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pipeline_utils.py
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146 lines (108 loc) · 5.1 KB
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# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
# Copyright (c) 2022, NVIDIA CORPORATION. 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
import os
from typing import Optional, Union
from huggingface_hub import snapshot_download
# CHANGE to diffusers.utils
from transformers.utils import logging
from .configuration_utils import ConfigMixin
from .dynamic_modules_utils import get_class_from_dynamic_module
INDEX_FILE = "diffusion_model.pt"
logger = logging.get_logger(__name__)
LOADABLE_CLASSES = {
"diffusers": {
"ModelMixin": ["save_pretrained", "from_pretrained"],
"GaussianDDPMScheduler": ["save_config", "from_config"],
},
"transformers": {
"ModelMixin": ["save_pretrained", "from_pretrained"],
},
}
class DiffusionPipeline(ConfigMixin):
config_name = "model_index.json"
def register_modules(self, **kwargs):
for name, module in kwargs.items():
# retrive library
library = module.__module__.split(".")[0]
# retrive class_name
class_name = module.__class__.__name__
register_dict = {name: (library, class_name)}
# save model index config
self.register(**register_dict)
# set models
setattr(self, name, module)
register_dict = {"_module" : self.__module__.split(".")[-1] + ".py"}
self.register(**register_dict)
def save_pretrained(self, save_directory: Union[str, os.PathLike]):
self.save_config(save_directory)
model_index_dict = self._dict_to_save
model_index_dict.pop("_class_name")
model_index_dict.pop("_module")
for name, (library_name, class_name) in self._dict_to_save.items():
importable_classes = LOADABLE_CLASSES[library_name]
# TODO: Suraj
if library_name == self.__module__:
library_name = self
library = importlib.import_module(library_name)
class_obj = getattr(library, class_name)
class_candidates = {c: getattr(library, c) for c in importable_classes.keys()}
save_method_name = None
for class_name, class_candidate in class_candidates.items():
if issubclass(class_obj, class_candidate):
save_method_name = importable_classes[class_name][0]
save_method = getattr(getattr(self, name), save_method_name)
save_method(os.path.join(save_directory, name))
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
# use snapshot download here to get it working from from_pretrained
if not os.path.isdir(pretrained_model_name_or_path):
cached_folder = snapshot_download(pretrained_model_name_or_path)
else:
cached_folder = pretrained_model_name_or_path
config_dict = cls.get_config_dict(cached_folder)
module_candidate = config_dict["_module"]
# if we load from explicit class, let's use it
if cls != DiffusionPipeline:
pipeline_class = cls
else:
# else we need to load the correct module from the Hub
class_name_ = config_dict["_class_name"]
module = module_candidate
pipeline_class = get_class_from_dynamic_module(cached_folder, module, class_name_, cached_folder)
init_dict, _ = pipeline_class.extract_init_dict(config_dict, **kwargs)
init_kwargs = {}
for name, (library_name, class_name) in init_dict.items():
importable_classes = LOADABLE_CLASSES[library_name]
if library_name == module_candidate:
# TODO(Suraj)
# for vq
pass
library = importlib.import_module(library_name)
class_obj = getattr(library, class_name)
class_candidates = {c: getattr(library, c) for c in importable_classes.keys()}
load_method_name = None
for class_name, class_candidate in class_candidates.items():
if issubclass(class_obj, class_candidate):
load_method_name = importable_classes[class_name][1]
load_method = getattr(class_obj, load_method_name)
if os.path.isdir(os.path.join(cached_folder, name)):
loaded_sub_model = load_method(os.path.join(cached_folder, name))
else:
loaded_sub_model = load_method(cached_folder)
init_kwargs[name] = loaded_sub_model # UNet(...), # DiffusionSchedule(...)
model = pipeline_class(**init_kwargs)
return model