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Commit ebcba9f0 authored by zhangqha's avatar zhangqha
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support MLAttention

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README.md
View file @ ebcba9f0
# MLAttention # MLAttention
## 简介
```
MLAttention is an efficient MLA decoding kernel , optimized for variable-length sequences serving.
目前支持的精度:
- BF16, FP16
目前支持的实现方式：
- OpenAI Triton
```
## 安装
### 源码方式安装
```bash
python3 -m pip install .
```
### 单测验证
```bash
pytest -s tests/test_triton_decode_attention.py
```
## 使用方式
```python
import triton
from triton_mla_op.triton_decode_attention import decode_attention_fwd
...
decode_attention_fwd(
q,
k_buffer,
v_buffer,
o,
req_to_token,
b_seq_len,
attn_logits,
num_kv_splits,
sm_scale,
)
...
```
## MLAttention开发进度
```
目前，基于 Cutlass的MLAttention版本正在积极开发中。
我们会及时在项目仓库中更新开发进度。欢迎关注我们的开发者社区以获取最新信息。
```
setup.py 0 → 100644
View file @ ebcba9f0
from setuptools import setup, find_packages
setup(
name='MLAttention',
packages=find_packages("triton_mla_op"),
package_dir={"": "triton_mla_op"},
include_package_data=True,
install_requires=[
'triton',
'torch'
]
)
tests/test_triton_decode_attention.py 0 → 100644
View file @ ebcba9f0
# SPDX-License-Identifier: Apache-2.0
import pytest
import torch
import triton
from triton_mla_op.triton_decode_attention import decode_attention_fwd
def cdiv(a, b):
return (a + b - 1) // b
@pytest.mark.parametrize("B", [3, 5])
@pytest.mark.parametrize("L", [1027, 1025])
@pytest.mark.parametrize("H_Q", [32])
@pytest.mark.parametrize("H_KV", [32, 8])
@pytest.mark.parametrize("D_QK", [128, 192, 576])
@pytest.mark.parametrize("D_V", [128, 512])
@pytest.mark.parametrize("CACHE_SIZE", [16384])
@pytest.mark.parametrize("PAGE_SIZE", [1, 16])
def test_decode_attention(B, L, H_Q, H_KV, D_QK, D_V, CACHE_SIZE, PAGE_SIZE):
assert CACHE_SIZE % PAGE_SIZE == 0
dtype = torch.bfloat16
seq_len = L # This represents the number of tokens already in the sequence
sm_scale = 1.0 / (D_QK**0.5)
num_kv_splits = 8
num_pages_per_batch = cdiv(seq_len, PAGE_SIZE) #这里为向上取整,65,(1027+16-1)//16
req_to_page = torch.randint(0,
CACHE_SIZE // PAGE_SIZE,
(B, num_pages_per_batch, 1), #shape为（B, num_pages_per_batch, 1）的tensor,大小取值为0 至cache_size//page_size
device="cuda")
req_to_token = req_to_page * PAGE_SIZE
req_to_token = req_to_token.expand(B, num_pages_per_batch, PAGE_SIZE) #这里是维度扩展，从torch.Size([3, 65, 1])扩展至torch.Size([3, 65, 16])
req_to_token = req_to_token + torch.arange(PAGE_SIZE, device="cuda").view(
1, 1, -1)
req_to_token = req_to_token.view(B, -1)
req_to_token = req_to_token[:, :seq_len].contiguous()
# q represents the new token being generated, one per batch
q = torch.randn(B, H_Q, D_QK, dtype=dtype, device="cuda")
# k_buffer and v_buffer represent all previous tokens
# Page size is 1.
k_buffer = torch.randn(CACHE_SIZE, H_KV, D_QK, dtype=dtype, device="cuda")
v_buffer = torch.randn(CACHE_SIZE, H_KV, D_V, dtype=dtype, device="cuda")
# o will have the same shape as q
o = torch.zeros(B, H_Q, D_V, dtype=dtype, device="cuda")
b_seq_len = torch.full((B, ), seq_len, device="cuda")
attn_logits = torch.empty(
(B, H_Q, num_kv_splits, D_V + 1),
dtype=torch.float32,
device="cuda",
)
# Call the original implementation.
decode_attention_fwd(
q,
k_buffer,
v_buffer,
o,
req_to_token,
b_seq_len,
attn_logits,
num_kv_splits,
sm_scale,
)
# Page size can be larger than 1.
k_buffer = k_buffer.view(CACHE_SIZE // PAGE_SIZE, PAGE_SIZE, H_KV, D_QK)
v_buffer = v_buffer.view(CACHE_SIZE // PAGE_SIZE, PAGE_SIZE, H_KV, D_V)
o1 = torch.zeros_like(o)
decode_attention_fwd(
q,
k_buffer,
v_buffer,
o1,
req_to_page,
b_seq_len,
attn_logits,
num_kv_splits,
sm_scale,
PAGE_SIZE,
)
assert torch.allclose(o, o1)
triton_mla_op/__init__.py 0 → 100644
View file @ ebcba9f0
from .triton_decode_attention import decode_attention_fwd
__all__ = ['decode_attention_fwd']
triton_mla_op/triton_decode_attention.py 0 → 100644
View file @ ebcba9f0
# SPDX-License-Identifier: Apache-2.0
# Adapted from
# https://github.com/sgl-project/sglang/blob/9f635ea50de920aa507f486daafba26a5b837574/python/sglang/srt/layers/attention/triton_ops/decode_attention.py
# which was originally adapted from
# https://github.com/ModelTC/lightllm/blob/96353e868a840db4d103138caf15ed9dbea8c186/lightllm/models/deepseek2/triton_kernel/gqa_flash_decoding_stage1.py
# https://github.com/ModelTC/lightllm/blob/96353e868a840db4d103138caf15ed9dbea8c186/lightllm/models/deepseek2/triton_kernel/gqa_flash_decoding_stage2.py
# Changes:
# - Add support for page size >= 1.
# Copyright 2025 vLLM Team
# Copyright 2023-2024 SGLang Team
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""
Memory-efficient attention for decoding.
