# xtrain

A from-scratch **Rust + CUDA** LLM **training** engine — the sibling of **xserv** (the
inference side). A learning project: hand-write the entire training-systems stack
(autograd → backward → optimizer → training loop → distributed → mixed precision →
gradient checkpointing), then use it to run a multi-version **scaling study** that maps
the data-vs-capacity frontier for a tiny model.

> **Status: complete — three phases.**
> **Phase 1** = the from-scratch full stack (T1–T13) + an 8-version scaling study (v0–v8):
> hand-write the whole training-systems stack, then map the data-vs-capacity frontier.
> **Phase 2** = systems-stack depth (T14–T18): hand-write the five deferred training-stack
> features — fused flash-attention, real GQA, gradient accumulation, process-per-GPU DDP,
> dropout. **Phase 3** = one Chinchilla-style double-axis run (v9): dim1280 true-GQA +
> 6.01B FineWeb tokens, validating the v8 conclusion that data and capacity must scale
> together. Trains Qwen3-compatible LMs whose weights load into **xserv**; deterministic
> gates stay byte-identical, while large BF16 checkpoints are served and checked for
> prompt-level drift. This README is the capstone; per-topic detail lives in [`docs/`](docs/).

---

## What got built (from scratch, by hand)

7 crates, no ML framework — only cuBLAS / NCCL / safetensors as deliberate "heavy-lifting"
borrows, the rest hand-written CUDA + Rust:

| crate | what's hand-written |
|---|---|
| `xtrain-cuda` | CUDA Runtime FFI, RAII `GpuBuffer`, **caching/pool allocator**, cuBLAS (sgemm + bf16 GemmEx) bindings |
| `xtrain-tensor` | tensor (dtype/shape/strides/storage), elementwise + transpose + embedding kernels |
| `xtrain-autodiff` | **tape autograd engine** (grad accumulation), per-op backward, finite-diff grad-check, **checkpoint** (recompute) primitive, **fused flash-attention** (online-softmax) fwd/bwd, **`repeat_kv`** broadcast (GQA), **`dropout`** (counter-based device RNG + mask) |
| `xtrain-model` | tiny **Qwen3-style** transformer (RoPE + RMSNorm + QK-norm + SwiGLU), batched forward, **GQA** (`num_kv_heads<num_heads`), residual/MLP **dropout** |
| `xtrain-optim` | hand-written **AdamW** (host + GPU kernels) |
| `xtrain-train` | training loop, LR schedule, grad clip, **gradient accumulation**, checkpoint, BPE corpus + cache, samplers, safetensors export |
| `xtrain-distributed` | **NCCL DDP** (thread-per-GPU + torchrun-style process-per-GPU launcher / cross-process `ncclUniqueId`, all-reduce) |

Every op's backward is verified against **finite differences** and against **PyTorch**
(forward + per-parameter grads, batch > 1). Trained weights export to HF-safetensors and
load into xserv (Qwen3, BF16); deterministic fixtures produce token-identical greedy output,
and large checkpoints are validated end-to-end in the serving path.

## The build journey — Phase 1 (T1–T13) + Phase 2 (T14–T18)

Each phase: design doc + implementation + tests + a scoped commit (see [`docs/`](docs/) and
[`docs/evolution.md`](docs/evolution.md) for the per-axis changelog). **Phase 1 (T1–T13)**
hand-built the stack and fixed the four real bottlenecks; **Phase 2 (T14–T18)** went back to
hand-write five deferred training-stack features — see the Phase-2 summary below the table.

