Support S3 Sharded loading with RunAI Model Streamer (vllm-project#16317)

Signed-off-by: Omer Dayan (SW-GPU) <[email protected]>
Co-authored-by: Cyrus Leung <[email protected]>
Signed-off-by: Zijing Liu <[email protected]>

Author: 2 people

vllm/config.py

@@ -1489,6 +1489,7 @@ class LoadFormat(str, enum.Enum):
     BITSANDBYTES = "bitsandbytes"
     MISTRAL = "mistral"
     RUNAI_STREAMER = "runai_streamer"
+    RUNAI_STREAMER_SHARDED = "runai_streamer_sharded"
     FASTSAFETENSORS = "fastsafetensors"
 
 

vllm/model_executor/model_loader/loader.py

@@ -611,8 +611,12 @@ class ShardedStateLoader(BaseModelLoader):
 
     DEFAULT_PATTERN = "model-rank-{rank}-part-{part}.safetensors"
 
-    def __init__(self, load_config: LoadConfig):
+    def __init__(self,
+                 load_config: LoadConfig,
+                 runai_model_streamer: bool = False):
         super().__init__(load_config)
+
+        self.runai_model_streamer = runai_model_streamer
         extra_config = ({} if load_config.model_loader_extra_config is None
                         else load_config.model_loader_extra_config.copy())
         self.pattern = extra_config.pop("pattern", self.DEFAULT_PATTERN)
@@ -659,7 +663,7 @@ def get_end_ptr(tensor: torch.Tensor) -> int:
 
     def _prepare_weights(self, model_name_or_path: str,
                          revision: Optional[str]):
-        if os.path.isdir(model_name_or_path):
+        if is_s3(model_name_or_path) or os.path.isdir(model_name_or_path):
             return model_name_or_path
         else:
             allow_patterns = ["*.safetensors"]
@@ -678,12 +682,13 @@ def load_model(self, vllm_config: VllmConfig) -> nn.Module:
         device_config = vllm_config.device_config
         model_config = vllm_config.model_config
         target_device = torch.device(device_config.device)
-        from safetensors.torch import safe_open
 
         from vllm.distributed import get_tensor_model_parallel_rank
 
-        local_model_path = self._prepare_weights(model_config.model,
-                                                 model_config.revision)
+        model_weights = model_config.model
+        if hasattr(model_config, "model_weights"):
+            model_weights = model_config.model_weights
+        local_model_path = model_weights
 
         with set_default_torch_dtype(model_config.dtype):
             with target_device:
@@ -695,40 +700,56 @@ def load_model(self, vllm_config: VllmConfig) -> nn.Module:
                 local_model_path,
                 self.pattern.format(rank=rank, part="*"),
             )
-            filepaths = glob.glob(pattern)
+
+            filepaths = []
+            if is_s3(local_model_path):
+                file_pattern = f"*{self.pattern.format(rank=rank, part=" * ")}"
+                filepaths = s3_glob(path=local_model_path,
+                                    allow_pattern=[file_pattern])
+            else:
+                filepaths = glob.glob(pattern)
             if not filepaths:
                 # TODO: support un-sharded checkpoints too
                 raise ValueError(
                     f"Could not find checkpoint files '{pattern}', only "
                     f"pre-sharded checkpoints are currently supported!")
             state_dict = self._filter_subtensors(model.state_dict())
-            for path in filepaths:
-                with safe_open(path, framework="pt") as f:
-                    for key in f.keys():  # noqa: SIM118
-                        tensor = f.get_tensor(key)
-                        # If loading with LoRA enabled, additional padding may
-                        # be added to certain parameters. We only load into a
-                        # narrowed view of the parameter data.
-                        param_data = state_dict[key].data
-                        param_shape = state_dict[key].shape
-                        for dim, size in enumerate(tensor.shape):
-                            if size < param_shape[dim]:
-                                param_data = param_data.narrow(dim, 0, size)
-                        if tensor.shape != param_shape:
-                            logger.warning(
-                                "loading tensor of shape %s into "
-                                "parameter '%s' of shape %s",
-                                tensor.shape,
-                                key,
-                                param_shape,
-                            )
-                        param_data.copy_(tensor)
-                        state_dict.pop(key)
+            for key, tensor in self.iterate_over_files(filepaths):
+                # If loading with LoRA enabled, additional padding may
+                # be added to certain parameters. We only load into a
+                # narrowed view of the parameter data.
+                param_data = state_dict[key].data
+                param_shape = state_dict[key].shape
+                for dim, size in enumerate(tensor.shape):
+                    if size < param_shape[dim]:
+                        param_data = param_data.narrow(dim, 0, size)
+                if tensor.shape != param_shape:
+                    logger.warning(
+                        "loading tensor of shape %s into "
+                        "parameter '%s' of shape %s",
+                        tensor.shape,
+                        key,
+                        param_shape,
+                    )
+                param_data.copy_(tensor)
+                state_dict.pop(key)
             if state_dict:
                 raise ValueError(
                     f"Missing keys {tuple(state_dict)} in loaded state!")
         return model.eval()
 
+    def iterate_over_files(
+            self, paths) -> Generator[Tuple[str, torch.Tensor], None, None]:
+        if self.runai_model_streamer:
+            yield from runai_safetensors_weights_iterator(paths, True)
+        else:
+            from safetensors.torch import safe_open
+            for path in paths:
+                with safe_open(path, framework="pt") as f:
+                    for key in f.keys():  # noqa: SIM118
+                        tensor = f.get_tensor(key)
+                        yield key, tensor
+
     @staticmethod
     def save_model(
         model: torch.nn.Module,
@@ -1515,4 +1536,7 @@ def get_model_loader(load_config: LoadConfig) -> BaseModelLoader:
     if load_config.load_format == LoadFormat.RUNAI_STREAMER:
         return RunaiModelStreamerLoader(load_config)
 
+    if load_config.load_format == LoadFormat.RUNAI_STREAMER_SHARDED:
+        return ShardedStateLoader(load_config, runai_model_streamer=True)
+
     return DefaultModelLoader(load_config)