[inductor] support linear+binary foldinig for freezing path #138807
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/138807
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| for use_bias, pytorch_op in itertools.product(linear_bias, ops): | ||
| # add | ||
| test_linear_fusion( | ||
| use_bias, | ||
| pytorch_op, | ||
| False, | ||
| add_tensor=torch.rand( | ||
| 4, | ||
| 32, | ||
| ).to(self.device), | ||
| expect_success=False, | ||
| ) | ||
|
|
||
| # broadcasting add | ||
| test_linear_fusion( | ||
| use_bias, | ||
| pytorch_op, | ||
| False, | ||
| add_tensor=torch.rand( | ||
| 4, | ||
| 1, | ||
| ).to(self.device), | ||
| expect_success=False, | ||
| ) |
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Please add note in the code on why you are expecting fusion failures.
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Added, thanks for your comments!
| torch.nn.ReLU6(): 3, | ||
| torch.nn.SiLU(): 3, | ||
| torch.nn.Hardsigmoid(): 5, | ||
| # torch.nn.Hardsigmoid(): 5, |
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Hardsigmoid will be decomposed into add, clamp_min, clamp_max, div. With this pr, scalar add will be folded with conv/linear, which will change the behavior of the following fusion. I haven't figured out how to fix this issue yet, do you have any ideas?
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I currently only support the float type scalar case to avoid this problem.
ghstack-source-id: 6839b91 Pull Request resolved: pytorch#138807
jgong5
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a small nit, others LGTM.
Co-authored-by: Jiong Gong <[email protected]>
Sorry, this was introduced by the last rebase. I have fixed it. Can you review it again? Thanks! |
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@pytorchbot merge |
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 |
…138807) Pull Request resolved: pytorch#138807 Approved by: https://github.com/jgong5, https://github.com/jansel Co-authored-by: Jiong Gong <[email protected]>
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