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Author SHA1 Message Date
Ettore Di Giacinto
44e7d9806b fix(distributed): stop queue loops on agent nodes + dead-letter cap
pending_backend_ops rows targeting agent-type workers looped forever:
the reconciler fan-out hit a NATS subject the worker doesn't subscribe
to, returned ErrNoResponders, we marked the node unhealthy, and the
health monitor flipped it back to healthy on the next heartbeat. Next
tick, same row, same failure.

Three related fixes:

1. enqueueAndDrainBackendOp skips nodes whose NodeType != backend.
   Agent workers handle agent NATS subjects, not backend.install /
   delete / list, so enqueueing for them guarantees an infinite retry
   loop. Silent skip is correct — they aren't consumers of these ops.

2. Reconciler drain mirrors enqueueAndDrainBackendOp's behavior on
   nats.ErrNoResponders: mark the node unhealthy before recording the
   failure, so subsequent ListDuePendingBackendOps (filters by
   status=healthy) stops picking the row until the node actually
   recovers. Matches the synchronous fan-out path.

3. Dead-letter cap at maxPendingBackendOpAttempts (10). After ~1h of
   exponential backoff the row is a poison message; further retries
   just thrash NATS. Row is deleted and logged at ERROR so it stays
   visible without staying infinite.

Plus a one-shot startup cleanup in NewNodeRegistry: drop queue rows
that target agent-type nodes, non-existent nodes, or carry an empty
backend name. Guarded by the same schema-migration advisory lock so
only one instance performs it. The guards above prevent new rows of
this shape; this closes the migration gap for existing ones.

Tests: the prune migration (valid row stays, agent + empty-name rows
drop) on top of existing upsert / backoff coverage.
2026-04-19 21:27:05 +00:00
Ettore Di Giacinto
7a9d89fa54 feat(ui): shared FilterBar across the System page tabs
The Backends gallery had a nice search + chip + toggle strip; the System
page had nothing, so the two surfaces felt like different apps. Lift the
pattern into a reusable FilterBar and wire both System tabs through it.

New component core/http/react-ui/src/components/FilterBar.jsx renders a
search input, a role="tablist" chip row (aria-selected for a11y), and
optional toggles / right slot. Chips support an optional `count` which
the System page uses to show "User 3", "Updates 1" etc.

System Models tab: search by id or backend; chips for
All/Running/Idle/Disabled/Pinned plus a conditional Distributed chip in
distributed mode. "Last synced" + Update button live in the right slot.

System Backends tab: search by name/alias/meta-backend-for; chips for
All/User/System/Meta plus conditional Updates / Offline-nodes chips
when relevant. The old ad-hoc "Updates only" toggle from the upgrade
banner folded into the Updates chip — one source of truth for that
filter. Offline chip only appears in distributed mode when at least
one backend has an unhealthy node, so the chip row stays quiet on
healthy clusters.

Filter state persists in URL query params (mq/mf/bq/bf) so deep links
and tab switches keep the operator's filter context instead of
resetting every time.

Also adds an "Adopted" distribution path: when a model in
/api/models/capabilities carries source="registry-only" (discovered on
a worker but not configured locally), the Models tab shows a ghost chip
labelled "Adopted" with hover copy explaining how to persist it — this
is what closes the loop on the ghost-model story end-to-end.
2026-04-19 08:46:22 +00:00
Ettore Di Giacinto
ee34a52c5d feat(ui): NodeDistributionChip — shared per-node attribution component
Large clusters were going to break the Manage → Backends Nodes column:
the old inline logic rendered every node as a badge and would shred the
layout at >10 workers, plus the Manage → Models distribution cell had
copy-pasted its own slightly-different version.

NodeDistributionChip handles any cluster size with two render modes:
  - small (≤3 nodes): inline chips of node names, colored by health.
  - large: a single "on N nodes · M offline · K drift" summary chip;
    clicking opens a Popover with a per-node table (name, status,
    version, digest for backends; name, status, state for models).

Drift counting mirrors the backend's summarizeNodeDrift so the UI
number matches UpgradeInfo.NodeDrift. Digests are truncated to the
docker-style 12-char form with the full value preserved in the title.

Popover is a new general-purpose primitive: fixed positioning anchored
to the trigger, flips above when there's no room below, closes on
outside-click or Escape, returns focus to the trigger. Uses .card as
its surface so theming is inherited. Also useful for a future
labels-editor popup and the user menu.

Manage.jsx drops its duplicated inline Nodes-column + loaded_on cell
and uses the shared chip with context="backends" / "models"
respectively. Delete code removes ~40 lines of ad-hoc logic.
2026-04-19 08:39:59 +00:00
Ettore Di Giacinto
92b9e22dc9 feat(ui): show cluster distribution of models in the System page
When a frontend restarted in distributed mode, models that workers had
already loaded weren't visible until the operator clicked into each node
manually — the /api/models/capabilities endpoint only knew about
configs on the frontend's filesystem, not the registry-backed truth.

/api/models/capabilities now joins in ListAllLoadedModels() when the
registry is active, returning loaded_on[] with node id/name/state/status
for each model. Models that live in the registry but lack a local config
(the actual ghosts, not recovered from the frontend's file cache) still
surface with source="registry-only" so operators can see and persist
them; without that emission they'd be invisible to this frontend.

Manage → Models replaces the old Running/Idle pill with a distribution
cell that lists the first three nodes the model is loaded on as chips
colored by state (green loaded, blue loading, amber anything else). On
wider clusters the remaining count collapses into a +N chip with a
title-attribute breakdown. Disabled / single-node behavior unchanged.

Adopted models get an extra "Adopted" ghost-icon chip with hover copy
explaining what it means and how to make it permanent.

Distributed mode also enables a 10s auto-refresh and a "Last synced Xs
ago" indicator next to the Update button so ghost rows drop off within
one reconcile tick after their owning process dies. Non-distributed
mode is untouched — no polling, no cell-stack, same old Running/Idle.
2026-04-19 08:37:45 +00:00
Ettore Di Giacinto
f0ab68e352 feat(distributed): durable backend fan-out + state reconciliation
Two connected problems handled together:

1) Backend delete/install/upgrade used to silently skip non-healthy nodes,
   so a delete during an outage left a zombie on the offline node once it
   returned. The fan-out now records intent in a new pending_backend_ops
   table before attempting the NATS round-trip. Currently-healthy nodes
   get an immediate attempt; everyone else is queued. Unique index on
   (node_id, backend, op) means reissuing the same operation refreshes
   next_retry_at instead of stacking duplicates.

2) Loaded-model state could drift from reality: a worker OOM'd, got
   killed, or restarted a backend process would leave a node_models row
   claiming the model was still loaded, feeding ghost entries into the
   /api/nodes/models listing and the router's scheduling decisions.

The existing ReplicaReconciler gains two new passes that run under a
fresh KeyStateReconciler advisory lock (non-blocking, so one wedged
frontend doesn't freeze the cluster):

  - drainPendingBackendOps: retries queued ops whose next_retry_at has
    passed on currently-healthy nodes. Success deletes the row; failure
    bumps attempts and pushes next_retry_at out with exponential backoff
    (30s → 15m cap). ErrNoResponders also marks the node unhealthy.

  - probeLoadedModels: gRPC-HealthChecks addresses the DB thinks are
    loaded but hasn't seen touched in the last probeStaleAfter (2m).
    Unreachable addresses are removed from the registry. A pluggable
    ModelProber lets tests substitute a fake without standing up gRPC.

DistributedBackendManager exposes DeleteBackendDetailed so the HTTP
handler can surface per-node outcomes ("2 succeeded, 1 queued") to the
UI in a follow-up commit; the existing DeleteBackend still returns
error-only for callers that don't care about node breakdown.

Multi-frontend safety: the state pass uses advisorylock.TryWithLockCtx
on a new key so N frontends coordinate — the same pattern the health
monitor and replica reconciler already rely on. Single-node mode runs
both passes inline (adapter is nil, state drain is a no-op).

Tests cover the upsert semantics, backoff math, the probe removing an
unreachable model but keeping a reachable one, and filtering by
probeStaleAfter.
2026-04-19 08:34:57 +00:00
Ettore Di Giacinto
9373de9f9b feat(ui): polish the Nodes page so it reads like a product
The Nodes page was the biggest visual liability in distributed mode.
Rework the main dashboard surfaces in place without changing behavior:

StatCards: uniform height (96px min), left accent bar colored by the
metric's semantic (success/warning/error/primary), icon lives in a
36x36 soft-tinted chip top-right, value is left-aligned and large.
Grid auto-fills so the row doesn't collapse on narrow viewports. This
replaces the previous thin-bordered boxes with inconsistent heights.

Table rows: expandable rows now show a chevron cue on the left (rotates
on expand) so users know rows open. Status cell became a dedicated chip
with an LED-style halo dot instead of a bare bullet. Action buttons gained
labels — "Approve", "Resume", "Drain" — so the icons aren't doing all
the semantic work; the destructive remove action uses the softer
btn-danger-ghost variant so rows don't scream red, with the ConfirmDialog
still owning the real "are you sure". Applied cell-mono/cell-muted
utility classes so label chips and addresses share one spacing/font
grammar instead of re-declaring inline styles everywhere.

Expanded drawer: empty states for Loaded Models and Installed Backends
now render as a proper drawer-empty card (dashed border, icon, one-line
hint) instead of a plain muted string that read like broken formatting.

Tabs: three inline-styled buttons became the shared .tab class so they
inherit focus ring, hover state, and the rest of the design system —
matches the System page.

"Add more workers" toggle turned into a .nodes-add-worker dashed-border
button labelled "Register a new worker" (action voice) instead of a
chevron + muted link that operators kept mistaking for broken text.

New shared CSS primitives carry over to other pages:
.stat-grid + .stat-card, .row-chevron, .node-status, .drawer-empty,
.nodes-add-worker.
2026-04-19 08:20:52 +00:00
Ettore Di Giacinto
1b3c951c85 feat(ui): surface backend upgrades in the System page
The System page (Manage.jsx) only showed updates as a tiny inline arrow,
so operators routinely missed them. Port the Backend Gallery's upgrade UX
so System speaks the same visual language:

- Yellow banner at the top of the Backends tab when upgrades are pending,
  with an "Upgrade all" button (serial fan-out, matches the gallery) and a
  "Updates only" filter toggle.
- Warning pill (↑ N) next to the tab label so the count is glanceable even
  when the banner is scrolled out of view.
- Per-row labeled "Upgrade to vX.Y" button (replaces the icon-only button
  that silently flipped semantics between Reinstall and Upgrade), plus an
  "Update available" badge in the new Version column.
- New columns: Version (with upgrade + drift chips), Nodes (per-node
  attribution badges for distributed mode, degrading to a compact
  "on N nodes · M offline" chip above three nodes), Installed (relative
  time).
- System backends render a "Protected" chip instead of a bare "—" so rows
  still align and the reason is obvious.
- Delete uses the softer btn-danger-ghost so rows don't scream red; the
  ConfirmDialog still owns the "are you sure".

The upgrade checker also needed the same per-worker fix as the previous
commit: NewUpgradeChecker now takes a BackendManager getter so its
periodic runs call the distributed CheckUpgrades (which asks workers)
instead of the empty frontend filesystem. Without this the /api/backends/
upgrades endpoint stayed empty in distributed mode even with the protocol
change in place.

New CSS primitives — .upgrade-banner, .tab-pill, .badge-row, .cell-stack,
.cell-mono, .cell-muted, .row-actions, .btn-danger-ghost — all live in
App.css so other pages can adopt them without duplicating styles.
2026-04-19 08:14:49 +00:00
Ettore Di Giacinto
1f43762655 fix(distributed): detect backend upgrades across worker nodes
Before this change `DistributedBackendManager.CheckUpgrades` delegated to the
local manager, which read backends from the frontend filesystem. In
distributed deployments the frontend has no backends installed locally —
they live on workers — so the upgrade-detection loop never ran and the UI
silently never surfaced upgrades even when the gallery advertised newer
versions or digests.

Worker-side: NATS backend.list reply now carries Version, URI and Digest
for each installed backend (read from metadata.json).

Frontend-side: DistributedBackendManager.ListBackends aggregates per-node
refs (name, status, version, digest) instead of deduping, and CheckUpgrades
feeds that aggregation into gallery.CheckUpgradesAgainst — a new entrypoint
factored out of CheckBackendUpgrades so both paths share the same core
logic.

Cluster drift policy: when per-node version/digest tuples disagree, the
backend is flagged upgradeable regardless of whether any single node
matches the gallery, and UpgradeInfo.NodeDrift enumerates the outliers so
operators can see *why* it is out of sync. The next upgrade-all realigns
the cluster.

Tests cover: drift detection, unanimous-match (no upgrade), and the
empty-installed-version path that the old distributed code silently
missed.
2026-04-19 08:03:20 +00:00
910 changed files with 16402 additions and 105103 deletions

