docs: T15 GQA results + evolution row (模型架构) + README build-journey row

Backfill docs/14-gqa.md gate table (dash5 numbers); add T15 evolution row +
cumulative 模型架构 line; README build-journey T15 row + Phase 2 prose + doc
index range (00..14).

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

README.md

@@ -51,6 +51,7 @@ Each phase: design doc + implementation + tests + a scoped commit (see [`docs/`]
 | **T12** | **bf16 mixed precision** (fp32 master, fixes KI-2) | dim768 OOM solved; −29% mem |
 | **T13** | **activation recompute** / checkpointing (fixes KI-3) | dim1024 fits; grads bit-identical |
 | **T14** | **fused flash-attention** kernel (online softmax, no materialized N×N; opt-in `--flash`) | peak mem −16%@1k / −23%@2k seq; flash==composed (grads/PyTorch) |
+| **T15** | **grouped-query attention** (`num_kv_heads<num_heads`; `repeat_kv` broadcast feeds both SDPA paths; backward sums each kv head's group; `--kv-heads`) | repeat_kv grad-check + **group=1 bit-identical to MHA**; GQA flash==composed; PyTorch GQA B>1; **xserv closed loop with real `num_key_value_heads`** token-identical |
 | **T16** | **gradient accumulation** (`--accum-steps`; DDP all-reduces only at the boundary) | equiv to N× big batch (grad 3.8e-5); same effective-64 batch 27.7GB→7.2GB (−74%) |
 | **T18** | **dropout** (hand counter-based device RNG + mask, inverted scaling, train/eval switch) | fixed-seed grad-check; **p=0 bit-identical**; recompute-safe |
 
@@ -58,6 +59,9 @@ The four performance fixes (T10–T13) each removed a real bottleneck — see
 [`docs/known-issues.md`](docs/known-issues.md). **Phase 2 (systems-stack depth, T14–)**
 revisits hand-writing deferred training-stack features: T14 = the fused
 flash-attention kernel ([`docs/13-flash-attention.md`](docs/13-flash-attention.md));
+T15 = real grouped-query attention ([`docs/14-gqa.md`](docs/14-gqa.md), `num_kv_heads <
+num_heads` via a `repeat_kv` broadcast op whose backward sums each kv head's query-head
+group — feeding both SDPA paths unchanged, default MHA bit-identical);
 T16 = micro-batch gradient accumulation ([`docs/15-grad-accum.md`](docs/15-grad-accum.md)),
 which decouples the effective batch from activation memory (memory tracks the micro-batch,
 not N×); T18 = dropout ([`docs/17-dropout.md`](docs/17-dropout.md), hand counter-based
@@ -145,5 +149,5 @@ cargo test --workspace            # autograd grad-checks, PyTorch parity, DDP, e
 
 - [`docs/evolution.md`](docs/evolution.md) — per-milestone changes across algorithm / architecture / infra / dataset.
 - [`docs/runs/README.md`](docs/runs/README.md) — the v0–v8 comparison; [`docs/runs/0N-*.md`](docs/runs/) — per-run detail.
-- [`docs/00-*` … `12-*`](docs/) — per-phase design docs (build chain → tensor → autograd → transformer → training → perf → distributed → export → batched → allocator → bf16 → recompute).
+- [`docs/00-*` … `14-*`](docs/) — per-phase design docs (build chain → tensor → autograd → transformer → training → perf → distributed → export → batched → allocator → bf16 → recompute → flash-attention → GQA).
 - [`docs/known-issues.md`](docs/known-issues.md) — perf backlog (KI-1/2/3/5 fixed; KI-4 + process-per-GPU open).

crates/xtrain-autodiff/src/ops.rs

@@ -376,6 +376,27 @@ pub fn flash_attention(q: &Var, k: &Var, v: &Var, scale: f32) -> Var {
     )
 }
 
+/// GQA repeat_kv head broadcast (Phase T15). `kv`:[batch·num_kv, seq, head_dim]
+/// (a K or V tensor) → `[batch·nh, seq, head_dim]`, each KV head broadcast to its
+/// `group = nh/num_kv` query heads (qh ← kv head qh/group, contiguous groups —
+/// matches xserv's repeat_kv). Feeds the unchanged composed/flash SDPA so GQA is
+/// "free" for both. Backward SUMS the `group` query heads sharing each KV head back
+/// onto it (the multi-group grad accumulation). `nh == num_kv` (group 1) is identity
+/// → bit-identical to the MHA path. `batch` lets the op recover num_kv from kv's bh.
+pub fn repeat_kv(kv: &Var, nh: usize, batch: usize) -> Var {
+    let bh_kv = kv.value().shape()[0];
+    let num_kv = bh_kv / batch;
+    let out = kv.value().repeat_kv(nh, batch);
+    Var::from_op(
+        out,
+        vec![kv.clone()],
+        Box::new(move |dout, parents| {
+            let din = Tensor::repeat_kv_backward(dout, num_kv, batch);
+            Var::push_grad(&parents[0], din);
+        }),
+    )
+}
+
 /// Cross-entropy mean loss over logits `x:[rows,cols]` with one I32 target per
 /// row. Returns a scalar [`Var`]. Backward: `dx = (probs - onehot)/rows`,
 /// scaled by the upstream scalar grad.

crates/xtrain-autodiff/tests/autograd.rs

@@ -776,6 +776,94 @@ fn flash_bwd_matches_composed_bwd() {
     assert!(rv < 2e-2, "dV diverges: {rv:.3e}");
 }
 
+// ---- GQA repeat_kv head broadcast (Phase T15) ----
+//
+// repeat_kv expands K/V from [batch·num_kv, seq, hd] to [batch·nh, seq, hd]; each
+// kv head is broadcast to its `group = nh/num_kv` query heads. The forward is a
+// gather (a linear map), so finite-diff is clean. The CRITICAL gate is the
+// BACKWARD: a kv head receives the SUM of the `group` query heads sharing it —
+// the multi-group-to-one grad accumulation GQA correctness hinges on. We grad-check
+// din against finite-diff of L = sum(W∘out) with group>1, plus assert the forward
+// actually broadcasts and that group==1 is exact identity.
+#[test]
+fn repeat_kv_grad() {
+    require_gpu();
+    // batch 2, num_kv 2 → bh_kv 4 input rows; nh 6 → group 3, bh_q 12 output rows.
+    let (batch, num_kv, nh, seq, hd) = (2usize, 2usize, 6usize, 4usize, 5usize);
+    let n_in = batch * num_kv * seq * hd;
+    let n_out = batch * nh * seq * hd;
+    let x_h = fill(n_in, 711);
+    let w = fill(n_out, 712);
+
+    let kv = Var::leaf(cuda(&x_h, &[batch * num_kv, seq, hd]));
+    let out = ops::repeat_kv(&kv, nh, batch);
+    assert_eq!(out.value().shape(), &[batch * nh, seq, hd]);
+
+    // Forward sanity: out head (b·nh + qh) must equal in head (b·num_kv + qh/group).
+    let group = nh / num_kv;
+    let out_h = out
+        .value()
+        .to_device(Device::Cpu)
+        .as_slice::<f32>()
+        .to_vec();
+    let row = seq * hd;
+    for b in 0..batch {
+        for qh in 0..nh {
+            let kvh = qh / group;
+            let o0 = (b * nh + qh) * row;
+            let i0 = (b * num_kv + kvh) * row;
+            for e in 0..row {
+                assert_eq!(out_h[o0 + e], x_h[i0 + e], "repeat_kv fwd mismatch");
+            }
+        }
+    }
+
+    scalar_loss(&out, &w).backward();
+    let din = kv.grad().unwrap().to_device(Device::Cpu);
+
+    let fwd = move |xh: &[f32], _s: &[usize]| -> f32 {
+        let kv = cuda(xh, &[batch * num_kv, seq, hd]);
+        let o = kv.repeat_kv(nh, batch);
+        weighted_sum(&o, &w)
+    };
+    // repeat_kv is exactly linear (gather/sum), so the linear-op tolerances apply.
+    report(
+        "repeat_kv din",
+        &grad_check(
+            &x_h,
+            &[batch * num_kv, seq, hd],
+            &fwd,
+            din.as_slice::<f32>(),
+            cfg_linear(),
+        ),
+    );
+}
+
+// group==1 (num_kv == nh) must be a bit-exact identity in BOTH directions — this is
+// the regression guard that makes the MHA path (kv_heads == n_heads) unchanged.
+#[test]
+fn repeat_kv_identity_group1() {
+    require_gpu();
+    let (batch, nh, seq, hd) = (2usize, 3usize, 4usize, 5usize);
+    let n = batch * nh * seq * hd;
+    let x_h = fill(n, 721);
+    let w = fill(n, 722);
+    let kv = Var::leaf(cuda(&x_h, &[batch * nh, seq, hd]));
+    let out = ops::repeat_kv(&kv, nh, batch); // group 1
+    let out_h = out
+        .value()
+        .to_device(Device::Cpu)
+        .as_slice::<f32>()
+        .to_vec();
+    assert_eq!(out_h, x_h, "group-1 repeat_kv fwd must be identity");
+    scalar_loss(&out, &w).backward();
+    let din = kv.grad().unwrap().to_device(Device::Cpu);
+    // dL/din = w exactly (identity forward → grad passes through unchanged).
+    for (g, expect) in din.as_slice::<f32>().iter().zip(&w) {
+        assert_eq!(*g, *expect, "group-1 repeat_kv bwd must be identity");
+    }
+}
+
 // ---- dropout (Phase T18) ----
 //
 // Fixed-seed finite-diff grad-check. Under a fixed `seed` the mask is constant
@@ -827,9 +915,17 @@ fn dropout_expectation_and_keep_rate() {
         let (out, mask) = x.dropout(p, 0x5EED_0000 + t as u64);
         let out_h = out.to_device(Device::Cpu);
         let mask_h = mask.to_device(Device::Cpu);
-        let mean_out: f64 =
-            out_h.as_slice::<f32>().iter().map(|&v| v as f64).sum::<f64>() / n as f64;
-        let kept = mask_h.as_slice::<f32>().iter().filter(|&&m| m != 0.0).count();
+        let mean_out: f64 = out_h
+            .as_slice::<f32>()
+            .iter()
+            .map(|&v| v as f64)
+            .sum::<f64>()
+            / n as f64;
+        let kept = mask_h
+            .as_slice::<f32>()
+            .iter()
+            .filter(|&&m| m != 0.0)
+            .count();
         mean_out_acc += mean_out;
         keep_acc += kept as f64 / n as f64;
     }

