switching to high quality piper tts and added label translations
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Matthias Hinrichs
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# --------------------------------------------------------------------------
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# Copyright (c) Microsoft, Intel Corporation. All rights reserved.
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# Licensed under the MIT License. See License.txt in the project root for
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# license information.
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# --------------------------------------------------------------------------
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import logging
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import tempfile
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import traceback
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from pathlib import Path
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import onnx
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import onnxruntime
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from onnxruntime.tools.symbolic_shape_infer import SymbolicShapeInference
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from onnxruntime.transformers.onnx_utils import extract_raw_data_from_model, has_external_data
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from .fusions import ReplaceUpsampleWithResize
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from .onnx_model import ONNXModel
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from .quant_utils import add_pre_process_metadata, save_and_reload_model_with_shape_infer
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logger = logging.getLogger(__name__)
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def quant_pre_process(
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input_model: str | Path | onnx.ModelProto | None = None,
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output_model_path: str | Path | None = None,
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skip_optimization: bool = False,
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skip_onnx_shape: bool = False,
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skip_symbolic_shape: bool = False,
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auto_merge: bool = False,
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int_max: int = 2**31 - 1,
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guess_output_rank: bool = False,
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verbose: int = 0,
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save_as_external_data: bool = False,
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all_tensors_to_one_file: bool = False,
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external_data_location: str | None = None,
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external_data_size_threshold: int = 1024,
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**deprecated_kwargs,
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) -> None:
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"""Shape inference and model optimization, in preparation for quantization.
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Args:
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input_model: Path to the input model file or ModelProto
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output_model_path: Path to the output model file
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skip_optimization: Skip model optimization step if true. This may result in ONNX shape
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inference failure for some models.
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skip_onnx_shape: Skip ONNX shape inference. Symbolic shape inference is most effective
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with transformer based models. Skipping all shape inferences may
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reduce the effectiveness of quantization, as a tensor with unknown
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shape can not be quantized.
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skip_symbolic_shape: Skip symbolic shape inference. Symbolic shape inference is most
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effective with transformer based models. Skipping all shape
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inferences may reduce the effectiveness of quantization, as a tensor
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with unknown shape can not be quantized.
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auto_merge: For symbolic shape inference, automatically merge symbolic dims when
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conflict happens.
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int_max: For symbolic shape inference, specify the maximum value for integer to be
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treated as boundless for ops like slice
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guess_output_rank: Guess output rank to be the same as input 0 for unknown ops
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verbose: Logs detailed info of inference, 0: turn off, 1: warnings, 3: detailed
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save_as_external_data: Saving an ONNX model to external data
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all_tensors_to_one_file: Saving all the external data to one file
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external_data_location: The file location to save the external file
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external_data_size_threshold: The size threshold for external data
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"""
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if input_model is None:
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input_model = deprecated_kwargs.pop("input_model_path", None)
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assert input_model is not None
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assert output_model_path is not None, "output_model_path is required."
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with tempfile.TemporaryDirectory(prefix="pre.quant.") as quant_tmp_dir:
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temp_path = Path(quant_tmp_dir)
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model = None
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if not skip_symbolic_shape:
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logger.info("Performing symbolic shape inference...")
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loaded_model = input_model if isinstance(input_model, onnx.ModelProto) else onnx.load(input_model)
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model = SymbolicShapeInference.infer_shapes(
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loaded_model,
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int_max,
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auto_merge,
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guess_output_rank,
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verbose,
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)
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# Since Upsample is deprecated after opset v10, and the model's opset will
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# be upgraded to at least v11 during quantization, we need to replace Upsample
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# with Resize first to avoid generating an invalid model.
