Gahow Wang
5353b38402
forward_batched(ids[B*S], batch)/loss_batched: run B equal-length sequences as ONE forward over flattened [B*S] ids, so every linear is one big [B*S,dim] GEMM. Attention reshapes to [B*nh,S,hd], runs the fused batched causal SDPA (per-seq mask + RoPE period=S, no cross-sequence attention), writes back [B*S,dim]. The old per-(batch,head) loop + host-round-tripping split/merge_heads + the additive causal_mask leaf are gone. forward(ids[seq]) is now forward_batched(ids,1), so the sampler / inference path (batch=1) is unchanged. +batched_ids_tensor helper. New batched.rs test: batched forward == looped single-sequence (logits identical 0.0, grads 6.4e-4, loss identical). PyTorch parity now exercises B>1 (B=2,S=4): loss 5e-8, logits 6.9e-6, all 25 param grads within rtol — verifying per-seq RoPE position + per-seq causal masking. 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).