diag: add FFN and pre_block diagnostics

single_shot_inference.py

@@ -425,6 +425,10 @@ def forward_layer(X_l, w, li, cfg, rope_cos, rope_sin,
 
     # -- mHC pre_block (attention) --
     x_in, attn_ctx = attn_mhc.pre_block(X_l)  # x_in: (T, H)
+    if li < 3:
+        A_l = None
+        B_l, C_l = attn_ctx
+        print(f"  L{li} pre_attn: |X_l|={X_l.abs().max().item():.2f} |x_in|={x_in.abs().max().item():.2f}", flush=True)
 
     # -- RMSNorm (pre-norm before attention) --
     x_normed = attn_norm.forward(x_in)  # (T, H) BF16
@@ -525,6 +529,9 @@ def forward_layer(X_l, w, li, cfg, rope_cos, rope_sin,
 
     # -- mHC post_block (FFN) --
     X_next = ffn_mhc.post_block(X_mid, F_ffn, ffn_ctx)  # (T, n_hc, H)
+    if li < 3:
+        B_l, C_l = ffn_ctx
+        print(f"  L{li} ffn: |X_mid|={X_mid.abs().max().item():.2f} |F_ffn|={F_ffn.abs().max().item():.2f} |B|={B_l.abs().max().item():.4f} |C|={C_l.abs().max().item():.4f} |X_next|={X_next.abs().max().item():.2f}", flush=True)
 
     return X_next