Wires cudarc::nccl into the TP worker lifecycle introduced in 7a-i.
With --features cuda the leader and its workers now establish a live
NCCL communicator end-to-end; without the feature the same code paths
return Error{kind="cuda_feature_not_enabled"} so a misconfigured
build is obvious instead of silently no-op.
NCCL state machine (harness/tp/nccl_state.rs) is shared between the
worker process and the leader's pool:
- generate_comm_id_hex() mints an Id::new() on the leader.
- NcclState::init parses 256 hex chars → [c_char; 128] → Id::uninit,
opens a CudaContext on the configured device, calls Comm::from_rank
with the supplied (rank, world_size, id). NCCL blocks until every
rank has joined.
- NcclState::sanity_check runs one all_reduce(1u32, Sum); the leader
asserts every rank reports observed_sum == world_size.
- NCCL handles serialised under Mutex; unsafe impl Send/Sync gates
the Comm across spawn_blocking boundaries (NCCL is move-safe; only
concurrent op issuance is unsafe).
WorkerPool::init_nccl orchestrates the rendezvous:
1. Write Init { comm_id } to every worker's stdin (no await yet).
2. Leader rank 0 calls its own Comm::from_rank in spawn_blocking,
concurrently with workers.
3. NCCL handshake completes for all ranks simultaneously.
4. Leader collects InitOk responses.
WorkerPool::nccl_sanity_check follows the same pattern over
all_reduce, validating world_size == observed_sum on every rank.
Worker.send_only / Worker.recv_only split out from the previous
monolithic Worker.request so the leader can interleave its own NCCL
work with the worker calls — required because NCCL blocks during
init.
Tests:
- 4 hex roundtrip unit tests for the wire encoding.
- The 7a-i "not implemented" expectation now reads
"cuda_feature_not_enabled" on the local dev box (no CUDA), or
accepts InitOk on a cuda-built test binary.
- New cuda-integration test in tp_worker_lifecycle_cuda.rs covers
the real init + sanity round-trip; gated on the cuda-integration
feature so default CI doesn't try to NCCL.
Verifiable on beast (2× RTX 5090):
cargo test -p neuron --features cuda-integration \
--test tp_worker_lifecycle_cuda
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Stage 4 of the candle-native pivot. /v1/chat/completions now switches
to text/event-stream when the request sets stream: true, emitting one
chat.completion.chunk per generated token followed by the OpenAI
[DONE] terminator.
Pipeline:
- chat_completion_stream creates a bounded mpsc::channel<ChatCompletionChunk>(32),
sends the leading role chunk, then spawns a blocking task that
acquires the per-model arch lock and runs the streaming generation
loop.
- run_inference_streaming tracks a cumulative decoded prefix so each
chunk's delta.content is the substring added since the last chunk —
safe across BPE byte-fallback boundaries that would otherwise split
multi-byte UTF-8 chars.
- The blocking task aborts cleanly if blocking_send fails (client
disconnected), so generation stops when the SSE consumer hangs up.
- Final chunk carries finish_reason ("stop" on EOS, "length" on
max_tokens). The handler appends data: [DONE] after the channel
closes.
The Stage 3 streaming 501 placeholder test is repurposed: with the
streaming path live, an unloaded model now hits the same 404 surface
as the non-streaming path (the model lookup happens first).
cortex-gateway's existing proxy is unchanged — it already forwards
SSE bytes verbatim from Phase 2 work, so the candle SSE format passes
through unmodified.
Neuron Cargo.toml gains futures + tokio-stream (both already in
workspace deps) for ReceiverStream and stream combinators.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds a manually-triggered workflow that builds CUDA-flavoured neuron
binaries and a CPU cortex binary, packages them as Fedora RPMs, signs
them, and rsyncs to the unstable channel at
https://rpm.lair.cafe/fedora/43/x86_64/unstable/. Mirrors the build
pipeline used by grenade/mistralrs-package.
Pipeline:
- prepare: derive {version,short_sha,commit_date} from the checkout;
the prerelease Release stamp "0.1.YYYYMMDDgitSHORTSHA" sorts below
the eventual "1" stable release.
- build-cortex: cargo build --release -p cortex-cli on a rust runner.
- build-neuron: matrix over ada (sm_89) and blackwell (sm_120) on
cuda-13.0 runners; cargo build with features "cuda cudnn flash-attn"
and CUDA_COMPUTE_CAP set per flavour.
