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Author SHA1 Message Date
dependabot[bot]
85f02497f2 chore(deps): bump the go_modules group across 1 directory with 8 updates
Bumps the go_modules group with 7 updates in the / directory:

| Package | From | To |
| --- | --- | --- |
| [github.com/containerd/containerd](https://github.com/containerd/containerd) | `1.7.31` | `1.7.32` |
| [github.com/in-toto/in-toto-golang](https://github.com/in-toto/in-toto-golang) | `0.9.0` | `0.11.0` |
| [github.com/sigstore/rekor](https://github.com/sigstore/rekor) | `1.4.3` | `1.5.0` |
| [github.com/sigstore/timestamp-authority/v2](https://github.com/sigstore/timestamp-authority) | `2.0.3` | `2.0.6` |
| [github.com/theupdateframework/go-tuf/v2](https://github.com/theupdateframework/go-tuf) | `2.3.0` | `2.4.1` |
| [github.com/go-git/go-git/v5](https://github.com/go-git/go-git) | `5.19.0` | `5.19.1` |
| [github.com/slack-go/slack](https://github.com/slack-go/slack) | `0.17.3` | `0.23.1` |



Updates `github.com/containerd/containerd` from 1.7.31 to 1.7.32
- [Release notes](https://github.com/containerd/containerd/releases)
- [Changelog](https://github.com/containerd/containerd/blob/main/RELEASES.md)
- [Commits](https://github.com/containerd/containerd/compare/v1.7.31...v1.7.32)

Updates `github.com/in-toto/in-toto-golang` from 0.9.0 to 0.11.0
- [Release notes](https://github.com/in-toto/in-toto-golang/releases)
- [Changelog](https://github.com/in-toto/in-toto-golang/blob/master/CHANGELOG.md)
- [Commits](https://github.com/in-toto/in-toto-golang/compare/v0.9.0...v0.11.0)

Updates `github.com/sigstore/rekor` from 1.4.3 to 1.5.0
- [Release notes](https://github.com/sigstore/rekor/releases)
- [Changelog](https://github.com/sigstore/rekor/blob/main/CHANGELOG.md)
- [Commits](https://github.com/sigstore/rekor/compare/v1.4.3...v1.5.0)

Updates `github.com/sigstore/sigstore` from 1.10.0 to 1.10.3
- [Release notes](https://github.com/sigstore/sigstore/releases)
- [Commits](https://github.com/sigstore/sigstore/compare/v1.10.0...v1.10.3)

Updates `github.com/sigstore/timestamp-authority/v2` from 2.0.3 to 2.0.6
- [Release notes](https://github.com/sigstore/timestamp-authority/releases)
- [Changelog](https://github.com/sigstore/timestamp-authority/blob/main/CHANGELOG.md)
- [Commits](https://github.com/sigstore/timestamp-authority/compare/v2.0.3...v2.0.6)

Updates `github.com/theupdateframework/go-tuf/v2` from 2.3.0 to 2.4.1
- [Release notes](https://github.com/theupdateframework/go-tuf/releases)
- [Commits](https://github.com/theupdateframework/go-tuf/compare/v2.3.0...v2.4.1)

Updates `github.com/go-git/go-git/v5` from 5.19.0 to 5.19.1
- [Release notes](https://github.com/go-git/go-git/releases)
- [Changelog](https://github.com/go-git/go-git/blob/main/HISTORY.md)
- [Commits](https://github.com/go-git/go-git/compare/v5.19.0...v5.19.1)

Updates `github.com/slack-go/slack` from 0.17.3 to 0.23.1
- [Release notes](https://github.com/slack-go/slack/releases)
- [Changelog](https://github.com/slack-go/slack/blob/master/CHANGELOG.md)
- [Commits](https://github.com/slack-go/slack/compare/v0.17.3...v0.23.1)

---
updated-dependencies:
- dependency-name: github.com/containerd/containerd
  dependency-version: 1.7.32
  dependency-type: direct:production
- dependency-name: github.com/go-git/go-git/v5
  dependency-version: 5.19.1
  dependency-type: indirect
- dependency-name: github.com/in-toto/in-toto-golang
  dependency-version: 0.11.0
  dependency-type: indirect
- dependency-name: github.com/sigstore/rekor
  dependency-version: 1.5.0
  dependency-type: indirect
- dependency-name: github.com/sigstore/sigstore
  dependency-version: 1.10.3
  dependency-type: indirect
- dependency-name: github.com/sigstore/timestamp-authority/v2
  dependency-version: 2.0.6
  dependency-type: indirect
- dependency-name: github.com/slack-go/slack
  dependency-version: 0.23.1
  dependency-type: indirect
- dependency-name: github.com/theupdateframework/go-tuf/v2
  dependency-version: 2.4.1
  dependency-type: indirect
...