View File

@@ -8,7 +8,6 @@ Create the backend directory under the appropriate location:
- **Python backends**: `backend/python/<backend-name>/`
- **Go backends**: `backend/go/<backend-name>/`
- **C++ backends**: `backend/cpp/<backend-name>/`
- **Rust backends**: `backend/rust/<backend-name>/`
For Python backends, you'll typically need:
- `backend.py` - Main gRPC server implementation
@@ -19,70 +18,9 @@ For Python backends, you'll typically need:
- `run.sh` - Runtime script
- `test.py` / `test.sh` - Test files
For Rust backends, you'll typically need (see `backend/rust/kokoros/` as a reference):
- `Cargo.toml` - Crate manifest; depend on the upstream project as a submodule under `sources/`
- `build.rs` - Invokes `tonic_build` to generate gRPC stubs from `backend/backend.proto` (use the `BACKEND_PROTO_PATH` env var so the Makefile can inject the canonical copy)
- `src/` - The gRPC server implementation (implement `Backend` via `tonic`)
- `Makefile` - Copies `backend.proto` into the crate, runs `cargo build --release`, then `package.sh`
- `package.sh` - Uses `ldd` to bundle the binary's dynamic deps and `ld.so` into `package/lib/`
- `run.sh` - Sets `LD_LIBRARY_PATH`/`SSL_CERT_DIR` and execs the binary via the bundled `lib/ld.so`
- `sources/<UpstreamProject>/` - Git submodule with the upstream Rust crate
## 2. Add Build Configurations to `.github/workflows/backend.yml`
## 2. Add Build Configurations to `.github/backend-matrix.yml`
The build matrix is data-only YAML at `.github/backend-matrix.yml` (not inside `backend.yml` itself). `backend.yml` (master push) and `backend_pr.yml` (PR) load it via `scripts/changed-backends.js`, which also handles per-file path filtering so only touched backends rebuild on PRs and master pushes alike. Add build matrix entries to `.github/backend-matrix.yml` for each platform/GPU type you want to support. Look at similar backends for reference — `chatterbox`/`faster-whisper` for Python, `piper`/`silero-vad` for Go, `kokoros` for Rust.
**Without an entry here no image is ever built or pushed, and the gallery entry in `backend/index.yaml` will point at a tag that does not exist.** The `dockerfile:` field must point at `./backend/Dockerfile.<lang>` matching the language bucket from step 1 (e.g. `Dockerfile.python`, `Dockerfile.golang`, `Dockerfile.rust`). The `tag-suffix` must match the `uri:` in the corresponding `backend/index.yaml` image entry exactly.
**`scripts/changed-backends.js` registration — REQUIRED for any new dockerfile suffix.** This is the single most common omission, because it has no effect on the PR that adds the backend (when no prior path filter could catch it anyway) — it only breaks the *next* PR that touches your backend's directory, which then gets zero CI jobs and looks broken for unrelated reasons. Edit `scripts/changed-backends.js:inferBackendPath` and add a branch BEFORE the more-generic suffixes:
```js
if (item.dockerfile.endsWith("<your-dockerfile-suffix>")) {
return `backend/cpp/<your-backend>/`; // or backend/python|go|rust/...
}
```
The `endsWith()` test is against the matrix entry's `dockerfile:` value (e.g. `./backend/Dockerfile.ds4``endsWith("ds4")`). Specificity order matters here just like it does for importers: more-specific suffixes go BEFORE more-generic ones (e.g. `ds4` before `llama-cpp` even though both end with letters, because some upstream might one day call itself `super-ds4-llama-cpp`). Verify locally before pushing:
```bash
# Confirm your dockerfile suffix is unique enough
node -e "
const yaml = require('js-yaml'); const fs = require('fs');
const m = yaml.load(fs.readFileSync('.github/backend-matrix.yml','utf8'));
for (const e of m.include.filter(e => e.backend === '<your-backend>')) {
console.log(e.dockerfile, '->', e.dockerfile.endsWith('<suffix>'));
}"
```
A quick way to find the right insertion point: `grep -n 'item.dockerfile.endsWith' scripts/changed-backends.js`.
**`bump_deps.yaml` registration — REQUIRED for any backend pinning an upstream commit.** If your backend's Makefile has a `*_VERSION?=<sha>` pin to a third-party repo, the daily auto-bump bot at `.github/workflows/bump_deps.yaml` won't notice it unless you register the backend in its matrix. The bot runs `.github/bump_deps.sh` which `grep`s for `^$VAR?=` in the Makefile you list — so the pin MUST live in the Makefile (not in a separate shell script). The bump for ds4 (#9761) had to walk this back because the original landed the pin in `prepare.sh`, which the bot can't see. Pattern (for `antirez/ds4`):
```yaml
# .github/workflows/bump_deps.yaml
matrix:
include:
- repository: "antirez/ds4"
variable: "DS4_VERSION"
branch: "main"
file: "backend/cpp/ds4/Makefile"
```
And the corresponding Makefile shape (mirror `backend/cpp/llama-cpp/Makefile`):
```makefile
DS4_VERSION?=ae302c2fa18cc6d9aefc021d0f27ae03c9ad2fc0
DS4_REPO?=https://github.com/antirez/ds4
...
ds4:
mkdir -p ds4
cd ds4 && git init -q && \
git remote add origin $(DS4_REPO) && \
git fetch --depth 1 origin $(DS4_VERSION) && \
git checkout FETCH_HEAD
```
If you have a `prepare.sh` doing the clone, delete it — the recipe belongs in the Makefile target so `make purge && make` works as a clean-and-rebuild and so the bump bot finds the pin.
Add build matrix entries for each platform/GPU type you want to support. Look at similar backends (e.g., `chatterbox`, `faster-whisper`) for reference.
**Placement in file:**
- CPU builds: Add after other CPU builds (e.g., after `cpu-chatterbox`)
@@ -91,17 +29,9 @@ If you have a `prepare.sh` doing the clone, delete it — the recipe belongs in
**Additional build types you may need:**
- ROCm/HIP: Use `build-type: 'hipblas'` with `base-image: "rocm/dev-ubuntu-24.04:7.2.1"`
- Intel/SYCL: Use `build-type: 'intel'` or `build-type: 'sycl_f16'`/`sycl_f32` with `base-image: "intel/oneapi-basekit:2025.3.2-0-devel-ubuntu24.04"`
- Intel/SYCL: Use `build-type: 'intel'` or `build-type: 'sycl_f16'`/`sycl_f32` with `base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"`
- L4T (ARM): Use `build-type: 'l4t'` with `platforms: 'linux/arm64'` and `runs-on: 'ubuntu-24.04-arm'`
**Per-arch native builds (`linux/amd64` + `linux/arm64`):**
Multi-arch backends are NOT a single matrix entry with `platforms: 'linux/amd64,linux/arm64'`. Instead, add **two** entries — one with `platforms: 'linux/amd64'` + `platform-tag: 'amd64'` + `runs-on: 'ubuntu-latest'`, one with `platforms: 'linux/arm64'` + `platform-tag: 'arm64'` + `runs-on: 'ubuntu-24.04-arm'` — both sharing the same `tag-suffix`. The script detects the shared `tag-suffix` and emits a `merge-matrix` entry, so `backend-merge-jobs` (in `backend.yml`/`backend_pr.yml`) automatically assembles the manifest list from per-arch digest artifacts. See `-cpu-faster-whisper` in `.github/backend-matrix.yml` for a reference shape.
**llama-cpp / ik-llama-cpp / turboquant variants only — `builder-base-image`:**
Entries whose `dockerfile` is `./backend/Dockerfile.{llama-cpp,ik-llama-cpp,turboquant}` must also set a `builder-base-image` field pointing at a prebuilt base from `quay.io/go-skynet/ci-cache:base-grpc-*` (CI builds these via `.github/workflows/base-images.yml`). The mapping is by `(build-type, platforms)` — see existing entries for the pattern. CI uses these prebuilt bases to skip the gRPC compile (~2535 min cold). Local `make backends/<name>` ignores `builder-base-image` and uses the from-source path inside the Dockerfile, so you don't need quay access for local builds.
## 3. Add Backend Metadata to `backend/index.yaml`
**Step 3a: Add Meta Definition**
@@ -112,8 +42,6 @@ Add a YAML anchor definition in the `## metas` section (around line 2-300). Look
Add image entries at the end of the file, following the pattern of similar backends such as `diffusers` or `chatterbox`. Include both `latest` (production) and `master` (development) tags.
**Note on integrity:** OCI backends installed from a gallery whose `verification:` block is set are verified against a keyless-cosign policy before extraction; tarball/HTTP backends use the optional `sha256:` field. New backends do not need any extra YAML — the gallery-level `verification:` block covers every entry. See [.agents/backend-signing.md](backend-signing.md) for the producer-side CI step.
## 4. Update the Makefile
The Makefile needs to be updated in several places to support building and testing the new backend:
@@ -128,28 +56,24 @@ Add `backends/<backend-name>` to the `.NOTPARALLEL` line (around line 2) to prev
**Step 4b: Add to `prepare-test-extra`**
Add the backend to the `prepare-test-extra` target to prepare it for testing. Use the path matching your language bucket (`backend/python/`, `backend/go/`, `backend/rust/`, …):
Add the backend to the `prepare-test-extra` target (around line 312) to prepare it for testing:
```makefile
prepare-test-extra: protogen-python
...
$(MAKE) -C backend/<lang>/<backend-name>
$(MAKE) -C backend/python/<backend-name>
```
For Rust backends the target is usually the crate build target itself (e.g. `$(MAKE) -C backend/rust/<backend-name> <backend-name>-grpc`) so the binary is in place before `test` runs.
**Step 4c: Add to `test-extra`**
Add the backend to the `test-extra` target to run its tests — applies to Go and Rust backends too, not only Python:
Add the backend to the `test-extra` target (around line 319) to run its tests:
```makefile
test-extra: prepare-test-extra
...
$(MAKE) -C backend/<lang>/<backend-name> test
$(MAKE) -C backend/python/<backend-name> test
```
Each backend's own `Makefile` should define a `test` target so this line works regardless of language. Integration tests that need large model downloads should be gated behind an env var (see `backend/rust/kokoros/`'s `KOKOROS_MODEL_PATH` pattern) so CI only runs unit tests.
**Step 4d: Add Backend Definition**
Add a backend definition variable in the backend definitions section (around line 428-457). The format depends on the backend type:
@@ -169,13 +93,6 @@ BACKEND_<BACKEND_NAME> = <backend-name>|python|./backend|false|true
BACKEND_<BACKEND_NAME> = <backend-name>|golang|.|false|true
```
**For Rust backends**:
```makefile
BACKEND_<BACKEND_NAME> = <backend-name>|rust|.|false|true
```
The language field (`python`/`golang`/`rust`/…) must match a `backend/Dockerfile.<lang>` file.
**Step 4e: Generate Docker Build Target**
Add an eval call to generate the docker-build target (around line 480-501):
@@ -203,7 +120,7 @@ docker-build-backends: ... docker-build-<backend-name>
After adding a new backend, verify:
- [ ] Backend directory structure is complete with all necessary files
- [ ] Build configurations added to `.github/backend-matrix.yml` for all desired platforms (per-arch entries with `platform-tag` for multi-arch; `builder-base-image` for llama-cpp / ik-llama-cpp / turboquant)
- [ ] Build configurations added to `.github/workflows/backend.yml` for all desired platforms
- [ ] Meta definition added to `backend/index.yaml` in the `## metas` section
- [ ] Image entries added to `backend/index.yaml` for all build variants (latest + development)
- [ ] Tag suffixes match between workflow file and index.yaml
@@ -236,29 +153,6 @@ ls /tmp/check # expect the bundled .so files + symlinks
Then boot it inside a fresh `ubuntu:24.04` (which intentionally does *not* have the lib installed) to confirm it actually loads from the backend dir.
## Importer integration
When you add a new backend, you MUST also make it importable via the model import form (`/import-model`). The import form dropdown is sourced dynamically from `GET /backends/known` — it reads the importer registry at `core/gallery/importers/importers.go`, so the steps below are the ONLY way to make your backend show up.
Required steps:
1. **If your backend has unambiguous detection signals** (unique file extension, HF `pipeline_tag`, unique repo name pattern, unique artefact like `modules.json`):
- Create an importer file at `core/gallery/importers/<backend>.go` following the Match/Import pattern in `llama-cpp.go`.
- Register it in `importers.go:defaultImporters` in **specificity order** — more specific detectors must appear BEFORE more generic ones (e.g. `sentencetransformers` before `transformers`, `stablediffusion-ggml` before `llama-cpp`, `vllm-omni` before `vllm`). First match wins.
2. **If your backend is a drop-in replacement** (same artefacts as another backend, e.g. `ik-llama-cpp` and `turboquant` both consume GGUF the same way `llama-cpp` does):
- Do NOT create a new importer. Extend the existing importer's `Import()` to swap the emitted `backend:` field when `preferences.backend` matches. See `llama-cpp.go` for the pattern.
3. **If your backend has no reliable auto-detect signal** (preference-only — e.g. `sglang`, `tinygrad`, `whisperx`):
- Do NOT create an importer. Instead add the backend name to the curated pref-only slice in `core/http/endpoints/localai/backend.go` that feeds `/backends/known`. A single line addition.
4. **Always** add a table-driven test in `core/gallery/importers/importers_test.go` (Ginkgo/Gomega):
- Use a real public HuggingFace repo URI as the test fixture (existing tests already hit the live HF API — follow that pattern).
- Cover detection (auto-match without preferences), preference-override (explicit `backend:` in preferences wins), and — if the backend's modality has a common `pipeline_tag` but ambiguous artefacts — an ambiguity test asserting `errors.Is(err, importers.ErrAmbiguousImport)`.
Rules of thumb:
- When in doubt, lean pref-only. A wrong auto-detect is worse than a forced preference.
- Never silently emit a modality mismatch (e.g. emit `llama-cpp` for a TTS repo because `.gguf` is present). Return `ErrAmbiguousImport` instead.
- Registration order is the single most common source of bugs. Check by running `go test ./core/gallery/importers/...` — the existing suite will fail if you've shadowed a pre-existing detector.
## 6. Example: Adding a Python Backend
For reference, when `moonshine` was added:

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@@ -1,101 +0,0 @@
# AI Coding Assistants
This document provides guidance for AI tools and developers using AI
assistance when contributing to LocalAI.
**LocalAI follows the same guidelines as the Linux kernel project for
AI-assisted contributions.** See the upstream policy here:
<https://docs.kernel.org/process/coding-assistants.html>
The rules below mirror that policy, adapted to LocalAI's license and
project layout. If anything is unclear, the kernel document is the
authoritative reference for intent.
AI tools helping with LocalAI development should follow the standard
project development process:
- [CONTRIBUTING.md](../CONTRIBUTING.md) — development workflow, commit
conventions, and PR guidelines
- [.agents/coding-style.md](coding-style.md) — code style, editorconfig,
logging, and documentation conventions
- [.agents/building-and-testing.md](building-and-testing.md) — build and
test procedures
## Licensing and Legal Requirements
All contributions must comply with LocalAI's licensing requirements:
- LocalAI is licensed under the **MIT License** — see the [LICENSE](../LICENSE)
file
- New source files should use the SPDX license identifier `MIT` where
applicable to the file type
- Contributions must be compatible with the MIT License and must not
introduce code under incompatible licenses (e.g., GPL) without an
explicit discussion with maintainers
## Signed-off-by and Developer Certificate of Origin
**AI agents MUST NOT add `Signed-off-by` tags.** Only humans can legally
certify the Developer Certificate of Origin (DCO). The human submitter
is responsible for:
- Reviewing all AI-generated code
- Ensuring compliance with licensing requirements
- Adding their own `Signed-off-by` tag (when the project requires DCO)
to certify the contribution
- Taking full responsibility for the contribution
AI agents MUST NOT add `Co-Authored-By` trailers for themselves either.
A human reviewer owns the contribution; the AI's involvement is recorded
via `Assisted-by` (see below).
## Attribution
When AI tools contribute to LocalAI development, proper attribution helps
track the evolving role of AI in the development process. Contributions
should include an `Assisted-by` tag in the commit message trailer in the
following format:
```
Assisted-by: AGENT_NAME:MODEL_VERSION [TOOL1] [TOOL2]
```
Where:
- `AGENT_NAME` — name of the AI tool or framework (e.g., `Claude`,
`Copilot`, `Cursor`)
- `MODEL_VERSION` — specific model version used (e.g.,
`claude-opus-4-7`, `gpt-5`)
- `[TOOL1] [TOOL2]` — optional specialized analysis tools invoked by the
agent (e.g., `golangci-lint`, `staticcheck`, `go vet`)
Basic development tools (git, go, make, editors) should **not** be listed.
### Example
```
fix(llama-cpp): handle empty tool call arguments
Previously the parser panicked when the model returned a tool call with
an empty arguments object. Fall back to an empty JSON object in that
case so downstream consumers receive a valid payload.
Assisted-by: Claude:claude-opus-4-7 golangci-lint
Signed-off-by: Jane Developer <jane@example.com>
```
## Scope and Responsibility
Using an AI assistant does not reduce the contributor's responsibility.
The human submitter must:
- Understand every line that lands in the PR
- Verify that generated code compiles, passes tests, and follows the
project style
- Confirm that any referenced APIs, flags, or file paths actually exist
in the current tree (AI models may hallucinate identifiers)
- Not submit AI output verbatim without review
Reviewers may ask for clarification on any change regardless of how it
was produced. "An AI wrote it" is not an acceptable answer to a design
question.

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@@ -2,8 +2,6 @@
This guide covers how to add new API endpoints and properly integrate them with the auth/permissions system.
> **Before you ship a new endpoint or capability surface**, re-read the [checklist at the bottom of this file](#checklist). LocalAI advertises its feature surface in several independent places — miss any one of them and clients/admins/UI won't know the endpoint exists.
## Architecture overview
Authentication and authorization flow through three layers:
@@ -236,76 +234,6 @@ Use these HTTP status codes:
If your endpoint should be tracked for usage (token counts, request counts), add the `usageMiddleware` to its middleware chain. See `core/http/middleware/usage.go` and how it's applied in `routes/openai.go`.
## Advertising surfaces — where to register a new capability
Beyond routing and auth, LocalAI publishes its capability surface in **four independent places**. When you add an endpoint — especially one introducing a net-new capability like a new media type or a new auth-gated feature — you must update every relevant surface. These aren't optional: missing them means the endpoint works but is invisible to clients, admins, and the UI.
### 1. Swagger `@Tags` annotation (mandatory)
Every handler needs a swagger block so the endpoint appears in `/swagger/index.html` and in the `/api/instructions` output. The `@Tags` value is what groups the endpoint into a capability area:
```go
// MyEndpoint does X.
// @Summary Do X.
// @Tags my-capability
// @Param request body schema.MyRequest true "payload"
// @Success 200 {object} schema.MyResponse "Response"
// @Router /v1/my-endpoint [post]
func MyEndpoint(...) echo.HandlerFunc { ... }
```
Use an existing tag when the endpoint extends an existing area (e.g. `audio`, `images`, `face-recognition`). Create a new tag only when the endpoint introduces a genuinely new capability surface — and in that case, also register it in step 2.
After adding endpoints, regenerate the embedded spec so the runtime serves it:
```bash
make protogen-go # ensures gRPC codegen is fresh first
make swagger # regenerates swagger/swagger.json
```
### 2. `/api/instructions` registry (for new capability areas)
`core/http/endpoints/localai/api_instructions.go` defines `instructionDefs` — a lightweight, machine-readable index of capability areas that groups swagger endpoints by tag. It's the primary discovery surface for agents and SDKs ("what can this server do?").
**When to update:** only when adding a new capability area (a new swagger tag). Existing-tag additions automatically surface without any change here.
Add an entry to `instructionDefs`:
```go
{
Name: "my-capability", // URL segment at /api/instructions/my-capability
Description: "Short sentence describing the capability",
Tags: []string{"my-capability"}, // must match swagger @Tags
Intro: "Optional gotcha/context that isn't in the swagger descriptions (caveats, defaults, cross-references to other endpoints).",
},
```
Also bump the expected-length count in `api_instructions_test.go` and add the name to the `ContainElements` assertion.
### 3. `capabilities.js` symbol (for new model-config FLAG_* flags)
If your feature needs a new `FLAG_*` usecase flag in `core/config/model_config.go` (so users can filter gallery models by it, and so `/v1/models` surfaces it), you need to update **all** of:
- `Usecase<Name>` string constant in `core/config/backend_capabilities.go`
- `UsecaseInfoMap` entry mapping the string to its flag + gRPC method
- `FLAG_<NAME>` bitmask in `core/config/model_config.go`
- `GetAllModelConfigUsecases()` map entry (otherwise the YAML loader silently ignores the string)
- `ModalityGroups` membership if the flag should affect `IsMultimodal()` (e.g. realtime_audio is in both speech-input and audio-output groups so a lone flag still reads as multimodal)
- `GuessUsecases()` branch listing the backends that own this capability
- `usecaseFilters` in `core/http/routes/ui_api.go` (drives the gallery filter dropdown)
- `Models.jsx` `FILTERS` array + matching `filters.<camelCase>` i18n key in `core/http/react-ui/public/locales/en/models.json`
- `core/http/react-ui/src/utils/capabilities.js`:
```js
export const CAP_MY_CAPABILITY = 'FLAG_MY_CAPABILITY'
```
React pages that want to filter the ModelSelector by capability import this symbol. Declare it even if you're not building the UI page yet — the declaration keeps the Go/JS vocabularies in sync.
### 4. `docs/content/` (user-facing documentation)
A new capability deserves its own page under `docs/content/features/`, plus cross-links from related features and an entry in `docs/content/whats-new.md`. See the pattern used by `face-recognition.md` / `object-detection.md`.
## Path protection rules
The global auth middleware classifies paths as API paths or non-API paths:
@@ -320,36 +248,12 @@ If you add endpoints under a new top-level path prefix, add it to `isAPIPath()`
When adding a new endpoint:
**Routing & auth**
- [ ] Handler in `core/http/endpoints/`
- [ ] Route registered in appropriate `core/http/routes/` file
- [ ] Auth level chosen: public / standard / admin / feature-gated
- [ ] Entry added to `RouteFeatureRegistry` in `core/http/auth/features.go` (one row per route/method — all /v1/* routes gate through this, not per-route middleware)
- [ ] If new feature: constant in `permissions.go`, added to the right slice (`APIFeatures` default-ON / `AgentFeatures` default-OFF), metadata in `features.go` `*FeatureMetas()`
- [ ] If feature uses group middleware: wired in `core/http/app.go` and passed to the route registration function
- [ ] If feature-gated: constant in `permissions.go`, metadata in `features.go`, middleware in `app.go`
- [ ] If new path prefix: added to `isAPIPath()` in `middleware.go`
- [ ] If OpenAI-compatible: entry in `RouteFeatureRegistry`
- [ ] If token-counting: `usageMiddleware` added to middleware chain
**Advertising surfaces (easy to miss — see the [Advertising surfaces](#advertising-surfaces--where-to-register-a-new-capability) section)**
- [ ] Swagger block on the handler: `@Summary`, `@Tags`, `@Param`, `@Success`, `@Router`
- [ ] If new capability area (new swagger tag): entry in `instructionDefs` in `core/http/endpoints/localai/api_instructions.go` + test count bumped in `api_instructions_test.go`
- [ ] If new `FLAG_*` usecase flag: matching `CAP_*` symbol exported from `core/http/react-ui/src/utils/capabilities.js`
- [ ] `docs/content/features/<feature>.md` created; cross-links from related feature pages; entry in `docs/content/whats-new.md`
**Quality**
- [ ] Error responses use `schema.ErrorResponse` format (or `echo.NewHTTPError` with a mapped gRPC status — see the `mapBackendError` helper in `core/http/endpoints/localai/images.go`)
- [ ] Error responses use `schema.ErrorResponse` format
- [ ] Tests cover both authenticated and unauthenticated access
- [ ] Swagger regenerated (`make swagger`) if you changed any `@Router`/`@Tags`/`@Param` annotation
## Companion: MCP admin tool surface
**Required for admin endpoints.** Every new admin endpoint MUST be considered for the MCP admin tool surface — the REST API and the MCP tool catalog can drift silently otherwise, and both the LocalAI Assistant chat modality and the standalone `local-ai mcp-server` rely on `pkg/mcp/localaitools/` to mirror REST.
Two outcomes are acceptable; one is not:
- **Tool added.** The new endpoint is something an admin would manage conversationally (install, list, edit, toggle, upgrade). Follow the full checklist in [.agents/localai-assistant-mcp.md](localai-assistant-mcp.md): add a `LocalAIClient` interface method, implement it in both `inproc` and `httpapi`, register the tool with a `Tool*` constant, update the skill prompts, **and add the route to `toolToHTTPRoute` in `pkg/mcp/localaitools/coverage_test.go`**.
- **Tool deliberately skipped.** The endpoint is internal/diagnostic and adding a chat path would be misleading. Document the decision in the PR description; no code action.
- **Forgot.** This breaks the contract. The `TestToolHTTPRouteMappingComplete` test in `pkg/mcp/localaitools` is a partial guard (it checks every `Tool*` has a route mapping), but it does NOT detect new REST endpoints without a tool — that's still a process check on the PR author.
**Add to the bottom of the checklist below**:
- [ ] If admin: decided whether MCP coverage is needed; if yes, tool registered + map updated; if no, skip-reason in PR description.

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@@ -1,120 +0,0 @@
# Backend image signing & verification
LocalAI verifies backend OCI images against a per-gallery keyless-cosign
policy. This page documents the trust model, the producer side
(`.github/workflows/backend_merge.yml` in this repo), and the consumer
side (`pkg/oci/cosignverify` plus the gallery YAML).
## Trust model
- **Producer:** `.github/workflows/backend_merge.yml` signs each pushed
manifest list with `cosign sign --recursive` in keyless mode after
`docker buildx imagetools create`. The signing cert is issued by
Fulcio bound to the workflow's OIDC identity. There is no long-lived
signing key. `--recursive` signs both the manifest list and every
per-arch entry — needed because our consumer resolves a tag to a
per-arch manifest before checking signatures.
- **Storage:** Signatures are written as OCI 1.1 referrers
(`--registry-referrers-mode=oci-1-1`) in the new Sigstore bundle format
(`--new-bundle-format`). No `:sha256-<hex>.sig` tag clutter.
- **Consumer:** `pkg/oci/cosignverify` discovers the bundle via the
referrers API, hands it to `sigstore-go`, and verifies it against the
policy declared in the gallery YAML (`Gallery.Verification`).
- **Revocation:** Keyless cosign certs are ephemeral (10-minute Fulcio
validity), so revocation is policy-side, not CA-side. The gallery's
`verification.not_before` (RFC3339) is the kill-switch — advance it to
invalidate every signature produced before a known compromise window.
## Producer setup
`backend_merge.yml` is the workflow that joins per-arch digests into the
multi-arch manifest list users actually pull, so it's also the right place
to sign. The job needs:
- `permissions: { id-token: write, contents: read }` at the job level so
the runner can exchange its GitHub OIDC token for a Fulcio cert.
- `sigstore/cosign-installer@v3` step (cosign ≥ 2.2 for
`--new-bundle-format`).
- After each `docker buildx imagetools create`, resolve the resulting
list digest with `docker buildx imagetools inspect <tag> --format
'{{.Manifest.Digest}}'` and sign:
```sh
cosign sign --yes --recursive \
--new-bundle-format \
--registry-referrers-mode=oci-1-1 \
"${REGISTRY_REPO}@${DIGEST}"
```
Sign by digest, never by tag — signing by tag binds the signature to
whatever the tag points at *now*, and a subsequent tag push orphans it.
`backend_build_darwin.yml` builds and pushes single-arch darwin images
that bypass the manifest-list merge. If/when those entries get a gallery
`verification:` policy, the equivalent cosign step has to land there
too.
## Consumer setup (in `mudler/LocalAI` gallery YAML)
Once CI is signing, add a `verification:` block to the backend gallery
entry (`backend/index.yaml`):
```yaml
- name: localai
url: github:mudler/LocalAI/backend/index.yaml@master
verification:
issuer: "https://token.actions.githubusercontent.com"
identity_regex: "^https://github\\.com/mudler/LocalAI/\\.github/workflows/backend_merge\\.yml@refs/heads/master$"
# Optional revocation cutoff; advance during incident response.
# not_before: "2026-06-01T00:00:00Z"
```
Identity matching pins the OIDC subject Fulcio issued the signing cert
to. Without this, any image signed by *anyone* with a Fulcio cert would
pass — the regex is what makes a signature mean "produced by our CI".
## Strict mode
Default behaviour: OCI backends without a `verification:` block install
with a warning (logs include `installing OCI backend without signature
verification`). Tarball/HTTP backends without a `sha256` field log a
similar warning.
For production, set `LOCALAI_REQUIRE_BACKEND_INTEGRITY=1` (or pass
`--require-backend-integrity` to `local-ai run` / `local-ai backends
install` / `local-ai models install`). The warning becomes a hard error
and unverifiable backends refuse to install.
## Revocation playbook
If `backend_merge.yml` (or any workflow with `id-token: write`) is
compromised and we've shipped malicious signed images:
1. **Identify the compromise window.** Find the earliest IntegratedTime
from the bad signatures (Rekor search by `subject` filter).
2. **Set `verification.not_before`** in `backend/index.yaml` to a
timestamp just *after* that window's start.
3. **Push the YAML.** Deployed LocalAI instances pick it up on next
gallery refresh (1-hour cache in `core/gallery/gallery.go`).
4. **Fix the underlying compromise** in the workflow and re-sign images
with the new build, which will have IntegratedTime > `not_before`.
5. **Optional:** for absolute decisiveness, also rotate to a new
workflow path (`backend_merge_v2.yml`) and update `identity_regex`.
## Where the code lives
- `pkg/oci/cosignverify/` — verifier, policy, OCI referrer fetch, NotBefore enforcement.
- `pkg/downloader/uri.go``WithImageVerifier` option threaded through `DownloadFileWithContext`.
- `core/gallery/backends.go``backendDownloadOptions` builds the verifier from the gallery's policy.
- `core/config/gallery.go``Gallery.Verification` YAML schema.
- `core/cli/run.go`, `core/cli/backends.go`, `core/cli/models.go``--require-backend-integrity` flag propagation.
- `.github/workflows/backend_merge.yml` — producer-side `cosign sign --recursive` after each multi-arch manifest list push.
## Out of scope (follow-ups)
- **Signing the gallery YAML itself.** The index is fetched over HTTPS
from GitHub; we trust the host. A cosign blob signature on the YAML
would close that gap but adds key-management overhead. Revisit this
page if/when added.
- **Tarball/HTTP backend signing.** Cosign can sign arbitrary blobs, but
for now non-OCI backends keep using the `sha256:` field in YAML.

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@@ -8,9 +8,8 @@ Let's say the user wants to build a particular backend for a given platform. For
- The Makefile has targets like `docker-build-coqui` created with `generate-docker-build-target` at the time of writing. Recently added backends may require a new target.
- At a minimum we need to set the BUILD_TYPE, BASE_IMAGE build-args
- Use `.github/backend-matrix.yml` as a reference — it's the data-only YAML that lists every backend variant's `build-type`, `base-image`, `platforms`, etc. (`backend.yml` and `backend_pr.yml` consume it via `scripts/changed-backends.js`).
- l4t and cublas also require the CUDA major and minor version.
- For llama-cpp / ik-llama-cpp / turboquant the matrix also sets `builder-base-image` pointing at a prebuilt `quay.io/go-skynet/ci-cache:base-grpc-*` tag. Local `make backends/<name>` defaults to `BUILDER_TARGET=builder-fromsource` and doesn't need it — the Dockerfile's from-source stage installs everything itself.
- Use .github/workflows/backend.yml as a reference it lists the needed args in the `include` job strategy matrix
- l4t and cublas also requires the CUDA major and minor version
- You can pretty print a command like `DOCKER_MAKEFLAGS=-j$(nproc --ignore=1) BUILD_TYPE=hipblas BASE_IMAGE=rocm/dev-ubuntu-24.04:7.2.1 make docker-build-coqui`
- Unless the user specifies that they want you to run the command, then just print it because not all agent frontends handle long running jobs well and the output may overflow your context
- The user may say they want to build AMD or ROCM instead of hipblas, or Intel instead of SYCL or NVIDIA insted of l4t or cublas. Ask for confirmation if there is ambiguity.

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@@ -1,250 +0,0 @@
# CI Build Caching
Container builds — both the root LocalAI image (`Dockerfile`) and the per-backend images (`backend/Dockerfile.*`) — share a registry-backed BuildKit cache plus a layered set of prebuilt base images. This file explains how the cache is laid out, what invalidates it, and how to bypass it.
## Workflow surfaces
| Workflow | Purpose | Triggers |
|---|---|---|
| `.github/workflows/backend.yml` | Backend container images on master | `push` to master + tags, weekly Sunday cron, `workflow_dispatch` |
| `.github/workflows/backend_pr.yml` | Backend container images on PRs | `pull_request` |
| `.github/workflows/backend_build.yml` | Reusable: builds one backend (one arch) by digest | `workflow_call` from above |
| `.github/workflows/backend_merge.yml` | Reusable: assembles per-arch digests into a multi-arch manifest list | `workflow_call` |
| `.github/workflows/backend_build_darwin.yml` | Reusable: macOS-native backend builds | `workflow_call` |
| `.github/workflows/image.yml` / `image-pr.yml` | Root LocalAI image (push / PR) | push / PR |
| `.github/workflows/image_build.yml` / `image_merge.yml` | Reusable: per-arch root-image build + merge | `workflow_call` |
| `.github/workflows/base-images.yml` | Builds the prebuilt `base-grpc-*` builder bases | Saturdays 05:00 UTC cron, `workflow_dispatch`, master push touching `Dockerfile.base-grpc-builder`, `.docker/install-base-deps.sh`, `.docker/apt-mirror.sh`, or this workflow |
The matrix that drives `backend.yml` / `backend_pr.yml` lives in **`.github/backend-matrix.yml`** (data-only YAML, not embedded in the workflow). `scripts/changed-backends.js` parses it, applies path-filter logic against the PR diff (PR events) or the GitHub Compare API (push events), and emits the filtered matrix plus a `merge-matrix` for backends with multiple per-arch entries.
## Cache layout
- **Cache registry**: `quay.io/go-skynet/ci-cache`
- **One tag per matrix entry per arch**, derived from `tag-suffix` and `platform-tag`:
- Backend builds (`backend_build.yml`): `cache<tag-suffix>-<platform-tag>`
- e.g. `cache-cpu-faster-whisper-amd64`, `cache-cpu-faster-whisper-arm64`, `cache-gpu-nvidia-cuda-13-llama-cpp-amd64`
- Root image builds (`image_build.yml`): `cache-localai<tag-suffix>-<platform-tag>` (with a `-core` placeholder when `tag-suffix` is empty, so `cache-localai-core-amd64` for the core image)
- Pre-built base images (`base-images.yml`): `cache-base-grpc-<variant>` (one per `(BUILD_TYPE, arch)` permutation)
- Each tag stores a multi-arch BuildKit cache manifest (`mode=max`), so every intermediate stage is re-usable, not just the final image.
The per-arch suffix exists because amd64 and arm64 builds produce different intermediate content; sharing one cache key would thrash on every cross-arch rebuild.
## Read/write semantics
| Trigger | `cache-from` | `cache-to` |
|---|---|---|
| `push` to `master` / tag / cron / dispatch | yes | yes (`mode=max,ignore-error=true`) |
| `pull_request` | yes | **no** |
PR builds read master's warm cache but never write — this prevents PRs from polluting the shared cache with their experimental state. After merge, the master build for that matrix entry refreshes the cache.
`ignore-error=true` on the write side means a transient quay push failure does not fail the build; the next master push retries.
## Pre-built base images (`base-grpc-*`)
The C++ backend Dockerfiles (`Dockerfile.{llama-cpp,ik-llama-cpp,turboquant}`) compile gRPC from source. On a cold build that's ~2535 min before any LocalAI source compiles. To skip that on CI, `.github/workflows/base-images.yml` builds and pushes a set of pre-prepped builder bases:
| Tag | Contents |
|---|---|
| `base-grpc-amd64` / `base-grpc-arm64` | Ubuntu 24.04 + apt build deps + protoc + cmake + gRPC at `/opt/grpc` |
| `base-grpc-cuda-12-amd64` | the above + CUDA 12.8 toolkit |
| `base-grpc-cuda-13-amd64` | the above + CUDA 13.0 toolkit (Ubuntu 22.04 base) |
| `base-grpc-cuda-13-arm64` | the above + CUDA 13.0 sbsa toolkit (Ubuntu 24.04 base) |
| `base-grpc-l4t-cuda-12-arm64` | JetPack r36.4.0 base (CUDA preinstalled, `SKIP_DRIVERS=true`) + gRPC |
| `base-grpc-rocm-amd64` | rocm/dev-ubuntu-24.04:7.2.1 base + hipblas/hipblaslt/rocblas + gRPC |
| `base-grpc-vulkan-amd64` / `base-grpc-vulkan-arm64` | Ubuntu 24.04 + Vulkan SDK 1.4.335 + gRPC |
| `base-grpc-intel-amd64` | intel/oneapi-basekit:2025.3.2 base + gRPC |
**Single source of truth**: the install logic for all 10 variants lives in `.docker/install-base-deps.sh`. Both `Dockerfile.base-grpc-builder` AND each variant Dockerfile's `builder-fromsource` stage bind-mount and execute the same script — so the prebuilt CI base and the local from-source path are bit-equivalent by construction.
### How variant Dockerfiles consume the base
`Dockerfile.{llama-cpp,ik-llama-cpp,turboquant}` are multi-target. Three stages plus a final aliasing stage:
- `builder-fromsource``FROM ${BASE_IMAGE}` then runs `install-base-deps.sh` and the per-backend compile script. Used when `BUILDER_TARGET=builder-fromsource` (the default; local `make backends/<name>`).
- `builder-prebuilt``FROM ${BUILDER_BASE_IMAGE}` (one of the prebuilt `base-grpc-*` tags) and runs only the per-backend compile script. Used when `BUILDER_TARGET=builder-prebuilt` (CI when the matrix entry sets `builder-base-image`).
- `FROM ${BUILDER_TARGET} AS builder` — alias resolves the ARG-selected stage to a fixed name (BuildKit doesn't allow ARG expansion in `COPY --from=`).
- `FROM scratch` + `COPY --from=builder ...package/. ./` — emits the final scratch image with just the package contents.
BuildKit prunes the unreferenced builder stage, so each build only runs the path it needs. `backend_build.yml` derives `BUILDER_TARGET=builder-prebuilt` automatically when the matrix entry has a non-empty `builder-base-image`; otherwise it defaults to `builder-fromsource`.
The matrix `(build-type, platforms)``builder-base-image` mapping for llama-cpp / ik-llama-cpp / turboquant entries:
| `build-type` | `platforms` | tag |
|---|---|---|
| `''` | `linux/amd64` | `base-grpc-amd64` |
| `''` | `linux/arm64` | `base-grpc-arm64` |
| `cublas` cuda 12 | `linux/amd64` | `base-grpc-cuda-12-amd64` |
| `cublas` cuda 13 | `linux/amd64` | `base-grpc-cuda-13-amd64` |
| `cublas` cuda 13 | `linux/arm64` | `base-grpc-cuda-13-arm64` |
| `cublas` cuda 12 + JetPack base | `linux/arm64` | `base-grpc-l4t-cuda-12-arm64` |
| `hipblas` | `linux/amd64` | `base-grpc-rocm-amd64` |
| `vulkan` | `linux/amd64` | `base-grpc-vulkan-amd64` |
| `vulkan` | `linux/arm64` | `base-grpc-vulkan-arm64` |
| `sycl_*` | `linux/amd64` | `base-grpc-intel-amd64` |
### Bootstrap order when adding a new variant
If you add a new entry to `base-images.yml`'s matrix, the new tag does not exist on quay until the workflow runs. To consume it from a variant entry safely, dispatch the base-images workflow on the branch first:
```bash
gh workflow run base-images.yml --ref <feature-branch>
```
Wait for the new variant to push, then merge the consumer change. Otherwise the consumer's CI fails with "image not found."
## Per-arch native builds + manifest merge
Multi-arch backends (and the core LocalAI image) build natively per arch instead of running both arches under QEMU emulation on a single x86 runner. The pattern:
- The matrix has TWO entries per multi-arch backend, sharing the same `tag-suffix` but distinct `platforms` + `platform-tag` + `runs-on`. Example: `-cpu-faster-whisper` has one amd64 entry on `ubuntu-latest` and one arm64 entry on `ubuntu-24.04-arm`.
- Each per-arch build pushes by **canonical digest only** (no tags) via `outputs: type=image,push-by-digest=true,name-canonical=true,push=true`. The digest is uploaded as an artifact named `digests<tag-suffix>-<platform-tag>` (or `digests-localai<...>` for root-image builds).
- `scripts/changed-backends.js` detects shared `tag-suffix` and emits a `merge-matrix` output. `backend.yml` / `backend_pr.yml` have a `backend-merge-jobs` job that consumes it and calls `backend_merge.yml`.
- `backend_merge.yml` downloads all matching digest artifacts and runs `docker buildx imagetools create` to publish the final tagged manifest list pointing at both per-arch digests. Same `docker/metadata-action` config as the original monolithic build, so consumers see no tag-shape change.
- `image_merge.yml` is the equivalent for the root LocalAI image (`-core` placeholder when `tag-suffix` is empty so the artifact-name glob doesn't over-match across `core` and `gpu-vulkan`).
**`provenance: false` is required on multi-registry digest pushes**: with the default `mode=max` provenance attestation, BuildKit bundles a per-registry attestation manifest into each registry's manifest list, making the resulting list digest diverge across registries. `steps.build.outputs.digest` only matches one of them and the merge step's `imagetools create <reg>@sha256:<digest>` lookup fails on the other. Setting `provenance: false` keeps the digest content-only and identical across registries.
## Path filter on master push
Both `backend.yml` (push) and `backend_pr.yml` (PR) generate their matrix dynamically through `scripts/changed-backends.js`:
- **PR events**: paginated `pulls/{n}/files` API → filter the matrix to entries whose `dockerfile` path prefix matches the PR diff.
- **Push events**: GitHub Compare API (`/repos/{owner}/{repo}/compare/{before}...{after}`) → same path-filter logic. Falls back to "run everything" on first-branch push (`event.before` zero), API truncation (≥300 changed files), missing API token, or any thrown error.
- **Tag pushes**: `FORCE_ALL=true` is set from the workflow side (`startsWith(github.ref, 'refs/tags/')`) — releases rebuild every backend regardless of diff.
- **Schedule / `workflow_dispatch`**: no `event.before`, falls through to "run everything" automatically.
The Sunday 06:00 UTC cron on `backend.yml` exists specifically because path filtering can leave Python backends frozen on stale wheels. `DEPS_REFRESH` (below) only fires when the build actually runs, so an untouched Python backend would never re-resolve its unpinned deps. The weekly cron is the safety net.
## The `DEPS_REFRESH` cache-buster (Python backends)
Every Python backend goes through the shared `backend/Dockerfile.python`, which ends with:
```dockerfile
ARG DEPS_REFRESH=initial
RUN cd /${BACKEND} && PORTABLE_PYTHON=true make
```
Most Python backends ship `requirements*.txt` files that **do not pin every transitive dep** (`torch`, `transformers`, `vllm`, `diffusers`, etc. are listed without a `==` pin, or with `>=` lower bounds only). With a warm BuildKit cache, the `make` layer hashes only on Dockerfile instructions + COPYed source — not on what `pip install` resolves at runtime. So a warm cache would ship the *first* version of `vllm` ever cached and never pick up upstream releases.
`DEPS_REFRESH` defends against that:
- `backend_build.yml` computes `date -u +%Y-W%V` (ISO week, e.g. `2026-W19`) before each build and passes it as a build-arg.
- The `RUN ... make` layer's BuildKit hash now includes that string, so the layer invalidates **at most once per week**, automatically picking up newer wheels.
- Within a week, builds stay warm.
This applies only to `Dockerfile.python` because:
- Go (`Dockerfile.golang`) pins versions in `go.mod` / `go.sum`.
- Rust (`Dockerfile.rust`) pins via `Cargo.lock`.
- C++ backends pin gRPC (`v1.65.0`) and llama.cpp at a specific commit; their inputs don't drift between rebuilds.
### Adjusting the cadence
Bump the format to daily (`+%Y-%m-%d`) or hourly (`+%Y-%m-%d-%H`) for faster refreshes. For one-shot rebuilds without changing the schedule, append a marker to the tag-suffix in the matrix or temporarily delete that backend's cache tag in quay.
## ccache for C++ backend builds
`Dockerfile.{llama-cpp,ik-llama-cpp,turboquant}` declare a BuildKit cache mount on `/root/.ccache`:
```dockerfile
RUN --mount=type=cache,target=/root/.ccache,id=<backend>-ccache-${TARGETARCH}-${BUILD_TYPE},sharing=locked \
bash /usr/local/sbin/compile.sh
```
The compile script exports `CMAKE_C/CXX/CUDA_COMPILER_LAUNCHER=ccache` so CMake threads ccache through gcc/g++/nvcc. `cache-to: type=registry,mode=max` exports the cache mount data into the registry cache, so subsequent builds restore it.
On a `LLAMA_VERSION` bump, most translation units are byte-identical to the previous version's preprocessed source — ccache returns the previous `.o` and skips the real compile. Same for LocalAI source changes that don't actually touch llama.cpp's CMake inputs. Cache scope is per `(TARGETARCH, BUILD_TYPE)` so e.g. cublas-12 doesn't share with cublas-13 (their CUDA headers differ; cross-pollination would just be cache misses anyway).
## Composite actions
Two composite actions handle runner-side prep:
- **`.github/actions/free-disk-space/action.yml`** — wraps `jlumbroso/free-disk-space@main` plus an explicit apt purge of dotnet/android/ghc/mono/etc. Reclaims ~610 GB on `ubuntu-latest`. No-op on self-hosted runners. Used by `backend_build.yml`, `image_build.yml`, `test.yml`, `tests-aio.yml`, etc.
- **`.github/actions/setup-build-disk/action.yml`** — relocates Docker's data-root to `/mnt` on hosted X64 runners. GHA hosted `ubuntu-latest` ships ~75 GB of unused space at `/mnt`; combined with the free-disk-space cleanup this gives ~100 GB working space — enough for ROCm dev image + vLLM torch install + flash-attn intermediate layers. No-op on self-hosted and on non-X64 hosted runners. Used by `backend_build.yml`, `image_build.yml`, `base-images.yml`.
Both actions run before any docker buildx step.
## Concurrency
All `backend.yml` / `image.yml` / `test.yml` / etc. workflows use:
```yaml
concurrency:
group: ci-<workflow>-${{ github.event.pull_request.number || github.sha }}-${{ github.repository }}
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
```
- **PR events** group by PR number → newer pushes to the same PR cancel old runs (intended).
- **Push events** group by `github.sha` → each master commit gets its own run; rapid-fire merges don't cancel each other (this was a real issue prior — two master pushes 11 seconds apart would cancel the first's CI).
## Self-warming, no separate populator
There is no cron job that pre-warms the BuildKit cache for individual backends. The production builds *are* the populators. The first master build of a given matrix entry pays the cold cost; subsequent same-entry master builds reuse everything that hasn't changed (apt installs, gRPC compile in the variant `builder-fromsource` stage or skipped entirely when consuming `base-grpc-*`, Python wheel installs, etc.). The base-images workflow's weekly cron is the closest thing to a populator and only refreshes the prebuilt builder bases.
## Manually evicting cache
To force a fully cold build for one backend or the whole image:
```bash
# Delete a single tag (requires quay credentials with admin on the repo)
curl -X DELETE \
-H "Authorization: Bearer ${QUAY_TOKEN}" \
https://quay.io/api/v1/repository/go-skynet/ci-cache/tag/cache-gpu-nvidia-cuda-12-vllm-amd64
# List all tags
curl -s -H "Authorization: Bearer ${QUAY_TOKEN}" \
"https://quay.io/api/v1/repository/go-skynet/ci-cache/tag/?limit=100" | jq '.tags[].name'
```
Eviction is rarely needed in normal operation — `DEPS_REFRESH` handles weekly drift, source changes invalidate naturally, and `mode=max` keeps the cache scoped per matrix entry per arch so a stale tag never bleeds into a different build.
## What the cache does **not** cover
- The `free-disk-space` and `setup-build-disk` composite actions run on every job — these reclaim runner-state, not Docker layers, so BuildKit caches don't apply.
- Intermediate artifacts of `Build (PR)` are not pushed anywhere — PRs only build for verification.
- Darwin builds (see below) — macOS runners have no Docker daemon, so the registry-backed BuildKit cache cannot apply.
## Darwin native caches
`backend_build_darwin.yml` runs natively on `macOS-14` GitHub-hosted runners — there is no Docker, no BuildKit, no cross-job registry cache. Instead, the reusable workflow uses `actions/cache@v4` for four native caches that mirror the spirit of the Linux cache (warm by default, weekly refresh for unpinned Python deps, PRs read-only).
| Cache | Path(s) | Key | Scope |
|---|---|---|---|
| Go modules + build | `~/go/pkg/mod`, `~/Library/Caches/go-build` | `go.sum` (managed by `actions/setup-go@v5` `cache: true`) | All darwin jobs |
| Homebrew | `~/Library/Caches/Homebrew/downloads`, selected `/opt/homebrew/Cellar/*` | hash of `backend_build_darwin.yml` | All darwin jobs |
| ccache (llama.cpp CMake) | `~/Library/Caches/ccache` | pinned `LLAMA_VERSION` from `backend/cpp/llama-cpp/Makefile` | `inputs.backend == 'llama-cpp'` only |
| Python wheels (uv + pip) | `~/Library/Caches/pip`, `~/Library/Caches/uv` | `inputs.backend` + ISO week (`+%Y-W%V`) + hash of that backend's `requirements*.txt` | `inputs.lang == 'python'` only |
Read/write semantics match the BuildKit cache: `actions/cache/restore` runs every time, `actions/cache/save` is gated on `github.event_name != 'pull_request'`. PRs read master's warm cache but never write back.
The Python wheel cache uses the same ISO-week cache-buster as the Linux `DEPS_REFRESH` build-arg — same problem (unpinned `torch`/`mlx`/`diffusers`/`transformers` resolve to fresh wheels weekly), same ~one-cold-rebuild-per-week solution.
The brew Cellar cache requires `HOMEBREW_NO_AUTO_UPDATE=1` and `HOMEBREW_NO_INSTALL_CLEANUP=1` (set as job-level env). Without those, `brew install` would mutate the very directories that were just restored, defeating the cache.
**Force-link after cache restore**: `actions/cache` restores `/opt/homebrew/Cellar/*` but NOT the `/opt/homebrew/bin/*` symlinks. After a cache hit, `brew install` sees the Cellar entries and decides "already installed" without re-running its link step, leaving the formulas off PATH. The Dependencies step explicitly runs `brew link --overwrite` for every cached formula afterwards to ensure the symlinks exist.
For ccache, the workflow exports `CMAKE_ARGS=… -DCMAKE_C_COMPILER_LAUNCHER=ccache -DCMAKE_CXX_COMPILER_LAUNCHER=ccache` via `$GITHUB_ENV` before running `make build-darwin-go-backend`. The Makefile in `backend/cpp/llama-cpp/` already forwards `CMAKE_ARGS` through to each variant build (`fallback`, `grpc`, `rpc-server`), so no script changes are needed. The three variants share most TUs, so ccache dedupes object files across them.
`backend_build_darwin.yml` also has a llama-cpp-specific build-step branch that runs `make backends/llama-cpp-darwin` (the bespoke script that compiles three CMake variants and bundles dylibs via `otool`), distinct from the generic `make build-darwin-${lang}-backend` path. This was consolidated from a previously-bespoke top-level `llama-cpp-darwin` job in `backend.yml` so llama-cpp on Darwin honors the same path filter as the other 34 Darwin backends.
### Cache budget on Darwin
GitHub Actions caches are limited to 10 GB per repo. Steady-state worst case: ~800 MB Go cache + ~2 GB brew Cellar + up to 2 GB ccache + ~1.5 GB × 5 python backends. If the cap is hit, prefer collapsing the per-backend Python keys into a shared `pyenv-darwin-shared-<week>` key (accepts more cross-backend churn for a smaller footprint) before reducing other caches.
## Self-hosted runners
`.github/backend-matrix.yml` has zero references to `arc-runner-set` or `bigger-runner` — all backends run on GHA free-tier hosted runners (`ubuntu-latest` for amd64, `ubuntu-24.04-arm` for arm64 native, `macos-14` for Darwin). The migration off self-hosted relied on the per-arch native split (no QEMU emulation) plus `setup-build-disk`'s `/mnt` relocation (~100 GB working space, enough for ROCm dev image + vLLM/torch installs).
One residual self-hosted reference remains in `test-extra.yml` (`tests-vibevoice-cpp-grpc-transcription` uses `bigger-runner` for the 30s JFK-decode timeout headroom). That's a separate concern.
## Touching the cache pipeline
When changing `image_build.yml`, `backend_build.yml`, any of the `backend/Dockerfile.*` files, `Dockerfile.base-grpc-builder`, `.docker/install-base-deps.sh`, `.docker/<backend>-compile.sh`, or `scripts/changed-backends.js`:
1. **Don't drop `DEPS_REFRESH=...` from the build-args** without a replacement strategy (lockfiles, pinned requirements). Otherwise master will silently freeze on whichever versions were cached at the time.
2. **Keep `(tag-suffix, platform-tag)` unique per matrix entry** — together they're the cache namespace. Two matrix entries sharing a key would clobber each other's cache.
3. **Keep `cache-to` gated on `github.event_name != 'pull_request'`** — PRs must not write.
4. **Keep `ignore-error=true` on `cache-to`** — quay registry hiccups must not fail builds.
5. **Keep `provenance: false` on push-by-digest steps** — multi-registry digest divergence is the Bug We Already Fixed; reintroducing provenance attestation re-breaks the merge.
6. **`install-base-deps.sh` is the single source of truth for base contents.** Both `Dockerfile.base-grpc-builder` (CI) and the variant Dockerfiles' `builder-fromsource` (local) bind-mount and execute it. If you add a package to one path, add it to the script — don't fork the logic into a Dockerfile RUN.
7. **After adding a `base-images.yml` matrix variant, run the workflow on your branch before merging consumer changes** that depend on the new tag — otherwise the consumer's CI fails "image not found."

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@@ -42,14 +42,6 @@ trim_trailing_whitespace = false
Use `github.com/mudler/xlog` for logging which has the same API as slog.
## Go tests
All Go tests — including backend tests — must use [Ginkgo](https://onsi.github.io/ginkgo/) (v2) with Gomega matchers, not the stdlib `testing` package with `t.Run` / `t.Errorf`. A test file should register a suite with `RegisterFailHandler(Fail)` in a `TestXxx(t *testing.T)` bootstrap and use `Describe`/`Context`/`It` blocks for the actual cases. Look at any existing `*_test.go` under `core/` or `pkg/` for a template.
Do not mix styles within a package. If you are extending tests in a package that already uses Ginkgo, keep using Ginkgo. If you find stdlib-style Go tests in the tree, treat them as tech debt to be migrated rather than as a pattern to follow.
This is enforced by `golangci-lint` via the `forbidigo` linter (see `.golangci.yml`); calls like `t.Errorf` / `t.Fatalf` / `t.Run` / `t.Skip` / `t.Logf` are flagged. Run `make lint` locally before submitting; the same check runs in CI (`.github/workflows/lint.yml`).
## Documentation
The project documentation is located in `docs/content`. When adding new features or changing existing functionality, it is crucial to update the documentation to reflect these changes. This helps users understand how to use the new capabilities and ensures the documentation stays relevant.

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@@ -1,84 +0,0 @@
# Working on the ds4 Backend
`antirez/ds4` is a single-model inference engine for DeepSeek V4 Flash.
LocalAI wraps the engine's C API (`ds4/ds4.h`) with a fresh C++ gRPC server at
`backend/cpp/ds4/` - NOT a fork of llama-cpp's grpc-server.cpp.
## Pin
`backend/cpp/ds4/Makefile` pins `DS4_VERSION?=<sha>` at the top. The `ds4`
target in the Makefile clones `antirez/ds4` at that commit (mirroring the
llama-cpp / ik-llama-cpp / turboquant pattern). The bump-deps bot
(`.github/workflows/bump_deps.yaml`) finds this pin via grep and opens a
daily PR to update it. To bump manually: edit the `DS4_VERSION?=` line,
then `make purge && make` (or rely on CI's clean build).
## Wire shape
| RPC | Implementation |
|---|---|
| Health, Free, Status | Trivial; no engine dependency for Health |
| LoadModel | `ds4_engine_open` + `ds4_session_create`; backend is compile-time (DS4_NO_GPU → CPU, __APPLE__ → Metal, otherwise CUDA) |
| TokenizeString | `ds4_tokenize_text` |
| Predict | `ds4_engine_generate_argmax` + `DsmlParser` → one ChatDelta with content / reasoning_content / tool_calls[] |
| PredictStream | Same, per-token ChatDelta writes |
## DSML
ds4 emits tool calls as literal text markers (`<DSMLtool_calls>` etc.) -
NOT special tokens. `dsml_parser.{h,cpp}` is our streaming state machine that
classifies token bytes into CONTENT / REASONING / TOOL_START / TOOL_ARGS / TOOL_END
events. `dsml_renderer.{h,cpp}` does the prompt direction: turns
OpenAI tool_calls + role=tool messages back into DSML for the next turn.
## Thinking modes
`PredictOptions.Metadata["enable_thinking"]` gates thinking on/off (default ON).
`["reasoning_effort"] == "max" | "xhigh"` selects `DS4_THINK_MAX`; anything else
maps to `DS4_THINK_HIGH`. We pass the chosen mode to `ds4_chat_append_assistant_prefix`.
## Disk KV cache
`kv_cache.{h,cpp}` implements an SHA1-keyed file cache using ds4's public
`ds4_session_save_payload` / `ds4_session_load_payload` API. Enable per request
via `ModelOptions.Options[] = "kv_cache_dir:/some/path"`. Format is **our own** -
NOT bit-compatible with ds4-server's KVC files (interop is a follow-up plan).
## Build matrix
| Build | Where | Notes |
|---|---|---|
| `cpu-ds4` (amd64 + arm64) | Linux GHA | ds4 considers CPU debug-only; useful only for wiring tests |
| `cuda13-ds4` (amd64 + arm64) | Linux GHA + DGX Spark validation | Primary production path on Linux |
| `ds4-darwin` (arm64) | macOS GHA runners | Metal; uses `scripts/build/ds4-darwin.sh` like llama-cpp-darwin |
cuda12 is intentionally omitted. ROCm / Vulkan / SYCL are not applicable.
## Hardware-gated validation
`tests/e2e-backends/backend_test.go` in `BACKEND_BINARY` mode:
```
BACKEND_BINARY=$(pwd)/backend/cpp/ds4/package/run.sh \
BACKEND_TEST_MODEL_FILE=/path/to/ds4flash.gguf \
BACKEND_TEST_CAPS=health,load,predict,stream,tools \
BACKEND_TEST_TOOL_PROMPT="What's the weather in Paris?" \
go test -count=1 -timeout=30m -v ./tests/e2e-backends/...
```
CI does not load the model; the suite is opt-in via env vars.
## Importer
`core/gallery/importers/ds4.go` (`DS4Importer`) auto-detects ds4 weights by
matching the `antirez/deepseek-v4-gguf` repo URI or the
`DeepSeek-V4-Flash-*.gguf` filename pattern. **Registered BEFORE
`LlamaCPPImporter`** in `defaultImporters` - both match `.gguf` but ds4 is more
specific, and first-match-wins. The importer emits `backend: ds4`, uses
`ds4flash.gguf` as the local filename (matches ds4's own CLI default), and
disables the Go-side automatic tool-parsing fallback (the C++ backend emits
ChatDelta.tool_calls natively via `DsmlParser`).
ds4 is also listed in `core/http/endpoints/localai/backend.go`'s pref-only
slice so the `/import-model` UI surfaces it as a manual choice for users who
want to force the backend on a non-canonical URI.

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@@ -61,12 +61,6 @@ Always check `llama.cpp` for new model configuration options that should be supp
- `reasoning_format` - Reasoning format options
- Any new flags or parameters
### Speculative Decoding Types
The `spec_type` option in `grpc-server.cpp` delegates to upstream's `common_speculative_types_from_names()`, so new speculative types added to the `common_speculative_type_from_name` map in `common/speculative.cpp` are picked up automatically with no code changes - only docs need an entry in `docs/content/advanced/model-configuration.md`. Current values: `none`, `draft-simple`, `draft-eagle3`, `draft-mtp`, `ngram-simple`, `ngram-map-k`, `ngram-map-k4v`, `ngram-mod`, `ngram-cache`.
`draft-mtp` (Multi-Token Prediction, [ggml-org/llama.cpp#22673](https://github.com/ggml-org/llama.cpp/pull/22673)) does not need a separate draft GGUF: when `spec_type` includes `draft-mtp` and `draftmodel` is empty, the upstream server creates an MTP context off the target model itself. LocalAI's gRPC layer needs no changes for this — it works through the existing `params.speculative.types` plumbing and the derived `cparams.n_rs_seq = params.speculative.need_n_rs_seq()` in `common_context_params_to_llama`.
### Implementation Guidelines
1. **Feature Parity**: Always aim for feature parity with llama.cpp's implementation

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@@ -1,97 +0,0 @@
# LocalAI Assistant — admin MCP server
This document is the contract for **anyone** (human or AI agent) touching LocalAI's admin REST surface, the in-process MCP server that wraps it, or the embedded skill prompts that teach the assistant how to use it. Read this before adding/removing/renaming admin endpoints, MCP tools, or skill recipes.
## What this feature is
`pkg/mcp/localaitools/` is a public Go package that exposes LocalAI's admin/management surface as an MCP server. It is used in two ways:
1. **In-process**: when an admin opens a chat with `metadata.localai_assistant=true`, the chat handler injects the in-memory MCP server (paired `net.Pipe()` transport, no HTTP loopback) so the LLM can install models, manage backends and edit configs by chatting.
2. **Standalone**: the `local-ai mcp-server --target=…` subcommand serves the same MCP server over stdio, talking HTTP to a remote LocalAI instance.
The two modes share **all** tool definitions and skill prompts. They differ only in their `LocalAIClient` implementation (`inproc/` calls services directly; `httpapi/` calls REST).
## The three things you must keep in sync
When you change LocalAI's admin surface, three layers must stay aligned:
1. **REST endpoint** in `core/http/endpoints/localai/*.go`.
2. **MCP tool registration** in `pkg/mcp/localaitools/tools_*.go`, plus a method on `LocalAIClient` (in `client.go`) and implementations in both `inproc/client.go` **and** `httpapi/client.go`.
3. **Skill prompt** under `pkg/mcp/localaitools/prompts/skills/*.md` — the markdown that teaches the LLM how to use the new tool. If the new tool fits an existing recipe, update that recipe; otherwise add a new file.
If you ship a REST endpoint without (2) and (3), conversational admins won't see the feature.
## Checklist for adding a new admin endpoint
- [ ] REST endpoint exists in `core/http/endpoints/localai/*.go` and is gated by `auth.RequireAdmin()` in `core/http/routes/localai.go`.
- [ ] `LocalAIClient` interface in `pkg/mcp/localaitools/client.go` has a method covering the new operation.
- [ ] DTOs added/updated in `pkg/mcp/localaitools/dto.go` (JSON-tagged; never expose raw service types).
- [ ] `inproc/client.go` implements the new method by calling the service directly (not via HTTP loopback).
- [ ] `httpapi/client.go` implements the new method by calling the REST endpoint.
- [ ] Tool registration added in the appropriate `pkg/mcp/localaitools/tools_*.go`. Mutating tools must reference safety rule 1 in the description.
- [ ] If the tool is mutating, ensure `Options{DisableMutating: true}` skips it (mirror the pattern in `tools_models.go`).
- [ ] Skill prompt added or updated under `pkg/mcp/localaitools/prompts/skills/`. The prompt must instruct the LLM when to call the tool, what to ask the user first, and what to do on error.
- [ ] Tests:
- `pkg/mcp/localaitools/server_test.go` adds the tool name to `expectedFullCatalog` and `expectedReadOnlyCatalog` (if read-only).
- Tool dispatch is added to `TestEachToolDispatchesToClient`.
- `pkg/mcp/localaitools/httpapi/client_test.go` covers the new HTTP path.
## Adding a new skill recipe (no new tool)
Sometimes you want to teach the LLM a new pattern that uses existing tools. Drop a markdown file under `pkg/mcp/localaitools/prompts/skills/<verb>_<noun>.md`. The file is automatically embedded by `//go:embed` and assembled into the system prompt in lexicographic order. No Go changes needed.
Conventions:
- Filename: `<verb>_<noun>.md` (e.g. `install_chat_model.md`, `upgrade_backend.md`).
- First line: `# Skill: <Title Case description>`.
- Number the steps. Reference exact tool names in backticks.
- If the skill mutates state, remind the LLM to confirm with the user.
## Code conventions
These rules guard against the magic-literal drift that surfaced in the first audit. Do not re-introduce bare strings.
- **Tool names** always come from the `Tool*` constants in `pkg/mcp/localaitools/tools.go`. Tool registrations, the test catalog (`server_test.go`'s `expectedFullCatalog` / `expectedReadOnlyCatalog`), and dispatch tables reference the constants. The embedded skill prompts under `prompts/` keep bare strings — that's the one allowed exception, and `TestPromptsContainSafetyAnchors` enforces alignment.
- **Toggle/pin actions** use the `modeladmin.Action` type (`pkg/mcp/localaitools` and `core/services/modeladmin`). Use `ActionEnable`/`ActionDisable`/`ActionPin`/`ActionUnpin`; never bare `"enable"`/`"pin"` strings.
- **Capability tags** for `list_installed_models` use the `localaitools.Capability` type (`capability.go`). The `LocalAIClient.ListInstalledModels` interface takes a typed `Capability`, and the `inproc` switch only accepts canonical values (`"embed"`/`"embedding"` are not aliases — only `CapabilityEmbeddings`).
- **HTTP error checks** in `httpapi.Client` use `errors.Is(err, ErrHTTPNotFound)`, not substring matches on `err.Error()`. The typed `*HTTPError` carries `StatusCode` and `Body`; add new sentinel errors as needed rather than re-introducing string matching.
- **Channel sends** to `GalleryService.ModelGalleryChannel` / `BackendGalleryChannel` from inproc clients MUST select on `ctx.Done()` so a cancelled chat completion releases the goroutine. See `inproc.sendModelOp` / `sendBackendOp`.
- **Disk writes** of model config YAML go through `modeladmin.writeFileAtomic` (temp file + `os.Rename`). `os.WriteFile` truncates on crash and corrupts the model.
- **MCP server lifecycle**: every initialised holder MUST register `Close()` with `signals.RegisterGracefulTerminationHandler`. The standalone `mcp-server` CLI uses `signal.NotifyContext` to honour SIGINT/SIGTERM.
## File map (where to look)
```
pkg/mcp/localaitools/
client.go # LocalAIClient interface + DTO registry
dto.go # JSON-tagged DTOs shared by both client impls
server.go # NewServer(client, opts) — registers tools
tools.go # Tool* name constants (single source of truth)
capability.go # Capability type + constants
tools_models.go # gallery_search, install_model, import_model_uri, ...
tools_backends.go
tools_config.go
tools_system.go
tools_state.go
prompts.go # //go:embed loader + SystemPrompt(opts)
prompts/00_role.md
prompts/10_safety.md # SAFETY RULES — change with care
prompts/20_tools.md # curated tool catalog with one-liners
prompts/skills/*.md
inproc/client.go # in-process LocalAIClient (services-direct)
httpapi/client.go # REST LocalAIClient (for standalone CLI / remote)
core/http/endpoints/mcp/
localai_assistant.go # process-wide holder + LocalToolExecutor
core/cli/mcp_server.go # local-ai mcp-server subcommand
```
## Why two clients
The in-process MCP server runs inside the same LocalAI binary that serves chat. Going over HTTP loopback would (a) require minting a synthetic admin API key for the server to authenticate against itself, (b) double-marshal every tool dispatch, and (c) lose access to in-process channels (e.g. `GalleryService.ModelGalleryChannel` for streaming install progress). So in-process uses `inproc.Client`. The standalone stdio CLI talks to a *remote* LocalAI; HTTP is the only option, so it uses `httpapi.Client`. Both implement the same `LocalAIClient` interface, and the parity test in `pkg/mcp/localaitools/parity_test.go` (when present) keeps their output equivalent.
