.global_avg:.3f}" - ) - total_time = encoder_time / 1000.0 - return ( - metric_logger.acc1.global_avg, - num_processed_samples.item() / total_time, - max_mem, - ) - - -def _get_cache_path(filepath): - import hashlib - - h = hashlib.sha1(filepath.encode()).hexdigest() - cache_path = os.path.join( - "~", ".torch", "vision", "datasets", "imagefolder", h[:10] + ".pt" - ) - cache_path = os.path.expanduser(cache_path) - return cache_path - - -def load_data(traindir, valdir, args): - # Data loading code - print("Loading data") - ( - val_resize_size, - val_crop_size, - ) = ( - args.val_resize_size, - args.val_crop_size, - ) - interpolation = InterpolationMode(args.interpolation) - if traindir is not None: - train_crop_size = args.train_crop_size - print("Loading training data") - st = time.time() - cache_path = _get_cache_path(traindir) - if args.cache_dataset and os.path.exists(cache_path): - # Attention, as the transforms are also cached! - print(f"Loading dataset_train from {cache_path}") - dataset, _ = torch.load(cache_path) - else: - auto_augment_policy = getattr(args, "auto_augment", None) - random_erase_prob = getattr(args, "random_erase", 0.0) - ra_magnitude = args.ra_magnitude - augmix_severity = args.augmix_severity - preprocessing = utils.ClassificationPresetTrain( - crop_size=train_crop_size, - interpolation=interpolation, - auto_augment_policy=auto_augment_policy, - random_erase_prob=random_erase_prob, - ra_magnitude=ra_magnitude, - augmix_severity=augmix_severity, - ) - dataset = torchvision.datasets.ImageFolder(traindir, preprocessing) - # ) if args.meta else torchvision.datasets.ImageNet( - # traindir, - # split="train", - # transform=preprocessing, - # ) - if args.cache_dataset: - print(f"Saving dataset_train to {cache_path}") - utils.mkdir(os.path.dirname(cache_path)) - utils.save_on_master((dataset, traindir), cache_path) - print("Took", time.time() - st) - print(f"Number of training images: {len(dataset)}") - if args.distributed: - if hasattr(args, "ra_sampler") and args.ra_sampler: - train_sampler = RASampler( - dataset, shuffle=True, repetitions=args.ra_reps - ) - else: - train_sampler = torch.utils.data.distributed.DistributedSampler(dataset) - else: - train_sampler = torch.utils.data.RandomSampler(dataset) - - print("Loading validation data") - cache_path = _get_cache_path(valdir) - if args.cache_dataset and os.path.exists(cache_path): - # Attention, as the transforms are also cached! - print(f"Loading dataset_test from {cache_path}") - dataset_test, test_sampler = torch.load(cache_path) - else: - if args.weights: - weights = torchvision.models.get_weight(args.weights) - preprocessing = weights.transforms() - else: - preprocessing = utils.ClassificationPresetEval( - crop_size=val_crop_size, - resize_size=val_resize_size, - interpolation=interpolation, - ) - dataset_test = ( - torchvision.datasets.ImageFolder( - valdir, - preprocessing, - ) - if args.meta - else torchvision.datasets.ImageNet( - valdir, split="val", transform=preprocessing - ) - ) - if args.cache_dataset: - print(f"Saving dataset_test to {cache_path}") - utils.mkdir(os.path.dirname(cache_path)) - utils.save_on_master((dataset_test, valdir), cache_path) - - print(f"Number of validation images: {len(dataset_test)}") - test_sampler = ( - torch.utils.data.distributed.DistributedSampler(dataset_test, shuffle=False) - if args.distributed - else torch.utils.data.SequentialSampler(dataset_test) - ) - - # for evaluation - if traindir is None: - return dataset_test, test_sampler - - return dataset, dataset_test, train_sampler, test_sampler - - -def main(args): - if args.output_dir: - utils.mkdir(args.output_dir) - - utils.init_distributed_mode(args) - print(args) - - device = torch.device(args.device) - - if args.use_deterministic_algorithms: - torch.backends.cudnn.benchmark = False - torch.use_deterministic_algorithms(True) - else: - torch.backends.cudnn.benchmark = True - - train_dir = os.path.join(args.data_path, "train_blurred") - val_dir = os.path.join(args.data_path, "val") - dataset, dataset_test, train_sampler, test_sampler = load_data( - train_dir, val_dir, args - ) - - collate_fn = None - num_classes = len(dataset.classes) - mixup_transforms = [] - if args.mixup_alpha > 0.0: - mixup_transforms.append( - utils.RandomMixup(num_classes, p=1.0, alpha=args.mixup_alpha) - ) - if args.cutmix_alpha > 0.0: - mixup_transforms.append( - utils.RandomCutmix(num_classes, p=1.0, alpha=args.cutmix_alpha) - ) - if mixup_transforms: - mixupcutmix = torchvision.transforms.RandomChoice(mixup_transforms) - - def collate_fn(batch): - return mixupcutmix(*default_collate(batch)) - - data_loader = torch.utils.data.DataLoader( - dataset, - batch_size=args.batch_size, - sampler=train_sampler, - num_workers=args.workers, - pin_memory=True, - collate_fn=collate_fn, - ) - data_loader_test = torch.utils.data.DataLoader( - dataset_test, - batch_size=args.batch_size, - sampler=test_sampler, - num_workers=args.workers, - pin_memory=True, - ) - - print("Creating model") - model = torchvision.models.get_model( - args.model, weights=args.weights, num_classes=num_classes - ) - - if args.weights_path is not None: - sd = torch.load(args.weights_path, map_location="cpu") - model.load_state_dict(sd) - - model.to(device) - if args.distributed and args.sync_bn: - model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model) - - sparsifier = simulate_sparsity(model, args) - criterion = nn.CrossEntropyLoss(label_smoothing=args.label_smoothing) - - custom_keys_weight_decay = [] - if args.bias_weight_decay is not None: - custom_keys_weight_decay.append(("bias", args.bias_weight_decay)) - if args.transformer_embedding_decay is not None: - for key in [ - "class_token", - "position_embedding", - "relative_position_bias_table", - ]: - custom_keys_weight_decay.append((key, args.transformer_embedding_decay)) - parameters = utils.set_weight_decay( - model, - args.weight_decay, - norm_weight_decay=args.norm_weight_decay, - custom_keys_weight_decay=( - custom_keys_weight_decay if len(custom_keys_weight_decay) > 0 else None - ), - ) - - opt_name = args.opt.lower() - if opt_name.startswith("sgd"): - optimizer = torch.optim.SGD( - parameters, - lr=args.lr, - momentum=args.momentum, - weight_decay=args.weight_decay, - nesterov="nesterov" in opt_name, - ) - elif opt_name == "rmsprop": - optimizer = torch.optim.RMSprop( - parameters, - lr=args.lr, - momentum=args.momentum, - weight_decay=args.weight_decay, - eps=0.0316, - alpha=0.9, - ) - elif opt_name == "adamw": - optimizer = torch.optim.AdamW( - parameters, lr=args.lr, weight_decay=args.weight_decay - ) - else: - raise RuntimeError( - f"Invalid optimizer {args.opt}. Only SGD, RMSprop and AdamW are supported." - ) - - scaler = torch.cuda.amp.GradScaler() if args.amp else None - - args.lr_scheduler = args.lr_scheduler.lower() - if args.lr_scheduler == "steplr": - main_lr_scheduler = torch.optim.lr_scheduler.StepLR( - optimizer, step_size=args.lr_step_size, gamma=args.lr_gamma - ) - elif args.lr_scheduler == "cosineannealinglr": - main_lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( - optimizer, T_max=args.epochs - args.lr_warmup_epochs, eta_min=args.lr_min - ) - elif args.lr_scheduler == "exponentiallr": - main_lr_scheduler = torch.optim.lr_scheduler.ExponentialLR( - optimizer, gamma=args.lr_gamma - ) - else: - raise RuntimeError( - f"Invalid lr scheduler '{args.lr_scheduler}'. Only StepLR, CosineAnnealingLR and ExponentialLR " - "are supported." - ) - - if args.lr_warmup_epochs > 0: - if args.lr_warmup_method == "linear": - warmup_lr_scheduler = torch.optim.lr_scheduler.LinearLR( - optimizer, - start_factor=args.lr_warmup_decay, - total_iters=args.lr_warmup_epochs, - ) - elif args.lr_warmup_method == "constant": - warmup_lr_scheduler = torch.optim.lr_scheduler.ConstantLR( - optimizer, - factor=args.lr_warmup_decay, - total_iters=args.lr_warmup_epochs, - ) - else: - raise RuntimeError( - f"Invalid warmup lr method '{args.lr_warmup_method}'. Only linear and constant are supported." - ) - lr_scheduler = torch.optim.lr_scheduler.SequentialLR( - optimizer, - schedulers=[warmup_lr_scheduler, main_lr_scheduler], - milestones=[args.lr_warmup_epochs], - ) - else: - lr_scheduler = main_lr_scheduler - - model_without_ddp = model - if args.distributed: - model = torch.nn.parallel.DistributedDataParallel( - model, device_ids=[args.gpu], find_unused_parameters=True - ) - model_without_ddp = model.module - - model_ema = None - if args.model_ema: - # Decay adjustment that aims to keep the decay independent from other hyper-parameters originally proposed at: - # https://github.com/facebookresearch/pycls/blob/f8cd9627/pycls/core/net.py#L123 - # - # total_ema_updates = (Dataset_size / n_GPUs) * epochs / (batch_size_per_gpu * EMA_steps) - # We consider constant = Dataset_size for a given dataset/setup and ommit it. Thus: - # adjust = 1 / total_ema_updates ~= n_GPUs * batch_size_per_gpu * EMA_steps / epochs - adjust = args.world_size * args.batch_size * args.model_ema_steps / args.epochs - alpha = 1.0 - args.model_ema_decay - alpha = min(1.0, alpha * adjust) - model_ema = utils.ExponentialMovingAverage( - model_without_ddp, device=device, decay=1.0 - alpha - ) - - # TODO: need to test resume functionality - if args.resume: - checkpoint_pattern = os.path.join(args.output_dir, "model_*.pth") - checkpoint_files = glob.glob(checkpoint_pattern) - epochs = [int(f.split("_")[-1].split(".")[0]) for f in checkpoint_files] - if epochs: - latest_epoch = max(epochs) - latest_checkpoint = os.path.join( - args.output_dir, f"model_{latest_epoch}.pth" - ) - try: - checkpoint = torch.load(latest_checkpoint, map_location="cpu") - model_without_ddp.load_state_dict(checkpoint["model"]) - optimizer.load_state_dict(checkpoint["optimizer"]) - lr_scheduler.load_state_dict(checkpoint["lr_scheduler"]) - args.start_epoch = checkpoint["epoch"] + 1 - if model_ema: - model_ema.load_state_dict(checkpoint["model_ema"]) - if scaler: - scaler.load_state_dict(checkpoint["scaler"]) - print(f"Resumed training from epoch {args.start_epoch}.") - except FileNotFoundError: - print( - f"No checkpoint found at {latest_checkpoint}. Starting training from scratch." - ) - args.start_epoch = 0 - else: - print("No checkpoint found. Starting training from scratch.") - args.start_epoch = 0 - else: - args.start_epoch = 0 - print("Zero-shot evaluation") - if model_ema: - evaluate( - model_ema, criterion, data_loader_test, device=device, log_suffix="EMA" - ) - else: - evaluate(model, criterion, data_loader_test, device=device) - - print("Start training") - start_time = time.time() - for epoch in range(args.start_epoch, args.epochs): - if args.distributed: - train_sampler.set_epoch(epoch) - train_one_epoch( - model, - criterion, - optimizer, - data_loader, - device, - epoch, - args, - model_ema, - scaler, - ) - lr_scheduler.step() - evaluate(model, criterion, data_loader_test, device=device) - if model_ema: - evaluate( - model_ema, criterion, data_loader_test, device=device, log_suffix="EMA" - ) - if args.output_dir: - checkpoint = { - "model": model_without_ddp.state_dict(), - "optimizer": optimizer.state_dict(), - "lr_scheduler": lr_scheduler.state_dict(), - "epoch": epoch, - "args": args, - } - if sparsifier: - checkpoint["sparsifier"] = sparsifier.state_dict() - if model_ema: - checkpoint["model_ema"] = model_ema.state_dict() - if scaler: - checkpoint["scaler"] = scaler.state_dict() - utils.save_on_master( - checkpoint, os.path.join(args.output_dir, f"model_{epoch}.pth") - ) - utils.save_on_master( - checkpoint, os.path.join(args.output_dir, "checkpoint.pth") - ) - - total_time = time.time() - start_time - total_time_str = str(datetime.timedelta(seconds=int(total_time))) - print(f"Training time {total_time_str}") - - -if __name__ == "__main__": - args = utils.get_args_parser(train=True).parse_args() - main(args) diff --git a/torchao/prototype/sparsity/superblock/utils.py b/torchao/prototype/sparsity/superblock/utils.py deleted file mode 100644 index d00cdea510..0000000000 --- a/torchao/prototype/sparsity/superblock/utils.py +++ /dev/null @@ -1,1297 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. - -import argparse -import copy -import datetime -import errno -import hashlib -import math -import os -import time -from collections import OrderedDict, defaultdict, deque -from typing import List, Optional, Tuple - -import torch -from torchvision.transforms import autoaugment, transforms -from torchvision.transforms import functional as F -from torchvision.transforms.functional import InterpolationMode - -from torchao.prototype.sparsity.sparsifier.weight_norm_sparsifier import ( - WeightNormSparsifier, -) -from torchao.prototype.sparsity.superblock.blocksparse import block_sparse_weight -from torchao.prototype.sparsity.superblock.supermask import ( - SupermaskLinear, - apply_supermask, -) -from torchao.quantization import int8_dynamic_activation_int8_weight, quantize_ -from torchao.sparsity import semi_sparse_weight, sparsify_ - - -def get_args_parser(train=False, evaluate=False, benchmark=False): - assert sum([train, evaluate, benchmark]) == 1, ( - "One and only one of training, evaluation, or benchmark can be true" - ) - - # Shared common args - parser = argparse.ArgumentParser( - description="SuperBlock Imagenet Training/Evaluation/Benchmarking Script", - add_help=True, - ) - parser.add_argument("--data-path", type=str, help="IMAGENET dataset path") - parser.add_argument( - "--model", - default="vit_b_16", - choices=["vit_b_16", "vit_h_14"], - type=str, - help="ViT base model", - ) - parser.add_argument( - "--device", default="cuda", type=str, help="device (Default: cuda)" - ) - parser.add_argument( - "-b", "--batch-size", default=32, type=int, help="per device batch size" - ) - parser.add_argument( - "--val-crop-size", - default=224, - type=int, - help="the central crop size used for validation (default: 224)", - ) - parser.add_argument( - "--sparsity", - choices=["bsr", "semi_structured"], - default=None, - help="weight sparsification to apply", - ) - parser.add_argument( - "--bsr", - type=int, - nargs="?", - const=256, - default=None, - help="Convert sparsified weights to BSR format with optional block size (default: 256)", - ) - parser.add_argument("--sparsity-linear", type=float, default=0.0) - parser.add_argument("--sparsity-conv1x1", type=float, default=0.0) - parser.add_argument("--sparsity-conv", type=float, default=0.0) - parser.add_argument( - "--skip-last-layer-sparsity", - action="store_true", - help="Skip applying sparsity to the last linear layer (for vit only)", - ) - parser.add_argument( - "--skip-first-transformer-sparsity", - action="store_true", - help="Skip applying sparsity to the first transformer layer (for vit only)", - ) - parser.add_argument( - "--quantization", action="store_true", help="Run with int8 dynamic quantization" - ) - parser.add_argument( - "--weights", default=None, type=str, help="the weights enum name to load" - ) - parser.add_argument( - "--weights-path", - type=str, - help="optional checkpoint to load weights after intialization", - ) - parser.add_argument( - "--header", action="store_true", help="Print header for first run" - ) - - # Eval a subset of training args - # lots of training args - if train or evaluate: - parser.add_argument( - "-j", - "--workers", - default=16, - type=int, - metavar="N", - help="number of data loading workers", - ) - parser.add_argument( - "--accumulation-steps", - default=1, - type=int, - help="Number of steps to accumulate gradients over", - ) - parser.add_argument( - "--epochs", - default=90, - type=int, - metavar="N", - help="number of total epochs to run", - ) - parser.add_argument("--opt", default="sgd", type=str, help="optimizer") - parser.add_argument( - "--lr", default=0.1, type=float, help="initial learning rate" - ) - parser.add_argument( - "--momentum", default=0.9, type=float, metavar="M", help="momentum" - ) - parser.add_argument( - "--wd", - "--weight-decay", - default=1e-4, - type=float, - metavar="W", - help="weight decay", - dest="weight_decay", - ) - parser.add_argument( - "--norm-weight-decay", - default=None, - type=float, - help="weight decay for Normalization layers (default: None, same value as --wd)", - ) - parser.add_argument( - "--bias-weight-decay", - default=None, - type=float, - help="weight decay for bias parameters of all layers (default: None, same value as --wd)", - ) - parser.add_argument( - "--transformer-embedding-decay", - default=None, - type=float, - help="weight decay for embedding parameters for vision transformer models (default: None, same value as --wd)", - ) - parser.add_argument( - "--label-smoothing", - default=0.0, - type=float, - help="label smoothing (default: 0.0)", - dest="label_smoothing", - ) - parser.add_argument( - "--mixup-alpha", default=0.0, type=float, help="mixup alpha (default: 0.0)" - ) - parser.add_argument( - "--cutmix-alpha", - default=0.0, - type=float, - help="cutmix alpha (default: 0.0)", - ) - parser.add_argument( - "--lr-scheduler", - default="steplr", - type=str, - help="the lr scheduler (default: steplr)", - ) - parser.add_argument( - "--lr-warmup-epochs", - default=0, - type=int, - help="the number of epochs to warmup (default: 0)", - ) - parser.add_argument( - "--lr-warmup-method", - default="constant", - type=str, - help="the warmup method (default: constant)", - ) - parser.add_argument( - "--lr-warmup-decay", default=0.01, type=float, help="the decay for lr" - ) - parser.add_argument( - "--lr-step-size", - default=30, - type=int, - help="decrease lr every step-size epochs", - ) - parser.add_argument( - "--lr-gamma", - default=0.1, - type=float, - help="decrease lr by a factor of lr-gamma", - ) - parser.add_argument( - "--lr-min", - default=0.0, - type=float, - help="minimum lr of lr schedule (default: 0.0)", - ) - parser.add_argument( - "--print-freq", default=10, type=int, help="print frequency" - ) - parser.add_argument( - "--output-dir", default=".", type=str, help="path to save outputs" - ) - parser.add_argument( - "--resume", - action="store_true", - help='Resumes training from latest available checkpoint ("model_.pth")', - ) - parser.add_argument( - "--start-epoch", default=0, type=int, metavar="N", help="start epoch" - ) - parser.add_argument( - "--cache-dataset", - dest="cache_dataset", - help="Cache the datasets for quicker initialization. It also serializes the transforms", - action="store_true", - ) - parser.add_argument( - "--sync-bn", dest="sync_bn", help="Use sync batch norm", action="store_true" - ) - parser.add_argument( - "--auto-augment", - default=None, - type=str, - help="auto augment policy (default: None)", - ) - parser.add_argument( - "--ra-magnitude", - default=9, - type=int, - help="magnitude of auto augment policy", - ) - parser.add_argument( - "--augmix-severity", default=3, type=int, help="severity of augmix policy" - ) - parser.add_argument( - "--random-erase", - default=0.0, - type=float, - help="random erasing probability (default: 0.0)", - ) - # Mixed precision training parameters - parser.add_argument( - "--amp", - action="store_true", - help="Use torch.cuda.amp for mixed precision training", - ) - # distributed training parameters - parser.add_argument( - "--world-size", default=1, type=int, help="number of distributed processes" - ) - parser.add_argument( - "--dist-url", - default="env://", - type=str, - help="url used to set up distributed training", - ) - parser.add_argument( - "--model-ema", - action="store_true", - help="enable tracking Exponential Moving Average of model parameters", - ) - parser.add_argument( - "--model-ema-steps", - type=int, - default=32, - help="the number of iterations that controls how often to update the EMA model (default: 32)", - ) - parser.add_argument( - "--model-ema-decay", - type=float, - default=0.99998, - help="decay factor for Exponential Moving Average of model parameters (default: 0.99998)", - ) - parser.add_argument( - "--use-deterministic-algorithms", - action="store_true", - help="Forces the use of deterministic algorithms only.", - ) - parser.add_argument( - "--interpolation", - default="bilinear", - type=str, - help="the interpolation method (default: bilinear)", - ) - parser.add_argument( - "--val-resize-size", - default=256, - type=int, - help="the resize size used for validation (default: 256)", - ) - parser.add_argument( - "--train-crop-size", - default=224, - type=int, - help="the random crop size used for training (default: 224)", - ) - parser.add_argument( - "--clip-grad-norm", - default=None, - type=float, - help="the maximum gradient norm (default None)", - ) - parser.add_argument( - "--ra-reps", - default=3, - type=int, - help="number of repetitions for Repeated Augmentation (default: 3)", - ) - parser.add_argument( - "--meta", action="store_true", help="Use Meta internal imagenet structure" - ) - - if benchmark: - parser.add_argument( - "--dtype", - choices=["float32", "bfloat16", "float16"], - help="Data type", - default="bfloat16", - ) - parser.add_argument( - "--tune-kernel-params", - action="store_true", - help="Tune kernel params for BSR", - ) - parser.add_argument( - "--profile", action="store_true", help="Dump Prefetto trace" - ) - - return parser - - -# filter functions -def mlp_0_only(mod, name): - return isinstance(mod, torch.nn.Linear) and "mlp.0" in name - - -def mlp_3_only(mod, name): - return isinstance(mod, torch.nn.Linear) and "mlp.3" in name - - -def mlp_only(mod, name): - return isinstance(mod, torch.nn.Linear) and "mlp" in name - - -def superblock_only(mod, name): - return isinstance(mod, SupermaskLinear) and "mlp" in name - - -def mlp_only_with_args( - mod, name, skip_last_layer_sparsity=False, skip_first_transformer_sparsity=False -): - if skip_last_layer_sparsity and "heads.head" in name: - return False - if skip_first_transformer_sparsity and "encoder.layers.encoder_layer_0" in name: - return False - if isinstance(mod, torch.nn.Linear) and "mlp" in name: - return True - return False - - -### Custom sparsification utils -def apply_sparsity(model): - for name, module in model.named_modules(): - if isinstance(module, SupermaskLinear) and "mlp" in name: - module.sparsify_offline() - - -def accelerate_with_sparsity(model, args): - if args.sparsity == "bsr": - apply_sparsity(model) - if args.quantization: - from torchao.dtypes import BlockSparseLayout - - quantize_( - model, - int8_dynamic_activation_int8_weight( - _layout=BlockSparseLayout(blocksize=args.bsr) - ), - superblock_only, - ) - else: - assert args.bsr is not None, "BSR requires a block size" - sparsify_(model, block_sparse_weight(blocksize=args.bsr), superblock_only) - elif args.sparsity == "semi_structured": - if args.quantization: - from torchao.dtypes import SemiSparseLayout - - quantize_( - model, - int8_dynamic_activation_int8_weight(layout=SemiSparseLayout()), - mlp_0_only, - ) - sparsify_(model, semi_sparse_weight(), mlp_3_only) - else: - sparsify_(model, semi_sparse_weight(), mlp_only) - else: - if args.quantization: - quantize_(model, int8_dynamic_activation_int8_weight(), mlp_only) - - -def simulate_sparsity(model, args): - if args.sparsity == "bsr": - apply_supermask( - model, - linear_sparsity=args.sparsity_linear, - linear_sp_tilesize=args.bsr, - conv1x1_sparsity=args.sparsity_conv1x1, - conv1x1_sp_tilesize=args.bsr, - conv_sparsity=args.sparsity_conv, - conv_sp_tilesize=args.bsr, - skip_last_layer_sparsity=args.skip_last_layer_sparsity, - skip_first_transformer_sparsity=args.skip_first_transformer_sparsity, - device=args.device, - verbose=False, - ) - elif args.sparsity == "semi_structured": - sparse_config = [] - for name, mod in model.named_modules(): - if mlp_only_with_args( - mod, - name, - skip_first_transformer_sparsity=args.skip_first_transformer_sparsity, - skip_last_layer_sparsity=args.skip_last_layer_sparsity, - ): - sparse_config.append({"tensor_fqn": f"{name}.weight"}) - - sparsifier = WeightNormSparsifier( - sparsity_level=1.0, sparse_block_shape=(1, 4), zeros_per_block=2 - ) - sparsifier.prepare(model, sparse_config) - sparsifier.step() - return sparsifier - - -# ------------------------------------------------------------ -# The following code contains torchvision reference code, -# largely copied from: https://github.com/pytorch/vision/tree/main/references/classification -# Please open issues in the original repository if you have questions. - - -class SmoothedValue: - """Track a series of values and provide access to smoothed values over a - window or the global series average. - """ - - def __init__(self, window_size=20, fmt=None): - if fmt is None: - fmt = "{median:.4f} ({global_avg:.4f})" - self.deque = deque(maxlen=window_size) - self.total = 0.0 - self.count = 0 - self.fmt = fmt - - def update(self, value, n=1): - self.deque.append(value) - self.count += n - self.total += value * n - - def synchronize_between_processes(self): - """ - Warning: does not synchronize the deque! - """ - t = reduce_across_processes([self.count, self.total]) - t = t.tolist() - self.count = int(t[0]) - self.total = t[1] - - @property - def median(self): - d = torch.tensor(list(self.deque)) - return d.median().item() - - @property - def avg(self): - d = torch.tensor(list(self.deque), dtype=torch.float32) - return d.mean().item() - - @property - def global_avg(self): - return self.total / self.count - - @property - def max(self): - return max(self.deque) - - @property - def value(self): - return self.deque[-1] - - def __str__(self): - return self.fmt.format( - median=self.median, - avg=self.avg, - global_avg=self.global_avg, - max=self.max, - value=self.value, - ) - - -class MetricLogger: - def __init__(self, delimiter="\t"): - self.meters = defaultdict(SmoothedValue) - self.delimiter = delimiter - - def update(self, **kwargs): - for k, v in kwargs.items(): - if isinstance(v, torch.Tensor): - v = v.item() - assert isinstance(v, (float, int)) - self.meters[k].update(v) - - def __getattr__(self, attr): - if attr in self.meters: - return self.meters[attr] - if attr in self.__dict__: - return self.__dict__[attr] - raise AttributeError( - f"'{type(self).__name__}' object has no attribute '{attr}'" - ) - - def __str__(self): - loss_str = [] - for name, meter in self.meters.items(): - loss_str.append(f"{name}: {str(meter)}") - return self.delimiter.join(loss_str) - - def synchronize_between_processes(self): - for meter in self.meters.values(): - meter.synchronize_between_processes() - - def add_meter(self, name, meter): - self.meters[name] = meter - - def log_every(self, iterable, print_freq, header=None): - i = 0 - if not header: - header = "" - start_time = time.time() - end = time.time() - iter_time = SmoothedValue(fmt="{avg:.4f}") - data_time = SmoothedValue(fmt="{avg:.4f}") - space_fmt = ":" + str(len(str(len(iterable)))) + "d" - if torch.cuda.is_available(): - log_msg = self.delimiter.join( - [ - header, - "[{0" + space_fmt + "}/{1}]", - "eta: {eta}", - "{meters}", - "time: {time}", - "data: {data}", - "max mem: {memory:.0f}", - ] - ) - else: - log_msg = self.delimiter.join( - [ - header, - "[{0" + space_fmt + "}/{1}]", - "eta: {eta}", - "{meters}", - "time: {time}", - "data: {data}", - ] - ) - MB = 1024.0 * 1024.0 - for obj in iterable: - data_time.update(time.time() - end) - yield obj - iter_time.update(time.time() - end) - if i % print_freq == 0: - eta_seconds = iter_time.global_avg * (len(iterable) - i) - eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) - if torch.cuda.is_available(): - print( - log_msg.format( - i, - len(iterable), - eta=eta_string, - meters=str(self), - time=str(iter_time), - data=str(data_time), - memory=torch.cuda.max_memory_allocated() / MB, - ) - ) - else: - print( - log_msg.format( - i, - len(iterable), - eta=eta_string, - meters=str(self), - time=str(iter_time), - data=str(data_time), - ) - ) - i += 1 - end = time.time() - total_time = time.time() - start_time - total_time_str = str(datetime.timedelta(seconds=int(total_time))) - print(f"{header} Total time: {total_time_str}") - - -class ExponentialMovingAverage(torch.optim.swa_utils.AveragedModel): - """Maintains moving averages of model parameters using an exponential decay. - ``ema_avg = decay * avg_model_param + (1 - decay) * model_param`` - `torch.optim.swa_utils.AveragedModel `_ - is used to compute the EMA. - """ - - def __init__(self, model, decay, device="cpu"): - def ema_avg(avg_model_param, model_param, num_averaged): - return decay * avg_model_param + (1 - decay) * model_param - - super().__init__(model, device, ema_avg, use_buffers=True) - - -def accuracy(output, target, topk=(1,)): - """Computes the accuracy over the k top predictions for the specified values of k""" - with torch.inference_mode(): - maxk = max(topk) - batch_size = target.size(0) - if target.ndim == 2: - target = target.max(dim=1)[1] - - _, pred = output.topk(maxk, 1, True, True) - pred = pred.t() - correct = pred.eq(target[None]) - - res = [] - for k in topk: - correct_k = correct[:k].flatten().sum(dtype=torch.float32) - res.append(correct_k * (100.0 / batch_size)) - return res - - -def mkdir(path): - try: - os.makedirs(path) - except OSError as e: - if e.errno != errno.EEXIST: - raise - - -def setup_for_distributed(is_master): - """ - This function disables printing when not in master process - """ - import builtins as __builtin__ - - builtin_print = __builtin__.print - - def print(*args, **kwargs): - force = kwargs.pop("force", False) - if is_master or force: - builtin_print(*args, **kwargs) - - __builtin__.print = print - - -def is_dist_avail_and_initialized(): - if not torch.distributed.is_available(): - return False - if not torch.distributed.is_initialized(): - return False - return True - - -def get_world_size(): - if not is_dist_avail_and_initialized(): - return 1 - return torch.distributed.get_world_size() - - -def get_rank(): - if not is_dist_avail_and_initialized(): - return 0 - return torch.distributed.get_rank() - - -def is_main_process(): - return get_rank() == 0 - - -def save_on_master(*args, **kwargs): - if is_main_process(): - torch.save(*args, **kwargs) - - -def init_distributed_mode(args): - if "RANK" in os.environ and "WORLD_SIZE" in os.environ: - args.rank = int(os.environ["RANK"]) - args.world_size = int(os.environ["WORLD_SIZE"]) - args.gpu = int(os.environ["LOCAL_RANK"]) - elif "SLURM_PROCID" in os.environ: - args.rank = int(os.environ["SLURM_PROCID"]) - args.gpu = args.rank % torch.cuda.device_count() - elif hasattr(args, "rank"): - pass - else: - print("Not using distributed mode") - args.distributed = False - return - - args.distributed = True - - torch.cuda.set_device(args.gpu) - args.dist_backend = "nccl" - print(f"| distributed init (rank {args.rank})", flush=True) - torch.distributed.init_process_group( - backend=args.dist_backend, - init_method=args.dist_url, - world_size=args.world_size, - rank=args.rank, - ) - torch.distributed.barrier() - setup_for_distributed(args.rank == 0) - - -def average_checkpoints(inputs): - """Loads checkpoints from inputs and returns a model with averaged weights. Original implementation taken from: - https://github.com/pytorch/fairseq/blob/a48f235636557b8d3bc4922a6fa90f3a0fa57955/scripts/average_checkpoints.py#L16 - - Args: - inputs (List[str]): An iterable of string paths of checkpoints to load from. - Returns: - A dict of string keys mapping to various values. The 'model' key - from the returned dict should correspond to an OrderedDict mapping - string parameter names to torch Tensors. - """ - params_dict = OrderedDict() - params_keys = None - new_state = None - num_models = len(inputs) - for fpath in inputs: - with open(fpath, "rb") as f: - state = torch.load( - f, - map_location=( - lambda s, _: torch.serialization.default_restore_location(s, "cpu") - ), - ) - # Copies over the settings from the first checkpoint - if new_state is None: - new_state = state - model_params = state["model"] - model_params_keys = list(model_params.keys()) - if params_keys is None: - params_keys = model_params_keys - elif params_keys != model_params_keys: - raise KeyError( - f"For checkpoint {f}, expected list of params: {params_keys}, but found: {model_params_keys}" - ) - for k in params_keys: - p = model_params[k] - if isinstance(p, torch.HalfTensor): - p = p.float() - if k not in params_dict: - params_dict[k] = p.clone() - # NOTE: clone() is needed in case of p is a shared parameter - else: - params_dict[k] += p - averaged_params = OrderedDict() - for k, v in params_dict.items(): - averaged_params[k] = v - if averaged_params[k].is_floating_point(): - averaged_params[k].div_(num_models) - else: - averaged_params[k] //= num_models - new_state["model"] = averaged_params - return new_state - - -def store_model_weights(model, checkpoint_path, checkpoint_key="model", strict=True): - """ - This method can be used to prepare weights files for new models. It receives as - input a model architecture and a checkpoint from the training script and produces - a file with the weights ready for release. - - Examples: - from torchvision import models as M - - # Classification - model = M.mobilenet_v3_large(weights=None) - print(store_model_weights(model, './class.pth')) - - # Quantized Classification - model = M.quantization.mobilenet_v3_large(weights=None, quantize=False) - model.fuse_model(is_qat=True) - model.qconfig = torch.ao.quantization.get_default_qat_qconfig('qnnpack') - _ = torch.ao.quantization.prepare_qat(model, inplace=True) - print(store_model_weights(model, './qat.pth')) - - # Object Detection - model = M.detection.fasterrcnn_mobilenet_v3_large_fpn(weights=None, weights_backbone=None) - print(store_model_weights(model, './obj.pth')) - - # Segmentation - model = M.segmentation.deeplabv3_mobilenet_v3_large(weights=None, weights_backbone=None, aux_loss=True) - print(store_model_weights(model, './segm.pth', strict=False)) - - Args: - model (pytorch.nn.Module): The model on which the weights will be loaded for validation purposes. - checkpoint_path (str): The path of the checkpoint we will load. - checkpoint_key (str, optional): The key of the checkpoint where the model weights are stored. - Default: "model". - strict (bool): whether to strictly enforce that the keys - in :attr:`state_dict` match the keys returned by this module's - :meth:`~torch.nn.Module.state_dict` function. Default: ``True`` - - Returns: - output_path (str): The location where the weights are saved. - """ - # Store the new model next to the checkpoint_path - checkpoint_path = os.path.abspath(checkpoint_path) - output_dir = os.path.dirname(checkpoint_path) - - # Deep copy to avoid side-effects on the model object. - model = copy.deepcopy(model) - checkpoint = torch.load(checkpoint_path, map_location="cpu") - - # Load the weights to the model to validate that everything works - # and remove unnecessary weights (such as auxiliaries, etc) - if checkpoint_key == "model_ema": - del checkpoint[checkpoint_key]["n_averaged"] - torch.nn.modules.utils.consume_prefix_in_state_dict_if_present( - checkpoint[checkpoint_key], "module." - ) - model.load_state_dict(checkpoint[checkpoint_key], strict=strict) - - tmp_path = os.path.join(output_dir, str(model.__hash__())) - torch.save(model.state_dict(), tmp_path) - - sha256_hash = hashlib.sha256() - with open(tmp_path, "rb") as f: - # Read and update hash string value in blocks of 4K - for byte_block in iter(lambda: f.read(4096), b""): - sha256_hash.update(byte_block) - hh = sha256_hash.hexdigest() - - output_path = os.path.join(output_dir, "weights-" + str(hh[:8]) + ".pth") - os.replace(tmp_path, output_path) - - return output_path - - -def reduce_across_processes(val): - if not is_dist_avail_and_initialized(): - # nothing to sync, but we still convert to tensor for consistency with the distributed case. - return torch.tensor(val) - - t = torch.tensor(val, device="cuda") - torch.distributed.barrier() - torch.distributed.all_reduce(t) - return t - - -def set_weight_decay( - model: torch.nn.Module, - weight_decay: float, - norm_weight_decay: Optional[float] = None, - norm_classes: Optional[List[type]] = None, - custom_keys_weight_decay: Optional[List[Tuple[str, float]]] = None, -): - if not norm_classes: - norm_classes = [ - torch.nn.modules.batchnorm._BatchNorm, - torch.nn.LayerNorm, - torch.nn.GroupNorm, - torch.nn.modules.instancenorm._InstanceNorm, - torch.nn.LocalResponseNorm, - ] - norm_classes = tuple(norm_classes) - - params = { - "other": [], - "norm": [], - } - params_weight_decay = { - "other": weight_decay, - "norm": norm_weight_decay, - } - custom_keys = [] - if custom_keys_weight_decay is not None: - for key, weight_decay in custom_keys_weight_decay: - params[key] = [] - params_weight_decay[key] = weight_decay - custom_keys.append(key) - - def _add_params(module, prefix=""): - for name, p in module.named_parameters(recurse=False): - if not p.requires_grad: - continue - is_custom_key = False - for key in custom_keys: - target_name = ( - f"{prefix}.{name}" if prefix != "" and "." in key else name - ) - if key == target_name: - params[key].append(p) - is_custom_key = True - break - if not is_custom_key: - if norm_weight_decay is not None and isinstance(module, norm_classes): - params["norm"].append(p) - else: - params["other"].append(p) - - for child_name, child_module in module.named_children(): - child_prefix = f"{prefix}.{child_name}" if prefix != "" else child_name - _add_params(child_module, prefix=child_prefix) - - _add_params(model) - - param_groups = [] - for key in params: - if len(params[key]) > 0: - param_groups.append( - {"params": params[key], "weight_decay": params_weight_decay[key]} - ) - return param_groups - - -# Presets for ImageNet training/eval taken from: https://github.com/pytorch/vision/blob/main/references/classification/presets.py - - -class ClassificationPresetTrain: - def __init__( - self, - *, - crop_size, - mean=(0.485, 0.456, 0.406), - std=(0.229, 0.224, 0.225), - interpolation=InterpolationMode.BILINEAR, - hflip_prob=0.5, - auto_augment_policy=None, - ra_magnitude=9, - augmix_severity=3, - random_erase_prob=0.0, - ): - trans = [transforms.RandomResizedCrop(crop_size, interpolation=interpolation)] - if hflip_prob > 0: - trans.append(transforms.RandomHorizontalFlip(hflip_prob)) - if auto_augment_policy is not None: - if auto_augment_policy == "ra": - trans.append( - autoaugment.RandAugment( - interpolation=interpolation, magnitude=ra_magnitude - ) - ) - elif auto_augment_policy == "ta_wide": - trans.append( - autoaugment.TrivialAugmentWide(interpolation=interpolation) - ) - elif auto_augment_policy == "augmix": - trans.append( - autoaugment.AugMix( - interpolation=interpolation, severity=augmix_severity - ) - ) - else: - aa_policy = autoaugment.AutoAugmentPolicy(auto_augment_policy) - trans.append( - autoaugment.AutoAugment( - policy=aa_policy, interpolation=interpolation - ) - ) - trans.extend( - [ - transforms.PILToTensor(), - transforms.ConvertImageDtype(torch.float), - transforms.Normalize(mean=mean, std=std), - ] - ) - if random_erase_prob > 0: - trans.append(transforms.RandomErasing(p=random_erase_prob)) - - self.transforms = transforms.Compose(trans) - - def __call__(self, img): - return self.transforms(img) - - -class ClassificationPresetEval: - def __init__( - self, - *, - crop_size, - resize_size=256, - mean=(0.485, 0.456, 0.406), - std=(0.229, 0.224, 0.225), - interpolation=InterpolationMode.BILINEAR, - ): - self.transforms = transforms.Compose( - [ - transforms.Resize(resize_size, interpolation=interpolation), - transforms.CenterCrop(crop_size), - transforms.PILToTensor(), - transforms.ConvertImageDtype(torch.float), - transforms.Normalize(mean=mean, std=std), - ] - ) - - def __call__(self, img): - return self.transforms(img) - - -# transforms taken from: https://github.com/pytorch/vision/blob/main/references/classification/transforms.py - - -class RandomMixup(torch.nn.Module): - """Randomly apply Mixup to the provided batch and targets. - The class implements the data augmentations as described in the paper - `"mixup: Beyond Empirical Risk Minimization" `_. - - Args: - num_classes (int): number of classes used for one-hot encoding. - p (float): probability of the batch being transformed. Default value is 0.5. - alpha (float): hyperparameter of the Beta distribution used for mixup. - Default value is 1.0. - inplace (bool): boolean to make this transform inplace. Default set to False. - """ - - def __init__( - self, - num_classes: int, - p: float = 0.5, - alpha: float = 1.0, - inplace: bool = False, - ) -> None: - super().__init__() - - if num_classes < 1: - raise ValueError( - f"Please provide a valid positive value for the num_classes. Got num_classes={num_classes}" - ) - - if alpha <= 0: - raise ValueError("Alpha param can't be zero.") - - self.num_classes = num_classes - self.p = p - self.alpha = alpha - self.inplace = inplace - - def forward( - self, batch: torch.Tensor, target: torch.Tensor - ) -> Tuple[torch.Tensor, torch.Tensor]: - """ - Args: - batch (Tensor): Float tensor of size (B, C, H, W) - target (Tensor): Integer tensor of size (B, ) - - Returns: - Tensor: Randomly transformed batch. - """ - if batch.ndim != 4: - raise ValueError(f"Batch ndim should be 4. Got {batch.ndim}") - if target.ndim != 1: - raise ValueError(f"Target ndim should be 1. Got {target.ndim}") - if not batch.is_floating_point(): - raise TypeError(f"Batch dtype should be a float tensor. Got {batch.dtype}.") - if target.dtype != torch.int64: - raise TypeError(f"Target dtype should be torch.int64. Got {target.dtype}") - - if not self.inplace: - batch = batch.clone() - target = target.clone() - - if target.ndim == 1: - target = torch.nn.functional.one_hot( - target, num_classes=self.num_classes - ).to(dtype=batch.dtype) - - if torch.rand(1).item() >= self.p: - return batch, target - - # It's faster to roll the batch by one instead of shuffling it to create image pairs - batch_rolled = batch.roll(1, 0) - target_rolled = target.roll(1, 0) - - # Implemented as on mixup paper, page 3. - lambda_param = float( - torch._sample_dirichlet(torch.tensor([self.alpha, self.alpha]))[0] - ) - batch_rolled.mul_(1.0 - lambda_param) - batch.mul_(lambda_param).add_(batch_rolled) - - target_rolled.mul_(1.0 - lambda_param) - target.mul_(lambda_param).add_(target_rolled) - - return batch, target - - def __repr__(self) -> str: - s = ( - f"{self.__class__.__name__}(" - f"num_classes={self.num_classes}" - f", p={self.p}" - f", alpha={self.alpha}" - f", inplace={self.inplace}" - f")" - ) - return s - - -class RandomCutmix(torch.nn.Module): - """Randomly apply Cutmix to the provided batch and targets. - The class implements the data augmentations as described in the paper - `"CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features" - `_. - - Args: - num_classes (int): number of classes used for one-hot encoding. - p (float): probability of the batch being transformed. Default value is 0.5. - alpha (float): hyperparameter of the Beta distribution used for cutmix. - Default value is 1.0. - inplace (bool): boolean to make this transform inplace. Default set to False. - """ - - def __init__( - self, - num_classes: int, - p: float = 0.5, - alpha: float = 1.0, - inplace: bool = False, - ) -> None: - super().__init__() - if num_classes < 1: - raise ValueError( - "Please provide a valid positive value for the num_classes." - ) - if alpha <= 0: - raise ValueError("Alpha param can't be zero.") - - self.num_classes = num_classes - self.p = p - self.alpha = alpha - self.inplace = inplace - - def forward( - self, batch: torch.Tensor, target: torch.Tensor - ) -> Tuple[torch.Tensor, torch.Tensor]: - """ - Args: - batch (Tensor): Float tensor of size (B, C, H, W) - target (Tensor): Integer tensor of size (B, ) - - Returns: - Tensor: Randomly transformed batch. - """ - if batch.ndim != 4: - raise ValueError(f"Batch ndim should be 4. Got {batch.ndim}") - if target.ndim != 1: - raise ValueError(f"Target ndim should be 1. Got {target.ndim}") - if not batch.is_floating_point(): - raise TypeError(f"Batch dtype should be a float tensor. Got {batch.dtype}.") - if target.dtype != torch.int64: - raise TypeError(f"Target dtype should be torch.int64. Got {target.dtype}") - - if not self.inplace: - batch = batch.clone() - target = target.clone() - - if target.ndim == 1: - target = torch.nn.functional.one_hot( - target, num_classes=self.num_classes - ).to(dtype=batch.dtype) - - if torch.rand(1).item() >= self.p: - return batch, target - - # It's faster to roll the batch by one instead of shuffling it to create image pairs - batch_rolled = batch.roll(1, 0) - target_rolled = target.roll(1, 0) - - # Implemented as on cutmix paper, page 12 (with minor corrections on typos). - lambda_param = float( - torch._sample_dirichlet(torch.tensor([self.alpha, self.alpha]))[0] - ) - _, H, W = F.get_dimensions(batch) - - r_x = torch.randint(W, (1,)) - r_y = torch.randint(H, (1,)) - - r = 0.5 * math.sqrt(1.0 - lambda_param) - r_w_half = int(r * W) - r_h_half = int(r * H) - - x1 = int(torch.clamp(r_x - r_w_half, min=0)) - y1 = int(torch.clamp(r_y - r_h_half, min=0)) - x2 = int(torch.clamp(r_x + r_w_half, max=W)) - y2 = int(torch.clamp(r_y + r_h_half, max=H)) - - batch[:, :, y1:y2, x1:x2] = batch_rolled[:, :, y1:y2, x1:x2] - lambda_param = float(1.0 - (x2 - x1) * (y2 - y1) / (W * H)) - - target_rolled.mul_(1.0 - lambda_param) - target.mul_(lambda_param).add_(target_rolled) - - return batch, target - - def __repr__(self) -> str: - s = ( - f"{self.__class__.__name__}(" - f"num_classes={self.num_classes}" - f", p={self.p}" - f", alpha={self.alpha}" - f", inplace={self.inplace}" - f")" - ) - return s - - -# RA Sampler implementaion taken from: https://github.com/pytorch/vision/blob/main/references/classification/sampler.py - - -class RASampler(torch.utils.data.Sampler): - """Sampler that restricts data loading to a subset of the dataset for distributed, - with repeated augmentation. - It ensures that different each augmented version of a sample will be visible to a - different process (GPU). - Heavily based on 'torch.utils.data.DistributedSampler'. - - This is borrowed from the DeiT Repo: - https://github.com/facebookresearch/deit/blob/main/samplers.py - """ - - def __init__( - self, dataset, num_replicas=None, rank=None, shuffle=True, seed=0, repetitions=3 - ): - if num_replicas is None: - if not torch.distributed.is_available(): - raise RuntimeError("Requires distributed package to be available!") - num_replicas = torch.distributed.get_world_size() - if rank is None: - if not torch.distributed.is_available(): - raise RuntimeError("Requires distributed package to be available!") - rank = torch.distributed.get_rank() - self.dataset = dataset - self.num_replicas = num_replicas - self.rank = rank - self.epoch = 0 - self.num_samples = int( - math.ceil(len(self.dataset) * float(repetitions) / self.num_replicas) - ) - self.total_size = self.num_samples * self.num_replicas - self.num_selected_samples = int( - math.floor(len(self.dataset) // 256 * 256 / self.num_replicas) - ) - self.shuffle = shuffle - self.seed = seed - self.repetitions = repetitions - - def __iter__(self): - if self.shuffle: - # Deterministically shuffle based on epoch - g = torch.Generator() - g.manual_seed(self.seed + self.epoch) - indices = torch.randperm(len(self.dataset), generator=g).tolist() - else: - indices = list(range(len(self.dataset))) - - # Add extra samples to make it evenly divisible - indices = [ele for ele in indices for i in range(self.repetitions)] - indices += indices[: (self.total_size - len(indices))] - assert len(indices) == self.total_size - - # Subsample - indices = indices[self.rank : self.total_size : self.num_replicas] - assert len(indices) == self.num_samples - - return iter(indices[: self.num_selected_samples]) - - def __len__(self): - return self.num_selected_samples - - def set_epoch(self, epoch): - self.epoch = epoch From 488c856ebf5e186d0487313b9599f91c7afc0e8c Mon Sep 17 00:00:00 2001 From: Lisa Jin Date: Mon, 16 Jun 2025 10:23:41 -0700 Subject: [PATCH 122/165] Test PARQ with torchao activation quantization (#2370) * Test PARQ with torchao activation quantization * Replace assertTrue with torch.testing.assert_close --- test/prototype/test_parq.py | 89 +++++++++++++++++-- .../prototype/parq/quant/uniform_torchao.py | 44 +++++---- 2 files changed, 102 insertions(+), 31 deletions(-) diff --git a/test/prototype/test_parq.py b/test/prototype/test_parq.py index 83c60ccf12..68c25821ee 100644 --- a/test/prototype/test_parq.py +++ b/test/prototype/test_parq.py @@ -27,8 +27,15 @@ ) from torchao.prototype.parq.quant.uniform_torchao import _BIT_WIDTH_TO_DTYPE from torchao.quantization.granularity import PerGroup +from torchao.quantization.qat import ( + FakeQuantizeConfig, + FromIntXQuantizationAwareTrainingConfig, + IntXQuantizationAwareTrainingConfig, +) from torchao.quantization.quant_api import ( + Int8DynamicActivationIntxWeightConfig, IntxWeightOnlyConfig, + MappingType, _is_linear, int4_weight_only, quantize_, @@ -68,9 +75,9 @@ def build_param_groups(model, b: int = 2, group_size: Optional[int] = None): class M(nn.Module): - def __init__(self, m=256, n=128, k=16, bias=False): + def __init__(self, m=256, n=128, k=16, bias=False, embedding=True): super().__init__() - self.embedding = nn.Embedding(10, m) + self.embedding = nn.Embedding(10, m) if embedding else nn.Identity() self.linear1 = nn.Linear(m, n, bias=bias) self.linear2 = nn.Linear(n, k, bias=bias) self.relu = nn.ReLU() @@ -83,7 +90,11 @@ def reset_parameters(self): nn.init.zeros_(module.bias) def example_inputs(self, device=None): - return torch.randint(1, 10, (1, 256), device=device) + return ( + torch.randint(1, 10, (1, self.linear1.in_features), device=device) + if isinstance(self.embedding, nn.Embedding) + else torch.randn(1, self.linear1.in_features, device=device) + ) def forward(self, x): x = self.embedding(x) @@ -150,11 +161,11 @@ def compare_quantized_models( p = p.view(-1, group_size) q, Q = quantizer.quantize(p, b=b, dim=-1) - q = q.view(original_shape) # compare to AffineQuantizedTensor instance + q = q.view(original_shape) ref = getattr(m_ref, n).weight.dequantize() - self.assertTrue(q.equal(ref)) + torch.testing.assert_close(q, ref, atol=0, rtol=0) def compare_parq_convert( self, @@ -182,13 +193,13 @@ def compare_parq_convert( p = module.weight.dequantize() # PARQ weight after quantize_ p_ref = getattr(m_ref, n).weight.dequantize() # native quantize_ - self.assertTrue(p_orig.equal(p_ref)) - self.assertTrue(p.equal(p_ref)) + torch.testing.assert_true(p_orig, p_ref, atol=0, rtol=0) + torch.testing.assert_true(p, p_ref, atol=0, rtol=0) @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_4, "Test only enabled for 2.4+") @common_utils.parametrize("group_size", [32, 256]) def test_int4_weight_only(self, group_size: int = 32): - model = M(m=512, n=512).to(torch.bfloat16).to(_DEVICE) + model = M(m=512, n=512).to(_DEVICE, dtype=torch.bfloat16) model.reset_parameters() m_ref = copy.deepcopy(model).eval().to(_DEVICE) @@ -265,8 +276,70 @@ def test_intx_weight_only_e2e(self, b: int = 2, group_size: int = 32): self.compare_parq_convert(model, m_ref, optimizer, config) +class TestInt8DynamicActivationTorchaoQuantizer(common_utils.TestCase): + def setUp(self): + torch.manual_seed(123) + + @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_6, "Test only enabled for 2.6+") + @common_utils.parametrize("b", [2, 3, 4, 8]) + @common_utils.parametrize("model_dtype", [torch.float16, torch.float32]) + @common_utils.parametrize("group_size", [32, 128]) + def test_int8_dynamic_activation_intx_e2e( + self, + b: int = 2, + model_dtype: torch.dtype = torch.float32, + group_size: int = 32, + ): + model = M(embedding=False).to(_DEVICE, dtype=model_dtype) + x = model.example_inputs(device=_DEVICE).to(model_dtype) + + # reference model using native quantization + m_ref = copy.deepcopy(model).eval().to(_DEVICE) + quantizer = UnifTorchaoQuantizer() + config = Int8DynamicActivationIntxWeightConfig( + weight_dtype=_BIT_WIDTH_TO_DTYPE[b], + weight_granularity=PerGroup(group_size), + weight_mapping_type=quantizer.mapping_type, + act_mapping_type=MappingType.ASYMMETRIC, + ) + quantize_(m_ref, config) + ref_out = m_ref(x) + + # quantize weights with PARQ + base_optimizer = torch.optim.SGD(build_param_groups(model, b, group_size)) + optimizer = QuantOptimizer( + base_optimizer, quantizer, ProxHardQuant(), quant_per_channel=True + ) + optimizer.zero_grad() + optimizer.step() + + # apply torchao quantized activations on top + activation_config = FakeQuantizeConfig( + torch.int8, + granularity="per_token", + mapping_type=config.act_mapping_type, + ) + filter_fn = optimizer.get_filter_fn(model) + quantize_( + model, + IntXQuantizationAwareTrainingConfig(activation_config=activation_config), + filter_fn=filter_fn, + ) + out = model(x) + torch.testing.assert_close(out, ref_out, atol=0, rtol=0) + + # equivalent to torchao's convert step + model.eval() + optimizer.restore_latent_params() + quantize_(model, FromIntXQuantizationAwareTrainingConfig(), filter_fn=filter_fn) + quantize_(model, config, filter_fn=filter_fn) + converted_out = model(x) + torch.testing.assert_close(converted_out, ref_out, atol=0, rtol=0) + + common_utils.instantiate_parametrized_tests(TestPARQuantization) common_utils.instantiate_parametrized_tests(TestUnifTorchaoQuantizer) +common_utils.instantiate_parametrized_tests(TestInt8DynamicActivationTorchaoQuantizer) if __name__ == "__main__": diff --git a/torchao/prototype/parq/quant/uniform_torchao.py b/torchao/prototype/parq/quant/uniform_torchao.py index a71ac8b5b3..4f90f9cb92 100644 --- a/torchao/prototype/parq/quant/uniform_torchao.py +++ b/torchao/prototype/parq/quant/uniform_torchao.py @@ -50,8 +50,23 @@ def __init__( self.quant_min = quant_min self.quant_max = quant_max self.eps = eps - self.preserve_zero = preserve_zero - self.zero_point_domain = zero_point_domain + + # defaults: zero_point_domain=ZeroPointDomain.INT, preserve_zero=True + self._choose_qparams = choose_qparams_affine + self._quantize = quantize_affine + self._dequantize = dequantize_affine + + if zero_point_domain == ZeroPointDomain.FLOAT and not preserve_zero: + self._choose_qparams = choose_qparams_affine_tinygemm + self._quantize = quantize_affine_tinygemm + self._dequantize = dequantize_affine_tinygemm + elif zero_point_domain == ZeroPointDomain.INT and not preserve_zero: + self._choose_qparams = choose_qparams_affine_dont_preserve_zero + self._quantize = quantize_affine + self._dequantize = dequantize_affine + elif zero_point_domain == ZeroPointDomain.NONE: + self._quantize = quantize_affine_no_zero_point + self._dequantize = dequantize_affine_no_zero_point def _init_quant_min_max(self, b: int) -> None: if self.quant_min is None or self.quant_max is None: @@ -74,24 +89,7 @@ def quantize( # assume that p has already been grouped in QuantOptimizer.step block_size = (1, p.size(-1)) if dim is not None else p.size() - if self.zero_point_domain == ZeroPointDomain.FLOAT and not self.preserve_zero: - _choose_qparams_affine = choose_qparams_affine_tinygemm - _quantize_affine = quantize_affine_tinygemm - _dequantize_affine = dequantize_affine_tinygemm - elif self.zero_point_domain == ZeroPointDomain.INT and not self.preserve_zero: - _choose_qparams_affine = choose_qparams_affine_dont_preserve_zero - _quantize_affine = quantize_affine - _dequantize_affine = dequantize_affine - else: # Default case: zero_point_domain == ZeroPointDomain.INT/NONE and preserve_zero - _choose_qparams_affine = choose_qparams_affine - if self.zero_point_domain == ZeroPointDomain.INT: - _quantize_affine = quantize_affine - _dequantize_affine = dequantize_affine - else: - _quantize_affine = quantize_affine_no_zero_point - _dequantize_affine = dequantize_affine_no_zero_point - - s, zero_point = _choose_qparams_affine( + s, zero_point = self._choose_qparams( p, self.mapping_type, block_size, @@ -101,13 +99,13 @@ def quantize( quant_max=self.quant_max, ) q_args = (block_size, s, zero_point, self.target_dtype) - q = _quantize_affine( + q = self._quantize( p, *q_args, quant_min=self.quant_min, quant_max=self.quant_max, ) - q = _dequantize_affine( + q = self._dequantize( q, *q_args, output_dtype=p.dtype, @@ -124,7 +122,7 @@ def quantize( else: block_size = Q.shape - Q = _dequantize_affine( + Q = self._dequantize( Q, block_size, *q_args[1:], From 5239ce7e64ff71f5b3f8affb95a137fe7200a6a0 Mon Sep 17 00:00:00 2001 From: andrewor14 Date: Mon, 16 Jun 2025 13:46:07 -0400 Subject: [PATCH 123/165] Revamp README (#2374) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Major changes: - Added performance highlights to the top - Added Latest News and Quick Start section - Moved Integrations to the top - Added key features - Updated overall messaging to match TorchAO paper - Added more tags to look more official - Condensed and hid some sections with too much detail - General formatting fixes and visual improvements ## Before: Screenshot 2025-06-13 at 5 07 41 PM ## After: Screenshot 2025-06-13 at 5 07 21 PM --- README.md | 274 +++++++++++++++++++++++++++++------------------------- 1 file changed, 146 insertions(+), 128 deletions(-) diff --git a/README.md b/README.md index 691594a933..d269c3974e 100644 --- a/README.md +++ b/README.md @@ -1,40 +1,133 @@ -# torchao: PyTorch Architecture Optimization +
+# TorchAO + +
+ +### PyTorch-Native Training-to-Serving Model Optimization +- Pre-train Llama-3.1-70B **1.5x faster** with float8 training +- Recover **77% of quantized perplexity degradation** on Llama-3.2-3B with QAT +- Quantize Llama-3-8B to int4 for **1.89x faster** inference with **58% less memory** + +
+ +[![](https://img.shields.io/badge/CodeML_%40_ICML-2025-blue)](https://codeml-workshop.github.io/codeml2025/) [![](https://dcbadge.vercel.app/api/server/gpumode?style=flat&label=TorchAO%20in%20GPU%20Mode)](https://discord.com/channels/1189498204333543425/1205223658021458100) +[![](https://img.shields.io/github/contributors-anon/pytorch/ao?color=yellow&style=flat-square)](https://github.com/pytorch/ao/graphs/contributors) +[![](https://img.shields.io/badge/torchao-documentation-blue?color=DE3412)](https://docs.pytorch.org/ao/stable/index.html) +[![license](https://img.shields.io/badge/license-BSD_3--Clause-lightgrey.svg)](./LICENSE) + +[Latest News](#-latest-news) | [Overview](#-overview) | [Quick Start](#-quick-start) | [Integrations](#-integrations) | [Inference](#-inference) | [Training](#-training) | [Videos](#-videos) | [Citation](#-citation) + +
+ + +## 📣 Latest News + +- [Jun 25] Our [TorchAO paper](https://codeml-workshop.github.io/codeml2025/) was accepted to CodeML @ ICML 2025! +- [Apr 25] Float8 rowwise training yielded [1.34-1.43x training speedup](https://pytorch.org/blog/accelerating-large-scale-training-and-convergence-with-pytorch-float8-rowwise-on-crusoe-2k-h200s/) at 2k H100 GPU scale +- [Apr 25] TorchAO is added as a [quantization backend to vLLM](https://docs.vllm.ai/en/latest/features/quantization/torchao.html) ([docs](https://docs.vllm.ai/en/latest/features/quantization/torchao.html))! +- [Mar 25] Our [2:4 Sparsity paper](https://openreview.net/pdf?id=O5feVk7p6Y) was accepted to SLLM @ ICLR 2025! +- [Jan 25] Our [integration with GemLite and SGLang](https://pytorch.org/blog/accelerating-llm-inference/) yielded 1.1-2x faster inference with int4 and float8 quantization across different batch sizes and tensor parallel sizes +- [Jan 25] We added [1-8 bit ARM CPU kernels](https://pytorch.org/blog/hi-po-low-bit-operators/) for linear and embedding ops + +
+ Older news +- [Nov 24] We achieved [1.43-1.51x faster pre-training](https://pytorch.org/blog/training-using-float8-fsdp2/) on Llama-3.1-70B and 405B using float8 training +- [Oct 24] TorchAO is added as a quantization backend to HF Transformers! +- [Sep 24] We officially launched TorchAO. Check out our blog [here](https://pytorch.org/blog/pytorch-native-architecture-optimization/)! +- [Jul 24] QAT [recovered up to 96% accuracy degradation](https://pytorch.org/blog/quantization-aware-training/) from quantization on Llama-3-8B +- [Jun 24] Semi-structured 2:4 sparsity [achieved 1.1x inference speedup and 1.3x training speedup](https://pytorch.org/blog/accelerating-neural-network-training/) on the SAM and ViT models respectively +- [Jun 24] Block sparsity [achieved 1.46x training speeedup](https://pytorch.org/blog/speeding-up-vits/) on the ViT model with <2% drop in accuracy -[Introduction](#introduction) | [Inference](#inference) | [Training](#training) | [Installation](#installation) |[Composability](#composability) | [Prototype Features](#prototype-features) | [Integrations](#integrations) | [Videos](#videos) | [For Developers](#for-developers) | [License](#license) | [Citation](#citation) +
-## Introduction -`torchao` accelerates PyTorch models with minimal code changes through advanced quantization and sparsification techniques. Optimize weights, gradients, activations, and more for both inference and training. +## 🌅 Overview -From the team that brought you the fast series +TorchAO is a PyTorch-native model optimization framework leveraging quantization and sparsity to provide an end-to-end, training-to-serving workflow +for AI models. TorchAO works out-of-the-box with `torch.compile()` and `FSDP2` across most HuggingFace PyTorch models. Key features include: +* Float8 [training](torchao/float8/README.md) and [inference](https://docs.pytorch.org/ao/main/generated/torchao.quantization.Float8DynamicActivationFloat8WeightConfig.html) for speedups without compromising accuracy +* [MX training and inference](torchao/prototype/mx_formats/README.md), provides MX tensor formats based on native PyTorch MX dtypes (prototype) +* [Quantization-Aware Training (QAT)](torchao/quantization/qat/README.md) for mitigating quantization degradation +* [Post-Training Quantization (PTQ)](torchao/quantization/README.md) for int4, int8, fp6 etc, with matching kernels targeting a variety of backends including CUDA, ARM CPU, and XNNPACK +* [Sparsity](torchao/sparsity/README.md), includes different techniques such as 2:4 sparsity and block sparsity + +Check out our [docs](https://docs.pytorch.org/ao/main/) for more details! + +From the team that brought you the fast series: * 9.5x inference speedups for Image segmentation models with [sam-fast](https://pytorch.org/blog/accelerating-generative-ai) * 10x inference speedups for Language models with [gpt-fast](https://pytorch.org/blog/accelerating-generative-ai-2) * 3x inference speedup for Diffusion models with [sd-fast](https://pytorch.org/blog/accelerating-generative-ai-3) * 2.7x inference speedup for FAIR’s Seamless M4T-v2 model with [seamlessv2-fast](https://pytorch.org/blog/accelerating-generative-ai-4/) -`torchao` isn't just for inference - it delivers substantial speedups at scale, from [up to 1.5x speedups](https://pytorch.org/blog/training-using-float8-fsdp2/) on 512 GPU clusters, to [1.34-1.43x speedups](https://pytorch.org/blog/accelerating-large-scale-training-and-convergence-with-pytorch-float8-rowwise-on-crusoe-2k-h200s/) on 2K H200 clusters with the latest `torchao.float8` rowwise -`torchao` works out-of-the-box with `torch.compile()` and `FSDP2` across most Hugging Face PyTorch models +## 🚀 Quick Start + +First, install TorchAO. We recommend installing the latest stable version: +``` +pip install torchao +``` + +
+ Other installation options + + ``` + # Nightly + pip install --pre torchao --index-url https://download.pytorch.org/whl/nightly/cu126 + + # Different CUDA versions + pip install torchao --index-url https://download.pytorch.org/whl/cu126 # CUDA 12.6 + pip install torchao --index-url https://download.pytorch.org/whl/cpu # CPU only + + # For developers + USE_CUDA=1 python setup.py develop + ``` +
-## Inference +Quantize your model weights to int4! +``` +from torchao.quantization import Int4WeightOnlyConfig, quantize_ +quantize_(model, Int4WeightOnlyConfig(group_size=32)) +``` +Compared to a `torch.compiled` bf16 baseline, your quantized model should be significantly smaller and faster on a single A100 GPU: +``` +int4 model size: 1.25 MB +bfloat16 model size: 4.00 MB +compression ratio: 3.2 + +bf16 mean time: 30.393 ms +int4 mean time: 4.410 ms +speedup: 6.9x +``` +For the full model setup and benchmark details, check out our [quick start guide](https://docs.pytorch.org/ao/stable/quick_start.html). Alternatively, try quantizing your favorite model using our [HuggingFace space](https://huggingface.co/spaces/pytorch/torchao-my-repo)! -`torchao` delivers substantial performance gains with minimal code changes: -### Performance Highlights +## 🔗 Integrations -- **INT4 Weight-Only Quantization**: 2x throughput (180 vs 107 tokens/sec) with 60% less memory (6.88 GB vs 16.43 GB) on LLaMA-3-7B -- **Float8 Dynamic Quantization**: 53.88% speedup on Flux.1-Dev* and 27.33% speedup on CogVideoX-5b on H100 GPU with preserved quality -- **INT4 + 2:4 Sparsity**: 2.4x throughput (226 vs 95 tokens/sec) with 80% memory reduction (5.3GB vs 16.4GB) on LLaMA-3-8B +TorchAO is integrated into some of the leading open-source libraries including: -[View detailed benchmarks](torchao/quantization/README.md) | [Learn about sparsity](torchao/sparsity/README.md) +* HuggingFace transformers with a [builtin inference backend](https://huggingface.co/docs/transformers/main/quantization/torchao) and [low bit optimizers](https://github.com/huggingface/transformers/pull/31865) +* HuggingFace diffusers best practices with `torch.compile` and TorchAO in a standalone repo [diffusers-torchao](https://github.com/huggingface/diffusers/blob/main/docs/source/en/quantization/torchao.md) +* Mobius HQQ backend leveraged our int4 kernels to get [195 tok/s on a 4090](https://github.com/mobiusml/hqq#faster-inference) +* TorchTune for our [QLoRA](https://docs.pytorch.org/torchtune/main/tutorials/qlora_finetune.html), [QAT](https://docs.pytorch.org/torchtune/main/recipes/qat_distributed.html), and [float8 quantized fine-tuning](https://github.com/pytorch/torchtune/pull/2546) recipes +* TorchTitan for [float8 pre-training](https://github.com/pytorch/torchtitan/blob/main/docs/float8.md) +* VLLM for LLM serving: [usage](https://docs.vllm.ai/en/latest/features/quantization/torchao.html), [detailed docs](https://docs.pytorch.org/ao/main/torchao_vllm_integration.html) +* SGLang for LLM serving: [usage](https://docs.sglang.ai/backend/server_arguments.html#server-arguments) and the major [PR](https://github.com/sgl-project/sglang/pull/1341). +* Axolotl for [QAT](https://docs.axolotl.ai/docs/qat.html) and [PTQ](https://docs.axolotl.ai/docs/quantize.html) -### Getting Started with Quantization -Quantize any model with `nn.Linear` layers (including HuggingFace models) in just one line: +## 🔎 Inference + +TorchAO delivers substantial performance gains with minimal code changes: + +- **Int4 weight-only**: [1.89x throughput with 58.1% less memory](torchao/quantization/README.md) on Llama-3-8B +- **Float8 dynamic quantization**: [1.54x and 1.27x speedup on Flux.1-Dev* and CogVideoX-5b respectively](https://github.com/sayakpaul/diffusers-torchao) on H100 with preserved quality +- **Int4 + 2:4 Sparsity**: [2.37x throughput with 67.7% memory reduction](torchao/sparsity/README.md) on Llama-3-8B + +Quantize any model with `nn.Linear` layers in just one line (Option 1), or load the quantized model directly from HuggingFace using our integration with HuggingFace transformers (Option 2): #### Option 1: Direct TorchAO API @@ -61,65 +154,43 @@ quantized_model = AutoModelForCausalLM.from_pretrained( ) ``` -### Deployment with vLLM - -Deploy quantized models with one command: +#### Deploy quantized models in vLLM with one command: ```shell vllm serve pytorch/Phi-4-mini-instruct-int4wo-hqq --tokenizer microsoft/Phi-4-mini-instruct -O3 ``` -**Benefits**: 67% VRAM reduction and 12-20% speedup on A100 GPUs while maintaining quality. - -[Step-by-step quantization guide](https://huggingface.co/pytorch/Phi-4-mini-instruct-int4wo-hqq#quantization-recipe) | [Pre-quantized models](https://huggingface.co/pytorch) +With this quantization flow, we achieve **67% VRAM reduction and 12-20% speedup** on A100 GPUs while maintaining model quality. For more detail, see this [step-by-step quantization guide](https://huggingface.co/pytorch/Phi-4-mini-instruct-int4wo-hqq#quantization-recipe). We also release some pre-quantized models [here](https://huggingface.co/pytorch). -## Training +## 🚅 Training -### Quantization Aware Training +### Quantization-Aware Training -Post-training quantization can result in a fast and compact model, but may also lead to accuracy degradation. We recommend exploring Quantization Aware Training (QAT) to overcome this limitation. In collaboration with [Torchtune](https://github.com/pytorch/torchtune/blob/main/recipes/quantization.md#quantization-aware-training-qat), we've developed a QAT recipe that demonstrates significant accuracy improvements over traditional PTQ, recovering **96% of the accuracy degradation on hellaswag and 68% of the perplexity degradation on wikitext** for Llama3 compared to post-training quantization (PTQ). And we've provided a full recipe [here](https://pytorch.org/blog/quantization-aware-training/). For more details, please see the [QAT README](./torchao/quantization/qat/README.md). +Post-training quantization can result in a fast and compact model, but may also lead to accuracy degradation. We recommend exploring Quantization-Aware Training (QAT) to overcome this limitation, especially for lower bit-width dtypes such as int4. In collaboration with [TorchTune](https://github.com/pytorch/torchtune/blob/main/recipes/quantization.md#quantization-aware-training-qat), we've developed a QAT recipe that demonstrates significant accuracy improvements over traditional PTQ, recovering **96% of the accuracy degradation on hellaswag and 68% of the perplexity degradation on wikitext** for Llama3 compared to post-training quantization (PTQ). For more details, please refer to the [QAT README](torchao/quantization/qat/README.md) and the [original blog](https://pytorch.org/blog/quantization-aware-training/): ```python -from torchao.quantization import ( - quantize_, - Int8DynamicActivationInt4WeightConfig, -) -from torchao.quantization.qat import ( - FakeQuantizeConfig, - FromIntXQuantizationAwareTrainingConfig, - IntXQuantizationAwareTrainingConfig, -) - -# Insert fake quantization +from torchao.quantization import quantize_ +from torchao.quantization.qat import FakeQuantizeConfig, IntXQuantizationAwareTrainingConfig activation_config = FakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False) weight_config = FakeQuantizeConfig(torch.int4, group_size=32) -quantize_( - my_model, - IntXQuantizationAwareTrainingConfig(activation_config, weight_config), -) +qat_config = IntXQuantizationAwareTrainingConfig(activation_config, weight_config), +quantize_(my_model, qat_config) +``` -# Run training... (not shown) +Users can also combine LoRA + QAT to speed up training by [1.89x](https://dev-discuss.pytorch.org/t/speeding-up-qat-by-1-89x-with-lora/2700) compared to vanilla QAT using this [fine-tuning recipe](https://github.com/pytorch/torchtune/blob/main/recipes/qat_lora_finetune_distributed.py). -# Convert fake quantization to actual quantized operations -quantize_(my_model, FromIntXQuantizationAwareTrainingConfig()) -quantize_(my_model, Int8DynamicActivationInt4WeightConfig(group_size=32)) -``` ### Float8 -[torchao.float8](torchao/float8) implements training recipes with the scaled float8 dtypes, as laid out in https://arxiv.org/abs/2209.05433 - -With ``torch.compile`` on, current results show throughput speedups of up to **1.5x on up to 512 GPU / 405B parameter count scale** ([details](https://pytorch.org/blog/training-using-float8-fsdp2/)) +[torchao.float8](torchao/float8) implements training recipes with the scaled float8 dtypes, as laid out in https://arxiv.org/abs/2209.05433. With ``torch.compile`` on, current results show throughput speedups of up to **1.5x on up to 512 GPU / 405B parameter count scale** ([details](https://pytorch.org/blog/training-using-float8-fsdp2/)): ```python from torchao.float8 import convert_to_float8_training -convert_to_float8_training(m, module_filter_fn=...) +convert_to_float8_training(m) ``` -And for an end-to-minimal training recipe of pretraining with float8, you can check out [torchtitan](https://github.com/pytorch/torchtitan/blob/main/docs/float8.md). - -#### Blog posts about float8 training - +Our float8 training is integrated into [TorchTitan's pre-training flows](https://github.com/pytorch/torchtitan/blob/main/docs/float8.md) so users can easily try it out. For more details, check out these blog posts about our float8 training support: +* [Accelerating Large Scale Training and Convergence with PyTorch Float8 Rowwise on Crusoe 2K H200s](https://pytorch.org/blog/accelerating-large-scale-training-and-convergence-with-pytorch-float8-rowwise-on-crusoe-2k-h200s/) * [Supercharging Training using float8 and FSDP2](https://pytorch.org/blog/training-using-float8-fsdp2/) * [Efficient Pre-training of Llama 3-like model architectures using torchtitan on Amazon SageMaker](https://aws.amazon.com/blogs/machine-learning/efficient-pre-training-of-llama-3-like-model-architectures-using-torchtitan-on-amazon-sagemaker/) * [Float8 in PyTorch](https://dev-discuss.pytorch.org/t/float8-in-pytorch-1-x/1815) @@ -127,13 +198,10 @@ And for an end-to-minimal training recipe of pretraining with float8, you can ch ### Sparse Training -We've added support for semi-structured 2:4 sparsity with **6% end-to-end speedups on ViT-L**. Full blog [here](https://pytorch.org/blog/accelerating-neural-network-training/) - -The code change is a 1 liner with the full example available [here](torchao/sparsity/training/) +We've added support for semi-structured 2:4 sparsity with **6% end-to-end speedups on ViT-L**. Full blog [here](https://pytorch.org/blog/accelerating-neural-network-training/). The code change is a 1 liner with the full example available [here](torchao/sparsity/training/): ```python from torchao.sparsity.training import SemiSparseLinear, swap_linear_with_semi_sparse_linear - swap_linear_with_semi_sparse_linear(model, {"seq.0": SemiSparseLinear}) ``` @@ -141,8 +209,7 @@ swap_linear_with_semi_sparse_linear(model, {"seq.0": SemiSparseLinear}) Optimizers like ADAM can consume substantial GPU memory - 2x as much as the model parameters themselves. TorchAO provides two approaches to reduce this overhead: -1. **Quantized optimizers**: Reduce optimizer state memory by 2-4x by quantizing to lower precision - +**1. Quantized optimizers**: Reduce optimizer state memory by 2-4x by quantizing to lower precision ```python from torchao.optim import AdamW8bit, AdamW4bit, AdamWFp8 @@ -150,7 +217,7 @@ optim = AdamW8bit(model.parameters()) # replace with Adam4bit and AdamFp8 for th ``` Our quantized optimizers are implemented in just a few hundred lines of PyTorch code and compiled for efficiency. While slightly slower than specialized kernels, they offer an excellent balance of memory savings and performance. See detailed [benchmarks here](https://github.com/pytorch/ao/tree/main/torchao/optim). -2. **CPU offloading**: Move optimizer state and gradients to CPU memory +**2. CPU offloading**: Move optimizer state and gradients to CPU memory For maximum memory savings, we support [single GPU CPU offloading](https://github.com/pytorch/ao/tree/main/torchao/optim#optimizer-cpu-offload) that efficiently moves both gradients and optimizer state to CPU memory. This approach can **reduce your VRAM requirements by 60%** with minimal impact on training speed: @@ -159,32 +226,10 @@ optim = CPUOffloadOptimizer(model.parameters(), torch.optim.AdamW, fused=True) optim.load_state_dict(ckpt["optim"]) ``` -## Installation - -`torchao` makes liberal use of several new features in PyTorch, it's recommended to use it with the current nightly or latest stable version of PyTorch, see [getting started](https://pytorch.org/get-started/locally/) for more details. - -Install the stable release (recommended): -```bash -pip install torchao -``` - -Other options: -```bash -# Nightly build -pip install --pre torchao --index-url https://download.pytorch.org/whl/nightly/cu124 - -# Different CUDA versions -pip install torchao --index-url https://download.pytorch.org/whl/cu118 # CUDA 11.8 -pip install torchao --index-url https://download.pytorch.org/whl/cpu # CPU only - -``` - -### Development Install -``` -USE_CPP=0 python setup.py develop # Skip C++/CUDA extensions -``` + -## License +## 🎥 Videos +* [Keynote talk at GPU MODE IRL](https://youtu.be/FH5wiwOyPX4?si=VZK22hHz25GRzBG1&t=1009) +* [Low precision dtypes at PyTorch conference](https://youtu.be/xcKwEZ77Cps?si=7BS6cXMGgYtFlnrA) +* [Slaying OOMs at the Mastering LLM's course](https://www.youtube.com/watch?v=UvRl4ansfCg) +* [Advanced Quantization at CUDA MODE](https://youtu.be/1u9xUK3G4VM?si=4JcPlw2w8chPXW8J) +* [Chip Huyen's GPU Optimization Workshop](https://www.youtube.com/live/v_q2JTIqE20?si=mf7HeZ63rS-uYpS6) +* [Cohere for AI community talk](https://www.youtube.com/watch?v=lVgrE36ZUw0) -`torchao` is released under the [BSD 3](https://github.com/pytorch-labs/ao/blob/main/LICENSE) license. -# Citation +## 💬 Citation If you find the torchao library useful, please cite it in your work as below. + ```bibtex @software{torchao, - title = {torchao: PyTorch native quantization and sparsity for training and inference}, - author = {torchao maintainers and contributors}, - url = {https://github.com/pytorch/torchao}, - license = {BSD-3-Clause}, - month = oct, - year = {2024} + title={TorchAO: PyTorch-Native Training-to-Serving Model Optimization}, + author={torchao}, + url={https://github.com/pytorch/torchao}, + license={BSD-3-Clause}, + month={oct}, + year={2024} } ``` From e4f2715e94c14454c61153c4a3dfc4c8f0758350 Mon Sep 17 00:00:00 2001 From: Jeff Daily Date: Mon, 16 Jun 2025 15:54:37 -0700 Subject: [PATCH 124/165] remove rocm source files when not building for rocm (#2385) * remove rocm source files when not building for rocm fixes #2297 * lint --------- Co-authored-by: Mark Saroufim --- setup.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/setup.py b/setup.py index 22b001b424..448eb344c1 100644 --- a/setup.py +++ b/setup.py @@ -491,6 +491,13 @@ def get_extensions(): print("Currently only gfx942 is supported. Compiling only for gfx942.") extra_compile_args["nvcc"].append("--offload-arch=gfx942") sources += rocm_sources + else: + # Remove ROCm-based sources from the sources list. + extensions_rocm_dir = os.path.join(extensions_dir, "rocm") + rocm_sources = list( + glob.glob(os.path.join(extensions_rocm_dir, "**/*.cpp"), recursive=True) + ) + sources = [s for s in sources if s not in rocm_sources] use_cutlass = False cutlass_90a_sources = None From 21a2d29e27692ac419f6ac64be1cc0a6786a2b66 Mon Sep 17 00:00:00 2001 From: XiaoWang Date: Tue, 17 Jun 2025 07:41:41 +0000 Subject: [PATCH 125/165] Enable Int4WeightOnlyGPTQQuantizer on Intel GPU. (#2200) Following https://github.com/pytorch/pytorch/issues/153019 requests, we enable int4wo-GPTQ for Intel GPU in pytorch/ao after RTN ready. How to run int4wo-GPTQ on Intel GPU: ```markdown from pathlib import Path import torch from torchao._models._eval import ( LMEvalInputRecorder, TransformerEvalWrapper, ) from torchao.quantization.GPTQ import Int4WeightOnlyGPTQQuantizer from torchao._models.llama.model import Transformer, prepare_inputs_for_model from torchao._models.llama.tokenizer import get_tokenizer from torchao.dtypes import ( Int4XPULayout, ) precision = torch.bfloat16 device = "xpu" checkpoint_path = Path("checkpoints/meta-llama/Llama-2-7b-chat-hf/model.pth") model = Transformer.from_name(checkpoint_path.parent.name) checkpoint = torch.load(str(checkpoint_path), mmap=True, weights_only=True) model.load_state_dict(checkpoint, assign=True) model = model.to(dtype=precision, device="cpu") model.eval() tokenizer_path = checkpoint_path.parent / "tokenizer.model" assert tokenizer_path.is_file(), tokenizer_path tokenizer = get_tokenizer( tokenizer_path, "Llama-2-7b-chat-hf", ) groupsize = 64 blocksize = 128 percdamp = 0.01 calibration_tasks = ["wikitext"] calibration_limit = 1 calibration_seq_length = 100 input_prep_func = prepare_inputs_for_model pad_calibration_inputs = False inputs = ( LMEvalInputRecorder( tokenizer, calibration_seq_length, input_prep_func, model.config.vocab_size, pad_calibration_inputs, device="cpu", ) .record_inputs( calibration_tasks, calibration_limit, ) .get_recorded_inputs() ) quantizer = Int4WeightOnlyGPTQQuantizer( groupsize, blocksize, percdamp, device=torch.device(device), layout=Int4XPULayout(), ) model.setup_caches(max_batch_size=1, max_seq_length=calibration_seq_length) model = quantizer.quantize(model, *inputs).xpu() model.reset_caches() limit = 1 result = TransformerEvalWrapper( model.xpu(), tokenizer, model.config.block_size, prepare_inputs_for_model, device, ).run_eval( ["wikitext"], limit, ) Pull Request resolved: https://github.com/pytorch/ao/pull/2200 Approved by: https://github.com/liangan1, https://github.com/jerryzh168 --- torchao/_models/_eval.py | 2 +- torchao/quantization/GPTQ/GPTQ.py | 95 ++++++++++++++++++++++--------- 2 files changed, 69 insertions(+), 28 deletions(-) diff --git a/torchao/_models/_eval.py b/torchao/_models/_eval.py index 0266417de5..faf059c400 100644 --- a/torchao/_models/_eval.py +++ b/torchao/_models/_eval.py @@ -35,7 +35,7 @@ def __init__( self, model, tokenizer, max_seq_length, input_prep_func=None, device="cuda" ): try: - super().__init__() + super().__init__(device=device) except TypeError: # lm_eval 0.4.2 removed the default init super().__init__("gpt2", device="cpu") diff --git a/torchao/quantization/GPTQ/GPTQ.py b/torchao/quantization/GPTQ/GPTQ.py index 8c3f791fd6..fe55ed19db 100644 --- a/torchao/quantization/GPTQ/GPTQ.py +++ b/torchao/quantization/GPTQ/GPTQ.py @@ -10,7 +10,11 @@ import torch.nn as nn from torch.utils._pytree import tree_flatten, tree_unflatten -from torchao.dtypes import TensorCoreTiledLayout, to_affine_quantized_intx_static +from torchao.dtypes import ( + Layout, + TensorCoreTiledLayout, + to_affine_quantized_intx_static, +) from torchao.quantization.quant_primitives import ( ZeroPointDomain, ) @@ -131,6 +135,7 @@ def configure_quantization_mode( group_size=-1, percdamp=0.01, blocksize=128, + device: torch.device = torch.device("cuda"), ): cls.get_qparams_func = get_qparams_func cls.quantize_func = quantize_func @@ -144,6 +149,7 @@ def configure_quantization_mode( cls.group_size = group_size cls.percdamp = percdamp cls.blocksize = blocksize + cls.device = device @classmethod def __torch_function__( @@ -178,6 +184,10 @@ def __torch_function__( # then we can do the fast thing. quantize_linear = not skip_gptq and cls.is_linear_layer(func) + if hasattr(cls, "device") and isinstance(cls.device, torch.device): + device = cls.device + else: + device = "cpu" # Determine if function is in-place # initialize function tracking @@ -199,7 +209,7 @@ def __torch_function__( # if we're not doing an in place op, move singular tensors to cuda now if not is_in_place: - flat_args = _tensors_to_cuda(flat_args) + flat_args = _tensors_to_device(flat_args, device=device) # convert [A, MultiTensor(b), MultiTensor(c1,c2,c3)] => [[A,b,c1], [A,b,c2] [A,b,c3]] # if its in place then instead we first pad i.e. MultiTensor(b) => MultiTensor(b1, b2, b3) @@ -208,7 +218,9 @@ def __torch_function__( with torch._C.DisableTorchFunctionSubclass(): if not quantize_linear: # normal function eval - out = cls._evaluate_function(func, grouped_args, spec, is_in_place) + out = cls._evaluate_function( + func, grouped_args, spec, is_in_place, device + ) # go back and unpad everything where possible. if not GPTQ_FUNC_LIST[func]["is_in_place"]: @@ -217,7 +229,7 @@ def __torch_function__( # GPTQ quantization for linear layers # Calculate Hessian approximation - H = _calculate_hessian(grouped_args, spec) + H = _calculate_hessian(grouped_args, spec, device) # turn weight MultiTensor into single cuda tensor W = args[1] @@ -225,7 +237,7 @@ def __torch_function__( W = W.values[0] W = W.to(H.device) - Q, DQ, all_qparams = cls.faster_quant(H, W.detach()) + Q, DQ, all_qparams = cls.faster_quant(H, W.detach(), device) # make quantized tensor subclass qtensor = cls.make_qtensor(Q, all_qparams) @@ -244,8 +256,8 @@ def __torch_function__( _do_unpad(flat_args, orig_counts=orig_counts) return out if args[0].debug: - act = args[0].values[0].to("cuda") - bias = args[2].values[0].to("cuda") if args[2] is not None else args[2] + act = args[0].values[0].to(device) + bias = args[2].values[0].to(device) if args[2] is not None else args[2] new_out = out.values[0].cpu() old_out = ( @@ -265,7 +277,7 @@ def __torch_function__( "SQNR for QDQ (this should be inf)", SQNR(DQ, DQ_after) ) # matches print( - "SQNR for weight (can be low)", SQNR(W, DQ.cuda()) + "SQNR for weight (can be low)", SQNR(W, DQ.to(device)) ) # fine to not match print( "SQNR for output with GPTQ (hopefully 35+)", @@ -318,14 +330,14 @@ def grouped_to_flat(cls, grouped: List[Tuple[Any, ...]]) -> Tuple[List[Any], boo return flattened, non_tensors_equal @classmethod - def _evaluate_function(cls, func, grouped_args, spec, is_in_place): + def _evaluate_function(cls, func, grouped_args, spec, is_in_place, device): outputs = [] for inp in grouped_args: # we move all remaining cpu tensors to cuda - cuda_inp = _tensors_to_cuda(inp) + device_inp = _tensors_to_device(inp, device) # return input to original structure - cur_args, cur_kwargs = tree_unflatten(cuda_inp, spec) + cur_args, cur_kwargs = tree_unflatten(device_inp, spec) out = func(*cur_args, **cur_kwargs) @@ -336,7 +348,7 @@ def _evaluate_function(cls, func, grouped_args, spec, is_in_place): # categortize func as in place. if is_in_place: detected_mutation = _maybe_copy_new_values( - inp, cuda_inp, force=GPTQ_FUNC_LIST[func]["is_in_place"] + inp, device_inp, force=GPTQ_FUNC_LIST[func]["is_in_place"] ) # if we already know its in place, don't compare, just copy if detected_mutation and GPTQ_FUNC_LIST[func]["is_in_place"] is None: GPTQ_FUNC_LIST[func]["is_in_place"] = True @@ -365,13 +377,14 @@ def _evaluate_function(cls, func, grouped_args, spec, is_in_place): return final_out @classmethod - def faster_quant(cls, H, W): + def faster_quant(cls, H, W, device): """ GPTQ quantization implementation. Args: H: Hessian matrix approximation W: Weight matrix to quantize + device: accelerator device Returns: Tuple containing: @@ -457,7 +470,12 @@ def faster_quant(cls, H, W): Hinv[block_start:block_end, block_end:] ) - torch.cuda.synchronize() + if "xpu" in device.type: + torch.xpu.synchronize() + elif "cuda" in device.type: + torch.cuda.synchronize() + else: + pass if all_qparams == []: all_qparams.append(cur_qparams) @@ -571,6 +589,7 @@ def __init__(self): self.make_qtensor = None self.skip_layer_func = None self.act_fake_quant_func = None + self.device = None def _check_functions(self): assert self.get_qparams_func is not None, "get_qparams_func must be set" @@ -611,6 +630,7 @@ def _create_quantized_state_dict( group_size=group_size, percdamp=percdamp, blocksize=blocksize, + device=self.device, ) # Set the state dict for the original model self.state_dict_manager.set_state_dict(model) @@ -639,6 +659,7 @@ def __init__( inner_k_tiles=8, padding_allowed=True, device: torch.device = torch.device("cuda"), + layout: Optional[Layout] = TensorCoreTiledLayout(inner_k_tiles=8), ): super().