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PyTorch MergeBot
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6ef70edd9a |
Revert "Inductor logging + analysis of torch.profile (#149697)"
This reverts commit
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Gabriel Ferns
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47f10d0ad0 |
Inductor logging + analysis of torch.profile (#149697)
Prereqs: - https://github.com/pytorch/pytorch/pull/152708 Features: 1. Adds inductor's estimate of flops and bandwidth to the json trace events that perfetto uses. 1. Only use the tflops estimation from triton if we don't have the info from the datasheet because Triton's estimates are inaccurate. I have a backlog item to fix triton flops estimation upstream. New `DeviceInfo` class, and new function `get_device_tflops`. 1. New helpers `countable_fx` and `count_flops_fx` helps get the flops of an `fx.Node`. 1. Extends Triton `torch.profiler` logging to `DebugAutotuner`. 1. New script `profile_analysis.py`: `--augment_trace` adds perf estimates to any perfetto json trace, `--analyze` creates a summary table of these perf estimates, and `--diff` will compare two traces side by side: ```python Device(NVIDIA H100, 0): Kernel Name | resnet Kernel Count | resnet FLOPS | resnet bw gbps | resnet Dur (ms) | resnet Achieved FLOPS % | resnet Achieved Bandwidth % | newresnet Kernel Count | newresnet FLOPS | newresnet bw gbps | newresnet Dur (ms) | newresnet Achieved FLOPS % | newresnet Achieved Bandwidth % --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- triton_poi_fused__native_batch_norm_legi | 24 | 0 | 0.11395268248131513 | 2.5919166666666666 | 0 | 0.003401572611382541 | 24 | 0 | 0.11395268248131513 | 2.5919166666666666 | 0 | 0.003401572611382541 sm90_xmma_fprop_implicit_gemm_f32f32_tf3 | 142 | 16932673552.422373 | 0.2585007824198784 | 12.441619718309857 | 0.08683422334575583 | 0.007716441266265022 | 142 | 16932673552.422373 | 0.2585007824198784 | 12.441619718309857 | 0.08683422334575583 | 0.007716441266265022 triton_red_fused__native_batch_norm_legi | 39 | 0 | 0.13990024992108846 | 5.752589743589743 | 0 | 0.004176126863316074 | 39 | 0 | 0.13990024992108846 | 5.752589743589743 | 0 | 0.004176126863316074 triton_poi_fused__native_batch_norm_legi | 25 | 0 | 0.31824055917536503 | 2.5291999999999994 | 0 | 0.009499718184339253 | 25 | 0 | 0.31824055917536503 | 2.5291999999999994 | 0 | 0.009499718184339253 void cutlass::Kernel2<cutlass_80_tensoro | 98 | 16211056473.596165 | 0.42972434051025826 | 7.130408163265306 | 0.08313362294151874 | 0.012827592254037562 | 98 | 16211056473.596165 | 0.42972434051025826 | 7.130408163265306 | 0.08313362294151874 | 0.012827592254037562 triton_red_fused__native_batch_norm_legi | 73 | 0 | 0.3225381327611705 | 9.987068493150682 | 0 | 0.009628003963020014 | 73 | 0 | 0.3225381327611705 | 9.987068493150682 | 0 | 0.009628003963020014 triton_poi_fused__native_batch_norm_legi | 15 | 0 | 1.4491211346487216 | 4.439333333333333 | 0 | 0.043257347302946926 | 15 | 0 | 1.4491211346487216 | 4.439333333333333 | 0 | 0.043257347302946926 void cutlass::Kernel2<cutlass_80_tensoro | 186 | 14501701145.337954 | 0.2667131401910989 | 7.873865591397849 | 0.07436769818122027 | 0.007961586274361157 | 186 | 14501701145.337954 | 0.2667131401910989 | 7.873865591397849 | 0.07436769818122027 | 0.007961586274361157 triton_poi_fused__native_batch_norm_legi | 33 | 0 | 1.4924556538193923 | 4.3101515151515155 | 0 | 0.044550915039384846 | 33 | 0 | 1.4924556538193923 | 4.3101515151515155 | 0 | 0.044550915039384846 triton_red_fused__native_batch_norm_legi | 29 | 0 | 0.25562590522631107 | 6.296275862068965 | 0 | 0.007630624036606301 | 29 | 0 | 0.25562590522631107 | 6.296275862068965 | 0 | 0.007630624036606301 triton_poi_fused__native_batch_norm_legi | 13 | 0 | 0.5870562174192726 | 2.7397692307692307 | 0 | 0.01752406619162008 | 13 | 0 | 0.5870562174192726 | 2.7397692307692307 | 0 | 0.01752406619162008 triton_poi_fused__native_batch_norm_legi | 34 | 0 | 0.41409928846284 | 2.853588235294117 | 0 | 0.012361172789935523 | 34 | 0 | 0.41409928846284 | 2.853588235294117 | 0 | 0.012361172789935523 triton_per_fused__native_batch_norm_legi | 34 | 0 | 0.11705315007018151 | 3.460647058823529 | 0 | 0.0034941238826919864 | 34 | 0 | 0.11705315007018151 | 3.460647058823529 | 0 | 0.0034941238826919864 triton_poi_fused__native_batch_norm_legi | 16 | 0 | 0.17207853197124584 | 2.3459375000000002 | 0 | 0.005136672596156592 | 16 | 0 | 0.17207853197124584 | 2.3459375000000002 | 0 | 0.005136672596156592 triton_per_fused__native_batch_norm_legi | 30 | 0 | 0.2639714322022256 | 6.131199999999999 | 0 | 0.007879744244842555 | 30 | 0 | 0.2639714322022256 | 6.131199999999999 | 0 | 0.007879744244842555 sm90_xmma_fprop_implicit_gemm_f32f32_tf3 | 100 | 11875430356.891787 | 0.19494470869421385 | 16.36534 | 0.06089964285585531 | 0.005819245035648175 | 100 | 11875430356.891787 | 0.19494470869421385 | 16.36534 | 0.06089964285585531 | 0.005819245035648175 triton_poi_fused__native_batch_norm_legi | 8 | 0 | 0.9854096626224687 | 3.2757500000000004 | 0 | 0.029415213809625928 | 8 | 0 | 0.9854096626224687 | 3.2757500000000004 | 0 | 0.029415213809625928 void cublasLt::splitKreduce_kernel<32, 1 | 56 | 34377923395.147064 | 0.8310300045762317 | 3.4199999999999986 | 0.17629704305203628 | 0.024806865808245714 | 56 | 34377923395.147064 | 0.8310300045762317 | 3.4199999999999986 | 0.17629704305203628 | 0.024806865808245714 triton_poi_fused__native_batch_norm_legi | 23 | 0 | 0.9944002965861103 | 3.2431304347826084 | 0 | 0.02968359094286896 | 23 | 0 | 0.9944002965861103 | 3.2431304347826084 | 0 | 0.02968359094286896 triton_per_fused__native_batch_norm_legi | 10 | 0 | 0.1826801058931057 | 4.428800000000001 | 0 | 0.00545313748934644 | 10 | 0 | 0.1826801058931057 | 4.428800000000001 | 0 | 0.00545313748934644 triton_poi_fused__native_batch_norm_legi | 10 | 0 | 0.3168973585366449 | 2.5471999999999997 | 0 | 0.009459622642884923 | 10 | 0 | 0.3168973585366449 | 2.5471999999999997 | 0 | 0.009459622642884923 triton_poi_fused__native_batch_norm_legi | 34 | 0 | 1.1463614897015777 | 4.124323529411764 | 0 | 0.03421974596124114 | 34 | 0 | 1.1463614897015777 | 4.124323529411764 | 0 | 0.03421974596124114 void cask_plugin_cudnn::xmma_cudnn::init | 44 | 44045510816.64277 | 2.0661232850348643 | 3.6887499999999993 | 0.22587441444432194 | 0.06167532194133924 | 44 | 44045510816.64277 | 2.0661232850348643 | 3.6887499999999993 | 0.22587441444432194 | 0.06167532194133924 sm90_xmma_fprop_implicit_gemm_f32f32_tf3 | 95 | 7876855400.165316 | 0.4694941555946739 | 18.224315789473682 | 0.04039413025725802 | 0.014014750913273854 | 95 | 7876855400.165316 | 0.4694941555946739 | 18.224315789473682 | 0.04039413025725802 | 0.014014750913273854 triton_per_fused__native_batch_norm_legi | 41 | 0 | 0.06825669875995298 | 3.0384146341463416 | 0 | 0.002037513395819492 | 41 | 0 | 0.06825669875995298 | 3.0384146341463416 | 0 | 0.002037513395819492 triton_poi_fused__native_batch_norm_legi | 23 | 0 | 0.08808154712430301 | 2.3275652173913044 | 0 | 0.0026292999141582997 | 23 | 0 | 0.08808154712430301 | 2.3275652173913044 | 0 | 0.0026292999141582997 triton_per_fused__native_batch_norm_legi | 40 | 0 | 0.18179321034952417 | 4.556825 | 0 | 0.005426662995508183 | 40 | 0 | 0.18179321034952417 | 4.556825 | 0 | 0.005426662995508183 triton_poi_fused__native_batch_norm_legi | 15 | 0 | 0.5887415155454232 | 2.783866666666667 | 0 | 0.017574373598370836 | 15 | 0 | 0.5887415155454232 | 2.783866666666667 | 0 | 0.017574373598370836 void cutlass::Kernel2<cutlass_80_tensoro | 38 | 14242013806.264643 | 0.256592404353939 | 7.217631578947369 | 0.0730359682372546 | 0.007659474756834 | 38 | 14242013806.264643 | 0.256592404353939 | 7.217631578947369 | 0.0730359682372546 | 0.007659474756834 triton_poi_fused__native_batch_norm_legi | 21 | 0 | 0.5842860973430516 | 2.7779047619047623 | 0 | 0.017441376040091088 | 21 | 0 | 0.5842860973430516 | 2.7779047619047623 | 0 | 0.017441376040091088 triton_per_fused__native_batch_norm_legi | 16 | 0 | 0.11509365173486417 | 3.5959375000000002 | 0 | 0.0034356313950705724 | 16 | 0 | 0.11509365173486417 | 3.5959375000000002 | 0 | 0.0034356313950705724 triton_poi_fused__native_batch_norm_legi | 14 | 0 | 0.1704672000243914 | 2.4044285714285714 | 0 | 0.00508857313505646 | 14 | 0 | 0.1704672000243914 | 2.4044285714285714 | 0 | 0.00508857313505646 triton_poi_fused__native_batch_norm_legi | 58 | 0 | 2.307520779930795 | 8.190706896551722 | 0 | 0.06888121731136704 | 58 | 0 | 2.307520779930795 | 8.190706896551722 | 0 | 0.06888121731136704 triton_per_fused__native_batch_norm_legi | 29 | 0 | 0.037243248971881276 | 3.0277586206896556 | 0 | 0.001111738775280038 | 29 | 0 | 0.037243248971881276 | 3.0277586206896556 | 0 | 0.001111738775280038 triton_poi_fused__native_batch_norm_legi | 20 | 0 | 0.04741699795428918 | 2.2911500000000005 | 0 | 0.0014154327747549007 | 20 | 0 | 0.04741699795428918 | 2.2911500000000005 | 0 | 0.0014154327747549007 triton_per_fused__native_batch_norm_legi | 25 | 0 | 0.13357016893727824 | 3.37536 | 0 | 0.003987169222008305 | 25 | 0 | 0.13357016893727824 | 3.37536 | 0 | 0.003987169222008305 triton_poi_fused__native_batch_norm_legi | 13 | 0 | 0.3089862268300253 | 2.8111538461538457 | 0 | 0.009223469457612694 | 13 | 0 | 0.3089862268300253 | 2.8111538461538457 | 0 | 0.009223469457612694 triton_poi_fused__native_batch_norm_legi | 17 | 0 | 0.3129385387909844 | 2.673 | 0 | 0.009341448919133863 | 17 | 0 | 0.3129385387909844 | 2.673 | 0 | 0.009341448919133863 triton_per_fused__native_batch_norm_legi | 19 | 0 | 0.2215568162533158 | 3.8837368421052636 | 0 | 0.0066136363060691275 | 19 | 0 | 0.2215568162533158 | 3.8837368421052636 | 0 | 0.0066136363060691275 std::enable_if<!