125 lines
5.0 KiB
Python
125 lines
5.0 KiB
Python
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import importlib.metadata
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from typing import TYPE_CHECKING
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from packaging import version
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from .base import HfQuantizer
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if TYPE_CHECKING:
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from ..modeling_utils import PreTrainedModel
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from ..utils import is_accelerate_available, is_auto_awq_available, is_torch_available, logging
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from ..utils.quantization_config import AWQLinearVersion
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if is_torch_available():
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import torch
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logger = logging.get_logger(__name__)
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class AwqQuantizer(HfQuantizer):
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"""
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4-bit quantization for Activation-aware Weight Quantization(AWQ) (https://arxiv.org/abs/2306.00978)
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"""
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# AWQ requires data callibration - we support only inference
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requires_calibration = True
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required_packages = ["awq", "accelerate"]
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def __init__(self, quantization_config, **kwargs):
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super().__init__(quantization_config, **kwargs)
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def validate_environment(self, device_map, **kwargs):
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if not torch.cuda.is_available():
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raise RuntimeError("GPU is required to run AWQ quantized model.")
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if not is_auto_awq_available():
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raise ImportError("Loading an AWQ quantized model requires auto-awq library (`pip install autoawq`)")
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if not is_accelerate_available():
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raise ImportError("Loading an AWQ quantized model requires accelerate (`pip install accelerate`)")
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if device_map is None:
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logger.warning_once(
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"You have loaded an AWQ model on CPU and have a CUDA device available, make sure to set "
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"your model on a GPU device in order to run your model."
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)
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elif device_map is not None:
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if isinstance(device_map, dict) and ("cpu" in device_map.values() or "disk" in device_map.values()):
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raise ValueError(
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"You are attempting to load an AWQ model with a device_map that contains a CPU or disk device."
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" This is not supported. Please remove the CPU or disk device from the device_map."
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)
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def update_torch_dtype(self, torch_dtype):
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if torch_dtype is None:
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torch_dtype = torch.float16
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elif torch_dtype != torch.float16:
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logger.warning("We suggest you to set `torch_dtype=torch.float16` for better efficiency with AWQ.")
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return torch_dtype
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def _process_model_before_weight_loading(self, model: "PreTrainedModel", **kwargs):
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from ..integrations import get_keys_to_not_convert, replace_with_awq_linear
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self.modules_to_not_convert = get_keys_to_not_convert(model)
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if self.quantization_config.modules_to_not_convert is not None:
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self.modules_to_not_convert.extend(self.quantization_config.modules_to_not_convert)
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model, has_been_replaced = replace_with_awq_linear(
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model, quantization_config=self.quantization_config, modules_to_not_convert=self.modules_to_not_convert
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)
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if not has_been_replaced:
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logger.warning(
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"You are loading an AWQ model but no linear modules were found in your model."
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" Please double check your model architecture, or submit an issue on github if you think this is a bug."
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)
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def _process_model_after_weight_loading(self, model):
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if self.quantization_config.do_fuse:
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from ..integrations import fuse_awq_modules
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model = fuse_awq_modules(model, self.quantization_config)
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model._awq_is_fused = True # TODO: consider storing this flag in model.config instead
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if self.quantization_config.version == AWQLinearVersion.EXLLAMA:
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from ..integrations import post_init_awq_exllama_modules
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model = post_init_awq_exllama_modules(model, self.quantization_config.exllama_config)
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@property
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def is_serializable(self):
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# AWQ through auto-awq has been always serializable, except if the model is fused.
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if self.quantization_config.do_fuse:
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logger.warning("You cannot save an AWQ model that uses fused modules!")
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return False
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if self.quantization_config.version == AWQLinearVersion.EXLLAMA:
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logger.warning("You cannot save an AWQ model that uses Exllama backend!")
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return False
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return True
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@property
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def is_trainable(self):
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# AWQ supports PEFT fine-tuning from version 0.2.0
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MIN_AWQ_VERSION_FOR_PEFT = "0.2.0"
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return version.parse(importlib.metadata.version("autoawq")) >= version.parse(MIN_AWQ_VERSION_FOR_PEFT)
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