rob thijssen d46d8d4f6c
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feat(tp): Stage 7b-iv — RPC + orchestration for TP load/inference
Wires the in-flight TP machinery (Stage 7a workers, 7b-iii sharded
Qwen3) end to end so a non-streaming chat completion can run across
multiple GPUs via NCCL.

RPC additions (tp/rpc.rs):
- LoadDenseShard{model_id, config_json, safetensors_paths}
- GenerateStep{model_id, tokens, offset}
- ClearKvCache{model_id}
- UnloadModel{model_id}
- LoadDenseShardOk / GenerateStepOk / KvCacheCleared / Unloaded

Worker side (tp/worker.rs):
- WorkerState gains a `models: HashMap<String, TpQwen3ForCausalLM>`
  keyed by model_id. LoadDenseShard mmaps safetensors via
  ShardedVarBuilder (only this rank's slice materialises), builds the
  TP model with the rank's NCCL Comm cloned from NcclState.
- GenerateStep runs the rank-local forward; the resulting logits are
  dropped (only the leader's are used for sampling). The forward's
  value here is the NCCL collectives inside the row-parallel layers
  letting the leader's rank-0 forward make progress.

Pool side (tp/mod.rs):
- WorkerPool::load_dense_shard fans LoadDenseShard out to every worker,
  builds rank 0's shard on the leader via spawn_blocking with a fresh
  SendComm wrapper at the move boundary (Comm is !Send at the type
  level), collects per-rank LoadDenseShardOk. Returns the leader's
  Arc<Mutex<TpQwen3ForCausalLM>>.
- WorkerPool::generate_step fans GenerateStep out, runs the leader's
  rank-0 forward in spawn_blocking (the AllReduce CustomOps inside
  row-parallel layers block until every worker issues the matching
  collective), returns the leader's last-position logits Tensor.
- WorkerPool::clear_kv_cache + unload_model follow the same pattern.

NcclState refactor (tp/nccl_state.rs):
- comm field becomes Option<Arc<Comm>> (was Option<Comm>) so callers
  can share a clone with TpQwen3ForCausalLM::load.
- new `comm()` accessor + `SendComm` wrapper for spawn_blocking moves.
- single allow(clippy::arc_with_non_send_sync) at the canonical
  construction site (Comm is !Send by type but the runtime invariant
  is enforced by SendComm + the pool's Mutex).

Harness side (candle.rs):
- LoadedHandle enum (Single | Tp) replaces the bare Arc<LoadedModel>
  in the harness's registry. list_models / unload_model /
  inference_endpoint walk the enum uniformly.
- TpLoadedModel holds the pool + leader_model + tokenizer + devices.
- load_model dispatches on `spec.tensor_parallel > 1` to a new
  cuda-gated load_tp path: resolve dense files via hf-hub, spawn the
  pool, init_nccl, load_dense_shard.
- chat_completion branches on the handle variant. The TP path mirrors
  run_inference: clear_kv_cache, prefill, sample, decode loop,
  detokenize. Acquires the pool Mutex for the whole request.
- Streaming through TP is deferred to Stage 7c (returns Other(err)).

Script (script/validate-neuron.sh):
- 4th positional arg `tp_size` (default 1). When >1, switches to the
  dense path (tp + GGUF is mutually exclusive — bails) and adds
  `tensor_parallel` + `devices` to the load payload. NEURON_DEVICES
  env overrides the default 0..N-1 device list.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-20 06:38:33 +03:00

cortex

A Rust reverse-proxy and fleet management layer for multi-node GPU inference clusters. Cortex sits in front of one or more neuron daemons (each running candle-based inference on a local GPU host) and presents a unified OpenAI + Anthropic compatible API surface.

Problem

Running local LLMs across multiple GPU nodes (different VRAM tiers, different model affinities) requires a unified API surface that:

  • Presents a single /v1/models catalogue merging every model that can be served by any neuron in the fleet.
  • Routes requests to the correct node based on where a model is loaded (or can be loaded), handling cold-load and eviction transparently.
  • Manages model lifecycle — load on demand, unload cold models, pin critical ones — by calling each neuron's /models/{load,unload} API.
  • Translates between OpenAI and Anthropic request/response envelopes so every client speaks whichever dialect it prefers.
  • Captures per-request metrics (tokens, tok/s, TTFT, latency) and exposes them as Prometheus counters/histograms.

Architecture

┌──────────────┐  ┌──────────┐  ┌────────────┐  ┌────────────┐
│ Claude Code  │  │ Zed/IDE  │  │ Tidal / mm │  │ curl / etc │
└──────┬───────┘  └─────┬────┘  └──────┬─────┘  └──────┬─────┘
       │                │              │               │
       └────────────────┴──────┬───────┴───────────────┘
                               │
                    ┌──────────▼──────────┐
                    │      cortex         │
                    │  (cortex-gateway)   │
                    │                     │
                    │  Router · Metrics   │
                    │  Evictor · Translate│
                    └──┬──────┬────────┬──┘
                       │      │        │
            ┌──────────▼┐  ┌──▼─────┐  ┌▼──────────┐
            │  neuron   │  │ neuron │  │  neuron   │
            │  :13131   │  │ :13131 │  │  :13131   │
            │  candle   │  │ candle │  │  candle   │
            └───────────┘  └────────┘  └───────────┘
                  private network (.internal)

Crates

Crate Purpose
cortex-core Shared types: config, node/model state, metrics, OpenAI/Anthropic envelopes, harness trait, discovery types
cortex-gateway Axum HTTP server: proxy, router, evictor, poller, metrics exporter
neuron Per-node daemon: GPU discovery, in-process candle inference, model lifecycle API
cortex-cli CLI entrypoint (cortex serve, cortex status, etc.)

Node setup

Each GPU node runs neuron (listening on :13131). Neuron uses huggingface/candle for in-process inference — there is no external inference subprocess to manage.

The neuron RPM (helexa-neuron) ships a systemd unit:

dnf copr enable helexa/helexa
dnf install helexa-neuron
systemctl enable --now neuron

Gateway config

# /etc/cortex/cortex.toml
[gateway]
listen = "0.0.0.0:31313"
metrics_listen = "0.0.0.0:31314"

[eviction]
strategy = "lru"        # lru | priority
defrag_after_cycles = 50

[[neurons]]
name = "beast"
endpoint = "http://beast.internal:13131"

[[neurons]]
name = "benjy"
endpoint = "http://benjy.internal:13131"

Model placement profiles live in models.toml — see models.example.toml.

Building

cargo build --release

CI

Every push triggers format, lint, and test checks. Ensure these pass locally before pushing:

cargo fmt --check --all                    # must be clean
cargo clippy --workspace -- -D warnings   # warnings are errors
cargo test --workspace                     # all tests must pass

Tagged releases (v*) additionally build SRPMs for both cortex and helexa-neuron and publish to COPR.

Running

# start the gateway
cortex serve --config /etc/cortex/cortex.toml

# check fleet status
cortex status

# list all models across nodes
curl http://localhost:31313/v1/models

License

GPL-3.0

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