Automatic fixes that replaces certain list comprehensions with generator ones where appropriate so that they are immediately consumed. This is preview functionality in ruff for rule C419 and it was automatically applied.
Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123960
Approved by: https://github.com/malfet
Refactor release only changes to two step execution.
1. Step ``tag-docker-images.sh`` . Tags latest docker images for current release. This step takes about 30min to complete. This step may fail due to space issues on the local host or http connection when pulling image. Hence should be rerun if failed.
2. Apply release only changes ``apply-release-changes.sh`` prepares a PR with release only changes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121728
Approved by: https://github.com/jeanschmidt
Fixes#120044
Should fix build from source instructions on release branch here: https://github.com/pytorch/pytorch#from-source
Please note we are using /test/ channel for release here to make sure it works, before actual release is completed.
Test main:
```
make triton
pip3 uninstall -y triton
WARNING: Skipping triton as it is not installed.
Looking in indexes: https://download.pytorch.org/whl/nightly/
Collecting pytorch-triton==3.0.0+a9bc1a3647
Downloading https://download.pytorch.org/whl/nightly/pytorch_triton-3.0.0%2Ba9bc1a3647-cp310-cp310-linux_x86_64.whl (239.0 MB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 239.0/239.0 MB 8.7 MB/s eta 0:00:00
Requirement already satisfied: filelock in /home/atalman/miniconda3/envs/py310/lib/python3.10/site-packages (from pytorch-triton==3.0.0+a9bc1a3647) (3.13.1)
Installing collected packages: pytorch-triton
Attempting uninstall: pytorch-triton
Found existing installation: pytorch-triton 2.2.0
Uninstalling pytorch-triton-2.2.0:
Successfully uninstalled pytorch-triton-2.2.0
Successfully installed pytorch-triton-3.0.0+a9bc1a3647
```
Test release/2.2:
```
make triton
pip3 uninstall -y triton
WARNING: Skipping triton as it is not installed.
Looking in indexes: https://download.pytorch.org/whl/test/
Collecting pytorch-triton==2.2.0
Using cached https://download.pytorch.org/whl/test/pytorch_triton-2.2.0-cp310-cp310-linux_x86_64.whl (183.1 MB)
Requirement already satisfied: filelock in /home/atalman/miniconda3/envs/py310/lib/python3.10/site-packages (from pytorch-triton==2.2.0) (3.13.1)
Installing collected packages: pytorch-triton
Attempting uninstall: pytorch-triton
Found existing installation: pytorch-triton 3.0.0+a9bc1a3647
Uninstalling pytorch-triton-3.0.0+a9bc1a3647:
Successfully uninstalled pytorch-triton-3.0.0+a9bc1a3647
Successfully installed pytorch-triton-2.2.0
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121169
Approved by: https://github.com/seemethere
It's a bit annoying to have to read through the test name in verbose mode just to see what the test's sentinel file is actually called when encountering an unexpected success. Now that we have sentinel files, we can directly list the file path from root in the error message.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120766
Approved by: https://github.com/Skylion007
Previously we were downloading all of (eager311, dynamo38, dynamo311).
Now we just download what's necessary. This is useful for
update_failures.py because the dynamo tests finish much faster than the
eager tests and it only needs the result from the dynamo tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119027
Approved by: https://github.com/jamesjwu
ghstack dependencies: #118874, #118882, #118931
Previously, you could run update_failures.py (with a commit hash) and it
would add new expected failures and skips for newly failing tests and
remove expected failures for newly passing tests.
This PR teaches update_failures.py to also remove skips for tests that
are now passing without them.
The way we do this is:
- dynamo_test_failures.py doesn't actually skip tests -- it runs the
test and then suppresses the signal.
- if the test actually passed, then the test gets skipped with a special
skip message
- we teach update_failures.py to look for the presence of that skip
message.
Test Plan:
- Used this to generate https://github.com/pytorch/pytorch/pull/118928
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118931
Approved by: https://github.com/yanboliang
ghstack dependencies: #118874, #118882
```
def f():
def g():
return ()
print(g.__name__)
f()
```
The following script should print `g` (with or without torch.compile),
but prints `f.<locals>.g` with torch.compile.
