# 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 Optional, Union, final import torch from vllm.lora.layers import LoRAMapping from vllm.lora.ops.ipex_ops import bgmv_expand, bgmv_expand_slice, bgmv_shrink from .punica_base import PunicaWrapperBase @final class PunicaWrapperXPU(PunicaWrapperBase): """ PunicaWrapperXPU 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 ipex 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) torch._dynamo.mark_dynamic(self._token_lora_indices, 0) torch._dynamo.mark_dynamic(self._embeddings_indices, 1) torch._dynamo.mark_dynamic(self._sampler_indices_padded, 0) def update_metadata(self, mapping: LoRAMapping, lora_index_to_id: list[Optional[int]], max_loras: int, vocab_size: int, extra_vocab_size: int, **kwargs): self.is_prefill = mapping.is_prefill self._update_base_metadata(mapping, lora_index_to_id, max_loras, vocab_size, extra_vocab_size) def _get_token_lora_indices(self, x: torch.Tensor) -> torch.IntTensor: return torch.narrow(self._token_lora_indices, 0, 0, x.size(0)) def _apply_shrink( self, y: torch.Tensor, x: torch.Tensor, w_t_all: torch.Tensor, scale: float, ): bgmv_shrink(x, w_t_all, y, self._get_token_lora_indices(x), scale) def _apply_expand( self, y: torch.Tensor, x: torch.Tensor, w_t_all: torch.Tensor, y_offset: int, y_slice_size: int, add_inputs: bool, ): token_lora_indices = self._get_token_lora_indices(x) bgmv_expand_slice(x, w_t_all, y, token_lora_indices, y_offset, y_slice_size, add_inputs) 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]) for slice_idx in range(len(lora_a_stacked)): self._apply_shrink(y[slice_idx], x, lora_a_stacked[slice_idx], 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 = self._get_token_lora_indices(y) self._apply_bias(token_lora_indices, y, output_slices, lora_bias_stacked) assert x.ndim == 3 assert x.size(0) == len(output_slices) # TODO fuse these kernels for slice_idx in range(len(lora_b_stacked)): self._apply_expand( y, x[slice_idx], lora_b_stacked[slice_idx], offset_start, output_slices[slice_idx], add_inputs=add_inputs, ) offset_start += output_slices[slice_idx] 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. """ token_lora_indices = self._get_token_lora_indices(x) bgmv_expand(x, lora_b_stacked, y, token_lora_indices, 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 = self._get_token_lora_indices(y) 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) bgmv_shrink(x, lora_a_stacked, buffer, self.sampler_indices, scale) bgmv_expand(buffer, lora_b_stacked, y, self.sampler_indices, add_inputs=True) return y.view_as(y_org)