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96 lines
3.6 KiB
96 lines
3.6 KiB
# 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_gptqmodel_available, is_torch_available, logging
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from ..utils.quantization_config import AwqBackend
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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://huggingface.co/papers/2306.00978)
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"""
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# AWQ requires data calibration - we support only inference
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requires_calibration = True
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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, **kwargs):
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if not is_gptqmodel_available():
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raise ImportError(
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"Loading an AWQ quantized model requires gptqmodel. Please install it with `pip install gptqmodel`"
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)
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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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def update_dtype(self, dtype):
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if dtype == torch.bfloat16 and (torch.cuda.is_available() or torch.xpu.is_available()):
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logger.warning(
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"`torch.bfloat16` is not supported for AWQ CUDA/XPU kernels yet. Casting to `torch.float16`."
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)
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dtype = torch.float16
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elif dtype != torch.float16 and (torch.cuda.is_available() or torch.xpu.is_available()):
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logger.warning("We suggest you to set `dtype=torch.float16` for better efficiency on CUDA/XPU with AWQ.")
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return dtype
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def _process_model_before_weight_loading(self, model: "PreTrainedModel", **kwargs):
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from ..integrations import replace_quantization_scales, replace_with_awq_linear
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self.modules_to_not_convert = self.get_modules_to_not_convert(
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model, self.quantization_config.modules_to_not_convert, model._keep_in_fp32_modules, add_default_skips=True
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)
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model = replace_with_awq_linear(
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model,
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quantization_config=self.quantization_config,
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modules_to_not_convert=self.modules_to_not_convert,
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device_map=kwargs.get("device_map"),
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)
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model = replace_quantization_scales(model, model.config.model_type)
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def _process_model_after_weight_loading(self, model, **kwargs):
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from gptqmodel.utils.model import hf_gptqmodel_post_init
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hf_gptqmodel_post_init(model, use_act_order=self.quantization_config.desc_act)
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def is_serializable(self):
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if self.quantization_config.backend in [AwqBackend.EXLLAMA_V1, AwqBackend.EXLLAMA_V2]:
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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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return version.parse(importlib.metadata.version("gptqmodel")) >= version.parse("5.0.0")
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