Signed-off-by: dependabot[bot] <support@github.com>
2026-06-03 08:45:00 +00:00
904 changed files with 11155 additions and 66250 deletions

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@@ -102,24 +102,6 @@ Multi-arch backends are NOT a single matrix entry with `platforms: 'linux/amd64,
Entries whose `dockerfile` is `./backend/Dockerfile.{llama-cpp,ik-llama-cpp,turboquant}` must also set a `builder-base-image` field pointing at a prebuilt base from `quay.io/go-skynet/ci-cache:base-grpc-*` (CI builds these via `.github/workflows/base-images.yml`). The mapping is by `(build-type, platforms)` — see existing entries for the pattern. CI uses these prebuilt bases to skip the gRPC compile (~2535 min cold). Local `make backends/<name>` ignores `builder-base-image` and uses the from-source path inside the Dockerfile, so you don't need quay access for local builds.
### Cover every OS the project supports (Linux **and** Darwin)
`.github/backend-matrix.yml` has two matrices, and they are the source of truth for which OS a backend ships on:
- `include:` — the **Linux** matrix (x86_64 + arm64; CPU and CUDA / ROCm / SYCL / Vulkan).
- `includeDarwin:` — the **macOS / Apple Silicon** matrix (arm64; Metal where the engine supports it, otherwise a native arm64 CPU build).
**A new backend must target every OS it can build for — do not ship Linux-only by default.** A backend that appears only under `include:` is silently unavailable on macOS even when its code would run there. Most C/C++/GGML engines build on Darwin out of the box (ggml defaults `GGML_METAL=ON` on Apple, so a plain build is Metal-enabled), and many Python backends do too (CPU / MPS wheels). If a backend genuinely cannot support an OS (e.g. CUDA-only, no CPU variant), state that in the PR description instead of omitting it silently.
Wiring a backend into `includeDarwin:` is more than the matrix entry:
1. **`includeDarwin:` entry** — `tag-suffix: "-metal-darwin-arm64-<backend>"`, `build-type: "metal"`, `lang: "go"` for go+ggml backends; omit `build-type` for the bespoke C++ ones (llama-cpp / ds4 / privacy-filter). Match an existing entry of the same shape.
2. **`backend/index.yaml`** — add `metal:` to the backend's `capabilities` map (main and `-development`) and concrete `metal-<backend>` / `metal-<backend>-development` image entries pointing at the `-metal-darwin-arm64-<backend>` images.
3. **C/C++ backends only** — add an `inferBackendPathDarwin` case in `scripts/changed-backends.js` returning `backend/cpp/<backend>/` (the generic fallthrough assumes `backend/<lang>/`, which is wrong for a C++ source tree driven with `lang: go`), and give `run.sh` a Darwin branch that exports `DYLD_LIBRARY_PATH` instead of `LD_LIBRARY_PATH`. If the build is bespoke (single `grpc-server` + dylib bundling), model it on `scripts/build/ds4-darwin.sh` and add a `backends/<backend>-darwin` make target plus a gated step in `.github/workflows/backend_build_darwin.yml`.
4. **C++ proto gotcha** — if the backend compiles the generated gRPC/protobuf in a separate CMake target (e.g. `hw_grpc_proto`), that target must link `protobuf::libprotobuf` + `gRPC::grpc++` so the Homebrew include dirs propagate; otherwise macOS fails with `google/protobuf/runtime_version.h not found` (Linux hides this because apt headers sit in `/usr/include`).
The CI path filter only builds a backend on a PR when a file under its directory changes, so a darwin-only YAML edit builds nothing — touch a file under `backend/<lang>/<backend>/` (a one-line comment is enough) in the same PR.
## 3. Add Backend Metadata to `backend/index.yaml`
**Step 3a: Add Meta Definition**
@@ -216,34 +198,12 @@ docker-build-backends: ... docker-build-<backend-name>
- If the backend is in `backend/python/<backend-name>/` but uses `.` as context in the workflow file, use `.` context
- Check similar backends to determine the correct context
## Documenting the backend (README + docs)
A backend is not "added" until it is discoverable. Update the user-facing docs:
- **`docs/content/features/backends.md`** - add the backend to the right
category in the "LocalAI supports various types of backends" list (and add a
new category if it introduces a new modality, e.g. sound classification).
- If the backend introduces a **new API surface** (a new endpoint or a realtime
capability), document it under `docs/content/` where its area lives (audio,
vision, etc.) and follow the api-endpoints checklist in
[api-endpoints-and-auth.md](api-endpoints-and-auth.md).
**If the backend is a native C/C++/GGML engine created and maintained by the
LocalAI team** (a from-scratch port like `parakeet.cpp`, `ced.cpp`,
`vibevoice.cpp`, `rf-detr.cpp`, not a wrapper around a third-party runtime), it
ALSO belongs in the top-level **`README.md`** table under "native C/C++/GGML
engines ... developed and maintained by the LocalAI project itself". Add a row
linking the upstream engine repo with a one-line description. This is the
project's showcase of its own engines; a new in-house backend that is missing
from it is a documentation bug.
## 5. Verification Checklist
After adding a new backend, verify:
- [ ] Backend directory structure is complete with all necessary files
- [ ] Build configurations added to `.github/backend-matrix.yml` for all desired platforms (per-arch entries with `platform-tag` for multi-arch; `builder-base-image` for llama-cpp / ik-llama-cpp / turboquant)
- [ ] **OS coverage considered**: added to `includeDarwin:` (macOS/Apple Silicon) if the backend can build there — with the `backend/index.yaml` `metal:` capability + `metal-<backend>` image entries, a `run.sh` Darwin/DYLD branch and `inferBackendPathDarwin` case for C++ backends — or the PR explains why an OS is unsupported. Do not ship Linux-only by default.
- [ ] Meta definition added to `backend/index.yaml` in the `## metas` section
- [ ] Image entries added to `backend/index.yaml` for all build variants (latest + development)
- [ ] Tag suffixes match between workflow file and index.yaml
@@ -251,8 +211,6 @@ After adding a new backend, verify:
- [ ] No YAML syntax errors (check with linter)
- [ ] No Makefile syntax errors (check with linter)
- [ ] Follows the same pattern as similar backends (e.g., if it's a transcription backend, follow `faster-whisper` pattern)
- [ ] Documented: added to the category list in `docs/content/features/backends.md` (and any new endpoint/realtime capability documented under `docs/content/`)
- [ ] If it is an in-house native C/C++/GGML engine, added to the maintained-engines table in the top-level `README.md`
## Bundling runtime shared libraries (`package.sh`)