(false), void>::type int | 23 | 504916805.19297093 | 1.0118296096314707 | 8.113913043478261 | 0.0025893169497075447 | 0.030203868944223014 | 23 | 504916805.19297093 | 1.0118296096314707 | 8.113913043478261 | 0.0025893169497075447 | 0.030203868944223014 triton_poi_fused_add_copy__38 | 56 | 0 | 0 | 2.132482142857143 | 0 | 0 | 56 | 0 | 0 | 2.132482142857143 | 0 | 0 triton_poi_fused_convolution_0 | 18 | 0 | 0.43458610794936897 | 2.773333333333334 | 0 | 0.012972719640279667 | 18 | 0 | 0.43458610794936897 | 2.773333333333334 | 0 | 0.012972719640279667 triton_poi_fused_convolution_1 | 17 | 0 | 0.028816312469162712 | 2.6145882352941174 | 0 | 0.0008601884319153051 | 17 | 0 | 0.028816312469162712 | 2.6145882352941174 | 0 | 0.0008601884319153051 void convolve_common_engine_float_NHWC<f | 44 | 8641868995.31118 | 0.024730540008465626 | 25.87327272727273 | 0.04431727689903169 | 0.0007382250748795709 | 44 | 8641868995.31118 | 0.024730540008465626 | 25.87327272727273 | 0.04431727689903169 | 0.0007382250748795709 triton_per_fused__native_batch_norm_legi | 12 | 0 | 0.6809930918986744 | 4.82675 | 0 | 0.020328151996975356 | 12 | 0 | 0.6809930918986744 | 4.82675 | 0 | 0.020328151996975356 triton_per_fused__native_batch_norm_legi | 14 | 0 | 0.02883030597936608 | 2.6651428571428575 | 0 | 0.0008606061486377935 | 14 | 0 | 0.02883030597936608 | 2.6651428571428575 | 0 | 0.0008606061486377935 triton_per_fused__native_batch_norm_legi | 16 | 0 | 0.0014658988233201874 | 2.098 | 0 | 4.375817383045335e-05 | 16 | 0 | 0.0014658988233201874 | 2.098 | 0 | 4.375817383045335e-05 triton_poi_fused__native_batch_norm_legi | 13 | 0 | 0.9926297180284697 | 3.2367692307692306 | 0 | 0.02963073785159611 | 13 | 0 | 0.9926297180284697 | 3.2367692307692306 | 0 | 0.02963073785159611 triton_poi_fused__native_batch_norm_legi | 9 | 0 | 1.3008817095666507 | 3.0863333333333336 | 0 | 0.03883228983781048 | 9 | 0 | 1.3008817095666507 | 3.0863333333333336 | 0 | 0.03883228983781048 void at::native::(anonymous namespace):: | 98 | 0 | 0.09174335613709389 | 4.408520408163265 | 0 | 0.0027386076458833994 | 98 | 0 | 0.09174335613709389 | 4.408520408163265 | 0 | 0.0027386076458833994 void at::native::vectorized_elementwise_ | 7 | 0 | 0 | 1.7278571428571428 | 0 | 0 | 7 | 0 | 0 | 1.7278571428571428 | 0 | 0 ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/149697 Approved by: https://github.com/eellison, https://github.com/shunting314 |
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PyTorch MergeBot
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c038719731 |
Revert "Inductor logging + analysis of torch.profile (#149697)"
This reverts commit
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Gabriel Ferns
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347ace4c7a |
Inductor logging + analysis of torch.profile (#149697)
Prereqs: - https://github.com/pytorch/pytorch/pull/152708 Features: 1. Adds inductor's estimate of flops and bandwidth to the json trace events that perfetto uses. 1. Only use the tflops estimation from triton if we don't have the info from the datasheet because Triton's estimates are inaccurate. I have a backlog item to fix triton flops estimation upstream. New `DeviceInfo` class, and new function `get_device_tflops`. 1. New helpers `countable_fx` and `count_flops_fx` helps get the flops of an `fx.Node`. 1. Extends Triton `torch.profiler` logging to `DebugAutotuner`. 1. New script `profile_analysis.py`: `--augment_trace` adds perf estimates to any perfetto json trace, `--analyze` creates a summary table of these perf estimates, and `--diff` will compare two traces side by side: ```python Device(NVIDIA H100, 0): Kernel Name | resnet Kernel Count | resnet FLOPS | resnet bw gbps | resnet Dur (ms) | resnet Achieved FLOPS % | resnet Achieved Bandwidth % | newresnet Kernel Count | newresnet FLOPS | newresnet bw gbps | newresnet Dur (ms) | newresnet Achieved FLOPS % | newresnet Achieved Bandwidth % --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- triton_poi_fused__native_batch_norm_legi | 24 | 0 | 0.11395268248131513 | 2.5919166666666666 | 0 | 0.003401572611382541 | 24 | 0 | 0.11395268248131513 | 2.5919166666666666 | 0 | 0.003401572611382541 sm90_xmma_fprop_implicit_gemm_f32f32_tf3 | 142 | 16932673552.422373 | 0.2585007824198784 | 12.441619718309857 | 0.08683422334575583 | 0.007716441266265022 | 142 | 16932673552.422373 | 0.2585007824198784 | 12.441619718309857 | 0.08683422334575583 | 0.007716441266265022 triton_red_fused__native_batch_norm_legi | 39 | 0 | 0.13990024992108846 | 5.752589743589743 | 0 | 0.004176126863316074 | 39 | 0 | 0.13990024992108846 | 5.752589743589743 | 0 | 0.004176126863316074 triton_poi_fused__native_batch_norm_legi | 25 | 0 | 0.31824055917536503 | 2.5291999999999994 | 0 | 0.009499718184339253 | 25 | 0 | 0.31824055917536503 | 2.5291999999999994 | 0 | 0.009499718184339253 void cutlass::Kernel2<cutlass_80_tensoro | 98 | 16211056473.596165 | 0.42972434051025826 | 7.130408163265306 | 0.08313362294151874 | 0.012827592254037562 | 98 | 16211056473.596165 | 0.42972434051025826 | 7.130408163265306 | 0.08313362294151874 | 0.012827592254037562 triton_red_fused__native_batch_norm_legi | 73 | 0 | 0.3225381327611705 | 9.987068493150682 | 0 | 0.009628003963020014 | 73 | 0 | 0.3225381327611705 | 9.987068493150682 | 0 | 0.009628003963020014 triton_poi_fused__native_batch_norm_legi | 15 | 0 | 1.4491211346487216 | 4.439333333333333 | 0 | 0.043257347302946926 | 15 | 0 | 1.4491211346487216 | 4.439333333333333 | 0 | 0.043257347302946926 void cutlass::Kernel2<cutlass_80_tensoro | 186 | 14501701145.337954 | 0.2667131401910989 | 7.873865591397849 | 0.07436769818122027 | 0.007961586274361157 | 186 | 14501701145.337954 | 0.2667131401910989 | 7.873865591397849 | 0.07436769818122027 | 0.007961586274361157 triton_poi_fused__native_batch_norm_legi | 33 | 0 | 1.4924556538193923 | 4.3101515151515155 | 0 | 0.044550915039384846 | 33 | 0 | 1.4924556538193923 | 4.3101515151515155 | 0 | 0.044550915039384846 triton_red_fused__native_batch_norm_legi | 29 | 0 | 0.25562590522631107 | 6.296275862068965 | 0 | 0.007630624036606301 | 29 | 0 | 0.25562590522631107 | 6.296275862068965 | 0 | 0.007630624036606301 triton_poi_fused__native_batch_norm_legi | 13 | 0 | 0.5870562174192726 | 2.7397692307692307 | 0 | 0.01752406619162008 | 13 | 0 | 0.5870562174192726 | 2.7397692307692307 | 0 | 0.01752406619162008 triton_poi_fused__native_batch_norm_legi | 34 | 0 | 0.41409928846284 | 2.853588235294117 | 0 | 0.012361172789935523 | 34 | 0 | 0.41409928846284 | 2.853588235294117 | 0 | 0.012361172789935523 triton_per_fused__native_batch_norm_legi | 34 | 0 | 0.11705315007018151 | 3.460647058823529 | 0 | 0.0034941238826919864 | 34 | 0 | 0.11705315007018151 | 3.460647058823529 | 0 | 0.0034941238826919864 triton_poi_fused__native_batch_norm_legi | 16 | 0 | 0.17207853197124584 | 2.3459375000000002 | 0 | 0.005136672596156592 | 16 | 0 | 0.17207853197124584 | 2.3459375000000002 | 0 | 0.005136672596156592 triton_per_fused__native_batch_norm_legi | 30 | 0 | 0.2639714322022256 | 6.131199999999999 | 0 | 0.007879744244842555 | 30 | 0 | 0.2639714322022256 | 6.131199999999999 | 0 | 0.007879744244842555 sm90_xmma_fprop_implicit_gemm_f32f32_tf3 | 100 | 11875430356.891787 | 0.19494470869421385 | 16.36534 | 0.06089964285585531 | 0.005819245035648175 | 100 | 11875430356.891787 | 0.19494470869421385 | 16.36534 | 0.06089964285585531 | 0.005819245035648175 triton_poi_fused__native_batch_norm_legi | 8 | 0 | 0.9854096626224687 | 3.2757500000000004 | 0 | 0.029415213809625928 | 8 | 0 | 0.9854096626224687 | 3.2757500000000004 | 0 | 0.029415213809625928 void cublasLt::splitKreduce_kernel<32, 1 | 56 | 34377923395.147064 | 0.8310300045762317 | 3.4199999999999986 | 0.17629704305203628 | 0.024806865808245714 | 56 | 34377923395.147064 | 0.8310300045762317 | 3.4199999999999986 | 0.17629704305203628 | 0.024806865808245714 triton_poi_fused__native_batch_norm_legi | 23 | 0 | 0.9944002965861103 | 3.2431304347826084 | 0 | 0.02968359094286896 | 23 | 0 | 0.9944002965861103 | 3.2431304347826084 | 0 | 0.02968359094286896 triton_per_fused__native_batch_norm_legi | 10 | 0 | 0.1826801058931057 | 4.428800000000001 | 0 | 0.00545313748934644 | 10 | 0 | 0.1826801058931057 | 4.428800000000001 | 0 | 0.00545313748934644 triton_poi_fused__native_batch_norm_legi | 10 | 0 | 0.3168973585366449 | 2.5471999999999997 | 0 | 0.009459622642884923 | 10 | 0 | 0.3168973585366449 | 2.5471999999999997 | 0 | 0.009459622642884923 triton_poi_fused__native_batch_norm_legi | 34 | 0 | 