4d6ada947c
Co-authored-by: Swapnil Parekh <swapnilp@ibm.com> Co-authored-by: Joe G <joseph.granados@h2o.ai> Co-authored-by: Antoni Baum <antoni.baum@protonmail.com>
840 lines
36 KiB
Python
840 lines
36 KiB
Python
import copy
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import json
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import math
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import os
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import re
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from dataclasses import dataclass, field
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from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
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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.adapter_commons.models import (AdapterLRUCache, AdapterModel,
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AdapterModelManager)
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from vllm.adapter_commons.utils import (add_adapter, deactivate_adapter,
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get_adapter, list_adapters,
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remove_adapter, set_adapter_mapping)
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from vllm.config import LoRAConfig
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from vllm.logger import init_logger
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from vllm.lora.layers import (BaseLayerWithLoRA,
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LinearScalingRotaryEmbeddingWithLora,
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LoRAMapping)
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from vllm.lora.lora import LoRALayerWeights, PackedLoRALayerWeights
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from vllm.lora.utils import (from_layer, from_layer_logits_processor,
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parse_fine_tuned_lora_name, replace_submodule)
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from vllm.model_executor.models.interfaces import SupportsLoRA
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from vllm.utils import is_pin_memory_available
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logger = init_logger(__name__)
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_GLOBAL_LORA_ID = 0
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@dataclass
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class LongContextLoRAContext:
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"""Context for lora adapters that support long context."""
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# The scaling factors to support long context lora fine tuned models.
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scaling_factors: List[float]
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# dimension to apply rotary embedding.
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rot_dim: int
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# offsets to the sin_cos_cache for each lora_id loaded.
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# This value is dynamically modified.
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offsets_by_lora_id: Dict[int, int] = field(default_factory=dict)
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def convert_mapping(
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mapping: LoRAMapping,
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lora_index_to_id: List[Optional[int]],
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max_loras: int,
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vocab_size: int,
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extra_vocab_size: int,
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long_lora_context: Optional[LongContextLoRAContext] = None,
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor,
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Optional[torch.Tensor], List[int]]:
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"""Converts LoRAMapping to index tensors.
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Args:
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mapping: LoRAMapping mapping rows in a batch to LoRA ids.
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lora_index_to_id: List mapping LoRA ids to LoRA indices.
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max_loras: Maximum number of LoRAs.
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vocab_size: Model vocab size.
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extra_vocab_size: Extra vocab size each LoRA can have.
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long_lora_context: Passed if there are long context lora in a batch.
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Returns:
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A tuple of tensors:
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base_indices: Tensor of shape [batch_size] mapping batch rows to
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LoRA indices.
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sampler_indices: Tensor of shape [batch_size] mapping requests to
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LoRA indices for sampler. For generation, this will be the
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same as base_indicies. For prefill, this will map requests
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to LoRA indices.
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sampler_indices_padded: Tensor of shape [batch_size] mapping
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requests to LoRA indices for sampler with padding.
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Same as sampler_indicies, but -1 is replaced with
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max_loras.
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embeddings_indices: Tensor of shape [2, batch_size] mapping
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requests to embedding indices. First row is for embeddings
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added by the LoRAs, second row is for the LoRA.lora_a
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embeddings.
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long_lora_indices: Tensor of shape [batch_size] mapping
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requests to RoPE offsets and rot dims for long LoRAs.
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None if long context lora doesn't exist.
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indices_len: List of lengths of the above tensors.
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Used to index into each tensor. It contains length for
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(base_indices, sampler_indices, sampler_indices_padded,
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embeddings_indices, long_lora_indices). If long_lora doesn't
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exist, it only contains first 4 entries.
