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Bugfix: support qk_head_dim != v_head_dim in FMHA #40

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Bugfix: support qk_head_dim != v_head_dim in FMHA #40

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53 changes: 31 additions & 22 deletions samples/AttentionFMHA.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,8 @@
def fmha_kernel(Q, K, V, Out,
qk_scale: float,
input_pos: int,
TILE_D: ConstInt, # TILE_D = hidden_size
Dqk: ConstInt, # Head dimension of Q and K
Dv: ConstInt, # Head dimension of V
H: ConstInt,
TILE_M: ConstInt,
TILE_N: ConstInt,
Expand Down Expand Up @@ -64,12 +65,12 @@ def fmha_kernel(Q, K, V, Out,
# Initialize online softmax accumulators in float32 for stability
m_i = ct.full((TILE_M, 1), -np.inf, dtype=np.float32)
l_i = ct.full((TILE_M, 1), 0.0, dtype=np.float32)
acc = ct.full((TILE_M, TILE_D), 0.0, dtype=np.float32)
acc = ct.full((TILE_M, Dv), 0.0, dtype=np.float32)

# Load query tile for this batch, head, and M-chunk
q = ct.load(
Q, index=(batch_idx, head_idx, bid_x, 0), shape=(1, 1, TILE_M, TILE_D)
).reshape((TILE_M, TILE_D)) # [TILE_M, TILE_D]
Q, index=(batch_idx, head_idx, bid_x, 0), shape=(1, 1, TILE_M, Dqk)
).reshape((TILE_M, Dqk)) # [TILE_M, Dqk]

# loop over k, v and update accumulator
m_end = input_pos + (bid_x + 1) * TILE_M
Expand All @@ -88,11 +89,11 @@ def fmha_kernel(Q, K, V, Out,
for j in range(0, Tc):
# --- Compute QK product ---
k = ct.load(
K, index=(batch_idx, off_kv_h, 0, j), shape=(1, 1, TILE_D, TILE_N),
K, index=(batch_idx, off_kv_h, 0, j), shape=(1, 1, Dqk, TILE_N),
order=(0, 1, 3, 2),
latency=2,
)
k = k.reshape((TILE_D, TILE_N)) # [TILE_D, TILE_N]
k = k.reshape((Dqk, TILE_N)) # [Dqk, TILE_N]
qk = ct.full((TILE_M, TILE_N), 0., dtype=np.float32)
qk = ct.mma(q, k, qk) # [TILE_M, TILE_N]

Expand Down Expand Up @@ -125,16 +126,16 @@ def fmha_kernel(Q, K, V, Out,

# --- Compute PV product ---
v = ct.load(
V, index=(batch_idx, off_kv_h, j, 0), shape=(1, 1, TILE_N, TILE_D),
V, index=(batch_idx, off_kv_h, j, 0), shape=(1, 1, TILE_N, Dv),
latency=4,
).reshape((TILE_N, TILE_D)) # [TILE_N, TILE_D]
).reshape((TILE_N, Dv)) # [TILE_N, Dv]
p = p.astype(Q.dtype)
acc = ct.mma(p, v, acc) # [TILE_M, TILE_N]
m_i = m_ij # [TILE_M, 1]

# --- Final Normalization and Store ---
acc = ct.truediv(acc, l_i, flush_to_zero=True, rounding_mode=RMd.APPROX)
acc = acc.reshape((1, 1, TILE_M, TILE_D)).astype(Out.dtype)
acc = acc.reshape((1, 1, TILE_M, Dv)).astype(Out.dtype)
ct.store(Out, index=(batch_idx, head_idx, bid_x, 0), tile=acc)


Expand Down Expand Up @@ -202,6 +203,7 @@ def cutile_fmha(Q: torch.Tensor, K: torch.Tensor, V: torch.Tensor,
qk_scale,
input_pos,
D_k,
D_v,
Heads,
tile_m,
tile_n,
Expand Down Expand Up @@ -273,12 +275,18 @@ def cutile_autotune_fmha(Q: torch.Tensor, K: torch.Tensor, V: torch.Tensor,


def torch_fmha(Q: torch.Tensor, K: torch.Tensor, V: torch.Tensor,
is_causal: bool, enable_gqa: bool) -> torch.Tensor:
backend = SDPBackend.CUDNN_ATTENTION \
if (Q.shape[2] == K.shape[2]) \
else SDPBackend.FLASH_ATTENTION
with sdpa_kernel(backend):
ret = scaled_dot_product_attention(Q, K, V,
is_causal: bool, enable_gqa: bool,
use_backend_selection_rule: bool = False) -> torch.Tensor:
if use_backend_selection_rule:
backend = SDPBackend.CUDNN_ATTENTION \
if (Q.shape[2] == K.shape[2]) \
else SDPBackend.FLASH_ATTENTION
with sdpa_kernel(backend):
ret = scaled_dot_product_attention(Q, K, V,
is_causal=is_causal,
enable_gqa=enable_gqa)
else:
ret = scaled_dot_product_attention(Q, K, V,
is_causal=is_causal,
enable_gqa=enable_gqa)
return ret
Expand All @@ -296,13 +304,14 @@ def torch_fmha(Q: torch.Tensor, K: torch.Tensor, V: torch.Tensor,