1.1463614897015777 | 4.124323529411764 | 0 | 0.03421974596124114 | 34 | 0 | 1.1463614897015777 | 4.124323529411764 | 0 | 0.03421974596124114 void cask_plugin_cudnn::xmma_cudnn::init | 44 | 44045510816.64277 | 2.0661232850348643 | 3.6887499999999993 | 0.22587441444432194 | 0.06167532194133924 | 44 | 44045510816.64277 | 2.0661232850348643 | 3.6887499999999993 | 0.22587441444432194 | 0.06167532194133924 sm90_xmma_fprop_implicit_gemm_f32f32_tf3 | 95 | 7876855400.165316 | 0.4694941555946739 | 18.224315789473682 | 0.04039413025725802 | 0.014014750913273854 | 95 | 7876855400.165316 | 0.4694941555946739 | 18.224315789473682 | 0.04039413025725802 | 0.014014750913273854 triton_per_fused__native_batch_norm_legi | 41 | 0 | 0.06825669875995298 | 3.0384146341463416 | 0 | 0.002037513395819492 | 41 | 0 | 0.06825669875995298 | 3.0384146341463416 | 0 | 0.002037513395819492 triton_poi_fused__native_batch_norm_legi | 23 | 0 | 0.08808154712430301 | 2.3275652173913044 | 0 | 0.0026292999141582997 | 23 | 0 | 0.08808154712430301 | 2.3275652173913044 | 0 | 0.0026292999141582997 triton_per_fused__native_batch_norm_legi | 40 | 0 | 0.18179321034952417 | 4.556825 | 0 | 0.005426662995508183 | 40 | 0 | 0.18179321034952417 | 4.556825 | 0 | 0.005426662995508183 triton_poi_fused__native_batch_norm_legi | 15 | 0 | 0.5887415155454232 | 2.783866666666667 | 0 | 0.017574373598370836 | 15 | 0 | 0.5887415155454232 | 2.783866666666667 | 0 | 0.017574373598370836 void cutlass::Kernel2<cutlass_80_tensoro | 38 | 14242013806.264643 | 0.256592404353939 | 7.217631578947369 | 0.0730359682372546 | 0.007659474756834 | 38 | 14242013806.264643 | 0.256592404353939 | 7.217631578947369 | 0.0730359682372546 | 0.007659474756834 triton_poi_fused__native_batch_norm_legi | 21 | 0 | 0.5842860973430516 | 2.7779047619047623 | 0 | 0.017441376040091088 | 21 | 0 | 0.5842860973430516 | 2.7779047619047623 | 0 | 0.017441376040091088 triton_per_fused__native_batch_norm_legi | 16 | 0 | 0.11509365173486417 | 3.5959375000000002 | 0 | 0.0034356313950705724 | 16 | 0 | 0.11509365173486417 | 3.5959375000000002 | 0 | 0.0034356313950705724 triton_poi_fused__native_batch_norm_legi | 14 | 0 | 0.1704672000243914 | 2.4044285714285714 | 0 | 0.00508857313505646 | 14 | 0 | 0.1704672000243914 | 2.4044285714285714 | 0 | 0.00508857313505646 triton_poi_fused__native_batch_norm_legi | 58 | 0 | 2.307520779930795 | 8.190706896551722 | 0 | 0.06888121731136704 | 58 | 0 | 2.307520779930795 | 8.190706896551722 | 0 | 0.06888121731136704 triton_per_fused__native_batch_norm_legi | 29 | 0 | 0.037243248971881276 | 3.0277586206896556 | 0 | 0.001111738775280038 | 29 | 0 | 0.037243248971881276 | 3.0277586206896556 | 0 | 0.001111738775280038 triton_poi_fused__native_batch_norm_legi | 20 | 0 | 0.04741699795428918 | 2.2911500000000005 | 0 | 0.0014154327747549007 | 20 | 0 | 0.04741699795428918 | 2.2911500000000005 | 0 | 0.0014154327747549007 triton_per_fused__native_batch_norm_legi | 25 | 0 | 0.13357016893727824 | 3.37536 | 0 | 0.003987169222008305 | 25 | 0 | 0.13357016893727824 | 3.37536 | 0 | 0.003987169222008305 triton_poi_fused__native_batch_norm_legi | 13 | 0 | 0.3089862268300253 | 2.8111538461538457 | 0 | 0.009223469457612694 | 13 | 0 | 0.3089862268300253 | 2.8111538461538457 | 0 | 0.009223469457612694 triton_poi_fused__native_batch_norm_legi | 17 | 0 | 0.3129385387909844 | 2.673 | 0 | 0.009341448919133863 | 17 | 0 | 0.3129385387909844 | 2.673 | 0 | 0.009341448919133863 triton_per_fused__native_batch_norm_legi | 19 | 0 | 0.2215568162533158 | 3.8837368421052636 | 0 | 0.0066136363060691275 | 19 | 0 | 0.2215568162533158 | 3.8837368421052636 | 0 | 0.0066136363060691275 std::enable_if<!(false), void>::type int | 23 | 504916805.19297093 | 1.0118296096314707 | 8.113913043478261 | 0.0025893169497075447 | 0.030203868944223014 | 23 | 504916805.19297093 | 1.0118296096314707 | 8.113913043478261 | 0.0025893169497075447 | 0.030203868944223014 triton_poi_fused_add_copy__38 | 56 | 0 | 0 | 2.132482142857143 | 0 | 0 | 56 | 0 | 0 | 2.132482142857143 | 0 | 0 triton_poi_fused_convolution_0 | 18 | 0 | 0.43458610794936897 | 2.773333333333334 | 0 | 0.012972719640279667 | 18 | 0 | 0.43458610794936897 | 2.773333333333334 | 0 | 0.012972719640279667 triton_poi_fused_convolution_1 | 17 | 0 | 0.028816312469162712 | 2.6145882352941174 | 0 | 0.0008601884319153051 | 17 | 0 | 0.028816312469162712 | 2.6145882352941174 | 0 | 0.0008601884319153051 void convolve_common_engine_float_NHWC<f | 44 | 8641868995.31118 | 0.024730540008465626 | 25.87327272727273 | 0.04431727689903169 | 0.0007382250748795709 | 44 | 8641868995.31118 | 0.024730540008465626 | 25.87327272727273 | 0.04431727689903169 | 0.0007382250748795709 triton_per_fused__native_batch_norm_legi | 12 | 0 | 0.6809930918986744 | 4.82675 | 0 | 0.020328151996975356 | 12 | 0 | 0.6809930918986744 | 4.82675 | 0 | 0.020328151996975356 triton_per_fused__native_batch_norm_legi | 14 | 0 | 0.02883030597936608 | 2.6651428571428575 | 0 | 0.0008606061486377935 | 14 | 0 | 0.02883030597936608 | 2.6651428571428575 | 0 | 0.0008606061486377935 triton_per_fused__native_batch_norm_legi | 16 | 0 | 0.0014658988233201874 | 2.098 | 0 | 4.375817383045335e-05 | 16 | 0 | 0.0014658988233201874 | 2.098 | 0 | 4.375817383045335e-05 triton_poi_fused__native_batch_norm_legi | 13 | 0 | 0.9926297180284697 | 3.2367692307692306 | 0 | 0.02963073785159611 | 13 | 0 | 0.9926297180284697 | 3.2367692307692306 | 0 | 0.02963073785159611 triton_poi_fused__native_batch_norm_legi | 9 | 0 | 1.3008817095666507 | 3.0863333333333336 | 0 | 0.03883228983781048 | 9 | 0 | 1.3008817095666507 | 3.0863333333333336 | 0 | 0.03883228983781048 void at::native::(anonymous namespace):: | 98 | 0 | 0.09174335613709389 | 4.408520408163265 | 0 | 0.0027386076458833994 | 98 | 0 | 0.09174335613709389 | 4.408520408163265 | 0 | 0.0027386076458833994 void at::native::vectorized_elementwise_ | 7 | 0 | 0 | 1.7278571428571428 | 0 | 0 | 7 | 0 | 0 | 1.7278571428571428 | 0 | 0 ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/149697 Approved by: https://github.com/eellison, https://github.com/shunting314 |
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PyTorch MergeBot
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eb152ab1dd |
Revert "Inductor logging + analysis of torch.profile (#149697)"
This reverts commit |
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Gabriel Ferns
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060838c231 |
Inductor logging + analysis of torch.profile (#149697)
Prereqs: - https://github.com/pytorch/pytorch/pull/152708 Features: 1. Adds inductor's estimate of flops and bandwidth to the json trace events that perfetto uses. 1. Only use the tflops estimation from triton if we don't have the info from the datasheet because Triton's estimates are inaccurate. I have a backlog item to fix triton flops estimation upstream. New `DeviceInfo` class, and new function `get_device_tflops`. 1. New helpers `countable_fx` and `count_flops_fx` helps get the flops of an `fx.Node`. 1. Extends Triton `torch.profiler` logging to `DebugAutotuner`. 1. New script `profile_analysis.py`: `--augment_trace` adds perf estimates to any perfetto json trace, `--analyze` creates a summary table of these perf estimates, and `--diff` will compare two traces side by side: ```python Device(NVIDIA H100, 0): Kernel Name | resnet Kernel Count | resnet FLOPS | resnet bw gbps | resnet Dur (ms) | resnet Achieved FLOPS % | resnet Achieved Bandwidth % | newresnet Kernel Count | newresnet FLOPS | newresnet bw gbps | newresnet Dur (ms) | newresnet Achieved FLOPS % | newresnet Achieved Bandwidth % --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- triton_poi_fused__native_batch_norm_legi | 24 | 0 | 0.11395268248131513 | 2.5919166666666666 | 0 | 0.003401572611382541 | 24 | 0 | 0.11395268248131513 | 2.5919166666666666 | 0 | 0.003401572611382541 sm90_xmma_fprop_implicit_gemm_f32f32_tf3 | 142 | 16932673552.422373 | 0.2585007824198784 | 12.441619718309857 | 0.08683422334575583 | 0.007716441266265022 | 142 | 16932673552.422373 | 0.2585007824198784 | 12.441619718309857 | 0.08683422334575583 | 0.007716441266265022 triton_red_fused__native_batch_norm_legi | 39 | 0 | 0.13990024992108846 | 5.752589743589743 | 0 | 0.004176126863316074 | 39 | 0 | 0.13990024992108846 | 5.752589743589743 | 0 | 0.004176126863316074 triton_poi_fused__native_batch_norm_legi | 25 | 0 | 0.31824055917536503 | 2.5291999999999994 | 0 | 0.009499718184339253 | 25 | 0 | 0.31824055917536503 | 2.5291999999999994 | 0 | 0.009499718184339253 void cutlass::Kernel2<cutlass_80_tensoro | 98 | 16211056473.596165 | 0.42972434051025826 | 7.130408163265306 | 0.08313362294151874 | 0.012827592254037562 | 98 | 16211056473.596165 | 0.42972434051025826 | 7.130408163265306 | 0.08313362294151874 | 0.012827592254037562 triton_red_fused__native_batch_norm_legi | 73 | 0 | 0.3225381327611705 | 9.987068493150682 | 0 | 0.009628003963020014 | 73 | 0 | 0.3225381327611705 | 9.987068493150682 | 0 | 0.009628003963020014 triton_poi_fused__native_batch_norm_legi | 15 | 0 | 1.4491211346487216 | 4.439333333333333 | 0 | 0.043257347302946926 | 15 | 0 | 1.4491211346487216 | 4.439333333333333 | 0 | 0.043257347302946926 void cutlass::Kernel2<cutlass_80_tensoro | 186 | 14501701145.337954 | 0.2667131401910989 | 7.873865591397849 | 0.07436769818122027 | 0.007961586274361157 | 186 | 14501701145.337954 | 0.2667131401910989 | 7.873865591397849 | 0.07436769818122027 | 0.007961586274361157 triton_poi_fused__native_batch_norm_legi | 33 | 0 | 1.4924556538193923 | 4.3101515151515155 | 0 | 0.044550915039384846 | 33 | 0 | 1.4924556538193923 | 4.3101515151515155 | 0 | 0.044550915039384846 triton_red_fused__native_batch_norm_legi | 29 | 0 | 0.25562590522631107 | 6.296275862068965 | 0 | 0.007630624036606301 | 29 | 0 | 0.25562590522631107 | 6.296275862068965 | 0 | 0.007630624036606301 triton_poi_fused__native_batch_norm_legi | 13 | 0 | 0.5870562174192726 | 2.7397692307692307 | 0 | 0.01752406619162008 | 13 | 0 | 0.5870562174192726 | 2.7397692307692307 | 0 | 0.01752406619162008 triton_poi_fused__native_batch_norm_legi | 34 | 0 | 0.41409928846284 | 2.853588235294117 | 0 | 0.012361172789935523 | 34 | 0 | 0.41409928846284 | 2.853588235294117 | 0 | 0.012361172789935523 triton_per_fused__native_batch_norm_legi | 34 | 0 | 0.11705315007018151 | 3.460647058823529 | 0 | 0.0034941238826919864 | 34 | 0 | 0.11705315007018151 | 3.460647058823529 | 0 | 0.0034941238826919864 triton_poi_fused__native_batch_norm_legi | 16 | 0 | 0.17207853197124584 | 2.3459375000000002 | 0 | 0.005136672596156592 | 16 | 0 | 0.17207853197124584 | 2.3459375000000002 | 0 | 0.005136672596156592 triton_per_fused__native_batch_norm_legi | 30 | 0 | 0.2639714322022256 | 6.131199999999999 | 0 | 0.007879744244842555 | 30 | 0 | 0.2639714322022256 | 6.131199999999999 | 0 | 0.007879744244842555 sm90_xmma_fprop_implicit_gemm_f32f32_tf3 | 100 | 11875430356.891787 | 0.19494470869421385 | 16.36534 | 0.06089964285585531 | 0.005819245035648175 | 100 | 11875430356.891787 | 0.19494470869421385 | 16.36534 | 0.06089964285585531 | 0.005819245035648175 triton_poi_fused__native_batch_norm_legi | 8 | 0 | 0.9854096626224687 | 3.2757500000000004 | 0 | 0.029415213809625928 | 8 | 0 | 0.9854096626224687 | 3.2757500000000004 | 0 | 0.029415213809625928 void cublasLt::splitKreduce_kernel<32, 1 | 56 | 34377923395.147064 | 0.8310300045762317 | 3.4199999999999986 | 0.17629704305203628 | 0.024806865808245714 | 56 | 34377923395.147064 | 0.8310300045762317 | 3.4199999999999986 | 0.17629704305203628 | 0.024806865808245714 triton_poi_fused__native_batch_norm_legi | 23 | 0 | 0.9944002965861103 | 3.2431304347826084 | 0 | 0.02968359094286896 | 23 | 0 | 0.9944002965861103 | 3.2431304347826084 | 0 | 0.02968359094286896 triton_per_fused__native_batch_norm_legi | 10 | 0 | 0.1826801058931057 | 4.428800000000001 | 0 | 0.00545313748934644 | 10 | 0 | 0.1826801058931057 | 4.428800000000001 | 0 | 0.00545313748934644 triton_poi_fused__native_batch_norm_legi | 10 | 0 | 0.3168973585366449 | 2.5471999999999997 | 0 | 0.009459622642884923 | 10 | 0 | 0.3168973585366449 | 2.5471999999999997 | 0 | 0.009459622642884923 triton_poi_fused__native_batch_norm_legi | 34 | 0 | 1.1463614897015777 | 4.124323529411764 | 0 | 0.03421974596124114 | 34 | 0 | 1.1463614897015777 | 4.124323529411764 | 0 | 0.03421974596124114 void cask_plugin_cudnn::xmma_cudnn::init | 44 | 44045510816.64277 | 2.0661232850348643 | 3.6887499999999993 | 0.22587441444432194 | 0.06167532194133924 | 44 | 44045510816.64277 | 2.0661232850348643 | 3.6887499999999993 | 0.22587441444432194 | 0.06167532194133924 sm90_xmma_fprop_implicit_gemm_f32f32_tf3 | 95 | 7876855400.165316 | 0.4694941555946739 | 18.224315789473682 | 0.04039413025725802 | 0.014014750913273854 | 95 | 7876855400.165316 | 0.4694941555946739 | 18.224315789473682 | 0.04039413025725802 | 0.014014750913273854 triton_per_fused__native_batch_norm_legi | 41 | 0 | 0.06825669875995298 | 3.0384146341463416 | 0 | 0.002037513395819492 | 41 | 0 | 0.06825669875995298 | 3.0384146341463416 | 0 | 0.002037513395819492 triton_poi_fused__native_batch_norm_legi | 23 | 0 | 0.08808154712430301 | 2.3275652173913044 | 0 | 0.0026292999141582997 | 23 | 0 | 0.08808154712430301 | 2.3275652173913044 | 0 | 0.0026292999141582997 triton_per_fused__native_batch_norm_legi | 40 | 0 | 0.18179321034952417 | 4.556825 | 0 | 0.005426662995508183 | 40 | 0 | 0.18179321034952417 | 4.556825 | 0 | 0.005426662995508183 triton_poi_fused__native_batch_norm_legi | 15 | 0 | 0.5887415155454232 | 2.783866666666667 | 0 | 0.017574373598370836 | 15 | 0 | 0.5887415155454232 | 2.783866666666667 | 0 | 0.017574373598370836 void cutlass::Kernel2<cutlass_80_tensoro | 38 | 14242013806.264643 | 0.256592404353939 | 7.217631578947369 | 0.0730359682372546 | 0.007659474756834 | 38 | 14242013806.264643 | 0.256592404353939 | 7.217631578947369 | 0.0730359682372546 | 0.007659474756834 triton_poi_fused__native_batch_norm_legi | 21 | 0 | 0.5842860973430516 | 2.7779047619047623 | 0 | 0.017441376040091088 | 21 | 0 | 0.5842860973430516 | 2.7779047619047623 | 0 | 0.017441376040091088 triton_per_fused__native_batch_norm_legi | 16 | 0 | 0.11509365173486417 | 3.5959375000000002 | 0 | 0.0034356313950705724 | 16 | 0 | 0.11509365173486417 | 3.5959375000000002 | 0 | 0.0034356313950705724 triton_poi_fused__native_batch_norm_legi | 14 | 0 | 0.1704672000243914 | 2.4044285714285714 | 0 | 0.00508857313505646 | 14 | 0 | 0.1704672000243914 | 2.4044285714285714 | 0 | 0.00508857313505646 triton_poi_fused__native_batch_norm_legi | 58 | 0 | 2.307520779930795 | 8.190706896551722 | 0 | 0.06888121731136704 | 58 | 0 | 2.307520779930795 | 8.190706896551722 | 0 | 0.06888121731136704 triton_per_fused__native_batch_norm_legi | 29 | 0 | 0.037243248971881276 | 3.0277586206896556 | 0 | 0.001111738775280038 | 29 | 0 | 0.037243248971881276 | 3.0277586206896556 | 0 | 0.001111738775280038 triton_poi_fused__native_batch_norm_legi | 20 | 0 | 0.04741699795428918 | 2.2911500000000005 | 0 | 0.0014154327747549007 | 20 | 0 | 0.04741699795428918 | 2.2911500000000005 | 0 | 0.0014154327747549007 triton_per_fused__native_batch_norm_legi | 25 | 0 | 0.13357016893727824 | 3.37536 | 0 | 0.003987169222008305 | 25 | 0 | 0.13357016893727824 | 3.37536 | 0 | 0.003987169222008305 triton_poi_fused__native_batch_norm_legi | 13 | 0 | 0.3089862268300253 | 2.8111538461538457 | 0 | 0.009223469457612694 | 13 | 0 | 0.3089862268300253 | 2.8111538461538457 | 0 | 0.009223469457612694 triton_poi_fused__native_batch_norm_legi | 17 | 0 | 0.3129385387909844 | 2.673 | 0 | 0.009341448919133863 | 17 | 0 | 0.3129385387909844 | 2.673 | 0 | 0.009341448919133863 triton_per_fused__native_batch_norm_legi | 19 | 0 | 0.2215568162533158 | 3.8837368421052636 | 0 | 0.0066136363060691275 | 19 | 0 | 0.2215568162533158 | 3.8837368421052636 | 0 | 0.0066136363060691275 std::enable_if<!(false), void>::type int | 23 | 504916805.19297093 | 1.0118296096314707 | 8.113913043478261 | 0.0025893169497075447 | 0.030203868944223014 | 23 | 504916805.19297093 | 1.0118296096314707 | 8.113913043478261 | 0.0025893169497075447 | 0.030203868944223014 triton_poi_fused_add_copy__38 | 56 | 0 | 0 | 2.132482142857143 | 0 | 0 | 56 | 0 | 0 | 2.132482142857143 | 0 | 0 triton_poi_fused_convolution_0 | 18 | 0 | 0.43458610794936897 | 2.773333333333334 | 0 | 0.012972719640279667 | 18 | 0 | 0.43458610794936897 | 2.773333333333334 | 0 | 0.012972719640279667 triton_poi_fused_convolution_1 | 17 | 0 | 0.028816312469162712 | 2.6145882352941174 | 0 | 0.0008601884319153051 | 17 | 0 | 0.028816312469162712 | 2.6145882352941174 | 0 | 0.0008601884319153051 void convolve_common_engine_float_NHWC<f | 44 | 8641868995.31118 | 0.024730540008465626 | 25.87327272727273 | 0.04431727689903169 | 0.0007382250748795709 | 44 | 8641868995.31118 | 0.024730540008465626 | 25.87327272727273 | 0.04431727689903169 | 0.0007382250748795709 triton_per_fused__native_batch_norm_legi | 12 | 0 | 0.6809930918986744 | 4.82675 | 0 | 0.020328151996975356 | 12 | 0 | 0.6809930918986744 | 4.82675 | 0 | 0.020328151996975356 triton_per_fused__native_batch_norm_legi | 14 | 0 | 0.02883030597936608 | 2.6651428571428575 | 0 | 0.0008606061486377935 | 14 | 0 | 0.02883030597936608 | 2.6651428571428575 | 0 | 0.0008606061486377935 triton_per_fused__native_batch_norm_legi | 16 | 0 | 0.0014658988233201874 | 2.098 | 0 | 4.375817383045335e-05 | 16 | 0 | 0.0014658988233201874 | 2.098 | 0 | 4.375817383045335e-05 triton_poi_fused__native_batch_norm_legi | 13 | 0 | 0.9926297180284697 | 3.2367692307692306 | 0 | 0.02963073785159611 | 13 | 0 | 0.9926297180284697 | 3.2367692307692306 | 0 | 0.02963073785159611 triton_poi_fused__native_batch_norm_legi | 9 | 0 | 1.3008817095666507 | 3.0863333333333336 | 0 | 0.03883228983781048 | 9 | 0 | 1.3008817095666507 | 3.0863333333333336 | 0 | 0.03883228983781048 void at::native::(anonymous namespace):: | 98 | 0 | 0.09174335613709389 | 4.408520408163265 | 0 | 0.0027386076458833994 | 98 | 0 | 0.09174335613709389 | 4.408520408163265 | 0 | 0.0027386076458833994 void at::native::vectorized_elementwise_ | 7 | 0 | 0 | 1.7278571428571428 | 0 | 0 | 7 | 0 | 0 | 1.7278571428571428 | 0 | 0 ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/149697 Approved by: https://github.com/eellison, https://github.com/shunting314 |
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PyTorch MergeBot
