8000 RNN sequence padding with batch_first · Issue #1176 · pytorch/pytorch · GitHub
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MaximumEntropy opened this issue Apr 3, 2017 · 2 comments
Closed

RNN sequence padding with batch_first #1176

MaximumEntropy opened this issue Apr 3, 2017 · 2 comments

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@MaximumEntropy
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I noticed that using batch_first with pack_padded_Sequence and pad_packed_sequence with an LSTM/GRU doesn't give me the expected output shape.

import torch.nn as nn
from torch.autograd import Variable
from torch.nn.utils.rnn import pad_packed_sequence, pack_padded_sequence

x = Variable(torch.randn(10, 20, 30))
lens = range(20)
lens = [z + 1 for z in lens]
lens = lens[::-1][:10]
x = pack_padded_sequence(x, lens, batch_first=True)

lstm = nn.LSTM(30, 50, batch_first=True)
h0 = Variable(torch.zeros(1, 10, 50))
c0 = Variable(torch.zeros(1, 10, 50))

packed_h, (packed_h_t, packed_c_t) = lstm(x, (h0, c0))
h, _ = pad_packed_sequence(packed_h)
print h.size() # Size 20 x 10 x 50 instead of 10 x 20 x 50
@apaszke
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apaszke commented Apr 3, 2017

pad_packed_sequence(packed_h, batch_first=True) does what you want.

@apaszke apaszke closed this as completed Apr 3, 2017
@MaximumEntropy
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MaximumEntropy commented Apr 3, 2017 via email

jjsjann123 added a commit to jjsjann123/pytorch that referenced this issue Nov 5, 2021
@jjsjann123
range-based for loop (pytorch#1176)
b4b1954 
code cleaning for clang-tidy
@jansel jansel mentioned this issue Feb 1, 2023
Closed
72 tasks
akashveramd pushed a commit to akashveramd/pytorch that referenced this issue Apr 9, 2025
@zjing14 @aska-0096
@samjwu @rocking5566 @geyyer @carlushuang @junliume @illsilin
Navi3 rel (pytorch#1176)
1837040 
* wmma_op + unit test

* add arch limitation to wmma test

* change arch limitation

* Refactor + Add all type unit test(int4 compile failed)

* Add f32_16x16x16_bf16 unit test

* tempsave

* tempsave

* tempsave

* runtime bug, cannot find symbol

* workaround for incorrect HIP warpSize return value

* debugging

* tempsave

* Correctness OK, waiting for optimization

* Tidy up + format

* temp save

* temp save, reproduce the v_bfi_b32 issue

* add inline asm for wmmaop test

* tidy up

* clean some debug purpose code

* discard some codes

* clang format

* clang format

* compiler issue fixed + increase tile size

* navi3x_multipleD+example

* temp save

* workable

* batchedgemm[OK], groupconv[debug]

* groupconv: Sanity check[OK], Performance[Bad]

* navi3x_groupconv_need_optimization

* create necessary files

* save progress

* Add Inter-Row thread transfer

* save progress

* save debugging progress

* sanity check pass

* fix a host tensor bug and clean up flash-attn code

* format

* cancel unnecessary change

* cancel unnecessary change

* cancel unnecessary change

* temp save, add asm backend flag to amd_wmma

* Mat-A LDS Bypass sanity pass

* temp save

* gemm sanity fix

* Porting new blockwise gemm to flash attention

* Example branch provide to compiler team

* tempsave

* Fix a bug

* batched gemm ported

* conv A-skip lds ported

* Skip B-Lds real gemm

* Skip B Lds Gemm + MulD

* batched gemm, conv, skip b lds

* format

* Attn, skip b lds

* Change GridwiseOp nam

* fix a typo caused bug

* Skip A_Lds sanity pass, Skip B_Lds scratch occured

* Bug found, intra-row permute off caused

* bug found

* a fix

* disable buffer load due to incorrect 3rd dword

* update fmha config, no scratch generated

* update 3rd dword

* fmha config update

* FMHA, add support to gfx1101/gfx1102

* Merge origin dev (pytorch#2)

