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support flashattention bwd #52

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40755fe
flashattention-2 fwd and bwd support (not support causal and gqa)
Dec 23, 2025
a47e7c7
flashattention-2 bwd support causal mask
Dec 30, 2025
4f3ef4b
support dq dk dv computation in accuracy atol=1e-2, rtol=5e-2
Dec 30, 2025
4858282
support gqa
Jan 5, 2026
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105 changes: 72 additions & 33 deletions test/bench_attention.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,8 +12,8 @@
import torch

from util import estimate_bench_iter
from kernels.attention import fmha_kernel

from kernels.attention import fmha_kernel, fmha_bwd_dq_kernel, fmha_bwd_dk_dv_kernel, fmha_bwd_preprocess_kernel
has_backward = True

def qkv_id(qkv_shape: tuple[tuple[int, ...], tuple[int, ...]]) -> str:
q_shape, kv_shape = qkv_shape
Expand All @@ -29,21 +29,27 @@ def qkv_id(qkv_shape: tuple[tuple[int, ...], tuple[int, ...]]) -> str:
@pytest.fixture(
params=[
# B, H, L, D
((1, 32, 1024, 128), (1, 32, 1024, 128)), # prefill
((1, 32, 1024, 128), (1, 8, 1024, 128)), # prefill + gqa
((1, 32, 8192, 128), (1, 32, 8192, 128)), # prefill
((1, 32, 8192, 128), (1, 8, 8192, 128)), # prefill + gqa
((1, 32, 1, 128), (1, 32, 1024, 128)), # decode
((8, 32, 1, 128), (8, 32, 1024, 128)), # decode
((1, 32, 1, 128), (1, 8, 1024, 128)), # decode + gqa
((8, 32, 1, 128), (8, 8, 1024, 128)), # decode + gqa
((6, 24, 67000, 64), (6, 24, 67000, 64)), # tanlan dim 64
((6, 24, 67000, 128), (6, 24, 67000, 128)), # tanlan dim 128
((6, 32, 1024, 128), (6, 32, 1024, 128)), # prefill
((1, 32, 1024, 64), (1, 32, 1024, 64)),
((1, 32, 1024, 64), (1, 8, 1024, 64)), # prefill + gqa
((1, 32, 1024, 128), (1, 8, 1024, 128)), # prefill + gqa
((1, 32, 8192, 64), (1, 32, 8192, 64)),
((1, 32, 8192, 128), (1, 32, 8192, 128)),
((1, 32, 8192, 128), (1, 8, 8192, 128)), # prefill + gqa
((1, 32, 1, 128), (1, 32, 1024, 128)), # decode
((1, 32, 1, 64), (1, 32, 1024, 64)), # decode
((8, 32, 1, 128), (8, 32, 1024, 128)), # decode
((1, 32, 1, 128), (1, 8, 1024, 128)), # decode + gqa
((8, 32, 1, 128), (8, 8, 1024, 128)), # decode + gqa
],
ids=qkv_id)
def qkv_shape(request):
return request.param


@pytest.fixture(params=[torch.float16, torch.bfloat16], ids=dtype_id)
@pytest.fixture(params=[torch.bfloat16], ids=dtype_id)
def dtype(request):
return request.param

Expand All @@ -52,25 +58,35 @@ def dtype(request):
def bench_fmha(qkv_shape, dtype, backend, benchmark):
q_shape, kv_shape = qkv_shape

q = torch.randn(q_shape, dtype=dtype, device='cuda')
k = torch.randn(kv_shape, dtype=dtype, device='cuda')
v = torch.randn(kv_shape, dtype=dtype, device='cuda')
o = torch.empty_like(q)
ref = torch.empty_like(q)
q = torch.randn(q_shape, dtype=dtype, device='cuda', requires_grad=True)
k = torch.randn(kv_shape, dtype=dtype, device='cuda', requires_grad=True)
v = torch.randn(kv_shape, dtype=dtype, device='cuda', requires_grad=True)
lse = torch.randn(q_shape[:-1], dtype=torch.float32, device='cuda')
grad = torch.randn(q_shape, dtype=dtype, device='cuda', requires_grad=True)
o = torch.zeros_like(q)
ref = torch.zeros_like(q)
is_causal = q_shape[2] == kv_shape[2]
enable_gqa = q_shape[1] != kv_shape[1]