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@@ -44,39 +44,6 @@ maps to `DS4_THINK_HIGH`. We pass the chosen mode to `ds4_chat_append_assistant_
via `ModelOptions.Options[] = "kv_cache_dir:/some/path"`. Format is **our own** -
NOT bit-compatible with ds4-server's KVC files (interop is a follow-up plan).
## Engine options (LoadModel)
`LoadModel` maps `ModelOptions.Options[]` (`"key:value"`, from model-YAML
`options:`) onto `ds4_engine_options` through a **declarative table**
(`kEngineOptSpecs` + `apply_engine_option` in `grpc-server.cpp`). The struct is
plain C with no reflection, so the field set is enumerated once in the table;
adding a future engine knob is a one-line table row, not a new branch. Unknown
keys are ignored (back-compat). A bare flag (`ssd_streaming` with no value)
means `true`. Path-type values (`mtp_path`, `expert_profile_path`,
`directional_steering_file`) resolve **relative to the model directory**, so a
gallery entry can reference a companion file it downloaded by bare filename;
absolute values pass through. `ds4_role` / `ds4_layers` / `ds4_listen` /
`ds4_route_timeout` / `kv_cache_dir` keep their dedicated handling (validation
+ coordinator wiring) and are not in the table.
Wired keys: `mtp_path`, `mtp_draft`, `mtp_margin`, `prefill_chunk`,
`power_percent`, `warm_weights`, `quality`, `ssd_streaming`,
`ssd_streaming_cold`, `ssd_streaming_preload_experts`,
`ssd_streaming_cache_experts` (count or `NGB`, sets both experts+bytes via
`ds4_parse_streaming_cache_experts_arg`), `simulate_used_memory` (`NGB` via
`ds4_parse_gib_arg`), `expert_profile_path`, `directional_steering_file`,
`directional_steering_attn`, `directional_steering_ffn`.
## SSD streaming (running models larger than RAM)
ds4's **SSD streaming** keeps non-routed weights resident and streams routed MoE
experts from the GGUF on cache misses, turning "does it fit in RAM" into a speed
spectrum. **Metal (Darwin) only** - it is a no-op on CUDA/CPU. Enable with
`options: ["ssd_streaming"]`; size the routed-expert cache with
`ssd_streaming_cache_experts:NGB` (omit for ds4's automatic 80%-of-working-set
budget). Gallery entries built on this: `deepseek-v4-flash-q4-ssd` (153 GB Flash
on a 128 GB Mac) and `deepseek-v4-pro-q2-ssd` (433 GB Pro, experimental).
## Build matrix
| Build | Where | Notes |

