Implement a Caffe2 standalone LSTM operator#17461
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ahhegazy wants to merge 1 commit intopytorch:masterfrom
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Implement a Caffe2 standalone LSTM operator#17461ahhegazy wants to merge 1 commit intopytorch:masterfrom
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Summary: Pull Request resolved: pytorch#17461 Implementing a standalone LSTM Operator in Caffe2 adopted from this Aten implementation: diffusion/FBS/browse/master/fbcode/caffe2/aten/src/ATen/native/RNN.cpp. The most tricky thing in this exercise was that caffe2::Tensor has no copy constructor that made it necessary to implement a custom templated copy constructor for the different Tensor containers used in the code. Also there was no way to use off-the-shelf C2 operators in my code easily so I had to copy some code that is doing basic matmul, cat, split, transpose and linear as utility functions. Two things missing: - Profiling this implementation against the current ONNXified LSTM op - Make this operator available to use in PyTorch Reviewed By: dzhulgakov Differential Revision: D14160172 fbshipit-source-id: 998189ed0741c057bfea2eb8f62aa3a87ae73f26
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Summary: Pull Request resolved: pytorch#17461 Implementing a standalone LSTM Operator in Caffe2 adopted from this Aten implementation: diffusion/FBS/browse/master/fbcode/caffe2/aten/src/ATen/native/RNN.cpp. The most tricky thing in this exercise was that caffe2::Tensor has no copy constructor that made it necessary to implement a custom templated copy constructor for the different Tensor containers used in the code. Also there was no way to use off-the-shelf C2 operators in my code easily so I had to copy some code that is doing basic matmul, cat, split, transpose and linear as utility functions. Two things missing: - Profiling this implementation against the current ONNXified LSTM op - Make this operator available to use in PyTorch Differential Revision: D14351575 fbshipit-source-id: 4a2d39559c02f3f0182f4a640e9729995bfb5767
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Summary: Pull Request resolved: pytorch#17461 Implementing a standalone LSTM Operator in Caffe2 adopted from this Aten implementation: diffusion/FBS/browse/master/fbcode/caffe2/aten/src/ATen/native/RNN.cpp. The most tricky thing in this exercise was that caffe2::Tensor has no copy constructor that made it necessary to implement a custom templated copy constructor for the different Tensor containers used in the code. Also there was no way to use off-the-shelf C2 operators in my code easily so I had to copy some code that is doing basic matmul, cat, split, transpose and linear as utility functions. Two things missing: - Profiling this implementation against the current ONNXified LSTM op - Make this operator available to use in PyTorch Differential Revision: D14351575 fbshipit-source-id: b07063a1fa9d0e491fda21f996b82ab7ea13f281
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Summary: Pull Request resolved: #17726 Pull Request resolved: #17725 Pull Request resolved: #17461 Implementing a standalone LSTM Operator in Caffe2 adopted from this Aten implementation: diffusion/FBS/browse/master/fbcode/caffe2/aten/src/ATen/native/RNN.cpp. The most tricky thing in this exercise was that caffe2::Tensor has no copy constructor that made it necessary to implement a custom templated copy constructor for the different Tensor containers used in the code. Also there was no way to use off-the-shelf C2 operators in my code easily so I had to copy some code that is doing basic matmul, cat, split, transpose and linear as utility functions. Two things missing: - Profiling this implementation against the current ONNXified LSTM op - Make this operator available to use in PyTorch Reviewed By: dzhulgakov Differential Revision: D14351575 fbshipit-source-id: 3b99b53212cf593c7a49e45580b5a07b90809e64
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Summary:
Implementing a standalone LSTM Operator in Caffe2 adopted from this Aten implementation: diffusion/FBS/browse/master/fbcode/caffe2/aten/src/ATen/native/RNN.cpp. The most tricky thing in this exercise was that caffe2::Tensor has no copy constructor that made it necessary to implement a custom templated copy constructor for the different Tensor containers used in the code. Also there was no way to use off-the-shelf C2 operators in my code easily so I had to copy some code that is doing basic matmul, cat, split, transpose and linear as utility functions.
Two things missing:
Differential Revision: D14160172