layers.py

# pylint: disable=unused-argument
import math
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PretrainedConfig

from vllm.adapter_commons.layers import AdapterMapping
from vllm.config import LoRAConfig
from vllm.distributed import (get_tensor_model_parallel_rank,
                              get_tensor_model_parallel_world_size,
                              split_tensor_along_last_dim,
                              tensor_model_parallel_all_gather,
                              tensor_model_parallel_all_reduce,
                              tensor_model_parallel_gather)
from vllm.distributed.utils import divide
from vllm.lora.punica import PunicaWrapper
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                               MergedColumnParallelLinear,
                                               QKVParallelLinear,
                                               ReplicatedLinear,
                                               RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.rotary_embedding import (
    LinearScalingRotaryEmbedding, RotaryEmbedding)
from vllm.model_executor.layers.vocab_parallel_embedding import (
    VocabParallelEmbedding)

if TYPE_CHECKING:
    pass


def _get_lora_device(base_layer: nn.Module) -> torch.device:
    # code borrowed from https://github.com/fmmoret/vllm/blob/fm-support-lora-on-quantized-models/vllm/lora/layers.py#L34
    """Returns the device for where to place the LoRA tensors."""
    # unquantizedLinear
    if hasattr(base_layer, "weight"):
        return base_layer.weight.device
    # GPTQ/AWQ
    elif hasattr(base_layer, "qweight"):
        return base_layer.qweight.device
    # marlin
    elif hasattr(base_layer, "B"):
        return base_layer.B.device
    else:
        raise ValueError(f"Unsupported base layer: {base_layer}")


def _not_fully_sharded_can_replace(can_replace):
    """
    decorator which adds the condition of not using fully sharded loras
    intended to wrap can_replace_layer()
    """

    def dec(*args, **kwargs):
        decorate = kwargs.pop("decorate") if "decorate" in kwargs else True
        condition = (not kwargs["lora_config"].fully_sharded_loras
                     if decorate else True)
        return can_replace(*args, **kwargs) and condition

    return dec


@dataclass
class LoRAMapping(AdapterMapping):
    is_prefill: bool = False


class BaseLayerWithLoRA(nn.Module):

    def slice_lora_a(
        self, lora_a: Union[torch.Tensor, List[Union[torch.Tensor, None]]]
    ) -> Union[torch.Tensor, List[Union[torch.Tensor, None]]]:
        """Slice lora a if splitting for tensor parallelism."""
        ...

    def slice_lora_b(
        self, lora_b: Union[torch.Tensor, List[Union[torch.Tensor, None]]]
    ) -> Union[torch.Tensor, List[Union[torch.Tensor, None]]]:
        """Slice lora b if splitting with tensor parallelism."""
        ...

    def create_lora_weights(
        self,
        max_loras: int,
        lora_config: LoRAConfig,
        model_config: Optional[PretrainedConfig] = None,
    ) -> None:
        """Initializes lora matrices."""
        ...

    def reset_lora(self, index: int):
        """Resets the lora weights at index back to 0."""
        ...

    def set_lora(
        self,
        index: int,
        lora_a: torch.Tensor,
        lora_b: torch.Tensor,
        embeddings_tensor: Optional[torch.Tensor],
    ):
        """Overwrites lora tensors at index."""
        ...

    def set_mapping(
        self,
        punica_wrapper: PunicaWrapper,
    ):
        self.punica_wrapper: PunicaWrapper = punica_wrapper

    @classmethod
    def can_replace_layer(
        cls,
        source_layer: nn.Module,
        lora_config: LoRAConfig,
        packed_modules_list: List,
        model_config: Optional[PretrainedConfig],
    ) -> bool:
        """Returns True if the layer can be replaced by this LoRA layer."""
        raise NotImplementedError


class VocabParallelEmbeddingWithLoRA(BaseLayerWithLoRA):

    def __init__(self, base_layer: VocabParallelEmbedding) -> None:
        super().__init__()
        self.base_layer = base_layer
        self.embeddings_slice: Optional[Tuple[int, int]]
        self.embeddings_weights: Optional[torch.Tensor]
        self.device = _get_lora_device(self.base_layer)

    def create_lora_weights(
            self,
            max_loras: int,
            lora_config: LoRAConfig,
            model_config: Optional[PretrainedConfig] = None) -> None:

        if self.base_layer.num_added_embeddings_per_partition > 0:
            # We can start adding lora weights
            self.embeddings_weights = self.base_layer.weight.data[
                self.base_layer.num_org_embeddings_per_partition:self.
                base_layer.num_org_embeddings_per_partition +
                self.base_layer.num_added_embeddings_per_partition]
            self.embeddings_slice = (
                self.base_layer.shard_indices.added_vocab_start_index -
                self.base_layer.org_vocab_size,
                self.base_layer.shard_indices.added_vocab_end_index -
                self.base_layer.org_vocab_size)
            self.base_layer.weight.data[
                self.base_layer.num_org_embeddings_per_partition:].fill_(0)
        else:
            self.embeddings_slice = None
            self.embeddings_weights = None

        self.embeddings_tensors = torch.zeros(
            (
                max_loras,
                lora_config.lora_extra_vocab_size,
                self.base_layer.embedding_dim,
            ),
            dtype=self.base_layer.weight.dtype,
            device=self.base_layer.weight.device,
        )
        self.lora_a_stacked = torch.zeros(
            (
                max_loras,
                self.base_layer.org_vocab_size +
                lora_config.lora_extra_vocab_size,
                lora_config.max_lora_rank,
            ),
            dtype=lora_config.lora_dtype,
            device=self.base_layer.weight.device,
        )
        self.lora_b_stacked = torch.zeros(
            (
                max_loras,
                1,
                self.base_layer.embedding_dim,
                lora_config.max_lora_rank,
            ),
            dtype=lora_config.lora_dtype,
            device=self.base_layer.weight.device,
        )
        self.lora_a_stacked_2d = self.lora_a_stacked.view(
            self.lora_a_stacked.shape[0] * self.lora_a_stacked.shape[1],
            self.lora_a_stacked.shape[2],
        )

