Starting integration

Signed-off-by: katielink <link.katie.e@gmail.com>

GANDLF/utils/modelio.py

@@ -1,7 +1,7 @@
 import hashlib
 import os
 import subprocess
-from typing import Any, Dict
+from typing import Any, Dict, Optional
 
 import torch
 
@@ -136,6 +136,7 @@ def save_model(
     params: Dict[str, Any],
     path: str,
     onnx_export: bool = True,
+    hf_hub_repo_id: Optional[str] = None,
 ):
     """
     Save the model dictionary to a file.
@@ -146,6 +147,7 @@ def save_model(
         params (dict): The parameter dictionary.
         path (str): The path to save the model dictionary to.
         onnx_export (bool): Whether to export to ONNX and OpenVINO.
+        hf_hub_repo_id (str): The Hugging Face Hub repo id to push to. Defaults to None (will not push to HF Hub).
     """
     model_dict["timestamp"] = get_unique_timestamp()
     model_dict["timestamp_hash"] = hashlib.sha256(
@@ -168,6 +170,9 @@ def save_model(
     # post-training optimization
     optimize_and_save_model(model, params, path, onnx_export=onnx_export)
 
+    if hf_hub_repo_id is not None:
+        push_model_to_hf_hub(model_path=path, repo_path=path, repo_id=hf_hub_repo_id)
+        #TODO: also push optimized models?
 
 def load_model(
     path: str, device: torch.device, full_sanity_check: bool = True
@@ -241,3 +246,115 @@ def load_ov_model(path: str, device: str = "CPU"):
     output_layer = compiled_model.outputs
 
     return compiled_model, input_layer, output_layer
+
+
+def load_model_from_hf_hub(
+        repo_id: str,
+        model_filename: str,
+        revision: str = None,
+        local_dir: str = None,
+        device: str = "CPU",
+    ) -> Dict[str, Any]:
+    """
+    Download and load model from Hugging Face Hub. If the repo is private, credentials must be set beforehand.
+
+    Args:
+        repo_id (str): The Hugging Face Hub repo id.
+        model_file_path (str): The model filename in the repo.
+        revision (Optional[str]): The revision of the model to load. Defaults to
+        the latest revision.
+        local_dir (Optional[str]): The local directory to download the model to. Defaults to None.
+        device (str): The device to run inference, can be "CPU", "GPU" or "MULTI:CPU,GPU". Default is "CPU".
+
+    Returns:
+        path: Path to model file. Can be used in `load_model`.
+    """
+    try:
+        import huggingface_hub
+    except ImportError:
+        print("WARNING: huggingface_hub is not present.")
+
+    local_model_path = huggingface_hub.hf_hub_download(
+        repo_id=repo_id, 
+        filename=model_filename,
+        revision=revision,
+        local_dir=local_dir
+    )
+
+    return load_model(local_model_path, device)
+
+
+def upload_model_repo_to_hf_hub(
+        repo_id: str,
+        local_path: str,
+        path_in_repo: str,
+        model_card_path: Optional[str] = None,
+        private: Optional[bool] = False,
+        upload_onnx: Optional[bool] = False,
+        upload_ov: Optional[bool] = False
+    ) -> str:
+    """
+    Upload model to repo on Hugging Face Hub. Must be logged in to Hugging Face Hub.
+
+    Args:
+        repo_id (str): The Hugging Face Hub repo id to upload to. Will create if does not already exist.
+        local_path (str): The path to the model to upload.
+        path_in_repo (str): The path to the model in the repo.
+        model_card_path (Optional[str]): The path to the model card to upload. Defaults to None.
+        private (Optional[bool]): Whether to make the repo private. Defaults to False.
+        upload_onnx (Optional[bool]): Whether to upload the ONNX model. Defaults to False.
+        upload_ov (Optional[bool]): Whether to upload the OpenVINO model. Defaults to False.
+    
+    Returns:
+        str: The revision of the model.
+    """
+    try:
+        import huggingface_hub
+    except ImportError:
+        print("WARNING: huggingface_hub is not present.")
+
+    huggingface_hub.create_repo(repo_id=repo_id, private=private)
+
+    api = huggingface_hub.HfApi()
+
+    api.upload_file(path_or_file_obj=local_path, path_in_repo=path_in_repo, repo_id=repo_id)
+
+    if model_card_path is not None:
+        #TODO: upload model card
+        pass
+    else:
+        #TODO: create new model card
+        pass
+
+    #TODO: upload optimized models?
+
+
+def push_model_to_hf_hub(
+    repo_id: str,
+    local_path: str,
+    path_in_repo: str
+) -> str:
+    """
+    Push model to repo on Hugging Face Hub. Must be logged in to Hugging Face Hub.
+
+    Args:
+        repo_id (str): The Hugging Face Hub repo id to push to.
+        model_path (str): The local path to the model to push.
+        path_in_repo (str): The path to the model in the repo.
+
+    Returns:
+        str: The URL to visualize the uploaded file on the hub.
+    """
+
+    try:
+        import huggingface_hub
+    except ImportError:
+        print("WARNING: huggingface_hub is not present.")
+
+    api = huggingface_hub.HfApi()
+
+    return api.upload_file(
+        path_or_file_obj=local_path,
+        path_in_repo=path_in_repo,
+        repo_id=repo_id
+    )