D1: SMEM-P test at hd=128

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2026-05-24 03:48:37 +00:00
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"""D1: Test SMEM-P at hd=128 with debug prints."""
import torch, math
import cutlass.cute as cute
import cutlass.torch as ct
import cuda.bindings.driver as cuda
from dsv4.kernels.attention.fmha import FmhaKernel
def test_smem_p(hd, n_kv=128):
m = 128
torch.manual_seed(42)
q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
k = torch.randn(n_kv, hd, 1, dtype=torch.bfloat16, device='cuda')
v = torch.randn(n_kv, hd, dtype=torch.bfloat16, device='cuda')
qf = q[:, :, 0].float()
kf = k[:, :, 0].float()
scale = 1.0 / math.sqrt(hd)
attn_max = (qf @ kf.T * scale).max(dim=-1, keepdim=True)[0]
attn_exp = torch.exp(qf @ kf.T * scale - attn_max)
attn_sum = attn_exp.sum(dim=-1, keepdim=True)
ref_unnorm = attn_exp @ v.float()
lse_tensor = torch.zeros(m, 1, 1, dtype=torch.float32, device='cuda')
kernel = FmhaKernel(head_dim=hd, s_k=n_kv, use_smem_p=True)
pv_n_tile = kernel.pv_n_tile
stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
v_tile = v[:, 0:pv_n_tile].contiguous().unsqueeze(-1)
c_tile = torch.zeros(m, pv_n_tile, 1, dtype=torch.bfloat16, device='cuda')
mQ = ct.from_dlpack(q).mark_layout_dynamic(leading_dim=ct.get_leading_dim(q))
mK = ct.from_dlpack(k).mark_layout_dynamic(leading_dim=ct.get_leading_dim(k))
mV = ct.from_dlpack(v_tile).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_tile))
mC = ct.from_dlpack(c_tile).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c_tile))
mLSE = ct.from_dlpack(lse_tensor).mark_layout_dynamic(leading_dim=ct.get_leading_dim(lse_tensor))
print(f'hd={hd} SMEM-P: Compiling...', flush=True)
compiled = cute.compile(kernel, mQ, mK, mV, mC, stream, mLSE)
compiled(mQ, mK, mV, mC, stream, mLSE)
torch.cuda.synchronize()
out = c_tile[:, :, 0].float()
cos = torch.nn.functional.cosine_similarity(out.flatten().unsqueeze(0), ref_unnorm.flatten().unsqueeze(0)).item()
print(f'hd={hd} SMEM-P: cos={cos:.6f} {"PASS" if cos >= 0.99 else "FAIL"}')
return cos
if __name__ == '__main__':
test_smem_p(128)