Add complex support for torch.nn.L1Loss #46640
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ghstack-source-id: d3a1d45b86cff29790b5aeb0b590cb3b54f49a5b Pull Request resolved: #46640
anjali411
added
module: nn
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| return apply_loss_reduction(loss, reduction); | ||
| } | ||
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| Tensor& l1_loss_out(Tensor&result, const Tensor& input, const Tensor& target, int64_t reduction) { | ||
| auto diff = input.sub(target); |
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This is interesting. In the previous code input - target is out of place if Reduction::None, but inplace otherwise. With this change it will always be out of place.
I'm not sure why that distinction existed previously, but it seems like this change can preserve the inplace subtraction in the else clause?
| @@ -7056,11 +7056,12 @@ def test_pointwise_loss_broadcast(self): | |||
| # https://github.com/pytorch/pytorch/issues/27692 reports | |||
| # that l1_loss get a wrong result for big batch size | |||
| def test_l1_loss_correct(self): | |||
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Is there no other L1 loss test that should be updated?
This is making me thing we should (eventually) create test_losses.py.
Summary: Building on top of the work of anjali411 (#46640) Things added in this PR: 1. Modify backward and double-backward formulas 2. Add complex support for `new module tests` and criterion tests (and add complex tests for L1) 3. Modify some existing tests to support complex Pull Request resolved: #49912 Reviewed By: zhangguanheng66 Differential Revision: D25853036 Pulled By: soulitzer fbshipit-source-id: df619f1b71c450ab2818eb17804e0c55990aa8ad
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Looks like this PR hasn't been updated in a while so we're going to go ahead and mark this as |
Stack from ghstack:
TODO:
l1_loss_backward