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https://git.datalinker.icu/vllm-project/vllm.git
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857 lines
33 KiB
Python
857 lines
33 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 math
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import os
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from collections.abc import Callable
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from typing import TypeVar
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import regex as re
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import safetensors.torch
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import torch
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from torch import nn
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from vllm.config.lora import LoRAConfig
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from vllm.logger import init_logger
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from vllm.lora.layers import BaseLayerWithLoRA, FusedMoEWithLoRA, LoRAMapping
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from vllm.lora.lora_weights import LoRALayerWeights, PackedLoRALayerWeights
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from vllm.lora.peft_helper import PEFTHelper
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from vllm.lora.punica_wrapper import get_punica_wrapper
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from vllm.lora.utils import (
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from_layer,
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from_layer_logits_processor,
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get_supported_lora_modules,
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is_base_embeddding_weights,
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is_regex_target_modules,
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parse_fine_tuned_lora_name,
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process_packed_modules_mapping,
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replace_submodule,
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)
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from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
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from vllm.model_executor.models import SupportsLoRA, supports_multimodal
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from vllm.model_executor.models.interfaces import is_pooling_model
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from vllm.model_executor.models.module_mapping import MultiModelKeys
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from vllm.model_executor.models.utils import PPMissingLayer, WeightsMapper
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from vllm.utils.cache import LRUCache
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from vllm.utils.platform_utils import is_pin_memory_available
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logger = init_logger(__name__)
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T = TypeVar("T")
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class AdapterLRUCache(LRUCache[int, T]):
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def __init__(self, capacity: int, deactivate_fn: Callable[[int], object]):
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super().__init__(capacity)
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self.deactivate_fn = deactivate_fn
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def _on_remove(self, key: int, value: T | None):
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logger.debug("Removing adapter int id: %d", key)
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self.deactivate_fn(key)
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return super()._on_remove(key, value)
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_GLOBAL_LORA_ID = 0
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def get_lora_id():
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global _GLOBAL_LORA_ID
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_GLOBAL_LORA_ID += 1
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return _GLOBAL_LORA_ID
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class LoRAModel:
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"""A LoRA fine-tuned model."""
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def __init__(
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self,
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lora_model_id: int,
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rank: int,
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loras: dict[str, LoRALayerWeights],
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) -> None:
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"""
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Args:
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lora_model_id: The integer id for the lora model.
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rank: lora rank.
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loras: module name -> weights for lora-replaced layers.
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"""
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self.id = lora_model_id
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assert lora_model_id > 0, (
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f"a valid lora id should be greater than 0, got {self.id}"
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)
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self.rank = rank
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self.loras: dict[str, LoRALayerWeights] = loras
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def clone(self, lora_model_id: int) -> "LoRAModel":
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"""Return a copy of the object with different ids.
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Will share the underlying tensors."""
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return self.__class__(
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lora_model_id,
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rank=self.rank,
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loras=self.loras.copy(),
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)
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def get_lora(self, module_name: str) -> LoRALayerWeights | None:
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"""Get LoRA for a given module by name"""
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return self.loras.get(module_name, None)
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def check_lora_name(self, lora_name: str) -> bool:
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return lora_name in self.loras
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# (yard1): TODO see if we can derive target_embedding_padding automatically
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@classmethod
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def from_lora_tensors(
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cls,
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lora_model_id: int,
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tensors: dict[str, torch.Tensor],
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peft_helper: PEFTHelper,
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device: str = "cuda",
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dtype: torch.dtype | None = None,
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target_embedding_padding: int | None = None,
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embedding_modules: dict[str, str] | None = None,
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embedding_padding_modules: list[str] | None = None,
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weights_mapper: WeightsMapper | None = None,
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) -> "LoRAModel":
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"""Create a LoRAModel from a dictionary of tensors."""
