Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67271
* [ONNX] Use Reciprocal operator instead of Div(1, x).
This is a more readable and perhaps more performant way to export
torch.reciprocal.
* Use Reciprocal in caffe to operator to import onnx
Test Plan: Imported from OSS
Reviewed By: msaroufim
Differential Revision: D31962519
Pulled By: malfet
fbshipit-source-id: d926e75b1c8312b9a980c9a1207a1a93ba0c71e0
Co-authored-by: take-cheeze <takechi101010@gmail.com>
Summary:
As GoogleTest `TEST` macro is non-compliant with it as well as `DEFINE_DISPATCH`
All changes but the ones to `.clang-tidy` are generated using following script:
```
for i in `find . -type f -iname "*.c*" -or -iname "*.h"|xargs grep cppcoreguidelines-avoid-non-const-global-variables|cut -f1 -d:|sort|uniq`; do sed -i "/\/\/ NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)/d" $i; done
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/62008
Reviewed By: driazati, r-barnes
Differential Revision: D29838584
Pulled By: malfet
fbshipit-source-id: 1b2f8602c945bd4ce50a9bfdd204755556e31d13
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/57080
ONNX optimizer is removed in ONNX 1.9
This PR removes ONNX optimizer from a C++ code path and uses `try-except` block in Python to keep it compatible with both ONNX-1.8 and 1.9.
Test Plan: Imported from OSS
Reviewed By: heitorschueroff
Differential Revision: D28467330
Pulled By: malfet
fbshipit-source-id: 5e4669dd0537648898e593f9e253da18d6dc7568
Co-authored-by: neginraoof <neginmr@utexas.edu>
Co-authored-by: Nikita Shulga <nshulga@fb.com>
Summary:
This is an automatic change generated by the following script:
```
#!/usr/bin/env python3
from subprocess import check_output, check_call
import os
def get_compiled_files_list():
import json
with open("build/compile_commands.json") as f:
data = json.load(f)
files = [os.path.relpath(node['file']) for node in data]
for idx, fname in enumerate(files):
if fname.startswith('build/') and fname.endswith('.DEFAULT.cpp'):
files[idx] = fname[len('build/'):-len('.DEFAULT.cpp')]
return files
def run_clang_tidy(fname):
check_call(["python3", "tools/clang_tidy.py", "-c", "build", "-x", fname,"-s"])
changes = check_output(["git", "ls-files", "-m"])
if len(changes) == 0:
return
check_call(["git", "commit","--all", "-m", f"NOLINT stubs for {fname}"])
def main():
git_files = check_output(["git", "ls-files"]).decode("ascii").split("\n")
compiled_files = get_compiled_files_list()
for idx, fname in enumerate(git_files):
if fname not in compiled_files:
continue
if fname.startswith("caffe2/contrib/aten/"):
continue
print(f"[{idx}/{len(git_files)}] Processing {fname}")
run_clang_tidy(fname)
if __name__ == "__main__":
main()
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56892
Reviewed By: H-Huang
Differential Revision: D27991944
Pulled By: malfet
fbshipit-source-id: 5415e1eb2c1b34319a4f03024bfaa087007d7179
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56759
```
caffe2/caffe2/onnx/onnx_exporter.cc:415:21: error: loop variable 'it' creates a copy from type 'const std::pair<const std::basic_string<char>, int>' [-Werror,-Wrange-loop-construct]
for (const auto it : blob_versions) {
^
caffe2/caffe2/onnx/onnx_exporter.cc:415:10: note: use reference type 'const std::pair<const std::basic_string<char>, int> &' to prevent copying
for (const auto it : blob_versions) {
^~~~~~~~~~~~~~~
&
```
Reviewed By: yfeldblum
Differential Revision: D27960126
fbshipit-source-id: fd46f37cf1aca9441209de8eb06add204046db95
Summary:
*Context:* https://github.com/pytorch/pytorch/issues/53406 added a lint for trailing whitespace at the ends of lines. However, in order to pass FB-internal lints, that PR also had to normalize the trailing newlines in four of the files it touched. This PR adds an OSS lint to normalize trailing newlines.
