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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING, Any, Dict, List, Optional
from .base import HfQuantizer
if TYPE_CHECKING:
from ..modeling_utils import PreTrainedModel
from ..utils import is_accelerate_available, is_eetq_available, is_torch_available, logging
from .quantizers_utils import get_module_from_name
if is_torch_available():
import torch
logger = logging.get_logger(__name__)
class EetqHfQuantizer(HfQuantizer):
"""
8-bit quantization from EETQ quantization method:
before loading: converts transformer layers into W8A16Linear during loading: load 16bit weight and pass to the
layer object after: quantizes individual weights in Linear8bitLt into 8bit at first .cuda() call
"""
requires_parameters_quantization = True
requires_calibration = False
required_packages = ["eetq", "accelerate"]
def __init__(self, quantization_config, **kwargs):
super().__init__(quantization_config, **kwargs)
self.quantization_config = quantization_config
def validate_environment(self, *args, **kwargs):
if not is_eetq_available():
raise ImportError(
"Using `eetq` 8-bit quantization requires eetq."
"Please install the latest version of eetq from : https://github.com/NetEase-FuXi/EETQ"
)
if not is_accelerate_available():
raise ImportError("Loading an EETQ quantized model requires accelerate (`pip install accelerate`)")
if kwargs.get("from_tf", False) or kwargs.get("from_flax", False):
raise ValueError(
"Converting into 8-bit weights from tf/flax weights is currently not supported, please make"
" sure the weights are in PyTorch format."
)
if not torch.cuda.is_available():
raise RuntimeError("No GPU found. A GPU is needed for quantization.")
device_map = kwargs.get("device_map", None)
if device_map is None:
logger.warning_once(
"You have loaded an EETQ model on CPU and have a CUDA device available, make sure to set "
"your model on a GPU device in order to run your model."
)
elif device_map is not None:
if isinstance(device_map, dict) and ("cpu" in device_map.values() or "disk" in device_map.values()):
raise ValueError(
"You are attempting to load an EETQ model with a device_map that contains a CPU or disk device."
" This is not supported. Please remove the CPU or disk device from the device_map."
)
def update_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
if torch_dtype is None:
torch_dtype = torch.float16
logger.info(
"Overriding torch_dtype=%s with `torch_dtype=torch.float16` due to "
"requirements of `eetq` to enable model loading in 8-bit. "
"Pass your own torch_dtype to specify the dtype of the remaining non-linear layers or pass"
" torch_dtype=torch.float16 to remove this warning.",
torch_dtype,
)
elif torch_dtype != torch.float16:
logger.info("We suggest you to set `torch_dtype=torch.float16` for better efficiency with EETQ.")
return torch_dtype
def check_quantized_param(
self,
model: "PreTrainedModel",
param_value: "torch.Tensor",
param_name: str,
state_dict: Dict[str, Any],
**kwargs,
):
from eetq import EetqLinear
module, tensor_name = get_module_from_name(model, param_name)
if isinstance(module, EetqLinear):
if self.pre_quantized or tensor_name == "bias":
if tensor_name == "weight" and param_value.dtype != torch.int8:
raise ValueError("Expect quantized weights but got an unquantized weight")
return False
else:
if tensor_name == "weight_scale":
raise ValueError("Expect unquantized weights but got a quantized weight_scale")
return True
return False
def create_quantized_param(
self,
model: "PreTrainedModel",
param_value: "torch.Tensor",
param_name: str,
target_device: "torch.device",
state_dict: Dict[str, Any],
unexpected_keys: Optional[List[str]] = None,
):
"""
quantizes weights into qweight and weight_scales
"""
from eetq import quantize_and_preprocess_weights
module, tensor_name = get_module_from_name(model, param_name)
new_value, weight_scale = quantize_and_preprocess_weights(param_value)
module._buffers[tensor_name] = new_value.to(target_device)
module.register("weight_scales", weight_scale.to(target_device))
def _process_model_after_weight_loading(self, model: "PreTrainedModel", **kwargs):
return model
def _process_model_before_weight_loading(
self,
model: "PreTrainedModel",
device_map,
keep_in_fp32_modules: List[str] = [],
**kwargs,
):
from ..integrations import get_keys_to_not_convert, replace_with_eetq_linear
self.modules_to_not_convert = get_keys_to_not_convert(model)
if self.quantization_config.modules_to_not_convert is not None:
self.modules_to_not_convert.extend(self.quantization_config.modules_to_not_convert)
model = replace_with_eetq_linear(
model,
modules_to_not_convert=self.modules_to_not_convert,
quantization_config=self.quantization_config,
pre_quantized=self.pre_quantized,
)
model.config.quantization_config = self.quantization_config
def is_serializable(self, safe_serialization=None):
return True
@property
def is_trainable(self) -> bool:
return True
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