    def reset_lora(self, index: int):
        self.lora_a_stacked[index] = 0
        self.lora_b_stacked[index] = 0
        self.embeddings_tensors[index] = 0

    def set_lora(
        self,
        index: int,
        lora_a: torch.Tensor,
        lora_b: torch.Tensor,
        embeddings_tensor: Optional[torch.Tensor],
    ):
        self.reset_lora(index)
        self.lora_a_stacked[index, :lora_a.shape[0], :lora_a.shape[1]].copy_(
            lora_a, non_blocking=True)
        self.lora_b_stacked[index,
                            0, :lora_b.shape[1], :lora_b.shape[0]].copy_(
                                lora_b.T, non_blocking=True)
        
        self.lora_a = lora_a.to(self.device)
        self.lora_b = lora_b.to(self.device)

        if self.lora_config.merge_lora:
            merged_weights = torch.matmul(self.lora_a, self.lora_b)
            if merged_weights.shape != self.base_layer.weight.data:
                merged_weights = merged_weights.T + self.base_layer.weight
            else:
                merged_weights = merged_weights + self.base_layer.weight
            self.base_layer.weight.data.copy_(merged_weights)

        if embeddings_tensor is not None:
            self.embeddings_tensors[
                index, :embeddings_tensor.shape[0], :embeddings_tensor.
                shape[1], ].copy_(embeddings_tensor, non_blocking=True)
            if self.embeddings_slice is not None:
                # TODO(yard1): Optimize this copy, we don't need to copy
                # everything, just the modified part
                embeddings = self.embeddings_tensors.view(
                    self.embeddings_tensors.shape[0] *
                    self.embeddings_tensors.shape[1],
                    self.embeddings_tensors.shape[2],
                )[self.embeddings_slice[0]:self.embeddings_slice[1]]
                assert self.embeddings_weights is not None
                self.embeddings_weights[:embeddings.shape[0]].copy_(embeddings)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        added_tokens_mask = x > self.base_layer.org_vocab_size - 1
        embeddings_indices = self.punica_wrapper.embeddings_indices            
        indices = embeddings_indices[0].view_as(x)

        if not self.lora_config.merge_lora:
            indices_0 = embeddings_indices[1].view_as(x)
            full_lora_a_embeddings = F.embedding(
                x + indices_0,
                self.lora_a_stacked_2d,
            )

            full_output = self.base_layer.forward(
                x.add_(indices * added_tokens_mask))

            full_output_org = full_output
            if full_output.ndim == 3:
                full_output = full_output.view(
                    full_output.shape[0] * full_output.shape[1], -1)
            if full_lora_a_embeddings.ndim == 3:
                full_lora_a_embeddings = full_lora_a_embeddings.view(
                    full_lora_a_embeddings.shape[0] *
                    full_lora_a_embeddings.shape[1],
                    -1,
                )

            # Embedding layer only need expand op
            self.punica_wrapper.add_expand(full_output,
                                        full_lora_a_embeddings,
                                        self.lora_b_stacked,
                                        add_input=True)
            return full_output.view_as(full_output_org)
        else:
            full_output = self.base_layer.forward(
                x.add_(indices * added_tokens_mask))
            return full_output

    @classmethod
    def can_replace_layer(
        cls,
        source_layer: nn.Module,
        lora_config: LoRAConfig,
        packed_modules_list: List,
        model_config: Optional[PretrainedConfig],
    ) -> bool:
        return type(source_layer) is VocabParallelEmbedding


class ReplicatedLinearWithLoRA(BaseLayerWithLoRA):

    def __init__(self, base_layer: ReplicatedLinear) -> None:
        super().__init__()
        self.base_layer = base_layer
        self.input_size = self.base_layer.input_size
        self.output_size = self.base_layer.output_size
        self.device = _get_lora_device(self.base_layer)

    def create_lora_weights(
        self,
        max_loras: int,
        lora_config: LoRAConfig,
        model_config: Optional[PretrainedConfig] = None,
    ) -> None:
        self.lora_config = lora_config
        lora_a_output_size = lora_config.max_lora_rank
        self.lora_a_stacked = torch.zeros(
            max_loras,
            1,
            lora_a_output_size,
            self.input_size,
            dtype=lora_config.lora_dtype,
            device=self.device,
        )
        self.lora_b_stacked = torch.zeros(
            max_loras,
            1,
            self.output_size,
            lora_config.max_lora_rank,
            dtype=lora_config.lora_dtype,
            device=self.device,
        )

    def reset_lora(self, index: int):
        self.lora_a_stacked[index] = 0
        self.lora_b_stacked[index] = 0

    def set_lora(
        self,
        index: int,
        lora_a: torch.Tensor,
        lora_b: torch.Tensor,
        embeddings_tensor: Optional[torch.Tensor],
    ):
        self.reset_lora(index)

        self.lora_a_stacked[index,
                            0, :lora_a.shape[1], :lora_a.shape[0]].copy_(
                                lora_a.T, non_blocking=True)
        self.lora_b_stacked[index,
                            0, :lora_b.shape[1], :lora_b.shape[0]].copy_(
                                lora_b.T, non_blocking=True)

    def apply(self, x: torch.Tensor,
              bias: Optional[torch.Tensor]) -> torch.Tensor:
        output = self.base_layer.quant_method.apply(self.base_layer, x, bias)
        self.punica_wrapper.add_lora(output, x, self.lora_a_stacked,
                                     self.lora_b_stacked, 1.0)
        return output

    def forward(self, input_):
        """Forward of ReplicatedLinearWithLoRA

        Args:
            input_: Tensor whose last dimension is `input_size`.

