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Unverified Commit 7dc66fcb authored by Ke Bao's avatar Ke Bao Committed by GitHub
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Optimize Triton decoding kernel for long context (#2394)

parent 1f09e84b
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Showing with 328 additions and 360 deletions
python/sglang/srt/layers/attention/triton_backend.py
View file @ 7dc66fcb
......@@ -40,6 +40,9 @@ class TritonAttnBackend(AttentionBackend):
else:
self.reduce_dtype = torch.float16
self.num_kv_splits = model_runner.server_args.triton_attention_num_kv_splits
self.v_head_dim = model_runner.token_to_kv_pool.get_value_buffer(0).shape[-1]
self.forward_metadata = None
self.cuda_graph_max_seq_len = model_runner.model_config.context_len
......@@ -53,10 +56,14 @@ class TritonAttnBackend(AttentionBackend):
start_loc = torch.zeros_like(forward_batch.seq_lens, dtype=torch.int32)
start_loc[1:] = torch.cumsum(forward_batch.seq_lens[:-1], dim=0)
total_num_tokens = forward_batch.seq_lens_sum
attn_logits = torch.empty(
(self.num_head, total_num_tokens),
dtype=self.reduce_dtype,
(
forward_batch.batch_size,
self.num_head,
self.num_kv_splits,
self.v_head_dim + 1,
),
dtype=torch.float32,
device=self.device,
)
......@@ -75,11 +82,8 @@ class TritonAttnBackend(AttentionBackend):
(max_bs,), dtype=torch.int32, device=self.device
)
self.cuda_graph_attn_logits = torch.empty(
(
self.num_head,
self.cuda_graph_max_total_num_tokens,
),
dtype=self.reduce_dtype,
(max_bs, self.num_head, self.num_kv_splits, self.v_head_dim + 1),
dtype=torch.float32,
device="cuda",
)
......@@ -189,6 +193,7 @@ class TritonAttnBackend(AttentionBackend):
forward_batch.seq_lens,
attn_logits,
max_seq_len,
self.num_kv_splits,
layer.scaling,
layer.logit_cap,
)
......
python/sglang/srt/layers/attention/triton_ops/decode_attention.py
View file @ 7dc66fcb
......@@ -17,8 +17,8 @@ It supports page size = 1.
"""
# 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
# 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
import triton
import triton.language as tl
......@@ -37,10 +37,10 @@ def tanh(x):
def _fwd_kernel_stage1(
Q,
K_Buffer,
V_Buffer,
sm_scale,
Req_to_tokens,
B_req_idx,
B_Start_Loc,
B_Seqlen,
Att_Out,
stride_req_to_tokens_b,
......@@ -48,152 +48,137 @@ def _fwd_kernel_stage1(
stride_qh,
stride_buf_kbs,
stride_buf_kh,
att_stride_h,
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,
SPLIT_K: tl.constexpr,
NUM_KV_SPLITS: 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_k_id = tl.program_id(2)
split_kv_id = tl.program_id(2)
reduce_dtype = Att_Out.dtype.element_ty
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_in_all_start_index = tl.load(B_Start_Loc + cur_batch)
cur_batch_req_idx = tl.load(B_req_idx + cur_batch)
off_q = cur_batch * stride_qbs + cur_head * stride_qh + offs_d
q = tl.load(Q + off_q).to(reduce_dtype)
q = tl.load(Q + off_q, mask=mask_d, other=0.0)
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)
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)
