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401 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
| 6c79530637 |
multi-kernel matmuls based on varying hint sizes (#156628)
The core idea is to generate multiple matmul kernels using different hints for symbolic variables, then select the most appropriate one at runtime for each unique shape we encounter. You can find some early experimentation details in these posts: https://fb.workplace.com/groups/8940092306109185/posts/9803850776399996/ https://fb.workplace.com/groups/8940092306109185/posts/9695805170537891/ https://fb.workplace.com/groups/257735836456307/posts/906589324904285/ Here’s a graph illustrating the empirically observed worst-case performance if an oracle always selected the least optimal hint for a given runtime size:  This graph illustrates the performance of a hint size of 64 relative to the worst case. Notice that as the runtime sizes increase, the performance gradually approaches the worst case:  This graph shows the performance of a hint size of 4096 — very poor for small sizes, and also suboptimal for some mid-sized shapes:  Finally, here’s the graph that motivated this PR. It illustrates the performance when selecting the best of three kernels generated with three different hints — 64, 256, and 4096:  ## How to review this PR At a high level, this extends @shunting314's multi-kernel abstraction to support varying GEMM choices driven by different hints. A few key points: 1. Unlike reduction kernels, triton template matmuls pass their grid as arguments to the kernel. This PR updates `MultiKernelCall` to support kernels with varying arguments. 2. The `V.graph.sizevars.size_hints` API is extended to accept a `hint_override`, allowing us to substitute the example input’s size hint with a custom value when generating multiple kernels. 3. The choice generation and benchmarking logic is updated to support multiple hint values. One kernel is generated per value in `torch._inductor.config.multi_kernel_hints`, and at runtime, we select the most suitable kernel for the current shape. 4. This PR does not add support for cpp wrapper codegen to keep it scoped. That will be added in the next PR. ## Results The following is a basic test that shows our basic multi kernel working where we no longer show significant variance based on the original hint size: https://gist.github.com/bobrenjc93/ba711d529e65fd65839b34799f6323ec Before ``` Hint\Runtime | 64 | 256 | 4096 --------------------------------------------------- 64 | 0.0948 | 0.3124 | 4.9477 256 | 0.2243 | 0.2256 | 3.3880 4096 | 0.3384 | 0.3404 | 3.3010 ``` After ``` Hint\Runtime | 64 | 256 | 4096 --------------------------------------------------- 64 | 0.0951 | 0.2289 | 3.3013 256 | 0.0952 | 0.2258 | 3.4045 4096 | 0.0957 | 0.2231 | 3.3146 ``` We also see an average speedup of 5.04% for the matrix of all hint/runtime pairs in [64, 4096] for every increment of 64: https://docs.google.com/spreadsheets/d/12TmYUDrAAFASGuP3POXTKPeAvQWIRzKzdrVSIb3vQkA/edit?gid=480268938#gid=480268938  NB: This is just the beginning and I plan on doing more investigation to see further improve on this initial result. For posterity the script used to generate that matrix is here: https://gist.github.com/bobrenjc93/c211fd0bd97fad8f46b91ad9dee76ad0 HUD benchmark runs: base: https://github.com/pytorch/pytorch/actions/runs/15889871988 head: https://github.com/pytorch/pytorch/actions/runs/15889876842 Pull Request resolved: https://github.com/pytorch/pytorch/pull/156628 Approved by: https://github.com/jansel |
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| 7a08755c5f |
[BE][Ez]: Update ruff to 0.12.2 (#157937)
Updates to the latest version of ruff and apply some fixes that it flagged and silence a few new lints Pull Request resolved: https://github.com/pytorch/pytorch/pull/157937 Approved by: https://github.com/ezyang |
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| ed508cc018 |
[inductor][triton] Add experimental use_tensor_descriptor config option (#157906)
Refactor to allow TMA descriptors to be used in general codegen. TMA descriptors can only be generated if the conditions listed in the triton documentation for [make_tensor_descriptor](https://triton-lang.org/main/python-api/generated/triton.language.make_tensor_descriptor.html) are met. Some implementation details: - The `TMACompatibilityChecker` class holds and checks the conditions required for a load / store operation to be represented by a tma descriptor load / store - The current TMA API requires that the innermost block size loads atleast 16 bytes of data. e.g. if the block shape is [YBLOCK, XBLOCK] and the tensor dtype is float32, this requires that XBLOCK >= 4. It is therefore required that the triton heuristics are aware of the minimum block sizes for the IO operations in the kernel. The minimum block sizes are determined in the `TMACompatibilityChecker` class and are passed to the triton heuristics when the block sizes are not static. The heuristic config options are then filtered to ensure that the minimum block size restriction is met. Testing: - Refactored test_torchinductor_strided_blocks.py to also test the `use_tensor_descriptor` option. This requires an upgrade to Triton version 3.4.0: https://github.com/pytorch/pytorch/issues/154206 Pull Request resolved: https://github.com/pytorch/pytorch/pull/157906 Approved by: https://github.com/jansel |