        Returns:
            - output
            - bias
        """
        bias = (self.base_layer.bias
                if not self.base_layer.skip_bias_add else None)

        # Matrix multiply.
        output = self.apply(input_, bias)

        output_bias = (self.base_layer.bias
                       if self.base_layer.skip_bias_add else None)
        return output, output_bias

    @classmethod
    @_not_fully_sharded_can_replace
    def can_replace_layer(
        cls,
        source_layer: nn.Module,
        lora_config: LoRAConfig,
        packed_modules_list: List,
        model_config: Optional[PretrainedConfig],
    ) -> bool:
        return type(source_layer) is ReplicatedLinear


class ColumnParallelLinearWithLoRA(BaseLayerWithLoRA):
    """
    LoRA on top of ColumnParallelLinear layer.

    LoRA B is sliced for tensor parallelism.
    """

    def __init__(self, base_layer: ColumnParallelLinear) -> None:
        super().__init__()
        self.base_layer = base_layer
        self.tp_size = get_tensor_model_parallel_world_size()
        self.input_size = self.base_layer.input_size
        self.output_size = self.base_layer.output_size_per_partition
        self.device = _get_lora_device(self.base_layer)

    def create_lora_weights(
        self,
        max_loras: int,
        lora_config: LoRAConfig,
        model_config: Optional[PretrainedConfig] = None,
    ) -> None:
        self.lora_config = lora_config
        self.tp_size = get_tensor_model_parallel_world_size()
        lora_a_output_size_per_partition = (
            lora_config.max_lora_rank if not lora_config.fully_sharded_loras
            else divide(lora_config.max_lora_rank, self.tp_size))
        self.lora_a_stacked = torch.zeros(
            max_loras,
            1,
            lora_a_output_size_per_partition,
            self.input_size,
            dtype=lora_config.lora_dtype,
            device=self.device,
        )
        self.lora_b_stacked = torch.zeros(
            max_loras,
            1,
            self.output_size,
            lora_config.max_lora_rank,
            dtype=lora_config.lora_dtype,
            device=self.device,
        )
        self.output_dim = self.lora_b_stacked.shape[2]

    def reset_lora(self, index: int):
        self.lora_a_stacked[index] = 0
        self.lora_b_stacked[index] = 0

    def slice_lora_a(self, lora_a: torch.Tensor) -> torch.Tensor:
        return lora_a

    def slice_lora_b(self, lora_b: torch.Tensor) -> torch.Tensor:
        tensor_model_parallel_rank = get_tensor_model_parallel_rank()
        shard_size = self.output_dim
        start_idx = tensor_model_parallel_rank * shard_size
        end_idx = (tensor_model_parallel_rank + 1) * shard_size
        lora_b = lora_b[:, start_idx:end_idx]
        return lora_b

    def set_lora(
        self,
        index: int,
        lora_a: torch.Tensor,
        lora_b: torch.Tensor,
        embeddings_tensor: Optional[torch.Tensor],
    ):
        self.reset_lora(index)

        if self.tp_size > 1:
            lora_a = self.slice_lora_a(lora_a)
            lora_b = self.slice_lora_b(lora_b)

        self.lora_a_stacked[index,
                            0, :lora_a.shape[1], :lora_a.shape[0]].copy_(
                                lora_a.T, non_blocking=True)
        self.lora_b_stacked[index,
                            0, :lora_b.shape[1], :lora_b.shape[0]].copy_(
                                lora_b.T, non_blocking=True)

    def apply(self, x: torch.Tensor,
              bias: Optional[torch.Tensor]) -> torch.Tensor:
        output = self.base_layer.quant_method.apply(self.base_layer, x, bias)
        self.punica_wrapper.add_lora(output, x, self.lora_a_stacked,
                                     self.lora_b_stacked, 1.0)
        return output

    def forward(self, input_):
        """Forward of ColumnParallelLinear

        Args:
            input_: Tensor whose last dimension is `input_size`.

        Returns:
            - output
            - bias
        """
        bias = (self.base_layer.bias
                if not self.base_layer.skip_bias_add else None)

        # Matrix multiply.
        output_parallel = self.apply(input_, bias)
        if self.base_layer.gather_output:
            # All-gather across the partitions.
            output = tensor_model_parallel_all_gather(output_parallel)
        else:
            output = output_parallel
        output_bias = (self.base_layer.bias
                       if self.base_layer.skip_bias_add else None)
        return output, output_bias

    @classmethod
    @_not_fully_sharded_can_replace
    def can_replace_layer(
        cls,
        source_layer: nn.Module,
        lora_config: LoRAConfig,
        packed_modules_list: List,
        model_config: Optional[PretrainedConfig],
    ) -> bool:
        return type(source_layer) is ColumnParallelLinear or (
            type(source_layer) is MergedColumnParallelLinear
            and len(packed_modules_list) == 1)


class MergedColumnParallelLinearWithLoRA(ColumnParallelLinearWithLoRA):
    """ColumnParallelLinear layer that is composed of 2 sublayers (slices)
    packed together (eg. gate_proj + up_proj -> gate_up_proj).

    This means we have 2 LoRAs, each applied to one half of the layer.

