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Enable GQA support in the prefix prefill kernels (#3007) 

Signed-off-by: Tao He <sighingnow@gmail.com>
This commit is contained in:
Tao He 2024-02-27 17:14:31 +08:00 committed by GitHub
parent 8b430d7dea
commit 71bcaf99e2
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3 changed files with 87 additions and 47 deletions
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61
tests/kernels/test_prefix_prefill.py
View File

@ -8,7 +8,8 @@ from vllm.model_executor.layers.triton_kernel.prefix_prefill import (
from xformers import ops as xops
from xformers.ops.fmha.attn_bias import BlockDiagonalCausalFromBottomRightMask
NUM_HEADS = [12]
NUM_HEADS = [64]
NUM_QUERIES_PER_KV = [1, 8, 64]
HEAD_SIZES = [128]
DTYPES = [torch.float16]
CUDA_DEVICES = [
@ -17,12 +18,14 @@ CUDA_DEVICES = [
@pytest.mark.parametrize("num_heads", NUM_HEADS)
@pytest.mark.parametrize("num_queries_per_kv", NUM_HEADS)
@pytest.mark.parametrize("head_size", HEAD_SIZES)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("device", CUDA_DEVICES)
@torch.inference_mode()
def test_contexted_kv_attention(
num_heads: int,
num_queries_per_kv: int,
head_size: int,
dtype: torch.dtype,
device: str,
@ -41,28 +44,29 @@ def test_contexted_kv_attention(
subquery_lens = [random.randint(16, MAX_SEQ_LEN) for _ in range(BS)]
ctx_lens = [random.randint(16, MAX_CTX_LEN) for _ in range(BS)]
seq_lens = [a + b for a, b in zip(subquery_lens, ctx_lens)]
num_kv_heads = num_heads // num_queries_per_kv
num_tokens = sum(subquery_lens)
query = torch.empty(num_tokens, num_heads, head_size, dtype=dtype)
query.uniform_(-1e-3, 1e-3)
output = torch.empty(num_tokens, num_heads, head_size, dtype=dtype)
kv = torch.empty(sum(seq_lens), 2, num_heads, head_size, dtype=dtype)
kv = torch.empty(sum(seq_lens), 2, num_kv_heads, head_size, dtype=dtype)
kv.uniform_(-1e-3, 1e-3)
key, value = kv.unbind(dim=1)
k_cache = torch.zeros(cache_size,
block_size,
num_heads,
num_kv_heads,
head_size,
dtype=dtype)
v_cache = torch.zeros(cache_size,
block_size,
num_heads,
num_kv_heads,
head_size,
dtype=dtype)
k = torch.zeros(sum(subquery_lens), num_heads, head_size, dtype=dtype)
v = torch.zeros(sum(subquery_lens), num_heads, head_size, dtype=dtype)
k = torch.zeros(sum(subquery_lens), num_kv_heads, head_size, dtype=dtype)
v = torch.zeros(sum(subquery_lens), num_kv_heads, head_size, dtype=dtype)
values = torch.arange(0, cache_size, dtype=torch.long)
values = values[torch.randperm(cache_size)]
block_table = values[:BS * max_block_per_request].view(
@ -93,19 +97,21 @@ def test_contexted_kv_attention(
end_loc = start_loc + block_size
start_slot = block_table[i, block_id] * block_size
end_slot = start_slot + end_loc - start_loc
k_cache.view(-1, num_heads, head_size)[start_slot:end_slot].copy_(
key[start_loc:end_loc])
v_cache.view(-1, num_heads, head_size)[start_slot:end_slot].copy_(
value[start_loc:end_loc])
k_cache.view(-1, num_kv_heads,
head_size)[start_slot:end_slot].copy_(
key[start_loc:end_loc])
v_cache.view(-1, num_kv_heads,
head_size)[start_slot:end_slot].copy_(
value[start_loc:end_loc])
cur_ctx += block_size
block_id += 1
# transpose K_cache[num_blocks, block_size, num_kv_heads, head_size]
# to K_cache[num_blocks, num_kv_heads, head_size/8, block_size, 8]
k_cache = k_cache.view(-1, block_size, num_heads, head_size // 8,
k_cache = k_cache.view(-1, block_size, num_kv_heads, head_size // 8,
8).permute(0, 2, 3, 1, 4).contiguous()
# transpose V_cache[num_blocks, block_size, num_kv_heads, head_size]
# to V_cache[num_blocks, num_kv_heads, head_size, block_size]
v_cache = v_cache.view(-1, block_size, num_heads,
v_cache = v_cache.view(-1, block_size, num_kv_heads,
head_size).permute(0, 2, 3, 1).contiguous()
# Warm up the Triton kernel by calling it once before actually measuring generation time
@ -123,12 +129,29 @@ def test_contexted_kv_attention(
attn_op = xops.fmha.cutlass.FwOp()
if num_kv_heads != num_heads:
