5ff0d32580
Signed-off-by: Varun Sundar Rabindranath <varun@neuralmagic.com> Co-authored-by: Varun Sundar Rabindranath <varun@neuralmagic.com>
448 lines
16 KiB
Python
448 lines
16 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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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, List, Optional, Tuple, Union, final
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import torch
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import vllm.envs as env
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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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if env.VLLM_USE_V1:
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from vllm.lora.ops.triton_ops.v1 import (V1KernelMeta, v1_expand,
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v1_shrink)
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else:
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from vllm.lora.ops.triton_ops import bgmv_expand
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from vllm.lora.ops.triton_ops import bgmv_expand_slice
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from vllm.lora.ops.triton_ops import bgmv_shrink
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from vllm.lora.ops.triton_ops import sgmv_expand
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from vllm.lora.ops.triton_ops import sgmv_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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class V1KernelMixin:
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def _v1_make_metadata(self, max_loras: int, max_num_batched_tokens: int,
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max_batches: int, device: Union[torch.device, str]):
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self.token_mapping_v1_meta = V1KernelMeta.make(max_loras,
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max_num_batched_tokens,
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device=device)
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self.prompt_mapping_v1_meta = V1KernelMeta.make(max_loras,
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max_batches,
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device=device)
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def _v1_prepare_metadata_tensors(self, token_lora_indices: torch.Tensor,
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sampler_indices: torch.Tensor):
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self.token_mapping_v1_meta.prepare_tensors(token_lora_indices)
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self.prompt_mapping_v1_meta.prepare_tensors(sampler_indices)
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def _v1_apply_shrink(
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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: Tuple[torch.Tensor, ...],
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scale: float,
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):
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v1_shrink(
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x,
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w_t_all,
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y,
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*self.token_mapping_v1_meta.meta_args(x.size(0)),
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scale,
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)
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def _v1_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: Tuple[torch.Tensor, ...],
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offset_start: int,
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add_inputs: bool,
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):
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v1_expand(
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x,
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w_t_all,
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y,
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*self.token_mapping_v1_meta.meta_args(x.size(0)),
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offset_start=offset_start,
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add_inputs=add_inputs,
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)
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@final
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class PunicaWrapperGPU(PunicaWrapperBase, V1KernelMixin):
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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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if env.VLLM_USE_V1:
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self._v1_make_metadata(self.max_loras, max_num_batched_tokens,
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max_batches, 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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if env.VLLM_USE_V1:
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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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self._v1_prepare_metadata_tensors(self.token_lora_indices,
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self.sampler_indices)
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else:
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# Forward to base class update_metadata
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PunicaWrapperBase.update_metadata(self, mapping, lora_index_to_id,
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max_loras, vocab_size,
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extra_vocab_size,
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long_lora_context, **kwargs)
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def _apply_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: Tuple[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 _apply_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 _apply_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: Tuple[torch.Tensor, ...],
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offset_start: 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(
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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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offset_start=offset_start,
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add_inputs=add_inputs,
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)
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def _apply_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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y_offset: Optional[int],
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y_slice_size: Optional[int],
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add_inputs: bool,
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):
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bgmv_expand_slice(x, w_t_all, y, self.token_lora_indices, y_offset,
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y_slice_size, add_inputs)
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def add_shrink(self, y: Union[Tuple[torch.Tensor, ...], torch.Tensor],
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x: torch.Tensor, 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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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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if env.VLLM_USE_V1:
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self._v1_apply_shrink(y, x, lora_a_stacked, scale) # type: ignore
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else:
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if self.is_prefill:
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# NOTE fused kernel
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self._apply_shrink_prefill(
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y, # type: ignore
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x,
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lora_a_stacked,
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scale)
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else:
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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_decode(y[slice_idx], x,
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lora_a_stacked[slice_idx], scale)
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def add_expand(self,
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y: torch.Tensor,
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x: Union[Tuple[torch.Tensor, ...], 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 (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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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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self._apply_bias(self.token_lora_indices, y, output_slices,
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lora_bias_stacked)
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if env.VLLM_USE_V1:
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# TODO (varun): Profile with add_inputs = False. i.e. move the
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# addition out of the kernel
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self._v1_apply_expand(
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y,
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x, # type: ignore
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lora_b_stacked,
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offset_start,
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add_inputs=True)
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else:
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if self.is_prefill:
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# NOTE fused kernel
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self._apply_expand_prefill(
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y,
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x, # type: ignore
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lora_b_stacked,
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offset_start,
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add_inputs=True)
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else:
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# TODO fuse these kernels
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for slice_idx in range(len(lora_b_stacked)):
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self._apply_expand_decode(
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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_start,
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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_start += output_slices[slice_idx]
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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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if env.VLLM_USE_V1:
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self._v1_apply_expand(y,
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x.unsqueeze(dim=0), (lora_b_stacked, ),
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offset_start=0,
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add_inputs=add_inputs)
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else:
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if self.is_prefill:
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sgmv_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.prefill_metadata,
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offset_start=0,
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add_inputs=add_inputs,
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)
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else:
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bgmv_expand(x, lora_b_stacked, y, self.token_lora_indices,
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add_inputs)
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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[Tuple[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[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 lora_bias_stacked is not None:
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assert len(lora_bias_stacked) == len(output_slices)
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y = self._apply_bias(self.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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if env.VLLM_USE_V1:
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v1_shrink(x, [lora_a_stacked], buffer.unsqueeze(dim=0),
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*self.prompt_mapping_v1_meta.meta_args(x.size(0)), scale)
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v1_expand(buffer.unsqueeze(dim=0), [lora_b_stacked],
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y,
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*self.prompt_mapping_v1_meta.meta_args(buffer.size(0)),
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add_inputs=True)
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else:
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# V0 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,
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lora_b_stacked,
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y,
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self.sampler_indices,
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add_inputs=True)
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y = y.view_as(y_org)
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