Add a new API torch.xpu.is_tf32_supported for Intel GPU#163141
Add a new API torch.xpu.is_tf32_supported for Intel GPU#163141guangyey wants to merge 9 commits intogh/guangyey/210/basefrom
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/163141
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Merge startedYour change will be merged while ignoring the following 2 checks: trunk / macos-py3-arm64 / build, xpu / linux-jammy-xpu-n-py3.10 / test (default, 2, 8, linux.idc.xpu) Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
# Motivation Aligned with other backends, this PR introduces a new API `torch.xpu.is_tf32_supported`, which should be used before `torch.backends.mkldnn.allow_tf32=True` or provide hardware capability information to the Triton # Additional Context On Intel Xe architecture and newer, TF32 operations can be accelerated through DPAS (Dot Product Accumulate Systolic) instructions. Therefore, TF32 support can be determined by checking whether the device supports subgroup matrix multiply-accumulate operations. Pull Request resolved: pytorch#163141 Approved by: https://github.com/EikanWang
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Motivation
Aligned with other backends, this PR introduces a new API
torch.xpu.is_tf32_supported, which should be used beforetorch.backends.mkldnn.allow_tf32=Trueor provide hardware capability information to the TritonAdditional Context
On Intel Xe architecture and newer, TF32 operations can be accelerated through DPAS (Dot Product Accumulate Systolic) instructions. Therefore, TF32 support can be determined by checking whether the device supports subgroup matrix multiply-accumulate operations.