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| 7e83d50845 |
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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| 6ef70edd9a |
Revert "Inductor logging + analysis of torch.profile (#149697)"
This reverts commit 47f10d0ad0dda281c886ff08ac2f938207027316. Reverted https://github.com/pytorch/pytorch/pull/149697 on behalf of https://github.com/malfet due to Looks like it's breaking ROCM tests, see https://hud.pytorch.org/hud/pytorch/pytorch/main/1?per_page=50&name_filter=rocm%20%2F%20linux-jammy ([comment](https://github.com/pytorch/pytorch/pull/149697#issuecomment-3025673908)) |
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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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| c038719731 |
Revert "Inductor logging + analysis of torch.profile (#149697)"
This reverts commit 347ace4c7ac2dbb14799089c30bd01a9ac312791. Reverted https://github.com/pytorch/pytorch/pull/149697 on behalf of https://github.com/huydhn due to Sorry for reverting your change but it seems to fail on ROCm ([comment](https://github.com/pytorch/pytorch/pull/149697#issuecomment-3020006655)) |
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| e3afbb0362 |
[inductor] Add typing to _inductor/ir.py (#149958)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149958 Approved by: https://github.com/Skylion007 |
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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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| 13efb2c858 |
[BE] Deprecate search_autotune_cache (#155302)
We haven't had the offline cache populated in > 1 year, this *should* be safe; if this passes, we can finally go through and rip out the offline cache logic Pull Request resolved: https://github.com/pytorch/pytorch/pull/155302 Approved by: https://github.com/masnesral |
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| dfc31b3345 |
[BE] comments + try to get rid of secondary make_autotune_fn (#156358)
Not sure this will work, but let's try it on the unit tests. The only thing I am worried about is the counters drifting off from their true values, so let the unit tests check that Pull Request resolved: https://github.com/pytorch/pytorch/pull/156358 Approved by: https://github.com/masnesral |
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| 3608737347 |
[ez] fix typo in comment (#156402)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156402 Approved by: https://github.com/BoyuanFeng ghstack dependencies: #156397 |
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| d8bb5ac260 |
[ez] fix typo in select_algorithm.py (#156625)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156625 Approved by: https://github.com/Skylion007, https://github.com/BoyuanFeng ghstack dependencies: #156445 |
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| 4491326fb0 |
[inductor] select_algorithm: add preprocessing fns (#156464)
Summary: # Why - keep code cleaner - modular way to hook up preprocessing steps - expand testability of flows that change which choices are provided e.g. to test performance models and lookup tables by running torch.compile # What - similar to feedback_saver_fns, now there are preprocessing_fns - the existing regex logic is exported into those as a proof of concept Test Plan: ``` buck2 run mode/opt scripts/coconutruben/torchmm:experiment 2>&1 | tee /tmp/epx038 ``` This does not exercise the logic, it just shows that it's safe right now Rollback Plan: Differential Revision: D76946993 Pull Request resolved: https://github.com/pytorch/pytorch/pull/156464 Approved by: https://github.com/masnesral |
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| 6ff6630375 |
[BE][3/16] fix typos in torch/ (torch/_inductor/) (#156313)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156313 Approved by: https://github.com/jingsh |
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| f1331f3f1b |
Revert "[BE][3/16] fix typos in torch/ (torch/_inductor/) (#156313)"
This reverts commit 3627270bdf17b0fb6f528ca1cb87d6f2ec32680a.
