reformulate bce_with_logits to not use abs#2195
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soumith merged 2 commits intopytorch:masterfrom Jul 24, 2017
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jjsjann123
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Nov 30, 2022
…#2195) * Add support for a symbolic output_shape for broadcast_in_dim in the Python Frontend * Fixed compilation of BroadcastInDimOpRecord with template specialization for defining expand sizes. Added SymbolicSizesRecord. Added python binding for symbolic_sizes(). * Fix up code with some suggestions from Ivan. Changed symbolic_sizes() to tensor_sizes(). * Add some tests and fix up python based Tensor and Scalar printing. * Added testing for tensor_sizes(). Made some minor changes to faciliate string captured definition testing. * Add comments and fix lint issues. * Add an output broadcast test with tensor_sizes(). * Added a test for tensor_size usage when each operand of a binary op has a broadcast. * Fix tensor_sizes() to reflect an expand in extent. Add appropriate test.
jagadish-amd
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Jun 2, 2025
- Changed to support new Hipblas 3.0.0 which is part of ROCm 7.0 is done. CP of ROCm@ec0c539 Co-authored-by: Pruthvi Madugundu <pruthvigithub@gmail.com>
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This is a fix for the bug reported here in which
binary_cross_entropy_with_logitsgives the wrong gradient withinputandtargetare0.After some investigation, this is because the gradient of
absat0is0(see here)In this PR I have reformulated the numerically stable
binary_cross_entropy_with_logitsto not useabs(originally I used what tensorflow does, which is use abs).I guess in general we should think about whether we want the grad of
abs(0)to be0?