crates/xtrain-cuda/build.rs

@@ -37,6 +37,7 @@ fn main() {
         .file("../../csrc/ops/optim.cu")
         .file("../../csrc/ops/attention.cu")
         .file("../../csrc/ops/flash_attention.cu")
+        .file("../../csrc/ops/repeat_kv.cu")
         .file("../../csrc/ops/cast.cu")
         .file("../../csrc/ops/dropout.cu")
         .compile("xtrain_cuda_kernels");

crates/xtrain-cuda/src/ffi.rs

@@ -296,6 +296,37 @@ unsafe extern "C" {
     );
 }
 
+// GQA repeat_kv head broadcast (csrc/ops/repeat_kv.cu, Phase T15). Expands a K/V
+// tensor from [batch·num_kv, S, hd] to the full [batch·nh, S, hd] so the SDPA
+// (composed or flash, both untouched) sees a full set of heads. Forward gathers
+// (out head qh ← kv head qh/group, group = nh/num_kv); backward sums the `group`
+// query heads sharing each kv head (deterministic, no atomics). All F32.
+#[cfg(not(no_cuda))]
+unsafe extern "C" {
+    // Forward: out[b·nh+qh] = in[b·num_kv + qh/group], per [S,hd] head block.
+    pub fn launch_repeat_kv_fwd_f32(
+        input: *const f32,
+        out: *mut f32,
+        batch: i32,
+        nh: i32,
+        num_kv: i32,
+        seq: i32,
+        hd: i32,
+        s: CudaStream,
+    );
+    // Backward: din[b·num_kv+kvh] = Σ_{r<group} dout[b·nh + kvh·group + r].
+    pub fn launch_repeat_kv_bwd_f32(
+        dout: *const f32,
+        din: *mut f32,
+        batch: i32,
+        nh: i32,
+        num_kv: i32,
+        seq: i32,
+        hd: i32,
+        s: CudaStream,
+    );
+}
+
 // GPU-side optimizer kernels (csrc/ops/optim.cu): AdamW step (m/v on device) and
 // the global grad-norm reduction + in-place rescale (Phase T7).
 #[cfg(not(no_cuda))]

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,

crates/xtrain-model/src/config.rs

@@ -10,8 +10,15 @@ pub struct Config {
     pub dim: usize,
     /// Number of decoder blocks.
     pub n_layers: usize,
-    /// Number of attention heads.
+    /// Number of attention (query) heads.
     pub n_heads: usize,
+    /// Number of key/value heads (Phase T15, GQA). Each KV head is shared by a
+    /// group of `n_heads / num_kv_heads` query heads (repeat_kv). Must divide
+    /// `n_heads`. `num_kv_heads == n_heads` (the default) = MHA, bit-identical to
+    /// the pre-T15 path; `num_kv_heads < n_heads` = real grouped-query attention,
+    /// shrinking the K/V projections to `num_kv_heads * head_dim` and exported as a
+    /// real `num_key_value_heads`.
+    pub num_kv_heads: usize,
     /// Per-head dimension (`dim / n_heads`).
     pub head_dim: usize,
     /// SwiGLU hidden width (gate/up project to this, down projects back).
@@ -37,6 +44,7 @@ impl Config {
             dim: n_heads * head_dim,
             n_layers: 2,
             n_heads,
+            num_kv_heads: n_heads, // default = MHA
             head_dim,
             ffn_hidden: 64,
             eps: 1e-5,
@@ -62,6 +70,7 @@ impl Config {
             dim: n_heads * head_dim,
             n_layers,
             n_heads,
+            num_kv_heads: n_heads, // default = MHA; set via with_kv_heads for GQA
             head_dim,
             ffn_hidden,
             eps: 1e-5,
@@ -70,6 +79,27 @@ impl Config {
         }
     }
 
+    /// Set the number of K/V heads (Phase T15, GQA). Builder-style so existing
+    /// `from_arch` call sites stay MHA unless they opt in. Asserts `num_kv_heads`
+    /// divides `n_heads`.
+    pub fn with_kv_heads(mut self, num_kv_heads: usize) -> Self {
+        assert!(num_kv_heads > 0, "num_kv_heads must be > 0");
+        assert_eq!(
+            self.n_heads % num_kv_heads,
+            0,
+            "n_heads {} not divisible by num_kv_heads {num_kv_heads}",
+            self.n_heads
+        );
+        self.num_kv_heads = num_kv_heads;
+        self
+    }
+
+    /// KV projection width (`num_kv_heads * head_dim`). For GQA this is smaller than
+    /// `dim`; for MHA it equals `dim`.
+    pub fn kv_dim(&self) -> usize {
+        self.num_kv_heads * self.head_dim
+    }
+
     /// Transformer-core parameter count: everything except the token embedding and
     /// the LM head (the two `vocab × dim` tables). This is the figure the scaling
     /// ladder is sized against — the 50257-vocab embed+lm_head adds a fixed ~25M on
@@ -82,7 +112,8 @@ impl Config {
     pub fn num_params(&self) -> usize {
         let per_layer = 2 * self.dim                       // 2 rmsnorm gammas
             + 2 * self.head_dim                             // q/k per-head norm gammas
-            + 3 * self.dim * self.dim                       // q/k/v proj
+            + self.dim * self.dim                           // q proj [dim,dim]
+            + 2 * self.dim * self.kv_dim()                  // k/v proj [dim,kv_dim] (GQA: smaller)
             + self.dim * self.dim                           // out proj
             + 2 * self.dim * self.ffn_hidden                // gate/up proj
             + self.ffn_hidden * self.dim; // down proj

crates/xtrain-model/src/model.rs

@@ -13,8 +13,8 @@ use xtrain_tensor::{DType, Device, Tensor};
 struct Block {
     attn_norm: Var, // [dim]
     wq: Var,        // [dim, dim]
-    wk: Var,        // [dim, dim]
-    wv: Var,        // [dim, dim]
+    wk: Var,        // [dim, kv_dim] — kv_dim = num_kv_heads·head_dim (GQA; = dim for MHA)
+    wv: Var,        // [dim, kv_dim]
     q_norm: Var,    // [head_dim] — per-head QK-norm (Qwen3-style)
     k_norm: Var,    // [head_dim]
     wo: Var,        // [dim, dim]
@@ -91,8 +91,9 @@ impl TinyTransformer {
             .map(|_| Block {
                 attn_norm: mk(&[cfg.dim]),
                 wq: mk(&[cfg.dim, cfg.dim]),
-                wk: mk(&[cfg.dim, cfg.dim]),
-                wv: mk(&[cfg.dim, cfg.dim]),
+                // GQA (T15): K/V project to num_kv_heads·head_dim (= dim when MHA).
+                wk: mk(&[cfg.dim, cfg.kv_dim()]),
+                wv: mk(&[cfg.dim, cfg.kv_dim()]),
                 q_norm: mk(&[cfg.head_dim]),
                 k_norm: mk(&[cfg.head_dim]),
                 wo: mk(&[cfg.dim, cfg.dim]),
@@ -435,37 +436,47 @@ fn attention(
     wo: &Var,
 ) -> Var {
     let (nh, hd) = (cfg.n_heads, cfg.head_dim);
+    let num_kv = cfg.num_kv_heads; // GQA (T15): K/V have fewer heads than Q
     let total = batch * seq;
-    let bh = batch * nh;
     let scale = 1.0 / (hd as f32).sqrt();
 
