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[triton] Support head_dim not 2^n in triton extend and decode attention (#1281)

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This commit is contained in:
Byron Hsu
2024-09-09 01:30:24 -07:00
committed by GitHub
parent 05bea6883c
commit 8e6bdf851c
3 changed files with 84 additions and 37 deletions
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50
python/sglang/srt/layers/decode_attention.py
View File

@@ -60,6 +60,7 @@ def _fwd_kernel_stage1(
BLOCK_DMODEL: tl.constexpr,
BLOCK_N: tl.constexpr,
logit_cap: tl.constexpr,
Lk: tl.constexpr,
):
cur_batch = tl.program_id(0)
cur_head = tl.program_id(1)
@@ -97,7 +98,7 @@ def _fwd_kernel_stage1(
)
k = tl.load(
K_Buffer + offs_buf_k,
mask=offs_n_new[:, None] < cur_batch_end_index,
mask=(offs_n_new[:, None] < cur_batch_end_index) & (offs_d[None, :] < Lk),
other=0.0,
).to(REDUCE_TRITON_TYPE)
att_value = tl.sum(q[None, :] * k, 1)
@@ -128,6 +129,7 @@ def _fwd_kernel_stage2(
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)
@@ -170,14 +172,16 @@ def _fwd_kernel_stage2(
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)
v = tl.load(
v_ptrs + v_index[:, None] * stride_buf_vbs, mask=(offs_d[None, :] < 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)
tl.store(out_ptrs, acc, mask=(offs_d < Lv))
def _decode_att_m_fwd(
@@ -196,7 +200,7 @@ def _decode_att_m_fwd(
# shape constraints
Lq, Lk = q.shape[-1], k_buffer.shape[-1]
assert Lq == Lk
assert Lk in {16, 32, 64, 128, 256}
assert Lk in {16, 32, 64, 96, 128, 256}
batch, head_num = B_req_idx.shape[0], q.shape[1]
@@ -208,6 +212,8 @@ def _decode_att_m_fwd(
else:
num_warps = 2
BLOCK_DMODEL = triton.next_power_of_2(Lk)
_fwd_kernel_stage1[grid](
q,
k_buffer,
@@ -224,11 +230,12 @@ def _decode_att_m_fwd(
k_buffer.stride(1),
att_out.stride(0),
kv_group_num=kv_group_num,
BLOCK_DMODEL=Lk,
BLOCK_DMODEL=BLOCK_DMODEL,
BLOCK_N=BLOCK,
logit_cap=logit_cap,
num_warps=num_warps,
num_stages=1,
Lk=Lk,
)
@@ -248,6 +255,9 @@ def _decode_softmax_reducev_fwd(
num_warps = 1
Lv = v_buffer.shape[-1]
BLOCK_DMODEL = triton.next_power_of_2(Lv)
_fwd_kernel_stage2[grid](
logics,
v_buffer,
@@ -263,10 +273,11 @@ def _decode_softmax_reducev_fwd(
o.stride(1),
req_to_tokens.stride(0),
kv_group_num=kv_group_num,
BLOCK_DMODEL=v_buffer.shape[-1],
BLOCK_DMODEL=BLOCK_DMODEL,
BLOCK_N=BLOCK,
num_warps=num_warps,
num_stages=3,
Lv=Lv,
)
@@ -293,6 +304,7 @@ def _fwd_grouped_kernel_stage1(
BLOCK_N: tl.constexpr,
BLOCK_H: tl.constexpr,
logit_cap: tl.constexpr,
Lk: tl.constexpr,
):
cur_batch = tl.program_id(0)
