214 lines
8.9 KiB
Python
214 lines
8.9 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 abc import ABC, abstractmethod
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
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from ..utils import is_torch_available
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from ..utils.quantization_config import QuantizationConfigMixin
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if TYPE_CHECKING:
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from ..modeling_utils import PreTrainedModel
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if is_torch_available():
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import torch
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class HfQuantizer(ABC):
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"""
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Abstract class of the HuggingFace quantizer. Supports for now quantizing HF transformers models for inference and/or quantization.
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This class is used only for transformers.PreTrainedModel.from_pretrained and cannot be easily used outside the scope of that method
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yet.
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Attributes
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quantization_config (`transformers.utils.quantization_config.QuantizationConfigMixin`):
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The quantization config that defines the quantization parameters of your model that you want to quantize.
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modules_to_not_convert (`List[str]`, *optional*):
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The list of module names to not convert when quantizing the model.
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required_packages (`List[str]`, *optional*):
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The list of required pip packages to install prior to using the quantizer
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requires_calibration (`bool`):
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Whether the quantization method requires to calibrate the model before using it.
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requires_parameters_quantization (`bool`):
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Whether the quantization method requires to create a new Parameter. For example, for bitsandbytes, it is
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required to create a new xxxParameter in order to properly quantize the model.
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"""
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requires_calibration = False
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required_packages = None
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requires_parameters_quantization = False
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def __init__(self, quantization_config: QuantizationConfigMixin, **kwargs):
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self.quantization_config = quantization_config
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# -- Handle extra kwargs below --
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self.modules_to_not_convert = kwargs.pop("modules_to_not_convert", [])
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self.pre_quantized = kwargs.pop("pre_quantized", True)
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if not self.pre_quantized and self.requires_calibration:
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raise ValueError(
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f"The quantization method {quantization_config.quant_method} does require the model to be pre-quantized."
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f" You explicitly passed `pre_quantized=False` meaning your model weights are not quantized. Make sure to "
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f"pass `pre_quantized=True` while knowing what you are doing."
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)
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def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
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"""
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Some quantization methods require to explicitly set the dtype of the model to a
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target dtype. You need to override this method in case you want to make sure that behavior is
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preserved
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Args:
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torch_dtype (`torch.dtype`):
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The input dtype that is passed in `from_pretrained`
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"""
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return torch_dtype
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def update_device_map(self, device_map: Optional[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
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"""
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Override this method if you want to pass a override the existing device map with a new
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one. E.g. for bitsandbytes, since `accelerate` is a hard requirement, if no device_map is
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passed, the device_map is set to `"auto"``
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Args:
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device_map (`Union[dict, str]`, *optional*):
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The device_map that is passed through the `from_pretrained` method.
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"""
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return device_map
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def adjust_target_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
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"""
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Override this method if you want to adjust the `target_dtype` variable used in `from_pretrained`
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to compute the device_map in case the device_map is a `str`. E.g. for bitsandbytes we force-set `target_dtype`
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to `torch.int8` and for 4-bit we pass a custom enum `accelerate.CustomDtype.int4`.
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Args:
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torch_dtype (`torch.dtype`, *optional*):
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The torch_dtype that is used to compute the device_map.
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"""
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return torch_dtype
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def update_missing_keys(self, model, missing_keys: List[str], prefix: str) -> List[str]:
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"""
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Override this method if you want to adjust the `missing_keys`.
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Args:
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missing_keys (`List[str]`, *optional*):
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The list of missing keys in the checkpoint compared to the state dict of the model
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"""
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return missing_keys
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def get_special_dtypes_update(self, model, torch_dtype: "torch.dtype") -> Dict[str, "torch.dtype"]:
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"""
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returns dtypes for modules that are not quantized - used for the computation of the device_map in case
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one passes a str as a device_map. The method will use the `modules_to_not_convert` that is modified
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in `_process_model_before_weight_loading`.
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Args:
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model (`~transformers.PreTrainedModel`):
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The model to quantize
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torch_dtype (`torch.dtype`):
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The dtype passed in `from_pretrained` method.
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"""
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return {
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name: torch_dtype
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for name, _ in model.named_parameters()
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if any(m in name for m in self.modules_to_not_convert)
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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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"""adjust max_memory argument for infer_auto_device_map() if extra memory is needed for quantization"""
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return max_memory
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def check_quantized_param(
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self,
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model: "PreTrainedModel",
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param_value: "torch.Tensor",
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param_name: str,
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state_dict: Dict[str, Any],
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**kwargs,
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) -> bool:
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"""
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checks if a loaded state_dict component is part of quantized param + some validation; only defined if
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requires_parameters_quantization == True for quantization methods that require to create a new parameters
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for quantization.
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"""
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return False
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def create_quantized_param(self, *args, **kwargs) -> "torch.nn.Parameter":
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"""
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takes needed components from state_dict and creates quantized param; only applicable if
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requires_parameters_quantization == True
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"""
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if not self.requires_parameters_quantization:
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raise AttributeError(
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f"`.create_quantized_param()` method is not supported by quantizer class {self.__class__.__name__}."
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)
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def validate_environment(self, *args, **kwargs):
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"""
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This method is used to potentially check for potential conflicts with arguments that are
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passed in `from_pretrained`. You need to define it for all future quantizers that are integrated with transformers.
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If no explicit check are needed, simply return nothing.
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"""
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return
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def preprocess_model(self, model: "PreTrainedModel", **kwargs):
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"""
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Setting model attributes and/or converting model before weights loading. At this point
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the model should be initialized on the meta device so you can freely manipulate the skeleton
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of the model in order to replace modules in-place. Make sure to override the abstract method `_process_model_before_weight_loading`.
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Args:
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model (`~transformers.PreTrainedModel`):
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The model to quantize
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kwargs (`dict`, *optional*):
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The keyword arguments that are passed along `_process_model_before_weight_loading`.
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"""
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model.is_quantized = True
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model.quantization_method = self.quantization_config.quant_method
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return self._process_model_before_weight_loading(model, **kwargs)
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def postprocess_model(self, model: "PreTrainedModel", **kwargs):
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"""
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Post-process the model post weights loading.
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Make sure to override the abstract method `_process_model_after_weight_loading`.
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Args:
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model (`~transformers.PreTrainedModel`):
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The model to quantize
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kwargs (`dict`, *optional*):
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The keyword arguments that are passed along `_process_model_after_weight_loading`.
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"""
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return self._process_model_after_weight_loading(model, **kwargs)
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@abstractmethod
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def _process_model_before_weight_loading(self, model, **kwargs):
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...
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@abstractmethod
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def _process_model_after_weight_loading(self, model, **kwargs):
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...
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@property
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@abstractmethod
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def is_serializable(self):
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...
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@property
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@abstractmethod
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def is_trainable(self):
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...
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