Add ModelProto support for transformers optimize_model #19990
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/azp run Windows ARM64 QNN CI Pipeline,Windows x64 QNN CI Pipeline,Windows CPU CI Pipeline,Windows GPU CI Pipeline,Windows GPU TensorRT CI Pipeline,ONNX Runtime Web CI Pipeline,Linux CPU CI Pipeline,Linux CPU Minimal Build E2E CI Pipeline,Linux GPU CI Pipeline,Linux GPU TensorRT CI Pipeline |
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/azp run Linux OpenVINO CI Pipeline,Linux QNN CI Pipeline,MacOS CI Pipeline,orttraining-amd-gpu-ci-pipeline,orttraining-linux-ci-pipeline,orttraining-linux-gpu-ci-pipeline,orttraining-ortmodule-distributed,onnxruntime-python-checks-ci-pipeline,onnxruntime-binary-size-checks-ci-pipeline |
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| from onnx_model_unet import UnetOnnxModel | |||
| from onnx_model_vae import VaeOnnxModel | |||
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| import onnxruntime | |||
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### Description Add `ModelProto` support as an input to transformers `optimize_model` API. ### Motivation and Context Currently, the `optimize_model` 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 `optimize_model` API, significant time can be saved.
Description
Add
ModelProtosupport as an input to transformersoptimize_modelAPI.Motivation and Context
Currently, the
optimize_modelAPI only accepts a model path as the input model. However, for large models, saving and loading from disk can be time-consuming. By addingModelProtoas an input option to theoptimize_modelAPI, significant time can be saved.