10d60cd41b
Co-authored-by: austindeng <austindeng@tencent.com> Co-authored-by: tianqilin.99 <tianqilin.99@bytedance.com> Co-authored-by: Qiaolin Yu <liin1211@outlook.com> Co-authored-by: ch-wan <cwan39@gatech.edu>
286 lines
9.6 KiB
Python
286 lines
9.6 KiB
Python
from __future__ import annotations
|
|
|
|
"""
|
|
Support attention backend for Cutlass MLA.
|
|
|
|
"""
|
|
|
|
from dataclasses import dataclass
|
|
from typing import TYPE_CHECKING, Optional, Union
|
|
|
|
import torch
|
|
import triton
|
|
|
|
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
|
|
from sglang.srt.utils import is_cuda
|
|
|
|
if TYPE_CHECKING:
|
|
from sglang.srt.layers.radix_attention import RadixAttention
|
|
from sglang.srt.model_executor.model_runner import ModelRunner
|
|
from sglang.srt.speculative.spec_info import SpecInfo
|
|
|
|
_is_cuda = is_cuda()
|
|
if _is_cuda:
|
|
from sgl_kernel import cutlass_mla_decode, cutlass_mla_get_workspace_size
|
|
|
|
|
|
# Cutlass MLA only supports pagesize=128
|
|
PAGE_SIZE = 128
|
|
|
|
|
|
@dataclass
|
|
class CutlassMLADecodeMetadata:
|
|
workspace: Optional[torch.Tensor] = None
|
|
block_kv_indices: Optional[torch.Tensor] = None
|
|
|
|
def __init__(
|
|
self,
|
|
workspace: Optional[torch.Tensor] = None,
|
|
block_kv_indices: Optional[torch.Tensor] = None,
|
|
):
|
|
self.workspace = workspace
|
|
self.block_kv_indices = block_kv_indices
|
|
|
|
|
|
class CutlassMLABackend(FlashInferMLAAttnBackend):
|
|
"""Cutlass 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.forward_metadata: Union[CutlassMLADecodeMetadata] = None
|
|
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 init_forward_metadata(self, forward_batch: ForwardBatch):
|
|
|
|
bs = forward_batch.batch_size
|
|
spec_info = forward_batch.spec_info
|
|
if forward_batch.forward_mode.is_decode_or_idle():
|
|
if spec_info is None:
|
|
max_seqlen_pad = triton.cdiv(
|
|
forward_batch.seq_lens_cpu.max().item(), PAGE_SIZE
|
|
)
|
|
block_kv_indices = torch.full(
|
|
(bs, max_seqlen_pad),
|
|
-1,
|
|
dtype=torch.int32,
|
|
device=forward_batch.seq_lens.device,
|
|
)
|
|
create_flashmla_kv_indices_triton[(bs,)](
|
|
self.req_to_token,
|
|
forward_batch.req_pool_indices,
|
|
forward_batch.seq_lens,
|
|
None,
|
|
block_kv_indices,
|
|
self.req_to_token.stride(0),
|
|
max_seqlen_pad,
|
|
PAGE_SIZE,
|
|
)
|
|
workspace_size = cutlass_mla_get_workspace_size(
|
|
max_seqlen_pad * PAGE_SIZE, bs, num_kv_splits=1
|
|
)
|
|
workspace = torch.empty(
|
|
workspace_size, device="cuda", dtype=torch.uint8
|
|
)
|
|
self.forward_metadata = CutlassMLADecodeMetadata(
|
|
workspace,
|
|
block_kv_indices,
|
|
)
|
|
else:
|
|
super().init_forward_metadata(forward_batch)
|
|
else:
|
|
super().init_forward_metadata(forward_batch)
|
|
|
|
def init_cuda_graph_state(
|
|
self,
|
|
max_bs: int,
|
|
max_num_tokens: int,
|
|
block_kv_indices: Optional[torch.Tensor] = None,
|
|
):
|
|
if block_kv_indices is None:
|
|
cuda_graph_kv_indices = torch.full(
|
|
(max_bs, (self.max_context_len + PAGE_SIZE) // PAGE_SIZE),
|
|
1,
|
|
dtype=torch.int32,
|
|
device="cuda",
|
|
)
|
|
else:
|
|
cuda_graph_kv_indices = block_kv_indices
|
|
|
|
workspace_size = cutlass_mla_get_workspace_size(
|
|
cuda_graph_kv_indices.shape[1] * PAGE_SIZE, max_bs, num_kv_splits=1
|
|
)
|
|
self.cuda_graph_mla_workspace = torch.empty(
|
|
workspace_size, device="cuda", dtype=torch.uint8
|
|
)
|
|
self.cuda_graph_kv_indices = cuda_graph_kv_indices
|
|
|
|
def init_forward_metadata_capture_cuda_graph(
|
|
self,
|
|
