Contiguous subgraph decomposition #161241

exclamaforte · 2025-08-22T03:25:19Z

Summary

Adds a subgraph decomposition for addmm and mm that performs well on large K compared to M and N, and functions well as an alternative to split-k on AMD (transposed only), which does not support AMD currently.

Background

On AMD (MI300x), for a matmul A * B, if B is non-contiguous, the resulting matmul is quite a bit slower.
For example:

  args[0]: TensorBox(StorageBox(
    InputBuffer(name='arg0_1', layout=FixedLayout('cuda:0', torch.float16, size=[1024, 178176], stride=[178176, 1]))
  ))
  args[1]: TensorBox(StorageBox(
    InputBuffer(name='arg1_1', layout=FixedLayout('cuda:0', torch.float16, size=[178176, 6144], stride=[1, 178176]))
  ))

is a lot slower than:

  args[0]: TensorBox(StorageBox(
    InputBuffer(name='arg0_1', layout=FixedLayout('cuda:0', torch.float16, size=[1024, 178176], stride=[178176, 1]))
  ))
  args[1]: TensorBox(StorageBox(
    InputBuffer(name='arg1_1', layout=FixedLayout('cuda:0', torch.float16, size=[178176, 6144], stride=[6144, 1]))
  ))

This PR adds a subgraph decomposition to test out whether making B contiguous is faster than just using the normal kernels.

Data

I ran this on unique non-contiguous shapes from torchbench/huggingface and got these speedups:

Parsed 420 unique shapes from benchmark output
addmm improvements when best:
  addmm_16448x512x2048: +0.14%
  addmm_128x2048x2048: +0.01%
  addmm_128x768x1000: +0.75%
  addmm_12672x3072x768: +1.08%
  addmm_512x768x32000: +0.62%
  addmm_12608x384x384: +0.00%
  addmm_4160x1024x4096: +0.90%
  addmm_16x768x2: +0.56%
  addmm_12608x3072x768: +0.09%
  addmm_64x4096x1000: +2.77%
  addmm_256x1024x512: +1.99%
  addmm_30x256x256: +1.12%
  addmm_100480x128x384: +0.91%
  addmm_6400x2048x512: +0.25%
  addmm_61568x1024x256: +0.08%
  addmm_1x768x768: +0.93%
  addmm_12544x384x384: +0.19%
  addmm_128x512x1000: +0.77%
  addmm_2048x128x128: +1.32%
  addmm_128x3072x1000: +0.24%
  addmm_7936x512x2048: +0.07%
  addmm_8192x512x2048: +0.33%
  addmm_64x1024x1000: +1.43%
  addmm_128x2304x1000: +0.01%
  addmm_32768x256x2: +0.75%
  addmm_64x384x1152: +0.79%
  addmm_64x640x1000: +0.01%
  addmm_100480x128x128: +0.87%
  addmm_1152x3072x768: +1.13%
  addmm_8192x256x2048: +1.40%
  addmm_4096x128x768: +0.01%
  addmm_128x2560x1000: +0.01%
  addmm_12544x2048x512: +0.43%
  addmm_200704x24x96: +0.14%
  addmm_8448x512x2048: +0.96%
  addmm_50176x256x1024: +0.62%
  addmm_4160x4096x1024: +0.22%
  addmm_4096x768x768: +0.32%
  addmm_220x2048x512: +0.56%
  addmm_8x2048x1000: +1.12%
  addmm_256x197951x512: +26.99%
  addmm_401536x64x192: +0.60%
  addmm_2040x2048x512: +0.47%
  addmm_512x1024x256: +1.32%
  addmm_128x4096x1000: +1.67%
  addmm_12672x768x768: +0.34%
  addmm_128x368x1000: +0.77%
  addmm_96x1280x1000: +0.01%
  addmm_12544x512x2048: +0.41%
  addmm_6272x320x1280: +0.76%
  addmm_12544x3072x768: +0.09%
  addmm_64x384x1000: +0.39%
mm improvements when best:
  mm_200704x128x512: +1.29%
  mm_663552x16x16: +0.80%
  mm_4096x768x768: +0.51%
  mm_131072x64x31: +0.24%
  mm_12544x1152x384: +0.11%
  mm_128x2048x2: +0.46%
  mm_262144x16x23: +0.62%
  mm_50176x576x192: +0.37%
  mm_131072x16x31: +0.26%
================================================================================
BENCHMARK ANALYSIS RESULTS
================================================================================

Operation: addmm
----------------------------------------
Total shapes analyzed: 247
Average Subgraph placement: 3.38
Median Subgraph placement: 2.0
Subgraph is best choice: 52/247 shapes (21.1%)
Average improvement when best: 1.15%
Median improvement when best: 0.58%
Largest improvement when best: +26.99%

Operation: bmm
----------------------------------------
Total shapes analyzed: 85
Average Subgraph placement: 24.00
Median Subgraph placement: 21.0
Subgraph is best choice: 0/85 shapes (0.0%)
Average improvement when best: N/A (never best)
Median improvement when best: N/A (never best)
Largest improvement when best: N/A (never best)

Operation: mm
----------------------------------------
Total shapes analyzed: 88
Average Subgraph placement: 15.08
Median Subgraph placement: 4.0
Subgraph is best choice: 9/88 shapes (10.2%)
Average improvement when best: 0.52%
Median improvement when best: 0.46%
Largest improvement when best: +1.29%

Results

The largest shape gain, 256,197951,512, seemed to be driven by a case where the extern kernel is way faster than the best triton configs on the recursive autotune:

addmm,Extern,extern_kernels.addmm,256,197951,512,0.38024500012397766
addmm,Triton,256,197951,512,32,256,16,2,2,4,2.005444049835205
addmm,Triton,256,197951,512,32,128,32,2,4,8,2.04189395904541
addmm,Triton,256,197951,512,64,128,16,2,4,8,2.1911399364471436
addmm,Triton,256,197951,512,64,128,32,2,4,8,2.496040105819702
addmm,Triton,256,197951,512,64,128,64,2,8,16,2.9306790828704834
addmm,Triton,256,197951,512,64,64,32,2,4,8,3.0347819328308105
...