* [Navi3x] Fix Gridwise_multiple_d operation (pytorch#649)

* Add CMake Option "USE_OPT_NAVI3X"

* fix bug

* standardize docs (pytorch#655)

* Separate bibtex requirement from rocm-docs-core (pytorch#656)

* separate bibtex requirement from rocm-docs-core

* point requirements to source rocm-docs-core repo

* Add CMake Option "USE_OPT_NAVI3X" (pytorch#647)

* Add CMake Option "USE_OPT_NAVI3X"

* remove navi3x opt compile option from cmake script

* Conv + quantization + tanh  (pytorch#645)

* Rename file. Prepare to support another activation

* Add comment for quantization

* Extract out_elementop

* Add tanh example

* Add conv + bias + tanh quantization instance

* Add missing parameter

* Refine cmake

* Add external api and client example

* Extract variable in example

* Fix the comment

---------

Co-authored-by: zjing14 <zhangjing14@gmail.com>

* Add a denorm test fix (pytorch#603)

* Add type_convert implementations for bf16

* Add the fix for conv_fwd

* Add the fix for conv_bwd_data

* Add the fix for conv_bwd_weight

* Format

* Format

* Another format

* Add a macro to use workaround on MI200 only

* Format

---------

Co-authored-by: Rosty Geyyer <rosty.geyyer@amd.com>
Co-authored-by: zjing14 <zhangjing14@gmail.com>

* simplify karg in device/grid of split-k op (pytorch#644)

* simplify karg in device/grid split-k op

* fix mk_kn_mn instances

* add more instances

* use name from tensor layout

* fix 3rd dword of buffer source descriptor (pytorch#659)

* add fp64 instances (pytorch#658)

Co-authored-by: root <root@ctr-ubbsmc15.amd.com>

* Issue pytorch#666: Revert "simplify karg in device/grid of split-k op (pytorch#644)" (pytorch#665)

This reverts commit bb5530a.

* Groupnorm + swish external api (pytorch#668)

* Rename to proper naming

* Add example of groupnorm + swish

* Extract duplicate code in example

* Add groupnorm + swish instances

* Ractor instance generation, split into multiple cpp file

* Add external api and client example

* Refine profiler message

* Use ck math version of exp

* Refine problem size in example

* Add host version of exp

* add a marco to turn on/off denorm fix (off by default) (pytorch#673)

* add a marco to turn off denorm fix by default

* expose the marco

---------

Co-authored-by: root <root@ctr-ubbsmc15.amd.com>

* fixed quant example (pytorch#672)

Co-authored-by: root <root@ctr-ubbsmc15.amd.com>

* Add dependabot config and pin rocm-docs-core (pytorch#663)

* [gtest] suppress unsafe buffer warn (pytorch#670)

ref: ROCm/MIOpen#1912

* Add memory index guard in wmma device ops (pytorch#667)

* Add more macros to turn on/off denorm fix (pytorch#678)

Co-authored-by: Rosty Geyyer <rosty.geyyer@amd.com>

* Fix a typo (pytorch#676)

* Add (pytorch#677)

* Allow using ROCm release candidate compilers. (pytorch#679)

* enable use of rocm5.5 release candidate 4

* upgrade to ROCM5.5 RC5

* try fix the PUB_KEY error, remove the cmake-data package

* upgrade to latest cmake version

* use private dockerhub repo for rocm5.5 rc5

* add missing bracket

* add vector load check

* solve conflicts

---------

Co-authored-by: Sam Wu <sjwu@ualberta.ca>
Co-authored-by: Sam Wu <sam.wu2@amd.com>
Co-authored-by: rocking5566 <ChunYu.Lai@amd.com>
Co-authored-by: zjing14 <zhangjing14@gmail.com>
Co-authored-by: Rostyslav Geyyer <46627076+geyyer@users.noreply.github.com>
Co-authored-by: Rosty Geyyer <rosty.geyyer@amd.com>
Co-authored-by: carlushuang <carlus.huang@amd.com>
Co-authored-by: root <root@ctr-ubbsmc15.amd.com>
Co-authored-by: Jun Liu <Liu.Jun@amd.com>
Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>

