[Triton] [Inductor] Add a Blackwell specific Template for persistent matmul#162916
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njriasan wants to merge 9 commits intopytorch:mainfrom
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[Triton] [Inductor] Add a Blackwell specific Template for persistent matmul#162916njriasan wants to merge 9 commits intopytorch:mainfrom
njriasan wants to merge 9 commits intopytorch:mainfrom
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/162916
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit 84da1da with merge base 6d64bc3 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
njriasan
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release notes: inductor
ciflow/h100
Blackwell
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Merge startedYour change will be merged once all checks pass (ETA 0-4 Hours). Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
markc-614
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Sep 17, 2025
…matmul (pytorch#162916) Summary: This adds the Triton Tutorial Matmul persistent matmul with device side TMA for Blackwell and adds it as a template option for blackwell. This uses newer Triton features such as automatic warp specialization and loop flattening, which while still containing flaws can improve performance on blackwell. This does not include the Epilogue subtiling section, as that will be a followup PR. This PR doesn't include any tuning. I am doing a larger benchmarking run to determine the best initial configs for tuning and will open a followup PR with better defaults soon. Test Plan: Tested on a Blackwell machine with test_max_autotune.py and confirmed the new tests pass. Pull Request resolved: pytorch#162916 Approved by: https://github.com/NikhilAPatel
mansiag05
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Sep 22, 2025
…matmul (pytorch#162916) Summary: This adds the Triton Tutorial Matmul persistent matmul with device side TMA for Blackwell and adds it as a template option for blackwell. This uses newer Triton features such as automatic warp specialization and loop flattening, which while still containing flaws can improve performance on blackwell. This does not include the Epilogue subtiling section, as that will be a followup PR. This PR doesn't include any tuning. I am doing a larger benchmarking run to determine the best initial configs for tuning and will open a followup PR with better defaults soon. Test Plan: Tested on a Blackwell machine with test_max_autotune.py and confirmed the new tests pass. Pull Request resolved: pytorch#162916 Approved by: https://github.com/NikhilAPatel
cleonard530
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Sep 22, 2025
…matmul (pytorch#162916) Summary: This adds the Triton Tutorial Matmul persistent matmul with device side TMA for Blackwell and adds it as a template option for blackwell. This uses newer Triton features such as automatic warp specialization and loop flattening, which while still containing flaws can improve performance on blackwell. This does not include the Epilogue subtiling section, as that will be a followup PR. This PR doesn't include any tuning. I am doing a larger benchmarking run to determine the best initial configs for tuning and will open a followup PR with better defaults soon. Test Plan: Tested on a Blackwell machine with test_max_autotune.py and confirmed the new tests pass. Pull Request resolved: pytorch#162916 Approved by: https://github.com/NikhilAPatel
dsashidh
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Sep 26, 2025
…matmul (pytorch#162916) Summary: This adds the Triton Tutorial Matmul persistent matmul with device side TMA for Blackwell and adds it as a template option for blackwell. This uses newer Triton features such as automatic warp specialization and loop flattening, which while still containing flaws can improve performance on blackwell. This does not include the Epilogue subtiling section, as that will be a followup PR. This PR doesn't include any tuning. I am doing a larger benchmarking run to determine the best initial configs for tuning and will open a followup PR with better defaults soon. Test Plan: Tested on a Blackwell machine with test_max_autotune.py and confirmed the new tests pass. Pull Request resolved: pytorch#162916 Approved by: https://github.com/NikhilAPatel
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Summary:
This adds the Triton Tutorial Matmul persistent matmul with device side TMA for Blackwell and adds it as a template option for blackwell. This uses newer Triton features such as automatic warp specialization and loop flattening, which while still containing flaws can improve performance on blackwell. This does not include the Epilogue subtiling section, as that will be a followup PR.
This PR doesn't include any tuning. I am doing a larger benchmarking run to determine the best initial configs for tuning and will open a followup PR with better defaults soon.
Test Plan:
Tested on a Blackwell machine with test_max_autotune.py and confirmed the new tests pass.
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