__init__() self.group_size = group_size @@ -647,14 +668,31 @@ def __init__( self.inner_k_tiles = inner_k_tiles self.padding_allowed = padding_allowed self.device = device + self.device = self.device self.act_fake_quant_func = None + self.layout = layout n_bit = 4 + + if "xpu" in self.device.type: + self.zero_point_domain = ZeroPointDomain.INT + self.zeros_precision = torch.int8 + else: + self.zero_point_domain = ZeroPointDomain.FLOAT + self.get_qparams_func = lambda w: get_groupwise_affine_qparams( - w, n_bit, group_size + w, + n_bit, + group_size, + zero_point_domain=self.zero_point_domain, ) self.quantize_func = ( lambda w, qparams: groupwise_affine_quantize_tensor_from_qparams( - w, qparams[0], qparams[1], n_bit, group_size + w, + qparams[0], + qparams[1], + n_bit, + group_size, + zero_point_domain=self.zero_point_domain, ) ) self.dequantize_func = ( @@ -664,6 +702,7 @@ def __init__( qparams[1], n_bit, group_size, + zero_point_domain=self.zero_point_domain, ) ) self.combine_qparams_list_func = lambda qparams_list: [ @@ -681,6 +720,8 @@ def make_qtensor(q, qparams): weight = self.dequantize_func(q, qparams) scale = qparams[0] zero_point = qparams[1] + if self.zero_point_domain == ZeroPointDomain.INT: + zero_point = zero_point.to(self.zeros_precision) # copied from quant_api apply_int4_weight_only_quant (this should probably be made into a utility fn at some point) # mapping_type = MappingType.ASYMMETRIC @@ -688,8 +729,6 @@ def make_qtensor(q, qparams): target_dtype = torch.int32 quant_min = 0 quant_max = 15 - zero_point_domain = ZeroPointDomain.FLOAT - _layout = TensorCoreTiledLayout(inner_k_tiles=8) # at least the big up to here should be a util quantized_tensor = to_affine_quantized_intx_static( @@ -700,8 +739,8 @@ def make_qtensor(q, qparams): target_dtype=target_dtype, quant_min=quant_min, quant_max=quant_max, - zero_point_domain=zero_point_domain, - _layout=_layout, + zero_point_domain=self.zero_point_domain, + _layout=self.layout, ) return quantized_tensor @@ -829,12 +868,13 @@ def _flat_to_grouped_and_pad( return grouped, orig_counts -def _tensors_to_cuda(args, move_all=False): +def _tensors_to_device(args, device=torch.device("cuda"), move_all=False): """ - Move tensors to CUDA for faster processing. + Move tensors to accelerator for faster processing. Args: args: Arguments that may contain tensors + device: accelerator device move_all: Whether to move all tensors or just single count tensors Returns: @@ -843,10 +883,10 @@ def _tensors_to_cuda(args, move_all=False): new_args = [] for x in args: if isinstance(x, MultiTensor) and (x.count == 1 or move_all): - new_args.append(x.__class__(x.values[0].cuda())) + new_args.append(x.__class__(x.values[0].to(device))) else: new_args.append( - x.cuda() + x.to(device) if isinstance(x, torch.Tensor) and not isinstance(x, MultiTensor) else x ) @@ -888,13 +928,14 @@ def _do_unpad(args, orig_counts): arg.unpad(count) -def _calculate_hessian(grouped_args, spec): +def _calculate_hessian(grouped_args, spec, device=torch.device("cuda")): """ Calculate the Hessian matrix for GPTQ. Args: grouped_args: Grouped arguments spec: Original structure specification + device: accelerator device Returns: torch.Tensor: Hessian matrix @@ -903,10 +944,10 @@ def _calculate_hessian(grouped_args, spec): total_batches = 0 for inp in grouped_args: # Move all remaining CPU tensors to CUDA - cuda_inp = [x.cuda() if isinstance(x, torch.Tensor) else x for x in inp] + device_inp = [x.to(device) if isinstance(x, torch.Tensor) else x for x in inp] # Return input to original structure - cur_args, _ = tree_unflatten(cuda_inp, spec) + cur_args, _ = tree_unflatten(device_inp, spec) # Setup x (activation tensor) x = cur_args[0].float() From d1c8118292b89b6110a4f428b83bb368eaabb46f Mon Sep 17 00:00:00 2001 From: Vasiliy Kuznetsov Date: Tue, 17 Jun 2025 07:53:20 -0400 Subject: [PATCH 126/165] remove torchao dependency from torchao build script (#2383) Summary: We should not use torchao to build torchao. Before this PR, I got stuck in a loop where I had a broken install (for some other reason) and could not uninstall torchao with this setup script, because the script required a working torchao to run. Commenting out the offending code in this PR, leaving for a future person to actually fix it. Test Plan: Reviewers: Subscribers: Tasks: Tags: --- setup.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/setup.py b/setup.py index 448eb344c1..cb6be7e1c1 100644 --- a/setup.py +++ b/setup.py @@ -85,8 +85,6 @@ def read_version(file_path="version.txt"): # └── TORCHAO_PARALLEL_BACKEND → Backend selection (aten_openmp, executorch, etc.) -from torchao.utils import TORCH_VERSION_AT_LEAST_2_7 - version_prefix = read_version() # Version is version.dev year month date if using nightlies and version if not version = ( @@ -388,16 +386,18 @@ def get_extensions(): ["-O3" if not debug_mode else "-O0", "-fdiagnostics-color=always"] ) - if use_cpu_kernels and is_linux and TORCH_VERSION_AT_LEAST_2_7: - if torch._C._cpu._is_avx512_supported(): - extra_compile_args["cxx"].extend( - [ - "-DCPU_CAPABILITY_AVX512", - "-march=native", - "-mfma", - "-fopenmp", - ] - ) + # TODO(future PR): make this work without using `TORCH_VERSION_AT_LEAST_2_7`, + # because we should not be using anything from `torchao` to build `torchao`. + # if use_cpu_kernels and is_linux and TORCH_VERSION_AT_LEAST_2_7: + # if torch._C._cpu._is_avx512_supported(): + # extra_compile_args["cxx"].extend( + # [ + # "-DCPU_CAPABILITY_AVX512", + # "-march=native", + # "-mfma", + # "-fopenmp", + # ] + # ) if debug_mode: extra_compile_args["cxx"].append("-g") From bc80a5da0396113544084b02f806c0cdda16de0c Mon Sep 17 00:00:00 2001 From: Scott Roy <161522778+metascroy@users.noreply.github.com> Date: Tue, 17 Jun 2025 10:11:11 -0700 Subject: [PATCH 127/165] turn off building tests with cpuinfo (#2324) --- torchao/experimental/CMakeLists.txt | 3 +++ 1 file changed, 3 insertions(+) diff --git a/torchao/experimental/CMakeLists.txt b/torchao/experimental/CMakeLists.txt index 7313a37e56..1d3c28508e 100644 --- a/torchao/experimental/CMakeLists.txt +++ b/torchao/experimental/CMakeLists.txt @@ -95,6 +95,9 @@ endif() if (NOT TARGET cpuinfo) # For some reason cpuinfo package has unused functions/variables # TODO (T215533422): fix upstream + set(CPUINFO_BUILD_UNIT_TESTS OFF CACHE BOOL "Disable unit tests" FORCE) + set(CPUINFO_BUILD_MOCK_TESTS OFF CACHE BOOL "Disable mock tests" FORCE) + set(CPUINFO_BUILD_BENCHMARKS OFF CACHE BOOL "Disable benchmarks" FORCE) add_compile_options(-Wno-unused-function -Wno-unused-variable) include(FetchContent) FetchContent_Declare(cpuinfo From 63a91d75f5705fb6ffff82112a7dd4e2363f8ad4 Mon Sep 17 00:00:00 2001 From: Daniel Vega-Myhre Date: Tue, 17 Jun 2025 11:19:27 -0700 Subject: [PATCH 128/165] [float8 training] update torchtitan benchmark script args (#2392) update torchtitan benchmark script args --- benchmarks/float8/training/README.md | 2 +- benchmarks/float8/training/torchtitan_benchmark.sh | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/benchmarks/float8/training/README.md b/benchmarks/float8/training/README.md index 5d8c2946d6..5897ea4962 100644 --- a/benchmarks/float8/training/README.md +++ b/benchmarks/float8/training/README.md @@ -12,7 +12,7 @@ Training parameters can be configured via environment variables. - `TORCHTITAN_ROOT`: Root directory of torchtitan in your local filesystem - Optional: - `FLOAT8_RECIPE_WITH_BEST_SETTINGS`: "rowwise" or "tensorwise". Applies float8 training with the specified scaling recipe, as well as additional training configs which are optimal for that scaling recipe. See `torchtitan_benchmark.sh` for more details. - - `BATCH_SIZE`: Defaults to 1. + - `LOCAL_BATCH_SIZE`: Defaults to 1. - `STEPS`: Defaults to 100. - `EXTRA_ARGS`: Extra arguments to pass to torchtitan training script. See [torchtitan](https://github.com/pytorch/torchtitan) docs for the full list of options. diff --git a/benchmarks/float8/training/torchtitan_benchmark.sh b/benchmarks/float8/training/torchtitan_benchmark.sh index d30b1eceb1..c1995ee39a 100755 --- a/benchmarks/float8/training/torchtitan_benchmark.sh +++ b/benchmarks/float8/training/torchtitan_benchmark.sh @@ -8,7 +8,7 @@ # with the given parameters, # script arguments -BATCH_SIZE=${BATCH_SIZE:-1} +LOCAL_BATCH_SIZE=${LOCAL_BATCH_SIZE:-1} STEPS=${STEPS:-100} # temporary log file which is deleted after performance data is parsed out and metrics are calculated. @@ -20,7 +20,7 @@ if [ -z "${TORCHTITAN_ROOT}" ]; then echo "Usage: TORCHTITAN_ROOT= ./float8_training_benchmark.sh" echo "Optional parameters configurable via environment variables:" echo " * FLOAT8_RECIPE_WITH_BEST_SETTINGS: "rowwise" or "tensorwise". if set, use float8 training in torchtitan with the specified recipe, including the additional settings which are optimal for that recipe. otherwise, use bf16 mixed precision training." - echo " * BATCH_SIZE: defaults to 1." + echo " * LOCAL_BATCH_SIZE: defaults to 1." echo " * STEPS: defaults to 100." echo " * EXTRA_ARGS: additional arguments to pass to the torchtitan training script." exit 1 @@ -45,7 +45,7 @@ cd ${TORCHTITAN_ROOT} echo "float8 args: ${FLOAT8_ARGS}" # run the command with the specified arguments -CONFIG_FILE="./torchtitan/models/llama3/train_configs/llama3_8b.toml" ${TORCHTITAN_ROOT}/run_train.sh --training.steps=${STEPS} --training.batch_size=${BATCH_SIZE} --training.compile ${FLOAT8_ARGS} ${EXTRA_ARGS} 2>&1 | tee ${LOG_FILE} +CONFIG_FILE="./torchtitan/models/llama3/train_configs/llama3_8b.toml" ${TORCHTITAN_ROOT}/run_train.sh --training.steps=${STEPS} --training.local-batch-size=${LOCAL_BATCH_SIZE} --training.compile ${FLOAT8_ARGS} ${EXTRA_ARGS} 2>&1 | tee ${LOG_FILE} # return to original working directory cd $original_dir From 7e7ea92ddfe442d3c1d3dfc748d085fdc33a7b25 Mon Sep 17 00:00:00 2001 From: Gasoonjia Date: Tue, 17 Jun 2025 13:17:50 -0700 Subject: [PATCH 129/165] deduplicate torch ao debugger tests between pytorch/ao and ExecuTorch (#2390) Summary: X-link: https://github.com/pytorch/executorch/pull/11735 This diff deduplicates numeric debugging tests on XnnPack quantizer between torchao and ExecuTorch. Reviewed By: jerryzh168 Differential Revision: D76634915 --- .../pt2e/test_numeric_debugger.py | 152 +----------------- torchao/testing/pt2e/utils.py | 72 ++++++++- 2 files changed, 74 insertions(+), 150 deletions(-) diff --git a/test/quantization/pt2e/test_numeric_debugger.py b/test/quantization/pt2e/test_numeric_debugger.py index 027d57d1b2..5f565767aa 100644 --- a/test/quantization/pt2e/test_numeric_debugger.py +++ b/test/quantization/pt2e/test_numeric_debugger.py @@ -12,22 +12,13 @@ import torch from torch.testing._internal.common_quantization import TestHelperModules -from torch.testing._internal.common_utils import IS_WINDOWS, TestCase, run_tests +from torch.testing._internal.common_utils import IS_WINDOWS, run_tests from torchao.quantization.pt2e import ( - CUSTOM_KEY, - NUMERIC_DEBUG_HANDLE_KEY, - compare_results, - extract_results_from_loggers, generate_numeric_debug_handle, prepare_for_propagation_comparison, ) -from torchao.quantization.pt2e.graph_utils import bfs_trace_with_node_process -from torchao.quantization.pt2e.quantize_pt2e import convert_pt2e, prepare_pt2e -from torchao.testing.pt2e._xnnpack_quantizer import ( - XNNPACKQuantizer, - get_symmetric_quantization_config, -) +from torchao.testing.pt2e.utils import PT2ENumericDebuggerTestCase from torchao.utils import TORCH_VERSION_AT_LEAST_2_7 if TORCH_VERSION_AT_LEAST_2_7: @@ -36,59 +27,7 @@ @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_7, "Requires torch 2.7+") @unittest.skipIf(IS_WINDOWS, "Windows not yet supported for torch.compile") -class TestNumericDebugger(TestCase): - def _assert_each_node_has_debug_handle(self, model) -> None: - def _assert_node_has_debug_handle(node): - self.assertTrue( - CUSTOM_KEY in node.meta - and NUMERIC_DEBUG_HANDLE_KEY in node.meta[CUSTOM_KEY], - f"Node {node} doesn't have debug handle", - ) - - bfs_trace_with_node_process(model, _assert_node_has_debug_handle) - - def _extract_debug_handles(self, model) -> dict[str, int]: - debug_handle_map: dict[str, int] = {} - - def _extract_debug_handles_from_node(node): - nonlocal debug_handle_map - if ( - CUSTOM_KEY in node.meta - and NUMERIC_DEBUG_HANDLE_KEY in node.meta[CUSTOM_KEY] - ): - debug_handle_map[str(node)] = node.meta[CUSTOM_KEY][ - NUMERIC_DEBUG_HANDLE_KEY - ] - - bfs_trace_with_node_process(model, _extract_debug_handles_from_node) - - return debug_handle_map - - def _extract_debug_handles_with_prev_decomp_op(self, model) -> dict[str, int]: - prev_decomp_op_to_debug_handle_map: dict[str, int] = {} - - def _extract_debug_handles_with_prev_decomp_op_from_node(node): - nonlocal prev_decomp_op_to_debug_handle_map - if ( - CUSTOM_KEY in node.meta - and NUMERIC_DEBUG_HANDLE_KEY in node.meta[CUSTOM_KEY] - ): - prev_decomp_op = str(node.meta.get("nn_module_stack")) - debug_handle = node.meta[CUSTOM_KEY][NUMERIC_DEBUG_HANDLE_KEY] - if prev_decomp_op not in prev_decomp_op_to_debug_handle_map: - prev_decomp_op_to_debug_handle_map[prev_decomp_op] = debug_handle - else: - assert ( - prev_decomp_op_to_debug_handle_map[prev_decomp_op] - == debug_handle - ), f"Node {node} has different debug handle {debug_handle}" - "than previous node sharing the same decomp op {prev_decomp_op}" - - bfs_trace_with_node_process( - model, _extract_debug_handles_with_prev_decomp_op_from_node - ) - return prev_decomp_op_to_debug_handle_map - +class TestNumericDebuggerInfra(PT2ENumericDebuggerTestCase): @unittest.skip( "torch._dynamo.exc.FailOnRecompileLimitHit: recompile_limit reached with one_graph=True. Excessive recompilations can degrade performance due to the compilation overhead of each recompilation. To monitor recom..." ) @@ -113,36 +52,6 @@ def test_control_flow(self): self.assertEqual(len(set(debug_handle_map.values())), len(debug_handle_map)) - def test_quantize_pt2e_preserve_handle(self): - m = TestHelperModules.Conv2dThenConv1d() - example_inputs = m.example_inputs() - ep = export_for_training(m, example_inputs, strict=True) - generate_numeric_debug_handle(ep) - m = ep.module() - - quantizer = XNNPACKQuantizer().set_global( - get_symmetric_quantization_config(is_per_channel=False) - ) - m = prepare_pt2e(m, quantizer) - debug_handle_map = self._extract_debug_handles(m) - res_counter = Counter(debug_handle_map.values()) - repeated_debug_handle_ids = [1, 2, 3] - # 3 ids were repeated because we copy over the id from node to its output observer - # torch.ops.aten.conv2d.default, torch.ops.aten.squeeze.dim and torch.ops.aten.conv1d.default - for dh_id in repeated_debug_handle_ids: - self.assertEqual(res_counter[dh_id], 2) - - m(*example_inputs) - m = convert_pt2e(m) - self._assert_each_node_has_debug_handle(ep) - debug_handle_map = self._extract_debug_handles(m) - res_counter = Counter(debug_handle_map.values()) - # same set of ids where repeated, because we copy over the id from observer/fake_quant to - # dequantize node - repeated_debug_handle_ids = [1, 2, 3] - for dh_id in repeated_debug_handle_ids: - self.assertEqual(res_counter[dh_id], 2) - def test_copy_preserve_handle(self): m = TestHelperModules.Conv2dThenConv1d() example_inputs = m.example_inputs() @@ -262,61 +171,6 @@ def test_prepare_for_propagation_comparison(self): self.assertTrue("conv2d" in [logger.node_name for logger in loggers]) self.assertEqual(res, ref) - def test_extract_results_from_loggers(self): - m = TestHelperModules.Conv2dThenConv1d() - example_inputs = m.example_inputs() - ep = export_for_training(m, example_inputs, strict=True) - generate_numeric_debug_handle(ep) - m = ep.module() - m_ref_logger = prepare_for_propagation_comparison(m) - - quantizer = XNNPACKQuantizer().set_global( - get_symmetric_quantization_config(is_per_channel=False) - ) - m = prepare_pt2e(m, quantizer) - m(*example_inputs) - m = convert_pt2e(m) - m_quant_logger = prepare_for_propagation_comparison(m) - - m_ref_logger(*example_inputs) - m_quant_logger(*example_inputs) - ref_results = extract_results_from_loggers(m_ref_logger) - quant_results = extract_results_from_loggers(m_quant_logger) - comparison_results = compare_results(ref_results, quant_results) - for node_summary in comparison_results.values(): - if len(node_summary.results) > 0: - self.assertGreaterEqual(node_summary.results[0].sqnr, 35) - - def test_extract_results_from_loggers_list_output(self): - m = TestHelperModules.Conv2dWithSplit() - example_inputs = m.example_inputs() - ep = export_for_training(m, example_inputs, strict=True) - generate_numeric_debug_handle(ep) - m = ep.module() - m_ref_logger = prepare_for_propagation_comparison(m) - - quantizer = XNNPACKQuantizer().set_global( - get_symmetric_quantization_config(is_per_channel=False) - ) - m = prepare_pt2e(m, quantizer) - m(*example_inputs) - m = convert_pt2e(m) - m_quant_logger = prepare_for_propagation_comparison(m) - - m_ref_logger(*example_inputs) - m_quant_logger(*example_inputs) - ref_results = extract_results_from_loggers(m_ref_logger) - quant_results = extract_results_from_loggers(m_quant_logger) - comparison_results = compare_results(ref_results, quant_results) - for node_summary in comparison_results.values(): - if len(node_summary.results) > 0: - sqnr = node_summary.results[0].sqnr - if isinstance(sqnr, list): - for sqnr_i in sqnr: - self.assertGreaterEqual(sqnr_i, 35) - else: - self.assertGreaterEqual(sqnr, 35) - def test_added_node_gets_unique_id(self) -> None: m = TestHelperModules.Conv2dThenConv1d() example_inputs = m.example_inputs() diff --git a/torchao/testing/pt2e/utils.py b/torchao/testing/pt2e/utils.py index ad49fec014..4342d81dc1 100644 --- a/torchao/testing/pt2e/utils.py +++ b/torchao/testing/pt2e/utils.py @@ -6,6 +6,7 @@ import copy import unittest +from typing import Dict import torch from torch.ao.quantization.backend_config import ( @@ -19,13 +20,19 @@ NodeSpec, QuantizationTestCase, ) +from torch.testing._internal.common_utils import TestCase +from torchao.quantization.pt2e import ( + CUSTOM_KEY, + NUMERIC_DEBUG_HANDLE_KEY, +) +from torchao.quantization.pt2e.graph_utils import bfs_trace_with_node_process from torchao.quantization.pt2e.quantize_pt2e import ( convert_pt2e, prepare_pt2e, prepare_qat_pt2e, ) -from torchao.utils import TORCH_VERSION_AT_LEAST_2_5 +from torchao.utils import TORCH_VERSION_AT_LEAST_2_5, TORCH_VERSION_AT_LEAST_2_7 if TORCH_VERSION_AT_LEAST_2_5: from torch.export import export_for_training @@ -133,3 +140,66 @@ def _test_quantizer( fx_quant_output = m_fx(*example_inputs) self.assertEqual(fx_quant_output, pt2_quant_output) return m + + +@unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_7, "Requires torch 2.7+") +class PT2ENumericDebuggerTestCase(TestCase): + """ + Base test case class for PT2E numeric debugger tests containing common utility functions + for numeric debugging functionality. + """ + + def _assert_each_node_has_debug_handle(self, model) -> None: + """Assert that each node in the model has a debug handle.""" + + def _assert_node_has_debug_handle(node): + self.assertTrue( + CUSTOM_KEY in node.meta + and NUMERIC_DEBUG_HANDLE_KEY in node.meta[CUSTOM_KEY], + f"Node {node} doesn't have debug handle", + ) + + bfs_trace_with_node_process(model, _assert_node_has_debug_handle) + + def _extract_debug_handles(self, model) -> Dict[str, int]: + """Extract debug handles from all nodes in the model.""" + debug_handle_map: Dict[str, int] = {} + + def _extract_debug_handles_from_node(node): + nonlocal debug_handle_map + if ( + CUSTOM_KEY in node.meta + and NUMERIC_DEBUG_HANDLE_KEY in node.meta[CUSTOM_KEY] + ): + debug_handle_map[str(node)] = node.meta[CUSTOM_KEY][ + NUMERIC_DEBUG_HANDLE_KEY + ] + + bfs_trace_with_node_process(model, _extract_debug_handles_from_node) + return debug_handle_map + + def _extract_debug_handles_with_prev_decomp_op(self, model) -> Dict[str, int]: + """Extract debug handles with previous decomposition operation mapping.""" + prev_decomp_op_to_debug_handle_map: Dict[str, int] = {} + + def _extract_debug_handles_with_prev_decomp_op_from_node(node): + nonlocal prev_decomp_op_to_debug_handle_map + if ( + CUSTOM_KEY in node.meta + and NUMERIC_DEBUG_HANDLE_KEY in node.meta[CUSTOM_KEY] + ): + prev_decomp_op = str(node.meta.get("nn_module_stack")) + debug_handle = node.meta[CUSTOM_KEY][NUMERIC_DEBUG_HANDLE_KEY] + if prev_decomp_op not in prev_decomp_op_to_debug_handle_map: + prev_decomp_op_to_debug_handle_map[prev_decomp_op] = debug_handle + else: + assert ( + prev_decomp_op_to_debug_handle_map[prev_decomp_op] + == debug_handle + ), f"Node {node} has different debug handle {debug_handle}" + "than previous node sharing the same decomp op {prev_decomp_op}" + + bfs_trace_with_node_process( + model, _extract_debug_handles_with_prev_decomp_op_from_node + ) + return prev_decomp_op_to_debug_handle_map From 0a93be7a3fca0269e4a4626ad2b055d00766a223 Mon Sep 17 00:00:00 2001 From: Jerry Zhang Date: Tue, 17 Jun 2025 15:17:14 -0700 Subject: [PATCH 130/165] Add pt2e tutorials to torchao doc page (#2384) Summary: att, after we migrate pt2e quant code from pytorch to torchao, now we also want to migrate the docs as well Test Plan: check generated docs Reviewers: Subscribers: Tasks: Tags: --- docs/source/index.rst | 15 +- docs/source/quick_start.rst | 95 ++- .../tutorials_source/pt2e_quant_openvino.rst | 251 ++++++++ .../tutorials_source/pt2e_quant_ptq.rst | 603 ++++++++++++++++++ .../tutorials_source/pt2e_quant_qat.rst | 487 ++++++++++++++ .../pt2e_quant_x86_inductor.rst | 305 +++++++++ .../pt2e_quant_xpu_inductor.rst | 238 +++++++ .../tutorials_source/pt2e_quantizer.rst | 381 +++++++++++ .../pt2e/quantizer/x86_inductor_quantizer.py | 4 +- 9 files changed, 2369 insertions(+), 10 deletions(-) create mode 100644 docs/source/tutorials_source/pt2e_quant_openvino.rst create mode 100644 docs/source/tutorials_source/pt2e_quant_ptq.rst create mode 100644 docs/source/tutorials_source/pt2e_quant_qat.rst create mode 100644 docs/source/tutorials_source/pt2e_quant_x86_inductor.rst create mode 100644 docs/source/tutorials_source/pt2e_quant_xpu_inductor.rst create mode 100644 docs/source/tutorials_source/pt2e_quantizer.rst diff --git a/docs/source/index.rst b/docs/source/index.rst index 20cd0748dc..c0fd2e7bf5 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -12,6 +12,7 @@ for an overall introduction to the library and recent highlight and updates. :caption: Getting Started quick_start + pt2e_quant .. toctree:: :glob: @@ -35,7 +36,7 @@ for an overall introduction to the library and recent highlight and updates. .. toctree:: :glob: :maxdepth: 1 - :caption: Tutorials + :caption: Eager Quantization Tutorials serialization subclass_basic @@ -43,3 +44,15 @@ for an overall introduction to the library and recent highlight and updates. static_quantization pretraining torchao_vllm_integration + +.. toctree:: + :glob: + :maxdepth: 1 + :caption: PT2E Quantization Tutorials + + tutorials_source/pt2e_quant_ptq + tutorials_source/pt2e_quant_qat + tutorials_source/pt2e_quant_x86_inductor + tutorials_source/pt2e_quant_xpu_inductor + tutorials_source/pt2e_quantizer + tutorials_source/openvino_quantizer diff --git a/docs/source/quick_start.rst b/docs/source/quick_start.rst index f92d960c45..2bd0744d0c 100644 --- a/docs/source/quick_start.rst +++ b/docs/source/quick_start.rst @@ -29,20 +29,20 @@ First, let's set up our toy model: import copy import torch - + class ToyLinearModel(torch.nn.Module): def __init__(self, m: int, n: int, k: int): super().__init__() self.linear1 = torch.nn.Linear(m, n, bias=False) self.linear2 = torch.nn.Linear(n, k, bias=False) - + def forward(self, x): x = self.linear1(x) x = self.linear2(x) return x - + model = ToyLinearModel(1024, 1024, 1024).eval().to(torch.bfloat16).to("cuda") - + # Optional: compile model for faster inference and generation model = torch.compile(model, mode="max-autotune", fullgraph=True) model_bf16 = copy.deepcopy(model) @@ -99,18 +99,18 @@ it is also much faster! benchmark_model, unwrap_tensor_subclass, ) - + # Temporary workaround for tensor subclass + torch.compile # Only needed for torch version < 2.5 if not TORCH_VERSION_AT_LEAST_2_5: unwrap_tensor_subclass(model) - + num_runs = 100 torch._dynamo.reset() example_inputs = (torch.randn(1, 1024, dtype=torch.bfloat16, device="cuda"),) bf16_time = benchmark_model(model_bf16, num_runs, example_inputs) int4_time = benchmark_model(model, num_runs, example_inputs) - + print("bf16 mean time: %0.3f ms" % bf16_time) print("int4 mean time: %0.3f ms" % int4_time) print("speedup: %0.1fx" % (bf16_time / int4_time)) @@ -121,6 +121,87 @@ On a single A100 GPU with 80GB memory, this prints:: int4 mean time: 4.410 ms speedup: 6.9x +PyTorch 2 Export Quantization +============================= +PyTorch 2 Export Quantization is a full graph quantization workflow mostly for static quantization. It targets hardwares that requires both input and output activation and weight to be quantized and relies of recognizing an operator pattern to make quantization decisions (such as linear - relu). PT2E quantization produces a pattern with quantize and dequantize ops inserted around the operators and during lowering quantized operator patterns will be fused into real quantized ops. Currently there are two typical lowering paths, 1. torch.compile through inductor lowering 2. ExecuTorch through delegation + +Here we show an example with X86InductorQuantizer + +API Example:: + + import torch + from torchao.quantization.pt2e.quantize_pt2e import prepare_pt2e + from torch.export import export + from torchao.quantization.pt2e.quantizer.x86_inductor_quantizer import ( + X86InductorQuantizer, + get_default_x86_inductor_quantization_config, + ) + + class M(torch.nn.Module): + def __init__(self): + super().__init__() + self.linear = torch.nn.Linear(5, 10) + + def forward(self, x): + return self.linear(x) + + # initialize a floating point model + float_model = M().eval() + + # define calibration function + def calibrate(model, data_loader): + model.eval() + with torch.no_grad(): + for image, target in data_loader: + model(image) + + # Step 1. program capture + m = export(m, *example_inputs).module() + # we get a model with aten ops + + # Step 2. quantization + # backend developer will write their own Quantizer and expose methods to allow + # users to express how they + # want the model to be quantized + quantizer = X86InductorQuantizer() + quantizer.set_global(xiq.get_default_x86_inductor_quantization_config()) + + # or prepare_qat_pt2e for Quantization Aware Training + m = prepare_pt2e(m, quantizer) + + # run calibration + # calibrate(m, sample_inference_data) + m = convert_pt2e(m) + + # Step 3. lowering + # lower to target backend + + # Optional: using the C++ wrapper instead of default Python wrapper + import torch._inductor.config as config + config.cpp_wrapper = True + + with torch.no_grad(): + optimized_model = torch.compile(converted_model) + + # Running some benchmark + optimized_model(*example_inputs) + + +Please follow these tutorials to get started on PyTorch 2 Export Quantization: + +Modeling Users: + +- `PyTorch 2 Export Post Training Quantization `_ +- `PyTorch 2 Export Quantization Aware Training `_ +- `PyTorch 2 Export Post Training Quantization with X86 Backend through Inductor `_ +- `PyTorch 2 Export Post Training Quantization with XPU Backend through Inductor `_ +- `PyTorch 2 Export Quantization for OpenVINO torch.compile Backend `_ + + +Backend Developers (please check out all Modeling Users docs as well): + +- `How to Write a Quantizer for PyTorch 2 Export Quantization `_ + Next Steps ========== diff --git a/docs/source/tutorials_source/pt2e_quant_openvino.rst b/docs/source/tutorials_source/pt2e_quant_openvino.rst new file mode 100644 index 0000000000..827023b300 --- /dev/null +++ b/docs/source/tutorials_source/pt2e_quant_openvino.rst @@ -0,0 +1,251 @@ +PyTorch 2 Export Quantization for OpenVINO torch.compile Backend +=========================================================================== + +**Authors**: `Daniil Lyakhov `_, `Aamir Nazir `_, `Alexander Suslov `_, `Yamini Nimmagadda `_, `Alexander Kozlov `_ + +Prerequisites +-------------- + +- `PyTorch 2 Export Post Training Quantization `_ +- `How to Write a Quantizer for PyTorch 2 Export Quantization `_ + +Introduction +-------------- + +.. note:: + + This is an experimental feature, the quantization API is subject to change. + +This tutorial demonstrates how to use ``OpenVINOQuantizer`` from `Neural Network Compression Framework (NNCF) `_ in PyTorch 2 Export Quantization flow to generate a quantized model customized for the `OpenVINO torch.compile backend `_ and explains how to lower the quantized model into the `OpenVINO `_ representation. +``OpenVINOQuantizer`` unlocks the full potential of low-precision OpenVINO kernels due to the placement of quantizers designed specifically for the OpenVINO. + +The PyTorch 2 export quantization flow uses ``torch.export`` to capture the model into a graph and performs quantization transformations on top of the ATen graph. +This approach is expected to have significantly higher model coverage, improved flexibility, and a simplified UX. +OpenVINO backend compiles the FX Graph generated by TorchDynamo into an optimized OpenVINO model. + +The quantization flow mainly includes four steps: + +- Step 1: Capture the FX Graph from the eager Model based on the `torch export mechanism `_. +- Step 2: Apply the PyTorch 2 Export Quantization flow with OpenVINOQuantizer based on the captured FX Graph. +- Step 3: Lower the quantized model into OpenVINO representation with the `torch.compile `_ API. +- Optional step 4: : Improve quantized model metrics via `quantize_pt2e `_ method. + +The high-level architecture of this flow could look like this: + +:: + + float_model(Python) Example Input + \ / + \ / + —-------------------------------------------------------- + | export | + —-------------------------------------------------------- + | + FX Graph in ATen + | + | OpenVINOQuantizer + | / + —-------------------------------------------------------- + | prepare_pt2e | + | | | + | Calibrate + | | | + | convert_pt2e | + —-------------------------------------------------------- + | + Quantized Model + | + —-------------------------------------------------------- + | Lower into Inductor | + —-------------------------------------------------------- + | + OpenVINO model + +Post Training Quantization +---------------------------- + +Now, we will walk you through a step-by-step tutorial for how to use it with `torchvision resnet18 model `_ +for post training quantization. + +Prerequisite: OpenVINO and NNCF installation +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +OpenVINO and NNCF could be easily installed via `pip distribution `_: + +.. code-block:: bash + + pip install -U pip + pip install openvino, nncf + + +1. Capture FX Graph +^^^^^^^^^^^^^^^^^^^^^ + +We will start by performing the necessary imports, capturing the FX Graph from the eager module. + +.. code-block:: python + + import copy + import openvino.torch + import torch + import torchvision.models as models + from torchao.quantization.pt2e.quantize_pt2e import convert_pt2e + from torchao.quantization.pt2e.quantize_pt2e import prepare_pt2e + + import nncf.torch + + # Create the Eager Model + model_name = "resnet18" + model = models.__dict__[model_name](pretrained=True) + + # Set the model to eval mode + model = model.eval() + + # Create the data, using the dummy data here as an example + traced_bs = 50 + x = torch.randn(traced_bs, 3, 224, 224) + example_inputs = (x,) + + # Capture the FX Graph to be quantized + with torch.no_grad(), nncf.torch.disable_patching(): + exported_model = torch.export.export(model, example_inputs).module() + + + +2. Apply Quantization +^^^^^^^^^^^^^^^^^^^^^^^ + +After we capture the FX Module to be quantized, we will import the OpenVINOQuantizer. + + +.. code-block:: python + + from nncf.experimental.torch.fx import OpenVINOQuantizer + + quantizer = OpenVINOQuantizer() + +``OpenVINOQuantizer`` has several optional parameters that allow tuning the quantization process to get a more accurate model. +Below is the list of essential parameters and their description: + + +* ``preset`` - defines quantization scheme for the model. Two types of presets are available: + + * ``PERFORMANCE`` (default) - defines symmetric quantization of weights and activations + + * ``MIXED`` - weights are quantized with symmetric quantization and the activations are quantized with asymmetric quantization. This preset is recommended for models with non-ReLU and asymmetric activation functions, e.g. ELU, PReLU, GELU, etc. + + .. code-block:: python + + OpenVINOQuantizer(preset=nncf.QuantizationPreset.MIXED) + +* ``model_type`` - used to specify quantization scheme required for specific type of the model. Transformer is the only supported special quantization scheme to preserve accuracy after quantization of Transformer models (BERT, Llama, etc.). None is default, i.e. no specific scheme is defined. + + .. code-block:: python + + OpenVINOQuantizer(model_type=nncf.ModelType.Transformer) + +* ``ignored_scope`` - this parameter can be used to exclude some layers from the quantization process to preserve the model accuracy. For example, when you want to exclude the last layer of the model from quantization. Below are some examples of how to use this parameter: + + .. code-block:: python + + #Exclude by layer name: + names = ['layer_1', 'layer_2', 'layer_3'] + OpenVINOQuantizer(ignored_scope=nncf.IgnoredScope(names=names)) + + #Exclude by layer type: + types = ['Conv2d', 'Linear'] + OpenVINOQuantizer(ignored_scope=nncf.IgnoredScope(types=types)) + + #Exclude by regular expression: + regex = '.*layer_.*' + OpenVINOQuantizer(ignored_scope=nncf.IgnoredScope(patterns=regex)) + + #Exclude by subgraphs: + # In this case, all nodes along all simple paths in the graph + # from input to output nodes will be excluded from the quantization process. + subgraph = nncf.Subgraph(inputs=['layer_1', 'layer_2'], outputs=['layer_3']) + OpenVINOQuantizer(ignored_scope=nncf.IgnoredScope(subgraphs=[subgraph])) + + +* ``target_device`` - defines the target device, the specificity of which will be taken into account during optimization. The following values are supported: ``ANY`` (default), ``CPU``, ``CPU_SPR``, ``GPU``, and ``NPU``. + + .. code-block:: python + + OpenVINOQuantizer(target_device=nncf.TargetDevice.CPU) + +For further details on `OpenVINOQuantizer` please see the `documentation `_. + +After we import the backend-specific Quantizer, we will prepare the model for post-training quantization. +``prepare_pt2e`` folds BatchNorm operators into preceding Conv2d operators, and inserts observers in appropriate places in the model. + +.. code-block:: python + + prepared_model = prepare_pt2e(exported_model, quantizer) + +Now, we will calibrate the ``prepared_model`` after the observers are inserted in the model. + +.. code-block:: python + + # We use the dummy data as an example here + prepared_model(*example_inputs) + +Finally, we will convert the calibrated Model to a quantized Model. ``convert_pt2e`` takes a calibrated model and produces a quantized model. + +.. code-block:: python + + quantized_model = convert_pt2e(prepared_model, fold_quantize=False) + +After these steps, we finished running the quantization flow, and we will get the quantized model. + + +3. Lower into OpenVINO representation +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +After that the FX Graph can utilize OpenVINO optimizations using `torch.compile(…, backend=”openvino”) `_ functionality. + +.. code-block:: python + + with torch.no_grad(), nncf.torch.disable_patching(): + optimized_model = torch.compile(quantized_model, backend="openvino") + + # Running some benchmark + optimized_model(*example_inputs) + + + +The optimized model is using low-level kernels designed specifically for Intel CPU. +This should significantly speed up inference time in comparison with the eager model. + +4. Optional: Improve quantized model metrics +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +NNCF implements advanced quantization algorithms like `SmoothQuant `_ and `BiasCorrection `_, which help +to improve the quantized model metrics while minimizing the output discrepancies between the original and compressed models. +These advanced NNCF algorithms can be accessed via the NNCF `quantize_pt2e` API: + +.. code-block:: python + + from nncf.experimental.torch.fx import quantize_pt2e + + calibration_loader = torch.utils.data.DataLoader(...) + + + def transform_fn(data_item): + images, _ = data_item + return images + + + calibration_dataset = nncf.Dataset(calibration_loader, transform_fn) + quantized_model = quantize_pt2e( + exported_model, quantizer, calibration_dataset, smooth_quant=True, fast_bias_correction=False + ) + + +For further details, please see the `documentation `_ +and a complete `example on Resnet18 quantization `_. + +Conclusion +------------ + +This tutorial introduces how to use torch.compile with the OpenVINO backend and the OpenVINO quantizer. +For more details on NNCF and the NNCF Quantization Flow for PyTorch models, refer to the `NNCF Quantization Guide `_. +For additional information, check out the `OpenVINO Deployment via torch.compile Documentation `_. diff --git a/docs/source/tutorials_source/pt2e_quant_ptq.rst b/docs/source/tutorials_source/pt2e_quant_ptq.rst new file mode 100644 index 0000000000..0b483697e3 --- /dev/null +++ b/docs/source/tutorials_source/pt2e_quant_ptq.rst @@ -0,0 +1,603 @@ +PyTorch 2 Export Post Training Quantization +================================================================ +**Author**: `Jerry Zhang `_ + +This tutorial introduces the steps to do post training static quantization in +graph mode based on +`torch._export.export `_. Compared +to `FX Graph Mode Quantization `_, +this flow is expected to have significantly higher model coverage +(`88% on 14K models `_), +better programmability, and a simplified UX. + +Exportable by `torch.export.export` is a prerequisite to use the flow, you can +find what are the constructs that's supported in `Export DB `_. + +The high level architecture of quantization 2 with quantizer could look like +this: + +:: + + float_model(Python) Example Input + \ / + \ / + —------------------------------------------------------- + | export | + —------------------------------------------------------- + | + FX Graph in ATen Backend Specific Quantizer + | / + —-------------------------------------------------------- + | prepare_pt2e | + —-------------------------------------------------------- + | + Calibrate/Train + | + —-------------------------------------------------------- + | convert_pt2e | + —-------------------------------------------------------- + | + Quantized Model + | + —-------------------------------------------------------- + | Lowering | + —-------------------------------------------------------- + | + Executorch, Inductor or + + +The PyTorch 2 export quantization API looks like this: + +.. code:: python + + import torch + class M(torch.nn.Module): + def __init__(self): + super().__init__() + self.linear = torch.nn.Linear(5, 10) + + def forward(self, x): + return self.linear(x) + + + example_inputs = (torch.randn(1, 5),) + m = M().eval() + + # Step 1. program capture + # This is available for pytorch 2.6+, for more details on lower pytorch versions + # please check `Export the model with torch.export` section + m = torch.export.export(m, example_inputs).module() + # we get a model with aten ops + + + # Step 2. quantization + from torchao.quantization.pt2e.quantize_pt2e import ( + prepare_pt2e, + convert_pt2e, + ) + + # install executorch: `pip install executorch` + from executorch.backends.xnnpack.quantizer.xnnpack_quantizer import ( + get_symmetric_quantization_config, + XNNPACKQuantizer, + ) + # backend developer will write their own Quantizer and expose methods to allow + # users to express how they + # want the model to be quantized + quantizer = XNNPACKQuantizer().set_global(get_symmetric_quantization_config()) + m = prepare_pt2e(m, quantizer) + + # calibration omitted + + m = convert_pt2e(m) + # we have a model with aten ops doing integer computations when possible + + +Motivation of PyTorch 2 Export Quantization +--------------------------------------------- + +In PyTorch versions prior to 2, we have FX Graph Mode Quantization that uses +`QConfigMapping `_ +and `BackendConfig `_ +for customizations. ``QConfigMapping`` allows modeling users to specify how +they want their model to be quantized, ``BackendConfig`` allows backend +developers to specify the supported ways of quantization in their backend. While +that API covers most use cases relatively well, it is not fully extensible. +There are two main limitations for the current API: + +* Limitation around expressing quantization intentions for complicated operator + patterns (how an operator pattern should be observed/quantized) using existing + objects: ``QConfig`` and ``QConfigMapping``. + +* Limited support on how user can express their intention of how they want + their model to be quantized. For example, if users want to quantize the every + other linear in the model, or the quantization behavior has some dependency on + the actual shape of the Tensor (for example, only observe/quantize inputs + and outputs when the linear has a 3D input), backend developer or modeling + users need to change the core quantization API/flow. + +A few improvements could make the existing flow better: + +* We use ``QConfigMapping`` and ``BackendConfig`` as separate objects, + ``QConfigMapping`` describes user’s intention of how they want their model to + be quantized, ``BackendConfig`` describes what kind of quantization a backend + supports. ``BackendConfig`` is backend-specific, but ``QConfigMapping`` is not, + and the user can provide a ``QConfigMapping`` that is incompatible with a specific + ``BackendConfig``, this is not a great UX. Ideally, we can structure this better + by making both configuration (``QConfigMapping``) and quantization capability + (``BackendConfig``) backend-specific, so there will be less confusion about + incompatibilities. +* In ``QConfig`` we are exposing observer/ ``fake_quant`` observer classes as an + object for the user to configure quantization, this increases the things that + the user may need to care about. For example, not only the ``dtype`` but also + how the observation should happen, these could potentially be hidden from the + user so that the user flow is simpler. + +Here is a summary of the benefits of the new API: + +- **Programmability** (addressing 1. and 2.): When a user’s quantization needs + are not covered by available quantizers, users can build their own quantizer and + compose it with other quantizers as mentioned above. +- **Simplified UX** (addressing 3.): Provides a single instance with which both + backend and users interact. Thus you no longer have the user facing quantization + config mapping to map users intent and a separate quantization config that + backends interact with to configure what backend support. We will still have a + method for users to query what is supported in a quantizer. With a single + instance, composing different quantization capabilities also becomes more + natural than previously. + + For example XNNPACK does not support ``embedding_byte`` + and we have natively support for this in ExecuTorch. Thus, if we had + ``ExecuTorchQuantizer`` that only quantized ``embedding_byte``, then it can be + composed with ``XNNPACKQuantizer``. (Previously, this used to be concatenating the + two ``BackendConfig`` together and since options in ``QConfigMapping`` are not + backend specific, user also need to figure out how to specify the configurations + by themselves that matches the quantization capabilities of the combined + backend. With a single quantizer instance, we can compose two quantizers and + query the composed quantizer for capabilities, which makes it less error prone + and cleaner, for example, ``composed_quantizer.quantization_capabilities())``. + +- **Separation of concerns** (addressing 4.): As we design the quantizer API, we + also decouple specification of quantization, as expressed in terms of ``dtype``, + min/max (# of bits), symmetric, and so on, from the observer concept. + Currently, the observer captures both quantization specification and how to + observe (Histogram vs MinMax observer). Modeling users are freed from + interacting with observer and fake quant objects with this change. + +Define Helper Functions and Prepare Dataset +------------------------------------------- + +We’ll start by doing the necessary imports, defining some helper functions and +prepare the data. These steps are identitcal to +`Static Quantization with Eager Mode in PyTorch `_. + +To run the code in this tutorial using the entire ImageNet dataset, first +download Imagenet by following the instructions at here +`ImageNet Data `_. Unzip the downloaded file +into the ``data_path`` folder. + +Download the `torchvision resnet18 model `_ +and rename it to ``data/resnet18_pretrained_float.pth``. + +.. code:: python + + import os + import sys + import time + import numpy as np + + import torch + import torch.nn as nn + from torch.utils.data import DataLoader + + import torchvision + from torchvision import datasets + from torchvision.models.resnet import resnet18 + import torchvision.transforms as transforms + + # Set up warnings + import warnings + warnings.filterwarnings( + action='ignore', + category=DeprecationWarning, + module=r'.*' + ) + warnings.filterwarnings( + action='default', + module=r'torchao.quantization.pt2e' + ) + + # Specify random seed for repeatable results + _ = torch.manual_seed(191009) + + + class AverageMeter(object): + """Computes and stores the average and current value""" + def __init__(self, name, fmt=':f'): + self.name = name + self.fmt = fmt + self.reset() + + def reset(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + + def __str__(self): + fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})' + return fmtstr.format(**self.__dict__) + + + def accuracy(output, target, topk=(1,)): + """ + Computes the accuracy over the k top predictions for the specified + values of k. + """ + with torch.no_grad(): + maxk = max(topk) + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.view(1, -1).expand_as(pred)) + + res = [] + for k in topk: + correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True) + res.append(correct_k.mul_(100.0 / batch_size)) + return res + + + def evaluate(model, criterion, data_loader): + model.eval() + top1 = AverageMeter('Acc@1', ':6.2f') + top5 = AverageMeter('Acc@5', ':6.2f') + cnt = 0 + with torch.no_grad(): + for image, target in data_loader: + output = model(image) + loss = criterion(output, target) + cnt += 1 + acc1, acc5 = accuracy(output, target, topk=(1, 5)) + top1.update(acc1[0], image.size(0)) + top5.update(acc5[0], image.size(0)) + print('') + + return top1, top5 + + def load_model(model_file): + model = resnet18(pretrained=False) + state_dict = torch.load(model_file, weights_only=True) + model.load_state_dict(state_dict) + model.to("cpu") + return model + + def print_size_of_model(model): + torch.save(model.state_dict(), "temp.p") + print("Size (MB):", os.path.getsize("temp.p")/1e6) + os.remove("temp.p") + + def prepare_data_loaders(data_path): + normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) + dataset = torchvision.datasets.ImageNet( + data_path, split="train", transform=transforms.Compose([ + transforms.RandomResizedCrop(224), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + normalize, + ])) + dataset_test = torchvision.datasets.ImageNet( + data_path, split="val", transform=transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + normalize, + ])) + + train_sampler = torch.utils.data.RandomSampler(dataset) + test_sampler = torch.utils.data.SequentialSampler(dataset_test) + + data_loader = torch.utils.data.DataLoader( + dataset, batch_size=train_batch_size, + sampler=train_sampler) + + data_loader_test = torch.utils.data.DataLoader( + dataset_test, batch_size=eval_batch_size, + sampler=test_sampler) + + return data_loader, data_loader_test + + data_path = '~/.data/imagenet' + saved_model_dir = 'data/' + float_model_file = 'resnet18_pretrained_float.pth' + + train_batch_size = 30 + eval_batch_size = 50 + + data_loader, data_loader_test = prepare_data_loaders(data_path) + example_inputs = (next(iter(data_loader))[0]) + criterion = nn.CrossEntropyLoss() + float_model = load_model(saved_model_dir + float_model_file).to("cpu") + float_model.eval() + + # create another instance of the model since + # we need to keep the original model around + model_to_quantize = load_model(saved_model_dir + float_model_file).to("cpu") + +Set the model to eval mode +-------------------------- + +For post training quantization, we'll need to set the model to the eval mode. + +.. code:: python + + model_to_quantize.eval() + +Export the model with torch.export +---------------------------------- + +Here is how you can use ``torch.export`` to export the model: + +.. code-block:: python + + example_inputs = (torch.rand(2, 3, 224, 224),) + # for pytorch 2.6+ + exported_model = torch.export.export(model_to_quantize, example_inputs).module() + + # for pytorch 2.5 and before + # from torch._export import capture_pre_autograd_graph + # exported_model = capture_pre_autograd_graph(model_to_quantize, example_inputs) + + # or capture with dynamic dimensions + # for pytorch 2.6+ + dynamic_shapes = tuple( + {0: torch.export.Dim("dim")} if i == 0 else None + for i in range(len(example_inputs)) + ) + exported_model = torch.export.export_for_training(model_to_quantize, example_inputs, dynamic_shapes=dynamic_shapes).module() + + # for pytorch 2.5 and before + # dynamic_shape API may vary as well + # from torch._export import dynamic_dim + # exported_model = capture_pre_autograd_graph(model_to_quantize, example_inputs, constraints=[dynamic_dim(example_inputs[0], 0)]) + + +Import the Backend Specific Quantizer and Configure how to Quantize the Model +----------------------------------------------------------------------------- + +The following code snippets describes how to quantize the model: + +.. code-block:: python + + from executorch.backends.xnnpack.quantizer.xnnpack_quantizer import ( + get_symmetric_quantization_config, + XNNPACKQuantizer, + ) + quantizer = XNNPACKQuantizer() + quantizer.set_global(get_symmetric_quantization_config()) + +``Quantizer`` is backend specific, and each ``Quantizer`` will provide their +own way to allow users to configure their model. Just as an example, here is +the different configuration APIs supported by ``XNNPackQuantizer``: + +.. code-block:: python + + quantizer.set_global(qconfig_opt) # qconfig_opt is an optional quantization config + .set_object_type(torch.nn.Conv2d, qconfig_opt) # can be a module type + .set_object_type(torch.nn.functional.linear, qconfig_opt) # or torch functional op + .set_module_name("foo.bar", qconfig_opt) + +.. note:: + + Check out our + `tutorial `_ + that describes how to write a new ``Quantizer``. + +Prepare the Model for Post Training Quantization +---------------------------------------------------------- + +``prepare_pt2e`` folds ``BatchNorm`` operators into preceding ``Conv2d`` +operators, and inserts observers in appropriate places in the model. + +.. code-block:: python + + prepared_model = prepare_pt2e(exported_model, quantizer) + print(prepared_model.graph) + +Calibration +-------------- + +The calibration function is run after the observers are inserted in the model. +The purpose for calibration is to run through some sample examples that is +representative of the workload (for example a sample of the training data set) +so that the observers in themodel are able to observe the statistics of the +Tensors and we can later use this information to calculate quantization +parameters. + +.. code-block:: python + + def calibrate(model, data_loader): + model.eval() + with torch.no_grad(): + for image, target in data_loader: + model(image) + calibrate(prepared_model, data_loader_test) # run calibration on sample data + +Convert the Calibrated Model to a Quantized Model +------------------------------------------------- + +``convert_pt2e`` takes a calibrated model and produces a quantized model. + +.. code-block:: python + + quantized_model = convert_pt2e(prepared_model) + print(quantized_model) + +At this step, we currently have two representations that you can choose from, but exact representation +we offer in the long term might change based on feedback from PyTorch users. + +* Q/DQ Representation (default) + + Previous documentation for `representations `_ all quantized operators are represented as ``dequantize -> fp32_op -> qauntize``. + +.. code-block:: python + + def quantized_linear(x_int8, x_scale, x_zero_point, weight_int8, weight_scale, weight_zero_point, bias_fp32, output_scale, output_zero_point): + x_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor( + x_i8, x_scale, x_zero_point, x_quant_min, x_quant_max, torch.int8) + weight_fp32 = torch.ops.quantized_decomposed.dequantize_per_tensor( + weight_i8, weight_scale, weight_zero_point, weight_quant_min, weight_quant_max, torch.int8) + weight_permuted = torch.ops.aten.permute_copy.default(weight_fp32, [1, 0]); + out_fp32 = torch.ops.aten.addmm.default(bias_fp32, x_fp32, weight_permuted) + out_i8 = torch.ops.quantized_decomposed.quantize_per_tensor( + out_fp32, out_scale, out_zero_point, out_quant_min, out_quant_max, torch.int8) + return out_i8 + +* Reference Quantized Model Representation + + We will have a special representation for selected ops, for example, quantized linear. Other ops are represented as ``dq -> float32_op -> q`` and ``q/dq`` are decomposed into more primitive operators. + You can get this representation by using ``convert_pt2e(..., use_reference_representation=True)``. + +.. code-block:: python + + # Reference Quantized Pattern for quantized linear + def quantized_linear(x_int8, x_scale, x_zero_point, weight_int8, weight_scale, weight_zero_point, bias_fp32, output_scale, output_zero_point): + x_int16 = x_int8.to(torch.int16) + weight_int16 = weight_int8.to(torch.int16) + acc_int32 = torch.ops.out_dtype(torch.mm, torch.int32, (x_int16 - x_zero_point), (weight_int16 - weight_zero_point)) + bias_scale = x_scale * weight_scale + bias_int32 = out_dtype(torch.ops.aten.div.Tensor, torch.int32, bias_fp32, bias_scale) + acc_int32 = acc_int32 + bias_int32 + acc_int32 = torch.ops.out_dtype(torch.ops.aten.mul.Scalar, torch.int32, acc_int32, x_scale * weight_scale / output_scale) + output_zero_point + out_int8 = torch.ops.aten.clamp(acc_int32, qmin, qmax).to(torch.int8) + return out_int8 + + +See `here `_ for the most up-to-date reference representations. + + +Checking Model Size and Accuracy Evaluation +---------------------------------------------- + +Now we can compare the size and model accuracy with baseline model. + +.. code-block:: python + + # Baseline model size and accuracy + print("Size of baseline model") + print_size_of_model(float_model) + + top1, top5 = evaluate(float_model, criterion, data_loader_test) + print("Baseline Float Model Evaluation accuracy: %2.2f, %2.2f"%(top1.avg, top5.avg)) + + # Quantized model size and accuracy + print("Size of model after quantization") + # export again to remove unused weights + quantized_model = torch.export.export_for_training(quantized_model, example_inputs).module() + print_size_of_model(quantized_model) + + top1, top5 = evaluate(quantized_model, criterion, data_loader_test) + print("[before serilaization] Evaluation accuracy on test dataset: %2.2f, %2.2f"%(top1.avg, top5.avg)) + + +.. note:: + We can't do performance evaluation now since the model is not lowered to + target device, it's just a representation of quantized computation in ATen + operators. + +.. note:: + The weights are still in fp32 right now, we may do constant propagation for quantize op to + get integer weights in the future. + +If you want to get better accuracy or performance, try configuring +``quantizer`` in different ways, and each ``quantizer`` will have its own way +of configuration, so please consult the documentation for the +quantizer you are using to learn more about how you can have more control +over how to quantize a model. + +Save and Load Quantized Model +--------------------------------- + +We'll show how to save and load the quantized model. + + +.. code-block:: python + + # 0. Store reference output, for example, inputs, and check evaluation accuracy: + example_inputs = (next(iter(data_loader))[0],) + ref = quantized_model(*example_inputs) + top1, top5 = evaluate(quantized_model, criterion, data_loader_test) + print("[before serialization] Evaluation accuracy on test dataset: %2.2f, %2.2f"%(top1.avg, top5.avg)) + + # 1. Export the model and Save ExportedProgram + pt2e_quantized_model_file_path = saved_model_dir + "resnet18_pt2e_quantized.pth" + # capture the model to get an ExportedProgram + quantized_ep = torch.export.export(quantized_model, example_inputs) + # use torch.export.save to save an ExportedProgram + torch.export.save(quantized_ep, pt2e_quantized_model_file_path) + + + # 2. Load the saved ExportedProgram + loaded_quantized_ep = torch.export.load(pt2e_quantized_model_file_path) + loaded_quantized_model = loaded_quantized_ep.module() + + # 3. Check results for example inputs and check evaluation accuracy again: + res = loaded_quantized_model(*example_inputs) + print("diff:", ref - res) + + top1, top5 = evaluate(loaded_quantized_model, criterion, data_loader_test) + print("[after serialization/deserialization] Evaluation accuracy on test dataset: %2.2f, %2.2f"%(top1.avg, top5.avg)) + + +Output: + + +.. code-block:: python + + [before serialization] Evaluation accuracy on test dataset: 79.82, 94.55 + diff: tensor([[0., 0., 0., ..., 0., 0., 0.], + [0., 0., 0., ..., 0., 0., 0.], + [0., 0., 0., ..., 0., 0., 0.], + ..., + [0., 0., 0., ..., 0., 0., 0.], + [0., 0., 0., ..., 0., 0., 0.], + [0., 0., 0., ..., 0., 0., 0.]]) + + [after serialization/deserialization] Evaluation accuracy on test dataset: 79.82, 94.55 + + +Debugging the Quantized Model +------------------------------ + +You can use `Numeric Suite `_ +that can help with debugging in eager mode and FX graph mode. The new version of +Numeric Suite working with PyTorch 2 Export models is still in development. + +Lowering and Performance Evaluation +------------------------------------ + +The model produced at this point is not the final model that runs on the device, +it is a reference quantized model that captures the intended quantized computation +from the user, expressed as ATen operators and some additional quantize/dequantize operators, +to get a model that runs on real devices, we'll need to lower the model. +For example, for the models that run on edge devices, we can lower with delegation and ExecuTorch runtime +operators. + +Conclusion +-------------- + +In this tutorial, we went through the overall quantization flow in PyTorch 2 +Export Quantization using ``XNNPACKQuantizer`` and got a quantized model that +could be further lowered to a backend that supports inference with XNNPACK +backend. To use this for your own backend, please first follow the +`tutorial `__ and +implement a ``Quantizer`` for your backend, and then quantize the model with +that ``Quantizer``. diff --git a/docs/source/tutorials_source/pt2e_quant_qat.rst b/docs/source/tutorials_source/pt2e_quant_qat.rst new file mode 100644 index 0000000000..cba870c668 --- /dev/null +++ b/docs/source/tutorials_source/pt2e_quant_qat.rst @@ -0,0 +1,487 @@ +PyTorch 2 Export Quantization-Aware Training (QAT) +================================================================ +**Author**: `Andrew Or `_ + +This tutorial shows how to perform quantization-aware training (QAT) in +graph mode based on `torch.export.export `_. +For more details about PyTorch 2 Export Quantization in general, refer +to the `post training quantization tutorial `_ + +The PyTorch 2 Export QAT flow looks like the following—it is similar +to the post training quantization (PTQ) flow for the most part: + +.. code:: python + + import torch + from torch._export import capture_pre_autograd_graph + from torchao.quantization.pt2e.quantize_pt2e import ( + prepare_qat_pt2e, + convert_pt2e, + ) + from executorch.backends.xnnpack.quantizer.xnnpack_quantizer import ( + get_symmetric_quantization_config, + XNNPACKQuantizer, + ) + + class M(torch.nn.Module): + def __init__(self): + super().__init__() + self.linear = torch.nn.Linear(5, 10) + + def forward(self, x): + return self.linear(x) + + + example_inputs = (torch.randn(1, 5),) + m = M() + + # Step 1. program capture + # This is available for pytorch 2.6+, for more details on lower pytorch versions + # please check `Export the model with torch.export` section + m = torch.export.export(m, example_inputs).module() + # we get a model with aten ops + + # Step 2. quantization-aware training + # backend developer will write their own Quantizer and expose methods to allow + # users to express how they want the model to be quantized + quantizer = XNNPACKQuantizer().set_global(get_symmetric_quantization_config()) + m = prepare_qat_pt2e(m, quantizer) + + # train omitted + + m = convert_pt2e(m) + # we have a model with aten ops doing integer computations when possible + + # move the quantized model to eval mode, equivalent to `m.eval()` + torchao.quantization.pt2e.move_exported_model_to_eval(m) + +Note that calling ``model.eval()`` or ``model.train()`` after program capture is +not allowed, because these methods no longer correctly change the behavior of +certain ops like dropout and batch normalization. Instead, please use +``torchao.quantization.pt2e.move_exported_model_to_eval()`` and +``torchao.quantization.pt2e.move_exported_model_to_train()`` (coming soon) +respectively. + + +Define Helper Functions and Prepare the Dataset +----------------------------------------------- + +To run the code in this tutorial using the entire ImageNet dataset, first +download ImageNet by following the instructions in +`ImageNet Data `_. Unzip the downloaded file +into the ``data_path`` folder. + +Next, download the `torchvision resnet18 model `_ +and rename it to ``data/resnet18_pretrained_float.pth``. + +We’ll start by doing the necessary imports, defining some helper functions and +prepare the data. These steps are very similar to the ones defined in the +`static eager mode post training quantization tutorial `_: + +.. code:: python + + import os + import sys + import time + import numpy as np + + import torch + import torch.nn as nn + from torch.utils.data import DataLoader + + import torchvision + from torchvision import datasets + from torchvision.models.resnet import resnet18 + import torchvision.transforms as transforms + + # Set up warnings + import warnings + warnings.filterwarnings( + action='ignore', + category=DeprecationWarning, + module=r'.*' + ) + warnings.filterwarnings( + action='default', + module=r'torchao.quantization.pt2e' + ) + + # Specify random seed for repeatable results + _ = torch.manual_seed(191009) + + class AverageMeter(object): + """Computes and stores the average and current value""" + def __init__(self, name, fmt=':f'): + self.name = name + self.fmt = fmt + self.reset() + + def reset(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + + def __str__(self): + fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})' + return fmtstr.format(**self.__dict__) + + def accuracy(output, target, topk=(1,)): + """ + Computes the accuracy over the k top predictions for the specified + values of k. + """ + with torch.no_grad(): + maxk = max(topk) + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.view(1, -1).expand_as(pred)) + + res = [] + for k in topk: + correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True) + res.append(correct_k.mul_(100.0 / batch_size)) + return res + + def evaluate(model, criterion, data_loader, device): + torchao.quantization.pt2e.move_exported_model_to_eval(model) + top1 = AverageMeter('Acc@1', ':6.2f') + top5 = AverageMeter('Acc@5', ':6.2f') + cnt = 0 + with torch.no_grad(): + for image, target in data_loader: + image = image.to(device) + target = target.to(device) + output = model(image) + loss = criterion(output, target) + cnt += 1 + acc1, acc5 = accuracy(output, target, topk=(1, 5)) + top1.update(acc1[0], image.size(0)) + top5.update(acc5[0], image.size(0)) + print('') + + return top1, top5 + + def load_model(model_file): + model = resnet18(pretrained=False) + state_dict = torch.load(model_file, weights_only=True) + model.load_state_dict(state_dict) + return model + + def print_size_of_model(model): + if isinstance(model, torch.jit.RecursiveScriptModule): + torch.jit.save(model, "temp.p") + else: + torch.jit.save(torch.jit.script(model), "temp.p") + print("Size (MB):", os.path.getsize("temp.p")/1e6) + os.remove("temp.p") + + def prepare_data_loaders(data_path): + normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) + dataset = torchvision.datasets.ImageNet( + data_path, split="train", transform=transforms.Compose([ + transforms.RandomResizedCrop(224), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + normalize, + ])) + dataset_test = torchvision.datasets.ImageNet( + data_path, split="val", transform=transforms.Compose([ + transforms.Resize(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + normalize, + ])) + + train_sampler = torch.utils.data.RandomSampler(dataset) + test_sampler = torch.utils.data.SequentialSampler(dataset_test) + + data_loader = torch.utils.data.DataLoader( + dataset, batch_size=train_batch_size, + sampler=train_sampler) + + data_loader_test = torch.utils.data.DataLoader( + dataset_test, batch_size=eval_batch_size, + sampler=test_sampler) + + return data_loader, data_loader_test + + def train_one_epoch(model, criterion, optimizer, data_loader, device, ntrain_batches): + # Note: do not call model.train() here, since this doesn't work on an exported model. + # Instead, call `torchao.quantization.pt2e.move_exported_model_to_train(model)`, which will + # be added in the near future + top1 = AverageMeter('Acc@1', ':6.2f') + top5 = AverageMeter('Acc@5', ':6.2f') + avgloss = AverageMeter('Loss', '1.5f') + + cnt = 0 + for image, target in data_loader: + start_time = time.time() + print('.', end = '') + cnt += 1 + image, target = image.to(device), target.to(device) + output = model(image) + loss = criterion(output, target) + optimizer.zero_grad() + loss.backward() + optimizer.step() + acc1, acc5 = accuracy(output, target, topk=(1, 5)) + top1.update(acc1[0], image.size(0)) + top5.update(acc5[0], image.size(0)) + avgloss.update(loss, image.size(0)) + if cnt >= ntrain_batches: + print('Loss', avgloss.avg) + + print('Training: * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}' + .format(top1=top1, top5=top5)) + return + + print('Full imagenet train set: * Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f}' + .format(top1=top1, top5=top5)) + return + + data_path = '~/.data/imagenet' + saved_model_dir = 'data/' + float_model_file = 'resnet18_pretrained_float.pth' + + train_batch_size = 32 + eval_batch_size = 32 + + data_loader, data_loader_test = prepare_data_loaders(data_path) + example_inputs = (next(iter(data_loader))[0]) + criterion = nn.CrossEntropyLoss() + float_model = load_model(saved_model_dir + float_model_file).to("cuda") + + +Export the model with torch.export +---------------------------------- + +Here is how you can use ``torch.export`` to export the model: + +.. code:: python + + from torch.export import export + + example_inputs = (torch.rand(2, 3, 224, 224),) + # for pytorch 2.6+ + exported_model = torch.export.export(float_model, example_inputs).module() + # for pytorch 2.5 and before + # from torch._export import capture_pre_autograd_graph + # exported_model = capture_pre_autograd_graph(model_to_quantize, example_inputs) + + +.. code:: python + + # or, to capture with dynamic dimensions: + + # for pytorch 2.6+ + dynamic_shapes = tuple( + {0: torch.export.Dim("dim")} if i == 0 else None + for i in range(len(example_inputs)) + ) + exported_model = torch.export.export(float_model, example_inputs, dynamic_shapes=dynamic_shapes).module() + + # for pytorch 2.5 and before + # dynamic_shape API may vary as well + # from torch._export import dynamic_dim + + # example_inputs = (torch.rand(2, 3, 224, 224),) + # exported_model = capture_pre_autograd_graph( + # float_model, + # example_inputs, + # constraints=[dynamic_dim(example_inputs[0], 0)], + # ) + + +Import the Backend Specific Quantizer and Configure how to Quantize the Model +----------------------------------------------------------------------------- + +The following code snippets describe how to quantize the model: + +.. code-block:: python + + from executorch.backends.xnnpack.quantizer.xnnpack_quantizer import ( + get_symmetric_quantization_config, + XNNPACKQuantizer, + ) + quantizer = XNNPACKQuantizer() + quantizer.set_global(get_symmetric_quantization_config(is_qat=True)) + +``Quantizer`` is backend specific, and each ``Quantizer`` will provide their +own way to allow users to configure their model. + +.. note:: + + Check out our + `tutorial `_ + that describes how to write a new ``Quantizer``. + + +Prepare the Model for Quantization-Aware Training +---------------------------------------------------------- + +``prepare_qat_pt2e`` inserts fake quantizes in appropriate places in the model +and performs the appropriate QAT "fusions", such as ``Conv2d`` + ``BatchNorm2d``, +for better training accuracies. The fused operations are represented as a subgraph +of ATen ops in the prepared graph. + +.. code-block:: python + + prepared_model = prepare_qat_pt2e(exported_model, quantizer) + print(prepared_model) + +.. note:: + + If your model contains batch normalization, the actual ATen ops you get + in the graph depend on the model's device when you export the model. + If the model is on CPU, then you'll get ``torch.ops.aten._native_batch_norm_legit``. + If the model is on CUDA, then you'll get ``torch.ops.aten.cudnn_batch_norm``. + However, this is not fundamental and may be subject to change in the future. + + Between these two ops, it has been shown that ``torch.ops.aten.cudnn_batch_norm`` + provides better numerics on models like MobileNetV2. To get this op, either + call ``model.cuda()`` before export, or run the following after prepare to manually + swap the ops: + + .. code:: python + + for n in prepared_model.graph.nodes: + if n.target == torch.ops.aten._native_batch_norm_legit.default: + n.target = torch.ops.aten.cudnn_batch_norm.default + prepared_model.recompile() + + In the future, we plan to consolidate the batch normalization ops such that + the above will no longer be necessary. + +Training Loop +----------------------------------------------------------------------------- + +The training loop is similar to the ones in previous versions of QAT. To achieve +better accuracies, you may optionally disable observers and updating batch +normalization statistics after a certain number of epochs, or evaluate the QAT +or the quantized model trained so far every ``N`` epochs. + +.. code:: python + + num_epochs = 10 + num_train_batches = 20 + num_eval_batches = 20 + num_observer_update_epochs = 4 + num_batch_norm_update_epochs = 3 + num_epochs_between_evals = 2 + + # QAT takes time and one needs to train over a few epochs. + # Train and check accuracy after each epoch + for nepoch in range(num_epochs): + train_one_epoch(prepared_model, criterion, optimizer, data_loader, "cuda", num_train_batches) + + # Optionally disable observer/batchnorm stats after certain number of epochs + if epoch >= num_observer_update_epochs: + print("Disabling observer for subseq epochs, epoch = ", epoch) + prepared_model.apply(torchao.quantization.pt2e.disable_observer) + if epoch >= num_batch_norm_update_epochs: + print("Freezing BN for subseq epochs, epoch = ", epoch) + for n in prepared_model.graph.nodes: + # Args: input, weight, bias, running_mean, running_var, training, momentum, eps + # We set the `training` flag to False here to freeze BN stats + if n.target in [ + torch.ops.aten._native_batch_norm_legit.default, + torch.ops.aten.cudnn_batch_norm.default, + ]: + new_args = list(n.args) + new_args[5] = False + n.args = new_args + prepared_model.recompile() + + # Check the quantized accuracy every N epochs + # Note: If you wish to just evaluate the QAT model (not the quantized model), + # then you can just call `torchao.quantization.pt2e.move_exported_model_to_eval/train`. + # However, the latter API is not ready yet and will be available in the near future. + if (nepoch + 1) % num_epochs_between_evals == 0: + prepared_model_copy = copy.deepcopy(prepared_model) + quantized_model = convert_pt2e(prepared_model_copy) + top1, top5 = evaluate(quantized_model, criterion, data_loader_test, neval_batches=num_eval_batches) + print('Epoch %d: Evaluation accuracy on %d images, %2.2f' % (nepoch, num_eval_batches * eval_batch_size, top1.avg)) + + +Saving and Loading Model Checkpoints +---------------------------------------------------------- + +Model checkpoints for the PyTorch 2 Export QAT flow are +the same as in any other training flow. They are useful for +pausing training and resuming it later, recovering from +failed training runs, and performing inference on different +machines at a later time. You can save model checkpoints +during or after training as follows: + +.. code:: python + + checkpoint_path = "/path/to/my/checkpoint_%s.pth" % nepoch + torch.save(prepared_model.state_dict(), "checkpoint_path") + +To load the checkpoints, you must export and prepare the +model the exact same way it was initially exported and +prepared. For example: + +.. code:: python + + from torch._export import capture_pre_autograd_graph + from executorch.backends.xnnpack.quantizer.xnnpack_quantizer import ( + get_symmetric_quantization_config, + XNNPACKQuantizer, + ) + from torchvision.models.resnet import resnet18 + + example_inputs = (torch.rand(2, 3, 224, 224),) + float_model = resnet18(pretrained=False) + exported_model = capture_pre_autograd_graph(float_model, example_inputs) + quantizer = XNNPACKQuantizer() + quantizer.set_global(get_symmetric_quantization_config(is_qat=True)) + prepared_model = prepare_qat_pt2e(exported_model, quantizer) + prepared_model.load_state_dict(torch.load(checkpoint_path)) + + # resume training or perform inference + + +Convert the Trained Model to a Quantized Model +---------------------------------------------------------- + +``convert_pt2e`` takes a calibrated model and produces a quantized model. +Note that, before inference, you must first call +``torchao.quantization.pt2e.move_exported_model_to_eval()`` to ensure certain ops +like dropout behave correctly in the eval graph. Otherwise, we would continue +to incorrectly apply dropout in the forward pass during inference, for example. + +.. code-block:: python + + quantized_model = convert_pt2e(prepared_model) + + # move certain ops like dropout to eval mode, equivalent to `m.eval()` + torchao.quantization.pt2e.move_exported_model_to_eval(m) + + print(quantized_model) + + top1, top5 = evaluate(quantized_model, criterion, data_loader_test, neval_batches=num_eval_batches) + print('Final evaluation accuracy on %d images, %2.2f' % (num_eval_batches * eval_batch_size, top1.avg)) + +.. TODO: add results here + + +Conclusion +-------------- + +In this tutorial, we demonstrated how to run Quantization-Aware Training (QAT) +flow in PyTorch 2 Export Quantization. After convert, the rest of the flow +is the same as Post-Training Quantization (PTQ); the user can +serialize/deserialize the model and further lower it to a backend that supports +inference with XNNPACK backend. For more detail, follow the +`PTQ tutorial `_. diff --git a/docs/source/tutorials_source/pt2e_quant_x86_inductor.rst b/docs/source/tutorials_source/pt2e_quant_x86_inductor.rst new file mode 100644 index 0000000000..e4faec469f --- /dev/null +++ b/docs/source/tutorials_source/pt2e_quant_x86_inductor.rst @@ -0,0 +1,305 @@ +PyTorch 2 Export Quantization with X86 Backend through Inductor +================================================================== + +**Author**: `Leslie Fang `_, `Weiwen Xia `_, `Jiong Gong `_, `Jerry Zhang `_ + +Prerequisites +--------------- + +- `PyTorch 2 Export Post Training Quantization `_ +- `PyTorch 2 Export Quantization Aware Training `_ +- `TorchInductor and torch.compile concepts in PyTorch `_ +- `Inductor C++ Wrapper concepts `_ + +Introduction +-------------- + +This tutorial introduces the steps for utilizing the PyTorch 2 Export Quantization flow to generate a quantized model customized +for the x86 inductor backend and explains how to lower the quantized model into the inductor. + +The pytorch 2 export quantization flow uses the torch.export to capture the model into a graph and perform quantization transformations on top of the ATen graph. +This approach is expected to have significantly higher model coverage, better programmability, and a simplified UX. +TorchInductor is the new compiler backend that compiles the FX Graphs generated by TorchDynamo into optimized C++/Triton kernels. + +This flow of quantization 2 with Inductor supports both static and dynamic quantization. Static quantization works best for CNN models, like ResNet-50. And dynamic quantization is more suitable for NLP models, like RNN and BERT. +For the difference between the two quantization types, please refer to the `following page `__. + +The quantization flow mainly includes three steps: + +- Step 1: Capture the FX Graph from the eager Model based on the `torch export mechanism `_. +- Step 2: Apply the Quantization flow based on the captured FX Graph, including defining the backend-specific quantizer, generating the prepared model with observers, + performing the prepared model's calibration or quantization-aware training, and converting the prepared model into the quantized model. +- Step 3: Lower the quantized model into inductor with the API ``torch.compile``. + +The high-level architecture of this flow could look like this: + +:: + + float_model(Python) Example Input + \ / + \ / + —-------------------------------------------------------- + | export | + —-------------------------------------------------------- + | + FX Graph in ATen + | X86InductorQuantizer + | / + —-------------------------------------------------------- + | prepare_pt2e | + | | | + | Calibrate/Train | + | | | + | convert_pt2e | + —-------------------------------------------------------- + | + Quantized Model + | + —-------------------------------------------------------- + | Lower into Inductor | + —-------------------------------------------------------- + | + Inductor + +Combining Quantization in PyTorch 2 Export and TorchInductor, we have flexibility and productivity with the new Quantization frontend +and outstanding out-of-box performance with the compiler backend. Especially on Intel fourth generation (SPR) Xeon processors which can +further boost the models' performance by leveraging the +`advanced-matrix-extensions `_ feature. + +Post Training Quantization +---------------------------- + +Now, we will walk you through a step-by-step tutorial for how to use it with `torchvision resnet18 model `_ +for post training quantization. + +1. Capture FX Graph +^^^^^^^^^^^^^^^^^^^^^ + +We will start by performing the necessary imports, capturing the FX Graph from the eager module. + +:: + + import torch + import torchvision.models as models + import copy + from torchao.quantization.pt2e.quantize_pt2e import prepare_pt2e, convert_pt2e + import torchao.quantization.pt2e.quantizer.x86_inductor_quantizer as xiq + from torchao.quantization.pt2e.quantizer.x86_inductor_quantizer import X86InductorQuantizer + from torch.export import export + + # Create the Eager Model + model_name = "resnet18" + model = models.__dict__[model_name](pretrained=True) + + # Set the model to eval mode + model = model.eval() + + # Create the data, using the dummy data here as an example + traced_bs = 50 + x = torch.randn(traced_bs, 3, 224, 224).contiguous(memory_format=torch.channels_last) + example_inputs = (x,) + + # Capture the FX Graph to be quantized + with torch.no_grad(): + # Note: requires torch >= 2.6 + exported_model = export( + model, + example_inputs + ) + + +Next, we will have the FX Module to be quantized. + +2. Apply Quantization +^^^^^^^^^^^^^^^^^^^^^^^ + +After we capture the FX Module to be quantized, we will import the Backend Quantizer for X86 CPU and configure how to +quantize the model. + +:: + + quantizer = X86InductorQuantizer() + quantizer.set_global(xiq.get_default_x86_inductor_quantization_config()) + +.. note:: + + The default quantization configuration in ``X86InductorQuantizer`` uses 8-bits for both activations and weights. + When Vector Neural Network Instruction is not available, the oneDNN backend silently chooses kernels that assume + `multiplications are 7-bit x 8-bit `_. In other words, potential + numeric saturation and accuracy issue may happen when running on CPU without Vector Neural Network Instruction. + +The quantization config is for static quantization by default. To apply dynamic quantization, add an argument ``is_dynamic=True`` when getting the config. + +.. code-block:: python + + quantizer = X86InductorQuantizer() + quantizer.set_global(xiq.get_default_x86_inductor_quantization_config(is_dynamic=True)) + + +After we import the backend-specific Quantizer, we will prepare the model for post-training quantization. +``prepare_pt2e`` folds BatchNorm operators into preceding Conv2d operators, and inserts observers in appropriate places in the model. + +:: + + prepared_model = prepare_pt2e(exported_model, quantizer) + +Now, we will calibrate the ``prepared_model`` after the observers are inserted in the model. This step is needed for static quantization only. + +:: + + # We use the dummy data as an example here + prepared_model(*example_inputs) + + # Alternatively: user can define the dataset to calibrate + # def calibrate(model, data_loader): + # model.eval() + # with torch.no_grad(): + # for image, target in data_loader: + # model(image) + # calibrate(prepared_model, data_loader_test) # run calibration on sample data + +Finally, we will convert the calibrated Model to a quantized Model. ``convert_pt2e`` takes a calibrated model and produces a quantized model. + +:: + + converted_model = convert_pt2e(prepared_model) + +After these steps, we finished running the quantization flow and we will get the quantized model. + + +3. Lower into Inductor +^^^^^^^^^^^^^^^^^^^^^^^^ + +After we get the quantized model, we will further lower it to the inductor backend. The default Inductor wrapper +generates Python code to invoke both generated kernels and external kernels. Additionally, Inductor supports +C++ wrapper that generates pure C++ code. This allows seamless integration of the generated and external kernels, +effectively reducing Python overhead. In the future, leveraging the C++ wrapper, we can extend the capability +to achieve pure C++ deployment. For more comprehensive details about C++ Wrapper in general, please refer to the +dedicated tutorial on `Inductor C++ Wrapper Tutorial `_. + +:: + + # Optional: using the C++ wrapper instead of default Python wrapper + import torch._inductor.config as config + config.cpp_wrapper = True + +:: + + with torch.no_grad(): + optimized_model = torch.compile(converted_model) + + # Running some benchmark + optimized_model(*example_inputs) + +In a more advanced scenario, int8-mixed-bf16 quantization comes into play. In this instance, +a Convolution or GEMM operator produces BFloat16 output data type instead of Float32 in the absence +of a subsequent quantization node. Subsequently, the BFloat16 tensor seamlessly propagates through +subsequent pointwise operators, effectively minimizing memory usage and potentially enhancing performance. +The utilization of this feature mirrors that of regular BFloat16 Autocast, as simple as wrapping the +script within the BFloat16 Autocast context. + +:: + + with torch.autocast(device_type="cpu", dtype=torch.bfloat16, enabled=True), torch.no_grad(): + # Turn on Autocast to use int8-mixed-bf16 quantization. After lowering into Inductor CPP Backend, + # For operators such as QConvolution and QLinear: + # * The input data type is consistently defined as int8, attributable to the presence of a pair + of quantization and dequantization nodes inserted at the input. + # * The computation precision remains at int8. + # * The output data type may vary, being either int8 or BFloat16, contingent on the presence + # of a pair of quantization and dequantization nodes at the output. + # For non-quantizable pointwise operators, the data type will be inherited from the previous node, + # potentially resulting in a data type of BFloat16 in this scenario. + # For quantizable pointwise operators such as QMaxpool2D, it continues to operate with the int8 + # data type for both input and output. + optimized_model = torch.compile(converted_model) + + # Running some benchmark + optimized_model(*example_inputs) + +Put all these codes together, we will have the toy example code. +Please note that since the Inductor ``freeze`` feature does not turn on by default yet, run your example code with ``TORCHINDUCTOR_FREEZING=1``. + +For example: + +:: + + TORCHINDUCTOR_FREEZING=1 python example_x86inductorquantizer_pytorch_2_1.py + +With PyTorch 2.1 release, all CNN models from TorchBench test suite have been measured and proven effective comparing with Inductor FP32 inference path. Please refer +to `this document `_ +for detail benchmark number. + +Quantization Aware Training +----------------------------- + +The PyTorch 2 Export Quantization-Aware Training (QAT) is now supported on X86 CPU using X86InductorQuantizer, +followed by the subsequent lowering of the quantized model into Inductor. +For a more in-depth understanding of PT2 Export Quantization-Aware Training, +we recommend referring to the dedicated `PyTorch 2 Export Quantization-Aware Training `_. + +The PyTorch 2 Export QAT flow is largely similar to the PTQ flow: + +.. code:: python + + import torch + from torch._export import capture_pre_autograd_graph + from torchao.quantization.pt2e.quantize_pt2e import ( + prepare_qat_pt2e, + convert_pt2e, + ) + from torch.export import export + import torchao.quantization.pt2e.quantizer.x86_inductor_quantizer as xiq + from torchao.quantization.pt2e.quantizer.x86_inductor_quantizer import X86InductorQuantizer + + class M(torch.nn.Module): + def __init__(self): + super().