wehub-resource-sync
74 lines
2.4 KiB
Python
74 lines
2.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import msgspec
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class LoRARequest(
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msgspec.Struct,
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omit_defaults=True, # type: ignore[call-arg]
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array_like=True,
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): # type: ignore[call-arg]
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"""
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Request for a LoRA adapter.
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lora_int_id must be globally unique for a given adapter.
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This is currently not enforced in vLLM.
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load_inplace: If True, forces reloading the adapter even if one
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with the same lora_int_id already exists in the cache. This replaces
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the existing adapter in-place. If False (default), only loads if the
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adapter is not already loaded.
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"""
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lora_name: str
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lora_int_id: int
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lora_path: str = ""
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base_model_name: str | None = msgspec.field(default=None)
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tensorizer_config_dict: dict | None = None
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load_inplace: bool = False
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is_3d_lora_weight: bool = False
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"""Whether this adapter's MoE weights are stored in the 3D fused
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`gate_up_proj` / `down_proj` layout (one fused tensor per layer) or the
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2D per-expert split layout (separate `gate_proj` / `up_proj` / `down_proj`
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tensors per expert). Only consulted when the engine is started with
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`enable_mixed_moe_lora_format=True`; otherwise it is ignored and the
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on-disk format is inferred from the base model."""
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def __post_init__(self):
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if self.lora_int_id < 1:
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raise ValueError(f"id must be > 0, got {self.lora_int_id}")
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# Ensure lora_path is not empty
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assert self.lora_path, "lora_path cannot be empty"
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@property
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def adapter_id(self):
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return self.lora_int_id
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@property
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def name(self):
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return self.lora_name
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@property
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def path(self):
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return self.lora_path
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def __eq__(self, value: object) -> bool:
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"""
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Overrides the equality method to compare LoRARequest
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instances based on lora_name. This allows for identification
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and comparison lora adapter across engines.
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"""
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return isinstance(value, self.__class__) and self.lora_name == value.lora_name
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def __hash__(self) -> int:
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"""
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Overrides the hash method to hash LoRARequest instances
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based on lora_name. This ensures that LoRARequest instances
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can be used in hash-based collections such as sets and dictionaries,
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identified by their names across engines.
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"""
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return hash(self.lora_name)
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