cur_kv_head = tl.program_id(1)
@@ -324,9 +336,9 @@ def _fwd_grouped_kernel_stage1(
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 + offs_q + start_mark, mask=mask_h[:, None]).to(
REDUCE_TRITON_TYPE
)
q = tl.load(
Q + offs_q + start_mark, mask=(mask_h[:, None]) & (offs_d[None, :] < Lk)
).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,
@@ -340,7 +352,7 @@ def _fwd_grouped_kernel_stage1(
)
k = tl.load(
K_Buffer + offs_buf_k,
mask=offs_n_new[None, :] < cur_batch_end_index,
mask=(offs_n_new[None, :] < cur_batch_end_index) & (offs_d[:, None] < Lk),
other=0.0,
).to(REDUCE_TRITON_TYPE)
qk = tl.dot(q, k)
@@ -395,6 +407,7 @@ def _fwd_grouped_kernel_stage2(
BLOCK_DMODEL: tl.constexpr,
BLOCK_N: tl.constexpr,
BLOCK_H: tl.constexpr,
Lv: tl.constexpr,
):
cur_batch = tl.program_id(0)
cur_kv_head = tl.program_id(1)
@@ -441,7 +454,9 @@ def _fwd_grouped_kernel_stage2(
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)
v = tl.load(
v_ptrs + v_index[:, None] * stride_buf_vbs, mask=(offs_d[None, :] < Lv)
)
p = p.to(v.dtype)
acc = acc * old_scale[:, None] + tl.dot(p, v)
e_max = n_e_max
@@ -449,7 +464,7 @@ def _fwd_grouped_kernel_stage2(
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])
tl.store(out_ptrs, acc, mask=(mask_h[:, None]) & (offs_d[None, :] < Lv))
def _decode_grouped_att_m_fwd(
@@ -468,13 +483,13 @@ def _decode_grouped_att_m_fwd(
# shape constraints
Lq, Lk = q.shape[-1], k_buffer.shape[-1]
assert Lq == Lk
assert Lk in {16, 32, 64, 128, 256, 576}
assert Lk in {16, 32, 64, 96, 128, 256, 576}
if Lk == 576:
BLOCK_DMODEL = 512
BLOCK_DPE = 64
else:
BLOCK_DMODEL = Lk
BLOCK_DMODEL = triton.next_power_of_2(Lk)
BLOCK_DPE = 0
batch, head_num = B_req_idx.shape[0], q.shape[1]
@@ -513,6 +528,7 @@ def _decode_grouped_att_m_fwd(
logit_cap=logit_cap,
num_warps=num_warps,
num_stages=1,
Lk=Lk,
)
@@ -533,6 +549,9 @@ def _decode_grouped_softmax_reducev_fwd(
num_warps = 8
Lv = v_buffer.shape[-1]
BLOCK_DMODEL = triton.next_power_of_2(Lv)
_fwd_grouped_kernel_stage2[grid](
logics,
v_buffer,
@@ -549,11 +568,12 @@ def _decode_grouped_softmax_reducev_fwd(
req_to_tokens.stride(0),
kv_group_num=kv_group_num,
q_head_num=head_num,
BLOCK_DMODEL=v_buffer.shape[-1],
BLOCK_DMODEL=BLOCK_DMODEL,
BLOCK_N=BLOCK,
BLOCK_H=BLOCK_H,
num_warps=num_warps,
num_stages=1,
Lv=Lv,
)

51
python/sglang/srt/layers/extend_attention.py
View File

@@ -15,7 +15,7 @@ limitations under the License.
"""
Memory-efficient attention for prefill.
It supporst page size = 1 and prefill with KV cache (i.e. extend).
It supports page size = 1 and prefill with KV cache (i.e. extend).