The problem looks like we use the co_qualname when reconstructing the
NestedUserFunctionVariable. I switched this over to use the co_name.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118768
Approved by: https://github.com/yanboliang, https://github.com/jansel
Changelog:
- Don't count running PYTORCH_TEST_WITH_DYNAMO=1 on dynamo/ tests in the pass
rate. This was a bug (we were counting all of these as failing, but in
reality, most of these pass). The net effect is that the passrate is (artifically)
6% higher.
- Have the histogram script filter out skips based on the passrate metric.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118752
Approved by: https://github.com/jamesjwu
Summary:
This diff implements a mechanism for safely update torch.export serialization schema, aka schema.py, which is the API surface having the strongest compatibility guarantee.
The diff is consist of 3 changes:
- Added a script to "build" or "materialize" schema.py into a platform neutral format (yaml), which serves as the committed form of the seialization schema.
- Added unittest to compare against schema.py and schema.yaml, so that it forces developers to execute the updater script when there is mismatch between two files.
- Added a checker inside the updater script, so that all the compatible change will result in a minor version bump, and all the incompatible changes will result in a major version bump.
torch.export's serialization BC/FC policy is (tentatively) documented here: https://docs.google.com/document/d/1EN7JrHbOPDhbpLDtiYG4_BPUs7PttpXlbZ27FuwKhxg/edit#heading=h.pup7ir8rqjhx , we will update the
As noted in the code doc, people should be able to run the following command to update schema properly from now on:
```
python scripts/export/update_schema.py --prefix <path_to_torch_development_diretory>
or
buck run caffe2:export_update_schema -- --prefix /data/users/$USER/fbsource/fbcode/caffe2/
```
Test Plan:
buck test mode/opt caffe2/test:test_export -- -r test_schema
buck run caffe2:update_export_schema -- --prefix /data/users/$USER/fbsource/fbcode/caffe2/
Differential Revision: D52971020
Pull Request resolved: https://github.com/pytorch/pytorch/pull/118424
Approved by: https://github.com/angelayi
- passrate.py: compute the pass rate
- update_failures.py: update `dynamo_test_failures.py`
Both of these scripts require you to download the test results from CI
locally. Maybe we can automate this more in the future. Checking these
in for now, with no tests :P.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117400
Approved by: https://github.com/voznesenskym
ghstack dependencies: #117391
Summary:
c2_protobuf_rule ([here](https://fburl.com/code/iyiulpmv)) is broken on buck2, ultimately due to the following error:
> .\./caffe2.proto: File does not reside within any path specified using --proto_path (or -I). You must specify a --proto_path which encompasses this file. Note that the proto_path must be an exact prefix of the .proto file names -- protoc is too dumb to figure out when two paths (e.g. absolute and relative) are equivalent (it's harder than you think).
The root cause is differences in how buck1 and buck2 handle `%SRCDIR%` (absolute versus relative paths). This diff fixes the build.
