v1.0

model_executor/utils.py

@@ -0,0 +1,94 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Utils for model executor."""
+
+import copy
+from typing import Any
+
+import torch
+
+from vllm.utils.torch_utils import is_torch_equal_or_newer
+
+
+def set_random_seed(seed: int) -> None:
+    from vllm.platforms import current_platform
+
+    current_platform.seed_everything(seed)
+
+
+def set_weight_attrs(
+    weight: torch.Tensor,
+    weight_attrs: dict[str, Any] | None,
+):
+    """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.
+        from vllm.platforms import current_platform
+
+        if current_platform.use_sync_weight_loader() and key == "weight_loader":
+            value = current_platform.make_synced_weight_loader(value)
+        setattr(weight, key, value)
+
+
+def get_packed_modules_mapping(model: torch.nn.Module) -> dict[str, list[str]]:
+    parent_map = getattr(model, "packed_modules_mapping", None)
+    parent_map = copy.deepcopy(parent_map) if parent_map is not None else {}
+
+    # don't infer mapping if the model has defined it explicitly.
+    if parent_map:
+        return parent_map
+
+    # We only check main components instead of whole model submodules
+    for child in model.children():
+        child_map = getattr(child, "packed_modules_mapping", None)
+        child_map = copy.deepcopy(child_map) if child_map is not None else {}
+
+        if any((k in parent_map and parent_map[k] != v) for k, v in child_map.items()):
+            raise ValueError(
+                f"Can't update {type(model).__name__}'s packed_modules_mapping "
+                f"safely because of conflicts from {type(child).__name__}."
+            )
+        else:
+            parent_map.update(child_map)
+    return parent_map
+
+
+def get_moe_expert_mapping(
+    model: torch.nn.Module,
+) -> list[tuple[str, str, int, str]]:
+    if parent_map := getattr(model, "get_expert_mapping", None):
+        return parent_map()
+    else:
+        # We only check main components instead of whole model submodules
+        for child in model.children():
+            child_map = getattr(child, "get_expert_mapping", None)
+            if child_map is not None:
+                return child_map()
+        return []
+
+
+def maybe_disable_graph_partition(current_backend: str) -> dict[str, bool]:
+    if current_backend == "inductor" and is_torch_equal_or_newer("2.9.0.dev"):
+        return {"graph_partition": False}
+    else:
+        return {}