quantize_extended.py

"""
Script to quantize ONNX models to additional formats: int4, int8, etc.
Based on transformers.js/scripts/quantize.py, extended for more quantization options.
"""

from enum import Enum
from tqdm import tqdm
from typing import Set, List, Optional
import onnx
import os
from dataclasses import dataclass, field
from transformers import HfArgumentParser
from onnxruntime.quantization import QuantType, QuantizationMode
from onnxruntime.quantization.onnx_quantizer import ONNXQuantizer
from onnxruntime.quantization.registry import IntegerOpsRegistry
from onnxruntime.quantization.matmul_nbits_quantizer import MatMulNBitsQuantizer
from onnxruntime.quantization.matmul_bnb4_quantizer import MatMulBnb4Quantizer
import float16
import utils

class QuantMode(Enum):
    FP16 = "fp16"
    Q8 = "q8"
    QI8 = "int8"
    QU8 = "uint8"
    Q4 = "q4"
    Q4F16 = "q4f16"
    BNB4 = "bnb4"
    INT4 = "int4"
    INT8 = "int8"

QUANTIZE_SUFFIX_MAPPING = {
    QuantMode.Q8: "quantized",
    QuantMode.INT4: "int4",
    QuantMode.INT8: "int8",
}

QUANTIZE_OPTIONS = tuple(x.value for x in QuantMode)
QUINT8_OPS = (
    "Conv",
    "GroupQueryAttention",
    "MultiHeadAttention",
)

@dataclass
class IOArguments:
    input_folder: str = field(metadata={"help": "Path of the input folder containing the .onnx models to quantize"})
    output_folder: str = field(metadata={"help": "Path of the output folder where the quantized .onnx models will be saved"})

@dataclass
class QuantizationArguments:
    modes: QuantMode = field(default=QUANTIZE_OPTIONS, metadata={"help": "Quantization mode to use.", "choices": QUANTIZE_OPTIONS, "nargs": "+",})
    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."})
    block_size: int = field(default=None, metadata={"help": "Block size for blockwise quantization."})
    is_symmetric: bool = field(default=True, metadata={"help": "Indicate whether to quantize the model symmetrically"})
    accuracy_level: int = field(default=None, metadata={"help": "Accuracy level of the 4-bit quantized MatMul computation."})
    quant_type: int = field(default=MatMulBnb4Quantizer.NF4, metadata={"help": "Quantization data type. 0: FP4, 1: NF4", "choices": [MatMulBnb4Quantizer.FP4, MatMulBnb4Quantizer.NF4],})
    op_block_list: List[str] = field(default=None, metadata={"help": "List of operators to exclude from quantization.", "nargs": "+",})

def quantize_int4(
    model: onnx.ModelProto,
    save_path: str,
    block_size: int = 32,
    is_symmetric: bool = True,
    accuracy_level: int = 4,
):
    """
    Quantize the weights of the model from float32 to 4-bit int using MatMulNBitsQuantizer
    """
    quantizer = MatMulNBitsQuantizer(
        model=model,
        block_size=block_size,
        is_symmetric=is_symmetric,
        accuracy_level=accuracy_level,
    )
    quantizer.process()
    utils.check_and_save_model(quantizer.model.model, save_path)
    return quantizer.model.model

def quantize_int8(
    model: onnx.ModelProto,
    save_path: str,
    per_channel: bool = False,
    reduce_range: bool = False,
    weight_type: QuantType = QuantType.QInt8,
    op_block_list: Optional[List[str]] = None,
):
    """
    Quantize the weights of the model from float32 to int8
    """
    op_types_to_quantize = set(IntegerOpsRegistry.keys())
    if op_block_list is not None:
        op_types_to_quantize.difference_update(op_block_list)

    quantizer = ONNXQuantizer(
        model,
        per_channel,
        reduce_range,
        mode=QuantizationMode.IntegerOps,
        static=False,
        weight_qType=weight_type,
        activation_qType=QuantType.QUInt8,
        tensors_range=None,
        nodes_to_quantize=[],
        nodes_to_exclude=[],
        op_types_to_quantize=op_types_to_quantize,
        extra_options=dict(EnableSubgraph=True, MatMulConstBOnly=True),
    )
    quantizer.quantize_model()
    utils.check_and_save_model(quantizer.model.model, save_path)
    return quantizer.model.model

def main():
    parser = HfArgumentParser((IOArguments, QuantizationArguments))
    io_args, quantization_args = parser.parse_args_into_dataclasses()
    input_folder = io_args.input_folder
    output_folder = io_args.output_folder
    if not quantization_args.modes:
        raise ValueError("At least one quantization mode must be specified")

    if not os.path.exists(input_folder):
        raise ValueError(f"Input folder {input_folder} does not exist")

    model_names_or_paths = [
        os.path.join(input_folder, file)
        for file in os.listdir(input_folder)
        if file.endswith(".onnx")
    ]
    if not model_names_or_paths:
        raise ValueError(f"No .onnx models found in {input_folder}")

    os.makedirs(output_folder, exist_ok=True)

    for model_path in tqdm(model_names_or_paths, desc="Models"):
        file_name_without_extension = os.path.splitext(os.path.basename(model_path))[0]
        model = onnx.load_model(model_path)
        for mode in tqdm(quantization_args.modes, desc="Modes"):
            try:
                suffix = QUANTIZE_SUFFIX_MAPPING.get(QuantMode(mode), mode)
            except Exception:
                suffix = mode
            save_path = os.path.join(output_folder, f"{file_name_without_extension}_{suffix}.onnx")
            mode_enum = QuantMode(mode)
            try:
                if mode_enum == QuantMode.FP16:
                    float16.convert_float_to_float16(
                        model,
                        keep_io_types=True,
                        disable_shape_infer=False,
                        op_block_list=quantization_args.op_block_list or []
                    )

                elif mode_enum == QuantMode.INT4 or mode_enum == QuantMode.Q4:
                    quantize_int4(
                        model,
                        save_path,
                        block_size=quantization_args.block_size or 32,
                        is_symmetric=quantization_args.is_symmetric,
                        accuracy_level=quantization_args.accuracy_level or 0,
                    )

                elif mode_enum == QuantMode.INT8 or mode_enum == QuantMode.QI8:
                    quantize_int8(
                        model,
                        save_path,
                        per_channel=quantization_args.per_channel or False,
                        reduce_range=quantization_args.reduce_range or False,
                        weight_type=QuantType.QInt8,
                        op_block_list=quantization_args.op_block_list,
                    )

                elif mode_enum == QuantMode.Q8:
                    quantize_int8(
                        model,
                        save_path,
                        per_channel=quantization_args.per_channel or False,
                        reduce_range=quantization_args.reduce_range or False,
                        weight_type=QuantType.QUInt8,
                        op_block_list=quantization_args.op_block_list,
                    )

                # Add other modes as needed (Q4F16, BNB4, QU8, etc.)
            except Exception as e:
                print(f"[WARN] Quantization mode '{mode}' failed for model '{model_path}': {e}")
                continue

if __name__ == "__main__":
    main()