aphrodite-engine/aphrodite/lora/models.py

import copy
import json
import math
import os
import re
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union

import safetensors.torch
import torch
from loguru import logger
from torch import nn

from aphrodite.adapter_commons.models import (AdapterLRUCache, AdapterModel,
                                              AdapterModelManager)
from aphrodite.adapter_commons.utils import (add_adapter, deactivate_adapter,
                                             get_adapter, list_adapters,
                                             remove_adapter,
                                             set_adapter_mapping)
from aphrodite.common.config import LoRAConfig
from aphrodite.common.utils import is_pin_memory_available
from aphrodite.lora.layers import (BaseLayerWithLoRA,
                                   LinearScalingRotaryEmbeddingWithLora,
                                   LoRAMapping)
from aphrodite.lora.lora import LoRALayerWeights, PackedLoRALayerWeights
from aphrodite.lora.utils import (from_layer, from_layer_logits_processor,
                                  parse_fine_tuned_lora_name,
                                  replace_submodule)
from aphrodite.modeling.models.interfaces import SupportsLoRA

_GLOBAL_LORA_ID = 0


@dataclass
class LongContextLoRAContext:
    """Context for lora adapters that support long context."""
    # The scaling factors to support long context lora fine tuned models.
    scaling_factors: List[float]
    # dimension to apply rotary embedding.
    rot_dim: int
    # offsets to the sin_cos_cache for each lora_id loaded.
    # This value is dynamically modified.
    offsets_by_lora_id: Dict[int, int] = field(default_factory=dict)


def convert_mapping(
    mapping: LoRAMapping,
    lora_index_to_id: List[Optional[int]],
    max_loras: int,
    vocab_size: int,
    extra_vocab_size: int,
    long_lora_context: Optional[LongContextLoRAContext] = None,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor,
           Optional[torch.Tensor], List[int]]:
    """Converts LoRAMapping to index tensors.

    Args:
        mapping: LoRAMapping mapping rows in a batch to LoRA ids.
        lora_index_to_id: List mapping LoRA ids to LoRA indices.
        max_loras: Maximum number of LoRAs.
        vocab_size: Model vocab size.
        extra_vocab_size: Extra vocab size each LoRA can have.
        long_lora_context: Passed if there are long context lora in a batch.

    Returns:
        A tuple of tensors:
            base_indices: Tensor of shape [batch_size] mapping batch rows to
                LoRA indices.
            sampler_indices: Tensor of shape [batch_size] mapping requests to
                LoRA indices for sampler. For generation, this will be the
                same as base_indicies. For prefill, this will map requests
                to LoRA indices.
            sampler_indices_padded: Tensor of shape [batch_size] mapping
                requests to LoRA indices for sampler with padding.
                Same as sampler_indicies, but -1 is replaced with
                max_loras.
            embeddings_indices: Tensor of shape [2, batch_size] mapping
                requests to embedding indices. First row is for embeddings
                added by the LoRAs, second row is for the LoRA.lora_a
                embeddings.
            long_lora_indices: Tensor of shape [batch_size] mapping
                requests to RoPE offsets and rot dims for long LoRAs.
                None if long context lora doesn't exist.
            indices_len: List of lengths of the above tensors.
                Used to index into each tensor. It contains length for
                (base_indices, sampler_indices, sampler_indices_padded,
                embeddings_indices, long_lora_indices). If long_lora doesn't
                exist, it only contains first 4 entries.
    """
    index_mapping_indices: List[int] = list(mapping.index_mapping).copy()
    embedding_indices = index_mapping_indices.copy()
    lora_indices = index_mapping_indices.copy()
    long_lora_offsets: Optional[torch.Tensor] = None
    if long_lora_context:
        long_lora_offsets = torch.zeros(len(index_mapping_indices),
                                        device="cuda",
                                        dtype=torch.long)
    prompt_mapping: List[int] = [
        lora_index_to_id.index(x) if x > 0 else -1
        for x in mapping.prompt_mapping
    ]
    lora_idx = None
    for i in range(len(index_mapping_indices)):
        # TODO index can be slow. optimize
        lora_idx = (lora_index_to_id.index(index_mapping_indices[i])
                    if index_mapping_indices[i] > 0 else -1)
        embedding_indices[i] = lora_idx if index_mapping_indices[i] > 0 else 0
        lora_indices[i] = lora_idx
        if long_lora_context:
            assert long_lora_offsets is not None
            lora_offset: int = long_lora_context.offsets_by_lora_id.get(
                index_mapping_indices[i], 0)
            long_lora_offsets[i] = lora_offset

