Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/41606
The previous diff (D22220798 (59294fbbb9) and D22220797) was recently reverted (D22492356 (28291d3cf8), D22492355) because of a bug associated with the op AsyncIf. The AsyncIf op has net_defs as args and the SSA rewriting didn't take that into account. It has a special path for the op If, but not for AsyncIf. Several changes I made to fix the bug:
1) Add op AsyncIf to the special path for If op in SSA rewriting
2) clear inputs/outputs of the netdefs that are args in If/AsyncIf ops because they're no longer valid
3) revert renamed inputs/outputs in the arg netdefs that are in the external_outputs in the parent netdef
2) and 3) are existing bugs in the `SsaRewrite` function that were just never exposed before.
The algorithm for `RemoveOpsByType` is the same as in my previous diff D22220798 (59294fbbb9). The only new changes in this diff are in `onnx::SsaRewrite` and a few newly added unit tests.
(Note: this ignores all push blocking failures!)
Reviewed By: yinghai
Differential Revision: D22588652
fbshipit-source-id: ebb68ecd1662ea2bae14d4be8f61a75cd8b7e3e6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/40649
The original implementation of RemoveOpsByType is pretty buggy and does not remove all instances of the ops that should be removed. It's also quite complicated and hard to modify. I reimplemented it by first converting the graph to its SSA form. The algorithm is quite simple once the graph is in SSA form. It's very similar to constant propagation with a few modifications. The hardest part is to deal with the case of removing an op with the output being an output of the predict net, because that output has to be preserved.
(Note: this ignores all push blocking failures!)
Reviewed By: yinghai, dzhulgakov
Differential Revision: D22220798
fbshipit-source-id: faf6ed5242f1e2f310125d964738c608c6c55c94
Summary:
Overal context: open-source BlackBoxPredictor as the entry
point for inference in Caffe2 (thread safe abstraction for Caffe2
inference). This should be used in ThroughputBenchmark for the purpose
of framework comparison
This specific diff:
There should be no harm in moving transformation code to
OSS. On the advantages side we will be able to compare production
Caffe2 setup with PyTorch in the most fair way via
ThroughputBenchmark. This approach avoid any complicated
transformation regirstries. Building those proper would be significant
engineering effort as well as production risk. In the past we had SEVs
related to transforms being turned off due to various refactors. Given
that we don't plan to build any other significant investments into
transformation logic except existing ones (like TVM and Glow), and
those also relate to open-source technologies, I came up to the
conclusion of moving to OSS the whole thing.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23350
ghstack-source-id: 87121538
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24928
Test Plan: waitforsandcastle
Differential Revision: D16445133
Pulled By: salexspb
fbshipit-source-id: a93106489611dfe427b0f144717bc720d04e47f3