- package-{cortex,neuron}: rpmbuild on the rpm runner against the new
prebuilt-binary specs in rpm/.
- publish: import signing key, sign RPMs, rsync to oolon, createrepo_c
--update, then regenerate packages.json for the UI.
New specs are prebuilt-binary variants — they consume the artifact
from the build job rather than running cargo at rpmbuild time. Each
helexa-neuron-{flavour} package Conflicts with the other flavours and
with helexa-neuron (the future source-build stable package) so one
flavour is installed at a time on a given host.
neuron crate gains cudnn and flash-attn feature flags forwarding to
the corresponding candle features, so the CI build command compiles
those kernels into the binary.
sccache is intentionally NOT used in the prerelease jobs — CUDA
compute cap isn't in its cache key, so flavours would mis-hit each
other. Each prerelease build is a clean cargo build.
Required Gitea secrets (already in place for cortex.spec / COPR
workflow):
- RPM_SIGNING_KEY, RPM_SIGNING_KEY_ID
- RSYNC_SSH_KEY
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Stage 3 of the candle-native pivot. neuron now serves
POST /v1/chat/completions backed by candle's quantized_qwen3 forward
pass on a per-model serialised generation loop, returning the standard
OpenAI ChatCompletionResponse envelope.
Pipeline per request:
- Look up the LoadedModel by request.model (404 if absent).
- Apply the Qwen3 chat template across all messages.
- Tokenize, then spawn_blocking onto tokio's blocking pool to acquire
the per-model arch lock and run prefill + greedy/temperature/top-p
sampling via LogitsProcessor.
- Stop on <|im_end|>/<|endoftext|> EOS or max_tokens (finish_reason
"stop" vs "length").
- Decode with skip_special_tokens=true, build OpenAI response with
prompt/completion/total usage counts.
Supporting changes:
- HarnessRegistry now stores Arc<dyn Harness> and caches a typed
Arc<CandleHarness> so inference routes bypass dyn-Trait dispatch.
- LoadedModel.arch becomes Arc<Mutex<ModelArch>> so the lock guard
can be moved into spawn_blocking.
- NeuronState gains an Option<Arc<CandleHarness>> field for the new
inference route.
- Typed InferenceError lets the handler map ModelNotLoaded → 404 and
other failures → 500 without string-matching anyhow messages.
- stream=true returns 501 until Stage 4 wires up SSE.
- Two leftover mistral.rs string references in proxy.rs and cortex-cli
(missed during the Stage 1 sweep) are corrected here.
Three new default-feature tests cover the no-candle 503, model-not-
loaded 404, and stream=true 501 paths. The cuda-integration test from
Stage 2 still covers real load/unload; a streaming-feature gated test
exercising actual generation will arrive with Stage 4.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Stage 2 of the candle-native pivot. Fleshes out CandleHarness with a
LoadedModel registry keyed by model_id, hf-hub-backed GGUF download,
and Qwen3 quantized weight construction via candle-transformers'
quantized_qwen3 module. unload_model drops the entry; Drop on the
candle ModelWeights frees device memory.
Device selection prefers CUDA (gated behind the new `cuda` feature),
falling back to CPU when CUDA is unavailable so default builds work
on non-GPU hosts. The candle CUDA toolchain isn't pulled in unless
`--features cuda` is passed, keeping CI green on CPU runners.
Config gains a [harness.candle] block with an optional hf_cache path.
HarnessRegistry::from_configs now takes HarnessSettings so per-harness
config flows through.
A gated tests/candle_lifecycle.rs exercises real load → list → unload
→ list-empty when run with `--features cuda-integration` against a
host with HF network access. The default-feature test in tests/api.rs
covers the wrong-harness rejection path without needing the network.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Package name, lib name, and binary all now just "neuron" without
the cortex- prefix.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- MistralRsHarness: Harness trait impl wrapping mistral.rs HTTP API
(list/load/unload models, health check, start/stop via systemd)
- HarnessRegistry: maps harness name -> Box<dyn Harness>, built from
neuron.toml config
- Neuron API endpoints: GET /models, POST /models/load,
POST /models/unload, GET /models/:id/endpoint
- NeuronConfig: figment-based config loading from neuron.toml
- Integration test: full model lifecycle through mock mistral.rs
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
