Introduce ORTSessionMixin and enable general io binding (works for diffusers as well) #2234
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…ConditionalGeneration with positional arguments.
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Pull Request Overview
This PR extends the IO binding functionality for multi‐part pipelines by introducing new mixin classes (ORTSessionMixin and ORTSessionsWrapper) and refactoring existing tests and utility functions for both diffusion and non‐diffusion workflows.
- Updates tests for onnxruntime modeling and diffusion pipelines to use updated function names and parameters.
- Refactors utility and exporter modules to adopt new patterns for provider and session handling, and removes deprecated or redundant code.
Reviewed Changes
Copilot reviewed 21 out of 21 changed files in this pull request and generated 1 comment.
Show a summary per file
| File | Description |
|---|---|
| tests/onnxruntime/test_modeling.py | Removed deprecated parameters and updated assertions to reflect changes in the IO binding interface and model naming. |
| tests/onnxruntime/test_diffusion.py | Renamed helper functions and added tests for IO binding functionality with diffusion pipelines. |
| optimum/utils/constant.py | Added and reorganized constants used across the codebase. |
| optimum/onnxruntime/utils.py | Refactored provider handling and added a new helper for inferring a session’s dtype. |
| optimum/onnxruntime/modeling_decoder.py | Updated deprecated code patterns to use instance configuration attributes consistently and improved IO binding calls. |
| Other files (e.g., optimization.py, exporters tasks, workflows) | Similar refactoring to align with the new multi-part pipeline design and clean up unused code. |
Comments suppressed due to low confidence (3)
tests/onnxruntime/test_modeling.py:140
- The 'use_io_binding' parameter has been removed in favor of a default behavior. Please ensure that this API change is clearly documented in the migration guide to avoid confusion among users.
model = model_cls.from_pretrained(self.LOCAL_MODEL_PATH, use_cache=False, use_io_binding=False)
optimum/onnxruntime/modeling_decoder.py:249
- Updating the condition to use 'self.config' instead of relying on a local 'config' variable improves maintainability. Confirm that similar updates are applied consistently throughout the file.
if self.config.model_type != "gpt_bigcode":
optimum/onnxruntime/utils.py:446
- [nitpick] Consider adding tests and a detailed docstring example for 'get_dtype_from_session' to verify that the correct torch dtype is returned based on the ONNX input/output types.
def get_dtype_from_session(session: ort.InferenceSession) -> torch.dtype:
echarlaix
approved these changes
May 16, 2025
optimum/onnxruntime/base.py
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| self.output_dtypes = {output.name: output.type for output in session.get_outputs()} | ||
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| self.model_path = Path(session._model_path) | ||
| self.model_name = self.model_path.name |
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yes talking about model_name which seems to be used in the tests (and can be easily extracted from model_path) so we can remove imo, not specific to this PR though so feel free to close!
Co-authored-by: Ella Charlaix <80481427+echarlaix@users.noreply.github.com>
…timum into ort-session-mixin
… cuda device, with specific dtype, etc)
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What does this PR do?
This PR tries to further extend the io binding feature to multi part pipelines and enables it more generally through two classes:
ORTSessionMixin: should be used whenever a class has an underlying inference session, it enables inference with and without io/binding, and handles the torch/onnxruntime interface like provider/device/dtype.ORTSessionsWrapper: should be used when a class has many models/parts (like encoder-decoders or diffusion pipelines), it usinfies the interface to these parts without compromising flexibility, for example one on can setpipeline.use_io_bindingto True/False which will set it to all parts, but can also target a specific partpipeline.unet.use_io_bindingdifferently. Parts can also be on different devices.I also added a special feature to diffusion pipelines and especially Unet/Tranformers component so that it reuses the same input tensor as output buffer (instead of creating a new one), this makes the diffusion operation more of an in-place operation, which should make it faster (theoretically), or at least uses less memory.
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