# -------------------------------------------------------------------------- # Copyright (c) Microsoft, Intel Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # -------------------------------------------------------------------------- import logging import tempfile import traceback from pathlib import Path from typing import Optional, Union import onnx import onnxruntime from onnxruntime.tools.symbolic_shape_infer import SymbolicShapeInference from onnxruntime.transformers.onnx_utils import extract_raw_data_from_model, has_external_data from .quant_utils import add_pre_process_metadata logger = logging.getLogger(__name__) def quant_pre_process( input_model: Optional[Union[str, Path, onnx.ModelProto]] = None, output_model_path: Optional[Union[str, Path]] = None, skip_optimization: bool = False, skip_onnx_shape: bool = False, skip_symbolic_shape: bool = False, auto_merge: bool = False, int_max: int = 2**31 - 1, guess_output_rank: bool = False, verbose: int = 0, save_as_external_data: bool = False, all_tensors_to_one_file: bool = False, external_data_location: Optional[str] = None, external_data_size_threshold: int = 1024, **deprecated_kwargs, ) -> None: """Shape inference and model optimization, in preparation for quantization. Args: input_model: Path to the input model file or ModelProto output_model_path: Path to the output model file skip_optimization: Skip model optimization step if true. This may result in ONNX shape inference failure for some models. skip_onnx_shape: Skip ONNX shape inference. Symbolic shape inference is most effective with transformer based models. Skipping all shape inferences may reduce the effectiveness of quantization, as a tensor with unknown shape can not be quantized. skip_symbolic_shape: Skip symbolic shape inference. Symbolic shape inference is most effective with transformer based models. Skipping all shape inferences may reduce the effectiveness of quantization, as a tensor with unknown shape can not be quantized. auto_merge: For symbolic shape inference, automatically merge symbolic dims when conflict happens. int_max: For symbolic shape inference, specify the maximum value for integer to be treated as boundless for ops like slice guess_output_rank: Guess output rank to be the same as input 0 for unknown ops verbose: Logs detailed info of inference, 0: turn off, 1: warnings, 3: detailed save_as_external_data: Saving an ONNX model to external data all_tensors_to_one_file: Saving all the external data to one file external_data_location: The file location to save the external file external_data_size_threshold: The size threshold for external data """ if input_model is None: input_model = deprecated_kwargs.pop("input_model_path", None) assert input_model is not None assert output_model_path is not None, "output_model_path is required." with tempfile.TemporaryDirectory(prefix="pre.quant.") as quant_tmp_dir: temp_path = Path(quant_tmp_dir) model = None if not skip_symbolic_shape: logger.info("Performing symbolic shape inference...") loaded_model = input_model if isinstance(input_model, onnx.ModelProto) else onnx.load(input_model) model = SymbolicShapeInference.infer_shapes( loaded_model, int_max, auto_merge, guess_output_rank, verbose, ) if not skip_optimization: # Use ORT optimizers (native code) to optimize model if not skip_symbolic_shape: # Need to save the inferenced model to file so as to run the optimizer input_model = str(temp_path / "symbolic_shape_inferred.onnx") if save_as_external_data: onnx.save_model( model, input_model, save_as_external_data=True, all_tensors_to_one_file=all_tensors_to_one_file, size_threshold=external_data_size_threshold, convert_attribute=False, ) else: onnx.save(model, input_model) model = None opt_model_path = str(temp_path / "optimized.onnx") try: sess_option = onnxruntime.SessionOptions() sess_option.optimized_model_filepath = opt_model_path sess_option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_BASIC # For large model, extract external data from model and add to session options if isinstance(input_model, onnx.ModelProto): if has_external_data(input_model): raise ValueError( "ModelProto has external data not loaded into memory, ORT cannot create session. " "Please load external data before calling this function. " "See https://onnx.ai/onnx/repo-docs/ExternalData.html for more information." ) external_names, external_values = extract_raw_data_from_model(input_model) sess_option.add_external_initializers(list(external_names), list(external_values)) input_model = input_model.SerializeToString() sess = onnxruntime.InferenceSession(input_model, sess_option, providers=["CPUExecutionProvider"]) # Close the session to avoid the cleanup error on Windows for temp folders # https://github.com/microsoft/onnxruntime/issues/17627 del sess except Exception: logger.error( "ONNX Runtime Model Optimization Failed! Consider rerun with option `--skip_optimization'." ) logger.error(traceback.format_exc()) input_model = opt_model_path if not skip_onnx_shape: # ONNX shape inference. # According to docs, infer_shapes_path should be used for 2G+ models. # If the skip optimization is specified, we could be dealing with a # large model. So be on the safe side, save the model if model is not None: input_model = str(temp_path / "symbolic_shape_inferred.onnx") if save_as_external_data: onnx.save_model( model, input_model, save_as_external_data=True, all_tensors_to_one_file=all_tensors_to_one_file, size_threshold=external_data_size_threshold, convert_attribute=False, ) else: onnx.save(model, input_model) model = None if isinstance(input_model, onnx.ModelProto): input_model = str(Path(quant_tmp_dir) / "model_input.onnx") onnx.save_model( model, input_model, save_as_external_data=True, all_tensors_to_one_file=all_tensors_to_one_file, size_threshold=external_data_size_threshold, convert_attribute=False, ) inferred_model_path = str(temp_path / "onnx_shape_inferred.onnx") onnx.shape_inference.infer_shapes_path(input_model, inferred_model_path) model = onnx.load(inferred_model_path) if model is None: model = input_model if isinstance(input_model, onnx.ModelProto) else onnx.load(input_model) add_pre_process_metadata(model) if save_as_external_data: onnx.save_model( model, output_model_path, save_as_external_data=True, all_tensors_to_one_file=all_tensors_to_one_file, location=external_data_location, size_threshold=external_data_size_threshold, convert_attribute=False, ) else: onnx.save(model, output_model_path)