[Feature] multi-source download (#2986)

* multi-source download

* multi-source download

* huggingface download revision

* requirement

* style

* add revision arg

* test

* pre-commit

fastdeploy/engine/args_utils.py

@@ -46,6 +46,10 @@ 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.
@@ -340,6 +344,12 @@ class EngineArgs:
             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

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

fastdeploy/entrypoints/openai/api_server.py

@@ -62,7 +62,7 @@ parser.add_argument("--metrics-port", default=8001, type=int, help="port for met
 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.model = retrive_model_from_server(args.model, args.revision)
 
 llm_engine = None
 

fastdeploy/envs.py

@@ -30,6 +30,8 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "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

@@ -31,7 +31,9 @@ from typing import Literal, TypeVar, Union
 
 import requests
 import yaml
-from aistudio_sdk.snapshot_download import snapshot_download
+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 tqdm import tqdm
 from typing_extensions import TypeIs, assert_never
 
@@ -494,21 +496,53 @@ def none_or_str(value):
 
 def retrive_model_from_server(model_name_or_path, revision="master"):
     """
-    Download pretrained model from AIStudio automatically
+    Download pretrained model from MODELSCOPE, AIStudio or HUGGINGFACE automatically
     """
     if os.path.exists(model_name_or_path):
         return model_name_or_path
-    try:
+    model_source = envs.FD_MODEL_SOURCE
-        repo_id = model_name_or_path
+    local_path = envs.FD_MODEL_CACHE
-        if repo_id.lower().strip().startswith("baidu"):
+    repo_id = model_name_or_path
-            repo_id = "PaddlePaddle" + repo_id.strip()[5:]
+    if model_source == "MODELSCOPE":
-        local_path = envs.FD_MODEL_CACHE
+        try:
-        if local_path is None:
+            if repo_id.lower().strip().startswith("baidu"):
-            local_path = f'{os.getenv("HOME")}/{repo_id}'
+                repo_id = "PaddlePaddle" + repo_id.strip()[5:]
-        snapshot_download(repo_id=repo_id, revision=revision, local_dir=local_path)
+            if local_path is None:
-        model_name_or_path = local_path
+                local_path = f'{os.getenv("HOME")}'
-    except Exception:
+            local_path = f"{local_path}/{repo_id}/{revision}"
-        raise Exception(f"The setting model_name_or_path:{model_name_or_path} is not exist.")
+            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']"
+        )
     return model_name_or_path
 
 

requirements.txt

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

test/utils/test_download.py

@@ -0,0 +1,43 @@
+import os
+import unittest
+
+from fastdeploy.utils import retrive_model_from_server
+
+
+class TestAistudioDownload(unittest.TestCase):
+    def test_retrive_model_from_server_MODELSCOPE(self):
+        os.environ["FD_MODEL_SOURCE"] = "MODELSCOPE"
+        os.environ["FD_MODEL_CACHE"] = "./models"
+
+        model_name_or_path = "baidu/ERNIE-4.5-0.3B-PT"
+        revision = "master"
+        expected_path = f"./models/PaddlePaddle/ERNIE-4.5-0.3B-PT/{revision}"
+        result = retrive_model_from_server(model_name_or_path, revision)
+        self.assertEqual(expected_path, result)
+
+        os.environ.clear()
+
+    def test_retrive_model_from_server_unsupported_source(self):
+        os.environ["FD_MODEL_SOURCE"] = "UNSUPPORTED_SOURCE"
+        os.environ["FD_MODEL_CACHE"] = "./models"
+
+        model_name_or_path = "baidu/ERNIE-4.5-0.3B-PT"
+        with self.assertRaises(ValueError):
+            retrive_model_from_server(model_name_or_path)
+
+        os.environ.clear()
+
+    def test_retrive_model_from_server_model_not_exist(self):
+        os.environ["FD_MODEL_SOURCE"] = "MODELSCOPE"
+        os.environ["FD_MODEL_CACHE"] = "./models"
+
+        model_name_or_path = "non_existing_model"
+
+        with self.assertRaises(Exception):
+            retrive_model_from_server(model_name_or_path)
+
+        os.environ.clear()
+
+
+if __name__ == "__main__":
+    unittest.main()