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>
