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Commit e37d6cc3 authored by zhuwenwen's avatar zhuwenwen
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接入新的concat算子，包含decode和prefill，并根据size的不同进行选择

parent 7f7e2f5a
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Showing with 331 additions and 12 deletions
vllm/v1/attention/backends/mla/common.py
View file @ e37d6cc3
......@@ -225,7 +225,7 @@ from vllm.v1.attention.backends.utils import (AttentionMetadataBuilder,
split_decodes_and_prefills)
from vllm.v1.kv_cache_interface import AttentionSpec
from vllm.v1.worker.block_table import BlockTable
from vllm.v1.attention.backends.mla.concatv3Tritonfinalv2 import concat_helper
from vllm.v1.attention.backends.mla.test_concat import lightop_concat_prefill_helper
try:
from vllm.vllm_flash_attn import flash_attn_varlen_func
......@@ -1396,7 +1396,11 @@ class MLACommonImpl(MLAAttentionImpl[M], Generic[M]):
.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
if envs.VLLM_USE_TRITON_CAT:
k = concat_helper(k_nope, k_pe.expand((*k_nope.shape[:-1], -1)),
if k_nope.shape[0] > 1024:
k = lightop_concat_prefill_helper(k_nope, k_pe.expand((*k_nope.shape[:-1], -1)),
dim=2)
else:
k = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))),
dim=-1)
else:
k = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))),
......@@ -1553,7 +1557,12 @@ class MLACommonImpl(MLAAttentionImpl[M], Generic[M]):
.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
if envs.VLLM_USE_TRITON_CAT:
k = concat_helper((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))), dim=-1)
if k_nope.shape[0] > 1024:
k = lightop_concat_prefill_helper(k_nope, k_pe.expand((*k_nope.shape[:-1], -1)),
dim=2)
else:
k = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))),
dim=-1)
else:
k = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))), dim=-1)
......
vllm/v1/attention/backends/mla/flashmla.py
View file @ e37d6cc3
......@@ -21,7 +21,7 @@ from vllm.v1.attention.backends.mla.common import (MLACommonBackend,
from vllm.v1.attention.backends.utils import AttentionCGSupport
from vllm.v1.kv_cache_interface import AttentionSpec
from vllm import envs
from vllm.v1.attention.backends.mla.concatv4_decode_only import concat_helper
from vllm.v1.attention.backends.mla.test_concat import concat_helper_decode
logger = init_logger(__name__)
......@@ -194,17 +194,11 @@ class FlashMLAImpl(MLACommonImpl[FlashMLAMetadata]):
assert attn_metadata.decode is not None
# if envs.VLLM_USE_TRITON_CAT:
# q = concat_helper(q_nope, q_pe, dim=-1)\
# .unsqueeze(1)
# if q_nope.shape[0] <= 1024:
# q = concat_helper(q_nope, q_pe, dim=-1)\
# q = concat_helper_decode(q_nope, q_pe, dim=2)\
# .unsqueeze(1)
# else:
# q = torch.cat([q_nope, q_pe], dim=-1)\
# .unsqueeze(1) # Add seqlen dim of 1 (decode)
# else:
# q = torch.cat([q_nope, q_pe], dim=-1)\
# .unsqueeze(1) # Add seqlen dim of 1 (decode)
if type(q) is tuple:
q = torch.cat(q, dim=-1)
......