Test Plan:
# Before
```
buck2 build arvr/mode/win/opt //xplat/caffe2:caffe2.pb.h
```
```
More details at https://www.internalfb.com/intern/buck/build/c6550454-ae6d-479e-9d08-016e544ef050
BUILD SUCCEEDED
```
```
Action failed: fbsource//xplat/caffe2:caffe2.pb.h (genrule)
Remote command returned non-zero exit code <no exit code>
Reproduce locally: frecli cas download-action 5df17cf64b7e2fc5ab090c91e1129f2f3cad36dc72c7c182ab052af23d3f32aa:145
stdout:
stderr:
OUTMISS: Missing outputs: buck-out/v2/gen/fbsource/dd87aacb8683145b/xplat/caffe2/caffe2.pb.h/out/caffe2.pb.h
```
# After
Buck1 still works
```
buck1 build arvr/mode/win/opt //xplat/caffe2:caffe2.pb.h
```
Buck2 works
```
buck2 build arvr/mode/win/opt //xplat/caffe2:caffe2.pb.h
```
```
Buck UI: https://www.internalfb.com/buck2/e5dae607-325a-4eab-b0c9-66fe4e9a6254
BUILD SUCCEEDED
```
Differential Revision: D52218365
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115954
Approved by: https://github.com/mcr229
1. This tags docker images using docker pull/tag/push for current release
2. Sets RELEASE_VERSION_TAG var and regenerates the workflows using the new docker tag
3. Remove conda token setting and Binary tests release changes these are already automated
4. Pin unstable and disabled jobs, autumate: https://github.com/pytorch/pytorch/pull/111675
Test:
```
RELEASE_VERSION=2.2 ./scripts/release/apply-release-changes.sh
Tagging pytorch/manylinux-builder:cuda11.8-main to pytorch/manylinux-builder:cuda11.8-2.2 , dry_run: enabled
Tagging pytorch/manylinux-builder:cuda12.1-main to pytorch/manylinux-builder:cuda12.1-2.2 , dry_run: enabled
Tagging pytorch/libtorch-cxx11-builder:cuda11.8-main to pytorch/libtorch-cxx11-builder:cuda11.8-2.2 , dry_run: enabled
Tagging pytorch/libtorch-cxx11-builder:cuda12.1-main to pytorch/libtorch-cxx11-builder:cuda12.1-2.2 , dry_run: enabled
Tagging pytorch/manylinux-builder:rocm5.6-main to pytorch/manylinux-builder:rocm5.6-2.2 , dry_run: enabled
Tagging pytorch/manylinux-builder:rocm5.7-main to pytorch/manylinux-builder:rocm5.7-2.2 , dry_run: enabled
Tagging pytorch/libtorch-cxx11-builder:rocm5.6-main to pytorch/libtorch-cxx11-builder:rocm5.6-2.2 , dry_run: enabled
Tagging pytorch/libtorch-cxx11-builder:rocm5.7-main to pytorch/libtorch-cxx11-builder:rocm5.7-2.2 , dry_run: enabled
Tagging pytorch/manylinux-builder:cpu-main to pytorch/manylinux-builder:cpu-2.2 , dry_run: enabled
Tagging pytorch/libtorch-cxx11-builder:cpu-main to pytorch/libtorch-cxx11-builder:cpu-2.2 , dry_run: enabled
Tagging pytorch/manylinuxcxx11-abi-builder:cpu-cxx11-abi-main to pytorch/manylinuxcxx11-abi-builder:cpu-cxx11-abi-2.2 , dry_run: enabled
Tagging pytorch/manylinuxaarch64-builder:cpu-aarch64-main to pytorch/manylinuxaarch64-builder:cpu-aarch64-2.2 , dry_run: enabled
Tagging pytorch/conda-builder:cuda11.8-main to pytorch/conda-builder:cuda11.8-2.2 , dry_run: enabled
Tagging pytorch/conda-builder:cuda12.1-main to pytorch/conda-builder:cuda12.1-2.2 , dry_run: enabled
Tagging pytorch/conda-builder:cpu-main to pytorch/conda-builder:cpu-2.2 , dry_run: enabled
/data/users/atalman/pytorch/.github/workflows/generated-linux-binary-manywheel-nightly.yml
/data/users/atalman/pytorch/.github/workflows/generated-linux-binary-conda-nightly.yml
/data/users/atalman/pytorch/.github/workflows/generated-linux-binary-libtorch-cxx11-abi-nightly.yml
/data/users/atalman/pytorch/.github/workflows/generated-linux-binary-libtorch-pre-cxx11-nightly.yml
/data/users/atalman/pytorch/.github/workflows/generated-linux-aarch64-binary-manywheel-nightly.yml
/data/users/atalman/pytorch/.github/workflows/generated-linux-binary-manywheel-main.yml
/data/users/atalman/pytorch/.github/workflows/generated-linux-binary-libtorch-cxx11-abi-main.yml
/data/users/atalman/pytorch/.github/workflows/generated-linux-binary-libtorch-pre-cxx11-main.yml
/data/users/atalman/pytorch/.github/workflows/generated-windows-binary-wheel-nightly.yml
/data/users/atalman/pytorch/.github/workflows/generated-windows-binary-conda-nightly.yml
/data/users/atalman/pytorch/.github/workflows/generated-windows-binary-libtorch-release-nightly.yml
/data/users/atalman/pytorch/.github/workflows/generated-windows-binary-libtorch-debug-nightly.yml
/data/users/atalman/pytorch/.github/workflows/generated-windows-binary-libtorch-release-main.yml
