13 Commits

Author	SHA1	Message	Date
rob thijssen	76ab24d98c	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
rob thijssen	800498f530	feat(neuron): bind listener before pre-warm, surface activation in /health ... Two coupled changes addressing the 2026-05-26 validate-neuron failure where a fresh deploy of beast had /health unreachable for ~5 minutes while Qwen3.6-27B q5k materialised, even though systemd reported the unit as active. 1. main.rs no longer awaits load_default_models before binding axum. The listener binds first; pre-warm runs in a spawned background task that holds a read lock on the harness registry for the duration of its sequential load loop. Concurrent on-demand /models/load and /v1/chat/completions traffic still flow. 2. /health gains an `activation` field carrying: state pre_warming | ready pending model ids queued but not started in_progress model id currently loading (Option) completed model ids loaded successfully this activation failed [{model_id, error}] for failed entries The field is `#[serde(default)]` so a pre-change cortex polling a new neuron — or vice versa — keeps working. `ActivationTracker` (new module `neuron::activation`) owns the RwLock-wrapped state; load_default_models takes a tracker reference and updates it per-model. NeuronState holds an Arc clone for the /health handler. Tests updated to construct trackers and assert state transitions (empty noop, two failures → ready with both in `failed`). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-26 15:18:04 +03:00
rob thijssen	da068ded6d	Stage 7a-ii: real NCCL handshake behind the worker pool ... Wires cudarc::nccl into the TP worker lifecycle introduced in 7a-i. With --features cuda the leader and its workers now establish a live NCCL communicator end-to-end; without the feature the same code paths return Error{kind="cuda_feature_not_enabled"} so a misconfigured build is obvious instead of silently no-op. NCCL state machine (harness/tp/nccl_state.rs) is shared between the worker process and the leader's pool: - generate_comm_id_hex() mints an Id::new() on the leader. - NcclState::init parses 256 hex chars → [c_char; 128] → Id::uninit, opens a CudaContext on the configured device, calls Comm::from_rank with the supplied (rank, world_size, id). NCCL blocks until every rank has joined. - NcclState::sanity_check runs one all_reduce(1u32, Sum); the leader asserts every rank reports observed_sum == world_size. - NCCL handles serialised under Mutex; unsafe impl Send/Sync gates the Comm across spawn_blocking boundaries (NCCL is move-safe; only concurrent op issuance is unsafe). WorkerPool::init_nccl orchestrates the rendezvous: 1. Write Init { comm_id } to every worker's stdin (no await yet). 2. Leader rank 0 calls its own Comm::from_rank in spawn_blocking, concurrently with workers. 3. NCCL handshake completes for all ranks simultaneously. 4. Leader collects InitOk responses. WorkerPool::nccl_sanity_check follows the same pattern over all_reduce, validating world_size == observed_sum on every rank. Worker.send_only / Worker.recv_only split out from the previous monolithic Worker.request so the leader can interleave its own NCCL work with the worker calls — required because NCCL blocks during init. Tests: - 4 hex roundtrip unit tests for the wire encoding. - The 7a-i "not implemented" expectation now reads "cuda_feature_not_enabled" on the local dev box (no CUDA), or accepts InitOk on a cuda-built test binary. - New cuda-integration test in tp_worker_lifecycle_cuda.rs covers the real init + sanity round-trip; gated on the cuda-integration feature so default CI doesn't try to NCCL. Verifiable on beast (2× RTX 5090): cargo test -p neuron --features cuda-integration \ --test tp_worker_lifecycle_cuda Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-19 16:40:01 +03:00
rob thijssen	2a7ede0232	Stage 7a-i: TP worker lifecycle scaffolding ... Leader → worker process plumbing for tensor parallelism. The neuron binary picks up two modes: default (the existing daemon, axum + HTTP) and `--worker` (a bare RPC loop driven over stdin/stdout). The leader spawns one worker per non-zero NCCL rank via tokio::process::Command on the same binary path (production: /proc/self/exe; tests: env!("CARGO_BIN_EXE_neuron")) and talks to each over newline- delimited JSON. Protocol (harness/tp/rpc.rs) is serde-tagged from the start — WorkerRequest::{Ping, Init, NcclSanityCheck, Shutdown} and WorkerResponse::{Pong, InitOk, NcclSanityResult, Bye, Error}, both `#[serde(tag = "op", rename_all = "snake_case")]`. Adding ops in 7b/7c is purely additive; unknown ops on the wire fail to parse (verified in unit tests). 7a-i scope: - WorkerPool::spawn(binary, world_size, devices) forks ranks 1..N as subprocesses, captures stdin/stdout, kills on drop. - ping_all() round-trips a Ping to every worker and validates the returned rank. - shutdown() sends Shutdown to each worker, awaits Bye, reaps. - Worker mode: parse Ping/Shutdown, return Pong/Bye; Init and NcclSanityCheck return Error{kind="not_implemented_7a_i"} so a 7a-ii binary speaking the same wire is a drop-in replacement (the kind field signals "real NCCL lands in the next commit"). - CandleHarness::load_model refuses tensor_parallel > 1 with a clear message until 7b is in. Three integration tests in tests/tp_worker_lifecycle.rs cover spawn/ ping/shutdown for 2- and 3-worker pools, plus the not_implemented_7a_i contract test for Init. Seven rpc serde unit tests assert the wire shape (op tags, field names, unknown-op rejection). All pass on the dev host; no CUDA required. Stage 7a-ii (next): the real NCCL Comm::from_rank wiring behind the existing Init/NcclSanityCheck op surface, CUDA-gated. Verifiable on beast's 2×5090. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-19 15:53:00 +03:00