__init__() + self.linear = torch.nn.Linear(1024, 1000) + + def forward(self, x): + return self.linear(x) + + example_inputs = (torch.randn(1, 1024),) + m = M() + + # Step 1. program capture + # NOTE: this API will be updated to torch.export API in the future, but the captured + # result shoud mostly stay the same + exported_model = export(m, example_inputs) + # we get a model with aten ops + + # Step 2. quantization-aware training + # Use Backend Quantizer for X86 CPU + # To apply dynamic quantization, add an argument ``is_dynamic=True`` when getting the config. + quantizer = X86InductorQuantizer() + quantizer.set_global(xiq.get_default_x86_inductor_quantization_config(is_qat=True)) + prepared_model = prepare_qat_pt2e(exported_model, quantizer) + + # train omitted + + converted_model = convert_pt2e(prepared_model) + # we have a model with aten ops doing integer computations when possible + + # move the quantized model to eval mode, equivalent to `m.eval()` + torchao.quantization.pt2e.move_exported_model_to_eval(converted_model) + + # Lower the model into Inductor + with torch.no_grad(): + optimized_model = torch.compile(converted_model) + _ = optimized_model(*example_inputs) + +Please note that the Inductor ``freeze`` feature is not enabled by default. +To use this feature, you need to run example code with ``TORCHINDUCTOR_FREEZING=1``. + +For example: + +:: + + TORCHINDUCTOR_FREEZING=1 python example_x86inductorquantizer_qat.py + +Conclusion +------------ + +With this tutorial, we introduce how to use Inductor with X86 CPU in PyTorch 2 Quantization. Users can learn about +how to use ``X86InductorQuantizer`` to quantize a model and lower it into the inductor with X86 CPU devices. diff --git a/docs/source/tutorials_source/pt2e_quant_xpu_inductor.rst b/docs/source/tutorials_source/pt2e_quant_xpu_inductor.rst new file mode 100644 index 0000000000..a0901291e9 --- /dev/null +++ b/docs/source/tutorials_source/pt2e_quant_xpu_inductor.rst @@ -0,0 +1,238 @@ +PyTorch 2 Export Quantization with Intel GPU Backend through Inductor +================================================================== + +**Author**: `Yan Zhiwei `_, `Wang Eikan `_, `Zhang Liangang `_, `Liu River `_, `Cui Yifeng `_ + +Prerequisites +--------------- + +- `PyTorch 2 Export Post Training Quantization `_ +- `TorchInductor and torch.compile concepts in PyTorch `_ +- PyTorch 2.7 or later + +Introduction +-------------- + +This tutorial introduces ``XPUInductorQuantizer``, which aims to serve quantized models for inference on Intel GPUs. +``XPUInductorQuantizer`` uses the PyTorch Export Quantization flow and lowers the quantized model into the inductor. + +The Pytorch 2 Export Quantization flow uses `torch.export` to capture the model into a graph and perform quantization transformations on top of the ATen graph. +This approach is expected to have significantly higher model coverage with better programmability and a simplified user experience. +TorchInductor is a compiler backend that transforms FX Graphs generated by ``TorchDynamo`` into optimized C++/Triton kernels. + +The quantization flow has three steps: + +- Step 1: Capture the FX Graph from the eager model based on the `torch export mechanism `_. +- Step 2: Apply the quantization flow based on the captured FX Graph, including defining the backend-specific quantizer, generating the prepared model with observers, + performing the prepared model's calibration, and converting the prepared model into the quantized model. +- Step 3: Lower the quantized model into inductor with the API ``torch.compile``, which would call Triton kernels or oneDNN GEMM/Convolution kernels. + + +The high-level architecture of this flow could look like this: + +.. image:: ../_static/img/pt2e_quant_xpu_inductor.png + :align: center + +Post Training Quantization +---------------------------- + +Static quantization is the only method we currently support. + +The following dependencies are recommended to be installed through the Intel GPU channel: + +:: + + pip3 install torch torchvision torchaudio pytorch-triton-xpu --index-url https://download.pytorch.org/whl/xpu + + +Please note that since the inductor ``freeze`` feature does not turn on by default yet, you must run your example code with ``TORCHINDUCTOR_FREEZING=1``. + +For example: + +:: + + TORCHINDUCTOR_FREEZING=1 python xpu_inductor_quantizer_example.py + + +1. Capture FX Graph +^^^^^^^^^^^^^^^^^^^^^ + +We will start by performing the necessary imports, capturing the FX Graph from the eager module. + +:: + + import torch + import torchvision.models as models + from torchao.quantization.pt2e.quantize_pt2e import prepare_pt2e, convert_pt2e + import torchao.quantization.pt2e.quantizer.xpu_inductor_quantizer as xpuiq + from torchao.quantization.pt2e.quantizer.xpu_inductor_quantizer import XPUInductorQuantizer + from torch.export import export + + # Create the Eager Model + model_name = "resnet18" + model = models.__dict__[model_name](weights=models.ResNet18_Weights.DEFAULT) + + # Set the model to eval mode + model = model.eval().to("xpu") + + # Create the data, using the dummy data here as an example + traced_bs = 50 + x = torch.randn(traced_bs, 3, 224, 224, device="xpu").contiguous(memory_format=torch.channels_last) + example_inputs = (x,) + + # Capture the FX Graph to be quantized + with torch.no_grad(): + exported_model = export( + model, + example_inputs, + ).module() + + +Next, we will quantize the FX Module. + +2. Apply Quantization +^^^^^^^^^^^^^^^^^^^^^^^ + +After we capture the FX Module, we will import the Backend Quantizer for Intel GPU and configure it to +quantize the model. + +:: + + quantizer = XPUInductorQuantizer() + quantizer.set_global(xpuiq.get_default_xpu_inductor_quantization_config()) + +The default quantization configuration in ``XPUInductorQuantizer`` uses signed 8-bits for both activations and weights. The tensors are per-tensor quantized, whereas the weights are signed 8-bit per-channel quantized. + +Optionally, in addition to the default quantization configuration using asymmetric quantized activation, signed 8-bits symmetric quantized activation is also supported, which has the potential to provide better performance. + +:: + + from torchao.quantization.pt2e.observer import HistogramObserver, PerChannelMinMaxObserver + from torchao.quantization.pt2e.quantizer.quantizer import QuantizationSpec + from torchao.quantization.pt2e.quantizer import QuantizationConfig + from typing import Any, Optional, TYPE_CHECKING + if TYPE_CHECKING: + from torchao.quantization.pt2e import ObserverOrFakeQuantizeConstructor + def get_xpu_inductor_symm_quantization_config(): + extra_args: dict[str, Any] = {"eps": 2**-12} + act_observer_or_fake_quant_ctr = HistogramObserver + act_quantization_spec = QuantizationSpec( + dtype=torch.int8, + quant_min=-128, + quant_max=127, + qscheme=torch.per_tensor_symmetric, # Change the activation quant config to symmetric + is_dynamic=False, + observer_or_fake_quant_ctr=act_observer_or_fake_quant_ctr.with_args( + **extra_args + ), + ) + + weight_observer_or_fake_quant_ctr: ObserverOrFakeQuantizeConstructor = ( + PerChannelMinMaxObserver + ) + + weight_quantization_spec = QuantizationSpec( + dtype=torch.int8, + quant_min=-128, + quant_max=127, + qscheme=torch.per_channel_symmetric, # Same as the default config, the only supported option for weight + ch_axis=0, # 0 corresponding to weight shape = (oc, ic, kh, kw) of conv + is_dynamic=False, + observer_or_fake_quant_ctr=weight_observer_or_fake_quant_ctr.with_args( + **extra_args + ), + ) + + bias_quantization_spec = None # will use placeholder observer by default + quantization_config = QuantizationConfig( + act_quantization_spec, + act_quantization_spec, + weight_quantization_spec, + bias_quantization_spec, + False, + ) + return quantization_config + + # Then, set the quantization configuration to the quantizer. + quantizer = XPUInductorQuantizer() + quantizer.set_global(get_xpu_inductor_symm_quantization_config()) + +After the backend-specific quantizer is imported, prepare the model for post-training quantization. +``prepare_pt2e`` folds ``BatchNorm`` operators into preceding Conv2d operators, and inserts observers into appropriate places in the model. + +:: + + prepared_model = prepare_pt2e(exported_model, quantizer) + +**(For static quantization only)** Calibrate the ``prepared_model`` after the observers are inserted into the model. + +:: + + # We use the dummy data as an example here + prepared_model(*example_inputs) + + # Alternatively: user can define the dataset to calibrate + # def calibrate(model, data_loader): + # model.eval() + # with torch.no_grad(): + # for image, target in data_loader: + # model(image) + # calibrate(prepared_model, data_loader_test) # run calibration on sample data + +Finally, convert the calibrated model to a quantized model. ``convert_pt2e`` takes a calibrated model and produces a quantized model. + +:: + + converted_model = convert_pt2e(prepared_model) + +After these steps, the quantization flow has been completed and the quantized model is available. + + +3. Lower into Inductor +^^^^^^^^^^^^^^^^^^^^^^^^ + +The quantized model will then be lowered into the inductor backend. + +:: + + with torch.no_grad(): + optimized_model = torch.compile(converted_model) + + # Running some benchmark + optimized_model(*example_inputs) + +In a more advanced scenario, int8-mixed-bf16 quantization comes into play. In this instance, +a convolution or GEMM operator produces the output in BFloat16 instead of Float32 in the absence +of a subsequent quantization node. Subsequently, the BFloat16 tensor seamlessly propagates through +subsequent pointwise operators, effectively minimizing memory usage and potentially enhancing performance. +The utilization of this feature mirrors that of regular BFloat16 Autocast, as simple as wrapping the +script within the BFloat16 Autocast context. + +:: + + with torch.amp.autocast(device_type="xpu", dtype=torch.bfloat16), torch.no_grad(): + # Turn on Autocast to use int8-mixed-bf16 quantization. After lowering into indcutor backend, + # For operators such as QConvolution and QLinear: + # * The input data type is consistently defined as int8, attributable to the presence of a pair + # of quantization and dequantization nodes inserted at the input. + # * The computation precision remains at int8. + # * The output data type may vary, being either int8 or BFloat16, contingent on the presence + # of a pair of quantization and dequantization nodes at the output. + # For non-quantizable pointwise operators, the data type will be inherited from the previous node, + # potentially resulting in a data type of BFloat16 in this scenario. + # For quantizable pointwise operators such as QMaxpool2D, it continues to operate with the int8 + # data type for both input and output. + optimized_model = torch.compile(converted_model) + + # Running some benchmark + optimized_model(*example_inputs) + + +Conclusion +------------ + +In this tutorial, we have learned how to utilize the ``XPUInductorQuantizer`` to perform post-training quantization on models for inference +on Intel GPUs, leveraging PyTorch 2's Export Quantization flow. We covered the step-by-step process of capturing an FX Graph, +applying quantization, and lowering the quantized model into the inductor backend using ``torch.compile``. Additionally, we explored +the benefits of using int8-mixed-bf16 quantization for improved memory efficiency and potential performance gains, +especially when using ``BFloat16`` autocast. diff --git a/docs/source/tutorials_source/pt2e_quantizer.rst b/docs/source/tutorials_source/pt2e_quantizer.rst new file mode 100644 index 0000000000..e669c5b986 --- /dev/null +++ b/docs/source/tutorials_source/pt2e_quantizer.rst @@ -0,0 +1,381 @@ +How to Write a ``Quantizer`` for PyTorch 2 Export Quantization +================================================================ + +**Author**: `Leslie Fang `_, `Weiwen Xia `__, `Jiong Gong `__, `Kimish Patel `__, `Jerry Zhang `__ + +Prerequisites: +^^^^^^^^^^^^^^^^ + +Required: + +- `Torchdynamo concepts in PyTorch `__ + +- `Quantization concepts in PyTorch `__ + +- `(prototype) PyTorch 2 Export Post Training Quantization `__ + +Optional: + +- `FX Graph Mode post training static quantization `__ + +- `BackendConfig in PyTorch Quantization FX Graph Mode `__ + +- `QConfig and QConfigMapping in PyTorch Quantization FX Graph Mode `__ + +Introduction +^^^^^^^^^^^^^ + +`(prototype) PyTorch 2 Export Post Training Quantization `__ introduced the overall API for pytorch 2 export quantization, main difference from fx graph mode quantization in terms of API is that we made it explicit that quantiation is targeting a specific backend. So to use the new flow, backend need to implement a ``Quantizer`` class that encodes: +(1). What is supported quantized operator or patterns in the backend +(2). How can users express the way they want their floating point model to be quantized, for example, quantized the whole model to be int8 symmetric quantization, or quantize only linear layers etc. + +Please see `here `__ For motivations for the new API and ``Quantizer``. + +An existing quantizer object defined for ``XNNPACK`` is in +`QNNPackQuantizer `__ + +Annotation API +^^^^^^^^^^^^^^^^^^^ + +``Quantizer`` uses annotation API to convey quantization intent for different operators/patterns. +Annotation API mainly consists of +`QuantizationSpec `__ +and +`QuantizationAnnotation `__. + +``QuantizationSpec`` is used to convey intent of how a tensor will be quantized, +e.g. dtype, bitwidth, min, max values, symmetric vs. asymmetric etc. +Furthermore, ``QuantizationSpec`` also allows quantizer to specify how a +tensor value should be observed, e.g. ``MinMaxObserver``, or ``HistogramObserver`` +, or some customized observer. + +``QuantizationAnnotation`` composed of ``QuantizationSpec`` objects is used to annotate input tensors +and output tensor of a pattern. Annotating input tensors is equivalent of annotating input edges, +while annotating output tensor is equivalent of annotating node. ``QuantizationAnnotation`` is a ``dataclass`` +with several fields: + +- ``input_qspec_map`` field is of class ``Dict`` to map each input tensor (as input edge) to a ``QuantizationSpec``. +- ``output_qspec`` field expresses the ``QuantizationSpec`` used to annotate the output tensor; +- ``_annotated`` field indicates if this node has already been annotated by quantizer. + +To conclude, annotation API requires quantizer to annotate edges (input tensors) or +nodes (output tensor) of the graph. Now, we will have a step-by-step tutorial for +how to use the annotation API with different types of ``QuantizationSpec``. + +1. Annotate Common Operator Patterns +-------------------------------------------------------- + +In order to use the quantized pattern/operators, e.g. ``quantized add``, +backend developers will have intent to quantize (as expressed by ``QuantizationSpec``) +inputs, output of the pattern. Following is an example flow (take ``add`` operator as example) +of how this intent is conveyed in the quantization workflow with annotation API. + +- Step 1: Identify the original floating point pattern in the FX graph. There are + several ways to identify this pattern: Quantizer may use a pattern matcher + to match the operator pattern; Quantizer may go through the nodes from start to the end and compare + the node's target type to match the operator pattern. In this example, we can use the + `get_source_partitions `__ + to match this pattern. The original floating point ``add`` pattern only contain a single ``add`` node. + +:: + + add_partitions = get_source_partitions(gm.graph, [operator.add, torch.add]) + add_partitions = list(itertools.chain(*add_partitions.values())) + for add_partition in add_partitions: + add_node = add_partition.output_nodes[0] + +- Step 2: Define the ``QuantizationSpec`` for inputs and output of the pattern. ``QuantizationSpec`` + defines the ``data type``, ``qscheme``, and other quantization parameters about users' intent of + how to observe or fake quantize a tensor. + +:: + + act_quantization_spec = QuantizationSpec( + dtype=torch.int8, + quant_min=-128, + quant_max=127, + qscheme=torch.per_tensor_affine, + is_dynamic=False, + observer_or_fake_quant_ctr=HistogramObserver.with_args(eps=2**-12), + ) + + input_act_qspec = act_quantization_spec + output_act_qspec = act_quantization_spec + +- Step 3: Annotate the inputs and output of the pattern with ``QuantizationAnnotation``. + In this example, we will create the ``QuantizationAnnotation`` object with the ``QuantizationSpec`` + created in above step 2 for two inputs and one output of the ``add`` node. + +:: + + input_qspec_map = {} + input_act0 = add_node.args[0] + input_qspec_map[input_act0] = input_act_qspec + + input_act1 = add_node.args[1] + input_qspec_map[input_act1] = input_act_qspec + + add_node.meta["quantization_annotation"] = QuantizationAnnotation( + input_qspec_map=input_qspec_map, + output_qspec=output_act_qspec, + _annotated=True, + ) + +After we annotate the ``add`` node like this, in the following up quantization flow, ``HistogramObserver`` will +be inserted at its two input nodes and one output node in prepare phase. And ``HistogramObserver`` will be substituted with +``quantize`` node and ``dequantize`` node in the convert phase. + +2. Annotate Operators that Shares Quantization Params +-------------------------------------------------------- + +It is natural that users want to annotate a quantized model where quantization +parameters can be shared among some tensors explicitly. Two typical use cases are: + +- Example 1: One example is for ``add`` where having both inputs sharing quantization + parameters makes operator implementation much easier. Without using of + `SharedQuantizationSpec `__, + we must annotate ``add`` as example in above section 1, in which two inputs of ``add`` + has different quantization parameters. +- Example 2: Another example is that of sharing quantization parameters between inputs and output. + This typically results from operators such as ``maxpool``, ``average_pool``, ``concat`` etc. + +``SharedQuantizationSpec`` is designed for this use case to annotate tensors whose quantization +parameters are shared with other tensors. Input of ``SharedQuantizationSpec`` is an ``EdgeOrNode`` object which +can be an input edge or an output value. + +.. note:: + + * Sharing is transitive + + Some tensors might be effectively using shared quantization spec due to: + + * Two nodes/edges are configured to use ``SharedQuantizationSpec``. + * There is existing sharing of some nodes. + + For example, let's say we have two ``conv`` nodes ``conv1`` and ``conv2``, and both of them are fed into a ``cat`` + node: ``cat([conv1_out, conv2_out], ...)``. Let's say the output of ``conv1``, ``conv2``, and the first input of ``cat`` are configured + with the same configurations of ``QuantizationSpec``. The second input of ``cat`` is configured to use ``SharedQuantizationSpec`` + with the first input. + + .. code-block:: + + conv1_out: qspec1(dtype=torch.int8, ...) + conv2_out: qspec1(dtype=torch.int8, ...) + cat_input0: qspec1(dtype=torch.int8, ...) + cat_input1: SharedQuantizationSpec((conv1, cat)) # conv1 node is the first input of cat + + First of all, the output of ``conv1`` is implicitly sharing quantization parameters (and observer object) + with the first input of ``cat``, and the same is true for the output of ``conv2`` and the second input of ``cat``. + Therefore, since the user configures the two inputs of ``cat`` to share quantization parameters, by transitivity, + ``conv2_out`` and ``conv1_out`` will also be sharing quantization parameters. In the observed graph, you + will see the following: + + .. code-block:: + + conv1 -> obs -> cat + conv2 -> obs / + + and both ``obs`` will be the same observer instance. + + +- Input edge is the connection between input node and the node consuming the input, + so it's a ``Tuple[Node, Node]``. +- Output value is an FX ``Node``. + +Now, if we want to rewrite ``add`` annotation example with ``SharedQuantizationSpec`` to indicate +two input tensors as sharing quantization parameters. We can define its ``QuantizationAnnotation`` +as this: + +- Step 1: Identify the original floating point pattern in the FX graph. We can use the same + methods introduced in ``QuantizationSpec`` example to identify the ``add`` pattern. +- Step 2: Annotate input_act0 of ``add`` with ``QuantizationSpec``. +- Step 3: Create a ``SharedQuantizationSpec`` object with input edge defined as ``(input_act0, add_node)`` which means to + share the observer used for this edge. Then, user can annotate input_act1 with this ``SharedQuantizationSpec`` + object. + +:: + + input_qspec_map = {} + share_qparams_with_input_act0_qspec = SharedQuantizationSpec((input_act0, add_node)) + input_qspec_map = {input_act0: act_quantization_spec, input_act1: share_qparams_with_input_act0_qspec} + + add_node.meta["quantization_annotation"] = QuantizationAnnotation( + input_qspec_map=input_qspec_map, + output_qspec=act_quantization_spec, + _annotated=True, + ) + +3. Annotate Operators with Fixed Quantization Parameters +--------------------------------------------------------- + +Another typical use case to annotate a quantized model is for tensors whose +quantization parameters are known beforehand. For example, operator like ``sigmoid``, which has +predefined and fixed scale/zero_point at input and output tensors. +`FixedQParamsQuantizationSpec `__ +is designed for this use case. To use ``FixedQParamsQuantizationSpec``, users need to pass in parameters +of ``scale`` and ``zero_point`` explicitly. + +- Step 1: Identify the original floating point pattern in the FX graph. We can use the same + methods introduced in ``QuantizationSpec`` example to identify the ``sigmoid`` pattern. +- Step 2: Create ``FixedQParamsQuantizationSpec`` object with inputs of fixed ``scale``, ``zero_point`` value. + These values will be used to create the ``quantize`` node and ``dequantize`` node in the convert phase. +- Step 3: Annotate inputs and output to use this ``FixedQParamsQuantizationSpec`` object. + +:: + + act_qspec = FixedQParamsQuantizationSpec( + dtype=torch.uint8, + quant_min=0, + quant_max=255, + qscheme=torch.per_tensor_affine, + scale=1.0 / 256.0, + zero_point=0, + ) + sigmoid_node.meta["quantization_annotation"] = QuantizationAnnotation( + input_qspec_map={input_act: act_qspec}, + output_qspec=act_qspec, + _annotated=True, + ) + +4. Annotate Tensors with Derived Quantization Parameters +--------------------------------------------------------------- + +Another use case is to define the constraint for tensors whose quantization parameters are derived from other tensors. +For example, if we want to annotate a convolution node, and define the ``scale`` of its bias input tensor +as product of the activation tensor's ``scale`` and weight tensor's ``scale``. We can use +`DerivedQuantizationSpec `__ +to annotate this conv node. + +- Step 1: Identify the original floating point pattern in the FX graph. We can use the same + methods introduced in ``QuantizationSpec`` example to identify the ``convolution`` pattern. +- Step 2: Define ``derive_qparams_fn`` function, it accepts list of ``ObserverOrFakeQuantize`` ( + `ObserverBase `__ + or `FakeQuantizeBase `__) + as input. From each ``ObserverOrFakeQuantize`` object, user can get the ``scale``, ``zero point`` value. + User can define its heuristic about how to derive new ``scale``, ``zero point`` value based on the + quantization parameters calculated from the observer or fake quant instances. +- Step 3: Define ``DerivedQuantizationSpec`` obejct, it accepts inputs of: list of ``EdgeOrNode`` objects. + The observer corresponding to each ``EdgeOrNode`` object will be passed into the ``derive_qparams_fn`` function; + ``derive_qparams_fn`` function; several other quantization parameters such as ``dtype``, ``qscheme``. +- Step 4: Annotate the inputs and output of this conv node with ``QuantizationAnnotation``. + +:: + + def derive_qparams_fn(obs_or_fqs: List[ObserverOrFakeQuantize]) -> Tuple[Tensor, Tensor]: + assert len(obs_or_fqs) == 2, \ + "Expecting two obs/fqs, one for activation and one for weight, got: {}".format(len(obs_or_fq)) + act_obs_or_fq = obs_or_fqs[0] + weight_obs_or_fq = obs_or_fqs[1] + act_scale, act_zp = act_obs_or_fq.calculate_qparams() + weight_scale, weight_zp = weight_obs_or_fq.calculate_qparams() + return torch.tensor([act_scale * weight_scale]).to(torch.float32), torch.tensor([0]).to(torch.int32) + + bias_qspec = DerivedQuantizationSpec( + derived_from=[(input_act, node), (weight, node)], + derive_qparams_fn=derive_qparams_fn, + dtype=torch.int32, + quant_min=-2**31, + quant_max=2**31 - 1, + qscheme=torch.per_tensor_symmetric, + ) + input_qspec_map = {input_act: act_quantization_spec, weight: weight_quantization_spec, bias: bias_qspec} + node.meta["quantization_annotation"] = QuantizationAnnotation( + input_qspec_map=input_qspec_map, + output_qspec=act_quantization_spec, + _annotated=True, + ) + +5. A Toy Example with Resnet18 +-------------------------------------------------------- + +After above annotation methods defined with ``QuantizationAnnotation API``, we can now put them together to construct a ``BackendQuantizer`` +and run a `toy example `__ +with ``Torchvision Resnet18``. To better understand the final example, here are the classes and utility +functions that are used in the example: + +- `QuantizationConfig `__ + consists of ``QuantizationSpec`` for activation, weight, and bias separately. +- When annotating the model, + `get_input_act_qspec `__, + `get_output_act_qspec `__, + `get_weight_qspec `__, and + `get_bias_qspec `__ + can be used to get the ``QuantizationSpec`` from ``QuantizationConfig`` for a specific pattern. + +A Note on IR for PT2E Quantization Flow +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +IR means the intermediate representation of the model, for example, ``torch`` IR (``torch.nn`` modules, ``torch.nn.functional`` ops) or ``aten`` IR (``torch.ops.aten.linear``, ...). PT2E Quantization Flow is using pre autograd aten IR (the output of `torch.export` API) so that we support training. As is shown before, we need to match the operator or operator patterns before we can attach annotations on them, So the question is how do we match the pattern? + +Motivation: Problem of Matching ``aten`` IR directly +-------------------------------------------------------- + +The most straightforward way might be matching ``aten`` IR directly. + +Example:: + + for n in gm.graph.nodes: + if n.op != "call_function" or n.target not in [ + torch.ops.aten.relu.default, + torch.ops.aten.relu_.default, + ]: + continue + relu_node = n + maybe_conv_node = n.args[0] + if ( + not isinstance(maybe_conv_node, Node) + or maybe_conv_node.op != "call_function" + or maybe_conv_node.target + not in [ + torch.ops.aten.conv1d.default, + torch.ops.aten.conv2d.default, + ] + ): + continue + + # annotate conv and relu nodes + ... + +However one problem for using this IR is that the representation might change if the PyTorch implementation for modules or functional ops changed. But this could be unexpected since modeling users typically assume that when the eager mode model code doesn't change, they should get the same model representation after program capture as well. One concrete effect for this problem is that if a ``Quantizer`` do annotations based on recognizing ``aten`` IR patterns, then it may fail to recognzing the pattern after PyTorch version update, and the same eager mode floating point may be left unquantized. + +Recommendation: Use ``SubgraphMatcherWithNameNodeMap`` for pattern matching +----------------------------------------------------------------------------- +Because of this, we recommend people to recognize the pattern through ``SubgraphMatcherWithNameNodeMap`` (an improved version of ``SubgraphMatcher`` that makes it easier to query the nodes that people want to annotate), through capturing a ``torch`` IR pattern (with the same program capture used for capturing the floating point model), instead of using the ``aten`` IR pattern directly. + +Example:: + + def conv_relu_pattern(input, weight, bias): + conv = torch.nn.functional.conv2d(input, weight, bias) + output = torch.nn.functional.relu(conv) + # returns an additional dict that includes a map from name to node that we want to annotate + return relu, {"input": input, "weight": weight, "bias": bias, "output": output} + + matcher = SubgraphMatcherWithNameNodeMap(conv_relu_pattern) + matches = matcher.match(model) + for match in matches: + # find input and output of the pattern + # annotate the nodes + name_node_map = match.name_node_map + input_node = name_node_map["input"] + weight_node = name_node_map["weight"] + bias_node = name_node_map["bias"] + output_node = name_node_map["relu"] + input_node.users[0].meta["quantization_annotation"] = ... + weight_node.users[0].meta["quantization_annotation"] = ... + bias_node.users[0].meta["quantization_annotation"] = ... + output_node.meta["quantization_annotation"] = ... + +With this, the ``Quantizer`` will still be valid even when the implementation for nn modules and functionals changes, the ``aten`` IR for floating point model will change, but since we capture the pattern again instead of hardcoding the ``aten`` IR for the pattern, we'll get the updated ``aten`` IR as well and will still be able to match the pattern. + +One caveat is that if inputs of the pattern has multiple users, we don't have a good way to identify which user node we want to annotate except for checking the aten op target. + +Another caveat is that we need to make sure we have an exhaustive list of examples (e.g. 2D, 3D, 4D inputs, real v.s. symbolic inputs, training=True v.s. training=False etc.) for the pattern to make sure cover different possible ``aten`` IR outcomes captured from the ``torch`` IR pattern. + +Note: We may provide some (pattern, list of example_inputs) or some pre-generated matcher object so people can just use them directly in the future. + +Conclusion +^^^^^^^^^^^^^^^^^^^ + +With this tutorial, we introduce the new quantization path in PyTorch 2. Users can learn about +how to define a ``BackendQuantizer`` with the ``QuantizationAnnotation API`` and integrate it into the PyTorch 2 Export Quantization flow. +Examples of ``QuantizationSpec``, ``SharedQuantizationSpec``, ``FixedQParamsQuantizationSpec``, and ``DerivedQuantizationSpec`` +are given for specific annotation use case. You can use `XNNPACKQuantizer `_ as an example to start implementing your own ``Quantizer``. After that please follow `this tutorial `_ to actually quantize your model. diff --git a/torchao/quantization/pt2e/quantizer/x86_inductor_quantizer.py b/torchao/quantization/pt2e/quantizer/x86_inductor_quantizer.py index cc296ebe33..84a66447c1 100644 --- a/torchao/quantization/pt2e/quantizer/x86_inductor_quantizer.py +++ b/torchao/quantization/pt2e/quantizer/x86_inductor_quantizer.py @@ -49,7 +49,7 @@ if TYPE_CHECKING: - from torchao.quantization.pt2e import _ObserverOrFakeQuantizeConstructor + from torchao.quantization.pt2e import ObserverOrFakeQuantizeConstructor __all__ = [ "X86InductorQuantizer", @@ -314,7 +314,7 @@ def get_default_x86_inductor_quantization_config( ), ) - weight_observer_or_fake_quant_ctr: _ObserverOrFakeQuantizeConstructor = ( + weight_observer_or_fake_quant_ctr: ObserverOrFakeQuantizeConstructor = ( FusedMovingAvgObsFakeQuantize if is_qat else PerChannelMinMaxObserver ) From e29b9bd0015f9c0b62c3982b32a485028c4da548 Mon Sep 17 00:00:00 2001 From: Jerry Zhang Date: Tue, 17 Jun 2025 15:18:27 -0700 Subject: [PATCH 131/165] Update index.rst (#2395) --- docs/source/index.rst | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/source/index.rst b/docs/source/index.rst index c0fd2e7bf5..d4d8580863 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -12,7 +12,6 @@ for an overall introduction to the library and recent highlight and updates. :caption: Getting Started quick_start - pt2e_quant .. toctree:: :glob: From 804fa1ed765db8d6852bed0c59f45a58faf010be Mon Sep 17 00:00:00 2001 From: cccclai Date: Tue, 17 Jun 2025 17:48:42 -0700 Subject: [PATCH 132/165] Add inplace quantizer examples Differential Revision: D76312488 Pull Request resolved: https://github.com/pytorch/ao/pull/2345 --- test/quantization/pt2e/test_quantize_pt2e.py | 91 ++++++++++++++++++++ torchao/quantization/pt2e/constant_fold.py | 25 ++++++ 2 files changed, 116 insertions(+) diff --git a/test/quantization/pt2e/test_quantize_pt2e.py b/test/quantization/pt2e/test_quantize_pt2e.py index 730969ba9c..be5a4dc537 100644 --- a/test/quantization/pt2e/test_quantize_pt2e.py +++ b/test/quantization/pt2e/test_quantize_pt2e.py @@ -2826,6 +2826,97 @@ def check_nn_module(node): if node.name == "mul": check_nn_module(node) + def test_quantize_in_place_ops(self): + class TestQuantizer(Quantizer): + example_inputs = None + + def set_example_inputs(self, example_inputs): + self.example_inputs = example_inputs + + def transform_for_annotation( + self, model: torch.fx.GraphModule + ) -> torch.fx.GraphModule: + # Make a copy of the graph to ensure that we are using the + # return value of this function. + ep = torch.export.export(model, self.example_inputs) + ep = ep.run_decompositions({}) + return ep.module() + + def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule: + act_qspec = QuantizationSpec( + dtype=torch.uint8, + quant_min=0, + quant_max=255, + qscheme=torch.per_tensor_affine, + is_dynamic=False, + observer_or_fake_quant_ctr=observer.default_observer, + ) + for node in model.graph.nodes: + if ( + node.op == "call_function" + and node.target == torch.ops.aten.add.Tensor + ): + input_act0 = node.args[0] + assert isinstance(input_act0, torch.fx.Node) + input_act1 = node.args[1] + assert isinstance(input_act1, torch.fx.Node) + print("input_act1 is a node") + node.meta["quantization_annotation"] = QuantizationAnnotation( + input_qspec_map={ + input_act0: act_qspec, + input_act1: act_qspec, + }, + output_qspec=act_qspec, + _annotated=True, + ) + + def validate(self, model: torch.fx.GraphModule) -> None: + pass + + class M(torch.nn.Module): + def __init__(self): + super().__init__() + self.register_buffer("buf", torch.randn(1, 2, 3, 3)) + + def forward(self, x): + self.buf.add_(x) + return self.buf + + def has_inplace_ops(graph_module: torch.fx.GraphModule) -> bool: + return ( + len( + [ + n + for n in graph_module.graph.nodes + if n.op == "call_function" + and n.name.endswith("_") + and n.name != "copy_" + ] + ) + > 0 + ) + + m = M().eval() + quantizer = TestQuantizer() + example_inputs = (torch.randn(1, 2, 3, 3),) + quantizer.set_example_inputs(example_inputs) + m = export_for_training(m, example_inputs, strict=True).module() + # Check that the model has in-place ops + self.assertTrue(has_inplace_ops(m)) + m = prepare_pt2e(m, quantizer) + # Check that the model no longer has in-place ops because the graph is funtionalized during annotate_to_tranform + self.assertFalse(has_inplace_ops(m)) + m(*example_inputs) + m = convert_pt2e(m, fold_quantize=True) + for node in m.graph.nodes: + if node.name == "quantize_per_tensor_default": + # Ensure the quant node is not fused with the mutable buffer + self.assertTrue(node.op == "call_function") + + # Verify the quantized model works + result = m(*example_inputs) + self.assertIsNotNone(result) + @skipIfNoQNNPACK @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_7, "Requires torch 2.7+") diff --git a/torchao/quantization/pt2e/constant_fold.py b/torchao/quantization/pt2e/constant_fold.py index 37a84c45bf..27f82e6757 100644 --- a/torchao/quantization/pt2e/constant_fold.py +++ b/torchao/quantization/pt2e/constant_fold.py @@ -93,6 +93,24 @@ def __init__( self.deferred_value = object() self.skip_folding_node_fn = skip_folding_node_fn + # Identify mutable buffers by finding copy_ operations + self.mutable_buffers = self._find_mutable_buffers() + + def _find_mutable_buffers(self) -> set[torch.fx.Node]: + """Find mutable buffers by identifying copy_ operations. + The first argument of copy_ op is the mutable buffer.""" + mutable_buffers = set() + for node in self.module.graph.nodes: + if ( + node.op == "call_function" + and hasattr(node.target, "_schema") + and "copy_" in str(node.target) + ): + # The first argument of copy_ is the mutable buffer + if len(node.args) > 0 and isinstance(node.args[0], torch.fx.Node): + mutable_buffers.add(node.args[0]) + return mutable_buffers + def _support_dynamic_shape(self) -> bool: # ConstantFolder not support dynamic shape now return False @@ -156,6 +174,13 @@ def is_woq_int8_pattern(node: torch.fx.node.Node) -> bool: # We only folding fp32_weight -> q # int8_weight and leave dq in graph to be fused return True + + # Check if any input to this node is a mutable buffer + # If so, prevent constant folding to avoid issues with quantize_per_tensor_default + for arg in node.args: + if isinstance(arg, torch.fx.Node) and arg in self.mutable_buffers: + return True + return False def node_to_last_non_output_use(self) -> dict[torch.fx.Node, list[torch.fx.Node]]: From 346baf65031d035641549fd3d59077baf78e4a77 Mon Sep 17 00:00:00 2001 From: Mark Saroufim Date: Tue, 17 Jun 2025 18:30:12 -0700 Subject: [PATCH 133/165] Delete Galore (#2397) * Delete Galore * restore cutlass changes --- benchmarks/bench_galore_fused_kernels.py | 65 --- benchmarks/fused_benchmark_utils.py | 261 ----------- test/galore/README.md | 170 ------- test/galore/profile_memory_usage.py | 297 ------------- test/kernel/galore_test_utils.py | 180 -------- test/kernel/test_fused_kernels.py | 119 ----- test/kernel/test_galore_downproj.py | 55 --- test/quantization/test_galore_quant.py | 116 ----- torchao/prototype/README.md | 3 - torchao/prototype/galore/README.md | 11 - torchao/prototype/galore/docs/README.md | 198 --------- .../prototype/galore/docs/galore_adam8bit.md | 35 -- .../galore/kernels/adam_downproj_fused.py | 365 --------------- torchao/prototype/galore/kernels/matmul.py | 417 ------------------ .../prototype/galore/optim/galore_torch.py | 401 ----------------- torchao/prototype/galore/utils.py | 114 ----- 16 files changed, 2807 deletions(-) delete mode 100644 benchmarks/bench_galore_fused_kernels.py delete mode 100644 benchmarks/fused_benchmark_utils.py delete mode 100644 test/galore/README.md delete mode 100644 test/galore/profile_memory_usage.py delete mode 100644 test/kernel/galore_test_utils.py delete mode 100644 test/kernel/test_fused_kernels.py delete mode 100644 test/kernel/test_galore_downproj.py delete mode 100644 test/quantization/test_galore_quant.py delete mode 100644 torchao/prototype/galore/README.md delete mode 100644 torchao/prototype/galore/docs/README.md delete mode 100644 torchao/prototype/galore/docs/galore_adam8bit.md delete mode 100644 torchao/prototype/galore/kernels/adam_downproj_fused.py delete mode 100644 torchao/prototype/galore/kernels/matmul.py delete mode 100644 torchao/prototype/galore/optim/galore_torch.py delete mode 100644 torchao/prototype/galore/utils.py diff --git a/benchmarks/bench_galore_fused_kernels.py b/benchmarks/bench_galore_fused_kernels.py deleted file mode 100644 index 3bfa9056bd..0000000000 --- a/benchmarks/bench_galore_fused_kernels.py +++ /dev/null @@ -1,65 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -import argparse -import os - -import torch -from fused_benchmark_utils import get_benchmark # , make_data - - -def run(args): - dtype = getattr(torch, args.dtype) - allow_tf32 = args.allow_tf32 - torch.backends.cuda.matmul.allow_tf32 = allow_tf32 - M, N = args.M, args.N - rank = args.rank - - # exp_avg, exp_avg2, grad, proj_matrix, params = make_data(M, N, rank, dtype) - - benchmark = get_benchmark(M, N, dtype, allow_tf32=allow_tf32) - save_path = ( - f"benchmark_{M}x{N}_{rank}_{args.dtype}_{'tf32' if allow_tf32 else 'no-tf32'}" - ) - if not os.path.exists(save_path): - os.makedirs(save_path) - print( - f"Running benchmark for {M}x{N}, dtype {args.dtype}, allow_tf32 {allow_tf32}", - flush=True, - ) - benchmark.run(show_plots=False, print_data=True, save_path=save_path) - print(f"Finished benchmark, results saved to {save_path}") - - -if __name__ == "__main__": - parser = argparse.ArgumentParser( - formatter_class=argparse.ArgumentDefaultsHelpFormatter - ) - parser.add_argument( - "--kernel", - choices=["hybrid", "fused", "compiled"], - default="hybrid", - type=str, - help="Kernel to test", - ) - - parser.add_argument( - "--allow_tf32", action="store_true", help="Allow tf32 for matmuls" - ) - parser.add_argument("--M", type=int, default=4096, help="Grad (param) shape M") - parser.add_argument("--N", type=int, default=4096, help="Grad (param) shape N") - parser.add_argument( - "--rank", type=int, default=128, help="Rank of GaLore projection" - ) - parser.add_argument( - "--dtype", - type=str, - choices=["float32", "float16", "bfloat16"], - default="float32", - help="Data type of grad (param) tensors", - ) - - args = parser.parse_args() - run(args) diff --git a/benchmarks/fused_benchmark_utils.py b/benchmarks/fused_benchmark_utils.py deleted file mode 100644 index c1ae0bfac2..0000000000 --- a/benchmarks/fused_benchmark_utils.py +++ /dev/null @@ -1,261 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -import torch -import triton -from triton.testing import do_bench - -from torchao.prototype.galore.kernels.adam_downproj_fused import fused_adam_mm_launcher -from torchao.prototype.galore.kernels.adam_step import triton_adam_launcher -from torchao.prototype.galore.kernels.matmul import triton_mm_launcher -from torchao.prototype.galore.utils import TestGaLoreProjector as GaLoreProjector - -torch.manual_seed(0) - -BETA1 = 0.9 -BETA2 = 0.999 -EPS = 1e-8 -STEP_SIZE = 1e-4 - - -def make_data(M, N, rank, dtype): - grad = torch.randn(M, N, device="cuda", dtype=dtype) - params = torch.randn(M, N, device="cuda", dtype=dtype) - - galore_proj = GaLoreProjector(rank=rank) - galore_proj.update_orthogonal_matrix(grad) - - if M >= N: - exp_avg = torch.randn(M, rank, device="cuda", dtype=dtype) - else: - exp_avg = torch.randn(rank, N, device="cuda", dtype=dtype) - exp_avg2 = exp_avg**2 - - return exp_avg, exp_avg2, grad, galore_proj.ortho_matrix, params - - -def make_copy(*args): - return [t.detach().clone() for t in args] - - -def _ref_op( - grad, - proj_matrix, - exp_avg, - exp_avg2, - params, - beta1=BETA1, - beta2=BETA2, - eps=EPS, - step_size=STEP_SIZE, - **kwargs, -): - # Step 1: Down proj grad - M, N = grad.shape - if M >= N: - a, b = grad, proj_matrix.t() - else: - a, b = proj_matrix.t(), grad - low_rank_grad = a @ b - - # Step 2: update adam state - exp_avg.mul_(beta1).add_(low_rank_grad, alpha=(1.0 - beta1)) - exp_avg2.mul_(beta2).addcmul_(low_rank_grad, low_rank_grad, value=1.0 - beta2) - denom = exp_avg2.sqrt().add_(eps) - low_rank_norm_grad = exp_avg / denom - - # Step 3: project normalized low rank grad to full rank - if M >= N: - a, b = low_rank_norm_grad, proj_matrix - else: - a, b = proj_matrix, low_rank_norm_grad - full_grad_norm = a @ b - - # Finally, update params with updated grad - params.add_(full_grad_norm, alpha=-step_size) - - return exp_avg, exp_avg2, params - - -def _tt_hybrid( - grad, - proj_matrix, - exp_avg, - exp_avg2, - params, - store=True, - step_size=STEP_SIZE, - fp8_fast_accum=False, - allow_tf32=False, -): - M, N = grad.shape - if M >= N: - a, b = grad, proj_matrix.t() - else: - a, b = proj_matrix.t(), grad - low_rank_grad = a @ b - - exp_avg, exp_avg2, norm_grad = triton_adam_launcher( - exp_avg, exp_avg2, low_rank_grad, store=store - ) - - if M >= N: - a, b = low_rank_grad, proj_matrix - else: - a, b = proj_matrix, low_rank_grad - params = triton_mm_launcher( - a, - b, - epilogue_alpha=-step_size, - epilogue_source=params, - allow_tf32=allow_tf32, - fp8_fast_accum=fp8_fast_accum, - ) - return exp_avg, exp_avg2, params - - -def _tt_fused( - grad, - proj_matrix, - exp_avg, - exp_avg2, - params, - store=True, - step_size=STEP_SIZE, - fp8_fast_accum=False, - allow_tf32=False, -): - M, N = grad.shape - - if M >= N: - a, b = grad, proj_matrix.t() - else: - a, b = proj_matrix.t(), grad - exp_avg, exp_avg2, low_rank_grad = fused_adam_mm_launcher( - a, - b, - exp_avg=exp_avg, - exp_avg2=exp_avg2, - store=store, - fp8_fast_accum=fp8_fast_accum, - allow_tf32=allow_tf32, - ) - - if M >= N: - a, b = low_rank_grad, proj_matrix - else: - a, b = proj_matrix, low_rank_grad - params = triton_mm_launcher( - a, - b, - epilogue_alpha=-step_size, - epilogue_source=params, - allow_tf32=allow_tf32, - fp8_fast_accum=fp8_fast_accum, - ) - return exp_avg, exp_avg2, params - - # logging.basicConfig(level=logging.INFO) - - -def get_kernel(kernel): - if kernel == "ref": - op = _ref_op - elif kernel == "ref": - op = torch.compile(_ref_op, fullgraph=True, mode="max-autotune") - elif kernel == "hybrid": - op = _tt_hybrid - elif kernel == "fused": - op = _tt_fused - else: - raise ValueError(f"Unknown kernel {kernel}") - - return lambda *args, **kwargs: op(*args, **kwargs) - - -def get_benchmark( - M, N, dtype, allow_tf32, fp8_fast_accum=False, quantiles=[0.5, 0.2, 0.8] -): - config = triton.testing.Benchmark( - x_names=["rank"], # Argument names to use as an x-axis for the plot - x_vals=[ - 32, - 64, - 128, - 256, - 512, - ], # Different possible values for `x_name` - line_arg="kernel", # Argument name whose value corresponds to a different line in the plot - # Possible values for `line_arg` - line_vals=["torch", "hybrid", "fused", "compiled"], - # Label name for the lines - line_names=["torch", "hybrid", "fused", "compiled"], - # Line styles - styles=[("black", "-"), ("blue", "-"), ("red", "-"), ("green", "-")], - ylabel="ms", # Label name for the y-axis - plot_name=f"Adam Kernel Comparison Grad shape: {M}x{N}, dtype: {dtype}, allow_tf32: {allow_tf32}\nMedian times (ms)", # Name for the plot, used also as a file name for saving the plot. - args={}, - ) - - def benchmark(rank, kernel): - torch.backends.cuda.matmul.allow_tf32 = allow_tf32 - - exp_avg, exp_avg2, grad, proj_matrix, params = make_data(M, N, rank, dtype) - - if kernel == "torch": - ms, min_ms, max_ms = do_bench( - lambda: _ref_op( - grad, - proj_matrix, - exp_avg, - exp_avg2, - params, - ), - quantiles=quantiles, - ) - if kernel == "hybrid": - ms, min_ms, max_ms = triton.testing.do_bench( - lambda: _tt_hybrid( - grad, - proj_matrix, - exp_avg, - exp_avg2, - params, - store=True, - allow_tf32=allow_tf32, - fp8_fast_accum=fp8_fast_accum, - ), - quantiles=quantiles, - ) - if kernel == "fused": - ms, min_ms, max_ms = triton.testing.do_bench( - lambda: _tt_fused( - grad, - proj_matrix, - exp_avg, - exp_avg2, - params, - store=True, - allow_tf32=allow_tf32, - fp8_fast_accum=fp8_fast_accum, - ), - quantiles=quantiles, - ) - if kernel == "compiled": - compiled_op = torch.compile(_ref_op, fullgraph=True, mode="max-autotune") - ms, min_ms, max_ms = triton.testing.do_bench( - lambda: compiled_op( - grad, - proj_matrix, - exp_avg, - exp_avg2, - params, - ), - quantiles=quantiles, - ) - - return ms, max_ms, min_ms - - return triton.testing.perf_report(config)(benchmark) diff --git a/test/galore/README.md b/test/galore/README.md deleted file mode 100644 index fc479267d8..0000000000 --- a/test/galore/README.md +++ /dev/null @@ -1,170 +0,0 @@ -### GaLore Memory Profiler - -Tests memory usage of `GaLore` optimizers. - -Uses `torch.profiler` under the hood with additional options for `nsys`, [`torch.cuda.memory`](https://pytorch.org/docs/stable/torch_cuda_memory.html) analyses. - -Runs an untrained Llama model with configs for various model sizes (see `configs`) from the original GaLore [repo](https://github.com/jiaweizzhao/GaLore/tree/master/configs) on a sample batch of data for a configurable set of iterations. - -The results of the profiler are saved and can be analyzed using the provided notebook. - -#### Examples - -Run memory profiler with `torch.optim.AdamW` - -``` -python galore_mem_prof.py -t --optimizer=adamw -``` - -Run profiler with `GaLoreAdamW` reference implementation with rank 128 - -``` -python galore_mem_prof.py -t --optimizer=galore_adamw --rank=128 -``` - -More options - -``` -python profile_memory_usage.py --help - -usage: profile_memory_usage.py [-h] [-t] [-m] [-ns] [--optimizer {adamw,galore_adamw}] [--rank RANK] [--update_proj_gap UPDATE_PROJ_GAP] - [--galore_scale GALORE_SCALE] [--wait_steps WAIT_STEPS] [--warmup_steps WARMUP_STEPS] [--profiler_steps PROFILER_STEPS] - [--max_steps MAX_STEPS] [--model_config MODEL_CONFIG] [--data_path DATA_PATH] [--output_dir OUTPUT_DIR] [-lr LEARNING_RATE] - [--weight_decay WEIGHT_DECAY] [--seed SEED] - -options: - -h, --help show this help message and exit - -t, --torch_profiler Enable torch profiler (default: False) - -m, --torch_memory_snapshot - Enable torch memory snapshot (default: False) - -ns, --nsys_profiler Enable nsys profiling context managerSurrounds training loop with cudaProfilerApi.{Start,Stop} (default: False) - --optimizer {adamw,galore_adamw} - Which optimizer to use (default: adamw) - --rank RANK - --update_proj_gap UPDATE_PROJ_GAP - --galore_scale GALORE_SCALE - --wait_steps WAIT_STEPS - Number of steps to run before starting torch profiler (default: 0) - --warmup_steps WARMUP_STEPS - Number of warmup steps for torch profiler (default: 0) - --profiler_steps PROFILER_STEPS - Number of active steps for torch profiler (default: 5) - --max_steps MAX_STEPS - Max number of train steps to run.Total train steps will be min of `max_steps` and the sum of torch profiler steps (`wait_steps` + - `warmup_steps` + `profiler_steps`). (default: 100) - --model_config MODEL_CONFIG - Path to Llama config file see `https://github.com/jiaweizzhao/GaLore/tree/master/configs` (default: ./configs/llama_100m.json) - --data_path DATA_PATH - Path to sample batch (default: ./data/sample_batch.pt) - --output_dir OUTPUT_DIR - Directory for profiler outputs (default: profiler_out) - -lr LEARNING_RATE, --learning_rate LEARNING_RATE - Learning rate (default: 0.001) - --weight_decay WEIGHT_DECAY - Weight decay for AdamW (default: 0.01) - --seed SEED Random seed for torch (default: 0) -``` - -#### Analysis - -After running the `profile_memory_usage`, the output directory (defaults to `profiler_out`) will have three types of files: - -- `*.{json,html} - these are the memory trace exports of `torch.profiler` - - the `html` contains the memory timeline plot - - the `json` file contains the raw data for this plot, which can be analyzed to extract summary stats. - - `galore_memory_analysis.py` along with `galore_memory_analysis_utils.py` demonstrate such analysis. -- `*.json.gz` - these are the complete `torch.profiler` traces which can be viewed using `perfetto`. - -#### Preliminary Observations - -- Memory Usage over Time - - - We can see a long delay between the first backwards step for `GaLoreAdamW` due to the calculation of the projection matrix (calls `torch.linalg.svd` on the `grad`). - - To visualize, paste the following into a jupyter notebook (replacing the filenames with the those after running the profiler script): - - ```python - adamW_html_trace = "./profiler_out/adamw_04-09-23.html" - adamW8bit_html_trace = "./profiler_out/adamw8bit_04-11-01.html" - galore_adamw_128_html_trace = "./profiler_out/galore_adamw-128-1.0-50_04-09-23.html" - galore_adamw8bit_128_html_trace = "./profiler_out/galore_adamw8bit-128-1.0-50_04-11-01.html" - - plot_memory_timeline(adamW_html_trace) - plot_memory_timeline(adamW8bit_html_trace) - plot_memory_timeline(galore_adamw_128_html_trace) - plot_memory_timeline(galore_adamw8bit_128_html_trace) - ``` - -- Memory Usage Stats - - - Summary stats for memory usage by type as well as total across all types can be viewed by running the following in jupyter notebook, again replacing the respective filepaths: - - ```python - adamW_trace = "./profiler_out/adamw_04-11-21-memory-timeline.json" - adamW8bit_trace = "./profiler_out/adamw8bit_04-11-21-memory-timeline.json" - galore_adamW_trace_128 = "./profiler_out/galore_adamw-128-1.0-50_04-11-21-memory-timeline.json" - galore_adamW8bit_trace_128 = "./profiler_out/galore_adamw8bit-128-1.0-50_04-11-21-memory-timeline.json" - - adamW_df = create_mem_df(adamW_trace, units="MB") - adamW8bit_df = create_mem_df(adamW8bit_trace, units="MB") - galore_adamW_df_128 = create_mem_df(galore_adamW_trace_128, units="MB") - galore_adamW8bit_df_128 = create_mem_df(galore_adamW8bit_trace_128, units="MB") - - show_memory_stats(adamW_df) - show_memory_stats(adamW8bit_df) - show_memory_stats(galore_adamW_df_128) - show_memory_stats(galore_adamW8bit_df_128) - ``` - - The following are results from sample runs of `Llama1B` model config with the following optimizers (all units in MB): - -- torch.optim.AdamW - - | | Parameter | Optimizer_State | Input | Temporary | Activation | Gradient | Autograd_Detail | Unknown | Total | - | ------ | --------- | --------------- | ----- | --------- | ---------- | -------- | --------------- | ------- | -------- | - | mean | 5,108.2 | 8,330.3 | 0.0 | 0.6 | 2,249.5 | 2,113.8 | 19.0 | 197.3 | 18,018.8 | - | min | 5,108.2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 5,108.2 | - | median | 5,108.2 | 10,216.4 | 0.0 | 0.0 | 2,151.1 | 1,930.1 | 10.0 | 16.3 | 20,306.5 | - | max | 5,108.3 | 10,216.4 | 0.3 | 20.0 | 5,946.4 | 5,108.2 | 312.2 | 5,124.4 | 25,557.3 | - -- GaLoreAdamW reference, rank 128 - - | | Parameter | Optimizer_State | Input | Temporary | Activation | Gradient | Autograd_Detail | Unknown | Total | - | ------ | --------- | --------------- | ----- | --------- | ---------- | -------- | --------------- | ------- | -------- | - | mean | 7,298.0 | 1,348.4 | 0.0 | 0.7 | 1,455.6 | 3,183.6 | 12.2 | 31.3 | 13,330.0 | - | min | 5,108.2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 5,108.2 | - | median | 7,796.2 | 1,576.7 | 0.0 | 0.0 | 545.4 | 3,898.2 | 0.0 | 26.2 | 14,422.8 | - | max | 8,047.2 | 1,576.7 | 0.3 | 42.7 | 5,960.0 | 5,108.2 | 312.2 | 518.2 | 15,349.2 | - -- bitsandbytes AdamW8bit - - | | Parameter | Optimizer_State | Input | Temporary | Activation | Gradient | Autograd_Detail | Unknown | Total | - | ------ | --------- | --------------- | ----- | --------- | ---------- | -------- | --------------- | ------- | -------- | - | mean | 5,108.2 | 2,047.4 | 0.0 | 0.7 | 2,390.0 | 1,925.2 | 20.1 | 20.3 | 11,511.9 | - | min | 5,108.2 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 5,108.2 | - | median | 5,108.2 | 2,560.4 | 0.0 | 0.0 | 2,351.0 | 1,738.1 | 10.0 | 16.3 | 12,621.3 | - | max | 5,108.3 | 2,560.4 | 0.3 | 20.0 | 5,946.4 | 5,108.2 | 312.2 | 46.9 | 13,631.3 | - -- GaLore AdamW8bit - - | | Parameter | Optimizer_State | Input | Temporary | Activation | Gradient | Autograd_Detail | Unknown | Total | - | ------ | --------- | --------------- | ----- | --------- | ---------- | -------- | --------------- | ------- | -------- | - | mean | 4,971.0 | 334.7 | 0.1 | 0.8 | 1,644.0 | 2,130.9 | 13.8 | 2,360.3 | 11,455.6 | - | min | 500.4 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 5,108.2 | - | median | 5,108.2 | 395.6 | 0.0 | 0.0 | 1,076.4 | 2,106.1 | 0.0 | 2,704.3 | 11,673.8 | - | max | 5,153.5 | 395.6 | 85.4 | 42.7 | 5,947.8 | 5,109.2 | 312.2 | 7,685.4 | 14,155.9 | - -- The `optimizer state` is indeed smaller for the `GaLoreAdamW` optimizer. -- Interestingly, the `Parameter` sizes balloons in the `GaLore` optimizer, likely due to extra data copies. Admittedly, the implementation is only a reference (per original repo) and leaves much room for optimization. -- The memory usage is in terms of memory allocated, which we can confirm by printing the max cuda memory allocated vs reserved (which the profiler script prints automatically). -- The `Total` column shows the allocation stats across all categories across all sampled timepoints. (Should not be interpreted as the row-wise sums). - -**NOTE**: The `json` output of the torch profiler memory trace is unlabeled. However, we can infer -- and confirm -- the labels by comparing the plots of the parsed dataframe with that of the direct `html` export of the profiler. - -- For example, after creating the dataframes per above, the following will plot the raw data, which should roughly reproduce the direct `html` export from `torch.profiler`, albeit with different timescale: - -```python -_ = adamW_df.plot(kind="area", stacked=True, ylabel="Memory (MB)" ) -_ = adamW8bit_df.plot(kind="area", stacked=True, ylabel="Memory (MB)" ) -_ = galore_adamW_df_128.plot(kind="area", stacked=True, ylabel="Memory (MB)" ) -_ = galore_adamW8bit_df_128.plot(kind="area", stacked=True, ylabel="Memory (MB)" ) -``` diff --git a/test/galore/profile_memory_usage.py b/test/galore/profile_memory_usage.py deleted file mode 100644 index 33fd746c39..0000000000 --- a/test/galore/profile_memory_usage.py +++ /dev/null @@ -1,297 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -import argparse -import contextlib -import logging -import os - -import model_configs -import profiling_utils -import torch -import torch.nn as nn -import torch.utils.data -from bitsandbytes.optim import AdamW8bit -from torch.profiler import record_function -from transformers import LlamaConfig, LlamaForCausalLM - -from torchao.prototype.galore.optim.galore_torch import AdamW as GaLoreAdamW -from torchao.prototype.galore.optim.galore_torch import AdamW8bit as GaLoreAdamW8bit - -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - - -def setup_galore(model, lr, weight_decay, rank, galore_scale, update_proj_gap): - galore_params = [] - target_modules_list = ["attn", "mlp"] - for module_name, module in model.named_modules(): - if not isinstance(module, nn.Linear): - continue - - if not any(target_key in module_name for target_key in target_modules_list): - continue - - logger.debug("Enabling GaLore for weights in module: ", module_name) - galore_params.append(module.weight) - id_galore_params = [id(p) for p in galore_params] - # make parameters without "rank" to another group - regular_params = [p for p in model.parameters() if id(p) not in id_galore_params] - # then call galore_adamw - - total_galore_params = sum(p.numel() for p in galore_params) - total_regular_params = sum(p.numel() for p in regular_params) - total_params = sum(p.numel() for p in model.parameters()) - assert total_galore_params + total_regular_params == total_params - - print( - f"Total params: {total_params} = GaLore params: {total_galore_params} + Regular params: {total_regular_params}" - ) - param_groups = [ - {"params": regular_params}, - { - "params": galore_params, - "rank": rank, - "update_proj_gap": update_proj_gap, - "scale": galore_scale, - "proj_type": "std", - }, - ] - if "adamw" in args.optimizer: - if "8bit" in args.optimizer: - optimizer = GaLoreAdamW8bit(param_groups, lr=lr, weight_decay=weight_decay) - else: - optimizer = GaLoreAdamW(param_groups, lr=lr, weight_decay=weight_decay) - else: - raise ValueError(f"Unknown optimizer: {args.optimizer}") - return optimizer - - -def train_step(model, batch, labels, optimizer, profiler=None): - with record_function("MODEL_FORWARD"): - loss = model(**batch, labels=labels).loss - - with record_function("MODEL_BACKWARD"): - loss.backward() - - with record_function("OPTIMIZER_STEP"): - optimizer.step() - optimizer.zero_grad(set_to_none=True) - - if profiler: - profiler.step() - - -def run(args, file_prefix): - torch.manual_seed(args.seed) - - # Initialize model from config dict - model_config = LlamaConfig() - try: - model_config_dict = getattr(model_configs, args.model_config.upper()) - except: - raise ValueError(f"Model config {args.model_config} not found") - model_config.update(model_config_dict) - model = LlamaForCausalLM(model_config).to("cuda") - - # Load sample batch - input_ids = torch.randint( - 0, - model_config.vocab_size, - size=(args.batch_size, args.max_seq_len), - dtype=torch.int64, - device="cuda", - ) - attention_mask = torch.ones_like(input_ids) - batch = dict(input_ids=input_ids, attention_mask=attention_mask) - labels = batch["input_ids"].clone() - - n_total_params = sum(p.numel() for p in model.parameters()) - trainable_params = [p for p in model.parameters() if p.requires_grad] - print( - f"Trainable params: {sum(p.numel() for p in trainable_params)} / {n_total_params}" - ) - - if args.optimizer.lower() == "adamw": - optimizer = torch.optim.AdamW( - trainable_params, lr=args.learning_rate, weight_decay=args.weight_decay - ) - - elif "galore" in args.optimizer.lower(): - optimizer = setup_galore( - model, - args.learning_rate, - args.weight_decay, - rank=args.rank, - galore_scale=args.galore_scale, - update_proj_gap=args.update_proj_gap, - ) - elif args.optimizer.lower() == "adamw8bit": - optimizer = AdamW8bit( - trainable_params, lr=args.learning_rate, weight_decay=args.weight_decay - ) - else: - raise "Unsupported optimizer" - - if args.torch_profiler: - prof_ctx = profiling_utils.get_torch_profiler( - name=file_prefix, - output_dir=args.output_dir, - wait_steps=args.wait_steps, - warmup_steps=args.warmup_steps, - active_steps=args.profiler_steps, - ) - elif args.nsys_profiler: - prof_ctx = profiling_utils.nsys_profiler() - else: - prof_ctx = contextlib.nullcontext() - - total_steps = min( - args.wait_steps + args.warmup_steps + args.profiler_steps, args.max_steps - ) - print( - f"Profiling {args.model_config} with {args.optimizer.upper()} for {total_steps} steps (wait_steps={args.wait_steps}, warmup_steps={args.warmup_steps}, profiler_steps={args.profiler_steps})" - ) - with prof_ctx as prof: - logger.debug(f"Profiler: {prof}") - for _ in range(total_steps): - with record_function("TRAIN_STEP"): - train_step( - model, - batch, - labels, - optimizer, - profiler=prof if args.torch_profiler else None, - ) - if args.torch_profiler: - print(f"Finished profiling, outputs saved to {args.output_dir}/{file_prefix}*") - else: - print("Finished profiling") - - -if __name__ == "__main__": - parser = argparse.ArgumentParser( - formatter_class=argparse.ArgumentDefaultsHelpFormatter - ) - parser.add_argument( - "-t", "--torch_profiler", action="store_true", help="Enable torch profiler" - ) - parser.add_argument( - "-m", - "--torch_memory_snapshot", - action="store_true", - help="Enable torch memory snapshot", - ) - - parser.add_argument( - "-ns", - "--nsys_profiler", - action="store_true", - help="Enable nsys profiling context manager" - "Surrounds training loop with cudaProfilerApi.{Start,Stop}", - ) - parser.add_argument( - "--optimizer", - default="adamw", - type=str, - choices=["adamw", "galore_adamw", "adamw8bit", "galore_adamw8bit"], - help="Which optimizer to use", - ) - parser.add_argument("--rank", type=int, default=128) - parser.add_argument("--update_proj_gap", type=int, default=50) - parser.add_argument("--galore_scale", type=float, default=1.0) - # parser.add_argument("--proj_type", type=str, default="std") - parser.add_argument( - "--wait_steps", - type=int, - default=0, - help="Number of steps to run before starting torch profiler", - ) - parser.add_argument( - "--warmup_steps", - type=int, - default=0, - help="Number of warmup steps for torch profiler", - ) - - parser.add_argument( - "--profiler_steps", - type=int, - default=5, - help="Number of active steps for torch profiler", - ) - parser.add_argument( - "--max_steps", - type=int, - default=100, - help="Max number of train steps to run." - "Total train steps will be min of `max_steps` and the sum of torch profiler steps (`wait_steps` + `warmup_steps` + `profiler_steps`).", - ) - parser.add_argument( - "--model_config", - default="llama100M", - type=str, - choices=["llama100M", "llama1B"], - help="Model configuration (see model_configs.py)", - ) - parser.add_argument( - "--batch_size", default=5, type=int, help="Batch size to use for train step" - ) - parser.add_argument( - "--max_seq_len", - default=256, - type=int, - help="Sequence length to use for train step, should be less than that in the specific model config", - ) - parser.add_argument( - "--output_dir", - default="profiler_out", - type=str, - help="Directory for profiler outputs", - ) - - parser.add_argument( - "-lr", - "--learning_rate", - default=1e-3, - type=float, - help="Learning rate", - ) - parser.add_argument( - "--weight_decay", - default=1e-2, - type=float, - help="Weight decay for AdamW", - ) - - parser.add_argument("--seed", default=0, type=int, help="Random seed for torch") - args = parser.parse_args() - output_dir = args.output_dir - # output_prefix = args.output_prefix - if not os.path.exists(output_dir): - os.makedirs(output_dir) - if "galore" not in args.optimizer.lower(): - file_prefix = args.optimizer.lower() - else: - file_prefix = "-".join( - [ - args.optimizer.lower(), - str(args.rank), - str(args.galore_scale), - str(args.update_proj_gap), - ] - ) - mem_ctx = ( - profiling_utils.memory_recorder( - file_name=os.path.join(output_dir, f"{file_prefix}-memory-snapshot") - ) - if args.torch_memory_snapshot - else contextlib.nullcontext() - ) - profiling_utils.flush_cuda_mem() - with mem_ctx: - run(args, file_prefix) - - profiling_utils.get_cuda_memory_usage(units="MB", show=True) diff --git a/test/kernel/galore_test_utils.py b/test/kernel/galore_test_utils.py deleted file mode 100644 index 2810941fe1..0000000000 --- a/test/kernel/galore_test_utils.py +++ /dev/null @@ -1,180 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -import torch - -from torchao.prototype.galore.kernels.adam_downproj_fused import fused_adam_mm_launcher -from torchao.prototype.galore.kernels.adam_downproj_fused import ( - set_tuner_top_k as adam_downproj_tuner_topk, -) -from torchao.prototype.galore.kernels.adam_step import triton_adam_launcher -from torchao.prototype.galore.kernels.matmul import set_tuner_top_k as matmul_tuner_topk -from torchao.prototype.galore.kernels.matmul import triton_mm_launcher -from torchao.prototype.galore.utils import TestGaLoreProjector as GaLoreProjector - -torch.manual_seed(0) - -adam_downproj_tuner_topk(10) -matmul_tuner_topk(10) - -BETA1 = 0.9 -BETA2 = 0.999 -EPS = 1e-8 -STEP_SIZE = 1e-4 - - -def make_data(M, N, rank, dtype): - grad = torch.randn(M, N, device="cuda", dtype=dtype) - params = torch.randn(M, N, device="cuda", dtype=dtype) - - galore_proj = GaLoreProjector(rank=rank) - galore_proj.update_orthogonal_matrix(grad) - - if M >= N: - exp_avg = torch.randn(M, rank, device="cuda", dtype=dtype) - else: - exp_avg = torch.randn(rank, N, device="cuda", dtype=dtype) - exp_avg2 = exp_avg**2 - - return exp_avg, exp_avg2, grad, galore_proj.ortho_matrix, params - - -def make_copy(*args): - return [t.detach().clone() for t in args] - - -def _ref_op( - grad, - proj_matrix, - exp_avg, - exp_avg2, - params, - beta1=BETA1, - beta2=BETA2, - eps=EPS, - step_size=STEP_SIZE, - **kwargs, -): - # Step 1: Down proj grad - M, N = grad.shape - if M >= N: - a, b = grad, proj_matrix.t() - else: - a, b = proj_matrix.t(), grad - low_rank_grad = a @ b - - # Step 2: update adam state - exp_avg.mul_(beta1).add_(low_rank_grad, alpha=(1.0 - beta1)) - exp_avg2.mul_(beta2).addcmul_(low_rank_grad, low_rank_grad, value=1.0 - beta2) - denom = exp_avg2.sqrt().add_(eps) - low_rank_norm_grad = exp_avg / denom - - # Step 3: project normalized low rank grad to full rank - if M >= N: - a, b = low_rank_norm_grad, proj_matrix - else: - a, b = proj_matrix, low_rank_norm_grad - full_grad_norm = a @ b - - # Finally, update params with updated grad - params.add_(full_grad_norm, alpha=-step_size) - - return exp_avg, exp_avg2, params - - -def _tt_hybrid( - grad, - proj_matrix, - exp_avg, - exp_avg2, - params, - store=True, - step_size=STEP_SIZE, - fp8_fast_accum=False, - allow_tf32=False, -): - M, N = grad.shape - if M >= N: - a, b = grad, proj_matrix.t() - else: - a, b = proj_matrix.t(), grad - low_rank_grad = a @ b - - exp_avg, exp_avg2, norm_grad = triton_adam_launcher( - exp_avg, exp_avg2, low_rank_grad, store=store - ) - - if M >= N: - a, b = low_rank_grad, proj_matrix - else: - a, b = proj_matrix, low_rank_grad - params = triton_mm_launcher( - a, - b, - epilogue_alpha=-step_size, - epilogue_source=params, - allow_tf32=allow_tf32, - fp8_fast_accum=fp8_fast_accum, - ) - return exp_avg, exp_avg2, params - - -def _tt_fused( - grad, - proj_matrix, - exp_avg, - exp_avg2, - params, - store=True, - step_size=STEP_SIZE, - fp8_fast_accum=False, - allow_tf32=False, -): - M, N = grad.shape - - if M >= N: - a, b = grad, proj_matrix.t() - else: - a, b = proj_matrix.t(), grad - exp_avg, exp_avg2, low_rank_grad = fused_adam_mm_launcher( - a, - b, - exp_avg=exp_avg, - exp_avg2=exp_avg2, - store=store, - fp8_fast_accum=fp8_fast_accum, - allow_tf32=allow_tf32, - ) - - if M >= N: - a, b = low_rank_grad, proj_matrix - else: - a, b = proj_matrix, low_rank_grad - params = triton_mm_launcher( - a, - b, - epilogue_alpha=-step_size, - epilogue_source=params, - allow_tf32=allow_tf32, - fp8_fast_accum=fp8_fast_accum, - ) - return exp_avg, exp_avg2, params - - # logging.basicConfig(level=logging.INFO) - - -def get_kernel(kernel): - if kernel == "ref": - op = _ref_op - elif kernel == "ref": - op = torch.compile(_ref_op, fullgraph=True, mode="max-autotune") - elif kernel == "hybrid": - op = _tt_hybrid - elif kernel == "fused": - op = _tt_fused - else: - raise ValueError(f"Unknown kernel {kernel}") - - return lambda *args, **kwargs: op(*args, **kwargs) diff --git a/test/kernel/test_fused_kernels.py b/test/kernel/test_fused_kernels.py deleted file mode 100644 index 3c51b78f1b..0000000000 --- a/test/kernel/test_fused_kernels.py +++ /dev/null @@ -1,119 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -import itertools - -import pytest - -# Skip entire test if triton is not available, otherwise CI failure -try: - import triton # noqa: F401 -except ImportError: - pytest.skip("triton is not installed", allow_module_level=True) - -import torch -from galore_test_utils import get_kernel, make_copy, make_data - -from torchao.testing.utils import skip_if_rocm - -torch.manual_seed(0) -MAX_DIFF_no_tf32 = 1e-5 -MAX_DIFF_tf32 = 1e-3 - - -def run_test(kernel, exp_avg, exp_avg2, grad, proj_matrix, params, allow_tf32): - # Copy to use for first run -- needed because of autotuning and inplace ops - ( - exp_avg_autotune_copy, - exp_avg2_autotune_copy, - grad_autotune_copy, - proj_matrix_autotune_copy, - params_autotune_copy, - ) = make_copy(exp_avg, exp_avg2, grad, proj_matrix, params) - - # Copy to use for second run to check accuracy - ( - exp_avg_test_copy, - exp_avg2_test_copy, - grad_test_copy, - proj_matrix_test_copy, - params_test_copy, - ) = make_copy(exp_avg, exp_avg2, grad, proj_matrix, params) - - print( - f"Running with {grad.shape[0]} x {grad.shape[1]} grad (param) shape, GaLore orthogonal matrix {list(proj_matrix.shape)}, dtype {grad.dtype} and allow_tf32 {allow_tf32}\n" - f"Kernel: {kernel}", - flush=True, - ) - - ref_op = get_kernel("ref") - test_op = get_kernel(kernel) - - # Reference run - ref_out = ref_op( - grad, - proj_matrix, - exp_avg, - exp_avg2, - params, - ) - - # Autotune - _ = test_op( - grad_autotune_copy, - proj_matrix_autotune_copy, - exp_avg_autotune_copy, - exp_avg2_autotune_copy, - params_autotune_copy, - store=False, - allow_tf32=allow_tf32, - ) - - # Accuracy run - test_out = test_op( - grad_test_copy, - proj_matrix_test_copy, - exp_avg_test_copy, - exp_avg2_test_copy, - params_test_copy, - store=True, - allow_tf32=allow_tf32, - ) - print("Accuracy:") - - output_names = [ - "adam state - running grad mean", - "adam state - running grad var", - "params (after update)", - ] - MAX_DIFF = MAX_DIFF_tf32 if allow_tf32 else MAX_DIFF_no_tf32 - for name, ref, tt in zip(output_names, ref_out, test_out): - max_diff = (ref - tt).abs().max() - print(f"-> {name}:\n Max err: {max_diff:.6f}") - assert max_diff < MAX_DIFF - - -KERNELS = ["hybrid"] # "fused"] -DTYPES = [torch.float32] # torch.float16 -ROW_DIMS = [4096] -COL_DIMS = [4096] # , 11008] -RANKS = [128] -ALLOW_TF32 = [False] # , True] - -TEST_CONFIGS = list( - itertools.product(KERNELS, DTYPES, ROW_DIMS, COL_DIMS, RANKS, ALLOW_TF32) -) - -# TEST_CONFIGS = TEST_CONFIGS[0:1] - - -@pytest.mark.skipif(not torch.cuda.is_available(), reason="requires GPU") -@pytest.mark.parametrize("kernel, dtype, M, N, rank, allow_tf32", TEST_CONFIGS) -@skip_if_rocm("ROCm enablement in progress") -def test_galore_fused_kernels(kernel, dtype, M, N, rank, allow_tf32): - torch.backends.cuda.matmul.allow_tf32 = allow_tf32 - - exp_avg, exp_avg2, grad, proj_matrix, params = make_data(M, N, rank, dtype) - run_test(kernel, exp_avg, exp_avg2, grad, proj_matrix, params, allow_tf32) diff --git a/test/kernel/test_galore_downproj.py b/test/kernel/test_galore_downproj.py deleted file mode 100644 index f0e135667e..0000000000 --- a/test/kernel/test_galore_downproj.py +++ /dev/null @@ -1,55 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -import pytest - -# Skip entire test if triton is not available, otherwise CI failure -try: - import triton # noqa: F401 -except ImportError: - pytest.skip("triton is not installed", allow_module_level=True) - -import torch -from galore_test_utils import make_data - -from torchao.prototype.galore.kernels.matmul import set_tuner_top_k as matmul_tuner_topk -from torchao.prototype.galore.kernels.matmul import triton_mm_launcher -from torchao.testing.utils import skip_if_rocm - -torch.manual_seed(0) - -matmul_tuner_topk(10) -MAX_DIFF_no_tf32 = 1e-4 -MAX_DIFF_tf32 = 1e-2 - - -TEST_CONFIGS = [ - # (4096, 4096, 128, True, False, torch.float32), - (4096, 4096, 128, False, False, torch.float32), - # (4096, 11008, 128, True, False, torch.float32), - (4096, 11008, 128, False, False, torch.float32), -] - - -@pytest.mark.skipif(not torch.cuda.is_available(), reason="requires GPU") -@pytest.mark.parametrize("M, N, rank, allow_tf32, fp8_fast_accum, dtype", TEST_CONFIGS) -@skip_if_rocm("ROCm enablement in progress") -def test_galore_downproj(M, N, rank, allow_tf32, fp8_fast_accum, dtype): - torch.backends.cuda.matmul.allow_tf32 = allow_tf32 - MAX_DIFF = MAX_DIFF_tf32 if allow_tf32 else MAX_DIFF_no_tf32 - exp_avg, exp_avg2, grad, galore_proj, params = make_data(M, N, rank, dtype) - - if M >= N: - a, b = grad, galore_proj.t() - else: - a, b = galore_proj.t(), grad - low_rank_ref = lambda: a @ b - low_rank_tt = lambda: triton_mm_launcher( - a, b, allow_tf32=allow_tf32, fp8_fast_accum=fp8_fast_accum - ) - diff = torch.max(torch.abs(low_rank_ref() - low_rank_tt())) - if not diff < MAX_DIFF: - print("diff: ", torch.max(torch.abs(low_rank_ref() - low_rank_tt()))) - assert diff < MAX_DIFF diff --git a/test/quantization/test_galore_quant.py b/test/quantization/test_galore_quant.py deleted file mode 100644 index cb2902d00f..0000000000 --- a/test/quantization/test_galore_quant.py +++ /dev/null @@ -1,116 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -import itertools - -import pytest - -from torchao.utils import TORCH_VERSION_AT_LEAST_2_7 - -# Skip entire test if triton is not available, otherwise CI failure -try: # noqa: F401 - import triton # noqa: F401 -except ImportError: # noqa: F401 - pytest.skip("triton is not installed", allow_module_level=True) # noqa: F401 -import torch - -# Skip entire test if CUDA is not available or ROCM is enabled -if not torch.cuda.is_available() or torch.version.hip is not None: - pytest.skip( - "CUDA is not available/ ROCM support is under development", - allow_module_level=True, - ) - -from bitsandbytes.functional import ( - create_dynamic_map, - dequantize_blockwise, - quantize_blockwise, -) - -from torchao.prototype.galore.kernels import ( - triton_dequant_blockwise, - triton_quantize_blockwise, -) -from torchao.testing.utils import skip_if_rocm - -SEED = 0 -torch.manual_seed(SEED) - -DIM1 = [64, 1024, 4096] -DIM2 = [1024, 2048, 4096] -SIGNS = [True, False] -DTYPES = [torch.float32] # , torch.float16] -BLOCKSIZE = [2048] - -TEST_CONFIGS = list(itertools.product(DIM1, DIM2, DTYPES, SIGNS, BLOCKSIZE)) - - -@pytest.mark.skip("skipping for now, see comments below") -@pytest.mark.skipif(not torch.cuda.is_available(), reason="Need CUDA available") -@pytest.mark.parametrize( - "dim1,dim2,dtype,signed,blocksize", - TEST_CONFIGS, -) -def test_galore_quantize_blockwise(dim1, dim2, dtype, signed, blocksize): - g = torch.randn(dim1, dim2, device="cuda", dtype=dtype) * 0.01 - - qmap = create_dynamic_map(signed).to(g.device) - - ref_bnb, qstate = quantize_blockwise(g, code=qmap, blocksize=blocksize) - bnb_norm = (g.reshape(-1, blocksize) / qstate.absmax[:, None]).reshape(g.shape) - - tt_q, tt_norm, tt_absmax = triton_quantize_blockwise( - g, qmap, group_size=blocksize, return_normalized=True - ) - tt_check = torch.allclose(ref_bnb, tt_q) - - # see notes.md under `prototype.galore.kernels` for an explanation of the following conditions - if not tt_check: - print( - f"Failed quantization check for {dim1} x {dim2}, {dtype}, signed {signed}" - ) - print(f"Absmax: {(qstate.absmax - tt_absmax).abs().max()}") - print(f"Norm diff: {(bnb_norm - tt_norm).abs().max()}") - - idx_diff = (ref_bnb != tt_q).to("cuda") - print(f"Num code idx diffs: {idx_diff.sum()}") - max_idx_diff = (ref_bnb - tt_q).abs().max() - print(f"Max code idx diff: {max_idx_diff}") - - # This below checks that the value being quantized falls half-way between two code buckets - # where bitsandbytes assigns to one and the triton implementation assigns to the other - # Since either bucket is technically valid, we only check that the distance between the value and the - # adjacent buckets are the same. I.e., we don't require that the triton implementation exactly matches - # bitsandbytes. - - bnb_code = qmap[ref_bnb[idx_diff].tolist()] - tt_code = qmap[tt_q[idx_diff].tolist()] - bnb_dist = torch.abs(bnb_code - bnb_norm[idx_diff]) - torch_dist = torch.abs(tt_code - bnb_norm[idx_diff]) - - dist_sum = torch.sum(bnb_dist - torch_dist) - print(f"Distance sum: {torch.sum(bnb_dist - torch_dist)}") - assert tt_check or (not tt_check and dist_sum < 1e-4) - - -@pytest.mark.parametrize( - "dim1,dim2,dtype,signed,blocksize", - TEST_CONFIGS, -) -@skip_if_rocm("ROCm enablement in progress") -@pytest.mark.skipif(not torch.cuda.is_available(), reason="Need CUDA available") -@pytest.mark.skipif( - TORCH_VERSION_AT_LEAST_2_7, reason="Failing in CI" -) # TODO: fix this -def test_galore_dequant_blockwise(dim1, dim2, dtype, signed, blocksize): - g = torch.randn(dim1, dim2, device="cuda", dtype=dtype) * 0.01 - - qmap = create_dynamic_map(signed).to(g.device) - - q, qstate = quantize_blockwise(g, code=qmap, blocksize=blocksize) - - dq_ref = dequantize_blockwise(q, qstate) - dq = triton_dequant_blockwise(q, qmap, qstate.absmax, group_size=blocksize) - assert torch.allclose(dq, dq_ref) diff --git a/torchao/prototype/README.md b/torchao/prototype/README.md index 70f9d87537..257ba4ffb8 100644 --- a/torchao/prototype/README.md +++ b/torchao/prototype/README.md @@ -6,9 +6,6 @@ #### Code structure -- `galore` - fused kernels for memory-efficient pre-training / fine-tuning per the [GaLore algorithm](https://arxiv.org/abs/2403.03507) - - `galore/kernels` - `triton` kernels that fuse various steps of the `GaLore` algorithm - - `galore/docs` - implementation notes and discussion of issues faced in kernel design. - [`quant_llm`](quant_llm) - FP16 x Floatx mixed matmul kernel per [FP6-LLM](https://arxiv.org/abs/2401.14112) - ~~`low_bit_optim`~~ - re-implementation of 8-bit optimizers from [bitsandbytes](https://github.com/TimDettmers/bitsandbytes) and 4-bit optimizers from [lpmm](https://github.com/thu-ml/low-bit-optimizers). **Promoted to `torchao.optim`.