The changes to the following files (made in 54847d0adb9be71be4979cead3d9d4c02160e4cd) are the only manually-written parts of this PR:
- `.github/workflows/lint.yml`
- `mypy-strict.ini`
- `tools/README.md`
- `tools/test/test_trailing_newlines.py`
- `tools/trailing_newlines.py`
I would have liked to make this just a shell one-liner like the other three similar lints, but nothing I could find quite fit the bill. Specifically, all the answers I tried from the following Stack Overflow questions were far too slow (at least a minute and a half to run on this entire repository):
- [How to detect file ends in newline?](https://stackoverflow.com/q/38746)
- [How do I find files that do not end with a newline/linefeed?](https://stackoverflow.com/q/4631068)
- [How to list all files in the Git index without newline at end of file](https://stackoverflow.com/q/27624800)
- [Linux - check if there is an empty line at the end of a file [duplicate]](https://stackoverflow.com/q/34943632)
- [git ensure newline at end of each file](https://stackoverflow.com/q/57770972)
To avoid giving false positives during the few days after this PR is merged, we should probably only merge it after https://github.com/pytorch/pytorch/issues/54967.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54737
Test Plan:
Running the shell script from the "Ensure correct trailing newlines" step in the `quick-checks` job of `.github/workflows/lint.yml` should print no output and exit in a fraction of a second with a status of 0. That was not the case prior to this PR, as shown by this failing GHA workflow run on an earlier draft of this PR:
- https://github.com/pytorch/pytorch/runs/2197446987?check_suite_focus=true
In contrast, this run (after correcting the trailing newlines in this PR) succeeded:
- https://github.com/pytorch/pytorch/pull/54737/checks?check_run_id=2197553241
To unit-test `tools/trailing_newlines.py` itself (this is run as part of our "Test tools" GitHub Actions workflow):
```
python tools/test/test_trailing_newlines.py
```
Reviewed By: malfet
Differential Revision: D27409736
Pulled By: samestep
fbshipit-source-id: 46f565227046b39f68349bbd5633105b2d2e9b19
Summary:
Since caffe2 and torch have been consolidated, CAFFE2_API should be merged with TORCH_API. Addresses a TODO.
Manually edited some references of the removed `CAFFE2_API`:
* `CONTRIBUTING.md`
* `caffe2/proto/CMakeLists.txt`
* `cmake/ProtoBuf.cmake`
* `c10/macros/Export.h`
* `torch/csrc/WindowsTorchApiMacro.h`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49496
Reviewed By: malfet, samestep
Differential Revision: D25600726
Pulled By: janeyx99
fbshipit-source-id: 7e068d959e397ac183c097d7e9a9afeca5ddd782
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46134
Make sure in-place ops stay in-place after SsaRewrite. This seems to break the premise of SSA, but it's necessary to ensure correctness. Note here we only preserve the inplace ops that enforce inplace. Ops like `Relu` don't enforce inplace, they allow inplace.
(Note: this ignores all push blocking failures!)
Reviewed By: yinghai
Differential Revision: D24234957
fbshipit-source-id: 274bd3ad6227fce6a98e615aad7e57cd2696aec3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42516
att. We need it for some scripts.
Reviewed By: houseroad
Differential Revision: D22918112
fbshipit-source-id: 8a1696ceeeda67a34114bc57cb52c925711cfb4c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42286
One more bug to fix. Operators such as If and AsyncIf need special treatment not just in `onnx::SsaRewrite`, but also in `RemoveOpsByType`. The solution needs two steps:
1) add external inputs/outputs of the subnets of If/AsyncIf op to the inputs/outputs of the op
2) if the inputs/outputs of the If/AsyncIf op need to be renamed as a result, the same inputs/outputs of the subnets need to be renamed as well.
I also added unit tests to cover this corner case.
Test Plan:
```
buck test //caffe2/caffe2/fb/predictor:black_box_predictor_test
mkdir /tmp/models
rm -rf /tmp/$USER/snntest
rm -rf /tmp/snntest
buck run mode/opt admarket/lib/ranking/prediction_replayer/snntest_replayer_test/tools:snntest_replay_test -- --serving_paradigm=USER_AD_PRECOMPUTATION_DSNN
```
Differential Revision: D22834028
fbshipit-source-id: c070707316cac694f452a96e5c80255abf4014bc
Summary:
the onnxifi path didn't handle the input/output name rewrite for ssa correctly for AsyncIf op. Add support for it.
Also fixed a place where we lose the net type while doing onnxifi transform.
Test Plan: Load 163357582_593 which is a multi feed model that uses AsyncIf. This used to fail with c2 not finding some blobs in workspace. Now it works.
