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>
This commit is contained in:
268
crates/xtrain-model/tests/gqa.rs
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268
crates/xtrain-model/tests/gqa.rs
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@@ -0,0 +1,268 @@
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// T15 GQA correctness gate. Real grouped-query attention (num_kv_heads <
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// num_heads): K/V project to num_kv_heads·head_dim and are repeat_kv-broadcast to
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// the full head set before the SDPA. This test pins three things:
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//
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// 1. GQA flash == GQA composed (forward logits + loss + EVERY param grad) — the
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// repeat_kv broadcast feeds both SDPA paths unchanged, so they must agree; in
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// particular the wk/wv grads (which flow back through repeat_kv's group-sum)
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// must match. Parameterised over fp32 (tight) and bf16 (rounding band).
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// 2. group==1 (num_kv_heads == n_heads) is BIT-IDENTICAL to the pre-T15 MHA path
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// (a model with num_kv_heads explicitly == n_heads vs the default config):
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// forward logits + every grad |Δ|=0. The regression guard.
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// 3. wk/wv really shrank to [dim, kv_dim] under GQA (shape check).
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#![cfg(not(no_cuda))]
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use xtrain_cuda::device;
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use xtrain_model::{Config, TinyTransformer, batched_ids_tensor};
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use xtrain_tensor::{DType, Device};
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fn fill(n: usize, seed: u64, scale: f32) -> Vec<f32> {
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let mut state = seed
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.wrapping_mul(2862933555777941757)
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.wrapping_add(3037000493);
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(0..n)
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.map(|_| {
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state = state
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.wrapping_mul(6364136223846793005)
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.wrapping_add(1442695040888963407);
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(((state >> 33) as f32 / (1u64 << 31) as f32) - 0.5) * 2.0 * scale
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})
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.collect()
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}
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fn build(cfg: Config, device: Device, dtype: DType, flash: bool) -> TinyTransformer {
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let mut seed = 1u64;
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let m = TinyTransformer::new(cfg, device, |shape| {
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seed = seed.wrapping_add(1);
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let n: usize = shape.iter().product();
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if shape.len() == 1 {
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fill(n, seed, 0.02).iter().map(|v| v + 1.0).collect()
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} else {
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fill(n, seed, 0.08)
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}
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});
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m.with_compute_dtype(dtype).with_flash(flash)
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}
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fn host(t: &xtrain_tensor::Tensor) -> Vec<f32> {
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t.to_dtype(DType::F32)
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.to_device(Device::Cpu)
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.as_slice::<f32>()
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.to_vec()
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}
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// A real GQA config: 8 query heads, 2 kv heads → group 4. seq=40 > FA_TILE=32 so
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// the flash online-softmax tile path is exercised too.
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fn gqa_cfg() -> Config {
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let mut cfg = Config::tiny();
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cfg.vocab = 16;
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cfg.n_layers = 3;
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// tiny() is 2 heads; rebuild with 8 query / 2 kv heads keeping head_dim=16.
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Config::from_arch(cfg.vocab, 8, cfg.head_dim, cfg.n_layers, cfg.ffn_hidden).with_kv_heads(2)
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}
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fn ids_targets(cfg: &Config, batch: usize, seq: usize) -> (Vec<Vec<i32>>, Vec<Vec<i32>>) {
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let seqs = (0..batch)
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.map(|b| {
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(0..seq)
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.map(|i| ((b * 7 + i * 3 + 1) % cfg.vocab) as i32)
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.collect()
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})
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.collect();
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let tgts = (0..batch)
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.map(|b| {
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(0..seq)
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.map(|i| ((b * 5 + i * 2 + 2) % cfg.vocab) as i32)
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.collect()
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})
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.collect();
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(seqs, tgts)
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}
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#[allow(clippy::type_complexity)]
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fn run_both(
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cfg: Config,
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dtype: DType,
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) -> (Vec<f32>, f32, Vec<Vec<f32>>, Vec<f32>, f32, Vec<Vec<f32>>) {
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device::set_device(0).unwrap();
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let device = Device::Cuda(0);
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let (batch, seq) = (3usize, 40usize);
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let (seqs, tgts) = ids_targets(&cfg, batch, seq);
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let ids = batched_ids_tensor(&seqs, device);
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let tgt = batched_ids_tensor(&tgts, device);
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let off = build(cfg, device, dtype, false);
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let off_logits = host(&off.forward_batched(&ids, batch).value());
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let off_loss = off.loss_batched(&ids, &tgt, batch);
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let off_loss_val = host(&off_loss.value())[0];
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off_loss.backward();
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let off_grads: Vec<Vec<f32>> = off
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.params()
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.iter()
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.map(|p| host(&p.grad().expect("off grad")))
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.collect();
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let on = build(cfg, device, dtype, true);
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let on_logits = host(&on.forward_batched(&ids, batch).value());
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let on_loss = on.loss_batched(&ids, &tgt, batch);
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let on_loss_val = host(&on_loss.value())[0];
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on_loss.backward();
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let on_grads: Vec<Vec<f32>> = on
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.params()
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.iter()
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.map(|p| host(&p.grad().expect("on grad")))
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.collect();
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(
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off_logits,
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off_loss_val,
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off_grads,
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on_logits,
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on_loss_val,
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on_grads,
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)
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}
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// GQA flash vs composed: same SDPA math on the same repeat_kv-broadcast K/V → fp32
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// agrees to reduction-order, bf16 to its rounding band.
