# 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.metadata from typing import TYPE_CHECKING, List, Optional from packaging import version from .base import HfQuantizer if TYPE_CHECKING: from ..modeling_utils import PreTrainedModel from ..utils import is_accelerate_available, is_auto_awq_available, is_torch_available, logging from ..utils.quantization_config import AWQLinearVersion if is_torch_available(): import torch logger = logging.get_logger(__name__) class AwqQuantizer(HfQuantizer): """ 4-bit quantization for Activation-aware Weight Quantization(AWQ) (https://arxiv.org/abs/2306.00978) """ # AWQ requires data callibration - we support only inference requires_calibration = True required_packages = ["awq", "accelerate"] def __init__(self, quantization_config, **kwargs): super().__init__(quantization_config, **kwargs) def validate_environment(self, device_map, **kwargs): if not is_auto_awq_available(): raise ImportError("Loading an AWQ quantized model requires auto-awq library (`pip install autoawq`)") if not is_accelerate_available(): raise ImportError("Loading an AWQ quantized model requires accelerate (`pip install accelerate`)") if self.quantization_config.version == AWQLinearVersion.GEMM and not torch.cuda.is_available(): logger.warning_once("No CUDA found, replace GEMM with IPEX version to support non-cuda AWQ model.") self.quantization_config.version = AWQLinearVersion.IPEX if self.quantization_config.version == AWQLinearVersion.IPEX: if version.parse(importlib.metadata.version("autoawq")) < version.parse("0.2.6"): raise RuntimeError( "To use IPEX backend, you need autoawq>0.2.6. Please install the latest version or from source." ) if device_map is None: logger.warning_once( "You have loaded an AWQ model without setting device_map, please set 'cpu' or 'xpu' or 'auto'" ) elif isinstance(device_map, dict) and "disk" in device_map.values(): raise ValueError( "You are attempting to load an IPEX version AWQ model with a device_map that contains disk device." " This is not supported. Please make sure only cpu and xpu in the device_map." ) else: if not torch.cuda.is_available(): raise RuntimeError( "GPU is required to run AWQ quantized model. You can use IPEX version AWQ if you have an Intel CPU" ) if device_map is None: logger.warning_once( "You have loaded an AWQ model on CPU and have a CUDA device available, make sure to set " "your model on a GPU device in order to run your model." ) elif device_map is not None: if isinstance(device_map, dict) and ("cpu" in device_map.values() or "disk" in device_map.values()): raise ValueError( "You are attempting to load an AWQ model with a device_map that contains a CPU or disk device." " This is not supported. Please remove the CPU or disk device from the device_map." ) def update_torch_dtype(self, torch_dtype): if torch_dtype is None: torch_dtype = torch.float16 logger.info("Loading the model in `torch.float16`. To overwrite it, set `torch_dtype` manually.") elif torch_dtype != torch.float16: logger.warning("We suggest you to set `torch_dtype=torch.float16` for better efficiency with AWQ.") return torch_dtype def _process_model_before_weight_loading( self, model: "PreTrainedModel", keep_in_fp32_modules: Optional[List[str]] = None, **kwargs ): from ..integrations import replace_quantization_scales, replace_with_awq_linear self.modules_to_not_convert = self.get_modules_to_not_convert( model, self.quantization_config.modules_to_not_convert, keep_in_fp32_modules ) model, has_been_replaced = replace_with_awq_linear( model, quantization_config=self.quantization_config, modules_to_not_convert=self.modules_to_not_convert ) model = replace_quantization_scales(model, model.config.model_type) if not has_been_replaced: logger.warning( "You are loading an AWQ model but no linear modules were found in your model." " Please double check your model architecture, or submit an issue on github if you think this is a bug." ) def _process_model_after_weight_loading(self, model, **kwargs): if self.quantization_config.do_fuse: from ..integrations import fuse_awq_modules model = fuse_awq_modules(model, self.quantization_config) model._awq_is_fused = True # TODO: consider storing this flag in model.config instead if self.quantization_config.version == AWQLinearVersion.EXLLAMA: from ..integrations import post_init_awq_exllama_modules model = post_init_awq_exllama_modules(model, self.quantization_config.exllama_config) if self.quantization_config.version == AWQLinearVersion.IPEX: from ..integrations import post_init_awq_ipex_modules model = post_init_awq_ipex_modules(model) def is_serializable(self, safe_serialization=None): # AWQ through auto-awq has been always serializable, except if the model is fused. if self.quantization_config.do_fuse: logger.warning("You cannot save an AWQ model that uses fused modules!") return False if self.quantization_config.version == AWQLinearVersion.EXLLAMA: logger.warning("You cannot save an AWQ model that uses Exllama backend!") return False return True @property def is_trainable(self): # AWQ supports PEFT fine-tuning from version 0.2.0 MIN_AWQ_VERSION_FOR_PEFT = "0.2.0" return version.parse(importlib.metadata.version("autoawq")) >= version.parse(MIN_AWQ_VERSION_FOR_PEFT)