364 lines
15 KiB
Python
364 lines
15 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, Optional, Union
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from ..utils import is_torch_available
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from ..utils.quantization_config import QuantizationConfigMixin, QuantizationMethod
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from .quantizers_utils import get_module_from_name
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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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from torch.nn import ModuleList
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else:
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ModuleList = str
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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 update_unexpected_keys(self, model, unexpected_keys: list[str], prefix: str) -> list[str]:
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"""
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Override this method if you want to adjust the `unexpected_keys`.
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Args:
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unexpected_keys (`list[str]`, *optional*):
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The list of unexpected keys in the checkpoint compared to the state dict of the model
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"""
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return unexpected_keys
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def update_missing_keys_after_loading(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` after loading the model params,
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but before the model is post-processed.
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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 update_expected_keys(self, model, expected_keys: list[str], loaded_keys: list[str]) -> list[str]:
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"""
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Override this method if you want to adjust the `update_expected_keys`.
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Args:
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expected_keys (`list[str]`, *optional*):
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The list of the expected keys in the initialized model.
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loaded_keys (`list[str]`, *optional*):
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The list of the loaded keys in the checkpoint.
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"""
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return expected_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 update_tp_plan(self, config):
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"updates the tp plan for the scales"
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return config
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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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if self.pre_quantized:
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self._convert_model_for_quantization(model)
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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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def dequantize(self, model):
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"""
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Potentially dequantize the model to retrieve the original model, with some loss in accuracy / performance.
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Note not all quantization schemes support this.
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"""
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model = self._dequantize(model)
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# Delete quantizer and quantization config
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del model.hf_quantizer
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del model.config.quantization_config
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del model.config._pre_quantization_dtype
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del model.quantization_method
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model.is_quantized = False
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return model
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def get_cuda_warm_up_factor(self):
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"""
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The factor to be used in `caching_allocator_warmup` to get the number of bytes to pre-allocate to warm up cuda.
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A factor of 2 means we allocate all bytes in the empty model (since we allocate in fp16), a factor of 4 means
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we allocate half the memory of the weights residing in the empty model, etc...
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"""
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# By default we return 4, i.e. half the model size (this corresponds to the case where the model is not
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# really pre-processed, i.e. we do not have the info that weights are going to be 8 bits before actual
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# weight loading)
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return 4
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def _dequantize(self, model):
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raise NotImplementedError(
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f"{self.quantization_config.quant_method} has no implementation of `dequantize`, please raise an issue on GitHub."
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)
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@staticmethod
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def get_modules_to_not_convert(
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model: "PreTrainedModel",
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skip_modules: Optional[list[str]] = None,
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keep_in_fp32_modules: Optional[list[str]] = None,
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add_default_skips: bool = False,
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):
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from ..integrations import get_keys_to_not_convert
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if skip_modules is None or add_default_skips:
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modules_to_not_convert = get_keys_to_not_convert(model)
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else:
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modules_to_not_convert = []
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if skip_modules is not None:
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modules_to_not_convert.extend(skip_modules)
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if keep_in_fp32_modules is not None:
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modules_to_not_convert.extend(keep_in_fp32_modules)
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return modules_to_not_convert
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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 False
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@property
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def is_compileable(self) -> bool:
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"""Flag indicating whether the quantized model can be compiled"""
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return False
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@abstractmethod
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def _process_model_before_weight_loading(self, model, **kwargs): ...
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@abstractmethod
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def _process_model_after_weight_loading(self, model, **kwargs): ...
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@abstractmethod
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def is_serializable(self, safe_serialization=None): ...
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@property
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@abstractmethod
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def is_trainable(self): ...
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def _convert_model_for_quantization(self, model):
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from accelerate import init_empty_weights
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for name, module in model.named_modules():
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module_class_name = module.__class__.__name__
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if module_class_name in MODULES_TO_PATCH_FOR_QUANTIZATION.keys() and (
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self.quantization_config.quant_method
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in MODULES_TO_PATCH_FOR_QUANTIZATION[module_class_name]["quantization_methods"]
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):
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with init_empty_weights():
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parent_module, name = get_module_from_name(model, name)
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parent_module._modules[name] = MODULES_TO_PATCH_FOR_QUANTIZATION[module_class_name]["module_name"](
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model.config.get_text_config()
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)
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class SequentialLlama4TextExperts(ModuleList):
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"""
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A module that implements a compressed version of a list of expert modules.
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This is specifically designed to work with Llama4TextExperts in MoE layers.
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"""
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def __init__(self, config):
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from transformers.models.llama4.modeling_llama4 import Llama4TextMLP
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super().__init__([Llama4TextMLP(config) for _ in range(config.num_local_experts)])
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self.num_experts = config.num_local_experts
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def forward(
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self,
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hidden_states: "torch.Tensor",
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) -> "torch.Tensor":
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hidden_states = hidden_states.reshape(self.num_experts, -1, hidden_states.shape[-1])
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routed_out = torch.zeros_like(hidden_states)
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for expert_idx in range(self.num_experts):
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routed_out[expert_idx] = self[expert_idx](hidden_states[expert_idx])
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return routed_out
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MODULES_TO_PATCH_FOR_QUANTIZATION = {
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"Llama4TextExperts": {
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"module_name": SequentialLlama4TextExperts,
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"quantization_methods": [
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QuantizationMethod.COMPRESSED_TENSORS,
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QuantizationMethod.BITS_AND_BYTES,
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],
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}
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}
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