Compared to the non-transposed autotune:

addmm,Subgraph,contiguous_addmm_1384,256,197951,512,0.5024129748344421
addmm,Extern,extern_kernels.addmm,256,197951,512,0.6881489753723145
addmm,Triton,256,197951,512,32,256,16,2,2,4,2.5115010738372803
addmm,Triton,256,197951,512,32,128,32,2,4,8,2.5167479515075684
addmm,Triton,256,197951,512,64,128,16,2,4,8,2.9507460594177246
addmm,Triton,256,197951,512,64,256,64,2,8,4,2.9673290252685547
addmm,Triton,256,197951,512,64,128,64,2,8,16,3.3906331062316895
addmm,Triton,256,197951,512,64,128,32,2,4,8,3.496859073638916

It seems to perform really well for high values of K vs N and M.
Testing this hypothesis with some custom shapes:

Parsed 64 unique shapes from benchmark output
addmm improvements when best:
  addmm_128x16384x128: +0.18%
  addmm_128x262144x256: +38.24%
  addmm_128x200000x512: +14.76%
  addmm_256x800000x128: +0.06%
  addmm_131072x128x256: +0.27%
  addmm_128x256x131072: +0.25%
  addmm_2048x200000x64: +12.45%
mm improvements when best:
  mm_128x16384x128: +0.18%
  mm_128x262144x256: +38.05%
  mm_128x200000x512: +9.47%
  mm_256x800000x128: +0.99%
  mm_512x6400000x256: +3.17%
  mm_524288x64x64: +0.29%
  mm_2048x200000x64: +11.19%
  mm_8192x1000000x256: +34.14%
  mm_128x4096x100000: +0.40%
  mm_128x3072x150000: +0.27%
================================================================================
BENCHMARK ANALYSIS RESULTS
================================================================================

Operation: addmm
----------------------------------------
Total shapes analyzed: 33
Average Subgraph placement: 4.39
Median Subgraph placement: 2.0
Subgraph is best choice: 7/33 shapes (21.2%)
Average improvement when best: 9.46%
Median improvement when best: 0.27%
Largest improvement when best: +38.24%

Operation: mm
----------------------------------------
Total shapes analyzed: 30
Average Subgraph placement: 7.63
Median Subgraph placement: 2.0
Subgraph is best choice: 10/30 shapes (33.3%)
Average improvement when best: 9.81%
Median improvement when best: 2.08%
Largest improvement when best: +38.05%

Conclusion

Contiguous Subgraph Decompositionseems worthwhile for mm and addmm, but not bmm, and has a very large improvment on low M, low N, and high K shapes.

Data gathering scripts:
https://gist.github.com/exclamaforte/4a896c064d301b27bf5ca0a4f8fc3866

Test Plan:

New unit tests.

Differential Revision: D80771648

cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov @coconutruben

pytorch-bot · 2025-08-22T03:25:23Z

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facebook-github-bot · 2025-08-22T03:25:30Z

This pull request was exported from Phabricator. Differential Revision: D80771648

facebook-github-bot · 2025-08-22T23:55:25Z

@exclamaforte has imported this pull request. If you are a Meta employee, you can view this in D80771648.

facebook-github-bot · 2025-08-26T22:22:28Z

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eellison

looks good ! cc @PaulZhang12

torch/_inductor/kernel/mm.py

test/inductor/test_max_autotune.py

coconutruben · 2025-09-02T18:53:44Z

@exclamaforte can you do this in the same way we did #161026 ? this will allow us to integrate with everything else nicely

make a contigous_mm template
add a template heuristic for it, can just yield a single dict with no kwargs

exclamaforte · 2025-09-03T07:15:55Z

@pytorchbot merge

facebook-github-bot · 2025-09-03T07:16:11Z

@exclamaforte has imported this pull request. If you are a Meta employee, you can view this in D80771648.

pytorchmergebot · 2025-09-03T07:17:44Z

Merge failed

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pytorchmergebot · 2025-09-04T00:14:25Z

@exclamaforte your PR has been successfully reverted.

jeffdaily · 2025-09-04T00:16:04Z

See also https://hud.pytorch.org/failure?name=rocm-mi300%20%2F%20linux-noble-rocm-py3.12-mi300%20%2F%20test%20(default%2C%202%2C%206%2C%20linux.rocm.gpu.gfx942.1)&jobName=undefined&failureCaptures=inductor%2Ftest_max_autotune.py%3A%3ATestMaxAutotune%3A%3Atest_max_autotune_contiguous_transform_mm_float32_sizes2.

facebook-github-bot · 2025-09-04T01:21:30Z

@exclamaforte has imported this pull request. If you are a Meta employee, you can view this in D80771648.

exclamaforte · 2025-09-04T02:11:02Z

Verified that I fixed this test now on an AMD devserver

exclamaforte · 2025-09-04T02:11:08Z

@pytorchbot merge

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exclamaforte · 2025-09-04T02:15:42Z