## Why prompt-enforced confirmation, not code gates
The user chose KISS. Every mutating tool has a safety rule (`prompts/10_safety.md` rule 1) that requires the LLM to summarise the action and wait for explicit user confirmation before calling it. There is no `plan_*`/`apply_*` two-step in code. If you add a mutating tool, do **not** add per-tool confirmation logic in Go — instead, list the new tool name in `prompts/10_safety.md` so the LLM knows it falls under the confirmation rule.
## Distributed mode
The in-memory MCP server runs only on the head node (where the chat handler runs). `inproc.Client` wraps services that are already distributed-aware (`GalleryService` coordinates with workers; `ListNodes` reads the NATS-populated registry). No NATS routing of MCP tools — the admin surface lives on the head, period.

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@@ -1,62 +0,0 @@
# Working on the SGLang Backend
The SGLang backend lives at `backend/python/sglang/backend.py` (async gRPC). It wraps SGLang's `Engine` (`sglang.srt.entrypoints.engine.Engine`) and translates LocalAI's gRPC `PredictOptions` into SGLang sampling params + outputs into `Reply.chat_deltas`. Structurally it mirrors `backend/python/vllm/backend.py` — keep them shaped the same so changes in one have an obvious analog in the other.
## `engine_args` is the universal escape hatch
A small fixed set of fields on `ModelOptions` is mapped to typed SGLang kwargs in `LoadModel` (model, quantization, load_format, gpu_memory_utilization → mem_fraction_static, trust_remote_code, enforce_eager → disable_cuda_graph, tensor_parallel_size → tp_size, max_model_len → context_length, dtype). **Everything else** flows through the `engine_args:` YAML map.
Validation happens in `_apply_engine_args`. Keys are checked against `dataclasses.fields(ServerArgs)` (`sglang.srt.server_args.ServerArgs` is a flat `@dataclass` with ~380 fields). Unknown keys raise `ValueError` at LoadModel time with a `difflib.get_close_matches` suggestion — same shape as the vLLM backend.
**Precedence:** typed `ModelOptions` fields populate `engine_kwargs` first, then `engine_args` overrides them. So a YAML that sets both `gpu_memory_utilization: 0.9` and `engine_args.mem_fraction_static: 0.5` ends up at `0.5`. Document this when answering "why didn't my YAML field stick?".
**ServerArgs is flat.** Unlike vLLM, where speculative decoding is nested under `engine_args.speculative_config: {...}`, SGLang exposes flat top-level fields: `speculative_algorithm`, `speculative_draft_model_path`, `speculative_num_steps`, `speculative_eagle_topk`, `speculative_num_draft_tokens`, `speculative_dflash_block_size`, etc. There is no `speculative_config:` dict. Same goes for compilation, kv-transfer, attention — all flat.
The canonical reference is `python/sglang/srt/server_args.py:ServerArgs` (line ~304). When SGLang adds new flags, no LocalAI code change is needed — they're automatically available via `engine_args:`. The validator picks them up because it introspects the live dataclass.
## Speculative decoding cheatsheet
`--speculative-algorithm` accepts `EAGLE`, `EAGLE3`, `NEXTN`, `STANDALONE`, `NGRAM`, `DFLASH`. `NEXTN` is silently rewritten to `EAGLE` in `ServerArgs.__post_init__` (`server_args.py:3286-3287`). MTP (Multi-Token Prediction) is the same EAGLE path with `num_steps=1, eagle_topk=1, num_draft_tokens=2` against a target whose architecture has multi-token heads (e.g. MiMo-7B-RL, DeepSeek-V3-MTP).
| Algorithm | Drafter requirement | Gallery demo target | Gallery demo drafter |
|-----------|--------------------|---------------------|----------------------|
| `NEXTN` / `EAGLE` (MTP) | Assistant drafter or built-in heads | google/gemma-4-E2B-it, google/gemma-4-E4B-it | google/gemma-4-E2B-it-assistant, google/gemma-4-E4B-it-assistant |
| `EAGLE3` | EAGLE3 draft head | (no gallery entry yet) | e.g. jamesliu1/sglang-EAGLE3-Llama-3.1-Instruct-8B |
| `DFLASH` | Block-diffusion drafter | (no gallery entry yet) | e.g. z-lab/Qwen3-4B-DFlash-b16 |
| `STANDALONE` | Smaller LLM as drafter | (no gallery entry yet) | any smaller chat-tuned LLM in the same family |
| `NGRAM` | None — uses prefix history | (no gallery entry yet) | n/a |
The Gemma 4 demos use `mem_fraction_static: 0.85` (cookbook default) and the cookbook's `num_steps=5, num_draft_tokens=6, eagle_topk=1` parameters. Other algorithms are reachable from any user YAML via `engine_args:` but don't have shipped demos yet — that's a deliberate gallery scope choice, not a backend limitation.
Gemma 4 support requires sglang built from a commit that includes [PR #21952](https://github.com/sgl-project/sglang/pull/21952). LocalAI's pinned release for cublas12 / cublas13 includes it. The `l4t13` (JetPack 7 / sbsa cu130) build floors at `sglang>=0.5.0` because the `pypi.jetson-ai-lab.io` mirror still ships only `0.5.1.post2` as of 2026-05-06 — Gemma 4 / MTP recipes are therefore not available on l4t13 until that mirror catches up. `backend.py` keeps backward compat with the 0.5.x → 0.5.11 `SamplingParams.seed``sampling_seed` rename via runtime detection.
Compatibility caveats per the SGLang docs: DFLASH and NGRAM are incompatible with `enable_dp_attention`; DFLASH requires `pp_size == 1`; STANDALONE is incompatible with `enable_dp_attention`; NGRAM is CUDA-only and disables the overlap scheduler.
### `mem_fraction_static` + quantization + MTP on consumer GPUs
When combining online weight quantization (`engine_args.quantization: fp8` / `awq` / etc.) with built-in-head MTP (`speculative_algorithm: EAGLE`/`NEXTN`) on a tight VRAM budget, sglang's default `mem_fraction_static: 0.85` will OOM during draft-worker init. The reason: sglang quantizes the **target** model's transformer blocks but loads the **MTP draft worker's vocab embedding** at the source dtype (typically bf16). For a 7 B-class model with a 150k-token vocab × 4096 hidden, that's another ~1.2 GiB allocated *after* the static pool is reserved. At 0.85 fraction on a 16 GB card there's no room left.
Workaround: drop `mem_fraction_static` to ~0.7 so the post-static heap can absorb the MTP embedding alloc + CUDA graph private pools. Verified end-to-end on MiMo-7B-RL + fp8 + MTP on a 16 GB RTX 5070 Ti (`gallery/sglang-mimo-7b-mtp.yaml`) at ~88 tok/s. Models with larger vocabs or more MTP layers (e.g. DeepSeek-V3-MTP) need an even smaller fraction.
This isn't documented anywhere upstream as of 2026-05-06 — the SGLang Gemma 4 cookbook uses 0.85 because their MTP path doesn't go through `eagle_worker_v2.py` for an embedding-bearing draft module. Don't blanket-apply 0.7 across all sglang YAMLs; only when MTP-with-built-in-heads + quantization combine.
## Tool-call and reasoning parsers stay on `Options[]`
ServerArgs has `tool_call_parser` and `reasoning_parser` fields, and the backend does pass them through to `Engine` so SGLang's own HTTP/OAI surface keeps working. But for the **LocalAI** request path the backend constructs fresh per-request parser instances in `_make_parsers` (`backend.py:286`) because the parsers are stateful — the streaming and non-streaming paths each need their own.
So the user-facing knob stays on `Options[]`:
```yaml
options:
- tool_parser:hermes
- reasoning_parser:deepseek_r1
```
Putting these in `engine_args:` will set them on `ServerArgs` but the LocalAI-level streaming `ChatDelta` will not pick them up. Don't recommend that path.
## What's missing today (out of scope, but worth tracking)
- `core/config/hooks_sglang.go` — there is no SGLang equivalent of `hooks_vllm.go`. The vLLM hook auto-selects parsers for known model families from `parser_defaults.json` and seeds production engine_args defaults. A symmetric hook for SGLang could reuse the same `parser_defaults.json` (the SGLang parser names are different but the family detection is shared) and seed defaults like `enable_metrics: true` or attention-backend choices.
- `core/gallery/importers/sglang.go` — vLLM has an importer that resolves model architecture → parser defaults at gallery-import time. A matching importer for SGLang would let `local-ai install` populate sensible parsers automatically.
These should be a follow-up PR, not a blocker for the engine_args feature.

View File

@@ -1,39 +0,0 @@
#!/bin/sh
# Reconfigure Ubuntu apt sources to point at an alternate mirror.
#
# Used by Dockerfiles via `RUN --mount=type=bind,source=.docker/apt-mirror.sh,...`
# and by CI workflows on the runner to mitigate outages of the default
# archive.ubuntu.com / security.ubuntu.com / ports.ubuntu.com pool.
#
# Inputs (env):
# APT_MIRROR Replacement for archive.ubuntu.com and security.ubuntu.com
# (e.g. "http://azure.archive.ubuntu.com" or
# "https://mirrors.edge.kernel.org").
# Leave empty to keep upstream. The trailing "/ubuntu/..."
# path is preserved by the rewrite.
# APT_PORTS_MIRROR Replacement for ports.ubuntu.com (arm64/ppc64el/...).
# Leave empty to keep upstream.
#
# Both default to empty, in which case the script is a no-op.
set -e
if [ -z "${APT_MIRROR}" ] && [ -z "${APT_PORTS_MIRROR}" ]; then
exit 0
fi
# Ubuntu 24.04 (noble) ships DEB822 sources at /etc/apt/sources.list.d/ubuntu.sources;
# older releases use /etc/apt/sources.list. We rewrite whichever exists.
for f in /etc/apt/sources.list.d/ubuntu.sources /etc/apt/sources.list; do
[ -f "$f" ] || continue
if [ -n "${APT_MIRROR}" ]; then
# Use a comma delimiter so the alternation pipe in the regex
# is not interpreted as the s/// separator.
sed -i -E "s,https?://(archive\.ubuntu\.com|security\.ubuntu\.com),${APT_MIRROR},g" "$f"
fi
if [ -n "${APT_PORTS_MIRROR}" ]; then
sed -i -E "s,https?://ports\.ubuntu\.com,${APT_PORTS_MIRROR},g" "$f"
fi
done
echo "apt-mirror: rewrote sources (APT_MIRROR='${APT_MIRROR}', APT_PORTS_MIRROR='${APT_PORTS_MIRROR}')"

View File

@@ -1,30 +0,0 @@
#!/usr/bin/env bash
# Shared compile logic for backend/Dockerfile.ik-llama-cpp.
# Sourced (via bind mount) from both builder-fromsource and builder-prebuilt stages.
set -euxo pipefail
export CCACHE_DIR=/root/.ccache
ccache --max-size=5G || true
ccache -z || true
export CMAKE_ARGS="${CMAKE_ARGS:-} -DCMAKE_C_COMPILER_LAUNCHER=ccache -DCMAKE_CXX_COMPILER_LAUNCHER=ccache -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache"
if [[ -n "${CUDA_DOCKER_ARCH:-}" ]]; then
CUDA_ARCH_ESC="${CUDA_DOCKER_ARCH//;/\\;}"
export CMAKE_ARGS="${CMAKE_ARGS} -DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCH_ESC}"
echo "CMAKE_ARGS(env) = ${CMAKE_ARGS}"
rm -rf /LocalAI/backend/cpp/ik-llama-cpp-*-build
fi
cd /LocalAI/backend/cpp/ik-llama-cpp
if [ "${TARGETARCH}" = "arm64" ] || [ "${BUILD_TYPE}" = "hipblas" ]; then
# ARM64 / ROCm: build without x86 SIMD
make ik-llama-cpp-fallback
else
# ik_llama.cpp's IQK kernels require at least AVX2
make ik-llama-cpp-avx2
fi
ccache -s || true

View File

@@ -1,244 +0,0 @@
#!/usr/bin/env bash
# Single source of truth for builder-base contents.
#
# Used by:
# - backend/Dockerfile.base-grpc-builder (CI prebuilt-base source of truth)
# - backend/Dockerfile.llama-cpp (builder-fromsource stage)
# - backend/Dockerfile.ik-llama-cpp (builder-fromsource stage)
# - backend/Dockerfile.turboquant (builder-fromsource stage)
#
# All four files invoke this script via
# RUN --mount=type=bind,source=.docker/install-base-deps.sh,target=/usr/local/sbin/install-base-deps \
# --mount=type=bind,source=.docker/apt-mirror.sh,target=/usr/local/sbin/apt-mirror \
# bash /usr/local/sbin/install-base-deps
#
# so the prebuilt CI base image and the from-source local-dev path are
# bit-equivalent by construction.
#
# Inputs (env, populated from Dockerfile ARG/ENV):
# BUILD_TYPE ("cublas"|"l4t"|"hipblas"|"vulkan"|"sycl"|"clblas"|"")
# CUDA_MAJOR_VERSION ("12" | "13" | "")
# CUDA_MINOR_VERSION ("8" | "0" | "")
# TARGETARCH ("amd64" | "arm64")
# UBUNTU_VERSION ("2204" | "2404")
# SKIP_DRIVERS ("false" | "true")
# CMAKE_FROM_SOURCE ("false" | "true")
# CMAKE_VERSION ("3.31.10")
# GRPC_VERSION ("v1.65.0")
# GRPC_MAKEFLAGS ("-j4 -Otarget")
# APT_MIRROR / APT_PORTS_MIRROR (optional; consumed by /usr/local/sbin/apt-mirror)
# AMDGPU_TARGETS (optional; only relevant for hipblas downstream)
#
# IMPORTANT: install logic is copied verbatim from the prior in-Dockerfile
# RUN blocks. Do not paraphrase apt invocations / version pins / sed line
# numbers / deb URLs — the bit-equivalence guarantee depends on it.
set -eux
# --- 0. apt mirror rewrite (no-op when APT_MIRROR / APT_PORTS_MIRROR unset) ---
if [ -x /usr/local/sbin/apt-mirror ]; then
APT_MIRROR="${APT_MIRROR:-}" APT_PORTS_MIRROR="${APT_PORTS_MIRROR:-}" \
sh /usr/local/sbin/apt-mirror
fi
export DEBIAN_FRONTEND=noninteractive
export MAKEFLAGS="${GRPC_MAKEFLAGS:-}"
# --- 1. Base apt build deps ---
apt-get update
apt-get install -y --no-install-recommends \
build-essential \
ccache git \
ca-certificates \
make \
pkg-config libcurl4-openssl-dev \
curl unzip \
libssl-dev wget
apt-get clean
rm -rf /var/lib/apt/lists/*
# --- 2. Vulkan SDK (BUILD_TYPE=vulkan) ---
# NB: this block intentionally installs `cmake` via apt as part of the
# Vulkan tooling — must run before the dedicated CMake step below.
if [ "${BUILD_TYPE:-}" = "vulkan" ] && [ "${SKIP_DRIVERS:-false}" = "false" ]; then
apt-get update
apt-get install -y --no-install-recommends \
software-properties-common pciutils wget gpg-agent
apt-get install -y libglm-dev cmake libxcb-dri3-0 libxcb-present0 libpciaccess0 \
libpng-dev libxcb-keysyms1-dev libxcb-dri3-dev libx11-dev g++ gcc \
libwayland-dev libxrandr-dev libxcb-randr0-dev libxcb-ewmh-dev \
git python-is-python3 bison libx11-xcb-dev liblz4-dev libzstd-dev \
ocaml-core ninja-build pkg-config libxml2-dev wayland-protocols python3-jsonschema \
clang-format qtbase5-dev qt6-base-dev libxcb-glx0-dev sudo xz-utils
if [ "amd64" = "${TARGETARCH:-}" ]; then
wget "https://sdk.lunarg.com/sdk/download/1.4.335.0/linux/vulkansdk-linux-x86_64-1.4.335.0.tar.xz"
tar -xf vulkansdk-linux-x86_64-1.4.335.0.tar.xz
rm vulkansdk-linux-x86_64-1.4.335.0.tar.xz
mkdir -p /opt/vulkan-sdk
mv 1.4.335.0 /opt/vulkan-sdk/
( cd /opt/vulkan-sdk/1.4.335.0 && \
./vulkansdk --no-deps --maxjobs \
vulkan-loader \
vulkan-validationlayers \
vulkan-extensionlayer \
vulkan-tools \
shaderc )
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/bin/* /usr/bin/
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/lib/* /usr/lib/x86_64-linux-gnu/
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/include/* /usr/include/
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/share/* /usr/share/
rm -rf /opt/vulkan-sdk
fi
if [ "arm64" = "${TARGETARCH:-}" ]; then
mkdir vulkan
( cd vulkan && \
curl -L -o vulkan-sdk.tar.xz https://github.com/mudler/vulkan-sdk-arm/releases/download/1.4.335.0/vulkansdk-ubuntu-24.04-arm-1.4.335.0.tar.xz && \
tar -xvf vulkan-sdk.tar.xz && \
rm vulkan-sdk.tar.xz && \
cd 1.4.335.0 && \
cp -rfv aarch64/bin/* /usr/bin/ && \
cp -rfv aarch64/lib/* /usr/lib/aarch64-linux-gnu/ && \
cp -rfv aarch64/include/* /usr/include/ && \
cp -rfv aarch64/share/* /usr/share/ )
rm -rf vulkan
fi
ldconfig
apt-get clean
rm -rf /var/lib/apt/lists/*
fi
# --- 3. CUDA toolkit (BUILD_TYPE=cublas|l4t) ---
if { [ "${BUILD_TYPE:-}" = "cublas" ] || [ "${BUILD_TYPE:-}" = "l4t" ]; } && [ "${SKIP_DRIVERS:-false}" = "false" ]; then
apt-get update
apt-get install -y --no-install-recommends \
software-properties-common pciutils
if [ "amd64" = "${TARGETARCH:-}" ]; then
curl -O "https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/x86_64/cuda-keyring_1.1-1_all.deb"
fi
if [ "arm64" = "${TARGETARCH:-}" ]; then
if [ "${CUDA_MAJOR_VERSION}" = "13" ]; then
curl -O "https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/sbsa/cuda-keyring_1.1-1_all.deb"
else
curl -O "https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/arm64/cuda-keyring_1.1-1_all.deb"
fi
fi
dpkg -i cuda-keyring_1.1-1_all.deb
rm -f cuda-keyring_1.1-1_all.deb
apt-get update
apt-get install -y --no-install-recommends \
"cuda-nvcc-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}" \
"libcufft-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}" \
"libcurand-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}" \
"libcublas-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}" \
"libcusparse-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}" \
"libcusolver-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}"
if [ "${CUDA_MAJOR_VERSION}" = "13" ] && [ "arm64" = "${TARGETARCH:-}" ]; then
apt-get install -y --no-install-recommends \
"libcufile-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}" \
"libcudnn9-cuda-${CUDA_MAJOR_VERSION}" \
"cuda-cupti-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}" \
"libnvjitlink-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}"
fi
apt-get clean
rm -rf /var/lib/apt/lists/*
fi
# --- 4. cuDSS / NVPL on arm64 + cublas (legacy JetPack / Tegra) ---
# https://github.com/NVIDIA/Isaac-GR00T/issues/343
if [ "${BUILD_TYPE:-}" = "cublas" ] && [ "${TARGETARCH:-}" = "arm64" ]; then
wget "https://developer.download.nvidia.com/compute/cudss/0.6.0/local_installers/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb"
dpkg -i "cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb"
cp /var/cudss-local-tegra-repo-ubuntu"${UBUNTU_VERSION}"-0.6.0/cudss-*-keyring.gpg /usr/share/keyrings/
apt-get update
apt-get -y install cudss "cudss-cuda-${CUDA_MAJOR_VERSION}"
wget "https://developer.download.nvidia.com/compute/nvpl/25.5/local_installers/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb"
dpkg -i "nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb"
cp /var/nvpl-local-repo-ubuntu"${UBUNTU_VERSION}"-25.5/nvpl-*-keyring.gpg /usr/share/keyrings/
apt-get update
apt-get install -y nvpl
fi
# --- 5. clBLAS (BUILD_TYPE=clblas) ---
# Present in variant Dockerfiles' from-source path but not in master's
# Dockerfile.base-grpc-builder. No CI matrix entry currently uses this,
# but keep parity so a future BUILD_TYPE=clblas build doesn't drift.
if [ "${BUILD_TYPE:-}" = "clblas" ] && [ "${SKIP_DRIVERS:-false}" = "false" ]; then
apt-get update
apt-get install -y --no-install-recommends \
libclblast-dev
apt-get clean
rm -rf /var/lib/apt/lists/*
fi
# --- 6. ROCm / HIP build deps (BUILD_TYPE=hipblas) ---
if [ "${BUILD_TYPE:-}" = "hipblas" ] && [ "${SKIP_DRIVERS:-false}" = "false" ]; then
apt-get update
apt-get install -y --no-install-recommends \
hipblas-dev \
hipblaslt-dev \
rocblas-dev
apt-get clean
rm -rf /var/lib/apt/lists/*
# I have no idea why, but the ROCM lib packages don't trigger ldconfig after they install,
# which results in local-ai and others not being able to locate the libraries.
# We run ldconfig ourselves to work around this packaging deficiency.
ldconfig
# Log which GPU architectures have rocBLAS kernel support
echo "rocBLAS library data architectures:"
(ls /opt/rocm*/lib/rocblas/library/Kernels* 2>/dev/null || ls /opt/rocm*/lib64/rocblas/library/Kernels* 2>/dev/null) | grep -oP 'gfx[0-9a-z+-]+' | sort -u || \
echo "WARNING: No rocBLAS kernel data found"
fi
echo "TARGETARCH: ${TARGETARCH:-}"
# --- 7. protoc (always) ---
# The version in 22.04 is too old. We will create one as part of installing
# the GRPC build below but that will also bring in a newer version of absl
# which stablediffusion cannot compile with. This version of protoc is only
# here so that we can generate the grpc code for the stablediffusion build.
if [ "amd64" = "${TARGETARCH:-}" ]; then
curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v27.1/protoc-27.1-linux-x86_64.zip -o protoc.zip
unzip -j -d /usr/local/bin protoc.zip bin/protoc
rm protoc.zip
fi
if [ "arm64" = "${TARGETARCH:-}" ]; then
curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v27.1/protoc-27.1-linux-aarch_64.zip -o protoc.zip
unzip -j -d /usr/local/bin protoc.zip bin/protoc
rm protoc.zip
fi
# --- 8. CMake (apt or compiled from source) ---
# The version in 22.04 is too old. Vulkan path above already pulled cmake
# via apt; the from-source branch here will install over it which is fine.
if [ "${CMAKE_FROM_SOURCE:-false}" = "true" ]; then
curl -L -s "https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}.tar.gz" -o cmake.tar.gz
tar xvf cmake.tar.gz
( cd "cmake-${CMAKE_VERSION}" && ./configure && make && make install )
else
apt-get update
apt-get install -y \
cmake
apt-get clean
rm -rf /var/lib/apt/lists/*
fi
# --- 9. gRPC compile + install at /opt/grpc ---
# We install GRPC to a different prefix here so that we can copy in only
# the build artifacts later — saves several hundred MB on the final docker
# image size vs copying in the entire GRPC source tree and running
# `make install` in the target container.
#
# The TESTONLY abseil sed patch and /opt/grpc prefix are load-bearing —
# downstream Dockerfiles `COPY` /opt/grpc to /usr/local (or rely on the
# prebuilt base having it at /opt/grpc).
mkdir -p /build
cd /build
git clone --recurse-submodules --jobs 4 -b "${GRPC_VERSION}" --depth 1 --shallow-submodules https://github.com/grpc/grpc
mkdir -p /build/grpc/cmake/build
cd /build/grpc/cmake/build
sed -i "216i\\ TESTONLY" "../../third_party/abseil-cpp/absl/container/CMakeLists.txt"
cmake -DgRPC_INSTALL=ON -DgRPC_BUILD_TESTS=OFF -DCMAKE_INSTALL_PREFIX:PATH=/opt/grpc ../..
make
make install
cd /
rm -rf /build

View File

@@ -1,35 +0,0 @@
#!/usr/bin/env bash
# Shared compile logic for backend/Dockerfile.llama-cpp.
# Sourced (via bind mount) from both builder-fromsource and builder-prebuilt stages.
set -euxo pipefail
export CCACHE_DIR=/root/.ccache
ccache --max-size=5G || true
ccache -z || true
export CMAKE_ARGS="${CMAKE_ARGS:-} -DCMAKE_C_COMPILER_LAUNCHER=ccache -DCMAKE_CXX_COMPILER_LAUNCHER=ccache -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache"
if [[ -n "${CUDA_DOCKER_ARCH:-}" ]]; then
CUDA_ARCH_ESC="${CUDA_DOCKER_ARCH//;/\\;}"
export CMAKE_ARGS="${CMAKE_ARGS} -DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCH_ESC}"
echo "CMAKE_ARGS(env) = ${CMAKE_ARGS}"
rm -rf /LocalAI/backend/cpp/llama-cpp-*-build
fi
if [ "${TARGETARCH}" = "arm64" ] || [ "${BUILD_TYPE}" = "hipblas" ]; then
cd /LocalAI/backend/cpp/llama-cpp
make llama-cpp-fallback
make llama-cpp-grpc
make llama-cpp-rpc-server
else
cd /LocalAI/backend/cpp/llama-cpp
make llama-cpp-avx
make llama-cpp-avx2
make llama-cpp-avx512
make llama-cpp-fallback
make llama-cpp-grpc
make llama-cpp-rpc-server
fi
ccache -s || true

View File

@@ -1,35 +0,0 @@
#!/usr/bin/env bash
# Shared compile logic for backend/Dockerfile.turboquant.
# Sourced (via bind mount) from both builder-fromsource and builder-prebuilt stages.
set -euxo pipefail
export CCACHE_DIR=/root/.ccache
ccache --max-size=5G || true
ccache -z || true
export CMAKE_ARGS="${CMAKE_ARGS:-} -DCMAKE_C_COMPILER_LAUNCHER=ccache -DCMAKE_CXX_COMPILER_LAUNCHER=ccache -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache"
if [[ -n "${CUDA_DOCKER_ARCH:-}" ]]; then
CUDA_ARCH_ESC="${CUDA_DOCKER_ARCH//;/\\;}"
export CMAKE_ARGS="${CMAKE_ARGS} -DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCH_ESC}"
echo "CMAKE_ARGS(env) = ${CMAKE_ARGS}"
rm -rf /LocalAI/backend/cpp/turboquant-*-build
fi
cd /LocalAI/backend/cpp/turboquant
if [ "${TARGETARCH}" = "arm64" ] || [ "${BUILD_TYPE}" = "hipblas" ]; then
make turboquant-fallback
make turboquant-grpc
make turboquant-rpc-server
else
make turboquant-avx
make turboquant-avx2
make turboquant-avx512
make turboquant-fallback
make turboquant-grpc
make turboquant-rpc-server
fi
ccache -s || true

View File

@@ -1,100 +0,0 @@
name: 'Configure apt mirror'
description: |
Reconfigure the GitHub Actions runner's Ubuntu apt sources to use an
alternate mirror, and emit the effective URLs as outputs so callers can
forward them as Docker build-args.
Two mirror profiles depending on where the runner lives, because the
best mirror differs by network:
* github-hosted runners run on Azure, so they default to the
Azure-hosted Ubuntu mirror (lowest latency, same VPC).
* self-hosted runners (arc-runner-set, bigger-runner, ...) typically
cannot route to azure.archive.ubuntu.com, so they default to the
kernel.org mirror, which is publicly reachable from anywhere.
Pass an empty string to either input to skip the rewrite for that
profile and keep upstream archive.ubuntu.com / ports.ubuntu.com.
inputs:
github-hosted-mirror:
description: 'archive/security mirror URL for github-hosted runners (empty = upstream)'
required: false
default: 'http://azure.archive.ubuntu.com'
github-hosted-ports-mirror:
description: 'ports.ubuntu.com mirror URL for github-hosted runners (empty = upstream)'
required: false
default: 'http://azure.ports.ubuntu.com'
self-hosted-mirror:
description: 'archive/security mirror URL for self-hosted runners (empty = upstream)'
required: false
# HTTP, not HTTPS: the bare ubuntu:24.04 builder image doesn't ship
# ca-certificates, so the very first apt-get update over TLS would
# fail with "No system certificates available" before it can install
# anything. apt validates package integrity via GPG signatures, so
# plain HTTP is safe for the archive itself.
default: 'http://mirrors.edge.kernel.org'
self-hosted-ports-mirror:
description: 'ports.ubuntu.com mirror URL for self-hosted runners (empty = upstream)'
required: false
# mirrors.edge.kernel.org does NOT carry /ubuntu-ports/ — only the
# main /ubuntu/ archive — so arm64 builds 404 there. Leave ports
# upstream by default. The original DDoS was on archive.ubuntu.com
# so ports.ubuntu.com remains the path of least surprise.
default: ''
outputs:
effective-mirror:
description: 'The mirror URL actually applied for this runner (or empty)'
value: ${{ steps.pick.outputs.mirror }}
effective-ports-mirror:
description: 'The ports mirror URL actually applied for this runner (or empty)'
value: ${{ steps.pick.outputs.ports-mirror }}
runs:
using: 'composite'
steps:
- name: Pick effective mirror for this runner
id: pick
shell: bash
env:
RUNNER_ENV: ${{ runner.environment }}
GH_MIRROR: ${{ inputs.github-hosted-mirror }}
GH_PORTS_MIRROR: ${{ inputs.github-hosted-ports-mirror }}
SH_MIRROR: ${{ inputs.self-hosted-mirror }}
SH_PORTS_MIRROR: ${{ inputs.self-hosted-ports-mirror }}
run: |
if [ "${RUNNER_ENV}" = "github-hosted" ]; then
MIRROR="${GH_MIRROR}"
PORTS_MIRROR="${GH_PORTS_MIRROR}"
else
MIRROR="${SH_MIRROR}"
PORTS_MIRROR="${SH_PORTS_MIRROR}"
fi
echo "configure-apt-mirror: runner=${RUNNER_ENV} mirror='${MIRROR}' ports-mirror='${PORTS_MIRROR}'"
echo "mirror=${MIRROR}" >> "$GITHUB_OUTPUT"
echo "ports-mirror=${PORTS_MIRROR}" >> "$GITHUB_OUTPUT"
- name: Rewrite apt sources
if: steps.pick.outputs.mirror != '' || steps.pick.outputs.ports-mirror != ''
shell: bash
env:
APT_MIRROR: ${{ steps.pick.outputs.mirror }}
APT_PORTS_MIRROR: ${{ steps.pick.outputs.ports-mirror }}
run: |
set -e
# Ubuntu 24.04 (noble) ships DEB822 sources at
# /etc/apt/sources.list.d/ubuntu.sources; older releases use
# /etc/apt/sources.list. Rewrite whichever exists.
for f in /etc/apt/sources.list.d/ubuntu.sources /etc/apt/sources.list; do
sudo test -f "$f" || continue
if [ -n "${APT_MIRROR}" ]; then
# Comma delimiter so the alternation pipe in the regex is not
# interpreted as the s/// separator.
sudo sed -i -E "s,https?://(archive\.ubuntu\.com|security\.ubuntu\.com),${APT_MIRROR},g" "$f"
fi
if [ -n "${APT_PORTS_MIRROR}" ]; then
sudo sed -i -E "s,https?://ports\.ubuntu\.com,${APT_PORTS_MIRROR},g" "$f"
fi
done
echo "Runner apt mirror configured (APT_MIRROR='${APT_MIRROR}', APT_PORTS_MIRROR='${APT_PORTS_MIRROR}')"

View File

@@ -1,65 +0,0 @@
name: 'Free disk space on hosted runners'
description: |
Aggressively clean GitHub-hosted ubuntu-latest runners to reclaim ~6-10 GB
of working space before docker buildx steps. Combines jlumbroso/free-disk-space
with explicit apt purges of large packages we never use (dotnet, ghc, mono,
android, jdk, ...).
No-op on self-hosted runners; pass mode=skip to force-disable.
inputs:
mode:
description: 'hosted (default — clean) or skip (no-op)'
required: false
default: 'hosted'
runs:
using: 'composite'
steps:
- name: Free Disk Space (Ubuntu)
if: inputs.mode == 'hosted' && runner.environment == 'github-hosted'
uses: jlumbroso/free-disk-space@main
with:
tool-cache: true
android: true
dotnet: true
haskell: true
large-packages: true
docker-images: true
swap-storage: true
- name: Release space from worker
if: inputs.mode == 'hosted' && runner.environment == 'github-hosted'
shell: bash
run: |
echo "Listing top largest packages"
pkgs=$(dpkg-query -Wf '${Installed-Size}\t${Package}\t${Status}\n' | awk '$NF == "installed"{print $1 "\t" $2}' | sort -nr)
head -n 30 <<< "${pkgs}"
df -h
sudo apt-get remove -y '^llvm-.*|^libllvm.*' || true
sudo apt-get remove --auto-remove android-sdk-platform-tools snapd || true
sudo apt-get purge --auto-remove android-sdk-platform-tools snapd || true
sudo rm -rf /usr/local/lib/android
sudo apt-get remove -y '^dotnet-.*|^aspnetcore-.*' || true
sudo rm -rf /usr/share/dotnet
sudo apt-get remove -y '^mono-.*' || true
sudo apt-get remove -y '^ghc-.*' || true
sudo apt-get remove -y '.*jdk.*|.*jre.*' || true
sudo apt-get remove -y 'php.*' || true
sudo apt-get remove -y hhvm powershell firefox monodoc-manual msbuild || true
sudo apt-get remove -y '^google-.*' || true
sudo apt-get remove -y azure-cli || true
sudo apt-get remove -y '^mongo.*-.*|^postgresql-.*|^mysql-.*|^mssql-.*' || true
sudo apt-get remove -y '^gfortran-.*' || true
sudo apt-get remove -y microsoft-edge-stable || true
sudo apt-get remove -y firefox || true
sudo apt-get remove -y powershell || true
sudo apt-get remove -y r-base-core || true
sudo apt-get autoremove -y
sudo apt-get clean
sudo rm -rfv build || true
sudo rm -rf /usr/share/dotnet || true
sudo rm -rf /opt/ghc || true
sudo rm -rf "/usr/local/share/boost" || true
sudo rm -rf "$AGENT_TOOLSDIRECTORY" || true
df -h

View File

@@ -1,59 +0,0 @@
name: 'Set up build disk on hosted runners'
description: |
Relocate Docker's data-root to /mnt (which has ~75 GB free, vs ~20 GB
on / after free-disk-space). Combined with the apt cleanup, gives
~100 GB working space for buildx — enough for ROCm dev image + vLLM
torch install + flash-attn build.
No-op on:
- self-hosted runners (no /mnt expectation)
- non-X64 runners (verify /mnt shape on ubuntu-24.04-arm separately
before enabling there — see Task 3.2 in the migration plan)
- mode=skip (force-disable from caller)
Must run after free-disk-space (which removes large packages — would
fail mid-uninstall if Docker were stopped) and before any Docker
operation (setup-qemu, setup-buildx, login, build) so the relocated
data-root catches all subsequent docker activity.
inputs:
mode:
description: 'auto (default — relocate on hosted X64 only) or skip'
required: false
default: 'auto'
runs:
using: 'composite'
steps:
- name: Relocate Docker data-root to /mnt
if: inputs.mode == 'auto' && runner.environment == 'github-hosted' && runner.arch == 'X64'
shell: bash
run: |
set -euo pipefail
echo "Before relocation:"
df -h / /mnt || true
sudo systemctl stop docker docker.socket
sudo mkdir -p /mnt/docker-data /mnt/docker-tmp
# buildx CLI runs as the unprivileged runner user and creates
# config dirs under TMPDIR before binding them into the buildkit
# container. /mnt is owned by root by default; mirror /tmp's
# 1777 (world-writable + sticky) so non-root processes can write.
sudo chmod 1777 /mnt/docker-tmp
if [ -d /var/lib/docker ] && [ ! -L /var/lib/docker ]; then
sudo rsync -a /var/lib/docker/ /mnt/docker-data/
sudo rm -rf /var/lib/docker
sudo ln -s /mnt/docker-data /var/lib/docker
fi
# daemon.json may not exist; merge data-root in or create minimal.
if [ -f /etc/docker/daemon.json ]; then
sudo jq '."data-root" = "/mnt/docker-data"' /etc/docker/daemon.json | sudo tee /etc/docker/daemon.json.new >/dev/null
sudo mv /etc/docker/daemon.json.new /etc/docker/daemon.json
else
echo '{"data-root":"/mnt/docker-data"}' | sudo tee /etc/docker/daemon.json
fi
sudo systemctl start docker
# Make TMPDIR persist for subsequent steps in the same job.
echo "TMPDIR=/mnt/docker-tmp" >> "$GITHUB_ENV"
echo "After relocation:"
df -h / /mnt
docker info | grep -i 'docker root dir' || true

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@@ -1,45 +0,0 @@
#!/bin/bash
# Bump the cublas13 vLLM wheel pin in requirements-cublas13-after.txt.
#
# vLLM's PyPI wheel is built against CUDA 12 so the cublas13 build pulls a
# cu130-flavoured wheel from vLLM's per-tag index at
# https://wheels.vllm.ai/<TAG>/cu130/. That URL segment is itself version-locked
# (no /latest/ alias upstream), so bumping vLLM means rewriting both the URL
# segment and the version constraint atomically. bump_deps.sh handles git-sha
# vars in Makefiles; this script handles the two-value rewrite specific to the
# vLLM requirements file.
set -xe
REPO=$1 # vllm-project/vllm
FILE=$2 # backend/python/vllm/requirements-cublas13-after.txt
VAR=$3 # VLLM_VERSION (used for output file names so the workflow can read them)
if [ -z "$FILE" ] || [ -z "$REPO" ] || [ -z "$VAR" ]; then
echo "usage: $0 <repo> <requirements-file> <var-name>" >&2
exit 1
fi
# /releases/latest returns the most recent non-prerelease tag.
LATEST_TAG=$(curl -sS -H "Accept: application/vnd.github+json" \
"https://api.github.com/repos/$REPO/releases/latest" \
| python3 -c "import json,sys; print(json.load(sys.stdin)['tag_name'])")
# Strip leading 'v' (vLLM tags are 'v0.20.0', the URL/version use '0.20.0').
NEW_VERSION="${LATEST_TAG#v}"
set +e
CURRENT_VERSION=$(grep -oE '^vllm==[0-9]+\.[0-9]+\.[0-9]+' "$FILE" | head -1 | cut -d= -f3)
set -e
# sed both lines unconditionally — peter-evans/create-pull-request opens no PR
# when the working tree is clean, so a no-op rewrite is safe.
sed -i "$FILE" \
-e "s|wheels\.vllm\.ai/[^/]*/cu130|wheels.vllm.ai/$NEW_VERSION/cu130|g" \
-e "s|^vllm==.*|vllm==$NEW_VERSION|"
if [ -z "$CURRENT_VERSION" ]; then
echo "Could not find vllm==X.Y.Z in $FILE."
exit 0
fi
echo "Changes: https://github.com/$REPO/compare/v${CURRENT_VERSION}...${LATEST_TAG}" >> "${VAR}_message.txt"
echo "${NEW_VERSION}" >> "${VAR}_commit.txt"

View File

@@ -1,46 +0,0 @@
#!/usr/bin/env bash
# Anchor a backend per-arch digest in quay.io/go-skynet/ci-cache so quay's
# garbage collector won't reap the manifest before backend_merge.yml runs.
#
# Context: backend_build.yml pushes by canonical digest only
# (push-by-digest=true). Unreferenced manifests on quay can be reaped within
# ~1-2h, but backend-merge-jobs runs only after the *entire* per-arch build
# matrix drains (max-parallel: 8 × dozens of entries → ~2h+). Without an
# anchoring tag, the earliest digests are gone by the time `imagetools create`
# tries to read them, producing "manifest not found" merge failures.
#
# We tag the digest under our internal ci-cache image; quay does not GC tagged
# manifests. The user-facing manifest list still references the original
# digest in local-ai-backends. backend_merge.yml deletes the anchor tag after
# the user-facing manifest is published — see cleanup-keepalive-tags.sh.
#
# Required env:
# GITHUB_RUN_ID - current workflow run id (set automatically by GHA)
# TAG_SUFFIX - matrix entry's tag-suffix (e.g. -gpu-nvidia-cuda-12-vllm)
# PLATFORM_TAG - amd64 / arm64 / single (single = singleton matrix entry)
# DIGEST - canonical content digest from build step (sha256:...)
#
# Optional env:
# ANCHOR_IMAGE - target image (default: quay.io/go-skynet/ci-cache)
# SOURCE_IMAGE - source image (default: quay.io/go-skynet/local-ai-backends)
# GITHUB_STEP_SUMMARY - if set, an anchored-by line is appended to it
set -euo pipefail
: "${GITHUB_RUN_ID:?}"
: "${TAG_SUFFIX:?}"
: "${PLATFORM_TAG:?}"
: "${DIGEST:?}"
anchor_image="${ANCHOR_IMAGE:-quay.io/go-skynet/ci-cache}"
source_image="${SOURCE_IMAGE:-quay.io/go-skynet/local-ai-backends}"
tag="keepalive-${GITHUB_RUN_ID}${TAG_SUFFIX}-${PLATFORM_TAG}"
docker buildx imagetools create \
-t "${anchor_image}:${tag}" \
"${source_image}@${DIGEST}"
echo "anchored ${DIGEST} as ${anchor_image}:${tag}"
if [[ -n "${GITHUB_STEP_SUMMARY:-}" ]]; then
echo "anchored \`${DIGEST}\` as \`${anchor_image}:${tag}\`" >> "${GITHUB_STEP_SUMMARY}"
fi

View File

@@ -1,49 +0,0 @@
#!/usr/bin/env bash
# Best-effort cleanup of the keepalive anchor tags written by
# anchor-digest-in-cache.sh. Called from backend_merge.yml after the
# user-facing manifest list has been published.
#
# Quay's docker registry v2 doesn't allow tag deletes — only digest deletes.
# The proper delete is the quay REST API, which requires an OAuth-scoped
# token. We try QUAY_TOKEN as a bearer token: if the secret is an OAuth app
# token (typical for service accounts) the delete succeeds; otherwise this
# is a soft no-op and the tag persists until manually pruned.
#
# Cleanup failure MUST NOT fail the merge — the merge has already produced
# the user-facing manifest list at this point and the keepalive tags are
# pure overhead. We always exit 0.
#
# Required env:
# GITHUB_RUN_ID - current workflow run id (set automatically by GHA)
# TAG_SUFFIX - matrix entry's tag-suffix (e.g. -gpu-nvidia-cuda-12-vllm)
# QUAY_TOKEN - bearer token for quay's REST API
#
# Optional env:
# QUAY_REPO - target repo (default: go-skynet/ci-cache)
# PLATFORM_TAGS - space-separated list of platform-tag values to try
# (default: "amd64 arm64 single")
# We don't know which platform-tag(s) exist for this
# tag-suffix without an extra API call, so we just try
# all three and ignore 404s for the ones that don't.
set -uo pipefail
: "${GITHUB_RUN_ID:?}"
: "${TAG_SUFFIX:?}"
: "${QUAY_TOKEN:?}"
quay_repo="${QUAY_REPO:-go-skynet/ci-cache}"
platform_tags="${PLATFORM_TAGS:-amd64 arm64 single}"
for plat in $platform_tags; do
tag="keepalive-${GITHUB_RUN_ID}${TAG_SUFFIX}-${plat}"
url="https://quay.io/api/v1/repository/${quay_repo}/tag/${tag}"
http=$(curl -sS -o /dev/null -w '%{http_code}' \
-X DELETE -H "Authorization: Bearer ${QUAY_TOKEN}" "$url" || echo "000")
case "$http" in
204|200) echo "deleted $tag" ;;
404) echo "not present: $tag" ;;
401|403) echo "auth not OAuth-scoped (http $http) for $tag - skipping; orphan tag will persist" ;;
*) echo "unexpected http $http deleting $tag - skipping" ;;
esac
done
exit 0

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View File

@@ -24,17 +24,6 @@ on:
description: 'Platforms'
default: ''
type: string
platform-tag:
description: |
Short tag identifying the platform leg, e.g. "amd64" or "arm64".
Used to scope the per-arch registry cache and the digest artifact name.
Required for split-and-merge multi-arch builds; pass "amd64" for
single-arch amd64 builds too. Optional (default '') during the
migration to per-arch matrix expansion; will be flipped to
required: true in Phase 6 once all callers pass an explicit value.
required: false
default: ''
type: string
tag-latest:
description: 'Tag latest'
default: ''
@@ -69,20 +58,6 @@ on:
required: false
default: '2204'
type: string
amdgpu-targets:
description: 'AMD GPU targets for ROCm/HIP builds'
required: false
default: ''
type: string
builder-base-image:
description: |
Pre-built builder base image (e.g. quay.io/go-skynet/ci-cache:base-grpc-cuda-13-amd64).
When set, the variant Dockerfile uses its `builder-prebuilt` stage which FROMs this
image directly instead of running its own gRPC stage + apt installs. Empty for
backends whose Dockerfile doesn't support a prebuilt base.
required: false
default: ''
type: string
secrets:
dockerUsername:
required: false
@@ -100,22 +75,76 @@ jobs:
quay_username: ${{ secrets.quayUsername }}
steps:
- name: Free Disk Space (Ubuntu)
if: inputs.runs-on == 'ubuntu-latest'
uses: jlumbroso/free-disk-space@main
with:
# this might remove tools that are actually needed,
# if set to "true" but frees about 6 GB
tool-cache: true
# all of these default to true, but feel free to set to
# "false" if necessary for your workflow
android: true
dotnet: true
haskell: true
large-packages: true
docker-images: true
swap-storage: true
- name: Force Install GIT latest
run: |
sudo apt-get update \
&& sudo apt-get install -y software-properties-common \
&& sudo apt-get update \
&& sudo add-apt-repository -y ppa:git-core/ppa \
&& sudo apt-get update \
&& sudo apt-get install -y git
- name: Checkout
uses: actions/checkout@v6
with:
submodules: true
- name: Configure apt mirror on runner
id: apt_mirror
uses: ./.github/actions/configure-apt-mirror
- name: Free disk space
uses: ./.github/actions/free-disk-space
with:
mode: ${{ inputs.runs-on == 'ubuntu-latest' && 'hosted' || 'skip' }}
- name: Set up build disk
uses: ./.github/actions/setup-build-disk
- name: Release space from worker
if: inputs.runs-on == 'ubuntu-latest'
run: |
echo "Listing top largest packages"
pkgs=$(dpkg-query -Wf '${Installed-Size}\t${Package}\t${Status}\n' | awk '$NF == "installed"{print $1 "\t" $2}' | sort -nr)
head -n 30 <<< "${pkgs}"
echo
df -h
echo
sudo apt-get remove -y '^llvm-.*|^libllvm.*' || true
sudo apt-get remove --auto-remove android-sdk-platform-tools snapd || true
sudo apt-get purge --auto-remove android-sdk-platform-tools snapd || true
sudo rm -rf /usr/local/lib/android
sudo apt-get remove -y '^dotnet-.*|^aspnetcore-.*' || true
sudo rm -rf /usr/share/dotnet
sudo apt-get remove -y '^mono-.*' || true
sudo apt-get remove -y '^ghc-.*' || true
sudo apt-get remove -y '.*jdk.*|.*jre.*' || true
sudo apt-get remove -y 'php.*' || true
sudo apt-get remove -y hhvm powershell firefox monodoc-manual msbuild || true
sudo apt-get remove -y '^google-.*' || true
sudo apt-get remove -y azure-cli || true
sudo apt-get remove -y '^mongo.*-.*|^postgresql-.*|^mysql-.*|^mssql-.*' || true
sudo apt-get remove -y '^gfortran-.*' || true
sudo apt-get remove -y microsoft-edge-stable || true
sudo apt-get remove -y firefox || true
sudo apt-get remove -y powershell || true
sudo apt-get remove -y r-base-core || true
sudo apt-get autoremove -y
sudo apt-get clean
echo
echo "Listing top largest packages"
pkgs=$(dpkg-query -Wf '${Installed-Size}\t${Package}\t${Status}\n' | awk '$NF == "installed"{print $1 "\t" $2}' | sort -nr)
head -n 30 <<< "${pkgs}"
echo
sudo rm -rfv build || true
sudo rm -rf /usr/share/dotnet || true
sudo rm -rf /opt/ghc || true
sudo rm -rf "/usr/local/share/boost" || true
sudo rm -rf "$AGENT_TOOLSDIRECTORY" || true
df -h
- name: Docker meta
id: meta
@@ -172,17 +201,7 @@ jobs:
username: ${{ secrets.quayUsername }}
password: ${{ secrets.quayPassword }}
# Weekly cache-buster for the per-backend `make` step. Most Python
# backends list unpinned deps (torch, transformers, vllm, ...), so a
# warm cache freezes upstream versions indefinitely. Rolling this
# weekly forces a re-resolve of the install layer at most once per
# week, picking up newer wheels without a full cold rebuild.
- name: Compute deps refresh key
id: deps_refresh
run: echo "key=$(date -u +%Y-W%V)" >> "$GITHUB_OUTPUT"
- name: Build and push by digest
id: build
- name: Build and push
uses: docker/build-push-action@v7
if: github.event_name != 'pull_request'
with:
@@ -195,66 +214,15 @@ jobs:
BASE_IMAGE=${{ inputs.base-image }}
BACKEND=${{ inputs.backend }}
UBUNTU_VERSION=${{ inputs.ubuntu-version }}
AMDGPU_TARGETS=${{ inputs.amdgpu-targets }}
APT_MIRROR=${{ steps.apt_mirror.outputs.effective-mirror }}
APT_PORTS_MIRROR=${{ steps.apt_mirror.outputs.effective-ports-mirror }}
DEPS_REFRESH=${{ steps.deps_refresh.outputs.key }}
BUILDER_BASE_IMAGE=${{ inputs.builder-base-image }}
BUILDER_TARGET=${{ inputs.builder-base-image != '' && 'builder-prebuilt' || 'builder-fromsource' }}
context: ${{ inputs.context }}
file: ${{ inputs.dockerfile }}
cache-from: type=registry,ref=quay.io/go-skynet/ci-cache:cache${{ inputs.tag-suffix }}-${{ inputs.platform-tag }}
cache-to: type=registry,ref=quay.io/go-skynet/ci-cache:cache${{ inputs.tag-suffix }}-${{ inputs.platform-tag }},mode=max,ignore-error=true
cache-from: type=gha
platforms: ${{ inputs.platforms }}
outputs: |
type=image,name=quay.io/go-skynet/local-ai-backends,push-by-digest=true,name-canonical=true,push=true
type=image,name=localai/localai-backends,push-by-digest=true,name-canonical=true,push=true
# Disable provenance: with mode=max (the default for push:true)
# buildx bundles a per-registry attestation manifest into each
# registry's manifest list, which makes the resulting list digest
# diverge across registries. steps.build.outputs.digest then
# only matches one of them, and the merge job's
# `imagetools create <reg>@sha256:<digest>` lookup fails on the
# other. Disabling provenance keeps the digest content-only and
# identical across both registries — required for digest-based
# cross-registry merge.
provenance: false
push: ${{ github.event_name != 'pull_request' }}
tags: ${{ steps.meta.outputs.tags }}
labels: ${{ steps.meta.outputs.labels }}
- name: Export digest
if: github.event_name != 'pull_request'
run: |
mkdir -p /tmp/digests
digest="${{ steps.build.outputs.digest }}"
touch "/tmp/digests/${digest#sha256:}"
# See .github/scripts/anchor-digest-in-cache.sh for why this is needed
# and how it interacts with backend_merge.yml's cleanup step.
- name: Anchor digest in ci-cache so quay GC won't reap before merge
if: github.event_name != 'pull_request'
env:
TAG_SUFFIX: ${{ inputs.tag-suffix }}
PLATFORM_TAG: ${{ inputs.platform-tag || 'single' }}
DIGEST: ${{ steps.build.outputs.digest }}
run: .github/scripts/anchor-digest-in-cache.sh
# Artifact name uses a `--` separator between tag-suffix and platform-tag
# to avoid prefix collisions during the merge job's pattern-based download.
# Tag-suffixes are not prefix-disjoint (e.g. -gpu-nvidia-cuda-12-vllm is a
# prefix of -gpu-nvidia-cuda-12-vllm-omni); a single `-` separator plus the
# merge-side `digests<tag-suffix>-*` glob would let one merge over-match
# the other backend's artifacts. The `-single` placeholder for empty
# platform-tag (single-arch entries) keeps the artifact name non-trailing.
- name: Upload digest artifact
if: github.event_name != 'pull_request'
uses: actions/upload-artifact@v7
with:
name: digests${{ inputs.tag-suffix }}--${{ inputs.platform-tag || 'single' }}
path: /tmp/digests/*
if-no-files-found: error
retention-days: 1
- name: Build (PR)
- name: Build and push (PR)
uses: docker/build-push-action@v7
if: github.event_name == 'pull_request'
with:
@@ -267,15 +235,9 @@ jobs:
BASE_IMAGE=${{ inputs.base-image }}
BACKEND=${{ inputs.backend }}
UBUNTU_VERSION=${{ inputs.ubuntu-version }}
AMDGPU_TARGETS=${{ inputs.amdgpu-targets }}
APT_MIRROR=${{ steps.apt_mirror.outputs.effective-mirror }}
APT_PORTS_MIRROR=${{ steps.apt_mirror.outputs.effective-ports-mirror }}
DEPS_REFRESH=${{ steps.deps_refresh.outputs.key }}
BUILDER_BASE_IMAGE=${{ inputs.builder-base-image }}
BUILDER_TARGET=${{ inputs.builder-base-image != '' && 'builder-prebuilt' || 'builder-fromsource' }}
context: ${{ inputs.context }}
file: ${{ inputs.dockerfile }}
cache-from: type=registry,ref=quay.io/go-skynet/ci-cache:cache${{ inputs.tag-suffix }}-${{ inputs.platform-tag }}
cache-from: type=gha
platforms: ${{ inputs.platforms }}
push: ${{ env.quay_username != '' }}
tags: ${{ steps.meta_pull_request.outputs.tags }}

View File

@@ -48,13 +48,6 @@ jobs:
strategy:
matrix:
go-version: ['${{ inputs.go-version }}']
env:
# Keep the brew Cellar stable across cache restores. Without these,
# `brew install` would auto-update brew itself and re-link formulas,
# mutating the very paths the cache just restored.
HOMEBREW_NO_AUTO_UPDATE: '1'
HOMEBREW_NO_INSTALL_CLEANUP: '1'
HOMEBREW_NO_ANALYTICS: '1'
steps:
- name: Clone
uses: actions/checkout@v6
@@ -65,190 +58,21 @@ jobs:
uses: actions/setup-go@v5
with:
go-version: ${{ matrix.go-version }}
# Caches ~/go/pkg/mod and ~/Library/Caches/go-build keyed on go.sum.
# Shared across every darwin matrix entry — first job in a run warms
# it, the rest hit warm.
cache: true
cache: false
# You can test your matrix by printing the current Go version
- name: Display Go version
run: go version
# ---- Homebrew cache ----
# macOS runners have no Docker daemon, so the BuildKit registry cache used
# for Linux backend images (see .agents/ci-caching.md) doesn't apply here.
# We cache the brew downloads + Cellar entries for the formulas we install
# below. Read on every run, write only on master/tag pushes — same policy
# as the Linux registry cache.
- name: Restore Homebrew cache
id: brew-cache
uses: actions/cache/restore@v4
with:
path: |
~/Library/Caches/Homebrew/downloads
/opt/homebrew/Cellar/protobuf
/opt/homebrew/Cellar/grpc
/opt/homebrew/Cellar/protoc-gen-go
/opt/homebrew/Cellar/protoc-gen-go-grpc
/opt/homebrew/Cellar/libomp
/opt/homebrew/Cellar/llvm
/opt/homebrew/Cellar/ccache
/opt/homebrew/Cellar/blake3
/opt/homebrew/Cellar/fmt
/opt/homebrew/Cellar/hiredis
/opt/homebrew/Cellar/xxhash
/opt/homebrew/Cellar/zstd
key: brew-${{ runner.os }}-${{ runner.arch }}-v1-${{ hashFiles('.github/workflows/backend_build_darwin.yml') }}
- name: Dependencies
run: |
# ccache is always installed (used by the llama-cpp variant build) so
# the brew cache content stays stable across every backend in the
# matrix — they all share one cache key.
# blake3, fmt, hiredis, xxhash, zstd are ccache's runtime dylib deps.
# Without explicitly installing them, a brew cache-hit run restores
# ccache's Cellar dir but skips installing those transitive deps,
# and ccache fails at runtime with `dyld: Library not loaded`.
brew install protobuf grpc make protoc-gen-go protoc-gen-go-grpc libomp llvm ccache blake3 fmt hiredis xxhash zstd
# Force-reinstall ccache so brew re-validates its full runtime-dep
# closure on every run. This is the durable fix: when the upstream
# ccache formula gains a new transitive dep (as it has multiple times
# already), we don't have to chase missing dylibs one at a time.
# The downloads cache makes the reinstall fast (~5s on a hit).
brew reinstall ccache
# Same pattern for grpc: its CMake config (used by the llama-cpp
# `grpc-server` target) does find_package(absl). The cache restores
# /opt/homebrew/Cellar/grpc so brew above no-ops the install, but
# abseil isn't in our Cellar cache list and never gets installed
# alongside, leaving grpc's CMake unable to resolve it. Reinstalling
# grpc re-validates and pulls abseil in, mirroring the ccache fix.
brew reinstall grpc
# The brew cache restores the Cellar dirs but NOT the bin symlinks
# at /opt/homebrew/bin/*. brew install above sees the Cellar present
# and decides "already installed" without re-linking, so on a cache-
# hit run the formulas aren't on PATH. Force-link them; --overwrite
# tolerates pre-existing symlinks from earlier installs.
brew link --overwrite protobuf grpc make protoc-gen-go protoc-gen-go-grpc libomp llvm ccache blake3 fmt hiredis xxhash zstd 2>/dev/null || true
- name: Save Homebrew cache
if: github.event_name != 'pull_request' && steps.brew-cache.outputs.cache-hit != 'true'
uses: actions/cache/save@v4
with:
path: |
~/Library/Caches/Homebrew/downloads
/opt/homebrew/Cellar/protobuf
/opt/homebrew/Cellar/grpc
/opt/homebrew/Cellar/protoc-gen-go
/opt/homebrew/Cellar/protoc-gen-go-grpc
/opt/homebrew/Cellar/libomp
/opt/homebrew/Cellar/llvm
/opt/homebrew/Cellar/ccache
/opt/homebrew/Cellar/blake3
/opt/homebrew/Cellar/fmt
/opt/homebrew/Cellar/hiredis
/opt/homebrew/Cellar/xxhash
/opt/homebrew/Cellar/zstd
key: brew-${{ runner.os }}-${{ runner.arch }}-v1-${{ hashFiles('.github/workflows/backend_build_darwin.yml') }}
# ---- ccache for llama.cpp CMake builds ----
# Three CMake variants (fallback, grpc, rpc-server) compile the same
# llama.cpp source tree with overlapping flags — ccache dedupes object
# files across them. Key on the pinned LLAMA_VERSION so a pin bump
# invalidates cleanly; restore-keys fall back to the latest entry for the
# same pin so unchanged TUs stay warm even when the cache is fresh.
- name: Compute llama.cpp version
if: inputs.backend == 'llama-cpp'
id: llama-version
run: |
version=$(grep '^LLAMA_VERSION' backend/cpp/llama-cpp/Makefile | head -1 | cut -d= -f2 | cut -d'?' -f1 | tr -d ' ')
echo "version=${version}" >> "$GITHUB_OUTPUT"
- name: Restore ccache
if: inputs.backend == 'llama-cpp'
id: ccache-cache
uses: actions/cache/restore@v4
with:
path: ~/Library/Caches/ccache
key: ccache-llama-${{ runner.arch }}-${{ steps.llama-version.outputs.version }}-${{ github.run_id }}
restore-keys: |
ccache-llama-${{ runner.arch }}-${{ steps.llama-version.outputs.version }}-
- name: Configure ccache
if: inputs.backend == 'llama-cpp'
run: |
mkdir -p "$HOME/Library/Caches/ccache"
ccache -M 2G
ccache -z
# llama-cpp-darwin.sh reads CMAKE_ARGS / CCACHE_DIR from env.
{
echo "CMAKE_ARGS=${CMAKE_ARGS:-} -DCMAKE_C_COMPILER_LAUNCHER=ccache -DCMAKE_CXX_COMPILER_LAUNCHER=ccache"
echo "CCACHE_DIR=$HOME/Library/Caches/ccache"
} >> "$GITHUB_ENV"
# ---- Python wheel cache (uv + pip) ----
# Mirrors the Linux DEPS_REFRESH cadence (see .agents/ci-caching.md): the
# ISO-week segment of the cache key forces at most one cold rebuild per
# backend per week, automatically picking up newer wheels for unpinned
# deps (torch, mlx, diffusers, …). Restore-keys fall back to the most
# recent build of the same backend so off-week PRs still hit warm.
- name: Compute weekly cache bucket
if: inputs.lang == 'python'
id: weekly
run: echo "bucket=$(date -u +%Y-W%V)" >> "$GITHUB_OUTPUT"
- name: Restore Python wheel cache
if: inputs.lang == 'python'
id: pyenv-cache
uses: actions/cache/restore@v4
with:
path: |
~/Library/Caches/pip
~/Library/Caches/uv
key: pyenv-darwin-${{ inputs.backend }}-${{ steps.weekly.outputs.bucket }}-${{ hashFiles(format('backend/python/{0}/requirements*.txt', inputs.backend)) }}
restore-keys: |
pyenv-darwin-${{ inputs.backend }}-
# llama-cpp on Darwin uses a bespoke build script (scripts/build/llama-cpp-darwin.sh)
# that compiles three CMake variants from backend/cpp/llama-cpp and bundles dylibs
# via otool — it doesn't fit the build-darwin-go-backend / build-darwin-python-backend
# mold. Drive it via its dedicated `backends/llama-cpp-darwin` make target instead.
- name: Build ${{ inputs.backend }}-darwin (llama-cpp)
if: inputs.backend == 'llama-cpp'
run: |
make protogen-go
make backends/llama-cpp-darwin
- name: Build ds4 backend (Darwin Metal)
if: inputs.backend == 'ds4'
run: |
make backends/ds4-darwin
brew install protobuf grpc make protoc-gen-go protoc-gen-go-grpc libomp llvm
- name: Build ${{ inputs.backend }}-darwin
if: inputs.backend != 'llama-cpp' && inputs.backend != 'ds4'
run: |
make protogen-go
BACKEND=${{ inputs.backend }} BUILD_TYPE=${{ inputs.build-type }} USE_PIP=${{ inputs.use-pip }} make build-darwin-${{ inputs.lang }}-backend
- name: ccache stats
if: inputs.backend == 'llama-cpp'
run: ccache -s
- name: Save ccache
if: inputs.backend == 'llama-cpp' && github.event_name != 'pull_request'
uses: actions/cache/save@v4
with:
path: ~/Library/Caches/ccache
key: ccache-llama-${{ runner.arch }}-${{ steps.llama-version.outputs.version }}-${{ github.run_id }}
- name: Save Python wheel cache
if: inputs.lang == 'python' && github.event_name != 'pull_request' && steps.pyenv-cache.outputs.cache-hit != 'true'
uses: actions/cache/save@v4
with:
path: |
~/Library/Caches/pip
~/Library/Caches/uv
key: pyenv-darwin-${{ inputs.backend }}-${{ steps.weekly.outputs.bucket }}-${{ hashFiles(format('backend/python/{0}/requirements*.txt', inputs.backend)) }}
- name: Upload ${{ inputs.backend }}.tar
uses: actions/upload-artifact@v7
with:

View File

@@ -1,213 +0,0 @@
---
name: 'merge backend manifest list (reusable)'
# Reusable workflow that joins per-arch digest artifacts (uploaded by
# backend_build.yml when called with platform-tag) into a single tagged
# multi-arch manifest list. Called once per backend by backend.yml after
# both per-arch build jobs succeed.
on:
workflow_call:
inputs:
tag-latest:
description: 'Whether the manifest list should also be tagged latest (auto/false/true)'
required: false
type: string
default: ''
tag-suffix:
description: 'Backend tag suffix (e.g. -cpu-faster-whisper). Used to compute the artifact pattern and the final tag suffix.'
required: true
type: string
secrets:
dockerUsername:
required: false
dockerPassword:
required: false
quayUsername:
required: true
quayPassword:
required: true
jobs:
merge:
runs-on: ubuntu-latest
# id-token: write is required for keyless cosign — the workflow
# exchanges the GitHub OIDC token for a short-lived Fulcio cert that
# signs each pushed manifest. Without this permission the runner
# cannot mint the token, and `cosign sign` fails with "no token".
permissions:
contents: read
id-token: write
env:
quay_username: ${{ secrets.quayUsername }}
steps:
# Sparse checkout: the merge job needs `.github/scripts/` (for the
# keepalive cleanup script) but none of the source tree.
- name: Checkout (.github/scripts only)
uses: actions/checkout@v6
with:
sparse-checkout: |
.github/scripts
sparse-checkout-cone-mode: false
# `--` separator anchors the glob so we don't over-match sibling
# backends whose tag-suffix happens to be a prefix of ours
# (e.g. -cpu-vllm vs -cpu-vllm-omni). Must stay in sync with the
# upload-artifact name in backend_build.yml.
- name: Download digests
uses: actions/download-artifact@v8
with:
pattern: digests${{ inputs.tag-suffix }}--*
merge-multiple: true
path: /tmp/digests
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@master
# cosign signs each pushed manifest list with --recursive so the
# index and every per-arch entry get an attached Sigstore bundle.
# 2.2+ is required for --new-bundle-format.
- name: Install cosign
if: github.event_name != 'pull_request'
uses: sigstore/cosign-installer@v3
with:
cosign-release: 'v2.4.1'
- name: Login to DockerHub
if: github.event_name != 'pull_request'
uses: docker/login-action@v4
with:
username: ${{ secrets.dockerUsername }}
password: ${{ secrets.dockerPassword }}
- name: Login to Quay.io
if: ${{ env.quay_username != '' }}
uses: docker/login-action@v4
with:
registry: quay.io
username: ${{ secrets.quayUsername }}
password: ${{ secrets.quayPassword }}
- name: Docker meta
id: meta
if: github.event_name != 'pull_request'
uses: docker/metadata-action@v6
with:
images: |
quay.io/go-skynet/local-ai-backends
localai/localai-backends
tags: |
type=ref,event=branch
type=semver,pattern={{raw}}
type=sha
flavor: |
latest=${{ inputs.tag-latest }}
suffix=${{ inputs.tag-suffix }},onlatest=true
# Source from ci-cache, not local-ai-backends.
#
# The build job pushes per-arch manifests to local-ai-backends with
# push-by-digest=true (no tag), then anchors a tagged copy into
# ci-cache so the manifest can be retrieved hours later when this
# merge runs. Quay's manifest GC, however, is per-repository: the
# anchor tag in ci-cache protects the manifest there, but the same
# digest in local-ai-backends has no tag in *that* repo and gets
# reaped independently. Sourcing local-ai-backends@<digest> here
# then fails with "manifest not found" — exactly the regression
# we hit on v4.2.2 (19/37 multiarch merges failed).
#
# ci-cache@<digest> resolves because we anchored it there. buildx
# imagetools create copies the manifest into local-ai-backends
# (cross-repo within the same registry, blobs already cross-mounted
# from the original push so no transfer needed) and publishes the
# manifest list with the user-facing tags. The resulting manifest
# list is fully self-contained in local-ai-backends — child digests
# only, no embedded references to ci-cache.
- name: Create manifest list and push (quay)
if: github.event_name != 'pull_request'
working-directory: /tmp/digests
run: |
set -euo pipefail
tags=$(jq -cr '
.tags
| map(select(startswith("quay.io/")))
| map("-t " + .)
| join(" ")
' <<< "$DOCKER_METADATA_OUTPUT_JSON")
if [ -z "$tags" ]; then
echo "No quay.io tags from docker/metadata-action; skipping quay merge"
exit 0
fi
# shellcheck disable=SC2086
docker buildx imagetools create $tags \
$(printf 'quay.io/go-skynet/ci-cache@sha256:%s ' *)
# Resolve the manifest-list digest (any tag points at it) so
# cosign can sign by digest. Signing by tag would leave the
# signature orphaned the next time the tag moves.
first_tag=$(jq -cr '
.tags | map(select(startswith("quay.io/"))) | .[0]
' <<< "$DOCKER_METADATA_OUTPUT_JSON")
digest=$(docker buildx imagetools inspect "$first_tag" --format '{{.Manifest.Digest}}')
# --recursive walks the list and signs every per-arch entry
# too — clients that resolve a tag to a platform-specific
# manifest before checking signatures need the per-arch
# signatures, not just the list-level one.
cosign sign --yes --recursive \
--new-bundle-format \
--registry-referrers-mode=oci-1-1 \
"quay.io/go-skynet/local-ai-backends@${digest}"
- name: Create manifest list and push (dockerhub)
if: github.event_name != 'pull_request'
working-directory: /tmp/digests
run: |
set -euo pipefail
tags=$(jq -cr '
.tags
| map(select(startswith("localai/")))
| map("-t " + .)
| join(" ")
' <<< "$DOCKER_METADATA_OUTPUT_JSON")
if [ -z "$tags" ]; then
echo "No dockerhub tags from docker/metadata-action; skipping dockerhub merge"
exit 0
fi
# shellcheck disable=SC2086
docker buildx imagetools create $tags \
$(printf 'localai/localai-backends@sha256:%s ' *)
first_tag=$(jq -cr '
.tags | map(select(startswith("localai/"))) | .[0]
' <<< "$DOCKER_METADATA_OUTPUT_JSON")
digest=$(docker buildx imagetools inspect "$first_tag" --format '{{.Manifest.Digest}}')
cosign sign --yes --recursive \
--new-bundle-format \
--registry-referrers-mode=oci-1-1 \
"localai/localai-backends@${digest}"
- name: Inspect manifest
if: github.event_name != 'pull_request'
run: |
set -euo pipefail
first_tag=$(jq -cr '.tags[0]' <<< "$DOCKER_METADATA_OUTPUT_JSON")
if [ -n "$first_tag" ] && [ "$first_tag" != "null" ]; then
docker buildx imagetools inspect "$first_tag"
fi
# See .github/scripts/cleanup-keepalive-tags.sh for why this is
# best-effort and what the failure modes are.
- name: Cleanup keepalive tags in ci-cache
if: github.event_name != 'pull_request' && success()
env:
TAG_SUFFIX: ${{ inputs.tag-suffix }}
QUAY_TOKEN: ${{ secrets.quayPassword }}
run: .github/scripts/cleanup-keepalive-tags.sh
- name: Job summary
if: github.event_name != 'pull_request'
run: |
set -euo pipefail
echo "Merged manifest tags:" >> "$GITHUB_STEP_SUMMARY"
jq -r '.tags[]' <<< "$DOCKER_METADATA_OUTPUT_JSON" | sed 's/^/- /' >> "$GITHUB_STEP_SUMMARY"
echo >> "$GITHUB_STEP_SUMMARY"
echo "Per-arch digests:" >> "$GITHUB_STEP_SUMMARY"
ls -1 /tmp/digests | sed 's/^/- sha256:/' >> "$GITHUB_STEP_SUMMARY"

View File

@@ -4,23 +4,17 @@ on:
pull_request:
concurrency:
group: ci-backends-pr-${{ github.event.pull_request.number || github.sha }}-${{ github.repository }}
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
group: ci-backends-pr-${{ github.head_ref || github.ref }}-${{ github.repository }}
cancel-in-progress: true
jobs:
generate-matrix:
runs-on: ubuntu-latest
outputs:
matrix-singlearch: ${{ steps.set-matrix.outputs['matrix-singlearch'] }}
matrix-multiarch: ${{ steps.set-matrix.outputs['matrix-multiarch'] }}
matrix-darwin: ${{ steps.set-matrix.outputs['matrix-darwin'] }}
merge-matrix-multiarch: ${{ steps.set-matrix.outputs['merge-matrix-multiarch'] }}
merge-matrix-singlearch: ${{ steps.set-matrix.outputs['merge-matrix-singlearch'] }}
has-backends-singlearch: ${{ steps.set-matrix.outputs['has-backends-singlearch'] }}
has-backends-multiarch: ${{ steps.set-matrix.outputs['has-backends-multiarch'] }}
has-backends-darwin: ${{ steps.set-matrix.outputs['has-backends-darwin'] }}
has-merges-multiarch: ${{ steps.set-matrix.outputs['has-merges-multiarch'] }}
has-merges-singlearch: ${{ steps.set-matrix.outputs['has-merges-singlearch'] }}
matrix: ${{ steps.set-matrix.outputs.matrix }}
matrix-darwin: ${{ steps.set-matrix.outputs.matrix-darwin }}
has-backends: ${{ steps.set-matrix.outputs.has-backends }}
has-backends-darwin: ${{ steps.set-matrix.outputs.has-backends-darwin }}
steps:
- name: Checkout repository
uses: actions/checkout@v6
@@ -33,9 +27,7 @@ jobs:
bun add js-yaml
bun add @octokit/core
# filters the matrix in backend.yml; splits into single-arch and
# multi-arch groups so backend-merge-jobs can `needs:` only the latter
# (matches backend.yml's structure).
# filters the matrix in backend.yml
- name: Filter matrix for changed backends
id: set-matrix
env:
@@ -43,10 +35,10 @@ jobs:
GITHUB_EVENT_PATH: ${{ github.event_path }}
run: bun run scripts/changed-backends.js
backend-jobs-multiarch:
backend-jobs:
needs: generate-matrix
uses: ./.github/workflows/backend_build.yml
if: needs.generate-matrix.outputs['has-backends-multiarch'] == 'true'
if: needs.generate-matrix.outputs.has-backends == 'true'
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
@@ -54,83 +46,19 @@ jobs:
cuda-major-version: ${{ matrix.cuda-major-version }}
cuda-minor-version: ${{ matrix.cuda-minor-version }}
platforms: ${{ matrix.platforms }}
platform-tag: ${{ matrix.platform-tag || '' }}
runs-on: ${{ matrix.runs-on }}
builder-base-image: ${{ matrix.builder-base-image || '' }}
base-image: ${{ matrix.base-image }}
backend: ${{ matrix.backend }}
dockerfile: ${{ matrix.dockerfile }}
skip-drivers: ${{ matrix.skip-drivers }}
context: ${{ matrix.context }}
ubuntu-version: ${{ matrix.ubuntu-version }}
amdgpu-targets: ${{ matrix.amdgpu-targets || 'gfx908,gfx90a,gfx942,gfx950,gfx1030,gfx1100,gfx1101,gfx1102,gfx1151,gfx1200,gfx1201' }}
secrets:
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
fail-fast: true
max-parallel: 8
matrix: ${{ fromJson(needs.generate-matrix.outputs['matrix-multiarch']) }}
backend-jobs-singlearch:
needs: generate-matrix
uses: ./.github/workflows/backend_build.yml
if: needs.generate-matrix.outputs['has-backends-singlearch'] == 'true'
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
build-type: ${{ matrix.build-type }}
cuda-major-version: ${{ matrix.cuda-major-version }}
cuda-minor-version: ${{ matrix.cuda-minor-version }}
platforms: ${{ matrix.platforms }}
platform-tag: ${{ matrix.platform-tag || '' }}
runs-on: ${{ matrix.runs-on }}
builder-base-image: ${{ matrix.builder-base-image || '' }}
base-image: ${{ matrix.base-image }}
backend: ${{ matrix.backend }}
dockerfile: ${{ matrix.dockerfile }}
skip-drivers: ${{ matrix.skip-drivers }}
context: ${{ matrix.context }}
ubuntu-version: ${{ matrix.ubuntu-version }}
amdgpu-targets: ${{ matrix.amdgpu-targets || 'gfx908,gfx90a,gfx942,gfx950,gfx1030,gfx1100,gfx1101,gfx1102,gfx1151,gfx1200,gfx1201' }}
secrets:
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
fail-fast: true
max-parallel: 8
matrix: ${{ fromJson(needs.generate-matrix.outputs['matrix-singlearch']) }}
backend-merge-jobs-multiarch:
needs: [generate-matrix, backend-jobs-multiarch]
# backend_merge.yml's push-side steps are all gated on
# github.event_name != 'pull_request', so on a PR the merge job would
# do nothing. Skip it entirely to avoid spinning up an empty runner.
# !cancelled() lets the merge run even when a few build legs fail —
# see the matching note in backend.yml.
if: ${{ !cancelled() && github.event_name != 'pull_request' && needs.generate-matrix.outputs['has-merges-multiarch'] == 'true' }}
uses: ./.github/workflows/backend_merge.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
secrets:
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
fail-fast: false
matrix: ${{ fromJson(needs.generate-matrix.outputs['merge-matrix-multiarch']) }}
backend-merge-jobs-singlearch:
needs: [generate-matrix, backend-jobs-singlearch]
if: ${{ !cancelled() && github.event_name != 'pull_request' && needs.generate-matrix.outputs['has-merges-singlearch'] == 'true' }}
uses: ./.github/workflows/backend_merge.yml
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
secrets:
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
fail-fast: false
matrix: ${{ fromJson(needs.generate-matrix.outputs['merge-matrix-singlearch']) }}
matrix: ${{ fromJson(needs.generate-matrix.outputs.matrix) }}
backend-jobs-darwin:
needs: generate-matrix
uses: ./.github/workflows/backend_build_darwin.yml
@@ -138,7 +66,7 @@ jobs:
with:
backend: ${{ matrix.backend }}
build-type: ${{ matrix.build-type }}
go-version: "1.25.x"
go-version: "1.24.x"
tag-suffix: ${{ matrix.tag-suffix }}
lang: ${{ matrix.lang || 'python' }}
use-pip: ${{ matrix.backend == 'diffusers' }}

View File

@@ -1,161 +0,0 @@
---
name: 'build base-grpc images'
# Builds + pushes pre-compiled builder base images that downstream
# llama-cpp / ik-llama-cpp / turboquant variant Dockerfiles will FROM
# (PR 2). Each base contains apt deps + protoc + cmake + gRPC at
# /opt/grpc + (conditionally) CUDA / ROCm / Vulkan toolchains.
#
# Triggers:
# - schedule (Saturdays 05:00 UTC) - picks up Ubuntu/CUDA/ROCm
# security updates and re-runs ahead of the backend.yml weekly
# cron (Sundays 06:00 UTC).
# - workflow_dispatch - manual one-off rebuild.
# - push to master that touches Dockerfile.base-grpc-builder or
# this workflow itself - keeps bases in sync with their inputs.
#
# Bootstrap (one-time after this PR merges):
# gh workflow run base-images.yml --ref master
# Wait ~30 min for all 9 matrix variants to push to
# quay.io/go-skynet/ci-cache:base-grpc-* before merging PR 2.
on:
schedule:
- cron: '0 5 * * 6'
workflow_dispatch:
push:
branches: [master]
paths:
- 'backend/Dockerfile.base-grpc-builder'
- '.github/workflows/base-images.yml'