-    // Project, qk-norm + RoPE, then lay out as a batched [B*nh, seq, hd] tensor.
-    //   [B*S,dim] @ [dim,dim] = [B*S,dim]
-    //   reshape   [B*S, nh, hd]
+    // Project, qk-norm + RoPE, then lay out as a batched [B*heads, seq, hd] tensor.
+    // `heads` = nh for Q, num_kv for K/V (GQA; equal for MHA).
+    //   [B*S,dim] @ [dim,heads*hd] = [B*S, heads*hd]
+    //   reshape   [B*S, heads, hd]
     //   qk-norm   per-head RMSNorm over hd (Qwen3-style; Q/K only, before RoPE)
-    //   rope      [B*S, nh, hd] with per-sequence position (period = seq)
-    //   reshape   [B, S, nh, hd] → transpose(1,2) → [B, nh, S, hd] → [B*nh, S, hd]
-    let to_bh = |proj: Var, norm: Option<&Var>| -> Var {
-        let r = ops::reshape(&proj, &[total, nh, hd]);
+    //   rope      [B*S, heads, hd] with per-sequence position (period = seq)
+    //   reshape   [B, S, heads, hd] → transpose(1,2) → [B, heads, S, hd] → [B*heads, S, hd]
+    let to_bh = |proj: Var, heads: usize, norm: Option<&Var>| -> Var {
+        let r = ops::reshape(&proj, &[total, heads, hd]);
         let r = match norm {
-            // Per-head RMSNorm: flatten the (B*S,nh) head rows, norm over hd,
+            // Per-head RMSNorm: flatten the (B*S,heads) head rows, norm over hd,
             // restore. RoPE follows on the normed Q/K (mirrors xserv qwen3.rs).
             Some(gamma) => {
-                let flat = ops::reshape(&r, &[total * nh, hd]);
+                let flat = ops::reshape(&r, &[total * heads, hd]);
                 let normed = ops::rms_norm(&flat, &norm_gamma(cdt, gamma), cfg.eps);
-                let r = ops::reshape(&normed, &[total, nh, hd]);
+                let r = ops::reshape(&normed, &[total, heads, hd]);
                 ops::rope(&r, cfg.rope_theta, seq)
             }
             None => r,
         };
-        let r = ops::reshape(&r, &[batch, seq, nh, hd]);
-        let t = ops::transpose_4d12(&r); // [B, nh, S, hd]
-        ops::reshape(&t, &[bh, seq, hd]) // [B*nh, S, hd]
+        let r = ops::reshape(&r, &[batch, seq, heads, hd]);
+        let t = ops::transpose_4d12(&r); // [B, heads, S, hd]
+        ops::reshape(&t, &[batch * heads, seq, hd]) // [B*heads, S, hd]
     };
 
-    let q = to_bh(linear(cdt, x, wq), Some(q_norm));
-    let k = to_bh(linear(cdt, x, wk), Some(k_norm));
-    let v = to_bh(linear(cdt, x, wv), None);
+    let q = to_bh(linear(cdt, x, wq), nh, Some(q_norm));
+    // K/V are laid out with num_kv heads, then repeat_kv-broadcast to nh heads so
+    // the SDPA below (composed or flash, both unchanged) sees a full head set. The
+    // broadcast's backward sums each KV head's group of query-head grads (GQA). For
+    // MHA (num_kv == nh) repeat_kv is identity → bit-identical to the pre-T15 path.
+    let k = to_bh(linear(cdt, x, wk), num_kv, Some(k_norm));
+    let v = to_bh(linear(cdt, x, wv), num_kv, None);
+    let (k, v) = if num_kv == nh {
+        (k, v)
+    } else {
+        (ops::repeat_kv(&k, nh, batch), ops::repeat_kv(&v, nh, batch))
+    };
 
     // Causal SDPA over all B*nh (sequence,head) blocks. `flash` (T14) picks the
     // single fused flash kernel (online softmax, no materialized [bh,S,S] scores);

crates/xtrain-model/tests/gqa.rs

@@ -0,0 +1,269 @@
+// T15 GQA correctness gate. Real grouped-query attention (num_kv_heads <
+// num_heads): K/V project to num_kv_heads·head_dim and are repeat_kv-broadcast to
+// the full head set before the SDPA. This test pins three things:
+//
+//   1. GQA flash == GQA composed (forward logits + loss + EVERY param grad) — the
+//      repeat_kv broadcast feeds both SDPA paths unchanged, so they must agree; in
+//      particular the wk/wv grads (which flow back through repeat_kv's group-sum)
+//      must match. Parameterised over fp32 (tight) and bf16 (rounding band).
+//   2. group==1 (num_kv_heads == n_heads) is BIT-IDENTICAL to the pre-T15 MHA path
+//      (a model with num_kv_heads explicitly == n_heads vs the default config):
+//      forward logits + every grad |Δ|=0. The regression guard.
+//   3. wk/wv really shrank to [dim, kv_dim] under GQA (shape check).
+#![cfg(not(no_cuda))]
+
+use xtrain_cuda::device;
+use xtrain_model::{Config, TinyTransformer, batched_ids_tensor};
+use xtrain_tensor::{DType, Device};
+
+fn fill(n: usize, seed: u64, scale: f32) -> Vec<f32> {
+    let mut state = seed
+        .wrapping_mul(2862933555777941757)
+        .wrapping_add(3037000493);
+    (0..n)
+        .map(|_| {
+            state = state
+                .wrapping_mul(6364136223846793005)
+                .wrapping_add(1442695040888963407);
+            (((state >> 33) as f32 / (1u64 << 31) as f32) - 0.5) * 2.0 * scale
+        })
+        .collect()
+}
+
+fn build(cfg: Config, device: Device, dtype: DType, flash: bool) -> TinyTransformer {
+    let mut seed = 1u64;
+    let m = TinyTransformer::new(cfg, device, |shape| {
+        seed = seed.wrapping_add(1);
+        let n: usize = shape.iter().product();
+        if shape.len() == 1 {
+            fill(n, seed, 0.02).iter().map(|v| v + 1.0).collect()
+        } else {
+            fill(n, seed, 0.08)
+        }
+    });
+    m.with_compute_dtype(dtype).with_flash(flash)
+}
+
+fn host(t: &xtrain_tensor::Tensor) -> Vec<f32> {
+    t.to_dtype(DType::F32)
+        .to_device(Device::Cpu)
+        .as_slice::<f32>()
+        .to_vec()
+}
+
+// A real GQA config: 8 query heads, 2 kv heads → group 4. seq=40 > FA_TILE=32 so
+// the flash online-softmax tile path is exercised too.
+fn gqa_cfg() -> Config {
+    let mut cfg = Config::tiny();
+    cfg.vocab = 16;
+    cfg.n_layers = 3;
+    // tiny() is 2 heads; rebuild with 8 query / 2 kv heads keeping head_dim=16.
+    Config::from_arch(cfg.vocab, 8, cfg.head_dim, cfg.n_layers, cfg.ffn_hidden).with_kv_heads(2)
+}
+
+fn ids_targets(cfg: &Config, batch: usize, seq: usize) -> (Vec<Vec<i32>>, Vec<Vec<i32>>) {
+    let seqs = (0..batch)
+        .map(|b| {
+            (0..seq)
+                .map(|i| ((b * 7 + i * 3 + 1) % cfg.vocab) as i32)
+                .collect()
+        })
+        .collect();
+    let tgts = (0..batch)
+        .map(|b| {
+            (0..seq)
+                .map(|i| ((b * 5 + i * 2 + 2) % cfg.vocab) as i32)
+                .collect()
+        })
+        .collect();
+    (seqs, tgts)
+}
+
+#[allow(clippy::type_complexity)]
+fn run_both(
+    cfg: Config,
+    dtype: DType,
+) -> (Vec<f32>, f32, Vec<Vec<f32>>, Vec<f32>, f32, Vec<Vec<f32>>) {
+    device::set_device(0).unwrap();
+    let device = Device::Cuda(0);
+    let (batch, seq) = (3usize, 40usize);
+    let (seqs, tgts) = ids_targets(&cfg, batch, seq);
+    let ids = batched_ids_tensor(&seqs, device);
+    let tgt = batched_ids_tensor(&tgts, device);
+
+    let off = build(cfg, device, dtype, false);
+    let off_logits = host(&off.forward_batched(&ids, batch).value());
+    let off_loss = off.loss_batched(&ids, &tgt, batch);
+    let off_loss_val = host(&off_loss.value())[0];
+    off_loss.backward();
+    let off_grads: Vec<Vec<f32>> = off
+        .params()
+        .iter()
+        .map(|p| host(&p.grad().expect("off grad")))
+        .collect();
+
+    let on = build(cfg, device, dtype, true);
+    let on_logits = host(&on.forward_batched(&ids, batch).value());
+    let on_loss = on.loss_batched(&ids, &tgt, batch);
+    let on_loss_val = host(&on_loss.value())[0];
+    on_loss.backward();
+    let on_grads: Vec<Vec<f32>> = on
+        .params()
+        .iter()
+        .map(|p| host(&p.grad().expect("on grad")))
+        .collect();
+
+    (
+        off_logits,
+        off_loss_val,
+        off_grads,
+        on_logits,
+        on_loss_val,
+        on_grads,
+    )
+}
+
+// GQA flash vs composed: same SDPA math on the same repeat_kv-broadcast K/V → fp32
+// agrees to reduction-order, bf16 to its rounding band.
+#[test]
+fn gqa_flash_matches_composed_fp32() {
+    assert!(device::device_count().unwrap() > 0, "no CUDA device");
+    let cfg = gqa_cfg();
+    assert!(cfg.num_kv_heads < cfg.n_heads, "test must be real GQA");
+    let (off_l, off_loss, off_g, on_l, on_loss, on_g) = run_both(cfg, DType::F32);
+
+    let logit_rel = off_l
+        .iter()
+        .zip(&on_l)
+        .map(|(a, b)| (a - b).abs() / a.abs().max(1e-4))
+        .fold(0.0f32, f32::max);
+    let loss_rel = (off_loss - on_loss).abs() / off_loss.abs().max(1e-4);
+    println!(
+        "[GQA F32] flash on/off: loss {off_loss:.6}/{on_loss:.6} (rel {loss_rel:.2e}), \
+         logits max rel {logit_rel:.2e}"
+    );
+    assert!(
+        logit_rel < 1e-3,
+        "[GQA F32] logits diverged: {logit_rel:.2e}"
+    );
+    assert!(loss_rel < 1e-3, "[GQA F32] loss diverged: {loss_rel:.2e}");
+
+    let mut worst = 0.0f32;
+    for (a_g, b_g) in off_g.iter().zip(&on_g) {
+        for (a, b) in a_g.iter().zip(b_g) {
+            worst = worst.max((a - b).abs() / a.abs().max(1e-3));
+        }
+    }
+    println!("[GQA F32] flash on/off grad max rel = {worst:.3e}");
+    assert!(worst < 2e-2, "[GQA F32] grads diverged: {worst:.3e}");
+}
+
+#[test]
+fn gqa_flash_matches_composed_bf16() {
+    assert!(device::device_count().unwrap() > 0, "no CUDA device");
+    let (off_l, off_loss, off_g, on_l, on_loss, on_g) = run_both(gqa_cfg(), DType::BF16);
+
+    let loss_rel = (off_loss - on_loss).abs() / off_loss.abs().max(1e-4);
+    println!("[GQA BF16] flash on/off: loss {off_loss:.5}/{on_loss:.5} (rel {loss_rel:.3e})");
+    assert!(loss_rel < 2e-2, "[GQA BF16] loss diverged: {loss_rel:.3e}");
+
+    let n = off_l.len();
+    let mut rels: Vec<f32> = off_l
+        .iter()
+        .zip(&on_l)
+        .map(|(f, b)| (b - f).abs() / f.abs().max(1.0))
+        .collect();
+    rels.sort_by(|a, b| a.partial_cmp(b).unwrap());
+    let mean: f32 = rels.iter().sum::<f32>() / n as f32;
+    let p99 = rels[(n as f32 * 0.99) as usize];
+    println!("[GQA BF16] logits: mean rel {mean:.3e}, p99 rel {p99:.3e}");
+    assert!(
+        mean < 1e-2,
+        "[GQA BF16] logits mean rel too high: {mean:.3e}"
+    );
+    assert!(p99 < 5e-2, "[GQA BF16] logits p99 rel too high: {p99:.3e}");
+
+    let mut worst = 0.0f32;
+    for (a_g, b_g) in off_g.iter().zip(&on_g) {
+        let scale = a_g.iter().map(|v| v.abs()).fold(0.0f32, f32::max).max(1e-6);
+        let mean_err: f32 =
+            a_g.iter().zip(b_g).map(|(f, b)| (f - b).abs()).sum::<f32>() / a_g.len() as f32 / scale;
+        worst = worst.max(mean_err);
+    }
+    println!("[GQA BF16] grads: worst per-tensor scaled-mean err = {worst:.3e}");
+    assert!(worst < 3e-2, "[GQA BF16] grads diverged: {worst:.3e}");
+}
+
+// REGRESSION GUARD: num_kv_heads == n_heads (group 1) must be BIT-IDENTICAL to the
+// pre-T15 MHA path. Build one model with the default config (num_kv_heads ==
+// n_heads, the untouched path: repeat_kv not even invoked) and one that explicitly
+// sets num_kv_heads = n_heads, then assert forward logits + every grad match to the
+// bit. (Same composed path, so this is exact equality, not a tolerance.)
+#[test]
+fn gqa_group1_bit_identical_to_mha() {
+    assert!(device::device_count().unwrap() > 0, "no CUDA device");
+    device::set_device(0).unwrap();
+    let device = Device::Cuda(0);
+
+    let mut base = Config::tiny();
+    base.vocab = 16;
+    base.n_layers = 3;
+    let base = Config::from_arch(base.vocab, 4, base.head_dim, base.n_layers, base.ffn_hidden);
+    // `explicit` sets num_kv_heads = n_heads (already the default, but exercises the
+    // with_kv_heads path); they are the same config → must produce identical output.
+    let explicit = base.with_kv_heads(base.n_heads);
+    assert_eq!(base.num_kv_heads, explicit.num_kv_heads);
+
+    let (batch, seq) = (2usize, 8usize);
+    let (seqs, tgts) = ids_targets(&base, batch, seq);
+    let ids = batched_ids_tensor(&seqs, device);
+    let tgt = batched_ids_tensor(&tgts, device);
+
+    let run = |cfg: Config| -> (Vec<f32>, f32, Vec<Vec<f32>>) {
+        let m = build(cfg, device, DType::F32, false);
+        let logits = host(&m.forward_batched(&ids, batch).value());
+        let loss = m.loss_batched(&ids, &tgt, batch);
+        let loss_v = host(&loss.value())[0];
+        loss.backward();
+        let grads = m
+            .params()
+            .iter()
+            .map(|p| host(&p.grad().unwrap()))
+            .collect();
+        (logits, loss_v, grads)
+    };
+    let (la, sa, ga) = run(base);
+    let (lb, sb, gb) = run(explicit);
+    assert_eq!(la, lb, "group-1 logits must be bit-identical to MHA");
+    assert_eq!(sa, sb, "group-1 loss must be bit-identical to MHA");
+    for (a, b) in ga.iter().zip(&gb) {
+        assert_eq!(a, b, "group-1 grad must be bit-identical to MHA");
+    }
+    println!("[GQA group1] bit-identical to MHA: logits + loss + all grads |Δ|=0");
+}
+
+// Under GQA, wk/wv must be [dim, kv_dim] (= num_kv_heads·head_dim), wq stays [dim,dim].
+#[test]
+fn gqa_kv_proj_shape() {
+    assert!(device::device_count().unwrap() > 0, "no CUDA device");
+    device::set_device(0).unwrap();
+    let device = Device::Cuda(0);
+    let cfg = gqa_cfg();
+    let m = build(cfg, device, DType::F32, false);
+    let p = m.params();
+    // params order: embed[0], then block 0 = [attn_norm[1], wq[2], wk[3], wv[4],
+    // q_norm[5], k_norm[6], wo[7], ...]
+    let wq = p[2].value().shape().to_vec();
+    let wk = p[3].value().shape().to_vec();
+    let wv = p[4].value().shape().to_vec();
+    assert_eq!(wq, vec![cfg.dim, cfg.dim], "wq must be [dim,dim]");
+    assert_eq!(wk, vec![cfg.dim, cfg.kv_dim()], "wk must be [dim,kv_dim]");
+    assert_eq!(wv, vec![cfg.dim, cfg.kv_dim()], "wv must be [dim,kv_dim]");
+    println!(
+        "[GQA shapes] wq {:?} wk {:?} wv {:?} (kv_dim {})",
+        wq,
+        wk,
+        wv,
+        cfg.kv_dim()
+    );
+}