It supports page size >= 1.
"""
import os
import logging
import torch
import triton
import triton.language as tl
from vllm.platforms import current_platform
is_hip_ = current_platform.is_rocm()
os.environ["TRITON_HIP_USE_NEW_STREAM_PIPELINE"] = f"0"
logger = logging.getLogger(__name__)
# TODO: Remove this when triton>=3.2.0. This issue will not affect performance
# and accuracy.
logger.warning(
"The following error message 'operation scheduled before its operands' "
"can be ignored.")
@triton.jit
def tanh(x):
# Tanh is just a scaled sigmoid
return 2 * tl.sigmoid(2 * x) - 1
@triton.jit
def _fwd_kernel_stage1(
Q,
K_Buffer,
V_Buffer,
sm_scale,
Req_to_tokens,
B_Seqlen,
Att_Out,
stride_req_to_tokens_b,
stride_qbs,
stride_qh,
stride_buf_kbs,
stride_buf_kh,
stride_buf_vbs,
stride_buf_vh,
stride_mid_ob,
stride_mid_oh,
stride_mid_os,
kv_group_num: tl.constexpr,
BLOCK_DMODEL: tl.constexpr,
BLOCK_DV: tl.constexpr,
BLOCK_N: tl.constexpr,
NUM_KV_SPLITS: tl.constexpr,
PAGE_SIZE: tl.constexpr,
logit_cap: tl.constexpr,
Lk: tl.constexpr,
Lv: tl.constexpr,
):
cur_batch = tl.program_id(0)
cur_head = tl.program_id(1)
split_kv_id = tl.program_id(2)
cur_kv_head = cur_head // kv_group_num
offs_d = tl.arange(0, BLOCK_DMODEL)
offs_dv = tl.arange(0, BLOCK_DV)
mask_d = offs_d < Lk
mask_dv = offs_dv < Lv
cur_batch_seq_len = tl.load(B_Seqlen + cur_batch)
cur_batch_req_idx = cur_batch
off_q = cur_batch * stride_qbs + cur_head * stride_qh + offs_d
q = tl.load(Q + off_q, mask=mask_d, other=0.0)
kv_len_per_split = tl.cdiv(cur_batch_seq_len, NUM_KV_SPLITS)
split_kv_start = kv_len_per_split * split_kv_id
split_kv_end = tl.minimum(split_kv_start + kv_len_per_split,
cur_batch_seq_len)
e_max = -float("inf")
e_sum = 0.0
acc = tl.zeros([BLOCK_DV], dtype=tl.float32)
if split_kv_end > split_kv_start:
for start_n in range(split_kv_start, split_kv_end, BLOCK_N):
offs_n = start_n + tl.arange(0, BLOCK_N)
kv_page_number = tl.load(
Req_to_tokens + stride_req_to_tokens_b * cur_batch_req_idx +
offs_n // PAGE_SIZE,
mask=offs_n < split_kv_end,
other=0,
)
kv_loc = kv_page_number * PAGE_SIZE + offs_n % PAGE_SIZE
offs_buf_k = (kv_loc[:, None] * stride_buf_kbs +
cur_kv_head * stride_buf_kh + offs_d[None, :])
k = tl.load(
K_Buffer + offs_buf_k,
mask=(offs_n[:, None] < split_kv_end) & (mask_d[None, :]),
other=0.0,
)
qk = tl.sum(q[None, :] * k, 1)
qk *= sm_scale
if logit_cap > 0:
qk = logit_cap * tanh(qk / logit_cap)
qk = tl.where(offs_n < split_kv_end, qk, float("-inf"))
offs_buf_v = (kv_loc[:, None] * stride_buf_vbs +
cur_kv_head * stride_buf_vh + offs_dv[None, :])
v = tl.load(
V_Buffer + offs_buf_v,
mask=(offs_n[:, None] < split_kv_end) & (mask_dv[None, :]),
other=0.0,
)
n_e_max = tl.maximum(tl.max(qk, 0), e_max)
re_scale = tl.exp(e_max - n_e_max)
p = tl.exp(qk - n_e_max)
acc *= re_scale
acc += tl.sum(p[:, None] * v, 0)
e_sum = e_sum * re_scale + tl.sum(p, 0)
e_max = n_e_max
offs_mid_o = (cur_batch * stride_mid_ob + cur_head * stride_mid_oh +
split_kv_id * stride_mid_os + offs_dv)
tl.store(
Att_Out + offs_mid_o,
acc / e_sum,
mask=(mask_dv),
)
offs_mid_o_1 = (cur_batch * stride_mid_ob + cur_head * stride_mid_oh +
split_kv_id * stride_mid_os + Lv)
tl.store(
Att_Out + offs_mid_o_1,
e_max + tl.log(e_sum),
)
def _decode_att_m_fwd(
q,
k_buffer,
v_buffer,
att_out,
Req_to_tokens,
B_Seqlen,
num_kv_splits,
sm_scale,
page_size,
logit_cap,
):
BLOCK = 64
NUM_KV_SPLITS = num_kv_splits
Lk = k_buffer.shape[-1]
Lv = v_buffer.shape[-1]
batch, head_num = q.shape[0], q.shape[1]
grid = (batch, head_num, NUM_KV_SPLITS)
kv_group_num = q.shape[1] // k_buffer.shape[-2]
num_warps = 4 if kv_group_num == 1 else 2
BLOCK_DMODEL = triton.next_power_of_2(Lk)
BLOCK_DV = triton.next_power_of_2(Lv)
_fwd_kernel_stage1[grid](
q,
k_buffer,
v_buffer,
sm_scale,
Req_to_tokens,
B_Seqlen,
att_out,
Req_to_tokens.stride(0),
q.stride(0),
q.stride(1),
k_buffer.stride(-3), # Assume (..., PAGE_SIZE, NUM_HEADS, HEAD_DIM)
k_buffer.stride(-2), # Assume (..., PAGE_SIZE, NUM_HEADS, HEAD_DIM)
v_buffer.stride(-3), # Assume (..., PAGE_SIZE, NUM_HEADS, HEAD_DIM)
v_buffer.stride(-2), # Assume (..., PAGE_SIZE, NUM_HEADS, HEAD_DIM)
att_out.stride(0),
att_out.stride(1),
