diff --git a/tests/unit/test_d5c_diag_ncomp96.py b/tests/unit/test_d5c_diag_ncomp96.py
deleted file mode 100644
index 1870d056..00000000
--- a/tests/unit/test_d5c_diag_ncomp96.py
+++ /dev/null
@@ -1,64 +0,0 @@
-"""
-Quick diagnostic: test FMHA with n_comp=96 and sink_bias directly.
-"""
-import torch
-import math
-import cutlass.cute as cute
-import cutlass.torch as ct
-import cuda.bindings.driver as cuda
-from dsv4.kernels.attention.fmha import FmhaKernel
-
-hd = 64; m = 128; n_comp = 96; n_swa = 32; n_total = 128
-swa_len = 32  # all SWA valid
-scale = 1.0 / math.sqrt(hd)
-torch.manual_seed(42)
-
-q = torch.randn(m, hd, 1, dtype=torch.bfloat16, device='cuda')
-k_comp = torch.randn(n_comp, hd, 1, dtype=torch.bfloat16, device='cuda')
-v_comp = torch.randn(n_comp, hd, dtype=torch.bfloat16, device='cuda')
-k_swa = torch.randn(n_swa, hd, 1, dtype=torch.bfloat16, device='cuda')
-v_swa = torch.randn(n_swa, hd, dtype=torch.bfloat16, device='cuda')
-attn_sink = torch.tensor([0.5], dtype=torch.float32, device='cuda')
-
-k_combined = torch.cat([k_comp, k_swa], dim=0)
-v_combined = torch.cat([v_comp, v_swa], dim=0)
-
-# Reference
-qf = q[:, :, 0].float()
-kf = k_combined[:, :, 0].float()
-vf = v_combined.float()
-scores = qf @ kf.T * scale
-scores[:, n_comp:] += 0.5  # sink bias on SWA
-ref = (torch.softmax(scores, dim=-1) @ vf).to(torch.bfloat16)
-lse_ref = torch.logsumexp(scores, dim=-1)
-
-# Kernel
-stream = cuda.CUstream(torch.cuda.current_stream().cuda_stream)
-kernel = FmhaKernel(head_dim=hd, s_k=n_total, normalize=False,
-                    apply_swa_mask=False, is_causal=False,
-                    n_comp=n_comp, apply_sink_bias=True)
-
-c_out = torch.zeros(m, hd, 1, dtype=torch.bfloat16, device='cuda')
-lse_out = torch.zeros(m, 1, 1, dtype=torch.float32, device='cuda')
-rs_out = torch.zeros(m, 1, 1, dtype=torch.float32, device='cuda')
-
-def to_cute(t): return ct.from_dlpack(t).mark_layout_dynamic(leading_dim=ct.get_leading_dim(t))
-mQ = to_cute(q); mK = to_cute(k_combined)
-mV = to_cute(v_combined.unsqueeze(-1).contiguous())
-mC = to_cute(c_out); mLSE = to_cute(lse_out); mRS = to_cute(rs_out)
-mSB = to_cute(attn_sink)
-
-compiled = cute.compile(kernel, mQ, mK, mV, mC, stream, mLSE,
-                        swa_len=swa_len, sink_bias=mSB, row_sums=mRS)
-compiled(mQ, mK, mV, mC, stream, mLSE,
-         swa_len=swa_len, sink_bias=mSB, row_sums=mRS)
-torch.cuda.synchronize()
-
-o_k = c_out[:, :, 0].float() / rs_out[:, 0, 0].float().unsqueeze(1).clamp(min=1e-30)
-cos = torch.nn.functional.cosine_similarity(o_k.flatten().unsqueeze(0), ref.flatten().unsqueeze(0).float()).item()
-lse_k = lse_out[:, 0, 0].float()
-print(f'n_comp=96, sink_bias=0.5: cos {cos:.6f}  {("PASS" if cos > 0.99 else "FAIL")}')
-print(f'LSE: kernel[0]={lse_k[0].item():.4f}  ref[0]={lse_ref[0].item():.4f}  diff={abs(lse_k[0].item()-lse_ref[0].item()):.6f}')
-if cos < 0.99:
-    print(f'  kernel[0,:4]={o_k[0,:4].tolist()}')
-    print(f'  ref[0,:4]={ref[0,:4].tolist()}')