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