- 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>
