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Create at::linear #10755
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| 'arange.*', 'range.*', '_gesv.*', '_getri.*', 'slice', | ||
| '_local_scalar', '_local_scalar_dense', | ||
| 'max_pool1d', 'max_pool2d', 'max_pool3d' | ||
| 'max_pool1d', 'max_pool2d', 'max_pool3d', 'linear' |
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ezyang
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yes please
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goldsborough has imported this pull request. If you are a Facebook employee, you can view this diff on Phabricator.
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Hey this seems to have broken ONNX export: https://ci.pytorch.org/jenkins/job/caffe2-builds/job/onnx-py2-gcc5-ubuntu16.04-test/2643/console I can try writing up a symbolic real quick |
Summary: The optimized code for `linear()` which uses `addmm` when a bias is given was duplicated three times in the ATen and the C++ API. Let's just have `at::linear` and use that everywhere. apaszke ezyang (who mentioned this in #10481) Pull Request resolved: pytorch/pytorch#10755 Differential Revision: D9443881 Pulled By: goldsborough fbshipit-source-id: a64862d1649b5961043d58401625ec267d97d9f3
Summary: Resubmission of pytorch/pytorch#10755 with fix for ONNX ezyang jamesr66a Pull Request resolved: pytorch/pytorch#10799 Differential Revision: D9482168 Pulled By: goldsborough fbshipit-source-id: 85d4bdfcf0d451f2e7a1c83c5f5415cdd6caacdc
Summary: The optimized code for `linear()` which uses `addmm` when a bias is given was duplicated three times in the ATen and the C++ API. Let's just have `at::linear` and use that everywhere. apaszke ezyang (who mentioned this in pytorch#10481) Pull Request resolved: pytorch#10755 Differential Revision: D9443881 Pulled By: goldsborough fbshipit-source-id: a64862d1649b5961043d58401625ec267d97d9f3
Summary: Resubmission of pytorch#10755 with fix for ONNX ezyang jamesr66a Pull Request resolved: pytorch#10799 Differential Revision: D9482168 Pulled By: goldsborough fbshipit-source-id: 85d4bdfcf0d451f2e7a1c83c5f5415cdd6caacdc
The optimized code for
linear()which usesaddmmwhen a bias is given was duplicated three times in the ATen and the C++ API. Let's just haveat::linearand use that everywhere.@apaszke @ezyang (who mentioned this in #10481)