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5e03433443 |
Revert "Inductor logging + analysis of torch.profile (#149697)"
This reverts commit |
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Gabriel Ferns
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e5afbe3124 |
Inductor logging + analysis of torch.profile (#149697)
Prereqs: - https://github.com/pytorch/pytorch/pull/152708 Features: 1. Adds inductor's estimate of flops and bandwidth to the json trace events that perfetto uses. 1. Only use the tflops estimation from triton if we don't have the info from the datasheet because Triton's estimates are inaccurate. I have a backlog item to fix triton flops estimation upstream. New `DeviceInfo` class, and new function `get_device_tflops`. 1. New helpers `countable_fx` and `count_flops_fx` helps get the flops of an `fx.Node`. 1. Extends Triton `torch.profiler` logging to `DebugAutotuner`. 1. New script `profile_analysis.py`: `--augment_trace` adds perf estimates to any perfetto json trace, `--analyze` creates a summary table of these perf estimates, and `--diff` will compare two traces side by side: ```python Device(NVIDIA H100, 0): Kernel Name | resnet Kernel Count | resnet FLOPS | resnet bw gbps | resnet Dur (ms) | resnet Achieved FLOPS % | resnet Achieved Bandwidth % | newresnet Kernel Count | newresnet FLOPS | newresnet bw gbps | newresnet Dur (ms) | newresnet Achieved FLOPS % | newresnet Achieved Bandwidth % --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- triton_poi_fused__native_batch_norm_legi | 24 | 0 | 0.11395268248131513 | 2.5919166666666666 | 0 | 0.003401572611382541 | 24 | 0 | 0.11395268248131513 | 2.5919166666666666 | 0 | 0.003401572611382541 sm90_xmma_fprop_implicit_gemm_f32f32_tf3 | 142 | 16932673552.422373 | 0.2585007824198784 | 12.441619718309857 | 0.08683422334575583 | 0.007716441266265022 | 142 | 16932673552.422373 | 0.2585007824198784 | 12.441619718309857 | 0.08683422334575583 | 0.007716441266265022 triton_red_fused__native_batch_norm_legi | 39 | 0 | 0.13990024992108846 | 5.752589743589743 | 0 | 0.004176126863316074 | 39 | 0 | 0.13990024992108846 | 5.752589743589743 | 0 | 0.004176126863316074 triton_poi_fused__native_batch_norm_legi | 25 | 0 | 0.31824055917536503 | 2.5291999999999994 | 0 | 0.009499718184339253 | 25 | 0 | 0.31824055917536503 | 2.5291999999999994 | 0 | 0.009499718184339253 void cutlass::Kernel2<cutlass_80_tensoro | 98 | 16211056473.596165 | 0.42972434051025826 | 7.130408163265306 | 0.08313362294151874 | 0.012827592254037562 | 98 | 16211056473.596165 | 0.42972434051025826 | 7.130408163265306 | 0.08313362294151874 | 0.012827592254037562 triton_red_fused__native_batch_norm_legi | 73 | 0 | 0.3225381327611705 | 9.987068493150682 | 0 | 0.009628003963020014 | 73 | 0 | 0.3225381327611705 | 9.987068493150682 | 0 | 0.009628003963020014 triton_poi_fused__native_batch_norm_legi | 15 | 0 | 1.4491211346487216 | 4.439333333333333 | 0 | 0.043257347302946926 | 15 | 0 | 1.4491211346487216 | 4.439333333333333 | 0 | 0.043257347302946926 void cutlass::Kernel2<cutlass_80_tensoro | 186 | 14501701145.337954 | 0.2667131401910989 | 7.873865591397849 | 0.07436769818122027 | 0.007961586274361157 | 186 | 14501701145.337954 | 0.2667131401910989 | 7.873865591397849 | 0.07436769818122027 | 0.007961586274361157 triton_poi_fused__native_batch_norm_legi | 33 | 0 | 1.4924556538193923 | 4.3101515151515155 | 0 | 0.044550915039384846 | 33 | 0 | 1.4924556538193923 | 4.3101515151515155 | 0 | 0.044550915039384846 triton_red_fused__native_batch_norm_legi | 29 | 0 | 0.25562590522631107 | 6.296275862068965 | 0 | 0.007630624036606301 | 29 | 0 | 0.25562590522631107 | 6.296275862068965 | 0 | 0.007630624036606301 triton_poi_fused__native_batch_norm_legi | 13 | 0 | 0.5870562174192726 | 2.7397692307692307 | 0 | 0.01752406619162008 | 13 | 0 | 0.5870562174192726 | 2.7397692307692307 | 0 | 0.01752406619162008 triton_poi_fused__native_batch_norm_legi | 34 | 0 | 0.41409928846284 | 2.853588235294117 | 0 | 0.012361172789935523 | 34 | 0 | 0.41409928846284 | 2.853588235294117 | 0 | 0.012361172789935523 triton_per_fused__native_batch_norm_legi | 34 | 0 | 0.11705315007018151 | 3.460647058823529 | 0 | 0.0034941238826919864 | 34 | 0 | 0.11705315007018151 | 3.460647058823529 | 0 | 0.0034941238826919864 triton_poi_fused__native_batch_norm_legi | 16 | 0 | 0.17207853197124584 | 2.3459375000000002 | 0 | 0.005136672596156592 | 16 | 0 | 0.17207853197124584 | 2.3459375000000002 | 0 | 0.005136672596156592 triton_per_fused__native_batch_norm_legi | 30 | 0 | 0.2639714322022256 | 6.131199999999999 | 0 | 0.007879744244842555 | 30 | 0 | 0.2639714322022256 | 6.131199999999999 | 0 | 0.007879744244842555 sm90_xmma_fprop_implicit_gemm_f32f32_tf3 | 100 | 11875430356.891787 | 0.19494470869421385 | 16.36534 | 0.06089964285585531 | 0.005819245035648175 | 100 | 11875430356.891787 | 0.19494470869421385 | 16.36534 | 0.06089964285585531 | 0.005819245035648175 triton_poi_fused__native_batch_norm_legi | 8 | 0 | 0.9854096626224687 | 3.2757500000000004 | 0 | 0.029415213809625928 | 8 | 0 | 0.9854096626224687 | 3.2757500000000004 | 0 | 0.029415213809625928 void cublasLt::splitKreduce_kernel<32, 1 | 56 | 34377923395.147064 | 0.8310300045762317 | 3.4199999999999986 | 0.17629704305203628 | 0.024806865808245714 | 56 | 34377923395.147064 | 0.8310300045762317 | 3.4199999999999986 | 0.17629704305203628 | 0.024806865808245714 triton_poi_fused__native_batch_norm_legi | 23 | 0 | 0.9944002965861103 | 3.2431304347826084 | 0 | 0.02968359094286896 | 23 | 0 | 0.9944002965861103 | 3.2431304347826084 | 0 | 0.02968359094286896 triton_per_fused__native_batch_norm_legi | 10 | 0 | 0.1826801058931057 | 4.428800000000001 | 0 | 0.00545313748934644 | 10 | 0 | 0.1826801058931057 | 4.428800000000001 | 0 | 0.00545313748934644 triton_poi_fused__native_batch_norm_legi | 10 | 0 | 0.3168973585366449 | 2.5471999999999997 | 0 | 0.009459622642884923 | 10 | 0 | 0.3168973585366449 | 2.5471999999999997 | 0 | 0.009459622642884923 triton_poi_fused__native_batch_norm_legi | 34 | 0 | 1.1463614897015777 | 4.124323529411764 | 0 | 0.03421974596124114 | 34 | 0 | 1.1463614897015777 | 4.124323529411764 | 0 | 0.03421974596124114 void cask_plugin_cudnn::xmma_cudnn::init | 44 | 44045510816.64277 | 2.0661232850348643 | 3.6887499999999993 | 0.22587441444432194 | 0.06167532194133924 | 44 | 44045510816.64277 | 2.0661232850348643 | 3.6887499999999993 | 0.22587441444432194 | 0.06167532194133924 sm90_xmma_fprop_implicit_gemm_f32f32_tf3 | 95 | 7876855400.165316 | 0.4694941555946739 | 18.224315789473682 | 0.04039413025725802 | 0.014014750913273854 | 95 | 7876855400.165316 | 0.4694941555946739 | 18.224315789473682 | 0.04039413025725802 | 0.014014750913273854 triton_per_fused__native_batch_norm_legi | 41 | 0 | 0.06825669875995298 | 3.0384146341463416 | 0 | 0.002037513395819492 | 41 | 0 | 0.06825669875995298 | 3.0384146341463416 | 0 | 0.002037513395819492 triton_poi_fused__native_batch_norm_legi | 23 | 0 | 0.08808154712430301 | 2.3275652173913044 | 0 | 0.0026292999141582997 | 23 | 0 | 0.08808154712430301 | 2.3275652173913044 | 0 | 0.0026292999141582997 triton_per_fused__native_batch_norm_legi | 40 | 0 | 0.18179321034952417 | 4.556825 | 0 | 0.005426662995508183 | 40 | 0 | 0.18179321034952417 | 4.556825 | 0 | 0.005426662995508183 triton_poi_fused__native_batch_norm_legi | 15 | 0 | 0.5887415155454232 | 2.783866666666667 | 0 | 0.017574373598370836 | 15 | 0 | 0.5887415155454232 | 2.783866666666667 | 0 | 0.017574373598370836 void cutlass::Kernel2<cutlass_80_tensoro | 38 | 14242013806.264643 | 0.256592404353939 | 7.217631578947369 | 0.0730359682372546 | 0.007659474756834 | 38 | 14242013806.264643 | 0.256592404353939 | 7.217631578947369 | 0.0730359682372546 | 0.007659474756834 triton_poi_fused__native_batch_norm_legi | 21 | 0 | 0.5842860973430516 | 2.7779047619047623 | 0 | 0.017441376040091088 | 21 | 0 | 0.5842860973430516 | 2.7779047619047623 | 0 | 0.017441376040091088 triton_per_fused__native_batch_norm_legi | 16 | 0 | 0.11509365173486417 | 3.5959375000000002 | 0 | 0.0034356313950705724 | 16 | 0 | 0.11509365173486417 | 3.5959375000000002 | 0 | 0.0034356313950705724 triton_poi_fused__native_batch_norm_legi | 14 | 0 | 0.1704672000243914 | 2.4044285714285714 | 0 | 0.00508857313505646 | 14 | 0 | 0.1704672000243914 | 2.4044285714285714 | 0 | 0.00508857313505646 triton_poi_fused__native_batch_norm_legi | 58 | 0 | 2.307520779930795 | 8.190706896551722 | 0 | 0.06888121731136704 | 58 | 0 | 2.307520779930795 | 8.190706896551722 | 0 | 0.06888121731136704 triton_per_fused__native_batch_norm_legi | 29 | 0 | 0.037243248971881276 | 3.0277586206896556 | 0 | 0.001111738775280038 | 29 | 0 | 0.037243248971881276 | 3.0277586206896556 | 0 | 0.001111738775280038 triton_poi_fused__native_batch_norm_legi | 20 | 0 | 0.04741699795428918 | 2.2911500000000005 | 0 | 0.0014154327747549007 | 20 | 0 | 0.04741699795428918 | 2.2911500000000005 | 0 | 0.0014154327747549007 triton_per_fused__native_batch_norm_legi | 25 | 0 | 0.13357016893727824 | 3.37536 | 0 | 0.003987169222008305 | 25 | 0 | 0.13357016893727824 | 3.37536 | 0 | 0.003987169222008305 triton_poi_fused__native_batch_norm_legi | 13 | 0 | 0.3089862268300253 | 2.8111538461538457 | 0 | 0.009223469457612694 | 13 | 0 | 0.3089862268300253 | 2.8111538461538457 | 0 | 0.009223469457612694 triton_poi_fused__native_batch_norm_legi | 17 | 0 | 0.3129385387909844 | 2.673 | 0 | 0.009341448919133863 | 17 | 0 | 0.3129385387909844 | 2.673 | 0 | 0.009341448919133863 triton_per_fused__native_batch_norm_legi | 19 | 0 | 0.2215568162533158 | 3.8837368421052636 | 0 | 0.0066136363060691275 | 19 | 0 | 0.2215568162533158 | 3.8837368421052636 | 0 | 0.0066136363060691275 std::enable_if<!