make from hub import work
This commit is contained in:
patil-suraj
parent
1a6196e8a2
commit
ce5666211e
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@ -23,7 +23,7 @@ class DDPM(DiffusionPipeline):
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modeling_file = "modeling_ddpm.py"
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def __init__(self, unet, noise_scheduler, vqvae):
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def __init__(self, unet, noise_scheduler):
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super().__init__()
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self.register_modules(unet=unet, noise_scheduler=noise_scheduler)
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@ -0,0 +1,339 @@
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# coding=utf-8
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# Copyright 2021 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Utilities to dynamically load objects from the Hub."""
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import importlib
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import os
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import re
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import shutil
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import sys
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from pathlib import Path
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from typing import Dict, Optional, Union
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from huggingface_hub import HfFolder, model_info
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from transformers.utils import (
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HF_MODULES_CACHE,
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TRANSFORMERS_DYNAMIC_MODULE_NAME,
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cached_path,
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hf_bucket_url,
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is_offline_mode,
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logging,
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)
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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def init_hf_modules():
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"""
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Creates the cache directory for modules with an init, and adds it to the Python path.
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"""
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# This function has already been executed if HF_MODULES_CACHE already is in the Python path.
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if HF_MODULES_CACHE in sys.path:
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return
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sys.path.append(HF_MODULES_CACHE)
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os.makedirs(HF_MODULES_CACHE, exist_ok=True)
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init_path = Path(HF_MODULES_CACHE) / "__init__.py"
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if not init_path.exists():
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init_path.touch()
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def create_dynamic_module(name: Union[str, os.PathLike]):
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"""
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Creates a dynamic module in the cache directory for modules.
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"""
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init_hf_modules()
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dynamic_module_path = Path(HF_MODULES_CACHE) / name
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# If the parent module does not exist yet, recursively create it.
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if not dynamic_module_path.parent.exists():
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create_dynamic_module(dynamic_module_path.parent)
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os.makedirs(dynamic_module_path, exist_ok=True)
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init_path = dynamic_module_path / "__init__.py"
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if not init_path.exists():
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init_path.touch()
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def get_relative_imports(module_file):
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"""
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Get the list of modules that are relatively imported in a module file.
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Args:
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module_file (`str` or `os.PathLike`): The module file to inspect.
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"""
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with open(module_file, "r", encoding="utf-8") as f:
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content = f.read()
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# Imports of the form `import .xxx`
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relative_imports = re.findall("^\s*import\s+\.(\S+)\s*$", content, flags=re.MULTILINE)
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# Imports of the form `from .xxx import yyy`
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relative_imports += re.findall("^\s*from\s+\.(\S+)\s+import", content, flags=re.MULTILINE)
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# Unique-ify
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return list(set(relative_imports))
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def get_relative_import_files(module_file):
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"""
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Get the list of all files that are needed for a given module. Note that this function recurses through the relative
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imports (if a imports b and b imports c, it will return module files for b and c).
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Args:
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module_file (`str` or `os.PathLike`): The module file to inspect.
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"""
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no_change = False
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files_to_check = [module_file]
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all_relative_imports = []
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# Let's recurse through all relative imports
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while not no_change:
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new_imports = []
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for f in files_to_check:
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new_imports.extend(get_relative_imports(f))
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module_path = Path(module_file).parent
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new_import_files = [str(module_path / m) for m in new_imports]
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new_import_files = [f for f in new_import_files if f not in all_relative_imports]
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files_to_check = [f"{f}.py" for f in new_import_files]
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no_change = len(new_import_files) == 0
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all_relative_imports.extend(files_to_check)
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return all_relative_imports
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def check_imports(filename):
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"""
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Check if the current Python environment contains all the libraries that are imported in a file.
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"""
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with open(filename, "r", encoding="utf-8") as f:
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content = f.read()
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# Imports of the form `import xxx`
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imports = re.findall("^\s*import\s+(\S+)\s*$", content, flags=re.MULTILINE)
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# Imports of the form `from xxx import yyy`
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imports += re.findall("^\s*from\s+(\S+)\s+import", content, flags=re.MULTILINE)
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# Only keep the top-level module
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imports = [imp.split(".")[0] for imp in imports if not imp.startswith(".")]