att_out.stride(2),
kv_group_num=kv_group_num,
BLOCK_DMODEL=BLOCK_DMODEL,
BLOCK_DV=BLOCK_DV,
BLOCK_N=BLOCK,
NUM_KV_SPLITS=NUM_KV_SPLITS,
PAGE_SIZE=page_size,
logit_cap=logit_cap,
num_warps=num_warps,
Lk=Lk,
Lv=Lv,
)
@triton.jit
def _fwd_kernel_stage2(
Mid_O,
o,
B_Seqlen,
stride_mid_ob,
stride_mid_oh,
stride_mid_os,
stride_obs,
stride_oh,
NUM_KV_SPLITS: tl.constexpr,
BLOCK_DV: tl.constexpr,
Lv: tl.constexpr,
):
cur_batch = tl.program_id(0)
cur_head = tl.program_id(1)
cur_batch_seq_len = tl.load(B_Seqlen + cur_batch)
offs_d = tl.arange(0, BLOCK_DV)
mask_d = offs_d < Lv
e_sum = 0.0
e_max = -float("inf")
acc = tl.zeros([BLOCK_DV], dtype=tl.float32)
offs_v = cur_batch * stride_mid_ob + cur_head * stride_mid_oh + offs_d
offs_logic = cur_batch * stride_mid_ob + cur_head * stride_mid_oh + Lv
for split_kv_id in range(0, NUM_KV_SPLITS):
kv_len_per_split = tl.cdiv(cur_batch_seq_len, NUM_KV_SPLITS)
split_kv_start = kv_len_per_split * split_kv_id
split_kv_end = tl.minimum(split_kv_start + kv_len_per_split,
cur_batch_seq_len)
if split_kv_end > split_kv_start:
tv = tl.load(Mid_O + offs_v + split_kv_id * stride_mid_os,
mask=mask_d,
other=0.0)
tlogic = tl.load(Mid_O + offs_logic + split_kv_id * stride_mid_os)
n_e_max = tl.maximum(tlogic, e_max)
old_scale = tl.exp(e_max - n_e_max)
acc *= old_scale
exp_logic = tl.exp(tlogic - n_e_max)
acc += exp_logic * tv
e_sum = e_sum * old_scale + exp_logic
e_max = n_e_max
tl.store(
o + cur_batch * stride_obs + cur_head * stride_oh + offs_d,
acc / e_sum,
mask=mask_d,
)
def _decode_softmax_reducev_fwd(
logits,
q,
o,
v_buffer,
b_seq_len,
num_kv_splits,
):
batch, head_num = q.shape[0], q.shape[1]
Lv = v_buffer.shape[-1]
BLOCK_DV = triton.next_power_of_2(Lv)
NUM_KV_SPLITS = num_kv_splits
extra_kargs = {}
if is_hip_:
# https://rocm.docs.amd.com/en/docs-6.2.0/how-to/llm-fine-tuning-optimization/optimizing-triton-kernel.html
# https://github.com/triton-lang/triton/blob/main/third_party/amd/backend/compiler.py
extra_kargs = {
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
}
grid = (batch, head_num)
_fwd_kernel_stage2[grid](
logits,
o,
b_seq_len,
logits.stride(0),
logits.stride(1),
logits.stride(2),
o.stride(0),
o.stride(1),
NUM_KV_SPLITS=NUM_KV_SPLITS,
BLOCK_DV=BLOCK_DV,
Lv=Lv,
num_warps=4,
**extra_kargs,
)
def decode_attention_fwd_normal(
q,
k_buffer,
v_buffer,
o,
req_to_token,
b_seq_len,
attn_logits,
num_kv_splits,
sm_scale,
page_size,
logit_cap=0.0,
):
_decode_att_m_fwd(
q,
k_buffer,
v_buffer,
attn_logits,
req_to_token,
b_seq_len,
num_kv_splits,
sm_scale,
page_size,
logit_cap,
)
_decode_softmax_reducev_fwd(attn_logits, q, o, v_buffer, b_seq_len,
num_kv_splits)
# opt
@triton.autotune(
configs=[
triton.Config({"BLOCK_N": 16}, num_warps=2, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 16}, num_warps=4, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 16}, num_warps=8, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 32}, num_warps=2, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 32}, num_warps=4, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 32}, num_warps=8, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 64}, num_warps=2, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 64}, num_warps=4, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 64}, num_warps=8, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 128}, num_warps=2, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 128}, num_warps=4, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 128}, num_warps=8, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 256}, num_warps=2, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 256}, num_warps=4, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 256}, num_warps=8, num_ldmatrixes=0, num_stages=1),
],
key=["B_Seqlen","stride_qbs","stride_buf_kbs","stride_buf_kh"]
)
@triton.jit
def _decode_v1_kernel_stage1_use_tc(
Q,
K_Buffer,
sm_scale,
Req_to_tokens,
#B_req_idx,
B_Start_Loc,
B_Seqlen,
Att_Out,
stride_req_to_tokens_b,
stride_qbs,
stride_qh,
stride_buf_kbs,
stride_buf_kh,
att_stride_h,
kv_group_num: tl.constexpr,
q_head_num: tl.constexpr,
BLOCK_DMODEL: tl.constexpr,
BLOCK_DPE: tl.constexpr,
