# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project """ Based on: Chen, L., Ye, Z., Wu, Y., Zhuo, D., Ceze, L., & Krishnamurthy, A. (2023). Punica: Multi-Tenant LoRA Serving. https://arxiv.org/abs/2310.18547 """ from typing import TYPE_CHECKING, Optional, Union, final import torch import vllm.envs as envs from vllm.lora.layers import LoRAMapping from vllm.triton_utils import HAS_TRITON if HAS_TRITON: from vllm.lora.ops.triton_ops import (LoRAKernelMeta, lora_expand, lora_shrink) from .punica_base import PunicaWrapperBase if TYPE_CHECKING: # avoid circuit import from vllm.lora.models import LongContextLoRAContext @final class PunicaWrapperGPU(PunicaWrapperBase): """ PunicaWrapperGPU is designed to manage and provide metadata for the punica kernel. The main function is to maintain the state information for Multi-LoRA, and to provide the interface for the punica triton kernel. """ def __init__(self, max_num_batched_tokens: int, max_batches: int, device: Union[torch.device, str], **kwargs): PunicaWrapperBase.__init__(self, max_num_batched_tokens, max_batches, device) self.max_loras = kwargs['max_loras'] self.token_mapping_meta = LoRAKernelMeta.make(self.max_loras, max_num_batched_tokens, device=device) # When cudagraph capture size is greater than max_num_seqs (max_batches, # here), V0 captures the graph as if max_num_seqs is set to # the capture size. # V1 doesn't have this problem and always respects max_num_seqs. max_num_prompts = (max_batches if envs.VLLM_USE_V1 else max_num_batched_tokens) self.prompt_mapping_meta = LoRAKernelMeta.make(self.max_loras, max_num_prompts, device=device) def update_metadata( self, mapping: LoRAMapping, lora_index_to_id: list[Optional[int]], max_loras: int, vocab_size: int, extra_vocab_size: int, long_lora_context: Optional["LongContextLoRAContext"] = None, **kwargs): self.is_prefill = mapping.is_prefill self._update_base_metadata(mapping, lora_index_to_id, max_loras, vocab_size, extra_vocab_size, long_lora_context) # Prepare cuda kernel metadata tensors self.token_mapping_meta.prepare_tensors(self.token_lora_indices) self.prompt_mapping_meta.prepare_tensors(self.sampler_indices) def add_shrink(self, y: torch.Tensor, x: torch.Tensor, lora_a_stacked: tuple[torch.Tensor, ...], scale: float, **kwargs): """ Performs GEMM for multiple slices of lora_a. Semantics: for i in range(len(lora_a_stacked)): y[i] += (x @ lora_a_stacked[i]) * scale Args: y (torch.Tensor): Output tensors x (torch.Tensor): Input tensor lora_a_stacked (tuple[torch.Tensor, ...]): lora_a's weights scale (float): Scaling factor for the operation """ x = x.view(-1, x.shape[-1]) lora_shrink( x, lora_a_stacked, y, *self.token_mapping_meta.meta_args(x.size(0)), scale, ) def add_expand(self, y: torch.Tensor, x: torch.Tensor, lora_b_stacked: tuple[torch.Tensor, ...], lora_bias_stacked: Optional[tuple[torch.Tensor, ...]], output_slices: tuple[int, ...], offset_start: int = 0, add_inputs=True, **kwargs) -> None: """ Performs GEMM and bias addition for multiple slices of lora_b. Semantics: for i in range(len(lora_b_stacked)): slice = output_slices[i] y[:, offset:offset+slice] += x[i] @ lora_b_stacked[i] + lora_bias_stacked[i] offset += slice Args: y (torch.Tensor): Output tensor. x (torch.Tensor): Input tensors lora_b_stacked (tuple[torch.Tensor, ...]): lora_b's weight lora_bias_stacked (Optional[tuple[torch.Tensor, ...]]): bias's weight output_slices (tuple[int, ...]): Every slice's size add_inputs (bool): Defaults to True. """ y_org = y y = y.view(-1, y.shape[-1]) if lora_bias_stacked is not None: token_lora_indices = torch.narrow(self._token_lora_indices, 0, 0, y.size(0)) self._apply_bias(token_lora_indices, y, output_slices, lora_bias_stacked) assert