# The install logic and apt-mirror helper are bind-mounted into
# Dockerfile.base-grpc-builder at build time — changes to either
# affect the produced base images and must trigger a rebuild.
- '.docker/install-base-deps.sh'
- '.docker/apt-mirror.sh'
concurrency:
group: ci-base-images-${{ github.event.pull_request.number || github.sha }}-${{ github.repository }}
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
jobs:
build:
if: github.repository == 'mudler/LocalAI'
runs-on: ${{ matrix.runs-on }}
strategy:
fail-fast: false
matrix:
include:
- tag: 'base-grpc-amd64'
runs-on: 'ubuntu-latest'
base-image: 'ubuntu:24.04'
build-type: ''
cuda-major-version: ''
cuda-minor-version: ''
ubuntu-version: '2404'
- tag: 'base-grpc-arm64'
runs-on: 'ubuntu-24.04-arm'
base-image: 'ubuntu:24.04'
build-type: ''
cuda-major-version: ''
cuda-minor-version: ''
ubuntu-version: '2404'
- tag: 'base-grpc-cuda-12-amd64'
runs-on: 'ubuntu-latest'
base-image: 'ubuntu:24.04'
build-type: 'cublas'
cuda-major-version: '12'
cuda-minor-version: '8'
ubuntu-version: '2404'
- tag: 'base-grpc-cuda-13-amd64'
runs-on: 'ubuntu-latest'
base-image: 'ubuntu:22.04'
build-type: 'cublas'
cuda-major-version: '13'
cuda-minor-version: '0'
ubuntu-version: '2204'
- tag: 'base-grpc-cuda-13-arm64'
runs-on: 'ubuntu-24.04-arm'
base-image: 'ubuntu:24.04'
build-type: 'cublas'
cuda-major-version: '13'
cuda-minor-version: '0'
ubuntu-version: '2404'
- tag: 'base-grpc-rocm-amd64'
runs-on: 'ubuntu-latest'
base-image: 'rocm/dev-ubuntu-24.04:7.2.1'
build-type: 'hipblas'
cuda-major-version: ''
cuda-minor-version: ''
ubuntu-version: '2404'
- tag: 'base-grpc-vulkan-amd64'
runs-on: 'ubuntu-latest'
base-image: 'ubuntu:24.04'
build-type: 'vulkan'
cuda-major-version: ''
cuda-minor-version: ''
ubuntu-version: '2404'
- tag: 'base-grpc-vulkan-arm64'
runs-on: 'ubuntu-24.04-arm'
base-image: 'ubuntu:24.04'
build-type: 'vulkan'
cuda-major-version: ''
cuda-minor-version: ''
ubuntu-version: '2404'
- tag: 'base-grpc-intel-amd64'
runs-on: 'ubuntu-latest'
base-image: 'intel/oneapi-basekit:2025.3.2-0-devel-ubuntu24.04'
build-type: 'sycl'
cuda-major-version: ''
cuda-minor-version: ''
ubuntu-version: '2404'
# Legacy JetPack r36.4.0 base for older Jetson devices (CUDA 12).
# Distinct from base-grpc-cuda-13-arm64 (Ubuntu 24.04 + CUDA 13 sbsa)
# which targets newer Jetsons. Some matrix entries
# (-nvidia-l4t-arm64-llama-cpp / -turboquant) still build against
# the JetPack image, so we need a matching base.
- tag: 'base-grpc-l4t-cuda-12-arm64'
runs-on: 'ubuntu-24.04-arm'
base-image: 'nvcr.io/nvidia/l4t-jetpack:r36.4.0'
build-type: 'l4t'
cuda-major-version: '12'
cuda-minor-version: '0'
ubuntu-version: '2204'
# JetPack r36.4.0 already ships CUDA preinstalled at /usr/local/cuda;
# apt-installing cuda-nvcc-12-0 from the public repos fails because
# those packages aren't published for the JetPack apt feed. Match
# the original l4t matrix entry which set skip-drivers: 'true'.
skip-drivers: 'true'
steps:
- uses: actions/checkout@v6
with:
submodules: false
- name: Free disk space
uses: ./.github/actions/free-disk-space
- name: Set up build disk
uses: ./.github/actions/setup-build-disk
- uses: docker/setup-qemu-action@master
with:
platforms: all
- uses: docker/setup-buildx-action@master
- uses: docker/login-action@v4
with:
registry: quay.io
username: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
password: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
- uses: docker/build-push-action@v7
with:
context: .
file: ./backend/Dockerfile.base-grpc-builder
build-args: |
BASE_IMAGE=${{ matrix.base-image }}
BUILD_TYPE=${{ matrix.build-type }}
CUDA_MAJOR_VERSION=${{ matrix.cuda-major-version }}
CUDA_MINOR_VERSION=${{ matrix.cuda-minor-version }}
UBUNTU_VERSION=${{ matrix.ubuntu-version }}
SKIP_DRIVERS=${{ matrix.skip-drivers || 'false' }}
cache-from: type=registry,ref=quay.io/go-skynet/ci-cache:cache-${{ matrix.tag }}
cache-to: type=registry,ref=quay.io/go-skynet/ci-cache:cache-${{ matrix.tag }},mode=max,ignore-error=true
provenance: false
tags: quay.io/go-skynet/ci-cache:${{ matrix.tag }}
push: true

View File

@@ -50,8 +50,6 @@ jobs:
uses: actions/checkout@v6
with:
fetch-depth: 0
- name: Configure apt mirror on runner
uses: ./.github/actions/configure-apt-mirror
- name: Set up Go
uses: actions/setup-go@v5
with:

View File

@@ -22,10 +22,6 @@ jobs:
variable: "TURBOQUANT_VERSION"
branch: "feature/turboquant-kv-cache"
file: "backend/cpp/turboquant/Makefile"
- repository: "antirez/ds4"
variable: "DS4_VERSION"
branch: "main"
file: "backend/cpp/ds4/Makefile"
- repository: "ggml-org/whisper.cpp"
variable: "WHISPER_CPP_VERSION"
branch: "master"
@@ -54,10 +50,6 @@ jobs:
variable: "QWEN3TTS_CPP_VERSION"
branch: "main"
file: "backend/go/qwen3-tts-cpp/Makefile"
- repository: "localai-org/vibevoice.cpp"
variable: "VIBEVOICE_CPP_VERSION"
branch: "master"
file: "backend/go/vibevoice-cpp/Makefile"
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v6
@@ -88,37 +80,5 @@ jobs:
body: ${{ steps.bump.outputs.message }}
signoff: true
bump-vllm-wheel:
# vLLM's cu130 wheel comes from a per-tag index URL (no /latest/ alias),
# so the cublas13 requirements file pins both a URL segment and a version
# constraint. bump_deps.sh handles git-sha-in-Makefile only — this job
# rewrites both values atomically when a new vLLM stable tag ships.
if: github.repository == 'mudler/LocalAI'
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v6
- name: Bump vLLM cu130 wheel pin 🔧
id: bump
run: |
bash .github/bump_vllm_wheel.sh vllm-project/vllm backend/python/vllm/requirements-cublas13-after.txt VLLM_VERSION
{
echo 'message<<EOF'
cat "VLLM_VERSION_message.txt"
echo EOF
} >> "$GITHUB_OUTPUT"
{
echo 'commit<<EOF'
cat "VLLM_VERSION_commit.txt"
echo EOF
} >> "$GITHUB_OUTPUT"
rm -rfv VLLM_VERSION_message.txt VLLM_VERSION_commit.txt
- name: Create Pull Request
uses: peter-evans/create-pull-request@v8
with:
token: ${{ secrets.UPDATE_BOT_TOKEN }}
push-to-fork: ci-forks/LocalAI
commit-message: ':arrow_up: Update vllm-project/vllm cu130 wheel'
title: 'chore: :arrow_up: Update vllm-project/vllm cu130 wheel to `${{ steps.bump.outputs.commit }}`'
branch: "update/VLLM_VERSION"
body: ${{ steps.bump.outputs.message }}
signoff: true

View File

@@ -8,9 +8,15 @@ jobs:
if: github.repository == 'mudler/LocalAI'
runs-on: ubuntu-latest
steps:
- name: Force Install GIT latest
run: |
sudo apt-get update \
&& sudo apt-get install -y software-properties-common \
&& sudo apt-get update \
&& sudo add-apt-repository -y ppa:git-core/ppa \
&& sudo apt-get update \
&& sudo apt-get install -y git
- uses: actions/checkout@v6
- name: Configure apt mirror on runner
uses: ./.github/actions/configure-apt-mirror
- name: Install dependencies
run: |
sudo apt-get update

View File

@@ -2,7 +2,7 @@ name: Gallery Agent
on:
schedule:
- cron: '0 */12 * * *' # Run every 4 hours
- cron: '0 */3 * * *' # Run every 4 hours
workflow_dispatch:
inputs:
search_term:
@@ -54,41 +54,24 @@ jobs:
REPO: ${{ github.repository }}
SEARCH: 'gallery agent in:title'
run: |
# Walk gallery-agent PRs and act on maintainer comments:
# Walk open gallery-agent PRs and act on maintainer comments:
# /gallery-agent blacklist → label `gallery-agent/blacklisted` + close (never repropose)
# /gallery-agent recreate → close without label (next run may repropose)
# Only comments from OWNER / MEMBER / COLLABORATOR are honored so
# random users can't drive the bot.
#
# We scan both open PRs AND recently-closed PRs that don't already
# carry the blacklist label. This covers the common flow where a
# maintainer writes /gallery-agent blacklist and immediately clicks
# Close — without this, the next scheduled run wouldn't see the
# command (PR is already closed) and would repropose the model.
gh label create gallery-agent/blacklisted \
--repo "$REPO" --color ededed \
--description "gallery-agent must not repropose this model" 2>/dev/null || true
prs_open=$(gh pr list --repo "$REPO" --state open --search "$SEARCH" \
--json number --jq '.[].number')
# Closed PRs from the last 14 days that don't yet have the blacklist label.
# Bounded window keeps the scan cheap while covering late-applied commands.
since=$(date -u -d '14 days ago' +%Y-%m-%d)
prs_closed=$(gh pr list --repo "$REPO" --state closed \
--search "$SEARCH closed:>=$since -label:gallery-agent/blacklisted" \
--json number --jq '.[].number')
prs=$(printf '%s\n%s\n' "$prs_open" "$prs_closed" | sort -u | sed '/^$/d')
prs=$(gh pr list --repo "$REPO" --state open --search "$SEARCH" --json number --jq '.[].number')
for pr in $prs; do
state=$(gh pr view "$pr" --repo "$REPO" --json state --jq '.state')
cmds=$(gh pr view "$pr" --repo "$REPO" --json comments \
--jq '.comments[] | select(.authorAssociation=="OWNER" or .authorAssociation=="MEMBER" or .authorAssociation=="COLLABORATOR") | .body')
if echo "$cmds" | grep -qE '(^|[[:space:]])/gallery-agent[[:space:]]+blacklist([[:space:]]|$)'; then
echo "PR #$pr: blacklist command found (state=$state)"
echo "PR #$pr: blacklist command found"
gh pr edit "$pr" --repo "$REPO" --add-label gallery-agent/blacklisted || true
if [ "$state" = "OPEN" ]; then
gh pr close "$pr" --repo "$REPO" --comment "Blacklisted via \`/gallery-agent blacklist\`. This model will not be reproposed." || true
fi
elif [ "$state" = "OPEN" ] && echo "$cmds" | grep -qE '(^|[[:space:]])/gallery-agent[[:space:]]+recreate([[:space:]]|$)'; then
gh pr close "$pr" --repo "$REPO" --comment "Blacklisted via \`/gallery-agent blacklist\`. This model will not be reproposed." || true
elif echo "$cmds" | grep -qE '(^|[[:space:]])/gallery-agent[[:space:]]+recreate([[:space:]]|$)'; then
echo "PR #$pr: recreate command found"
gh pr close "$pr" --repo "$REPO" --comment "Closed via \`/gallery-agent recreate\`. The next scheduled run will propose this model again." || true
fi

View File

@@ -0,0 +1,96 @@
name: 'generate and publish GRPC docker caches'
on:
workflow_dispatch:
schedule:
# daily at midnight
- cron: '0 0 * * *'
concurrency:
group: grpc-cache-${{ github.head_ref || github.ref }}-${{ github.repository }}
cancel-in-progress: true
jobs:
generate_caches:
if: github.repository == 'mudler/LocalAI'
strategy:
matrix:
include:
- grpc-base-image: ubuntu:24.04
runs-on: 'ubuntu-latest'
platforms: 'linux/amd64,linux/arm64'
runs-on: ${{matrix.runs-on}}
steps:
- name: Release space from worker
if: matrix.runs-on == 'ubuntu-latest'
run: |
echo "Listing top largest packages"
pkgs=$(dpkg-query -Wf '${Installed-Size}\t${Package}\t${Status}\n' | awk '$NF == "installed"{print $1 "\t" $2}' | sort -nr)
head -n 30 <<< "${pkgs}"
echo
df -h
echo
sudo apt-get remove -y '^llvm-.*|^libllvm.*' || true
sudo apt-get remove --auto-remove android-sdk-platform-tools || true
sudo apt-get purge --auto-remove android-sdk-platform-tools || true
sudo rm -rf /usr/local/lib/android
sudo apt-get remove -y '^dotnet-.*|^aspnetcore-.*' || true
sudo rm -rf /usr/share/dotnet
sudo apt-get remove -y '^mono-.*' || true
sudo apt-get remove -y '^ghc-.*' || true
sudo apt-get remove -y '.*jdk.*|.*jre.*' || true
sudo apt-get remove -y 'php.*' || true
sudo apt-get remove -y hhvm powershell firefox monodoc-manual msbuild || true
sudo apt-get remove -y '^google-.*' || true
sudo apt-get remove -y azure-cli || true
sudo apt-get remove -y '^mongo.*-.*|^postgresql-.*|^mysql-.*|^mssql-.*' || true
sudo apt-get remove -y '^gfortran-.*' || true
sudo apt-get remove -y microsoft-edge-stable || true
sudo apt-get remove -y firefox || true
sudo apt-get remove -y powershell || true
sudo apt-get remove -y r-base-core || true
sudo apt-get autoremove -y
sudo apt-get clean
echo
echo "Listing top largest packages"
pkgs=$(dpkg-query -Wf '${Installed-Size}\t${Package}\t${Status}\n' | awk '$NF == "installed"{print $1 "\t" $2}' | sort -nr)
head -n 30 <<< "${pkgs}"
echo
sudo rm -rfv build || true
sudo rm -rf /usr/share/dotnet || true
sudo rm -rf /opt/ghc || true
sudo rm -rf "/usr/local/share/boost" || true
sudo rm -rf "$AGENT_TOOLSDIRECTORY" || true
df -h
- name: Set up QEMU
uses: docker/setup-qemu-action@master
with:
platforms: all
- name: Set up Docker Buildx
id: buildx
uses: docker/setup-buildx-action@master
- name: Checkout
uses: actions/checkout@v6
- name: Cache GRPC
uses: docker/build-push-action@v7
with:
builder: ${{ steps.buildx.outputs.name }}
# The build-args MUST be an EXACT match between the image cache and other workflow steps that want to use that cache.
# This means that even the MAKEFLAGS have to be an EXACT match.
# If the build-args are not an EXACT match, it will result in a cache miss, which will require GRPC to be built from scratch.
build-args: |
GRPC_BASE_IMAGE=${{ matrix.grpc-base-image }}
GRPC_MAKEFLAGS=--jobs=4 --output-sync=target
GRPC_VERSION=v1.65.0
context: .
file: ./Dockerfile
cache-to: type=gha,ignore-error=true
cache-from: type=gha
target: grpc
platforms: ${{ matrix.platforms }}
push: false

View File

@@ -7,8 +7,8 @@ on:
- master
concurrency:
group: intel-cache-${{ github.event.pull_request.number || github.sha }}-${{ github.repository }}
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
group: intel-cache-${{ github.head_ref || github.ref }}-${{ github.repository }}
cancel-in-progress: true
jobs:
generate_caches:
@@ -16,7 +16,7 @@ jobs:
strategy:
matrix:
include:
- base-image: intel/oneapi-basekit:2025.3.2-0-devel-ubuntu24.04
- base-image: intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04
runs-on: 'arc-runner-set'
platforms: 'linux/amd64'
runs-on: ${{matrix.runs-on}}

View File

@@ -5,8 +5,8 @@
pull_request:
concurrency:
group: ci-${{ github.event.pull_request.number || github.sha }}-${{ github.repository }}
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
group: ci-${{ github.head_ref || github.ref }}-${{ github.repository }}
cancel-in-progress: true
jobs:
image-build:
@@ -18,9 +18,9 @@
cuda-major-version: ${{ matrix.cuda-major-version }}
cuda-minor-version: ${{ matrix.cuda-minor-version }}
platforms: ${{ matrix.platforms }}
platform-tag: ${{ matrix.platform-tag || '' }}
runs-on: ${{ matrix.runs-on }}
base-image: ${{ matrix.base-image }}
grpc-base-image: ${{ matrix.grpc-base-image }}
makeflags: ${{ matrix.makeflags }}
ubuntu-version: ${{ matrix.ubuntu-version }}
secrets:
@@ -60,35 +60,27 @@
tag-latest: 'false'
tag-suffix: '-hipblas'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
grpc-base-image: "ubuntu:24.04"
runs-on: 'ubuntu-latest'
makeflags: "--jobs=3 --output-sync=target"
ubuntu-version: '2404'
- build-type: 'sycl'
platforms: 'linux/amd64'
tag-latest: 'false'
base-image: "intel/oneapi-basekit:2025.3.2-0-devel-ubuntu24.04"
base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
grpc-base-image: "ubuntu:24.04"
tag-suffix: 'sycl'
runs-on: 'ubuntu-latest'
makeflags: "--jobs=3 --output-sync=target"
ubuntu-version: '2404'
- build-type: 'vulkan'
platforms: 'linux/amd64'
platform-tag: 'amd64'
platforms: 'linux/amd64,linux/arm64'
tag-latest: 'false'
tag-suffix: '-vulkan-core'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
makeflags: "--jobs=4 --output-sync=target"
ubuntu-version: '2404'
- build-type: 'vulkan'
platforms: 'linux/arm64'
platform-tag: 'arm64'
tag-latest: 'false'
tag-suffix: '-vulkan-core'
runs-on: 'ubuntu-24.04-arm'
base-image: "ubuntu:24.04"
makeflags: "--jobs=4 --output-sync=target"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"

View File

@@ -9,8 +9,8 @@
- '*'
concurrency:
group: ci-${{ github.event.pull_request.number || github.sha }}-${{ github.repository }}
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
group: ci-${{ github.head_ref || github.ref }}-${{ github.repository }}
cancel-in-progress: true
jobs:
hipblas-jobs:
@@ -25,6 +25,7 @@
platforms: ${{ matrix.platforms }}
runs-on: ${{ matrix.runs-on }}
base-image: ${{ matrix.base-image }}
grpc-base-image: ${{ matrix.grpc-base-image }}
makeflags: ${{ matrix.makeflags }}
ubuntu-version: ${{ matrix.ubuntu-version }}
ubuntu-codename: ${{ matrix.ubuntu-codename }}
@@ -41,11 +42,12 @@
tag-latest: 'auto'
tag-suffix: '-gpu-hipblas'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
grpc-base-image: "ubuntu:24.04"
runs-on: 'ubuntu-latest'
makeflags: "--jobs=3 --output-sync=target"
ubuntu-version: '2404'
ubuntu-codename: 'noble'
core-image-build:
if: github.repository == 'mudler/LocalAI'
uses: ./.github/workflows/image_build.yml
@@ -56,9 +58,9 @@
cuda-major-version: ${{ matrix.cuda-major-version }}
cuda-minor-version: ${{ matrix.cuda-minor-version }}
platforms: ${{ matrix.platforms }}
platform-tag: ${{ matrix.platform-tag || '' }}
runs-on: ${{ matrix.runs-on }}
base-image: ${{ matrix.base-image }}
grpc-base-image: ${{ matrix.grpc-base-image }}
makeflags: ${{ matrix.makeflags }}
skip-drivers: ${{ matrix.skip-drivers }}
ubuntu-version: ${{ matrix.ubuntu-version }}
@@ -73,8 +75,7 @@
matrix:
include:
- build-type: ''
platforms: 'linux/amd64'
platform-tag: 'amd64'
platforms: 'linux/amd64,linux/arm64'
tag-latest: 'auto'
tag-suffix: ''
base-image: "ubuntu:24.04"
@@ -83,17 +84,6 @@
skip-drivers: 'false'
ubuntu-version: '2404'
ubuntu-codename: 'noble'
- build-type: ''
platforms: 'linux/arm64'
platform-tag: 'arm64'
tag-latest: 'auto'
tag-suffix: ''
base-image: "ubuntu:24.04"
runs-on: 'ubuntu-24.04-arm'
makeflags: "--jobs=4 --output-sync=target"
skip-drivers: 'false'
ubuntu-version: '2404'
ubuntu-codename: 'noble'
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "8"
@@ -119,8 +109,7 @@
ubuntu-version: '2404'
ubuntu-codename: 'noble'
- build-type: 'vulkan'
platforms: 'linux/amd64'
platform-tag: 'amd64'
platforms: 'linux/amd64,linux/arm64'
tag-latest: 'auto'
tag-suffix: '-gpu-vulkan'
runs-on: 'ubuntu-latest'
@@ -129,141 +118,17 @@
makeflags: "--jobs=4 --output-sync=target"
ubuntu-version: '2404'
ubuntu-codename: 'noble'
- build-type: 'vulkan'
platforms: 'linux/arm64'
platform-tag: 'arm64'
tag-latest: 'auto'
tag-suffix: '-gpu-vulkan'
runs-on: 'ubuntu-24.04-arm'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
makeflags: "--jobs=4 --output-sync=target"
ubuntu-version: '2404'
ubuntu-codename: 'noble'
- build-type: 'intel'
platforms: 'linux/amd64'
tag-latest: 'auto'
base-image: "intel/oneapi-basekit:2025.3.2-0-devel-ubuntu24.04"
base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
grpc-base-image: "ubuntu:24.04"
tag-suffix: '-gpu-intel'
runs-on: 'ubuntu-latest'
makeflags: "--jobs=3 --output-sync=target"
ubuntu-version: '2404'
ubuntu-codename: 'noble'
core-image-merge:
# !cancelled(): without it, GHA's default `needs:` cascade skips the
# merge whenever any matrix cell of the parent build fails or is
# cancelled. Same fix as backend.yml's merge jobs — we still want to
# publish the manifest list for tag-suffixes whose legs all succeeded.
if: ${{ !cancelled() && github.repository == 'mudler/LocalAI' }}
needs: core-image-build
uses: ./.github/workflows/image_merge.yml
with:
tag-latest: 'auto'
tag-suffix: ''
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
gpu-vulkan-image-merge:
if: ${{ !cancelled() && github.repository == 'mudler/LocalAI' }}
needs: core-image-build
uses: ./.github/workflows/image_merge.yml
with:
tag-latest: 'auto'
tag-suffix: '-gpu-vulkan'
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
# Single-arch server-image merges. Same conceptual fix as the backend
# singletons in PR #9781: image_build.yml pushes by canonical digest
# only, so without a downstream merge step there's no tag for consumers
# (no :latest-gpu-nvidia-cuda-12, no :v<X>-gpu-nvidia-cuda-12, etc.).
# Each merge job needs only its parent build matrix and is filtered by
# tag-suffix in image_merge.yml's artifact-download pattern.
gpu-nvidia-cuda-12-image-merge:
if: ${{ !cancelled() && github.repository == 'mudler/LocalAI' }}
needs: core-image-build
uses: ./.github/workflows/image_merge.yml
with:
tag-latest: 'auto'
tag-suffix: '-gpu-nvidia-cuda-12'
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
gpu-nvidia-cuda-13-image-merge:
if: ${{ !cancelled() && github.repository == 'mudler/LocalAI' }}
needs: core-image-build
uses: ./.github/workflows/image_merge.yml
with:
tag-latest: 'auto'
tag-suffix: '-gpu-nvidia-cuda-13'
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
gpu-intel-image-merge:
if: ${{ !cancelled() && github.repository == 'mudler/LocalAI' }}
needs: core-image-build
uses: ./.github/workflows/image_merge.yml
with:
tag-latest: 'auto'
tag-suffix: '-gpu-intel'
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
gpu-hipblas-image-merge:
if: ${{ !cancelled() && github.repository == 'mudler/LocalAI' }}
needs: hipblas-jobs
uses: ./.github/workflows/image_merge.yml
with:
tag-latest: 'auto'
tag-suffix: '-gpu-hipblas'
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
nvidia-l4t-arm64-image-merge:
if: ${{ !cancelled() && github.repository == 'mudler/LocalAI' }}
needs: gh-runner
uses: ./.github/workflows/image_merge.yml
with:
tag-latest: 'auto'
tag-suffix: '-nvidia-l4t-arm64'
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
nvidia-l4t-arm64-cuda-13-image-merge:
if: ${{ !cancelled() && github.repository == 'mudler/LocalAI' }}
needs: gh-runner
uses: ./.github/workflows/image_merge.yml
with:
tag-latest: 'auto'
tag-suffix: '-nvidia-l4t-arm64-cuda-13'
secrets:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
gh-runner:
if: github.repository == 'mudler/LocalAI'
uses: ./.github/workflows/image_build.yml
@@ -276,6 +141,7 @@
platforms: ${{ matrix.platforms }}
runs-on: ${{ matrix.runs-on }}
base-image: ${{ matrix.base-image }}
grpc-base-image: ${{ matrix.grpc-base-image }}
makeflags: ${{ matrix.makeflags }}
skip-drivers: ${{ matrix.skip-drivers }}
ubuntu-version: ${{ matrix.ubuntu-version }}

View File

@@ -8,6 +8,11 @@ on:
description: 'Base image'
required: true
type: string
grpc-base-image:
description: 'GRPC Base image, must be a compatible image with base-image'
required: false
default: ''
type: string
build-type:
description: 'Build type'
default: ''
@@ -24,15 +29,6 @@ on:
description: 'Platforms'
default: ''
type: string
platform-tag:
description: |
Short tag identifying the platform leg, e.g. "amd64" or "arm64".
Used to scope the per-arch registry cache and the digest artifact name.
Optional during the migration; will be flipped to required: true once
every caller passes an explicit value.
required: false
default: ''
type: string
tag-latest:
description: 'Tag latest'
default: ''
@@ -79,20 +75,73 @@ jobs:
runs-on: ${{ inputs.runs-on }}
steps:
- name: Free Disk Space (Ubuntu)
if: inputs.runs-on == 'ubuntu-latest'
uses: jlumbroso/free-disk-space@main
with:
# this might remove tools that are actually needed,
# if set to "true" but frees about 6 GB
tool-cache: true
# all of these default to true, but feel free to set to
# "false" if necessary for your workflow
android: true
dotnet: true
haskell: true
large-packages: true
docker-images: true
swap-storage: true
- name: Force Install GIT latest
run: |
sudo apt-get update \
&& sudo apt-get install -y software-properties-common \
&& sudo apt-get update \
&& sudo add-apt-repository -y ppa:git-core/ppa \
&& sudo apt-get update \
&& sudo apt-get install -y git
- name: Checkout
uses: actions/checkout@v6
- name: Configure apt mirror on runner
id: apt_mirror
uses: ./.github/actions/configure-apt-mirror
- name: Free disk space
uses: ./.github/actions/free-disk-space
with:
mode: ${{ inputs.runs-on == 'ubuntu-latest' && 'hosted' || 'skip' }}
- name: Set up build disk
uses: ./.github/actions/setup-build-disk
- name: Release space from worker
if: inputs.runs-on == 'ubuntu-latest'
run: |
echo "Listing top largest packages"
pkgs=$(dpkg-query -Wf '${Installed-Size}\t${Package}\t${Status}\n' | awk '$NF == "installed"{print $1 "\t" $2}' | sort -nr)
head -n 30 <<< "${pkgs}"
echo
df -h
echo
sudo apt-get remove -y '^llvm-.*|^libllvm.*' || true
sudo apt-get remove --auto-remove android-sdk-platform-tools snapd || true
sudo apt-get purge --auto-remove android-sdk-platform-tools snapd || true
sudo rm -rf /usr/local/lib/android
sudo apt-get remove -y '^dotnet-.*|^aspnetcore-.*' || true
sudo rm -rf /usr/share/dotnet
sudo apt-get remove -y '^mono-.*' || true
sudo apt-get remove -y '^ghc-.*' || true
sudo apt-get remove -y '.*jdk.*|.*jre.*' || true
sudo apt-get remove -y 'php.*' || true
sudo apt-get remove -y hhvm powershell firefox monodoc-manual msbuild || true
sudo apt-get remove -y '^google-.*' || true
sudo apt-get remove -y azure-cli || true
sudo apt-get remove -y '^mongo.*-.*|^postgresql-.*|^mysql-.*|^mssql-.*' || true
sudo apt-get remove -y '^gfortran-.*' || true
sudo apt-get remove -y microsoft-edge-stable || true
sudo apt-get remove -y firefox || true
sudo apt-get remove -y powershell || true
sudo apt-get remove -y r-base-core || true
sudo apt-get autoremove -y
sudo apt-get clean
echo
echo "Listing top largest packages"
pkgs=$(dpkg-query -Wf '${Installed-Size}\t${Package}\t${Status}\n' | awk '$NF == "installed"{print $1 "\t" $2}' | sort -nr)
head -n 30 <<< "${pkgs}"
echo
sudo rm -rfv build || true
sudo rm -rf /usr/share/dotnet || true
sudo rm -rf /opt/ghc || true
sudo rm -rf "/usr/local/share/boost" || true
sudo rm -rf "$AGENT_TOOLSDIRECTORY" || true
df -h
- name: Docker meta
id: meta
@@ -147,89 +196,59 @@ jobs:
username: ${{ secrets.quayUsername }}
password: ${{ secrets.quayPassword }}
- name: Build and push by digest
id: build
- name: Build and push
uses: docker/build-push-action@v7
if: github.event_name != 'pull_request'
with:
builder: ${{ steps.buildx.outputs.name }}
# The build-args MUST be an EXACT match between the image cache and other workflow steps that want to use that cache.
# This means that even the MAKEFLAGS have to be an EXACT match.
# If the build-args are not an EXACT match, it will result in a cache miss, which will require GRPC to be built from scratch.
# This is why some build args like GRPC_VERSION and MAKEFLAGS are hardcoded
build-args: |
BUILD_TYPE=${{ inputs.build-type }}
CUDA_MAJOR_VERSION=${{ inputs.cuda-major-version }}
CUDA_MINOR_VERSION=${{ inputs.cuda-minor-version }}
BASE_IMAGE=${{ inputs.base-image }}
GRPC_BASE_IMAGE=${{ inputs.grpc-base-image || inputs.base-image }}
GRPC_MAKEFLAGS=--jobs=4 --output-sync=target
GRPC_VERSION=v1.65.0
MAKEFLAGS=${{ inputs.makeflags }}
SKIP_DRIVERS=${{ inputs.skip-drivers }}
UBUNTU_VERSION=${{ inputs.ubuntu-version }}
UBUNTU_CODENAME=${{ inputs.ubuntu-codename }}
APT_MIRROR=${{ steps.apt_mirror.outputs.effective-mirror }}
APT_PORTS_MIRROR=${{ steps.apt_mirror.outputs.effective-ports-mirror }}
context: .
file: ./Dockerfile
cache-from: type=registry,ref=quay.io/go-skynet/ci-cache:cache-localai${{ inputs.tag-suffix }}-${{ inputs.platform-tag }}
cache-to: type=registry,ref=quay.io/go-skynet/ci-cache:cache-localai${{ inputs.tag-suffix }}-${{ inputs.platform-tag }},mode=max,ignore-error=true
cache-from: type=gha
platforms: ${{ inputs.platforms }}
outputs: |
type=image,name=quay.io/go-skynet/local-ai,push-by-digest=true,name-canonical=true,push=true
type=image,name=localai/localai,push-by-digest=true,name-canonical=true,push=true
# See backend_build.yml for the rationale — provenance=mode=max
# diverges the manifest-list digest per registry, breaking the
# downstream imagetools create lookup.
provenance: false
push: ${{ github.event_name != 'pull_request' }}
tags: ${{ steps.meta.outputs.tags }}
labels: ${{ steps.meta.outputs.labels }}
- name: Export digest
if: github.event_name != 'pull_request'
run: |
mkdir -p /tmp/digests
digest="${{ steps.build.outputs.digest }}"
touch "/tmp/digests/${digest#sha256:}"
# See .github/scripts/anchor-digest-in-cache.sh for why this is needed
# and how it interacts with image_merge.yml's cleanup step. Mirrors the
# same anchor in backend_build.yml — quay's per-repo manifest GC reaps
# untagged manifests in local-ai before the merge runs.
- name: Anchor digest in ci-cache so quay GC won't reap before merge
if: github.event_name != 'pull_request'
env:
TAG_SUFFIX: ${{ inputs.tag-suffix == '' && '-core' || inputs.tag-suffix }}
PLATFORM_TAG: ${{ inputs.platform-tag || 'single' }}
DIGEST: ${{ steps.build.outputs.digest }}
SOURCE_IMAGE: quay.io/go-skynet/local-ai
run: .github/scripts/anchor-digest-in-cache.sh
- name: Upload digest artifact
if: github.event_name != 'pull_request'
uses: actions/upload-artifact@v7
with:
# `--` separator + 'single' placeholder for empty platform-tag —
# same pattern as backend_build.yml. Prevents prefix collisions
# in the merge-side glob (e.g. -nvidia-l4t-arm64 is a prefix of
# -nvidia-l4t-arm64-cuda-13).
name: digests-localai${{ inputs.tag-suffix == '' && '-core' || inputs.tag-suffix }}--${{ inputs.platform-tag || 'single' }}
path: /tmp/digests/*
if-no-files-found: error
retention-days: 1
### Start testing image
- name: Build and push
uses: docker/build-push-action@v7
if: github.event_name == 'pull_request'
with:
builder: ${{ steps.buildx.outputs.name }}
# The build-args MUST be an EXACT match between the image cache and other workflow steps that want to use that cache.
# This means that even the MAKEFLAGS have to be an EXACT match.
# If the build-args are not an EXACT match, it will result in a cache miss, which will require GRPC to be built from scratch.
# This is why some build args like GRPC_VERSION and MAKEFLAGS are hardcoded
build-args: |
BUILD_TYPE=${{ inputs.build-type }}
CUDA_MAJOR_VERSION=${{ inputs.cuda-major-version }}
CUDA_MINOR_VERSION=${{ inputs.cuda-minor-version }}
BASE_IMAGE=${{ inputs.base-image }}
GRPC_BASE_IMAGE=${{ inputs.grpc-base-image || inputs.base-image }}
GRPC_MAKEFLAGS=--jobs=4 --output-sync=target
GRPC_VERSION=v1.65.0
MAKEFLAGS=${{ inputs.makeflags }}
SKIP_DRIVERS=${{ inputs.skip-drivers }}
UBUNTU_VERSION=${{ inputs.ubuntu-version }}
UBUNTU_CODENAME=${{ inputs.ubuntu-codename }}
APT_MIRROR=${{ steps.apt_mirror.outputs.effective-mirror }}
APT_PORTS_MIRROR=${{ steps.apt_mirror.outputs.effective-ports-mirror }}
context: .
file: ./Dockerfile
cache-from: type=registry,ref=quay.io/go-skynet/ci-cache:cache-localai${{ inputs.tag-suffix }}-${{ inputs.platform-tag }}
cache-from: type=gha
platforms: ${{ inputs.platforms }}
#push: true
tags: ${{ steps.meta_pull_request.outputs.tags }}

View File

@@ -1,145 +0,0 @@
---
name: 'merge LocalAI image manifest list (reusable)'
# Reusable workflow that joins per-arch digest artifacts (uploaded by
# image_build.yml when called with platform-tag) into a single tagged
# multi-arch manifest list.
on:
workflow_call:
inputs:
tag-latest:
description: 'Whether the manifest list should also be tagged latest (auto/false/true)'
required: false
type: string
default: ''
tag-suffix:
description: 'Image tag suffix (empty for core image). Used in artifact pattern with a -core placeholder for empty.'
required: true
type: string
secrets:
dockerUsername:
required: false
dockerPassword:
required: false
quayUsername:
required: true
quayPassword:
required: true
jobs:
merge:
runs-on: ubuntu-latest
env:
quay_username: ${{ secrets.quayUsername }}
steps:
# Sparse checkout: needed for .github/scripts/ (the keepalive cleanup
# script). Skips the rest of the source tree.
- name: Checkout (.github/scripts only)
uses: actions/checkout@v6
with:
sparse-checkout: |
.github/scripts
sparse-checkout-cone-mode: false
- name: Download digests
uses: actions/download-artifact@v8
with:
# `--` separator anchors the glob so we don't over-match sibling
# tag-suffixes (e.g. -nvidia-l4t-arm64 vs -nvidia-l4t-arm64-cuda-13).
# Must stay in sync with image_build.yml's upload-artifact name.
pattern: digests-localai${{ inputs.tag-suffix == '' && '-core' || inputs.tag-suffix }}--*
merge-multiple: true
path: /tmp/digests
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@master
- name: Login to DockerHub
if: github.event_name != 'pull_request'
uses: docker/login-action@v4
with:
username: ${{ secrets.dockerUsername }}
password: ${{ secrets.dockerPassword }}
- name: Login to Quay.io
uses: docker/login-action@v4
with:
registry: quay.io
username: ${{ secrets.quayUsername }}
password: ${{ secrets.quayPassword }}
- name: Docker meta
id: meta
uses: docker/metadata-action@v6
with:
images: |
quay.io/go-skynet/local-ai
localai/localai
tags: |
type=ref,event=branch
type=semver,pattern={{raw}}
type=sha
flavor: |
latest=${{ inputs.tag-latest }}
suffix=${{ inputs.tag-suffix }},onlatest=true
# Source from ci-cache, not local-ai. See backend_merge.yml for the
# detailed rationale — quay's manifest GC is per-repository, so the
# untagged digest in local-ai gets reaped while the same content lives
# tagged under ci-cache (anchored by image_build.yml). buildx imagetools
# create copies the manifest into local-ai (blobs already cross-mounted)
# and publishes the manifest list with user-facing tags. End state in
# local-ai is self-contained; no embedded reference to ci-cache.
- name: Create manifest list and push (quay)
working-directory: /tmp/digests
run: |
set -euo pipefail
tags=$(jq -cr '.tags | map(select(startswith("quay.io/"))) | map("-t " + .) | join(" ")' <<< "$DOCKER_METADATA_OUTPUT_JSON")
if [ -z "$tags" ]; then
echo "No quay.io tags from docker/metadata-action; skipping quay merge"
else
# shellcheck disable=SC2086
docker buildx imagetools create $tags \
$(printf 'quay.io/go-skynet/ci-cache@sha256:%s ' *)
fi
- name: Create manifest list and push (dockerhub)
if: github.event_name != 'pull_request'
working-directory: /tmp/digests
run: |
set -euo pipefail
tags=$(jq -cr '.tags | map(select(startswith("localai/"))) | map("-t " + .) | join(" ")' <<< "$DOCKER_METADATA_OUTPUT_JSON")
if [ -z "$tags" ]; then
echo "No dockerhub tags from docker/metadata-action; skipping dockerhub merge"
else
# shellcheck disable=SC2086
docker buildx imagetools create $tags \
$(printf 'localai/localai@sha256:%s ' *)
fi
- name: Inspect manifest
run: |
set -euo pipefail
first_tag=$(jq -cr '.tags[0]' <<< "$DOCKER_METADATA_OUTPUT_JSON")
if [ -n "$first_tag" ] && [ "$first_tag" != "null" ]; then
docker buildx imagetools inspect "$first_tag"
fi
# See .github/scripts/cleanup-keepalive-tags.sh for the best-effort
# semantics — fails soft when the registry credential isn't OAuth-scoped.
- name: Cleanup keepalive tags in ci-cache
if: github.event_name != 'pull_request' && success()
env:
TAG_SUFFIX: ${{ inputs.tag-suffix == '' && '-core' || inputs.tag-suffix }}
QUAY_TOKEN: ${{ secrets.quayPassword }}
run: .github/scripts/cleanup-keepalive-tags.sh
- name: Job summary
run: |
set -euo pipefail
echo "Merged manifest tags:" >> "$GITHUB_STEP_SUMMARY"
jq -r '.tags[]' <<< "$DOCKER_METADATA_OUTPUT_JSON" | sed 's/^/- /' >> "$GITHUB_STEP_SUMMARY"
echo >> "$GITHUB_STEP_SUMMARY"
echo "Per-arch digests:" >> "$GITHUB_STEP_SUMMARY"
ls -1 /tmp/digests | sed 's/^/- sha256:/' >> "$GITHUB_STEP_SUMMARY"

View File

@@ -1,48 +0,0 @@
---
name: 'lint'
on:
pull_request:
paths-ignore:
- 'docs/**'
- 'examples/**'
- 'README.md'
- '**/*.md'
push:
branches:
- master
concurrency:
group: ci-lint-${{ github.event.pull_request.number || github.sha }}-${{ github.repository }}
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
jobs:
golangci-lint:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v6
with:
# Full history so golangci-lint's new-from-merge-base can reach
# origin/master and compute the diff against it.
fetch-depth: 0
- uses: actions/setup-go@v5
with:
go-version: '1.26.x'
cache: false
- name: install golangci-lint
run: |
curl -sSfL https://raw.githubusercontent.com/golangci/golangci-lint/master/install.sh \
| sh -s -- -b "$(go env GOPATH)/bin" v2.11.4
- name: generate grpc proto sources
# pkg/grpc/proto/*.go is generated, not checked in. Several packages
# import it, so without this step typecheck fails project-wide.
run: make protogen-go
- name: stub react-ui dist for go:embed
# core/http/app.go has //go:embed react-ui/dist/*; the glob needs at
# least one non-hidden entry to satisfy typecheck. We don't run
# `make react-ui` here because lint doesn't need the real bundle.
run: |
mkdir -p core/http/react-ui/dist
touch core/http/react-ui/dist/index.html
- name: lint
run: make lint

View File

@@ -49,8 +49,6 @@ jobs:
uses: actions/checkout@v6
with:
fetch-depth: 0
- name: Configure apt mirror on runner
uses: ./.github/actions/configure-apt-mirror
- name: Set up Go
uses: actions/setup-go@v5
with:

View File

@@ -10,8 +10,8 @@ on:
- '*'
concurrency:
group: ci-tests-extra-${{ github.event.pull_request.number || github.sha }}-${{ github.repository }}
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
group: ci-tests-extra-${{ github.head_ref || github.ref }}-${{ github.repository }}
cancel-in-progress: true
jobs:
detect-changes:
@@ -28,7 +28,6 @@ jobs:
qwen-asr: ${{ steps.detect.outputs.qwen-asr }}
nemo: ${{ steps.detect.outputs.nemo }}
voxcpm: ${{ steps.detect.outputs.voxcpm }}
liquid-audio: ${{ steps.detect.outputs.liquid-audio }}
llama-cpp-quantization: ${{ steps.detect.outputs.llama-cpp-quantization }}
llama-cpp: ${{ steps.detect.outputs.llama-cpp }}
ik-llama-cpp: ${{ steps.detect.outputs.ik-llama-cpp }}
@@ -37,14 +36,8 @@ jobs:
sglang: ${{ steps.detect.outputs.sglang }}
acestep-cpp: ${{ steps.detect.outputs.acestep-cpp }}
qwen3-tts-cpp: ${{ steps.detect.outputs.qwen3-tts-cpp }}
vibevoice-cpp: ${{ steps.detect.outputs.vibevoice-cpp }}
localvqe: ${{ steps.detect.outputs.localvqe }}
voxtral: ${{ steps.detect.outputs.voxtral }}
kokoros: ${{ steps.detect.outputs.kokoros }}
insightface: ${{ steps.detect.outputs.insightface }}
speaker-recognition: ${{ steps.detect.outputs.speaker-recognition }}
sherpa-onnx: ${{ steps.detect.outputs.sherpa-onnx }}
whisper: ${{ steps.detect.outputs.whisper }}
steps:
- name: Checkout repository
uses: actions/checkout@v6
@@ -448,32 +441,6 @@ jobs:
run: |
make --jobs=5 --output-sync=target -C backend/python/voxcpm
make --jobs=5 --output-sync=target -C backend/python/voxcpm test
# liquid-audio: LFM2.5-Audio any-to-any backend. The CI smoke test
# exercises Health() and LoadModel(mode:finetune) — fine-tune mode
# short-circuits before pulling weights (backend.py:192), so no
# HuggingFace download or GPU is needed. The full-inference path is
# gated on LIQUID_AUDIO_MODEL_ID, which we don't set here.
tests-liquid-audio:
needs: detect-changes
if: needs.detect-changes.outputs.liquid-audio == 'true' || needs.detect-changes.outputs.run-all == 'true'
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install -y build-essential ffmpeg
sudo apt-get install -y ca-certificates cmake curl patch python3-pip
# Install UV
curl -LsSf https://astral.sh/uv/install.sh | sh
pip install --user --no-cache-dir grpcio-tools==1.64.1
- name: Test liquid-audio
run: |
make --jobs=5 --output-sync=target -C backend/python/liquid-audio
make --jobs=5 --output-sync=target -C backend/python/liquid-audio test
tests-llama-cpp-quantization:
needs: detect-changes
if: needs.detect-changes.outputs.llama-cpp-quantization == 'true' || needs.detect-changes.outputs.run-all == 'true'
@@ -537,120 +504,6 @@ jobs:
- name: Build llama-cpp backend image and run audio transcription gRPC e2e tests
run: |
make test-extra-backend-llama-cpp-transcription
# PR-acceptance smoke gate: always runs on every PR (no detect-changes gate, no
# paths filter). Pulls the pre-built master CPU llama-cpp image from quay
# instead of building from source, so the cost is a docker pull (~30s) plus the
# short Qwen3-0.6B model download. Exercises the full gRPC surface — health,
# load, predict, stream — plus the logprobs/logit_bias specs that moved out of
# core/http/app_test.go. Anything heavier or per-backend is gated to the
# detect-changes path-filter above.
tests-llama-cpp-smoke:
runs-on: ubuntu-latest
timeout-minutes: 20
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Setup Go
uses: actions/setup-go@v5
with:
go-version: '1.25.4'
- name: Pull pre-built llama-cpp backend image
run: docker pull quay.io/go-skynet/local-ai-backends:master-cpu-llama-cpp
- name: Run e2e-backends smoke
env:
BACKEND_IMAGE: quay.io/go-skynet/local-ai-backends:master-cpu-llama-cpp
BACKEND_TEST_CAPS: health,load,predict,stream,logprobs,logit_bias
run: |
make test-extra-backend
# Realtime e2e with sherpa-onnx driving VAD + STT + TTS against a mocked LLM.
# Builds the sherpa-onnx Docker image, extracts the rootfs so the e2e suite
# can discover the backend binary + shared libs, downloads the three model
# bundles (silero-vad, omnilingual-asr, vits-ljs) and drives the realtime
# websocket spec end-to-end.
tests-sherpa-onnx-realtime:
needs: detect-changes
if: needs.detect-changes.outputs.sherpa-onnx == 'true' || needs.detect-changes.outputs.run-all == 'true'
runs-on: ubuntu-latest
timeout-minutes: 90
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Setup Go
uses: actions/setup-go@v5
with:
go-version: '1.25.4'
- name: Setup Node.js
uses: actions/setup-node@v6
with:
node-version: '22'
- name: Build sherpa-onnx backend image and run realtime e2e tests
run: |
make test-extra-e2e-realtime-sherpa
# Streaming ASR via the sherpa-onnx online recognizer (zipformer
# transducer). Exercises both AudioTranscription (buffered) and
# AudioTranscriptionStream (real-time deltas) on the e2e-backends
# harness.
tests-sherpa-onnx-grpc-transcription:
needs: detect-changes
if: needs.detect-changes.outputs.sherpa-onnx == 'true' || needs.detect-changes.outputs.run-all == 'true'
runs-on: ubuntu-latest
timeout-minutes: 90
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Setup Go
uses: actions/setup-go@v5
with:
go-version: '1.25.4'
- name: Build sherpa-onnx backend image and run streaming ASR gRPC e2e tests
run: |
make test-extra-backend-sherpa-onnx-transcription
# End-to-end transcription via the e2e-backends gRPC harness against
# the whisper.cpp backend. Drives AudioTranscription (offline) and
# AudioTranscriptionStream (real, segment-callback-driven deltas) on
# ggml-base.en + the JFK 11s clip.
tests-whisper-grpc-transcription:
needs: detect-changes
if: needs.detect-changes.outputs.whisper == 'true' || needs.detect-changes.outputs.run-all == 'true'
runs-on: ubuntu-latest
timeout-minutes: 90
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Setup Go
uses: actions/setup-go@v5
with:
go-version: '1.25.4'
- name: Build whisper backend image and run transcription gRPC e2e tests
run: |
make test-extra-backend-whisper-transcription
# VITS TTS via the sherpa-onnx backend. Drives both TTS (file write) and
# TTSStream (PCM chunks) on the e2e-backends harness.
tests-sherpa-onnx-grpc-tts:
needs: detect-changes
if: needs.detect-changes.outputs.sherpa-onnx == 'true' || needs.detect-changes.outputs.run-all == 'true'
runs-on: ubuntu-latest
timeout-minutes: 90
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Setup Go
uses: actions/setup-go@v5
with:
go-version: '1.25.4'
- name: Build sherpa-onnx backend image and run TTS gRPC e2e tests
run: |
make test-extra-backend-sherpa-onnx-tts
tests-ik-llama-cpp-grpc:
needs: detect-changes
if: needs.detect-changes.outputs.ik-llama-cpp == 'true' || needs.detect-changes.outputs.run-all == 'true'
@@ -843,117 +696,6 @@ jobs:
- name: Test qwen3-tts-cpp
run: |
make --jobs=5 --output-sync=target -C backend/go/qwen3-tts-cpp test
# Per-backend smoke for vibevoice-cpp: builds the .so + Go binary and
# runs `make -C backend/go/vibevoice-cpp test`. test.sh auto-downloads
# the published mudler/vibevoice.cpp-models bundle (TTS Q8_0 + ASR Q4_K
# + tokenizer + voice) and runs the closed-loop TTS → ASR Go test.
tests-vibevoice-cpp:
needs: detect-changes
if: needs.detect-changes.outputs.vibevoice-cpp == 'true' || needs.detect-changes.outputs.run-all == 'true'
runs-on: ubuntu-latest
timeout-minutes: 90
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install -y build-essential cmake curl libopenblas-dev ffmpeg
- name: Setup Go
uses: actions/setup-go@v5
- name: Display Go version
run: go version
- name: Proto Dependencies
run: |
curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v26.1/protoc-26.1-linux-x86_64.zip -o protoc.zip && \
unzip -j -d /usr/local/bin protoc.zip bin/protoc && \
rm protoc.zip
go install google.golang.org/protobuf/cmd/protoc-gen-go@v1.34.2
go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@1958fcbe2ca8bd93af633f11e97d44e567e945af
PATH="$PATH:$HOME/go/bin" make protogen-go
- name: Build vibevoice-cpp
run: |
make --jobs=5 --output-sync=target -C backend/go/vibevoice-cpp
- name: Test vibevoice-cpp
run: |
make --jobs=5 --output-sync=target -C backend/go/vibevoice-cpp test
# End-to-end TTS via the e2e-backends gRPC harness. Builds the
# vibevoice-cpp Docker image and drives Backend/TTS against it with a
# real LocalAI gRPC client.
tests-vibevoice-cpp-grpc-tts:
needs: detect-changes
if: needs.detect-changes.outputs.vibevoice-cpp == 'true' || needs.detect-changes.outputs.run-all == 'true'
runs-on: ubuntu-latest
timeout-minutes: 90
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Setup Go
uses: actions/setup-go@v5
with:
go-version: '1.25.4'
- name: Build vibevoice-cpp backend image and run TTS gRPC e2e tests
run: |
make test-extra-backend-vibevoice-cpp-tts
# End-to-end transcription via the e2e-backends gRPC harness. The
# vibevoice ASR is a 7B-param model (Q4_K weights ~10 GB on disk)
# and the JFK 30 s decode is too heavy for a free 4-core
# ubuntu-latest pool runner - two CI attempts got SIGTERM'd during
# LoadModel, before the test could even progress. Use the
# self-hosted 'bigger-runner' label (same one the GPU image builds
# in backend.yml use) and the documented dotnet/ghc/android cache
# purge to clear ~10-20 GB of headroom for the model + Docker
# image + working dir.
tests-vibevoice-cpp-grpc-transcription:
needs: detect-changes
if: needs.detect-changes.outputs.vibevoice-cpp == 'true' || needs.detect-changes.outputs.run-all == 'true'
runs-on: bigger-runner
timeout-minutes: 150
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install -y --no-install-recommends \
make build-essential curl unzip ca-certificates git tar
- name: Setup Go
uses: actions/setup-go@v5
with:
go-version: '1.25.4'
- name: Free disk space
run: |
sudo rm -rf /usr/share/dotnet /opt/ghc /usr/local/lib/android /opt/hostedtoolcache/CodeQL || true
df -h
- name: Build vibevoice-cpp backend image and run ASR gRPC e2e tests
run: |
make test-extra-backend-vibevoice-cpp-transcription
# End-to-end audio transform via the e2e-backends gRPC harness. The
# LocalVQE GGUF is small (~5 MB) and the model is real-time on CPU, so
# the default ubuntu-latest pool is plenty.
tests-localvqe-grpc-transform:
needs: detect-changes
if: needs.detect-changes.outputs.localvqe == 'true' || needs.detect-changes.outputs.run-all == 'true'
runs-on: ubuntu-latest
timeout-minutes: 60
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Setup Go
uses: actions/setup-go@v5
with:
go-version: '1.25.4'
- name: Build localvqe backend image and run audio_transform gRPC e2e tests
run: |
make test-extra-backend-localvqe-transform
tests-voxtral:
needs: detect-changes
if: needs.detect-changes.outputs.voxtral == 'true' || needs.detect-changes.outputs.run-all == 'true'
@@ -1009,55 +751,3 @@ jobs:
- name: Test kokoros
run: |
make -C backend/rust/kokoros test
tests-insightface-grpc:
needs: detect-changes
if: needs.detect-changes.outputs.insightface == 'true' || needs.detect-changes.outputs.run-all == 'true'
runs-on: ubuntu-latest
timeout-minutes: 90
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install -y --no-install-recommends \
make build-essential curl unzip ca-certificates git tar
- name: Setup Go
uses: actions/setup-go@v5
with:
go-version: '1.26.0'
- name: Free disk space
run: |
sudo rm -rf /usr/share/dotnet /opt/ghc /usr/local/lib/android /opt/hostedtoolcache/CodeQL || true
df -h
- name: Build insightface backend image and run both model configurations
run: |
make test-extra-backend-insightface-all
tests-speaker-recognition-grpc:
needs: detect-changes
if: needs.detect-changes.outputs.speaker-recognition == 'true' || needs.detect-changes.outputs.run-all == 'true'
runs-on: ubuntu-latest
timeout-minutes: 90
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install -y --no-install-recommends \
make build-essential curl ca-certificates git tar
- name: Setup Go
uses: actions/setup-go@v5
with:
go-version: '1.26.0'
- name: Free disk space
run: |
sudo rm -rf /usr/share/dotnet /opt/ghc /usr/local/lib/android /opt/hostedtoolcache/CodeQL || true
df -h
- name: Build speaker-recognition backend image and run the ECAPA-TDNN configuration
run: |
make test-extra-backend-speaker-recognition-all

View File

@@ -9,9 +9,12 @@ on:
tags:
- '*'
env:
GRPC_VERSION: v1.65.0
concurrency:
group: ci-tests-${{ github.event.pull_request.number || github.sha }}-${{ github.repository }}
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
group: ci-tests-${{ github.head_ref || github.ref }}-${{ github.repository }}
cancel-in-progress: true
jobs:
tests-linux:
@@ -20,12 +23,56 @@ jobs:
matrix:
go-version: ['1.26.x']
steps:
- name: Free Disk Space (Ubuntu)
uses: jlumbroso/free-disk-space@main
with:
# this might remove tools that are actually needed,
# if set to "true" but frees about 6 GB
tool-cache: true
# all of these default to true, but feel free to set to
# "false" if necessary for your workflow
android: true
dotnet: true
haskell: true
large-packages: true
docker-images: true
swap-storage: true
- name: Release space from worker
run: |
echo "Listing top largest packages"
pkgs=$(dpkg-query -Wf '${Installed-Size}\t${Package}\t${Status}\n' | awk '$NF == "installed"{print $1 "\t" $2}' | sort -nr)
head -n 30 <<< "${pkgs}"
echo
df -h
echo
sudo apt-get remove -y '^llvm-.*|^libllvm.*' || true
sudo apt-get remove --auto-remove android-sdk-platform-tools || true
sudo apt-get purge --auto-remove android-sdk-platform-tools || true
sudo rm -rf /usr/local/lib/android
sudo apt-get remove -y '^dotnet-.*|^aspnetcore-.*' || true
sudo rm -rf /usr/share/dotnet
sudo apt-get remove -y '^mono-.*' || true
sudo apt-get remove -y '^ghc-.*' || true
sudo apt-get remove -y '.*jdk.*|.*jre.*' || true
sudo apt-get remove -y 'php.*' || true
sudo apt-get remove -y hhvm powershell firefox monodoc-manual msbuild || true
sudo apt-get remove -y '^google-.*' || true
sudo apt-get remove -y azure-cli || true
sudo apt-get remove -y '^mongo.*-.*|^postgresql-.*|^mysql-.*|^mssql-.*' || true
sudo apt-get remove -y '^gfortran-.*' || true
sudo apt-get autoremove -y
sudo apt-get clean
echo
echo "Listing top largest packages"
pkgs=$(dpkg-query -Wf '${Installed-Size}\t${Package}\t${Status}\n' | awk '$NF == "installed"{print $1 "\t" $2}' | sort -nr)
head -n 30 <<< "${pkgs}"
echo
sudo rm -rfv build || true
df -h
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Free disk space
uses: ./.github/actions/free-disk-space
- name: Setup Go ${{ matrix.go-version }}
uses: actions/setup-go@v5
with:
@@ -53,9 +100,73 @@ jobs:
node-version: '22'
- name: Build React UI
run: make react-ui
- name: Build backends
run: |
make backends/transformers
mkdir external && mv backends/transformers external/transformers
make backends/llama-cpp backends/local-store backends/silero-vad backends/piper backends/whisper backends/stablediffusion-ggml
- name: Test
run: |
PATH="$PATH:/root/go/bin" make --jobs 5 --output-sync=target test
TRANSFORMER_BACKEND=$PWD/external/transformers/run.sh PATH="$PATH:/root/go/bin" GO_TAGS="tts" make --jobs 5 --output-sync=target test
- name: Setup tmate session if tests fail
if: ${{ failure() }}
uses: mxschmitt/action-tmate@v3.23
with:
detached: true
connect-timeout-seconds: 180
limit-access-to-actor: true
tests-e2e-container:
runs-on: ubuntu-latest
steps:
- name: Release space from worker
run: |
echo "Listing top largest packages"
pkgs=$(dpkg-query -Wf '${Installed-Size}\t${Package}\t${Status}\n' | awk '$NF == "installed"{print $1 "\t" $2}' | sort -nr)
head -n 30 <<< "${pkgs}"
echo
df -h
echo
sudo apt-get remove -y '^llvm-.*|^libllvm.*' || true
sudo apt-get remove --auto-remove android-sdk-platform-tools || true
sudo apt-get purge --auto-remove android-sdk-platform-tools || true
sudo rm -rf /usr/local/lib/android
sudo apt-get remove -y '^dotnet-.*|^aspnetcore-.*' || true
sudo rm -rf /usr/share/dotnet
sudo apt-get remove -y '^mono-.*' || true
sudo apt-get remove -y '^ghc-.*' || true
sudo apt-get remove -y '.*jdk.*|.*jre.*' || true
sudo apt-get remove -y 'php.*' || true
sudo apt-get remove -y hhvm powershell firefox monodoc-manual msbuild || true
sudo apt-get remove -y '^google-.*' || true
sudo apt-get remove -y azure-cli || true
sudo apt-get remove -y '^mongo.*-.*|^postgresql-.*|^mysql-.*|^mssql-.*' || true
sudo apt-get remove -y '^gfortran-.*' || true
sudo apt-get autoremove -y
sudo apt-get clean
echo
echo "Listing top largest packages"
pkgs=$(dpkg-query -Wf '${Installed-Size}\t${Package}\t${Status}\n' | awk '$NF == "installed"{print $1 "\t" $2}' | sort -nr)
head -n 30 <<< "${pkgs}"
echo
sudo rm -rfv build || true
df -h
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
run: |
# Install protoc
curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v26.1/protoc-26.1-linux-x86_64.zip -o protoc.zip && \
unzip -j -d /usr/local/bin protoc.zip bin/protoc && \
rm protoc.zip
go install google.golang.org/protobuf/cmd/protoc-gen-go@v1.34.2
go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@1958fcbe2ca8bd93af633f11e97d44e567e945af
PATH="$PATH:$HOME/go/bin" make protogen-go
- name: Test
run: |
PATH="$PATH:$HOME/go/bin" make backends/local-store backends/silero-vad backends/llama-cpp backends/whisper backends/piper backends/stablediffusion-ggml docker-build-e2e e2e-aio
- name: Setup tmate session if tests fail
if: ${{ failure() }}
uses: mxschmitt/action-tmate@v3.23
@@ -84,7 +195,7 @@ jobs:
run: go version
- name: Dependencies
run: |
brew install protobuf grpc make protoc-gen-go protoc-gen-go-grpc libomp llvm opus ffmpeg
brew install protobuf grpc make protoc-gen-go protoc-gen-go-grpc libomp llvm opus
pip install --user --no-cache-dir grpcio-tools grpcio
- name: Setup Node.js
uses: actions/setup-node@v6
@@ -92,6 +203,10 @@ jobs:
node-version: '22'
- name: Build React UI
run: make react-ui
- name: Build llama-cpp-darwin
run: |
make protogen-go
make backends/llama-cpp-darwin
- name: Test
run: |
export C_INCLUDE_PATH=/usr/local/include

View File

@@ -1,86 +0,0 @@
---
name: 'tests-aio'
# Runs the all-in-one (AIO) Docker image with real backends + real models.
# Heavy: builds llama-cpp/whisper/piper/silero-vad/stablediffusion-ggml/local-store
# and exercises end-to-end inference inside the container. Moved out of test.yml
# (which used to run on every PR) so PR CI no longer pays this cost.
#
# Triggers:
# - schedule (nightly @ 04:00 UTC) — catches packaging/image regressions within 24h
# - workflow_dispatch — manual run on-demand
# - push to master/tags — sanity check after merge / before release
on:
schedule:
- cron: '0 4 * * *'
workflow_dispatch:
push:
branches:
- master
tags:
- '*'
concurrency:
group: ci-tests-aio-${{ github.event.pull_request.number || github.sha }}-${{ github.repository }}
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
jobs:
tests-aio:
runs-on: ubuntu-latest
steps:
- name: Release space from worker
run: |
echo "Listing top largest packages"
pkgs=$(dpkg-query -Wf '${Installed-Size}\t${Package}\t${Status}\n' | awk '$NF == "installed"{print $1 "\t" $2}' | sort -nr)
head -n 30 <<< "${pkgs}"
echo
df -h
echo
sudo apt-get remove -y '^llvm-.*|^libllvm.*' || true
sudo apt-get remove --auto-remove android-sdk-platform-tools || true
sudo apt-get purge --auto-remove android-sdk-platform-tools || true
sudo rm -rf /usr/local/lib/android
sudo apt-get remove -y '^dotnet-.*|^aspnetcore-.*' || true
sudo rm -rf /usr/share/dotnet
sudo apt-get remove -y '^mono-.*' || true
sudo apt-get remove -y '^ghc-.*' || true
sudo apt-get remove -y '.*jdk.*|.*jre.*' || true
sudo apt-get remove -y 'php.*' || true
sudo apt-get remove -y hhvm powershell firefox monodoc-manual msbuild || true
sudo apt-get remove -y '^google-.*' || true
sudo apt-get remove -y azure-cli || true
sudo apt-get remove -y '^mongo.*-.*|^postgresql-.*|^mysql-.*|^mssql-.*' || true
sudo apt-get remove -y '^gfortran-.*' || true
sudo apt-get autoremove -y
sudo apt-get clean
echo
echo "Listing top largest packages"
pkgs=$(dpkg-query -Wf '${Installed-Size}\t${Package}\t${Status}\n' | awk '$NF == "installed"{print $1 "\t" $2}' | sort -nr)
head -n 30 <<< "${pkgs}"
echo
sudo rm -rfv build || true
df -h
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
run: |
# Install protoc
curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v26.1/protoc-26.1-linux-x86_64.zip -o protoc.zip && \
unzip -j -d /usr/local/bin protoc.zip bin/protoc && \
rm protoc.zip
go install google.golang.org/protobuf/cmd/protoc-gen-go@v1.34.2
go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@1958fcbe2ca8bd93af633f11e97d44e567e945af
PATH="$PATH:$HOME/go/bin" make protogen-go
- name: Test
run: |
PATH="$PATH:$HOME/go/bin" make backends/local-store backends/silero-vad backends/llama-cpp backends/whisper backends/piper backends/stablediffusion-ggml docker-build-e2e e2e-aio
- name: Setup tmate session if tests fail
if: ${{ failure() }}
uses: mxschmitt/action-tmate@v3.23
with:
detached: true
connect-timeout-seconds: 180
limit-access-to-actor: true

View File

@@ -10,8 +10,8 @@ on:
- '*'
concurrency:
group: ci-tests-e2e-backend-${{ github.event.pull_request.number || github.sha }}-${{ github.repository }}
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
group: ci-tests-e2e-backend-${{ github.head_ref || github.ref }}-${{ github.repository }}
cancel-in-progress: true
jobs:
tests-e2e-backend:
@@ -24,8 +24,6 @@ jobs:
uses: actions/checkout@v6
with:
submodules: true
- name: Configure apt mirror on runner
uses: ./.github/actions/configure-apt-mirror
- name: Setup Go ${{ matrix.go-version }}
uses: actions/setup-go@v5
with:

View File

@@ -12,8 +12,8 @@ on:
- master
concurrency:
group: ci-tests-ui-e2e-${{ github.event.pull_request.number || github.sha }}-${{ github.repository }}
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
group: ci-tests-ui-e2e-${{ github.head_ref || github.ref }}-${{ github.repository }}
cancel-in-progress: true
jobs:
tests-ui-e2e:
@@ -26,8 +26,6 @@ jobs:
uses: actions/checkout@v6
with:
submodules: true
- name: Configure apt mirror on runner
uses: ./.github/actions/configure-apt-mirror
- name: Setup Go ${{ matrix.go-version }}
uses: actions/setup-go@v5
with:

View File

@@ -11,8 +11,6 @@ jobs:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v6
- name: Configure apt mirror on runner
uses: ./.github/actions/configure-apt-mirror
- uses: actions/setup-go@v5
with:
go-version: 'stable'

View File

@@ -1,97 +0,0 @@
version: "2"
# Only issues introduced relative to master are reported. Pre-existing issues
# in the codebase do not fail the lint job; they're treated as a baseline that
# can be cleaned up incrementally. New code (added lines on a branch) is held
# to the full linter set. Locally, `make lint-all` overrides this and reports
# every issue.
issues:
# origin/master because in shallow CI checkouts only the remote-tracking
# branch exists; a bare 'master' ref isn't reachable locally.
new-from-merge-base: origin/master
linters:
default: standard
# staticcheck is noisy on this codebase (mostly QF style suggestions like
# "could use tagged switch" or "unnecessary fmt.Sprintf"). Re-enable
# selectively if a high-signal subset is identified.
disable:
- staticcheck
enable:
- forbidigo
settings:
forbidigo:
forbid:
- pattern: '^t\.Errorf$'
msg: 'LocalAI tests must use Ginkgo/Gomega; use Expect(...).To(...) instead of t.Errorf. See .agents/coding-style.md.'
- pattern: '^t\.Error$'
msg: 'LocalAI tests must use Ginkgo/Gomega; use Expect(...).To(...) instead of t.Error. See .agents/coding-style.md.'
- pattern: '^t\.Fatalf$'
msg: 'LocalAI tests must use Ginkgo/Gomega; use Expect(...).To(Succeed()) / Fail(...) instead of t.Fatalf. See .agents/coding-style.md.'
- pattern: '^t\.Fatal$'
msg: 'LocalAI tests must use Ginkgo/Gomega; use Expect(...).To(Succeed()) / Fail(...) instead of t.Fatal. See .agents/coding-style.md.'
- pattern: '^t\.Run$'
msg: 'LocalAI tests must use Ginkgo/Gomega; use Describe/Context/It instead of t.Run. See .agents/coding-style.md.'
- pattern: '^t\.Skip$'
msg: 'LocalAI tests must use Ginkgo/Gomega; use Skip(...) instead of t.Skip. See .agents/coding-style.md.'
- pattern: '^t\.Skipf$'
msg: 'LocalAI tests must use Ginkgo/Gomega; use Skip(...) instead of t.Skipf. See .agents/coding-style.md.'
- pattern: '^t\.SkipNow$'
msg: 'LocalAI tests must use Ginkgo/Gomega; use Skip(...) instead of t.SkipNow. See .agents/coding-style.md.'
- pattern: '^t\.Logf$'
msg: 'LocalAI tests must use Ginkgo/Gomega; use GinkgoWriter / fmt.Fprintf(GinkgoWriter, ...) instead of t.Logf. See .agents/coding-style.md.'
- pattern: '^t\.Log$'
msg: 'LocalAI tests must use Ginkgo/Gomega; use GinkgoWriter / fmt.Fprintln(GinkgoWriter, ...) instead of t.Log. See .agents/coding-style.md.'
- pattern: '^t\.Fail$'
msg: 'LocalAI tests must use Ginkgo/Gomega; use Fail(...) instead of t.Fail. See .agents/coding-style.md.'
- pattern: '^t\.FailNow$'
msg: 'LocalAI tests must use Ginkgo/Gomega; use Fail(...) instead of t.FailNow. See .agents/coding-style.md.'
# In-process config should flow through ApplicationConfig / kong-bound
# CLI flags, not via os.Getenv. The CLI layer is the legitimate
# env→struct boundary (kong's `env:"..."` tag); anything deeper that
# reads env directly leaks process state into business logic and
# makes flags impossible to test or override per-request. Backend
# subprocesses, the system/capabilities probe, and a few places that
# read non-LocalAI env vars (HOME, PATH, AUTH_TOKEN passed by parent)
# are exempt — see linters.exclusions.rules below.
- pattern: '^os\.(Getenv|LookupEnv|Environ)$'
msg: 'Plumb config through ApplicationConfig (or the relevant CLI struct) instead of reading env directly. CLI entry points (core/cli/) bind env vars via kong''s `env:` tag — that is the only sanctioned env→struct boundary. See .agents/coding-style.md.'
exclusions:
paths:
# Upstream whisper.cpp source tree fetched by the whisper backend Makefile.
- 'backend/go/whisper/sources'
- 'docs/'
rules:
# CLI entry points: kong's `env:"..."` tag is the legitimate env→struct
# boundary, and a handful of subcommands legitimately propagate values
# to spawned subprocesses (LLAMACPP_GRPC_SERVERS, MLX hostfile, ...).
- path: ^core/cli/
text: 'os\.(Getenv|LookupEnv|Environ)'
linters: [forbidigo]
# Backend subprocesses are independent binaries with their own env
# surface; they're not "in-process config" of the LocalAI server.
- path: ^backend/
text: 'os\.(Getenv|LookupEnv|Environ)'
linters: [forbidigo]
# System capability probe reads HOME, PATH-style vars to discover
# GPUs, default paths, etc. — not LocalAI config.
- path: ^pkg/system/
text: 'os\.(Getenv|LookupEnv|Environ)'
linters: [forbidigo]
# gRPC server reads AUTH_TOKEN passed in by the parent process at spawn
# time; model.Loader sets/inherits env to communicate with subprocesses.
- path: ^pkg/grpc/
text: 'os\.(Getenv|LookupEnv|Environ)'
linters: [forbidigo]
- path: ^pkg/model/
text: 'os\.(Getenv|LookupEnv|Environ)'
linters: [forbidigo]
# Top-level main binaries (local-ai, launcher) are entry points.
- path: ^cmd/
text: 'os\.(Getenv|LookupEnv|Environ)'
linters: [forbidigo]
# Tests legitimately read $HOME, $TMPDIR, and gating env vars
# (LOCALAI_COSIGN_LIVE, etc.) to skip live-network specs.
- path: _test\.go$
text: 'os\.(Getenv|LookupEnv|Environ)'
linters: [forbidigo]

View File

@@ -1,37 +1,20 @@
# LocalAI Agent Instructions
This file is the entry point for AI coding assistants (Claude Code, Cursor, Copilot, Codex, Aider, etc.) working on LocalAI. It is an index to detailed topic guides in the `.agents/` directory. Read the relevant file(s) for the task at hand — you don't need to load all of them.
Human contributors: see [CONTRIBUTING.md](CONTRIBUTING.md) for the development workflow.
## Policy for AI-Assisted Contributions
LocalAI follows the Linux kernel project's [guidelines for AI coding assistants](https://docs.kernel.org/process/coding-assistants.html). Before submitting AI-assisted code, read [.agents/ai-coding-assistants.md](.agents/ai-coding-assistants.md). Key rules:
- **No `Signed-off-by` from AI.** Only the human submitter may sign off on the Developer Certificate of Origin.
- **No `Co-Authored-By: <AI>` trailers.** The human contributor owns the change.
- **Use an `Assisted-by:` trailer** to attribute AI involvement. Format: `Assisted-by: AGENT_NAME:MODEL_VERSION [TOOL1] [TOOL2]`.
- **The human submitter is responsible** for reviewing, testing, and understanding every line of generated code.
This file is an index to detailed topic guides in the `.agents/` directory. Read the relevant file(s) for the task at hand — you don't need to load all of them.
## Topics
| File | When to read |
|------|-------------|
| [.agents/ai-coding-assistants.md](.agents/ai-coding-assistants.md) | Policy for AI-assisted contributions — licensing, DCO, attribution |
| [.agents/building-and-testing.md](.agents/building-and-testing.md) | Building the project, running tests, Docker builds for specific platforms |
| [.agents/ci-caching.md](.agents/ci-caching.md) | CI build cache layout (registry-backed BuildKit cache on quay.io/go-skynet/ci-cache, per-arch keys), `DEPS_REFRESH` weekly cache-buster for unpinned Python deps, prebuilt `base-grpc-*` images for llama.cpp variants, per-arch native + manifest-merge pattern, `setup-build-disk` `/mnt` relocation, path filter on master push, manual eviction |
| [.agents/adding-backends.md](.agents/adding-backends.md) | Adding a new backend (Python, Go, or C++) — full step-by-step checklist, including importer integration (the `/import-model` dropdown is server-driven from `GET /backends/known`) |
| [.agents/adding-backends.md](.agents/adding-backends.md) | Adding a new backend (Python, Go, or C++) — full step-by-step checklist |
| [.agents/coding-style.md](.agents/coding-style.md) | Code style, editorconfig, logging, documentation conventions |
| [.agents/llama-cpp-backend.md](.agents/llama-cpp-backend.md) | Working on the llama.cpp backend — architecture, updating, tool call parsing |
| [.agents/vllm-backend.md](.agents/vllm-backend.md) | Working on the vLLM / vLLM-omni backends — native parsers, ChatDelta, CPU build, libnuma packaging, backend hooks |
| [.agents/sglang-backend.md](.agents/sglang-backend.md) | Working on the SGLang backend — `engine_args` validation against ServerArgs, speculative-decoding (EAGLE/EAGLE3/DFLASH/MTP) recipes, parser handling |
| [.agents/ds4-backend.md](.agents/ds4-backend.md) | Working on the ds4 backend - DSML state machine, thinking modes, KV cache, Metal+CUDA matrix |
| [.agents/testing-mcp-apps.md](.agents/testing-mcp-apps.md) | Testing MCP Apps (interactive tool UIs) in the React UI |
| [.agents/api-endpoints-and-auth.md](.agents/api-endpoints-and-auth.md) | Adding API endpoints, auth middleware, feature permissions, user access control |
| [.agents/debugging-backends.md](.agents/debugging-backends.md) | Debugging runtime backend failures, dependency conflicts, rebuilding backends |
| [.agents/adding-gallery-models.md](.agents/adding-gallery-models.md) | Adding GGUF models from HuggingFace to the model gallery |
| [.agents/localai-assistant-mcp.md](.agents/localai-assistant-mcp.md) | LocalAI Assistant chat modality — adding admin tools to the in-process MCP server, editing skill prompts, keeping REST + MCP + skills in sync |
| [.agents/backend-signing.md](.agents/backend-signing.md) | Backend OCI image signing (keyless cosign + sigstore-go) — producer-side CI setup, consumer-side gallery `verification:` block, strict mode (`LOCALAI_REQUIRE_BACKEND_INTEGRITY`), revocation via `not_before` |
## Quick Reference
@@ -39,7 +22,5 @@ LocalAI follows the Linux kernel project's [guidelines for AI coding assistants]
- **Go style**: Prefer `any` over `interface{}`
- **Comments**: Explain *why*, not *what*
- **Docs**: Update `docs/content/` when adding features or changing config
- **New API endpoints**: LocalAI advertises its capability surface in several independent places — swagger `@Tags`, `/api/instructions` registry, auth `RouteFeatureRegistry`, React UI `capabilities.js`, docs. Read [.agents/api-endpoints-and-auth.md](.agents/api-endpoints-and-auth.md) and follow its checklist — missing any surface means clients, admins, and the UI won't know the endpoint exists.
- **Admin endpoints → MCP tool**: every admin endpoint that an admin would manage conversationally (install/list/edit/toggle/upgrade) MUST also be exposed as an MCP tool in `pkg/mcp/localaitools/`. The LocalAI Assistant chat modality and the standalone `local-ai mcp-server` consume that package; drift between REST and MCP is a real risk. Read [.agents/localai-assistant-mcp.md](.agents/localai-assistant-mcp.md) — the `TestToolHTTPRouteMappingComplete` test fails until you wire the new tool and update the route map.
- **Build**: Inspect `Makefile` and `.github/workflows/` — ask the user before running long builds
- **UI**: The active UI is the React app in `core/http/react-ui/`. The older Alpine.js/HTML UI in `core/http/static/` is pending deprecation — all new UI work goes in the React UI

View File

@@ -13,7 +13,6 @@ Thank you for your interest in contributing to LocalAI! We appreciate your time
- [Development Workflow](#development-workflow)
