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Author SHA1 Message Date
Ettore Di Giacinto
5fe87cb0d5 feat: upgrade banner with Upgrade All button, detect pre-existing backends
- Add upgrade banner on Backends page showing count and Upgrade All button
- Fix upgrade detection for backends installed before version tracking:
  flag as upgradeable when gallery has a version but installed has none
- Fix OCI digest check to flag backends with no stored digest as upgradeable
2026-04-11 22:11:23 +00:00
Ettore Di Giacinto
6dd37a95c4 test: add e2e tests for backend upgrade API
- Test GET /api/backends/upgrades returns 200 (even with no upgrade checker)
- Test POST /api/backends/upgrade/:name accepts request and returns job ID
- Test full upgrade flow: trigger upgrade via API, wait for job completion,
  verify run.sh updated to v2 and metadata.json has version 2.0.0
- Test POST /api/backends/upgrades/check returns 200
- Fix nil check for applicationInstance in upgrade API routes
2026-04-11 11:43:56 +00:00
Ettore Di Giacinto
ee00a10836 fix: use advisory lock for upgrade checker in distributed mode
In distributed mode with multiple frontend instances, use PostgreSQL
advisory lock (KeyBackendUpgradeCheck) so only one instance runs
periodic upgrade checks and auto-upgrades. Prevents duplicate
upgrade operations across replicas.

Standalone mode is unchanged (simple ticker loop).
2026-04-11 11:43:56 +00:00
Ettore Di Giacinto
948f3bfaa4 feat: add upgrade checker service, API endpoints, and CLI command
- UpgradeChecker background service: checks every 6h, auto-upgrades when enabled
- API endpoints: GET /backends/upgrades, POST /backends/upgrades/check, POST /backends/upgrade/:name
- CLI: `localai backends upgrade` command, version display in `backends list`
- BackendManager interface: add UpgradeBackend and CheckUpgrades methods
- Wire upgrade op through GalleryService backend handler
- Distributed mode: fan-out upgrade to worker nodes via NATS
2026-04-11 11:43:56 +00:00
Ettore Di Giacinto
1e083cd870 feat(ui): add backend version display and upgrade support
- Add upgrade check/trigger API endpoints to config and api module
- Backends page: version badge, upgrade indicator, upgrade button
- Manage page: version in metadata, context-aware upgrade/reinstall button
- Settings page: auto-upgrade backends toggle
2026-04-11 11:43:56 +00:00
Ettore Di Giacinto
b19e60d03a feat: add AutoUpgradeBackends config and runtime settings
Add configuration and runtime settings for backend auto-upgrade:
- RuntimeSettings field for dynamic config via API/JSON
- ApplicationConfig field, option func, and roundtrip conversion
- CLI flag with LOCALAI_AUTO_UPGRADE_BACKENDS env var
- Config file watcher support for runtime_settings.json
- Tests for ToRuntimeSettings, ApplyRuntimeSettings, and roundtrip
2026-04-11 11:43:56 +00:00
Ettore Di Giacinto
4d463e9f0d feat: add backend upgrade detection and execution logic
Add CheckBackendUpgrades() to compare installed backend versions/digests
against gallery entries, and UpgradeBackend() to perform atomic upgrades
with backup-based rollback on failure. Includes Agent A's data model
changes (Version/URI/Digest fields, GetImageDigest).
2026-04-11 11:43:56 +00:00
Ettore Di Giacinto
ae4ae5f425 feat: add backend versioning data model foundation
Add Version, URI, and Digest fields to BackendMetadata for tracking
installed backend versions and enabling upgrade detection. Add Version
field to GalleryBackend. Add UpgradeAvailable/AvailableVersion fields
to SystemBackend. Implement GetImageDigest() for lightweight OCI digest
lookups via remote.Head. Record version, URI, and digest at install time
in InstallBackend() and propagate version through meta backends.
2026-04-11 11:43:56 +00:00
768 changed files with 7151 additions and 75102 deletions

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@@ -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
@@ -19,22 +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`
Add build matrix entries 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.
If you add a new language bucket, `scripts/changed-backends.js` also needs a branch in `inferBackendPath` so PR change-detection routes file edits correctly.
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`)
@@ -42,8 +28,8 @@ If you add a new language bucket, `scripts/changed-backends.js` also needs a bra
- CUDA 13 builds: Add after other CUDA 13 builds (e.g., after `gpu-nvidia-cuda-13-chatterbox`)
**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"`
- ROCm/HIP: Use `build-type: 'hipblas'` with `base-image: "rocm/dev-ubuntu-24.04:6.4.4"`
- 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'`
## 3. Add Backend Metadata to `backend/index.yaml`
@@ -70,28 +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
$(MAKE) -C backend/python/<backend-name> test
```
Each backend's own `Makefile` should define a `test` target so this line works regardless of language. Integration tests that need large model downloads should be gated behind an env var (see `backend/rust/kokoros/`'s `KOKOROS_MODEL_PATH` pattern) so CI only runs unit tests.
**Step 4d: Add Backend Definition**
Add a backend definition variable in the backend definitions section (around line 428-457). The format depends on the backend type:
@@ -111,13 +93,6 @@ BACKEND_<BACKEND_NAME> = <backend-name>|python|./backend|false|true
BACKEND_<BACKEND_NAME> = <backend-name>|golang|.|false|true
```
**For Rust backends**:
```makefile
BACKEND_<BACKEND_NAME> = <backend-name>|rust|.|false|true
```
The language field (`python`/`golang`/`rust`/…) must match a `backend/Dockerfile.<lang>` file.
**Step 4e: Generate Docker Build Target**
Add an eval call to generate the docker-build target (around line 480-501):
@@ -154,53 +129,6 @@ After adding a new backend, verify:
- [ ] No Makefile syntax errors (check with linter)
- [ ] Follows the same pattern as similar backends (e.g., if it's a transcription backend, follow `faster-whisper` pattern)
## Bundling runtime shared libraries (`package.sh`)
The final `Dockerfile.python` stage is `FROM scratch` — there is no system `libc`, no `apt`, no fallback library path. Only files explicitly copied from the builder stage end up in the backend image. That means any runtime `dlopen` your backend (or its Python deps) needs **must** be packaged into `${BACKEND}/lib/`.
Pattern:
1. Make sure the library is installed in the builder stage of `backend/Dockerfile.python` (add it to the top-level `apt-get install`).
2. Drop a `package.sh` in your backend directory that copies the library — and its soname symlinks — into `$(dirname $0)/lib`. See `backend/python/vllm/package.sh` for a reference implementation that walks `/usr/lib/x86_64-linux-gnu`, `/usr/lib/aarch64-linux-gnu`, etc.
3. `Dockerfile.python` already runs `package.sh` automatically if it exists, after `package-gpu-libs.sh`.
4. `libbackend.sh` automatically prepends `${EDIR}/lib` to `LD_LIBRARY_PATH` at run time, so anything packaged this way is found by `dlopen`.
How to find missing libs: when a Python module silently fails to register torch ops or you see `AttributeError: '_OpNamespace' '...' object has no attribute '...'`, run the backend image's Python with `LD_DEBUG=libs` to see which `dlopen` failed. The filename in the error message (e.g. `libnuma.so.1`) is what you need to package.
To verify packaging works without trusting the host:
```bash
make docker-build-<backend>
CID=$(docker create --entrypoint=/run.sh local-ai-backend:<backend>)
docker cp $CID:/lib /tmp/check && docker rm $CID
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:
@@ -236,66 +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), also declare the matching symbol in `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:
@@ -310,36 +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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@@ -10,7 +10,7 @@ Let's say the user wants to build a particular backend for a given platform. For
- At a minimum we need to set the BUILD_TYPE, BASE_IMAGE build-args
- 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$(nproc --ignore=1) BUILD_TYPE=hipblas BASE_IMAGE=rocm/dev-ubuntu-24.04:7.2.1 make docker-build-coqui`
- You can pretty print a command like `DOCKER_MAKEFLAGS=-j$(nproc --ignore=1) BUILD_TYPE=hipblas BASE_IMAGE=rocm/dev-ubuntu-24.04:6.4.4 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.
- Sometimes the user may need extra parameters to be added to `docker build` (e.g. `--platform` for cross-platform builds or `--progress` to view the full logs), in which case you can generate the `docker build` command directly.

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@@ -1,111 +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. This file explains how that cache is laid out, what invalidates it, and how to bypass it.
## Cache layout
- **Cache registry**: `quay.io/go-skynet/ci-cache`
- **One tag per matrix entry**, derived from the existing `tag-suffix`:
- Backend builds (`backend_build.yml`): `cache<tag-suffix>`
- e.g. `cache-gpu-nvidia-cuda-12-llama-cpp`, `cache-cpu-vllm`, `cache-nvidia-l4t-cuda-13-arm64-vllm`
- Root image builds (`image_build.yml`): `cache-localai<tag-suffix>`
- e.g. `cache-localai-gpu-nvidia-cuda-12`, `cache-localai-gpu-vulkan`
- Each tag stores a multi-arch BuildKit cache manifest (`mode=max`), so every intermediate stage is re-usable, not just the final image.
## Read/write semantics
| Trigger | `cache-from` | `cache-to` |
|---|---|---|
| `push` to `master` / tag | 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.
## Self-warming, no separate populator
There is no cron job that pre-warms the cache. The production builds *are* the populator. 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 `Dockerfile.{llama-cpp,ik-llama-cpp,turboquant}`, Python wheel installs, etc.).
Historically there was a `generate_grpc_cache.yaml` cron that targeted a `grpc` stage in the root Dockerfile. That stage was removed in July 2025 and the cron silently failed every night for 9 months without writing anything. It was deleted along with the registry-cache rollout.
## 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-W17`) 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 (`Dockerfile.{llama-cpp,ik-llama-cpp,turboquant}`) clone gRPC at a pinned tag (`v1.65.0`) and llama.cpp at a pinned commit; their inputs don't drift between rebuilds.
### Adjusting the cadence
If you need a faster refresh (e.g. while debugging an upstream flake), bump the format to daily (`+%Y-%m-%d`) or hourly (`+%Y-%m-%d-%H`). If you need a one-shot rebuild for a specific backend without changing the schedule, append a marker to the tag-suffix in the matrix or temporarily delete that backend's cache tag in quay.
## 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
# 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 so a stale tag never bleeds into a different build.
## What the cache **does not** cover
- The "Free Disk Space" / "Release space from worker" steps run on every job — these reclaim ~6 GB on `ubuntu-latest` runners. They are runner-state cleanup, not Docker, and BuildKit caches don't apply.
- Intermediate artifacts of `Build and push (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.
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.
### 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.
## Touching the cache pipeline
When changing `image_build.yml`, `backend_build.yml`, or any of the `backend/Dockerfile.*` files:
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` unique per matrix entry** — it's the cache namespace. Two matrix entries sharing a tag-suffix 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.

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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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@@ -1,97 +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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@@ -1,115 +0,0 @@
# Working on the vLLM Backend
The vLLM backend lives at `backend/python/vllm/backend.py` (async gRPC) and the multimodal variant at `backend/python/vllm-omni/backend.py` (sync gRPC). Both wrap vLLM's `AsyncLLMEngine` / `Omni` and translate the LocalAI gRPC `PredictOptions` into vLLM `SamplingParams` + outputs into `Reply.chat_deltas`.
This file captures the non-obvious bits — most of the bring-up was a single PR (`feat/vllm-parity`) and the things below are easy to get wrong.
## Tool calling and reasoning use vLLM's *native* parsers
Do not write regex-based tool-call extractors for vLLM. vLLM ships:
- `vllm.tool_parsers.ToolParserManager` — 50+ registered parsers (`hermes`, `llama3_json`, `llama4_pythonic`, `mistral`, `qwen3_xml`, `deepseek_v3`, `granite4`, `openai`, `kimi_k2`, `glm45`, …)
- `vllm.reasoning.ReasoningParserManager` — 25+ registered parsers (`deepseek_r1`, `qwen3`, `mistral`, `gemma4`, …)
Both can be used standalone: instantiate with a tokenizer, call `extract_tool_calls(text, request=None)` / `extract_reasoning(text, request=None)`. The backend stores the parser *classes* on `self.tool_parser_cls` / `self.reasoning_parser_cls` at LoadModel time and instantiates them per request.
**Selection:** vLLM does *not* auto-detect parsers from model name — neither does the LocalAI backend. The user (or `core/config/hooks_vllm.go`) must pick one and pass it via `Options[]`:
```yaml
options:
- tool_parser:hermes
- reasoning_parser:qwen3
```
Auto-defaults for known model families live in `core/config/parser_defaults.json` and are applied:
- at gallery import time by `core/gallery/importers/vllm.go`
- at model load time by the `vllm` / `vllm-omni` backend hook in `core/config/hooks_vllm.go`
User-supplied `tool_parser:`/`reasoning_parser:` in the config wins over defaults — the hook checks for existing entries before appending.
**When to update `parser_defaults.json`:** any time vLLM ships a new tool or reasoning parser, or you onboard a new model family that LocalAI users will pull from HuggingFace. The file is keyed by *family pattern* matched against `normalizeModelID(cfg.Model)` (lowercase, org-prefix stripped, `_``-`). Patterns are checked **longest-first** — keep `qwen3.5` before `qwen3`, `llama-3.3` before `llama-3`, etc., or the wrong family wins. Add a covering test in `core/config/hooks_test.go`.
**Sister file — `core/config/inference_defaults.json`:** same pattern but for sampling parameters (temperature, top_p, top_k, min_p, repeat_penalty, presence_penalty). Loaded by `core/config/inference_defaults.go` and applied by `ApplyInferenceDefaults()`. The schema is `map[string]float64` only — *strings don't fit*, which is why parser defaults needed their own JSON file. The inference file is **auto-generated from unsloth** via `go generate ./core/config/` (see `core/config/gen_inference_defaults/`) — don't hand-edit it; instead update the upstream source or regenerate. Both files share `normalizeModelID()` and the longest-first pattern ordering.
**Constructor compatibility gotcha:** the abstract `ToolParser.__init__` accepts `tools=`, but several concrete parsers (Hermes2ProToolParser, etc.) override `__init__` and *only* accept `tokenizer`. Always:
```python
try:
tp = self.tool_parser_cls(self.tokenizer, tools=tools)
except TypeError:
tp = self.tool_parser_cls(self.tokenizer)
```
## ChatDelta is the streaming contract
The Go side (`core/backend/llm.go`, `pkg/functions/chat_deltas.go`) consumes `Reply.chat_deltas` to assemble the OpenAI response. For tool calls to surface in `chat/completions`, the Python backend **must** populate `Reply.chat_deltas[].tool_calls` with `ToolCallDelta{index, id, name, arguments}`. Returning the raw `<tool_call>...</tool_call>` text in `Reply.message` is *not* enough — the Go regex fallback exists for llama.cpp, not for vllm.
Same story for `reasoning_content` — emit it on `ChatDelta.reasoning_content`, not as part of `content`.
## Message conversion to chat templates
`tokenizer.apply_chat_template()` expects a list of dicts, not proto Messages. The shared helper in `backend/python/common/vllm_utils.py` (`messages_to_dicts`) handles the mapping including:
- `tool_call_id` and `name` for `role="tool"` messages
- `tool_calls` JSON-string field → parsed Python list for `role="assistant"`
- `reasoning_content` for thinking models
Pass `tools=json.loads(request.Tools)` and (when `request.Metadata.get("enable_thinking") == "true"`) `enable_thinking=True` to `apply_chat_template`. Wrap in `try/except TypeError` because not every tokenizer template accepts those kwargs.
## CPU support and the SIMD/library minefield
vLLM publishes prebuilt CPU wheels at `https://github.com/vllm-project/vllm/releases/...`. The pin lives in `backend/python/vllm/requirements-cpu-after.txt`.
**Version compatibility — important:** newer vllm CPU wheels (≥ 0.15) declare `torch==2.10.0+cpu` as a hard dep, but `torch==2.10.0` only exists on the PyTorch test channel and pulls in an incompatible `torchvision`. Stay on **`vllm 0.14.1+cpu` + `torch 2.9.1+cpu`** until both upstream catch up. Bumping requires verifying torchvision/torchaudio match.
`requirements-cpu.txt` uses `--extra-index-url https://download.pytorch.org/whl/cpu`. `install.sh` adds `--index-strategy=unsafe-best-match` for the `cpu` profile so uv resolves transformers/vllm from PyPI while pulling torch from the PyTorch index.
**SIMD baseline:** the prebuilt CPU wheel is compiled with AVX-512 VNNI/BF16. On a CPU without those instructions, importing `vllm.model_executor.models.registry` SIGILLs at `_run_in_subprocess` time during model inspection. There is no runtime flag to disable it. Workarounds:
1. **Run on a host with the right SIMD baseline** (default — fast)
2. **Build from source** with `FROM_SOURCE=true` env var. Plumbing exists end-to-end:
- `install.sh` hides `requirements-cpu-after.txt`, runs `installRequirements` for the base deps, then clones vllm and `VLLM_TARGET_DEVICE=cpu uv pip install --no-deps .`
- `backend/Dockerfile.python` declares `ARG FROM_SOURCE` + `ENV FROM_SOURCE`
- `Makefile` `docker-build-backend` macro forwards `--build-arg FROM_SOURCE=$(FROM_SOURCE)` when set
- Source build takes 3050 minutes — too slow for per-PR CI but fine for local.
**Runtime shared libraries:** vLLM's `vllm._C` extension `dlopen`s `libnuma.so.1` at import time. If missing, the C extension silently fails and `torch.ops._C_utils.init_cpu_threads_env` is never registered → `EngineCore` crashes on `init_device` with:
```
AttributeError: '_OpNamespace' '_C_utils' object has no attribute 'init_cpu_threads_env'
```
`backend/python/vllm/package.sh` bundles `libnuma.so.1` and `libgomp.so.1` into `${BACKEND}/lib/`, which `libbackend.sh` adds to `LD_LIBRARY_PATH` at run time. The builder stage in `backend/Dockerfile.python` installs `libnuma1`/`libgomp1` so package.sh has something to copy. Do *not* assume the production host has these — backend images are `FROM scratch`.
## Backend hook system (`core/config/backend_hooks.go`)
Per-backend defaults that used to be hardcoded in `ModelConfig.Prepare()` now live in `core/config/hooks_*.go` files and self-register via `init()`:
- `hooks_llamacpp.go` → GGUF metadata parsing, context size, GPU layers, jinja template
- `hooks_vllm.go` → tool/reasoning parser auto-selection from `parser_defaults.json`
Hook keys:
- `"llama-cpp"`, `"vllm"`, `"vllm-omni"`, … — backend-specific
- `""` — runs only when `cfg.Backend` is empty (auto-detect case)
- `"*"` — global catch-all, runs for every backend before specific hooks
Multiple hooks per key are supported and run in registration order. Adding a new backend default:
```go
// core/config/hooks_<backend>.go
func init() {
RegisterBackendHook("<backend>", myDefaults)
}
func myDefaults(cfg *ModelConfig, modelPath string) {
// only fill in fields the user didn't set
}
```
## The `Messages.ToProto()` fields you need to set
`core/schema/message.go:ToProto()` must serialize:
- `ToolCallID``proto.Message.ToolCallId` (for `role="tool"` messages — links result back to the call)
- `Reasoning``proto.Message.ReasoningContent`
- `ToolCalls``proto.Message.ToolCalls` (JSON-encoded string)
These were originally not serialized and tool-calling conversations broke silently — the C++ llama.cpp backend reads them but always got empty strings. Any new field added to `schema.Message` *and* `proto.Message` needs a matching line in `ToProto()`.

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@@ -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

@@ -1,91 +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
default: 'https://mirrors.edge.kernel.org'
self-hosted-ports-mirror:
description: 'ports.ubuntu.com mirror URL for self-hosted runners (empty = upstream)'
required: false
default: 'https://mirrors.edge.kernel.org'
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

@@ -1,45 +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"

446
.github/gallery-agent/agent.go vendored Normal file
View File

@@ -0,0 +1,446 @@
package main
import (
"context"
"encoding/json"
"fmt"
"io"
"net/http"
"os"
"regexp"
"slices"
"strings"
"github.com/ghodss/yaml"
hfapi "github.com/mudler/LocalAI/pkg/huggingface-api"
"github.com/mudler/cogito"
"github.com/mudler/cogito/clients"
"github.com/mudler/cogito/structures"
"github.com/sashabaranov/go-openai/jsonschema"
)
var (
openAIModel = os.Getenv("OPENAI_MODEL")
openAIKey = os.Getenv("OPENAI_KEY")
openAIBaseURL = os.Getenv("OPENAI_BASE_URL")
galleryIndexPath = os.Getenv("GALLERY_INDEX_PATH")
//defaultclient
llm = clients.NewOpenAILLM(openAIModel, openAIKey, openAIBaseURL)
)
// cleanTextContent removes trailing spaces, tabs, and normalizes line endings
// to prevent YAML linting issues like trailing spaces and multiple empty lines
func cleanTextContent(text string) string {
lines := strings.Split(text, "\n")
var cleanedLines []string
var prevEmpty bool
for _, line := range lines {
// Remove all trailing whitespace (spaces, tabs, etc.)
trimmed := strings.TrimRight(line, " \t\r")
// Avoid multiple consecutive empty lines
if trimmed == "" {
if !prevEmpty {
cleanedLines = append(cleanedLines, "")
}
prevEmpty = true
} else {
cleanedLines = append(cleanedLines, trimmed)
prevEmpty = false
}
}
// Remove trailing empty lines from the result
result := strings.Join(cleanedLines, "\n")
return stripThinkingTags(strings.TrimRight(result, "\n"))
}
type galleryModel struct {
Name string `yaml:"name"`
Urls []string `yaml:"urls"`
}
// isModelExisting checks if a specific model ID exists in the gallery using text search
func isModelExisting(modelID string) (bool, error) {
indexPath := getGalleryIndexPath()
content, err := os.ReadFile(indexPath)
if err != nil {
return false, fmt.Errorf("failed to read %s: %w", indexPath, err)
}
var galleryModels []galleryModel
err = yaml.Unmarshal(content, &galleryModels)
if err != nil {
return false, fmt.Errorf("failed to unmarshal %s: %w", indexPath, err)
}
for _, galleryModel := range galleryModels {
if slices.Contains(galleryModel.Urls, modelID) {
return true, nil
}
}
return false, nil
}
// filterExistingModels removes models that already exist in the gallery
func filterExistingModels(models []ProcessedModel) ([]ProcessedModel, error) {
var filteredModels []ProcessedModel
for _, model := range models {
exists, err := isModelExisting(model.ModelID)
if err != nil {
fmt.Printf("Error checking if model %s exists: %v, skipping\n", model.ModelID, err)
continue
}
if !exists {
filteredModels = append(filteredModels, model)
} else {
fmt.Printf("Skipping existing model: %s\n", model.ModelID)
}
}
fmt.Printf("Filtered out %d existing models, %d new models remaining\n",
len(models)-len(filteredModels), len(filteredModels))
return filteredModels, nil
}
// getGalleryIndexPath returns the gallery index file path, with a default fallback
func getGalleryIndexPath() string {
if galleryIndexPath != "" {
return galleryIndexPath
}
return "gallery/index.yaml"
}
func stripThinkingTags(content string) string {
// Remove content between <thinking> and </thinking> (including multi-line)
content = regexp.MustCompile(`(?s)<thinking>.*?</thinking>`).ReplaceAllString(content, "")
// Remove content between <think> and </think> (including multi-line)
content = regexp.MustCompile(`(?s)<think>.*?</think>`).ReplaceAllString(content, "")
// Clean up any extra whitespace
content = strings.TrimSpace(content)
return content
}
func getRealReadme(ctx context.Context, repository string) (string, error) {
// Create a conversation fragment
fragment := cogito.NewEmptyFragment().
AddMessage("user",
`Your task is to get a clear description of a large language model from huggingface by using the provided tool. I will share with you a repository that might be quantized, and as such probably not by the original model author. We need to get the real description of the model, and not the one that might be quantized. You will have to call the tool to get the readme more than once by figuring out from the quantized readme which is the base model readme. This is the repository: `+repository)
// Execute with tools
result, err := cogito.ExecuteTools(llm, fragment,
cogito.WithIterations(3),
cogito.WithMaxAttempts(3),
cogito.DisableSinkState,
cogito.WithTools(&HFReadmeTool{client: hfapi.NewClient()}))
if err != nil {
return "", err
}
result = result.AddMessage("user", "Describe the model in a clear and concise way that can be shared in a model gallery.")
// Get a response
_, err = llm.Ask(ctx, result)
if err != nil {
return "", err
}
content := result.LastMessage().Content
return cleanTextContent(content), nil
}
func selectMostInterestingModels(ctx context.Context, searchResult *SearchResult) ([]ProcessedModel, error) {
if len(searchResult.Models) == 1 {
return searchResult.Models, nil
}
// Create a conversation fragment
fragment := cogito.NewEmptyFragment().
AddMessage("user",
`Your task is to analyze a list of AI models and select the most interesting ones for a model gallery. You will be given detailed information about multiple models including their metadata, file information, and README content.
Consider the following criteria when selecting models:
1. Model popularity (download count)
2. Model recency (last modified date)
3. Model completeness (has preferred model file, README, etc.)
4. Model uniqueness (not duplicates or very similar models)
5. Model quality (based on README content and description)
6. Model utility (practical applications)
You should select models that would be most valuable for users browsing a model gallery. Prioritize models that are:
- Well-documented with clear READMEs
- Recently updated
- Popular (high download count)
- Have the preferred quantization format available
- Offer unique capabilities or are from reputable authors
Return your analysis and selection reasoning.`)
// Add the search results as context
modelsInfo := fmt.Sprintf("Found %d models matching '%s' with quantization preference '%s':\n\n",
searchResult.TotalModelsFound, searchResult.SearchTerm, searchResult.Quantization)
for i, model := range searchResult.Models {
modelsInfo += fmt.Sprintf("Model %d:\n", i+1)
modelsInfo += fmt.Sprintf(" ID: %s\n", model.ModelID)
modelsInfo += fmt.Sprintf(" Author: %s\n", model.Author)
modelsInfo += fmt.Sprintf(" Downloads: %d\n", model.Downloads)
modelsInfo += fmt.Sprintf(" Last Modified: %s\n", model.LastModified)
modelsInfo += fmt.Sprintf(" Files: %d files\n", len(model.Files))
if model.PreferredModelFile != nil {
modelsInfo += fmt.Sprintf(" Preferred Model File: %s (%d bytes)\n",
model.PreferredModelFile.Path, model.PreferredModelFile.Size)
} else {
modelsInfo += " No preferred model file found\n"
}
if model.ReadmeContent != "" {
modelsInfo += fmt.Sprintf(" README: %s\n", model.ReadmeContent)
}
if model.ProcessingError != "" {
modelsInfo += fmt.Sprintf(" Processing Error: %s\n", model.ProcessingError)
}
modelsInfo += "\n"
}
fragment = fragment.AddMessage("user", modelsInfo)
fragment = fragment.AddMessage("user", "Based on your analysis, select the top 5 most interesting models and provide a brief explanation for each selection. Also, create a filtered SearchResult with only the selected models. Return just a list of repositories IDs, you will later be asked to output it as a JSON array with the json tool.")
// Get a response
newFragment, err := llm.Ask(ctx, fragment)
if err != nil {
return nil, err
}
fmt.Println(newFragment.LastMessage().Content)
repositories := struct {
Repositories []string `json:"repositories"`
}{}
s := structures.Structure{
Schema: jsonschema.Definition{
Type: jsonschema.Object,
AdditionalProperties: false,
Properties: map[string]jsonschema.Definition{
"repositories": {
Type: jsonschema.Array,
Items: &jsonschema.Definition{Type: jsonschema.String},
Description: "The trending repositories IDs",
},
},
Required: []string{"repositories"},
},
Object: &repositories,
}
err = newFragment.ExtractStructure(ctx, llm, s)
if err != nil {
return nil, err
}
filteredModels := []ProcessedModel{}
for _, m := range searchResult.Models {
if slices.Contains(repositories.Repositories, m.ModelID) {
filteredModels = append(filteredModels, m)
}
}
return filteredModels, nil
}
// ModelMetadata represents extracted metadata from a model
type ModelMetadata struct {
Tags []string `json:"tags"`
License string `json:"license"`
}
// extractModelMetadata extracts tags and license from model README and documentation
func extractModelMetadata(ctx context.Context, model ProcessedModel) ([]string, string, error) {
// Create a conversation fragment
fragment := cogito.NewEmptyFragment().
AddMessage("user",
`Your task is to extract metadata from an AI model's README and documentation. You will be provided with:
1. Model information (ID, author, description)
2. README content
You need to extract:
1. **Tags**: An array of relevant tags that describe the model. Use common tags from the gallery such as:
- llm, gguf, gpu, cpu, multimodal, image-to-text, text-to-text, text-to-speech, tts
- thinking, reasoning, chat, instruction-tuned, code, vision
- Model family names (e.g., llama, qwen, mistral, gemma) if applicable
- Any other relevant descriptive tags
Select 3-8 most relevant tags.
2. **License**: The license identifier (e.g., "apache-2.0", "mit", "llama2", "gpl-3.0", "bsd", "cc-by-4.0").
If no license is found, return an empty string.