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"""
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index_mapping_indices: List[int] = list(mapping.index_mapping).copy()
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embedding_indices = index_mapping_indices.copy()
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lora_indices = index_mapping_indices.copy()
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long_lora_offsets: Optional[torch.Tensor] = None
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if long_lora_context:
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long_lora_offsets = torch.zeros(len(index_mapping_indices),
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device="cuda",
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dtype=torch.long)
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prompt_mapping: List[int] = [
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lora_index_to_id.index(x) if x > 0 else -1
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for x in mapping.prompt_mapping
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]
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lora_idx = None
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for i in range(len(index_mapping_indices)):
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# TODO index can be slow. optimize
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lora_idx = (lora_index_to_id.index(index_mapping_indices[i])
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if index_mapping_indices[i] > 0 else -1)
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embedding_indices[i] = lora_idx if index_mapping_indices[i] > 0 else 0
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lora_indices[i] = lora_idx
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if long_lora_context:
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assert long_lora_offsets is not None
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lora_offset: int = long_lora_context.offsets_by_lora_id.get(
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index_mapping_indices[i], 0)
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long_lora_offsets[i] = lora_offset
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indices_list: List[Union[List[int], torch.Tensor]] = [
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index_mapping_indices, lora_indices, embedding_indices
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]
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if long_lora_context:
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assert long_lora_offsets is not None
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indices_list.append(long_lora_offsets)
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indices = torch.tensor(indices_list, dtype=torch.long, device="cuda")
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prompt_mapping_tensor = torch.tensor(prompt_mapping,
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device="cuda",
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dtype=torch.long)
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embeddings_indices = torch.stack([
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indices[2] * extra_vocab_size,
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indices[2] * (vocab_size + extra_vocab_size)
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])
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embeddings_indices[embeddings_indices == -1] = max_loras - 1
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base_indices = indices[1]
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sampler_indices = prompt_mapping_tensor
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sampler_indices_padded = sampler_indices.clone()
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sampler_indices_padded[sampler_indices_padded == -1] = max_loras - 1
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sampler_indices_padded = (
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torch.arange(
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0, len(sampler_indices_padded), device="cuda", dtype=torch.long) +
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(sampler_indices_padded * len(sampler_indices_padded)))
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long_lora_indices = None
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long_lora_indices_len: Optional[int] = None
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if long_lora_context:
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long_lora_indices = indices[3]
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long_lora_indices_len = long_lora_indices.shape[-1]
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# Contain length of indices tensors. Used to index into each tensor.
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indices_len = [
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base_indices.shape[-1], sampler_indices.shape[-1],
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sampler_indices_padded.shape[-1], embeddings_indices.shape[-1]
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]
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if long_lora_indices_len is not None:
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indices_len.append(long_lora_indices_len)
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return (base_indices, sampler_indices, sampler_indices_padded,
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embeddings_indices, long_lora_indices, indices_len)
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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(AdapterModel):
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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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scaling_factor: Optional[float] = None,
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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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scaling_factor: Scaling factor to support long context lora model.
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None if the lora is not tuned for long context support.
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"""
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self.id = lora_model_id
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# Scaling factor for long context lora model. None if it is not
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# fine tuned for the long context.
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self.scaling_factor = scaling_factor
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assert (lora_model_id >
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0), f"a valid lora id should be greater than 0, got {self.id}"
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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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@property
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def extra_vocab_size(self) -> int:
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return max(lora.extra_vocab_size
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for lora in self.loras.values()) if self.loras else 0
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def get_lora(self, module_name: str) -> Optional[LoRALayerWeights]:
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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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# (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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rank: int,
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lora_alpha: int,
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tensors: Dict[str, torch.Tensor],
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device: str = "cuda",
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dtype: Optional[torch.dtype] = None,
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embeddings: Optional[Dict[str, torch.Tensor]] = None,
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target_embedding_padding: Optional[int] = None,
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scaling_factor: Optional[float] = None,
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embedding_modules: Optional[Dict[str, str]] = None,
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embedding_padding_modules: Optional[List[str]] = 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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module_name, is_lora_a = parse_fine_tuned_lora_name(tensor_name)
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if module_name not in loras:
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lora_embeddings_tensor = None
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if embeddings:
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assert embedding_modules is not None
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embeddings_module = next(
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(k for k in embedding_modules if k in module_name),
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None)
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if embeddings_module:
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lora_embeddings_tensor = embeddings[
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embedding_modules[embeddings_module]].to(
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device=device, dtype=dtype)
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if pin_memory:
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lora_embeddings_tensor = (
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lora_embeddings_tensor.pin_memory())
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loras[module_name] = LoRALayerWeights(module_name, rank,
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lora_alpha, None, None,
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lora_embeddings_tensor)