(false), void>::type int | 23 | 504916805.19297093 | 1.0118296096314707 | 8.113913043478261 | 0.0025893169497075447 | 0.030203868944223014 | 23 | 504916805.19297093 | 1.0118296096314707 | 8.113913043478261 | 0.0025893169497075447 | 0.030203868944223014 triton_poi_fused_add_copy__38 | 56 | 0 | 0 | 2.132482142857143 | 0 | 0 | 56 | 0 | 0 | 2.132482142857143 | 0 | 0 triton_poi_fused_convolution_0 | 18 | 0 | 0.43458610794936897 | 2.773333333333334 | 0 | 0.012972719640279667 | 18 | 0 | 0.43458610794936897 | 2.773333333333334 | 0 | 0.012972719640279667 triton_poi_fused_convolution_1 | 17 | 0 | 0.028816312469162712 | 2.6145882352941174 | 0 | 0.0008601884319153051 | 17 | 0 | 0.028816312469162712 | 2.6145882352941174 | 0 | 0.0008601884319153051 void convolve_common_engine_float_NHWC<f | 44 | 8641868995.31118 | 0.024730540008465626 | 25.87327272727273 | 0.04431727689903169 | 0.0007382250748795709 | 44 | 8641868995.31118 | 0.024730540008465626 | 25.87327272727273 | 0.04431727689903169 | 0.0007382250748795709 triton_per_fused__native_batch_norm_legi | 12 | 0 | 0.6809930918986744 | 4.82675 | 0 | 0.020328151996975356 | 12 | 0 | 0.6809930918986744 | 4.82675 | 0 | 0.020328151996975356 triton_per_fused__native_batch_norm_legi | 14 | 0 | 0.02883030597936608 | 2.6651428571428575 | 0 | 0.0008606061486377935 | 14 | 0 | 0.02883030597936608 | 2.6651428571428575 | 0 | 0.0008606061486377935 triton_per_fused__native_batch_norm_legi | 16 | 0 | 0.0014658988233201874 | 2.098 | 0 | 4.375817383045335e-05 | 16 | 0 | 0.0014658988233201874 | 2.098 | 0 | 4.375817383045335e-05 triton_poi_fused__native_batch_norm_legi | 13 | 0 | 0.9926297180284697 | 3.2367692307692306 | 0 | 0.02963073785159611 | 13 | 0 | 0.9926297180284697 | 3.2367692307692306 | 0 | 0.02963073785159611 triton_poi_fused__native_batch_norm_legi | 9 | 0 | 1.3008817095666507 | 3.0863333333333336 | 0 | 0.03883228983781048 | 9 | 0 | 1.3008817095666507 | 3.0863333333333336 | 0 | 0.03883228983781048 void at::native::(anonymous namespace):: | 98 | 0 | 0.09174335613709389 | 4.408520408163265 | 0 | 0.0027386076458833994 | 98 | 0 | 0.09174335613709389 | 4.408520408163265 | 0 | 0.0027386076458833994 void at::native::vectorized_elementwise_ | 7 | 0 | 0 | 1.7278571428571428 | 0 | 0 | 7 | 0 | 0 | 1.7278571428571428 | 0 | 0 ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/149697 Approved by: https://github.com/eellison, https://github.com/shunting314 |
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David Berard
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43496e9b90 |
[NJT] fix flop counter for SDPA & test (#147032)
Fixes 3 issues: 1. The test wasn't actually testing SDPA: both were checking cuda, and the inputs to SDPA were not transposed. 2. FlopCounterMode has been renamed _FlopCounterMode (and a wrapper named FlopCounterMode has been added) 3. offsets_to_list also needs to ignore the actual offset values if offsets is a meta tensor. Differential Revision: [D69558785](https://our.internmc.facebook.com/intern/diff/D69558785) Pull Request resolved: https://github.com/pytorch/pytorch/pull/147032 Approved by: https://github.com/jbschlosser |
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Aaron Orenstein
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2f9d378f7b |
PEP585 update - torch/utils (#145201)
See #145101 for details. Pull Request resolved: https://github.com/pytorch/pytorch/pull/145201 Approved by: https://github.com/bobrenjc93 |
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Luca Wehrstedt
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a0d2c09115 |
Add flop formula for _scaled_mm (#144973)
This will make it work correctly with the partitioner's AutoAC Pull Request resolved: https://github.com/pytorch/pytorch/pull/144973 Approved by: https://github.com/jeffdaily |
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PyTorch MergeBot
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6559374494 |
Revert "Add flop formula for _scaled_mm (#144872)"
This reverts commit
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Luca Wehrstedt
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f31452268b |
Add flop formula for _scaled_mm (#144872)
This will make it work correctly with the partitioner's AutoAC Pull Request resolved: https://github.com/pytorch/pytorch/pull/144872 Approved by: https://github.com/vkuzo |
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Aaron Orenstein
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45ef3309e3 |
[BE] typing for decorators (#144161)
Summary: Untyped decorators strip annotations from the decorated items. - _compile - _inductor/fx_passes/post_grad - _inductor/lowering - _library/custom_ops - _meta_registrations - _ops - _refs/nn/functional - ao/quantization/quantizer/xnnpack_quantizer_utils - distributed/_composable/contract - fx/experimental/graph_gradual_typechecker - fx/experimental/migrate_gradual_types/constraint_generator - optim/optimizer - signal/windows/windows - testing/_internal/common_device_type - torch/_inductor/decomposition - utils/flop_counter Test Plan: unit tests Differential Revision: D62302684 Pull Request resolved: https://github.com/pytorch/pytorch/pull/144161 Approved by: https://github.com/Skylion007, https://github.com/albanD |
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Florian (Feuermagier)
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4fa72168ea |
FlopCounterMode: Decompose ops for inference mode (#138508)
Fixes #126268 I've basically followed @ezyang suggestion (I think) to use `func.decompose(...)`. Since `__torch_dispatch__` won't be called a second time for the same op, I've added a second `TorchDispatchMode` (`_DecomposedCounterMode`) that simpy dispatches to the parent flop counter. Using `self` as the inner context manager is not possible, since the second call to `__enter__` would re-initialize the counter's tracking state. Let me know if there's something wrong with this implementation, since I'm quite unsure how the decomposition thing actually works :D Pull Request resolved: https://github.com/pytorch/pytorch/pull/138508 Approved by: https://github.com/ezyang Co-authored-by: Edward Z. Yang <ezyang@meta.com> |
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PyTorch MergeBot
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727f1a6da9 |
Revert "FlopCounterMode: Decompose ops for inference mode (#138508)"
This reverts commit
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Florian (Feuermagier)
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f915409c26 |
FlopCounterMode: Decompose ops for inference mode (#138508)
Fixes #126268 I've basically followed @ezyang suggestion (I think) to use `func.decompose(...)`. Since `__torch_dispatch__` won't be called a second time for the same op, I've added a second `TorchDispatchMode` (`_DecomposedCounterMode`) that simpy dispatches to the parent flop counter. Using `self` as the inner context manager is not possible, since the second call to `__enter__` would re-initialize the counter's tracking state. Let me know if there's something wrong with this implementation, since I'm quite unsure how the decomposition thing actually works :D Pull Request resolved: https://github.com/pytorch/pytorch/pull/138508 Approved by: https://github.com/ezyang |
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Tom Ritchford
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c0582fd0f8 |
Remove unused Python variables in torch/[b-z]* (#136963)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/136963 Approved by: https://github.com/ezyang |
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Aaron Gokaslan
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afaa5fcecb |
[BE][Ez]: FURB142,FURB92 misc preview fixes (#133880)
Fixes some miscellaneous code quality issues with some refurb rules that have not been enabled yet. Pull Request resolved: https://github.com/pytorch/pytorch/pull/133880 Approved by: https://github.com/soulitzer, https://github.com/malfet |
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David Berard
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1962f9475f |
[NJT][flop counter] attention: if offsets are fake, use max seqlen (#132356)
