17 Commits

Author	SHA1	Message	Date
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	18ae3c30ee	post-validation cleanup: cuDNN runtime + repetition penalty ... Two followups from the live single-GPU validation pass. 1. deploy.sh now ensures libcudnn.so.9 is available on each neuron host before installing/upgrading the package. Probes ldconfig first so hosts with a manual (tar/runfile) cuDNN install are untouched, then adds NVIDIA's RHEL9 CUDA repo (the Fedora 43 CUDA repo doesn't ship cuDNN; only the RHEL9 one does) and installs libcudnn9-cuda-13. benjy hit "cannot open shared object file: libcudnn.so.9" during validation; this prevents that recurring. 2. candle.rs applies a 1.1 repetition penalty over the last 64 generated tokens before sampling, in both the non-streaming chat_completion path and the streaming chat_completion_stream path. Without it small Q4_K_M models degenerate into "Wait, no, no..." loops once they hit a confident-but-wrong path; with it sampling stays coherent. Defaults match mistral.rs and llama.cpp; exposing the value via the OpenAI request (frequency/presence penalty mapping) is Stage 8 territory. Both routes through a new sample_with_penalty() helper so future sampling tweaks land in one place. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-19 14:48:08 +03:00
rob thijssen	602e8e1471	fix(neuron/candle): source tokenizer.json from base repo when GGUF ... GGUF-only HF repos (unsloth/Qwen3-*-GGUF, Qwen/Qwen3-*-GGUF) ship the .gguf file but not tokenizer.json — the tokenizer data is embedded in the GGUF metadata itself, and the standalone tokenizer.json lives in the base non-GGUF repo (unsloth/Qwen3-0.6B, Qwen/Qwen3-0.6B, etc.). Live validation against quadbrat hit: HTTP 400 fetch tokenizer.json from unsloth/Qwen3-0.6B-GGUF: HTTP status client error (404 Not Found) resolve_files now derives the tokenizer repo by stripping a `-GGUF` or `-gguf` suffix from the model_id; non-GGUF ids fall through to fetching from the same repo. The error message includes the attempted tokenizer repo id so the next failure (e.g. base repo doesn't exist) is unambiguous. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-19 13:16:39 +03:00
rob thijssen	6cf87e328f	chore(neuron): log load_model failures server-side with full chain ... The HTTP handler now emits a tracing::warn on load_model failures with the expanded anyhow chain (format!("{e:#}")) before returning the 400. journalctl -u neuron will surface the underlying hf-hub / materialisation error without needing to capture the curl response body separately. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-19 13:08:54 +03:00
rob thijssen	f9f5fa41b6	fix(neuron): surface full anyhow chain + ensure $HOME exists at start ... Two fixes uncovered by the live validation against beast/benjy/quadbrat: 1. api.rs swallowed everything beyond the outermost anyhow context. The validation script reported '{"error":"fetch GGUF ...gguf"}' but the actual underlying hf-hub failure (cache dir creation, network, auth, etc.) was hidden. Switching every error response to format!("{e:#}") expands the full cause chain via anyhow's alternate Display format. 2. The neuron systemd unit declared the service user but never ensured /var/lib/neuron (its $HOME) existed. hf-hub defaults its cache to ~/.cache/huggingface/hub — when $HOME is absent the cache dir creation fails and the download aborts. Adding `StateDirectory=neuron` makes systemd create + chown that directory at activation; no spec change needed. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-19 08:17:37 +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	5c957d08ec	ci: add build-prerelease workflow for CUDA RPMs on rpm.lair.cafe ... Adds a manually-triggered workflow that builds CUDA-flavoured neuron binaries and a CPU cortex binary, packages them as Fedora RPMs, signs them, and rsyncs to the unstable channel at https://rpm.lair.cafe/fedora/43/x86_64/unstable/. Mirrors the build pipeline used by grenade/mistralrs-package. Pipeline: - prepare: derive {version,short_sha,commit_date} from the checkout; the prerelease Release stamp "0.1.YYYYMMDDgitSHORTSHA" sorts below the eventual "1" stable release. - build-cortex: cargo build --release -p cortex-cli on a rust runner. - build-neuron: matrix over ada (sm_89) and blackwell (sm_120) on cuda-13.0 runners; cargo build with features "cuda cudnn flash-attn" and CUDA_COMPUTE_CAP set per flavour. - package-{cortex,neuron}: rpmbuild on the rpm runner against the new prebuilt-binary specs in rpm/. - publish: import signing key, sign RPMs, rsync to oolon, createrepo_c --update, then regenerate packages.json for the UI. New specs are prebuilt-binary variants — they consume the artifact from the build job rather than running cargo at rpmbuild time. Each helexa-neuron-{flavour} package Conflicts with the other flavours and with helexa-neuron (the future source-build stable package) so one flavour is installed at a time on a given host. neuron crate gains cudnn and flash-attn feature flags forwarding to the corresponding candle features, so the CI build command compiles those kernels into the binary. sccache is intentionally NOT used in the prerelease jobs — CUDA compute cap isn't in its cache key, so flavours would mis-hit each other. Each prerelease build is a clean cargo build. Required Gitea secrets (already in place for cortex.spec / COPR workflow): - RPM_SIGNING_KEY, RPM_SIGNING_KEY_ID - RSYNC_SSH_KEY Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-18 17:01:35 +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	3f94c50817	chore: move default ports out of common-collision ranges ... Previous defaults collided with well-trodden infra services and with the Linux ephemeral port range: - cortex API 8000 — common dev-server default (Django, minio UI) - cortex metrics 9100 — Prometheus node_exporter default - neuron API 9090 — Cockpit default on Fedora, Prometheus self Move to helexa-themed palindromic ports, all below Linux's 32768-60999 ephemeral range and not registered to any well-known service: - cortex API 31313 - cortex metrics 31314 - neuron API 13131 Updated places: - cortex.example.toml, neuron.example.toml defaults - default impls in cortex-core and neuron config - cortex-cli --endpoint default for the status subcommand - doc comments citing example URLs - README.md and CLAUDE.md snippets Consumers already on the old ports need a one-line edit in their /etc/cortex/cortex.toml or /etc/neuron/neuron.toml to match; firewall rules and prometheus scrape configs will also need updating. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>	2026-04-16 17:45:25 +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