train+ddp: micro-batch gradient accumulation (--accum-steps)

Accumulate grads over N micro-batches, then one AdamW step + zero_grad,
for an effective batch of N×micro at one micro-batch's activation cost.
Each micro-loss is scaled by 1/N before backward (the tape SUM-accumulates
the scaled grads) so the boundary grad equals a single step over an N×
batch. accum==1 skips the scale → bit-identical to the pre-T16 path.

DDP: the cross-rank all-reduce fires ONLY at the accumulation boundary
(intermediate micro-steps are local-only, no NCCL); the /world average is
orthogonal to the per-micro 1/N, so the boundary grad is the effective
global-batch mean. New --accum-steps flag in both train binaries; effective
batch is printed.

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

crates/xtrain-train/src/bin/train.rs

@@ -101,6 +101,10 @@ fn main() {
     // Optimization knobs.
     let steps: usize = flag(&args, "--steps", 2000);
     let batch_size: usize = flag(&args, "--batch", 8);
+    // Micro-batch gradient accumulation (Phase T16): effective batch =
+    // accum_steps × batch, at one micro-batch's activation-memory cost. Default 1
+    // = no accumulation (bit-identical to the pre-T16 path).
+    let accum_steps: usize = flag(&args, "--accum-steps", 1).max(1);
     let seq_len: usize = flag(&args, "--seq", 64);
     let max_lr: f32 = flag(&args, "--max-lr", 3e-3);
     let min_lr: f32 = flag(&args, "--min-lr", max_lr * 0.1);
@@ -201,6 +205,7 @@ fn main() {
     let tcfg = TrainConfig {
         seq_len,
         batch_size,
+        accum_steps,
         steps,
         schedule: LrSchedule {
             max_lr,
@@ -219,10 +224,13 @@ fn main() {
     };
 
     println!(
-        "training: {} steps, seq {}, batch {}, lr {:.1e}→{:.1e}, eval every {}",
+        "training: {} steps, seq {}, batch {} × accum {} = effective batch {}, \
+         lr {:.1e}→{:.1e}, eval every {}",
         tcfg.steps,
         tcfg.seq_len,
         tcfg.batch_size,
+        tcfg.accum_steps,
+        tcfg.batch_size * tcfg.accum_steps,
         tcfg.schedule.max_lr,
         tcfg.schedule.min_lr,
         tcfg.eval_every