Reverted https://github.com/pytorch/pytorch/pull/156313 on behalf of https://github.com/atalman due to export/test_torchbind.py::TestCompileTorchbind::test_compile_error_on_input_aliasing_contents_backend_aot_eager [GH job link](https://github.com/pytorch/pytorch/actions/runs/15804799771/job/44548489912) [HUD commit link](
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| 3627270bdf |
[BE][3/16] fix typos in torch/ (torch/_inductor/) (#156313)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/156313 Approved by: https://github.com/jingsh |
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| 9afee0fa96 |
[inductor] Set num_workers to number of available cpu divided by number of available gpu (#156201)
internal: https://fb.workplace.com/groups/1075192433118967/posts/1689562705015267/?comment_id=1690284241609780¬if_id=1749770611538976¬if_t=work_group_comment&ref=notif Right now it doesn't have the divided by 2 logic yet. Not sure how to tell if we are on a dev machine. Pull Request resolved: https://github.com/pytorch/pytorch/pull/156201 Approved by: https://github.com/masnesral |
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| bb462a6237 |
[cutlass backend] Fix prescreening non-deterministic problem (#156144)
Differential Revision: [D76642615](https://our.internmc.facebook.com/intern/diff/D76642615/) What do we expect to see when we run two identical matmul back to back? We expect to see the second one spending no time in precompilation, autotuning and prescreening. However, the introduction of prescreening bring some non-deterministics-ness. Basically, we have 1. prescreening of first matmul chooses a set of kernels to advance to autotuning 2. autotuning re-does the autotuning of the winners, potentially changing their timings a bit 3. second prescreening results in a slightly different set of kernels 4. since not all timings are present, an autotune is re-done. With this diff: ``` SingleProcess AUTOTUNE benchmarking takes 3.8633 seconds and 134.7364 seconds precompiling for 32 choices and 24.4472 seconds prescreening SingleProcess AUTOTUNE benchmarking takes 0.0003 seconds and 0.0027 seconds precompiling for 32 choices and 0.0006 seconds prescreening ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/156144 Approved by: https://github.com/mlazos |
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| a2a75be0f8 |
Rename inductor cache (#156128)
Requested by Simon on a different PR Pull Request resolved: https://github.com/pytorch/pytorch/pull/156128 Approved by: https://github.com/xmfan |
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| fdf5d97fa8 |
[cutlass backend][ez] Log timings from prescreening (#155757)
Differential Revision: [D76474669](https://our.internmc.facebook.com/intern/diff/D76474669/) Pull Request resolved: https://github.com/pytorch/pytorch/pull/155757 Approved by: https://github.com/ColinPeppler |
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| 6c0b42fd2f |
[inductor][cutlass backend] Log prescreening elpase (#155508)
Differential Revision: [D76311352](https://our.internmc.facebook.com/intern/diff/D76311352/) Pull Request resolved: https://github.com/pytorch/pytorch/pull/155508 Approved by: https://github.com/jingsh |
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| d1947a8707 |
Migrate from lru_cache to cache (#155613)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155613 Approved by: https://github.com/ezyang ghstack dependencies: #155612 |
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| d2f06d2b06 |
[logs] Change autotune data into separate items (#155525)
Summary: Split the autotune data into multiple keys and items : this is better for storage of the data and easier querying. Test Plan: ``` TORCHINDUCTOR_MAX_AUTOTUNE=1 tlp buck run (sample) ``` Rollback Plan: Differential Revision: D76303514 Pull Request resolved: https://github.com/pytorch/pytorch/pull/155525 Approved by: https://github.com/jamesjwu, https://github.com/masnesral |
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| 0b677560e6 |
[inductor] use int64 for large index (#154575)
Split reduction may need add an extra mask to avoid invalid index. Previously we always uses torch.int32 dtype. That causes problem when the tensor numel exceeds 2^31. Fix https://github.com/pytorch/pytorch/issues/154168 Pull Request resolved: https://github.com/pytorch/pytorch/pull/154575 Approved by: https://github.com/ngimel, https://github.com/jansel |
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| eb152ab1dd |
Revert "Inductor logging + analysis of torch.profile (#149697)"
This reverts commit 060838c2312ad207c7afe2c86f8a484afea5f328.