** - [`spinquant`](spinquant) - re-implementation of [SpinQuant](https://arxiv.org/abs/2405.16406) diff --git a/torchao/prototype/galore/README.md b/torchao/prototype/galore/README.md deleted file mode 100644 index 2a7ae1f7d9..0000000000 --- a/torchao/prototype/galore/README.md +++ /dev/null @@ -1,11 +0,0 @@ -## Fused GaLore - -### Experimental kernels for fusing various parts of the GaLore algorithm - -#### AdamW - -See `docs/galore_adam.md` for implementation notes. - -#### AdamW8bit - -See `docs/galore_adam8bit.md` for implementation notes. diff --git a/torchao/prototype/galore/docs/README.md b/torchao/prototype/galore/docs/README.md deleted file mode 100644 index 74b077c4a9..0000000000 --- a/torchao/prototype/galore/docs/README.md +++ /dev/null @@ -1,198 +0,0 @@ -## Fused GaLore Adam (WIP) - -### Various fused implementations of `Adam` update step per [Gradient Low-Rank Projection](https://arxiv.org/abs/2403.03507) - -This is an initial attempt at optimizing the update step of the `GaLore Adam` optimizer. - -#### Overview - -The `GaLore` `Adam` optimizer introduces additional ops to the traditional `adam` update step. - -Specifically: - -1. `grad` is projected to low rank --> additional matmul -2. `adam` states are updated with `grad` elementwise (same as `Adam` except in low-rank) -3. normalized `grad` is projected to full rank --> additional matmul -4. `params` are updated with the normalized full rank grad - -#### Implementation - -Various fusions were attempted across 2 kernel implementations: - -- `Fused` - - Steps 1 & 2 are fused: the `adam` state updates are loaded and updated (inplace) during the first `matmul` - - Steps 3 & 4 are fused: the param update is folded as an epilogue into the second `matmul` -- `Hybrid` - - Step 1 is performed using standard `torch matmul` (i.e., `cuBlas`) - - Step 2 is fused as an elementwise kernel - - Steps 3 & 4 per `Fused` - -#### Performance - -Below are benchmarks for various kernels: - -- `torch` - reference `torch` implementation where each of the steps are implemented verbatim per above -- `hybrid` - see above -- `fused` - see above -- `compiled` - `torch` reference implementation compiled using `torch.compile` with `fullgraph=True` and `mode="max-autotune"`. - -Configs for each benchmark are the `grad (param)` shape, `dtype` of `grad` and `adam` states, and `allow_tf32`, whether `torch` and `triton` matmuls are allowed to use `TF32` tensor cores (see `Discussion`). - -`Grad shape`: `4096x4096`, `dtype`: `torch.float32`, `allow_tf32`: `False` - -``` -Median times (ms): - rank torch hybrid fused compiled -0 32.0 0.560128 0.347136 0.505856 0.534528 -1 64.0 0.627712 0.404480 0.600960 0.615424 -2 128.0 0.825232 0.583168 0.985072 0.833536 -3 256.0 1.378304 1.126400 1.489920 1.375232 -4 512.0 2.286080 2.101760 2.969600 2.302976 -``` - -`Grad shape`: `4096x4096`, `dtype`: `torch.float32`, `allow_tf32`: `True` - -``` -Median times (ms): - rank torch hybrid fused compiled -0 32.0 0.540672 0.321536 0.316416 0.508928 -1 64.0 0.612240 0.337728 0.345024 0.538624 -2 128.0 0.640000 0.395264 0.393216 0.693248 -3 256.0 0.777216 0.489472 0.548784 1.102848 -4 512.0 1.216512 0.864256 0.960512 1.968128 -``` - -`Grad shape`: `4096x11008`, `dtype`: `torch.float32`, `allow_tf32`: `False` - -``` -Median times (ms): - rank torch hybrid fused compiled -0 32.0 1.538672 0.915456 0.835584 1.364032 -1 64.0 1.546240 0.940032 1.022976 1.486848 -2 128.0 2.116608 1.498112 1.613312 2.098176 -3 256.0 3.423744 2.719744 2.881536 3.227136 -4 512.0 5.499904 5.036544 5.450752 5.508096 -``` - -`Grad shape`: `4096x11008`, `dtype`: `torch.float32`, `allow_tf32`: `True` - -``` -Median times (ms): - rank torch hybrid fused compiled -0 32.0 1.413120 0.871424 0.817152 1.353184 -1 64.0 1.489920 0.916480 0.854016 1.389568 -2 128.0 1.679360 0.996352 1.005568 1.563648 -3 256.0 2.152448 1.415168 1.470464 2.185216 -4 512.0 3.210240 2.460672 2.580480 3.477504 -``` - -##### Accuracy - -Comparison to reference `torch` implementation: - -``` -Running with 4096 x 4096 grad (param) shape, GaLore orthogonal matrix [128, 4096], dtype torch.float32, and allow_tf32 True -Kernel: hybrid -Accuracy: --> adam state - running grad mean: - Max err: 0.000000 Relative err: 0.000001 --> adam state - running grad var: - Max err: 0.000002 Relative err: 0.000002 --> params (after update): - Max err: 0.000000 Relative err: 0.000001 -``` - -``` -Running with 4096 x 4096 grad (param) shape, GaLore orthogonal matrix [128, 4096], dtype torch.float32 and allow_tf32 False -Kernel: hybrid -Accuracy: --> adam state - running grad mean: - Max err: 0.000000 Relative err: 0.000000 --> adam state - running grad var: - Max err: 0.000002 Relative err: 0.000002 --> params (after update): - Max err: 0.000000 Relative err: 0.000000 -``` - -``` -Running with 4096 x 4096 grad (param) shape, GaLore orthogonal matrix [128, 4096], dtype torch.float32 and allow_tf32 True -Kernel: fused -Accuracy: --> adam state - running grad mean: - Max err: 0.000845 Relative err: 0.001152 --> adam state - running grad var: - Max err: 0.000162 Relative err: 0.000161 --> params (after update): - Max err: 0.000000 Relative err: 0.000001 -``` - -``` -Running with 4096 x 4096 grad (param) shape, GaLore orthogonal matrix [128, 4096], dtype torch.float32 and allow_tf32 False -Kernel: fused -Accuracy: --> adam state - running grad mean: -Max err: 0.000003 Relative err: 0.000004 --> adam state - running grad var: -Max err: 0.000002 Relative err: 0.000002 --> params (after update): -Max err: 0.000000 Relative err: 0.000000 -``` - -#### Discussion - -##### Down Projection GEMM Shape - -The motivation for the `hybrid` approach is the unconventional matrix shapes of the down projection (Step 1): - -- The projection is always done such that the larger dimension of the `grad` matrix is maintained while other is projected to low rank per the `GaLore` algorithm - - E.g., if `M >= N`, the GEMM is of shape (`M x N`) x (`N x rank`) = (`M x rank`), (`rank x M`) x (`M x N`) = (`rank x N`) otherwise -- Since `{M, N} >> rank` by definition, this results in a large reduction dimension relative to one of the output dimensions (output matrix is either fat or skinny) -- This does not fit cleanly into the `split-k / parallel reduction` `GEMM` paradigm which is more tailored for shapes where both output dims are smaller than the reduction dimension. -- Consequently, I had trouble finding an optimal kernel config using `triton` `autotuner` for the down projection step, despite tuning across many compute and io-bound configs (see `fused.triton_utils.kernels.matmul.py`). -- Benchmarking `triton`-tuned `matmul` against default `torch.matmul` for these shapes showed worse performance, for `torch.float32` - -#### Effect of `TF32` tensor cores - -`allow_tf32`: this has significant impact on relative performance of `triton` vs `torch` matmuls: - -- Quick benchmarks of the downprojection `matmul` show that: - - with `allow_tf32=True` for both, triton exhibits `~1.30x` performance improvement over `torch`. - - with `allow_tf32=False`, performance of `triton` degrades significantly to `~.50x` of `torch`. - -See this [`torch note`](https://pytorch.org/docs/stable/notes/cuda.html#tf32-on-ampere) for more details on this feature. - -**Note**: This might be less of a concern given this incoming triton [PR](https://github.com/openai/triton/pull/3234), which implements a fast `TF32` trick that improves both performance and accuracy. - -#### Repro - -_Accuracy_ - -- Test accuracy of `torch` vs `hybrid` for `M=4096`, `N=4096`, `rank=128`, and `tf32` switched on: - - ```python - pytest test/kernel/test_fused_kernels.py - ``` - -_Benchmark_ - -- Benchmark across all kernels without `tf32`: - - ```python - python benchmarks/bench_galore_fused_kernels.py - ``` - -For additional benchmarking options: - -```python - python benchmarks/bench_galore_fused_kernels.py --help -``` - -#### Test Env - -- GPU Device Props: - - Name: `NVIDIA RTX A6000` - - CC: `86` - - Total_memory: `48676MB` - - SM count: `84` -- Torch: `2.2.2` -- Triton: `2.2.0` diff --git a/torchao/prototype/galore/docs/galore_adam8bit.md b/torchao/prototype/galore/docs/galore_adam8bit.md deleted file mode 100644 index ddb45c29b8..0000000000 --- a/torchao/prototype/galore/docs/galore_adam8bit.md +++ /dev/null @@ -1,35 +0,0 @@ -## GaLore AdamW8bit Optimizer - -### Overview - -`GaLore` AdamW8bit optimizer utilizes `bitsandbytes` `AdamW8bit` optimizer to additionally quantize the optimizer states. - -In addition to the additional ops introduced by `GaLore` to the standard `Adam` update step (see the `galore_adam.md` for details), additional dequantize / quantize steps are needed: - -- one to to dequantize the quantized states for the state update -- after the states are updated, they need to quantized along and `quant_state` updated -- For `bitsandbytes` `AdamW8bit`, the `quant_state` consists of group-wise (`blocksize`) scaling factors. - -The `bitsandbytes` 8bit optimizer is implemented in CUDA, with handcrafted logic for implementing each of these steps. - -> The motivation for re-implementing this optimizer purely in `triton` / `torch` is to enable exploration of various fusion / optimization strategies that would be difficult with the current CUDA impl. - -#### Quantization Algorithm - -1. Weights are quantized in contiguous `blocksize` segments -2. Given tensor `M x N`, reshape to `-1 x blocksize` -3. Find columnwise `absmax` and normalize tensor by dividing by `absmax` -4. Reshape normalized tensor back to original shape -5. `bitsandbytes` then uses an `8-bit` [quantization code](https://github.com/TimDettmers/bitsandbytes/blob/76885a41df9e6c94b3f80b1c37374c8441b6933e/bitsandbytes/optim/optimizer.py#L146-L151), which can either be signed or unsigned -- signed for tracking `mean`, unsigned for tracking `var`. -6. The normalized tensor is then assigned to the code it is closest to: - - E.g., given normalized value `.0412` and buckets `.0402` and `.0416`, it will be assigned the latter code. -7. **IMPORTANT**: This gives rise to a small number of edge-case errors when trying to reproduce `bitsandbytes` quantization - - Specifically, if a normalized value falls directly between two codes there is a degree of indeterminism. - - E.g., in the previous example, if the normalized value is `.0409`, it would be equidistant to the codes `.0402` and `.0416`. - - See the assertions in the `test_galore_quant.py` unittest that checks that these are the only discrepancies arising from the triton implementation (run with `pytest -sv -k` flags to see the output from this test). - -### bitsandbytes CUDA Source - -- Adam[W]8bit [update step](https://github.com/TimDettmers/bitsandbytes/blob/fd9d072e02b74348004f197e686e168448883a9e/csrc/kernels.cu#L1770) -- Adam blockwise [quantization](https://github.com/TimDettmers/bitsandbytes/blob/fd9d072e02b74348004f197e686e168448883a9e/csrc/kernels.cu#L413) after update -- [Blockwise](https://github.com/TimDettmers/bitsandbytes/blob/fd9d072e02b74348004f197e686e168448883a9e/csrc/kernels.cu#L726) [Quantization](https://github.com/TimDettmers/bitsandbytes/blob/fd9d072e02b74348004f197e686e168448883a9e/csrc/kernels.cu#L339) kernel diff --git a/torchao/prototype/galore/kernels/adam_downproj_fused.py b/torchao/prototype/galore/kernels/adam_downproj_fused.py deleted file mode 100644 index c45fc5d238..0000000000 --- a/torchao/prototype/galore/kernels/adam_downproj_fused.py +++ /dev/null @@ -1,365 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -import logging - -import torch -import triton -import triton.language as tl - -from torchao.prototype.common.triton.matmul_perf_model import ( - early_config_prune, - estimate_matmul_time, -) - -from .adam_step import BETA1, BETA2, EPS -from .custom_autotune import Config, autotune -from .matmul import ( - TRITON_ACC_TYPES, - get_higher_dtype, - get_mm_heuristics, - init_to_zero, - to_tl_type, -) -from .matmul import get_autotuner as default_mm_autotuner - -logger = logging.getLogger(__name__) - -AUTOTUNER_TOP_K = 50 - - -def set_tuner_top_k(k): - global AUTOTUNER_TOP_K - AUTOTUNER_TOP_K = k - - -@triton.jit -def _fused_adam_mm_kernel( - # matmul args - A, - B, - C, - M, - N, - K, # - stride_am, - stride_ak, # - stride_bk, - stride_bn, # - stride_cm, - stride_cn, # - # adam epilogue, - exp_avg_ptr, # these will be updated inplace - exp_avg2_ptr, - store, - # grad_ptr, # low rank grad output -- not needed, C is the output - # meta params - BLOCK_M: tl.constexpr, - BLOCK_N: tl.constexpr, - BLOCK_K: tl.constexpr, # - SPLIT_K: tl.constexpr, - EVEN_K: tl.constexpr, - GROUP_M: tl.constexpr, - # Adam-specific params - BETA1: tl.constexpr = BETA1, - BETA2: tl.constexpr = BETA2, - EPS: tl.constexpr = EPS, - # matmul kernel settings - acc_dtype: tl.constexpr = tl.float32, # - allow_tf32: tl.constexpr = False, # higher precision for this phase - fp8_fast_accum: tl.constexpr = False, # - AB_DTYPE: tl.constexpr = None, # -): - # matrix multiplication - pid = tl.program_id(0) - pid_z = tl.program_id(1) - grid_m = tl.cdiv(M, BLOCK_M) - grid_n = tl.cdiv(N, BLOCK_N) - # re-order program ID for better L2 performance - width = GROUP_M * grid_n - group_id = pid // width - group_size = min(grid_m - group_id * GROUP_M, GROUP_M) - pid_m = group_id * GROUP_M + (pid % group_size) - pid_n = (pid % width) // (group_size) - # do matrix multiplication - rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) - rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) - ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M) - rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N) - rk = pid_z * BLOCK_K + tl.arange(0, BLOCK_K) - # pointers - A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak) - B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn) - acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=acc_dtype) - for k in range(0, tl.cdiv(K, BLOCK_K * SPLIT_K)): - if EVEN_K: - a = tl.load(A) - b = tl.load(B) - else: - k_remaining = K - k * (BLOCK_K * SPLIT_K) - _0 = tl.zeros((1, 1), dtype=C.dtype.element_ty) - a = tl.load(A, mask=rk[None, :] < k_remaining, other=_0) - b = tl.load(B, mask=rk[:, None] < k_remaining, other=_0) - if AB_DTYPE is not None: - a = a.to(AB_DTYPE) - b = b.to(AB_DTYPE) - if fp8_fast_accum: - acc = tl.dot(a, b, acc, out_dtype=acc_dtype, allow_tf32=allow_tf32) - else: - acc += tl.dot(a, b, out_dtype=acc_dtype, allow_tf32=allow_tf32) - A += BLOCK_K * SPLIT_K * stride_ak - B += BLOCK_K * SPLIT_K * stride_bk - # acc = acc.to(C.dtype.element_ty) - - # rematerialize rm and rn to save registers - rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) - rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) - - epilogue_offsets = rm[:, None] * stride_cm + rn[None, :] * stride_cn - mask = (rm < M)[:, None] & (rn < N)[None, :] - - # Load adam state - exp_avg = tl.load(exp_avg_ptr + epilogue_offsets, mask=mask) - exp_avg2 = tl.load(exp_avg2_ptr + epilogue_offsets, mask=mask) - - # Perform update - exp_avg = BETA1 * exp_avg.to(acc.dtype) + (1.0 - BETA1) * acc - exp_avg2 = BETA2 * exp_avg2.to(acc.dtype) + (1.0 - BETA2) * (acc * acc) - denom = tl.sqrt(exp_avg2) + EPS - norm_grad = exp_avg / denom - # Convert to output type - norm_grad = norm_grad.to(C.dtype.element_ty) - - # acc = acc.to(C.dtype.element_ty) - C = C + epilogue_offsets - - # handles write-back with reduction-splitting - if SPLIT_K == 1: - tl.store(C, norm_grad, mask=mask) - else: - tl.atomic_add(C, norm_grad, mask=mask) - - if store: - tl.store( - exp_avg_ptr + epilogue_offsets, - exp_avg, - mask=mask, - ) - tl.store( - exp_avg2_ptr + epilogue_offsets, - exp_avg2, - mask=mask, - ) - - -def _get_configs_splitk_all(): - """ - Configs specific to split-k matmuls - Not used currently - """ - configs = [] - for num_stages in [2, 3, 4, 5]: - for block_m in [16, 32, 64, 128]: - for block_k in [16, 32, 64, 128, 256]: - for block_n in [16, 32, 64, 128]: - num_warps = 2 if block_n <= 64 else 4 - configs.append( - Config( - { - "BLOCK_M": block_m, - "BLOCK_N": block_n, - "BLOCK_K": block_k, - "SPLIT_K": 1, - }, - num_stages=num_stages, - num_warps=num_warps, - ) - ) - # split_k - for split_k in [2, 4, 8]: - configs.append( - Config( - { - "BLOCK_M": block_m, - "BLOCK_N": block_n, - "BLOCK_K": block_k, - "SPLIT_K": split_k, - }, - num_stages=num_stages, - num_warps=num_warps, - pre_hook=init_to_zero("C"), - ) - ) - return configs - - -def _get_configs_splitk_small(): - """Configs for split-k, smaller version than above - Not used currently - """ - configs = [] - for num_stages in [2, 3, 4]: - for block_m in [64, 128]: - for block_k in [16, 32, 64]: - for block_n in [64, 128]: - num_warps = 2 if block_n <= 64 else 4 - configs.append( - Config( - { - "BLOCK_M": block_m, - "BLOCK_N": block_n, - "BLOCK_K": block_k, - "SPLIT_K": 1, - }, - num_stages=num_stages, - num_warps=num_warps, - ) - ) - # split_k - for split_k in [2, 4, 8]: - configs.append( - Config( - { - "BLOCK_M": block_m, - "BLOCK_N": block_n, - "BLOCK_K": block_k, - "SPLIT_K": split_k, - }, - num_stages=num_stages, - num_warps=num_warps, - pre_hook=init_to_zero("C"), - ) - ) - return configs - - -def _splitk_autotuner( - configs=_get_configs_splitk_small(), - key=["M", "N", "K"], - early_config_prune=early_config_prune, - perf_model=estimate_matmul_time, - top_k=AUTOTUNER_TOP_K, -): - """Autotuner for splitk matmuls - Not used currently - """ - autotuner = autotune( - configs=configs, - key=key, - prune_configs_by={ - "early_config_prune": early_config_prune, - "perf_model": perf_model, - "top_k": top_k, - }, - ) - - return autotuner - - -def _get_kernel( - tuner_fn=default_mm_autotuner, heuristics_fn=get_mm_heuristics, topk=AUTOTUNER_TOP_K -): - tuner = tuner_fn() - tuner.topk = topk - heuristics = heuristics_fn() - return tuner(heuristics(_fused_adam_mm_kernel)) - - -DEFAULT_KERNEL = _get_kernel() - - -def fused_adam_mm_launcher( - a, - b, - *, - exp_avg, - exp_avg2, - store=True, - BETA1=BETA1, - BETA2=BETA2, - EPS=EPS, - allow_tf32=False, - fp8_fast_accum=False, - acc_dtype=None, - output_dtype=None, - kernel=None, -): - device = a.device - # handle non-contiguous inputs if necessary - # a = grad - # b = galore_proj.ortho_matrix.t() - if a.stride(0) > 1 and a.stride(1) > 1: - a = a.contiguous() - if b.stride(0) > 1 and b.stride(1) > 1: - b = b.contiguous() - # checks constraints - assert a.shape[1] == b.shape[0], "incompatible dimensions" - M, K = a.shape - _, N = b.shape - - # common type between a and b - ab_dtype = get_higher_dtype(a.dtype, b.dtype) - - # allocates output - if output_dtype is None: - output_dtype = ab_dtype - - c = torch.empty((M, N), device=device, dtype=output_dtype) - - if acc_dtype is None: - acc_dtype = [ab_dtype][0] - else: - assert isinstance(acc_dtype, torch.dtype), "acc_dtype must be a torch.dtype" - assert acc_dtype in TRITON_ACC_TYPES[a.dtype], ( - "acc_dtype not compatible with the type of a" - ) - assert acc_dtype in TRITON_ACC_TYPES[b.dtype], ( - "acc_dtype not compatible with the type of b" - ) - - acc_dtype = to_tl_type(acc_dtype) - ab_dtype = to_tl_type(ab_dtype) - output_dtype = to_tl_type(output_dtype) - - # Tensor cores support input with mixed float8 types. - if a.dtype in [tl.float8e4nv, tl.float8e5] and b.dtype in [ - tl.float8e4nv, - tl.float8e5, - ]: - ab_dtype = None - - grid = lambda META: ( - triton.cdiv(M, META["BLOCK_M"]) * triton.cdiv(N, META["BLOCK_N"]), - META["SPLIT_K"], - ) - - if kernel is None: - kernel = DEFAULT_KERNEL - kernel[grid]( - a, - b, - c, - M, - N, - K, # - a.stride(0), - a.stride(1), # - b.stride(0), - b.stride(1), # - c.stride(0), - c.stride(1), # - exp_avg, - exp_avg2, - store=store, - BETA1=BETA1, # , # - BETA2=BETA2, # , # - EPS=EPS, # - acc_dtype=acc_dtype, # - allow_tf32=allow_tf32, # - fp8_fast_accum=fp8_fast_accum, # - GROUP_M=8, - AB_DTYPE=ab_dtype, - ) - return exp_avg, exp_avg2, c # c -> normalized low rank grad diff --git a/torchao/prototype/galore/kernels/matmul.py b/torchao/prototype/galore/kernels/matmul.py deleted file mode 100644 index 0a7c830f02..0000000000 --- a/torchao/prototype/galore/kernels/matmul.py +++ /dev/null @@ -1,417 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -import torch -import triton -import triton.language as tl - -from torchao.prototype.common.triton.matmul_perf_model import ( - early_config_prune, - estimate_matmul_time, -) - -from .custom_autotune import Config, autotune, heuristics - -# Allowed types for acc_type given the types of a and b. -TRITON_ACC_TYPES = { - torch.float16: (torch.float32, torch.float16), - torch.bfloat16: (torch.float32, torch.bfloat16), - torch.float32: (torch.float32,), - torch.int8: (torch.int32,), -} - -AUTOTUNER_TOP_K = 50 -_ordered_datatypes = [torch.int8, torch.float16, torch.bfloat16, torch.float32] - - -def upcast_if_fp8(a): - if "fp8" in str(a): - return torch.float16 - return a - - -def get_higher_dtype(a, b): - a = upcast_if_fp8(a) - b = upcast_if_fp8(b) - if a is b: - return a - - assert a in _ordered_datatypes - assert b in _ordered_datatypes - - for d in _ordered_datatypes: - if a is d: - return b - if b is d: - return a - - -def init_to_zero(name): - return lambda nargs: nargs[name].zero_() - - -def set_tuner_top_k(k): - global AUTOTUNER_TOP_K - AUTOTUNER_TOP_K = k - - -def to_tl_type(ty): - return getattr(tl, str(ty).split(".")[-1]) - - -def get_configs_io_bound(): - configs = [] - for num_stages in [2, 3, 4, 5, 6]: - for block_m in [16, 32]: - for block_k in [32, 64]: - for block_n in [32, 64, 128, 256]: - num_warps = 2 if block_n <= 64 else 4 - configs.append( - Config( - { - "BLOCK_M": block_m, - "BLOCK_N": block_n, - "BLOCK_K": block_k, - "SPLIT_K": 1, - }, - num_stages=num_stages, - num_warps=num_warps, - ) - ) - # split_k - for split_k in [2, 4, 8, 16]: - configs.append( - Config( - { - "BLOCK_M": block_m, - "BLOCK_N": block_n, - "BLOCK_K": block_k, - "SPLIT_K": split_k, - }, - num_stages=num_stages, - num_warps=num_warps, - pre_hook=init_to_zero("C"), - ) - ) - return configs - - -def get_configs_compute_bound(): - configs = [ - # basic configs for compute-bound matmuls - Config( - {"BLOCK_M": 128, "BLOCK_N": 256, "BLOCK_K": 32, "SPLIT_K": 1}, - num_stages=3, - num_warps=8, - ), - Config( - {"BLOCK_M": 256, "BLOCK_N": 128, "BLOCK_K": 32, "SPLIT_K": 1}, - num_stages=3, - num_warps=8, - ), - Config( - {"BLOCK_M": 256, "BLOCK_N": 64, "BLOCK_K": 32, "SPLIT_K": 1}, - num_stages=4, - num_warps=4, - ), - Config( - {"BLOCK_M": 64, "BLOCK_N": 256, "BLOCK_K": 32, "SPLIT_K": 1}, - num_stages=4, - num_warps=4, - ), - Config( - {"BLOCK_M": 128, "BLOCK_N": 128, "BLOCK_K": 32, "SPLIT_K": 1}, - num_stages=4, - num_warps=4, - ), - Config( - {"BLOCK_M": 128, "BLOCK_N": 64, "BLOCK_K": 32, "SPLIT_K": 1}, - num_stages=4, - num_warps=4, - ), - Config( - {"BLOCK_M": 64, "BLOCK_N": 128, "BLOCK_K": 32, "SPLIT_K": 1}, - num_stages=4, - num_warps=4, - ), - Config( - {"BLOCK_M": 128, "BLOCK_N": 32, "BLOCK_K": 32, "SPLIT_K": 1}, - num_stages=4, - num_warps=4, - ), - Config( - {"BLOCK_M": 64, "BLOCK_N": 32, "BLOCK_K": 32, "SPLIT_K": 1}, - num_stages=5, - num_warps=2, - ), - # good for int8 - Config( - {"BLOCK_M": 128, "BLOCK_N": 256, "BLOCK_K": 128, "SPLIT_K": 1}, - num_stages=3, - num_warps=8, - ), - Config( - {"BLOCK_M": 256, "BLOCK_N": 128, "BLOCK_K": 128, "SPLIT_K": 1}, - num_stages=3, - num_warps=8, - ), - Config( - {"BLOCK_M": 256, "BLOCK_N": 64, "BLOCK_K": 128, "SPLIT_K": 1}, - num_stages=4, - num_warps=4, - ), - Config( - {"BLOCK_M": 64, "BLOCK_N": 256, "BLOCK_K": 128, "SPLIT_K": 1}, - num_stages=4, - num_warps=4, - ), - Config( - {"BLOCK_M": 128, "BLOCK_N": 128, "BLOCK_K": 128, "SPLIT_K": 1}, - num_stages=4, - num_warps=4, - ), - Config( - {"BLOCK_M": 128, "BLOCK_N": 64, "BLOCK_K": 64, "SPLIT_K": 1}, - num_stages=4, - num_warps=4, - ), - Config( - {"BLOCK_M": 64, "BLOCK_N": 128, "BLOCK_K": 64, "SPLIT_K": 1}, - num_stages=4, - num_warps=4, - ), - Config( - {"BLOCK_M": 128, "BLOCK_N": 32, "BLOCK_K": 64, "SPLIT_K": 1}, - num_stages=4, - num_warps=4, - ), - Config( - {"BLOCK_M": 64, "BLOCK_N": 32, "BLOCK_K": 64, "SPLIT_K": 1}, - num_stages=5, - num_warps=2, - ), - ] - return configs - - -def get_autotuner( - configs=get_configs_compute_bound() + get_configs_io_bound(), - key=["M", "N", "K"], - early_config_prune=early_config_prune, - perf_model=estimate_matmul_time, - top_k=AUTOTUNER_TOP_K, -): - autotuner = autotune( - configs=configs, - key=key, - prune_configs_by={ - "early_config_prune": early_config_prune, - "perf_model": perf_model, - "top_k": top_k, - }, - ) - - return autotuner - - -def get_mm_heuristics(): - return heuristics( - { - "EVEN_K": lambda args: args["K"] % (args["BLOCK_K"] * args["SPLIT_K"]) == 0, - } - ) - - -@triton.jit -def _matmul_kernel( - A, - B, - C, - M, - N, - K, # - stride_am, - stride_ak, # - stride_bk, - stride_bn, # - stride_cm, - stride_cn, # - # meta params - BLOCK_M: tl.constexpr, - BLOCK_N: tl.constexpr, - BLOCK_K: tl.constexpr, # - SPLIT_K: tl.constexpr, - EVEN_K: tl.constexpr, - GROUP_M: tl.constexpr, - # epilogue - epilogue_alpha=None, - epilogue_beta=None, - epilogue_source=None, # Corresponds to C in GEMM convention of D = AB + C - # matmul kernel settings - acc_dtype: tl.constexpr = tl.float32, # - allow_tf32: tl.constexpr = True, # - fp8_fast_accum: tl.constexpr = True, # - AB_DTYPE: tl.constexpr = None, # - EPILOGUE: tl.constexpr = False, -): - # matrix multiplication - pid = tl.program_id(0) - pid_z = tl.program_id(1) - grid_m = tl.cdiv(M, BLOCK_M) - grid_n = tl.cdiv(N, BLOCK_N) - # re-order program ID for better L2 performance - width = GROUP_M * grid_n - group_id = pid // width - group_size = min(grid_m - group_id * GROUP_M, GROUP_M) - pid_m = group_id * GROUP_M + (pid % group_size) - pid_n = (pid % width) // (group_size) - # do matrix multiplication - rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) - rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) - ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M) - rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N) - rk = pid_z * BLOCK_K + tl.arange(0, BLOCK_K) - # pointers - A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak) - B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn) - acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=acc_dtype) - for k in range(0, tl.cdiv(K, BLOCK_K * SPLIT_K)): - if EVEN_K: - a = tl.load(A) - b = tl.load(B) - else: - k_remaining = K - k * (BLOCK_K * SPLIT_K) - _0 = tl.zeros((1, 1), dtype=C.dtype.element_ty) - a = tl.load(A, mask=rk[None, :] < k_remaining, other=_0) - b = tl.load(B, mask=rk[:, None] < k_remaining, other=_0) - if AB_DTYPE is not None: - a = a.to(AB_DTYPE) - b = b.to(AB_DTYPE) - if fp8_fast_accum: - acc = tl.dot(a, b, acc, out_dtype=acc_dtype, allow_tf32=allow_tf32) - else: - acc += tl.dot(a, b, out_dtype=acc_dtype, allow_tf32=allow_tf32) - A += BLOCK_K * SPLIT_K * stride_ak - B += BLOCK_K * SPLIT_K * stride_bk - # acc = acc.to(C.dtype.element_ty) - - # rematerialize rm and rn to save registers - rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) - rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) - - if EPILOGUE: - if epilogue_alpha is not None: - acc = epilogue_alpha.to(acc_dtype) * acc - if epilogue_source is not None: - epilogue_src = tl.load( - epilogue_source + rm[:, None] * stride_cm + rn[None, :] * stride_cn - ) - if epilogue_beta is not None: - epilogue_src = epilogue_src.to(acc_dtype) * epilogue_beta.to(acc_dtype) - acc = acc + epilogue_src - - acc = acc.to(C.dtype.element_ty) - C = C + (rm[:, None] * stride_cm + rn[None, :] * stride_cn) - mask = (rm < M)[:, None] & (rn < N)[None, :] - # handles write-back with reduction-splitting - if SPLIT_K == 1: - tl.store(C, acc, mask=mask) - else: - tl.atomic_add(C, acc, mask=mask) - - -_autotuner = get_autotuner() -_heuristics = get_mm_heuristics() -matmul = _autotuner(_heuristics(_matmul_kernel)) - - -def triton_mm_launcher( - a, - b, - epilogue_alpha=None, - epilogue_beta=None, - epilogue_source=None, - allow_tf32=True, - fp8_fast_accum=True, - acc_dtype=None, - output_dtype=None, - kernel=matmul, -): - device = a.device - # handle non-contiguous inputs if necessary - # a = grad - # b = galore_proj.ortho_matrix.t() - if a.stride(0) > 1 and a.stride(1) > 1: - a = a.contiguous() - if b.stride(0) > 1 and b.stride(1) > 1: - b = b.contiguous() - # checks constraints - assert a.shape[1] == b.shape[0], "incompatible dimensions" - M, K = a.shape - _, N = b.shape - - # common type between a and b - ab_dtype = get_higher_dtype(a.dtype, b.dtype) - - # allocates output - if output_dtype is None: - output_dtype = ab_dtype - - c = torch.empty((M, N), device=device, dtype=output_dtype) - - if acc_dtype is None: - acc_dtype = [ab_dtype][0] - else: - assert isinstance(acc_dtype, torch.dtype), "acc_dtype must be a torch.dtype" - assert acc_dtype in TRITON_ACC_TYPES[a.dtype], ( - "acc_dtype not compatible with the type of a" - ) - assert acc_dtype in TRITON_ACC_TYPES[b.dtype], ( - "acc_dtype not compatible with the type of b" - ) - - acc_dtype = to_tl_type(acc_dtype) - ab_dtype = to_tl_type(ab_dtype) - output_dtype = to_tl_type(output_dtype) - - # Tensor cores support input with mixed float8 types. - if a.dtype in [tl.float8e4nv, tl.float8e5] and b.dtype in [ - tl.float8e4nv, - tl.float8e5, - ]: - ab_dtype = None - # launch kernel - # print( - # f"{__file__} triton matmul args: (AB dtype {ab_dtype}) (C dtype {c.dtype}) (allow_tf32 {allow_tf32}) (fp8_fast_accum {fp8_fast_accum})" - # ) - grid = lambda META: ( - triton.cdiv(M, META["BLOCK_M"]) * triton.cdiv(N, META["BLOCK_N"]), - META["SPLIT_K"], - ) - - matmul[grid]( - a, - b, - c, - M, - N, - K, # - a.stride(0), - a.stride(1), # - b.stride(0), - b.stride(1), # - c.stride(0), - c.stride(1), # - epilogue_alpha=epilogue_alpha, # - epilogue_beta=epilogue_beta, # - epilogue_source=epilogue_source, # - acc_dtype=acc_dtype, # - allow_tf32=allow_tf32, # - fp8_fast_accum=fp8_fast_accum, # - GROUP_M=8, - AB_DTYPE=ab_dtype, - EPILOGUE=any([epilogue_alpha, epilogue_beta, epilogue_source]), - ) - return c diff --git a/torchao/prototype/galore/optim/galore_torch.py b/torchao/prototype/galore/optim/galore_torch.py deleted file mode 100644 index 876c40292d..0000000000 --- a/torchao/prototype/galore/optim/galore_torch.py +++ /dev/null @@ -1,401 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -"""Reference implementations -See https://github.com/jiaweizzhao/GaLore/tree/master/galore_torch -""" - -# copy dependencies from transformers/optimization.py -import math -import warnings -from typing import Callable, Iterable, Tuple - -import torch -from bitsandbytes.optim.optimizer import Optimizer2State -from torch import nn -from torch.optim import Optimizer - - -class GaLoreProjector: - def __init__( - self, rank, verbose=False, update_proj_gap=200, scale=1.0, proj_type="std" - ): - self.rank = rank - self.verbose = verbose - self.update_proj_gap = update_proj_gap - self.scale = scale - self.ortho_matrix = None - self.proj_type = proj_type - - def project(self, full_rank_grad, iter): - if self.proj_type == "std": - if full_rank_grad.shape[0] >= full_rank_grad.shape[1]: - if self.ortho_matrix is None or iter % self.update_proj_gap == 0: - self.ortho_matrix = self.get_orthogonal_matrix( - full_rank_grad, self.rank, type="right" - ) - low_rank_grad = torch.matmul(full_rank_grad, self.ortho_matrix.t()) - else: - if self.ortho_matrix is None or iter % self.update_proj_gap == 0: - self.ortho_matrix = self.get_orthogonal_matrix( - full_rank_grad, self.rank, type="left" - ) - low_rank_grad = torch.matmul(self.ortho_matrix.t(), full_rank_grad) - elif self.proj_type == "reverse_std": - if full_rank_grad.shape[0] >= full_rank_grad.shape[1]: - if self.ortho_matrix is None or iter % self.update_proj_gap == 0: - self.ortho_matrix = self.get_orthogonal_matrix( - full_rank_grad, self.rank, type="left" - ) - low_rank_grad = torch.matmul(self.ortho_matrix.t(), full_rank_grad) - else: - if self.ortho_matrix is None or iter % self.update_proj_gap == 0: - self.ortho_matrix = self.get_orthogonal_matrix( - full_rank_grad, self.rank, type="right" - ) - low_rank_grad = torch.matmul(full_rank_grad, self.ortho_matrix.t()) - elif self.proj_type == "right": - if self.ortho_matrix is None or iter % self.update_proj_gap == 0: - self.ortho_matrix = self.get_orthogonal_matrix( - full_rank_grad, self.rank, type="right" - ) - low_rank_grad = torch.matmul(full_rank_grad, self.ortho_matrix.t()) - elif self.proj_type == "left": - if self.ortho_matrix is None or iter % self.update_proj_gap == 0: - self.ortho_matrix = self.get_orthogonal_matrix( - full_rank_grad, self.rank, type="left" - ) - low_rank_grad = torch.matmul(self.ortho_matrix.t(), full_rank_grad) - elif self.proj_type == "full": - if self.ortho_matrix is None or iter % self.update_proj_gap == 0: - self.ortho_matrix = self.get_orthogonal_matrix( - full_rank_grad, self.rank, type="full" - ) - low_rank_grad = ( - torch.matmul(self.ortho_matrix[0].t(), full_rank_grad) - @ self.ortho_matrix[1].t() - ) - - return low_rank_grad - - def project_back(self, low_rank_grad): - if self.proj_type == "std": - if low_rank_grad.shape[0] >= low_rank_grad.shape[1]: - full_rank_grad = torch.matmul(low_rank_grad, self.ortho_matrix) - else: - full_rank_grad = torch.matmul(self.ortho_matrix, low_rank_grad) - elif self.proj_type == "reverse_std": - if ( - low_rank_grad.shape[0] <= low_rank_grad.shape[1] - ): # note this is different from std - full_rank_grad = torch.matmul(self.ortho_matrix, low_rank_grad) - else: - full_rank_grad = torch.matmul(low_rank_grad, self.ortho_matrix) - elif self.proj_type == "right": - full_rank_grad = torch.matmul(low_rank_grad, self.ortho_matrix) - elif self.proj_type == "left": - full_rank_grad = torch.matmul(self.ortho_matrix, low_rank_grad) - elif self.proj_type == "full": - full_rank_grad = ( - torch.matmul(self.ortho_matrix[0], low_rank_grad) @ self.ortho_matrix[1] - ) - - return full_rank_grad * self.scale - - # svd decomposition - def get_orthogonal_matrix(self, weights, rank, type): - module_params = weights - - if module_params.data.dtype != torch.float: - float_data = False - original_type = module_params.data.dtype - original_device = module_params.data.device - matrix = module_params.data.float() - else: - float_data = True - matrix = module_params.data - - U, s, Vh = torch.linalg.svd(matrix, full_matrices=False) - - # make the smaller matrix always to be orthogonal matrix - if type == "right": - # A = U[:, :rank] @ torch.diag(s[:rank]) - B = Vh[:rank, :] - - if not float_data: - B = B.to(original_device).type(original_type) - return B - elif type == "left": - A = U[:, :rank] - # B = torch.diag(s[:rank]) @ Vh[:rank, :] - if not float_data: - A = A.to(original_device).type(original_type) - return A - elif type == "full": - A = U[:, :rank] - B = Vh[:rank, :] - if not float_data: - A = A.to(original_device).type(original_type) - B = B.to(original_device).type(original_type) - return [A, B] - else: - raise ValueError("type should be left, right or full") - - -class AdamW(Optimizer): - """ - Implements Adam algorithm with weight decay fix as introduced in [Decoupled Weight Decay - Regularization](https://arxiv.org/abs/1711.05101). - - Parameters: - params (`Iterable[nn.parameter.Parameter]`): - Iterable of parameters to optimize or dictionaries defining parameter groups. - lr (`float`, *optional*, defaults to 0.001): - The learning rate to use. - betas (`Tuple[float,float]`, *optional*, defaults to `(0.9, 0.999)`): - Adam's betas parameters (b1, b2). - eps (`float`, *optional*, defaults to 1e-06): - Adam's epsilon for numerical stability. - weight_decay (`float`, *optional*, defaults to 0.0): - Decoupled weight decay to apply. - correct_bias (`bool`, *optional*, defaults to `True`): - Whether or not to correct bias in Adam (for instance, in Bert TF repository they use `False`). - no_deprecation_warning (`bool`, *optional*, defaults to `False`): - A flag used to disable the deprecation warning (set to `True` to disable the warning). - """ - - def __init__( - self, - params: Iterable[nn.parameter.Parameter], - lr: float = 1e-3, - betas: Tuple[float, float] = (0.9, 0.999), - eps: float = 1e-6, - weight_decay: float = 0.0, - correct_bias: bool = True, - no_deprecation_warning: bool = False, - ): - if not no_deprecation_warning: - warnings.warn( - "This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch" - " implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this" - " warning", - FutureWarning, - ) - if lr < 0.0: - raise ValueError(f"Invalid learning rate: {lr} - should be >= 0.0") - if not 0.0 <= betas[0] < 1.0: - raise ValueError( - f"Invalid beta parameter: {betas[0]} - should be in [0.0, 1.0)" - ) - if not 0.0 <= betas[1] < 1.0: - raise ValueError( - f"Invalid beta parameter: {betas[1]} - should be in [0.0, 1.0)" - ) - if not 0.0 <= eps: - raise ValueError(f"Invalid epsilon value: {eps} - should be >= 0.0") - defaults = { - "lr": lr, - "betas": betas, - "eps": eps, - "weight_decay": weight_decay, - "correct_bias": correct_bias, - } - super().__init__(params, defaults) - - @torch.no_grad() - def step(self, closure: Callable = None): - """ - Performs a single optimization step. - - Arguments: - closure (`Callable`, *optional*): A closure that reevaluates the model and returns the loss. - """ - loss = None - if closure is not None: - loss = closure() - - for group in self.param_groups: - for p in group["params"]: - if p.grad is None: - continue - grad = p.grad - if grad.is_sparse: - raise RuntimeError( - "Adam does not support sparse gradients, please consider SparseAdam instead" - ) - - state = self.state[p] - - if "step" not in state: - state["step"] = 0 - - # GaLore Projection - if "rank" in group: - if "projector" not in state: - state["projector"] = GaLoreProjector( - group["rank"], - update_proj_gap=group["update_proj_gap"], - scale=group["scale"], - proj_type=group["proj_type"], - ) - - grad = state["projector"].project(grad, state["step"]) - - # State initialization - if "exp_avg" not in state: - # Exponential moving average of gradient values - state["exp_avg"] = torch.zeros_like(grad) - # Exponential moving average of squared gradient values - state["exp_avg_sq"] = torch.zeros_like(grad) - - exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"] - beta1, beta2 = group["betas"] - - state["step"] += 1 - - # Decay the first and second moment running average coefficient - # In-place operations to update the averages at the same time - exp_avg.mul_(beta1).add_(grad, alpha=(1.0 - beta1)) - exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1.0 - beta2) - denom = exp_avg_sq.sqrt().add_(group["eps"]) - - step_size = group["lr"] - if group["correct_bias"]: # No bias correction for Bert - bias_correction1 = 1.0 - beta1 ** state["step"] - bias_correction2 = 1.0 - beta2 ** state["step"] - step_size = ( - step_size * math.sqrt(bias_correction2) / bias_correction1 - ) - - # compute norm gradient - norm_grad = exp_avg / denom - - # GaLore Projection Back - if "rank" in group: - norm_grad = state["projector"].project_back(norm_grad) - - p.add_(norm_grad, alpha=-step_size) - - # Just adding the square of the weights to the loss function is *not* - # the correct way of using L2 regularization/weight decay with Adam, - # since that will interact with the m and v parameters in strange ways. - # - # Instead we want to decay the weights in a manner that doesn't interact - # with the m/v parameters. This is equivalent to adding the square - # of the weights to the loss with plain (non-momentum) SGD. - # Add weight decay at the end (fixed version) - if group["weight_decay"] > 0.0: - p.add_(p, alpha=(-group["lr"] * group["weight_decay"])) - - return loss - - -class AdamW8bit(Optimizer2State): - def __init__( - self, - params, - lr=1e-3, - betas=(0.9, 0.999), - eps=1e-8, - weight_decay=1e-2, - amsgrad=False, - optim_bits=32, - args=None, - min_8bit_size=4096, - percentile_clipping=100, - block_wise=True, - is_paged=False, - ): - super().__init__( - "adam", - params, - lr, - betas, - eps, - weight_decay, - 8, - args, - min_8bit_size, - percentile_clipping, - block_wise, - is_paged=is_paged, - ) - - @torch.no_grad() - def step(self, closure=None): - """Performs a single optimization step. - - Arguments: - closure (callable, optional): A closure that reevaluates the model - and returns the loss. - """ - loss = None - if closure is not None: - with torch.enable_grad(): - loss = closure() - - if not self.initialized: - self.check_overrides() - self.to_gpu() # needed for fairseq pure fp16 training - self.initialized = True - - # if self.is_paged: self.page_mng.prefetch_all() - for gindex, group in enumerate(self.param_groups): - for pindex, p in enumerate(group["params"]): - if p.grad is None: - continue - state = self.state[p] - - if "step" not in state: - state["step"] = 0 - - # GaLore Projection - if "rank" in group: - if "projector" not in state: - state["projector"] = GaLoreProjector( - group["rank"], - update_proj_gap=group["update_proj_gap"], - scale=group["scale"], - proj_type=group["proj_type"], - ) - - if "weight_decay" in group and group["weight_decay"] > 0: - # ensure that the weight decay is not applied to the norm grad - group["weight_decay_saved"] = group["weight_decay"] - group["weight_decay"] = 0 - - grad = state["projector"].project(p.grad, state["step"]) - - # suboptimal implementation - p.saved_data = p.data.clone() - p.data = grad.clone().to(p.data.dtype).to(p.data.device) - p.data.zero_() - p.grad = grad - - if "state1" not in state: - self.init_state(group, p, gindex, pindex) - - self.prefetch_state(p) - self.update_step(group, p, gindex, pindex) - torch.cuda.synchronize() - - # GaLore Projection Back - if "rank" in group: - p.data = p.saved_data.add_(state["projector"].project_back(p.data)) - - # apply weight decay - if "weight_decay_saved" in group: - p.data.add_( - p.data, alpha=-group["lr"] * group["weight_decay_saved"] - ) - group["weight_decay"] = group["weight_decay_saved"] - del group["weight_decay_saved"] - - if self.is_paged: - # all paged operation are asynchronous, we need - # to sync to make sure all tensors are in the right state - torch.cuda.synchronize() - - return loss diff --git a/torchao/prototype/galore/utils.py b/torchao/prototype/galore/utils.py deleted file mode 100644 index 6e9db05d30..0000000000 --- a/torchao/prototype/galore/utils.py +++ /dev/null @@ -1,114 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -import torch - - -def get_orthogonal_matrix(weights, rank, type): - module_params = weights - - if module_params.data.dtype != torch.float: - float_data = False - original_type = module_params.data.dtype - original_device = module_params.data.device - matrix = module_params.data.float() - else: - float_data = True - matrix = module_params.data - - U, s, Vh = torch.linalg.svd(matrix, full_matrices=False) - - # make the smaller matrix always to be orthogonal matrix - if type == "right": - # A = U[:, :rank] @ torch.diag(s[:rank]) - B = Vh[:rank, :] - - if not float_data: - B = B.to(original_device).type(original_type) - return B - elif type == "left": - A = U[:, :rank] - # B = torch.diag(s[:rank]) @ Vh[:rank, :] - if not float_data: - A = A.to(original_device).type(original_type) - return A - elif type == "full": - A = U[:, :rank] - B = Vh[:rank, :] - if not float_data: - A = A.to(original_device).type(original_type) - B = B.to(original_device).type(original_type) - return [A, B] - else: - raise ValueError("type should be left, right or full") - - -class TestGaLoreProjector: - def __init__( - self, - rank=128, - scale=1.0, - proj_type="std", - ): - self.rank = rank - self.scale = scale - - if proj_type != "std": - raise ("Only std projection is supported") - - self.proj_type = proj_type - - self.ortho_matrix = None - - def update_orthogonal_matrix(self, full_rank_grad): - if full_rank_grad.shape[0] >= full_rank_grad.shape[1]: - self.ortho_matrix = get_orthogonal_matrix( - full_rank_grad, self.rank, type="right" - ) - else: - self.ortho_matrix = get_orthogonal_matrix( - full_rank_grad, self.rank, type="left" - ) - - def project(self, full_rank_grad): - if full_rank_grad.shape[0] >= full_rank_grad.shape[1]: - low_rank_grad = torch.matmul(full_rank_grad, self.ortho_matrix.t()) - else: - low_rank_grad = torch.matmul(self.ortho_matrix.t(), full_rank_grad) - - return low_rank_grad - - def project_back(self, low_rank_grad): - if low_rank_grad.shape[0] >= low_rank_grad.shape[1]: - full_rank_grad = torch.matmul(low_rank_grad, self.ortho_matrix) - else: - full_rank_grad = torch.matmul(self.ortho_matrix, low_rank_grad) - - return full_rank_grad * self.scale - - -def make_copy(*args): - return [t.detach().clone() for t in args] - - -# def adam_step( -# exp_avg, -# exp_avg2, -# grad, -# galore_proj, -# params, -# step_size=1e-4, -# beta1=BETA1, -# beta2=BETA2, -# eps=EPS, -# ): -# grad = galore_proj.project(grad) -# exp_avg = beta1 * exp_avg + (1 - beta1) * grad -# exp_avg2 = beta2 * exp_avg2 + (1 - beta2) * torch.square(grad) -# denom = exp_avg2.sqrt() + eps -# norm_grad = exp_avg / denom -# norm_grad = galore_proj.project_back(norm_grad) -# # params = params - step_size * norm_grad -# return exp_avg, exp_avg2, denom, norm_grad From 282d04f923f1646eb2fa0ec19d11300286339760 Mon Sep 17 00:00:00 2001 From: Apurva Jain Date: Tue, 17 Jun 2025 19:32:19 -0700 Subject: [PATCH 134/165] [BE] Convert quant_primitives methods private (#2350) --- docs/source/api_ref_quantization.rst | 6 - test/dtypes/test_affine_quantized_float.py | 20 +- test/dtypes/test_floatx.py | 8 +- test/prototype/test_gguf_quant.py | 4 +- test/quantization/test_marlin_qqq.py | 4 +- test/quantization/test_qat.py | 4 +- test/quantization/test_quant_primitives.py | 12 +- test/test_ops.py | 6 +- torchao/dtypes/affine_quantized_tensor.py | 58 +-- torchao/dtypes/affine_quantized_tensor_ops.py | 8 +- .../floatx/floatx_tensor_core_layout.py | 2 +- torchao/dtypes/uintx/int4_cpu_layout.py | 4 +- torchao/dtypes/uintx/int4_xpu_layout.py | 4 +- torchao/dtypes/uintx/marlin_qqq_tensor.py | 12 +- .../dtypes/uintx/tensor_core_tiled_layout.py | 4 +- .../prototype/parq/quant/uniform_torchao.py | 24 +- .../gguf/gguf_quantized_tensor.py | 12 +- torchao/quantization/__init__.py | 16 - .../qat/affine_fake_quantized_tensor.py | 12 +- torchao/quantization/qat/utils.py | 6 +- torchao/quantization/quant_primitives.py | 365 ++++++++++++++---- torchao/quantization/utils.py | 24 +- tutorials/calibration_flow/gptq_like.py | 6 +- 23 files changed, 399 insertions(+), 222 deletions(-) diff --git a/docs/source/api_ref_quantization.rst b/docs/source/api_ref_quantization.rst index 5293684ab9..f2fad00b69 100644 --- a/docs/source/api_ref_quantization.rst +++ b/docs/source/api_ref_quantization.rst @@ -63,14 +63,8 @@ Quantization Primitives choose_qparams_affine choose_qparams_affine_with_min_max - choose_qparams_affine_floatx quantize_affine - quantize_affine_floatx dequantize_affine - dequantize_affine_floatx - choose_qparams_and_quantize_affine_hqq - fake_quantize_affine - fake_quantize_affine_cachemask safe_int_mm int_scaled_matmul MappingType diff --git a/test/dtypes/test_affine_quantized_float.py b/test/dtypes/test_affine_quantized_float.py index 879551fc0a..b63a406715 100644 --- a/test/dtypes/test_affine_quantized_float.py +++ b/test/dtypes/test_affine_quantized_float.py @@ -42,10 +42,10 @@ ) from torchao.quantization.quant_primitives import ( MappingType, + _choose_qparams_affine_float8, + _dequantize_affine_float8, + _quantize_affine_float8, choose_qparams_affine, - choose_qparams_affine_float8, - dequantize_affine_float8, - quantize_affine_float8, ) from torchao.utils import ( is_sm_at_least_89, @@ -358,21 +358,21 @@ def test_mm_float8dq_per_row( @common_utils.parametrize("output_dtype", [torch.float32, torch.bfloat16]) @common_utils.parametrize("block_size", [None, (1, 32), (2, 16), (4, 8)]) def test_dequantize_affine_float8(self, float8_dtype, output_dtype, block_size): - """Test dequantize_affine_float8 with various configurations""" + """Test _dequantize_affine_float8 with various configurations""" device = "cuda" input_tensor = torch.randn(8, 64, device=device, dtype=torch.float32) # Choose quantization parameters - scale = choose_qparams_affine_float8( + scale = _choose_qparams_affine_float8( input_tensor, float8_dtype=float8_dtype, block_size=block_size ) # Quantize - quantized = quantize_affine_float8(input_tensor, scale, float8_dtype) + quantized = _quantize_affine_float8(input_tensor, scale, float8_dtype) # Dequantize - dequantized = dequantize_affine_float8(quantized, scale, output_dtype) + dequantized = _dequantize_affine_float8(quantized, scale, output_dtype) # Verify output properties self.assertEqual(dequantized.dtype, output_dtype) @@ -395,7 +395,7 @@ def test_dequantize_affine_float8_scale_broadcasting(self): block_size = (2, 16) # 2x2 blocks in first dim, 2x16 blocks in second dim # Choose quantization parameters - scale = choose_qparams_affine_float8( + scale = _choose_qparams_affine_float8( input_tensor, float8_dtype=torch.float8_e4m3fn, block_size=block_size ) @@ -407,10 +407,10 @@ def test_dequantize_affine_float8_scale_broadcasting(self): self.assertEqual(scale.shape, expected_scale_shape) # Quantize - quantized = quantize_affine_float8(input_tensor, scale, torch.float8_e4m3fn) + quantized = _quantize_affine_float8(input_tensor, scale, torch.float8_e4m3fn) # Dequantize - dequantized = dequantize_affine_float8(quantized, scale, torch.float32) + dequantized = _dequantize_affine_float8(quantized, scale, torch.float32) # Verify shapes match self.assertEqual(dequantized.shape, input_tensor.shape) diff --git a/test/dtypes/test_floatx.py b/test/dtypes/test_floatx.py index 956ef9a03e..237bc2bd92 100644 --- a/test/dtypes/test_floatx.py +++ b/test/dtypes/test_floatx.py @@ -91,13 +91,13 @@ def test_from_scaled_tc_floatx_compile(self, ebits, mbits, device): @parametrize("ebits,mbits", _Floatx_DTYPES) def test_to_copy_device(self, ebits, mbits): from torchao.quantization.quant_primitives import ( - choose_qparams_affine_floatx, - quantize_affine_floatx, + _choose_qparams_affine_floatx, + _quantize_affine_floatx, ) x = torch.randn(256, 64) - scale = choose_qparams_affine_floatx(x, ebits, mbits) - x = quantize_affine_floatx(x, scale, ebits, mbits) + scale = _choose_qparams_affine_floatx(x, ebits, mbits) + x = _quantize_affine_floatx(x, scale, ebits, mbits) _layout = FloatxTensorCoreLayout(ebits, mbits) floatx_tensor_impl = FloatxTensorCoreAQTTensorImpl.from_plain( x, scale, None, _layout diff --git a/test/prototype/test_gguf_quant.py b/test/prototype/test_gguf_quant.py index b68d84b101..af44243fe4 100644 --- a/test/prototype/test_gguf_quant.py +++ b/test/prototype/test_gguf_quant.py @@ -13,7 +13,7 @@ GGUFWeightOnlyConfig, ) from torchao.quantization import quantize_ -from torchao.quantization.quant_primitives import choose_qparams_gguf +from torchao.quantization.quant_primitives import _choose_qparams_gguf from torchao.quantization.utils import compute_error @@ -31,7 +31,7 @@ def test_choose_qparams_gguf(self): super_block_min_scale, quantized_block_scale, quantized_block_min, - ) = choose_qparams_gguf(self.input, self.block_size, self.dtype) + ) = _choose_qparams_gguf(self.input, self.block_size, self.dtype) assert super_block_scale_scale.shape, (2, 8) assert super_block_min_scale.shape, (2, 8) diff --git a/test/quantization/test_marlin_qqq.py b/test/quantization/test_marlin_qqq.py index cff46ad329..8fe21c6bd3 100644 --- a/test/quantization/test_marlin_qqq.py +++ b/test/quantization/test_marlin_qqq.py @@ -21,7 +21,7 @@ ) from torchao.quantization.quant_primitives import ( MappingType, - choose_qparams_and_quantize_affine_qqq, + _choose_qparams_and_quantize_affine_qqq, ) from torchao.testing.utils import skip_if_rocm from torchao.utils import TORCH_VERSION_AT_LEAST_2_5 @@ -102,7 +102,7 @@ def test_pack_unpack_equivalence(self): for group_size in [-1, 128]: # Quantize weights - q_w, s_group, s_channel, _ = choose_qparams_and_quantize_affine_qqq( + q_w, s_group, s_channel, _ = _choose_qparams_and_quantize_affine_qqq( w, num_bits, group_size ) diff --git a/test/quantization/test_qat.py b/test/quantization/test_qat.py index 323802757d..f0404a2ac2 100644 --- a/test/quantization/test_qat.py +++ b/test/quantization/test_qat.py @@ -64,9 +64,9 @@ MappingType, TorchAODType, ZeroPointDomain, + _fake_quantize_affine, choose_qparams_affine, dequantize_affine, - fake_quantize_affine, quantize_affine, ) from torchao.quantization.unified import ( @@ -637,7 +637,7 @@ def test_qat_4w_primitives(self): group_size, scales_precision, ) - w_fq = fake_quantize_affine( + w_fq = _fake_quantize_affine( weight, block_size, scales, diff --git a/test/quantization/test_quant_primitives.py b/test/quantization/test_quant_primitives.py index e69d68b27f..ac2a42b9cf 100644 --- a/test/quantization/test_quant_primitives.py +++ b/test/quantization/test_quant_primitives.py @@ -13,11 +13,11 @@ from torchao.quantization.quant_primitives import ( MappingType, ZeroPointDomain, + _choose_qparams_affine_tinygemm, + _fake_quantize_affine, + _fake_quantize_affine_cachemask, choose_qparams_affine, - choose_qparams_affine_tinygemm, dequantize_affine, - fake_quantize_affine, - fake_quantize_affine_cachemask, quantize_affine, ) @@ -672,7 +672,7 @@ def test_get_groupwise_affine_qparams(self): zero_point_domain=zero_point_domain, ) if zero_point_domain == ZeroPointDomain.FLOAT: - scale, zero_point = choose_qparams_affine_tinygemm( + scale, zero_point = _choose_qparams_affine_tinygemm( input, mapping_type, block_size, @@ -780,7 +780,7 @@ def test_fake_quantize_affine(self): dequantized = dequantize_affine( quantized, block_size, scale, zero_point, dtype, quant_min, quant_max ) - fake_quantized = fake_quantize_affine( + fake_quantized = _fake_quantize_affine( input, block_size, scale, zero_point, dtype, quant_min, quant_max ) torch.testing.assert_close(dequantized, fake_quantized) @@ -816,7 +816,7 @@ def test_fake_quantize_affine_cachemask(self): dequantized = dequantize_affine( quantized, block_size, scale, zero_point, dtype, quant_min, quant_max ) - (fake_quantized, mask) = fake_quantize_affine_cachemask( + (fake_quantized, mask) = _fake_quantize_affine_cachemask( input, block_size, scale, diff --git a/test/test_ops.py b/test/test_ops.py index 012a4d562d..faec689a69 100644 --- a/test/test_ops.py +++ b/test/test_ops.py @@ -23,7 +23,9 @@ marlin_qqq_workspace, pack_to_marlin_qqq, ) -from torchao.quantization.quant_primitives import choose_qparams_and_quantize_affine_qqq +from torchao.quantization.quant_primitives import ( + _choose_qparams_and_quantize_affine_qqq, +) from torchao.sparsity.marlin import inject_24, marlin_24_workspace, pack_to_marlin_24 from torchao.utils import ( TORCH_VERSION_AT_LEAST_2_5, @@ -713,7 +715,7 @@ def test_marlin_qqq(batch_size, k_chunk, n_chunk, num_bits, group_size, mnk_fact ) # Quantize weights - q_w, s_group, s_channel, w_ref = choose_qparams_and_quantize_affine_qqq( + q_w, s_group, s_channel, w_ref = _choose_qparams_and_quantize_affine_qqq( b_weight, num_bits, group_size ) q_w = q_w.t() diff --git a/torchao/dtypes/affine_quantized_tensor.py b/torchao/dtypes/affine_quantized_tensor.py index 132ac0f28e..39f9131a9e 100644 --- a/torchao/dtypes/affine_quantized_tensor.py +++ b/torchao/dtypes/affine_quantized_tensor.py @@ -18,22 +18,22 @@ FP8_TYPES, MappingType, ZeroPointDomain, + _choose_qparams_affine_dont_preserve_zero, + _choose_qparams_affine_float8, + _choose_qparams_affine_floatx, + _choose_qparams_affine_tinygemm, + _choose_qparams_and_quantize_affine_hqq, + _dequantize_affine_float8, + _dequantize_affine_floatx, + _dequantize_affine_no_zero_point, + _dequantize_affine_tinygemm, + _quantize_affine_float8, + _quantize_affine_floatx, + _quantize_affine_no_zero_point, + _quantize_affine_tinygemm, choose_qparams_affine, - choose_qparams_affine_dont_preserve_zero, - choose_qparams_affine_float8, - choose_qparams_affine_floatx, - choose_qparams_affine_tinygemm, - choose_qparams_and_quantize_affine_hqq, dequantize_affine, - dequantize_affine_float8, - dequantize_affine_floatx, - dequantize_affine_no_zero_point, - dequantize_affine_tinygemm, quantize_affine, - quantize_affine_float8, - quantize_affine_floatx, - quantize_affine_no_zero_point, - quantize_affine_tinygemm, ) from torchao.utils import ( TORCH_VERSION_AT_LEAST_2_5, @@ -142,7 +142,7 @@ def dequantize(self, output_dtype: Optional[torch.dtype] = None) -> torch.Tensor if isinstance(self._layout, FloatxTensorCoreLayout): int_data, scale = self.tensor_impl.get_plain() - return dequantize_affine_floatx( + return _dequantize_affine_floatx( int_data, scale, self._layout.ebits, @@ -151,11 +151,11 @@ def dequantize(self, output_dtype: Optional[torch.dtype] = None) -> torch.Tensor ) elif isinstance(self._layout, Float8Layout): data, scale, _ = self.tensor_impl.get_plain() - return dequantize_affine_float8(data, scale, output_dtype) + return _dequantize_affine_float8(data, scale, output_dtype) else: data, scale, zero_point = self.tensor_impl.get_plain() if self.zero_point_domain == ZeroPointDomain.FLOAT: - dq = dequantize_affine_tinygemm( + dq = _dequantize_affine_tinygemm( data, self.block_size, scale, @@ -166,7 +166,7 @@ def dequantize(self, output_dtype: Optional[torch.dtype] = None) -> torch.Tensor output_dtype=output_dtype, ) elif self.zero_point_domain == ZeroPointDomain.NONE: - dq = dequantize_affine_no_zero_point( + dq = _dequantize_affine_no_zero_point( data, self.block_size, scale, @@ -270,7 +270,7 @@ def from_hp_to_intx( from torchao.dtypes import Int4CPULayout from torchao.dtypes.uintx import TensorCoreTiledLayout - data, scale, zero_point, _ = choose_qparams_and_quantize_affine_hqq( + data, scale, zero_point, _ = _choose_qparams_and_quantize_affine_hqq( input_float, nbits=nbits, group_size=group_size, @@ -291,7 +291,7 @@ def from_hp_to_intx( data = data.to(target_dtype) else: if zero_point_domain == ZeroPointDomain.FLOAT and not preserve_zero: - scale, zero_point = choose_qparams_affine_tinygemm( + scale, zero_point = _choose_qparams_affine_tinygemm( input_float, mapping_type, block_size, @@ -303,7 +303,7 @@ def from_hp_to_intx( zero_point_dtype, ) elif zero_point_domain == ZeroPointDomain.INT and not preserve_zero: - scale, zero_point = choose_qparams_affine_dont_preserve_zero( + scale, zero_point = _choose_qparams_affine_dont_preserve_zero( input_float, mapping_type, block_size, @@ -329,7 +329,7 @@ def from_hp_to_intx( # choose_qparams_affine is a custom op that does support returning optional Tensors. We thus set the zero_point to None if its domain is None if zero_point_domain == ZeroPointDomain.NONE: zero_point = None - data = quantize_affine_no_zero_point( + data = _quantize_affine_no_zero_point( input_float, block_size, scale, @@ -339,7 +339,7 @@ def from_hp_to_intx( quant_max, ) elif zero_point_domain == ZeroPointDomain.FLOAT: - data = quantize_affine_tinygemm( + data = _quantize_affine_tinygemm( input_float, block_size, scale, @@ -400,7 +400,7 @@ def from_hp_to_intx_static( if zero_point_domain == ZeroPointDomain.NONE: zero_point = None - int_data = quantize_affine_no_zero_point( + int_data = _quantize_affine_no_zero_point( input_float, block_size, scale, @@ -410,7 +410,7 @@ def from_hp_to_intx_static( quant_max, ) elif zero_point_domain == ZeroPointDomain.FLOAT: - int_data = quantize_affine_tinygemm( + int_data = _quantize_affine_tinygemm( input_float, block_size, scale, @@ -462,10 +462,10 @@ def from_hp_to_floatx( if target_dtype in FP8_TYPES: original_shape = input_float.shape input_float = _layout.pre_process(input_float) - scale = choose_qparams_affine_float8( + scale = _choose_qparams_affine_float8( input_float, float8_dtype=target_dtype, block_size=block_size ) - data = quantize_affine_float8(input_float, scale, target_dtype) + data = _quantize_affine_float8(input_float, scale, target_dtype) data, scale, zero_point = _layout.post_process( data, scale, None, block_size ) @@ -499,7 +499,7 @@ def from_hp_to_floatx_static( input_float, scale, ZeroPointDomain.NONE, block_size ) - data = quantize_affine_float8( + data = _quantize_affine_float8( input_float, scale, target_dtype, @@ -545,8 +545,8 @@ def from_hp_to_fpx( ebits, mbits = _layout.ebits, _layout.mbits # Note: these ops are hardcoded to have per axis quantization (axis=1) right now - scale = choose_qparams_affine_floatx(input_float, ebits, mbits) - floatx_unpacked = quantize_affine_floatx(input_float, scale, ebits, mbits) + scale = _choose_qparams_affine_floatx(input_float, ebits, mbits) + floatx_unpacked = _quantize_affine_floatx(input_float, scale, ebits, mbits) floatx_packed, scale, _ = _layout.post_process( floatx_unpacked, scale, None, block_size ) diff --git a/torchao/dtypes/affine_quantized_tensor_ops.py b/torchao/dtypes/affine_quantized_tensor_ops.py index a76b4daa23..02a2d3004a 100644 --- a/torchao/dtypes/affine_quantized_tensor_ops.py +++ b/torchao/dtypes/affine_quantized_tensor_ops.py @@ -92,9 +92,9 @@ ) from torchao.quantization.quant_primitives import ( ZeroPointDomain, + _dequantize_affine_no_zero_point, + _dequantize_affine_tinygemm, dequantize_affine, - dequantize_affine_no_zero_point, - dequantize_affine_tinygemm, ) from torchao.utils import ( fill_defaults, @@ -318,9 +318,9 @@ def _(func, types, args, kwargs): # we need to increase block size to correct dim new_blocks = idx.dim() - 1 if args[1].zero_point_domain == ZeroPointDomain.FLOAT: - _dequantize_affine = dequantize_affine_tinygemm + _dequantize_affine = _dequantize_affine_tinygemm elif args[1].zero_point_domain == ZeroPointDomain.NONE: - _dequantize_affine = dequantize_affine_no_zero_point + _dequantize_affine = _dequantize_affine_no_zero_point else: _dequantize_affine = dequantize_affine diff --git a/torchao/dtypes/floatx/floatx_tensor_core_layout.py b/torchao/dtypes/floatx/floatx_tensor_core_layout.py index 6871033f1a..c7fb1e1a7c 100644 --- a/torchao/dtypes/floatx/floatx_tensor_core_layout.py +++ b/torchao/dtypes/floatx/floatx_tensor_core_layout.py @@ -467,7 +467,7 @@ class FloatxTensorCoreLayout(Layout): class FloatxTensorCoreAQTTensorImpl(AQTTensorImpl): """FloatxTensorCoreAQTTensorImpl represents a Tensor with dtype floatx(ebits=a, mbits=b), it has a internal tensor field of "packed_floatx_data", which is packed from the - uint8 unpacked data (the output of `quantize_affine_floatx` operator) + uint8 unpacked data (the output of `_quantize_affine_floatx` operator) The packing is optimized for TensorCore, from the fp6-llm paper: https://arxiv.org/abs/2401.14112 github repo: https://github.com/usyd-fsalab/fp6_llm, now renamed to quant-llm diff --git a/torchao/dtypes/uintx/int4_cpu_layout.py b/torchao/dtypes/uintx/int4_cpu_layout.py index 6c89f98ff7..bf9446d265 100644 --- a/torchao/dtypes/uintx/int4_cpu_layout.py +++ b/torchao/dtypes/uintx/int4_cpu_layout.py @@ -19,7 +19,7 @@ from torchao.dtypes.utils import AQTTensorImpl, Layout, is_device from torchao.quantization.quant_primitives import ( ZeroPointDomain, - quantize_affine_tinygemm, + _quantize_affine_tinygemm, ) from torchao.utils import ( TORCH_VERSION_AT_LEAST_2_5, @@ -266,7 +266,7 @@ def get_plain(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: # TODO: move this to `unpack_tinygemm_scales_and_zeros`? scale = scale.reshape(scale.shape[:-1]).contiguous() zero = zero.reshape(zero.shape[:-1]).contiguous() - int_data = quantize_affine_tinygemm( + int_data = _quantize_affine_tinygemm( dequantized, block_size, scale, diff --git a/torchao/dtypes/uintx/int4_xpu_layout.py b/torchao/dtypes/uintx/int4_xpu_layout.py index c67eebd747..955a7a8610 100644 --- a/torchao/dtypes/uintx/int4_xpu_layout.py +++ b/torchao/dtypes/uintx/int4_xpu_layout.py @@ -377,8 +377,8 @@ def __torch_dispatch__(cls, func, types, args, kwargs): def get_plain(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: from torchao.quantization.quant_primitives import ( + _quantize_affine_tinygemm, quantize_affine, - quantize_affine_tinygemm, ) from torchao.quantization.utils import unpack_tinygemm_scales_and_zeros @@ -429,7 +429,7 @@ def get_plain(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: # TODO: move this to `unpack_tinygemm_scales_and_zeros`? scale = scale.reshape(scale.shape[:-1]).contiguous() zero = zero.reshape(zero.shape[:-1]).contiguous() - int_data = quantize_affine_tinygemm( + int_data = _quantize_affine_tinygemm( dequantized, block_size, scale, diff --git a/torchao/dtypes/uintx/marlin_qqq_tensor.py b/torchao/dtypes/uintx/marlin_qqq_tensor.py index 3f3f4fa075..04066a6c65 100644 --- a/torchao/dtypes/uintx/marlin_qqq_tensor.py +++ b/torchao/dtypes/uintx/marlin_qqq_tensor.py @@ -24,8 +24,8 @@ from torchao.dtypes.utils import AQTTensorImpl, Layout from torchao.quantization.quant_primitives import ( ZeroPointDomain, - choose_qparams_and_quantize_affine_qqq, - dequantize_affine_qqq, + _choose_qparams_and_quantize_affine_qqq, + _dequantize_affine_qqq, ) logger = logging.getLogger(__name__) @@ -36,9 +36,9 @@ class MarlinQQQTensor(AffineQuantizedTensor): """MarlinQQQ quantized tensor subclass which inherits AffineQuantizedTensor class. - To see what happens during choose_qparams_and_quantize_affine_qqq, quantization and dequantization for marlin qqq quantization, + To see what happens during _choose_qparams_and_quantize_affine_qqq, quantization and dequantization for marlin qqq quantization, please checkout https://github.com/pytorch/ao/blob/main/torchao/quantization/quant_primitives.py - and check the two quant primitive ops: choose_qparams_and_quantize_affine_qqq and dequantize_affine_qqq + and check the two quant primitive ops: _choose_qparams_and_quantize_affine_qqq and _dequantize_affine_qqq """ def dequantize(self, output_dtype: Optional[torch.dtype] = None) -> torch.Tensor: @@ -48,7 +48,7 @@ def dequantize(self, output_dtype: Optional[torch.dtype] = None) -> torch.Tensor int_data, s_group, s_channel = self.tensor_impl.get_plain() nbits = int(math.log2(self.quant_max - self.quant_min + 1)) group_size = max(self.block_size) - return dequantize_affine_qqq( + return _dequantize_affine_qqq( int_data, s_group, s_channel, nbits, group_size, output_dtype ) @@ -69,7 +69,7 @@ def from_hp_to_intx( input_float = _layout.pre_process(input_float) nbits = int(math.log2(quant_max - quant_min + 1)) group_size = max(block_size) - data, s_group, s_channel, _ = choose_qparams_and_quantize_affine_qqq( + data, s_group, s_channel, _ = _choose_qparams_and_quantize_affine_qqq( input_float, nbits, group_size ) tensor_impl_ctr = get_tensor_impl_constructor(type(_layout)) diff --git a/torchao/dtypes/uintx/tensor_core_tiled_layout.py b/torchao/dtypes/uintx/tensor_core_tiled_layout.py index 0856d22fee..591d9a9be1 100644 --- a/torchao/dtypes/uintx/tensor_core_tiled_layout.py +++ b/torchao/dtypes/uintx/tensor_core_tiled_layout.py @@ -21,7 +21,7 @@ from torchao.quantization.quant_primitives import ( ZeroPointDomain, _get_reduction_params, - quantize_affine_tinygemm, + _quantize_affine_tinygemm, ) from torchao.utils import ( TORCH_VERSION_AT_LEAST_2_5, @@ -511,7 +511,7 @@ def dequant_4d(self): target_dtype = torch.int32 quant_min = 0 quant_max = 15 - int_data = quantize_affine_tinygemm( + int_data = _quantize_affine_tinygemm( dequantized, self.block_size, scale, diff --git a/torchao/prototype/parq/quant/uniform_torchao.py b/torchao/prototype/parq/quant/uniform_torchao.py index 4f90f9cb92..ebe4e775e6 100644 --- a/torchao/prototype/parq/quant/uniform_torchao.py +++ b/torchao/prototype/parq/quant/uniform_torchao.py @@ -14,15 +14,15 @@ _DTYPE_TO_QVALUE_BOUNDS, MappingType, ZeroPointDomain, + _choose_qparams_affine_dont_preserve_zero, + _choose_qparams_affine_tinygemm, + _dequantize_affine_no_zero_point, + _dequantize_affine_tinygemm, + _quantize_affine_no_zero_point, + _quantize_affine_tinygemm, choose_qparams_affine, - choose_qparams_affine_dont_preserve_zero, - choose_qparams_affine_tinygemm, dequantize_affine, - dequantize_affine_no_zero_point, - dequantize_affine_tinygemm, quantize_affine, - quantize_affine_no_zero_point, - quantize_affine_tinygemm, ) from .quantizer import Quantizer @@ -57,16 +57,16 @@ def __init__( self._dequantize = dequantize_affine if zero_point_domain == ZeroPointDomain.FLOAT and not preserve_zero: - self._choose_qparams = choose_qparams_affine_tinygemm - self._quantize = quantize_affine_tinygemm - self._dequantize = dequantize_affine_tinygemm + self._choose_qparams = _choose_qparams_affine_tinygemm + self._quantize = _quantize_affine_tinygemm + self._dequantize = _dequantize_affine_tinygemm elif zero_point_domain == ZeroPointDomain.INT and not preserve_zero: - self._choose_qparams = choose_qparams_affine_dont_preserve_zero + self._choose_qparams = _choose_qparams_affine_dont_preserve_zero self._quantize = quantize_affine self._dequantize = dequantize_affine elif zero_point_domain == ZeroPointDomain.NONE: - self._quantize = quantize_affine_no_zero_point - self._dequantize = dequantize_affine_no_zero_point + self._quantize = _quantize_affine_no_zero_point + self._dequantize = _dequantize_affine_no_zero_point def _init_quant_min_max(self, b: int) -> None: if self.quant_min is None or self.quant_max is None: diff --git a/torchao/prototype/quantization/gguf/gguf_quantized_tensor.py b/torchao/prototype/quantization/gguf/gguf_quantized_tensor.py index 9757769d16..c1272fceb6 100644 --- a/torchao/prototype/quantization/gguf/gguf_quantized_tensor.py +++ b/torchao/prototype/quantization/gguf/gguf_quantized_tensor.py @@ -10,9 +10,9 @@ from torch.utils._python_dispatch import return_and_correct_aliasing from torchao.quantization.quant_primitives import ( - choose_qparams_gguf, - dequantize_gguf, - quantize_gguf, + _choose_qparams_gguf, + _dequantize_gguf, + _quantize_gguf, ) from torchao.utils import ( TORCH_VERSION_AT_LEAST_2_5, @@ -130,7 +130,7 @@ def dequantize(self, output_dtype: Optional[torch.dtype] = None) -> torch.Tensor block_size = tuple( [1] * (self.int_data.ndim - 1) + [_QK_K // self.n_blocks_per_superblock] ) - return dequantize_gguf( + return _dequantize_gguf( self.int_data, block_size, self.dtype, @@ -198,9 +198,9 @@ def from_float(cls, input_float, n_blocks_per_superblock, target_dtype): super_block_min_scale, quantized_block_scale, quantized_block_min, - ) = choose_qparams_gguf(input_float, block_size, target_dtype) + ) = _choose_qparams_gguf(input_float, block_size, target_dtype) - int_data = quantize_gguf( + int_data = _quantize_gguf( input_float, block_size, target_dtype, diff --git a/torchao/quantization/__init__.py b/torchao/quantization/__init__.py index 44fc6c8397..d9aba0bcc5 100644 --- a/torchao/quantization/__init__.py +++ b/torchao/quantization/__init__.py @@ -83,17 +83,9 @@ TorchAODType, ZeroPointDomain, choose_qparams_affine, - choose_qparams_affine_dont_preserve_zero, - choose_qparams_affine_floatx, - choose_qparams_affine_tinygemm, choose_qparams_affine_with_min_max, - choose_qparams_and_quantize_affine_hqq, dequantize_affine, - dequantize_affine_floatx, - fake_quantize_affine, - fake_quantize_affine_cachemask, quantize_affine, - quantize_affine_floatx, ) from .smoothquant import ( SmoothFakeDynamicallyQuantizedLinear, @@ -172,17 +164,9 @@ "AffineQuantizedObserverBase", # quant primitive ops "choose_qparams_affine", - "choose_qparams_affine_tinygemm", - "choose_qparams_affine_dont_preserve_zero", "choose_qparams_affine_with_min_max", - "choose_qparams_affine_floatx", "quantize_affine", - "quantize_affine_floatx", "dequantize_affine", - "dequantize_affine_floatx", - "choose_qparams_and_quantize_affine_hqq", - "fake_quantize_affine", - "fake_quantize_affine_cachemask", # operators/kernels "safe_int_mm", "int_scaled_matmul", diff --git a/torchao/quantization/qat/affine_fake_quantized_tensor.py b/torchao/quantization/qat/affine_fake_quantized_tensor.py index 6896588971..80ecd173c2 100644 --- a/torchao/quantization/qat/affine_fake_quantized_tensor.py +++ b/torchao/quantization/qat/affine_fake_quantized_tensor.py @@ -12,11 +12,11 @@ from torchao.quantization.quant_primitives import ( MappingType, ZeroPointDomain, + _choose_qparams_affine_dont_preserve_zero, + _choose_qparams_affine_tinygemm, + _fake_quantize_affine, _get_and_check_qmin_qmax, choose_qparams_affine, - choose_qparams_affine_dont_preserve_zero, - choose_qparams_affine_tinygemm, - fake_quantize_affine, ) from torchao.utils import TorchAOBaseTensor @@ -55,7 +55,7 @@ def apply_fake_quant_fn(t: torch.Tensor): assert isinstance(t, AffineFakeQuantizedTensor) qmin, qmax = _get_and_check_qmin_qmax(target_dtype, quant_min, quant_max) if zero_point_domain == ZeroPointDomain.FLOAT and not preserve_zero: - scale, zero_point = choose_qparams_affine_tinygemm( + scale, zero_point = _choose_qparams_affine_tinygemm( t.original_tensor, mapping_type, block_size, @@ -67,7 +67,7 @@ def apply_fake_quant_fn(t: torch.Tensor): zero_point_dtype, ) elif zero_point_domain == ZeroPointDomain.INT and not preserve_zero: - scale, zero_point = choose_qparams_affine_dont_preserve_zero( + scale, zero_point = _choose_qparams_affine_dont_preserve_zero( t.original_tensor, mapping_type, block_size, @@ -90,7 +90,7 @@ def apply_fake_quant_fn(t: torch.Tensor): scale_dtype, zero_point_dtype, ) - fq = fake_quantize_affine( + fq = _fake_quantize_affine( t, block_size, scale, diff --git a/torchao/quantization/qat/utils.py b/torchao/quantization/qat/utils.py index 132020499c..4f3323a1e8 100644 --- a/torchao/quantization/qat/utils.py +++ b/torchao/quantization/qat/utils.py @@ -9,7 +9,7 @@ from torchao.quantization.quant_primitives import ( ZeroPointDomain, - fake_quantize_affine, + _fake_quantize_affine, ) from torchao.quantization.utils import ( _get_per_token_block_size, @@ -87,7 +87,7 @@ def _fake_quantize_per_channel_group( assert input.shape[-1] % group_size == 0 assert input.dim() == 2 block_size = (1, group_size) - return fake_quantize_affine( + return _fake_quantize_affine( input, block_size, scales, @@ -110,7 +110,7 @@ def _fake_quantize_per_token( _per_token_quant_qparam_dim_check(input, scales, zero_points) block_size = _get_per_token_block_size(input) - fq = fake_quantize_affine( + fq = _fake_quantize_affine( input, block_size, scales, diff --git a/torchao/quantization/quant_primitives.py b/torchao/quantization/quant_primitives.py index 9e0c6447c8..df136bc06e 100644 --- a/torchao/quantization/quant_primitives.py +++ b/torchao/quantization/quant_primitives.py @@ -24,32 +24,32 @@ __all__ = [ "choose_qparams_affine", - "choose_qparams_affine_tinygemm", - "choose_qparams_affine_dont_preserve_zero", "choose_qparams_affine_with_min_max", - "choose_qparams_affine_floatx", "quantize_affine", - "quantize_affine_no_zero_point", - "quantize_affine_tinygemm", "dequantize_affine", - "dequantize_affine_no_zero_point", - "dequantize_affine_tinygemm", - "quantize_affine_floatx", - "dequantize_affine_floatx", - "fake_quantize_affine", - "fake_quantize_affine_cachemask", - "choose_qparams_and_quantize_affine_hqq", - "choose_qparams_and_quantize_affine_qqq", - "dequantize_affine_qqq", "MappingType", "ZeroPointDomain", "TorchAODType", - "choose_qparams_affine_float8", - "quantize_affine_float8", - "dequantize_affine_float8", - "choose_qparams_gguf", - "quantize_gguf", - "dequantize_gguf", + "_choose_qparams_affine_tinygemm", + "_choose_qparams_affine_dont_preserve_zero", + "_choose_qparams_affine_floatx", + "_choose_qparams_and_quantize_affine_hqq", + "_choose_qparams_and_quantize_affine_qqq", + "_choose_qparams_affine_float8", + "_choose_qparams_gguf", + "_quantize_affine_no_zero_point", + "_quantize_affine_tinygemm", + "_quantize_affine_floatx", + "_quantize_affine_float8", + "_quantize_gguf", + "_dequantize_affine_no_zero_point", + "_dequantize_affine_tinygemm", + "_dequantize_affine_floatx", + "_dequantize_affine_qqq", + "_dequantize_affine_float8", + "_dequantize_gguf", + "_fake_quantize_affine", + "_fake_quantize_affine_cachemask", ] @@ -228,9 +228,19 @@ def backward(ctx, gy: torch.Tensor) -> torch.Tensor: # TODO: decide on if we want to allow custom quant_min/quant_max here def _get_and_check_qmin_qmax(dtype, quant_min, quant_max): - """Get quant_min and quant_max args based on dtype and also - verify that they are within the range of possible quant_min/quant_max - for dtype + """Get quant_min and quant_max args based on dtype and also verify bounds. + + Args: + dtype: Target quantization dtype (e.g., torch.uint8, torch.int8, or FP8 types) + quant_min: Minimum quantized value, or None to use dtype default + quant_max: Maximum quantized value, or None to use dtype default + + Returns: + Tuple[int/float, int/float]: Validated (quant_min, quant_max) values + + Raises: + ValueError: If dtype is unsupported + AssertionError: If quant_min/quant_max are out of bounds for dtype """ if dtype in FP8_TYPES: quant_min_lower_bound, quant_max_upper_bound = ( @@ -357,11 +367,25 @@ def _quantize_affine( quant_min: Optional[Union[int, float, bool]] = None, quant_max: Optional[Union[int, float, bool]] = None, ) -> torch.Tensor: - """op definition that has compatible signatures with custom op library + """Quantize tensor using affine quantization with integer zero point domain. + + Op definition that has compatible signatures with custom op library. + + Args: + input: Input tensor to quantize (float32, float16, or bfloat16) + block_size: Granularity of quantization - size of tensor elements sharing same qparam + scale: Quantization scale parameter + zero_point: Quantization zero point parameter (optional) + output_dtype: Target quantized dtype (e.g., torch.uint8, torch.int8) + quant_min: Minimum quantized value, derived from dtype if None + quant_max: Maximum quantized value, derived from dtype if None + + Returns: + Quantized tensor with requested dtype Note: - zero_point_domain is pre-defined specifies how we quantize the floating point to quantized data: - INT: quantized_val = (float_val / scale) (integer) + zero_point (integer) + zero_point_domain is pre-defined as INT, meaning: + quantized_val = (float_val / scale) (integer) + zero_point (integer) """ quant_min, quant_max = _get_and_check_qmin_qmax(output_dtype, quant_min, quant_max) # workaround for uintx dtypes, since we don't have native Uintx dtype connected with @@ -386,12 +410,26 @@ def _quantize_affine_no_dtype_cast( quant_min: Union[int, float], quant_max: Union[int, float], ) -> torch.Tensor: - """ + """Quantize tensor using affine quantization without dtype casting. + + Performs quantization with integer zero point domain without casting to target dtype. + + Args: + input: Input tensor to quantize (float32, float16, or bfloat16) + block_size: Granularity of quantization - size of tensor elements sharing same qparam + scale: Quantization scale parameter + zero_point: Quantization zero point parameter (optional) + quant_min: Minimum quantized value + quant_max: Maximum quantized value + + Returns: + Quantized tensor without dtype casting + The op does the following: - 1. figure out the dimension for reduction based on block_size, also reshape the input to align with + 1. Figure out the dimension for reduction based on block_size, also reshape the input to align with the shape after reduction - 2. quantize the input based on the quantization parameters scale and zero_point and zero_point_domain = INT - 3. reshape the quantized result to origianl shape + 2. Quantize the input based on the quantization parameters scale and zero_point with zero_point_domain = INT + 3. Reshape the quantized result to original shape """ # TODO: validations # TODO: validate scale/zero_point dimensions are compatible with block_size @@ -428,7 +466,7 @@ def _quantize_affine_no_dtype_cast( return quant -def quantize_affine_tinygemm( +def _quantize_affine_tinygemm( input: torch.Tensor, block_size: List[int], scale: torch.Tensor, @@ -437,16 +475,31 @@ def quantize_affine_tinygemm( quant_min: Optional[Union[int, float, bool]] = None, quant_max: Optional[Union[int, float, bool]] = None, ) -> torch.Tensor: - """ + """Quantize tensor using affine quantization with float zero point domain for tinygemm. + + Specialized quantization for tinygemm int4mm kernel where zero point is in floating point domain. + + Args: + input: Input tensor to quantize (float32, float16, or bfloat16) + block_size: Granularity of quantization - size of tensor elements sharing same qparam + scale: Quantization scale parameter + zero_point: Quantization zero point parameter (optional) + output_dtype: Target quantized dtype (e.g., torch.uint8, torch.int8) + quant_min: Minimum quantized value, derived from dtype if None + quant_max: Maximum quantized value, derived from dtype if None + + Returns: + Quantized tensor with requested dtype + The op does the following: - 1. figure out the dimension for reduction based on block_size, also reshape the input to align with + 1. Figure out the dimension for reduction based on block_size, also reshape the input to align with the shape after reduction - 2. quantize the input based on the quantization parameters scale and zero_point and zero_point_domain = FLOAT - 3. reshape the quantized result to origianl shape + 2. Quantize the input based on the quantization parameters scale and zero_point with zero_point_domain = FLOAT + 3. Reshape the quantized result to original shape Note: - zero_point_domain is pre-defined specifies how we quantize the floating point to quantized data: - FLOAT: quantized_val = (float_val - (zero_point (float) - scale * mid_point)) / scale + zero_point_domain is pre-defined as FLOAT, meaning: + quantized_val = (float_val - (zero_point (float) - scale * mid_point)) / scale """ quant_min, quant_max = _get_and_check_qmin_qmax(output_dtype, quant_min, quant_max) # workaround for uintx dtypes, since we don't have native Uintx dtype connected with @@ -471,12 +524,26 @@ def _quantize_affine_tinygemm_no_dtype_cast( quant_min: Optional[Union[int, float]] = None, quant_max: Optional[Union[int, float]] = None, ) -> torch.Tensor: - """ + """Quantize tensor using affine quantization with float zero point domain without dtype casting. + + Specialized quantization for tinygemm int4mm kernel where zero point is in floating point domain. + + Args: + input: Input tensor to quantize (float32, float16, or bfloat16) + block_size: Granularity of quantization - size of tensor elements sharing same qparam + scale: Quantization scale parameter + zero_point: Quantization zero point parameter (optional) + quant_min: Minimum quantized value + quant_max: Maximum quantized value + + Returns: + Quantized tensor without dtype casting + The op does the following: - 1. figure out the dimension for reduction based on block_size, also reshape the input to align with + 1. Figure out the dimension for reduction based on block_size, also reshape the input to align with the shape after reduction - 2. quantize the input based on the quantization parameters scale and zero_point and zero_point_domain = FLOAT - 3. reshape the quantized result to origianl shape + 2. Quantize the input based on the quantization parameters scale and zero_point with zero_point_domain = FLOAT + 3. Reshape the quantized result to original shape """ # TODO: validations # TODO: validate scale/zero_point dimensions are compatible with block_size @@ -513,7 +580,7 @@ def _quantize_affine_tinygemm_no_dtype_cast( return quant -def quantize_affine_no_zero_point( +def _quantize_affine_no_zero_point( input: torch.Tensor, block_size: List[int], scale: torch.Tensor, @@ -522,17 +589,32 @@ def quantize_affine_no_zero_point( quant_min: Optional[Union[int, float, bool]] = None, quant_max: Optional[Union[int, float, bool]] = None, ) -> torch.Tensor: - """ + """Quantize tensor using affine quantization without zero point. + + Specialized quantization for cases where zero point is not needed (e.g., floatx quantization). + + Args: + input: Input tensor to quantize (float32, float16, or bfloat16) + block_size: Granularity of quantization - size of tensor elements sharing same qparam + scale: Quantization scale parameter + zero_point: Quantization zero point parameter (ignored, should be None) + output_dtype: Target quantized dtype (e.g., torch.uint8, torch.int8) + quant_min: Minimum quantized value, derived from dtype if None + quant_max: Maximum quantized value, derived from dtype if None + + Returns: + Quantized tensor with requested dtype + The op does the following: - 1. figure out the dimension for reduction based on block_size, also reshape the input to align with + 1. Figure out the dimension for reduction based on block_size, also reshape the input to align with the shape after reduction - 2. quantize the input based on the quantization parameters scale and zero_point and zero_point_domain = NONE - 3. reshape the quantized result to origianl shape + 2. Quantize the input based on the quantization parameters scale with zero_point_domain = NONE + 3. Reshape the quantized result to original shape Note: - zero_point_domain is pre-defined specifies how we quantize the floating point to quantized data: - None: quantized_val = (float_val / scale) | this is primarily used for floatx quantization - Where we do not want to round values to nearest integer and instead scale and cast. + zero_point_domain is pre-defined as NONE, meaning: + quantized_val = (float_val / scale) | This is primarily used for floatx quantization + where we do not want to round values to nearest integer and instead scale and cast. """ quant_min, quant_max = _get_and_check_qmin_qmax(output_dtype, quant_min, quant_max) # workaround for uintx dtypes, since we don't have native Uintx dtype connected with @@ -557,12 +639,26 @@ def _quantize_affine_no_zero_point_no_dtype_cast( quant_min: Optional[Union[int, float]] = None, quant_max: Optional[Union[int, float]] = None, ) -> torch.Tensor: - """ + """Quantize tensor using affine quantization without zero point and without dtype casting. + + Specialized quantization for cases where zero point is not needed without casting to target dtype. + + Args: + input: Input tensor to quantize (float32, float16, or bfloat16) + block_size: Granularity of quantization - size of tensor elements sharing same qparam + scale: Quantization scale parameter + zero_point: Quantization zero point parameter (ignored, should be None) + quant_min: Minimum quantized value + quant_max: Maximum quantized value + + Returns: + Quantized tensor without dtype casting + The op does the following: - 1. figure out the dimension for reduction based on block_size, also reshape the input to align with + 1. Figure out the dimension for reduction based on block_size, also reshape the input to align with the shape after reduction - 2. quantize the input based on the quantization parameters scale and zero_point and zero_point_domain = NONE - 3. reshape the quantized result to origianl shape + 2. Quantize the input based on the quantization parameters scale with zero_point_domain = NONE + 3. Reshape the quantized result to original shape """ # TODO: validations # TODO: validate scale/zero_point dimensions are compatible with block_size @@ -648,7 +744,23 @@ def _dequantize_affine( quant_max: Optional[Union[int, float, bool]] = None, output_dtype: torch.dtype = torch.float32, ) -> torch.Tensor: - """op definition that has compatible signatures with custom op library""" + """Dequantize tensor using affine dequantization with integer zero point domain. + + Op definition that has compatible signatures with custom op library. + + Args: + input: Quantized tensor to dequantize + block_size: Granularity of quantization - size of tensor elements sharing same qparam + scale: Quantization scale parameter + zero_point: Quantization zero point parameter (optional) + input_dtype: Expected dtype of input tensor (e.g., torch.uint8, torch.int8) + quant_min: Minimum quantized value for input tensor + quant_max: Maximum quantized value for input tensor + output_dtype: Target output dtype (default: torch.float32) + + Returns: + Dequantized tensor with requested output dtype + """ # TODO: validate scale/zero_point dimensions are compatible with block_size if input_dtype not in _SUB_BYTE_UINT_BOUNDS: assert input.dtype == input_dtype, ( @@ -680,13 +792,27 @@ def _dequantize_affine_no_dtype_check( quant_max: Union[int, float], output_dtype: torch.dtype = torch.float32, ) -> torch.Tensor: - """This function converts AQT tensors to their high precision floating point representation + """Dequantize tensor using affine dequantization without dtype checking. + + Converts quantized tensors to their high precision floating point representation. + + Args: + input: Quantized tensor to dequantize + block_size: Granularity of quantization - size of tensor elements sharing same qparam + scale: Quantization scale parameter + zero_point: Quantization zero point parameter (optional) + quant_min: Minimum quantized value for input tensor + quant_max: Maximum quantized value for input tensor + output_dtype: Target output dtype (default: torch.float32) + + Returns: + Dequantized tensor with requested output dtype The op does the following: - 1. figure out the dimension for reduction based on block_size, also reshape the input to align with + 1. Figure out the dimension for reduction based on block_size, also reshape the input to align with the shape after reduction - 2. dequantize the input based on the quantization parameters scale and zero_point and args like zero_point_domain - 3. reshape the quantized result to origianl shape and change dtype to the output_dtype + 2. Dequantize the input based on the quantization parameters scale and zero_point + 3. Reshape the quantized result to original shape and change dtype to the output_dtype """ assert len(block_size) == input.dim(), ( f"Got input dim:{input.dim()}, block_size: {block_size}" @@ -723,13 +849,27 @@ def _dequantize_affine_no_zero_point_no_dtype_check( quant_max: Union[int, float], output_dtype: torch.dtype = torch.float32, ) -> torch.Tensor: - """This function converts AQT tensors to their high precision floating point representation + """Dequantize tensor using affine dequantization without zero point and without dtype checking. + + Converts quantized tensors to their high precision floating point representation without zero point. + + Args: + input: Quantized tensor to dequantize + block_size: Granularity of quantization - size of tensor elements sharing same qparam + scale: Quantization scale parameter + zero_point: Quantization zero point parameter (ignored, should be None) + quant_min: Minimum quantized value for input tensor + quant_max: Maximum quantized value for input tensor + output_dtype: Target output dtype (default: torch.float32) + + Returns: + Dequantized tensor with requested output dtype The op does the following: - 1. figure out the dimension for reduction based on block_size, also reshape the input to align with + 1. Figure out the dimension for reduction based on block_size, also reshape the input to align with the shape after reduction - 2. dequantize the input based on the quantization parameters scale and zero_point and args like zero_point_domain - 3. reshape the quantized result to origianl shape and change dtype to the output_dtype + 2. Dequantize the input based on the quantization parameters scale (no zero point) + 3. Reshape the quantized result to original shape and change dtype to the output_dtype """ assert len(block_size) == input.dim(), ( f"Got input dim:{input.dim()}, block_size: {block_size}" @@ -745,7 +885,7 @@ def _dequantize_affine_no_zero_point_no_dtype_check( scale = scale.view(shape_after_reduction) assert zero_point is None, ( - "zero_point should be None for dequantize_affine_no_zero_point" + "zero_point should be None for _dequantize_affine_no_zero_point" ) dequant = input.to(output_dtype) dequant = dequant * scale @@ -753,7 +893,7 @@ def _dequantize_affine_no_zero_point_no_dtype_check( return dequant.view(original_shape).to(output_dtype) -def dequantize_affine_no_zero_point( +def _dequantize_affine_no_zero_point( input: torch.Tensor, block_size: Tuple[int, ...], scale: torch.Tensor, @@ -848,7 +988,7 @@ def _dequantize_affine_tinygemm_no_dtype_check( return dequant.view(original_shape).to(output_dtype) -def dequantize_affine_tinygemm( +def _dequantize_affine_tinygemm( input: torch.Tensor, block_size: Tuple[int, ...], scale: torch.Tensor, @@ -898,7 +1038,7 @@ def dequantize_affine_tinygemm( ) -def fake_quantize_affine( +def _fake_quantize_affine( input: torch.Tensor, block_size: Tuple[int, ...], scale: torch.Tensor, @@ -946,7 +1086,7 @@ def fake_quantize_affine( return fq -def fake_quantize_affine_cachemask( +def _fake_quantize_affine_cachemask( input: torch.Tensor, block_size: Tuple[int, ...], scale: torch.Tensor, @@ -961,12 +1101,12 @@ def fake_quantize_affine_cachemask( This is equivalent to calling `quantize_affine` + `dequantize_affine` but without the dtype casts. - Note: Compared to :func:`~torchao.quantization.quant_primitives.fake_quantize_affine`, + Note: Compared to :func:`~torchao.quantization.quant_primitives._fake_quantize_affine`, this consumes more memory and returns an additional outlier mask for intermediate quantized values. Args: - Same as :func:`~torchao.quantization.quant_primitives.fake_quantize_affine`. + Same as :func:`~torchao.quantization.quant_primitives._fake_quantize_affine`. Returns: A 2-tuple of ( @@ -1003,8 +1143,25 @@ def _do_fake_quantize_affine( quant_max: Optional[Union[int, float]] = None, zero_point_domain: ZeroPointDomain = ZeroPointDomain.INT, ) -> Tuple[torch.Tensor, torch.Tensor]: - """ - Helper function for `fake_quantize_affine` that returns both the + """Helper function for fake quantization that returns both intermediate and final values. + + Performs quantization followed by dequantization without dtype casting, returning both + the intermediate quantized values and the final dequantized values. + + Args: + input: Input tensor to fake quantize (float32, float16, or bfloat16) + block_size: Granularity of quantization - size of tensor elements sharing same qparam + scale: Quantization scale parameter + zero_point: Quantization zero point parameter (optional) + quant_dtype: Target quantized dtype for determining quant_min/quant_max + quant_min: Minimum quantized value, derived from dtype if None + quant_max: Maximum quantized value, derived from dtype if None + zero_point_domain: Domain of zero point (INT, FLOAT, or NONE) + + Returns: + Tuple of (intermediate quantized values, final dequantized values) + + Helper function for `_fake_quantize_affine` that returns both the intermediate quantized values and the final dequantized values. """ input_dtype = input.dtype @@ -1086,7 +1243,7 @@ def choose_qparams_affine( # TODO: lower this op to custom op library @torch.no_grad() -def choose_qparams_affine_tinygemm( +def _choose_qparams_affine_tinygemm( input: torch.Tensor, mapping_type: MappingType, block_size: Tuple[int], @@ -1157,7 +1314,7 @@ def choose_qparams_affine_tinygemm( # TODO: lower this op to custom op library -def choose_qparams_affine_dont_preserve_zero( +def _choose_qparams_affine_dont_preserve_zero( input: torch.Tensor, mapping_type: MappingType, block_size: Tuple[int], @@ -1427,7 +1584,7 @@ def _choose_qparams_affine( ) -def choose_qparams_and_quantize_affine_qqq( +def _choose_qparams_and_quantize_affine_qqq( w: torch.Tensor, num_bits: int, group_size: int, @@ -1497,7 +1654,7 @@ def reshape_w(w): return q_w, s_group, s_channel, w_ref -def choose_qparams_gguf( +def _choose_qparams_gguf( input: Optional[torch.Tensor], block_size: List[int], target_dtype: torch.dtype, @@ -1580,7 +1737,7 @@ def choose_qparams_gguf( ) -def quantize_gguf( +def _quantize_gguf( input: torch.Tensor, block_size: List[int], target_dtype: torch.dtype, @@ -1642,7 +1799,7 @@ def quantize_gguf( return int_data -def dequantize_gguf( +def _dequantize_gguf( input: torch.Tensor, block_size: List[int], target_dtype: torch.dtype, @@ -1705,7 +1862,7 @@ def dequantize_gguf( return dequant -def dequantize_affine_qqq( +def _dequantize_affine_qqq( w: torch.Tensor, s_group: torch.Tensor, s_channel: torch.Tensor, @@ -1845,7 +2002,7 @@ def _convert_to_affinequantized_format( # Main hqq quantizer function -def choose_qparams_and_quantize_affine_hqq( +def _choose_qparams_and_quantize_affine_hqq( tensor: torch.Tensor, nbits: float = 4, group_size: int = 64, @@ -1857,6 +2014,28 @@ def choose_qparams_and_quantize_affine_hqq( raw_output: bool = False, # If True, it will return the quant params in hqq lib format optimize_weights: Callable = optimize_weights_proximal_legacy, # weights proximal optimizer function ) -> tuple: + """Choose quantization parameters and quantize tensor using HQQ (Half-Quadratic Quantization). + + Performs quantization using HQQ method with optional weight optimization via proximal solver. + + Args: + tensor: Input tensor to quantize (float32, float16, or bfloat16) + nbits: Number of bits for quantization (default: 4) + group_size: Size of quantization groups (default: 64) + optimize: Whether to optimize weights using proximal solver (default: True) + axis: Axis along which to perform quantization (0 or 1, default: 1) + compute_dtype: Target compute dtype (default: torch.float16) + device: Target device for computation (default: "cuda") + verbose: Whether to print optimization error information (default: False) + raw_output: If True, return params in HQQ library format (default: False) + optimize_weights: Weight optimization function (default: optimize_weights_proximal_legacy) + + Returns: + Tuple of (quantized_weights, scale, zero_point, original_shape) + + Note: + Uses proximal solver to minimize ||W - dequantize(quantize(W))||_p^p for weight optimization. + """ assert axis in [0, 1], "axis should be either 0 or 1" if group_size is not None: assert _is_divisible(tensor.numel(), group_size), ( @@ -1939,9 +2118,25 @@ def choose_qparams_and_quantize_affine_hqq( return W_q, scale, zero, shape -def choose_qparams_affine_floatx( +def _choose_qparams_affine_floatx( tensor: torch.Tensor, ebits: int, mbits: int ) -> torch.Tensor: + """Choose quantization parameters for floatx quantization. + + Calculates scale parameter for quantizing to custom floating point format. + + Args: + tensor: Input tensor to quantize (float32, float16, or bfloat16) + ebits: Number of exponent bits in target floatx format + mbits: Number of mantissa bits in target floatx format + + Returns: + Scale tensor for floatx quantization + + Note: + Uses global lookup table as workaround for torch.compile() compatibility + since _n_ones() is not compatible due to << operator. + """ # _n_ones() is not compatible with torch.compile() due to << operator # https://github.com/pytorch/pytorch/issues/119152 # exp_bias = _n_ones(ebits - 1) @@ -1959,7 +2154,7 @@ def choose_qparams_affine_floatx( return scale.to(dtype) -def quantize_affine_floatx( +def _quantize_affine_floatx( tensor: torch.Tensor, scale: torch.Tensor, ebits: int, mbits: int ) -> torch.Tensor: """Quantizes the float32 high precision floating point tensor to low precision floating point number and @@ -1970,7 +2165,7 @@ def quantize_affine_floatx( return tensor_floatx -def dequantize_affine_floatx( +def _dequantize_affine_floatx( tensor: torch.Tensor, scale: torch.Tensor, ebits: int, @@ -1983,7 +2178,7 @@ def dequantize_affine_floatx( return tensor -def choose_qparams_affine_float8( +def _choose_qparams_affine_float8( tensor: torch.Tensor, float8_dtype: torch.dtype = torch.float8_e4m3fn, scale_dtype: torch.dtype = torch.float32, @@ -2075,7 +2270,7 @@ def _expand_scale_to_tensor_shape( return expanded_scale -def quantize_affine_float8( +def _quantize_affine_float8( tensor: torch.Tensor, scale: torch.Tensor, float8_dtype: torch.dtype = torch.float8_e4m3fn, @@ -2095,7 +2290,7 @@ def quantize_affine_float8( return fp8_tensor -def dequantize_affine_float8( +def _dequantize_affine_float8( tensor: torch.Tensor, scale: torch.Tensor, output_dtype: torch.dtype = torch.float32, diff --git a/torchao/quantization/utils.py b/torchao/quantization/utils.py index 3c968e2d40..c7dd92d55c 100644 --- a/torchao/quantization/utils.py +++ b/torchao/quantization/utils.py @@ -15,15 +15,15 @@ from torchao.quantization.quant_primitives import ( MappingType, ZeroPointDomain, + _choose_qparams_affine_dont_preserve_zero, + _choose_qparams_affine_tinygemm, + _dequantize_affine_no_zero_point, + _dequantize_affine_tinygemm, + _quantize_affine_no_zero_point, + _quantize_affine_tinygemm, choose_qparams_affine, - choose_qparams_affine_dont_preserve_zero, - choose_qparams_affine_tinygemm, dequantize_affine, - dequantize_affine_no_zero_point, - dequantize_affine_tinygemm, quantize_affine, - quantize_affine_no_zero_point, - quantize_affine_tinygemm, ) from torchao.utils import ( TORCH_VERSION_AT_LEAST_2_5, @@ -357,7 +357,7 @@ def get_groupwise_affine_qparams( ) if zero_point_domain == ZeroPointDomain.FLOAT and not preserve_zero: - scale, zero_point = choose_qparams_affine_tinygemm( + scale, zero_point = _choose_qparams_affine_tinygemm( w, mapping_type, block_size, @@ -369,7 +369,7 @@ def get_groupwise_affine_qparams( zero_point_dtype=zero_point_dtype, ) elif zero_point_domain == ZeroPointDomain.INT and not preserve_zero: - scale, zero_point = choose_qparams_affine_dont_preserve_zero( + scale, zero_point = _choose_qparams_affine_dont_preserve_zero( w, mapping_type, block_size, @@ -439,9 +439,9 @@ def groupwise_affine_quantize_tensor_from_qparams( if zero_point_domain == ZeroPointDomain.INT: _quantize_affine = quantize_affine elif zero_point_domain == ZeroPointDomain.FLOAT: - _quantize_affine = quantize_affine_tinygemm + _quantize_affine = _quantize_affine_tinygemm elif ZeroPointDomain == ZeroPointDomain.NONE: - _quantize_affine = quantize_affine_no_zero_point + _quantize_affine = _quantize_affine_no_zero_point else: raise ValueError(f"Unrecognized zero point domain: {zero_point_domain}") @@ -508,9 +508,9 @@ def groupwise_affine_dequantize_tensor_from_qparams( if zero_point_domain == ZeroPointDomain.INT: _dequantize_affine = dequantize_affine elif zero_point_domain == ZeroPointDomain.FLOAT: - _dequantize_affine = dequantize_affine_tinygemm + _dequantize_affine = _dequantize_affine_tinygemm else: - _dequantize_affine = dequantize_affine_no_zero_point + _dequantize_affine = _dequantize_affine_no_zero_point return _dequantize_affine( w_int32, block_size, diff --git a/tutorials/calibration_flow/gptq_like.py b/tutorials/calibration_flow/gptq_like.py index ab7a2b4f37..df824e506f 100644 --- a/tutorials/calibration_flow/gptq_like.py +++ b/tutorials/calibration_flow/gptq_like.py @@ -48,7 +48,7 @@ LinearActivationQuantizedTensor, MappingType, PerTensor, - fake_quantize_affine, + _fake_quantize_affine, quantize_, to_linear_activation_quantized, ) @@ -237,7 +237,9 @@ def forward_pre_hook( new_input = [] for inp in args[0]: new_input.append( - fake_quantize_affine(inp, inp.shape, input_scale, input_zp, torch.uint8) + _fake_quantize_affine( + inp, inp.shape, input_scale, input_zp, torch.uint8 + ) ) mt = MultiTensor(new_input) From 6a8887f942e2ce65ed274dcd497e830af5f406bf Mon Sep 17 00:00:00 2001 From: qizixi <22851944+zixi-qi@users.noreply.github.com> Date: Tue, 17 Jun 2025 20:28:01 -0700 Subject: [PATCH 135/165] fix torchao quantized model in fbcode (#2396) Summary: Without this change, ran into: > AttributeError: module 'fbgemm_gpu' has no attribute '__version__' Differential Revision: D76858513 --- torchao/quantization/quant_api.