    Both slices must have the same size.
    """

    def __init__(self, base_layer: MergedColumnParallelLinear) -> None:
        super().__init__(base_layer)

    def create_lora_weights(
        self,
        max_loras: int,
        lora_config: LoRAConfig,
        model_config: Optional[PretrainedConfig] = None,
    ) -> None:
        self.lora_config = lora_config
        n_slices = 2
        if not (len(self.base_layer.output_sizes) == n_slices
                and self.base_layer.output_sizes[0]
                == self.base_layer.output_sizes[1]):
            raise ValueError(
                "LoRAColumnParallelLinear2Slice requires 2 slices with "
                "the same size.")
        self.tp_size = get_tensor_model_parallel_world_size()
        self.tp_rank = get_tensor_model_parallel_rank()

        lora_a_output_size_per_partition = (
            lora_config.max_lora_rank if not lora_config.fully_sharded_loras
            else divide(lora_config.max_lora_rank, self.tp_size))

        self.lora_a_stacked = tuple(
            torch.zeros(
                max_loras,
                1,
                lora_a_output_size_per_partition,
                self.input_size,
                dtype=lora_config.lora_dtype,
                device=self.device,
            ) for _ in range(n_slices))
        self.lora_b_stacked = tuple(
            torch.zeros(
                max_loras,
                1,
                self.output_size // 2,
                lora_config.max_lora_rank,
                dtype=lora_config.lora_dtype,
                device=self.device,
            ) for _ in range(n_slices))

        self.output_dim = self.lora_b_stacked[0].shape[2]

    def reset_lora(self, index: int):
        self.lora_a_stacked[0][index] = 0
        self.lora_a_stacked[1][index] = 0
        self.lora_b_stacked[0][index] = 0
        self.lora_b_stacked[1][index] = 0

    def slice_lora_a(
        self, lora_a: List[Union[torch.Tensor, None]]
    ) -> List[Union[torch.Tensor, None]]:
        return lora_a

    def slice_lora_b(
        self, lora_b: List[Union[torch.Tensor, None]]
    ) -> List[Union[torch.Tensor, None]]:
        if lora_b[0] is None or lora_b[1] is None:
            return lora_b
        shard_size = self.output_dim
        start_idx = self.tp_rank * shard_size
        end_idx = (self.tp_rank + 1) * shard_size
        lora_b = [
            lora_b[0][:, start_idx:end_idx],
            lora_b[1][:, start_idx:end_idx],
        ]
        return lora_b

    def set_lora(
        self,
        index: int,
        lora_a: torch.Tensor,
        lora_b: torch.Tensor,
        embeddings_tensor: Optional[torch.Tensor],
    ):
        self.reset_lora(index)

        if self.tp_size > 1:
            lora_a = self.slice_lora_a(lora_a)
            lora_b = self.slice_lora_b(lora_b)

        if lora_a[0] is not None:
            self.lora_a_stacked[0][
                index, 0, :lora_a[0].shape[1], :lora_a[0].shape[0]].copy_(
                    lora_a[0].T, non_blocking=True)
            self.lora_b_stacked[0][
                index, 0, :lora_b[0].shape[1], :lora_b[0].shape[0]].copy_(
                    lora_b[0].T, non_blocking=True)
        if lora_a[1] is not None:
            self.lora_a_stacked[1][
                index, 0, :lora_a[1].shape[1], :lora_a[1].shape[0]].copy_(
                    lora_a[1].T, non_blocking=True)
            self.lora_b_stacked[1][
                index, 0, :lora_b[1].shape[1], :lora_b[1].shape[0]].copy_(
                    lora_b[1].T, non_blocking=True)

    def apply(self, x: torch.Tensor,
              bias: Optional[torch.Tensor]) -> torch.Tensor:
        output = self.base_layer.quant_method.apply(self.base_layer, x, bias)
        self.punica_wrapper.add_lora_packed_nslice(
            output, x, self.lora_a_stacked, self.lora_b_stacked, 1.0,
            (self.output_dim, self.output_dim))
        return output

    @classmethod
    @_not_fully_sharded_can_replace
    def can_replace_layer(
        cls,
        source_layer: nn.Module,
        lora_config: LoRAConfig,
        packed_modules_list: List,
        model_config: Optional[PretrainedConfig],
    ) -> bool:
        return (type(source_layer) is MergedColumnParallelLinear
                and len(packed_modules_list) == 2)


class QKVParallelLinearWithLora(ColumnParallelLinearWithLoRA):
    """
    ColumnParallelLinear layer that is specifically designed for
    qkv_proj. Certain models, such as chtglm3 and baichuan-7b,
    only contains a single LoRA within their qkv_proj layer.

    During inference with Tensor Parallel, the weights of lora_b
    must be accurately partitioned according to the respective ranks.

    Q slice may have different shape than K and V slices (which both have
    the same shape).
    """

    def __init__(self, base_layer: QKVParallelLinear) -> None:
        super().__init__(base_layer)
        self.tp_size = get_tensor_model_parallel_world_size()
        self.q_proj_total_size = (self.base_layer.total_num_heads *
                                  self.base_layer.head_size)
        self.q_proj_shard_size = (self.base_layer.num_heads *
                                  self.base_layer.head_size)
        self.kv_proj_shard_size = (self.base_layer.num_kv_heads *
                                   self.base_layer.head_size)
        self.kv_proj_total_size = (self.base_layer.total_num_kv_heads *
                                   self.base_layer.head_size)

    def slice_lora_b(self, lora_b: torch.Tensor) -> torch.Tensor:
        tp_rank = get_tensor_model_parallel_rank()
        self.q_shard_id = tp_rank
        self.kv_shard_id = tp_rank // self.base_layer.num_kv_head_replicas
        lora_b_q = lora_b[:, self.q_proj_shard_size *
                          self.q_shard_id:self.q_proj_shard_size *
                          (self.q_shard_id + 1)]
        k_offset = self.q_proj_total_size
        lora_b_k = lora_b[:, k_offset +
                          self.kv_proj_shard_size * self.kv_shard_id:k_offset +
                          self.kv_proj_shard_size * (self.kv_shard_id + 1)]
        v_offset = k_offset + self.kv_proj_total_size
        lora_b_v = lora_b[:, v_offset +
                          self.kv_proj_shard_size * self.kv_shard_id:v_offset +
                          self.kv_proj_shard_size * (self.kv_shard_id + 1)]
        lora_b = torch.cat([lora_b_q, lora_b_k, lora_b_v], dim=1)
        return lora_b

    def set_lora(
        self,
        index: int,
        lora_a: torch.Tensor,
        lora_b: torch.Tensor,
        embeddings_tensor: Optional[torch.Tensor],
    ):
        self.reset_lora(index)
        if self.tp_size > 1:
            lora_a = self.slice_lora_a(lora_a)
            lora_b = self.slice_lora_b(lora_b)

        self.lora_a_stacked[index,
                            0, :lora_a.shape[1], :lora_a.shape[0]].copy_(
                                lora_a.T, non_blocking=True)
        self.lora_b_stacked[index,
                            0, :lora_b.shape[1], :lora_b.shape[0]].copy_(
                                lora_b.T, non_blocking=True)