View File

@@ -70,12 +70,6 @@ if [ "${BUILD_TYPE:-}" = "vulkan" ] && [ "${SKIP_DRIVERS:-false}" = "false" ]; t
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
# Mesa Vulkan ICD drivers (ANV/RADV/lavapipe + Arm SoC) and their ICD
# manifests. The LunarG SDK below only provides the loader and shader
# tooling, not hardware drivers — without Mesa the packaged Vulkan backend
# would ship a loader that finds no GPU. package-gpu-libs.sh bundles these
# .so files plus their deps into the backend so it stays self-contained.
apt-get install -y mesa-vulkan-drivers libdrm2
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

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@@ -17,29 +17,19 @@ if [[ -n "${CUDA_DOCKER_ARCH:-}" ]]; then
rm -rf /LocalAI/backend/cpp/llama-cpp-*-build
fi
cd /LocalAI/backend/cpp/llama-cpp
if [ -z "${BUILD_TYPE:-}" ]; then
# Pure CPU image (BUILD_TYPE empty): one build with ggml CPU_ALL_VARIANTS replaces the
# per-microarch binaries (x86: avx/avx2/avx512/fallback; arm64: armv8.x/armv9.x). ggml
# dlopens the best libggml-cpu-*.so at runtime by probing host CPU features.
#
# arm64: the CPU_ALL_VARIANTS table includes armv9.2 SME variants whose -march=...+sme is
# rejected by the Ubuntu 24.04 default gcc-13. gcc-14 accepts it, so build the arm64
# variants with it (the host never *selects* SME unless it has it, but every variant must
# still compile).
if [ "${TARGETARCH}" = "arm64" ]; then
apt-get update -qq && apt-get install -y -qq gcc-14 g++-14
export CC=gcc-14 CXX=g++-14
fi
make llama-cpp-cpu-all
else
# GPU build (cublas/hipblas/sycl/vulkan/...): the accelerator does the compute, so a
# single fallback CPU build is enough - no per-microarch CPU variants needed. (This also
# keeps the heavy GPU backend compile from also building the whole CPU variant matrix,
# and avoids the gcc-14 apt step on GPU base images such as nvidia l4t.)
if [ "${TARGETARCH}" = "arm64" ] || [ "${BUILD_TYPE}" = "hipblas" ]; then
cd /LocalAI/backend/cpp/llama-cpp
make llama-cpp-fallback
make llama-cpp-grpc
make llama-cpp-rpc-server
else
cd /LocalAI/backend/cpp/llama-cpp
make llama-cpp-avx
make llama-cpp-avx2
make llama-cpp-avx512
make llama-cpp-fallback
make llama-cpp-grpc
make llama-cpp-rpc-server
fi
make llama-cpp-grpc
make llama-cpp-rpc-server
ccache -s || true