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#[test]
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fn gqa_flash_matches_composed_fp32() {
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assert!(device::device_count().unwrap() > 0, "no CUDA device");
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let cfg = gqa_cfg();
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assert!(cfg.num_kv_heads < cfg.n_heads, "test must be real GQA");
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let (off_l, off_loss, off_g, on_l, on_loss, on_g) = run_both(cfg, DType::F32);
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let logit_rel = off_l
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.iter()
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.zip(&on_l)
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.map(|(a, b)| (a - b).abs() / a.abs().max(1e-4))
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.fold(0.0f32, f32::max);
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let loss_rel = (off_loss - on_loss).abs() / off_loss.abs().max(1e-4);
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println!(
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"[GQA F32] flash on/off: loss {off_loss:.6}/{on_loss:.6} (rel {loss_rel:.2e}), \
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logits max rel {logit_rel:.2e}"
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);
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assert!(
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logit_rel < 1e-3,
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"[GQA F32] logits diverged: {logit_rel:.2e}"
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);
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assert!(loss_rel < 1e-3, "[GQA F32] loss diverged: {loss_rel:.2e}");
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let mut worst = 0.0f32;
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for (a_g, b_g) in off_g.iter().zip(&on_g) {
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for (a, b) in a_g.iter().zip(b_g) {
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worst = worst.max((a - b).abs() / a.abs().max(1e-3));
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}
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}
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println!("[GQA F32] flash on/off grad max rel = {worst:.3e}");
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assert!(worst < 2e-2, "[GQA F32] grads diverged: {worst:.3e}");
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}
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#[test]
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fn gqa_flash_matches_composed_bf16() {
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assert!(device::device_count().unwrap() > 0, "no CUDA device");
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let (off_l, off_loss, off_g, on_l, on_loss, on_g) = run_both(gqa_cfg(), DType::BF16);
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let loss_rel = (off_loss - on_loss).abs() / off_loss.abs().max(1e-4);
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println!("[GQA BF16] flash on/off: loss {off_loss:.5}/{on_loss:.5} (rel {loss_rel:.3e})");
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assert!(loss_rel < 2e-2, "[GQA BF16] loss diverged: {loss_rel:.3e}");
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let n = off_l.len();
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let mut rels: Vec<f32> = off_l
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.iter()
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.zip(&on_l)
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.map(|(f, b)| (b - f).abs() / f.abs().max(1.0))
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.collect();
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rels.sort_by(|a, b| a.partial_cmp(b).unwrap());
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let mean: f32 = rels.iter().sum::<f32>() / n as f32;
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let p99 = rels[(n as f32 * 0.99) as usize];
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println!("[GQA BF16] logits: mean rel {mean:.3e}, p99 rel {p99:.3e}");
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assert!(
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mean < 1e-2,
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"[GQA BF16] logits mean rel too high: {mean:.3e}"
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);
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assert!(p99 < 5e-2, "[GQA BF16] logits p99 rel too high: {p99:.3e}");
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let mut worst = 0.0f32;
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for (a_g, b_g) in off_g.iter().zip(&on_g) {
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let scale = a_g.iter().map(|v| v.abs()).fold(0.0f32, f32::max).max(1e-6);
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let mean_err: f32 =
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a_g.iter().zip(b_g).map(|(f, b)| (f - b).abs()).sum::<f32>() / a_g.len() as f32 / scale;
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worst = worst.max(mean_err);
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}
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println!("[GQA BF16] grads: worst per-tensor scaled-mean err = {worst:.3e}");
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assert!(worst < 3e-2, "[GQA BF16] grads diverged: {worst:.3e}");
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}
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// REGRESSION GUARD: num_kv_heads == n_heads (group 1) must be BIT-IDENTICAL to the
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// pre-T15 MHA path. Build one model with the default config (num_kv_heads ==
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// n_heads, the untouched path: repeat_kv not even invoked) and one that explicitly
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// sets num_kv_heads = n_heads, then assert forward logits + every grad match to the
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// bit. (Same composed path, so this is exact equality, not a tolerance.)
