Lightstar/venv/Lib/site-packages/transformers/quantizers/quantizer_gptq.py

# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import importlib
from typing import TYPE_CHECKING, Optional

from packaging import version

from .base import HfQuantizer


if TYPE_CHECKING:
    from ..modeling_utils import PreTrainedModel

from ..utils import is_auto_gptq_available, is_optimum_available, is_torch_available, logging
from ..utils.quantization_config import GPTQConfig, QuantizationConfigMixin


if is_torch_available():
    import torch

logger = logging.get_logger(__name__)


class GptqHfQuantizer(HfQuantizer):
    """
    Quantizer of the GPTQ method - for GPTQ the quantizer support calibration of the model through
    `auto_gptq` package. Quantization is done under the hood for users if they load a non-prequantized model.
    """

    requires_calibration = False
    required_packages = ["optimum", "auto_gptq"]
    optimum_quantizer = None

    def __init__(self, quantization_config: QuantizationConfigMixin, **kwargs):
        super().__init__(quantization_config, **kwargs)
        from optimum.gptq import GPTQQuantizer

        self.optimum_quantizer = GPTQQuantizer.from_dict(self.quantization_config.to_dict_optimum())

    def validate_environment(self, *args, **kwargs):
        gptq_supports_cpu = version.parse(importlib.metadata.version("auto-gptq")) > version.parse("0.4.2")
        if not gptq_supports_cpu and not torch.cuda.is_available():
            raise RuntimeError("GPU is required to quantize or run quantize model.")
        elif not (is_optimum_available() and is_auto_gptq_available()):
            raise ImportError(
                "Loading a GPTQ quantized model requires optimum (`pip install optimum`) and auto-gptq library (`pip install auto-gptq`)"
            )
        elif version.parse(importlib.metadata.version("auto_gptq")) < version.parse("0.4.2"):
            raise ImportError(
                "You need a version of auto_gptq >= 0.4.2 to use GPTQ: `pip install --upgrade auto-gptq`"
            )

    def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
        if torch_dtype is None:
            torch_dtype = torch.float16
        elif torch_dtype != torch.float16:
            logger.info("We suggest you to set `torch_dtype=torch.float16` for better efficiency with GPTQ.")
        return torch_dtype

    def _process_model_before_weight_loading(self, model: "PreTrainedModel", **kwargs):
        if model.__class__.main_input_name != "input_ids":
            raise RuntimeError("We can only quantize pure text model.")

        if self.pre_quantized:
            model = self.optimum_quantizer.convert_model(model)

    def _process_model_after_weight_loading(self, model: "PreTrainedModel", **kwargs):
        if self.pre_quantized:
            model = self.optimum_quantizer.post_init_model(model)
        else:
            if self.quantization_config.tokenizer is None:
                self.quantization_config.tokenizer = model.name_or_path

            self.optimum_quantizer.quantize_model(model, self.quantization_config.tokenizer)
            model.config.quantization_config = GPTQConfig.from_dict(self.optimum_quantizer.to_dict())

    @property
    def is_trainable(self, model: Optional["PreTrainedModel"] = None):
        return True

    def is_serializable(self, safe_serialization=None):
        return True