Return the extracted metadata in a structured format.`)
// Add model information
modelInfo := "Model Information:\n"
modelInfo += fmt.Sprintf(" ID: %s\n", model.ModelID)
modelInfo += fmt.Sprintf(" Author: %s\n", model.Author)
modelInfo += fmt.Sprintf(" Downloads: %d\n", model.Downloads)
if model.ReadmeContent != "" {
modelInfo += fmt.Sprintf(" README Content:\n%s\n", model.ReadmeContent)
} else if model.ReadmeContentPreview != "" {
modelInfo += fmt.Sprintf(" README Preview: %s\n", model.ReadmeContentPreview)
}
fragment = fragment.AddMessage("user", modelInfo)
fragment = fragment.AddMessage("user", "Extract the tags and license from the model information. Return the metadata as a JSON object with 'tags' (array of strings) and 'license' (string).")
// Get a response
newFragment, err := llm.Ask(ctx, fragment)
if err != nil {
return nil, "", err
}
// Extract structured metadata
metadata := ModelMetadata{}
s := structures.Structure{
Schema: jsonschema.Definition{
Type: jsonschema.Object,
AdditionalProperties: false,
Properties: map[string]jsonschema.Definition{
"tags": {
Type: jsonschema.Array,
Items: &jsonschema.Definition{Type: jsonschema.String},
Description: "Array of relevant tags describing the model",
},
"license": {
Type: jsonschema.String,
Description: "License identifier (e.g., apache-2.0, mit, llama2). Empty string if not found.",
},
},
Required: []string{"tags", "license"},
},
Object: &metadata,
}
err = newFragment.ExtractStructure(ctx, llm, s)
if err != nil {
return nil, "", err
}
return metadata.Tags, metadata.License, nil
}
// extractIconFromReadme scans the README content for image URLs and returns the first suitable icon URL found
func extractIconFromReadme(readmeContent string) string {
if readmeContent == "" {
return ""
}
// Regular expressions to match image URLs in various formats (case-insensitive)
// Match markdown image syntax: ![alt](url) - case insensitive extensions
markdownImageRegex := regexp.MustCompile(`(?i)!\[[^\]]*\]\(([^)]+\.(png|jpg|jpeg|svg|webp|gif))\)`)
// Match HTML img tags: <img src="url">
htmlImageRegex := regexp.MustCompile(`(?i)<img[^>]+src=["']([^"']+\.(png|jpg|jpeg|svg|webp|gif))["']`)
// Match plain URLs ending with image extensions
plainImageRegex := regexp.MustCompile(`(?i)https?://[^\s<>"']+\.(png|jpg|jpeg|svg|webp|gif)`)
// Try markdown format first
matches := markdownImageRegex.FindStringSubmatch(readmeContent)
if len(matches) > 1 && matches[1] != "" {
url := strings.TrimSpace(matches[1])
// Prefer HuggingFace CDN URLs or absolute URLs
if strings.HasPrefix(strings.ToLower(url), "http") {
return url
}
}
// Try HTML img tags
matches = htmlImageRegex.FindStringSubmatch(readmeContent)
if len(matches) > 1 && matches[1] != "" {
url := strings.TrimSpace(matches[1])
if strings.HasPrefix(strings.ToLower(url), "http") {
return url
}
}
// Try plain URLs
matches = plainImageRegex.FindStringSubmatch(readmeContent)
if len(matches) > 0 {
url := strings.TrimSpace(matches[0])
if strings.HasPrefix(strings.ToLower(url), "http") {
return url
}
}
return ""
}
// getHuggingFaceAvatarURL attempts to get the HuggingFace avatar URL for a user
func getHuggingFaceAvatarURL(author string) string {
if author == "" {
return ""
}
// Try to fetch user info from HuggingFace API
// HuggingFace API endpoint: https://huggingface.co/api/users/{username}
baseURL := "https://huggingface.co"
userURL := fmt.Sprintf("%s/api/users/%s", baseURL, author)
req, err := http.NewRequest("GET", userURL, nil)
if err != nil {
return ""
}
client := &http.Client{}
resp, err := client.Do(req)
if err != nil {
return ""
}
defer resp.Body.Close()
if resp.StatusCode != http.StatusOK {
return ""
}
// Parse the response to get avatar URL
var userInfo map[string]any
body, err := io.ReadAll(resp.Body)
if err != nil {
return ""
}
if err := json.Unmarshal(body, &userInfo); err != nil {
return ""
}
// Try to extract avatar URL from response
if avatar, ok := userInfo["avatarUrl"].(string); ok && avatar != "" {
return avatar
}
if avatar, ok := userInfo["avatar"].(string); ok && avatar != "" {
return avatar
}
return ""
}
// extractModelIcon extracts icon URL from README or falls back to HuggingFace avatar
func extractModelIcon(model ProcessedModel) string {
// First, try to extract icon from README
if icon := extractIconFromReadme(model.ReadmeContent); icon != "" {
return icon
}
// Fallback: Try to get HuggingFace user avatar
if model.Author != "" {
if avatar := getHuggingFaceAvatarURL(model.Author); avatar != "" {
return avatar
}
}
return ""
}

View File

@@ -7,8 +7,8 @@ import (
"os"
"strings"
"github.com/ghodss/yaml"
"github.com/mudler/LocalAI/core/gallery/importers"
"sigs.k8s.io/yaml"
)
func formatTextContent(text string) string {

View File

@@ -1,301 +0,0 @@
package main
import (
"encoding/json"
"fmt"
"io"
"net/http"
"os"
"regexp"
"strings"
hfapi "github.com/mudler/LocalAI/pkg/huggingface-api"
"sigs.k8s.io/yaml"
)
var galleryIndexPath = os.Getenv("GALLERY_INDEX_PATH")
// getGalleryIndexPath returns the gallery index file path, with a default fallback
func getGalleryIndexPath() string {
if galleryIndexPath != "" {
return galleryIndexPath
}
return "gallery/index.yaml"
}
type galleryModel struct {
Name string `yaml:"name"`
Urls []string `yaml:"urls"`
}
// loadGalleryURLSet parses gallery/index.yaml once and returns the set of
// HuggingFace model URLs already present in the gallery.
func loadGalleryURLSet() (map[string]struct{}, error) {
indexPath := getGalleryIndexPath()
content, err := os.ReadFile(indexPath)
if err != nil {
return nil, fmt.Errorf("failed to read %s: %w", indexPath, err)
}
var galleryModels []galleryModel
if err := yaml.Unmarshal(content, &galleryModels); err != nil {
return nil, fmt.Errorf("failed to unmarshal %s: %w", indexPath, err)
}
set := make(map[string]struct{}, len(galleryModels))
for _, gm := range galleryModels {
for _, u := range gm.Urls {
set[u] = struct{}{}
}
}
// Also skip URLs already proposed in open (unmerged) gallery-agent PRs.
// The workflow injects these via EXTRA_SKIP_URLS so we don't keep
// re-proposing the same model every run while a PR is waiting to merge.
for _, line := range strings.FieldsFunc(os.Getenv("EXTRA_SKIP_URLS"), func(r rune) bool {
return r == '\n' || r == ',' || r == ' '
}) {
u := strings.TrimSpace(line)
if u != "" {
set[u] = struct{}{}
}
}
return set, nil
}
// modelAlreadyInGallery checks whether a HuggingFace model repo is already
// referenced in the gallery URL set.
func modelAlreadyInGallery(set map[string]struct{}, modelID string) bool {
_, ok := set["https://huggingface.co/"+modelID]
return ok
}
// baseModelFromTags returns the first `base_model:<repo>` value found in the
// tag list, or "" if none is present. HuggingFace surfaces the base model
// declared in the model card's YAML frontmatter as such a tag.
func baseModelFromTags(tags []string) string {
for _, t := range tags {
if strings.HasPrefix(t, "base_model:") {
return strings.TrimPrefix(t, "base_model:")
}
}
return ""
}
// licenseFromTags returns the `license:<id>` value from the tag list, or "".
func licenseFromTags(tags []string) string {
for _, t := range tags {
if strings.HasPrefix(t, "license:") {
return strings.TrimPrefix(t, "license:")
}
}
return ""
}
// curatedTags produces the gallery tag list from HuggingFace's raw tag set.
// Always includes llm + gguf, then adds whitelisted family / capability
// markers when they appear in the HF tag list.
func curatedTags(hfTags []string) []string {
whitelist := []string{
"gpu", "cpu",
"llama", "mistral", "mixtral", "qwen", "qwen2", "qwen3",
"gemma", "gemma2", "gemma3", "phi", "phi3", "phi4",
"deepseek", "yi", "falcon", "command-r",
"vision", "multimodal", "code", "chat",
"instruction-tuned", "reasoning", "thinking",
}
seen := map[string]struct{}{}
out := []string{"llm", "gguf"}
seen["llm"] = struct{}{}
seen["gguf"] = struct{}{}
hfSet := map[string]struct{}{}
for _, t := range hfTags {
hfSet[strings.ToLower(t)] = struct{}{}
}
for _, w := range whitelist {
if _, ok := hfSet[w]; ok {
if _, dup := seen[w]; !dup {
out = append(out, w)
seen[w] = struct{}{}
}
}
}
return out
}
// resolveReadme fetches a description-quality README for a (possibly
// quantized) repo: if a `base_model:` tag is present, fetch the base repo's
// README; otherwise fall back to the repo's own README.
func resolveReadme(client *hfapi.Client, modelID string, hfTags []string) (string, error) {
if base := baseModelFromTags(hfTags); base != "" && base != modelID {
if content, err := client.GetReadmeContent(base, "README.md"); err == nil && strings.TrimSpace(content) != "" {
return cleanTextContent(content), nil
}
}
content, err := client.GetReadmeContent(modelID, "README.md")
if err != nil {
return "", err
}
return cleanTextContent(content), nil
}
// extractDescription turns a raw HuggingFace README into a concise plain-text
// description suitable for embedding in gallery/index.yaml: strips YAML
// frontmatter, HTML tags/comments, markdown images, link URLs (keeping the
// link text), markdown tables, and then truncates at a paragraph boundary
// around ~1200 characters. Raw README should still be used for icon
// extraction — call this only for the `description:` field.
func extractDescription(readme string) string {
s := readme
// Strip leading YAML frontmatter: `---\n...\n---\n` at start of file.
if strings.HasPrefix(strings.TrimLeft(s, " \t\n"), "---") {
trimmed := strings.TrimLeft(s, " \t\n")
rest := strings.TrimPrefix(trimmed, "---")
if idx := strings.Index(rest, "\n---"); idx >= 0 {
after := rest[idx+len("\n---"):]
after = strings.TrimPrefix(after, "\n")
s = after
}
}
// Strip HTML comments and tags.
s = regexp.MustCompile(`(?s)<!--.*?-->`).ReplaceAllString(s, "")
s = regexp.MustCompile(`(?is)<[^>]+>`).ReplaceAllString(s, "")
// Strip markdown images entirely.
s = regexp.MustCompile(`!\[[^\]]*\]\([^)]*\)`).ReplaceAllString(s, "")
// Replace markdown links `[text](url)` with just `text`.
s = regexp.MustCompile(`\[([^\]]+)\]\([^)]+\)`).ReplaceAllString(s, "$1")
// Drop table lines and horizontal rules, and flatten all leading
// whitespace: generateYAMLEntry embeds this under a `description: |`
// literal block whose indentation is set by the first non-empty line.
// If any line has extra leading whitespace (e.g. from an indented
// `<p align="center">` block in the original README), YAML will pick
// that up as the block's indent and every later line at a smaller
// indent blows the block scalar. Stripping leading whitespace here
// guarantees uniform 4-space indentation after formatTextContent runs.
var kept []string
for _, line := range strings.Split(s, "\n") {
t := strings.TrimLeft(line, " \t")
ts := strings.TrimSpace(t)
if strings.HasPrefix(ts, "|") {
continue
}
if strings.HasPrefix(ts, ":--") || strings.HasPrefix(ts, "---") || strings.HasPrefix(ts, "===") {
continue
}
kept = append(kept, t)
}
s = strings.Join(kept, "\n")
// Normalise whitespace and drop any leading blank lines so the literal
// block in YAML doesn't start with a blank first line (which would
// break the indentation detector the same way).
s = cleanTextContent(s)
s = strings.TrimLeft(s, " \t\n")
// Truncate at a paragraph boundary around maxLen chars.
const maxLen = 1200
if len(s) > maxLen {
cut := strings.LastIndex(s[:maxLen], "\n\n")
if cut < maxLen/3 {
cut = maxLen
}
s = strings.TrimRight(s[:cut], " \t\n") + "\n\n..."
}
return s
}
// cleanTextContent removes trailing spaces/tabs and collapses multiple empty
// lines so README content embeds cleanly into YAML without lint noise.
func cleanTextContent(text string) string {
lines := strings.Split(text, "\n")
var cleaned []string
var prevEmpty bool
for _, line := range lines {
trimmed := strings.TrimRight(line, " \t\r")
if trimmed == "" {
if !prevEmpty {
cleaned = append(cleaned, "")
}
prevEmpty = true
} else {
cleaned = append(cleaned, trimmed)
prevEmpty = false
}
}
return strings.TrimRight(strings.Join(cleaned, "\n"), "\n")
}
// extractIconFromReadme scans README content for an image URL usable as a
// gallery entry icon.
func extractIconFromReadme(readmeContent string) string {
if readmeContent == "" {
return ""
}
markdownImageRegex := regexp.MustCompile(`(?i)!\[[^\]]*\]\(([^)]+\.(png|jpg|jpeg|svg|webp|gif))\)`)
htmlImageRegex := regexp.MustCompile(`(?i)<img[^>]+src=["']([^"']+\.(png|jpg|jpeg|svg|webp|gif))["']`)
plainImageRegex := regexp.MustCompile(`(?i)https?://[^\s<>"']+\.(png|jpg|jpeg|svg|webp|gif)`)
if m := markdownImageRegex.FindStringSubmatch(readmeContent); len(m) > 1 && strings.HasPrefix(strings.ToLower(m[1]), "http") {
return strings.TrimSpace(m[1])
}
if m := htmlImageRegex.FindStringSubmatch(readmeContent); len(m) > 1 && strings.HasPrefix(strings.ToLower(m[1]), "http") {
return strings.TrimSpace(m[1])
}
if m := plainImageRegex.FindStringSubmatch(readmeContent); len(m) > 0 && strings.HasPrefix(strings.ToLower(m[0]), "http") {
return strings.TrimSpace(m[0])
}
return ""
}
// getHuggingFaceAvatarURL returns the HF avatar URL for a user, or "".
func getHuggingFaceAvatarURL(author string) string {
if author == "" {
return ""
}
userURL := fmt.Sprintf("https://huggingface.co/api/users/%s/overview", author)
resp, err := http.Get(userURL)
if err != nil {
return ""
}
defer resp.Body.Close()
if resp.StatusCode != http.StatusOK {
return ""
}
body, err := io.ReadAll(resp.Body)
if err != nil {
return ""
}
var info map[string]any
if err := json.Unmarshal(body, &info); err != nil {
return ""
}
if v, ok := info["avatarUrl"].(string); ok && v != "" {
return v
}
if v, ok := info["avatar"].(string); ok && v != "" {
return v
}
return ""
}
// extractModelIcon extracts an icon URL from the README, falling back to the
// HuggingFace user avatar.
func extractModelIcon(model ProcessedModel) string {
if icon := extractIconFromReadme(model.ReadmeContent); icon != "" {
return icon
}
if model.Author != "" {
if avatar := getHuggingFaceAvatarURL(model.Author); avatar != "" {
return avatar
}
}
return ""
}

View File

@@ -6,6 +6,7 @@ import (
"fmt"
"os"
"strconv"
"strings"
"time"
hfapi "github.com/mudler/LocalAI/pkg/huggingface-api"
@@ -38,6 +39,16 @@ type ProcessedModel struct {
Icon string `json:"icon,omitempty"`
}
// SearchResult represents the complete result of searching and processing models
type SearchResult struct {
SearchTerm string `json:"search_term"`
Limit int `json:"limit"`
Quantization string `json:"quantization"`
TotalModelsFound int `json:"total_models_found"`
Models []ProcessedModel `json:"models"`
FormattedOutput string `json:"formatted_output"`
}
// AddedModelSummary represents a summary of models added to the gallery
type AddedModelSummary struct {
SearchTerm string `json:"search_term"`
@@ -52,16 +63,19 @@ type AddedModelSummary struct {
func main() {
startTime := time.Now()
// Synthetic mode for local testing
if sm := os.Getenv("SYNTHETIC_MODE"); sm == "true" || sm == "1" {
// Check for synthetic mode
syntheticMode := os.Getenv("SYNTHETIC_MODE")
if syntheticMode == "true" || syntheticMode == "1" {
fmt.Println("Running in SYNTHETIC MODE - generating random test data")
if err := runSyntheticMode(); err != nil {
err := runSyntheticMode()
if err != nil {
fmt.Fprintf(os.Stderr, "Error in synthetic mode: %v\n", err)
os.Exit(1)
}
return
}
// Get configuration from environment variables
searchTerm := os.Getenv("SEARCH_TERM")
if searchTerm == "" {
searchTerm = "GGUF"
@@ -69,7 +83,7 @@ func main() {
limitStr := os.Getenv("LIMIT")
if limitStr == "" {
limitStr = "15"
limitStr = "5"
}
limit, err := strconv.Atoi(limitStr)
if err != nil {
@@ -78,197 +92,287 @@ func main() {
}
quantization := os.Getenv("QUANTIZATION")
if quantization == "" {
quantization = "Q4_K_M"
}
maxModelsStr := os.Getenv("MAX_MODELS")
if maxModelsStr == "" {
maxModelsStr = "1"
maxModels := os.Getenv("MAX_MODELS")
if maxModels == "" {
maxModels = "1"
}
maxModels, err := strconv.Atoi(maxModelsStr)
maxModelsInt, err := strconv.Atoi(maxModels)
if err != nil {
fmt.Fprintf(os.Stderr, "Error parsing MAX_MODELS: %v\n", err)
os.Exit(1)
}
// Print configuration
fmt.Printf("Gallery Agent Configuration:\n")
fmt.Printf(" Search Term: %s\n", searchTerm)
fmt.Printf(" Limit: %d\n", limit)
fmt.Printf(" Quantization: %s\n", quantization)
fmt.Printf(" Max Models to Add: %d\n", maxModels)
fmt.Printf(" Gallery Index Path: %s\n", getGalleryIndexPath())
fmt.Printf(" Max Models to Add: %d\n", maxModelsInt)
fmt.Printf(" Gallery Index Path: %s\n", os.Getenv("GALLERY_INDEX_PATH"))
fmt.Println()
// Phase 1: load current gallery and query HuggingFace.
gallerySet, err := loadGalleryURLSet()
result, err := searchAndProcessModels(searchTerm, limit, quantization)
if err != nil {
fmt.Fprintf(os.Stderr, "Error loading gallery index: %v\n", err)
fmt.Fprintf(os.Stderr, "Error: %v\n", err)
os.Exit(1)
}
fmt.Printf("Loaded %d existing gallery entries\n", len(gallerySet))
client := hfapi.NewClient()
fmt.Println(result.FormattedOutput)
var models []ProcessedModel
fmt.Println("Searching for trending models on HuggingFace...")
rawModels, err := client.GetTrending(searchTerm, limit)
if err != nil {
fmt.Fprintf(os.Stderr, "Error fetching models: %v\n", err)
os.Exit(1)
}
fmt.Printf("Found %d trending models matching %q\n", len(rawModels), searchTerm)
totalFound := len(rawModels)
// Phase 2: drop anything already in the gallery *before* any expensive
// per-model work (GetModelDetails, README fetches, icon lookups).
fresh := rawModels[:0]
for _, m := range rawModels {
if modelAlreadyInGallery(gallerySet, m.ModelID) {
fmt.Printf("Skipping existing model: %s\n", m.ModelID)
continue
if len(result.Models) > 1 {
fmt.Println("More than one model found (", len(result.Models), "), using AI agent to select the most interesting models")
for _, model := range result.Models {
fmt.Println("Model: ", model.ModelID)
}
fresh = append(fresh, m)
// Use AI agent to select the most interesting models
fmt.Println("Using AI agent to select the most interesting models...")
models, err = selectMostInterestingModels(context.Background(), result)
if err != nil {
fmt.Fprintf(os.Stderr, "Error in model selection: %v\n", err)
// Continue with original result if selection fails
models = result.Models
}
} else if len(result.Models) == 1 {
models = result.Models
fmt.Println("Only one model found, using it directly")
}
fmt.Printf("%d candidates after gallery dedup\n", len(fresh))
// Phase 3: HuggingFace already returned these in trendingScore order —
// just cap to MAX_MODELS.
if len(fresh) > maxModels {
fresh = fresh[:maxModels]
fmt.Print(models)
// Filter out models that already exist in the gallery
fmt.Println("Filtering out existing models...")
models, err = filterExistingModels(models)
if err != nil {
fmt.Fprintf(os.Stderr, "Error filtering existing models: %v\n", err)
os.Exit(1)
}
if len(fresh) == 0 {
// Limit to maxModelsInt after filtering
if len(models) > maxModelsInt {
models = models[:maxModelsInt]
}
// Track added models for summary
var addedModelIDs []string
var addedModelURLs []string
// Generate YAML entries and append to gallery/index.yaml
if len(models) > 0 {
for _, model := range models {
addedModelIDs = append(addedModelIDs, model.ModelID)
// Generate Hugging Face URL for the model
modelURL := fmt.Sprintf("https://huggingface.co/%s", model.ModelID)
addedModelURLs = append(addedModelURLs, modelURL)
}
fmt.Println("Generating YAML entries for selected models...")
err = generateYAMLForModels(context.Background(), models, quantization)
if err != nil {
fmt.Fprintf(os.Stderr, "Error generating YAML entries: %v\n", err)
os.Exit(1)
}
} else {
fmt.Println("No new models to add to the gallery.")
writeSummary(AddedModelSummary{
SearchTerm: searchTerm,
TotalFound: totalFound,
ModelsAdded: 0,
Quantization: quantization,
ProcessingTime: time.Since(startTime).String(),
})
return
}
// Phase 4: fetch details and build ProcessedModel entries for survivors.
var processed []ProcessedModel
quantPrefs := []string{quantization, "Q4_K_M", "Q4_K_S", "Q3_K_M", "Q2_K", "Q8_0"}
for _, m := range fresh {
fmt.Printf("Processing model: %s (downloads=%d)\n", m.ModelID, m.Downloads)
pm := ProcessedModel{
ModelID: m.ModelID,
Author: m.Author,
Downloads: m.Downloads,
LastModified: m.LastModified,
QuantizationPreferences: quantPrefs,
}
details, err := client.GetModelDetails(m.ModelID)
if err != nil {
fmt.Printf(" Error getting model details: %v (skipping)\n", err)
continue
}
preferred := hfapi.FindPreferredModelFile(details.Files, quantPrefs)
if preferred == nil {
fmt.Printf(" No GGUF file matching %v — skipping\n", quantPrefs)
continue
}
pm.Files = make([]ProcessedModelFile, len(details.Files))
for j, f := range details.Files {
fileType := "other"
if f.IsReadme {
fileType = "readme"
} else if f.Path == preferred.Path {
fileType = "model"
}
pm.Files[j] = ProcessedModelFile{
Path: f.Path,
Size: f.Size,
SHA256: f.SHA256,
IsReadme: f.IsReadme,
FileType: fileType,
}
if f.Path == preferred.Path {
copyFile := pm.Files[j]
pm.PreferredModelFile = &copyFile
}
if f.IsReadme {
copyFile := pm.Files[j]
pm.ReadmeFile = &copyFile
}
}
// Deterministic README resolution: follow base_model tag if set.
// Keep the raw (HTML-bearing) README around while we extract the
// icon, then strip it down to a plain-text description for the
// `description:` YAML field.
readme, err := resolveReadme(client, m.ModelID, m.Tags)
if err != nil {
fmt.Printf(" Warning: failed to fetch README: %v\n", err)
}
pm.ReadmeContent = readme
pm.License = licenseFromTags(m.Tags)
pm.Tags = curatedTags(m.Tags)
pm.Icon = extractModelIcon(pm)
if pm.ReadmeContent != "" {
pm.ReadmeContent = extractDescription(pm.ReadmeContent)
pm.ReadmeContentPreview = truncateString(pm.ReadmeContent, 200)
}
fmt.Printf(" License: %s, Tags: %v, Icon: %s\n", pm.License, pm.Tags, pm.Icon)
processed = append(processed, pm)
}
if len(processed) == 0 {
fmt.Println("No processable models after detail fetch.")
writeSummary(AddedModelSummary{
SearchTerm: searchTerm,
TotalFound: totalFound,
ModelsAdded: 0,
Quantization: quantization,
ProcessingTime: time.Since(startTime).String(),
})
return
}
// Phase 5: write YAML entries.
var addedIDs, addedURLs []string
for _, pm := range processed {
addedIDs = append(addedIDs, pm.ModelID)
addedURLs = append(addedURLs, "https://huggingface.co/"+pm.ModelID)
}
fmt.Println("Generating YAML entries for selected models...")
if err := generateYAMLForModels(context.Background(), processed, quantization); err != nil {
fmt.Fprintf(os.Stderr, "Error generating YAML entries: %v\n", err)
os.Exit(1)
}
writeSummary(AddedModelSummary{
// Create and write summary
processingTime := time.Since(startTime).String()
summary := AddedModelSummary{
SearchTerm: searchTerm,
TotalFound: totalFound,
ModelsAdded: len(addedIDs),
AddedModelIDs: addedIDs,
AddedModelURLs: addedURLs,
TotalFound: result.TotalModelsFound,
ModelsAdded: len(addedModelIDs),
AddedModelIDs: addedModelIDs,
AddedModelURLs: addedModelURLs,
Quantization: quantization,
ProcessingTime: time.Since(startTime).String(),
})
}
ProcessingTime: processingTime,
}
func writeSummary(summary AddedModelSummary) {
data, err := json.MarshalIndent(summary, "", " ")
// Write summary to file
summaryData, err := json.MarshalIndent(summary, "", " ")
if err != nil {
fmt.Fprintf(os.Stderr, "Error marshaling summary: %v\n", err)
return
} else {
err = os.WriteFile("gallery-agent-summary.json", summaryData, 0644)
if err != nil {
fmt.Fprintf(os.Stderr, "Error writing summary file: %v\n", err)
} else {
fmt.Printf("Summary written to gallery-agent-summary.json\n")
}
}
if err := os.WriteFile("gallery-agent-summary.json", data, 0644); err != nil {
fmt.Fprintf(os.Stderr, "Error writing summary file: %v\n", err)
return
}
func searchAndProcessModels(searchTerm string, limit int, quantization string) (*SearchResult, error) {
client := hfapi.NewClient()
var outputBuilder strings.Builder
fmt.Println("Searching for models...")
// Initialize the result struct
result := &SearchResult{
SearchTerm: searchTerm,
Limit: limit,
Quantization: quantization,
Models: []ProcessedModel{},
}
fmt.Println("Summary written to gallery-agent-summary.json")
models, err := client.GetLatest(searchTerm, limit)
if err != nil {
return nil, fmt.Errorf("failed to fetch models: %w", err)
}
fmt.Println("Models found:", len(models))
result.TotalModelsFound = len(models)
if len(models) == 0 {
outputBuilder.WriteString("No models found.\n")
result.FormattedOutput = outputBuilder.String()
return result, nil
}
outputBuilder.WriteString(fmt.Sprintf("Found %d models matching '%s':\n\n", len(models), searchTerm))
// Process each model
for i, model := range models {
outputBuilder.WriteString(fmt.Sprintf("%d. Processing Model: %s\n", i+1, model.ModelID))
outputBuilder.WriteString(fmt.Sprintf(" Author: %s\n", model.Author))
outputBuilder.WriteString(fmt.Sprintf(" Downloads: %d\n", model.Downloads))
outputBuilder.WriteString(fmt.Sprintf(" Last Modified: %s\n", model.LastModified))
// Initialize processed model struct
processedModel := ProcessedModel{
ModelID: model.ModelID,
Author: model.Author,
Downloads: model.Downloads,
LastModified: model.LastModified,
QuantizationPreferences: []string{quantization, "Q4_K_M", "Q4_K_S", "Q3_K_M", "Q2_K"},
}
// Get detailed model information
details, err := client.GetModelDetails(model.ModelID)
if err != nil {
errorMsg := fmt.Sprintf(" Error getting model details: %v\n", err)
outputBuilder.WriteString(errorMsg)
processedModel.ProcessingError = err.Error()
result.Models = append(result.Models, processedModel)
continue
}
// Define quantization preferences (in order of preference)
quantizationPreferences := []string{quantization, "Q4_K_M", "Q4_K_S", "Q3_K_M", "Q2_K"}
// Find preferred model file
preferredModelFile := hfapi.FindPreferredModelFile(details.Files, quantizationPreferences)
// Process files
processedFiles := make([]ProcessedModelFile, len(details.Files))
for j, file := range details.Files {
fileType := "other"
if file.IsReadme {
fileType = "readme"
} else if preferredModelFile != nil && file.Path == preferredModelFile.Path {
fileType = "model"
}
processedFiles[j] = ProcessedModelFile{
Path: file.Path,
Size: file.Size,
SHA256: file.SHA256,
IsReadme: file.IsReadme,
FileType: fileType,
}
}
processedModel.Files = processedFiles
// Set preferred model file
if preferredModelFile != nil {
for _, file := range processedFiles {
if file.Path == preferredModelFile.Path {
processedModel.PreferredModelFile = &file
break
}
}
}
// Print file information
outputBuilder.WriteString(fmt.Sprintf(" Files found: %d\n", len(details.Files)))
if preferredModelFile != nil {
outputBuilder.WriteString(fmt.Sprintf(" Preferred Model File: %s (SHA256: %s)\n",
preferredModelFile.Path,
preferredModelFile.SHA256))
} else {
outputBuilder.WriteString(fmt.Sprintf(" No model file found with quantization preferences: %v\n", quantizationPreferences))
}
if details.ReadmeFile != nil {
outputBuilder.WriteString(fmt.Sprintf(" README File: %s\n", details.ReadmeFile.Path))
// Find and set readme file
for _, file := range processedFiles {
if file.IsReadme {
processedModel.ReadmeFile = &file
break
}
}
fmt.Println("Getting real readme for", model.ModelID, "waiting...")