| phase | what | result |
|---|---|---|
| T1–T2 | Rust↔CUDA build chain · tensor abstraction | vector-add verified · roundtrip |
| T3–T4 | hand GEMM fwd/bwd + finite-diff · **tape autograd** + 11 op backwards | grads vs cuBLAS 1e-7 / finite-diff |
| T5 | tiny transformer (RoPE+RMSNorm+SwiGLU) | overfit + **PyTorch parity** |
| T6 | AdamW + training loop + checkpoint · GPT-2 BPE + TinyStories | first **coherent English** |
| T7 | cuBLAS + GPU optimizer + drop syncs | ~3× (2.7K→8.5K tok/s) |
| T8 | NCCL DDP | multi-GPU (weak scaling, then) |
| T9 | + per-head **QK-norm** (Qwen3-compat) + safetensors export | **xserv closed loop, token-identical** |
| **T10** | **batched multi-sequence forward** (fixes KI-1) | **single-GPU 15–24×**; MFU 0.4%→14% |
| **T11** | **device caching allocator** (fixes KI-5) | single-GPU 2.3×; **8-GPU 461K tok/s** |
| **T12** | **bf16 mixed precision** (fp32 master, fixes KI-2) | dim768 OOM solved; −29% mem |
| **T13** | **activation recompute** / checkpointing (fixes KI-3) | dim1024 fits; grads bit-identical |
| **T14** | **fused flash-attention** kernel (online softmax, no materialized N×N; opt-in `--flash`) | peak mem −16%@1k / −23%@2k seq; flash==composed (grads/PyTorch) |
| **T15** | **grouped-query attention** (`num_kv_heads<num_heads`; `repeat_kv` broadcast feeds both SDPA paths; backward sums each kv head's group; `--kv-heads`) | repeat_kv grad-check + **group=1 bit-identical to MHA**; GQA flash==composed; PyTorch GQA B>1; **xserv closed loop with real `num_key_value_heads`** token-identical |
| **T16** | **gradient accumulation** (`--accum-steps`; DDP all-reduces only at the boundary) | equiv to N× big batch (grad 3.8e-5); same effective-64 batch 27.7GB→7.2GB (−74%) |
| **T17** | **process-per-GPU** DDP (torchrun-style: 1 worker process / CUDA context per GPU; launcher mints `ncclUniqueId` → hex env injection; `train_rank` reused unchanged; thread-per-GPU path kept) | proc==thread loss 1.5e-7, cross-rank 1.2e-7, xserv md5 identical · **measured no-op on throughput**: thread 5.27× vs proc 5.31×@8 (8 GPUs 95–99% util) → residual non-linearity is NCCL/PCIe, *not* CUDA-context serialization (falsifies the old KI-5 hypothesis) |
| **T18** | **dropout** (hand counter-based device RNG + mask, inverted scaling, train/eval switch) | fixed-seed grad-check; **p=0 bit-identical**; recompute-safe |

The four performance fixes (T10–T13) each removed a real bottleneck — see
[`docs/known-issues.md`](docs/known-issues.md) — which is where **Phase 1** closed.

## Phase 2 — systems-stack depth (T14–T18)

Phase 1 fixed bottlenecks; Phase 2 went back to hand-write the five training-stack features
that had been **explicitly deferred** earlier (project's actual goal = learn the whole stack).
Each is opt-in, kept the default path **bit-identical**, and held a **hard correctness gate**:

