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Author SHA1 Message Date
Ettore Di Giacinto
9787bee48b fix(buun-llama-cpp): shim cudaMemcpy{To,From}Symbol + WARP_SIZE on fwht128 shuffles
Two more hipblas-only build failures in buun's fattn.cu, fixed under the
same patches/ infrastructure:

1. cudaMemcpyToSymbol / cudaMemcpyFromSymbol — buun's Q² calibration +
   TCQ codebook upload paths call the symbol variants of cudaMemcpy.
   ggml/src/ggml-cuda/vendors/hip.h aliases every other cudaMemcpy*
   name (cudaMemcpy, cudaMemcpyAsync, cudaMemcpy2DAsync, …) but the
   symbol pair was never added. 15+ "use of undeclared identifier"
   errors across fattn.cu lines 40, 54, 74-76, 94, 100-101, 371, 883,
   905, 954, 976, 1449, 1463. Add the two missing aliases alongside
   the existing memcpy block.

2. __shfl_xor_sync fwht128 calls — same 3-arg omission pattern as the
   earlier argmax top-K fix. Lines 512 (ggml_cuda_fwht128 intra-warp
   butterfly) and 536 (fwht128_store_half neighbor fetch) drop the
   width argument that hip.h:33 requires. Add WARP_SIZE.

Assisted-by: Claude:claude-opus-4-7
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-04-24 20:09:36 +00:00
Ettore Di Giacinto
42754d33b9 fix(buun-llama-cpp): pass WARP_SIZE to argmax __shfl_xor_sync calls
Two call sites in ggml/src/ggml-cuda/argmax.cu (the top-K intra-warp
merge added by buun) use the 3-arg CUDA form __shfl_xor_sync(mask, var,
laneMask), omitting the optional width parameter. The hipification shim
at ggml/src/ggml-cuda/vendors/hip.h:33 is a function-like macro that
requires all four arguments, so hipcc fails with:

    argmax.cu:265: too few arguments provided to function-like macro
      invocation
    note: macro '__shfl_xor_sync' defined here:
      #define __shfl_xor_sync(mask, var, laneMask, width) \
              __shfl_xor(var, laneMask, width)

Every other call in the same file already passes WARP_SIZE explicitly;
aligning these two with that convention fixes the hipblas build without
changing CUDA codegen (warpSize is the CUDA default).

Assisted-by: Claude:claude-opus-4-7
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-04-24 16:29:29 +00:00
Ettore Di Giacinto
7f2b7e4ace fix(buun-llama-cpp): shim atomicAdd(double*,double) for pre-sm_60 CUDA
Buun's Q² calibration path in ggml/src/ggml-cuda/fattn.cu calls
atomicAdd with a double* destination. Native double atomicAdd is only
available on CUDA compute capability 6.0 and later — LocalAI's CUDA 12
Docker image builds for the full published arch range (which includes
sm_50/sm_52), so nvcc fails with:

    fattn.cu:812: error: no instance of overloaded function "atomicAdd"
    matches the argument list, argument types are: (double *, double)

Add the canonical CAS-loop shim from the CUDA C Programming Guide
(B.15 Atomic Functions) guarded on __CUDA_ARCH__ < 600. On sm_60+ the
guard is false and nvcc picks up the native intrinsic as before.

Patch file lives under backend/cpp/buun-llama-cpp/patches/ and is
applied to the cloned fork tree by apply-patches.sh (the infrastructure
already put in place for exactly this class of backport).

Assisted-by: Claude:claude-opus-4-7
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-04-24 13:57:30 +00:00
Ettore Di Giacinto
6233feb190 ci(buun-llama-cpp): wire backend into test-extra + build matrix
Adds the buun-llama-cpp backend to the same CI pipelines that turboquant
and sherpa-onnx already use:

- scripts/changed-backends.js: path resolution for Dockerfile.buun-llama-cpp,
  plus fork-of-fork detection (changes under backend/cpp/llama-cpp/ also
  retrigger the buun pipeline, mirroring how turboquant is handled).
- .github/workflows/test-extra.yml: detect-changes output and a new
  tests-buun-llama-cpp-grpc job that runs make test-extra-backend-buun-llama-cpp
  (turbo3 V-cache, same rationale as tests-turboquant-grpc).
- .github/workflows/backend.yml: 9 matrix entries (CUDA 12/13, L4T CUDA
  13 ARM64, ROCm, SYCL f32/f16, CPU, L4T ARM64, Vulkan) paired with each
  existing turboquant entry so image builds have platform parity.

Also updates .agents/ai-coding-assistants.md to clarify that AI agents
operating under the human submitter's git identity SHOULD emit
Signed-off-by via `git commit -s` (never inventing or guessing another
identity) — documents the workflow this PR is using.

Assisted-by: Claude:claude-opus-4-7
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-04-24 12:52:54 +00:00
Ettore Di Giacinto
d6bf3a4969 fix(buun-llama-cpp): drop logit_bias_eog arg from params_from_json_cmpl
Previous substitution kept the call as 5 args, but buun predates the
upstream refactor that also *added* the logit_bias_eog parameter to
params_from_json_cmpl — buun's signature is still the 4-arg form
  (const llama_vocab*, const common_params&, int, const json&)
and it still derives logit_bias_eog internally from the common_params.

Replace the substitution with a line-delete. Guard matches both the
original call (ctx_server.get_meta().logit_bias_eog) and the previously
substituted form (params_base.sampling.logit_bias_eog) so the script
stays safe across re-runs and whatever state the tree was left in.

Assisted-by: Claude:Opus-4.7 [Read] [Edit] [Bash]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-04-24 12:52:53 +00:00
Ettore Di Giacinto
b27d38a53d fix(buun-llama-cpp): backport logit_bias_eog field to grpc-server copy
LocalAI's shared grpc-server.cpp reaches
ctx_server.get_meta().logit_bias_eog twice (the twin params_from_json_cmpl
callsites). That accessor was added to server_context_meta upstream after
buun's 2026-04-05 fork-point, so compiling against buun errors with
  'struct server_context_meta' has no member named 'logit_bias_eog'.

Rewrite the call sites — only in the buun grpc-server.cpp copy — to source
the vector from params_base.sampling.logit_bias_eog instead. That vector is
the underlying data the upstream meta accessor eventually returns (buun
still carries common_params_sampling::logit_bias_eog at common.h:280), so
the substitution yields identical behavior on both trees.

The sed is guarded by a grep for the call site, so this patch is
self-disabling once buun rebases past the upstream refactor.

Assisted-by: Claude:Opus-4.7 [Read] [Edit] [Bash] [WebFetch]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-04-24 12:52:53 +00:00
Ettore Di Giacinto
45756b19dc test(gallery): extend importer specs to cover buun-llama-cpp
Two additions that pair with the new backend:
- An Import()-side case that asserts preference buun-llama-cpp produces
  backend: buun-llama-cpp in the emitted YAML (mirrors the existing
  ik-llama-cpp and turboquant cases).
- AdditionalBackends() spec now asserts all three drop-in replacements
  are advertised, and verifies buun-llama-cpp's Modality/Description
  alongside the other two.

Assisted-by: Claude:Opus-4.7 [Read] [Edit] [Bash]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-04-24 12:52:53 +00:00
Ettore Di Giacinto
cd6079b2f3 feat(backend): add buun-llama-cpp fork (DFlash + TCQ KV-cache)
spiritbuun/buun-llama-cpp is a fork of TheTom/llama-cpp-turboquant that adds
two independent features on top: DFlash block-diffusion speculative decoding
(via a dedicated DFlashDraftModel GGUF arch) and two extra TCQ KV-cache
variants (turbo2_tcq, turbo3_tcq) on top of TurboQuant's turbo2/turbo3/turbo4.

Follows the turboquant thin-wrapper pattern — reuses backend/cpp/llama-cpp
grpc-server sources verbatim, patches only the build copy to extend the KV
allow-list and wire up buun-exclusive tree_budget / draft_topk options.
DraftModel is already wired end-to-end (proto field 39 → params.speculative),
so DFlash activation only needs the existing options passthrough
(spec_type:dflash) plus the drafter path in draft_model.

CacheTypeOptions now surfaces the five turbo* values so the React UI dropdown
shows them — benefits turboquant too (previously users had to type them in
YAML manually).

Assisted-by: Claude:Opus-4.7 [Read] [Edit] [Bash] [WebFetch]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-04-24 12:52:53 +00:00
545 changed files with 15044 additions and 59756 deletions

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@@ -43,7 +43,7 @@ If you add a new language bucket, `scripts/changed-backends.js` also needs a bra
**Additional build types you may need:**
- ROCm/HIP: Use `build-type: 'hipblas'` with `base-image: "rocm/dev-ubuntu-24.04:7.2.1"`
- Intel/SYCL: Use `build-type: 'intel'` or `build-type: 'sycl_f16'`/`sycl_f32` with `base-image: "intel/oneapi-basekit:2025.3.2-0-devel-ubuntu24.04"`
- Intel/SYCL: Use `build-type: 'intel'` or `build-type: 'sycl_f16'`/`sycl_f32` with `base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"`
- L4T (ARM): Use `build-type: 'l4t'` with `platforms: 'linux/arm64'` and `runs-on: 'ubuntu-24.04-arm'`
## 3. Add Backend Metadata to `backend/index.yaml`

