[misc] only tqdm for first rank (#6672)

vllm/model_executor/model_loader/weight_utils.py

@@ -313,6 +313,13 @@ def filter_files_not_needed_for_inference(
     return hf_weights_files
 
 
+# explicitly use pure text format, with a newline at the end
+# this makes it impossible to see the animation in the progress bar
+# but will avoid messing up with ray or multiprocessing, which wraps
+# each line of output with some prefix.
+_BAR_FORMAT = "{desc}: {percentage:3.0f}% Completed | {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}]\n"  # noqa: E501
+
+
 def np_cache_weights_iterator(
     model_name_or_path: str, cache_dir: Optional[str], hf_folder: str,
     hf_weights_files: List[str]
@@ -321,6 +328,8 @@ def np_cache_weights_iterator(
 
     Will dump the model weights to numpy files if they are not already dumped.
     """
+    enable_tqdm = not torch.distributed.is_initialized(
+    ) or torch.distributed.get_rank() == 0
     # Convert the model weights from torch tensors to numpy arrays for
     # faster loading.
     np_folder = os.path.join(hf_folder, "np")
@@ -331,8 +340,12 @@ def np_cache_weights_iterator(
     with get_lock(model_name_or_path, cache_dir):
         if not os.path.exists(weight_names_file):
             weight_names: List[str] = []
-            for bin_file in tqdm(hf_weights_files,
-                                 desc="Loading np_cache checkpoint shards"):
+            for bin_file in tqdm(
+                    hf_weights_files,
+                    desc="Loading np_cache checkpoint shards",
+                    disable=not enable_tqdm,
+                    bar_format=_BAR_FORMAT,
+            ):
                 state = torch.load(bin_file, map_location="cpu")
                 for name, param in state.items():
                     param_path = os.path.join(np_folder, name)
@@ -356,8 +369,14 @@ def safetensors_weights_iterator(
     hf_weights_files: List[str]
 ) -> Generator[Tuple[str, torch.Tensor], None, None]:
     """Iterate over the weights in the model safetensor files."""
-    for st_file in tqdm(hf_weights_files,
-                        desc="Loading safetensors checkpoint shards"):
+    enable_tqdm = not torch.distributed.is_initialized(
+    ) or torch.distributed.get_rank() == 0
+    for st_file in tqdm(
+            hf_weights_files,
+            desc="Loading safetensors checkpoint shards",
+            disable=not enable_tqdm,
+            bar_format=_BAR_FORMAT,
+    ):
         with safe_open(st_file, framework="pt") as f:
             for name in f.keys():  # noqa: SIM118
                 param = f.get_tensor(name)
@@ -368,8 +387,14 @@ def pt_weights_iterator(
     hf_weights_files: List[str]
 ) -> Generator[Tuple[str, torch.Tensor], None, None]:
     """Iterate over the weights in the model bin/pt files."""
-    for bin_file in tqdm(hf_weights_files,
-                         desc="Loading pt checkpoint shards"):
+    enable_tqdm = not torch.distributed.is_initialized(
+    ) or torch.distributed.get_rank() == 0
+    for bin_file in tqdm(
+            hf_weights_files,
+            desc="Loading pt checkpoint shards",
+            disable=not enable_tqdm,
+            bar_format=_BAR_FORMAT,
+    ):
         state = torch.load(bin_file, map_location="cpu")
         for name, param in state.items():
             yield name, param