### Description
`lintrunner` is a linter runner successfully used by pytorch, onnx and
onnx-script. It provides a uniform experience running linters locally
and in CI. It supports all major dev systems: Windows, Linux and MacOs.
The checks are enforced by the `Python format` workflow.
This PR adopts `lintrunner` to onnxruntime and fixed ~2000 flake8 errors
in Python code. `lintrunner` now runs all required python lints
including `ruff`(replacing `flake8`), `black` and `isort`. Future lints
like `clang-format` can be added.
Most errors are auto-fixed by `ruff` and the fixes should be considered
robust.
Lints that are more complicated to fix are applied `# noqa` for now and
should be fixed in follow up PRs.
### Notable changes
1. This PR **removed some suboptimal patterns**:
- `not xxx in` -> `xxx not in` membership checks
- bare excepts (`except:` -> `except Exception`)
- unused imports
The follow up PR will remove:
- `import *`
- mutable values as default in function definitions (`def func(a=[])`)
- more unused imports
- unused local variables
2. Use `ruff` to replace `flake8`. `ruff` is much (40x) faster than
flake8 and is more robust. We are using it successfully in onnx and
onnx-script. It also supports auto-fixing many flake8 errors.
3. Removed the legacy flake8 ci flow and updated docs.
4. The added workflow supports SARIF code scanning reports on github,
example snapshot:
5. Removed `onnxruntime-python-checks-ci-pipeline` as redundant
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
Unified linting experience in CI and local.
Replacing https://github.com/microsoft/onnxruntime/pull/14306
---------
Signed-off-by: Justin Chu <justinchu@microsoft.com>
- Update Gradle version used in most places from 6.8.3 to 8.0.1. Update Android Gradle Plugin version where applicable.
Not updated in this change: React Native Android projects (under `js/react_native/`). That can be done later along with updating the React Native projects.
- Add Gradle wrapper in `java/` to make it easier to consistently use a specific Gradle version.
### Description
<!-- Describe your changes. -->
I fixed some broken links in the C API documentation, but then did a
quick pass over all of the links I could find and then fixed those.
### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->
I got some 404's when exploring the documentation and wanted to fix it.
Add the ability to get and set tuning results of an inference session.
Also add tool to manipulate onnx file to embed the results into the
model file and automatically load it on session initialization.
A tool to convert ONNX model to tfevents so that we can use tensorboard
to open it for visualization. This is especially useful for debugging
when the ONNX model is too large to open by Netron.
usage: onnx2tfevents.py [-h] [--logdir LOGDIR] [--model MODEL]
Record more info from the React Native CI E2E test. In particular, log the view hierarchy when exiting the test and dump logs from Android emulator to the build output.
**Description**:
Use the onnx headers to find the latest opset for each operator. This
allows the script to detect optimizers with
`graph_utils::IsSupportedOptypeVersionAndDomain` calls that need
updating when run during the update of the onnx commit id. Without this
change issues are not detected until a new kernel is registered.
**Motivation and Context**
Detect optimizers that need updates as part of the ONNX update process.
# Motivation
Currently, ORT minimal builds use kernel def hashes to map from nodes to
kernels to execute when loading the model. As the kernel def hashes must
be known ahead of time, this works for statically registered kernels.
This works well for the CPU EP.
For this approach to work, the kernel def hashes must also be known at
ORT format model conversion time, which means the EP with statically
registered kernels must also be enabled then. This is not an issue for
the always-available CPU EP. However, we do not want to require that any
EP which statically registers kernels is always available too.
Consequently, we explore another approach to match nodes to kernels that
does not rely on kernel def hashes. An added benefit of this is the
possibility of moving away from kernel def hashes completely, which
would eliminate the maintenance burden of keeping the hashes stable.
# Approach
In a full build, ORT uses some information from the ONNX op schema to
match a node to a kernel. We want to avoid including the ONNX op schema
in a minimal build to reduce binary size. Essentially, we take the
necessary information from the ONNX op schema and make it available in a
minimal build.
We decouple the ONNX op schema from the kernel matching logic. The
kernel matching logic instead relies on per-op information which can
either be obtained from the ONNX op schema or another source.
This per-op information must be available in a minimal build when there
are no ONNX op schemas. We put it in the ORT format model.
Existing uses of kernel def hashes to look up kernels are replaced
with the updated kernel matching logic. We no longer store
kernel def hashes in the ORT format model’s session state and runtime
optimization representations. We no longer keep the logic to
generate and ensure stability of kernel def hashes.
* Update to handle multiline declarations for the kernels which are typical these days.
* Update to new path for the cpu contrib_op kernel registrations.
* Update tools/python/find_optimizer_opset_version_updates_required.py
Co-authored-by: Justin Chu <justinchuby@users.noreply.github.com>
Description: Set black's target version to be py37 - py310
Motivation and Context
Black by default targets its format for py3.10. Since our project supports python 3.7, we need to target version to all the python versions supported.
Re-ran black. 13 files reformatted.
