Simplify weight loading logic (#2133)

vllm/config.py

@@ -122,15 +122,10 @@ class ModelConfig:
 
         # TODO: Remove this check once HF updates the pt weights of Mixtral.
         architectures = getattr(self.hf_config, "architectures", [])
-        if "MixtralForCausalLM" in architectures:
-            if load_format == "pt":
+        if "MixtralForCausalLM" in architectures and load_format == "pt":
             raise ValueError(
                 "Currently, the 'pt' format is not supported for Mixtral. "
                 "Please use the 'safetensors' format instead. ")
-            elif load_format == "auto":
-                # Do not fall back to pt weights.
-                load_format = "safetensors"
-
         self.load_format = load_format
 
     def _verify_tokenizer_mode(self) -> None:

vllm/model_executor/models/mixtral.py

@@ -412,7 +412,11 @@ class MixtralForCausalLM(nn.Module):
 
         params_dict = dict(self.named_parameters())
         for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, load_format, revision):
+                model_name_or_path,
+                cache_dir,
+                load_format,
+                revision,
+                fall_back_to_pt=False):
             if "rotary_emb.inv_freq" in name:
                 continue
             for (param_name, weight_name, shard_id) in stacked_params_mapping:

vllm/model_executor/weight_utils.py

@@ -125,15 +125,29 @@ def get_quant_config(
 def prepare_hf_model_weights(
     model_name_or_path: str,
     cache_dir: Optional[str] = None,
-    use_safetensors: bool = False,
+    load_format: str = "auto",
     fall_back_to_pt: bool = True,
     revision: Optional[str] = None,
 ) -> Tuple[str, List[str], bool]:
     # Download model weights from huggingface.
     is_local = os.path.isdir(model_name_or_path)
+    use_safetensors = False
     # Some quantized models use .pt files for storing the weights.
-    allow_patterns = ["*.safetensors"
-                      ] if use_safetensors else ["*.bin", "*.pt"]
+    if load_format == "auto":
+        allow_patterns = ["*.safetensors", "*.bin"]
+    elif load_format == "safetensors":
+        use_safetensors = True
+        allow_patterns = ["*.safetensors"]
+    elif load_format == "pt":
+        allow_patterns = ["*.pt"]
+    elif load_format == "npcache":
+        allow_patterns = ["*.bin"]
+    else:
+        raise ValueError(f"Unknown load_format: {load_format}")
+
+    if fall_back_to_pt:
+        allow_patterns += [".pt"]
+
     if not is_local:
         # Use file lock to prevent multiple processes from
         # downloading the same model weights at the same time.
@@ -148,6 +162,10 @@ def prepare_hf_model_weights(
     hf_weights_files: List[str] = []
     for pattern in allow_patterns:
         hf_weights_files += glob.glob(os.path.join(hf_folder, pattern))
+        if len(hf_weights_files) > 0:
+            if pattern == "*.safetensors":
+                use_safetensors = True
+            break
     if not use_safetensors:
         # Exclude files that are not needed for inference.
         # https://github.com/huggingface/transformers/blob/v4.34.0/src/transformers/trainer.py#L227-L233
@@ -163,13 +181,6 @@ def prepare_hf_model_weights(
             if not any(f.endswith(x) for x in blacklist)
         ]
 
-    if len(hf_weights_files) == 0 and use_safetensors and fall_back_to_pt:
-        return prepare_hf_model_weights(model_name_or_path,
-                                        cache_dir=cache_dir,
-                                        use_safetensors=False,
-                                        fall_back_to_pt=False,
-                                        revision=revision)
-
     if len(hf_weights_files) == 0:
         raise RuntimeError(
             f"Cannot find any model weights with `{model_name_or_path}`")
@@ -182,30 +193,16 @@ def hf_model_weights_iterator(
     cache_dir: Optional[str] = None,
     load_format: str = "auto",
     revision: Optional[str] = None,
+    fall_back_to_pt: Optional[bool] = True,
 ) -> Iterator[Tuple[str, torch.Tensor]]:
-    use_safetensors = False
-    use_np_cache = False
-    fall_back_to_pt = False
-    if load_format == "auto":
-        use_safetensors = True
-        fall_back_to_pt = True
-    elif load_format == "safetensors":
-        use_safetensors = True
-    elif load_format == "pt":
-        pass
-    elif load_format == "npcache":
-        use_np_cache = True
-    else:
-        raise ValueError(f"Unknown load_format: {load_format}")
-
     hf_folder, hf_weights_files, use_safetensors = prepare_hf_model_weights(
         model_name_or_path,
         cache_dir=cache_dir,
-        use_safetensors=use_safetensors,
+        load_format=load_format,
         fall_back_to_pt=fall_back_to_pt,
         revision=revision)
 
-    if use_np_cache:
+    if load_format == "npcache":
         # Currently np_cache only support *.bin checkpoints
         assert use_safetensors is False