// Use agent to get the real readme and prepare the model description
readmeContent, err := getRealReadme(context.Background(), model.ModelID)
if err == nil {
processedModel.ReadmeContent = readmeContent
processedModel.ReadmeContentPreview = truncateString(readmeContent, 200)
outputBuilder.WriteString(fmt.Sprintf(" README Content Preview: %s\n",
processedModel.ReadmeContentPreview))
} else {
fmt.Printf(" Warning: Failed to get real readme: %v\n", err)
}
fmt.Println("Real readme got", readmeContent)
// Extract metadata (tags, license) from README using LLM
fmt.Println("Extracting metadata for", model.ModelID, "waiting...")
tags, license, err := extractModelMetadata(context.Background(), processedModel)
if err == nil {
processedModel.Tags = tags
processedModel.License = license
outputBuilder.WriteString(fmt.Sprintf(" Tags: %v\n", tags))
outputBuilder.WriteString(fmt.Sprintf(" License: %s\n", license))
} else {
fmt.Printf(" Warning: Failed to extract metadata: %v\n", err)
}
// Extract icon from README or use HuggingFace avatar
icon := extractModelIcon(processedModel)
if icon != "" {
processedModel.Icon = icon
outputBuilder.WriteString(fmt.Sprintf(" Icon: %s\n", icon))
}
// Get README content
// readmeContent, err := client.GetReadmeContent(model.ModelID, details.ReadmeFile.Path)
// if err == nil {
// processedModel.ReadmeContent = readmeContent
// processedModel.ReadmeContentPreview = truncateString(readmeContent, 200)
// outputBuilder.WriteString(fmt.Sprintf(" README Content Preview: %s\n",
// processedModel.ReadmeContentPreview))
// }
}
// Print all files with their checksums
outputBuilder.WriteString(" All Files:\n")
for _, file := range processedFiles {
outputBuilder.WriteString(fmt.Sprintf(" - %s (%s, %d bytes", file.Path, file.FileType, file.Size))
if file.SHA256 != "" {
outputBuilder.WriteString(fmt.Sprintf(", SHA256: %s", file.SHA256))
}
outputBuilder.WriteString(")\n")
}
outputBuilder.WriteString("\n")
result.Models = append(result.Models, processedModel)
}
result.FormattedOutput = outputBuilder.String()
return result, nil
}
func truncateString(s string, maxLen int) string {
@@ -277,4 +381,3 @@ func truncateString(s string, maxLen int) string {
}
return s[:maxLen] + "..."
}

46
.github/gallery-agent/tools.go vendored Normal file
View File

@@ -0,0 +1,46 @@
package main
import (
"fmt"
hfapi "github.com/mudler/LocalAI/pkg/huggingface-api"
openai "github.com/sashabaranov/go-openai"
jsonschema "github.com/sashabaranov/go-openai/jsonschema"
)
// Get repository README from HF
type HFReadmeTool struct {
client *hfapi.Client
}
func (s *HFReadmeTool) Execute(args map[string]any) (string, any, error) {
q, ok := args["repository"].(string)
if !ok {
return "", nil, fmt.Errorf("no query")
}
readme, err := s.client.GetReadmeContent(q, "README.md")
if err != nil {
return "", nil, err
}
return readme, nil, nil
}
func (s *HFReadmeTool) Tool() openai.Tool {
return openai.Tool{
Type: openai.ToolTypeFunction,
Function: &openai.FunctionDefinition{
Name: "hf_readme",
Description: "A tool to get the README content of a huggingface repository",
Parameters: jsonschema.Definition{
Type: jsonschema.Object,
Properties: map[string]jsonschema.Definition{
"repository": {
Type: jsonschema.String,
Description: "The huggingface repository to get the README content of",
},
},
Required: []string{"repository"},
},
},
}
}

View File

@@ -30,7 +30,6 @@ jobs:
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:
dockerUsername: ${{ secrets.DOCKERHUB_USERNAME }}
dockerPassword: ${{ secrets.DOCKERHUB_PASSWORD }}
@@ -54,32 +53,6 @@ jobs:
dockerfile: "./backend/Dockerfile.python"
context: "./"
ubuntu-version: '2204'
- build-type: ''
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-cpu-vllm'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'true'
backend: "vllm"
dockerfile: "./backend/Dockerfile.python"
context: "./"
ubuntu-version: '2404'
- build-type: ''
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-cpu-sglang'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'true'
backend: "sglang"
dockerfile: "./backend/Dockerfile.python"
context: "./"
ubuntu-version: '2404'
- build-type: ''
cuda-major-version: ""
cuda-minor-version: ""
@@ -119,30 +92,11 @@ jobs:
dockerfile: "./backend/Dockerfile.python"
context: "./"
ubuntu-version: '2404'
# tinygrad ships a single image — its CPU device uses bundled
# libLLVM, and its CUDA / HIP / Metal devices dlopen the host
# driver libraries at runtime via tinygrad's ctypes autogen
# wrappers. There is no toolkit-version split because tinygrad
# generates kernels itself (PTX renderer for CUDA) and never
# links against cuDNN/cuBLAS/torch.
- build-type: ''
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-tinygrad'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'true'
backend: "tinygrad"
dockerfile: "./backend/Dockerfile.python"
context: "./"
ubuntu-version: '2404'
- build-type: ''
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64,linux/arm64'
tag-latest: 'auto'
tag-suffix: '-cpu-whisperx'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
@@ -154,7 +108,7 @@ jobs:
- build-type: ''
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64,linux/arm64'
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-cpu-faster-whisper'
runs-on: 'ubuntu-latest'
@@ -386,19 +340,6 @@ jobs:
dockerfile: "./backend/Dockerfile.llama-cpp"
context: "./"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "8"
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-nvidia-cuda-12-turboquant'
runs-on: 'bigger-runner'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "turboquant"
dockerfile: "./backend/Dockerfile.turboquant"
context: "./"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "8"
@@ -425,19 +366,6 @@ jobs:
dockerfile: "./backend/Dockerfile.python"
context: "./"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "8"
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-nvidia-cuda-12-sglang'
runs-on: 'arc-runner-set'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "sglang"
dockerfile: "./backend/Dockerfile.python"
context: "./"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "8"
@@ -685,32 +613,6 @@ jobs:
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "8"
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-nvidia-cuda-12-qwen3-tts-cpp'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "qwen3-tts-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "8"
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-nvidia-cuda-12-vibevoice-cpp'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "vibevoice-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "8"
@@ -724,32 +626,6 @@ jobs:
dockerfile: "./backend/Dockerfile.python"
context: "./"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "8"
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-nvidia-cuda-12-insightface'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "insightface"
dockerfile: "./backend/Dockerfile.python"
context: "./"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "8"
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-nvidia-cuda-12-speaker-recognition'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "speaker-recognition"
dockerfile: "./backend/Dockerfile.python"
context: "./"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "8"
@@ -894,19 +770,6 @@ jobs:
dockerfile: "./backend/Dockerfile.llama-cpp"
context: "./"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-nvidia-cuda-13-turboquant'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "turboquant"
dockerfile: "./backend/Dockerfile.turboquant"
context: "./"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
@@ -920,45 +783,6 @@ jobs:
backend: "llama-cpp"
dockerfile: "./backend/Dockerfile.llama-cpp"
context: "./"
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
platforms: 'linux/arm64'
skip-drivers: 'false'
tag-latest: 'auto'
tag-suffix: '-nvidia-l4t-cuda-13-arm64-turboquant'
base-image: "ubuntu:24.04"
runs-on: 'ubuntu-24.04-arm'
ubuntu-version: '2404'
backend: "turboquant"
dockerfile: "./backend/Dockerfile.turboquant"
context: "./"
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-nvidia-cuda-13-vllm'
runs-on: 'arc-runner-set'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "vllm"
dockerfile: "./backend/Dockerfile.python"
context: "./"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-nvidia-cuda-13-vllm-omni'
runs-on: 'arc-runner-set'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "vllm-omni"
dockerfile: "./backend/Dockerfile.python"
context: "./"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
@@ -1115,45 +939,6 @@ jobs:
backend: "diffusers"
dockerfile: "./backend/Dockerfile.python"
context: "./"
- build-type: 'l4t'
cuda-major-version: "13"
cuda-minor-version: "0"
platforms: 'linux/arm64'
tag-latest: 'auto'
tag-suffix: '-nvidia-l4t-cuda-13-arm64-vllm'
runs-on: 'ubuntu-24.04-arm'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
ubuntu-version: '2404'
backend: "vllm"
dockerfile: "./backend/Dockerfile.python"
context: "./"
- build-type: 'l4t'
cuda-major-version: "13"
cuda-minor-version: "0"
platforms: 'linux/arm64'
tag-latest: 'auto'
tag-suffix: '-nvidia-l4t-cuda-13-arm64-vllm-omni'
runs-on: 'ubuntu-24.04-arm'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
ubuntu-version: '2404'
backend: "vllm-omni"
dockerfile: "./backend/Dockerfile.python"
context: "./"
- build-type: 'l4t'
cuda-major-version: "13"
cuda-minor-version: "0"
platforms: 'linux/arm64'
tag-latest: 'auto'
tag-suffix: '-nvidia-l4t-cuda-13-arm64-sglang'
runs-on: 'ubuntu-24.04-arm'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
ubuntu-version: '2404'
backend: "sglang"
dockerfile: "./backend/Dockerfile.python"
context: "./"
- build-type: 'l4t'
cuda-major-version: "13"
cuda-minor-version: "0"
@@ -1440,32 +1225,6 @@ jobs:
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-nvidia-cuda-13-qwen3-tts-cpp'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "qwen3-tts-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-nvidia-cuda-13-vibevoice-cpp'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "vibevoice-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
@@ -1479,32 +1238,6 @@ jobs:
backend: "acestep-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
platforms: 'linux/arm64'
skip-drivers: 'false'
tag-latest: 'auto'
tag-suffix: '-nvidia-l4t-cuda-13-arm64-qwen3-tts-cpp'
base-image: "ubuntu:24.04"
ubuntu-version: '2404'
runs-on: 'ubuntu-24.04-arm'
backend: "qwen3-tts-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
platforms: 'linux/arm64'
skip-drivers: 'false'
tag-latest: 'auto'
tag-suffix: '-nvidia-l4t-cuda-13-arm64-vibevoice-cpp'
base-image: "ubuntu:24.04"
ubuntu-version: '2404'
runs-on: 'ubuntu-24.04-arm'
backend: "vibevoice-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
@@ -1526,7 +1259,7 @@ jobs:
tag-latest: 'auto'
tag-suffix: '-gpu-rocm-hipblas-rerankers'
runs-on: 'ubuntu-latest'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
skip-drivers: 'false'
backend: "rerankers"
dockerfile: "./backend/Dockerfile.python"
@@ -1539,25 +1272,12 @@ jobs:
tag-latest: 'auto'
tag-suffix: '-gpu-rocm-hipblas-llama-cpp'
runs-on: 'ubuntu-latest'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
skip-drivers: 'false'
backend: "llama-cpp"
dockerfile: "./backend/Dockerfile.llama-cpp"
context: "./"
ubuntu-version: '2404'
- build-type: 'hipblas'
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-rocm-hipblas-turboquant'
runs-on: 'ubuntu-latest'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
skip-drivers: 'false'
backend: "turboquant"
dockerfile: "./backend/Dockerfile.turboquant"
context: "./"
ubuntu-version: '2404'
- build-type: 'hipblas'
cuda-major-version: ""
cuda-minor-version: ""
@@ -1565,7 +1285,7 @@ jobs:
tag-latest: 'auto'
tag-suffix: '-gpu-rocm-hipblas-vllm'
runs-on: 'arc-runner-set'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
skip-drivers: 'false'
backend: "vllm"
dockerfile: "./backend/Dockerfile.python"
@@ -1578,25 +1298,12 @@ jobs:
tag-latest: 'auto'
tag-suffix: '-gpu-rocm-hipblas-vllm-omni'
runs-on: 'arc-runner-set'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
skip-drivers: 'false'
backend: "vllm-omni"
dockerfile: "./backend/Dockerfile.python"
context: "./"
ubuntu-version: '2404'
- build-type: 'hipblas'
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-rocm-hipblas-sglang'
runs-on: 'arc-runner-set'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
skip-drivers: 'false'
backend: "sglang"
dockerfile: "./backend/Dockerfile.python"
context: "./"
ubuntu-version: '2404'
- build-type: 'hipblas'
cuda-major-version: ""
cuda-minor-version: ""
@@ -1604,7 +1311,7 @@ jobs:
tag-latest: 'auto'
tag-suffix: '-gpu-rocm-hipblas-transformers'
runs-on: 'arc-runner-set'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
skip-drivers: 'false'
backend: "transformers"
dockerfile: "./backend/Dockerfile.python"
@@ -1617,7 +1324,7 @@ jobs:
tag-latest: 'auto'
tag-suffix: '-gpu-rocm-hipblas-diffusers'
runs-on: 'arc-runner-set'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
skip-drivers: 'false'
backend: "diffusers"
dockerfile: "./backend/Dockerfile.python"
@@ -1630,7 +1337,7 @@ jobs:
tag-latest: 'auto'
tag-suffix: '-gpu-rocm-hipblas-ace-step'
runs-on: 'arc-runner-set'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
skip-drivers: 'false'
backend: "ace-step"
dockerfile: "./backend/Dockerfile.python"
@@ -1644,7 +1351,7 @@ jobs:
tag-latest: 'auto'
tag-suffix: '-gpu-rocm-hipblas-kokoro'
runs-on: 'arc-runner-set'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
skip-drivers: 'false'
backend: "kokoro"
dockerfile: "./backend/Dockerfile.python"
@@ -1657,7 +1364,7 @@ jobs:
tag-latest: 'auto'
tag-suffix: '-gpu-rocm-hipblas-vibevoice'
runs-on: 'arc-runner-set'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
skip-drivers: 'false'
backend: "vibevoice"
dockerfile: "./backend/Dockerfile.python"
@@ -1670,7 +1377,7 @@ jobs:
tag-latest: 'auto'
tag-suffix: '-gpu-rocm-hipblas-qwen-asr'
runs-on: 'arc-runner-set'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
skip-drivers: 'false'
backend: "qwen-asr"
dockerfile: "./backend/Dockerfile.python"
@@ -1683,7 +1390,7 @@ jobs:
tag-latest: 'auto'
tag-suffix: '-gpu-rocm-hipblas-nemo'
runs-on: 'arc-runner-set'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
skip-drivers: 'false'
backend: "nemo"
dockerfile: "./backend/Dockerfile.python"
@@ -1696,7 +1403,7 @@ jobs:
tag-latest: 'auto'
tag-suffix: '-gpu-rocm-hipblas-qwen-tts'
runs-on: 'arc-runner-set'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
skip-drivers: 'false'
backend: "qwen-tts"
dockerfile: "./backend/Dockerfile.python"
@@ -1709,7 +1416,7 @@ jobs:
tag-latest: 'auto'
tag-suffix: '-gpu-rocm-hipblas-fish-speech'
runs-on: 'arc-runner-set'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
skip-drivers: 'false'
backend: "fish-speech"
dockerfile: "./backend/Dockerfile.python"
@@ -1722,7 +1429,7 @@ jobs:
tag-latest: 'auto'
tag-suffix: '-gpu-rocm-hipblas-voxcpm'
runs-on: 'arc-runner-set'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
skip-drivers: 'false'
backend: "voxcpm"
dockerfile: "./backend/Dockerfile.python"
@@ -1735,7 +1442,7 @@ jobs:
tag-latest: 'auto'
tag-suffix: '-gpu-rocm-hipblas-pocket-tts'
runs-on: 'arc-runner-set'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
skip-drivers: 'false'
backend: "pocket-tts"
dockerfile: "./backend/Dockerfile.python"
@@ -1748,12 +1455,25 @@ jobs:
tag-latest: 'auto'
tag-suffix: '-gpu-rocm-hipblas-faster-whisper'
runs-on: 'bigger-runner'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
skip-drivers: 'false'
backend: "faster-whisper"
dockerfile: "./backend/Dockerfile.python"
context: "./"
ubuntu-version: '2404'
- build-type: 'hipblas'
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-rocm-hipblas-whisperx'
runs-on: 'bigger-runner'
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
skip-drivers: 'false'
backend: "whisperx"
dockerfile: "./backend/Dockerfile.python"
context: "./"
ubuntu-version: '2404'
- build-type: 'hipblas'
cuda-major-version: ""
cuda-minor-version: ""
@@ -1761,7 +1481,7 @@ jobs:
tag-latest: 'auto'
tag-suffix: '-gpu-rocm-hipblas-coqui'
runs-on: 'bigger-runner'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
skip-drivers: 'false'
backend: "coqui"
dockerfile: "./backend/Dockerfile.python"
@@ -1775,7 +1495,7 @@ jobs:
tag-latest: 'auto'
tag-suffix: '-gpu-intel-rerankers'
runs-on: 'ubuntu-latest'
base-image: "intel/oneapi-basekit:2025.3.2-0-devel-ubuntu24.04"
base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
skip-drivers: 'false'
backend: "rerankers"
dockerfile: "./backend/Dockerfile.python"
@@ -1788,25 +1508,12 @@ jobs:
tag-latest: 'auto'
tag-suffix: '-gpu-intel-sycl-f32-llama-cpp'
runs-on: 'ubuntu-latest'
base-image: "intel/oneapi-basekit:2025.3.2-0-devel-ubuntu24.04"
base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
skip-drivers: 'false'
backend: "llama-cpp"
dockerfile: "./backend/Dockerfile.llama-cpp"
context: "./"
ubuntu-version: '2404'
- build-type: 'sycl_f32'
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-intel-sycl-f32-turboquant'
runs-on: 'ubuntu-latest'
base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
skip-drivers: 'false'
backend: "turboquant"
dockerfile: "./backend/Dockerfile.turboquant"
context: "./"
ubuntu-version: '2404'
- build-type: 'sycl_f16'
cuda-major-version: ""
cuda-minor-version: ""
@@ -1820,19 +1527,6 @@ jobs:
dockerfile: "./backend/Dockerfile.llama-cpp"
context: "./"
ubuntu-version: '2404'
- build-type: 'sycl_f16'
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-intel-sycl-f16-turboquant'
runs-on: 'ubuntu-latest'
base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
skip-drivers: 'false'
backend: "turboquant"
dockerfile: "./backend/Dockerfile.turboquant"
context: "./"
ubuntu-version: '2404'
- build-type: 'intel'
cuda-major-version: ""
cuda-minor-version: ""
@@ -1846,19 +1540,6 @@ jobs:
dockerfile: "./backend/Dockerfile.python"
context: "./"
ubuntu-version: '2404'
- build-type: 'intel'
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-intel-sglang'
runs-on: 'arc-runner-set'
base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
skip-drivers: 'false'
backend: "sglang"
dockerfile: "./backend/Dockerfile.python"
context: "./"
ubuntu-version: '2404'
- build-type: 'intel'
cuda-major-version: ""
cuda-minor-version: ""
@@ -2225,32 +1906,6 @@ jobs:
dockerfile: "./backend/Dockerfile.llama-cpp"
context: "./"
ubuntu-version: '2404'
- build-type: ''
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64,linux/arm64'
tag-latest: 'auto'
tag-suffix: '-cpu-turboquant'
runs-on: 'bigger-runner'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "turboquant"
dockerfile: "./backend/Dockerfile.turboquant"
context: "./"
ubuntu-version: '2404'
- build-type: ''
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-cpu-ik-llama-cpp'
runs-on: 'bigger-runner'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "ik-llama-cpp"
dockerfile: "./backend/Dockerfile.ik-llama-cpp"
context: "./"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "0"
@@ -2264,19 +1919,6 @@ jobs:
dockerfile: "./backend/Dockerfile.llama-cpp"
context: "./"
ubuntu-version: '2204'
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "0"
platforms: 'linux/arm64'
skip-drivers: 'false'
tag-latest: 'auto'
tag-suffix: '-nvidia-l4t-arm64-turboquant'
base-image: "nvcr.io/nvidia/l4t-jetpack:r36.4.0"
runs-on: 'ubuntu-24.04-arm'
backend: "turboquant"
dockerfile: "./backend/Dockerfile.turboquant"
context: "./"
ubuntu-version: '2204'
- build-type: 'vulkan'
cuda-major-version: ""
cuda-minor-version: ""
@@ -2290,19 +1932,6 @@ jobs:
dockerfile: "./backend/Dockerfile.llama-cpp"
context: "./"
ubuntu-version: '2404'
- build-type: 'vulkan'
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64,linux/arm64'
tag-latest: 'auto'
tag-suffix: '-gpu-vulkan-turboquant'
runs-on: 'bigger-runner'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "turboquant"
dockerfile: "./backend/Dockerfile.turboquant"
context: "./"
ubuntu-version: '2404'
# Stablediffusion-ggml
- build-type: ''
cuda-major-version: ""
@@ -2507,7 +2136,7 @@ jobs:
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-rocm-hipblas-whisper'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
runs-on: 'ubuntu-latest'
skip-drivers: 'false'
backend: "whisper"
@@ -2586,171 +2215,13 @@ jobs:
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-rocm-hipblas-acestep-cpp'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
runs-on: 'ubuntu-latest'
skip-drivers: 'false'
backend: "acestep-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
# qwen3-tts-cpp
- build-type: ''
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64,linux/arm64'
tag-latest: 'auto'
tag-suffix: '-cpu-qwen3-tts-cpp'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "qwen3-tts-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
- build-type: 'sycl_f32'
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-intel-sycl-f32-qwen3-tts-cpp'
runs-on: 'ubuntu-latest'
base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
skip-drivers: 'false'
backend: "qwen3-tts-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
- build-type: 'sycl_f16'
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-intel-sycl-f16-qwen3-tts-cpp'
runs-on: 'ubuntu-latest'
base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
skip-drivers: 'false'
backend: "qwen3-tts-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
- build-type: 'vulkan'
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64,linux/arm64'
tag-latest: 'auto'
tag-suffix: '-gpu-vulkan-qwen3-tts-cpp'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "qwen3-tts-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "0"
platforms: 'linux/arm64'
skip-drivers: 'false'
tag-latest: 'auto'
tag-suffix: '-nvidia-l4t-arm64-qwen3-tts-cpp'
base-image: "nvcr.io/nvidia/l4t-jetpack:r36.4.0"
runs-on: 'ubuntu-24.04-arm'
backend: "qwen3-tts-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2204'
- build-type: 'hipblas'
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-rocm-hipblas-qwen3-tts-cpp'
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
runs-on: 'ubuntu-latest'
skip-drivers: 'false'
backend: "qwen3-tts-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
# vibevoice-cpp
- build-type: ''
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64,linux/arm64'
tag-latest: 'auto'
tag-suffix: '-cpu-vibevoice-cpp'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "vibevoice-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
- build-type: 'sycl_f32'
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-intel-sycl-f32-vibevoice-cpp'
runs-on: 'ubuntu-latest'
base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
skip-drivers: 'false'
backend: "vibevoice-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
- build-type: 'sycl_f16'
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-intel-sycl-f16-vibevoice-cpp'
runs-on: 'ubuntu-latest'
base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
skip-drivers: 'false'
backend: "vibevoice-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
- build-type: 'vulkan'
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64,linux/arm64'
tag-latest: 'auto'
tag-suffix: '-gpu-vulkan-vibevoice-cpp'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "vibevoice-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "0"
platforms: 'linux/arm64'
skip-drivers: 'false'
tag-latest: 'auto'
tag-suffix: '-nvidia-l4t-arm64-vibevoice-cpp'
base-image: "nvcr.io/nvidia/l4t-jetpack:r36.4.0"
runs-on: 'ubuntu-24.04-arm'
backend: "vibevoice-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2204'
- build-type: 'hipblas'
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-rocm-hipblas-vibevoice-cpp'
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
runs-on: 'ubuntu-latest'
skip-drivers: 'false'
backend: "vibevoice-cpp"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
# voxtral
- build-type: ''
cuda-major-version: ""
@@ -2793,20 +2264,6 @@ jobs:
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
# kokoros (Rust TTS)
- build-type: ''
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-cpu-kokoros'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "kokoros"
dockerfile: "./backend/Dockerfile.rust"
context: "./"
ubuntu-version: '2404'
# local-store
- build-type: ''
cuda-major-version: ""
@@ -2835,34 +2292,6 @@ jobs:
dockerfile: "./backend/Dockerfile.python"
context: "./"
ubuntu-version: '2404'
# insightface (face recognition)
- build-type: ''
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64,linux/arm64'
tag-latest: 'auto'
tag-suffix: '-cpu-insightface'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "insightface"
dockerfile: "./backend/Dockerfile.python"
context: "./"
ubuntu-version: '2404'
# speaker-recognition (voice/speaker biometrics)
- build-type: ''
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64,linux/arm64'
tag-latest: 'auto'
tag-suffix: '-cpu-speaker-recognition'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "speaker-recognition"
dockerfile: "./backend/Dockerfile.python"
context: "./"
ubuntu-version: '2404'
- build-type: 'intel'
cuda-major-version: ""
cuda-minor-version: ""
@@ -2909,7 +2338,7 @@ jobs:
# platforms: 'linux/amd64'
# tag-latest: 'auto'
# tag-suffix: '-gpu-hipblas-rfdetr'
# base-image: "rocm/dev-ubuntu-24.04:7.2.1"
# base-image: "rocm/dev-ubuntu-24.04:6.4.4"
# runs-on: 'ubuntu-latest'
# skip-drivers: 'false'
# backend: "rfdetr"
@@ -2950,7 +2379,7 @@ jobs:
tag-latest: 'auto'
tag-suffix: '-gpu-rocm-hipblas-neutts'
runs-on: 'arc-runner-set'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
skip-drivers: 'false'
backend: "neutts"
dockerfile: "./backend/Dockerfile.python"
@@ -3060,49 +2489,6 @@ jobs:
dockerfile: "./backend/Dockerfile.python"
context: "./"
ubuntu-version: '2404'
# sherpa-onnx CPU
- build-type: ''
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64,linux/arm64'
tag-latest: 'auto'
tag-suffix: '-cpu-sherpa-onnx'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "sherpa-onnx"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
# sherpa-onnx CUDA 12
- build-type: 'cublas'
cuda-major-version: "12"
cuda-minor-version: "8"
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-nvidia-cuda-12-sherpa-onnx'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "sherpa-onnx"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
# sherpa-onnx CUDA 13 — requires onnxruntime 1.24.x+ for the
# gpu_cuda13 tarball; sherpa-onnx SHERPA_COMMIT pins to v1.12.39.
- build-type: 'cublas'
cuda-major-version: "13"
cuda-minor-version: "0"
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-nvidia-cuda-13-sherpa-onnx'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "sherpa-onnx"
dockerfile: "./backend/Dockerfile.golang"
context: "./"
ubuntu-version: '2404'
backend-jobs-darwin:
uses: ./.github/workflows/backend_build_darwin.yml
strategy:
@@ -3141,14 +2527,6 @@ jobs:
tag-suffix: "-metal-darwin-arm64-acestep-cpp"
build-type: "metal"
lang: "go"
- backend: "qwen3-tts-cpp"
tag-suffix: "-metal-darwin-arm64-qwen3-tts-cpp"
build-type: "metal"
lang: "go"
- backend: "vibevoice-cpp"
tag-suffix: "-metal-darwin-arm64-vibevoice-cpp"
build-type: "metal"
lang: "go"
- backend: "voxtral"
tag-suffix: "-metal-darwin-arm64-voxtral"
build-type: "metal"

View File

@@ -58,11 +58,6 @@ on:
required: false
default: '2204'
type: string
amdgpu-targets:
description: 'AMD GPU targets for ROCm/HIP builds'
required: false
default: ''
type: string
secrets:
dockerUsername:
required: false
@@ -80,14 +75,6 @@ jobs:
quay_username: ${{ secrets.quayUsername }}
steps:
- 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 (Ubuntu)
if: inputs.runs-on == 'ubuntu-latest'
@@ -105,6 +92,18 @@ jobs:
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: Release space from worker
if: inputs.runs-on == 'ubuntu-latest'
run: |
@@ -202,15 +201,6 @@ 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)" >> "$GITHUB_OUTPUT"
- name: Build and push
uses: docker/build-push-action@v7
if: github.event_name != 'pull_request'
@@ -224,14 +214,9 @@ jobs:
BASE_IMAGE=${{ 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 }}
context: ${{ inputs.context }}
file: ${{ inputs.dockerfile }}
cache-from: type=registry,ref=quay.io/go-skynet/ci-cache:cache${{ inputs.tag-suffix }}
cache-to: type=registry,ref=quay.io/go-skynet/ci-cache:cache${{ inputs.tag-suffix }},mode=max,ignore-error=true
cache-from: type=gha
platforms: ${{ inputs.platforms }}
push: ${{ github.event_name != 'pull_request' }}
tags: ${{ steps.meta.outputs.tags }}
@@ -250,13 +235,9 @@ jobs:
BASE_IMAGE=${{ 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 }}
context: ${{ inputs.context }}
file: ${{ inputs.dockerfile }}
cache-from: type=registry,ref=quay.io/go-skynet/ci-cache:cache${{ inputs.tag-suffix }}
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
@@ -65,141 +58,21 @@ jobs:
uses: actions/setup-go@v5
with:
go-version: ${{ matrix.go-version }}
# Caches ~/go/pkg/mod and ~/Library/Caches/go-build keyed on go.sum.
# Shared across every darwin matrix entry — first job in a run warms
# it, the rest hit warm.
cache: true
cache: false
# You can test your matrix by printing the current Go version
- name: Display Go version
run: go version
# ---- Homebrew cache ----
# macOS runners have no Docker daemon, so the BuildKit registry cache used
# for Linux backend images (see .agents/ci-caching.md) doesn't apply here.
# We cache the brew downloads + Cellar entries for the formulas we install
# below. Read on every run, write only on master/tag pushes — same policy
# as the Linux registry cache.
- name: Restore Homebrew cache
id: brew-cache
uses: actions/cache/restore@v4
with:
path: |
~/Library/Caches/Homebrew/downloads
/opt/homebrew/Cellar/protobuf
/opt/homebrew/Cellar/grpc
/opt/homebrew/Cellar/protoc-gen-go
/opt/homebrew/Cellar/protoc-gen-go-grpc
/opt/homebrew/Cellar/libomp
/opt/homebrew/Cellar/llvm
/opt/homebrew/Cellar/ccache
key: brew-${{ 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.
