### Description
Add ModelProto support for `quantize` api
### Motivation and Context
Currently, the `quantize` API only accepts a model path as the input
model. However, for large models, saving and loading from disk can be
time-consuming. By adding `ModelProto` as an input option to the
`quantize` API, significant time can be saved.
### Description
<!-- Describe your changes. -->
Fix for this issue which raise the error of FileNotAccessd in windows
when the context of TemporaryDirectory finished.
https://github.com/microsoft/onnxruntime/issues/17627
### 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. -->
https://github.com/microsoft/onnxruntime/issues/17627
Stack from [ghstack](https://github.com/ezyang/ghstack) (oldest at
bottom):
* __->__ #16789
Bump ruff to 0.0.278 and fix new lint errors. I added noqa to all
existing RUF012 errors which requires mutable class variables to be
annotated with `ClassVar`, as well as all PERF issues.
Signed-off-by: Justin Chu <justinchu@microsoft.com>
### 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>
Shape Inference and Model Optimization before Quantization
Model quantization with QDQ format, i.e. inserting QuantizeLinear/DeQuantizeLinear on
the tensor, requires tensor shape information to perform its best. Currently, shape inferencing
works best with optimized model. As a result, it is highly recommended to run quantization
on optimized model with shape information.
This change adds code for model optimization and shape inferencing of the following three steps:
1. Symbolic shape inference.
2. Model optimization
3. ONNX shape inference
At the same time we should recommend model optimization should be turned off during quantization.
As the optimization might change the computation graph, making it harder for the QDQ debugger
to locate matching tensors between original and the quantized models.