/data/users/atalman/pytorch/.github/workflows/generated-windows-binary-libtorch-debug-main.yml
/data/users/atalman/pytorch/.github/workflows/generated-macos-binary-wheel-nightly.yml
/data/users/atalman/pytorch/.github/workflows/generated-macos-binary-conda-nightly.yml
/data/users/atalman/pytorch/.github/workflows/generated-macos-binary-libtorch-cxx11-abi-nightly.yml
/data/users/atalman/pytorch/.github/workflows/generated-macos-arm64-binary-libtorch-cxx11-abi-nightly.yml
/data/users/atalman/pytorch/.github/workflows/generated-macos-arm64-binary-wheel-nightly.yml
/data/users/atalman/pytorch/.github/workflows/generated-macos-arm64-binary-conda-nightly.yml
````
Result of pinning unstable and disabled jobs:
```
# The link to the published list of disabled jobs
DISABLED_JOBS_URL = "https://ossci-metrics.s3.amazonaws.com/disabled-jobs.json?versionid=kKJlAXdrUbk3CilXbKu.6OwNTGQB8a.B"
# and unstable jobs
UNSTABLE_JOBS_URL = "https://ossci-metrics.s3.amazonaws.com/unstable-jobs.json?versionid=vzaicOxSsh55iXBXwgGrW6dFeVtPfrhr"
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114355
Approved by: https://github.com/malfet
Enables two ruff rules derived from pylint:
* PLR1722 replaces any exit() calls with sys.exit(). exit() is only designed to be used in repl contexts as may not always be imported by default. This always use the version in the sys module which is better
* PLW3301 replaces nested min / max calls with simplified versions (ie. `min(a, min(b, c))` => `min(a, b. c)`). The new version is more idiomatic and more efficient.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109461
Approved by: https://github.com/ezyang
# Summary
This PR made some significant changes to the scripts around Release Scripts. At a high level:
- Turned the quips into docs and updated links
- Update the common.categorizes list in the hopes to make this the source of truth for releases- This is hard since the release_notes labels can be changed at will. An alternative would be to poll from github api. However, I think that is overkill. The notebook does a set compare and will show you knew categories. I think we want this to be manual so that the release note engineer will decided how to categorize.
- Create cateogry group from speaking with folks on distributed and AO that told me these different release categories can be merged.
- I am the newest person to Core and don't use ghstack soo made token getting a lil more generic.
- Added a classifier.py file. This file will train a commit categorizer for you, hopefully with decent accuracy. I was able to achieve 75% accuracy. I drop the highest frequency class - "skip" since this creates a more useful cateogrizer.
- I updated the categorize.py script so that the prompt will be what the classifier thinks, gated by a flag.
- Added a readme that will hopefully help future release notes engineers.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94560
Approved by: https://github.com/albanD
Merges startswith, endswith calls to into a single call that feeds in a tuple. Not only are these calls more readable, but it will be more efficient as it iterates through each string only once.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96754
Approved by: https://github.com/ezyang
Applies the remaining flake8-comprehension fixes and checks. This changes replace all remaining unnecessary generator expressions with list/dict/set comprehensions which are more succinct, performant, and better supported by our torch.jit compiler. It also removes useless generators such as 'set(a for a in b)`, resolving it into just the set call.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94676
Approved by: https://github.com/ezyang
I applied some flake8 fixes and enabled checking for them in the linter. I also enabled some checks for my previous comprehensions PR.
This is a follow up to #94323 where I enable the flake8 checkers for the fixes I made and fix a few more of them.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94601
Approved by: https://github.com/ezyang
Preferring dash over underscore in command-line options. Add `--command-arg-name` to the argument parser. The old arguments with underscores `--command_arg_name` are kept for backward compatibility.