# As of Nov 2023, xformers only supports MHA. For MQA/GQA,
# project the key and value tensors to the desired number of
# heads.
#
# see also: vllm/model_executor/layers/attention.py
query = query.view(query.shape[0], num_kv_heads, num_queries_per_kv,
query.shape[-1])
key = key[:, :, None, :].expand(key.shape[0], num_kv_heads,
num_queries_per_kv, key.shape[-1])
value = value[:, :,
None, :].expand(value.shape[0], num_kv_heads,
num_queries_per_kv, value.shape[-1])
query = query.unsqueeze(0)
key = key.unsqueeze(0)
value = value.unsqueeze(0)
attn_bias = BlockDiagonalCausalFromBottomRightMask.from_seqlens(
subquery_lens, seq_lens)
output_ref = xops.memory_efficient_attention_forward(
query.unsqueeze(0),
key.unsqueeze(0),
value.unsqueeze(0),
query,
key,
value,
attn_bias=attn_bias,
p=0.0,
scale=scale,
@ -137,9 +160,9 @@ def test_contexted_kv_attention(
torch.cuda.synchronize()
start_time = time.time()
output_ref = xops.memory_efficient_attention_forward(
query.unsqueeze(0),
key.unsqueeze(0),
value.unsqueeze(0),
query,
key,
value,
attn_bias=attn_bias,
p=0.0,
scale=scale,
@ -148,5 +171,5 @@ def test_contexted_kv_attention(
torch.cuda.synchronize()
end_time = time.time()
print(f"xformers Time: {(end_time - start_time)*1000:.2f} ms")
output_ref = output_ref.squeeze(0)
output_ref = output_ref.squeeze(0, 2)
assert torch.allclose(output_ref, output, atol=1e-6, rtol=0)

34
vllm/model_executor/layers/attention.py
View File

@ -137,25 +137,27 @@ class PagedAttention(nn.Module):
)
if input_metadata.is_prompt:
# Prompt run.
if self.num_kv_heads != self.num_heads:
# As of Nov 2023, xformers only supports MHA. For MQA/GQA,
# project the key and value tensors to the desired number of
# heads.
# TODO(woosuk): Use MQA/GQA kernels for higher performance.
query = query.view(query.shape[0], self.num_kv_heads,
self.num_queries_per_kv, query.shape[-1])
key = key[:, :,
None, :].expand(key.shape[0], self.num_kv_heads,
self.num_queries_per_kv,
key.shape[-1])
value = value[:, :, None, :].expand(value.shape[0],
self.num_kv_heads,
self.num_queries_per_kv,
value.shape[-1])
# normal attention
if (key_cache is None or value_cache is None
or input_metadata.block_tables.numel() == 0):
if self.num_kv_heads != self.num_heads:
