Sync from v0.13
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351
vllm/lora/punica_wrapper/punica_cpu.py
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351
vllm/lora/punica_wrapper/punica_cpu.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from collections.abc import Callable
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import torch
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from vllm.lora.ops.torch_ops import (
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bgmv_expand,
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bgmv_expand_slice,
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bgmv_shrink,
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sgmv_expand,
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sgmv_expand_slice,
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sgmv_shrink,
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)
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from .punica_base import PunicaWrapperBase
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# The platforms that are compatible with the PyTorch-native implementation can
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# inherit this class
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class PunicaWrapperCPU(PunicaWrapperBase):
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"""
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PunicaWrapperCPU 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 pytorch punica ops.
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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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PunicaWrapperBase.__init__(self, max_num_batched_tokens, max_batches, device)
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def _shrink_prefill(
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self,
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y: torch.Tensor,
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x: torch.Tensor,
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w_t_all: torch.Tensor,
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scale: float,
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):
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# No LoRA request, so return directly
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if self.no_lora:
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return
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sgmv_shrink(
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x,
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w_t_all,
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y,
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*self.prefill_metadata,
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scale,
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)
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def _shrink_decode(
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self,
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y: torch.Tensor,
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x: torch.Tensor,
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w_t_all: torch.Tensor,
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scale: float,
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):
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bgmv_shrink(x, w_t_all, y, self.token_lora_indices, scale)
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def _expand_prefill(
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self,
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y: torch.Tensor,
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x: torch.Tensor,
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w_t_all: torch.Tensor,
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add_inputs: bool,
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):
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# No LoRA request, so return directly
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if self.no_lora:
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return
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sgmv_expand(
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x,
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w_t_all,
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y,
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*self.prefill_metadata,
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add_inputs,
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)
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def _expand_decode(
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self,
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y: torch.Tensor,
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x: torch.Tensor,
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w_t_all: torch.Tensor,
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add_inputs: bool,
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):
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bgmv_expand(x, w_t_all, y, self.token_lora_indices, add_inputs)
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def _expand_slice_prefill(
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self,
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y: torch.Tensor,
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x: torch.Tensor,
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w_t_all: torch.Tensor,
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y_offset: int,
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y_slice_size: int,
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add_inputs: bool,
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):
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# No LoRA request, so return directly
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if self.no_lora:
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return
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sgmv_expand_slice(
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x,
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w_t_all,
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y,
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*self.prefill_metadata,
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y_offset,
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y_slice_size,
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add_inputs,
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)
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def _expand_slice_decode(
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self,
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y: torch.Tensor,
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x: torch.Tensor,
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w_t_all: torch.Tensor,
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y_offset: int,
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y_slice_size: int,
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add_inputs: bool,
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):
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bgmv_expand_slice(
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x, w_t_all, y, self.token_lora_indices, y_offset, y_slice_size, add_inputs
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)
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def _apply_expand(
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self,
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y: torch.Tensor,
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x: torch.Tensor,
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w_t_all: torch.Tensor,
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y_offset: int,
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y_slice_size: int,
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add_inputs: bool = True,
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):
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"""
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Perform the ` y[:,y_offset:y_offset+y_slice_size]+=x@w_t_all`
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computation, which is suitable for the
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GEMM of lora'b.
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"""
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expand_slice_fun: Callable = (
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self._expand_slice_prefill if self.is_prefill else self._expand_slice_decode
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)
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expand_slice_fun(y, x, w_t_all, y_offset, y_slice_size, add_inputs)
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def _apply_shrink(
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self, y: torch.Tensor, x: torch.Tensor, w_t_all: torch.Tensor, scale: float
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):
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"""
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Perform the ` y+=x@w_t_all` computation, which is suitable for the
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GEMM of lora'a.
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When `is_prefill is` true, it indicates that it is currently the
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prefill stage, and the `_shrink_prefill` function should be called.
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Otherwise, it is the decode stage, and the _shrink_decode function
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should be called.
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"""
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y_org = y
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y = y.view(-1, y.shape[-1])
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shrink_fun: Callable = (
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self._shrink_prefill if self.is_prefill else self._shrink_decode
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)
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shrink_fun(y, x, w_t_all, scale)
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y = y.view_as(y_org)
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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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):
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"""
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Performs GEMM for multiple slices of lora_a.
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When `is_prefill is` true, it indicates that it is currently the
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prefill stage, and the `_shrink_prefill` function should be called.
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Otherwise, it is the decode stage, and the _shrink_decode function
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should be called.
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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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x = x.view(-1, x.shape[-1])
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# TODO fuse these kernels
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for slice_idx in range(len(lora_a_stacked)):
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self._apply_shrink(y[slice_idx], x, lora_a_stacked[slice_idx], scale)
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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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) -> 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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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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add_inputs (bool): Defaults to True.
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"""
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y_org = y
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y = y.view(-1, y.shape[-1])
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offset_left = offset_start
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for slice_idx in range(len(lora_b_stacked)):
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self._apply_expand(
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y,
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x[slice_idx],
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lora_b_stacked[slice_idx],
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offset_left,
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output_slices[slice_idx],
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add_inputs=add_inputs,
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)
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offset_left += output_slices[slice_idx]
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y = y.view_as(y_org)
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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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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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# Embedding layer only need expand op
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expand_fun: Callable = (
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self._expand_prefill if self.is_prefill else self._expand_decode
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)
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expand_fun(y, x, lora_b_stacked, add_inputs)
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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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) -> 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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assert len(lora_a_stacked) == len(lora_b_stacked) == len(output_slices)
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if buffer is None:
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r = lora_b_stacked[0].size(-1)
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# We set the buffer to be float32 by default, consistent with the
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# triton op
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buffer = tuple(
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torch.zeros((x.size(0), r), dtype=torch.float32, device=x.device)
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for _ in range(len(output_slices))
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)
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self.add_shrink(buffer, x, lora_a_stacked, scale, **kwargs)
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self.add_expand(
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y, buffer, lora_b_stacked, output_slices, add_inputs=True, **kwargs
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)
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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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) -> 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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y_org = y
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y = y.view(-1, y.shape[-1])
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x = x.view(-1, x.shape[-1])
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r = lora_b_stacked.size(-1)
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if buffer is None:
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# We set the buffer to be float32 by default, consistent with the
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# triton op
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buffer = torch.zeros((x.size(0), r), dtype=torch.float32, device=x.device)
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# LogitsProcessorWithLoRA always using bgmv.
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bgmv_shrink(x, lora_a_stacked, buffer, self.sampler_indices, scale)
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bgmv_expand(buffer, lora_b_stacked, y, self.sampler_indices, add_inputs=True)
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y = y.view_as(y_org)
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