Reviewed By: dhe95
Differential Revision: D21268230
fbshipit-source-id: ce7ec0e952513d0f251df1bfcfb2b0250f51fd94
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:
This directory is opted-in to clang-format but is not format-clean. This blocks continuous formatting from being enabled on fbcode, and causes hassle for other codemods that leave inconsistent formatting. This diff runs clang-format, which is widely used and considered safe.
If you are unhappy with the formatting of a particular block, please *accept this diff* and then in a stacked commit undo the change and wrap that code in `// clang-format off` and `// clang-format on`, or `/* clang-format off */` and `/* clang-format on */`.
drop-conflicts
Test Plan: sandcastleit
Reviewed By: jerryzh168
Differential Revision: D22311706
fbshipit-source-id: 1ca59a82e96156a4a5dfad70ba3e64d44c5e762a
Summary:
To fix caffe2 model with Copy OP cannot export to onnx model
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37144
Reviewed By: houseroad
Differential Revision: D21252421
Pulled By: yinghai
fbshipit-source-id: 4f1077188f36b0691d199e418880bbb27f11032d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30915
Since we now have C++14, we don't need these c10::guts helpers anymore
ghstack-source-id: 95777609
Test Plan: waitforsandcastle
Differential Revision: D18869639
fbshipit-source-id: 97716f932297c64c6e814410ac47b444c33d4e2e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30887
Support to convert quantized concat from pytorch to caffe2
Test Plan:
python test/onnx/test_pytorch_onnx_caffe2_quantized.py TestQuantizedOps.test_cat
Imported from OSS
Differential Revision: D18855676
fbshipit-source-id: 5d0cf3f03c61819e168b080afa368b1255d0419c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30490
Add symbolic mapping to Int8AvgPool2d and Int8Reshape op in C2
Test Plan:
python test/onnx/test_pytorch_onnx_caffe2_quantized.py TestQuantizedOps
Imported from OSS
Differential Revision: D18740520
fbshipit-source-id: 1606125500c4b549fbc984e7929b7fd5204396a0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/28855
Resubmit:
OfflineTensor will be a shell to just carry the shape and dtype. No data will be stored. This should help us plumb through the onnxifi process.
Test Plan:
```
buck test caffe2/caffe2/fb/opt:onnxifi_with_offline_tensor_test
```
Reviewed By: ipiszy, ChunliF
Differential Revision: D18212824
fbshipit-source-id: 5c8aaed2ef11d719dfa2a2901875efd66806ea56
Summary: OfflineTensor will be a shell to just carry the shape and dtype. No data will be stored. This should help us plumb through the onnxifi process.
Test Plan:
```
buck test caffe2/caffe2/fb/opt:onnxifi_with_offline_tensor_test
```
Reviewed By: ChunliF, zrphercule
Differential Revision: D18187208
fbshipit-source-id: 57c70f6f9897a5fc66580c81295db108acd03862
Summary:
Exporting torch.select when index = negative one (x[:,-1]) was broken. This PR has the fix in symbolic function for select.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/25273
Reviewed By: hl475
Differential Revision: D17159707
Pulled By: houseroad
fbshipit-source-id: 2c3b275421082758f1b63c1c9b6e578f03ca9f76
Summary:
Support exporting
* Standard tensor indexing like
```
x = torch.ones(4, 5)
ind = torch.tensor([0, 1])
return x[ind]
```
* [Advanced indexing](https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#advanced-indexing) like
```
x = torch.ones(4,5,6,7,8)
ind1 = torch.tensor([0, 1])
ind2 = torch.tensor([[3], [2]])
ind3 = torch.tensor([[2, 2], [4, 5]])
return x[2:4, ind1, None, ind2, ind3, :]
```
It would be ideal if ONNX can natively support indexing in future opsets, but for opset <= 10 it will always need this kind of workarounds.
There are still various limitations, such as not supporting advanced indexing with negative indices, not supporting mask indices of rank > 1, etc. My feeling is that these are less common cases that requires great effort to support using current opset, and it's better to not make the index export more cumbersome than it already is.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21716
Reviewed By: zrphercule
Differential Revision: D15902199
Pulled By: houseroad
fbshipit-source-id: 5f1cc687fc9f97da18732f6a2c9dfe8f6fdb34a6
Summary:
Some overlap with https://github.com/pytorch/pytorch/pull/21716 regarding caffe2 nonzero. Will rebase the other one accordingly whichever gets merged first.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22601
Reviewed By: zrphercule
Differential Revision: D16224660
Pulled By: houseroad
fbshipit-source-id: dbfd1b8776cb626601e0bf83b3fcca291806e653
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21233
It is possible that OnnxifiOp is created in a thread where weights have been cleaned from the workspace, which is legit use case as we can create the backend once and lower all the weights. So we need to extract the weight shape info the first time we create the backend and save it.