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# Unique-ify and test we got them all
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imports = list(set(imports))
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missing_packages = []
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for imp in imports:
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try:
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importlib.import_module(imp)
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except ImportError:
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missing_packages.append(imp)
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if len(missing_packages) > 0:
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raise ImportError(
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"This modeling file requires the following packages that were not found in your environment: "
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f"{', '.join(missing_packages)}. Run `pip install {' '.join(missing_packages)}`"
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)
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return get_relative_imports(filename)
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def get_class_in_module(class_name, module_path):
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"""
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Import a module on the cache directory for modules and extract a class from it.
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"""
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module_path = module_path.replace(os.path.sep, ".")
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module = importlib.import_module(module_path)
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return getattr(module, class_name)
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def get_cached_module_file(
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pretrained_model_name_or_path: Union[str, os.PathLike],
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module_file: str,
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cache_dir: Optional[Union[str, os.PathLike]] = None,
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force_download: bool = False,
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resume_download: bool = False,
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proxies: Optional[Dict[str, str]] = None,
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use_auth_token: Optional[Union[bool, str]] = None,
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revision: Optional[str] = None,
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local_files_only: bool = False,
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):
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"""
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Prepares Downloads a module from a local folder or a distant repo and returns its path inside the cached
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Transformers module.
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Args:
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pretrained_model_name_or_path (`str` or `os.PathLike`):
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This can be either:
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- a string, the *model id* of a pretrained model configuration hosted inside a model repo on
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huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced
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under a user or organization name, like `dbmdz/bert-base-german-cased`.
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- a path to a *directory* containing a configuration file saved using the
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[`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.
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module_file (`str`):
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The name of the module file containing the class to look for.
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cache_dir (`str` or `os.PathLike`, *optional*):
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Path to a directory in which a downloaded pretrained model configuration should be cached if the standard
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cache should not be used.
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force_download (`bool`, *optional*, defaults to `False`):
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Whether or not to force to (re-)download the configuration files and override the cached versions if they
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exist.
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resume_download (`bool`, *optional*, defaults to `False`):
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Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
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proxies (`Dict[str, str]`, *optional*):
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A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
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'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
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use_auth_token (`str` or *bool*, *optional*):
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The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
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when running `transformers-cli login` (stored in `~/.huggingface`).
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revision (`str`, *optional*, defaults to `"main"`):
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
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identifier allowed by git.
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local_files_only (`bool`, *optional*, defaults to `False`):
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If `True`, will only try to load the tokenizer configuration from local files.
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<Tip>
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Passing `use_auth_token=True` is required when you want to use a private model.
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</Tip>
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Returns:
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`str`: The path to the module inside the cache.
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"""
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# Download and cache module_file from the repo `pretrained_model_name_or_path` of grab it if it's a local file.
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pretrained_model_name_or_path = str(pretrained_model_name_or_path)
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module_file_or_url = os.path.join(pretrained_model_name_or_path, module_file)
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submodule = "local"
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try:
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# Load from URL or cache if already cached
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resolved_module_file = cached_path(
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module_file_or_url,
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cache_dir=cache_dir,
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force_download=force_download,
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proxies=proxies,
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resume_download=resume_download,
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local_files_only=local_files_only,
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use_auth_token=use_auth_token,
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)
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except EnvironmentError:
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logger.error(f"Could not locate the {module_file} inside {pretrained_model_name_or_path}.")
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raise
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# Check we have all the requirements in our environment
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modules_needed = check_imports(resolved_module_file)
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# Now we move the module inside our cached dynamic modules.
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full_submodule = TRANSFORMERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
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create_dynamic_module(full_submodule)
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submodule_path = Path(HF_MODULES_CACHE) / full_submodule
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# We always copy local files (we could hash the file to see if there was a change, and give them the name of
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# that hash, to only copy when there is a modification but it seems overkill for now).
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# The only reason we do the copy is to avoid putting too many folders in sys.path.
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shutil.copy(resolved_module_file, submodule_path / module_file)
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for module_needed in modules_needed:
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module_needed = f"{module_needed}.py"
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shutil.copy(os.path.join(pretrained_model_name_or_path, module_needed), submodule_path / module_needed)
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return os.path.join(full_submodule, module_file)
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def get_class_from_dynamic_module(
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pretrained_model_name_or_path: Union[str, os.PathLike],
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module_file: str,
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class_name: str,
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cache_dir: Optional[Union[str, os.PathLike]] = None,
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force_download: bool = False,
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resume_download: bool = False,
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proxies: Optional[Dict[str, str]] = None,
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use_auth_token: Optional[Union[bool, str]] = None,
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revision: Optional[str] = None,
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local_files_only: bool = False,
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**kwargs,
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):
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"""
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Extracts a class from a module file, present in the local folder or repository of a model.