The flop counter is used by the partitioner, in which case the tensors passed in can be fake. The flop computations for nested attention use the offsets to determine the actual amount of compute that will be done. But when the offsets are fake, we end up with unbacked symints (from `(offsets[1:] - offsets[:-1]).to_list()`). If we find that the offsets are fake or functional tensors, then use the max sequence length instead. Repro: https://gist.github.com/davidberard98/903fb3e586edb6d1d466786e1a610eba Differential Revision: [D60597463](https://our.internmc.facebook.com/intern/diff/D60597463) Pull Request resolved: https://github.com/pytorch/pytorch/pull/132356 Approved by: https://github.com/soulitzer |
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Michael Lazos
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93979e7063 |
Skip frame if torch dispatch mode enabled (#131828)
Fixes https://github.com/pytorch/pytorch/issues/105929 We now skip frames if a dispatch mode is enabled. Pull Request resolved: https://github.com/pytorch/pytorch/pull/131828 Approved by: https://github.com/bdhirsh, https://github.com/anijain2305 |
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PyTorch MergeBot
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5ced63a005 |
Revert "[BE] typing for decorators - utils/flop_counter (#131580)"
This reverts commit
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rzou
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a3cdbd8189 |
[FlopCounterMode] Fix register_flop_formula (#131777)
Previously, FlopCounterMode would ignore any custom ops registered through `register_flop_formula`. The problem was: - register_flop_formula(target) requires target to be an OpOverloadPacket. - register_flop_formula used register_decomposition to populate its registry - register_decomposition decomposes the OpOverloadPacket into OpOverload before putting it into the registry - FlopCounterMode ignores OpOverloads in its registry (it assumes the registry is a dictionary mapping OpOverloadPacket to flop formula). register_decomposition is too heavy of a hammer, plus this isn't a decomposition, so I changed the registration mechanism. Test Plan: - new tests Pull Request resolved: https://github.com/pytorch/pytorch/pull/131777 Approved by: https://github.com/Chillee |
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Aaron Orenstein
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81c26ba5ae |
[BE] typing for decorators - utils/flop_counter (#131580)
See #131429 Pull Request resolved: https://github.com/pytorch/pytorch/pull/131580 Approved by: https://github.com/oulgen, https://github.com/zou3519 ghstack dependencies: #131568, #131569, #131570, #131571, #131572, #131573, #131574, #131575, #131576, #131577, #131578, #131579 |
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Aaron Orenstein
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5a0068cc69 |
[BE] mypy: disallow untyped decorators (#131428)
Untyped decorators strip the types from their decorated function so even if the underlying function is fully typed then callers to it don't get any benefit from type annotations. Step 1 - Enable the error and override in all the offending files. #131429 Pull Request resolved: https://github.com/pytorch/pytorch/pull/131428 Approved by: https://github.com/justinchuby, https://github.com/oulgen |
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eqy
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f845a7a91a |
[cuDNN][SDPA] Remove TORCH_CUDNN_SDPA_ENABLED=1, enable cuDNN SDPA by default on H100 and 2nd on other archs >= sm80 (#125343)
Looks like one of the first failures seen is `test_causal_variants_compile_causal_variant_CausalVariant_LOWER_RIGHT_shape0_cuda` when `test_causal_variants_causal_variant_CausalVariant_LOWER_RIGHT_shape0_cuda` passes. What seems interesting here is that the `torch.compile` version fails while the eager version passes. Not sure what the difference would be here... Nevertheless, is there a recommended mechanism to skip cuDNN SDPA as a backend for this test? CC @drisspg Pull Request resolved: https://github.com/pytorch/pytorch/pull/125343 Approved by: https://github.com/Skylion007 |
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PyTorch MergeBot
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999eec8dea |
Revert "[cuDNN][SDPA] Remove TORCH_CUDNN_SDPA_ENABLED=1, enable cuDNN SDPA by default on H100 and 2nd on other archs >= sm80 (#125343)"
This reverts commit
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Eddie Yan
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b7e7a4cb01 |
[cuDNN][SDPA] Remove TORCH_CUDNN_SDPA_ENABLED=1, enable cuDNN SDPA by default on H100 and 2nd on other archs >= sm80 (#125343)
Looks like one of the first failures seen is `test_causal_variants_compile_causal_variant_CausalVariant_LOWER_RIGHT_shape0_cuda` when `test_causal_variants_causal_variant_CausalVariant_LOWER_RIGHT_shape0_cuda` passes. What seems interesting here is that the `torch.compile` version fails while the eager version passes. Not sure what the difference would be here... Nevertheless, is there a recommended mechanism to skip cuDNN SDPA as a backend for this test? CC @drisspg Pull Request resolved: https://github.com/pytorch/pytorch/pull/125343 Approved by: https://github.com/Skylion007 |
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PyTorch MergeBot
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817ce6835b |
Revert "[cuDNN][SDPA] Remove TORCH_CUDNN_SDPA_ENABLED=1, enable cuDNN SDPA by default on H100 and 2nd on other archs >= sm80 (#125343)"
This reverts commit
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eqy
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4c971932e8 |
[cuDNN][SDPA] Remove TORCH_CUDNN_SDPA_ENABLED=1, enable cuDNN SDPA by default on H100 and 2nd on other archs >= sm80 (#125343)
Looks like one of the first failures seen is `test_causal_variants_compile_causal_variant_CausalVariant_LOWER_RIGHT_shape0_cuda` when `test_causal_variants_causal_variant_CausalVariant_LOWER_RIGHT_shape0_cuda` passes. What seems interesting here is that the `torch.compile` version fails while the eager version passes. Not sure what the difference would be here... Nevertheless, is there a recommended mechanism to skip cuDNN SDPA as a backend for this test? CC @drisspg Pull Request resolved: https://github.com/pytorch/pytorch/pull/125343 Approved by: https://github.com/Skylion007 |
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Aaron Orenstein
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8db9dfa2d7 |
Flip default value for mypy disallow_untyped_defs [9/11] (#127846)
See #127836 for details. Pull Request resolved: https://github.com/pytorch/pytorch/pull/127846 Approved by: https://github.com/ezyang ghstack dependencies: #127842, #127843, #127844, #127845 |
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David Berard
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9e85d3d830 |
Add "accurate" FlopCounter implementations for NestedTensor SDPA kernels (#125776)
This adds implementations for: * _flash_attention_forward * _efficient_attention_forward * _flash_attention_backward * _efficient_attention_backward These flop counts are implemented as follows: * Unbind the batch elements * Calculate flops individually for each element in the batch * Sum the final result This means that we are accessing the concrete sequence lengths (which could be slow, and may trigger a GPU/CPU sync); but, the FLOP numbers will vary with the sparsity of the NestedTensor - more accurate than if we just assumed we padded everything. Differential Revision: [D57120139](https://our.internmc.facebook.com/intern/diff/D57120139) Pull Request resolved: https://github.com/pytorch/pytorch/pull/125776 Approved by: https://github.com/Chillee |
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albanD
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76a26a885d |
Add module tracker (#125352)
This does a few things that were originally a few PRs but I am on a new machine and don't have ghstack. If it is too problematic to review, I can re-split, just let me know. This does: - Cleanup context manager use in test_flop_counter - Remove need for mod argument in FlopCounterMode, warning about it - Re-implement a Module tracker from scratch using global forward Module use and multi_grad_hook (we cannot use global backward Module hook because they don't look for nested Tensor and they're custom Function based instead of multi_grad_hook). - Update FlopCouterMode to use the new ModuleTracker. All the existing test suite passes as-is (only changes there are new tests and refactoring mentioned above) Pull Request resolved: https://github.com/pytorch/pytorch/pull/125352 Approved by: https://github.com/mikaylagawarecki |
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Manish Rajpal
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2bbfb70831 |
ignore unsupported module from flop counter (#125346)
Summary: Torchscript modules do not support forward hooks and thus can't work with flop_counter context manager for hierarchical output by passing a module to FlopCounterMode on construction. Currently any module that includes a script module causes an exception to be thrown so adding a try/catch to ignore any script modules for forward hooks. Test Plan: CI Signals Differential Revision: D56850661 Pull Request resolved: https://github.com/pytorch/pytorch/pull/125346 Approved by: https://github.com/842974287 |
