452 lines
16 KiB
Python
452 lines
16 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from collections.abc import Callable
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import torch
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from vllm.lora.punica_wrapper.punica_base import PunicaWrapperBase
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from vllm_ascend.lora.utils import refresh_all_lora_classes
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from vllm_ascend.utils import AscendDeviceType, get_ascend_device_type
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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 PunicaWrapperNPU(PunicaWrapperBase):
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"""
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PunicaWrapperNPU 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__(self, max_num_batched_tokens: int, max_batches: int, device: torch.device | str, **kwargs):
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PunicaWrapperBase.__init__(self, max_num_batched_tokens, max_batches, device)
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refresh_all_lora_classes()
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self.lora_config = kwargs.get("lora_config")
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if get_ascend_device_type() == AscendDeviceType._310P or (
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self.lora_config is not None and self.lora_config.max_lora_rank >= 128
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):
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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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else:
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from vllm_ascend.lora.lora_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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self.bgmv_expand = bgmv_expand
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self.bgmv_expand_slice = bgmv_expand_slice
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self.bgmv_shrink = bgmv_shrink
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self.sgmv_expand = sgmv_expand
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self.sgmv_expand_slice = sgmv_expand_slice
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self.sgmv_shrink = sgmv_shrink
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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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self.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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self.bgmv_shrink(x, w_t_all, y, self._get_token_lora_indices(x), 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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self.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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self.bgmv_expand(x, w_t_all, y, self._get_token_lora_indices(x), 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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self.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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self.bgmv_expand_slice(
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x,
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w_t_all,
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y,
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self._get_token_lora_indices(x),
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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 _get_token_lora_indices(self, x: torch.Tensor) -> torch.Tensor:
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return torch.narrow(self._token_lora_indices, 0, 0, x.size(0))
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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 = self._expand_slice_prefill if self.is_prefill else self._expand_slice_decode
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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(self, y: torch.Tensor, x: torch.Tensor, w_t_all: torch.Tensor, scale: float):
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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 = self._shrink_prefill if self.is_prefill else self._shrink_decode
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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 and bias addition 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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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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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, y: torch.Tensor, x: torch.Tensor, lora_b_stacked: torch.Tensor, add_inputs: bool = True, **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 = self._expand_prefill if self.is_prefill else self._expand_decode
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x = x.to(torch.float32)
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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) @ lora_a_stacked[
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indices[i], layer_idx, :, :] @ lora_b_stacked[
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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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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) 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(y, buffer, lora_b_stacked, output_slices, add_inputs=True, **kwargs)
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def add_lora_fused_moe(
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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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*,
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topk_weights: torch.Tensor | None = None,
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sorted_token_ids: torch.Tensor | None = None,
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expert_ids: torch.Tensor,
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num_tokens_post_padded: torch.Tensor | None = None,
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max_lora_rank: int = 0,
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top_k_num: int = 1,
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shrink_config=None,
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expand_config=None,
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adapter_enabled: torch.Tensor,
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mul_routed_weight: bool = False,
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fully_sharded: bool = False,
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offset: int = 0,
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token_lora_mapping: torch.Tensor | None = None,
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) -> None:
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"""
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Ascend-native fused MoE LoRA (v2): static-shape per-row gather via the
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same bgmv_shrink/bgmv_expand AscendC kernels (csrc/kernels/bgmv_*.cpp)
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used by the dense Linear LoRA layers, instead of grouping rows by a
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data-dependent ``torch.unique`` over active LoRA ids. The previous
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``torch.unique``/``nonzero`` version produced output whose *shape*
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depended on tensor values, which ACL Graph capture cannot record
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(it failed with an `aclnnUnique2` error as soon as `enforce_eager`
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was turned off) -- every tensor below has a shape that depends only
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on input shapes, never on values, so this stays graph-capturable.
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Rows are already one-token-per-row (top_k_num=1). Each row needs the
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LoRA slot for (lora_id, expert_id), so we fold both into a single
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gather index into a ``[max_loras * num_experts, ...]`` view of the
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existing per-(lora, expert) weight stacks:
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combined_idx[row] = lora_id[row] * num_experts + expert_id[row]
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or -1 when the row has no active adapter, mirroring the -1 sentinel
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``PunicaWrapperBase.token_lora_indices`` already uses. bgmv_shrink/
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bgmv_expand skip any row whose index is negative (leaving the
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zero-initialized shrink buffer / unmodified ``y`` in place), so
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inactive rows get a zero delta for free -- no Python-level branching
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needed.
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"""
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del sorted_token_ids, num_tokens_post_padded, max_lora_rank
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del shrink_config, expand_config, fully_sharded
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assert top_k_num == 1, "Ascend MoE LoRA v1 expects pre-expanded rows (top_k_num=1)."
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if token_lora_mapping is None:
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token_lora_mapping = self.token_lora_indices
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x2d = x.view(-1, x.shape[-1])
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y2d = y.view(-1, y.shape[-1])
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expert_idx = expert_ids.view(-1).to(torch.long)
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num_experts = lora_a_stacked[0].shape[1]
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lora_idx_safe = token_lora_mapping.clamp(min=0)
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enabled = (token_lora_mapping >= 0) & adapter_enabled[lora_idx_safe].bool()
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combined_idx = torch.where(
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enabled,
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lora_idx_safe * num_experts + expert_idx,
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torch.full_like(token_lora_mapping, -1),
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).contiguous()
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# bgmv_shrink writes fp32 (its Y_T); bgmv_expand reads fp32 (its X_T),
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# so the shrink buffer is fp32.
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rank = lora_a_stacked[0].shape[-2]
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shrink_out = torch.zeros((x2d.shape[0], rank), dtype=torch.float32, device=x2d.device)
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cur_offset = offset
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for slice_idx in range(len(lora_a_stacked)):
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# lora_a_stacked[s]/lora_b_stacked[s]: [max_loras, num_experts, rank, *].
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# Flattening the leading two dims turns "gather by (lora, expert)"
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# into "the plain per-row gather" to reuse bgmv_shrink/bgmv_expand.
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a = lora_a_stacked[slice_idx]
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b = lora_b_stacked[slice_idx]
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out_size = b.shape[-2]
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a_flat = a.view(-1, rank, a.shape[-1])
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b_flat = b.view(-1, out_size, rank)
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self.bgmv_shrink(x2d, a_flat, shrink_out, combined_idx, 1.0)
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delta = shrink_out
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if mul_routed_weight and topk_weights is not None:
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delta = shrink_out * topk_weights.view(-1, 1)
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self.bgmv_expand_slice(delta, b_flat, y2d, combined_idx, cur_offset, out_size, add_inputs=True)
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cur_offset += out_size
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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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buffer = torch.zeros((x.size(0), r), dtype=torch.float32, device=x.device)
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indices = torch.narrow(self._sampler_indices, 0, 0, x.size(0))
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self.bgmv_shrink(x, lora_a_stacked, buffer, indices, scale)
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self.bgmv_expand(buffer, lora_b_stacked, y, indices, add_inputs=True)
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y = y.view_as(y_org)
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