Both dashes and underscores are used in the PyTorch codebase. Some argument parsers only have dashes or only have underscores in arguments. For example, the `torchrun` utility for distributed training only accepts underscore arguments (e.g., `--master_port`). The dashes are more common in other command-line tools. And it looks to be the default choice in the Python standard library:
`argparse.BooleanOptionalAction`: 4a9dff0e5a/Lib/argparse.py (L893-L895)
```python
class BooleanOptionalAction(Action):
def __init__(...):
if option_string.startswith('--'):
option_string = '--no-' + option_string[2:]
_option_strings.append(option_string)
```
It adds `--no-argname`, not `--no_argname`. Also typing `_` need to press the shift or the caps-lock key than `-`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94505
Approved by: https://github.com/ezyang, https://github.com/seemethere
This change introduces a mechanism to test onnx export based on sample inputs registered in OpInfo, similar to how MPS and other components of pytorch are tested. It provides test coverage on ops and dtypes previously unattainable with manually created test models. This is the best way for us to discover gaps in the exporter support, especially for ops with partial existing support.
This test is adapted from https://github.com/pytorch/pytorch/blob/master/test/test_mps.py
This PR also
- Update sqrt to support integer inputs to match pytorch behavior
- Add pytest-subtests for unittest subtests support in the new test file
I only enabled very few ops: `t`, `ceil` and `sqrt` because otherwise too many things will fail due to (1) unsupported dtypes in the exporter (2) unimplemented dtype support in onnxruntime (3) unexpected input to verification.verify.
Subsequent PRs should improve `verification.verify` first for it to accept any legal input to a pytorch model, then incrementally fix the symbolic functions to enable more test cases.
Fixes#85363
Design #88118
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86182
Approved by: https://github.com/BowenBao
Summary: Currently, build_mobile.sh doesn't allow lite interpreter builds or tracing based selective builds. build_mobile.sh is used for host builds of PyTorch for Mobile deployment.
Additionally, certain flags such as `USE_BLAS` were not being respected as they should be. This change addresses that as well.
Test Plan: Build using:
```
cat /tmp/selected_ops.yaml
- aten::add
- aten::sub
```
```
BUILD_PYTORCH_MOBILE_WITH_HOST_TOOLCHAIN=1 USE_LIGHTWEIGHT_DISPATCH=0 BUILD_LITE_INTERPRETER=1 SELECTED_OP_LIST=/tmp/selected_ops.yaml ./scripts/build_mobile.sh
```
```
cat /tmp/main.cpp
int main() {
auto m = torch::jit::_load_for_mobile("/tmp/path_to_model.ptl");
auto res = m.forward({});
return 0;
}
```
Test using:
```
g++ /tmp/main.cpp -L build_mobile/lib/ -I build_mobile/install/include/ -lpthread -lc10 -ltorch_cpu -ltorch -lXNNPACK -lpytorch_qnnpack -lcpuinfo -lclog -lpthreadpool -lgloo -lkineto -lfmt -ldl -lc10
```
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84647
Approved by: https://github.com/JacobSzwejbka, https://github.com/cccclai
We're no longer building Caffe2 mobile as part of our CI, and it adds a lot of clutter to our make files. Any lingering internal dependencies will use the buck build and so wont be effected.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84338
Approved by: https://github.com/dreiss
This PR adds an internal wrapper on the [beartype](https://github.com/beartype/beartype) library to perform runtime type checking in `torch.onnx`. It uses beartype when it is found in the environment and is reduced to a no-op when beartype is not found.
Setting the env var `TORCH_ONNX_EXPERIMENTAL_RUNTIME_TYPE_CHECK=ERRORS` will turn on the feature. setting `TORCH_ONNX_EXPERIMENTAL_RUNTIME_TYPE_CHECK=DISABLED` will disable all checks. When not set and `beartype` is installed, a warning message is emitted.
Now when users call an api with invalid arguments e.g.