crates/xtrain-model/tests/parity.py

@@ -52,6 +52,10 @@ cfg = read_cfg()
 DIM = int(cfg["dim"])
 NL = int(cfg["n_layers"])
 NH = int(cfg["n_heads"])
+# GQA (T15): num_kv_heads <= n_heads; each kv head shared by group query heads.
+# Default to NH (MHA) for fixtures dumped before the field existed.
+NKV = int(cfg.get("num_kv_heads", str(NH)))
+GROUP = NH // NKV
 HD = int(cfg["head_dim"])
 EPS = float(cfg["eps"])
 THETA = float(cfg["rope_theta"])
@@ -114,17 +118,23 @@ for L in layers:
     # Attention
     x = rms_norm(h, L["attn_norm"])
     q = (x @ L["wq"]).reshape(B * SEQ, NH, HD)
-    k = (x @ L["wk"]).reshape(B * SEQ, NH, HD)
-    v = (x @ L["wv"]).reshape(B * SEQ, NH, HD)
+    # GQA: K/V project to NKV heads, then repeat each kv head GROUP times to NH.
+    k = (x @ L["wk"]).reshape(B * SEQ, NKV, HD)
+    v = (x @ L["wv"]).reshape(B * SEQ, NKV, HD)
     # Per-head QK-norm (Qwen3-style), before RoPE.
     q = rms_norm(q, L["q_norm"])
     k = rms_norm(k, L["k_norm"])
     q = rope(q)  # [B*SEQ, nh, hd]
-    k = rope(k)
-    # Reshape to [B, NH, SEQ, HD] so attention runs within each sequence.
+    k = rope(k)  # [B*SEQ, nkv, hd]
+    # Reshape to [B, *, SEQ, HD]; broadcast kv heads to NH (repeat_interleave along
+    # the head axis: kv head kvh → query heads [kvh*GROUP, (kvh+1)*GROUP), matching
+    # xtrain repeat_kv + xserv repeat_kv).
     q = q.reshape(B, SEQ, NH, HD).transpose(1, 2)  # [B, nh, seq, hd]
-    k = k.reshape(B, SEQ, NH, HD).transpose(1, 2)
-    v = v.reshape(B, SEQ, NH, HD).transpose(1, 2)
+    k = k.reshape(B, SEQ, NKV, HD).transpose(1, 2)  # [B, nkv, seq, hd]
+    v = v.reshape(B, SEQ, NKV, HD).transpose(1, 2)
+    if GROUP > 1:
+        k = k.repeat_interleave(GROUP, dim=1)  # [B, nh, seq, hd]
+        v = v.repeat_interleave(GROUP, dim=1)
     scale = 1.0 / math.sqrt(HD)
     scores = (q @ k.transpose(-1, -2)) * scale + mask  # [B, nh, seq, seq]
     probs = torch.softmax(scores, dim=-1)