BLOCK_N: tl.constexpr,
BLOCK_H: tl.constexpr,
SPLIT_K: tl.constexpr,
PAGE_SIZE: tl.constexpr,
logit_cap: tl.constexpr,
Lk: tl.constexpr,
):
cur_batch = tl.program_id(0)
cur_head_id = tl.program_id(1)
cur_kv_head = cur_head_id // tl.cdiv(kv_group_num, BLOCK_H)
split_k_id = tl.program_id(2)
reduce_dtype = Att_Out.dtype.element_ty
if BLOCK_H < kv_group_num:
VALID_BLOCK_H: tl.constexpr = BLOCK_H
else:
VALID_BLOCK_H: tl.constexpr = kv_group_num
cur_head = cur_head_id * VALID_BLOCK_H + tl.arange(0, BLOCK_H)
mask_h = cur_head < (cur_head_id + 1) * VALID_BLOCK_H
mask_h = mask_h & (cur_head < q_head_num)
offs_d = tl.arange(0, BLOCK_DMODEL)
cur_batch_seq_len = tl.load(B_Seqlen + cur_batch)
cur_batch_in_all_start_index = tl.load(B_Start_Loc + cur_batch)
# cur_batch_req_idx = tl.load(B_req_idx + cur_batch)
cur_batch_req_idx = cur_batch
offs_q = cur_batch * stride_qbs + cur_head[:, None] * stride_qh + offs_d[None, :]
q = tl.load(
Q + offs_q, mask=(mask_h[:, None]) & (offs_d[None, :] < Lk), other=0.0
).to(reduce_dtype)
if BLOCK_DPE > 0:
offs_dpe = BLOCK_DMODEL + tl.arange(0, BLOCK_DPE)
off_qpe = (
cur_batch * stride_qbs + cur_head[:, None] * stride_qh + offs_dpe[None, :]
)
qpe = tl.load(Q + off_qpe, mask=mask_h[:, None], other=0.0).to(reduce_dtype)
kv_len_per_split = tl.cdiv(cur_batch_seq_len, SPLIT_K)
split_k_start = kv_len_per_split * split_k_id
split_k_end = tl.minimum(split_k_start + kv_len_per_split, cur_batch_seq_len)
for start_n in range(split_k_start, split_k_end, BLOCK_N):
offs_n = start_n + tl.arange(0, BLOCK_N)
kv_page_number = tl.load(
Req_to_tokens + stride_req_to_tokens_b * cur_batch_req_idx +
offs_n // PAGE_SIZE,
mask=offs_n < split_k_end,
other=0,
)
k_loc = kv_page_number * PAGE_SIZE + offs_n % PAGE_SIZE
offs_buf_k = (
k_loc[None, :] * stride_buf_kbs
+ cur_kv_head * stride_buf_kh
+ offs_d[:, None]
)
k = tl.load(
K_Buffer + offs_buf_k,
mask=(offs_n[None, :] < split_k_end) & (offs_d[:, None] < Lk),
other=0.0,
).to(reduce_dtype)
qk = tl.dot(q, k)
if BLOCK_DPE > 0:
offs_buf_kpe = (
k_loc[None, :] * stride_buf_kbs
+ cur_kv_head * stride_buf_kh
+ offs_dpe[:, None]
)
kpe = tl.load(
K_Buffer + offs_buf_kpe,
mask=offs_n[None, :] < split_k_end,
other=0.0,
).to(reduce_dtype)
qk += tl.dot(qpe, kpe)
qk *= sm_scale
if logit_cap > 0:
qk = logit_cap * tanh(qk / logit_cap)
offs_o = cur_head[:, None] * att_stride_h + (
cur_batch_in_all_start_index + offs_n[None, :]
)
tl.store(
Att_Out + offs_o,
qk,
mask=mask_h[:, None] & (offs_n[None, :] < split_k_end),
)
@triton.autotune(
configs=[
triton.Config({"BLOCK_N": 32}, num_warps=1, num_ldmatrixes=1, num_stages=1),
triton.Config({"BLOCK_N": 32}, num_warps=2, num_ldmatrixes=1, num_stages=1),
triton.Config({"BLOCK_N": 32}, num_warps=4, num_ldmatrixes=1, num_stages=1),
triton.Config({"BLOCK_N": 32}, num_warps=8, num_ldmatrixes=1, num_stages=1),
triton.Config({"BLOCK_N": 64}, num_warps=1, num_ldmatrixes=1, num_stages=1),
triton.Config({"BLOCK_N": 64}, num_warps=2, num_ldmatrixes=1, num_stages=1),
triton.Config({"BLOCK_N": 64}, num_warps=4, num_ldmatrixes=1, num_stages=1),
triton.Config({"BLOCK_N": 64}, num_warps=8, num_ldmatrixes=1, num_stages=1),
triton.Config({"BLOCK_N": 8}, num_warps=1, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 8}, num_warps=2, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 8}, num_warps=4, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 8}, num_warps=8, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 16}, num_warps=1, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 16}, num_warps=2, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 16}, num_warps=4, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 16}, num_warps=8, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 32}, num_warps=1, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 32}, num_warps=2, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 32}, num_warps=4, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 32}, num_warps=8, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 64}, num_warps=1, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 64}, num_warps=2, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 64}, num_warps=4, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 64}, num_warps=8, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 128}, num_warps=1, num_ldmatrixes=1, num_stages=1),