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@@ -19,21 +19,17 @@ fi
cd /LocalAI/backend/cpp/turboquant
if [ -z "${BUILD_TYPE:-}" ]; then
# Pure CPU image: one ggml CPU_ALL_VARIANTS build replaces the per-microarch binaries.
# arm64: the armv9.2 SME variants need gcc-14 (gcc-13 rejects +sme).
if [ "${TARGETARCH}" = "arm64" ]; then
apt-get update -qq && apt-get install -y -qq gcc-14 g++-14
export CC=gcc-14 CXX=g++-14
fi
make turboquant-cpu-all
else
# GPU build (cublas/hipblas/sycl/vulkan/...): single fallback CPU build, the accelerator
# does the compute. Keeps the GPU compile from also building the CPU variant matrix and
# avoids the gcc-14 apt step on GPU base images such as nvidia l4t.
if [ "${TARGETARCH}" = "arm64" ] || [ "${BUILD_TYPE}" = "hipblas" ]; then
make turboquant-fallback
make turboquant-grpc
make turboquant-rpc-server
else
make turboquant-avx
make turboquant-avx2
make turboquant-avx512
make turboquant-fallback
make turboquant-grpc
make turboquant-rpc-server
fi
make turboquant-grpc
make turboquant-rpc-server
ccache -s || true

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@@ -31,15 +31,6 @@ backend/python/**/source
backend/cpp/llama-cpp/llama.cpp
backend/cpp/llama-cpp-*-build
# privacy-filter: same in-place pattern. The Makefile fetches privacy-filter.cpp
# at the pinned commit (or symlinks a PRIVACY_FILTER_SRC checkout for local dev).
# A stale dir/symlink COPY'd into the image makes the clone step fail (dangling
# symlink) or compile against the wrong commit, so keep host build state out.
backend/cpp/privacy-filter/privacy-filter.cpp
backend/cpp/privacy-filter/build
backend/cpp/privacy-filter/grpc-server
backend/cpp/privacy-filter/package
# Rust backend build output (sources are tracked; target/ is generated)
backend/rust/*/target

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File diff suppressed because it is too large Load Diff

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@@ -1,55 +0,0 @@
#!/bin/bash
# Bump the single vllm-metal pin (VLLM_METAL_VERSION) in the vLLM backend's
# darwin (Apple Silicon) install path. The macOS/Metal build
# (backend/python/vllm/install.sh, Darwin branch) installs vllm-metal, which is
# version-locked to a specific vLLM source release. install.sh derives that vLLM
# version at build time from vllm-metal's own installer (`vllm_v=`) at the pinned
# tag, so there is only ONE value to bump here -- mirroring bump_vllm_wheel.sh,
# which bumps the Linux cu130 wheel pin.
#
# This deliberately tracks vllm-project/vllm-metal, NOT vllm-project/vllm: the
# darwin build can only use the exact vLLM version vllm-metal supports, so it may
# lag the Linux pin (requirements-cublas13-after.txt) until vllm-metal catches up.
set -xe
REPO=$1 # vllm-project/vllm-metal
FILE=$2 # backend/python/vllm/install.sh
VAR=$3 # VLLM_METAL_VERSION (used for the workflow's output file names)
if [ -z "$FILE" ] || [ -z "$REPO" ] || [ -z "$VAR" ]; then
echo "usage: $0 <repo> <install-file> <var-name>" >&2
exit 1
fi
# vllm-metal ships frequent dev releases, all flagged as non-prerelease, so
# /releases/latest returns the newest one (with its cp312 wheel asset).
LATEST_TAG=$(curl -sS -H "Accept: application/vnd.github+json" \
"https://api.github.com/repos/$REPO/releases/latest" \
| python3 -c "import json,sys; print(json.load(sys.stdin)['tag_name'])")
# The coupled vLLM source version lives in vllm-metal's installer at that tag.
NEW_VLLM_VERSION=$(curl -fsSL \
"https://raw.githubusercontent.com/$REPO/$LATEST_TAG/install.sh" \
| grep -oE 'vllm_v="[0-9]+\.[0-9]+\.[0-9]+"' | head -1 | cut -d'"' -f2)
if [ -z "$LATEST_TAG" ] || [ -z "$NEW_VLLM_VERSION" ]; then
echo "Could not resolve vllm-metal tag ($LATEST_TAG) or its vllm_v ($NEW_VLLM_VERSION)." >&2
exit 1
fi
set +e
CURRENT_TAG=$(grep -oE 'VLLM_METAL_VERSION="[^"]*"' "$FILE" | head -1 | cut -d'"' -f2)
set -e
# Rewrite the single pin. install.sh derives VLLM_VERSION from this tag at build
# time, so there is nothing else to touch. peter-evans/create-pull-request opens
# no PR on a clean tree, so a no-op rewrite (already current) is safe.
sed -i "$FILE" \
-e "s|VLLM_METAL_VERSION=\"[^\"]*\"|VLLM_METAL_VERSION=\"$LATEST_TAG\"|"
if [ -z "$CURRENT_TAG" ]; then
echo "Could not find VLLM_METAL_VERSION=\"...\" in $FILE." >&2
exit 0
fi
echo "vllm-metal ${CURRENT_TAG} -> ${LATEST_TAG} (builds vLLM ${NEW_VLLM_VERSION}): https://github.com/$REPO/releases/tag/${LATEST_TAG}" >> "${VAR}_message.txt"
echo "${LATEST_TAG}" >> "${VAR}_commit.txt"

