333 lines
8.3 KiB
Python
333 lines
8.3 KiB
Python
# Adapted from
|
|
# https://github.com/ModelTC/lightllm/blob/f2a54f0912293f683bf1d1695fd12c4098a5bf82/lightllm/models/llama/triton_kernel/token_attention_nopad_att1.py
|
|
# https://github.com/ModelTC/lightllm/blob/f2a54f0912293f683bf1d1695fd12c4098a5bf82/lightllm/models/llama/triton_kernel/token_attention_softmax_and_reducev.py
|
|
import torch
|
|
import triton
|
|
import triton.language as tl
|
|
from sglang.srt.managers.router.model_runner import global_server_args
|
|
from sglang.srt.utils import wrap_kernel_launcher
|
|
|
|
if global_server_args.attention_reduce_in_fp32:
|
|
REDUCE_TRITON_TYPE = tl.float32
|
|
REDUCE_TORCH_TYPE = torch.float32
|
|
else:
|
|
REDUCE_TRITON_TYPE = tl.float16
|
|
REDUCE_TORCH_TYPE = torch.float16
|
|
|
|
|
|
@triton.jit
|
|
def _fwd_kernel_stage1(
|
|
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,
|
|
BLOCK_DMODEL: tl.constexpr,
|
|
BLOCK_N: tl.constexpr,
|
|
):
|
|
cur_batch = tl.program_id(0)
|
|
cur_head = tl.program_id(1)
|
|
start_n = tl.program_id(2)
|
|
|
|
cur_kv_head = cur_head // kv_group_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_start_index = 0
|
|
cur_batch_end_index = cur_batch_seq_len
|
|
|
|
off_q = cur_batch * stride_qbs + cur_head * stride_qh + offs_d
|
|
|
|
offs_n = start_n * BLOCK_N + tl.arange(0, BLOCK_N)
|
|
|
|
block_stard_index = start_n * BLOCK_N
|
|
block_mask = tl.where(block_stard_index < cur_batch_seq_len, 1, 0)
|
|
|
|
for start_mark in range(0, block_mask, 1):
|
|
q = tl.load(Q + off_q + start_mark).to(REDUCE_TRITON_TYPE)
|
|
offs_n_new = cur_batch_start_index + offs_n
|
|
k_loc = tl.load(
|
|
Req_to_tokens + stride_req_to_tokens_b * cur_batch_req_idx + offs_n_new,
|
|
mask=offs_n_new < cur_batch_end_index,
|
|
other=0,
|
|
)
|
|
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_new[:, None] < cur_batch_end_index,
|
|
other=0.0,
|
|
).to(REDUCE_TRITON_TYPE)
|
|
att_value = tl.sum(q[None, :] * k, 1)
|
|
att_value *= sm_scale
|
|
off_o = cur_head * att_stride_h + (cur_batch_in_all_start_index + offs_n)
|
|
tl.store(Att_Out + off_o, att_value, mask=offs_n_new < cur_batch_end_index)
|
|
|
|
|
|
@triton.jit
|
|
def _fwd_kernel_stage2(
|
|
Logics,
|
|
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,
|
|
other_kv_index, # To fix a NAN issue
|
|
kv_group_num: tl.constexpr,
|
|
BLOCK_DMODEL: tl.constexpr,
|
|
BLOCK_N: tl.constexpr,
|
|
):
|
|
cur_batch = tl.program_id(0)
|
|
cur_head = tl.program_id(1)
|
|
|
|
cur_kv_head = cur_head // kv_group_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 = 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 = float("-inf")
|
|
e_sum = 0.0
|
|
acc = tl.zeros([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_index = tl.load(
|
|
Req_to_tokens
|
|
+ cur_batch_req_idx * stride_req_to_token_b
|
|
+ (start_n + offs_n),
|
|
mask=(start_n + offs_n) < cur_batch_seq_len,
|
|
other=other_kv_index,
|
|
)
|
|
|
|
qk = tl.load(
|
|
Logics
|
|
+ cur_head * stride_logic_h
|
|
+ (cur_batch_start_loc + start_n + offs_n),
|
|
mask=start_n + offs_n < cur_batch_seq_len,
|
|
other=float("-inf"),
|
|
)
|
|
|
|
n_e_max = tl.maximum(tl.max(qk, 0), e_max)
|
|
old_scale = tl.exp(e_max - n_e_max)
|
|
p = tl.exp(qk - n_e_max)
|
|
e_sum = e_sum * old_scale + tl.sum(p, 0)
|
|
v = tl.load(v_ptrs + v_index[:, None] * stride_buf_vbs)
