Two forward-only Tensor primitives the KV-cache decode engine is built on,
each gated by an isolated correctness test:
- rope_at(theta, pos0): RoPE at an absolute position (pos = pos0 + row, no
modulo) for a single decode token, vs the training rope_k (pos = row %
period) left untouched. New forward-only CUDA kernel, no training-path risk.
Gate: bit-identical to the full-sequence rope's corresponding row.
- decode_attention(k, v, scale): single-query × cached-K/V SDPA, composed from
the existing strided batched GEMM + plain (non-causal) softmax — no new
kernel. Gate: equals the full causal attention's last query row (max |Δ| 6e-8).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Enable assistant-only supervised fine-tuning and a fixed chat-prompt eval path
used by the v12 SFT runs:
- cross_entropy ignores negative targets (-100 ignore-index), normalizing by
valid rows instead of all rows; CUDA fwd/bwd skip t<0 (ops.rs, nn.cu).
- Corpus gains optional labels + load_sft_tsv_cached: two-column TSV is
formatted as 'User: .. \nAssistant:' + answer + <|endoftext|>, prompt tokens
masked to -100 while answer+EOS are supervised; i32 label cache alongside the
u16 token cache; sample() retries windows that are fully masked; eval uses
target_window so masking applies to val loss too (data.rs, train_loop.rs).
- train + train_ddp: --sft-tsv selects the TSV loader, --init-ckpt continues
training from a base checkpoint.
- greedy_sample: --prompts-file/--prompt/--temperature for fixed chat-prompt
generation eval.
Test fixtures updated for the new Corpus.labels field; dropout.rs carries
incidental rustfmt. Not rebuilt locally (no CUDA toolchain on this checkout);
correctness rests on the documented v12 base+SFT runs on the GPU box.
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>
add/mul/add_bias(+sum_rows)/rms_norm/silu/rope/softmax/cross_entropy,
each with its analytic backward, in csrc/ops/nn.cu (inlined warp/block
reductions). FFI declarations + nn.cu in build.rs (no_cuda gated). Tensor
gains the matching thin wrappers; DType grows I32 for cross-entropy targets.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
