124 lines
4.6 KiB
Python
124 lines
4.6 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, Optional, Union
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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_torch_available, logging
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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 BitNetHfQuantizer(HfQuantizer):
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"""
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1.58-bit quantization from BitNet quantization method:
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Before loading: it converts the linear layers into BitLinear layers during loading.
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Check out the paper introducing this method: https://huggingface.co/papers/2402.17764
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"""
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requires_parameters_quantization = False
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requires_calibration = True
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required_packages = ["accelerate"]
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def __init__(self, quantization_config, **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("Loading a BitNet quantized model requires accelerate (`pip install accelerate`)")
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if kwargs.get("from_tf", False) or kwargs.get("from_flax", False):
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raise ValueError(
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"Loading ternary weights from tf/flax is currently not supported, please make"
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" sure the weights are in PyTorch format."
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)
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if not torch.cuda.is_available():
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logger.warning_once(
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"You don't have a GPU available to load the model, the inference will be slow because of weight unpacking"
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)
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return
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device_map = kwargs.get("device_map", None)
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if device_map is None:
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logger.warning_once(
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"You have loaded a BitNet 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 a BitNet 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 _process_model_after_weight_loading(self, model: "PreTrainedModel", **kwargs):
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return model
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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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keep_in_fp32_modules: Optional[list[str]] = None,
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**kwargs,
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):
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from ..integrations import replace_with_bitnet_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, keep_in_fp32_modules
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)
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model = replace_with_bitnet_linear(
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model,
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modules_to_not_convert=self.modules_to_not_convert,
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quantization_config=self.quantization_config,
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pre_quantized=self.pre_quantized,
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)
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def adjust_max_memory(self, max_memory: dict[str, Union[int, str]]) -> dict[str, Union[int, str]]:
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max_memory = {key: val * 0.90 for key, val in max_memory.items()}
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return max_memory
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def adjust_target_dtype(self, target_dtype: "torch.dtype") -> "torch.dtype":
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target_dtype = torch.int8
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return target_dtype
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def is_serializable(self, safe_serialization=None):
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return True
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@property
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def is_trainable(self) -> bool:
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return (
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self.quantization_config.linear_class == "autobitlinear"
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and self.quantization_config.quantization_mode == "online"
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)
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@property
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def is_qat_trainable(self) -> bool:
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"""Flag indicating whether the quantized model can carry out quantization aware training"""
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return (
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self.quantization_config.linear_class == "autobitlinear"
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and self.quantization_config.quantization_mode == "online"
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)
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