Description: Format all python files under onnxruntime with black and isort.
After checking in, we can use .git-blame-ignore-revs to ignore the formatting PR in git blame.
#11315, #11316
Follow up to #10904.
- Move node EP assignment for ORT format into SessionState::FinalizeSessionState().
- Add unit test for #10904.
- Make convert_onnx_models_to_ort.py optimization level configurable via environment variable.
* Tweaks to the model utils
* Add handling for a dim_value of -1 when replacing the entire input shape. This occurs in models exported from PaddlePaddle
* make pytorch helpers accessible in package
* make QDQ helpers accessible in package
Add runtime optimization support to ONNX -> ORT format conversion script.
Replace `--optimization_level`, `--use_nnapi`, and `--use_coreml` with a new `--optimization_style` option.
* Add initial helper for optimizing a QDQ format model for usage with ORT.
If a DQ node has multiple consumers it will end up in multiple QDQ node units. This is complicated to handle as each qdq unit could end up being handled by different execution providers. By duplicating the DQ node we simplify this logic.
Generally the duplicate nodes will disappear when the qdq node unit is converted to a single node with a quantized operator. If there are qdq node units that are not able to be converted to use a quantized operator the ORT cleanup (pending) to drop remaining Q->DQ pairs between fp32 nodes can remove any remaining DQ nodes.
* Fix pep8 warning
Co-authored-by: Guoyu Wang <wanggy@outlook.com>
* Update required operators for prebuilt package to add opsets 14 and 15.
Add helper script to check if the prebuilt package will support the model and if not why not.
* Add support for multiple opsets being specified on a single line in the required operators config. This makes it easier to update the pre-built package config.
It's also required for validation tools to work as they only have a single opset from the model and not per-operator opsets. If we only list the incremental ops we could merge in the ops from the previous opset, but that wouldn't give a way to drop an operator from being supported.
Left the info on which ops changed though so we have a better feel for the cost of supporting each opset.
* schema change
* cc channges
* remove temp debug code
* Adding fbs namespace to session_state_flatbuffers_utils.h
* Add fbs namepsace to all ort format utils
* Include pytorch_export_contrib_ops in inference builds
Rename / move it from tools/python/register_custom_ops_pytorch_exporter
to onnxruntime/python/tools/pytorch_export_contrib_ops.
Rationale for inclusion in inference builds:
This code is potentially useful for anyone using ORT, not just training.
Rationale for new name:
"Contrib op" is the nomenclature used within ORT to refer to the set of
ops that are not in the standard op set but are included by default with
ORT. This is more specific than "custom op", which is what the PyTorch
exporter uses to refer to any non-standard op.
Step 1 of addressing #8818. After this is merged I will update the docs.
* Enable test_pytorch_export_contrib_ops.py in CI
Fixes AB#1342330
* Change the strided copy to switch on data size not data type.
Move to header so we can reduce on the enabled types.
Setup type reduction for Concat now that it's using this implementation.
Support of sparse initializers with smaller indices data type to save space.
Make the script more efficient by selecting indices data type and checking resulting sparse bytes
Exclude new code from SPARSE_TENSORS
* GridSample OP implementation for CPU and CUDA
**Description**: This change contains implementation for torch grid_sample OP.
Cuda implementation contains contribution from Muscle Wu.
* Use interpolation for out-of-bound points in zero padding mode
Out-of-bound points in zeros padding mode changed from constant 0 to
interpolation of surrounding pixels. This aligns with Pytorch implementation.
A bug in CUDA batch offset calculation is fixed.
Custom op exporter type is added.
* Fix nearest bug in CPU
* Update per CI build finding and review comments
* Force float to avoid potential integer T issue
* Style update
* PR update
* Remove c++17 feature from cuda code
* atenop for inference
* assert if dtype mismatch
* atenop config in frontend
* fix orttrainer test
* gradient def not only for ATenOp
* bugfix
* fix gradient input shape and type issue
* fix after merge master
**Description**: As title.
**Motivation and Context**
- PyTorch ONNX exporter heavily depends on ONNX shape inference to export accurate and efficient model. Custom symbolic function exports the op as contrib ops, thus exporter is unable to perform standard onnx shape inference. Models with dynamic shape inputs are affected.
* Update the operator documentation generation
- Make layout a little nicer
- Update to latest supported operators including training
- Fix some links that are broken when the docs content is copied to github-pages
- Fix incorrect usage of 'onnx.ai.ml' as the default domain
- ML ops are now separated from the real default domain of 'onnx.ai'
- Include CPU, CUDA and training kernels
- exclude DNNL as it's not an EP we own
* There are separate paths for CUDA and CUDNN as they are not guaranteed to be in the same location on a Windows machine. Use the CUDNN path when looking for the CUDNN library.
* Enable validation of both contrib ops and operator kernels in build
Filter generation so it's deterministic
Add ability for CI to publish the md files as build artifacts if they differ so a developer can download and add to their PR to resolve any diffs.
Remove workarounds for github-pages as that will now link to the github docs which display correctly