# As of Nov 2023, xformers only supports MHA. For MQA/GQA,
# project the key and value tensors to the desired number of
# heads.
# TODO(woosuk): Use MQA/GQA kernels for higher performance.
query = query.view(query.shape[0], self.num_kv_heads,
self.num_queries_per_kv,
query.shape[-1])
key = key[:, :,
None, :].expand(key.shape[0], self.num_kv_heads,
self.num_queries_per_kv,
key.shape[-1])
value = value[:, :,
None, :].expand(value.shape[0],
self.num_kv_heads,
self.num_queries_per_kv,
value.shape[-1])
# Set attention bias if not provided. This typically happens at
# the very attention layer of every iteration.
# FIXME(woosuk): This is a hack.

39
vllm/model_executor/layers/triton_kernel/prefix_prefill.py
View File

@ -45,6 +45,7 @@ if triton.__version__ >= "2.1.0":
stride_v_cache_h,
stride_v_cache_d,
stride_v_cache_bl,
num_queries_per_kv: int,
BLOCK_M: tl.constexpr,
BLOCK_DMODEL: tl.constexpr,
BLOCK_N: tl.constexpr,
@ -53,6 +54,8 @@ if triton.__version__ >= "2.1.0":
cur_head = tl.program_id(1)
start_m = tl.program_id(2)
cur_kv_head = cur_head // num_queries_per_kv
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)
@ -85,13 +88,14 @@ if triton.__version__ >= "2.1.0":
mask=(start_n + offs_n) < cur_batch_ctx_len,
other=0)
off_k = (bn[None, :] * stride_k_cache_bs +
cur_head * stride_k_cache_h +
cur_kv_head * stride_k_cache_h +
(offs_d[:, None] // x) * stride_k_cache_d +
((start_n + offs_n[None, :]) % block_size) *
stride_k_cache_bl +
(offs_d[:, None] % x) * stride_k_cache_x)
off_v = (
bn[:, None] * stride_v_cache_bs + cur_head * stride_v_cache_h +
bn[:, None] * stride_v_cache_bs +
cur_kv_head * stride_v_cache_h +
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,
@ -131,9 +135,9 @@ if triton.__version__ >= "2.1.0":
l_i = l_i_new
m_i = m_i_new
off_k = (offs_n[None, :] * stride_kbs + cur_head * stride_kh +
off_k = (offs_n[None, :] * stride_kbs + cur_kv_head * stride_kh +
offs_d[:, None] * stride_kd)
off_v = (offs_n[:, None] * stride_vbs + cur_head * stride_vh +
off_v = (offs_n[:, None] * stride_vbs + cur_kv_head * stride_vh +
offs_d[None, :] * stride_vd)
k_ptrs = K + off_k
v_ptrs = V + off_v
@ -232,6 +236,7 @@ if triton.__version__ >= "2.1.0":
stride_v_cache_h,
stride_v_cache_d,
stride_v_cache_bl,
num_queries_per_kv: int,
BLOCK_M: tl.constexpr,
BLOCK_DMODEL: tl.constexpr,
BLOCK_N: tl.constexpr,
@ -240,6 +245,8 @@ if triton.__version__ >= "2.1.0":
cur_head = tl.program_id(1)
start_m = tl.program_id(2)
cur_kv_head = cur_head // num_queries_per_kv
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)
@ -272,13 +279,14 @@ if triton.__version__ >= "2.1.0":
mask=(start_n + offs_n) < cur_batch_ctx_len,
other=0)
off_k = (bn[None, :] * stride_k_cache_bs +
cur_head * stride_k_cache_h +
cur_kv_head * stride_k_cache_h +
(offs_d[:, None] // x) * stride_k_cache_d +
((start_n + offs_n[None, :]) % block_size) *
stride_k_cache_bl +