Reverted https://github.com/pytorch/pytorch/pull/149697 on behalf of https://github.com/clee2000 due to broke a bunch of tests internally D76299454, probably also broke rocm inductor/test_analysis.py::TestAnalysisCUDA::test_augment_trace_against_flop_counter_maxat0_cuda_float16 [GH job link](https://github.com/pytorch/pytorch/actions/runs/15545277599/job/43766911025) [HUD commit link](
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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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| 27df0c56b7 |
Revert "[inductor] use int64 for large index (#154575)"
This reverts commit 2596e3d0617852469241be8777cf46db5c83928c.
Reverted https://github.com/pytorch/pytorch/pull/154575 on behalf of https://github.com/clee2000 due to broke inductor/test_op_dtype_prop.py::TestCaseCUDA::test_op_dtype_propagation_add_cuda_int32 [GH job link](https://github.com/pytorch/pytorch/actions/runs/15510656657/job/43673763835) [HUD commit link](
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| 2596e3d061 |
[inductor] use int64 for large index (#154575)
Split reduction may need add an extra mask to avoid invalid index. Previously we always uses torch.int32 dtype. That causes problem when the tensor numel exceeds 2^31. Fix https://github.com/pytorch/pytorch/issues/154168 Pull Request resolved: https://github.com/pytorch/pytorch/pull/154575 Approved by: https://github.com/ngimel, https://github.com/jansel |
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| 400f439670 |
[pt][easy] Rename metadata column (#155365)
Summary: Fixing typo: our logging requires autotuning_data instead of autotune_data, making it consistent Test Plan: Run benchmark, observe in perfetto trace proper name Rollback Plan: Differential Revision: D76159393 Pull Request resolved: https://github.com/pytorch/pytorch/pull/155365 Approved by: https://github.com/masnesral, https://github.com/Skylion007 |
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| 64436c38c9 |
[logs] Add autotuning data (#154771)
Summary: Add autotuning logging data to scuba/chrome trace. Test Plan: ``` TORCHINDUCTOR_MAX_AUTOTUNE=1 tlp buck run //scripts/sashko:compilation_sample ``` Open https://interncache-all.fbcdn.net/manifold/perfetto-artifacts/tree/ui/index.html#!/viewer?local_cache_key=00000000-0000-0000-92db-f23383ebf5b5, search for template_autotuning, see in metadata strides (see screenshot) Differential Revision: D75457770 Pull Request resolved: https://github.com/pytorch/pytorch/pull/154771 Approved by: https://github.com/masnesral, https://github.com/PaulZhang12 |
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| 7e4c097b07 |
Revert "[inductor] Add typing to _inductor/ir.py (#149958)"
This reverts commit 529e0357c6c4e74f8cd32c29198c5f1c9f6e329d.
Reverted https://github.com/pytorch/pytorch/pull/149958 on behalf of https://github.com/malfet due to Looks like it broke inductor_torchbind tests, due to more graphbreaks, see
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| 529e0357c6 |
[inductor] Add typing to _inductor/ir.py (#149958)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149958 Approved by: https://github.com/Skylion007 |
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| fa705f7912 |
[BE] minor refactor + some comments on behavior (#154695)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154695 Approved by: https://github.com/masnesral, https://github.com/eellison |
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| 5e03433443 |
Revert "Inductor logging + analysis of torch.profile (#149697)"
This reverts commit e5afbe31245287a92fe328c404b3557e5c5eca73.