    @classmethod
    @_not_fully_sharded_can_replace
    def can_replace_layer(cls, source_layer: nn.Module,
                          lora_config: LoRAConfig, packed_modules_list: List,
                          model_config: Optional[PretrainedConfig]) -> bool:
        return type(source_layer) is QKVParallelLinear and len(
            packed_modules_list) == 1


class MergedQKVParallelLinearWithLora(ColumnParallelLinearWithLoRA):
    """ColumnParallelLinear layer that is composed of 3 sublayers (slices)
    packed together in qkv proj fashion
    (q_proj + k_proj + v_proj -> qkv_proj).

    This means we have 3 LoRAs, each applied to one slice of the layer.

    Q slice may have different shape than K and V slices (which both have
    the same shape).
    """

    def __init__(self, base_layer: QKVParallelLinear) -> None:
        super().__init__(base_layer)

    def create_lora_weights(
        self,
        max_loras: int,
        lora_config: LoRAConfig,
        model_config: Optional[PretrainedConfig] = None,
    ) -> None:
        self.lora_config = lora_config
        self.tp_size = get_tensor_model_parallel_world_size()
        self.tp_rank = get_tensor_model_parallel_rank()
        self.q_proj_shard_size = (self.base_layer.num_heads *
                                  self.base_layer.head_size)
        self.kv_proj_shard_size = (self.base_layer.num_kv_heads *
                                   self.base_layer.head_size)
        self.q_shard_id = self.tp_rank
        self.kv_shard_id = self.tp_rank // self.base_layer.num_kv_head_replicas

        lora_a_output_size_per_partition = (
            lora_config.max_lora_rank if not lora_config.fully_sharded_loras
            else divide(lora_config.max_lora_rank, self.tp_size))
        # q, k, v
        self.lora_a_stacked = (
            torch.zeros(
                max_loras,
                1,
                lora_a_output_size_per_partition,
                self.input_size,
                dtype=lora_config.lora_dtype,
                device=self.device,
            ),
            torch.zeros(
                max_loras,
                1,
                lora_a_output_size_per_partition,
                self.input_size,
                dtype=lora_config.lora_dtype,
                device=self.device,
            ),
            torch.zeros(
                max_loras,
                1,
                lora_a_output_size_per_partition,
                self.input_size,
                dtype=lora_config.lora_dtype,
                device=self.device,
            ),
        )
        self.lora_b_stacked = (
            torch.zeros(
                max_loras,
                1,
                self.q_proj_shard_size,
                lora_config.max_lora_rank,
                dtype=lora_config.lora_dtype,
                device=self.device,
            ),
            torch.zeros(
                max_loras,
                1,
                self.kv_proj_shard_size,
                lora_config.max_lora_rank,
                dtype=lora_config.lora_dtype,
                device=self.device,
            ),
            torch.zeros(
                max_loras,
                1,
                self.kv_proj_shard_size,
                lora_config.max_lora_rank,
                dtype=lora_config.lora_dtype,
                device=self.device,
            ),
        )

        self.output_slices = (
            self.q_proj_shard_size,
            self.kv_proj_shard_size,
            self.kv_proj_shard_size,
        )
        self.packed_indices: Optional[torch.Tensor] = None
        self.standard_indices: Optional[torch.Tensor] = None
        # lazily initialized.
        self.indices: torch.Tensor
        self.indices_len: List[int]

    def reset_lora(self, index: int):
        if not self.lora_config.merge_lora:
            self.lora_a_stacked[0][index] = 0
            self.lora_b_stacked[0][index] = 0
            self.lora_a_stacked[1][index] = 0
            self.lora_b_stacked[1][index] = 0
            self.lora_a_stacked[2][index] = 0
            self.lora_b_stacked[2][index] = 0

    def slice_lora_a(
        self, lora_a: List[Union[torch.Tensor, None]]
    ) -> List[Union[torch.Tensor, None]]:
        return lora_a

    def slice_lora_b(
        self, lora_b: List[Union[torch.Tensor, None]]
    ) -> List[Union[torch.Tensor, None]]:
        lora_b_q, lora_b_k, lora_b_v = None, None, None
        if lora_b[0] is not None:
            lora_b_q = lora_b[0][:, self.q_proj_shard_size *
                                 self.q_shard_id:self.q_proj_shard_size *
                                 (self.q_shard_id + 1), ]
        if lora_b[1] is not None:
            lora_b_k = lora_b[1][:, self.kv_proj_shard_size *
                                 self.kv_shard_id:self.kv_proj_shard_size *
                                 (self.kv_shard_id + 1), ]
        if lora_b[2] is not None:
            lora_b_v = lora_b[2][:, self.kv_proj_shard_size *
                                 self.kv_shard_id:self.kv_proj_shard_size *
                                 (self.kv_shard_id + 1), ]
        lora_b = [lora_b_q, lora_b_k, lora_b_v]
        return lora_b

    def set_lora(
        self,
        index: int,
        lora_a: torch.Tensor,
        lora_b: torch.Tensor,
        embeddings_tensor: Optional[torch.Tensor],
    ):
        self.reset_lora(index)

        if self.tp_size > 1:
            lora_a = self.slice_lora_a(lora_a)
            lora_b = self.slice_lora_b(lora_b)