    indices_list: List[Union[List[int], torch.Tensor]] = [
        index_mapping_indices, lora_indices, embedding_indices
    ]
    if long_lora_context:
        assert long_lora_offsets is not None
        indices_list.append(long_lora_offsets)
    indices = torch.tensor(indices_list, dtype=torch.long, device="cuda")
    prompt_mapping_tensor = torch.tensor(prompt_mapping,
                                         device="cuda",
                                         dtype=torch.long)
    embeddings_indices = torch.stack([
        indices[2] * extra_vocab_size,
        indices[2] * (vocab_size + extra_vocab_size)
    ])
    embeddings_indices[embeddings_indices == -1] = max_loras - 1
    base_indices = indices[1]
    sampler_indices = prompt_mapping_tensor
    sampler_indices_padded = sampler_indices.clone()
    sampler_indices_padded[sampler_indices_padded == -1] = max_loras - 1
    sampler_indices_padded = (
        torch.arange(
            0, len(sampler_indices_padded), device="cuda", dtype=torch.long) +
        (sampler_indices_padded * len(sampler_indices_padded)))
    long_lora_indices = None
    long_lora_indices_len: Optional[int] = None
    if long_lora_context:
        long_lora_indices = indices[3]
        long_lora_indices_len = long_lora_indices.shape[-1]
    # Contain length of indices tensors. Used to index into each tensor.
    indices_len = [
        base_indices.shape[-1], sampler_indices.shape[-1],
        sampler_indices_padded.shape[-1], embeddings_indices.shape[-1]
    ]
    if long_lora_indices_len is not None:
        indices_len.append(long_lora_indices_len)

    return (base_indices, sampler_indices, sampler_indices_padded,
            embeddings_indices, long_lora_indices, indices_len)


def get_lora_id():
    global _GLOBAL_LORA_ID
    _GLOBAL_LORA_ID += 1
    return _GLOBAL_LORA_ID


class LoRAModel(AdapterModel):
    """A LoRA fine-tuned model."""

    def __init__(
        self,
        lora_model_id: int,
        rank: int,
        loras: Dict[str, LoRALayerWeights],
        scaling_factor: Optional[float] = None,
    ) -> None:
        """
        Args:
            lora_model_id: The integer id for the lora model.
            rank: lora rank.
            loras: module name -> weights for lora-replaced layers.
            scaling_factor: Scaling factor to support long context lora model.
                None if the lora is not tuned for long context support.
        """
        self.id = lora_model_id
        # Scaling factor for long context lora model. None if it is not
        # fine tuned for the long context.
        self.scaling_factor = scaling_factor
        assert (lora_model_id >
                0), f"a valid lora id should be greater than 0, got {self.id}"
        self.rank = rank
        self.loras: Dict[str, LoRALayerWeights] = loras

    def clone(self, lora_model_id: int) -> "LoRAModel":
        """Return a copy of the object with different ids.

        Will share the underlying tensors."""
        return self.__class__(
            lora_model_id,
            rank=self.rank,
            loras=self.loras.copy(),
        )

    @property
    def extra_vocab_size(self) -> int:
        return max(lora.extra_vocab_size
                   for lora in self.loras.values()) if self.loras else 0

    def get_lora(self, module_name: str) -> Optional[LoRALayerWeights]:
        """Get LoRA for a given module by name"""
        return self.loras.get(module_name, None)