brew install protobuf grpc make protoc-gen-go protoc-gen-go-grpc libomp llvm ccache
- 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
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 }}-
brew install protobuf grpc make protoc-gen-go protoc-gen-go-grpc libomp llvm
- name: Build ${{ inputs.backend }}-darwin
run: |
make protogen-go
BACKEND=${{ 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

@@ -53,7 +53,6 @@ jobs:
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 }}

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

@@ -14,14 +14,6 @@ jobs:
variable: "LLAMA_VERSION"
branch: "master"
file: "backend/cpp/llama-cpp/Makefile"
- repository: "ikawrakow/ik_llama.cpp"
variable: "IK_LLAMA_VERSION"
branch: "main"
file: "backend/cpp/ik-llama-cpp/Makefile"
- repository: "TheTom/llama-cpp-turboquant"
variable: "TURBOQUANT_VERSION"
branch: "feature/turboquant-kv-cache"
file: "backend/cpp/turboquant/Makefile"
- repository: "ggml-org/whisper.cpp"
variable: "WHISPER_CPP_VERSION"
branch: "master"
@@ -46,10 +38,6 @@ jobs:
variable: "SAM3_VERSION"
branch: "main"
file: "backend/go/sam3-cpp/Makefile"
- repository: "predict-woo/qwen3-tts.cpp"
variable: "QWEN3TTS_CPP_VERSION"
branch: "main"
file: "backend/go/qwen3-tts-cpp/Makefile"
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v6
@@ -80,37 +68,5 @@ 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:
@@ -48,88 +48,21 @@ jobs:
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: Process gallery-agent PR commands
env:
GH_TOKEN: ${{ secrets.UPDATE_BOT_TOKEN }}
REPO: ${{ github.repository }}
SEARCH: 'gallery agent in:title'
run: |
# Walk gallery-agent PRs and act on maintainer comments:
# /gallery-agent blacklist → label `gallery-agent/blacklisted` + close (never repropose)
# /gallery-agent recreate → close without label (next run may repropose)
# Only comments from OWNER / MEMBER / COLLABORATOR are honored so
# random users can't drive the bot.
#
# We scan both open PRs AND recently-closed PRs that don't already
# carry the blacklist label. This covers the common flow where a
# maintainer writes /gallery-agent blacklist and immediately clicks
# Close — without this, the next scheduled run wouldn't see the
# command (PR is already closed) and would repropose the model.
gh label create gallery-agent/blacklisted \
--repo "$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')
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=$state)"
gh pr edit "$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:]]|$)'; then
echo "PR #$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
done
- name: Collect skip URLs for the gallery agent
id: open_prs
env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
REPO: ${{ github.repository }}
SEARCH: 'gallery agent in:title'
run: |
# Skip set =
# URLs from any open gallery-agent PR (avoid duplicate PRs for the same model while one is pending)
# + URLs from closed PRs carrying the `gallery-agent/blacklisted` label (hard blacklist)
# Plain-closed PRs without the label are ignored — closing a PR is
# not by itself a "never propose again" signal; maintainers must
# opt in via the /gallery-agent blacklist comment command.
urls_open=$(gh pr list --repo "$REPO" --state open --search "$SEARCH" \
--json body --jq '[.[].body] | join("\n")' \
| grep -oE 'https://huggingface\.co/[^ )]+' || true)
urls_blacklist=$(gh pr list --repo "$REPO" --state closed --search "$SEARCH" \
--label gallery-agent/blacklisted \
--json body --jq '[.[].body] | join("\n")' \
| grep -oE 'https://huggingface\.co/[^ )]+' || true)
urls=$(printf '%s\n%s\n' "$urls_open" "$urls_blacklist" | sort -u | sed '/^$/d')
echo "Skip URLs:"
echo "$urls"
{
echo "urls<<EOF"
echo "$urls"
echo "EOF"
} >> "$GITHUB_OUTPUT"
- uses: mudler/localai-github-action@v1.1
with:
model: 'https://huggingface.co/unsloth/Qwen3.5-2B-GGUF'
- name: Run gallery agent
env:
#OPENAI_MODEL: ${{ secrets.OPENAI_MODEL }}
OPENAI_MODEL: Qwen3.5-2B-GGUF
OPENAI_BASE_URL: "http://localhost:8080"
OPENAI_KEY: ${{ secrets.OPENAI_KEY }}
#OPENAI_BASE_URL: ${{ secrets.OPENAI_BASE_URL }}
SEARCH_TERM: ${{ github.event.inputs.search_term || 'GGUF' }}
LIMIT: ${{ github.event.inputs.limit || '15' }}
QUANTIZATION: ${{ github.event.inputs.quantization || 'Q4_K_M' }}
MAX_MODELS: ${{ github.event.inputs.max_models || '1' }}
EXTRA_SKIP_URLS: ${{ steps.open_prs.outputs.urls }}
run: |
export GALLERY_INDEX_PATH=$PWD/gallery/index.yaml
go run ./.github/gallery-agent
@@ -191,21 +124,7 @@ jobs:
**Added Models:**
${{ steps.read_summary.outputs.added_models || '- No models added' }}
### Bot commands
Maintainers (owner / member / collaborator) can control this PR
by leaving a comment with one of:
- `/gallery-agent recreate` — close this PR; the next scheduled
run will propose this model again (useful if the entry needs
to be regenerated with fresh metadata).
- `/gallery-agent blacklist` — close this PR and permanently
prevent the gallery agent from ever reproposing this model.
Plain "Close" (without a command) is treated as a no-op: the
model may be reproposed by a future run.
**Workflow Details:**
- Triggered by: `${{ github.event_name }}`
- Run ID: `${{ github.run_id }}`

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

@@ -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

@@ -59,7 +59,7 @@ jobs:
hugo --minify --baseURL "${{ steps.pages.outputs.base_url }}/"
- name: Upload artifact
uses: actions/upload-pages-artifact@v5
uses: actions/upload-pages-artifact@v4
with:
path: docs/public

View File

@@ -20,6 +20,7 @@
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 }}
secrets:
@@ -58,14 +59,16 @@
platforms: 'linux/amd64'
tag-latest: 'false'
tag-suffix: '-hipblas'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
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"

View File

@@ -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 }}
@@ -40,12 +41,13 @@
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-hipblas'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
base-image: "rocm/dev-ubuntu-24.04:6.4.4"
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
@@ -58,6 +60,7 @@
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 }}
@@ -118,7 +121,8 @@
- 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"
@@ -137,6 +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: ''
@@ -70,13 +75,6 @@ jobs:
runs-on: ${{ inputs.runs-on }}
steps:
- 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 (Ubuntu)
if: inputs.runs-on == 'ubuntu-latest'
uses: jlumbroso/free-disk-space@main
@@ -92,6 +90,16 @@ jobs:
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: Release space from worker
if: inputs.runs-on == 'ubuntu-latest'
@@ -193,21 +201,25 @@ jobs:
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${{ inputs.tag-suffix }}
cache-to: type=registry,ref=quay.io/go-skynet/ci-cache:cache-localai${{ inputs.tag-suffix }},mode=max,ignore-error=true
cache-from: type=gha
platforms: ${{ inputs.platforms }}
push: ${{ github.event_name != 'pull_request' }}
tags: ${{ steps.meta.outputs.tags }}
@@ -218,20 +230,25 @@ jobs:
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 }}
cache-from: type=gha
platforms: ${{ inputs.platforms }}
#push: true
tags: ${{ steps.meta_pull_request.outputs.tags }}

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-${{ github.head_ref || github.ref }}-${{ github.repository }}
cancel-in-progress: true
jobs:
golangci-lint:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
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

@@ -39,7 +39,7 @@ jobs:
run: |
make build-launcher-darwin
- name: Upload DMG to Release
uses: softprops/action-gh-release@v3
uses: softprops/action-gh-release@v2
with:
files: ./dist/LocalAI.dmg
launcher-build-linux:
@@ -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:
@@ -61,6 +59,6 @@ jobs:
sudo apt-get install golang gcc libgl1-mesa-dev xorg-dev libxkbcommon-dev
make build-launcher-linux
- name: Upload Linux launcher artifacts
uses: softprops/action-gh-release@v3
uses: softprops/action-gh-release@v2
with:
files: ./local-ai-launcher-linux.tar.xz

View File

@@ -29,19 +29,9 @@ jobs:
nemo: ${{ steps.detect.outputs.nemo }}
voxcpm: ${{ steps.detect.outputs.voxcpm }}
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 }}
turboquant: ${{ steps.detect.outputs.turboquant }}
vllm: ${{ steps.detect.outputs.vllm }}
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 }}
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 }}
steps:
- name: Checkout repository
uses: actions/checkout@v6
@@ -474,261 +464,6 @@ jobs:
- name: Test llama-cpp-quantization
run: |
make --jobs=5 --output-sync=target -C backend/python/llama-cpp-quantization test
tests-llama-cpp-grpc:
needs: detect-changes
if: needs.detect-changes.outputs.llama-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 llama-cpp backend image and run gRPC e2e tests
run: |
make test-extra-backend-llama-cpp
tests-llama-cpp-grpc-transcription:
needs: detect-changes
if: needs.detect-changes.outputs.llama-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 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
# 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'
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 ik-llama-cpp backend image and run gRPC e2e tests
run: |
make test-extra-backend-ik-llama-cpp
tests-turboquant-grpc:
needs: detect-changes
if: needs.detect-changes.outputs.turboquant == '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'
# Exercises the turboquant (llama.cpp fork) backend with KV-cache
# quantization enabled. The convenience target sets
# BACKEND_TEST_CACHE_TYPE_K / _V=q8_0, which are plumbed into the
# ModelOptions.CacheTypeKey/Value gRPC fields. LoadModel-success +
# backend stdout/stderr (captured by the Ginkgo suite) prove the
# cache-type config path reaches the fork's KV-cache init.
- name: Build turboquant backend image and run gRPC e2e tests
run: |
make test-extra-backend-turboquant
# tests-vllm-grpc is currently disabled in CI.
#
# The prebuilt vllm CPU wheel is compiled with AVX-512 VNNI/BF16
# instructions, and neither ubuntu-latest nor the bigger-runner pool
# offers a stable CPU baseline that supports them — runners come
# back with different hardware between runs and SIGILL on import of
# vllm.model_executor.models.registry. Compiling vllm from source
# via FROM_SOURCE=true works on any CPU but takes 30-50 minutes per
# run, which is too slow for a smoke test.
#
# The test itself (tests/e2e-backends + make test-extra-backend-vllm)
# is fully working and validated locally on a host with the right
# SIMD baseline. Run it manually with:
#
# make test-extra-backend-vllm
#
# Re-enable this job once we have a self-hosted runner label with
# guaranteed AVX-512 VNNI/BF16 support, or once the vllm project
# publishes a CPU wheel with a wider baseline.
#
# tests-vllm-grpc:
# needs: detect-changes
# if: needs.detect-changes.outputs.vllm == 'true' || needs.detect-changes.outputs.run-all == 'true'
# runs-on: bigger-runner
# 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: '1.25.4'
# - 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 vllm (cpu) backend image and run gRPC e2e tests
# run: |
# make test-extra-backend-vllm
# tests-sglang-grpc is currently disabled in CI for the same reason as
# tests-vllm-grpc: sglang's CPU kernel (sgl-kernel) uses __m512 AVX-512
# intrinsics unconditionally in shm.cpp, so the from-source build
# requires `-march=sapphirerapids` (already set in install.sh) and the
# resulting binary SIGILLs at import on CPUs without AVX-512 VNNI/BF16.
# The ubuntu-latest runner pool does not guarantee that ISA baseline.
#
# The test itself (tests/e2e-backends + make test-extra-backend-sglang)
# is fully working and validated locally on a host with the right
# SIMD baseline. Run it manually with:
#
# make test-extra-backend-sglang
#
# Re-enable this job once we have a self-hosted runner label with
# guaranteed AVX-512 VNNI/BF16 support.
#
# tests-sglang-grpc:
# needs: detect-changes
# if: needs.detect-changes.outputs.sglang == 'true' || needs.detect-changes.outputs.run-all == 'true'
# runs-on: bigger-runner
# 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: '1.25.4'
# - 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 sglang (cpu) backend image and run gRPC e2e tests
# run: |
# make test-extra-backend-sglang
tests-acestep-cpp:
needs: detect-changes
if: needs.detect-changes.outputs.acestep-cpp == 'true' || needs.detect-changes.outputs.run-all == 'true'
@@ -761,129 +496,6 @@ jobs:
- name: Test acestep-cpp
run: |
make --jobs=5 --output-sync=target -C backend/go/acestep-cpp test
tests-qwen3-tts-cpp:
needs: detect-changes
if: needs.detect-changes.outputs.qwen3-tts-cpp == '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 cmake curl libopenblas-dev ffmpeg
- name: Setup Go
uses: actions/setup-go@v5
- name: Display Go version
run: go version
- name: Proto 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:$HOME/go/bin" make protogen-go
- name: Build qwen3-tts-cpp
run: |
make --jobs=5 --output-sync=target -C backend/go/qwen3-tts-cpp
- name: Test qwen3-tts-cpp
run: |
make --jobs=5 --output-sync=target -C backend/go/qwen3-tts-cpp test
# Per-backend smoke for vibevoice-cpp: builds the .so + Go binary and
# runs `make -C backend/go/vibevoice-cpp test`. test.sh auto-downloads
# the published mudler/vibevoice.cpp-models bundle (TTS Q8_0 + ASR Q4_K
# + tokenizer + voice) and runs the closed-loop TTS → ASR Go test.
tests-vibevoice-cpp:
needs: detect-changes
if: needs.detect-changes.outputs.vibevoice-cpp == 'true' || needs.detect-changes.outputs.run-all == 'true'
runs-on: ubuntu-latest
timeout-minutes: 90
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install -y build-essential cmake curl libopenblas-dev ffmpeg
- name: Setup Go
uses: actions/setup-go@v5
- name: Display Go version
run: go version
- name: Proto Dependencies
run: |
curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v26.1/protoc-26.1-linux-x86_64.zip -o protoc.zip && \
unzip -j -d /usr/local/bin protoc.zip bin/protoc && \
rm protoc.zip
go install google.golang.org/protobuf/cmd/protoc-gen-go@v1.34.2
go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@1958fcbe2ca8bd93af633f11e97d44e567e945af
PATH="$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
tests-voxtral:
needs: detect-changes
if: needs.detect-changes.outputs.voxtral == 'true' || needs.detect-changes.outputs.run-all == 'true'
@@ -939,55 +551,3 @@ 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

@@ -3,18 +3,15 @@ name: 'tests'
on:
pull_request:
paths-ignore:
- 'docs/**'
- 'examples/**'
- 'README.md'
- '**/*.md'
- 'backend/**'
push:
branches:
- master
tags:
- '*'
env:
GRPC_VERSION: v1.65.0
concurrency:
group: ci-tests-${{ github.head_ref || github.ref }}-${{ github.repository }}
cancel-in-progress: true
@@ -103,9 +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
@@ -134,7 +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
@@ -142,6 +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-${{ github.head_ref || github.ref }}-${{ github.repository }}
cancel-in-progress: true
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

@@ -3,12 +3,6 @@ name: 'E2E Backend Tests'
on:
pull_request:
paths-ignore:
- 'docs/**'
- 'examples/**'
- 'README.md'
- '**/*.md'
- 'backend/**'
push:
branches:
- master
@@ -30,8 +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

@@ -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

@@ -1,53 +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.'
exclusions:
paths:
# Upstream whisper.cpp source tree fetched by the whisper backend Makefile.
- 'backend/go/whisper/sources'
- 'docs/'

View File

@@ -1,34 +1,19 @@
# 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), `DEPS_REFRESH` weekly cache-buster for unpinned Python deps, 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/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 |
## Quick Reference
@@ -36,7 +21,5 @@ 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 && \

578
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/tinygrad backends/sherpa-onnx
.NOTPARALLEL: backends/diffusers backends/llama-cpp 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/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
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 .
@@ -245,8 +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 .
@@ -271,12 +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'
@@ -288,8 +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 \
@@ -299,13 +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 $(TEST_FLAKES) -v -r ./tests/e2e
# --- Container-based real-model testing ---
@@ -331,8 +311,6 @@ test-realtime-models-docker: build-mock-backend
--build-arg BUILD_TYPE=$(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) \
@@ -416,13 +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)
@@ -447,7 +419,6 @@ prepare-test-extra: protogen-python
$(MAKE) -C backend/python/chatterbox
$(MAKE) -C backend/python/vllm
$(MAKE) -C backend/python/vllm-omni
$(MAKE) -C backend/python/sglang
$(MAKE) -C backend/python/vibevoice
$(MAKE) -C backend/python/moonshine
$(MAKE) -C backend/python/pocket-tts
@@ -461,9 +432,6 @@ prepare-test-extra: protogen-python
$(MAKE) -C backend/python/whisperx
$(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
@@ -486,421 +454,8 @@ test-extra: prepare-test-extra
$(MAKE) -C backend/python/whisperx test
$(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
##
## End-to-end gRPC tests that exercise a built backend container image.
##
## The test suite in tests/e2e-backends is backend-agnostic. You drive it via env
## vars (see tests/e2e-backends/backend_test.go for the full list) and the
## capability-driven harness picks which gRPC RPCs to exercise:
##
## BACKEND_IMAGE Required. Docker image to test, e.g. local-ai-backend:llama-cpp.
## BACKEND_TEST_MODEL_URL URL of a model file to download and load.
## BACKEND_TEST_MODEL_FILE Path to an already-downloaded model (skips download).
## BACKEND_TEST_MODEL_NAME HuggingFace repo id (e.g. Qwen/Qwen2.5-0.5B-Instruct).
## Use this instead of MODEL_URL for backends that
## resolve HF model ids natively (vllm, vllm-omni).
## BACKEND_TEST_CAPS Comma-separated capabilities, default "health,load,predict,stream".
## Adds "tools" to exercise ChatDelta tool call extraction.
## BACKEND_TEST_PROMPT Override the prompt used in predict/stream specs.
## BACKEND_TEST_OPTIONS Comma-separated Options[] entries forwarded to LoadModel,
## e.g. "tool_parser:hermes,reasoning_parser:qwen3".
##
## Direct usage (image already built, no docker-build-* dependency):
##
## make test-extra-backend BACKEND_IMAGE=local-ai-backend:llama-cpp \
## BACKEND_TEST_MODEL_URL=https://.../model.gguf
##
## Convenience wrappers below build a specific backend image first, then run the
## suite against it.
##
BACKEND_TEST_MODEL_URL?=https://huggingface.co/Qwen/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B-Q8_0.gguf
## Generic target — runs the suite against whatever BACKEND_IMAGE points at.
## Depends on protogen-go so pkg/grpc/proto is generated before `go test`.
test-extra-backend: protogen-go
@test -n "$$BACKEND_IMAGE" || { echo "BACKEND_IMAGE must be set" >&2; exit 1; }
BACKEND_IMAGE="$$BACKEND_IMAGE" \
BACKEND_TEST_MODEL_URL="$${BACKEND_TEST_MODEL_URL:-$(BACKEND_TEST_MODEL_URL)}" \
BACKEND_TEST_MODEL_FILE="$$BACKEND_TEST_MODEL_FILE" \
BACKEND_TEST_MODEL_NAME="$$BACKEND_TEST_MODEL_NAME" \
BACKEND_TEST_MMPROJ_URL="$$BACKEND_TEST_MMPROJ_URL" \
BACKEND_TEST_MMPROJ_FILE="$$BACKEND_TEST_MMPROJ_FILE" \
BACKEND_TEST_AUDIO_URL="$$BACKEND_TEST_AUDIO_URL" \
BACKEND_TEST_AUDIO_FILE="$$BACKEND_TEST_AUDIO_FILE" \
BACKEND_TEST_CAPS="$$BACKEND_TEST_CAPS" \
BACKEND_TEST_PROMPT="$$BACKEND_TEST_PROMPT" \
BACKEND_TEST_OPTIONS="$$BACKEND_TEST_OPTIONS" \
BACKEND_TEST_TOOL_PROMPT="$$BACKEND_TEST_TOOL_PROMPT" \
BACKEND_TEST_TOOL_NAME="$$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
test-extra-backend-ik-llama-cpp: docker-build-ik-llama-cpp
BACKEND_IMAGE=local-ai-backend:ik-llama-cpp $(MAKE) test-extra-backend
## turboquant: exercises the llama.cpp-fork backend with the fork's
## *TurboQuant-specific* KV-cache types (turbo3 for both K and V). turbo3
## is what makes this backend distinct from stock llama-cpp — picking q8_0
## here would only test the standard llama.cpp code path that the upstream
## llama-cpp backend already covers. The fork auto-enables flash_attention
## when turbo3/turbo4 are active, so we don't need to set it explicitly.
test-extra-backend-turboquant: docker-build-turboquant
BACKEND_IMAGE=local-ai-backend:turboquant \
BACKEND_TEST_CACHE_TYPE_K=q8_0 \
BACKEND_TEST_CACHE_TYPE_V=turbo3 \
$(MAKE) test-extra-backend
## Audio transcription wrapper for the llama-cpp backend.
## Drives the new AudioTranscription / AudioTranscriptionStream RPCs against
## ggml-org/Qwen3-ASR-0.6B-GGUF (a small ASR model that requires its mmproj
## audio encoder companion). The audio fixture is a short public-domain
## "jfk.wav" clip ggml-org bundles with whisper.cpp's CI assets.
test-extra-backend-llama-cpp-transcription: docker-build-llama-cpp
BACKEND_IMAGE=local-ai-backend:llama-cpp \
BACKEND_TEST_MODEL_URL=https://huggingface.co/ggml-org/Qwen3-ASR-0.6B-GGUF/resolve/main/Qwen3-ASR-0.6B-Q8_0.gguf \
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 \
$(MAKE) test-extra-backend
## vllm is resolved from a HuggingFace model id (no file download) and
## exercises Predict + streaming + tool-call extraction via the hermes parser.
## Requires a host CPU with the SIMD instructions the prebuilt vllm CPU
## wheel was compiled against (AVX-512 VNNI/BF16); older CPUs will SIGILL
## on import — on CI this means using the bigger-runner label.
test-extra-backend-vllm: docker-build-vllm
BACKEND_IMAGE=local-ai-backend:vllm \
BACKEND_TEST_MODEL_NAME=Qwen/Qwen2.5-0.5B-Instruct \
BACKEND_TEST_CAPS=health,load,predict,stream,tools \
BACKEND_TEST_OPTIONS=tool_parser:hermes \
$(MAKE) test-extra-backend
## 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
## embeddings, Stable Diffusion and Whisper — run them individually or via
## the `test-extra-backend-tinygrad-all` aggregate.
test-extra-backend-tinygrad: docker-build-tinygrad
BACKEND_IMAGE=local-ai-backend:tinygrad \
BACKEND_TEST_MODEL_NAME=Qwen/Qwen3-0.6B \
BACKEND_TEST_CAPS=health,load,predict,stream,tools \
BACKEND_TEST_OPTIONS=tool_parser:hermes \
$(MAKE) test-extra-backend
## tinygrad — embeddings via LLM last-hidden-state pooling. Reuses the same
## Qwen3-0.6B as the chat target so we don't need a separate BERT vendor;
## the Embedding RPC mean-pools and L2-normalizes the last-layer hidden
## state.
test-extra-backend-tinygrad-embeddings: docker-build-tinygrad
BACKEND_IMAGE=local-ai-backend:tinygrad \
BACKEND_TEST_MODEL_NAME=Qwen/Qwen3-0.6B \
BACKEND_TEST_CAPS=health,load,embeddings \
$(MAKE) test-extra-backend
## tinygrad — Stable Diffusion 1.5. The original CompVis/runwayml repos have
## been gated, so we use the community-maintained mirror at
## stable-diffusion-v1-5/stable-diffusion-v1-5 with the EMA-only pruned
## checkpoint (~4.3GB). Step count is kept low (4) so a CPU-only run finishes
## in a few minutes; bump BACKEND_TEST_IMAGE_STEPS for higher quality.
test-extra-backend-tinygrad-sd: docker-build-tinygrad
BACKEND_IMAGE=local-ai-backend:tinygrad \
BACKEND_TEST_MODEL_NAME=stable-diffusion-v1-5/stable-diffusion-v1-5 \
BACKEND_TEST_CAPS=health,load,image \
$(MAKE) test-extra-backend
## tinygrad — Whisper. Loads OpenAI's tiny.en checkpoint (smallest at ~75MB)
## from the original azure CDN through tinygrad's `fetch` helper, and
## transcribes the canonical jfk.wav fixture from whisper.cpp's CI samples.
## Exercises both AudioTranscription and AudioTranscriptionStream.
test-extra-backend-tinygrad-whisper: docker-build-tinygrad
BACKEND_IMAGE=local-ai-backend:tinygrad \
BACKEND_TEST_MODEL_NAME=openai/whisper-tiny.en \
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
test-extra-backend-tinygrad-all: \
test-extra-backend-tinygrad \
test-extra-backend-tinygrad-embeddings \
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
## 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).
test-extra-backend-sglang: docker-build-sglang
BACKEND_IMAGE=local-ai-backend:sglang \
BACKEND_TEST_MODEL_NAME=Qwen/Qwen2.5-0.5B-Instruct \
BACKEND_TEST_CAPS=health,load,predict,stream,tools \
BACKEND_TEST_OPTIONS=tool_parser:qwen \
$(MAKE) test-extra-backend
## mlx is Apple-Silicon-first — the MLX backend auto-detects the right tool
## parser from the chat template, so no tool_parser: option is needed (it
## would be ignored at runtime). Run this on macOS / arm64 with Metal; the
## Linux/CPU mlx variant is untested in CI.
test-extra-backend-mlx: docker-build-mlx
BACKEND_IMAGE=local-ai-backend:mlx \
BACKEND_TEST_MODEL_NAME=mlx-community/Qwen2.5-0.5B-Instruct-4bit \
BACKEND_TEST_CAPS=health,load,predict,stream,tools \
$(MAKE) test-extra-backend
test-extra-backend-mlx-vlm: docker-build-mlx-vlm
BACKEND_IMAGE=local-ai-backend:mlx-vlm \
BACKEND_TEST_MODEL_NAME=mlx-community/Qwen2.5-0.5B-Instruct-4bit \
BACKEND_TEST_CAPS=health,load,predict,stream,tools \
$(MAKE) test-extra-backend
DOCKER_IMAGE?=local-ai
IMAGE_TYPE?=core
BASE_IMAGE?=ubuntu:24.04
@@ -916,8 +471,6 @@ docker:
--build-arg CUDA_MINOR_VERSION=$(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 $(DOCKER_IMAGE) .
docker-cuda12:
@@ -931,13 +484,11 @@ docker-cuda12:
--build-arg BUILD_TYPE=$(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)" \
@@ -946,8 +497,6 @@ 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) .