        if self.lora_config.merge_lora:
            qkv_weights_list = []
            for i in range(len(self.output_slices)):
                if lora_a[i] is not None:
                    if lora_a[i].numel() == 0 or lora_b[i].numel() == 0:
                        continue
                    weight_A = lora_a[i].to(self.device)
                    weight_B = lora_b[i].to(self.device)
                    delta_weight = torch.matmul(weight_A, weight_B)
                    qkv_weights_list.append(delta_weight)
                else:
                    if i == 0:
                        qkv_weights_list.append(torch.zeros(self.input_size, self.q_proj_shard_size, 
                                                                dtype=self.base_layer.weight.dtype, device=self.device))
                    else:
                        qkv_weights_list.append(torch.zeros(self.input_size, self.kv_proj_shard_size, 
                                                                dtype=self.base_layer.weight.dtype, device=self.device))

            if len(qkv_weights_list) > 0:
                qkv_weights = torch.cat(qkv_weights_list, dim=-1)
                if qkv_weights.shape != self.base_layer.weight.shape:
                    qkv_weights = qkv_weights.T + self.base_layer.weight.data
                else:
                    qkv_weights = qkv_weights + self.base_layer.weight.data
                
                self.base_layer.weight.data.copy_(qkv_weights)
        else:
            if lora_b[0] is not None:
                lora_b_q = lora_b[0]
                self.lora_b_stacked[0][
                    index, 0, :lora_b_q.shape[1], :lora_b_q.shape[0]].copy_(
                        lora_b_q.T, non_blocking=True)
            if lora_b[1] is not None:
                lora_b_k = lora_b[1]
                self.lora_b_stacked[1][
                    index, 0, :lora_b_k.shape[1], :lora_b_k.shape[0]].copy_(
                        lora_b_k.T, non_blocking=True)
            if lora_b[2] is not None:
                lora_b_v = lora_b[2]
                self.lora_b_stacked[2][
                    index, 0, :lora_b_v.shape[1], :lora_b_v.shape[0]].copy_(
                        lora_b_v.T, non_blocking=True)

            if lora_a[0] is not None:
                self.lora_a_stacked[0][
                    index, 0, :lora_a[0].shape[1], :lora_a[0].shape[0]].copy_(
                        lora_a[0].T, non_blocking=True)
            if lora_a[1] is not None:
                self.lora_a_stacked[1][
                    index, 0, :lora_a[1].shape[1], :lora_a[1].shape[0]].copy_(
                        lora_a[1].T, non_blocking=True)
            if lora_a[2] is not None:
                self.lora_a_stacked[2][
                    index, 0, :lora_a[2].shape[1], :lora_a[2].shape[0]].copy_(
                        lora_a[2].T, non_blocking=True)

    def apply(self, x: torch.Tensor,
              bias: Optional[torch.Tensor]) -> torch.Tensor:
        if not self.lora_config.merge_lora:
            output = self.base_layer.quant_method.apply(self.base_layer, x, bias)            
            self.punica_wrapper.add_lora_packed_nslice(output, x,
                                                       self.lora_a_stacked,
                                                       self.lora_b_stacked, 1.0,
                                                       self.output_slices)
        else:
            output = self.base_layer.quant_method.apply(self.base_layer, x, bias)          
        return output

    @classmethod
    @_not_fully_sharded_can_replace
    def can_replace_layer(
        cls,
        source_layer: nn.Module,
        lora_config: LoRAConfig,
        packed_modules_list: List,
        model_config: Optional[PretrainedConfig],
    ) -> bool:
        return (type(source_layer) is QKVParallelLinear
                and len(packed_modules_list) == 3)


class RowParallelLinearWithLoRA(BaseLayerWithLoRA):

    def __init__(self, base_layer: RowParallelLinear) -> None:
        super().__init__()
        self.base_layer = base_layer
        self.input_size = self.base_layer.input_size_per_partition
        self.output_size = self.base_layer.output_size
        self.device = _get_lora_device(self.base_layer)

    def create_lora_weights(
        self,
        max_loras: int,
        lora_config: LoRAConfig,
        model_config: Optional[PretrainedConfig] = None,
    ) -> None:
        self.lora_config = lora_config
        self.tp_rank = get_tensor_model_parallel_rank()
        self.lora_a_stacked = torch.zeros(
            (
                max_loras,
                1,
                lora_config.max_lora_rank,
                self.input_size,
            ),
            dtype=lora_config.lora_dtype,
            device=self.device,
        )
        tp_size = get_tensor_model_parallel_world_size()
        lora_b_output_size_per_partition = (
            self.output_size if not lora_config.fully_sharded_loras else
            divide(self.output_size, tp_size))

        self.lora_b_stacked = torch.zeros(
            (
                max_loras,
                1,
                lora_b_output_size_per_partition,
                lora_config.max_lora_rank,
            ),
            dtype=lora_config.lora_dtype,
            device=self.device,
        )

    def reset_lora(self, index: int):
        self.lora_a_stacked[index] = 0
        self.lora_b_stacked[index] = 0

    def slice_lora_a(self, lora_a: torch.Tensor) -> torch.Tensor:
        tensor_model_parallel_rank = get_tensor_model_parallel_rank()
        shard_size = self.input_size
        start_idx = tensor_model_parallel_rank * shard_size
        end_idx = (tensor_model_parallel_rank + 1) * shard_size
        lora_a = lora_a[start_idx:end_idx, :]
        return lora_a

    def slice_lora_b(self, lora_b: torch.Tensor) -> torch.Tensor:
        return lora_b

    def set_lora(
        self,
        index: int,
        lora_a: torch.Tensor,
        lora_b: torch.Tensor,
        embeddings_tensor: Optional[torch.Tensor],
    ):
        self.reset_lora(index)

        if self.base_layer.tp_size > 1:
            lora_a = self.slice_lora_a(lora_a)
            lora_b = self.slice_lora_b(lora_b)

        self.lora_a_stacked[index,
                            0, :lora_a.shape[1], :lora_a.shape[0]].copy_(
                                lora_a.T, non_blocking=True)
        self.lora_b_stacked[index,
                            0, :lora_b.shape[1], :lora_b.shape[0]].copy_(
                                lora_b.T, non_blocking=True)

    def apply(self, x: torch.Tensor) -> torch.Tensor:
        output = self.base_layer.quant_method.apply(self.base_layer, x)
        self.punica_wrapper.add_lora(output, x, self.lora_a_stacked,
                                     self.lora_b_stacked, 1.0)
        return output

    def forward(self, input_):
        """Forward of RowParallelLinear

        Args:
            input_: tensor whose last dimension is `input_size`. If
                    `input_is_parallel` is set, then the last dimension
                    is `input_size // tp_size`.

