Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17726
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17725
Pull Request resolved: https://github.com/pytorch/pytorch/pull/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
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/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: c33e3f9e8aeae578b64d97593cb031a251216029
