[Bugfix] Add file lock for ModelScope download

benchmarks/backend_request_func.py

@@ -14,6 +14,8 @@
 from transformers import (AutoTokenizer, PreTrainedTokenizer,
                           PreTrainedTokenizerFast)
 
+from vllm.model_executor.model_loader.weight_utils import get_lock
+
 AIOHTTP_TIMEOUT = aiohttp.ClientTimeout(total=6 * 60 * 60)
 
 
@@ -430,12 +432,15 @@ def get_model(pretrained_model_name_or_path: str) -> str:
     if os.getenv('VLLM_USE_MODELSCOPE', 'False').lower() == 'true':
         from modelscope import snapshot_download
 
-        model_path = snapshot_download(
-            model_id=pretrained_model_name_or_path,
-            local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
-            ignore_file_pattern=[".*.pt", ".*.safetensors", ".*.bin"])
+        # Use file lock to prevent multiple processes from
+        # downloading the same model weights at the same time.
+        with get_lock(pretrained_model_name_or_path):
+            model_path = snapshot_download(
+                model_id=pretrained_model_name_or_path,
+                local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
+                ignore_file_pattern=[".*.pt", ".*.safetensors", ".*.bin"])
 
-        return model_path
+            return model_path
     return pretrained_model_name_or_path
 
 

vllm/model_executor/model_loader/loader.py

@@ -49,7 +49,7 @@
 from vllm.model_executor.model_loader.weight_utils import (
     download_safetensors_index_file_from_hf, download_weights_from_hf,
     filter_duplicate_safetensors_files, filter_files_not_needed_for_inference,
-    get_gguf_extra_tensor_names, gguf_quant_weights_iterator,
+    get_gguf_extra_tensor_names, get_lock, gguf_quant_weights_iterator,
     initialize_dummy_weights, np_cache_weights_iterator, pt_weights_iterator,
     runai_safetensors_weights_iterator, safetensors_weights_iterator)
 from vllm.model_executor.utils import set_weight_attrs
@@ -235,13 +235,17 @@ def _maybe_download_from_modelscope(
             from modelscope.hub.snapshot_download import snapshot_download
 
             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,
-                )
+                # 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):
+                    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

vllm/model_executor/model_loader/weight_utils.py

@@ -8,6 +8,7 @@
 import tempfile
 import time
 from collections import defaultdict
+from pathlib import Path
 from typing import Any, Callable, Dict, Generator, List, Optional, Tuple, Union
 
 import filelock
@@ -67,8 +68,10 @@ def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs, disable=True)
 
 
-def get_lock(model_name_or_path: str, cache_dir: Optional[str] = None):
+def get_lock(model_name_or_path: Union[str, Path],
+             cache_dir: Optional[str] = None):
     lock_dir = cache_dir or temp_dir
+    model_name_or_path = str(model_name_or_path)
     os.makedirs(os.path.dirname(lock_dir), exist_ok=True)
     model_name = model_name_or_path.replace("/", "-")
     hash_name = hashlib.sha256(model_name.encode()).hexdigest()

vllm/transformers_utils/tokenizer.py

@@ -150,16 +150,22 @@ def get_tokenizer(
         # pylint: disable=C.
         from modelscope.hub.snapshot_download import snapshot_download
 
+        # avoid circuit import
+        from vllm.model_executor.model_loader.weight_utils import get_lock
+
         # Only set the tokenizer here, model will be downloaded on the workers.
         if not os.path.exists(tokenizer_name):
-            tokenizer_path = snapshot_download(
-                model_id=tokenizer_name,
-                cache_dir=download_dir,
-                revision=revision,
-                local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
-                # Ignore weights - we only need the tokenizer.
-                ignore_file_pattern=[".*.pt", ".*.safetensors", ".*.bin"])
-            tokenizer_name = tokenizer_path
+            # Use file lock to prevent multiple processes from
+            # downloading the same file at the same time.
+            with get_lock(tokenizer_name, download_dir):
+                tokenizer_path = snapshot_download(
+                    model_id=tokenizer_name,
+                    cache_dir=download_dir,
+                    revision=revision,
+                    local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE,
+                    # Ignore weights - we only need the tokenizer.
+                    ignore_file_pattern=[".*.pt", ".*.safetensors", ".*.bin"])
+                tokenizer_name = tokenizer_path
 
     if tokenizer_mode == "slow":
         if kwargs.get("use_fast", False):