Adding mixin class for ease saving, uploading, downloading (as discus…

…sed in issue #9). (#11)

* work initiated

* start upload_to_hub

* add changes

* final-push

* i feel this is better.

* updated for Repositary class

* small updates

* fix mutiple calling

* small fix

* make style

* add everything

* minor fix

* minor fix

* done evrything

* small fix

* [doc] remove mention of TF support

* Fix typings (i think)

* We do NOT want to have a hard requirement on torch

* Fix flake8

* Fix CI

Co-authored-by: Julien Chaumond <[email protected]>

src/huggingface_hub/__init__.py

@@ -30,4 +30,5 @@
 )
 from .file_download import cached_download, hf_hub_url
 from .hf_api import HfApi, HfFolder
+from .hub_mixin import ModelHubMixin
 from .repository import Repository

src/huggingface_hub/hub_mixin.py

@@ -0,0 +1,246 @@
+import json
+import logging
+import os
+from typing import Dict, Optional
+
+import requests
+
+from .constants import CONFIG_NAME, PYTORCH_WEIGHTS_NAME
+from .file_download import cached_download, hf_hub_url, is_torch_available
+from .hf_api import HfApi, HfFolder
+from .repository import Repository
+
+
+if is_torch_available():
+    import torch
+
+
+logger = logging.getLogger(__name__)
+
+
+class ModelHubMixin(object):
+    def __init__(self, *args, **kwargs):
+        """
+        Mix this class with your torch-model class for ease process of saving & loading from huggingface-hub
+
+        Example::
+
+            >>> from huggingface_hub import ModelHubMixin
+
+            >>> class MyModel(nn.Module, ModelHubMixin):
+            ...    def __init__(self, **kwargs):
+            ...        super().__init__()
+            ...        self.config = kwargs.pop("config", None)
+            ...        self.layer = ...
+            ...    def forward(self, ...)
+            ...        return ...
+
+            >>> model = MyModel()
+            >>> model.save_pretrained("mymodel", push_to_hub=False) # Saving model weights in the directory
+            >>> model.push_to_hub("mymodel", "model-1") # Pushing model-weights to hf-hub
+
+            >>> # Downloading weights from hf-hub & model will be initialized from those weights
+            >>> model = MyModel.from_pretrained("username/mymodel@main")
+        """
+
+    def save_pretrained(
+        self,
+        save_directory: str,
+        config: Optional[dict] = None,
+        push_to_hub: bool = False,
+        **kwargs,
+    ):
+        """
+        Saving weights in local directory.
+
+        Parameters:
+            save_directory (:obj:`str`):
+                Specify directory in which you want to save weights.
+            config (:obj:`dict`, `optional`):
+                specify config (must be dict) incase you want to save it.
+            push_to_hub (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Set it to `True` in case you want to push your weights to huggingface_hub
+            model_id (:obj:`str`, `optional`, defaults to :obj:`save_directory`):
+                Repo name in huggingface_hub. If not specified, repo name will be same as `save_directory`
+            kwargs (:obj:`Dict`, `optional`):
+                kwargs will be passed to `push_to_hub`
+        """
+
+        os.makedirs(save_directory, exist_ok=True)
+
+        # saving config
+        if isinstance(config, dict):
+            path = os.path.join(save_directory, CONFIG_NAME)
+            with open(path, "w") as f:
+                json.dump(config, f)
+
+        # saving model weights
+        path = os.path.join(save_directory, PYTORCH_WEIGHTS_NAME)
+        self._save_pretrained(path)
+
+        if push_to_hub:
+            return self.push_to_hub(save_directory, **kwargs)
+
+    def _save_pretrained(self, path):
+        """
+        Overwrite this method in case you don't want to save complete model, rather some specific layers
+        """
+        model_to_save = self.module if hasattr(self, "module") else self
+        torch.save(model_to_save.state_dict(), path)
+
+    @classmethod
+    def from_pretrained(
+        cls,
+        pretrained_model_name_or_path: Optional[str],
+        strict: bool = True,
+        map_location: Optional[str] = "cpu",
+        force_download: bool = False,
+        resume_download: bool = False,
+        proxies: Dict = None,
+        use_auth_token: Optional[str] = None,
+        cache_dir: Optional[str] = None,
+        local_files_only: bool = False,
+        **model_kwargs,
+    ):
+        r"""
+        Instantiate a pretrained pytorch model from a pre-trained model configuration from huggingface-hub.
+        The model is set in evaluation mode by default using ``model.eval()`` (Dropout modules are deactivated). To
+        train the model, you should first set it back in training mode with ``model.train()``.
+
+        Parameters:
+            pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`, `optional`):
+                Can be either:
+                    - A string, the `model id` of a pretrained model hosted inside a model repo on huggingface.co.
+                      Valid model ids can be located at the root-level, like ``bert-base-uncased``, or namespaced under
+                      a user or organization name, like ``dbmdz/bert-base-german-cased``.
+                    - You can add `revision` by appending `@` at the end of model_id simply like this: ``dbmdz/bert-base-german-cased@main``
+                      Revision is the specific model version to use. It can be a branch name, a tag name, or a commit id,
+                      since we use a git-based system for storing models and other artifacts on huggingface.co, so ``revision`` can be any identifier allowed by git.
+                    - A path to a `directory` containing model weights saved using
+                      :func:`~transformers.PreTrainedModel.save_pretrained`, e.g., ``./my_model_directory/``.
