# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import math from typing import TYPE_CHECKING, Optional, Union import torch import torch.nn.functional as F import torch_xla.core.xla_model as xm from vllm.lora.ops.xla_ops import bgmv_expand, bgmv_expand_slice, bgmv_shrink from vllm.lora.punica_wrapper.utils import convert_mapping if TYPE_CHECKING: # avoid circuit import from vllm.lora.layers import LoRAMapping from .punica_base import PunicaWrapperBase class PunicaWrapperTPU(PunicaWrapperBase): """ PunicaWrapperTPU 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 pytorch punica ops. """ 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) # PunicaWrapperBase defines some tensors with dtype=torch.int64, which # isn't supported by the TPU. So convert those tensors to int32. # Not all of them are used by the TPU so only convert the useful ones. self._token_lora_indices = self._token_lora_indices.to( dtype=torch.int32) self._sampler_indices = self._sampler_indices.to(dtype=torch.int32) self._sampler_indices_padded = self._sampler_indices_padded.to( dtype=torch.int32) torch.ops.xla.dynamo_set_buffer_donor_(self._token_lora_indices, True) torch.ops.xla.dynamo_set_buffer_donor_(self._sampler_indices, True) torch.ops.xla.dynamo_set_buffer_donor_(self._sampler_indices_padded, True) torch.ops.xla.dynamo_set_buffer_donor_(self._embeddings_indices, True) torch.ops.xla.dynamo_set_buffer_donor_(self._lora_indices_per_batch, True) 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 _get_token_lora_indices(self, x: torch.Tensor) -> torch.IntTensor: return torch.narrow(self._token_lora_indices, 0, 0, x.size(0)) @property def embeddings_indices(self) -> torch.Tensor: """ This property provides access to the indices used for lora embeddings, specifically for VocabParallelEmbeddingWithLoRA. """ return self._embeddings_indices[:] @property def sampler_indices_padded(self) -> torch.Tensor: """ This property provides access to padded sampler indices. """ return self._sampler_indices_padded[:] def shrink( self, x: torch.Tensor, w_t_all: torch.Tensor, scale: float, ): return bgmv_shrink(x, w_t_all, self._get_token_lora_indices(x), scale) def expand(self, y: torch.Tensor, x: torch.Tensor, w_t_all: torch.Tensor, add_inputs: bool): return bgmv_expand(x, w_t_all, y, self._get_token_lora_indices(x), add_inputs) def expand_slice(self, y: torch.Tensor, x: torch.Tensor, w_t_all: torch.Tensor, y_offset: int, y_slice_size: int, add_inputs: bool) -> torch.Tensor: return bgmv_expand_slice(x, w_t_all, y, self._get_token_lora_indices(x), y_offset, y_slice_size, add_inputs) def add_shrink(self, y: Union[tuple[torch.Tensor, ...], torch.Tensor], x: torch.Tensor, lora_a_stacked: tuple[torch.Tensor, ...], scale: float, **kwargs) -> Optional[torch.Tensor]: """ 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 (Union[tuple[torch.Tensor, ...], 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 """ torch.ops.xla.dynamo_set_buffer_donor_(y, True) x = x.view(-1, x.shape[-1]) for slice_idx in range(len(lora_a_stacked)): lora_s = lora_a_stacked[slice_idx] y_s = self.shrink(x, lora_s, scale) y[slice_idx, :, :] = y_s # type: ignore[index] return y def add_expand(self, y: torch.Tensor, x: Union[tuple[torch.Tensor, ...], 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) -> torch.Tensor: """ 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 (Union[tuple[torch.Tensor, ...], 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]) offset_left = 0 if lora_bias_stacked is not None: y = self._apply_bias(self._get_token_lora_indices(y), y, output_slices, lora_bias_stacked) for slice_idx in range(len(lora_b_stacked)): y = self.expand_slice(y, x[slice_idx], lora_b_stacked[slice_idx], offset_left, output_slices[slice_idx], add_inputs=add_inputs) offset_left += output_slices[slice_idx] return 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) -> torch.Tensor: """ 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. """ # Embedding layer only needs the expand op return self.expand(y, x, lora_b_stacked, 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[tuple[torch.Tensor, ...]] = None, **kwargs) -> torch.Tensor: """ 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 not be changed in-place. x (torch.Tensor): Input tensor (T, E) 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[tuple[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) y = self._apply_bias(self._get_token_lora_indices(y), y, output_slices, lora_bias_stacked) if buffer is None: r = lora_b_stacked[0].size(-1) T = x.size(0) buffer = torch.zeros( (len(output_slices), T, r), dtype=x.dtype, device=x.device, ) buffer = self.add_shrink(buffer, x, lora_a_stacked, scale, **kwargs) return self.add_expand(y, buffer, 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) -> torch.Tensor: """ 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]) sampler_indices = torch.narrow(self._sampler_indices, 0, 0, x.size(0)) buffer = bgmv_shrink(x, lora_a_stacked, sampler_indices, scale) y = bgmv_expand(buffer, lora_b_stacked, y, sampler_indices, add_inputs=True) return y.view_as(y_org) def _apply_bias( self, indices: torch.Tensor, output: torch.Tensor, output_slices: tuple[int, ...], lora_bias_stacked: tuple[Optional[torch.Tensor], ...], ): """Applies bias to output Input shapes: lora_bias_stacked: 3 element tuple of (num_loras, output_dim) indices: (batch_size) output: (batch_size, q_slice_size + 2*kv_slice_size) output_slices: n-1 element tuple of (slice_size...), where n is number of slices """ org_output = output output = output.view(-1, output.shape[-1]) indices = indices.view(-1) offset_left = 0 for slice_idx, slice in enumerate(output_slices): bias = lora_bias_stacked[slice_idx] if bias is not None: bias = bias.view(-1, bias.shape[-1]) bias = bias[indices] bias = torch.where(indices[:, None] == -1, 0, bias) bias = F.pad(bias, (offset_left, output.shape[1] - (offset_left + slice), 0, 0)) output += bias offset_left += slice return output.view_as(org_output) # This performs the same tensor ops as the base method, except it does them # on the CPU then transfers the results to the TPU def _update_base_metadata( self, mapping: "LoRAMapping", lora_index_to_id: list[Optional[int]], max_loras: int, vocab_size: int, extra_vocab_size: int, ): # Make sure we don't accidentally collect outside operations xm.mark_step() # Pad the prompt mapping to avoid running into recompiles on the TPU # TODO: Should this happen inside mapping internally? If so how can we # avoid having backend specific LoRAMapping classes? mapping.prompt_mapping = self._pad_prompt_mapping( mapping.prompt_mapping) ( base_indices, sampler_indices, sampler_indices_padded, embeddings_indices, indices_len, ) = convert_mapping( mapping, lora_index_to_id, max_loras, vocab_size, extra_vocab_size, "cpu", ) self._token_lora_indices = self._pad_to_shape( base_indices, self._token_lora_indices.shape, dims=1).to(self.device) self._sampler_indices = self._pad_to_shape(sampler_indices, self._sampler_indices.shape, dims=1).to(self.device) self._sampler_indices_padded = self._pad_to_shape( sampler_indices_padded, self._sampler_indices_padded.shape, dims=1).to(self.device) self._embeddings_indices = self._pad_to_shape( embeddings_indices, self._embeddings_indices.shape, dims=2).to(self.device) self.indices_len[:] = indices_len def _update_prefill_metadata(self, token_lora_tensor: torch.Tensor) -> None: self.batch_size = 1 self._lora_indices_per_batch[:self. batch_size] = token_lora_tensor[:self. batch_size] def _pad_prompt_mapping( self, prompt_mapping: tuple[int, ...]) -> tuple[int, ...]: num_reqs = len(prompt_mapping) # From vllm/v1/worker/tpu_model_runner:51, but need to avoid a circular # import MIN_NUM_SEQS = 8 padded_num_reqs = max(2**math.ceil(math.log2(num_reqs)), MIN_NUM_SEQS) pad_len = padded_num_reqs - num_reqs padding = [-1] * pad_len return tuple(list(prompt_mapping) + padding) def _pad_to_shape(self, src, target_shape, dims=1): if dims == 1: pad_len = target_shape[0] - src.shape[0] return F.pad(src, (0, pad_len), value=0).to(torch.int32) else: pad_rows = target_shape[0] - src.shape[0] pad_cols = target_shape[1] - src.shape[1] return F.pad(src, (0, pad_cols, 0, pad_rows), value=0).to(torch.int32)