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87 lines
3.2 KiB
Python
87 lines
3.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Utils for model executor."""
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import copy
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from typing import Any, Optional
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import torch
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def set_random_seed(seed: int) -> None:
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from vllm.platforms import current_platform
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current_platform.seed_everything(seed)
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def set_weight_attrs(
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weight: torch.Tensor,
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weight_attrs: Optional[dict[str, Any]],
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):
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"""Set attributes on a weight tensor.
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This method is used to set attributes on a weight tensor. This method
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will not overwrite existing attributes.
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Args:
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weight: The weight tensor.
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weight_attrs: A dictionary of attributes to set on the weight tensor.
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"""
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if weight_attrs is None:
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return
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for key, value in weight_attrs.items():
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assert not hasattr(
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weight, key), f"Overwriting existing tensor attribute: {key}"
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# NOTE(woosuk): During weight loading, we often do something like:
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# narrowed_tensor = param.data.narrow(0, offset, len)
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# narrowed_tensor.copy_(real_weight)
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# expecting narrowed_tensor and param.data to share the same storage.
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# However, on TPUs, narrowed_tensor will lazily propagate to the base
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# tensor, which is param.data, leading to the redundant memory usage.
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# This sometimes causes OOM errors during model loading. To avoid this,
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# we sync the param tensor after its weight loader is called.
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# TODO(woosuk): Remove this hack once we have a better solution.
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from vllm.platforms import current_platform
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if current_platform.use_sync_weight_loader(
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) and key == "weight_loader":
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value = current_platform.make_synced_weight_loader(value)
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setattr(weight, key, value)
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def get_packed_modules_mapping(model: torch.nn.Module) -> dict[str, list[str]]:
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parent_map = getattr(model, "packed_modules_mapping", None)
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parent_map = copy.deepcopy(parent_map) if parent_map is not None else {}
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# don't infer mapping if the model has defined it explicitly.
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if parent_map:
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return parent_map
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# We only check main components instead of whole model submodules
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for child in model.children():
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child_map = getattr(child, "packed_modules_mapping", None)
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child_map = copy.deepcopy(child_map) if child_map is not None else {}
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if any((k in parent_map and parent_map[k] != v)
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for k, v in child_map.items()):
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raise ValueError(
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f"Can't update {type(model).__name__}'s packed_modules_mapping "
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f"safely because of conflicts from {type(child).__name__}.")
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else:
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parent_map.update(child_map)
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return parent_map
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def get_moe_expert_mapping(
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model: torch.nn.Module, ) -> list[tuple[str, str, int, str]]:
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if parent_map := getattr(model, "get_expert_mapping", None):
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return parent_map()
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else:
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# We only check main components instead of whole model submodules
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for child in model.children():
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child_map = getattr(child, "get_expert_mapping", None)
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if child_map is not None:
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return child_map()
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return []
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