pytorch
d5ca53c9 - Performance fix for torch.cat operator on ROCm (#46097)
Commit
3 years agoPerformance fix for torch.cat operator on ROCm (#46097)
Summary:
This pull request is a partial revert of https://github.com/pytorch/pytorch/pull/44833 for ROCm to fix the performance of the concatenate operator. The changes only affect execution on ROCm and are guarded by the define `__HIP_PLATFORM_HCC__`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46097
Test Plan:
Benchmark
`python -m pt.cat_test --tag_filter all --device cuda`
Results on ROCm before the PR:
```
# ----------------------------------------
# PyTorch/Caffe2 Operator Micro-benchmarks
# ----------------------------------------
# Tag : all
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes(1,1,1)_N2_dim0_cuda
# Input: sizes: (1, 1, 1), N: 2, dim: 0, device: cuda
Forward Execution Time (us) : 10828.314
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes(512,512,2)_N2_dim1_cuda
# Input: sizes: (512, 512, 2), N: 2, dim: 1, device: cuda
Forward Execution Time (us) : 11888.028
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes(128,1024,2)_N2_dim1_cuda
# Input: sizes: (128, 1024, 2), N: 2, dim: 1, device: cuda
Forward Execution Time (us) : 11898.945
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes(1024,1024,2)_N2_dim0_cuda
# Input: sizes: (1024, 1024, 2), N: 2, dim: 0, device: cuda
Forward Execution Time (us) : 11787.744
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes(1025,1023,2)_N2_dim1_cuda
# Input: sizes: (1025, 1023, 2), N: 2, dim: 1, device: cuda
Forward Execution Time (us) : 11792.479
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes(1024,1024,2)_N2_dim2_cuda
# Input: sizes: (1024, 1024, 2), N: 2, dim: 2, device: cuda
Forward Execution Time (us) : 11769.718
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes[<function<lambda>at0x7f989e5c2510>,111,65]_N5_dim0_cuda
# Input: sizes: [<function <lambda> at 0x7f989e5c2510>, 111, 65], N: 5, dim: 0, device: cuda
Forward Execution Time (us) : 11633.882
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes[96,<function<lambda>at0x7f989e5c2620>,64]_N5_dim1_cuda
# Input: sizes: [96, <function <lambda> at 0x7f989e5c2620>, 64], N: 5, dim: 1, device: cuda
Forward Execution Time (us) : 11617.768
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes[128,64,<function<lambda>at0x7f96eee4df28>]_N5_dim2_cuda
# Input: sizes: [128, 64, <function <lambda> at 0x7f96eee4df28>], N: 5, dim: 2, device: cuda
Forward Execution Time (us) : 11625.143
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes[<function<lambda>at0x7f96ef874048>,32,64]_N50_dim0_cuda
# Input: sizes: [<function <lambda> at 0x7f96ef874048>, 32, 64], N: 50, dim: 0, device: cuda
Forward Execution Time (us) : 13079.204
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes[32,<function<lambda>at0x7f96ef8740d0>,64]_N50_dim1_cuda
# Input: sizes: [32, <function <lambda> at 0x7f96ef8740d0>, 64], N: 50, dim: 1, device: cuda
Forward Execution Time (us) : 13095.620
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes[33,65,<function<lambda>at0x7f96ef874158>]_N50_dim2_cuda
# Input: sizes: [33, 65, <function <lambda> at 0x7f96ef874158>], N: 50, dim: 2, device: cuda
Forward Execution Time (us) : 13403.086
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes(64,32,4,16,32)_N2_dim2_cuda
# Input: sizes: (64, 32, 4, 16, 32), N: 2, dim: 2, device: cuda
Forward Execution Time (us) : 118.704
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes(16,32,4,16,32)_N8_dim2_cuda
# Input: sizes: (16, 32, 4, 16, 32), N: 8, dim: 2, device: cuda
Forward Execution Time (us) : 263.273
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes(9,31,5,15,33)_N17_dim4_cuda
# Input: sizes: (9, 31, 5, 15, 33), N: 17, dim: 4, device: cuda
Forward Execution Time (us) : 463.024
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes[<function<lambda>at0x7f96ef8741e0>]_N100_dim0_cuda
# Input: sizes: [<function <lambda> at 0x7f96ef8741e0>], N: 100, dim: 0, device: cuda
Forward Execution Time (us) : 23818.032
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes[<function<lambda>at0x7f96ef874268>]_N1000_dim0_cuda
# Input: sizes: [<function <lambda> at 0x7f96ef874268>], N: 1000, dim: 0, device: cuda
Forward Execution Time (us) : 234778.296
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes[<function<lambda>at0x7f96ef8742f0>]_N2000_dim0_cuda
# Input: sizes: [<function <lambda> at 0x7f96ef8742f0>], N: 2000, dim: 0, device: cuda
Forward Execution Time (us) : 470288.132
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes[<function<lambda>at0x7f96ef874378>]_N3000_dim0_cuda
