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https://git.datalinker.icu/vllm-project/vllm.git
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494 lines
15 KiB
Python
494 lines
15 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""
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Based on:
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Chen, L., Ye, Z., Wu, Y., Zhuo, D., Ceze, L., & Krishnamurthy, A. (2023).
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Punica: Multi-Tenant LoRA Serving.
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https://arxiv.org/abs/2310.18547
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"""
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from abc import ABC, abstractmethod
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from typing import TYPE_CHECKING
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import torch
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from .utils import compute_meta, convert_mapping
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if TYPE_CHECKING:
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# avoid circuit import
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from vllm.lora.layers import LoRAMapping
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class PunicaWrapperABC(ABC):
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"""
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PunicaWrapper ABC.
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"""
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@abstractmethod
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def update_metadata(
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self,
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mapping: "LoRAMapping",
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lora_index_to_id: list[int | None],
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max_loras: int,
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vocab_size: int,
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**kwargs,
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) -> None:
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"""
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Update the lora-related metadata
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"""
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raise NotImplementedError
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@abstractmethod
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def add_shrink(
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self,
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y: tuple[torch.Tensor, ...] | torch.Tensor,
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x: torch.Tensor,
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lora_a_stacked: tuple[torch.Tensor, ...],
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scale: float,
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**kwargs,
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) -> torch.Tensor | None:
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"""
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Performs GEMM for multiple slices of lora_a.
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"""
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raise NotImplementedError
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@abstractmethod
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def add_expand(
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self,
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y: torch.Tensor,
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x: tuple[torch.Tensor, ...] | torch.Tensor,
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lora_b_stacked: tuple[torch.Tensor, ...],
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output_slices: tuple[int, ...],
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offset_start: int = 0,
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add_inputs=True,
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**kwargs,
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) -> torch.Tensor | None:
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"""
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Performs GEMM for multiple slices of lora_b.
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"""
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raise NotImplementedError
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@abstractmethod
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def add_lora_embedding(
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self,
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y: torch.Tensor,
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x: torch.Tensor,
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lora_b_stacked: torch.Tensor,
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add_inputs: bool = True,
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**kwargs,
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) -> torch.Tensor | None:
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"""
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Applies lora specifically for VocabParallelEmbeddingWithLoRA,
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and this layer only requires the expand operation.
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"""
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raise NotImplementedError
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@abstractmethod
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def add_lora_linear(
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self,
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y: torch.Tensor,
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x: torch.Tensor,
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lora_a_stacked: tuple[torch.Tensor, ...],
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lora_b_stacked: tuple[torch.Tensor, ...],
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scale: float,
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output_slices: tuple[int, ...],
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*,
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buffer: tuple[torch.Tensor, ...] | None = None,
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**kwargs,
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) -> torch.Tensor | None:
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"""
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Applicable to linear-related lora.
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"""
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raise NotImplementedError
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@abstractmethod
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def add_lora_logits(
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self,
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y: torch.Tensor,
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x: torch.Tensor,
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lora_a_stacked: torch.Tensor,
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lora_b_stacked: torch.Tensor,
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scale,
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*,
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buffer: torch.Tensor | None = None,
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**kwargs,
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) -> torch.Tensor | None:
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"""
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Applies lora specifically for LogitsProcessorWithLoRA.
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"""
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raise NotImplementedError
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class PunicaWrapperBase(PunicaWrapperABC):
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"""
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PunicaWrapperBase is designed to manage and provide metadata for the punica
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kernel. The main function is to maintain the state information for
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Multi-LoRA, and to provide the interface for the punica.