rob thijssen	aad314cdfa	feat(neuron): graceful unload-on-shutdown via SIGTERM/SIGINT ... Stage 6 of the candle-native pivot. Adds first-class deactivation: neuron now drains in-flight requests on SIGTERM (systemd stop) or SIGINT (Ctrl-C), then unloads every loaded model before the process exits — releasing CUDA contexts and VRAM cleanly rather than leaving the OS to reclaim them. Mechanism: - startup::shutdown_signal() resolves on either ctrl_c() or a SIGTERM listener. - axum::serve(...).with_graceful_shutdown(shutdown_signal()) stops accepting new connections, lets active requests finish, then returns control to main. - startup::unload_all_models(&registry) iterates list_all_models() and calls unload per entry. Per-model failures are logged warnings; cleanup continues. Empty registry is a fast no-op. - main holds an Arc<NeuronState> reference past axum's lifetime so the registry is still reachable for the unload sweep. data/neuron.service: - TimeoutStopSec=120s — generous bound for big-model unloads before systemd escalates to SIGKILL. - KillSignal=SIGTERM — explicit, matches the handler. Two non-gated tests cover the empty-registry no-op and the no-models- loaded path. Real load-then-unload-on-shutdown is exercised by the cuda-integration test from Stage 2 (which calls unload_model directly) and observable on a real GPU host by stopping the service and watching nvidia-smi. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-18 17:58:07 +03:00
rob thijssen	6779b7526a	feat(neuron): load default_models on service activation ... Stage 5 of the candle-native pivot. Adds first-class support for auto-loading a configured set of models when the neuron service activates. Config: - NeuronConfig.default_models: Vec<ModelSpec> (defaults to []). - neuron.example.toml ships a commented [[default_models]] example. Activation flow (crates/neuron/src/startup.rs::load_default_models): - Sequential — VRAM contention makes parallel loads risky. - Per-entry timing logged at info level on success. - Failures logged as warnings; the next entry is still attempted. - An empty list short-circuits without log noise. Called from main.rs after the registry is built and before the axum listener binds, so /models reflects the loaded state from the very first request. data/neuron.service gains TimeoutStartSec=1800s. With activation blocked on potentially slow first-time HF downloads + GGUF materialisation, systemd's default 90s would kill larger model loads mid-flight. Two non-gated tests in tests/activation.rs cover the continues-past-failure and empty-list paths using a synthetically unknown harness name to fail loads fast without touching the network. The cuda-integration test from earlier stages still exercises the real load/unload lifecycle. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-18 17:56:08 +03:00
rob thijssen	84f5662df1	feat(neuron): OpenAI-compatible SSE streaming chat completions ... Stage 4 of the candle-native pivot. /v1/chat/completions now switches to text/event-stream when the request sets stream: true, emitting one chat.completion.chunk per generated token followed by the OpenAI [DONE] terminator. Pipeline: - chat_completion_stream creates a bounded mpsc::channel<ChatCompletionChunk>(32), sends the leading role chunk, then spawns a blocking task that acquires the per-model arch lock and runs the streaming generation loop. - run_inference_streaming tracks a cumulative decoded prefix so each chunk's delta.content is the substring added since the last chunk — safe across BPE byte-fallback boundaries that would otherwise split multi-byte UTF-8 chars. - The blocking task aborts cleanly if blocking_send fails (client disconnected), so generation stops when the SSE consumer hangs up. - Final chunk carries finish_reason ("stop" on EOS, "length" on max_tokens). The handler appends data: [DONE] after the channel closes. The Stage 3 streaming 501 placeholder test is repurposed: with the streaming path live, an unloaded model now hits the same 404 surface as the non-streaming path (the model lookup happens first). cortex-gateway's existing proxy is unchanged — it already forwards SSE bytes verbatim from Phase 2 work, so the candle SSE format passes through unmodified. Neuron Cargo.toml gains futures + tokio-stream (both already in workspace deps) for ReceiverStream and stream combinators. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-18 17:53:14 +03:00
