Performance fix for torch.cat operator on ROCm (#46097) #46323

ashishfarmer · 2020-10-14T16:48:01Z

Cherry-picking the commit for 1.7 release
@malfet @jeffdaily @sunway513

Summary:
This pull request is a partial revert of #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: #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

Fixes #{issue number}

Summary: This pull request is a partial revert of pytorch#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: pytorch#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

facebook-github-bot · 2020-10-14T16:48:13Z

💊 CI failures summary and remediations

As of commit 5238f36 (more details on the Dr. CI page):

1/1 failures possibly* introduced in this PR
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Extra GitHub checks: 1 failed

Failed: GitHub Actions - clang-tidy

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dr-ci · 2020-10-14T16:58:28Z

💊 CI failures summary and remediations

As of commit 5238f36 (more details on the Dr. CI page):

2/2 failures possibly* introduced in this PR
- 2/2 non-CircleCI failure(s)

Extra GitHub checks: 1 failed

Failed: GitHub Actions - clang-tidy

ci.pytorch.org: 1 failed

Failed: pr/pytorch-linux-bionic-rocm3.8-py3.6

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jeffdaily

LGTM. Cherry-picked from master #46097, and performance for ROCm was demonstrated as recovered using the indicated benchmark.

codecov · 2020-10-14T21:05:33Z

Codecov Report

Merging #46323 into release/1.7 will not change coverage.
The diff coverage is n/a.

@@             Coverage Diff              @@
##           release/1.7   #46323   +/-   ##
============================================
  Coverage        68.43%   68.43%           
============================================
  Files              406      406           
  Lines            52318    52318           
============================================
  Hits             35803    35803           
  Misses           16515    16515

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pytorchbot added the open source label Oct 14, 2020

jeffdaily mentioned this pull request Oct 14, 2020

[v1.7.0] Release Tracker #45592

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heitorschueroff requested review from heitorschueroff and jeffdaily October 14, 2020 17:28

heitorschueroff added module: rocm AMD GPU support for Pytorch module: performance Issues related to performance, either of kernel code or framework glue triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module labels Oct 14, 2020

jeffdaily approved these changes Oct 14, 2020

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malfet approved these changes Oct 14, 2020

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malfet merged commit c52a61f into pytorch:release/1.7 Oct 14, 2020

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Performance fix for torch.cat operator on ROCm (#46097) #46323

Performance fix for torch.cat operator on ROCm (#46097) #46323

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ashishfarmer commented Oct 14, 2020 •

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Performance fix for torch.cat operator on ROCm (#46097) #46323

Performance fix for torch.cat operator on ROCm (#46097) #46323

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