for start_n in range(split_k_start, split_k_end, BLOCK_N):
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)
k_loc = tl.load(
kv_loc = tl.load(
Req_to_tokens + stride_req_to_tokens_b * cur_batch_req_idx + offs_n,
mask=offs_n < split_k_end,
mask=offs_n < split_kv_end,
other=0,
)
offs_buf_k = (
k_loc[:, None] * stride_buf_kbs
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_k_end) & (offs_d[None, :] < Lk),
mask=(offs_n[:, None] < split_kv_end) & (mask_d[None, :]),
other=0.0,
).to(reduce_dtype)
att_value = tl.sum(q[None, :] * k, 1)
att_value *= sm_scale
)
qk = tl.sum(q[None, :] * k, 1)
qk *= sm_scale
if logit_cap > 0:
att_value = logit_cap * tanh(att_value / logit_cap)
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 < split_k_end)
qk = logit_cap * tanh(qk / logit_cap)
@triton.jit
def _fwd_kernel_stage2(
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,
BLOCK_DMODEL: tl.constexpr,
BLOCK_N: tl.constexpr,
Lv: tl.constexpr,
):
cur_batch = tl.program_id(0)
cur_head = tl.program_id(1)
qk = tl.where(offs_n < split_kv_end, qk, float("-inf"))
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_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,
)
offs_n = tl.arange(0, BLOCK_N)
offs_d = tl.arange(0, BLOCK_DMODEL)
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)
offs_buf_v = cur_kv_head * stride_buf_vh + offs_d[None, :]
v_ptrs = V_Buffer + offs_buf_v
e_sum = e_sum * re_scale + tl.sum(p, 0)
e_max = n_e_max
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=0,
offs_mid_o = (
cur_batch * stride_mid_ob
+ cur_head * stride_mid_oh
+ split_kv_id * stride_mid_os
+ offs_dv
)
qk = tl.load(
logits
+ 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"),
tl.store(
Att_Out + offs_mid_o,
acc / e_sum,
mask=(mask_dv),
)
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, mask=(offs_d[None, :] < Lv)
offs_mid_o_1 = (
cur_batch * stride_mid_ob
+ cur_head * stride_mid_oh
+ split_kv_id * stride_mid_os
+ Lv
)
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, mask=(offs_d < 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_req_idx,
B_Start_Loc,
B_Seqlen,
max_len_in_batch,
num_kv_splits,
sm_scale,
logit_cap,
):
BLOCK = 32
SPLIT_K = 8
BLOCK = 64
NUM_KV_SPLITS = num_kv_splits
Lk = k_buffer.shape[-1]
Lv = v_buffer.shape[-1]
batch, head_num = B_req_idx.shape[0], q.shape[1]
grid = (batch, head_num, SPLIT_K)
grid = (batch, head_num, NUM_KV_SPLITS)
kv_group_num = q.shape[1] // k_buffer.shape[1]
if kv_group_num == 1:
......@@ -202,14 +187,15 @@ def _decode_att_m_fwd(
num_warps = 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_req_idx,
B_Start_Loc,
B_Seqlen,
att_out,
Req_to_tokens.stride(0),
......@@ -217,56 +203,20 @@ def _decode_att_m_fwd(
q.stride(1),
k_buffer.stride(0),
k_buffer.stride(1),
v_buffer.stride(0),
v_buffer.stride(1),
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,
SPLIT_K=SPLIT_K,
NUM_KV_SPLITS=NUM_KV_SPLITS,
logit_cap=logit_cap,
num_warps=num_warps,
num_stages=1,
num_stages=2,
Lk=Lk,
)
def _decode_softmax_reducev_fwd(
logits,
v_buffer,
o,
req_to_tokens,
b_req_idx,
b_start_loc,
b_seq_len,
