422 lines
13 KiB
Python
422 lines
13 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
"""
|
|
Based on:
|
|
Chen, L., Ye, Z., Wu, Y., Zhuo, D., Ceze, L., & Krishnamurthy, A. (2023).
|
|
Punica: Multi-Tenant LoRA Serving.
|
|
https://arxiv.org/abs/2310.18547
|
|
"""
|
|
|
|
from typing import final
|
|
|
|
import torch
|
|
|
|
from vllm import _custom_ops as ops
|
|
from vllm.lora.layers import LoRAMapping
|
|
from vllm.lora.ops.xpu_ops import bgmv_expand, bgmv_expand_slice, bgmv_shrink
|
|
from vllm.triton_utils import HAS_TRITON, triton
|
|
from vllm.utils.math_utils import round_up
|
|
|
|
if HAS_TRITON:
|
|
from vllm.lora.ops.triton_ops import (
|
|
LoRAKernelMeta,
|
|
fused_moe_lora,
|
|
)
|
|
|
|
from .punica_base import PunicaWrapperBase
|
|
|
|
|
|
@final
|
|
class PunicaWrapperXPU(PunicaWrapperBase):
|
|
"""
|
|
PunicaWrapperXPU is designed to manage and provide metadata for the punica
|
|
kernel. The main function is to maintain the state information for
|
|
Multi-LoRA, and to provide the interface for the punica ipex kernel.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
max_num_batched_tokens: int,
|
|
max_batches: int,
|
|
device: torch.device | str,
|
|
**kwargs,
|
|
):
|
|
PunicaWrapperBase.__init__(self, max_num_batched_tokens, max_batches, device)
|
|
torch._dynamo.mark_dynamic(self._token_lora_indices, 0)
|
|
torch._dynamo.mark_dynamic(self._embeddings_indices, 1)
|
|
torch._dynamo.mark_dynamic(self._sampler_indices_padded, 0)
|
|
|
|
self.lora_config = kwargs["lora_config"]
|
|
self.max_loras = self.lora_config.max_loras
|
|
self.token_mapping_meta = LoRAKernelMeta.make(
|
|
self.max_loras, max_num_batched_tokens, device=device
|
|
)
|
|
|
|
def update_metadata(
|
|
self,
|
|
mapping: LoRAMapping,
|
|
lora_index_to_id: list[int | None],
|
|
max_loras: int,
|
|
vocab_size: int,
|
|
**kwargs,
|
|
):
|
|
self.is_prefill = mapping.is_prefill
|
|
self._update_base_metadata(mapping, lora_index_to_id, max_loras, vocab_size)
|
|
|
|
def _get_token_lora_indices(self, x: torch.Tensor) -> torch.IntTensor:
|
|
return torch.narrow(self._token_lora_indices, 0, 0, x.size(0))
|
|
|
|
def _apply_shrink(
|
|
self,
|
|
y: torch.Tensor,
|
|
x: torch.Tensor,
|
|
w_t_all: torch.Tensor,
|
|
scale: float,
|
|
):
|
|
bgmv_shrink(x, w_t_all, y, self._get_token_lora_indices(x), scale)
|
|
|
|
def _apply_expand(
|
|
self,
|
|
y: torch.Tensor,
|
|
x: torch.Tensor,
|
|
w_t_all: torch.Tensor,
|
|
y_offset: int,
|
|
y_slice_size: int,
|
|
add_inputs: bool,
|
|
):
|
|
token_lora_indices = self._get_token_lora_indices(x)
|
|
bgmv_expand_slice(
|
|
x, w_t_all, y, token_lora_indices, y_offset, y_slice_size, add_inputs
|
|
)
|
|
|
|
def add_shrink(
|
|
self,
|
|
y: torch.Tensor,
|
|
x: torch.Tensor,
|
|
lora_a_stacked: tuple[torch.Tensor, ...],
|
|
scale: float,
|
|
**kwargs,
|
|
):
|
|
"""
|
|
Performs GEMM for multiple slices of lora_a.