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"""
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def __init__(
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self,
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max_num_batched_tokens: int,
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max_batches: int,
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device: torch.device | str,
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**kwargs,
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):
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self._token_lora_indices = torch.empty(
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max_num_batched_tokens, dtype=torch.long, device=device
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)
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self._sampler_indices = torch.empty(
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max_num_batched_tokens, dtype=torch.long, device=device
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)
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self._sampler_indices_padded = torch.empty(
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max_num_batched_tokens, dtype=torch.long, device=device
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)
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self._embeddings_indices = torch.empty(
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2, max_num_batched_tokens, dtype=torch.long, device=device
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)
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# 4 is the number of indices tensors.
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# base_indices, sampler_indices, sampler_indices_padded,
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# embeddings_indices
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self.indices_len: list[int | None] = [None] * 4
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# these attributes are the information required for sgmv kernel
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self._seq_start_locs = torch.empty(max_batches, dtype=torch.long, device=device)
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self._seq_lengths = torch.empty(max_batches, dtype=torch.long, device=device)
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self._lora_indices_per_batch = torch.empty(
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max_batches, dtype=torch.long, device=device
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)
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self.device: torch.device = device
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self.max_length: int = 0
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self.token_nums: int = 0
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self.batch_size: int = -1
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self.is_prefill = False
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self.no_lora = False
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def _update_base_metadata(
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self,
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mapping: "LoRAMapping",
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lora_index_to_id: list[int | None],
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max_loras: int,
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vocab_size: int,
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):
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# NOTE We have remove lora extra vocab support for now. So we set
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# extra_vocab_size always to 0, and extra_vocab_size will be removed.
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extra_vocab_size = 0
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(
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base_indices,
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sampler_indices,
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sampler_indices_padded,
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embeddings_indices,
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indices_len,
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) = convert_mapping(
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mapping,
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lora_index_to_id,
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max_loras,
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vocab_size,
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extra_vocab_size,
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self.device,
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)
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self._token_lora_indices[: base_indices.shape[0]].copy_(base_indices)
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self._sampler_indices[: sampler_indices.shape[0]].copy_(sampler_indices)
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self._sampler_indices_padded[: sampler_indices_padded.shape[0]].copy_(
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sampler_indices_padded
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)
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self._embeddings_indices[
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: embeddings_indices.shape[0], : embeddings_indices.shape[1]
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].copy_(embeddings_indices)
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self.indices_len[:] = indices_len
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def _update_prefill_metadata(self, token_lora_tensor: torch.Tensor) -> None:
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(
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b_seq_start_tensor,
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seq_length_tensor,
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lora_indices_tensor,
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batch_size,
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max_length,
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token_nums,
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no_lora,
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) = compute_meta(token_lora_tensor)
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self._seq_start_locs[: b_seq_start_tensor.shape[0]].copy_(b_seq_start_tensor)
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self._seq_lengths[: seq_length_tensor.shape[0]].copy_(seq_length_tensor)
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self._lora_indices_per_batch[: lora_indices_tensor.shape[0]].copy_(
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lora_indices_tensor
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)
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self.batch_size = batch_size
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self.max_length = max_length
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self.token_nums = token_nums
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self.no_lora = no_lora
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@property
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def prefill_metadata(
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self,
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, int, int, int]:
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"""
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This property provides a convenient way to access the necessary
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metadata for prefill-related kernel computations.
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1. seq_start_locs: Tensor of sequence start positions.
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2. seq_lengths: Tensor of sequence lengths.
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3. lora_indices_per_batch: Tensor of lora indices, and an index of
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-1 means no lora should be applied.
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4. batch_size: Batch size after clustering identical lora indices.
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5. max_length: The maximum sequence length in the batch.
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6. token_nums: The token numbers in the batch.
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"""
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return (
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self._seq_start_locs[: self.batch_size],
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self._seq_lengths[: self.batch_size],
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self._lora_indices_per_batch[: self.batch_size],
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self.batch_size,
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self.max_length,
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self.token_nums,
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)
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@property
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def token_lora_indices(self) -> torch.Tensor:
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"""
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This property provides the lora indices corresponding to each token
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in the batch. An index of -1 means no lora should be applied.