# --- User Configuration ---
BATCH_SIZE = 2
NUM_HEADS = 8
NUM_HEADS = 32
SEQ_LEN_Q = 128
SEQ_LEN_KV = 128
D_K = 64
SEQ_LEN_KV = 256
D_K = 128
D_V = 64

QUERY_GROUP_SIZE = 1
QUERY_GROUP_SIZE = 8
enable_gqa = QUERY_GROUP_SIZE > 1

DTYPE = torch.float16

Expand Down Expand Up @@ -336,7 +345,7 @@ def torch_fmha(Q: torch.Tensor, K: torch.Tensor, V: torch.Tensor,
dtype:{output_fmha_cutile_non_causal.dtype}""")
if args.correctness_check:
ref_fmha = torch_fmha(Q_input, K_input, V_input,
is_causal=False, enable_gqa=False)
is_causal=False, enable_gqa=enable_gqa)
torch.testing.assert_close(output_fmha_cutile_non_causal, ref_fmha, atol=1e-3, rtol=1e-3)
print("Correctness check passed")
else:
Expand All @@ -354,7 +363,7 @@ def torch_fmha(Q: torch.Tensor, K: torch.Tensor, V: torch.Tensor,
dtype: {output_fmha_cutile_causal.dtype}""")
if args.correctness_check:
ref_fmha = torch_fmha(Q_input, K_input, V_input,
is_causal=True, enable_gqa=False)
is_causal=True, enable_gqa=enable_gqa)
torch.testing.assert_close(output_fmha_cutile_causal, ref_fmha, atol=1e-3, rtol=1e-3)
print("Correctness check passed")
else:
Expand Down Expand Up @@ -394,7 +403,7 @@ def torch_fmha(Q: torch.Tensor, K: torch.Tensor, V: torch.Tensor,
dtype: {output_fmha_cutile_autotune_causal.dtype}""")
print(f"Tuned config: {tuned_config}")
if args.correctness_check:
ref_fmha = torch_fmha(Q_input, K_input, V_input, is_causal=True, enable_gqa=False)
ref_fmha = torch_fmha(Q_input, K_input, V_input, is_causal=True, enable_gqa=enable_gqa)
torch.testing.assert_close(
output_fmha_cutile_autotune_causal, ref_fmha, atol=1e-2, rtol=5e-2
)
Expand Down
1 change: 1 addition & 0 deletions samples/templates/AttentionFMHA.py
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Original file line number Diff line number Diff line change
Expand Up @@ -83,6 +83,7 @@ def cutile_fmha(Q: torch.Tensor, K: torch.Tensor, V: torch.Tensor,
qk_scale,
input_pos,
D_k,
D_v,
Heads,
tile_m,
tile_n,
Expand Down
16 changes: 9 additions & 7 deletions test/bench_attention.py
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Original file line number Diff line number Diff line change
Expand Up @@ -74,10 +74,11 @@ def bench_fmha(qkv_shape, dtype, backend, benchmark):
rounds=rounds, warmup_rounds=warmup_rounds, iterations=iterations,
)

B, H, L, D = q.shape
# first gemm mma(q, k): 2 * B * H * L * L * D
# second gemm mma(p, v): 2 * B * H * L * L * D
flop_count = 4 * B * H * L * L * D
B, H, L, Dqk = q.shape
_, _, _, Dv = v.shape
# first gemm mma(q, k): 2 * B * H * L * L * Dqk
# second gemm mma(p, v): 2 * B * H * L * L * Dv
flop_count = 2 * B * H * L * L * (Dqk + Dv)

if is_causal:
flop_count /= 2
Expand All @@ -88,9 +89,10 @@ def bench_fmha(qkv_shape, dtype, backend, benchmark):


def cutile_fmha(q, k, v, o, is_causal, enable_gqa):
b, qh, q_len, d = q.shape
b, qh, q_len, dqk = q.shape
_, kh, k_len, _ = k.shape
qk_scale = 1 / sqrt(d)
_, _, _, dv = v.shape
qk_scale = 1 / sqrt(dqk)
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)
Expand All @@ -100,7 +102,7 @@ def cutile_fmha(q, k, v, o, is_causal, enable_gqa):
(q, k, v, o,
qk_scale,
input_pos,
d, qh,
dqk, dv, qh,
TILE_M, TILE_N,
query_group_size, is_causal, EVEN_K))

Expand Down
19 changes: 10 additions & 9 deletions test/kernels/attention.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,8 @@
def fmha_kernel(Q, K, V, Out,
qk_scale: float,
input_pos: int,
TILE_D: ConstInt, # TILE_D = hidden_size
Dqk: ConstInt, # Head dimension of Q and K
Dv: ConstInt, # Head dimension of V
H: ConstInt,
TILE_M: ConstInt,
TILE_N: ConstInt,
Expand Down Expand Up @@ -54,12 +55,12 @@ def fmha_kernel(Q, K, V, Out,
# Initialize online softmax accumulators in float32 for stability
m_i = ct.full((TILE_M, 1), -np.inf, dtype=np.float32)
l_i = ct.full((TILE_M, 1), 0.0, dtype=np.float32)
acc = ct.full((TILE_M, TILE_D), 0.0, dtype=np.float32)
acc = ct.full((TILE_M, Dv), 0.0, dtype=np.float32)