triton.Config({"BLOCK_N": 128}, num_warps=2, num_ldmatrixes=1, num_stages=1),
triton.Config({"BLOCK_N": 128}, num_warps=4, num_ldmatrixes=1, num_stages=1),
triton.Config({"BLOCK_N": 128}, num_warps=8, num_ldmatrixes=1, num_stages=1),
triton.Config({"BLOCK_N": 256}, num_warps=1, num_ldmatrixes=1, num_stages=1),
triton.Config({"BLOCK_N": 256}, num_warps=2, num_ldmatrixes=1, num_stages=1),
triton.Config({"BLOCK_N": 256}, num_warps=4, num_ldmatrixes=1, num_stages=1),
triton.Config({"BLOCK_N": 256}, num_warps=8, num_ldmatrixes=1, num_stages=1),
triton.Config({"BLOCK_N": 512}, num_warps=1, num_ldmatrixes=1, num_stages=1),
triton.Config({"BLOCK_N": 512}, num_warps=2, num_ldmatrixes=1, num_stages=1),
triton.Config({"BLOCK_N": 512}, num_warps=4, num_ldmatrixes=1, num_stages=1),
triton.Config({"BLOCK_N": 512}, num_warps=8, num_ldmatrixes=1, num_stages=1),
triton.Config({"BLOCK_N": 128}, num_warps=1, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 128}, num_warps=2, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 128}, num_warps=4, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 128}, num_warps=8, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 256}, num_warps=1, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 256}, num_warps=2, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 256}, num_warps=4, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 256}, num_warps=8, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 512}, num_warps=1, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 512}, num_warps=2, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 512}, num_warps=4, num_ldmatrixes=0, num_stages=1),
triton.Config({"BLOCK_N": 512}, num_warps=8, num_ldmatrixes=0, num_stages=1),
],
key=["B_Seqlen","stride_logic_h","stride_buf_vbs","stride_buf_vh"]
)
@triton.jit
def _decode_v1_kernel_stage2_use_tc(
logits,
V_Buffer,
Out,
Req_to_tokens,
#B_req_idx,
B_Start_Loc,
B_Seqlen,
stride_logic_h,
stride_buf_vbs,
stride_buf_vh,
stride_obs,
stride_oh,
stride_req_to_token_b,
kv_group_num: tl.constexpr,
q_head_num: tl.constexpr,
BLOCK_DMODEL: tl.constexpr,
BLOCK_H: tl.constexpr,
PAGE_SIZE: tl.constexpr,
Lv: tl.constexpr,
BLOCK_N: tl.constexpr,
):
cur_batch = tl.program_id(0)
cur_kv_head = tl.program_id(1)
cur_head = cur_kv_head * kv_group_num + tl.arange(0, BLOCK_H)
mask_h = cur_head < (cur_kv_head + 1) * kv_group_num
mask_h = mask_h & (cur_head < q_head_num)
cur_batch_seq_len = tl.load(B_Seqlen + cur_batch)
cur_batch_start_loc = tl.load(B_Start_Loc + cur_batch)
cur_batch_req_idx = cur_batch #tl.load(B_req_idx + cur_batch)
offs_n = tl.arange(0, BLOCK_N)
offs_d = tl.arange(0, BLOCK_DMODEL)
offs_buf_v = cur_kv_head * stride_buf_vh + offs_d[None, :]
v_ptrs = V_Buffer + offs_buf_v
e_max = tl.zeros([BLOCK_H], dtype=tl.float32) - float("inf")
e_sum = tl.zeros([BLOCK_H], dtype=tl.float32)
acc = tl.zeros([BLOCK_H, BLOCK_DMODEL], dtype=tl.float32)
for start_n in range(0, cur_batch_seq_len, BLOCK_N):
start_n = tl.multiple_of(start_n, BLOCK_N)
v_page_number = tl.load(
Req_to_tokens
+ cur_batch_req_idx * stride_req_to_token_b
+ (start_n + offs_n) // PAGE_SIZE,
mask=(start_n + offs_n) < cur_batch_seq_len,
other=0,
)
v_loc = v_page_number * PAGE_SIZE + (start_n + offs_n) % PAGE_SIZE
offs_qk = cur_head[:, None] * stride_logic_h + (
cur_batch_start_loc + start_n + offs_n[None, :]
)
qk = tl.load(
logits + offs_qk,
mask=mask_h[:, None] & (start_n + offs_n[None, :] < cur_batch_seq_len),
other=float("-inf"),
) #[head, block_n]
n_e_max = tl.maximum(tl.max(qk, 1), e_max)
old_scale = tl.exp(e_max - n_e_max)
p = tl.exp(qk - n_e_max[:, None])
e_sum = e_sum * old_scale + tl.sum(p, 1)
v = tl.load(
v_ptrs + v_loc[:, None] * stride_buf_vbs, mask=(offs_d[None, :] < Lv)
) #[block_n,head_dim]
p = p.to(v.dtype)
acc = acc * old_scale[:, None] + tl.dot(p, v)
e_max = n_e_max
acc = acc / e_sum[:, None]
off_o = cur_batch * stride_obs + cur_head[:, None] * stride_oh + offs_d[None, :]