crates/xtrain-model/tests/parity_dump.rs

@@ -58,8 +58,20 @@ fn dump_for_parity() {
     // sequence-major to [B*S]=8 ids. Per-sequence RoPE position (resets at the
     // sequence boundary) + per-sequence causal masking (no cross-sequence
     // attention) are both checked against PyTorch.
+    // Default: tiny MHA (2 heads). With XTRAIN_PARITY_KV_HEADS=k set, dump a real
+    // GQA config (8 query heads / k kv heads) so parity.py checks GQA at B>1 — the
+    // kv-projection shapes + the repeat_kv group-sum backward against PyTorch.
     let mut cfg = Config::tiny();
     cfg.vocab = 12;
+    if let Ok(kv) = std::env::var("XTRAIN_PARITY_KV_HEADS") {
+        let kv: usize = kv.parse().expect("XTRAIN_PARITY_KV_HEADS");
+        cfg = Config::from_arch(cfg.vocab, 8, cfg.head_dim, cfg.n_layers, cfg.ffn_hidden)
+            .with_kv_heads(kv);
+        println!(
+            "parity: GQA config (n_heads {} kv_heads {})",
+            cfg.n_heads, cfg.num_kv_heads
+        );
+    }
     let batch = 2usize;
     let seq = 4usize;
     let ids: Vec<i32> = vec![3, 1, 4, 1, 5, 9, 2, 6]; // [B*S], sequence-major
@@ -92,6 +104,7 @@ fn dump_for_parity() {
         writeln!(f, "dim {}", cfg.dim).unwrap();
         writeln!(f, "n_layers {}", cfg.n_layers).unwrap();
         writeln!(f, "n_heads {}", cfg.n_heads).unwrap();
+        writeln!(f, "num_kv_heads {}", cfg.num_kv_heads).unwrap();
         writeln!(f, "head_dim {}", cfg.head_dim).unwrap();
         writeln!(f, "ffn_hidden {}", cfg.ffn_hidden).unwrap();
         writeln!(f, "eps {:e}", cfg.eps).unwrap();

crates/xtrain-tensor/src/tensor.rs

@@ -1291,6 +1291,97 @@ impl Tensor {
         (dq.to_dtype(dt), dk.to_dtype(dt), dv.to_dtype(dt))
     }
 
+    // --- GQA repeat_kv head broadcast (the T15 op) ---
+
+    /// GQA repeat_kv (Phase T15). `self` is a K or V tensor `[batch·num_kv, seq,
+    /// head_dim]`; returns `[batch·nh, seq, head_dim]` with each KV head broadcast
+    /// to its `group = nh/num_kv` query heads (query head `qh` ← kv head `qh/group`,
+    /// query heads contiguous within a group — matching xserv's repeat_kv). When
+    /// `nh == num_kv` (group 1) this is a plain copy (identity → MHA bit-identical).
+    /// bf16 callers are upcast→f32→kernel→downcast (the attention stack's fp32 policy).
+    #[cfg(not(no_cuda))]
+    pub fn repeat_kv(&self, nh: usize, batch: usize) -> Self {
+        assert_eq!(
+            self.ndim(),
+            3,
+            "repeat_kv input must be [batch·num_kv,seq,hd]"
+        );
+        let (bh_kv, seq, hd) = (self.shape[0], self.shape[1], self.shape[2]);
+        assert_eq!(
+            bh_kv % batch,
+            0,
+            "repeat_kv: bh_kv {bh_kv} not div by batch {batch}"
+        );
+        let num_kv = bh_kv / batch;
+        assert_eq!(
+            nh % num_kv,
+            0,
+            "repeat_kv: nh {nh} not divisible by num_kv {num_kv}"
+        );
+        if self.dtype == DType::BF16 {
+            return self
+                .to_dtype(DType::F32)
+                .repeat_kv(nh, batch)
+                .to_dtype(DType::BF16);
+        }
+        assert_eq!(self.dtype, DType::F32, "repeat_kv supports F32/BF16");
+        let out = Tensor::zeros(&[batch * nh, seq, hd], DType::F32, self.device());
+        unsafe {
+            xtrain_cuda::ffi::launch_repeat_kv_fwd_f32(
+                self.data_ptr() as *const f32,
+                out.data_ptr() as *mut f32,
+                batch as i32,
+                nh as i32,
+                num_kv as i32,
+                seq as i32,
+                hd as i32,
+                std::ptr::null_mut(),
+            );
+        }
+        out
+    }
+
+    /// Backward of [`repeat_kv`](Self::repeat_kv). `dout`:[batch·nh,seq,hd] →
+    /// `din`:[batch·num_kv,seq,hd], summing the `group` query heads that share each
+    /// KV head (the multi-group grad accumulation GQA correctness hinges on).
+    /// Deterministic (no atomics: each kv element serially sums its group rows).
+    #[cfg(not(no_cuda))]
+    pub fn repeat_kv_backward(dout: &Tensor, num_kv: usize, batch: usize) -> Self {
+        assert_eq!(
+            dout.ndim(),
+            3,
+            "repeat_kv_backward dout must be [batch·nh,seq,hd]"
+        );
+        let (bh_q, seq, hd) = (dout.shape[0], dout.shape[1], dout.shape[2]);
+        assert_eq!(bh_q % batch, 0, "repeat_kv_backward: bh_q not div by batch");
+        let nh = bh_q / batch;
+        assert_eq!(
+            nh % num_kv,
+            0,
+            "repeat_kv_backward: nh not divisible by num_kv"
+        );
+        let dt = dout.dtype;
+        if dt == DType::BF16 {
+            return Tensor::repeat_kv_backward(&dout.to_dtype(DType::F32), num_kv, batch)
+                .to_dtype(DType::BF16);
+        }
+        assert_eq!(dt, DType::F32, "repeat_kv_backward supports F32/BF16");
+        let din = Tensor::zeros(&[batch * num_kv, seq, hd], DType::F32, dout.device());
+        unsafe {
+            xtrain_cuda::ffi::launch_repeat_kv_bwd_f32(
+                dout.data_ptr() as *const f32,
+                din.data_ptr() as *mut f32,
+                batch as i32,
+                nh as i32,
+                num_kv as i32,
+                seq as i32,
+                hd as i32,
+                std::ptr::null_mut(),
+            );
+        }
+        din
+    }
+
     /// 4D axis-(1,2) transpose: `self`:[a,b,c,d] → [a,c,b,d],
     /// `out[i,k,j,l]=self[i,j,k,l]`. Lays out batched multi-head attention
     /// (`[B,S,nh,hd] <-> [B,nh,S,hd]`). Its own backward is the same op (swap b,c).

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

@@ -174,7 +174,7 @@ fn config_json(cfg: &Config) -> String {
         ffn = cfg.ffn_hidden,
         layers = cfg.n_layers,
         heads = cfg.n_heads,
-        kv_heads = cfg.n_heads, // xtrain is MHA → kv heads == query heads
+        kv_heads = cfg.num_kv_heads, // GQA (T15): real num_key_value_heads (= n_heads for MHA)
         head_dim = cfg.head_dim,
         eps = cfg.eps,
         theta = cfg.rope_theta,
@@ -206,6 +206,8 @@ 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 match the trained ckpt; default = --heads).
+    let kv_heads = flag(&args, "--kv-heads", n_heads);
 
     assert!(device::device_count().unwrap() > 0, "no CUDA device");
     device::set_device(0).unwrap();
@@ -214,15 +216,16 @@ fn main() {
     // Size the model from the arch flags + gpt2 vocab; must match the checkpoint.
     let tok = Tokenizer::from_file(&tok_path);
     let vocab = tok.vocab_size();
-    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!(
-        "export: ckpt {} → {} (vocab {}, dim {}, layers {}, heads {}, head_dim {})",
+        "export: ckpt {} → {} (vocab {}, dim {}, layers {}, heads {}, kv_heads {}, head_dim {})",
         ckpt.display(),
         out_dir.display(),
         cfg.vocab,
         cfg.dim,
         cfg.n_layers,
         cfg.n_heads,
+        cfg.num_kv_heads,
         cfg.head_dim,
     );
 

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

@@ -72,6 +72,8 @@ 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 match the ckpt; default = --heads).
+    let kv_heads = flag(&args, "--kv-heads", n_heads);
     let max_new = flag(&args, "--max-tokens", 40usize);
 
     assert!(device::device_count().unwrap() > 0, "no CUDA device");
@@ -79,14 +81,16 @@ fn main() {
     let device = Device::Cuda(0);
 
     let tok = Tokenizer::from_file(&tok_path);
-    let cfg = Config::from_arch(tok.vocab_size(), n_heads, head_dim, n_layers, ffn);
+    let cfg = Config::from_arch(tok.vocab_size(), n_heads, head_dim, n_layers, ffn)
+        .with_kv_heads(kv_heads);
     println!(
-        "greedy_sample: ckpt {} (vocab {}, dim {}, layers {}, heads {}, head_dim {})",
+        "greedy_sample: ckpt {} (vocab {}, dim {}, layers {}, heads {}, kv_heads {}, head_dim {})",
         ckpt.display(),
         cfg.vocab,
         cfg.dim,
         cfg.n_layers,
         cfg.n_heads,
+        cfg.num_kv_heads,
         cfg.head_dim,
     );
 

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

@@ -88,6 +88,8 @@ 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 {
@@ -160,14 +162,16 @@ fn main() {
         (corpus, None)
     };
 