x.ndim == 3 assert x.size(0) == len(output_slices) num_tokens = x.size(1) # first dimension is the num slices lora_expand( x, lora_b_stacked, y, *self.token_mapping_meta.meta_args(num_tokens), offset_start=offset_start, add_inputs=True, ) y = y.view_as(y_org) def add_lora_embedding(self, y: torch.Tensor, x: torch.Tensor, lora_b_stacked: torch.Tensor, add_inputs: bool = True, **kwargs) -> None: """ Applies lora specifically for VocabParallelEmbeddingWithLoRA. Semantics: y += x @ lora_b_stacked Args: y (torch.Tensor): Output tensor. x (torch.Tensor): Input tensor. lora_b_stacked (torch.Tensor): lora_b's weights. add_inputs (bool): Default to True. """ lora_expand( x.unsqueeze(dim=0), (lora_b_stacked, ), y, *self.token_mapping_meta.meta_args(x.size(0)), offset_start=0, add_inputs=add_inputs, ) def add_lora_linear(self, y: torch.Tensor, x: torch.Tensor, lora_a_stacked: tuple[torch.Tensor, ...], lora_b_stacked: tuple[torch.Tensor, ...], lora_bias_stacked: Optional[tuple[torch.Tensor, ...]], scale: float, output_slices: tuple[int, ...], *, buffer: Optional[torch.Tensor] = None, **kwargs) -> None: """ Applicable to linear-related lora. Semantics: for i in range(len(lora_a_stacked)): y[i] += ( x[i].unsqueeze(0) @ lora_a_stacked[indices[i], layer_idx, :, :] @ lora_b_stacked[indices[i], layer_idx, :, :] * scale ).squeeze(0)+lora_bias_stacked[i] Args: y (torch.Tensor): Output tensor. Will be changed in-place. x (torch.Tensor): Input tensor lora_a_stacked (tuple[torch.Tensor, ...]): lora_a's weight. lora_b_stacked (tuple[torch.Tensor, ...]): lora_b's weight. lora_bias_stacked (Optional[tuple[torch.Tensor, ...]]): lora's bias. scale (float): Scaling factor. output_slices (tuple[int, ...]): Every slice's size. buffer (Optional[torch.Tensor]): Defaults to None. """ assert len(lora_a_stacked) == len(lora_b_stacked) == len(output_slices) if lora_bias_stacked is not None: assert len(lora_bias_stacked) == len(output_slices) token_lora_indices = torch.narrow(self._token_lora_indices, 0, 0, y.size(0)) y = self._apply_bias(token_lora_indices, y, output_slices, lora_bias_stacked) if buffer is None: r = lora_b_stacked[0].size(-1) # We set the buffer to be float32 by default, refer to: # https://github.com/triton-lang/triton/issues/1387 buffer = torch.zeros( # type: ignore (len(output_slices), x.size(0), r), dtype=torch.float32, device=x.device, ) self.add_shrink( buffer, # type: ignore x, lora_a_stacked, scale, **kwargs) self.add_expand( y, buffer, # type: ignore lora_b_stacked, None, output_slices, add_inputs=True, **kwargs) def add_lora_logits(self, y: torch.Tensor, x: torch.Tensor, lora_a_stacked: torch.Tensor, lora_b_stacked: torch.Tensor, scale, *, buffer: Optional[torch.Tensor] = None, **kwargs) -> None: """ Applies lora specifically for LogitsProcessorWithLoRA. Semantics: buffer = (x @ lora_a_stacked) * scale y += buffer @ lora_b_stacked Args: y (torch.Tensor): Output tensor. x (torch.Tensor): Input tensor. lora_a_stacked (torch.Tensor): lora_a's weights. lora_b_stacked (torch.Tensor): lora_b's weights. scale (float): Scaling factor. buffer (Optional[torch.Tensor]): Default to None. """ y_org = y y = y.view(-1, y.shape[-1]) x = x.view(-1, x.shape[-1]) r = lora_b_stacked.size(-1) if buffer is None: # We set the buffer to be float32 by default, refer to: # https://github.com/triton-lang/triton/issues/1387 buffer = torch.zeros((x.size(0), r), dtype=torch.float32, device=x.device) lora_shrink(x, [lora_a_stacked], buffer.unsqueeze(dim=0), *self.prompt_mapping_meta.meta_args(x.size(0)), scale) lora_expand(buffer.unsqueeze(dim=0), [lora_b_stacked], y, *self.prompt_mapping_meta.meta_args(buffer.size(0)), add_inputs=True) y = y.view_as(y_org)