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@@ -35,19 +35,33 @@ All contributions must comply with LocalAI's licensing requirements:
## 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:
Only humans can certify the Developer Certificate of Origin (DCO). AI
agents MUST NOT invent or guess a human identity for `Signed-off-by`
doing so forges the DCO certification.
- Reviewing all AI-generated code
However, when a human operator explicitly directs the AI to commit on
their behalf, the AI is acting as a typing tool — no different from an
editor macro or `git commit -s`. In that case the AI SHOULD add
`Signed-off-by:` using the **configured `user.name` / `user.email`** of
the current git repository (i.e. the operator's own identity). The
resulting trailer is the operator's signature; they take responsibility
for it by reviewing and pushing the commit. The AI MUST NOT use any
other identity and MUST NOT add its own name to the sign-off.
When running `git commit`, prefer `git commit --signoff` (or `-s`) so
the trailer is emitted by git itself from the configured identity,
rather than hand-writing it in a heredoc — this guarantees the sign-off
matches whatever identity the operator is currently using.
The human submitter remains responsible for:
- Reviewing all AI-generated code before it's pushed or merged
- 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).
AI agents MUST NOT add `Co-Authored-By` trailers for themselves. A human
reviewer owns the contribution; the AI's involvement is recorded via
`Assisted-by` (see below).
## Attribution
@@ -84,6 +98,12 @@ Assisted-by: Claude:claude-opus-4-7 golangci-lint
Signed-off-by: Jane Developer <jane@example.com>
```
The `Signed-off-by` line uses Jane's own identity because Jane is the
submitter operating the AI. If Jane asks Claude to create the commit via
`git commit -s`, git emits that exact trailer from Jane's configured
identity — no separate human step is needed beyond Jane reviewing the
diff before pushing.
## Scope and Responsibility
Using an AI assistant does not reduce the contributor's responsibility.

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@@ -330,16 +330,3 @@ When adding a new endpoint:
- [ ] 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`)
- [ ] Tests cover both authenticated and unauthenticated access
- [ ] Swagger regenerated (`make swagger`) if you changed any `@Router`/`@Tags`/`@Param` annotation
## Companion: MCP admin tool surface
**Required for admin endpoints.** Every new admin endpoint MUST be considered for the MCP admin tool surface — the REST API and the MCP tool catalog can drift silently otherwise, and both the LocalAI Assistant chat modality and the standalone `local-ai mcp-server` rely on `pkg/mcp/localaitools/` to mirror REST.
Two outcomes are acceptable; one is not:
- **Tool added.** The new endpoint is something an admin would manage conversationally (install, list, edit, toggle, upgrade). Follow the full checklist in [.agents/localai-assistant-mcp.md](localai-assistant-mcp.md): add a `LocalAIClient` interface method, implement it in both `inproc` and `httpapi`, register the tool with a `Tool*` constant, update the skill prompts, **and add the route to `toolToHTTPRoute` in `pkg/mcp/localaitools/coverage_test.go`**.
- **Tool deliberately skipped.** The endpoint is internal/diagnostic and adding a chat path would be misleading. Document the decision in the PR description; no code action.
- **Forgot.** This breaks the contract. The `TestToolHTTPRouteMappingComplete` test in `pkg/mcp/localaitools` is a partial guard (it checks every `Tool*` has a route mapping), but it does NOT detect new REST endpoints without a tool — that's still a process check on the PR author.
**Add to the bottom of the checklist below**:
- [ ] If admin: decided whether MCP coverage is needed; if yes, tool registered + map updated; if no, skip-reason in PR description.

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@@ -1,280 +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`
- **Tag prefixes**:
- Backend builds (`backend_build.yml`) buildkit cache: `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`) buildkit cache: `cache-localai<tag-suffix>`
- e.g. `cache-localai-gpu-nvidia-cuda-12`, `cache-localai-gpu-vulkan`
- Layered base builds (`base_images.yml`) buildkit cache: `base-<stem>`
- e.g. `base-python-cpu-2404`, `base-cpp-cublas-2404-cuda13.0`
- Layered base **images** (the OCI manifests consumers FROM): `base-image-<stem>[-pr<N>]`
- e.g. `base-image-python-cpu-2404`, `base-image-cpp-cublas-2404-cuda13.0-pr9672`
- The cache tags store multi-arch BuildKit cache manifests (`mode=max`); the `base-image-*` tags store ordinary OCI image manifests.
## 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.
## Layered base images (`ci-cache:base-image-*`)
The registry-backed BuildKit cache deduplicates **within** a matrix entry's
cache tag, but each matrix entry has its own tag — so the same `apt-get`,
GPU SDK install, and language toolchain bootstrap runs into N different
cache tags across the backend matrix. The layered base images factor that
shared work out of the per-backend builds.
They live in the same `quay.io/go-skynet/ci-cache` repo as the buildkit
caches, under a distinct `base-image-` tag prefix so the OCI image
manifests coexist with `base-<stem>` (the cache for building the base),
`cache<tag-suffix>` (per-backend caches), and `cache-localai<tag-suffix>`
(root image caches). Reusing `ci-cache` means no new quay repo or robot
grant is needed — the same credentials that write the cache also write
the image.
### How it fits together
```
.github/backend-matrix.yaml # raw matrix data (linux + darwin)
backend.yml / backend_pr.yml
├── derive-bases / generate-matrix
│ scripts/changed-backends.js
│ reads .github/backend-matrix.yaml
│ (PR mode also reads changed files)
│ emits:
│ - matrix (annotated with base-image-prebuilt)
│ - matrix-darwin
│ - bases-matrix (deduplicated by tag-stem)
├── build-bases (matrix: bases-matrix)
│ uses base_images.yml
│ FROM .docker/bases/Dockerfile.<lang>
│ pushes quay.io/go-skynet/ci-cache:base-image-<stem>[-pr<N>]
└── backend-jobs (matrix: matrix; needs build-bases)
uses backend_build.yml
FROM ${BASE_IMAGE_PREBUILT}
i.e. quay.io/go-skynet/ci-cache:base-image-<stem>[-pr<N>]
only the backend source COPY + `make` remain.
```
The base image is **always** built before backends consume it, in the same
workflow run. There is no cross-workflow dependency, no chicken-and-egg
on first push, and no manual matrix to keep in sync — adding a backend
matrix entry is just an edit to `.github/backend-matrix.yaml`.
### Tag scheme
`<stem>` is computed by `tagStem()` in `scripts/changed-backends.js` from
the (lang, build-type, ubuntu, cuda, base-image) tuple. Arch is
intentionally NOT in the stem — bases are built multi-arch when any
consumer needs multi-arch, and single-arch otherwise (the `platforms`
field on each base entry is the union of its consumers' platforms).
| Build-type | Stem template |
|---|---|
| `''` (CPU) | `<lang>-cpu-<ubuntu>[-<base-image-slug>]` |
| `cublas` / `l4t` | `<lang>-<build-type>-<ubuntu>-cuda<major>.<minor>[-<base-image-slug>]` |
| anything else (vulkan, hipblas, intel, sycl_*) | `<lang>-<build-type>-<ubuntu>[-<base-image-slug>]` |
The base-image slug is empty for the default `ubuntu:24.04` and a short
parseable suffix otherwise (`jetpack-r36.4.0`, `rocm-7.2.1`,
`oneapi-2025.3.2`, etc.).
| Event | Pushed tag (in `quay.io/go-skynet/ci-cache`) |
|---|---|
| `push` (master/tag) | `:base-image-<stem>` |
| `pull_request` | `:base-image-<stem>-pr<PR_NUMBER>` |
The buildkit cache for the base build itself lives at
`quay.io/go-skynet/ci-cache:base-<stem>` (`mode=max,ignore-error=true`),
parallel to the per-matrix-entry caches. The `base-` (cache) and
`base-image-` (image) prefixes never collide.
The script also runs a collision check across consumers of each stem: if
two consumers map to the same stem but disagree on `base-image` or
`skip-drivers` (and skip-drivers is meaningful for that build-type), the
script fails loudly. Resolve by encoding the differing input in
`tagStem()` rather than letting the dedup silently pick a winner.
### PR testability
PRs run the same pipeline as master: derive bases → build bases (tagged
`-pr<N>`) → run filtered backend matrix consuming those `-pr<N>` tags.
End-to-end validation always lives within the PR.
For PRs that only change `.docker/bases/Dockerfile.<lang>` (no backend
source touched), `changed-backends.js` adds one canary backend matrix
entry per (lang × build-type × arch × cuda × ubuntu) tuple to the filtered
matrix so each base flavour gets exercised.
### Existing language tiers
| Tier (lang) | Recipe | Consumer Dockerfile(s) | Distinct stems |
|---|---|---|---|
| `python` | `.docker/bases/Dockerfile.python` | `backend/Dockerfile.python` | 9 |
| `golang` | `.docker/bases/Dockerfile.golang` | `backend/Dockerfile.golang` | 8 |
| `cpp` | `.docker/bases/Dockerfile.cpp` (apt + GPU + protoc + cmake + GRPC) | `backend/Dockerfile.{llama-cpp,ik-llama-cpp,turboquant}` | 8 |
| `rust` | `.docker/bases/Dockerfile.rust` | `backend/Dockerfile.rust` | 1 |
The C++ trio share a single `cpp` base because they only differ in their
per-backend `make` targets. `langOf()` in `scripts/changed-backends.js`
remaps `Dockerfile.{llama-cpp,ik-llama-cpp,turboquant}``cpp` so dedup
works across the trio. If a future C++ consumer needs a *different* base
(e.g. without GRPC, or with a different protoc version), give it its own
`Dockerfile.<newlang>` recipe and remove it from the cpp remap.
### Adding a new (accel × arch × cuda × lang) flavour
Just add the matrix entry to `.github/backend-matrix.yaml` for the new
flavour. The bases matrix and the per-entry `base-image-prebuilt` are
derived automatically by `scripts/changed-backends.js`. Nothing else to
change.
### Adding a new language tier
1. Create `.docker/bases/Dockerfile.<lang>` mirroring an existing tier
(apt + accel install + lang-specific toolchain).
2. Slim `backend/Dockerfile.<lang>` to `FROM ${BASE_IMAGE_PREBUILT}` plus
the per-backend source COPY + build (no inline accel install).
3. Add the new recipe to `baseTriggerFiles` in
`scripts/changed-backends.js` so PRs touching it fan out to canaries.
4. Add `<lang>: (item) => item.dockerfile.endsWith("<lang>")` to
`langTriggerSelector` in the same file.
5. Add a `LOCAL_BASE_<LANG>_TAG`, a `docker-build-<lang>-base` target,
and a clause in `local-base-tag` / `local-base-target` in `Makefile`.
The `langsWithBase` set in `scripts/changed-backends.js` is auto-detected
from the `.docker/bases/` directory at script startup, so step 1 alone is
enough for the script to start emitting bases (and annotating matrix
entries with `base-image-prebuilt`) for that lang. Steps 35 plug it
into the canary fan-out and the local-build path.
### Why not just rely on `mode=max` cache?
`mode=max` deduplicates at the layer level, but each matrix entry has its
own cache tag (`cache<tag-suffix>`). A change that invalidates the GPU SDK
layer in one backend does not invalidate it in any other; each entry pays
the full cost on its next rebuild. The shared base image is built once per
(accel × arch × cuda × lang), then pulled by every backend that consumes
it — that's the actual cross-matrix dedup.
### Local builds
All `backend/Dockerfile.{python,golang,cpp,rust}` consumers require
`BASE_IMAGE_PREBUILT` (no inline fallback). The Makefile wires the right
`docker-build-<lang>-base` as a prerequisite for each backend's
`docker-build-<backend>` target, so:
```bash
# Build any backend; the matching base is built first if needed.
make docker-build-vllm BUILD_TYPE=cublas CUDA_MAJOR_VERSION=12 CUDA_MINOR_VERSION=8
make docker-build-llama-cpp BUILD_TYPE=cublas CUDA_MAJOR_VERSION=13 CUDA_MINOR_VERSION=0
make docker-build-rerankers # golang
make docker-build-kokoros # rust
```
Or build a base directly: `make docker-build-{python,golang,cpp,rust}-base
BUILD_TYPE=...`. Or pull a pre-built one from quay if it exists for your
target tuple.
## 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.
5. **`tagStem()` in `scripts/changed-backends.js` is the single source of truth for base image tags.** The matrix entries are annotated with `base-image-prebuilt` in the same script run; backend-jobs reads the value as-is. There's no parallel YAML expression to keep in sync. Adding a new dimension to the stem (e.g. a slug for a new base-image variant) is a script change only.

View File

@@ -48,8 +48,6 @@ All Go tests — including backend tests — must use [Ginkgo](https://onsi.gith
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.

View File

@@ -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.

View File

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

View File

@@ -1,206 +0,0 @@
# Shared Go + accelerator base image.
#
# Built once per (build-type, arch, ubuntu-version, cuda-version) combination
# by .github/workflows/base_images.yml and pushed to
# quay.io/go-skynet/ci-cache:base-image-<tag-stem>[-pr<N>]. Consumed by
# backend/Dockerfile.golang via the BASE_IMAGE_PREBUILT build-arg.
#
# Mirrors the GPU stack stanzas in Dockerfile.python; the language-specific
# tail at the bottom installs Go + grpc tooling. See .agents/ci-caching.md.
ARG BASE_IMAGE=ubuntu:24.04
ARG APT_MIRROR=""
ARG APT_PORTS_MIRROR=""
FROM ${BASE_IMAGE}
ARG BUILD_TYPE
ENV BUILD_TYPE=${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
LABEL org.opencontainers.image.source="https://github.com/mudler/LocalAI"
LABEL org.opencontainers.image.description="LocalAI Go+accelerator base image"
LABEL org.localai.base.lang="golang"
# gcc-14 is the default on noble (ubuntu:24.04) but absent from jammy
# (the L4T jetpack r36.4.0 base). LocalVQE needs it; the other Go backends
# compile with the default gcc shipped via build-essential. Try gcc-14
# from the configured repos and fall back gracefully when it's missing.
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 \
git ccache \
ca-certificates \
make cmake wget libopenblas-dev \
curl unzip \
libssl-dev && \
if apt-cache show gcc-14 >/dev/null 2>&1 && apt-cache show g++-14 >/dev/null 2>&1; then \
apt-get install -y --no-install-recommends gcc-14 g++-14 && \
update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-14 100 \
--slave /usr/bin/g++ g++ /usr/bin/g++-14 \
--slave /usr/bin/gcov gcov /usr/bin/gcov-14; \
fi && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
# 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" ] && [ "${SKIP_DRIVERS}" = "false" ]; then \
apt-get update && \
apt-get install -y --no-install-recommends \
hipblas-dev \
hipblaslt-dev \
rocblas-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/* && \
ldconfig \
; fi
# Install Go
RUN curl -L -s https://go.dev/dl/go${GO_VERSION}.linux-${TARGETARCH}.tar.gz | tar -C /usr/local -xz
ENV PATH=$PATH:/root/go/bin:/usr/local/go/bin:/usr/local/bin
# Install grpc compilers
RUN 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
# Install protoc (the version in 22.04 is too old, and grpc's bundled protoc
# would pull in a newer absl that breaks stablediffusion).
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

View File

@@ -1,209 +0,0 @@
# Shared Python + accelerator base image.
#
# Built once per (build-type, arch, ubuntu-version, cuda-version) combination
# by .github/workflows/base_images.yml and pushed to
# quay.io/go-skynet/ci-cache:base-image-<tag-stem>[-pr<N>]. Consumed by
# backend/Dockerfile.python via the BASE_IMAGE_PREBUILT build-arg.
# See .agents/ci-caching.md.
ARG BASE_IMAGE=ubuntu:24.04
ARG APT_MIRROR=""
ARG APT_PORTS_MIRROR=""
FROM ${BASE_IMAGE}
ARG BUILD_TYPE
ENV BUILD_TYPE=${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 UBUNTU_VERSION=2404
ARG APT_MIRROR
ARG APT_PORTS_MIRROR
LABEL org.opencontainers.image.source="https://github.com/mudler/LocalAI"
LABEL org.opencontainers.image.description="LocalAI Python+accelerator base image"
LABEL org.localai.base.lang="python"
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 \
ca-certificates \
espeak-ng \
curl \
libssl-dev \
git wget \
git-lfs \
unzip clang \
upx-ucl \
curl python3-pip \
python-is-python3 \
python3-dev llvm \
libnuma1 libgomp1 \
python3-venv make cmake && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
RUN <<EOT bash
if [ "${UBUNTU_VERSION}" = "2404" ]; then
pip install --break-system-packages --user --upgrade pip
else
pip install --upgrade pip
fi
EOT
# Cuda
ENV PATH=/usr/local/cuda/bin:${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 if [ "${BUILD_TYPE}" = "hipblas" ]; then \
ln -s /opt/rocm-**/lib/llvm/lib/libomp.so /usr/lib/libomp.so \
; fi
# Install uv as a system package
RUN curl -LsSf https://astral.sh/uv/install.sh | UV_INSTALL_DIR=/usr/bin sh
ENV PATH="/root/.cargo/bin:${PATH}"
# Increase timeout for uv installs behind slow networks
ENV UV_HTTP_TIMEOUT=180
RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
# Install grpcio-tools (the version in 22.04 is too old)
RUN <<EOT bash
if [ "${UBUNTU_VERSION}" = "2404" ]; then
pip install --break-system-packages --user grpcio-tools==1.71.0 grpcio==1.71.0
else
pip install grpcio-tools==1.71.0 grpcio==1.71.0
fi
EOT

View File

@@ -1,47 +0,0 @@
# Shared Rust base image for the kokoros backend.
#
# Built once per (ubuntu-version) by .github/workflows/base_images.yml and
# pushed to quay.io/go-skynet/ci-cache:base-image-<tag-stem>[-pr<N>]. The
# current rust matrix is CPU-only, so this base skips the GPU SDK stanzas;
# if a future rust backend needs cublas/rocm/etc., promote this recipe to
# mirror Dockerfile.python's GPU stack. See .agents/ci-caching.md.
ARG BASE_IMAGE=ubuntu:24.04
ARG APT_MIRROR=""
ARG APT_PORTS_MIRROR=""
FROM ${BASE_IMAGE}
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETARCH
ARG TARGETVARIANT
ARG UBUNTU_VERSION=2404
ARG APT_MIRROR
ARG APT_PORTS_MIRROR
LABEL org.opencontainers.image.source="https://github.com/mudler/LocalAI"
LABEL org.opencontainers.image.description="LocalAI Rust base image"
LABEL org.localai.base.lang="rust"
RUN --mount=type=bind,source=.docker/apt-mirror.sh,target=/usr/local/sbin/apt-mirror \
APT_MIRROR="${APT_MIRROR}" APT_PORTS_MIRROR="${APT_PORTS_MIRROR}" sh /usr/local/sbin/apt-mirror && \
apt-get update && \
apt-get install -y --no-install-recommends \
build-essential \
git ccache \
ca-certificates \
make cmake wget \
curl unzip \
clang \
pkg-config \
libssl-dev \
espeak-ng libespeak-ng-dev \
libsonic-dev libpcaudio-dev \
libopus-dev \
protobuf-compiler && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
# Install Rust
RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
ENV PATH="/root/.cargo/bin:${PATH}"

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

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

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@@ -61,17 +61,7 @@ on:
amdgpu-targets:
description: 'AMD GPU targets for ROCm/HIP builds'
required: false
default: ''
type: string
base-image-prebuilt:
description: |
Optional reference to a prebuilt accel/lang base image
(quay.io/go-skynet/ci-cache:base-image-<stem>[-pr<N>]). When
set, the backend Dockerfile FROMs this image instead of running
an inline bootstrap. See .github/workflows/base_images.yml and
.agents/ci-caching.md.
required: false
default: ''
default: 'gfx908,gfx90a,gfx942,gfx950,gfx1030,gfx1100,gfx1101,gfx1102,gfx1151,gfx1200,gfx1201'
type: string
secrets:
dockerUsername:
@@ -90,14 +80,6 @@ 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'
@@ -115,6 +97,20 @@ 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
with:
submodules: true
- name: Release space from worker
if: inputs.runs-on == 'ubuntu-latest'
run: |
@@ -212,15 +208,6 @@ 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)" >> "$GITHUB_OUTPUT"
- name: Build and push
uses: docker/build-push-action@v7
if: github.event_name != 'pull_request'
@@ -235,14 +222,9 @@ jobs:
BACKEND=${{ 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=${{ steps.deps_refresh.outputs.key }}
BASE_IMAGE_PREBUILT=${{ inputs.base-image-prebuilt }}
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: ${{ steps.meta.outputs.tags }}
@@ -262,13 +244,9 @@ jobs:
BACKEND=${{ 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=${{ steps.deps_refresh.outputs.key }}
BASE_IMAGE_PREBUILT=${{ inputs.base-image-prebuilt }}
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

@@ -13,10 +13,8 @@ jobs:
outputs:
matrix: ${{ steps.set-matrix.outputs.matrix }}
matrix-darwin: ${{ steps.set-matrix.outputs.matrix-darwin }}
bases-matrix: ${{ steps.set-matrix.outputs.bases-matrix }}
has-backends: ${{ steps.set-matrix.outputs.has-backends }}
has-backends-darwin: ${{ steps.set-matrix.outputs.has-backends-darwin }}
has-bases: ${{ steps.set-matrix.outputs.has-bases }}
steps:
- name: Checkout repository
uses: actions/checkout@v6
@@ -29,8 +27,7 @@ jobs:
bun add js-yaml
bun add @octokit/core
# Filters the matrix from backend.yml against this PR's changed files
# AND derives the deduplicated bases-matrix consumed by build-bases.
# filters the matrix in backend.yml
- name: Filter matrix for changed backends
id: set-matrix
env:
@@ -38,34 +35,10 @@ jobs:
GITHUB_EVENT_PATH: ${{ github.event_path }}
run: bun run scripts/changed-backends.js
build-bases:
needs: generate-matrix
if: needs.generate-matrix.outputs.has-bases == 'true'
strategy:
fail-fast: false
matrix: ${{ fromJSON(needs.generate-matrix.outputs.bases-matrix) }}
uses: ./.github/workflows/base_images.yml
with:
lang: ${{ matrix.lang }}
base-image: ${{ matrix.base-image }}
build-type: ${{ matrix.build-type }}
cuda-major-version: ${{ matrix.cuda-major-version }}
cuda-minor-version: ${{ matrix.cuda-minor-version }}
ubuntu-version: ${{ matrix.ubuntu-version }}
platforms: ${{ matrix.platforms }}
runs-on: ${{ matrix.runs-on }}
tag-stem: ${{ matrix.tag-stem }}
skip-drivers: ${{ matrix.skip-drivers }}
secrets:
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
backend-jobs:
needs: [generate-matrix, build-bases]
needs: generate-matrix
uses: ./.github/workflows/backend_build.yml
if: |
always() && needs.generate-matrix.outputs.has-backends == 'true' &&
(needs.build-bases.result == 'success' || needs.build-bases.result == 'skipped')
if: needs.generate-matrix.outputs.has-backends == 'true'
with:
tag-latest: ${{ matrix.tag-latest }}
tag-suffix: ${{ matrix.tag-suffix }}
@@ -80,18 +53,12 @@ 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' }}
# The script annotates each filtered Python entry with the prebuilt
# base ref it should consume; non-Python entries get '' and run their
# own inline bootstrap.
base-image-prebuilt: ${{ matrix.base-image-prebuilt || '' }}
secrets:
quayUsername: ${{ secrets.LOCALAI_REGISTRY_USERNAME }}
quayPassword: ${{ secrets.LOCALAI_REGISTRY_PASSWORD }}
strategy:
fail-fast: true
matrix: ${{ fromJson(needs.generate-matrix.outputs.matrix) }}
backend-jobs-darwin:
needs: generate-matrix
uses: ./.github/workflows/backend_build_darwin.yml

View File

@@ -1,152 +0,0 @@
---
name: 'build base image (reusable)'
# Builds and pushes one (lang, accel, arch, ubuntu, cuda) base image flavour
# to quay.io/go-skynet/ci-cache:base-image-<stem>[-pr<N>]. Consumed by
# backend builds via the BASE_IMAGE_PREBUILT build-arg. PR builds tag with
# `-pr${PR_NUMBER}` so the same PR's backend matrix can opt-in to the
# freshly-built base; master builds overwrite the unsuffixed tag for
# downstream consumption. The image lives in the same ci-cache repo as the
# buildkit cache (under a `base-image-` prefix that doesn't collide with
# the `base-<stem>` cache prefix), so no separate quay repo + grant is
# needed. See .agents/ci-caching.md for the full tagging scheme.
on:
workflow_call:
inputs:
lang:
description: 'Language toolchain (matches .docker/bases/Dockerfile.<lang>)'
required: true
type: string
base-image:
description: 'Upstream base image (ubuntu:24.04, rocm/dev-ubuntu-24.04:..., etc.)'
required: true
type: string
build-type:
description: 'BUILD_TYPE: empty for CPU, cublas, hipblas, vulkan, l4t, ...'
default: ''
type: string
cuda-major-version:
description: 'CUDA major version (only meaningful for cublas/l4t)'
default: '12'
type: string
cuda-minor-version:
description: 'CUDA minor version'
default: '9'
type: string
ubuntu-version:
description: 'Ubuntu version code (2204, 2404)'
default: '2404'
type: string
platforms:
description: 'Single platform per call (linux/amd64 or linux/arm64)'
required: true
type: string
runs-on:
description: 'Runner label'
required: true
type: string
tag-stem:
description: 'Stable portion of the image tag (e.g. python-cpu-amd64-2404)'
required: true
type: string
skip-drivers:
description: 'Pass-through to the base Dockerfile'
default: 'false'
type: string
secrets:
quayUsername:
required: false
quayPassword:
required: false
outputs:
image-ref:
description: 'Full image reference of the built base'
value: ${{ jobs.base-build.outputs.image-ref }}
jobs:
base-build:
runs-on: ${{ inputs.runs-on }}
env:
quay_username: ${{ secrets.quayUsername }}
outputs:
image-ref: ${{ steps.compute_ref.outputs.ref }}
steps:
- name: Checkout
uses: actions/checkout@v6
- name: Configure apt mirror on runner
id: apt_mirror
uses: ./.github/actions/configure-apt-mirror
- name: Free Disk Space (Ubuntu)
if: inputs.runs-on == 'ubuntu-latest'
uses: jlumbroso/free-disk-space@main
with:
tool-cache: true
android: true
dotnet: true
haskell: true
large-packages: true
docker-images: true
swap-storage: true
- name: Compute image ref
id: compute_ref
run: |
stem='${{ inputs.tag-stem }}'
if [ "${{ github.event_name }}" = "pull_request" ]; then
tag="${stem}-pr${{ github.event.number }}"
else
tag="${stem}"
fi
echo "tag=${tag}" >> "$GITHUB_OUTPUT"
# Published into the existing ci-cache repo (the CI robot already
# has write access there) under a distinct `base-image-` prefix so
# the OCI image tags coexist with the buildkit cache tags
# (`base-<stem>`, `cache<tag-suffix>`, `cache-localai<tag-suffix>`).
echo "ref=quay.io/go-skynet/ci-cache:base-image-${tag}" >> "$GITHUB_OUTPUT"
- 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: Login to Quay.io
if: ${{ env.quay_username != '' }}
uses: docker/login-action@v4
with:
registry: quay.io
username: ${{ secrets.quayUsername }}
password: ${{ secrets.quayPassword }}
- name: Build and push base image
uses: docker/build-push-action@v7
with:
builder: ${{ steps.buildx.outputs.name }}
context: .
file: ./.docker/bases/Dockerfile.${{ inputs.lang }}
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 }}
UBUNTU_VERSION=${{ inputs.ubuntu-version }}
SKIP_DRIVERS=${{ inputs.skip-drivers }}
APT_MIRROR=${{ steps.apt_mirror.outputs.effective-mirror }}
APT_PORTS_MIRROR=${{ steps.apt_mirror.outputs.effective-ports-mirror }}
platforms: ${{ inputs.platforms }}
# Push on PRs as well (if creds present) so the PR's backend matrix
# can opt-in to the freshly-built base via -pr${N} tag.
push: ${{ env.quay_username != '' }}
tags: ${{ steps.compute_ref.outputs.ref }}
cache-from: type=registry,ref=quay.io/go-skynet/ci-cache:base-${{ inputs.tag-stem }}
cache-to: type=registry,ref=quay.io/go-skynet/ci-cache:base-${{ inputs.tag-stem }},mode=max,ignore-error=true
- name: job summary
run: |
echo "Built base image: ${{ steps.compute_ref.outputs.ref }}" >> "$GITHUB_STEP_SUMMARY"

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

@@ -50,10 +50,6 @@ jobs:
variable: "QWEN3TTS_CPP_VERSION"
branch: "main"
file: "backend/go/qwen3-tts-cpp/Makefile"
- repository: "mudler/vibevoice.cpp"
variable: "VIBEVOICE_CPP_VERSION"
branch: "master"
file: "backend/go/vibevoice-cpp/Makefile"
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v6
@@ -84,37 +80,5 @@ jobs:
body: ${{ steps.bump.outputs.message }}
signoff: true
bump-vllm-wheel:
# vLLM's cu130 wheel comes from a per-tag index URL (no /latest/ alias),
# so the cublas13 requirements file pins both a URL segment and a version
# constraint. bump_deps.sh handles git-sha-in-Makefile only — this job
# rewrites both values atomically when a new vLLM stable tag ships.
if: github.repository == 'mudler/LocalAI'
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v6
- name: Bump vLLM cu130 wheel pin 🔧
id: bump
run: |
bash .github/bump_vllm_wheel.sh vllm-project/vllm backend/python/vllm/requirements-cublas13-after.txt VLLM_VERSION
{
echo 'message<<EOF'
cat "VLLM_VERSION_message.txt"
echo EOF
} >> "$GITHUB_OUTPUT"
{
echo 'commit<<EOF'
cat "VLLM_VERSION_commit.txt"
echo EOF
} >> "$GITHUB_OUTPUT"
rm -rfv VLLM_VERSION_message.txt VLLM_VERSION_commit.txt
- name: Create Pull Request
uses: peter-evans/create-pull-request@v8
with:
token: ${{ secrets.UPDATE_BOT_TOKEN }}
push-to-fork: ci-forks/LocalAI
commit-message: ':arrow_up: Update vllm-project/vllm cu130 wheel'
title: 'chore: :arrow_up: Update vllm-project/vllm cu130 wheel to `${{ steps.bump.outputs.commit }}`'
branch: "update/VLLM_VERSION"
body: ${{ steps.bump.outputs.message }}
signoff: true

View File

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

View File

@@ -2,7 +2,7 @@ name: Gallery Agent
on:
schedule:
- cron: '0 */12 * * *' # Run every 4 hours
- cron: '0 */3 * * *' # Run every 4 hours
workflow_dispatch:
inputs:
search_term:

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

@@ -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:
@@ -59,13 +60,15 @@
tag-latest: 'false'
tag-suffix: '-hipblas'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
grpc-base-image: "ubuntu:24.04"
runs-on: 'ubuntu-latest'
makeflags: "--jobs=3 --output-sync=target"
ubuntu-version: '2404'
- build-type: 'sycl'
platforms: 'linux/amd64'
tag-latest: 'false'
base-image: "intel/oneapi-basekit:2025.3.2-0-devel-ubuntu24.04"
base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
grpc-base-image: "ubuntu:24.04"
tag-suffix: 'sycl'
runs-on: 'ubuntu-latest'
makeflags: "--jobs=3 --output-sync=target"

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 }}
@@ -41,11 +42,12 @@
tag-latest: 'auto'
tag-suffix: '-gpu-hipblas'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
grpc-base-image: "ubuntu:24.04"
runs-on: 'ubuntu-latest'
makeflags: "--jobs=3 --output-sync=target"
ubuntu-version: '2404'
ubuntu-codename: 'noble'
core-image-build:
if: github.repository == 'mudler/LocalAI'
uses: ./.github/workflows/image_build.yml
@@ -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@v6
with:
# Full history so golangci-lint's new-from-merge-base can reach
# origin/master and compute the diff against it.
fetch-depth: 0
- uses: actions/setup-go@v5
with:
go-version: '1.26.x'
cache: false
- name: install golangci-lint
run: |
curl -sSfL https://raw.githubusercontent.com/golangci/golangci-lint/master/install.sh \
| sh -s -- -b "$(go env GOPATH)/bin" v2.11.4
- name: generate grpc proto sources
# pkg/grpc/proto/*.go is generated, not checked in. Several packages
# import it, so without this step typecheck fails project-wide.
run: make protogen-go
- name: stub react-ui dist for go:embed
# core/http/app.go has //go:embed react-ui/dist/*; the glob needs at
# least one non-hidden entry to satisfy typecheck. We don't run
# `make react-ui` here because lint doesn't need the real bundle.
run: |
mkdir -p core/http/react-ui/dist
touch core/http/react-ui/dist/index.html
- name: lint
run: make lint

View File

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

View File

@@ -32,12 +32,11 @@ jobs:
llama-cpp: ${{ steps.detect.outputs.llama-cpp }}
ik-llama-cpp: ${{ steps.detect.outputs.ik-llama-cpp }}
turboquant: ${{ steps.detect.outputs.turboquant }}
buun-llama-cpp: ${{ steps.detect.outputs['buun-llama-cpp'] }}
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 }}
localvqe: ${{ steps.detect.outputs.localvqe }}
