Implements the recurrent-path Gated DeltaNet block that occupies 48 of
Qwen3.6's 64 decoder layers (`layer_types[i] == "linear_attention"`).
Ported from `huggingface/transformers/models/qwen3_5/modeling_qwen3_5.py`
(`Qwen3_5GatedDeltaNet`, `torch_recurrent_gated_delta_rule`,
`Qwen3_5RMSNormGated`, `l2norm`).
Layout: `arch/qwen3_5.rs` becomes `arch/qwen3_5/` with submodules
- `mod.rs` — Config + (still-stub) ForCausalLM
- `linear_attn.rs` — GatedDeltaNet + GatedDeltaNetState
- `rmsnorm.rs` — Qwen3_5RmsNorm `(1+w)*x`, Qwen3_5RmsNormGated, l2norm
Architecture pieces in this commit:
- Block: in_proj_qkv + in_proj_z + in_proj_b + in_proj_a + out_proj
(all bias=False); depthwise causal Conv1d (k=4) with state-aware
prepend; SiLU; per-head reshape; L2norm on q,k.
- Discretisation: g = -exp(A_log) * softplus(a + dt_bias); beta = σ(b).
All computed in f32 to avoid the -inf underflow in fp16 that the
reference notes.
- Delta rule (recurrent, per-token):
state *= exp(g_t)
kv_mem = state^T · k_t
delta = (v_t - kv_mem) * beta_t
state += outer(k_t, delta)
out_t = state^T · q_t
- Output: RMSNormGated(core_attn_out, z) reshape out_proj.
State (`GatedDeltaNetState`) lives inline on the layer:
- conv_state: (B, conv_dim, conv_kernel_size) — left-padded tail.
- recurrent_state: (B, num_v_heads, head_k_dim, head_v_dim) — the
delta-rule outer-product memory.
Cleared via `clear_kv_cache` at the start of every new request.
Config extended with the qwen3_5-specific fields:
- linear_num_value_heads (48 in Qwen3.6-27B)
- linear_num_key_heads (16)
- linear_key_head_dim (128)
- linear_value_head_dim (128)
- linear_conv_kernel_dim (4)
- hidden_act ("silu")
Performance note: this is the **recurrent** delta-rule (PyTorch's
`torch_recurrent_gated_delta_rule`), correct for any seq_len but O(L)
prefill. The chunked algorithm (`torch_chunk_gated_delta_rule`,
chunk_size=64) is a follow-up perf optimisation; surface stays the
same.
8 unit tests:
- softplus small/large branches
- l2norm hand-calc + zero-vector stability
- repeat_interleave round-trip
- forward_smoke on tiny dims (4-head fixture) — verifies shape +
no NaN/Inf propagation through the f32-promotion pipeline. Doesn't
validate numerical correctness against the Python reference; that
requires a fixed-weight fixture and is the next step.
cargo clippy CPU + --features cuda both clean; 32 lib tests pass.
The ForCausalLM stub still bails on forward — wrapping
attention/MLP/decoder layer + lm_head is the next sub-stage.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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/modelscatalogue 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