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/torchao/quantization/quant_api.py b/torchao/quantization/quant_api.py index 7b40f388ed..1ee092eff3 100644 --- a/torchao/quantization/quant_api.py +++ b/torchao/quantization/quant_api.py @@ -81,6 +81,7 @@ is_MI300, is_sm_at_least_89, is_sm_at_least_90, + is_fbcode, ) from .autoquant import AutoQuantizableLinearWeight, autoquant @@ -2010,7 +2011,7 @@ def _(module: torch.nn.Module, config: FbgemmConfig) -> torch.nn.Module: import fbgemm_gpu.experimental.gen_ai # noqa: F401 - if fbgemm_gpu.__version__ < "1.2.0": + if not is_fbcode() and fbgemm_gpu.__version__ < "1.2.0": raise ImportError("Requires fbgemm-gpu-genai >= 1.2.0") _SUPPORTED_DTYPES = { From 8b12ddf7735ab82026e8fcd99e5232f44e5e779f Mon Sep 17 00:00:00 2001 From: andrewor14 Date: Wed, 18 Jun 2025 09:52:55 -0400 Subject: [PATCH 136/165] Fix ruff broken on main (#2404) --- torchao/quantization/quant_api.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/torchao/quantization/quant_api.py b/torchao/quantization/quant_api.py index 1ee092eff3..8b66ac84ce 100644 --- a/torchao/quantization/quant_api.py +++ b/torchao/quantization/quant_api.py @@ -78,10 +78,10 @@ TORCH_VERSION_AT_LEAST_2_4, TORCH_VERSION_AT_LEAST_2_5, TORCH_VERSION_AT_LEAST_2_6, + is_fbcode, is_MI300, is_sm_at_least_89, is_sm_at_least_90, - is_fbcode, ) from .autoquant import AutoQuantizableLinearWeight, autoquant From c561d263dbe16b0852cc72ef9c1e773f2e74eb40 Mon Sep 17 00:00:00 2001 From: andrewor14 Date: Wed, 18 Jun 2025 12:24:12 -0400 Subject: [PATCH 137/165] Add part 2 of end-to-end tutorial: fine-tuning (#2394) This commit adds the QAT tutorial and a general structure for the fine-tuning tutorial, which all also include QLoRA and float8 quantized fine-tuning. It also connects the 3 tutorial parts (pre-training, fine-tuning, and serving) into one cohesive end-to-end flow with some visuals and text. --- docs/source/finetuning.rst | 293 +++++++++++++++++++++++++++++++++ docs/source/index.rst | 8 +- docs/source/pretraining.rst | 43 +++-- docs/source/serving.rst | 12 ++ docs/static/e2e_flow_part1.png | Bin 0 -> 258271 bytes docs/static/e2e_flow_part2.png | Bin 0 -> 265822 bytes docs/static/e2e_flow_part3.png | Bin 0 -> 279562 bytes docs/static/qat_eval.png | Bin 0 -> 224659 bytes 8 files changed, 338 insertions(+), 18 deletions(-) create mode 100644 docs/source/finetuning.rst create mode 100644 docs/source/serving.rst create mode 100644 docs/static/e2e_flow_part1.png create mode 100644 docs/static/e2e_flow_part2.png create mode 100644 docs/static/e2e_flow_part3.png create mode 100644 docs/static/qat_eval.png diff --git a/docs/source/finetuning.rst b/docs/source/finetuning.rst new file mode 100644 index 0000000000..00e2471e7f --- /dev/null +++ b/docs/source/finetuning.rst @@ -0,0 +1,293 @@ +(Part 2) Fine-tuning with QAT, QLoRA, and float8 +------------------------------------------------ + +TorchAO provides an end-to-end pre-training, fine-tuning, and serving +model optimization flow by leveraging our quantization and sparsity +techniques integrated into our partner frameworks. This is part 2 of 3 +such tutorials showcasing this end-to-end flow, focusing on the +fine-tuning step. + +.. image:: ../static/e2e_flow_part2.png + +Fine-tuning is an important step for adapting your pre-trained model +to more domain-specific data. In this tutorial, we demonstrate 3 model +optimization techniques that can be applied to your model during fine-tuning: + +1. **Quantization-Aware Training (QAT)**, for adapting your model to +quantization numerics during fine-tuning, with the goal of mitigating +quantization degradations in your fine-tuned model when it is quantized +eventually, e.g. in the serving step. Check out `our blog `__ +and `README `__ for more details! + +2. **Quantized Low-Rank Adaptation (QLoRA)**, for reducing the resource +requirement of fine-tuning by introducing small, trainable low-rank +matrices and freezing the original pre-trained checkpoint, a type of +Parameter-Efficient Fine-Tuning (PEFT). Please refer to the `original +paper `__ for more details. + +3. **Float8 Quantized Fine-tuning**, for speeding up fine-tuning by +dynamically quantizing high precision weights and activations to float8, +similar to `pre-training in float8 `__. + + +Quantization-Aware Training (QAT) +################################## + +The goal of Quantization-Aware Training is to adapt the model to +quantization numerics during training or fine-tuning, so as to mitigate +the inevitable quantization degradation when the model is actually +quantized eventually, presumably during the serving step after fine-tuning. +TorchAO's QAT support has been used successfully for the recent release of +the `Llama-3.2 quantized 1B/3B `__ +and the `LlamaGuard-3-8B `__ models to improve the quality of the quantized models. + +TorchAO's QAT support involves two separate steps: prepare and convert. +The prepare step "fake" quantizes activations and/or weights during +training, which means, the high precision values (e.g. bf16) are mapped +to their corresponding quantized values *without* actually casting them +to the target lower precision dtype (e.g. int4). The convert step, +applied after training, replaces "fake" quantization operations in the +model with "real" quantization that does perform the dtype casting: + +.. image:: ../../torchao/quantization/qat/images/qat_diagram.png + +There are multiple options for using TorchAO's QAT for fine-tuning: + +1. Use our integration with `TorchTune `__ +2. Use our integration with `Axolotl `__ +3. Directly use our QAT APIs with your own training loop + + +Option 1: TorchTune QAT Integration +=================================== + +TorchAO's QAT support is integrated into TorchTune's distributed fine-tuning recipe. +Instead of the following command, which applies full distributed fine-tuning without QAT: + +.. code:: + + # Regular fine-tuning without QAT + tune run --nnodes 1 --nproc_per_node 4 full_finetune_distributed --config llama3_2/3B_full batch_size=16 + +Users can run the following equivalent command instead. Note that specifying the quantizer +is optional: + +.. code:: + + # Fine-tuning with QAT, by default: + # activations are fake quantized to asymmetric per token int8 + # weights are fake quantized to symmetric per group int4 + # configurable through "quantizer._component_" in the command + tune run --nnodes 1 --nproc_per_node 4 qat_distributed --config llama3_2/3B_qat_full batch_size=16 + +After fine-tuning, users can quantize and evaluate the resulting model as follows. +This is the same whether or not QAT was used during the fine-tuning process: + +.. code:: + + # Quantize model weights to int4 + tune run quantize --config quantization \ + model._component_=torchtune.models.llama3_2.llama3_2_3b \ + checkpointer._component_=torchtune.training.FullModelHFCheckpointer \ + 'checkpointer.checkpoint_files=[model-00001-of-00002.safetensors,model-00002-of-00002.safetensors]' \ + checkpointer.model_type=LLAMA3 \ + quantizer._component_=torchtune.training.quantization.Int8DynActInt4WeightQuantizer \ + quantizer.groupsize=32 + + # Evaluate the int4 model on hellaswag and wikitext + tune run eleuther_eval --config eleuther_evaluation \ + batch_size=1 \ + 'tasks=[hellaswag, wikitext]' \ + model._component_=torchtune.models.llama3_2.llama3_2_3b \ + checkpointer._component_=torchtune.training.FullModelTorchTuneCheckpointer \ + 'checkpointer.checkpoint_files=[model-00001-of-00002-8da4w.ckpt]' \ + checkpointer.model_type=LLAMA3 \ + tokenizer._component_=torchtune.models.llama3.llama3_tokenizer \ + tokenizer.path=/tmp/Meta-Llama-3-8B-Instruct/original/tokenizer.model \ + quantizer._component_=torchtune.training.quantization.Int8DynActInt4WeightQuantizer \ + quantizer.groupsize=32 + +This should print the following after fine-tuning: + +.. code:: + + | Tasks |Version|Filter|n-shot| Metric | |Value | |Stderr| + |---------|------:|------|------|--------|---|-----:|---|-----:| + |hellaswag| 1|none |None |acc |↑ |0.5021|± |0.0050| + | | |none |None |acc_norm|↑ |0.6797|± |0.0047| + + | Tasks |Version|Filter|n-shot| Metric | | Value | |Stderr| + |--------|------:|------|------|---------------|---|------:|---|------| + |wikitext| 2|none |None |bits_per_byte |↓ | 0.6965|± | N/A| + | | |none |None |byte_perplexity|↓ | 1.6206|± | N/A| + | | |none |None |word_perplexity|↓ |13.2199|± | N/A| + +You can compare these values with and without QAT to see how much QAT helped mitigate quantization degradation! +For example, when fine-tuning Llama-3.2-3B on the +`OpenAssistant Conversations (OASST1) `__ +dataset, we find that the quantized model achieved 3.4% higher accuracy +with QAT than without, recovering 69.8% of the overall accuracy degradation +from quantization: + +.. image:: ../static/qat_eval.png + +In addition to vanilla QAT as in the above example, TorchAO's QAT can also be composed with LoRA to yield a `1.89x training speedup `__ and lower memory usage by 36.1%. This is implemented in TorchTune's `QAT + LoRA fine-tuning recipe `__, which can be run using the following command: + +.. code:: + + # Fine-tuning with QAT + LoRA + tune run --nnodes 1 --nproc_per_node 4 qat_lora_finetune_distributed --config llama3_2/3B_qat_lora batch_size=16 + +For more details about how QAT is set up in TorchTune, please refer to `this tutorial `__. + + +Option 2: Axolotl QAT Integration +================================= + +Axolotl also recently added a QAT fine-tuning recipe that leverages TorchAO's QAT support. +To get started, try fine-tuning Llama-3.2-3B with QAT using the following command: + +.. code:: + + axolotl train examples/llama-3/3b-qat-fsdp2.yaml + # once training is complete, perform the quantization step + + axolotl quantize examples/llama-3/3b-qat-fsdp2.yaml + # you should now have a quantized model saved in ./outputs/qat_out/quatized + +Please refer to the `Axolotl QAT documentation `__ for full details. + + +Option 3: TorchAO QAT API +========================= + +If you prefer to use a different training framework or your own custom training loop, +you can call TorchAO's QAT APIs directly to transform the model before fine-tuning. +These APIs are what the TorchTune and Axolotl QAT integrations call under the hood. + +In this example, we will fine-tune a mini version of Llama3 on a single GPU: + +.. code:: py + + import torch + from torchtune.models.llama3 import llama3 + + # Set up a smaller version of llama3 to fit in a single A100 GPU + # For smaller GPUs, adjust the model attributes accordingly + def get_model(): + return llama3( + vocab_size=4096, + num_layers=16, + num_heads=16, + num_kv_heads=4, + embed_dim=2048, + max_seq_len=2048, + ).cuda() + + # Example training loop + def train_loop(m: torch.nn.Module): + optimizer = torch.optim.SGD(m.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-5) + loss_fn = torch.nn.CrossEntropyLoss() + for i in range(10): + example = torch.randint(0, 4096, (2, 16)).cuda() + target = torch.randn((2, 16, 4096)).cuda() + output = m(example) + loss = loss_fn(output, target) + loss.backward() + optimizer.step() + optimizer.zero_grad() + +Next, run the prepare step, which fake quantizes the model. In this example, +we use int8 per token dynamic activations and int4 symmetric per group weights +as our quantization scheme. Note that although we are targeting lower integer +precisions, training still performs arithmetic in higher float precision (float32) +because we are not actually casting the fake quantized values. + +.. code:: py + + from torchao.quantization import ( + quantize_, + ) + from torchao.quantization.qat import ( + FakeQuantizeConfig, + IntXQuantizationAwareTrainingConfig, + ) + model = get_model() + + # prepare: insert fake quantization ops + # swaps `torch.nn.Linear` with `FakeQuantizedLinear` + activation_config = FakeQuantizeConfig(torch.int8, "per_token", is_symmetric=False) + weight_config = FakeQuantizeConfig(torch.int4, group_size=32) + qat_config = IntXQuantizationAwareTrainingConfig(activation_config, weight_config) + quantize_(model, qat_config) + + # fine-tune + train_loop(model) + +After fine-tuning, we end up with a model in the original high precision. +This fine-tuned model has the exact same structure as the original model. +The only difference is the QAT fine-tuned model has weights that are more +attuned to quantization, which will be beneficial later during inference. +The next step is to actually quantize the model: + +.. code:: py + + from torchao.quantization import ( + Int8DynamicActivationInt4WeightConfig, + ) + from torchao.quantization.qat import ( + FromIntXQuantizationAwareTrainingConfig, + ) + + # convert: transform fake quantization ops into actual quantized ops + # swap `FakeQuantizedLinear` back to `torch.nn.Linear` and inserts + # quantized activation and weight tensor subclasses + quantize_(model, FromIntXQuantizationAwareTrainingConfig()) + quantize_(model, Int8DynamicActivationInt4WeightConfig(group_size=32)) + +Now our model is ready for serving, and will typically have higher quantized +accuracy than if we did not apply the prepare step (fake quantization) during +fine-tuning. + +For full details of using TorchAO's QAT API, please refer to the `QAT README `__. + +.. raw:: html + + + + +Quantized Low-Rank Adaptation (QLoRA) +##################################### + +(Coming soon!) + + +Float8 Quantized Fine-tuning +############################ + +(Coming soon!) diff --git a/docs/source/index.rst b/docs/source/index.rst index d4d8580863..aac72590fd 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -37,12 +37,14 @@ for an overall introduction to the library and recent highlight and updates. :maxdepth: 1 :caption: Eager Quantization Tutorials + pretraining + finetuning + serving + torchao_vllm_integration serialization + static_quantization subclass_basic subclass_advanced - static_quantization - pretraining - torchao_vllm_integration .. toctree:: :glob: diff --git a/docs/source/pretraining.rst b/docs/source/pretraining.rst index 441b8c4a4b..da9659b9a0 100644 --- a/docs/source/pretraining.rst +++ b/docs/source/pretraining.rst @@ -1,21 +1,29 @@ -Pretraining with float8 +(Part 1) Pre-training with float8 --------------------------------- -Pretraining with float8 using torchao can provide `up to 1.5x speedups `__ on 512 GPU clusters, +TorchAO provides an end-to-end pre-training, fine-tuning, and serving +model optimization flow by leveraging our quantization and sparsity +techniques integrated into our partner frameworks. This is part 1 of 3 +such tutorials showcasing this end-to-end flow, focusing on the +pre-training step. + +.. image:: ../static/e2e_flow_part1.png + +Pre-training with float8 using torchao can provide `up to 1.5x speedups `__ on 512 GPU clusters, and up to `1.34-1.43x speedups `__ on 2K H200 clusters with the latest `torchao.float8` rowwise recipe. -In this tutorial, we will show 2 ways to use the **torchao.float8** recipes for pretraining: +In this tutorial, we will show 2 ways to use the **torchao.float8** recipes for pre-training: -1. :ref:`Pretraining with torchtitan`, the offical PyTorch pretraining framework with native torchao integration. -2. :ref:`Pretraining with torchao directly`, to integrate torchao's float8 training recipes into your own pretraining code. +1. :ref:`Pre-training with torchtitan`, the offical PyTorch pre-training framework with native torchao integration. +2. :ref:`Pre-training with torchao directly`, to integrate torchao's float8 training recipes into your own pre-training code. -Pretraining with torchtitan +Pre-training with torchtitan ########################### -In this tutorial we'll pretrain Llama3 8b using torchtitan with torchao's float8 training recipes: rowwise scaling and tensorwise scaling. +In this tutorial we'll pre-train Llama3-8B using torchtitan with torchao's float8 training recipes: rowwise scaling and tensorwise scaling. -`Torchtitan `__ is PyTorch's official pretraining framework that is natively integrated with torchao, and supports +`Torchtitan `__ is PyTorch's official pre-training framework that is natively integrated with torchao, and supports several popular flagship models with common forms of parallelism, float8 training, distributed checkpointing and more. See the torchtitan `docs `__ for additional details. @@ -29,12 +37,12 @@ Prerequisites 2. `Install torchao `__. 3. `Install torchtitan `__, including the "downloading a tokenizer" step. -You're now ready to start a pretraining job using one of the recipes below! +You're now ready to start a pre-training job using one of the recipes below! Rowwise scaling =============== -Run the following command from torchtitan root directory to launch a Llama3 8b training job on 8 GPUs with float8 rowwise training: +Run the following command from torchtitan root directory to launch a Llama3-8B training job on 8 GPUs with float8 rowwise training: .. code:: console @@ -104,10 +112,10 @@ Picking a recipe The higher throughput of tensorwise scaling comes at the cost of slightly higher quantization error (i.e., reduced numerical integrity vs bfloat16) compared to rowwise scaling. This is because rowwise scaling using a more granular scaling factor (per row, instead of per tensor), which limits the impact of outliers that can cause underflow during scaling. -Below you can see the loss curves comparing bfloat16, float8 tensorwise, and float8 rowwise training for training Llama3 8b on 8xH100 GPUs: +Below you can see the loss curves comparing bfloat16, float8 tensorwise, and float8 rowwise training for training Llama3-8B on 8xH100 GPUs: .. image:: ../static/fp8-loss-curves.png - :alt: Loss curves for training Llama3 8b on 8xH100s with torchtitan using bfloat16, float8 tensorwise, and float8 rowwise training. + :alt: Loss curves for training Llama3-8B on 8xH100s with torchtitan using bfloat16, float8 tensorwise, and float8 rowwise training. Important notes @@ -117,12 +125,12 @@ Important notes * You must use :code:`--training.compile` to achieve high performance. torchao float8 training recipes are built natively on top of :code:`torch.compile`, so it will work out of the box! -Pretraining with torchao directly +Pre-training with torchao directly ################################# -In this tutorial we'll pretrain a toy model using torchao APIs directly. +In this tutorial we'll pre-train a toy model using torchao APIs directly. -You can use this workflow to integrate torchao into your own custom pretraining code directly. +You can use this workflow to integrate torchao into your own custom pre-training code directly. Prerequisites ================ @@ -200,3 +208,8 @@ Below is a code snippet showing how to use it: 'model_state_dict': m.state_dict(), 'optimizer_state_dict': optimizer.state_dict(), }, 'checkpoint.pth') + + +After pre-training your model, you can optionally fine-tune it to more domain-specific datasets +and adapt it for eventual quantization during serving. In the `next part `__ of +this tutorial, we will explore a few model optimization options during the fine-tuning step. diff --git a/docs/source/serving.rst b/docs/source/serving.rst new file mode 100644 index 0000000000..cb61b159c4 --- /dev/null +++ b/docs/source/serving.rst @@ -0,0 +1,12 @@ +(Part 3) Serving on vLLM, SGLang, ExecuTorch +------------------------------------------------ + +TorchAO provides an end-to-end pre-training, fine-tuning, and serving +model optimization flow by leveraging our quantization and sparsity +techniques integrated into our partner frameworks. This is part 3 of 3 +such tutorials showcasing this end-to-end flow, focusing on the +serving step. + +.. image:: ../static/e2e_flow_part3.png + +(Coming soon!) diff --git a/docs/static/e2e_flow_part1.png b/docs/static/e2e_flow_part1.png new file mode 100644 index 0000000000000000000000000000000000000000..bb996f7e2fcf039b2fb9efdc517aa01f1db1338f GIT binary patch literal 258271 zcmeFZXH-*b*Def*im*l4G^GnF(xpqNN|UZ4y($9IrT3;N2m&h7t5hKnYCu9$s?s5n zNN57md+%`W#oqgUp7DJ@%pPZq^Xp{{Oi0$cbC)@khyo%91YZR@-Q19{h zHVH*&;OpWzRyOv)*AzNG7;Zhd{U__|i&W3T7&vu`9Voxt>*bPj(v}S)L)v6tUovL= zcpa%1Zn(=w+|%cHP}jb^bhJC_a_o~jUF*R}u)$aNATivA=ncoWxaKqaW!E@zi|n6mm-M<$iq}7E6$^z%Owfc_e;h8XkN13LQb9yj(Khd$KjwBT9lA zZPu8{Q?l=GkNE1;J$Qw^Rlxq7{I*eMQv%mee$yAJU+Y3j#ayvp zZj)2AXICAFA6vnWJifJS|55btJls2$=ZelHp^m}K_z;mltyc`%EL!)TcPBpNK2H%u>$8bsdvDW*`N=M>dOs*)`o(e zE1`K5XQ@M}*e_!OZ{AO(q7!`_Z4loa!C3K@^HEHRo-7ZQ^0PzjJw30NJlEa7*70dd zGfKEis&<4kHpH!!jmtf#YG6LM^Zck$uUPtRaP*A=_IH}(ERhP=Nl6|wM@B2W3%*Gc 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z1K*)iOg}d!bmF~nqd=v?>W9E|t#NJr_ISgZLNte-wt6-fM|yFR*7o)Q<=17-1R9uy z-)T%Zztwp@39qR~o4B;5B18pAIsi#pRI0O=^qS1&cmGj$Z^bg&d*f)1O|rI-gcaq- z+~^2=j`wS<+r`fHO`pd{eWkC!q0);n?q^WCS|7E%fy{$i9^7hRKsV2bvFf{;ha$IY zP_EkB2&kKCSC7uw!ywH+hz0vuJyibaoB9ja9%(*K=vm7jVX)wcu@_?RedwRFA_gR3g%4SdeZz zu*cmzvWHX80X9VB4-=yNDYQ1zUQ652yJRjkem>pBPIwMq2N>-5~Q;m47q)yUuv+B7- z?-!Uf^-zJoj|W@LN$dCM0g45aUlfbaI%+n6E@0rrByRtXXw4=XGn6RYkR`6w@tu4g zGVr}58ANR4?!iePin$C6X&a=^Jx)M^vMuQ~V*(jn$ z2j;M~?Zny4fKiHvme&UrnwA%`6y_~ALMnV(&bML(kjec9pa8O20NKP+7BkFOWaS~i Pz{h@*qwPf-=J9_3lvlZB literal 0 HcmV?d00001 From 101c03903577256b6463154c87e10d5094ac2482 Mon Sep 17 00:00:00 2001 From: Daniel Vega-Myhre Date: Wed, 18 Jun 2025 11:09:45 -0700 Subject: [PATCH 138/165] [float8 moe training] make using triton kernels for per-group scaling configurable (#2405) * improve moe training benchmarking * lint * readability improvements * grab use_triton for args instead of class attribute * add comment --- .../benchmarks/benchmark_scaled_grouped_mm.py | 72 +++++++++++++------ .../moe_training/conversion_utils.py | 11 ++- .../moe_training/scaled_grouped_mm.py | 48 +++++++++++-- torchao/prototype/moe_training/tensor.py | 22 +++++- 4 files changed, 122 insertions(+), 31 deletions(-) diff --git a/torchao/prototype/moe_training/benchmarks/benchmark_scaled_grouped_mm.py b/torchao/prototype/moe_training/benchmarks/benchmark_scaled_grouped_mm.py index af1a652fc0..a347763fe6 100644 --- a/torchao/prototype/moe_training/benchmarks/benchmark_scaled_grouped_mm.py +++ b/torchao/prototype/moe_training/benchmarks/benchmark_scaled_grouped_mm.py @@ -4,7 +4,7 @@ # This source code is licensed under the BSD 3-Clause license found in the # LICENSE file in the root directory of this source tree. # this benchmarking script is a modified version of the original script from: https://github.com/drisspg/transformer_nuggets/blob/main/transformer_nuggets/utils/benchmark.py - +import argparse import itertools import time from dataclasses import dataclass @@ -31,7 +31,9 @@ class ExperimentConfig: @dataclass(frozen=True) class ExperimentResult: - time_us: float + torch_time_us: float + triton_time_us: bool + triton_speedup: float @dataclass(frozen=True) @@ -41,12 +43,14 @@ class Experiment: def get_configs() -> List[ExperimentConfig]: - A_shapes = [(2**8, 4096), (2**12, 4096), (2**16, 4096)] - B_shapes = [(4, 4096, 4096), (8, 4096, 4096), (16, 4096, 4096)] + A_shapes = [(2**8, 8192), (2**12, 8192), (2**16, 8192)] + B_shapes = [(4, 8192, 8192), (8, 8192, 8192), (16, 8192, 8192)] high_precision_dtypes = [torch.bfloat16] configs = [] for A_shape, B_shape, high_precision_dtype in itertools.product( - A_shapes, B_shapes, high_precision_dtypes + A_shapes, + B_shapes, + high_precision_dtypes, ): configs.append( ExperimentConfig( @@ -58,7 +62,9 @@ def get_configs() -> List[ExperimentConfig]: return configs -def run_experiment(config: ExperimentConfig) -> ExperimentResult: +def run_experiment( + config: ExperimentConfig, args: argparse.Namespace +) -> ExperimentResult: # define test inputs A = torch.randn( *config.A_shape, @@ -92,26 +98,46 @@ def warmup(func, *args, **kwargs): for _ in range(10): func(*args, **kwargs) - def forward_backward(A, B_t, offs): - out = _scaled_grouped_mm(A, B_t, offs=offs, out_dtype=torch.bfloat16) + def forward_backward(A, B_t, offs, use_triton=True): + out = _scaled_grouped_mm( + A, + B_t, + offs=offs, + out_dtype=torch.bfloat16, + use_triton_for_per_group_scales=use_triton, + ) out.sum().backward() + torch.cuda.synchronize() - # bench triton - warmup(forward_backward, A, B_t, offs) + # benchmark torch + torch_func = torch.compile(forward_backward) if args.compile else forward_backward + warmup(torch_func, A, B_t, offs, use_triton=False) start_time_ns = time.perf_counter_ns() - forward_backward(A, B_t, offs) - time_ns = time.perf_counter_ns() - start_time_ns - time_us = time_ns / 1e3 + torch_func(A, B_t, offs, use_triton=False) + torch_time_ns = time.perf_counter_ns() - start_time_ns + torch_time_us = torch_time_ns / 1e3 - return ExperimentResult(time_us=time_us) + # benchmark triton + warmup(forward_backward, A, B_t, offs, use_triton=True) + start_time_ns = time.perf_counter_ns() + forward_backward(A, B_t, offs, use_triton=True) + triton_time_ns = time.perf_counter_ns() - start_time_ns + triton_time_us = triton_time_ns / 1e3 + + return ExperimentResult( + torch_time_us=round(torch_time_us, 3), + triton_time_us=round(triton_time_us, 3), + triton_speedup=round(torch_time_us / triton_time_us, 3), + ) def print_results(experiments: List[Experiment]): headers = [ "A_shape", "B_shape", - "high_precision_dtype", - "time_us", + "torch_time_us", + "triton_time_us", + "triton_speedup", ] rows = [] for experiment in experiments: @@ -121,19 +147,20 @@ def print_results(experiments: List[Experiment]): [ A_shape, B_shape, - experiment.config.high_precision_dtype, - experiment.result.time_us, + experiment.result.torch_time_us, + experiment.result.triton_time_us, + experiment.result.triton_speedup, ] ) print(tabulate(rows, headers=headers)) -def main(): +def main(args: argparse.Namespace): torch.random.manual_seed(123) configs = get_configs() results = [] for config in tqdm(configs): - result = run_experiment(config) + result = run_experiment(config, args) results.append(Experiment(config=config, result=result)) # Use Tabulate to print results @@ -141,4 +168,7 @@ def main(): if __name__ == "__main__": - main() + arg_parser = argparse.ArgumentParser() + arg_parser.add_argument("--compile", action="store_true") + args = arg_parser.parse_args() + main(args) diff --git a/torchao/prototype/moe_training/conversion_utils.py b/torchao/prototype/moe_training/conversion_utils.py index 928af1cf2e..4d65303b89 100644 --- a/torchao/prototype/moe_training/conversion_utils.py +++ b/torchao/prototype/moe_training/conversion_utils.py @@ -28,7 +28,8 @@ class MoETrainingConfig(AOBaseConfig): For all other ops, ScaledGroupedMMTensor behaves like a regular torch.Tensor. """ - pass + # temporary config flag for testing/benchmarking, will remove before graduating out of prototype + use_triton_for_per_group_scales: bool = True @register_quantize_module_handler(MoETrainingConfig) @@ -46,7 +47,7 @@ def _moe_training_transform( Returns: nn.Module: The modified module with swapped parameters. """ - out = _swap_params(module) + out = _swap_params(module, config=config) return out @@ -54,6 +55,7 @@ def _swap_params( module: nn.Module, *, module_filter_fn: Optional[Callable[[nn.Module, str], bool]] = None, + config: Optional[MoETrainingConfig] = None, ) -> nn.Module: """ Recurses through the nn.Module, recursively swapping the data tensor of @@ -69,6 +71,7 @@ def _swap_params( Returns: nn.Module: The modified module with swapped linear layers. """ + use_triton = config.use_triton_for_per_group_scales if config is not None else False if isinstance(module, nn.Parameter) and ( module_filter_fn is None or module_filter_fn(module, "") ): @@ -77,7 +80,9 @@ def _swap_params( f"Does not support a root nn.Parameter with children: {module}" ) if not isinstance(module.data, ScaledGroupedMMTensor): - new_data = ScaledGroupedMMTensor(module.data) + new_data = ScaledGroupedMMTensor( + module.data, use_triton_for_per_group_scales=use_triton + ) return nn.Parameter(new_data, requires_grad=module.requires_grad) return module diff --git a/torchao/prototype/moe_training/scaled_grouped_mm.py b/torchao/prototype/moe_training/scaled_grouped_mm.py index d3aaf615db..f7d470e556 100644 --- a/torchao/prototype/moe_training/scaled_grouped_mm.py +++ b/torchao/prototype/moe_training/scaled_grouped_mm.py @@ -14,7 +14,11 @@ triton_fp8_col_major_jagged_colwise_scales, triton_fp8_row_major_jagged_rowwise_scales, ) -from torchao.prototype.moe_training.utils import _is_column_major +from torchao.prototype.moe_training.utils import ( + _is_column_major, + _to_2d_jagged_float8_tensor_colwise, + _to_2d_jagged_float8_tensor_rowwise, +) def _scaled_grouped_mm( @@ -22,6 +26,7 @@ def _scaled_grouped_mm( B_t: torch.Tensor, offs: torch.Tensor, out_dtype: Optional[torch.dtype] = torch.bfloat16, + use_triton_for_per_group_scales: bool = True, ) -> torch.Tensor: """ This function performs dynamic float8 quantization with row-wise scaling @@ -34,6 +39,7 @@ def _scaled_grouped_mm( and in column-major memory layout. offs (int32 torch.Tensor): The offsets to use to mark the starting index of each group along dim0 of the A tensor. out_dtype (Optional[torch.dtype]): The dtype of the output tensor. Currently only torch.bfloat16 is supported. + use_triton_for_per_group_scales (bool): Whether to use custom triton kernels to compute per-group scales. Default is True. """ return _Float8GroupedMM.apply( A, @@ -53,6 +59,7 @@ def forward( B_t: torch.Tensor, offs: torch.Tensor, out_dtype: Optional[torch.dtype] = torch.bfloat16, + use_triton_for_per_group_scales: bool = True, ) -> torch.Tensor: # torchao _scaled_grouped_mm only supports A=2D, B=3D. assert A.ndim == 2, "A must be 2D" @@ -136,9 +143,16 @@ def forward( # Store what we need for backward. ctx.save_for_backward(A, B_fp8_col_major, B_scales, offs) ctx.out_dtype = out_dtype + ctx.use_triton_for_per_group_scales = use_triton_for_per_group_scales # Perform scaled grouped GEMM and return result. # output shape: scaled grouped mm of (M,K) @ (B,K,N) = (M,N) + assert not _is_column_major(A_fp8_row_major), ( + "A must be row-major for output = A @ B" + ) + assert _is_column_major(B_t_fp8_col_major), ( + "B must be column-major for output = A @ B" + ) return torch._scaled_grouped_mm( A_fp8_row_major, B_t_fp8_col_major, @@ -153,6 +167,7 @@ def forward( def backward(ctx, grad_output: torch.Tensor): A, B_fp8_col_major, B_scales, offs = ctx.saved_tensors out_dtype = ctx.out_dtype + use_triton_for_per_group_scales = ctx.use_triton_for_per_group_scales # Convert grad_output to float8, row-major for left operand of grouped GEMM # needed for grad_A: grad_output @ B @@ -175,6 +190,12 @@ def backward(ctx, grad_output: torch.Tensor): # # grad_A = grad_output @ B # grad_A = scaled grouped mm of (M,N) @ (B,N,K) = (M,K) + assert not _is_column_major(grad_output_fp8_row_major), ( + "grad_output must be row-major for grad_A = grad_output @ B" + ) + assert _is_column_major(B_fp8_col_major), ( + "B must be column-major for grad_A = grad_output @ B" + ) grad_A = torch._scaled_grouped_mm( grad_output_fp8_row_major, B_fp8_col_major, @@ -195,25 +216,42 @@ def backward(ctx, grad_output: torch.Tensor): # grad_B is a special case. both operands of the grouped gemm will be 2D with offsets determing the "groups." # Compute scales for grad_output_t and A, which are both 2D tensors with offsets which define the "jagged" groups. + per_group_rowwise_scale_func = ( + triton_fp8_row_major_jagged_rowwise_scales + if use_triton_for_per_group_scales + else _to_2d_jagged_float8_tensor_rowwise + ) + per_group_colwise_scale_func = ( + triton_fp8_col_major_jagged_colwise_scales + if use_triton_for_per_group_scales + else _to_2d_jagged_float8_tensor_colwise + ) + grad_output_t_fp8_row_major, grad_output_t_scales = ( - triton_fp8_row_major_jagged_rowwise_scales( + per_group_rowwise_scale_func( grad_output_t_row_major, offs, - output_dtype=torch.float8_e4m3fn, + torch.float8_e4m3fn, round_scales_to_power_of_2=True, ) ) - A_fp8_col_major, A_scales = triton_fp8_col_major_jagged_colwise_scales( + A_fp8_col_major, A_scales = per_group_colwise_scale_func( A_col_major, offs, - output_dtype=torch.float8_e4m3fn, + torch.float8_e4m3fn, round_scales_to_power_of_2=True, ) # Compute grad_B = grad_output_t @ A. # grad_B = grad_output_t @ A # grad_B = (N,M) @ (M,K) = (N,K) + assert not _is_column_major(grad_output_t_fp8_row_major), ( + "grad_output_t must be row-major for grad_B = grad_output_t @ A" + ) + assert _is_column_major(A_fp8_col_major), ( + "A must be column-major for grad_B = grad_output_t @ A" + ) grad_B = torch._scaled_grouped_mm( grad_output_t_fp8_row_major, A_fp8_col_major, diff --git a/torchao/prototype/moe_training/tensor.py b/torchao/prototype/moe_training/tensor.py index 2a929d3b76..8d7a8f815b 100644 --- a/torchao/prototype/moe_training/tensor.py +++ b/torchao/prototype/moe_training/tensor.py @@ -12,9 +12,16 @@ class ScaledGroupedMMTensor(torch.Tensor): grouped_mm_func_name = "_grouped_mm" offs_arg_name = "offs" + use_triton_for_per_group_scales = True - def __init__(self, data: torch.Tensor): + def __init__( + self, data: torch.Tensor, use_triton_for_per_group_scales: bool = True + ): self._data = data + self._use_triton_for_per_group_scales = use_triton_for_per_group_scales + + def __repr__(self): + return f"ScaledGroupedMMTensor(use_triton_for_per_group_scales={self._use_triton_for_per_group_scales}, {self._data})" @classmethod def __torch_function__(cls, func, types, args, kwargs={}): @@ -31,5 +38,16 @@ def __torch_function__(cls, func, types, args, kwargs={}): B_is_3d = B.dim() == 3 has_offs = kwargs.get(cls.offs_arg_name) is not None if A_is_2d and B_is_3d and has_offs: - return _scaled_grouped_mm(*args, **kwargs) + # prefer to use B to check use_triton, as that will be the weight/nn.Parameter + # that is converted to ScaledGroupedMMTensor + use_triton = ( + B._use_triton_for_per_group_scales + if isinstance(B, cls) + else A._use_triton_for_per_group_scales + ) + return _scaled_grouped_mm( + *args, + use_triton_for_per_group_scales=use_triton, + **kwargs, + ) return super().__torch_function__(func, types, args, kwargs) From 809af2e69ad9a37d5a9d099c0e1ff7b714d86cbf Mon Sep 17 00:00:00 2001 From: Gasoonjia Date: Wed, 18 Jun 2025 15:04:38 -0700 Subject: [PATCH 139/165] Replace debug handle with `from_node` to trace operator transformation (#2339) Replace debug handle with `from_node` to trace operator transformation (#2339) Summary: X-link: https://github.com/pytorch/executorch/pull/11532 Pull Request resolved: https://github.com/pytorch/ao/pull/2339 This diff replace the debug handle with `from_node` infrastructure, which is a first class citizen in exported program and used to trace the node-level transformation by recording every ancestor of given node. N6213836 is a demonstration of how `from_node` infra records the node transformation after unlifting and re-exporting exported graph. For simplify the progress, we are trying to reuse the debug handle infrastructure by generating debug handle with hashing their greatest ancestor's node. After this change user no longer need to invoke `generate_numeric_debug_handle` for debugging. Also the original pipeline will still work under current scenario. Reviewed By: jerryzh168 Differential Revision: D76168997 --- .../pt2e/test_numeric_debugger.py | 75 ++++++++++--------- test/quantization/pt2e/test_quantize_pt2e.py | 5 +- torchao/quantization/pt2e/__init__.py | 2 + .../quantization/pt2e/_numeric_debugger.py | 75 ++++++++++++++++--- torchao/quantization/pt2e/convert.py | 43 +++++++---- torchao/quantization/pt2e/prepare.py | 14 ++-- torchao/testing/pt2e/utils.py | 35 +++------ 7 files changed, 153 insertions(+), 96 deletions(-) diff --git a/test/quantization/pt2e/test_numeric_debugger.py b/test/quantization/pt2e/test_numeric_debugger.py index 5f565767aa..040cd1edcf 100644 --- a/test/quantization/pt2e/test_numeric_debugger.py +++ b/test/quantization/pt2e/test_numeric_debugger.py @@ -15,7 +15,6 @@ from torch.testing._internal.common_utils import IS_WINDOWS, run_tests from torchao.quantization.pt2e import ( - generate_numeric_debug_handle, prepare_for_propagation_comparison, ) from torchao.testing.pt2e.utils import PT2ENumericDebuggerTestCase @@ -35,20 +34,21 @@ def test_simple(self): m = TestHelperModules.Conv2dThenConv1d() example_inputs = m.example_inputs() ep = export_for_training(m, example_inputs, strict=True) - generate_numeric_debug_handle(ep) - self._assert_each_node_has_debug_handle(ep) - debug_handle_map = self._extract_debug_handles(ep) + m = ep.module() + self._assert_each_node_has_debug_handle(m) + debug_handle_map = self._extract_debug_handles(m) self.assertEqual(len(set(debug_handle_map.values())), len(debug_handle_map)) + @unittest.skip("debug flow not working on model with conditional control flow") def test_control_flow(self): m = TestHelperModules.ControlFlow() example_inputs = m.example_inputs() ep = export_for_training(m, example_inputs, strict=True) - generate_numeric_debug_handle(ep) + m = ep.module() - self._assert_each_node_has_debug_handle(ep) - debug_handle_map = self._extract_debug_handles(ep) + self._assert_each_node_has_debug_handle(m) + debug_handle_map = self._extract_debug_handles(m) self.assertEqual(len(set(debug_handle_map.values())), len(debug_handle_map)) @@ -56,13 +56,13 @@ def test_copy_preserve_handle(self): m = TestHelperModules.Conv2dThenConv1d() example_inputs = m.example_inputs() ep = torch.export.export(m, example_inputs, strict=True) - generate_numeric_debug_handle(ep) + m = ep.module() - self._assert_each_node_has_debug_handle(ep) - debug_handle_map_ref = self._extract_debug_handles(ep) + self._assert_each_node_has_debug_handle(m) + debug_handle_map_ref = self._extract_debug_handles(m) ep_copy = copy.copy(ep) - debug_handle_map = self._extract_debug_handles(ep_copy) + debug_handle_map = self._extract_debug_handles(ep_copy.module()) self._assert_each_node_has_debug_handle(ep) self.assertEqual(debug_handle_map, debug_handle_map_ref) @@ -71,13 +71,12 @@ def test_deepcopy_preserve_handle(self): m = TestHelperModules.Conv2dThenConv1d() example_inputs = m.example_inputs() ep = torch.export.export(m, example_inputs, strict=True) - generate_numeric_debug_handle(ep) - debug_handle_map_ref = self._extract_debug_handles(ep) + debug_handle_map_ref = self._extract_debug_handles(ep.module()) ep_copy = copy.deepcopy(ep) - debug_handle_map = self._extract_debug_handles(ep_copy) + debug_handle_map = self._extract_debug_handles(ep_copy.module()) - self._assert_each_node_has_debug_handle(ep) + self._assert_each_node_has_debug_handle(ep.module()) self.assertEqual(debug_handle_map, debug_handle_map_ref) @unittest.skip( @@ -87,16 +86,16 @@ def test_re_export_preserve_handle(self): m = TestHelperModules.Conv2dThenConv1d() example_inputs = m.example_inputs() ep = export_for_training(m, example_inputs, strict=True) - generate_numeric_debug_handle(ep) m = ep.module() - self._assert_each_node_has_debug_handle(ep) - debug_handle_map_ref = self._extract_debug_handles(ep) + self._assert_each_node_has_debug_handle(m) + debug_handle_map_ref = self._extract_debug_handles(m) ep_reexport = export_for_training(m, example_inputs, strict=True) + m_reexport = ep_reexport.module() - self._assert_each_node_has_debug_handle(ep_reexport) - debug_handle_map = self._extract_debug_handles(ep_reexport) + self._assert_each_node_has_debug_handle(m_reexport) + debug_handle_map = self._extract_debug_handles(m_reexport) self.assertEqual(debug_handle_map, debug_handle_map_ref) @@ -107,16 +106,17 @@ def test_run_decompositions_same_handle_id(self): m = TestHelperModules.Conv2dThenConv1d() example_inputs = m.example_inputs() ep = export_for_training(m, example_inputs, strict=True) - generate_numeric_debug_handle(ep) + m = ep.module() - self._assert_each_node_has_debug_handle(ep) - debug_handle_map_ref = self._extract_debug_handles(ep) + self._assert_each_node_has_debug_handle(m) + debug_handle_map_ref = self._extract_debug_handles(m) ep_copy = copy.copy(ep) ep_copy = ep_copy.run_decompositions() + m_decomposed = ep_copy.module() - self._assert_each_node_has_debug_handle(ep_copy) - debug_handle_map = self._extract_debug_handles(ep_copy) + self._assert_each_node_has_debug_handle(m_decomposed) + debug_handle_map = self._extract_debug_handles(m_decomposed) # checking the map still has the same ids, the node may change self.assertEqual( @@ -135,18 +135,19 @@ def test_run_decompositions_map_handle_to_new_nodes(self): for m in test_models: example_inputs = m.example_inputs() ep = export_for_training(m, example_inputs, strict=True) - generate_numeric_debug_handle(ep) + m = ep.module() - self._assert_each_node_has_debug_handle(ep) + self._assert_each_node_has_debug_handle(m) pre_decomp_to_debug_handle_map_ref = ( - self._extract_debug_handles_with_prev_decomp_op(ep) + self._extract_debug_handles_with_prev_decomp_op(m) ) ep_copy = copy.copy(ep) ep_copy = ep_copy.run_decompositions() - self._assert_each_node_has_debug_handle(ep_copy) + m_decomposed = ep_copy.module() + self._assert_each_node_has_debug_handle(m_decomposed) pre_decomp_to_debug_handle_map = ( - self._extract_debug_handles_with_prev_decomp_op(ep_copy) + self._extract_debug_handles_with_prev_decomp_op(m_decomposed) ) # checking the map still has the same ids, the node may change @@ -158,7 +159,6 @@ def test_prepare_for_propagation_comparison(self): m = TestHelperModules.Conv2dThenConv1d() example_inputs = m.example_inputs() ep = export_for_training(m, example_inputs, strict=True) - generate_numeric_debug_handle(ep) m = ep.module() m_logger = prepare_for_propagation_comparison(m) ref = m(*example_inputs) @@ -175,9 +175,10 @@ def test_added_node_gets_unique_id(self) -> None: m = TestHelperModules.Conv2dThenConv1d() example_inputs = m.example_inputs() ep = export_for_training(m, example_inputs, strict=True) - generate_numeric_debug_handle(ep) - ref_handles = self._extract_debug_handles(ep) + + ref_handles = self._extract_debug_handles(ep.module()) ref_counter = Counter(ref_handles.values()) + for k, v in ref_counter.items(): self.assertEqual( v, @@ -199,10 +200,10 @@ def test_added_node_gets_unique_id(self) -> None: # Regenerate handles, make sure only the new relu node has a new id, and # it doesn't clash with any of the existing ids. - generate_numeric_debug_handle(ep) - self._assert_each_node_has_debug_handle(ep) - handles_after_modification = self._extract_debug_handles(ep) + m = ep.module() + self._assert_each_node_has_debug_handle(m) + handles_after_modification = self._extract_debug_handles(m) handles_counter = Counter(handles_after_modification.values()) for name, handle in ref_handles.items(): self.assertIn(name, handles_after_modification) @@ -219,7 +220,7 @@ def test_added_node_gets_unique_id(self) -> None: # Check for relu specifically. Avoid hardcoding the handle id since it # may change with future node ordering changes. - self.assertNotEqual(handles_after_modification["relu_default"], 0) + self.assertNotIn(handles_after_modification["relu_default"], ref_counter) self.assertEqual(handles_counter[handles_after_modification["relu_default"]], 1) diff --git a/test/quantization/pt2e/test_quantize_pt2e.py b/test/quantization/pt2e/test_quantize_pt2e.py index be5a4dc537..19f208a55c 100644 --- a/test/quantization/pt2e/test_quantize_pt2e.py +++ b/test/quantization/pt2e/test_quantize_pt2e.py @@ -36,7 +36,7 @@ ) import torchao -from torchao.quantization.pt2e import ObserverOrFakeQuantize, observer +from torchao.quantization.pt2e import FROM_NODE_KEY, ObserverOrFakeQuantize, observer from torchao.quantization.pt2e.quantize_pt2e import ( convert_pt2e, prepare_pt2e, @@ -1499,7 +1499,8 @@ def forward(self, x): for n in m.graph.nodes: if n.op == "get_attr" and "frozen_param" in n.target: for key in n.meta: - self.assertEqual(n.meta[key], weight_meta[key]) + if key != FROM_NODE_KEY: + self.assertEqual(n.meta[key], weight_meta[key]) def test_save_load(self): """Test save/load a quantized model""" diff --git a/torchao/quantization/pt2e/__init__.py b/torchao/quantization/pt2e/__init__.py index b6b8a728a3..8b6a99337b 100644 --- a/torchao/quantization/pt2e/__init__.py +++ b/torchao/quantization/pt2e/__init__.py @@ -7,6 +7,7 @@ from torchao.quantization.pt2e._numeric_debugger import ( # noqa: F401 CUSTOM_KEY, + FROM_NODE_KEY, NUMERIC_DEBUG_HANDLE_KEY, compare_results, extract_results_from_loggers, @@ -132,6 +133,7 @@ "generate_numeric_debug_handle", "CUSTOM_KEY", "NUMERIC_DEBUG_HANDLE_KEY", + "FROM_NODE_KEY", "prepare_for_propagation_comparison", "extract_results_from_loggers", "compare_results", diff --git a/torchao/quantization/pt2e/_numeric_debugger.py b/torchao/quantization/pt2e/_numeric_debugger.py index 0d66ca71ee..de1e1eee84 100644 --- a/torchao/quantization/pt2e/_numeric_debugger.py +++ b/torchao/quantization/pt2e/_numeric_debugger.py @@ -16,10 +16,16 @@ from torch.fx import GraphModule, Node from torch.nn import functional as F +from torchao.utils import TORCH_VERSION_AT_LEAST_2_6 + +if TORCH_VERSION_AT_LEAST_2_6: + from torch.fx.traceback import NodeSource + from .graph_utils import bfs_trace_with_node_process NUMERIC_DEBUG_HANDLE_KEY = "numeric_debug_handle" CUSTOM_KEY = "custom" +FROM_NODE_KEY = "from_node" log = logging.getLogger(__name__) @@ -78,6 +84,56 @@ def _assign_debug_handle(node: torch.fx.Node) -> None: bfs_trace_with_node_process(ep, _assign_debug_handle) +def _get_greatest_ancestor_node_source(node: Node) -> Optional["NodeSource"]: + if (node_source := node.meta.get(FROM_NODE_KEY)) is None: + return None + + node_source = node_source[-1] + + while len(node_source.from_node) > 0: + node_source = node_source.from_node[-1] + + return node_source + + +def _generate_debug_handle_from_node(node: Node) -> Optional[int]: + """ + Generate a debug handle based on node's oldest ancestor node's name + and graph id, or return None if the node does not need to be traced. + + This is a temporary function for migrating node tracing infra from + using debug handle to node.meta["from_node"]. The infrastructure will + depend on node.meta["from_node"] directly in the future, without the need + of debug handle as intermediate variable. + """ + + if node.op == "placeholder" or node.op == "output": + # placeholder and output nodes don't have debug handle + return None + + if ( + FROM_NODE_KEY not in node.meta + or node.meta[FROM_NODE_KEY] is None + or node.meta[FROM_NODE_KEY][-1].pass_name == "ExportedProgram.module().unlift()" + ): + # This node is not part of the ExportedProgram.module().graph, so it doesn't have a debug handle + return None + + greatest_ancestor_node_source = _get_greatest_ancestor_node_source(node) + + if greatest_ancestor_node_source is None: + # This node is not part of the ExportedProgram.module().graph, so it doesn't have a debug handle + return None + + if greatest_ancestor_node_source.pass_name == "ExportedProgram.module().unlift()": + # uplifted nodes don't have debug handle + return None + + return hash( + greatest_ancestor_node_source.name + str(greatest_ancestor_node_source.graph_id) + ) + + def _detach(x: object) -> object: detached: object = None if isinstance(x, torch.Tensor): @@ -187,23 +243,24 @@ def _insert_logger(model: GraphModule, node: Node, debug_handle: int) -> Node: def prepare_for_propagation_comparison(model: GraphModule) -> GraphModule: - """Add output loggers to node that has numeric_debug_handle + """Add output loggers to unlifted node Args: model (GraphModule): original model Returns: - a model with output loggers for all nodes that has numeric_debug_handle_id + a model with output loggers for all unlifted nodes """ + if not TORCH_VERSION_AT_LEAST_2_6: + log.warning( + "prepare_for_propagation_comparison is only supported for PyTorch 2.6+" + ) + return model + # don't change the original model model = copy.deepcopy(model) for n in model.graph.nodes: - if ( - CUSTOM_KEY not in n.meta - or NUMERIC_DEBUG_HANDLE_KEY not in n.meta[CUSTOM_KEY] - ): - continue - numeric_debug_handle = n.meta[CUSTOM_KEY][NUMERIC_DEBUG_HANDLE_KEY] - _insert_logger(model, n, numeric_debug_handle) + if (numeric_debug_handle := _generate_debug_handle_from_node(n)) is not None: + _insert_logger(model, n, numeric_debug_handle) model.recompile() return model diff --git a/torchao/quantization/pt2e/convert.py b/torchao/quantization/pt2e/convert.py index 2f3e6e0461..99516ac4c3 100644 --- a/torchao/quantization/pt2e/convert.py +++ b/torchao/quantization/pt2e/convert.py @@ -71,8 +71,12 @@ from torch.fx.graph_module import _USER_PRESERVED_ATTRIBUTES_KEY from torch.nn.utils.parametrize import type_before_parametrizations -from torchao.quantization.pt2e import CUSTOM_KEY, NUMERIC_DEBUG_HANDLE_KEY +from torchao.quantization.pt2e import FROM_NODE_KEY from torchao.quantization.pt2e.observer import _is_activation_post_process +from torchao.utils import TORCH_VERSION_AT_LEAST_2_6 + +if TORCH_VERSION_AT_LEAST_2_6: + from torch.fx.traceback import NodeSource, NodeSourceAction __all__ = [ "convert", @@ -182,6 +186,18 @@ def add_dequantize_op_kwargs(dequantize_op, input_node): dequantize_op_kwargs = {"out_dtype": dq_out_dtype} return dequantize_op_kwargs + def add_quantize_dequantize_node_info(qdq_node, original_node): + # propagate from_node info from observer/fake_quant node to quantize/dequantize node + if not TORCH_VERSION_AT_LEAST_2_6: + return + qdq_node.meta[FROM_NODE_KEY] = [ + NodeSource( + original_node, + "replace_observer_with_quantize_dequantize_node", + [NodeSourceAction.CREATE], + ) + ] + if dtype in SUPPORTED_QDTYPES and (not is_dynamic): # TODO: probably should cleanup this condition check, it's hard # to reason about this if and the following elif @@ -254,6 +270,8 @@ def add_dequantize_op_kwargs(dequantize_op, input_node): quantized_node = graph.create_node( node_type, quantize_op, tuple(quantize_op_inputs), {} ) + add_quantize_dequantize_node_info(quantized_node, node) + # use the same qparams from quantize op dq_inputs = [quantized_node] + quantize_op_inputs[1:] dequantized_node = graph.call_function( @@ -263,16 +281,8 @@ def add_dequantize_op_kwargs(dequantize_op, input_node): ) node.replace_all_uses_with(dequantized_node) - # propagate numeric debug handle from observer/fake_quant node to dequantize node - if ( - CUSTOM_KEY in node.meta - and NUMERIC_DEBUG_HANDLE_KEY in node.meta[CUSTOM_KEY] - ): - if CUSTOM_KEY not in dequantized_node.meta: - dequantized_node.meta[CUSTOM_KEY] = {} - dequantized_node.meta[CUSTOM_KEY][NUMERIC_DEBUG_HANDLE_KEY] = node.meta[ - CUSTOM_KEY - ][NUMERIC_DEBUG_HANDLE_KEY] + + add_quantize_dequantize_node_info(dequantized_node, node) graph.erase_node(node) elif is_dynamic: # uint8/int8/fp16 dynamic quantization @@ -353,6 +363,9 @@ def add_dequantize_op_kwargs(dequantize_op, input_node): quantized_node = graph.create_node( node_type, quantize_op, tuple(quantize_op_inputs), {} ) + + add_quantize_dequantize_node_info(quantized_node, node) + # use the same qparams from quantize op dq_inputs = [quantized_node] + quantize_op_inputs[1:] # need to use the tensor variant of this op, since scale and zero_point @@ -366,11 +379,9 @@ def add_dequantize_op_kwargs(dequantize_op, input_node): ) node.replace_all_uses_with(dequantized_node) - # propagate numeric debug handle from observer/fake_quant node to dequantize node - if NUMERIC_DEBUG_HANDLE_KEY in node.meta: - dequantized_node.meta[NUMERIC_DEBUG_HANDLE_KEY] = node.meta[ - NUMERIC_DEBUG_HANDLE_KEY - ] + + add_quantize_dequantize_node_info(dequantized_node, node) + graph.erase_node(node) elif dtype == torch.float16: # Insert to_fp16 -> to_fp32 node diff --git a/torchao/quantization/pt2e/prepare.py b/torchao/quantization/pt2e/prepare.py index 22542c09e2..97801f993c 100644 --- a/torchao/quantization/pt2e/prepare.py +++ b/torchao/quantization/pt2e/prepare.py @@ -22,8 +22,7 @@ from torch.fx.node import Argument from torchao.quantization.pt2e import ( - CUSTOM_KEY, - NUMERIC_DEBUG_HANDLE_KEY, + FROM_NODE_KEY, DerivedObserverOrFakeQuantize, ObserverOrFakeQuantize, ) @@ -43,6 +42,7 @@ QuantizationSpecBase, SharedQuantizationSpec, ) +from torchao.utils import TORCH_VERSION_AT_LEAST_2_6 # TODO: make pt2e folder private? __all__ = [ @@ -556,14 +556,10 @@ def _maybe_insert_output_observer_for_node( if ( isinstance(node, Node) and isinstance(new_output, Node) - and CUSTOM_KEY in node.meta - and NUMERIC_DEBUG_HANDLE_KEY in node.meta[CUSTOM_KEY] + and FROM_NODE_KEY in node.meta + and TORCH_VERSION_AT_LEAST_2_6 ): - if CUSTOM_KEY not in new_output.meta: - new_output.meta[CUSTOM_KEY] = {} - new_output.meta[CUSTOM_KEY][NUMERIC_DEBUG_HANDLE_KEY] = node.meta[ - CUSTOM_KEY - ][NUMERIC_DEBUG_HANDLE_KEY] + new_output.meta[FROM_NODE_KEY] = node.meta[FROM_NODE_KEY] return new_output return None diff --git a/torchao/testing/pt2e/utils.py b/torchao/testing/pt2e/utils.py index 4342d81dc1..5d903a4a15 100644 --- a/torchao/testing/pt2e/utils.py +++ b/torchao/testing/pt2e/utils.py @@ -22,10 +22,8 @@ ) from torch.testing._internal.common_utils import TestCase -from torchao.quantization.pt2e import ( - CUSTOM_KEY, - NUMERIC_DEBUG_HANDLE_KEY, -) +from torchao.quantization.pt2e import FROM_NODE_KEY +from torchao.quantization.pt2e._numeric_debugger import _generate_debug_handle_from_node from torchao.quantization.pt2e.graph_utils import bfs_trace_with_node_process from torchao.quantization.pt2e.quantize_pt2e import ( convert_pt2e, @@ -153,10 +151,10 @@ def _assert_each_node_has_debug_handle(self, model) -> None: """Assert that each node in the model has a debug handle.""" def _assert_node_has_debug_handle(node): - self.assertTrue( - CUSTOM_KEY in node.meta - and NUMERIC_DEBUG_HANDLE_KEY in node.meta[CUSTOM_KEY], - f"Node {node} doesn't have debug handle", + self.assertIn( + FROM_NODE_KEY, + node.meta, + f"Node {node} doesn't have from_node info", ) bfs_trace_with_node_process(model, _assert_node_has_debug_handle) @@ -167,29 +165,20 @@ def _extract_debug_handles(self, model) -> Dict[str, int]: def _extract_debug_handles_from_node(node): nonlocal debug_handle_map - if ( - CUSTOM_KEY in node.meta - and NUMERIC_DEBUG_HANDLE_KEY in node.meta[CUSTOM_KEY] - ): - debug_handle_map[str(node)] = node.meta[CUSTOM_KEY][ - NUMERIC_DEBUG_HANDLE_KEY - ] + if (dh := _generate_debug_handle_from_node(node)) is not None: + debug_handle_map[str(node)] = dh bfs_trace_with_node_process(model, _extract_debug_handles_from_node) return debug_handle_map - def _extract_debug_handles_with_prev_decomp_op(self, model) -> Dict[str, int]: - """Extract debug handles with previous decomposition operation mapping.""" - prev_decomp_op_to_debug_handle_map: Dict[str, int] = {} + def _extract_debug_handles_with_prev_decomp_op(self, model) -> dict[str, int]: + prev_decomp_op_to_debug_handle_map: dict[str, int] = {} def _extract_debug_handles_with_prev_decomp_op_from_node(node): nonlocal prev_decomp_op_to_debug_handle_map - if ( - CUSTOM_KEY in node.meta - and NUMERIC_DEBUG_HANDLE_KEY in node.meta[CUSTOM_KEY] - ): + if FROM_NODE_KEY in node.meta: prev_decomp_op = str(node.meta.get("nn_module_stack")) - debug_handle = node.meta[CUSTOM_KEY][NUMERIC_DEBUG_HANDLE_KEY] + debug_handle = _generate_debug_handle_from_node(node) if prev_decomp_op not in prev_decomp_op_to_debug_handle_map: prev_decomp_op_to_debug_handle_map[prev_decomp_op] = debug_handle else: From 4e3d0198f93602d88db9d5ba6bb6020454fc5fde Mon Sep 17 00:00:00 2001 From: Xuan Liao Date: Thu, 19 Jun 2025 10:51:28 +0800 Subject: [PATCH 140/165] Enable cpp kernel building (#2402) enable cpp kernl building --- setup.py | 27 ++++++++++++++------------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/setup.py b/setup.py index cb6be7e1c1..5560ab877e 100644 --- a/setup.py +++ b/setup.py @@ -385,19 +385,20 @@ def get_extensions(): extra_compile_args["cxx"].extend( ["-O3" if not debug_mode else "-O0", "-fdiagnostics-color=always"] ) - - # TODO(future PR): make this work without using `TORCH_VERSION_AT_LEAST_2_7`, - # because we should not be using anything from `torchao` to build `torchao`. - # if use_cpu_kernels and is_linux and TORCH_VERSION_AT_LEAST_2_7: - # if torch._C._cpu._is_avx512_supported(): - # extra_compile_args["cxx"].extend( - # [ - # "-DCPU_CAPABILITY_AVX512", - # "-march=native", - # "-mfma", - # "-fopenmp", - # ] - # ) + if ( + use_cpu_kernels + and is_linux + and hasattr(torch._C._cpu, "_is_avx512_supported") + and torch._C._cpu._is_avx512_supported() + ): + extra_compile_args["cxx"].extend( + [ + "-DCPU_CAPABILITY_AVX512", + "-march=native", + "-mfma", + "-fopenmp", + ] + ) if debug_mode: extra_compile_args["cxx"].append("-g") From eb8617756682b35f98727ea43e5c6bc3d37e416e Mon Sep 17 00:00:00 2001 From: Mark Saroufim Date: Fri, 20 Jun 2025 18:55:30 -0700 Subject: [PATCH 141/165] Remove more Galore bits (#2417) --- test/galore/memory_analysis_utils.py | 78 ---- test/galore/model_configs.py | 181 -------- test/galore/profiling_utils.py | 196 --------- torchao/prototype/galore/__init__.py | 1 - torchao/prototype/galore/kernels/__init__.py | 12 - torchao/prototype/galore/kernels/adam_step.py | 181 -------- .../galore/kernels/custom_autotune.py | 395 ------------------ torchao/prototype/galore/kernels/quant.py | 189 --------- torchao/prototype/galore/optim/__init__.py | 0 9 files changed, 1233 deletions(-) delete mode 100644 test/galore/memory_analysis_utils.py delete mode 100644 test/galore/model_configs.py delete mode 100644 test/galore/profiling_utils.py delete mode 100644 torchao/prototype/galore/__init__.py delete mode 100644 torchao/prototype/galore/kernels/__init__.py delete mode 100644 torchao/prototype/galore/kernels/adam_step.py delete mode 100644 torchao/prototype/galore/kernels/custom_autotune.py delete mode 100644 torchao/prototype/galore/kernels/quant.py delete mode 100644 torchao/prototype/galore/optim/__init__.py diff --git a/test/galore/memory_analysis_utils.py b/test/galore/memory_analysis_utils.py deleted file mode 100644 index 41f601e3a2..0000000000 --- a/test/galore/memory_analysis_utils.py +++ /dev/null @@ -1,78 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -from functools import partial - -import pandas as pd -from IPython.display import HTML - - -def plot_memory_timeline(trace_file): - """Plots html output of torch profiler memory trace - For use within Jupyter Notebook only! - See https://pytorch.org/docs/main/profiler.html#torch.profiler._KinetoProfile.export_memory_timeline - - Args: - trace_file: path to html export of torch profiler memory timeline - """ - with open(trace_file) as f: - return HTML(f.read()) - - -# These are the (unlabeled) columns in the json export of a torch profiler memory timeline trace -COL_NAMES = [ - "Parameter", - "Optimizer_State", - "Input", - "Temporary", - "Activation", - "Gradient", - "Autograd_Detail", - "Unknown", -] - - -def create_mem_df(mem_trace, units="GB"): - """Create dataframe from json export of torch profiler CUDA memory timeline trace - Columns per COL_NAMES, in units of MB - These are the (unlabeled) columns in the json export of a torch profiler memory timeline trace but can be - inferred (and confirmed) by comparing the plots of the json export with the plots of the html export - - E.g., df.plot(kind="area", stacked=True, ylabel="MB") - - See https://pytorch.org/docs/main/profiler.html#torch.profiler._KinetoProfile.export_memory_timeline - Args: - mem_trace: path to json export of torch profiler memory timeline - units: "MB" or "GB" - """ - df = pd.read_json(mem_trace).T.explode(0) - - def _convert_to_units(df, col): - return df[col] / 1024 ** (3 if units == "GB" else 2) - - convert_cols_to_MB = {col: partial(_convert_to_units, col=col) for col in COL_NAMES} - - df = pd.DataFrame( - [l[1:] for l in df.iloc[:, 1].to_list()], columns=COL_NAMES - ).assign(**convert_cols_to_MB) - df["Total"] = df.sum(axis=1) - return df - - -def show_memory_stats(df, stats=["mean", "min", "50%", "max"]): - """Show summary statistics for torch profiler CUDA memory timeline trace - Args: - df: dataframe created by create_mem_df - stats: list of statistics to show. Valid stats are "mean", "min", "25%", "50%", "75%", "max" - - """ - mem_sum = ( - df.describe() - .loc[stats] - .rename(index={"50%": "median"}) - .style.format(precision=1, thousands=",") - ) - - return mem_sum diff --git a/test/galore/model_configs.py b/test/galore/model_configs.py deleted file mode 100644 index 1efb4a131e..0000000000 --- a/test/galore/model_configs.py +++ /dev/null @@ -1,181 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -# LLAMA100M = { -# "architectures": ["LLaMAForCausalLM"], -# "attention_bias": False, -# "attention_dropout": 0.0, -# "bos_token_id": 0, -# "eos_token_id": 1, -# "hidden_act": "silu", -# "hidden_size": 640, -# "initializer_range": 0.02, -# "intermediate_size": 1708, -# "max_position_embeddings": 2048, -# "max_sequence_length": 1024, -# "model_type": "llama", -# "num_attention_heads": 10, -# "num_hidden_layers": 12, -# "num_key_value_heads": 10, -# "pad_token_id": -1, -# "pretraining_tp": 1, -# "rms_norm_eps": 1e-06, -# "rope_scaling": None, -# "rope_theta": 10000.0, -# "tie_word_embeddings": False, -# "transformers_version": "4.39.3", -# "use_cache": True, -# "vocab_size": 32100, -# } -LLAMA1B = { - "vocab_size": 32000, - "max_position_embeddings": 2048, - "hidden_size": 2048, - "intermediate_size": 5461, - "num_hidden_layers": 24, - "num_attention_heads": 32, - "num_key_value_heads": 32, - "hidden_act": "silu", - "initializer_range": 0.02, - "rms_norm_eps": 1e-06, - "pretraining_tp": 1, - "use_cache": True, - "rope_theta": 10000.0, - "rope_scaling": None, - "attention_bias": False, - "attention_dropout": 0.0, - "return_dict": True, - "output_hidden_states": False, - "output_attentions": False, - "torchscript": False, - "torch_dtype": None, - "use_bfloat16": False, - "tf_legacy_loss": False, - "pruned_heads": {}, - "tie_word_embeddings": False, - "chunk_size_feed_forward": 0, - "is_encoder_decoder": False, - "is_decoder": False, - "cross_attention_hidden_size": None, - "add_cross_attention": False, - "tie_encoder_decoder": False, - "max_length": 20, - "min_length": 0, - "do_sample": False, - "early_stopping": False, - "num_beams": 1, - "num_beam_groups": 1, - "diversity_penalty": 0.0, - "temperature": 1.0, - "top_k": 50, - "top_p": 1.0, - "typical_p": 1.0, - "repetition_penalty": 1.0, - "length_penalty": 1.0, - "no_repeat_ngram_size": 0, - "encoder_no_repeat_ngram_size": 0, - "bad_words_ids": None, - "num_return_sequences": 1, - "output_scores": False, - "return_dict_in_generate": False, - "forced_bos_token_id": None, - "forced_eos_token_id": None, - "remove_invalid_values": False, - "exponential_decay_length_penalty": None, - "suppress_tokens": None, - "begin_suppress_tokens": None, - "architectures": ["LLaMAForCausalLM"], - "finetuning_task": None, - "id2label": {0: "LABEL_0", 1: "LABEL_1"}, - "label2id": {"LABEL_0": 0, "LABEL_1": 1}, - "tokenizer_class": None, - "prefix": None, - "bos_token_id": 0, - "pad_token_id": -1, - "eos_token_id": 1, - "sep_token_id": None, - "decoder_start_token_id": None, - "task_specific_params": None, - "problem_type": None, - "_name_or_path": "./configs/llama_1b.json", - "transformers_version": "4.39.3", - "max_sequence_length": 1024, - "model_type": "llama", -} -LLAMA100M = { - "vocab_size": 32100, - "max_position_embeddings": 2048, - "hidden_size": 640, - "intermediate_size": 1708, - "num_hidden_layers": 12, - "num_attention_heads": 10, - "num_key_value_heads": 10, - "hidden_act": "silu", - "initializer_range": 0.02, - "rms_norm_eps": 1e-06, - "pretraining_tp": 1, - "use_cache": True, - "rope_theta": 10000.0, - "rope_scaling": None, - "attention_bias": False, - "attention_dropout": 0.0, - "return_dict": True, - "output_hidden_states": False, - "output_attentions": False, - "torchscript": False, - "torch_dtype": None, - "use_bfloat16": False, - "tf_legacy_loss": False, - "pruned_heads": {}, - "tie_word_embeddings": False, - "chunk_size_feed_forward": 0, - "is_encoder_decoder": False, - "is_decoder": False, - "cross_attention_hidden_size": None, - "add_cross_attention": False, - "tie_encoder_decoder": False, - "max_length": 20, - "min_length": 0, - "do_sample": False, - "early_stopping": False, - "num_beams": 1, - "num_beam_groups": 1, - "diversity_penalty": 0.0, - "temperature": 1.0, - "top_k": 50, - "top_p": 1.0, - "typical_p": 1.0, - "repetition_penalty": 1.0, - "length_penalty": 1.0, - "no_repeat_ngram_size": 0, - "encoder_no_repeat_ngram_size": 0, - "bad_words_ids": None, - "num_return_sequences": 1, - "output_scores": False, - "return_dict_in_generate": False, - "forced_bos_token_id": None, - "forced_eos_token_id": None, - "remove_invalid_values": False, - "exponential_decay_length_penalty": None, - "suppress_tokens": None, - "begin_suppress_tokens": None, - "architectures": ["LLaMAForCausalLM"], - "finetuning_task": None, - "id2label": {0: "LABEL_0", 1: "LABEL_1"}, - "label2id": {"LABEL_0": 0, "LABEL_1": 1}, - "tokenizer_class": None, - "prefix": None, - "bos_token_id": 0, - "pad_token_id": -1, - "eos_token_id": 1, - "sep_token_id": None, - "decoder_start_token_id": None, - "task_specific_params": None, - "problem_type": None, - "_name_or_path": "./configs/llama_100m.json", - "transformers_version": "4.39.3", - "max_sequence_length": 1024, - "model_type": "llama", -} diff --git a/test/galore/profiling_utils.py b/test/galore/profiling_utils.py deleted file mode 100644 index 9f59ae68e2..0000000000 --- a/test/galore/profiling_utils.py +++ /dev/null @@ -1,196 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -import gc -import logging -import os -from contextlib import contextmanager -from datetime import datetime -from functools import partial - -import torch - -logging.basicConfig( - format="%(levelname)s:%(asctime)s %(message)s", - level=logging.INFO, - datefmt="%Y-%m-%d %H:%M:%S", -) -logger: logging.Logger = logging.getLogger(__name__) -logger.setLevel(level=logging.INFO) - -TIME_FORMAT_STR: str = "%m-%d-%H" - -# Keep a max of 100,000 alloc/free events in the recorded history -# leading up to the snapshot. -MAX_NUM_OF_MEM_EVENTS_PER_SNAPSHOT: int = 100000 - - -def flush_cuda_mem(): - gc.collect() - torch.cuda.empty_cache() - torch.cuda.reset_peak_memory_stats() - torch.cuda.reset_max_memory_allocated() - torch.cuda.reset_max_memory_cached() - torch.cuda.reset_accumulated_memory_stats() - - -@contextmanager -def cuda_max_memory(): - try: - flush_cuda_mem() - yield - - finally: - mem_miB = torch.cuda.max_memory_allocated() // (1024 * 1024) - print(f"{mem_miB} MB of CUDA memory allocated") - flush_cuda_mem() - return mem_miB - - -def get_cuda_memory_usage(units="MB", show=True): - """ - Get maximum allocated / reserved CUDA memory in given units - - Args: - units: MB, GB, or B - """ - units = units.upper() - if units == "MB": - divisor = 1024**2 - elif units == "GB": - divisor = 1024**3 - else: - units = "B" - divisor = 1 - max_memory_allocated = torch.cuda.max_memory_allocated() / divisor - max_memory_reserved = torch.cuda.max_memory_reserved() / divisor - if show: - print( - "Max Memory Allocated:", - f"{max_memory_allocated:,.1f} {units}", - ) - print( - "Max Memory Reserved:", - f"{max_memory_reserved:,.1f} {units}", - ) - - return max_memory_allocated, max_memory_reserved - - -def export_memory_snapshot(prefix) -> None: - # Prefix for file names. - timestamp = datetime.now().strftime(TIME_FORMAT_STR) - file_prefix = f"{prefix}_{timestamp}" - - try: - logger.info(f"Saving snapshot to local file: {file_prefix}.pickle") - torch.cuda.memory._dump_snapshot(f"{file_prefix}.pickle") - except Exception as e: - logger.error(f"Failed to capture memory snapshot {e}") - return - - -@contextmanager -def memory_recorder(file_name="cuda_memory_snapshot", export=False) -> None: - assert torch.cuda.is_available(), ( - "Memory profiler requires GPU, check torch.cuda.is_available()" - ) - try: - logger.info("Starting snapshot record_memory_history") - torch.cuda.memory._record_memory_history( - max_entries=MAX_NUM_OF_MEM_EVENTS_PER_SNAPSHOT - ) - yield - finally: - logger.info("Stopping snapshot record_memory_history") - torch.cuda.memory._record_memory_history(enabled=None) - if export: - export_memory_snapshot(file_name) - - -def trace_handler( - prof: torch.profiler.profile, - prefix: str = "profile", - output_dir="./", - sort_key="cuda_time_total", - export_trace=True, - export_memory_timeline=True, - print_table=True, -): - timestamp = datetime.now().strftime(TIME_FORMAT_STR) - file_prefix = os.path.join(output_dir, f"{prefix}_{timestamp}") - - if export_trace: - prof.export_chrome_trace(f"{file_prefix}-trace.json.gz") - - if export_memory_timeline: - prof.export_memory_timeline(f"{file_prefix}.html", device="cuda:0") - prof.export_memory_timeline( - f"{file_prefix}-memory-timeline.json", device="cuda:0" - ) - if print_table: - print(prof.key_averages().table(sort_by=sort_key, row_limit=10)) - - -def get_torch_profiler( - name: str = "profile", - output_dir: str = "./profiler_out", - activities=[ - torch.profiler.ProfilerActivity.CPU, - torch.profiler.ProfilerActivity.CUDA, - ], - record_shapes=True, - profile_memory=True, - with_stack=True, - wait_steps=1, - warmup_steps=1, - active_steps=10, - repeat=1, - # options for profiler outputs - on_trace_ready=trace_handler, - export_trace=True, - export_memory_timeline=True, - print_table=True, -): - """ - Args: - name: name of the profiler, used for output files - table_key: key to sort profiler table by: one of `cpu_time`, `cuda_time`, `cpu_time_total`, - `cuda_time_total`, `cpu_memory_usage`, `cuda_memory_usage`, - `self_cpu_memory_usage`, `self_cuda_memory_usage`, `count`. - - """ - if not os.path.exists(output_dir): - os.makedirs(output_dir) - return torch.profiler.profile( - activities=activities, - schedule=torch.profiler.schedule( - wait=wait_steps, warmup=warmup_steps, active=active_steps, repeat=repeat - ), - record_shapes=record_shapes, - profile_memory=profile_memory, - with_stack=with_stack, - on_trace_ready=partial( - on_trace_ready, - prefix=name, - output_dir=output_dir, - export_trace=export_trace, - export_memory_timeline=export_memory_timeline, - print_table=print_table, - ), - ) - - -@contextmanager -def nsys_profiler(): - try: - torch.cuda.cudart().cudaProfilerStart() - free, total = torch.cuda.mem_get_info() - print(f"Start, Memory Usage: Free {free:.2e}, Used {(total - free):.2e}") - yield "nsys" - finally: - free, total = torch.cuda.mem_get_info() - print(f"End, Memory Usage: Free {free:.2e}, Used {(total - free):.2e}") - torch.cuda.cudart().cudaProfilerStop() diff --git a/torchao/prototype/galore/__init__.py b/torchao/prototype/galore/__init__.py deleted file mode 100644 index a769e11a16..0000000000 --- a/torchao/prototype/galore/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .kernels import * # noqa: F403 diff --git a/torchao/prototype/galore/kernels/__init__.py b/torchao/prototype/galore/kernels/__init__.py deleted file mode 100644 index 71129d34a2..0000000000 --- a/torchao/prototype/galore/kernels/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -from .adam_downproj_fused import fused_adam_mm_launcher -from .adam_step import triton_adam_launcher -from .matmul import triton_mm_launcher -from .quant import triton_dequant_blockwise, triton_quantize_blockwise - -__all__ = [ - "fused_adam_mm_launcher", - "triton_adam_launcher", - "triton_mm_launcher", - "triton_dequant_blockwise", - "triton_quantize_blockwise", -] diff --git a/torchao/prototype/galore/kernels/adam_step.py b/torchao/prototype/galore/kernels/adam_step.py deleted file mode 100644 index fcb554bd1d..0000000000 --- a/torchao/prototype/galore/kernels/adam_step.py +++ /dev/null @@ -1,181 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -import torch -import triton -import triton.language as tl -from triton.language.math import sqrt -from triton.runtime.autotuner import heuristics - -from .custom_autotune import Config, autotune - -BETA1, BETA2 = 0.9, 0.999 -EPS = 1e-8 - -AUTOTUNER_TOP_K = 10 - - -def get_configs_for_adam(num_warps=[2, 4, 8], block_sizes=[512, 1024, 2048]): - configs = [] - for w in num_warps: - for bs in block_sizes: - configs.append(Config({"BLOCK_SIZE": bs}, num_warps=w)) - return configs - - -def early_adam_prune(configs, named_args): - numels = named_args["numels"] - pruned_configs = [cfg for cfg in configs if numels % cfg.kwargs["BLOCK_SIZE"] == 0] - # print("Pruned configs:\n") - for cfg in pruned_configs: - print(f"{cfg}\n") - return pruned_configs - - -def get_adam_tuner( - configs=get_configs_for_adam(), - early_config_prune=None, # early_adam_prune, - top_k=AUTOTUNER_TOP_K, -): - return autotune( - configs=configs, - prune_configs_by={ - "early_config_prune": early_config_prune, - "top_k": top_k, - }, - key=["numels"], - ) - - -def get_adam_heuristics(): - return { - "USE_MASK": lambda args: args["numels"] % args["BLOCK_SIZE"] != 0, - } - - -@autotune(configs=get_configs_for_adam(), key=["numels"]) -@heuristics(get_adam_heuristics()) -@triton.jit -def _adam_update( - avg_ptr, - avg2_ptr, - grad_ptr, - # avg_out_ptr, - # avg2_out_ptr, - # grad_out_ptr, - numels, - store, - BLOCK_SIZE: tl.constexpr, - USE_MASK: tl.constexpr, - BETA1: tl.constexpr = BETA1, - BETA2: tl.constexpr = BETA2, - EPS: tl.constexpr = EPS, -): - pid_m = tl.program_id(0) - offset = pid_m * BLOCK_SIZE - offset = offset + tl.arange(0, BLOCK_SIZE) - # load_idx = offset + tl.arange(0, BLOCK_SIZE) - load_idx = tl.max_contiguous(tl.multiple_of(offset, BLOCK_SIZE), BLOCK_SIZE) - - mask = None - if USE_MASK: - mask = load_idx < numels - avg = tl.load(avg_ptr + load_idx, mask=mask) - avg2 = tl.load(avg2_ptr + load_idx, mask=mask) - grad = tl.load(grad_ptr + load_idx, mask=mask) - - avg = BETA1 * avg + (1.0 - BETA1) * grad - avg2 = BETA2 * avg2 + (1.0 - BETA2) * (grad * grad) - - denom = sqrt(avg2) + EPS - # denom = tl.sqrt(avg2) + EPS - - norm_grad = avg / denom - - if store: - tl.store(avg_ptr + load_idx, avg, mask=mask) - tl.store(avg2_ptr + load_idx, avg2, mask=mask) - tl.store(grad_ptr + load_idx, norm_grad, mask=mask) - # tl.store(avg_out_ptr + load_idx, avg, mask=mask) - # tl.store(avg2_out_ptr + load_idx, avg2, mask=mask) - # tl.store(grad_out_ptr + load_idx, norm_grad, mask=mask) - - -adam_update = _adam_update - - -def triton_adam_launcher( - avg, - avg2, - grad, - store=True, - beta1=BETA1, - beta2=BETA2, - eps=EPS, -): - M, N = avg.shape - # avg_out = torch.empty_like(avg) - # avg2_out = torch.empty_like(avg2) - # grad_out = torch.empty_like(grad) - - grid = lambda META: (triton.cdiv(M * N, META["BLOCK_SIZE"]),) - adam_update[grid]( - avg, - avg2, - grad, - # avg_out, - # avg2_out, - # grad_out, - avg.numel(), - store=store, - BETA1=beta1, - BETA2=beta2, - EPS=eps, - # BLOCK_SIZE=1024, - # USE_MASK=USE_MASK, - ) - return avg, avg2, grad - - -def ref_adam_step(exp_avg, exp_avg2, grad, beta1=BETA1, beta2=BETA2, eps=EPS): - exp_avg = beta1 * exp_avg + (1 - beta1) * grad - exp_avg2 = beta2 * exp_avg2 + (1 - beta2) * torch.square(grad) - denom = exp_avg2.sqrt() + eps - norm_grad = exp_avg / denom - return exp_avg, exp_avg2, norm_grad - - -def make_data(M, N, rank, dtype): - # full_grad = torch.randn(M, N, device="cuda", dtype=dtype) - params = torch.randn(M, N, device="cuda", dtype=dtype) - - if M >= N: - exp_avg = torch.randn(M, rank, device="cuda", dtype=dtype) - else: - exp_avg = torch.randn(rank, N, device="cuda", dtype=dtype) - exp_avg2 = exp_avg**2 - down_grad = torch.randn_like(exp_avg) - - return exp_avg, exp_avg2, down_grad, params - - -if __name__ == "__main__": - M = N = 4096 - rank = 128 - dtype = torch.float32 - exp_avg, exp_avg2, grad, params = make_data(M, N, rank, dtype=dtype) - exp_avg_copy, exp_avg2_copy, grad_copy = ( - exp_avg.clone(), - exp_avg2.clone(), - grad.clone(), - ) - ref_out = ref_adam_step(exp_avg, exp_avg2, grad) - - # Autotune run -- changes exp_avg, exp_avg2, grad in-place - _ = triton_adam_launcher(exp_avg, exp_avg2, grad) - triton_out = triton_adam_launcher(exp_avg_copy, exp_avg2_copy, grad_copy) - - for ref, tt in zip(ref_out, triton_out): - print(torch.max(torch.abs(ref - tt))) diff --git a/torchao/prototype/galore/kernels/custom_autotune.py b/torchao/prototype/galore/kernels/custom_autotune.py deleted file mode 100644 index 8ecfabf849..0000000000 --- a/torchao/prototype/galore/kernels/custom_autotune.py +++ /dev/null @@ -1,395 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -from __future__ import annotations - -import builtins -import logging -import os -import time -from typing import Dict - -import numpy as np -from triton.runtime.cache import default_cache_dir -from triton.runtime.errors import OutOfResources -from triton.runtime.jit import KernelInterface -from triton.testing import do_bench - -logger = logging.getLogger(__file__) - - -class Autotuner(KernelInterface): - def __init__( - self, - fn, - arg_names, - configs, - key, - reset_to_zero, - restore_value, - prune_configs_by: Dict = None, - warmup=25, - rep=100, - ): - """ - :param prune_configs_by: a dict of functions that are used to prune configs, fields: - 'perf_model': performance model used to predicate running time with different configs, returns running time - 'top_k': number of configs to bench - 'prune_num_stages_by'(optional): a function used to prune num_stages. It takes configs:List[Config] as its input, and returns pruned configs. - """ - if not configs: - self.configs = [Config({}, num_warps=4, num_stages=2, num_ctas=1)] - else: - self.configs = configs - self.key_idx = [arg_names.index(k) for k in key] - self.cache = {} - self.arg_names = arg_names - - # Reset to zero or restore values - self.reset_idx = [] - if reset_to_zero is not None: - self.reset_idx = [arg_names.index(k) for k in reset_to_zero] - self.restore_idx = [] - if restore_value is not None: - self.restore_idx = [arg_names.index(k) for k in restore_value] - - # Hook to reset or restore for required tensors - self.pre_hook = lambda args, reset_only=False: 0 - self.post_hook = lambda args: 0 - if len(self.reset_idx) > 0 or len(self.restore_idx) > 0: - - def _pre_hook(args, reset_only=False): - for i in self.reset_idx: - args[i].zero_() - if not reset_only: - self.restore_copies = [args[i].clone() for i in self.restore_idx] - - self.pre_hook = _pre_hook - if len(self.restore_idx) > 0: - - def _post_hook(args): - for i, j in enumerate(self.restore_idx): - args[j].copy_(self.restore_copies[i]) - self.restore_copies = [] - - self.post_hook = _post_hook - - self.perf_model = None - self.configs_top_k = 1.0 - self.early_config_prune = None - if prune_configs_by: - self.perf_model = prune_configs_by.get("perf_model", self.perf_model) - self.configs_top_k = prune_configs_by.get("top_k", self.configs_top_k) - self.early_config_prune = prune_configs_by.get( - "early_config_prune", self.early_config_prune - ) - - self.fn = fn - self.num_warmups = warmup - self.num_reps = rep - # self.autotune_log_path = os.path.join(default_cache_dir(), autotune_log_file) - self.kernel_name = self._find_kernel_name() - - def _find_kernel_name(self): - try: - kernel_name = self.fn.__name__ - except AttributeError: - try: # in case JITfn is wrapped in both autotune and heuristic - kernel_name = self.fn.fn.__name__ - except: # noqa - kernel_name = self.fn.