+                    - :obj:`None` if you are both providing the configuration and state dictionary (resp. with keyword
+                      arguments ``config`` and ``state_dict``).
+            cache_dir (:obj:`Union[str, os.PathLike]`, `optional`):
+                Path to a directory in which a downloaded pretrained model configuration should be cached if the
+                standard cache should not be used.
+            force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Whether or not to force the (re-)download of the model weights and configuration files, overriding the
+                cached versions if they exist.
+            resume_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Whether or not to delete incompletely received files. Will attempt to resume the download if such a
+                file exists.
+            proxies (:obj:`Dict[str, str], `optional`):
+                A dictionary of proxy servers to use by protocol or endpoint, e.g., :obj:`{'http': 'foo.bar:3128',
+                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
+            local_files_only(:obj:`bool`, `optional`, defaults to :obj:`False`):
+                Whether or not to only look at local files (i.e., do not try to download the model).
+            use_auth_token (:obj:`str` or `bool`, `optional`):
+                The token to use as HTTP bearer authorization for remote files. If :obj:`True`, will use the token
+                generated when running :obj:`transformers-cli login` (stored in :obj:`~/.huggingface`).
+            model_kwargs (:obj:`Dict`, `optional`)::
+                model_kwargs will be passed to the model during initialization
+        .. note::
+            Passing :obj:`use_auth_token=True` is required when you want to use a private model.
+        """
+
+        model_id = pretrained_model_name_or_path
+        map_location = torch.device(map_location)
+
+        revision = None
+        if len(model_id.split("@")) == 2:
+            model_id, revision = model_id.split("@")
+
+        if model_id in os.listdir() and CONFIG_NAME in os.listdir(model_id):
+            config_file = os.path.join(model_id, CONFIG_NAME)
+        else:
+            try:
+                config_url = hf_hub_url(
+                    model_id, filename=CONFIG_NAME, revision=revision
+                )
+                config_file = cached_download(
+                    config_url,
+                    cache_dir=cache_dir,
+                    force_download=force_download,
+                    proxies=proxies,
+                    resume_download=resume_download,
+                    local_files_only=local_files_only,
+                    use_auth_token=use_auth_token,
+                )
+            except requests.exceptions.RequestException:
+                logger.warning("config.json NOT FOUND in HuggingFace Hub")
+                config_file = None
+
+        if model_id in os.listdir():
+            print("LOADING weights from local directory")
+            model_file = os.path.join(model_id, PYTORCH_WEIGHTS_NAME)
+        else:
+            model_url = hf_hub_url(
+                model_id, filename=PYTORCH_WEIGHTS_NAME, revision=revision
+            )
+            model_file = cached_download(
+                model_url,
+                cache_dir=cache_dir,
+                force_download=force_download,
+                proxies=proxies,
+                resume_download=resume_download,
+                local_files_only=local_files_only,
+                use_auth_token=use_auth_token,
+            )
+
+        if config_file is not None:
+            with open(config_file, "r", encoding="utf-8") as f:
+                config = json.load(f)
+            model_kwargs.update({"config": config})
+
+        model = cls(**model_kwargs)
+
+        state_dict = torch.load(model_file, map_location=map_location)
+        model.load_state_dict(state_dict, strict=strict)
+        model.eval()
+
+        return model
+
+    @staticmethod
+    def push_to_hub(
+        save_directory: Optional[str],
+        model_id: Optional[str] = None,
+        repo_url: Optional[str] = None,
+        commit_message: Optional[str] = "add model",
+        organization: Optional[str] = None,
+        private: bool = None,
+    ) -> str:
+        """
+        Parameters:
+            save_directory (:obj:`Union[str, os.PathLike]`):
+                Directory having model weights & config.
+            model_id (:obj:`str`, `optional`, defaults to :obj:`save_directory`):
+                Repo name in huggingface_hub. If not specified, repo name will be same as `save_directory`
+            repo_url (:obj:`str`, `optional`):
+                Specify this in case you want to push to existing repo in hub.
+            organization (:obj:`str`, `optional`):
+                Organization in which you want to push your model.
+            private (:obj:`bool`, `optional`):
+                private: Whether the model repo should be private (requires a paid huggingface.co account)
+            commit_message (:obj:`str`, `optional`, defaults to :obj:`add model`):
+                Message to commit while pushing
+
+        Returns:
+            url to commit on remote repo.
+        """
+        if model_id is None:
+            model_id = save_directory
+
+        token = HfFolder.get_token()
+        if repo_url is None:
+            repo_url = HfApi().create_repo(
+                token,
+                model_id,
+                organization=organization,
+                private=private,
+                repo_type=None,
+                exist_ok=True,
+            )
+
+        repo = Repository(save_directory, clone_from=repo_url, use_auth_token=token)
+
+        return repo.push_to_hub(commit_message=commit_message)

