Gahow Wang
7821bd9c34
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>
xtrain
A from-scratch Rust + CUDA LLM training engine — the sibling of xserv (the inference side). GPU-first.
The goal is to learn the full training-systems stack by hand: autograd / backward passes / optimizers (AdamW) / the training loop / distributed logic. Heavy lifting is borrowed where it makes sense (GEMM → cuBLAS after a hand-written version, multi-GPU comms → NCCL, tokenizer → reused from xserv), but the core is written from scratch. The target architecture is a tiny modern transformer (RoPE + RMSNorm + SwiGLU, ~1–30M params) whose forward aligns with xserv's Qwen3, so the backward passes map one-to-one onto xserv's existing forward kernels and trained weights can flow back into xserv.
Status
Bootstrapping (P0). This repo currently contains only the project skeleton and a working Rust↔CUDA build chain, verified by a trivial vector-add CUDA kernel.
Layout
xtrain/
├── Cargo.toml # workspace
├── csrc/ # CUDA sources (.cu)
│ └── test/vecadd.cu # trivial element-wise vector-add (smoke test)
└── crates/
└── xtrain-cuda/ # CUDA Runtime FFI + build.rs (nvcc → sm_120)
├── build.rs # compiles csrc/*.cu via the `cc` crate, links cudart
├── src/ # ffi / error / device / memory
└── tests/ # vecadd smoke test
The build mirrors xserv's approach: build.rs invokes nvcc (via the cc crate)
to compile csrc/*.cu targeting sm_120 (RTX 5090) and links them into the Rust
crate over hand-written extern "C" FFI.
Building & testing
CUDA compilation and execution happen on a GPU box (dash5, 8× RTX 5090, sm_120):
export PATH=/usr/local/cuda/bin:$HOME/.cargo/bin:$PATH
cargo build
cargo test -p xtrain-cuda -- --nocapture # runs the vecadd smoke test
On a machine without nvcc/GPU, build.rs detects the missing toolchain, skips
CUDA compilation, and sets a no_cuda cfg — so host-side cargo check still
works (the GPU smoke test is compiled out).