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if is_lora_a:
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loras[module_name].lora_a = tensor.to(device=device,
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dtype=dtype).t()
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if pin_memory:
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loras[module_name].lora_a = loras[
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module_name].lora_a.pin_memory()
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else:
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loras[module_name].lora_b = tensor.to(device=device,
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dtype=dtype).t()
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assert embedding_padding_modules is not None
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if any(name in module_name
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for name in embedding_padding_modules
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) and target_embedding_padding is not None:
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lora_b = loras[module_name].lora_b
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assert target_embedding_padding >= lora_b.shape[1]
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addition = target_embedding_padding - lora_b.shape[1]
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loras[module_name].lora_b = torch.nn.functional.pad(
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lora_b, (0, addition))
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if pin_memory:
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loras[module_name].lora_b = loras[
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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, rank, loras, scaling_factor=scaling_factor)
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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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*,
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max_position_embeddings: Optional[int] = None,
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lora_model_id: Optional[int] = None,
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device: str = "cuda",
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dtype: Optional[torch.dtype] = None,
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target_embedding_padding: Optional[int] = None,
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embedding_modules: Optional[Dict[str, str]] = None,
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embedding_padding_modules: Optional[List[str]] = 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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max_position_embeddings: Max position embedding length. Used to
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scaling the largest context length. If None, the lora model's
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context length is not scaled.
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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_config_path = os.path.join(lora_dir, "adapter_config.json")
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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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new_embeddings_tensor_path = os.path.join(
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lora_dir, "new_embeddings.safetensors")
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new_embeddings_bin_file_path = os.path.join(lora_dir,
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"new_embeddings.bin")
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with open(lora_config_path) as f:
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config = json.load(f)
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if os.path.isfile(lora_tensor_path):
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tensors: Dict[str, torch.Tensor] = {}
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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,
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framework="pt") as f: # type: ignore
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for lora_module in f.keys(): # noqa
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module_name, _ = parse_fine_tuned_lora_name(lora_module)
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part_name = module_name.split(".")[-1]
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if part_name 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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# Load tensors if there are only expected modules.
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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):
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# When a bin file is provided, we rely on config to find unexpected
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# modules.
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unexpected_modules = []
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target_modules = config["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:
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print(unexpected_modules, "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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tensors = torch.load(lora_bin_file_path)
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else:
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raise ValueError(f"{lora_dir} doesn't contain tensors")
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embeddings = None
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if os.path.isfile(new_embeddings_tensor_path):
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embeddings = safetensors.torch.load_file(
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new_embeddings_tensor_path)
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elif os.path.isfile(new_embeddings_bin_file_path):
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embeddings = torch.load(new_embeddings_bin_file_path)
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rank = config["r"]
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lora_alpha = config["lora_alpha"]
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context_length = config.get("context_length", None)
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scaling_factor = None
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if context_length:
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if max_position_embeddings is None:
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max_position_embeddings = context_length
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scaling_factor = float(
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math.ceil(context_length / max_position_embeddings))
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return cls.from_lora_tensors(
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lora_model_id=get_lora_id()
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if lora_model_id is None else lora_model_id,
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rank=rank,
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lora_alpha=lora_alpha,
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tensors=tensors,
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device=device,
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dtype=dtype,
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embeddings=embeddings,
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target_embedding_padding=target_embedding_padding,
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scaling_factor=scaling_factor,
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embedding_modules=embedding_modules,
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embedding_padding_modules=embedding_padding_modules,
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)
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class LoRAModelManager(AdapterModelManager):
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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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):
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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.lora_config = lora_config
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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[Optional[int]] = [None] * self.lora_slots
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self.vocab_size = vocab_size
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self.long_lora_context: Optional[LongContextLoRAContext] = None
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self.base_indices = torch.empty(self.max_num_batched_tokens,
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dtype=torch.long,
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device="cuda")
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self.sampler_indices = torch.empty(self.max_num_batched_tokens,
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dtype=torch.long,
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device="cuda")
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self.sampler_indices_padded = torch.empty(self.max_num_batched_tokens,
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dtype=torch.long,
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device="cuda")
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self.embeddings_indices = torch.empty(2,
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self.max_num_batched_tokens,
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dtype=torch.long,
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device="cuda")
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self.long_lora_indices = torch.empty(self.max_num_batched_tokens,
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dtype=torch.long,
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device="cuda")