__name__ - return kernel_name - - def _get_key_combination(self, args, as_str=True, sep=" "): - key_vals = [f"{self.arg_names[i]}={args[i]}" for i in self.key_idx] - return f"{sep}".join(key_vals) if as_str else key_vals - - def _bench(self, *args, config, **meta): - # check for conflicts, i.e. meta-parameters both provided - # as kwargs and by the autotuner - conflicts = meta.keys() & config.kwargs.keys() - if conflicts: - raise ValueError( - f"Conflicting meta-parameters: {', '.join(conflicts)}." - " Make sure that you don't re-define auto-tuned symbols." - ) - # augment meta-parameters with tunable ones - current = dict(meta, **config.kwargs) - full_nargs = {**self.nargs, **current} - - def kernel_call(): - if config.pre_hook: - config.pre_hook(full_nargs) - self.pre_hook(args) - self.fn.run( - *args, - num_warps=config.num_warps, - num_stages=config.num_stages, - num_ctas=config.num_ctas, - **current, - ) - self.post_hook(args) - - try: - return do_bench( - kernel_call, - warmup=self.num_warmups, - rep=self.num_reps, - quantiles=(0.5, 0.2, 0.8), - ) - except OutOfResources: - return [float("inf"), float("inf"), float("inf")] - - def run(self, *args, **kwargs): - self.nargs = dict(zip(self.arg_names, args)) - if len(self.configs) > 1: - all_args = {**self.nargs, **kwargs} - _args = [] - for name in self.arg_names: - if name in all_args: - _args.append(all_args[name]) - key = [_args[i] for i in self.key_idx] - for arg in _args: - if hasattr(arg, "dtype"): - key.append(str(arg.dtype)) - key = tuple(key) - if key not in self.cache: - logger.debug("Cache miss!\n") - logger.info( - f"\n==== Autotune ====\nRunning autotune for {self.kernel_name} for {len(self.configs)} total configs" - f" for key combination {self._get_key_combination(args)}..." - ) - # prune configs - pruned_configs = self.prune_configs(kwargs) - logger.info(f"\nNum configs after pruning {len(pruned_configs)}") - bench_start = time.time() - timings = {} - for config in pruned_configs: - timings[config] = self._bench(*args, config=config, **kwargs) - # timings = { - # config: self._bench(*args, config=config, **kwargs) - # for config in pruned_configs - # } - bench_end = time.time() - self.bench_time = bench_end - bench_start - self.cache[key] = builtins.min(timings, key=timings.get) - self.pre_hook(args, reset_only=True) - self.configs_timings = timings - - sorted_timings = dict( - sorted(timings.items(), key=lambda x: np.mean(x[1])) - ) - _key_suffix = self._get_key_combination(args, sep="-") - autotune_file = f"autotune_{self.kernel_name}_{_key_suffix}.log" - autotune_log_path = os.path.join(default_cache_dir(), autotune_file) - - logger.info(f"\nFinished autotune, writing log to {autotune_log_path}") - - with open(f"{autotune_log_path}", "w") as f: - f.write( - f" ==== Autotune Results ====\nKernel name: {self.kernel_name}\nArgs: {self.arg_names}\nKeys: {self._get_key_combination(args)}\n" - ) - f.write("\nPruned configs:\n") - for cfg in pruned_configs: - f.write(f"{cfg}\n") - f.write("Timings:\n") - for cfg, timing in sorted_timings.items(): - f.write(f"{cfg} {timing} \n") - f.write(f"Best config: {self.cache[key]}\n") - config = self.cache[key] - logger.debug("\nAutotune: Cache hit! Running best config...") - else: - config = self.configs[0] - self.best_config = config - logger.info(f"\nAutotune Best Config: {config}\n") - - full_nargs = {**self.nargs, **kwargs, **self.best_config.kwargs} - if config.pre_hook is not None: - config.pre_hook(full_nargs) - ret = self.fn.run( - *args, - num_warps=config.num_warps, - num_stages=config.num_stages, - num_ctas=config.num_ctas, - **kwargs, - **config.kwargs, - ) - self.nargs = None - return ret - - def prune_configs(self, kwargs): - pruned_configs = self.configs - if self.early_config_prune: - pruned_configs = self.early_config_prune(self.configs, self.nargs) - if self.perf_model: - top_k = self.configs_top_k - if isinstance(top_k, float) and top_k <= 1.0: - top_k = int(len(self.configs) * top_k) - if len(pruned_configs) > top_k: - est_timing = { - config: self.perf_model( - **self.nargs, - **kwargs, - **config.kwargs, - num_stages=config.num_stages, - num_warps=config.num_warps, - num_ctas=config.num_ctas, - ) - for config in pruned_configs - } - pruned_configs = sorted(est_timing.keys(), key=lambda x: est_timing[x])[ - :top_k - ] - return pruned_configs - - def warmup(self, *args, **kwargs): - self.nargs = dict(zip(self.arg_names, args)) - ret = [] - for config in self.prune_configs(kwargs): - ret.append( - self.fn.warmup( - *args, - num_warps=config.num_warps, - num_ctas=config.num_ctas, - num_stages=config.num_stages, - **kwargs, - **config.kwargs, - ) - ) - self.nargs = None - return ret - - -class Config: - """ - An object that represents a possible kernel configuration for the auto-tuner to try. - - :ivar meta: a dictionary of meta-parameters to pass to the kernel as keyword arguments. - :type meta: dict[Str, Any] - :ivar num_warps: the number of warps to use for the kernel when compiled for GPUs. For example, if - `num_warps=8`, then each kernel instance will be automatically parallelized to - cooperatively execute using `8 * 32 = 256` threads. - :type num_warps: int - :ivar num_stages: the number of stages that the compiler should use when software-pipelining loops. - Mostly useful for matrix multiplication workloads on SM80+ GPUs. - :type num_ctas: int - :ivar num_ctas: number of blocks in a block cluster. SM90+ only. - :ivar pre_hook: a function that will be called before the kernel is called. Parameters of this - function are args. - """ - - def __init__(self, kwargs, num_warps=4, num_stages=2, num_ctas=1, pre_hook=None): - self.kwargs = kwargs - self.num_warps = num_warps - self.num_ctas = num_ctas - self.num_stages = num_stages - self.pre_hook = pre_hook - - def __str__(self): - res = [] - for k, v in self.kwargs.items(): - res.append(f"{k}: {v}") - res.append(f"num_warps: {self.num_warps}") - res.append(f"num_ctas: {self.num_ctas}") - res.append(f"num_stages: {self.num_stages}") - return ", ".join(res) - - -def autotune( - configs, - key, - prune_configs_by=None, - reset_to_zero=None, - restore_value=None, - warmup=25, - rep=100, -): - """ - Decorator for auto-tuning a :code:`triton.jit`'d function. - - .. highlight:: python - .. code-block:: python - - @triton.autotune(configs=[ - triton.Config(meta={'BLOCK_SIZE': 128}, num_warps=4), - triton.Config(meta={'BLOCK_SIZE': 1024}, num_warps=8), - ], - key=['x_size'] # the two above configs will be evaluated anytime - # the value of x_size changes - ) - @triton.jit - def kernel(x_ptr, x_size, **META): - BLOCK_SIZE = META['BLOCK_SIZE'] - :note: When all the configurations are evaluated, the kernel will run multiple times. - This means that whatever value the kernel updates will be updated multiple times. - To avoid this undesired behavior, you can use the `reset_to_zero` argument, which - resets the value of the provided tensor to `zero` before running any configuration. - :param configs: a list of :code:`triton.Config` objects - :type configs: list[triton.Config] - :param key: a list of argument names whose change in value will trigger the evaluation of all provided configs. - :type key: list[str] - :param prune_configs_by: a dict of functions that are used to prune configs, fields: - 'perf_model': performance model used to predicate running time with different configs, returns running time - 'top_k': number of configs to bench - 'early_config_prune'(optional): a function used to do early prune (eg, num_stages). It takes configs:List[Config] as its input, and returns pruned configs. - :param reset_to_zero: a list of argument names whose value will be reset to zero before evaluating any configs. - :type reset_to_zero: list[str] - :param restore_value: a list of argument names whose value will be restored after evaluating any configs. - :type restore_value: list[str] - :param warmup: Warmup time (in ms) to pass to benchmarking, defaults to 25. - :type warmup: int - :param rep: Repetition time (in ms) to pass to benchmarking, defaults to 100. - :type rep: int - """ - - def decorator(fn): - return Autotuner( - fn, - fn.arg_names, - configs, - key, - reset_to_zero, - restore_value, - prune_configs_by, - warmup, - rep, - ) - - return decorator - - -class Heuristics(KernelInterface): - def __init__(self, fn, arg_names, values) -> None: - self.fn = fn - self.values = values - self.arg_names = arg_names - - def run(self, *args, **kwargs): - for v, heur in self.values.items(): - kwargs[v] = heur({**dict(zip(self.arg_names, args)), **kwargs}) - return self.fn.run(*args, **kwargs) - - -def heuristics(values): - """ - Decorator for specifying how the values of certain meta-parameters may be computed. - This is useful for cases where auto-tuning is prohibitevely expensive, or just not applicable. - - .. highlight:: python - .. code-block:: python - - @triton.heuristics(values={'BLOCK_SIZE': lambda args: 2 ** int(math.ceil(math.log2(args[1])))}) - @triton.jit - def kernel(x_ptr, x_size, **META): - BLOCK_SIZE = META['BLOCK_SIZE'] # smallest power-of-two >= x_size - :param values: a dictionary of meta-parameter names and functions that compute the value of the meta-parameter. - each such function takes a list of positional arguments as input. - :type values: dict[str, Callable[[list[Any]], Any]] - """ - - def decorator(fn): - return Heuristics(fn, fn.arg_names, values) - - return decorator diff --git a/torchao/prototype/galore/kernels/quant.py b/torchao/prototype/galore/kernels/quant.py deleted file mode 100644 index 7c8a58e864..0000000000 --- a/torchao/prototype/galore/kernels/quant.py +++ /dev/null @@ -1,189 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD 3-Clause license found in the -# LICENSE file in the root directory of this source tree. -import torch -import triton -import triton.language as tl - - -@triton.jit -def _dequant_kernel( - q_idx_ptr, - absmax_ptr, - qmap_ptr, - dq_ptr, - stride_qm, - stride_qn, - M, - N, - GROUP_SIZE: tl.constexpr, - BLOCK_M: tl.constexpr, - BLOCK_N: tl.constexpr, -): - pid_m = tl.program_id(0) - pid_n = tl.program_id(1) - rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) - rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) - # rm = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M) - # rn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N) - offsets = rm[:, None] * stride_qm + rn[None, :] * stride_qn - mask = (rm[:, None] < M) & (rn[None, :] < N) - tl.static_print(offsets) - group_offsets = offsets // GROUP_SIZE - tl.static_print("group_offsets", group_offsets) - q_idx = tl.load(q_idx_ptr + offsets, mask=mask) - tl.static_print(q_idx) - # NOTE: Must upcast q_idx to int32 (q_idx is tl.uint8, which does not work for pointer indexing) - q_vals = tl.load(qmap_ptr + q_idx.to(tl.int32)) - absmax = tl.load( - absmax_ptr + group_offsets, mask=group_offsets < (M * N // GROUP_SIZE) - ) - - dq = q_vals * absmax - tl.store(dq_ptr + offsets, dq, mask=mask) - - -def triton_dequant_blockwise( - q: torch.Tensor, qmap: torch.Tensor, absmax: torch.Tensor, group_size: int -): - M, N = q.shape - dq = torch.empty_like(q).to(absmax.dtype) - grid = lambda META: ( - triton.cdiv(M, META["BLOCK_M"]), - triton.cdiv(N, META["BLOCK_N"]), - ) - _dequant_kernel[grid]( - q, - absmax, - qmap, - dq, - q.stride(0), - q.stride(1), - M, - N, - BLOCK_M=1, - BLOCK_N=group_size, - GROUP_SIZE=group_size, - ) - return dq - - -@triton.heuristics( - values={ - "USE_MASK": lambda args: args["numels"] % args["BLOCK_SIZE"] != 0, - "NUM_GROUPS": lambda args: triton.cdiv(args["numels"], args["BLOCK_SIZE"]), - } -) -@triton.jit -def _quantize_blockwise_kernel( - t_ptr, - cutoffs_ptr, - q_ptr, - absmax_ptr, - norm_ptr, - numels, - BLOCK_SIZE: tl.constexpr, - NUM_BUCKETS: tl.constexpr, - USE_MASK: tl.constexpr, - NUM_GROUPS: tl.constexpr, - RETURN_NORM: tl.constexpr = False, -): - pid = tl.program_id(0) - offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) - mask = None - absmax_mask = None - if USE_MASK: - mask = offsets < numels - absmax_mask = pid < NUM_GROUPS - t = tl.load(t_ptr + offsets, mask=mask) - - absmax = tl.max(tl.abs(t), axis=0) - normalized = t / absmax - - # Load code buckets - cutoffs = tl.load(cutoffs_ptr + tl.arange(0, NUM_BUCKETS)) - q = tl.reshape(normalized, (BLOCK_SIZE, 1)) > cutoffs - - # NOTE: explicit cast is needed, addition on tl.int1 (bool) does not work as per torch / numpy - q = q.to(tl.uint8) - q = tl.sum(q, axis=1) - - tl.store(q_ptr + offsets, q, mask=mask) - # Each block processes one group_size number of elements, hence 1 absmax - tl.store(absmax_ptr + pid, absmax, mask=absmax_mask) - - if RETURN_NORM: - tl.store(norm_ptr + offsets, normalized, mask=mask) - - -# NOTE: Each block processes one group_size number of elements, hence BLOCK_SIZE = group_size -# where group_size corresponds to the groupwise quantization blocksize -def triton_quantize_blockwise( - t: torch.Tensor, code, group_size=2048, return_normalized=False -): - """ - Params: - t: torch.Tensor, tensor to quantize - code: torch.Tensor, quantization codebook for bitsandbytes, output of `bitsandbytes.functional.create_dynamic_map` - # absmax: torch.Tensor, absolute max values for each block, if None, will be calculated from the input tensor - group_size: int, groupwise quantization blocksize, default 2048, the hardcoded blocksize for bitsandbytes 8-bit optimizers - return_normalized: bool, if True, will return the normalized tensor, primarily for debugging - """ - numel = t.numel() - q = torch.empty(numel, dtype=torch.uint8, device=t.device) - normalized = torch.empty_like(t) if return_normalized else None - num_groups = numel // group_size - abs_max = torch.empty(num_groups, dtype=t.dtype, device="cuda") - # Cutoffs for quantization - # code corresponds to actual (normalized) quant codes - # Cutoffs are used to calculate which of these codes a value belongs to - # E.g., for consecutive codes C1 and C2, the corresponding cutoff is C1 + C2 / 2 - # Hence, if a value is greater is assigned C1 if it is less than all cutoffs up to this cutoff - cutoffs = (code[:-1] + code[1:]) / 2 - - # Need to make cutoffs multiple of 2 for triton reduce - MAX_CUTOFF = torch.tensor( - torch.finfo(cutoffs.dtype).max, dtype=cutoffs.dtype, device=cutoffs.device - ).reshape( - 1, - ) - cutoffs = torch.cat([cutoffs, MAX_CUTOFF], dim=-1) - assert cutoffs.numel() % 2 == 0 - - grid = lambda META: (triton.cdiv(t.numel(), META["BLOCK_SIZE"]),) - # assert t.numel() % group_size == 0 - _quantize_blockwise_kernel[grid]( - t.view(-1), - cutoffs, - q, - abs_max, - normalized.view(-1) if return_normalized else None, - numel, - NUM_BUCKETS=len(cutoffs), - BLOCK_SIZE=group_size, - RETURN_NORM=return_normalized, - ) - return ( - q.reshape(t.shape), - normalized.reshape(t.shape) if return_normalized else None, - abs_max, - ) - - -# Reference implementation -def _torch_quantize_blockwise(tensor: torch.Tensor, code, absmax=None, blocksize=2048): - # Flatten values first - - # If not absmax, need to first normalize -> reshape to (-1, blocksize) -> max over the last dim - - # Quantize by flattening A to [numels, 1] > code[:, None], sum, then reshape back to original shape - if absmax is None: - absmax = tensor.reshape(-1, blocksize).abs().max(dim=-1).values - - normalized = tensor.reshape(-1, blocksize) / absmax[:, None] - buckets = (code[:-1] + code[1:]) / 2 - q = normalized.reshape(normalized.numel(), 1) > buckets - q = q.sum(dim=1).reshape(tensor.shape) - return q.to(torch.uint8), normalized.reshape(tensor.shape), absmax diff --git a/torchao/prototype/galore/optim/__init__.py b/torchao/prototype/galore/optim/__init__.py deleted file mode 100644 index e69de29bb2..0000000000 From e73a142fa6d0561f40daa5a3c1a727bae3bd2202 Mon Sep 17 00:00:00 2001 From: Thien Tran Date: Sat, 21 Jun 2025 23:44:47 +0700 Subject: [PATCH 142/165] Build mxfp4 kernel for sm120a (#2285) --- benchmarks/float8/bench_matmul.py | 60 ++++-- benchmarks/float8/utils.py | 9 +- setup.py | 81 +++++--- test/prototype/mx_formats/test_mx_mm.py | 5 +- torchao/__init__.py | 13 ++ ...els.cu => mx_fp_cutlass_kernels_sm100a.cu} | 0 .../mx_fp_cutlass_kernels_sm120a.cu | 195 ++++++++++++++++++ torchao/testing/float8/roofline_utils.py | 7 + 8 files changed, 320 insertions(+), 50 deletions(-) rename torchao/csrc/cuda/mx_kernels/{mx_fp_cutlass_kernels.cu => mx_fp_cutlass_kernels_sm100a.cu} (100%) create mode 100644 torchao/csrc/cuda/mx_kernels/mx_fp_cutlass_kernels_sm120a.cu diff --git a/benchmarks/float8/bench_matmul.py b/benchmarks/float8/bench_matmul.py index e3f19d8f49..d11c233ec0 100644 --- a/benchmarks/float8/bench_matmul.py +++ b/benchmarks/float8/bench_matmul.py @@ -16,6 +16,8 @@ get_name_to_shapes_iter, ) +from torchao.ops import mx_fp4_bf16 +from torchao.prototype.mx_formats.mx_tensor import to_mx from torchao.testing.float8.roofline_utils import get_specs @@ -62,13 +64,19 @@ def run( ): device = "cuda" # TODO(future PR): this is ugly - assert recipe in ("tensorwise", "rowwise", "mxfp8_cublas"), "unsupported" + assert recipe in ("tensorwise", "rowwise", "mxfp8_cublas", "mxfp4_cutlass"), ( + "unsupported" + ) + use_fp4 = recipe == "mxfp4_cutlass" specs = get_specs() bf16_peak_tops = specs["bf16_peak_tops"] fp8_peak_tops = specs["fp8_peak_tops"] + fp4_peak_tops = specs["fp4_peak_tops"] print(f"gpu_name: {torch.cuda.get_device_name(0)}") - print(f"peak tops: bf16 {bf16_peak_tops:.2e}, fp8 {fp8_peak_tops:.2e}") + print( + f"peak tops: bf16 {bf16_peak_tops:.2e}, fp8 {fp8_peak_tops:.2e}, fp4 {fp4_peak_tops:.2e}" + ) headers = ( "fast_accum", @@ -77,14 +85,14 @@ def run( "K", "N", "ref_time_s", - "fp8_time_s", - "fp8_speedup", + "time_s", + "speedup", ) results = [] dtype = torch.bfloat16 name_to_shapes = get_name_to_shapes_iter(shape_gen_name, M, K, N) - fast_accum_vals = [True, False] + fast_accum_vals = [False] if use_fp4 else [True, False] for idx, (fast_accum, (name, (M, K, N))) in enumerate( itertools.product(fast_accum_vals, name_to_shapes) @@ -107,35 +115,53 @@ def run( del A - # raw float8 matmul (upper bound for what we can achive in eager mode) - # TODO(future): add e5m2 - d1, d2, d3 = torch.float8_e4m3fn, torch.float8_e4m3fn, dtype - A = torch.zeros(M, K, device=device, dtype=d1) - B = torch.zeros(K, N, device=device, dtype=d2).t().contiguous().t() + A_hp = torch.randn(M, K, device=device) + B_hp_t = torch.randn(N, K, device=device) + + if use_fp4: + _, A = to_mx(A_hp, torch.float4_e2m1fn_x2, 32) + _, Bt = to_mx(B_hp_t, torch.float4_e2m1fn_x2, 32) + B = Bt.contiguous().T + peak_tops = fp4_peak_tops + else: + # raw float8 matmul (upper bound for what we can achive in eager mode) + # TODO(future): add e5m2 + d1, d2, d3 = torch.float8_e4m3fn, torch.float8_e4m3fn, dtype + A = A_hp.to(d1) + B = B_hp_t.to(d2).contiguous().T + peak_tops = fp8_peak_tops + if recipe == "tensorwise": scale_a = torch.tensor([1.0], device=device) scale_b = torch.tensor([1.0], device=device) elif recipe == "rowwise": scale_a = torch.ones(M, 1, device=device) scale_b = torch.ones(1, N, device=device) - elif recipe == "mxfp8_cublas": + elif recipe in ("mxfp8_cublas", "mxfp4_cutlass"): scale_a = torch.ones(M, K // 32, device=device, dtype=torch.float8_e8m0fnu) scale_b = torch.ones(N, K // 32, device=device, dtype=torch.float8_e8m0fnu) else: assert False, f"unknown recipe {recipe}" - def do_matmul(A, B): + def do_matmul_fp8(A, B): nonlocal scale_a nonlocal scale_b return torch._scaled_mm( A, B, scale_a, scale_b, out_dtype=d3, use_fast_accum=fast_accum ) - fp8_time_sec, fp8_tops_sec, fp8_pct_top_peak = do_benchmarks( - tops, fp8_peak_tops, use_gpu_kernel_time, do_matmul, A, B + def do_matmul_mxfp4(A, B): + nonlocal scale_a + nonlocal scale_b + return mx_fp4_bf16(A, B, scale_a, scale_b) + + do_matmul = do_matmul_mxfp4 if use_fp4 else do_matmul_fp8 + + time_sec, tops_sec, pct_top_peak = do_benchmarks( + tops, peak_tops, use_gpu_kernel_time, do_matmul, A, B ) print( - f"fp8 time_sec {fp8_time_sec:.2E}, tops/sec {fp8_tops_sec:.2E}, pct_peak {fp8_pct_top_peak:.3f}" + f"time_sec {time_sec:.2E}, tops/sec {tops_sec:.2E}, pct_peak {pct_top_peak:.3f}" ) del A, B, scale_a, scale_b @@ -148,8 +174,8 @@ def do_matmul(A, B): K, N, ref_time_sec, - fp8_time_sec, - ref_time_sec / fp8_time_sec, + time_sec, + ref_time_sec / time_sec, ] ) diff --git a/benchmarks/float8/utils.py b/benchmarks/float8/utils.py index 0ee2b922fc..6c3051937d 100644 --- a/benchmarks/float8/utils.py +++ b/benchmarks/float8/utils.py @@ -352,9 +352,6 @@ def get_gpu_kernel_gemm_time_s(f, *args, **kwargs): ) # there is only 1 key, aten::mm or aten::_scaled_mm, with unit nanoseconds assert len(data) == 1 - if "aten::mm" in data: - return data["aten::mm"] / 1e6 / n_iter - elif "aten::_scaled_mm" in data: - return data["aten::_scaled_mm"] / 1e6 / n_iter - else: - raise AssertionError("unexpected format of data") + key, value = next(iter(data.items())) + assert key in ("aten::mm", "aten::_scaled_mm", "torchao::mx_fp4_bf16") + return value / 1e6 / n_iter diff --git a/setup.py b/setup.py index 5560ab877e..65590b3cff 100644 --- a/setup.py +++ b/setup.py @@ -272,15 +272,18 @@ def get_cutlass_build_flags(): raise ValueError("No CUDA version found") major, minor = map(int, cuda_version.split(".")[:2]) - build_sm90a = major > 12 or (major == 12 and minor >= 6) - build_sm100a = major > 12 or (major == 12 and minor >= 8) + build_sm90a = (major, minor) >= (12, 6) + build_sm100a = (major, minor) >= (12, 8) + build_sm120a = (major, minor) >= (12, 8) if build_sm90a: print(f"CUDA {cuda_version}: Enabling SM90a CUTLASS kernels") if build_sm100a: print(f"CUDA {cuda_version}: Enabling SM100a CUTLASS kernels") + if build_sm120a: + print(f"CUDA {cuda_version}: Enabling SM120a CUTLASS kernels") - return build_sm90a, build_sm100a + return build_sm90a, build_sm100a, build_sm120a except: # Fallback to architecture flags cuda_arch_flags = _get_cuda_arch_flags() @@ -340,6 +343,11 @@ def __init__( self.cmake_args = cmake_args +def remove_items(a: list, b: list) -> list: + """Remove items in list b from list a""" + return [x for x in a if x not in b] + + def get_extensions(): # Skip building C++ extensions if USE_CPP is set to "0" if use_cpp == "0": @@ -454,7 +462,7 @@ def get_extensions(): excluded_sources = list( glob.glob(os.path.join(extensions_dir, "cpu/*.cpp"), recursive=True) ) - sources = [s for s in sources if s not in excluded_sources] + sources = remove_items(sources, excluded_sources) # Collect CUDA source files extensions_cuda_dir = os.path.join(extensions_dir, "cuda") @@ -498,22 +506,24 @@ def get_extensions(): rocm_sources = list( glob.glob(os.path.join(extensions_rocm_dir, "**/*.cpp"), recursive=True) ) - sources = [s for s in sources if s not in rocm_sources] + sources = remove_items(sources, rocm_sources) - use_cutlass = False + use_cutlass = use_cuda and not IS_WINDOWS cutlass_90a_sources = None cutlass_100a_sources = None + cutlass_120a_sources = None build_for_sm90a = False build_for_sm100a = False - if use_cuda and not IS_WINDOWS: - use_cutlass = True + build_for_sm120a = False + + if use_cutlass: cutlass_dir = os.path.join(third_party_path, "cutlass") cutlass_include_dir = os.path.join(cutlass_dir, "include") cutlass_tools_include_dir = os.path.join( cutlass_dir, "tools", "util", "include" ) cutlass_extensions_include_dir = os.path.join(cwd, extensions_cuda_dir) - if use_cutlass: + extra_compile_args["nvcc"].extend( [ "-DTORCHAO_USE_CUTLASS", @@ -533,7 +543,7 @@ def get_extensions(): ] ) - build_for_sm90a, build_for_sm100a = get_cutlass_build_flags() + build_for_sm90a, build_for_sm100a, build_for_sm120a = get_cutlass_build_flags() # Define sm90a sources cutlass_90a_sources = [ os.path.join( @@ -557,40 +567,40 @@ def get_extensions(): "rowwise_scaled_linear_sparse_cutlass_" + dtypes + ".cu", ) ) - # Always remove sm90a sources from main sources - sources = [s for s in sources if s not in cutlass_90a_sources] + sources = remove_items(sources, cutlass_90a_sources) # Always compile mx_fp_cutlass_kernels.cu ONLY with sm100a architecture cutlass_100a_sources = [ os.path.join( extensions_cuda_dir, "mx_kernels", - "mx_fp_cutlass_kernels.cu", + "mx_fp_cutlass_kernels_sm100a.cu", ), ] - # Remove from main sources to prevent compilation with other architectures - sources = [ - s for s in sources if os.path.basename(s) != "mx_fp_cutlass_kernels.cu" + sources = remove_items(sources, cutlass_100a_sources) + + # Always compile mx_fp_cutlass_kernels.cu ONLY with sm120a architecture + cutlass_120a_sources = [ + os.path.join( + extensions_cuda_dir, + "mx_kernels", + "mx_fp_cutlass_kernels_sm120a.cu", + ), ] + sources = remove_items(sources, cutlass_120a_sources) else: - # Remove CUTLASS-based kernels from the sources list. An - # assumption is that these files will have "cutlass" in its - # name. + # Remove CUTLASS-based kernels from the sources list. An assumption is that + # these files will have "cutlass" in its name. cutlass_sources = list( glob.glob( os.path.join(extensions_cuda_dir, "**/*cutlass*.cu"), recursive=True ) ) - sources = [s for s in sources if s not in cutlass_sources] + sources = remove_items(sources, cutlass_sources) ext_modules = [] if len(sources) > 0: - # Double-check to ensure mx_fp_cutlass_kernels.cu is not in sources - sources = [ - s for s in sources if os.path.basename(s) != "mx_fp_cutlass_kernels.cu" - ] - ext_modules.append( extension( "torchao._C", @@ -643,6 +653,27 @@ def get_extensions(): ) ) + # Only build the cutlass_120a extension if sm120a is in the architecture flags + if ( + cutlass_120a_sources is not None + and len(cutlass_120a_sources) > 0 + and build_for_sm120a + ): + cutlass_120a_extra_compile_args = copy.deepcopy(extra_compile_args) + # Only use sm120a architecture for these sources, ignoring cuda_arch_flags + cutlass_120a_extra_compile_args["nvcc"].append( + "-gencode=arch=compute_120a,code=sm_120a" + ) + ext_modules.append( + extension( + "torchao._C_cutlass_120a", + cutlass_120a_sources, + py_limited_api=True, + extra_compile_args=cutlass_120a_extra_compile_args, + extra_link_args=extra_link_args, + ) + ) + # Build CMakeLists from /torchao/experimental - additional options become available : TORCHAO_BUILD_CPU_AARCH64, TORCHAO_BUILD_KLEIDIAI, TORCHAO_BUILD_MPS_OPS, TORCHAO_PARALLEL_BACKEND if build_macos_arm_auto or os.getenv("BUILD_TORCHAO_EXPERIMENTAL") == "1": build_options = BuildOptions() diff --git a/test/prototype/mx_formats/test_mx_mm.py b/test/prototype/mx_formats/test_mx_mm.py index 46380cfb55..bf383446cd 100644 --- a/test/prototype/mx_formats/test_mx_mm.py +++ b/test/prototype/mx_formats/test_mx_mm.py @@ -14,7 +14,7 @@ from torchao.prototype.mx_formats.utils import to_blocked from torchao.utils import ( TORCH_VERSION_AT_LEAST_2_8, - is_sm_at_least_100, + is_sm_version, ) if not TORCH_VERSION_AT_LEAST_2_8: @@ -59,7 +59,8 @@ def run_matrix_test(M: int, K: int, N: int, format) -> float: @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") @pytest.mark.skipif( - not is_sm_at_least_100(), reason="CUDA capability >= 10.0 required for mxfloat8" + not (is_sm_version(10, 0) or is_sm_version(12, 0)), + reason="CUDA capability 10.0 or 12.0 is required for mxfloat8", ) @pytest.mark.parametrize( "size", diff --git a/torchao/__init__.py b/torchao/__init__.py index e6e291309f..2d40be2ac4 100644 --- a/torchao/__init__.py +++ b/torchao/__init__.py @@ -25,8 +25,21 @@ so_files = list(Path(__file__).parent.glob("_C*.so")) if len(so_files) > 0: + compute_capability = ( + torch.cuda.get_device_capability() if torch.cuda.is_available() else None + ) + for file in so_files: + # only load architecture-specific target if the current GPU matches that target + if ( + ("cutlass_90a" in file.name and compute_capability != (9, 0)) + or ("cutlass_100a" in file.name and compute_capability != (10, 0)) + or ("cutlass_120a" in file.name and compute_capability != (12, 0)) + ): + continue + torch.ops.load_library(str(file)) + from . import ops # The following library contains CPU kernels from torchao/experimental diff --git a/torchao/csrc/cuda/mx_kernels/mx_fp_cutlass_kernels.cu b/torchao/csrc/cuda/mx_kernels/mx_fp_cutlass_kernels_sm100a.cu similarity index 100% rename from torchao/csrc/cuda/mx_kernels/mx_fp_cutlass_kernels.cu rename to torchao/csrc/cuda/mx_kernels/mx_fp_cutlass_kernels_sm100a.cu diff --git a/torchao/csrc/cuda/mx_kernels/mx_fp_cutlass_kernels_sm120a.cu b/torchao/csrc/cuda/mx_kernels/mx_fp_cutlass_kernels_sm120a.cu new file mode 100644 index 0000000000..0436124cbb --- /dev/null +++ b/torchao/csrc/cuda/mx_kernels/mx_fp_cutlass_kernels_sm120a.cu @@ -0,0 +1,195 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// All rights reserved. +// +// This source code is licensed under the BSD 3-Clause license found in the +// LICENSE file in the root directory of this source tree. + +#include + +#include +#include +#include +#include +#include +#include + +#if defined(TORCHAO_USE_CUTLASS) && !defined(_WIN32) && \ + defined(CUDA_VERSION) && (CUDA_VERSION >= 12080) +#define BUILD_MX_KERNELS_CUTLASS +#endif + +#if defined(BUILD_MX_KERNELS_CUTLASS) + +#include "cute/tensor.hpp" +#include "cutlass/detail/sm100_blockscaled_layout.hpp" +#include "cutlass/epilogue/collective/collective_builder.hpp" +#include "cutlass/epilogue/thread/linear_combination.h" +#include "cutlass/gemm/collective/collective_builder.hpp" +#include "cutlass/gemm/device/gemm_universal_adapter.h" +#include "cutlass/util/packed_stride.hpp" + + +#endif + +namespace torchao { + +#if defined(BUILD_MX_KERNELS_CUTLASS) +namespace { + +using namespace cute; + +void run_gemm(at::Tensor& a, at::Tensor& b, at::Tensor& a_scale, + at::Tensor& b_scale, at::Tensor& out, int M, int K, int N) { + + using MmaTileShape = Shape<_128,_128,_128>; + using ClusterShape = Shape<_1,_1,_1>; + using PerSmTileShape_MNK = Shape<_128,_128,_128>; + + // A matrix configuration + using ElementA = cutlass::mx_float4_t; + using LayoutATag = cutlass::layout::RowMajor; // Layout type for A matrix operand + constexpr int AlignmentA = 32; // Memory access granularity/alignment of A matrix in units of elements (up to 16 bytes) + + // B matrix configuration + using ElementB = cutlass::mx_float4_t; + using LayoutBTag = cutlass::layout::ColumnMajor; // Layout type for B matrix operand + constexpr int AlignmentB = 32; // Memory access granularity/alignment of B matrix in units of elements (up to 16 bytes) + + // C/D matrix configuration + using ElementC = cutlass::bfloat16_t; // Element type for C matrix operand + using ElementD = cutlass::bfloat16_t; + using LayoutCTag = cutlass::layout::RowMajor; // Layout type for C matrix operand + using LayoutDTag = cutlass::layout::RowMajor; // Layout type for D matrix operand + constexpr int AlignmentD = 128 / cutlass::sizeof_bits::value; // Memory access granularity/alignment of D matrix in units of elements (up to 16 bytes) + constexpr int AlignmentC = 128 / cutlass::sizeof_bits::value; // Memory access granularity/alignment of C matrix in units of elements (up to 16 bytes) + // Kernel functional config + using ElementAccumulator = float; // Element type for internal accumulation + using ArchTag = cutlass::arch::Sm120; // Tag indicating the minimum SM that supports the intended feature + using OperatorClass = cutlass::arch::OpClassBlockScaledTensorOp; // Operator class tag + + using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder< + ArchTag, OperatorClass, + PerSmTileShape_MNK, ClusterShape, + cutlass::epilogue::collective::EpilogueTileAuto, + ElementAccumulator, ElementAccumulator, + ElementC, LayoutCTag, AlignmentC, + ElementD, LayoutDTag, AlignmentD, + cutlass::epilogue::collective::EpilogueScheduleAuto // Epilogue schedule policy + >::CollectiveOp; + + using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder< + ArchTag, OperatorClass, + ElementA, LayoutATag, AlignmentA, + ElementB, LayoutBTag, AlignmentB, + ElementAccumulator, + MmaTileShape, ClusterShape, + cutlass::gemm::collective::StageCountAutoCarveout(sizeof(typename CollectiveEpilogue::SharedStorage))>, + cutlass::gemm::collective::KernelScheduleAuto // Kernel schedule policy. Auto or using targeted scheduling policy + >::CollectiveOp; + + using GemmKernel = cutlass::gemm::kernel::GemmUniversal< + Shape, // Indicates ProblemShape + CollectiveMainloop, + CollectiveEpilogue, + void>; + + using Gemm = cutlass::gemm::device::GemmUniversalAdapter; + + // Reference device GEMM implementation type + using StrideA = typename Gemm::GemmKernel::StrideA; + using StrideB = typename Gemm::GemmKernel::StrideB; + using StrideC = typename Gemm::GemmKernel::StrideC; + using StrideD = typename Gemm::GemmKernel::StrideD; + using LayoutSFA = typename Gemm::GemmKernel::CollectiveMainloop::LayoutSFA; + using LayoutSFB = typename Gemm::GemmKernel::CollectiveMainloop::LayoutSFB; + using Sm1xxBlkScaledConfig = typename Gemm::GemmKernel::CollectiveMainloop::Sm1xxBlkScaledConfig; + + // Initialize strides using packed stride configuration + auto stride_A = cutlass::make_cute_packed_stride(StrideA{}, make_shape(M, K, 1)); + auto stride_B = cutlass::make_cute_packed_stride(StrideB{}, make_shape(N, K, 1)); + auto stride_D = cutlass::make_cute_packed_stride(StrideD{}, make_shape(M, N, 1)); + + // Initialize scale factor layouts using block scaled configuration + auto layout_SFA = Sm1xxBlkScaledConfig::tile_atom_to_shape_SFA(make_shape(M, N, K, 1)); + auto layout_SFB = Sm1xxBlkScaledConfig::tile_atom_to_shape_SFB(make_shape(M, N, K, 1)); + + using DtypeA = typename ElementA::DataType; + using DtypeB = typename ElementB::DataType; + using DtypeScaleA = typename ElementA::ScaleFactorType; + using DtypeScaleB = typename ElementB::ScaleFactorType; + using DtypeOut = ElementD; + + Gemm gemm; + + auto A_ptr = reinterpret_cast(a.data_ptr()); + auto B_ptr = reinterpret_cast(b.data_ptr()); + auto SFA_ptr = reinterpret_cast(a_scale.data_ptr()); + auto SFB_ptr = reinterpret_cast(b_scale.data_ptr()); + auto out_ptr = reinterpret_cast(out.data_ptr()); + + typename Gemm::Arguments arguments{ + cutlass::gemm::GemmUniversalMode::kGemm, + {M, N, K, 1}, + { // Mainloop arguments + A_ptr, stride_A, + B_ptr, stride_B, + SFA_ptr, layout_SFA, + SFB_ptr, layout_SFB + }, + { // Epilogue arguments + {1.0, 0.0}, + nullptr, StrideC{}, // No bias for now + out_ptr, stride_D + } + }; + + // Check the problem size is supported or not + cutlass::Status status = gemm.can_implement(arguments); + TORCH_CHECK(status == cutlass::Status::kSuccess, "Cutlass cannot implement"); + // Allocate workspace memory + size_t workspace_size = Gemm::get_workspace_size(arguments); + auto workspace = a.new_empty( + {static_cast(workspace_size)}, + at::TensorOptions().dtype(at::kByte)); + + // Initialize CUTLASS kernel with arguments and workspace pointer + status = gemm.initialize(arguments, workspace.data_ptr()); + TORCH_CHECK(status == cutlass::Status::kSuccess, "Cutlass cannot initialize"); + + status = gemm.run(at::cuda::getCurrentCUDAStream()); + TORCH_CHECK(status == cutlass::Status::kSuccess, "Cutlass cannot run", cutlass::cutlassGetStatusString(status)); + + C10_CUDA_KERNEL_LAUNCH_CHECK(); + +} +} +#endif + +at::Tensor mx_fp4_bf16(at::Tensor a, at::Tensor b, at::Tensor a_scale, + at::Tensor b_scale) { +#if defined(BUILD_MX_KERNELS_CUTLASS) + TORCH_CHECK(a.is_cuda(), "a must be CUDA tensor"); + TORCH_CHECK(b.is_cuda(), "b must be CUDA tensor"); + TORCH_CHECK(a_scale.is_cuda(), "a_scale must be CUDA tensor"); + TORCH_CHECK(b_scale.is_cuda(), "b_scale must be CUDA tensor"); + + auto M = a.size(0); + auto K = a.size(1) * 2; + auto N = b.size(1); + + auto out = + at::empty({M, N}, a.options().dtype(at::kBFloat16)); + + run_gemm(a, b, a_scale, b_scale, out, M, K, N); + return out; +#else + TORCH_CHECK_NOT_IMPLEMENTED(false, __func__); + return at::Tensor{}; +#endif +} + +TORCH_LIBRARY_IMPL(torchao, CUDA, m) { + m.impl("torchao::mx_fp4_bf16", &mx_fp4_bf16); +} + +} // namespace torchao diff --git a/torchao/testing/float8/roofline_utils.py b/torchao/testing/float8/roofline_utils.py index 7bfb9887df..286803dbf2 100644 --- a/torchao/testing/float8/roofline_utils.py +++ b/torchao/testing/float8/roofline_utils.py @@ -54,6 +54,13 @@ # TODO(future): run measurement on hardware "pct_achievable_mem_bw": 0.92, }, + "NVIDIA GeForce RTX 5090": { + # https://images.nvidia.com/aem-dam/Solutions/geforce/blackwell/nvidia-rtx-blackwell-gpu-architecture.pdf + "bf16_peak_tops": 209.5e12, + "fp8_peak_tops": 419e12, + "fp4_peak_tops": 1676e12, + "peak_mem_bw_bytes_sec": 1.792e15, + }, # TODO(future): more GPU names } From 28989031568710ec6739eb901708eb392782cfd1 Mon Sep 17 00:00:00 2001 From: Daniel Vega-Myhre Date: Sat, 21 Jun 2025 09:54:00 -0700 Subject: [PATCH 143/165] [float8 moe training] FSDP support (#2413) * fsdp support in moe training * unwrap args and kwargs * disable func * fsdp working * roll back use_triton flag * add fsdp test for moe training --- test/prototype/moe_training/test_fsdp.py | 156 ++++++++++++++++++ .../benchmarks/benchmark_scaled_grouped_mm.py | 32 +--- .../moe_training/conversion_utils.py | 8 +- .../moe_training/scaled_grouped_mm.py | 20 +-- torchao/prototype/moe_training/tensor.py | 102 ++++++++++-- 5 files changed, 253 insertions(+), 65 deletions(-) create mode 100644 test/prototype/moe_training/test_fsdp.py diff --git a/test/prototype/moe_training/test_fsdp.py b/test/prototype/moe_training/test_fsdp.py new file mode 100644 index 0000000000..4994a76854 --- /dev/null +++ b/test/prototype/moe_training/test_fsdp.py @@ -0,0 +1,156 @@ +import copy +import os + +import pytest +import torch +from torch import distributed as dist +from torch import nn +from torch.distributed._composable.fsdp import fully_shard +from torch.nn import functional as F + +# this feature requires CUDA and SM89+ +if not torch.cuda.is_available() or torch.cuda.get_device_capability() < (8, 9): + pytest.skip( + "CUDA not available or compute capability < 8.9", allow_module_level=True + ) + +from torchao.float8.float8_utils import compute_error +from torchao.prototype.moe_training.conversion_utils import MoETrainingConfig +from torchao.prototype.moe_training.tensor import ScaledGroupedMMTensor +from torchao.quantization.quant_api import quantize_ + +# this test requires torchtitan +try: + from torchtitan.experiments.llama4.model.args import TransformerModelArgs + from torchtitan.experiments.llama4.model.moe import MoE +except ImportError: + import warnings + + warnings.warn("torchtitan not installed, skipping MoE tests.") + pytest.skip(allow_module_level=True) + + +def test_moe_float8_training_fsdp(): + assert torch.cuda.is_available() + + # setup distributed for fsdp + setup_distributed() + + # define model args + target_fqns = ["experts"] + model_args = TransformerModelArgs( + moe_enabled=True, + num_experts=8, + dim=256, + ) + init_std = 0.02 + device = torch.device("cuda") + + # reference bf16 MoE + ref_model = MoE(model_args).to(torch.bfloat16).cuda() + torch.manual_seed(42) + ref_model.init_weights(init_std, device) + + # target MoE for testing conversion + model = copy.deepcopy(ref_model) + + # assert starting params are identical for both models + for param1, param2 in zip(model.parameters(), ref_model.parameters()): + assert torch.equal(param1, param2) + + # convert MoE to float8 training + def moe_module_filter_fn(mod: nn.Module, cur_fqn: str) -> bool: + for target_fqn in target_fqns: + if target_fqn in cur_fqn: + return True + return False + + # quantize test model + config = MoETrainingConfig() + quantize_(model, config=config, filter_fn=moe_module_filter_fn) + + # validate that only the experts were converted + _validate_model_conversion( + model, + target_fqns=target_fqns, + ) + + # FSDP2 + fully_shard(model) + fully_shard(ref_model) + + # inputs + batch, seq, dim = 8, 2048, 256 + ref_x = torch.randn( + batch, seq, dim, dtype=torch.bfloat16, requires_grad=True, device=device + ) + x = ref_x.detach().clone().requires_grad_(True) + + # forward pass + ref_out = ref_model(ref_x) + out = model(x) + + # validate output + out_sqnr = compute_error(out, ref_out) + assert out_sqnr.item() >= 30.0, f"SQNR must be >= 30.0, got {out_sqnr.item()}." + + # compute loss + labels = torch.ones_like(ref_out) + ref_loss = F.mse_loss(ref_out, labels) + out_loss = F.mse_loss(out, labels) + + # backward pass + ref_loss.backward() + out_loss.backward() + + # validate input gradient + input_grad_sqnr = compute_error(x.grad, ref_x.grad) + assert input_grad_sqnr.item() >= 30.0, ( + f"SQNR must be >= 30.0, got {input_grad_sqnr.item()}." + ) + + # validate param gradients + for param1, param2 in zip(model.parameters(), ref_model.parameters()): + param_grad_sqnr = compute_error(param1.grad, param2.grad) + assert param_grad_sqnr.item() >= 25.0, ( + f"SQNR must be >= 25.0, got {param_grad_sqnr.item()}." + ) + + dist.destroy_process_group() + + +def _validate_model_conversion( + root_module: nn.Module, + target_fqns: list[str], +): + def _recursive_validate( + module: nn.Module, + cur_fqn: str, + ): + is_allowed_module = cur_fqn in target_fqns + + # check current module params + for param_name, param in module.named_parameters(recurse=False): + is_converted_type = isinstance(param, ScaledGroupedMMTensor) + if is_converted_type: + assert is_allowed_module, ( + f"Module {cur_fqn} is not in target_fqns, but has converted param {param_name}." + ) + if not is_allowed_module: + assert not is_converted_type, ( + f"Module {cur_fqn} is not in target_fqns, but has converted param {param_name}." + ) + + # recursively check child modules + for child_name, child_module in module.named_children(): + child_fqn = f"{cur_fqn}.{child_name}" if cur_fqn else child_name + _recursive_validate(child_module, child_fqn) + + _recursive_validate(root_module, "") + + +def setup_distributed(): + rank = int(os.environ["RANK"]) + world_size = int(os.environ["WORLD_SIZE"]) + dist.init_process_group("nccl", rank=rank, world_size=world_size) + torch.cuda.set_device(rank) diff --git a/torchao/prototype/moe_training/benchmarks/benchmark_scaled_grouped_mm.py b/torchao/prototype/moe_training/benchmarks/benchmark_scaled_grouped_mm.py index a347763fe6..c229eaeb71 100644 --- a/torchao/prototype/moe_training/benchmarks/benchmark_scaled_grouped_mm.py +++ b/torchao/prototype/moe_training/benchmarks/benchmark_scaled_grouped_mm.py @@ -31,9 +31,7 @@ class ExperimentConfig: @dataclass(frozen=True) class ExperimentResult: - torch_time_us: float - triton_time_us: bool - triton_speedup: float + time_us: float @dataclass(frozen=True) @@ -98,36 +96,26 @@ def warmup(func, *args, **kwargs): for _ in range(10): func(*args, **kwargs) - def forward_backward(A, B_t, offs, use_triton=True): + def forward_backward(A, B_t, offs): out = _scaled_grouped_mm( A, B_t, offs=offs, out_dtype=torch.bfloat16, - use_triton_for_per_group_scales=use_triton, ) out.sum().backward() torch.cuda.synchronize() # benchmark torch torch_func = torch.compile(forward_backward) if args.compile else forward_backward - warmup(torch_func, A, B_t, offs, use_triton=False) + warmup(torch_func, A, B_t, offs) start_time_ns = time.perf_counter_ns() - torch_func(A, B_t, offs, use_triton=False) + torch_func(A, B_t, offs) torch_time_ns = time.perf_counter_ns() - start_time_ns - torch_time_us = torch_time_ns / 1e3 - - # benchmark triton - warmup(forward_backward, A, B_t, offs, use_triton=True) - start_time_ns = time.perf_counter_ns() - forward_backward(A, B_t, offs, use_triton=True) - triton_time_ns = time.perf_counter_ns() - start_time_ns - triton_time_us = triton_time_ns / 1e3 + time_us = torch_time_ns / 1e3 return ExperimentResult( - torch_time_us=round(torch_time_us, 3), - triton_time_us=round(triton_time_us, 3), - triton_speedup=round(torch_time_us / triton_time_us, 3), + time_us=round(time_us, 3), ) @@ -135,9 +123,7 @@ def print_results(experiments: List[Experiment]): headers = [ "A_shape", "B_shape", - "torch_time_us", - "triton_time_us", - "triton_speedup", + "time_us", ] rows = [] for experiment in experiments: @@ -147,9 +133,7 @@ def print_results(experiments: List[Experiment]): [ A_shape, B_shape, - experiment.result.torch_time_us, - experiment.result.triton_time_us, - experiment.result.triton_speedup, + experiment.result.time_us, ] ) print(tabulate(rows, headers=headers)) diff --git a/torchao/prototype/moe_training/conversion_utils.py b/torchao/prototype/moe_training/conversion_utils.py index 4d65303b89..51af0fd956 100644 --- a/torchao/prototype/moe_training/conversion_utils.py +++ b/torchao/prototype/moe_training/conversion_utils.py @@ -28,9 +28,6 @@ class MoETrainingConfig(AOBaseConfig): For all other ops, ScaledGroupedMMTensor behaves like a regular torch.Tensor. """ - # temporary config flag for testing/benchmarking, will remove before graduating out of prototype - use_triton_for_per_group_scales: bool = True - @register_quantize_module_handler(MoETrainingConfig) def _moe_training_transform( @@ -71,7 +68,6 @@ def _swap_params( Returns: nn.Module: The modified module with swapped linear layers. """ - use_triton = config.use_triton_for_per_group_scales if config is not None else False if isinstance(module, nn.Parameter) and ( module_filter_fn is None or module_filter_fn(module, "") ): @@ -80,9 +76,7 @@ def _swap_params( f"Does not support a root nn.Parameter with children: {module}" ) if not isinstance(module.data, ScaledGroupedMMTensor): - new_data = ScaledGroupedMMTensor( - module.data, use_triton_for_per_group_scales=use_triton - ) + new_data = ScaledGroupedMMTensor(module.data) return nn.Parameter(new_data, requires_grad=module.requires_grad) return module diff --git a/torchao/prototype/moe_training/scaled_grouped_mm.py b/torchao/prototype/moe_training/scaled_grouped_mm.py index f7d470e556..29adffd831 100644 --- a/torchao/prototype/moe_training/scaled_grouped_mm.py +++ b/torchao/prototype/moe_training/scaled_grouped_mm.py @@ -16,8 +16,6 @@ ) from torchao.prototype.moe_training.utils import ( _is_column_major, - _to_2d_jagged_float8_tensor_colwise, - _to_2d_jagged_float8_tensor_rowwise, ) @@ -26,7 +24,6 @@ def _scaled_grouped_mm( B_t: torch.Tensor, offs: torch.Tensor, out_dtype: Optional[torch.dtype] = torch.bfloat16, - use_triton_for_per_group_scales: bool = True, ) -> torch.Tensor: """ This function performs dynamic float8 quantization with row-wise scaling @@ -143,7 +140,6 @@ def forward( # Store what we need for backward. ctx.save_for_backward(A, B_fp8_col_major, B_scales, offs) ctx.out_dtype = out_dtype - ctx.use_triton_for_per_group_scales = use_triton_for_per_group_scales # Perform scaled grouped GEMM and return result. # output shape: scaled grouped mm of (M,K) @ (B,K,N) = (M,N) @@ -167,7 +163,6 @@ def forward( def backward(ctx, grad_output: torch.Tensor): A, B_fp8_col_major, B_scales, offs = ctx.saved_tensors out_dtype = ctx.out_dtype - use_triton_for_per_group_scales = ctx.use_triton_for_per_group_scales # Convert grad_output to float8, row-major for left operand of grouped GEMM # needed for grad_A: grad_output @ B @@ -216,19 +211,8 @@ def backward(ctx, grad_output: torch.Tensor): # grad_B is a special case. both operands of the grouped gemm will be 2D with offsets determing the "groups." # Compute scales for grad_output_t and A, which are both 2D tensors with offsets which define the "jagged" groups. - per_group_rowwise_scale_func = ( - triton_fp8_row_major_jagged_rowwise_scales - if use_triton_for_per_group_scales - else _to_2d_jagged_float8_tensor_rowwise - ) - per_group_colwise_scale_func = ( - triton_fp8_col_major_jagged_colwise_scales - if use_triton_for_per_group_scales - else _to_2d_jagged_float8_tensor_colwise - ) - grad_output_t_fp8_row_major, grad_output_t_scales = ( - per_group_rowwise_scale_func( + triton_fp8_row_major_jagged_rowwise_scales( grad_output_t_row_major, offs, torch.float8_e4m3fn, @@ -236,7 +220,7 @@ def backward(ctx, grad_output: torch.Tensor): ) ) - A_fp8_col_major, A_scales = per_group_colwise_scale_func( + A_fp8_col_major, A_scales = triton_fp8_col_major_jagged_colwise_scales( A_col_major, offs, torch.float8_e4m3fn, diff --git a/torchao/prototype/moe_training/tensor.py b/torchao/prototype/moe_training/tensor.py index 8d7a8f815b..3ea9529237 100644 --- a/torchao/prototype/moe_training/tensor.py +++ b/torchao/prototype/moe_training/tensor.py @@ -1,7 +1,24 @@ +from typing import Any, Optional, Tuple + import torch +import torch.utils._pytree as pytree +from torch._prims_common import suggest_memory_format from torchao.prototype.moe_training import _scaled_grouped_mm +_ops_to_preserve_subclass = { + torch.ops.aten.empty_like.default, + torch.ops.aten.new_zeros.default, + torch.ops.aten.slice.Tensor, + torch.ops.aten.copy_.default, + torch.ops.aten.view.default, + torch.ops.aten.as_strided.default, + torch.ops.aten._to_copy.default, + torch.ops.aten._pin_memory.default, + torch.ops.aten.split.Tensor, + torch.ops.aten.clone.default, +} + class ScaledGroupedMMTensor(torch.Tensor): """ @@ -12,19 +29,34 @@ class ScaledGroupedMMTensor(torch.Tensor): grouped_mm_func_name = "_grouped_mm" offs_arg_name = "offs" - use_triton_for_per_group_scales = True - def __init__( - self, data: torch.Tensor, use_triton_for_per_group_scales: bool = True + @staticmethod + def __new__( + cls, + tensor: torch.Tensor, ): - self._data = data - self._use_triton_for_per_group_scales = use_triton_for_per_group_scales + return torch.Tensor._make_wrapper_subclass( + cls, + tensor.size(), + strides=tensor.stride(), + storage_offset=tensor.storage_offset(), + memory_format=suggest_memory_format(tensor), + dtype=tensor.dtype, + layout=tensor.layout, + device=tensor.device, + pin_memory=tensor.is_pinned(), + requires_grad=tensor.requires_grad, + ) - def __repr__(self): - return f"ScaledGroupedMMTensor(use_triton_for_per_group_scales={self._use_triton_for_per_group_scales}, {self._data})" + def __init__( + self, + tensor: torch.Tensor, + ): + self._data = tensor @classmethod def __torch_function__(cls, func, types, args, kwargs={}): + # override the grouped mm op to use the differentiable _scaled_grouped_mm if func.__name__ == cls.grouped_mm_func_name: # Use torchao scaled grouped mm with dynamic quant for # "2d x 3d with offsets" case (used for routed experts). @@ -38,16 +70,54 @@ def __torch_function__(cls, func, types, args, kwargs={}): B_is_3d = B.dim() == 3 has_offs = kwargs.get(cls.offs_arg_name) is not None if A_is_2d and B_is_3d and has_offs: - # prefer to use B to check use_triton, as that will be the weight/nn.Parameter - # that is converted to ScaledGroupedMMTensor - use_triton = ( - B._use_triton_for_per_group_scales - if isinstance(B, cls) - else A._use_triton_for_per_group_scales - ) return _scaled_grouped_mm( *args, - use_triton_for_per_group_scales=use_triton, **kwargs, ) - return super().__torch_function__(func, types, args, kwargs) + + # Disable torch_function by hand because we don't want + # the wrapping behavior of the super() impl, go directly to dispatch + with torch._C.DisableTorchFunctionSubclass(): + return func(*args, **kwargs) + + @classmethod + def __torch_dispatch__(cls, func, types, args, kwargs={}): + # detach is special case + if func == torch.ops.aten.detach.default: + return ScaledGroupedMMTensor(args[0]._data) + + # unwrap args and kwargs + unwrap = lambda tensor: tensor._data + args, kwargs = pytree.tree_map_only( + ScaledGroupedMMTensor, unwrap, (args, kwargs or {}) + ) + + # perform op + out = func(*args, **kwargs) + + # return regular tensors for ops that don't preserve subclass + if func not in _ops_to_preserve_subclass: + return out + + # wrap outputs back into ScaledGroupedMMTensor for ops that do preserve subclass + return pytree.tree_map_only( + torch.Tensor, + lambda x: ScaledGroupedMMTensor(x), + out, + ) + + def fsdp_pre_all_gather(self, mesh): + return (self._data,), () + + def fsdp_post_all_gather( + self, + all_gather_outputs: Tuple[torch.Tensor, ...], + metadata: Any, + param_dtype: torch.dtype, + *, + out: Optional[torch.Tensor] = None, + ): + (data,) = all_gather_outputs + return ScaledGroupedMMTensor( + data, + ), (data,) From 4e25496f10073c0ca8f9f174046080eea842d45e Mon Sep 17 00:00:00 2001 From: Gasoonjia Date: Mon, 23 Jun 2025 13:58:09 -0700 Subject: [PATCH 144/165] mitigate the numeric test issue (#2426) mitigate the numeric test issue (#2426) Summary: this update mitigate the existing numeric debug ppl issue. Will further investigate it and reenable the test Reviewed By: jerryzh168 Differential Revision: D77162495 --- test/quantization/pt2e/test_numeric_debugger.py | 1 + 1 file changed, 1 insertion(+) diff --git a/test/quantization/pt2e/test_numeric_debugger.py b/test/quantization/pt2e/test_numeric_debugger.py index 040cd1edcf..e935c25d68 100644 --- a/test/quantization/pt2e/test_numeric_debugger.py +++ b/test/quantization/pt2e/test_numeric_debugger.py @@ -24,6 +24,7 @@ from torch.export import export_for_training +@unittest.skip("skip for now, need to fix") @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_7, "Requires torch 2.7+") @unittest.skipIf(IS_WINDOWS, "Windows not yet supported for torch.compile") class TestNumericDebuggerInfra(PT2ENumericDebuggerTestCase): From d506cc7831e2b7da5a44b4360cc3c94d8f3d4972 Mon Sep 17 00:00:00 2001 From: Jerry Zhang Date: Mon, 23 Jun 2025 18:35:12 -0700 Subject: [PATCH 145/165] Revert "Build mxfp4 kernel for sm120a" (#2428) Revert "Build mxfp4 kernel for sm120a (#2285)" This reverts commit e73a142fa6d0561f40daa5a3c1a727bae3bd2202. --- benchmarks/float8/bench_matmul.py | 60 ++---- benchmarks/float8/utils.py | 9 +- setup.py | 81 +++----- test/prototype/mx_formats/test_mx_mm.py | 5 +- torchao/__init__.py | 13 -- ...els_sm100a.cu => mx_fp_cutlass_kernels.cu} | 0 .../mx_fp_cutlass_kernels_sm120a.cu | 195 ------------------ torchao/testing/float8/roofline_utils.py | 7 - 8 files changed, 50 insertions(+), 320 deletions(-) rename torchao/csrc/cuda/mx_kernels/{mx_fp_cutlass_kernels_sm100a.cu => mx_fp_cutlass_kernels.cu} (100%) delete mode 100644 torchao/csrc/cuda/mx_kernels/mx_fp_cutlass_kernels_sm120a.cu diff --git a/benchmarks/float8/bench_matmul.py b/benchmarks/float8/bench_matmul.py index d11c233ec0..e3f19d8f49 100644 --- a/benchmarks/float8/bench_matmul.py +++ b/benchmarks/float8/bench_matmul.py @@ -16,8 +16,6 @@ get_name_to_shapes_iter, ) -from torchao.ops import mx_fp4_bf16 -from torchao.prototype.mx_formats.mx_tensor import to_mx from torchao.testing.float8.roofline_utils import get_specs @@ -64,19 +62,13 @@ def run( ): device = "cuda" # TODO(future PR): this is ugly - assert recipe in ("tensorwise", "rowwise", "mxfp8_cublas", "mxfp4_cutlass"), ( - "unsupported" - ) - use_fp4 = recipe == "mxfp4_cutlass" + assert recipe in ("tensorwise", "rowwise", "mxfp8_cublas"), "unsupported" specs = get_specs() bf16_peak_tops = specs["bf16_peak_tops"] fp8_peak_tops = specs["fp8_peak_tops"] - fp4_peak_tops = specs["fp4_peak_tops"] print(f"gpu_name: {torch.cuda.get_device_name(0)}") - print( - f"peak tops: bf16 {bf16_peak_tops:.2e}, fp8 {fp8_peak_tops:.2e}, fp4 {fp4_peak_tops:.2e}" - ) + print(f"peak tops: bf16 {bf16_peak_tops:.2e}, fp8 {fp8_peak_tops:.2e}") headers = ( "fast_accum", @@ -85,14 +77,14 @@ def run( "K", "N", "ref_time_s", - "time_s", - "speedup", + "fp8_time_s", + "fp8_speedup", ) results = [] dtype = torch.bfloat16 name_to_shapes = get_name_to_shapes_iter(shape_gen_name, M, K, N) - fast_accum_vals = [False] if use_fp4 else [True, False] + fast_accum_vals = [True, False] for idx, (fast_accum, (name, (M, K, N))) in enumerate( itertools.product(fast_accum_vals, name_to_shapes) @@ -115,53 +107,35 @@ def run( del A - A_hp = torch.randn(M, K, device=device) - B_hp_t = torch.randn(N, K, device=device) - - if use_fp4: - _, A = to_mx(A_hp, torch.float4_e2m1fn_x2, 32) - _, Bt = to_mx(B_hp_t, torch.float4_e2m1fn_x2, 32) - B = Bt.contiguous().T - peak_tops = fp4_peak_tops - else: - # raw float8 matmul (upper bound for what we can achive in eager mode) - # TODO(future): add e5m2 - d1, d2, d3 = torch.float8_e4m3fn, torch.float8_e4m3fn, dtype - A = A_hp.to(d1) - B = B_hp_t.to(d2).contiguous().T - peak_tops = fp8_peak_tops - + # raw float8 matmul (upper bound for what we can achive in eager mode) + # TODO(future): add e5m2 + d1, d2, d3 = torch.float8_e4m3fn, torch.float8_e4m3fn, dtype + A = torch.zeros(M, K, device=device, dtype=d1) + B = torch.zeros(K, N, device=device, dtype=d2).t().contiguous().t() if recipe == "tensorwise": scale_a = torch.tensor([1.0], device=device) scale_b = torch.tensor([1.0], device=device) elif recipe == "rowwise": scale_a = torch.ones(M, 1, device=device) scale_b = torch.ones(1, N, device=device) - elif recipe in ("mxfp8_cublas", "mxfp4_cutlass"): + elif recipe == "mxfp8_cublas": scale_a = torch.ones(M, K // 32, device=device, dtype=torch.float8_e8m0fnu) scale_b = torch.ones(N, K // 32, device=device, dtype=torch.float8_e8m0fnu) else: assert False, f"unknown recipe {recipe}" - def do_matmul_fp8(A, B): + def do_matmul(A, B): nonlocal scale_a nonlocal scale_b return torch._scaled_mm( A, B, scale_a, scale_b, out_dtype=d3, use_fast_accum=fast_accum ) - def do_matmul_mxfp4(A, B): - nonlocal scale_a - nonlocal scale_b - return mx_fp4_bf16(A, B, scale_a, scale_b) - - do_matmul = do_matmul_mxfp4 if use_fp4 else do_matmul_fp8 - - time_sec, tops_sec, pct_top_peak = do_benchmarks( - tops, peak_tops, use_gpu_kernel_time, do_matmul, A, B + fp8_time_sec, fp8_tops_sec, fp8_pct_top_peak = do_benchmarks( + tops, fp8_peak_tops, use_gpu_kernel_time, do_matmul, A, B ) print( - f"time_sec {time_sec:.2E}, tops/sec {tops_sec:.2E}, pct_peak {pct_top_peak:.3f}" + f"fp8 time_sec {fp8_time_sec:.2E}, tops/sec {fp8_tops_sec:.2E}, pct_peak {fp8_pct_top_peak:.3f}" ) del A, B, scale_a, scale_b @@ -174,8 +148,8 @@ def do_matmul_mxfp4(A, B): K, N, ref_time_sec, - time_sec, - ref_time_sec / time_sec, + fp8_time_sec, + ref_time_sec / fp8_time_sec, ] ) diff --git a/benchmarks/float8/utils.py b/benchmarks/float8/utils.py index 6c3051937d..0ee2b922fc 100644 --- a/benchmarks/float8/utils.py +++ b/benchmarks/float8/utils.py @@ -352,6 +352,9 @@ def get_gpu_kernel_gemm_time_s(f, *args, **kwargs): ) # there is only 1 key, aten::mm or aten::_scaled_mm, with unit nanoseconds assert len(data) == 1 - key, value = next(iter(data.items())) - assert key in ("aten::mm", "aten::_scaled_mm", "torchao::mx_fp4_bf16") - return value / 1e6 / n_iter + if "aten::mm" in data: + return data["aten::mm"] / 1e6 / n_iter + elif "aten::_scaled_mm" in data: + return data["aten::_scaled_mm"] / 1e6 / n_iter + else: + raise AssertionError("unexpected format of data") diff --git a/setup.py b/setup.py index 65590b3cff..5560ab877e 100644 --- a/setup.py +++ b/setup.py @@ -272,18 +272,15 @@ def get_cutlass_build_flags(): raise ValueError("No CUDA version found") major, minor = map(int, cuda_version.split(".")[:2]) - build_sm90a = (major, minor) >= (12, 6) - build_sm100a = (major, minor) >= (12, 8) - build_sm120a = (major, minor) >= (12, 8) + build_sm90a = major > 12 or (major == 12 and minor >= 6) + build_sm100a = major > 12 or (major == 12 and minor >= 8) if build_sm90a: print(f"CUDA {cuda_version}: Enabling SM90a CUTLASS kernels") if build_sm100a: print(f"CUDA {cuda_version}: Enabling SM100a CUTLASS kernels") - if build_sm120a: - print(f"CUDA {cuda_version}: Enabling SM120a CUTLASS kernels") - return build_sm90a, build_sm100a, build_sm120a + return build_sm90a, build_sm100a except: # Fallback to architecture flags cuda_arch_flags = _get_cuda_arch_flags() @@ -343,11 +340,6 @@ def __init__( self.cmake_args = cmake_args -def remove_items(a: list, b: list) -> list: - """Remove items in list b from list a""" - return [x for x in a if x not in b] - - def get_extensions(): # Skip building C++ extensions if USE_CPP is set to "0" if use_cpp == "0": @@ -462,7 +454,7 @@ def get_extensions(): excluded_sources = list( glob.glob(os.path.join(extensions_dir, "cpu/*.cpp"), recursive=True) ) - sources = remove_items(sources, excluded_sources) + sources = [s for s in sources if s not in excluded_sources] # Collect CUDA source files extensions_cuda_dir = os.path.join(extensions_dir, "cuda") @@ -506,24 +498,22 @@ def get_extensions(): rocm_sources = list( glob.glob(os.path.join(extensions_rocm_dir, "**/*.cpp"), recursive=True) ) - sources = remove_items(sources, rocm_sources) + sources = [s for s in sources if s not in rocm_sources] - use_cutlass = use_cuda and not IS_WINDOWS + use_cutlass = False cutlass_90a_sources = None cutlass_100a_sources = None - cutlass_120a_sources = None build_for_sm90a = False build_for_sm100a = False - build_for_sm120a = False - - if use_cutlass: + if use_cuda and not IS_WINDOWS: + use_cutlass = True cutlass_dir = os.path.join(third_party_path, "cutlass") cutlass_include_dir = os.path.join(cutlass_dir, "include") cutlass_tools_include_dir = os.path.join( cutlass_dir, "tools", "util", "include" ) cutlass_extensions_include_dir = os.path.join(cwd, extensions_cuda_dir) - + if use_cutlass: extra_compile_args["nvcc"].extend( [ "-DTORCHAO_USE_CUTLASS", @@ -543,7 +533,7 @@ def get_extensions(): ] ) - build_for_sm90a, build_for_sm100a, build_for_sm120a = get_cutlass_build_flags() + build_for_sm90a, build_for_sm100a = get_cutlass_build_flags() # Define sm90a sources cutlass_90a_sources = [ os.path.join( @@ -567,40 +557,40 @@ def get_extensions(): "rowwise_scaled_linear_sparse_cutlass_" + dtypes + ".cu", ) ) - sources = remove_items(sources, cutlass_90a_sources) + # Always remove sm90a sources from main sources + sources = [s for s in sources if s not in cutlass_90a_sources] # Always compile mx_fp_cutlass_kernels.cu ONLY with sm100a architecture cutlass_100a_sources = [ os.path.join( extensions_cuda_dir, "mx_kernels", - "mx_fp_cutlass_kernels_sm100a.cu", + "mx_fp_cutlass_kernels.cu", ), ] - sources = remove_items(sources, cutlass_100a_sources) - - # Always compile mx_fp_cutlass_kernels.cu ONLY with sm120a architecture - cutlass_120a_sources = [ - os.path.join( - extensions_cuda_dir, - "mx_kernels", - "mx_fp_cutlass_kernels_sm120a.cu", - ), + # Remove from main sources to prevent compilation with other architectures + sources = [ + s for s in sources if os.path.basename(s) != "mx_fp_cutlass_kernels.cu" ] - sources = remove_items(sources, cutlass_120a_sources) else: - # Remove CUTLASS-based kernels from the sources list. An assumption is that - # these files will have "cutlass" in its name. + # Remove CUTLASS-based kernels from the sources list. An + # assumption is that these files will have "cutlass" in its + # name. cutlass_sources = list( glob.glob( os.path.join(extensions_cuda_dir, "**/*cutlass*.cu"), recursive=True ) ) - sources = remove_items(sources, cutlass_sources) + sources = [s for s in sources if s not in cutlass_sources] ext_modules = [] if len(sources) > 0: + # Double-check to ensure mx_fp_cutlass_kernels.cu is not in sources + sources = [ + s for s in sources if os.path.basename(s) != "mx_fp_cutlass_kernels.cu" + ] + ext_modules.append( extension( "torchao._C", @@ -653,27 +643,6 @@ def get_extensions(): ) ) - # Only build the cutlass_120a extension if sm120a is in the architecture flags - if ( - cutlass_120a_sources is not None - and len(cutlass_120a_sources) > 0 - and build_for_sm120a - ): - cutlass_120a_extra_compile_args = copy.deepcopy(extra_compile_args) - # Only use sm120a architecture for these sources, ignoring cuda_arch_flags - cutlass_120a_extra_compile_args["nvcc"].append( - "-gencode=arch=compute_120a,code=sm_120a" - ) - ext_modules.append( - extension( - "torchao._C_cutlass_120a", - cutlass_120a_sources, - py_limited_api=True, - extra_compile_args=cutlass_120a_extra_compile_args, - extra_link_args=extra_link_args, - ) - ) - # Build CMakeLists from /torchao/experimental - additional options become available : TORCHAO_BUILD_CPU_AARCH64, TORCHAO_BUILD_KLEIDIAI, TORCHAO_BUILD_MPS_OPS, TORCHAO_PARALLEL_BACKEND if build_macos_arm_auto or os.getenv("BUILD_TORCHAO_EXPERIMENTAL") == "1": build_options = BuildOptions() diff --git a/test/prototype/mx_formats/test_mx_mm.py b/test/prototype/mx_formats/test_mx_mm.py index bf383446cd..46380cfb55 100644 --- a/test/prototype/mx_formats/test_mx_mm.py +++ b/test/prototype/mx_formats/test_mx_mm.py @@ -14,7 +14,7 @@ from torchao.prototype.mx_formats.utils import to_blocked from torchao.utils import ( TORCH_VERSION_AT_LEAST_2_8, - is_sm_version, + is_sm_at_least_100, ) if not TORCH_VERSION_AT_LEAST_2_8: @@ -59,8 +59,7 @@ def run_matrix_test(M: int, K: int, N: int, format) -> float: @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") @pytest.mark.skipif( - not (is_sm_version(10, 0) or is_sm_version(12, 0)), - reason="CUDA capability 10.0 or 12.0 is required for mxfloat8", + not is_sm_at_least_100(), reason="CUDA capability >= 10.0 required for mxfloat8" ) @pytest.mark.parametrize( "size", diff --git a/torchao/__init__.py b/torchao/__init__.py index 2d40be2ac4..e6e291309f 100644 --- a/torchao/__init__.py +++ b/torchao/__init__.py @@ -25,21 +25,8 @@ so_files = list(Path(__file__).parent.glob("_C*.so")) if len(so_files) > 0: - compute_capability = ( - torch.cuda.get_device_capability() if torch.cuda.is_available() else None - ) - for file in so_files: - # only load architecture-specific target if the current GPU matches that target - if ( - ("cutlass_90a" in file.name and compute_capability != (9, 0)) - or ("cutlass_100a" in file.name and compute_capability != (10, 0)) - or ("cutlass_120a" in file.name and compute_capability != (12, 0)) - ): - continue - torch.ops.load_library(str(file)) - from . import ops # The following library contains CPU kernels from torchao/experimental diff --git a/torchao/csrc/cuda/mx_kernels/mx_fp_cutlass_kernels_sm100a.cu b/torchao/csrc/cuda/mx_kernels/mx_fp_cutlass_kernels.cu similarity index 100% rename from torchao/csrc/cuda/mx_kernels/mx_fp_cutlass_kernels_sm100a.cu rename to torchao/csrc/cuda/mx_kernels/mx_fp_cutlass_kernels.cu diff --git a/torchao/csrc/cuda/mx_kernels/mx_fp_cutlass_kernels_sm120a.cu b/torchao/csrc/cuda/mx_kernels/mx_fp_cutlass_kernels_sm120a.cu deleted file mode 100644 index 0436124cbb..0000000000 --- a/torchao/csrc/cuda/mx_kernels/mx_fp_cutlass_kernels_sm120a.cu +++ /dev/null @@ -1,195 +0,0 @@ -// Copyright (c) Meta Platforms, Inc. and affiliates. -// All rights reserved. -// -// This source code is licensed under the BSD 3-Clause license found in the -// LICENSE file in the root directory of this source tree. - -#include - -#include -#include -#include -#include -#include -#include - -#if defined(TORCHAO_USE_CUTLASS) && !defined(_WIN32) && \ - defined(CUDA_VERSION) && (CUDA_VERSION >= 12080) -#define BUILD_MX_KERNELS_CUTLASS -#endif - -#if defined(BUILD_MX_KERNELS_CUTLASS) - -#include "cute/tensor.hpp" -#include "cutlass/detail/sm100_blockscaled_layout.hpp" -#include "cutlass/epilogue/collective/collective_builder.hpp" -#include "cutlass/epilogue/thread/linear_combination.h" -#include "cutlass/gemm/collective/collective_builder.hpp" -#include "cutlass/gemm/device/gemm_universal_adapter.h" -#include "cutlass/util/packed_stride.hpp" - - -#endif - -namespace torchao { - -#if defined(BUILD_MX_KERNELS_CUTLASS) -namespace { - -using namespace cute; - -void run_gemm(at::Tensor& a, at::Tensor& b, at::Tensor& a_scale, - at::Tensor& b_scale, at::Tensor& out, int M, int K, int N) { - - using MmaTileShape = Shape<_128,_128,_128>; - using ClusterShape = Shape<_1,_1,_1>; - using PerSmTileShape_MNK = Shape<_128,_128,_128>; - - // A matrix configuration - using ElementA = cutlass::mx_float4_t; - using LayoutATag = cutlass::layout::RowMajor; // Layout type for A matrix operand - constexpr int AlignmentA = 32; // Memory access granularity/alignment of A matrix in units of elements (up to 16 bytes) - - // B matrix configuration - using ElementB = cutlass::mx_float4_t; - using LayoutBTag = cutlass::layout::ColumnMajor; // Layout type for B matrix operand - constexpr int AlignmentB = 32; // Memory access granularity/alignment of B matrix in units of elements (up to 16 bytes) - - // C/D matrix configuration - using ElementC = cutlass::bfloat16_t; // Element type for C matrix operand - using ElementD = cutlass::bfloat16_t; - using LayoutCTag = cutlass::layout::RowMajor; // Layout type for C matrix operand - using LayoutDTag = cutlass::layout::RowMajor; // Layout type for D matrix operand - constexpr int AlignmentD = 128 / cutlass::sizeof_bits::value; // Memory access granularity/alignment of D matrix in units of elements (up to 16 bytes) - constexpr int AlignmentC = 128 / cutlass::sizeof_bits::value; // Memory access granularity/alignment of C matrix in units of elements (up to 16 bytes) - // Kernel functional config - using ElementAccumulator = float; // Element type for internal accumulation - using ArchTag = cutlass::arch::Sm120; // Tag indicating the minimum SM that supports the intended feature - using OperatorClass = cutlass::arch::OpClassBlockScaledTensorOp; // Operator class tag - - using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder< - ArchTag, OperatorClass, - PerSmTileShape_MNK, ClusterShape, - cutlass::epilogue::collective::EpilogueTileAuto, - ElementAccumulator, ElementAccumulator, - ElementC, LayoutCTag, AlignmentC, - ElementD, LayoutDTag, AlignmentD, - cutlass::epilogue::collective::EpilogueScheduleAuto // Epilogue schedule policy - >::CollectiveOp; - - using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder< - ArchTag, OperatorClass, - ElementA, LayoutATag, AlignmentA, - ElementB, LayoutBTag, AlignmentB, - ElementAccumulator, - MmaTileShape, ClusterShape, - cutlass::gemm::collective::StageCountAutoCarveout(sizeof(typename CollectiveEpilogue::SharedStorage))>, - cutlass::gemm::collective::KernelScheduleAuto // Kernel schedule policy. Auto or using targeted scheduling policy - >::CollectiveOp; - - using GemmKernel = cutlass::gemm::kernel::GemmUniversal< - Shape, // Indicates ProblemShape - CollectiveMainloop, - CollectiveEpilogue, - void>; - - using Gemm = cutlass::gemm::device::GemmUniversalAdapter; - - // Reference device GEMM implementation type - using StrideA = typename Gemm::GemmKernel::StrideA; - using StrideB = typename Gemm::GemmKernel::StrideB; - using StrideC = typename Gemm::GemmKernel::StrideC; - using StrideD = typename Gemm::GemmKernel::StrideD; - using LayoutSFA = typename Gemm::GemmKernel::CollectiveMainloop::LayoutSFA; - using LayoutSFB = typename Gemm::GemmKernel::CollectiveMainloop::LayoutSFB; - using Sm1xxBlkScaledConfig = typename Gemm::GemmKernel::CollectiveMainloop::Sm1xxBlkScaledConfig; - - // Initialize strides using packed stride configuration - auto stride_A = cutlass::make_cute_packed_stride(StrideA{}, make_shape(M, K, 1)); - auto stride_B = cutlass::make_cute_packed_stride(StrideB{}, make_shape(N, K, 1)); - auto stride_D = cutlass::make_cute_packed_stride(StrideD{}, make_shape(M, N, 1)); - - // Initialize scale factor layouts using block scaled configuration - auto layout_SFA = Sm1xxBlkScaledConfig::tile_atom_to_shape_SFA(make_shape(M, N, K, 1)); - auto layout_SFB = Sm1xxBlkScaledConfig::tile_atom_to_shape_SFB(make_shape(M, N, K, 1)); - - using DtypeA = typename ElementA::DataType; - using DtypeB = typename ElementB::DataType; - using DtypeScaleA = typename ElementA::ScaleFactorType; - using DtypeScaleB = typename ElementB::ScaleFactorType; - using DtypeOut = ElementD; - - Gemm gemm; - - auto A_ptr = reinterpret_cast(a.data_ptr()); - auto B_ptr = reinterpret_cast(b.data_ptr()); - auto SFA_ptr = reinterpret_cast(a_scale.data_ptr()); - auto SFB_ptr = reinterpret_cast(b_scale.data_ptr()); - auto out_ptr = reinterpret_cast(out.data_ptr()); - - typename Gemm::Arguments arguments{ - cutlass::gemm::GemmUniversalMode::kGemm, - {M, N, K, 1}, - { // Mainloop arguments - A_ptr, stride_A, - B_ptr, stride_B, - SFA_ptr, layout_SFA, - SFB_ptr, layout_SFB - }, - { // Epilogue arguments - {1.0, 0.0}, - nullptr, StrideC{}, // No bias for now - out_ptr, stride_D - } - }; - - // Check the problem size is supported or not - cutlass::Status status = gemm.can_implement(arguments); - TORCH_CHECK(status == cutlass::Status::kSuccess, "Cutlass cannot implement"); - // Allocate workspace memory - size_t workspace_size = Gemm::get_workspace_size(arguments); - auto workspace = a.new_empty( - {static_cast(workspace_size)}, - at::TensorOptions().dtype(at::kByte)); - - // Initialize CUTLASS kernel with arguments and workspace pointer - status = gemm.initialize(arguments, workspace.data_ptr()); - TORCH_CHECK(status == cutlass::Status::kSuccess, "Cutlass cannot initialize"); - - status = gemm.run(at::cuda::getCurrentCUDAStream()); - TORCH_CHECK(status == cutlass::Status::kSuccess, "Cutlass cannot run", cutlass::cutlassGetStatusString(status)); - - C10_CUDA_KERNEL_LAUNCH_CHECK(); - -} -} -#endif - -at::Tensor mx_fp4_bf16(at::Tensor a, at::Tensor b, at::Tensor a_scale, - at::Tensor b_scale) { -#if defined(BUILD_MX_KERNELS_CUTLASS) - TORCH_CHECK(a.is_cuda(), "a must be CUDA tensor"); - TORCH_CHECK(b.is_cuda(), "b must be CUDA tensor"); - TORCH_CHECK(a_scale.is_cuda(), "a_scale must be CUDA tensor"); - TORCH_CHECK(b_scale.is_cuda(), "b_scale must be CUDA tensor"); - - auto M = a.size(0); - auto K = a.size(1) * 2; - auto N = b.size(1); - - auto out = - at::empty({M, N}, a.options().dtype(at::kBFloat16)); - - run_gemm(a, b, a_scale, b_scale, out, M, K, N); - return out; -#else - TORCH_CHECK_NOT_IMPLEMENTED(false, __func__); - return at::Tensor{}; -#endif -} - -TORCH_LIBRARY_IMPL(torchao, CUDA, m) { - m.impl("torchao::mx_fp4_bf16", &mx_fp4_bf16); -} - -} // namespace torchao diff --git a/torchao/testing/float8/roofline_utils.py b/torchao/testing/float8/roofline_utils.py index 286803dbf2..7bfb9887df 100644 --- a/torchao/testing/float8/roofline_utils.py +++ b/torchao/testing/float8/roofline_utils.py @@ -54,13 +54,6 @@ # TODO(future): run measurement on hardware "pct_achievable_mem_bw": 0.92, }, - "NVIDIA GeForce RTX 5090": { - # https://images.nvidia.com/aem-dam/Solutions/geforce/blackwell/nvidia-rtx-blackwell-gpu-architecture.pdf - "bf16_peak_tops": 209.5e12, - "fp8_peak_tops": 419e12, - "fp4_peak_tops": 1676e12, - "peak_mem_bw_bytes_sec": 1.792e15, - }, # TODO(future): more GPU names } From 8b051b49dd2931d4518b6bb6d8ea4f5d3704aeca Mon Sep 17 00:00:00 2001 From: Vasiliy Kuznetsov Date: Tue, 24 Jun 2025 10:16:36 -0400 Subject: [PATCH 146/165] fix float8 training TP+SP integration tests (#2414) Update [ghstack-poisoned] --- test/float8/test_dtensor.py | 2 ++ test/float8/test_fsdp2_tp.py | 2 ++ 2 files changed, 4 insertions(+) diff --git a/test/float8/test_dtensor.py b/test/float8/test_dtensor.py index 9db046b749..a9ccb35b79 100644 --- a/test/float8/test_dtensor.py +++ b/test/float8/test_dtensor.py @@ -67,6 +67,8 @@ def setup_distributed(): device_mesh = init_device_mesh("cuda", (world_size,)) # seed must be the same in all processes torch.manual_seed(1) + local_rank = torch.distributed.get_rank() + torch.cuda.set_device(local_rank) return device_mesh diff --git a/test/float8/test_fsdp2_tp.py b/test/float8/test_fsdp2_tp.py index fa3d30410b..f04b791273 100644 --- a/test/float8/test_fsdp2_tp.py +++ b/test/float8/test_fsdp2_tp.py @@ -46,6 +46,8 @@ def setup_distributed(): ) # seed must be the same in all processes torch.manual_seed(1) + local_rank = torch.distributed.get_rank() + torch.cuda.set_device(local_rank) return device_mesh From 1a701e6d3f9cc50acb829f73aa645b01fd7bdbb1 Mon Sep 17 00:00:00 2001 From: Vasiliy Kuznetsov Date: Tue, 24 Jun 2025 10:17:34 -0400 Subject: [PATCH 147/165] rename `torchao.testing.float8` to `torchao.testing.training` (#2415) * Update [ghstack-poisoned] * Update [ghstack-poisoned] --- benchmarks/float8/bench_matmul.py | 2 +- benchmarks/float8/float8_roofline.py | 2 +- test/float8/test_base.py | 2 +- test/float8/test_compile.py | 2 +- test/float8/test_dtensor.py | 2 +- test/float8/test_fsdp2/test_fsdp2.py | 5 ++++- test/float8/test_fsdp2_tp.py | 2 +- test/float8/test_numerics_integration.py | 2 +- torchao/testing/{float8 => training}/__init__.py | 0 torchao/testing/{float8 => training}/dtensor_utils.py | 0 torchao/testing/{float8 => training}/fsdp2_utils.py | 0 torchao/testing/{float8 => training}/roofline_utils.py | 0 torchao/testing/{float8 => training}/test_utils.py | 0 13 files changed, 11 insertions(+), 8 deletions(-) rename torchao/testing/{float8 => training}/__init__.py (100%) rename torchao/testing/{float8 => training}/dtensor_utils.py (100%) rename torchao/testing/{float8 => training}/fsdp2_utils.py (100%) rename torchao/testing/{float8 => training}/roofline_utils.py (100%) rename torchao/testing/{float8 => training}/test_utils.py (100%) diff --git a/benchmarks/float8/bench_matmul.py b/benchmarks/float8/bench_matmul.py index e3f19d8f49..cf844fa51b 100644 --- a/benchmarks/float8/bench_matmul.py +++ b/benchmarks/float8/bench_matmul.py @@ -16,7 +16,7 @@ get_name_to_shapes_iter, ) -from torchao.testing.float8.roofline_utils import get_specs +from torchao.testing.training.roofline_utils import get_specs def benchmark_fn_in_sec(f, *args, **kwargs): diff --git a/benchmarks/float8/float8_roofline.py b/benchmarks/float8/float8_roofline.py index f9374f835e..5a8419cde8 100644 --- a/benchmarks/float8/float8_roofline.py +++ b/benchmarks/float8/float8_roofline.py @@ -63,7 +63,7 @@ ) from torchao.prototype.mx_formats import MXLinearConfig from torchao.quantization import quantize_ -from torchao.testing.float8.roofline_utils import ( +from torchao.testing.training.roofline_utils import ( get_float8_mem_sympy, get_gemm_time_sympy, ) diff --git a/test/float8/test_base.py b/test/float8/test_base.py index 8e3efeab60..15099dc2c1 100644 --- a/test/float8/test_base.py +++ b/test/float8/test_base.py @@ -55,7 +55,7 @@ fp8_tensor_statistics, tensor_to_scale, ) -from torchao.testing.float8.test_utils import get_test_float8_linear_config +from torchao.testing.training.test_utils import get_test_float8_linear_config from torchao.utils import is_MI300, is_ROCM random.seed(0) diff --git a/test/float8/test_compile.py b/test/float8/test_compile.py index ac5d1f8d96..aaf9d3d3f5 100644 --- a/test/float8/test_compile.py +++ b/test/float8/test_compile.py @@ -37,7 +37,7 @@ hp_tensor_to_float8_dynamic, ) from torchao.float8.float8_tensor import GemmInputRole, LinearMMConfig, ScaledMMConfig -from torchao.testing.float8.test_utils import get_test_float8_linear_config +from torchao.testing.training.test_utils import get_test_float8_linear_config def _test_compile_base( diff --git a/test/float8/test_dtensor.py b/test/float8/test_dtensor.py index a9ccb35b79..e7220bff9f 100644 --- a/test/float8/test_dtensor.py +++ b/test/float8/test_dtensor.py @@ -57,7 +57,7 @@ ) from torchao.float8.float8_utils import tensor_to_scale from torchao.float8.fsdp_utils import WeightWithDynamicFloat8CastTensor -from torchao.testing.float8.dtensor_utils import ToyModel +from torchao.testing.training.dtensor_utils import ToyModel torch.set_float32_matmul_precision("high") diff --git a/test/float8/test_fsdp2/test_fsdp2.py b/test/float8/test_fsdp2/test_fsdp2.py index 6f0cfecf41..b4c7f9fd15 100644 --- a/test/float8/test_fsdp2/test_fsdp2.py +++ b/test/float8/test_fsdp2/test_fsdp2.py @@ -43,7 +43,10 @@ from torchao.float8.float8_scaling_utils import hp_tensor_to_float8_dynamic from torchao.float8.float8_tensor import GemmInputRole from torchao.float8.fsdp_utils import WeightWithDynamicFloat8CastTensor -from torchao.testing.float8.fsdp2_utils import check_parity_bf16_mp, check_parity_no_mp +from torchao.testing.training.fsdp2_utils import ( + check_parity_bf16_mp, + check_parity_no_mp, +) if not is_sm_at_least_89(): pytest.skip("Unsupported CUDA device capability version", allow_module_level=True) diff --git a/test/float8/test_fsdp2_tp.py b/test/float8/test_fsdp2_tp.py index f04b791273..93c7735149 100644 --- a/test/float8/test_fsdp2_tp.py +++ b/test/float8/test_fsdp2_tp.py @@ -32,7 +32,7 @@ Float8ColwiseParallel, Float8RowwiseParallel, ) -from torchao.testing.float8.dtensor_utils import ToyModel +from torchao.testing.training.dtensor_utils import ToyModel def setup_distributed(): diff --git a/test/float8/test_numerics_integration.py b/test/float8/test_numerics_integration.py index f25c876189..db02444109 100644 --- a/test/float8/test_numerics_integration.py +++ b/test/float8/test_numerics_integration.py @@ -33,7 +33,7 @@ convert_to_float8_training, ) from torchao.float8.float8_utils import IS_ROCM, compute_error -from torchao.testing.float8.test_utils import get_test_float8_linear_config +from torchao.testing.training.test_utils import get_test_float8_linear_config torch.manual_seed(0) diff --git a/torchao/testing/float8/__init__.py b/torchao/testing/training/__init__.py similarity index 100% rename from torchao/testing/float8/__init__.py rename to torchao/testing/training/__init__.py diff --git a/torchao/testing/float8/dtensor_utils.py b/torchao/testing/training/dtensor_utils.py similarity index 100% rename from torchao/testing/float8/dtensor_utils.py rename to torchao/testing/training/dtensor_utils.py diff --git a/torchao/testing/float8/fsdp2_utils.py b/torchao/testing/training/fsdp2_utils.py similarity index 100% rename from torchao/testing/float8/fsdp2_utils.py rename to torchao/testing/training/fsdp2_utils.py diff --git a/torchao/testing/float8/roofline_utils.py b/torchao/testing/training/roofline_utils.py similarity index 100% rename from torchao/testing/float8/roofline_utils.py rename to torchao/testing/training/roofline_utils.py diff --git a/torchao/testing/float8/test_utils.py b/torchao/testing/training/test_utils.py similarity index 100% rename from torchao/testing/float8/test_utils.py rename to torchao/testing/training/test_utils.py From b96354087db6d0480ebbc10d5a63a9ca49c19dfa Mon Sep 17 00:00:00 2001 From: Vasiliy Kuznetsov Date: Tue, 24 Jun 2025 10:19:02 -0400 Subject: [PATCH 148/165] make dtensor shared test util more generic (#2416) * Update [ghstack-poisoned] * Update [ghstack-poisoned] * Update [ghstack-poisoned] --- test/float8/test_dtensor.py | 160 ++++------------------ torchao/testing/training/dtensor_utils.py | 138 +++++++++++++++++++ 2 files changed, 161 insertions(+), 137 deletions(-) diff --git a/test/float8/test_dtensor.py b/test/float8/test_dtensor.py index e7220bff9f..5509eb1cc2 100644 --- a/test/float8/test_dtensor.py +++ b/test/float8/test_dtensor.py @@ -10,7 +10,6 @@ TODO(future): make this run in CI """ -import copy import os import pytest @@ -23,12 +22,6 @@ from torch.distributed._tensor import DTensor, Replicate, Shard, distribute_tensor from torch.distributed.device_mesh import DeviceMesh, init_device_mesh -from torch.distributed.tensor.parallel import ( - ColwiseParallel, - PrepareModuleInput, - RowwiseParallel, - parallelize_module, -) from torch.testing._internal.distributed._tensor.common_dtensor import ( ModelArgs, Transformer, @@ -50,14 +43,11 @@ LinearMMConfig, hp_tensor_and_scale_to_float8, ) -from torchao.float8.float8_tensor_parallel import ( - Float8ColwiseParallel, - Float8RowwiseParallel, - PrepareFloat8ModuleInput, -) from torchao.float8.float8_utils import tensor_to_scale from torchao.float8.fsdp_utils import WeightWithDynamicFloat8CastTensor -from torchao.testing.training.dtensor_utils import ToyModel +from torchao.testing.training.dtensor_utils import ( + _test_lowp_mlp_tensor_parallelism_base, +) torch.set_float32_matmul_precision("high") @@ -193,140 +183,36 @@ def _test_dtensor_fp8_autograd(mesh: DeviceMesh, size=16): loss.backward() -def _test_fp8_mlp_tensor_parallelism_base( - mesh: DeviceMesh, size=16, compile: bool = False, rowwise: bool = False -): - device = mesh.device_type - - if rowwise: - config = Float8LinearConfig.from_recipe_name(Float8LinearRecipeName.ROWWISE) - # hack around config being frozen - # TODO(future PR): we should make this nicer at the config level - object.__setattr__(config, "emulate", True) - else: - config = Float8LinearConfig(emulate=True) - - toy_model = ToyModel().to(device) - toy_model_fp8 = convert_to_float8_training(toy_model, config=config) - - tp_model = copy.deepcopy(toy_model) - tp_model = convert_to_float8_training(tp_model, config=config) - sp_model = copy.deepcopy(toy_model) - sp_model = convert_to_float8_training(sp_model, config=config) - - # For tensorwise scaling, enable float8 all_gather. - # For rowwise scaling, keep high precision all_gather. Motivation for - # not doing float8 all-gather for rowwise: tensors need to be scaled both ways, - # so for float8 all-gather we'd need to send two float8 copies per tensor, - # which is similar # bytes over the wire than just doing bfloat16 all-gather. - if rowwise: - colwise_parallel_cls = ColwiseParallel - rowwise_parallel_cls = RowwiseParallel - prepare_input_cls = PrepareModuleInput - else: - colwise_parallel_cls = Float8ColwiseParallel - rowwise_parallel_cls = Float8RowwiseParallel - prepare_input_cls = PrepareFloat8ModuleInput - - # vanilla TP - tp_model = parallelize_module( - tp_model, - mesh, - { - "ffn.w1": colwise_parallel_cls(), - "ffn.w2": colwise_parallel_cls(), - "ffn.out_proj": rowwise_parallel_cls(), - }, +def _test_fp8_mlp_tensor_parallelism_eager(mesh: DeviceMesh, size=16): + tensorwise_config = Float8LinearConfig(emulate=True) + _test_lowp_mlp_tensor_parallelism_base( + mesh, tensorwise_config, size, compile=False, allgather_in_lowp=True ) - # "sequence parallel" mlp computation - sp_model = parallelize_module( - sp_model, - mesh, - { - "ffn": prepare_input_cls( - input_layouts=Shard(1), desired_input_layouts=Replicate() - ), - "ffn.w1": colwise_parallel_cls(), - "ffn.w2": colwise_parallel_cls(), - "ffn.out_proj": rowwise_parallel_cls( - output_layouts=Shard(1), use_local_output=False - ), - }, + rowwise_config = Float8LinearConfig.from_recipe_name(Float8LinearRecipeName.ROWWISE) + # hack around config being frozen + # TODO(future PR): we should make this nicer at the config level + object.__setattr__(rowwise_config, "emulate", True) + _test_lowp_mlp_tensor_parallelism_base( + mesh, rowwise_config, size, compile=False, allgather_in_lowp=False ) - # prepare_input_cls with specific submodule fqn - sp_model2 = copy.deepcopy(toy_model) - sp_model2 = convert_to_float8_training(sp_model2, config=config) - if rowwise: - prepare_input = prepare_input_cls( - input_layouts=Shard(1), - desired_input_layouts=Replicate(), - ) - else: - prepare_input = prepare_input_cls( - input_layouts=Shard(1), - desired_input_layouts=Replicate(), - fwd_config_submodule_fqn="w2", - ) - - sp_model2 = parallelize_module( - sp_model2, - mesh, - { - "ffn": prepare_input, - "ffn.w1": colwise_parallel_cls(), - "ffn.w2": colwise_parallel_cls(), - "ffn.out_proj": rowwise_parallel_cls( - output_layouts=Shard(1), use_local_output=False - ), - }, - ) - - if compile: - tp_model = torch.compile(tp_model) - sp_model = torch.compile(sp_model) - sp_model2 = torch.compile(sp_model2) - - x_fp32 = torch.rand(size, size * 2, size, device=device, requires_grad=False) - x_fp32_tp_input = x_fp32.clone() - x_fp32_sp_input = distribute_tensor(x_fp32.clone(), mesh, [Shard(0)]) - - tp_out = tp_model(x_fp32_tp_input) - tp_out.sum().backward() - sp_out = sp_model(x_fp32_sp_input) - sp_out.sum().backward() - global_out = toy_model_fp8(x_fp32) - global_out.sum().backward() - torch.testing.assert_close(tp_out, global_out) - torch.testing.assert_close(sp_out.full_tensor(), global_out) - torch.testing.assert_close(tp_model.ffn.w1.weight.grad, sp_model.ffn.w1.weight.grad) - torch.testing.assert_close( - tp_model.ffn.out_proj.weight.grad, sp_model.ffn.out_proj.weight.grad +def _test_fp8_mlp_tensor_parallelism_compile(mesh: DeviceMesh, size=16): + tensorwise_config = Float8LinearConfig(emulate=True) + _test_lowp_mlp_tensor_parallelism_base( + mesh, tensorwise_config, size, compile=True, allgather_in_lowp=True ) - sp_out2 = sp_model2(x_fp32_sp_input) - sp_out2.sum().backward() - torch.testing.assert_close(sp_out2.full_tensor(), global_out) - torch.testing.assert_close( - tp_model.ffn.w1.weight.grad, sp_model2.ffn.w1.weight.grad - ) - torch.testing.assert_close( - tp_model.ffn.out_proj.weight.grad, sp_model2.ffn.out_proj.weight.grad + rowwise_config = Float8LinearConfig.from_recipe_name(Float8LinearRecipeName.ROWWISE) + # hack around config being frozen + # TODO(future PR): we should make this nicer at the config level + object.