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>
Eliminate the per-step GPU↔host roundtrip of every parameter/gradient.
- optim.cu: adamw_step (m/v on device, in-place param update), sumsq_accum
(block-reduced global grad sum-of-squares), scale_inplace.
- GpuAdamW: device m/v state per param; step launches the kernel reading
each param's .grad() and rewriting the param buffer in place — no host
roundtrip. Host AdamW kept as the torch-parity reference.
- clip_grad_norm_gpu: device sum-of-squares reduction (only the scalar norm
comes back), in-place rescale of grads by pre_scale·clip_factor.
- train_loop: use GpuAdamW + clip_grad_norm_gpu.
- test: GPU AdamW vs host reference parity (max abs err < 1e-6).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Phase T5 structural ops on top of the T4 set, needed to assemble the
tiny transformer:
- embedding: gather rows by I32 ids (CUDA kernel) / scatter-add backward
(atomic, so repeated ids accumulate). csrc/ops/model.cu + ffi.
- reshape: contiguous metadata-only view (Tensor::reshape), no kernel.
- transpose_3d01: [a,b,c]->[b,a,c] for the multi-head layout (kernel).
- autograd nodes: embedding/reshape/transpose_3d01/transpose_2d, plus
split_heads (->Vec<Var>) / merge_heads for per-head attention.
- tape: Var::zero_grad + set_value so a hand-written GD step can update
params and clear grads between steps.
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>
Hand-written tiled GEMM (csrc/ops/gemm.cu, TILE_SIZE=32, FP32 accumulate,
boundary-masked) plus an out-of-place transpose kernel. Wire both through
xtrain-cuda FFI (no_cuda-gated) and expose at the tensor level:
Tensor::matmul, transpose_2d, and matmul_backward computing
dA = dC·Bᵀ and dB = Aᵀ·dC by materializing transposes and reusing the
forward. Also declare cuBLAS sgemm FFI + link cublas, used only as a
correctness reference in tests.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
New csrc/ops/elementwise.cu (out[i]=in[i]*alpha), compiled by
xtrain-cuda/build.rs and exposed via launch_scale_f32 FFI, gated behind
not(no_cuda) like the existing vecadd smoke test.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
