quantize.py

import json
import os
import shutil
from dataclasses import dataclass, field
from typing import Optional, Set
from tqdm import tqdm

from transformers import (
    AutoConfig,
    AutoTokenizer,
    HfArgumentParser
)

import onnx
from optimum.exporters.onnx import main_export, export_models
from optimum.exporters.tasks import TasksManager
from onnxruntime.quantization import (
    quantize_dynamic,
    QuantType
)

DEFAULT_QUANTIZE_PARAMS = {
    'per_channel': True,
    'reduce_range': True,
}

MODEL_SPECIFIC_QUANTIZE_PARAMS = {
    'whisper': {
        'per_channel': False,
        'reduce_range': False,
    }
}

MODELS_WITHOUT_TOKENIZERS = [
    'wav2vec2'
]


@dataclass
class ConversionArguments:
    """
    Arguments used for converting HuggingFace models to onnx.
    """

    model_id: str = field(
        metadata={
            "help": "Model identifier"
        }
    )
    quantize: bool = field(
        default=False,
        metadata={
            "help": "Whether to quantize the model."
        }
    )
    output_parent_dir: str = field(
        default='./models/',
        metadata={
            "help": "Path where the converted model will be saved to."
        }
    )

    task: Optional[str] = field(
        default='auto',
        metadata={
            "help": (
                "The task to export the model for. If not specified, the task will be auto-inferred based on the model. Available tasks depend on the model, but are among:"
                f" {str(list(TasksManager._TASKS_TO_AUTOMODELS.keys()))}. For decoder models, use `xxx-with-past` to export the model using past key values in the decoder."
            )
        }
    )

    opset: int = field(
        default=None,
        metadata={
            "help": (
                "If specified, ONNX opset version to export the model with. Otherwise, the default opset will be used."
            )
        }
    )

    device: str = field(
        default='cpu',
        metadata={
            "help": 'The device to use to do the export.'
        }
    )
    skip_validation: bool = field(
        default=False,
        metadata={
            "help": "Whether to skip validation of the converted model"
        }
    )

    per_channel: bool = field(
        default=None,
        metadata={
            "help": "Whether to quantize weights per channel"
        }
    )
    reduce_range: bool = field(
        default=None,
        metadata={
            "help": "Whether to quantize weights with 7-bits. It may improve the accuracy for some models running on non-VNNI machine, especially for per-channel mode"
        }
    )

    output_attentions: bool = field(
        default=False,
        metadata={
            "help": "Whether to output attentions from the model. NOTE: This is only supported for whisper models right now."
        }
    )

    split_modalities: bool = field(
        default=False,
        metadata={
            "help": "Whether to split multimodal models. NOTE: This is only supported for CLIP models right now."
        }
    )


def get_operators(model: onnx.ModelProto) -> Set[str]:
    operators = set()

    def traverse_graph(graph):
        for node in graph.node:
            operators.add(node.op_type)
            for attr in node.attribute:
                if attr.type == onnx.AttributeProto.GRAPH:
                    subgraph = attr.g
                    traverse_graph(subgraph)

    traverse_graph(model.graph)
    return operators


def quantize(model_names_or_paths, **quantize_kwargs):
    """
    Quantize the weights of the model from float32 to int8 to allow very efficient inference on modern CPU

    Uses unsigned ints for activation values, signed ints for weights, per
    https://onnxruntime.ai/docs/performance/quantization.html#data-type-selection
    it is faster on most CPU architectures
    Args:
        onnx_model_path: Path to location the exported ONNX model is stored
    Returns: The Path generated for the quantized
    """

    quantize_config = dict(
        **quantize_kwargs,
        per_model_config={}
    )

    for model in tqdm(model_names_or_paths, desc='Quantizing'):
        directory_path = os.path.dirname(model)
        file_name_without_extension = os.path.splitext(
            os.path.basename(model))[0]

        # NOTE:
        # As of 2023/04/20, the current latest version of onnxruntime-web is 1.14.0, and does not support INT8 weights for Conv layers.
        # For this reason, we choose model weight types to ensure compatibility with onnxruntime-web.
        #
        # As per docs, signed weight type (QInt8) is faster on most CPUs, so, we use that unless the model contains a Conv layer.
        # For more information, see:
        #  - https://github.com/microsoft/onnxruntime/issues/3130#issuecomment-1105200621
        #  - https://github.com/microsoft/onnxruntime/issues/2339

        loaded_model = onnx.load_model(model)
        op_types = get_operators(loaded_model)
        weight_type = QuantType.QUInt8 if 'Conv' in op_types else QuantType.QInt8

        quantize_dynamic(
            model_input=model,
            model_output=os.path.join(
                directory_path, f'{file_name_without_extension}_quantized.onnx'),

            weight_type=weight_type,
            optimize_model=False,

            # TODO allow user to specify these
            # op_types_to_quantize=['MatMul', 'Add', 'Conv'],
            extra_options=dict(
                EnableSubgraph=True
            ),
            **quantize_kwargs
        )

        quantize_config['per_model_config'][file_name_without_extension] = dict(
            op_types=list(op_types),
            weight_type=str(weight_type),
        )

    # Save quantization config
    with open(os.path.join(directory_path, 'quantize_config.json'), 'w') as fp:
        json.dump(quantize_config, fp, indent=4)


def main():
    """
    Example usage:
    python quantize.py --model_id sentence-transformers/all-MiniLM-L6-v2-unquantized  --quantize --task default
    """
    parser = HfArgumentParser(
        (ConversionArguments, )
    )
    conv_args, = parser.parse_args_into_dataclasses()

    model_id = conv_args.model_id

    output_model_folder = os.path.join(conv_args.output_parent_dir, model_id)

    # Create output folder
    os.makedirs(output_model_folder, exist_ok=True)

    # Saving the model config
    config = AutoConfig.from_pretrained(model_id)

    tokenizer = None
    try:
        # Load tokenizer
        tokenizer = AutoTokenizer.from_pretrained(model_id)

    except KeyError:
        pass  # No Tokenizer

    except Exception as e:
        if config.model_type not in MODELS_WITHOUT_TOKENIZERS:
            raise e

    # model_name_or_path can be local path or huggingface id
    export_kwargs = dict(
        model_name_or_path=model_id,
        output=output_model_folder,
        task=conv_args.task,
        opset=conv_args.opset,
        device=conv_args.device,
        do_validation=not conv_args.skip_validation,
    )


    # Step 1. convert huggingface model to onnx
    main_export(**export_kwargs)


    # Step 2. (optional, recommended) quantize the converted model for fast inference and to reduce model size.
    if conv_args.quantize:
        # Update quantize config with model specific defaults
        quantize_config = MODEL_SPECIFIC_QUANTIZE_PARAMS.get(
            config.model_type, DEFAULT_QUANTIZE_PARAMS)

        quantize([
            os.path.join(output_model_folder, x)
            for x in os.listdir(output_model_folder)
            if x.endswith('.onnx') and not x.endswith('_quantized.onnx')
        ], **quantize_config)

    # Step 3. Move .onnx files to the 'onnx' subfolder
    os.makedirs(os.path.join(output_model_folder, 'onnx'), exist_ok=True)
    for file in os.listdir(output_model_folder):
        if file.endswith(('.onnx', '.onnx_data')):
            shutil.move(os.path.join(output_model_folder, file),
                        os.path.join(output_model_folder, 'onnx', file))

if __name__ == '__main__':
    main()