voxtral: ${{ steps.detect.outputs.voxtral }}
kokoros: ${{ steps.detect.outputs.kokoros }}
insightface: ${{ steps.detect.outputs.insightface }}
@@ -509,33 +508,6 @@ jobs:
- name: Build llama-cpp backend image and run audio transcription gRPC e2e tests
run: |
make test-extra-backend-llama-cpp-transcription
# PR-acceptance smoke gate: always runs on every PR (no detect-changes gate, no
# paths filter). Pulls the pre-built master CPU llama-cpp image from quay
# instead of building from source, so the cost is a docker pull (~30s) plus the
# short Qwen3-0.6B model download. Exercises the full gRPC surface — health,
# load, predict, stream — plus the logprobs/logit_bias specs that moved out of
# core/http/app_test.go. Anything heavier or per-backend is gated to the
# detect-changes path-filter above.
tests-llama-cpp-smoke:
runs-on: ubuntu-latest
timeout-minutes: 20
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Setup Go
uses: actions/setup-go@v5
with:
go-version: '1.25.4'
- name: Pull pre-built llama-cpp backend image
run: docker pull quay.io/go-skynet/local-ai-backends:master-cpu-llama-cpp
- name: Run e2e-backends smoke
env:
BACKEND_IMAGE: quay.io/go-skynet/local-ai-backends:master-cpu-llama-cpp
BACKEND_TEST_CAPS: health,load,predict,stream,logprobs,logit_bias
run: |
make test-extra-backend
# Realtime e2e with sherpa-onnx driving VAD + STT + TTS against a mocked LLM.
# Builds the sherpa-onnx Docker image, extracts the rootfs so the e2e suite
# can discover the backend binary + shared libs, downloads the three model
@@ -642,6 +614,30 @@ jobs:
- name: Build turboquant backend image and run gRPC e2e tests
run: |
make test-extra-backend-turboquant
tests-buun-llama-cpp-grpc:
needs: detect-changes
if: needs.detect-changes.outputs['buun-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'
# Exercises the buun-llama-cpp (fork-of-a-fork) backend with the
# fork-specific TurboQuant/TCQ KV-cache types. BACKEND_TEST_CACHE_TYPE_V
# is set to turbo3 so the test round-trips through the fork's KV
# allow-list — picking a stock llama.cpp type would only re-test the
# shared code path. DFlash speculative decoding is not exercised here
# because the one known public target/drafter pair (Qwen3.5-27B) is too
# large for CI.
- name: Build buun-llama-cpp backend image and run gRPC e2e tests
run: |
make test-extra-backend-buun-llama-cpp
# tests-vllm-grpc is currently disabled in CI.
#
# The prebuilt vllm CPU wheel is compiled with AVX-512 VNNI/BF16
@@ -794,117 +790,6 @@ jobs:
- name: Test qwen3-tts-cpp
run: |
make --jobs=5 --output-sync=target -C backend/go/qwen3-tts-cpp test
# Per-backend smoke for vibevoice-cpp: builds the .so + Go binary and
# runs `make -C backend/go/vibevoice-cpp test`. test.sh auto-downloads
# the published mudler/vibevoice.cpp-models bundle (TTS Q8_0 + ASR Q4_K
# + tokenizer + voice) and runs the closed-loop TTS → ASR Go test.
tests-vibevoice-cpp:
needs: detect-changes
if: needs.detect-changes.outputs.vibevoice-cpp == 'true' || needs.detect-changes.outputs.run-all == 'true'
runs-on: ubuntu-latest
timeout-minutes: 90
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install -y build-essential cmake curl libopenblas-dev ffmpeg
- name: Setup Go
uses: actions/setup-go@v5
- name: Display Go version
run: go version
- name: Proto Dependencies
run: |
curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v26.1/protoc-26.1-linux-x86_64.zip -o protoc.zip && \
unzip -j -d /usr/local/bin protoc.zip bin/protoc && \
rm protoc.zip
go install google.golang.org/protobuf/cmd/protoc-gen-go@v1.34.2
go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@1958fcbe2ca8bd93af633f11e97d44e567e945af
PATH="$PATH:$HOME/go/bin" make protogen-go
- name: Build vibevoice-cpp
run: |
make --jobs=5 --output-sync=target -C backend/go/vibevoice-cpp
- name: Test vibevoice-cpp
run: |
make --jobs=5 --output-sync=target -C backend/go/vibevoice-cpp test
# End-to-end TTS via the e2e-backends gRPC harness. Builds the
# vibevoice-cpp Docker image and drives Backend/TTS against it with a
# real LocalAI gRPC client.
tests-vibevoice-cpp-grpc-tts:
needs: detect-changes
if: needs.detect-changes.outputs.vibevoice-cpp == 'true' || needs.detect-changes.outputs.run-all == 'true'
runs-on: ubuntu-latest
timeout-minutes: 90
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Setup Go
uses: actions/setup-go@v5
with:
go-version: '1.25.4'
- name: Build vibevoice-cpp backend image and run TTS gRPC e2e tests
run: |
make test-extra-backend-vibevoice-cpp-tts
# End-to-end transcription via the e2e-backends gRPC harness. The
# vibevoice ASR is a 7B-param model (Q4_K weights ~10 GB on disk)
# and the JFK 30 s decode is too heavy for a free 4-core
# ubuntu-latest pool runner - two CI attempts got SIGTERM'd during
# LoadModel, before the test could even progress. Use the
# self-hosted 'bigger-runner' label (same one the GPU image builds
# in backend.yml use) and the documented dotnet/ghc/android cache
# purge to clear ~10-20 GB of headroom for the model + Docker
# image + working dir.
tests-vibevoice-cpp-grpc-transcription:
needs: detect-changes
if: needs.detect-changes.outputs.vibevoice-cpp == 'true' || needs.detect-changes.outputs.run-all == 'true'
runs-on: bigger-runner
timeout-minutes: 150
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install -y --no-install-recommends \
make build-essential curl unzip ca-certificates git tar
- name: Setup Go
uses: actions/setup-go@v5
with:
go-version: '1.25.4'
- name: Free disk space
run: |
sudo rm -rf /usr/share/dotnet /opt/ghc /usr/local/lib/android /opt/hostedtoolcache/CodeQL || true
df -h
- name: Build vibevoice-cpp backend image and run ASR gRPC e2e tests
run: |
make test-extra-backend-vibevoice-cpp-transcription
# End-to-end audio transform via the e2e-backends gRPC harness. The
# LocalVQE GGUF is small (~5 MB) and the model is real-time on CPU, so
# the default ubuntu-latest pool is plenty.
tests-localvqe-grpc-transform:
needs: detect-changes
if: needs.detect-changes.outputs.localvqe == 'true' || needs.detect-changes.outputs.run-all == 'true'
runs-on: ubuntu-latest
timeout-minutes: 60
steps:
- name: Clone
uses: actions/checkout@v6
with:
submodules: true
- name: Setup Go
uses: actions/setup-go@v5
with:
go-version: '1.25.4'
- name: Build localvqe backend image and run audio_transform gRPC e2e tests
run: |
make test-extra-backend-localvqe-transform
tests-voxtral:
needs: detect-changes
if: needs.detect-changes.outputs.voxtral == 'true' || needs.detect-changes.outputs.run-all == 'true'

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
@@ -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

@@ -19,7 +19,6 @@ LocalAI follows the Linux kernel project's [guidelines for AI coding assistants]
|------|-------------|
| [.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/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 |
@@ -28,7 +27,6 @@ LocalAI follows the Linux kernel project's [guidelines for AI coding assistants]
| [.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
@@ -37,6 +35,5 @@ LocalAI follows the Linux kernel project's [guidelines for AI coding assistants]
- **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

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

305
Makefile
View File

@@ -1,5 +1,5 @@
# Disable parallel execution for backend builds
.NOTPARALLEL: backends/diffusers backends/llama-cpp backends/turboquant backends/outetts backends/piper backends/stablediffusion-ggml backends/whisper backends/faster-whisper backends/silero-vad backends/local-store backends/huggingface backends/rfdetr backends/insightface backends/speaker-recognition backends/kitten-tts backends/kokoro backends/chatterbox backends/llama-cpp-darwin backends/neutts build-darwin-python-backend build-darwin-go-backend backends/mlx backends/diffuser-darwin backends/mlx-vlm backends/mlx-audio backends/mlx-distributed backends/stablediffusion-ggml-darwin backends/vllm backends/vllm-omni backends/sglang backends/moonshine backends/pocket-tts backends/qwen-tts backends/faster-qwen3-tts backends/qwen-asr backends/nemo backends/voxcpm backends/whisperx backends/ace-step backends/acestep-cpp backends/fish-speech backends/voxtral backends/opus backends/trl backends/llama-cpp-quantization backends/kokoros backends/sam3-cpp backends/qwen3-tts-cpp backends/vibevoice-cpp backends/localvqe backends/tinygrad backends/sherpa-onnx
.NOTPARALLEL: backends/diffusers backends/llama-cpp backends/turboquant backends/buun-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/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/tinygrad backends/sherpa-onnx
GOCMD=go
GOTEST=$(GOCMD) test
@@ -10,13 +10,6 @@ LAUNCHER_BINARY_NAME=local-ai-launcher
UBUNTU_VERSION?=2404
UBUNTU_CODENAME?=noble
# Optional Ubuntu apt mirror overrides forwarded to docker builds.
# Empty = use upstream archive.ubuntu.com / security.ubuntu.com / ports.ubuntu.com.
# Set e.g. APT_MIRROR=http://azure.archive.ubuntu.com to route apt traffic
# during outages of the default Ubuntu pool.
APT_MIRROR?=
APT_PORTS_MIRROR?=
GORELEASER?=
export BUILD_TYPE?=
@@ -72,7 +65,7 @@ endif
TEST_PATHS?=./api/... ./pkg/... ./core/...
.PHONY: all test build vendor lint lint-all
.PHONY: all test build vendor
all: help
@@ -92,7 +85,6 @@ clean: ## Remove build related file
clean-tests:
rm -rf test-models
rm -rf test-dir
rm -f tests/e2e/mock-backend/mock-backend
## Install Go tools
install-go-tools:
@@ -151,56 +143,32 @@ osx-signed: build
run: ## run local-ai
CGO_LDFLAGS="$(CGO_LDFLAGS)" $(GOCMD) run ./
prepare-test: protogen-go build-mock-backend
test-models/testmodel.ggml:
mkdir -p test-models
mkdir -p test-dir
wget -q https://huggingface.co/mradermacher/gpt2-alpaca-gpt4-GGUF/resolve/main/gpt2-alpaca-gpt4.Q4_K_M.gguf -O test-models/testmodel.ggml
wget -q https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-base.en.bin -O test-models/whisper-en
wget -q https://cdn.openai.com/whisper/draft-20220913a/micro-machines.wav -O test-dir/audio.wav
cp tests/models_fixtures/* test-models
prepare-test: protogen-go
cp tests/models_fixtures/* test-models
########################################################
## Tests
########################################################
## Test targets
## After the test-suite reorg (see plans/test-reorg) the default `make test`
## no longer downloads multi-GB GGUF/whisper fixtures or builds llama-cpp /
## transformers / piper / whisper / stablediffusion-ggml. core/http/app_test.go
## now drives the mock-backend binary built by build-mock-backend; real-backend
## inference moved into tests/e2e-backends/ (per-backend, path-filtered) and
## tests/e2e-aio/ (nightly).
test: prepare-test
test: test-models/testmodel.ggml protogen-go
@echo 'Running tests'
export GO_TAGS="debug"
$(MAKE) prepare-test
OPUS_SHIM_LIBRARY=$(abspath ./pkg/opus/shim/libopusshim.so) \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --flake-attempts $(TEST_FLAKES) --fail-fast -v -r $(TEST_PATHS)
########################################################
## Lint
########################################################
## Runs golangci-lint with config from .golangci.yml. Includes the standard
## linter set plus forbidigo, which enforces the Ginkgo/Gomega-only test
## convention documented in .agents/coding-style.md.
##
## LINT_EXCLUDE_DIRS_RE matches directories whose Go packages can't typecheck
## without C/C++ headers we don't install in the lint runner (cgo wrappers
## around llama.cpp, piper/spdlog, silero-vad/onnxruntime, and Fyne/OpenGL for
## the launcher). Their compile-time correctness is enforced by their own
## build pipelines. Keep this as a deny list — `go list ./...` discovers
## everything else automatically, so new packages are scanned by default.
LINT_EXCLUDE_DIRS_RE=/(backend/go/(piper|silero-vad|llm)|cmd/launcher)(/|$$)
lint:
@command -v golangci-lint >/dev/null 2>&1 || { \
echo 'golangci-lint not installed. Install: go install github.com/golangci/golangci-lint/v2/cmd/golangci-lint@latest'; \
exit 1; \
}
golangci-lint run $$(go list -e -f '{{.Dir}}' ./... | grep -vE '$(LINT_EXCLUDE_DIRS_RE)')
## Like `lint` but reports every issue, including the pre-existing baseline
## that `lint` ignores via .golangci.yml's new-from-merge-base. Use this to
## see what's available to clean up.
lint-all:
@command -v golangci-lint >/dev/null 2>&1 || { \
echo 'golangci-lint not installed. Install: go install github.com/golangci/golangci-lint/v2/cmd/golangci-lint@latest'; \
exit 1; \
}
golangci-lint run --new=false --new-from-merge-base= --new-from-rev= $$(go list -e -f '{{.Dir}}' ./... | grep -vE '$(LINT_EXCLUDE_DIRS_RE)')
HUGGINGFACE_GRPC=$(abspath ./)/backend/python/transformers/run.sh TEST_DIR=$(abspath ./)/test-dir/ FIXTURES=$(abspath ./)/tests/fixtures CONFIG_FILE=$(abspath ./)/test-models/config.yaml MODELS_PATH=$(abspath ./)/test-models BACKENDS_PATH=$(abspath ./)/backends \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter="!llama-gguf" --flake-attempts $(TEST_FLAKES) --fail-fast -v -r $(TEST_PATHS)
$(MAKE) test-llama-gguf
$(MAKE) test-tts
$(MAKE) test-stablediffusion
########################################################
## E2E AIO tests (uses standard image with pre-configured models)
@@ -216,8 +184,6 @@ docker-build-e2e:
--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
--build-arg APT_MIRROR=$(APT_MIRROR) \
--build-arg APT_PORTS_MIRROR=$(APT_PORTS_MIRROR) \
--build-arg GO_TAGS="$(GO_TAGS)" \
-t local-ai:tests -f Dockerfile .
@@ -232,20 +198,6 @@ run-e2e-aio: protogen-go
@echo 'Running e2e AIO tests'
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --flake-attempts $(TEST_FLAKES) -v -r ./tests/e2e-aio
# vLLM multi-node DP smoke (CPU). Builds local-ai:tests and the
# cpu-vllm backend from the current working tree, then drives a
# head + headless follower via testcontainers-go and asserts a chat
# completion. BuildKit caches both images, so re-runs only rebuild
# what changed. The test lives under tests/e2e/distributed and is
# selected by the VLLMMultinode label so it doesn't run alongside
# the other distributed-suite tests by default.
test-e2e-vllm-multinode: docker-build-e2e extract-backend-vllm protogen-go
@echo 'Running e2e vLLM multi-node DP test'
LOCALAI_IMAGE=local-ai \
LOCALAI_IMAGE_TAG=tests \
LOCALAI_VLLM_BACKEND_DIR=$(abspath ./local-backends/vllm) \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter='VLLMMultinode' -v -r ./tests/e2e/distributed
########################################################
## E2E tests
########################################################
@@ -259,8 +211,6 @@ prepare-e2e:
--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
--build-arg UBUNTU_VERSION=$(UBUNTU_VERSION) \
--build-arg UBUNTU_CODENAME=$(UBUNTU_CODENAME) \
--build-arg APT_MIRROR=$(APT_MIRROR) \
--build-arg APT_PORTS_MIRROR=$(APT_PORTS_MIRROR) \
--build-arg GO_TAGS="$(GO_TAGS)" \
--build-arg MAKEFLAGS="$(DOCKER_MAKEFLAGS)" \
-t localai-tests .
@@ -285,12 +235,20 @@ teardown-e2e:
## Integration and unit tests
########################################################
## Storage / vector-store integration. Requires the local-store backend to
## be available — we build it on demand and pass its location via
## BACKENDS_PATH (the model loader looks there for the gRPC binary).
test-stores: backends/local-store
BACKENDS_PATH=$(abspath ./)/backends \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --flake-attempts $(TEST_FLAKES) -v -r tests/integration
test-llama-gguf: prepare-test
TEST_DIR=$(abspath ./)/test-dir/ FIXTURES=$(abspath ./)/tests/fixtures CONFIG_FILE=$(abspath ./)/test-models/config.yaml MODELS_PATH=$(abspath ./)/test-models BACKENDS_PATH=$(abspath ./)/backends \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter="llama-gguf" --flake-attempts $(TEST_FLAKES) -v -r $(TEST_PATHS)
test-tts: prepare-test
TEST_DIR=$(abspath ./)/test-dir/ FIXTURES=$(abspath ./)/tests/fixtures CONFIG_FILE=$(abspath ./)/test-models/config.yaml MODELS_PATH=$(abspath ./)/test-models BACKENDS_PATH=$(abspath ./)/backends \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter="tts" --flake-attempts $(TEST_FLAKES) -v -r $(TEST_PATHS)
test-stablediffusion: prepare-test
TEST_DIR=$(abspath ./)/test-dir/ FIXTURES=$(abspath ./)/tests/fixtures CONFIG_FILE=$(abspath ./)/test-models/config.yaml MODELS_PATH=$(abspath ./)/test-models BACKENDS_PATH=$(abspath ./)/backends \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter="stablediffusion" --flake-attempts $(TEST_FLAKES) -v -r $(TEST_PATHS)
test-stores:
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter="stores" --flake-attempts $(TEST_FLAKES) -v -r tests/integration
test-opus:
@echo 'Running opus backend tests'
@@ -302,8 +260,6 @@ test-opus-docker:
docker build --target builder \
--build-arg BUILD_TYPE=$(or $(BUILD_TYPE),) \
--build-arg BASE_IMAGE=$(or $(BASE_IMAGE),ubuntu:24.04) \
--build-arg APT_MIRROR=$(APT_MIRROR) \
--build-arg APT_PORTS_MIRROR=$(APT_PORTS_MIRROR) \
--build-arg BACKEND=opus \
-t localai-opus-test -f backend/Dockerfile.golang .
docker run --rm localai-opus-test \
@@ -313,13 +269,23 @@ test-realtime: build-mock-backend
@echo 'Running realtime e2e tests (mock backend)'
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter="Realtime && !real-models" --flake-attempts $(TEST_FLAKES) -v -r ./tests/e2e
# Container-based real-model realtime testing. Build env vars / pipeline
# definition kept here so test-realtime-models-docker can drive a fully wired
# pipeline (VAD + STT + LLM + TTS) from inside a containerised runner.
# Real-model realtime tests. Set REALTIME_TEST_MODEL to use your own pipeline,
# or leave unset to auto-build one from the component env vars below.
REALTIME_VAD?=silero-vad-ggml
REALTIME_STT?=whisper-1
REALTIME_LLM?=qwen3-0.6b
REALTIME_TTS?=tts-1
REALTIME_BACKENDS_PATH?=$(abspath ./)/backends
test-realtime-models: build-mock-backend
@echo 'Running realtime e2e tests (real models)'
REALTIME_TEST_MODEL=$${REALTIME_TEST_MODEL:-realtime-test-pipeline} \
REALTIME_VAD=$(REALTIME_VAD) \
REALTIME_STT=$(REALTIME_STT) \
REALTIME_LLM=$(REALTIME_LLM) \
REALTIME_TTS=$(REALTIME_TTS) \
REALTIME_BACKENDS_PATH=$(REALTIME_BACKENDS_PATH) \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter="Realtime" --flake-attempts $(TEST_FLAKES) -v -r ./tests/e2e
# --- Container-based real-model testing ---
@@ -333,7 +299,7 @@ local-backends:
extract-backend-%: docker-build-% local-backends
@echo "Extracting backend $*..."
@CID=$$(docker create --entrypoint=/run.sh local-ai-backend:$*) && \
@CID=$$(docker create local-ai-backend:$*) && \
rm -rf local-backends/$* && mkdir -p local-backends/$* && \
docker cp $$CID:/ - | tar -xf - -C local-backends/$* && \
docker rm $$CID > /dev/null
@@ -345,8 +311,6 @@ test-realtime-models-docker: build-mock-backend
--build-arg BUILD_TYPE=$(or $(BUILD_TYPE),cublas) \
--build-arg CUDA_MAJOR_VERSION=$(or $(CUDA_MAJOR_VERSION),13) \
--build-arg CUDA_MINOR_VERSION=$(or $(CUDA_MINOR_VERSION),0) \
--build-arg APT_MIRROR=$(APT_MIRROR) \
--build-arg APT_PORTS_MIRROR=$(APT_PORTS_MIRROR) \
-t localai-test-runner .
docker run --rm \
$(REALTIME_DOCKER_FLAGS) \
@@ -564,9 +528,7 @@ test-extra-backend: protogen-go
## Convenience wrappers: build the image, then exercise it.
test-extra-backend-llama-cpp: docker-build-llama-cpp
BACKEND_IMAGE=local-ai-backend:llama-cpp \
BACKEND_TEST_CAPS=health,load,predict,stream,logprobs,logit_bias \
$(MAKE) test-extra-backend
BACKEND_IMAGE=local-ai-backend:llama-cpp $(MAKE) test-extra-backend
test-extra-backend-ik-llama-cpp: docker-build-ik-llama-cpp
BACKEND_IMAGE=local-ai-backend:ik-llama-cpp $(MAKE) test-extra-backend
@@ -583,6 +545,19 @@ test-extra-backend-turboquant: docker-build-turboquant
BACKEND_TEST_CACHE_TYPE_V=turbo3 \
$(MAKE) test-extra-backend
## buun-llama-cpp: exercises the fork-of-a-fork backend (spiritbuun/buun-llama-cpp)
## with the *TurboQuant/TCQ-specific* KV-cache types (turbo3 for V). Same rationale
## as turboquant above: picking a standard llama.cpp type would only re-test the
## shared code path. buun inherits turboquant's turbo2/turbo3/turbo4 and adds
## turbo2_tcq / turbo3_tcq on top. DFlash speculative decoding is not exercised
## here because no small DFlash drafter model exists (the known public pair is
## Qwen3.5-27B, ~54 GB).
test-extra-backend-buun-llama-cpp: docker-build-buun-llama-cpp
BACKEND_IMAGE=local-ai-backend:buun-llama-cpp \
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
@@ -608,14 +583,6 @@ test-extra-backend-vllm: docker-build-vllm
BACKEND_TEST_OPTIONS=tool_parser:hermes \
$(MAKE) test-extra-backend
## vllm multi-node data-parallel smoke test. Runs LocalAI head + a
## `local-ai p2p-worker vllm` follower in docker compose against
## Qwen2.5-0.5B with data_parallel_size=2. Requires 2 NVIDIA GPUs and
## nvidia-container-runtime on the host — vLLM v1's DP coordinator is
## not viable on CPU so this cannot run in CI without GPU.
test-extra-backend-vllm-multinode:
./tests/e2e/vllm-multinode/smoke.sh
## tinygrad mirrors the vllm target (same model, same caps, same parser) so
## the two backends are directly comparable. The LLM path covers Predict,
## streaming and native tool-call extraction. Companion targets below cover
@@ -870,42 +837,6 @@ test-extra-backend-sherpa-onnx-tts: docker-build-sherpa-onnx
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
## LocalVQE audio transform (joint AEC + noise suppression + dereverb).
## Exercises the audio_transform capability end-to-end: batch transform
## of a real WAV fixture and bidi streaming of synthetic silent frames.
test-extra-backend-localvqe-transform: docker-build-localvqe
BACKEND_IMAGE=local-ai-backend:localvqe \
BACKEND_TEST_MODEL_URL='https://huggingface.co/LocalAI-io/LocalVQE/resolve/main/localvqe-v1-1.3M-f32.gguf#localvqe-v1-1.3M-f32.gguf' \
BACKEND_TEST_AUDIO_URL=https://github.com/ggml-org/whisper.cpp/raw/master/samples/jfk.wav \
BACKEND_TEST_CAPS=health,load,audio_transform \
$(MAKE) test-extra-backend
## sglang mirrors the vllm setup: HuggingFace model id, same tiny Qwen,
## tool-call extraction via sglang's native qwen parser. CPU builds use
## sglang's upstream pyproject_cpu.toml recipe (see backend/python/sglang/install.sh).
@@ -948,8 +879,6 @@ docker:
--build-arg CUDA_MINOR_VERSION=$(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 $(DOCKER_IMAGE) .
docker-cuda12:
@@ -963,13 +892,11 @@ docker-cuda12:
--build-arg BUILD_TYPE=$(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)" \
@@ -978,8 +905,6 @@ 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) .
########################################################
@@ -1037,6 +962,11 @@ 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
# buun-llama-cpp is a fork-of-a-fork (spiritbuun/buun-llama-cpp forks
# TheTom/llama-cpp-turboquant) that adds DFlash block-diffusion speculative
# decoding and extra TCQ KV-cache variants on top of TurboQuant. Same thin
# wrapper pattern as turboquant — reuses backend/cpp/llama-cpp grpc-server.
BACKEND_BUUN_LLAMA_CPP = buun-llama-cpp|buun-llama-cpp|.|false|false
# Golang backends
BACKEND_PIPER = piper|golang|.|false|true
@@ -1048,8 +978,6 @@ BACKEND_WHISPER = whisper|golang|.|false|true
BACKEND_VOXTRAL = voxtral|golang|.|false|true
BACKEND_ACESTEP_CPP = acestep-cpp|golang|.|false|true
BACKEND_QWEN3_TTS_CPP = qwen3-tts-cpp|golang|.|false|true
BACKEND_VIBEVOICE_CPP = vibevoice-cpp|golang|.|false|true
BACKEND_LOCALVQE = localvqe|golang|.|false|true
BACKEND_OPUS = opus|golang|.|false|true
BACKEND_SHERPA_ONNX = sherpa-onnx|golang|.|false|true
@@ -1094,90 +1022,6 @@ BACKEND_KOKOROS = kokoros|rust|.|false|true
# C++ backends (Go wrapper with purego)
BACKEND_SAM3_CPP = sam3-cpp|golang|.|false|true
# Tag stem for the local prebuilt base images. Mirrors tagStem() in
# scripts/changed-backends.js and the inline expression in
# .github/workflows/backend.yml, so a `make docker-build-X` produces the
# same FROM ref shape that CI uses.
LOCAL_BASE_BUILD_TYPE := $(or $(BUILD_TYPE),cpu)
LOCAL_BASE_UBUNTU_VERSION := $(or $(UBUNTU_VERSION),2404)
LOCAL_BASE_CUDA_SUFFIX := $(if $(filter cublas l4t,$(BUILD_TYPE)),-cuda$(CUDA_MAJOR_VERSION).$(CUDA_MINOR_VERSION))
LOCAL_BASE_PYTHON_TAG := localai-base:python-$(LOCAL_BASE_BUILD_TYPE)-$(LOCAL_BASE_UBUNTU_VERSION)$(LOCAL_BASE_CUDA_SUFFIX)
LOCAL_BASE_GOLANG_TAG := localai-base:golang-$(LOCAL_BASE_BUILD_TYPE)-$(LOCAL_BASE_UBUNTU_VERSION)$(LOCAL_BASE_CUDA_SUFFIX)
LOCAL_BASE_CPP_TAG := localai-base:cpp-$(LOCAL_BASE_BUILD_TYPE)-$(LOCAL_BASE_UBUNTU_VERSION)$(LOCAL_BASE_CUDA_SUFFIX)
LOCAL_BASE_RUST_TAG := localai-base:rust-$(LOCAL_BASE_BUILD_TYPE)-$(LOCAL_BASE_UBUNTU_VERSION)
# Per-(lang) base image build targets. Each backend's docker-build-X target
# depends on the matching base via generate-docker-build-target below.
# PHONY so docker handles its own layer caching.
.PHONY: docker-build-python-base docker-build-golang-base docker-build-cpp-base docker-build-rust-base
docker-build-python-base:
docker build \
--build-arg BUILD_TYPE=$(BUILD_TYPE) \
--build-arg BASE_IMAGE=$(or $(BASE_IMAGE),ubuntu:24.04) \
--build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) \
--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
--build-arg UBUNTU_VERSION=$(LOCAL_BASE_UBUNTU_VERSION) \
--build-arg APT_MIRROR=$(APT_MIRROR) \
--build-arg APT_PORTS_MIRROR=$(APT_PORTS_MIRROR) \
$(if $(SKIP_DRIVERS),--build-arg SKIP_DRIVERS=$(SKIP_DRIVERS)) \
-t $(LOCAL_BASE_PYTHON_TAG) \
-f .docker/bases/Dockerfile.python \
.
docker-build-golang-base:
docker build \
--build-arg BUILD_TYPE=$(BUILD_TYPE) \
--build-arg BASE_IMAGE=$(or $(BASE_IMAGE),ubuntu:24.04) \
--build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) \
--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
--build-arg UBUNTU_VERSION=$(LOCAL_BASE_UBUNTU_VERSION) \
--build-arg APT_MIRROR=$(APT_MIRROR) \
--build-arg APT_PORTS_MIRROR=$(APT_PORTS_MIRROR) \
$(if $(SKIP_DRIVERS),--build-arg SKIP_DRIVERS=$(SKIP_DRIVERS)) \
-t $(LOCAL_BASE_GOLANG_TAG) \
-f .docker/bases/Dockerfile.golang \
.
docker-build-cpp-base:
docker build \
--build-arg BUILD_TYPE=$(BUILD_TYPE) \
--build-arg BASE_IMAGE=$(or $(BASE_IMAGE),ubuntu:24.04) \
--build-arg CUDA_MAJOR_VERSION=$(CUDA_MAJOR_VERSION) \
--build-arg CUDA_MINOR_VERSION=$(CUDA_MINOR_VERSION) \
--build-arg UBUNTU_VERSION=$(LOCAL_BASE_UBUNTU_VERSION) \
--build-arg APT_MIRROR=$(APT_MIRROR) \
--build-arg APT_PORTS_MIRROR=$(APT_PORTS_MIRROR) \
$(if $(SKIP_DRIVERS),--build-arg SKIP_DRIVERS=$(SKIP_DRIVERS)) \
-t $(LOCAL_BASE_CPP_TAG) \
-f .docker/bases/Dockerfile.cpp \
.
docker-build-rust-base:
docker build \
--build-arg BASE_IMAGE=$(or $(BASE_IMAGE),ubuntu:24.04) \
--build-arg UBUNTU_VERSION=$(LOCAL_BASE_UBUNTU_VERSION) \
--build-arg APT_MIRROR=$(APT_MIRROR) \
--build-arg APT_PORTS_MIRROR=$(APT_PORTS_MIRROR) \
-t $(LOCAL_BASE_RUST_TAG) \
-f .docker/bases/Dockerfile.rust \
.
# Map a consumer dockerfile-type to the base-image tag it should consume.
# Mirrors langOf() in scripts/changed-backends.js: the C++ trio
# (llama-cpp/ik-llama-cpp/turboquant) all consume the shared cpp base.
local-base-tag = $(strip \
$(if $(filter python,$(1)),$(LOCAL_BASE_PYTHON_TAG), \
$(if $(filter golang,$(1)),$(LOCAL_BASE_GOLANG_TAG), \
$(if $(filter llama-cpp ik-llama-cpp turboquant,$(1)),$(LOCAL_BASE_CPP_TAG), \
$(if $(filter rust,$(1)),$(LOCAL_BASE_RUST_TAG))))))
local-base-target = $(strip \
$(if $(filter python,$(1)),docker-build-python-base, \
$(if $(filter golang,$(1)),docker-build-golang-base, \
$(if $(filter llama-cpp ik-llama-cpp turboquant,$(1)),docker-build-cpp-base, \
$(if $(filter rust,$(1)),docker-build-rust-base)))))
# Helper function to build docker image for a backend
# Usage: $(call docker-build-backend,BACKEND_NAME,DOCKERFILE_TYPE,BUILD_CONTEXT,PROGRESS_FLAG,NEEDS_BACKEND_ARG)
define docker-build-backend
@@ -1188,20 +1032,14 @@ define docker-build-backend
--build-arg CUDA_MINOR_VERSION=$(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 $(call local-base-tag,$(2)),--build-arg BASE_IMAGE_PREBUILT=$(call local-base-tag,$(2))) \
$(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) $(3)
endef
# Generate docker-build targets from backend definitions. Each consumer
# gets the matching layered base as a prerequisite so the FROM in the
# slimmed Dockerfile resolves locally. The map lives in local-base-target.
# Generate docker-build targets from backend definitions
define generate-docker-build-target
docker-build-$(word 1,$(subst |, ,$(1))): $(call local-base-target,$(word 2,$(subst |, ,$(1))))
docker-build-$(word 1,$(subst |, ,$(1))):
$$(call docker-build-backend,$(word 1,$(subst |, ,$(1))),$(word 2,$(subst |, ,$(1))),$(word 3,$(subst |, ,$(1))),$(word 4,$(subst |, ,$(1))),$(word 5,$(subst |, ,$(1))))
endef
@@ -1209,6 +1047,7 @@ endef
$(eval $(call generate-docker-build-target,$(BACKEND_LLAMA_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_IK_LLAMA_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_TURBOQUANT)))
$(eval $(call generate-docker-build-target,$(BACKEND_BUUN_LLAMA_CPP)))
$(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)))
@@ -1246,8 +1085,6 @@ $(eval $(call generate-docker-build-target,$(BACKEND_WHISPERX)))
$(eval $(call generate-docker-build-target,$(BACKEND_ACE_STEP)))
$(eval $(call generate-docker-build-target,$(BACKEND_ACESTEP_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_QWEN3_TTS_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_VIBEVOICE_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_LOCALVQE)))
$(eval $(call generate-docker-build-target,$(BACKEND_MLX)))
$(eval $(call generate-docker-build-target,$(BACKEND_MLX_VLM)))
$(eval $(call generate-docker-build-target,$(BACKEND_MLX_DISTRIBUTED)))
@@ -1262,7 +1099,7 @@ $(eval $(call generate-docker-build-target,$(BACKEND_SHERPA_ONNX)))
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-localvqe docker-build-insightface docker-build-speaker-recognition docker-build-sherpa-onnx
docker-build-backends: docker-build-llama-cpp docker-build-ik-llama-cpp docker-build-turboquant docker-build-buun-llama-cpp 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-insightface docker-build-speaker-recognition docker-build-sherpa-onnx
########################################################
### Mock Backend for E2E Tests

View File

@@ -38,7 +38,7 @@
- **Built-in AI agents** — autonomous agents with tool use, RAG, MCP, and skills
- **Privacy-first** — your data never leaves your infrastructure
Created by [Ettore Di Giacinto](https://github.com/mudler) and maintained by the [LocalAI team](#team).
Created and maintained by [Ettore Di Giacinto](https://github.com/mudler).
> [:book: Documentation](https://localai.io/) | [:speech_balloon: Discord](https://discord.gg/uJAeKSAGDy) | [💻 Quickstart](https://localai.io/basics/getting_started/) | [🖼️ Models](https://models.localai.io/) | [❓FAQ](https://localai.io/faq/)
@@ -201,14 +201,13 @@ 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 +250,7 @@ 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

@@ -1,34 +1,23 @@
# Shared C++ + accelerator base image for the llama-cpp / ik-llama-cpp /
# turboquant trio. They differ only in their Makefile targets at build
# time; the apt + GPU SDK + protoc + cmake + GRPC install is identical.
#
# Built once per (build-type, arch, ubuntu-version, cuda-version) combination
# by .github/workflows/base_images.yml and pushed to
# quay.io/go-skynet/ci-cache:base-image-<tag-stem>[-pr<N>]. Consumed by
# backend/Dockerfile.{llama-cpp,ik-llama-cpp,turboquant} via the
# BASE_IMAGE_PREBUILT build-arg. See .agents/ci-caching.md.
ARG BASE_IMAGE=ubuntu:24.04
ARG APT_MIRROR=""
ARG APT_PORTS_MIRROR=""
ARG GRPC_BASE_IMAGE=${BASE_IMAGE}
FROM ${BASE_IMAGE} AS grpc
# 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 && \
RUN apt-get update && \