|
||
# Scaling factor -> offset to the sin_cos_cache to it.
|
||
# Used for long context lora.
|
||
self.scaling_factor_to_offset: Dict[float, int] = {}
|
||
# 4 is the number of indicies tensors defined above
|
||
# base_indices, sampler_indices, sampler_indices_padded,
|
||
# embeddings_indices
|
||
self.indices_len: List[Optional[int]] = [None] * 4
|
||
super().__init__(model)
|
||
if hasattr(self.model, "supported_lora_modules"):
|
||
self.supported_lora_modules = copy.deepcopy(
|
||
self.model.supported_lora_modules)
|
||
if lora_config.long_lora_scaling_factors:
|
||
# We need to replace rotary emb layer to do batch computation
|
||
# for long lora.
|
||
self.supported_lora_modules.append("rotary_emb")
|
||
self.packed_modules_mapping = copy.deepcopy(
|
||
self.model.packed_modules_mapping)
|
||
self.packed_modules: Dict[str, List[str]] = {}
|
||
self.modules: Dict[str, "BaseLayerWithLoRA"] = {}
|
||
# Dict instead of a Set for compatibility with LRUCache.
|
||
self._last_mapping: Optional[LoRAMapping] = None
|
||
self._create_lora_modules()
|
||
self.model.lora_manager = self
|
||
self.adapter_type = 'LoRa'
|
||
|
||
@property
|
||
def capacity(self) -> int:
|
||
return self.lora_config.max_cpu_loras
|
||
|
||
@property
|
||
def lora_slots(self) -> int:
|
||
return self.lora_config.max_loras
|
||
|
||
@property
|
||
def adapter_slots(self) -> int:
|
||
return self.lora_slots
|
||
|
||
def activate_adapter(
|
||
self,
|
||
lora_id: int,
|
||
) -> bool:
|
||
"""Move LoRA into a GPU buffer to be used in the forward pass."""
|
||
if lora_id in self._active_adapters:
|
||
return False
|
||
first_free_slot = next(
|
||
((i, lora_id) for i, lora_id in enumerate(self.lora_index_to_id)
|
||
if lora_id is None), None)
|
||
if first_free_slot is None:
|
||
raise ValueError("No free lora slots")
|
||
index, _ = first_free_slot
|
||
self._active_adapters[lora_id] = None
|
||
lora_model = self._registered_adapters[lora_id]
|
||
logger.debug("Activating LoRA. int id: %d, slot index: %d",
|
||
lora_model.id, index)
|
||
self.lora_index_to_id[index] = lora_model.id
|
||
for module_name, module in self.modules.items():
|
||
module_lora = lora_model.get_lora(module_name)
|
||
if module_lora:
|
||
module_lora.optimize()
|
||
module.set_lora(index, module_lora.lora_a, module_lora.lora_b,
|
||
module_lora.embeddings_tensor)
|
||
else:
|
||
module.reset_lora(index)
|
||
return True
|
||
|
||
def _deactivate_adapter(self, lora_id: int):
|
||
try:
|
||
index = self.lora_index_to_id.index(lora_id)
|
||
self.lora_index_to_id[index] = None
|
||
except ValueError:
|
||
pass
|
||
|
||
def _set_long_lora_context(self, lora: LoRAModel):
|
||
if self.long_lora_context is None:
|
||
return
|
||
|
||
if lora.scaling_factor is None:
|
||
return
|
||
|
||
if (lora.scaling_factor not in self.scaling_factor_to_offset):
|
||
raise ValueError(f"Long LoRA scaling factor {lora.scaling_factor}"
|
||
" has not been initialized.")
