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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.
import importlib
from typing import TYPE_CHECKING, Any, Dict, List, 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_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() or is_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_torch_dtype(self, torch_dtype: "torch.dtype") -> "torch.dtype":
if torch_dtype is None:
logger.info("You did not specify `torch_dtype` in `from_pretrained`. Setting it to `torch.float32`.")
torch_dtype = torch.float32
return torch_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
elif is_quanto_available():
logger.warning_once(
"Importing from quanto will be deprecated in v4.47. Please install optimum-quanto instrad `pip install optimum-quanto`"
)
from 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 check_quantized_param(
self,
model: "PreTrainedModel",
param_value: "torch.Tensor",
param_name: str,
state_dict: Dict[str, Any],
**kwargs,
) -> bool:
"""
Check if a parameter needs to be quantized.
"""
if is_optimum_quanto_available():
from optimum.quanto import QModuleMixin
elif is_quanto_available():
logger.warning_once(
"Importing from quanto will be deprecated in v4.47. Please install optimum-quanto instrad `pip install optimum-quanto`"
)
from quanto import QModuleMixin
device_map = kwargs.get("device_map", None)
param_device = kwargs.get("param_device", None)
# we don't quantize the model if the module is going to be offloaded to the cpu
if device_map is not None and param_device is not None:
device_map_values = set(device_map.values())
if param_device == "cpu" and len(device_map_values) > 1:
if not (device_map_values == {"cpu"} or device_map_values == {"cpu", "disk"}):
return False
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",
*args,
**kwargs,
):
"""
Create the quantized parameter by calling .freeze() after setting it to the module.
"""
from accelerate.utils import set_module_tensor_to_device
set_module_tensor_to_device(model, param_name, target_device, param_value)
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: List[str] = [], **kwargs
):
from ..integrations import get_keys_to_not_convert, replace_with_quanto_layers
# We keep some modules such as the lm_head in their original dtype for numerical stability reasons
if self.quantization_config.modules_to_not_convert is None:
self.modules_to_not_convert = get_keys_to_not_convert(model)
else:
self.modules_to_not_convert = self.quantization_config.modules_to_not_convert
if not isinstance(self.modules_to_not_convert, list):
self.modules_to_not_convert = [self.modules_to_not_convert]
self.modules_to_not_convert.extend(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):
return model
@property
def is_trainable(self, model: Optional["PreTrainedModel"] = None):
return True
def is_serializable(self, safe_serialization=None):
return False
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