):
BLOCK = 64
batch, head = b_seq_len.shape[0], logits.shape[0]
grid = (batch, head, 1)
kv_group_num = logits.shape[0] // v_buffer.shape[1]
num_warps = 1
Lv = v_buffer.shape[-1]
BLOCK_DMODEL = triton.next_power_of_2(Lv)
_fwd_kernel_stage2[grid](
logits,
v_buffer,
o,
req_to_tokens,
b_req_idx,
b_start_loc,
b_seq_len,
logits.stride(0),
v_buffer.stride(0),
v_buffer.stride(1),
o.stride(0),
o.stride(1),
req_to_tokens.stride(0),
kv_group_num=kv_group_num,
BLOCK_DMODEL=BLOCK_DMODEL,
BLOCK_N=BLOCK,
num_warps=num_warps,
num_stages=3,
Lv=Lv,
)
......@@ -275,10 +225,10 @@ def _decode_softmax_reducev_fwd(
def _fwd_grouped_kernel_stage1(
Q,
K_Buffer,
V_Buffer,
sm_scale,
Req_to_tokens,
B_req_idx,
B_Start_Loc,
B_Seqlen,
Att_Out,
stride_req_to_tokens_b,
......@@ -286,23 +236,27 @@ def _fwd_grouped_kernel_stage1(
stride_qh,
stride_buf_kbs,
stride_buf_kh,
att_stride_h,
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,
SPLIT_K: tl.constexpr,
NUM_KV_SPLITS: 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_k_id = tl.program_id(2)
reduce_dtype = Att_Out.dtype.element_ty
split_kv_id = tl.program_id(2)
if BLOCK_H < kv_group_num:
VALID_BLOCK_H: tl.constexpr = BLOCK_H
......@@ -313,171 +267,136 @@ def _fwd_grouped_kernel_stage1(
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_in_all_start_index = tl.load(B_Start_Loc + cur_batch)
cur_batch_req_idx = tl.load(B_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)
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], other=0.0).to(reduce_dtype)
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, 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)
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)
for start_n in range(split_k_start, split_k_end, BLOCK_N):
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)
k_loc = tl.load(
kv_loc = tl.load(
Req_to_tokens + stride_req_to_tokens_b * cur_batch_req_idx + offs_n,
mask=offs_n < split_k_end,
mask=offs_n < split_kv_end,
other=0,
)
offs_buf_k = (
k_loc[None, :] * stride_buf_kbs
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_k_end) & (offs_d[:, None] < Lk),
mask=(offs_n[None, :] < split_kv_end) & (mask_d[:, None]),
other=0.0,
).to(reduce_dtype)
qk = tl.dot(q, k)
)
qk = tl.dot(q, k.to(q.dtype))
if BLOCK_DPE > 0:
offs_buf_kpe = (
k_loc[None, :] * stride_buf_kbs
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_k_end,
mask=(offs_n[None, :] < split_kv_end) & (mask_dpe[:, None]),
other=0.0,
).to(reduce_dtype)
qk += tl.dot(qpe, kpe)
)
qk += tl.dot(qpe, kpe.to(qpe.dtype))
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, :]
qk = tl.where(
mask_h[:, None] & (offs_n[None, :] < split_kv_end), qk, float("-inf")
)
tl.store(
Att_Out + offs_o,
qk,
mask=mask_h[:, None] & (offs_n[None, :] < split_k_end),
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)
@triton.jit
def _fwd_grouped_kernel_stage2(
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_N: tl.constexpr,
BLOCK_H: 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)
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)