```python
torch.onnx.export(conv, y, path, export_params=True, training=False)
# traning should take TrainingModel, not bool
```
they get
```
Traceback (most recent call last):
File "bisect_m1_error.py", line 63, in <module>
main()
File "bisect_m1_error.py", line 59, in main
reveal_error()
File "bisect_m1_error.py", line 32, in reveal_error
torch.onnx.export(conv, y, cpu_model_path, export_params=True, training=False)
File "<@beartype(torch.onnx.utils.export) at 0x1281f5a60>", line 136, in export
File "pytorch/venv/lib/python3.9/site-packages/beartype/_decor/_error/errormain.py", line 301, in raise_pep_call_exception
raise exception_cls( # type: ignore[misc]
beartype.roar.BeartypeCallHintParamViolation: @beartyped export() parameter training=False violates type hint <class 'torch._C._onnx.TrainingMode'>, as False not instance of <protocol "torch._C._onnx.TrainingMode">.
```
when `TORCH_ONNX_EXPERIMENTAL_RUNTIME_TYPE_CHECK` is not set and `beartype` is installed, a warning message is emitted.
```
>>> torch.onnx.export("foo", "bar", "f")
<stdin>:1: CallHintViolationWarning: Traceback (most recent call last):
File "/home/justinchu/dev/pytorch/torch/onnx/_internal/_beartype.py", line 54, in _coerce_beartype_exceptions_to_warnings
return beartyped(*args, **kwargs)
File "<@beartype(torch.onnx.utils.export) at 0x7f1d4ab35280>", line 39, in export
File "/home/justinchu/anaconda3/envs/pytorch/lib/python3.9/site-packages/beartype/_decor/_error/errormain.py", line 301, in raise_pep_call_exception
raise exception_cls( # type: ignore[misc]
beartype.roar.BeartypeCallHintParamViolation: @beartyped export() parameter model='foo' violates type hint typing.Union[torch.nn.modules.module.Module, torch.jit._script.ScriptModule, torch.jit.ScriptFunction], as 'foo' not <protocol "torch.jit.ScriptFunction">, <protocol "torch.nn.modules.module.Module">, or <protocol "torch.jit._script.ScriptModule">.
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/justinchu/dev/pytorch/torch/onnx/_internal/_beartype.py", line 63, in _coerce_beartype_exceptions_to_warnings
return func(*args, **kwargs)
File "/home/justinchu/dev/pytorch/torch/onnx/utils.py", line 482, in export
_export(
File "/home/justinchu/dev/pytorch/torch/onnx/utils.py", line 1422, in _export
with exporter_context(model, training, verbose):
File "/home/justinchu/anaconda3/envs/pytorch/lib/python3.9/contextlib.py", line 119, in __enter__
return next(self.gen)
File "/home/justinchu/dev/pytorch/torch/onnx/utils.py", line 177, in exporter_context
with select_model_mode_for_export(
File "/home/justinchu/anaconda3/envs/pytorch/lib/python3.9/contextlib.py", line 119, in __enter__
return next(self.gen)
File "/home/justinchu/dev/pytorch/torch/onnx/utils.py", line 95, in select_model_mode_for_export
originally_training = model.training
AttributeError: 'str' object has no attribute 'training'
```
We see the error is caught right when the type mismatch happens, improving from what otherwise would become `AttributeError: 'str' object has no attribute 'training'`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83673
Approved by: https://github.com/BowenBao
* Move memory heavy tests from `test_pytorch_onnx_onnxruntime.py` to
`test_models_onnxruntime.py`. The former is run in parallel in CI,
while the latter is not. A change is that the moved tests are now
only covered in default opset export.
* Refactor and create base class for tests that export model to ONNX
and verify with ONNX Runtime. The new base class are parameterized
with `opset_version` and `is_script`. Further work can be done to
refactor existing test classes in `test_pytorch_onnx_onnxruntime.py`.
See #75630
* Reduce unnecessarily large tensor size in
`test_pytorch_onnx_onnxruntime.py` to further reduce memory usage
and test time.
After this PR, the running time for `test_pytorch_onnx_onnxruntime.py`
is reduced from `1338.82s (0:22:18)` to `225.07s (0:03:45)`,
benchmarked on 10900x with `-n 10`.
Fixes#79179
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79640
Approved by: https://github.com/justinchuby, https://github.com/garymm
Should fix#78844
Custom op related tests utilize inline cpp extension to build custom
operator from c++ source snippet. Only two test cases become flaky after
parallel run, and both use inline cpp extension. Reverting to run these
tests in single process to try resolve the flakiness.