out_ptrs = Out + off_o
tl.store(out_ptrs, acc, mask=(mask_h[:, None]) & (offs_d[None, :] < Lv))
def _decode_v1_stage1_use_tc(
q,
k_buffer,
att_out,
Req_to_tokens,
#B_req_idx,
B_Start_Loc,
B_Seqlen,
sm_scale,
page_size,
num_kv_splits,
logit_cap,
):
Lk = k_buffer.shape[-1]
if Lk == 576:
BLOCK_DMODEL = 512
BLOCK_DPE = 64
elif Lk == 288:
BLOCK_DMODEL = 256
BLOCK_DPE = 32
else:
BLOCK_DMODEL = triton.next_power_of_2(Lk)
BLOCK_DPE = 0
# batch, head_num = B_req_idx.shape[0], q.shape[1]
batch, head_num = q.shape[0], q.shape[1]
kv_group_num = q.shape[1] // k_buffer.shape[-2]
SPLIT_K = num_kv_splits
BLOCK_H = max(16, min(64, triton.next_power_of_2(kv_group_num)))
grid = lambda META: (
batch,
triton.cdiv(head_num, min(BLOCK_H, kv_group_num)),
SPLIT_K,
)
_decode_v1_kernel_stage1_use_tc[grid](
q,
k_buffer,
sm_scale,
Req_to_tokens,
#B_req_idx,
B_Start_Loc,
B_Seqlen,
att_out,
Req_to_tokens.stride(0),
q.stride(0),
q.stride(1),
k_buffer.stride(-3),
k_buffer.stride(-2),
att_out.stride(0),
kv_group_num=kv_group_num,
q_head_num=head_num,
BLOCK_DMODEL=BLOCK_DMODEL,
BLOCK_DPE=BLOCK_DPE,
BLOCK_H=BLOCK_H,
SPLIT_K=SPLIT_K,
PAGE_SIZE=page_size,
logit_cap=logit_cap,
Lk=Lk,
kpack=2,
)
return _decode_v1_kernel_stage1_use_tc.best_config
def _decode_v1_stage2_use_tc(
logits,
v_buffer,
o,
req_to_tokens,
#b_req_idx,
b_start_loc,
b_seq_len,
page_size,
):
batch, head_num = b_seq_len.shape[0], logits.shape[0]
kv_group_num = logits.shape[0] // v_buffer.shape[-2]
BLOCK_H = max(16, triton.next_power_of_2(kv_group_num))
grid = (batch, triton.cdiv(head_num, min(BLOCK_H, kv_group_num)), 1)
Lv = v_buffer.shape[-1]
BLOCK_DMODEL = triton.next_power_of_2(Lv)
_decode_v1_kernel_stage2_use_tc[grid](
logits,
v_buffer,
o,
req_to_tokens,
#b_req_idx,
b_start_loc,
b_seq_len,
logits.stride(0),
v_buffer.stride(-3),
v_buffer.stride(-2),
o.stride(0),
o.stride(1),
req_to_tokens.stride(0),
kv_group_num=kv_group_num,
q_head_num=head_num,
BLOCK_DMODEL=BLOCK_DMODEL,
BLOCK_H=BLOCK_H,
PAGE_SIZE=page_size,
Lv=Lv,
)
return _decode_v1_kernel_stage2_use_tc.best_config
def decode_attention_v1(
q,
k_buffer,
v_buffer,
o,
req_to_token,
#b_req_idx,
b_start_loc,
b_seq_len,
attn_logits,
num_kv_splits,
sm_scale,
page_size,
logit_cap=0.0,
):
# GQA/MQA/MLA
_decode_v1_stage1_best_config = _decode_v1_stage1_use_tc(
q,
k_buffer,
attn_logits,
req_to_token,
#b_req_idx,
b_start_loc,
b_seq_len,
sm_scale,
page_size,
num_kv_splits,
logit_cap,
)
_decode_v1_stage2_best_config = _decode_v1_stage2_use_tc(
attn_logits,
v_buffer,
o,
req_to_token,
#b_req_idx,
b_start_loc,
b_seq_len,
page_size,
)
return _decode_v1_stage1_best_config, _decode_v1_stage2_best_config
@triton.autotune(
configs=[
triton.Config({"BLOCK_N": 16}, num_warps=2, num_stages=1),
triton.Config({"BLOCK_N": 16}, num_warps=4, num_stages=1),
triton.Config({"BLOCK_N": 16}, num_warps=8, num_stages=1),
triton.Config({"BLOCK_N": 32}, num_warps=2, num_stages=1),
triton.Config({"BLOCK_N": 32}, num_warps=4, num_stages=1),
triton.Config({"BLOCK_N": 32}, num_warps=8, num_stages=1),
triton.Config({"BLOCK_N": 64}, num_warps=2, num_stages=1),
triton.Config({"BLOCK_N": 64}, num_warps=4, num_stages=1),
triton.Config({"BLOCK_N": 64}, num_warps=8, num_stages=1),
triton.Config({"BLOCK_N": 128}, num_warps=2, num_stages=1),
triton.Config({"BLOCK_N": 128}, num_warps=4, num_stages=1),
triton.Config({"BLOCK_N": 128}, num_warps=8, num_stages=1),
triton.Config({"BLOCK_N": 256}, num_warps=2, num_stages=1),
triton.Config({"BLOCK_N": 256}, num_warps=4, num_stages=1),
triton.Config({"BLOCK_N": 256}, num_warps=8, num_stages=1),
],
key=["B_Seqlen","stride_qbs","stride_buf_kbs","stride_buf_kh", "stride_buf_vbs", "stride_buf_vh"]
)
@triton.jit
def _decode_v2_kernel_stage1_use_tc(
Q,
K_Buffer,
V_Buffer,
sm_scale,
Req_to_tokens,
# B_req_idx,
B_Seqlen,
Att_Out,
stride_req_to_tokens_b,
stride_qbs,
stride_qh,
stride_buf_kbs,
stride_buf_kh,
stride_buf_vbs,
stride_buf_vh,
stride_mid_ob,
stride_mid_oh,
stride_mid_os,
kv_group_num: tl.constexpr,
q_head_num: tl.constexpr,
BLOCK_DMODEL: tl.constexpr,
BLOCK_DPE: tl.constexpr,
BLOCK_DV: tl.constexpr,
BLOCK_N: tl.constexpr,
BLOCK_H: tl.constexpr,
NUM_KV_SPLITS: tl.constexpr,