View File

@@ -3,7 +3,6 @@ package main
import (
"context"
"encoding/json"
"errors"
"fmt"
"os"
"strconv"
@@ -114,17 +113,6 @@ func main() {
fmt.Println("Searching for trending models on HuggingFace...")
rawModels, err := client.GetTrending(searchTerm, limit)
if err != nil {
if errors.Is(err, hfapi.ErrRateLimited) {
fmt.Printf("HuggingFace API is rate limited after retries, skipping this run: %v\n", err)
writeSummary(AddedModelSummary{
SearchTerm: searchTerm,
TotalFound: 0,
ModelsAdded: 0,
Quantization: quantization,
ProcessingTime: time.Since(startTime).String(),
})
return
}
fmt.Fprintf(os.Stderr, "Error fetching models: %v\n", err)
os.Exit(1)
}
@@ -289,3 +277,4 @@ func truncateString(s string, maxLen int) string {
}
return s[:maxLen] + "..."
}

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@@ -44,7 +44,7 @@ jobs:
has-merges-singlearch: ${{ steps.set-matrix.outputs['has-merges-singlearch'] }}
steps:
- name: Checkout repository
uses: actions/checkout@v7
uses: actions/checkout@v6
- name: Setup Bun
uses: oven-sh/setup-bun@v2

View File

@@ -101,7 +101,7 @@ jobs:
steps:
- name: Checkout
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true

View File

@@ -57,7 +57,7 @@ jobs:
HOMEBREW_NO_ANALYTICS: '1'
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
@@ -98,8 +98,6 @@ jobs:
/opt/homebrew/Cellar/hiredis
/opt/homebrew/Cellar/xxhash
/opt/homebrew/Cellar/zstd
/opt/homebrew/Cellar/nlohmann-json
/opt/homebrew/Cellar/opus
key: brew-${{ runner.os }}-${{ runner.arch }}-v1-${{ hashFiles('.github/workflows/backend_build_darwin.yml') }}
- name: Dependencies
@@ -111,15 +109,7 @@ jobs:
# Without explicitly installing them, a brew cache-hit run restores
# ccache's Cellar dir but skips installing those transitive deps,
# and ccache fails at runtime with `dyld: Library not loaded`.
# nlohmann-json is header-only and required by the ds4 backend
# (dsml_renderer.cpp includes <nlohmann/json.hpp>); on Linux it comes
# from the apt-installed nlohmann-json3-dev in the build image.
# opus + pkg-config are required by the opus go backend: its
# Makefile/package.sh call `pkg-config --cflags/--libs opus` to build
# libopusshim.dylib and to locate libopus.dylib for bundling. brew's
# pkg-config defaults its search path to the Homebrew prefix so the
# opus.pc is found.
brew install protobuf grpc make protoc-gen-go protoc-gen-go-grpc libomp llvm ccache blake3 fmt hiredis xxhash zstd nlohmann-json opus pkg-config
brew install protobuf grpc make protoc-gen-go protoc-gen-go-grpc libomp llvm ccache blake3 fmt hiredis xxhash zstd
# Force-reinstall ccache so brew re-validates its full runtime-dep
# closure on every run. This is the durable fix: when the upstream
# ccache formula gains a new transitive dep (as it has multiple times
@@ -138,7 +128,7 @@ jobs:
# and decides "already installed" without re-linking, so on a cache-
# hit run the formulas aren't on PATH. Force-link them; --overwrite
# tolerates pre-existing symlinks from earlier installs.
brew link --overwrite protobuf grpc make protoc-gen-go protoc-gen-go-grpc libomp llvm ccache blake3 fmt hiredis xxhash zstd nlohmann-json opus pkg-config 2>/dev/null || true
brew link --overwrite protobuf grpc make protoc-gen-go protoc-gen-go-grpc libomp llvm ccache blake3 fmt hiredis xxhash zstd 2>/dev/null || true
- name: Save Homebrew cache
if: github.event_name != 'pull_request' && steps.brew-cache.outputs.cache-hit != 'true'
@@ -158,8 +148,6 @@ jobs:
/opt/homebrew/Cellar/hiredis
/opt/homebrew/Cellar/xxhash
/opt/homebrew/Cellar/zstd
/opt/homebrew/Cellar/nlohmann-json
/opt/homebrew/Cellar/opus
key: brew-${{ runner.os }}-${{ runner.arch }}-v1-${{ hashFiles('.github/workflows/backend_build_darwin.yml') }}
# ---- ccache for llama.cpp CMake builds ----
@@ -235,17 +223,8 @@ jobs:
run: |
make backends/ds4-darwin
# privacy-filter is a C++/ggml backend like ds4 - a single grpc-server with
# otool dylib bundling - so it gets its own bespoke darwin script rather than
# the generic build-darwin-go-backend path.
- name: Build privacy-filter backend (Darwin Metal)
if: inputs.backend == 'privacy-filter'
run: |
make protogen-go
make backends/privacy-filter-darwin
- name: Build ${{ inputs.backend }}-darwin
if: inputs.backend != 'llama-cpp' && inputs.backend != 'ds4' && inputs.backend != 'privacy-filter'
if: inputs.backend != 'llama-cpp' && inputs.backend != 'ds4'
run: |
make protogen-go
BACKEND=${{ inputs.backend }} BUILD_TYPE=${{ inputs.build-type }} USE_PIP=${{ inputs.use-pip }} make build-darwin-${{ inputs.lang }}-backend

