diff --git a/single_shot_inference.py b/single_shot_inference.py
index 453fc083..41852c18 100644
--- a/single_shot_inference.py
+++ b/single_shot_inference.py
@@ -486,10 +486,23 @@ def forward_layer(X_l, w, li, cfg, rope_cos, rope_sin,
     k_full, v_full = kv_cache.get()  # (1, seq_len, hd) each — RoPE'd, K=V
 
     # -- FMHA: (n_h, T, hd) × (1, seq_len, hd) → (n_h, T, hd) --
-    from dsv4.kernels.attention.production import dsv4_attention
-    q_input = q_heads.permute(1, 0, 2)  # (n_h, T, hd)
-    # k_full, v_full are (1, seq_len, hd) — already in (n_kv, N, hd) format
-    attn_out = dsv4_attention(q_input, k_full, v_full)  # (n_h, T, hd)
+    # Use PyTorch SDPA for correctness verification (production FMHA has no sink bias)
+    # dsv4_attention can be swapped back once sink bias is integrated into the kernel
+    USE_SDPA = True  # Set False to use production FMHA kernel
+    if USE_SDPA:
+        # PyTorch scaled_dot_product_attention
+        # q: (n_h, T, hd), k: (1, seq_len, hd), v: (1, seq_len, hd)
+        # Need to expand K/V for GQA: (1, seq_len, hd) → (n_h, seq_len, hd)
+        k_expanded = k_full.expand(n_h, -1, -1)  # (n_h, seq_len, hd)
+        v_expanded = v_full.expand(n_h, -1, -1)
+        scale = 1.0 / math.sqrt(hd)
+        # For decode (T=1), use SDPA with is_causal=False (no causal mask needed)
+        attn_out = torch.nn.functional.scaled_dot_product_attention(
+            q_input, k_expanded, v_expanded,
+            scale=scale, is_causal=False)  # (n_h, T, hd)
+    else:
+        from dsv4.kernels.attention.production import dsv4_attention
+        attn_out = dsv4_attention(q_input, k_full, v_full)  # (n_h, T, hd)
     attn_out = attn_out.permute(1, 0, 2)  # (T, n_h, hd)
 
     # -- Inverse RoPE on attention output (paper §2.3.3) --