    # (yard1): TODO see if we can derive target_embedding_padding automatically
    @classmethod
    def from_lora_tensors(
        cls,
        lora_model_id: int,
        rank: int,
        lora_alpha: int,
        tensors: Dict[str, torch.Tensor],
        device: str = "cuda",
        dtype: Optional[torch.dtype] = None,
        embeddings: Optional[Dict[str, torch.Tensor]] = None,
        target_embedding_padding: Optional[int] = None,
        scaling_factor: Optional[float] = None,
        embedding_modules: Optional[Dict[str, str]] = None,
        embedding_padding_modules: Optional[List[str]] = None,
    ) -> "LoRAModel":
        """Create a LoRAModel from a dictionary of tensors."""
        pin_memory = str(device) == "cpu" and is_pin_memory_available()
        loras: Dict[str, LoRALayerWeights] = {}
        for tensor_name, tensor in tensors.items():
            module_name, is_lora_a = parse_fine_tuned_lora_name(tensor_name)
            if module_name not in loras:
                lora_embeddings_tensor = None
                if embeddings:
                    assert embedding_modules is not None
                    embeddings_module = next(
                        (k for k in embedding_modules if k in module_name),
                        None)
                    if embeddings_module:
                        lora_embeddings_tensor = embeddings[
                            embedding_modules[embeddings_module]].to(
                                device=device, dtype=dtype)
                        if pin_memory:
                            lora_embeddings_tensor = (
                                lora_embeddings_tensor.pin_memory())
                loras[module_name] = LoRALayerWeights(module_name, rank,
                                                      lora_alpha, None, None,
                                                      lora_embeddings_tensor)
            if is_lora_a:
                loras[module_name].lora_a = tensor.to(device=device,
                                                      dtype=dtype).t()
                if pin_memory:
                    loras[module_name].lora_a = loras[
                        module_name].lora_a.pin_memory()
            else:
                loras[module_name].lora_b = tensor.to(device=device,
                                                      dtype=dtype).t()
                assert embedding_padding_modules is not None
                if any(name in module_name
                       for name in embedding_padding_modules
                       ) and target_embedding_padding is not None:
                    lora_b = loras[module_name].lora_b
                    assert target_embedding_padding >= lora_b.shape[1]
                    addition = target_embedding_padding - lora_b.shape[1]
                    loras[module_name].lora_b = torch.nn.functional.pad(
                        lora_b, (0, addition))
                if pin_memory:
                    loras[module_name].lora_b = loras[
                        module_name].lora_b.pin_memory()

        for lora in loras.values():
            lora.optimize()
        return cls(lora_model_id, rank, loras, scaling_factor=scaling_factor)

    @classmethod
    def from_local_checkpoint(
        cls,
        lora_dir: str,
        expected_lora_modules: List[str],
        *,
        max_position_embeddings: Optional[int] = None,
        lora_model_id: Optional[int] = None,
        device: str = "cuda",
        dtype: Optional[torch.dtype] = None,
        target_embedding_padding: Optional[int] = None,
        embedding_modules: Optional[Dict[str, str]] = None,
        embedding_padding_modules: Optional[List[str]] = None,
    ) -> "LoRAModel":
        """Create a LoRAModel from a local checkpoint.
        
        Args:
            lora_dir: The local path that has lora data.
            expected_lora_modules: Name of modules that are expected to be
                replaced by lora.
            max_position_embeddings: Max position embedding length. Used to
                scaling the largest context length. If None, the lora model's
                context length is not scaled.
            lora_model_id: Lora model id. If not given, automatically set by
                a global counter.
            device: Device where the lora model is loaded.
            dtype: dtype of the lora model weights.

        Returns:
            Loaded LoRA Model.
        """
        lora_config_path = os.path.join(lora_dir, "adapter_config.json")
        lora_tensor_path = os.path.join(lora_dir, "adapter_model.safetensors")
        lora_bin_file_path = os.path.join(lora_dir, "adapter_model.bin")
        new_embeddings_tensor_path = os.path.join(
            lora_dir, "new_embeddings.safetensors")
        new_embeddings_bin_file_path = os.path.join(lora_dir,
                                                    "new_embeddings.bin")
        with open(lora_config_path) as f:
            config = json.load(f)
        if os.path.isfile(lora_tensor_path):
            tensors: Dict[str, torch.Tensor] = {}
            # Find unexpected modules.
            # Use safetensor key as a source of truth to find expected modules.
            # in peft if you have target_modules A, B, C and C does not exist
            # in the model it won’t error and model will be trained with A, B
            # loraified. C won’t exist in the safetensor but it will exist in
            # the target_modules of the adapter_config.json.
            unexpected_modules = []
            with safetensors.safe_open(lora_tensor_path,
                                       framework="pt") as f:  # type: ignore
                for lora_module in f.keys():  # noqa
                    module_name, _ = parse_fine_tuned_lora_name(lora_module)
                    part_name = module_name.split(".")[-1]
                    if part_name not in expected_lora_modules:
                        unexpected_modules.append(module_name)
                if unexpected_modules:
                    raise ValueError(
                        f"While loading {lora_dir}, expected"
                        f" target modules in {expected_lora_modules}"
                        f" but received {unexpected_modules}."
                        f" Please verify that the loaded LoRA module is correct"
                    )
                # Load tensors if there are only expected modules.
                for module in f.keys():  # noqa
                    tensors[module] = f.get_tensor(module)
        elif os.path.isfile(lora_bin_file_path):
            # When a bin file is provided, we rely on config to find unexpected
            # modules.
            unexpected_modules = []
            target_modules = config["target_modules"]
            for module in target_modules:
                # Compatible with more modules,
                # such as:layers.11.self_attn.k_proj
                part_name = module.split(".")[-1]
                if part_name not in expected_lora_modules:
                    unexpected_modules.append(module)
            # loaded lora's target modules must be a subset of
            # expected_lora_modules. It is not reliable. But there's no
            # other better mechanism.
            if unexpected_modules:
                print(unexpected_modules, "modules")
                raise ValueError(
                    f"While loading {lora_dir}, expected"
                    f" target modules in {expected_lora_modules}"
                    f" but received {unexpected_modules}."
                    f" Please verify that the loaded LoRA module is correct")
            tensors = torch.load(lora_bin_file_path)
        else:
            raise ValueError(f"{lora_dir} doesn't contain tensors")

