"""Quick D1 regression test: HEAD_DIM=64 only, must match Stage C."""
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():
    torch.manual_seed(42)
    hd, n = 64, 128
    m = 128
    q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
    k = torch.randn(n, hd, 1, dtype=torch.bfloat16, device='cuda')
    v = torch.randn(n, hd, dtype=torch.bfloat16, device='cuda')
    v_kernel = v.unsqueeze(-1)
    c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device='cuda')

    qf = q[:, :, 0].float()
    kf = k[:, :, 0].float()
    scale = 1.0 / math.sqrt(hd)
    attn = qf @ kf.T * scale
    attn = torch.softmax(attn, dim=-1)
    ref = attn @ v.float()

    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_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
    mC = ct.from_dlpack(c).mark_layout_dynamic(leading_dim=ct.get_leading_dim(c))
    stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)

    kernel = FmhaKernel(head_dim=hd, s_k=n)
    print(f'hd={hd}, n={n}: Compiling...', flush=True)
    compiled = cute.compile(kernel, mQ, mK, mV, mC, stream)
    compiled(mQ, mK, mV, mC, stream)
    torch.cuda.synchronize()

    out = c[:, :, 0].float()
    cos = torch.nn.functional.cosine_similarity(
        out.flatten().unsqueeze(0), ref.flatten().unsqueeze(0)
    ).item()
    max_abs = (out - ref).abs().max().item()
    print(f'hd={hd}, n={n}: cos {cos:.6f}  max_abs {max_abs:.4f}  {"PASS" if cos >= 0.97 else "FAIL"}')

if __name__ == '__main__':
    test()