Revert "[Feature] multi-source download"

fastdeploy/engine/args_utils.py

@@ -46,10 +46,6 @@ class EngineArgs:
     """
     The name or path of the model to be used.
     """
-    revision: Optional[str] = "master"
-    """
-    The revision for downloading models.
-    """
     model_config_name: Optional[str] = "config.json"
     """
     The name of the model configuration file.
@@ -344,12 +340,6 @@ def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
             default=EngineArgs.model,
             help="Model name or path to be used.",
         )
-        model_group.add_argument(
-            "--revision",
-            type=nullable_str,
-            default=EngineArgs.revision,
-            help="Revision for downloading models",
-        )
         model_group.add_argument(
             "--model-config-name",
             type=nullable_str,

fastdeploy/entrypoints/llm.py

@@ -66,11 +66,10 @@ class LLM:
     def __init__(
         self,
         model: str,
-        revision: Optional[str] = "master",
         tokenizer: Optional[str] = None,
         **kwargs,
     ):
-        model = retrive_model_from_server(model, revision)
+        model = retrive_model_from_server(model)
         engine_args = EngineArgs(
             model=model,
             tokenizer=tokenizer,

fastdeploy/entrypoints/openai/api_server.py

@@ -62,7 +62,7 @@
 parser.add_argument("--controller-port", default=-1, type=int, help="port for controller server")
 parser = EngineArgs.add_cli_args(parser)
 args = parser.parse_args()
-args.model = retrive_model_from_server(args.model, args.revision)
+args.model = retrive_model_from_server(args.model)
 
 llm_engine = None
 

fastdeploy/envs.py

@@ -30,8 +30,6 @@
     "FD_DEBUG": lambda: os.getenv("FD_DEBUG", "0"),
     # Number of days to keep fastdeploy logs.
     "FD_LOG_BACKUP_COUNT": lambda: os.getenv("FD_LOG_BACKUP_COUNT", "7"),
-    # Model download source, can set "AISTUDIO", "MODELSCOPE" or "HUGGINGFACE".
-    "FD_MODEL_SOURCE": lambda: os.getenv("FD_MODEL_SOURCE", "MODELSCOPE"),
     # Model download cache directory.
     "FD_MODEL_CACHE": lambda: os.getenv("FD_MODEL_CACHE", None),
     # Maximum number of stop sequences.

fastdeploy/utils.py

@@ -31,9 +31,7 @@
 
 import requests
 import yaml
-from aistudio_sdk.snapshot_download import snapshot_download as aistudio_download
-from huggingface_hub._snapshot_download import snapshot_download as huggingface_download
-from modelscope.hub.snapshot_download import snapshot_download as modelscope_download
+from aistudio_sdk.snapshot_download import snapshot_download
 from tqdm import tqdm
 from typing_extensions import TypeIs, assert_never
 
@@ -496,53 +494,21 @@ def none_or_str(value):
 
 def retrive_model_from_server(model_name_or_path, revision="master"):
     """
-    Download pretrained model from MODELSCOPE, AIStudio or HUGGINGFACE automatically
+    Download pretrained model from AIStudio automatically
     """
     if os.path.exists(model_name_or_path):
         return model_name_or_path
-    model_source = envs.FD_MODEL_SOURCE
-    local_path = envs.FD_MODEL_CACHE
-    repo_id = model_name_or_path
-    if model_source == "MODELSCOPE":
-        try:
-            if repo_id.lower().strip().startswith("baidu"):
-                repo_id = "PaddlePaddle" + repo_id.strip()[5:]
-            if local_path is None:
-                local_path = f'{os.getenv("HOME")}'
-            local_path = f"{local_path}/{repo_id}/{revision}"
-            modelscope_download(repo_id=repo_id, revision=revision, local_dir=local_path)
-            model_name_or_path = local_path
-        except Exception:
-            raise Exception(f"The setting model_name_or_path:{model_name_or_path} is not exist.")
-    elif model_source == "AISTUDIO":
-        try:
-            if repo_id.lower().strip().startswith("baidu"):
-                repo_id = "PaddlePaddle" + repo_id.strip()[5:]
-            if local_path is None:
-                local_path = f'{os.getenv("HOME")}'
-            local_path = f"{local_path}/{repo_id}/{revision}"
-            aistudio_download(repo_id=repo_id, revision=revision, local_dir=local_path)
-            model_name_or_path = local_path
-        except Exception:
-            raise Exception(f"The setting model_name_or_path:{model_name_or_path} is not exist.")
-    elif model_source == "HUGGINGFACE":
-        try:
-            if revision == "master":
-                revision = "main"
-            repo_id = model_name_or_path
-            if repo_id.lower().strip().startswith("PaddlePaddle"):
-                repo_id = "baidu" + repo_id.strip()[12:]
-            if local_path is None:
-                local_path = f'{os.getenv("HOME")}'
-            local_path = f"{local_path}/{repo_id}/{revision}"
-            huggingface_download(repo_id=repo_id, revision=revision, local_dir=local_path)
-            model_name_or_path = local_path
-        except Exception:
-            raise Exception(f"The setting model_name_or_path:{model_name_or_path} is not exist.")
-    else:
-        raise ValueError(
-            f"Unsupported model source: {model_source}, please choose one of ['MODELSCOPE', 'AISTUDIO', 'HUGGINGFACE']"
-        )
+    try:
+        repo_id = model_name_or_path
+        if repo_id.lower().strip().startswith("baidu"):
+            repo_id = "PaddlePaddle" + repo_id.strip()[5:]
+        local_path = envs.FD_MODEL_CACHE
+        if local_path is None:
+            local_path = f'{os.getenv("HOME")}/{repo_id}'
+        snapshot_download(repo_id=repo_id, revision=revision, local_dir=local_path)
+        model_name_or_path = local_path
+    except Exception:
+        raise Exception(f"The setting model_name_or_path:{model_name_or_path} is not exist.")
     return model_name_or_path
 
 

requirements.txt

@@ -30,7 +30,6 @@ use-triton-in-paddle
 crcmod
 fastsafetensors==0.1.14
 msgpack
-modelscope
 opentelemetry-api>=1.24.0
 opentelemetry-sdk>=1.24.0
 opentelemetry-instrumentation-redis