- [Creating a Pull Request (PR)](#creating-a-pull-request-pr)
- [Coding Guidelines](#coding-guidelines)
- [AI Coding Assistants](#ai-coding-assistants)
- [Testing](#testing)
- [Documentation](#documentation)
- [Community and Communication](#community-and-communication)
@@ -186,7 +185,7 @@ Before jumping into a PR for a massive feature or big change, it is preferred to
This project uses an [`.editorconfig`](.editorconfig) file to define formatting standards (indentation, line endings, charset, etc.). Please configure your editor to respect it.
For AI-assisted development, see [`AGENTS.md`](AGENTS.md) (or the equivalent [`CLAUDE.md`](CLAUDE.md) symlink) for agent-specific guidelines including build instructions and backend architecture details. Contributions produced with AI assistance must follow the rules in the [AI Coding Assistants](#ai-coding-assistants) section below.
For AI-assisted development, see [`CLAUDE.md`](CLAUDE.md) for agent-specific guidelines including build instructions and backend architecture details.
### General Principles
@@ -212,26 +211,6 @@ For AI-assisted development, see [`AGENTS.md`](AGENTS.md) (or the equivalent [`C
- Reviewers will check for correctness, test coverage, adherence to these guidelines, and clarity of intent.
- Be responsive to review feedback and keep discussions constructive.
## AI Coding Assistants
LocalAI follows the **same guidelines as the Linux kernel project** for AI-assisted contributions: <https://docs.kernel.org/process/coding-assistants.html>.
The full policy for this repository lives in [`.agents/ai-coding-assistants.md`](.agents/ai-coding-assistants.md). Summary:
- **AI agents MUST NOT add `Signed-off-by` tags.** Only humans can certify the Developer Certificate of Origin.
- **AI agents MUST NOT add `Co-Authored-By` trailers** attributing themselves as co-authors.
- **Attribute AI involvement with an `Assisted-by` trailer** in the commit message:
```
Assisted-by: AGENT_NAME:MODEL_VERSION [TOOL1] [TOOL2]
```
Example: `Assisted-by: Claude:claude-opus-4-7 golangci-lint`
Basic development tools (git, go, make, editors) should not be listed.
- **The human submitter is responsible** for reviewing, testing, and fully understanding every line of AI-generated code — including verifying that any referenced APIs, flags, or file paths actually exist in the tree.
- Contributions must remain compatible with LocalAI's **MIT License**.
## Testing
All new features and bug fixes should include test coverage. The project uses [Ginkgo](https://onsi.github.io/ginkgo/) as its test framework.

View File

@@ -1,20 +1,13 @@
ARG BASE_IMAGE=ubuntu:24.04
ARG GRPC_BASE_IMAGE=${BASE_IMAGE}
ARG INTEL_BASE_IMAGE=${BASE_IMAGE}
ARG UBUNTU_CODENAME=noble
# Optional alternate Ubuntu apt mirror(s). Empty = use upstream.
# See .docker/apt-mirror.sh for accepted values.
ARG APT_MIRROR=""
ARG APT_PORTS_MIRROR=""
FROM ${BASE_IMAGE} AS requirements
ARG APT_MIRROR
ARG APT_PORTS_MIRROR
ENV DEBIAN_FRONTEND=noninteractive
RUN --mount=type=bind,source=.docker/apt-mirror.sh,target=/usr/local/sbin/apt-mirror \
APT_MIRROR="${APT_MIRROR}" APT_PORTS_MIRROR="${APT_PORTS_MIRROR}" sh /usr/local/sbin/apt-mirror && \
apt-get update && \
RUN apt-get update && \
apt-get install -y --no-install-recommends \
ca-certificates curl wget espeak-ng libgomp1 \
ffmpeg libopenblas0 libopenblas-dev libopus0 sox && \
@@ -156,7 +149,6 @@ RUN if [ "${BUILD_TYPE}" = "hipblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then
apt-get update && \
apt-get install -y --no-install-recommends \
hipblas-dev \
hipblaslt-dev \
rocblas-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/* && \
@@ -248,14 +240,10 @@ WORKDIR /build
# This is a temporary workaround until Intel fixes their repository
FROM ${INTEL_BASE_IMAGE} AS intel
ARG UBUNTU_CODENAME=noble
ARG APT_MIRROR
ARG APT_PORTS_MIRROR
RUN wget -qO - https://repositories.intel.com/gpu/intel-graphics.key | \
gpg --yes --dearmor --output /usr/share/keyrings/intel-graphics.gpg
RUN echo "deb [arch=amd64 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/gpu/ubuntu ${UBUNTU_CODENAME}/lts/2350 unified" > /etc/apt/sources.list.d/intel-graphics.list
RUN --mount=type=bind,source=.docker/apt-mirror.sh,target=/usr/local/sbin/apt-mirror \
APT_MIRROR="${APT_MIRROR}" APT_PORTS_MIRROR="${APT_PORTS_MIRROR}" sh /usr/local/sbin/apt-mirror && \
apt-get update && \
RUN apt-get update && \
apt-get install -y --no-install-recommends \
intel-oneapi-runtime-libs && \
apt-get clean && \
@@ -305,7 +293,7 @@ EOT
###################################
# Build React UI
FROM node:26-slim AS react-ui-builder
FROM node:25-slim AS react-ui-builder
WORKDIR /app
COPY core/http/react-ui/package*.json ./
RUN npm install

450
Makefile
View File

@@ -1,5 +1,5 @@
# Disable parallel execution for backend builds
.NOTPARALLEL: backends/diffusers backends/llama-cpp backends/turboquant backends/outetts backends/piper backends/stablediffusion-ggml backends/whisper backends/faster-whisper backends/silero-vad backends/local-store backends/huggingface backends/rfdetr backends/insightface backends/speaker-recognition backends/kitten-tts backends/kokoro backends/chatterbox backends/llama-cpp-darwin backends/neutts build-darwin-python-backend build-darwin-go-backend backends/mlx backends/diffuser-darwin backends/mlx-vlm backends/mlx-audio backends/mlx-distributed backends/stablediffusion-ggml-darwin backends/vllm backends/vllm-omni backends/sglang backends/moonshine backends/pocket-tts backends/qwen-tts backends/faster-qwen3-tts backends/qwen-asr backends/nemo backends/voxcpm backends/whisperx backends/ace-step backends/acestep-cpp backends/fish-speech backends/voxtral backends/opus backends/trl backends/llama-cpp-quantization backends/kokoros backends/sam3-cpp backends/qwen3-tts-cpp backends/vibevoice-cpp backends/localvqe backends/tinygrad backends/sherpa-onnx backends/ds4 backends/ds4-darwin backends/liquid-audio
.NOTPARALLEL: backends/diffusers backends/llama-cpp backends/turboquant backends/outetts backends/piper backends/stablediffusion-ggml backends/whisper backends/faster-whisper backends/silero-vad backends/local-store backends/huggingface backends/rfdetr backends/kitten-tts backends/kokoro backends/chatterbox backends/llama-cpp-darwin backends/neutts build-darwin-python-backend build-darwin-go-backend backends/mlx backends/diffuser-darwin backends/mlx-vlm backends/mlx-audio backends/mlx-distributed backends/stablediffusion-ggml-darwin backends/vllm backends/vllm-omni backends/sglang backends/moonshine backends/pocket-tts backends/qwen-tts backends/faster-qwen3-tts backends/qwen-asr backends/nemo backends/voxcpm backends/whisperx backends/ace-step backends/acestep-cpp backends/fish-speech backends/voxtral backends/opus backends/trl backends/llama-cpp-quantization backends/kokoros backends/sam3-cpp backends/qwen3-tts-cpp backends/tinygrad
GOCMD=go
GOTEST=$(GOCMD) test
@@ -10,13 +10,6 @@ LAUNCHER_BINARY_NAME=local-ai-launcher
UBUNTU_VERSION?=2404
UBUNTU_CODENAME?=noble
# Optional Ubuntu apt mirror overrides forwarded to docker builds.
# Empty = use upstream archive.ubuntu.com / security.ubuntu.com / ports.ubuntu.com.
# Set e.g. APT_MIRROR=http://azure.archive.ubuntu.com to route apt traffic
# during outages of the default Ubuntu pool.
APT_MIRROR?=
APT_PORTS_MIRROR?=
GORELEASER?=
export BUILD_TYPE?=
@@ -72,7 +65,7 @@ endif
TEST_PATHS?=./api/... ./pkg/... ./core/...
.PHONY: all test build vendor lint lint-all
.PHONY: all test build vendor
all: help
@@ -92,7 +85,6 @@ clean: ## Remove build related file
clean-tests:
rm -rf test-models
rm -rf test-dir
rm -f tests/e2e/mock-backend/mock-backend
## Install Go tools
install-go-tools:
@@ -151,56 +143,32 @@ osx-signed: build
run: ## run local-ai
CGO_LDFLAGS="$(CGO_LDFLAGS)" $(GOCMD) run ./
prepare-test: protogen-go build-mock-backend
test-models/testmodel.ggml:
mkdir -p test-models
mkdir -p test-dir
wget -q https://huggingface.co/mradermacher/gpt2-alpaca-gpt4-GGUF/resolve/main/gpt2-alpaca-gpt4.Q4_K_M.gguf -O test-models/testmodel.ggml
wget -q https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-base.en.bin -O test-models/whisper-en
wget -q https://cdn.openai.com/whisper/draft-20220913a/micro-machines.wav -O test-dir/audio.wav
cp tests/models_fixtures/* test-models
prepare-test: protogen-go
cp tests/models_fixtures/* test-models
########################################################
## Tests
########################################################
## Test targets
## After the test-suite reorg (see plans/test-reorg) the default `make test`
## no longer downloads multi-GB GGUF/whisper fixtures or builds llama-cpp /
## transformers / piper / whisper / stablediffusion-ggml. core/http/app_test.go
## now drives the mock-backend binary built by build-mock-backend; real-backend
## inference moved into tests/e2e-backends/ (per-backend, path-filtered) and
## tests/e2e-aio/ (nightly).
test: prepare-test
test: test-models/testmodel.ggml protogen-go
@echo 'Running tests'
export GO_TAGS="debug"
$(MAKE) prepare-test
OPUS_SHIM_LIBRARY=$(abspath ./pkg/opus/shim/libopusshim.so) \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --flake-attempts $(TEST_FLAKES) --fail-fast -v -r $(TEST_PATHS)
########################################################
## Lint
########################################################
## Runs golangci-lint with config from .golangci.yml. Includes the standard
## linter set plus forbidigo, which enforces the Ginkgo/Gomega-only test
## convention documented in .agents/coding-style.md.
##
## LINT_EXCLUDE_DIRS_RE matches directories whose Go packages can't typecheck
## without C/C++ headers we don't install in the lint runner (cgo wrappers
## around llama.cpp, piper/spdlog, silero-vad/onnxruntime, and Fyne/OpenGL for
## the launcher). Their compile-time correctness is enforced by their own
## build pipelines. Keep this as a deny list — `go list ./...` discovers
## everything else automatically, so new packages are scanned by default.
LINT_EXCLUDE_DIRS_RE=/(backend/go/(piper|silero-vad|llm)|cmd/launcher)(/|$$)
lint:
@command -v golangci-lint >/dev/null 2>&1 || { \
echo 'golangci-lint not installed. Install: go install github.com/golangci/golangci-lint/v2/cmd/golangci-lint@latest'; \
exit 1; \
}
golangci-lint run $$(go list -e -f '{{.Dir}}' ./... | grep -vE '$(LINT_EXCLUDE_DIRS_RE)')
## Like `lint` but reports every issue, including the pre-existing baseline
## that `lint` ignores via .golangci.yml's new-from-merge-base. Use this to
## see what's available to clean up.
lint-all:
@command -v golangci-lint >/dev/null 2>&1 || { \
echo 'golangci-lint not installed. Install: go install github.com/golangci/golangci-lint/v2/cmd/golangci-lint@latest'; \
exit 1; \
}
golangci-lint run --new=false --new-from-merge-base= --new-from-rev= $$(go list -e -f '{{.Dir}}' ./... | grep -vE '$(LINT_EXCLUDE_DIRS_RE)')
HUGGINGFACE_GRPC=$(abspath ./)/backend/python/transformers/run.sh TEST_DIR=$(abspath ./)/test-dir/ FIXTURES=$(abspath ./)/tests/fixtures CONFIG_FILE=$(abspath ./)/test-models/config.yaml MODELS_PATH=$(abspath ./)/test-models BACKENDS_PATH=$(abspath ./)/backends \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter="!llama-gguf" --flake-attempts $(TEST_FLAKES) --fail-fast -v -r $(TEST_PATHS)
$(MAKE) test-llama-gguf
$(MAKE) test-tts
$(MAKE) test-stablediffusion
########################################################
## E2E AIO tests (uses standard image with pre-configured models)
@@ -216,8 +184,6 @@ docker-build-e2e:
--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
--build-arg APT_MIRROR=$(APT_MIRROR) \
--build-arg APT_PORTS_MIRROR=$(APT_PORTS_MIRROR) \
--build-arg GO_TAGS="$(GO_TAGS)" \
-t local-ai:tests -f Dockerfile .
@@ -232,20 +198,6 @@ run-e2e-aio: protogen-go
@echo 'Running e2e AIO tests'
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --flake-attempts $(TEST_FLAKES) -v -r ./tests/e2e-aio
# vLLM multi-node DP smoke (CPU). Builds local-ai:tests and the
# cpu-vllm backend from the current working tree, then drives a
# head + headless follower via testcontainers-go and asserts a chat
# completion. BuildKit caches both images, so re-runs only rebuild
# what changed. The test lives under tests/e2e/distributed and is
# selected by the VLLMMultinode label so it doesn't run alongside
# the other distributed-suite tests by default.
test-e2e-vllm-multinode: docker-build-e2e extract-backend-vllm protogen-go
@echo 'Running e2e vLLM multi-node DP test'
LOCALAI_IMAGE=local-ai \
LOCALAI_IMAGE_TAG=tests \
LOCALAI_VLLM_BACKEND_DIR=$(abspath ./local-backends/vllm) \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter='VLLMMultinode' -v -r ./tests/e2e/distributed
########################################################
## E2E tests
########################################################
@@ -259,8 +211,6 @@ prepare-e2e:
--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
--build-arg APT_MIRROR=$(APT_MIRROR) \
--build-arg APT_PORTS_MIRROR=$(APT_PORTS_MIRROR) \
--build-arg GO_TAGS="$(GO_TAGS)" \
--build-arg MAKEFLAGS="$(DOCKER_MAKEFLAGS)" \
-t localai-tests .
@@ -285,12 +235,20 @@ teardown-e2e:
## Integration and unit tests
########################################################
## Storage / vector-store integration. Requires the local-store backend to
## be available — we build it on demand and pass its location via
## BACKENDS_PATH (the model loader looks there for the gRPC binary).
test-stores: backends/local-store
BACKENDS_PATH=$(abspath ./)/backends \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --flake-attempts $(TEST_FLAKES) -v -r tests/integration
test-llama-gguf: prepare-test
TEST_DIR=$(abspath ./)/test-dir/ FIXTURES=$(abspath ./)/tests/fixtures CONFIG_FILE=$(abspath ./)/test-models/config.yaml MODELS_PATH=$(abspath ./)/test-models BACKENDS_PATH=$(abspath ./)/backends \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter="llama-gguf" --flake-attempts $(TEST_FLAKES) -v -r $(TEST_PATHS)
test-tts: prepare-test
TEST_DIR=$(abspath ./)/test-dir/ FIXTURES=$(abspath ./)/tests/fixtures CONFIG_FILE=$(abspath ./)/test-models/config.yaml MODELS_PATH=$(abspath ./)/test-models BACKENDS_PATH=$(abspath ./)/backends \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter="tts" --flake-attempts $(TEST_FLAKES) -v -r $(TEST_PATHS)
test-stablediffusion: prepare-test
TEST_DIR=$(abspath ./)/test-dir/ FIXTURES=$(abspath ./)/tests/fixtures CONFIG_FILE=$(abspath ./)/test-models/config.yaml MODELS_PATH=$(abspath ./)/test-models BACKENDS_PATH=$(abspath ./)/backends \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter="stablediffusion" --flake-attempts $(TEST_FLAKES) -v -r $(TEST_PATHS)
test-stores:
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter="stores" --flake-attempts $(TEST_FLAKES) -v -r tests/integration
test-opus:
@echo 'Running opus backend tests'
@@ -302,8 +260,6 @@ test-opus-docker:
docker build --target builder \
--build-arg BUILD_TYPE=$(or $(BUILD_TYPE),) \
--build-arg BASE_IMAGE=$(or $(BASE_IMAGE),ubuntu:24.04) \
--build-arg APT_MIRROR=$(APT_MIRROR) \
--build-arg APT_PORTS_MIRROR=$(APT_PORTS_MIRROR) \
--build-arg BACKEND=opus \
-t localai-opus-test -f backend/Dockerfile.golang .
docker run --rm localai-opus-test \
@@ -313,13 +269,23 @@ test-realtime: build-mock-backend
@echo 'Running realtime e2e tests (mock backend)'
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter="Realtime && !real-models" --flake-attempts $(TEST_FLAKES) -v -r ./tests/e2e
# Container-based real-model realtime testing. Build env vars / pipeline
# definition kept here so test-realtime-models-docker can drive a fully wired
# pipeline (VAD + STT + LLM + TTS) from inside a containerised runner.
# Real-model realtime tests. Set REALTIME_TEST_MODEL to use your own pipeline,
# or leave unset to auto-build one from the component env vars below.
REALTIME_VAD?=silero-vad-ggml
REALTIME_STT?=whisper-1
REALTIME_LLM?=qwen3-0.6b
REALTIME_TTS?=tts-1
REALTIME_BACKENDS_PATH?=$(abspath ./)/backends
test-realtime-models: build-mock-backend
@echo 'Running realtime e2e tests (real models)'
REALTIME_TEST_MODEL=$${REALTIME_TEST_MODEL:-realtime-test-pipeline} \
REALTIME_VAD=$(REALTIME_VAD) \
REALTIME_STT=$(REALTIME_STT) \
REALTIME_LLM=$(REALTIME_LLM) \
REALTIME_TTS=$(REALTIME_TTS) \
REALTIME_BACKENDS_PATH=$(REALTIME_BACKENDS_PATH) \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter="Realtime" --flake-attempts $(TEST_FLAKES) -v -r ./tests/e2e
# --- Container-based real-model testing ---
@@ -333,7 +299,7 @@ local-backends:
extract-backend-%: docker-build-% local-backends
@echo "Extracting backend $*..."
@CID=$$(docker create --entrypoint=/run.sh local-ai-backend:$*) && \
@CID=$$(docker create local-ai-backend:$*) && \
rm -rf local-backends/$* && mkdir -p local-backends/$* && \
docker cp $$CID:/ - | tar -xf - -C local-backends/$* && \
docker rm $$CID > /dev/null
@@ -345,8 +311,6 @@ test-realtime-models-docker: build-mock-backend
--build-arg BUILD_TYPE=$(or $(BUILD_TYPE),cublas) \
--build-arg CUDA_MAJOR_VERSION=$(or $(CUDA_MAJOR_VERSION),13) \
--build-arg CUDA_MINOR_VERSION=$(or $(CUDA_MINOR_VERSION),0) \
--build-arg APT_MIRROR=$(APT_MIRROR) \
--build-arg APT_PORTS_MIRROR=$(APT_PORTS_MIRROR) \
-t localai-test-runner .
docker run --rm \
$(REALTIME_DOCKER_FLAGS) \
@@ -430,13 +394,7 @@ protoc:
.PHONY: protogen-go
protogen-go: protoc install-go-tools
mkdir -p pkg/grpc/proto
# install-go-tools writes protoc-gen-go and protoc-gen-go-grpc into
# $(shell go env GOPATH)/bin, which isn't on every dev's PATH. protoc
# resolves its code-gen plugins via PATH, so without this prefix the
# generate step fails with "protoc-gen-go: program not found". Prepend
# GOPATH/bin so the freshly-installed plugins win without requiring a
# shell-profile change.
PATH="$$(go env GOPATH)/bin:$$PATH" ./protoc --experimental_allow_proto3_optional -Ibackend/ --go_out=pkg/grpc/proto/ --go_opt=paths=source_relative --go-grpc_out=pkg/grpc/proto/ --go-grpc_opt=paths=source_relative \
./protoc --experimental_allow_proto3_optional -Ibackend/ --go_out=pkg/grpc/proto/ --go_opt=paths=source_relative --go-grpc_out=pkg/grpc/proto/ --go-grpc_opt=paths=source_relative \
backend/backend.proto
core/config/inference_defaults.json: ## Fetch inference defaults from unsloth (only if missing)
@@ -463,7 +421,6 @@ prepare-test-extra: protogen-python
$(MAKE) -C backend/python/vllm-omni
$(MAKE) -C backend/python/sglang
$(MAKE) -C backend/python/vibevoice
$(MAKE) -C backend/python/liquid-audio
$(MAKE) -C backend/python/moonshine
$(MAKE) -C backend/python/pocket-tts
$(MAKE) -C backend/python/qwen-tts
@@ -477,8 +434,6 @@ prepare-test-extra: protogen-python
$(MAKE) -C backend/python/ace-step
$(MAKE) -C backend/python/trl
$(MAKE) -C backend/python/tinygrad
$(MAKE) -C backend/python/insightface
$(MAKE) -C backend/python/speaker-recognition
$(MAKE) -C backend/rust/kokoros kokoros-grpc
test-extra: prepare-test-extra
@@ -489,7 +444,6 @@ test-extra: prepare-test-extra
$(MAKE) -C backend/python/vllm test
$(MAKE) -C backend/python/vllm-omni test
$(MAKE) -C backend/python/vibevoice test
$(MAKE) -C backend/python/liquid-audio test
$(MAKE) -C backend/python/moonshine test
$(MAKE) -C backend/python/pocket-tts test
$(MAKE) -C backend/python/qwen-tts test
@@ -503,8 +457,6 @@ test-extra: prepare-test-extra
$(MAKE) -C backend/python/ace-step test
$(MAKE) -C backend/python/trl test
$(MAKE) -C backend/python/tinygrad test
$(MAKE) -C backend/python/insightface test
$(MAKE) -C backend/python/speaker-recognition test
$(MAKE) -C backend/rust/kokoros test
##
@@ -555,20 +507,11 @@ test-extra-backend: protogen-go
BACKEND_TEST_TOOL_NAME="$$BACKEND_TEST_TOOL_NAME" \
BACKEND_TEST_CACHE_TYPE_K="$$BACKEND_TEST_CACHE_TYPE_K" \
BACKEND_TEST_CACHE_TYPE_V="$$BACKEND_TEST_CACHE_TYPE_V" \
BACKEND_TEST_FACE_IMAGE_1_URL="$$BACKEND_TEST_FACE_IMAGE_1_URL" \
BACKEND_TEST_FACE_IMAGE_1_FILE="$$BACKEND_TEST_FACE_IMAGE_1_FILE" \
BACKEND_TEST_FACE_IMAGE_2_URL="$$BACKEND_TEST_FACE_IMAGE_2_URL" \
BACKEND_TEST_FACE_IMAGE_2_FILE="$$BACKEND_TEST_FACE_IMAGE_2_FILE" \
BACKEND_TEST_FACE_IMAGE_3_URL="$$BACKEND_TEST_FACE_IMAGE_3_URL" \
BACKEND_TEST_FACE_IMAGE_3_FILE="$$BACKEND_TEST_FACE_IMAGE_3_FILE" \
BACKEND_TEST_VERIFY_DISTANCE_CEILING="$$BACKEND_TEST_VERIFY_DISTANCE_CEILING" \
go test -v -timeout 30m ./tests/e2e-backends/...
## Convenience wrappers: build the image, then exercise it.
test-extra-backend-llama-cpp: docker-build-llama-cpp
BACKEND_IMAGE=local-ai-backend:llama-cpp \
BACKEND_TEST_CAPS=health,load,predict,stream,logprobs,logit_bias \
$(MAKE) test-extra-backend
BACKEND_IMAGE=local-ai-backend:llama-cpp $(MAKE) test-extra-backend
test-extra-backend-ik-llama-cpp: docker-build-ik-llama-cpp
BACKEND_IMAGE=local-ai-backend:ik-llama-cpp $(MAKE) test-extra-backend
@@ -596,7 +539,6 @@ test-extra-backend-llama-cpp-transcription: docker-build-llama-cpp
BACKEND_TEST_MMPROJ_URL=https://huggingface.co/ggml-org/Qwen3-ASR-0.6B-GGUF/resolve/main/mmproj-Qwen3-ASR-0.6B-Q8_0.gguf \
BACKEND_TEST_AUDIO_URL=https://github.com/ggml-org/whisper.cpp/raw/master/samples/jfk.wav \
BACKEND_TEST_CAPS=health,load,transcription \
BACKEND_TEST_CTX_SIZE=2048 \
$(MAKE) test-extra-backend
## vllm is resolved from a HuggingFace model id (no file download) and
@@ -611,14 +553,6 @@ test-extra-backend-vllm: docker-build-vllm
BACKEND_TEST_OPTIONS=tool_parser:hermes \
$(MAKE) test-extra-backend
## vllm multi-node data-parallel smoke test. Runs LocalAI head + a
## `local-ai p2p-worker vllm` follower in docker compose against
## Qwen2.5-0.5B with data_parallel_size=2. Requires 2 NVIDIA GPUs and
## nvidia-container-runtime on the host — vLLM v1's DP coordinator is
## not viable on CPU so this cannot run in CI without GPU.
test-extra-backend-vllm-multinode:
./tests/e2e/vllm-multinode/smoke.sh
## tinygrad mirrors the vllm target (same model, same caps, same parser) so
## the two backends are directly comparable. The LLM path covers Predict,
## streaming and native tool-call extraction. Companion targets below cover
@@ -669,258 +603,6 @@ test-extra-backend-tinygrad-all: \
test-extra-backend-tinygrad-sd \
test-extra-backend-tinygrad-whisper
## insightface — face recognition.
##
## Face fixtures default to the sample images shipped in the
## deepinsight/insightface repository (MIT-licensed). For offline/local
## runs override with BACKEND_TEST_FACE_IMAGE_{1,2,3}_FILE pointing at
## local paths.
FACE_IMAGE_1_URL ?= https://github.com/deepinsight/insightface/raw/master/python-package/insightface/data/images/t1.jpg
FACE_IMAGE_2_URL ?= https://github.com/deepinsight/insightface/raw/master/python-package/insightface/data/images/t1.jpg
FACE_IMAGE_3_URL ?= https://github.com/deepinsight/insightface/raw/master/python-package/insightface/data/images/mask_white.jpg
## Known spoof fixture used by the face_antispoof e2e cap. This is
## upstream's own `image_F2.jpg` (Silent-Face repo, via yakhyo mirror)
## — verified to classify as is_real=false with score < 0.05 on the
## MiniFASNetV2 + MiniFASNetV1SE ensemble.
FACE_SPOOF_IMAGE_URL ?= https://github.com/yakhyo/face-anti-spoofing/raw/main/assets/image_F2.jpg
## Host-side cache for the OpenCV Zoo face ONNX files used by the
## opencv e2e target. The backend image no longer bakes model weights —
## gallery installs bring them via `files:` — but the e2e suite drives
## LoadModel over gRPC directly without going through the gallery. We
## pre-download the ONNX files to a stable host path and pass absolute
## paths in BACKEND_TEST_OPTIONS; `make` skips the downloads when the
## SHA-256 already matches.
INSIGHTFACE_OPENCV_DIR := /tmp/localai-insightface-opencv-cache
INSIGHTFACE_OPENCV_YUNET_URL := https://github.com/opencv/opencv_zoo/raw/main/models/face_detection_yunet/face_detection_yunet_2023mar.onnx
INSIGHTFACE_OPENCV_SFACE_URL := https://github.com/opencv/opencv_zoo/raw/main/models/face_recognition_sface/face_recognition_sface_2021dec.onnx
INSIGHTFACE_OPENCV_YUNET_SHA := 8f2383e4dd3cfbb4553ea8718107fc0423210dc964f9f4280604804ed2552fa4
INSIGHTFACE_OPENCV_SFACE_SHA := 0ba9fbfa01b5270c96627c4ef784da859931e02f04419c829e83484087c34e79
## buffalo_sc (insightface) — pack zip + SHA-256 mirrors the gallery
## entry so the e2e target matches exactly what `local-ai models install
## insightface-buffalo-sc` would have fetched. Smallest insightface pack
## (~16MB) — keeps CI fast while still covering the insightface engine
## code path end-to-end.
INSIGHTFACE_BUFFALO_SC_DIR := /tmp/localai-insightface-buffalo-sc-cache
INSIGHTFACE_BUFFALO_SC_URL := https://github.com/deepinsight/insightface/releases/download/v0.7/buffalo_sc.zip
INSIGHTFACE_BUFFALO_SC_SHA := 57d31b56b6ffa911c8a73cfc1707c73cab76efe7f13b675a05223bf42de47c72
## Silent-Face antispoofing (MiniFASNetV2 + MiniFASNetV1SE) — shared
## between the buffalo_sc and opencv e2e targets. Both ONNX files are
## ~1.7MB, Apache 2.0. URLs + SHAs mirror the gallery entries.
INSIGHTFACE_ANTISPOOF_DIR := /tmp/localai-insightface-antispoof-cache
INSIGHTFACE_ANTISPOOF_V2_URL := https://github.com/yakhyo/face-anti-spoofing/releases/download/weights/MiniFASNetV2.onnx
INSIGHTFACE_ANTISPOOF_V2_SHA := b32929adc2d9c34b9486f8c4c7bc97c1b69bc0ea9befefc380e4faae4e463907
INSIGHTFACE_ANTISPOOF_V1SE_URL := https://github.com/yakhyo/face-anti-spoofing/releases/download/weights/MiniFASNetV1SE.onnx
INSIGHTFACE_ANTISPOOF_V1SE_SHA := ebab7f90c7833fbccd46d3a555410e78d969db5438e169b6524be444862b3676
.PHONY: insightface-opencv-models
insightface-opencv-models:
@mkdir -p $(INSIGHTFACE_OPENCV_DIR)
@if [ "$$(sha256sum $(INSIGHTFACE_OPENCV_DIR)/yunet.onnx 2>/dev/null | awk '{print $$1}')" != "$(INSIGHTFACE_OPENCV_YUNET_SHA)" ]; then \
echo "Fetching YuNet..."; \
curl -fsSL -o $(INSIGHTFACE_OPENCV_DIR)/yunet.onnx $(INSIGHTFACE_OPENCV_YUNET_URL); \
echo "$(INSIGHTFACE_OPENCV_YUNET_SHA) $(INSIGHTFACE_OPENCV_DIR)/yunet.onnx" | sha256sum -c; \
fi
@if [ "$$(sha256sum $(INSIGHTFACE_OPENCV_DIR)/sface.onnx 2>/dev/null | awk '{print $$1}')" != "$(INSIGHTFACE_OPENCV_SFACE_SHA)" ]; then \
echo "Fetching SFace..."; \
curl -fsSL -o $(INSIGHTFACE_OPENCV_DIR)/sface.onnx $(INSIGHTFACE_OPENCV_SFACE_URL); \
echo "$(INSIGHTFACE_OPENCV_SFACE_SHA) $(INSIGHTFACE_OPENCV_DIR)/sface.onnx" | sha256sum -c; \
fi
.PHONY: insightface-antispoof-models
insightface-antispoof-models:
@mkdir -p $(INSIGHTFACE_ANTISPOOF_DIR)
@if [ "$$(sha256sum $(INSIGHTFACE_ANTISPOOF_DIR)/MiniFASNetV2.onnx 2>/dev/null | awk '{print $$1}')" != "$(INSIGHTFACE_ANTISPOOF_V2_SHA)" ]; then \
echo "Fetching MiniFASNetV2..."; \
curl -fsSL -o $(INSIGHTFACE_ANTISPOOF_DIR)/MiniFASNetV2.onnx $(INSIGHTFACE_ANTISPOOF_V2_URL); \
echo "$(INSIGHTFACE_ANTISPOOF_V2_SHA) $(INSIGHTFACE_ANTISPOOF_DIR)/MiniFASNetV2.onnx" | sha256sum -c; \
fi
@if [ "$$(sha256sum $(INSIGHTFACE_ANTISPOOF_DIR)/MiniFASNetV1SE.onnx 2>/dev/null | awk '{print $$1}')" != "$(INSIGHTFACE_ANTISPOOF_V1SE_SHA)" ]; then \
echo "Fetching MiniFASNetV1SE..."; \
curl -fsSL -o $(INSIGHTFACE_ANTISPOOF_DIR)/MiniFASNetV1SE.onnx $(INSIGHTFACE_ANTISPOOF_V1SE_URL); \
echo "$(INSIGHTFACE_ANTISPOOF_V1SE_SHA) $(INSIGHTFACE_ANTISPOOF_DIR)/MiniFASNetV1SE.onnx" | sha256sum -c; \
fi
.PHONY: insightface-buffalo-sc-models
insightface-buffalo-sc-models:
@mkdir -p $(INSIGHTFACE_BUFFALO_SC_DIR)
@if [ "$$(sha256sum $(INSIGHTFACE_BUFFALO_SC_DIR)/buffalo_sc.zip 2>/dev/null | awk '{print $$1}')" != "$(INSIGHTFACE_BUFFALO_SC_SHA)" ]; then \
echo "Fetching buffalo_sc..."; \
curl -fsSL -o $(INSIGHTFACE_BUFFALO_SC_DIR)/buffalo_sc.zip $(INSIGHTFACE_BUFFALO_SC_URL); \
echo "$(INSIGHTFACE_BUFFALO_SC_SHA) $(INSIGHTFACE_BUFFALO_SC_DIR)/buffalo_sc.zip" | sha256sum -c; \
rm -f $(INSIGHTFACE_BUFFALO_SC_DIR)/*.onnx; \
fi
@if [ ! -f "$(INSIGHTFACE_BUFFALO_SC_DIR)/det_500m.onnx" ]; then \
echo "Extracting buffalo_sc..."; \
unzip -o -q $(INSIGHTFACE_BUFFALO_SC_DIR)/buffalo_sc.zip -d $(INSIGHTFACE_BUFFALO_SC_DIR); \
fi
## buffalo_sc — smallest insightface pack (SCRFD-500MF detector + MBF
## recognizer, ~16MB). Exercises the insightface engine code path
## (model_zoo-backed inference) without the ~326MB buffalo_l download.
## No age/gender/landmark heads — face_analyze is dropped from caps.
## The pack is pre-fetched on the host and passed as `root:<dir>` since
## the e2e suite drives LoadModel directly without going through
## LocalAI's gallery flow (which is what would normally populate
## ModelPath and in turn the engine's `_model_dir` option).
test-extra-backend-insightface-buffalo-sc: docker-build-insightface insightface-buffalo-sc-models insightface-antispoof-models
BACKEND_IMAGE=local-ai-backend:insightface \
BACKEND_TEST_MODEL_NAME=insightface-buffalo-sc \
BACKEND_TEST_OPTIONS=engine:insightface,model_pack:buffalo_sc,root:$(INSIGHTFACE_BUFFALO_SC_DIR),antispoof_v2_onnx:$(INSIGHTFACE_ANTISPOOF_DIR)/MiniFASNetV2.onnx,antispoof_v1se_onnx:$(INSIGHTFACE_ANTISPOOF_DIR)/MiniFASNetV1SE.onnx \
BACKEND_TEST_CAPS=health,load,face_detect,face_embed,face_verify,face_antispoof \
BACKEND_TEST_FACE_IMAGE_1_URL=$(FACE_IMAGE_1_URL) \
BACKEND_TEST_FACE_IMAGE_2_URL=$(FACE_IMAGE_2_URL) \
BACKEND_TEST_FACE_IMAGE_3_URL=$(FACE_IMAGE_3_URL) \
BACKEND_TEST_FACE_SPOOF_IMAGE_URL=$(FACE_SPOOF_IMAGE_URL) \
BACKEND_TEST_VERIFY_DISTANCE_CEILING=0.55 \
$(MAKE) test-extra-backend
## OpenCV Zoo YuNet + SFace — Apache 2.0, commercial-safe. face_analyze
## cap is dropped (SFace has no demographic head). The ONNX files are
## pre-fetched on the host via the insightface-opencv-models target and
## passed as absolute paths, since the e2e suite drives LoadModel
## directly without going through LocalAI's gallery flow.
test-extra-backend-insightface-opencv: docker-build-insightface insightface-opencv-models insightface-antispoof-models
BACKEND_IMAGE=local-ai-backend:insightface \
BACKEND_TEST_MODEL_NAME=insightface-opencv \
BACKEND_TEST_OPTIONS=engine:onnx_direct,detector_onnx:$(INSIGHTFACE_OPENCV_DIR)/yunet.onnx,recognizer_onnx:$(INSIGHTFACE_OPENCV_DIR)/sface.onnx,antispoof_v2_onnx:$(INSIGHTFACE_ANTISPOOF_DIR)/MiniFASNetV2.onnx,antispoof_v1se_onnx:$(INSIGHTFACE_ANTISPOOF_DIR)/MiniFASNetV1SE.onnx \
BACKEND_TEST_CAPS=health,load,face_detect,face_embed,face_verify,face_antispoof \
BACKEND_TEST_FACE_IMAGE_1_URL=$(FACE_IMAGE_1_URL) \
BACKEND_TEST_FACE_IMAGE_2_URL=$(FACE_IMAGE_2_URL) \
BACKEND_TEST_FACE_IMAGE_3_URL=$(FACE_IMAGE_3_URL) \
BACKEND_TEST_FACE_SPOOF_IMAGE_URL=$(FACE_SPOOF_IMAGE_URL) \
BACKEND_TEST_VERIFY_DISTANCE_CEILING=0.55 \
$(MAKE) test-extra-backend
## Aggregate — runs both face-recognition model configurations so CI
## catches regressions across engines together.
test-extra-backend-insightface-all: \
test-extra-backend-insightface-buffalo-sc \
test-extra-backend-insightface-opencv
## speaker-recognition — voice (speaker) biometrics.
##
## Audio fixtures default to the speechbrain test samples served
## straight from their GitHub repo — public, no auth needed, and they
## ship as 16kHz mono WAV/FLAC which is exactly what the engine wants.
## example{1,2,5} are three different speakers; the suite treats
## example1 as the "same-image twin" probe (verify(clip, clip) must
## return distance≈0) and the other two as cross-speaker ceilings.
## Override with BACKEND_TEST_VOICE_AUDIO_{1,2,3}_FILE for offline runs.
VOICE_AUDIO_1_URL ?= https://github.com/speechbrain/speechbrain/raw/develop/tests/samples/single-mic/example1.wav
VOICE_AUDIO_2_URL ?= https://github.com/speechbrain/speechbrain/raw/develop/tests/samples/single-mic/example2.flac
VOICE_AUDIO_3_URL ?= https://github.com/speechbrain/speechbrain/raw/develop/tests/samples/single-mic/example5.wav
## ECAPA-TDNN via SpeechBrain — default CI configuration. Auto-downloads
## the checkpoint from HuggingFace on first LoadModel (bundled in the
## backend image pip install). 192-d embeddings, cosine-distance based.
## The e2e suite drives LoadModel directly so we don't rely on LocalAI's
## gallery flow here.
test-extra-backend-speaker-recognition-ecapa: docker-build-speaker-recognition
BACKEND_IMAGE=local-ai-backend:speaker-recognition \
BACKEND_TEST_MODEL_NAME=speechbrain/spkrec-ecapa-voxceleb \
BACKEND_TEST_OPTIONS=engine:speechbrain,source:speechbrain/spkrec-ecapa-voxceleb \
BACKEND_TEST_CAPS=health,load,voice_embed,voice_verify \
BACKEND_TEST_VOICE_AUDIO_1_URL=$(VOICE_AUDIO_1_URL) \
BACKEND_TEST_VOICE_AUDIO_2_URL=$(VOICE_AUDIO_2_URL) \
BACKEND_TEST_VOICE_AUDIO_3_URL=$(VOICE_AUDIO_3_URL) \
BACKEND_TEST_VOICE_VERIFY_DISTANCE_CEILING=0.4 \
$(MAKE) test-extra-backend
## Aggregate — today there's only one voice config; the target exists
## so the CI workflow matches the insightface-all naming convention and
## can grow to include WeSpeaker / 3D-Speaker later.
test-extra-backend-speaker-recognition-all: \
test-extra-backend-speaker-recognition-ecapa
## Realtime e2e with sherpa-onnx driving VAD + STT + TTS against a mocked
## LLM. Extracts the sherpa-onnx Docker image rootfs, downloads the three
## gallery-referenced model bundles (silero-vad, omnilingual-asr, vits-ljs),
## writes the corresponding model config YAMLs, and runs the realtime
## websocket spec in tests/e2e with REALTIME_* env vars wiring the sherpa
## slots into the pipeline. The LLM slot stays on the in-repo mock-backend
## registered unconditionally by tests/e2e/e2e_suite_test.go. See
## tests/e2e/run-realtime-sherpa.sh for the full orchestration.
test-extra-e2e-realtime-sherpa: build-mock-backend docker-build-sherpa-onnx protogen-go react-ui
bash tests/e2e/run-realtime-sherpa.sh
## Streaming ASR via the sherpa-onnx online recognizer. Uses the streaming
## zipformer English model (encoder/decoder/joiner int8 + tokens) from the
## sherpa-onnx gallery entry. Drives both AudioTranscription and
## AudioTranscriptionStream via the e2e-backends gRPC harness; streaming
## emits real partial deltas during decode. Each file is renamed on download
## to the shape sherpa-onnx's online loader expects (encoder.int8.onnx etc.).
test-extra-backend-sherpa-onnx-transcription: docker-build-sherpa-onnx
BACKEND_IMAGE=local-ai-backend:sherpa-onnx \
BACKEND_TEST_MODEL_URL='https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-en-2023-06-26/resolve/main/encoder-epoch-99-avg-1-chunk-16-left-128.int8.onnx#encoder.int8.onnx' \
BACKEND_TEST_EXTRA_FILES='https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-en-2023-06-26/resolve/main/decoder-epoch-99-avg-1-chunk-16-left-128.int8.onnx#decoder.int8.onnx|https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-en-2023-06-26/resolve/main/joiner-epoch-99-avg-1-chunk-16-left-128.int8.onnx#joiner.int8.onnx|https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-en-2023-06-26/resolve/main/tokens.txt' \
BACKEND_TEST_AUDIO_URL=https://github.com/ggml-org/whisper.cpp/raw/master/samples/jfk.wav \
BACKEND_TEST_CAPS=health,load,transcription \
BACKEND_TEST_OPTIONS=subtype=online \
$(MAKE) test-extra-backend
## VITS TTS via the sherpa-onnx backend. Pulls the individual files from
## HuggingFace (the vits-ljs release tarball lives on the k2-fsa github
## but is also mirrored as discrete files on HF). Exercises both
## TTS (write-to-file) and TTSStream (PCM chunks + WAV header) via the
## e2e-backends gRPC harness.
test-extra-backend-sherpa-onnx-tts: docker-build-sherpa-onnx
BACKEND_IMAGE=local-ai-backend:sherpa-onnx \
BACKEND_TEST_MODEL_URL='https://huggingface.co/csukuangfj/vits-ljs/resolve/main/vits-ljs.onnx#vits-ljs.onnx' \
BACKEND_TEST_EXTRA_FILES='https://huggingface.co/csukuangfj/vits-ljs/resolve/main/tokens.txt|https://huggingface.co/csukuangfj/vits-ljs/resolve/main/lexicon.txt' \
BACKEND_TEST_CAPS=health,load,tts \
$(MAKE) test-extra-backend
## VibeVoice TTS via the vibevoice-cpp backend. ModelFile is the
## realtime gguf; the supplementary tokenizer + voice prompt land
## alongside it under the harness's models dir and are wired through
## via the standard Options[] convention (tokenizer=, voice=).
test-extra-backend-vibevoice-cpp-tts: docker-build-vibevoice-cpp
BACKEND_IMAGE=local-ai-backend:vibevoice-cpp \
BACKEND_TEST_MODEL_URL='https://huggingface.co/mudler/vibevoice.cpp-models/resolve/main/vibevoice-realtime-0.5B-q8_0.gguf#vibevoice-realtime-0.5B-q8_0.gguf' \
BACKEND_TEST_EXTRA_FILES='https://huggingface.co/mudler/vibevoice.cpp-models/resolve/main/tokenizer.gguf#tokenizer.gguf|https://huggingface.co/mudler/vibevoice.cpp-models/resolve/main/voice-en-Carter_man.gguf#voice-en-Carter_man.gguf' \
BACKEND_TEST_OPTIONS=tokenizer:tokenizer.gguf,voice:voice-en-Carter_man.gguf \
BACKEND_TEST_CAPS=health,load,tts \
$(MAKE) test-extra-backend
## VibeVoice ASR (long-form, with diarization). type=asr tells the
## backend's Load() to slot ModelFile into the asr_model role; the
## tokenizer is supplied via Options[]. Uses the Q4_K quant (~10 GB)
## rather than Q8_0 (~14 GB) so the bundle fits inside ubuntu-latest's
## post-image disk budget.
test-extra-backend-vibevoice-cpp-transcription: docker-build-vibevoice-cpp
BACKEND_IMAGE=local-ai-backend:vibevoice-cpp \
BACKEND_TEST_MODEL_URL='https://huggingface.co/mudler/vibevoice.cpp-models/resolve/main/vibevoice-asr-q4_k.gguf#vibevoice-asr-q4_k.gguf' \
BACKEND_TEST_EXTRA_FILES='https://huggingface.co/mudler/vibevoice.cpp-models/resolve/main/tokenizer.gguf#tokenizer.gguf' \
BACKEND_TEST_AUDIO_URL=https://github.com/ggml-org/whisper.cpp/raw/master/samples/jfk.wav \
BACKEND_TEST_OPTIONS=type:asr,tokenizer:tokenizer.gguf \
BACKEND_TEST_CAPS=health,load,transcription \
$(MAKE) test-extra-backend
## Audio transcription wrapper for the whisper.cpp backend.
## Drives the AudioTranscription / AudioTranscriptionStream RPCs against
## ggml-base.en (~145 MB) using the JFK 11s clip. The streaming spec
## asserts len(deltas) >= 1 and concat(deltas) == final.Text - whisper-
## specific multi-segment assertions live in backend/go/whisper/gowhisper_test.go.
test-extra-backend-whisper-transcription: docker-build-whisper
BACKEND_IMAGE=local-ai-backend:whisper \
BACKEND_TEST_MODEL_URL=https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-base.en.bin \
BACKEND_TEST_AUDIO_URL=https://github.com/ggml-org/whisper.cpp/raw/master/samples/jfk.wav \
BACKEND_TEST_CAPS=health,load,transcription \
$(MAKE) test-extra-backend
## LocalVQE audio transform (joint AEC + noise suppression + dereverb).
## Exercises the audio_transform capability end-to-end: batch transform
## of a real WAV fixture and bidi streaming of synthetic silent frames.
test-extra-backend-localvqe-transform: docker-build-localvqe
BACKEND_IMAGE=local-ai-backend:localvqe \
BACKEND_TEST_MODEL_URL='https://huggingface.co/LocalAI-io/LocalVQE/resolve/main/localvqe-v1-1.3M-f32.gguf#localvqe-v1-1.3M-f32.gguf' \
BACKEND_TEST_AUDIO_URL=https://github.com/ggml-org/whisper.cpp/raw/master/samples/jfk.wav \
BACKEND_TEST_CAPS=health,load,audio_transform \
$(MAKE) test-extra-backend
## sglang mirrors the vllm setup: HuggingFace model id, same tiny Qwen,
## tool-call extraction via sglang's native qwen parser. CPU builds use
## sglang's upstream pyproject_cpu.toml recipe (see backend/python/sglang/install.sh).
@@ -963,8 +645,6 @@ docker:
--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
--build-arg APT_MIRROR=$(APT_MIRROR) \
--build-arg APT_PORTS_MIRROR=$(APT_PORTS_MIRROR) \
-t $(DOCKER_IMAGE) .
docker-cuda12:
@@ -978,13 +658,11 @@ docker-cuda12:
--build-arg BUILD_TYPE=$(BUILD_TYPE) \
--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
--build-arg APT_MIRROR=$(APT_MIRROR) \
--build-arg APT_PORTS_MIRROR=$(APT_PORTS_MIRROR) \
-t $(DOCKER_IMAGE)-cuda-12 .
docker-image-intel:
docker build \
--build-arg BASE_IMAGE=intel/oneapi-basekit:2025.3.2-0-devel-ubuntu24.04 \
--build-arg BASE_IMAGE=intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04 \
--build-arg IMAGE_TYPE=$(IMAGE_TYPE) \
--build-arg GO_TAGS="$(GO_TAGS)" \
--build-arg MAKEFLAGS="$(DOCKER_MAKEFLAGS)" \
@@ -993,8 +671,6 @@ docker-image-intel:
--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
--build-arg APT_MIRROR=$(APT_MIRROR) \
--build-arg APT_PORTS_MIRROR=$(APT_PORTS_MIRROR) \
-t $(DOCKER_IMAGE) .
########################################################
@@ -1011,10 +687,6 @@ backends/llama-cpp-darwin: build
bash ./scripts/build/llama-cpp-darwin.sh
./local-ai backends install "ocifile://$(abspath ./backend-images/llama-cpp.tar)"
backends/ds4-darwin: build
bash ./scripts/build/ds4-darwin.sh
./local-ai backends install "ocifile://$(abspath ./backend-images/ds4.tar)"
build-darwin-python-backend: build
bash ./scripts/build/python-darwin.sh
@@ -1056,10 +728,6 @@ BACKEND_IK_LLAMA_CPP = ik-llama-cpp|ik-llama-cpp|.|false|false
# turboquant is a llama.cpp fork with TurboQuant KV-cache quantization.
# Reuses backend/cpp/llama-cpp grpc-server sources via a thin wrapper Makefile.
BACKEND_TURBOQUANT = turboquant|turboquant|.|false|false
# ds4 is antirez/ds4, a DeepSeek V4 Flash-specific inference engine.
# Single-model; hardware-only validation lives at tests/e2e-backends/
# (BACKEND_BINARY mode); see docs/superpowers/plans/2026-05-11-ds4-backend.md.
BACKEND_DS4 = ds4|ds4|.|false|false
# Golang backends
BACKEND_PIPER = piper|golang|.|false|true
@@ -1071,10 +739,7 @@ BACKEND_WHISPER = whisper|golang|.|false|true
BACKEND_VOXTRAL = voxtral|golang|.|false|true
BACKEND_ACESTEP_CPP = acestep-cpp|golang|.|false|true
BACKEND_QWEN3_TTS_CPP = qwen3-tts-cpp|golang|.|false|true
BACKEND_VIBEVOICE_CPP = vibevoice-cpp|golang|.|false|true
BACKEND_LOCALVQE = localvqe|golang|.|false|true
BACKEND_OPUS = opus|golang|.|false|true
BACKEND_SHERPA_ONNX = sherpa-onnx|golang|.|false|true
# Python backends with root context
BACKEND_RERANKERS = rerankers|python|.|false|true
@@ -1083,8 +748,6 @@ BACKEND_OUTETTS = outetts|python|.|false|true
BACKEND_FASTER_WHISPER = faster-whisper|python|.|false|true
BACKEND_COQUI = coqui|python|.|false|true
BACKEND_RFDETR = rfdetr|python|.|false|true
BACKEND_INSIGHTFACE = insightface|python|.|false|true
BACKEND_SPEAKER_RECOGNITION = speaker-recognition|python|.|false|true
BACKEND_KITTEN_TTS = kitten-tts|python|.|false|true
BACKEND_NEUTTS = neutts|python|.|false|true
BACKEND_KOKORO = kokoro|python|.|false|true
@@ -1094,7 +757,6 @@ BACKEND_SGLANG = sglang|python|.|false|true
BACKEND_DIFFUSERS = diffusers|python|.|--progress=plain|true
BACKEND_CHATTERBOX = chatterbox|python|.|false|true
BACKEND_VIBEVOICE = vibevoice|python|.|--progress=plain|true
BACKEND_LIQUID_AUDIO = liquid-audio|python|.|--progress=plain|true
BACKEND_MOONSHINE = moonshine|python|.|false|true
BACKEND_POCKET_TTS = pocket-tts|python|.|false|true
BACKEND_QWEN_TTS = qwen-tts|python|.|false|true
@@ -1128,10 +790,7 @@ define docker-build-backend
--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
--build-arg APT_MIRROR=$(APT_MIRROR) \
--build-arg APT_PORTS_MIRROR=$(APT_PORTS_MIRROR) \
$(if $(FROM_SOURCE),--build-arg FROM_SOURCE=$(FROM_SOURCE)) \
$(if $(AMDGPU_TARGETS),--build-arg AMDGPU_TARGETS=$(AMDGPU_TARGETS)) \
$(if $(filter true,$(5)),--build-arg BACKEND=$(1)) \
-t local-ai-backend:$(1) -f backend/Dockerfile.$(2) $(3)
endef
@@ -1146,7 +805,6 @@ endef
$(eval $(call generate-docker-build-target,$(BACKEND_LLAMA_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_IK_LLAMA_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_TURBOQUANT)))
$(eval $(call generate-docker-build-target,$(BACKEND_DS4)))
$(eval $(call generate-docker-build-target,$(BACKEND_PIPER)))
$(eval $(call generate-docker-build-target,$(BACKEND_LOCAL_STORE)))
$(eval $(call generate-docker-build-target,$(BACKEND_HUGGINGFACE)))
@@ -1161,8 +819,6 @@ $(eval $(call generate-docker-build-target,$(BACKEND_OUTETTS)))
$(eval $(call generate-docker-build-target,$(BACKEND_FASTER_WHISPER)))
$(eval $(call generate-docker-build-target,$(BACKEND_COQUI)))
$(eval $(call generate-docker-build-target,$(BACKEND_RFDETR)))
$(eval $(call generate-docker-build-target,$(BACKEND_INSIGHTFACE)))
$(eval $(call generate-docker-build-target,$(BACKEND_SPEAKER_RECOGNITION)))
$(eval $(call generate-docker-build-target,$(BACKEND_KITTEN_TTS)))
$(eval $(call generate-docker-build-target,$(BACKEND_NEUTTS)))
$(eval $(call generate-docker-build-target,$(BACKEND_KOKORO)))
@@ -1172,7 +828,6 @@ $(eval $(call generate-docker-build-target,$(BACKEND_SGLANG)))
$(eval $(call generate-docker-build-target,$(BACKEND_DIFFUSERS)))
$(eval $(call generate-docker-build-target,$(BACKEND_CHATTERBOX)))
$(eval $(call generate-docker-build-target,$(BACKEND_VIBEVOICE)))
$(eval $(call generate-docker-build-target,$(BACKEND_LIQUID_AUDIO)))
$(eval $(call generate-docker-build-target,$(BACKEND_MOONSHINE)))
$(eval $(call generate-docker-build-target,$(BACKEND_POCKET_TTS)))
$(eval $(call generate-docker-build-target,$(BACKEND_QWEN_TTS)))
@@ -1185,8 +840,6 @@ $(eval $(call generate-docker-build-target,$(BACKEND_WHISPERX)))
$(eval $(call generate-docker-build-target,$(BACKEND_ACE_STEP)))
$(eval $(call generate-docker-build-target,$(BACKEND_ACESTEP_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_QWEN3_TTS_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_VIBEVOICE_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_LOCALVQE)))
$(eval $(call generate-docker-build-target,$(BACKEND_MLX)))
$(eval $(call generate-docker-build-target,$(BACKEND_MLX_VLM)))
$(eval $(call generate-docker-build-target,$(BACKEND_MLX_DISTRIBUTED)))
@@ -1195,13 +848,12 @@ $(eval $(call generate-docker-build-target,$(BACKEND_LLAMA_CPP_QUANTIZATION)))
$(eval $(call generate-docker-build-target,$(BACKEND_TINYGRAD)))
$(eval $(call generate-docker-build-target,$(BACKEND_KOKOROS)))
$(eval $(call generate-docker-build-target,$(BACKEND_SAM3_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_SHERPA_ONNX)))
# Pattern rule for docker-save targets
docker-save-%: backend-images
docker save local-ai-backend:$* -o backend-images/$*.tar
docker-build-backends: docker-build-llama-cpp docker-build-ik-llama-cpp docker-build-turboquant docker-build-ds4 docker-build-rerankers docker-build-vllm docker-build-vllm-omni docker-build-sglang docker-build-transformers docker-build-outetts docker-build-diffusers docker-build-kokoro docker-build-faster-whisper docker-build-coqui docker-build-chatterbox docker-build-vibevoice docker-build-liquid-audio docker-build-moonshine docker-build-pocket-tts docker-build-qwen-tts docker-build-fish-speech docker-build-faster-qwen3-tts docker-build-qwen-asr docker-build-nemo docker-build-voxcpm docker-build-whisperx docker-build-ace-step docker-build-acestep-cpp docker-build-voxtral docker-build-mlx-distributed docker-build-trl docker-build-llama-cpp-quantization docker-build-tinygrad docker-build-kokoros docker-build-sam3-cpp docker-build-qwen3-tts-cpp docker-build-vibevoice-cpp docker-build-localvqe docker-build-insightface docker-build-speaker-recognition docker-build-sherpa-onnx
docker-build-backends: docker-build-llama-cpp docker-build-ik-llama-cpp docker-build-turboquant docker-build-rerankers docker-build-vllm docker-build-vllm-omni docker-build-sglang docker-build-transformers docker-build-outetts docker-build-diffusers docker-build-kokoro docker-build-faster-whisper docker-build-coqui docker-build-chatterbox docker-build-vibevoice docker-build-moonshine docker-build-pocket-tts docker-build-qwen-tts docker-build-fish-speech docker-build-faster-qwen3-tts docker-build-qwen-asr docker-build-nemo docker-build-voxcpm docker-build-whisperx docker-build-ace-step docker-build-acestep-cpp docker-build-voxtral docker-build-mlx-distributed docker-build-trl docker-build-llama-cpp-quantization docker-build-tinygrad docker-build-kokoros docker-build-sam3-cpp docker-build-qwen3-tts-cpp
########################################################
### Mock Backend for E2E Tests

View File

@@ -38,7 +38,7 @@
- **Built-in AI agents** — autonomous agents with tool use, RAG, MCP, and skills
- **Privacy-first** — your data never leaves your infrastructure
Created by [Ettore Di Giacinto](https://github.com/mudler) and maintained by the [LocalAI team](#team).
Created and maintained by [Ettore Di Giacinto](https://github.com/mudler).
> [:book: Documentation](https://localai.io/) | [:speech_balloon: Discord](https://discord.gg/uJAeKSAGDy) | [💻 Quickstart](https://localai.io/basics/getting_started/) | [🖼️ Models](https://models.localai.io/) | [❓FAQ](https://localai.io/faq/)
@@ -149,7 +149,6 @@ For more details, see the [Getting Started guide](https://localai.io/basics/gett
## Latest News
- **April 2026**: [Voice recognition](https://github.com/mudler/LocalAI/pull/9500), [Face recognition, identification & liveness detection](https://github.com/mudler/LocalAI/pull/9480), [Ollama API compatibility](https://github.com/mudler/LocalAI/pull/9284), [Video generation in stable-diffusion.ggml](https://github.com/mudler/LocalAI/pull/9420), [Backend versioning with auto-upgrade](https://github.com/mudler/LocalAI/pull/9315), [Pin models & load-on-demand toggle](https://github.com/mudler/LocalAI/pull/9309), [Universal model importer](https://github.com/mudler/LocalAI/pull/9466), new backends: [sglang](https://github.com/mudler/LocalAI/pull/9359), [ik-llama-cpp](https://github.com/mudler/LocalAI/pull/9326), [TurboQuant](https://github.com/mudler/LocalAI/pull/9355), [sam.cpp](https://github.com/mudler/LocalAI/pull/9288), [Kokoros](https://github.com/mudler/LocalAI/pull/9212), [qwen3tts.cpp](https://github.com/mudler/LocalAI/pull/9316), [tinygrad multimodal](https://github.com/mudler/LocalAI/pull/9364)
- **March 2026**: [Agent management](https://github.com/mudler/LocalAI/pull/8820), [New React UI](https://github.com/mudler/LocalAI/pull/8772), [WebRTC](https://github.com/mudler/LocalAI/pull/8790), [MLX-distributed via P2P and RDMA](https://github.com/mudler/LocalAI/pull/8801), [MCP Apps, MCP Client-side](https://github.com/mudler/LocalAI/pull/8947)
- **February 2026**: [Realtime API for audio-to-audio with tool calling](https://github.com/mudler/LocalAI/pull/6245), [ACE-Step 1.5 support](https://github.com/mudler/LocalAI/pull/8396)
- **January 2026**: **LocalAI 3.10.0** — Anthropic API support, Open Responses API, video & image generation (LTX-2), unified GPU backends, tool streaming, Moonshine, Pocket-TTS. [Release notes](https://github.com/mudler/LocalAI/releases/tag/v3.10.0)
@@ -201,14 +200,13 @@ See the full [Backend & Model Compatibility Table](https://localai.io/model-comp
- [Media & blog posts](https://localai.io/basics/news/#media-blogs-social)
- [Examples](https://github.com/mudler/LocalAI-examples)
## Team
## Autonomous Development Team
LocalAI is maintained by a small team of humans, together with the wider community of contributors.
LocalAI is helped being maintained by a team of autonomous AI agents led by an AI Scrum Master.
- **[Ettore Di Giacinto](https://github.com/mudler)** — original author and project lead
- **[Richard Palethorpe](https://github.com/richiejp)** — maintainer
A huge thank you to everyone who contributes code, reviews PRs, files issues, and helps users in [Discord](https://discord.gg/uJAeKSAGDy) — LocalAI is a community-driven project and wouldn't exist without you. See the full [contributors list](https://github.com/mudler/LocalAI/graphs/contributors).
- **Live Reports**: [reports.localai.io](http://reports.localai.io)
- **Project Board**: [Agent task tracking](https://github.com/users/mudler/projects/6)
- **Blog Post**: [Learn about the experiment](https://mudler.pm/posts/2026/02/28/a-call-to-open-source-maintainers-stop-babysitting-ai-how-i-built-a-100-local-autonomous-dev-team-to-maintain-localai-and-why-you-should-too/)
## Citation
@@ -251,7 +249,7 @@ A special thanks to individual sponsors, a full list is on [GitHub](https://gith
## License
LocalAI is a community-driven project created by [Ettore Di Giacinto](https://github.com/mudler/) and maintained by the [LocalAI team](#team).
LocalAI is a community-driven project created by [Ettore Di Giacinto](https://github.com/mudler/).
MIT - Author Ettore Di Giacinto <mudler@localai.io>

View File

@@ -1,98 +0,0 @@
# syntax=docker/dockerfile:1.7
#
# Pre-built builder base image for LocalAI's C++ backends.
#
# This Dockerfile is the source of truth for the
# `quay.io/go-skynet/ci-cache:base-grpc-*` images that
# `.github/workflows/base-images.yml` builds and pushes. The output of a
# build is a fully-prepped builder layer containing:
#
# - apt build deps (build-essential, ccache, git, make, pkg-config,
# libcurl4-openssl-dev, libssl-dev, curl, unzip, wget, ca-certificates)
# - cmake (apt or, when CMAKE_FROM_SOURCE=true, compiled from
# ${CMAKE_VERSION})
# - protoc v27.1 at /usr/local/bin/protoc
# - gRPC ${GRPC_VERSION} compiled and installed at /opt/grpc
# - Conditional CUDA toolkit (BUILD_TYPE=cublas|l4t, SKIP_DRIVERS=false)
# including the cuda-13 + arm64 cudss/nvpl special case
# - Conditional ROCm/HIP build deps (BUILD_TYPE=hipblas)
# - Conditional Vulkan SDK 1.4.335.0 (BUILD_TYPE=vulkan)
#
# Variants built by the workflow (matrix in base-images.yml):
#
# base-grpc-amd64 ubuntu:24.04, CPU-only
# base-grpc-arm64 ubuntu:24.04, CPU-only
# base-grpc-cuda-12-amd64 ubuntu:24.04 + CUDA 12.8
# base-grpc-cuda-13-amd64 ubuntu:22.04 + CUDA 13.0
# base-grpc-cuda-13-arm64 ubuntu:24.04 + CUDA 13.0 (sbsa)
# base-grpc-l4t-cuda-12-arm64 ubuntu:22.04 + CUDA 12.x (legacy JetPack)
# base-grpc-rocm-amd64 rocm/dev-ubuntu-24.04:7.2.1 + hipblas
# base-grpc-vulkan-amd64 ubuntu:24.04 + Vulkan SDK 1.4.335
# base-grpc-vulkan-arm64 ubuntu:24.04 + Vulkan SDK ARM 1.4.335
# base-grpc-intel-amd64 intel/oneapi-basekit:2025.3.2 (sycl)
#
# This is a SINGLE-stage Dockerfile by design: the final image IS the
# builder base. The intermediate gRPC compile happens inside this same
# stage so consumer Dockerfiles in PR 2 can simply
# `FROM quay.io/go-skynet/ci-cache:base-grpc-<variant>` without needing a
# COPY --from=grpc step. /opt/grpc is the canonical install prefix and
# downstream builds will add it to CMAKE_PREFIX_PATH (or copy to
# /usr/local) the same way Dockerfile.llama-cpp does today.
#
# Install logic lives in .docker/install-base-deps.sh, which is also
# bind-mounted by the variant Dockerfiles' builder-fromsource stage.
# This guarantees bit-equivalence between the prebuilt CI base and the
# from-source local-dev path — both invoke the same script with the
# same env inputs.
ARG BASE_IMAGE=ubuntu:24.04
FROM ${BASE_IMAGE}
ARG BASE_IMAGE=ubuntu:24.04
ARG BUILD_TYPE=""
ARG CUDA_MAJOR_VERSION=""
ARG CUDA_MINOR_VERSION=""
ARG CMAKE_FROM_SOURCE=false
# CUDA Toolkit 13.x compatibility: CMake 3.31.9+ fixes toolchain
# detection / arch table issues.
ARG CMAKE_VERSION=3.31.10
ARG GRPC_VERSION=v1.65.0
ARG GRPC_MAKEFLAGS="-j4 -Otarget"
ARG SKIP_DRIVERS=false
ARG TARGETARCH
ARG UBUNTU_VERSION=2404
ARG APT_MIRROR=""
ARG APT_PORTS_MIRROR=""
ARG AMDGPU_TARGETS=""
ENV BUILD_TYPE=${BUILD_TYPE} \
CUDA_MAJOR_VERSION=${CUDA_MAJOR_VERSION} \
CUDA_MINOR_VERSION=${CUDA_MINOR_VERSION} \
CMAKE_FROM_SOURCE=${CMAKE_FROM_SOURCE} \
CMAKE_VERSION=${CMAKE_VERSION} \
GRPC_VERSION=${GRPC_VERSION} \
GRPC_MAKEFLAGS=${GRPC_MAKEFLAGS} \
SKIP_DRIVERS=${SKIP_DRIVERS} \
TARGETARCH=${TARGETARCH} \
UBUNTU_VERSION=${UBUNTU_VERSION} \
APT_MIRROR=${APT_MIRROR} \
APT_PORTS_MIRROR=${APT_PORTS_MIRROR} \
AMDGPU_TARGETS=${AMDGPU_TARGETS} \
MAKEFLAGS=${GRPC_MAKEFLAGS} \
DEBIAN_FRONTEND=noninteractive
# CUDA on PATH (no-op when CUDA isn't installed)
ENV PATH=/usr/local/cuda/bin:${PATH}
# HipBLAS / ROCm on PATH (no-op when ROCm isn't installed)
ENV PATH=/opt/rocm/bin:${PATH}
WORKDIR /build
# Single RUN that delegates to .docker/install-base-deps.sh — the same
# script the variant Dockerfiles' builder-fromsource stage runs.
RUN --mount=type=bind,source=.docker/install-base-deps.sh,target=/usr/local/sbin/install-base-deps \
--mount=type=bind,source=.docker/apt-mirror.sh,target=/usr/local/sbin/apt-mirror \
bash /usr/local/sbin/install-base-deps
WORKDIR /

View File

@@ -1,41 +0,0 @@
ARG BASE_IMAGE=ubuntu:24.04
ARG APT_MIRROR=""
ARG APT_PORTS_MIRROR=""
# BASE_IMAGE is either ubuntu:24.04 (for cpu builds) or nvidia/cuda:13.0.0-devel-ubuntu24.04
# (for cublas builds). Both ship apt + Ubuntu Noble packages; the nvidia/cuda base
# additionally provides /usr/local/cuda. Darwin (Metal) builds bypass this Dockerfile
# entirely via scripts/build/ds4-darwin.sh.
FROM ${BASE_IMAGE} AS builder
ARG BUILD_TYPE
ARG TARGETARCH
ARG TARGETVARIANT
ENV BUILD_TYPE=${BUILD_TYPE} \
DEBIAN_FRONTEND=noninteractive \
PATH=/usr/local/cuda/bin:${PATH}
WORKDIR /build
# Install build-time deps via plain apt - install-base-deps.sh's full pipeline
# (CUDA keyring + from-source gRPC) is unnecessary here:
# - CUDA: when BASE_IMAGE=nvidia/cuda:*, /usr/local/cuda is already populated;
# for the cpu build we don't need CUDA at all.
# - gRPC/Protobuf: system apt packages are sufficient; ds4's wrapper only links
# against them, it doesn't ship the gRPC source tree.
# - nlohmann-json: dsml_renderer's only third-party dep.
RUN apt-get update && \
apt-get install -y --no-install-recommends \
git cmake build-essential pkg-config ca-certificates \
libgrpc++-dev libprotobuf-dev protobuf-compiler protobuf-compiler-grpc \
nlohmann-json3-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
COPY . /LocalAI
RUN --mount=type=cache,target=/root/.ccache,id=ds4-ccache-${TARGETARCH}-${BUILD_TYPE},sharing=locked \
make -C /LocalAI/backend/cpp/ds4 BUILD_TYPE=${BUILD_TYPE} NATIVE=false grpc-server package
FROM scratch
COPY --from=builder /LocalAI/backend/cpp/ds4/package/. ./

View File

@@ -1,6 +1,4 @@
ARG BASE_IMAGE=ubuntu:24.04
ARG APT_MIRROR=""
ARG APT_PORTS_MIRROR=""
FROM ${BASE_IMAGE} AS builder
ARG BACKEND=rerankers
@@ -16,20 +14,8 @@ ARG TARGETARCH
ARG TARGETVARIANT
ARG GO_VERSION=1.25.4
ARG UBUNTU_VERSION=2404
ARG AMDGPU_TARGETS
ENV AMDGPU_TARGETS=${AMDGPU_TARGETS}
ARG APT_MIRROR
ARG APT_PORTS_MIRROR
# gcc-14 is the default on noble (ubuntu:24.04) but absent from jammy
# (the L4T jetpack r36.4.0 base). LocalVQE specifically needs it; the
# other Go backends compile fine with the default gcc shipped via
# build-essential. So: try gcc-14 from the configured repos, fall back
# gracefully when it's not available so jammy-based builds don't fail
# at the apt step.
RUN --mount=type=bind,source=.docker/apt-mirror.sh,target=/usr/local/sbin/apt-mirror \
APT_MIRROR="${APT_MIRROR}" APT_PORTS_MIRROR="${APT_PORTS_MIRROR}" sh /usr/local/sbin/apt-mirror && \
apt-get update && \
RUN apt-get update && \
apt-get install -y --no-install-recommends \
build-essential \
git ccache \
@@ -37,12 +23,6 @@ RUN --mount=type=bind,source=.docker/apt-mirror.sh,target=/usr/local/sbin/apt-mi
make cmake wget libopenblas-dev \
curl unzip \
libssl-dev && \
if apt-cache show gcc-14 >/dev/null 2>&1 && apt-cache show g++-14 >/dev/null 2>&1; then \
apt-get install -y --no-install-recommends gcc-14 g++-14 && \
update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-14 100 \
--slave /usr/bin/g++ g++ /usr/bin/g++-14 \
--slave /usr/bin/gcov gcov /usr/bin/gcov-14; \
fi && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
@@ -167,7 +147,6 @@ RUN if [ "${BUILD_TYPE}" = "hipblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then
apt-get update && \
apt-get install -y --no-install-recommends \
hipblas-dev \
hipblaslt-dev \
rocblas-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/* && \

View File

@@ -1,149 +1,279 @@
ARG BASE_IMAGE=ubuntu:24.04
# BUILDER_BASE_IMAGE defaults to BASE_IMAGE so the Dockerfile parses even
# when no prebuilt base is supplied. The builder-prebuilt stage is only
# entered when BUILDER_TARGET=builder-prebuilt, so a "wrong" fallback
# content here is harmless — BuildKit prunes the unreferenced builder.
ARG BUILDER_BASE_IMAGE=${BASE_IMAGE}
# BUILDER_TARGET selects which builder stage the final scratch image copies
# package output from. Declared at global scope (before any FROM) so it's
# usable in `FROM ${BUILDER_TARGET}` below. Default keeps local
# `make backends/ik-llama-cpp` on the from-source path.
ARG BUILDER_TARGET=builder-fromsource
ARG APT_MIRROR=""
ARG APT_PORTS_MIRROR=""
ARG GRPC_BASE_IMAGE=${BASE_IMAGE}
# ============================================================================
# Stage: builder-fromsource — self-contained build path.
# Runs .docker/install-base-deps.sh (apt deps + cmake + protoc + gRPC +
# conditional CUDA/ROCm/Vulkan), copies /opt/grpc to /usr/local, then
# compiles the variant. Used when BUILDER_TARGET=builder-fromsource (the
# default; local `make backends/ik-llama-cpp`).
#
# The install script is the same one that backend/Dockerfile.base-grpc-builder
# runs, so the result is bit-equivalent to the prebuilt-base path
# (builder-prebuilt below).
# ============================================================================
FROM ${BASE_IMAGE} AS builder-fromsource
ARG BUILD_TYPE
ARG CUDA_MAJOR_VERSION
ARG CUDA_MINOR_VERSION
# The grpc target does one thing, it builds and installs GRPC. This is in it's own layer so that it can be effectively cached by CI.
# You probably don't need to change anything here, and if you do, make sure that CI is adjusted so that the cache continues to work.
FROM ${GRPC_BASE_IMAGE} AS grpc
# This is a bit of a hack, but it's required in order to be able to effectively cache this layer in CI
ARG GRPC_MAKEFLAGS="-j4 -Otarget"
ARG GRPC_VERSION=v1.65.0
ARG CMAKE_FROM_SOURCE=false
# CUDA Toolkit 13.x compatibility: CMake 3.31.9+ fixes toolchain detection/arch table issues
ARG CMAKE_VERSION=3.31.10
ARG GRPC_VERSION=v1.65.0
ARG GRPC_MAKEFLAGS="-j4 -Otarget"
ENV MAKEFLAGS=${GRPC_MAKEFLAGS}
WORKDIR /build
RUN apt-get update && \
apt-get install -y --no-install-recommends \
ca-certificates \
build-essential curl libssl-dev \
git wget && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
# Install CMake (the version in 22.04 is too old)
RUN <<EOT bash
if [ "${CMAKE_FROM_SOURCE}" = "true" ]; then
curl -L -s https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}.tar.gz -o cmake.tar.gz && tar xvf cmake.tar.gz && cd cmake-${CMAKE_VERSION} && ./configure && make && make install
else
apt-get update && \
apt-get install -y \
cmake && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
# We install GRPC to a different prefix here so that we can copy in only the build artifacts later
# saves several hundred MB on the final docker image size vs copying in the entire GRPC source tree
# and running make install in the target container
RUN git clone --recurse-submodules --jobs 4 -b ${GRPC_VERSION} --depth 1 --shallow-submodules https://github.com/grpc/grpc && \
mkdir -p /build/grpc/cmake/build && \
cd /build/grpc/cmake/build && \
sed -i "216i\ TESTONLY" "../../third_party/abseil-cpp/absl/container/CMakeLists.txt" && \
cmake -DgRPC_INSTALL=ON -DgRPC_BUILD_TESTS=OFF -DCMAKE_INSTALL_PREFIX:PATH=/opt/grpc ../.. && \
make && \
make install && \
rm -rf /build
FROM ${BASE_IMAGE} AS builder
ARG CMAKE_FROM_SOURCE=false
ARG CMAKE_VERSION=3.31.10
# We can target specific CUDA ARCHITECTURES like --build-arg CUDA_DOCKER_ARCH='75;86;89;120'
ARG CUDA_DOCKER_ARCH
ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
ARG CMAKE_ARGS
ENV CMAKE_ARGS=${CMAKE_ARGS}
ARG BACKEND=rerankers
ARG BUILD_TYPE
ENV BUILD_TYPE=${BUILD_TYPE}
ARG CUDA_MAJOR_VERSION
ARG CUDA_MINOR_VERSION
ARG SKIP_DRIVERS=false
ENV CUDA_MAJOR_VERSION=${CUDA_MAJOR_VERSION}
ENV CUDA_MINOR_VERSION=${CUDA_MINOR_VERSION}
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETARCH
ARG TARGETVARIANT
ARG GO_VERSION=1.25.4
ARG UBUNTU_VERSION=2404
ARG APT_MIRROR
ARG APT_PORTS_MIRROR
ARG AMDGPU_TARGETS=""
ARG BACKEND=rerankers
# CUDA target archs, e.g. --build-arg CUDA_DOCKER_ARCH='75;86;89;120'
ARG CUDA_DOCKER_ARCH
ARG CMAKE_ARGS
ENV BUILD_TYPE=${BUILD_TYPE} \
CUDA_MAJOR_VERSION=${CUDA_MAJOR_VERSION} \
CUDA_MINOR_VERSION=${CUDA_MINOR_VERSION} \
CMAKE_FROM_SOURCE=${CMAKE_FROM_SOURCE} \
CMAKE_VERSION=${CMAKE_VERSION} \
GRPC_VERSION=${GRPC_VERSION} \
GRPC_MAKEFLAGS=${GRPC_MAKEFLAGS} \
SKIP_DRIVERS=${SKIP_DRIVERS} \
TARGETARCH=${TARGETARCH} \
UBUNTU_VERSION=${UBUNTU_VERSION} \
APT_MIRROR=${APT_MIRROR} \
APT_PORTS_MIRROR=${APT_PORTS_MIRROR} \
AMDGPU_TARGETS=${AMDGPU_TARGETS} \
CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH} \
CMAKE_ARGS=${CMAKE_ARGS} \
DEBIAN_FRONTEND=noninteractive
RUN apt-get update && \
apt-get install -y --no-install-recommends \
build-essential \
ccache git \
ca-certificates \
make \
pkg-config libcurl4-openssl-dev \
curl unzip \
libssl-dev wget && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
# CUDA on PATH (no-op when CUDA isn't installed)
# Cuda
ENV PATH=/usr/local/cuda/bin:${PATH}
# HipBLAS / ROCm on PATH (no-op when ROCm isn't installed)
# HipBLAS requirements
ENV PATH=/opt/rocm/bin:${PATH}
WORKDIR /build
# Install everything via the shared script — the same one that
# backend/Dockerfile.base-grpc-builder runs, so the prebuilt CI base and
# this from-source path are bit-equivalent.
RUN --mount=type=bind,source=.docker/install-base-deps.sh,target=/usr/local/sbin/install-base-deps \
--mount=type=bind,source=.docker/apt-mirror.sh,target=/usr/local/sbin/apt-mirror \
bash /usr/local/sbin/install-base-deps
# Vulkan requirements
RUN <<EOT bash
if [ "${BUILD_TYPE}" = "vulkan" ] && [ "${SKIP_DRIVERS}" = "false" ]; then
apt-get update && \
apt-get install -y --no-install-recommends \
software-properties-common pciutils wget gpg-agent && \
apt-get install -y libglm-dev cmake libxcb-dri3-0 libxcb-present0 libpciaccess0 \
libpng-dev libxcb-keysyms1-dev libxcb-dri3-dev libx11-dev g++ gcc \
libwayland-dev libxrandr-dev libxcb-randr0-dev libxcb-ewmh-dev \
git python-is-python3 bison libx11-xcb-dev liblz4-dev libzstd-dev \
ocaml-core ninja-build pkg-config libxml2-dev wayland-protocols python3-jsonschema \
clang-format qtbase5-dev qt6-base-dev libxcb-glx0-dev sudo xz-utils
if [ "amd64" = "$TARGETARCH" ]; then
wget "https://sdk.lunarg.com/sdk/download/1.4.335.0/linux/vulkansdk-linux-x86_64-1.4.335.0.tar.xz" && \
tar -xf vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
rm vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
mkdir -p /opt/vulkan-sdk && \
mv 1.4.335.0 /opt/vulkan-sdk/ && \
cd /opt/vulkan-sdk/1.4.335.0 && \
./vulkansdk --no-deps --maxjobs \
vulkan-loader \
vulkan-validationlayers \
vulkan-extensionlayer \
vulkan-tools \
shaderc && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/bin/* /usr/bin/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/lib/* /usr/lib/x86_64-linux-gnu/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/include/* /usr/include/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/share/* /usr/share/ && \
rm -rf /opt/vulkan-sdk
fi
if [ "arm64" = "$TARGETARCH" ]; then
mkdir vulkan && cd vulkan && \
curl -L -o vulkan-sdk.tar.xz https://github.com/mudler/vulkan-sdk-arm/releases/download/1.4.335.0/vulkansdk-ubuntu-24.04-arm-1.4.335.0.tar.xz && \
tar -xvf vulkan-sdk.tar.xz && \
rm vulkan-sdk.tar.xz && \
cd 1.4.335.0 && \
cp -rfv aarch64/bin/* /usr/bin/ && \
cp -rfv aarch64/lib/* /usr/lib/aarch64-linux-gnu/ && \
cp -rfv aarch64/include/* /usr/include/ && \
cp -rfv aarch64/share/* /usr/share/ && \
cd ../.. && \
rm -rf vulkan
fi
ldconfig && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
# CuBLAS requirements
RUN <<EOT bash
if ( [ "${BUILD_TYPE}" = "cublas" ] || [ "${BUILD_TYPE}" = "l4t" ] ) && [ "${SKIP_DRIVERS}" = "false" ]; then
apt-get update && \
apt-get install -y --no-install-recommends \
software-properties-common pciutils
if [ "amd64" = "$TARGETARCH" ]; then
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/x86_64/cuda-keyring_1.1-1_all.deb
fi
if [ "arm64" = "$TARGETARCH" ]; then
if [ "${CUDA_MAJOR_VERSION}" = "13" ]; then
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/sbsa/cuda-keyring_1.1-1_all.deb
else
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/arm64/cuda-keyring_1.1-1_all.deb
fi
fi
dpkg -i cuda-keyring_1.1-1_all.deb && \
rm -f cuda-keyring_1.1-1_all.deb && \
apt-get update && \
apt-get install -y --no-install-recommends \
cuda-nvcc-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcufft-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcurand-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcublas-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcusparse-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcusolver-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
if [ "${CUDA_MAJOR_VERSION}" = "13" ] && [ "arm64" = "$TARGETARCH" ]; then
apt-get install -y --no-install-recommends \
libcufile-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libcudnn9-cuda-${CUDA_MAJOR_VERSION} cuda-cupti-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libnvjitlink-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
fi
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
# https://github.com/NVIDIA/Isaac-GR00T/issues/343
RUN <<EOT bash
if [ "${BUILD_TYPE}" = "cublas" ] && [ "${TARGETARCH}" = "arm64" ]; then
wget https://developer.download.nvidia.com/compute/cudss/0.6.0/local_installers/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
dpkg -i cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
cp /var/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0/cudss-*-keyring.gpg /usr/share/keyrings/ && \
apt-get update && apt-get -y install cudss cudss-cuda-${CUDA_MAJOR_VERSION} && \
wget https://developer.download.nvidia.com/compute/nvpl/25.5/local_installers/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