@pytorchbot merge

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## Summary Adds a subgraph decomposition for addmm and mm that performs well on large `K` compared to `M` and `N`, and functions well as an alternative to `split-k` on AMD (transposed only), which does not support AMD currently. ## Background On AMD (MI300x), for a matmul A * B, if B is non-contiguous, the resulting matmul is quite a bit slower. For example: ``` args[0]: TensorBox(StorageBox( InputBuffer(name='arg0_1', layout=FixedLayout('cuda:0', torch.float16, size=[1024, 178176], stride=[178176, 1])) )) args[1]: TensorBox(StorageBox( InputBuffer(name='arg1_1', layout=FixedLayout('cuda:0', torch.float16, size=[178176, 6144], stride=[1, 178176])) )) ``` is a lot slower than: ``` args[0]: TensorBox(StorageBox( InputBuffer(name='arg0_1', layout=FixedLayout('cuda:0', torch.float16, size=[1024, 178176], stride=[178176, 1])) )) args[1]: TensorBox(StorageBox( InputBuffer(name='arg1_1', layout=FixedLayout('cuda:0', torch.float16, size=[178176, 6144], stride=[6144, 1])) )) ``` This PR adds a subgraph decomposition to test out whether making B contiguous is faster than just using the normal kernels. ## Data I ran this on unique non-contiguous shapes from torchbench/huggingface and got these speedups: ``` Parsed 420 unique shapes from benchmark output addmm improvements when best: addmm_16448x512x2048: +0.14% addmm_128x2048x2048: +0.01% addmm_128x768x1000: +0.75% addmm_12672x3072x768: +1.08% addmm_512x768x32000: +0.62% addmm_12608x384x384: +0.00% addmm_4160x1024x4096: +0.90% addmm_16x768x2: +0.56% addmm_12608x3072x768: +0.09% addmm_64x4096x1000: +2.77% addmm_256x1024x512: +1.99% addmm_30x256x256: +1.12% addmm_100480x128x384: +0.91% addmm_6400x2048x512: +0.25% addmm_61568x1024x256: +0.08% addmm_1x768x768: +0.93% addmm_12544x384x384: +0.19% addmm_128x512x1000: +0.77% addmm_2048x128x128: +1.32% addmm_128x3072x1000: +0.24% addmm_7936x512x2048: +0.07% addmm_8192x512x2048: +0.33% addmm_64x1024x1000: +1.43% addmm_128x2304x1000: +0.01% addmm_32768x256x2: +0.75% addmm_64x384x1152: +0.79% addmm_64x640x1000: +0.01% addmm_100480x128x128: +0.87% addmm_1152x3072x768: +1.13% addmm_8192x256x2048: +1.40% addmm_4096x128x768: +0.01% addmm_128x2560x1000: +0.01% addmm_12544x2048x512: +0.43% addmm_200704x24x96: +0.14% addmm_8448x512x2048: +0.96% addmm_50176x256x1024: +0.62% addmm_4160x4096x1024: +0.22% addmm_4096x768x768: +0.32% addmm_220x2048x512: +0.56% addmm_8x2048x1000: +1.12% addmm_256x197951x512: +26.99% addmm_401536x64x192: +0.60% addmm_2040x2048x512: +0.47% addmm_512x1024x256: +1.32% addmm_128x4096x1000: +1.67% addmm_12672x768x768: +0.34% addmm_128x368x1000: +0.77% addmm_96x1280x1000: +0.01% addmm_12544x512x2048: +0.41% addmm_6272x320x1280: +0.76% addmm_12544x3072x768: +0.09% addmm_64x384x1000: +0.39% mm improvements when best: mm_200704x128x512: +1.29% mm_663552x16x16: +0.80% mm_4096x768x768: +0.51% mm_131072x64x31: +0.24% mm_12544x1152x384: +0.11% mm_128x2048x2: +0.46% mm_262144x16x23: +0.62% mm_50176x576x192: +0.37% mm_131072x16x31: +0.26% ================================================================================ BENCHMARK ANALYSIS RESULTS ================================================================================ Operation: addmm ---------------------------------------- Total shapes analyzed: 247 Average Subgraph placement: 3.38 Median Subgraph placement: 2.0 Subgraph is best choice: 52/247 shapes (21.1%) Average improvement when best: 1.15% Median improvement when best: 0.58% Largest improvement when best: +26.99% Operation: bmm ---------------------------------------- Total shapes analyzed: 85 Average Subgraph placement: 24.00 Median Subgraph placement: 21.0 Subgraph is best choice: 0/85 shapes (0.0%) Average improvement when best: N/A (never best) Median improvement when best: N/A (never best) Largest improvement when best: N/A (never best) Operation: mm ---------------------------------------- Total shapes analyzed: 88 Average Subgraph placement: 15.08 Median Subgraph placement: 4.0 Subgraph is best choice: 9/88 shapes (10.2%) Average improvement when best: 0.52% Median improvement when best: 0.46% Largest improvement when best: +1.29% ``` ## Results The largest shape gain, `256,197951,512`, seemed to be driven by a case where the extern kernel is way faster than the best triton configs on the recursive autotune: ``` addmm,Extern,extern_kernels.addmm,256,197951,512,0.38024500012397766 addmm,Triton,256,197951,512,32,256,16,2,2,4,2.005444049835205 addmm,Triton,256,197951,512,32,128,32,2,4,8,2.04189395904541 addmm,Triton,256,197951,512,64,128,16,2,4,8,2.1911399364471436 addmm,Triton,256,197951,512,64,128,32,2,4,8,2.496040105819702 addmm,Triton,256,197951,512,64,128,64,2,8,16,2.9306790828704834 addmm,Triton,256,197951,512,64,64,32,2,4,8,3.0347819328308105 ... ``` Compared to the non-transposed autotune: ``` addmm,Subgraph,contiguous_addmm_1384,256,197951,512,0.5024129748344421 addmm,Extern,extern_kernels.addmm,256,197951,512,0.6881489753723145 addmm,Triton,256,197951,512,32,256,16,2,2,4,2.5115010738372803 addmm,Triton,256,197951,512,32,128,32,2,4,8,2.5167479515075684 addmm,Triton,256,197951,512,64,128,16,2,4,8,2.9507460594177246 addmm,Triton,256,197951,512,64,256,64,2,8,4,2.9673290252685547 addmm,Triton,256,197951,512,64,128,64,2,8,16,3.3906331062316895 addmm,Triton,256,197951,512,64,128,32,2,4,8,3.496859073638916 ``` It seems to perform really well for high values of `K` vs `N` and `M`. Testing this hypothesis with some custom shapes: ``` Parsed 64 unique shapes from benchmark output addmm improvements when best: addmm_128x16384x128: +0.18% addmm_128x262144x256: +38.24% addmm_128x200000x512: +14.76% addmm_256x800000x128: +0.06% addmm_131072x128x256: +0.27% addmm_128x256x131072: +0.25% addmm_2048x200000x64: +12.45% mm improvements when best: mm_128x16384x128: +0.18% mm_128x262144x256: +38.05% mm_128x200000x512: +9.47% mm_256x800000x128: +0.99% mm_512x6400000x256: +3.17% mm_524288x64x64: +0.29% mm_2048x200000x64: +11.19% mm_8192x1000000x256: +34.14% mm_128x4096x100000: +0.40% mm_128x3072x150000: +0.27% ================================================================================ BENCHMARK ANALYSIS RESULTS ================================================================================ Operation: addmm ---------------------------------------- Total shapes analyzed: 33 Average Subgraph placement: 4.39 Median Subgraph placement: 2.0 Subgraph is best choice: 7/33 shapes (21.2%) Average improvement when best: 9.46% Median improvement when best: 0.27% Largest improvement when best: +38.24% Operation: mm ---------------------------------------- Total shapes analyzed: 30 Average Subgraph placement: 7.63 Median Subgraph placement: 2.0 Subgraph is best choice: 10/30 shapes (33.3%) Average improvement when best: 9.81% Median improvement when best: 2.08% Largest improvement when best: +38.05% ``` ## Conclusion Contiguous Subgraph Decompositionseems worthwhile for `mm` and `addmm`, but not `bmm`, and has a very large improvment on low `M`, low `N`, and high `K` shapes. Data gathering scripts: https://gist.github.com/exclamaforte/4a896c064d301b27bf5ca0a4f8fc3866 ## Test Plan: New unit tests. Differential Revision: D80771648 Pull Request resolved: pytorch#161241 Approved by: https://github.com/eellison