* Disable SkipLDS & Align AIT api (pytorch#3)

* fix layernorm, reduction Ops (pytorch#4)

* [Navi3x] Fix Gridwise_multiple_d operation (pytorch#649)

* Add CMake Option "USE_OPT_NAVI3X"

* fix bug

* standardize docs (pytorch#655)

* Separate bibtex requirement from rocm-docs-core (pytorch#656)

* separate bibtex requirement from rocm-docs-core

* point requirements to source rocm-docs-core repo

* Add CMake Option "USE_OPT_NAVI3X" (pytorch#647)

* Add CMake Option "USE_OPT_NAVI3X"

* remove navi3x opt compile option from cmake script

* Conv + quantization + tanh  (pytorch#645)

* Rename file. Prepare to support another activation

* Add comment for quantization

* Extract out_elementop

* Add tanh example

* Add conv + bias + tanh quantization instance

* Add missing parameter

* Refine cmake

* Add external api and client example

* Extract variable in example

* Fix the comment

---------

Co-authored-by: zjing14 <zhangjing14@gmail.com>

* Add a denorm test fix (pytorch#603)

* Add type_convert implementations for bf16

* Add the fix for conv_fwd

* Add the fix for conv_bwd_data

* Add the fix for conv_bwd_weight

* Format

* Format

* Another format

* Add a macro to use workaround on MI200 only

* Format

---------

Co-authored-by: Rosty Geyyer <rosty.geyyer@amd.com>
Co-authored-by: zjing14 <zhangjing14@gmail.com>

* simplify karg in device/grid of split-k op (pytorch#644)

* simplify karg in device/grid split-k op

* fix mk_kn_mn instances

* add more instances

* use name from tensor layout

* fix 3rd dword of buffer source descriptor (pytorch#659)

* add fp64 instances (pytorch#658)

Co-authored-by: root <root@ctr-ubbsmc15.amd.com>

* Issue pytorch#666: Revert "simplify karg in device/grid of split-k op (pytorch#644)" (pytorch#665)

This reverts commit bb5530a.

* Groupnorm + swish external api (pytorch#668)

* Rename to proper naming

* Add example of groupnorm + swish

* Extract duplicate code in example

* Add groupnorm + swish instances

* Ractor instance generation, split into multiple cpp file

* Add external api and client example

* Refine profiler message

* Use ck math version of exp

* Refine problem size in example

* Add host version of exp

* add a marco to turn on/off denorm fix (off by default) (pytorch#673)

* add a marco to turn off denorm fix by default

* expose the marco

---------

Co-authored-by: root <root@ctr-ubbsmc15.amd.com>

* fixed quant example (pytorch#672)

Co-authored-by: root <root@ctr-ubbsmc15.amd.com>

* Add dependabot config and pin rocm-docs-core (pytorch#663)

* [gtest] suppress unsafe buffer warn (pytorch#670)

ref: ROCm/MIOpen#1912

* Add memory index guard in wmma device ops (pytorch#667)

* Add more macros to turn on/off denorm fix (pytorch#678)

Co-authored-by: Rosty Geyyer <rosty.geyyer@amd.com>

* Fix a typo (pytorch#676)

* Add (pytorch#677)

* Allow using ROCm release candidate compilers. (pytorch#679)

* enable use of rocm5.5 release candidate 4

* upgrade to ROCM5.5 RC5

* try fix the PUB_KEY error, remove the cmake-data package

* upgrade to latest cmake version

* use private dockerhub repo for rocm5.5 rc5

* add missing bracket

* Disable SkipLDS & Align AIT api

* Update dependabot config (pytorch#682)

