"""
D1: Debug O rescale at s_k=256 with diagnostic 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_multi_kv_debug(hd=64, s_k=256):
    m = 128
    n_kv_tiles = s_k // 128
    torch.manual_seed(42)

    q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
    k = torch.randn(s_k, hd, 1, dtype=torch.bfloat16, device='cuda')
    v = torch.randn(s_k, hd, dtype=torch.bfloat16, device='cuda')
    c = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device='cuda')

    # FP32 reference
    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()
    ref_norm = (attn_exp / attn_sum) @ v.float()

    # Also compute per-tile references
    for kt in range(n_kv_tiles):
        k_start = kt * 128
        k_end = k_start + 128
        kf_t = k[k_start:k_end, :, 0].float()
        vf_t = v[k_start:k_end].float()
        attn_t = qf @ kf_t.T * scale
        print(f"  kt={kt}: K[{k_start}:{k_end}] attn range [{attn_t.min().item():.4f}, {attn_t.max().item():.4f}]")

    lse_tensor = torch.zeros(m, 1, 1, dtype=torch.float32, device='cuda')

    kernel = FmhaKernel(head_dim=hd, s_k=s_k, use_smem_p=False, normalize=False)
    pv_n_tile = kernel.pv_n_tile
    n_pv_tiles = kernel.n_pv_tiles
    print(f"  n_kv_tiles={kernel.n_kv_tiles}, pv_n_tile={pv_n_tile}, n_pv_tiles={n_pv_tiles}")
    # tmem_o0_offset is set in _setup, not __init__

    stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)

    # Compile with first PV tile
    v_tile = v[:, 0:pv_n_tile].contiguous()
    v_kernel = v_tile.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_kernel).mark_layout_dynamic(leading_dim=ct.get_leading_dim(v_kernel))
    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'  Compiling...', flush=True)
    compiled = cute.compile(kernel, mQ, mK, mV, mC, stream, mLSE)

    lse_val = None
    for nt in range(n_pv_tiles):
        v_start = nt * pv_n_tile
        v_end = v_start + pv_n_tile
        v_tile = v[:, v_start:v_end].contiguous()
        v_kernel = v_tile.unsqueeze(-1)
        c_tile = torch.zeros(m, pv_n_tile, 1, dtype=torch.bfloat16, device='cuda')
        lse_tensor.zero_()

        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_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))

        compiled(mQ, mK, mV, mC, stream, mLSE)
        torch.cuda.synchronize()

        c[:, v_start:v_end, :] = c_tile
        if nt == 0:
            lse_val = lse_tensor[0, 0, 0].item()

    out_unnorm = c[:, :, 0].float()
    out_norm = out_unnorm / attn_sum

    # Compare per-row
    row_cos = []
    for i in range(min(8, m)):
        rc = torch.nn.functional.cosine_similarity(
            out_unnorm[i].unsqueeze(0), ref_unnorm[i].unsqueeze(0)
        ).item()
        row_cos.append(rc)

    cos_unnorm = torch.nn.functional.cosine_similarity(
        out_unnorm.flatten().unsqueeze(0), ref_unnorm.flatten().unsqueeze(0)
    ).item()

    print(f"  cos_unnorm={cos_unnorm:.6f}")
    print(f"  row 0 cos={row_cos[0]:.6f}  row 1 cos={row_cos[1]:.6f}")
    print(f"  out[0,:8]={out_unnorm[0,:8].tolist()}")
    print(f"  ref[0,:8]={ref_unnorm[0,:8].tolist()}")
    print(f"  lse_val={lse_val}, ref_lse={(torch.log(attn_sum[0,0]) + attn_max[0,0]).item()}")
    return cos_unnorm


def test():
    print("=== D1: Multi-KV Debug ===\n")
    test_multi_kv_debug(64, 256)


if __name__ == '__main__':
    test()