"""
import torch
@@ -67,6 +67,8 @@ def _fwd_kernel(
BLOCK_M: tl.constexpr,
BLOCK_N: tl.constexpr,
logit_cap: tl.constexpr,
Lq: tl.constexpr,
Lv: tl.constexpr,
):
cur_seq = tl.program_id(0)
cur_head = tl.program_id(1)
@@ -86,13 +88,18 @@ def _fwd_kernel(
offs_m = tl.arange(0, BLOCK_M)
mask_m = (cur_block_m * BLOCK_M + offs_m) < cur_seq_len_extend
mask_d = offs_d < Lq
mask_dv = offs_dv < Lv
offs_q = (
(cur_seq_extend_start_contiguous + cur_block_m * BLOCK_M + offs_m[:, None])
* stride_qbs
+ cur_head * stride_qh
+ offs_d[None, :]
)
q = tl.load(Q_Extend + offs_q, mask=mask_m[:, None], other=0.0)
q = tl.load(
Q_Extend + offs_q, mask=(mask_m[:, None]) & (mask_d[None, :]), other=0.0
)
if BLOCK_DPE > 0:
offs_dpe = BLOCK_DMODEL + tl.arange(0, BLOCK_DPE)
@@ -125,7 +132,9 @@ def _fwd_kernel(
+ cur_kv_head * stride_buf_kh
+ offs_d[:, None]
)
k = tl.load(K_Buffer + offs_buf_k, mask=mask_n[None, :], other=0.0)
k = tl.load(
K_Buffer + offs_buf_k, mask=(mask_n[None, :]) & (mask_d[:, None]), other=0.0
)
qk = tl.dot(q.to(k.dtype), k)
if BLOCK_DPE > 0:
@@ -157,7 +166,9 @@ def _fwd_kernel(
+ cur_kv_head * stride_buf_vh
+ offs_dv[None, :]
)
v = tl.load(V_Buffer + offs_buf_v, mask=mask_n[:, None], other=0.0)
v = tl.load(
V_Buffer + offs_buf_v, mask=mask_n[:, None] & mask_dv[None, :], other=0.0
)
p = p.to(v.dtype)
acc = acc * re_scale[:, None] + tl.dot(p, v)
@@ -176,7 +187,9 @@ def _fwd_kernel(
+ cur_kv_head * stride_kh
+ offs_d[:, None]
)
k = tl.load(K_Extend + offs_k, mask=mask_n[None, :], other=0.0)
k = tl.load(
K_Extend + offs_k, mask=(mask_n[None, :]) & (mask_d[:, None]), other=0.0
)
qk = tl.dot(q, k, out_dtype=tl.float32)
if BLOCK_DPE > 0:
@@ -214,7 +227,9 @@ def _fwd_kernel(
+ cur_kv_head * stride_vh
+ offs_dv[None, :]
)
v = tl.load(V_Extend + offs_v, mask=mask_n[:, None], other=0.0)
v = tl.load(
V_Extend + offs_v, mask=mask_n[:, None] & mask_dv[None, :], other=0.0
)
p = p.to(v.dtype)
acc = acc * re_scale[:, None] + tl.dot(p, v)
@@ -226,7 +241,9 @@ def _fwd_kernel(
+ cur_head * stride_oh
+ offs_dv[None, :]
)
tl.store(O_Extend + offs_o, acc / deno[:, None], mask=mask_m[:, None])
tl.store(
O_Extend + offs_o, acc / deno[:, None], mask=mask_m[:, None] & mask_dv[None, :]
)
def extend_attention_fwd(
@@ -261,16 +278,18 @@ def extend_attention_fwd(
)
