Gahow Wang
163f567c80
DDP training step (train_rank) on top of DdpContext: each rank advances the SAME RNG, draws the whole global batch, and runs forward+backward only on its shard (i % world == rank) so the union over ranks is the single-GPU batch in the same order. After backward, all-reduce-average the device grads, then finish the mean with clip(pre_scale = 1/b_local) -> Sigma_global/B_global, identical to the single-GPU clip(1/B). Each rank then runs its own GpuAdamW.step; same init + same averaged grad + same optimizer state keep params bit-identical across ranks. Adds a deterministic build_model (same LCG init as bin/train) shared by ranks + baseline, a per-step loss all-reduce for the reported global-mean loss, and the thread-per-GPU launch() helper (thread::scope; Var graph is !Send so each rank builds its model thread-locally, only UniqueId/config/&Corpus cross threads). 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).