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109 lines
3.8 KiB
109 lines
3.8 KiB
# 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
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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_kernels_available, is_torch_available, logging
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from .quantizers_utils import get_module_from_name
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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 EetqHfQuantizer(HfQuantizer):
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"""
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8-bit quantization from EETQ quantization method
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"""
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requires_calibration = False
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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_kernels_available():
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raise ImportError("Loading an EETQ quantized model requires kernels (`pip install kernels`)")
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if not is_accelerate_available():
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raise ImportError("Loading an EETQ quantized model requires accelerate (`pip install accelerate`)")
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if not torch.cuda.is_available():
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raise RuntimeError("No GPU found. A GPU is needed for quantization.")
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device_map = kwargs.get("device_map")
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if device_map is None:
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logger.warning_once(
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"You have loaded an EETQ 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 isinstance(device_map, dict):
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if len(device_map) > 1 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 an EETQ 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 update_dtype(self, dtype: "torch.dtype") -> "torch.dtype":
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if dtype != torch.float16:
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logger.info("We suggest you to set `dtype=torch.float16` for better efficiency with EETQ.")
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return dtype
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def param_needs_quantization(self, model: "PreTrainedModel", param_name: str, **kwargs) -> bool:
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from ..integrations.eetq import EetqLinear
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module, tensor_name = get_module_from_name(model, param_name)
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if isinstance(module, EetqLinear):
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if self.pre_quantized or tensor_name == "bias":
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return False
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else:
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return True
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return False
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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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from ..integrations import replace_with_eetq_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, model._keep_in_fp32_modules
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)
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model = replace_with_eetq_linear(
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model, modules_to_not_convert=self.modules_to_not_convert, pre_quantized=self.pre_quantized
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)
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def is_serializable(self):
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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 True
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def get_quantize_ops(self):
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from ..integrations.eetq import EetqQuantize
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return EetqQuantize(self)
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