########################################################
@@ -1000,11 +549,6 @@ backend-images:
# Backend metadata: BACKEND_NAME | DOCKERFILE_TYPE | BUILD_CONTEXT | PROGRESS_FLAG | NEEDS_BACKEND_ARG
# llama-cpp is special - uses llama-cpp Dockerfile and doesn't need BACKEND arg
BACKEND_LLAMA_CPP = llama-cpp|llama-cpp|.|false|false
# ik-llama-cpp is a fork of llama.cpp with superior CPU performance
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
# Golang backends
BACKEND_PIPER = piper|golang|.|false|true
@@ -1015,10 +559,7 @@ BACKEND_STABLEDIFFUSION_GGML = stablediffusion-ggml|golang|.|--progress=plain|tr
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_OPUS = opus|golang|.|false|true
BACKEND_SHERPA_ONNX = sherpa-onnx|golang|.|false|true
# Python backends with root context
BACKEND_RERANKERS = rerankers|python|.|false|true
@@ -1027,14 +568,11 @@ 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
BACKEND_VLLM = vllm|python|.|false|true
BACKEND_VLLM_OMNI = vllm-omni|python|.|false|true
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
@@ -1048,12 +586,9 @@ BACKEND_NEMO = nemo|python|.|false|true
BACKEND_VOXCPM = voxcpm|python|.|false|true
BACKEND_WHISPERX = whisperx|python|.|false|true
BACKEND_ACE_STEP = ace-step|python|.|false|true
BACKEND_MLX = mlx|python|.|false|true
BACKEND_MLX_VLM = mlx-vlm|python|.|false|true
BACKEND_MLX_DISTRIBUTED = mlx-distributed|python|./|false|true
BACKEND_TRL = trl|python|.|false|true
BACKEND_LLAMA_CPP_QUANTIZATION = llama-cpp-quantization|python|.|false|true
BACKEND_TINYGRAD = tinygrad|python|.|false|true
# Rust backends
BACKEND_KOKOROS = kokoros|rust|.|false|true
@@ -1071,10 +606,6 @@ 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
@@ -1087,8 +618,6 @@ endef
# Generate all docker-build targets
$(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_PIPER)))
$(eval $(call generate-docker-build-target,$(BACKEND_LOCAL_STORE)))
$(eval $(call generate-docker-build-target,$(BACKEND_HUGGINGFACE)))
@@ -1103,14 +632,11 @@ $(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)))
$(eval $(call generate-docker-build-target,$(BACKEND_VLLM)))
$(eval $(call generate-docker-build-target,$(BACKEND_VLLM_OMNI)))
$(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)))
@@ -1125,23 +651,17 @@ $(eval $(call generate-docker-build-target,$(BACKEND_VOXCPM)))
$(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_MLX)))
$(eval $(call generate-docker-build-target,$(BACKEND_MLX_VLM)))
$(eval $(call generate-docker-build-target,$(BACKEND_MLX_DISTRIBUTED)))
$(eval $(call generate-docker-build-target,$(BACKEND_TRL)))
$(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-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 docker-build-vibevoice-cpp docker-build-insightface docker-build-speaker-recognition docker-build-sherpa-onnx
docker-build-backends: docker-build-llama-cpp docker-build-rerankers docker-build-vllm docker-build-vllm-omni 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-kokoros docker-build-sam3-cpp
########################################################
### Mock Backend for E2E Tests

View File

@@ -32,13 +32,13 @@
**LocalAI** is the open-source AI engine. Run any model - LLMs, vision, voice, image, video - on any hardware. No GPU required.
- **Drop-in API compatibility** — OpenAI, Anthropic, ElevenLabs APIs
- **36+ backends** — llama.cpp, vLLM, transformers, whisper, diffusers, MLX...
- **35+ backends** — llama.cpp, vLLM, transformers, whisper, diffusers, MLX...
- **Any hardware** — NVIDIA, AMD, Intel, Apple Silicon, Vulkan, or CPU-only
- **Multi-user ready** — API key auth, user quotas, role-based access
- **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)
@@ -186,7 +185,7 @@ For older news and full release notes, see [GitHub Releases](https://github.com/
## Supported Backends & Acceleration
LocalAI supports **36+ backends** including llama.cpp, vLLM, transformers, whisper.cpp, diffusers, MLX, MLX-VLM, and many more. Hardware acceleration is available for **NVIDIA** (CUDA 12/13), **AMD** (ROCm), **Intel** (oneAPI/SYCL), **Apple Silicon** (Metal), **Vulkan**, and **NVIDIA Jetson** (L4T). All backends can be installed on-the-fly from the [Backend Gallery](https://localai.io/backends/).
LocalAI supports **35+ backends** including llama.cpp, vLLM, transformers, whisper.cpp, diffusers, MLX, MLX-VLM, and many more. Hardware acceleration is available for **NVIDIA** (CUDA 12/13), **AMD** (ROCm), **Intel** (oneAPI/SYCL), **Apple Silicon** (Metal), **Vulkan**, and **NVIDIA Jetson** (L4T). All backends can be installed on-the-fly from the [Backend Gallery](https://localai.io/backends/).
See the full [Backend & Model Compatibility Table](https://localai.io/model-compatibility/) and [GPU Acceleration guide](https://localai.io/features/gpu-acceleration/).
@@ -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

@@ -1,6 +1,4 @@
ARG BASE_IMAGE=ubuntu:24.04
ARG APT_MIRROR=""
ARG APT_PORTS_MIRROR=""
FROM ${BASE_IMAGE} AS builder
ARG BACKEND=rerankers
@@ -16,14 +14,8 @@ ARG TARGETARCH
ARG TARGETVARIANT
ARG GO_VERSION=1.25.4
ARG UBUNTU_VERSION=2404
ARG AMDGPU_TARGETS
ENV AMDGPU_TARGETS=${AMDGPU_TARGETS}
ARG APT_MIRROR
ARG APT_PORTS_MIRROR
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 \
@@ -155,7 +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

@@ -1,292 +0,0 @@
ARG BASE_IMAGE=ubuntu:24.04
ARG GRPC_BASE_IMAGE=${BASE_IMAGE}
ARG APT_MIRROR=""
ARG APT_PORTS_MIRROR=""
# 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 ${GRPC_BASE_IMAGE} AS grpc
# This is a bit of a hack, but it's required in order to be able to effectively cache this layer in CI
ARG GRPC_MAKEFLAGS="-j4 -Otarget"
ARG GRPC_VERSION=v1.65.0
ARG CMAKE_FROM_SOURCE=false
# CUDA Toolkit 13.x compatibility: CMake 3.31.9+ fixes toolchain detection/arch table issues
ARG CMAKE_VERSION=3.31.10
ARG APT_MIRROR
ARG APT_PORTS_MIRROR
ENV MAKEFLAGS=${GRPC_MAKEFLAGS}
WORKDIR /build
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 && \
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
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 && \
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 \
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 \
; 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-${CMAKE_VERSION} && ./configure && make && make install
else
apt-get update && \
apt-get install -y \
cmake && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
COPY --from=grpc /opt/grpc /usr/local
COPY . /LocalAI
RUN <<'EOT' bash
set -euxo pipefail
if [[ -n "${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
FROM scratch
# Copy all available binaries (the build process only creates the appropriate ones for the target architecture)
COPY --from=builder /LocalAI/backend/cpp/ik-llama-cpp/package/. ./

View File

@@ -1,7 +1,5 @@
ARG BASE_IMAGE=ubuntu:24.04
ARG GRPC_BASE_IMAGE=${BASE_IMAGE}
ARG APT_MIRROR=""
ARG APT_PORTS_MIRROR=""
# The grpc target does one thing, it builds and installs GRPC. This is in it's own layer so that it can be effectively cached by CI.
@@ -14,16 +12,12 @@ ARG GRPC_VERSION=v1.65.0
ARG CMAKE_FROM_SOURCE=false
# CUDA Toolkit 13.x compatibility: CMake 3.31.9+ fixes toolchain detection/arch table issues
ARG CMAKE_VERSION=3.31.10
ARG APT_MIRROR
ARG APT_PORTS_MIRROR
ENV MAKEFLAGS=${GRPC_MAKEFLAGS}
WORKDIR /build
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 \
build-essential curl libssl-dev \
@@ -64,8 +58,6 @@ 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}
@@ -79,12 +71,8 @@ ARG TARGETARCH
ARG TARGETVARIANT
ARG GO_VERSION=1.25.4
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 git \
@@ -216,17 +204,12 @@ 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/* && \
# 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" \
ldconfig \
; fi
RUN echo "TARGETARCH: $TARGETARCH"

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="${APT_PORTS_MIRROR}" sh /usr/local/sbin/apt-mirror && \
apt-get update && \
RUN apt-get update && \
apt-get install -y --no-install-recommends \
build-essential \
ccache \
@@ -35,7 +29,6 @@ RUN --mount=type=bind,source=.docker/apt-mirror.sh,target=/usr/local/sbin/apt-mi
curl python3-pip \
python-is-python3 \
python3-dev llvm \
libnuma1 libgomp1 \
python3-venv make cmake && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
@@ -168,7 +161,6 @@ RUN if [ "${BUILD_TYPE}" = "hipblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then
apt-get update && \
apt-get install -y --no-install-recommends \
hipblas-dev \
hipblaslt-dev \
rocblas-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/* && \
@@ -203,19 +195,6 @@ COPY backend/backend.proto /${BACKEND}/backend.proto
COPY backend/python/common/ /${BACKEND}/common
COPY scripts/build/package-gpu-libs.sh /package-gpu-libs.sh
# Optional per-backend source build toggle (e.g. vllm on CPU can set
# FROM_SOURCE=true to compile against the build host SIMD instead of
# pulling a prebuilt wheel). Default empty — most backends ignore it.
ARG FROM_SOURCE=""
ENV FROM_SOURCE=${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 /${BACKEND} && PORTABLE_PYTHON=true make
# Package GPU libraries into the backend's lib directory
@@ -230,4 +209,4 @@ RUN if [ -f "/${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

@@ -1,301 +0,0 @@
ARG BASE_IMAGE=ubuntu:24.04
ARG GRPC_BASE_IMAGE=${BASE_IMAGE}
ARG APT_MIRROR=""
ARG APT_PORTS_MIRROR=""
# 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 ${GRPC_BASE_IMAGE} AS grpc
# This is a bit of a hack, but it's required in order to be able to effectively cache this layer in CI
ARG GRPC_MAKEFLAGS="-j4 -Otarget"
ARG GRPC_VERSION=v1.65.0
ARG CMAKE_FROM_SOURCE=false
# CUDA Toolkit 13.x compatibility: CMake 3.31.9+ fixes toolchain detection/arch table issues
ARG CMAKE_VERSION=3.31.10
ARG APT_MIRROR
ARG APT_PORTS_MIRROR
ENV MAKEFLAGS=${GRPC_MAKEFLAGS}
WORKDIR /build
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 && \
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
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 && \
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 \
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
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-${CMAKE_VERSION} && ./configure && make && make install
else
apt-get update && \
apt-get install -y \
cmake && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
COPY --from=grpc /opt/grpc /usr/local
COPY . /LocalAI
RUN <<'EOT' bash
set -euxo pipefail
if [[ -n "${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
FROM scratch
# Copy all available binaries (the build process only creates the appropriate ones for the target architecture)
COPY --from=builder /LocalAI/backend/cpp/turboquant/package/. ./

View File

@@ -17,18 +17,12 @@ service Backend {
rpc GenerateImage(GenerateImageRequest) returns (Result) {}
rpc GenerateVideo(GenerateVideoRequest) returns (Result) {}
rpc AudioTranscription(TranscriptRequest) returns (TranscriptResult) {}
rpc AudioTranscriptionStream(TranscriptRequest) returns (stream TranscriptStreamResponse) {}
rpc TTS(TTSRequest) returns (Result) {}
rpc TTSStream(TTSRequest) returns (stream Reply) {}
rpc SoundGeneration(SoundGenerationRequest) returns (Result) {}
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) {}
@@ -310,11 +304,6 @@ 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 {
@@ -333,21 +322,11 @@ message TranscriptRequest {
bool translate = 5;
bool diarize = 6;
string prompt = 7;
float temperature = 8;
repeated string timestamp_granularities = 9;
bool stream = 10;
}
message TranscriptResult {
repeated TranscriptSegment segments = 1;
string text = 2;
string language = 3;
float duration = 4;
}
message TranscriptStreamResponse {
string delta = 1;
TranscriptResult final_result = 2;
}
message TranscriptSegment {
@@ -485,112 +464,6 @@ 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"
@@ -673,7 +546,6 @@ message ModelMetadataResponse {
bool supports_thinking = 1;
string rendered_template = 2; // The rendered chat template with enable_thinking=true (empty if not applicable)
ToolFormatMarkers tool_format = 3; // Auto-detected tool format markers from differential template analysis
string media_marker = 4; // Marker the backend expects in the prompt for each multimodal input (images/audio/video). Empty when the backend does not use a marker.
}
// Fine-tuning messages

View File

@@ -1,78 +0,0 @@
## Clip/LLaVA library for multimodal support — built locally from copied sources
set(TARGET myclip)
add_library(${TARGET} clip.cpp clip.h llava.cpp llava.h)
install(TARGETS ${TARGET} LIBRARY)
target_include_directories(myclip PUBLIC .)
target_include_directories(myclip PUBLIC ../..)
target_include_directories(myclip PUBLIC ../../common)
target_link_libraries(${TARGET} PRIVATE common ggml llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)
if (NOT MSVC)
target_compile_options(${TARGET} PRIVATE -Wno-cast-qual)
endif()
set(TARGET grpc-server)
set(CMAKE_CXX_STANDARD 17)
cmake_minimum_required(VERSION 3.15)
set(TARGET grpc-server)
set(_PROTOBUF_LIBPROTOBUF libprotobuf)
set(_REFLECTION grpc++_reflection)
if (${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
if (CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "arm64")
set(HOMEBREW_DEFAULT_PREFIX "/opt/homebrew")
else()
set(HOMEBREW_DEFAULT_PREFIX "/usr/local")
endif()
link_directories("${HOMEBREW_DEFAULT_PREFIX}/lib")
include_directories("${HOMEBREW_DEFAULT_PREFIX}/include")
endif()
find_package(absl CONFIG REQUIRED)
find_package(Protobuf CONFIG REQUIRED)
find_package(gRPC CONFIG REQUIRED)
find_program(_PROTOBUF_PROTOC protoc)
set(_GRPC_GRPCPP grpc++)
find_program(_GRPC_CPP_PLUGIN_EXECUTABLE grpc_cpp_plugin)
include_directories(${CMAKE_CURRENT_BINARY_DIR})
include_directories(${Protobuf_INCLUDE_DIRS})
message(STATUS "Using protobuf version ${Protobuf_VERSION} | Protobuf_INCLUDE_DIRS: ${Protobuf_INCLUDE_DIRS} | CMAKE_CURRENT_BINARY_DIR: ${CMAKE_CURRENT_BINARY_DIR}")
# Proto file
get_filename_component(hw_proto "../../../../../../backend/backend.proto" ABSOLUTE)
get_filename_component(hw_proto_path "${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 ${_PROTOBUF_PROTOC}
ARGS --grpc_out "${CMAKE_CURRENT_BINARY_DIR}"
--cpp_out "${CMAKE_CURRENT_BINARY_DIR}"
-I "${hw_proto_path}"
--plugin=protoc-gen-grpc="${_GRPC_CPP_PLUGIN_EXECUTABLE}"
"${hw_proto}"
DEPENDS "${hw_proto}")
add_library(hw_grpc_proto
${hw_grpc_srcs}
${hw_grpc_hdrs}
${hw_proto_srcs}
${hw_proto_hdrs} )
add_executable(${TARGET} grpc-server.cpp json.hpp)
target_link_libraries(${TARGET} PRIVATE common llama myclip ${CMAKE_THREAD_LIBS_INIT} absl::flags hw_grpc_proto
absl::flags_parse
gRPC::${_REFLECTION}
gRPC::${_GRPC_GRPCPP}
protobuf::${_PROTOBUF_LIBPROTOBUF})
target_compile_features(${TARGET} PRIVATE cxx_std_11)
if(TARGET BUILD_INFO)
add_dependencies(${TARGET} BUILD_INFO)
endif()

View File

@@ -1,167 +0,0 @@
IK_LLAMA_VERSION?=a8aecbf15933295af96504f9a693998322185b5c
LLAMA_REPO?=https://github.com/ikawrakow/ik_llama.cpp
CMAKE_ARGS?=
BUILD_TYPE?=
NATIVE?=false
ONEAPI_VARS?=/opt/intel/oneapi/setvars.sh
TARGET?=--target grpc-server
JOBS?=$(shell nproc 2>/dev/null || sysctl -n hw.ncpu 2>/dev/null || echo 1)
ARCH?=$(shell uname -m)
# Disable Shared libs as we are linking on static gRPC and we can't mix shared and static
CMAKE_ARGS+=-DBUILD_SHARED_LIBS=OFF -DLLAMA_CURL=OFF
CURRENT_MAKEFILE_DIR := $(dir $(abspath $(lastword $(MAKEFILE_LIST))))
ifeq ($(NATIVE),false)
CMAKE_ARGS+=-DGGML_NATIVE=OFF -DLLAMA_OPENSSL=OFF
endif
# If build type is cublas, then we set -DGGML_CUDA=ON to CMAKE_ARGS automatically
ifeq ($(BUILD_TYPE),cublas)
CMAKE_ARGS+=-DGGML_CUDA=ON
# If build type is openblas then we set -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS
# to CMAKE_ARGS automatically
else ifeq ($(BUILD_TYPE),openblas)
CMAKE_ARGS+=-DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS
# If build type is clblas (openCL) we set -DGGML_CLBLAST=ON -DCLBlast_DIR=/some/path
else ifeq ($(BUILD_TYPE),clblas)
CMAKE_ARGS+=-DGGML_CLBLAST=ON -DCLBlast_DIR=/some/path
# If it's hipblas we do have also to set CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++
else ifeq ($(BUILD_TYPE),hipblas)
ROCM_HOME ?= /opt/rocm
ROCM_PATH ?= /opt/rocm
export CXX=$(ROCM_HOME)/llvm/bin/clang++
export CC=$(ROCM_HOME)/llvm/bin/clang
AMDGPU_TARGETS?=gfx803,gfx900,gfx906,gfx908,gfx90a,gfx942,gfx1010,gfx1030,gfx1032,gfx1100,gfx1101,gfx1102,gfx1200,gfx1201
CMAKE_ARGS+=-DGGML_HIP=ON -DAMDGPU_TARGETS=$(AMDGPU_TARGETS)
else ifeq ($(BUILD_TYPE),vulkan)
CMAKE_ARGS+=-DGGML_VULKAN=1
else ifeq ($(OS),Darwin)
ifeq ($(BUILD_TYPE),)
BUILD_TYPE=metal
endif
ifneq ($(BUILD_TYPE),metal)
CMAKE_ARGS+=-DGGML_METAL=OFF
else
CMAKE_ARGS+=-DGGML_METAL=ON
CMAKE_ARGS+=-DGGML_METAL_EMBED_LIBRARY=ON
CMAKE_ARGS+=-DGGML_METAL_USE_BF16=ON
CMAKE_ARGS+=-DGGML_OPENMP=OFF
endif
TARGET+=--target ggml-metal
endif
ifeq ($(BUILD_TYPE),sycl_f16)
CMAKE_ARGS+=-DGGML_SYCL=ON \
-DCMAKE_C_COMPILER=icx \
-DCMAKE_CXX_COMPILER=icpx \
-DCMAKE_CXX_FLAGS="-fsycl" \
-DGGML_SYCL_F16=ON
endif
ifeq ($(BUILD_TYPE),sycl_f32)
CMAKE_ARGS+=-DGGML_SYCL=ON \
-DCMAKE_C_COMPILER=icx \
-DCMAKE_CXX_COMPILER=icpx \
-DCMAKE_CXX_FLAGS="-fsycl"
endif
INSTALLED_PACKAGES=$(CURDIR)/../grpc/installed_packages
INSTALLED_LIB_CMAKE=$(INSTALLED_PACKAGES)/lib/cmake
ADDED_CMAKE_ARGS=-Dabsl_DIR=${INSTALLED_LIB_CMAKE}/absl \
-DProtobuf_DIR=${INSTALLED_LIB_CMAKE}/protobuf \
-Dutf8_range_DIR=${INSTALLED_LIB_CMAKE}/utf8_range \
-DgRPC_DIR=${INSTALLED_LIB_CMAKE}/grpc \
-DCMAKE_CXX_STANDARD_INCLUDE_DIRECTORIES=${INSTALLED_PACKAGES}/include
build-ik-llama-cpp-grpc-server:
# Conditionally build grpc for the backend to use if needed
ifdef BUILD_GRPC_FOR_BACKEND_LLAMA
$(MAKE) -C ../../grpc build
_PROTOBUF_PROTOC=${INSTALLED_PACKAGES}/bin/proto \
_GRPC_CPP_PLUGIN_EXECUTABLE=${INSTALLED_PACKAGES}/bin/grpc_cpp_plugin \
PATH="${INSTALLED_PACKAGES}/bin:${PATH}" \
CMAKE_ARGS="${CMAKE_ARGS} ${ADDED_CMAKE_ARGS}" \
IK_LLAMA_VERSION=$(IK_LLAMA_VERSION) \
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../$(VARIANT) grpc-server
else
echo "BUILD_GRPC_FOR_BACKEND_LLAMA is not defined."
IK_LLAMA_VERSION=$(IK_LLAMA_VERSION) $(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../$(VARIANT) grpc-server
endif
ik-llama-cpp-avx2: llama.cpp
cp -rf $(CURRENT_MAKEFILE_DIR)/../ik-llama-cpp $(CURRENT_MAKEFILE_DIR)/../ik-llama-cpp-avx2-build
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../ik-llama-cpp-avx2-build purge
$(info ${GREEN}I ik-llama-cpp build info:avx2${RESET})
CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=on -DGGML_AVX2=on -DGGML_AVX512=off -DGGML_FMA=on -DGGML_F16C=on" $(MAKE) VARIANT="ik-llama-cpp-avx2-build" build-ik-llama-cpp-grpc-server
cp -rfv $(CURRENT_MAKEFILE_DIR)/../ik-llama-cpp-avx2-build/grpc-server ik-llama-cpp-avx2
ik-llama-cpp-avx512: llama.cpp
cp -rf $(CURRENT_MAKEFILE_DIR)/../ik-llama-cpp $(CURRENT_MAKEFILE_DIR)/../ik-llama-cpp-avx512-build
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../ik-llama-cpp-avx512-build purge
$(info ${GREEN}I ik-llama-cpp build info:avx512${RESET})
CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=on -DGGML_AVX2=off -DGGML_AVX512=on -DGGML_FMA=on -DGGML_F16C=on" $(MAKE) VARIANT="ik-llama-cpp-avx512-build" build-ik-llama-cpp-grpc-server
cp -rfv $(CURRENT_MAKEFILE_DIR)/../ik-llama-cpp-avx512-build/grpc-server ik-llama-cpp-avx512
ik-llama-cpp-avx: llama.cpp
cp -rf $(CURRENT_MAKEFILE_DIR)/../ik-llama-cpp $(CURRENT_MAKEFILE_DIR)/../ik-llama-cpp-avx-build
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../ik-llama-cpp-avx-build purge
$(info ${GREEN}I ik-llama-cpp build info:avx${RESET})
CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=on -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off" $(MAKE) VARIANT="ik-llama-cpp-avx-build" build-ik-llama-cpp-grpc-server
cp -rfv $(CURRENT_MAKEFILE_DIR)/../ik-llama-cpp-avx-build/grpc-server ik-llama-cpp-avx
ik-llama-cpp-fallback: llama.cpp
cp -rf $(CURRENT_MAKEFILE_DIR)/../ik-llama-cpp $(CURRENT_MAKEFILE_DIR)/../ik-llama-cpp-fallback-build
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../ik-llama-cpp-fallback-build purge
$(info ${GREEN}I ik-llama-cpp build info:fallback${RESET})
CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off" $(MAKE) VARIANT="ik-llama-cpp-fallback-build" build-ik-llama-cpp-grpc-server
cp -rfv $(CURRENT_MAKEFILE_DIR)/../ik-llama-cpp-fallback-build/grpc-server ik-llama-cpp-fallback
ik-llama-cpp-grpc: llama.cpp
cp -rf $(CURRENT_MAKEFILE_DIR)/../ik-llama-cpp $(CURRENT_MAKEFILE_DIR)/../ik-llama-cpp-grpc-build
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../ik-llama-cpp-grpc-build purge
$(info ${GREEN}I ik-llama-cpp build info:grpc${RESET})
CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_RPC=ON -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off" TARGET="--target grpc-server --target rpc-server" $(MAKE) VARIANT="ik-llama-cpp-grpc-build" build-ik-llama-cpp-grpc-server
cp -rfv $(CURRENT_MAKEFILE_DIR)/../ik-llama-cpp-grpc-build/grpc-server ik-llama-cpp-grpc
ik-llama-cpp-rpc-server: ik-llama-cpp-grpc
cp -rf $(CURRENT_MAKEFILE_DIR)/../ik-llama-cpp-grpc-build/llama.cpp/build/bin/rpc-server ik-llama-cpp-rpc-server
llama.cpp:
mkdir -p llama.cpp
cd llama.cpp && \
git init && \
git remote add origin $(LLAMA_REPO) && \
git fetch origin && \
git checkout -b build $(IK_LLAMA_VERSION) && \
git submodule update --init --recursive --depth 1 --single-branch
llama.cpp/examples/grpc-server: llama.cpp
mkdir -p llama.cpp/examples/grpc-server
bash prepare.sh
rebuild:
bash prepare.sh
rm -rf grpc-server
$(MAKE) grpc-server
package:
bash package.sh
purge:
rm -rf llama.cpp/build
rm -rf llama.cpp/examples/grpc-server
rm -rf grpc-server
clean: purge
rm -rf llama.cpp
grpc-server: llama.cpp llama.cpp/examples/grpc-server
@echo "Building grpc-server with $(BUILD_TYPE) build type and $(CMAKE_ARGS)"
ifneq (,$(findstring sycl,$(BUILD_TYPE)))
+bash -c "source $(ONEAPI_VARS); \
cd llama.cpp && mkdir -p build && cd build && cmake .. $(CMAKE_ARGS) && cmake --build . --config Release -j $(JOBS) $(TARGET)"
else
+cd llama.cpp && mkdir -p build && cd build && cmake .. $(CMAKE_ARGS) && cmake --build . --config Release -j $(JOBS) $(TARGET)
endif
cp llama.cpp/build/bin/grpc-server .

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@@ -1,58 +0,0 @@
#!/bin/bash
# Script to copy the appropriate libraries based on architecture
# This script is used in the final stage of the Dockerfile
set -e
CURDIR=$(dirname "$(realpath $0)")
REPO_ROOT="${CURDIR}/../../.."
# Create lib directory
mkdir -p $CURDIR/package/lib
cp -avrf $CURDIR/ik-llama-cpp-* $CURDIR/package/
cp -rfv $CURDIR/run.sh $CURDIR/package/
# Detect architecture and copy appropriate libraries
if [ -f "/lib64/ld-linux-x86-64.so.2" ]; then
# x86_64 architecture
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
# ARM64 architecture
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 $CURDIR/package/lib/libpthread.so.0
else
echo "Error: Could not detect architecture"
exit 1
fi
# Package GPU libraries based on BUILD_TYPE
# The GPU library packaging script will detect BUILD_TYPE and copy appropriate GPU libraries
GPU_LIB_SCRIPT="${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/

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@@ -1,10 +0,0 @@
--- a/ggml/src/iqk/iqk_common.h
+++ b/ggml/src/iqk/iqk_common.h
@@ -9,6 +9,7 @@
#pragma once
#include "iqk_config.h"
+#include <cstdint>
#if defined IQK_IMPLEMENT

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@@ -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;

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@@ -1,49 +0,0 @@
#!/bin/bash
## Patches
## Apply patches from the `patches` directory
if [ -d "patches" ]; then
for patch in $(ls patches); do
echo "Applying patch $patch"
patch -d llama.cpp/ -p1 < patches/$patch
done
fi
set -e
cp -r CMakeLists.txt llama.cpp/examples/grpc-server/
cp -r grpc-server.cpp llama.cpp/examples/grpc-server/
cp -r utils.hpp llama.cpp/examples/grpc-server/
cp -rfv llama.cpp/vendor/nlohmann/json.hpp llama.cpp/examples/grpc-server/
## Copy clip/llava files for multimodal support (built as myclip library)
cp -rfv llama.cpp/examples/llava/clip.h llama.cpp/examples/grpc-server/clip.h
cp -rfv llama.cpp/examples/llava/clip.cpp llama.cpp/examples/grpc-server/clip.cpp
cp -rfv llama.cpp/examples/llava/llava.cpp llama.cpp/examples/grpc-server/llava.cpp
# Prepend llama.h include to llava.h
echo '#include "llama.h"' > llama.cpp/examples/grpc-server/llava.h
cat llama.cpp/examples/llava/llava.h >> llama.cpp/examples/grpc-server/llava.h
# Copy clip-impl.h if it exists
if [ -f llama.cpp/examples/llava/clip-impl.h ]; then
cp -rfv llama.cpp/examples/llava/clip-impl.h llama.cpp/examples/grpc-server/clip-impl.h
fi
# Copy stb_image.h
if [ -f llama.cpp/vendor/stb/stb_image.h ]; then
cp -rfv llama.cpp/vendor/stb/stb_image.h llama.cpp/examples/grpc-server/stb_image.h
elif [ -f llama.cpp/common/stb_image.h ]; then
cp -rfv llama.cpp/common/stb_image.h llama.cpp/examples/grpc-server/stb_image.h
fi
## Fix API compatibility in llava.cpp (llama_n_embd -> llama_model_n_embd)
if [ -f llama.cpp/examples/grpc-server/llava.cpp ]; then
sed -i 's/llama_n_embd(/llama_model_n_embd(/g' llama.cpp/examples/grpc-server/llava.cpp
fi
set +e
if grep -q "grpc-server" llama.cpp/examples/CMakeLists.txt; then
echo "grpc-server already added"
else
echo "add_subdirectory(grpc-server)" >> llama.cpp/examples/CMakeLists.txt
fi
set -e

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@@ -1,40 +0,0 @@
#!/bin/bash
set -ex
# Get the absolute current dir where the script is located
CURDIR=$(dirname "$(realpath $0)")
cd /
echo "CPU info:"
grep -e "model\sname" /proc/cpuinfo | head -1
grep -e "flags" /proc/cpuinfo | head -1
# ik_llama.cpp requires AVX2 — default to avx2 binary
BINARY=ik-llama-cpp-avx2
if [ -e $CURDIR/ik-llama-cpp-fallback ] && ! grep -q -e "\savx2\s" /proc/cpuinfo ; then
echo "CPU: AVX2 NOT found, using fallback"
BINARY=ik-llama-cpp-fallback
fi
# Extend ld library path with the dir where this script is located/lib
if [ "$(uname)" == "Darwin" ]; then
export DYLD_LIBRARY_PATH=$CURDIR/lib:$DYLD_LIBRARY_PATH
#export DYLD_FALLBACK_LIBRARY_PATH=$CURDIR/lib:$DYLD_FALLBACK_LIBRARY_PATH
else
export LD_LIBRARY_PATH=$CURDIR/lib:$LD_LIBRARY_PATH
fi
# If there is a lib/ld.so, use it
if [ -f $CURDIR/lib/ld.so ]; then
echo "Using lib/ld.so"
echo "Using binary: $BINARY"
exec $CURDIR/lib/ld.so $CURDIR/$BINARY "$@"
fi
echo "Using binary: $BINARY"
exec $CURDIR/$BINARY "$@"
# We should never reach this point, however just in case we do, run fallback
exec $CURDIR/ik-llama-cpp-fallback "$@"

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@@ -1,483 +0,0 @@
// https://github.com/ggerganov/llama.cpp/blob/master/examples/server/utils.hpp
#pragma once
#include <string>
#include <vector>
#include <set>
#include <mutex>
#include <condition_variable>
#include <unordered_map>
#include "json.hpp"
#include "clip.h"
using json = nlohmann::json;
extern bool server_verbose;
#ifndef SERVER_VERBOSE
#define SERVER_VERBOSE 1
#endif
#if SERVER_VERBOSE != 1
#define LOG_VERBOSE(MSG, ...)