This reverts commit d647185. Reverted pytorch#161241 on behalf of https://github.com/jeffdaily due to breaks rocm mi300 tests ([comment](pytorch#161241 (comment)))

## Summary Adds a subgraph decomposition for addmm and mm that performs well on large `K` compared to `M` and `N`, and functions well as an alternative to `split-k` on AMD (transposed only), which does not support AMD currently. ## Background On AMD (MI300x), for a matmul A * B, if B is non-contiguous, the resulting matmul is quite a bit slower. For example: ``` args[0]: TensorBox(StorageBox( InputBuffer(name='arg0_1', layout=FixedLayout('cuda:0', torch.float16, size=[1024, 178176], stride=[178176, 1])) )) args[1]: TensorBox(StorageBox( InputBuffer(name='arg1_1', layout=FixedLayout('cuda:0', torch.float16, size=[178176, 6144], stride=[1, 178176])) )) ``` is a lot slower than: ``` args[0]: TensorBox(StorageBox( InputBuffer(name='arg0_1', layout=FixedLayout('cuda:0', torch.float16, size=[1024, 178176], stride=[178176, 1])) )) args[1]: TensorBox(StorageBox( InputBuffer(name='arg1_1', layout=FixedLayout('cuda:0', torch.float16, size=[178176, 6144], stride=[6144, 1])) )) ``` This PR adds a subgraph decomposition to test out whether making B contiguous is faster than just using the normal kernels. ## Data I ran this on unique non-contiguous shapes from torchbench/huggingface and got these speedups: ``` Parsed 420 unique shapes from benchmark output addmm improvements when best: addmm_16448x512x2048: +0.14% addmm_128x2048x2048: +0.01% addmm_128x768x1000: +0.75% addmm_12672x3072x768: +1.08% addmm_512x768x32000: +0.62% addmm_12608x384x384: +0.00% addmm_4160x1024x4096: +0.90% addmm_16x768x2: +0.56% addmm_12608x3072x768: +0.09% addmm_64x4096x1000: +2.77% addmm_256x1024x512: +1.99% addmm_30x256x256: +1.12% addmm_100480x128x384: +0.91% addmm_6400x2048x512: +0.25% addmm_61568x1024x256: +0.08% addmm_1x768x768: +0.93% addmm_12544x384x384: +0.19% addmm_128x512x1000: +0.77% addmm_2048x128x128: +1.32% addmm_128x3072x1000: +0.24% addmm_7936x512x2048: +0.07% addmm_8192x512x2048: +0.33% addmm_64x1024x1000: +1.43% addmm_128x2304x1000: +0.01% addmm_32768x256x2: +0.75% addmm_64x384x1152: +0.79% addmm_64x640x1000: +0.01% addmm_100480x128x128: +0.87% addmm_1152x3072x768: +1.13% addmm_8192x256x2048: +1.40% addmm_4096x128x768: +0.01% addmm_128x2560x1000: +0.01% addmm_12544x2048x512: +0.43% addmm_200704x24x96: +0.14% addmm_8448x512x2048: +0.96% addmm_50176x256x1024: +0.62% addmm_4160x4096x1024: +0.22% addmm_4096x768x768: +0.32% addmm_220x2048x512: +0.56% addmm_8x2048x1000: +1.12% addmm_256x197951x512: +26.99% addmm_401536x64x192: +0.60% addmm_2040x2048x512: +0.47% addmm_512x1024x256: +1.32% addmm_128x4096x1000: +1.67% addmm_12672x768x768: +0.34% addmm_128x368x1000: +0.77% addmm_96x1280x1000: +0.01% addmm_12544x512x2048: +0.41% addmm_6272x320x1280: +0.76% addmm_12544x3072x768: +0.09% addmm_64x384x1000: +0.39% mm improvements when best: mm_200704x128x512: +1.29% mm_663552x16x16: +0.80% mm_4096x768x768: +0.51% mm_131072x64x31: +0.24% mm_12544x1152x384: +0.11% mm_128x2048x2: +0.46% mm_262144x16x23: +0.62% mm_50176x576x192: +0.37% mm_131072x16x31: +0.26% ================================================================================ BENCHMARK ANALYSIS RESULTS ================================================================================ Operation: addmm ---------------------------------------- Total shapes analyzed: 247 Average Subgraph placement: 3.38 Median Subgraph placement: 2.0 Subgraph is best choice: 52/247 shapes (21.1%) Average improvement when best: 1.15% Median improvement when best: 0.58% Largest improvement when best: +26.99% Operation: bmm ---------------------------------------- Total shapes analyzed: 85 Average Subgraph placement: 24.00 Median Subgraph placement: 