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Support FlashMLA backend (#4472)

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Co-authored-by: yinfan98 <1106310035@qq.com>
This commit is contained in:
lukec
2025-03-17 00:07:06 +08:00
committed by GitHub
parent 1b859295f4
commit a53fe428f9
6 changed files with 209 additions and 1 deletions
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128
python/sglang/srt/layers/attention/flashmla_backend.py Normal file
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@@ -0,0 +1,128 @@
from __future__ import annotations
"""
Support attention backend for flashMLA.
Current initial integration of FlashMLA shows normal accuracy, but performance is slightly lacking.
#TODO
Support FlashMLA decode with cudagraph
Enable speculative sampling in FlashMLA
Integrate FA3 prefill
"""
from typing import TYPE_CHECKING, Optional, Union
import torch
import triton
from flash_mla import flash_mla_with_kvcache, get_mla_metadata
from sglang.global_config import global_config
from sglang.srt.layers.attention.base_attn_backend import AttentionBackend
from sglang.srt.layers.attention.flashinfer_mla_backend import FlashInferMLAAttnBackend
from sglang.srt.layers.attention.utils import create_flashmla_kv_indices_triton
from sglang.srt.layers.dp_attention import get_attention_tp_size
from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMode
if TYPE_CHECKING:
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.model_executor.model_runner import ModelRunner
from sglang.srt.speculative.eagle_utils import EagleDraftInput, EagleVerifyInput
# FlashMLA only supports pagesize=64
PAGE_SIZE = 64
class FlashMLABackend(FlashInferMLAAttnBackend):
"""Flashinfer attention kernels."""
def __init__(
self,
model_runner: ModelRunner,
skip_prefill: bool = False,
kv_indptr_buf: Optional[torch.Tensor] = None,
kv_last_page_len_buf: Optional[torch.Tensor] = None,
):
super().__init__(
model_runner, skip_prefill, kv_indptr_buf, kv_last_page_len_buf
)
self.num_q_heads = (
model_runner.model_config.num_attention_heads // get_attention_tp_size()
)
self.num_kv_heads = model_runner.model_config.get_num_kv_heads(
get_attention_tp_size()
)
self.req_to_token = model_runner.req_to_token_pool.req_to_token
self.num_local_heads = (
model_runner.model_config.num_attention_heads // get_attention_tp_size()
)
self.kv_lora_rank = model_runner.model_config.kv_lora_rank
self.qk_nope_head_dim = model_runner.model_config.qk_nope_head_dim
self.qk_rope_head_dim = model_runner.model_config.qk_rope_head_dim
self.v_head_dim = model_runner.model_config.v_head_dim
self.scaling = model_runner.model_config.scaling
self.data_type = model_runner.kv_cache_dtype
self.q_data_type = model_runner.dtype
self.kv_cache_dim = self.kv_lora_rank + self.qk_rope_head_dim
def forward_decode(
self,
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
layer: RadixAttention,
forward_batch: ForwardBatch,
save_kv_cache: bool = True,
):
cache_loc = forward_batch.out_cache_loc
if k is not None:
assert v is not None
if save_kv_cache:
forward_batch.token_to_kv_pool.set_kv_buffer(
layer,
cache_loc,
k,
v,
)
bs = forward_batch.batch_size
max_seqlen_pad = triton.cdiv(forward_batch.seq_lens.max().item(), PAGE_SIZE)
flashmla_index = torch.full(
(bs, max_seqlen_pad), -1, dtype=torch.int32, device=q.device
)
create_flashmla_kv_indices_triton[(bs,)](
self.indices_updater_decode.req_to_token,
forward_batch.req_pool_indices,
forward_batch.seq_lens,
None,
flashmla_index,
self.indices_updater_decode.req_to_token.size(1),
flashmla_index.size(1),
max_seqlen_pad,
)
mla_metadata, mla_splits = get_mla_metadata(
forward_batch.seq_lens.to(torch.int32),
1 * self.num_q_heads // self.num_kv_heads,
self.num_kv_heads,
)
k_cache = forward_batch.token_to_kv_pool.get_key_buffer(layer.layer_id)
reshape_q = q.view(bs, -1, layer.tp_q_head_num, layer.head_dim)
o, _ = flash_mla_with_kvcache(
q=reshape_q,
k_cache=k_cache.view(-1, PAGE_SIZE, 1, self.kv_cache_dim),
block_table=flashmla_index,
cache_seqlens=forward_batch.seq_lens.to(torch.int32),
head_dim_v=self.kv_lora_rank, # TODO Retrieve from config.
tile_scheduler_metadata=mla_metadata,
num_splits=mla_splits,
softmax_scale=layer.scaling,
causal=False,
)
return o.view(-1, layer.tp_q_head_num * layer.v_head_dim)

54
python/sglang/srt/layers/attention/utils.py
View File

@@ -15,6 +15,7 @@ def create_flashinfer_kv_indices_triton(
BLOCK_SIZE: tl.constexpr = 512
pid = tl.program_id(axis=0)
# find the req pool idx, this is for batch to token
req_pool_index = tl.load(req_pool_indices_ptr + pid)
kv_indices_offset = tl.load(kv_indptr + pid)
@@ -37,3 +38,56 @@ def create_flashinfer_kv_indices_triton(
mask=mask,
)
tl.store(kv_indices_ptr + kv_indices_offset + offset, data, mask=mask)
@triton.jit
def create_flashmla_kv_indices_triton(
req_to_token_ptr, # [max_batch, max_context_len]
req_pool_indices_ptr,
page_kernel_lens_ptr,
kv_start_idx,
kv_indices_ptr,
req_to_token_ptr_stride: tl.constexpr,
kv_indices_ptr_stride: tl.constexpr,
max_pagesize: tl.constexpr,
):
PAGED_SIZE: tl.constexpr = 64
BLOCK_SIZE: tl.constexpr = 4096
NUM_PAGE_PER_BLOCK: tl.constexpr = 64
pid = tl.program_id(axis=0)
# find the req pool idx, this is for batch to token
req_pool_index = tl.load(req_pool_indices_ptr + pid)
kv_start = 0
kv_end = 0
if kv_start_idx:
kv_start = tl.load(kv_start_idx + pid).to(tl.int32)
kv_end = kv_start
kv_end += tl.load(page_kernel_lens_ptr + pid).to(tl.int32)
num_paged = tl.cdiv(kv_end - kv_start, PAGED_SIZE)
num_pages_loop = tl.cdiv(kv_end - kv_start, BLOCK_SIZE)
for i in range(num_pages_loop):
paged_offset = (
tl.arange(0, NUM_PAGE_PER_BLOCK) + i * NUM_PAGE_PER_BLOCK
) * PAGED_SIZE
paged_offset_out = tl.arange(0, NUM_PAGE_PER_BLOCK) + i * NUM_PAGE_PER_BLOCK
mask = paged_offset <= num_paged * PAGED_SIZE
mask_out = paged_offset_out <= num_paged
data = tl.load(
req_to_token_ptr
+ req_pool_index * req_to_token_ptr_stride
+ kv_start
+ paged_offset,
mask=mask,
)
tl.store(
kv_indices_ptr + pid * kv_indices_ptr_stride + paged_offset_out,
data // PAGED_SIZE,
mask=mask_out,
)