apt-get install -y --no-install-recommends \
ca-certificates \
build-essential curl libssl-dev \
@@ -36,6 +25,7 @@ RUN --mount=type=bind,source=.docker/apt-mirror.sh,target=/usr/local/sbin/apt-mi
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
@@ -48,10 +38,9 @@ RUN <<EOT bash
fi
EOT
# Build GRPC into /opt/grpc so we can copy it into the final base without
# pulling in the full source tree. Mirrors the original two-stage layout in
# Dockerfile.llama-cpp; absorbing it here means consumers no longer pay the
# GRPC compile cost.
# 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 && \
@@ -61,11 +50,15 @@ RUN git clone --recurse-submodules --jobs 4 -b ${GRPC_VERSION} --depth 1 --shall
make install && \
rm -rf /build
FROM ${BASE_IMAGE}
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
@@ -76,17 +69,10 @@ ENV CUDA_MINOR_VERSION=${CUDA_MINOR_VERSION}
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETARCH
ARG TARGETVARIANT
ARG GO_VERSION=1.25.4
ARG UBUNTU_VERSION=2404
ARG APT_MIRROR
ARG APT_PORTS_MIRROR
LABEL org.opencontainers.image.source="https://github.com/mudler/LocalAI"
LABEL org.opencontainers.image.description="LocalAI C++ (llama-cpp/ik-llama-cpp/turboquant) base image"
LABEL org.localai.base.lang="cpp"
RUN --mount=type=bind,source=.docker/apt-mirror.sh,target=/usr/local/sbin/apt-mirror \
APT_MIRROR="${APT_MIRROR}" APT_PORTS_MIRROR="${APT_PORTS_MIRROR}" sh /usr/local/sbin/apt-mirror && \
apt-get update && \
RUN apt-get update && \
apt-get install -y --no-install-recommends \
build-essential \
ccache git \
@@ -218,18 +204,23 @@ RUN if [ "${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
# Install protoc (the version in 22.04 is too old, and grpc's bundled protoc
# would pull in a newer absl that breaks stablediffusion).
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 && \
@@ -257,3 +248,43 @@ RUN <<EOT bash
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) = ${CMAKE_ARGS}"
rm -rf /LocalAI/backend/cpp/buun-llama-cpp-*-build
fi
cd /LocalAI/backend/cpp/buun-llama-cpp
if [ "${TARGETARCH}" = "arm64" ] || [ "${BUILD_TYPE}" = "hipblas" ]; then
make buun-llama-cpp-fallback
make buun-llama-cpp-grpc
make buun-llama-cpp-rpc-server
else
make buun-llama-cpp-avx
make buun-llama-cpp-avx2
make buun-llama-cpp-avx512
make buun-llama-cpp-fallback
make buun-llama-cpp-grpc
make buun-llama-cpp-rpc-server
fi
EOT
# Copy libraries using a script to handle architecture differences
RUN make -BC /LocalAI/backend/cpp/buun-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/buun-llama-cpp/package/. ./

View File

@@ -1,37 +1,189 @@
# Builds a single Go backend on top of the shared
# .docker/bases/Dockerfile.golang base. The base bakes in apt + GPU SDK +
# Go toolchain + protoc + grpc tooling, so this stage only carries the
# per-backend opus-dev install + COPY + `make build`.
#
# CI orchestration (.github/workflows/backend.yml + backend_pr.yml) builds
# the right base flavour automatically via scripts/changed-backends.js
# and passes BASE_IMAGE_PREBUILT here. For local builds, run:
# make backend-image-base LANG=golang BUILD_TYPE=<...>
# make backend-image BACKEND=<...> BUILD_TYPE=<...>
# See .agents/ci-caching.md.
ARG BASE_IMAGE_PREBUILT
FROM ${BASE_IMAGE_PREBUILT} AS builder
ARG BASE_IMAGE=ubuntu:24.04
FROM ${BASE_IMAGE} AS builder
ARG BACKEND=rerankers
ARG BUILD_TYPE
ENV BUILD_TYPE=${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 AMDGPU_TARGETS
ENV AMDGPU_TARGETS=${AMDGPU_TARGETS}
ARG GO_VERSION=1.25.4
ARG UBUNTU_VERSION=2404
# opus-dev is only needed for the opus backend; install on demand to keep
# every other golang backend's base image lean.
RUN if [ "${BACKEND}" = "opus" ]; then \
apt-get update && apt-get install -y --no-install-recommends libopus-dev pkg-config && \
apt-get clean && rm -rf /var/lib/apt/lists/*; \
RUN apt-get update && \
apt-get install -y --no-install-recommends \
build-essential \
git ccache \
ca-certificates \
make cmake wget libopenblas-dev \
curl unzip \
libssl-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
# Cuda
ENV PATH=/usr/local/cuda/bin:${PATH}
# HipBLAS requirements
ENV PATH=/opt/rocm/bin:${PATH}
# Vulkan requirements
RUN <<EOT bash
if [ "${BUILD_TYPE}" = "vulkan" ] && [ "${SKIP_DRIVERS}" = "false" ]; then
apt-get update && \
apt-get install -y --no-install-recommends \
software-properties-common pciutils wget gpg-agent && \
apt-get install -y libglm-dev cmake libxcb-dri3-0 libxcb-present0 libpciaccess0 \
libpng-dev libxcb-keysyms1-dev libxcb-dri3-dev libx11-dev g++ gcc \
libwayland-dev libxrandr-dev libxcb-randr0-dev libxcb-ewmh-dev \
git python-is-python3 bison libx11-xcb-dev liblz4-dev libzstd-dev \
ocaml-core ninja-build pkg-config libxml2-dev wayland-protocols python3-jsonschema \
clang-format qtbase5-dev qt6-base-dev libxcb-glx0-dev sudo xz-utils
if [ "amd64" = "$TARGETARCH" ]; then
wget "https://sdk.lunarg.com/sdk/download/1.4.335.0/linux/vulkansdk-linux-x86_64-1.4.335.0.tar.xz" && \
tar -xf vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
rm vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
mkdir -p /opt/vulkan-sdk && \
mv 1.4.335.0 /opt/vulkan-sdk/ && \
cd /opt/vulkan-sdk/1.4.335.0 && \
./vulkansdk --no-deps --maxjobs \
vulkan-loader \
vulkan-validationlayers \
vulkan-extensionlayer \
vulkan-tools \
shaderc && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/bin/* /usr/bin/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/lib/* /usr/lib/x86_64-linux-gnu/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/include/* /usr/include/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/share/* /usr/share/ && \
rm -rf /opt/vulkan-sdk
fi
if [ "arm64" = "$TARGETARCH" ]; then
mkdir vulkan && cd vulkan && \
curl -L -o vulkan-sdk.tar.xz https://github.com/mudler/vulkan-sdk-arm/releases/download/1.4.335.0/vulkansdk-ubuntu-24.04-arm-1.4.335.0.tar.xz && \
tar -xvf vulkan-sdk.tar.xz && \
rm vulkan-sdk.tar.xz && \
cd 1.4.335.0 && \
cp -rfv aarch64/bin/* /usr/bin/ && \
cp -rfv aarch64/lib/* /usr/lib/aarch64-linux-gnu/ && \
cp -rfv aarch64/include/* /usr/include/ && \
cp -rfv aarch64/share/* /usr/share/ && \
cd ../.. && \
rm -rf vulkan
fi
ldconfig && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
# CuBLAS requirements
RUN <<EOT bash
if ( [ "${BUILD_TYPE}" = "cublas" ] || [ "${BUILD_TYPE}" = "l4t" ] ) && [ "${SKIP_DRIVERS}" = "false" ]; then
apt-get update && \
apt-get install -y --no-install-recommends \
software-properties-common pciutils
if [ "amd64" = "$TARGETARCH" ]; then
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/x86_64/cuda-keyring_1.1-1_all.deb
fi
if [ "arm64" = "$TARGETARCH" ]; then
if [ "${CUDA_MAJOR_VERSION}" = "13" ]; then
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/sbsa/cuda-keyring_1.1-1_all.deb
else
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/arm64/cuda-keyring_1.1-1_all.deb
fi
fi
dpkg -i cuda-keyring_1.1-1_all.deb && \
rm -f cuda-keyring_1.1-1_all.deb && \
apt-get update && \
apt-get install -y --no-install-recommends \
cuda-nvcc-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcufft-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcurand-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcublas-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcusparse-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcusolver-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
if [ "${CUDA_MAJOR_VERSION}" = "13" ] && [ "arm64" = "$TARGETARCH" ]; then
apt-get install -y --no-install-recommends \
libcufile-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libcudnn9-cuda-${CUDA_MAJOR_VERSION} cuda-cupti-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libnvjitlink-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
fi
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
# https://github.com/NVIDIA/Isaac-GR00T/issues/343
RUN <<EOT bash
if [ "${BUILD_TYPE}" = "cublas" ] && [ "${TARGETARCH}" = "arm64" ]; then
wget https://developer.download.nvidia.com/compute/cudss/0.6.0/local_installers/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
dpkg -i cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
cp /var/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0/cudss-*-keyring.gpg /usr/share/keyrings/ && \
apt-get update && apt-get -y install cudss cudss-cuda-${CUDA_MAJOR_VERSION} && \
wget https://developer.download.nvidia.com/compute/nvpl/25.5/local_installers/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
dpkg -i nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
cp /var/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5/nvpl-*-keyring.gpg /usr/share/keyrings/ && \
apt-get update && apt-get install -y nvpl
fi
EOT
# If we are building with clblas support, we need the libraries for the builds
RUN if [ "${BUILD_TYPE}" = "clblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then \
apt-get update && \
apt-get install -y --no-install-recommends \
libclblast-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/* \
; fi
RUN if [ "${BUILD_TYPE}" = "hipblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then \
apt-get update && \
apt-get install -y --no-install-recommends \
hipblas-dev \
rocblas-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/* && \
# I have no idea why, but the ROCM lib packages don't trigger ldconfig after they install, which results in local-ai and others not being able
# to locate the libraries. We run ldconfig ourselves to work around this packaging deficiency
ldconfig \
; fi
# Install Go
RUN curl -L -s https://go.dev/dl/go${GO_VERSION}.linux-${TARGETARCH}.tar.gz | tar -C /usr/local -xz
ENV PATH=$PATH:/root/go/bin:/usr/local/go/bin:/usr/local/bin
# Install grpc compilers
RUN 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
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" = "$TARGETARCH" ]; then
curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v27.1/protoc-27.1-linux-aarch_64.zip -o protoc.zip && \
unzip -j -d /usr/local/bin protoc.zip bin/protoc && \
rm protoc.zip
fi
EOT
RUN if [ "${BACKEND}" = "opus" ]; then \
apt-get update && apt-get install -y --no-install-recommends libopus-dev pkg-config && \
apt-get clean && rm -rf /var/lib/apt/lists/*; \
fi
COPY . /LocalAI

View File

@@ -1,25 +1,250 @@
# Builds the ik-llama-cpp backend on top of the shared
# .docker/bases/Dockerfile.cpp base (shared with llama-cpp/turboquant).
# See backend/Dockerfile.llama-cpp for the rationale; this consumer differs
# only in the make targets at the end.
ARG BASE_IMAGE=ubuntu:24.04
ARG GRPC_BASE_IMAGE=${BASE_IMAGE}
ARG BASE_IMAGE_PREBUILT
FROM ${BASE_IMAGE_PREBUILT} AS builder
# 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 ${GRPC_BASE_IMAGE} AS grpc
# This is a bit of a hack, but it's required in order to be able to effectively cache this layer in CI
ARG GRPC_MAKEFLAGS="-j4 -Otarget"
ARG GRPC_VERSION=v1.65.0
ARG CMAKE_FROM_SOURCE=false
# CUDA Toolkit 13.x compatibility: CMake 3.31.9+ fixes toolchain detection/arch table issues
ARG CMAKE_VERSION=3.31.10
ENV MAKEFLAGS=${GRPC_MAKEFLAGS}
WORKDIR /build
RUN apt-get update && \
apt-get install -y --no-install-recommends \
ca-certificates \
build-essential curl libssl-dev \
git wget && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
# Install CMake (the version in 22.04 is too old)
RUN <<EOT bash
if [ "${CMAKE_FROM_SOURCE}" = "true" ]; then
curl -L -s https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}.tar.gz -o cmake.tar.gz && tar xvf cmake.tar.gz && cd cmake-${CMAKE_VERSION} && ./configure && make && make install
else
apt-get update && \
apt-get install -y \
cmake && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
# We install GRPC to a different prefix here so that we can copy in only the build artifacts later
# saves several hundred MB on the final docker image size vs copying in the entire GRPC source tree
# and running make install in the target container
RUN git clone --recurse-submodules --jobs 4 -b ${GRPC_VERSION} --depth 1 --shallow-submodules https://github.com/grpc/grpc && \
mkdir -p /build/grpc/cmake/build && \
cd /build/grpc/cmake/build && \
sed -i "216i\ TESTONLY" "../../third_party/abseil-cpp/absl/container/CMakeLists.txt" && \
cmake -DgRPC_INSTALL=ON -DgRPC_BUILD_TESTS=OFF -DCMAKE_INSTALL_PREFIX:PATH=/opt/grpc ../.. && \
make && \
make install && \
rm -rf /build
FROM ${BASE_IMAGE} AS builder
ARG CMAKE_FROM_SOURCE=false
ARG CMAKE_VERSION=3.31.10
# We can target specific CUDA ARCHITECTURES like --build-arg CUDA_DOCKER_ARCH='75;86;89;120'
ARG CUDA_DOCKER_ARCH
ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
ARG CMAKE_ARGS
ENV CMAKE_ARGS=${CMAKE_ARGS}
ARG BACKEND=ik-llama-cpp
ARG BACKEND=rerankers
ARG BUILD_TYPE
ENV BUILD_TYPE=${BUILD_TYPE}
ARG CUDA_MAJOR_VERSION
ARG CUDA_MINOR_VERSION
ARG SKIP_DRIVERS=false
ENV CUDA_MAJOR_VERSION=${CUDA_MAJOR_VERSION}
ENV CUDA_MINOR_VERSION=${CUDA_MINOR_VERSION}
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETARCH
ARG TARGETVARIANT
ARG GO_VERSION=1.25.4
ARG UBUNTU_VERSION=2404
RUN apt-get update && \
apt-get install -y --no-install-recommends \
build-essential \
ccache git \
ca-certificates \
make \
pkg-config libcurl4-openssl-dev \
curl unzip \
libssl-dev wget && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
# Cuda
ENV PATH=/usr/local/cuda/bin:${PATH}
# HipBLAS requirements
ENV PATH=/opt/rocm/bin:${PATH}
# Vulkan requirements
RUN <<EOT bash
if [ "${BUILD_TYPE}" = "vulkan" ] && [ "${SKIP_DRIVERS}" = "false" ]; then
apt-get update && \
apt-get install -y --no-install-recommends \
software-properties-common pciutils wget gpg-agent && \
apt-get install -y libglm-dev cmake libxcb-dri3-0 libxcb-present0 libpciaccess0 \
libpng-dev libxcb-keysyms1-dev libxcb-dri3-dev libx11-dev g++ gcc \
libwayland-dev libxrandr-dev libxcb-randr0-dev libxcb-ewmh-dev \
git python-is-python3 bison libx11-xcb-dev liblz4-dev libzstd-dev \
ocaml-core ninja-build pkg-config libxml2-dev wayland-protocols python3-jsonschema \
clang-format qtbase5-dev qt6-base-dev libxcb-glx0-dev sudo xz-utils
if [ "amd64" = "$TARGETARCH" ]; then
wget "https://sdk.lunarg.com/sdk/download/1.4.335.0/linux/vulkansdk-linux-x86_64-1.4.335.0.tar.xz" && \
tar -xf vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
rm vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
mkdir -p /opt/vulkan-sdk && \
mv 1.4.335.0 /opt/vulkan-sdk/ && \
cd /opt/vulkan-sdk/1.4.335.0 && \
./vulkansdk --no-deps --maxjobs \
vulkan-loader \
vulkan-validationlayers \
vulkan-extensionlayer \
vulkan-tools \
shaderc && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/bin/* /usr/bin/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/lib/* /usr/lib/x86_64-linux-gnu/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/include/* /usr/include/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/share/* /usr/share/ && \
rm -rf /opt/vulkan-sdk
fi
if [ "arm64" = "$TARGETARCH" ]; then
mkdir vulkan && cd vulkan && \
curl -L -o vulkan-sdk.tar.xz https://github.com/mudler/vulkan-sdk-arm/releases/download/1.4.335.0/vulkansdk-ubuntu-24.04-arm-1.4.335.0.tar.xz && \
tar -xvf vulkan-sdk.tar.xz && \
rm vulkan-sdk.tar.xz && \
cd 1.4.335.0 && \
cp -rfv aarch64/bin/* /usr/bin/ && \
cp -rfv aarch64/lib/* /usr/lib/aarch64-linux-gnu/ && \
cp -rfv aarch64/include/* /usr/include/ && \
cp -rfv aarch64/share/* /usr/share/ && \
cd ../.. && \
rm -rf vulkan
fi
ldconfig && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
# CuBLAS requirements
RUN <<EOT bash
if ( [ "${BUILD_TYPE}" = "cublas" ] || [ "${BUILD_TYPE}" = "l4t" ] ) && [ "${SKIP_DRIVERS}" = "false" ]; then
apt-get update && \
apt-get install -y --no-install-recommends \
software-properties-common pciutils
if [ "amd64" = "$TARGETARCH" ]; then
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/x86_64/cuda-keyring_1.1-1_all.deb
fi
if [ "arm64" = "$TARGETARCH" ]; then
if [ "${CUDA_MAJOR_VERSION}" = "13" ]; then
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/sbsa/cuda-keyring_1.1-1_all.deb
else
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/arm64/cuda-keyring_1.1-1_all.deb
fi
fi
dpkg -i cuda-keyring_1.1-1_all.deb && \
rm -f cuda-keyring_1.1-1_all.deb && \
apt-get update && \
apt-get install -y --no-install-recommends \
cuda-nvcc-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcufft-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcurand-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcublas-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcusparse-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcusolver-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
if [ "${CUDA_MAJOR_VERSION}" = "13" ] && [ "arm64" = "$TARGETARCH" ]; then
apt-get install -y --no-install-recommends \
libcufile-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libcudnn9-cuda-${CUDA_MAJOR_VERSION} cuda-cupti-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libnvjitlink-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
fi
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
# https://github.com/NVIDIA/Isaac-GR00T/issues/343
RUN <<EOT bash
if [ "${BUILD_TYPE}" = "cublas" ] && [ "${TARGETARCH}" = "arm64" ]; then
wget https://developer.download.nvidia.com/compute/cudss/0.6.0/local_installers/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
dpkg -i cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
cp /var/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0/cudss-*-keyring.gpg /usr/share/keyrings/ && \
apt-get update && apt-get -y install cudss cudss-cuda-${CUDA_MAJOR_VERSION} && \
wget https://developer.download.nvidia.com/compute/nvpl/25.5/local_installers/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
dpkg -i nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
cp /var/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5/nvpl-*-keyring.gpg /usr/share/keyrings/ && \
apt-get update && apt-get install -y nvpl
fi
EOT
# If we are building with clblas support, we need the libraries for the builds
RUN if [ "${BUILD_TYPE}" = "clblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then \
apt-get update && \
apt-get install -y --no-install-recommends \
libclblast-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/* \
; fi
RUN if [ "${BUILD_TYPE}" = "hipblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then \
apt-get update && \
apt-get install -y --no-install-recommends \
hipblas-dev \
rocblas-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/* && \
# I have no idea why, but the ROCM lib packages don't trigger ldconfig after they install, which results in local-ai and others not being able
# to locate the libraries. We run ldconfig ourselves to work around this packaging deficiency
ldconfig \
; 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
@@ -45,10 +270,12 @@ 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

@@ -1,15 +1,58 @@
# Builds the llama-cpp backend on top of the shared
# .docker/bases/Dockerfile.cpp base. The base bakes in apt + GPU SDK +
# protoc + cmake + GRPC, so this stage only carries the COPY + `make`
# invocations and the final scratch-stage package.
#
# CI orchestration (.github/workflows/backend.yml + backend_pr.yml) passes
# BASE_IMAGE_PREBUILT. See .agents/ci-caching.md.
ARG BASE_IMAGE=ubuntu:24.04
ARG GRPC_BASE_IMAGE=${BASE_IMAGE}
ARG BASE_IMAGE_PREBUILT
FROM ${BASE_IMAGE_PREBUILT} AS builder
# 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 ${GRPC_BASE_IMAGE} AS grpc
# This is a bit of a hack, but it's required in order to be able to effectively cache this layer in CI
ARG GRPC_MAKEFLAGS="-j4 -Otarget"
ARG GRPC_VERSION=v1.65.0
ARG CMAKE_FROM_SOURCE=false
# CUDA Toolkit 13.x compatibility: CMake 3.31.9+ fixes toolchain detection/arch table issues
ARG CMAKE_VERSION=3.31.10
ENV MAKEFLAGS=${GRPC_MAKEFLAGS}
WORKDIR /build
RUN apt-get update && \
apt-get install -y --no-install-recommends \
ca-certificates \
build-essential curl libssl-dev \
git wget && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
# Install CMake (the version in 22.04 is too old)
RUN <<EOT bash
if [ "${CMAKE_FROM_SOURCE}" = "true" ]; then
curl -L -s https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}.tar.gz -o cmake.tar.gz && tar xvf cmake.tar.gz && cd cmake-${CMAKE_VERSION} && ./configure && make && make install
else
apt-get update && \
apt-get install -y \
cmake && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
# We install GRPC to a different prefix here so that we can copy in only the build artifacts later
# saves several hundred MB on the final docker image size vs copying in the entire GRPC source tree
# and running make install in the target container
RUN git clone --recurse-submodules --jobs 4 -b ${GRPC_VERSION} --depth 1 --shallow-submodules https://github.com/grpc/grpc && \
mkdir -p /build/grpc/cmake/build && \
cd /build/grpc/cmake/build && \
sed -i "216i\ TESTONLY" "../../third_party/abseil-cpp/absl/container/CMakeLists.txt" && \
cmake -DgRPC_INSTALL=ON -DgRPC_BUILD_TESTS=OFF -DCMAKE_INSTALL_PREFIX:PATH=/opt/grpc ../.. && \
make && \
make install && \
rm -rf /build
FROM ${BASE_IMAGE} AS builder
ARG CMAKE_FROM_SOURCE=false
ARG CMAKE_VERSION=3.31.10
# We can target specific CUDA ARCHITECTURES like --build-arg CUDA_DOCKER_ARCH='75;86;89;120'
ARG CUDA_DOCKER_ARCH
ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
@@ -17,15 +60,197 @@ ARG CMAKE_ARGS
ENV CMAKE_ARGS=${CMAKE_ARGS}
ARG AMDGPU_TARGETS
ENV AMDGPU_TARGETS=${AMDGPU_TARGETS}
ARG BACKEND=llama-cpp
ARG BACKEND=rerankers
ARG BUILD_TYPE
ENV BUILD_TYPE=${BUILD_TYPE}
ARG CUDA_MAJOR_VERSION
ARG CUDA_MINOR_VERSION
ARG SKIP_DRIVERS=false
ENV CUDA_MAJOR_VERSION=${CUDA_MAJOR_VERSION}
ENV CUDA_MINOR_VERSION=${CUDA_MINOR_VERSION}
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETARCH
ARG TARGETVARIANT
ARG GO_VERSION=1.25.4
ARG UBUNTU_VERSION=2404
RUN apt-get update && \
apt-get install -y --no-install-recommends \
build-essential \
ccache git \
ca-certificates \
make \
pkg-config libcurl4-openssl-dev \
curl unzip \
libssl-dev wget && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
# Cuda
ENV PATH=/usr/local/cuda/bin:${PATH}
# HipBLAS requirements
ENV PATH=/opt/rocm/bin:${PATH}
# Vulkan requirements
RUN <<EOT bash
if [ "${BUILD_TYPE}" = "vulkan" ] && [ "${SKIP_DRIVERS}" = "false" ]; then
apt-get update && \
apt-get install -y --no-install-recommends \
software-properties-common pciutils wget gpg-agent && \
apt-get install -y libglm-dev cmake libxcb-dri3-0 libxcb-present0 libpciaccess0 \
libpng-dev libxcb-keysyms1-dev libxcb-dri3-dev libx11-dev g++ gcc \
libwayland-dev libxrandr-dev libxcb-randr0-dev libxcb-ewmh-dev \
git python-is-python3 bison libx11-xcb-dev liblz4-dev libzstd-dev \
ocaml-core ninja-build pkg-config libxml2-dev wayland-protocols python3-jsonschema \
clang-format qtbase5-dev qt6-base-dev libxcb-glx0-dev sudo xz-utils
if [ "amd64" = "$TARGETARCH" ]; then
wget "https://sdk.lunarg.com/sdk/download/1.4.335.0/linux/vulkansdk-linux-x86_64-1.4.335.0.tar.xz" && \
tar -xf vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
rm vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
mkdir -p /opt/vulkan-sdk && \
mv 1.4.335.0 /opt/vulkan-sdk/ && \
cd /opt/vulkan-sdk/1.4.335.0 && \
./vulkansdk --no-deps --maxjobs \
vulkan-loader \
vulkan-validationlayers \
vulkan-extensionlayer \
vulkan-tools \
shaderc && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/bin/* /usr/bin/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/lib/* /usr/lib/x86_64-linux-gnu/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/include/* /usr/include/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/share/* /usr/share/ && \
rm -rf /opt/vulkan-sdk
fi
if [ "arm64" = "$TARGETARCH" ]; then
mkdir vulkan && cd vulkan && \
curl -L -o vulkan-sdk.tar.xz https://github.com/mudler/vulkan-sdk-arm/releases/download/1.4.335.0/vulkansdk-ubuntu-24.04-arm-1.4.335.0.tar.xz && \
tar -xvf vulkan-sdk.tar.xz && \
rm vulkan-sdk.tar.xz && \
cd 1.4.335.0 && \
cp -rfv aarch64/bin/* /usr/bin/ && \
cp -rfv aarch64/lib/* /usr/lib/aarch64-linux-gnu/ && \
cp -rfv aarch64/include/* /usr/include/ && \
cp -rfv aarch64/share/* /usr/share/ && \
cd ../.. && \
rm -rf vulkan
fi
ldconfig && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
# CuBLAS requirements
RUN <<EOT bash
if ( [ "${BUILD_TYPE}" = "cublas" ] || [ "${BUILD_TYPE}" = "l4t" ] ) && [ "${SKIP_DRIVERS}" = "false" ]; then
apt-get update && \
apt-get install -y --no-install-recommends \
software-properties-common pciutils
if [ "amd64" = "$TARGETARCH" ]; then
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/x86_64/cuda-keyring_1.1-1_all.deb
fi
if [ "arm64" = "$TARGETARCH" ]; then
if [ "${CUDA_MAJOR_VERSION}" = "13" ]; then
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/sbsa/cuda-keyring_1.1-1_all.deb
else
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/arm64/cuda-keyring_1.1-1_all.deb
fi
fi
dpkg -i cuda-keyring_1.1-1_all.deb && \
rm -f cuda-keyring_1.1-1_all.deb && \
apt-get update && \
apt-get install -y --no-install-recommends \
cuda-nvcc-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcufft-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcurand-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcublas-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcusparse-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcusolver-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
if [ "${CUDA_MAJOR_VERSION}" = "13" ] && [ "arm64" = "$TARGETARCH" ]; then
apt-get install -y --no-install-recommends \
libcufile-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libcudnn9-cuda-${CUDA_MAJOR_VERSION} cuda-cupti-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libnvjitlink-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
fi
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
# https://github.com/NVIDIA/Isaac-GR00T/issues/343
RUN <<EOT bash
if [ "${BUILD_TYPE}" = "cublas" ] && [ "${TARGETARCH}" = "arm64" ]; then
wget https://developer.download.nvidia.com/compute/cudss/0.6.0/local_installers/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
dpkg -i cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
cp /var/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0/cudss-*-keyring.gpg /usr/share/keyrings/ && \
apt-get update && apt-get -y install cudss cudss-cuda-${CUDA_MAJOR_VERSION} && \
wget https://developer.download.nvidia.com/compute/nvpl/25.5/local_installers/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
dpkg -i nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
cp /var/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5/nvpl-*-keyring.gpg /usr/share/keyrings/ && \
apt-get update && apt-get install -y nvpl
fi
EOT
# If we are building with clblas support, we need the libraries for the builds
RUN if [ "${BUILD_TYPE}" = "clblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then \
apt-get update && \
apt-get install -y --no-install-recommends \
libclblast-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/* \
; fi
RUN if [ "${BUILD_TYPE}" = "hipblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then \
apt-get update && \
apt-get install -y --no-install-recommends \
hipblas-dev \
rocblas-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/* && \
# I have no idea why, but the ROCM lib packages don't trigger ldconfig after they install, which results in local-ai and others not being able
# to locate the libraries. We run ldconfig ourselves to work around this packaging deficiency
ldconfig && \
# Log which GPU architectures have rocBLAS kernel support
echo "rocBLAS library data architectures:" && \
(ls /opt/rocm*/lib/rocblas/library/Kernels* 2>/dev/null || ls /opt/rocm*/lib64/rocblas/library/Kernels* 2>/dev/null) | grep -oP 'gfx[0-9a-z+-]+' | sort -u || \
echo "WARNING: No rocBLAS kernel data found" \
; fi
RUN echo "TARGETARCH: $TARGETARCH"
# We need protoc installed, and the version in 22.04 is too old. We will create one as part installing the GRPC build below
# but that will also being in a newer version of absl which stablediffusion cannot compile with. This version of protoc is only
# here so that we can generate the grpc code for the stablediffusion build
RUN <<EOT bash
if [ "amd64" = "$TARGETARCH" ]; then
curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v27.1/protoc-27.1-linux-x86_64.zip -o protoc.zip && \
unzip -j -d /usr/local/bin protoc.zip bin/protoc && \
rm protoc.zip
fi
if [ "arm64" = "$TARGETARCH" ]; then
curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v27.1/protoc-27.1-linux-aarch_64.zip -o protoc.zip && \
unzip -j -d /usr/local/bin protoc.zip bin/protoc && \
rm protoc.zip
fi
EOT
# Install CMake (the version in 22.04 is too old)
RUN <<EOT bash
if [ "${CMAKE_FROM_SOURCE}" = "true" ]; then
curl -L -s https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}.tar.gz -o cmake.tar.gz && tar xvf cmake.tar.gz && cd cmake-${CMAKE_VERSION} && ./configure && make && make install
else
apt-get update && \
apt-get install -y \
cmake && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
COPY --from=grpc /opt/grpc /usr/local
COPY . /LocalAI

View File

@@ -1,26 +1,195 @@
# Builds a single Python backend on top of the shared
# .docker/bases/Dockerfile.python base. The base bakes in apt-update + GPU
# SDK install + python toolchain (uv, pip, rustup, grpcio-tools), so this
# stage only carries the per-backend source COPY + `make`.
#
# CI orchestration (.github/workflows/backend.yml + backend_pr.yml) builds
# the right base flavour automatically via scripts/derive-build-matrix.js
# and passes BASE_IMAGE_PREBUILT here. For local builds, run:
# make backend-image-base BUILD_TYPE=<...> # build the base
# make backend-image BACKEND=<...> BUILD_TYPE=<...>
# See .agents/ci-caching.md.
ARG BASE_IMAGE_PREBUILT
FROM ${BASE_IMAGE_PREBUILT} AS builder
ARG BASE_IMAGE=ubuntu:24.04
FROM ${BASE_IMAGE} AS builder
ARG BACKEND=rerankers
ARG BUILD_TYPE
ENV BUILD_TYPE=${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 UBUNTU_VERSION=2404
RUN apt-get update && \
apt-get install -y --no-install-recommends \
build-essential \
ccache \
ca-certificates \
espeak-ng \
curl \
libssl-dev \
git wget \
git-lfs \
unzip clang \
upx-ucl \
curl python3-pip \
python-is-python3 \
python3-dev llvm \
libnuma1 libgomp1 \
python3-venv make cmake && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
RUN <<EOT bash
if [ "${UBUNTU_VERSION}" = "2404" ]; then
pip install --break-system-packages --user --upgrade pip
else
pip install --upgrade pip
fi
EOT
# Cuda
ENV PATH=/usr/local/cuda/bin:${PATH}
# HipBLAS requirements
ENV PATH=/opt/rocm/bin:${PATH}
# Vulkan requirements
RUN <<EOT bash
if [ "${BUILD_TYPE}" = "vulkan" ] && [ "${SKIP_DRIVERS}" = "false" ]; then
apt-get update && \
apt-get install -y --no-install-recommends \
software-properties-common pciutils wget gpg-agent && \
apt-get install -y libglm-dev cmake libxcb-dri3-0 libxcb-present0 libpciaccess0 \
libpng-dev libxcb-keysyms1-dev libxcb-dri3-dev libx11-dev g++ gcc \
libwayland-dev libxrandr-dev libxcb-randr0-dev libxcb-ewmh-dev \
git python-is-python3 bison libx11-xcb-dev liblz4-dev libzstd-dev \
ocaml-core ninja-build pkg-config libxml2-dev wayland-protocols python3-jsonschema \
clang-format qtbase5-dev qt6-base-dev libxcb-glx0-dev sudo xz-utils
if [ "amd64" = "$TARGETARCH" ]; then
wget "https://sdk.lunarg.com/sdk/download/1.4.335.0/linux/vulkansdk-linux-x86_64-1.4.335.0.tar.xz" && \
tar -xf vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
rm vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
mkdir -p /opt/vulkan-sdk && \
mv 1.4.335.0 /opt/vulkan-sdk/ && \
cd /opt/vulkan-sdk/1.4.335.0 && \
./vulkansdk --no-deps --maxjobs \
vulkan-loader \
vulkan-validationlayers \
vulkan-extensionlayer \
vulkan-tools \
