Single-sequence KV-cache decode (xtrain-model/src/decode.rs): per-layer K/V
cache + single-token incremental forward (prefill = first prompt.len() decode
steps, one code path). Mirrors model::block_forward at the raw-Tensor level (no
autograd tape — inference needs no grads), using rope_at + decode_attention.
Cache is host-accumulated token-major f32, rebuilt per step (the honest M2a
baseline; M2b moves it device-side + batched ragged).
Gate (the M2 centerpiece): KV-cache greedy decode is TOKEN-IDENTICAL to the
naive full-recompute greedy — tests/decode_kv.rs (small GQA model, F32, 24
tokens) and corroborated on the v12 1.05B SFT checkpoint (cached eval =
naive eval byte-for-byte: format 100/100, correct 8/100).
eval_arith --cached A/Bs the two paths + reports decode tok/s. Measured on v12
(1.05B, batch 1, F32): the cache win is sequence-length-dependent —
max_new=32 naive 108 vs cached 111 tok/s (~1.0x; overhead-bound)
max_new=128 naive 69 vs cached 133 tok/s (~1.9x)
max_new=256 naive OOM vs cached 129 tok/s
Cached throughput stays ~constant (O(1)/token) while naive decays (O(t)/token,
O(seq^2) graph → OOM at length). Short eval prompts are overhead-bound, so the
cache matters for long rollouts (DPO/GRPO), not the arithmetic eval itself.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Enable assistant-only supervised fine-tuning and a fixed chat-prompt eval path
used by the v12 SFT runs:
- cross_entropy ignores negative targets (-100 ignore-index), normalizing by
valid rows instead of all rows; CUDA fwd/bwd skip t<0 (ops.rs, nn.cu).
- Corpus gains optional labels + load_sft_tsv_cached: two-column TSV is
formatted as 'User: .. \nAssistant:' + answer + <|endoftext|>, prompt tokens
masked to -100 while answer+EOS are supervised; i32 label cache alongside the
u16 token cache; sample() retries windows that are fully masked; eval uses
target_window so masking applies to val loss too (data.rs, train_loop.rs).
- train + train_ddp: --sft-tsv selects the TSV loader, --init-ckpt continues
training from a base checkpoint.
- greedy_sample: --prompts-file/--prompt/--temperature for fixed chat-prompt
generation eval.
Test fixtures updated for the new Corpus.labels field; dropout.rs carries
incidental rustfmt. Not rebuilt locally (no CUDA toolchain on this checkout);
correctness rests on the documented v12 base+SFT runs on the GPU box.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- grad_accum.rs: accum=N×B grads bit-close to a single N·B big batch;
accum_steps=1 bit-identical (max|Δ|==0) to no-accum; real train() loop
with accum tracks a big-batch baseline over 20 AdamW steps.
- ddp_correctness.rs: world=2 + accum=2 matches a single-GPU big batch of
the same effective size (loss + cross-rank + vs-baseline).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- Corpus::load_cached: tokenize the (large) corpus ONCE, cache the id stream to
<corpus>.u16.bin (gpt2 vocab 50257 < 65536 → exact u16), read cache on reruns.
- Corpus::split_tail: hold out a tail slice as a validation corpus.
- train(): take an optional valid corpus + eval_every/eval_batches; periodic
deterministic val-loss eval that checkpoints the BEST val model; returns
TrainResult{train_losses, evals, best_val}. T6 fixed-cadence path preserved.
- bin/train + bin/export_safetensors: read architecture (--heads/--head-dim/
--layers/--ffn) + opt knobs (--steps/--batch/--seq/--max-lr/--val-tokens/
--eval-every) from CLI flags; defaults reproduce the v0-baseline tiny config.
- gitignore the multi-GB corpus + *.u16.bin caches + *.ckpt (dash5-only).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
xserv's Qwen3 forward unconditionally applies per-head RMSNorm to Q and K
(q_norm/k_norm, shape [head_dim]) before RoPE — even gamma=1 is a real RMS
divide, not identity. xtrain never had this, so an exact xserv<->xtrain loop
was structurally impossible. Add it (reusing the 2D rms_norm op on the
[seq*nh, hd] head rows, inserted between reshape and rope to mirror
qwen3.rs's order) so the trained model is genuinely Qwen3-compatible.
params() inserts q_norm,k_norm after wv; num_params() counts them; the
PyTorch parity refs (parity.py / adamw_parity.py) + their name lists add the
same step so the dumps stay self-consistent.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
cargo runs tests with cwd = crate dir, so the bare relative default
data/tinystories-valid-3mb.txt didn't resolve. Anchor it to the repo root via
CARGO_MANIFEST_DIR so the test runs out of the box (still overridable with
XTRAIN_CORPUS).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The loss trajectory already matched torch.optim.AdamW (worst relerr ~2e-4),
but the float64 torch reference diverged per-weight from the f32 GPU training
after the model memorised the batch (flat region: weights underdetermined,
loss identical). Fixes: run the torch reference in float32 (match engine
precision), shorten to 10 steps (weights still well-determined), and compare
final params with an allclose-style rtol+atol metric (a pure relative metric is
misleading on near-zero weights).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Acceptance tests (GPU-gated not(no_cuda), run on dash5):
- adamw_parity_dump.rs + adamw_parity.py: build the tiny model with fixed init,
run N AdamW steps on a fixed batch, dump the loss trajectory + final params;
the Python side rebuilds the identical model and runs torch.optim.AdamW with
matched lr/wd/betas/eps, comparing trajectory + final params within rtol.
- checkpoint_roundtrip.rs: train a few steps, save, load into a fresh model with
a DIFFERENT init, assert identical logits/loss on a fixed input.
- real_training.rs (#[ignore], --release): train on TinyStories for a bounded
budget; assert loss drops substantially and print greedy samples.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