- **T14 · fused flash-attention** ([`docs/13-flash-attention.md`](docs/13-flash-attention.md)) —
  a single hand-written kernel: **online (streaming) softmax, tiled over KV, never materializes
  the `N×N` scores**; flash-style backward recomputes scores + the `D=ΣdO·O` Jacobian
  simplification for dQ/dK/dV. Opt-in `--flash`, default off. **The win is memory, not
  wall-clock**: peak activation **−16%@seq1024 / −23%@seq2048** (grows with seq, since the
  `N×N` never lands), but **~2.3× slower** at head-dim 64 (a hand kernel can't beat cuBLAS
  tensor-cores on a small head). Gate: flash == composed (loss rel `0.0`, grad `4.4e-5`),
  PyTorch B>1 `7.9e-6`.
- **T15 · real GQA** ([`docs/14-gqa.md`](docs/14-gqa.md)) — `num_kv_heads < num_heads` via a new
  `repeat_kv` **broadcast op** that copies K/V `group = nh/num_kv` times to feed **both**
  (composed + flash) SDPA paths **unchanged**; its **backward is a deterministic group-sum**
  (no atomics) collapsing each kv head's query-head group. Gate: `repeat_kv` grad-check +
  **group=1 bit-identical to MHA** (regression guard); **xserv closed loop with real
  `num_key_value_heads`** token-identical.
- **T16 · gradient accumulation** ([`docs/15-grad-accum.md`](docs/15-grad-accum.md)) — N
  micro-steps scaled by `1/N` accumulate on the tape, then one AdamW step; DDP **all-reduces
  only at the accumulation boundary**. Decouples effective batch from activation memory: same
  effective batch 64, big-batch **27.7GB (OOM)** → accum 4×16 **7.2GB (−74%)**. Gate: `accum=N`
  ≡ one N× batch (grad `3.8e-5`); `accum=1` bit-identical.
- **T18 · dropout** ([`docs/17-dropout.md`](docs/17-dropout.md)) — a **stateless counter-based
  device RNG** (Philox-style bit-mix) → Bernoulli mask, inverted `1/(1−p)` scaling in train,
  identity in eval; wired at the two residual sites (attn-out, mlp-out). Stateless RNG is what
  makes it **compose bit-exactly with T13 activation recompute** — the backward re-run
  regenerates the *same* mask from `(seed, index)`. Gate: fixed-seed grad-check; **p=0
  bit-identical**.
- **T17 · process-per-GPU** ([`docs/16-process-per-gpu.md`](docs/16-process-per-gpu.md)) — a
  torchrun-style launcher: one worker process + CUDA context per GPU, the launcher mints one
  `ncclUniqueId` and **hex-injects it into each child's env** (no shared FS/TCP, no race); the
  worker reuses the T8 `train_rank` **unchanged**. Built and **correct** (proc vs thread loss
  `1.5e-7`, cross-rank `1.2e-7`, xserv md5 identical) — but **measured throughput-neutral**:
  8-GPU thread **491K (5.27×)** vs proc **493K (5.31×)**, `<1%`. This **falsifies** the
  long-standing KI-5/T11 hypothesis that thread-per-GPU's shared CUDA context caused the
  residual ~5×@8; with all 8 GPUs at 95–99% util, the residual is the **NCCL all-reduce + PCIe
  topology wall**, not context serialization. The third profile-first falsification (see below).

## The scaling study — v0 → v10

Same Qwen3-style architecture throughout; we scaled **dim** and **data** and read out val
loss (full per-run detail in [`docs/runs/`](docs/runs/)).

| ver | data (trained tok / epoch) | dim / core params | val loss | axis explored |
|---|---|---|---|---|
| v0–v3 | TinyStories (↑) | 32→512 / 41K→67M | 3.80 → 1.30 | bring-up |
| v4 | TinyStories 1.54ep | 768 / 127M | 1.17 | — |
| v5 | TinyStories 5.33ep | 768 / 127M | **1.11** | **data volume → saturates** |
| v6 | FineWeb-edu 1.02ep | 768 / 127M | 3.07\* | **corpus swap → graduates to real text** |
| v7 | FineWeb-edu 1.45ep | 768 / 127M | 3.01\* | same subset, more epochs → near-ceiling |
| **v8** | FineWeb-edu 1.05ep | **1024 / 226M** | **2.98\*** | **capacity → helps** |
| **v9** | FineWeb-edu 6.01B / ~1ep | **1280 / 357M + GQA** | **2.89\*** | **data + capacity → helps** |
| **v10** | FineWeb-edu 6.76B / ~1ep | **1280 / 357M + GQA** | **2.88\*** | **data-only top-up → small gain** |

\* FineWeb-edu val is a different (harder) distribution — **not comparable** to the
TinyStories val of v0–v5. Judge v6+ by sample quality + transfer, not the number.

### Four findings

1. **Data volume saturates.** TinyStories at dim768: 3.5× more tokens (v4→v5) bought only
   −5% val, curve flat. The narrow synthetic corpus is exhausted at this model size.
2. **Corpus > more-of-the-same.** Swapping TinyStories → FineWeb-edu (v5→v6) was a
   *qualitative* jump: the model went from only-writes-kid-stories to writing genuine
   historical/scientific expository prose. (Cost: TinyStories transfer val 1.11 → 2.75.)
3. **Capacity helps.** v8 (dim1024, ~1 epoch) beats both v6 (dim768, same epoch, by 0.085)
   and v7 (dim768, *more* data, by 0.035) → the dim768 runs were partly capacity-limited.
4. **Double-axis scale helps.** v9 scales both axes (dim1280/core357M + 6.01B FineWeb tokens)
   and beats v8 by another 0.095 val loss (~3.2%). The direction is validated, but the gain is
   still incremental and greedy decoding still repeats.
5. **Moving validation tails must stop.** v10 added one more FineWeb shard and got moving-tail
   val 2.8816, but appending data moves the held-out tail. A fixed eval v1 was created from the
   shard010 tail: v6/v7/v8/v9/v10 = 3.2328 / 3.1850 / 3.1515 / 2.9278 / 2.8814. Future runs
   should report this fixed eval first.

