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