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Description
🐛 Describe the bug
Since I upgraded torch from 1.13.0+cu117 to 2.0.0+cu117, the following code isn't logging nor printing the stack trace.
import torch
from torch.profiler import profile, record_function
a = torch.rand(100, 100)
b = torch.rand(100, 100)
with profile(with_stack=True, profile_memory=True) as prof:
with record_function("model_inference"):
torch.add(a, b)
prof.export_stacks("myFile", "self_cpu_time_total")
print(prof.key_averages(group_by_stack_n=5))for 1.13.0+cu117 the file is logged correctly:
test_prof.py(8):_<module>;torch/autograd/profiler.py(478):___init__;<built-in_method_zeros_of_type_object_at_0x7f55776329c0> 45
test_prof.py(8):_<module>;torch/autograd/profiler.py(478):___init__;<built-in_method_zeros_of_type_object_at_0x7f55776329c0> 30
test_prof.py(8):_<module>;torch/autograd/profiler.py(478):___init__;<built-in_method_zeros_of_type_object_at_0x7f55776329c0> 5
test_prof.py(8):_<module>;torch/autograd/profiler.py(487):___enter__;torch/_ops.py(437):___call__;<built-in_method__record_function_enter_of_PyCapsule_object_at_0x7f549a9189f0> 143
test_prof.py(8):_<module>;torch/autograd/profiler.py(487):___enter__;torch/_ops.py(437):___call__;<built-in_method__record_function_enter_of_PyCapsule_object_at_0x7f549a9189f0> 3
test_prof.py(8):_<module>;<built-in_method_add_of_type_object_at_0x7f55776329c0> 85
test_prof.py(8):_<module>;torch/profiler/profiler.py(475):___exit__;torch/profiler/profiler.py(484):_stop;torch/profiler/profiler.py(511):__transit_action;torch/profiler/profiler.py(117):_stop_trace;torch/autograd/profiler.py(223):___exit__;torch/cuda/__init__.py(556):_synchronize;torch/cuda/__init__.py(201):__lazy_init;<built-in_function__cuda_init> 1269
test_prof.py(8):_<module>;torch/profiler/profiler.py(475):___exit__;torch/profiler/profiler.py(484):_stop;torch/profiler/profiler.py(511):__transit_action;torch/profiler/profiler.py(117):_stop_trace;torch/autograd/profiler.py(223):___exit__;torch/cuda/__init__.py(556):_synchronize;torch/cuda/__init__.py(201):__lazy_init;torch/cuda/__init__.py(108):__check_capability;torch/cuda/__init__.py(344):_get_device_capability;torch/cuda/__init__.py(361):_get_device_properties;<built-in_method__get_device_properties_of_PyCapsule_object_at_0x7f5597a3d8d0> 175
test_prof.py(8):_<module>;torch/profiler/profiler.py(475):___exit__;torch/profiler/profiler.py(484):_stop;torch/profiler/profiler.py(511):__transit_action;torch/profiler/profiler.py(117):_stop_trace;torch/autograd/profiler.py(223):___exit__;torch/cuda/__init__.py(556):_synchronize;<built-in_function__cuda_synchronize> 252937
for 2.0.0+117cu however, I get an empty file. Is there something I'm missing?
Versions
For the env with torch 1.13.0
Collecting environment information...
PyTorch version: 1.13.0+cu117
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A
OS: Ubuntu 18.04.6 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: Could not collect
CMake version: version 3.10.2
Libc version: glibc-2.27
Python version: 3.9.15 (main, Nov 24 2022, 14:31:59) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.4.0-147-generic-x86_64-with-glibc2.27
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce GTX 1650
Nvidia driver version: 525.105.17
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.8.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.8.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.8.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.8.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.8.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.8.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.8.1
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn.so.8
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_adv_train.so.8
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_ops_train.so.8
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
CPU(s): 16
On-line CPU(s) list: 0-15
Thread(s) per core: 2
Core(s) per socket: 8
Socket(s): 1
NUMA node(s): 1
Vendor ID: GenuineIntel
CPU family: 6
Model: 158
Model name: Intel(R) Core(TM) i9-9980HK CPU @ 2.40GHz
Stepping: 13
CPU MHz: 964.671
CPU max MHz: 5000.0000
CPU min MHz: 800.0000
BogoMIPS: 4800.00
Virtualisation: VT-x
L1d cache: 32K
L1i cache: 32K
L2 cache: 256K
L3 cache: 16384K
NUMA node0 CPU(s): 0-15
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx rdseed adx smap clflushopt intel_pt xsaveopt xsavec xgetbv1 xsaves dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp md_clear flush_l1d arch_capabilities
Versions of relevant libraries:
[pip3] numpy==1.23.5
[pip3] torch==1.13.0
[pip3] torchaudio==0.13.0
[pip3] torchvision==0.14.0
[conda] cudatoolkit 11.0.3 h88f8997_10 conda-forge
[conda] numpy 1.23.5 pypi_0 pypi
[conda] torch 1.13.0 pypi_0 pypi
[conda] torchaudio 0.13.0 pypi_0 pypi
[conda] torchvision 0.14.0 pypi_0 pypi
For torch 2.0.0:
Collecting environment information...
PyTorch version: 2.0.0+cu117
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A
OS: Ubuntu 18.04.6 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: Could not collect
CMake version: version 3.26.3
Libc version: glibc-2.27
Python version: 3.9.16 (main, Mar 8 2023, 14:00:05) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.4.0-147-generic-x86_64-with-glibc2.27
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce GTX 1650
Nvidia driver version: 525.105.17
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.8.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.8.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.8.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.8.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.8.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.8.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.8.1
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn.so.8
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_adv_train.so.8
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudnn_ops_train.so.8
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
CPU(s): 16
On-line CPU(s) list: 0-15
Thread(s) per core: 2
Core(s) per socket: 8
Socket(s): 1
NUMA node(s): 1
Vendor ID: GenuineIntel
CPU family: 6
Model: 158
Model name: Intel(R) Core(TM) i9-9980HK CPU @ 2.40GHz
Stepping: 13
CPU MHz: 1672.804
CPU max MHz: 5000.0000
CPU min MHz: 800.0000
BogoMIPS: 4800.00
Virtualisation: VT-x
L1d cache: 32K
L1i cache: 32K
L2 cache: 256K
L3 cache: 16384K
NUMA node0 CPU(s): 0-15
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx rdseed adx smap clflushopt intel_pt xsaveopt xsavec xgetbv1 xsaves dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp md_clear flush_l1d arch_capabilities
Versions of relevant libraries:
[pip3] numpy==1.24.3
[pip3] torch==2.0.0
[pip3] torchvision==0.15.1
[pip3] triton==2.0.0
[conda] numpy 1.24.3 pypi_0 pypi
[conda] torch 2.0.0 pypi_0 pypi
[conda] torchvision 0.15.1 pypi_0 pypi
[conda] triton 2.0.0 pypi_0 pypi
cc @ezyang @gchanan @zou3519 @robieta @chaekit @aaronenyeshi @ngimel @nbcsm @guotuofeng @guyang3532 @gaoteng-git @tiffzhaofb @dzhulgakov @davidberard98