**Meta-finding:** every lever is now in the **~3% or smaller** regime. Single-axis moves were
exhausted by v8; v9 confirms Chinchilla-style double-axis scale works; v10 shows a data-only
top-up mostly adapts to the new shard. The next useful run should change model/context, not just
append another shard.

## Efficiency — throughput & MFU

The throughput story is the perf-infra report card (RTX 5090, bf16/fp32):

| | v1 | v2 | v3 | v4 | v5 |
|---|---|---|---|---|---|
| tok/s | 3.3K (1 GPU) | 3.6K (4 GPU) | 26K (1 GPU) | 145K (8 GPU) | 217K (8 GPU) |
| **MFU** | 0.4% | 0.2% | 14% | 17% | 13% |
| enabled by | — | DDP (weak) | **batched (T10)** | **alloc (T11)** | **bf16 (T12)** |

v1/v2 ran at **<0.5% MFU** — the single-sequence design left the GPU idle (launch-bound).
**Batched forward (T10) was the single biggest unlock** (~35× MFU jump). 6ND is an accurate
FLOPs count, but predicting *time* needs the *realized* MFU, which varied ~40× across
versions — a fixed-MFU estimate is off by up to ~100× for the early launch-bound runs.

## Engineering lessons

- **Profile before optimizing.** *Three* "known" fixes were *falsified by measurement*: (1)
  "bigger batch fixes DDP scaling" (real cause: single-seq launch-bound → T10); (2) "bucket the
  all-reduce" (real cause: per-op `cudaMalloc` serialization → T11 caching allocator); and (3)
  "process-per-GPU would fix the residual ~5×@8" (T17 — built the torchrun-style launcher and
  measured it **throughput-neutral**: the residual is the NCCL/PCIe communication wall, not
  shared-context serialization). All three would have been no-ops; each got measured and either
  reverted or recorded as a deliberate negative result instead of shipped on faith.
- **Honest correctness.** QK-norm was *added* to match xserv's Qwen3 (not faked); every change
  kept a hard correctness gate, and **no tolerance was ever loosened to go green**. Phase 2 held
  the line: flash == composed SDPA (grads/PyTorch), GQA group=1 bit-identical to MHA, gradient
  accumulation `accum=1` bit-identical, dropout p=0 bit-identical *and* dropout × recompute
  bit-exact, the default path unchanged on every feature, and the **xserv closed-loop md5
  byte-identical (`b04fc9f9`) throughout the deterministic gates**.
- **The closed loop matters.** Exporting to xserv and checking generated continuations caught
  real bugs and proved the whole stack end-to-end.

## Running it

Everything trains on a remote 8× RTX 5090 box; model artifacts live in a registry
(`tiny-models/v0…v10`). Serve any trained version in xserv:

```bash
# on the GPU box
cargo run -p xserv-model --release --bin xserv-cli -- <registry>/v10-fineweb-edu-dim1280-gqa-data6765 --max-tokens 100
# then type a prompt, e.g.  In science,
```

Build/test the engine itself (CUDA compiles + runs on the GPU box; host-side `cargo check`
works anywhere via the `no_cuda` cfg):

```sh
export PATH=/usr/local/cuda/bin:$HOME/.cargo/bin:$PATH
cargo test --workspace            # autograd grad-checks, PyTorch parity, DDP, etc.
```

## Doc index

- [`docs/evolution.md`](docs/evolution.md) — per-milestone changes across algorithm / architecture / infra / dataset.
- [`docs/runs/README.md`](docs/runs/README.md) — the v0–v10 comparison; [`docs/runs/0N-*.md`](docs/runs/) — per-run detail.
- [`docs/00-*` … `17-*`](docs/) — per-phase design docs (build chain → tensor → autograd → transformer → training → perf → distributed → export → batched → allocator → bf16 → recompute → flash-attention → GQA → grad-accum → process-per-GPU → dropout).
- [`docs/known-issues.md`](docs/known-issues.md) — perf backlog (KI-1/2/3/5 fixed; process-per-GPU CLOSED = measured no-op; KI-4 = accepted modeling tradeoff).