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_sum = e_sum * re_scale + tl.sum(p, 1)
e_max = n_e_max
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_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=0,
offs_mid_o = (
cur_batch * stride_mid_ob
+ cur_head[:, None] * stride_mid_oh
+ split_kv_id * stride_mid_os
+ offs_dv[None, :]
)
offs_qk = cur_head[:, None] * stride_logic_h + (
cur_batch_start_loc + start_n + offs_n[None, :]
tl.store(
Att_Out + offs_mid_o,
acc / e_sum[:, None],
mask=(mask_h[:, None]) & (mask_dv[None, :]),
)
qk = tl.load(
logits + offs_qk,
mask=mask_h[:, None] & (start_n + offs_n[None, :] < cur_batch_seq_len),
other=float("-inf"),
offs_mid_o_1 = (
cur_batch * stride_mid_ob
+ cur_head * stride_mid_oh
+ split_kv_id * stride_mid_os
+ Lv
)
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_index[:, None] * stride_buf_vbs, mask=(offs_d[None, :] < Lv)
tl.store(
Att_Out + offs_mid_o_1,
e_max + tl.log(e_sum),
mask=mask_h,
)
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_grouped_att_m_fwd(
q,
k_buffer,
v_buffer,
att_out,
Req_to_tokens,
B_req_idx,
B_Start_Loc,
B_Seqlen,
max_len_in_batch,
num_kv_splits,
sm_scale,
logit_cap,
):
BLOCK = 64
BLOCK = 32
Lk = k_buffer.shape[-1]
Lv = v_buffer.shape[-1]
if Lk == 576:
BLOCK_DMODEL = 512
......@@ -488,20 +407,19 @@ def _decode_grouped_att_m_fwd(
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]
kv_group_num = q.shape[1] // k_buffer.shape[1]
BLOCK_H = max(16, min(64, triton.next_power_of_2(kv_group_num)))
SPLIT_K = 8
BLOCK_H = 16
NUM_KV_SPLITS = num_kv_splits
grid = (
batch,
triton.cdiv(head_num, min(BLOCK_H, kv_group_num)),
SPLIT_K,
NUM_KV_SPLITS,
)
num_warps = 4
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
......@@ -511,10 +429,10 @@ def _decode_grouped_att_m_fwd(
_fwd_grouped_kernel_stage1[grid](
q,
k_buffer,
v_buffer,
sm_scale,
Req_to_tokens,
B_req_idx,
B_Start_Loc,
B_Seqlen,
att_out,
Req_to_tokens.stride(0),
......@@ -522,41 +440,88 @@ def _decode_grouped_att_m_fwd(
q.stride(1),
k_buffer.stride(0),
k_buffer.stride(1),
v_buffer.stride(0),
v_buffer.stride(1),
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_N=BLOCK,
BLOCK_H=BLOCK_H,
SPLIT_K=SPLIT_K,
NUM_KV_SPLITS=NUM_KV_SPLITS,
logit_cap=logit_cap,
num_warps=num_warps,
num_stages=1,
num_warps=4,
num_stages=2,
Lk=Lk,
Lv=Lv,
**extra_kargs,
)
def _decode_grouped_softmax_reducev_fwd(
logits,
v_buffer,
o,
req_to_tokens,
b_req_idx,
b_start_loc,
b_seq_len,
@triton.jit
def _fwd_kernel_stage2(
Mid_O,
O,
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,
):
BLOCK = 128
batch, head_num = b_seq_len.shape[0], logits.shape[0]
kv_group_num = logits.shape[0] // v_buffer.shape[1]
BLOCK_H = max(16, min(64, triton.next_power_of_2(kv_group_num)))
grid = (batch, triton.cdiv(head_num, min(BLOCK_H, kv_group_num)), 1)
cur_batch = tl.program_id(0)
cur_head = tl.program_id(1)
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)
num_warps = 8
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):
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,
num_kv_splits,
):
batch, head_num = q.shape[0], q.shape[1]
