[hpc]optimize the torch.cat cuda kernel #44833
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Summary: Pull Request resolved: pytorch#44833 Current cat cuda kernel employs the pin memory to pass the tensor data. 1) It is much slower than passing through argument using constant memory 2) the H2D sometimes overlaps with other H2D in training, and thus generates some random delay and leads to desync issue. For small N, we actually saw 2X improvements. Test Plan: benchmark ``` ./buck-out/opt/gen/caffe2/benchmarks/operator_benchmark/pt/cat_test.par --tag_filter all --device cuda ``` ``` # ---------------------------------------- # 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) : 38.825 # 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) : 45.440 # 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) : 38.765 # 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) : 60.075 # 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) : 65.203 # 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) : 83.941 # Benchmarking PyTorch: cat # Mode: Eager # Name: cat_sizes[<function<lambda>at0x7f0d50fc2440>,111,65]_N5_dim0_cuda # Input: sizes: [<function <lambda> at 0x7f0d50fc2440>, 111, 65], N: 5, dim: 0, device: cuda Forward Execution Time (us) : 51.059 # Benchmarking PyTorch: cat # Mode: Eager # Name: cat_sizes[96,<function<lambda>at0x7f0d50fc2b90>,64]_N5_dim1_cuda # Input: sizes: [96, <function <lambda> at 0x7f0d50fc2b90>, 64], N: 5, dim: 1, device: cuda Forward Execution Time (us) : 42.134 # Benchmarking PyTorch: cat # Mode: Eager # Name: cat_sizes[128,64,<function<lambda>at0x7f0b22b7e3b0>]_N5_dim2_cuda # Input: sizes: [128, 64, <function <lambda> at 0x7f0b22b7e3b0>], N: 5, dim: 2, device: cuda Forward Execution Time (us) : 78.333 # Benchmarking PyTorch: cat # Mode: Eager # Name: cat_sizes[<function<lambda>at0x7f0b22b7e5f0>,32,64]_N50_dim0_cuda # Input: sizes: [<function <lambda> at 0x7f0b22b7e5f0>, 32, 64], N: 50, dim: 0, device: cuda Forward Execution Time (us) : 77.065 # Benchmarking PyTorch: cat # Mode: Eager # Name: cat_sizes[32,<function<lambda>at0x7f0b22b7e680>,64]_N50_dim1_cuda # Input: sizes: [32, <function <lambda> at 0x7f0b22b7e680>, 64], N: 50, dim: 1, device: cuda Forward Execution Time (us) : 74.632 # Benchmarking PyTorch: cat # Mode: Eager # Name: cat_sizes[33,65,<function<lambda>at0x7f0b22b7e710>]_N50_dim2_cuda # Input: sizes: [33, 65, <function <lambda> at 0x7f0b22b7e710>], N: 50, dim: 2, device: cuda Forward Execution Time (us) : 81.846 # 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) : 99.291 # 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) : 114.060 # 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) : 478.777 # Benchmarking PyTorch: cat # Mode: Eager # Name: cat_sizes[<function<lambda>at0x7f0b22b7e7a0>]_N100_dim0_cuda # Input: sizes: [<function <lambda> at 0x7f0b22b7e7a0>], N: 100, dim: 0, device: cuda Forward Execution Time (us) : 80.165 # Benchmarking PyTorch: cat # Mode: Eager # Name: cat_sizes[<function<lambda>at0x7f0b22b7e830>]_N1000_dim0_cuda # Input: sizes: [<function <lambda> at 0x7f0b22b7e830>], N: 1000, dim: 0, device: cuda Forward Execution Time (us) : 491.983 # Benchmarking PyTorch: cat # Mode: Eager # Name: cat_sizes[<function<lambda>at0x7f0b22b7e8c0>]_N2000_dim0_cuda # Input: sizes: [<function <lambda> at 0x7f0b22b7e8c0>], N: 2000, dim: 0, device: cuda Forward Execution Time (us) : 966.613 # Benchmarking PyTorch: cat # Mode: Eager # Name: cat_sizes[<function<lambda>at0x7f0b22b7e950>]_N3000_dim0_cuda # Input: sizes: [<function <lambda> at 0x7f0b22b7e950>], N: 3000, dim: 0, device: cuda Forward Execution Time (us) : 1500.133 ``` After optimization ``` # ---------------------------------------- # 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) : 22.168 # 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) : 33.430 # 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) : 19.884 # 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) : 