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<Tip warning={true}>
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Calling this function will execute the code in the module file found locally or downloaded from the Hub. It should
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therefore only be called on trusted repos.
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</Tip>
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Args:
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pretrained_model_name_or_path (`str` or `os.PathLike`):
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This can be either:
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- a string, the *model id* of a pretrained model configuration hosted inside a model repo on
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huggingface.co. Valid model ids can be located at the root-level, like `bert-base-uncased`, or namespaced
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under a user or organization name, like `dbmdz/bert-base-german-cased`.
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- a path to a *directory* containing a configuration file saved using the
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[`~PreTrainedTokenizer.save_pretrained`] method, e.g., `./my_model_directory/`.
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module_file (`str`):
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The name of the module file containing the class to look for.
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class_name (`str`):
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The name of the class to import in the module.
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cache_dir (`str` or `os.PathLike`, *optional*):
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Path to a directory in which a downloaded pretrained model configuration should be cached if the standard
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cache should not be used.
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force_download (`bool`, *optional*, defaults to `False`):
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Whether or not to force to (re-)download the configuration files and override the cached versions if they
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exist.
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resume_download (`bool`, *optional*, defaults to `False`):
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Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
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proxies (`Dict[str, str]`, *optional*):
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A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
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'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
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use_auth_token (`str` or `bool`, *optional*):
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The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
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when running `transformers-cli login` (stored in `~/.huggingface`).
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revision (`str`, *optional*, defaults to `"main"`):
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The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
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git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
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identifier allowed by git.
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local_files_only (`bool`, *optional*, defaults to `False`):
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If `True`, will only try to load the tokenizer configuration from local files.
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<Tip>
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Passing `use_auth_token=True` is required when you want to use a private model.
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</Tip>
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Returns:
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`type`: The class, dynamically imported from the module.
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Examples:
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```python
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# Download module `modeling.py` from huggingface.co and cache then extract the class `MyBertModel` from this
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# module.
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cls = get_class_from_dynamic_module("sgugger/my-bert-model", "modeling.py", "MyBertModel")
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```"""
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# And lastly we get the class inside our newly created module
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final_module = get_cached_module_file(
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pretrained_model_name_or_path,
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module_file,
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cache_dir=cache_dir,
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force_download=force_download,
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resume_download=resume_download,
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proxies=proxies,
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use_auth_token=use_auth_token,
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revision=revision,
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local_files_only=local_files_only,
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)
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return get_class_in_module(class_name, final_module.replace(".py", ""))
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@ -16,6 +16,7 @@
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import importlib
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import os
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from pathlib import Path
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from typing import Optional, Union
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from huggingface_hub import snapshot_download
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@ -23,6 +24,7 @@ from huggingface_hub import snapshot_download
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from transformers.utils import logging
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from .configuration_utils import ConfigMixin
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from .dynamic_modules_utils import get_class_from_dynamic_module
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INDEX_FILE = "diffusion_model.pt"
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@ -91,12 +93,10 @@ class DiffusionPipeline(ConfigMixin):
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def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs):
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# use snapshot download here to get it working from from_pretrained
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cached_folder = snapshot_download(pretrained_model_name_or_path)
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config_dict, pipeline_kwargs = cls.get_config_dict(cached_folder)
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_, config_dict = cls.get_config_dict(cached_folder)
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module = pipeline_kwargs["_module"]
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# TODO(Suraj) - make from hub import work
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# Make `ddpm = DiffusionPipeline.from_pretrained("fusing/ddpm-lsun-bedroom-pipe")` work
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# Add Sylvains code from transformers
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module = config_dict.pop("_module", None)
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class_name_ = config_dict.pop("_class_name")
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init_kwargs = {}
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@ -122,5 +122,6 @@ class DiffusionPipeline(ConfigMixin):
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init_kwargs[name] = loaded_sub_model # UNet(...), # DiffusionSchedule(...)
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model = cls(**init_kwargs)
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class_obj = get_class_from_dynamic_module(cached_folder, module, class_name_, cached_folder)
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model = class_obj(**init_kwargs)
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return model
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