Lv = v_buffer.shape[-1]
BLOCK_DMODEL = triton.next_power_of_2(Lv)
BLOCK_DV = triton.next_power_of_2(Lv)
NUM_KV_SPLITS = num_kv_splits
extra_kargs = {}
if is_hip_:
......@@ -564,28 +529,20 @@ def _decode_grouped_softmax_reducev_fwd(
# https://github.com/triton-lang/triton/blob/main/third_party/amd/backend/compiler.py
extra_kargs = {"waves_per_eu": 4, "matrix_instr_nonkdim": 16, "kpack": 2}
_fwd_grouped_kernel_stage2[grid](
grid = (batch, head_num)
_fwd_kernel_stage2[grid](
logits,
v_buffer,
o,
req_to_tokens,
b_req_idx,
b_start_loc,
b_seq_len,
logits.stride(0),
v_buffer.stride(0),
v_buffer.stride(1),
logits.stride(1),
logits.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_N=BLOCK,
BLOCK_H=BLOCK_H,
NUM_KV_SPLITS=NUM_KV_SPLITS,
BLOCK_DV=BLOCK_DV,
Lv=Lv,
num_warps=num_warps,
num_stages=1,
num_warps=4,
num_stages=2,
**extra_kargs,
)
......@@ -597,34 +554,27 @@ def decode_attention_fwd_normal(
o,
req_to_token,
b_req_idx,
b_start_loc,
b_seq_len,
attn_logits,
max_len_in_batch,
num_kv_splits,
sm_scale,
logit_cap=0.0,
):
_decode_att_m_fwd(
q,
k_buffer,
v_buffer,
attn_logits,
req_to_token,
b_req_idx,
b_start_loc,
b_seq_len,
max_len_in_batch,
num_kv_splits,
sm_scale,
logit_cap,
)
_decode_softmax_reducev_fwd(
attn_logits,
v_buffer,
o,
req_to_token,
b_req_idx,
b_start_loc,
b_seq_len,
)
_decode_softmax_reducev_fwd(attn_logits, q, o, v_buffer, num_kv_splits)
def decode_attention_fwd_grouped(
......@@ -634,34 +584,27 @@ def decode_attention_fwd_grouped(
o,
req_to_token,
b_req_idx,
b_start_loc,
b_seq_len,
attn_logits,
max_len_in_batch,
num_kv_splits,
sm_scale,
logit_cap=0.0,
):
_decode_grouped_att_m_fwd(
q,
k_buffer,
v_buffer,
attn_logits,
req_to_token,
b_req_idx,
b_start_loc,
b_seq_len,
max_len_in_batch,
num_kv_splits,
sm_scale,
logit_cap,
)
_decode_grouped_softmax_reducev_fwd(
attn_logits,
v_buffer,
o,
req_to_token,
b_req_idx,
b_start_loc,
b_seq_len,
)
_decode_softmax_reducev_fwd(attn_logits, q, o, v_buffer, num_kv_splits)
def decode_attention_fwd(
......@@ -675,9 +618,11 @@ def decode_attention_fwd(
b_seq_len,
attn_logits,
max_len_in_batch,
num_kv_splits,
sm_scale,
logit_cap=0.0,
):
assert num_kv_splits == attn_logits.shape[2]
kv_group_num = q.shape[1] // v_buffer.shape[1]
if kv_group_num == 1:
......@@ -689,10 +634,10 @@ def decode_attention_fwd(
o,
req_to_token,
b_req_idx,
b_start_loc,
b_seq_len,
attn_logits,
max_len_in_batch,
num_kv_splits,
sm_scale,
logit_cap,
)
......@@ -705,10 +650,10 @@ def decode_attention_fwd(
o,
req_to_token,
b_req_idx,
b_start_loc,
b_seq_len,
attn_logits,
max_len_in_batch,
num_kv_splits,
sm_scale,
logit_cap,
)
python/sglang/srt/server_args.py
View file @ 7dc66fcb
......@@ -141,6 +141,7 @@ class ServerArgs:
enable_nan_detection: bool = False
enable_p2p_check: bool = False
triton_attention_reduce_in_fp32: bool = False
triton_attention_num_kv_splits: int = 8
num_continuous_decode_steps: int = 1
delete_ckpt_after_loading: bool = False
......@@ -753,6 +754,12 @@ class ServerArgs:
help="Cast the intermidiate attention results to fp32 to avoid possible crashes related to fp16."