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pin_memory = str(device) == "cpu" and is_pin_memory_available()
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loras: dict[str, LoRALayerWeights] = {}
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for tensor_name, tensor in tensors.items():
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if is_base_embeddding_weights(tensor_name):
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continue
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module_name, is_lora_a = parse_fine_tuned_lora_name(
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tensor_name, weights_mapper
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)
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if module_name not in loras:
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loras[module_name] = LoRALayerWeights.from_config(
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module_name, peft_helper
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)
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if is_lora_a:
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loras[module_name].lora_a = tensor.to(device=device, dtype=dtype)
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if pin_memory:
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loras[module_name].lora_a = loras[module_name].lora_a.pin_memory()
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else:
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loras[module_name].lora_b = tensor.to(device=device, dtype=dtype)
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assert embedding_padding_modules is not None
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if (
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any(name in module_name for name in embedding_padding_modules)
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and target_embedding_padding is not None
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):
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lora_b = loras[module_name].lora_b
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assert target_embedding_padding >= lora_b.shape[0]
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addition = target_embedding_padding - lora_b.shape[0]
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loras[module_name].lora_b = torch.nn.functional.pad(
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lora_b, (0, 0, 0, addition)
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)
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if pin_memory:
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loras[module_name].lora_b = loras[module_name].lora_b.pin_memory()
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for lora in loras.values():
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lora.optimize()
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return cls(lora_model_id, peft_helper.r, loras)
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@classmethod
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def from_local_checkpoint(
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cls,
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lora_dir: str,
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expected_lora_modules: list[str],
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peft_helper: PEFTHelper,
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*,
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lora_model_id: int | None = None,
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device: str = "cuda",
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dtype: torch.dtype | None = None,
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target_embedding_padding: int | None = None,
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embedding_modules: dict[str, str] | None = None,
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embedding_padding_modules: list[str] | None = None,
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weights_mapper: WeightsMapper | None = None,
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tensorizer_config_dict: dict | None = None,
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) -> "LoRAModel":
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"""Create a LoRAModel from a local checkpoint.
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Args:
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lora_dir: The local path that has lora data.
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expected_lora_modules: Name of modules that are expected to be
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replaced by lora.
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peft_helper: Loaded lora configuration information.
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lora_model_id: LoRA model id. If not given, automatically set by
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a global counter.
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device: Device where the lora model is loaded.
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dtype: dtype of the lora model weights.
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Returns:
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Loaded LoRA Model.
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"""
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lora_tensor_path = os.path.join(lora_dir, "adapter_model.safetensors")
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lora_bin_file_path = os.path.join(lora_dir, "adapter_model.bin")
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lora_pt_file_path = os.path.join(lora_dir, "adapter_model.pt")
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# new_embeddings_tensor_path = os.path.join(
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# lora_dir, "new_embeddings.safetensors"
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# )
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# new_embeddings_bin_file_path = os.path.join(lora_dir, "new_embeddings.bin")
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tensors: dict[str, torch.Tensor] = {}
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unexpected_modules: list[list[str] | str] = []
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def check_unexpected_modules(modules: dict):
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for lora_module in modules.keys(): # noqa
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if is_base_embeddding_weights(lora_module):
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continue
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module_name, _ = parse_fine_tuned_lora_name(lora_module, weights_mapper)
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# Handle FSDP file format where experts.base_layer is the
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# gate_up_proj and experts is the down_proj
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if "base_layer" in lora_module:
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continue
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# Case for expert lora weights
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if ".experts" in module_name:
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if not any(
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module_name.endswith(ele) for ele in expected_lora_modules
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):
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unexpected_modules.append(module_name)
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elif module_name.split(".")[-1] not in expected_lora_modules:
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unexpected_modules.append(module_name)
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if unexpected_modules:
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raise ValueError(
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f"While loading {lora_dir}, expected"
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f" target modules in {expected_lora_modules}"
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f" but received {unexpected_modules}."
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f" Please verify that the loaded LoRA module is correct"
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)
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if tensorizer_config_dict:
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from tensorizer import TensorDeserializer
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tensorizer_config = TensorizerConfig(**tensorizer_config_dict)
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lora_tensor_path = os.path.join(
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tensorizer_config.tensorizer_dir, "adapter_model.tensors"
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)
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tensorizer_args = tensorizer_config._construct_tensorizer_args()
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tensors = TensorDeserializer(
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lora_tensor_path,
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dtype=tensorizer_config.dtype,
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**tensorizer_args.deserialization_kwargs,
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)
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check_unexpected_modules(tensors)
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elif os.path.isfile(lora_tensor_path):
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# Find unexpected modules.
