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feat(stage-8c): TP-aware Qwen3-Next (tp_qwen3_5)
Adds `harness/tp/tp_qwen3_5.rs` — the tensor-parallel variant of the
Qwen3-Next architecture — plus the dispatch wiring needed to route a
load through it on both the leader and the workers.

Architecture pieces (all per-rank, follow `tp_qwen3.rs` patterns for
the full-attention layers + a new pattern for linear-attention):

- TpQwen3_5GatedDeltaNet: V-head-dim sharded. `num_v_heads / world_size`
  V-heads per rank, `num_k_heads / world_size` K-heads. `in_proj_z`,
  `in_proj_b`, `in_proj_a`, `A_log`, `dt_bias` shard uniformly along
  the V-head dim. `out_proj` is row-parallel + AllReduce (the only
  collective inside the block). The recurrent state shards 1:1 with
  V-heads — no cross-rank sync inside the delta-rule loop.

  `in_proj_qkv` and `conv1d.weight` are FUSED tensors with three
  regions along dim 0 (`[first key_dim, second key_dim, value_dim]`).
  Standard uniform-slicing doesn't align with the head boundaries —
  rank 0 would end up with `[first half of K_0, full K_1, first half
  of V]`. New `load_fused_qkv_slice_{2d,3d}` helpers load the full
  tensor, narrow per-region per-rank, and `Tensor::cat` the three
  slices into a per-rank fused weight. Transient peak of one full
  tensor per layer during construction; net memory is properly per-
  rank after the full drops.

- TpQwen3_5Attention: column-parallel `q_proj` (the widened
  `2 * num_heads * head_dim` output, including the gate half — shards
  along the head axis so both query AND gate halves stay consistent
  per rank), `k_proj`, `v_proj`; row-parallel `o_proj` with AllReduce.
  Otherwise mirrors `tp_qwen3.rs`'s attention.

- TpQwen3_5MLP, TpQwen3_5DecoderLayer (dispatches on layer_types),
  TpQwen3_5Model (with `model.language_model.*` prefix), and
  TpQwen3_5ForCausalLM (with tied or separate `lm_head` at top level).

Dispatch wiring:

- New `tp::TpLeaderModel` enum holds either Qwen3 or Qwen3_5 variant.
  `WorkerPool::load_dense_shard` now dispatches on `model_type` from
  the config JSON and returns `Arc<Mutex<TpLeaderModel>>`. The two
  downstream methods (`generate_step`, `clear_kv_cache`) thread this
  enum through — the inner forward+clear_kv_cache dispatch happens
  via the enum's pub methods. Adding another TP architecture later is
  one more enum variant + match arms.

- Worker side gets a parallel `WorkerModel` enum + dispatch in
  `handle_load_dense_shard`, branching on the same `model_type`.

- Harness gate `TP_SUPPORTED_MODEL_TYPES` now `["qwen3", "qwen3_5"]`.
  `TpLoadedModel.leader_model` retyped to the enum.

Helpers in `arch/qwen3_5/linear_attn.rs`:
- `softplus` and `repeat_interleave` made `pub(crate)` so the TP
  module reuses them rather than duplicating.

Reuses unchanged: `Qwen3_5RmsNorm` (replicated weight), the gated
`Qwen3_5RmsNormGated` tail, `l2norm`, the `RotaryEmbedding` (partial
RoPE with `partial_rotary_factor` already correct).

CPU build + clippy + 32 lib tests pass; `cargo clippy --features cuda`
also clean inside the patched runner container.

Single inflight risk to call out: tensor names. For full-attention
layers the per-layer prefix is `model.language_model.layers.<i>.self_attn.*`
and for linear-attention layers `model.language_model.layers.<i>.linear_attn.*`
— the same as the single-GPU path. lm_head sits at the top level (not
under `language_model`) — consistent with the single-GPU path that
validated against Qwen3.5-0.8B.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-20 22:02:42 +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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