PAGE_SIZE: tl.constexpr,
logit_cap: tl.constexpr,
Lk: tl.constexpr,
Lv: tl.constexpr,
):
cur_batch = tl.program_id(0)
cur_head_id = tl.program_id(1)
cur_kv_head = cur_head_id // tl.cdiv(kv_group_num, BLOCK_H)
split_kv_id = tl.program_id(2)
if BLOCK_H < kv_group_num:
VALID_BLOCK_H: tl.constexpr = BLOCK_H
else:
VALID_BLOCK_H: tl.constexpr = kv_group_num
cur_head = cur_head_id * VALID_BLOCK_H + tl.arange(0, BLOCK_H)
mask_h = cur_head < (cur_head_id + 1) * VALID_BLOCK_H
mask_h = mask_h & (cur_head < q_head_num)
offs_d = tl.arange(0, BLOCK_DMODEL)
offs_dv = tl.arange(0, BLOCK_DV)
mask_d = offs_d < Lk
mask_dv = offs_dv < Lv
cur_batch_seq_len = tl.load(B_Seqlen + cur_batch)
# cur_batch_req_idx = tl.load(B_req_idx + cur_batch)
cur_batch_req_idx = cur_batch
offs_q = cur_batch * stride_qbs + cur_head[:, None] * stride_qh + offs_d[None, :]
q = tl.load(Q + offs_q, mask=(mask_h[:, None]) & (mask_d[None, :]), other=0.0)
if BLOCK_DPE > 0:
offs_dpe = BLOCK_DMODEL + tl.arange(0, BLOCK_DPE)
mask_dpe = offs_dpe < Lk
off_qpe = (
cur_batch * stride_qbs + cur_head[:, None] * stride_qh + offs_dpe[None, :]
)
qpe = tl.load(
Q + off_qpe, mask=(mask_h[:, None]) & (mask_dpe[None, :]), other=0.0
)
kv_len_per_split = tl.cdiv(cur_batch_seq_len, NUM_KV_SPLITS)
split_kv_start = kv_len_per_split * split_kv_id
split_kv_end = tl.minimum(split_kv_start + kv_len_per_split, cur_batch_seq_len)
e_max = tl.zeros([BLOCK_H], dtype=tl.float32) - float("inf")
e_sum = tl.zeros([BLOCK_H], dtype=tl.float32)
acc = tl.zeros([BLOCK_H, BLOCK_DV], dtype=tl.float32)
if split_kv_end > split_kv_start:
for start_n in range(split_kv_start, split_kv_end, BLOCK_N):
offs_n = start_n + tl.arange(0, BLOCK_N)
kv_page_number = tl.load(
Req_to_tokens + stride_req_to_tokens_b * cur_batch_req_idx + offs_n // PAGE_SIZE,
mask=offs_n < split_kv_end,
other=0,
)
kv_loc = kv_page_number * PAGE_SIZE + offs_n % PAGE_SIZE
offs_buf_k = (
kv_loc[None, :] * stride_buf_kbs
+ cur_kv_head * stride_buf_kh
+ offs_d[:, None]
)
k = tl.load(
K_Buffer + offs_buf_k,
mask=(offs_n[None, :] < split_kv_end) & (mask_d[:, None]),
other=0.0,
)
qk = tl.dot(q, k.to(q.dtype))
if BLOCK_DPE > 0:
offs_buf_kpe = (
kv_loc[None, :] * stride_buf_kbs
+ cur_kv_head * stride_buf_kh
+ offs_dpe[:, None]
)
kpe = tl.load(
K_Buffer + offs_buf_kpe,
mask=(offs_n[None, :] < split_kv_end) & (mask_dpe[:, None]),
other=0.0,
)
qk += tl.dot(qpe, kpe.to(qpe.dtype))
qk *= sm_scale
if logit_cap > 0:
qk = logit_cap * tanh(qk / logit_cap)
qk = tl.where(
mask_h[:, None] & (offs_n[None, :] < split_kv_end), qk, float("-inf")
)
offs_buf_v = (
kv_loc[:, None] * stride_buf_vbs
+ cur_kv_head * stride_buf_vh
+ offs_dv[None, :]
)
v = tl.load(
V_Buffer + offs_buf_v,
mask=(offs_n[:, None] < split_kv_end) & (mask_dv[None, :]),
other=0.0,
)
n_e_max = tl.maximum(tl.max(qk, 1), e_max)
re_scale = tl.exp(e_max - n_e_max)
p = tl.exp(qk - n_e_max[:, None])
acc *= re_scale[:, None]
acc += tl.dot(p.to(v.dtype), v)
e_sum = e_sum * re_scale + tl.sum(p, 1)
e_max = n_e_max
offs_mid_o = (
cur_batch * stride_mid_ob
+ cur_head[:, None] * stride_mid_oh
+ split_kv_id * stride_mid_os
+ offs_dv[None, :]
)
tl.store(
Att_Out + offs_mid_o,
acc / e_sum[:, None],
mask=(mask_h[:, None]) & (mask_dv[None, :]),
)
offs_mid_o_1 = (
cur_batch * stride_mid_ob
+ cur_head * stride_mid_oh
+ split_kv_id * stride_mid_os
+ Lv
)
tl.store(
Att_Out + offs_mid_o_1,
e_max + tl.log(e_sum),
mask=mask_h,
)
def _decode_v2_stage1_use_tc(
q,
k_buffer,
v_buffer,
att_out,
Req_to_tokens,
# B_req_idx,
B_Seqlen,
num_kv_splits,
sm_scale,
page_size,
logit_cap,
):
Lk = k_buffer.shape[-1]
Lv = v_buffer.shape[-1]
if Lk == 576:
BLOCK_DMODEL = 512
BLOCK_DPE = 64
elif Lk == 288:
BLOCK_DMODEL = 256
BLOCK_DPE = 32
else:
BLOCK_DMODEL = triton.next_power_of_2(Lk)
BLOCK_DPE = 0
BLOCK_DV = triton.next_power_of_2(Lv)
# batch, head_num = B_req_idx.shape[0], q.shape[1]
batch, head_num = q.shape[0], q.shape[1]
kv_group_num = q.shape[1] // k_buffer.shape[-2]
BLOCK_H = 16
NUM_KV_SPLITS = num_kv_splits
grid = lambda META: (
batch,
triton.cdiv(head_num, min(BLOCK_H, kv_group_num)),
NUM_KV_SPLITS,
)
_decode_v2_kernel_stage1_use_tc[grid](
q,
k_buffer,
v_buffer,
sm_scale,
Req_to_tokens,