# Input: sizes: [<function <lambda> at 0x7f96ef874378>], N: 3000, dim: 0, device: cuda
Forward Execution Time (us) : 704361.221
```
Results on ROCm after the PR:
```
# ----------------------------------------
# PyTorch/Caffe2 Operator Micro-benchmarks
# ----------------------------------------
# Tag : all
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes(1,1,1)_N2_dim0_cuda
# Input: sizes: (1, 1, 1), N: 2, dim: 0, device: cuda
Forward Execution Time (us) : 29.292
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes(512,512,2)_N2_dim1_cuda
# Input: sizes: (512, 512, 2), N: 2, dim: 1, device: cuda
Forward Execution Time (us) : 46.320
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes(128,1024,2)_N2_dim1_cuda
# Input: sizes: (128, 1024, 2), N: 2, dim: 1, device: cuda
Forward Execution Time (us) : 36.969
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes(1024,1024,2)_N2_dim0_cuda
# Input: sizes: (1024, 1024, 2), N: 2, dim: 0, device: cuda
Forward Execution Time (us) : 92.816
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes(1025,1023,2)_N2_dim1_cuda
# Input: sizes: (1025, 1023, 2), N: 2, dim: 1, device: cuda
Forward Execution Time (us) : 93.943
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes(1024,1024,2)_N2_dim2_cuda
# Input: sizes: (1024, 1024, 2), N: 2, dim: 2, device: cuda
Forward Execution Time (us) : 163.914
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes[<function<lambda>at0x7f1da3186510>,111,65]_N5_dim0_cuda
# Input: sizes: [<function <lambda> at 0x7f1da3186510>, 111, 65], N: 5, dim: 0, device: cuda
Forward Execution Time (us) : 75.475
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes[96,<function<lambda>at0x7f1da3186620>,64]_N5_dim1_cuda
# Input: sizes: [96, <function <lambda> at 0x7f1da3186620>, 64], N: 5, dim: 1, device: cuda
Forward Execution Time (us) : 68.880
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes[128,64,<function<lambda>at0x7f1bf3c50f28>]_N5_dim2_cuda
# Input: sizes: [128, 64, <function <lambda> at 0x7f1bf3c50f28>], N: 5, dim: 2, device: cuda
Forward Execution Time (us) : 85.268
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes[<function<lambda>at0x7f1bf4669048>,32,64]_N50_dim0_cuda
# Input: sizes: [<function <lambda> at 0x7f1bf4669048>, 32, 64], N: 50, dim: 0, device: cuda
Forward Execution Time (us) : 111.543
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes[32,<function<lambda>at0x7f1bf46690d0>,64]_N50_dim1_cuda
# Input: sizes: [32, <function <lambda> at 0x7f1bf46690d0>, 64], N: 50, dim: 1, device: cuda
Forward Execution Time (us) : 110.644
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes[33,65,<function<lambda>at0x7f1bf4669158>]_N50_dim2_cuda
# Input: sizes: [33, 65, <function <lambda> at 0x7f1bf4669158>], N: 50, dim: 2, device: cuda
Forward Execution Time (us) : 116.201
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes(64,32,4,16,32)_N2_dim2_cuda
# Input: sizes: (64, 32, 4, 16, 32), N: 2, dim: 2, device: cuda
Forward Execution Time (us) : 117.708
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes(16,32,4,16,32)_N8_dim2_cuda
# Input: sizes: (16, 32, 4, 16, 32), N: 8, dim: 2, device: cuda
Forward Execution Time (us) : 264.953
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes(9,31,5,15,33)_N17_dim4_cuda
# Input: sizes: (9, 31, 5, 15, 33), N: 17, dim: 4, device: cuda
Forward Execution Time (us) : 480.304
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes[<function<lambda>at0x7f1bf46691e0>]_N100_dim0_cuda
# Input: sizes: [<function <lambda> at 0x7f1bf46691e0>], N: 100, dim: 0, device: cuda
Forward Execution Time (us) : 116.385
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes[<function<lambda>at0x7f1bf4669268>]_N1000_dim0_cuda
# Input: sizes: [<function <lambda> at 0x7f1bf4669268>], N: 1000, dim: 0, device: cuda
Forward Execution Time (us) : 913.591
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes[<function<lambda>at0x7f1bf46692f0>]_N2000_dim0_cuda
# Input: sizes: [<function <lambda> at 0x7f1bf46692f0>], N: 2000, dim: 0, device: cuda
Forward Execution Time (us) : 2003.212
# Benchmarking PyTorch: cat
# Mode: Eager
# Name: cat_sizes[<function<lambda>at0x7f1bf4669378>]_N3000_dim0_cuda
# Input: sizes: [<function <lambda> at 0x7f1bf4669378>], N: 3000, dim: 0, device: cuda
Forward Execution Time (us) : 3004.174
```
Reviewed By: bdhirsh
Differential Revision: D24286324
Pulled By: malfet
fbshipit-source-id: 291f3f3f80f9d2f9ba52a455a942f3fb0406e7d2
Author
ashishfarmer
Committer
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