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# Use safetensor key as a source of truth to find expected modules.
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# in peft if you have target_modules A, B, C and C does not exist
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# in the model it won’t error and model will be trained with A, B
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# loraified. C won’t exist in the safetensor but it will exist in
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# the target_modules of the adapter_config.json.
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unexpected_modules = []
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with safetensors.safe_open(lora_tensor_path, framework="pt") as f: # type: ignore
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# Load tensors if there are only expected modules.
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check_unexpected_modules(f)
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for module in f.keys(): # noqa
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tensors[module] = f.get_tensor(module)
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elif os.path.isfile(lora_bin_file_path) or os.path.isfile(lora_pt_file_path):
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# When a bin/pt file is provided, we rely on config to find
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# unexpected modules.
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unexpected_modules = []
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target_modules = peft_helper.target_modules
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if not isinstance(target_modules, list):
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target_modules = [target_modules]
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for module in target_modules:
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# Compatible with more modules,
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# such as:layers.11.self_attn.k_proj
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part_name = module.split(".")[-1]
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if part_name not in expected_lora_modules:
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unexpected_modules.append(module)
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# loaded lora's target modules must be a subset of
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# expected_lora_modules. It is not reliable. See
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# https://github.com/vllm-project/vllm/pull/5909. But there's no
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# other better mechanism.
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if unexpected_modules and not is_regex_target_modules(
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peft_helper.target_modules, expected_lora_modules
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):
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raise ValueError(
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f"While loading {lora_dir}, expected"
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f" target modules in {expected_lora_modules}"
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f" but received {unexpected_modules}."
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f" Please verify that the loaded LoRA module is correct"
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)
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lora_file_path = (
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lora_bin_file_path
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if os.path.isfile(lora_bin_file_path)
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else lora_pt_file_path
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)
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tensors = torch.load(lora_file_path, map_location=device, weights_only=True)
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else:
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raise ValueError(f"{lora_dir} doesn't contain tensors")
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return cls.from_lora_tensors(
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lora_model_id=get_lora_id() if lora_model_id is None else lora_model_id,
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tensors=tensors,
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peft_helper=peft_helper,
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device=device,
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dtype=dtype,
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target_embedding_padding=target_embedding_padding,
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embedding_modules=embedding_modules,
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embedding_padding_modules=embedding_padding_modules,
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weights_mapper=weights_mapper,
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)
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class LoRAModelManager:
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"""A manager that manages multiple LoRA-fine-tuned models."""
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def __init__(
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self,
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model: SupportsLoRA,
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max_num_seqs: int,
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max_num_batched_tokens: int,
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vocab_size: int,
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lora_config: LoRAConfig,
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device: torch.device,
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):
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"""Create a LoRAModelManager and adapter for a given model.
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Args:
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model: the model to be adapted.
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max_num_seqs: the maximum number of sequences model can run in a
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single batch.
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max_num_batched_tokens: the maximum number of tokens model can run
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in a single batch.
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vocab_size: the vocab size of the model.
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lora_config: the LoRA configuration.
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"""
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self.model: SupportsLoRA = model
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self._registered_adapters: dict[int, LoRAModel] = {}
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# Dict instead of a set for compatibility with LRUCache.
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self._active_adapters: dict[int, None] = {}
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self.adapter_type = "LoRA"
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self.lora_config = lora_config
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self.device = device
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self.max_num_seqs = max_num_seqs
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assert self.capacity >= self.lora_slots
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self.max_num_batched_tokens = math.ceil(max_num_batched_tokens / 8) * 8
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self.lora_index_to_id: list[int | None] = [None] * self.lora_slots
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self.vocab_size = vocab_size
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self.punica_wrapper = get_punica_wrapper(
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max_num_batched_tokens,
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max_batches=self.max_num_seqs,
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device=self.device,
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max_loras=self.lora_config.max_loras,
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)
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self.supported_lora_modules = get_supported_lora_modules(self.model)
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assert self.supported_lora_modules, "No supported LoRA modules found in"
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f" {self.model.__class__.__name__}."
