Gahow Wang
d2a585c5cb
v3 tested the documented mitigation (raise global_batch to amortize the per-step all-reduce). Isolated back-to-back A/B on 4× RTX 5090, dim384/12L, seq256: global_batch 32 (8/rank) → 3163 tok/s global_batch 256 (64/rank)→ 3200 tok/s (8× batch, +1.2%, within noise) 8× larger batch = 1/8 the all-reduces per token, yet no speedup → all-reduce is NOT the bottleneck. GPU util 0–15%, mem ~2–3 GB/32 GB → the workload is launch-bound: the single-sequence model design (each sequence its own tiny forward/backward, per-op kernel launches) starves the GPU, and batching only adds proportionally more serial launches. Real fix is batched (multi-sequence) forward so GEMMs fill the GPU — a T4/T5 autograd/model change, not a batch knob. Bucketed/overlapped all-reduce stays deferred (no value until launch-bound is fixed). KI-1 kept Open with the corrected root cause. 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).