__setattr__(rowwise_config, "emulate", True) + _test_lowp_mlp_tensor_parallelism_base( + mesh, rowwise_config, size, compile=True, allgather_in_lowp=False ) -def _test_fp8_mlp_tensor_parallelism_eager(mesh: DeviceMesh, size=16): - _test_fp8_mlp_tensor_parallelism_base(mesh, size, compile=False, rowwise=False) - _test_fp8_mlp_tensor_parallelism_base(mesh, size, compile=False, rowwise=True) - - -def _test_fp8_mlp_tensor_parallelism_compile(mesh: DeviceMesh, size=16): - _test_fp8_mlp_tensor_parallelism_base(mesh, size, compile=True, rowwise=False) - _test_fp8_mlp_tensor_parallelism_base(mesh, size, compile=True, rowwise=True) - - def _test_distribute_fsdp_tensor_subclass(tp_mesh: DeviceMesh): torch.manual_seed(42) model = Transformer(ModelArgs(dropout_p=0.0, weight_tying=False)).cuda() diff --git a/torchao/testing/training/dtensor_utils.py b/torchao/testing/training/dtensor_utils.py index 84e4095263..7ac0360363 100644 --- a/torchao/testing/training/dtensor_utils.py +++ b/torchao/testing/training/dtensor_utils.py @@ -3,9 +3,27 @@ # # This source code is licensed under the BSD 3-Clause license found in the # LICENSE file in the root directory of this source tree. +import copy +import torch import torch.nn as nn import torch.nn.functional as F +from torch.distributed._tensor import Replicate, Shard, distribute_tensor +from torch.distributed.device_mesh import DeviceMesh +from torch.distributed.tensor.parallel import ( + ColwiseParallel, + PrepareModuleInput, + RowwiseParallel, + parallelize_module, +) + +from torchao.float8 import Float8LinearConfig +from torchao.float8.float8_linear_utils import convert_to_float8_training +from torchao.float8.float8_tensor_parallel import ( + Float8ColwiseParallel, + Float8RowwiseParallel, + PrepareFloat8ModuleInput, +) class FeedForward(nn.Module): @@ -28,3 +46,123 @@ def __init__(self): def forward(self, x): return self.ffn(x) + + +def _test_lowp_mlp_tensor_parallelism_base( + mesh: DeviceMesh, + config: Float8LinearConfig, + size=16, + compile: bool = False, + allgather_in_lowp: bool = False, +): + device = mesh.device_type + + toy_model = ToyModel().to(device) + toy_model_fp8 = convert_to_float8_training(toy_model, config=config) + + tp_model = copy.deepcopy(toy_model) + tp_model = convert_to_float8_training(tp_model, config=config) + sp_model = copy.deepcopy(toy_model) + sp_model = convert_to_float8_training(sp_model, config=config) + + # For tensorwise scaling, enable float8 all_gather. + # For rowwise scaling, keep high precision all_gather. Motivation for + # not doing float8 all-gather for rowwise: tensors need to be scaled both ways, + # so for float8 all-gather we'd need to send two float8 copies per tensor, + # which is similar # bytes over the wire than just doing bfloat16 all-gather. + if not allgather_in_lowp: + colwise_parallel_cls = ColwiseParallel + rowwise_parallel_cls = RowwiseParallel + prepare_input_cls = PrepareModuleInput + else: + colwise_parallel_cls = Float8ColwiseParallel + rowwise_parallel_cls = Float8RowwiseParallel + prepare_input_cls = PrepareFloat8ModuleInput + + # vanilla TP + tp_model = parallelize_module( + tp_model, + mesh, + { + "ffn.w1": colwise_parallel_cls(), + "ffn.w2": colwise_parallel_cls(), + "ffn.out_proj": rowwise_parallel_cls(), + }, + ) + + # "sequence parallel" mlp computation + sp_model = parallelize_module( + sp_model, + mesh, + { + "ffn": prepare_input_cls( + input_layouts=Shard(1), desired_input_layouts=Replicate() + ), + "ffn.w1": colwise_parallel_cls(), + "ffn.w2": colwise_parallel_cls(), + "ffn.out_proj": rowwise_parallel_cls( + output_layouts=Shard(1), use_local_output=False + ), + }, + ) + + # prepare_input_cls with specific submodule fqn + sp_model2 = copy.deepcopy(toy_model) + sp_model2 = convert_to_float8_training(sp_model2, config=config) + + if not allgather_in_lowp: + prepare_input = prepare_input_cls( + input_layouts=Shard(1), + desired_input_layouts=Replicate(), + ) + else: + prepare_input = prepare_input_cls( + input_layouts=Shard(1), + desired_input_layouts=Replicate(), + fwd_config_submodule_fqn="w2", + ) + + sp_model2 = parallelize_module( + sp_model2, + mesh, + { + "ffn": prepare_input, + "ffn.w1": colwise_parallel_cls(), + "ffn.w2": colwise_parallel_cls(), + "ffn.out_proj": rowwise_parallel_cls( + output_layouts=Shard(1), use_local_output=False + ), + }, + ) + + if compile: + tp_model = torch.compile(tp_model) + sp_model = torch.compile(sp_model) + sp_model2 = torch.compile(sp_model2) + + x_fp32 = torch.rand(size, size * 2, size, device=device, requires_grad=False) + x_fp32_tp_input = x_fp32.clone() + x_fp32_sp_input = distribute_tensor(x_fp32.clone(), mesh, [Shard(0)]) + + tp_out = tp_model(x_fp32_tp_input) + tp_out.sum().backward() + sp_out = sp_model(x_fp32_sp_input) + sp_out.sum().backward() + global_out = toy_model_fp8(x_fp32) + global_out.sum().backward() + torch.testing.assert_close(tp_out, global_out) + torch.testing.assert_close(sp_out.full_tensor(), global_out) + torch.testing.assert_close(tp_model.ffn.w1.weight.grad, sp_model.ffn.w1.weight.grad) + torch.testing.assert_close( + tp_model.ffn.out_proj.weight.grad, sp_model.ffn.out_proj.weight.grad + ) + + sp_out2 = sp_model2(x_fp32_sp_input) + sp_out2.sum().backward() + torch.testing.assert_close(sp_out2.full_tensor(), global_out) + torch.testing.assert_close( + tp_model.ffn.w1.weight.grad, sp_model2.ffn.w1.weight.grad + ) + torch.testing.assert_close( + tp_model.ffn.out_proj.weight.grad, sp_model2.ffn.out_proj.weight.grad + ) From 9eeb101efe2e4d4631d8db7a3cab158d26c7728d Mon Sep 17 00:00:00 2001 From: Daniel Vega-Myhre Date: Tue, 24 Jun 2025 12:08:18 -0600 Subject: [PATCH 149/165] [float8] add _auto_filter_for_recipe to float8 (#2410) * add auto_filter_for_recipe to float8 * lint * address comments * add tests --- torchao/float8/__init__.py | 6 +- torchao/float8/float8_linear_utils.py | 87 ++++++++++++++++++++++++++- 2 files changed, 90 insertions(+), 3 deletions(-) diff --git a/torchao/float8/__init__.py b/torchao/float8/__init__.py index a97a46fa1c..4f90292918 100644 --- a/torchao/float8/__init__.py +++ b/torchao/float8/__init__.py @@ -6,7 +6,10 @@ ScalingGranularity, ScalingType, ) -from torchao.float8.float8_linear_utils import convert_to_float8_training +from torchao.float8.float8_linear_utils import ( + _auto_filter_for_recipe, + convert_to_float8_training, +) from torchao.float8.float8_tensor import ( Float8Tensor, GemmInputRole, @@ -44,6 +47,7 @@ # top level UX "convert_to_float8_training", "precompute_float8_dynamic_scale_for_fsdp", + "_auto_filter_for_recipe", # types "FP8Granularity", # note: Float8Tensor and Float8Linear are not public APIs diff --git a/torchao/float8/float8_linear_utils.py b/torchao/float8/float8_linear_utils.py index 230bfd881f..0d9674e6c3 100644 --- a/torchao/float8/float8_linear_utils.py +++ b/torchao/float8/float8_linear_utils.py @@ -4,11 +4,12 @@ # This source code is licensed under the BSD 3-Clause license found in the # LICENSE file in the root directory of this source tree. import logging -from typing import Callable, Optional +from functools import partial +from typing import Callable, List, Optional, Union import torch.nn as nn -from torchao.float8.config import Float8LinearConfig +from torchao.float8.config import Float8LinearConfig, Float8LinearRecipeName from torchao.float8.float8_linear import Float8Linear log = logging.getLogger(__name__) @@ -113,3 +114,85 @@ def convert_to_float8_training( from_float, module_filter_fn=module_filter_fn, ) + + +def _auto_filter_for_recipe( + recipe: Union[str, Float8LinearRecipeName], filter_fqns: List[str] +) -> Callable[[nn.Module, str], bool]: + """Returns function which automatically filters nn.Linear modules that meet at least one of the following criteria: + + 1. Dims not divisible by 16 (hardware requirement for float8). + 2. Dim sizes below certain thresholds, which may result in worse performance. + + NOTE: the thresholds are simple heuristics based on performance testing, and may not be optimal + for your model. For the best performance, we recommend defining your own module_filter_fn customized for + your module, using the performance tables for the given float8 recipe here: + https://github.com/pytorch/ao/tree/main/torchao/float8#performance). These benchmarks referenced for + auto filtering layers were run on H100 GPUs, and may not be representative of other hardware. + + This is an experimental API, the design may change in the future. + """ + if isinstance(recipe, str): + recipe = Float8LinearRecipeName(recipe) + if recipe == Float8LinearRecipeName.TENSORWISE: + return partial(_auto_filter_for_tensorwise, filter_fqns=filter_fqns) + elif recipe == Float8LinearRecipeName.ROWWISE: + return partial(_auto_filter_for_rowwise, filter_fqns=filter_fqns) + elif recipe == Float8LinearRecipeName.ROWWISE_WITH_GW_HP: + raise NotImplementedError(f"Unsupported recipe: {recipe}") + else: + raise ValueError(f"Invalid recipe: {recipe}") + + +def _auto_filter_for_rowwise(mod: nn.Module, fqn: str, filter_fqns: List[str]) -> bool: + if not isinstance(mod, nn.Linear): + return False + + # If the fqn matches any filtered fqn, then we should not convert this module. + is_filtered_fqn = any(filter_fqn in fqn for filter_fqn in filter_fqns) + if is_filtered_fqn: + return False + + # All dims must be divisible by 16 due to float8 hardware requirements. + N, K = mod.weight.shape + dims_multiples_of_16 = K % 16 == 0 and N % 16 == 0 + if not dims_multiples_of_16: + return False + + # Dims below these thresholds may result in worse performance + # (see https://github.com/pytorch/ao/tree/main/torchao/float8#rowwise-scaling) + # Note that these benchmarks referenced for auto filtering layers were run on + # H100 GPUs, and may not be representative of other hardware. + if N <= 2048: + return False + elif K <= 1024: + return False + elif N <= 4096 and K <= 2048: + return False + return True + + +def _auto_filter_for_tensorwise( + mod: nn.Module, fqn: str, filter_fqns: List[str] +) -> bool: + if not isinstance(mod, nn.Linear): + return False + + # If the fqn matches any filtered fqn, then we should not convert this module. + is_filtered_fqn = any(filter_fqn in fqn for filter_fqn in filter_fqns) + if is_filtered_fqn: + return False + + # All dims must be divisible by 16 due to float8 hardware requirements. + N, K = mod.weight.shape + dims_multiples_of_16 = K % 16 == 0 and N % 16 == 0 + if not dims_multiples_of_16: + return False + + # Dims below these thresholds may result in worse performance + # (see https://github.com/pytorch/ao/tree/main/torchao/float8#tensorwise-scaling) + # Note that these benchmarks referenced for auto filtering layers were run on + # H100 GPUs, and may not be representative of other hardware. + if K <= 4096 and N <= 1024: + return False + return True From a743e9be832457b25dedf5d2bec53dda6bcc6865 Mon Sep 17 00:00:00 2001 From: Scott Roy <161522778+metascroy@users.noreply.github.com> Date: Tue, 24 Jun 2025 11:14:35 -0700 Subject: [PATCH 150/165] Update github links in torchao pt2e tutorial (#2435) update github links in torchao pt2e tutorial --- .../tutorials_source/pt2e_quantizer.rst | 28 +++++++++---------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/docs/source/tutorials_source/pt2e_quantizer.rst b/docs/source/tutorials_source/pt2e_quantizer.rst index e669c5b986..3fa6909da2 100644 --- a/docs/source/tutorials_source/pt2e_quantizer.rst +++ b/docs/source/tutorials_source/pt2e_quantizer.rst @@ -32,16 +32,16 @@ Introduction Please see `here `__ For motivations for the new API and ``Quantizer``. An existing quantizer object defined for ``XNNPACK`` is in -`QNNPackQuantizer `__ +`XNNPackQuantizer `__ Annotation API ^^^^^^^^^^^^^^^^^^^ ``Quantizer`` uses annotation API to convey quantization intent for different operators/patterns. Annotation API mainly consists of -`QuantizationSpec `__ +`QuantizationSpec `__ and -`QuantizationAnnotation `__. +`QuantizationAnnotation `__. ``QuantizationSpec`` is used to convey intent of how a tensor will be quantized, e.g. dtype, bitwidth, min, max values, symmetric vs. asymmetric etc. @@ -133,7 +133,7 @@ parameters can be shared among some tensors explicitly. Two typical use cases ar - Example 1: One example is for ``add`` where having both inputs sharing quantization parameters makes operator implementation much easier. Without using of - `SharedQuantizationSpec `__, + `SharedQuantizationSpec `__, we must annotate ``add`` as example in above section 1, in which two inputs of ``add`` has different quantization parameters. - Example 2: Another example is that of sharing quantization parameters between inputs and output. @@ -211,7 +211,7 @@ as this: Another typical use case to annotate a quantized model is for tensors whose quantization parameters are known beforehand. For example, operator like ``sigmoid``, which has predefined and fixed scale/zero_point at input and output tensors. -`FixedQParamsQuantizationSpec `__ +`FixedQParamsQuantizationSpec `__ is designed for this use case. To use ``FixedQParamsQuantizationSpec``, users need to pass in parameters of ``scale`` and ``zero_point`` explicitly. @@ -243,14 +243,14 @@ of ``scale`` and ``zero_point`` explicitly. Another use case is to define the constraint for tensors whose quantization parameters are derived from other tensors. For example, if we want to annotate a convolution node, and define the ``scale`` of its bias input tensor as product of the activation tensor's ``scale`` and weight tensor's ``scale``. We can use -`DerivedQuantizationSpec `__ +`DerivedQuantizationSpec `__ to annotate this conv node. - Step 1: Identify the original floating point pattern in the FX graph. We can use the same methods introduced in ``QuantizationSpec`` example to identify the ``convolution`` pattern. - Step 2: Define ``derive_qparams_fn`` function, it accepts list of ``ObserverOrFakeQuantize`` ( - `ObserverBase `__ - or `FakeQuantizeBase `__) + `ObserverBase `__ + or `FakeQuantizeBase `__) as input. From each ``ObserverOrFakeQuantize`` object, user can get the ``scale``, ``zero point`` value. User can define its heuristic about how to derive new ``scale``, ``zero point`` value based on the quantization parameters calculated from the observer or fake quant instances. @@ -293,13 +293,13 @@ and run a `toy example `__ +- `QuantizationConfig `__ consists of ``QuantizationSpec`` for activation, weight, and bias separately. - When annotating the model, - `get_input_act_qspec `__, - `get_output_act_qspec `__, - `get_weight_qspec `__, and - `get_bias_qspec `__ + `get_input_act_qspec `__, + `get_output_act_qspec `__, + `get_weight_qspec `__, and + `get_bias_qspec `__ can be used to get the ``QuantizationSpec`` from ``QuantizationConfig`` for a specific pattern. A Note on IR for PT2E Quantization Flow @@ -378,4 +378,4 @@ Conclusion With this tutorial, we introduce the new quantization path in PyTorch 2. Users can learn about how to define a ``BackendQuantizer`` with the ``QuantizationAnnotation API`` and integrate it into the PyTorch 2 Export Quantization flow. Examples of ``QuantizationSpec``, ``SharedQuantizationSpec``, ``FixedQParamsQuantizationSpec``, and ``DerivedQuantizationSpec`` -are given for specific annotation use case. You can use `XNNPACKQuantizer `_ as an example to start implementing your own ``Quantizer``. After that please follow `this tutorial `_ to actually quantize your model. +are given for specific annotation use case. You can use `XNNPACKQuantizer `_ as an example to start implementing your own ``Quantizer``. After that please follow `this tutorial `_ to actually quantize your model. From 7d6bb6a3e481f83d9a9bfeb1c0f79bfa49cc89cf Mon Sep 17 00:00:00 2001 From: Vasiliy Kuznetsov Date: Tue, 24 Jun 2025 15:17:13 -0400 Subject: [PATCH 151/165] enable to_mxfp8 cast for DTensor (#2420) * Update [ghstack-poisoned] * Update [ghstack-poisoned] * Update [ghstack-poisoned] * Update [ghstack-poisoned] * Update [ghstack-poisoned] * Update [ghstack-poisoned] * Update [ghstack-poisoned] * Update [ghstack-poisoned] --- test/prototype/mx_formats/test_mx_dtensor.py | 98 ++++++++++++++++++++ test/prototype/mx_formats/test_mx_dtensor.sh | 17 ++++ torchao/prototype/mx_formats/kernels.py | 3 - torchao/prototype/mx_formats/mx_tensor.py | 45 +++++++-- torchao/testing/training/dtensor_utils.py | 23 +++-- 5 files changed, 168 insertions(+), 18 deletions(-) create mode 100644 test/prototype/mx_formats/test_mx_dtensor.py create mode 100755 test/prototype/mx_formats/test_mx_dtensor.sh diff --git a/test/prototype/mx_formats/test_mx_dtensor.py b/test/prototype/mx_formats/test_mx_dtensor.py new file mode 100644 index 0000000000..bfc930c579 --- /dev/null +++ b/test/prototype/mx_formats/test_mx_dtensor.py @@ -0,0 +1,98 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD 3-Clause license found in the +# LICENSE file in the root directory of this source tree. +""" +Test numerics of manually defined float16 TP vs mxfp8 TP of toy models + +Note: for now, this does not run in CI. +TODO(future): make this run in CI +""" + +import os + +import pytest +import torch + +from torchao.utils import TORCH_VERSION_AT_LEAST_2_7 + +if not TORCH_VERSION_AT_LEAST_2_7: + pytest.skip("Unsupported PyTorch version", allow_module_level=True) + +from torch.distributed._tensor import DTensor, Shard, distribute_tensor +from torch.distributed.device_mesh import DeviceMesh, init_device_mesh +from tqdm import tqdm + +from torchao.prototype.mx_formats import MXLinearConfig +from torchao.prototype.mx_formats.mx_tensor import MXTensor +from torchao.testing.training.dtensor_utils import ( + _test_lowp_mlp_tensor_parallelism_base, +) + +torch.set_float32_matmul_precision("high") + + +def setup_distributed(): + world_size = int(os.environ.get("WORLD_SIZE", -1)) + device_mesh = init_device_mesh("cuda", (world_size,)) + # seed must be the same in all processes + torch.manual_seed(1) + local_rank = torch.distributed.get_rank() + torch.cuda.set_device(local_rank) + return device_mesh + + +def _test_dtensor_cast_to_mxfp8(mesh: DeviceMesh, size=4): + device = mesh.device_type + + x_fp32 = torch.rand(size, size, device=device) + x_fp8 = MXTensor.to_mx(x_fp32, torch.float8_e4m3fn, block_size=size // 2) + + dist_x_fp32 = distribute_tensor(x_fp32, mesh, [Shard(0)]) + dist_x_fp8 = MXTensor.to_mx(dist_x_fp32, torch.float8_e4m3fn, block_size=size // 2) + assert isinstance(dist_x_fp8, DTensor) + + # Verify that the result of to_mx with DTensor matches the slice of the + # result of to_mx without DTensor. This will fail on numeric op mismatches. + local_rank = torch.distributed.get_rank() + world_size = torch.distributed.get_world_size() + assert size % world_size == 0, "unsupported" + x_fp8_fp32 = x_fp8.to_dtype(torch.float32) + rows_per_slice = size // world_size + slice_start = local_rank * rows_per_slice + slice_end = (local_rank + 1) * rows_per_slice + x_fp8_fp32_slice = x_fp8_fp32[slice_start:slice_end] + torch.testing.assert_close( + x_fp8_fp32_slice, dist_x_fp8.to_local().to_dtype(torch.float32), atol=0, rtol=0 + ) + + +def _test_mxfp8_mlp_tensor_parallelism_eager(mesh: DeviceMesh, size=16): + config = MXLinearConfig.from_recipe_name("mxfp8_emulated") + # TODO(future PR): assert that the K dim must be divisible by block size, + # today this is silently incorrect if block_size is greater than K + config.block_size = 16 + _test_lowp_mlp_tensor_parallelism_base( + mesh, config, size, compile=False, allgather_in_lowp=False + ) + + # TODO(future PR): compile + + +if __name__ == "__main__": + device_mesh = setup_distributed() + tests = [ + _test_dtensor_cast_to_mxfp8, + # TODO(next PR): enable this (current PR got too large, so splitting) + # _test_mxfp8_mlp_tensor_parallelism_eager, + ] + + for test in tqdm(tests, desc="Running tests"): + try: + test(device_mesh) + except Exception as e: + print(f"Test {test.__name__} failed with error: {e}") + raise e + + torch.distributed.destroy_process_group() diff --git a/test/prototype/mx_formats/test_mx_dtensor.sh b/test/prototype/mx_formats/test_mx_dtensor.sh new file mode 100755 index 0000000000..abf9424e3c --- /dev/null +++ b/test/prototype/mx_formats/test_mx_dtensor.sh @@ -0,0 +1,17 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD 3-Clause license found in the +# LICENSE file in the root directory of this source tree. +#!/bin/bash + +# terminate script on first error +set -e + +if python -c 'import torch;print(torch.cuda.is_available())' | grep -q "False"; then + echo "Skipping test_dtensor.sh because no CUDA devices are available." + exit +fi + +# integration tests for TP/SP +NCCL_DEBUG=WARN torchrun --nproc_per_node 2 test/prototype/mx_formats/test_mx_dtensor.py diff --git a/torchao/prototype/mx_formats/kernels.py b/torchao/prototype/mx_formats/kernels.py index eacf0ac5df..f96e73a55a 100644 --- a/torchao/prototype/mx_formats/kernels.py +++ b/torchao/prototype/mx_formats/kernels.py @@ -1102,15 +1102,12 @@ def _triton_calculate_scale(x, axis): bf16_mbits = 7 bf16_exp_bias = 127 fp32_mbits = 23 - # We use a small epsilon to avoid division by zero - epsilon = 1e-10 # Find the maximum absolute value for each row max_abs = tl.max(x, axis=axis) # Calculate the e8m0 scale by extracting the exponent (floor) # TODO(future PR): support other exponent extraction types (ceil, RNE) - max_abs = max_abs + epsilon max_abs = max_abs.to(tl.bfloat16) max_abs_int16 = max_abs.to(tl.int16, bitcast=True) extracted_pow2 = ((max_abs_int16 >> bf16_mbits) & 0b11111111) - bf16_exp_bias diff --git a/torchao/prototype/mx_formats/mx_tensor.py b/torchao/prototype/mx_formats/mx_tensor.py index 784d3eda6d..ef9ae42fcd 100644 --- a/torchao/prototype/mx_formats/mx_tensor.py +++ b/torchao/prototype/mx_formats/mx_tensor.py @@ -21,6 +21,7 @@ from typing import Callable, Dict, Union import torch +from torch.distributed._tensor import DTensor from torchao.prototype.mx_formats.config import MXGemmKernelChoice from torchao.prototype.mx_formats.constants import ( @@ -166,6 +167,8 @@ def to_mx( # calculate the scale in e8m0 format orig_shape = data_hp.shape + # TODO(future PR): fix this line for TP, currently this reshape does not work + # for rank 3 tensor where dim1 is sharded data_hp = data_hp.reshape(-1, block_size) # find max value of the data @@ -174,10 +177,6 @@ def to_mx( # section 6.3. max_abs = torch.amax(torch.abs(data_hp), 1) - # Add an epsilon to prevent the log2 function call for returning -inf - # where the values are zero. - eps = F32_MIN_NORMAL * (max_abs == 0).type(max_abs.dtype) - # Set X to be the largest power-of-two less than or equal to # max_abs(v), divided by the largest power of two representable # in the element data type, and get the mbits at the same time @@ -233,8 +232,12 @@ def to_mx( ) # Calculate the scale for different modes - max_abs_int32 = (max_abs + eps).view(hp_int_dtype) - extracted_pow2 = ((max_abs_int32 >> hp_mbits) & 0b11111111) - hp_exp_bias + max_abs_int32 = max_abs.view(hp_int_dtype) + # For now, use `torch.bitwise_right_shift` instead of `>>` to support DTensor + # See https://github.com/pytorch/pytorch/issues/156533. + extracted_pow2 = ( + (torch.bitwise_right_shift(max_abs_int32, hp_mbits)) & 0b11111111 + ) - hp_exp_bias if scaling_mode in (ScaleCalculationMode.FLOOR, ScaleCalculationMode.EVEN): scale_e8m0_unbiased = extracted_pow2 - target_max_pow2 @@ -266,9 +269,11 @@ def to_mx( ) # For now, calculate the scale in floating point. - scale_fp32 = (scale_e8m0_biased.to(torch.int32) << MBITS_F32).view( - torch.float32 - ) + # For now, use `torch.bitwise_left_shift` instead of `<<` to support DTensor + # See https://github.com/pytorch/pytorch/issues/156533. + scale_fp32 = ( + torch.bitwise_left_shift(scale_e8m0_biased.to(torch.int32), MBITS_F32) + ).view(torch.float32) # Today, 2**-127 returns 0 in compile+inductor+triton because it is in the # float32 denormal range. For now, manually adjust the fp scale. This is @@ -597,6 +602,28 @@ def to_mx( scale_e8m0_biased, data_lp = to_mx( data_hp, elem_dtype, block_size, scaling_mode, pack_fp6 ) + if isinstance(scale_e8m0_biased, DTensor): + assert isinstance(data_lp, DTensor), "unsupported" + local_scale_e8m0_biased = scale_e8m0_biased.to_local() + local_data_lp = data_lp.to_local() + inner_mx_tensor = MXTensor( + local_scale_e8m0_biased, + local_data_lp, + elem_dtype, + block_size, + data_hp.dtype, + use_fp4_custom_triton_dequant_kernel, + gemm_kernel_choice, + pack_fp6, + ) + return DTensor.from_local( + inner_mx_tensor, + data_lp.device_mesh, + data_lp.placements, + run_check=False, + shape=data_lp.size(), + stride=data_lp.stride(), + ) return MXTensor( scale_e8m0_biased, data_lp, diff --git a/torchao/testing/training/dtensor_utils.py b/torchao/testing/training/dtensor_utils.py index 7ac0360363..815ee20969 100644 --- a/torchao/testing/training/dtensor_utils.py +++ b/torchao/testing/training/dtensor_utils.py @@ -4,6 +4,7 @@ # This source code is licensed under the BSD 3-Clause license found in the # LICENSE file in the root directory of this source tree. import copy +from typing import Union import torch import torch.nn as nn @@ -24,6 +25,8 @@ Float8RowwiseParallel, PrepareFloat8ModuleInput, ) +from torchao.prototype.mx_formats.config import MXLinearConfig +from torchao.quantization import quantize_ class FeedForward(nn.Module): @@ -36,7 +39,9 @@ def __init__(self): self.out_proj = nn.Linear(32, 16, bias=False) def forward(self, x): - return self.out_proj(F.silu(self.w1(x)) * self.w2(x)) + x = F.silu(self.w1(x)) * self.w2(x) + x = self.out_proj(x) + return x class ToyModel(nn.Module): @@ -50,20 +55,26 @@ def forward(self, x): def _test_lowp_mlp_tensor_parallelism_base( mesh: DeviceMesh, - config: Float8LinearConfig, + config: Union[Float8LinearConfig, MXLinearConfig], size=16, compile: bool = False, allgather_in_lowp: bool = False, ): device = mesh.device_type + # TODO(future): remove this once float8 training works with `quantize_` API + convert_model_func = convert_to_float8_training + if isinstance(config, MXLinearConfig): + convert_model_func = quantize_ + toy_model = ToyModel().to(device) - toy_model_fp8 = convert_to_float8_training(toy_model, config=config) + toy_model_fp8 = copy.deepcopy(toy_model) + convert_model_func(toy_model_fp8, config=config) tp_model = copy.deepcopy(toy_model) - tp_model = convert_to_float8_training(tp_model, config=config) + convert_model_func(tp_model, config=config) sp_model = copy.deepcopy(toy_model) - sp_model = convert_to_float8_training(sp_model, config=config) + convert_model_func(sp_model, config=config) # For tensorwise scaling, enable float8 all_gather. # For rowwise scaling, keep high precision all_gather. Motivation for @@ -108,7 +119,7 @@ def _test_lowp_mlp_tensor_parallelism_base( # prepare_input_cls with specific submodule fqn sp_model2 = copy.deepcopy(toy_model) - sp_model2 = convert_to_float8_training(sp_model2, config=config) + convert_model_func(sp_model2, config=config) if not allgather_in_lowp: prepare_input = prepare_input_cls( From 7ca9f10d048bd7ff3b5a9cd14a66c264be8b22e6 Mon Sep 17 00:00:00 2001 From: Driss Guessous <32754868+drisspg@users.noreply.github.com> Date: Tue, 24 Jun 2025 12:20:00 -0700 Subject: [PATCH 152/165] NVfp4 (#2408) --- test/prototype/mx_formats/test_mx_linear.py | 138 ++++- test/prototype/mx_formats/test_mx_tensor.py | 66 ++ torchao/prototype/mx_formats/__init__.py | 8 +- torchao/prototype/mx_formats/config.py | 6 +- torchao/prototype/mx_formats/mx_subclass.py | 73 ++- torchao/prototype/mx_formats/nvfp4_tensor.py | 617 +++++++++++++++++++ 6 files changed, 894 insertions(+), 14 deletions(-) create mode 100644 torchao/prototype/mx_formats/nvfp4_tensor.py diff --git a/test/prototype/mx_formats/test_mx_linear.py b/test/prototype/mx_formats/test_mx_linear.py index bfb6742d14..0e39264742 100644 --- a/test/prototype/mx_formats/test_mx_linear.py +++ b/test/prototype/mx_formats/test_mx_linear.py @@ -9,6 +9,7 @@ import pytest import torch import torch.nn as nn +import torch.nn.functional as F from torchao.prototype.mx_formats.config import ( MXGemmKernelChoice, @@ -25,7 +26,11 @@ MXInferenceLinear, MXLinear, ) -from torchao.prototype.mx_formats.mx_subclass import MXFPInferenceConfig +from torchao.prototype.mx_formats.mx_subclass import ( + MXFPInferenceConfig, + NVFP4InferenceConfig, + NVFP4MMConfig, +) from torchao.quantization import quantize_ from torchao.quantization.utils import compute_error from torchao.testing.utils import skip_if_rocm @@ -404,6 +409,7 @@ def test_inference_print_str(): @skip_if_rocm( "ROCm float4 gemm require gfx950" ) # TODO(future): deploy gfx950 in ROCM CI +@pytest.mark.skipif(not is_sm_at_least_100(), reason="CUDA capability >= 10.0 required") def test_inference_subclass(elem_dtype, bias: bool, compile: bool): """ Smoke test for inference compile @@ -441,3 +447,133 @@ def test_inference_subclass(elem_dtype, bias: bool, compile: bool): assert sqnr >= SQNR_THRESHOLD, ( f"Got a sqnr of {sqnr} for {elem_dtype} and bias={bias}" ) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +@pytest.mark.skipif( + not TORCH_VERSION_AT_LEAST_2_8, reason="torch.compile requires PyTorch 2.8+" +) +@pytest.mark.parametrize("bias", [True, False]) +@pytest.mark.parametrize("compile", [True, False]) +@pytest.mark.parametrize( + "mm_config", [NVFP4MMConfig.DYNAMIC, NVFP4MMConfig.WEIGHT_ONLY] +) +@pytest.mark.parametrize("inpt_dtype", [torch.bfloat16, torch.float32]) +@torch.no_grad() +@skip_if_rocm("ROCm float4 gemm require gfx950") +def test_inference_subclass_nvfp4( + bias: bool, compile: bool, mm_config: NVFP4MMConfig, inpt_dtype: torch.dtype +): + """ + Test NVFP4 recipe with scale_dtype=float8_e4m3fn and block_size=16 + Tests both DYNAMIC and WEIGHT_ONLY mm_config modes + """ + # DYNAMIC mode requires SM100+, but WEIGHT_ONLY works on older GPUs + if mm_config == NVFP4MMConfig.DYNAMIC and not is_sm_at_least_100(): + pytest.skip("CUDA capability >= 10.0 required for DYNAMIC float4 gemm") + + if bias and inpt_dtype == torch.float32: + pytest.xfail("Bias is not supported when module weight is in fp32") + + if mm_config == NVFP4MMConfig.WEIGHT_ONLY and compile: + pytest.skip("TODO: NVFP4MMConfig.WEIGHT_ONLY currently errors w/ compile") + m = nn.Linear(64, 256, bias=bias, dtype=inpt_dtype, device="cuda") + m_mx = copy.deepcopy(m) + + config = NVFP4InferenceConfig(mm_config=mm_config) + quantize_(m_mx, config=config) + + if compile: + m_mx = torch.compile(m_mx, fullgraph=True, backend="aot_eager") + + x = torch.randn(128, 64, device="cuda", dtype=inpt_dtype) + y_ref = m(x) + y_mx = m_mx(x) + sqnr = compute_error(y_ref, y_mx) + + if mm_config == NVFP4MMConfig.WEIGHT_ONLY: + SQNR_THRESHOLD = 18.0 + else: + SQNR_THRESHOLD = 15.0 + + assert y_mx.dtype == inpt_dtype, f"Got {y_mx.dtype} for inpt_dtype={inpt_dtype}" + assert sqnr >= SQNR_THRESHOLD, ( + f"Got a sqnr of {sqnr} for NVFP4 recipe with bias={bias}, mm_config={mm_config}" + ) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +@pytest.mark.skipif( + not TORCH_VERSION_AT_LEAST_2_8, reason="torch.compile requires PyTorch 2.8+" +) +@pytest.mark.parametrize("use_gelu", [True, False]) +@pytest.mark.parametrize( + "mm_config", [NVFP4MMConfig.DYNAMIC, NVFP4MMConfig.WEIGHT_ONLY] +) +@pytest.mark.parametrize("compile", [False]) +@pytest.mark.parametrize("bias", [True, False]) +@pytest.mark.parametrize("inpt_dtype", [torch.bfloat16, torch.float32]) +@torch.no_grad() +@skip_if_rocm("ROCm float4 gemm require gfx950") +def test_nvfp4_matmul_with_amax( + use_gelu: bool, + mm_config: NVFP4MMConfig, + compile: bool, + bias: bool, + inpt_dtype: torch.dtype, +): + from torchao.prototype.mx_formats.nvfp4_tensor import ( + NVFP4Tensor, + per_tensor_amax_to_scale, + ) + + # DYNAMIC mode requires SM100+, but WEIGHT_ONLY works on older GPUs + if mm_config == NVFP4MMConfig.DYNAMIC and not is_sm_at_least_100(): + pytest.skip("CUDA capability >= 10.0 required for DYNAMIC float4 gemm") + + if bias and inpt_dtype == torch.float32: + pytest.xfail("Bias is not supported when module weight is in fp32") + + if mm_config == NVFP4MMConfig.WEIGHT_ONLY and compile: + pytest.skip("TODO: NVFP4MMConfig.WEIGHT_ONLY currently errors w/ compile") + + m, k, n = 64, 256, 128 + + # Create activation tensor + if use_gelu: + x = torch.randn(m, k, dtype=inpt_dtype, device="cuda") + A = torch.nn.functional.gelu(x) + else: + A = torch.randn(m, k, dtype=inpt_dtype, device="cuda") + + B = torch.randn(n, k, dtype=inpt_dtype, device="cuda") + bias_tensor = torch.randn(n, dtype=inpt_dtype, device="cuda") if bias else None + + # Compute reference + C_ref = F.linear(A, B, bias_tensor) + + a_scale = per_tensor_amax_to_scale(torch.amax(torch.abs(A))) + b_scale = per_tensor_amax_to_scale(torch.amax(torch.abs(B))) + A_nvfp4 = NVFP4Tensor.to_nvfp4( + A, + per_tensor_scale=a_scale, + mm_config=mm_config, + ) + B_nvfp4 = NVFP4Tensor.to_nvfp4( + B, + per_tensor_scale=b_scale, + mm_config=mm_config, + ) + + func = torch.compile(F.linear, fullgraph=True) if compile else F.linear + + C_nvfp4 = func(A_nvfp4, B_nvfp4, bias_tensor) + assert C_nvfp4.dtype == inpt_dtype, ( + f"Got {C_nvfp4.dtype} for inpt_dtype={inpt_dtype}" + ) + + sqnr = compute_error(C_ref, C_nvfp4) + SQNR_THRESHOLD = 16.0 + assert sqnr >= SQNR_THRESHOLD, ( + f"SQNR {sqnr:.2f} < {SQNR_THRESHOLD}, use_gelu={use_gelu}, mm_config={mm_config}, compile={compile}, bias={bias}" + ) diff --git a/test/prototype/mx_formats/test_mx_tensor.py b/test/prototype/mx_formats/test_mx_tensor.py index 6dfd33f9c7..0490b0b1ee 100644 --- a/test/prototype/mx_formats/test_mx_tensor.py +++ b/test/prototype/mx_formats/test_mx_tensor.py @@ -14,6 +14,7 @@ from torchao.prototype.mx_formats.constants import ( DTYPE_FP6_E2M3, DTYPE_FP6_E3M2, + F4_E2M1_MAX, SUPPORTED_ELEM_DTYPES, ) from torchao.prototype.mx_formats.kernels import pack_uint4, pack_uint6 @@ -591,3 +592,68 @@ def to_f8(x): torch.testing.assert_close( data_in_range_f8_c, data_out_of_range_f8_c, atol=0, rtol=0 ) + + +@pytest.mark.parametrize( + "dtype,shape,use_per_tensor_scale", + [ + (torch.bfloat16, (32, 64), False), + (torch.float32, (64, 128), False), + (torch.bfloat16, (128, 256), False), + (torch.bfloat16, (64, 128), True), + ], +) +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +@pytest.mark.skipif( + not TORCH_VERSION_AT_LEAST_2_8, reason="torch.compile requires PyTorch 2.8+" +) +def test_nvfp4_reconstruction(dtype, shape, use_per_tensor_scale): + from torchao.prototype.mx_formats.nvfp4_tensor import ( + NVFP4Tensor, + per_tensor_amax_to_scale, + ) + + x = torch.randn(shape, dtype=dtype, device="cuda") + if use_per_tensor_scale: + tensor_amax = torch.max(torch.abs(x)) + scale = per_tensor_amax_to_scale(tensor_amax) + else: + scale = None + + x_nvfp4 = NVFP4Tensor.to_nvfp4(x, per_tensor_scale=scale) + x_reconstructed = x_nvfp4.to_dtype(dtype) + + def assert_sqnr_gt_threshold(orig, new, threshold): + sqnr = compute_error(orig, new) + if torch.all(torch.isnan(sqnr)): + # if both operands are full of zeroes, sqnr is nan and this is ok + # test for this explicitly + assert torch.all(orig == 0) and torch.all(new == 0) + else: + assert sqnr >= threshold + + reconstructed_amax = x_nvfp4.get_hp_scales().view(shape[0], -1, 1) * F4_E2M1_MAX + max_abs = torch.amax( + torch.abs(x.reshape(shape[0], -1, x_nvfp4._block_size)), dim=-1 + ).unsqueeze(-1) + + assert_sqnr_gt_threshold(max_abs, reconstructed_amax, 30.0) + assert_sqnr_gt_threshold(x, x_reconstructed, 8.0) + + assert x.shape == x_reconstructed.shape, ( + f"Shape mismatch: {x.shape} vs {x_reconstructed.shape}" + ) + assert x.dtype == x_reconstructed.dtype, ( + f"Dtype mismatch: {x.dtype} vs {x_reconstructed.dtype}" + ) + + x_nvfp4_t = x_nvfp4.t() + x_reconstructed_t = x_nvfp4_t.to_dtype(dtype) + assert_sqnr_gt_threshold(x.t(), x_reconstructed_t, 8.0) + + assert x.t().shape == x_reconstructed_t.shape, ( + f"Transpose shape mismatch: {x.t().shape} vs {x_reconstructed_t.shape}" + ) + assert x.t().dtype == x_reconstructed_t.dtype, ( + f"Transpose dtype mismatch: {x.t().dtype} vs {x_reconstructed_t.dtype}" + ) diff --git a/torchao/prototype/mx_formats/__init__.py b/torchao/prototype/mx_formats/__init__.py index 7c1f0ace55..5947d616be 100644 --- a/torchao/prototype/mx_formats/__init__.py +++ b/torchao/prototype/mx_formats/__init__.py @@ -6,7 +6,11 @@ ) # Note: Prototype and subject to change -from torchao.prototype.mx_formats.mx_subclass import MXFPInferenceConfig +from torchao.prototype.mx_formats.mx_subclass import ( + MXFPInferenceConfig, + NVFP4InferenceConfig, + NVFP4MMConfig, +) # import mx_linear here to register the quantize_ transform logic # ruff: noqa: I001 @@ -18,4 +22,6 @@ "MXLinearConfig", "MXLinearRecipeName", "MXFPInferenceConfig", + "NVFP4InferenceConfig", + "NVFP4MMConfig", ] diff --git a/torchao/prototype/mx_formats/config.py b/torchao/prototype/mx_formats/config.py index eb1b15228d..525bf21fc6 100644 --- a/torchao/prototype/mx_formats/config.py +++ b/torchao/prototype/mx_formats/config.py @@ -57,10 +57,10 @@ def _validate_gemm_kernel_choice(gemm_kernel_choice, block_size, elem_dtype): f"elem_dtype must be one of {valid_dtypes} to use the CUTLASS MX gemm kernels, got {elem_dtype}" ) elif gemm_kernel_choice == MXGemmKernelChoice.CUBLAS: - assert block_size == 32, ( - f"block_size must be 32 to use the cuBLAS MX gemm kernels, got {block_size}" + assert block_size in [16, 32], ( + f"block_size must be in [16, 32] to use the cuBLAS MX gemm kernels, got {block_size}" ) - valid_dtypes = [torch.float8_e4m3fn] + valid_dtypes = [torch.float8_e4m3fn, torch.float4_e2m1fn_x2] assert elem_dtype in valid_dtypes, ( f"elem_dtype must be one of {valid_dtypes} to use the CUTLASS MX gemm kernels, got {elem_dtype}" ) diff --git a/torchao/prototype/mx_formats/mx_subclass.py b/torchao/prototype/mx_formats/mx_subclass.py index 2173c97002..d1be8a04f4 100644 --- a/torchao/prototype/mx_formats/mx_subclass.py +++ b/torchao/prototype/mx_formats/mx_subclass.py @@ -10,7 +10,6 @@ import torch -import torchao from torchao.core.config import AOBaseConfig from torchao.prototype.mx_formats import ( MXGemmKernelChoice, @@ -20,13 +19,19 @@ _validate_gemm_kernel_choice, ) from torchao.prototype.mx_formats.mx_tensor import MXTensor +from torchao.prototype.mx_formats.nvfp4_tensor import NVFP4MMConfig, NVFP4Tensor from torchao.quantization.quant_api import to_linear_activation_quantized from torchao.quantization.transform_module import ( register_quantize_module_handler, ) -from torchao.utils import TORCH_VERSION_AT_LEAST_2_5, is_sm_at_least_100 +from torchao.utils import ( + TORCH_VERSION_AT_LEAST_2_5, + TORCH_VERSION_AT_LEAST_2_8, + is_sm_at_least_100, +) +# TODO The naming for these configs is a little weird, rename before moving to public API # Note: This API is extra prototype and will change in the future @dataclass class MXFPInferenceConfig(AOBaseConfig): @@ -63,16 +68,13 @@ class MXFPInferenceConfig(AOBaseConfig): block_size: int = 32 - # Dtypes for Input and Weights + # Dtypes for Input and Weights, supports Fp8 and Fp4 formats activation_dtype: torch.dtype = torch.float8_e4m3fn weight_dtype: torch.dtype = torch.float8_e4m3fn # Which kernel to run for mm gemm_kernel_choice: MXGemmKernelChoice = MXGemmKernelChoice.CUBLAS - # Set some magic perf settings - set_inductor_config: bool = False - def __post_init__(self): assert self.activation_dtype == self.weight_dtype, ( "For now - we only support matching input/weight dtypes." @@ -115,8 +117,6 @@ def _mx_inference_linear_transform( # TODO Sm120 has slightly more restrictive reqs # TODO handle AMD assert is_sm_at_least_100(), "MXFP is only supported on sm100 machiens for now" - if config.set_inductor_config: - torchao.quantization.utils.recommended_inductor_config_setter() activation_dtype = config.activation_dtype weight_dtype = config.weight_dtype @@ -151,7 +151,62 @@ def _mx_inference_linear_transform( return module +@dataclass +class NVFP4InferenceConfig(AOBaseConfig): + """ + NVIDIA FP4 (NVFP4) Inference Quantization Configuration + + This is a specialized configuration for NVIDIA's FP4 format. + All parameters are fixed in the NVFP4 implementation except mm_config: + - mm_config: NVFP4MMConfig, which can be set to DYNAMIC or WEIGHT_ONLY (emulated mm in high precision) + - Data: float4_e2m1fn_x2 + - Scales: float8_e4m3fn + - Block size: 16 along the reduction dim + """ + + mm_config: NVFP4MMConfig = NVFP4MMConfig.DYNAMIC + + def __post_init__(self): + # Validate PyTorch version + if not TORCH_VERSION_AT_LEAST_2_8: + raise RuntimeError("NVFP4InferenceConfig requires PyTorch 2.8 or later") + + +@register_quantize_module_handler(NVFP4InferenceConfig) +def _nvfp4_inference_linear_transform( + module: torch.nn.Linear, config: NVFP4InferenceConfig +): + """Quantization handler for NVFP4InferenceConfig""" + if config.mm_config == NVFP4MMConfig.DYNAMIC: + assert is_sm_at_least_100(), ( + "NVFP4 DYNAMIC mode is only supported on sm100+ machines" + ) + + weight = module.weight + + if module.bias is not None and weight.dtype == torch.float32: + raise RuntimeError( + "Bias is not supported when module weight is in fp32 (out_dtype=Float32). " + "Please use bfloat16 or float16 weights, or remove the bias from the linear layer." + ) + + quantized_weight = NVFP4Tensor.to_nvfp4( + weight, + mm_config=config.mm_config, + ) + + module.weight = torch.nn.Parameter(quantized_weight, requires_grad=False) + module.extra_repr = types.MethodType(_linear_extra_repr, module) + return module + + if TORCH_VERSION_AT_LEAST_2_5: torch.serialization.add_safe_globals( - [MXTensor, MXGemmKernelChoice, _input_activation_quant_func_mxfp] + [ + MXTensor, + NVFP4Tensor, + NVFP4MMConfig, + MXGemmKernelChoice, + _input_activation_quant_func_mxfp, + ] ) diff --git a/torchao/prototype/mx_formats/nvfp4_tensor.py b/torchao/prototype/mx_formats/nvfp4_tensor.py new file mode 100644 index 0000000000..ed1b5df1d0 --- /dev/null +++ b/torchao/prototype/mx_formats/nvfp4_tensor.py @@ -0,0 +1,617 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD 3-Clause license found in the +# LICENSE file in the root directory of this source tree. + +from enum import Enum +from typing import Any, Callable, Dict, Optional + +import torch +from torch.utils._python_dispatch import return_and_correct_aliasing + +from torchao.prototype.mx_formats.constants import F4_E2M1_MAX, F8E4M3_MAX +from torchao.prototype.mx_formats.kernels import ( + f4_unpacked_to_f32, + f32_to_f4_unpacked, + pack_uint4, + unpack_uint4, +) +from torchao.prototype.mx_formats.mx_tensor import ( + tensor_size_fp4x2_to_hp, + tensor_size_hp_to_fp4x2, +) +from torchao.prototype.mx_formats.utils import to_blocked +from torchao.utils import fill_defaults + +E4M3_EPS = torch.finfo(torch.float8_e4m3fn).tiny + +aten = torch.ops.aten + +NVFP4_OPS_TABLE: Dict[Any, Any] = {} + + +class NVFP4MMConfig(Enum): + DYNAMIC = "dynamic" + WEIGHT_ONLY = "weight_only" + + +def implements(aten_ops): + """Register aten ops to the NVFP4 op table""" + + def decorator(func): + for op in aten_ops: + NVFP4_OPS_TABLE[op] = func + return func + + return decorator + + +class NVFP4Tensor(torch.Tensor): + """NVIDIA FP4 (NVFP4) Tensor subclass. + + This implements the NVIDIA variant of MX FP4 format, which uses a specific + quantization algorithm for FP4 data with UE4M3 scales. + + Attributes: + _scale_e4m3: Blockwise scales in float8_e4m3fn format + _per_tensor_scale: Optional global per-tensor scale in float32 format + _data: Packed FP4 data (2 values per byte) + _block_size: Block size for quantization (fixed at 16) + _orig_dtype: Original tensor dtype before quantization + mm_config: Matrix multiplication configuration + """ + + _scale_e4m3: torch.Tensor + _per_tensor_scale: Optional[torch.Tensor] + _data: torch.Tensor + _block_size: int + _orig_dtype: torch.dtype + mm_config: NVFP4MMConfig + + def __new__( + cls, + blockwise_scales, + per_tensor_scale, + data_bits, + block_size, + orig_dtype, + mm_config=NVFP4MMConfig.DYNAMIC, + ): + # FP4 tensor size handling + new_size = data_bits.size() + new_size = tensor_size_fp4x2_to_hp( + new_size, + data_bits.is_contiguous(), + ) + + self = torch.Tensor._make_wrapper_subclass( + cls, + new_size, + dtype=orig_dtype, + device=data_bits.device, + requires_grad=False, + ) + + self._scale_e4m3 = blockwise_scales + self._per_tensor_scale = per_tensor_scale + self._data = data_bits + self._block_size = block_size + self._orig_dtype = orig_dtype + self.mm_config = mm_config + return self + + def __repr__(self): + return f"NVFP4Tensor: blockwise_scales: {self._scale_e4m3}, per_tensor_scale: {self._per_tensor_scale}, d: {self._data}, d_hp: {self.to_dtype(self._orig_dtype)}" + + @classmethod + def __torch_dispatch__(cls, func, types, args, kwargs=None): + # Use NVFP4-specific ops table + if func in NVFP4_OPS_TABLE: + return NVFP4_OPS_TABLE[func](func, types, args, kwargs) + + raise NotImplementedError(f"{func} not implemented for NVFP4Tensor") + + @staticmethod + def to_nvfp4( + data_hp: torch.Tensor, + block_size: int = 16, + per_tensor_scale: Optional[torch.Tensor] = None, + mm_config: NVFP4MMConfig = NVFP4MMConfig.DYNAMIC, + ): + """Convert high precision tensor to NVFP4 format. + + Args: + data_hp: High precision input tensor (bfloat16 or float32) + block_size: Block size for quantization (must be 16) + per_tensor_amax: Optional pre-computed absolute maximum for calibration. + If provided, uses per-tensor scaling. If None, uses block-wise scaling only. + + Returns: + NVFP4Tensor: Quantized tensor in NVFP4 format + """ + blockwise_scales, data_lp = nvfp4_quantize( + data_hp, block_size, per_tensor_scale + ) + return NVFP4Tensor( + blockwise_scales, + per_tensor_scale, + data_lp, + block_size, + data_hp.dtype, + mm_config, + ) + + def __tensor_flatten__(self): + ctx = { + "_block_size": self._block_size, + "_orig_dtype": self._orig_dtype, + "mm_config": self.mm_config, + } + tensor_list = ["_scale_e4m3", "_data"] + if self._per_tensor_scale is not None: + tensor_list.append("_per_tensor_scale") + return tensor_list, ctx + + def _apply_fn_to_data(self, fn: Callable): + """Applies a fn to all tensor components stored on this class""" + tensor_names, ctx = self.__tensor_flatten__() + new_tensors = {} + for name in tensor_names: + new_tensors[name] = fn(getattr(self, name)) + if "_per_tensor_scale" not in tensor_names: + new_tensors["_per_tensor_scale"] = None + return self.__class__.__tensor_unflatten__( + new_tensors, + ctx, + None, + None, + ) + + @staticmethod + def __tensor_unflatten__( + inner_tensors, + metadata, + outer_size, + outer_stride, + ): + return NVFP4Tensor( + inner_tensors["_scale_e4m3"], + inner_tensors.get("_per_tensor_scale", None), + inner_tensors["_data"], + metadata["_block_size"], + metadata["_orig_dtype"], + metadata["mm_config"], + ) + + # Do not force the NVFP4Tensor type on the returned tensor + __torch_function__ = torch._C._disabled_torch_function_impl + + def to_dtype(self, target_dtype: torch.dtype) -> torch.Tensor: + """Convert NVFP4Tensor back to high precision dtype. + + Args: + target_dtype: Target dtype for dequantization (e.g., torch.float32, torch.bfloat16) + + Returns: + torch.Tensor: Dequantized tensor in the target dtype + """ + is_transposed = not self._data.is_contiguous() + if is_transposed: + M, K = self.shape[1], self.shape[0] + else: + M, K = self.shape[0], self.shape[1] + data = self._data.t() if is_transposed else self._data + data_unpacked = unpack_uint4(data.contiguous().view(torch.uint8)) + data_f32 = f4_unpacked_to_f32(data_unpacked) + + data_f32 = data_f32.view(M, K // self._block_size, self._block_size) + scale_e4m3_reshaped = self.get_hp_scales().view(M, K // self._block_size, 1) + data_scaled = data_f32 * scale_e4m3_reshaped.to(torch.float32) + result = data_scaled.view(M, K).to(target_dtype) + + if is_transposed: + result = result.t() + + return result + + def get_hp_scales(self) -> torch.Tensor: + """Get the scales of the NVFP4Tensor in original dtype. + + Returns: + torch.Tensor: Scales of the NVFP4Tensor + """ + return ( + self._scale_e4m3.to(self._orig_dtype) + if not self._per_tensor_scale + else self._per_tensor_scale * self._scale_e4m3.to(self._orig_dtype) + ) + + @classmethod + def _same_metadata(cls, self: "NVFP4Tensor", src: "NVFP4Tensor") -> bool: + """Check if two NVFP4Tensors have the same metadata. + + Args: + self: First NVFP4Tensor to compare + src: Second NVFP4Tensor to compare + + Returns: + bool: True if both tensors have identical metadata, False otherwise + """ + # Check per_tensor_scale equality + per_tensor_scale_equal = ( + self._per_tensor_scale is None and src._per_tensor_scale is None + ) or (self._per_tensor_scale.shape == src._per_tensor_scale.shape) + + return ( + isinstance(self, NVFP4Tensor) + and isinstance(src, NVFP4Tensor) + and self._block_size == src._block_size + and self._orig_dtype == src._orig_dtype + and self._scale_e4m3.shape == src._scale_e4m3.shape + and per_tensor_scale_equal + and self._data.shape == src._data.shape + ) + + +@implements([aten.detach.default, aten.alias.default]) +def nvfp4_detach_alias(func, types, args, kwargs): + return return_and_correct_aliasing( + func, args, kwargs, args[0]._apply_fn_to_data(func) + ) + + +@implements([aten._to_copy.default]) +def nvfp4_to_copy(func, types, args, kwargs): + """Autocast + device movement""" + assert isinstance(args[0], NVFP4Tensor) + + # Handle dtype parameter + dtype = kwargs.pop("dtype", None) + if dtype is not None: + assert dtype in { + torch.float16, + torch.bfloat16, + torch.float32, + }, "Only support floating point conversion for autocast w/ NVFP4Tensor" + + # Handle device parameter + device = kwargs.pop("device", None) + if device is not None: + # Apply device change using _apply_fn_to_data + tensor = args[0]._apply_fn_to_data(lambda x: func(x, device=device)) + tensor = return_and_correct_aliasing(func, args, {}, tensor) + else: + tensor = args[0] + + if dtype is not None: + res = NVFP4Tensor( + tensor._scale_e4m3, + tensor._per_tensor_scale, + tensor._data, + tensor._block_size, + dtype, + tensor.mm_config, + ) + return res + + return tensor + + +@implements([aten.copy_.default]) +def nvfp4_copy_(func, types, args, kwargs): + self = args[0] + src = args[1] + if NVFP4Tensor._same_metadata(self, src): + self_tensors = self.__tensor_flatten__()[0] + for tensor_name in self_tensors: + getattr(self, tensor_name).copy_(getattr(src, tensor_name)) + return self + raise ValueError( + f"Not supported args for copy_ due to metadata mismatch: {self}, {src}" + ) + + +@implements([aten.clone.default]) +def nvfp4_clone(func, types, args, kwargs): + self = args[0] + memory_format = kwargs.get("memory_format", None) + + if memory_format is not None: + clone_fn = lambda x: x.clone(memory_format=memory_format) + else: + clone_fn = lambda x: x.clone() + + return self._apply_fn_to_data(clone_fn) + + +@implements([aten.slice.Tensor]) +def nvfp4_slice(func, types, args, kwargs): + x, dim, start, end, step = fill_defaults(args, 5, [0, None, None, 1]) + + if step != 1: + raise ValueError("Only support aten.slice with step=1") + + assert x._data.is_contiguous(), "Only support contiguous data for now" + + M, K = x.shape[0], x.shape[1] + scale_shaped = x._scale_e4m3.view(M, K // x._block_size) + + if dim == 0: + # Slicing along the first dimension (rows) + sliced_scale = aten.slice.Tensor(scale_shaped, dim, start, end, step).flatten() + sliced_data = aten.slice.Tensor(x._data, dim, start, end, step) + elif dim == 1: + # Slicing along reduction dim - must align with block boundaries + if start is not None: + assert start % x._block_size == 0, ( + f"Start index {start} must be a multiple of block_size {x._block_size}" + ) + + if end is not None: + assert end % x._block_size == 0, ( + f"End index {end} must be a multiple of block_size {x._block_size}" + ) + + sliced_data = aten.slice.Tensor(x._data, dim, start, end, step) + + # Calculate which scale blocks to keep + start_block = 0 if start is None else start // x._block_size + end_block = None if end is None else end // x._block_size + + # Slice the scale tensor accordingly + sliced_scale = aten.slice.Tensor(scale_shaped, 1, start_block, end_block, step) + else: + raise ValueError( + f"NVFP4Tensor only supports slicing along dimensions 0 and 1, got dim={dim}" + ) + + return NVFP4Tensor( + sliced_scale, + x._per_tensor_scale, # Unchanged per-tensor scale + sliced_data, + x._block_size, + x._orig_dtype, + x.mm_config, + ) + + +@implements([aten.t.default]) +def nvfp4_t(func, types, args, kwargs): + # For now, only transpose(input, 0, 1) is supported. + old = args[0] + new = NVFP4Tensor( + old._scale_e4m3, + old._per_tensor_scale, + old._data.t(), + old._block_size, + old._orig_dtype, + old.mm_config, + ) + return new + + +@implements([aten.view.default]) +def nvfp4_view_op(func, types, args, kwargs): + data = args[0]._data + new_size = args[1] + new_size = tensor_size_hp_to_fp4x2(new_size, data.is_contiguous()) + new_data = func(data, new_size, *args[2:], **kwargs) + return NVFP4Tensor( + args[0]._scale_e4m3, + args[0]._per_tensor_scale, + new_data, + args[0]._block_size, + args[0]._orig_dtype, + args[0].mm_config, + ) + + +def _addmm_nvfp4_dispatch( + a: NVFP4Tensor, b: NVFP4Tensor, aten_op, bias: Optional[torch.Tensor] = None +) -> torch.Tensor: + """ + Core implementation shared between nvfp4_mm, nvfp4_addmm, and nvfp4_linear. + The only difference is whether bias is None or not. + """ + assert a._data.is_contiguous() + assert b._data.t().is_contiguous() + assert a._block_size == 16, f"NVFP4 requires block_size=16, got {a._block_size}" + assert b._block_size == 16, f"NVFP4 requires block_size=16, got {b._block_size}" + + M, K = a.shape[0], a.shape[1] + N = b.shape[1] + + # Swizzle Dizzle + a_scale = a._scale_e4m3.view(M, K // a._block_size) + b_scale = b._scale_e4m3.view(N, K // b._block_size) + a_scale_blocked = to_blocked(a_scale) + b_scale_blocked = to_blocked(b_scale) + + # Merge double quant scales into 1 scale for Scale_In^D + if a._per_tensor_scale is not None: + assert b._per_tensor_scale is not None + scale_result = a._per_tensor_scale * b._per_tensor_scale + else: + assert b._per_tensor_scale is None and a._per_tensor_scale is None + scale_result = None + + # THIS IS A WORKAROUND: + # RuntimeError: CUDA error: CUBLAS_STATUS_INVALID_VALUE when calling + # When we have per-tensor scaling, we need to apply it before bias + # since bias is not quantized + should_add_bias_separately = (scale_result is not None) and (bias is not None) + + result = torch._scaled_mm( + a._data.view(torch.float4_e2m1fn_x2), + b._data.view(torch.float4_e2m1fn_x2), + a_scale_blocked.view(torch.float8_e4m3fn), + b_scale_blocked.view(torch.float8_e4m3fn), + bias=None if should_add_bias_separately else bias, + out_dtype=a._orig_dtype, + # scale_result=scale_result, # Not supported yet + ) + + if scale_result is not None: + result = result * scale_result.to(a._orig_dtype) + + # Add bias after scaling if needed + if should_add_bias_separately: + result = result + bias + + return result + + +@implements([torch.nn.functional.linear, aten.linear.default]) +def nvfp4_linear(func, types, args, kwargs): + input_tensor, weight_tensor, bias = ( + args[0], + args[1], + args[2] if len(args) > 2 else None, + ) + + if not isinstance(weight_tensor, NVFP4Tensor): + raise NotImplementedError("NVFP4Tensor: weight must be NVFP4Tensor") + + config = weight_tensor.mm_config + + if config == NVFP4MMConfig.WEIGHT_ONLY: + weight_dequant = weight_tensor.to_dtype(weight_tensor._orig_dtype) + return torch.nn.functional.linear(input_tensor, weight_dequant, bias) + else: + input_quant = NVFP4Tensor.to_nvfp4(input_tensor, mm_config=config) + return _addmm_nvfp4_dispatch(input_quant, weight_tensor, func, bias=bias) + + +@implements([aten.mm.default, aten.matmul.default]) +def nvfp4_mm(func, types, args, kwargs): + input_tensor, weight_tensor = args[0], args[1] + + if not isinstance(weight_tensor, NVFP4Tensor): + raise NotImplementedError("NVFP4Tensor: weight must be NVFP4Tensor") + + config = weight_tensor.mm_config + + if config == NVFP4MMConfig.WEIGHT_ONLY: + weight_dequant = weight_tensor.to_dtype(weight_tensor._orig_dtype) + if isinstance(input_tensor, NVFP4Tensor): + input_dequant = input_tensor.to_dtype(input_tensor._orig_dtype) + return func(input_dequant, weight_dequant) + else: + return func(input_tensor, weight_dequant) + else: + if not isinstance(input_tensor, NVFP4Tensor): + input_tensor = NVFP4Tensor.to_nvfp4(input_tensor, mm_config=config) + return _addmm_nvfp4_dispatch(input_tensor, weight_tensor, func) + + +@implements([aten.addmm.default]) +def nvfp4_addmm(func, types, args, kwargs): + bias, input_tensor, weight_tensor = args[0], args[1], args[2] + + if not isinstance(weight_tensor, NVFP4Tensor): + raise NotImplementedError("NVFP4Tensor: weight must be NVFP4Tensor") + + config = weight_tensor.mm_config + + if config == NVFP4MMConfig.WEIGHT_ONLY: + weight_dequant = weight_tensor.to_dtype(weight_tensor._orig_dtype) + if isinstance(input_tensor, NVFP4Tensor): + input_dequant = input_tensor.to_dtype(input_tensor._orig_dtype) + return torch.addmm(bias, input_dequant, weight_dequant) + else: + return torch.addmm(bias, input_tensor, weight_dequant) + else: + if not isinstance(input_tensor, NVFP4Tensor): + input_tensor = NVFP4Tensor.to_nvfp4(input_tensor, mm_config=config) + return _addmm_nvfp4_dispatch(input_tensor, weight_tensor, func, bias=bias) + + +def per_tensor_amax_to_scale(amax: torch.Tensor) -> torch.Tensor: + """Convert per-tensor amax to per-tensor scale. + Used to scale fp32 scales down to fp8 scales + + Args: + amax: Per-tensor amax tensor + + Returns: + torch.Tensor: Per-tensor scale tensor + """ + return torch.clamp(amax / F8E4M3_MAX, min=E4M3_EPS, max=F8E4M3_MAX).to( + torch.float32 + ) + + +def nvfp4_quantize( + data_hp: torch.Tensor, + block_size: int = 16, + per_tensor_scale: Optional[torch.Tensor] = None, +) -> tuple[torch.Tensor, torch.Tensor]: + """NVIDIA FP4 quantization with UE4M3 scales. + + Implements the NVIDIA algorithm for quantizing tensors to FP4 format + with unsigned E4M3 (UE4M3) scales. + + Args: + data_hp: High precision input tensor (bfloat16 or float32) + block_size: Block size for quantization (must be 16) + per_tensor_amax: Optional pre-computed absolute maximum for calibration. + If provided, uses per-tensor scaling. If None, uses block-wise scaling only. + + Returns: + tuple: A tuple containing: + - total_scale_fp8: Blockwise scales in float8_e4m3fn format + - per_tensor_scale: Global per-tensor scale if per_tensor_amax provided, else None + - data_lp: Packed FP4 data (2 values per byte) + + Raises: + AssertionError: If input dtype is not supported, tensor size is not + divisible by block_size, tensor is not contiguous, or block_size != 16 + """ + assert data_hp.dtype in (torch.bfloat16, torch.float), ( + f"{data_hp.dtype} not supported" + ) + assert data_hp.numel() % block_size == 0, "unsupported" + assert data_hp.is_contiguous(), "unsupported" + assert block_size == 16, "NVFP4 requires block_size=16" + + orig_shape = data_hp.shape + data_hp = data_hp.reshape(orig_shape[0], -1, block_size) + + max_abs = torch.amax(torch.abs(data_hp), dim=-1) + # These scales are currently in fp32, we are going to `quantize` them to e4m3 + block_scale = max_abs / F4_E2M1_MAX + + out_scales = None + if per_tensor_scale is None: + # We are doing single level scaling + block_scale_fp8 = torch.clamp(block_scale, min=E4M3_EPS, max=F8E4M3_MAX).to( + torch.float8_e4m3fn + ) + block_scale_fp32 = block_scale_fp8.to(torch.float32) + data_scaled = data_hp / block_scale_fp32.unsqueeze(-1) + out_scales = block_scale_fp8 + else: + # We are doing two level scaling, + # This will likely be calibrated but + # we want the per_tensor_scale ~= amax of the block_scale_fp32 + block_scale_fp32 = block_scale.to(torch.float32) + # Quantize the blockwise scales w/ the per_tensor_scale + scaled_block_scales = block_scale_fp32 / per_tensor_scale + scaled_block_scales_fp8 = torch.clamp( + scaled_block_scales, min=E4M3_EPS, max=F8E4M3_MAX + ).to(torch.float8_e4m3fn) + scaled_block_scales_fp32 = scaled_block_scales_fp8.to(torch.float32) + # We "temporarily" dequant the scaled_block_scales_fp32 to get the per_tensor_scale + # To apply to data + total_scale = per_tensor_scale * scaled_block_scales_fp32 + data_scaled = data_hp / total_scale.unsqueeze(-1) + out_scales = scaled_block_scales_fp8 + + data_scaled = torch.clamp(data_scaled, -F4_E2M1_MAX, F4_E2M1_MAX) + data_scaled = data_scaled.view(orig_shape) + data_lp = f32_to_f4_unpacked(data_scaled.float()) + # TODO: NotImplementedError: "copy_kernel" not implemented for 'Float4_e2m1fn_x2' + # data_lp = pack_uint4(data_lp).view(torch.float4_e2m1fn_x2) + data_lp = pack_uint4(data_lp) + return out_scales, data_lp From 32599bee3dadf9ba9b6e55f70b2e7ca49fba2d96 Mon Sep 17 00:00:00 2001 From: Vasiliy Kuznetsov Date: Tue, 24 Jun 2025 15:26:47 -0400 Subject: [PATCH 153/165] enable tensor parallelism for MXLinear (#2434) * Update [ghstack-poisoned] * Update [ghstack-poisoned] * Update [ghstack-poisoned] * Update [ghstack-poisoned] * Update [ghstack-poisoned] * Update [ghstack-poisoned] * Update [ghstack-poisoned] * Update [ghstack-poisoned] * Update [ghstack-poisoned] * Update [ghstack-poisoned] --- test/prototype/mx_formats/test_mx_dtensor.py | 12 ++--- test/prototype/mx_formats/test_mx_linear.py | 4 +- test/prototype/mx_formats/test_mx_tensor.py | 2 +- torchao/prototype/mx_formats/kernels.py | 8 ++-- torchao/prototype/mx_formats/mx_tensor.py | 50 ++++++++++---------- torchao/testing/training/dtensor_utils.py | 11 +++-- 6 files changed, 45 insertions(+), 42 deletions(-) diff --git a/test/prototype/mx_formats/test_mx_dtensor.py b/test/prototype/mx_formats/test_mx_dtensor.py index bfc930c579..4aefb3874e 100644 --- a/test/prototype/mx_formats/test_mx_dtensor.py +++ b/test/prototype/mx_formats/test_mx_dtensor.py @@ -68,24 +68,22 @@ def _test_dtensor_cast_to_mxfp8(mesh: DeviceMesh, size=4): ) -def _test_mxfp8_mlp_tensor_parallelism_eager(mesh: DeviceMesh, size=16): +def _test_mxfp8_mlp_tensor_parallelism(mesh: DeviceMesh, size=16): config = MXLinearConfig.from_recipe_name("mxfp8_emulated") - # TODO(future PR): assert that the K dim must be divisible by block size, - # today this is silently incorrect if block_size is greater than K config.block_size = 16 _test_lowp_mlp_tensor_parallelism_base( mesh, config, size, compile=False, allgather_in_lowp=False ) - - # TODO(future PR): compile + _test_lowp_mlp_tensor_parallelism_base( + mesh, config, size, compile=True, allgather_in_lowp=False + ) if __name__ == "__main__": device_mesh = setup_distributed() tests = [ _test_dtensor_cast_to_mxfp8, - # TODO(next PR): enable this (current PR got too large, so splitting) - # _test_mxfp8_mlp_tensor_parallelism_eager, + _test_mxfp8_mlp_tensor_parallelism, ] for test in tqdm(tests, desc="Running tests"): diff --git a/test/prototype/mx_formats/test_mx_linear.py b/test/prototype/mx_formats/test_mx_linear.py index 0e39264742..8a69737889 100644 --- a/test/prototype/mx_formats/test_mx_linear.py +++ b/test/prototype/mx_formats/test_mx_linear.py @@ -195,8 +195,8 @@ def test_linear_eager_emulated_vs_real_gemm(recipe_name, mkn): # TODO(future): enable compile support @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") def test_activation_checkpointing(): - input_shape = (2, 4) - grad_shape = (2, 8) + input_shape = (16, 4) + grad_shape = (16, 8) elem_dtype = torch.float8_e4m3fn m = nn.Sequential( diff --git a/test/prototype/mx_formats/test_mx_tensor.py b/test/prototype/mx_formats/test_mx_tensor.py index 0490b0b1ee..7294590b57 100644 --- a/test/prototype/mx_formats/test_mx_tensor.py +++ b/test/prototype/mx_formats/test_mx_tensor.py @@ -73,7 +73,7 @@ def assert_sqnr_gt_threshold(orig, new, threshold): @pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") @pytest.mark.parametrize("elem_dtype", SUPPORTED_ELEM_DTYPES) def test_hello_world(elem_dtype): - data = torch.randn(4, 4, device="cuda", dtype=torch.bfloat16) + data = torch.randn(8, 8, device="cuda", dtype=torch.bfloat16) block_size = 4 _test_mx(data, elem_dtype, block_size) diff --git a/torchao/prototype/mx_formats/kernels.py b/torchao/prototype/mx_formats/kernels.py index f96e73a55a..72cbba1802 100644 --- a/torchao/prototype/mx_formats/kernels.py +++ b/torchao/prototype/mx_formats/kernels.py @@ -1056,7 +1056,7 @@ def pack_uint6(uint8_data: torch.Tensor) -> torch.Tensor: # effective mx block size since we're packing 2 fp4 into 1 uint8 packed_mx_block_size = 3 * mx_block_size // 4 - packed_shape = [uint8_data.shape[0], packed_mx_block_size] + packed_shape = [*uint8_data.shape[:-1], packed_mx_block_size] n_mx_blocks = uint8_data.numel() // mx_block_size grid = lambda meta: (triton.cdiv(n_mx_blocks, meta["BLOCK_SIZE_IN"]),) @@ -1337,7 +1337,9 @@ def triton_to_mxfp8_dim1( # Create scale tensors col_scale = torch.empty( - (n_cols * n_rows // inner_block_size, 1), dtype=torch.uint8, device=x.device + (n_cols, n_rows // inner_block_size, 1), + dtype=torch.uint8, + device=x.device, ) # Calculate grid dimensions based on tile size @@ -1374,7 +1376,7 @@ def triton_to_mxfp8_dim1_reference( scale_e8m0_dim1, x_hp_d1_normalized = to_mx( x_hp_d1, torch.float8_e4m3fn, block_size ) - scale_e8m0_dim1 = scale_e8m0_dim1.unsqueeze(1).view(torch.float8_e8m0fnu) + scale_e8m0_dim1 = scale_e8m0_dim1.view(torch.float8_e8m0fnu) return ( x_hp_d1_normalized.t(), scale_e8m0_dim1, diff --git a/torchao/prototype/mx_formats/mx_tensor.py b/torchao/prototype/mx_formats/mx_tensor.py index ef9ae42fcd..e98878af77 100644 --- a/torchao/prototype/mx_formats/mx_tensor.py +++ b/torchao/prototype/mx_formats/mx_tensor.py @@ -25,7 +25,6 @@ from torchao.prototype.mx_formats.config import MXGemmKernelChoice from torchao.prototype.mx_formats.constants import ( - BF16_EXP_BIAS, BLOCK_SIZE_DEFAULT, DTYPE_FP6_E2M3, DTYPE_FP6_E3M2, @@ -62,7 +61,6 @@ # TODO(later): read from somewhere else? SBITS, EBITS_F32, MBITS_F32 = 1, 8, 23 -EBITS_BF16, MBITS_BF16 = 8, 7 EBITS_F4_E2M1, MBITS_F4_E2M1 = 2, 1 EBITS_F6_E2M3, MBITS_F6_E2M3 = 2, 3 EBITS_F6_E3M2, MBITS_F6_E3M2 = 3, 2 @@ -137,9 +135,7 @@ def _to_mx_rceil( ) # scale and saturated cast the data elements to max of target dtype - data_lp = torch.clamp( - data_hp * descale_fp.unsqueeze(1), min=-1 * max_pos, max=max_pos - ) + data_lp = torch.clamp(data_hp * descale_fp, min=-1 * max_pos, max=max_pos) return exponent, data_lp @@ -160,22 +156,33 @@ def to_mx( torch.float, ), f"{data_hp.dtype} is not supported yet" # TODO(future PR): consider supporting padding - assert data_hp.numel() % block_size == 0, "unsupported" + assert data_hp.shape[-1] % block_size == 0, ( + f"the last dimension of shape {data_hp.shape} must be divisible by block_size {block_size}" + ) assert data_hp.is_contiguous(), "unsupported" assert elem_dtype in SUPPORTED_ELEM_DTYPES, "unsupported" - # calculate the scale in e8m0 format - orig_shape = data_hp.shape - # TODO(future PR): fix this line for TP, currently this reshape does not work - # for rank 3 tensor where dim1 is sharded - data_hp = data_hp.reshape(-1, block_size) + data_hp = data_hp.reshape( + *orig_shape[:-1], orig_shape[-1] // block_size, block_size + ) # find max value of the data # Note: this only implements the `minimally supported` version of # https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf # section 6.3. - max_abs = torch.amax(torch.abs(data_hp), 1) + max_abs = torch.amax(torch.abs(data_hp), -1).unsqueeze(-1) + + # We cast to float32 here because + # in the `max_abs_int32 = max_abs.view(hp_int_dtype)` line below, + # if tensor parallel is enabled then the resulting shape is 2x larger + # than it should be under some conditions, likely because of a bug in + # the `view` op with DTensor and target dtype int16. I reproduce in + # torchtitan but not in a unit test, so not enough info to file a good + # issue in pytorch/pytorch. For now, work around. In the future we should + # debug and fix this properly. + data_hp = data_hp.to(torch.float32) + max_abs = max_abs.to(torch.float32) # Set X to be the largest power-of-two less than or equal to # max_abs(v), divided by the largest power of two representable @@ -206,17 +213,11 @@ def to_mx( if scaling_mode == ScaleCalculationMode.RCEIL: scale_e8m0_biased, data_lp = _to_mx_rceil(data_hp, max_abs, max_pos) else: - if data_hp.dtype is torch.float32: - hp_int_dtype = torch.int32 - hp_mbits = MBITS_F32 - hp_ebits = EBITS_F32 - hp_exp_bias = F32_EXP_BIAS - else: - assert data_hp.dtype is torch.bfloat16 - hp_int_dtype = torch.int16 - hp_mbits = MBITS_BF16 - hp_ebits = EBITS_BF16 - hp_exp_bias = BF16_EXP_BIAS + assert data_hp.dtype is torch.float32 + hp_int_dtype = torch.int32 + hp_mbits = MBITS_F32 + hp_ebits = EBITS_F32 + hp_exp_bias = F32_EXP_BIAS # rounding before calculating the largest power of 2 # X = 2^(floor(log2(rounding(max_abs(v)))-max_exp)) @@ -285,7 +286,7 @@ def to_mx( scale_fp32 = torch.clamp(scale_fp32, min=F32_MIN_NORMAL) # scale and saturated cast the data elements to max of target dtype - data_lp = data_hp / scale_fp32.unsqueeze(1) + data_lp = data_hp / scale_fp32 if ( elem_dtype in (torch.float8_e4m3fn, torch.float8_e5m2) @@ -511,7 +512,6 @@ def __new__( assert scale_e8m0_bits.dtype == torch.float8_e8m0fnu, ( f"scale_e8m0_bits.dtype must be `torch.float8_e8m0fnu`, got {scale_e8m0_bits.dtype}" ) - assert len(scale_e8m0_bits.shape) == 1, "unsupported" assert data_bits.dtype in ( torch.float8_e4m3fn, torch.float8_e5m2, diff --git a/torchao/testing/training/dtensor_utils.py b/torchao/testing/training/dtensor_utils.py index 815ee20969..7ebf67d53c 100644 --- a/torchao/testing/training/dtensor_utils.py +++ b/torchao/testing/training/dtensor_utils.py @@ -152,15 +152,18 @@ def _test_lowp_mlp_tensor_parallelism_base( sp_model2 = torch.compile(sp_model2) x_fp32 = torch.rand(size, size * 2, size, device=device, requires_grad=False) + go_fp32 = torch.rand(size, size * 2, size, device=device, requires_grad=False) x_fp32_tp_input = x_fp32.clone() + go_fp32_tp = go_fp32.clone() x_fp32_sp_input = distribute_tensor(x_fp32.clone(), mesh, [Shard(0)]) + go_fp32_sp = distribute_tensor(go_fp32.clone(), mesh, [Shard(0)]) tp_out = tp_model(x_fp32_tp_input) - tp_out.sum().backward() + tp_out.backward(go_fp32_tp) sp_out = sp_model(x_fp32_sp_input) - sp_out.sum().backward() + sp_out.backward(go_fp32_sp) global_out = toy_model_fp8(x_fp32) - global_out.sum().backward() + global_out.backward(go_fp32) torch.testing.assert_close(tp_out, global_out) torch.testing.assert_close(sp_out.full_tensor(), global_out) torch.testing.assert_close(tp_model.ffn.w1.weight.grad, sp_model.ffn.w1.weight.grad) @@ -169,7 +172,7 @@ def _test_lowp_mlp_tensor_parallelism_base( ) sp_out2 = sp_model2(x_fp32_sp_input) - sp_out2.sum().backward() + sp_out2.backward(go_fp32_sp) torch.testing.assert_close(sp_out2.full_tensor(), global_out) torch.testing.assert_close( tp_model.ffn.w1.weight.grad, sp_model2.ffn.w1.weight.grad From 7a846d5578a0c9c0111b82f367cb426d2639812e Mon Sep 17 00:00:00 2001 From: mobicham <37179323+mobicham@users.noreply.github.com> Date: Tue, 24 Jun 2025 21:29:21 +0200 Subject: [PATCH 154/165] Gemlite generate.py fix (#2372) * fix get_plain() with FMA mode * update * fix in_features/out_feature meta-data mismatch * update gemlite slice test * add packing_bitwidth support * add packing_bitwidth support and cleanup * update default gemlite layout * cleanup * fix symmetric use-case and relax _same_meta_data * _copy() meta data * fix (4,) in autoquant * Add dynamic mode in gemlite layout * mode explanation Signed-off-by: mobicham * use weights_only instead of static * generate fix Signed-off-by: mobicham * remove set_packing_bitwidth --------- Signed-off-by: mobicham --- torchao/_models/llama/benchmarks.sh | 18 +++++-------- torchao/_models/llama/generate.py | 39 +++++++++++------------------ 2 files changed, 20 insertions(+), 37 deletions(-) diff --git a/torchao/_models/llama/benchmarks.sh b/torchao/_models/llama/benchmarks.sh index 4c11b193d5..ed01500c33 100644 --- a/torchao/_models/llama/benchmarks.sh +++ b/torchao/_models/llama/benchmarks.sh @@ -97,19 +97,13 @@ python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --co python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --compile_prefill --quantization sparse-marlin --write_result benchmark_results.txt --prefill_size 8000 --precision float16 --sparsity semi-structured # gemlite benchmarks -python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --precision float16 --quantization gemlite-8-4-64 --write_result benchmark_results.txt -python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --precision float16 --quantization gemlite-32-4-64 --write_result benchmark_results.txt -python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --precision float16 --quantization gemlite-8-4-None --write_result benchmark_results.txt -python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --precision float16 --quantization gemlite-32-4-None --write_result benchmark_results.txt -python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --precision float16 --quantization gemlite-8-8-None --write_result benchmark_results.txt -python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --precision float16 --quantization gemlite-32-8-None --write_result benchmark_results.txt +python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --precision float16 --quantization gemlite-4-64-wo --write_result benchmark_results.txt +python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --precision float16 --quantization gemlite-4-128-wo --write_result benchmark_results.txt +python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --precision float16 --quantization gemlite-8-None-dq --write_result benchmark_results.txt -python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --precision float16 --quantization gemlite-8-4-64 --write_result benchmark_results.txt --batch_size 32 -python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --precision float16 --quantization gemlite-32-4-64 --write_result benchmark_results.txt --batch_size 32 -python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --precision float16 --quantization gemlite-8-4-None --write_result benchmark_results.txt --batch_size 32 -python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --precision float16 --quantization gemlite-32-4-None --write_result benchmark_results.txt --batch_size 32 -python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --precision float16 --quantization gemlite-8-8-None --write_result benchmark_results.txt --batch_size 32 -python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --precision float16 --quantization gemlite-32-8-None --write_result benchmark_results.txt --batch_size 32 +python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --precision float16 --quantization gemlite-4-64-wo --write_result benchmark_results.txt --batch_size 32 +python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --precision float16 --quantization gemlite-4-128-wo --write_result benchmark_results.txt --batch_size 32 +python generate.py --checkpoint_path $CHECKPOINT_PATH/$MODEL_REPO/model.pth --compile --precision float16 --quantization gemlite-8-None-dq --write_result benchmark_results.txt --batch_size 32 # 2:4 sparse model export MODEL_REPO=nm-testing/SparseLlama-3-8B-pruned_50.2of4 diff --git a/torchao/_models/llama/generate.py b/torchao/_models/llama/generate.py index 0cf166103b..8f02e83a99 100644 --- a/torchao/_models/llama/generate.py +++ b/torchao/_models/llama/generate.py @@ -244,7 +244,7 @@ def encode_tokens(tokenizer, string, bos=True, device=default_device): def _load_model(checkpoint_path, device, precision): checkpoint = torch.load( - str(checkpoint_path), mmap=True, weights_only=True, map_location=device + str(checkpoint_path), mmap=True, weights_only=True, map_location="cpu" ) if "model" in checkpoint and "stories" in str(checkpoint_path): checkpoint = checkpoint["model"] @@ -366,34 +366,24 @@ def ffn_or_attn_only(mod, fqn): import os import pwd - from gemlite.core import GemLiteLinearTriton + import gemlite + + gemlite.set_autotune("max") + config_file = f"/tmp/{pwd.getpwuid(os.getuid()).pw_gecos}_gemlite.json" _quant_args = quantization.split("-") - bit_width = int(_quant_args[-2]) - group_size = None if _quant_args[-1] == "None" else int(_quant_args[-1]) - try: - packing_bitwidth = int(_quant_args[-3]) - except: - # if only 2 inputs found, use default value - packing_bitwidth = 32 + bit_width = int(_quant_args[1]) + group_size = None if _quant_args[2] == "None" else int(_quant_args[2]) + mode = "dynamic" if _quant_args[3] == "dq" else "weight_only" quantize_( model, - gemlite_uintx_weight_only(group_size, bit_width, packing_bitwidth), + gemlite_uintx_weight_only( + bit_width=bit_width, group_size=group_size, mode=mode + ), ) - # try to load gemlite kernel config - try: - GemLiteLinearTriton.load_config( - f"/tmp/{pwd.getpwuid(os.getuid()).pw_gecos}_gemlite.json" - ) - print( - f"loaded gemlite kernel cache /tmp/{pwd.getpwuid(os.getuid()).pw_gecos}_gemlite.json" - ) - except: - print( - f"unable to load gemlite kernel cache /tmp/{pwd.getpwuid(os.getuid()).pw_gecos}_gemlite.json" - ) + gemlite.load_config(config_file) print("running gemlite warmup") generate( @@ -405,9 +395,8 @@ def ffn_or_attn_only(mod, fqn): temperature=temperature, top_k=top_k, ) - GemLiteLinearTriton.cache_config( - f"/tmp/{pwd.getpwuid(os.getuid()).pw_gecos}_gemlite.json" - ) + gemlite.cache_config(config_file) + if "int8wo" in quantization: quantize_(model, int8_weight_only()) if "int8dq" in quantization: From faf788a1752dd8fbe3c529e6fe3612e68324651f Mon Sep 17 00:00:00 2001 From: Driss Guessous <32754868+drisspg@users.noreply.github.com> Date: Tue, 24 Jun 2025 12:59:38 -0700 Subject: [PATCH 155/165] add-to-benchmarks (#2427) --- benchmarks/float8/bench_matmul.py | 93 +++++++++++++++++----- benchmarks/float8/utils.py | 9 +-- torchao/testing/training/roofline_utils.py | 7 ++ 3 files changed, 84 insertions(+), 25 deletions(-) diff --git a/benchmarks/float8/bench_matmul.py b/benchmarks/float8/bench_matmul.py index cf844fa51b..30ea2eab39 100644 --- a/benchmarks/float8/bench_matmul.py +++ b/benchmarks/float8/bench_matmul.py @@ -16,6 +16,8 @@ get_name_to_shapes_iter, ) +from torchao.ops import mx_fp4_bf16 +from torchao.prototype.mx_formats.mx_tensor import to_mx from torchao.testing.training.roofline_utils import get_specs @@ -62,29 +64,38 @@ def run( ): device = "cuda" # TODO(future PR): this is ugly - assert recipe in ("tensorwise", "rowwise", "mxfp8_cublas"), "unsupported" + assert recipe in ( + "tensorwise", + "rowwise", + "mxfp8_cublas", + "mxfp4_cutlass", + "nvfp4", + ), "unsupported" + use_fp4 = recipe in ("mxfp4_cutlass", "nvfp4") specs = get_specs() bf16_peak_tops = specs["bf16_peak_tops"] fp8_peak_tops = specs["fp8_peak_tops"] + fp4_peak_tops = specs["fp4_peak_tops"] print(f"gpu_name: {torch.cuda.get_device_name(0)}") - print(f"peak tops: bf16 {bf16_peak_tops:.2e}, fp8 {fp8_peak_tops:.2e}") - + print( + f"peak tops: bf16 {bf16_peak_tops:.2e}, fp8 {fp8_peak_tops:.2e}, fp4 {fp4_peak_tops:.2e}" + ) headers = ( "fast_accum", "name", "M", "K", "N", - "ref_time_s", - "fp8_time_s", + "time_s", + "speedup", "fp8_speedup", ) results = [] dtype = torch.bfloat16 name_to_shapes = get_name_to_shapes_iter(shape_gen_name, M, K, N) - fast_accum_vals = [True, False] + fast_accum_vals = [False] if use_fp4 else [True, False] for idx, (fast_accum, (name, (M, K, N))) in enumerate( itertools.product(fast_accum_vals, name_to_shapes) @@ -107,38 +118,82 @@ def run( del A - # raw float8 matmul (upper bound for what we can achive in eager mode) - # TODO(future): add e5m2 - d1, d2, d3 = torch.float8_e4m3fn, torch.float8_e4m3fn, dtype - A = torch.zeros(M, K, device=device, dtype=d1) - B = torch.zeros(K, N, device=device, dtype=d2).t().contiguous().t() + A_hp = torch.randn(M, K, device=device) + B_hp_t = torch.randn(N, K, device=device) + + if recipe == "mxfp4_cutlass": + _, A = to_mx(A_hp, torch.float4_e2m1fn_x2, 32) + _, Bt = to_mx(B_hp_t, torch.float4_e2m1fn_x2, 32) + B = Bt.contiguous().T + peak_tops = fp4_peak_tops + elif recipe == "nvfp4": + from torchao.prototype.mx_formats.nvfp4_tensor import nvfp4_quantize + + A_scales, A_data = nvfp4_quantize(A_hp, block_size=16) + B_scales, B_data = nvfp4_quantize(B_hp_t, block_size=16) + A = A_data.view(torch.float4_e2m1fn_x2) + B = B_data.view(torch.float4_e2m1fn_x2).T + peak_tops = fp4_peak_tops + else: + # raw float8 matmul (upper bound for what we can achive in eager mode) + # TODO(future): add e5m2 + d1, d2, d3 = torch.float8_e4m3fn, torch.float8_e4m3fn, dtype + A = A_hp.to(d1) + B = B_hp_t.to(d2).contiguous().T + peak_tops = fp8_peak_tops + if recipe == "tensorwise": scale_a = torch.tensor([1.0], device=device) scale_b = torch.tensor([1.0], device=device) elif recipe == "rowwise": scale_a = torch.ones(M, 1, device=device) scale_b = torch.ones(1, N, device=device) - elif recipe == "mxfp8_cublas": + elif recipe in ("mxfp8_cublas", "mxfp4_cutlass"): scale_a = torch.ones(M, K // 32, device=device, dtype=torch.float8_e8m0fnu) scale_b = torch.ones(N, K // 32, device=device, dtype=torch.float8_e8m0fnu) + elif recipe == "nvfp4": + # Use the blockwise scales from nvfp4_quantize + scale_a = A_scales.view(torch.float8_e4m3fn) + scale_b = B_scales.view(torch.float8_e4m3fn) else: assert False, f"unknown recipe {recipe}" - def do_matmul(A, B): + def do_matmul_fp8(A, B): nonlocal scale_a nonlocal scale_b return torch._scaled_mm( A, B, scale_a, scale_b, out_dtype=d3, use_fast_accum=fast_accum ) - fp8_time_sec, fp8_tops_sec, fp8_pct_top_peak = do_benchmarks( - tops, fp8_peak_tops, use_gpu_kernel_time, do_matmul, A, B + def do_matmul_mxfp4(A, B): + nonlocal scale_a + nonlocal scale_b + return mx_fp4_bf16(A, B, scale_a, scale_b) + + def do_matmul_nvfp4(A, B): + nonlocal scale_a + nonlocal scale_b + return torch._scaled_mm(A, B, scale_a, scale_b, out_dtype=dtype) + + if recipe == "mxfp4_cutlass": + do_matmul = do_matmul_mxfp4 + elif recipe == "nvfp4": + do_matmul = do_matmul_nvfp4 + else: + do_matmul = do_matmul_fp8 + + time_sec, tops_sec, pct_top_peak = do_benchmarks( + tops, peak_tops, use_gpu_kernel_time, do_matmul, A, B ) print( - f"fp8 time_sec {fp8_time_sec:.2E}, tops/sec {fp8_tops_sec:.2E}, pct_peak {fp8_pct_top_peak:.3f}" + f"time_sec {time_sec:.2E}, tops/sec {tops_sec:.2E}, pct_peak {pct_top_peak:.3f}" ) - del A, B, scale_a, scale_b + del A, B + if scale_a is not None: + del scale_a + if scale_b is not None: + del scale_b results.append( [ @@ -148,8 +203,8 @@ def do_matmul(A, B): K, N, ref_time_sec, - fp8_time_sec, - ref_time_sec / fp8_time_sec, + time_sec, + ref_time_sec / time_sec, ] ) diff --git a/benchmarks/float8/utils.py b/benchmarks/float8/utils.py index 0ee2b922fc..6c3051937d 100644 --- a/benchmarks/float8/utils.py +++ b/benchmarks/float8/utils.py @@ -352,9 +352,6 @@ def get_gpu_kernel_gemm_time_s(f, *args, **kwargs): ) # there is only 1 key, aten::mm or aten::_scaled_mm, with unit nanoseconds assert len(data) == 1 - if "aten::mm" in data: - return data["aten::mm"] / 1e6 / n_iter - elif "aten::_scaled_mm" in data: - return data["aten::_scaled_mm"] / 1e6 / n_iter - else: - raise AssertionError("unexpected format of data") + key, value = next(iter(data.items())) + assert key in ("aten::mm", "aten::_scaled_mm", "torchao::mx_fp4_bf16") + return value / 1e6 / n_iter diff --git a/torchao/testing/training/roofline_utils.py b/torchao/testing/training/roofline_utils.py index 7bfb9887df..286803dbf2 100644 --- a/torchao/testing/training/roofline_utils.py +++ b/torchao/testing/training/roofline_utils.py @@ -54,6 +54,13 @@ # TODO(future): run measurement on hardware "pct_achievable_mem_bw": 0.92, }, + "NVIDIA GeForce RTX 5090": { + # https://images.nvidia.com/aem-dam/Solutions/geforce/blackwell/nvidia-rtx-blackwell-gpu-architecture.pdf + "bf16_peak_tops": 209.5e12, + "fp8_peak_tops": 419e12, + "fp4_peak_tops": 1676e12, + "peak_mem_bw_bytes_sec": 1.792e15, + }, # TODO(future): more GPU names } From 4ebc9c042565e16af249b7cec8ebb2dc9fa0274f Mon Sep 17 00:00:00 2001 From: Gasoonjia Date: Tue, 24 Jun 2025 13:19:45 -0700 Subject: [PATCH 156/165] solve the test issue (#2432) bring numeric debug test back (#2432) Summary: torchao numeric debug infra have to rely on pytorch 2.8 nightly and beyond; update the test decorator to skip the tests on lower version pytorch Differential Revision: D77191199 --- test/quantization/pt2e/test_numeric_debugger.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/test/quantization/pt2e/test_numeric_debugger.py b/test/quantization/pt2e/test_numeric_debugger.py index e935c25d68..80648f6c77 100644 --- a/test/quantization/pt2e/test_numeric_debugger.py +++ b/test/quantization/pt2e/test_numeric_debugger.py @@ -18,14 +18,15 @@ prepare_for_propagation_comparison, ) from torchao.testing.pt2e.utils import PT2ENumericDebuggerTestCase -from torchao.utils import TORCH_VERSION_AT_LEAST_2_7 +from torchao.utils import TORCH_VERSION_AT_LEAST_2_8 -if TORCH_VERSION_AT_LEAST_2_7: +if TORCH_VERSION_AT_LEAST_2_8: from torch.export import export_for_training -@unittest.skip("skip for now, need to fix") -@unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_7, "Requires torch 2.7+") +@unittest.skipIf( + not TORCH_VERSION_AT_LEAST_2_8, "Requires torch 2.8 and above, including nightly" +) @unittest.skipIf(IS_WINDOWS, "Windows not yet supported for torch.compile") class TestNumericDebuggerInfra(PT2ENumericDebuggerTestCase): @unittest.skip( From 8940aa72b182afe70f95e33500f01fc270c9f7cd Mon Sep 17 00:00:00 2001 From: shiyang-weng Date: Wed, 25 Jun 2025 16:22:23 +0800 Subject: [PATCH 157/165] [float8] Prevent quantize_affine_float8/dequantize_affine_float8 decomposed on inductor (#2379) * quantize_affine_float8/dequantize_affine_float8 not decomposed on inductor * remove redundant unittest.skipIf * fix rebase issue * change dispatch key to a flag decomposed * To be more explicit, use name inductor_decomposed instead * Change ut path --- test/dtypes/test_affine_quantized_float.py | 40 ++++++++++++++++++++++ torchao/quantization/quant_primitives.py | 20 +++++++++++ torchao/utils.py | 13 +++++-- 3 files changed, 70 insertions(+), 3 deletions(-) diff --git a/test/dtypes/test_affine_quantized_float.py b/test/dtypes/test_affine_quantized_float.py index b63a406715..33a1fe66a7 100644 --- a/test/dtypes/test_affine_quantized_float.py +++ b/test/dtypes/test_affine_quantized_float.py @@ -675,6 +675,46 @@ def test_preprocess_scale_3d_reshape(self): expected_shape = (8, 1) # Flattened (2*2*2, 1) self.assertEqual(result.shape, expected_shape) + @common_utils.parametrize("float8_dtype", [torch.float8_e4m3fn, torch.float8_e5m2]) + @common_utils.parametrize("hp_dtype", [torch.float32, torch.bfloat16]) + def test_quantize_dequantize_fp8_inductor(self, float8_dtype, hp_dtype): + quantize_affine_float8 = torch.ops.torchao.quantize_affine_float8 + dequantize_affine_float8 = torch.ops.torchao.dequantize_affine_float8 + input = torch.randn(10, 10) + with torch.no_grad(): + torch._dynamo.reset() + expected_scale = torch.tensor(2.0) + expected_quantized = quantize_affine_float8( + input, + expected_scale, + float8_dtype=float8_dtype, + ) + expected_dequantized = dequantize_affine_float8( + expected_quantized, + expected_scale, + output_dtype=hp_dtype, + ) + test_q, (code_q,) = torch._inductor.utils.run_and_get_code( + torch.compile(quantize_affine_float8), + input, + expected_scale, + float8_dtype=float8_dtype, + ) + torch.testing.FileCheck().check( + "torch.ops.torchao.quantize_affine_float8.default" + ).run(code_q) + test_dq, (code_dq,) = torch._inductor.utils.run_and_get_code( + torch.compile(dequantize_affine_float8), + test_q, + expected_scale, + hp_dtype, + ) + torch.testing.FileCheck().check( + "torch.ops.torchao.dequantize_affine_float8.default" + ).run(code_dq) + torch.testing.assert_close(expected_quantized, test_q) + torch.testing.assert_close(expected_dequantized, test_dq) + common_utils.instantiate_parametrized_tests(TestAffineQuantizedFloat8Compile) diff --git a/torchao/quantization/quant_primitives.py b/torchao/quantization/quant_primitives.py index df136bc06e..56e8422197 100644 --- a/torchao/quantization/quant_primitives.py +++ b/torchao/quantization/quant_primitives.py @@ -2270,6 +2270,7 @@ def _expand_scale_to_tensor_shape( return expanded_scale +@_register_custom_op(quant_lib, False) def _quantize_affine_float8( tensor: torch.Tensor, scale: torch.Tensor, @@ -2290,6 +2291,16 @@ def _quantize_affine_float8( return fp8_tensor +@torch.library.impl(quant_lib, "quantize_affine_float8", "Meta") +def _quantize_affine_float8_meta( + tensor: torch.Tensor, + scale: torch.Tensor, + float8_dtype: torch.dtype = torch.float8_e4m3fn, +) -> torch.Tensor: + return torch.empty_like(tensor, dtype=float8_dtype) + + +@_register_custom_op(quant_lib, False) def _dequantize_affine_float8( tensor: torch.Tensor, scale: torch.Tensor, @@ -2305,3 +2316,12 @@ def _dequantize_affine_float8( hp_tensor = fp8_tensor * scale_expanded return hp_tensor.to(output_dtype) + + +@torch.library.impl(quant_lib, "dequantize_affine_float8", "Meta") +def _dequantize_affine_float8_meta( + tensor: torch.Tensor, + scale: torch.Tensor, + output_dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + return torch.empty_like(tensor, dtype=output_dtype) diff --git a/torchao/utils.py b/torchao/utils.py index 416d23d785..1a12fb0668 100644 --- a/torchao/utils.py +++ b/torchao/utils.py @@ -179,7 +179,7 @@ def find_multiple(n: int, *args: int) -> int: return n + k - (n % k) -def _register_custom_op(lib): +def _register_custom_op(lib, inductor_decomposed=True): """This decorator is used to preserve some high level operators for torch.export.export while still allow them to be decomposed for inductor path @@ -206,6 +206,12 @@ def _the_op_that_needs_to_be_preserved(...) """ from torch._inductor.decomposition import register_decomposition + dispatch_key = ( + "CompositeImplicitAutograd" + if inductor_decomposed + else "CompositeExplicitAutograd" + ) + def decorator(fn): if TORCH_VERSION_AT_LEAST_2_5: from torch._library.infer_schema import infer_schema @@ -221,11 +227,12 @@ def decorator(fn): op_name = fn.