shaderc && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/bin/* /usr/bin/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/lib/* /usr/lib/x86_64-linux-gnu/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/include/* /usr/include/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/share/* /usr/share/ && \
rm -rf /opt/vulkan-sdk
fi
if [ "arm64" = "$TARGETARCH" ]; then
mkdir vulkan && cd vulkan && \
curl -L -o vulkan-sdk.tar.xz https://github.com/mudler/vulkan-sdk-arm/releases/download/1.4.335.0/vulkansdk-ubuntu-24.04-arm-1.4.335.0.tar.xz && \
tar -xvf vulkan-sdk.tar.xz && \
rm vulkan-sdk.tar.xz && \
cd 1.4.335.0 && \
cp -rfv aarch64/bin/* /usr/bin/ && \
cp -rfv aarch64/lib/* /usr/lib/aarch64-linux-gnu/ && \
cp -rfv aarch64/include/* /usr/include/ && \
cp -rfv aarch64/share/* /usr/share/ && \
cd ../.. && \
rm -rf vulkan
fi
ldconfig && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
# CuBLAS requirements
RUN <<EOT bash
if ( [ "${BUILD_TYPE}" = "cublas" ] || [ "${BUILD_TYPE}" = "l4t" ] ) && [ "${SKIP_DRIVERS}" = "false" ]; then
apt-get update && \
apt-get install -y --no-install-recommends \
software-properties-common pciutils
if [ "amd64" = "$TARGETARCH" ]; then
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/x86_64/cuda-keyring_1.1-1_all.deb
fi
if [ "arm64" = "$TARGETARCH" ]; then
if [ "${CUDA_MAJOR_VERSION}" = "13" ]; then
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/sbsa/cuda-keyring_1.1-1_all.deb
else
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/arm64/cuda-keyring_1.1-1_all.deb
fi
fi
dpkg -i cuda-keyring_1.1-1_all.deb && \
rm -f cuda-keyring_1.1-1_all.deb && \
apt-get update && \
apt-get install -y --no-install-recommends \
cuda-nvcc-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcufft-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcurand-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcublas-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcusparse-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcusolver-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
if [ "${CUDA_MAJOR_VERSION}" = "13" ] && [ "arm64" = "$TARGETARCH" ]; then
apt-get install -y --no-install-recommends \
libcufile-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libcudnn9-cuda-${CUDA_MAJOR_VERSION} cuda-cupti-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libnvjitlink-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
fi
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
# https://github.com/NVIDIA/Isaac-GR00T/issues/343
RUN <<EOT bash
if [ "${BUILD_TYPE}" = "cublas" ] && [ "${TARGETARCH}" = "arm64" ]; then
wget https://developer.download.nvidia.com/compute/cudss/0.6.0/local_installers/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
dpkg -i cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
cp /var/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0/cudss-*-keyring.gpg /usr/share/keyrings/ && \
apt-get update && apt-get -y install cudss cudss-cuda-${CUDA_MAJOR_VERSION} && \
wget https://developer.download.nvidia.com/compute/nvpl/25.5/local_installers/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
dpkg -i nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
cp /var/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5/nvpl-*-keyring.gpg /usr/share/keyrings/ && \
apt-get update && apt-get install -y nvpl
fi
EOT
# If we are building with clblas support, we need the libraries for the builds
RUN if [ "${BUILD_TYPE}" = "clblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then \
apt-get update && \
apt-get install -y --no-install-recommends \
libclblast-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/* \
; fi
RUN if [ "${BUILD_TYPE}" = "hipblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then \
apt-get update && \
apt-get install -y --no-install-recommends \
hipblas-dev \
rocblas-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/* && \
# I have no idea why, but the ROCM lib packages don't trigger ldconfig after they install, which results in local-ai and others not being able
# to locate the libraries. We run ldconfig ourselves to work around this packaging deficiency
ldconfig \
; fi
RUN if [ "${BUILD_TYPE}" = "hipblas" ]; then \
ln -s /opt/rocm-**/lib/llvm/lib/libomp.so /usr/lib/libomp.so \
; fi
# Install uv as a system package
RUN curl -LsSf https://astral.sh/uv/install.sh | UV_INSTALL_DIR=/usr/bin sh
ENV PATH="/root/.cargo/bin:${PATH}"
# Increase timeout for uv installs behind slow networks
ENV UV_HTTP_TIMEOUT=180
RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
# Install grpcio-tools (the version in 22.04 is too old)
RUN <<EOT bash
if [ "${UBUNTU_VERSION}" = "2404" ]; then
pip install --break-system-packages --user grpcio-tools==1.71.0 grpcio==1.71.0
else
pip install grpcio-tools==1.71.0 grpcio==1.71.0
fi
EOT
COPY backend/python/${BACKEND} /${BACKEND}
COPY backend/backend.proto /${BACKEND}/backend.proto
@@ -33,13 +202,6 @@ COPY scripts/build/package-gpu-libs.sh /package-gpu-libs.sh
ARG FROM_SOURCE=""
ENV FROM_SOURCE=${FROM_SOURCE}
# Cache-buster for the per-backend `make` step. Most Python backends list
# unpinned deps (torch, transformers, vllm, ...), so a warm registry cache
# would otherwise freeze upstream versions indefinitely. CI passes a value
# that rolls weekly so the install layer is rebuilt at most once per week
# and picks up newer wheels from PyPI / nightly indexes.
ARG DEPS_REFRESH=initial
RUN cd /${BACKEND} && PORTABLE_PYTHON=true make
# Package GPU libraries into the backend's lib directory
@@ -54,4 +216,4 @@ RUN if [ -f "/${BACKEND}/package.sh" ]; then \
FROM scratch
ARG BACKEND=rerankers
COPY --from=builder /${BACKEND}/ /
COPY --from=builder /${BACKEND}/ /

View File

@@ -1,15 +1,31 @@
# Builds a single Rust backend on top of the shared
# .docker/bases/Dockerfile.rust base. The base bakes in apt + Rust +
# protobuf-compiler + audio dev libs (espeak/sonic/pcaudio/opus), so this
# stage only carries the per-backend COPY + `make build`.
#
# CI orchestration (.github/workflows/backend.yml + backend_pr.yml) passes
# BASE_IMAGE_PREBUILT. See .agents/ci-caching.md.
ARG BASE_IMAGE=ubuntu:24.04
ARG BASE_IMAGE_PREBUILT
FROM ${BASE_IMAGE_PREBUILT} AS builder
FROM ${BASE_IMAGE} AS builder
ARG BACKEND=kokoros
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETARCH
ARG TARGETVARIANT
RUN apt-get update && \
apt-get install -y --no-install-recommends \
build-essential \
git ccache \
ca-certificates \
make cmake wget \
curl unzip \
clang \
pkg-config \
libssl-dev \
espeak-ng libespeak-ng-dev \
libsonic-dev libpcaudio-dev \
libopus-dev \
protobuf-compiler && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
# Install Rust
RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
ENV PATH="/root/.cargo/bin:${PATH}"
COPY . /LocalAI

View File

@@ -1,25 +1,254 @@
# Builds the turboquant backend on top of the shared
# .docker/bases/Dockerfile.cpp base (shared with llama-cpp/ik-llama-cpp).
# See backend/Dockerfile.llama-cpp for the rationale; this consumer differs
# only in the make targets at the end.
ARG BASE_IMAGE=ubuntu:24.04
ARG GRPC_BASE_IMAGE=${BASE_IMAGE}
ARG BASE_IMAGE_PREBUILT
FROM ${BASE_IMAGE_PREBUILT} AS builder
# 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 ${GRPC_BASE_IMAGE} AS grpc
# This is a bit of a hack, but it's required in order to be able to effectively cache this layer in CI
ARG GRPC_MAKEFLAGS="-j4 -Otarget"
ARG GRPC_VERSION=v1.65.0
ARG CMAKE_FROM_SOURCE=false
# CUDA Toolkit 13.x compatibility: CMake 3.31.9+ fixes toolchain detection/arch table issues
ARG CMAKE_VERSION=3.31.10
ENV MAKEFLAGS=${GRPC_MAKEFLAGS}
WORKDIR /build
RUN apt-get update && \
apt-get install -y --no-install-recommends \
ca-certificates \
build-essential curl libssl-dev \
git wget && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
# Install CMake (the version in 22.04 is too old)
RUN <<EOT bash
if [ "${CMAKE_FROM_SOURCE}" = "true" ]; then
curl -L -s https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}.tar.gz -o cmake.tar.gz && tar xvf cmake.tar.gz && cd cmake-${CMAKE_VERSION} && ./configure && make && make install
else
apt-get update && \
apt-get install -y \
cmake && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
# We install GRPC to a different prefix here so that we can copy in only the build artifacts later
# saves several hundred MB on the final docker image size vs copying in the entire GRPC source tree
# and running make install in the target container
RUN git clone --recurse-submodules --jobs 4 -b ${GRPC_VERSION} --depth 1 --shallow-submodules https://github.com/grpc/grpc && \
mkdir -p /build/grpc/cmake/build && \
cd /build/grpc/cmake/build && \
sed -i "216i\ TESTONLY" "../../third_party/abseil-cpp/absl/container/CMakeLists.txt" && \
cmake -DgRPC_INSTALL=ON -DgRPC_BUILD_TESTS=OFF -DCMAKE_INSTALL_PREFIX:PATH=/opt/grpc ../.. && \
make && \
make install && \
rm -rf /build
FROM ${BASE_IMAGE} AS builder
ARG CMAKE_FROM_SOURCE=false
ARG CMAKE_VERSION=3.31.10
# We can target specific CUDA ARCHITECTURES like --build-arg CUDA_DOCKER_ARCH='75;86;89;120'
ARG CUDA_DOCKER_ARCH
ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
ARG CMAKE_ARGS
ENV CMAKE_ARGS=${CMAKE_ARGS}
ARG BACKEND=turboquant
ARG BACKEND=rerankers
ARG BUILD_TYPE
ENV BUILD_TYPE=${BUILD_TYPE}
ARG CUDA_MAJOR_VERSION
ARG CUDA_MINOR_VERSION
ARG SKIP_DRIVERS=false
ENV CUDA_MAJOR_VERSION=${CUDA_MAJOR_VERSION}
ENV CUDA_MINOR_VERSION=${CUDA_MINOR_VERSION}
ENV DEBIAN_FRONTEND=noninteractive
ARG TARGETARCH
ARG TARGETVARIANT
ARG GO_VERSION=1.25.4
ARG UBUNTU_VERSION=2404
RUN apt-get update && \
apt-get install -y --no-install-recommends \
build-essential \
ccache git \
ca-certificates \
make \
pkg-config libcurl4-openssl-dev \
curl unzip \
libssl-dev wget && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
# Cuda
ENV PATH=/usr/local/cuda/bin:${PATH}
# HipBLAS requirements
ENV PATH=/opt/rocm/bin:${PATH}
# Vulkan requirements
RUN <<EOT bash
if [ "${BUILD_TYPE}" = "vulkan" ] && [ "${SKIP_DRIVERS}" = "false" ]; then
apt-get update && \
apt-get install -y --no-install-recommends \
software-properties-common pciutils wget gpg-agent && \
apt-get install -y libglm-dev cmake libxcb-dri3-0 libxcb-present0 libpciaccess0 \
libpng-dev libxcb-keysyms1-dev libxcb-dri3-dev libx11-dev g++ gcc \
libwayland-dev libxrandr-dev libxcb-randr0-dev libxcb-ewmh-dev \
git python-is-python3 bison libx11-xcb-dev liblz4-dev libzstd-dev \
ocaml-core ninja-build pkg-config libxml2-dev wayland-protocols python3-jsonschema \
clang-format qtbase5-dev qt6-base-dev libxcb-glx0-dev sudo xz-utils
if [ "amd64" = "$TARGETARCH" ]; then
wget "https://sdk.lunarg.com/sdk/download/1.4.335.0/linux/vulkansdk-linux-x86_64-1.4.335.0.tar.xz" && \
tar -xf vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
rm vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
mkdir -p /opt/vulkan-sdk && \
mv 1.4.335.0 /opt/vulkan-sdk/ && \
cd /opt/vulkan-sdk/1.4.335.0 && \
./vulkansdk --no-deps --maxjobs \
vulkan-loader \
vulkan-validationlayers \
vulkan-extensionlayer \
vulkan-tools \
shaderc && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/bin/* /usr/bin/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/lib/* /usr/lib/x86_64-linux-gnu/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/include/* /usr/include/ && \
cp -rfv /opt/vulkan-sdk/1.4.335.0/x86_64/share/* /usr/share/ && \
rm -rf /opt/vulkan-sdk
fi
if [ "arm64" = "$TARGETARCH" ]; then
mkdir vulkan && cd vulkan && \
curl -L -o vulkan-sdk.tar.xz https://github.com/mudler/vulkan-sdk-arm/releases/download/1.4.335.0/vulkansdk-ubuntu-24.04-arm-1.4.335.0.tar.xz && \
tar -xvf vulkan-sdk.tar.xz && \
rm vulkan-sdk.tar.xz && \
cd 1.4.335.0 && \
cp -rfv aarch64/bin/* /usr/bin/ && \
cp -rfv aarch64/lib/* /usr/lib/aarch64-linux-gnu/ && \
cp -rfv aarch64/include/* /usr/include/ && \
cp -rfv aarch64/share/* /usr/share/ && \
cd ../.. && \
rm -rf vulkan
fi
ldconfig && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
# CuBLAS requirements
RUN <<EOT bash
if ( [ "${BUILD_TYPE}" = "cublas" ] || [ "${BUILD_TYPE}" = "l4t" ] ) && [ "${SKIP_DRIVERS}" = "false" ]; then
apt-get update && \
apt-get install -y --no-install-recommends \
software-properties-common pciutils
if [ "amd64" = "$TARGETARCH" ]; then
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/x86_64/cuda-keyring_1.1-1_all.deb
fi
if [ "arm64" = "$TARGETARCH" ]; then
if [ "${CUDA_MAJOR_VERSION}" = "13" ]; then
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/sbsa/cuda-keyring_1.1-1_all.deb
else
curl -O https://developer.download.nvidia.com/compute/cuda/repos/ubuntu${UBUNTU_VERSION}/arm64/cuda-keyring_1.1-1_all.deb
fi
fi
dpkg -i cuda-keyring_1.1-1_all.deb && \
rm -f cuda-keyring_1.1-1_all.deb && \
apt-get update && \
apt-get install -y --no-install-recommends \
cuda-nvcc-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcufft-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcurand-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcublas-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcusparse-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
libcusolver-dev-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
if [ "${CUDA_MAJOR_VERSION}" = "13" ] && [ "arm64" = "$TARGETARCH" ]; then
apt-get install -y --no-install-recommends \
libcufile-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libcudnn9-cuda-${CUDA_MAJOR_VERSION} cuda-cupti-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libnvjitlink-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
fi
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
# https://github.com/NVIDIA/Isaac-GR00T/issues/343
RUN <<EOT bash
if [ "${BUILD_TYPE}" = "cublas" ] && [ "${TARGETARCH}" = "arm64" ]; then
wget https://developer.download.nvidia.com/compute/cudss/0.6.0/local_installers/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
dpkg -i cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0_0.6.0-1_arm64.deb && \
cp /var/cudss-local-tegra-repo-ubuntu${UBUNTU_VERSION}-0.6.0/cudss-*-keyring.gpg /usr/share/keyrings/ && \
apt-get update && apt-get -y install cudss cudss-cuda-${CUDA_MAJOR_VERSION} && \
wget https://developer.download.nvidia.com/compute/nvpl/25.5/local_installers/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
dpkg -i nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5_1.0-1_arm64.deb && \
cp /var/nvpl-local-repo-ubuntu${UBUNTU_VERSION}-25.5/nvpl-*-keyring.gpg /usr/share/keyrings/ && \
apt-get update && apt-get install -y nvpl
fi
EOT
# If we are building with clblas support, we need the libraries for the builds
RUN if [ "${BUILD_TYPE}" = "clblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then \
apt-get update && \
apt-get install -y --no-install-recommends \
libclblast-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/* \
; fi
RUN if [ "${BUILD_TYPE}" = "hipblas" ] && [ "${SKIP_DRIVERS}" = "false" ]; then \
apt-get update && \
apt-get install -y --no-install-recommends \
hipblas-dev \
rocblas-dev && \
apt-get clean && \
rm -rf /var/lib/apt/lists/* && \
# I have no idea why, but the ROCM lib packages don't trigger ldconfig after they install, which results in local-ai and others not being able
# to locate the libraries. We run ldconfig ourselves to work around this packaging deficiency
ldconfig && \
# Log which GPU architectures have rocBLAS kernel support
echo "rocBLAS library data architectures:" && \
(ls /opt/rocm*/lib/rocblas/library/Kernels* 2>/dev/null || ls /opt/rocm*/lib64/rocblas/library/Kernels* 2>/dev/null) | grep -oP 'gfx[0-9a-z+-]+' | sort -u || \
echo "WARNING: No rocBLAS kernel data found" \
; fi
RUN echo "TARGETARCH: $TARGETARCH"
# We need protoc installed, and the version in 22.04 is too old. We will create one as part installing the GRPC build below
# but that will also being in a newer version of absl which stablediffusion cannot compile with. This version of protoc is only
# here so that we can generate the grpc code for the stablediffusion build
RUN <<EOT bash
if [ "amd64" = "$TARGETARCH" ]; then
curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v27.1/protoc-27.1-linux-x86_64.zip -o protoc.zip && \
unzip -j -d /usr/local/bin protoc.zip bin/protoc && \
rm protoc.zip
fi
if [ "arm64" = "$TARGETARCH" ]; then
curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v27.1/protoc-27.1-linux-aarch_64.zip -o protoc.zip && \
unzip -j -d /usr/local/bin protoc.zip bin/protoc && \
rm protoc.zip
fi
EOT
# Install CMake (the version in 22.04 is too old)
RUN <<EOT bash
if [ "${CMAKE_FROM_SOURCE}" = "true" ]; then
curl -L -s https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}.tar.gz -o cmake.tar.gz && tar xvf cmake.tar.gz && cd cmake-${CMAKE_VERSION} && ./configure && make && make install
else
apt-get update && \
apt-get install -y \
cmake && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
fi
EOT
COPY --from=grpc /opt/grpc /usr/local
COPY . /LocalAI

View File

@@ -41,14 +41,9 @@ service Backend {
rpc VAD(VADRequest) returns (VADResponse) {}
rpc Diarize(DiarizeRequest) returns (DiarizeResponse) {}
rpc AudioEncode(AudioEncodeRequest) returns (AudioEncodeResult) {}
rpc AudioDecode(AudioDecodeRequest) returns (AudioDecodeResult) {}
rpc AudioTransform(AudioTransformRequest) returns (AudioTransformResult) {}
rpc AudioTransformStream(stream AudioTransformFrameRequest) returns (stream AudioTransformFrameResponse) {}
rpc ModelMetadata(ModelOptions) returns (ModelMetadataResponse) {}
// Fine-tuning RPCs
@@ -315,11 +310,6 @@ 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 {
@@ -355,12 +345,6 @@ message TranscriptStreamResponse {
TranscriptResult final_result = 2;
}
message TranscriptWord {
int64 start = 1;
int64 end = 2;
string text = 3;
}
message TranscriptSegment {
int32 id = 1;
int64 start = 2;
@@ -368,7 +352,6 @@ message TranscriptSegment {
string text = 4;
repeated int32 tokens = 5;
string speaker = 6;
repeated TranscriptWord words = 7;
}
message GenerateImageRequest {
@@ -425,43 +408,6 @@ message VADResponse {
repeated VADSegment segments = 1;
}
// --- Speaker diarization messages ---
//
// Pure speaker diarization: "who spoke when". Returns time-stamped segments
// labelled with cluster IDs (the same string for the same speaker across
// segments). Some backends (e.g. vibevoice.cpp) produce diarization as a
// by-product of ASR and may also fill in `text` per segment; backends with a
// dedicated diarization pipeline (e.g. sherpa-onnx pyannote) leave `text`
// empty and emit only the segmentation.
message DiarizeRequest {
string dst = 1; // path to audio file (HTTP layer materialises uploads to a temp file)
uint32 threads = 2;
string language = 3; // optional; only meaningful for transcription-bundling backends
int32 num_speakers = 4; // exact speaker count if known (>0 forces); 0 = auto
int32 min_speakers = 5; // hint when auto-detecting; 0 = unset
int32 max_speakers = 6; // hint when auto-detecting; 0 = unset
float clustering_threshold = 7; // distance threshold when num_speakers unknown; 0 = backend default
float min_duration_on = 8; // discard segments shorter than this (seconds); 0 = backend default
float min_duration_off = 9; // merge gaps shorter than this (seconds); 0 = backend default
bool include_text = 10; // when the backend can emit per-segment transcript for free, ask it to populate `text`
}
message DiarizeSegment {
int32 id = 1;
float start = 2; // seconds
float end = 3; // seconds
string speaker = 4; // backend-emitted speaker label (e.g. "0", "SPEAKER_00")
string text = 5; // optional per-segment transcript (empty unless include_text and supported)
}
message DiarizeResponse {
repeated DiarizeSegment segments = 1;
int32 num_speakers = 2; // count of distinct speaker labels in `segments`
float duration = 3; // total audio duration in seconds (0 if unknown)
string language = 4; // optional, when the backend bundles transcription
}
message SoundGenerationRequest {
string text = 1;
string model = 2;
@@ -718,56 +664,6 @@ message AudioDecodeResult {
int32 samples_per_frame = 3;
}
// Generic audio transform: an audio-in, audio-out operation, optionally
// conditioned on a second reference signal. Concrete transforms include
// AEC + noise suppression + dereverberation (LocalVQE), voice conversion
// (reference = target speaker), pitch shifting, etc.
message AudioTransformRequest {
string audio_path = 1; // required, primary input file path
string reference_path = 2; // optional auxiliary; empty => zero-fill
string dst = 3; // required, output file path
map<string, string> params = 4; // backend-specific tuning
}
message AudioTransformResult {
string dst = 1;
int32 sample_rate = 2;
int32 samples = 3;
bool reference_provided = 4;
}
// Bidirectional streaming audio transform. The first message MUST carry a
// Config; subsequent messages carry Frames. A second Config mid-stream
// resets streaming state before the next frame.
message AudioTransformFrameRequest {
oneof payload {
AudioTransformStreamConfig config = 1;
AudioTransformFrame frame = 2;
}
}
message AudioTransformStreamConfig {
enum SampleFormat {
F32_LE = 0;
S16_LE = 1;
}
SampleFormat sample_format = 1;
int32 sample_rate = 2; // 0 => backend default
int32 frame_samples = 3; // 0 => backend default
map<string, string> params = 4;
bool reset = 5; // reset streaming state before next frame
}
message AudioTransformFrame {
bytes audio_pcm = 1; // frame_samples samples in stream's format
bytes reference_pcm = 2; // empty => zero-fill (silent reference)
}
message AudioTransformFrameResponse {
bytes pcm = 1;
int64 frame_index = 2;
}
message ModelMetadataResponse {
bool supports_thinking = 1;
string rendered_template = 2; // The rendered chat template with enable_thinking=true (empty if not applicable)

View File

@@ -0,0 +1,85 @@
# Pinned to the HEAD of master on https://github.com/spiritbuun/buun-llama-cpp.
# Auto-bumped nightly by .github/workflows/bump_deps.yaml.
BUUN_LLAMA_VERSION?=22464d0848b87c5d56b52fdf6af2e5da46bf803e
LLAMA_REPO?=https://github.com/spiritbuun/buun-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)
CURRENT_MAKEFILE_DIR := $(dir $(abspath $(lastword $(MAKEFILE_LIST))))
LLAMA_CPP_DIR := $(CURRENT_MAKEFILE_DIR)/../llama-cpp
GREEN := \033[0;32m
RESET := \033[0m
# buun-llama-cpp is a llama.cpp fork-of-a-fork (spiritbuun/buun-llama-cpp forked
# TheTom/llama-cpp-turboquant, which itself forked ggml-org/llama.cpp). 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
# ../buun-llama-cpp-<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 buun-llama-cpp-<flavor>-build directory;
# 2. clones the buun fork into buun-llama-cpp-<flavor>-build/llama.cpp via the
# copy's own `llama.cpp` target, overriding LLAMA_REPO/LLAMA_VERSION;
# 3. applies patches from backend/cpp/buun-llama-cpp/patches/ to the cloned
# fork sources (for backporting upstream commits the fork hasn't pulled);
# 4. runs the copy's `grpc-server` target, which produces the binary we copy
# up as buun-llama-cpp-<flavor>.
define buun-llama-cpp-build
rm -rf $(CURRENT_MAKEFILE_DIR)/../buun-llama-cpp-$(1)-build
cp -rf $(LLAMA_CPP_DIR) $(CURRENT_MAKEFILE_DIR)/../buun-llama-cpp-$(1)-build
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../buun-llama-cpp-$(1)-build purge
# Augment the copied grpc-server.cpp's KV-cache allow-list with the
# fork's turbo2/turbo3/turbo4/turbo2_tcq/turbo3_tcq types and wire up the
# DFlash-specific option handlers (tree_budget / draft_topk). 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 $(CURRENT_MAKEFILE_DIR)/../buun-llama-cpp-$(1)-build/grpc-server.cpp
$(info $(GREEN)I buun-llama-cpp build info:$(1)$(RESET))
LLAMA_REPO=$(LLAMA_REPO) LLAMA_VERSION=$(BUUN_LLAMA_VERSION) \
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../buun-llama-cpp-$(1)-build llama.cpp
bash $(CURRENT_MAKEFILE_DIR)/apply-patches.sh $(CURRENT_MAKEFILE_DIR)/../buun-llama-cpp-$(1)-build/llama.cpp $(PATCHES_DIR)
CMAKE_ARGS="$(CMAKE_ARGS) $(2)" TARGET="$(3)" \
LLAMA_REPO=$(LLAMA_REPO) LLAMA_VERSION=$(BUUN_LLAMA_VERSION) \
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../buun-llama-cpp-$(1)-build grpc-server
cp -rfv $(CURRENT_MAKEFILE_DIR)/../buun-llama-cpp-$(1)-build/grpc-server buun-llama-cpp-$(1)
endef
buun-llama-cpp-avx2:
$(call buun-llama-cpp-build,avx2,-DGGML_AVX=on -DGGML_AVX2=on -DGGML_AVX512=off -DGGML_FMA=on -DGGML_F16C=on,--target grpc-server)
buun-llama-cpp-avx512:
$(call buun-llama-cpp-build,avx512,-DGGML_AVX=on -DGGML_AVX2=off -DGGML_AVX512=on -DGGML_FMA=on -DGGML_F16C=on,--target grpc-server)
buun-llama-cpp-avx:
$(call buun-llama-cpp-build,avx,-DGGML_AVX=on -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off,--target grpc-server)
buun-llama-cpp-fallback:
$(call buun-llama-cpp-build,fallback,-DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off,--target grpc-server)
buun-llama-cpp-grpc:
$(call buun-llama-cpp-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)
buun-llama-cpp-rpc-server: buun-llama-cpp-grpc
cp -rf $(CURRENT_MAKEFILE_DIR)/../buun-llama-cpp-grpc-build/llama.cpp/build/bin/rpc-server buun-llama-cpp-rpc-server
package:
bash package.sh
purge:
rm -rf $(CURRENT_MAKEFILE_DIR)/../buun-llama-cpp-*-build
rm -rf buun-llama-cpp-* package
clean: purge

View File

@@ -0,0 +1,50 @@
#!/bin/bash
# Apply the buun-llama-cpp patch series to a cloned buun-llama-cpp checkout.
#
# buun-llama-cpp is a fork-of-a-fork that branched off upstream llama.cpp
# before some API changes the shared backend/cpp/llama-cpp/grpc-server.cpp
# depends on. We carry those upstream commits as patch files under
# backend/cpp/buun-llama-cpp/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 buun-llama-cpp patches applied successfully"

View File

@@ -1,22 +1,22 @@
#!/bin/bash
# Bundle the vibevoice-cpp binary, the per-variant .so files, and the
# runtime libs the binary depends on so the package is self-contained.
# Mirrors backend/go/qwen3-tts-cpp/package.sh.
# 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/vibevoice-cpp $CURDIR/package/
cp -fv $CURDIR/libgovibevoicecpp-*.so $CURDIR/package/
cp -fv $CURDIR/run.sh $CURDIR/package/
cp -avrf $CURDIR/buun-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
@@ -28,6 +28,7 @@ if [ -f "/lib64/ld-linux-x86-64.so.2" ]; then
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
@@ -38,8 +39,6 @@ elif [ -f "/lib/ld-linux-aarch64.so.1" ]; then
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

View File

@@ -0,0 +1,162 @@
#!/bin/bash
# Patch the shared backend/cpp/llama-cpp/grpc-server.cpp *copy* used by the
# buun-llama-cpp build to account for three 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 plus the buun
# additions `turbo2_tcq` / `turbo3_tcq`.
#
# 2. Wire up buun-exclusive speculative-decoding option handlers
# (tree_budget / draft_topk) alongside the existing spec_* handlers.
# These reference struct fields (common_params.speculative.tree_budget
# and .draft_topk) that only exist in buun's common/common.h — adding
# them to the shared backend/cpp/llama-cpp/grpc-server.cpp would break
# the stock llama-cpp build, so we inject them only into the buun copy.
#
# 3. 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 buun path.
#
# We patch the *copy* sitting in buun-llama-cpp-<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 $SRC" >&2
exit 2
fi
if grep -q 'GGML_TYPE_TURBO2_TCQ' "$SRC"; then
echo "==> $SRC already has buun cache types, skipping KV allow-list patch"
else
echo "==> patching $SRC to allow turbo2/turbo3/turbo4/turbo2_tcq/turbo3_tcq KV-cache types"
# Insert the five TURBO entries right after the first ` GGML_TYPE_Q5_1,`
# line (the kv_cache_types[] allow-list). Using awk because the builder
# image does not ship python3, and GNU sed's multi-line `a\` quoting is
# awkward.
awk '
/^ GGML_TYPE_Q5_1,$/ && !done {
print
print " // buun-llama-cpp fork extras — added by patch-grpc-server.sh"
print " GGML_TYPE_TURBO2_0,"
print " GGML_TYPE_TURBO3_0,"
print " GGML_TYPE_TURBO4_0,"
print " GGML_TYPE_TURBO2_TCQ,"
print " GGML_TYPE_TURBO3_TCQ,"
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 'optname, "tree_budget"' "$SRC"; then
echo "==> $SRC already has DFlash option handlers, skipping"
else
echo "==> patching $SRC to add tree_budget / draft_topk option handlers"
# Insert two new `else if` handlers between the inner close-brace of the
# `spec_p_split` block and the next `} else if (…spec_ngram_size_n…)` line.
# Upstream writes each `} else if` as a single physical line, so we don't
# emit an outer `}` ourselves — the existing next line provides both the
# close of our `draft_topk` block and the open of `spec_ngram_size_n`.
# Anchor on the exact 3-line body of spec_p_split so we can't drift.
awk '
prev2 == " } else if (!strcmp(optname, \"spec_p_split\")) {" &&
prev1 ~ /^ +if \(optval != NULL\) \{$/ &&
$0 ~ /^ +try \{ params\.speculative\.p_split = std::stof\(optval_str\); \} catch \(\.\.\.\) \{\}$/ &&
!done {
print # print the try-line itself
getline inner_close # read " }" closing the inner if
print inner_close # print it — this closes spec_p_split body
print " // buun-llama-cpp DFlash options — added by patch-grpc-server.sh"
print " } else if (!strcmp(optname, \"tree_budget\")) {"
print " if (optval != NULL) {"
print " try { params.speculative.tree_budget = std::stoi(optval_str); } catch (...) {}"
print " }"
print " } else if (!strcmp(optname, \"draft_topk\")) {"
print " if (optval != NULL) {"
print " try { params.speculative.draft_topk = std::stoi(optval_str); } catch (...) {}"
print " }"
# The next source line (`} else if (…spec_ngram_size_n…) {`) closes
# our draft_topk block and continues the chain naturally; fall back
# into the main loop to emit it and everything after.
done = 1
prev2 = prev1
prev1 = inner_close
next
}
{ print; prev2 = prev1; prev1 = $0 }
END {
if (!done) {
print "patch-grpc-server.sh: spec_p_split anchor not found" > "/dev/stderr"
exit 1
}
}
' "$SRC" > "$SRC.tmp"
mv "$SRC.tmp" "$SRC"
echo "==> DFlash option-handler patch OK"
fi
if grep -qE 'ctx_server\.get_meta\(\)\.logit_bias_eog|params_base\.sampling\.logit_bias_eog,' "$SRC"; then
echo "==> patching $SRC to drop the logit_bias_eog arg from params_from_json_cmpl() callsites (buun still uses the pre-refactor 4-arg signature)"
# Upstream llama.cpp refactored params_from_json_cmpl to take a precomputed
# logit_bias_eog vector after buun's 2026-04-05 fork-point — simultaneously
# adding server_context_meta::logit_bias_eog as the supplier. Buun carries
# neither change: its params_from_json_cmpl is still 4-arg, and internally
# derives logit_bias_eog from the common_params it's passed. So we just
# delete the argument line entirely — the remaining 4 args match buun's
# signature and the resulting behavior matches upstream bit-for-bit
# (upstream's 5th arg is the same data buun derives internally).
#
# Guard is broad so this works whether the line has been run through this
# block before (leaving params_base.sampling.logit_bias_eog,) or not
# (leaving the original ctx_server.get_meta().logit_bias_eog,).
sed -E '/^[[:space:]]+(ctx_server\.get_meta\(\)\.logit_bias_eog|params_base\.sampling\.logit_bias_eog),$/d' "$SRC" > "$SRC.tmp"
mv "$SRC.tmp" "$SRC"
echo "==> logit_bias_eog arg drop OK"
else
echo "==> $SRC has no logit_bias_eog arg line, skipping"
fi
if grep -q 'get_media_marker()' "$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
echo "==> all patches applied"

View File

@@ -0,0 +1,46 @@
Subject: [PATCH] ggml-cuda/fattn: provide atomicAdd(double*,double) shim for pre-sm_60
Buun's Q² calibration path in ggml_cuda_turbo_scale_q calls
atomicAdd(&d_q_channel_sq_fattn[threadIdx.x], (double)(val * val));
but native double atomicAdd is only available on compute capability 6.0
and newer. Compiling against a CUDA arch list that includes older
architectures (LocalAI's CUDA 12 Docker image builds for the full