Reverted https://github.com/pytorch/pytorch/pull/149697 on behalf of https://github.com/malfet due to Broke rocm, see
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| b0a2ca65ef |
support more prologue functions in generated templates cache (#154892)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154892 Approved by: https://github.com/jansel, https://github.com/eellison ghstack dependencies: #154891 |
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| 51b4c51973 |
add missing check for caching triton template caching (#154891)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154891 Approved by: https://github.com/eellison |
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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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| 295ea202f6 |
[inductor] Add kernel_hash_key to ChoiceCaller (#154470)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154470 Approved by: https://github.com/mlazos |
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| 69e22301da |
Revert "[inductor] Add kernel_hash_key to ChoiceCaller (#154470)"
This reverts commit 7a79de1c0f31200f95a48a9e69fbd2df2a3c735d. Reverted https://github.com/pytorch/pytorch/pull/154470 on behalf of https://github.com/seemethere due to Failing internal inductor tests, author is aware and suggested revert. D75767762 ([comment](https://github.com/pytorch/pytorch/pull/154470#issuecomment-2931717432)) |
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| 7a79de1c0f |
[inductor] Add kernel_hash_key to ChoiceCaller (#154470)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154470 Approved by: https://github.com/mlazos |
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| c7e8e8ee19 |
Add torch.profile benchmarking function to feedback_fns (#153579)
Summary: Updates some benchmarking code to have the option to use torch.profile, and passes in a thunk to benchmark_fns to get this information (this will be a different result from `timings`, which are already passed into those functions). Test Plan: Existing unit tests. Differential Revision: D74444990 Pull Request resolved: https://github.com/pytorch/pytorch/pull/153579 Approved by: https://github.com/coconutruben, https://github.com/masnesral, https://github.com/nmacchioni |
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| 6f992e1b3f |
[BE][AT] cleanup my old todo (#154542)
Summary: this todo is very old, and probably not needed anymore. let's have CI figure out if removing this breaks anything Test Plan: CI Differential Revision: D75491068 Pull Request resolved: https://github.com/pytorch/pytorch/pull/154542 Approved by: https://github.com/Skylion007 |
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| e904d01c16 |
Make inductor UT to be generic (#154196)
# Motivation https://github.com/pytorch/pytorch/pull/151773 introduces UT `test_triton_template_generated_code_caching` failed on XPU; https://github.com/pytorch/pytorch/pull/153895 introduces UT `test_mutation_rename` failed on XPU; fix https://github.com/pytorch/pytorch/issues/154218 # Additional Context With this PR, both failed UTs passed on local machine. Pull Request resolved: https://github.com/pytorch/pytorch/pull/154196 Approved by: https://github.com/jansel |
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| e927ba6dbd |
[inductor][cutlass backend] Add 2 stage autotuning aka prescreening (#153335)
Motivation:
By default, we are tuning the cutlass backend kernels on 3 swizzles. There are runtime params, so they share the same underlying kernel, which saves a lot of compilation time. However, autotuning all combinations of {configs} x {swizzles} is still expensive.
Observations:
Winner of the {configs} x {swizzles} autotuning is the same as if we do a greedy search: first find the top X winners of {configs} with swizzle 2 (hardcoded), then autotune on the {top X winner configs} x {swizzles}. In other words, we can use a Greedy algorithm to reduce autotuning time.
I attach the logs below. This somewhat depends on what X is, but a number like 5-10 works pretty well from empirical observations.
Logs:
Baseline:
https://gist.github.com/henrylhtsang/9a604f150a270dc19524f72a5d4dfac2
```
AUTOTUNE mm(2048x2048, 2048x2048)
strides: [2048, 1], [1, 2048]
dtypes: torch.bfloat16, torch.bfloat16
cuda_cutlass_gemm_1776 0.0291 ms 100.0% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_1777 0.0291 ms 100.0% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_1778 0.0291 ms 100.0% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_1800 0.0293 ms 99.2% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_1801 0.0293 ms 99.2% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_1802 0.0293 ms 99.2% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_9012 0.0294 ms 98.9% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_9013 0.0294 ms 98.9% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_9014 0.0294 ms 98.9% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_8940 0.0296 ms 98.3% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_8941 0.0296 ms 98.3% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_8942 0.0296 ms 98.3% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_8934 0.0297 ms 98.1% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_8935 0.0297 ms 98.1% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_8936 0.0297 ms 98.1% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_2001 0.0297 ms 97.8% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_2x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_2002 0.0297 ms 97.8% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_2x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_2003 0.0297 ms 97.8% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_2x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_1848 0.0298 ms 97.6% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_1849 0.0298 ms 97.6% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_1850 0.0298 ms 97.6% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_8964 0.0298 ms 97.6% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_8965 0.0298 ms 97.6% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_8966 0.0298 ms 97.6% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_8958 0.0298 ms 