vllm/v1/attention/backends/mla/test_concat.py 0 → 100644
View file @ e37d6cc3
import triton
import triton.language as tl
import torch
from functools import reduce
import pytest
import torch
import math
from lightop import ds_cat
def test_concat_Acc_prefill(shape_pair, dim):
torch.manual_seed(1)
shape1, shape2 = shape_pair
M = shape1[0]
N = shape1[1]
x_sizes = [M, N, 128]
x_strides = [N//8 * 2048, 256, 1]
x_max_index = N//8 * 2048 * M
x_required_length = x_max_index
x_data = torch.arange(x_required_length,device='cuda').bfloat16()
x = torch.as_strided(x_data, size=x_sizes, stride=x_strides)
y_sizes = [M, N, 64]
y_strides = [576, 0, 1]
y_max_index = 576 * M
y_required_length = y_max_index
y_data = torch.arange(y_required_length,device='cuda').bfloat16()
y = torch.as_strided(y_data, size=y_sizes, stride=y_strides)
expected = torch.cat([x,y], dim=dim)
result = concat_prefill_helper_Triton(x, y, dim=dim)
result_lightop = lightop_concat_prefill_helper(x, y, dim=dim)
assert torch.allclose(result, expected, rtol=1e-5, atol=1e-5), "Mismatch"
# print("精度验证通过")
# print("expected",expected)
# print("result_lightop",result_lightop)
assert torch.allclose(result, result_lightop, rtol=1e-5, atol=1e-5), "result_lightop Mismatch Triton error"
assert torch.allclose(expected, result_lightop, rtol=1e-5, atol=1e-5), "result_lightop Mismatch torch error"
print("prefill 精度验证通过")
def test_concat_Acc_decode(shape_pair, dim):
torch.manual_seed(1)
shape1, shape2 = shape_pair
M = shape1[0]
N = shape1[1]
x_sizes = [M, N, 512]
x_strides = [512, 512*M, 1]
x_max_index = M * N * 512
x_required_length = x_max_index
x_data = torch.arange(x_required_length,device='cuda').bfloat16()
x = torch.as_strided(x_data, size=x_sizes, stride=x_strides)
# print("形状:", x.shape)
# print("步幅:", x.stride())
y_sizes = [M, N, 64]
y_strides = [1536*(N//8), 192, 1]
y_max_index = 1536*(N//8) * M
y_required_length = y_max_index
y_data = torch.arange(y_required_length,device='cuda').bfloat16()
y = torch.as_strided(y_data, size=y_sizes, stride=y_strides)
# print("形状:", y.shape)
# print("步幅:", y.stride())
expected = torch.cat([x,y], dim=dim)
result = concat_helper_decode(x, y, dim=dim)
assert torch.allclose(result, expected, rtol=1e-5, atol=1e-5), "Mismatch"
print("decode 精度正常")
@triton.jit
def concat_kernel(
A_ptr, B_ptr, C_ptr,
A_section_numel, B_section_numel, C_section_numel,
Per_block_A,
Per_block_B,
section_numA,
section_numB,
M,
N,
Astride_0,
Astride_1,
Astride_2,
Bstride_0,
Bstride_1,
Bstride_2,
BLOCK_SIZE: tl.constexpr
):
block_idx = tl.program_id(0)
numA = section_numA // Per_block_A
if (block_idx < numA):
#处理A的block
for sub_section_index in range(Per_block_A):
sub_offset = block_idx * Per_block_A + sub_section_index
if sub_offset <= section_numA-1:
M_idx = sub_offset // N
N_idx = sub_offset % N
C_ptr_block_start = C_ptr + sub_offset * C_section_numel
A_ptr_block_start = A_ptr + M_idx * Astride_0 + N_idx * Astride_1
for offset in range(0, A_section_numel, BLOCK_SIZE):
offset_idx = offset + tl.arange(0, BLOCK_SIZE)
mask = offset_idx < A_section_numel
val_from_A = tl.load(A_ptr_block_start + offset_idx, mask=mask)
tl.store(C_ptr_block_start + offset_idx, val_from_A, mask=mask)
else:
#处理B的block
#shape是1024*8*64，实际上只有1024 * 64 块数据，开了1024/4=256个线程块来处理。每个线程块处理1块连续的数据
#需要注意C的分块也是有M * N 大小的，而这里只有M大小个线程块，每个线程块需要写入N次数据到C中。
for sub_section_index in range(Per_block_B):
sub_offset = (block_idx - numA) * Per_block_B + sub_section_index
if sub_offset <= section_numB-1:
C_ptr_block_start = C_ptr + sub_offset * N * C_section_numel
B_ptr_block_start = B_ptr + sub_offset * Bstride_0