"This only affects Triton attention kernels.",
)
parser.add_argument(
"--triton-attention-num-kv-splits",
type=int,
default=ServerArgs.triton_attention_num_kv_splits,
help="The number of KV splits in flash decoding Triton kernel. Larger value is better in longer context scenarios. The default value is 8.",
)
parser.add_argument(
"--num-continuous-decode-steps",
type=int,
......
test/srt/test_triton_attention_kernels.py
View file @ 7dc66fcb
......@@ -182,6 +182,7 @@ class TestTritonAttention(unittest.TestCase):
seq_len = 10 # This represents the number of tokens already in the sequence
total_tokens = B * seq_len
sm_scale = 1.0 / (D**0.5)
num_kv_splits = 8
# q represents the new token being generated, one per batch
q = torch.randn(B, H_Q, D, dtype=dtype, device="cuda")
......@@ -199,8 +200,8 @@ class TestTritonAttention(unittest.TestCase):
b_seq_len = torch.full((B,), seq_len, device="cuda")
attn_logits = torch.empty(
(H_Q, total_tokens),
dtype=dtype,
(B, H_Q, num_kv_splits, D + 1),
dtype=torch.float32,
device="cuda",
)
......@@ -215,6 +216,7 @@ class TestTritonAttention(unittest.TestCase):
b_seq_len,
attn_logits,
seq_len,
num_kv_splits,
sm_scale,
)
......@@ -235,9 +237,10 @@ class TestTritonAttention(unittest.TestCase):
def _test_grouped_decode_attention_once(self, B, H_Q, H_KV, D, D_V):
dtype = torch.bfloat16
seq_len = 10 # This represents the number of tokens already in the sequence
seq_len = 128 # This represents the number of tokens already in the sequence
total_tokens = B * seq_len
sm_scale = 1.0 / (D**0.5)
num_kv_splits = 8
# q represents the new token being generated, one per batch
q = torch.randn(B, H_Q, D, dtype=dtype, device="cuda")
......@@ -247,8 +250,8 @@ class TestTritonAttention(unittest.TestCase):
v_buffer = torch.randn(total_tokens, H_KV, D_V, dtype=dtype, device="cuda")
# o will have the same shape as q
o = torch.zeros(B, H_Q, D, dtype=dtype, device="cuda")
o_grouped = torch.zeros(B, H_Q, D, dtype=dtype, device="cuda")
o = torch.zeros(B, H_Q, D_V, dtype=dtype, device="cuda")
o_grouped = torch.zeros(B, H_Q, D_V, dtype=dtype, device="cuda")
req_to_token = torch.arange(total_tokens, device="cuda").reshape(B, seq_len)
b_req_idx = torch.arange(B, device="cuda")
......@@ -256,8 +259,8 @@ class TestTritonAttention(unittest.TestCase):
b_seq_len = torch.full((B,), seq_len, device="cuda")
attn_logits = torch.empty(
(H_Q, total_tokens),
dtype=dtype,
(B, H_Q, num_kv_splits, D_V + 1),
dtype=torch.float32,
device="cuda",
)
......@@ -268,13 +271,19 @@ class TestTritonAttention(unittest.TestCase):
o,
req_to_token,
b_req_idx,
b_start_loc,
b_seq_len,
attn_logits,
seq_len,
num_kv_splits,
sm_scale,
)
attn_logits1 = torch.empty(
(B, H_Q, num_kv_splits, D_V + 1),
dtype=torch.float32,
device="cuda",
)
decode_attention_fwd_grouped(
q,
k_buffer,
......@@ -282,21 +291,23 @@ class TestTritonAttention(unittest.TestCase):
o_grouped,
req_to_token,
b_req_idx,
b_start_loc,
b_seq_len,
attn_logits,
attn_logits1,
seq_len,
num_kv_splits,
sm_scale,
)
cos_sim = torch.nn.functional.cosine_similarity(
o.flatten(), o_grouped.flatten(), dim=0
)
print(cos_sim.item())
self.assertTrue(cos_sim.item() > 0.99)
self.assertTrue(torch.allclose(o, o_grouped, atol=3e-2))
def test_grouped_decode_attention(self):
configs = [
(2, 16, 16, 64, 64),
(2, 16, 1, 64, 64),
(2, 64, 1, 13, 13),
(2, 128, 1, 80, 80),
......
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