gqa: real grouped-query attention (repeat_kv op + both SDPA paths + wiring + tests)

- repeat_kv CUDA kernel: fwd head-block gather, bwd DETERMINISTIC group-sum (each
  kv head sums its group of query-head grads; no atomics) + Tensor/ops node.
- Config gains num_kv_heads (default = n_heads → MHA); wk/wv project to kv_dim;
  attention() repeat_kv-broadcasts K/V to nh heads before the UNCHANGED composed
  & flash SDPA → GQA on both paths. group=1 is identity → MHA bit-identical.
- --kv-heads flag on train/train_ddp/export_safetensors/greedy_sample; export
  writes real num_key_value_heads (xserv repeat_kv grouping aligned).
- Tests: repeat_kv grad-check (group>1 grad-sum + group=1 identity); model gqa.rs
  (GQA flash==composed fp32/bf16, group=1 bit-identical to MHA, kv-proj shape);
  parity_dump+parity.py GQA path (repeat_interleave) via XTRAIN_PARITY_KV_HEADS.

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

crates/xtrain-distributed/src/bin/train_ddp.rs

@@ -61,6 +61,8 @@ fn main() {
     let head_dim = flag(&args, "--head-dim", 16usize);
     let n_layers = flag(&args, "--layers", 4usize);
     let ffn = flag(&args, "--ffn", 64usize);
+    // GQA (Phase T15): num K/V heads (must divide --heads). Default = --heads (MHA).
+    let kv_heads = flag(&args, "--kv-heads", n_heads);
     // `--dim` is informational; dim is always n_heads*head_dim. Warn on mismatch.
     let dim_flag = flag(&args, "--dim", 0usize);
     if dim_flag != 0 && dim_flag != n_heads * head_dim {
@@ -137,13 +139,14 @@ fn main() {
         (corpus, None)
     };
 
-    let cfg = Config::from_arch(vocab, n_heads, head_dim, n_layers, ffn);
+    let cfg = Config::from_arch(vocab, n_heads, head_dim, n_layers, ffn).with_kv_heads(kv_heads);
     println!(
-        "model: dim {} layers {} heads {} head_dim {} ffn {} → core {:.3}M params \
+        "model: dim {} layers {} heads {} kv_heads {} head_dim {} ffn {} → core {:.3}M params \
          (+ embed/lm {:.2}M = {:.2}M total)",
         cfg.dim,
         cfg.n_layers,
         cfg.n_heads,
+        cfg.num_kv_heads,
         cfg.head_dim,
         cfg.ffn_hidden,
         cfg.core_params() as f32 / 1e6,