8000 support scaled mm on inductor by shiyang-weng · Pull Request #153602 · pytorch/pytorch · GitHub
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support scaled mm on inductor #153602

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support scaled mm on inductor #153602

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@shiyang-weng shiyang-weng commented May 15, 2025 •
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Support scaled_mm on inductor
Fuse following pattern to scaled_mm

   #   + - - - - | - - - - - - | - - - - - +
   #   |    dq_per_tensor  dq_per_tensor   |
   #   |         |              |          |
   #   |    OPT(to_bf16)    OPT(to_bf16)   |
   #   |          \             |          |
   #   |                     permute       |
   #   |                     /             |
   #   |             addmm/mm              |
   #   |                |                  |
   #   |      OPT(quant_per_tensor)        |

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

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pytorch-bot bot commented May 15, 2025 •
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🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/153602

Note: Links to docs will display an error until the docs builds have been completed.

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@shiyang-weng shiyang-weng marked this pull request as draft May 15, 2025 08:21
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This PR needs a release notes: label

If your changes are user facing and intended to be a part of release notes, please use a label starting with release notes:.

If not, please add the topic: not user facing label.

To add a label, you can comment to pytorchbot, for example
@pytorchbot label "topic: not user facing"

For more information, see
https://github.com/pytorch/pytorch/wiki/PyTorch-AutoLabel-Bot#why-categorize-for-release-notes-and-how-does-it-work.

@shiyang-weng shiyang-weng marked this pull request as ready for review May 16, 2025 07:14
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jerryzh168 commented May 16, 2025 •
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how does quantization pattern got produced for this?

we have moved the pt2e flow to torchao recently, would it be better for this to be added in torchao: https://github.com/pytorch/ao/tree/main/torchao/quantization/pt2e/inductor_passes?

@jerryzh168 jerryzh168 added the triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module label May 16, 2025
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we have moved the pt2e flow to torchao recently, would it be better for this to be added in torchao: https://github.com/pytorch/ao/tree/main/torchao/quantization/pt2e/inductor_passes?

Yeah I agree. I probably need to move this to Torchao.

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@leslie-fang-intel leslie-fang-intel Awaiting requested review from leslie-fang-intel

@sanchitintel sanchitintel Awaiting requested review from sanchitintel

@Xia-Weiwen Xia-Weiwen Awaiting requested review from Xia-Weiwen

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