Co-authored-by: samjwu <samjwu@users.noreply.github.com>

* update attn api

* solve type_convert bug + enable

---------

Co-authored-by: Sam Wu <sjwu@ualberta.ca>
Co-authored-by: Sam Wu <sam.wu2@amd.com>
Co-authored-by: rocking5566 <ChunYu.Lai@amd.com>
Co-authored-by: zjing14 <zhangjing14@gmail.com>
Co-authored-by: Rostyslav Geyyer <46627076+geyyer@users.noreply.github.com>
Co-authored-by: Rosty Geyyer <rosty.geyyer@amd.com>
Co-authored-by: carlushuang <carlus.huang@amd.com>
Co-authored-by: root <root@ctr-ubbsmc15.amd.com>
Co-authored-by: Jun Liu <Liu.Jun@amd.com>
Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
Co-authored-by: samjwu <samjwu@users.noreply.github.com>
Co-authored-by: haocwang <Haocong.WANG@amd.com>

* fix typo

* Fix attention with causal mask

* multiple fix, try ait compile

* Add A/B not use LDS pipeline

* Clang format, Add gfx1101, gfx1102 support of FMHA example

* cancel change of format script

* 1. Enable 2-stage global Prefetch ( May cause VGPR spilling)
2. Enable FP16 accumulator blockwise_gemm

* clang-format

* 1. change blockwise gemm loopover direction from kmn to mnk ( ~1% improvement)
2. change kernel timing mode to 50 warmup + 50 timed repeat

* Update low level abstration of blockwise gemm wmma

* (2/5) bilinear gemm pass, perf bug: skip a lds has lower performance than skip b lds

* (3/5) batched gemm pass, perf bug: skip a lds has lower performance than skip b lds

* (4/5) grouped conv pass

* (5/5) attention pass, todo: debug lds perf bug

* AIT Attention API refactor (pytorch#8)

* sanity pass

* sanity pass 2

* confirm significant performance regression.

* turn on all instances

* turn off instance format

* Fix bug & tunning & format

* DML meta, self_attn+cross_attn

* sanity pass

* remove useless flag

* update tile and problem size used in AIT attention

* bug fix in grouped conv supporting check

* deprecate inline asm wmma

* Bug fix: double lds skip

* clang-format

* Fix errors in
1. example, fmha
2. gridwise pipeline
3. deviceop, fmha, change some containers from vector to array

* part2 of previous commit

* clang format

* API fix of gridwisegemmpipeline

* separate array base and vector base attention tensor transformation

* fix gemm

* clang format

* add gemm fp16 instances

* Temp save

* fpAintB kernel compile pass

* Sanity pass.

* Temp save

* debug code enabled

* Fp16AInt8B_GEMM sanity

* MQA implementation

* GQA-4 example

* tempsave

* Compile pass

* New implementation of fp16Aint8B Gemm, Acheieve similar math throughput with native fp16 Gemm

* format

* Todo: fix gemm_bilinear_wmma instances compilation bug

* Solve a bug when K1=16

* remove unnecessary changes

* Remove tensor layout limitation to LDS usage in tesnor contraction

* update self-attention and cross-attention

* fix a typo of name

* Add arch limiter for fp8 gemm

* enable fp8 gemm_xdl for all gfx9 targets

* temporarily disable gemm_xdl_fp16_fp8 on MI100/200

* fix the cmake logic for gemm_xdl_fp16_fp8

* re-enable the gemm_xdl_fp16_fp8 on MI100/200

---------

Co-authored-by: aska-0096 <haocwang@amd.com>
Co-authored-by: Sam Wu <sjwu@ualberta.ca>
Co-authored-by: Sam Wu <sam.wu2@amd.com>
Co-authored-by: rocking5566 <ChunYu.Lai@amd.com>
Co-authored-by: Rostyslav Geyyer <46627076+geyyer@users.noreply.github.com>
Co-authored-by: Rosty Geyyer <rosty.geyyer@amd.com>
Co-authored-by: carlushuang <carlus.huang@amd.com>
Co-authored-by: root <root@ctr-ubbsmc15.amd.com>
Co-authored-by: Jun Liu <Liu.Jun@amd.com>
Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
Co-authored-by: samjwu <samjwu@users.noreply.github.com>
Co-authored-by: haocwang <Haocong.WANG@amd.com>
Co-authored-by: illsilin <Illia.Silin@amd.com>
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@apaszke @MaximumEntropy
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