Estimates batch size from free GPU memory using the checkpoint's
hidden_dim/n_blocks, same as the train command.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Calibrated against a measured reference point (hidden_dim=512,
n_blocks=6, batch_size=131072 -> ~8 GiB VRAM), assuming activation
memory scales linearly with batch_size * hidden_dim * n_blocks.
CUDA-only for now since it relies on torch.cuda.mem_get_info.
The Typer-based giant/cli.py train command now has full feature
parity (dropout, warmup-epochs, validate-steps, shorthand flags),
making the standalone argparse script redundant.
validate_marginals and collect_samples could already vary flow ODE
steps for inference (giant predict --steps), but training-time
marginal validation and DDIM evaluation were stuck at hardcoded
defaults. Add a validate_steps config/CLI option and forward steps to
sample_ddim consistently.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Provides stratified marginal comparisons, joint-structure checks (correlation
matrices, physically-coupled pairwise plots, direction alignment), and
physical-constraint validation (unit-norm directions, non-negative raw
targets) for a trained model's generated samples, building on the aggregate
marginal/KL check already in giant.validate.
Supports two entry points: live sampling against a checkpoint + val data
(load_model_bundle/collect_samples), or loading a precomputed
`giant predict --coord local` parquet directly (load_predicted_local) without
needing the checkpoint at all. Predict output is now tagged with parquet
schema metadata so the loader can verify a file's format and reject
coord=global or untagged files with a clear error instead of guessing from
column names.
Also extends the config git-hash mismatch warning (added for --config
loading) to checkpoint loading: both `giant predict` and
analysis.load_model_bundle now look for a config.toml next to the checkpoint
and warn (without failing) if it was generated from a different git commit.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
validate_marginals now estimates a per-dimension KL(real || generated) via
a shared histogram, alongside the existing mean/std comparison, so
distribution-shape drift shows up even when the first two moments match.
Wire it into giant/train.py: every validate_every epochs (default 10, 0
disables), the training loop runs validate_marginals against val_loader and
prints the table. validate_every flows through DEFAULT_CONFIG/config.toml
and is exposed as --validate-every on both giant train and scripts/train.py.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
cli.py and scripts/train.py duplicated ~140 lines of training setup and had
drifted (scripts/train.py forgot to save model_config, breaking predict on
those checkpoints). Extract shared logic into giant/constants.py (X_DIM,
target names), giant/config.py (device/git/TOML/seeding helpers, run
metadata), and giant/pipeline.py (the actual training-job orchestration),
so both entry points become thin CLI wrappers around the same code path.
Also adds --seed/--resume support (checkpoints now carry optimizer/scheduler
state, epoch, and best_val_loss), a richer [meta] section in the saved
config.toml (git hash, seed, versions, timestamp, invocation, dataset
stats), and a metrics.csv (train/val loss, lr, epoch time) written every
epoch and append-safe across resumes.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Outputs the model's 9D prediction (denormalised only — still local
frame, log-scaled scalars) alongside the matching ground-truth target
for the same input rows, so they're directly comparable in the space
the loss is actually computed in. Also fixes mat_map keys being cast
with int() instead of str() when loading a checkpoint in predict.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
step_length already encodes |post_pos - pre_pos| by definition, so a raw
post_pos target would duplicate that magnitude and could drift inconsistent
with step_length during sampling. Instead add travel_dir, a unit vector
(local frame) giving only the direction of pre_pos->post_pos; post_pos is
reconstructed at inference as pre_pos + step_length * travel_dir, keeping
the two self-consistent. Target grows from 6D to 9D.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
The dataset yielded one row at a time, forcing DataLoader's default
collate to Python-loop over every row to assemble each batch. That
loop scales with batch size and was pinning a CPU core at 100% while
the GPU sat idle. Now the dataset yields whole batches via vectorized
numpy slicing, used with DataLoader(batch_size=None).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- iter_cond_chunks: column-projected row-group streaming; post-step
variables are never read from disk during inference
- build_cond_features: assembles conditioning arrays without any target
or post-step fields
- inv_local_frame_rotation: Rodrigues R^T (negative angle) to rotate
predicted post_dir back from local frame to world frame
- giant predict: loads checkpoint, streams input, runs flow matching
sampler, inverse-normalises and inverse-rotates outputs, writes
predictions incrementally as parquet via PyArrow ParquetWriter
- train now saves model_config in checkpoint so predict can reconstruct
the architecture without extra CLI flags
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Streaming pipeline: row-group-level parquet reading (PyArrow) so
large files never fully land in RAM; Welford online algorithm for
normalizer fitting; StreamingStepsDataset with shuffle buffer and
multi-worker file striping; event-ID scan and vocab scan via cheap
single-column reads
- giant/cli.py: typer-based CLI with `giant train` subcommand, mirroring
scripts/train.py; --shuffle-buffer flag for RAM control
- pyproject.toml: add typer>=0.12 dependency and giant entry point
- train.py: replace len(loader.dataset) with local counters (compatible
with IterableDataset which has no __len__)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
lars