# Load query tile for this batch, head, and M-chunk
q = ct.load(
Q, index=(batch_idx, head_idx, bid_x, 0), shape=(1, 1, TILE_M, TILE_D)
).reshape((TILE_M, TILE_D)) # [TILE_M, TILE_D]
Q, index=(batch_idx, head_idx, bid_x, 0), shape=(1, 1, TILE_M, Dqk)
).reshape((TILE_M, Dqk)) # [TILE_M, Dqk]

# loop over k, v and update accumulator
m_end = input_pos + (bid_x + 1) * TILE_M
Expand All @@ -78,11 +79,11 @@ def fmha_kernel(Q, K, V, Out,
for j in range(0, Tc):
# --- Compute QK product ---
k = ct.load(
K, index=(batch_idx, off_kv_h, 0, j), shape=(1, 1, TILE_D, TILE_N),
K, index=(batch_idx, off_kv_h, 0, j), shape=(1, 1, Dqk, TILE_N),
order=(0, 1, 3, 2),
latency=2,
)
k = k.reshape((TILE_D, TILE_N)) # [TILE_D, TILE_N]
k = k.reshape((Dqk, TILE_N)) # [Dqk, TILE_N]
qk = ct.full((TILE_M, TILE_N), 0., dtype=np.float32)
qk = ct.mma(q, k, qk) # [TILE_M, TILE_N]

Expand Down Expand Up @@ -115,14 +116,14 @@ def fmha_kernel(Q, K, V, Out,

# --- Compute PV product ---
v = ct.load(
V, index=(batch_idx, off_kv_h, j, 0), shape=(1, 1, TILE_N, TILE_D),
V, index=(batch_idx, off_kv_h, j, 0), shape=(1, 1, TILE_N, Dv),
latency=4,
).reshape((TILE_N, TILE_D)) # [TILE_N, TILE_D]
).reshape((TILE_N, Dv)) # [TILE_N, Dv]
p = p.astype(Q.dtype)
acc = ct.mma(p, v, acc) # [TILE_M, TILE_N]
m_i = m_ij # [TILE_M, 1]

# --- Final Normalization and Store ---
acc = ct.truediv(acc, l_i, flush_to_zero=True, rounding_mode=RMd.APPROX)
acc = acc.reshape((1, 1, TILE_M, TILE_D)).astype(Out.dtype)
acc = acc.reshape((1, 1, TILE_M, Dv)).astype(Out.dtype)
ct.store(Out, index=(batch_idx, head_idx, bid_x, 0), tile=acc)
14 changes: 7 additions & 7 deletions test/test_attention.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,21 +15,21 @@
@pytest.mark.parametrize("k_heads", [8])
@pytest.mark.parametrize("q_len", [1, 15, 32])
@pytest.mark.parametrize("k_len", [32, 63])
@pytest.mark.parametrize("hidden_size", [32])
@pytest.mark.parametrize("head_dim", [32])
@pytest.mark.parametrize("tile_size", [(8, 16)])
@pytest.mark.parametrize("is_causal", [True, False])
@pytest.mark.parametrize("use_input_pos", [True, False])
def test_flash_attention(batch_size, q_heads, k_heads,
q_len, k_len,
hidden_size, tile_size, is_causal,
head_dim, tile_size, is_causal,
use_input_pos,
float_dtype):
query_group_size = q_heads // k_heads
TILE_M, TILE_N = tile_size
qk_scale = 1 / math.sqrt(hidden_size)
q = torch.randn((batch_size, q_heads, q_len, hidden_size), dtype=float_dtype, device='cuda')
k = torch.randn((batch_size, k_heads, k_len, hidden_size), dtype=float_dtype, device='cuda')
v = torch.randn((batch_size, k_heads, k_len, hidden_size), dtype=float_dtype, device='cuda')
qk_scale = 1 / math.sqrt(head_dim)
q = torch.randn((batch_size, q_heads, q_len, head_dim), dtype=float_dtype, device='cuda')
k = torch.randn((batch_size, k_heads, k_len, head_dim), dtype=float_dtype, device='cuda')
v = torch.randn((batch_size, k_heads, k_len, head_dim), dtype=float_dtype, device='cuda')
o = torch.zeros_like(q)
grid = (math.ceil(q_len / TILE_M), batch_size * q_heads, 1)
if use_input_pos:
Expand All @@ -43,7 +43,7 @@ def test_flash_attention(batch_size, q_heads, k_heads,
(q, k, v, o,
qk_scale,
input_pos,
hidden_size, q_heads,
head_dim, head_dim, q_heads,
TILE_M, TILE_N,
query_group_size, is_causal, EVEN_K))
if is_causal:
Expand Down
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