Reverts test skip added previously #78936.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78944
Approved by: https://github.com/janeyx99, https://github.com/garymm
Currently `torch.onnx.export(.., operator_export_type=OperatorExportTypes.ONNX_ATEN_FALLBACK)` only issues ATen ops through explicit requests (e.g. `g.at()`) calls inside each op symbolic function. This is done based on specific conditions such as `operator_export_type==OperatorExportTypes.ONNX_ATEN_FALLBACK)` or `is_caffe2_aten_fallback()`
This PR extends the ATen fallback mechanism for scenarios when the symbolic function raises `RuntimeError` during export. The idea is that partial implementation of existing ONNX ops can fallback to ATen as a last resort. That is valuable because each operator can have many input combinations and not all are always implemented.
A minor fix was done to make sure the `overload_name` attribute is added to explicit ATen op fallback requests when a symbolic is not registered to a particular op.
ps: The behavior for builds with BUILD_CAFFE2=1 is not changed to ensure BC.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74759
Approved by: https://github.com/garymm, https://github.com/msaroufim
`torch.cuda.synchronize()` is a heavy hammer and distorts benchmarking results a lot. Timer provides results that are closer to kernel times observed in profiler.
If you want, instead of `blocked_autorange` you can use `timeit` that repeats the stmt fixed number of times.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75393
Approved by: https://github.com/davidberard98
Summary:
And add a new tool to update it in the future, which follows the policy
of using "latest as of 18 months ago". This policy is meant to balance:
* recent enough to increase the odds of being able to successfully
export
* old enough to increase the odds of exported model being runnable by
different ONNX implementations
Related changes:
* test_models.py: explicitly fix opset_version to 9 rather than relying on default. Caffe2 doesn't support newer versions.
* symbolic_helper.py:
* Remove a misleading comment
* Remove unnecessary check in `_set_opset_version`
* Use a range to define `_onnx_stable_opsets`
* test_pytorch_common.py:
* Rename a variable from min -> max. I think it was a copy-paste error.
* Make skip test messages more informative.
* Remove unused `skipIfONNXShapeInference`. More on that below.
* test_pytorch_onnx_onnxruntime.py:
* Make all the `TestCase` classes explicitly specify opset version.
* Make `test_unsupported_pad` respect `opset_version` by using `run_test`
* Unrelated simplification: make it obvious that all tests run with `onnx_shape_inference=True`. AFAICT this was already the case.
* There was one test that was entirely disabled (test_tolist) because it was asking to be skipped whenever `onnx_shape_inference=True`, but it was always True. I changed the model being tested so as to preserve the intended test coverage but still have the test actually pass.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73898
Reviewed By: msaroufim
Differential Revision: D35264615
Pulled By: malfet
fbshipit-source-id: cda8fbdffe4cc8210d8d96e659e3a9adf1b5f1d2
(cherry picked from commit b5e639e88828d34442282d0b50c977e610a2ba3a)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75108
- Add option to only run some graphs
- Add NNC Static vs Dynamic
- Update make_tensor bc it wasnt using strides
Test Plan: Imported from OSS
Reviewed By: ejguan
Differential Revision: D35374000
Pulled By: eellison
fbshipit-source-id: df16b8647f2309a8837207cacba55d30f46845ce
(cherry picked from commit 19feb54db049186972b47548cf3d83e76512adfd)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/74076
Extends the repro script to cpu and NNC. As in file:
Usage:
```
1. Run your script and pipe into a log file
PYTORCH_JIT_LOG_LEVEL=">>tensorexpr_fuser" python3 my_test.py &> log.txt
2. Run log_extract:
log_extract.py log.txt --baseline --nnc
```
Test Plan: Imported from OSS
Reviewed By: gchanan
Differential Revision: D34946883
Pulled By: eellison
fbshipit-source-id: 644012dbbca0b490820ef83e761c06b0dd009e52
(cherry picked from commit 5256c8f3ff8545033d1335cc96d34194abda1370)