View File

@@ -49,7 +49,7 @@ jobs:
# Sparse checkout: the merge job needs `.github/scripts/` (for the
# keepalive cleanup script) but none of the source tree.
- name: Checkout (.github/scripts only)
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
sparse-checkout: |
.github/scripts

View File

@@ -23,7 +23,7 @@ jobs:
has-merges-singlearch: ${{ steps.set-matrix.outputs['has-merges-singlearch'] }}
steps:
- name: Checkout repository
uses: actions/checkout@v7
uses: actions/checkout@v6
- name: Setup Bun
uses: oven-sh/setup-bun@v2

View File

@@ -127,7 +127,7 @@ jobs:
# the original l4t matrix entry which set skip-drivers: 'true'.
skip-drivers: 'true'
steps:
- uses: actions/checkout@v7
- uses: actions/checkout@v6
with:
submodules: false
- name: Free disk space

View File

@@ -11,7 +11,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
fetch-depth: 0
- name: Set up Go
@@ -25,7 +25,7 @@ jobs:
runs-on: macos-latest
steps:
- name: Checkout
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
fetch-depth: 0
- name: Set up Go
@@ -47,7 +47,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
fetch-depth: 0
- name: Configure apt mirror on runner

View File

@@ -14,7 +14,7 @@ jobs:
bump:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v7
- uses: actions/checkout@v6
- uses: actions/setup-go@v5
with:

