pipeline_utils.py

# 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

from .configuration_utils import ConfigMixin
from .dynamic_modules_utils import get_class_from_dynamic_module
from .utils import DIFFUSERS_CACHE, logging


INDEX_FILE = "diffusion_model.pt"


logger = logging.get_logger(__name__)


LOADABLE_CLASSES = {
    "diffusers": {
        "ModelMixin": ["save_pretrained", "from_pretrained"],
        "DiffusionPipeline": ["save_pretrained", "from_pretrained"],
        "GaussianDDPMScheduler": ["save_config", "from_config"],
        "ClassifierFreeGuidanceScheduler": ["save_config", "from_config"],
        "GlideDDIMScheduler": ["save_config", "from_config"],
        "DDIMScheduler": ["save_config", "from_config"],
    },
    "transformers": {
        "PreTrainedTokenizer": ["save_pretrained", "from_pretrained"],
        "PreTrainedTokenizerFast": ["save_pretrained", "from_pretrained"],
        "PreTrainedModel": ["save_pretrained", "from_pretrained"],
    },
}

ALL_IMPORTABLE_CLASSES = {}
for library in LOADABLE_CLASSES:
    ALL_IMPORTABLE_CLASSES.update(LOADABLE_CLASSES[library])


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]
            # if library is not in LOADABLE_CLASSES, then it is a custom module
            if library not in LOADABLE_CLASSES:
                library = module.__module__.split(".")[-1]

            # 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]}
        self.register(**register_dict)

    def save_pretrained(self, save_directory: Union[str, os.PathLike]):
        self.save_config(save_directory)

        model_index_dict = self.config
        model_index_dict.pop("_class_name")
        model_index_dict.pop("_diffusers_version")
        model_index_dict.pop("_module")

        for pipeline_component_name in model_index_dict.keys():
            sub_model = getattr(self, pipeline_component_name)
            model_cls = sub_model.__class__

            save_method_name = None
            # search for the model's base class in LOADABLE_CLASSES
            for library_name, library_classes in LOADABLE_CLASSES.items():
                library = importlib.import_module(library_name)
                for base_class, save_load_methods in library_classes.items():
                    class_candidate = getattr(library, base_class)
                    if issubclass(model_cls, class_candidate):
                        # if we found a suitable base class in LOADABLE_CLASSES then grab its save method
                        save_method_name = save_load_methods[0]
                        break
                if save_method_name is not None:
                    break

            save_method = getattr(sub_model, save_method_name)
            save_method(os.path.join(save_directory, pipeline_component_name))

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
        r"""
        Add docstrings
        """
        cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
        resume_download = kwargs.pop("resume_download", False)
        proxies = kwargs.pop("proxies", None)
        local_files_only = kwargs.pop("local_files_only", False)
        use_auth_token = kwargs.pop("use_auth_token", None)

        # 1. Download the checkpoints and configs
        # 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,
                cache_dir=cache_dir,
                resume_download=resume_download,
                proxies=proxies,
                local_files_only=local_files_only,
                use_auth_token=use_auth_token,
            )
        else:
            cached_folder = pretrained_model_name_or_path

        config_dict = cls.get_config_dict(cached_folder)

        # 2. Get class name and module candidates to load custom models
        module_candidate_name = config_dict["_module"]
        module_candidate = module_candidate_name + ".py"

        # 3. Load the pipeline class, if using custom module then load it from the hub
        # if we load from explicit class, let's use it
        if cls != DiffusionPipeline:
            pipeline_class = cls
        else:
            diffusers_module = importlib.import_module(cls.__module__.split(".")[0])
            pipeline_class = getattr(diffusers_module, config_dict["_class_name"])

            # (TODO - we should allow to load custom pipelines
            # else we need to load the correct module from the Hub
            # 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 = {}

        # 4. Load each module in the pipeline
        for name, (library_name, class_name) in init_dict.items():
            # if the model is not in diffusers or transformers, we need to load it from the hub
            # assumes that it's a subclass of ModelMixin
            if library_name == module_candidate_name:
                class_obj = get_class_from_dynamic_module(cached_folder, module_candidate, class_name, cached_folder)
                # since it's not from a library, we need to check class candidates for all importable classes
                importable_classes = ALL_IMPORTABLE_CLASSES
                class_candidates = {c: class_obj for c in ALL_IMPORTABLE_CLASSES.keys()}
            else:
                # else we just import it from the library.
                library = importlib.import_module(library_name)
                class_obj = getattr(library, class_name)
                importable_classes = LOADABLE_CLASSES[library_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)

            # check if the module is in a subdirectory
            if os.path.isdir(os.path.join(cached_folder, name)):
                loaded_sub_model = load_method(os.path.join(cached_folder, name))
            else:
                # else load from the root directory
                loaded_sub_model = load_method(cached_folder)

            init_kwargs[name] = loaded_sub_model  # UNet(...), # DiffusionSchedule(...)

        # 5. Instantiate the pipeline
        model = pipeline_class(**init_kwargs)
        return model