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#[test]
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fn gqa_group1_bit_identical_to_mha() {
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assert!(device::device_count().unwrap() > 0, "no CUDA device");
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device::set_device(0).unwrap();
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let device = Device::Cuda(0);
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let mut base = Config::tiny();
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base.vocab = 16;
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base.n_layers = 3;
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let base = Config::from_arch(base.vocab, 4, base.head_dim, base.n_layers, base.ffn_hidden);
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// `explicit` sets num_kv_heads = n_heads (already the default, but exercises the
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// with_kv_heads path); they are the same config → must produce identical output.
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let explicit = base.with_kv_heads(base.n_heads);
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assert_eq!(base.num_kv_heads, explicit.num_kv_heads);
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let (batch, seq) = (2usize, 8usize);
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let (seqs, tgts) = ids_targets(&base, batch, seq);
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let ids = batched_ids_tensor(&seqs, device);
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let tgt = batched_ids_tensor(&tgts, device);
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let run = |cfg: Config| -> (Vec<f32>, f32, Vec<Vec<f32>>) {
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let m = build(cfg, device, DType::F32, false);
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let logits = host(&m.forward_batched(&ids, batch).value());
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let loss = m.loss_batched(&ids, &tgt, batch);
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let loss_v = host(&loss.value())[0];
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loss.backward();
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let grads = m
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.params()
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.iter()
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.map(|p| host(&p.grad().unwrap()))
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.collect();
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(logits, loss_v, grads)
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};
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let (la, sa, ga) = run(base);
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let (lb, sb, gb) = run(explicit);
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assert_eq!(la, lb, "group-1 logits must be bit-identical to MHA");
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assert_eq!(sa, sb, "group-1 loss must be bit-identical to MHA");
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for (a, b) in ga.iter().zip(&gb) {
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assert_eq!(a, b, "group-1 grad must be bit-identical to MHA");
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}
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println!("[GQA group1] bit-identical to MHA: logits + loss + all grads |Δ|=0");
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}
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// Under GQA, wk/wv must be [dim, kv_dim] (= num_kv_heads·head_dim), wq stays [dim,dim].
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#[test]
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fn gqa_kv_proj_shape() {
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assert!(device::device_count().unwrap() > 0, "no CUDA device");
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device::set_device(0).unwrap();
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let device = Device::Cuda(0);
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let cfg = gqa_cfg();
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let m = build(cfg, device, DType::F32, false);
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let p = m.params();
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// params order: embed, then per block [attn_norm, wq, wk, wv, q_norm, k_norm, wo, ...]
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let wq = p[1].value().shape().to_vec();
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let wk = p[2].value().shape().to_vec();
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let wv = p[3].value().shape().to_vec();
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assert_eq!(wq, vec![cfg.dim, cfg.dim], "wq must be [dim,dim]");
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assert_eq!(wk, vec![cfg.dim, cfg.kv_dim()], "wk must be [dim,kv_dim]");
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assert_eq!(wv, vec![cfg.dim, cfg.kv_dim()], "wv must be [dim,kv_dim]");
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println!(
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"[GQA shapes] wq {:?} wk {:?} wv {:?} (kv_dim {})",
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wq,
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wk,
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wv,
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cfg.kv_dim()
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);
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}
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@@ -52,6 +52,10 @@ cfg = read_cfg()
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DIM = int(cfg["dim"])
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NL = int(cfg["n_layers"])
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NH = int(cfg["n_heads"])
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# GQA (T15): num_kv_heads <= n_heads; each kv head shared by group query heads.
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# Default to NH (MHA) for fixtures dumped before the field existed.