#else
#define LOG_VERBOSE(MSG, ...) \
do \
{ \
if (server_verbose) \
{ \
server_log("VERBOSE", __func__, __LINE__, MSG, __VA_ARGS__); \
} \
} while (0)
#endif
#define LOG_ERROR( MSG, ...) server_log("ERROR", __func__, __LINE__, MSG, __VA_ARGS__)
#define LOG_WARNING(MSG, ...) server_log("WARNING", __func__, __LINE__, MSG, __VA_ARGS__)
#define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__)
//
// parallel
//
enum server_state {
SERVER_STATE_LOADING_MODEL, // Server is starting up, model not fully loaded yet
SERVER_STATE_READY, // Server is ready and model is loaded
SERVER_STATE_ERROR // An error occurred, load_model failed
};
enum task_type {
TASK_TYPE_COMPLETION,
TASK_TYPE_CANCEL,
TASK_TYPE_NEXT_RESPONSE
};
struct task_server {
int id = -1; // to be filled by llama_server_queue
int target_id;
task_type type;
json data;
bool infill_mode = false;
bool embedding_mode = false;
int multitask_id = -1;
};
struct task_result {
int id;
int multitask_id = -1;
bool stop;
bool error;
json result_json;
};
struct task_multi {
int id;
std::set<int> subtasks_remaining{};
std::vector<task_result> results{};
};
// TODO: can become bool if we can't find use of more states
enum slot_state
{
IDLE,
PROCESSING,
};
enum slot_command
{
NONE,
LOAD_PROMPT,
RELEASE,
};
struct slot_params
{
bool stream = true;
bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt
uint32_t seed = -1; // RNG seed
int32_t n_keep = 0; // number of tokens to keep from initial prompt
int32_t n_predict = -1; // new tokens to predict
std::vector<std::string> antiprompt;
json input_prefix;
json input_suffix;
};
struct slot_image
{
int32_t id;
bool request_encode_image = false;
float * image_embedding = nullptr;
int32_t image_tokens = 0;
clip_image_u8 * img_data;
std::string prefix_prompt; // before of this image
};
// completion token output with probabilities
struct completion_token_output
{
struct token_prob
{
llama_token tok;
float prob;
};
std::vector<token_prob> probs;
llama_token tok;
std::string text_to_send;
};
static inline void server_log(const char *level, const char *function, int line,
const char *message, const nlohmann::ordered_json &extra)
{
nlohmann::ordered_json log
{
{"timestamp", time(nullptr)},
{"level", level},
{"function", function},
{"line", line},
{"message", message},
};
if (!extra.empty())
{
log.merge_patch(extra);
}
const std::string str = log.dump(-1, ' ', false, json::error_handler_t::replace);
printf("%.*s\n", (int)str.size(), str.data());
fflush(stdout);
}
//
// server utils
//
template <typename T>
static T json_value(const json &body, const std::string &key, const T &default_value)
{
// Fallback null to default value
return body.contains(key) && !body.at(key).is_null()
? body.value(key, default_value)
: default_value;
}
inline std::string format_chatml(std::vector<json> messages)
{
std::ostringstream chatml_msgs;
for (auto it = messages.begin(); it != messages.end(); ++it) {
chatml_msgs << "<|im_start|>"
<< json_value(*it, "role", std::string("user")) << '\n';
chatml_msgs << json_value(*it, "content", std::string(""))
<< "<|im_end|>\n";
}
chatml_msgs << "<|im_start|>assistant" << '\n';
return chatml_msgs.str();
}
//
// work queue utils
//
struct llama_server_queue {
int id = 0;
std::mutex mutex_tasks;
// queues
std::vector<task_server> queue_tasks;
std::vector<task_server> queue_tasks_deferred;
std::vector<task_multi> queue_multitasks;
std::condition_variable condition_tasks;
// callback functions
std::function<void(task_server&)> callback_new_task;
std::function<void(task_multi&)> callback_finish_multitask;
std::function<void(void)> callback_all_task_finished;
// Add a new task to the end of the queue
int post(task_server task) {
std::unique_lock<std::mutex> lock(mutex_tasks);
if (task.id == -1) {
task.id = id++;
}
queue_tasks.push_back(std::move(task));
condition_tasks.notify_one();
return task.id;
}
// Add a new task, but defer until one slot is available
void defer(task_server task) {
std::unique_lock<std::mutex> lock(mutex_tasks);
queue_tasks_deferred.push_back(std::move(task));
}
// Get the next id for creating anew task
int get_new_id() {
std::unique_lock<std::mutex> lock(mutex_tasks);
return id++;
}
// Register function to process a new task
void on_new_task(std::function<void(task_server&)> callback) {
callback_new_task = callback;
}
// Register function to process a multitask
void on_finish_multitask(std::function<void(task_multi&)> callback) {
callback_finish_multitask = callback;
}
// Register the function to be called when the batch of tasks is finished
void on_all_tasks_finished(std::function<void(void)> callback) {
callback_all_task_finished = callback;
}
// Call when the state of one slot is changed
void notify_slot_changed() {
// move deferred tasks back to main loop
std::unique_lock<std::mutex> lock(mutex_tasks);
for (auto & task : queue_tasks_deferred) {
queue_tasks.push_back(std::move(task));
}
queue_tasks_deferred.clear();
}
// Start the main loop. This call is blocking
[[noreturn]]
void start_loop() {
while (true) {
// new task arrived
LOG_VERBOSE("have new task", {});
{
while (true)
{
std::unique_lock<std::mutex> lock(mutex_tasks);
if (queue_tasks.empty()) {
lock.unlock();
break;
}
task_server task = queue_tasks.front();
queue_tasks.erase(queue_tasks.begin());
lock.unlock();
LOG_VERBOSE("callback_new_task", {});
callback_new_task(task);
}
LOG_VERBOSE("callback_all_task_finished", {});
// process and update all the multitasks
auto queue_iterator = queue_multitasks.begin();
while (queue_iterator != queue_multitasks.end())
{
if (queue_iterator->subtasks_remaining.empty())
{
// all subtasks done == multitask is done
task_multi current_multitask = *queue_iterator;
callback_finish_multitask(current_multitask);
// remove this multitask
queue_iterator = queue_multitasks.erase(queue_iterator);
}
else
{
++queue_iterator;
}
}
// all tasks in the current loop is finished
callback_all_task_finished();
}
LOG_VERBOSE("wait for new task", {});
// wait for new task
{
std::unique_lock<std::mutex> lock(mutex_tasks);
if (queue_tasks.empty()) {
condition_tasks.wait(lock, [&]{
return !queue_tasks.empty();
});
}
}
}
}
//
// functions to manage multitasks
//
// add a multitask by specifying the id of all subtask (subtask is a task_server)
void add_multitask(int multitask_id, std::vector<int>& sub_ids)
{
std::lock_guard<std::mutex> lock(mutex_tasks);
task_multi multi;
multi.id = multitask_id;
std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end()));
queue_multitasks.push_back(multi);
}
// updatethe remaining subtasks, while appending results to multitask
void update_multitask(int multitask_id, int subtask_id, task_result& result)
{
std::lock_guard<std::mutex> lock(mutex_tasks);
for (auto& multitask : queue_multitasks)
{
if (multitask.id == multitask_id)
{
multitask.subtasks_remaining.erase(subtask_id);
multitask.results.push_back(result);
}
}
}
};
struct llama_server_response {
typedef std::function<void(int, int, task_result&)> callback_multitask_t;
callback_multitask_t callback_update_multitask;
// for keeping track of all tasks waiting for the result
std::set<int> waiting_task_ids;
// the main result queue
std::vector<task_result> queue_results;
std::mutex mutex_results;
std::condition_variable condition_results;
void add_waiting_task_id(int task_id) {
std::unique_lock<std::mutex> lock(mutex_results);
waiting_task_ids.insert(task_id);
}
void remove_waiting_task_id(int task_id) {
std::unique_lock<std::mutex> lock(mutex_results);
waiting_task_ids.erase(task_id);
}
// This function blocks the thread until there is a response for this task_id
task_result recv(int task_id) {
while (true)
{
std::unique_lock<std::mutex> lock(mutex_results);
condition_results.wait(lock, [&]{
return !queue_results.empty();
});
LOG_VERBOSE("condition_results unblock", {});
for (int i = 0; i < (int) queue_results.size(); i++)
{
if (queue_results[i].id == task_id)
{
assert(queue_results[i].multitask_id == -1);
task_result res = queue_results[i];
queue_results.erase(queue_results.begin() + i);
return res;
}
}
}
// should never reach here
}
// Register the function to update multitask
void on_multitask_update(callback_multitask_t callback) {
callback_update_multitask = callback;
}
// Send a new result to a waiting task_id
void send(task_result result) {
std::unique_lock<std::mutex> lock(mutex_results);
LOG_VERBOSE("send new result", {});
for (auto& task_id : waiting_task_ids) {
// LOG_TEE("waiting task id %i \n", task_id);
// for now, tasks that have associated parent multitasks just get erased once multitask picks up the result
if (result.multitask_id == task_id)
{
LOG_VERBOSE("callback_update_multitask", {});
callback_update_multitask(task_id, result.id, result);
continue;
}
if (result.id == task_id)
{
LOG_VERBOSE("queue_results.push_back", {});
queue_results.push_back(result);
condition_results.notify_one();
return;
}
}
}
};
//
// base64 utils (TODO: move to common in the future)
//
static const std::string base64_chars =
"ABCDEFGHIJKLMNOPQRSTUVWXYZ"
"abcdefghijklmnopqrstuvwxyz"
"0123456789+/";
static inline bool is_base64(uint8_t c)
{
return (isalnum(c) || (c == '+') || (c == '/'));
}
static inline std::vector<uint8_t> base64_decode(const std::string & encoded_string)
{
int i = 0;
int j = 0;
int in_ = 0;
int in_len = encoded_string.size();
uint8_t char_array_4[4];
uint8_t char_array_3[3];
std::vector<uint8_t> ret;
while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_]))
{
char_array_4[i++] = encoded_string[in_]; in_++;
if (i == 4)
{
for (i = 0; i <4; i++)
{
char_array_4[i] = base64_chars.find(char_array_4[i]);
}
char_array_3[0] = ((char_array_4[0] ) << 2) + ((char_array_4[1] & 0x30) >> 4);
char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2);
char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3];
for (i = 0; (i < 3); i++)
{
ret.push_back(char_array_3[i]);
}
i = 0;
}
}
if (i)
{
for (j = i; j <4; j++)
{
char_array_4[j] = 0;
}
for (j = 0; j <4; j++)
{
char_array_4[j] = base64_chars.find(char_array_4[j]);
}
char_array_3[0] = ((char_array_4[0] ) << 2) + ((char_array_4[1] & 0x30) >> 4);
char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2);
char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3];
for (j = 0; (j < i - 1); j++)
{
ret.push_back(char_array_3[j]);
}
}
return ret;
}

View File

@@ -62,18 +62,7 @@ add_executable(${TARGET} grpc-server.cpp json.hpp httplib.h)
target_include_directories(${TARGET} PRIVATE ../llava)
target_include_directories(${TARGET} PRIVATE ${CMAKE_SOURCE_DIR})
# Upstream llama.cpp renamed the `common` helpers library to `llama-common`.
# Forks that branched before the rename (e.g. llama-cpp-turboquant) still
# expose it as `common`. Detect which one is present so the same CMakeLists
# drives both builds — otherwise an unresolved name silently degrades to a
# plain `-l` flag and the PUBLIC include dir (where common.h lives) is lost.
if (TARGET llama-common)
set(_LLAMA_COMMON_TARGET llama-common)
else()
set(_LLAMA_COMMON_TARGET common)
endif()
target_link_libraries(${TARGET} PRIVATE ${_LLAMA_COMMON_TARGET} llama mtmd ${CMAKE_THREAD_LIBS_INIT} absl::flags hw_grpc_proto
target_link_libraries(${TARGET} PRIVATE common llama mtmd ${CMAKE_THREAD_LIBS_INIT} absl::flags hw_grpc_proto
absl::flags_parse
gRPC::${_REFLECTION}
gRPC::${_GRPC_GRPCPP}

View File

@@ -1,5 +1,5 @@
LLAMA_VERSION?=beb42fffa45eded44804a1fd4916146222371581
LLAMA_VERSION?=e62fa13c2497b2cd1958cb496e9489e86bbd5182
LLAMA_REPO?=https://github.com/ggerganov/llama.cpp
CMAKE_ARGS?=
@@ -33,10 +33,7 @@ else ifeq ($(BUILD_TYPE),hipblas)
ROCM_PATH ?= /opt/rocm
export CXX=$(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)),)
$(error AMDGPU_TARGETS is emptyset it to a comma-separated list of gfx targets e.g. gfx1100,gfx1101)
endif
AMDGPU_TARGETS?=gfx803,gfx900,gfx906,gfx908,gfx90a,gfx942,gfx1010,gfx1030,gfx1032,gfx1100,gfx1101,gfx1102,gfx1200,gfx1201
CMAKE_ARGS+=-DGGML_HIP=ON -DAMDGPU_TARGETS=$(AMDGPU_TARGETS)
else ifeq ($(BUILD_TYPE),vulkan)
CMAKE_ARGS+=-DGGML_VULKAN=1
@@ -135,7 +132,7 @@ llama.cpp:
cd llama.cpp && \
git init && \
git remote add origin $(LLAMA_REPO) && \
git fetch --all --tags && \
git fetch origin && \
git checkout -b build $(LLAMA_VERSION) && \
git submodule update --init --recursive --depth 1 --single-branch

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
@@ -34,8 +26,6 @@
#include <regex>
#include <atomic>
#include <cstdlib>
#include <fstream>
#include <iterator>
#include <mutex>
#include <signal.h>
#include <thread>
@@ -86,27 +76,6 @@ static grpc::Status checkAuth(grpc::ServerContext* context) {
return grpc::Status(grpc::StatusCode::UNAUTHENTICATED, "invalid token");
}
// Minimal base64 encoder. The C++ backend already pulls in base64_decode from
// llama.cpp's server-common.cpp, but no encoder is exposed — and we need one to
// hand audio bytes to the existing PredictOptions.audios path (which expects
// base64-encoded strings, just like images).
static std::string base64_encode_bytes(const unsigned char* data, size_t len) {
static const char tbl[] =
"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/";
std::string out;
out.reserve(((len + 2) / 3) * 4);
for (size_t i = 0; i < len; i += 3) {
uint32_t triple = (uint32_t(data[i]) << 16);
if (i + 1 < len) triple |= (uint32_t(data[i + 1]) << 8);
if (i + 2 < len) triple |= uint32_t(data[i + 2]);
out.push_back(tbl[(triple >> 18) & 0x3F]);
out.push_back(tbl[(triple >> 12) & 0x3F]);
out.push_back(i + 1 < len ? tbl[(triple >> 6) & 0x3F] : '=');
out.push_back(i + 2 < len ? tbl[triple & 0x3F] : '=');
}
return out;
}
// END LocalAI
@@ -442,7 +411,7 @@ 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();
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;
@@ -642,21 +611,6 @@ 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;
@@ -679,39 +633,39 @@ static void params_parse(server_context& /*ctx_server*/, const backend::ModelOpt
}
} 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")) {
if (optval != NULL) {
try { params.speculative.draft.n_ctx = std::stoi(optval_str); } catch (...) {}
try { params.speculative.n_ctx = std::stoi(optval_str); } catch (...) {}
}
}
}
@@ -933,8 +887,8 @@ 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
@@ -2837,13 +2791,6 @@ public:
return grpc::Status(grpc::StatusCode::FAILED_PRECONDITION, "Model not loaded");
}
// Report the active multimodal media marker so the Go layer can emit the
// same string when rendering prompts outside the tokenizer-template path.
// Only meaningful when an mtmd context was initialized (vision/audio models).
if (ctx_server.impl->mctx != nullptr) {
response->set_media_marker(get_media_marker());
}
// Check if chat templates are initialized
if (ctx_server.impl->chat_params.tmpls == nullptr) {
// If templates are not initialized, we can't detect thinking support
@@ -2984,119 +2931,6 @@ public:
return grpc::Status::OK;
}
// runTranscriptionAsCompletion implements OAI /v1/audio/transcriptions on
// top of the existing chat-completion + multimodal-audio pipeline, exactly
// the way upstream llama.cpp's server does it (see
// tools/server/server-context.cpp post_transcriptions_oai → forwards into
// handle_completions_impl with a single user message attaching the audio
// file via the mtmd marker).
//
// We synthesize a backend::PredictOptions with one user message
// ("Transcribe audio to text" + optional language hint) and the audio
// bytes attached via the existing PredictOptions.audios field, then
// delegate to our own Predict() handler. This keeps every multimodal
// codepath identical to the chat path and avoids duplicating ~700 lines
// of task-construction logic.
grpc::Status runTranscriptionAsCompletion(grpc::ServerContext* context,
const backend::TranscriptRequest* request,
backend::Reply* out_reply) {
if (params_base.model.path.empty()) {
return grpc::Status(grpc::StatusCode::FAILED_PRECONDITION, "Model not loaded");
}
if (request->dst().empty()) {
return grpc::Status(grpc::StatusCode::INVALID_ARGUMENT, "dst (audio file path) is required");
}
// Read audio bytes from the path LocalAI's HTTP layer wrote.
std::ifstream f(request->dst(), std::ios::binary);
if (!f.is_open()) {
return grpc::Status(grpc::StatusCode::INVALID_ARGUMENT, "failed to open audio file: " + request->dst());
}
std::vector<unsigned char> bytes((std::istreambuf_iterator<char>(f)),
std::istreambuf_iterator<char>());
f.close();
if (bytes.empty()) {
return grpc::Status(grpc::StatusCode::INVALID_ARGUMENT, "audio file is empty: " + request->dst());
}
std::string b64 = base64_encode_bytes(bytes.data(), bytes.size());
// Build the same prompt upstream uses in convert_transcriptions_to_chatcmpl.
std::string user_prompt = "Transcribe audio to text";
if (!request->language().empty()) {
user_prompt += " (language: " + request->language() + ")";
}
if (!request->prompt().empty()) {
// Optional context hint from the caller.
user_prompt += "\n" + request->prompt();
}
backend::PredictOptions synthetic;
synthetic.set_usetokenizertemplate(true);
synthetic.set_temperature(request->temperature());
// Generation length: leave at 0 so parse_options uses -1 (model default).
// The model's stop tokens / EOS handle termination naturally for ASR.
backend::Message* msg = synthetic.add_messages();
msg->set_role("user");
msg->set_content(user_prompt);
synthetic.add_audios(b64);
return Predict(context, &synthetic, out_reply);
}
grpc::Status AudioTranscription(ServerContext* context,
const backend::TranscriptRequest* request,
backend::TranscriptResult* response) override {
auto auth = checkAuth(context);
if (!auth.ok()) return auth;
backend::Reply reply;
grpc::Status st = runTranscriptionAsCompletion(context, request, &reply);
if (!st.ok()) {
return st;
}
response->set_text(reply.message());
if (!request->language().empty()) {
response->set_language(request->language());
}
return grpc::Status::OK;
}
grpc::Status AudioTranscriptionStream(ServerContext* context,
const backend::TranscriptRequest* request,
grpc::ServerWriter<backend::TranscriptStreamResponse>* writer) override {
auto auth = checkAuth(context);
if (!auth.ok()) return auth;
// Buffered streaming: run the transcription as a normal chat
// completion, then emit one delta + one final event. Real
// token-by-token streaming would require refactoring PredictStream's
// 700-line writer-coupled body; the HTTP/SSE contract is identical
// either way, and clients that only consume the assembled text don't
// notice the difference.
backend::Reply reply;
grpc::Status st = runTranscriptionAsCompletion(context, request, &reply);
if (!st.ok()) {
return st;
}
const std::string& text = reply.message();
if (!text.empty()) {
backend::TranscriptStreamResponse delta_chunk;
delta_chunk.set_delta(text);
writer->Write(delta_chunk);
}
backend::TranscriptStreamResponse final_chunk;
backend::TranscriptResult* final_result = final_chunk.mutable_final_result();
final_result->set_text(text);
if (!request->language().empty()) {
final_result->set_language(request->language());
}
writer->Write(final_chunk);
return grpc::Status::OK;
}
};

View File

@@ -46,10 +46,6 @@ if [ "$(uname)" == "Darwin" ]; then
#export DYLD_FALLBACK_LIBRARY_PATH=$CURDIR/lib:$DYLD_FALLBACK_LIBRARY_PATH
else
export LD_LIBRARY_PATH=$CURDIR/lib:$LD_LIBRARY_PATH
# Tell rocBLAS where to find TensileLibrary data (GPU kernel tuning files)
if [ -d "$CURDIR/lib/rocblas/library" ]; then
export ROCBLAS_TENSILE_LIBPATH=$CURDIR/lib/rocblas/library
fi
fi
# If there is a lib/ld.so, use it

View File

@@ -1,81 +0,0 @@
# 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?=11a241d0db78a68e0a5b99fe6f36de6683100f6a
LLAMA_REPO?=https://github.com/TheTom/llama-cpp-turboquant
CMAKE_ARGS?=
BUILD_TYPE?=
NATIVE?=false
ONEAPI_VARS?=/opt/intel/oneapi/setvars.sh
TARGET?=--target grpc-server
JOBS?=$(shell nproc 2>/dev/null || sysctl -n hw.ncpu 2>/dev/null || echo 1)
ARCH?=$(shell uname -m)
CURRENT_MAKEFILE_DIR := $(dir $(abspath $(lastword $(MAKEFILE_LIST))))
LLAMA_CPP_DIR := $(CURRENT_MAKEFILE_DIR)/../llama-cpp
GREEN := \033[0;32m
RESET := \033[0m
# turboquant is a llama.cpp fork. Rather than duplicating grpc-server.cpp / CMakeLists.txt /
# prepare.sh we reuse the ones in backend/cpp/llama-cpp, and only swap which repo+sha the
# fetch step pulls. Each flavor target copies ../llama-cpp into a sibling ../turboquant-<flavor>-build
# directory, then invokes llama-cpp's own build-llama-cpp-grpc-server with LLAMA_REPO/LLAMA_VERSION
# overridden to point at the fork.
PATCHES_DIR := $(CURRENT_MAKEFILE_DIR)/patches
# Each flavor target:
# 1. copies backend/cpp/llama-cpp/ (grpc-server.cpp + prepare.sh + CMakeLists.txt + Makefile)
# into a sibling turboquant-<flavor>-build directory;
# 2. clones the turboquant fork into turboquant-<flavor>-build/llama.cpp via the copy's
# own `llama.cpp` target, overriding LLAMA_REPO/LLAMA_VERSION;
# 3. applies patches from backend/cpp/turboquant/patches/ to the cloned fork sources
# (needed until the fork catches up with upstream server-context.cpp changes);
# 4. runs the copy's `grpc-server` target, which produces the binary we copy up as
# turboquant-<flavor>.
define turboquant-build
rm -rf $(CURRENT_MAKEFILE_DIR)/../turboquant-$(1)-build
cp -rf $(LLAMA_CPP_DIR) $(CURRENT_MAKEFILE_DIR)/../turboquant-$(1)-build
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../turboquant-$(1)-build purge
# Augment the copied grpc-server.cpp's KV-cache allow-list with the
# fork's turbo2/turbo3/turbo4 types. We patch the *copy*, never the
# original under backend/cpp/llama-cpp/, so the stock llama-cpp build
# stays compiling against vanilla upstream.
bash $(CURRENT_MAKEFILE_DIR)/patch-grpc-server.sh $(CURRENT_MAKEFILE_DIR)/../turboquant-$(1)-build/grpc-server.cpp
$(info $(GREEN)I turboquant build info:$(1)$(RESET))
LLAMA_REPO=$(LLAMA_REPO) LLAMA_VERSION=$(TURBOQUANT_VERSION) \
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../turboquant-$(1)-build llama.cpp
bash $(CURRENT_MAKEFILE_DIR)/apply-patches.sh $(CURRENT_MAKEFILE_DIR)/../turboquant-$(1)-build/llama.cpp $(PATCHES_DIR)
CMAKE_ARGS="$(CMAKE_ARGS) $(2)" TARGET="$(3)" \
LLAMA_REPO=$(LLAMA_REPO) LLAMA_VERSION=$(TURBOQUANT_VERSION) \
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../turboquant-$(1)-build grpc-server
cp -rfv $(CURRENT_MAKEFILE_DIR)/../turboquant-$(1)-build/grpc-server turboquant-$(1)
endef
turboquant-avx2:
$(call turboquant-build,avx2,-DGGML_AVX=on -DGGML_AVX2=on -DGGML_AVX512=off -DGGML_FMA=on -DGGML_F16C=on,--target grpc-server)
turboquant-avx512:
$(call turboquant-build,avx512,-DGGML_AVX=on -DGGML_AVX2=off -DGGML_AVX512=on -DGGML_FMA=on -DGGML_F16C=on,--target grpc-server)
turboquant-avx:
$(call turboquant-build,avx,-DGGML_AVX=on -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off,--target grpc-server)
turboquant-fallback:
$(call turboquant-build,fallback,-DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off,--target grpc-server)
turboquant-grpc:
$(call turboquant-build,grpc,-DGGML_RPC=ON -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off,--target grpc-server --target rpc-server)
turboquant-rpc-server: turboquant-grpc
cp -rf $(CURRENT_MAKEFILE_DIR)/../turboquant-grpc-build/llama.cpp/build/bin/rpc-server turboquant-rpc-server
package:
bash package.sh
purge:
rm -rf $(CURRENT_MAKEFILE_DIR)/../turboquant-*-build
rm -rf turboquant-* package
clean: purge

View File

@@ -1,50 +0,0 @@
#!/bin/bash
# Apply the turboquant patch series to a cloned llama-cpp-turboquant checkout.
#
# The turboquant fork branched from upstream llama.cpp before a few API changes
# that the shared backend/cpp/llama-cpp/grpc-server.cpp depends on. We carry
# those upstream commits as patch files under backend/cpp/turboquant/patches/
# and apply them here so the reused grpc-server source compiles against the
# fork unmodified.
#
# Drop the corresponding patch from patches/ whenever the fork catches up with
# upstream — the build will fail fast if a patch stops applying, which is the
# signal to retire it.
set -euo pipefail
if [[ $# -ne 2 ]]; then
echo "usage: $0 <llama.cpp-src-dir> <patches-dir>" >&2
exit 2
fi
SRC_DIR=$1
PATCHES_DIR=$2
if [[ ! -d "$SRC_DIR" ]]; then
echo "source dir does not exist: $SRC_DIR" >&2
exit 2
fi
if [[ ! -d "$PATCHES_DIR" ]]; then
echo "no patches dir at $PATCHES_DIR, nothing to apply"
exit 0
fi
shopt -s nullglob
patches=("$PATCHES_DIR"/*.patch)
shopt -u nullglob
if [[ ${#patches[@]} -eq 0 ]]; then
echo "no .patch files in $PATCHES_DIR, nothing to apply"
exit 0
fi
cd "$SRC_DIR"
for patch in "${patches[@]}"; do
echo "==> applying $patch"
git apply --verbose "$patch"
done
echo "all turboquant patches applied successfully"

View File

@@ -1,57 +0,0 @@
#!/bin/bash
# Script to copy the appropriate libraries based on architecture
# This script is used in the final stage of the Dockerfile
set -e
CURDIR=$(dirname "$(realpath $0)")
REPO_ROOT="${CURDIR}/../../.."
