support ignore patterns in model loader (#6673)

vllm/config.py

@@ -599,12 +599,16 @@ class LoadConfig:
                 mainly for profiling.
             "tensorizer" will use CoreWeave's tensorizer library for
                 fast weight loading.
+        ignore_patterns: The list of patterns to ignore when loading the model.
+            Default to "original/**/*" to avoid repeated loading of llama's 
+            checkpoints.
     """
 
     load_format: Union[str, LoadFormat, "BaseModelLoader"] = LoadFormat.AUTO
     download_dir: Optional[str] = None
     model_loader_extra_config: Optional[Union[str, dict]] = field(
         default_factory=dict)
+    ignore_patterns: Optional[Union[List[str], str]] = None
 
     def __post_init__(self):
         model_loader_extra_config = self.model_loader_extra_config or {}
@@ -613,6 +617,13 @@ class LoadConfig:
                 model_loader_extra_config)
         self._verify_load_format()
 
+        if self.ignore_patterns is not None and len(self.ignore_patterns) > 0:
+            logger.info(
+                "Ignoring the following patterns when downloading weights: %s",
+                self.ignore_patterns)
+        else:
+            self.ignore_patterns = ["original/**/*"]
+
     def _verify_load_format(self) -> None:
         if not isinstance(self.load_format, str):
             return
@@ -801,7 +812,9 @@ class SchedulerConfig:
                 # for higher throughput.
                 self.max_num_batched_tokens = max(max_model_len, 2048)
         if enable_chunked_prefill:
-            logger.info("Chunked prefill is enabled (EXPERIMENTAL).")
+            logger.info(
+                "Chunked prefill is enabled with max_num_batched_tokens=%d.",
+                max_num_batched_tokens)
 
         self.max_num_seqs = max_num_seqs
         self.max_model_len = max_model_len