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self.packed_modules_mapping = process_packed_modules_mapping(self.model)
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# Used to indicate whether the model is a multimodal model
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self.supports_mm: bool = (
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supports_multimodal(self.model)
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# In case the model only supports LoRA for
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# text modules (e.g. ChatGLM)
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and hasattr(self.model, "get_mm_mapping")
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)
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self.is_pooling_model = is_pooling_model(self.model)
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self.packed_modules: dict[str, list[str]] = {}
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self.modules: dict[str, BaseLayerWithLoRA] = {}
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# Dict instead of a set for compatibility with LRUCache.
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self._last_mapping: LoRAMapping | None = None
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self._create_lora_modules()
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self.model.lora_manager = self
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def __len__(self) -> int:
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return len(self._registered_adapters)
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@property
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def capacity(self) -> int:
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return self.lora_config.max_cpu_loras
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@property
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def lora_slots(self) -> int:
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return self.lora_config.max_loras
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@property
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def adapter_slots(self) -> int:
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return self.lora_slots
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def activate_adapter(
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self,
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lora_id: int,
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) -> bool:
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"""Move LoRA into a GPU buffer to be used in the forward pass."""
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if lora_id in self._active_adapters:
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return False
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first_free_slot = next(
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(
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(i, lora_id)
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for i, lora_id in enumerate(self.lora_index_to_id)
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if lora_id is None
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),
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None,
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)
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if first_free_slot is None:
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raise ValueError("No free lora slots")
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index, _ = first_free_slot
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self._active_adapters[lora_id] = None
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lora_model = self._registered_adapters[lora_id]
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logger.debug(
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"Activating LoRA. int id: %d, slot index: %d", lora_model.id, index
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)
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self.lora_index_to_id[index] = lora_model.id
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for module_name, module in self.modules.items():
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module_lora = self._get_lora_layer_weights(lora_model, module_name)
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if module_lora:
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# Note (gnovack) - If MOE lora weights are not split into
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# num_experts chunks, we split them here
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if isinstance(module, FusedMoEWithLoRA) and torch.is_tensor(
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module_lora.lora_a
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):
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# Handle FSDP file format where experts.base_layer is the
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# gate_up_proj and experts is the down_proj
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gate_up_proj_lora = self._get_lora_layer_weights(
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lora_model, module_name + ".base_layer"
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)
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assert gate_up_proj_lora is not None
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assert module_lora is not None
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down_proj_lora = module_lora
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num_experts = module_lora.lora_a.shape[0] // module_lora.rank
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gate_proj_a = gate_up_proj_lora.lora_a.chunk(num_experts, dim=0)
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up_proj_a = gate_up_proj_lora.lora_a.chunk(num_experts, dim=0)
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gate_proj_b = gate_up_proj_lora.lora_b[::2, ...].chunk(
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num_experts, dim=-1
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)
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up_proj_b = gate_up_proj_lora.lora_b[1::2, ...].chunk(
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num_experts, dim=-1
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)
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down_proj_a = down_proj_lora.lora_a.chunk(num_experts, dim=0)
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down_proj_b = down_proj_lora.lora_b.chunk(num_experts, dim=-1)
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lora_a = []
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lora_b = []
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for i in range(num_experts):
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lora_a.append(gate_proj_a[i])
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lora_a.append(down_proj_a[i])
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lora_a.append(up_proj_a[i])
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lora_b.append(gate_proj_b[i])
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lora_b.append(down_proj_b[i])
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lora_b.append(up_proj_b[i])
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module_lora.lora_a = lora_a
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module_lora.lora_b = lora_b
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module.set_lora(
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index,
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module_lora.lora_a,
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module_lora.lora_b,
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)
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else:
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module.reset_lora(index)
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return True
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def _deactivate_adapter(self, lora_id: int):
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try:
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index = self.lora_index_to_id.index(lora_id)
|
||
self.lora_index_to_id[index] = None
|
||
except ValueError:
|
||
pass
|
||
|
||
def _add_adapter(self, lora: LoRAModel):
|
||
self._create_merged_loras_inplace(lora)
|
||
self._registered_adapters[lora.id] = lora
|
||
|
||
def pin_adapter(self, lora_id: int) -> bool:
|
||
"""Pin a LoRAModel in the manager cache."""