21.0 Subgraph is best choice: 0/85 shapes (0.0%) Average improvement when best: N/A (never best) Median improvement when best: N/A (never best) Largest improvement when best: N/A (never best) Operation: mm ---------------------------------------- Total shapes analyzed: 88 Average Subgraph placement: 15.08 Median Subgraph placement: 4.0 Subgraph is best choice: 9/88 shapes (10.2%) Average improvement when best: 0.52% Median improvement when best: 0.46% Largest improvement when best: +1.29% ``` ## Results The largest shape gain, `256,197951,512`, seemed to be driven by a case where the extern kernel is way faster than the best triton configs on the recursive autotune: ``` addmm,Extern,extern_kernels.addmm,256,197951,512,0.38024500012397766 addmm,Triton,256,197951,512,32,256,16,2,2,4,2.005444049835205 addmm,Triton,256,197951,512,32,128,32,2,4,8,2.04189395904541 addmm,Triton,256,197951,512,64,128,16,2,4,8,2.1911399364471436 addmm,Triton,256,197951,512,64,128,32,2,4,8,2.496040105819702 addmm,Triton,256,197951,512,64,128,64,2,8,16,2.9306790828704834 addmm,Triton,256,197951,512,64,64,32,2,4,8,3.0347819328308105 ... ``` Compared to the non-transposed autotune: ``` addmm,Subgraph,contiguous_addmm_1384,256,197951,512,0.5024129748344421 addmm,Extern,extern_kernels.addmm,256,197951,512,0.6881489753723145 addmm,Triton,256,197951,512,32,256,16,2,2,4,2.5115010738372803 addmm,Triton,256,197951,512,32,128,32,2,4,8,2.5167479515075684 addmm,Triton,256,197951,512,64,128,16,2,4,8,2.9507460594177246 addmm,Triton,256,197951,512,64,256,64,2,8,4,2.9673290252685547 addmm,Triton,256,197951,512,64,128,64,2,8,16,3.3906331062316895 addmm,Triton,256,197951,512,64,128,32,2,4,8,3.496859073638916 ``` It seems to perform really well for high values of `K` vs `N` and `M`. Testing this hypothesis with some custom shapes: ``` Parsed 64 unique shapes from benchmark output addmm improvements when best: addmm_128x16384x128: +0.18% addmm_128x262144x256: +38.24% addmm_128x200000x512: +14.76% addmm_256x800000x128: +0.06% addmm_131072x128x256: +0.27% addmm_128x256x131072: +0.25% addmm_2048x200000x64: +12.45% mm improvements when best: mm_128x16384x128: +0.18% mm_128x262144x256: +38.05% mm_128x200000x512: +9.47% mm_256x800000x128: +0.99% mm_512x6400000x256: +3.17% mm_524288x64x64: +0.29% mm_2048x200000x64: +11.19% mm_8192x1000000x256: +34.14% mm_128x4096x100000: +0.40% mm_128x3072x150000: +0.27% ================================================================================ BENCHMARK ANALYSIS RESULTS ================================================================================ Operation: addmm ---------------------------------------- Total shapes analyzed: 33 Average Subgraph placement: 4.39 Median Subgraph placement: 2.0 Subgraph is best choice: 7/33 shapes (21.2%) Average improvement when best: 9.46% Median improvement when best: 0.27% Largest improvement when best: +38.24% Operation: mm ---------------------------------------- Total shapes analyzed: 30 Average Subgraph placement: 7.63 Median Subgraph placement: 2.0 Subgraph is best choice: 10/30 shapes (33.3%) Average improvement when best: 9.81% Median improvement when best: 2.08% Largest improvement when best: +38.05% ``` ## Conclusion Contiguous Subgraph Decompositionseems worthwhile for `mm` and `addmm`, but not `bmm`, and has a very large improvment on low `M`, low `N`, and high `K` shapes. Data gathering scripts: https://gist.github.com/exclamaforte/4a896c064d301b27bf5ca0a4f8fc3866 ## Test Plan: New unit tests. Differential Revision: D80771648 Pull Request resolved: pytorch#161241 Approved by: https://github.com/eellison

This reverts commit d647185. Reverted pytorch#161241 on behalf of https://github.com/jeffdaily due to breaks rocm mi300 tests ([comment](pytorch#161241 (comment)))

