File size: 6,793 Bytes
6f0b660 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 |
# 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.
import importlib
from typing import TYPE_CHECKING, Optional, Union
from packaging import version
from .base import HfQuantizer
from .quantizers_utils import get_module_from_name
if TYPE_CHECKING:
from ..modeling_utils import PreTrainedModel
from ..utils import (
is_accelerate_available,
is_optimum_quanto_available,
is_torch_available,
logging,
)
from ..utils.quantization_config import QuantoConfig
if is_torch_available():
import torch
logger = logging.get_logger(__name__)
class QuantoHfQuantizer(HfQuantizer):
"""
Quantizer for the quanto library
"""
required_packages = ["quanto", "accelerate"]
requires_parameters_quantization = True
requires_calibration = False
def __init__(self, quantization_config: QuantoConfig, **kwargs):
super().__init__(quantization_config, **kwargs)
self.post_init()
def post_init(self):
r"""
Safety checker
"""
if self.quantization_config.activations is not None and not self.pre_quantized:
raise ValueError(
"We don't support quantizing the activations with transformers library."
"Use quanto library for more complex use cases such as activations quantization, calibration and quantization aware training."
)
def validate_environment(self, *args, **kwargs):
if not is_optimum_quanto_available():
raise ImportError(
"Loading an optimum-quanto quantized model requires optimum-quanto library (`pip install optimum-quanto`)"
)
if not is_accelerate_available():
raise ImportError(
"Loading an optimum-quanto quantized model requires accelerate library (`pip install accelerate`)"
)
def update_device_map(self, device_map):
if device_map is None:
device_map = {"": "cpu"}
logger.info(
"The device_map was not initialized. "
"Setting device_map to {'':'cpu'}. "
"If you want to use the model for inference, please set device_map ='auto'"
)
return device_map
def update_dtype(self, dtype: "torch.dtype") -> "torch.dtype":
if dtype is None:
logger.info("You did not specify `dtype` in `from_pretrained`. Setting it to `torch.float32`.")
dtype = torch.float32
return dtype
def update_missing_keys(self, model, missing_keys: list[str], prefix: str) -> list[str]:
if is_optimum_quanto_available():
from optimum.quanto import QModuleMixin
not_missing_keys = []
for name, module in model.named_modules():
if isinstance(module, QModuleMixin):
for missing in missing_keys:
if (
(name in missing or name in f"{prefix}.{missing}")
and not missing.endswith(".weight")
and not missing.endswith(".bias")
):
not_missing_keys.append(missing)
return [k for k in missing_keys if k not in not_missing_keys]
def param_needs_quantization(self, model: "PreTrainedModel", param_name: str, **kwargs) -> bool:
if is_optimum_quanto_available():
from optimum.quanto import QModuleMixin
module, tensor_name = get_module_from_name(model, param_name)
# We only quantize the weights and the bias is not quantized.
if isinstance(module, QModuleMixin) and "weight" in tensor_name:
# if the weights are quantized, don't need to recreate it again with `create_quantized_param`
return not module.frozen
else:
return False
def adjust_max_memory(self, max_memory: dict[str, Union[int, str]]) -> dict[str, Union[int, str]]:
max_memory = {key: val * 0.90 for key, val in max_memory.items()}
return max_memory
def create_quantized_param(
self,
model: "PreTrainedModel",
param_value: "torch.Tensor",
param_name: str,
target_device: "torch.device",
**kwargs,
):
from ..modeling_utils import _load_parameter_into_model
_load_parameter_into_model(model, param_name, param_value.to(target_device))
module, _ = get_module_from_name(model, param_name)
module.freeze()
module.weight.requires_grad = False
def adjust_target_dtype(self, target_dtype: "torch.dtype") -> "torch.dtype":
if version.parse(importlib.metadata.version("accelerate")) > version.parse("0.27.0"):
from accelerate.utils import CustomDtype
mapping = {
"int8": torch.int8,
"float8": CustomDtype.FP8,
"int4": CustomDtype.INT4,
"int2": CustomDtype.INT2,
}
target_dtype = mapping[self.quantization_config.weights]
return target_dtype
else:
raise ValueError(
"You are using `device_map='auto'` on an optimum-quanto quantized model. To automatically compute"
" the appropriate device map, you should upgrade your `accelerate` library,"
"`pip install --upgrade accelerate` or install it from source."
)
def _process_model_before_weight_loading(
self, model: "PreTrainedModel", keep_in_fp32_modules: Optional[list[str]] = None, **kwargs
):
from ..integrations import replace_with_quanto_layers
self.modules_to_not_convert = self.get_modules_to_not_convert(
model, self.quantization_config.modules_to_not_convert, keep_in_fp32_modules
)
model, _ = replace_with_quanto_layers(
model, modules_to_not_convert=self.modules_to_not_convert, quantization_config=self.quantization_config
)
model.config.quantization_config = self.quantization_config
def _process_model_after_weight_loading(self, model, **kwargs):
return model
@property
def is_trainable(self) -> bool:
return True
def is_serializable(self, safe_serialization=None):
return False
|