dpkg -i nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
cp /var/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5/nvpl-*-keyring.gpg /usr/share/keyrings/ && \
apt-get update && apt-get install -y nvpl
fi
EOT
# If we are building with clblas support, we need the libraries for the builds
RUN if [ "${BUILD_TYPE}" = "clblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then \
apt-get update && \
apt-get install -y --no-install-recommends \
libclblast-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/* \
; fi
RUN if [ "${BUILD_TYPE}" = "hipblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then \
apt-get update && \
apt-get install -y --no-install-recommends \
hipblas-dev \
rocblas-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/* && \
# I have no idea why, but the ROCM lib packages don't trigger ldconfig after they install, which results in local-ai and others not being able
# to locate the libraries. We run ldconfig ourselves to work around this packaging deficiency
ldconfig \
; fi
RUN echo "TARGETARCH: $TARGETARCH"
# We need protoc installed, and the version in 22.04 is too old. We will create one as part installing the GRPC build below
# but that will also being in a newer version of absl which stablediffusion cannot compile with. This version of protoc is only
# here so that we can generate the grpc code for the stablediffusion build
RUN <<EOT bash
if [ "amd64" = "$TARGETARCH" ]; then
curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v27.1/protoc-27.1-linux-x86_64.zip -o protoc.zip && \
unzip -j -d /usr/local/bin protoc.zip bin/protoc && \
rm protoc.zip
fi
if [ "arm64" = "$TARGETARCH" ]; then
curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v27.1/protoc-27.1-linux-aarch_64.zip -o protoc.zip && \
unzip -j -d /usr/local/bin protoc.zip bin/protoc && \
rm protoc.zip
fi
EOT
# Install CMake (the version in 22.04 is too old)
RUN <<EOT bash
if [ "${CMAKE_FROM_SOURCE}" = "true" ]; then
curl -L -s https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}.tar.gz -o cmake.tar.gz && tar xvf cmake.tar.gz && cd cmake-${CMAKE_VERSION} && ./configure && make && make install
else
apt-get update && \
apt-get install -y \
cmake && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
COPY --from=grpc /opt/grpc /usr/local
# Mirror builder-prebuilt: copy gRPC from /opt/grpc to /usr/local so
# CMake's find_package finds it at the canonical prefix the Makefile expects.
RUN cp -a /opt/grpc/. /usr/local/
COPY . /LocalAI
# BuildKit cache mount for ccache. See Dockerfile.llama-cpp (commit 9228e5b4)
# for the rationale. Distinct mount id so ik-llama-cpp's cache doesn't
# overlap with llama-cpp's — ik_llama.cpp is a different fork with
# different source.
#
# The compile body is shared with builder-prebuilt via .docker/ik-llama-cpp-compile.sh.
RUN --mount=type=bind,source=.docker/ik-llama-cpp-compile.sh,target=/usr/local/sbin/compile.sh \
--mount=type=cache,target=/root/.ccache,id=ik-llama-cpp-ccache-${TARGETARCH}-${BUILD_TYPE},sharing=locked \
bash /usr/local/sbin/compile.sh
RUN <<'EOT' bash
set -euxo pipefail
if [[ -n "${CUDA_DOCKER_ARCH:-}" ]]; then
CUDA_ARCH_ESC="${CUDA_DOCKER_ARCH//;/\\;}"
export CMAKE_ARGS="${CMAKE_ARGS:-} -DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCH_ESC}"
echo "CMAKE_ARGS(env) = ${CMAKE_ARGS}"
rm -rf /LocalAI/backend/cpp/ik-llama-cpp-*-build
fi
cd /LocalAI/backend/cpp/ik-llama-cpp
if [ "${TARGETARCH}" = "arm64" ] || [ "${BUILD_TYPE}" = "hipblas" ]; then
# ARM64 / ROCm: build without x86 SIMD
make ik-llama-cpp-fallback
else
# ik_llama.cpp's IQK kernels require at least AVX2
make ik-llama-cpp-avx2
fi
EOT
# Copy libraries using a script to handle architecture differences
RUN make -BC /LocalAI/backend/cpp/ik-llama-cpp package
# ============================================================================
# Stage: builder-prebuilt — uses the pre-built base from
# quay.io/go-skynet/ci-cache:base-grpc-* (built by .github/workflows/base-images.yml).
# That image already has gRPC at /opt/grpc + apt deps + CUDA/ROCm/Vulkan
# pre-installed, so we just copy gRPC to /usr/local and compile. Used when
# BUILDER_TARGET=builder-prebuilt (CI when the matrix entry sets
# builder-base-image).
# ============================================================================
FROM ${BUILDER_BASE_IMAGE} AS builder-prebuilt
ARG BUILD_TYPE
ENV BUILD_TYPE=${BUILD_TYPE}
ARG CUDA_DOCKER_ARCH
ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
ARG CMAKE_ARGS
ENV CMAKE_ARGS=${CMAKE_ARGS}
ARG TARGETARCH
ARG TARGETVARIANT
# The base-grpc-* image installs gRPC to /opt/grpc but doesn't copy it to
# /usr/local. Mirror what the from-source path does so the compile step
# can find gRPC at the canonical prefix the Makefile expects.
RUN cp -a /opt/grpc/. /usr/local/
COPY . /LocalAI
RUN --mount=type=bind,source=.docker/ik-llama-cpp-compile.sh,target=/usr/local/sbin/compile.sh \
--mount=type=cache,target=/root/.ccache,id=ik-llama-cpp-ccache-${TARGETARCH}-${BUILD_TYPE},sharing=locked \
bash /usr/local/sbin/compile.sh
RUN make -BC /LocalAI/backend/cpp/ik-llama-cpp package
# ============================================================================
# Final stage — copies package output from one of the two builders.
# BUILDER_TARGET selects which one. BuildKit prunes the unreferenced builder.
#
# BuildKit doesn't support variable expansion in `COPY --from=` directly,
# so we resolve the ARG by aliasing the chosen builder to a fixed stage
# name via `FROM ${BUILDER_TARGET} AS builder` and then COPY --from=builder.
# BUILDER_TARGET itself is declared as a global ARG at the top of this
# file (required for use in FROM), so we just re-import it into this
# stage's scope before the FROM directive.
# ============================================================================
FROM ${BUILDER_TARGET} AS builder
FROM scratch

View File

@@ -1,155 +1,290 @@
ARG BASE_IMAGE=ubuntu:24.04
# BUILDER_BASE_IMAGE defaults to BASE_IMAGE so the Dockerfile parses even
# when no prebuilt base is supplied. The builder-prebuilt stage is only
# entered when BUILDER_TARGET=builder-prebuilt, so a "wrong" fallback
# content here is harmless — BuildKit prunes the unreferenced builder.
ARG BUILDER_BASE_IMAGE=${BASE_IMAGE}
# BUILDER_TARGET selects which builder stage the final scratch image copies
# package output from. Declared at global scope (before any FROM) so it's
# usable in `FROM ${BUILDER_TARGET}` below. Default keeps local
# `make backends/llama-cpp` on the from-source path.
ARG BUILDER_TARGET=builder-fromsource
ARG APT_MIRROR=""
ARG APT_PORTS_MIRROR=""
ARG GRPC_BASE_IMAGE=${BASE_IMAGE}
# ============================================================================
# Stage: builder-fromsource — self-contained build path.
# Runs .docker/install-base-deps.sh (apt deps + cmake + protoc + gRPC +
# conditional CUDA/ROCm/Vulkan), copies /opt/grpc to /usr/local, then
# compiles the variant. Used when BUILDER_TARGET=builder-fromsource (the
# default; local `make backends/llama-cpp`).
#
# The install script is the same one that backend/Dockerfile.base-grpc-builder
# runs, so the result is bit-equivalent to the prebuilt-base path
# (builder-prebuilt below).
# ============================================================================
FROM ${BASE_IMAGE} AS builder-fromsource
ARG BUILD_TYPE
ARG CUDA_MAJOR_VERSION
ARG CUDA_MINOR_VERSION
# The grpc target does one thing, it builds and installs GRPC. This is in it's own layer so that it can be effectively cached by CI.
# You probably don't need to change anything here, and if you do, make sure that CI is adjusted so that the cache continues to work.
FROM ${GRPC_BASE_IMAGE} AS grpc
# This is a bit of a hack, but it's required in order to be able to effectively cache this layer in CI
ARG GRPC_MAKEFLAGS="-j4 -Otarget"
ARG GRPC_VERSION=v1.65.0
ARG CMAKE_FROM_SOURCE=false
# CUDA Toolkit 13.x compatibility: CMake 3.31.9+ fixes toolchain detection/arch table issues
ARG CMAKE_VERSION=3.31.10
ARG GRPC_VERSION=v1.65.0
ARG GRPC_MAKEFLAGS="-j4 -Otarget"
ARG SKIP_DRIVERS=false
ARG TARGETARCH
ARG TARGETVARIANT
ARG GO_VERSION=1.25.4
ARG UBUNTU_VERSION=2404
ARG APT_MIRROR
ARG APT_PORTS_MIRROR
ARG AMDGPU_TARGETS
# CUDA target archs, e.g. --build-arg CUDA_DOCKER_ARCH='75;86;89;120'
ARG CUDA_DOCKER_ARCH
ARG CMAKE_ARGS
ENV BUILD_TYPE=${BUILD_TYPE} \
CUDA_MAJOR_VERSION=${CUDA_MAJOR_VERSION} \
CUDA_MINOR_VERSION=${CUDA_MINOR_VERSION} \
CMAKE_FROM_SOURCE=${CMAKE_FROM_SOURCE} \
CMAKE_VERSION=${CMAKE_VERSION} \
GRPC_VERSION=${GRPC_VERSION} \
GRPC_MAKEFLAGS=${GRPC_MAKEFLAGS} \
SKIP_DRIVERS=${SKIP_DRIVERS} \
TARGETARCH=${TARGETARCH} \
UBUNTU_VERSION=${UBUNTU_VERSION} \
APT_MIRROR=${APT_MIRROR} \
APT_PORTS_MIRROR=${APT_PORTS_MIRROR} \
AMDGPU_TARGETS=${AMDGPU_TARGETS} \
CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH} \
CMAKE_ARGS=${CMAKE_ARGS} \
DEBIAN_FRONTEND=noninteractive
# CUDA on PATH (no-op when CUDA isn't installed)
ENV PATH=/usr/local/cuda/bin:${PATH}
# HipBLAS / ROCm on PATH (no-op when ROCm isn't installed)
ENV PATH=/opt/rocm/bin:${PATH}
ENV MAKEFLAGS=${GRPC_MAKEFLAGS}
WORKDIR /build
# Install everything via the shared script — the same one that
# backend/Dockerfile.base-grpc-builder runs, so the prebuilt CI base and
# this from-source path are bit-equivalent.
RUN --mount=type=bind,source=.docker/install-base-deps.sh,target=/usr/local/sbin/install-base-deps \
--mount=type=bind,source=.docker/apt-mirror.sh,target=/usr/local/sbin/apt-mirror \
bash /usr/local/sbin/install-base-deps
RUN apt-get update && \
apt-get install -y --no-install-recommends \
ca-certificates \
build-essential curl libssl-dev \
git wget && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
# Mirror builder-prebuilt: copy gRPC from /opt/grpc to /usr/local so
# CMake's find_package finds it at the canonical prefix the Makefile expects.
RUN cp -a /opt/grpc/. /usr/local/
# Install CMake (the version in 22.04 is too old)
RUN <<EOT bash
if [ "${CMAKE_FROM_SOURCE}" = "true" ]; then
curl -L -s https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}.tar.gz -o cmake.tar.gz && tar xvf cmake.tar.gz && cd cmake-${CMAKE_VERSION} && ./configure && make && make install
else
apt-get update && \
apt-get install -y \
cmake && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
COPY . /LocalAI
# We install GRPC to a different prefix here so that we can copy in only the build artifacts later
# saves several hundred MB on the final docker image size vs copying in the entire GRPC source tree
# and running make install in the target container
RUN git clone --recurse-submodules --jobs 4 -b ${GRPC_VERSION} --depth 1 --shallow-submodules https://github.com/grpc/grpc && \
mkdir -p /build/grpc/cmake/build && \
cd /build/grpc/cmake/build && \
sed -i "216i\ TESTONLY" "../../third_party/abseil-cpp/absl/container/CMakeLists.txt" && \
cmake -DgRPC_INSTALL=ON -DgRPC_BUILD_TESTS=OFF -DCMAKE_INSTALL_PREFIX:PATH=/opt/grpc ../.. && \
make && \
make install && \
rm -rf /build
# BuildKit cache mount for ccache. Persists compiler outputs across builds
# via the registry cache (cache-to: type=registry,mode=max in CI). On a
# LLAMA_VERSION bump most TUs are byte-identical to the previous version's
# preprocessed source — ccache returns the previous .o file and skips the
# real compile. Same for LocalAI source changes that don't touch llama.cpp.
# CMAKE_*_COMPILER_LAUNCHER threads ccache through CMake to wrap gcc/g++/nvcc.
# sharing=locked serializes concurrent writes if multiple matrix variants
# share the same cache mount id.
#
# The compile body is shared with builder-prebuilt via .docker/llama-cpp-compile.sh.
RUN --mount=type=bind,source=.docker/llama-cpp-compile.sh,target=/usr/local/sbin/compile.sh \
--mount=type=cache,target=/root/.ccache,id=llama-cpp-ccache-${TARGETARCH}-${BUILD_TYPE},sharing=locked \
bash /usr/local/sbin/compile.sh
# Copy libraries using a script to handle architecture differences
RUN make -BC /LocalAI/backend/cpp/llama-cpp package
# ============================================================================
# Stage: builder-prebuilt — uses the pre-built base from
# quay.io/go-skynet/ci-cache:base-grpc-* (built by .github/workflows/base-images.yml).
# That image already has gRPC at /opt/grpc + apt deps + CUDA/ROCm/Vulkan
# pre-installed, so we just copy gRPC to /usr/local and compile. Used when
# BUILDER_TARGET=builder-prebuilt (CI when the matrix entry sets
# builder-base-image).
# ============================================================================
FROM ${BUILDER_BASE_IMAGE} AS builder-prebuilt
ARG BUILD_TYPE
ENV BUILD_TYPE=${BUILD_TYPE}
FROM ${BASE_IMAGE} AS builder
ARG CMAKE_FROM_SOURCE=false
ARG CMAKE_VERSION=3.31.10
# We can target specific CUDA ARCHITECTURES like --build-arg CUDA_DOCKER_ARCH='75;86;89;120'
ARG CUDA_DOCKER_ARCH
ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
ARG CMAKE_ARGS
ENV CMAKE_ARGS=${CMAKE_ARGS}
ARG AMDGPU_TARGETS
ENV AMDGPU_TARGETS=${AMDGPU_TARGETS}
ARG BACKEND=rerankers
ARG BUILD_TYPE
ENV BUILD_TYPE=${BUILD_TYPE}
ARG CUDA_MAJOR_VERSION
ARG CUDA_MINOR_VERSION
ARG SKIP_DRIVERS=false
ENV CUDA_MAJOR_VERSION=${CUDA_MAJOR_VERSION}
ENV CUDA_MINOR_VERSION=${CUDA_MINOR_VERSION}
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETARCH
ARG TARGETVARIANT
ARG GO_VERSION=1.25.4
ARG UBUNTU_VERSION=2404
RUN apt-get update && \
apt-get install -y --no-install-recommends \
build-essential \
ccache git \
ca-certificates \
make \
pkg-config libcurl4-openssl-dev \
curl unzip \
libssl-dev wget && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
# Cuda
ENV PATH=/usr/local/cuda/bin:${PATH}
# HipBLAS requirements
ENV PATH=/opt/rocm/bin:${PATH}
# Vulkan requirements
RUN <<EOT bash
if [ "${BUILD_TYPE}" = "vulkan" ] && [ "${SKIP_DRIVERS}" = "false" ]; then
apt-get update && \
apt-get install -y --no-install-recommends \
software-properties-common pciutils wget gpg-agent && \
apt-get install -y libglm-dev cmake libxcb-dri3-0 libxcb-present0 libpciaccess0 \
libpng-dev libxcb-keysyms1-dev libxcb-dri3-dev libx11-dev g++ gcc \
libwayland-dev libxrandr-dev libxcb-randr0-dev libxcb-ewmh-dev \
git python-is-python3 bison libx11-xcb-dev liblz4-dev libzstd-dev \
ocaml-core ninja-build pkg-config libxml2-dev wayland-protocols python3-jsonschema \
clang-format qtbase5-dev qt6-base-dev libxcb-glx0-dev sudo xz-utils
if [ "amd64" = "$TARGETARCH" ]; then
wget "https://sdk.lunarg.com/sdk/download/1.4.335.0/linux/vulkansdk-linux-x86_64-1.4.335.0.tar.xz" && \
tar -xf vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
rm vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
mkdir -p /opt/vulkan-sdk && \
mv 1.4.335.0 /opt/vulkan-sdk/ && \
cd /opt/vulkan-sdk/1.4.335.0 && \
./vulkansdk --no-deps --maxjobs \
vulkan-loader \
vulkan-validationlayers \
vulkan-extensionlayer \
vulkan-tools \
shaderc && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/bin/* /usr/bin/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/lib/* /usr/lib/x86_64-linux-gnu/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/include/* /usr/include/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/share/* /usr/share/ && \
rm -rf /opt/vulkan-sdk
fi
if [ "arm64" = "$TARGETARCH" ]; then
mkdir vulkan && cd vulkan && \
curl -L -o vulkan-sdk.tar.xz https://github.com/mudler/vulkan-sdk-arm/releases/download/1.4.335.0/vulkansdk-ubuntu-24.04-arm-1.4.335.0.tar.xz && \
tar -xvf vulkan-sdk.tar.xz && \
rm vulkan-sdk.tar.xz && \
cd 1.4.335.0 && \
cp -rfv aarch64/bin/* /usr/bin/ && \
cp -rfv aarch64/lib/* /usr/lib/aarch64-linux-gnu/ && \
cp -rfv aarch64/include/* /usr/include/ && \
cp -rfv aarch64/share/* /usr/share/ && \
cd ../.. && \
rm -rf vulkan
fi
ldconfig && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
# CuBLAS requirements
RUN <<EOT bash
if ( [ "${BUILD_TYPE}" = "cublas" ] || [ "${BUILD_TYPE}" = "l4t" ] ) && [ "${SKIP_DRIVERS}" = "false" ]; then
apt-get update && \
apt-get install -y --no-install-recommends \
software-properties-common pciutils
if [ "amd64" = "$TARGETARCH" ]; then
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/x86_64/cuda-keyring_1.1-1_all.deb
fi
if [ "arm64" = "$TARGETARCH" ]; then
if [ "${CUDA_MAJOR_VERSION}" = "13" ]; then
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/sbsa/cuda-keyring_1.1-1_all.deb
else
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/arm64/cuda-keyring_1.1-1_all.deb
fi
fi
dpkg -i cuda-keyring_1.1-1_all.deb && \
rm -f cuda-keyring_1.1-1_all.deb && \
apt-get update && \
apt-get install -y --no-install-recommends \
cuda-nvcc-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcufft-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcurand-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcublas-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcusparse-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcusolver-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
if [ "${CUDA_MAJOR_VERSION}" = "13" ] && [ "arm64" = "$TARGETARCH" ]; then
apt-get install -y --no-install-recommends \
libcufile-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libcudnn9-cuda-${CUDA_MAJOR_VERSION} cuda-cupti-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libnvjitlink-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
fi
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
# https://github.com/NVIDIA/Isaac-GR00T/issues/343
RUN <<EOT bash
if [ "${BUILD_TYPE}" = "cublas" ] && [ "${TARGETARCH}" = "arm64" ]; then
wget https://developer.download.nvidia.com/compute/cudss/0.6.0/local_installers/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
dpkg -i cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
cp /var/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0/cudss-*-keyring.gpg /usr/share/keyrings/ && \
apt-get update && apt-get -y install cudss cudss-cuda-${CUDA_MAJOR_VERSION} && \
wget https://developer.download.nvidia.com/compute/nvpl/25.5/local_installers/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
dpkg -i nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
cp /var/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5/nvpl-*-keyring.gpg /usr/share/keyrings/ && \
apt-get update && apt-get install -y nvpl
fi
EOT
# If we are building with clblas support, we need the libraries for the builds
RUN if [ "${BUILD_TYPE}" = "clblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then \
apt-get update && \
apt-get install -y --no-install-recommends \
libclblast-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/* \
; fi
RUN if [ "${BUILD_TYPE}" = "hipblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then \
apt-get update && \
apt-get install -y --no-install-recommends \
hipblas-dev \
rocblas-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/* && \
# I have no idea why, but the ROCM lib packages don't trigger ldconfig after they install, which results in local-ai and others not being able
# to locate the libraries. We run ldconfig ourselves to work around this packaging deficiency
ldconfig && \
# Log which GPU architectures have rocBLAS kernel support
echo "rocBLAS library data architectures:" && \
(ls /opt/rocm*/lib/rocblas/library/Kernels* 2>/dev/null || ls /opt/rocm*/lib64/rocblas/library/Kernels* 2>/dev/null) | grep -oP 'gfx[0-9a-z+-]+' | sort -u || \
echo "WARNING: No rocBLAS kernel data found" \
; fi
RUN echo "TARGETARCH: $TARGETARCH"
# We need protoc installed, and the version in 22.04 is too old. We will create one as part installing the GRPC build below
# but that will also being in a newer version of absl which stablediffusion cannot compile with. This version of protoc is only
# here so that we can generate the grpc code for the stablediffusion build
RUN <<EOT bash
if [ "amd64" = "$TARGETARCH" ]; then
curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v27.1/protoc-27.1-linux-x86_64.zip -o protoc.zip && \
unzip -j -d /usr/local/bin protoc.zip bin/protoc && \
rm protoc.zip
fi
if [ "arm64" = "$TARGETARCH" ]; then
curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v27.1/protoc-27.1-linux-aarch_64.zip -o protoc.zip && \
unzip -j -d /usr/local/bin protoc.zip bin/protoc && \
rm protoc.zip
fi
EOT
# Install CMake (the version in 22.04 is too old)
RUN <<EOT bash
if [ "${CMAKE_FROM_SOURCE}" = "true" ]; then
curl -L -s https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}.tar.gz -o cmake.tar.gz && tar xvf cmake.tar.gz && cd cmake-${CMAKE_VERSION} && ./configure && make && make install
else
apt-get update && \
apt-get install -y \
cmake && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
COPY --from=grpc /opt/grpc /usr/local
# The base-grpc-* image installs gRPC to /opt/grpc but doesn't copy it to
# /usr/local. The variant Dockerfile's from-source path does that too;
# mirror it here so the compile step can find gRPC at the canonical
# prefix the Makefile expects.
RUN cp -a /opt/grpc/. /usr/local/
COPY . /LocalAI
RUN --mount=type=bind,source=.docker/llama-cpp-compile.sh,target=/usr/local/sbin/compile.sh \
--mount=type=cache,target=/root/.ccache,id=llama-cpp-ccache-${TARGETARCH}-${BUILD_TYPE},sharing=locked \
bash /usr/local/sbin/compile.sh
RUN <<'EOT' bash
set -euxo pipefail
if [[ -n "${CUDA_DOCKER_ARCH:-}" ]]; then
CUDA_ARCH_ESC="${CUDA_DOCKER_ARCH//;/\\;}"
export CMAKE_ARGS="${CMAKE_ARGS:-} -DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCH_ESC}"
echo "CMAKE_ARGS(env) = ${CMAKE_ARGS}"
rm -rf /LocalAI/backend/cpp/llama-cpp-*-build
fi
if [ "${TARGETARCH}" = "arm64" ] || [ "${BUILD_TYPE}" = "hipblas" ]; then
cd /LocalAI/backend/cpp/llama-cpp
make llama-cpp-fallback
make llama-cpp-grpc
make llama-cpp-rpc-server
else
cd /LocalAI/backend/cpp/llama-cpp
make llama-cpp-avx
make llama-cpp-avx2
make llama-cpp-avx512
make llama-cpp-fallback
make llama-cpp-grpc
make llama-cpp-rpc-server
fi
EOT
# Copy libraries using a script to handle architecture differences
RUN make -BC /LocalAI/backend/cpp/llama-cpp package
# ============================================================================
# Final stage — copies package output from one of the two builders.
# BUILDER_TARGET selects which one. BuildKit prunes the unreferenced builder.
#
# BuildKit doesn't support variable expansion in `COPY --from=` directly,
# so we resolve the ARG by aliasing the chosen builder to a fixed stage
# name via `FROM ${BUILDER_TARGET} AS builder` and then COPY --from=builder.
# BUILDER_TARGET itself is declared as a global ARG at the top of this
# file (required for use in FROM), so we just re-import it into this
# stage's scope before the FROM directive.
# ============================================================================
FROM ${BUILDER_TARGET} AS builder
FROM scratch

View File

@@ -1,6 +1,4 @@
ARG BASE_IMAGE=ubuntu:24.04
ARG APT_MIRROR=""
ARG APT_PORTS_MIRROR=""
FROM ${BASE_IMAGE} AS builder
ARG BACKEND=rerankers
@@ -15,12 +13,8 @@ ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETARCH
ARG TARGETVARIANT
ARG UBUNTU_VERSION=2404
ARG APT_MIRROR
ARG APT_PORTS_MIRROR
RUN --mount=type=bind,source=.docker/apt-mirror.sh,target=/usr/local/sbin/apt-mirror \
APT_MIRROR="${APT_MIRROR}" APT_PORTS_MIRROR="${APT_PORTS_MIRROR}" sh /usr/local/sbin/apt-mirror && \
apt-get update && \
RUN apt-get update && \
apt-get install -y --no-install-recommends \
build-essential \
ccache \
@@ -168,7 +162,6 @@ RUN if [ "${BUILD_TYPE}" = "hipblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then
apt-get update && \
apt-get install -y --no-install-recommends \
hipblas-dev \
hipblaslt-dev \
rocblas-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/* && \
@@ -209,13 +202,6 @@ COPY scripts/build/package-gpu-libs.sh /package-gpu-libs.sh
ARG FROM_SOURCE=""
ENV FROM_SOURCE=${FROM_SOURCE}
# Cache-buster for the per-backend `make` step. Most Python backends list
# unpinned deps (torch, transformers, vllm, ...), so a warm registry cache
# would otherwise freeze upstream versions indefinitely. CI passes a value
# that rolls weekly so the install layer is rebuilt at most once per week
# and picks up newer wheels from PyPI / nightly indexes.
ARG DEPS_REFRESH=initial
RUN cd /${BACKEND} && PORTABLE_PYTHON=true make
# Package GPU libraries into the backend's lib directory
@@ -230,4 +216,4 @@ RUN if [ -f "/${BACKEND}/package.sh" ]; then \
FROM scratch
ARG BACKEND=rerankers
COPY --from=builder /${BACKEND}/ /
COPY --from=builder /${BACKEND}/ /

View File

@@ -1,18 +1,12 @@
ARG BASE_IMAGE=ubuntu:24.04
ARG APT_MIRROR=""
ARG APT_PORTS_MIRROR=""
FROM ${BASE_IMAGE} AS builder
ARG BACKEND=kokoros
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETARCH
ARG TARGETVARIANT
ARG APT_MIRROR
ARG APT_PORTS_MIRROR
RUN --mount=type=bind,source=.docker/apt-mirror.sh,target=/usr/local/sbin/apt-mirror \
APT_MIRROR="${APT_MIRROR}" APT_PORTS_MIRROR="${APT_PORTS_MIRROR}" sh /usr/local/sbin/apt-mirror && \
apt-get update && \
RUN apt-get update && \
apt-get install -y --no-install-recommends \
build-essential \
git ccache \

View File

@@ -1,158 +1,288 @@
ARG BASE_IMAGE=ubuntu:24.04
# BUILDER_BASE_IMAGE defaults to BASE_IMAGE so the Dockerfile parses even
# when no prebuilt base is supplied. The builder-prebuilt stage is only
# entered when BUILDER_TARGET=builder-prebuilt, so a "wrong" fallback
# content here is harmless — BuildKit prunes the unreferenced builder.
ARG BUILDER_BASE_IMAGE=${BASE_IMAGE}
# BUILDER_TARGET selects which builder stage the final scratch image copies
# package output from. Declared at global scope (before any FROM) so it's
# usable in `FROM ${BUILDER_TARGET}` below. Default keeps local
# `make backends/turboquant` on the from-source path.
ARG BUILDER_TARGET=builder-fromsource
ARG APT_MIRROR=""
ARG APT_PORTS_MIRROR=""
ARG GRPC_BASE_IMAGE=${BASE_IMAGE}
# ============================================================================
# Stage: builder-fromsource — self-contained build path.
# Runs .docker/install-base-deps.sh (apt deps + cmake + protoc + gRPC +
# conditional CUDA/ROCm/Vulkan), copies /opt/grpc to /usr/local, then
# compiles the variant. Used when BUILDER_TARGET=builder-fromsource (the
# default; local `make backends/turboquant`).
#
# The install script is the same one that backend/Dockerfile.base-grpc-builder
# runs, so the result is bit-equivalent to the prebuilt-base path
# (builder-prebuilt below).
# ============================================================================
FROM ${BASE_IMAGE} AS builder-fromsource
ARG BUILD_TYPE
ARG CUDA_MAJOR_VERSION
ARG CUDA_MINOR_VERSION
# The grpc target does one thing, it builds and installs GRPC. This is in it's own layer so that it can be effectively cached by CI.
# You probably don't need to change anything here, and if you do, make sure that CI is adjusted so that the cache continues to work.
FROM ${GRPC_BASE_IMAGE} AS grpc
# This is a bit of a hack, but it's required in order to be able to effectively cache this layer in CI
ARG GRPC_MAKEFLAGS="-j4 -Otarget"
ARG GRPC_VERSION=v1.65.0
ARG CMAKE_FROM_SOURCE=false
# CUDA Toolkit 13.x compatibility: CMake 3.31.9+ fixes toolchain detection/arch table issues
ARG CMAKE_VERSION=3.31.10
ARG GRPC_VERSION=v1.65.0
ARG GRPC_MAKEFLAGS="-j4 -Otarget"
ENV MAKEFLAGS=${GRPC_MAKEFLAGS}
WORKDIR /build
RUN apt-get update && \
apt-get install -y --no-install-recommends \
ca-certificates \
build-essential curl libssl-dev \
git wget && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
# Install CMake (the version in 22.04 is too old)
RUN <<EOT bash
if [ "${CMAKE_FROM_SOURCE}" = "true" ]; then
curl -L -s https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}.tar.gz -o cmake.tar.gz && tar xvf cmake.tar.gz && cd cmake-${CMAKE_VERSION} && ./configure && make && make install
else
apt-get update && \
apt-get install -y \
cmake && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
# We install GRPC to a different prefix here so that we can copy in only the build artifacts later
# saves several hundred MB on the final docker image size vs copying in the entire GRPC source tree
# and running make install in the target container
RUN git clone --recurse-submodules --jobs 4 -b ${GRPC_VERSION} --depth 1 --shallow-submodules https://github.com/grpc/grpc && \
mkdir -p /build/grpc/cmake/build && \
cd /build/grpc/cmake/build && \
sed -i "216i\ TESTONLY" "../../third_party/abseil-cpp/absl/container/CMakeLists.txt" && \
cmake -DgRPC_INSTALL=ON -DgRPC_BUILD_TESTS=OFF -DCMAKE_INSTALL_PREFIX:PATH=/opt/grpc ../.. && \
make && \
make install && \
rm -rf /build
FROM ${BASE_IMAGE} AS builder
ARG CMAKE_FROM_SOURCE=false
ARG CMAKE_VERSION=3.31.10
# We can target specific CUDA ARCHITECTURES like --build-arg CUDA_DOCKER_ARCH='75;86;89;120'
ARG CUDA_DOCKER_ARCH
ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
ARG CMAKE_ARGS
ENV CMAKE_ARGS=${CMAKE_ARGS}
ARG BACKEND=rerankers
ARG BUILD_TYPE
ENV BUILD_TYPE=${BUILD_TYPE}
ARG CUDA_MAJOR_VERSION
ARG CUDA_MINOR_VERSION
ARG SKIP_DRIVERS=false
ENV CUDA_MAJOR_VERSION=${CUDA_MAJOR_VERSION}
ENV CUDA_MINOR_VERSION=${CUDA_MINOR_VERSION}
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETARCH
ARG TARGETVARIANT
ARG GO_VERSION=1.25.4
ARG UBUNTU_VERSION=2404
ARG APT_MIRROR
ARG APT_PORTS_MIRROR
ARG AMDGPU_TARGETS=""
ARG BACKEND=rerankers
# CUDA target archs, e.g. --build-arg CUDA_DOCKER_ARCH='75;86;89;120'
ARG CUDA_DOCKER_ARCH
ARG CMAKE_ARGS
ENV BUILD_TYPE=${BUILD_TYPE} \
CUDA_MAJOR_VERSION=${CUDA_MAJOR_VERSION} \
CUDA_MINOR_VERSION=${CUDA_MINOR_VERSION} \
CMAKE_FROM_SOURCE=${CMAKE_FROM_SOURCE} \
CMAKE_VERSION=${CMAKE_VERSION} \
GRPC_VERSION=${GRPC_VERSION} \
GRPC_MAKEFLAGS=${GRPC_MAKEFLAGS} \
SKIP_DRIVERS=${SKIP_DRIVERS} \
TARGETARCH=${TARGETARCH} \
UBUNTU_VERSION=${UBUNTU_VERSION} \
APT_MIRROR=${APT_MIRROR} \
APT_PORTS_MIRROR=${APT_PORTS_MIRROR} \
AMDGPU_TARGETS=${AMDGPU_TARGETS} \
CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH} \
CMAKE_ARGS=${CMAKE_ARGS} \
DEBIAN_FRONTEND=noninteractive
RUN apt-get update && \
apt-get install -y --no-install-recommends \
build-essential \
ccache git \
ca-certificates \
make \
pkg-config libcurl4-openssl-dev \
curl unzip \
libssl-dev wget && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
# CUDA on PATH (no-op when CUDA isn't installed)
# Cuda
ENV PATH=/usr/local/cuda/bin:${PATH}
# HipBLAS / ROCm on PATH (no-op when ROCm isn't installed)
# HipBLAS requirements
ENV PATH=/opt/rocm/bin:${PATH}
WORKDIR /build
# Install everything via the shared script — the same one that
# backend/Dockerfile.base-grpc-builder runs, so the prebuilt CI base and
# this from-source path are bit-equivalent.
RUN --mount=type=bind,source=.docker/install-base-deps.sh,target=/usr/local/sbin/install-base-deps \
--mount=type=bind,source=.docker/apt-mirror.sh,target=/usr/local/sbin/apt-mirror \
bash /usr/local/sbin/install-base-deps
# Vulkan requirements
RUN <<EOT bash
if [ "${BUILD_TYPE}" = "vulkan" ] && [ "${SKIP_DRIVERS}" = "false" ]; then
apt-get update && \
apt-get install -y --no-install-recommends \
software-properties-common pciutils wget gpg-agent && \
apt-get install -y libglm-dev cmake libxcb-dri3-0 libxcb-present0 libpciaccess0 \
libpng-dev libxcb-keysyms1-dev libxcb-dri3-dev libx11-dev g++ gcc \
libwayland-dev libxrandr-dev libxcb-randr0-dev libxcb-ewmh-dev \
git python-is-python3 bison libx11-xcb-dev liblz4-dev libzstd-dev \
ocaml-core ninja-build pkg-config libxml2-dev wayland-protocols python3-jsonschema \
clang-format qtbase5-dev qt6-base-dev libxcb-glx0-dev sudo xz-utils
if [ "amd64" = "$TARGETARCH" ]; then
wget "https://sdk.lunarg.com/sdk/download/1.4.335.0/linux/vulkansdk-linux-x86_64-1.4.335.0.tar.xz" && \
tar -xf vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
rm vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
mkdir -p /opt/vulkan-sdk && \
mv 1.4.335.0 /opt/vulkan-sdk/ && \
cd /opt/vulkan-sdk/1.4.335.0 && \
./vulkansdk --no-deps --maxjobs \
vulkan-loader \
vulkan-validationlayers \
vulkan-extensionlayer \
vulkan-tools \
shaderc && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/bin/* /usr/bin/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/lib/* /usr/lib/x86_64-linux-gnu/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/include/* /usr/include/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/share/* /usr/share/ && \
rm -rf /opt/vulkan-sdk
fi
if [ "arm64" = "$TARGETARCH" ]; then
mkdir vulkan && cd vulkan && \
curl -L -o vulkan-sdk.tar.xz https://github.com/mudler/vulkan-sdk-arm/releases/download/1.4.335.0/vulkansdk-ubuntu-24.04-arm-1.4.335.0.tar.xz && \
tar -xvf vulkan-sdk.tar.xz && \
rm vulkan-sdk.tar.xz && \
cd 1.4.335.0 && \
cp -rfv aarch64/bin/* /usr/bin/ && \
cp -rfv aarch64/lib/* /usr/lib/aarch64-linux-gnu/ && \
cp -rfv aarch64/include/* /usr/include/ && \
cp -rfv aarch64/share/* /usr/share/ && \
cd ../.. && \
rm -rf vulkan
fi
ldconfig && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
# CuBLAS requirements
RUN <<EOT bash
if ( [ "${BUILD_TYPE}" = "cublas" ] || [ "${BUILD_TYPE}" = "l4t" ] ) && [ "${SKIP_DRIVERS}" = "false" ]; then
apt-get update && \
apt-get install -y --no-install-recommends \
software-properties-common pciutils
if [ "amd64" = "$TARGETARCH" ]; then
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/x86_64/cuda-keyring_1.1-1_all.deb
fi
if [ "arm64" = "$TARGETARCH" ]; then
if [ "${CUDA_MAJOR_VERSION}" = "13" ]; then
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/sbsa/cuda-keyring_1.1-1_all.deb
else
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/arm64/cuda-keyring_1.1-1_all.deb
fi
fi
dpkg -i cuda-keyring_1.1-1_all.deb && \
rm -f cuda-keyring_1.1-1_all.deb && \
apt-get update && \
apt-get install -y --no-install-recommends \
cuda-nvcc-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcufft-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcurand-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcublas-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcusparse-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcusolver-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
if [ "${CUDA_MAJOR_VERSION}" = "13" ] && [ "arm64" = "$TARGETARCH" ]; then
apt-get install -y --no-install-recommends \
libcufile-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libcudnn9-cuda-${CUDA_MAJOR_VERSION} cuda-cupti-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libnvjitlink-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
fi
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
# https://github.com/NVIDIA/Isaac-GR00T/issues/343
RUN <<EOT bash
if [ "${BUILD_TYPE}" = "cublas" ] && [ "${TARGETARCH}" = "arm64" ]; then
wget https://developer.download.nvidia.com/compute/cudss/0.6.0/local_installers/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
dpkg -i cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
cp /var/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0/cudss-*-keyring.gpg /usr/share/keyrings/ && \
apt-get update && apt-get -y install cudss cudss-cuda-${CUDA_MAJOR_VERSION} && \
wget https://developer.download.nvidia.com/compute/nvpl/25.5/local_installers/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
dpkg -i nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
cp /var/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5/nvpl-*-keyring.gpg /usr/share/keyrings/ && \
apt-get update && apt-get install -y nvpl
fi
EOT
# If we are building with clblas support, we need the libraries for the builds
RUN if [ "${BUILD_TYPE}" = "clblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then \
apt-get update && \
apt-get install -y --no-install-recommends \
libclblast-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/* \
; fi
RUN if [ "${BUILD_TYPE}" = "hipblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then \
apt-get update && \
apt-get install -y --no-install-recommends \
hipblas-dev \
rocblas-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/* && \
# I have no idea why, but the ROCM lib packages don't trigger ldconfig after they install, which results in local-ai and others not being able
# to locate the libraries. We run ldconfig ourselves to work around this packaging deficiency
ldconfig && \
# Log which GPU architectures have rocBLAS kernel support
echo "rocBLAS library data architectures:" && \
(ls /opt/rocm*/lib/rocblas/library/Kernels* 2>/dev/null || ls /opt/rocm*/lib64/rocblas/library/Kernels* 2>/dev/null) | grep -oP 'gfx[0-9a-z+-]+' | sort -u || \
echo "WARNING: No rocBLAS kernel data found" \
; fi
RUN echo "TARGETARCH: $TARGETARCH"
# We need protoc installed, and the version in 22.04 is too old. We will create one as part installing the GRPC build below
# but that will also being in a newer version of absl which stablediffusion cannot compile with. This version of protoc is only
# here so that we can generate the grpc code for the stablediffusion build
RUN <<EOT bash
if [ "amd64" = "$TARGETARCH" ]; then
curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v27.1/protoc-27.1-linux-x86_64.zip -o protoc.zip && \
unzip -j -d /usr/local/bin protoc.zip bin/protoc && \
rm protoc.zip
fi
if [ "arm64" = "$TARGETARCH" ]; then
curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v27.1/protoc-27.1-linux-aarch_64.zip -o protoc.zip && \
unzip -j -d /usr/local/bin protoc.zip bin/protoc && \
rm protoc.zip
fi
EOT
# Install CMake (the version in 22.04 is too old)
RUN <<EOT bash
if [ "${CMAKE_FROM_SOURCE}" = "true" ]; then
curl -L -s https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}.tar.gz -o cmake.tar.gz && tar xvf cmake.tar.gz && cd cmake-${CMAKE_VERSION} && ./configure && make && make install
else
apt-get update && \
apt-get install -y \
cmake && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
COPY --from=grpc /opt/grpc /usr/local
# Mirror builder-prebuilt: copy gRPC from /opt/grpc to /usr/local so
# CMake's find_package finds it at the canonical prefix the Makefile expects.
RUN cp -a /opt/grpc/. /usr/local/
COPY . /LocalAI
# BuildKit cache mount for ccache. See Dockerfile.llama-cpp (commit 9228e5b4)
# for rationale. turboquant is a llama.cpp fork that reuses
# backend/cpp/llama-cpp source via a thin wrapper Makefile, so MOST TUs
# are content-identical to the upstream llama-cpp build. Sharing a cache
# id with llama-cpp could give cross-fork hits — but for now keep them
# separate so a regression in one doesn't poison the other. Revisit
# sharing after measuring the actual hit rate.
#
# The compile body is shared with builder-prebuilt via .docker/turboquant-compile.sh.
RUN --mount=type=bind,source=.docker/turboquant-compile.sh,target=/usr/local/sbin/compile.sh \
--mount=type=cache,target=/root/.ccache,id=turboquant-ccache-${TARGETARCH}-${BUILD_TYPE},sharing=locked \
bash /usr/local/sbin/compile.sh
RUN <<'EOT' bash
set -euxo pipefail
if [[ -n "${CUDA_DOCKER_ARCH:-}" ]]; then
CUDA_ARCH_ESC="${CUDA_DOCKER_ARCH//;/\\;}"
export CMAKE_ARGS="${CMAKE_ARGS:-} -DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCH_ESC}"
echo "CMAKE_ARGS(env) = ${CMAKE_ARGS}"
rm -rf /LocalAI/backend/cpp/turboquant-*-build
fi
cd /LocalAI/backend/cpp/turboquant
if [ "${TARGETARCH}" = "arm64" ] || [ "${BUILD_TYPE}" = "hipblas" ]; then
make turboquant-fallback
make turboquant-grpc
make turboquant-rpc-server
else
make turboquant-avx
make turboquant-avx2
make turboquant-avx512
make turboquant-fallback
make turboquant-grpc
make turboquant-rpc-server
fi
EOT
# Copy libraries using a script to handle architecture differences
RUN make -BC /LocalAI/backend/cpp/turboquant package
# ============================================================================
# Stage: builder-prebuilt — uses the pre-built base from
# quay.io/go-skynet/ci-cache:base-grpc-* (built by .github/workflows/base-images.yml).
# That image already has gRPC at /opt/grpc + apt deps + CUDA/ROCm/Vulkan
# pre-installed, so we just copy gRPC to /usr/local and compile. Used when
# BUILDER_TARGET=builder-prebuilt (CI when the matrix entry sets
# builder-base-image).
# ============================================================================
FROM ${BUILDER_BASE_IMAGE} AS builder-prebuilt
ARG BUILD_TYPE
ENV BUILD_TYPE=${BUILD_TYPE}
ARG CUDA_DOCKER_ARCH
ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
ARG CMAKE_ARGS
ENV CMAKE_ARGS=${CMAKE_ARGS}
# AMDGPU_TARGETS must be forwarded into the env here too — backend/cpp/llama-cpp/Makefile
# (which the turboquant Makefile reuses via a sibling build dir) errors out when the var
# is empty on a hipblas build, and the prebuilt path is what CI exercises most of the
# time. The builder-fromsource stage above already does this; mirror it here.
ARG AMDGPU_TARGETS
ENV AMDGPU_TARGETS=${AMDGPU_TARGETS}
ARG TARGETARCH
ARG TARGETVARIANT
# The base-grpc-* image installs gRPC to /opt/grpc but doesn't copy it to
# /usr/local. Mirror what the from-source path does so the compile step
# can find gRPC at the canonical prefix the Makefile expects.
RUN cp -a /opt/grpc/. /usr/local/
COPY . /LocalAI
RUN --mount=type=bind,source=.docker/turboquant-compile.sh,target=/usr/local/sbin/compile.sh \
--mount=type=cache,target=/root/.ccache,id=turboquant-ccache-${TARGETARCH}-${BUILD_TYPE},sharing=locked \
bash /usr/local/sbin/compile.sh
RUN make -BC /LocalAI/backend/cpp/turboquant package
# ============================================================================
# Final stage — copies package output from one of the two builders.
# BUILDER_TARGET selects which one. BuildKit prunes the unreferenced builder.
#
# BuildKit doesn't support variable expansion in `COPY --from=` directly,
# so we resolve the ARG by aliasing the chosen builder to a fixed stage
# name via `FROM ${BUILDER_TARGET} AS builder` and then COPY --from=builder.
# BUILDER_TARGET itself is declared as a global ARG at the top of this
# file (required for use in FROM), so we just re-import it into this
# stage's scope before the FROM directive.
# ============================================================================
FROM ${BUILDER_TARGET} AS builder
FROM scratch

View File

@@ -24,11 +24,6 @@ service Backend {
rpc TokenizeString(PredictOptions) returns (TokenizationResponse) {}
rpc Status(HealthMessage) returns (StatusResponse) {}
rpc Detect(DetectOptions) returns (DetectResponse) {}
rpc FaceVerify(FaceVerifyRequest) returns (FaceVerifyResponse) {}
rpc FaceAnalyze(FaceAnalyzeRequest) returns (FaceAnalyzeResponse) {}
rpc VoiceVerify(VoiceVerifyRequest) returns (VoiceVerifyResponse) {}
rpc VoiceAnalyze(VoiceAnalyzeRequest) returns (VoiceAnalyzeResponse) {}
rpc VoiceEmbed(VoiceEmbedRequest) returns (VoiceEmbedResponse) {}
rpc StoresSet(StoresSetOptions) returns (Result) {}
rpc StoresDelete(StoresDeleteOptions) returns (Result) {}
@@ -41,19 +36,9 @@ service Backend {
rpc VAD(VADRequest) returns (VADResponse) {}
rpc Diarize(DiarizeRequest) returns (DiarizeResponse) {}
rpc AudioEncode(AudioEncodeRequest) returns (AudioEncodeResult) {}
rpc AudioDecode(AudioDecodeRequest) returns (AudioDecodeResult) {}
rpc AudioTransform(AudioTransformRequest) returns (AudioTransformResult) {}
rpc AudioTransformStream(stream AudioTransformFrameRequest) returns (stream AudioTransformFrameResponse) {}
// AudioToAudioStream is the bidirectional any-to-any S2S RPC. Backends
// that load a speech-to-speech model consume input audio frames and emit
// interleaved audio + transcript + tool-call deltas as typed events.
// Backends without S2S support return UNIMPLEMENTED.
rpc AudioToAudioStream(stream AudioToAudioRequest) returns (stream AudioToAudioResponse) {}
rpc ModelMetadata(ModelOptions) returns (ModelMetadataResponse) {}
// Fine-tuning RPCs
@@ -320,11 +305,6 @@ message ModelOptions {
bool Reranking = 71;
repeated string Overrides = 72;
// EngineArgs carries a JSON-encoded map of backend-native engine arguments
// applied verbatim to the backend's engine constructor (e.g. vLLM AsyncEngineArgs).
// Unknown keys produce an error at LoadModel time.
string EngineArgs = 73;
}
message Result {
@@ -360,12 +340,6 @@ message TranscriptStreamResponse {
TranscriptResult final_result = 2;
}
message TranscriptWord {
int64 start = 1;
int64 end = 2;
string text = 3;
}
message TranscriptSegment {
int32 id = 1;
int64 start = 2;
@@ -373,7 +347,6 @@ message TranscriptSegment {
string text = 4;
repeated int32 tokens = 5;
string speaker = 6;
repeated TranscriptWord words = 7;
}
message GenerateImageRequest {
@@ -430,43 +403,6 @@ message VADResponse {
repeated VADSegment segments = 1;
}
// --- Speaker diarization messages ---
//
// Pure speaker diarization: "who spoke when". Returns time-stamped segments
// labelled with cluster IDs (the same string for the same speaker across
// segments). Some backends (e.g. vibevoice.cpp) produce diarization as a
// by-product of ASR and may also fill in `text` per segment; backends with a
// dedicated diarization pipeline (e.g. sherpa-onnx pyannote) leave `text`
// empty and emit only the segmentation.
message DiarizeRequest {
string dst = 1; // path to audio file (HTTP layer materialises uploads to a temp file)
uint32 threads = 2;
string language = 3; // optional; only meaningful for transcription-bundling backends
int32 num_speakers = 4; // exact speaker count if known (>0 forces); 0 = auto
int32 min_speakers = 5; // hint when auto-detecting; 0 = unset
int32 max_speakers = 6; // hint when auto-detecting; 0 = unset
float clustering_threshold = 7; // distance threshold when num_speakers unknown; 0 = backend default
float min_duration_on = 8; // discard segments shorter than this (seconds); 0 = backend default
float min_duration_off = 9; // merge gaps shorter than this (seconds); 0 = backend default
bool include_text = 10; // when the backend can emit per-segment transcript for free, ask it to populate `text`
}
message DiarizeSegment {
int32 id = 1;
float start = 2; // seconds
float end = 3; // seconds
string speaker = 4; // backend-emitted speaker label (e.g. "0", "SPEAKER_00")
string text = 5; // optional per-segment transcript (empty unless include_text and supported)
}
message DiarizeResponse {
repeated DiarizeSegment segments = 1;
int32 num_speakers = 2; // count of distinct speaker labels in `segments`
float duration = 3; // total audio duration in seconds (0 if unknown)
string language = 4; // optional, when the backend bundles transcription
}
message SoundGenerationRequest {
string text = 1;
string model = 2;
@@ -539,112 +475,6 @@ message DetectResponse {
repeated Detection Detections = 1;
}
// --- Face recognition messages ---
message FacialArea {
float x = 1;
float y = 2;
float w = 3;
float h = 4;
}
message FaceVerifyRequest {
string img1 = 1; // base64-encoded image
string img2 = 2; // base64-encoded image
float threshold = 3; // cosine-distance threshold; 0 = use backend default
bool anti_spoofing = 4; // run MiniFASNet liveness on each image; failed liveness forces verified=false
}
message FaceVerifyResponse {
bool verified = 1;
float distance = 2; // 1 - cosine_similarity
float threshold = 3;
float confidence = 4; // 0-100
string model = 5; // e.g. "buffalo_l"
FacialArea img1_area = 6;
FacialArea img2_area = 7;
float processing_time_ms = 8;
bool img1_is_real = 9; // anti-spoofing result when enabled
float img1_antispoof_score = 10;
bool img2_is_real = 11;
float img2_antispoof_score = 12;
}
message FaceAnalyzeRequest {
string img = 1; // base64-encoded image
repeated string actions = 2; // subset of ["age","gender","emotion","race"]; empty = all-supported
bool anti_spoofing = 3;
}
message FaceAnalysis {
FacialArea region = 1;
float face_confidence = 2;
float age = 3;
string dominant_gender = 4; // "Man" | "Woman"
map<string, float> gender = 5;
string dominant_emotion = 6; // reserved; empty in MVP
map<string, float> emotion = 7;
string dominant_race = 8; // not populated
map<string, float> race = 9;
bool is_real = 10; // anti-spoofing result when enabled
float antispoof_score = 11;
}
message FaceAnalyzeResponse {
repeated FaceAnalysis faces = 1;
}
// --- Voice (speaker) recognition messages ---
//
// Analogous to the Face* messages above, but for speaker biometrics.
// Audio fields accept a filesystem path (same convention as
// TranscriptRequest.dst). The HTTP layer materialises base64 / URL /
// data-URI inputs to a temp file before calling the gRPC backend.
message VoiceVerifyRequest {
string audio1 = 1; // path to first audio clip
string audio2 = 2; // path to second audio clip
float threshold = 3; // cosine-distance threshold; 0 = use backend default
bool anti_spoofing = 4; // reserved for future AASIST bolt-on
}
message VoiceVerifyResponse {
bool verified = 1;
float distance = 2; // 1 - cosine_similarity
float threshold = 3;
float confidence = 4; // 0-100
string model = 5; // e.g. "speechbrain/spkrec-ecapa-voxceleb"
float processing_time_ms = 6;
}
message VoiceAnalyzeRequest {
string audio = 1; // path to audio clip
repeated string actions = 2; // subset of ["age","gender","emotion"]; empty = all-supported
}
message VoiceAnalysis {
float start = 1; // segment start time in seconds (0 if single-utterance)
float end = 2; // segment end time in seconds
float age = 3;
string dominant_gender = 4;
map<string, float> gender = 5;
string dominant_emotion = 6;
map<string, float> emotion = 7;
}
message VoiceAnalyzeResponse {
repeated VoiceAnalysis segments = 1;
}
message VoiceEmbedRequest {
string audio = 1; // path to audio clip
}
message VoiceEmbedResponse {
repeated float embedding = 1;
string model = 2;
}
message ToolFormatMarkers {
string format_type = 1; // "json_native", "tag_with_json", "tag_with_tagged"
@@ -723,143 +553,6 @@ message AudioDecodeResult {
int32 samples_per_frame = 3;
}
// Generic audio transform: an audio-in, audio-out operation, optionally
// conditioned on a second reference signal. Concrete transforms include
// AEC + noise suppression + dereverberation (LocalVQE), voice conversion
// (reference = target speaker), pitch shifting, etc.
message AudioTransformRequest {
string audio_path = 1; // required, primary input file path
string reference_path = 2; // optional auxiliary; empty => zero-fill
string dst = 3; // required, output file path
map<string, string> params = 4; // backend-specific tuning
}
message AudioTransformResult {
string dst = 1;
int32 sample_rate = 2;
int32 samples = 3;
bool reference_provided = 4;
}
// Bidirectional streaming audio transform. The first message MUST carry a
// Config; subsequent messages carry Frames. A second Config mid-stream
// resets streaming state before the next frame.
message AudioTransformFrameRequest {
oneof payload {
AudioTransformStreamConfig config = 1;
AudioTransformFrame frame = 2;
}
}
message AudioTransformStreamConfig {
enum SampleFormat {
F32_LE = 0;
S16_LE = 1;
}
SampleFormat sample_format = 1;
int32 sample_rate = 2; // 0 => backend default
int32 frame_samples = 3; // 0 => backend default
map<string, string> params = 4;
bool reset = 5; // reset streaming state before next frame
}
message AudioTransformFrame {
bytes audio_pcm = 1; // frame_samples samples in stream's format
bytes reference_pcm = 2; // empty => zero-fill (silent reference)
}
message AudioTransformFrameResponse {
bytes pcm = 1;
int64 frame_index = 2;
}
// === AudioToAudioStream messages =========================================
//
// Bidirectional stream between the LocalAI core and an any-to-any audio
// model. The client opens the stream with a Config payload, then alternates
// Frame (input audio) and Control (turn boundaries, function-call results,
// session updates) payloads. The server streams back typed events: audio
// frames carry PCM in `pcm`; transcript / tool-call deltas carry JSON in
// `meta`; the stream ends with a `response.done` (success) or `error` event.
message AudioToAudioRequest {
oneof payload {
AudioToAudioConfig config = 1;
AudioToAudioFrame frame = 2;
AudioToAudioControl control = 3;
}
}
message AudioToAudioConfig {
// PCM format for client→server audio. 0 => backend default
// (16 kHz for the LFM2-Audio Conformer encoder).
int32 input_sample_rate = 1;
// Preferred server→client audio rate. 0 => backend default
// (24 kHz for the LFM2-Audio vocoder).
int32 output_sample_rate = 2;
// Optional system prompt override. Empty => backend chooses based on
// mode (e.g. "Respond with interleaved text and audio.").
string system_prompt = 3;
// Optional baked-voice id. Models that only ship a fixed set of
// voices (e.g. LFM2-Audio: us_male/us_female/uk_male/uk_female) match
// this against their voice table; an empty string keeps the default.
string voice = 4;
// JSON-encoded array of tool definitions in OpenAI Chat Completions
// format. Empty => no tools.
string tools = 5;
// Free-form sampling / decoding parameters (temperature, top_k,
// max_new_tokens, audio_top_k, etc).
map<string, string> params = 6;
// True => reset any session-scoped state before processing further
// frames on this stream. The first Config implicitly resets.
bool reset = 7;
}
message AudioToAudioFrame {
// Raw PCM s16le mono at config.input_sample_rate. Empty pcm + end_of_input
// is a valid "user finished speaking" marker without trailing audio.
bytes pcm = 1;
// Marks the last frame of a user turn. The backend may begin emitting
// a response immediately after seeing this.
bool end_of_input = 2;
}
message AudioToAudioControl {
// Free-form control event names. Initial set:
// "input_audio_buffer.commit" — user finished speaking
// "response.cancel" — abort in-flight generation
// "conversation.item.create" — inject a non-audio item (e.g.
// function_call_output as JSON in
// `payload`)
// "session.update" — re-configure mid-stream
string event = 1;
// Event-specific JSON payload.
bytes payload = 2;
}
message AudioToAudioResponse {
// Event identifies what this frame carries. Mirrors the OpenAI Realtime
// API server-event names where applicable. Initial set:
// "response.audio.delta"
// "response.audio_transcript.delta"
// "response.function_call_arguments.delta"
// "response.function_call_arguments.done"
// "response.done"
// "error"
string event = 1;
// Populated when event = response.audio.delta.
bytes pcm = 2;
// Populated alongside pcm to identify its rate. 0 => same as the
// session's negotiated output_sample_rate.
int32 sample_rate = 3;
// JSON payload for non-PCM events (transcript chunk, tool args, error
// body).
bytes meta = 4;
// Monotonic per-stream counter, useful for client reordering and
// debugging.
int64 sequence = 5;
}
message ModelMetadataResponse {
bool supports_thinking = 1;
string rendered_template = 2; // The rendered chat template with enable_thinking=true (empty if not applicable)

View File

@@ -1,9 +0,0 @@
ds4/
build/
package/
grpc-server
*.o
backend.pb.cc
backend.pb.h
backend.grpc.pb.cc
backend.grpc.pb.h

View File

@@ -1,101 +0,0 @@
cmake_minimum_required(VERSION 3.15)
project(ds4-grpc-server LANGUAGES CXX C)
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
set(TARGET grpc-server)
option(DS4_NATIVE "Compile with -march=native / -mcpu=native" ON)
set(DS4_GPU "cpu" CACHE STRING "GPU backend: cpu, cuda, or metal")
set(DS4_DIR "${CMAKE_CURRENT_SOURCE_DIR}/ds4" CACHE PATH "Path to cloned ds4 source")
find_package(Threads REQUIRED)
find_package(Protobuf CONFIG QUIET)
if(NOT Protobuf_FOUND)
find_package(Protobuf REQUIRED)
endif()
find_package(gRPC CONFIG QUIET)
if(NOT gRPC_FOUND)
# Ubuntu's apt-installed grpc++ does not ship a CMake config - fall back.
find_library(GRPCPP_LIB grpc++ REQUIRED)
find_library(GRPCPP_REFLECTION_LIB grpc++_reflection REQUIRED)
add_library(gRPC::grpc++ INTERFACE IMPORTED)
set_target_properties(gRPC::grpc++ PROPERTIES INTERFACE_LINK_LIBRARIES "${GRPCPP_LIB}")
add_library(gRPC::grpc++_reflection INTERFACE IMPORTED)
set_target_properties(gRPC::grpc++_reflection PROPERTIES INTERFACE_LINK_LIBRARIES "${GRPCPP_REFLECTION_LIB}")
endif()
find_program(_PROTOC NAMES protoc REQUIRED)
find_program(_GRPC_CPP_PLUGIN NAMES grpc_cpp_plugin REQUIRED)
get_filename_component(HW_PROTO "${CMAKE_CURRENT_SOURCE_DIR}/../../backend.proto" ABSOLUTE)
get_filename_component(HW_PROTO_PATH "${HW_PROTO}" PATH)
set(HW_PROTO_SRCS "${CMAKE_CURRENT_BINARY_DIR}/backend.pb.cc")
set(HW_PROTO_HDRS "${CMAKE_CURRENT_BINARY_DIR}/backend.pb.h")
set(HW_GRPC_SRCS "${CMAKE_CURRENT_BINARY_DIR}/backend.grpc.pb.cc")
set(HW_GRPC_HDRS "${CMAKE_CURRENT_BINARY_DIR}/backend.grpc.pb.h")
add_custom_command(
OUTPUT "${HW_PROTO_SRCS}" "${HW_PROTO_HDRS}" "${HW_GRPC_SRCS}" "${HW_GRPC_HDRS}"
COMMAND ${_PROTOC}
ARGS --grpc_out "${CMAKE_CURRENT_BINARY_DIR}"
--cpp_out "${CMAKE_CURRENT_BINARY_DIR}"
-I "${HW_PROTO_PATH}"
--plugin=protoc-gen-grpc="${_GRPC_CPP_PLUGIN}"
"${HW_PROTO}"
DEPENDS "${HW_PROTO}")
add_library(hw_grpc_proto STATIC
${HW_GRPC_SRCS} ${HW_GRPC_HDRS}
${HW_PROTO_SRCS} ${HW_PROTO_HDRS})
target_include_directories(hw_grpc_proto PUBLIC ${CMAKE_CURRENT_BINARY_DIR})
set(DS4_OBJS "${DS4_DIR}/ds4.o")
if(DS4_GPU STREQUAL "cuda")
list(APPEND DS4_OBJS "${DS4_DIR}/ds4_cuda.o")
elseif(DS4_GPU STREQUAL "metal")
list(APPEND DS4_OBJS "${DS4_DIR}/ds4_metal.o")
elseif(DS4_GPU STREQUAL "cpu")
set(DS4_OBJS "${DS4_DIR}/ds4_cpu.o")
endif()
add_executable(${TARGET}
grpc-server.cpp
dsml_parser.cpp
dsml_renderer.cpp
kv_cache.cpp)
target_include_directories(${TARGET} PRIVATE ${DS4_DIR})
foreach(obj ${DS4_OBJS})
target_sources(${TARGET} PRIVATE ${obj})
set_source_files_properties(${obj} PROPERTIES EXTERNAL_OBJECT TRUE GENERATED TRUE)
endforeach()
target_link_libraries(${TARGET} PRIVATE
hw_grpc_proto
gRPC::grpc++
gRPC::grpc++_reflection
protobuf::libprotobuf
Threads::Threads
m)
if(DS4_GPU STREQUAL "cuda")
find_package(CUDAToolkit REQUIRED)
target_link_libraries(${TARGET} PRIVATE CUDA::cudart CUDA::cublas)
elseif(DS4_GPU STREQUAL "metal")
find_library(FOUNDATION_LIB Foundation REQUIRED)
find_library(METAL_LIB Metal REQUIRED)
target_link_libraries(${TARGET} PRIVATE ${FOUNDATION_LIB} ${METAL_LIB})
elseif(DS4_GPU STREQUAL "cpu")
target_compile_definitions(${TARGET} PRIVATE DS4_NO_GPU)
endif()
if(DS4_NATIVE)
if(APPLE)
target_compile_options(${TARGET} PRIVATE -mcpu=native)
else()
target_compile_options(${TARGET} PRIVATE -march=native)
endif()
endif()

View File

@@ -1,78 +0,0 @@
# ds4 backend Makefile.
#
# Upstream pin lives below as DS4_VERSION?=599e49d253971451f710cb8323344e789906ed6c
# (.github/bump_deps.sh) can find and update it - matches the
# llama-cpp / ik-llama-cpp / turboquant convention.
DS4_VERSION?=599e49d253971451f710cb8323344e789906ed6c
DS4_REPO?=https://github.com/antirez/ds4
CURRENT_MAKEFILE_DIR := $(dir $(abspath $(lastword $(MAKEFILE_LIST))))
BUILD_DIR := build
BUILD_TYPE ?=
NATIVE ?= false
JOBS ?= $(shell nproc 2>/dev/null || sysctl -n hw.ncpu 2>/dev/null || echo 4)
UNAME_S := $(shell uname -s)
CMAKE_ARGS ?= -DCMAKE_BUILD_TYPE=Release
ifeq ($(BUILD_TYPE),cublas)
CMAKE_ARGS += -DDS4_GPU=cuda
DS4_OBJ_TARGET := ds4.o ds4_cuda.o
else ifeq ($(UNAME_S),Darwin)
CMAKE_ARGS += -DDS4_GPU=metal
DS4_OBJ_TARGET := ds4.o ds4_metal.o
else
# CPU reference path (Linux only - macOS CPU path is broken by VM bug per ds4 README).
CMAKE_ARGS += -DDS4_GPU=cpu
DS4_OBJ_TARGET := ds4_cpu.o
endif
ifneq ($(NATIVE),true)
CMAKE_ARGS += -DDS4_NATIVE=OFF
endif
.PHONY: grpc-server package clean purge test all
all: grpc-server
# Clone the upstream ds4 source at the pinned commit. Directory acts as the
# target so make only re-clones when missing. After a DS4_VERSION bump,
# run 'make purge && make' to refetch (or rely on CI's clean build).
ds4:
mkdir -p ds4
cd ds4 && \
git init -q && \
git remote add origin $(DS4_REPO) && \
git fetch --depth 1 origin $(DS4_VERSION) && \
git checkout FETCH_HEAD
# Build ds4's engine object files via its own Makefile, which already encodes
# the right per-platform compile flags (Objective-C/Metal on Darwin, nvcc on Linux+CUDA).
ds4/ds4.o: ds4
ifeq ($(BUILD_TYPE),cublas)
+$(MAKE) -C ds4 ds4.o ds4_cuda.o
else ifeq ($(UNAME_S),Darwin)
+$(MAKE) -C ds4 ds4.o ds4_metal.o
else
+$(MAKE) -C ds4 ds4_cpu.o
endif
grpc-server: ds4/ds4.o
mkdir -p $(BUILD_DIR)
cd $(BUILD_DIR) && cmake $(CMAKE_ARGS) $(CURRENT_MAKEFILE_DIR) && cmake --build . --config Release -j $(JOBS)
cp $(BUILD_DIR)/grpc-server grpc-server
package: grpc-server
bash package.sh
test:
@echo "ds4 backend: e2e coverage at tests/e2e-backends/ (BACKEND_BINARY mode)"
clean:
rm -rf $(BUILD_DIR) grpc-server package
if [ -d ds4 ]; then $(MAKE) -C ds4 clean; fi
purge: clean
rm -rf ds4

View File

@@ -1,359 +0,0 @@
#include "dsml_parser.h"
#include <algorithm>
#include <cstdio>
#include <cstring>
#include <chrono>
#include <random>
#include <string>
#include <vector>
namespace ds4cpp {
namespace {
constexpr const char *kThinkOpen = "<think>";
constexpr const char *kThinkClose = "</think>";
constexpr const char *kToolsOpen = "<\xef\xbd\x9c" "DSML\xef\xbd\x9c" "tool_calls>"; // <DSMLtool_calls>
constexpr const char *kToolsClose = "</\xef\xbd\x9c" "DSML\xef\xbd\x9c" "tool_calls>"; // </DSMLtool_calls>
constexpr const char *kInvokeOpenPfx = "<\xef\xbd\x9c" "DSML\xef\xbd\x9c" "invoke name=\""; // <DSMLinvoke name="
constexpr const char *kInvokeClose = "</\xef\xbd\x9c" "DSML\xef\xbd\x9c" "invoke>"; // </DSMLinvoke>
constexpr const char *kParamOpenPfx = "<\xef\xbd\x9c" "DSML\xef\xbd\x9c" "parameter name=\""; // <DSMLparameter name="
constexpr const char *kParamClose = "</\xef\xbd\x9c" "DSML\xef\xbd\x9c" "parameter>"; // </DSMLparameter>
// All structural markers the parser might encounter - used to detect "buf
// might be a partial marker, don't drain yet" conditions.
const std::vector<std::string> &all_markers() {
static const std::vector<std::string> v = {
kThinkOpen, kThinkClose,
kToolsOpen, kToolsClose,
kInvokeOpenPfx, kInvokeClose,
kParamOpenPfx, kParamClose,
};
return v;
}
// Returns true if `buf` could be a *prefix* of any marker (i.e., we should
// wait for more text before draining as plain content). The marker-prefix
// loop handles fixed markers exactly. For markers with variable-length
// internal data (kInvokeOpenPfx, kParamOpenPfx have an open quote, then the
// tool/param name, then a closing quote and `>`), we also wait while buf
// starts with `<` and has not yet seen a `>`: the leading `<` could be the
// start of one of those open markers, or a literal that we can confirm only
// once we know what follows. Anything after the first `>` arrives is either
// consumed by TryConsumeMarker or emitted as a literal `<` by the caller.
bool looks_like_prefix(const std::string &buf) {
for (const auto &m : all_markers()) {
if (m.size() > buf.size() && m.compare(0, buf.size(), buf) == 0) return true;
}
if (!buf.empty() && buf[0] == '<' && buf.find('>') == std::string::npos) {
return true;
}
return false;
}
bool consume_literal(std::string &buf, const std::string &lit) {
if (buf.compare(0, lit.size(), lit) == 0) {
buf.erase(0, lit.size());
return true;
}
return false;
}
// Find the next '<' in buf starting at offset; returns std::string::npos if none.
size_t next_tag(const std::string &buf, size_t off = 0) {
return buf.find('<', off);
}
std::string json_escape(const std::string &in) {
std::string out;
out.reserve(in.size() + 2);
for (char c : in) {
switch (c) {
case '"': out += "\\\""; break;
case '\\': out += "\\\\"; break;
case '\b': out += "\\b"; break;
case '\f': out += "\\f"; break;
case '\n': out += "\\n"; break;
case '\r': out += "\\r"; break;
case '\t': out += "\\t"; break;
default:
if (static_cast<unsigned char>(c) < 0x20) {
char tmp[8];
std::snprintf(tmp, sizeof(tmp), "\\u%04x", c);
out += tmp;
} else {
out += c;
}
}
}
return out;
}
} // namespace
DsmlParser::DsmlParser() = default;
bool DsmlParser::IsInDsmlStructural() const {
switch (state_) {
case State::TOOL_CALLS:
case State::INVOKE:
return true;
case State::PARAM_VALUE: // payload bytes; user sampling applies
case State::TEXT:
case State::THINK:
return false;
}
return false;
}
void DsmlParser::EmitArgsChunk(const std::string &chunk, std::vector<ParserEvent> &out) {
if (chunk.empty()) return;
ParserEvent e;
e.type = ParserEvent::TOOL_ARGS;
e.text = chunk;
e.index = tool_index_;
out.push_back(std::move(e));
}
void DsmlParser::FinishCurrentToolCall(std::vector<ParserEvent> &out) {
if (tool_index_ < 0) return;
// Close the JSON object that was opened on the first parameter.
if (args_emitted_open_brace_) {
EmitArgsChunk("}", out);
} else {
EmitArgsChunk("{}", out);
}
ParserEvent e;
e.type = ParserEvent::TOOL_END;
e.index = tool_index_;
out.push_back(std::move(e));
current_tool_name_.clear();
args_emitted_open_brace_ = false;
args_param_count_ = 0;
}
bool DsmlParser::TryConsumeMarker(std::vector<ParserEvent> &out) {
switch (state_) {
case State::TEXT: {
if (consume_literal(buf_, kThinkOpen)) { state_ = State::THINK; return true; }
if (consume_literal(buf_, kToolsOpen)) { state_ = State::TOOL_CALLS; return true; }
return false;
}
case State::THINK: {
if (consume_literal(buf_, kThinkClose)) { state_ = State::TEXT; return true; }
return false;
}
case State::TOOL_CALLS: {
if (consume_literal(buf_, kToolsClose)) { state_ = State::TEXT; return true; }
// <DSMLinvoke name="X">
if (buf_.compare(0, std::strlen(kInvokeOpenPfx), kInvokeOpenPfx) == 0) {
size_t close_q = buf_.find('"', std::strlen(kInvokeOpenPfx));
if (close_q == std::string::npos) return false; // need more bytes
size_t close_gt = buf_.find('>', close_q);
if (close_gt == std::string::npos) return false;
current_tool_name_ = buf_.substr(std::strlen(kInvokeOpenPfx),
close_q - std::strlen(kInvokeOpenPfx));
tool_index_++;
buf_.erase(0, close_gt + 1);
ParserEvent e;
e.type = ParserEvent::TOOL_START;
e.tool_name = current_tool_name_;
e.tool_id = RandomToolId();
e.index = tool_index_;
out.push_back(std::move(e));
args_emitted_open_brace_ = false;
args_param_count_ = 0;
state_ = State::INVOKE;
return true;
}
return false;
}
case State::INVOKE: {
if (consume_literal(buf_, kInvokeClose)) {
FinishCurrentToolCall(out);
state_ = State::TOOL_CALLS;
return true;
}
// <DSMLparameter name="K" string="true|false">
if (buf_.compare(0, std::strlen(kParamOpenPfx), kParamOpenPfx) == 0) {
size_t close_q = buf_.find('"', std::strlen(kParamOpenPfx));
if (close_q == std::string::npos) return false;
size_t string_attr = buf_.find("string=\"", close_q);
if (string_attr == std::string::npos) return false;
size_t string_q = buf_.find('"', string_attr + 8);
if (string_q == std::string::npos) return false;
size_t close_gt = buf_.find('>', string_q);
if (close_gt == std::string::npos) return false;
param_name_ = buf_.substr(std::strlen(kParamOpenPfx),
close_q - std::strlen(kParamOpenPfx));
std::string string_val = buf_.substr(string_attr + 8,
string_q - (string_attr + 8));
param_is_string_ = (string_val == "true");
param_value_.clear();
buf_.erase(0, close_gt + 1);
// Emit args JSON opener / separator.
std::string opener;
if (!args_emitted_open_brace_) { opener = "{"; args_emitted_open_brace_ = true; }
else { opener = ","; }
opener += "\"" + json_escape(param_name_) + "\":";
if (param_is_string_) opener += "\"";
EmitArgsChunk(opener, out);
args_param_count_++;
state_ = State::PARAM_VALUE;
return true;
}
return false;
}
case State::PARAM_VALUE: {
if (consume_literal(buf_, kParamClose)) {
if (param_is_string_) EmitArgsChunk("\"", out);
state_ = State::INVOKE;
return true;
}
return false;
}
}
return false;
}
void DsmlParser::DrainPlain(std::vector<ParserEvent> &out) {
// Drain everything up to the next '<' that *might* start a marker.
// Anything before the next '<' is safe to emit; the '<...' tail stays buffered.
while (!buf_.empty()) {
size_t lt = next_tag(buf_, 0);
if (lt == std::string::npos) {
// No tag at all - emit (or accumulate) the whole buffer.
ParserEvent e;
if (state_ == State::PARAM_VALUE) {
std::string esc = param_is_string_ ? json_escape(buf_) : buf_;
EmitArgsChunk(esc, out);
} else if (state_ == State::THINK) {
e.type = ParserEvent::REASONING;
e.text = buf_;
out.push_back(std::move(e));
} else if (state_ == State::TEXT) {
e.type = ParserEvent::CONTENT;
e.text = buf_;
out.push_back(std::move(e));
}
// Inside INVOKE / TOOL_CALLS with no marker, raw bytes are
// structural whitespace - discard.
buf_.clear();
return;
}
if (lt > 0) {
std::string chunk = buf_.substr(0, lt);
buf_.erase(0, lt);
ParserEvent e;
if (state_ == State::PARAM_VALUE) {
std::string esc = param_is_string_ ? json_escape(chunk) : chunk;
EmitArgsChunk(esc, out);
} else if (state_ == State::THINK) {
e.type = ParserEvent::REASONING;
e.text = chunk;
out.push_back(std::move(e));
} else if (state_ == State::TEXT) {
e.type = ParserEvent::CONTENT;
e.text = chunk;
out.push_back(std::move(e));
}
}
// buf_[0] == '<' - try consuming a marker. If we consumed one, loop again.
if (!TryConsumeMarker(out)) {
// Could be a partial marker - wait for more bytes.
if (looks_like_prefix(buf_)) return;
// Otherwise this '<' is a literal - emit one char and continue.
std::string one(1, buf_[0]);
buf_.erase(0, 1);
ParserEvent e;
if (state_ == State::PARAM_VALUE) {
std::string esc = param_is_string_ ? json_escape(one) : one;
EmitArgsChunk(esc, out);
} else if (state_ == State::THINK) {
e.type = ParserEvent::REASONING;
e.text = one;
out.push_back(std::move(e));
} else if (state_ == State::TEXT) {
e.type = ParserEvent::CONTENT;
e.text = one;
out.push_back(std::move(e));
}
}
}
}
void DsmlParser::Feed(const std::string &chunk, std::vector<ParserEvent> &out) {
buf_ += chunk;
DrainPlain(out);
}
void DsmlParser::Flush(std::vector<ParserEvent> &out) {