|
||
raise NotImplementedError(
|
||
"Pinning is not supported in LoRAModelManager. "
|
||
"Use LRUCacheLoRAModelManager for pinning"
|
||
) # type: ignore
|
||
|
||
def _set_adapter_mapping(self, mapping: LoRAMapping) -> None:
|
||
# update lora states
|
||
self.punica_wrapper.update_metadata(
|
||
mapping,
|
||
self.lora_index_to_id,
|
||
self.lora_slots + 1,
|
||
self.vocab_size,
|
||
)
|
||
|
||
def remove_all_adapters(self):
|
||
"""Remove all LoRAModels from the manager."""
|
||
self._registered_adapters.clear()
|
||
self.lora_index_to_id = [None] * self.lora_slots
|
||
self._active_adapters.clear()
|
||
|
||
def _create_lora_modules(self):
|
||
def _parent_module(module_name: str) -> str:
|
||
# module name is a dot separated name.
|
||
# for example:
|
||
# - given an input 'x.y.z' return 'x.y'
|
||
# - given an input 'x' return ''
|
||
return module_name.rpartition(".")[0]
|
||
|
||
for module_name, module in self.model.named_modules(remove_duplicate=False):
|
||
if isinstance(module, PPMissingLayer):
|
||
continue
|
||
|
||
if not self._match_target_modules(module_name):
|
||
continue
|
||
# A temporary approach for multimodal models to support LoRA
|
||
# TODO: Remove this restriction
|
||
if self._filter_unsupported_mm_module(module_name):
|
||
logger.warning(
|
||
"Regarding multimodal models, vLLM currently only supports "
|
||
"adding LoRA to language model, %s will be ignored.",
|
||
module_name,
|
||
)
|
||
continue
|
||
parts = module_name.split(".")[-1]
|
||
packed_moduled_lst = self.packed_modules_mapping.get(parts, [])
|
||
new_module = replace_submodule(
|
||
self.model,
|
||
module_name,
|
||
from_layer(
|
||
module,
|
||
self.lora_slots,
|
||
self.lora_config,
|
||
packed_moduled_lst,
|
||
self.model.config,
|
||
),
|
||
)
|
||
|
||
# (yard1): TODO make this more robust
|
||
if "lm_head" in module_name:
|
||
logits_processor_module_name = "logits_processor"
|
||
parent_module = _parent_module(module_name)
|
||
if parent_module:
|
||
logits_processor_module_name = (
|
||
f"{parent_module}.{logits_processor_module_name}"
|
||
)
|
||
|
||
logits_processor_module = self.model.get_submodule(
|
||
logits_processor_module_name
|
||
)
|
||
|
||
new_module = replace_submodule(
|
||
self.model,
|
||
logits_processor_module_name,
|
||
from_layer_logits_processor(
|
||
logits_processor_module,
|
||
module,
|
||
self.lora_slots,
|
||
self.lora_config,
|
||
self.model.config,
|
||
),
|
||
)
|
||
|
||
# In some models, especially multimodal ones, layers with the same
|
||
# name may have different types, such as nn.Linear and
|
||
# ReplicatedLinear. The nn.Linear layers cannot be replaced with
|
||
# LoRA layers, leading to assertion error. The following check
|
||
# aims to prevent this error
|
||
if self.supports_mm and not isinstance(new_module, BaseLayerWithLoRA):
|
||
continue
|
||
self.register_module(module_name, new_module)
|
||
self._register_packed_modules(module_name)
|
||
# All lora layers share the same punica_wrapper based on reference.
|
||
new_module.set_mapping(self.punica_wrapper)
|
||
|
||
def register_module(self, module_name: str, module: "BaseLayerWithLoRA"):
|
||
assert isinstance(module, BaseLayerWithLoRA), (
|
||
f"Module {module_name} must be a BaseLayerWithLoRA instance,"
|
||
)
|
||
f" got {type(module)}"
|
||
self.modules[module_name] = module
|
||
|
||
def create_dummy_lora(
|
||
self,
|
||
lora_id: int,
|
||
rank: int,
|
||
embedding_modules: dict[str, str] | None = None,
|
||
) -> LoRAModel:
|
||
"""Create zero-initialized LoRAModel for warmup."""