## Summary Adds a subgraph decomposition for addmm and mm that performs well on large `K` compared to `M` and `N`, and functions well as an alternative to `split-k` on AMD (transposed only), which does not support AMD currently. ## Background On AMD (MI300x), for a matmul A * B, if B is non-contiguous, the resulting matmul is quite a bit slower. For example: ``` args[0]: TensorBox(StorageBox( InputBuffer(name='arg0_1', layout=FixedLayout('cuda:0', torch.float16, size=[1024, 178176], stride=[178176, 1])) )) args[1]: TensorBox(StorageBox( InputBuffer(name='arg1_1', layout=FixedLayout('cuda:0', torch.float16, size=[178176, 6144], stride=[1, 178176])) )) ``` is a lot slower than: ``` args[0]: TensorBox(StorageBox( InputBuffer(name='arg0_1', layout=FixedLayout('cuda:0', torch.float16, size=[1024, 178176], stride=[178176, 1])) )) args[1]: TensorBox(StorageBox( InputBuffer(name='arg1_1', layout=FixedLayout('cuda:0', torch.float16, size=[178176, 6144], stride=[6144, 1])) )) ``` This PR adds a subgraph decomposition to test out whether making B contiguous is faster than just using the normal kernels. ## Data I ran this on unique non-contiguous shapes from torchbench/huggingface and got these speedups: ``` Parsed 420 unique shapes from benchmark output addmm improvements when best: addmm_16448x512x2048: +0.14% addmm_128x2048x2048: +0.01% addmm_128x768x1000: +0.75% addmm_12672x3072x768: +1.08% addmm_512x768x32000: +0.62% addmm_12608x384x384: +0.00% addmm_4160x1024x4096: +0.90% addmm_16x768x2: +0.56% addmm_12608x3072x768: +0.09% addmm_64x4096x1000: +2.77% addmm_256x1024x512: +1.99% addmm_30x256x256: +1.12% addmm_100480x128x384: +0.91% addmm_6400x2048x512: +0.25% addmm_61568x1024x256: +0.08% addmm_1x768x768: +0.93% addmm_12544x384x384: +0.19% addmm_128x512x1000: +0.77% addmm_2048x128x128: +1.32% addmm_128x3072x1000: +0.24% addmm_7936x512x2048: +0.07% addmm_8192x512x2048: +0.33% addmm_64x1024x1000: +1.43% addmm_128x2304x1000: +0.01% addmm_32768x256x2: +0.75% addmm_64x384x1152: +0.79% addmm_64x640x1000: +0.01% addmm_100480x128x128: +0.87% addmm_1152x3072x768: +1.13% addmm_8192x256x2048: +1.40% addmm_4096x128x768: +0.01% addmm_128x2560x1000: +0.01% addmm_12544x2048x512: +0.43% addmm_200704x24x96: +0.14% addmm_8448x512x2048: +0.96% addmm_50176x256x1024: +0.62% addmm_4160x4096x1024: +0.22% addmm_4096x768x768: +0.32% addmm_220x2048x512: +0.56% addmm_8x2048x1000: +1.12% addmm_256x197951x512: +26.99% addmm_401536x64x192: +0.60% addmm_2040x2048x512: +0.47% addmm_512x1024x256: +1.32% addmm_128x4096x1000: +1.67% addmm_12672x768x768: +0.34% addmm_128x368x1000: +0.77% addmm_96x1280x1000: +0.01% addmm_12544x512x2048: +0.41% addmm_6272x320x1280: +0.76% addmm_12544x3072x768: +0.09% addmm_64x384x1000: +0.39% mm improvements when best: mm_200704x128x512: +1.29% mm_663552x16x16: +0.80% mm_4096x768x768: +0.51% mm_131072x64x31: +0.24% mm_12544x1152x384: +0.11% mm_128x2048x2: +0.46% mm_262144x16x23: +0.62% mm_50176x576x192: +0.37% mm_131072x16x31: +0.26% ================================================================================ BENCHMARK ANALYSIS RESULTS ================================================================================ Operation: addmm ---------------------------------------- Total shapes analyzed: 247 Average Subgraph placement: 3.38 Median Subgraph placement: 2.0 Subgraph is best choice: 52/247 shapes (21.1%) Average improvement when best: 1.15% Median improvement when best: 0.58% Largest improvement when best: +26.99% Operation: bmm ---------------------------------------- Total shapes analyzed: 85 Average Subgraph placement: 24.00 Median Subgraph placement: 21.0 Subgraph is best choice: 0/85 shapes (0.0%) Average improvement when best: N/A (never best) Median improvement when best: N/A (never best) Largest improvement when best: N/A (never best) Operation: mm ---------------------------------------- Total shapes analyzed: 88 Average Subgraph placement: 15.08 Median Subgraph placement: 4.0 Subgraph is best choice: 9/88 shapes (10.2%) Average improvement when best: 0.52% Median improvement when best: 0.46% Largest improvement when best: +1.29% ``` ## Results The largest shape gain, `256,197951,512`, seemed to be driven by a case where the extern kernel is way faster than the best triton configs on the recursive autotune: ``` addmm,Extern,extern_kernels.addmm,256,197951,512,0.38024500012397766 addmm,Triton,256,197951,512,32,256,16,2,2,4,2.005444049835205 addmm,Triton,256,197951,512,32,128,32,2,4,8,2.04189395904541 addmm,Triton,256,197951,512,64,128,16,2,4,8,2.1911399364471436 addmm,Triton,256,197951,512,64,128,32,2,4,8,2.496040105819702 addmm,Triton,256,197951,512,64,128,64,2,8,16,2.9306790828704834 addmm,Triton,256,197951,512,64,64,32,2,4,8,3.0347819328308105 ... ``` Compared to the non-transposed autotune: ``` addmm,Subgraph,contiguous_addmm_1384,256,197951,512,0.5024129748344421 addmm,Extern,extern_kernels.addmm,256,197951,512,0.6881489753723145 addmm,Triton,256,197951,512,32,256,16,2,2,4,2.5115010738372803 addmm,Triton,256,197951,512,32,128,32,2,4,8,2.5167479515075684 addmm,Triton,256,197951,512,64,128,16,2,4,8,2.9507460594177246 addmm,Triton,256,197951,512,64,256,64,2,8,4,2.9673290252685547 addmm,Triton,256,197951,512,64,128,64,2,8,16,3.3906331062316895 addmm,Triton,256,197951,512,64,128,32,2,4,8,3.496859073638916 ``` It seems to perform really well for high values of `K` vs `N` and `M`. Testing this hypothesis with some custom shapes: ``` Parsed 64 unique shapes from benchmark output addmm improvements when best: addmm_128x16384x128: +0.18% addmm_128x262144x256: +38.24% addmm_128x200000x512: +14.76% addmm_256x800000x128: +0.06% addmm_131072x128x256: +0.27% addmm_128x256x131072: +0.25% addmm_2048x200000x64: +12.45% mm improvements when best: mm_128x16384x128: +0.18% mm_128x262144x256: +38.05% mm_128x200000x512: +9.47% mm_256x800000x128: +0.99% mm_512x6400000x256: +3.17% mm_524288x64x64: +0.29% mm_2048x200000x64: +11.19% mm_8192x1000000x256: +34.14% mm_128x4096x100000: +0.40% mm_128x3072x150000: +0.27% ================================================================================ BENCHMARK ANALYSIS RESULTS ================================================================================ Operation: addmm ---------------------------------------- Total shapes analyzed: 33 Average Subgraph placement: 4.39 Median Subgraph placement: 2.0 Subgraph is best choice: 7/33 shapes (21.2%) Average improvement when best: 9.46% Median improvement when best: 0.27% Largest improvement when best: +38.24% Operation: mm ---------------------------------------- Total shapes analyzed: 30 Average Subgraph placement: 7.63 Median Subgraph placement: 2.0 Subgraph is best choice: 10/30 shapes (33.3%) Average improvement when best: 9.81% Median improvement when best: 2.08% Largest improvement when best: +38.05% ``` ## Conclusion Contiguous Subgraph Decompositionseems worthwhile for `mm` and `addmm`, but not `bmm`, and has a very large improvment on low `M`, low `N`, and high `K` shapes. Data gathering scripts: https://gist.github.com/exclamaforte/4a896c064d301b27bf5ca0a4f8fc3866 ## Test Plan: New unit tests. Differential Revision: D80771648 Pull Request resolved: pytorch#161241 Approved by: https://github.com/eellison

