V9-PILOT caught a launcher-level integration gap: T18 wired dropout into
the single-GPU bin/train, but the DDP path never did. train_ddp had no
--dropout flag and never set cfg.dropout, and ddp.rs::train_rank never
called model.train() — so under DDP every forward ran in the default eval
mode and dropout was a silent identity, regardless of config.
Fix, mirroring the single-GPU train/eval discipline:
- train_ddp.rs: add a --dropout <p> flag (default 0 = off, matching the
prior behavior) and set cfg.dropout from it; log it when on.
- ddp.rs::train_rank: call model.train() at the start of each step (before
the micro-batch loop). eval_loss() flips the model to eval mode and does
not restore it, so re-asserting train() each step keeps dropout live
across eval boundaries.
--dropout 0 (default) is bit-identical to the prior DDP path: cfg.dropout
stays 0 and ops::dropout(p=0) is a clone no-op regardless of training mode.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- repeat_kv CUDA kernel: fwd head-block gather, bwd DETERMINISTIC group-sum (each
kv head sums its group of query-head grads; no atomics) + Tensor/ops node.
- Config gains num_kv_heads (default = n_heads → MHA); wk/wv project to kv_dim;
attention() repeat_kv-broadcasts K/V to nh heads before the UNCHANGED composed
& flash SDPA → GQA on both paths. group=1 is identity → MHA bit-identical.
- --kv-heads flag on train/train_ddp/export_safetensors/greedy_sample; export
writes real num_key_value_heads (xserv repeat_kv grouping aligned).
- Tests: repeat_kv grad-check (group>1 grad-sum + group=1 identity); model gqa.rs
(GQA flash==composed fp32/bf16, group=1 bit-identical to MHA, kv-proj shape);
parity_dump+parity.py GQA path (repeat_interleave) via XTRAIN_PARITY_KV_HEADS.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Accumulate grads over N micro-batches, then one AdamW step + zero_grad,
for an effective batch of N×micro at one micro-batch's activation cost.
Each micro-loss is scaled by 1/N before backward (the tape SUM-accumulates
the scaled grads) so the boundary grad equals a single step over an N×
batch. accum==1 skips the scale → bit-identical to the pre-T16 path.
DDP: the cross-rank all-reduce fires ONLY at the accumulation boundary
(intermediate micro-steps are local-only, no NCCL); the /world average is
orthogonal to the per-micro 1/N, so the boundary grad is the effective
global-batch mean. New --accum-steps flag in both train binaries; effective
batch is printed.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
autograd: flash_attention_batched_bwd (dQ/dK/dV finite-diff, seq>tile)
+ flash_matches_composed_fwd. model/tests/flash.rs: flash==composed
on-vs-off (logits/loss/every param grad), fp32 + bf16. parity_dump:
XTRAIN_PARITY_FLASH dumps the flash path for the same parity.py oracle
(PyTorch SDPA parity at B>1). train + train_ddp get the --flash flag.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Wrap each transformer block's forward in the checkpoint primitive when
recompute is enabled (Phase T13 / KI-3). To make the block forward a pure
segment fn (no `&self` borrow, so it can re-run in the backward closure),
extract the block body + its helpers (linear / norm_gamma / attention /
swiglu_mlp) into free functions parameterised by (cfg, compute_dtype) and add
`Block::block_params()` (the 11 leaves in the params() per-block order). The
non-recompute path calls `block_forward` directly — identical graph to before.
- `TinyTransformer::with_recompute(bool)` builder (opt-in; default off keeps the
unchanged tape / bit-identical numerics).
- `--recompute` flag wired into bin/train and bin/train_ddp (DDP: each rank
checkpoints independently).
Correctness gate: tests/recompute.rs builds two identical models (recompute
on/off), runs the same batched loss+backward, and asserts the forward logits,
the loss, and EVERY parameter grad match within tight fp tol — parameterised
over fp32 and bf16 (T12 composition).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- TinyTransformer::with_compute_dtype(BF16): embedding stays fp32
master then casts to bf16; each linear casts its fp32 weight to bf16
on the fly; logits cast back to fp32 for cross-entropy. Default F32
reproduces the v0-v4 forward graph bit-for-bit.
- --bf16 flag on bin/train and bin/train_ddp (off by default).
- tests/bf16.rs: same fp32 master weights run fp32 vs bf16; assert
loss/logits/grads within a loose bf16 tol, no NaN, and grads are
fp32 (master untouched).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The T8 DDP path now matches the single-GPU `bin/train`: CLI-tunable arch
(scaling-ladder rung), the cached token-id stream (`load_cached`), held-out
val-loss eval + best-val checkpointing, and LR warmup→cosine. Rank 0 owns the
val corpus and runs the no-grad eval / writes the best checkpoint (params are
bit-identical across ranks). The eval/checkpoint logic is reused from
`xtrain-train` (`eval_loss`, `checkpoint::save`) rather than duplicated.
- DdpConfig gains eval_every / eval_batches / ckpt_path.
- train_rank takes `valid: Option<&Corpus>` and returns DdpResult
(losses + evals + best_val); launch threads the val corpus to rank 0 only.
- bin/train_ddp reworked to the bin/train CLI (positional tokenizer/corpus +
--dim/--heads/--head-dim/--layers/--ffn/--steps/--batch/--seq/--max-lr/
--val-tokens/--eval-every/--ckpt), reusing the u16 cache.
- DDP correctness test updated to the new signatures (semantics unchanged).
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>
