99 lines
3.6 KiB
Python
99 lines
3.6 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 ..integrations import replace_with_aqlm_linear
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from ..utils import is_accelerate_available, is_aqlm_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 AqlmHfQuantizer(HfQuantizer):
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"""
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Quantizer of the AQLM method. Enables the loading of prequantized models.
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"""
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requires_calibration = True
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required_packages = ["aqlm"]
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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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self.quantization_config = quantization_config
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def validate_environment(self, *args, **kwargs):
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if not is_accelerate_available():
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raise ImportError("Using `aqlm` quantization requires Accelerate: `pip install accelerate`")
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if not is_aqlm_available():
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raise ImportError("Using `aqlm` quantization requires AQLM: `pip install aqlm[gpu,cpu]`")
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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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if torch.cuda.is_available():
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torch_dtype = torch.float16
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logger.info(
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"CUDA available. Assuming AQLM inference on GPU and loading the model in `torch.float16`. To overwrite it, set `torch_dtype` manually."
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)
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else:
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torch_dtype = torch.float32
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logger.info(
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"CUDA is unavailable. Assuming AQLM inference on CPU and loading the model in `torch.float32`. To overwrite it, set `torch_dtype` manually."
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)
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return torch_dtype
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def _process_model_before_weight_loading(
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self,
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model: "PreTrainedModel",
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**kwargs,
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):
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replace_with_aqlm_linear(
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model,
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quantization_config=self.quantization_config,
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linear_weights_not_to_quantize=self.quantization_config.linear_weights_not_to_quantize,
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)
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model.config.quantization_config = self.quantization_config
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def _process_model_after_weight_loading(self, model: "PreTrainedModel", **kwargs):
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return model
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@property
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def is_trainable(self, model: Optional["PreTrainedModel"] = None):
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aqlm_supports_training = version.parse(importlib.metadata.version("aqlm")) >= version.parse("1.0.2")
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if aqlm_supports_training:
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return True
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else:
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logger.warning(
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f"Currently installed `aqlm` version ({importlib.metadata.version('aqlm')}) doesn't support training. If you wish to train a quantized model, please update `aqlm` with `pip install aqlm>=1.0.2`"
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)
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return False
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@property
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def is_serializable(self):
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return True
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