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Add ModelProto support for transformers optimize_model (#19990)
### 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.
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| # ------------------------------------------------------------------------- | ||
| # Copyright (c) Microsoft Corporation. All rights reserved. | ||
| # Licensed under the MIT License. | ||
| # -------------------------------------------------------------------------- | ||
| from fusion_utils import NumpyHelper | ||
| from onnx import ModelProto, TensorProto | ||
| from onnx.external_data_helper import set_external_data | ||
| from onnx_model import OnnxModel | ||
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| from onnxruntime import OrtValue | ||
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| def extract_raw_data_from_model(model: ModelProto): | ||
| """ | ||
| Extract external data from model and return the external data as a list of tuples (name, value). | ||
| Note this function does not handle external data that is not loaded into the model as raw data. | ||
| Args: | ||
| model (ModelProto): the model proto to extract external data from. | ||
| Returns: | ||
| (external_names, external_values): a tuple of two lists of external data names and values. | ||
| """ | ||
| external_data = [] | ||
| onnx_model = OnnxModel(model) | ||
| for graph in onnx_model.graphs(): | ||
| for initializer in graph.initializer: | ||
| name = initializer.name | ||
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| if initializer.HasField("raw_data"): | ||
| numpy_tensor = NumpyHelper.to_array(initializer) | ||
| ort_value = OrtValue.ortvalue_from_numpy(numpy_tensor) | ||
| external_data.append((name, ort_value)) | ||
| # mimic set_external_data | ||
| set_external_data(initializer, location="foo.bin") | ||
| initializer.name = name | ||
| initializer.ClearField("raw_data") | ||
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| return zip(*external_data) | ||
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| def has_external_data(model: ModelProto): | ||
| """ | ||
| Check if the model has external data. | ||
| Args: | ||
| model (ModelProto): the model proto to check for external data. | ||
| Returns: | ||
| bool: True if the model has external data, False otherwise. | ||
| """ | ||
| onnx_model = OnnxModel(model) | ||
| for graph in onnx_model.graphs(): | ||
| for initializer in graph.initializer: | ||
| if initializer.HasField("data_location") and initializer.data_location == TensorProto.EXTERNAL: | ||
| return True | ||
| return False |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,38 @@ | ||
| # ------------------------------------------------------------------------- | ||
| # Copyright (c) Microsoft Corporation. All rights reserved. | ||
| # Licensed under the MIT License. | ||
| # -------------------------------------------------------------------------- | ||
| import unittest | ||
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| import numpy | ||
| from onnx import ModelProto, TensorProto, helper | ||
| from onnx.external_data_helper import set_external_data | ||
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| from onnxruntime.transformers.onnx_utils import extract_raw_data_from_model, has_external_data | ||
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| class TestOnnxUtils(unittest.TestCase): | ||
| def test_extract_raw_data_from_model(self): | ||
| model = self._get_model_proto_with_raw_data(False) | ||
| external_names, external_values = extract_raw_data_from_model(model) | ||
| self.assertEqual(list(external_names), ["inputs"]) | ||
| self.assertEqual(len(external_values), 1) | ||
| self.assertEqual(external_values[0].numpy(), [0.0]) | ||
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| def test_has_external_data(self): | ||
| model = self._get_model_proto_with_raw_data() | ||
| self.assertTrue(has_external_data(model)) | ||
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| def test_has_external_data_with_no_external_data(self): | ||
| model = self._get_model_proto_with_raw_data(False) | ||
| self.assertFalse(has_external_data(model)) | ||
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| def _get_model_proto_with_raw_data(self, has_external_data: bool = True) -> ModelProto: | ||
| input = helper.make_tensor_value_info("inputs", TensorProto.FLOAT, [None]) | ||
| output = helper.make_tensor_value_info("outputs", TensorProto.FLOAT, [None]) | ||
| raw_data = numpy.array([0.0], dtype=numpy.float32).tobytes() | ||
| tensor = helper.make_tensor("inputs", TensorProto.FLOAT, [1], raw_data, True) | ||
| if has_external_data: | ||
| set_external_data(tensor, location="foo.bin") | ||
| node = helper.make_node("Identity", inputs=["inputs"], outputs=["outputs"]) | ||
| return helper.make_model(helper.make_graph([node], "graph", [input], [output], initializer=[tensor])) |