|
||
model = LoRAModel(lora_id, rank, {})
|
||
for module_name, module in self.model.named_modules():
|
||
if (
|
||
not self._match_target_modules(module_name)
|
||
or not isinstance(module, BaseLayerWithLoRA)
|
||
or self._filter_unsupported_mm_module(module_name)
|
||
):
|
||
continue
|
||
parts = module_name.split(".")
|
||
if module_name not in self.packed_modules:
|
||
assert embedding_modules is not None
|
||
if parts[-1] in embedding_modules:
|
||
input_dim = (
|
||
module.base_layer.org_vocab_size
|
||
if hasattr(module.base_layer, "org_vocab_size")
|
||
else module.base_layer.weight.shape[1]
|
||
)
|
||
output_dim = (
|
||
module.base_layer.embedding_dim
|
||
if hasattr(module.base_layer, "embedding_dim")
|
||
else module.base_layer.weight.shape[0]
|
||
)
|
||
lora = LoRALayerWeights.create_dummy_lora_weights(
|
||
module_name,
|
||
input_dim,
|
||
output_dim,
|
||
rank,
|
||
module.lora_a_stacked[0].dtype,
|
||
"cpu",
|
||
)
|
||
else:
|
||
lora = LoRALayerWeights.create_dummy_lora_weights(
|
||
module_name,
|
||
module.lora_a_stacked[0].shape[-1],
|
||
module.lora_b_stacked[0].shape[-2],
|
||
rank,
|
||
module.lora_a_stacked[0].dtype,
|
||
"cpu",
|
||
)
|
||
else:
|
||
parts = module_name.split(".")
|
||
replacements = self.packed_modules_mapping[parts[-1]]
|
||
subloras: list[LoRALayerWeights | None] = []
|
||
for i, r in enumerate(replacements):
|
||
lora = LoRALayerWeights.create_dummy_lora_weights(
|
||
module_name + "." + r,
|
||
module.lora_a_stacked[i].shape[-1],
|
||
module.lora_b_stacked[i].shape[-2],
|
||
rank,
|
||
module.lora_a_stacked[i].dtype,
|
||
"cpu",
|
||
)
|
||
subloras.append(lora)
|
||
lora = PackedLoRALayerWeights.pack(subloras)
|
||
model.loras[module_name] = lora
|
||
return model
|
||
|
||
def _match_target_modules(self, module_name: str):
|
||
return any(
|
||
re.match(
|
||
r".*\.{target_module}$".format(target_module=target_module), module_name
|
||
)
|
||
or target_module == module_name
|
||
for target_module in self.supported_lora_modules
|
||
)
|
||
|
||
def _filter_unsupported_mm_module(self, module_name: str) -> bool:
|
||
"""
|
||
Regarding multimodal models, vLLM currently only supports adding LoRA to
|
||
language model. LoRA for other modules, such as the vision tower, will
|
||
be filtered out.
|
||
"""
|
||
if self.supports_mm:
|
||
module_mapping: MultiModelKeys = self.model.get_mm_mapping()
|
||
prefix_lst = module_mapping.connector + module_mapping.tower_model
|
||
return any([module_name.startswith(prefix) for prefix in prefix_lst])
|
||
return False
|
||
|
||
def _register_packed_modules(self, module_full_name: str) -> None:
|
||
parts = module_full_name.split(".")