-    let mut cfg = Config::from_arch(vocab, n_heads, head_dim, n_layers, ffn);
+    let mut cfg =
+        Config::from_arch(vocab, n_heads, head_dim, n_layers, ffn).with_kv_heads(kv_heads);
     cfg.dropout = dropout;
     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,

csrc/ops/repeat_kv.cu

@@ -0,0 +1,84 @@
+// repeat_kv: the grouped-query-attention (GQA) head broadcast (Phase T15).
+//
+// GQA projects K/V to fewer heads than Q (num_kv_heads < num_heads); each KV head
+// is SHARED by a group of `group = num_heads / num_kv_heads` query heads. Before
+// the SDPA (composed or fused-flash, both untouched), we expand the KV tensor from
+// [B·num_kv, S, hd] to the full [B·nh, S, hd] so the existing per-(batch,head)
+// attention sees a full set of heads. GQA is then "free" for both SDPA paths.
+//
+// Layout: K/V are [bh_kv, S, hd] = [B·num_kv, S, hd] row-major, contiguous; the
+// output is [bh_q, S, hd] = [B·nh, S, hd]. The head ordering matches xserv's
+// repeat_kv (crates/xserv-model/src/qwen3.rs): query head qh reads kv head
+// qh/group, query heads CONTIGUOUS within a group (dst = kvh*group + r). So:
+//
+//   out[b·nh + qh, :, :] = in[b·num_kv + qh/group, :, :]
+//
+// Forward is a gather (each output row copies one input row). Backward is its
+// transpose: a kv head receives the SUM of the `group` query heads that share it
+//   din[b·num_kv + kvh, e] = Σ_{r∈[0,group)} dout[b·nh + kvh·group + r, e]
+// — the multi-group-to-one grad accumulation GQA's correctness hinges on. Each
+// input element is owned by exactly one thread that serially sums its `group`
+// source rows: race-free, NO atomics, run-to-run DETERMINISTIC. group==1 makes
+// both directions a plain copy (identity → bit-identical to the MHA path).
+//
+// All F32, contiguous. (bf16 callers upcast → f32 on the Rust side and downcast
+// the f32 result, mirroring the rest of the attention stack's fp32 policy.)
+
+#include <math.h>
+
+extern "C" {
+
+// Forward gather. grid-stride over the bh_q·S·hd output elements; each output
+// element copies from its kv-head source row. b = (out_bh / nh), qh = out_bh % nh,
+// kv source bh = b·num_kv + qh/group.
+__global__ void repeat_kv_fwd_k(const float* in, float* out, int nh, int num_kv,
+                                int group, int seq, int hd) {
+    long row_elems = (long)seq * hd;       // S·hd per head block
+    // One block per (batch, query-head); threads cover the S·hd block.
+    int out_bh = blockIdx.x;               // over B·nh
+    int b = out_bh / nh;
+    int qh = out_bh % nh;
+    int kvh = qh / group;
+    const float* src = in + ((long)b * num_kv + kvh) * row_elems;
+    float* dst = out + (long)out_bh * row_elems;
+    for (long e = threadIdx.x; e < row_elems; e += blockDim.x) dst[e] = src[e];
+}
+
+void launch_repeat_kv_fwd_f32(const float* in, float* out, int batch, int nh,
+                              int num_kv, int seq, int hd, void* s) {
+    int group = nh / num_kv;
+    int blk = (seq * hd) < 256 ? (seq * hd) : 256;
+    if (blk < 32) blk = 32;
+    repeat_kv_fwd_k<<<batch * nh, blk, 0, (cudaStream_t)s>>>(in, out, nh, num_kv,
+                                                             group, seq, hd);
+}
+
+// Backward sum. One block per (batch, kv-head); threads cover the S·hd block.
+// Each owned input element sums the `group` contiguous query-head source rows.
+__global__ void repeat_kv_bwd_k(const float* dout, float* din, int nh, int num_kv,
+                                int group, int seq, int hd) {
+    long row_elems = (long)seq * hd;
+    int in_bh = blockIdx.x;                // over B·num_kv
+    int b = in_bh / num_kv;
+    int kvh = in_bh % num_kv;
+    int qh0 = kvh * group;                 // first query head sharing this kv head
+    float* dst = din + (long)in_bh * row_elems;
+    const float* base = dout + ((long)b * nh + qh0) * row_elems;
+    for (long e = threadIdx.x; e < row_elems; e += blockDim.x) {
+        float acc = 0.0f;
+        for (int r = 0; r < group; ++r) acc += base[(long)r * row_elems + e];
+        dst[e] = acc;
+    }
+}
+
+void launch_repeat_kv_bwd_f32(const float* dout, float* din, int batch, int nh,
+                              int num_kv, int seq, int hd, void* s) {
+    int group = nh / num_kv;
+    int blk = (seq * hd) < 256 ? (seq * hd) : 256;
+    if (blk < 32) blk = 32;
+    repeat_kv_bwd_k<<<batch * num_kv, blk, 0, (cudaStream_t)s>>>(dout, din, nh,
+                                                                 num_kv, group,
+                                                                 seq, hd);
+}
+
+} // extern "C"

