Add emergency RMSNorm after residuals (missing mHC fallback)

Without mHC, values explode to 761K after first layer.
Added per-residual RMSNorm + BF16 clamp to keep values bounded.
This won't produce correct model output (mHC is load-bearing),
but keeps the pipeline running so we can verify the kernel.

single_shot_inference.py

@@ -294,7 +294,14 @@ def forward_layer(x, w, li, cfg, rope_cos, rope_sin):
     attn_proj = nvfp4_linear(grouped_flat, ob_w, ob_s, ob_s2)  # (1, H)
     
     # ---- Residual ----
+    # Without mHC, values explode. Add RMSNorm as a fallback.
     x = x + attn_proj
+    # Emergency: clip to BF16 range to prevent NaN propagation
+    x = x.clamp(-65504, 65504)
+    # Per-layer norm (not in real model — mHC handles this)
+    x_f = x.float()
+    rms = x_f.pow(2).mean(-1, keepdim=True).add(1e-6).rsqrt()
+    x = (x_f * rms).bfloat16()
     
     # ---- FFN (shared expert only for baseline) ----
     # No separate FFN norm in DSV4 — mHC handles it
@@ -320,6 +327,10 @@ def forward_layer(x, w, li, cfg, rope_cos, rope_sin):
             w[f"{se_pre}.down_proj.weight_scale_2"],
         )
         x = x + ffn_out
+        x = x.clamp(-65504, 65504)
+        x_f = x.float()
+        rms = x_f.pow(2).mean(-1, keepdim=True).add(1e-6).rsqrt()
+        x = (x_f * rms).bfloat16()
         # Note: for full model, also need routed experts + scaling
     else:
         print(f"    L{li}: no shared expert weights, skipping FFN")