- 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>
xtrain-side top-k next-token logit dump (f32 forward, same model/config/ckpt
as the exporter) mirroring xserv's dump-logits, so the closed-loop check can
compare both sides numerically for the same prompt + weights.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
New bin export_safetensors: load an xtrain checkpoint, map every param to its
HF Qwen3 tensor name, transpose 2D projection weights [in,out]->[out,in]
(1D norms + [vocab,dim] embed/lm_head kept), cast to BF16 (xserv's qwen3
forward is BF16-only), and write config.json + model.safetensors + a copy of
the gpt2 tokenizer.json. Sized exactly like bin/train.rs. safetensors 0.5 to
match xserv. GPU body gated behind not(no_cuda).
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>
Eliminate the per-step GPU↔host roundtrip of every parameter/gradient.
- optim.cu: adamw_step (m/v on device, in-place param update), sumsq_accum
(block-reduced global grad sum-of-squares), scale_inplace.
- GpuAdamW: device m/v state per param; step launches the kernel reading
each param's .grad() and rewriting the param buffer in place — no host
roundtrip. Host AdamW kept as the torch-parity reference.
- clip_grad_norm_gpu: device sum-of-squares reduction (only the scalar norm
comes back), in-place rescale of grads by pre_scale·clip_factor.
- train_loop: use GpuAdamW + clip_grad_norm_gpu.
- test: GPU AdamW vs host reference parity (max abs err < 1e-6).
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>
Training loop (train_loop.rs): sample batch_size sequences, forward loss +
backward (tape SUMs grads), clip_grad_norm with ×1/batch averaging, AdamW step
with scheduled lr, zero_grad; logs loss/lr/gnorm/tok-s and checkpoints
periodically; returns the loss trace.
Checkpoint (checkpoint.rs): flat little-endian dump of params() in order
(magic/version/count + per-param ndim/dims/f32 data); load_into validates and
overwrites a matching model's params via set_value (exact f32 round-trip).
Sampler (sample.rs): autoregressive greedy / temperature generation — re-runs
forward on the growing prefix (model is single-sequence, RoPE pos=row).
bin/train.rs: end-to-end entry — load tokenizer+corpus, train a tiny 4-layer
model for a bounded budget, checkpoint, print samples. no_cuda stub keeps it
buildable on a GPU-less host.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
New xtrain-train crate scaffold. Data pipeline reuses xserv's from-scratch
GPT-2/Qwen BPE via a path-dep (../../../xserv/crates/xserv-tokenizer, resolves
on both ~/projects and dash5 /opt/wjh/projects): Corpus::load tokenizes the
corpus into one id stream and samples fixed-length (input, target) next-token
windows (LCG-seeded, reproducible). Trims a range-downloaded file to whole
stories (<|endoftext|> boundaries).
Also the host-only training math: LrSchedule (linear warmup + cosine decay)
and global L2 grad-norm + clip scale, each with a local unit test.
Corpus: data/tinystories-valid-3mb.txt — first ~3MB of TinyStories-valid
(fetched on dash5 via hf-mirror.com; HF direct unreachable). Substitution
noted: a real TinyStories subset, not the full set.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