|
|
|
|
Semantics:
|
|
for i in range(len(lora_a_stacked)):
|
|
y[i] += (x @ lora_a_stacked[i]) * scale
|
|
|
|
Args:
|
|
y (torch.Tensor): Output tensors
|
|
x (torch.Tensor): Input tensor
|
|
lora_a_stacked (tuple[torch.Tensor, ...]): lora_a's weights
|
|
scale (float): Scaling factor for the operation
|
|
"""
|
|
|
|
x = x.view(-1, x.shape[-1])
|
|
for slice_idx in range(len(lora_a_stacked)):
|
|
self._apply_shrink(y[slice_idx], x, lora_a_stacked[slice_idx], scale)
|
|
|
|
def add_expand(
|
|
self,
|
|
y: torch.Tensor,
|
|
x: torch.Tensor,
|
|
lora_b_stacked: tuple[torch.Tensor, ...],
|
|
output_slices: tuple[int, ...],
|
|
offset_start: int = 0,
|
|
add_inputs=True,
|
|
**kwargs,
|
|
) -> None:
|
|
"""
|
|
Performs GEMM for multiple slices of lora_b.
|
|
|
|
Semantics:
|
|
for i in range(len(lora_b_stacked)):
|
|
slice = output_slices[i]
|
|
y[:, offset:offset+slice] += x[i] @ lora_b_stacked[i]
|
|
offset += slice
|
|
|
|
Args:
|
|
y (torch.Tensor): Output tensor.
|
|
x (torch.Tensor): Input tensors
|
|
lora_b_stacked (tuple[torch.Tensor, ...]): lora_b's weight
|
|
output_slices (tuple[int, ...]): Every slice's size
|
|
add_inputs (bool): Defaults to True.
|
|
"""
|
|
y_org = y
|
|
y = y.view(-1, y.shape[-1])
|
|
|
|
assert x.ndim == 3
|
|
assert x.size(0) == len(output_slices)
|
|
|
|
# TODO fuse these kernels
|
|
for slice_idx in range(len(lora_b_stacked)):
|
|
self._apply_expand(
|
|
y,
|
|
x[slice_idx],
|
|
lora_b_stacked[slice_idx],
|
|
offset_start,
|
|
output_slices[slice_idx],
|
|
add_inputs=add_inputs,
|
|
)
|
|
offset_start += output_slices[slice_idx]
|
|
y.view_as(y_org)
|
|
|
|
def add_lora_embedding(
|
|
self,
|
|
y: torch.Tensor,
|
|
x: torch.Tensor,
|
|
lora_b_stacked: torch.Tensor,
|
|
add_inputs: bool = True,
|
|
**kwargs,
|
|
) -> None:
|
|
"""
|
|
Applies lora specifically for VocabParallelEmbeddingWithLoRA.
|
|
|
|
Semantics:
|
|
y += x @ lora_b_stacked
|
|
|
|
Args:
|
|
y (torch.Tensor): Output tensor.
|
|
x (torch.Tensor): Input tensor.
|
|
lora_b_stacked (torch.Tensor): lora_b's weights.
|
|
add_inputs (bool): Default to True.
|
|
"""
|
|
token_lora_indices = self._get_token_lora_indices(x)
|
|
bgmv_expand(x, lora_b_stacked, y, token_lora_indices, add_inputs)
|
|
|
|
def add_lora_linear(
|
|
self,
|
|
y: torch.Tensor,
|
|
x: torch.Tensor,
|
|
lora_a_stacked: tuple[torch.Tensor, ...],
|
|
lora_b_stacked: tuple[torch.Tensor, ...],
|
|
scale: float,
|
|
output_slices: tuple[int, ...],
|
|
*,
|
|
buffer: torch.Tensor | None = None,
|
|
**kwargs,
|
|
) -> None:
|
|
"""
|
|
Applicable to linear-related lora.
|
|
|
|
Semantics:
|
|
for i in range(len(lora_a_stacked)):
|
|
y[i] += (
|
|
x[i].unsqueeze(0)
|
|
@ lora_a_stacked[indices[i], layer_idx, :, :]
|
|
@ lora_b_stacked[indices[i], layer_idx, :, :]
|
|
* scale
|
|
).squeeze(0)
|
|
|
|
Args:
|
|
y (torch.Tensor): Output tensor. Will be changed in-place.
|
|
x (torch.Tensor): Input tensor
|
|
lora_a_stacked (tuple[torch.Tensor, ...]): lora_a's weight.
|
|
lora_b_stacked (tuple[torch.Tensor, ...]): lora_b's weight.