bs: int,
|
|
num_tokens: int,
|
|
req_pool_indices: torch.Tensor,
|
|
seq_lens: torch.Tensor,
|
|
encoder_lens: Optional[torch.Tensor],
|
|
forward_mode: ForwardMode,
|
|
spec_info: Optional[SpecInfo],
|
|
):
|
|
if forward_mode.is_decode_or_idle():
|
|
if spec_info is None:
|
|
max_seqlen_pad = self.cuda_graph_kv_indices.shape[1]
|
|
|
|
create_flashmla_kv_indices_triton[(bs,)](
|
|
self.req_to_token,
|
|
req_pool_indices,
|
|
seq_lens,
|
|
None,
|
|
self.cuda_graph_kv_indices,
|
|
self.req_to_token.stride(0),
|
|
self.cuda_graph_kv_indices.stride(0),
|
|
PAGE_SIZE,
|
|
)
|
|
self.forward_metadata = CutlassMLADecodeMetadata(
|
|
self.cuda_graph_mla_workspace,
|
|
self.cuda_graph_kv_indices[:bs, :max_seqlen_pad],
|
|
)
|
|
else:
|
|
super().init_forward_metadata_capture_cuda_graph(
|
|
bs,
|
|
num_tokens,
|
|
req_pool_indices,
|
|
seq_lens,
|
|
encoder_lens,
|
|
forward_mode,
|
|
spec_info,
|
|
)
|
|
|
|
def init_forward_metadata_replay_cuda_graph(
|
|
self,
|
|
bs: int,
|
|
req_pool_indices: torch.Tensor,
|
|
seq_lens: torch.Tensor,
|
|
seq_lens_sum: int,
|
|
encoder_lens: Optional[torch.Tensor],
|
|
forward_mode: ForwardMode,
|
|
spec_info: Optional[SpecInfo],
|
|
seq_lens_cpu: Optional[torch.Tensor],
|
|
):
|
|
|
|
if forward_mode.is_decode_or_idle():
|
|
assert seq_lens_cpu is not None
|
|
seq_lens = seq_lens[:bs]
|
|
|
|
create_flashmla_kv_indices_triton[(bs,)](
|
|
self.req_to_token,
|
|
req_pool_indices[:bs],
|
|
seq_lens,
|
|
None,
|
|
self.cuda_graph_kv_indices,
|
|
self.req_to_token.stride(0),
|
|
self.cuda_graph_kv_indices.stride(0),
|
|
PAGE_SIZE,
|
|
)
|
|
else:
|
|
super().init_forward_metadata_replay_cuda_graph(
|
|
bs,
|
|
req_pool_indices,
|
|
seq_lens,
|
|
seq_lens_sum,
|
|
encoder_lens,
|
|
forward_mode,
|
|
spec_info,
|
|
seq_lens_cpu,
|
|
)
|
|
|
|
def get_cuda_graph_seq_len_fill_value(self):
|
|
return 1
|
|
|
|
def forward_decode(
|
|
self,
|
|
q: torch.Tensor,
|
|
k: torch.Tensor,
|
|
v: torch.Tensor,
|
|
layer: RadixAttention,
|
|
forward_batch: ForwardBatch,
|
|
save_kv_cache: bool = True,
|
|
# For multi-head latent attention
|
|
q_rope: Optional[torch.Tensor] = None,
|
|
k_rope: Optional[torch.Tensor] = None,
|
|
):
|
|
cache_loc = forward_batch.out_cache_loc
|
|
|
|
if k is not None:
|
|
assert v is not None
|
|
if save_kv_cache:
|
|
if k_rope is not None:
|
|
forward_batch.token_to_kv_pool.set_mla_kv_buffer(
|
|
layer,
|
|
cache_loc,
|
|
k,
|
|
k_rope,
|
|
)
|
|
else:
|
|
forward_batch.token_to_kv_pool.set_kv_buffer(
|
|
layer,
|
|
cache_loc,
|
|
k,
|
|
v,
|
|
)
|
|
|
|
# Reshape inputs
|
|
if q_rope is not None:
|
|
q_nope = q.view(-1, layer.tp_q_head_num, layer.v_head_dim)
|
|
q_rope = q_rope.view(
|
|
-1, layer.tp_q_head_num, layer.head_dim - layer.v_head_dim
|
|
)
|
|
else:
|
|
reshaped_q = q.view(-1, layer.tp_q_head_num, layer.head_dim)
|
|
q_nope = reshaped_q[:, :, : layer.v_head_dim]
|
|
q_rope = reshaped_q[:, :, layer.v_head_dim :]
|
|
|
|
q_nope = q_nope.to(self.q_data_type)
|
|
q_rope = q_rope.to(self.q_data_type)
|
|
|
|
k_cache = forward_batch.token_to_kv_pool.get_key_buffer(layer.layer_id)
|
|
|
|
o = cutlass_mla_decode(
|
|
q_nope=q_nope,
|
|
q_pe=q_rope,
|
|
kv_c_and_k_pe_cache=k_cache.view(-1, PAGE_SIZE, self.kv_cache_dim),
|
|
seq_lens=forward_batch.seq_lens.to(torch.int32),
|
|
page_table=self.forward_metadata.block_kv_indices,
|
|
workspace=self.forward_metadata.workspace,
|
|
sm_scale=layer.scaling,
|
|
num_kv_splits=1,
|
|
)
|
|
|
|
return o.view(-1, layer.tp_q_head_num * layer.v_head_dim)
|