# B_req_idx,
B_Seqlen,
att_out,
Req_to_tokens.stride(0),
q.stride(0),
q.stride(1),
k_buffer.stride(-3),
k_buffer.stride(-2),
v_buffer.stride(-3),
v_buffer.stride(-2),
att_out.stride(0),
att_out.stride(1),
att_out.stride(2),
kv_group_num=kv_group_num,
q_head_num=head_num,
BLOCK_DMODEL=BLOCK_DMODEL,
BLOCK_DPE=BLOCK_DPE,
BLOCK_DV=BLOCK_DV,
BLOCK_H=BLOCK_H,
NUM_KV_SPLITS=NUM_KV_SPLITS,
PAGE_SIZE=page_size,
logit_cap=logit_cap,
Lk=Lk,
Lv=Lv,
kpack=2,
)
return _decode_v2_kernel_stage1_use_tc.best_config
@triton.autotune(
configs=[
triton.Config({}, num_warps=1, num_stages=1),
triton.Config({}, num_warps=2, num_stages=1),
triton.Config({}, num_warps=4, num_stages=1),
triton.Config({}, num_warps=8, num_stages=1),
],
key=["B_Seqlen", "stride_mid_ob", "stride_mid_oh", "stride_mid_os"]
)
@triton.jit
def _decode_v2_kernel_stage2(
Mid_O,
O,
B_Seqlen,
stride_mid_ob,
stride_mid_oh,
stride_mid_os,
stride_obs,
stride_oh,
NUM_KV_SPLITS: tl.constexpr,
BLOCK_DV: tl.constexpr,
Lv: tl.constexpr,
):
cur_batch = tl.program_id(0)
cur_head = tl.program_id(1)
cur_batch_seq_len = tl.load(B_Seqlen + cur_batch)
offs_d = tl.arange(0, BLOCK_DV)
mask_d = offs_d < Lv
e_sum = 0.0
e_max = -float("inf")
acc = tl.zeros([BLOCK_DV], dtype=tl.float32)
offs_v = cur_batch * stride_mid_ob + cur_head * stride_mid_oh + offs_d
offs_logic = cur_batch * stride_mid_ob + cur_head * stride_mid_oh + Lv
for split_kv_id in range(0, NUM_KV_SPLITS):
kv_len_per_split = tl.cdiv(cur_batch_seq_len, NUM_KV_SPLITS)
split_kv_start = kv_len_per_split * split_kv_id
split_kv_end = tl.minimum(split_kv_start + kv_len_per_split, cur_batch_seq_len)
if split_kv_end > split_kv_start:
tv = tl.load(
Mid_O + offs_v + split_kv_id * stride_mid_os, mask=mask_d, other=0.0
)
tlogic = tl.load(Mid_O + offs_logic + split_kv_id * stride_mid_os)
n_e_max = tl.maximum(tlogic, e_max)
old_scale = tl.exp(e_max - n_e_max)
acc *= old_scale
exp_logic = tl.exp(tlogic - n_e_max)
acc += exp_logic * tv
e_sum = e_sum * old_scale + exp_logic
e_max = n_e_max
tl.store(
O + cur_batch * stride_obs + cur_head * stride_oh + offs_d,
acc / e_sum,
mask=mask_d,
)
def _decode_v2_stage2_use_tc(
logits,
q,
o,
v_buffer,
b_seq_len,
num_kv_splits,
):
batch, head_num = q.shape[0], q.shape[1]
Lv = v_buffer.shape[-1]
BLOCK_DV = triton.next_power_of_2(Lv)
NUM_KV_SPLITS = num_kv_splits
grid = (batch, head_num)
_decode_v2_kernel_stage2[grid](
logits,
o,
b_seq_len,
logits.stride(0),
logits.stride(1),
logits.stride(2),
o.stride(0),
o.stride(1),
NUM_KV_SPLITS=NUM_KV_SPLITS,
BLOCK_DV=BLOCK_DV,
Lv=Lv,
)
return _decode_v2_kernel_stage2.best_config
def decode_attention_v2(
q,
k_buffer,
v_buffer,
o,
req_to_token,
# b_req_idx,
b_seq_len,
attn_logits,
num_kv_splits,
sm_scale,
page_size,
logit_cap=0.0,
):
_decode_v2_stage1_best_config = _decode_v2_stage1_use_tc(
q,
k_buffer,
v_buffer,
attn_logits,
req_to_token,
# b_req_idx,
b_seq_len,
num_kv_splits,
sm_scale,
page_size,
logit_cap,
)
_decode_v2_stage2_best_config = _decode_v2_stage2_use_tc(attn_logits, q, o, v_buffer, b_seq_len, num_kv_splits)
return _decode_v2_stage1_best_config, _decode_v2_stage2_best_config
def decode_attention_fwd(
q,
k_buffer,
v_buffer,
o,
req_to_token,
b_seq_len,
attn_logits,
num_kv_splits,
sm_scale,
page_size=1,
logit_cap=0.0,
):
assert num_kv_splits == attn_logits.shape[2]
kv_group_num = q.shape[1] // v_buffer.shape[-2]
b_start_loc = torch.arange(0, k_buffer.shape[0] * page_size, k_buffer.shape[0] * page_size // q.shape[0], device="cuda").to(torch.int32)
if kv_group_num == 1:
# MHA
decode_attention_fwd_normal(
q,
k_buffer,
v_buffer,
o,
req_to_token,
b_seq_len,
attn_logits,
num_kv_splits,
sm_scale,
page_size,
logit_cap,
)
else:
# GQA/MQA/MLA
decode_attention_v2(
q,
k_buffer,
v_buffer,
o,
req_to_token,
b_seq_len,
attn_logits,
num_kv_splits,
sm_scale,
page_size,
logit_cap,
)
# attn_logits_v1 = torch.empty(
# (q.shape[1],k_buffer.shape[0]*page_size),
# dtype=torch.float16,
# device="cuda")
# decode_attention_v1(
# q,
# k_buffer,
# v_buffer,
# o,
# req_to_token,
# b_start_loc,
# b_seq_len,
# attn_logits_v1,
# num_kv_splits,
# sm_scale,
# page_size,
# logit_cap,
# )
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