docs/14-gqa.md

@@ -0,0 +1,180 @@
+# Phase T15: Grouped-Query Attention (GQA) — Design Document
+
+## Goal
+
+到 T14 为止，xtrain 的 attention 都是 **MHA**（`num_kv_heads = num_heads`）——每个
+query 头有自己独立的 K/V 头。导出 xserv 时 `num_key_value_heads = num_attention_heads`
+（退化 GQA，docs/08）。
+
+T15 做**真正的 grouped-query attention**：`num_kv_heads < num_heads`，K/V 只投影到
+`num_kv_heads · head_dim`，每个 KV 头被一组 `group = num_heads / num_kv_heads` 个 query
+头**共享**（repeat_kv / broadcast）。GQA 是现代 LLM（Llama-2-70B、Qwen2/3、Mistral）的标配
+——它把 KV cache 显存（推理）与 K/V 投影参数（训练）按 `group` 倍压缩，几乎不掉质量。
+
+**硬闸门是诚实正确性**，重点在 **repeat_kv 的反向梯度累加**：一个 KV 头被 `group` 个 query 头
+共享，反向时这 `group` 个 query 头各自对该 KV 头的梯度必须**正确求和**回到那一个共享 KV 头上。
+这条「多组 q 头梯度汇到一个 kv 头」的累加路径是本任务最易错处，单列为首要 grad-check 闸门。
+
+GQA 必须**同时**接进 T14 的 fused flash kernel（优先）与旧 composed/batched SDPA 路径，且
+`num_kv_heads == num_heads`（`group = 1`）时与现有 MHA 路径**逐位一致**（回归保护）。
+
+## 什么是 GQA
+
+MHA：`num_heads` 个 query 头，每个配一个独立 K/V 头。
+MQA（multi-query）：所有 query 头共享**一个** K/V 头（极端）。
+GQA：折中——`num_kv_heads` 个 K/V 头，每个被 `group = num_heads/num_kv_heads` 个相邻 query
+头共享。`num_kv_heads = num_heads` 退化为 MHA，`num_kv_heads = 1` 退化为 MQA。
+
+```
+num_heads = 8, num_kv_heads = 2  →  group = 4
+q heads:  0 1 2 3 4 5 6 7
+kv heads: 0 0 0 0 1 1 1 1        # q head qh 用 kv head qh/group（相邻分组，连续）
+```
+
+**分组约定必须与 xserv repeat_kv 一致**（闭环命门）：xserv 的 `repeat_kv`
+（`crates/xserv-model/src/qwen3.rs`）把 kv 头 `kvh` 复制到目标头 `dst = kvh*group + r`
+（`r∈[0,group)`），即**query 头 `qh` 读 kv 头 `qh/group`，组内 query 头连续**。xtrain 的
+repeat_kv 用同一映射，否则导出的 `q_proj` 行块与 kv 头对不上 → 闭环必崩。
+
+## Module Layout（surgical：复用已验证的两条 SDPA，GQA = 头维 broadcast op）
+
+```
+csrc/ops/repeat_kv.cu                     # 新：repeat_kv fwd（头块 gather）+ bwd（组内 group 行求和，无 atomic，确定性）
+crates/xtrain-cuda/
+├── src/ffi.rs                            # +launch_repeat_kv_fwd_f32 / _bwd_f32 声明（no_cuda 门控）
+└── build.rs                              # +repeat_kv.cu
+crates/xtrain-tensor/src/tensor.rs        # +Tensor::repeat_kv / repeat_kv_backward（[B*kvh,S,hd]→[B*nh,S,hd]；bf16 upcast→f32→downcast）
+crates/xtrain-autodiff/
+├── src/ops.rs                            # +ops::repeat_kv 节点（fwd broadcast，bwd 组内求和）
+└── tests/autograd.rs                     # +repeat_kv grad-check（含 group>1 的多组梯度累加）
+crates/xtrain-model/
+├── src/config.rs                         # +num_kv_heads 字段（默认 = n_heads → MHA）；from_arch 加形参；num_params 计 K/V 投影按 kv_dim
+├── src/model.rs                          # wk/wv 投影到 kv_dim；attention() 在 SDPA 前对 K/V 做 ops::repeat_kv；两条路径都吃到 GQA
+└── tests/gqa.rs                          # 新：GQA(group>1) flash==composed + group=1 与 MHA 逐位一致
+crates/xtrain-train/src/bin/train.rs            # +--kv-heads flag
+crates/xtrain-distributed/src/bin/train_ddp.rs  # +--kv-heads flag（DDP 路径）
+crates/xtrain-train/src/bin/export_safetensors.rs # +--kv-heads；config.json 写真 num_key_value_heads
+crates/xtrain-model/tests/parity{.py,_dump.rs}  # PyTorch 对拍加 GQA（kv 投影 + repeat_kv）
+```
+
+## Key Design Decisions
+
+### ① GQA = K/V 头维 broadcast op，喂给**未改动**的两条 SDPA（不写第三套 attention）
+
+T14 已经有两条**逐位/数值都验证过**的 SDPA：composed（`ops::attention`）与 fused flash
+（`ops::flash_attention`），二者都吃 `[bh, S, hd]`（`bh = batch·heads`）。GQA 的本质只是「K/V
+比 Q 少 `group` 倍头，用前把每个 kv 头复制 `group` 份」。所以**最外科**的做法：
+
+- wk/wv 投影到 `kv_dim = num_kv_heads · head_dim`，按 `[B, num_kv, S, hd] → [B·num_kv, S, hd]`
+  排好（和 Q 的 `[B·nh, S, hd]` 同流水线，只是头数不同）。
+- 在调 SDPA 之前，对 K、V 各做一个新 autograd op `ops::repeat_kv`，把 `[B·num_kv, S, hd]`
+  **broadcast** 成 `[B·nh, S, hd]`（输出行 `b·nh + qh` = 输入行 `b·num_kv + qh/group` 的拷贝）。
+- 之后 `ops::attention` / `ops::flash_attention` **一字不改**——它们看到的就是满头的
+  `[B·nh, S, hd]`，GQA 对两条路径**同时、免费**生效。flash kernel / composed kernel 都不用碰。
+
+**为什么不在 kernel 内做 GQA**：那要给 flash fwd/bwd 两个 kernel 各加 kv-head 索引、给 composed
+的两次 strided GEMM 各算 GQA stride，且两套都要重测——是「第三套 attention 改动」。而 broadcast-op
+方案：(a) 两条 SDPA 路径零改动、其 T14 闸门不回归；(b) repeat_kv 的 fwd/bwd 是独立可 grad-check
+的小 op，正确性风险隔离在一处；(c) 关键的「多组 q 头梯度汇到一个 kv 头」就是 repeat_kv 的**反向**，
+干净地落在一个 op 上单测。代价是 K/V 在显存里被物化成满头 `[B·nh,S,hd]`（多 `group` 倍）——本规模
+（训练、seq 不极端）可接受；真要省这份显存是 follow-up（kernel 内 GQA 读取），记进逃生舱不在 T15 做。
+
+> 备选（不采纳）：flash/composed kernel 内直接按 `kv_head = q_head/group` 索引 K/V。省 broadcast
+> 物化，但动两套已验证 kernel + 重写两套 backward 的 kv 累加，违反「不写第三套 attention」与回归保护。
+> escape hatch：先 broadcast-op 把正确性 + 闭环钉死，kernel-内 GQA（省显存）留 follow-up。
+
+### ② repeat_kv 的反向 = 组内求和（确定性，无 atomic）
+
+`repeat_kv` 前向：`out[b·nh + qh] = in[b·num_kv + qh/group]`（按 `S·hd` 整行拷贝）。
+
+反向是它的**转置**：一个 kv 头收到它那 `group` 个 query 头的梯度之**和**：
+```
+din[b·num_kv + kvh] = Σ_{r=0}^{group-1} dout[b·nh + kvh·group + r]
+```
+这正是闸门要求的「多组 q 头梯度累加到一个 kv 头」。实现上**不用 atomicAdd**：每个输入
+（kv-head, 元素）由唯一一个 block 负责，它**串行累加自己那 group 个连续源行**——天然 race-free
+且**run-to-run 确定**（不像 flash bwd 的跨行 atomic 反向有归约序不确定问题）。`group=1` 时
+反向退化为单行拷贝（identity）。
+
+autograd 层面其实也可以靠引擎的扇出 SUM（把一个 kv Var 喂给 group 个下游），但那样图里要
+显式建 group 份 view、且 flash/composed 的 batched 布局不是按头切的——做成一个专门的
+broadcast op，fwd/bwd 各一发 kernel，最简且能单独 grad-check。
+
+### ③ `num_kv_heads` 进 Config（它改模型尺寸/导出），默认 = n_heads → 退化 MHA
+
+不同于 T14 的 `use_flash`（运行时旗标，不进 Config），`num_kv_heads` **改 K/V 投影的形状、改参数量、
+改导出的 `num_key_value_heads`**——它是**架构**的一部分，必须进 `Config` 并落进 checkpoint/导出。
+
+- `Config` 加 `num_kv_heads: usize`；`from_arch` 加该形参；`Config::tiny()` 默认 `num_kv_heads =
+  n_heads`（MHA）。约束：`num_heads % num_kv_heads == 0`（断言）。
+- `num_params()`：K/V 投影从 `2·dim·dim` 改成 `2·dim·(num_kv_heads·head_dim)`；QK-norm 的
+  `k_norm` 仍是 `[head_dim]`（per-head，作用在单个 head 向量上，与头数无关）→ 不变。
+- **`num_kv_heads == n_heads` 时 `group=1`**：`ops::repeat_kv` 是 identity（fwd 单行拷贝、bwd 单行
+  拷贝），wk/wv 形状回到 `[dim,dim]` → 整条图与 T14 的 MHA 路径**逐位一致**（回归保护闸门）。
+
+> wk/wv 形状从 `[dim,dim]` 变成 `[dim, kv_dim]`：`Block` 里 wk/wv 的 `mk(&[dim, kv_dim])`，
+> `params()`/`block_params()` 顺序不变（还是 attn_norm,wq,wk,wv,q_norm,k_norm,wo,...），只是
+> wk/wv 的 shape 跟着 Config。导出转置照旧按各自 shape 走（`transpose` 读 `v.value().shape()`）。
+
+### ④ bf16 / recompute / dropout / DDP 全部自动兼容
+
+- **bf16**：`Tensor::repeat_kv` 沿用全 repo 一致的 cast 策略——bf16 入则 upcast f32 → kernel →
+  downcast；kernel 只一份 f32。`ops::repeat_kv` 的 fwd/bwd 都在 SDPA 之前/之后，dtype 与 K/V 流一致。
+- **recompute（T13）**：repeat_kv 在 `block_forward` 内、`attention()` 里，重算段重跑 `attention()`
+  自然重跑 repeat_kv（无额外状态，确定性）→ 梯度仍逐位一致。
+- **dropout（T18）**：dropout 接在 attn/mlp 子块**输出**，与 attention 内部的 repeat_kv 正交，不交互。
+- **DDP**：repeat_kv 不引入新参数；wk/wv 变小（kv_dim）只是参数张量小一圈，`params()` 泛化迭代
+  + all-reduce 照旧；跨 rank 一致性不受影响。
+
+### ⑤ 导出 xserv：写真 `num_key_value_heads`，分组约定对齐 repeat_kv
+
+`export_safetensors.rs` 的 `config.json` 把 `num_key_value_heads` 从「= num_attention_heads」改成
+**真 `cfg.num_kv_heads`**；`--kv-heads` flag 传入（须与训练 ckpt 一致）。q/k/v_proj 各自按其 shape
+转置导出（k/v_proj 现在是 `[kv_dim, dim]`，xserv loader 期望的 GQA 形状）。xserv 的 `repeat_kv`
+用 `dst = kvh·group + r` 分组，与 ① 的 xtrain 约定**逐头对齐** → 同一份权重在两侧前向数学一致，
+闭环（贪心逐 token 一致）成立。
+
+## 验证方法
+
+全部 `#![cfg(not(no_cuda))]` 门控，本地 `cargo check`/`fmt`，构建+实跑在 dash5（8× RTX 5090）。
+
+### 1. 正确性（硬闸门全绿，dash5 实跑 capture）
+
+- **repeat_kv finite-diff grad-check**（`autograd.rs::repeat_kv_grad`）：**核心闸门**——`group>1`
+  （如 bh: 2 kv 头 → 6 q 头）下 grad-check `din`，验证「多组 q 头梯度求和到一个 kv 头」的反向。
+  外加 `group=1` identity 自检。
+- **GQA flash==composed**（`gqa.rs`）：真 GQA 配置（`num_kv_heads < n_heads`，如 8 头/2 kv 头）下，
+  flash on/off 两个同 init 模型，断 forward logits / loss / **每参数梯度**一致（fp32 紧容差 + bf16
+  舍入带）——尤其 wk/wv 的梯度（它们经过 repeat_kv 反向的组内求和）。
+- **group=1 与 MHA 逐位一致**（`gqa.rs`）：`num_kv_heads = n_heads` 的模型对 T14 的 MHA 模型，
+  forward + 每参数梯度 `|Δ|=0`（回归保护）。
+- **PyTorch GQA 对拍 B>1**（`parity_dump.rs` + `parity.py`）：等价 PyTorch 模型加 GQA（k/v 投影到
+  kv_dim + `repeat_interleave(group)` 分组，与 xserv/xtrain 约定一致），对拍 forward logits + 全部
+  参数梯度（composed 与 flash 两条都跑，共用同一 oracle）。
+- **小 GQA 配置短训收敛**：一个真 GQA 小模型短训，loss 单调降、无 NaN、采样连贯。
+- **全回归套开/关**：autograd / structural / batched==looped / bf16 / recompute（逐位）/ overfit 27/27 /
+  AdamW（GPU bit-exact + host 对 torch）/ DDP loss-match + 跨 rank（`--test-threads=1`）/ flash /
+  grad_accum / dropout / **xserv 闭环 md5**。MHA 默认（kv=heads）图不变 → 不回归。
+
+### 2. 闭环（payoff）—— 真 GQA 端到端
+
+导出一个 `num_key_value_heads < num_attention_heads` 的 GQA checkpoint → xserv 加载 → 贪心生成
+**对 xtrain 自身逐 token 一致**（BF16 推理 vs f32 训练，与 v1–v8 同款判据）。这是 GQA 真正落地的证明：
+训练侧的分组、导出的分组、推理侧 xserv 的 repeat_kv 分组三方对齐。
+
+## 实测结果（dash5 1× / 2× RTX 5090）
+
+**硬闸门全绿：**
+
+| 闸门 | 结果 |
+|---|---|
+| ① repeat_kv grad-check（**多组 q 头梯度求和到一个 kv 头**，group=3） | **过** — din max_rel **2.05e-4**；group=1 identity 双向**逐位**（fwd/bwd |Δ|=0） |
+| GQA flash==composed（model 级 8h/2kv，logits/loss/每参数梯度） | fp32: loss rel **0.0**、logits 3.0e-4、grad **4.1e-5**；bf16: loss 9.0e-5、logits mean 2.9e-3/p99 1.0e-2、grad scaled-mean 8.9e-3 |
+| group=1 对 MHA**逐位一致**（回归保护） | **过** — logits + loss + 全部梯度 |Δ|=0 |
+| ② PyTorch GQA 对拍 B>1（composed & flash，repeat_interleave 分组对齐） | composed: loss **1.74e-8**/logits 2.04e-5/25 grad 进 rtol；flash: loss 1.74e-8/logits 2.28e-5/25 grad 进 rtol |
+| ③ 小 GQA 配置短训收敛（8h/2kv/hd32/4L/ffn1024，600 步） | train **10.90→3.15** 无 NaN、gnorm 稳 ~1.2、采样连贯英文（~200K tok/s） |
+| ④ **xserv 闭环真 GQA**（导出 `num_key_value_heads=2 < num_attention_heads=8`，xserv 加载 `heads=8/2 kv`，贪心） | "One day"/"The little" 两 prompt **逐 token 一致**；"Once upon a time" 在 `...Lily's mommy ` 处 BF16 漂移晚分叉（said vs came）——与 v1/v2/v3/T14 同款判据 |
+| ⑤ 回归套：autograd 23（含 repeat_kv 2）/ structural 5 / batched / bf16 / flash 2 / **gqa 4** / overfit 27/27 / recompute 2 / dropout 6 / grad_accum 3 / checkpoint-roundtrip / AdamW(host 对 torch 4.8e-6) / DDP 3(`--test-threads=1`, loss 5.67e-7+跨 rank 一致) / GEMM / tensor | **全绿** |
+| ⑤ MHA 默认 export md5（v3 ckpt 用 T15 代码重导 safetensors） | **逐位一致** `b04fc9f9a0c9af04c47d9ca649aea12e`（与 registry/T14 同）→ 默认（kv=heads）export 零漂移 |
+
+> **诚实记录**：闭环 2/3 prompt 完全 token-identical、1/3 在 BF16 漂移点晚分叉——这恰证明 GQA 分组**正确**：若 kv→q 头映射错，attention 会从第一个生成 token 起就崩（不会是深处近-tie 的晚分叉）。GQA 把 K/V 在显存里物化成满头 `[B·nh,S,hd]`（broadcast-op 方案的代价）——本规模可接受，kernel-内 GQA（省这份显存）留 follow-up。未为凑绿放宽任何容差。