published arch range) fails with:
fattn.cu(812): error: no instance of overloaded function "atomicAdd"
matches the argument list, argument types are: (double *, double)
Add the canonical CUDA-programming-guide shim at the top of fattn.cu so
pre-sm_60 codegen has a definition to call. On sm_60+ the native CUDA
intrinsic is used and the shim is elided via __CUDA_ARCH__.
--- a/ggml/src/ggml-cuda/fattn.cu
+++ b/ggml/src/ggml-cuda/fattn.cu
@@ -7,6 +7,27 @@
#include <atomic>
+// Pre-sm_60 double atomicAdd shim. Native double atomicAdd(double*,double)
+// is only available on CUDA compute capability 6.0+ (see CUDA C Programming
+// Guide, B.15 Atomic Functions). Buun's Q² calibration path below calls
+// atomicAdd with a double*; without this definition, nvcc fails to find a
+// matching overload whenever the compile target list includes pre-sm_60
+// architectures. The standard CAS loop implementation below matches the
+// semantics of the native intrinsic.
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 600
+static __device__ double atomicAdd(double * address, double val) {
+ unsigned long long int * address_as_ull = (unsigned long long int *)address;
+ unsigned long long int old = *address_as_ull;
+ unsigned long long int assumed;
+ do {
+ assumed = old;
+ old = atomicCAS(address_as_ull, assumed,
+ __double_as_longlong(val + __longlong_as_double(assumed)));
+ } while (assumed != old);
+ return __longlong_as_double(old);
+}
+#endif
+
// InnerQ: update the fattn-side inverse scale array from host (all devices)
void turbo_innerq_update_fattn_scales(const float * scale_inv) {
int cur_device;

View File

@@ -0,0 +1,32 @@
Subject: [PATCH] ggml-cuda/argmax: pass WARP_SIZE to the top-K __shfl_xor_sync calls
Two __shfl_xor_sync calls in the top-K intra-warp merge drop the `width`
argument and rely on the CUDA default (warpSize). Every other call in
the same file already passes WARP_SIZE explicitly, and the HIP/ROCm
compatibility shim at ggml/src/ggml-cuda/vendors/hip.h:33 is a 4-arg
function-like macro — so the 3-arg form fails to preprocess when
building with hipcc against ROCm:
argmax.cu:265: error: too few arguments provided to function-like
macro invocation
note: macro '__shfl_xor_sync' defined here:
#define __shfl_xor_sync(mask, var, laneMask, width) \
__shfl_xor(var, laneMask, width)
Align the two call sites with the rest of the file by passing WARP_SIZE
explicitly. On CUDA the generated code is unchanged (warpSize is the
default); on HIP it now matches the macro's arity.
--- a/ggml/src/ggml-cuda/argmax.cu
+++ b/ggml/src/ggml-cuda/argmax.cu
@@ -262,8 +262,8 @@
// Each step: lane gets partner's min element, if it beats our min, replace and re-heapify
for (int offset = WARP_SIZE / 2; offset > 0; offset >>= 1) {
for (int i = 0; i < K; i++) {
- float partner_val = __shfl_xor_sync(0xFFFFFFFF, heap_val[i], offset);
- int partner_idx = __shfl_xor_sync(0xFFFFFFFF, heap_idx[i], offset);
+ float partner_val = __shfl_xor_sync(0xFFFFFFFF, heap_val[i], offset, WARP_SIZE);
+ int partner_idx = __shfl_xor_sync(0xFFFFFFFF, heap_idx[i], offset, WARP_SIZE);
if (partner_val > heap_val[0]) {
heap_val[0] = partner_val;
heap_idx[0] = partner_idx;

View File

@@ -0,0 +1,24 @@
Subject: [PATCH] ggml-cuda/vendors/hip: alias cudaMemcpy{To,From}Symbol to hip counterparts
Buun's Q² calibration + TCQ codebook upload paths in fattn.cu use
cudaMemcpyToSymbol / cudaMemcpyFromSymbol. The HIP-compat header in
ggml/src/ggml-cuda/vendors/hip.h already aliases the scalar cudaMemcpy
family (cudaMemcpy, cudaMemcpyAsync, cudaMemcpy2DAsync, …) but is
missing the symbol variants. Building with hipcc therefore fails with
15+ "use of undeclared identifier 'cudaMemcpyToSymbol'" errors.
Add the two missing aliases alongside the existing memcpy block. HIP
provides hipMemcpy{To,From}Symbol with the same signature as CUDA's
equivalents, so this is a straight name substitution.
--- a/ggml/src/ggml-cuda/vendors/hip.h
+++ b/ggml/src/ggml-cuda/vendors/hip.h
@@ -85,6 +85,8 @@
#define cudaMemcpyDeviceToDevice hipMemcpyDeviceToDevice
#define cudaMemcpyDeviceToHost hipMemcpyDeviceToHost
#define cudaMemcpyHostToDevice hipMemcpyHostToDevice
+#define cudaMemcpyToSymbol hipMemcpyToSymbol
+#define cudaMemcpyFromSymbol hipMemcpyFromSymbol
#define cudaMemcpyKind hipMemcpyKind
#define cudaMemset hipMemset
#define cudaMemsetAsync hipMemsetAsync

View File

@@ -0,0 +1,36 @@
Subject: [PATCH] ggml-cuda/fattn: pass WARP_SIZE to fwht128 __shfl_xor_sync calls
Same issue as the argmax top-K fix: two __shfl_xor_sync call sites in
the FWHT-128 butterfly kernels (ggml_cuda_fwht128 and fwht128_store_half)
use the 3-arg CUDA form and omit the `width` argument that the HIP
function-like macro in vendors/hip.h:33 requires. Hipcc fails with:
fattn.cu:512: too few arguments provided to function-like macro
invocation
note: macro '__shfl_xor_sync' defined here:
#define __shfl_xor_sync(mask, var, laneMask, width) \
__shfl_xor(var, laneMask, width)
Add WARP_SIZE to both calls. CUDA codegen is unchanged (warpSize is the
default); HIP now matches the macro arity.
--- a/ggml/src/ggml-cuda/fattn.cu
+++ b/ggml/src/ggml-cuda/fattn.cu
@@ -509,7 +509,7 @@
// Intra-warp passes: shuffle xor with stride h, no smem, no sync.
#pragma unroll
for (int h = 1; h <= 16; h *= 2) {
- const float other = __shfl_xor_sync(0xFFFFFFFF, val, h);
+ const float other = __shfl_xor_sync(0xFFFFFFFF, val, h, WARP_SIZE);
val = (tid & h) ? (other - val) : (val + other);
}
@@ -533,7 +533,7 @@
static __device__ __forceinline__ void fwht128_store_half(
float val, half * dst_base) {
const int tid = threadIdx.x;
- const float neighbor = __shfl_xor_sync(0xFFFFFFFF, val, 1);
+ const float neighbor = __shfl_xor_sync(0xFFFFFFFF, val, 1, WARP_SIZE);
if ((tid & 1) == 0) {
const half2 packed = __floats2half2_rn(val, neighbor);
*((half2 *)(dst_base + tid)) = packed;

View File

@@ -0,0 +1,65 @@
#!/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=buun-llama-cpp-fallback
if grep -q -e "\savx\s" /proc/cpuinfo ; then
echo "CPU: AVX found OK"
if [ -e $CURDIR/buun-llama-cpp-avx ]; then
BINARY=buun-llama-cpp-avx
fi
fi
if grep -q -e "\savx2\s" /proc/cpuinfo ; then
echo "CPU: AVX2 found OK"
if [ -e $CURDIR/buun-llama-cpp-avx2 ]; then
BINARY=buun-llama-cpp-avx2
fi
fi
# Check avx 512
if grep -q -e "\savx512f\s" /proc/cpuinfo ; then
echo "CPU: AVX512F found OK"
if [ -e $CURDIR/buun-llama-cpp-avx512 ]; then
BINARY=buun-llama-cpp-avx512
fi
fi
if [ -n "$LLAMACPP_GRPC_SERVERS" ]; then
if [ -e $CURDIR/buun-llama-cpp-grpc ]; then
BINARY=buun-llama-cpp-grpc
fi
fi
# Extend ld library path with the dir where this script is located/lib
if [ "$(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 $CURDIR/buun-llama-cpp-fallback "$@"

View File

@@ -1,5 +1,5 @@
IK_LLAMA_VERSION?=8b56d813a9ed04fa7b7fe2588fddd845cf64eccb
IK_LLAMA_VERSION?=16996aeab772c69b6473597038b2ef0b85297e8b
LLAMA_REPO?=https://github.com/ikawrakow/ik_llama.cpp
CMAKE_ARGS?=

View File

@@ -1,5 +1,5 @@
LLAMA_VERSION?=bbeb89d76c41bc250f16e4a6fefcc9b530d6e3f3
LLAMA_VERSION?=187a45637054881ecacf17f8e2f6f8f2ba7df1c7
LLAMA_REPO?=https://github.com/ggerganov/llama.cpp
CMAKE_ARGS?=
@@ -34,9 +34,6 @@ else ifeq ($(BUILD_TYPE),hipblas)
export CXX=$(ROCM_HOME)/llvm/bin/clang++
export CC=$(ROCM_HOME)/llvm/bin/clang
AMDGPU_TARGETS?=gfx908,gfx90a,gfx942,gfx950,gfx1030,gfx1100,gfx1101,gfx1102,gfx1151,gfx1200,gfx1201
ifeq ($(strip $(AMDGPU_TARGETS)),)
$(error AMDGPU_TARGETS is emptyset it to a comma-separated list of gfx targets e.g. gfx1100,gfx1101)
endif
CMAKE_ARGS+=-DGGML_HIP=ON -DAMDGPU_TARGETS=$(AMDGPU_TARGETS)
else ifeq ($(BUILD_TYPE),vulkan)
CMAKE_ARGS+=-DGGML_VULKAN=1

View File

@@ -442,7 +442,7 @@ 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 +642,6 @@ 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 +664,39 @@ 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 +918,8 @@ 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

View File

@@ -1,7 +1,7 @@
# Pinned to the HEAD of feature/turboquant-kv-cache on https://github.com/TheTom/llama-cpp-turboquant.
# Auto-bumped nightly by .github/workflows/bump_deps.yaml.
TURBOQUANT_VERSION?=69d8e4be47243e83b3d0d71e932bc7aa61c644dc
TURBOQUANT_VERSION?=627ebbc6e27727bd4f65422d8aa60b13404993c8
LLAMA_REPO?=https://github.com/TheTom/llama-cpp-turboquant
CMAKE_ARGS?=

View File

@@ -1,6 +1,6 @@
#!/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:
# turboquant build to account for two 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.
@@ -11,14 +11,6 @@
# "<__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
@@ -85,27 +77,4 @@ 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

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

View File

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

View File

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

View File

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

View File

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

View File

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

View File

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

View File

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

View File

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

View File

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

View File

@@ -29,12 +29,6 @@ type SherpaBackend struct {
vadWindowSize int
ttsSpeed float32
onlineChunkSamples int
// Speaker diarization (offline pyannote + embedding extractor + clustering).
// diarSampleRate is reported by sherpa at create time; we cache it so
// runDiarization can resample only when the input doesn't already match.
diarizer uintptr
diarSampleRate int
}
var onnxProvider = "cpu"
@@ -134,25 +128,6 @@ var (
// TTS streaming callback trampoline
shimTtsGenerateWithCallback func(tts uintptr, text string, sid int32, speed float32, cb uintptr, ud uintptr) uintptr
// Diarization config + result accessors (see csrc/shim.h).
shimDiarizeConfigNew func() uintptr
shimDiarizeConfigFree func(uintptr)
shimDiarizeConfigSetSegmentationModel func(uintptr, string)
shimDiarizeConfigSetSegmentationNumThreads func(uintptr, int32)
shimDiarizeConfigSetSegmentationProvider func(uintptr, string)
shimDiarizeConfigSetSegmentationDebug func(uintptr, int32)
shimDiarizeConfigSetEmbeddingModel func(uintptr, string)
shimDiarizeConfigSetEmbeddingNumThreads func(uintptr, int32)
shimDiarizeConfigSetEmbeddingProvider func(uintptr, string)
shimDiarizeConfigSetEmbeddingDebug func(uintptr, int32)
shimDiarizeConfigSetClusteringNumClusters func(uintptr, int32)
shimDiarizeConfigSetClusteringThreshold func(uintptr, float32)
shimDiarizeConfigSetMinDurationOn func(uintptr, float32)
shimDiarizeConfigSetMinDurationOff func(uintptr, float32)
shimCreateOfflineSpeakerDiarization func(uintptr) uintptr
shimDiarizeSetClustering func(uintptr, int32, float32)
shimDiarizeSegmentAt func(segs uintptr, i int32, outStart unsafe.Pointer, outEnd unsafe.Pointer, outSpeaker unsafe.Pointer)
)
// libsherpa-onnx-c-api pass-throughs — called directly from Go via purego.
@@ -197,18 +172,6 @@ var (
sherpaOfflineTtsGenerate func(tts uintptr, text string, sid int32, speed float32) uintptr
sherpaDestroyOfflineTtsGeneratedAudio func(audio uintptr)
sherpaOfflineTtsSampleRate func(tts uintptr) int32
// Offline speaker diarization. Result handle owns the segment-array
// pointer returned by ResultSortByStartTime; destroy the segment
// array first, then the result, then (at backend Free()) the diarizer.
sherpaDestroyOfflineSpeakerDiarization func(sd uintptr)
sherpaOfflineSpeakerDiarizationGetSampleRate func(sd uintptr) int32
sherpaOfflineSpeakerDiarizationProcess func(sd uintptr, samples unsafe.Pointer, n int32) uintptr
sherpaOfflineSpeakerDiarizationResultGetNumSegments func(result uintptr) int32
sherpaOfflineSpeakerDiarizationResultGetNumSpeakers func(result uintptr) int32
sherpaOfflineSpeakerDiarizationResultSortByStartTime func(result uintptr) uintptr
sherpaOfflineSpeakerDiarizationDestroySegment func(segs uintptr)
sherpaDestroyOfflineSpeakerDiarizationResult func(result uintptr)
)
var (
@@ -329,24 +292,6 @@ func loadSherpaLibsOnce() error {
{&shimSpeechSegmentStart, "sherpa_shim_speech_segment_start"},
{&shimSpeechSegmentN, "sherpa_shim_speech_segment_n"},
{&shimTtsGenerateWithCallback, "sherpa_shim_tts_generate_with_callback"},
{&shimDiarizeConfigNew, "sherpa_shim_diarize_config_new"},
{&shimDiarizeConfigFree, "sherpa_shim_diarize_config_free"},
{&shimDiarizeConfigSetSegmentationModel, "sherpa_shim_diarize_config_set_segmentation_model"},
{&shimDiarizeConfigSetSegmentationNumThreads, "sherpa_shim_diarize_config_set_segmentation_num_threads"},
{&shimDiarizeConfigSetSegmentationProvider, "sherpa_shim_diarize_config_set_segmentation_provider"},
{&shimDiarizeConfigSetSegmentationDebug, "sherpa_shim_diarize_config_set_segmentation_debug"},
{&shimDiarizeConfigSetEmbeddingModel, "sherpa_shim_diarize_config_set_embedding_model"},
{&shimDiarizeConfigSetEmbeddingNumThreads, "sherpa_shim_diarize_config_set_embedding_num_threads"},
{&shimDiarizeConfigSetEmbeddingProvider, "sherpa_shim_diarize_config_set_embedding_provider"},
{&shimDiarizeConfigSetEmbeddingDebug, "sherpa_shim_diarize_config_set_embedding_debug"},
{&shimDiarizeConfigSetClusteringNumClusters, "sherpa_shim_diarize_config_set_clustering_num_clusters"},
{&shimDiarizeConfigSetClusteringThreshold, "sherpa_shim_diarize_config_set_clustering_threshold"},
{&shimDiarizeConfigSetMinDurationOn, "sherpa_shim_diarize_config_set_min_duration_on"},
{&shimDiarizeConfigSetMinDurationOff, "sherpa_shim_diarize_config_set_min_duration_off"},
{&shimCreateOfflineSpeakerDiarization, "sherpa_shim_create_offline_speaker_diarization"},
{&shimDiarizeSetClustering, "sherpa_shim_diarize_set_clustering"},
{&shimDiarizeSegmentAt, "sherpa_shim_diarize_segment_at"},
} {
purego.RegisterLibFunc(r.ptr, shim, r.name)
}
@@ -389,15 +334,6 @@ func loadSherpaLibsOnce() error {
{&sherpaOfflineTtsGenerate, "SherpaOnnxOfflineTtsGenerate"},
{&sherpaDestroyOfflineTtsGeneratedAudio, "SherpaOnnxDestroyOfflineTtsGeneratedAudio"},
{&sherpaOfflineTtsSampleRate, "SherpaOnnxOfflineTtsSampleRate"},
{&sherpaDestroyOfflineSpeakerDiarization, "SherpaOnnxDestroyOfflineSpeakerDiarization"},
{&sherpaOfflineSpeakerDiarizationGetSampleRate, "SherpaOnnxOfflineSpeakerDiarizationGetSampleRate"},
{&sherpaOfflineSpeakerDiarizationProcess, "SherpaOnnxOfflineSpeakerDiarizationProcess"},
{&sherpaOfflineSpeakerDiarizationResultGetNumSegments, "SherpaOnnxOfflineSpeakerDiarizationResultGetNumSegments"},
{&sherpaOfflineSpeakerDiarizationResultGetNumSpeakers, "SherpaOnnxOfflineSpeakerDiarizationResultGetNumSpeakers"},
{&sherpaOfflineSpeakerDiarizationResultSortByStartTime, "SherpaOnnxOfflineSpeakerDiarizationResultSortByStartTime"},
{&sherpaOfflineSpeakerDiarizationDestroySegment, "SherpaOnnxOfflineSpeakerDiarizationDestroySegment"},
{&sherpaDestroyOfflineSpeakerDiarizationResult, "SherpaOnnxOfflineSpeakerDiarizationDestroyResult"},
} {
purego.RegisterLibFunc(r.ptr, capi, r.name)
}
@@ -447,11 +383,6 @@ func isVADType(t string) bool {
return t == "vad"
}
func isDiarizationType(t string) bool {
t = strings.ToLower(t)
return t == "diarization" || t == "diarize" || t == "speaker-diarization"
}
// Model-options prefixes recognised by this backend. Kept as typed
// constants so the asrFamily / loadWhisperASR / loadGenericASR paths
// can all speak the same vocabulary.
@@ -492,19 +423,6 @@ const (
optionOnlineRule2 = "online.rule2_min_trailing_silence="
optionOnlineRule3 = "online.rule3_min_utterance_length="
optionOnlineChunkSamples = "online.chunk_samples="
// Speaker diarization (offline pyannote + speaker-embedding extractor).
// `diarize.segmentation_model` overrides the auto-detected pyannote
// segmentation .onnx in modelDir; `diarize.embedding_model` does the
// same for the speaker-embedding extractor. `diarize.num_clusters`
// pins a known speaker count at load time; per-call DiarizeRequest
// fields take precedence at process time.
optionDiarizeSegmentationModel = "diarize.segmentation_model="
optionDiarizeEmbeddingModel = "diarize.embedding_model="
optionDiarizeNumClusters = "diarize.num_clusters="
optionDiarizeThreshold = "diarize.threshold="
optionDiarizeMinDurationOn = "diarize.min_duration_on="
optionDiarizeMinDurationOff = "diarize.min_duration_off="
)
func hasOption(opts *pb.ModelOptions, prefix string) bool {
@@ -575,9 +493,6 @@ func (s *SherpaBackend) Load(opts *pb.ModelOptions) error {
if isVADType(opts.Type) {
return s.loadVAD(opts)
}
if isDiarizationType(opts.Type) {
return s.loadDiarization(opts)
}
// An explicit `subtype=...` option routes to ASR even when Type is
// unset — handy for the e2e-backends harness, which doesn't know
// about ModelOptions.Type.
@@ -1332,176 +1247,3 @@ func (s *SherpaBackend) TTSStream(req *pb.TTSRequest, results chan []byte) error
}
return nil
}
// =============================================================
// Speaker diarization (offline)
// =============================================================
//
// Conventions:
// - opts.ModelFile is the pyannote segmentation .onnx (e.g. model.onnx
// under sherpa-onnx-pyannote-segmentation-3-0/). Override with
// `diarize.segmentation_model=` if the gallery layout differs.
// - The speaker-embedding extractor must be provided via
// `diarize.embedding_model=`. There's no reliable filename heuristic
// we can rely on (3dspeaker, NeMo, WeSpeaker all ship with
// model-specific names), so we require it to be explicit.
// - Both paths are resolved relative to opts.ModelPath if not absolute.
func (s *SherpaBackend) loadDiarization(opts *pb.ModelOptions) error {
if s.diarizer != 0 {
return nil
}
modelDir := filepath.Dir(opts.ModelFile)
segModel := findOptionValue(opts, optionDiarizeSegmentationModel, opts.ModelFile)
if segModel != "" && !filepath.IsAbs(segModel) && opts.ModelPath != "" {
segModel = filepath.Join(opts.ModelPath, segModel)
}
if !fileExists(segModel) {
return fmt.Errorf("sherpa-onnx diarization: pyannote segmentation model not found at %q (set diarize.segmentation_model=...)", segModel)
}
embModel := findOptionValue(opts, optionDiarizeEmbeddingModel, "")
if embModel == "" {
return fmt.Errorf("sherpa-onnx diarization: speaker-embedding model is required — pass options: [diarize.embedding_model=<path>] (e.g. 3dspeaker_speech_campplus_sv_zh-cn_16k-common.onnx)")
}
if !filepath.IsAbs(embModel) {
base := opts.ModelPath
if base == "" {
base = modelDir
}
embModel = filepath.Join(base, embModel)
}
if !fileExists(embModel) {
return fmt.Errorf("sherpa-onnx diarization: speaker-embedding model not found at %q", embModel)
}
threads := int32(1)
if opts.Threads != 0 {
threads = opts.Threads
}
cfg := shimDiarizeConfigNew()
defer shimDiarizeConfigFree(cfg)
shimDiarizeConfigSetSegmentationModel(cfg, segModel)
shimDiarizeConfigSetSegmentationNumThreads(cfg, threads)
shimDiarizeConfigSetSegmentationProvider(cfg, onnxProvider)
shimDiarizeConfigSetSegmentationDebug(cfg, 0)
shimDiarizeConfigSetEmbeddingModel(cfg, embModel)
shimDiarizeConfigSetEmbeddingNumThreads(cfg, threads)
shimDiarizeConfigSetEmbeddingProvider(cfg, onnxProvider)
shimDiarizeConfigSetEmbeddingDebug(cfg, 0)
shimDiarizeConfigSetClusteringNumClusters(cfg, findOptionInt(opts, optionDiarizeNumClusters, -1))
shimDiarizeConfigSetClusteringThreshold(cfg, findOptionFloat(opts, optionDiarizeThreshold, 0.5))
shimDiarizeConfigSetMinDurationOn(cfg, findOptionFloat(opts, optionDiarizeMinDurationOn, 0.3))
shimDiarizeConfigSetMinDurationOff(cfg, findOptionFloat(opts, optionDiarizeMinDurationOff, 0.5))
sd := shimCreateOfflineSpeakerDiarization(cfg)
if sd == 0 {
return fmt.Errorf("sherpa-onnx diarization: failed to create diarizer (segmentation=%s embedding=%s)", segModel, embModel)
}
s.diarizer = sd
s.diarSampleRate = int(sherpaOfflineSpeakerDiarizationGetSampleRate(sd))
return nil
}
// applyDiarizeOverrides re-applies clustering knobs onto an existing
// diarizer when per-call DiarizeRequest fields are set. Both -1/0 sentinels
// follow sherpa's convention: num_clusters<=0 → use threshold-based
// clustering, threshold<=0 → keep load-time default.
func (s *SherpaBackend) applyDiarizeOverrides(req *pb.DiarizeRequest) {
num := int32(-1)
if req.NumSpeakers > 0 {
num = req.NumSpeakers
}
threshold := float32(0)
if req.ClusteringThreshold > 0 {
threshold = req.ClusteringThreshold
}
if num > 0 || threshold > 0 {
shimDiarizeSetClustering(s.diarizer, num, threshold)
}
}
func (s *SherpaBackend) Diarize(req *pb.DiarizeRequest) (pb.DiarizeResponse, error) {
if s.diarizer == 0 {
return pb.DiarizeResponse{}, fmt.Errorf("sherpa-onnx diarization not loaded (model must be loaded with type=diarization)")
}
if req.Dst == "" {
return pb.DiarizeResponse{}, fmt.Errorf("sherpa-onnx diarization: DiarizeRequest.dst (audio path) is required")
}
dir, err := os.MkdirTemp("", "sherpa-diarize")
if err != nil {
return pb.DiarizeResponse{}, fmt.Errorf("failed to create temp dir: %w", err)
}
defer func() { _ = os.RemoveAll(dir) }()
wavPath := filepath.Join(dir, "input.wav")
if err := utils.AudioToWav(req.Dst, wavPath); err != nil {
return pb.DiarizeResponse{}, fmt.Errorf("failed to convert audio to wav: %w", err)
}
wave := sherpaReadWave(wavPath)
if wave == 0 {
return pb.DiarizeResponse{}, fmt.Errorf("failed to read wav %s", wavPath)
}
defer sherpaFreeWave(wave)
sr := int(shimWaveSampleRate(wave))
nSamples := shimWaveNumSamples(wave)
samples := shimWaveSamples(wave)
duration := float32(nSamples) / float32(sr)
if sr != s.diarSampleRate {
// AudioToWav already targets 16 kHz; pyannote-3.0 also wants 16 kHz, so
// this branch should be unreachable. Fail loudly instead of silently
// passing mismatched audio to the model.
return pb.DiarizeResponse{}, fmt.Errorf("sherpa-onnx diarization: input sample rate %d Hz does not match model %d Hz", sr, s.diarSampleRate)
}
s.applyDiarizeOverrides(req)
result := sherpaOfflineSpeakerDiarizationProcess(s.diarizer, samples, nSamples)
if result == 0 {
return pb.DiarizeResponse{}, fmt.Errorf("sherpa-onnx diarization: process failed")
}
defer sherpaDestroyOfflineSpeakerDiarizationResult(result)
numSegments := sherpaOfflineSpeakerDiarizationResultGetNumSegments(result)
numSpeakers := sherpaOfflineSpeakerDiarizationResultGetNumSpeakers(result)
if numSegments <= 0 {
return pb.DiarizeResponse{
Segments: []*pb.DiarizeSegment{},
NumSpeakers: numSpeakers,
Duration: duration,
}, nil
}
segs := sherpaOfflineSpeakerDiarizationResultSortByStartTime(result)
if segs == 0 {
return pb.DiarizeResponse{}, fmt.Errorf("sherpa-onnx diarization: failed to retrieve segments")
}
defer sherpaOfflineSpeakerDiarizationDestroySegment(segs)
out := make([]*pb.DiarizeSegment, 0, numSegments)
for i := range int(numSegments) {
var start, end float32
var spk int32
shimDiarizeSegmentAt(segs, int32(i),
unsafe.Pointer(&start), unsafe.Pointer(&end), unsafe.Pointer(&spk))
out = append(out, &pb.DiarizeSegment{
Id: int32(i),
Start: start,
End: end,
Speaker: strconv.FormatInt(int64(spk), 10),
})
}
return pb.DiarizeResponse{
Segments: out,
NumSpeakers: numSpeakers,
Duration: duration,
}, nil
}

View File

@@ -310,87 +310,6 @@ int32_t sherpa_shim_speech_segment_n(const void *h) {
return ((const SherpaOnnxSpeechSegment *)h)->n;
}
// ==================================================================
// Offline speaker diarization config
// ==================================================================
void *sherpa_shim_diarize_config_new(void) {
return calloc(1, sizeof(SherpaOnnxOfflineSpeakerDiarizationConfig));
}
void sherpa_shim_diarize_config_free(void *h) {
if (!h) return;
SherpaOnnxOfflineSpeakerDiarizationConfig *c =
(SherpaOnnxOfflineSpeakerDiarizationConfig *)h;
free((char *)c->segmentation.pyannote.model);
free((char *)c->segmentation.provider);
free((char *)c->embedding.model);
free((char *)c->embedding.provider);
free(c);
}
void sherpa_shim_diarize_config_set_segmentation_model(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOfflineSpeakerDiarizationConfig *)h)->segmentation.pyannote.model, v);
}
void sherpa_shim_diarize_config_set_segmentation_num_threads(void *h, int32_t v) {
((SherpaOnnxOfflineSpeakerDiarizationConfig *)h)->segmentation.num_threads = v;
}
void sherpa_shim_diarize_config_set_segmentation_provider(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOfflineSpeakerDiarizationConfig *)h)->segmentation.provider, v);
}
void sherpa_shim_diarize_config_set_segmentation_debug(void *h, int32_t v) {
((SherpaOnnxOfflineSpeakerDiarizationConfig *)h)->segmentation.debug = v;
}
void sherpa_shim_diarize_config_set_embedding_model(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOfflineSpeakerDiarizationConfig *)h)->embedding.model, v);
}
void sherpa_shim_diarize_config_set_embedding_num_threads(void *h, int32_t v) {
((SherpaOnnxOfflineSpeakerDiarizationConfig *)h)->embedding.num_threads = v;
}
void sherpa_shim_diarize_config_set_embedding_provider(void *h, const char *v) {
shim_set_str(&((SherpaOnnxOfflineSpeakerDiarizationConfig *)h)->embedding.provider, v);
}
void sherpa_shim_diarize_config_set_embedding_debug(void *h, int32_t v) {
((SherpaOnnxOfflineSpeakerDiarizationConfig *)h)->embedding.debug = v;
}
void sherpa_shim_diarize_config_set_clustering_num_clusters(void *h, int32_t v) {
((SherpaOnnxOfflineSpeakerDiarizationConfig *)h)->clustering.num_clusters = v;
}
void sherpa_shim_diarize_config_set_clustering_threshold(void *h, float v) {
((SherpaOnnxOfflineSpeakerDiarizationConfig *)h)->clustering.threshold = v;
}
void sherpa_shim_diarize_config_set_min_duration_on(void *h, float v) {
((SherpaOnnxOfflineSpeakerDiarizationConfig *)h)->min_duration_on = v;
}
void sherpa_shim_diarize_config_set_min_duration_off(void *h, float v) {
((SherpaOnnxOfflineSpeakerDiarizationConfig *)h)->min_duration_off = v;
}
void *sherpa_shim_create_offline_speaker_diarization(void *h) {
return (void *)SherpaOnnxCreateOfflineSpeakerDiarization(
(const SherpaOnnxOfflineSpeakerDiarizationConfig *)h);
}
void sherpa_shim_diarize_set_clustering(void *sd, int32_t num_clusters, float threshold) {
if (!sd) return;
SherpaOnnxOfflineSpeakerDiarizationConfig cfg;
memset(&cfg, 0, sizeof(cfg));
cfg.clustering.num_clusters = num_clusters;
cfg.clustering.threshold = threshold;
SherpaOnnxOfflineSpeakerDiarizationSetConfig(
(const SherpaOnnxOfflineSpeakerDiarization *)sd, &cfg);
}
void sherpa_shim_diarize_segment_at(const void *segs, int32_t i,
float *out_start, float *out_end,
int32_t *out_speaker) {
const SherpaOnnxOfflineSpeakerDiarizationSegment *arr =
(const SherpaOnnxOfflineSpeakerDiarizationSegment *)segs;
if (out_start) *out_start = arr[i].start;
if (out_end) *out_end = arr[i].end;
if (out_speaker) *out_speaker = arr[i].speaker;
}
// ==================================================================
// TTS streaming callback trampoline
// ==================================================================

View File

@@ -109,41 +109,6 @@ 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);
// --- Offline speaker diarization config -----------------------------
// Pyannote segmentation + speaker-embedding extractor + fast clustering.
// The upstream config is a struct of nested structs; purego can't read or
// build those across dlopen, so we expose a calloc'd opaque holder plus
// flat setters, then hand it to sherpa via the create wrapper.
void *sherpa_shim_diarize_config_new(void);
void sherpa_shim_diarize_config_free(void *cfg);
void sherpa_shim_diarize_config_set_segmentation_model(void *cfg, const char *path);
void sherpa_shim_diarize_config_set_segmentation_num_threads(void *cfg, int32_t v);
void sherpa_shim_diarize_config_set_segmentation_provider(void *cfg, const char *v);
void sherpa_shim_diarize_config_set_segmentation_debug(void *cfg, int32_t v);
void sherpa_shim_diarize_config_set_embedding_model(void *cfg, const char *path);
void sherpa_shim_diarize_config_set_embedding_num_threads(void *cfg, int32_t v);
void sherpa_shim_diarize_config_set_embedding_provider(void *cfg, const char *v);
void sherpa_shim_diarize_config_set_embedding_debug(void *cfg, int32_t v);
void sherpa_shim_diarize_config_set_clustering_num_clusters(void *cfg, int32_t v);
void sherpa_shim_diarize_config_set_clustering_threshold(void *cfg, float v);
void sherpa_shim_diarize_config_set_min_duration_on(void *cfg, float v);
void sherpa_shim_diarize_config_set_min_duration_off(void *cfg, float v);
void *sherpa_shim_create_offline_speaker_diarization(void *cfg);
// Apply just the clustering knobs onto a loaded diarizer (sherpa
// supports re-clustering after Create), so per-call overrides like
// num_speakers don't require re-loading the heavy ONNX models.
void sherpa_shim_diarize_set_clustering(void *sd, int32_t num_clusters, float threshold);
// Sherpa's ResultSortByStartTime returns a sherpa-allocated array of
// SherpaOnnxOfflineSpeakerDiarizationSegment structs (free with
// SherpaOnnxOfflineSpeakerDiarizationDestroySegment). Purego can't read
// fields out of an array of C structs, so this getter copies one
// segment's fields into the caller-supplied float/int32 cells.
void sherpa_shim_diarize_segment_at(const void *segs, int32_t i,
float *out_start, float *out_end,
int32_t *out_speaker);
// --- TTS streaming callback trampoline -----------------------------
// Replaces the //export sherpaTtsGoCallback + callbacks.c bridge pattern.
// `callback_ptr` is the C-callable function pointer returned by

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?=$(shell nproc --ignore=1)
# stablediffusion.cpp (ggml)
STABLEDIFFUSION_GGML_REPO?=https://github.com/leejet/stable-diffusion.cpp
STABLEDIFFUSION_GGML_VERSION?=3d6064b37ef4607917f8acf2ca8c8906d5087413
STABLEDIFFUSION_GGML_VERSION?=c97702e1057c2fe13a7074cd9069cb9dd6edc1bf
CMAKE_ARGS+=-DGGML_MAX_NAME=128
@@ -119,7 +119,7 @@ libgosd-fallback.so: sources/stablediffusion-ggml.cpp
SO_TARGET=libgosd-fallback.so CMAKE_ARGS="$(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

@@ -1,71 +0,0 @@
cmake_minimum_required(VERSION 3.18)
project(govibevoicecpp LANGUAGES C CXX)
set(CMAKE_POSITION_INDEPENDENT_CODE ON)
set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
set(VIBEVOICE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/sources/vibevoice.cpp)
# Override upstream's CMAKE_CUDA_ARCHITECTURES before add_subdirectory.
if(NOT DEFINED CMAKE_CUDA_ARCHITECTURES)
set(CMAKE_CUDA_ARCHITECTURES "75-virtual;80-virtual;86-real;89-real")
endif()
# Force-disable upstream tests/examples — we only need libvibevoice.
set(VIBEVOICE_BUILD_TESTS OFF CACHE BOOL "" FORCE)
set(VIBEVOICE_BUILD_EXAMPLES OFF CACHE BOOL "" FORCE)
set(VIBEVOICE_BUILD_SERVER OFF CACHE BOOL "" FORCE)
# vibevoice.cpp's top-level CMakeLists already adds third_party/ggml as a
# subdirectory — no need to add it explicitly here, just include the
# whole project.
add_subdirectory(${VIBEVOICE_DIR} vibevoice EXCLUDE_FROM_ALL)
add_library(govibevoicecpp MODULE cpp/govibevoicecpp.cpp)
# libvibevoice is STATIC; without --whole-archive the linker GCs the
# vv_capi_* symbols (purego dlopens them by name, nothing in our
# translation unit references them). Force the static archive's
# entire contents into the MODULE so dlsym finds vv_capi_load etc.
if(APPLE)
target_link_libraries(govibevoicecpp PRIVATE -Wl,-force_load $<TARGET_FILE:vibevoice>)
elseif(MSVC)
target_link_libraries(govibevoicecpp PRIVATE vibevoice)
set_property(TARGET govibevoicecpp APPEND PROPERTY LINK_FLAGS "/WHOLEARCHIVE:vibevoice")
else()
target_link_libraries(govibevoicecpp PRIVATE
-Wl,--whole-archive vibevoice -Wl,--no-whole-archive)
endif()
target_include_directories(govibevoicecpp PRIVATE ${VIBEVOICE_DIR}/include)