src/huggingface_hub/repository.py

@@ -159,14 +159,26 @@ def clone_from(self, repo_url: str, use_auth_token: Union[bool, str, None] = Non
                     encoding="utf-8",
                     cwd=self.local_dir,
                 )
-                subprocess.run(
-                    ["git", "remote", "add", "origin", repo_url],
+
+                output = subprocess.run(
+                    "git remote -v".split(),
                     stderr=subprocess.PIPE,
                     stdout=subprocess.PIPE,
                     check=True,
                     encoding="utf-8",
                     cwd=self.local_dir,
                 )
+
+                if "origin" not in output.stdout.split():
+                    subprocess.run(
+                        ["git", "remote", "add", "origin", repo_url],
+                        stderr=subprocess.PIPE,
+                        stdout=subprocess.PIPE,
+                        check=True,
+                        encoding="utf-8",
+                        cwd=self.local_dir,
+                    )
+
                 subprocess.run(
                     "git fetch".split(),
                     stderr=subprocess.PIPE,
@@ -183,15 +195,27 @@ def clone_from(self, repo_url: str, use_auth_token: Union[bool, str, None] = Non
                     check=True,
                     cwd=self.local_dir,
                 )
-                # TODO(check if we really want the --force flag)
-                subprocess.run(
-                    "git checkout origin/main -ft".split(),
+
+                output = subprocess.run(
+                    "git branch".split(),
                     stderr=subprocess.PIPE,
                     stdout=subprocess.PIPE,
-                    encoding="utf-8",
                     check=True,
+                    encoding="utf-8",
                     cwd=self.local_dir,
                 )
+
+                if "main" not in output.stdout.split():
+                    # TODO(check if we really want the --force flag)
+                    subprocess.run(
+                        "git checkout origin/main -ft".split(),
+                        stderr=subprocess.PIPE,
+                        stdout=subprocess.PIPE,
+                        encoding="utf-8",
+                        check=True,
+                        cwd=self.local_dir,
+                    )
+
         except subprocess.CalledProcessError as exc:
             raise EnvironmentError(exc.stderr)
 

tests/test_hubmixin.py

@@ -0,0 +1,66 @@
+import unittest
+
+from huggingface_hub.file_download import is_torch_available
+from huggingface_hub.hub_mixin import ModelHubMixin
+
+
+if is_torch_available():
+    import torch.nn as nn
+
+
+HUGGINGFACE_ID = "vasudevgupta"
+DUMMY_REPO_NAME = "dummy"
+
+
+def require_torch(test_case):
+    """
+    Decorator marking a test that requires PyTorch.
+
+    These tests are skipped when PyTorch isn't installed.
+
+    """
+    if not is_torch_available():
+        return unittest.skip("test requires PyTorch")(test_case)
+    else:
+        return test_case
+
+
+@require_torch
+class DummyModel(ModelHubMixin):
+    def __init__(self, **kwargs):
+        super().__init__()
+        self.config = kwargs.pop("config", None)
+        self.l1 = nn.Linear(2, 2)
+
+    def forward(self, x):
+        return self.l1(x)
+
+
+@require_torch
+class DummyModelTest(unittest.TestCase):
+    def test_save_pretrained(self):
+        model = DummyModel()
+        model.save_pretrained(DUMMY_REPO_NAME)
+        model.save_pretrained(
+            DUMMY_REPO_NAME, config={"num": 12, "act": "gelu"}, push_to_hub=True
+        )
+        model.save_pretrained(
+            DUMMY_REPO_NAME, config={"num": 24, "act": "relu"}, push_to_hub=True
+        )
+        model.save_pretrained(
+            "dummy-wts", config=None, push_to_hub=True, model_id=DUMMY_REPO_NAME
+        )
+
+    def test_from_pretrained(self):
+        model = DummyModel()
+        model.save_pretrained(
+            DUMMY_REPO_NAME, config={"num": 7, "act": "gelu_fast"}, push_to_hub=True
+        )
+
+        model = DummyModel.from_pretrained(f"{HUGGINGFACE_ID}/{DUMMY_REPO_NAME}@main")
+        self.assertTrue(model.config == {"num": 7, "act": "gelu_fast"})
+
+    def test_push_to_hub(self):
+        model = DummyModel()
+        model.save_pretrained("dummy-wts", push_to_hub=False)
+        model.push_to_hub("dummy-wts", model_id=DUMMY_REPO_NAME)