(offs_d[:, None] % x) * stride_k_cache_x)
off_v = (
bn[:, None] * stride_v_cache_bs + cur_head * stride_v_cache_h +
bn[:, None] * stride_v_cache_bs +
cur_kv_head * stride_v_cache_h +
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,
@ -317,9 +325,9 @@ if triton.__version__ >= "2.1.0":
l_i = l_i_new
m_i = m_i_new
off_k = (offs_n[None, :] * stride_kbs + cur_head * stride_kh +
off_k = (offs_n[None, :] * stride_kbs + cur_kv_head * stride_kh +
offs_d[:, None] * stride_kd)
off_v = (offs_n[:, None] * stride_vbs + cur_head * stride_vh +
off_v = (offs_n[:, None] * stride_vbs + cur_kv_head * stride_vh +
offs_d[None, :] * stride_vd)
k_ptrs = K + off_k
v_ptrs = V + off_v
@ -420,6 +428,7 @@ if triton.__version__ >= "2.1.0":
stride_v_cache_h,
stride_v_cache_d,
stride_v_cache_bl,
num_queries_per_kv: int,
BLOCK_M: tl.constexpr,
BLOCK_DMODEL: tl.constexpr,
BLOCK_N: tl.constexpr,
@ -429,6 +438,8 @@ if triton.__version__ >= "2.1.0":
cur_head = tl.program_id(1)
start_m = tl.program_id(2)
cur_kv_head = cur_head // num_queries_per_kv
# cur_batch_seq_len: the length of prompts
# cur_batch_ctx_len: the length of prefix
# cur_batch_in_all_start_index: the start id of the dim=0
@ -468,13 +479,14 @@ if triton.__version__ >= "2.1.0":
mask=(start_n + offs_n) < cur_batch_ctx_len,
other=0)
off_k = (bn[None, :] * stride_k_cache_bs +
cur_head * stride_k_cache_h +
cur_kv_head * stride_k_cache_h +
(offs_d[:, None] // x) * stride_k_cache_d +
((start_n + offs_n[None, :]) % block_size) *
stride_k_cache_bl +
(offs_d[:, None] % x) * stride_k_cache_x)
off_v = (
bn[:, None] * stride_v_cache_bs + cur_head * stride_v_cache_h +
bn[:, None] * stride_v_cache_bs +
cur_kv_head * stride_v_cache_h +
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,
@ -522,9 +534,9 @@ if triton.__version__ >= "2.1.0":
l_i = l_i_new
m_i = m_i_new
off_k = (offs_n[None, :] * stride_kbs + cur_head * stride_kh +
off_k = (offs_n[None, :] * stride_kbs + cur_kv_head * stride_kh +
offs_d[:, None] * stride_kd)
off_v = (offs_n[:, None] * stride_vbs + cur_head * stride_vh +
off_v = (offs_n[:, None] * stride_vbs + cur_kv_head * stride_vh +
offs_d[None, :] * stride_vd)
k_ptrs = K + off_k
v_ptrs = V + off_v
@ -628,6 +640,7 @@ if triton.__version__ >= "2.1.0":
sm_scale = 1.0 / (Lq**0.5)
batch, head = b_seq_len.shape[0], q.shape[1]
num_queries_per_kv = q.shape[1] // k.shape[1]
grid = (batch, head, triton.cdiv(max_input_len, BLOCK)) # batch, head,
@ -674,6 +687,7 @@ if triton.__version__ >= "2.1.0":
v_cache.stride(2),
v_cache.stride(
3), #[num_blocks, num_kv_heads, head_size, block_size]
num_queries_per_kv=num_queries_per_kv,
BLOCK_M=BLOCK,
BLOCK_DMODEL=Lk,
BLOCK_N=BLOCK,
@ -721,6 +735,7 @@ if triton.__version__ >= "2.1.0":
v_cache.stride(2),
v_cache.stride(
3), #[num_blocks, num_kv_heads, head_size, block_size]
num_queries_per_kv=num_queries_per_kv,
BLOCK_M=BLOCK,
BLOCK_DMODEL=Lk,
BLOCK_N=BLOCK,