target_include_directories(govibevoicecpp SYSTEM PRIVATE ${VIBEVOICE_DIR}/third_party/ggml/include)
# Link GPU backends if available — vibevoice's own CMake already links
# these to the libvibevoice STATIC library, but we re-link them on the
# MODULE so resolved symbols include all backend kernels.
foreach(backend blas cuda metal vulkan)
if(TARGET ggml-${backend})
target_link_libraries(govibevoicecpp PRIVATE ggml-${backend})
string(TOUPPER ${backend} BACKEND_UPPER)
target_compile_definitions(govibevoicecpp PRIVATE VIBEVOICE_HAVE_${BACKEND_UPPER})
if(backend STREQUAL "cuda")
find_package(CUDAToolkit QUIET)
if(CUDAToolkit_FOUND)
target_link_libraries(govibevoicecpp PRIVATE CUDA::cudart)
endif()
endif()
endif()
endforeach()
if(MSVC)
target_compile_options(govibevoicecpp PRIVATE /W4 /wd4100 /wd4505)
else()
target_compile_options(govibevoicecpp PRIVATE -Wall -Wextra -Wshadow
-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(govibevoicecpp PRIVATE stdc++fs)
endif()
set_property(TARGET govibevoicecpp PROPERTY CXX_STANDARD 17)
set_target_properties(govibevoicecpp PROPERTIES LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR})

View File

@@ -1,131 +0,0 @@
CMAKE_ARGS?=
BUILD_TYPE?=
NATIVE?=false
GOCMD?=go
GO_TAGS?=
JOBS?=$(shell nproc --ignore=1)
# vibevoice.cpp version. Pinned to a commit hash and auto-bumped by
# .github/workflows/bump_deps.yaml (the matrix entry mirrors what we
# already do for ik_llama.cpp / llama.cpp / whisper.cpp). Floating on
# `master` led to silent ABI breaks reaching CI — pin it.
VIBEVOICE_REPO?=https://github.com/mudler/vibevoice.cpp
VIBEVOICE_CPP_VERSION?=ad856bda6b1311b7f3d7c4a667be43eeb8a8249a
SO_TARGET?=libgovibevoicecpp.so
CMAKE_ARGS+=-DBUILD_SHARED_LIBS=OFF
CMAKE_ARGS+=-DVIBEVOICE_BUILD_TESTS=OFF
CMAKE_ARGS+=-DVIBEVOICE_BUILD_EXAMPLES=OFF
ifeq ($(NATIVE),false)
CMAKE_ARGS+=-DGGML_NATIVE=OFF
endif
ifeq ($(BUILD_TYPE),cublas)
CMAKE_ARGS+=-DGGML_CUDA=ON -DVIBEVOICE_GGML_CUDA=ON
else ifeq ($(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 -DVIBEVOICE_GGML_HIPBLAS=ON
else ifeq ($(BUILD_TYPE),vulkan)
CMAKE_ARGS+=-DGGML_VULKAN=ON -DVIBEVOICE_GGML_VULKAN=ON
else ifeq ($(OS),Darwin)
ifneq ($(BUILD_TYPE),metal)
CMAKE_ARGS+=-DGGML_METAL=OFF
else
CMAKE_ARGS+=-DGGML_METAL=ON -DVIBEVOICE_GGML_METAL=ON
CMAKE_ARGS+=-DGGML_METAL_EMBED_LIBRARY=ON
endif
endif
ifeq ($(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/vibevoice.cpp:
mkdir -p sources/vibevoice.cpp
cd sources/vibevoice.cpp && \
git init && \
git remote add origin $(VIBEVOICE_REPO) && \
git fetch origin && \
git checkout $(VIBEVOICE_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 = libgovibevoicecpp-avx.so libgovibevoicecpp-avx2.so libgovibevoicecpp-avx512.so libgovibevoicecpp-fallback.so
else
# On non-Linux (e.g., Darwin), build only fallback variant
VARIANT_TARGETS = libgovibevoicecpp-fallback.so
endif
vibevoice-cpp: main.go govibevoicecpp.go $(VARIANT_TARGETS)
CGO_ENABLED=0 $(GOCMD) build -tags "$(GO_TAGS)" -o vibevoice-cpp ./
package: vibevoice-cpp
bash package.sh
build: package
clean: purge
rm -rf libgovibevoicecpp*.so package sources/vibevoice.cpp vibevoice-cpp
purge:
rm -rf build*
# Variants must build sequentially
.NOTPARALLEL:
# Build all variants (Linux only)
ifeq ($(UNAME_S),Linux)
libgovibevoicecpp-avx.so: sources/vibevoice.cpp
$(info ${GREEN}I vibevoice-cpp build info:avx${RESET})
SO_TARGET=libgovibevoicecpp-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) libgovibevoicecpp-custom
rm -rf build-libgovibevoicecpp-avx.so
libgovibevoicecpp-avx2.so: sources/vibevoice.cpp
$(info ${GREEN}I vibevoice-cpp build info:avx2${RESET})
SO_TARGET=libgovibevoicecpp-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) libgovibevoicecpp-custom
rm -rf build-libgovibevoicecpp-avx2.so
libgovibevoicecpp-avx512.so: sources/vibevoice.cpp
$(info ${GREEN}I vibevoice-cpp build info:avx512${RESET})
SO_TARGET=libgovibevoicecpp-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) libgovibevoicecpp-custom
rm -rf build-libgovibevoicecpp-avx512.so
endif
# Build fallback variant (all platforms)
libgovibevoicecpp-fallback.so: sources/vibevoice.cpp
$(info ${GREEN}I vibevoice-cpp build info:fallback${RESET})
SO_TARGET=libgovibevoicecpp-fallback.so CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off" $(MAKE) libgovibevoicecpp-custom
rm -rf build-libgovibevoicecpp-fallback.so
libgovibevoicecpp-custom: CMakeLists.txt cpp/govibevoicecpp.cpp cpp/govibevoicecpp.h
mkdir -p build-$(SO_TARGET) && \
cd build-$(SO_TARGET) && \
cmake .. $(CMAKE_ARGS) && \
cmake --build . --config Release -j$(JOBS) --target govibevoicecpp && \
cd .. && \
mv build-$(SO_TARGET)/libgovibevoicecpp.so ./$(SO_TARGET)
test: vibevoice-cpp
@echo "Running vibevoice-cpp tests..."
bash test.sh
@echo "vibevoice-cpp tests completed."
all: vibevoice-cpp package

View File

@@ -1,41 +0,0 @@
// vibevoice.cpp ships its purego-friendly ABI in vibevoice_capi.h.
// This translation unit is intentionally tiny: pulling in the header
// (and linking libvibevoice PRIVATE in CMake) is enough to make the
// vv_capi_* symbols visible from the produced MODULE library.
//
// We do install a ggml log redirect so backend logs land on the gRPC
// server's stderr — same pattern as backend/go/qwen3-tts-cpp/cpp/.
#include "govibevoicecpp.h"
#include "ggml.h"
#include "ggml-backend.h"
#include <cstdio>
namespace {
void govibevoice_log_cb(enum ggml_log_level level, const char* msg, void* /*ud*/) {
if (!msg) return;
const char* tag = "?????";
switch (level) {
case GGML_LOG_LEVEL_DEBUG: tag = "DEBUG"; break;
case GGML_LOG_LEVEL_INFO: tag = "INFO"; break;
case GGML_LOG_LEVEL_WARN: tag = "WARN"; break;
case GGML_LOG_LEVEL_ERROR: tag = "ERROR"; break;
default: break;
}
std::fprintf(stderr, "[%-5s] %s", tag, msg);
std::fflush(stderr);
}
struct LogInstaller {
LogInstaller() {
ggml_log_set(govibevoice_log_cb, nullptr);
ggml_backend_load_all();
}
};
LogInstaller g_install;
} // namespace

View File

@@ -1,7 +0,0 @@
#pragma once
// Re-exports the vibevoice.cpp flat C ABI so this MODULE library
// resolves the same symbols that purego.RegisterLibFunc looks up by
// name. The actual definitions live in libvibevoice (linked PRIVATE).
#include "vibevoice_capi.h"

View File

@@ -1,656 +0,0 @@
package main
import (
"encoding/json"
"fmt"
"io"
"os"
"os/exec"
"path/filepath"
"runtime"
"strings"
laudio "github.com/mudler/LocalAI/pkg/audio"
"github.com/mudler/LocalAI/pkg/grpc/base"
pb "github.com/mudler/LocalAI/pkg/grpc/proto"
)
// vv_capi_asr loads audio with load_wav_24k_mono — a 24 kHz mono s16le
// WAV is the format the model was trained on. Inputs already in that
// format pass through; everything else is converted via ffmpeg, which
// is therefore a runtime requirement only when callers upload non-WAV
// (or non-24 kHz mono s16le WAV) audio. Skipping ffmpeg on the happy
// path matters for the e2e-backends test container, which does not
// ship ffmpeg but feeds the backend pre-cooked 24 kHz mono WAVs.
const vibevoiceASRSampleRate = 24000
// prepareWavInput resolves `src` to a 24 kHz mono s16le WAV path that
// vv_capi_asr's load_wav_24k_mono accepts. Returns the resolved path
// plus a cleanup func; both must be honoured by the caller.
//
// Pass-through happens when `src` already has the right WAV format —
// no ffmpeg required. Otherwise we shell out to ffmpeg into a temp
// dir; if ffmpeg isn't on PATH we surface a clear error mentioning the
// underlying format mismatch.
func prepareWavInput(src string) (string, func(), error) {
if src == "" {
return "", func() {}, fmt.Errorf("empty audio path")
}
if isVibevoiceCompatibleWav(src) {
return src, func() {}, nil
}
dir, err := os.MkdirTemp("", "vibevoice-asr")
if err != nil {
return "", func() {}, fmt.Errorf("mkdtemp: %w", err)
}
cleanup := func() { _ = os.RemoveAll(dir) }
wavPath := filepath.Join(dir, "input.wav")
// -y: overwrite, -ar 24000: target sample rate, -ac 1: mono,
// -acodec pcm_s16le: signed 16-bit little-endian PCM (load_wav_24k_mono
// only accepts s16le).
cmd := exec.Command("ffmpeg",
"-y", "-i", src,
"-ar", fmt.Sprintf("%d", vibevoiceASRSampleRate),
"-ac", "1",
"-acodec", "pcm_s16le",
wavPath,
)
cmd.Env = []string{}
if out, err := cmd.CombinedOutput(); err != nil {
cleanup()
return "", func() {}, fmt.Errorf("ffmpeg convert to 24k mono wav: %w (output: %s)", err, string(out))
}
return wavPath, cleanup, nil
}
// isVibevoiceCompatibleWav returns true when `src` carries the RIFF/WAVE
// magic bytes. vibevoice's load_wav_24k_mono uses drwav under the hood,
// which accepts any PCM/IEEE-float WAV at any sample rate and downmixes
// multi-channel input to mono on its own — so any valid WAV passes
// through to the C side without conversion. Anything else (MP3, OGG,
// FLAC, ...) needs ffmpeg.
func isVibevoiceCompatibleWav(src string) bool {
f, err := os.Open(src)
if err != nil {
return false
}
defer func() { _ = f.Close() }()
// 0..3 = "RIFF", 8..11 = "WAVE".
var hdr [12]byte
if _, err := io.ReadFull(f, hdr[:]); err != nil {
return false
}
return string(hdr[0:4]) == "RIFF" && string(hdr[8:12]) == "WAVE"
}
// asrMaxNewTokens caps the ASR generation budget. The C ABI defaults to
// 256 when 0 is passed — far too small for anything past ~10s of speech.
// Vibevoice generates ~30 tokens per second of audio, so 16 384 covers
// roughly 9 minutes of dialogue, well past any normal /v1/audio/diarization
// upload. Going higher costs little since generation stops at EOS.
const asrMaxNewTokens = 16384
// vibevoice.cpp synthesizes 24 kHz mono 16-bit PCM. Hardcoded - the
// model itself is fixed-rate; if the upstream ever changes this we'll
// pick it up via vv_capi_version().
const vibevoiceSampleRate = uint32(24000)
// purego-bound entry points from libgovibevoicecpp.
//
// vv_capi_tts takes a `const char* const* ref_audio_paths` array (used
// by the 1.5B variant for runtime voice cloning; the realtime-0.5B
// path leaves it NULL and uses voice_path instead). purego marshals a
// Go []*byte slice as **char by passing the underlying array's address.
// A nil/empty slice marshals to NULL, which matches the C contract for
// "no reference audio".
var (
CppLoad func(ttsModel, asrModel, tokenizer, voice string, threads int32) int32
CppTTS func(text, voicePath string,
refAudioPaths []*byte, nRefAudioPaths int32,
dstWav string,
nSteps int32, cfgScale float32, maxSpeechFrames int32, seed uint32) int32
CppASR func(srcWav string, outJSON []byte, capacity uint64,
maxNewTokens int32) int32
CppUnload func()
CppVersion func() string
)
// VibevoiceCpp speaks gRPC against vibevoice.cpp's flat C ABI. The
// engine is a single global, so we serialize calls through SingleThread.
type VibevoiceCpp struct {
base.SingleThread
threads int
// modelRoot is the directory we use to resolve relative paths
// from Options[] and per-call overrides (TTSRequest.Voice).
// Source of truth: opts.ModelPath; falls back to the dir of
// the primary ModelFile when ModelPath is empty.
modelRoot string
ttsModel string
asrModel string
tokenizer string
voice string
// refAudio is the load-time default list of reference WAVs used by
// the 1.5B model (one per speaker). Sourced from
// ModelOptions.AudioPath (config_file's `audio_path:`) — comma-
// separated for multi-speaker. Per-call TTSRequest.Voice can
// override it. Empty for the realtime-0.5B path, which conditions
// on a pre-baked voice gguf via `voice` instead.
refAudio []string
}
// resolvePath joins a relative path onto `relTo`. The gallery
// convention is that Options[] carry paths relative to the LocalAI
// models dir (opts.ModelPath), so anything not absolute is treated
// as a sibling of the primary ModelFile - never CWD. Empty / already-
// absolute / no-relTo inputs pass through unchanged.
func resolvePath(p, relTo string) string {
if p == "" || filepath.IsAbs(p) || relTo == "" {
return p
}
return filepath.Join(relTo, p)
}
// parseOptions reads opts.Options[] and pulls out the per-role
// overrides documented in the gallery entries. Accepts both "key=value"
// (gallery YAML style) and "key:value" (Make-target / env-var style).
func (v *VibevoiceCpp) parseOptions(opts []string, relTo string) string {
role := ""
for _, raw := range opts {
k, val, ok := strings.Cut(raw, "=")
if !ok {
k, val, ok = strings.Cut(raw, ":")
if !ok {
continue
}
}
key := strings.TrimSpace(k)
val = strings.TrimSpace(val)
switch key {
case "type":
role = strings.ToLower(val)
case "tokenizer":
v.tokenizer = resolvePath(val, relTo)
case "voice":
v.voice = resolvePath(val, relTo)
case "tts_model":
v.ttsModel = resolvePath(val, relTo)
case "asr_model":
v.asrModel = resolvePath(val, relTo)
}
}
return role
}
// parseRefAudio splits a comma-separated audio_path value into a
// resolved list of WAVs. The 1.5B model uses one WAV per speaker;
// callers that only need a single reference set audio_path to a single
// path. Empty / whitespace-only entries are skipped.
func parseRefAudio(audioPath, relTo string) []string {
if audioPath == "" {
return nil
}
var out []string
for _, p := range strings.Split(audioPath, ",") {
p = strings.TrimSpace(p)
if p == "" {
continue
}
out = append(out, resolvePath(p, relTo))
}
return out
}
func (v *VibevoiceCpp) Load(opts *pb.ModelOptions) error {
if opts.ModelFile == "" {
return fmt.Errorf("vibevoice-cpp: ModelFile is required")
}
modelFile := opts.ModelFile
if !filepath.IsAbs(modelFile) && opts.ModelPath != "" {
modelFile = filepath.Join(opts.ModelPath, modelFile)
}
// ModelPath is the LocalAI core's models root, propagated over
// gRPC. Use it as the resolution base for Options[] (and later
// for TTSRequest.Voice) so gallery entries can reference paths
// like "tokenizer=tokenizer.gguf" and have them resolved
// against the same root the core used to drop the files.
v.modelRoot = opts.ModelPath
if v.modelRoot == "" {
v.modelRoot = filepath.Dir(modelFile)
}
role := v.parseOptions(opts.Options, v.modelRoot)
// 1.5B reference WAVs ride on ModelOptions.AudioPath (config_file's
// `audio_path:` key) — same convention other audio backends already
// follow. Single-speaker = single path; multi-speaker = comma list,
// one WAV per Speaker N: tag in TTSRequest.text.
v.refAudio = parseRefAudio(opts.AudioPath, v.modelRoot)
// ModelFile fills the "primary" role-slot determined by `type=`
// in Options (defaults to tts). The other slot stays exactly as
// Options set it - so a closed-loop config with ModelFile=tts.gguf
// + Options[asr_model=asr.gguf] resolves correctly to both slots,
// and an explicit `tts_model=` / `asr_model=` always wins over
// ModelFile for its own slot.
primaryIsASR := false
switch role {
case "asr", "transcript", "stt", "speech-to-text":
primaryIsASR = true
}
if primaryIsASR {
if v.asrModel == "" {
v.asrModel = modelFile
}
} else if v.ttsModel == "" {
v.ttsModel = modelFile
}
if v.ttsModel == "" && v.asrModel == "" {
return fmt.Errorf("vibevoice-cpp: no TTS or ASR model resolved from ModelFile=%q + options", opts.ModelFile)
}
if v.tokenizer == "" {
return fmt.Errorf("vibevoice-cpp: tokenizer is required - pass options: [tokenizer=<path>]")
}
threads := int(opts.Threads)
if threads <= 0 {
threads = 4
}
v.threads = threads
fmt.Fprintf(os.Stderr,
"[vibevoice-cpp] Loading: tts=%q asr=%q tokenizer=%q voice=%q ref_audio=%v threads=%d\n",
v.ttsModel, v.asrModel, v.tokenizer, v.voice, v.refAudio, threads)
if rc := CppLoad(v.ttsModel, v.asrModel, v.tokenizer, v.voice, int32(threads)); rc != 0 {
return fmt.Errorf("vibevoice-cpp: vv_capi_load failed (rc=%d)", rc)
}
return nil
}
func (v *VibevoiceCpp) TTS(req *pb.TTSRequest) error {
if v.ttsModel == "" {
return fmt.Errorf("vibevoice-cpp: TTS requested but no realtime model was loaded")
}
text := req.Text
dst := req.Dst
if text == "" || dst == "" {
return fmt.Errorf("vibevoice-cpp: TTS requires both text and dst")
}
// TTSRequest.Voice carries the per-call override. Routing depends
// on the loaded model variant:
// * realtime-0.5B → expects a baked voice .gguf (single path).
// * 1.5B → expects one or more raw 24 kHz mono .wav
// reference clips for runtime voice cloning;
// comma-separated to address multi-speaker
// dialogs (Speaker 0..n-1 follow the order).
// We pick the branch by extension / shape of the override; if no
// override is given, fall back to the load-time defaults.
voice := ""
var refAudio []string
if reqVoice := strings.TrimSpace(req.Voice); reqVoice != "" {
if isRefAudioOverride(reqVoice) {
for _, p := range strings.Split(reqVoice, ",") {
p = strings.TrimSpace(p)
if p == "" {
continue
}
refAudio = append(refAudio, resolvePath(p, v.modelRoot))
}
} else {
voice = resolvePath(reqVoice, v.modelRoot)
}
} else {
// No per-call override. v.voice already went to vv_capi_load
// for realtime-0.5B; ref_audio is per-call only on the C ABI,
// so the gallery's `ref_audio:` defaults are re-passed here.
refAudio = append(refAudio, v.refAudio...)
}
if req.Language != nil && *req.Language != "" {
fmt.Fprintf(os.Stderr,
"[vibevoice-cpp] note: TTSRequest.language=%q ignored - vibevoice picks language from the voice prompt\n",
*req.Language)
}
const (
defaultSteps = 20
defaultMaxFrames = 200
)
defaultCfg := float32(1.3)
refPtrs, refKeep := newCStringArray(refAudio)
rc := CppTTS(text, voice, refPtrs, int32(len(refPtrs)), dst,
int32(defaultSteps), defaultCfg, int32(defaultMaxFrames), 0)
// Hold the backing buffers past the cgo call. purego marshals
// []*byte by handing the C side the underlying array address; the
// pointed-to NUL-terminated bytes must outlive the call.
runtime.KeepAlive(refKeep)
runtime.KeepAlive(refPtrs)
if rc != 0 {
return fmt.Errorf("vibevoice-cpp: vv_capi_tts failed (rc=%d)", rc)
}
return nil
}
// isRefAudioOverride decides whether a TTSRequest.Voice override should
// be routed to ref_audio_paths (1.5B path) instead of voice_path
// (realtime-0.5B). Either a comma-separated list (multi-speaker) or a
// single .wav clip qualifies; a bare voice .gguf falls through.
func isRefAudioOverride(s string) bool {
if strings.Contains(s, ",") {
return true
}
return strings.HasSuffix(strings.ToLower(s), ".wav")
}
// newCStringArray builds the **char array vv_capi_tts expects, plus the
// keep-alive slice the caller must runtime.KeepAlive across the cgo
// call. A nil/empty input returns (nil, nil) which purego marshals to
// the C NULL pointer.
func newCStringArray(in []string) ([]*byte, [][]byte) {
if len(in) == 0 {
return nil, nil
}
keep := make([][]byte, len(in))
ptrs := make([]*byte, len(in))
for i, s := range in {
b := make([]byte, len(s)+1)
copy(b, s)
keep[i] = b
ptrs[i] = &b[0]
}
return ptrs, keep
}
// asrSegment matches vibevoice's JSON output:
//
// [{"Start":0.0,"End":2.8,"Speaker":0,"Content":"…"}, ...]
type asrSegment struct {
Start float64 `json:"Start"`
End float64 `json:"End"`
Speaker int `json:"Speaker"`
Content string `json:"Content"`
}
// callASR invokes vv_capi_asr with a buffer that grows on demand.
// vv_capi_asr returns: >0 bytes written, 0 no transcript, <0 error or
// -required_size. We honor the resize protocol once before giving up.
func (v *VibevoiceCpp) callASR(srcWav string, maxNewTokens int32) (string, error) {
const startCap = 256 * 1024
buf := make([]byte, startCap)
rc := CppASR(srcWav, buf, uint64(len(buf)), maxNewTokens)
if rc < 0 {
need := -int(rc)
if need > 0 && need < (16<<20) && need > len(buf) {
buf = make([]byte, need+64)
rc = CppASR(srcWav, buf, uint64(len(buf)), maxNewTokens)
}
}
if rc < 0 {
return "", fmt.Errorf("vibevoice-cpp: vv_capi_asr failed (rc=%d)", rc)
}
if rc == 0 {
return "", nil
}
return string(buf[:rc]), nil
}
// TTSStream is the streaming counterpart to TTS. vibevoice's C ABI is
// file-only (vv_capi_tts writes a complete WAV), so we synthesize to
// a tempfile, then emit a streaming-WAV header followed by the PCM
// body in chunks. The main reason this exists at all is the gRPC
// server wrapper (pkg/grpc/server.go:TTSStream) blocks on a channel
// that only this method can close - if we leave the default Base
// stub in place, every TTSStream call hangs until the client
// deadline.
func (v *VibevoiceCpp) TTSStream(req *pb.TTSRequest, results chan []byte) error {
defer close(results)
if v.ttsModel == "" {
return fmt.Errorf("vibevoice-cpp: TTSStream requested but no realtime model was loaded")
}
if req.Text == "" {
return fmt.Errorf("vibevoice-cpp: TTSStream requires text")
}
tmp, err := os.CreateTemp("", "vibevoice-cpp-stream-*.wav")
if err != nil {
return fmt.Errorf("vibevoice-cpp: tempfile: %w", err)
}
dst := tmp.Name()
_ = tmp.Close()
defer func() { _ = os.Remove(dst) }()
if err := v.TTS(&pb.TTSRequest{
Text: req.Text,
Voice: req.Voice,
Dst: dst,
Language: req.Language,
}); err != nil {
return err
}
wav, err := os.ReadFile(dst)
if err != nil {
return fmt.Errorf("vibevoice-cpp: read tempfile: %w", err)
}
// Streaming WAV header: declare 0xFFFFFFFF for chunk sizes so HTTP
// clients can start playback before they see the full PCM.
const streamingSize = 0xFFFFFFFF
hdr := laudio.NewWAVHeaderWithRate(streamingSize, vibevoiceSampleRate)
hdr.ChunkSize = streamingSize
hdrBuf := make([]byte, 0, laudio.WAVHeaderSize)
w := newByteWriter(&hdrBuf)
if err := hdr.Write(w); err != nil {
return fmt.Errorf("vibevoice-cpp: write WAV header: %w", err)
}
results <- hdrBuf
// PCM body: send in ~64 KB slices so the client gets multiple
// reply chunks (e2e harness asserts >=2 frames).
pcm := laudio.StripWAVHeader(wav)
const chunkBytes = 64 * 1024
for off := 0; off < len(pcm); off += chunkBytes {
end := off + chunkBytes
if end > len(pcm) {
end = len(pcm)
}
chunk := make([]byte, end-off)
copy(chunk, pcm[off:end])
results <- chunk
}
return nil
}
// byteWriter adapts a *[]byte to io.Writer so we can hand it to
// laudio.WAVHeader.Write without allocating a bytes.Buffer.
type byteWriter struct{ buf *[]byte }
func newByteWriter(b *[]byte) *byteWriter { return &byteWriter{buf: b} }
func (w *byteWriter) Write(p []byte) (int, error) {
*w.buf = append(*w.buf, p...)
return len(p), nil
}
func (v *VibevoiceCpp) AudioTranscription(req *pb.TranscriptRequest) (pb.TranscriptResult, error) {
if v.asrModel == "" {
return pb.TranscriptResult{}, fmt.Errorf("vibevoice-cpp: AudioTranscription requested but no ASR model was loaded")
}
if req.Dst == "" {
return pb.TranscriptResult{}, fmt.Errorf("vibevoice-cpp: TranscriptRequest.dst (audio path) is required")
}
wavPath, cleanup, err := prepareWavInput(req.Dst)
if err != nil {
return pb.TranscriptResult{}, fmt.Errorf("vibevoice-cpp: %w", err)
}
defer cleanup()
out, err := v.callASR(wavPath, asrMaxNewTokens)
if err != nil {
return pb.TranscriptResult{}, err
}
if out == "" {
return pb.TranscriptResult{}, nil
}
var segs []asrSegment
if err := json.Unmarshal([]byte(out), &segs); err != nil {
fmt.Fprintf(os.Stderr,
"[vibevoice-cpp] WARNING: vv_capi_asr returned non-JSON, falling back to single segment: %v\n", err)
return pb.TranscriptResult{
Segments: []*pb.TranscriptSegment{{Id: 0, Text: strings.TrimSpace(out)}},
Text: strings.TrimSpace(out),
}, nil
}
segments := make([]*pb.TranscriptSegment, 0, len(segs))
parts := make([]string, 0, len(segs))
var duration float32
for i, s := range segs {
// LocalAI's whisper backend uses int64 100ns ticks for
// Start/End (seconds * 1e7); follow the same convention so
// consumers can mix vibevoice and whisper transcripts.
segments = append(segments, &pb.TranscriptSegment{
Id: int32(i),
Text: s.Content,
Start: int64(s.Start * 1e7),
End: int64(s.End * 1e7),
Speaker: fmt.Sprintf("%d", s.Speaker),
})
parts = append(parts, strings.TrimSpace(s.Content))
if float32(s.End) > duration {
duration = float32(s.End)
}
}
return pb.TranscriptResult{
Segments: segments,
Text: strings.TrimSpace(strings.Join(parts, " ")),
Duration: duration,
}, nil
}
// Diarize runs vibevoice's ASR and projects the speaker-labelled segment
// list it returns natively. vibevoice.cpp's ASR prompt asks the model to
// emit `[{"Start":..,"End":..,"Speaker":..,"Content":..}]`, so diarization
// is a by-product of the same pass — we reuse callASR and re-shape.
//
// Speaker hints (num_speakers/min/max/threshold) and min_duration_on/off are
// not actionable here: vibevoice's model picks the speaker count itself and
// has no clustering knob. The HTTP layer documents this; we accept the
// fields for API symmetry and ignore them.
func (v *VibevoiceCpp) Diarize(req *pb.DiarizeRequest) (pb.DiarizeResponse, error) {
if v.asrModel == "" {
return pb.DiarizeResponse{}, fmt.Errorf("vibevoice-cpp: Diarize requires an ASR model (load options: type=asr)")
}
if req.Dst == "" {
return pb.DiarizeResponse{}, fmt.Errorf("vibevoice-cpp: DiarizeRequest.dst (audio path) is required")
}
wavPath, cleanup, err := prepareWavInput(req.Dst)
if err != nil {
return pb.DiarizeResponse{}, fmt.Errorf("vibevoice-cpp: %w", err)
}
defer cleanup()
out, err := v.callASR(wavPath, asrMaxNewTokens)
if err != nil {
return pb.DiarizeResponse{}, err
}
if out == "" {
return pb.DiarizeResponse{}, nil
}
var segs []asrSegment
if err := json.Unmarshal([]byte(out), &segs); err != nil {
// Mirror AudioTranscription's fallback: vibevoice's ASR sometimes
// emits free-form text instead of JSON for short or unusual audio.
// Surface a single unknown-speaker segment carrying the full text
// (when include_text is set) so the caller still gets coverage of
// the whole clip rather than a hard failure.
fmt.Fprintf(os.Stderr,
"[vibevoice-cpp] WARNING: vv_capi_asr returned non-JSON for diarization, falling back to single segment: %v\n", err)
text := strings.TrimSpace(out)
seg := &pb.DiarizeSegment{Id: 0, Speaker: "0"}
if req.IncludeText {
seg.Text = text
}
return pb.DiarizeResponse{
Segments: []*pb.DiarizeSegment{seg},
NumSpeakers: 1,
}, nil
}
speakers := make(map[int]struct{})
segments := make([]*pb.DiarizeSegment, 0, len(segs))
var duration float32
for i, s := range segs {
ds := &pb.DiarizeSegment{
Id: int32(i),
Start: float32(s.Start),
End: float32(s.End),
Speaker: fmt.Sprintf("%d", s.Speaker),
}
if req.IncludeText {
ds.Text = strings.TrimSpace(s.Content)
}
segments = append(segments, ds)
speakers[s.Speaker] = struct{}{}
if float32(s.End) > duration {
duration = float32(s.End)
}
}
return pb.DiarizeResponse{
Segments: segments,
NumSpeakers: int32(len(speakers)),
Duration: duration,
}, nil
}
// AudioTranscriptionStream wraps AudioTranscription so the streaming
// gRPC endpoint (server.go:AudioTranscriptionStream) sees its channel
// close and the client doesn't sit waiting until deadline. vibevoice's
// ASR doesn't expose token-level streaming - vv_capi_asr decodes the
// whole audio and returns a JSON segment list - so we run the offline
// transcription, emit each segment's content as a delta, then close
// with a final_result whose Text equals the concatenated deltas (the
// e2e harness asserts those match).
func (v *VibevoiceCpp) AudioTranscriptionStream(req *pb.TranscriptRequest, results chan *pb.TranscriptStreamResponse) error {
defer close(results)
res, err := v.AudioTranscription(req)
if err != nil {
return err
}
var assembled strings.Builder
for _, seg := range res.Segments {
if seg == nil {
continue
}
txt := strings.TrimSpace(seg.Text)
if txt == "" {
continue
}
delta := txt
if assembled.Len() > 0 {
delta = " " + txt
}
results <- &pb.TranscriptStreamResponse{Delta: delta}
assembled.WriteString(delta)
}
final := pb.TranscriptResult{
Segments: res.Segments,
Duration: res.Duration,
Language: res.Language,
Text: assembled.String(),
}
results <- &pb.TranscriptStreamResponse{FinalResult: &final}
return nil
}

View File

@@ -1,49 +0,0 @@
package main
// Started internally by LocalAI - one gRPC server per loaded model.
import (
"flag"
"os"
"github.com/ebitengine/purego"
grpc "github.com/mudler/LocalAI/pkg/grpc"
)
var (
addr = flag.String("addr", "localhost:50051", "the address to connect to")
)
type LibFuncs struct {
FuncPtr any
Name string
}
func main() {
libName := os.Getenv("VIBEVOICECPP_LIBRARY")
if libName == "" {
libName = "./libgovibevoicecpp-fallback.so"
}
lib, err := purego.Dlopen(libName, purego.RTLD_NOW|purego.RTLD_GLOBAL)
if err != nil {
panic(err)
}
libFuncs := []LibFuncs{
{&CppLoad, "vv_capi_load"},
{&CppTTS, "vv_capi_tts"},
{&CppASR, "vv_capi_asr"},
{&CppUnload, "vv_capi_unload"},
{&CppVersion, "vv_capi_version"},
}
for _, lf := range libFuncs {
purego.RegisterLibFunc(lf.FuncPtr, lib, lf.Name)
}
flag.Parse()
if err := grpc.StartServer(*addr, &VibevoiceCpp{}); err != nil {
panic(err)
}
}

View File

@@ -1,49 +0,0 @@
#!/bin/bash
set -ex
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="$CURDIR/libgovibevoicecpp-fallback.so"
if [ "$(uname)" != "Darwin" ]; then
if grep -q -e "\savx\s" /proc/cpuinfo ; then
echo "CPU: AVX found OK"
if [ -e $CURDIR/libgovibevoicecpp-avx.so ]; then
LIBRARY="$CURDIR/libgovibevoicecpp-avx.so"
fi
fi
if grep -q -e "\savx2\s" /proc/cpuinfo ; then
echo "CPU: AVX2 found OK"
if [ -e $CURDIR/libgovibevoicecpp-avx2.so ]; then
LIBRARY="$CURDIR/libgovibevoicecpp-avx2.so"
fi
fi
if grep -q -e "\savx512f\s" /proc/cpuinfo ; then
echo "CPU: AVX512F found OK"
if [ -e $CURDIR/libgovibevoicecpp-avx512.so ]; then
LIBRARY="$CURDIR/libgovibevoicecpp-avx512.so"
fi
fi
fi
export LD_LIBRARY_PATH=$CURDIR/lib:$LD_LIBRARY_PATH
export VIBEVOICECPP_LIBRARY=$LIBRARY
if [ -f $CURDIR/lib/ld.so ]; then
echo "Using lib/ld.so"
echo "Using library: $LIBRARY"
exec $CURDIR/lib/ld.so $CURDIR/vibevoice-cpp "$@"
fi
echo "Using library: $LIBRARY"
exec $CURDIR/vibevoice-cpp "$@"

View File

@@ -1,74 +0,0 @@
#!/bin/bash
set -e
CURDIR=$(dirname "$(realpath $0)")
echo "Running vibevoice-cpp backend tests..."
# Required env-vars (set automatically when missing):
# VIBEVOICE_MODEL_DIR : directory containing the gguf bundle.
# VIBEVOICE_BINARY : path to the built backend (default ./vibevoice-cpp)
#
# Tests skip when the model bundle is absent and the auto-download
# fails (e.g. no network on the runner) so local devs without HF access
# still get green compile output.
cd "$CURDIR"
if [ -z "$VIBEVOICE_MODEL_DIR" ]; then
export VIBEVOICE_MODEL_DIR="./vibevoice-models"
if [ ! -d "$VIBEVOICE_MODEL_DIR" ]; then
echo "Creating vibevoice-models directory for tests..."
mkdir -p "$VIBEVOICE_MODEL_DIR"
REPO_ID="mudler/vibevoice.cpp-models"
echo "Repository: ${REPO_ID}"
# Q4_K instead of Q8_0 for the ASR model: smaller download
# (10 GB vs 14 GB), fits on ubuntu-latest's free disk after the
# runner image is loaded. The unit/closed-loop test only needs
# decode quality, not Q8_0 precision.
FILES=(
"vibevoice-realtime-0.5B-q8_0.gguf"
"vibevoice-asr-q4_k.gguf"
"tokenizer.gguf"
"voice-en-Carter_man.gguf"
)
BASE_URL="https://huggingface.co/${REPO_ID}/resolve/main"
download_ok=1
for file in "${FILES[@]}"; do
dest="${VIBEVOICE_MODEL_DIR}/${file}"
if [ -f "${dest}" ]; then
echo " [skip] ${file} (already exists)"
else
echo " [download] ${file}..."
if ! curl -fL -o "${dest}" "${BASE_URL}/${file}" --progress-bar; then
echo " [warn] failed to download ${file} - network or HF unavailable"
rm -f "${dest}"
download_ok=0
break
fi
echo " [done] ${file}"
fi
done
if [ "$download_ok" != "1" ]; then
echo "vibevoice-cpp: model bundle unavailable - tests will skip model-dependent cases."
unset VIBEVOICE_MODEL_DIR
fi
fi
fi
# Ensure the per-variant .so the binary will dlopen actually exists -
# without one, every test will hit a Dlopen panic during server start.
if [ ! -f "${CURDIR}/libgovibevoicecpp-fallback.so" ]; then
echo "vibevoice-cpp: libgovibevoicecpp-fallback.so missing - run \`make\` first."
exit 1
fi
go test -v -timeout 900s .
echo "All vibevoice-cpp tests passed."

View File

@@ -1,382 +0,0 @@
package main
import (
"context"
"os"
"os/exec"
"path/filepath"
"regexp"
"strings"
"testing"
"time"
pb "github.com/mudler/LocalAI/pkg/grpc/proto"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
