# xtrain

A from-scratch **Rust + CUDA** LLM **training** engine — the sibling of
[xserv](https://github.com/) (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):

```sh
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).