// At flush time we no longer wait for marker completion - drain everything
// (the trailing bytes won't grow). Mirror DrainPlain's state-aware
// classification: PARAM_VALUE bytes become TOOL_ARGS, THINK bytes become
// REASONING, TEXT bytes become CONTENT, and INVOKE/TOOL_CALLS bytes are
// structural whitespace (discarded).
auto emit_plain = [&](const std::string &chunk) {
if (chunk.empty()) return;
if (state_ == State::PARAM_VALUE) {
std::string esc = param_is_string_ ? json_escape(chunk) : chunk;
EmitArgsChunk(esc, out);
return;
}
if (state_ == State::THINK) {
ParserEvent e;
e.type = ParserEvent::REASONING;
e.text = chunk;
out.push_back(std::move(e));
return;
}
if (state_ == State::TEXT) {
ParserEvent e;
e.type = ParserEvent::CONTENT;
e.text = chunk;
out.push_back(std::move(e));
return;
}
// INVOKE / TOOL_CALLS: structural whitespace, discard.
};
while (!buf_.empty()) {
size_t lt = next_tag(buf_, 0);
if (lt == std::string::npos) {
emit_plain(buf_);
buf_.clear();
return;
}
if (lt > 0) {
std::string chunk = buf_.substr(0, lt);
buf_.erase(0, lt);
emit_plain(chunk);
}
if (!TryConsumeMarker(out)) {
// Definitely a literal '<' now (no chance of more bytes arriving).
std::string one(1, buf_[0]);
buf_.erase(0, 1);
emit_plain(one);
}
}
// If we ended mid-tool-call (model truncated), close it cleanly.
if (state_ == State::INVOKE || state_ == State::PARAM_VALUE) {
if (state_ == State::PARAM_VALUE && param_is_string_) EmitArgsChunk("\"", out);
FinishCurrentToolCall(out);
state_ = State::TEXT;
}
}
std::string RandomToolId() {
static thread_local std::mt19937_64 rng{
static_cast<uint64_t>(std::chrono::system_clock::now().time_since_epoch().count())};
const char *alphabet =
"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789";
std::string out = "call_";
for (int i = 0; i < 16; ++i) {
out += alphabet[rng() % 62];
}
return out;
}
} // namespace ds4cpp

View File

@@ -1,77 +0,0 @@
#pragma once
#include <functional>
#include <string>
#include <vector>
namespace ds4cpp {
struct ParserEvent {
enum Type { CONTENT, REASONING, TOOL_START, TOOL_ARGS, TOOL_END };
Type type;
std::string text; // CONTENT, REASONING, TOOL_ARGS
std::string tool_name; // TOOL_START
std::string tool_id; // TOOL_START (caller-assigned)
int index = 0; // TOOL_START / TOOL_ARGS / TOOL_END
};
// Streaming parser. Stateless across instances; one per Predict call.
class DsmlParser {
public:
DsmlParser();
// Feed a chunk of raw model-emitted text. Appends classified events to
// `out`. May buffer the tail of `chunk` internally if it looks like a
// marker prefix.
void Feed(const std::string &chunk, std::vector<ParserEvent> &out);
// Flush any remaining buffered text as CONTENT (called at generation end).
void Flush(std::vector<ParserEvent> &out);
// True when the parser is inside a DSML structural position - that is,
// tags/markers between tool-call boundaries where the model is expected
// to emit protocol bytes verbatim. Mirrors ds4_server.c's "force
// temperature=0 unless dsml_decode_state_uses_payload_sampling" rule:
//
// TEXT / THINK -> false (user sampling applies)
// PARAM_VALUE -> false (payload uses user sampling)
// TOOL_CALLS / INVOKE -> true (structural; force greedy)
//
// Callers should use this BEFORE the next sample() call to pick the
// effective temperature; the parser's state reflects what's already
// been consumed, so it predicts the next token's classification.
bool IsInDsmlStructural() const;
private:
enum class State { TEXT, THINK, TOOL_CALLS, INVOKE, PARAM_VALUE };
State state_ = State::TEXT;
std::string buf_;
std::string current_tool_name_;
int tool_index_ = -1;
// While parsing a parameter value:
std::string param_name_;
bool param_is_string_ = true;
std::string param_value_;
// Incrementally-built arguments JSON for the active tool call.
std::string args_json_so_far_;
bool args_emitted_open_brace_ = false;
int args_param_count_ = 0;
// Try to consume one structural marker starting at buf_[0]. Returns true
// and advances state if a complete marker was consumed; false if the
// buffer is ambiguous (could be a marker prefix).
bool TryConsumeMarker(std::vector<ParserEvent> &out);
// Drain plain text from buf_ as far as we're sure it's not a marker prefix.
// Emits CONTENT or REASONING depending on current state.
void DrainPlain(std::vector<ParserEvent> &out);
// Emit the next chunk of arguments JSON to the consumer.
void EmitArgsChunk(const std::string &chunk, std::vector<ParserEvent> &out);
void FinishCurrentToolCall(std::vector<ParserEvent> &out);
};
// Generate a random tool call ID (e.g. "call_AbCdEf"). Used by the gRPC layer
// when assigning IDs to streamed tool calls.
std::string RandomToolId();
} // namespace ds4cpp

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@@ -1,140 +0,0 @@
#include "dsml_renderer.h"
// We accept either nlohmann::json (if available) or fall back to a tiny
// hand-rolled parser. The LocalAI tree already has nlohmann/json bundled
// in vendor paths; we use the apt-installed nlohmann-json3-dev (installed
// in Task 11 step 1) when present, otherwise the bundled copy.
#if __has_include(<nlohmann/json.hpp>)
#include <nlohmann/json.hpp>
using json = nlohmann::json;
#else
#error "nlohmann/json.hpp not found; install nlohmann-json3-dev"
#endif
#include <sstream>
namespace ds4cpp {
namespace {
void render_param(std::ostringstream &os, const std::string &name,
const json &value) {
bool is_string = value.is_string();
os << "<\xef\xbd\x9c" "DSML\xef\xbd\x9c" "parameter name=\"" << name
<< "\" string=\"" << (is_string ? "true" : "false") << "\">";
if (is_string) {
os << value.get<std::string>();
} else {
os << value.dump();
}
os << "</\xef\xbd\x9c" "DSML\xef\xbd\x9c" "parameter>\n";
}
} // namespace
std::string RenderAssistantToolCalls(const std::string &tool_calls_json) {
if (tool_calls_json.empty()) return "";
json arr;
try {
arr = json::parse(tool_calls_json);
} catch (const std::exception &) {
return "";
}
if (!arr.is_array() || arr.empty()) return "";
std::ostringstream os;
os << "\n\n<\xef\xbd\x9c" "DSML\xef\xbd\x9c" "tool_calls>\n";
for (const auto &call : arr) {
// OpenAI shape: { id, type, function: { name, arguments (JSON string) } }
// Anthropic shape comes through normalized by LocalAI.
std::string name;
std::string args_str;
if (call.contains("function")) {
const auto &fn = call["function"];
if (fn.contains("name") && fn["name"].is_string())
name = fn["name"].get<std::string>();
if (fn.contains("arguments") && fn["arguments"].is_string())
args_str = fn["arguments"].get<std::string>();
}
os << "<\xef\xbd\x9c" "DSML\xef\xbd\x9c" "invoke name=\"" << name << "\">\n";
if (!args_str.empty()) {
json args;
try {
args = json::parse(args_str);
} catch (...) {
args = json{};
}
if (args.is_object()) {
for (auto it = args.begin(); it != args.end(); ++it) {
render_param(os, it.key(), it.value());
}
}
}
os << "</\xef\xbd\x9c" "DSML\xef\xbd\x9c" "invoke>\n";
}
os << "</\xef\xbd\x9c" "DSML\xef\xbd\x9c" "tool_calls>";
return os.str();
}
std::string RenderToolResult(const std::string &tool_call_id, const std::string &content) {
std::ostringstream os;
// ds4_server.c wraps tool results in a "tool_result" DSML tag carrying
// the tool_call_id. Match that shape.
os << "<\xef\xbd\x9c" "DSML\xef\xbd\x9c" "tool_result id=\"" << tool_call_id << "\">"
<< content
<< "</\xef\xbd\x9c" "DSML\xef\xbd\x9c" "tool_result>";
return os.str();
}
std::string RenderToolsManifest(const std::string &tools_json) {
if (tools_json.empty()) return "";
json arr;
try {
arr = json::parse(tools_json);
} catch (const std::exception &) {
return "";
}
if (!arr.is_array() || arr.empty()) return "";
// Extract each OpenAI tool's `function` object, dump as compact JSON, one
// per line. Mirrors openai_function_schema_from_tool() in ds4_server.c.
std::ostringstream schemas;
for (const auto &tool : arr) {
if (tool.contains("function") && tool["function"].is_object()) {
schemas << tool["function"].dump() << "\n";
} else if (tool.is_object()) {
// Anthropic / direct-schema form: pass through.
schemas << tool.dump() << "\n";
}
}
if (schemas.tellp() == std::streampos(0)) return "";
// Verbatim text from ds4_server.c append_tools_prompt_text. Do NOT
// paraphrase - the model was trained on these exact bytes.
std::ostringstream os;
os << "## Tools\n\n"
"You have access to a set of tools to help answer the user question. "
"You can invoke tools by writing a \"<\xef\xbd\x9c" "DSML\xef\xbd\x9c" "tool_calls>\" block like the following:\n\n"
"<\xef\xbd\x9c" "DSML\xef\xbd\x9c" "tool_calls>\n"
"<\xef\xbd\x9c" "DSML\xef\xbd\x9c" "invoke name=\"$TOOL_NAME\">\n"
"<\xef\xbd\x9c" "DSML\xef\xbd\x9c" "parameter name=\"$PARAMETER_NAME\" string=\"true|false\">$PARAMETER_VALUE</\xef\xbd\x9c" "DSML\xef\xbd\x9c" "parameter>\n"
"...\n"
"</\xef\xbd\x9c" "DSML\xef\xbd\x9c" "invoke>\n"
"<\xef\xbd\x9c" "DSML\xef\xbd\x9c" "invoke name=\"$TOOL_NAME2\">\n"
"...\n"
"</\xef\xbd\x9c" "DSML\xef\xbd\x9c" "invoke>\n"
"</\xef\xbd\x9c" "DSML\xef\xbd\x9c" "tool_calls>\n\n"
"String parameters should be specified as raw text and set `string=\"true\"`. "
"Preserve characters such as `>`, `&`, and `&&` exactly; never replace normal string characters with XML or HTML entity escapes. "
"Only if a string value itself contains the exact closing parameter tag `</\xef\xbd\x9c" "DSML\xef\xbd\x9c" "parameter>`, write that tag as `&lt;/\xef\xbd\x9c" "DSML\xef\xbd\x9c" "parameter>` inside the value. "
"For all other types (numbers, booleans, arrays, objects), pass the value in JSON format and set `string=\"false\"`.\n\n"
"If thinking_mode is enabled (triggered by <think>), you MUST output your complete reasoning inside <think>...</think> BEFORE any tool calls or final response.\n\n"
"Otherwise, output directly after </think> with tool calls or final response.\n\n"
"### Available Tool Schemas\n\n"
<< schemas.str()
<< "\nYou MUST strictly follow the above defined tool name and parameter schemas to invoke tool calls. "
"Use the exact parameter names from the schemas.";
return os.str();
}
} // namespace ds4cpp

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@@ -1,27 +0,0 @@
#pragma once
#include <string>
namespace ds4cpp {
// Render an assistant message's tool_calls JSON array into the DSML block
// that ds4 expects in its prompt. `tool_calls_json` is the value of
// proto.Message.tool_calls (OpenAI shape: array of {id, type, function:{name, arguments}}).
// Returns the DSML text to append after the assistant's content.
std::string RenderAssistantToolCalls(const std::string &tool_calls_json);
// Render a role="tool" message into the DSML "tool result" block. ds4's
// prompt template expects tool results inside a specific tag; we wrap the
// `content` with that tag and include the `tool_call_id` so the model can
// correlate.
std::string RenderToolResult(const std::string &tool_call_id, const std::string &content);
// Render the "## Tools" manifest that ds4 expects in the SYSTEM prompt when
// tools are available. Without this preamble the model has no idea tools
// exist and will not emit DSML tool calls. Mirrors append_tools_prompt_text()
// in ds4_server.c (~line 1646): a fixed preamble + "### Available Tool
// Schemas" section + one JSON schema per line (extracted from each OpenAI
// tool's .function object) + a fixed closing instruction. Returns empty
// when tools_json is empty / unparseable.
std::string RenderToolsManifest(const std::string &tools_json);
} // namespace ds4cpp

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@@ -1,696 +0,0 @@
// ds4 LocalAI gRPC backend.
//
// Wraps antirez/ds4's `ds4_engine_*` / `ds4_session_*` public API
// (see ds4/ds4.h) over LocalAI's backend.proto. Tool calls, thinking
// mode, and disk KV cache are wired in follow-up commits; this commit
// is just the bind/listen/Health/Free skeleton.
#include "backend.pb.h"
#include "backend.grpc.pb.h"
#include "dsml_parser.h" // populated in Task 12
#include "dsml_renderer.h" // populated in Task 16
#include "kv_cache.h" // populated in Task 17
extern "C" {
#include "ds4.h"
}
#include <grpcpp/grpcpp.h>
#include <grpcpp/server.h>
#include <grpcpp/server_builder.h>
#include <grpcpp/ext/proto_server_reflection_plugin.h>
#include <atomic>
#include <chrono>
#include <csignal>
#include <cstring>
#include <iostream>
#include <memory>
#include <mutex>
#include <string>
#include <thread>
#include <vector>
using grpc::Server;
using grpc::ServerBuilder;
using grpc::ServerContext;
using grpc::ServerWriter;
// NOTE: do NOT alias `grpc::Status` as `Status` - the Status RPC method below
// would shadow the type, breaking the other RPC method declarations that use
// it as a return type. Use GStatus instead.
using GStatus = ::grpc::Status;
using grpc::StatusCode;
namespace {
// Global state - ds4 is single-engine-per-process by design.
std::mutex g_engine_mu;
ds4_engine *g_engine = nullptr;
ds4_session *g_session = nullptr;
int g_ctx_size = 32768;
std::string g_kv_cache_dir; // empty disables disk cache
std::atomic<Server *> g_server{nullptr};
// Parse a "key:value" option string. Returns empty when no colon.
static std::pair<std::string, std::string> split_option(const std::string &opt) {
auto colon = opt.find(':');
if (colon == std::string::npos) return {opt, ""};
return {opt.substr(0, colon), opt.substr(colon + 1)};
}
static void append_token_text(ds4_engine *engine, int token, std::string &out) {
size_t len = 0;
const char *text = ds4_token_text(engine, token, &len);
if (text && len > 0) out.append(text, len);
}
struct CollectCtx {
ds4_engine *engine;
std::string raw_buf; // exact raw bytes for Reply.message
ds4cpp::DsmlParser parser;
backend::Reply *reply;
int tokens;
// Per-tool aggregation: accumulate ChatDelta tool_calls so we emit one
// delta with all calls, mirroring how vllm's non-streaming path returns.
struct Pending {
std::string id;
std::string name;
std::string args;
};
std::vector<Pending> pending;
std::string content_buf;
std::string reasoning_buf;
};
static void apply_events(CollectCtx *c, const std::vector<ds4cpp::ParserEvent> &events) {
for (const auto &e : events) {
switch (e.type) {
case ds4cpp::ParserEvent::CONTENT:
c->content_buf += e.text;
break;
case ds4cpp::ParserEvent::REASONING:
c->reasoning_buf += e.text;
break;
case ds4cpp::ParserEvent::TOOL_START:
if ((int)c->pending.size() <= e.index)
c->pending.resize(e.index + 1);
c->pending[e.index].id = e.tool_id;
c->pending[e.index].name = e.tool_name;
break;
case ds4cpp::ParserEvent::TOOL_ARGS:
if ((int)c->pending.size() > e.index)
c->pending[e.index].args += e.text;
break;
case ds4cpp::ParserEvent::TOOL_END:
// No-op for non-streaming: the final delta is emitted at the end.
break;
}
}
}
static void collect_emit(void *ud, int token) {
auto *c = static_cast<CollectCtx *>(ud);
if (token == ds4_token_eos(c->engine)) return;
size_t len = 0;
const char *text = ds4_token_text(c->engine, token, &len);
if (!text || len == 0) return;
std::string chunk(text, len);
c->raw_buf += chunk;
std::vector<ds4cpp::ParserEvent> events;
c->parser.Feed(chunk, events);
apply_events(c, events);
c->tokens++;
}
static void collect_done(void *) {}
struct StreamCtx {
ds4_engine *engine;
ServerWriter<backend::Reply> *writer;
ds4cpp::DsmlParser parser;
int tokens;
bool aborted;
// Track which tool indices we've seen TOOL_START for, so subsequent
// ARGS deltas can elide the redundant id/name fields.
std::vector<bool> tool_started;
};
static void stream_emit(void *ud, int token) {
auto *s = static_cast<StreamCtx *>(ud);
if (s->aborted) return;
if (token == ds4_token_eos(s->engine)) return;
size_t len = 0;
const char *text = ds4_token_text(s->engine, token, &len);
if (!text || len == 0) return;
std::string chunk(text, len);
std::vector<ds4cpp::ParserEvent> events;
s->parser.Feed(chunk, events);
if (events.empty()) { s->tokens++; return; }
backend::Reply reply;
auto *delta = reply.add_chat_deltas();
bool any_field = false;
for (const auto &e : events) {
switch (e.type) {
case ds4cpp::ParserEvent::CONTENT:
delta->set_content(delta->content() + e.text);
any_field = true;
break;
case ds4cpp::ParserEvent::REASONING:
delta->set_reasoning_content(delta->reasoning_content() + e.text);
any_field = true;
break;
case ds4cpp::ParserEvent::TOOL_START: {
if ((int)s->tool_started.size() <= e.index)
s->tool_started.resize(e.index + 1, false);
s->tool_started[e.index] = true;
auto *tc = delta->add_tool_calls();
tc->set_index(e.index);
tc->set_id(e.tool_id);
tc->set_name(e.tool_name);
any_field = true;
break;
}
case ds4cpp::ParserEvent::TOOL_ARGS: {
auto *tc = delta->add_tool_calls();
tc->set_index(e.index);
tc->set_arguments(e.text);
any_field = true;
break;
}
case ds4cpp::ParserEvent::TOOL_END:
// No marker delta needed - the Go side closes the tool call on
// the final aggregator pass.
break;
}
}
reply.set_message(chunk);
reply.set_tokens(1);
if (any_field) {
if (!s->writer->Write(reply)) s->aborted = true;
}
s->tokens++;
}
static void stream_done(void *) {}
// Per-thread RNG seed for ds4_session_sample. Initialized lazily from
// system_clock; ds4 owns the random walk after that.
static uint64_t *get_rng() {
static thread_local uint64_t seed = 0;
if (seed == 0) {
seed = static_cast<uint64_t>(
std::chrono::system_clock::now().time_since_epoch().count());
if (seed == 0) seed = 1;
}
return &seed;
}
struct SampleParams {
float temperature;
int top_k;
float top_p;
float min_p;
};
// Compute the effective sampling parameters for the next token, mirroring
// ds4_server.c:7102-7115:
// - thinking mode enabled -> override (T=1, top_k=0, top_p=1, min_p=0)
// - inside DSML structural position (tool-call markers) -> force T=0
// - otherwise -> the request's user-supplied sampling settings
// The parser argument carries state from tokens emitted so far; its
// IsInDsmlStructural() predicts the next token's classification.
static SampleParams compute_sample_params(const backend::PredictOptions *request,
const ds4cpp::DsmlParser &parser,
bool think_enabled);
static ds4_think_mode parse_think_mode(const backend::PredictOptions *request) {
// Per the vllm backend convention, "enable_thinking" gates thinking on/off,
// and "reasoning_effort" picks the strength when on.
const auto &md = request->metadata();
auto et = md.find("enable_thinking");
bool enabled = true; // default ON per ds4-server
if (et != md.end()) enabled = (et->second == "true" || et->second == "1");
if (!enabled) return DS4_THINK_NONE;
auto re = md.find("reasoning_effort");
if (re != md.end() && (re->second == "max" || re->second == "xhigh"))
return DS4_THINK_MAX;
return DS4_THINK_HIGH;
}
static SampleParams compute_sample_params(const backend::PredictOptions *request,
const ds4cpp::DsmlParser &parser,
bool think_enabled) {
SampleParams p = {
request->temperature(),
request->topk(),
request->topp(),
request->minp(),
};
if (think_enabled) {
// Match ds4-server: thinking mode wants creativity in the reasoning
// pass and the trailing content, so the entire generation overrides
// sampling unless DSML structural bytes take over below.
p.temperature = 1.0f;
p.top_k = 0;
p.top_p = 1.0f;
p.min_p = 0.0f;
}
if (parser.IsInDsmlStructural()) {
// Tool-call structural bytes (tags, markers, headers) must parse
// cleanly. Force greedy regardless of user/thinking settings.
p.temperature = 0.0f;
}
return p;
}
// Build the rendered text for cache keying. We feed the same text the model
// will see; that lets the cache survive small client-side reformatting of
// chat history (the cache is keyed on bytes, not tokens).
static std::string render_prompt_text(const backend::PredictOptions *request) {
// Two-mode: either the raw prompt or the chat-template path. We mirror
// build_prompt's branching but accumulate text (not tokens) so we can
// SHA1 it for the cache key. ds4_session caches a tokens-indexed
// checkpoint, but the disk format keys on bytes per ds4-server's design.
if (!request->usetokenizertemplate() || request->messages_size() == 0) {
return request->prompt();
}
std::string out;
const std::string sys_role = "system";
for (const auto &m : request->messages()) {
if (m.role() == sys_role) { out += "[sys] " + m.content() + "\n"; break; }
}
for (const auto &m : request->messages()) {
if (m.role() == sys_role) continue;
out += "[" + m.role() + "] " + m.content() + "\n";
}
return out;
}
ds4cpp::KvCache g_kv_cache;
// Try to recover prefill state for `rendered`. Returns the matched prefix length.
static size_t maybe_load_cache(const std::string &rendered) {
if (!g_kv_cache.enabled() || !g_session) return 0;
return g_kv_cache.LoadLongestPrefix(g_session, rendered, g_ctx_size);
}
static void maybe_save_cache(const std::string &rendered) {
if (g_kv_cache.enabled() && g_session) {
g_kv_cache.Save(g_session, rendered, g_ctx_size);
}
}
static void build_prompt(ds4_engine *engine, const backend::PredictOptions *request,
ds4_tokens *out) {
if (!request->usetokenizertemplate() || request->messages_size() == 0) {
ds4_tokenize_text(engine, request->prompt().c_str(), out);
return;
}
// Chat-template path: render via ds4's helpers.
ds4_chat_begin(engine, out);
ds4_think_mode think = parse_think_mode(request);
// ds4_encode_chat_prompt is convenient when there is exactly one
// system+user pair, but for arbitrary turn lists we use the granular
// append helpers. Pull the first system message (if any), then append
// every other message in order.
const std::string sys_role = "system";
std::string system_text;
for (const auto &m : request->messages()) {
if (m.role() == sys_role) { system_text = m.content(); break; }
}
// Inject the tools manifest into the system prompt when tools are present.
// ds4 was trained to emit DSML tool calls ONLY when this preamble is in
// the system message - without it, the model has no idea tools exist and
// the e2e tool-call test will fail. The renderer lives in dsml_renderer
// and is a verbatim port of ds4_server.c's append_tools_prompt_text.
std::string tools_manifest;
if (!request->tools().empty()) {
tools_manifest = ds4cpp::RenderToolsManifest(request->tools());
}
if (!system_text.empty() || !tools_manifest.empty()) {
std::string combined = system_text;
if (!tools_manifest.empty()) {
if (!combined.empty()) combined += "\n\n";
combined += tools_manifest;
}
ds4_chat_append_message(engine, out, "system", combined.c_str());
}
for (const auto &m : request->messages()) {
if (m.role() == sys_role) continue;
if (m.role() == "assistant" && !m.tool_calls().empty()) {
std::string combined = m.content();
combined += ds4cpp::RenderAssistantToolCalls(m.tool_calls());
ds4_chat_append_message(engine, out, "assistant", combined.c_str());
} else if (m.role() == "tool") {
std::string body = ds4cpp::RenderToolResult(m.tool_call_id(), m.content());
ds4_chat_append_message(engine, out, "user", body.c_str());
} else {
ds4_chat_append_message(engine, out, m.role().c_str(), m.content().c_str());
}
}
ds4_chat_append_assistant_prefix(engine, out, think);
}
class DS4Backend final : public backend::Backend::Service {
public:
GStatus Health(ServerContext *, const backend::HealthMessage *,
backend::Reply *reply) override {
reply->set_message(std::string("OK"));
return GStatus::OK;
}
GStatus Free(ServerContext *, const backend::HealthMessage *,
backend::Result *result) override {
std::lock_guard<std::mutex> lock(g_engine_mu);
if (g_session) { ds4_session_free(g_session); g_session = nullptr; }
if (g_engine) { ds4_engine_close(g_engine); g_engine = nullptr; }
result->set_success(true);
return GStatus::OK;
}
GStatus LoadModel(ServerContext *, const backend::ModelOptions *request,
backend::Result *result) override {
std::lock_guard<std::mutex> lock(g_engine_mu);
if (g_engine) {
if (g_session) { ds4_session_free(g_session); g_session = nullptr; }
ds4_engine_close(g_engine);
g_engine = nullptr;
}
std::string model_path = request->modelfile();
if (model_path.empty()) model_path = request->model();
if (model_path.empty()) {
result->set_success(false);
result->set_message("ds4: ModelOptions.Model or .ModelFile must be set");
return GStatus::OK;
}
std::string mtp_path;
int mtp_draft = 0;
float mtp_margin = 3.0f;
for (const auto &opt : request->options()) {
auto [k, v] = split_option(opt);
if (k == "mtp_path") mtp_path = v;
else if (k == "mtp_draft") mtp_draft = std::stoi(v);
else if (k == "mtp_margin") mtp_margin = std::stof(v);
else if (k == "kv_cache_dir") g_kv_cache_dir = v;
}
g_kv_cache.SetDir(g_kv_cache_dir);
ds4_engine_options opt = {};
opt.model_path = model_path.c_str();
opt.mtp_path = mtp_path.empty() ? nullptr : mtp_path.c_str();
opt.n_threads = request->threads() > 0 ? request->threads() : 0;
opt.mtp_draft_tokens = mtp_draft;
opt.mtp_margin = mtp_margin;
opt.directional_steering_file = nullptr;
opt.warm_weights = false;
opt.quality = false;
#if defined(DS4_NO_GPU)
opt.backend = DS4_BACKEND_CPU;
#elif defined(__APPLE__)
opt.backend = DS4_BACKEND_METAL;
#else
opt.backend = DS4_BACKEND_CUDA;
#endif
int rc = ds4_engine_open(&g_engine, &opt);
if (rc != 0 || !g_engine) {
result->set_success(false);
result->set_message("ds4_engine_open failed (rc=" + std::to_string(rc) + ")");
return GStatus::OK;
}
g_ctx_size = request->contextsize() > 0 ? request->contextsize() : 32768;
rc = ds4_session_create(&g_session, g_engine, g_ctx_size);
if (rc != 0 || !g_session) {
ds4_engine_close(g_engine);
g_engine = nullptr;
result->set_success(false);
result->set_message("ds4_session_create failed (rc=" + std::to_string(rc) + ")");
return GStatus::OK;
}
result->set_success(true);
result->set_message("loaded " + model_path);
return GStatus::OK;
}
GStatus TokenizeString(ServerContext *, const backend::PredictOptions *request,
backend::TokenizationResponse *response) override {
std::lock_guard<std::mutex> lock(g_engine_mu);
if (!g_engine) return GStatus(StatusCode::FAILED_PRECONDITION, "ds4: model not loaded");
ds4_tokens out = {};
ds4_tokenize_text(g_engine, request->prompt().c_str(), &out);
for (int i = 0; i < out.len; ++i) response->add_tokens(out.v[i]);
response->set_length(out.len);
ds4_tokens_free(&out);
return GStatus::OK;
}
GStatus Predict(ServerContext *, const backend::PredictOptions *request,
backend::Reply *reply) override {
std::lock_guard<std::mutex> lock(g_engine_mu);
if (!g_engine || !g_session) {
return GStatus(StatusCode::FAILED_PRECONDITION, "ds4: model not loaded");
}
ds4_tokens prompt = {};
build_prompt(g_engine, request, &prompt);
int n_predict = request->tokens() > 0 ? request->tokens() : 256;
CollectCtx collect = {g_engine, "", {}, reply, 0, {}, "", ""};
std::string cache_key = render_prompt_text(request);
size_t cache_hit = maybe_load_cache(cache_key);
(void)cache_hit; // future: skip prompt prefix if hit covers full prompt
// Manual generation loop on g_session. When MTP speculative weights
// were loaded (LoadModel option 'mtp_path:'), we use the
// ds4_session_eval_speculative_argmax path which may accept N>1
// tokens per outer iteration. Otherwise per-token argmax + eval.
// Either way g_session advances so the disk KV cache picks up a
// real checkpoint after the call (see maybe_save_cache below).
char err[256] = {0};
int rc = ds4_session_sync(g_session, &prompt, err, sizeof(err));
int prompt_len = prompt.len;
ds4_tokens_free(&prompt);
if (rc == 0) {
const int eos = ds4_token_eos(g_engine);
const int draft_max = ds4_engine_mtp_draft_tokens(g_engine);
const bool think_enabled = ds4_think_mode_enabled(parse_think_mode(request));
int produced = 0;
while (produced < n_predict) {
SampleParams sp = compute_sample_params(request, collect.parser, think_enabled);
int first;
if (sp.temperature <= 0.0f) {
first = ds4_session_argmax(g_session);
} else {
first = ds4_session_sample(g_session,
sp.temperature, sp.top_k,
sp.top_p, sp.min_p, get_rng());
}
if (first == eos) break;
// MTP only when sampling is greedy (ds4-server gate).
if (draft_max > 0 && sp.temperature <= 0.0f) {
constexpr int kAcceptedMax = 8;
int accepted[kAcceptedMax];
int cap = std::min(kAcceptedMax, draft_max + 1);
int n = ds4_session_eval_speculative_argmax(
g_session, first, draft_max, eos,
accepted, cap, err, sizeof(err));
if (n < 0) { rc = -1; break; }
bool stop = false;
for (int j = 0; j < n; ++j) {
if (accepted[j] == eos) { stop = true; break; }
collect_emit(&collect, accepted[j]);
if (++produced >= n_predict) { stop = true; break; }
}
if (stop) break;
} else {
collect_emit(&collect, first);
if (++produced >= n_predict) break;
rc = ds4_session_eval(g_session, first, err, sizeof(err));
if (rc != 0) break;
}
}
collect_done(&collect);
}
maybe_save_cache(cache_key);
// Flush any buffered parser state.
std::vector<ds4cpp::ParserEvent> events;
collect.parser.Flush(events);
apply_events(&collect, events);
if (rc != 0) {
return GStatus(StatusCode::INTERNAL,
std::string("ds4 generation failed: ") + err);
}
// Emit one ChatDelta with content/reasoning/tool_calls.
auto *delta = reply->add_chat_deltas();
delta->set_content(collect.content_buf);
delta->set_reasoning_content(collect.reasoning_buf);
for (size_t i = 0; i < collect.pending.size(); ++i) {
auto *tc = delta->add_tool_calls();
tc->set_index(static_cast<int32_t>(i));
tc->set_id(collect.pending[i].id);
tc->set_name(collect.pending[i].name);
tc->set_arguments(collect.pending[i].args);
}
reply->set_message(collect.raw_buf);
reply->set_tokens(collect.tokens);
reply->set_prompt_tokens(prompt_len);
return GStatus::OK;
}
GStatus PredictStream(ServerContext *, const backend::PredictOptions *request,
ServerWriter<backend::Reply> *writer) override {
std::lock_guard<std::mutex> lock(g_engine_mu);
if (!g_engine || !g_session) {
return GStatus(StatusCode::FAILED_PRECONDITION, "ds4: model not loaded");
}
ds4_tokens prompt = {};
build_prompt(g_engine, request, &prompt);
int n_predict = request->tokens() > 0 ? request->tokens() : 256;
StreamCtx s = {g_engine, writer, {}, 0, false, {}};
std::string cache_key = render_prompt_text(request);
size_t cache_hit = maybe_load_cache(cache_key);
(void)cache_hit;
// Manual loop on g_session - see Predict() above for the rationale.
// MTP speculative path used when ds4_engine_mtp_draft_tokens > 0.
char err[256] = {0};
int rc = ds4_session_sync(g_session, &prompt, err, sizeof(err));
ds4_tokens_free(&prompt);
if (rc == 0) {
const int eos = ds4_token_eos(g_engine);
const int draft_max = ds4_engine_mtp_draft_tokens(g_engine);
const bool think_enabled = ds4_think_mode_enabled(parse_think_mode(request));
int produced = 0;
while (produced < n_predict && !s.aborted) {
SampleParams sp = compute_sample_params(request, s.parser, think_enabled);
int first;
if (sp.temperature <= 0.0f) {
first = ds4_session_argmax(g_session);
} else {
first = ds4_session_sample(g_session,
sp.temperature, sp.top_k,
sp.top_p, sp.min_p, get_rng());
}
if (first == eos) break;
if (draft_max > 0 && sp.temperature <= 0.0f) {
constexpr int kAcceptedMax = 8;
int accepted[kAcceptedMax];
int cap = std::min(kAcceptedMax, draft_max + 1);
int n = ds4_session_eval_speculative_argmax(
g_session, first, draft_max, eos,
accepted, cap, err, sizeof(err));
if (n < 0) { rc = -1; break; }
bool stop = false;
for (int j = 0; j < n; ++j) {
if (accepted[j] == eos) { stop = true; break; }
stream_emit(&s, accepted[j]);
if (s.aborted) { stop = true; break; }
if (++produced >= n_predict) { stop = true; break; }
}
if (stop) break;
} else {
stream_emit(&s, first);
if (s.aborted || ++produced >= n_predict) break;
rc = ds4_session_eval(g_session, first, err, sizeof(err));
if (rc != 0) break;
}
}
stream_done(&s);
}
maybe_save_cache(cache_key);
// Flush parser state.
std::vector<ds4cpp::ParserEvent> events;
s.parser.Flush(events);
if (!events.empty() && !s.aborted) {
backend::Reply reply;
auto *delta = reply.add_chat_deltas();
for (const auto &e : events) {
if (e.type == ds4cpp::ParserEvent::CONTENT) {
delta->set_content(delta->content() + e.text);
} else if (e.type == ds4cpp::ParserEvent::REASONING) {
delta->set_reasoning_content(delta->reasoning_content() + e.text);
}
}
s.writer->Write(reply);
}
if (rc != 0 && !s.aborted) {
return GStatus(StatusCode::INTERNAL,
std::string("ds4 generation failed: ") + err);
}
return GStatus::OK;
}
GStatus Status(ServerContext *, const backend::HealthMessage *,
backend::StatusResponse *response) override {
std::lock_guard<std::mutex> lock(g_engine_mu);
response->set_state(g_engine ? backend::StatusResponse::READY
: backend::StatusResponse::UNINITIALIZED);
return GStatus::OK;
}
};
void RunServer(const std::string &addr) {
DS4Backend service;
grpc::EnableDefaultHealthCheckService(true);
grpc::reflection::InitProtoReflectionServerBuilderPlugin();
ServerBuilder builder;
builder.AddListeningPort(addr, grpc::InsecureServerCredentials());
builder.RegisterService(&service);
builder.SetMaxReceiveMessageSize(64 * 1024 * 1024);
builder.SetMaxSendMessageSize(64 * 1024 * 1024);
std::unique_ptr<Server> server(builder.BuildAndStart());
if (!server) {
std::cerr << "ds4 grpc-server: failed to bind " << addr << "\n";
std::exit(1);
}
g_server = server.get();
std::cerr << "ds4 grpc-server listening on " << addr << "\n";
server->Wait();
}
void signal_handler(int) {
if (auto *srv = g_server.load()) {
srv->Shutdown(std::chrono::system_clock::now() +
std::chrono::seconds(3));
}
}
} // namespace
int main(int argc, char *argv[]) {
std::string addr = "127.0.0.1:50051";
for (int i = 1; i < argc; ++i) {
std::string a = argv[i];
const std::string addr_flag = "--addr=";
if (a.rfind(addr_flag, 0) == 0) addr = a.substr(addr_flag.size());
else if (a == "--addr" && i + 1 < argc) addr = argv[++i];
else if (a == "--help" || a == "-h") {
std::cout << "Usage: grpc-server --addr=HOST:PORT\n";
return 0;
}
}
std::signal(SIGINT, signal_handler);
std::signal(SIGTERM, signal_handler);
RunServer(addr);
return 0;
}

View File

@@ -1,205 +0,0 @@
#include "kv_cache.h"
#include <cerrno>
#include <cstdio>
#include <cstring>
#include <dirent.h>
#include <fstream>
#include <sys/stat.h>
#include <vector>
namespace ds4cpp {
namespace {
// Minimal SHA1 (public domain reference). 30 lines; used only here.
struct Sha1 {
uint32_t h[5];
uint64_t bits;
uint8_t block[64];
size_t used;
Sha1() { h[0]=0x67452301; h[1]=0xEFCDAB89; h[2]=0x98BADCFE; h[3]=0x10325476; h[4]=0xC3D2E1F0; bits=0; used=0; }
static uint32_t rol(uint32_t x, int n){ return (x<<n)|(x>>(32-n)); }
void transform(const uint8_t *b) {
uint32_t w[80];
for (int i=0;i<16;i++) w[i] = (uint32_t)b[i*4]<<24 | (uint32_t)b[i*4+1]<<16 | (uint32_t)b[i*4+2]<<8 | b[i*4+3];
for (int i=16;i<80;i++) w[i] = rol(w[i-3]^w[i-8]^w[i-14]^w[i-16], 1);
uint32_t a=h[0],bb=h[1],c=h[2],d=h[3],e=h[4];
for (int i=0;i<80;i++) {
uint32_t f,k;
if (i<20) { f=(bb&c)|((~bb)&d); k=0x5A827999; }
else if (i<40) { f=bb^c^d; k=0x6ED9EBA1; }
else if (i<60) { f=(bb&c)|(bb&d)|(c&d); k=0x8F1BBCDC; }
else { f=bb^c^d; k=0xCA62C1D6; }
uint32_t t = rol(a,5)+f+e+k+w[i];
e=d; d=c; c=rol(bb,30); bb=a; a=t;
}
h[0]+=a; h[1]+=bb; h[2]+=c; h[3]+=d; h[4]+=e;
}
void update(const void *p, size_t n) {
const uint8_t *bp = (const uint8_t*)p;
bits += (uint64_t)n*8;
while (n) {
size_t take = 64-used;
if (take>n) take=n;
std::memcpy(block+used, bp, take);
used += take; bp += take; n -= take;
if (used == 64) { transform(block); used = 0; }
}
}
void final(uint8_t out[20]) {
uint8_t pad[64] = {0x80};
size_t padlen = (used < 56) ? (56-used) : (120-used);
uint64_t lb = bits;
uint8_t len[8];
for (int i=0;i<8;i++) len[7-i] = (uint8_t)(lb >> (i*8));
update(pad, padlen);
update(len, 8);
for (int i=0;i<5;i++) {
out[i*4] = h[i]>>24;
out[i*4+1] = h[i]>>16;
out[i*4+2] = h[i]>>8;
out[i*4+3] = h[i];
}
}
};
std::string mkdir_p(const std::string &d) {
if (d.empty()) return d;
struct stat st{};
if (stat(d.c_str(), &st) == 0) return d;
mkdir(d.c_str(), 0755);
return d;
}
bool file_exists(const std::string &p) {
struct stat st{};
return stat(p.c_str(), &st) == 0;
}
} // namespace
std::string Sha1Hex(const void *data, size_t len) {
Sha1 s;
s.update(data, len);
uint8_t out[20];
s.final(out);
char hex[41];
for (int i = 0; i < 20; ++i) std::snprintf(hex + i*2, 3, "%02x", out[i]);
hex[40] = 0;
return std::string(hex);
}
KvCache::KvCache() = default;
void KvCache::SetDir(const std::string &dir) {
dir_ = dir;
if (!dir_.empty()) {
mkdir_p(dir_);
std::fprintf(stderr, "ds4 KvCache: enabled at %s\n", dir_.c_str());
} else {
std::fprintf(stderr, "ds4 KvCache: disabled (no dir set)\n");
}
}
std::string KvCache::Path(const std::string &rendered_text) const {
if (dir_.empty()) return "";
return dir_ + "/" + Sha1Hex(rendered_text.data(), rendered_text.size()) + ".kv";
}
size_t KvCache::LoadLongestPrefix(ds4_session *session,
const std::string &rendered_text,
int ctx_size) {
if (dir_.empty() || !session) return 0;
// Strategy: enumerate all .kv files in dir, read their stored prefix
// header, pick the longest one that is also a prefix of rendered_text.
DIR *d = opendir(dir_.c_str());
if (!d) return 0;
struct dirent *de;
size_t best_len = 0;
std::string best_path;
while ((de = readdir(d)) != nullptr) {
std::string name = de->d_name;
if (name.size() < 4 || name.substr(name.size()-3) != ".kv") continue;
std::string path = dir_ + "/" + name;
std::ifstream f(path, std::ios::binary);
if (!f) continue;
char magic[4]; f.read(magic, 4);
if (f.gcount() != 4 || std::memcmp(magic, "DS4G", 4) != 0) continue;
uint32_t version=0, file_ctx=0, prefix_len=0;
f.read((char*)&version, 4); f.read((char*)&file_ctx, 4); f.read((char*)&prefix_len, 4);
if (version != 1) continue;
if ((int)file_ctx != ctx_size) continue;
if (prefix_len > rendered_text.size()) continue;
std::vector<char> prefix(prefix_len);
f.read(prefix.data(), prefix_len);
if (std::memcmp(prefix.data(), rendered_text.data(), prefix_len) != 0) continue;
if (prefix_len > best_len) {
best_len = prefix_len;
best_path = path;
}
}
closedir(d);
if (best_len == 0) return 0;
// Load best_path's payload into session.
std::ifstream f(best_path, std::ios::binary);
char magic[4]; f.read(magic, 4);
uint32_t version, file_ctx, prefix_len;
f.read((char*)&version, 4); f.read((char*)&file_ctx, 4); f.read((char*)&prefix_len, 4);
f.seekg(prefix_len, std::ios::cur);
uint64_t payload_bytes = 0;
f.read((char*)&payload_bytes, 8);
// ds4_session_load_payload reads from a FILE*; reopen via fopen.
FILE *fp = std::fopen(best_path.c_str(), "rb");
if (!fp) return 0;
// Seek past header + prefix + payload_bytes field.
std::fseek(fp, 4 + 4 + 4 + 4 + prefix_len + 8, SEEK_SET);
char errbuf[256] = {0};
int rc = ds4_session_load_payload(session, fp, payload_bytes, errbuf, sizeof(errbuf));
std::fclose(fp);
if (rc != 0) return 0;
return best_len;
}
void KvCache::Save(ds4_session *session, const std::string &rendered_text, int ctx_size) {
if (dir_.empty()) {
std::fprintf(stderr, "ds4 KvCache::Save: skipped (dir empty)\n");
return;
}
if (!session) {
std::fprintf(stderr, "ds4 KvCache::Save: skipped (session null)\n");
return;
}
std::string path = Path(rendered_text);
uint64_t payload_bytes = ds4_session_payload_bytes(session);
std::fprintf(stderr, "ds4 KvCache::Save: path=%s payload_bytes=%llu prefix_len=%zu\n",
path.c_str(), (unsigned long long)payload_bytes, rendered_text.size());
FILE *fp = std::fopen(path.c_str(), "wb");
if (!fp) {
std::fprintf(stderr, "ds4 KvCache::Save: fopen failed: %s\n", std::strerror(errno));
return;
}
char magic[4] = {'D','S','4','G'};
uint32_t version = 1;
uint32_t ctx = static_cast<uint32_t>(ctx_size);
uint32_t prefix_len = static_cast<uint32_t>(rendered_text.size());
std::fwrite(magic, 4, 1, fp);
std::fwrite(&version, 4, 1, fp);
std::fwrite(&ctx, 4, 1, fp);
std::fwrite(&prefix_len, 4, 1, fp);
std::fwrite(rendered_text.data(), prefix_len, 1, fp);
std::fwrite(&payload_bytes, 8, 1, fp);
char errbuf[256] = {0};
int rc = ds4_session_save_payload(session, fp, errbuf, sizeof(errbuf));
std::fclose(fp);
if (rc != 0) {
std::fprintf(stderr, "ds4 KvCache::Save: ds4_session_save_payload rc=%d err=%s; removing %s\n",
rc, errbuf, path.c_str());
std::remove(path.c_str());
} else {
std::fprintf(stderr, "ds4 KvCache::Save: wrote %s ok\n", path.c_str());
}
}
} // namespace ds4cpp

View File

@@ -1,44 +0,0 @@
#pragma once
#include <string>
extern "C" {
#include "ds4.h"
}
namespace ds4cpp {
// Disk-backed KV cache for ds4 sessions. Keyed by SHA1(rendered prompt prefix).
// Format (our own, NOT bit-compatible with ds4-server's KVC files - interop
// is a follow-up plan):
//
// "DS4G" (4 bytes magic) + u32 version=1 + u32 ctx_size +
// u32 prefix_text_len + prefix_text + u64 payload_bytes + payload
class KvCache {
public:
KvCache(); // disabled (dir empty)
// Set the cache directory. Empty disables.
void SetDir(const std::string &dir);
// Returns the cache file path for a given rendered text prefix.
std::string Path(const std::string &rendered_text) const;
// Look up the longest cached prefix that is also a prefix of
// `rendered_text`. Loads it into `session` if found. Returns the
// matched prefix length in bytes (0 if no hit).
size_t LoadLongestPrefix(ds4_session *session,
const std::string &rendered_text,
int ctx_size);
// Save the current session, associated with this rendered text prefix.
void Save(ds4_session *session, const std::string &rendered_text, int ctx_size);
bool enabled() const { return !dir_.empty(); }
private:
std::string dir_;
};
// Compute SHA1 of arbitrary bytes; returns 40-char hex.
std::string Sha1Hex(const void *data, size_t len);
} // namespace ds4cpp

View File

@@ -1,39 +0,0 @@
#!/bin/bash
set -e
CURDIR=$(dirname "$(realpath "$0")")
REPO_ROOT="${CURDIR}/../../.."
mkdir -p "$CURDIR/package/lib"
cp -avf "$CURDIR/grpc-server" "$CURDIR/package/"
cp -rfv "$CURDIR/run.sh" "$CURDIR/package/"
UNAME_S=$(uname -s)
if [ "$UNAME_S" = "Darwin" ]; then
# Darwin: bundle dylibs via otool -L (handled by scripts/build/ds4-darwin.sh).
echo "package.sh: Darwin handled by ds4-darwin.sh"
exit 0
fi
if [ -f "/lib64/ld-linux-x86-64.so.2" ]; then
cp -arfLv /lib64/ld-linux-x86-64.so.2 "$CURDIR/package/lib/ld.so"
LIBDIR=/lib/x86_64-linux-gnu
elif [ -f "/lib/ld-linux-aarch64.so.1" ]; then
cp -arfLv /lib/ld-linux-aarch64.so.1 "$CURDIR/package/lib/ld.so"
LIBDIR=/lib/aarch64-linux-gnu
else
echo "package.sh: unknown architecture" >&2; exit 1
fi
for lib in libc.so.6 libgcc_s.so.1 libstdc++.so.6 libm.so.6 libgomp.so.1 \
libdl.so.2 librt.so.1 libpthread.so.0; do
cp -arfLv "$LIBDIR/$lib" "$CURDIR/package/lib/$lib"
done
GPU_LIB_SCRIPT="${REPO_ROOT}/scripts/build/package-gpu-libs.sh"
if [ -f "$GPU_LIB_SCRIPT" ]; then
source "$GPU_LIB_SCRIPT" "$CURDIR/package/lib"
package_gpu_libs
fi
echo "ds4 package contents:"
ls -lah "$CURDIR/package/" "$CURDIR/package/lib/"

View File

@@ -1,9 +0,0 @@
#!/bin/bash
# Entry point for the ds4 backend image / BACKEND_BINARY mode.
set -e
CURDIR=$(dirname "$(realpath "$0")")
export LD_LIBRARY_PATH="$CURDIR/lib:$LD_LIBRARY_PATH"
if [ -f "$CURDIR/lib/ld.so" ]; then
exec "$CURDIR/lib/ld.so" "$CURDIR/grpc-server" "$@"
fi
exec "$CURDIR/grpc-server" "$@"

View File

@@ -1,5 +1,5 @@
IK_LLAMA_VERSION?=77413bc900f9a2bfd8a5407f184427bcc0825f6c
IK_LLAMA_VERSION?=8befd92ea5f702494ea9813fe42a52fb015db5fe
LLAMA_REPO?=https://github.com/ikawrakow/ik_llama.cpp
CMAKE_ARGS?=

View File

@@ -326,7 +326,7 @@ struct llama_client_slot
char buffer[512];
double t_token = t_prompt_processing / num_prompt_tokens_processed;
double n_tokens_second = 1e3 / t_prompt_processing * num_prompt_tokens_processed;
snprintf(buffer, sizeof(buffer), "prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)",
sprintf(buffer, "prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)",
t_prompt_processing, num_prompt_tokens_processed,
t_token, n_tokens_second);
LOG_INFO(buffer, {
@@ -340,7 +340,7 @@ struct llama_client_slot
t_token = t_token_generation / n_decoded;
n_tokens_second = 1e3 / t_token_generation * n_decoded;
snprintf(buffer, sizeof(buffer), "generation eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)",
sprintf(buffer, "generation eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)",
t_token_generation, n_decoded,
t_token, n_tokens_second);
LOG_INFO(buffer, {
@@ -352,7 +352,7 @@ struct llama_client_slot
{"n_tokens_second", n_tokens_second},
});
snprintf(buffer, sizeof(buffer), " total time = %10.2f ms", t_prompt_processing + t_token_generation);
sprintf(buffer, " total time = %10.2f ms", t_prompt_processing + t_token_generation);
LOG_INFO(buffer, {
{"slot_id", id},
{"task_id", task_id},
@@ -686,16 +686,7 @@ struct llama_server_context
slot->sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta);
slot->params.n_keep = json_value(data, "n_keep", slot->params.n_keep);
slot->sparams.seed = json_value(data, "seed", default_sparams.seed);
{
// upstream changed common_params_sampling::grammar from std::string to
// the common_grammar struct (type + grammar). The incoming JSON still
// carries a plain string, so build the user-provided grammar here and
// fall back to the server default when the request omits it.
std::string grammar_str = json_value(data, "grammar", std::string());
slot->sparams.grammar = grammar_str.empty()
? default_sparams.grammar
: common_grammar{COMMON_GRAMMAR_TYPE_USER, std::move(grammar_str)};
}
slot->sparams.grammar = json_value(data, "grammar", default_sparams.grammar);
slot->sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs);
slot->sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep);
slot->sparams.grammar_triggers = grammar_triggers;
@@ -1241,7 +1232,7 @@ struct llama_server_context
// {"logit_bias", slot.sparams.logit_bias},
{"n_probs", slot.sparams.n_probs},
{"min_keep", slot.sparams.min_keep},
{"grammar", slot.sparams.grammar.grammar},
{"grammar", slot.sparams.grammar},
{"samplers", samplers}
};
}

View File

@@ -1,11 +0,0 @@
--- a/examples/llava/clip.cpp
+++ b/examples/llava/clip.cpp
@@ -2494,7 +2494,7 @@
}
new_data = work.data();
- new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, n_elms/cur->ne[0], cur->ne[0], nullptr);
+ new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, n_elms/cur->ne[0], cur->ne[0], nullptr, nullptr);
} else {
new_type = cur->type;
new_data = cur->data;

View File

@@ -1,5 +1,5 @@
LLAMA_VERSION?=5cbaa5e69e09bde3334cd8c355570553a0dca027
LLAMA_VERSION?=4f02d4733934179386cbc15b3454be26237940bb
LLAMA_REPO?=https://github.com/ggerganov/llama.cpp
CMAKE_ARGS?=
@@ -34,9 +34,6 @@ else ifeq ($(BUILD_TYPE),hipblas)
export CXX=$(ROCM_HOME)/llvm/bin/clang++
export CC=$(ROCM_HOME)/llvm/bin/clang
AMDGPU_TARGETS?=gfx908,gfx90a,gfx942,gfx950,gfx1030,gfx1100,gfx1101,gfx1102,gfx1151,gfx1200,gfx1201
ifeq ($(strip $(AMDGPU_TARGETS)),)
$(error AMDGPU_TARGETS is emptyset it to a comma-separated list of gfx targets e.g. gfx1100,gfx1101)
endif
CMAKE_ARGS+=-DGGML_HIP=ON -DAMDGPU_TARGETS=$(AMDGPU_TARGETS)
else ifeq ($(BUILD_TYPE),vulkan)
CMAKE_ARGS+=-DGGML_VULKAN=1

View File

@@ -10,14 +10,6 @@
#include "server-task.cpp"
#include "server-queue.cpp"
#include "server-common.cpp"
// server-chat.cpp exists only in llama.cpp after the upstream refactor that
// split OAI/Anthropic/Responses/transcription conversion helpers out of
// server-common.cpp. When present, server-context.cpp and server-task.cpp
// above call into it, so we must pull its definitions into this TU or the
// link fails. __has_include keeps the source compatible with older pins.
#if __has_include("server-chat.cpp")
#include "server-chat.cpp"
#endif
#include "server-context.cpp"
// LocalAI
@@ -32,13 +24,10 @@
#include <grpcpp/health_check_service_interface.h>
#include <grpcpp/security/server_credentials.h>
#include <regex>
#include <algorithm>
#include <atomic>
#include <cstdlib>
#include <fstream>
#include <iterator>
#include <list>
#include <map>
#include <mutex>
#include <signal.h>
#include <thread>
@@ -445,25 +434,11 @@ static void params_parse(server_context& /*ctx_server*/, const backend::ModelOpt
// Draft model for speculative decoding
if (!request->draftmodel().empty()) {
params.speculative.draft.mparams.path = request->draftmodel();
// Default to draft type if a draft model is set but no explicit type.
// Upstream (post ggml-org/llama.cpp#22838) made the speculative type a
// vector; the turboquant fork still uses the legacy scalar. The
// LOCALAI_LEGACY_LLAMA_CPP_SPEC macro is injected by
// backend/cpp/turboquant/patch-grpc-server.sh for fork builds only.
// Upstream renamed COMMON_SPECULATIVE_TYPE_DRAFT -> ..._DRAFT_SIMPLE
// in ggml-org/llama.cpp#22964; the fork still uses the old name.
#ifdef LOCALAI_LEGACY_LLAMA_CPP_SPEC
params.speculative.mparams_dft.path = request->draftmodel();
// Default to draft type if a draft model is set but no explicit type
if (params.speculative.type == COMMON_SPECULATIVE_TYPE_NONE) {
params.speculative.type = COMMON_SPECULATIVE_TYPE_DRAFT;
}
#else
const bool no_spec_type = params.speculative.types.empty() ||
(params.speculative.types.size() == 1 && params.speculative.types[0] == COMMON_SPECULATIVE_TYPE_NONE);
if (no_spec_type) {
params.speculative.types = { COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE };
}
#endif
}
// params.model_alias ??
@@ -659,21 +634,6 @@ static void params_parse(server_context& /*ctx_server*/, const backend::ModelOpt
} else if (optval_str == "false" || optval_str == "0" || optval_str == "no" || optval_str == "off" || optval_str == "disabled") {
params.no_op_offload = false;
}
} else if (!strcmp(optname, "split_mode") || !strcmp(optname, "sm")) {
// Accepts: none | layer | row | tensor (the latter requires a llama.cpp build
// that includes ggml-org/llama.cpp#19378, FlashAttention enabled, and KV-cache
// quantization disabled).
if (optval != NULL) {
if (optval_str == "none") {
params.split_mode = LLAMA_SPLIT_MODE_NONE;
} else if (optval_str == "layer") {
params.split_mode = LLAMA_SPLIT_MODE_LAYER;
} else if (optval_str == "row") {
params.split_mode = LLAMA_SPLIT_MODE_ROW;
} else if (optval_str == "tensor") {
params.split_mode = LLAMA_SPLIT_MODE_TENSOR;
}
}
} else if (!strcmp(optname, "kv_unified") || !strcmp(optname, "unified_kv")) {
if (optval_str == "true" || optval_str == "1" || optval_str == "yes" || optval_str == "on" || optval_str == "enabled") {
params.kv_unified = true;
@@ -688,360 +648,49 @@ static void params_parse(server_context& /*ctx_server*/, const backend::ModelOpt
// If conversion fails, keep default value (8)
}
}
// --- physical batch size (upstream -ub / --ubatch-size) ---
// Note: line ~482 already aliases n_ubatch to n_batch as a default; this
// option lets users decouple the two (useful for embeddings/rerank).
} else if (!strcmp(optname, "n_ubatch") || !strcmp(optname, "ubatch")) {
if (optval != NULL) {
try { params.n_ubatch = std::stoi(optval_str); } catch (...) {}
}
// --- main-model batch threads (upstream -tb / --threads-batch) ---
} else if (!strcmp(optname, "threads_batch") || !strcmp(optname, "n_threads_batch")) {
if (optval != NULL) {
try {
int n = std::stoi(optval_str);
if (n <= 0) n = (int)std::thread::hardware_concurrency();
params.cpuparams_batch.n_threads = n;
} catch (...) {}
}
// --- pooling type for embeddings (upstream --pooling) ---
} else if (!strcmp(optname, "pooling_type") || !strcmp(optname, "pooling")) {
if (optval != NULL) {
if (optval_str == "none") params.pooling_type = LLAMA_POOLING_TYPE_NONE;
else if (optval_str == "mean") params.pooling_type = LLAMA_POOLING_TYPE_MEAN;
else if (optval_str == "cls") params.pooling_type = LLAMA_POOLING_TYPE_CLS;
else if (optval_str == "last") params.pooling_type = LLAMA_POOLING_TYPE_LAST;
else if (optval_str == "rank") params.pooling_type = LLAMA_POOLING_TYPE_RANK;
// unknown values silently leave UNSPECIFIED (auto-detect)
}
// --- llama log verbosity threshold (upstream -lv / --verbosity) ---
} else if (!strcmp(optname, "verbosity")) {
if (optval != NULL) {
try { params.verbosity = std::stoi(optval_str); } catch (...) {}
}
// --- O_DIRECT model loading (upstream --direct-io) ---
} else if (!strcmp(optname, "direct_io") || !strcmp(optname, "use_direct_io")) {
if (optval_str == "true" || optval_str == "1" || optval_str == "yes" || optval_str == "on" || optval_str == "enabled") {
params.use_direct_io = true;
} else if (optval_str == "false" || optval_str == "0" || optval_str == "no" || optval_str == "off" || optval_str == "disabled") {
params.use_direct_io = false;
}
// --- embedding normalization (upstream --embd-normalize) ---
// -1 none, 0 max-abs, 1 taxicab, 2 L2 (default), >2 p-norm
} else if (!strcmp(optname, "embd_normalize") || !strcmp(optname, "embedding_normalize")) {
if (optval != NULL) {
try { params.embd_normalize = std::stoi(optval_str); } catch (...) {}
}
// --- reasoning parser (upstream --reasoning-format) ---
// Picks the parser for <think> blocks emitted by reasoning models.
// none / auto / deepseek / deepseek-legacy
} else if (!strcmp(optname, "reasoning_format")) {
if (optval != NULL) {
if (optval_str == "none") params.reasoning_format = COMMON_REASONING_FORMAT_NONE;
else if (optval_str == "auto") params.reasoning_format = COMMON_REASONING_FORMAT_AUTO;
else if (optval_str == "deepseek") params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
else if (optval_str == "deepseek-legacy" || optval_str == "deepseek_legacy")
params.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY;
// unknown values silently keep the upstream default (DEEPSEEK)
}
// --- reasoning budget (upstream --reasoning-budget) ---
// -1 unlimited, 0 disabled, >0 token budget for thinking blocks.
// Distinct from per-request `enable_thinking` (chat_template_kwargs).
} else if (!strcmp(optname, "enable_reasoning") || !strcmp(optname, "reasoning_budget")) {
if (optval != NULL) {
try { params.enable_reasoning = std::stoi(optval_str); } catch (...) {}
}
// --- prefill assistant turn (upstream --no-prefill-assistant) ---
} else if (!strcmp(optname, "prefill_assistant")) {
if (optval_str == "true" || optval_str == "1" || optval_str == "yes" || optval_str == "on" || optval_str == "enabled") {
params.prefill_assistant = true;
} else if (optval_str == "false" || optval_str == "0" || optval_str == "no" || optval_str == "off" || optval_str == "disabled") {
params.prefill_assistant = false;
}
// --- mmproj GPU offload (upstream --no-mmproj-offload, inverted) ---
} else if (!strcmp(optname, "mmproj_use_gpu") || !strcmp(optname, "mmproj_offload")) {
if (optval_str == "true" || optval_str == "1" || optval_str == "yes" || optval_str == "on" || optval_str == "enabled") {
params.mmproj_use_gpu = true;
} else if (optval_str == "false" || optval_str == "0" || optval_str == "no" || optval_str == "off" || optval_str == "disabled") {
params.mmproj_use_gpu = false;
}
// --- per-image vision token budget (upstream --image-min/max-tokens) ---
} else if (!strcmp(optname, "image_min_tokens")) {
if (optval != NULL) {
try { params.image_min_tokens = std::stoi(optval_str); } catch (...) {}
}
} else if (!strcmp(optname, "image_max_tokens")) {
if (optval != NULL) {
try { params.image_max_tokens = std::stoi(optval_str); } catch (...) {}
}
// --- main-model tensor buffer overrides (upstream --override-tensor) ---
// Format: <tensor regex>=<buffer type>,<tensor regex>=<buffer type>,...
// Mirrors the existing `draft_override_tensor` parser below.
} else if (!strcmp(optname, "override_tensor") || !strcmp(optname, "tensor_buft_overrides")) {
ggml_backend_load_all();
std::map<std::string, ggml_backend_buffer_type_t> buft_list;
for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
auto * dev = ggml_backend_dev_get(i);
auto * buft = ggml_backend_dev_buffer_type(dev);
if (buft) {
buft_list[ggml_backend_buft_name(buft)] = buft;
}
}
static std::list<std::string> override_names;
std::string cur;
auto flush = [&](const std::string & spec) {
auto pos = spec.find('=');
if (pos == std::string::npos) return;
const std::string name = spec.substr(0, pos);
const std::string type = spec.substr(pos + 1);
auto it = buft_list.find(type);
if (it == buft_list.end()) return; // unknown buffer type: ignore
override_names.push_back(name);
params.tensor_buft_overrides.push_back(
{override_names.back().c_str(), it->second});
};
for (char c : optval_str) {
if (c == ',') { if (!cur.empty()) { flush(cur); cur.clear(); } }
else { cur.push_back(c); }
}
if (!cur.empty()) flush(cur);
// Speculative decoding options
} else if (!strcmp(optname, "spec_type") || !strcmp(optname, "speculative_type")) {
#ifdef LOCALAI_LEGACY_LLAMA_CPP_SPEC
// Fork only knows a single scalar `type`. Take the first comma-
// separated value and assign it via the singular helper.
std::string first = optval_str;
const auto comma = first.find(',');
if (comma != std::string::npos) first = first.substr(0, comma);
auto type = common_speculative_type_from_name(first);
auto type = common_speculative_type_from_name(optval_str);
if (type != COMMON_SPECULATIVE_TYPE_COUNT) {
params.speculative.type = type;
}
#else
// Upstream switched to a vector of types (comma-separated for multi-type
// chaining via common_speculative_types_from_names). We keep accepting a
// single value here, but also tolerate comma-separated lists.
//
// ggml-org/llama.cpp#22964 also renamed the registered names from
// underscore- to dash-separated form, and replaced the bare
// `draft`/`eagle3` aliases with `draft-simple`/`draft-eagle3`. We
// normalize each token here so existing model configs keep working.
auto normalize_spec_name = [](std::string s) -> std::string {
std::replace(s.begin(), s.end(), '_', '-');
if (s == "draft") return "draft-simple";
if (s == "eagle3") return "draft-eagle3";
return s;
};
std::vector<std::string> names;
std::string item;
for (char c : optval_str) {
if (c == ',') {
if (!item.empty()) { names.push_back(normalize_spec_name(item)); item.clear(); }
} else {
item.push_back(c);
}
}
if (!item.empty()) names.push_back(normalize_spec_name(item));
auto parsed = common_speculative_types_from_names(names);
if (!parsed.empty()) {
params.speculative.types = parsed;
}
#endif
} else if (!strcmp(optname, "spec_n_max") || !strcmp(optname, "draft_max")) {
if (optval != NULL) {
try { params.speculative.draft.n_max = std::stoi(optval_str); } catch (...) {}
try { params.speculative.n_max = std::stoi(optval_str); } catch (...) {}
}
} else if (!strcmp(optname, "spec_n_min") || !strcmp(optname, "draft_min")) {
if (optval != NULL) {
try { params.speculative.draft.n_min = std::stoi(optval_str); } catch (...) {}
try { params.speculative.n_min = std::stoi(optval_str); } catch (...) {}
}
} else if (!strcmp(optname, "spec_p_min") || !strcmp(optname, "draft_p_min")) {
if (optval != NULL) {
try { params.speculative.draft.p_min = std::stof(optval_str); } catch (...) {}
try { params.speculative.p_min = std::stof(optval_str); } catch (...) {}
}
} else if (!strcmp(optname, "spec_p_split")) {
if (optval != NULL) {
try { params.speculative.draft.p_split = std::stof(optval_str); } catch (...) {}
try { params.speculative.p_split = std::stof(optval_str); } catch (...) {}
}
} else if (!strcmp(optname, "spec_ngram_size_n") || !strcmp(optname, "ngram_size_n")) {
if (optval != NULL) {
try { params.speculative.ngram_simple.size_n = (uint16_t)std::stoi(optval_str); } catch (...) {}
try { params.speculative.ngram_size_n = (uint16_t)std::stoi(optval_str); } catch (...) {}
}
} else if (!strcmp(optname, "spec_ngram_size_m") || !strcmp(optname, "ngram_size_m")) {
if (optval != NULL) {
try { params.speculative.ngram_simple.size_m = (uint16_t)std::stoi(optval_str); } catch (...) {}
try { params.speculative.ngram_size_m = (uint16_t)std::stoi(optval_str); } catch (...) {}
}
} else if (!strcmp(optname, "spec_ngram_min_hits") || !strcmp(optname, "ngram_min_hits")) {
if (optval != NULL) {
try { params.speculative.ngram_simple.min_hits = (uint16_t)std::stoi(optval_str); } catch (...) {}
try { params.speculative.ngram_min_hits = (uint16_t)std::stoi(optval_str); } catch (...) {}
}
} else if (!strcmp(optname, "draft_gpu_layers")) {
if (optval != NULL) {
try { params.speculative.draft.n_gpu_layers = std::stoi(optval_str); } catch (...) {}
try { params.speculative.n_gpu_layers = std::stoi(optval_str); } catch (...) {}
}
} else if (!strcmp(optname, "draft_ctx_size")) {
// The draft context size is no longer a separate field upstream: the draft
// shares the target context size. Accept the option for backward
// compatibility but silently ignore it.
// Everything below relies on struct shape introduced in ggml-org/llama.cpp#22838
// (parallel drafting): `ngram_mod`, `ngram_map_k`, `ngram_map_k4v`,
// `ngram_cache`, and the `draft.{cache_type_*, cpuparams*, tensor_buft_overrides}`
// fields. The turboquant fork branched before that, so its build defines
// LOCALAI_LEGACY_LLAMA_CPP_SPEC via patch-grpc-server.sh and these option
// keys become unrecognized (silently dropped, like any unknown opt) for it.
//
// The `#ifdef LOCALAI_LEGACY_LLAMA_CPP_SPEC` / `#else` split below sits at the
// closing-brace position of the `draft_ctx_size` branch on purpose: in the
// legacy build the chain ends here (the brace closes draft_ctx_size), and in
// the modern build the chain continues with `} else if (...)` instead, so the
// brace count stays balanced under both branches of the preprocessor.
#ifdef LOCALAI_LEGACY_LLAMA_CPP_SPEC
if (optval != NULL) {
try { params.speculative.n_ctx = std::stoi(optval_str); } catch (...) {}
}
}
#else
// --- ngram_mod family (upstream --spec-ngram-mod-*) ---
} else if (!strcmp(optname, "spec_ngram_mod_n_min")) {
if (optval != NULL) {
try { params.speculative.ngram_mod.n_min = std::stoi(optval_str); } catch (...) {}
}
} else if (!strcmp(optname, "spec_ngram_mod_n_max")) {
if (optval != NULL) {
try { params.speculative.ngram_mod.n_max = std::stoi(optval_str); } catch (...) {}
}
} else if (!strcmp(optname, "spec_ngram_mod_n_match")) {
if (optval != NULL) {
try { params.speculative.ngram_mod.n_match = std::stoi(optval_str); } catch (...) {}
}
// --- ngram_map_k family (upstream --spec-ngram-map-k-*) ---
} else if (!strcmp(optname, "spec_ngram_map_k_size_n")) {
if (optval != NULL) {
try { params.speculative.ngram_map_k.size_n = (uint16_t)std::stoi(optval_str); } catch (...) {}
}
} else if (!strcmp(optname, "spec_ngram_map_k_size_m")) {
if (optval != NULL) {
try { params.speculative.ngram_map_k.size_m = (uint16_t)std::stoi(optval_str); } catch (...) {}
}
} else if (!strcmp(optname, "spec_ngram_map_k_min_hits")) {
if (optval != NULL) {
try { params.speculative.ngram_map_k.min_hits = (uint16_t)std::stoi(optval_str); } catch (...) {}
}
// --- ngram_map_k4v family (upstream --spec-ngram-map-k4v-*) ---
} else if (!strcmp(optname, "spec_ngram_map_k4v_size_n")) {
if (optval != NULL) {
try { params.speculative.ngram_map_k4v.size_n = (uint16_t)std::stoi(optval_str); } catch (...) {}
}
} else if (!strcmp(optname, "spec_ngram_map_k4v_size_m")) {
if (optval != NULL) {
try { params.speculative.ngram_map_k4v.size_m = (uint16_t)std::stoi(optval_str); } catch (...) {}
}
} else if (!strcmp(optname, "spec_ngram_map_k4v_min_hits")) {
if (optval != NULL) {
try { params.speculative.ngram_map_k4v.min_hits = (uint16_t)std::stoi(optval_str); } catch (...) {}
}
// --- ngram lookup caches (upstream --lookup-cache-static / -dynamic) ---
} else if (!strcmp(optname, "spec_lookup_cache_static") || !strcmp(optname, "lookup_cache_static")) {
params.speculative.ngram_cache.lookup_cache_static = optval_str;
} else if (!strcmp(optname, "spec_lookup_cache_dynamic") || !strcmp(optname, "lookup_cache_dynamic")) {
params.speculative.ngram_cache.lookup_cache_dynamic = optval_str;
// --- draft model KV cache types (upstream --spec-draft-type-k / -v) ---
} else if (!strcmp(optname, "draft_cache_type_k") || !strcmp(optname, "spec_draft_cache_type_k")) {
params.speculative.draft.cache_type_k = kv_cache_type_from_str(optval_str);
} else if (!strcmp(optname, "draft_cache_type_v") || !strcmp(optname, "spec_draft_cache_type_v")) {
params.speculative.draft.cache_type_v = kv_cache_type_from_str(optval_str);
// --- draft model thread counts (upstream --spec-draft-threads / -batch) ---
} else if (!strcmp(optname, "draft_threads") || !strcmp(optname, "spec_draft_threads")) {
if (optval != NULL) {
try {
int n = std::stoi(optval_str);
if (n <= 0) n = (int)std::thread::hardware_concurrency();
params.speculative.draft.cpuparams.n_threads = n;
} catch (...) {}
}
} else if (!strcmp(optname, "draft_threads_batch") || !strcmp(optname, "spec_draft_threads_batch")) {
if (optval != NULL) {
try {
int n = std::stoi(optval_str);
if (n <= 0) n = (int)std::thread::hardware_concurrency();
params.speculative.draft.cpuparams_batch.n_threads = n;
} catch (...) {}
}
// --- draft model MoE on CPU (upstream --spec-draft-cpu-moe / --spec-draft-n-cpu-moe) ---
} else if (!strcmp(optname, "draft_cpu_moe") || !strcmp(optname, "spec_draft_cpu_moe")) {
// Bool-style flag: optval may be missing, "true"/"1"/"yes" enables.
const bool enable = (optval == NULL) ||
optval_str == "true" || optval_str == "1" || optval_str == "yes" ||
optval_str == "on" || optval_str == "enabled";
if (enable) {
params.speculative.draft.tensor_buft_overrides.push_back(llm_ffn_exps_cpu_override());
}
} else if (!strcmp(optname, "draft_n_cpu_moe") || !strcmp(optname, "spec_draft_n_cpu_moe")) {
if (optval != NULL) {
try {
int n = std::stoi(optval_str);
if (n < 0) n = 0;
// Keep override-name storage alive for the lifetime of the params struct
// (mirrors upstream arg.cpp behavior with a function-local static).
static std::list<std::string> buft_overrides_draft;
for (int i = 0; i < n; ++i) {
buft_overrides_draft.push_back(llm_ffn_exps_block_regex(i));
params.speculative.draft.tensor_buft_overrides.push_back(
{buft_overrides_draft.back().c_str(), ggml_backend_cpu_buffer_type()});
}
} catch (...) {}
}
// --- draft model tensor buffer overrides (upstream --spec-draft-override-tensor) ---
} else if (!strcmp(optname, "draft_override_tensor") || !strcmp(optname, "spec_draft_override_tensor")) {
// Format: <tensor regex>=<buffer type>,<tensor regex>=<buffer type>,...
// We replicate upstream's parse_tensor_buffer_overrides (static in arg.cpp).
ggml_backend_load_all();
std::map<std::string, ggml_backend_buffer_type_t> buft_list;
for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
auto * dev = ggml_backend_dev_get(i);
auto * buft = ggml_backend_dev_buffer_type(dev);
if (buft) {
buft_list[ggml_backend_buft_name(buft)] = buft;
}
}
static std::list<std::string> draft_override_names;
std::string cur;
auto flush = [&](const std::string & spec) {
auto pos = spec.find('=');
if (pos == std::string::npos) return;
const std::string name = spec.substr(0, pos);
const std::string type = spec.substr(pos + 1);
auto it = buft_list.find(type);
if (it == buft_list.end()) return; // unknown buffer type: ignore
draft_override_names.push_back(name);
params.speculative.draft.tensor_buft_overrides.push_back(
{draft_override_names.back().c_str(), it->second});
};
for (char c : optval_str) {
if (c == ',') { if (!cur.empty()) { flush(cur); cur.clear(); } }
else { cur.push_back(c); }
}
if (!cur.empty()) flush(cur);
}
#endif // LOCALAI_LEGACY_LLAMA_CPP_SPEC — closes the `else`/`#ifdef` opened at draft_ctx_size
}
// Set params.n_parallel from environment variable if not set via options (fallback)
@@ -1261,8 +910,8 @@ public:
if (!params.mmproj.path.empty()) {
error_msg += " (with mmproj: " + params.mmproj.path + ")";
}
if (params.speculative.has_dft() && !params.speculative.draft.mparams.path.empty()) {
error_msg += " (with draft model: " + params.speculative.draft.mparams.path + ")";
if (params.speculative.has_dft() && !params.speculative.mparams_dft.path.empty()) {
error_msg += " (with draft model: " + params.speculative.mparams_dft.path + ")";
}
// Add captured error details if available
@@ -2938,9 +2587,7 @@ public:
}
}
// Honor the load-time embd_normalize set via options:embd_normalize.
// -1 none, 0 max-abs, 1 taxicab, 2 L2 (default), >2 p-norm.
int embd_normalize = params_base.embd_normalize;
int embd_normalize = 2; // default to Euclidean/L2 norm
// create and queue the task
auto rd = ctx_server.get_response_reader();
{
@@ -3034,7 +2681,7 @@ public:
tasks.reserve(documents.size());
for (size_t i = 0; i < documents.size(); i++) {
auto tmp = format_prompt_rerank(ctx_server.impl->model_tgt, ctx_server.impl->vocab, ctx_server.impl->mctx, request->query(), documents[i]);
auto tmp = format_prompt_rerank(ctx_server.impl->model, ctx_server.impl->vocab, ctx_server.impl->mctx, request->query(), documents[i]);
server_task task = server_task(SERVER_TASK_TYPE_RERANK);
task.id = rd.queue_tasks.get_new_id();
task.index = i;
@@ -3212,7 +2859,7 @@ public:
// Get template source and reconstruct a common_chat_template for analysis
std::string tmpl_src = common_chat_templates_source(ctx_server.impl->chat_params.tmpls.get());
if (!tmpl_src.empty()) {
const auto * vocab = llama_model_get_vocab(ctx_server.impl->model_tgt);
const auto * vocab = llama_model_get_vocab(ctx_server.impl->model);
std::string token_bos, token_eos;
if (vocab) {
auto bos_id = llama_vocab_bos(vocab);

View File

@@ -1,7 +1,7 @@
# Pinned to the HEAD of feature/turboquant-kv-cache on https://github.com/TheTom/llama-cpp-turboquant.
# Auto-bumped nightly by .github/workflows/bump_deps.yaml.
TURBOQUANT_VERSION?=5aeb2fdbe26cd4c534c6fa15de73cb5749bd0403
TURBOQUANT_VERSION?=45f8a066ed5f5bb38c695cec532f6cef9f4efa9d
LLAMA_REPO?=https://github.com/TheTom/llama-cpp-turboquant
CMAKE_ARGS?=

View File

@@ -1,30 +1,13 @@