This reverts commit d647185. Reverted pytorch#161241 on behalf of https://github.com/jeffdaily due to breaks rocm mi300 tests ([comment](pytorch#161241 (comment)))

## Summary Adds a subgraph decomposition for addmm and mm that performs well on large `K` compared to `M` and `N`, and functions well as an alternative to `split-k` on AMD (transposed only), which does not support AMD currently. ## Background On AMD (MI300x), for a matmul A * B, if B is non-contiguous, the resulting matmul is quite a bit slower. For example: ``` args[0]: TensorBox(StorageBox( InputBuffer(name='arg0_1', layout=FixedLayout('cuda:0', torch.float16, size=[1024, 178176], stride=[178176, 1])) )) args[1]: TensorBox(StorageBox( InputBuffer(name='arg1_1', layout=FixedLayout('cuda:0', torch.float16, size=[178176, 6144], stride=[1, 178176])) )) ``` is a lot slower than: ``` args[0]: TensorBox(StorageBox( InputBuffer(name='arg0_1', layout=FixedLayout('cuda:0', torch.float16, size=[1024, 178176], stride=[178176, 1])) )) args[1]: TensorBox(StorageBox( InputBuffer(name='arg1_1', layout=FixedLayout('cuda:0', torch.float16, size=[178176, 6144], stride=[6144, 1])) )) ``` This PR adds a subgraph decomposition to test out whether making B contiguous is faster than just using the normal kernels. ## Data I ran this on unique non-contiguous shapes from torchbench/huggingface and got these speedups: ``` Parsed 420 unique shapes from benchmark output addmm improvements when best: addmm_16448x512x2048: +0.14% addmm_128x2048x2048: +0.01% addmm_128x768x1000: +0.75% addmm_12672x3072x768: +1.08% addmm_512x768x32000: +0.62% addmm_12608x384x384: +0.00% addmm_4160x1024x4096: +0.90% addmm_16x768x2: +0.56% addmm_12608x3072x768: +0.09% addmm_64x4096x1000: +2.77% addmm_256x1024x512: +1.99% addmm_30x256x256: +1.12% addmm_100480x128x384: +0.91% addmm_6400x2048x512: +0.25% addmm_61568x1024x256: +0.08% addmm_1x768x768: +0.93% addmm_12544x384x384: +0.19% addmm_128x512x1000: +0.77% addmm_2048x128x128: +1.32% addmm_128x3072x1000: +0.24% addmm_7936x512x2048: +0.07% addmm_8192x512x2048: +0.33% addmm_64x1024x1000: +1.43% addmm_128x2304x1000: +0.01% addmm_32768x256x2: +0.75% addmm_64x384x1152: +0.79% addmm_64x640x1000: +0.01% addmm_100480x128x128: +0.87% addmm_1152x3072x768: +1.13% addmm_8192x256x2048: +1.40% addmm_4096x128x768: +0.01% addmm_128x2560x1000: +0.01% addmm_12544x2048x512: +0.43% addmm_200704x24x96: +0.14% addmm_8448x512x2048: +0.96% addmm_50176x256x1024: +0.62% addmm_4160x4096x1024: +0.22% addmm_4096x768x768: +0.32% addmm_220x2048x512: +0.56% addmm_8x2048x1000: +1.12% addmm_256x197951x512: +26.99% addmm_401536x64x192: +0.60% addmm_2040x2048x512: +0.47% addmm_512x1024x256: +1.32% addmm_128x4096x1000: +1.67% addmm_12672x768x768: +0.34% addmm_128x368x1000: +0.77% addmm_96x1280x1000: +0.01% addmm_12544x512x2048: +0.41% addmm_6272x320x1280: +0.76% addmm_12544x3072x768: +0.09% addmm_64x384x1000: +0.39% mm improvements when best: mm_200704x128x512: +1.29% mm_663552x16x16: +0.80% mm_4096x768x768: +0.51% mm_131072x64x31: +0.24% mm_12544x1152x384: +0.11% mm_128x2048x2: +0.46% mm_262144x16x23: +0.62% mm_50176x576x192: +0.37% mm_131072x16x31: +0.26% ================================================================================ BENCHMARK ANALYSIS RESULTS ================================================================================ Operation: addmm ---------------------------------------- Total shapes analyzed: 247 Average Subgraph placement: 3.38 Median Subgraph placement: 2.0 Subgraph is best choice: 52/247 shapes (21.1%) Average improvement when best: 1.15% Median improvement when best: 0.58% Largest improvement when best: +26.99% Operation: bmm ---------------------------------------- Total shapes analyzed: 85 Average Subgraph placement: 24.00 Median Subgraph