|
|
scale (float): Scaling factor.
|
|
output_slices (tuple[int, ...]): Every slice's size.
|
|
buffer (Optional[torch.Tensor]): Defaults to None.
|
|
"""
|
|
|
|
assert len(lora_a_stacked) == len(lora_b_stacked) == len(output_slices)
|
|
|
|
if buffer is None:
|
|
r = lora_b_stacked[0].size(-1)
|
|
buffer = torch.zeros( # type: ignore
|
|
(len(output_slices), x.size(0), r),
|
|
dtype=x.dtype,
|
|
device=x.device,
|
|
)
|
|
self.add_shrink(
|
|
buffer, # type: ignore
|
|
x,
|
|
lora_a_stacked,
|
|
scale,
|
|
**kwargs,
|
|
)
|
|
self.add_expand(
|
|
y,
|
|
buffer, # type: ignore
|
|
lora_b_stacked,
|
|
output_slices,
|
|
add_inputs=True,
|
|
**kwargs,
|
|
)
|
|
|
|
@property
|
|
def sampler_indices_padded(self) -> torch.Tensor:
|
|
"""
|
|
This property provides access to padded sampler indices.
|
|
"""
|
|
return self._sampler_indices_padded[:]
|
|
|
|
def add_lora_logits(
|
|
self,
|
|
y: torch.Tensor,
|
|
x: torch.Tensor,
|
|
lora_a_stacked: torch.Tensor,
|
|
lora_b_stacked: torch.Tensor,
|
|
scale,
|
|
*,
|
|
buffer: torch.Tensor | None = None,
|
|
**kwargs,
|
|
) -> None:
|
|
"""
|
|
Applies lora specifically for LogitsProcessorWithLoRA.
|
|
|
|
Semantics:
|
|
buffer = (x @ lora_a_stacked) * scale
|
|
y += buffer @ lora_b_stacked
|
|
|
|
Args:
|
|
y (torch.Tensor): Output tensor.
|
|
x (torch.Tensor): Input tensor.
|
|
lora_a_stacked (torch.Tensor): lora_a's weights.
|
|
lora_b_stacked (torch.Tensor): lora_b's weights.
|
|
scale (float): Scaling factor.
|
|
buffer (Optional[torch.Tensor]): Default to None.
|
|
"""
|
|
y_org = y
|
|
y = y.view(-1, y.shape[-1])
|
|
x = x.view(-1, x.shape[-1])
|
|
r = lora_b_stacked.size(-1)
|
|
if buffer is None:
|
|
buffer = torch.zeros((x.size(0), r), dtype=x.dtype, device=x.device)
|
|
sampler_indices = torch.narrow(self._sampler_indices, 0, 0, x.size(0))
|
|
bgmv_shrink(x, lora_a_stacked, buffer, sampler_indices, scale)
|
|
bgmv_expand(buffer, lora_b_stacked, y, sampler_indices, add_inputs=True)
|
|
return y.view_as(y_org)
|
|
|
|
def moe_lora_align_block_size(
|
|
self,
|
|
topk_ids: torch.Tensor,
|
|
num_tokens: int,
|
|
block_size: int,
|
|
num_experts: int,
|
|
max_loras: int,
|
|
adapter_enabled: torch.Tensor,
|
|
expert_map: torch.Tensor | None = None,
|
|
pad_sorted_ids: bool = False,
|
|
naive_block_assignment: bool = False,
|
|
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
|
|
"""
|
|
Aligns tokens and experts into block-sized chunks for LoRA-based
|
|
mixture-of-experts (MoE) execution.
