dropout: wire into model (residual sites) + train/eval switch + flag (T18)

Config.dropout (default 0). TinyTransformer gets a Cell<bool> training switch
(train()/eval()/with_training, default eval = safe) + a Cell<u64> step_seed bumped
once per training forward. forward_batched derives a per-layer block_seed (pure fn
of step_seed×layer) and block_forward derives two per-site seeds, inserting
ops::dropout at the attn and ffn sub-block outputs (before each residual). The
seed is a pure function of (step_seed, layer, site) so the checkpoint (T13)
recompute re-derives the same masks → grads stay exact. p=0 or eval → no dropout
node → graph bit-identical to pre-T18.

train_loop: model.train() per step (restored after eval flips to eval); eval_loss
runs model.eval(). bin/train: --dropout flag → cfg.dropout. Export/sampling run in
eval (default), so exported weights are dropout-free (xserv closed loop unaffected).

Model-level tests (dropout.rs): p=0 bit-identical to no-dropout (logits/loss/grads);
eval(p>0) == p=0 identity; train differs from eval + finite; recompute-with-dropout
grads match non-recompute (fp32 + bf16).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

crates/xtrain-model/src/config.rs

@@ -20,6 +20,11 @@ pub struct Config {
     pub eps: f32,
     /// RoPE base frequency (theta).
     pub rope_theta: f32,
+    /// Dropout probability `p` (Phase T18). Applied at the attention/MLP sub-block
+    /// outputs (before each residual add) at TRAINING time, with inverted scaling
+    /// `1/(1-p)`; disabled (identity) at eval. Default `0.0` = no dropout, and the
+    /// forward graph is then bit-identical to the pre-T18 path.
+    pub dropout: f32,
 }
 
 impl Config {
@@ -36,6 +41,7 @@ impl Config {
             ffn_hidden: 64,
             eps: 1e-5,
             rope_theta: 10000.0,
+            dropout: 0.0,
         }
     }
 
@@ -60,6 +66,7 @@ impl Config {
             ffn_hidden,
             eps: 1e-5,
             rope_theta: 10000.0,
+            dropout: 0.0,
         }
     }