for offset in range(0, B_section_numel, BLOCK_SIZE):
offset_idx = offset + tl.arange(0, BLOCK_SIZE)
mask = offset_idx < B_section_numel
val_from_B = tl.load(B_ptr_block_start + offset_idx, mask=mask)
for idx in range(0,N,1):
tl.store(C_ptr_block_start + idx * C_section_numel + A_section_numel + offset_idx, val_from_B, mask=mask)
def concat_prefill_helper_Triton(A:torch.Tensor, B:torch.Tensor, dim:int):
output_shape = list(A.shape)
output_shape[dim] = A.shape[dim] + B.shape[dim] #128+64=192
C = torch.empty(output_shape, device=A.device, dtype=A.dtype)
if dim!=0 :
#分开计算block块A需要
Per_block_A = 64
Per_block_B = 1
#128 \64 \192
unit_offset_A, unit_offset_B, unit_offset_C = A.shape[dim],B.shape[dim],C.shape[dim]
#A的分块数是：M * N 这里的demo是1024 * 8
block_numA = reduce(lambda x, y: x * y, output_shape[:dim])
#B的分块数是：M 这里的demo是1024
block_numB = output_shape[0]
#A的每个分块可以处理多份数据的读取和写入，这是因为单次的任务量太小。假设这里Per_block = 8 那么A就开启了1024个线程块，每个线程块处理8份数据的读取和写入
#B的每个分块处理1次B的读取和8次C的写入，L2 cache复用率高
block_num = block_numA // Per_block_A + block_numB // Per_block_B
num_blocks = math.ceil(block_num)
concat_kernel[(num_blocks,)](
A, B, C,
unit_offset_A, unit_offset_B, unit_offset_C,
Per_block_A,
Per_block_B,
block_numA,
block_numB,
output_shape[0],
output_shape[1],
A.stride(0),
A.stride(1),
A.stride(2),
B.stride(0),
B.stride(1),
B.stride(2),
BLOCK_SIZE=1024)
return C
assert False, "not support"
def concat_helper_decode(A:torch.Tensor, B:torch.Tensor, dim:int):
assert dim==2 , "tensor dim must be 3 and concat dim must be 2"
output_shape = list(A.shape)
output_shape[dim] = A.shape[dim] + B.shape[dim]
C = torch.empty(output_shape, device=A.device, dtype=A.dtype)
mode = 0
if dim!=0 :
ds_cat( A, B, C, mode)
return C
assert False, "not support"
def lightop_concat_prefill_helper(A:torch.Tensor, B:torch.Tensor, dim:int):
assert dim==2 , "tensor dim must be 3 and concat dim must be 2"
output_shape = list(A.shape)
output_shape[dim] = A.shape[dim] + B.shape[dim]
C = torch.empty(output_shape, device=A.device, dtype=A.dtype)
mode = 6
if dim!=0 :
ds_cat( A, B, C, mode)
return C
assert False, "not support"
configs = []
configs.append(
triton.testing.Benchmark(
x_names=['M','N'],
x_vals=[(1024,8),(2048,8),(3072,8),(4096,8),(6144,8),(8192,8),\
(1024,16),(2048,16),(3072,16),(4096,16),(6144,16),(8192,16),\
(1024,32),(2048,32),(3072,32),(4096,32),(6144,32),(8192,32)
],
x_log=True,
line_arg='provider',
line_vals=['triton', 'torch', 'lightop'],
line_names=['Triton', 'Torch','Lightop'],
styles=[('blue', '-'), ('green', '-'), ('yellow', '-')],
ylabel='s',
plot_name='concat-dim2',
args={"dim":2},
),
)
configs_decode = []
configs_decode.append(
triton.testing.Benchmark(
x_names=['M','N'],
x_vals=[(4,8),(8,8),(16,8),(32,8),(64,8),(96,8),(128,8),(256,8),(512,8),(768,8),(767,8),(765,8),(766,8), \
(4,16),(8,16),(16,16),(32,16),(64,16),(96,16),(128,16),(256,16),(512,16),(768,16),(767,16),(765,16),(766,16), \
(4,32),(8,32),(16,32),(32,32),(64,32),(96,32),(128,32),(256,32),(512,32),(768,32),(767,32),(765,32),(766,32)],
x_log=True,
line_arg='provider',
line_vals=['lightop', 'torch'],
line_names=['Lightop', 'Torch'],
styles=[('blue', '-'), ('green', '-')],
ylabel='s',
plot_name='concat-dim2',
args={"dim":2},
),
)
@triton.testing.perf_report(configs)
def benchmark_prefill(M, N, provider, dim):
x_sizes = [M, N, 128]
x_strides = [N//8 * 2048, 256, 1]
x_max_index = N//8 * 2048 * M
x_required_length = x_max_index
x_data = torch.arange(x_required_length,device='cuda').bfloat16()
x = torch.as_strided(x_data, size=x_sizes, stride=x_strides)
y_sizes = [M, N, 64]
y_strides = [576, 0, 1]