97.5% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_8959 0.0298 ms 97.5% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_8960 0.0298 ms 97.5% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_1929 0.0302 ms 96.4% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_1930 0.0302 ms 96.4% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_1931 0.0302 ms 96.4% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_1770 0.0302 ms 96.2% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_1771 0.0302 ms 96.2% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_1772 0.0302 ms 96.2% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_1953 0.0302 ms 96.2% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x2x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_1954 0.0302 ms 96.2% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x2x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_1955 0.0302 ms 96.2% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x2x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_1995 0.0303 ms 96.0% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_2x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_1996 0.0303 ms 96.0% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_2x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_1997 0.0303 ms 96.0% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_2x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_1794 0.0303 ms 95.9% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_1795 0.0303 ms 95.9% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_1796 0.0303 ms 95.9% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_1842 0.0303 ms 95.9% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_1843 0.0303 ms 95.9% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_1844 0.0303 ms 95.9% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_9006 0.0304 ms 95.7% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_9007 0.0304 ms 95.7% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_9008 0.0304 ms 95.7% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_1923 0.0306 ms 95.2% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=1
```
with prescreening:
```
AUTOTUNE mm(147456x6144, 6144x2048)
strides: [6144, 1], [2048, 1]
dtypes: torch.bfloat16, torch.bfloat16
cutlass_1a5e81af 4.5469 ms 100.0% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_ttn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=1
cutlass_aa6f899c 4.6328 ms 98.1% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=1
cutlass_aa6f899c 4.6836 ms 97.1% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=4
cutlass_161b8b81 4.7224 ms 96.3% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=1
cutlass_161b8b81 4.7234 ms 96.3% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=2
cutlass_161b8b81 4.7274 ms 96.2% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=4
cutlass_853b6347 4.7369 ms 96.0% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x2x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=1
cutlass_aa6f899c 4.7404 ms 95.9% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=2
cutlass_161b8b81 4.7711 ms 95.3% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=8
cutlass_8bc6fbda 4.8148 ms 94.4% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=8
cutlass_8bc6fbda 4.8159 ms 94.4% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=1
cutlass_8bc6fbda 4.8214 ms 94.3% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=4
cutlass_8bc6fbda 4.8302 ms 94.1% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=2
cutlass_0a1c55af 4.8487 ms 93.8% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x2x1_0_ttn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=8
cutlass_0a1c55af 4.8527 ms 93.7% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x2x1_0_ttn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=2
cutlass_02780d72 4.8617 ms 93.5% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_2x1x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=4
cutlass_0a1c55af 4.8737 ms 93.3% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x2x1_0_ttn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=1
cutlass_0a1c55af 4.8738 ms 93.3% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x2x1_0_ttn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=4
cutlass_02780d72 4.9348 ms 92.1% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_2x1x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=1
cutlass_02780d72 4.9763 ms 91.4% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_2x1x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=2
cutlass_853b6347 4.9805 ms 91.3% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x2x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=2
cutlass_1a5e81af 5.0225 ms 90.5% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_ttn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=8
cutlass_853b6347 5.0271 ms 90.4% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x2x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=8
cutlass_02780d72 5.0595 ms 89.9% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_2x1x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=8
cutlass_853b6347 5.1434 ms 88.4% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x2x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=4
cutlass_c1ffa14b 5.1574 ms 88.2% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_2x1x1_0_ttn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=8
cutlass_1a5e81af 5.1916 ms 87.6% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_ttn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=4
cutlass_c1ffa14b 5.2018 ms 87.4% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_2x1x1_0_ttn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=4
cutlass_c1ffa14b 5.2019 ms 87.4% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_2x1x1_0_ttn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=1
cutlass_c1ffa14b 5.2037 ms 87.4% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_2x1x1_0_ttn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=2
cutlass_1a5e81af 5.5329 ms 82.2% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_ttn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=2
cutlass_aa6f899c 11.5046 ms 39.5% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=8
