rob thijssen c97a8654f5
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feat(neuron): dynamic-resolution images via Qwen smart_resize (#14)
Replace the fixed 448×448-square preprocess with native-aspect
`smart_resize`, and thread the resulting per-image grid through the LM
so spatial structure survives non-square images (documents, screenshots,
charts, panoramas, OCR) instead of being squished into a square.

- preprocess.rs: port Qwen `smart_resize` (factor = patch×merge = 32;
  pixel budget [min,max], default 256²–1024² → 64–1024 LM tokens).
  `PreprocessProfile` drops the fixed target dims for `factor`/`min_pixels`/
  `max_pixels`; `preprocess`/`preprocess_data_uri` now return the resized
  `(h, w)`; add `resized_dims_for_uri` (decode + resize, no normalize) for
  the TP leader's token count.
- rope.rs: `compute_mrope_index`/`get_rope_index` take per-image
  `grids: &[(lm_gh, lm_gw)]` instead of assuming a square `isqrt(run)`.
  Walk image runs in order, validate `run == gh*gw`, emit row-major
  positions, resume the shared counter at `base + max(gh,gw)`. Correct
  for multiple images of differing grids interleaved with text.
- candle.rs: `VisionMeta`/`LoadedModel`/`TpLoadedModel` carry the
  `image_grid_factor` (patch×merge) instead of the constant 196; all four
  prompt-build sites compute per-image counts from each image's resized
  grid (single-GPU from the extracted `ImageInput.h/w`, TP from
  `resized_dims_for_uri`). `ModelArch` gains `vision_grid_factor`.
- single-GPU (`mod.rs`, `dispatch.rs`) and TP
  (`tp_qwen3_5.rs::prefill_with_images_chunked`, `dispatch.rs`,
  `tp/worker.rs`) thread the grids into `get_rope_index`. Each TP rank
  recomputes grids from its own deterministic preprocess — no rpc.rs
  change, single source of truth.

The vision tower itself was already grid-general (recent pos-embed
interpolation + 2D rotary fix). No patch-count cap: pos-embed is
interpolated to any grid; `max_pixels` bounds cost (O(patches²) ViT
attention + prefill) instead.

Tests: smart_resize (aspect/cap/floor/reject), `compute_mrope_index`
non-square + two-image + mismatch cases, square-grid regression guard.
Non-cuda build + clippy + full workspace tests green; TP load/dispatch
paths are cuda-gated → Gitea CUDA type-check. Operator pixel-budget
config + remaining doc cleanup follow in C5.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-04 22:47:27 +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.

Inside the daemon, every CUDA device gets one dedicated OS thread (named cuda-dev-N) that owns the device's CUDA context for the daemon's lifetime. Model loads, forward passes, KV-cache resets, NCCL collectives, VRAM queries, and unloads all route through that thread via a job channel; tensors never escape it alive. This pins context binding to a known thread, makes the CUDA Drop contract structurally safe, and isolates driver-error poisoning to one worker rather than the whole process. See CLAUDE.md for the design rationale and crates/neuron/src/harness/device_worker/ for the code.

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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