291 lines
11 KiB
Python
291 lines
11 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""
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Based on:
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Chen, L., Ye, Z., Wu, Y., Zhuo, D., Ceze, L., & Krishnamurthy, A. (2023).
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Punica: Multi-Tenant LoRA Serving.
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https://arxiv.org/abs/2310.18547
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"""
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from typing import TYPE_CHECKING, Optional, Union, final
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import torch
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import vllm.envs as envs
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from vllm.lora.layers import LoRAMapping
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from vllm.triton_utils import HAS_TRITON
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if HAS_TRITON:
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from vllm.lora.ops.triton_ops import (LoRAKernelMeta, lora_expand,
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lora_shrink)
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from .punica_base import PunicaWrapperBase
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if TYPE_CHECKING:
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# avoid circuit import
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from vllm.lora.models import LongContextLoRAContext
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@final
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class PunicaWrapperGPU(PunicaWrapperBase):
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"""
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PunicaWrapperGPU 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 punica triton kernel.
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"""
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def __init__(self, max_num_batched_tokens: int, max_batches: int,
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device: Union[torch.device, str], **kwargs):
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PunicaWrapperBase.__init__(self, max_num_batched_tokens, max_batches,
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device)
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self.max_loras = kwargs['max_loras']
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self.token_mapping_meta = LoRAKernelMeta.make(self.max_loras,
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max_num_batched_tokens,
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device=device)
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# When cudagraph capture size is greater than max_num_seqs (max_batches,
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# here), V0 captures the graph as if max_num_seqs is set to
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# the capture size.
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# V1 doesn't have this problem and always respects max_num_seqs.
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max_num_prompts = (max_batches
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if envs.VLLM_USE_V1 else max_num_batched_tokens)
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self.prompt_mapping_meta = LoRAKernelMeta.make(self.max_loras,
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max_num_prompts,
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device=device)
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def update_metadata(
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self,
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mapping: LoRAMapping,
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lora_index_to_id: list[Optional[int]],
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max_loras: int,
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vocab_size: int,
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extra_vocab_size: int,
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long_lora_context: Optional["LongContextLoRAContext"] = None,
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**kwargs):
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self.is_prefill = mapping.is_prefill
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self._update_base_metadata(mapping, lora_index_to_id, max_loras,
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vocab_size, extra_vocab_size,
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long_lora_context)
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# Prepare cuda kernel metadata tensors
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self.token_mapping_meta.prepare_tensors(self.token_lora_indices)
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self.prompt_mapping_meta.prepare_tensors(self.sampler_indices)
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def add_shrink(self, y: torch.Tensor, x: torch.Tensor,
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lora_a_stacked: tuple[torch.Tensor,
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...], scale: float, **kwargs):
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"""
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Performs GEMM for multiple slices of lora_a.
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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 (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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lora_shrink(
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x,
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lora_a_stacked,
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y,
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*self.token_mapping_meta.meta_args(x.size(0)),
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scale,
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)
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def add_expand(self,
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y: torch.Tensor,
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x: torch.Tensor,
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lora_b_stacked: tuple[torch.Tensor, ...],
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lora_bias_stacked: Optional[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) -> 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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lora_bias_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 (torch.Tensor): Input tensors
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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, ...]]):
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bias'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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if lora_bias_stacked is not None:
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token_lora_indices = torch.narrow(self._token_lora_indices, 0, 0,
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y.size(0))
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self._apply_bias(token_lora_indices, y, output_slices,
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lora_bias_stacked)
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assert x.ndim == 3
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assert x.size(0) == len(output_slices)
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num_tokens = x.size(1) # first dimension is the num slices
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lora_expand(
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x,
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lora_b_stacked,
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y,
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*self.token_mapping_meta.meta_args(num_tokens),
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offset_start=offset_start,
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add_inputs=True,
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)
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y = y.view_as(y_org)
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def add_lora_embedding(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) -> 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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lora_expand(
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x.unsqueeze(dim=0),
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(lora_b_stacked, ),
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y,
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*self.token_mapping_meta.meta_args(x.size(0)),
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offset_start=0,
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add_inputs=add_inputs,
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)
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def add_lora_linear(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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lora_bias_stacked: Optional[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: Optional[torch.Tensor] = None,
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**kwargs) -> 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)+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[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 lora_bias_stacked is not None:
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assert len(lora_bias_stacked) == len(output_slices)
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token_lora_indices = torch.narrow(self._token_lora_indices, 0, 0,
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y.size(0))
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y = self._apply_bias(token_lora_indices, y, output_slices,
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lora_bias_stacked)
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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, refer to:
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# https://github.com/triton-lang/triton/issues/1387
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buffer = torch.zeros( # type: ignore
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(len(output_slices), x.size(0), r),
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dtype=torch.float32,
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device=x.device,
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)
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self.add_shrink(
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buffer, # type: ignore
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x,
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lora_a_stacked,
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scale,
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**kwargs)
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self.add_expand(
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y,
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buffer, # type: ignore
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lora_b_stacked,
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None,
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output_slices,
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add_inputs=True,
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**kwargs)
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def add_lora_logits(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: Optional[torch.Tensor] = None,
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**kwargs) -> 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, refer to:
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# https://github.com/triton-lang/triton/issues/1387
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buffer = torch.zeros((x.size(0), r),
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dtype=torch.float32,
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device=x.device)
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lora_shrink(x, [lora_a_stacked], buffer.unsqueeze(dim=0),
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*self.prompt_mapping_meta.meta_args(x.size(0)), scale)
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lora_expand(buffer.unsqueeze(dim=0), [lora_b_stacked],
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y,
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*self.prompt_mapping_meta.meta_args(buffer.size(0)),
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add_inputs=True)
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y = y.view_as(y_org)
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