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"""
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token_lora_len = self.indices_len[0]
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return self._token_lora_indices[:token_lora_len]
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@property
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def sampler_indices(self) -> torch.Tensor:
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"""
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This property is used to access the lora indices specifically for
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LogitsProcessorWithLoRA.
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"""
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sampler_indices_len = self.indices_len[1]
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return self._sampler_indices[:sampler_indices_len]
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@property
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def sampler_indices_padded(self) -> torch.Tensor:
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"""
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This property provides access to padded sampler indices.
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"""
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indices_padded_len = self.indices_len[2]
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return self._sampler_indices_padded[:indices_padded_len]
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@property
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def embeddings_indices(self) -> torch.Tensor:
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"""
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This property provides access to the indices used for lora embeddings,
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specifically for VocabParallelEmbeddingWithLoRA.
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"""
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embeddings_indices_len = self.indices_len[3]
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return self._embeddings_indices[:, :embeddings_indices_len]
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def update_metadata(
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self,
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mapping: "LoRAMapping",
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lora_index_to_id: list[int | None],
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max_loras: int,
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vocab_size: int,
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**kwargs,
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):
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self._update_base_metadata(mapping, lora_index_to_id, max_loras, vocab_size)
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if mapping.is_prefill:
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# Update metadata required for prefill-related operators.
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self._update_prefill_metadata(self.token_lora_indices)
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self.is_prefill = True
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else:
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self.is_prefill = False
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@abstractmethod
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def add_shrink(
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self,
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y: tuple[torch.Tensor, ...] | torch.Tensor,
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x: torch.Tensor,
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lora_a_stacked: tuple[torch.Tensor, ...],
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scale: float,
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**kwargs,
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) -> torch.Tensor | None:
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"""
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Performs GEMM for multiple slices of lora_a.
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Semantics:
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for i in range(len(lora_a_stacked)):
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y[i] += (x @ lora_a_stacked[i]) * scale
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Args:
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y (Union[tuple[torch.Tensor, ...], torch.Tensor]): Output tensors
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x (torch.Tensor): Input tensor
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lora_a_stacked (tuple[torch.Tensor, ...]): lora_a's weights
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scale (float): Scaling factor for the operation
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"""
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# TODO: implement it based on torch ops
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raise NotImplementedError
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@abstractmethod
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def add_expand(
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self,
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y: torch.Tensor,
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x: tuple[torch.Tensor, ...] | torch.Tensor,
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lora_b_stacked: tuple[torch.Tensor, ...],
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output_slices: tuple[int, ...],
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offset_start: int = 0,
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add_inputs=True,
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**kwargs,
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) -> torch.Tensor | None:
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"""
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Performs GEMM for multiple slices of lora_b.
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Semantics:
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offset = offset_start
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for i in range(len(lora_b_stacked)):
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slice = output_slices[i]
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y[:, offset:offset+slice] += x[i] @ lora_b_stacked[i]
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offset += slice
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Args:
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y (torch.Tensor): Output tensor.
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x (Union[tuple[torch.Tensor, ...], torch.Tensor]): Input tensors
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lora_b_stacked (tuple[torch.Tensor, ...]): lora_b's weight
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output_slices (tuple[int, ...]): Every slice's size
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offset_start (int): The starting position of y, defaults to 0
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add_inputs (bool): Defaults to True.
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"""
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# TODO: implement it based on torch ops
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raise NotImplementedError
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@abstractmethod
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def add_lora_embedding(
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self,
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y: torch.Tensor,
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x: torch.Tensor,
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lora_b_stacked: torch.Tensor,
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add_inputs: bool = True,
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**kwargs,
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) -> torch.Tensor | None:
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"""
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Applies lora specifically for VocabParallelEmbeddingWithLoRA.
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and this layer only requires the expand operation.
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Semantics:
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y += x @ lora_b_stacked
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Args:
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y (torch.Tensor): Output tensor.