|
|
"""
|
|
(token_lora_mapping, _, _, _, lora_ids, _, _) = (
|
|
self.token_mapping_meta.meta_args(
|
|
num_tokens, self.lora_config.specialize_active_lora
|
|
)
|
|
)
|
|
if naive_block_assignment:
|
|
expert_ids = topk_ids.reshape(-1)
|
|
sorted_ids = None
|
|
num_tokens_post_pad = None
|
|
else:
|
|
max_num_tokens_padded = topk_ids.numel() + num_experts * (block_size - 1)
|
|
if pad_sorted_ids:
|
|
max_num_tokens_padded = round_up(max_num_tokens_padded, block_size)
|
|
sorted_ids = torch.empty(
|
|
(max_loras * max_num_tokens_padded,),
|
|
dtype=torch.int32,
|
|
device=topk_ids.device,
|
|
)
|
|
max_num_m_blocks = triton.cdiv(max_num_tokens_padded, block_size)
|
|
# Expert ids must be set default to -1 to prevent a blank block
|
|
expert_ids = torch.empty(
|
|
(max_loras * max_num_m_blocks,),
|
|
dtype=torch.int32,
|
|
device=topk_ids.device,
|
|
)
|
|
num_tokens_post_pad = torch.empty(
|
|
(max_loras), dtype=torch.int32, device=topk_ids.device
|
|
)
|
|
|
|
ops.moe_lora_align_block_size(
|
|
topk_ids,
|
|
token_lora_mapping,
|
|
num_experts,
|
|
block_size,
|
|
max_loras,
|
|
max_num_tokens_padded,
|
|
max_num_m_blocks,
|
|
sorted_ids,
|
|
expert_ids,
|
|
num_tokens_post_pad,
|
|
adapter_enabled,
|
|
lora_ids,
|
|
)
|
|
if expert_map is not None:
|
|
expert_ids = expert_map[expert_ids]
|
|
|
|
return None, sorted_ids, expert_ids, num_tokens_post_pad
|
|
|
|
def add_lora_fused_moe(
|
|
self,
|
|
y: torch.Tensor,
|
|
x: torch.Tensor,
|
|
lora_a_stacked: tuple[torch.Tensor, ...],
|
|
lora_b_stacked: tuple[torch.Tensor, ...],
|
|
topk_weights: torch.Tensor,
|
|
sorted_token_ids: torch.Tensor | None,
|
|
expert_ids: torch.Tensor,
|
|
num_tokens_post_padded: torch.Tensor | None,
|
|
max_lora_rank: int,
|
|
top_k_num: int,
|
|
shrink_config,
|
|
expand_config,
|
|
adapter_enabled: torch.Tensor,
|
|
mul_routed_weight=False,
|
|
fully_sharded: bool = False,
|
|
offset: int = 0,
|
|
token_lora_mapping: torch.Tensor | None = None,
|
|
):
|
|
"""
|
|
Performs a fused forward computation for LoRA of Mixture-of-Experts (MoE) layer.
|
|
"""
|
|
(
|
|
token_lora_mapping_meta,
|
|
_,
|
|
_,
|
|
_,
|
|
lora_ids,
|
|
_,
|
|
num_active_loras,
|
|
) = self.token_mapping_meta.meta_args(
|
|
x.size(0), self.lora_config.specialize_active_lora
|
|
)
|
|
if token_lora_mapping is None:
|
|
token_lora_mapping = token_lora_mapping_meta
|
|
fused_moe_lora(
|
|
y,
|
|
x,
|
|
lora_a_stacked,
|
|
lora_b_stacked,
|
|
topk_weights,
|
|
sorted_token_ids,
|
|
expert_ids,
|
|
num_tokens_post_padded,
|
|
token_lora_mapping,
|
|
max_lora_rank,
|
|
top_k_num,
|
|
lora_ids,
|
|
num_active_loras,
|
|
adapter_enabled,
|
|
shrink_config.get("BLOCK_SIZE_M", 64),
|
|
shrink_config.get("BLOCK_SIZE_N", 64),
|
|
shrink_config.get("BLOCK_SIZE_K", 32),
|
|
shrink_config.get("GROUP_SIZE_M", 8),
|
|
shrink_config.get("NUM_WARPS", 4),
|
|
shrink_config.get("NUM_STAGES", 3),
|
|
shrink_config.get("SPLIT_K", 1),
|
|
expand_config.get("BLOCK_SIZE_M", 64),
|
|
expand_config.get("BLOCK_SIZE_N", 64),
|
|
expand_config.get("BLOCK_SIZE_K", 32),
|
|
expand_config.get("GROUP_SIZE_M", 8),
|
|
expand_config.get("NUM_WARPS", 4),
|
|
expand_config.get("NUM_STAGES", 3),
|
|
expand_config.get("SPLIT_K", 1),
|
|
mul_routed_weight,
|
|
fully_sharded,
|
|
offset,
|
|
)
|