assert Lq == Lk and Lv == Lo
assert Lq in {16, 32, 64, 128, 256, 576}
assert Lv in {16, 32, 64, 128, 256, 512}
# TODO: is the assertion necessary?
assert Lq in {16, 32, 64, 96, 128, 256, 576}
assert Lv in {16, 32, 64, 96, 128, 256, 512}
if Lq == 576:
BLOCK_DMODEL = 512
BLOCK_DPE = 64
else:
BLOCK_DMODEL = Lq
BLOCK_DMODEL = triton.next_power_of_2(Lq)
BLOCK_DPE = 0
BLOCK_DV = Lv
BLOCK_DV = triton.next_power_of_2(Lv)
if CUDA_CAPABILITY[0] >= 9:
if Lq <= 256:
@@ -330,6 +349,8 @@ def extend_attention_fwd(
num_warps=num_warps,
num_stages=num_stages,
logit_cap=logit_cap,
Lq=Lq,
Lv=Lv,
)
@@ -373,10 +394,7 @@ def redundant_attention(
pt += cur_seq_len_extend
def test():
torch.manual_seed(0)
B, N_CTX, H_Q, H_KV, D = 19, 12331, 12, 4, 128
def test_once(B, N_CTX, H_Q, H_KV, D):
dtype = torch.float16
b_seq_len_prefix = torch.randint(
@@ -473,4 +491,5 @@ def test():
if __name__ == "__main__":
test()
test_once(19, 12331, 12, 4, 128)
test_once(19, 12331, 12, 4, 96)

20
python/sglang/srt/layers/prefill_attention.py
View File

@@ -48,6 +48,7 @@ def _fwd_kernel(
BLOCK_M: tl.constexpr,
BLOCK_DMODEL: tl.constexpr,
BLOCK_N: tl.constexpr,
Lk: tl.constexpr,
):
cur_batch = tl.program_id(0)
cur_head = tl.program_id(1)
@@ -72,7 +73,11 @@ def _fwd_kernel(
off_k = offs_n[None, :] * stride_kbs + cur_kv_head * stride_kh + offs_d[:, None]
off_v = offs_n[:, None] * stride_vbs + cur_kv_head * stride_vh + offs_d[None, :]
q = tl.load(Q + off_q, mask=offs_m[:, None] < cur_batch_seq_len, other=0.0)
mask_d = offs_d < Lk
q = tl.load(
Q + off_q, mask=(offs_m[:, None] < cur_batch_seq_len) & (mask_d), other=0.0
)
k_ptrs = K + off_k
v_ptrs = V + off_v
@@ -89,7 +94,7 @@ def _fwd_kernel(
# -- compute qk ----
k = tl.load(
k_ptrs + (cur_batch_in_all_start_index + start_n) * stride_kbs,
mask=(start_n + offs_n[None, :]) < cur_batch_seq_len,
mask=((start_n + offs_n[None, :]) < cur_batch_seq_len) & (mask_d[:, None]),
other=0.0,
)
# mask = tl.load(mask_ptrs + start_n, mask=start_n + offs_n < cur_batch_end_loc, other=0.0)
@@ -118,7 +123,7 @@ def _fwd_kernel(
# update acc
v = tl.load(
v_ptrs + (cur_batch_in_all_start_index + start_n) * stride_vbs,
mask=(start_n + offs_n[:, None]) < cur_batch_seq_len,
mask=((start_n + offs_n[:, None]) < cur_batch_seq_len) & (mask_d[None, :]),
other=0.0,
)
@@ -134,7 +139,9 @@ def _fwd_kernel(
+ offs_d[None, :]
)
out_ptrs = Out + off_o
tl.store(out_ptrs, acc, mask=offs_m[:, None] < cur_batch_seq_len)
tl.store(
out_ptrs, acc, mask=(offs_m[:, None] < cur_batch_seq_len) & (mask_d[None, :])
)
def context_attention_fwd(q, k, v, o, b_start_loc, b_seq_len, max_input_len):
@@ -145,7 +152,7 @@ def context_attention_fwd(q, k, v, o, b_start_loc, b_seq_len, max_input_len):
Lq, Lk, Lv = q.shape[-1], k.shape[-1], v.shape[-1]
assert Lq == Lk and Lk == Lv
assert Lk in {16, 32, 64, 128, 256}
assert Lk in {16, 32, 64, 96, 128, 256}
sm_scale = 1.0 / (Lq**0.5)
batch, head = b_seq_len.shape[0], q.shape[1]
@@ -172,8 +179,9 @@ def context_attention_fwd(q, k, v, o, b_start_loc, b_seq_len, max_input_len):
o.stride(1),
kv_group_num=kv_group_num,
BLOCK_M=BLOCK,
BLOCK_DMODEL=Lk,
BLOCK_DMODEL=triton.next_power_of_2(Lk),
BLOCK_N=BLOCK,
num_warps=num_warps,
num_stages=1,
Lk=Lk,
)