Gahow Wang
c396b39483
Add `xtrain_autodiff::checkpoint::checkpoint(segment_fn, input, params)`, a higher-order autograd node (à la torch.utils.checkpoint) for activation recomputation (Phase T13 / KI-3): - forward: run `segment_fn` on detached leaves so its internal ops are NOT recorded on the outer tape; keep only the output value (the local sub-tape — and thus the segment's intermediate activations — drops immediately). The checkpoint node's parents are [input, ..params]. - backward: re-run `segment_fn` from the saved input + (unchanged) param values into a fresh local tape, seed the recomputed output with the upstream grad, backprop, then push the recovered input/param grads to the real parents. Local tape drops at the end → recomputed activations freed. Exact by construction (same deterministic kernels, same inputs) → grads match the non-checkpointed path. Composes with bf16 (T12, same path on recompute) and DDP (T8, per-rank). Supporting change: `Var::backward_seeded(seed)` — backward from an explicit non-scalar upstream grad (the segment output is generally not a scalar); `backward()` is now the scalar wrapper that seeds ones. 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).