docs/evolution.md

@@ -25,6 +25,7 @@
 | T12 | 算法/Infra | **bf16 混合精度**（fp32 master，cuBLAS GemmEx，norm/softmax/CE 保 fp32） | dim768 OOM 解除，−29% 显存/+13% tok/s（修 KI-2） |
 | T13 | 算法/Infra | **激活重计算**（per-block gradient checkpointing：前向 no-tape + 反向重算，`backward_seeded`） | 梯度对非重计算版**逐位一致**(0.00)；dim768 31.1→14.6GB；**dim1024 batch32 OOM→16.6GB 装下**（修 KI-3，解锁 v8） |
 | T14 | 算法/Infra | **融合 flash-attention kernel**（手写单 kernel：online softmax、tiled over KV、**不物化 N×N scores**；flash 式 bwd：重算 scores + `D=ΣdO·O` 化简雅可比 + dQ/dK/dV）；opt-in `--flash`，默认保 composed（Phase 2） | fwd 对 composed 6.7e-5、bwd 对 composed dQ 1.7e-5、PyTorch B>1 7.9e-6、flash==composed loss rel 0.0；**峰值显存 −16%@seq1024 / −23%@seq2048**（不物化 N×N，收益随 seq 增长）；tok/s ~2.3–2.8× 慢（hd=64 小头维干不过 cuBLAS tensor-core，flash 已知权衡=胜场在显存）；md5 闭环逐位一致 |
+| T15 | 模型架构 | **真 GQA**（`num_kv_heads<num_heads`：wk/wv 投影到 `kv_dim`，新 `repeat_kv` broadcast 算子把 K/V 复制 `group=nh/num_kv` 份喂给**未改动**的 composed/flash 两条 SDPA；分组约定对齐 xserv repeat_kv `dst=kvh·group+r`）；`repeat_kv` 反向=组内 group 行**确定性求和**（无 atomic）→ 多组 q 头梯度汇一个 kv 头；`num_kv_heads` 进 Config(默认=nh→MHA)、`--kv-heads` flag、导出写真 `num_key_value_heads`（Phase 2） | repeat_kv grad-check 2.1e-4(group3)+group1 identity 逐位；GQA flash==composed fp32 grad 4.1e-5/bf16 在带；**group1 对 MHA 逐位一致**(回归保护)；PyTorch GQA B>1 对拍 composed/flash 各 loss 1.7e-8/logits 2.3e-5/25 grad 进 rtol；小 GQA(8h/2kv) 训 600 步 10.9→3.15 连贯；**xserv 闭环真 GQA**(num_kv 2<8)：2/3 prompt token-identical、1 在 BF16 漂移处晚分叉；MHA 默认 export md5 逐位一致(b04fc9f9) |
 | T16 | 算法/Infra | **梯度累积**（N 个 micro-step：每个 micro-loss `×1/N` 再 backward，tape SUM 累加 → 一次 AdamW step+zero；`--accum-steps`）；**DDP 只在累积边界 all-reduce**（中间 micro-step 不发 NCCL，`/world` 与 `1/N` 正交）；显存随 micro 不随有效 batch | 等效大 batch**逐位贴合**（loss rel 8.5e-8、grad rel 3.8e-5）；`accum=1` 逐位回归(0.00)；DDP+accum 对单卡 loss 5.7e-7/跨 rank 一致；**显存平**：同有效 batch 64，big-batch 27.7GB→accum(4×16) **7.2GB(−74%)**（big-batch OOM 而 accum 装下）；全回归+xserv 闭环 md5 一致 |
 | T18 | 算法 | **dropout**（手写 counter-based 设备 RNG → Bernoulli mask，训练 inverted 1/(1-p) scaling、eval 恒等）；新 autodiff `dropout` 算子（fwd 生成+施加 mask，bwd 用同 mask），接 residual/ffn 两处；`--dropout` flag 默认 0 | 固定 seed grad-check 过；E[out]≈input + keep≈1-p；**p=0 与无 dropout 逐位一致**；recompute(T13) 组合下梯度仍逐位一致（counter-based seed 重算复现同 mask）；全回归 + xserv 闭环绿（导出/推理 dropout 关） |
 
@@ -53,7 +54,7 @@
 ## 三、各维度的累积演进（轴向看一条线怎么走的）
 
 - **算法**：手写 autograd(tape)+扇出累加 → AdamW/LR-sched/grad-clip → +QK-norm(Qwen3) → batched forward → bf16 混合精度(fp32 master) → 激活重计算(T13) → 融合 flash-attention(T14，online softmax + flash 式 bwd) → 梯度累积(T16，复用 tape SUM，等效大 batch 而显存随 micro) → dropout(T18，counter-based 设备 RNG + inverted scaling，train/eval 切换)。
-- **模型架构**：固定 Qwen3-style；dim **32→256→384→512→768→1024**（v8 首拨容量轴，头数 24→32）；核心参数 **41K→226M**（总 3.26M→329M）。
+- **模型架构**：固定 Qwen3-style；dim **32→256→384→512→768→1024**（v8 首拨容量轴，头数 24→32）；核心参数 **41K→226M**（总 3.26M→329M）。+QK-norm(T9，Qwen3 兼容) → **真 GQA(T15，`num_kv_heads<num_heads`，repeat_kv broadcast + 组内梯度求和；默认=nh→MHA 逐位回归)**——架构补齐到现代 LLM 标配（MHA/GQA/MQA 一条 `num_kv_heads` 轴），两条 SDPA(composed/flash) 共用同一 broadcast，导出真 `num_key_value_heads` 且 xserv 闭环。
 - **Infra**：单卡 fp32 → cuBLAS/GPU-optim(T7) → NCCL DDP(T8) → batched forward(T10) → caching allocator(T11) → bf16(T12) → 激活重计算(T13，解锁 dim1024) → flash-attention(T14，不物化 N×N，attention 显存收益随 seq 增长) → 梯度累积(T16，DDP 只在累积边界通信，显存随 micro 不随有效 batch)。吞吐 **3.3K→217K tok/s**（dim768 bf16），dim1024+重算 ~129K（重算税）；MFU **0.4%→17%**（每次提升都对应一块 perf 基建，详见 known-issues + MFU 分析）。T13/T14/T16 是三条**显存杠杆**（重计算压激活峰值、flash 不物化 N×N attention scores、梯度累积解耦有效 batch 与激活显存），可叠加放大有效 batch。
 - **数据集**：TinyStories 3MB 切片 → 全量 TinyStories（epoch 0.01→5.33，**至饱和**）→ **v6 毕业到 FineWeb-edu 真实网页**（2.255B 语料，1.02ep）→ **v7 同子集多 epoch（1.45ep，近顶）→ v8 同子集换大模型**（dim1024，1.05ep）。tokenizer 全程 gpt2 BPE（复用 xserv-tokenizer；v6 刻意不换 tokenizer 以隔离「数据来源」变量，KI-4 留后续版本）。
   - **v5→v6 数据轴的质变**：v0–v5 都吃合成幼儿故事（TinyStories，低熵、词汇受控），v5 证明同尺寸模型在它上面已饱和；v6 第一版换成**真实教育类网页文本**（FineWeb-edu），语言种类发生质变——采样从「只会写小故事」变成「能写历史/科学/说明文」。