SingleProcess AUTOTUNE benchmarking takes 1.9526 seconds and 0.0352 seconds precompiling for 32 choices
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153335
Approved by: https://github.com/eellison
|
|||
| 025c5cc048 |
Revert "[inductor][cutlass backend] Add 2 stage autotuning aka prescreening (#153335)"
This reverts commit d23762974eae105aad837188d5d2254ea9783b37. Reverted https://github.com/pytorch/pytorch/pull/153335 on behalf of https://github.com/yangw-dev due to sorry the pr is failed internally [D75155648](https://www.internalfb.com/diff/D75155648) ([comment](https://github.com/pytorch/pytorch/pull/153335#issuecomment-2901916364)) |
|||
| 482e5b6660 |
[inductor] Added precompilation_timeout_seconds into a config instead of hardcoded (#153788)
Fixes #153392 - Updated config.py to add the timeout as a config var to be tuned dynamically (default is 3600s). - Passed the var as a kwarg during call on instance. Pull Request resolved: https://github.com/pytorch/pytorch/pull/153788 Approved by: https://github.com/henrylhtsang |
|||
| 4bcff4af99 |
Move prologue_supported_inputs computations to def_kernal (#150869)
This avoid replaying load_input on a cache hit on the generate_code_cache. the idea is that if a template have prologue_loads_all_inputs = True, it means that all all inputs are loaded and hence no need to replay Effect on the current benchmark on a local run on dev server. 18549985383 -> 15072230073 25697270062 -> 20738613297 Pull Request resolved: https://github.com/pytorch/pytorch/pull/150869 Approved by: https://github.com/eellison |
|||
| 11c0ffefcd |
Cache code generation during triton template expansion and enable it for mm_template. (#151773)
In a model, we see ~~ 40% of the time in mm/addmm tuning. The model have 2000 mm, many of which receives the same input shapes. with autotune enabled, this become expensive, while we already cache auto tuning results, we did not used to cache the generation of the python code and the loading for each config that we autotune on. This diff handles the code generation part (template expansions) a previous diff handled the loading part. This is expected to save 20% of the model I am working on. How do we do the caching? For a given configurations and input layout, the generated code is always the same. One caveat is that some other information collected during code generation are input dependent (namely depends on inputs names and symbol names in inputs). and not just layout. ! To handle those we use a record and replay approach, where we record the functions that are called during code generation that effect those outputs and replay them at a cache hit. Effect on the current benchmark on a local run on dev server. mm_loop. 24115830838 -> 18362098019 mm_loop_dynamic 30506097176-> 25697270062 Pull Request resolved: https://github.com/pytorch/pytorch/pull/151773 Approved by: https://github.com/eellison |
|||
| d23762974e |
[inductor][cutlass backend] Add 2 stage autotuning aka prescreening (#153335)
Motivation:
By default, we are tuning the cutlass backend kernels on 3 swizzles. There are runtime params, so they share the same underlying kernel, which saves a lot of compilation time. However, autotuning all combinations of {configs} x {swizzles} is still expensive.
Observations:
Winner of the {configs} x {swizzles} autotuning is the same as if we do a greedy search: first find the top X winners of {configs} with swizzle 2 (hardcoded), then autotune on the {top X winner configs} x {swizzles}. In other words, we can use a Greedy algorithm to reduce autotuning time.
I attach the logs below. This somewhat depends on what X is, but a number like 5-10 works pretty well from empirical observations.
Logs:
Baseline:
https://gist.github.com/henrylhtsang/9a604f150a270dc19524f72a5d4dfac2
```
AUTOTUNE mm(2048x2048, 2048x2048)
strides: [2048, 1], [1, 2048]
dtypes: torch.bfloat16, torch.bfloat16
cuda_cutlass_gemm_1776 0.0291 ms 100.0% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_1777 0.0291 ms 100.0% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_1778 0.0291 ms 100.0% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_1800 0.0293 ms 99.2% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_1801 0.0293 ms 99.2% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_1802 0.0293 ms 99.2% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_9012 0.0294 ms 98.9% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_9013 0.0294 ms 98.9% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_9014 0.0294 ms 98.9% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_8940 0.0296 ms 98.3% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_8941 0.0296 ms 98.3% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_8942 0.0296 ms 98.3% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_8934 0.0297 ms 98.1% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_8935 0.0297 ms 98.1% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_8936 0.0297 ms 98.1% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_2001 0.0297 ms 97.8% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_2x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_2002 0.0297 ms 97.8% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_2x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_2003 0.0297 ms 97.8% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_2x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_1848 0.0298 ms 97.6% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_1849 0.0298 ms 97.6% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_1850 0.0298 ms 97.6% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_8964 0.0298 ms 97.6% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_8965 0.0298 ms 97.6% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_8966 0.0298 ms 97.6% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_8958 0.0298 ms 97.5% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_8959 0.0298 ms 97.5% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_8960 0.0298 ms 97.5% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_1929 0.0302 ms 96.4% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_1930 0.0302 ms 96.4% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_1931 0.0302 ms 96.4% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_1770 