test+bins: flash grad-check, flash==composed, PyTorch parity, --flash flag

autograd: flash_attention_batched_bwd (dQ/dK/dV finite-diff, seq>tile)
+ flash_matches_composed_fwd. model/tests/flash.rs: flash==composed
on-vs-off (logits/loss/every param grad), fp32 + bf16. parity_dump:
XTRAIN_PARITY_FLASH dumps the flash path for the same parity.py oracle
(PyTorch SDPA parity at B>1). train + train_ddp get the --flash flag.

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

crates/xtrain-model/tests/parity_dump.rs

@@ -67,7 +67,7 @@ fn dump_for_parity() {
 
     // Same deterministic init as the overfit test.
     let mut seed = 1u64;
-    let model = TinyTransformer::new(cfg, device, |shape| {
+    let mut model = TinyTransformer::new(cfg, device, |shape| {
         seed = seed.wrapping_add(1);
         let n: usize = shape.iter().product();
         if shape.len() == 1 {
@@ -76,6 +76,14 @@ fn dump_for_parity() {
             fill(n, seed, 0.08)
         }
     });
+    // T14: with XTRAIN_PARITY_FLASH set, dump from the fused flash-attention path.
+    // flash is the SAME SDPA math, so the SAME parity.py PyTorch oracle is the
+    // reference for both paths — running this once per path checks flash against
+    // PyTorch at B>1 (forward logits + every parameter grad).
+    if std::env::var("XTRAIN_PARITY_FLASH").is_ok() {
+        model = model.with_flash(true);
+        println!("parity: FLASH attention path");
+    }
 
     // config + ids
     {