Reviewed By: bertmaher, rdzhabarov
Differential Revision: D15587237
fbshipit-source-id: 1f264dc32c0398c42b618e9c41c119eb13e1c9f1
Summary:
As a work around for dynamic shape case.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20093
Reviewed By: zrphercule
Differential Revision: D15220661
Pulled By: houseroad
fbshipit-source-id: de271fce542be380bd49a3c74032c61f9aed3b67
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19585
Originally we will unroll all If op to many different subnets;
Now we will not unroll it anymore, but just add all external input of its subnet to the If op, and ssa-rewrite all external input/outputs. That would be enough.
Reviewed By: yinghai
Differential Revision: D15038139
fbshipit-source-id: 8532216d8749068acd5558ad0d8cb1d98463a063
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18512
Ceil and Floor have been supported since version 6 of ONNX: export them using the native onnx ops instead of an Aten op.
Similarly, support for the Where op has been added in version 9, so we don't need to wrap these op in an Aten op.
Reviewed By: houseroad
Differential Revision: D14635130
fbshipit-source-id: d54a2b6e295074a6214b5939b21051a6735c9958
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17545
This diff avoids renaming boundary inputs of net during onnxifi transform.
It also removes adding mappings for the initializer during onnxifi op creation.
Thus gets read of the mapped ws creation during onnxifi op creation.
Reviewed By: zrphercule
Differential Revision: D14243161
fbshipit-source-id: 6eafa920c45f6a6bfacbbb443e8e84cf9778644c
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17481
Usually, feature macros are either defined or undefined and checked accordingly.
C10_MOBILE was a weird special case that was always defined but either defined to 1 or to 0.
This caused a lot of confusion for me when trying to disable something from mobile build and it also disabled it
from the server build (because I was using ifdef). Also, I found a place in the existing code base that made
that wrong assumption and used the macro wrongly, see https://fburl.com/y4icohts
Reviewed By: dzhulgakov
Differential Revision: D14214825
fbshipit-source-id: f3a155b6d43d334e8839e2b2e3c40ed2c773eab6
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17375
Previously we create the onnxGraph first and take it to the onnx manager for registration. It doesn't work well in practice. This diff takes "bring your own constructor" approach to reduce the resource spent doing backend compilation.
Reviewed By: kimishpatel, rdzhabarov
Differential Revision: D14173793
fbshipit-source-id: cbc4fe99fc522f017466b2fce88ffc67ae6757cf
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17198
We come to the point that we need to apply some rules to bind certain ops together to avoid un-inferrable intermediate shapes. We either lower them together to backend or neither. This diff adds a pass for us to add rules like this. The first one is to bind `Gather` with `SparseLengthsWeighted*`.
Reviewed By: ipiszy
Differential Revision: D14118326
fbshipit-source-id: 14bc62e1feddae02a3dd8eae93b8f553d52ac951
Summary:
Add support of count_include_pad end to end test for AveragePool
We can export AveragePool from PyTorch with count_include_pad attribute. However, we don't directly support it in Caffe2's ONNX backend.
We also want to check whether we can pass the end to end test for average pool operator with count_include_pad attribute (pytorch => onnx => caffe2)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17034
Reviewed By: houseroad
Differential Revision: D14060186
Pulled By: dwarakrajagopal
fbshipit-source-id: 10dae532611c71f8c8cfc3fa701cc7c1c1c02695
Summary:
For >2D input, previously the code uses static shape captured during tracing and reshape before/after `Gemm`.
Now we add `-1` to the first `Reshape`, and uses `Shape(X) => Slice(outer) => Concat(with -1 for inner) => Reshape` for the second.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16184
Differential Revision: D14070754
Pulled By: ezyang
fbshipit-source-id: 86c69e9b254945b3406c07e122e57a00dfeba3df
Summary:
The second input (`lengths`) is not supported.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16727
Differential Revision: D14054105
Pulled By: houseroad
fbshipit-source-id: 36b8d00460f9623696439e1bd2a6bc60b7bb263c
