Let model precision for XPU device align with CUDA #2587
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torchbenchmark/util/extra_args.py
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| @@ -37,19 +37,19 @@ def check_precision( | |||
| if precision == "bypass": | |||
| return True | |||
| if precision == "fp16": | |||
| return model.device == "cuda" and hasattr(model, "enable_fp16") | |||
| return model.device == "cuda" or model.device == "xpu" and hasattr(model, "enable_fp16") | |||
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| return model.device == "cuda" or model.device == "xpu" and hasattr(model, "enable_fp16") | |
| return model.device in ["cuda", "xpu"] and hasattr(model, "enable_fp16") |
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Hi @xuzhao9, could you please help to review this PR? |
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Hi @chuanqi129 , my focus is now moved to Triton and operator level benchmarking at https://github.com/pytorch-labs/tritonbench/tree/main. I can review this PR, but we need to sort out how to proceed other PR reviews with the new Torchbench owner cc @atalman |
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I don't think the failures in cpu ci are related to this pr. |
Thanks @xuzhao9 for the information and help to review this PR! Hi @atalman could you please help to check the PR check failure and whether we can land this PR? |
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@atalman Could you please help review this pr |
torchbench by default load some models in fp16 if uses gpu. We align such behavior on xpu devices. Also aligned with cuda in nanogpt to use fused adam optimizer