Two exact correctness gates (composed = the end-to-end batched GRPO step == looped):
- xtrain-model forward_batched_ragged_matches_looped: forward_batched on RIGHT-padded
ragged sequences == per-sequence single-seq forward on the real rows. fp32
max|Δlogit| = 3.7e-7, bf16 = 0.0, both composed + flash SDPA. Pins "right-pad is
free under causal".
- xtrain-autodiff clipped_pg_loss_batched_matches_looped: batched op == looped
Σ_s (1/N)·clipped_pg_loss_s. loss Δ=1.5e-8, grad max|Δ|=7.5e-9 (f32).
bench_grpo_batch: weight-independent micro-bench of the per-sample training forwards
(loads v12 base as policy, N realistic ragged samples, teacher-forced argmax targets
so the closeness smoke isn't −log-amplified by random low-prob tokens). Measured on
dash5 (v12 1.05B, N=48, micro=16): capture 622→71 ms (8.7×), inner 1907→208 ms
(9.2×), training forwards 2526→280 ms (9.0×).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The GRPO (M4) token-level loss op + the one primitive it needs:
- scale_rows(x[r,c], s[r]): per-row scale (new ~5-line CUDA kernel). The
clipped-PG backward scales each completion token's row of (probs − onehot) by
its own per-token coefficient, which cross_entropy_backward's single scalar
scale can't express.
- clipped_pg_loss(logits, target, logp_old, logp_ref, A, eps, beta): per-token
ρ_t = exp(logπθ_t − logp_old_t), L = −mean min(ρA, clip(ρ,1±ε)A) + β·mean KL
(k3 estimator), masked to completion tokens. Backward reuses the CE machinery
(probs − onehot) + scale_rows. Gates: grad-check the active PG path + the A=0
(KL-only) path; degenerate value checks ε→∞ ⇒ vanilla PG, β=0 ⇒ no KL.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Two new ops for DPO (M3), both reusing existing kernels (no new CUDA):
- seq_logprob(logits, target): Σ log πθ(target) over non-ignored (target≥0)
positions — the per-sequence logprob DPO compares between policy and
reference. = −Σ per_row of cross_entropy (ignored rows already 0, like SFT
masking); backward = cross_entropy_backward(probs, target, −upstream) (sum,
no mean division). Gate: finite-diff grad-check with a -100 completion mask.
- dpo_loss(lpθ_chosen, lpθ_rejected, lpref_chosen, lpref_rejected, β): scalar
L = −log σ(Δ) = softplus(−Δ) with the two policy logprobs as parents (ref
logprobs constant). Gate: grad-check both parents + degenerate points
(policy==ref ⇒ Δ=0, L=log2, grads ∓β/2; β=0 ⇒ grads 0). Same formula as TRL.
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>
ops::dropout(x,p,seed): fwd runs Tensor::dropout, caches the mask in the backward
closure, bwd pushes dx=d⊙mask. p==0 returns x.clone() (no node) so the default
graph is unchanged. Tests in autograd.rs: fixed-seed finite-diff grad-check (mask
held constant across the ± perturbation — dropout is a fixed elementwise linear
map of x); E[out]≈input + keep-rate≈1-p over a seed sweep; p=0 kernel identity.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Match the trusted composed grad-check dims (seq=5<FA_TILE); the multi-tile
online-softmax path is gated by flash_bwd_matches_composed_bwd (seq=40),
sharper than finite-diff on the near-zero grads a long softmax produces.
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>
Add the batched-forward primitives. Linears/norms/elementwise/embedding/CE
already act on flat [rows,dim], so they work unchanged on [B*S,dim]; only
attention + RoPE need sequence awareness:
- RoPE: kernel takes a `period` (= seq len) so position = row % period, i.e.
per-sequence position on a flattened batch (period == tokens = single seq).
- Fused batched causal attention: new `Tensor::attention`/`attention_backward`
+ ops node, running QKᵀ and PV as cublasSgemmStridedBatched over the B*nh
(sequence,head) blocks (new sgemm_strided_batched binding) and a causal
softmax kernel (scale + per-row causal mask inline) — the whole attention is
3 launches regardless of B*nh, no per-head/per-seq loop, no host round-trip.
- transpose_4d12 ([B,S,nh,hd] <-> [B,nh,S,hd]) to lay out the batched heads.
grad-checks: new batched-rope, transpose_4d12, batched-attention dQ/dK/dV all
pass finite-diff (attn dK 1.5e-2, dQ 7.5e-3, dV 2.9e-4; rest tighter) alongside
the existing 12.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Finite-diff grad-checks (same L=sum(W∘out) harness as autograd.rs) for
embedding (incl. repeated ids), reshape, transpose_3d01, transpose_2d,
and split/merge_heads round-trip. Gated #![cfg(not(no_cuda))].
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
ops.rs wraps each Tensor op as a Var node with its backward closure (forward
caches captured by move). swiglu = mul(silu(gate), up); attention is composed
(matmul+scale+softmax+matmul), no fused kernel. tests/autograd.rs grad-checks
every op via the L=sum(W∘out) template, plus a fan-out grad-accumulation test
(dL/dx=4x) and an end-to-end composed-attention grad-check (dQ/dK/dV). Adds
xtrain-cuda dev-dep for device selection in tests.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