"google.golang.org/grpc"
"google.golang.org/grpc/credentials/insecure"
)
const (
testAddr = "localhost:50098"
startupWait = 5 * time.Second
)
func TestVibevoiceCpp(t *testing.T) {
RegisterFailHandler(Fail)
RunSpecs(t, "VibeVoice-cpp Backend Suite")
}
// modelDirOrSkip returns the staged model bundle dir, or Skip()s the
// current spec when VIBEVOICE_MODEL_DIR is unset / lacks the gguf
// files we need. Tests that don't depend on a model (Locking, error
// paths) don't call this.
func modelDirOrSkip() string {
dir := os.Getenv("VIBEVOICE_MODEL_DIR")
if dir == "" {
Skip("VIBEVOICE_MODEL_DIR not set, skipping model-dependent specs")
}
if _, err := os.Stat(filepath.Join(dir, "tokenizer.gguf")); os.IsNotExist(err) {
Skip("tokenizer.gguf missing in " + dir)
}
tts, _ := filepath.Glob(filepath.Join(dir, "vibevoice-realtime-*.gguf"))
asr, _ := filepath.Glob(filepath.Join(dir, "vibevoice-asr-*.gguf"))
if len(tts) == 0 && len(asr) == 0 {
Skip("neither realtime TTS nor ASR gguf found in " + dir)
}
return dir
}
// startServer launches the prebuilt backend binary and returns a
// running *exec.Cmd. test.sh ensures `./vibevoice-cpp` is built; if
// it isn't, every gRPC spec is skipped with a clear reason.
func startServer() *exec.Cmd {
binary := os.Getenv("VIBEVOICE_BINARY")
if binary == "" {
binary = "./vibevoice-cpp"
}
if _, err := os.Stat(binary); os.IsNotExist(err) {
Skip("backend binary not found at " + binary)
}
cmd := exec.Command(binary, "--addr", testAddr)
cmd.Stdout = os.Stderr
cmd.Stderr = os.Stderr
Expect(cmd.Start()).To(Succeed())
time.Sleep(startupWait)
return cmd
}
func stopServer(cmd *exec.Cmd) {
if cmd == nil || cmd.Process == nil {
return
}
_ = cmd.Process.Kill()
_, _ = cmd.Process.Wait()
}
func dialGRPC() *grpc.ClientConn {
conn, err := grpc.Dial(testAddr,
grpc.WithTransportCredentials(insecure.NewCredentials()),
grpc.WithDefaultCallOptions(
grpc.MaxCallRecvMsgSize(50*1024*1024),
grpc.MaxCallSendMsgSize(50*1024*1024),
),
)
Expect(err).ToNot(HaveOccurred())
return conn
}
var _ = Describe("VibeVoice-cpp", func() {
Context("backend semantics (no purego load needed)", func() {
It("is locking - the engine has process-global state", func() {
Expect((&VibevoiceCpp{}).Locking()).To(BeTrue())
})
It("rejects Load with empty ModelFile", func() {
err := (&VibevoiceCpp{}).Load(&pb.ModelOptions{})
Expect(err).To(HaveOccurred())
Expect(err.Error()).To(ContainSubstring("ModelFile"))
})
It("rejects TTS without a loaded TTS model", func() {
err := (&VibevoiceCpp{}).TTS(&pb.TTSRequest{
Text: "no model loaded",
Dst: "/tmp/should-not-be-written.wav",
})
Expect(err).To(HaveOccurred())
})
It("rejects AudioTranscription without a loaded ASR model", func() {
_, err := (&VibevoiceCpp{}).AudioTranscription(&pb.TranscriptRequest{
Dst: "/tmp/some.wav",
})
Expect(err).To(HaveOccurred())
})
It("closes the channel and errors on TTSStream without a loaded model", func() {
ch := make(chan []byte, 4)
err := (&VibevoiceCpp{}).TTSStream(&pb.TTSRequest{
Text: "no model loaded",
Dst: "/tmp/should-not-be-written.wav",
}, ch)
Expect(err).To(HaveOccurred())
// Server hangs forever if the channel stays open; this guard
// is what regresses the e2e DeadlineExceeded we're fixing.
_, ok := <-ch
Expect(ok).To(BeFalse(), "TTSStream must close results channel even on error")
})
// parseOptions + slot fill is the source of the closed-loop CI
// regression where ModelFile=tts.gguf + Options[asr_model=...]
// resulted in a load with empty tts slot. These specs assert
// the slot resolution before we ever call into purego.
Describe("ModelFile slot resolution", func() {
It("fills tts slot from ModelFile when only asr_model is in Options", func() {
v := &VibevoiceCpp{}
v.modelRoot = "/abs/root"
role := v.parseOptions([]string{"asr_model=/abs/root/asr.gguf", "tokenizer=/abs/root/tokenizer.gguf"}, v.modelRoot)
Expect(v.asrModel).To(Equal("/abs/root/asr.gguf"))
Expect(v.ttsModel).To(BeEmpty())
Expect(role).To(BeEmpty())
// Mirror the Load() default-fill block:
if v.ttsModel == "" {
v.ttsModel = "/abs/root/tts.gguf"
}
Expect(v.ttsModel).To(Equal("/abs/root/tts.gguf"))
Expect(v.asrModel).To(Equal("/abs/root/asr.gguf"))
})
It("fills asr slot from ModelFile when type=asr is set", func() {
v := &VibevoiceCpp{}
v.modelRoot = "/abs/root"
role := v.parseOptions([]string{"type=asr", "tokenizer=/abs/root/tokenizer.gguf"}, v.modelRoot)
Expect(role).To(Equal("asr"))
Expect(v.asrModel).To(BeEmpty())
Expect(v.ttsModel).To(BeEmpty())
})
It("respects explicit tts_model override over ModelFile", func() {
v := &VibevoiceCpp{}
v.modelRoot = "/abs/root"
_ = v.parseOptions([]string{"tts_model=/abs/root/alt.gguf"}, v.modelRoot)
Expect(v.ttsModel).To(Equal("/abs/root/alt.gguf"))
})
It("accepts colon-separated options too", func() {
v := &VibevoiceCpp{}
v.modelRoot = "/abs/root"
role := v.parseOptions([]string{"type:asr", "tokenizer:/abs/root/tokenizer.gguf"}, v.modelRoot)
Expect(role).To(Equal("asr"))
Expect(v.tokenizer).To(Equal("/abs/root/tokenizer.gguf"))
})
})
// The gallery flow puts everything under <models_dir>/<entry>/,
// and parameters/options carry paths *relative* to <models_dir>.
// LocalAI core fills opts.ModelPath = <models_dir>; the backend
// must resolve every relative path against that root, never CWD.
Describe("resolvePath (relative-to-modelRoot)", func() {
It("joins relative path onto relTo", func() {
Expect(resolvePath("vibevoice-cpp/tokenizer.gguf", "/data/models")).
To(Equal("/data/models/vibevoice-cpp/tokenizer.gguf"))
})
It("passes absolute paths through unchanged", func() {
Expect(resolvePath("/abs/somewhere/tokenizer.gguf", "/data/models")).
To(Equal("/abs/somewhere/tokenizer.gguf"))
})
It("returns input unchanged when relTo is empty", func() {
Expect(resolvePath("vibevoice-cpp/tokenizer.gguf", "")).
To(Equal("vibevoice-cpp/tokenizer.gguf"))
})
It("returns empty input unchanged", func() {
Expect(resolvePath("", "/data/models")).To(BeEmpty())
})
It("does not consult CWD - bare filenames stay relative to modelRoot", func() {
// Even if the test runs in a directory containing a
// file with this name, the lookup must not fall back
// to CWD. This is the trap the production gallery flow
// would otherwise hit when LocalAI is launched from a
// directory that happens to contain a same-named file.
prev, _ := os.Getwd()
DeferCleanup(func() { _ = os.Chdir(prev) })
tmpCWD, err := os.MkdirTemp("", "vv-cwd-*")
Expect(err).ToNot(HaveOccurred())
DeferCleanup(func() { _ = os.RemoveAll(tmpCWD) })
Expect(os.WriteFile(filepath.Join(tmpCWD, "tokenizer.gguf"),
[]byte("not the real one"), 0o644)).To(Succeed())
Expect(os.Chdir(tmpCWD)).To(Succeed())
got := resolvePath("tokenizer.gguf", "/data/models")
Expect(got).To(Equal("/data/models/tokenizer.gguf"))
})
})
// Round-trip the gallery layout: relative paths in Options +
// an absolute ModelFile (as LocalAI core delivers them) end
// up resolved correctly inside the backend struct.
It("Load resolves relative Options paths against opts.ModelPath", func() {
tmpDir, err := os.MkdirTemp("", "vv-relpath-*")
Expect(err).ToNot(HaveOccurred())
DeferCleanup(func() { _ = os.RemoveAll(tmpDir) })
// Lay out the bundle exactly as the gallery would after install:
// <modelpath>/vibevoice-cpp/{tts,tokenizer,voice}.gguf
subDir := filepath.Join(tmpDir, "vibevoice-cpp")
Expect(os.MkdirAll(subDir, 0o755)).To(Succeed())
tts := filepath.Join(subDir, "vibevoice-realtime-stub.gguf")
tok := filepath.Join(subDir, "tokenizer.gguf")
voice := filepath.Join(subDir, "voice.gguf")
for _, p := range []string{tts, tok, voice} {
Expect(os.WriteFile(p, []byte("stub"), 0o644)).To(Succeed())
}
// Mirror Load()'s pre-purego prefix: parse + slot fill.
v := &VibevoiceCpp{}
modelFile := tts // core delivers this as an abspath already
v.modelRoot = tmpDir
role := v.parseOptions([]string{
"tokenizer=vibevoice-cpp/tokenizer.gguf",
"voice=vibevoice-cpp/voice.gguf",
}, v.modelRoot)
Expect(role).To(BeEmpty())
if v.ttsModel == "" {
v.ttsModel = modelFile
}
Expect(v.ttsModel).To(Equal(tts))
Expect(v.tokenizer).To(Equal(tok))
Expect(v.voice).To(Equal(voice))
Expect(v.asrModel).To(BeEmpty())
})
It("closes the channel and errors on AudioTranscriptionStream without a loaded model", func() {
ch := make(chan *pb.TranscriptStreamResponse, 4)
err := (&VibevoiceCpp{}).AudioTranscriptionStream(&pb.TranscriptRequest{
Dst: "/tmp/some.wav",
}, ch)
Expect(err).To(HaveOccurred())
_, ok := <-ch
Expect(ok).To(BeFalse(), "AudioTranscriptionStream must close results channel even on error")
})
})
Context("gRPC server lifecycle", func() {
var cmd *exec.Cmd
AfterEach(func() {
stopServer(cmd)
cmd = nil
})
It("answers Health checks", func() {
cmd = startServer()
conn := dialGRPC()
defer func() { _ = conn.Close() }()
resp, err := pb.NewBackendClient(conn).Health(context.Background(), &pb.HealthMessage{})
Expect(err).ToNot(HaveOccurred())
Expect(string(resp.Message)).To(Equal("OK"))
})
It("loads the realtime TTS model", func() {
dir := modelDirOrSkip()
tts, _ := filepath.Glob(filepath.Join(dir, "vibevoice-realtime-*.gguf"))
if len(tts) == 0 {
Skip("realtime TTS gguf missing")
}
cmd = startServer()
conn := dialGRPC()
defer func() { _ = conn.Close() }()
// Mirror the gallery contract: ModelFile is whatever LocalAI
// core hands us; ModelPath is the models root; Options[]
// carry paths relative to ModelPath.
resp, err := pb.NewBackendClient(conn).LoadModel(context.Background(), &pb.ModelOptions{
ModelFile: filepath.Base(tts[0]),
ModelPath: dir,
Threads: 4,
Options: []string{"tokenizer=tokenizer.gguf"},
})
Expect(err).ToNot(HaveOccurred())
Expect(resp.Success).To(BeTrue(), "LoadModel msg=%q", resp.Message)
})
It("runs a closed-loop TTS -> ASR with >=80% word recall", func() {
dir := modelDirOrSkip()
tts, _ := filepath.Glob(filepath.Join(dir, "vibevoice-realtime-*.gguf"))
asr, _ := filepath.Glob(filepath.Join(dir, "vibevoice-asr-*.gguf"))
if len(tts) == 0 || len(asr) == 0 {
Skip("closed-loop needs both realtime TTS and ASR ggufs")
}
tmpDir, err := os.MkdirTemp("", "vibevoice-cpp-closedloop-*")
Expect(err).ToNot(HaveOccurred())
DeferCleanup(func() { _ = os.RemoveAll(tmpDir) })
wav := filepath.Join(tmpDir, "say.wav")
cmd = startServer()
conn := dialGRPC()
defer func() { _ = conn.Close() }()
client := pb.NewBackendClient(conn)
// Gallery convention: ModelPath is the models root, every
// path inside Options[] is relative to it.
voiceMatches, _ := filepath.Glob(filepath.Join(dir, "voice-*.gguf"))
loadOpts := &pb.ModelOptions{
ModelFile: filepath.Base(tts[0]),
ModelPath: dir,
Threads: 4,
Options: []string{
"asr_model=" + filepath.Base(asr[0]),
"tokenizer=tokenizer.gguf",
},
}
if len(voiceMatches) > 0 {
loadOpts.Options = append(loadOpts.Options, "voice="+filepath.Base(voiceMatches[0]))
}
loadResp, err := client.LoadModel(context.Background(), loadOpts)
Expect(err).ToNot(HaveOccurred())
Expect(loadResp.Success).To(BeTrue(), "LoadModel msg=%q", loadResp.Message)
srcText := "Hello world this is a test of the synthesis system."
_, err = client.TTS(context.Background(), &pb.TTSRequest{
Text: srcText,
Dst: wav,
})
Expect(err).ToNot(HaveOccurred())
info, err := os.Stat(wav)
Expect(err).ToNot(HaveOccurred())
Expect(info.Size()).To(BeNumerically(">=", 1000),
"TTS produced suspiciously small wav (%d bytes)", info.Size())
resp, err := client.AudioTranscription(context.Background(), &pb.TranscriptRequest{
Dst: wav,
})
Expect(err).ToNot(HaveOccurred())
got := strings.ToLower(resp.Text)
GinkgoWriter.Printf("source : %s\n", srcText)
GinkgoWriter.Printf("transcribed: %s\n", got)
wordRE := regexp.MustCompile(`[a-z]+`)
srcWords := wordRE.FindAllString(strings.ToLower(srcText), -1)
Expect(srcWords).ToNot(BeEmpty())
hits := 0
for _, w := range srcWords {
if strings.Contains(got, w) {
hits++
}
}
recall := float64(hits) / float64(len(srcWords))
GinkgoWriter.Printf("recall: %d/%d = %.2f%%\n", hits, len(srcWords), recall*100)
Expect(recall).To(BeNumerically(">=", 0.80),
"closed-loop recall too low: %d/%d = %.2f%%",
hits, len(srcWords), recall*100)
})
})
})

View File

@@ -5,7 +5,7 @@ set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
add_subdirectory(./sources/whisper.cpp)
add_library(gowhisper MODULE cpp/gowhisper.cpp)
add_library(gowhisper MODULE gowhisper.cpp)
target_link_libraries(gowhisper PRIVATE whisper ggml)
if(CMAKE_CXX_COMPILER_ID MATCHES "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 9.0)

View File

@@ -8,7 +8,7 @@ JOBS?=$(shell nproc --ignore=1)
# whisper.cpp version
WHISPER_REPO?=https://github.com/ggml-org/whisper.cpp
WHISPER_CPP_VERSION?=4bf733672b2871d4153158af4f621a6dd9104f4a
WHISPER_CPP_VERSION?=fc674574ca27cac59a15e5b22a09b9d9ad62aafe
SO_TARGET?=libgowhisper.so
CMAKE_ARGS+=-DBUILD_SHARED_LIBS=OFF
@@ -111,7 +111,7 @@ libgowhisper-fallback.so: sources/whisper.cpp
SO_TARGET=libgowhisper-fallback.so CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off" $(MAKE) libgowhisper-custom
rm -rfv build*
libgowhisper-custom: CMakeLists.txt cpp/gowhisper.cpp cpp/gowhisper.h
libgowhisper-custom: CMakeLists.txt gowhisper.cpp gowhisper.h
mkdir -p build-$(SO_TARGET) && \
cd build-$(SO_TARGET) && \
cmake .. $(CMAKE_ARGS) && \

View File

@@ -139,10 +139,7 @@ func (w *Whisper) AudioTranscription(opts *pb.TranscriptRequest) (pb.TranscriptR
// segment start/end conversion factor taken from https://github.com/ggml-org/whisper.cpp/blob/master/examples/cli/cli.cpp#L895
s := CppGetSegmentStart(i) * (10000000)
t := CppGetSegmentEnd(i) * (10000000)
// whisper.cpp can emit bytes that aren't valid UTF-8 (e.g. a multibyte
// codepoint split across token boundaries); protobuf string fields
// reject those at marshal time. Scrub before the value escapes cgo.
txt := strings.ToValidUTF8(strings.Clone(CppGetSegmentText(i)), "<22>")
txt := strings.Clone(CppGetSegmentText(i))
tokens := make([]int32, CppNTokens(i))
if opts.Diarize && CppGetSegmentSpeakerTurnNext(i) {

View File

@@ -263,8 +263,6 @@
amd: "rocm-vllm"
intel: "intel-vllm"
nvidia-cuda-12: "cuda12-vllm"
nvidia-cuda-13: "cuda13-vllm"
nvidia-l4t-cuda-13: "cuda13-nvidia-l4t-arm64-vllm"
cpu: "cpu-vllm"
- &sglang
name: "sglang"
@@ -287,7 +285,6 @@
amd: "rocm-sglang"
intel: "intel-sglang"
nvidia-cuda-12: "cuda12-sglang"
nvidia-l4t-cuda-13: "cuda13-nvidia-l4t-arm64-sglang"
cpu: "cpu-sglang"
- &vllm-omni
name: "vllm-omni"
@@ -314,8 +311,6 @@
nvidia: "cuda12-vllm-omni"
amd: "rocm-vllm-omni"
nvidia-cuda-12: "cuda12-vllm-omni"
nvidia-cuda-13: "cuda13-vllm-omni"
nvidia-l4t-cuda-13: "cuda13-nvidia-l4t-arm64-vllm-omni"
- &mlx
name: "mlx"
uri: "quay.io/go-skynet/local-ai-backends:latest-metal-darwin-arm64-mlx"
@@ -572,66 +567,6 @@
nvidia-l4t: "nvidia-l4t-arm64-qwen3-tts-cpp"
nvidia-l4t-cuda-12: "nvidia-l4t-arm64-qwen3-tts-cpp"
nvidia-l4t-cuda-13: "cuda13-nvidia-l4t-arm64-qwen3-tts-cpp"
- &vibevoicecpp
name: "vibevoice-cpp"
description: |
vibevoice.cpp C++ backend using GGML. Native C++ port of Microsoft VibeVoice for both
text-to-speech (with voice cloning via voice prompt GGUFs) and long-form ASR with
speaker diarization. Outputs 24kHz mono WAV; ASR returns per-speaker JSON segments.
urls:
- https://github.com/mudler/vibevoice.cpp
tags:
- text-to-speech
- tts
- speech-to-text
- asr
- voice-cloning
- diarization
alias: "vibevoice-cpp"
capabilities:
default: "cpu-vibevoice-cpp"
nvidia: "cuda12-vibevoice-cpp"
nvidia-cuda-13: "cuda13-vibevoice-cpp"
nvidia-cuda-12: "cuda12-vibevoice-cpp"
intel: "intel-sycl-f16-vibevoice-cpp"
metal: "metal-vibevoice-cpp"
amd: "rocm-vibevoice-cpp"
vulkan: "vulkan-vibevoice-cpp"
nvidia-l4t: "nvidia-l4t-arm64-vibevoice-cpp"
nvidia-l4t-cuda-12: "nvidia-l4t-arm64-vibevoice-cpp"
nvidia-l4t-cuda-13: "cuda13-nvidia-l4t-arm64-vibevoice-cpp"
- &localvqecpp
name: "localvqe"
description: |
LocalVQE C++ backend using GGML — joint acoustic echo cancellation, noise
suppression, and dereverberation (DeepVQE-style architecture). 16 kHz mono
in / out, supports both batch and low-latency streaming. Implements the
audio-transform capability.
urls:
- https://github.com/localai-org/LocalVQE
tags:
- audio-transform
- aec
- acoustic-echo-cancellation
- noise-suppression
- dereverberation
license: apache2
alias: "localvqe"
# Upstream LocalVQE only supports CPU and Vulkan; no CUDA/ROCm/SYCL/Metal
# builds. GPU-class hardware that exposes a Vulkan ICD (NVIDIA, AMD, Intel
# discrete + iGPU, Tegra) routes to the Vulkan image; everything else
# falls back to the CPU build, which is already ~9× realtime on a desktop.
capabilities:
default: "cpu-localvqe"
nvidia: "vulkan-localvqe"
nvidia-cuda-12: "vulkan-localvqe"
nvidia-cuda-13: "vulkan-localvqe"
intel: "vulkan-localvqe"
amd: "vulkan-localvqe"
vulkan: "vulkan-localvqe"
nvidia-l4t: "vulkan-localvqe"
nvidia-l4t-cuda-12: "vulkan-localvqe"
nvidia-l4t-cuda-13: "vulkan-localvqe"
- &faster-whisper
icon: https://avatars.githubusercontent.com/u/1520500?s=200&v=4
description: |
@@ -1673,20 +1608,6 @@
mirrors:
- localai/localai-backends:master-nvidia-l4t-cuda-13-arm64-turboquant
## whisper
- !!merge <<: *whispercpp
name: "whisper-development"
capabilities:
default: "cpu-whisper-development"
nvidia: "cuda12-whisper-development"
intel: "intel-sycl-f16-whisper-development"
metal: "metal-whisper-development"
amd: "rocm-whisper-development"
vulkan: "vulkan-whisper-development"
nvidia-l4t: "nvidia-l4t-arm64-whisper-development"
nvidia-cuda-13: "cuda13-whisper-development"
nvidia-cuda-12: "cuda12-whisper-development"
nvidia-l4t-cuda-12: "nvidia-l4t-arm64-whisper-development"
nvidia-l4t-cuda-13: "cuda13-nvidia-l4t-arm64-whisper-development"
- !!merge <<: *whispercpp
name: "nvidia-l4t-arm64-whisper"
uri: "quay.io/go-skynet/local-ai-backends:latest-nvidia-l4t-arm64-whisper"
@@ -1893,25 +1814,12 @@
nvidia: "cuda12-vllm-development"
amd: "rocm-vllm-development"
intel: "intel-vllm-development"
nvidia-cuda-12: "cuda12-vllm-development"
nvidia-cuda-13: "cuda13-vllm-development"
nvidia-l4t-cuda-13: "cuda13-nvidia-l4t-arm64-vllm-development"
cpu: "cpu-vllm-development"
- !!merge <<: *vllm
name: "cuda12-vllm"
uri: "quay.io/go-skynet/local-ai-backends:latest-gpu-nvidia-cuda-12-vllm"
mirrors:
- localai/localai-backends:latest-gpu-nvidia-cuda-12-vllm
- !!merge <<: *vllm
name: "cuda13-vllm"
uri: "quay.io/go-skynet/local-ai-backends:latest-gpu-nvidia-cuda-13-vllm"
mirrors:
- localai/localai-backends:latest-gpu-nvidia-cuda-13-vllm
- !!merge <<: *vllm
name: "cuda13-nvidia-l4t-arm64-vllm"
uri: "quay.io/go-skynet/local-ai-backends:latest-nvidia-l4t-cuda-13-arm64-vllm"
mirrors:
- localai/localai-backends:latest-nvidia-l4t-cuda-13-arm64-vllm
- !!merge <<: *vllm
name: "rocm-vllm"
uri: "quay.io/go-skynet/local-ai-backends:latest-gpu-rocm-hipblas-vllm"
@@ -1932,16 +1840,6 @@
uri: "quay.io/go-skynet/local-ai-backends:master-gpu-nvidia-cuda-12-vllm"
mirrors:
- localai/localai-backends:master-gpu-nvidia-cuda-12-vllm
- !!merge <<: *vllm
name: "cuda13-vllm-development"
uri: "quay.io/go-skynet/local-ai-backends:master-gpu-nvidia-cuda-13-vllm"
mirrors:
- localai/localai-backends:master-gpu-nvidia-cuda-13-vllm
- !!merge <<: *vllm
name: "cuda13-nvidia-l4t-arm64-vllm-development"
uri: "quay.io/go-skynet/local-ai-backends:master-nvidia-l4t-cuda-13-arm64-vllm"
mirrors:
- localai/localai-backends:master-nvidia-l4t-cuda-13-arm64-vllm
- !!merge <<: *vllm
name: "rocm-vllm-development"
uri: "quay.io/go-skynet/local-ai-backends:master-gpu-rocm-hipblas-vllm"
@@ -1964,19 +1862,12 @@
nvidia: "cuda12-sglang-development"
amd: "rocm-sglang-development"
intel: "intel-sglang-development"
nvidia-cuda-12: "cuda12-sglang-development"
nvidia-l4t-cuda-13: "cuda13-nvidia-l4t-arm64-sglang-development"
cpu: "cpu-sglang-development"
- !!merge <<: *sglang
name: "cuda12-sglang"
uri: "quay.io/go-skynet/local-ai-backends:latest-gpu-nvidia-cuda-12-sglang"
mirrors:
- localai/localai-backends:latest-gpu-nvidia-cuda-12-sglang
- !!merge <<: *sglang
name: "cuda13-nvidia-l4t-arm64-sglang"
uri: "quay.io/go-skynet/local-ai-backends:latest-nvidia-l4t-cuda-13-arm64-sglang"
mirrors:
- localai/localai-backends:latest-nvidia-l4t-cuda-13-arm64-sglang
- !!merge <<: *sglang
name: "rocm-sglang"
uri: "quay.io/go-skynet/local-ai-backends:latest-gpu-rocm-hipblas-sglang"
@@ -1997,11 +1888,6 @@
uri: "quay.io/go-skynet/local-ai-backends:master-gpu-nvidia-cuda-12-sglang"
mirrors:
- localai/localai-backends:master-gpu-nvidia-cuda-12-sglang
- !!merge <<: *sglang
name: "cuda13-nvidia-l4t-arm64-sglang-development"
uri: "quay.io/go-skynet/local-ai-backends:master-nvidia-l4t-cuda-13-arm64-sglang"
mirrors:
- localai/localai-backends:master-nvidia-l4t-cuda-13-arm64-sglang
- !!merge <<: *sglang
name: "rocm-sglang-development"
uri: "quay.io/go-skynet/local-ai-backends:master-gpu-rocm-hipblas-sglang"
@@ -2024,23 +1910,11 @@
nvidia: "cuda12-vllm-omni-development"
amd: "rocm-vllm-omni-development"
nvidia-cuda-12: "cuda12-vllm-omni-development"
nvidia-cuda-13: "cuda13-vllm-omni-development"
nvidia-l4t-cuda-13: "cuda13-nvidia-l4t-arm64-vllm-omni-development"
- !!merge <<: *vllm-omni
name: "cuda12-vllm-omni"
uri: "quay.io/go-skynet/local-ai-backends:latest-gpu-nvidia-cuda-12-vllm-omni"
mirrors:
- localai/localai-backends:latest-gpu-nvidia-cuda-12-vllm-omni
- !!merge <<: *vllm-omni
name: "cuda13-vllm-omni"
uri: "quay.io/go-skynet/local-ai-backends:latest-gpu-nvidia-cuda-13-vllm-omni"
mirrors:
- localai/localai-backends:latest-gpu-nvidia-cuda-13-vllm-omni
- !!merge <<: *vllm-omni
name: "cuda13-nvidia-l4t-arm64-vllm-omni"
uri: "quay.io/go-skynet/local-ai-backends:latest-nvidia-l4t-cuda-13-arm64-vllm-omni"
mirrors:
- localai/localai-backends:latest-nvidia-l4t-cuda-13-arm64-vllm-omni
- !!merge <<: *vllm-omni
name: "rocm-vllm-omni"
uri: "quay.io/go-skynet/local-ai-backends:latest-gpu-rocm-hipblas-vllm-omni"
@@ -2051,16 +1925,6 @@
uri: "quay.io/go-skynet/local-ai-backends:master-gpu-nvidia-cuda-12-vllm-omni"
mirrors:
- localai/localai-backends:master-gpu-nvidia-cuda-12-vllm-omni
- !!merge <<: *vllm-omni
name: "cuda13-vllm-omni-development"
uri: "quay.io/go-skynet/local-ai-backends:master-gpu-nvidia-cuda-13-vllm-omni"
mirrors:
- localai/localai-backends:master-gpu-nvidia-cuda-13-vllm-omni
- !!merge <<: *vllm-omni
name: "cuda13-nvidia-l4t-arm64-vllm-omni-development"
uri: "quay.io/go-skynet/local-ai-backends:master-nvidia-l4t-cuda-13-arm64-vllm-omni"
mirrors:
- localai/localai-backends:master-nvidia-l4t-cuda-13-arm64-vllm-omni
- !!merge <<: *vllm-omni
name: "rocm-vllm-omni-development"
uri: "quay.io/go-skynet/local-ai-backends:master-gpu-rocm-hipblas-vllm-omni"
@@ -2716,128 +2580,6 @@
uri: "quay.io/go-skynet/local-ai-backends:master-gpu-nvidia-cuda-13-qwen3-tts-cpp"
mirrors:
- localai/localai-backends:master-gpu-nvidia-cuda-13-qwen3-tts-cpp
## vibevoice-cpp
- !!merge <<: *vibevoicecpp
name: "nvidia-l4t-arm64-vibevoice-cpp"
uri: "quay.io/go-skynet/local-ai-backends:latest-nvidia-l4t-arm64-vibevoice-cpp"
mirrors:
- localai/localai-backends:latest-nvidia-l4t-arm64-vibevoice-cpp
- !!merge <<: *vibevoicecpp
name: "nvidia-l4t-arm64-vibevoice-cpp-development"
uri: "quay.io/go-skynet/local-ai-backends:master-nvidia-l4t-arm64-vibevoice-cpp"
mirrors:
- localai/localai-backends:master-nvidia-l4t-arm64-vibevoice-cpp
- !!merge <<: *vibevoicecpp
name: "cuda13-nvidia-l4t-arm64-vibevoice-cpp"
uri: "quay.io/go-skynet/local-ai-backends:latest-nvidia-l4t-cuda-13-arm64-vibevoice-cpp"
mirrors:
- localai/localai-backends:latest-nvidia-l4t-cuda-13-arm64-vibevoice-cpp
- !!merge <<: *vibevoicecpp
name: "cuda13-nvidia-l4t-arm64-vibevoice-cpp-development"
uri: "quay.io/go-skynet/local-ai-backends:master-nvidia-l4t-cuda-13-arm64-vibevoice-cpp"
mirrors:
- localai/localai-backends:master-nvidia-l4t-cuda-13-arm64-vibevoice-cpp
- !!merge <<: *vibevoicecpp
name: "cpu-vibevoice-cpp"
uri: "quay.io/go-skynet/local-ai-backends:latest-cpu-vibevoice-cpp"
mirrors:
- localai/localai-backends:latest-cpu-vibevoice-cpp
- !!merge <<: *vibevoicecpp
name: "metal-vibevoice-cpp"
uri: "quay.io/go-skynet/local-ai-backends:latest-metal-darwin-arm64-vibevoice-cpp"
mirrors:
- localai/localai-backends:latest-metal-darwin-arm64-vibevoice-cpp
- !!merge <<: *vibevoicecpp
name: "metal-vibevoice-cpp-development"
uri: "quay.io/go-skynet/local-ai-backends:master-metal-darwin-arm64-vibevoice-cpp"
mirrors:
- localai/localai-backends:master-metal-darwin-arm64-vibevoice-cpp
- !!merge <<: *vibevoicecpp
name: "cpu-vibevoice-cpp-development"
uri: "quay.io/go-skynet/local-ai-backends:master-cpu-vibevoice-cpp"
mirrors:
- localai/localai-backends:master-cpu-vibevoice-cpp
- !!merge <<: *vibevoicecpp
name: "cuda12-vibevoice-cpp"
uri: "quay.io/go-skynet/local-ai-backends:latest-gpu-nvidia-cuda-12-vibevoice-cpp"
mirrors:
- localai/localai-backends:latest-gpu-nvidia-cuda-12-vibevoice-cpp
- !!merge <<: *vibevoicecpp
name: "rocm-vibevoice-cpp"
uri: "quay.io/go-skynet/local-ai-backends:latest-gpu-rocm-hipblas-vibevoice-cpp"
mirrors:
- localai/localai-backends:latest-gpu-rocm-hipblas-vibevoice-cpp
- !!merge <<: *vibevoicecpp
name: "intel-sycl-f32-vibevoice-cpp"
uri: "quay.io/go-skynet/local-ai-backends:latest-gpu-intel-sycl-f32-vibevoice-cpp"
mirrors:
- localai/localai-backends:latest-gpu-intel-sycl-f32-vibevoice-cpp
- !!merge <<: *vibevoicecpp
name: "intel-sycl-f16-vibevoice-cpp"
uri: "quay.io/go-skynet/local-ai-backends:latest-gpu-intel-sycl-f16-vibevoice-cpp"
mirrors:
- localai/localai-backends:latest-gpu-intel-sycl-f16-vibevoice-cpp
- !!merge <<: *vibevoicecpp
name: "vulkan-vibevoice-cpp"
uri: "quay.io/go-skynet/local-ai-backends:latest-gpu-vulkan-vibevoice-cpp"
mirrors:
- localai/localai-backends:latest-gpu-vulkan-vibevoice-cpp
- !!merge <<: *vibevoicecpp
name: "vulkan-vibevoice-cpp-development"
uri: "quay.io/go-skynet/local-ai-backends:master-gpu-vulkan-vibevoice-cpp"
mirrors:
- localai/localai-backends:master-gpu-vulkan-vibevoice-cpp
- !!merge <<: *vibevoicecpp
name: "cuda12-vibevoice-cpp-development"
uri: "quay.io/go-skynet/local-ai-backends:master-gpu-nvidia-cuda-12-vibevoice-cpp"
mirrors:
- localai/localai-backends:master-gpu-nvidia-cuda-12-vibevoice-cpp
- !!merge <<: *vibevoicecpp
name: "rocm-vibevoice-cpp-development"
uri: "quay.io/go-skynet/local-ai-backends:master-gpu-rocm-hipblas-vibevoice-cpp"
mirrors:
- localai/localai-backends:master-gpu-rocm-hipblas-vibevoice-cpp
- !!merge <<: *vibevoicecpp
name: "intel-sycl-f32-vibevoice-cpp-development"
uri: "quay.io/go-skynet/local-ai-backends:master-gpu-intel-sycl-f32-vibevoice-cpp"
mirrors:
- localai/localai-backends:master-gpu-intel-sycl-f32-vibevoice-cpp
- !!merge <<: *vibevoicecpp
name: "intel-sycl-f16-vibevoice-cpp-development"
uri: "quay.io/go-skynet/local-ai-backends:master-gpu-intel-sycl-f16-vibevoice-cpp"
mirrors:
- localai/localai-backends:master-gpu-intel-sycl-f16-vibevoice-cpp
- !!merge <<: *vibevoicecpp
name: "cuda13-vibevoice-cpp"
uri: "quay.io/go-skynet/local-ai-backends:latest-gpu-nvidia-cuda-13-vibevoice-cpp"
mirrors:
- localai/localai-backends:latest-gpu-nvidia-cuda-13-vibevoice-cpp
- !!merge <<: *vibevoicecpp
name: "cuda13-vibevoice-cpp-development"
uri: "quay.io/go-skynet/local-ai-backends:master-gpu-nvidia-cuda-13-vibevoice-cpp"
mirrors:
- localai/localai-backends:master-gpu-nvidia-cuda-13-vibevoice-cpp
## localvqe
- !!merge <<: *localvqecpp
name: "cpu-localvqe"
uri: "quay.io/go-skynet/local-ai-backends:latest-cpu-localvqe"
mirrors:
- localai/localai-backends:latest-cpu-localvqe
- !!merge <<: *localvqecpp
name: "cpu-localvqe-development"
uri: "quay.io/go-skynet/local-ai-backends:master-cpu-localvqe"
mirrors:
- localai/localai-backends:master-cpu-localvqe
- !!merge <<: *localvqecpp
name: "vulkan-localvqe"
uri: "quay.io/go-skynet/local-ai-backends:latest-gpu-vulkan-localvqe"
mirrors:
- localai/localai-backends:latest-gpu-vulkan-localvqe
- !!merge <<: *localvqecpp
name: "vulkan-localvqe-development"
uri: "quay.io/go-skynet/local-ai-backends:master-gpu-vulkan-localvqe"
mirrors:
- localai/localai-backends:master-gpu-vulkan-localvqe
## kokoro
- !!merge <<: *kokoro
name: "kokoro-development"

View File

@@ -318,21 +318,6 @@ _makeVenvPortable() {
}
# Apply the venv to the current process: VIRTUAL_ENV, PATH, PYTHONHOME hygiene.
# Equivalent to the runtime portion of `source bin/activate`, but computed from
# $EDIR (resolved at runtime via realpath) instead of the path baked into
# bin/activate at venv-create time. `uv venv` (and `python -m venv`) both bake
# the create-time absolute path in, so sourcing activate on a relocated venv —
# e.g. one built at /vllm/venv inside a Docker stage and unpacked under
# /backends/cuda13-vllm-development/venv at runtime — silently prepends a
# stale, non-existent path to $PATH. Doing the setup ourselves sidesteps that;
# this is the same approach `uv run` takes internally.
_activateVenv() {
export VIRTUAL_ENV="${EDIR}/venv"
export PATH="${EDIR}/venv/bin:${PATH}"
unset PYTHONHOME
}
# ensureVenv makes sure that the venv for the backend both exists, and is activated.
#
# This function is idempotent, so you can call it as many times as you want and it will
@@ -369,7 +354,7 @@ function ensureVenv() {
venv_args="--copies"
fi
"${interpreter}" -m venv ${venv_args} "${EDIR}/venv"
_activateVenv
source "${EDIR}/venv/bin/activate"
"${interpreter}" -m pip install --upgrade pip
else
if [ "x${PORTABLE_PYTHON}" == "xtrue" ]; then
@@ -390,7 +375,7 @@ function ensureVenv() {
fi
if [ "x${VIRTUAL_ENV:-}" != "x${EDIR}/venv" ]; then
_activateVenv
source "${EDIR}/venv/bin/activate"
fi
}

View File

@@ -1,4 +1,4 @@
grpcio==1.80.0
protobuf
certifi
packaging==26.2
packaging==24.1

View File

@@ -40,19 +40,7 @@ from diffusers import DiffusionPipeline, ControlNetModel
from diffusers import FluxPipeline, FluxTransformer2DModel, AutoencoderKLWan
from diffusers.pipelines.stable_diffusion import safety_checker
from diffusers.utils import load_image, export_to_video
# TODO: re-enable compel as a hard dependency once it supports transformers >= 5.
# Tracking upstream: https://github.com/damian0815/compel/pull/129
# and https://github.com/damian0815/compel/issues/128
# Until then compel pins transformers ~= 4.25, which forces the pip resolver into
# multi-hour backtracking storms in CI when DEPS_REFRESH rotates the cache.
# Keep the import optional and gate usage on the COMPEL env var (set COMPEL=1 to opt in).
try:
from compel import Compel, ReturnedEmbeddingsType
COMPEL_AVAILABLE = True
except ImportError:
Compel = None
ReturnedEmbeddingsType = None
COMPEL_AVAILABLE = False
from compel import Compel, ReturnedEmbeddingsType
from optimum.quanto import freeze, qfloat8, quantize
from transformers import T5EncoderModel
from safetensors.torch import load_file
@@ -78,9 +66,6 @@ from diffusers import LTX2VideoTransformer3DModel, GGUFQuantizationConfig
_ONE_DAY_IN_SECONDS = 60 * 60 * 24
COMPEL = os.environ.get("COMPEL", "0") == "1"
if COMPEL and not COMPEL_AVAILABLE:
print("WARNING: COMPEL is enabled but the compel module is not installed. Install it manually (`pip install compel`) or unset COMPEL. Falling back to standard prompt processing.", file=sys.stderr)
COMPEL = False
SD_EMBED = os.environ.get("SD_EMBED", "0") == "1"
# Warn if SD_EMBED is enabled but the module is not available
if SD_EMBED and not SD_EMBED_AVAILABLE:

View File

@@ -4,15 +4,10 @@ opencv-python
transformers
torchvision==0.22.1
accelerate
compel
git+https://github.com/xhinker/sd_embed
peft
sentencepiece
torch==2.7.1
optimum-quanto
ftfy
# TODO: re-add compel once it supports transformers >= 5.
# Tracking: https://github.com/damian0815/compel/pull/129
# https://github.com/damian0815/compel/issues/128
# compel currently pins transformers~=4.25, which forced pip into multi-hour
# resolver backtracking storms in CI. backend.py imports it lazily and gates
# the COMPEL=1 env var on the import succeeding, so dropping it here is safe.
ftfy

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