rob thijssen 8d3194f992
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Stage 7b-iii (1/2): AllReduce CustomOp + ShardedVarBuilder-backed TP linears
Ports the canonical
candle-examples/examples/llama_multiprocess/model.rs pattern into
the harness. Two new files, one deletion:

- harness/tp/all_reduce.rs — AllReduce wraps Arc<cudarc::nccl::Comm>
  and implements candle's CustomOp1 trait. cuda_fwd extracts the
  rank's CudaSlice<dtype> from a CudaStorage, asserts the input is
  contiguous (a strided activation hitting all_reduce is almost
  always a model construction bug), allocates an output CudaSlice
  on the same device, calls Comm::all_reduce(Sum), and wraps the
  result back as a CudaStorage. Handles BF16, F16, F32. NcclError
  surfaces via {e:?} (no Display impl in cudarc 0.19.x). Send/Sync
  hand-impl'd with the same NCCL-thread-safety caveat candle's
  example documents.

- harness/tp/tp_linear.rs — ColumnParallelLinear and
  RowParallelLinear, both built on candle's ShardedVarBuilder +
  Shard hints. `vb.get_with_hints((), "weight", shard(dim, rank, ws))`
  reads JUST the rank's slice from the safetensors view; no full-
  tensor host materialisation. ColumnParallel.forward is a plain
  local matmul (output is naturally sharded). RowParallel.forward =
  local matmul + apply_op1_no_bwd(&self.all_reduce). On CPU /
  world_size == 1, the AllReduce is skipped and the partial output
  is returned as-is. Both layers are no-bias — every Qwen3-family
  target sets attention_bias=false; bias-aware sharding is a
  future-model concern.

- Deletes harness/tp/sharded_linear.rs from 7b-ii. That commit's
  hand-rolled "load full + narrow" approach was useful exploration
  but candle's ShardedVarBuilder does the same work without
  materialising the full tensor on host. The 5 unit tests there
  verified the slicing math against an unsharded reference; that
  math now lives inside candle and is covered by candle's own tests.

Next (7b-iii 2/2): TpQwen3Attention + TpQwen3MLP composing the
column/row pair, then a TpQwen3Model that runs the full forward.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-19 18:14:54 +03:00
2026-05-18 17:50:35 +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

Description
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