|
||
module_name = parts[-1]
|
||
replacements = self.packed_modules_mapping.get(module_name, [])
|
||
# When replacements is less than or equal to 1, it indicates that this
|
||
# module is not a packed module.
|
||
if len(replacements) <= 1:
|
||
return
|
||
prefix = ".".join(parts[:-1])
|
||
self.packed_modules[module_full_name] = [
|
||
prefix + "." + r if prefix else r for r in replacements
|
||
]
|
||
|
||
def _create_merged_loras_inplace(self, lora_model: LoRAModel) -> None:
|
||
for module_name, new_module_names in self.packed_modules.items():
|
||
replacement_loras: list[LoRALayerWeights | None] = []
|
||
replaced_module: set[str] = set()
|
||
has_replacement = False
|
||
for r in new_module_names:
|
||
lora = self._get_lora_layer_weights(lora_model, r)
|
||
replacement_loras.append(lora)
|
||
if lora:
|
||
has_replacement = True
|
||
replaced_module.add(r)
|
||
if not has_replacement:
|
||
continue
|
||
for i in range(len(replacement_loras)):
|
||
if replacement_loras[i]:
|
||
continue
|
||
replacement_loras[i] = None
|
||
# HACK Temporary solution for the pool model.
|
||
if self.is_pooling_model and not lora_model.check_lora_name(module_name):
|
||
replaced_module_name = module_name.replace("model.", "")
|
||
if lora_model.check_lora_name(module_name):
|
||
module_name = replaced_module_name
|
||
lora_model.loras[module_name] = PackedLoRALayerWeights.pack(
|
||
replacement_loras
|
||
)
|
||
# Remove the modules that have been replaced.
|
||
for module in replaced_module:
|
||
lora_model.loras.pop(module, None)
|
||
|
||
def _get_lora_layer_weights(
|
||
self, lora_model: LoRAModel, module_name: str
|
||
) -> LoRALayerWeights | None:
|
||
org_module_name = module_name
|
||
if self.is_pooling_model and not lora_model.check_lora_name(module_name):
|
||
# If it's a pool model, and the layer name is not found,
|
||
# remove the prefix 'model.' and search again.
|
||
module_name = module_name.replace("model.", "")
|
||
if lora_model.check_lora_name(module_name):
|
||
org_module_name = module_name
|
||
logger.info_once(
|
||
"For the pool model, successfully loaded the LoRA weights "
|
||
"after removing the prefix 'model.'."
|
||
)
|
||
return lora_model.get_lora(org_module_name)
|
||
|
||
def deactivate_adapter(self, adapter_id: int) -> bool:
|
||
if adapter_id not in self._active_adapters:
|
||
return False
|
||
self._deactivate_adapter(adapter_id)
|
||
self._active_adapters.pop(adapter_id, None)
|
||
return True
|
||
|
||
def add_adapter(self, adapter: LoRAModel) -> bool:
|
||
logger.debug("Adding lora. Model id: %d, int id: %d", adapter.id, adapter.id)
|
||
if adapter.id in self._registered_adapters:
|
||
return False
|
||
if len(self._registered_adapters) >= self.capacity:
|
||
raise RuntimeError("No free adapter slots.")
|
||
self._add_adapter(adapter)
|
||
return True
|
||
|
||
def set_adapter_mapping(self, mapping: LoRAMapping) -> None:
|
||
if self._last_mapping != mapping:
|
||
self._set_adapter_mapping(mapping)
|
||
self._last_mapping = mapping
|
||
|
||
def remove_adapter(self, adapter_id: int) -> bool:
|
||
self.deactivate_adapter(adapter_id)
|
||
if adapter_id not in self._registered_adapters:
|
||
return False
|
||
self._registered_adapters.pop(adapter_id, None)
|
||
return True
|
||
|
||
def list_adapters(self) -> dict[int, LoRAModel]:
|
||
return dict(self._registered_adapters)
|
||
|
||
def get_adapter(self, adapter_id: int) -> LoRAModel | None:
|
||
return self._registered_adapters.get(adapter_id)
|
||
|
||
|
||
class LoRALRUCache(AdapterLRUCache[LoRAModel]):
|
||
def __init__(self, capacity: int, deactivate_lora_fn: Callable[[int], bool]):
|
||
super().__init__(capacity, deactivate_lora_fn)
|
||
|
||
|
||
class LRUCacheLoRAModelManager(LoRAModelManager):
|
||
"""A model manager that manages multiple LoRAs with LRU cache."""