        Returns:
            - output
            - bias
        """
        # Set up backprop all-reduce.
        if self.base_layer.input_is_parallel:
            input_parallel = input_
        else:
            # TODO: simplify code below
            tp_rank = get_tensor_model_parallel_rank()
            splitted_input = split_tensor_along_last_dim(
                input_, num_partitions=self.base_layer.tp_size)
            input_parallel = splitted_input[tp_rank].contiguous()

        # Matrix multiply.
        output_parallel = self.apply(input_parallel)
        if self.base_layer.reduce_results and self.base_layer.tp_size > 1:
            output_ = tensor_model_parallel_all_reduce(output_parallel)
        else:
            output_ = output_parallel

        if not self.base_layer.skip_bias_add:
            output = (output_ + self.base_layer.bias
                      if self.base_layer.bias is not None else output_)
            output_bias = None
        else:
            output = output_
            output_bias = self.base_layer.bias
        return output, output_bias

    @property
    def weight(self):
        return (self.base_layer.weight if hasattr(self.base_layer, "weight")
                else self.base_layer.qweight)

    @classmethod
    @_not_fully_sharded_can_replace
    def can_replace_layer(
        cls,
        source_layer: nn.Module,
        lora_config: LoRAConfig,
        packed_modules_list: List,
        model_config: Optional[PretrainedConfig],
    ) -> bool:
        return type(source_layer) is RowParallelLinear


class LogitsProcessorWithLoRA(BaseLayerWithLoRA):
    """
    LoRA wrapper for LogitsProcessor, with extra logic to handle the
    application of the LoRA adapter and added LoRA vocabulary.

    Args:
        base_layer: LogitsProcessor layer
        hidden_size: hidden size of the model
        dtype: data type of the model
        device: device of the model
        sharded_to_full_mapping: index mapping from sharded vocab to full vocab
            received from base_layer.get_sharded_to_full_mapping(). If None,
            no reindexing will be done.
    """

    def __init__(self, base_layer: LogitsProcessor, hidden_size: int,
                 dtype: torch.dtype, device: torch.device,
                 sharded_to_full_mapping: Optional[List[int]]) -> None:
        super().__init__()
        self.base_layer = base_layer
        self.hidden_size = hidden_size
        self.dtype = dtype
        self.device = device
        self.tp_size = get_tensor_model_parallel_world_size()
        self.tp_rank = get_tensor_model_parallel_rank()
        self.sharded_to_full_mapping = sharded_to_full_mapping

    @property
    def logits_as_input(self):
        return self.base_layer.logits_as_input

    @property
    def vocab_size(self):
        return self.base_layer.vocab_size

    @property
    def scale(self):
        return self.base_layer.scale

    @property
    def soft_cap(self):
        return self.base_layer.soft_cap

    @property
    def use_gather(self):
        return self.base_layer.use_gather

    @property
    def org_vocab_size(self):
        return self.base_layer.org_vocab_size

    @property
    def include_gpu_probs_tensor(self):
        return self.base_layer.include_gpu_probs_tensor

    @property
    def should_modify_greedy_probs_inplace(self):
        return self.base_layer.should_modify_greedy_probs_inplace

    def create_lora_weights(
        self,
        max_loras: int,
        lora_config: LoRAConfig,
        model_config: Optional[PretrainedConfig] = None,
    ) -> None:
        # TODO: Verify if this condition can be further relaxed
        if 32000 < self.base_layer.vocab_size > 257024:
            raise ValueError("When using LoRA, vocab size must be "
                             "32000 >= vocab_size <= 257024")
        self.lora_a_stacked = torch.zeros(
            (
                max_loras,
                1,
                lora_config.max_lora_rank,
                self.hidden_size,
            ),
            dtype=lora_config.lora_dtype,
            device=self.device,
        )
        self.lora_b_stacked = torch.zeros(
            (
                max_loras,
                1,
                # Pad for kernel compatibility
                math.ceil(self.base_layer.vocab_size /
                          lora_config.lora_vocab_padding_size) *
                lora_config.lora_vocab_padding_size,
                lora_config.max_lora_rank,
            ),
            dtype=lora_config.lora_dtype,
            device=self.device,
        )
        self.embeddings_tensors = torch.full(
            (max_loras, lora_config.lora_extra_vocab_size, self.hidden_size),
            fill_value=float("-inf"),
            dtype=self.dtype,
            device=self.device,
        )
        if self.sharded_to_full_mapping is not None:
            self.sharded_to_full_mapping_gpu = torch.tensor(
                self.sharded_to_full_mapping,
                device=self.device,
                dtype=torch.long)
        else:
            self.sharded_to_full_mapping_gpu = None

    def reset_lora(self, index: int):
        self.lora_a_stacked[index] = 0
        self.lora_b_stacked[index] = 0
        self.embeddings_tensors[index] = float("-inf")

    def set_lora(
        self,
        index: int,
        lora_a: torch.Tensor,
        lora_b: torch.Tensor,
        embeddings_tensor: Optional[torch.Tensor],
    ):
        self.reset_lora(index)
        self.lora_a_stacked[index,
                            0, :lora_a.shape[1], :lora_a.shape[0]].copy_(
                                lora_a.T, non_blocking=True)
        self.lora_b_stacked[index,
                            0, :lora_b.shape[1], :lora_b.shape[0]].copy_(
                                lora_b.T, non_blocking=True)
        if embeddings_tensor is not None:
            self.embeddings_tensors[
                index, :embeddings_tensor.shape[0], :embeddings_tensor.
                shape[1], ] = embeddings_tensor

