184 lines
7.2 KiB
Python
184 lines
7.2 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, Union
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from packaging import version
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from .base import HfQuantizer
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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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from typing import Any, Dict, List
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from ..utils import is_torch_available, is_torchao_available, logging
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if is_torch_available():
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import torch
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if is_torchao_available():
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from torchao.quantization import quantize_
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logger = logging.get_logger(__name__)
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# Finds the parent of a node module named "name"
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def find_parent(model, name):
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module_tree = name.split(".")[:-1]
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parent = model
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for m in module_tree:
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parent = parent._modules[m]
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return parent
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class TorchAoHfQuantizer(HfQuantizer):
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"""
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Quantizer for torchao: https://github.com/pytorch/ao/
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"""
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requires_parameters_quantization = True
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requires_calibration = False
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required_packages = ["torchao"]
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def __init__(self, quantization_config, **kwargs):
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super().__init__(quantization_config, **kwargs)
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def validate_environment(self, *args, **kwargs):
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if not is_torchao_available():
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raise ImportError("Loading an torchao quantized model requires torchao library (`pip install torchao`)")
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self.offload = False
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device_map = kwargs.get("device_map", None)
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if isinstance(device_map, dict):
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if "cpu" in device_map.values() or "disk" in device_map.values():
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if self.pre_quantized:
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raise ValueError(
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"You are attempting to perform cpu/disk offload with a pre-quantized torchao model "
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"This is not supported yet . Please remove the CPU or disk device from the device_map."
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)
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else:
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self.offload = True
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def update_torch_dtype(self, torch_dtype):
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if self.quantization_config.quant_type == "int4_weight_only":
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if torch_dtype is not None and torch_dtype != torch.bfloat16:
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logger.warning_once(
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f"Setting torch_dtype to {torch_dtype} for int4_weight_only quantization, but only bfloat16 is supported right now. Please set the torch_dtype to bfloat16."
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)
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if torch_dtype is None:
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logger.warning_once(
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"Setting torch_dtype to torch.bfloat16 for int4_weight_only quantization since only bfloat16 is supported right now. Please set torch_dtype=torch.bfloat16 to remove this warning."
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)
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torch_dtype = torch.bfloat16
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return torch_dtype
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def adjust_target_dtype(self, target_dtype: "torch.dtype") -> "torch.dtype":
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if version.parse(importlib.metadata.version("accelerate")) > version.parse("0.19.0"):
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from accelerate.utils import CustomDtype
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map_to_target_dtype = {
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"int4_weight_only": CustomDtype.INT4,
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"int8_weight_only": torch.int8,
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"int8_dynamic_activation_int8_weight": torch.int8,
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}
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return map_to_target_dtype[self.quantization_config.quant_type]
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else:
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raise ValueError(
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"You are using `device_map='auto'` on a torchao quantized model. To automatically compute"
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" the appropriate device map, you should upgrade your `accelerate` library with "
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"`pip install --upgrade accelerate`"
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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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# need more space for the quantization parameters (e.g. scale). Tested with int4 wo and group size = 128
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max_memory = {key: val * 0.9 for key, val in max_memory.items()}
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return max_memory
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def _process_model_before_weight_loading(self, model: "PreTrainedModel", **kwargs):
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from ..integrations import get_keys_to_not_convert
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self.modules_to_not_convert = get_keys_to_not_convert(model)
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if self.quantization_config.modules_to_not_convert is not None:
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self.modules_to_not_convert.extend(self.quantization_config.modules_to_not_convert)
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return
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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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param_device = kwargs.pop("param_device", None)
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# check if the param_name is not in self.modules_to_not_convert
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if any((key + "." in param_name) or (key == param_name) for key in self.modules_to_not_convert):
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return False
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elif param_device == "cpu" and self.offload:
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# We don't quantize weights that we offload
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return False
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else:
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# we only quantize the weight of nn.Linear
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module, tensor_name = get_module_from_name(model, param_name)
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return isinstance(module, torch.nn.Linear) and (tensor_name == "weight")
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def create_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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target_device: "torch.device",
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state_dict: Dict[str, Any],
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unexpected_keys: List[str],
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):
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"""
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Each nn.Linear layer that needs to be quantized is processsed here.
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First, we set the value the weight tensor, then we move it to the target device. Finally, we quantize the module.
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"""
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module, tensor_name = get_module_from_name(model, param_name)
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module._parameters[tensor_name] = torch.nn.Parameter(param_value).to(device=target_device)
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quantize_(module, self.quantization_config.get_apply_tensor_subclass())
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def _process_model_after_weight_loading(self, model):
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"""No process required for torchao quantized model"""
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return
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def is_serializable(self, safe_serialization=None):
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if safe_serialization:
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logger.warning(
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"torchao quantized model does not support safe serialization, "
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"please set `safe_serialization` to False"
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)
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return False
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_is_torchao_serializable = version.parse(importlib.metadata.version("huggingface_hub")) >= version.parse(
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"0.25.0"
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)
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if not _is_torchao_serializable:
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logger.warning("torchao quantized model is only serializable after huggingface_hub >= 0.25.0 ")
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return _is_torchao_serializable
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@property
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def is_trainable(self):
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supported_quant_types_for_training = [
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"int8_weight_only",
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"int8_dynamic_activation_int8_weight",
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]
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return self.quantization_config.quant_type in supported_quant_types_for_training
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