Fix mHCContext attribute access (not tuple unpacking) and enable attention diag

single_shot_inference.py

@@ -370,8 +370,7 @@ 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 MHC_DIAG:  # mHC diagnostics
-        A_l = None
-        B_l, C_l = attn_ctx
+        B_l, C_l = attn_ctx.B_l, attn_ctx.C_l
         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) --
@@ -461,7 +460,7 @@ def forward_layer(X_l, w, li, cfg, rope_cos, rope_sin,
                 q_input, k_expanded, v_expanded, scale=scale, is_causal=False)
         attn_out = attn_out.permute(1, 0, 2)  # (T, n_h, hd)
         # Diagnostic: check attention entropy (how spread out the attention is)
-        if False:  # MHC_DIAG
+        if MHC_DIAG and li < 3:
             with torch.no_grad():
                 scores = torch.matmul(q_input, k_expanded.transpose(-1, -2)) * scale  # (n_h, T, seq_len)
                 weights = torch.softmax(scores.float(), dim=-1)  # (n_h, 1, seq_len)
@@ -520,7 +519,7 @@ def forward_layer(X_l, w, li, cfg, rope_cos, rope_sin,
     X_mid = attn_mhc.post_block(X_l, F_attn, attn_ctx)  # (T, n_hc, H)
     # Diagnostic: check mHC is stabilizing the residual
     if MHC_DIAG:  # mHC diagnostics
-        B_l, C_l = attn_ctx
+        B_l, C_l = attn_ctx.B_l, attn_ctx.C_l
         print(f"  L{li} attn: |X_l|={X_l.abs().max().item():.2f} |F_attn|={F_attn.abs().max().item():.2f} |B|={B_l.abs().max().item():.4f} |C|={C_l.abs().max().item():.4f} |X_mid|={X_mid.abs().max().item():.2f}")
         # Check B_l is doubly stochastic (rows sum to 1.0)
         B_row_sums = B_l.sum(dim=-1)  # (T, n_hc)
@@ -544,7 +543,7 @@ 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 MHC_DIAG:  # ffn mHC diagnostics
-        B_l_ffn, C_l_ffn = ffn_ctx
+        B_l_ffn, C_l_ffn = ffn_ctx.B_l, ffn_ctx.C_l
         print(f"  L{li} ffn: |X_mid|={X_mid.abs().max().item():.2f} |F_ffn|={F_ffn.abs().max().item():.2f} |B|={B_l_ffn.abs().max().item():.4f} |C|={C_l_ffn.abs().max().item():.4f} |X_next|={X_next.abs().max().item():.2f}", flush=True)
 
     return X_next