    def _get_logits(
        self,
        hidden_states: torch.Tensor,
        lm_head: VocabParallelEmbedding,
        embedding_bias: Optional[torch.Tensor] = None,
    ) -> Optional[torch.Tensor]:
        # Get the logits for the next tokens.
        logits = lm_head.linear_method.apply(lm_head, hidden_states)
        if embedding_bias is not None:
            logits += embedding_bias
        logits = tensor_model_parallel_gather(logits)
        if logits is None:
            return None

        if self.sharded_to_full_mapping_gpu is not None:
            # Reindex full logits tensor to ensure 1:1 mapping between
            # index and token_id
            # Example for:
            #   org_vocab_size = 4
            #   added_vocab_size = 2
            #   pad_to_size = 8
            #   tp_size = 2

            # indices:  [0, 1, 2,  3, 4, 5, 6,  7]
            # token_id: [0, 1, 4, -1, 2, 3, 5, -1]

            # Therefore, the mapping is expected to be:
            # [0, 1, 4, 6, 2, 3, 5, 7] so that when we reindex,
            # we get:
            # indices:  [0, 1, 2, 3, 4, 5,  6,  7]
            # token_id: [0, 1, 2, 3, 4, 5, -1, -1]
            logits = logits[:, self.sharded_to_full_mapping_gpu]

        lora_logits = torch.empty(
            self.embeddings_tensors.shape[0] + 1,
            self.embeddings_tensors.shape[1],
            hidden_states.shape[0],
            dtype=self.embeddings_tensors.dtype,
            device=self.embeddings_tensors.device,
        )
        torch.matmul(self.embeddings_tensors,
                     hidden_states.T,
                     out=lora_logits[:-1])
        lora_logits[-1] = float("-inf")
        lora_logits = lora_logits.mT
        indices_padded = self.punica_wrapper.sampler_indices_padded
        lora_logits = (lora_logits.reshape(
            lora_logits.shape[0] * lora_logits.shape[1],
            lora_logits.shape[2],
        ).index_select(0, indices_padded).nan_to_num_(nan=float("-inf"),
                                                      posinf=float("inf"),
                                                      neginf=float("-inf")))
        logits[:,
               self.base_layer.org_vocab_size:self.base_layer.org_vocab_size +
               lora_logits.shape[1], ] = lora_logits

        # LogitsProcessorWithLoRA always using bgmv
        self.punica_wrapper.add_lora_logits(logits, hidden_states,
                                            self.lora_a_stacked,
                                            self.lora_b_stacked, 1.0)

        # Remove paddings in vocab (if any).
        logits = logits[:, :self.base_layer.vocab_size]
        return logits

    def forward(self, *args, **kwargs):
        return type(self.base_layer).forward(self, *args, **kwargs)

    @classmethod
    def can_replace_layer(
        cls,
        source_layer: nn.Module,
        lora_config: LoRAConfig,
        packed_modules_list: List,
        model_config: Optional[PretrainedConfig],
    ) -> bool:
        # Special handling for the LogitsProcessor.
        return False


class LinearScalingRotaryEmbeddingWithLora(BaseLayerWithLoRA):
    """Implements RoPE-scaled embeddings with linear scaling for
    multiple LoRA adapters with a specialized kernel.

    Replace LinearScalingRotaryEmbedding with MultiLinearScalingRotaryEmbedding
    which can handle multi lora adapters in a specialied kernel.
    """

    def __init__(self, base_layer: RotaryEmbedding) -> None:
        super().__init__()
        self.base_layer = base_layer

    @property
    def scaling_factors(self):
        return self.base_layer.scaling_factors

    @property
    def rotary_dim(self):
        return self.base_layer.rotary_dim

    def create_lora_weights(
        self,
        max_loras: int,
        lora_config: LoRAConfig,
        model_config: Optional[PretrainedConfig] = None,
    ) -> None:
        scaling_factors = (list(lora_config.long_lora_scaling_factors)
                           if lora_config.long_lora_scaling_factors else [])
        base_scaling_factor = (self.base_layer.scaling_factor if isinstance(
            self.base_layer, LinearScalingRotaryEmbedding) else 1.0)
        scaling_factors = sorted(
            list(set([base_scaling_factor] + scaling_factors)))
        self.base_layer = LinearScalingRotaryEmbedding(
            self.base_layer.head_size,
            self.base_layer.rotary_dim,
            self.base_layer.max_position_embeddings,
            self.base_layer.base,
            self.base_layer.is_neox_style,
            scaling_factors,
            self.base_layer.dtype,
        )

    def reset_lora(self, index: int):
        ...

    def set_lora(
        self,
        index: int,
        lora_a: torch.Tensor,
        lora_b: torch.Tensor,
        embeddings_tensor: Optional[torch.Tensor],
    ):
        ...

    def forward(
        self,
        positions: torch.Tensor,
        query: torch.Tensor,
        key: torch.Tensor,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        return self.base_layer(
            positions,
            query,
            key,
            offsets=self.punica_wrapper.long_lora_indices,
        )

    @property
    def scaling_factor_to_offset(self) -> Dict[float, int]:
        return self.base_layer.scaling_factor_to_offset

    @classmethod
    def can_replace_layer(
        cls,
        source_layer: nn.Module,
        lora_config: LoRAConfig,
        packed_modules_list: List,
        model_config: Optional[PretrainedConfig],
    ) -> bool:
        """Returns True if the layer can be replaced by this LoRA layer."""
        return (type(source_layer) is LinearScalingRotaryEmbedding
                or type(source_layer) is RotaryEmbedding)

    def extra_repr(self) -> str:
        return self.base_layer.extra_repr()