| |
| |
| from __future__ import annotations |
|
|
| import pathlib |
| import tempfile |
| import unittest |
|
|
| import numpy as np |
| import onnx |
| import onnx.inliner |
| import onnxruntime |
| import parameterized |
|
|
| from onnxscript import optimizer |
| from onnxscript.rewriter import onnxruntime as ort_rewriter |
| from onnxscript.utils import evaluation_utils |
|
|
| _SKIP_TABLE = {} |
|
|
| model_folder_path = ( |
| pathlib.Path(__file__).resolve().parent.parent.parent / "testdata" / "e2e_models" |
| ) |
|
|
| |
| model_names = [entry.name for entry in model_folder_path.iterdir() if entry.is_dir()] |
|
|
|
|
| class ModelTest(unittest.TestCase): |
| @parameterized.parameterized.expand(model_names) |
| def test_model_runs_and_matches_accuracy_after_optimization(self, model_name): |
| test_id = model_name |
| if (skip_reason := _SKIP_TABLE.get(test_id)) is not None: |
| self.skipTest(skip_reason) |
|
|
| model_dir = pathlib.Path(model_folder_path) / model_name / "dynamo" |
| model_path = model_dir / f"{model_name}_dynamo.onnx" |
| if not model_path.exists(): |
| self.skipTest(f"Model {model_name!r} does not exist") |
| model = onnx.load(model_path) |
| model = optimizer.optimize(model, onnx_shape_inference=False) |
|
|
| with tempfile.TemporaryDirectory() as tmp_folder: |
| tmp_folder = pathlib.Path(tmp_folder) |
| optimized_model_path = tmp_folder / f"{model_name}_opt.onnx" |
| onnx.save( |
| model, |
| optimized_model_path, |
| save_as_external_data=True, |
| all_tensors_to_one_file=True, |
| ) |
|
|
| session = onnxruntime.InferenceSession( |
| optimized_model_path, providers=("CPUExecutionProvider",) |
| ) |
|
|
| inputs, expected_outputs = evaluation_utils.load_test_data( |
| model_dir, [i.name for i in model.graph.input] |
| ) |
|
|
| input_names = [i.name for i in session.get_inputs()] |
| assert set(input_names) == set(inputs.keys()) |
|
|
| outputs = session.run(None, inputs) |
| |
| del session |
|
|
| for output, expected_output in zip(outputs, expected_outputs): |
| np.testing.assert_allclose(output, expected_output, rtol=1e-3, atol=1e-3) |
|
|
| def test_optimizer_after_inlining(self): |
| model_dir = pathlib.Path(model_folder_path) / ".." / "dort_models" |
| filename = model_dir / "llama_forward.onnx" |
|
|
| onnx_model = onnx.load(filename) |
| onnxruntime.InferenceSession( |
| onnx_model.SerializeToString(), providers=["CPUExecutionProvider"] |
| ) |
|
|
| |
| onnx_model = optimizer.optimize(onnx_model) |
| onnxruntime.InferenceSession( |
| onnx_model.SerializeToString(), providers=["CPUExecutionProvider"] |
| ) |
| onnx_model = ort_rewriter.rewrite(onnx_model) |
| onnxruntime.InferenceSession( |
| onnx_model.SerializeToString(), providers=["CPUExecutionProvider"] |
| ) |
|
|
| |
| onnx_model = onnx.inliner.inline_local_functions(onnx_model) |
| onnxruntime.InferenceSession( |
| onnx_model.SerializeToString(), providers=["CPUExecutionProvider"] |
| ) |
|
|
| |
| onnx_model = optimizer.optimize(onnx_model) |
| onnxruntime.InferenceSession( |
| onnx_model.SerializeToString(), providers=["CPUExecutionProvider"] |
| ) |
| onnx_model = ort_rewriter.rewrite(onnx_model) |
| onnxruntime.InferenceSession( |
| onnx_model.SerializeToString(), providers=["CPUExecutionProvider"] |
| ) |
|
|
|
|
| if __name__ == "__main__": |
| unittest.main(verbosity=2) |
|
|