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NKV = int(cfg.get("num_kv_heads", str(NH)))
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GROUP = NH // NKV
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HD = int(cfg["head_dim"])
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EPS = float(cfg["eps"])
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THETA = float(cfg["rope_theta"])
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@@ -114,17 +118,23 @@ for L in layers:
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# Attention
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x = rms_norm(h, L["attn_norm"])
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q = (x @ L["wq"]).reshape(B * SEQ, NH, HD)
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k = (x @ L["wk"]).reshape(B * SEQ, NH, HD)
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v = (x @ L["wv"]).reshape(B * SEQ, NH, HD)
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# GQA: K/V project to NKV heads, then repeat each kv head GROUP times to NH.
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k = (x @ L["wk"]).reshape(B * SEQ, NKV, HD)
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v = (x @ L["wv"]).reshape(B * SEQ, NKV, HD)
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# Per-head QK-norm (Qwen3-style), before RoPE.
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q = rms_norm(q, L["q_norm"])
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k = rms_norm(k, L["k_norm"])
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q = rope(q) # [B*SEQ, nh, hd]
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k = rope(k)
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# Reshape to [B, NH, SEQ, HD] so attention runs within each sequence.
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k = rope(k) # [B*SEQ, nkv, hd]
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# Reshape to [B, *, SEQ, HD]; broadcast kv heads to NH (repeat_interleave along
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# the head axis: kv head kvh → query heads [kvh*GROUP, (kvh+1)*GROUP), matching
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# xtrain repeat_kv + xserv repeat_kv).
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q = q.reshape(B, SEQ, NH, HD).transpose(1, 2) # [B, nh, seq, hd]
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k = k.reshape(B, SEQ, NH, HD).transpose(1, 2)
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v = v.reshape(B, SEQ, NH, HD).transpose(1, 2)
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k = k.reshape(B, SEQ, NKV, HD).transpose(1, 2) # [B, nkv, seq, hd]
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v = v.reshape(B, SEQ, NKV, HD).transpose(1, 2)
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if GROUP > 1:
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k = k.repeat_interleave(GROUP, dim=1) # [B, nh, seq, hd]
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v = v.repeat_interleave(GROUP, dim=1)
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scale = 1.0 / math.sqrt(HD)
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scores = (q @ k.transpose(-1, -2)) * scale + mask # [B, nh, seq, seq]
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probs = torch.softmax(scores, dim=-1)
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@@ -58,8 +58,20 @@ fn dump_for_parity() {
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// sequence-major to [B*S]=8 ids. Per-sequence RoPE position (resets at the
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// sequence boundary) + per-sequence causal masking (no cross-sequence
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// attention) are both checked against PyTorch.
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// Default: tiny MHA (2 heads). With XTRAIN_PARITY_KV_HEADS=k set, dump a real
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// GQA config (8 query heads / k kv heads) so parity.py checks GQA at B>1 — the
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// kv-projection shapes + the repeat_kv group-sum backward against PyTorch.
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let mut cfg = Config::tiny();
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cfg.vocab = 12;
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if let Ok(kv) = std::env::var("XTRAIN_PARITY_KV_HEADS") {
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let kv: usize = kv.parse().expect("XTRAIN_PARITY_KV_HEADS");
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cfg = Config::from_arch(cfg.vocab, 8, cfg.head_dim, cfg.n_layers, cfg.ffn_hidden)
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.with_kv_heads(kv);
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println!(
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"parity: GQA config (n_heads {} kv_heads {})",
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cfg.n_heads, cfg.num_kv_heads
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);
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}
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let batch = 2usize;
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let seq = 4usize;
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let ids: Vec<i32> = vec![3, 1, 4, 1, 5, 9, 2, 6]; // [B*S], sequence-major
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@@ -92,6 +104,7 @@ fn dump_for_parity() {
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writeln!(f, "dim {}", cfg.dim).unwrap();
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writeln!(f, "n_layers {}", cfg.n_layers).unwrap();
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writeln!(f, "n_heads {}", cfg.n_heads).unwrap();
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writeln!(f, "num_kv_heads {}", cfg.num_kv_heads).unwrap();
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writeln!(f, "head_dim {}", cfg.head_dim).unwrap();
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writeln!(f, "ffn_hidden {}", cfg.ffn_hidden).unwrap();
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writeln!(f, "eps {:e}", cfg.eps).unwrap();
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