team-10/venv/Lib/site-packages/transformers/quantizers/quantizer_auto_round.py
2025-08-02 02:00:33 +02:00

81 lines
3 KiB
Python

# 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.
from typing import TYPE_CHECKING
from .base import HfQuantizer
if TYPE_CHECKING:
from ..modeling_utils import PreTrainedModel
from ..utils import is_auto_round_available, is_torch_available, logging
from ..utils.quantization_config import QuantizationConfigMixin
if is_torch_available():
import torch
logger = logging.get_logger(__name__)
class AutoRoundQuantizer(HfQuantizer):
"""
Quantizer of the AutoRound method. (https://huggingface.co/papers/2309.05516)
"""
# AutoRound requires data calibration - we support only inference
requires_calibration = True
required_packages = ["auto_round"]
def __init__(self, quantization_config: QuantizationConfigMixin, **kwargs):
super().__init__(quantization_config, **kwargs)
def validate_environment(self, *args, **kwargs):
self.device_map = kwargs.get("device_map", None)
if not is_auto_round_available():
raise ImportError(
"Loading an AutoRound quantized model requires auto-round library (`pip install 'auto-round>=0.5'`)"
)
def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
if torch_dtype is None:
torch_dtype = torch.bfloat16
logger.info("Loading the model in `torch.bfloat16`. To overwrite it, set `torch_dtype` manually.")
return torch_dtype
def _process_model_before_weight_loading(self, model: "PreTrainedModel", **kwargs):
if model.__class__.main_input_name != "input_ids":
logger.warning("AutoRound offers only limited support for models that are not strictly text-based.")
from auto_round.inference.convert_model import convert_hf_model, infer_target_device
if self.pre_quantized:
target_device = infer_target_device(self.device_map)
model, used_backends = convert_hf_model(model, target_device)
self.used_backends = used_backends
def _process_model_after_weight_loading(self, model: "PreTrainedModel", **kwargs):
if self.pre_quantized:
from auto_round.inference.convert_model import post_init
post_init(model, self.used_backends)
else:
raise ValueError("AutoRound only sports pre-quantized models.")
@property
def is_trainable(self) -> bool:
return False
def is_serializable(self, safe_serialization=None):
## for gptq/awq models, the quantization config will be changed
return True