95 lines
3.8 KiB
Python
95 lines
3.8 KiB
Python
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# 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
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from typing import TYPE_CHECKING, Optional
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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_auto_gptq_available, is_optimum_available, is_torch_available, logging
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from ..utils.quantization_config import GPTQConfig, 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 GptqHfQuantizer(HfQuantizer):
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"""
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Quantizer of the GPTQ method - for GPTQ the quantizer support calibration of the model through
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`auto_gptq` package. Quantization is done under the hood for users if they load a non-prequantized model.
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"""
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requires_calibration = False
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required_packages = ["optimum", "auto_gptq"]
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optimum_quantizer = None
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def __init__(self, quantization_config: QuantizationConfigMixin, **kwargs):
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super().__init__(quantization_config, **kwargs)
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from optimum.gptq import GPTQQuantizer
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self.optimum_quantizer = GPTQQuantizer.from_dict(self.quantization_config.to_dict_optimum())
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def validate_environment(self, *args, **kwargs):
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gptq_supports_cpu = version.parse(importlib.metadata.version("auto-gptq")) > version.parse("0.4.2")
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if not gptq_supports_cpu and not torch.cuda.is_available():
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raise RuntimeError("GPU is required to quantize or run quantize model.")
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elif not (is_optimum_available() and is_auto_gptq_available()):
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raise ImportError(
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"Loading a GPTQ quantized model requires optimum (`pip install optimum`) and auto-gptq library (`pip install auto-gptq`)"
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)
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elif version.parse(importlib.metadata.version("auto_gptq")) < version.parse("0.4.2"):
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raise ImportError(
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"You need a version of auto_gptq >= 0.4.2 to use GPTQ: `pip install --upgrade auto-gptq`"
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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.float16
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elif torch_dtype != torch.float16:
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logger.info("We suggest you to set `torch_dtype=torch.float16` for better efficiency with GPTQ.")
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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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raise RuntimeError("We can only quantize pure text model.")
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if self.pre_quantized:
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model = self.optimum_quantizer.convert_model(model)
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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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model = self.optimum_quantizer.post_init_model(model)
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else:
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if self.quantization_config.tokenizer is None:
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self.quantization_config.tokenizer = model.name_or_path
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self.optimum_quantizer.quantize_model(model, self.quantization_config.tokenizer)
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model.config.quantization_config = GPTQConfig.from_dict(self.optimum_quantizer.to_dict())
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@property
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def is_trainable(self, model: Optional["PreTrainedModel"] = None):
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return True
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@property
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def is_serializable(self):
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return True
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