diff --git a/tests/unit/test_d1_lse_verify.py b/tests/unit/test_d1_lse_verify.py
new file mode 100644
index 00000000..01c2a6d1
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+++ b/tests/unit/test_d1_lse_verify.py
@@ -0,0 +1,80 @@
+"""
+D1: Verify per-row LSE correctness.
+"""
+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_lse(hd=64):
+    m = 128
+    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')
+
+    # FP32 reference LSE
+    qf = q[:, :, 0].float()
+    kf = k[:, :, 0].float()
+    scale = 1.0 / math.sqrt(hd)
+    attn = qf @ kf.T * scale
+    attn_max = attn.max(dim=-1)[0]  # (m,)
+    attn_exp = torch.exp(attn - attn_max.unsqueeze(-1))
+    attn_sum = attn_exp.sum(dim=-1)  # (m,)
+    ref_lse = torch.log(attn_sum) + attn_max  # (m,) natural log domain
+
+    # Our kernel LSE: lse = ln(row_sum) + row_max * ln(2)
+    # row_max is in scale_log2 domain: max(S * scale * log2(e))
+    # So row_max * ln(2) converts back to natural domain.
+    # Thus lse = ln(row_sum) + row_max * ln(2)
+    # But row_max = max(S * scale * log2(e)) = max(S * scale) * log2(e)
+    # So row_max * ln(2) = max(S * scale) * log2(e) * ln(2) = max(S * scale) 
+    # Therefore lse = ln(row_sum) + max(S * scale)
+    # And our ref: lse = ln(sum(exp(S * scale - max))) + max = ln(sum) + max
+    # These should be the same.
+
+    kernel = FmhaKernel(head_dim=hd, s_k=s_k, use_smem_p=False, normalize=False)
+    pv_n_tile = kernel.pv_n_tile
+
+    stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
+
+    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')
+    lse_tensor = torch.zeros(m, dtype=torch.float32, 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)
+    compiled(mQ, mK, mV, mC, stream, mLSE)
+    torch.cuda.synchronize()
+
+    kernel_lse = lse_tensor.cpu()
+
+    # Compare
+    lse_err = (kernel_lse - ref_lse).abs()
+    print(f'  LSE max err: {lse_err.max().item():.6f}')
+    print(f'  LSE mean err: {lse_err.mean().item():.6f}')
+    print(f'  Kernel LSE[:8]: {kernel_lse[:8].tolist()}')
+    print(f'  Ref LSE[:8]:    {ref_lse[:8].tolist()}')
+
+    # Check O too
+    ref_unnorm = attn_exp @ v.float()
+    out = c_tile[:, :, 0].float()
+    cos = torch.nn.functional.cosine_similarity(
+        out.flatten().unsqueeze(0), ref_unnorm.flatten().unsqueeze(0)
+    ).item()
+    print(f'  O cos_unnorm: {cos:.6f}')
+
+
+if __name__ == '__main__':
+    test_lse()