Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66742
Modified loops in files under fbsource/fbcode/caffe2/ from the format
`for(TYPE var=x0;var<x_max;x++)`
to the format
`for(const auto var: irange(xmax))`
This was achieved by running r-barnes's loop upgrader script (D28874212) with some modification to exclude all files under /torch/jit and a number of reversions or unused variable suppression warnings added by hand.
Test Plan: Sandcastle
Reviewed By: malfet
Differential Revision: D31705366
fbshipit-source-id: be58222426c192406a7f93c21582c3f6f2082401
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66234
Modified loops in files under fbsource/fbcode/caffe2/ from the format
`for(TYPE var=x0;var<x_max;x++)`
to the format
`for(const auto var: irange(xmax))`
This was achieved by running r-barnes's loop upgrader script (D28874212) with some modification to exclude all files under /torch/jit and a number of reversions or unused variable suppression warnings added by hand.
bypass_size_limit
allow-large-files
Test Plan: Sandcastle
Reviewed By: ngimel
Differential Revision: D30652629
fbshipit-source-id: 0ae6c4bbbb554bad42e372792a6430e1acf15e3e
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