__name__[1:] schema = op_name + infer_schema(fn, mutates_args={}) lib.define(schema) - lib.impl(op_name, fn, "CompositeImplicitAutograd") + lib.impl(op_name, fn, dispatch_key) lib_namespace = lib.ns op = getattr(getattr(torch.ops, lib_namespace), op_name) - register_decomposition([op])(fn) + if inductor_decomposed: + register_decomposition([op])(fn) return op else: return fn From 8b57afe6b38dccb2b2045ca32c2e35e818142eaa Mon Sep 17 00:00:00 2001 From: Xia Weiwen Date: Wed, 25 Jun 2025 16:34:41 +0800 Subject: [PATCH 158/165] [CPU] Enable DA8W4 on CPU (#2128) * [CPU] enable int8_dynamic_activation_int4_weight with Int4CPULayout * Fix format issue * Add Int8DynamicActInt4WeightCPULayout * remove dispatch for t() * Add cpp kernel for weight packing and GEMM * Register ATQ linear dispatch for da8w4 linear * Fix issues with torch.compile * Fix DA8W4CPUAQTTensorImpl.get_plain * Test DA8W4CPUAQTTensorImpl.get_plain in UT * Skip UT if CPP kernel not built * Add AVX512_VNNI implementation for small M * improve performance * Support symmetric quantization of activation * Refine code * Refine code * Put in a separate file * Bug fix * refine code --- setup.py | 37 +- test/quantization/test_quant_api.py | 68 ++ torchao/csrc/cpu/da8w4_linear.cpp | 745 ++++++++++++++++++ torchao/dtypes/__init__.py | 2 + torchao/dtypes/affine_quantized_tensor_ops.py | 8 + torchao/dtypes/uintx/__init__.py | 4 + .../uintx/dyn_int8_act_int4_wei_cpu_layout.py | 312 ++++++++ torchao/dtypes/uintx/int4_cpu_layout.py | 18 +- torchao/ops.py | 84 ++ torchao/quantization/quant_api.py | 48 +- 10 files changed, 1296 insertions(+), 30 deletions(-) create mode 100644 torchao/csrc/cpu/da8w4_linear.cpp create mode 100644 torchao/dtypes/uintx/dyn_int8_act_int4_wei_cpu_layout.py diff --git a/setup.py b/setup.py index 5560ab877e..88669e7b3b 100644 --- a/setup.py +++ b/setup.py @@ -385,20 +385,29 @@ def get_extensions(): extra_compile_args["cxx"].extend( ["-O3" if not debug_mode else "-O0", "-fdiagnostics-color=always"] ) - if ( - use_cpu_kernels - and is_linux - and hasattr(torch._C._cpu, "_is_avx512_supported") - and torch._C._cpu._is_avx512_supported() - ): - extra_compile_args["cxx"].extend( - [ - "-DCPU_CAPABILITY_AVX512", - "-march=native", - "-mfma", - "-fopenmp", - ] - ) + + if use_cpu_kernels and is_linux: + if ( + hasattr(torch._C._cpu, "_is_avx512_supported") + and torch._C._cpu._is_avx512_supported() + ): + extra_compile_args["cxx"].extend( + [ + "-DCPU_CAPABILITY_AVX512", + "-march=native", + "-mfma", + "-fopenmp", + ] + ) + if ( + hasattr(torch._C._cpu, "_is_avx512_vnni_supported") + and torch._C._cpu._is_avx512_vnni_supported() + ): + extra_compile_args["cxx"].extend( + [ + "-DCPU_CAPABILITY_AVX512_VNNI", + ] + ) if debug_mode: extra_compile_args["cxx"].append("-g") diff --git a/test/quantization/test_quant_api.py b/test/quantization/test_quant_api.py index 0435a6c59b..2bb20d5afd 100644 --- a/test/quantization/test_quant_api.py +++ b/test/quantization/test_quant_api.py @@ -29,6 +29,7 @@ AffineQuantizedTensor, Int4CPULayout, Int4XPULayout, + Int8DynamicActInt4WeightCPULayout, PlainLayout, QDQLayout, TensorCoreTiledLayout, @@ -70,6 +71,7 @@ TORCH_VERSION_AT_LEAST_2_4, TORCH_VERSION_AT_LEAST_2_5, TORCH_VERSION_AT_LEAST_2_6, + TORCH_VERSION_AT_LEAST_2_7, TORCH_VERSION_AT_LEAST_2_8, is_sm_at_least_89, is_sm_at_least_90, @@ -695,6 +697,72 @@ def test_int4wo_cpu(self, dtype, x_dim, use_hqq): assert "_weight_int4pack_mm_for_cpu" in code[0] assert "aten.mm.default" not in code[0] + @unittest.skipIf( + "CPU" not in torch._C._dispatch_dump("torchao::da8w4_linear_cpu"), + reason="cpp kernels not built", + ) + @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_7, "Test only enabled for 2.7+") + @common_utils.parametrize("dtype", [torch.float, torch.bfloat16, torch.half]) + @common_utils.parametrize("x_dim", [2, 3]) + @common_utils.parametrize("bias", [True, False]) + @common_utils.parametrize("bs", [1, 160]) + @common_utils.parametrize("sym_quant_a", [True, False]) + def test_8da4w_cpu(self, dtype, x_dim, bias, bs, sym_quant_a): + if sym_quant_a and not TORCH_VERSION_AT_LEAST_2_8: + # not supported until PT 2.8 + return + device = "cpu" + m = ToyLinearModel(bias=bias).eval().to(dtype).to(device) + m2 = copy.deepcopy(m) + example_inputs = m.example_inputs(batch_size=bs, dtype=dtype, device=device) + if x_dim == 3: + example_inputs = (example_inputs[0].unsqueeze(0),) + + with torch.no_grad(): + # Currently, the difference between Int8DynamicActInt4WeightCPULayout and PlainLayout + # is that the former packs two int4 weights into one int8, while the latter does not. + quantize_( + m, + Int8DynamicActivationInt4WeightConfig( + group_size=32, + layout=Int8DynamicActInt4WeightCPULayout(), + act_mapping_type=MappingType.SYMMETRIC + if sym_quant_a + else MappingType.ASYMMETRIC, + ), + ) + y, code = torch._inductor.utils.run_and_get_code( + torch.compile(m, fullgraph=True, dynamic=True), + *example_inputs, + ) + # ensure the expected op is in the code + assert "torch.ops.torchao.da8w4_linear_cpu.default" in code[0] + quantize_( + m2, + int8_dynamic_activation_int4_weight( + group_size=32, + layout=PlainLayout(), + act_mapping_type=MappingType.SYMMETRIC + if sym_quant_a + else MappingType.ASYMMETRIC, + ), + ) + torch._dynamo.reset() # may segfault without this + y2 = torch.compile(m2, fullgraph=True, dynamic=True)(*example_inputs) + atol, rtol = 4e-7, 1e-5 + if dtype == torch.bfloat16: + atol, rtol = 1e-2, 3e-3 + elif dtype == torch.half: + atol, rtol = 6e-3, 2e-3 + assert torch.allclose(y, y2, atol=atol, rtol=rtol) + # Test get_plain by dequantize() + dqw1 = m.linear1.weight.original_weight_tensor.dequantize() + dqw2 = m.linear2.weight.original_weight_tensor.dequantize() + dqw1_ref = m2.linear1.weight.original_weight_tensor.dequantize() + dqw2_ref = m2.linear2.weight.original_weight_tensor.dequantize() + assert torch.allclose(dqw1, dqw1_ref) + assert torch.allclose(dqw2, dqw2_ref) + # TODO(#1690): move to new config names @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_4, "Test only enabled for 2.4+") @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available") diff --git a/torchao/csrc/cpu/da8w4_linear.cpp b/torchao/csrc/cpu/da8w4_linear.cpp new file mode 100644 index 0000000000..537aa0fce9 --- /dev/null +++ b/torchao/csrc/cpu/da8w4_linear.cpp @@ -0,0 +1,745 @@ +#include +#include +#include +#include + +namespace torchao { + +namespace { + +#define BLOCK_N 32 + +static bool cpublas_checked = false; +static bool cpublas_can_pack = false; + +bool cpublas_could_pack() { + // the could_pack check requires AMX support implicitly + if (cpublas_checked) { + return cpublas_can_pack; + } + cpublas_can_pack = at::native::cpublas::could_pack(at::kByte); + cpublas_checked = true; + return cpublas_can_pack; +} + +/* +return: packed_weight, packed_scales, packed_qzeros, compensation +*/ +std::tuple +da8w4_linear_prepack_impl( + const at::Tensor& weight, + const at::Tensor& scales, + const at::Tensor& qzeros) { + // weight shape = [N, K] + // scales shape = [N, G] + // qzeros shape = [N, G] + TORCH_CHECK(weight.dim() == 2, + "DA8W4 CPU: Weight should be a 2D tensor for packing"); + TORCH_CHECK(weight.size(1) % 2 == 0, + "DA8W4 CPU: Weight should have even number of columns for packing"); + + auto new_scales = scales; + auto new_qzeros = qzeros; + if (new_scales.dim() == 1) { + new_scales.unsqueeze_(1); + } + new_scales = new_scales.to(at::kFloat); + if (new_qzeros.dim() == 1) { + new_qzeros.unsqueeze_(1); + } + new_qzeros = new_qzeros.to(at::kChar); + int N = weight.size(0); + int K = weight.size(1); + int G = scales.size(1); + int group_size = K / G; + int block_k = group_size > 128 ? 128 : group_size; + constexpr int block_n = BLOCK_N; + int Nc = N / block_n; + int Kc = K / block_k; + + // Reorder weight to [N/block_n, K/block_k, block_k, block_n] + // Reorder scales/qzeros to [N/block_n, G, block_n] + auto weight_view = weight.view({Nc, block_n, Kc, block_k}); + at::Tensor weight_reordered = weight_view.permute({0, 2, 3, 1}).contiguous(); + at::Tensor blocked_weight; + at::Tensor blocked_scales = new_scales.view({Nc, block_n, G}).permute({0, 2, 1}).contiguous(); + at::Tensor blocked_qzeros = new_qzeros.view({Nc, block_n, G}).permute({0, 2, 1}).contiguous(); + // Compensation = Σ(k)(W[k][n] - ZP[n]) for each block. + auto weight_sub_qzero = weight.view({Nc, block_n, G, -1}).to(at::kInt) - new_qzeros.view({Nc, block_n, G, -1}); + weight_sub_qzero = weight_sub_qzero.view({Nc, block_n, Kc, block_k}); + at::Tensor compensation = weight_sub_qzero.sum(-1); + compensation = compensation.permute({0, 2, 1}).contiguous().to(at::kInt); + + if (cpublas_could_pack()) { + blocked_weight = at::empty({Nc, Kc, block_k, block_n / 2}, weight.options()); + auto weight_ptr = weight_reordered.data_ptr(); + auto blocked_weight_ptr = blocked_weight.data_ptr(); + int64_t num_blocks = Nc * Kc; + at::parallel_for(0, num_blocks, 1, [&](int64_t begin, int64_t end) { + for (const auto i : c10::irange(begin, end)) { + auto in_ptr = weight_ptr + i * block_k * block_n; + auto out_ptr = blocked_weight_ptr + i * block_k * block_n / 2; + + // Reorder weight block to VNNI4 and pack two lanes along N + // N=16 viewed as two lanes: a0, ...a7, b0, ...b7 + // pack two lanes: [a0, b0], ..., [a7, b7] + // plain shape = [block_k, block_n] + // packed shape = [block_k / 4, block_n / 2, 4] viewed as [block_k, block_n / 2] + constexpr int n_group_size = 8; + constexpr int vnni_size = 4; + constexpr int n_group = block_n / n_group_size; // 4 + for (int nb = 0; nb < n_group; nb += 2) { + for (int k = 0; k < block_k; k += vnni_size) { + for (int ni = 0; ni < n_group_size; ++ni) { + for (int ki = 0; ki < vnni_size; ++ki) { + int src_idx_1 = nb * n_group_size + ni + (k + ki) * block_n; + int src_idx_2 = (nb + 1) * n_group_size + ni + (k + ki) * block_n; + int dst_idx = (nb / 2 * n_group_size + ni) * vnni_size + k * block_n / 2 + ki; + uint8_t src_1 = *(in_ptr + src_idx_1); + uint8_t src_2 = *(in_ptr + src_idx_2); + uint8_t dst = (src_1 & 0x0f) | ((src_2 & 0x0f) << 4); + *(out_ptr + dst_idx) = dst; + } + } + } + } + } + }); + } else { + // Pack weight: two int4 -> one int8 + using namespace at::indexing; + at::Tensor even_columns = + weight_reordered.index({Slice(), Slice(), Slice(), Slice(1, None, 2)}); + even_columns = even_columns.bitwise_left_shift(4); + at::Tensor odd_columns = + weight_reordered.index({Slice(), Slice(), Slice(), Slice(None, None, 2)}); + blocked_weight = even_columns.bitwise_or(odd_columns); + } + + return std::make_tuple(std::move(blocked_weight), std::move(blocked_scales), std::move(blocked_qzeros), std::move(compensation)); +} + +template +struct ActDtype; +template<> +struct ActDtype { + using type = int8_t; +}; + +template<> +struct ActDtype { + using type = uint8_t; +}; + + +#if defined(CPU_CAPABILITY_AVX512) +inline std::array<__m256i, 2> load_zps_4vnni(const int8_t* __restrict__ zps) { + // broadcast 01234567 to + // 01234567012345670123456701234567 + __m256i vzps_low = _mm256_set1_epi64x(*reinterpret_cast(zps)); + __m256i vzps_high = _mm256_set1_epi64x(*reinterpret_cast(zps + 8)); + // shuffle from + // 01234567012345670123456701234567 + // to + // 00001111222233334444555566667777 + __m256i shuffle_mask = _mm256_set_epi8( + 7, + 7, + 7, + 7, + 6, + 6, + 6, + 6, + 5, + 5, + 5, + 5, + 4, + 4, + 4, + 4, + 3, + 3, + 3, + 3, + 2, + 2, + 2, + 2, + 1, + 1, + 1, + 1, + 0, + 0, + 0, + 0); + vzps_low = _mm256_shuffle_epi8(vzps_low, shuffle_mask); + vzps_high = _mm256_shuffle_epi8(vzps_high, shuffle_mask); + return {vzps_low, vzps_high}; +} + +inline std::array<__m256i, 2> load_uint4_as_int8(const uint8_t* __restrict__ qB) { + __m256i packed = _mm256_loadu_si256(reinterpret_cast(qB)); + const __m256i low_mask = _mm256_set1_epi8(0x0f); + __m256i high = _mm256_srli_epi16(packed, 4); + high = _mm256_and_si256(high, low_mask); + __m256i low = _mm256_and_si256(packed, low_mask); + return {low, high}; +} + +template +void _dequant_weight_zp_only( + const uint8_t* __restrict__ B, + int8_t* dqB, + const int8_t* __restrict__ qzeros, + int64_t K) { + // unpack weight int8 -> two int4 + // subtract zero point + // B shape = [K, ldb] = [K, N / 2], actual shape = [K / 4, N / 2, 4] + // dqB shape = [K, N], actual shape = [K / 4, N, 4] +#pragma GCC unroll 2 + for (int n = 0; n < N; n += 16) { + auto [zps_low, zps_high] = load_zps_4vnni(&qzeros[n]); + for (int k = 0; k < K; k += 4) { + auto [vb_low, vb_high] = load_uint4_as_int8(B + ldb * k + n / 2 * 4); + vb_high = _mm256_sub_epi8(vb_high, zps_high); + vb_low = _mm256_sub_epi8(vb_low, zps_low); + // store vb to B + _mm256_storeu_si256(reinterpret_cast<__m256i_u*>(dqB + N * k + n * 4), vb_low); + _mm256_storeu_si256(reinterpret_cast<__m256i_u*>(dqB + N * k + (n + 8) * 4), vb_high); + } + } +} + +template +void _dequant_and_store( + float* __restrict__ output, + const int32_t* __restrict__ input, + const float* __restrict__ scale_a, + const int32_t* __restrict__ zp_a, + const float* __restrict__ scale_b, + const int32_t* __restrict__ comp_b, + int M, + int ldi, + int ldo, + int ldsa = 1) { + for (int m = 0; m < M; ++m) { + float a_scale = *(scale_a + m * ldsa); + __m512 va_scale = _mm512_set1_ps(a_scale); + int32_t a_zp; + __m512i va_zp; + if constexpr (!sym_quant_a) { + a_zp = *(zp_a + m * ldsa); + va_zp = _mm512_set1_epi32(a_zp); + } + int n = 0; +#pragma GCC unroll 2 + for (; n < N; n += 16) { + __m512i vc = _mm512_loadu_si512(input + m * ldi + n); + if constexpr (!sym_quant_a) { + __m512i vb_comp = _mm512_loadu_si512(comp_b + n); + vc = _mm512_sub_epi32(vc, _mm512_mullo_epi32(vb_comp, va_zp)); + } + __m512 vc_f = _mm512_cvtepi32_ps(vc); + __m512 vc_f_mul = _mm512_mul_ps(vc_f, va_scale); + __m512 vb_s = _mm512_loadu_ps(scale_b + n); + vc_f_mul = _mm512_mul_ps(vc_f_mul, vb_s); + if constexpr (accum) { + __m512 vo = _mm512_loadu_ps(output + m * ldo + n); + _mm512_storeu_ps(output + m * ldo + n, _mm512_add_ps(vo, vc_f_mul)); + } else { + _mm512_storeu_ps(output + m * ldo + n, vc_f_mul); + } + } + for (; n < N; ++n) { + float dq_val; + if constexpr (sym_quant_a) { + dq_val = (float)input[m * ldi + n] * a_scale * scale_b[n]; + } else { + dq_val = + (float)(input[m * ldi + n] - a_zp * comp_b[n]) * a_scale * scale_b[n]; + } + if constexpr (accum) { + output[m * ldo + n] += dq_val; + } else { + output[m * ldo + n] = dq_val; + } + } + } +} + +#else +template +void _dequant_weight_zp_only( + const uint8_t* B, + int8_t* dqB, + const int8_t* qzeros, + int64_t K) { + // B shape = [K, N / 2] + // dqB shape = [K, N] + for (int k = 0; k < K; ++k) { + for (int n = 0; n < N / 2; ++n) { + int32_t b = (int32_t)B[k * ldb + n]; + dqB[k * N + n * 2] = (b & 0xf) - qzeros[n]; + dqB[k * N + n * 2 + 1] = (b >> 4) - qzeros[n]; + } + } +} +#endif + +#if defined(CPU_CAPABILITY_AVX512_VNNI) +inline __m512i combine_m256i(__m256i a, __m256i b) { + __m512i c = _mm512_castsi256_si512(a); + return _mm512_inserti64x4(c, b, 1); +} + +inline __m512i combine_m256i(std::array<__m256i, 2> two_256) { + return combine_m256i(two_256[0], two_256[1]); +} + +// negate elements in a according to b's sign +static inline __m512i _mm512_sign_epi8(__m512i a, __m512i b) { + __m512i zero = _mm512_setzero_si512(); + __mmask64 blt0 = _mm512_movepi8_mask(b); + return _mm512_mask_sub_epi8(a, blt0, zero, a); +} + +template +void _dequant_gemm_accum_small_M( + float* __restrict__ C, + const uint8_t* A, + const float* scales_a, + const int32_t* qzeros_a, + const uint8_t* B, + const float* scales_b, + const int8_t* qzeros_b, + int64_t K, + int64_t lda, + int64_t ldc) { + // if sym_quant_a is true, A pointer type is passed in as uint8_t* but actually int8_t*. + + constexpr int COLS = N / 16; + // Computing compensation is faster than loading it for small M + // because it's memory bound. + __m512i ones = _mm512_set1_epi8(1); // used for computing compensation + __m512i va; + __m512i vb[COLS]; + __m512i vc[M * COLS]; + __m512 vscales[COLS]; + __m512i vzps[COLS]; + __m512i vcompensate[COLS]; + + // Load scales and zps + c10::ForcedUnroll{}([&](auto i) { + vscales[i] = _mm512_loadu_ps(scales_b + i * 16); + vzps[i] = combine_m256i(load_zps_4vnni(qzeros_b + i * 16)); + if constexpr (!sym_quant_a) { + vcompensate[i] = _mm512_setzero_epi32(); + } + }); + c10::ForcedUnroll{}( + [&](auto i) { vc[i] = _mm512_setzero_epi32(); }); + + auto compute = [&](auto i, int k) { + constexpr const int row = i / COLS; + constexpr const int col = i % COLS; + + if constexpr (col == 0) { + va = _mm512_set1_epi32(*(int32_t*)(A + row * lda + k)); + } + + if constexpr (row == 0) { + int B_offset = k * ldb + col * 16 * 2; + vb[col] = combine_m256i(load_uint4_as_int8(B + B_offset)); + vb[col] = _mm512_sub_epi8(vb[col], vzps[col]); + if constexpr (!sym_quant_a) { + vcompensate[col] = + _mm512_dpbusd_epi32(vcompensate[col], ones, vb[col]); + } + _mm_prefetch(B + B_offset + 128 * ldb, _MM_HINT_T0); + } + if constexpr (sym_quant_a) { + auto vsb = _mm512_sign_epi8(vb[col], va); + auto vabsa = _mm512_sign_epi8(va, va); + vc[i] = _mm512_dpbusds_epi32(vc[i], vabsa, vsb); + } else { + vc[i] = _mm512_dpbusd_epi32(vc[i], va, vb[col]); + } + }; + + // Accumulate along k + constexpr const int unroll = 4; + int k = 0; + for (; k < K / 4 / unroll; k++) { + c10::ForcedUnroll{}([&](auto i) { + c10::ForcedUnroll{}(compute, 4 * (k * unroll + i)); + }); + } + k *= 4 * unroll; + for (; k < K; k += 4) { + c10::ForcedUnroll{}(compute, k); + } + + // Store to C + auto store = [&](auto i) { + constexpr const int row = i / COLS; + constexpr const int col = i % COLS; + // compute (qC - compensate * zp_a) * scale_a * scale_b + __m512 vc_float; + if constexpr (!sym_quant_a) { + vc[i] = _mm512_sub_epi32( + vc[i], + _mm512_mullo_epi32( + vcompensate[col], _mm512_set1_epi32(*(qzeros_a + row)))); + } + vc_float = _mm512_cvtepi32_ps(vc[i]); + vc_float = _mm512_mul_ps(vc_float, _mm512_set1_ps(*(scales_a + row))); + + vc_float = _mm512_mul_ps(vc_float, vscales[col]); + auto vc_old = _mm512_loadu_ps(C + row * ldc + col * 16); + vc_float = _mm512_add_ps(vc_float, vc_old); + _mm512_storeu_ps(C + row * ldc + col * 16, vc_float); + }; + c10::ForcedUnroll{}(store); + +} + +#define call_dequant_gemm_accum_small_M(M) \ + _dequant_gemm_accum_small_M( \ + C, \ + A, \ + scales_a, \ + qzeros_a, \ + B, \ + scales_b, \ + qzeros_b, \ + K, \ + lda, \ + ldc); +#endif + +template +void _dequant_gemm_accum( + float* C, + const uint8_t* A, + const float* scales_a, + const int32_t* qzeros_a, + const uint8_t* B, + const float* scales_b, + const int8_t* qzeros_b, + const int32_t* compensation, + int64_t M, + int64_t K, + int64_t lda, + int64_t ldc) { + // Compute GEMM int8 * int8 -> int32 + // dequant result to float by applying scales/qzeros +#if defined(CPU_CAPABILITY_AVX512_VNNI) + if (M <= 4 && cpublas_can_pack) { + switch (M) { + case 1: + call_dequant_gemm_accum_small_M(1); + return; + case 2: + call_dequant_gemm_accum_small_M(2); + return; + case 3: + call_dequant_gemm_accum_small_M(3); + return; + case 4: + call_dequant_gemm_accum_small_M(4); + return; + } + } +#endif + + int8_t dqB[K * N]; + _dequant_weight_zp_only(B, dqB, qzeros_b, K); + using Tin = typename ActDtype::type; + Tin* A_ptr = (Tin*)A; +#if defined(CPU_CAPABILITY_AVX512) + if constexpr (cpublas_can_pack) { + int32_t C_i32[M * N]; + at::native::cpublas::brgemm( + M, + N, + K, + lda, + N /*ldb*/, + N /*ldc*/, + false /* add_C */, + A_ptr, + dqB, + C_i32, + true /* is_vnni */); + _mm_prefetch(B + N * K / 2, _MM_HINT_T0); + _mm_prefetch(A + K, _MM_HINT_T0); + _dequant_and_store( + C, + C_i32, + scales_a, + qzeros_a, + scales_b, + compensation, + M, + N /*ldi*/, + ldc, + 1 /*ldsa*/); + } else +#endif + { + for (int64_t i = 0; i < M; ++i) { + for (int64_t j = 0; j < N; ++j) { + float sum = 0; + for (int64_t k = 0; k < K; ++k) { + if constexpr (sym_quant_a) { + sum += ((int32_t)A_ptr[i * lda + k] * dqB[k * N + j]); + } else { + sum += ((int32_t)A_ptr[i * lda + k] - qzeros_a[i]) * (int32_t)dqB[k * N + j]; + } + } + C[i * ldc + j] += sum * scales_a[i] * scales_b[j]; + } + } + } +} + +template +inline void copy_bias(const float* bias_ptr, float* y_buf, int64_t m) { + if (bias_ptr) { + for (int i = 0; i < m; ++i) { + int j = 0; +#if defined(CPU_CAPABILITY_AVX512) +#pragma GCC unroll 2 + for (; j < N; j += 16) { + __m512 bias_vec = _mm512_loadu_ps(bias_ptr + j); + _mm512_storeu_ps(y_buf + i * N + j, bias_vec); + } +#endif + for (; j < N; ++j) { + y_buf[i * N + j] = bias_ptr[j]; + } + } + } else { // initialize to zero + for (int i = 0; i < m; ++i) { + int j = 0; +#if defined(CPU_CAPABILITY_AVX512) +#pragma GCC unroll 2 + for (; j < N; j += 16) { + __m512 zero_vec = _mm512_setzero_ps(); + _mm512_storeu_ps(y_buf + i * N + j, zero_vec); + } +#endif + for (; j < N; ++j) { + y_buf[i * N + j] = 0; + } + } + } +} + +template +inline void store_out(const float* y_buf, out_dtype* c_ptr, int64_t m, /* int64_t n, */ int64_t lda) { + for (int i = 0; i < m; ++i) { + int j = 0; + if constexpr (std::is_same::value) { +#if defined(CPU_CAPABILITY_AVX512) +#pragma GCC unroll 2 + for (; j < N; j += 16) { + __m512 y_vec = _mm512_loadu_ps(y_buf + i * N + j); + _mm512_storeu_ps(c_ptr + i * lda + j, y_vec); + } +#endif + for (; j < N; ++j) { + c_ptr[i * lda + j] = y_buf[i * N + j]; + } + } else if constexpr (std::is_same::value) { +#if defined(CPU_CAPABILITY_AVX512) +#pragma GCC unroll 2 + for (; j < N; j += 16) { + __m512 y_vec = _mm512_loadu_ps(y_buf + i * N + j); + __m256i y_bf16_vec = at::vec::cvtfp32_bf16(y_vec); + _mm256_storeu_si256(reinterpret_cast<__m256i*>(c_ptr + i * lda + j), y_bf16_vec); + } +#endif + for (; j < N; ++j) { + c_ptr[i * lda + j] = at::BFloat16(y_buf[i * N + j]); + } + } else if constexpr (std::is_same::value) { +#if defined(CPU_CAPABILITY_AVX512) +#pragma GCC unroll 2 + for (; j < N; j += 16) { + __m512 y_vec = _mm512_loadu_ps(y_buf + i * N + j); + __m256i y_fp16_vec = at::vec::cvtfp32_fp16(y_vec); + _mm256_storeu_si256(reinterpret_cast<__m256i*>(c_ptr + i * lda + j), y_fp16_vec); + } +#endif + for (; j < N; ++j) { + c_ptr[i * lda + j] = at::Half(y_buf[i * N + j]); + } + } else { + TORCH_CHECK(false, "Unsupported output dtype"); + } + } +} + +template +void _da8w4_linear_impl( + const at::Tensor& input, + const at::Tensor& input_scales, + const at::Tensor& input_qzeros, + const at::Tensor& weight, + const at::Tensor& weight_scales, + const at::Tensor& weight_qzeros, + const at::Tensor& compensation, + const std::optional& bias, + at::Tensor& output) { + // input shape = [..., K] + // input is per token quantized + int64_t K = input.size(-1); + auto input_view = input.view({-1, K}); + int64_t M = input_view.size(0); + TORCH_CHECK(input_scales.numel() == M, "DA8W4: unexpected input scales shape"); + TORCH_CHECK(input_scales.sizes() == input_qzeros.sizes(), "DA8W4: unexpected input qzeros shape"); + + // weight shape = [Nc, Kc, block_k, block_n/2] + // scales/qzeros shape = [Nc, G, block_n] + // compensation shape = [Nc, Kc, block_n] + int64_t Nc = weight.size(0); + int64_t Kc = weight.size(1); + int64_t block_k = weight.size(2); + constexpr int64_t block_n = BLOCK_N; + TORCH_CHECK(weight.size(3) * 2 == block_n, "DA8W4: unexpected weight shape"); + int64_t N = Nc * block_n; + TORCH_CHECK(K == Kc * block_k, "DA8W4: weight and input shapes mismatch"); + int64_t block_m = [&]() -> long { + if (M <= 48) { + return M; + } else if (M < 64) { + return 32; + } else if (M < 96) { + return 48; + } else { + return 64; + } + }(); + int64_t Mc = (M + block_m - 1) / block_m; + bool parallel_on_M = M > 128; + int64_t num_blocks = parallel_on_M ? Mc * Nc : Nc; + + // scales/qzeros shape = [Nc, G, block_n] + int64_t num_groups = weight_scales.size(1); + int64_t group_size = K / num_groups; + TORCH_CHECK(group_size % block_k == 0, + "DA8W4 CPU: group_size should be divisible by block_k"); + int64_t block_per_group = group_size / block_k; + + using Tin = typename ActDtype::type; + const Tin* a_ptr = input_view.data_ptr(); + const float* a_scales_ptr = input_scales.data_ptr(); + const int32_t* a_qzeros_ptr = sym_quant_a ? nullptr : input_qzeros.data_ptr(); + const uint8_t* b_ptr = weight.data_ptr(); + const float* b_scales_ptr = weight_scales.data_ptr(); + const int8_t* b_qzeros_ptr = weight_qzeros.data_ptr(); + const int32_t* compensation_ptr = sym_quant_a ? nullptr : compensation.data_ptr(); + out_dtype* c_ptr = output.data_ptr(); + const float* bias_ptr = bias.has_value() ? bias.value().data_ptr() : nullptr; + + at::parallel_for(0, num_blocks, 1, [&](int64_t begin, int64_t end) { + for (const auto i : c10::irange(begin, end)) { + int64_t mc = parallel_on_M ? i / Nc : 0; + int64_t nc = parallel_on_M ? i % Nc : i; + int64_t mc_end = parallel_on_M ? mc + 1 : Mc; + + for (int mci = mc; mci < mc_end; ++mci) { + int64_t m_size = mci * block_m + block_m > M ? M - mci * block_m : block_m; + alignas(64) float y_buf[m_size][block_n]; + // copy bias to y_buf if bias is not None + auto bias_data = bias_ptr ? bias_ptr + nc * block_n : nullptr; + copy_bias(bias_data, y_buf[0], m_size); + for (int kci = 0; kci < Kc; ++kci) { + _dequant_gemm_accum( + y_buf[0] /*C*/, + (uint8_t*)a_ptr + mci * block_m * K + kci * block_k /*A*/, + a_scales_ptr + mci * block_m /*scales_a*/, + a_qzeros_ptr + mci * block_m /*qzeros_a*/, + b_ptr + (nc * Kc + kci) * block_n * block_k / 2 /*B*/, + b_scales_ptr + nc * block_n * num_groups + kci / block_per_group * block_n /*scales_b*/, + b_qzeros_ptr + nc * block_n * num_groups + kci / block_per_group * block_n /*qzeros_b*/, + compensation_ptr + nc * block_n * Kc + kci * block_n /*compensation*/, + m_size /*M*/, + block_k /*K*/, + K /*lda*/, + block_n /*ldc*/); + } + // store y_buf to output with dtype conversion + store_out( + y_buf[0], + c_ptr + mci * block_m * N + nc * block_n, + m_size, + N /*lda*/); + } + } + if constexpr (cpublas_can_pack) { + at::native::cpublas::brgemm_release(); + } + }); +} + +at::Tensor da8w4_linear_impl( + const at::Tensor& input, + const at::Tensor& input_scales, + const at::Tensor& input_qzeros, + const at::Tensor& weight, + const at::Tensor& weight_scales, + const at::Tensor& weight_qzeros, + const at::Tensor& compensation, + const std::optional& bias, + at::ScalarType output_dtype) { + static bool cpublas_can_pack = cpublas_could_pack(); + bool sym_quant_a = input.scalar_type() == c10::kChar; + auto out_sizes = input.sizes().vec(); + int64_t N = weight.size(0) * weight.size(-1) * 2; + out_sizes.back() = N; + auto output = at::empty(out_sizes, input.options().dtype(output_dtype)); + +#define call__da8w4_linear_impl(cpublas_can_pack, sym_quant_act) \ + AT_DISPATCH_FLOATING_TYPES_AND2( \ + at::ScalarType::BFloat16, at::ScalarType::Half, output_dtype, "da8w4_linear_cpu", [&] { \ + _da8w4_linear_impl( \ + input, \ + input_scales, \ + input_qzeros, \ + weight, \ + weight_scales, \ + weight_qzeros, \ + compensation, \ + bias, \ + output); \ + }); + + if (cpublas_can_pack) { + if (sym_quant_a) { + call__da8w4_linear_impl(true, true); + } else { + call__da8w4_linear_impl(true, false); + } + } else { + if (sym_quant_a) { + call__da8w4_linear_impl(false, true); + } else { + call__da8w4_linear_impl(false, false); + } + } + return output; +} + +} // anonymous namespace + +TORCH_LIBRARY_IMPL(torchao, CPU, m) { + m.impl("torchao::da8w4_linear_prepack_cpu", &da8w4_linear_prepack_impl); + m.impl("torchao::da8w4_linear_cpu", &da8w4_linear_impl); +} + +} // namespace torchao diff --git a/torchao/dtypes/__init__.py b/torchao/dtypes/__init__.py index 581c3e4ecb..b0dde2cf10 100644 --- a/torchao/dtypes/__init__.py +++ b/torchao/dtypes/__init__.py @@ -20,6 +20,7 @@ CutlassInt4PackedLayout, Int4CPULayout, Int4XPULayout, + Int8DynamicActInt4WeightCPULayout, MarlinQQQLayout, MarlinQQQTensor, MarlinSparseLayout, @@ -67,4 +68,5 @@ "FbgemmInt4Tensor", "to_fbgemm_fp8", "FbgemmFp8Tensor", + "Int8DynamicActInt4WeightCPULayout", ] diff --git a/torchao/dtypes/affine_quantized_tensor_ops.py b/torchao/dtypes/affine_quantized_tensor_ops.py index 02a2d3004a..8b028352e4 100644 --- a/torchao/dtypes/affine_quantized_tensor_ops.py +++ b/torchao/dtypes/affine_quantized_tensor_ops.py @@ -35,6 +35,10 @@ _linear_int8_act_int4_weight_cutlass_check, _linear_int8_act_int4_weight_cutlass_impl, ) +from torchao.dtypes.uintx.dyn_int8_act_int4_wei_cpu_layout import ( + _linear_int8_act_int4_weight_cpu_check, + _linear_int8_act_int4_weight_cpu_impl, +) from torchao.dtypes.uintx.gemlite_layout import ( _linear_fp_act_int4_weight_gemlite_check, _linear_fp_act_int4_weight_gemlite_impl, @@ -247,6 +251,10 @@ def _register_aqt_quantized_linear_dispatches(): _linear_bf16_act_uint4_weight_float_zero_check, _linear_bf16_act_uint4_weight_float_zero_impl, ), + ( + _linear_int8_act_int4_weight_cpu_check, + _linear_int8_act_int4_weight_cpu_impl, + ), ]: register_aqt_quantized_linear_dispatch(dispatch_condition, impl) diff --git a/torchao/dtypes/uintx/__init__.py b/torchao/dtypes/uintx/__init__.py index fee6141164..6d1bc95653 100644 --- a/torchao/dtypes/uintx/__init__.py +++ b/torchao/dtypes/uintx/__init__.py @@ -4,6 +4,9 @@ from .cutlass_int4_packed_layout import ( CutlassInt4PackedLayout, ) +from .dyn_int8_act_int4_wei_cpu_layout import ( + Int8DynamicActInt4WeightCPULayout, +) from .int4_cpu_layout import ( Int4CPULayout, ) @@ -48,4 +51,5 @@ "PackedLinearInt8DynamicActivationIntxWeightLayout", "QDQLayout", "Int4XPULayout", + "Int8DynamicActInt4WeightCPULayout", ] diff --git a/torchao/dtypes/uintx/dyn_int8_act_int4_wei_cpu_layout.py b/torchao/dtypes/uintx/dyn_int8_act_int4_wei_cpu_layout.py new file mode 100644 index 0000000000..ced7ec0dd8 --- /dev/null +++ b/torchao/dtypes/uintx/dyn_int8_act_int4_wei_cpu_layout.py @@ -0,0 +1,312 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD 3-Clause license found in the +# LICENSE file in the root directory of this source tree. +from dataclasses import dataclass +from typing import Tuple + +import torch +from torch.utils._python_dispatch import ( + return_and_correct_aliasing, +) + +from torchao.dtypes.affine_quantized_tensor import ( + AffineQuantizedTensor, + register_layout, +) +from torchao.dtypes.utils import Layout, PlainLayout, is_device +from torchao.utils import ( + TORCH_VERSION_AT_LEAST_2_7, + TORCH_VERSION_AT_LEAST_2_8, +) + +from .int4_cpu_layout import ( + Int4CPUAQTTensorImpl, + _is_float, +) + +aten = torch.ops.aten + + +@dataclass(frozen=True) +class Int8DynamicActInt4WeightCPULayout(Layout): + """Layout class for da8w4 CPU layout for affine quantized tensor""" + + pass + + +@register_layout(Int8DynamicActInt4WeightCPULayout) +class DA8W4CPUAQTTensorImpl(Int4CPUAQTTensorImpl): + """TensorImpl for da8w4 CPU layout for affine quantized tensor + It stores the original tensor of dimension [n][k] (int32 dtype) as packed weight of 2-d tensor of + dimension: [n][k / 2] (uint8 dtype) + It is similar to Int4CPUAQTTensorImpl but with a different memory layout of weight data + fields: + packed_weight (torch.Tensor): the 2-d packed tensor in a Int4 CPU layout + scales (torch.Tensor): the scales Tensor used to map between floating point tensor to quantized tensor + qzeros (torch.Tensor): the zero_point Tensor used to map between floating point tensor to quantized tensor + """ + + def __new__( + cls, + packed_weight: torch.Tensor, + scales: torch.Tensor, + qzeros: torch.Tensor, + compensation: torch.Tensor, + transposed: bool, + _layout: Layout, + ): + kwargs = {} + kwargs["device"] = packed_weight.device + kwargs["layout"] = ( + kwargs.get("layout") + if kwargs.get("layout", False) + else packed_weight.layout + ) + kwargs["dtype"] = packed_weight.dtype + kwargs["requires_grad"] = False + shape = packed_weight.shape + return torch.Tensor._make_wrapper_subclass(cls, shape, **kwargs) # type: ignore[attr-defined] + + def __init__( + self, + packed_weight: torch.Tensor, + scales: torch.Tensor, + qzeros: torch.Tensor, + compensation: torch.Tensor, + transposed: bool, + _layout: Layout, + ): + self.packed_weight = packed_weight + self.scales = scales + self.qzeros = qzeros + self.compensation = compensation + self.transposed = transposed + self._layout = _layout + + def __tensor_flatten__(self): + return ["packed_weight", "scales", "qzeros", "compensation"], [ + self.transposed, + self._layout, + ] + + @classmethod + def __tensor_unflatten__( + cls, tensor_data_dict, tensor_attributes, outer_size, outer_stride + ): + packed_weight, scales, qzeros, compensation = ( + tensor_data_dict["packed_weight"], + tensor_data_dict["scales"], + tensor_data_dict["qzeros"], + tensor_data_dict["compensation"], + ) + ( + transposed, + _layout, + ) = tensor_attributes + return cls(packed_weight, scales, qzeros, compensation, transposed, _layout) + + @classmethod + def from_plain( + cls, + int_data: torch.Tensor, + scale: torch.Tensor, + zero_point: torch.Tensor, + _layout: Layout, + ): + assert isinstance(_layout, Int8DynamicActInt4WeightCPULayout) + assert int_data.dtype == torch.uint8, "DA8W4 CPU: expects uint8 weight" + assert int_data.shape[1] % 2 == 0, "DA8W4 CPU: expects even number of columns" + if scale.dim() == 1: + scale.unsqueeze_(-1) + scale = scale.to(torch.float) + if zero_point.dim() == 1: + zero_point.unsqueeze_(-1) + + weight_int4, scales, qzeros, compensation = ( + torch.ops.torchao.da8w4_linear_prepack_cpu(int_data, scale, zero_point) + ) + return cls(weight_int4, scales, qzeros, compensation, False, _layout) + + def _apply_fn_to_data(self, fn): + return self.__class__( + fn(self.packed_weight), + fn(self.scales), + fn(self.qzeros), + fn(self.compensation), + self.transposed, + self._layout, + ) + + @classmethod + def __torch_dispatch__(cls, func, types, args, kwargs): + kwargs = {} if kwargs is None else kwargs + if func is aten.t.default: + """we don't need to repack the weight and just rely on external + shape being changed and record the status of transpose/no-transpose + """ + transposed = DA8W4CPUAQTTensorImpl( + args[0].packed_weight, + args[0].scales, + args[0].qzeros, + args[0].compensation, + not args[0].transposed, + args[0]._layout, + ) + return return_and_correct_aliasing(func, args, kwargs, transposed) + else: + return super().__torch_dispatch__(func, types, args, kwargs) + + __torch_function__ = torch._C._disabled_torch_function_impl + + @property + def block_size(self): + assert len(self.packed_weight.shape) == 2 + weight_shape = self.packed_weight.shape + N = weight_shape[0] + K = weight_shape[1] * 2 + groups = self.scales.numel() // N + group_size = K // groups + return (1, group_size) + + def get_plain(self) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + # Unpack weight by linear(eye(K), packed_weight).t() + packed_w_shape = self.packed_weight.shape + if len(packed_w_shape) == 4: + K = packed_w_shape[1] * packed_w_shape[2] + else: + K = packed_w_shape[1] + x = torch.eye(K).to(torch.uint8) + x_scale = torch.ones(K).float() + x_qzero = torch.zeros(K).to(torch.int32) + w_scale = torch.ones_like(self.scales).float() + w_qzero = torch.zeros_like(self.qzeros).to(torch.int8) + plain_weight = torch.ops.torchao.da8w4_linear_cpu.default( + x, + x_scale, + x_qzero, + self.packed_weight, + w_scale, + w_qzero, + self.compensation, + None, # bias + torch.float, # out_dtype + ) + plain_weight = plain_weight.t().contiguous() + plain_weight = plain_weight.to(torch.int8) + + if self.scales.dim() == 2: + assert self.qzeros.dim() == 2 + plain_scales = self.scales + plain_qzeros = self.qzeros + else: + assert self.scales.dim() == 3 and self.qzeros.dim() == 3 + packed_shape = self.scales.shape # [Nc, G, block_n] + plain_scales = ( + self.scales.permute([0, 2, 1]).contiguous().view([-1, packed_shape[1]]) + ) + plain_qzeros = ( + self.qzeros.permute([0, 2, 1]).contiguous().view([-1, packed_shape[1]]) + ) + + return plain_weight, plain_scales, plain_qzeros + + +def _aqt_is_uint8(aqt): + """Check if an AffineQuantizedTensor is uint8 quantized Tensor""" + return ( + aqt.tensor_impl.dtype == torch.uint8 + and aqt.quant_min == 0 + and aqt.quant_max == 255 + ) + + +def _aqt_is_int8(aqt): + """Check if an AffineQuantizedTensor is uint8 quantized Tensor""" + return ( + aqt.tensor_impl.dtype == torch.int8 + and aqt.quant_min == -127 + and aqt.quant_max == 127 + ) + + +def _aqt_is_uint4(aqt): + """Check if an AffineQuantizedTensor is uint4 quantized Tensor""" + return ( + aqt.tensor_impl.dtype == torch.uint8 + and aqt.quant_min == 0 + and aqt.quant_max == 15 + ) + + +def _linear_int8_act_int4_weight_cpu_check(input_tensor, weight_tensor, bias): + return ( + TORCH_VERSION_AT_LEAST_2_7 + and is_device(input_tensor.device.type, "cpu") + and is_device(weight_tensor.device.type, "cpu") + and (bias is None or is_device(bias.device.type, "cpu")) + and isinstance(input_tensor, AffineQuantizedTensor) + and (_aqt_is_uint8(input_tensor) or _aqt_is_int8(input_tensor)) + and _is_float(input_tensor.dtype) + and isinstance(input_tensor._layout, PlainLayout) + and isinstance(weight_tensor, AffineQuantizedTensor) + and _aqt_is_uint4(weight_tensor) + and _is_float(weight_tensor.dtype) + and isinstance(weight_tensor._layout, Int8DynamicActInt4WeightCPULayout) + ) + + +def _linear_int8_act_int4_weight_cpu_impl(input_tensor, weight_tensor, bias): + assert TORCH_VERSION_AT_LEAST_2_7, ( + f"Requires PyTorch version at least 2.7, but got: {torch.__version__}" + ) + if _aqt_is_int8(input_tensor): + assert TORCH_VERSION_AT_LEAST_2_8, ( + f"Requires PyTorch version at least 2.8, but got: {torch.__version__}" + ) + assert is_device(input_tensor.device.type, "cpu"), ( + f"For CPU device only but got: {input_tensor.device}" + ) + assert weight_tensor.block_size[0] == 1, ( + f"Requires groupwise quantization, got block_size: {weight_tensor.block_size}" + ) + assert input_tensor.shape[-1] == weight_tensor.shape[1], ( + f"need input_tensor shape: {input_tensor.shape} final" + f"dim to match weight_tensor shape: {weight_tensor.shape} second dim " + ) + + act_mat = input_tensor + act = act_mat.tensor_impl.int_data + act_scales = act_mat.tensor_impl.scale + act_qzeros = act_mat.tensor_impl.zero_point + + packed_weight = weight_tensor.tensor_impl.packed_weight + wei_scales = weight_tensor.tensor_impl.scales + wei_qzeros = weight_tensor.tensor_impl.qzeros + compensation = weight_tensor.tensor_impl.compensation + + orig_act_size = act_mat.size() + orig_dtype = act_mat.dtype + + # reshape to 2D + act = act.reshape(-1, act.shape[-1]) + + y = torch.ops.torchao.da8w4_linear_cpu.default( + act.contiguous(), + act_scales, + act_qzeros, + packed_weight, + wei_scales, + wei_qzeros, + compensation, + bias.float() if bias is not None else bias, # requires bias to be float + orig_dtype, # out_dtype + ) + + # remove out_feature padding + orig_out_features = weight_tensor.shape[-2] + y = y[:, :orig_out_features] + y = y.reshape(*orig_act_size[:-1], orig_out_features) + + return y.to(orig_dtype) diff --git a/torchao/dtypes/uintx/int4_cpu_layout.py b/torchao/dtypes/uintx/int4_cpu_layout.py index bf9446d265..da19bbc259 100644 --- a/torchao/dtypes/uintx/int4_cpu_layout.py +++ b/torchao/dtypes/uintx/int4_cpu_layout.py @@ -150,7 +150,7 @@ def to(self, *args, **kwargs): device = kwargs["device"] if not is_device(torch.device(self.device).type, device): raise ValueError( - f"Int4CPUAQTTensorImpl does not support conversion from {self.device} to {device}" + f"{self.__class__.__name__} does not support conversion from {self.device} to {device}" ) return self.__class__( self.packed_weight.to(device), @@ -181,18 +181,6 @@ def __torch_dispatch__(cls, func, types, args, kwargs): func, args, kwargs, args[0]._apply_fn_to_data(torch.clone) ) - if func is aten.t.default: - """we don't need to repack the weight and just rely on external - shape being changed and record the status of transpose/no-transpose - """ - transposed = Int4CPUAQTTensorImpl( - args[0].packed_weight, - args[0].scale_and_zero, - not args[0].transposed, - args[0]._layout, - ) - return return_and_correct_aliasing(func, args, kwargs, transposed) - if func is aten.slice.Tensor: self, dim, start, end, step = fill_defaults(args, 5, [0, None, None, 1]) if dim in [0, 1]: @@ -217,11 +205,11 @@ def __torch_dispatch__(cls, func, types, args, kwargs): return return_and_correct_aliasing(func, args, kwargs, sliced) else: raise NotImplementedError( - f"Int4CPUAQTTensorImpl dispatch: attempting to run {func}, with dim={dim}, that is not supported" + f"{cls.__name__} dispatch: attempting to run {func}, with dim={dim}, that is not supported" ) raise NotImplementedError( - f"Int4CPUAQTTensorImpl dispatch: attempting to run {func}, this is not supported" + f"{cls.__name__} dispatch: attempting to run {func}, this is not supported" ) __torch_function__ = torch._C._disabled_torch_function_impl diff --git a/torchao/ops.py b/torchao/ops.py index cda3746624..babe5506c0 100644 --- a/torchao/ops.py +++ b/torchao/ops.py @@ -64,6 +64,12 @@ lib.define( "qscaled_dot_product(Tensor query, Tensor key, Tensor value, Tensor? attn_mask=None, float dropout_p=0.0, bool is_causal=False, float? scale=None, float q_scale=1.0, int q_zp=0, float k_scale=1.0, int k_zp=0, float v_scale=1.0, int v_zp=0, float a_scale=1.0, int a_zp=0, float o_scale=1.0, int o_zp=0) -> Tensor" ) +lib.define( + "da8w4_linear_prepack_cpu(Tensor weight, Tensor scales, Tensor qzeros) -> (Tensor, Tensor, Tensor, Tensor)" +) +lib.define( + "da8w4_linear_cpu(Tensor input, Tensor input_scales, Tensor input_qzeros, Tensor weight, Tensor weight_scales, Tensor weight_qzeros, Tensor compensation, Tensor? bias, ScalarType output_dtype) -> Tensor" +) def register_custom_op(name): @@ -1022,3 +1028,81 @@ def meta_mx_fp4_bf16(A: Tensor, B: Tensor, A_scale: Tensor, B_scale: Tensor): """Meta impl for mx_fp4_bf16""" # Assume that the contraction happens in the K dim thus M,N are perserved post bit pack return torch.empty((A.size(0), B.size(1)), dtype=torch.bfloat16, device=A.device) + + +def da8w4_linear_prepack_cpu( + weight: Tensor, + scales: Tensor, + qzeros: Tensor, +) -> Tensor: + """ + Prepack weights for DA8W4 linear operator on CPU. + Args: + weight: weight tensor. + scales: scales for weight tensor. + qzeros: zero points for weight tensor. + Returns: + packed weight, scales, and zero points. + """ + return torch.ops.torchao.da8w4_linear_prepack_cpu.default(weight, scales, qzeros) + + +@register_custom_op("torchao::da8w4_linear_prepack_cpu") +def _(weight: Tensor, scales: Tensor, qzeros: Tensor) -> Tensor: + return weight, scales, qzeros, torch.Tensor() + + +def da8w4_linear_cpu( + input: Tensor, + input_scales: Tensor, + input_qzeros: Tensor, + weight: Tensor, + weight_scales: Tensor, + weight_qzeros: Tensor, + compensation: Tensor, + bias: Optional[Tensor], + out_dtype: torch.dtype, +): + """ + DA8W4 linear operator on CPU. + Args: + input: input tensor. + input_scales: scales for input tensor. + input_qzeros: zero points for input tensor. + weight: weight tensor. + weight_scales: scales for weight tensor. + weight_qzeros: zero points for weight tensor. + compensation: compensation tensor for weight. + bias: optional bias tensor. + out_dtype: output data type. + Returns: + output tensor in out_dtype. + """ + return torch.ops.torchao.da8w4_linear_cpu.default( + input, + input_scales, + input_qzeros, + weight, + weight_scales, + weight_qzeros, + compensation, + bias, + out_dtype, + ) + + +@register_custom_op("torchao::da8w4_linear_cpu") +def _( + input: Tensor, + input_scales: Tensor, + input_qzeros: Tensor, + weight: Tensor, + weight_scales: Tensor, + weight_qzeros: Tensor, + compensation: Tensor, + bias: Optional[Tensor], + out_dtype: torch.dtype, +) -> Tensor: + assert weight.dim() == 4 + N = weight.size(0) * weight.size(3) * 2 + return input.new_empty(*input.shape[:-1], N, dtype=out_dtype) diff --git a/torchao/quantization/quant_api.py b/torchao/quantization/quant_api.py index 8b66ac84ce..7287ae2bc0 100644 --- a/torchao/quantization/quant_api.py +++ b/torchao/quantization/quant_api.py @@ -35,6 +35,7 @@ Float8Layout, Int4CPULayout, Int4XPULayout, + Int8DynamicActInt4WeightCPULayout, MarlinQQQLayout, MarlinSparseLayout, PackedLinearInt8DynamicActivationIntxWeightLayout, @@ -660,6 +661,38 @@ def _int8_asymm_per_token_quant(x: torch.Tensor) -> torch.Tensor: ) +def _uint8_asymm_per_token_quant(x: torch.Tensor) -> torch.Tensor: + mapping_type = MappingType.ASYMMETRIC + target_dtype = torch.uint8 + scale_dtype = torch.float32 + eps = torch.finfo(torch.float32).eps + zero_point_dtype = torch.int32 + quant_min = 0 + quant_max = 255 + if TORCH_VERSION_AT_LEAST_2_6: + out = to_affine_quantized_intx( + x, + mapping_type, + _get_per_token_block_size(x), + target_dtype, + quant_min=quant_min, + quant_max=quant_max, + eps=eps, + scale_dtype=scale_dtype, + zero_point_dtype=zero_point_dtype, + ) + else: + out = to_affine_quantized_intx( + x, + mapping_type, + _get_per_token_block_size(x), + target_dtype, + quant_min=quant_min, + quant_max=quant_max, + ) + return out + + def _int8_symm_per_token_quant(x: torch.Tensor) -> torch.Tensor: mapping_type = MappingType.SYMMETRIC target_dtype = torch.int8 @@ -731,7 +764,10 @@ def _int8_dynamic_activation_int4_weight_transform( # input settings if act_mapping_type == MappingType.ASYMMETRIC: - input_quant_func = _int8_asymm_per_token_quant + if isinstance(layout, Int8DynamicActInt4WeightCPULayout): + input_quant_func = _uint8_asymm_per_token_quant + else: + input_quant_func = _int8_asymm_per_token_quant elif act_mapping_type == MappingType.SYMMETRIC: if isinstance(layout, MarlinQQQLayout): input_quant_func = _int8_symm_per_token_quant @@ -748,6 +784,16 @@ def _int8_dynamic_activation_int4_weight_transform( ) elif isinstance(layout, CutlassInt4PackedLayout): weight = _int4_symm_cutlass_quant(weight) + elif isinstance(layout, Int8DynamicActInt4WeightCPULayout): + weight = to_affine_quantized_intx( + weight, + mapping_type, + block_size, + target_dtype=torch.uint8, + quant_min=0, + quant_max=15, + _layout=layout, + ) else: weight = to_affine_quantized_intx( weight, From 5a5066735d6336bc770cb487811a681b99f612e2 Mon Sep 17 00:00:00 2001 From: andrewor14 Date: Wed, 25 Jun 2025 15:29:30 -0400 Subject: [PATCH 159/165] Call out axolotl + QAT integration on README (#2442) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index d269c3974e..fffb640cc7 100644 --- a/README.md +++ b/README.md @@ -25,6 +25,7 @@ ## 📣 Latest News - [Jun 25] Our [TorchAO paper](https://codeml-workshop.github.io/codeml2025/) was accepted to CodeML @ ICML 2025! +- [May 25] QAT is now integrated into [Axolotl](https://github.com/axolotl-ai-cloud/axolotl) for fine-tuning ([docs](https://docs.axolotl.ai/docs/qat.html))! - [Apr 25] Float8 rowwise training yielded [1.34-1.43x training speedup](https://pytorch.org/blog/accelerating-large-scale-training-and-convergence-with-pytorch-float8-rowwise-on-crusoe-2k-h200s/) at 2k H100 GPU scale - [Apr 25] TorchAO is added as a [quantization backend to vLLM](https://docs.vllm.ai/en/latest/features/quantization/torchao.html) ([docs](https://docs.vllm.ai/en/latest/features/quantization/torchao.html))! - [Mar 25] Our [2:4 Sparsity paper](https://openreview.net/pdf?id=O5feVk7p6Y) was accepted to SLLM @ ICLR 2025! From 420f782b5769256cb25575927566a39387bb3467 Mon Sep 17 00:00:00 2001 From: Xia Weiwen Date: Thu, 26 Jun 2025 18:06:39 +0800 Subject: [PATCH 160/165] [CPU] Fix ref path of DA8W4 cpp kernel (#2444) --- torchao/csrc/cpu/da8w4_linear.cpp | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/torchao/csrc/cpu/da8w4_linear.cpp b/torchao/csrc/cpu/da8w4_linear.cpp index 537aa0fce9..df2f60b4c7 100644 --- a/torchao/csrc/cpu/da8w4_linear.cpp +++ b/torchao/csrc/cpu/da8w4_linear.cpp @@ -70,6 +70,7 @@ da8w4_linear_prepack_impl( at::Tensor compensation = weight_sub_qzero.sum(-1); compensation = compensation.permute({0, 2, 1}).contiguous().to(at::kInt); +#if defined(CPU_CAPABILITY_AVX512) if (cpublas_could_pack()) { blocked_weight = at::empty({Nc, Kc, block_k, block_n / 2}, weight.options()); auto weight_ptr = weight_reordered.data_ptr(); @@ -105,7 +106,9 @@ da8w4_linear_prepack_impl( } } }); - } else { + } else +#endif + { // Pack weight: two int4 -> one int8 using namespace at::indexing; at::Tensor even_columns = From 353dd44926fb155895d5288bc57b6ed3129429d6 Mon Sep 17 00:00:00 2001 From: Vasiliy Kuznetsov Date: Thu, 26 Jun 2025 13:03:04 -0400 Subject: [PATCH 161/165] float8 readme: remove duplication (#2447) We had two duplicate example training loops in float8 readme, removing and making the same example work for all recipes --- torchao/float8/README.md | 64 ++++++---------------------------------- 1 file changed, 9 insertions(+), 55 deletions(-) diff --git a/torchao/float8/README.md b/torchao/float8/README.md index 8533a05779..578fea0d1f 100644 --- a/torchao/float8/README.md +++ b/torchao/float8/README.md @@ -12,16 +12,12 @@ and composable with key systems such as autograd, ```torch.compile``` and distri # Single GPU User API -## float8 linear with dynamic tensorwise scaling - -This is the default recipe, with a good balance of performance and accuracy. - ```python import time import torch import torch.nn as nn -from torchao.float8 import convert_to_float8_training +from torchao.float8 import convert_to_float8_training, Float8LinearConfig from torchao.utils import TORCH_VERSION_AT_LEAST_2_5 if not TORCH_VERSION_AT_LEAST_2_5: @@ -47,8 +43,15 @@ def module_filter_fn(mod: torch.nn.Module, fqn: str): return False return True +# configure float8 recipe +# valid recipe names: "tensorwise", "rowwise", "rowwise_with_gw_hp" +config = Float8LinearConfig.from_recipe_name("tensorwise") + # convert specified `torch.nn.Linear` modules to `Float8Linear` -convert_to_float8_training(m, module_filter_fn=module_filter_fn) +convert_to_float8_training(m, config=config, module_filter_fn=module_filter_fn) + +# display converted model +print(m) # enable torch.compile for competitive performance m = torch.compile(m) @@ -75,55 +78,6 @@ end_time = time.time() print("Training time:", end_time - start_time) ``` -## float8 linear with rowwise scaling - -This is a more accurate recipe compared to tensorwise, with more granular scaling. - -```python -import torch -import torch.nn as nn -from torchao.float8 import convert_to_float8_training, Float8LinearConfig -from torchao.utils import TORCH_VERSION_AT_LEAST_2_5 - -if not TORCH_VERSION_AT_LEAST_2_5: - raise AssertionError("torchao.float8 requires PyTorch version 2.5 or greater") - -# create model and sample input -m = nn.Sequential( - nn.Linear(2048, 4096), - nn.Linear(4096, 128), -).bfloat16().cuda() -x = torch.randn(4096, 2048, device="cuda", dtype=torch.bfloat16) -optimizer = torch.optim.SGD(m.parameters(), lr=0.1) - -# optional: filter modules from being eligible for float8 conversion -def module_filter_fn(mod: torch.nn.Module, fqn: str): - # don't convert the last module - if fqn == "1": - return False - # don't convert linear modules with weight dimensions not divisible by 16 - if isinstance(mod, torch.nn.Linear): - if mod.in_features % 16 != 0 or mod.out_features % 16 != 0: - return False - return True - -# configure rowwise scaling -config = Float8LinearConfig.from_recipe_name("rowwise") - -# convert specified `torch.nn.Linear` modules to `Float8Linear` -convert_to_float8_training(m, config=config, module_filter_fn=module_filter_fn) - -# enable torch.compile for competitive performance -m = torch.compile(m) - -# toy training loop -for _ in range(10): - optimizer.zero_grad() - y = m(x) - y.sum().backward() - optimizer.step() -``` - # Multi GPU User API We compose with the `DTensor` based [distributed APIs](https://pytorch.org/docs/stable/distributed.tensor.parallel.html), From de5707176df2ff3b8cf5f93ef6f0afcdab9e6a60 Mon Sep 17 00:00:00 2001 From: Vasiliy Kuznetsov Date: Thu, 26 Jun 2025 13:04:03 -0400 Subject: [PATCH 162/165] float8 readme: add key features section (#2448) Adds a section summarizing the key features of float8 training --- torchao/float8/README.md | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/torchao/float8/README.md b/torchao/float8/README.md index 578fea0d1f..7234840560 100644 --- a/torchao/float8/README.md +++ b/torchao/float8/README.md @@ -6,6 +6,15 @@ and up to [**1.25x at 8 GPU / 8B parameter count scale**](#training-benchmarks). The codebase strives to stay small, hackable, debuggable with native PyTorch tooling and composable with key systems such as autograd, ```torch.compile``` and distributed. +## Key features + +* e2e pretraining speedups of up to [**1.5x at 512 GPU / 405B parameter count scale**](https://pytorch.org/blog/training-using-float8-fsdp2/), +and up to [**1.25x at 8 GPU / 8B parameter count scale**](#training-benchmarks), with performance and accuracy validated on up to [**2k GPUs**](https://pytorch.org/blog/accelerating-large-scale-training-and-convergence-with-pytorch-float8-rowwise-on-crusoe-2k-h200s/), via [torchtitan's float8 integration](https://github.com/pytorch/torchtitan/blob/main/docs/float8.md) +* seamless composability with [torch.compile](https://docs.pytorch.org/docs/stable/torch.compiler.html) +* seamless composability with [DTensor](https://docs.pytorch.org/docs/stable/distributed.tensor.html), including [FSDP2 with float8 weight all-gather](https://dev-discuss.pytorch.org/t/enabling-float8-all-gather-in-fsdp2/2359) and [Async TP](https://discuss.pytorch.org/t/distributed-w-torchtitan-introducing-async-tensor-parallelism-in-pytorch/209487) +* seamless composability with [PyTorch Activation Checkpointing](https://pytorch.org/blog/activation-checkpointing-techniques/) +* three different scaling recipes to trade off performance vs accuracy: tensorwise (fastest), rowwise, rowwise_with_gw_hp (most accurate) + ℹ️ See the [feature tracker](https://github.com/pytorch/ao/issues/556) for upcoming features. ℹ️ These APIs are training-only and float8-only, and we plan to [unify them with the rest of torchao](https://github.com/pytorch/ao/issues/894) in the future. From 6f9f9692b8b708fb737b8ed6d42cf195907e48c2 Mon Sep 17 00:00:00 2001 From: Scott Roy <161522778+metascroy@users.noreply.github.com> Date: Thu, 26 Jun 2025 10:13:00 -0700 Subject: [PATCH 163/165] Improve tiling params to speed up prefill (#2406) init --- .../linear_8bit_act_xbit_weight.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/torchao/experimental/ops/linear_8bit_act_xbit_weight/linear_8bit_act_xbit_weight.cpp b/torchao/experimental/ops/linear_8bit_act_xbit_weight/linear_8bit_act_xbit_weight.cpp index 96bfe17b5a..8caffe4342 100644 --- a/torchao/experimental/ops/linear_8bit_act_xbit_weight/linear_8bit_act_xbit_weight.cpp +++ b/torchao/experimental/ops/linear_8bit_act_xbit_weight/linear_8bit_act_xbit_weight.cpp @@ -203,7 +203,8 @@ void linear_operator( nc = tiling_params->nc; } else { auto params = LinearTilingParams::from_target_tiles_per_thread( - m, + // We process m sequentially, so m_step is the "m" for the purpose of computing tiling params + m_step, m_step, n, n_step, From b1163dc63dfa22d403586672fd3648cd661c5003 Mon Sep 17 00:00:00 2001 From: Abdourrahmane Kabbaj <145877572+Akabbaj@users.noreply.github.com> Date: Thu, 26 Jun 2025 10:22:33 -0700 Subject: [PATCH 164/165] Fixes issue #156414: Fixes bug in implementation of _combine_histogram (Follow up) (#2418) Fixes issue #156414: Fixes bug in implementation of _combine_histograms in torchao/. --- torchao/quantization/pt2e/observer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/torchao/quantization/pt2e/observer.py b/torchao/quantization/pt2e/observer.py index b781f5a07e..4115040669 100644 --- a/torchao/quantization/pt2e/observer.py +++ b/torchao/quantization/pt2e/observer.py @@ -1248,7 +1248,7 @@ def _combine_histograms( # If the orig hist only has one value (i.e., the min and max are the same) # we can just add it into new histogram if orig_min == orig_max: - bin_value = torch.sum(update_hist) + bin_value = torch.sum(orig_hist) transformed_orig_hist = ( torch.histc(orig_min, bins=self.bins, min=update_min, max=update_max) # type: ignore[arg-type] * bin_value From 994a4ba6c869854fcaa6ca7e118fcbd75e6c28cc Mon Sep 17 00:00:00 2001 From: Driss Guessous <32754868+drisspg@users.noreply.github.com> Date: Thu, 26 Jun 2025 12:50:32 -0700 Subject: [PATCH 165/165] Store NVFP4 block scales in swwizzled layout on tensor (#2438) --- test/prototype/mx_formats/test_mx_linear.py | 2 + test/prototype/mx_formats/test_mx_tensor.py | 298 +++++++++++++++++++ torchao/prototype/mx_formats/mx_subclass.py | 7 +- torchao/prototype/mx_formats/nvfp4_tensor.py | 243 ++++++++++++--- torchao/prototype/mx_formats/utils.py | 32 ++ torchao/utils.py | 4 + 6 files changed, 543 insertions(+), 43 deletions(-) diff --git a/test/prototype/mx_formats/test_mx_linear.py b/test/prototype/mx_formats/test_mx_linear.py index 8a69737889..4e24cfc482 100644 --- a/test/prototype/mx_formats/test_mx_linear.py +++ b/test/prototype/mx_formats/test_mx_linear.py @@ -558,11 +558,13 @@ def test_nvfp4_matmul_with_amax( A, per_tensor_scale=a_scale, mm_config=mm_config, + is_swizzled_scales=True, ) B_nvfp4 = NVFP4Tensor.to_nvfp4( B, per_tensor_scale=b_scale, mm_config=mm_config, + is_swizzled_scales=True, ) func = torch.compile(F.linear, fullgraph=True) if compile else F.linear diff --git a/test/prototype/mx_formats/test_mx_tensor.py b/test/prototype/mx_formats/test_mx_tensor.py index 7294590b57..3c4dc7c7b6 100644 --- a/test/prototype/mx_formats/test_mx_tensor.py +++ b/test/prototype/mx_formats/test_mx_tensor.py @@ -657,3 +657,301 @@ def assert_sqnr_gt_threshold(orig, new, threshold): assert x.t().dtype == x_reconstructed_t.dtype, ( f"Transpose dtype mismatch: {x.t().dtype} vs {x_reconstructed_t.dtype}" ) + + +@pytest.mark.parametrize( + "shape", + [ + (128, 4), + (256, 8), + (100, 3), + (4, 4), + (50, 10), + (384, 12), + ], +) +@pytest.mark.parametrize( + "use_triton_kernel", [False, True] if torch.cuda.is_available() else [False] +) +@pytest.mark.skipif( + not TORCH_VERSION_AT_LEAST_2_8, reason="torch.compile requires PyTorch 2.8+" +) +def test_to_blocked_from_blocked_roundtrip(shape, use_triton_kernel: bool): + from torchao.prototype.mx_formats.utils import from_blocked, to_blocked + + rows, cols = shape + device = "cuda" if torch.cuda.is_available() else "cpu" + + original = torch.randint(0, 255, (rows, cols), device=device, dtype=torch.uint8) + + blocked = to_blocked(original, use_triton_kernel=use_triton_kernel) + reconstructed = from_blocked(blocked, rows, cols) + + torch.testing.assert_close( + original, + reconstructed, + atol=0.0, + rtol=0.0, + msg=f"Roundtrip failed for shape {shape} with use_triton_kernel={use_triton_kernel}", + ) + + +@pytest.mark.parametrize("is_swizzled_scales", [False, True]) +@pytest.mark.parametrize( + "shape", + [ + (32, 64), + (16, 32), + (64, 128), + (384, 128), + ], +) +@pytest.mark.skipif( + not TORCH_VERSION_AT_LEAST_2_8, reason="torch.compile requires PyTorch 2.8+" +) +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +def test_nvfp4_swizzled_scales_construction(is_swizzled_scales, shape): + """ + Test that NVFP4Tensor can be constructed with swizzled scales and + that the _is_swizzled_scales flag is set correctly. + """ + from torchao.prototype.mx_formats.nvfp4_tensor import NVFP4Tensor + + M, K = shape + data = torch.randn(M, K, device="cuda", dtype=torch.bfloat16) + + tensor = NVFP4Tensor.to_nvfp4(data, is_swizzled_scales=is_swizzled_scales) + assert tensor._is_swizzled_scales == is_swizzled_scales + reconstructed = tensor.to_dtype(torch.bfloat16) + assert reconstructed.shape == data.shape + + +@pytest.mark.parametrize( + "slice_dim,slice_spec", + [ + # Row slicing - must align with 128-row boundaries + pytest.param(0, slice(0, 128), id="slice_rows[0:128]"), + pytest.param(0, slice(128, 256), id="slice_rows[128:256]"), + # Column slicing - must align with 64-column boundaries (4 scale columns * 16 block_size) + pytest.param(1, slice(0, 64), id="slice_cols[0:64]"), + pytest.param(1, slice(64, 128), id="slice_cols[64:128]"), + pytest.param(1, slice(0, 128), id="slice_cols[0:128]_full_width"), + # Test tensor parallelism patterns (half splits) + pytest.param(1, slice(0, 2048), id="slice_cols[0:2048]_tp_first_half"), + pytest.param(1, slice(2048, 4096), id="slice_cols[2048:4096]_tp_second_half"), + # Test quarter splits + pytest.param(1, slice(0, 1024), id="slice_cols[0:1024]_quarter"), + pytest.param(1, slice(1024, 2048), id="slice_cols[1024:2048]_quarter"), + ], +) +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +@pytest.mark.skipif( + not TORCH_VERSION_AT_LEAST_2_8, reason="NVFP4 requires PyTorch 2.8+" +) +def test_nvfp4_swizzled_scales_slicing(slice_dim, slice_spec): + """ + Test that slicing works correctly with swizzled scales and maintains + the swizzled state in the output tensor. + """ + from torchao.prototype.mx_formats.nvfp4_tensor import NVFP4Tensor + + # Use larger tensor sizes that align with swizzled requirements + if slice_dim == 0: + # For row slicing, need at least 256 rows to test 128-row boundaries + M, K = 256, 4096 + else: + # For column slicing, need multiples of 64 columns for alignment + M, K = 128, 4096 + + data = torch.randn(M, K, device="cuda", dtype=torch.bfloat16) + + tensor = NVFP4Tensor.to_nvfp4(data, is_swizzled_scales=True) + assert tensor._is_swizzled_scales == True + + if slice_dim == 0: + sliced_tensor = tensor[slice_spec, :] + else: + sliced_tensor = tensor[:, slice_spec] + + # Verify sliced tensor maintains swizzled state + assert sliced_tensor._is_swizzled_scales == True + + # Verify sliced tensor can be dequantized + sliced_reconstructed = sliced_tensor.to_dtype(torch.bfloat16) + + # Compare with direct slicing of original data + original_reconstructed = tensor.to_dtype(torch.bfloat16) + if slice_dim == 0: + expected = original_reconstructed[slice_spec, :] + else: + expected = original_reconstructed[:, slice_spec] + + torch.testing.assert_close(sliced_reconstructed, expected, atol=1e-6, rtol=1e-6) + + +@pytest.mark.parametrize( + "slice_dim,slice_spec,expected_error", + [ + # Row slicing with misaligned boundaries + pytest.param( + 0, + slice(0, 100), + "Row slicing of NVFP4Tensor with swizzled scales requires", + id="misaligned_row_end", + ), + pytest.param( + 0, + slice(50, 150), + "Row slicing of NVFP4Tensor with swizzled scales requires", + id="misaligned_row_start", + ), + # Column slicing with misaligned boundaries + pytest.param( + 1, + slice(0, 32), + "Column slicing of NVFP4Tensor with swizzled scales requires", + id="misaligned_col_32", + ), + pytest.param( + 1, + slice(16, 80), + "Column slicing of NVFP4Tensor with swizzled scales requires", + id="misaligned_col_start", + ), + pytest.param( + 1, + slice(0, 100), + "Column slicing of NVFP4Tensor with swizzled scales requires", + id="misaligned_col_end", + ), + # Odd column boundaries (FP4 packing requirement) + pytest.param( + 1, + slice(1, 65), + "start index to be a multiple of 64, got 1", + id="odd_start", + ), + pytest.param( + 1, + slice(0, 65), + " multiple of 64 or equal to tensor size 4096, got 65", + id="odd_end", + ), + ], +) +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +@pytest.mark.skipif( + not TORCH_VERSION_AT_LEAST_2_8, reason="NVFP4 requires PyTorch 2.8+" +) +def test_nvfp4_swizzled_scales_slicing_errors(slice_dim, slice_spec, expected_error): + """ + Test that slicing raises appropriate errors for misaligned boundaries. + """ + from torchao.prototype.mx_formats.nvfp4_tensor import NVFP4Tensor + + M, K = 256, 4096 + data = torch.randn(M, K, device="cuda", dtype=torch.bfloat16) + tensor = NVFP4Tensor.to_nvfp4(data, is_swizzled_scales=True) + + with pytest.raises(RuntimeError, match=expected_error): + if slice_dim == 0: + _ = tensor[slice_spec, :] + else: + _ = tensor[:, slice_spec] + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +@pytest.mark.skipif( + not TORCH_VERSION_AT_LEAST_2_8, reason="NVFP4 requires PyTorch 2.8+" +) +def test_nvfp4_swizzled_scales_view_semantics(): + """ + Test that slicing maintains proper view semantics where possible. + """ + from torchao.prototype.mx_formats.nvfp4_tensor import NVFP4Tensor + + M, K = 256, 4096 + data = torch.randn(M, K, device="cuda", dtype=torch.bfloat16) + tensor = NVFP4Tensor.to_nvfp4(data, is_swizzled_scales=True) + + # Test row slicing (should maintain views) + sliced_tensor = tensor[0:128, :] + + # Test that the sliced tensor shares storage with original for data + # (Note: scales might not share storage due to swizzled layout complexity) + assert sliced_tensor._data.data_ptr() == tensor._data.data_ptr() + + # Test full-width column slicing (should maintain views) + full_width_slice = tensor[:, 0:K] + assert full_width_slice._scale_e4m3.data_ptr() == tensor._scale_e4m3.data_ptr() + assert full_width_slice._data.data_ptr() == tensor._data.data_ptr() + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +@pytest.mark.skipif( + not TORCH_VERSION_AT_LEAST_2_8, reason="NVFP4 requires PyTorch 2.8+" +) +def test_nvfp4_swizzled_scales_serialization(): + """ + Test that tensor flatten/unflatten preserves the swizzled scales state. + """ + from torchao.prototype.mx_formats.nvfp4_tensor import NVFP4Tensor + + M, K = 32, 64 + data = torch.randn(M, K, device="cuda", dtype=torch.bfloat16) + + # Create tensor with swizzled scales + original_tensor = NVFP4Tensor.to_nvfp4(data, is_swizzled_scales=True) + + # Test serialization + tensor_list, ctx = original_tensor.__tensor_flatten__() + + # Verify swizzled flag is preserved in context + assert "_is_swizzled_scales" in ctx + assert ctx["_is_swizzled_scales"] == True + + # Test deserialization + inner_tensors = {} + for name in tensor_list: + inner_tensors[name] = getattr(original_tensor, name) + + reconstructed_tensor = NVFP4Tensor.__tensor_unflatten__( + inner_tensors, ctx, None, None + ) + + # Verify the swizzled state is preserved + assert reconstructed_tensor._is_swizzled_scales == True + + # Verify functionality is preserved + original_dq = original_tensor.to_dtype(torch.bfloat16) + reconstructed_dq = reconstructed_tensor.to_dtype(torch.bfloat16) + + torch.testing.assert_close(original_dq, reconstructed_dq, atol=1e-6, rtol=1e-6) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA not available") +@pytest.mark.skipif( + not TORCH_VERSION_AT_LEAST_2_8, reason="NVFP4 requires PyTorch 2.8+" +) +def test_nvfp4_swizzled_scales_get_scales_method(): + """ + Test that the get_scales() method correctly unswizzles scales when needed. + """ + from torchao.prototype.mx_formats.nvfp4_tensor import NVFP4Tensor + + M, K = 32, 64 + data = torch.randn(M, K, device="cuda", dtype=torch.bfloat16) + + # Create tensors with both storage methods + regular_tensor = NVFP4Tensor.to_nvfp4(data, is_swizzled_scales=False) + swizzled_tensor = NVFP4Tensor.to_nvfp4(data, is_swizzled_scales=True) + + # Get scales from both tensors and verify they are equal + regular_scales = regular_tensor.get_hp_scales() + swizzled_scales = swizzled_tensor.get_hp_scales() + torch.testing.assert_close(regular_scales, swizzled_scales, atol=0.0, rtol=0.0) + + # Verify scales have the expected shape + expected_shape = (M, K // 16) + assert regular_scales.shape == expected_shape + assert swizzled_scales.shape == expected_shape diff --git a/torchao/prototype/mx_formats/mx_subclass.py b/torchao/prototype/mx_formats/mx_subclass.py index d1be8a04f4..e70930cd55 100644 --- a/torchao/prototype/mx_formats/mx_subclass.py +++ b/torchao/prototype/mx_formats/mx_subclass.py @@ -184,6 +184,11 @@ def _nvfp4_inference_linear_transform( weight = module.weight + if weight.shape[0] % 16 != 0 or weight.shape[1] % 16 != 0: + raise RuntimeError( + f"NVFP4 only supports weight shape divisible by 16, got {weight.shape}" + ) + if module.bias is not None and weight.dtype == torch.float32: raise RuntimeError( "Bias is not supported when module weight is in fp32 (out_dtype=Float32). " @@ -193,8 +198,8 @@ def _nvfp4_inference_linear_transform( quantized_weight = NVFP4Tensor.to_nvfp4( weight, mm_config=config.mm_config, + is_swizzled_scales=True, ) - module.weight = torch.nn.Parameter(quantized_weight, requires_grad=False) module.extra_repr = types.MethodType(_linear_extra_repr, module) return module diff --git a/torchao/prototype/mx_formats/nvfp4_tensor.py b/torchao/prototype/mx_formats/nvfp4_tensor.py index ed1b5df1d0..1545b1bc94 100644 --- a/torchao/prototype/mx_formats/nvfp4_tensor.py +++ b/torchao/prototype/mx_formats/nvfp4_tensor.py @@ -4,6 +4,7 @@ # This source code is licensed under the BSD 3-Clause license found in the # LICENSE file in the root directory of this source tree. +import sys from enum import Enum from typing import Any, Callable, Dict, Optional @@ -21,8 +22,8 @@ tensor_size_fp4x2_to_hp, tensor_size_hp_to_fp4x2, ) -from torchao.prototype.mx_formats.utils import to_blocked -from torchao.utils import fill_defaults +from torchao.prototype.mx_formats.utils import from_blocked, to_blocked +from torchao.utils import ceil_div, fill_defaults E4M3_EPS = torch.finfo(torch.float8_e4m3fn).tiny @@ -54,11 +55,12 @@ class NVFP4Tensor(torch.Tensor): quantization algorithm for FP4 data with UE4M3 scales. Attributes: - _scale_e4m3: Blockwise scales in float8_e4m3fn format + _scale_e4m3: Blockwise scales in float8_e4m3fn format (may be swizzled) _per_tensor_scale: Optional global per-tensor scale in float32 format _data: Packed FP4 data (2 values per byte) _block_size: Block size for quantization (fixed at 16) _orig_dtype: Original tensor dtype before quantization + _is_swizzled_scales: Whether scales are stored in swizzled (blocked) format mm_config: Matrix multiplication configuration """ @@ -67,6 +69,7 @@ class NVFP4Tensor(torch.Tensor): _data: torch.Tensor _block_size: int _orig_dtype: torch.dtype + _is_swizzled_scales: bool mm_config: NVFP4MMConfig def __new__( @@ -77,12 +80,14 @@ def __new__( block_size, orig_dtype, mm_config=NVFP4MMConfig.DYNAMIC, + is_swizzled_scales=False, ): - # FP4 tensor size handling + # FP4 tensor size handling two paths, contiguous or not new_size = data_bits.size() + new_size = tensor_size_fp4x2_to_hp( new_size, - data_bits.is_contiguous(), + data_bits.stride(0) > data_bits.stride(1), ) self = torch.Tensor._make_wrapper_subclass( @@ -94,6 +99,7 @@ def __new__( ) self._scale_e4m3 = blockwise_scales + self._is_swizzled_scales = is_swizzled_scales self._per_tensor_scale = per_tensor_scale self._data = data_bits self._block_size = block_size @@ -118,14 +124,17 @@ def to_nvfp4( block_size: int = 16, per_tensor_scale: Optional[torch.Tensor] = None, mm_config: NVFP4MMConfig = NVFP4MMConfig.DYNAMIC, + is_swizzled_scales: bool = False, ): """Convert high precision tensor to NVFP4 format. Args: data_hp: High precision input tensor (bfloat16 or float32) block_size: Block size for quantization (must be 16) - per_tensor_amax: Optional pre-computed absolute maximum for calibration. + per_tensor_scale: Optional pre-computed absolute maximum for calibration. If provided, uses per-tensor scaling. If None, uses block-wise scaling only. + mm_config: Matrix multiplication configuration + is_swizzled_scales: If True, store scales in swizzled format for faster matrix multiplication Returns: NVFP4Tensor: Quantized tensor in NVFP4 format @@ -133,6 +142,12 @@ def to_nvfp4( blockwise_scales, data_lp = nvfp4_quantize( data_hp, block_size, per_tensor_scale ) + + if is_swizzled_scales: + M, K = data_hp.shape[0], data_hp.shape[1] + scale_shape = (M, K // block_size) + blockwise_scales = to_blocked(blockwise_scales.view(scale_shape)).flatten() + return NVFP4Tensor( blockwise_scales, per_tensor_scale, @@ -140,12 +155,14 @@ def to_nvfp4( block_size, data_hp.dtype, mm_config, + is_swizzled_scales, ) def __tensor_flatten__(self): ctx = { "_block_size": self._block_size, "_orig_dtype": self._orig_dtype, + "_is_swizzled_scales": self._is_swizzled_scales, "mm_config": self.mm_config, } tensor_list = ["_scale_e4m3", "_data"] @@ -182,6 +199,7 @@ def __tensor_unflatten__( metadata["_block_size"], metadata["_orig_dtype"], metadata["mm_config"], + metadata.get("_is_swizzled_scales", False), ) # Do not force the NVFP4Tensor type on the returned tensor @@ -196,7 +214,7 @@ def to_dtype(self, target_dtype: torch.dtype) -> torch.Tensor: Returns: torch.Tensor: Dequantized tensor in the target dtype """ - is_transposed = not self._data.is_contiguous() + is_transposed = self._data.stride(0) < self._data.stride(1) if is_transposed: M, K = self.shape[1], self.shape[0] else: @@ -221,10 +239,21 @@ def get_hp_scales(self) -> torch.Tensor: Returns: torch.Tensor: Scales of the NVFP4Tensor """ + is_transposed = self._data.stride(0) < self._data.stride(1) + if is_transposed: + M, K = self.shape[1], self.shape[0] + else: + M, K = self.shape[0], self.shape[1] + + if self._is_swizzled_scales: + scale_e4m3 = from_blocked(self._scale_e4m3, M, K // self._block_size) + else: + scale_e4m3 = self._scale_e4m3 + return ( - self._scale_e4m3.to(self._orig_dtype) + scale_e4m3.to(self._orig_dtype) if not self._per_tensor_scale - else self._per_tensor_scale * self._scale_e4m3.to(self._orig_dtype) + else self._per_tensor_scale * scale_e4m3.to(self._orig_dtype) ) @classmethod @@ -238,7 +267,6 @@ def _same_metadata(cls, self: "NVFP4Tensor", src: "NVFP4Tensor") -> bool: Returns: bool: True if both tensors have identical metadata, False otherwise """ - # Check per_tensor_scale equality per_tensor_scale_equal = ( self._per_tensor_scale is None and src._per_tensor_scale is None ) or (self._per_tensor_scale.shape == src._per_tensor_scale.shape) @@ -248,6 +276,7 @@ def _same_metadata(cls, self: "NVFP4Tensor", src: "NVFP4Tensor") -> bool: and isinstance(src, NVFP4Tensor) and self._block_size == src._block_size and self._orig_dtype == src._orig_dtype + and self._is_swizzled_scales == src._is_swizzled_scales and self._scale_e4m3.shape == src._scale_e4m3.shape and per_tensor_scale_equal and self._data.shape == src._data.shape @@ -292,6 +321,7 @@ def nvfp4_to_copy(func, types, args, kwargs): tensor._block_size, dtype, tensor.mm_config, + tensor._is_swizzled_scales, ) return res @@ -335,46 +365,166 @@ def nvfp4_slice(func, types, args, kwargs): assert x._data.is_contiguous(), "Only support contiguous data for now" M, K = x.shape[0], x.shape[1] - scale_shaped = x._scale_e4m3.view(M, K // x._block_size) - - if dim == 0: - # Slicing along the first dimension (rows) - sliced_scale = aten.slice.Tensor(scale_shaped, dim, start, end, step).flatten() - sliced_data = aten.slice.Tensor(x._data, dim, start, end, step) - elif dim == 1: - # Slicing along reduction dim - must align with block boundaries - if start is not None: - assert start % x._block_size == 0, ( - f"Start index {start} must be a multiple of block_size {x._block_size}" - ) - if end is not None: - assert end % x._block_size == 0, ( - f"End index {end} must be a multiple of block_size {x._block_size}" + if x._is_swizzled_scales: + scale_rows = M + scale_cols = K // x._block_size + n_row_blocks = ceil_div(scale_rows, 128) + n_col_blocks = ceil_div(scale_cols, 4) + elements_per_block = 32 * 16 # 512 elements + + if dim == 0: + # Row slicing + # Handle sys.maxsize (default slice end) + if end == sys.maxsize: + end = M + + # Check if start/end align with 128-row boundaries + if start is not None and start % 128 != 0: + raise RuntimeError( + f"Row slicing of NVFP4Tensor with swizzled scales requires " + f"start index to be a multiple of 128, got {start}" + ) + if end is not None and end != M and end % 128 != 0: + raise RuntimeError( + f"Row slicing of NVFP4Tensor with swizzled scales requires " + f"end index to be a multiple of 128 or equal to tensor size {M}, got {end}" + ) + + # Calculate which row blocks to keep + start_block = 0 if start is None else start // 128 + end_block = n_row_blocks if end is None or end >= M else end // 128 + + # The swizzled tensor has shape (n_row_blocks * n_col_blocks * 32 * 16,) + blocks_per_row = n_col_blocks + start_idx = start_block * blocks_per_row * elements_per_block + end_idx = ( + end_block * blocks_per_row * elements_per_block + if end_block < n_row_blocks + else None ) - sliced_data = aten.slice.Tensor(x._data, dim, start, end, step) + sliced_scale = aten.slice.Tensor(x._scale_e4m3, 0, start_idx, end_idx, 1) + sliced_data = aten.slice.Tensor(x._data, 0, start, end, step) + + elif dim == 1: + # Column slicing + # Handle sys.maxsize (default slice end) + if end == sys.maxsize: + end = K + + # Check if start/end align with 64-column boundaries (4 scale columns * 16 block_size) + if start is not None and start % 64 != 0: + raise RuntimeError( + f"Column slicing of NVFP4Tensor with swizzled scales requires " + f"start index to be a multiple of 64, got {start}" + ) + if end is not None and end != K and end % 64 != 0: + raise RuntimeError( + f"Column slicing of NVFP4Tensor with swizzled scales requires " + f"end index to be a multiple of 64 or equal to tensor size {K}, got {end}" + ) + + # Also check FP4 packing alignment + if start is not None and start % 2 != 0: + raise RuntimeError(f"Start index {start} must be even for FP4 packing") + if end is not None and end != K and end % 2 != 0: + raise RuntimeError(f"End index {end} must be even for FP4 packing") + + # Calculate which column blocks to keep + start_scale_col = 0 if start is None else start // 16 + end_scale_col = scale_cols if end is None or end >= K else end // 16 + + start_col_block = start_scale_col // 4 + end_col_block = end_scale_col // 4 + + # Verify the end aligns with block boundary + if end_scale_col % 4 != 0: + raise RuntimeError( + f"Column slicing end index {end} does not align with scale block boundaries. " + f"End must result in a multiple of 4 scale columns (64 data columns)." + ) + + if start_col_block == 0 and end_col_block == n_col_blocks: + # Full width - no slicing needed + sliced_scale = x._scale_e4m3 + else: + # Extract specific column blocks from each row block + # Each row block in swizzled format contains n_col_blocks chunks of (32, 16) + elements_per_row_block = n_col_blocks * elements_per_block + + # Build list of slices to extract + slices_to_extract = [] + for row_block in range(n_row_blocks): + row_start = row_block * elements_per_row_block + col_start = row_start + start_col_block * elements_per_block + col_end = row_start + end_col_block * elements_per_block + slices_to_extract.append(x._scale_e4m3[col_start:col_end]) + + # Concatenate all the slices + sliced_scale = torch.cat(slices_to_extract, dim=0) + + # Slice the data tensor + packed_start = None if start is None else start // 2 + packed_end = None if end is None else end // 2 + sliced_data = aten.slice.Tensor( + x._data, dim, packed_start, packed_end, step + ) - # Calculate which scale blocks to keep - start_block = 0 if start is None else start // x._block_size - end_block = None if end is None else end // x._block_size + else: + raise ValueError( + f"NVFP4Tensor only supports slicing along dimensions 0 and 1, got dim={dim}" + ) - # Slice the scale tensor accordingly - sliced_scale = aten.slice.Tensor(scale_shaped, 1, start_block, end_block, step) else: - raise ValueError( - f"NVFP4Tensor only supports slicing along dimensions 0 and 1, got dim={dim}" - ) + scale_shaped = x._scale_e4m3.view(M, K // x._block_size) + + if dim == 0: + sliced_scale = aten.slice.Tensor(scale_shaped, dim, start, end, step) + sliced_data = aten.slice.Tensor(x._data, dim, start, end, step) + + elif dim == 1: + if start is not None: + assert start % x._block_size == 0, ( + f"Start index {start} must be a multiple of block_size {x._block_size}" + ) + assert start % 2 == 0, ( + f"Start index {start} must be even for FP4 packing" + ) + + if end is not None and end != sys.maxsize: + assert end % x._block_size == 0, ( + f"End index {end} must be a multiple of block_size {x._block_size}" + ) + assert end % 2 == 0, f"End index {end} must be even for FP4 packing" + + packed_start = None if start is None else start // 2 + packed_end = None if end is None else end // 2 + sliced_data = aten.slice.Tensor( + x._data, dim, packed_start, packed_end, step + ) - return NVFP4Tensor( + start_block = 0 if start is None else start // x._block_size + end_block = None if end is None else end // x._block_size + sliced_scale = aten.slice.Tensor( + scale_shaped, 1, start_block, end_block, step + ) + + sliced_scale = sliced_scale.flatten() + + # Create result tensor + result = NVFP4Tensor( sliced_scale, - x._per_tensor_scale, # Unchanged per-tensor scale + x._per_tensor_scale, sliced_data, x._block_size, x._orig_dtype, x.mm_config, + x._is_swizzled_scales, ) + return return_and_correct_aliasing(func, args, kwargs, result) + @implements([aten.t.default]) def nvfp4_t(func, types, args, kwargs): @@ -387,6 +537,7 @@ def nvfp4_t(func, types, args, kwargs): old._block_size, old._orig_dtype, old.mm_config, + old._is_swizzled_scales, ) return new @@ -404,6 +555,7 @@ def nvfp4_view_op(func, types, args, kwargs): args[0]._block_size, args[0]._orig_dtype, args[0].mm_config, + args[0]._is_swizzled_scales, ) @@ -423,10 +575,17 @@ def _addmm_nvfp4_dispatch( N = b.shape[1] # Swizzle Dizzle - a_scale = a._scale_e4m3.view(M, K // a._block_size) - b_scale = b._scale_e4m3.view(N, K // b._block_size) - a_scale_blocked = to_blocked(a_scale) - b_scale_blocked = to_blocked(b_scale) + if a._is_swizzled_scales: + a_scale_blocked = a._scale_e4m3 # Already swizzled + else: + a_scale = a._scale_e4m3.view(M, K // a._block_size) + a_scale_blocked = to_blocked(a_scale) + + if b._is_swizzled_scales: + b_scale_blocked = b._scale_e4m3 # Already swizzled + else: + b_scale = b._scale_e4m3.view(N, K // b._block_size) + b_scale_blocked = to_blocked(b_scale) # Merge double quant scales into 1 scale for Scale_In^D if a._per_tensor_scale is not None: @@ -571,8 +730,8 @@ def nvfp4_quantize( assert data_hp.dtype in (torch.bfloat16, torch.float), ( f"{data_hp.dtype} not supported" ) - assert data_hp.numel() % block_size == 0, "unsupported" - assert data_hp.is_contiguous(), "unsupported" + assert data_hp.size(-1) % block_size == 0, "K dim must be divisible by block_size" + assert data_hp.is_contiguous(), "Only support contiguous data for now" assert block_size == 16, "NVFP4 requires block_size=16" orig_shape = data_hp.shape diff --git a/torchao/prototype/mx_formats/utils.py b/torchao/prototype/mx_formats/utils.py index e4777d3899..1a48dd4592 100644 --- a/torchao/prototype/mx_formats/utils.py +++ b/torchao/prototype/mx_formats/utils.py @@ -58,6 +58,38 @@ def to_blocked(input_matrix, use_triton_kernel: bool = True) -> Tensor: return rearranged.flatten() +def from_blocked( + blocked_tensor: Tensor, original_rows: int, original_cols: int +) -> Tensor: + """ + Inverse of to_blocked: convert from blocked layout back to regular row-major layout. + + Args: + blocked_tensor: Flattened blocked tensor from to_blocked() + original_rows: Original number of rows before blocking + original_cols: Original number of columns before blocking + + Returns: + Tensor of shape (original_rows, original_cols) in regular layout + """ + n_row_blocks = ceil_div(original_rows, 128) + n_col_blocks = ceil_div(original_cols, 4) + + rearranged = blocked_tensor.view(n_row_blocks * n_col_blocks, 32, 16) + + temp = rearranged.reshape(n_row_blocks * n_col_blocks, 32, 4, 4) + + temp = temp.transpose(1, 2) + + blocks = temp.reshape(n_row_blocks, n_col_blocks, 128, 4) + + padded_view = blocks.permute(0, 2, 1, 3) + + padded = padded_view.reshape(n_row_blocks * 128, n_col_blocks * 4) + + return padded[:original_rows, :original_cols] + + def _to_blocked_single(scales: Tensor) -> Tensor: """Assume that we have a 128x4 block of scales in K Major order diff --git a/torchao/utils.py b/torchao/utils.py index 1a12fb0668..677bad2718 100644 --- a/torchao/utils.py +++ b/torchao/utils.py @@ -705,6 +705,10 @@ def check_xpu_version(device, version="2.8.0"): return device == "xpu" and compare_versions(torch.__version__, version) >= 0 +def ceil_div(a, b): + return (a + b - 1) // b + + TORCH_VERSION_AFTER_2_5 = _torch_version_at_least("2.5.0.dev") TORCH_VERSION_AFTER_2_4 = _torch_version_at_least("2.4.0.dev") TORCH_VERSION_AFTER_2_3 = _torch_version_at_least("2.3.0.dev")

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+ Alternative Legacy API + +The above `quantize_` API is the recommended flow for using TorchAO QAT. +We also offer an alternative legacy "quantizer" API for specific quantization +schemes, but these are not customizable unlike the above example. + +.. code:: + + from torchao.quantization.qat import Int8DynActInt4WeightQATQuantizer + qat_quantizer = Int8DynActInt4WeightQATQuantizer(group_size=32) + + # prepare: insert fake quantization ops + # swaps `torch.nn.Linear` with `Int8DynActInt4WeightQATLinear` + model = qat_quantizer.prepare(model) + + # train + train_loop(model) + + # convert: transform fake quantization ops into actual quantized ops + # swaps `Int8DynActInt4WeightQATLinear` with `Int8DynActInt4WeightLinear` + model = qat_quantizer.convert(model) + +.. raw:: html + +

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