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[Bugfix][Kernel] allow non-power-of-two head sizes in prefix prefill (#4128)

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Michał Moskal 2024-04-18 00:51:28 -07:00 committed by GitHub
parent 53b018edcb
commit e8cc7967ff
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2 changed files with 28 additions and 18 deletions
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2
tests/kernels/test_prefix_prefill.py
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@ -10,7 +10,7 @@ from vllm.attention.ops.prefix_prefill import context_attention_fwd
NUM_HEADS = [64]
NUM_QUERIES_PER_KV = [1, 8, 64]
HEAD_SIZES = [128]
HEAD_SIZES = [128, 96]
DTYPES = [torch.float16]
CUDA_DEVICES = [
f"cuda:{i}" for i in range(1 if torch.cuda.device_count() == 1 else 2)

44
vllm/attention/ops/prefix_prefill.py
View File

@ -47,7 +47,8 @@ if triton.__version__ >= "2.1.0":
stride_v_cache_bl,
num_queries_per_kv: int,
BLOCK_M: tl.constexpr,
BLOCK_DMODEL: tl.constexpr,
BLOCK_DMODEL: tl.constexpr, # head size
BLOCK_DMODEL_PADDED: tl.constexpr, # head size padded to a power of 2
BLOCK_N: tl.constexpr,
):
cur_batch = tl.program_id(0)
@ -59,26 +60,30 @@ if triton.__version__ >= "2.1.0":
cur_batch_ctx_len = tl.load(B_Ctxlen + cur_batch)
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_query_len = cur_batch_seq_len - cur_batch_ctx_len
block_start_loc = BLOCK_M * start_m
# initialize offsets
offs_n = tl.arange(0, BLOCK_N)
offs_d = tl.arange(0, BLOCK_DMODEL)
offs_d = tl.arange(0, BLOCK_DMODEL_PADDED)
offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
off_q = (
(cur_batch_in_all_start_index + offs_m[:, None]) * stride_qbs +
cur_head * stride_qh + offs_d[None, :] * stride_qd)
q = tl.load(
Q + off_q,
mask=offs_m[:, None] < cur_batch_seq_len - cur_batch_ctx_len,
other=0.0)
dim_mask = tl.where(
tl.arange(0, BLOCK_DMODEL_PADDED) < BLOCK_DMODEL, 1, 0).to(tl.int1)
q = tl.load(Q + off_q,
mask=dim_mask[None, :] &
(offs_m[:, None] < cur_batch_query_len),
other=0.0)
# # initialize pointer to m and l
m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
l_i = tl.zeros([BLOCK_M], dtype=tl.float32)
acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32)
acc = tl.zeros([BLOCK_M, BLOCK_DMODEL_PADDED], dtype=tl.float32)
for start_n in range(0, cur_batch_ctx_len, BLOCK_N):
start_n = tl.multiple_of(start_n, BLOCK_N)
@ -99,7 +104,8 @@ if triton.__version__ >= "2.1.0":
offs_d[None, :] * stride_v_cache_d +
(start_n + offs_n[:, None]) % block_size * stride_v_cache_bl)
k = tl.load(K_cache + off_k,
mask=(start_n + offs_n[None, :]) < cur_batch_ctx_len,
mask=dim_mask[:, None] &
((start_n + offs_n[None, :]) < cur_batch_ctx_len),
other=0.0)
qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
@ -126,7 +132,8 @@ if triton.__version__ >= "2.1.0":
acc = acc * acc_scale[:, None]
# update acc
v = tl.load(V_cache + off_v,
mask=(start_n + offs_n[:, None]) < cur_batch_ctx_len,
mask=dim_mask[None, :] &
((start_n + offs_n[:, None]) < cur_batch_ctx_len),
other=0.0)
p = p.to(v.dtype)
@ -142,16 +149,15 @@ if triton.__version__ >= "2.1.0":
k_ptrs = K + off_k
v_ptrs = V + off_v
block_mask = tl.where(
block_start_loc < cur_batch_seq_len - cur_batch_ctx_len, 1, 0)
block_mask = tl.where(block_start_loc < cur_batch_query_len, 1, 0)
for start_n in range(0, block_mask * (start_m + 1) * BLOCK_M, BLOCK_N):
start_n = tl.multiple_of(start_n, BLOCK_N)
# -- 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 - cur_batch_ctx_len,
mask=dim_mask[:, None] &
((start_n + offs_n[None, :]) < cur_batch_query_len),
other=0.0)
qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
@ -179,8 +185,8 @@ if triton.__version__ >= "2.1.0":
# 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 - cur_batch_ctx_len,
mask=dim_mask[None, :] &
((start_n + offs_n[:, None]) < cur_batch_query_len),
other=0.0)
p = p.to(v.dtype)
@ -195,7 +201,8 @@ if triton.__version__ >= "2.1.0":
out_ptrs = Out + off_o
tl.store(out_ptrs,
acc,
mask=offs_m[:, None] < cur_batch_seq_len - cur_batch_ctx_len)
mask=dim_mask[None, :] &
(offs_m[:, None] < cur_batch_query_len))
return
@triton.jit
@ -636,7 +643,8 @@ if triton.__version__ >= "2.1.0":
# shape constraints
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}
# round up Lk to a power of 2 - this is required for Triton block size
Lk_padded = 2**((Lk - 1).bit_length())
sm_scale = 1.0 / (Lq**0.5)
batch, head = b_seq_len.shape[0], q.shape[1]
@ -646,6 +654,7 @@ if triton.__version__ >= "2.1.0":
num_warps = 8 if Lk <= 64 else 8
if alibi_slopes is not None:
assert Lk == Lk_padded
_fwd_kernel_alibi[grid](
q,
k,
@ -738,6 +747,7 @@ if triton.__version__ >= "2.1.0":
num_queries_per_kv=num_queries_per_kv,
BLOCK_M=BLOCK,
BLOCK_DMODEL=Lk,
BLOCK_DMODEL_PADDED=Lk_padded,
BLOCK_N=BLOCK,
num_warps=num_warps,
num_stages=1,