rob thijssen	729317d1ef	feat(neuron): OpenAI-compatible non-streaming chat completion ... Stage 3 of the candle-native pivot. neuron now serves POST /v1/chat/completions backed by candle's quantized_qwen3 forward pass on a per-model serialised generation loop, returning the standard OpenAI ChatCompletionResponse envelope. Pipeline per request: - Look up the LoadedModel by request.model (404 if absent). - Apply the Qwen3 chat template across all messages. - Tokenize, then spawn_blocking onto tokio's blocking pool to acquire the per-model arch lock and run prefill + greedy/temperature/top-p sampling via LogitsProcessor. - Stop on <|im_end|>/<|endoftext|> EOS or max_tokens (finish_reason "stop" vs "length"). - Decode with skip_special_tokens=true, build OpenAI response with prompt/completion/total usage counts. Supporting changes: - HarnessRegistry now stores Arc<dyn Harness> and caches a typed Arc<CandleHarness> so inference routes bypass dyn-Trait dispatch. - LoadedModel.arch becomes Arc<Mutex<ModelArch>> so the lock guard can be moved into spawn_blocking. - NeuronState gains an Option<Arc<CandleHarness>> field for the new inference route. - Typed InferenceError lets the handler map ModelNotLoaded → 404 and other failures → 500 without string-matching anyhow messages. - stream=true returns 501 until Stage 4 wires up SSE. - Two leftover mistral.rs string references in proxy.rs and cortex-cli (missed during the Stage 1 sweep) are corrected here. Three new default-feature tests cover the no-candle 503, model-not- loaded 404, and stream=true 501 paths. The cuda-integration test from Stage 2 still covers real load/unload; a streaming-feature gated test exercising actual generation will arrive with Stage 4. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-18 16:47:58 +03:00
rob thijssen	5c2bd1a1da	feat(neuron): wire candle harness load/unload via GGUF ... Stage 2 of the candle-native pivot. Fleshes out CandleHarness with a LoadedModel registry keyed by model_id, hf-hub-backed GGUF download, and Qwen3 quantized weight construction via candle-transformers' quantized_qwen3 module. unload_model drops the entry; Drop on the candle ModelWeights frees device memory. Device selection prefers CUDA (gated behind the new `cuda` feature), falling back to CPU when CUDA is unavailable so default builds work on non-GPU hosts. The candle CUDA toolchain isn't pulled in unless `--features cuda` is passed, keeping CI green on CPU runners. Config gains a [harness.candle] block with an optional hf_cache path. HarnessRegistry::from_configs now takes HarnessSettings so per-harness config flows through. A gated tests/candle_lifecycle.rs exercises real load → list → unload → list-empty when run with `--features cuda-integration` against a host with HF network access. The default-feature test in tests/api.rs covers the wrong-harness rejection path without needing the network. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-18 16:02:49 +03:00
rob thijssen	3cccc2c56b	refactor(neuron): cut mistralrs/llamacpp, scaffold candle harness ... Stage 1 of the candle-native pivot. Replaces the external-process harness model (mistralrs over HTTP, llamacpp placeholder) with an in-process Harness trait whose sole implementation is candle. The trait keeps its shape so future engines slot in additively, but start/stop default to no-ops and HarnessConfig drops endpoint and systemd_unit since no harness needs external supervision. Behaviour is unchanged on the wire: load_model returns a "not implemented yet (Stage 2)" error and list_models is empty. The gateway-side proxy, poller, and router are untouched. CLAUDE.md Phase 11 (llama.cpp) and Phase 12 (mistral.rs COPR) are marked superseded; the staged plan lives in ~/.claude/plans/create-a-more-aggressive-calm-naur.md. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-18 15:53:04 +03:00
rob thijssen	6c238f4557	refactor: rename cortex-neuron binary and crate to neuron ... Package name, lib name, and binary all now just "neuron" without the cortex- prefix. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-04-15 15:51:15 +03:00
rob thijssen	26e5e7ead8	feat: implement mistral.rs harness and neuron model API ... - MistralRsHarness: Harness trait impl wrapping mistral.rs HTTP API (list/load/unload models, health check, start/stop via systemd) - HarnessRegistry: maps harness name -> Box<dyn Harness>, built from neuron.toml config - Neuron API endpoints: GET /models, POST /models/load, POST /models/unload, GET /models/:id/endpoint - NeuronConfig: figment-based config loading from neuron.toml - Integration test: full model lifecycle through mock mistral.rs Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-04-15 14:29:42 +03:00
rob thijssen	6dc717ebcd	feat: add neuron daemon with GPU discovery and health endpoints ... Replace cortex-agent stub with neuron (cortex-neuron binary). cortex-core additions: - discovery.rs: DeviceInfo, DiscoveryResponse, DeviceHealth, HealthResponse - harness.rs: Harness async trait, HarnessConfig, ModelSpec, ModelInfo neuron crate (crates/neuron/): - discovery.rs: nvidia-smi CSV parsing (pure functions) + system discovery via uname/nvidia-smi/nvcc - health.rs: cached GPU health polling every 5s - api.rs: GET /discovery and GET /health axum handlers - main.rs: CLI entrypoint with --port flag (default 9090) - harness stubs for mistralrs (Phase 8) and llamacpp (Phase 11) 12 new tests (9 unit + 3 integration), 35 total. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-04-15 14:23:42 +03:00