Refactor dynamo converter and ir.Model support

[justinchuby]

Describe your changes

• Avoid redundant disk IO when saving a dynamo exported model by saving the ir.Model directly
• Refactor torch.onnx conversion passes
• Fixed the neural compressor pass when models contain additional value info protos for initializers because neural-compressor doesn't handle them properly.

Checklist before requesting a review

• Add unit tests for this change.
• Make sure all tests can pass.
• Update documents if necessary.
• Lint and apply fixes to your code by running lintrunner -a
• Is this a user-facing change? If yes, give a description of this change to be included in the release notes.
• Is this PR including examples changes? If yes, please remember to update example documentation in a follow-up PR.

[Copilot]

Pull Request Overview

This PR refactors the model conversion flow to avoid redundant disk IO when saving a Dynamo exported model and enables direct saving of ONNX IR models by introducing the ir_model_to_olive_model function.

• Updated onnxscript_fusion.py to replace model_proto_to_olive_model with ir_model_to_olive_model.
• Refactored conversion.py to add separate export methods for torchscript and dynamo exporters.
• Added ir_model_to_olive_model in onnx/common.py to support saving IR models.

Reviewed Changes

Copilot reviewed 3 out of 3 changed files in this pull request and generated 1 comment.

File	Description
olive/passes/onnx/onnxscript_fusion.py	Updated the model conversion call to use ir_model_to_olive_model.
olive/passes/onnx/conversion.py	Refactored export methods and introduced dynamo exporter changes.
olive/passes/onnx/common.py	Added the new function ir_model_to_olive_model for saving IR models.

[justinchuby]

https://aiinfra.visualstudio.com/PublicPackages/_build/results?buildId=758867&view=logs&j=168020a6-ab1b-592e-b9e6-a6cd0eb1939c&t=f9534d71-ad75-594d-34df-0afc963a2a4d&l=10801

@titaiwangms do you have an idea about this error in _rename_dynamic_shapes_with_model_inputs?

[titaiwangms]

https://aiinfra.visualstudio.com/PublicPackages/_build/results?buildId=758867&view=logs&j=168020a6-ab1b-592e-b9e6-a6cd0eb1939c&t=f9534d71-ad75-594d-34df-0afc963a2a4d&l=10801

@titaiwangms do you have an idea about this error in _rename_dynamic_shapes_with_model_inputs?

Yes, it's a bug and the function will be deleted in 2.7. Because I didn't consider the mismatch input names between onnx graph and nn.Module, it causes KeyError to the matching. (That's why I set it to 2.7)

[Copilot]

Pull Request Overview

This PR refactors the dynamo exporter flow to avoid redundant disk IO and updates the torch.onnx conversion passes while improving type annotations and generic typing.

• Update test conditions to account for torch version 2.7.0 and dynamic shape handling.
• Refactor type hints in IoConfig by marking several fields as Optional.
• Enhance generic typing in config_utils with a type variable.

Reviewed Changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated no comments.

File	Description
test/unit_test/passes/onnx/test_conversion.py	Adjusted test skip conditions for Windows and dynamic shapes based on torch version.
olive/model/config/io_config.py	Updated type annotations to use Optional for various input/output fields.
olive/common/config_utils.py	Modified the config union type to use a generic type variable T.

Comments suppressed due to low confidence (1)

olive/common/config_utils.py:317

• Ensure that the type variable T is properly defined (e.g., via 'from typing import TypeVar' and 'T = TypeVar("T")') to support the generic type annotation.

config: Union[dict[str, Any], T, None],

[titaiwangms]

@justinchuby @jambayk Are we merging this soon? I am working on a PR to refactor the patch and dynamic_shapes to the latest version for better support on LLMs.

[justinchuby]

I think the PR is ready. I would need some reviews

[Copilot]

Pull Request Overview

This PR refactors the dynamo exporter and onnx conversion passes by reducing redundant disk IO and updating type annotations.

• Updated test conditions for dynamo export based on torch versions
• Changed type annotations in IoConfig to use Optional
• Updated generic type hints in config_utils

Reviewed Changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated 1 comment.

File	Description
test/unit_test/passes/onnx/test_conversion.py	Adjusted test conditions with additional torch version checks
olive/model/config/io_config.py	Updated type hints to use Optional for better type clarity and safety
olive/common/config_utils.py	Modified function signature to use generic type T for configuration input

Comments suppressed due to low confidence (2)

test/unit_test/passes/onnx/test_conversion.py:36

• [nitpick] Consider adding an inline comment to explain why skipping is conditioned on the torch version for Windows platforms, which can help future maintainers understand the rationale.

if platform.system() == "Windows" and use_dynamo_exporter and _torch_is_older_than("2.7.0"):

olive/model/config/io_config.py:37

• [nitpick] Updating type annotations to Optional is a good improvement. It would be helpful to update the corresponding docstrings to reflect these changes.

input_shapes: Optional[list[list[int]]] = None

[justinchuby]

=================================== FAILURES ===================================
____________________________ test_inc_quantization _____________________________
test/unit_test/passes/inc/test_inc_quantization.py:43: in test_inc_quantization
quantized_model = p.run(ov_model, output_folder)
/passes/_pass.py:242: in run
output_model = self._run_for_config(model, self.config, output_model_path)
***/passes/onnx/inc_quantization.py:536: in _run_for_config
if q_model.is_large_model:
E AttributeError: 'NoneType' object has no attribute 'is_large_model'

[jambayk]

closing since it has become stale

[justinchuby]

I will continue on this. Thanks