[Inductor] Set prop_kind to forward_inference when grad is not needed for mkldnn_linear_pointwise and mkldnn_convolution_pointwise #147072
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/147072
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leslie-fang-intel
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Feb 14, 2025
CaoE
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Feb 17, 2025
jansel
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Feb 18, 2025
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| auto aprop_kind = ideep::prop_kind::forward; | ||
| bool maybe_backward = GradMode::is_enabled() && | ||
| (input_t.requires_grad() || weight_t.requires_grad() || | ||
| (bias_opt.has_value() && bias_opt->defined() && | ||
| bias_opt->requires_grad())); | ||
| if (!maybe_backward) { | ||
| aprop_kind = ideep::prop_kind::forward_inference; | ||
| } |
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define a helper method for this repeated code block
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I modified the code, I directly set aprop_kind in mkldnn_linear_pointwise_binary function to forward_inference since binary fusion is only for inference. Could you please review it again? Thanks!
leslie-fang-intel
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Mar 19, 2025
jansel
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Mar 20, 2025
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amathewc
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Apr 17, 2025
… for mkldnn_linear_pointwise and mkldnn_convolution_pointwise (pytorch#147072) Summary: The `prop_kind` of `mkldnn._linear_pointwise`, `mkldnn._linear_pointwise.binary`, `mkldnn._convolution_pointwise.binary` and `mkldnn._convolution_pointwise_.binary` are always `dnnl_forward`, i.e., `dnnl_forward_training` , regardless of whether `grad` is needed. Setting `prop_kind` to `dnnl_forward_inference` for these ops when `grad` is not needed could have better performance. Pull Request resolved: pytorch#147072 Approved by: https://github.com/leslie-fang-intel, https://github.com/CaoE, https://github.com/jansel
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Stack from ghstack (oldest at bottom):
Summary:
The
prop_kindofmkldnn._linear_pointwise,mkldnn._linear_pointwise.binary,mkldnn._convolution_pointwise.binaryandmkldnn._convolution_pointwise_.binaryare alwaysdnnl_forward, i.e.,dnnl_forward_training, regardless of whethergradis needed. Settingprop_kindtodnnl_forward_inferencefor these ops whengradis not needed could have better performance.cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10