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x (torch.Tensor): Input tensor.
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lora_b_stacked (torch.Tensor): lora_b's weights.
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add_inputs (bool): Default to True.
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"""
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# TODO: implement it based on torch ops
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raise NotImplementedError
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@abstractmethod
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def add_lora_linear(
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self,
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y: torch.Tensor,
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x: torch.Tensor,
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lora_a_stacked: tuple[torch.Tensor, ...],
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lora_b_stacked: tuple[torch.Tensor, ...],
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scale: float,
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output_slices: tuple[int, ...],
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*,
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buffer: tuple[torch.Tensor, ...] | None = None,
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**kwargs,
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) -> torch.Tensor | None:
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"""
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Applicable to linear-related lora.
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Semantics:
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for i in range(len(lora_a_stacked)):
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y[i] += (
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x[i].unsqueeze(0)
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@ lora_a_stacked[indices[i], layer_idx, :, :]
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@ lora_b_stacked[indices[i], layer_idx, :, :]
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* scale
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).squeeze(0)
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Args:
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y (torch.Tensor): Output tensor. Will be changed in-place.
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x (torch.Tensor): Input tensor
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lora_a_stacked (tuple[torch.Tensor, ...]): lora_a's weight.
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lora_b_stacked (tuple[torch.Tensor, ...]): lora_b's weight.
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scale (float): Scaling factor.
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output_slices (tuple[int, ...]): Every slice's size.
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buffer (Optional[tuple[torch.Tensor, ...]]): Defaults to None.
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"""
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# TODO: implement it based on torch ops
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raise NotImplementedError
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@abstractmethod
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def add_lora_logits(
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self,
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y: torch.Tensor,
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x: torch.Tensor,
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lora_a_stacked: torch.Tensor,
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lora_b_stacked: torch.Tensor,
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scale,
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*,
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buffer: torch.Tensor | None = None,
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**kwargs,
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) -> torch.Tensor | None:
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"""
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Applies lora specifically for LogitsProcessorWithLoRA.
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Semantics:
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buffer = (x @ lora_a_stacked) * scale
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y += buffer @ lora_b_stacked
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Args:
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y (torch.Tensor): Output tensor.
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x (torch.Tensor): Input tensor.
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lora_a_stacked (torch.Tensor): lora_a's weights.
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lora_b_stacked (torch.Tensor):lora_b's weights.
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scale (float): Scaling factor.
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buffer (Optional[torch.Tensor]):Default to None.
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"""
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# TODO: implement it based on torch ops
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raise NotImplementedError
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def moe_lora_align_block_size(
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self,
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topk_ids: torch.Tensor,
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num_tokens: int,
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block_size: int,
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num_experts: int,
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max_loras: int,
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adapter_enabled: torch.Tensor,
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expert_map: torch.Tensor | None = None,
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pad_sorted_ids: bool = False,
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) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""
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Aligns tokens and experts into block-sized chunks for LoRA-based
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mixture-of-experts (MoE) execution.
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"""
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# TODO: implement it based on torch ops
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raise NotImplementedError
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def add_lora_fused_moe(
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self,
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y: torch.Tensor,
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x: torch.Tensor,
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lora_a_stacked: tuple[torch.Tensor, ...],
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lora_b_stacked: tuple[torch.Tensor, ...],
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topk_weights: torch.Tensor,
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sorted_token_ids: torch.Tensor,
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expert_ids: torch.Tensor,
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num_tokens_post_padded: torch.Tensor,
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max_lora_rank: int,
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top_k_num: int,
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shrink_config,
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expand_config,
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adapter_enabled: torch.Tensor,
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mul_routed_weight=False,
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fully_sharded: bool = False,
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offset: int = 0,
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):
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"""
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Performs a fused forward computation for LoRA of
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Mixture-of-Experts (MoE) layer.
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"""
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# TODO: implement it based on torch ops
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raise NotImplementedError
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