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helexa/crates/neuron/tests/tp_worker_lifecycle.rs
rob thijssen 76ab24d98c
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refactor(neuron): phase 3 — TP forward + NCCL state move onto device worker
Third slice of the per-device CUDA context-ownership refactor planned at
~/.claude/plans/plan-the-per-device-worker-abstract-micali.md. The
leader's `NcclState`, every `Comm::all_reduce` issued by the TP layers,
the leader-side KV cache reset, and the TP forward step itself now all
run on the per-device worker thread — the same OS thread that bound
the leader's `CudaContext` at startup.

What this phase changes:

- `Job` gains `NcclInit`, `NcclSanity`, `CloneLeaderComm` (Phase 3
  bridge — Phase 4 removes), `TransferInTp`, `DropTp`, `TpClearKv`,
  `TpForwardLogits`. Plus a new `TpHandle(u64)` opaque key.
- `DeviceWorkerState` gains `nccl: NcclState` and
  `tp_models: HashMap<TpHandle, Box<TpLeaderModel>>` (+ counter).
- `WorkerPool` loses its `leader_nccl` field; gains a
  `leader_worker: Arc<DeviceWorkerHandle>` passed at construction.
  `init_nccl`, `nccl_sanity_check`, `load_dense_shard`,
  `generate_step`, `clear_kv_cache` all route their leader-side ops
  through `Job::Nccl*` / `Job::Tp*` instead of spawn_blocking against
  a Mutex-wrapped state. `generate_step` returns `Vec<f32>` instead
  of a device-resident `Tensor` — the worker copies logits to CPU
  before reply so the async caller can sample on a CPU candle
  tensor with zero device-context touch.
- `TpLoadedModel.leader_model: Arc<Mutex<TpLeaderModel>>` → opaque
  `leader_handle: TpHandle`. The boxed `TpLeaderModel` lives in the
  worker thread's slab; both the model's CUDA tensors and the
  embedded `Arc<Comm>` clones release on the same thread that
  allocated them (the Drop semantics constraint cudarc forces).
- `Job::CloneLeaderComm` is a Phase 3 bridge: the TP shard load still
  runs in spawn_blocking and needs the leader's `Arc<Comm>` to build
  the row-parallel layers' AllReduce ops. The Job clones the Comm
  out of the worker's NcclState and ships it back as `SendComm`.
  Phase 4 deletes this bridge when the load itself moves onto the
  worker.
- `Job::NcclInit` and `Job::NcclSanity` are ungated by `cuda` so the
  no-cuda `NcclState` stubs (which reply with `cuda_feature_not_enabled`)
  still flow through the same channel uniformly; the cuda-only
  TP variants (CloneLeaderComm, Transfer/Drop/Clear/Forward Tp)
  remain gated.

What this phase doesn't touch (yet):

- TP shard load itself — still spawn_blocking, bridged via
  `CloneLeaderComm`. Phase 4 moves it to `Job::TpLoadShard` and
  reads `state.nccl.comm()` directly inside the worker.
- Single-GPU model loads — still spawn_blocking, transferred via
  `Job::TransferIn`. Phase 4 moves them.
- `device_vram_mb` / `cuda_mem_mb` / `log_construction_complete`
  helpers — still present, used inside spawn_blocking load closures.
  Phase 4 cleanup folds them into `dispatch.rs`.

`tp/mod.rs::WorkerPool::spawn` gained a required
`leader_worker: Arc<DeviceWorkerHandle>` argument. Three external
callers were updated: `CandleHarness::load_tp` (passes the cached
device worker), `main.rs::tp_smoke` (spawns a fresh worker), and
the two `tp_worker_lifecycle*.rs` integration tests.

Public API unchanged. fmt + clippy clean; 37 lib tests + all
integration tests pass. CUDA-only TP integration smoke deferred to
the next deploy on beast.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-27 10:16:02 +03:00

