[Inductor][ROCm] Composable Kernel backend for Inductor #125453
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Test Plan: pytest -rpfs test/inductor/test_cudacodecache.py
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…#130576) Add functional support for torch.addmm with CK backend. See also #125453 # Implementation details 1. It turns out we can use the same template between addmm and matmul; essentially, matmul is addmm with empty bias 2. The Python generator in CK was updated to generate the shared cpp template. The pip package can be installed from `pip install git+https://github.com/rocm/composable_kernel@add-addmm` and will be merged into `develop` branch after this PR lands to avoid breaking the current matmul # Testing `pytest test/inductor/test_ck_backend.py -k addmm` Pull Request resolved: #130576 Approved by: https://github.com/chenyang78
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… autotune (#133285) This PR enables dynamic shapes for the CK backend for gemm max autotune (see #125453). This is achieved via unhardcoding the problem sizes from the template body and passing them as parameters instead. We handle passing the problem sizes for the kernel call as well as for the benchmark call. # Testing `pytest test/inductor/test_ck_backend.py [-k dynamic]` Pull Request resolved: #133285 Approved by: https://github.com/ColinPeppler
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MakeArgument signature was changed in ROCm/composable_kernel#1453 adding splitK argument to universal gemm templates which are used to codegen addmm and matmul (part of the series started at #125453 ) # Testing `pytest test/inductor/test_ck_backend.py` Pull Request resolved: #134483 Approved by: https://github.com/ColinPeppler
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…34483) MakeArgument signature was changed in ROCm/composable_kernel#1453 adding splitK argument to universal gemm templates which are used to codegen addmm and matmul (part of the series started at pytorch#125453 ) # Testing `pytest test/inductor/test_ck_backend.py` Pull Request resolved: pytorch#134483 Approved by: https://github.com/ColinPeppler
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… autotune (pytorch#133285) This PR enables dynamic shapes for the CK backend for gemm max autotune (see pytorch#125453). This is achieved via unhardcoding the problem sizes from the template body and passing them as parameters instead. We handle passing the problem sizes for the kernel call as well as for the benchmark call. # Testing `pytest test/inductor/test_ck_backend.py [-k dynamic]` Pull Request resolved: pytorch#133285 Approved by: https://github.com/ColinPeppler
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This PR adds an alternative backend for Inductor, adding Composable Kernel Universal GEMM instances to the autotune instance selection.
The implementation is heavily influenced by the series of PRs which adds CUTLASS backend (#106991). The main differences are
(1) customizing compiler for the ROCm platform
(2) customizing template code generation for Composable Kernel Universal GEMM instances.
We provide config tuning knobs for balancing between instance sources compilation time and finding the best instance.
Testing
Install the ck library
Run the test
cc @jeffdaily @sunway513 @jithunnair-amd @pruthvistony @ROCmSupport @dllehr-amd @jataylo @hongxiayang @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @peterbell10 @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @chauhang