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# Copyright 2024 The HuggingFace 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 inspect
from typing import Dict, List, Optional, Union
from ..utils import is_transformers_available, logging
from .auto import DiffusersAutoQuantizer
from .base import DiffusersQuantizer
from .quantization_config import QuantizationConfigMixin as DiffQuantConfigMixin
try:
from transformers.utils.quantization_config import QuantizationConfigMixin as TransformersQuantConfigMixin
except ImportError:
class TransformersQuantConfigMixin:
pass
logger = logging.get_logger(__name__)
class PipelineQuantizationConfig:
"""
Configuration class to be used when applying quantization on-the-fly to [`~DiffusionPipeline.from_pretrained`].
Args:
quant_backend (`str`): Quantization backend to be used. When using this option, we assume that the backend
is available to both `diffusers` and `transformers`.
quant_kwargs (`dict`): Params to initialize the quantization backend class.
components_to_quantize (`list`): Components of a pipeline to be quantized.
quant_mapping (`dict`): Mapping defining the quantization specs to be used for the pipeline
components. When using this argument, users are not expected to provide `quant_backend`, `quant_kawargs`,
and `components_to_quantize`.
"""
def __init__(
self,
quant_backend: str = None,
quant_kwargs: Dict[str, Union[str, float, int, dict]] = None,
components_to_quantize: Optional[List[str]] = None,
quant_mapping: Dict[str, Union[DiffQuantConfigMixin, "TransformersQuantConfigMixin"]] = None,
):
self.quant_backend = quant_backend
# Initialize kwargs to be {} to set to the defaults.
self.quant_kwargs = quant_kwargs or {}
self.components_to_quantize = components_to_quantize
self.quant_mapping = quant_mapping
self.post_init()
def post_init(self):
quant_mapping = self.quant_mapping
self.is_granular = True if quant_mapping is not None else False
self._validate_init_args()
def _validate_init_args(self):
if self.quant_backend and self.quant_mapping:
raise ValueError("Both `quant_backend` and `quant_mapping` cannot be specified at the same time.")
if not self.quant_mapping and not self.quant_backend:
raise ValueError("Must provide a `quant_backend` when not providing a `quant_mapping`.")
if not self.quant_kwargs and not self.quant_mapping:
raise ValueError("Both `quant_kwargs` and `quant_mapping` cannot be None.")
if self.quant_backend is not None:
self._validate_init_kwargs_in_backends()
if self.quant_mapping is not None:
self._validate_quant_mapping_args()
def _validate_init_kwargs_in_backends(self):
quant_backend = self.quant_backend
self._check_backend_availability(quant_backend)
quant_config_mapping_transformers, quant_config_mapping_diffusers = self._get_quant_config_list()
if quant_config_mapping_transformers is not None:
init_kwargs_transformers = inspect.signature(quant_config_mapping_transformers[quant_backend].__init__)
init_kwargs_transformers = {name for name in init_kwargs_transformers.parameters if name != "self"}
else:
init_kwargs_transformers = None
init_kwargs_diffusers = inspect.signature(quant_config_mapping_diffusers[quant_backend].__init__)
init_kwargs_diffusers = {name for name in init_kwargs_diffusers.parameters if name != "self"}
if init_kwargs_transformers != init_kwargs_diffusers:
raise ValueError(
"The signatures of the __init__ methods of the quantization config classes in `diffusers` and `transformers` don't match. "
f"Please provide a `quant_mapping` instead, in the {self.__class__.__name__} class. Refer to [the docs](https://huggingface.co/docs/diffusers/main/en/quantization/overview#pipeline-level-quantization) to learn more about how "
"this mapping would look like."
)
def _validate_quant_mapping_args(self):
quant_mapping = self.quant_mapping
transformers_map, diffusers_map = self._get_quant_config_list()
available_transformers = list(transformers_map.values()) if transformers_map else None
available_diffusers = list(diffusers_map.values())
for module_name, config in quant_mapping.items():
if any(isinstance(config, cfg) for cfg in available_diffusers):
continue
if available_transformers and any(isinstance(config, cfg) for cfg in available_transformers):
continue
if available_transformers:
raise ValueError(
f"Provided config for module_name={module_name} could not be found. "
f"Available diffusers configs: {available_diffusers}; "
f"Available transformers configs: {available_transformers}."
)
else:
raise ValueError(
f"Provided config for module_name={module_name} could not be found. "
f"Available diffusers configs: {available_diffusers}."
)
def _check_backend_availability(self, quant_backend: str):
quant_config_mapping_transformers, quant_config_mapping_diffusers = self._get_quant_config_list()
available_backends_transformers = (
list(quant_config_mapping_transformers.keys()) if quant_config_mapping_transformers else None
)
available_backends_diffusers = list(quant_config_mapping_diffusers.keys())
if (
available_backends_transformers and quant_backend not in available_backends_transformers
) or quant_backend not in quant_config_mapping_diffusers:
error_message = f"Provided quant_backend={quant_backend} was not found."
if available_backends_transformers:
error_message += f"\nAvailable ones (transformers): {available_backends_transformers}."
error_message += f"\nAvailable ones (diffusers): {available_backends_diffusers}."
raise ValueError(error_message)
def _resolve_quant_config(self, is_diffusers: bool = True, module_name: str = None):
quant_config_mapping_transformers, quant_config_mapping_diffusers = self._get_quant_config_list()
quant_mapping = self.quant_mapping
components_to_quantize = self.components_to_quantize
# Granular case
if self.is_granular and module_name in quant_mapping:
logger.debug(f"Initializing quantization config class for {module_name}.")
config = quant_mapping[module_name]
return config
# Global config case
else:
should_quantize = False
# Only quantize the modules requested for.
if components_to_quantize and module_name in components_to_quantize:
should_quantize = True
# No specification for `components_to_quantize` means all modules should be quantized.
elif not self.is_granular and not components_to_quantize:
should_quantize = True
if should_quantize:
logger.debug(f"Initializing quantization config class for {module_name}.")
mapping_to_use = quant_config_mapping_diffusers if is_diffusers else quant_config_mapping_transformers
quant_config_cls = mapping_to_use[self.quant_backend]
quant_kwargs = self.quant_kwargs
return quant_config_cls(**quant_kwargs)
# Fallback: no applicable configuration found.
return None
def _get_quant_config_list(self):
if is_transformers_available():
from transformers.quantizers.auto import (
AUTO_QUANTIZATION_CONFIG_MAPPING as quant_config_mapping_transformers,
)
else:
quant_config_mapping_transformers = None
from ..quantizers.auto import AUTO_QUANTIZATION_CONFIG_MAPPING as quant_config_mapping_diffusers
return quant_config_mapping_transformers, quant_config_mapping_diffusers