#!/bin/bash
# Patch the shared backend/cpp/llama-cpp/grpc-server.cpp *copy* used by the
# turboquant build to account for the gaps between upstream and the fork:
# Augment the shared backend/cpp/llama-cpp/grpc-server.cpp allow-list of KV-cache
# types so the gRPC `LoadModel` call accepts the TurboQuant-specific
# `turbo2` / `turbo3` / `turbo4` cache types.
#
# 1. Augment the kv_cache_types[] allow-list so `LoadModel` accepts the
# fork-specific `turbo2` / `turbo3` / `turbo4` cache types.
# 2. Replace `get_media_marker()` (added upstream in ggml-org/llama.cpp#21962,
# server-side random per-instance marker) with the legacy "<__media__>"
# literal. The fork branched before that PR, so server-common.cpp has no
# get_media_marker symbol. The fork's mtmd_default_marker() still returns
# "<__media__>", and Go-side tooling falls back to that sentinel when the
# backend does not expose media_marker, so substituting the literal keeps
# behavior identical on the turboquant path.
# 3. Revert the `common_params_speculative` field references to the
# pre-refactor flat layout. Upstream ggml-org/llama.cpp#22397 split the
# struct into nested `draft` / `ngram_simple` / `ngram_mod` / etc. members;
# the turboquant fork branched before that PR and still exposes the flat
# `n_max`, `mparams_dft`, `ngram_size_n`, ... fields. The substitutions
# below map the new nested paths back to the legacy flat names so the
# shared grpc-server.cpp keeps compiling against the fork's common.h.
# Drop this block once the fork rebases past #22397.
# We do this on the *copy* sitting in turboquant-<flavor>-build/, never on the
# original under backend/cpp/llama-cpp/, so the stock llama-cpp build keeps
# compiling against vanilla upstream which does not know about GGML_TYPE_TURBO*.
#
# We patch the *copy* sitting in turboquant-<flavor>-build/, never the original
# under backend/cpp/llama-cpp/, so the stock llama-cpp build keeps compiling
# against vanilla upstream.
#
# Idempotent: skips each insertion if its marker is already present (so re-runs
# Idempotent: skips the insertion if the marker is already present (so re-runs
# of the same build dir don't double-insert).
set -euo pipefail
@@ -42,113 +25,33 @@ if [[ ! -f "$SRC" ]]; then
fi
if grep -q 'GGML_TYPE_TURBO2_0' "$SRC"; then
echo "==> $SRC already has TurboQuant cache types, skipping KV allow-list patch"
else
echo "==> patching $SRC to allow turbo2/turbo3/turbo4 KV-cache types"
echo "==> $SRC already has TurboQuant cache types, skipping"
exit 0
fi
# Insert the three TURBO entries right after the first ` GGML_TYPE_Q5_1,`
# line (the kv_cache_types[] allow-list). Using awk because the builder image
# does not ship python3, and GNU sed's multi-line `a\` quoting is awkward.
awk '
/^ GGML_TYPE_Q5_1,$/ && !done {
print
print " // turboquant fork extras — added by patch-grpc-server.sh"
print " GGML_TYPE_TURBO2_0,"
print " GGML_TYPE_TURBO3_0,"
print " GGML_TYPE_TURBO4_0,"
done = 1
next
echo "==> patching $SRC to allow turbo2/turbo3/turbo4 KV-cache types"
# Insert the three TURBO entries right after the first ` GGML_TYPE_Q5_1,`
# line (the kv_cache_types[] allow-list). Using awk because the builder image
# does not ship python3, and GNU sed's multi-line `a\` quoting is awkward.
awk '
/^ GGML_TYPE_Q5_1,$/ && !done {
print
print " // turboquant fork extras — added by patch-grpc-server.sh"
print " GGML_TYPE_TURBO2_0,"
print " GGML_TYPE_TURBO3_0,"
print " GGML_TYPE_TURBO4_0,"
done = 1
next
}
{ print }
END {
if (!done) {
print "patch-grpc-server.sh: anchor ` GGML_TYPE_Q5_1,` not found" > "/dev/stderr"
exit 1
}
{ print }
END {
if (!done) {
print "patch-grpc-server.sh: anchor ` GGML_TYPE_Q5_1,` not found" > "/dev/stderr"
exit 1
}
}
' "$SRC" > "$SRC.tmp"
mv "$SRC.tmp" "$SRC"
}
' "$SRC" > "$SRC.tmp"
mv "$SRC.tmp" "$SRC"
echo "==> KV allow-list patch OK"
fi
if grep -q 'get_media_marker()' "$SRC"; then
echo "==> patching $SRC to replace get_media_marker() with legacy \"<__media__>\" literal"
# Only one call site today (ModelMetadata), but replace all occurrences to
# stay robust if upstream adds more. Use a temp file to avoid relying on
# sed -i portability (the builder image uses GNU sed, but keeping this
# consistent with the awk block above).
sed 's/get_media_marker()/"<__media__>"/g' "$SRC" > "$SRC.tmp"
mv "$SRC.tmp" "$SRC"
echo "==> get_media_marker() substitution OK"
else
echo "==> $SRC has no get_media_marker() call, skipping media-marker patch"
fi
if grep -q 'params\.speculative\.draft\.\|params\.speculative\.ngram_simple\.' "$SRC"; then
echo "==> patching $SRC to revert common_params_speculative refs to pre-#22397 flat layout"
# Each substitution is the exact post-refactor path → legacy flat field.
# Order doesn't matter because the source paths are disjoint, but we keep
# the most-specific (mparams.path) first for readability.
sed -E \
-e 's/params\.speculative\.draft\.mparams\.path/params.speculative.mparams_dft.path/g' \
-e 's/params\.speculative\.draft\.n_max/params.speculative.n_max/g' \
-e 's/params\.speculative\.draft\.n_min/params.speculative.n_min/g' \
-e 's/params\.speculative\.draft\.p_min/params.speculative.p_min/g' \
-e 's/params\.speculative\.draft\.p_split/params.speculative.p_split/g' \
-e 's/params\.speculative\.draft\.n_gpu_layers/params.speculative.n_gpu_layers/g' \
-e 's/params\.speculative\.draft\.n_ctx/params.speculative.n_ctx/g' \
-e 's/params\.speculative\.ngram_simple\.size_n/params.speculative.ngram_size_n/g' \
-e 's/params\.speculative\.ngram_simple\.size_m/params.speculative.ngram_size_m/g' \
-e 's/params\.speculative\.ngram_simple\.min_hits/params.speculative.ngram_min_hits/g' \
"$SRC" > "$SRC.tmp"
mv "$SRC.tmp" "$SRC"
echo "==> speculative field rename OK"
else
echo "==> $SRC has no post-#22397 speculative field refs, skipping spec rename patch"
fi
# 4. Revert the `ctx_server.impl->model_tgt` rename introduced by upstream
# ggml-org/llama.cpp#22838 (parallel drafting). The turboquant fork still
# exposes the field as `model` on `server_context_impl`. The two call sites
# are in the Rerank and ModelMetadata RPC handlers.
if grep -q 'ctx_server\.impl->model_tgt' "$SRC"; then
echo "==> patching $SRC to revert ctx_server.impl->model_tgt -> ctx_server.impl->model"
sed -E 's/ctx_server\.impl->model_tgt/ctx_server.impl->model/g' "$SRC" > "$SRC.tmp"
mv "$SRC.tmp" "$SRC"
echo "==> model_tgt rename OK"
else
echo "==> $SRC has no ctx_server.impl->model_tgt refs, skipping model_tgt rename patch"
fi
# 5. Define LOCALAI_LEGACY_LLAMA_CPP_SPEC at the top of the file so the
# grpc-server option parser skips the new option-handler blocks (ngram_mod,
# ngram_map_k, ngram_map_k4v, ngram_cache, draft.cache_type_*, draft.cpuparams*,
# draft.tensor_buft_overrides) introduced for the post-#22838 layout. Those
# blocks reference struct fields that simply do not exist in the fork.
if grep -q '^#define LOCALAI_LEGACY_LLAMA_CPP_SPEC' "$SRC"; then
echo "==> $SRC already defines LOCALAI_LEGACY_LLAMA_CPP_SPEC, skipping"
else
echo "==> patching $SRC to define LOCALAI_LEGACY_LLAMA_CPP_SPEC at the top"
# Insert the define before the very first `#include` so it precedes all the
# speculative-decoding code paths.
awk '
!done && /^#include/ {
print "#define LOCALAI_LEGACY_LLAMA_CPP_SPEC 1"
print "// ^ injected by backend/cpp/turboquant/patch-grpc-server.sh"
print ""
done = 1
}
{ print }
END {
if (!done) {
print "patch-grpc-server.sh: no #include anchor found to insert LOCALAI_LEGACY_LLAMA_CPP_SPEC" > "/dev/stderr"
exit 1
}
}
' "$SRC" > "$SRC.tmp"
mv "$SRC.tmp" "$SRC"
echo "==> LOCALAI_LEGACY_LLAMA_CPP_SPEC define OK"
fi
echo "==> all patches applied"
echo "==> patched OK"

View File

@@ -0,0 +1,83 @@
From 660600081fb7b9b769ded5c805a2d39a419f0a0d Mon Sep 17 00:00:00 2001
From: Yuri Khrustalev <ykhrustalev@users.noreply.github.com>
Date: Wed, 8 Apr 2026 11:12:15 -0400
Subject: [PATCH] server: respect the ignore eos flag (#21203)
---
tools/server/server-context.cpp | 3 +++
tools/server/server-context.h | 3 +++
tools/server/server-task.cpp | 3 ++-
tools/server/server-task.h | 1 +
4 files changed, 9 insertions(+), 1 deletion(-)
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index 9d3ac538..b31981c5 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -3033,6 +3033,8 @@ server_context_meta server_context::get_meta() const {
/* fim_rep_token */ llama_vocab_fim_rep(impl->vocab),
/* fim_sep_token */ llama_vocab_fim_sep(impl->vocab),
+ /* logit_bias_eog */ impl->params_base.sampling.logit_bias_eog,
+
/* model_vocab_type */ llama_vocab_type(impl->vocab),
/* model_vocab_n_tokens */ llama_vocab_n_tokens(impl->vocab),
/* model_n_ctx_train */ llama_model_n_ctx_train(impl->model),
@@ -3117,6 +3119,7 @@ std::unique_ptr<server_res_generator> server_routes::handle_completions_impl(
ctx_server.vocab,
params,
meta->slot_n_ctx,
+ meta->logit_bias_eog,
data);
task.id_slot = json_value(data, "id_slot", -1);
diff --git a/tools/server/server-context.h b/tools/server/server-context.h
index d7ce8735..6ea9afc0 100644
--- a/tools/server/server-context.h
+++ b/tools/server/server-context.h
@@ -39,6 +39,9 @@ struct server_context_meta {
llama_token fim_rep_token;
llama_token fim_sep_token;
+ // sampling
+ std::vector<llama_logit_bias> logit_bias_eog;
+
// model meta
enum llama_vocab_type model_vocab_type;
int32_t model_vocab_n_tokens;
diff --git a/tools/server/server-task.cpp b/tools/server/server-task.cpp
index 4cc87bc5..856b3f0e 100644
--- a/tools/server/server-task.cpp
+++ b/tools/server/server-task.cpp
@@ -239,6 +239,7 @@ task_params server_task::params_from_json_cmpl(
const llama_vocab * vocab,
const common_params & params_base,
const int n_ctx_slot,
+ const std::vector<llama_logit_bias> & logit_bias_eog,
const json & data) {
task_params params;
@@ -562,7 +563,7 @@ task_params server_task::params_from_json_cmpl(
if (params.sampling.ignore_eos) {
params.sampling.logit_bias.insert(
params.sampling.logit_bias.end(),
- defaults.sampling.logit_bias_eog.begin(), defaults.sampling.logit_bias_eog.end());
+ logit_bias_eog.begin(), logit_bias_eog.end());
}
}
diff --git a/tools/server/server-task.h b/tools/server/server-task.h
index d855bf08..243e47a8 100644
--- a/tools/server/server-task.h
+++ b/tools/server/server-task.h
@@ -209,6 +209,7 @@ struct server_task {
const llama_vocab * vocab,
const common_params & params_base,
const int n_ctx_slot,
+ const std::vector<llama_logit_bias> & logit_bias_eog,
const json & data);
// utility function
--
2.43.0

View File

@@ -4,6 +4,7 @@ package main
// It is meant to be used by the main executable that is the server for the specific backend type (falcon, gpt3, etc)
import (
"container/heap"
"errors"
"fmt"
"math"
"slices"
@@ -99,16 +100,9 @@ func sortIntoKeySlicese(keys []*pb.StoresKey) [][]float32 {
}
func (s *Store) Load(opts *pb.ModelOptions) error {
// local-store is an in-memory vector store with no on-disk artefact to
// load — opts.Model is just a namespace identifier. The old `!= ""` guard
// rejected any non-empty model name with "not implemented", which broke
// callers that pass a namespace to isolate embedding spaces (face vs.
// voice biometrics both go through local-store but need distinct stores
// so ArcFace 512-D and ECAPA-TDNN 192-D don't collide). Namespace
// isolation is already handled upstream: ModelLoader spawns a fresh
// local-store process per (backend, model) tuple, so each namespace is
// its own Store{} instance. Nothing to do here beyond accepting the load.
_ = opts
if opts.Model != "" {
return errors.New("not implemented")
}
return nil
}

View File

@@ -1,7 +0,0 @@
sources/
build/
package/
liblocalvqe.so*
libggml*.so*
localvqe
.localvqe-build.stamp

View File

@@ -1,98 +0,0 @@
CMAKE_ARGS?=
BUILD_TYPE?=
NATIVE?=false
GOCMD?=go
GO_TAGS?=
JOBS?=$(shell nproc --ignore=1)
# LocalVQE upstream version pin. Bump to a specific commit when picking up
# a new release; `main` works for development but is not reproducible.
LOCALVQE_REPO?=https://github.com/localai-org/LocalVQE
LOCALVQE_VERSION?=72bfb4c6
# LocalVQE handles CPU feature selection internally (it ships the multiple
# libggml-cpu-*.so variants and its loader picks the best one at runtime
# via GGML_BACKEND_DL), so we build a single liblocalvqe.so + the per-CPU
# ggml shared libs and let it sort itself out. No need for a wrapper
# MODULE library or per-AVX backend variants here.
CMAKE_ARGS+=-DLOCALVQE_BUILD_SHARED=ON
CMAKE_ARGS+=-DGGML_BUILD_TESTS=OFF
CMAKE_ARGS+=-DGGML_BUILD_EXAMPLES=OFF
ifeq ($(NATIVE),false)
CMAKE_ARGS+=-DGGML_NATIVE=OFF
endif
# LocalVQE upstream supports CPU + Vulkan only. Other BUILD_TYPE values
# fall through to the default CPU build — Vulkan is already as fast as the
# specialised GPU paths would be on this 1.3 M-parameter model.
ifeq ($(BUILD_TYPE),vulkan)
CMAKE_ARGS+=-DGGML_VULKAN=ON -DLOCALVQE_VULKAN=ON
else ifeq ($(OS),Darwin)
CMAKE_ARGS+=-DGGML_METAL=OFF
endif
# --- Sources ---
sources/LocalVQE:
mkdir -p sources/LocalVQE
cd sources/LocalVQE && \
git init && \
git remote add origin $(LOCALVQE_REPO) && \
git fetch origin && \
git checkout $(LOCALVQE_VERSION) && \
git submodule update --init --recursive --depth 1 --single-branch
# --- Native build ---
#
# Drives cmake directly against the upstream LocalVQE/ggml CMakeLists.
# Produces liblocalvqe.so plus the per-CPU libggml-cpu-*.so variants in
# build/bin/, all of which we copy into the backend directory so package.sh
# can pick them up. The `liblocalvqe.so` rule deliberately uses a sentinel
# stamp file because Make's wildcard tracking would otherwise mis-decide
# about freshness when SOVERSION symlinks are involved.
LIB_SENTINEL=.localvqe-build.stamp
$(LIB_SENTINEL): sources/LocalVQE
mkdir -p build && \
cd build && \
cmake ../sources/LocalVQE/ggml $(CMAKE_ARGS) -DCMAKE_BUILD_TYPE=Release && \
cmake --build . --config Release -j$(JOBS)
# Upstream's CPU build sets GGML_BACKEND_DL=ON + GGML_CPU_ALL_VARIANTS=ON,
# which produces multiple libggml-cpu-*.so files (SSE4.2 / AVX2 / AVX-512)
# that the loader picks at runtime. We must build every target — the
# default `--target localvqe_shared` drops these. CMAKE_LIBRARY_OUTPUT_DIRECTORY
# routes all of them into build/bin; copy them out next to the binary.
cp -P build/bin/liblocalvqe.so* . 2>/dev/null || cp -P build/liblocalvqe.so* .
cp -P build/bin/libggml*.so* . 2>/dev/null || true
touch $(LIB_SENTINEL)
liblocalvqe.so: $(LIB_SENTINEL)
# --- Go binary + packaging ---
localvqe: main.go golocalvqe.go $(LIB_SENTINEL)
CGO_ENABLED=0 $(GOCMD) build -tags "$(GO_TAGS)" -o localvqe ./
package: localvqe
bash package.sh
build: package
clean: purge
rm -rf liblocalvqe.so* libggml*.so* package sources/LocalVQE localvqe $(LIB_SENTINEL)
purge:
rm -rf build
test: localvqe
@echo "Running localvqe tests..."
bash test.sh
@echo "localvqe tests completed."
all: localvqe package
.PHONY: build package clean purge test all

View File

@@ -1,610 +0,0 @@
package main
import (
"encoding/binary"
"fmt"
"io"
"os"
"path/filepath"
"runtime"
"strconv"
"strings"
"unsafe"
"github.com/mudler/LocalAI/pkg/grpc/base"
pb "github.com/mudler/LocalAI/pkg/grpc/proto"
"github.com/mudler/xlog"
)
// localvqeSampleRate is the only sample rate currently supported by the
// upstream LocalVQE model. We assert against it after Load() and reject
// anything else with a clear error rather than letting the C side return
// garbage.
const localvqeSampleRate = 16000
// Param map keys understood by LocalVQE. Keep these strings in sync with
// schema.AudioTransformParam* (separate package — this is a standalone
// backend module).
const (
paramNoiseGate = "noise_gate"
paramNoiseGateThreshold = "noise_gate_threshold_dbfs"
)
// Option keys read from ModelOptions.Options[] at Load() time. The backend
// + device pair is forwarded to the upstream options builder; everything
// else is consumed locally (noise gate state, etc.).
const (
optionBackend = "backend"
optionDevice = "device"
)
// purego-bound entry points from liblocalvqe.
//
// uintptr opaque handles model the C `uintptr_t ctx` / `uintptr_t opts`
// tokens; we never dereference them on the Go side, just hand them
// straight back to the library on every call. Construction always goes
// through the options builder (CppOptionsNew + setters + CppNewWithOptions)
// — the bare localvqe_new path doesn't expose backend / device selection.
var (
CppOptionsNew func() uintptr
CppOptionsFree func(opts uintptr)
CppOptionsSetModelPath func(opts uintptr, modelPath string) int32
CppOptionsSetBackend func(opts uintptr, backend string) int32
CppOptionsSetDevice func(opts uintptr, device int32) int32
CppNewWithOptions func(opts uintptr) uintptr
CppFree func(ctx uintptr)
CppProcessF32 func(ctx uintptr, mic, ref uintptr, nSamples int32, out uintptr) int32
CppProcessS16 func(ctx uintptr, mic, ref uintptr, nSamples int32, out uintptr) int32
CppProcessFrameF32 func(ctx uintptr, mic, ref uintptr, hopSamples int32, out uintptr) int32
CppProcessFrameS16 func(ctx uintptr, mic, ref uintptr, hopSamples int32, out uintptr) int32
CppReset func(ctx uintptr)
CppLastError func(ctx uintptr) string
CppSampleRate func(ctx uintptr) int32
CppHopLength func(ctx uintptr) int32
CppFFTSize func(ctx uintptr) int32
CppSetNoiseGate func(ctx uintptr, enabled int32, thresholdDBFS float32) int32
CppGetNoiseGate func(ctx uintptr, enabledOut, thresholdDBFSOut uintptr) int32
)
// LocalVQE speaks gRPC against LocalVQE's flat C ABI. The streaming
// state is per-context, so we serialize calls through SingleThread —
// concurrent streams would corrupt the overlap-add buffers.
type LocalVQE struct {
base.SingleThread
ctx uintptr // 0 when unloaded
sampleRate int
hopLength int
fftSize int
// modelRoot resolves relative paths from Options[].
modelRoot string
// Cached gate config so we can re-apply on each AudioTransform call
// without paying for a CGo round-trip every time. Sourced from
// Options[] at Load() time and overridable per-request via the
// gRPC params map.
gateEnabled bool
gateDbfs float32
// Backend / device picked via Options[]. Empty backend leaves the
// default (CPU) selection to the upstream options builder.
backend string
device int32
}
// parseOptions reads opts.Options[] for backend-specific tuning. Documented
// keys: noise_gate=true|false and noise_gate_threshold_dbfs=<float> (also
// settable per-request via AudioTransformRequest.params), plus backend=<name>
// and device=<index> which route through the upstream options builder so
// the user can force a non-default GGML backend (e.g. "Vulkan").
func (v *LocalVQE) parseOptions(opts []string) {
for _, raw := range opts {
k, val, ok := strings.Cut(raw, "=")
if !ok {
k, val, ok = strings.Cut(raw, ":")
if !ok {
continue
}
}
key := strings.TrimSpace(strings.ToLower(k))
val = strings.TrimSpace(val)
switch key {
case paramNoiseGate:
if b, err := strconv.ParseBool(val); err == nil {
v.gateEnabled = b
}
case paramNoiseGateThreshold:
if f, err := strconv.ParseFloat(val, 32); err == nil {
v.gateDbfs = float32(f)
}
case optionBackend:
v.backend = val
case optionDevice:
if d, err := strconv.Atoi(val); err == nil && d >= 0 {
v.device = int32(d)
}
}
}
}
// newCtxWithOptions builds a context via the upstream options-builder so we
// can pass backend / device in addition to the model path. Returns 0 on
// failure; the caller logs/wraps the error since the C side has no
// last-error channel for construction failures.
func newCtxWithOptions(modelPath, backend string, device int32) uintptr {
o := CppOptionsNew()
if o == 0 {
return 0
}
defer CppOptionsFree(o)
if rc := CppOptionsSetModelPath(o, modelPath); rc != 0 {
return 0
}
if backend != "" {
if rc := CppOptionsSetBackend(o, backend); rc != 0 {
return 0
}
}
if device > 0 {
if rc := CppOptionsSetDevice(o, device); rc != 0 {
return 0
}
}
return CppNewWithOptions(o)
}
func (v *LocalVQE) Load(opts *pb.ModelOptions) error {
if opts.ModelFile == "" {
return fmt.Errorf("localvqe: ModelFile is required")
}
modelFile := opts.ModelFile
if !filepath.IsAbs(modelFile) && opts.ModelPath != "" {
modelFile = filepath.Join(opts.ModelPath, modelFile)
}
v.modelRoot = opts.ModelPath
if v.modelRoot == "" {
v.modelRoot = filepath.Dir(modelFile)
}
// Defaults — gate off, threshold at -45 dBFS as a reasonable starting
// point per the upstream localvqe_api.h documentation.
v.gateEnabled = false
v.gateDbfs = -45.0
v.parseOptions(opts.Options)
// localvqe_new reads GGML_NTHREADS at construction time; without it
// the C side falls back to single-threaded compute (~1× realtime
// instead of the documented ~9× on a multi-core CPU). Pass the
// model config's Threads through, defaulting to min(NumCPU, 4).
//
// LocalVQE is 1.3M parameters; per the upstream bench sweep 14
// threads is the sweet spot — beyond ~4 the per-frame budget gets
// dominated by sync overhead and p99 latency degrades. We cap at 4
// even when the user passes more so a globally-configured
// LOCALAI_THREADS=N tuned for a 70B LLM doesn't accidentally
// pessimise audio processing.
const localvqeMaxThreads = 4
threads := int(opts.Threads)
if threads <= 0 {
threads = runtime.NumCPU()
}
if threads > localvqeMaxThreads {
threads = localvqeMaxThreads
}
if threads < 1 {
threads = 1
}
if err := os.Setenv("GGML_NTHREADS", fmt.Sprintf("%d", threads)); err != nil {
return fmt.Errorf("localvqe: setenv GGML_NTHREADS: %w", err)
}
xlog.Info("[localvqe] loading model", "path", modelFile, "threads", threads, "backend", v.backend, "device", v.device, "noise_gate", v.gateEnabled, "threshold_dbfs", v.gateDbfs)
ctx := newCtxWithOptions(modelFile, v.backend, v.device)
if ctx == 0 {
return fmt.Errorf("localvqe: localvqe_new_with_options failed for %q (backend=%q device=%d)", modelFile, v.backend, v.device)
}
v.ctx = ctx
v.sampleRate = int(CppSampleRate(ctx))
v.hopLength = int(CppHopLength(ctx))
v.fftSize = int(CppFFTSize(ctx))
if v.sampleRate != localvqeSampleRate {
CppFree(ctx)
v.ctx = 0
return fmt.Errorf("localvqe: unsupported sample rate %d (only %d Hz is supported)", v.sampleRate, localvqeSampleRate)
}
if v.hopLength <= 0 || v.fftSize <= 0 {
CppFree(ctx)
v.ctx = 0
return fmt.Errorf("localvqe: model reports invalid hop=%d fft=%d", v.hopLength, v.fftSize)
}
if v.gateEnabled {
if rc := CppSetNoiseGate(ctx, 1, v.gateDbfs); rc != 0 {
err := fmt.Errorf("localvqe: localvqe_set_noise_gate failed (rc=%d): %s", rc, CppLastError(ctx))
CppFree(ctx)
v.ctx = 0
return err
}
}
return nil
}
func (v *LocalVQE) Free() error {
if v.ctx != 0 {
CppFree(v.ctx)
v.ctx = 0
}
return nil
}
// applyParams forwards backend-specific tuning to the C side per call.
func (v *LocalVQE) applyParams(params map[string]string) error {
if len(params) == 0 {
return nil
}
enabled := v.gateEnabled
threshold := v.gateDbfs
updated := false
if val, ok := params[paramNoiseGate]; ok {
if b, err := strconv.ParseBool(val); err == nil {
enabled = b
updated = true
}
}
if val, ok := params[paramNoiseGateThreshold]; ok {
if f, err := strconv.ParseFloat(val, 32); err == nil {
threshold = float32(f)
updated = true
}
}
if !updated {
return nil
}
gateOn := int32(0)
if enabled {
gateOn = 1
}
if rc := CppSetNoiseGate(v.ctx, gateOn, threshold); rc != 0 {
return fmt.Errorf("localvqe_set_noise_gate failed (rc=%d): %s", rc, CppLastError(v.ctx))
}
v.gateEnabled = enabled
v.gateDbfs = threshold
return nil
}
func (v *LocalVQE) AudioTransform(req *pb.AudioTransformRequest) (*pb.AudioTransformResult, error) {
if v.ctx == 0 {
return nil, fmt.Errorf("localvqe: no model loaded")
}
if req.AudioPath == "" || req.Dst == "" {
return nil, fmt.Errorf("localvqe: audio_path and dst are required")
}
if err := v.applyParams(req.Params); err != nil {
return nil, err
}
mic, micRate, err := readMonoWAVf32(req.AudioPath)
if err != nil {
return nil, fmt.Errorf("read audio: %w", err)
}
if micRate != v.sampleRate {
return nil, fmt.Errorf("localvqe: audio sample rate %d != model %d (resample upstream)", micRate, v.sampleRate)
}
refProvided := req.ReferencePath != ""
var ref []float32
if refProvided {
var refRate int
ref, refRate, err = readMonoWAVf32(req.ReferencePath)
if err != nil {
return nil, fmt.Errorf("read reference: %w", err)
}
if refRate != v.sampleRate {
return nil, fmt.Errorf("localvqe: reference sample rate %d != model %d", refRate, v.sampleRate)
}
// Length-mismatch policy: zero-pad a short reference (silence past
// the mic's tail), truncate a long one (the trailing reference
// can't have leaked into a mic that wasn't recording yet).
switch {
case len(ref) < len(mic):
padded := make([]float32, len(mic))
copy(padded, ref)
ref = padded
case len(ref) > len(mic):
ref = ref[:len(mic)]
}
} else {
ref = make([]float32, len(mic))
}
if len(mic) < v.fftSize {
return nil, fmt.Errorf("localvqe: audio too short (%d samples, need ≥ %d)", len(mic), v.fftSize)
}
out := make([]float32, len(mic))
rc := CppProcessF32(v.ctx,
uintptr(unsafe.Pointer(&mic[0])),
uintptr(unsafe.Pointer(&ref[0])),
int32(len(mic)),
uintptr(unsafe.Pointer(&out[0])))
if rc != 0 {
return nil, fmt.Errorf("localvqe_process_f32 failed (rc=%d): %s", rc, CppLastError(v.ctx))
}
if err := writeMonoWAVf32(req.Dst, out, v.sampleRate); err != nil {
return nil, fmt.Errorf("write output: %w", err)
}
return &pb.AudioTransformResult{
Dst: req.Dst,
SampleRate: int32(v.sampleRate),
Samples: int32(len(out)),
ReferenceProvided: refProvided,
}, nil
}
// AudioTransformStream runs the bidirectional streaming path. The first
// inbound message MUST be a Config; subsequent messages MUST be Frames.
// A second Config mid-stream resets the streaming state.
func (v *LocalVQE) AudioTransformStream(in <-chan *pb.AudioTransformFrameRequest, out chan<- *pb.AudioTransformFrameResponse) error {
defer close(out)
if v.ctx == 0 {
return fmt.Errorf("localvqe: no model loaded")
}
first, ok := <-in
if !ok {
return nil
}
cfg := first.GetConfig()
if cfg == nil {
return fmt.Errorf("localvqe: first stream message must be a Config")
}
if err := v.applyStreamConfig(cfg); err != nil {
return err
}
hop := v.hopLength
if cfg.FrameSamples != 0 && int(cfg.FrameSamples) != hop {
return fmt.Errorf("localvqe: frame_samples=%d != hop_length=%d", cfg.FrameSamples, hop)
}
// Pre-allocated scratch buffers for the C-side process call. The
// per-frame output []byte stays a fresh allocation: the response
// channel is buffered, so reusing one backing array would race with
// the gRPC send goroutine flushing prior queued frames.
micF32 := make([]float32, hop)
refF32 := make([]float32, hop)
outF32 := make([]float32, hop)
micS16 := make([]int16, hop)
refS16 := make([]int16, hop)
outS16 := make([]int16, hop)
useS16 := cfg.SampleFormat == pb.AudioTransformStreamConfig_S16_LE
frameSize := hop * 4
if useS16 {
frameSize = hop * 2
}
frameIndex := int64(0)
for req := range in {
switch payload := req.Payload.(type) {
case *pb.AudioTransformFrameRequest_Config:
if err := v.applyStreamConfig(payload.Config); err != nil {
return err
}
if payload.Config.Reset_ {
CppReset(v.ctx)
frameIndex = 0
}
continue
case *pb.AudioTransformFrameRequest_Frame:
if len(payload.Frame.AudioPcm) != frameSize {
return fmt.Errorf("localvqe: frame audio bytes=%d expected=%d", len(payload.Frame.AudioPcm), frameSize)
}
refBuf := payload.Frame.ReferencePcm
if len(refBuf) != 0 && len(refBuf) != frameSize {
return fmt.Errorf("localvqe: frame reference bytes=%d expected=%d (or 0)", len(refBuf), frameSize)
}
var outBytes []byte
if useS16 {
if err := decodeS16LE(payload.Frame.AudioPcm, micS16); err != nil {
return err
}
if len(refBuf) > 0 {
if err := decodeS16LE(refBuf, refS16); err != nil {
return err
}
} else {
zeroS16(refS16)
}
rc := CppProcessFrameS16(v.ctx,
uintptr(unsafe.Pointer(&micS16[0])),
uintptr(unsafe.Pointer(&refS16[0])),
int32(hop),
uintptr(unsafe.Pointer(&outS16[0])))
if rc != 0 {
return fmt.Errorf("localvqe_process_frame_s16 (rc=%d): %s", rc, CppLastError(v.ctx))
}
outBytes = make([]byte, hop*2)
encodeS16LE(outS16, outBytes)
} else {
if err := decodeF32LE(payload.Frame.AudioPcm, micF32); err != nil {
return err
}
if len(refBuf) > 0 {
if err := decodeF32LE(refBuf, refF32); err != nil {
return err
}
} else {
zeroF32(refF32)
}
rc := CppProcessFrameF32(v.ctx,
uintptr(unsafe.Pointer(&micF32[0])),
uintptr(unsafe.Pointer(&refF32[0])),
int32(hop),
uintptr(unsafe.Pointer(&outF32[0])))
if rc != 0 {
return fmt.Errorf("localvqe_process_frame_f32 (rc=%d): %s", rc, CppLastError(v.ctx))
}
outBytes = make([]byte, hop*4)
encodeF32LE(outF32, outBytes)
}
out <- &pb.AudioTransformFrameResponse{Pcm: outBytes, FrameIndex: frameIndex}
frameIndex++
default:
return fmt.Errorf("localvqe: unexpected stream payload %T", payload)
}
}
return nil
}
func zeroS16(s []int16) {
for i := range s {
s[i] = 0
}
}
func zeroF32(s []float32) {
for i := range s {
s[i] = 0
}
}
func (v *LocalVQE) applyStreamConfig(cfg *pb.AudioTransformStreamConfig) error {
if cfg.SampleRate != 0 && int(cfg.SampleRate) != v.sampleRate {
return fmt.Errorf("localvqe: sample_rate=%d != model %d", cfg.SampleRate, v.sampleRate)
}
return v.applyParams(cfg.Params)
}
// ---- WAV I/O ----------------------------------------------------------
//
// Minimal mono PCM WAV reader/writer. Only handles the subset LocalVQE
// cares about (mono, 16-bit signed, no extensible chunks). For broader
// audio support the HTTP layer's `audio.NormalizeAudioFile` already
// converts arbitrary input to a canonical WAV before we see it; this
// reader just decodes the canonical shape.
func readMonoWAVf32(path string) ([]float32, int, error) {
f, err := os.Open(path)
if err != nil {
return nil, 0, err
}
defer func() { _ = f.Close() }()
header := make([]byte, 44)
if _, err := io.ReadFull(f, header); err != nil {
return nil, 0, err
}
if string(header[0:4]) != "RIFF" || string(header[8:12]) != "WAVE" {
return nil, 0, fmt.Errorf("not a WAV file")
}
channels := binary.LittleEndian.Uint16(header[22:24])
sampleRate := binary.LittleEndian.Uint32(header[24:28])
bitsPerSample := binary.LittleEndian.Uint16(header[34:36])
if channels != 1 {
return nil, 0, fmt.Errorf("only mono WAV supported (got %d channels)", channels)
}
if bitsPerSample != 16 {
return nil, 0, fmt.Errorf("only 16-bit PCM supported (got %d bits)", bitsPerSample)
}
rest, err := io.ReadAll(f)
if err != nil {
return nil, 0, err
}
n := len(rest) / 2
out := make([]float32, n)
for i := 0; i < n; i++ {
s := int16(binary.LittleEndian.Uint16(rest[i*2 : i*2+2]))
out[i] = float32(s) / 32768.0
}
return out, int(sampleRate), nil
}
func writeMonoWAVf32(path string, samples []float32, sampleRate int) error {
f, err := os.Create(path)
if err != nil {
return err
}
defer func() { _ = f.Close() }()
dataLen := uint32(len(samples) * 2)
header := make([]byte, 44)
copy(header[0:4], []byte("RIFF"))
binary.LittleEndian.PutUint32(header[4:8], 36+dataLen)
copy(header[8:12], []byte("WAVE"))
copy(header[12:16], []byte("fmt "))
binary.LittleEndian.PutUint32(header[16:20], 16) // fmt chunk size
binary.LittleEndian.PutUint16(header[20:22], 1) // PCM
binary.LittleEndian.PutUint16(header[22:24], 1) // mono
binary.LittleEndian.PutUint32(header[24:28], uint32(sampleRate))
binary.LittleEndian.PutUint32(header[28:32], uint32(sampleRate*2)) // byte rate
binary.LittleEndian.PutUint16(header[32:34], 2) // block align
binary.LittleEndian.PutUint16(header[34:36], 16) // bits per sample
copy(header[36:40], []byte("data"))
binary.LittleEndian.PutUint32(header[40:44], dataLen)
if _, err := f.Write(header); err != nil {
return err
}
body := make([]byte, len(samples)*2)
for i, s := range samples {
clamped := s * 32768.0
if clamped > 32767 {
clamped = 32767
} else if clamped < -32768 {
clamped = -32768
}
binary.LittleEndian.PutUint16(body[i*2:i*2+2], uint16(int16(clamped)))
}
_, err = f.Write(body)
return err
}
// ---- PCM endec helpers ------------------------------------------------
func decodeS16LE(buf []byte, out []int16) error {
if len(buf) != len(out)*2 {
return fmt.Errorf("decodeS16LE: buf=%d out=%d", len(buf), len(out))
}
for i := range out {
out[i] = int16(binary.LittleEndian.Uint16(buf[i*2 : i*2+2]))
}
return nil
}
func encodeS16LE(in []int16, out []byte) {
for i, s := range in {
binary.LittleEndian.PutUint16(out[i*2:i*2+2], uint16(s))
}
}
func decodeF32LE(buf []byte, out []float32) error {
if len(buf) != len(out)*4 {
return fmt.Errorf("decodeF32LE: buf=%d out=%d", len(buf), len(out))
}
for i := range out {
bits := binary.LittleEndian.Uint32(buf[i*4 : i*4+4])
out[i] = *(*float32)(unsafe.Pointer(&bits))
}
return nil
}
func encodeF32LE(in []float32, out []byte) {
for i, s := range in {
bits := *(*uint32)(unsafe.Pointer(&s))
binary.LittleEndian.PutUint32(out[i*4:i*4+4], bits)
}
}

View File

@@ -1,120 +0,0 @@
package main
import (
"os"
"testing"
pb "github.com/mudler/LocalAI/pkg/grpc/proto"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
func TestLocalVQE(t *testing.T) {
RegisterFailHandler(Fail)
RunSpecs(t, "LocalVQE-cpp Backend Suite")
}
// modelPathOrSkip returns the LocalVQE GGUF path or Skip()s the current
// spec when LOCALVQE_MODEL_PATH is unset / unreadable.
func modelPathOrSkip() string {
path := os.Getenv("LOCALVQE_MODEL_PATH")
if path == "" {
Skip("LOCALVQE_MODEL_PATH not set, skipping model-dependent specs")
}
if _, err := os.Stat(path); err != nil {
Skip("LOCALVQE_MODEL_PATH unreadable: " + err.Error())
}
return path
}
var _ = Describe("LocalVQE-cpp", func() {
Context("backend semantics (no purego load needed)", func() {
It("is locking - the engine has per-context streaming state", func() {
Expect((&LocalVQE{}).Locking()).To(BeTrue())
})
It("rejects Load with empty ModelFile", func() {
err := (&LocalVQE{}).Load(&pb.ModelOptions{})
Expect(err).To(HaveOccurred())
Expect(err.Error()).To(ContainSubstring("ModelFile"))
})
It("rejects AudioTransform without a loaded model", func() {
_, err := (&LocalVQE{}).AudioTransform(&pb.AudioTransformRequest{
AudioPath: "/tmp/audio.wav",
Dst: "/tmp/out.wav",
})
Expect(err).To(HaveOccurred())
Expect(err.Error()).To(ContainSubstring("no model loaded"))
})
It("closes the output channel and errors on AudioTransformStream without a loaded model", func() {
in := make(chan *pb.AudioTransformFrameRequest, 1)
out := make(chan *pb.AudioTransformFrameResponse, 1)
close(in)
err := (&LocalVQE{}).AudioTransformStream(in, out)
Expect(err).To(HaveOccurred())
_, ok := <-out
Expect(ok).To(BeFalse(), "AudioTransformStream must close results channel even on error")
})
It("rejects AudioTransform with empty audio_path", func() {
v := &LocalVQE{ctx: 1, sampleRate: localvqeSampleRate, hopLength: 256, fftSize: 512}
_, err := v.AudioTransform(&pb.AudioTransformRequest{Dst: "/tmp/out.wav"})
Expect(err).To(HaveOccurred())
Expect(err.Error()).To(ContainSubstring("audio_path"))
})
})
Context("parseOptions", func() {
It("reads noise_gate=true (=)", func() {
v := &LocalVQE{}
v.parseOptions([]string{"noise_gate=true"})
Expect(v.gateEnabled).To(BeTrue())
})
It("reads noise_gate_threshold_dbfs=-50 (:)", func() {
v := &LocalVQE{}
v.parseOptions([]string{"noise_gate_threshold_dbfs:-50"})
Expect(v.gateDbfs).To(BeNumerically("==", -50.0))
})
It("ignores unknown keys without error", func() {
v := &LocalVQE{}
v.parseOptions([]string{"unknown=value", "another:thing"})
Expect(v.gateEnabled).To(BeFalse())
})
It("is case-insensitive on keys", func() {
v := &LocalVQE{}
v.parseOptions([]string{"NOISE_GATE=true"})
Expect(v.gateEnabled).To(BeTrue())
})
})
Context("model-gated integration (LOCALVQE_MODEL_PATH)", func() {
It("load + sample rate + hop + fft", func() {
path := modelPathOrSkip()
v := &LocalVQE{}
Expect(v.Load(&pb.ModelOptions{ModelFile: path})).To(Succeed())
defer func() { _ = v.Free() }()
Expect(v.sampleRate).To(Equal(localvqeSampleRate))
Expect(v.hopLength).To(Equal(256))
Expect(v.fftSize).To(Equal(512))
})
It("sets reference_provided correctly", func() {
// This spec is best exercised against a real model + WAV
// fixture, which the e2e harness drives separately. Here
// we just assert the expectation when ref is empty.
path := modelPathOrSkip()
v := &LocalVQE{}
Expect(v.Load(&pb.ModelOptions{ModelFile: path})).To(Succeed())
defer func() { _ = v.Free() }()
// Synthetic input; the C side handles a constant-zero ref
// just fine. Skip writing the WAV: this spec is a smoke
// check — the SNR-improvement assertion lives in the e2e
// harness where we have a real fixture.
})
})
})

View File

@@ -1,62 +0,0 @@
package main
// Started internally by LocalAI - one gRPC server per loaded model.
import (
"flag"
"os"
"github.com/ebitengine/purego"
grpc "github.com/mudler/LocalAI/pkg/grpc"
)
var (
addr = flag.String("addr", "localhost:50051", "the address to connect to")
)
type LibFuncs struct {
FuncPtr any
Name string
}
func main() {
libName := os.Getenv("LOCALVQE_LIBRARY")
if libName == "" {
libName = "./liblocalvqe.so"
}
lib, err := purego.Dlopen(libName, purego.RTLD_NOW|purego.RTLD_GLOBAL)
if err != nil {
panic(err)
}
libFuncs := []LibFuncs{
{&CppOptionsNew, "localvqe_options_new"},
{&CppOptionsFree, "localvqe_options_free"},
{&CppOptionsSetModelPath, "localvqe_options_set_model_path"},
{&CppOptionsSetBackend, "localvqe_options_set_backend"},
{&CppOptionsSetDevice, "localvqe_options_set_device"},
{&CppNewWithOptions, "localvqe_new_with_options"},
{&CppFree, "localvqe_free"},
{&CppProcessF32, "localvqe_process_f32"},
{&CppProcessS16, "localvqe_process_s16"},
{&CppProcessFrameF32, "localvqe_process_frame_f32"},
{&CppProcessFrameS16, "localvqe_process_frame_s16"},
{&CppReset, "localvqe_reset"},
{&CppLastError, "localvqe_last_error"},
{&CppSampleRate, "localvqe_sample_rate"},
{&CppHopLength, "localvqe_hop_length"},
{&CppFFTSize, "localvqe_fft_size"},
{&CppSetNoiseGate, "localvqe_set_noise_gate"},
{&CppGetNoiseGate, "localvqe_get_noise_gate"},
}
for _, lf := range libFuncs {
purego.RegisterLibFunc(lf.FuncPtr, lib, lf.Name)
}
flag.Parse()
if err := grpc.StartServer(*addr, &LocalVQE{}); err != nil {
panic(err)
}
}

View File

@@ -1,61 +0,0 @@
#!/bin/bash
# Bundle the localvqe binary, the upstream liblocalvqe.so + the per-CPU
# libggml-*.so runtime variants, the run wrapper, and the runtime libs the
# binary depends on so the package is self-contained.
set -e
CURDIR=$(dirname "$(realpath $0)")
REPO_ROOT="${CURDIR}/../../.."
mkdir -p $CURDIR/package/lib
cp -avf $CURDIR/localvqe $CURDIR/package/
# liblocalvqe.so* (with SOVERSION symlinks) and the libggml-*.so runtime
# variants — LocalVQE picks the matching CPU variant at load time.
cp -P $CURDIR/liblocalvqe.so* $CURDIR/package/ 2>/dev/null || true
cp -P $CURDIR/libggml*.so* $CURDIR/package/ 2>/dev/null || true
cp -fv $CURDIR/run.sh $CURDIR/package/
# Detect architecture and copy appropriate libraries
if [ -f "/lib64/ld-linux-x86-64.so.2" ]; then
echo "Detected x86_64 architecture, copying x86_64 libraries..."
cp -arfLv /lib64/ld-linux-x86-64.so.2 $CURDIR/package/lib/ld.so
cp -arfLv /lib/x86_64-linux-gnu/libc.so.6 $CURDIR/package/lib/libc.so.6
cp -arfLv /lib/x86_64-linux-gnu/libgcc_s.so.1 $CURDIR/package/lib/libgcc_s.so.1
cp -arfLv /lib/x86_64-linux-gnu/libstdc++.so.6 $CURDIR/package/lib/libstdc++.so.6
cp -arfLv /lib/x86_64-linux-gnu/libm.so.6 $CURDIR/package/lib/libm.so.6
cp -arfLv /lib/x86_64-linux-gnu/libgomp.so.1 $CURDIR/package/lib/libgomp.so.1
cp -arfLv /lib/x86_64-linux-gnu/libdl.so.2 $CURDIR/package/lib/libdl.so.2
cp -arfLv /lib/x86_64-linux-gnu/librt.so.1 $CURDIR/package/lib/librt.so.1
cp -arfLv /lib/x86_64-linux-gnu/libpthread.so.0 $CURDIR/package/lib/libpthread.so.0
elif [ -f "/lib/ld-linux-aarch64.so.1" ]; then
echo "Detected ARM64 architecture, copying ARM64 libraries..."
cp -arfLv /lib/ld-linux-aarch64.so.1 $CURDIR/package/lib/ld.so
cp -arfLv /lib/aarch64-linux-gnu/libc.so.6 $CURDIR/package/lib/libc.so.6
cp -arfLv /lib/aarch64-linux-gnu/libgcc_s.so.1 $CURDIR/package/lib/libgcc_s.so.1
cp -arfLv /lib/aarch64-linux-gnu/libstdc++.so.6 $CURDIR/package/lib/libstdc++.so.6
cp -arfLv /lib/aarch64-linux-gnu/libm.so.6 $CURDIR/package/lib/libm.so.6
cp -arfLv /lib/aarch64-linux-gnu/libgomp.so.1 $CURDIR/package/lib/libgomp.so.1
cp -arfLv /lib/aarch64-linux-gnu/libdl.so.2 $CURDIR/package/lib/libdl.so.2
cp -arfLv /lib/aarch64-linux-gnu/librt.so.1 $CURDIR/package/lib/librt.so.1
cp -arfLv /lib/aarch64-linux-gnu/libpthread.so.0 $CURDIR/package/lib/libpthread.so.0
elif [ $(uname -s) = "Darwin" ]; then
echo "Detected Darwin"
else
echo "Error: Could not detect architecture"
exit 1
fi
# Package GPU libraries based on BUILD_TYPE
GPU_LIB_SCRIPT="${REPO_ROOT}/scripts/build/package-gpu-libs.sh"
if [ -f "$GPU_LIB_SCRIPT" ]; then
echo "Packaging GPU libraries for BUILD_TYPE=${BUILD_TYPE:-cpu}..."
source "$GPU_LIB_SCRIPT" "$CURDIR/package/lib"
package_gpu_libs
fi
echo "Packaging completed successfully"
ls -liah $CURDIR/package/
ls -liah $CURDIR/package/lib/

View File

@@ -1,23 +0,0 @@
#!/bin/bash
set -ex
CURDIR=$(dirname "$(realpath $0)")
# LocalVQE's runtime CPU-variant loader (ggml_backend_load_all) searches
# get_executable_path() and current_path() — the second one is what saves us
# when /proc/self/exe resolves to lib/ld.so under the bundled-loader path.
# So we cd into $CURDIR (where all the libggml-cpu-*.so files live) before
# exec'ing the binary.
cd "$CURDIR"
export LD_LIBRARY_PATH=$CURDIR:$CURDIR/lib:$LD_LIBRARY_PATH
export LOCALVQE_LIBRARY=$CURDIR/liblocalvqe.so
if [ -f $CURDIR/lib/ld.so ]; then
echo "Using lib/ld.so"
echo "Using library: $LOCALVQE_LIBRARY"
exec $CURDIR/lib/ld.so $CURDIR/localvqe "$@"
fi
echo "Using library: $LOCALVQE_LIBRARY"
exec $CURDIR/localvqe "$@"

View File

@@ -1,14 +0,0 @@
#!/bin/bash
set -e
CURDIR=$(dirname "$(realpath $0)")
cd "$CURDIR"
# The Go test suite uses a built localvqe binary for end-to-end
# specs. It also opportunistically runs the integration tests when
# LOCALVQE_MODEL_PATH points at a real GGUF; otherwise those specs Skip().
export LOCALVQE_BINARY="${LOCALVQE_BINARY:-$CURDIR/localvqe}"
export LD_LIBRARY_PATH="$CURDIR:$LD_LIBRARY_PATH"
go test -v ./...

View File

@@ -10,7 +10,7 @@ set(SAM3_BUILD_TESTS OFF CACHE BOOL "Disable sam3.cpp tests" FORCE)
add_subdirectory(./sources/sam3.cpp)
add_library(gosam3 MODULE cpp/gosam3.cpp)
add_library(gosam3 MODULE gosam3.cpp)
target_link_libraries(gosam3 PRIVATE sam3 ggml)
if(CMAKE_CXX_COMPILER_ID MATCHES "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 9.0)

View File

@@ -111,7 +111,7 @@ libgosam3-fallback.so: sources/sam3.cpp
SO_TARGET=libgosam3-fallback.so CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off" $(MAKE) libgosam3-custom
rm -rfv build*
libgosam3-custom: CMakeLists.txt cpp/gosam3.cpp cpp/gosam3.h
libgosam3-custom: CMakeLists.txt gosam3.cpp gosam3.h
mkdir -p build-$(SO_TARGET) && \
cd build-$(SO_TARGET) && \
cmake .. $(CMAKE_ARGS) && \

View File

@@ -1,11 +0,0 @@
.cache/
sources/
build*/
package/
backend-assets/
sherpa-onnx
*.so
compile_commands.json
sherpa-onnx-whisper-*
vits-ljs/
streaming-zipformer-en/

View File

@@ -1,120 +0,0 @@
CURRENT_DIR=$(abspath ./)
GOCMD=go
ONNX_VERSION?=1.24.4
# v1.12.39 — includes upstream's onnxruntime 1.24.4 bump (#3501). Earlier
# pinned commits only support onnxruntime 1.23.2, which has no CUDA 13
# pre-built tarball, blocking the -gpu-nvidia-cuda-13 build matrix entry.
SHERPA_COMMIT?=7288d15e3e31a7bd589b2ba88828d521e7a6b140
ONNX_ARCH?=x64
ONNX_OS?=linux
ifneq (,$(findstring aarch64,$(shell uname -m)))
ONNX_ARCH=aarch64
endif
ifeq ($(OS),Darwin)
ONNX_OS=osx
ifneq (,$(findstring aarch64,$(shell uname -m)))
ONNX_ARCH=arm64
else ifneq (,$(findstring arm64,$(shell uname -m)))
ONNX_ARCH=arm64
else
ONNX_ARCH=x86_64
endif
endif
# Upstream onnxruntime ships CUDA 12 and CUDA 13 variants under different
# names: -gpu-<ver>.tgz for CUDA 12, -gpu_cuda13-<ver>.tgz for CUDA 13
# (note underscore vs dash). CUDA 13 tarballs only exist from 1.24.x onward.
ifeq ($(BUILD_TYPE),cublas)
SHERPA_GPU=ON
ONNX_PROVIDER=cuda
ifeq ($(CUDA_MAJOR_VERSION),13)
ONNX_VARIANT=-gpu_cuda13
else
ONNX_VARIANT=-gpu
endif
else
ONNX_VARIANT=
SHERPA_GPU=OFF
ONNX_PROVIDER=cpu
endif
JOBS?=$(shell nproc --ignore=1 2>/dev/null || sysctl -n hw.ncpu 2>/dev/null || echo 4)
sources/onnxruntime:
mkdir -p sources/onnxruntime
curl -L https://github.com/microsoft/onnxruntime/releases/download/v$(ONNX_VERSION)/onnxruntime-$(ONNX_OS)-$(ONNX_ARCH)$(ONNX_VARIANT)-$(ONNX_VERSION).tgz \
-o sources/onnxruntime/onnxruntime.tgz
cd sources/onnxruntime && tar -xf onnxruntime.tgz --strip-components=1 && rm onnxruntime.tgz
sources/sherpa-onnx: sources/onnxruntime
git clone https://github.com/k2-fsa/sherpa-onnx.git sources/sherpa-onnx
cd sources/sherpa-onnx && git checkout $(SHERPA_COMMIT)
mkdir -p sources/sherpa-onnx/build
# sherpa-onnx's cmake detects a pre-installed onnxruntime via the
# SHERPA_ONNXRUNTIME_{INCLUDE,LIB}_DIR env vars (not via -D flags).
# Point them at our locally-downloaded Microsoft tarball — without
# this, sherpa-onnx falls through to download_onnxruntime() which
# fetches from csukuangfj/onnxruntime-libs. For the GPU 1.24.4
# build that release mirror publishes `-patched.zip` instead of the
# expected `.tgz`, so the download 404s and the build fails.
cd sources/sherpa-onnx/build && \
SHERPA_ONNXRUNTIME_INCLUDE_DIR=$(CURRENT_DIR)/sources/onnxruntime/include \
SHERPA_ONNXRUNTIME_LIB_DIR=$(CURRENT_DIR)/sources/onnxruntime/lib \
cmake \
-DCMAKE_BUILD_TYPE=Release \
-DCMAKE_C_FLAGS="-Wno-error=format-security" \
-DCMAKE_CXX_FLAGS="-Wno-error=format-security" \
-DSHERPA_ONNX_ENABLE_GPU=$(SHERPA_GPU) \
-DSHERPA_ONNX_ENABLE_TTS=ON \
-DSHERPA_ONNX_ENABLE_BINARY=OFF \
-DSHERPA_ONNX_ENABLE_PYTHON=OFF \
-DSHERPA_ONNX_ENABLE_TESTS=OFF \
-DSHERPA_ONNX_ENABLE_C_API=ON \
-DBUILD_SHARED_LIBS=ON \
-DSHERPA_ONNX_USE_PRE_INSTALLED_ONNXRUNTIME_IF_AVAILABLE=ON \
..
cd sources/sherpa-onnx/build && make -j$(JOBS)
backend-assets/lib: sources/sherpa-onnx sources/onnxruntime
mkdir -p backend-assets/lib
cp -rfLv sources/onnxruntime/lib/* backend-assets/lib/
cp -rfLv sources/sherpa-onnx/build/lib/*.so* backend-assets/lib/ 2>/dev/null || true
cp -rfLv sources/sherpa-onnx/build/lib/*.dylib backend-assets/lib/ 2>/dev/null || true
# libsherpa-shim wraps sherpa-onnx's nested config structs and TTS
# callback plumbing behind a purego-friendly API: opaque handles plus
# fixed-signature setters/getters/trampoline. Plain C compile — no cgo.
SHIM_EXT=so
ifeq ($(OS),Darwin)
SHIM_EXT=dylib
endif
backend-assets/lib/libsherpa-shim.$(SHIM_EXT): csrc/shim.c csrc/shim.h backend-assets/lib
$(CC) -shared -fPIC -O2 \
-I$(CURRENT_DIR)/sources/sherpa-onnx/sherpa-onnx/c-api \
-o $@ csrc/shim.c \
-L$(CURRENT_DIR)/backend-assets/lib \
-lsherpa-onnx-c-api \
-Wl,-rpath,'$$ORIGIN'
sherpa-onnx: backend-assets/lib backend-assets/lib/libsherpa-shim.$(SHIM_EXT)
CGO_ENABLED=0 $(GOCMD) build \
-ldflags "$(LD_FLAGS) -X main.onnxProvider=$(ONNX_PROVIDER)" \
-tags "$(GO_TAGS)" -o sherpa-onnx ./
package:
bash package.sh
build: sherpa-onnx package
clean:
rm -rf sherpa-onnx sources/ backend-assets/ package/ vits-ljs/ sherpa-onnx-whisper-*/
test: sherpa-onnx
LD_LIBRARY_PATH=$(CURRENT_DIR)/backend-assets/lib \
bash test.sh
.PHONY: build package clean test

View File

File diff suppressed because it is too large Load Diff

View File

@@ -1,170 +0,0 @@
package main
import (
"context"
"os"
"path/filepath"
"testing"
pb "github.com/mudler/LocalAI/pkg/grpc/proto"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
func TestSherpaBackend(t *testing.T) {
RegisterFailHandler(Fail)
RunSpecs(t, "Sherpa-ONNX Backend Suite")
}
// Load libsherpa-shim + libsherpa-onnx-c-api via purego before any spec
// runs — otherwise any Load/TTS/VAD/AudioTranscription call hits a nil
// function pointer. LD_LIBRARY_PATH must contain the directory holding
// both .so files; test.sh sets this.
var _ = BeforeSuite(func() {
Expect(loadSherpaLibs()).To(Succeed())
})
var _ = Describe("Sherpa-ONNX", func() {
Context("lifecycle", func() {
It("is locking (C API is not thread safe)", func() {
Expect((&SherpaBackend{}).Locking()).To(BeTrue())
})
It("errors loading a non-existent model", func() {
tmpDir, err := os.MkdirTemp("", "sherpa-test-nonexistent")
Expect(err).ToNot(HaveOccurred())
defer os.RemoveAll(tmpDir)
err = (&SherpaBackend{}).Load(&pb.ModelOptions{
ModelFile: filepath.Join(tmpDir, "non-existent-model.onnx"),
})
Expect(err).To(HaveOccurred())
})
It("errors loading a non-existent ASR model", func() {
tmpDir, err := os.MkdirTemp("", "sherpa-test-asr")
Expect(err).ToNot(HaveOccurred())
defer os.RemoveAll(tmpDir)
err = (&SherpaBackend{}).Load(&pb.ModelOptions{
ModelFile: filepath.Join(tmpDir, "model.onnx"),
Type: "asr",
})
Expect(err).To(HaveOccurred())
})
It("dispatches Load by Type", func() {
tmpDir, err := os.MkdirTemp("", "sherpa-test-dispatch")
Expect(err).ToNot(HaveOccurred())
defer os.RemoveAll(tmpDir)
modelFile := filepath.Join(tmpDir, "model.onnx")
for _, typ := range []string{"", "asr", "vad"} {
err := (&SherpaBackend{}).Load(&pb.ModelOptions{ModelFile: modelFile, Type: typ})
Expect(err).To(HaveOccurred(), "Type=%q", typ)
}
})
})
Context("method errors without loaded model", func() {
It("rejects TTS", func() {
tmpDir, err := os.MkdirTemp("", "sherpa-test-tts")
Expect(err).ToNot(HaveOccurred())
defer os.RemoveAll(tmpDir)
err = (&SherpaBackend{}).TTS(&pb.TTSRequest{
Text: "should fail — no model loaded",
Dst: filepath.Join(tmpDir, "output.wav"),
})
Expect(err).To(HaveOccurred())
})
It("rejects AudioTranscription", func() {
_, err := (&SherpaBackend{}).AudioTranscription(context.Background(), &pb.TranscriptRequest{
Dst: "/tmp/nonexistent.wav",
})
Expect(err).To(HaveOccurred())
})
It("rejects VAD", func() {
_, err := (&SherpaBackend{}).VAD(&pb.VADRequest{
Audio: []float32{0.1, 0.2, 0.3},
})
Expect(err).To(HaveOccurred())
})
})
Context("type detection", func() {
DescribeTable("isASRType",
func(input string, want bool) {
Expect(isASRType(input)).To(Equal(want))
},
Entry("asr", "asr", true),
Entry("ASR", "ASR", true),
Entry("Asr", "Asr", true),
Entry("transcription", "transcription", true),
Entry("Transcription", "Transcription", true),
Entry("transcribe", "transcribe", true),
Entry("Transcribe", "Transcribe", true),
Entry("tts", "tts", false),
Entry("empty", "", false),
Entry("other", "other", false),
Entry("vad", "vad", false),
)
DescribeTable("isVADType",
func(input string, want bool) {
Expect(isVADType(input)).To(Equal(want))
},
Entry("vad", "vad", true),
Entry("VAD", "VAD", true),
Entry("Vad", "Vad", true),
Entry("asr", "asr", false),
Entry("tts", "tts", false),
Entry("empty", "", false),
Entry("other", "other", false),
)
})
Context("option parsing", func() {
It("parses float options with fallback on bad input", func() {
opts := &pb.ModelOptions{Options: []string{
"vad.threshold=0.3",
"tts.length_scale=1.25",
"bad.number=not-a-float",
}}
Expect(findOptionFloat(opts, "vad.threshold=", 0.5)).To(BeNumerically("~", 0.3, 1e-6))
Expect(findOptionFloat(opts, "tts.length_scale=", 1.0)).To(BeNumerically("~", 1.25, 1e-6))
Expect(findOptionFloat(opts, "missing.key=", 0.7)).To(BeNumerically("~", 0.7, 1e-6))
Expect(findOptionFloat(opts, "bad.number=", 9.9)).To(BeNumerically("~", 9.9, 1e-6))
})
It("parses int options with fallback on bad input", func() {
opts := &pb.ModelOptions{Options: []string{
"asr.sample_rate=22050",
"online.chunk_samples=800",
"bad.int=4.2",
}}
Expect(findOptionInt(opts, "asr.sample_rate=", 16000)).To(Equal(int32(22050)))
Expect(findOptionInt(opts, "online.chunk_samples=", 1600)).To(Equal(int32(800)))
Expect(findOptionInt(opts, "missing.key=", 42)).To(Equal(int32(42)))
Expect(findOptionInt(opts, "bad.int=", 100)).To(Equal(int32(100)))
})
It("parses bool options (0/1, true/false, yes/no, on/off)", func() {
opts := &pb.ModelOptions{Options: []string{
"online.enable_endpoint=0",
"asr.sense_voice.use_itn=True",
"feature.on=yes",
"feature.off=Off",
"feature.bad=maybe",
}}
Expect(findOptionBool(opts, "online.enable_endpoint=", 1)).To(Equal(int32(0)))
Expect(findOptionBool(opts, "asr.sense_voice.use_itn=", 0)).To(Equal(int32(1)))
Expect(findOptionBool(opts, "feature.on=", 0)).To(Equal(int32(1)))
Expect(findOptionBool(opts, "feature.off=", 1)).To(Equal(int32(0)))
Expect(findOptionBool(opts, "feature.bad=", 1)).To(Equal(int32(1)))
Expect(findOptionBool(opts, "missing.key=", 1)).To(Equal(int32(1)))
})
})
})

View File

@@ -1,406 +0,0 @@
#include "shim.h"
#include "c-api.h"
#include <stdlib.h>
#include <string.h>
// Replace the char* field pointed to by `slot` with a strdup of `s`
// (or NULL if s is NULL). Frees any prior value. Silently no-ops when
// strdup fails — the caller will see a Create* failure downstream.
static void shim_set_str(const char **slot, const char *s) {
free((char *)*slot);
*slot = s ? strdup(s) : NULL;
}
// ==================================================================
// VAD config
// ==================================================================
void *sherpa_shim_vad_config_new(void) {
return calloc(1, sizeof(SherpaOnnxVadModelConfig));
}
void sherpa_shim_vad_config_free(void *h) {
if (!h) return;
SherpaOnnxVadModelConfig *c = (SherpaOnnxVadModelConfig *)h;
free((char *)c->silero_vad.model);
free((char *)c->provider);
free(c);
}
void sherpa_shim_vad_config_set_silero_model(void *h, const char *v) {
shim_set_str(&((SherpaOnnxVadModelConfig *)h)->silero_vad.model, v);
}
void sherpa_shim_vad_config_set_silero_threshold(void *h, float v) {
((SherpaOnnxVadModelConfig *)h)->silero_vad.threshold = v;
}
void sherpa_shim_vad_config_set_silero_min_silence_duration(void *h, float v) {
((SherpaOnnxVadModelConfig *)h)->silero_vad.min_silence_duration = v;
}
void sherpa_shim_vad_config_set_silero_min_speech_duration(void *h, float v) {
((SherpaOnnxVadModelConfig *)h)->silero_vad.min_speech_duration = v;
}
void sherpa_shim_vad_config_set_silero_window_size(void *h, int32_t v) {
((SherpaOnnxVadModelConfig *)h)->silero_vad.window_size = v;
}
void sherpa_shim_vad_config_set_silero_max_speech_duration(void *h, float v) {
((SherpaOnnxVadModelConfig *)h)->silero_vad.max_speech_duration = v;
}
void sherpa_shim_vad_config_set_sample_rate(void *h, int32_t v) {
((SherpaOnnxVadModelConfig *)h)->sample_rate = v;
}
void sherpa_shim_vad_config_set_num_threads(void *h, int32_t v) {
((SherpaOnnxVadModelConfig *)h)->num_threads = v;
}
void sherpa_shim_vad_config_set_provider(void *h, const char *v) {
shim_set_str(&((SherpaOnnxVadModelConfig *)h)->provider, v);
}
void sherpa_shim_vad_config_set_debug(void *h, int32_t v) {
((SherpaOnnxVadModelConfig *)h)->debug = v;
}
void *sherpa_shim_create_vad(void *h, float buffer_size_seconds) {
return (void *)SherpaOnnxCreateVoiceActivityDetector(
(const SherpaOnnxVadModelConfig *)h, buffer_size_seconds);
}
// ==================================================================
// Offline TTS config (VITS)
// ==================================================================
void *sherpa_shim_tts_config_new(void) {
return calloc(1, sizeof(SherpaOnnxOfflineTtsConfig));
}
void sherpa_shim_tts_config_free(void *h) {
if (!h) return;
SherpaOnnxOfflineTtsConfig *c = (SherpaOnnxOfflineTtsConfig *)h;
free((char *)c->model.vits.model);
free((char *)c->model.vits.tokens);
free((char *)c->model.vits.lexicon);
free((char *)c->model.vits.data_dir);
free((char *)c->model.provider);
free(c);
}
void sherpa_shim_tts_config_set_vits_model(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOfflineTtsConfig *)h)->model.vits.model, v);
}
void sherpa_shim_tts_config_set_vits_tokens(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOfflineTtsConfig *)h)->model.vits.tokens, v);
}
void sherpa_shim_tts_config_set_vits_lexicon(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOfflineTtsConfig *)h)->model.vits.lexicon, v);
}
void sherpa_shim_tts_config_set_vits_data_dir(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOfflineTtsConfig *)h)->model.vits.data_dir, v);
}
void sherpa_shim_tts_config_set_vits_noise_scale(void *h, float v) {
((SherpaOnnxOfflineTtsConfig *)h)->model.vits.noise_scale = v;
}
void sherpa_shim_tts_config_set_vits_noise_scale_w(void *h, float v) {
((SherpaOnnxOfflineTtsConfig *)h)->model.vits.noise_scale_w = v;
}
void sherpa_shim_tts_config_set_vits_length_scale(void *h, float v) {
((SherpaOnnxOfflineTtsConfig *)h)->model.vits.length_scale = v;
}
void sherpa_shim_tts_config_set_num_threads(void *h, int32_t v) {
((SherpaOnnxOfflineTtsConfig *)h)->model.num_threads = v;
}
void sherpa_shim_tts_config_set_debug(void *h, int32_t v) {
((SherpaOnnxOfflineTtsConfig *)h)->model.debug = v;
}
void sherpa_shim_tts_config_set_provider(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOfflineTtsConfig *)h)->model.provider, v);
}
void sherpa_shim_tts_config_set_max_num_sentences(void *h, int32_t v) {
((SherpaOnnxOfflineTtsConfig *)h)->max_num_sentences = v;
}
void *sherpa_shim_create_offline_tts(void *h) {
return (void *)SherpaOnnxCreateOfflineTts(
(const SherpaOnnxOfflineTtsConfig *)h);
}
// ==================================================================
// Offline recognizer config
// ==================================================================
void *sherpa_shim_offline_recog_config_new(void) {
return calloc(1, sizeof(SherpaOnnxOfflineRecognizerConfig));
}
void sherpa_shim_offline_recog_config_free(void *h) {
if (!h) return;
SherpaOnnxOfflineRecognizerConfig *c = (SherpaOnnxOfflineRecognizerConfig *)h;
free((char *)c->model_config.provider);
free((char *)c->model_config.tokens);
free((char *)c->model_config.whisper.encoder);
free((char *)c->model_config.whisper.decoder);
free((char *)c->model_config.whisper.language);
free((char *)c->model_config.whisper.task);
free((char *)c->model_config.paraformer.model);
free((char *)c->model_config.sense_voice.model);
free((char *)c->model_config.sense_voice.language);
free((char *)c->model_config.omnilingual.model);
free((char *)c->decoding_method);
free(c);
}
void sherpa_shim_offline_recog_config_set_num_threads(void *h, int32_t v) {
((SherpaOnnxOfflineRecognizerConfig *)h)->model_config.num_threads = v;
}
void sherpa_shim_offline_recog_config_set_debug(void *h, int32_t v) {
((SherpaOnnxOfflineRecognizerConfig *)h)->model_config.debug = v;
}
void sherpa_shim_offline_recog_config_set_provider(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOfflineRecognizerConfig *)h)->model_config.provider, v);
}
void sherpa_shim_offline_recog_config_set_tokens(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOfflineRecognizerConfig *)h)->model_config.tokens, v);
}
void sherpa_shim_offline_recog_config_set_feat_sample_rate(void *h, int32_t v) {
((SherpaOnnxOfflineRecognizerConfig *)h)->feat_config.sample_rate = v;
}
void sherpa_shim_offline_recog_config_set_feat_feature_dim(void *h, int32_t v) {
((SherpaOnnxOfflineRecognizerConfig *)h)->feat_config.feature_dim = v;
}
void sherpa_shim_offline_recog_config_set_decoding_method(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOfflineRecognizerConfig *)h)->decoding_method, v);
}
void sherpa_shim_offline_recog_config_set_whisper_encoder(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOfflineRecognizerConfig *)h)->model_config.whisper.encoder, v);
}
void sherpa_shim_offline_recog_config_set_whisper_decoder(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOfflineRecognizerConfig *)h)->model_config.whisper.decoder, v);
}
void sherpa_shim_offline_recog_config_set_whisper_language(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOfflineRecognizerConfig *)h)->model_config.whisper.language, v);
}
void sherpa_shim_offline_recog_config_set_whisper_task(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOfflineRecognizerConfig *)h)->model_config.whisper.task, v);
}
void sherpa_shim_offline_recog_config_set_whisper_tail_paddings(void *h, int32_t v) {
((SherpaOnnxOfflineRecognizerConfig *)h)->model_config.whisper.tail_paddings = v;
}
void sherpa_shim_offline_recog_config_set_paraformer_model(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOfflineRecognizerConfig *)h)->model_config.paraformer.model, v);
}
void sherpa_shim_offline_recog_config_set_sense_voice_model(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOfflineRecognizerConfig *)h)->model_config.sense_voice.model, v);
}
void sherpa_shim_offline_recog_config_set_sense_voice_language(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOfflineRecognizerConfig *)h)->model_config.sense_voice.language, v);
}
void sherpa_shim_offline_recog_config_set_sense_voice_use_itn(void *h, int32_t v) {
((SherpaOnnxOfflineRecognizerConfig *)h)->model_config.sense_voice.use_itn = v;
}
void sherpa_shim_offline_recog_config_set_omnilingual_model(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOfflineRecognizerConfig *)h)->model_config.omnilingual.model, v);
}
void *sherpa_shim_create_offline_recognizer(void *h) {
return (void *)SherpaOnnxCreateOfflineRecognizer(
(const SherpaOnnxOfflineRecognizerConfig *)h);
}
// ==================================================================
// Online recognizer config
// ==================================================================
void *sherpa_shim_online_recog_config_new(void) {
return calloc(1, sizeof(SherpaOnnxOnlineRecognizerConfig));
}
void sherpa_shim_online_recog_config_free(void *h) {
if (!h) return;
SherpaOnnxOnlineRecognizerConfig *c = (SherpaOnnxOnlineRecognizerConfig *)h;
free((char *)c->model_config.transducer.encoder);
free((char *)c->model_config.transducer.decoder);
free((char *)c->model_config.transducer.joiner);
free((char *)c->model_config.tokens);
free((char *)c->model_config.provider);
free((char *)c->decoding_method);
free(c);
}
void sherpa_shim_online_recog_config_set_transducer_encoder(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOnlineRecognizerConfig *)h)->model_config.transducer.encoder, v);
}
void sherpa_shim_online_recog_config_set_transducer_decoder(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOnlineRecognizerConfig *)h)->model_config.transducer.decoder, v);
}
void sherpa_shim_online_recog_config_set_transducer_joiner(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOnlineRecognizerConfig *)h)->model_config.transducer.joiner, v);
}
void sherpa_shim_online_recog_config_set_tokens(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOnlineRecognizerConfig *)h)->model_config.tokens, v);
}
void sherpa_shim_online_recog_config_set_num_threads(void *h, int32_t v) {
((SherpaOnnxOnlineRecognizerConfig *)h)->model_config.num_threads = v;
}
void sherpa_shim_online_recog_config_set_debug(void *h, int32_t v) {
((SherpaOnnxOnlineRecognizerConfig *)h)->model_config.debug = v;
}
void sherpa_shim_online_recog_config_set_provider(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOnlineRecognizerConfig *)h)->model_config.provider, v);
}
void sherpa_shim_online_recog_config_set_feat_sample_rate(void *h, int32_t v) {
((SherpaOnnxOnlineRecognizerConfig *)h)->feat_config.sample_rate = v;
}
void sherpa_shim_online_recog_config_set_feat_feature_dim(void *h, int32_t v) {
((SherpaOnnxOnlineRecognizerConfig *)h)->feat_config.feature_dim = v;
}
void sherpa_shim_online_recog_config_set_decoding_method(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOnlineRecognizerConfig *)h)->decoding_method, v);
}
void sherpa_shim_online_recog_config_set_enable_endpoint(void *h, int32_t v) {
((SherpaOnnxOnlineRecognizerConfig *)h)->enable_endpoint = v;
}
void sherpa_shim_online_recog_config_set_rule1_min_trailing_silence(void *h, float v) {
((SherpaOnnxOnlineRecognizerConfig *)h)->rule1_min_trailing_silence = v;
}
void sherpa_shim_online_recog_config_set_rule2_min_trailing_silence(void *h, float v) {
((SherpaOnnxOnlineRecognizerConfig *)h)->rule2_min_trailing_silence = v;
}
void sherpa_shim_online_recog_config_set_rule3_min_utterance_length(void *h, float v) {
((SherpaOnnxOnlineRecognizerConfig *)h)->rule3_min_utterance_length = v;
}
void *sherpa_shim_create_online_recognizer(void *h) {
return (void *)SherpaOnnxCreateOnlineRecognizer(
(const SherpaOnnxOnlineRecognizerConfig *)h);
}
// ==================================================================
// Result-struct accessors
// ==================================================================
int32_t sherpa_shim_wave_sample_rate(const void *h) {
return ((const SherpaOnnxWave *)h)->sample_rate;
}
int32_t sherpa_shim_wave_num_samples(const void *h) {
return ((const SherpaOnnxWave *)h)->num_samples;
}
const float *sherpa_shim_wave_samples(const void *h) {
return ((const SherpaOnnxWave *)h)->samples;
}
const char *sherpa_shim_offline_result_text(const void *h) {
return ((const SherpaOnnxOfflineRecognizerResult *)h)->text;
}
const char *sherpa_shim_online_result_text(const void *h) {
return ((const SherpaOnnxOnlineRecognizerResult *)h)->text;
}
int32_t sherpa_shim_generated_audio_sample_rate(const void *h) {
return ((const SherpaOnnxGeneratedAudio *)h)->sample_rate;
}
int32_t sherpa_shim_generated_audio_n(const void *h) {
return ((const SherpaOnnxGeneratedAudio *)h)->n;
}
const float *sherpa_shim_generated_audio_samples(const void *h) {
return ((const SherpaOnnxGeneratedAudio *)h)->samples;
}
int32_t sherpa_shim_speech_segment_start(const void *h) {
return ((const SherpaOnnxSpeechSegment *)h)->start;
}
int32_t sherpa_shim_speech_segment_n(const void *h) {
return ((const SherpaOnnxSpeechSegment *)h)->n;
}
// ==================================================================
// Offline speaker diarization config
// ==================================================================
void *sherpa_shim_diarize_config_new(void) {
return calloc(1, sizeof(SherpaOnnxOfflineSpeakerDiarizationConfig));
}
void sherpa_shim_diarize_config_free(void *h) {
if (!h) return;
SherpaOnnxOfflineSpeakerDiarizationConfig *c =
(SherpaOnnxOfflineSpeakerDiarizationConfig *)h;
free((char *)c->segmentation.pyannote.model);
free((char *)c->segmentation.provider);
free((char *)c->embedding.model);
free((char *)c->embedding.provider);
free(c);
}
void sherpa_shim_diarize_config_set_segmentation_model(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOfflineSpeakerDiarizationConfig *)h)->segmentation.pyannote.model, v);
}
void sherpa_shim_diarize_config_set_segmentation_num_threads(void *h, int32_t v) {
((SherpaOnnxOfflineSpeakerDiarizationConfig *)h)->segmentation.num_threads = v;
}
void sherpa_shim_diarize_config_set_segmentation_provider(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOfflineSpeakerDiarizationConfig *)h)->segmentation.provider, v);
}
void sherpa_shim_diarize_config_set_segmentation_debug(void *h, int32_t v) {
((SherpaOnnxOfflineSpeakerDiarizationConfig *)h)->segmentation.debug = v;
}
void sherpa_shim_diarize_config_set_embedding_model(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOfflineSpeakerDiarizationConfig *)h)->embedding.model, v);
}
void sherpa_shim_diarize_config_set_embedding_num_threads(void *h, int32_t v) {
((SherpaOnnxOfflineSpeakerDiarizationConfig *)h)->embedding.num_threads = v;
}
void sherpa_shim_diarize_config_set_embedding_provider(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOfflineSpeakerDiarizationConfig *)h)->embedding.provider, v);
}
void sherpa_shim_diarize_config_set_embedding_debug(void *h, int32_t v) {
((SherpaOnnxOfflineSpeakerDiarizationConfig *)h)->embedding.debug = v;
}
void sherpa_shim_diarize_config_set_clustering_num_clusters(void *h, int32_t v) {
((SherpaOnnxOfflineSpeakerDiarizationConfig *)h)->clustering.num_clusters = v;
}
void sherpa_shim_diarize_config_set_clustering_threshold(void *h, float v) {
((SherpaOnnxOfflineSpeakerDiarizationConfig *)h)->clustering.threshold = v;
}
void sherpa_shim_diarize_config_set_min_duration_on(void *h, float v) {
((SherpaOnnxOfflineSpeakerDiarizationConfig *)h)->min_duration_on = v;
}
void sherpa_shim_diarize_config_set_min_duration_off(void *h, float v) {
((SherpaOnnxOfflineSpeakerDiarizationConfig *)h)->min_duration_off = v;
}
void *sherpa_shim_create_offline_speaker_diarization(void *h) {
return (void *)SherpaOnnxCreateOfflineSpeakerDiarization(
(const SherpaOnnxOfflineSpeakerDiarizationConfig *)h);
}
void sherpa_shim_diarize_set_clustering(void *sd, int32_t num_clusters, float threshold) {
if (!sd) return;
SherpaOnnxOfflineSpeakerDiarizationConfig cfg;
memset(&cfg, 0, sizeof(cfg));
cfg.clustering.num_clusters = num_clusters;
cfg.clustering.threshold = threshold;
SherpaOnnxOfflineSpeakerDiarizationSetConfig(
(const SherpaOnnxOfflineSpeakerDiarization *)sd, &cfg);
}
void sherpa_shim_diarize_segment_at(const void *segs, int32_t i,
float *out_start, float *out_end,
int32_t *out_speaker) {
const SherpaOnnxOfflineSpeakerDiarizationSegment *arr =
(const SherpaOnnxOfflineSpeakerDiarizationSegment *)segs;
if (out_start) *out_start = arr[i].start;
if (out_end) *out_end = arr[i].end;
if (out_speaker) *out_speaker = arr[i].speaker;
}
// ==================================================================
// TTS streaming callback trampoline
// ==================================================================
void *sherpa_shim_tts_generate_with_callback(
void *tts, const char *text, int32_t sid, float speed,
uintptr_t callback_ptr, uintptr_t user_data) {
SherpaOnnxGeneratedAudioCallbackWithArg cb =
(SherpaOnnxGeneratedAudioCallbackWithArg)callback_ptr;
return (void *)SherpaOnnxOfflineTtsGenerateWithCallbackWithArg(
(const SherpaOnnxOfflineTts *)tts, text, sid, speed, cb,
(void *)user_data);
}

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