placement: 21.0 Subgraph is best choice: 0/85 shapes (0.0%) Average improvement when best: N/A (never best) Median improvement when best: N/A (never best) Largest improvement when best: N/A (never best) Operation: mm ---------------------------------------- Total shapes analyzed: 88 Average Subgraph placement: 15.08 Median Subgraph placement: 4.0 Subgraph is best choice: 9/88 shapes (10.2%) Average improvement when best: 0.52% Median improvement when best: 0.46% Largest improvement when best: +1.29% ``` ## Results The largest shape gain, `256,197951,512`, seemed to be driven by a case where the extern kernel is way faster than the best triton configs on the recursive autotune: ``` addmm,Extern,extern_kernels.addmm,256,197951,512,0.38024500012397766 addmm,Triton,256,197951,512,32,256,16,2,2,4,2.005444049835205 addmm,Triton,256,197951,512,32,128,32,2,4,8,2.04189395904541 addmm,Triton,256,197951,512,64,128,16,2,4,8,2.1911399364471436 addmm,Triton,256,197951,512,64,128,32,2,4,8,2.496040105819702 addmm,Triton,256,197951,512,64,128,64,2,8,16,2.9306790828704834 addmm,Triton,256,197951,512,64,64,32,2,4,8,3.0347819328308105 ... ``` Compared to the non-transposed autotune: ``` addmm,Subgraph,contiguous_addmm_1384,256,197951,512,0.5024129748344421 addmm,Extern,extern_kernels.addmm,256,197951,512,0.6881489753723145 addmm,Triton,256,197951,512,32,256,16,2,2,4,2.5115010738372803 addmm,Triton,256,197951,512,32,128,32,2,4,8,2.5167479515075684 addmm,Triton,256,197951,512,64,128,16,2,4,8,2.9507460594177246 addmm,Triton,256,197951,512,64,256,64,2,8,4,2.9673290252685547 addmm,Triton,256,197951,512,64,128,64,2,8,16,3.3906331062316895 addmm,Triton,256,197951,512,64,128,32,2,4,8,3.496859073638916 ``` It seems to perform really well for high values of `K` vs `N` and `M`. Testing this hypothesis with some custom shapes: ``` Parsed 64 unique shapes from benchmark output addmm improvements when best: addmm_128x16384x128: +0.18% addmm_128x262144x256: +38.24% addmm_128x200000x512: +14.76% addmm_256x800000x128: +0.06% addmm_131072x128x256: +0.27% addmm_128x256x131072: +0.25% addmm_2048x200000x64: +12.45% mm improvements when best: mm_128x16384x128: +0.18% mm_128x262144x256: +38.05% mm_128x200000x512: +9.47% mm_256x800000x128: +0.99% mm_512x6400000x256: +3.17% mm_524288x64x64: +0.29% mm_2048x200000x64: +11.19% mm_8192x1000000x256: +34.14% mm_128x4096x100000: +0.40% mm_128x3072x150000: +0.27% ================================================================================ BENCHMARK ANALYSIS RESULTS ================================================================================ Operation: addmm ---------------------------------------- Total shapes analyzed: 33 Average Subgraph placement: 4.39 Median Subgraph placement: 2.0 Subgraph is best choice: 7/33 shapes (21.2%) Average improvement when best: 9.46% Median improvement when best: 0.27% Largest improvement when best: +38.24% Operation: mm ---------------------------------------- Total shapes analyzed: 30 Average Subgraph placement: 7.63 Median Subgraph placement: 2.0 Subgraph is best choice: 10/30 shapes (33.3%) Average improvement when best: 9.81% Median improvement when best: 2.08% Largest improvement when best: +38.05% ``` ## Conclusion Contiguous Subgraph Decompositionseems worthwhile for `mm` and `addmm`, but not `bmm`, and has a very large improvment on low `M`, low `N`, and high `K` shapes. Data gathering scripts: https://gist.github.com/exclamaforte/4a896c064d301b27bf5ca0a4f8fc3866 ## Test Plan: New unit tests. Differential Revision: D80771648 Pull Request resolved: pytorch#161241 Approved by: https://github.com/eellison

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Contiguous subgraph decomposition #161241

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