48.082 # 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) : 53.261 # 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) : 71.294 # Benchmarking PyTorch: cat # Mode: Eager # Name: cat_sizes[<function<lambda>at0x7f837a135200>,111,65]_N5_dim0_cuda # Input: sizes: [<function <lambda> at 0x7f837a135200>, 111, 65], N: 5, dim: 0, device: cuda Forward Execution Time (us) : 40.165 # Benchmarking PyTorch: cat # Mode: Eager # Name: cat_sizes[96,<function<lambda>at0x7f837a135950>,64]_N5_dim1_cuda # Input: sizes: [96, <function <lambda> at 0x7f837a135950>, 64], N: 5, dim: 1, device: cuda Forward Execution Time (us) : 32.666 # Benchmarking PyTorch: cat # Mode: Eager # Name: cat_sizes[128,64,<function<lambda>at0x7f82e50e2440>]_N5_dim2_cuda # Input: sizes: [128, 64, <function <lambda> at 0x7f82e50e2440>], N: 5, dim: 2, device: cuda Forward Execution Time (us) : 67.003 # Benchmarking PyTorch: cat # Mode: Eager # Name: cat_sizes[<function<lambda>at0x7f82e50e24d0>,32,64]_N50_dim0_cuda # Input: sizes: [<function <lambda> at 0x7f82e50e24d0>, 32, 64], N: 50, dim: 0, device: cuda Forward Execution Time (us) : 67.035 # Benchmarking PyTorch: cat # Mode: Eager # Name: cat_sizes[32,<function<lambda>at0x7f82e50e2560>,64]_N50_dim1_cuda # Input: sizes: [32, <function <lambda> at 0x7f82e50e2560>, 64], N: 50, dim: 1, device: cuda Forward Execution Time (us) : 63.803 # Benchmarking PyTorch: cat # Mode: Eager # Name: cat_sizes[33,65,<function<lambda>at0x7f82e50e25f0>]_N50_dim2_cuda # Input: sizes: [33, 65, <function <lambda> at 0x7f82e50e25f0>], N: 50, dim: 2, device: cuda Forward Execution Time (us) : 69.969 # 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) : 98.327 # 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) : 112.363 # 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) : 478.224 # Benchmarking PyTorch: cat # Mode: Eager # Name: cat_sizes[<function<lambda>at0x7f82e50e2680>]_N100_dim0_cuda # Input: sizes: [<function <lambda> at 0x7f82e50e2680>], N: 100, dim: 0, device: cuda Forward Execution Time (us) : 63.269 # Benchmarking PyTorch: cat # Mode: Eager # Name: cat_sizes[<function<lambda>at0x7f82e50e2710>]_N1000_dim0_cuda # Input: sizes: [<function <lambda> at 0x7f82e50e2710>], N: 1000, dim: 0, device: cuda Forward Execution Time (us) : 470.141 # Benchmarking PyTorch: cat # Mode: Eager # Name: cat_sizes[<function<lambda>at0x7f82e50e27a0>]_N2000_dim0_cuda # Input: sizes: [<function <lambda> at 0x7f82e50e27a0>], N: 2000, dim: 0, device: cuda Forward Execution Time (us) : 966.668 # Benchmarking PyTorch: cat # Mode: Eager # Name: cat_sizes[<function<lambda>at0x7f82e50e2830>]_N3000_dim0_cuda # Input: sizes: [<function <lambda> at 0x7f82e50e2830>], N: 3000, dim: 0, device: cuda Forward Execution Time (us) : 1485.309 ``` Reviewed By: ngimel Differential Revision: D23727275 fbshipit-source-id: d7ec013db4ccdd9ca09d7b45ea614e88d63e88c0
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This pull request was exported from Phabricator. Differential Revision: D23727275 |
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This pull request has been merged in 20f52cd. |
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This reverts commit 20f52cd.
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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
ashishfarmer
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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
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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
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Summary:
Current cat cuda kernel employs the pin memory to pass the tensor data. 1) It is much slower than passing through argument using constant memory 2) the H2D sometimes overlaps with other H2D in training, and thus generates some random delay and leads to desync issue.
For small N, we actually saw 2X improvements.
Test Plan:
benchmark
After optimization
Differential Revision: D23727275