|
||
|
||
offsets = self.scaling_factor_to_offset.get(lora.scaling_factor)
|
||
if offsets:
|
||
self.long_lora_context.offsets_by_lora_id[lora.id] = offsets
|
||
|
||
def _add_adapter(self, lora: LoRAModel):
|
||
self._create_merged_loras_inplace(lora)
|
||
self._registered_adapters[lora.id] = lora
|
||
self._set_long_lora_context(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
|
||
|
||
# TODO see if this can be vectorized
|
||
def _set_adapter_mapping(self, mapping: LoRAMapping) -> None:
|
||
(base_indices, sampler_indices, sampler_indices_padded,
|
||
embeddings_indices, long_lora_offsets_tensor,
|
||
indices_len) = convert_mapping(mapping, self.lora_index_to_id,
|
||
self.lora_slots + 1, self.vocab_size,
|
||
self.lora_config.lora_extra_vocab_size,
|
||
self.long_lora_context)
|
||
self.base_indices[:base_indices.shape[0]].copy_(base_indices)
|
||
self.sampler_indices[:sampler_indices.shape[0]].copy_(sampler_indices)
|
||
self.sampler_indices_padded[:sampler_indices_padded.shape[0]].copy_(
|
||
sampler_indices_padded)
|
||
self.embeddings_indices[:embeddings_indices.
|
||
shape[0], :embeddings_indices.shape[1]].copy_(
|
||
embeddings_indices)
|
||
if long_lora_offsets_tensor is not None:
|
||
self.long_lora_indices[:long_lora_offsets_tensor.shape[0]].copy_(
|
||
long_lora_offsets_tensor)
|
||
else:
|
||
self.long_lora_indices.zero_()
|
||
# Maintain the reference
|
||
self.indices_len[:] = indices_len
|
||
|
||
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):
|
||
for module_name, module in self.model.named_modules(
|
||
remove_duplicate=False):
|
||
if not self._match_target_modules(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))
|
||
# LinearScalingRotaryEmbeddingWithLora is used to handle
|
||
# long context lora. Register relevant metadata.
|
||
if isinstance(new_module, LinearScalingRotaryEmbeddingWithLora):
|
||
self.long_lora_context = LongContextLoRAContext(
|
||
new_module.scaling_factors, new_module.rotary_dim)
|
||
self.scaling_factor_to_offset = \
|
||
new_module.scaling_factor_to_offset
|
||
# (yard1): TODO make this more robust
|
||
if "lm_head" in module_name:
|
||
logits_processor_module = self.model.get_submodule(
|
||
"logits_processor")
|
||
new_module = replace_submodule(
|
||
self.model, "logits_processor",
|
||
from_layer_logits_processor(logits_processor_module,
|
||
module, self.lora_slots,
|
||
self.lora_config,
|
||
self.model.config))
|
||
self.register_module(module_name, new_module)
|
||
self._register_packed_modules(module_name)
|
||
new_module.set_mapping(self.base_indices, self.sampler_indices,
|
||
self.sampler_indices_padded,
|
||
self.embeddings_indices,
|
||
self.long_lora_indices, self.indices_len)
|
||
|
||
def register_module(self, module_name: str, module: "BaseLayerWithLoRA"):
|
||
assert isinstance(module, BaseLayerWithLoRA)
|
||
self.modules[module_name] = module
|
||
|
||
def create_dummy_lora(
|
||
self,
|
||
lora_id: int,
|
||
rank: int,
|
||
scaling_factor: Optional[float],
|
||
embedding_modules: Optional[Dict[str, str]] = None) -> LoRAModel:
|
||
"""Create zero-initialized LoRAModel for warmup."""