0.0302 ms 96.2% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_1771 0.0302 ms 96.2% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_1772 0.0302 ms 96.2% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_1953 0.0302 ms 96.2% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x2x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_1954 0.0302 ms 96.2% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x2x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_1955 0.0302 ms 96.2% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x2x1_0_tnn_align8_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_1995 0.0303 ms 96.0% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_2x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_1996 0.0303 ms 96.0% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_2x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_1997 0.0303 ms 96.0% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_2x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_1794 0.0303 ms 95.9% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_1795 0.0303 ms 95.9% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_1796 0.0303 ms 95.9% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_1842 0.0303 ms 95.9% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_1843 0.0303 ms 95.9% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_1844 0.0303 ms 95.9% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_9006 0.0304 ms 95.7% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=1
cuda_cutlass_gemm_9007 0.0304 ms 95.7% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=2
cuda_cutlass_gemm_9008 0.0304 ms 95.7% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=4
cuda_cutlass_gemm_1923 0.0306 ms 95.2% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=1
```
with prescreening:
```
AUTOTUNE mm(147456x6144, 6144x2048)
strides: [6144, 1], [2048, 1]
dtypes: torch.bfloat16, torch.bfloat16
cutlass_1a5e81af 4.5469 ms 100.0% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_ttn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=1
cutlass_aa6f899c 4.6328 ms 98.1% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=1
cutlass_aa6f899c 4.6836 ms 97.1% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=4
cutlass_161b8b81 4.7224 ms 96.3% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=1
cutlass_161b8b81 4.7234 ms 96.3% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=2
cutlass_161b8b81 4.7274 ms 96.2% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=4
cutlass_853b6347 4.7369 ms 96.0% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x2x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=1
cutlass_aa6f899c 4.7404 ms 95.9% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=2
cutlass_161b8b81 4.7711 ms 95.3% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=8
cutlass_8bc6fbda 4.8148 ms 94.4% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=8
cutlass_8bc6fbda 4.8159 ms 94.4% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=1
cutlass_8bc6fbda 4.8214 ms 94.3% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=4
cutlass_8bc6fbda 4.8302 ms 94.1% cutlass3x_sm90_tensorop_s64x256x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=2
cutlass_0a1c55af 4.8487 ms 93.8% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x2x1_0_ttn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=8
cutlass_0a1c55af 4.8527 ms 93.7% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x2x1_0_ttn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=2
cutlass_02780d72 4.8617 ms 93.5% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_2x1x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=4
cutlass_0a1c55af 4.8737 ms 93.3% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x2x1_0_ttn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=1
cutlass_0a1c55af 4.8738 ms 93.3% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x2x1_0_ttn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=4
cutlass_02780d72 4.9348 ms 92.1% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_2x1x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=1
cutlass_02780d72 4.9763 ms 91.4% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_2x1x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=2
cutlass_853b6347 4.9805 ms 91.3% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x2x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=2
cutlass_1a5e81af 5.0225 ms 90.5% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_ttn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=8
cutlass_853b6347 5.0271 ms 90.4% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x2x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=8
cutlass_02780d72 5.0595 ms 89.9% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_2x1x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=8
cutlass_853b6347 5.1434 ms 88.4% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_1x2x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=4
cutlass_c1ffa14b 5.1574 ms 88.2% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_2x1x1_0_ttn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=8
cutlass_1a5e81af 5.1916 ms 87.6% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_ttn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=4
cutlass_c1ffa14b 5.2018 ms 87.4% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_2x1x1_0_ttn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=4
cutlass_c1ffa14b 5.2019 ms 87.4% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_2x1x1_0_ttn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=1
cutlass_c1ffa14b 5.2037 ms 87.4% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_256x128x64_2x1x1_0_ttn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=2
cutlass_1a5e81af 5.5329 ms 82.2% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_2x1x1_0_ttn_align8_stream_k_warpspecialized_cooperative_epi_tma swizzle=2
cutlass_aa6f899c 11.5046 ms 39.5% cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_void_bf16_128x256x64_1x2x1_0_ttn_align8_warpspecialized_cooperative_epi_tma swizzle=8
SingleProcess AUTOTUNE benchmarking takes 1.9526 seconds and 0.0352 seconds precompiling for 32 choices
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/153335
Approved by: https://github.com/eellison
|
