Implement a Caffe2 standalone LSTM operator (#17461)#17726
Closed
ahhegazy wants to merge 1 commit intopytorch:masterfrom
Closed
Implement a Caffe2 standalone LSTM operator (#17461)#17726ahhegazy wants to merge 1 commit intopytorch:masterfrom
ahhegazy wants to merge 1 commit intopytorch:masterfrom
Conversation
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
petrex
pushed a commit
to petrex/pytorch
that referenced
this pull request
Mar 7, 2019
* upstream/master: (24 commits) Automatic update of fbcode/onnx to 96c58ceeacf0f2b73d752e413e4fd78787a12da3 (pytorch#17676) Set the default ONNX opset to the latest stable opset (i.e., 9) (pytorch#17736) Add module attributes (pytorch#17309) - refactoring serialization of ONNX initializers to be name-based (pytorch#17420) ONNX Export for Max and Average Pooling in CEIL_MODE use flake8-mypy (pytorch#17721) use fp16<->fp32 intrinsic (pytorch#17496) Implement a Caffe2 standalone LSTM operator (pytorch#17726) caffe2:libtorch_cuda depends on caffe2:caffe2_gpu (pytorch#17729) add tensor and cost inference functions (pytorch#17684) ONNX Export Narrow op Keep the dim_type of hinted shape as BATCH if possible (pytorch#17734) fix different round behavior on CPU and GPU pytorch#16498 (pytorch#17443) Warn about memory overlaps on expanded tensors (pytorch#17576) fix exp fam. formula refactor caffe2 operator constructors - 10/9 (pytorch#17659) Improve ONNX symbolic for logsoftmax and softmax (pytorch#17672) Enable using CMD when building cpp extensions on Windows Do not rename net boundary inputs/outputs during ssaRewrite. (pytorch#17545) Reapply D14078519 (pytorch#17596) ...
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
2 participants
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
Summary:
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:
Differential Revision: D14351575