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Manish Rajpal
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da44d2f7fb |
split out flop counting its own method (#125061)
Summary: Modularizing code for reuse by splitting __torch_dispatch__ to move flop counting to its own method. Test Plan: unit tests Differential Revision: D56644523 Pull Request resolved: https://github.com/pytorch/pytorch/pull/125061 Approved by: https://github.com/842974287 |
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chilli
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84580f76d9 |
fix flop counter issue with out parameters (#123768)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123768 Approved by: https://github.com/zou3519 |
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chilli
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8bfc87ce74 |
fixed flop counter formula for conv transposed backwards pass (#119874)
Fixes #119806 Pull Request resolved: https://github.com/pytorch/pytorch/pull/119874 Approved by: https://github.com/zou3519 ghstack dependencies: #119521 |
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Aaron Gokaslan
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3fe437b24b |
[BE]: Update flake8 to v6.1.0 and fix lints (#116591)
Updates flake8 to v6.1.0 and fixes a few lints using sed and some ruff tooling.
- Replace `assert(0)` with `raise AssertionError()`
- Remove extraneous parenthesis i.e.
- `assert(a == b)` -> `assert a == b`
- `if(x > y or y < z):`->`if x > y or y < z:`
- And `return('...')` -> `return '...'`
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116591
Approved by: https://github.com/albanD, https://github.com/malfet
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Aaron Gokaslan
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ee5d981249 |
[BE]: Enable RUFF PERF402 and apply fixes (#115505)
* Enable PERF402. Makes code more efficient and succinct by removing useless list copies that could be accomplished either via a list constructor or extend call. All test cases have noqa added since performance is not as sensitive in that folder. Pull Request resolved: https://github.com/pytorch/pytorch/pull/115505 Approved by: https://github.com/malfet |
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ChanBong
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5e10dd2c78 |
fix docstring issues in torch.utils (#113335)
Fixes #112634 Fixes all the issues listed except in `torch/utils/_pytree.py` as the file no longer exists. ### Error counts |File | Count Before | Count now| |---- | ---- | ---- | |`torch/utils/collect_env.py` | 39 | 25| |`torch/utils/cpp_extension.py` | 51 | 13| |`torch/utils/flop_counter.py` | 25 | 8| |`torch/utils/_foreach_utils.py.py` | 2 | 0| |`torch/utils/_python_dispatch.py.py` | 26 | 25| |`torch/utils/backend_registration.py` | 15 | 4| |`torch/utils/checkpoint.py` | 29 | 21| Pull Request resolved: https://github.com/pytorch/pytorch/pull/113335 Approved by: https://github.com/ezyang |
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chilli
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74adb4cccc |
Updated flop counter to accept pytree inputs/outputs (#111990)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111990 Approved by: https://github.com/ezyang |
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chilli
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ada65508d2 |
Add option to flop counter formula registration to get raw values (#110591)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110591 Approved by: https://github.com/awgu ghstack dependencies: #110501, #110504 |
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chilli
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e686341f64 |
Consider that ops can be fused into cat in the min-cut partitioner (#110501)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110501 Approved by: https://github.com/eellison |
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Justin Chu
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4cc1745b13 |
[BE] f-stringify torch/ and scripts (#105538)
This PR is a follow up on the pyupgrade series to convert more strings to use f-strings using `flynt`. - https://docs.python.org/3/reference/lexical_analysis.html#f-strings - https://pypi.org/project/flynt/ Command used: ``` flynt torch/ -ll 120 flynt scripts/ -ll 120 flynt tools/ -ll 120 ``` and excluded `collect_env.py` Pull Request resolved: https://github.com/pytorch/pytorch/pull/105538 Approved by: https://github.com/ezyang, https://github.com/malfet |
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Nikita Shulga
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5837e95d30 |
[Reland] Update mypy to 1.4.1 (#105227)
This PR re-lands - [Typing] Fix PEP 484 Violation (#105022) - Update mypy to 1.4.1 (#91983) That were reverted due to the conflict with internal source repo. Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional) Plus few real fixes: - Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi` - Add missing return statement to `torch._export. deserialize_graph` - Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights` - Add assert it `torch/optim/optimizer.py` that Optional list is not None TODO (in followup PR): - Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py` Unrelated, to bypass CI failures due to the gcc9 dependency update in Ubuntu-18.04: - Add hack to squash older libstdc++ from conda environment in favor one from OS to `.ci/docker/install_conda.sh` - Update bazel cuda builds to focal, as with libstdc++-6.0.32 bazel builds loose the ability to catch exceptions (probably because they link with cupti statically, but I could not found where it is done) Pull Request resolved: https://github.com/pytorch/pytorch/pull/105227 Approved by: https://github.com/atalman, https://github.com/albanD, https://github.com/Skylion007 |
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PyTorch MergeBot
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15fd1ea118 |
Revert "[Reland] Update mypy to 1.4.1 (#105227)"
This reverts commit
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Nikita Shulga
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c9c4f8efc3 |
[Reland] Update mypy to 1.4.1 (#105227)
This PR re-lands - [Typing] Fix PEP 484 Violation (#105022) - Update mypy to 1.4.1 (#91983) That were reverted due to the conflict with internal source repo. Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional) Plus few real fixes: - Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi` - Add missing return statement to `torch._export. deserialize_graph` - Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights` - Add assert it `torch/optim/optimizer.py` that Optional list is not None TODO (in followup PR): - Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py` Pull Request resolved: https://github.com/pytorch/pytorch/pull/105227 Approved by: https://github.com/atalman, https://github.com/albanD, https://github.com/Skylion007 |
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PyTorch MergeBot
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3c5a494d7a |
Revert "Update mypy to 1.4.1 (#91983)"
This reverts commit
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Nikita Shulga
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634659e262 |
Update mypy to 1.4.1 (#91983)
Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional) Plus few real fixes: - Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi` - Add missing return statement to `torch._export. deserialize_graph` - Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights` - TODO (in followup PR): - Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py` Pull Request resolved: https://github.com/pytorch/pytorch/pull/91983 Approved by: https://github.com/kit1980, https://github.com/ZainRizvi, https://github.com/huydhn, https://github.com/thiagocrepaldi, https://github.com/aaronenyeshi |
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Andrew Gu
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974525c053 |
De-register forward hooks upon exiting flop counter context (#103744)
This PR fixes https://github.com/pytorch/pytorch/issues/103684. - Instead of registering forward hooks in `__init__()`, do it upon `__enter__()`. - De-register those forward hooks upon `__exit__()`. - Achieve this by saving an additional mapping `_module_to_forward_hook_handles: Dict[nn.Module, _ForwardHookHandles]`. Only the values in the mapping (i.e. not the keys) are useful for this change. (A `List[_ForwardHookHandles]` would suffice.) - The unit test accesses private attributes `_forward_hooks` and `_forward_pre_hooks` :/ Note that this PR is technically not backward compatible since it does not register the hooks upon `__init__()`, which means that you will not get the flops counting without the context manager. Pull Request resolved: https://github.com/pytorch/pytorch/pull/103744 Approved by: https://github.com/Chillee |