[Bugfix] Fix get_quant_config when using modelscope (#24421)

Signed-off-by: wangli <wangli858794774@gmail.com>

vllm/model_executor/model_loader/default_loader.py

@@ -7,20 +7,19 @@ import time
 from collections.abc import Generator, Iterable
 from typing import Optional, cast
 
-import huggingface_hub
 import torch
 from torch import nn
 from transformers.utils import SAFE_WEIGHTS_INDEX_NAME
 
-from vllm import envs
 from vllm.config import LoadConfig, ModelConfig
 from vllm.logger import init_logger
 from vllm.model_executor.model_loader.base_loader import BaseModelLoader
 from vllm.model_executor.model_loader.weight_utils import (
     download_safetensors_index_file_from_hf, download_weights_from_hf,
     fastsafetensors_weights_iterator, filter_duplicate_safetensors_files,
-    filter_files_not_needed_for_inference, get_lock, np_cache_weights_iterator,
+    filter_files_not_needed_for_inference, maybe_download_from_modelscope,
-    pt_weights_iterator, safetensors_weights_iterator)
+    np_cache_weights_iterator, pt_weights_iterator,
+    safetensors_weights_iterator)
 from vllm.platforms import current_platform
 
 logger = init_logger(__name__)
@@ -57,35 +56,6 @@ class DefaultModelLoader(BaseModelLoader):
             raise ValueError(f"Model loader extra config is not supported for "
                              f"load format {load_config.load_format}")
 
-    def _maybe_download_from_modelscope(
-            self, model: str, revision: Optional[str]) -> Optional[str]:
-        """Download model from ModelScope hub if VLLM_USE_MODELSCOPE is True.
-
-        Returns the path to the downloaded model, or None if the model is not
-        downloaded from ModelScope."""
-        if envs.VLLM_USE_MODELSCOPE:
-            # download model from ModelScope hub,
-            # lazy import so that modelscope is not required for normal use.
-            # pylint: disable=C.
-            from modelscope.hub.snapshot_download import snapshot_download
-
-            # Use file lock to prevent multiple processes from
-            # downloading the same model weights at the same time.
-            with get_lock(model, self.load_config.download_dir):
-                if not os.path.exists(model):
-                    model_path = snapshot_download(
-                        model_id=model,
-                        cache_dir=self.load_config.download_dir,
-                        local_files_only=huggingface_hub.constants.
-                        HF_HUB_OFFLINE,
-                        revision=revision,
-                        ignore_file_pattern=self.load_config.ignore_patterns,
-                    )
-                else:
-                    model_path = model
-            return model_path
-        return None
-
     def _prepare_weights(
         self,
         model_name_or_path: str,
@@ -96,7 +66,7 @@ class DefaultModelLoader(BaseModelLoader):
         """Prepare weights for the model.
 
         If the model is not local, it will be downloaded."""
-        model_name_or_path = (self._maybe_download_from_modelscope(
+        model_name_or_path = (maybe_download_from_modelscope(
             model_name_or_path, revision) or model_name_or_path)
 
         is_local = os.path.isdir(model_name_or_path)

vllm/model_executor/model_loader/weight_utils.py

@@ -21,6 +21,7 @@ from huggingface_hub import HfFileSystem, hf_hub_download, snapshot_download
 from safetensors.torch import load_file, safe_open, save_file
 from tqdm.auto import tqdm
 
+from vllm import envs
 from vllm.config import LoadConfig, ModelConfig
 from vllm.distributed import get_tensor_model_parallel_rank
 from vllm.logger import init_logger
@@ -95,6 +96,41 @@ def get_lock(model_name_or_path: Union[str, Path],
     return lock
 
 
+def maybe_download_from_modelscope(
+        model: str,
+        revision: Optional[str] = None,
+        download_dir: Optional[str] = None,
+        ignore_patterns: Optional[Union[str, list[str]]] = None,
+        allow_patterns: Optional[Union[list[str],
+                                       str]] = None) -> Optional[str]:
+    """Download model from ModelScope hub if VLLM_USE_MODELSCOPE is True.
+
+        Returns the path to the downloaded model, or None if the model is not
+        downloaded from ModelScope."""
+    if envs.VLLM_USE_MODELSCOPE:
+        # download model from ModelScope hub,
+        # lazy import so that modelscope is not required for normal use.
+        # pylint: disable=C.
+        from modelscope.hub.snapshot_download import snapshot_download
+
+        # Use file lock to prevent multiple processes from
+        # downloading the same model weights at the same time.
+        with get_lock(model, download_dir):
+            if not os.path.exists(model):
+                model_path = snapshot_download(
+                    model_id=model,
+                    cache_dir=download_dir,
+                    local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
+                    revision=revision,
+                    ignore_file_pattern=ignore_patterns,
+                    allow_patterns=allow_patterns,
+                )
+            else:
+                model_path = model
+        return model_path
+    return None
+
+
 def _shared_pointers(tensors):
     ptrs = defaultdict(list)
     for k, v in tensors.items():
@@ -169,7 +205,13 @@ def get_quant_config(model_config: ModelConfig,
     # Inflight BNB quantization
     if model_config.quantization == "bitsandbytes":
         return quant_cls.from_config({})
-    is_local = os.path.isdir(model_config.model)
+    model_name_or_path = maybe_download_from_modelscope(
+        model_config.model,
+        revision=model_config.revision,
+        download_dir=load_config.download_dir,
+        allow_patterns=["*.json"],
+    ) or model_config.model
+    is_local = os.path.isdir(model_name_or_path)
     if not is_local:
         # Download the config files.
         with get_lock(model_config.model, load_config.download_dir):
@@ -182,7 +224,7 @@ def get_quant_config(model_config: ModelConfig,
                 tqdm_class=DisabledTqdm,
             )
     else:
-        hf_folder = model_config.model
+        hf_folder = model_name_or_path
 
     possible_config_filenames = quant_cls.get_config_filenames()