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if model:
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ai_onnx_domain = [opset for opset in model.opset_import if not opset.domain or opset.domain == "ai.onnx"]
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if len(ai_onnx_domain) == 1:
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opset_version = ai_onnx_domain[0].version
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if opset_version < 10:
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ReplaceUpsampleWithResize(ONNXModel(model), opset_version).apply()
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model.opset_import.remove(ai_onnx_domain[0])
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opset_version = 11
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model.opset_import.extend([onnx.helper.make_opsetid("", opset_version)])
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model = onnx.version_converter.convert_version(model, opset_version)
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model = save_and_reload_model_with_shape_infer(model)
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if not skip_optimization:
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# Use ORT optimizers (native code) to optimize model
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if not skip_symbolic_shape:
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# Need to save the inferenced model to file so as to run the optimizer
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input_model = str(temp_path / "symbolic_shape_inferred.onnx")
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if save_as_external_data:
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onnx.save_model(
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model,
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input_model,
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save_as_external_data=True,
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all_tensors_to_one_file=all_tensors_to_one_file,
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size_threshold=external_data_size_threshold,
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convert_attribute=False,
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)
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else:
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onnx.save(model, input_model)
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model = None
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opt_model_path = str(temp_path / "optimized.onnx")
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try:
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sess_option = onnxruntime.SessionOptions()
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sess_option.optimized_model_filepath = opt_model_path
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sess_option.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_BASIC
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# For large model, extract external data from model and add to session options
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if isinstance(input_model, onnx.ModelProto):
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if has_external_data(input_model):
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raise ValueError(
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"ModelProto has external data not loaded into memory, ORT cannot create session. "
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"Please load external data before calling this function. "
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"See https://onnx.ai/onnx/repo-docs/ExternalData.html for more information."
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)
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external_names, external_values = extract_raw_data_from_model(input_model)
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sess_option.add_external_initializers(list(external_names), list(external_values))
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input_model = input_model.SerializeToString()
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# the saved optimized model otherwise points to the original external data file name
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# which is not available relative to the optimized model file
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elif skip_symbolic_shape and save_as_external_data:
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sess_option.add_session_config_entry(
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"session.optimized_model_external_initializers_file_name", "optimized.onnx.data"
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)
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sess = onnxruntime.InferenceSession(input_model, sess_option, providers=["CPUExecutionProvider"])
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# Close the session to avoid the cleanup error on Windows for temp folders
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# https://github.com/microsoft/onnxruntime/issues/17627
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del sess
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except Exception:
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logger.error(
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"ONNX Runtime Model Optimization Failed! Consider rerun with option `--skip_optimization'."
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)
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logger.error(traceback.format_exc())
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input_model = opt_model_path
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if not skip_onnx_shape:
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# ONNX shape inference.
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# According to docs, infer_shapes_path should be used for 2G+ models.
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# If the skip optimization is specified, we could be dealing with a
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# large model. So be on the safe side, save the model
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if model is not None:
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input_model = str(temp_path / "symbolic_shape_inferred.onnx")
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if save_as_external_data:
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onnx.save_model(
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model,
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input_model,
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save_as_external_data=True,
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all_tensors_to_one_file=all_tensors_to_one_file,
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size_threshold=external_data_size_threshold,
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convert_attribute=False,
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)
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else:
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onnx.save(model, input_model)
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model = None
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if isinstance(input_model, onnx.ModelProto):
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input_model = str(Path(quant_tmp_dir) / "model_input.onnx")
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onnx.save_model(
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model,
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input_model,
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save_as_external_data=True,
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all_tensors_to_one_file=all_tensors_to_one_file,
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size_threshold=external_data_size_threshold,
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convert_attribute=False,
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)
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inferred_model_path = str(temp_path / "onnx_shape_inferred.onnx")
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onnx.shape_inference.infer_shapes_path(input_model, inferred_model_path)
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model = onnx.load(inferred_model_path)
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if model is None:
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model = input_model if isinstance(input_model, onnx.ModelProto) else onnx.load(input_model)
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add_pre_process_metadata(model)
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if save_as_external_data:
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onnx.save_model(
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model,
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output_model_path,
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save_as_external_data=True,
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all_tensors_to_one_file=all_tensors_to_one_file,
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location=external_data_location,
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size_threshold=external_data_size_threshold,
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convert_attribute=False,
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)
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else:
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onnx.save(model, output_model_path)
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