add qwen3

vllm-v0.6.2/vllm/model_executor/utils.py

@@ -0,0 +1,52 @@
+"""Utils for model executor."""
+from typing import Any, Dict, Optional
+
+import torch
+
+from vllm.platforms import current_platform
+
+
+def set_random_seed(seed: int) -> None:
+    current_platform.seed_everything(seed)
+
+
+def set_weight_attrs(
+    weight: torch.Tensor,
+    weight_attrs: Optional[Dict[str, Any]],
+):
+    """Set attributes on a weight tensor.
+
+    This method is used to set attributes on a weight tensor. This method
+    will not overwrite existing attributes.
+
+    Args:
+        weight: The weight tensor.
+        weight_attrs: A dictionary of attributes to set on the weight tensor.
+    """
+    if weight_attrs is None:
+        return
+    for key, value in weight_attrs.items():
+        assert not hasattr(
+            weight, key), (f"Overwriting existing tensor attribute: {key}")
+
+        # NOTE(woosuk): During weight loading, we often do something like:
+        # narrowed_tensor = param.data.narrow(0, offset, len)
+        # narrowed_tensor.copy_(real_weight)
+        # expecting narrowed_tensor and param.data to share the same storage.
+        # However, on TPUs, narrowed_tensor will lazily propagate to the base
+        # tensor, which is param.data, leading to the redundant memory usage.
+        # This sometimes causes OOM errors during model loading. To avoid this,
+        # we sync the param tensor after its weight loader is called.
+        # TODO(woosuk): Remove this hack once we have a better solution.
+        if current_platform.is_tpu() and key == "weight_loader":
+            value = _make_synced_weight_loader(value)
+        setattr(weight, key, value)
+
+
+def _make_synced_weight_loader(original_weight_loader):
+
+    def _synced_weight_loader(param, *args, **kwargs):
+        original_weight_loader(param, *args, **kwargs)
+        torch._sync(param)
+
+    return _synced_weight_loader