y_max_index = 576 * M
y_required_length = y_max_index
y_data = torch.arange(y_required_length,device='cuda').bfloat16()
y = torch.as_strided(y_data, size=y_sizes, stride=y_strides)
quantiles = [0.5, 0.2, 0.8]
if provider == 'torch':
ms, min_ms, max_ms = triton.testing.do_bench(lambda: torch.cat([x,y],dim=dim), quantiles=quantiles)
if provider == 'triton':
ms, min_ms, max_ms = triton.testing.do_bench(lambda: concat_prefill_helper_Triton(x, y,dim=dim), quantiles=quantiles)
if provider == 'lightop':
ms, min_ms, max_ms = triton.testing.do_bench(lambda: lightop_concat_prefill_helper(x, y,dim=dim), quantiles=quantiles)
return (ms*1000), (max_ms*1000), (min_ms*1000)
@triton.testing.perf_report(configs_decode)
def benchmark_decode(M, N, provider, dim):
x_sizes = [M, N, 512]
x_strides = [512, 512*M, 1]
x_max_index = M * N * 512
x_required_length = x_max_index
x_data = torch.arange(x_required_length,device='cuda').bfloat16()
x = torch.as_strided(x_data, size=x_sizes, stride=x_strides)
# print("形状:", x.shape)
# print("步幅:", x.stride())
y_sizes = [M, N, 64]
y_strides = [1536*(N//8), 192, 1]
y_max_index = 1536*(N//8) * M
y_required_length = y_max_index
y_data = torch.arange(y_required_length,device='cuda').bfloat16()
y = torch.as_strided(y_data, size=y_sizes, stride=y_strides)
# print("形状:", y.shape)
# print("步幅:", y.stride())
quantiles = [0.5, 0.2, 0.8]
if provider == 'torch':
ms, min_ms, max_ms = triton.testing.do_bench(lambda: torch.cat([x,y],dim=dim), quantiles=quantiles)
if provider == 'lightop':
ms, min_ms, max_ms = triton.testing.do_bench(lambda: concat_helper_decode(x, y,dim=dim), quantiles=quantiles)
return (ms*1000), (max_ms*1000), (min_ms*1000)
if __name__ == '__main__':
benchmark_prefill.run(save_path="./triton_test",print_data=True)
test_concat_Acc_prefill(((1024, 8, 128), (1024, 8, 64)), 2)
test_concat_Acc_prefill(((2048, 8, 128), (2048, 8, 64)), 2)
test_concat_Acc_prefill(((4096, 8, 128), (4096, 8, 64)), 2)
test_concat_Acc_prefill(((8192, 8, 128), (8192, 8, 64)), 2)
test_concat_Acc_prefill(((1024, 16, 128), (1024, 16, 64)), 2)
test_concat_Acc_prefill(((2048, 16, 128), (2048, 16, 64)), 2)
test_concat_Acc_prefill(((4096, 16, 128), (4096, 16, 64)), 2)
test_concat_Acc_prefill(((8192, 16, 128), (8192, 16, 64)), 2)
test_concat_Acc_prefill(((1024, 32, 128), (1024, 32, 64)), 2)
test_concat_Acc_prefill(((2048, 32, 128), (2048, 32, 64)), 2)
test_concat_Acc_prefill(((4096, 32, 128), (4096, 32, 64)), 2)
test_concat_Acc_prefill(((8192, 32, 128), (8192, 32, 64)), 2)
benchmark_decode.run(save_path="./cat_triton_test",print_data=True)
test_concat_Acc_decode(((16, 8, 512), (16, 8, 64)), 2)
test_concat_Acc_decode(((32, 8, 512), (32, 8, 64)), 2)
test_concat_Acc_decode(((128, 8, 512), (128, 8, 64)), 2)
test_concat_Acc_decode(((768, 8, 512), (768, 8, 64)), 2)
test_concat_Acc_decode(((32, 16, 512), (32, 16, 64)), 2)
test_concat_Acc_decode(((32, 32, 512), (32, 32, 64)), 2)
test_concat_Acc_decode(((768, 32, 512), (768, 32, 64)), 2)
test_concat_Acc_decode(((128, 32, 512), (128, 32, 64)), 2)
test_concat_Acc_decode(((512, 32, 512), (512, 32, 64)), 2)
test_concat_Acc_decode(((765, 8, 512), (765, 8, 64)), 2)
test_concat_Acc_decode(((766, 8, 512), (766, 8, 64)), 2)
test_concat_Acc_decode(((767, 8, 512), (767, 8, 64)), 2)
test_concat_Acc_decode(((765, 16, 512), (765, 16, 64)), 2)
test_concat_Acc_decode(((766, 16, 512), (766, 16, 64)), 2)
test_concat_Acc_decode(((765, 32, 512), (765, 32, 64)), 2)
test_concat_Acc_decode(((767, 32, 512), (767, 32, 64)), 2)
\ No newline at end of file
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