# Create lib directory
mkdir -p $CURDIR/package/lib
cp -avrf $CURDIR/turboquant-* $CURDIR/package/
cp -rfv $CURDIR/run.sh $CURDIR/package/
# Detect architecture and copy appropriate libraries
if [ -f "/lib64/ld-linux-x86-64.so.2" ]; then
# x86_64 architecture
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
# ARM64 architecture
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 $CURDIR/package/lib/libpthread.so.0
else
echo "Error: Could not detect architecture"
exit 1
fi
# Package GPU libraries based on BUILD_TYPE
GPU_LIB_SCRIPT="${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

@@ -1,111 +0,0 @@
#!/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:
#
# 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 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
# of the same build dir don't double-insert).
set -euo pipefail
if [[ $# -ne 1 ]]; then
echo "usage: $0 <grpc-server.cpp>" >&2
exit 2
fi
SRC=$1
if [[ ! -f "$SRC" ]]; then
echo "grpc-server.cpp not found at $SRC" >&2
exit 2
fi
if grep -q 'GGML_TYPE_TURBO2_0' "$SRC"; then
echo "==> $SRC already has TurboQuant cache types, skipping KV allow-list patch"
else
echo "==> patching $SRC to allow turbo2/turbo3/turbo4 KV-cache types"
# Insert the three TURBO entries right after the first ` GGML_TYPE_Q5_1,`
# line (the kv_cache_types[] allow-list). Using awk because the builder image
# does not ship python3, and GNU sed's multi-line `a\` quoting is awkward.
awk '
/^ GGML_TYPE_Q5_1,$/ && !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
}
}
' "$SRC" > "$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
echo "==> all patches applied"

View File

@@ -1,65 +0,0 @@
#!/bin/bash
set -ex
# Get the absolute current dir where the script is located
CURDIR=$(dirname "$(realpath $0)")
cd /
echo "CPU info:"
grep -e "model\sname" /proc/cpuinfo | head -1
grep -e "flags" /proc/cpuinfo | head -1
BINARY=turboquant-fallback
if grep -q -e "\savx\s" /proc/cpuinfo ; then
echo "CPU: AVX found OK"
if [ -e $CURDIR/turboquant-avx ]; then
BINARY=turboquant-avx
fi
fi
if grep -q -e "\savx2\s" /proc/cpuinfo ; then
echo "CPU: AVX2 found OK"
if [ -e $CURDIR/turboquant-avx2 ]; then
BINARY=turboquant-avx2
fi
fi
# Check avx 512
if grep -q -e "\savx512f\s" /proc/cpuinfo ; then
echo "CPU: AVX512F found OK"
if [ -e $CURDIR/turboquant-avx512 ]; then
BINARY=turboquant-avx512
fi
fi
if [ -n "$LLAMACPP_GRPC_SERVERS" ]; then
if [ -e $CURDIR/turboquant-grpc ]; then
BINARY=turboquant-grpc
fi
fi
# Extend ld library path with the dir where this script is located/lib
if [ "$(uname)" == "Darwin" ]; then
export DYLD_LIBRARY_PATH=$CURDIR/lib:$DYLD_LIBRARY_PATH
else
export LD_LIBRARY_PATH=$CURDIR/lib:$LD_LIBRARY_PATH
# Tell rocBLAS where to find TensileLibrary data (GPU kernel tuning files)
if [ -d "$CURDIR/lib/rocblas/library" ]; then
export ROCBLAS_TENSILE_LIBPATH=$CURDIR/lib/rocblas/library
fi
fi
# If there is a lib/ld.so, use it
if [ -f $CURDIR/lib/ld.so ]; then
echo "Using lib/ld.so"
echo "Using binary: $BINARY"
exec $CURDIR/lib/ld.so $CURDIR/$BINARY "$@"
fi
echo "Using binary: $BINARY"
exec $CURDIR/$BINARY "$@"
# We should never reach this point, however just in case we do, run fallback
exec $CURDIR/turboquant-fallback "$@"

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

@@ -1,56 +0,0 @@
cmake_minimum_required(VERSION 3.14)
project(goqwen3ttscpp LANGUAGES C CXX)
set(CMAKE_POSITION_INDEPENDENT_CODE ON)
set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
set(QWEN3TTS_DIR ${CMAKE_CURRENT_SOURCE_DIR}/sources/qwen3-tts.cpp)
# Override upstream's CMAKE_CUDA_ARCHITECTURES before add_subdirectory.
if(NOT DEFINED CMAKE_CUDA_ARCHITECTURES)
set(CMAKE_CUDA_ARCHITECTURES "75-virtual;80-virtual;86-real;89-real")
endif()
# Build ggml from the upstream's submodule FIRST, so that ggml/ggml-base/ggml-cpu
# CMake targets exist when the upstream project references them by name.
# The upstream CMakeLists.txt uses target_link_libraries(... ggml ggml-base ggml-cpu)
# with target_link_directories pointing at a pre-built ggml/build/. By adding ggml
# as a subdirectory here, CMake resolves those names as targets instead.
add_subdirectory(${QWEN3TTS_DIR}/ggml ggml EXCLUDE_FROM_ALL)
# Now add the upstream project
add_subdirectory(${QWEN3TTS_DIR} qwen3tts EXCLUDE_FROM_ALL)
add_library(goqwen3ttscpp MODULE cpp/goqwen3ttscpp.cpp)
target_link_libraries(goqwen3ttscpp PRIVATE qwen3_tts)
target_include_directories(goqwen3ttscpp PRIVATE ${QWEN3TTS_DIR}/src)
target_include_directories(goqwen3ttscpp SYSTEM PRIVATE ${QWEN3TTS_DIR}/ggml/include)
# Link GPU backends if available
foreach(backend blas cuda metal vulkan)
if(TARGET ggml-${backend})
target_link_libraries(goqwen3ttscpp PRIVATE ggml-${backend})
string(TOUPPER ${backend} BACKEND_UPPER)
target_compile_definitions(goqwen3ttscpp PRIVATE QWEN3TTS_HAVE_${BACKEND_UPPER})
if(backend STREQUAL "cuda")
find_package(CUDAToolkit QUIET)
if(CUDAToolkit_FOUND)
target_link_libraries(goqwen3ttscpp PRIVATE CUDA::cudart)
endif()
endif()
endif()
endforeach()
if(MSVC)
target_compile_options(goqwen3ttscpp PRIVATE /W4 /wd4100 /wd4505)
else()
target_compile_options(goqwen3ttscpp PRIVATE -Wall -Wextra -Wshadow -Wconversion
-Wno-unused-parameter -Wno-unused-function -Wno-sign-conversion)
endif()
if(CMAKE_CXX_COMPILER_ID MATCHES "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 9.0)
target_link_libraries(goqwen3ttscpp PRIVATE stdc++fs)
endif()
set_property(TARGET goqwen3ttscpp PROPERTY CXX_STANDARD 17)
set_target_properties(goqwen3ttscpp PROPERTIES LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR})

View File

@@ -1,126 +0,0 @@
CMAKE_ARGS?=
BUILD_TYPE?=
NATIVE?=false
GOCMD?=go
GO_TAGS?=
JOBS?=$(shell nproc --ignore=1)
# qwen3-tts.cpp version
QWEN3TTS_REPO?=https://github.com/predict-woo/qwen3-tts.cpp
QWEN3TTS_CPP_VERSION?=7a762e2ad4bacc6fdda81d81bf10a09ffb546f29
SO_TARGET?=libgoqwen3ttscpp.so
CMAKE_ARGS+=-DBUILD_SHARED_LIBS=OFF
ifeq ($(NATIVE),false)
CMAKE_ARGS+=-DGGML_NATIVE=OFF
endif
ifeq ($(BUILD_TYPE),cublas)
CMAKE_ARGS+=-DGGML_CUDA=ON
else ifeq ($(BUILD_TYPE),openblas)
CMAKE_ARGS+=-DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS
else ifeq ($(BUILD_TYPE),clblas)
CMAKE_ARGS+=-DGGML_CLBLAST=ON -DCLBlast_DIR=/some/path
else ifeq ($(BUILD_TYPE),hipblas)
CMAKE_ARGS+=-DGGML_HIPBLAS=ON
else ifeq ($(BUILD_TYPE),vulkan)
CMAKE_ARGS+=-DGGML_VULKAN=ON
else ifeq ($(OS),Darwin)
ifneq ($(BUILD_TYPE),metal)
CMAKE_ARGS+=-DGGML_METAL=OFF
else
CMAKE_ARGS+=-DGGML_METAL=ON
CMAKE_ARGS+=-DGGML_METAL_EMBED_LIBRARY=ON
endif
endif
ifeq ($(BUILD_TYPE),sycl_f16)
CMAKE_ARGS+=-DGGML_SYCL=ON \
-DCMAKE_C_COMPILER=icx \
-DCMAKE_CXX_COMPILER=icpx \
-DGGML_SYCL_F16=ON
endif
ifeq ($(BUILD_TYPE),sycl_f32)
CMAKE_ARGS+=-DGGML_SYCL=ON \
-DCMAKE_C_COMPILER=icx \
-DCMAKE_CXX_COMPILER=icpx
endif
sources/qwen3-tts.cpp:
mkdir -p sources/qwen3-tts.cpp
cd sources/qwen3-tts.cpp && \
git init && \
git remote add origin $(QWEN3TTS_REPO) && \
git fetch origin && \
git checkout $(QWEN3TTS_CPP_VERSION) && \
git submodule update --init --recursive --depth 1 --single-branch
# Detect OS
UNAME_S := $(shell uname -s)
# Only build CPU variants on Linux
ifeq ($(UNAME_S),Linux)
VARIANT_TARGETS = libgoqwen3ttscpp-avx.so libgoqwen3ttscpp-avx2.so libgoqwen3ttscpp-avx512.so libgoqwen3ttscpp-fallback.so
else
# On non-Linux (e.g., Darwin), build only fallback variant
VARIANT_TARGETS = libgoqwen3ttscpp-fallback.so
endif
qwen3-tts-cpp: main.go goqwen3ttscpp.go $(VARIANT_TARGETS)
CGO_ENABLED=0 $(GOCMD) build -tags "$(GO_TAGS)" -o qwen3-tts-cpp ./
package: qwen3-tts-cpp
bash package.sh
build: package
clean: purge
rm -rf libgoqwen3ttscpp*.so package sources/qwen3-tts.cpp qwen3-tts-cpp
purge:
rm -rf build*
# Variants must build sequentially
.NOTPARALLEL:
# Build all variants (Linux only)
ifeq ($(UNAME_S),Linux)
libgoqwen3ttscpp-avx.so: sources/qwen3-tts.cpp
$(info ${GREEN}I qwen3-tts-cpp build info:avx${RESET})
SO_TARGET=libgoqwen3ttscpp-avx.so CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=on -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off" $(MAKE) libgoqwen3ttscpp-custom
rm -rf build-libgoqwen3ttscpp-avx.so
libgoqwen3ttscpp-avx2.so: sources/qwen3-tts.cpp
$(info ${GREEN}I qwen3-tts-cpp build info:avx2${RESET})
SO_TARGET=libgoqwen3ttscpp-avx2.so CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=on -DGGML_AVX2=on -DGGML_AVX512=off -DGGML_FMA=on -DGGML_F16C=on -DGGML_BMI2=on" $(MAKE) libgoqwen3ttscpp-custom
rm -rf build-libgoqwen3ttscpp-avx2.so
libgoqwen3ttscpp-avx512.so: sources/qwen3-tts.cpp
$(info ${GREEN}I qwen3-tts-cpp build info:avx512${RESET})
SO_TARGET=libgoqwen3ttscpp-avx512.so CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=on -DGGML_AVX2=on -DGGML_AVX512=on -DGGML_FMA=on -DGGML_F16C=on -DGGML_BMI2=on" $(MAKE) libgoqwen3ttscpp-custom
rm -rf build-libgoqwen3ttscpp-avx512.so
endif
# Build fallback variant (all platforms)
libgoqwen3ttscpp-fallback.so: sources/qwen3-tts.cpp
$(info ${GREEN}I qwen3-tts-cpp build info:fallback${RESET})
SO_TARGET=libgoqwen3ttscpp-fallback.so CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off" $(MAKE) libgoqwen3ttscpp-custom
rm -rf build-libgoqwen3ttscpp-fallback.so
libgoqwen3ttscpp-custom: CMakeLists.txt cpp/goqwen3ttscpp.cpp cpp/goqwen3ttscpp.h
mkdir -p build-$(SO_TARGET) && \
cd build-$(SO_TARGET) && \
cmake .. $(CMAKE_ARGS) && \
cmake --build . --config Release -j$(JOBS) --target goqwen3ttscpp && \
cd .. && \
mv build-$(SO_TARGET)/libgoqwen3ttscpp.so ./$(SO_TARGET)
test: qwen3-tts-cpp
@echo "Running qwen3-tts-cpp tests..."
bash test.sh
@echo "qwen3-tts-cpp tests completed."
all: qwen3-tts-cpp package

View File

@@ -1,161 +0,0 @@
#include "goqwen3ttscpp.h"
#include "ggml-backend.h"
#include "qwen3_tts.h"
#include <cmath>
#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <string>
using namespace qwen3_tts;
// Global engine (loaded once, reused across requests)
static Qwen3TTS *g_engine = nullptr;
static bool g_loaded = false;
static int g_threads = 4;
static void ggml_log_cb(enum ggml_log_level level, const char *log, void *data) {
const char *level_str;
if (!log)
return;
switch (level) {
case GGML_LOG_LEVEL_DEBUG:
level_str = "DEBUG";
break;
case GGML_LOG_LEVEL_INFO:
level_str = "INFO";
break;
case GGML_LOG_LEVEL_WARN:
level_str = "WARN";
break;
case GGML_LOG_LEVEL_ERROR:
level_str = "ERROR";
break;
default:
level_str = "?????";
break;
}
fprintf(stderr, "[%-5s] ", level_str);
fputs(log, stderr);
fflush(stderr);
}
// Map language string to language_id token used by the model
static int language_to_id(const char *lang) {
if (!lang || lang[0] == '\0')
return 2050; // default: English
std::string l(lang);
if (l == "en")
return 2050;
if (l == "ru")
return 2069;
if (l == "zh")
return 2055;
if (l == "ja")
return 2058;
if (l == "ko")
return 2064;
if (l == "de")
return 2053;
if (l == "fr")
return 2061;
if (l == "es")
return 2054;
if (l == "it")
return 2056;
if (l == "pt")
return 2057;
fprintf(stderr, "[qwen3-tts-cpp] Unknown language '%s', defaulting to English\n",
lang);
return 2050;
}
int load_model(const char *model_dir, int n_threads) {
ggml_log_set(ggml_log_cb, nullptr);
ggml_backend_load_all();
if (n_threads <= 0)
n_threads = 4;
g_threads = n_threads;
fprintf(stderr, "[qwen3-tts-cpp] Loading models from %s (threads=%d)\n",
model_dir, n_threads);
g_engine = new Qwen3TTS();
if (!g_engine->load_models(model_dir)) {
fprintf(stderr, "[qwen3-tts-cpp] FATAL: failed to load models from %s\n",
model_dir);
delete g_engine;
g_engine = nullptr;
return 1;
}
g_loaded = true;
fprintf(stderr, "[qwen3-tts-cpp] Models loaded successfully\n");
return 0;
}
int synthesize(const char *text, const char *ref_audio_path, const char *dst,
const char *language, float temperature, float top_p,
int top_k, float repetition_penalty, int max_audio_tokens,
int n_threads) {
if (!g_loaded || !g_engine) {
fprintf(stderr, "[qwen3-tts-cpp] ERROR: models not loaded\n");
return 1;
}
if (!text || !dst) {
fprintf(stderr, "[qwen3-tts-cpp] ERROR: text and dst are required\n");
return 2;
}
tts_params params;
params.max_audio_tokens = max_audio_tokens > 0 ? max_audio_tokens : 4096;
params.temperature = temperature;
params.top_p = top_p;
params.top_k = top_k;
params.repetition_penalty = repetition_penalty;
params.n_threads = n_threads > 0 ? n_threads : g_threads;
params.language_id = language_to_id(language);
fprintf(stderr, "[qwen3-tts-cpp] Synthesizing: text='%.50s%s', lang_id=%d, "
"temp=%.2f, threads=%d\n",
text, (strlen(text) > 50 ? "..." : ""), params.language_id,
temperature, params.n_threads);
tts_result result;
bool has_ref = ref_audio_path && ref_audio_path[0] != '\0';
if (has_ref) {
fprintf(stderr, "[qwen3-tts-cpp] Voice cloning with ref: %s\n",
ref_audio_path);
result = g_engine->synthesize_with_voice(text, ref_audio_path, params);
} else {
result = g_engine->synthesize(text, params);
}
if (!result.success) {
fprintf(stderr, "[qwen3-tts-cpp] ERROR: synthesis failed: %s\n",
result.error_msg.c_str());
return 3;
}
int n_samples = (int)result.audio.size();
if (n_samples == 0) {
fprintf(stderr, "[qwen3-tts-cpp] ERROR: synthesis produced no samples\n");
return 4;
}
fprintf(stderr,
"[qwen3-tts-cpp] Synthesis done: %d samples (%.2fs @ 24kHz)\n",
n_samples, (float)n_samples / 24000.0f);
if (!save_audio_file(dst, result.audio, result.sample_rate)) {
fprintf(stderr, "[qwen3-tts-cpp] ERROR: failed to write %s\n", dst);
return 5;
}
fprintf(stderr, "[qwen3-tts-cpp] Wrote %s\n", dst);
return 0;
}

View File

@@ -1,12 +0,0 @@
#pragma once
#include <cstddef>
#include <cstdint>
extern "C" {
int load_model(const char *model_dir, int n_threads);
int synthesize(const char *text, const char *ref_audio_path, const char *dst,
const char *language, float temperature, float top_p,
int top_k, float repetition_penalty, int max_audio_tokens,
int n_threads);
}

View File

@@ -1,74 +0,0 @@
package main
import (
"fmt"
"os"
"path/filepath"
"github.com/mudler/LocalAI/pkg/grpc/base"
pb "github.com/mudler/LocalAI/pkg/grpc/proto"
)
var (
CppLoadModel func(modelDir string, nThreads int) int
CppSynthesize func(text, refAudioPath, dst, language string,
temperature, topP float32, topK int,
repetitionPenalty float32, maxAudioTokens, nThreads int) int
)
type Qwen3TtsCpp struct {
base.SingleThread
threads int
}
func (q *Qwen3TtsCpp) Load(opts *pb.ModelOptions) error {
// ModelFile is the model directory path (containing GGUF files)
modelDir := opts.ModelFile
if modelDir == "" {
modelDir = opts.ModelPath
}
// Resolve relative paths
if !filepath.IsAbs(modelDir) && opts.ModelPath != "" {
modelDir = filepath.Join(opts.ModelPath, modelDir)
}
threads := int(opts.Threads)
if threads <= 0 {
threads = 4
}
q.threads = threads
fmt.Fprintf(os.Stderr, "[qwen3-tts-cpp] Loading models from: %s (threads=%d)\n", modelDir, threads)
if ret := CppLoadModel(modelDir, threads); ret != 0 {
return fmt.Errorf("failed to load qwen3-tts model (error code: %d)", ret)
}
return nil
}
func (q *Qwen3TtsCpp) TTS(req *pb.TTSRequest) error {
text := req.Text
voice := req.Voice // reference audio path for voice cloning (empty = no cloning)
dst := req.Dst
language := ""
if req.Language != nil {
language = *req.Language
}
// Synthesis parameters with sensible defaults
temperature := float32(0.9)
topP := float32(0.8)
topK := 50
repetitionPenalty := float32(1.05)
maxAudioTokens := 4096
if ret := CppSynthesize(text, voice, dst, language,
temperature, topP, topK, repetitionPenalty,
maxAudioTokens, q.threads); ret != 0 {
return fmt.Errorf("failed to synthesize audio (error code: %d)", ret)
}
return nil
}

View File

@@ -1,47 +0,0 @@
package main
// Note: this is started internally by LocalAI and a server is allocated for each 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() {
// Get library name from environment variable, default to fallback
libName := os.Getenv("QWEN3TTS_LIBRARY")
if libName == "" {
libName = "./libgoqwen3ttscpp-fallback.so"
}
gosd, err := purego.Dlopen(libName, purego.RTLD_NOW|purego.RTLD_GLOBAL)
if err != nil {
panic(err)
}
libFuncs := []LibFuncs{
{&CppLoadModel, "load_model"},
{&CppSynthesize, "synthesize"},
}
for _, lf := range libFuncs {
purego.RegisterLibFunc(lf.FuncPtr, gosd, lf.Name)
}
flag.Parse()
if err := grpc.StartServer(*addr, &Qwen3TtsCpp{}); err != nil {
panic(err)
}
}

View File

@@ -1,64 +0,0 @@
#!/bin/bash
# Script to copy the appropriate libraries based on architecture
# This script is used in the final stage of the Dockerfile
set -e
CURDIR=$(dirname "$(realpath $0)")
REPO_ROOT="${CURDIR}/../../.."
# Create lib directory
mkdir -p $CURDIR/package/lib
cp -avf $CURDIR/qwen3-tts-cpp $CURDIR/package/
cp -fv $CURDIR/libgoqwen3ttscpp-*.so $CURDIR/package/
cp -fv $CURDIR/run.sh $CURDIR/package/
# Detect architecture and copy appropriate libraries
if [ -f "/lib64/ld-linux-x86-64.so.2" ]; then
# x86_64 architecture
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/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/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
# ARM64 architecture
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/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/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

@@ -1,173 +0,0 @@
package main
import (
"context"
"os"
"os/exec"
"path/filepath"
"testing"
"time"
pb "github.com/mudler/LocalAI/pkg/grpc/proto"
"google.golang.org/grpc"
"google.golang.org/grpc/credentials/insecure"
)
const (
testAddr = "localhost:50051"
startupWait = 5 * time.Second
)
func skipIfNoModel(t *testing.T) string {
t.Helper()
modelDir := os.Getenv("QWEN3TTS_MODEL_DIR")
if modelDir == "" {
t.Skip("QWEN3TTS_MODEL_DIR not set, skipping test (set to directory with GGUF models)")
}
if _, err := os.Stat(filepath.Join(modelDir, "qwen3-tts-0.6b-f16.gguf")); os.IsNotExist(err) {
t.Skipf("TTS model file not found in %s, skipping", modelDir)
}
if _, err := os.Stat(filepath.Join(modelDir, "qwen3-tts-tokenizer-f16.gguf")); os.IsNotExist(err) {
t.Skipf("Tokenizer model file not found in %s, skipping", modelDir)
}
return modelDir
}
func startServer(t *testing.T) *exec.Cmd {
t.Helper()
binary := os.Getenv("QWEN3TTS_BINARY")
if binary == "" {
binary = "./qwen3-tts-cpp"
}
if _, err := os.Stat(binary); os.IsNotExist(err) {
t.Skipf("Backend binary not found at %s, skipping", binary)
}
cmd := exec.Command(binary, "--addr", testAddr)
cmd.Stdout = os.Stderr
cmd.Stderr = os.Stderr
if err := cmd.Start(); err != nil {
t.Fatalf("Failed to start server: %v", err)
}
time.Sleep(startupWait)
return cmd
}
func stopServer(cmd *exec.Cmd) {
if cmd != nil && cmd.Process != nil {
cmd.Process.Kill()
cmd.Wait()
}
}
func dialGRPC(t *testing.T) *grpc.ClientConn {
t.Helper()
conn, err := grpc.Dial(testAddr,
grpc.WithTransportCredentials(insecure.NewCredentials()),
grpc.WithDefaultCallOptions(
grpc.MaxCallRecvMsgSize(50*1024*1024),
grpc.MaxCallSendMsgSize(50*1024*1024),
),
)
if err != nil {
t.Fatalf("Failed to dial gRPC: %v", err)
}
return conn
}
func TestServerHealth(t *testing.T) {
cmd := startServer(t)
defer stopServer(cmd)
conn := dialGRPC(t)
defer conn.Close()
client := pb.NewBackendClient(conn)
resp, err := client.Health(context.Background(), &pb.HealthMessage{})
if err != nil {
t.Fatalf("Health check failed: %v", err)
}
if string(resp.Message) != "OK" {
t.Fatalf("Expected OK, got %s", string(resp.Message))
}
}
func TestLoadModel(t *testing.T) {
modelDir := skipIfNoModel(t)
cmd := startServer(t)
defer stopServer(cmd)
conn := dialGRPC(t)
defer conn.Close()
client := pb.NewBackendClient(conn)
resp, err := client.LoadModel(context.Background(), &pb.ModelOptions{
ModelFile: modelDir,
Threads: 4,
})
if err != nil {
t.Fatalf("LoadModel failed: %v", err)
}
if !resp.Success {
t.Fatalf("LoadModel returned failure: %s", resp.Message)
}
}
func TestTTS(t *testing.T) {
modelDir := skipIfNoModel(t)
tmpDir, err := os.MkdirTemp("", "qwen3tts-test")
if err != nil {
t.Fatal(err)
}
t.Cleanup(func() { os.RemoveAll(tmpDir) })
outputFile := filepath.Join(tmpDir, "output.wav")
cmd := startServer(t)
defer stopServer(cmd)
conn := dialGRPC(t)
defer conn.Close()
client := pb.NewBackendClient(conn)
// Load models
loadResp, err := client.LoadModel(context.Background(), &pb.ModelOptions{
ModelFile: modelDir,
Threads: 4,
})
if err != nil {
t.Fatalf("LoadModel failed: %v", err)
}
if !loadResp.Success {
t.Fatalf("LoadModel returned failure: %s", loadResp.Message)
}
// Synthesize speech
language := "en"
_, err = client.TTS(context.Background(), &pb.TTSRequest{
Text: "Hello, this is a test of the Qwen3 text to speech system.",
Dst: outputFile,
Language: &language,
})
if err != nil {
t.Fatalf("TTS failed: %v", err)
}
// Verify output file exists and has content
info, err := os.Stat(outputFile)
if os.IsNotExist(err) {
t.Fatal("Output audio file was not created")
}
if err != nil {
t.Fatalf("Failed to stat output file: %v", err)
}
t.Logf("Output file size: %d bytes", info.Size())
// WAV header is 44 bytes minimum; any real audio should be much larger
if info.Size() < 1000 {
t.Errorf("Output file too small (%d bytes), expected real audio data", info.Size())
}
}

View File

@@ -1,52 +0,0 @@
#!/bin/bash
set -ex
# Get the absolute current dir where the script is located
CURDIR=$(dirname "$(realpath $0)")
cd /
echo "CPU info:"
if [ "$(uname)" != "Darwin" ]; then
grep -e "model\sname" /proc/cpuinfo | head -1
grep -e "flags" /proc/cpuinfo | head -1
fi
LIBRARY="$CURDIR/libgoqwen3ttscpp-fallback.so"
if [ "$(uname)" != "Darwin" ]; then
if grep -q -e "\savx\s" /proc/cpuinfo ; then
echo "CPU: AVX found OK"
if [ -e $CURDIR/libgoqwen3ttscpp-avx.so ]; then
LIBRARY="$CURDIR/libgoqwen3ttscpp-avx.so"
fi
fi
if grep -q -e "\savx2\s" /proc/cpuinfo ; then
echo "CPU: AVX2 found OK"
if [ -e $CURDIR/libgoqwen3ttscpp-avx2.so ]; then
LIBRARY="$CURDIR/libgoqwen3ttscpp-avx2.so"
fi
fi
# Check avx 512
if grep -q -e "\savx512f\s" /proc/cpuinfo ; then
echo "CPU: AVX512F found OK"
if [ -e $CURDIR/libgoqwen3ttscpp-avx512.so ]; then
LIBRARY="$CURDIR/libgoqwen3ttscpp-avx512.so"
fi
fi
fi
export LD_LIBRARY_PATH=$CURDIR/lib:$LD_LIBRARY_PATH
export QWEN3TTS_LIBRARY=$LIBRARY
# If there is a lib/ld.so, use it
if [ -f $CURDIR/lib/ld.so ]; then
echo "Using lib/ld.so"
echo "Using library: $LIBRARY"
exec $CURDIR/lib/ld.so $CURDIR/qwen3-tts-cpp "$@"
fi
echo "Using library: $LIBRARY"
exec $CURDIR/qwen3-tts-cpp "$@"

View File

@@ -1,52 +0,0 @@
#!/bin/bash
set -e
CURDIR=$(dirname "$(realpath $0)")
echo "Running qwen3-tts-cpp backend tests..."
# The test requires:
# - QWEN3TTS_MODEL_DIR: path to directory containing GGUF model files
# - QWEN3TTS_BINARY: path to the qwen3-tts-cpp binary (defaults to ./qwen3-tts-cpp)
#
# Tests that require the model will be skipped if QWEN3TTS_MODEL_DIR is not set
# or the directory does not contain the required model files.
cd "$CURDIR"
# Only auto-download models when QWEN3TTS_MODEL_DIR is not explicitly set
if [ -z "$QWEN3TTS_MODEL_DIR" ]; then
export QWEN3TTS_MODEL_DIR="./qwen3-tts-models"
if [ ! -d "$QWEN3TTS_MODEL_DIR" ]; then
echo "Creating qwen3-tts-models directory for tests..."
mkdir -p "$QWEN3TTS_MODEL_DIR"
REPO_ID="endo5501/qwen3-tts.cpp"
echo "Repository: ${REPO_ID}"
echo ""
# Files to download (smallest model for testing)
FILES=(
"qwen3-tts-0.6b-f16.gguf"
"qwen3-tts-tokenizer-f16.gguf"
)
BASE_URL="https://huggingface.co/${REPO_ID}/resolve/main"
for file in "${FILES[@]}"; do
dest="${QWEN3TTS_MODEL_DIR}/${file}"
if [ -f "${dest}" ]; then
echo " [skip] ${file} (already exists)"
else
echo " [download] ${file}..."
curl -L -o "${dest}" "${BASE_URL}/${file}" --progress-bar
echo " [done] ${file}"
fi
done
fi
fi
# Run Go tests
go test -v -timeout 600s .
echo "All qwen3-tts-cpp tests passed."

