30-step bench charged the one-time NCCL init + 4 model builds (present at world=4,
absent at world=1) against the wall clock, understating steady-state scaling
(in-loop tok/s already showed ~53k at 4 GPUs). Bump to 150 steps.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
dash5 verify: loss trajectory matches single-GPU to max_rel 1.16e-7 and
cross-rank params are bit-identical (0.0), but DDP-vs-single-GPU per-param rel
diff is ~2.8e-3 after 20 AdamW steps — expected, since the two differ only in
gradient summation order (fp add isn't associative) and that rounding compounds.
Bump check (c) 1e-3 -> 1e-2 (a/b stay tight). Also remove an unused DType import.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
bin/train_ddp: spawn one thread per visible GPU (CUDA_VISIBLE_DEVICES selects
the set), NCCL all-reduce gradients each step, train the tiny transformer on
TinyStories; doubles as the throughput driver (prints global tok/s). no_cuda
build keeps a stub main.
tests/ddp_correctness: (1) 2-rank DDP vs single-GPU over the same synthetic data
-> loss trajectory max_rel < 1e-3, cross-rank params bit-identical (==0.0), DDP
vs single-GPU params rel < 1e-3; (2) 1/2/4-GPU throughput table on a fixed
per-GPU workload. Gated #[cfg(not(no_cuda))], auto-skips with < 2 GPUs.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
