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>
Default-stream kernels run in order and every host read goes through a
stream-ordered cudaMemcpy (to_device), so the per-op cudaDeviceSynchronize
after each kernel was pure overhead — remove all 21 in tensor.rs. Host
data is still correctly ordered by the D2H memcpy that reads it.
Also zero op-output buffers with cudaMemset (device-side, async) instead of
a blocking H2D memcpy of a host zero buffer on every allocation — that
copy was itself a hidden per-op sync point.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Route Tensor::matmul and matmul_backward through cuBLAS Sgemm instead of
the hand-written tiled kernel. fp32 → same GEMM up to rounding order, so
the T3 cuBLAS tolerance and downstream grad-checks are preserved.
- cublas.rs: thread-local persistent handle + row-major sgemm helper with
transpose flags (col-major⟺row-major as the T3 oracle does).
- matmul_backward: dA/dB via cuBLAS OP_T, dropping the two transpose
kernels + their allocations the T3 version ran.
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 xtrain-tensor crate: DType (F32), shape/stride helpers, Arc-counted
host/device Storage with CPU↔CUDA copy, and a contiguous Tensor with
creation, host↔device transfer, and a scale() op driving the elementwise
kernel. GPU integration tests (host↔device roundtrip + scale correctness)
gated behind not(no_cuda); a thin build.rs emits the no_cuda cfg so the
kernel call sites compile out locally.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