|
||
model = LoRAModel(lora_id, rank, {}, scaling_factor)
|
||
for module_name, module in self.model.named_modules():
|
||
if not self._match_target_modules(module_name) or not isinstance(
|
||
module, BaseLayerWithLoRA) or isinstance(
|
||
module, LinearScalingRotaryEmbeddingWithLora):
|
||
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 +
|
||
self.lora_config.lora_extra_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]
|
||
embeddings_tensor_dim = (module.base_layer.embedding_dim if
|
||
hasattr(module.base_layer,
|
||
"embedding_dim") else
|
||
module.base_layer.weight.shape[1])
|
||
lora = LoRALayerWeights.create_dummy_lora_weights(
|
||
module_name,
|
||
input_dim,
|
||
output_dim,
|
||
rank,
|
||
module.lora_a_stacked.dtype,
|
||
"cpu",
|
||
embeddings_tensor_dim=embeddings_tensor_dim)
|
||
else:
|
||
lora = LoRALayerWeights.create_dummy_lora_weights(
|
||
module_name,
|
||
module.lora_a_stacked.shape[-1],
|
||
module.lora_b_stacked.shape[-2],
|
||
rank,
|
||
module.lora_a_stacked.dtype,
|
||
"cpu",
|
||
)
|
||
lora.optimize()
|
||
else:
|
||
parts = module_name.split(".")
|
||
replacements = self.packed_modules_mapping[parts[-1]]
|
||
subloras: List[Optional["LoRALayerWeights"]] = []
|
||
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",
|
||
)
|
||
lora.optimize()
|
||
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 _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[Optional[LoRALayerWeights]] = []
|
||
has_replacement = False
|
||
for r in new_module_names:
|
||
lora = lora_model.get_lora(r)
|
||
replacement_loras.append(lora)
|
||
if lora:
|
||
has_replacement = True
|
||
if not has_replacement:
|
||
continue
|
||
for i in range(len(replacement_loras)):
|
||
if replacement_loras[i]:
|
||
continue
|
||
replacement_loras[i] = None
|
||
lora_model.loras[module_name] = PackedLoRALayerWeights.pack(
|
||
replacement_loras)
|
||
|
||
def deactivate_adapter(self, adapter_id: int) -> bool:
|
||
return deactivate_adapter(adapter_id, self._active_adapters,
|
||
self._deactivate_adapter)
|
||
|
||
def add_adapter(self, adapter: LoRAModel) -> bool:
|
||
logger.debug(
|
||
"Adding lora. Model id: %d, "
|
||
"int id: %d, "
|
||
"scaling factor: %s", adapter.id, adapter.id,
|
||
adapter.scaling_factor)
|
||
return add_adapter(adapter, self._registered_adapters, self.capacity,
|
||
self._add_adapter)
|
||
|
||
def set_adapter_mapping(self, mapping: LoRAMapping) -> None:
|
||
self._last_mapping = set_adapter_mapping(mapping, self._last_mapping,
|
||
self._set_adapter_mapping)
|
||
|
||
def remove_adapter(self, adapter_id: int) -> bool:
|
||
return remove_adapter(adapter_id, self._registered_adapters,
|
||
self.deactivate_adapter)
|
||
|
||
def list_adapters(self) -> Dict[int, Any]:
|
||
return list_adapters(self._registered_adapters)
|
||
|
||
def get_adapter(self, adapter_id: int) -> Optional[Any]:
|
||
return get_adapter(adapter_id, self._registered_adapters)
|
||
|
||
|
||
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,
|
||
):
|
||
super().__init__(model, max_num_seqs, max_num_batched_tokens,
|
||
vocab_size, lora_config)
|
||
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, "
|
||
"scaling factor: %s", lora.id, lora.id, lora.scaling_factor)
|
||
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("Pinning failed. "
|
||
f"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,
|
||
lora_manager_cls: Type[LoRAModelManager] = LoRAModelManager,
|
||
**kwargs) -> LoRAModelManager:
|
||
"""Create a LoRA adapter for a given model."""
|
||
if not hasattr(model, "supported_lora_modules"):
|
||
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,
|
||
**kwargs)
|
||
return lora_manager
|