149 lines
5.5 KiB
Rust

//! Stage 7a-i: confirm the TP worker subprocess lifecycle round-trips.
//!
//! Spawns two worker subprocesses via the leader→worker stdio RPC,
//! pings each, and cleanly shuts them down. No CUDA required —
//! `Init` and `NcclSanityCheck` are stubbed in 7a-i, so this test
//! runs on any host the workspace builds on.
use neuron::harness::device_worker::DeviceWorkerHandle;
use neuron::harness::tp::{WorkerPool, rpc::WorkerResponse};
/// Path to the neuron binary built by cargo for this test process.
/// cargo populates `CARGO_BIN_EXE_neuron` at compile time for sibling-
/// binary tests; production paths in main.rs use `/proc/self/exe`.
const NEURON_BIN: &str = env!("CARGO_BIN_EXE_neuron");
/// Two workers (so we spawn one subprocess: rank 0 is in-process,
/// rank 1 is the child). Verify the spawned worker responds to Ping
/// with its own identity, then shut it down cleanly.
#[tokio::test]
async fn test_spawn_ping_shutdown() {
// cuda_devices: rank 0 → device 0 (leader, unused here),
// rank 1 → device 1 (worker; not actually opened in 7a-i).
let leader_worker = DeviceWorkerHandle::spawn(0).expect("spawn device worker");
let mut pool = WorkerPool::spawn(NEURON_BIN.as_ref(), 2, &[0, 1], leader_worker)
.await
.expect("spawn worker pool");
let pongs = pool.ping_all().await.expect("ping all workers");
assert_eq!(pongs.len(), 1, "expected one Pong (rank 1 only)");
match &pongs[0] {
WorkerResponse::Pong {
rank,
world_size,
cuda_device,
} => {
assert_eq!(*rank, 1);
assert_eq!(*world_size, 2);
assert_eq!(*cuda_device, 1);
}
other => panic!("expected Pong, got {other:?}"),
}
pool.shutdown().await.expect("clean shutdown");
}
/// Three workers — exercise the loop in `ping_all` / `shutdown`.
#[tokio::test]
async fn test_spawn_three_workers() {
let leader_worker = DeviceWorkerHandle::spawn(0).expect("spawn device worker");
let mut pool = WorkerPool::spawn(NEURON_BIN.as_ref(), 3, &[0, 1, 2], leader_worker)
.await
.expect("spawn worker pool");
let pongs = pool.ping_all().await.expect("ping all workers");
assert_eq!(pongs.len(), 2, "expected two Pongs (ranks 1 and 2)");
for (i, resp) in pongs.iter().enumerate() {
match resp {
WorkerResponse::Pong {
rank,
world_size,
cuda_device,
} => {
let expected_rank = (i + 1) as u32;
assert_eq!(*rank, expected_rank);
assert_eq!(*world_size, 3);
assert_eq!(*cuda_device, expected_rank);
}
other => panic!("expected Pong, got {other:?}"),
}
}
pool.shutdown().await.expect("clean shutdown");
}
/// 7a-ii: without the cuda feature, Init must fail with a clear
/// `cuda_feature_not_enabled` marker rather than silently succeeding.
/// This is the local-dev-box test; the real NCCL handshake is exercised
/// by `tp_worker_lifecycle_cuda.rs` (gated on `cuda-integration`).
#[tokio::test]
async fn test_init_returns_cuda_feature_not_enabled_without_cuda() {
use neuron::harness::tp::rpc::WorkerRequest;
use std::process::Stdio;
use tokio::io::{AsyncBufReadExt, AsyncWriteExt, BufReader};
use tokio::process::Command;
// Spawn a single worker by hand to send Init directly (the pool's
// public API doesn't expose Init yet — that lands in 7a-ii).
let mut child = Command::new(NEURON_BIN)
.arg("--worker")
.arg("--rank")
.arg("1")
.arg("--tp-size")
.arg("2")
.arg("--cuda-device")
.arg("1")
.stdin(Stdio::piped())
.stdout(Stdio::piped())
.stderr(Stdio::null())
.kill_on_drop(true)
.spawn()
.expect("spawn worker");
let mut stdin = child.stdin.take().expect("stdin");
let stdout = child.stdout.take().expect("stdout");
let mut lines = BufReader::new(stdout).lines();
let req = WorkerRequest::Init {
comm_id: "ff".repeat(128),
};
let mut payload = serde_json::to_string(&req).unwrap();
payload.push('\n');
stdin.write_all(payload.as_bytes()).await.unwrap();
stdin.flush().await.unwrap();
let reply = lines
.next_line()
.await
.expect("read line")
.expect("got line");
let resp: WorkerResponse = serde_json::from_str(&reply).expect("parse reply");
match resp {
WorkerResponse::Error { kind, .. } => {
#[cfg(feature = "cuda")]
{
// With cuda enabled the response depends on whether
// CUDA hardware is actually present. Accept either
// the success contract or a real NCCL failure.
let _ = kind;
}
#[cfg(not(feature = "cuda"))]
assert_eq!(kind, "cuda_feature_not_enabled");
}
WorkerResponse::InitOk => {
// Real NCCL succeeded — only possible with cuda feature
// AND a working NCCL stack AND another rank actually
// joining. Don't fail; just acknowledge.
#[cfg(not(feature = "cuda"))]
panic!("InitOk without cuda feature is impossible");
}
other => panic!("expected Error or InitOk, got {other:?}"),
}
// Clean shutdown.
stdin.write_all(b"{\"op\":\"shutdown\"}\n").await.unwrap();
stdin.flush().await.unwrap();
let _ = lines.next_line().await; // Bye
let _ = child.wait().await;
}