|
|
acc = acc * old_scale + tl.sum(p[:, None] * v, 0)
|
|
e_max = n_e_max
|
|
|
|
acc = acc / e_sum
|
|
off_o = cur_batch * stride_obs + cur_head * stride_oh + offs_d
|
|
out_ptrs = Out + off_o
|
|
tl.store(out_ptrs, acc)
|
|
|
|
|
|
cached_kernel_stage1 = None
|
|
cached_kernel_stage2 = None
|
|
|
|
|
|
def _token_att_m_fwd(
|
|
q,
|
|
k_buffer,
|
|
att_out,
|
|
Req_to_tokens,
|
|
B_req_idx,
|
|
B_Start_Loc,
|
|
B_Seqlen,
|
|
max_len_in_batch,
|
|
):
|
|
BLOCK = 32
|
|
# shape constraints
|
|
Lq, Lk = q.shape[-1], k_buffer.shape[-1]
|
|
assert Lq == Lk
|
|
assert Lk in {16, 32, 64, 128, 256}
|
|
sm_scale = 1.0 / (Lk**0.5)
|
|
|
|
batch, head_num = B_req_idx.shape[0], q.shape[1]
|
|
|
|
grid = (batch, head_num, triton.cdiv(max_len_in_batch, BLOCK))
|
|
kv_group_num = q.shape[1] // k_buffer.shape[1]
|
|
|
|
if kv_group_num == 1:
|
|
num_warps = 4
|
|
else:
|
|
num_warps = 2
|
|
|
|
global cached_kernel_stage1
|
|
if cached_kernel_stage1:
|
|
cached_kernel_stage1(
|
|
grid,
|
|
num_warps,
|
|
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(0),
|
|
k_buffer.stride(1),
|
|
att_out.stride(0),
|
|
)
|
|
return
|
|
|
|
_fwd_kernel_stage1[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(0),
|
|
k_buffer.stride(1),
|
|
att_out.stride(0),
|
|
kv_group_num=kv_group_num,
|
|
BLOCK_DMODEL=Lk,
|
|
BLOCK_N=BLOCK,
|
|
num_warps=num_warps,
|
|
num_stages=1,
|
|
)
|
|
cached_kernel_stage1 = wrap_kernel_launcher(_fwd_kernel_stage1)
|
|
|
|
|
|
def _token_softmax_reducev_fwd(
|
|
logics,
|
|
v_buffer,
|
|
o,
|
|
req_to_tokens,
|
|
b_req_idx,
|
|
b_start_loc,
|
|
b_seq_len,
|
|
other_kv_index,
|
|
):
|
|
BLOCK = 64
|
|
batch, head = b_seq_len.shape[0], logics.shape[0]
|
|
grid = (batch, head, 1)
|
|
kv_group_num = logics.shape[0] // v_buffer.shape[1]
|
|
|
|
num_warps = 1
|
|
|
|
global cached_kernel_stage2
|
|
if cached_kernel_stage2:
|
|
cached_kernel_stage2(
|
|
grid,
|
|
num_warps,
|
|
logics,
|
|
v_buffer,
|
|
o,
|
|
req_to_tokens,
|
|
b_req_idx,
|
|
b_start_loc,
|
|
b_seq_len,
|
|
logics.stride(0),
|
|
v_buffer.stride(0),
|
|
v_buffer.stride(1),
|
|
o.stride(0),
|
|
o.stride(1),
|
|
req_to_tokens.stride(0),
|
|
other_kv_index,
|
|
)
|
|
return
|
|
|
|
_fwd_kernel_stage2[grid](
|
|
logics,
|
|
v_buffer,
|
|
o,
|
|
req_to_tokens,
|
|
b_req_idx,
|
|
b_start_loc,
|
|
b_seq_len,
|
|
logics.stride(0),
|
|
v_buffer.stride(0),
|
|
v_buffer.stride(1),
|
|
o.stride(0),
|
|
o.stride(1),
|
|
req_to_tokens.stride(0),
|
|
other_kv_index,
|
|
kv_group_num=kv_group_num,
|
|
BLOCK_DMODEL=v_buffer.shape[-1],
|
|
BLOCK_N=BLOCK,
|
|
num_warps=num_warps,
|
|
num_stages=3,
|
|
)
|
|
cached_kernel_stage2 = wrap_kernel_launcher(_fwd_kernel_stage2)
|
|
|
|
|
|
def token_attention_fwd(
|
|
q,
|
|
k_buffer,
|
|
v_buffer,
|
|
o,
|
|
req_to_token,
|
|
b_req_idx,
|
|
b_start_loc,
|
|
b_seq_len,
|
|
max_len_in_batch,
|
|
other_kv_index,
|
|
total_num_tokens,
|
|
att_m=None,
|
|
):
|
|
if att_m is None:
|
|
att_m = torch.empty(
|
|
(q.shape[-2], total_num_tokens), dtype=REDUCE_TORCH_TYPE, device="cuda"
|
|
)
|
|
|
|
_token_att_m_fwd(
|
|
q,
|
|
k_buffer,
|
|
att_m,
|
|
req_to_token,
|
|
b_req_idx,
|
|
b_start_loc,
|
|
b_seq_len,
|
|
max_len_in_batch,
|
|
)
|
|
_token_softmax_reducev_fwd(
|
|
att_m,
|
|
v_buffer,
|
|
o,
|
|
req_to_token,
|
|
b_req_idx,
|
|
b_start_loc,
|
|
b_seq_len,
|
|
other_kv_index,
|
|
)
|