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484 lines
18 KiB
Python
484 lines
18 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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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, List, Optional, Tuple, Union
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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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from vllm.lora.models import LongContextLoRAContext
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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[Optional[int]],
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max_loras: int,
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vocab_size: int,
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extra_vocab_size: int,
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long_lora_context: Optional["LongContextLoRAContext"] = None,
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**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: Union[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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) -> 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: Union[Tuple[torch.Tensor, ...], torch.Tensor],
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lora_b_stacked: Tuple[torch.Tensor, ...],
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lora_bias_stacked: Optional[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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) -> None:
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"""
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Performs GEMM and bias addition 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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) -> 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(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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lora_bias_stacked: Optional[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: Optional[Tuple[torch.Tensor, ...]] = None,
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**kwargs) -> 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(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: Optional[torch.Tensor] = None,
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**kwargs) -> 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__(self, max_num_batched_tokens: int, max_batches: int,
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device: Union[torch.device, str], **kwargs):
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self._token_lora_indices = torch.empty(max_num_batched_tokens,
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dtype=torch.long,
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device=device)
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self._sampler_indices = torch.empty(max_num_batched_tokens,
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dtype=torch.long,
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device=device)
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self._sampler_indices_padded = torch.empty(max_num_batched_tokens,
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dtype=torch.long,
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device=device)
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self._embeddings_indices = torch.empty(2,
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max_num_batched_tokens,
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dtype=torch.long,
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device=device)
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self._long_lora_indices = torch.empty(max_num_batched_tokens,
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dtype=torch.long,
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device=device)
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# 5 is the number of indices tensors.
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# base_indices, sampler_indices, sampler_indices_padded,
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# embeddings_indices,long_lora_indices
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self.indices_len: List[Optional[int]] = [None] * 5
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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,
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dtype=torch.long,
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device=device)
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self._seq_lengths = torch.empty(max_batches,
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dtype=torch.long,
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device=device)
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self._lora_indices_per_batch = torch.empty(max_batches,
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dtype=torch.long,
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device=device)
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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[Optional[int]],
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max_loras: int,
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vocab_size: int,
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extra_vocab_size: int,
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long_lora_context: Optional["LongContextLoRAContext"] = None,
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):
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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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long_lora_offsets_tensor,
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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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long_lora_context,
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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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self._embeddings_indices[:embeddings_indices.
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shape[0], :embeddings_indices.shape[1]].copy_(
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embeddings_indices)
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if long_lora_offsets_tensor is not None:
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self._long_lora_indices[:long_lora_offsets_tensor.shape[0]].copy_(
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long_lora_offsets_tensor)
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else:
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self._long_lora_indices.zero_()
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self.indices_len[:] = indices_len
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def _update_prefill_metada(self, token_lora_tensor: torch.Tensor) -> None:
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(b_seq_start_tensor, seq_length_tensor, lora_indices_tensor,
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batch_size, max_length, token_nums,
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no_lora) = compute_meta(token_lora_tensor)
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self._seq_start_locs[:b_seq_start_tensor.shape[0]].copy_(
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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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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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def _apply_bias(
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self,
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indices: torch.Tensor,
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output: torch.Tensor,
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output_slices: Tuple[int, ...],
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lora_bias_stacked: Tuple[Optional[torch.Tensor], ...],
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):
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"""Applies bias to output
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Input shapes:
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lora_bias_stacked: 3 element tuple of (num_loras, output_dim)
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indices: (batch_size)
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output: (batch_size, q_slice_size + 2*kv_slice_size)
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output_slices: n-1 element tuple of (slice_size...),
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where n is number of slices
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"""
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org_output = output
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output = output.view(-1, output.shape[-1])
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indices = indices.view(-1)
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offset_left = 0
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for slice_idx, slice in enumerate(output_slices):
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bias = lora_bias_stacked[slice_idx]
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if bias is not None:
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bias = bias.view(-1, bias.shape[-1])
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bias = bias[indices]
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bias[indices == -1] = 0
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output[:, offset_left:offset_left + slice] += bias
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offset_left += slice
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return output.view_as(org_output)
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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 (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, self.max_length, self.token_nums)
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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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@property
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def long_lora_indices(self) -> torch.Tensor:
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"""
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This property provides access to the indices used for long context
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lora, specifically for LinearScalingRotaryEmbeddingWithLora.
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"""
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long_lora_len = self.indices_len[4]
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return self._long_lora_indices[:long_lora_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[Optional[int]],
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max_loras: int,
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vocab_size: int,
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extra_vocab_size: int,
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long_lora_context: Optional["LongContextLoRAContext"] = None,
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**kwargs):
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self._update_base_metadata(mapping, lora_index_to_id, max_loras,
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vocab_size, extra_vocab_size,
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long_lora_context)
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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_metada(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(self, y: Union[Tuple[torch.Tensor, ...], torch.Tensor],
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x: torch.Tensor, lora_a_stacked: Tuple[torch.Tensor, ...],
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scale: float, **kwargs) -> 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(self,
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y: torch.Tensor,
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x: Union[Tuple[torch.Tensor, ...], torch.Tensor],
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lora_b_stacked: Tuple[torch.Tensor, ...],
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lora_bias_stacked: Optional[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) -> None:
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"""
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Performs GEMM and bias addition 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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lora_bias_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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lora_bias_stacked (Optional[Tuple[torch.Tensor, ...]]):
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bias'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(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) -> 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(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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lora_bias_stacked: Optional[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: Optional[Tuple[torch.Tensor, ...]] = None,
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**kwargs) -> 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)+lora_bias_stacked[i]
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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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lora_bias_stacked (Optional[Tuple[torch.Tensor, ...]]): lora's bias.
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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(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: Optional[torch.Tensor] = None,
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**kwargs) -> 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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