backend(q, k, v, o, is_causal, enable_gqa)
ref_fmha(q, k, v, ref, is_causal, enable_gqa)
torch.testing.assert_close(o, ref, atol=1e-2, rtol=5e-2)
if has_backward:
dq, dk, dv = backend(q, k, v, o, grad, lse, is_causal, enable_gqa)
dq_ref, dk_ref, dv_ref = ref_fmha(q, k, v, ref, grad, is_causal, enable_gqa)
torch.testing.assert_close(o, ref, atol=1e-2, rtol=5e-2)
torch.testing.assert_close(dq, dq_ref, atol=1e-2, rtol=5e-2)
torch.testing.assert_close(dk, dk_ref, atol=1e-2, rtol=5e-2)
torch.testing.assert_close(dv, dv_ref, atol=1e-2, rtol=5e-2)
else:
backend(q, k, v, o, grad, lse, is_causal, enable_gqa)
ref_fmha(q, k, v, ref, grad, is_causal, enable_gqa)
torch.testing.assert_close(o, ref, atol=1e-2, rtol=5e-2)
torch.cuda.synchronize()

warmup_rounds, iterations, rounds = estimate_bench_iter(
backend, (q, k, v, o, is_causal, enable_gqa),
backend, (q, k, v, o, grad, lse, is_causal, enable_gqa),
)

benchmark.pedantic(
backend, (q, k, v, o, is_causal, enable_gqa),
backend, (q, k, v, o, grad, lse, is_causal, enable_gqa),
rounds=rounds, warmup_rounds=warmup_rounds, iterations=iterations,
)

Expand All @@ -87,25 +103,40 @@ def bench_fmha(qkv_shape, dtype, backend, benchmark):
benchmark.extra_info['bytes_rw'] = bytes_rw


def cutile_fmha(q, k, v, o, is_causal, enable_gqa):
def cutile_fmha(q, k, v, o, grad, lse, is_causal, enable_gqa):
b, qh, q_len, d = q.shape
_, kh, k_len, _ = k.shape
qk_scale = 1 / sqrt(d)
TILE_M, TILE_N = (256, 128) if is_causal else (64, 128)
query_group_size = qh // kh
grid = (ceil(q_len / TILE_M), b * qh, 1)
input_pos = 0 if q_len == k_len else (k_len - 1)
EVEN_K = (k_len % TILE_N) == 0
ct.launch(torch.cuda.current_stream(), grid, fmha_kernel,
(q, k, v, o,
qk_scale,
input_pos,
d, qh,
TILE_M, TILE_N,
ct.launch(torch.cuda.current_stream(), (ceil(q_len / TILE_M), b * qh, 1),
fmha_kernel,
(q, k, v, o, lse,
qk_scale, input_pos, d, qh, TILE_M, TILE_N,
query_group_size, is_causal, EVEN_K))


def torch_fmha(q, k, v, o, is_causal, enable_gqa):
if has_backward:
dq = torch.zeros_like(q)
dk = torch.zeros_like(k)
dv = torch.zeros_like(v)
delta = torch.zeros_like(lse)
ct.launch(torch.cuda.current_stream(), (ceil(q_len / TILE_M), b * qh, 1),
fmha_bwd_preprocess_kernel,
(o, grad, delta, qh, TILE_M, d))
ct.launch(torch.cuda.current_stream(), (ceil(k_len / TILE_N), b * kh, 1),
fmha_bwd_dk_dv_kernel,
(q, k, v, grad, delta, lse, dk, dv,
qk_scale, input_pos, d, kh, TILE_M, TILE_N,
query_group_size, is_causal, EVEN_K))
ct.launch(torch.cuda.current_stream(), (ceil(q_len / TILE_M), b * qh, 1),
fmha_bwd_dq_kernel,
(q, k, v, grad, delta, lse, dq,
qk_scale, input_pos, d, qh, TILE_M, TILE_N,
query_group_size, is_causal, EVEN_K))
return dq, dk, dv

def torch_fmha(q, k, v, o, grad, lse, is_causal, enable_gqa):
backend = SDPBackend.CUDNN_ATTENTION \
if (q.shape[2] == k.shape[2]) \
else SDPBackend.FLASH_ATTENTION
Expand All @@ -114,11 +145,19 @@ def torch_fmha(q, k, v, o, is_causal, enable_gqa):
is_causal=is_causal,
enable_gqa=enable_gqa)
o.copy_(ret)
if has_backward:
ret.backward(grad, retain_graph=True)
dq, dk, dv = q.grad, k.grad, v.grad
return dq, dk, dv


def ref_fmha(q, k, v, o, is_causal, enable_gqa):
def ref_fmha(q, k, v, o, grad, is_causal, enable_gqa):
with sdpa_kernel(SDPBackend.FLASH_ATTENTION):
ret = scaled_dot_product_attention(q, k, v,
is_causal=is_causal,
enable_gqa=enable_gqa)
o.copy_(ret)
if has_backward:
ret.backward(grad, retain_graph=True)
dq, dk, dv = q.grad, k.grad, v.grad
return dq, dk, dv
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