        embeddings = None
        if os.path.isfile(new_embeddings_tensor_path):
            embeddings = safetensors.torch.load_file(
                new_embeddings_tensor_path)
        elif os.path.isfile(new_embeddings_bin_file_path):
            embeddings = torch.load(new_embeddings_bin_file_path)

        rank = config["r"]
        lora_alpha = config["lora_alpha"]
        context_length = config.get("context_length", None)
        scaling_factor = None
        if context_length:
            if max_position_embeddings is None:
                max_position_embeddings = context_length
            scaling_factor = float(
                math.ceil(context_length / max_position_embeddings))

        return cls.from_lora_tensors(
            lora_model_id=get_lora_id()
            if lora_model_id is None else lora_model_id,
            rank=rank,
            lora_alpha=lora_alpha,
            tensors=tensors,
            device=device,
            dtype=dtype,
            embeddings=embeddings,
            target_embedding_padding=target_embedding_padding,
            scaling_factor=scaling_factor,
            embedding_modules=embedding_modules,
            embedding_padding_modules=embedding_padding_modules,
        )


class LoRAModelManager(AdapterModelManager):
    """A manager that manages multiple LoRA-fine-tuned models."""

    def __init__(
        self,
        model: SupportsLoRA,
        max_num_seqs: int,
        max_num_batched_tokens: int,
        vocab_size: int,
        lora_config: LoRAConfig,
    ):
        """Create a LoRAModelManager and adapter for a given model.

        Args:
            model: the model to be adapted.
            max_num_seqs: the maximum number of sequences model can run in a
                single batch.
            max_num_batched_tokens: the maximum number of tokens model can run
                in a single batch.
            vocab_size: the vocab size of the model.
            lora_config: the LoRA configuration.
        """
        self.lora_config = lora_config
        self.max_num_seqs = max_num_seqs
        assert self.capacity >= self.lora_slots
        self.max_num_batched_tokens = math.ceil(max_num_batched_tokens / 8) * 8
        self.lora_index_to_id: List[Optional[int]] = [None] * self.lora_slots
        self.vocab_size = vocab_size
        self.long_lora_context: Optional[LongContextLoRAContext] = None
        self.base_indices = torch.empty(self.max_num_batched_tokens,
                                        dtype=torch.long,
                                        device="cuda")
        self.sampler_indices = torch.empty(self.max_num_batched_tokens,
                                           dtype=torch.long,
                                           device="cuda")
        self.sampler_indices_padded = torch.empty(self.max_num_batched_tokens,
                                                  dtype=torch.long,
                                                  device="cuda")
        self.embeddings_indices = torch.empty(2,
                                              self.max_num_batched_tokens,
                                              dtype=torch.long,
                                              device="cuda")
        self.long_lora_indices = torch.empty(self.max_num_batched_tokens,
                                             dtype=torch.long,
                                             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(f"Activating LoRA. int id: {lora_model.id}, "
                     f"slot index: {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(f"Adding lora. Model id: {adapter.id}, "
                     f"int id: {adapter.id}, "
                     f"scaling factor: {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(f"Adding lora. Model id: {lora.id}, "
                     f"int id: {lora.id}, "
                     f"scaling factor: {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