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

@@ -26,7 +26,7 @@ else ifeq ($(BUILD_TYPE),hipblas)
ROCM_PATH ?= /opt/rocm
export CXX=$(ROCM_HOME)/llvm/bin/clang++
export CC=$(ROCM_HOME)/llvm/bin/clang
AMDGPU_TARGETS?=gfx908,gfx90a,gfx942,gfx950,gfx1030,gfx1100,gfx1101,gfx1102,gfx1200,gfx1201
AMDGPU_TARGETS?=gfx803,gfx900,gfx906,gfx908,gfx90a,gfx942,gfx1010,gfx1030,gfx1032,gfx1100,gfx1101,gfx1102,gfx1200,gfx1201
CMAKE_ARGS+=-DGGML_HIPBLAS=ON -DAMDGPU_TARGETS=$(AMDGPU_TARGETS)
else ifeq ($(BUILD_TYPE),vulkan)
CMAKE_ARGS+=-DGGML_VULKAN=ON
@@ -111,7 +111,7 @@ libgosam3-fallback.so: sources/sam3.cpp
SO_TARGET=libgosam3-fallback.so CMAKE_ARGS="$(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

@@ -1,169 +0,0 @@
package main
import (
"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(&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

@@ -1,325 +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;
}
// ==================================================================
// 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);
}

View File

@@ -1,129 +0,0 @@
#ifndef LOCALAI_SHERPA_ONNX_SHIM_H
#define LOCALAI_SHERPA_ONNX_SHIM_H
#include <stdint.h>
// libsherpa-shim: purego-friendly wrapper around sherpa-onnx's C API.
// Purego can't access C struct fields and can't route C callbacks to Go
// funcs directly. Every function here is a fixed-signature trampoline
// that replaces one field read/write or callback handoff that the Go
// backend would otherwise have to do through cgo.
//
// String lifetime: setters strdup; _free walks every owned string and
// frees it. Callers may discard their input buffers the moment a setter
// returns.
//
// Opaque handles are `void *` in both directions. Nothing here holds a
// reference across calls except config handles (freed via _free) and
// sherpa-allocated results (freed via sherpa's own Destroy* entry
// points, which Go calls through purego pass-through).
#ifdef __cplusplus
extern "C" {
#endif
// --- VAD config -----------------------------------------------------
void *sherpa_shim_vad_config_new(void);
void sherpa_shim_vad_config_free(void *cfg);
void sherpa_shim_vad_config_set_silero_model(void *cfg, const char *path);
void sherpa_shim_vad_config_set_silero_threshold(void *cfg, float v);
void sherpa_shim_vad_config_set_silero_min_silence_duration(void *cfg, float v);
void sherpa_shim_vad_config_set_silero_min_speech_duration(void *cfg, float v);
void sherpa_shim_vad_config_set_silero_window_size(void *cfg, int32_t v);
void sherpa_shim_vad_config_set_silero_max_speech_duration(void *cfg, float v);
void sherpa_shim_vad_config_set_sample_rate(void *cfg, int32_t v);
void sherpa_shim_vad_config_set_num_threads(void *cfg, int32_t v);
void sherpa_shim_vad_config_set_provider(void *cfg, const char *v);
void sherpa_shim_vad_config_set_debug(void *cfg, int32_t v);
void *sherpa_shim_create_vad(void *cfg, float buffer_size_seconds);
// --- Offline TTS config (VITS path — the only TTS family the backend uses) ---
void *sherpa_shim_tts_config_new(void);
void sherpa_shim_tts_config_free(void *cfg);
void sherpa_shim_tts_config_set_vits_model(void *cfg, const char *v);
void sherpa_shim_tts_config_set_vits_tokens(void *cfg, const char *v);
void sherpa_shim_tts_config_set_vits_lexicon(void *cfg, const char *v);
void sherpa_shim_tts_config_set_vits_data_dir(void *cfg, const char *v);
void sherpa_shim_tts_config_set_vits_noise_scale(void *cfg, float v);
void sherpa_shim_tts_config_set_vits_noise_scale_w(void *cfg, float v);
void sherpa_shim_tts_config_set_vits_length_scale(void *cfg, float v);
void sherpa_shim_tts_config_set_num_threads(void *cfg, int32_t v);
void sherpa_shim_tts_config_set_debug(void *cfg, int32_t v);
void sherpa_shim_tts_config_set_provider(void *cfg, const char *v);
void sherpa_shim_tts_config_set_max_num_sentences(void *cfg, int32_t v);
void *sherpa_shim_create_offline_tts(void *cfg);
// --- Offline recognizer config (Whisper / Paraformer / SenseVoice / Omnilingual) ---
void *sherpa_shim_offline_recog_config_new(void);
void sherpa_shim_offline_recog_config_free(void *cfg);
void sherpa_shim_offline_recog_config_set_num_threads(void *cfg, int32_t v);
void sherpa_shim_offline_recog_config_set_debug(void *cfg, int32_t v);
void sherpa_shim_offline_recog_config_set_provider(void *cfg, const char *v);
void sherpa_shim_offline_recog_config_set_tokens(void *cfg, const char *v);
void sherpa_shim_offline_recog_config_set_feat_sample_rate(void *cfg, int32_t v);
void sherpa_shim_offline_recog_config_set_feat_feature_dim(void *cfg, int32_t v);
void sherpa_shim_offline_recog_config_set_decoding_method(void *cfg, const char *v);
void sherpa_shim_offline_recog_config_set_whisper_encoder(void *cfg, const char *v);
void sherpa_shim_offline_recog_config_set_whisper_decoder(void *cfg, const char *v);
void sherpa_shim_offline_recog_config_set_whisper_language(void *cfg, const char *v);
void sherpa_shim_offline_recog_config_set_whisper_task(void *cfg, const char *v);
void sherpa_shim_offline_recog_config_set_whisper_tail_paddings(void *cfg, int32_t v);
void sherpa_shim_offline_recog_config_set_paraformer_model(void *cfg, const char *v);
void sherpa_shim_offline_recog_config_set_sense_voice_model(void *cfg, const char *v);
void sherpa_shim_offline_recog_config_set_sense_voice_language(void *cfg, const char *v);
void sherpa_shim_offline_recog_config_set_sense_voice_use_itn(void *cfg, int32_t v);
void sherpa_shim_offline_recog_config_set_omnilingual_model(void *cfg, const char *v);
void *sherpa_shim_create_offline_recognizer(void *cfg);
// --- Online recognizer config (streaming zipformer transducer) ---
void *sherpa_shim_online_recog_config_new(void);
void sherpa_shim_online_recog_config_free(void *cfg);
void sherpa_shim_online_recog_config_set_transducer_encoder(void *cfg, const char *v);
void sherpa_shim_online_recog_config_set_transducer_decoder(void *cfg, const char *v);
void sherpa_shim_online_recog_config_set_transducer_joiner(void *cfg, const char *v);
void sherpa_shim_online_recog_config_set_tokens(void *cfg, const char *v);
void sherpa_shim_online_recog_config_set_num_threads(void *cfg, int32_t v);
void sherpa_shim_online_recog_config_set_debug(void *cfg, int32_t v);
void sherpa_shim_online_recog_config_set_provider(void *cfg, const char *v);
void sherpa_shim_online_recog_config_set_feat_sample_rate(void *cfg, int32_t v);
void sherpa_shim_online_recog_config_set_feat_feature_dim(void *cfg, int32_t v);
void sherpa_shim_online_recog_config_set_decoding_method(void *cfg, const char *v);
void sherpa_shim_online_recog_config_set_enable_endpoint(void *cfg, int32_t v);
void sherpa_shim_online_recog_config_set_rule1_min_trailing_silence(void *cfg, float v);
void sherpa_shim_online_recog_config_set_rule2_min_trailing_silence(void *cfg, float v);
void sherpa_shim_online_recog_config_set_rule3_min_utterance_length(void *cfg, float v);
void *sherpa_shim_create_online_recognizer(void *cfg);
// --- Result accessors (sherpa-allocated; caller destroys via sherpa's own Destroy*) ---
int32_t sherpa_shim_wave_sample_rate(const void *wave);
int32_t sherpa_shim_wave_num_samples(const void *wave);
const float *sherpa_shim_wave_samples(const void *wave);
const char *sherpa_shim_offline_result_text(const void *result);
const char *sherpa_shim_online_result_text(const void *result);
int32_t sherpa_shim_generated_audio_sample_rate(const void *audio);
int32_t sherpa_shim_generated_audio_n(const void *audio);
const float *sherpa_shim_generated_audio_samples(const void *audio);
int32_t sherpa_shim_speech_segment_start(const void *seg);
int32_t sherpa_shim_speech_segment_n(const void *seg);
// --- TTS streaming callback trampoline -----------------------------
// Replaces the //export sherpaTtsGoCallback + callbacks.c bridge pattern.
// `callback_ptr` is the C-callable function pointer returned by
// purego.NewCallback. `user_data` is an integer the Go side uses to
// look up its state (sync.Map keyed by uint64).
//
// Returns the sherpa-allocated SherpaOnnxGeneratedAudio. Destroy with
// SherpaOnnxDestroyOfflineTtsGeneratedAudio (callable directly from
// Go via purego).
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);
#ifdef __cplusplus
}
#endif
#endif

View File

@@ -1,23 +0,0 @@
package main
import (
"flag"
grpc "github.com/mudler/LocalAI/pkg/grpc"
)
var (
addr = flag.String("addr", "localhost:50051", "the address to connect to")
)
func main() {
flag.Parse()
if err := loadSherpaLibs(); err != nil {
panic(err)
}
if err := grpc.StartServer(*addr, &SherpaBackend{}); err != nil {
panic(err)
}
}

View File

@@ -1,51 +0,0 @@
#!/bin/bash
set -e
CURDIR=$(dirname "$(realpath $0)")
REPO_ROOT="${CURDIR}/../../.."
mkdir -p $CURDIR/package/lib
cp -avf $CURDIR/sherpa-onnx $CURDIR/package/
cp -avf $CURDIR/run.sh $CURDIR/package/
cp -rfLv $CURDIR/backend-assets/lib/* $CURDIR/package/lib/
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 [ $(uname -s) = "Darwin" ]; then
echo "Detected Darwin"
else
echo "Error: Could not detect architecture"
exit 1
fi
GPU_LIB_SCRIPT="${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

@@ -1,13 +0,0 @@
#!/bin/bash
set -ex
CURDIR=$(dirname "$(realpath $0)")
export LD_LIBRARY_PATH=$CURDIR/lib:$LD_LIBRARY_PATH
if [ -f $CURDIR/lib/ld.so ]; then
echo "Using lib/ld.so"
exec $CURDIR/lib/ld.so $CURDIR/sherpa-onnx "$@"
fi
exec $CURDIR/sherpa-onnx "$@"

View File

@@ -1,12 +0,0 @@
#!/bin/bash
# Unit tests for the sherpa-onnx backend. Exercises error-path and
# dispatch logic via SherpaBackend directly (no gRPC). Integration
# coverage (gRPC TTS / streaming ASR / realtime pipeline) lives in
# tests/e2e-backends and tests/e2e and runs against the Docker image.
set -e
CURDIR=$(dirname "$(realpath $0)")
cd "$CURDIR"
PACKAGES=$(go list ./... | grep -v /sources/)
go test -v -timeout 60s $PACKAGES

View File

@@ -4,7 +4,7 @@ set(CMAKE_POSITION_INDEPENDENT_CODE ON)
add_subdirectory(./sources/stablediffusion-ggml.cpp)
add_library(gosd MODULE cpp/gosd.cpp)
add_library(gosd MODULE gosd.cpp)
target_link_libraries(gosd PRIVATE stable-diffusion ggml)
if(CMAKE_CXX_COMPILER_ID MATCHES "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 9.0)

View File

@@ -8,7 +8,7 @@ JOBS?=$(shell nproc --ignore=1)
# stablediffusion.cpp (ggml)
STABLEDIFFUSION_GGML_REPO?=https://github.com/leejet/stable-diffusion.cpp
STABLEDIFFUSION_GGML_VERSION?=3d6064b37ef4607917f8acf2ca8c8906d5087413
STABLEDIFFUSION_GGML_VERSION?=e8323cabb0e4511ba18a50b1cb34cf1f87fc71ef
CMAKE_ARGS+=-DGGML_MAX_NAME=128
@@ -32,7 +32,7 @@ else ifeq ($(BUILD_TYPE),hipblas)
ROCM_PATH ?= /opt/rocm
export CXX=$(ROCM_HOME)/llvm/bin/clang++
export CC=$(ROCM_HOME)/llvm/bin/clang
AMDGPU_TARGETS?=gfx908,gfx90a,gfx942,gfx950,gfx1030,gfx1100,gfx1101,gfx1102,gfx1200,gfx1201
AMDGPU_TARGETS?=gfx803,gfx900,gfx906,gfx908,gfx90a,gfx942,gfx1010,gfx1030,gfx1032,gfx1100,gfx1101,gfx1102,gfx1200,gfx1201
CMAKE_ARGS+=-DSD_HIPBLAS=ON -DGGML_HIPBLAS=ON -DAMDGPU_TARGETS=$(AMDGPU_TARGETS)
else ifeq ($(BUILD_TYPE),vulkan)
CMAKE_ARGS+=-DSD_VULKAN=ON -DGGML_VULKAN=ON
@@ -119,7 +119,7 @@ libgosd-fallback.so: sources/stablediffusion-ggml.cpp
SO_TARGET=libgosd-fallback.so CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off" $(MAKE) libgosd-custom
rm -rfv build*
libgosd-custom: CMakeLists.txt cpp/gosd.cpp cpp/gosd.h
libgosd-custom: CMakeLists.txt gosd.cpp gosd.h
mkdir -p build-$(SO_TARGET) && \
cd build-$(SO_TARGET) && \
cmake .. $(CMAKE_ARGS) && \

View File

@@ -26,10 +26,6 @@
#include "stb_image_resize.h"
#include <stdlib.h>
#include <regex>
#include <errno.h>
#include <signal.h>
#include <unistd.h>
#include <sys/wait.h>
@@ -984,256 +980,6 @@ int gen_image(sd_img_gen_params_t *p, int steps, char *dst, float cfg_scale, cha
return !ret;
}
// ---------------- Video generation ----------------
sd_vid_gen_params_t* sd_vid_gen_params_new(void) {
sd_vid_gen_params_t *params = (sd_vid_gen_params_t *)std::malloc(sizeof(sd_vid_gen_params_t));
sd_vid_gen_params_init(params);
sd_sample_params_init(&params->sample_params);
sd_sample_params_init(&params->high_noise_sample_params);
sd_cache_params_init(&params->cache);
return params;
}
// Persistent storage for cleaned video prompts (kept alive for the duration of generation)
static std::string cleaned_vid_prompt_storage;
static std::string cleaned_vid_negative_prompt_storage;
void sd_vid_gen_params_set_prompts(sd_vid_gen_params_t *params, const char *prompt, const char *negative_prompt) {
lora_vec.clear();
lora_strings.clear();
std::string prompt_str = prompt ? prompt : "";
std::string negative_prompt_str = negative_prompt ? negative_prompt : "";
const char* lora_dir_to_use = lora_dir_path.empty() ? nullptr : lora_dir_path.c_str();
auto [loras, cleaned_prompt] = parse_loras_from_prompt(prompt_str, lora_dir_to_use);
lora_vec = loras;
cleaned_vid_prompt_storage = cleaned_prompt;
auto [neg_loras, cleaned_negative] = parse_loras_from_prompt(negative_prompt_str, lora_dir_to_use);
cleaned_vid_negative_prompt_storage = cleaned_negative;
params->prompt = cleaned_vid_prompt_storage.c_str();
params->negative_prompt = cleaned_vid_negative_prompt_storage.c_str();
params->loras = lora_vec.empty() ? nullptr : lora_vec.data();
params->lora_count = static_cast<uint32_t>(lora_vec.size());
}
void sd_vid_gen_params_set_dimensions(sd_vid_gen_params_t *params, int width, int height) {
params->width = width;
params->height = height;
}
void sd_vid_gen_params_set_seed(sd_vid_gen_params_t *params, int64_t seed) {
params->seed = seed;
}
void sd_vid_gen_params_set_video_frames(sd_vid_gen_params_t *params, int n) {
params->video_frames = n;
}
// Load an image file into an sd_image_t, resizing to target dims if needed.
// Returns a heap-allocated buffer the caller must free (or nullptr on failure).
static uint8_t* load_and_resize_image(const char* path, int target_width, int target_height, sd_image_t* out) {
if (!path || strlen(path) == 0) {
*out = {0, 0, 0, nullptr};
return nullptr;
}
int c = 0, img_w = 0, img_h = 0;
uint8_t* buf = stbi_load(path, &img_w, &img_h, &c, 3);
if (!buf) {
fprintf(stderr, "Failed to load image from '%s'\n", path);
*out = {0, 0, 0, nullptr};
return nullptr;
}
if (img_w != target_width || img_h != target_height) {
fprintf(stderr, "Resizing image from %dx%d to %dx%d\n", img_w, img_h, target_width, target_height);
uint8_t* resized = (uint8_t*)malloc((size_t)target_width * target_height * 3);
if (!resized) { free(buf); *out = {0, 0, 0, nullptr}; return nullptr; }
stbir_resize(buf, img_w, img_h, 0,
resized, target_width, target_height, 0, STBIR_TYPE_UINT8,
3, STBIR_ALPHA_CHANNEL_NONE, 0,
STBIR_EDGE_CLAMP, STBIR_EDGE_CLAMP,
STBIR_FILTER_BOX, STBIR_FILTER_BOX,
STBIR_COLORSPACE_SRGB, nullptr);
free(buf);
buf = resized;
}
*out = {(uint32_t)target_width, (uint32_t)target_height, 3, buf};
return buf;
}
// Pipe raw RGB/RGBA frames to ffmpeg stdin and let it produce an MP4 at dst.
// Uses fork+execvp to avoid shell interpretation of dst.
static int ffmpeg_mux_raw_to_mp4(sd_image_t* frames, int num_frames, int fps, const char* dst) {
if (num_frames <= 0 || !frames || !frames[0].data) {
fprintf(stderr, "ffmpeg_mux: empty frames\n");
return 1;
}
int width = (int)frames[0].width;
int height = (int)frames[0].height;
int channels = (int)frames[0].channel;
const char* pix_fmt_in = (channels == 4) ? "rgba" : "rgb24";
char size_str[32];
char fps_str[32];
snprintf(size_str, sizeof(size_str), "%dx%d", width, height);
snprintf(fps_str, sizeof(fps_str), "%d", fps);
int pipefd[2];
if (pipe(pipefd) != 0) { perror("pipe"); return 1; }
pid_t pid = fork();
if (pid < 0) { perror("fork"); close(pipefd[0]); close(pipefd[1]); return 1; }
if (pid == 0) {
// child
close(pipefd[1]);
if (dup2(pipefd[0], STDIN_FILENO) < 0) { perror("dup2"); _exit(127); }
close(pipefd[0]);
std::vector<char*> argv = {
const_cast<char*>("ffmpeg"),
const_cast<char*>("-y"),
const_cast<char*>("-hide_banner"),
const_cast<char*>("-loglevel"), const_cast<char*>("warning"),
const_cast<char*>("-f"), const_cast<char*>("rawvideo"),
const_cast<char*>("-pix_fmt"), const_cast<char*>(pix_fmt_in),
const_cast<char*>("-s"), size_str,
const_cast<char*>("-framerate"), fps_str,
const_cast<char*>("-i"), const_cast<char*>("-"),
const_cast<char*>("-c:v"), const_cast<char*>("libx264"),
const_cast<char*>("-pix_fmt"), const_cast<char*>("yuv420p"),
const_cast<char*>("-movflags"), const_cast<char*>("+faststart"),
// Force MP4 container. Distributed LocalAI hands us a staging
// path (e.g. /staging/localai-output-NNN.tmp) with a non-standard
// extension; relying on filename suffix makes ffmpeg bail with
// "Unable to choose an output format".
const_cast<char*>("-f"), const_cast<char*>("mp4"),
const_cast<char*>(dst),
nullptr
};
execvp(argv[0], argv.data());
perror("execvp ffmpeg");
_exit(127);
}
// parent
close(pipefd[0]);
// Ignore SIGPIPE so a dying ffmpeg surfaces via write() errno instead of killing us.
signal(SIGPIPE, SIG_IGN);
for (int i = 0; i < num_frames; i++) {
if (!frames[i].data) continue;
size_t frame_bytes = (size_t)frames[i].width * frames[i].height * frames[i].channel;
const uint8_t* p = frames[i].data;
size_t remaining = frame_bytes;
while (remaining > 0) {
ssize_t n = write(pipefd[1], p, remaining);
if (n < 0) {
if (errno == EINTR) continue;
perror("write frame to ffmpeg");
close(pipefd[1]);
int status;
waitpid(pid, &status, 0);
return 1;
}
p += n;
remaining -= (size_t)n;
}
}
close(pipefd[1]);
int status = 0;
while (waitpid(pid, &status, 0) < 0) {
if (errno != EINTR) { perror("waitpid"); return 1; }
}
if (!WIFEXITED(status) || WEXITSTATUS(status) != 0) {
fprintf(stderr, "ffmpeg exited with status %d\n", status);
return 1;
}
return 0;
}
int gen_video(sd_vid_gen_params_t *p, int steps, char *dst, float cfg_scale, int fps, char *init_image, char *end_image) {
if (!p) return 1;
if (!dst || strlen(dst) == 0) {
fprintf(stderr, "gen_video: dst is empty\n");
std::free(p);
return 1;
}
std::vector<int> skip_layers = {7, 8, 9};
fprintf(stderr, "Generating video: %dx%d, frames=%d, fps=%d, steps=%d, cfg=%.2f\n",
p->width, p->height, p->video_frames, fps, steps, cfg_scale);
// Sample params (shared by both low and high-noise passes — MoE models use the high-noise
// set during the first phase; single-model Wan2.1 ignores it. Same defaults for both is fine.)
p->sample_params.guidance.txt_cfg = cfg_scale;
p->sample_params.guidance.slg.layers = skip_layers.data();
p->sample_params.guidance.slg.layer_count = skip_layers.size();
p->sample_params.sample_method = sample_method;
p->sample_params.sample_steps = steps;
p->sample_params.scheduler = scheduler;
p->sample_params.flow_shift = flow_shift;
p->high_noise_sample_params.guidance.txt_cfg = cfg_scale;
p->high_noise_sample_params.guidance.slg.layers = skip_layers.data();
p->high_noise_sample_params.guidance.slg.layer_count = skip_layers.size();
p->high_noise_sample_params.sample_method = sample_method;
p->high_noise_sample_params.sample_steps = steps;
p->high_noise_sample_params.scheduler = scheduler;
p->high_noise_sample_params.flow_shift = flow_shift;
// Load init/end reference images if provided (resized to output dims).
uint8_t* init_buf = nullptr;
uint8_t* end_buf = nullptr;
sd_image_t init_img = {0, 0, 0, nullptr};
sd_image_t end_img = {0, 0, 0, nullptr};
if (init_image && strlen(init_image) > 0) {
init_buf = load_and_resize_image(init_image, p->width, p->height, &init_img);
if (!init_buf) { std::free(p); return 1; }
}
if (end_image && strlen(end_image) > 0) {
end_buf = load_and_resize_image(end_image, p->width, p->height, &end_img);
if (!end_buf) { if (init_buf) free(init_buf); std::free(p); return 1; }
}
p->init_image = init_img;
p->end_image = end_img;
// Generate
int num_frames_out = 0;
sd_image_t* frames = generate_video(sd_c, p, &num_frames_out);
std::free(p);
if (!frames || num_frames_out == 0) {
fprintf(stderr, "generate_video produced no frames\n");
if (init_buf) free(init_buf);
if (end_buf) free(end_buf);
return 1;
}
fprintf(stderr, "Generated %d frames, muxing to %s via ffmpeg\n", num_frames_out, dst);
int rc = ffmpeg_mux_raw_to_mp4(frames, num_frames_out, fps, dst);
for (int i = 0; i < num_frames_out; i++) {
if (frames[i].data) free(frames[i].data);
}
free(frames);
if (init_buf) free(init_buf);
if (end_buf) free(end_buf);
if (rc == 0) {
fprintf(stderr, "gen_video done: %s\n", dst);
}
fflush(stderr);
return rc;
}
int unload() {
free_sd_ctx(sd_c);
return 0;

View File

@@ -23,7 +23,6 @@ type SDGGML struct {
var (
LoadModel func(model, model_apth string, options []uintptr, threads int32, diff int) int
GenImage func(params uintptr, steps int, dst string, cfgScale float32, srcImage string, strength float32, maskImage string, refImages []uintptr, refImagesCount int) int
GenVideo func(params uintptr, steps int, dst string, cfgScale float32, fps int, initImage string, endImage string) int
TilingParamsSetEnabled func(params uintptr, enabled bool)
TilingParamsSetTileSizes func(params uintptr, tileSizeX int, tileSizeY int)
@@ -35,12 +34,6 @@ var (
ImgGenParamsSetDimensions func(params uintptr, width int, height int)
ImgGenParamsSetSeed func(params uintptr, seed int64)
ImgGenParamsGetVaeTilingParams func(params uintptr) uintptr
VidGenParamsNew func() uintptr
VidGenParamsSetPrompts func(params uintptr, prompt string, negativePrompt string)
VidGenParamsSetDimensions func(params uintptr, width int, height int)
VidGenParamsSetSeed func(params uintptr, seed int64)
VidGenParamsSetVideoFrames func(params uintptr, n int)
)
// Copied from Purego internal/strings
@@ -160,58 +153,3 @@ func (sd *SDGGML) GenerateImage(opts *pb.GenerateImageRequest) error {
return nil
}
func (sd *SDGGML) GenerateVideo(opts *pb.GenerateVideoRequest) error {
dst := opts.Dst
if dst == "" {
return fmt.Errorf("dst is empty")
}
width := int(opts.Width)
height := int(opts.Height)
if width == 0 {
width = 512
}
if height == 0 {
height = 512
}
numFrames := int(opts.NumFrames)
if numFrames <= 0 {
numFrames = 16
}
fps := int(opts.Fps)
if fps <= 0 {
fps = 16
}
steps := int(opts.Step)
if steps <= 0 {
steps = 20
}
cfg := opts.CfgScale
if cfg == 0 {
cfg = sd.cfgScale
}
if cfg == 0 {
cfg = 5.0
}
// sd_vid_gen_params_new allocates; gen_video frees it after the generation call.
p := VidGenParamsNew()
VidGenParamsSetPrompts(p, opts.Prompt, opts.NegativePrompt)
VidGenParamsSetDimensions(p, width, height)
VidGenParamsSetSeed(p, int64(opts.Seed))
VidGenParamsSetVideoFrames(p, numFrames)
fmt.Fprintf(os.Stderr, "GenerateVideo: dst=%s size=%dx%d frames=%d fps=%d steps=%d cfg=%.2f\n",
dst, width, height, numFrames, fps, steps, cfg)
ret := GenVideo(p, steps, dst, cfg, fps, opts.StartImage, opts.EndImage)
if ret != 0 {
return fmt.Errorf("video inference failed (code %d)", ret)
}
return nil
}

View File

@@ -18,13 +18,6 @@ void sd_img_gen_params_set_seed(sd_img_gen_params_t *params, int64_t seed);
int load_model(const char *model, char *model_path, char* options[], int threads, int diffusionModel);
int gen_image(sd_img_gen_params_t *p, int steps, char *dst, float cfg_scale, char *src_image, float strength, char *mask_image, char* ref_images[], int ref_images_count);
sd_vid_gen_params_t* sd_vid_gen_params_new(void);
void sd_vid_gen_params_set_prompts(sd_vid_gen_params_t *params, const char *prompt, const char *negative_prompt);
void sd_vid_gen_params_set_dimensions(sd_vid_gen_params_t *params, int width, int height);
void sd_vid_gen_params_set_seed(sd_vid_gen_params_t *params, int64_t seed);
void sd_vid_gen_params_set_video_frames(sd_vid_gen_params_t *params, int n);
int gen_video(sd_vid_gen_params_t *p, int steps, char *dst, float cfg_scale, int fps, char *init_image, char *end_image);
#ifdef __cplusplus
}
#endif

View File

@@ -32,7 +32,6 @@ func main() {
libFuncs := []LibFuncs{
{&LoadModel, "load_model"},
{&GenImage, "gen_image"},
{&GenVideo, "gen_video"},
{&TilingParamsSetEnabled, "sd_tiling_params_set_enabled"},
{&TilingParamsSetTileSizes, "sd_tiling_params_set_tile_sizes"},
{&TilingParamsSetRelSizes, "sd_tiling_params_set_rel_sizes"},
@@ -43,12 +42,6 @@ func main() {
{&ImgGenParamsSetDimensions, "sd_img_gen_params_set_dimensions"},
{&ImgGenParamsSetSeed, "sd_img_gen_params_set_seed"},
{&ImgGenParamsGetVaeTilingParams, "sd_img_gen_params_get_vae_tiling_params"},
{&VidGenParamsNew, "sd_vid_gen_params_new"},
{&VidGenParamsSetPrompts, "sd_vid_gen_params_set_prompts"},
{&VidGenParamsSetDimensions, "sd_vid_gen_params_set_dimensions"},
{&VidGenParamsSetSeed, "sd_vid_gen_params_set_seed"},
{&VidGenParamsSetVideoFrames, "sd_vid_gen_params_set_video_frames"},
}
for _, lf := range libFuncs {

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