|
||
|
||
def __init__(
|
||
self,
|
||
model: nn.Module,
|
||
max_num_seqs: int,
|
||
max_num_batched_tokens: int,
|
||
vocab_size: int,
|
||
lora_config: LoRAConfig,
|
||
device: torch.device,
|
||
):
|
||
super().__init__(
|
||
model, max_num_seqs, max_num_batched_tokens, vocab_size, lora_config, device
|
||
)
|
||
self._registered_adapters: LoRALRUCache = LoRALRUCache(
|
||
self.capacity, self.deactivate_adapter
|
||
)
|
||
self._active_adapters: LoRALRUCache = LoRALRUCache(
|
||
self.lora_slots, self._deactivate_adapter
|
||
)
|
||
|
||
def list_adapters(self) -> dict[int, LoRAModel]:
|
||
"""List all registered LoRAModels."""
|
||
return dict(self._registered_adapters.cache)
|
||
|
||
def add_adapter(self, lora: LoRAModel) -> bool:
|
||
"""Add a LoRAModel to the manager."""
|
||
logger.debug("Adding lora. Model id: %d, int id: %d", lora.id, lora.id)
|
||
if lora.id not in self._registered_adapters:
|
||
self._add_adapter(lora)
|
||
was_added = True
|
||
else:
|
||
# We always touch to update the LRU cache order
|
||
self._registered_adapters.touch(lora.id)
|
||
was_added = False
|
||
return was_added
|
||
|
||
def activate_adapter(
|
||
self,
|
||
lora_id: int,
|
||
) -> bool:
|
||
if (
|
||
lora_id not in self._active_adapters
|
||
and len(self._active_adapters) >= self.lora_slots
|
||
):
|
||
self._active_adapters.remove_oldest()
|
||
result = super().activate_adapter(lora_id)
|
||
# We always touch to update the LRU cache order
|
||
self._active_adapters.touch(lora_id)
|
||
return result
|
||
|
||
def remove_oldest_adapter(self) -> bool:
|
||
if len(self._registered_adapters) > 0:
|
||
self._registered_adapters.remove_oldest()
|
||
return True
|
||
return False
|
||
|
||
def pin_adapter(self, lora_id: int) -> bool:
|
||
"""Pin a LoRAModel in the manager cache."""
|
||
self._pin_lora_in_cpu_cache(lora_id)
|
||
self._pin_lora_in_gpu_cache(lora_id)
|
||
return True
|
||
|
||
def _pin_lora_in_cpu_cache(self, lora_id: int):
|
||
try:
|
||
self._registered_adapters.pin(lora_id)
|
||
except ValueError as err:
|
||
raise ValueError(
|
||
f"Pinning failed. LoRA {lora_id} is not registered."
|
||
) from err
|
||
|
||
def _pin_lora_in_gpu_cache(self, lora_id: int):
|
||
if lora_id not in self._active_adapters:
|
||
# move lora to gpu if not already active
|
||
self.activate_adapter(lora_id)
|
||
|
||
self._active_adapters.pin(lora_id)
|
||
|
||
|
||
def create_lora_manager(
|
||
model: nn.Module,
|
||
max_num_seqs: int,
|
||
max_num_batched_tokens: int,
|
||
vocab_size: int,
|
||
lora_config: LoRAConfig,
|
||
device: torch.device,
|
||
lora_manager_cls: type[LoRAModelManager] = LoRAModelManager,
|
||
**kwargs,
|
||
) -> LoRAModelManager:
|
||
"""Create a LoRA adapter for a given model."""
|
||
if not isinstance(model, SupportsLoRA):
|
||
raise ValueError(f"Model {type(model)} is not supported for LoRA.")
|
||
lora_manager = lora_manager_cls(
|
||
model=model,
|
||
max_num_seqs=max_num_seqs,
|
||
max_num_batched_tokens=max_num_batched_tokens,
|
||
vocab_size=vocab_size,
|
||
lora_config=lora_config,
|
||
device=device,
|
||
**kwargs,
|
||
)
|
||
return lora_manager
|