View File

@@ -26,10 +26,6 @@ jobs:
variable: "DS4_VERSION"
branch: "main"
file: "backend/cpp/ds4/Makefile"
- repository: "localai-org/privacy-filter.cpp"
variable: "PRIVACY_FILTER_VERSION"
branch: "master"
file: "backend/cpp/privacy-filter/Makefile"
- repository: "ggml-org/whisper.cpp"
variable: "WHISPER_CPP_VERSION"
branch: "master"
@@ -42,22 +38,6 @@ jobs:
variable: "PARAKEET_VERSION"
branch: "master"
file: "backend/go/parakeet-cpp/Makefile"
- repository: "mudler/ced.cpp"
variable: "CED_VERSION"
branch: "master"
file: "backend/go/ced/Makefile"
- repository: "mudler/voice-detect.cpp"
variable: "VOICEDETECT_VERSION"
branch: "master"
file: "backend/go/voice-detect/Makefile"
- repository: "mudler/face-detect.cpp"
variable: "FACEDETECT_VERSION"
branch: "master"
file: "backend/go/face-detect/Makefile"
- repository: "mudler/depth-anything.cpp"
variable: "DEPTHANYTHING_VERSION"
branch: "master"
file: "backend/go/depth-anything-cpp/Makefile"
- repository: "leejet/stable-diffusion.cpp"
variable: "STABLEDIFFUSION_GGML_VERSION"
branch: "master"
@@ -82,25 +62,17 @@ jobs:
variable: "RFDETR_VERSION"
branch: "main"
file: "backend/go/rfdetr-cpp/Makefile"
- repository: "mudler/locate-anything.cpp"
variable: "LOCATEANYTHING_VERSION"
branch: "master"
file: "backend/go/locate-anything-cpp/Makefile"
- repository: "ServeurpersoCom/qwentts.cpp"
- repository: "predict-woo/qwen3-tts.cpp"
variable: "QWEN3TTS_CPP_VERSION"
branch: "master"
branch: "main"
file: "backend/go/qwen3-tts-cpp/Makefile"
- repository: "ServeurpersoCom/omnivoice.cpp"
variable: "OMNIVOICE_VERSION"
branch: "master"
file: "backend/go/omnivoice-cpp/Makefile"
- repository: "localai-org/vibevoice.cpp"
variable: "VIBEVOICE_CPP_VERSION"
branch: "master"
file: "backend/go/vibevoice-cpp/Makefile"
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v7
- uses: actions/checkout@v6
- name: Bump dependencies 🔧
id: bump
run: |
@@ -136,7 +108,7 @@ jobs:
if: github.repository == 'mudler/LocalAI'
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v7
- uses: actions/checkout@v6
- name: Bump vLLM cu130 wheel pin 🔧
id: bump
run: |
@@ -162,39 +134,3 @@ jobs:
branch: "update/VLLM_VERSION"
body: ${{ steps.bump.outputs.message }}
signoff: true
bump-vllm-metal:
# The darwin (Apple Silicon) vLLM build installs vllm-metal, which is locked
# to a specific vLLM source release. install.sh pins both VLLM_METAL_VERSION
# (the wheel release) and VLLM_VERSION (the vLLM it builds against); this job
# tracks vllm-project/vllm-metal and rewrites both atomically. Separate from
# bump-vllm-wheel because darwin follows vllm-metal, not vllm/vllm latest.
if: github.repository == 'mudler/LocalAI'
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v7
- name: Bump vllm-metal pin 🔧
id: bump
run: |
bash .github/bump_vllm_metal.sh vllm-project/vllm-metal backend/python/vllm/install.sh VLLM_METAL_VERSION
{
echo 'message<<EOF'
cat "VLLM_METAL_VERSION_message.txt"
echo EOF
} >> "$GITHUB_OUTPUT"
{
echo 'commit<<EOF'
cat "VLLM_METAL_VERSION_commit.txt"
echo EOF
} >> "$GITHUB_OUTPUT"
rm -rfv VLLM_METAL_VERSION_message.txt VLLM_METAL_VERSION_commit.txt
- name: Create Pull Request
uses: peter-evans/create-pull-request@v8
with:
token: ${{ secrets.UPDATE_BOT_TOKEN }}
push-to-fork: ci-forks/LocalAI
commit-message: ':arrow_up: Update vllm-project/vllm-metal (darwin)'
title: 'chore: :arrow_up: Update vllm-metal (darwin) to `${{ steps.bump.outputs.commit }}`'
branch: "update/VLLM_METAL_VERSION"
body: ${{ steps.bump.outputs.message }}
signoff: true

View File

@@ -13,7 +13,7 @@ jobs:
- repository: "mudler/LocalAI"
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v7
- uses: actions/checkout@v6
- name: Bump dependencies 🔧
run: |
bash .github/bump_docs.sh ${{ matrix.repository }}

View File

@@ -8,7 +8,7 @@ jobs:
if: github.repository == 'mudler/LocalAI'
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v7
- uses: actions/checkout@v6
- name: Configure apt mirror on runner
uses: ./.github/actions/configure-apt-mirror
- name: Install dependencies

View File

@@ -16,7 +16,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- uses: actions/setup-go@v5

View File

@@ -31,7 +31,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Checkout repository
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
token: ${{ secrets.GITHUB_TOKEN }}

View File

@@ -44,7 +44,7 @@ jobs:
uses: docker/setup-buildx-action@master
- name: Checkout
uses: actions/checkout@v7
uses: actions/checkout@v6
- name: Cache Intel images
uses: docker/build-push-action@v7

View File

@@ -28,7 +28,7 @@ jobs:
HUGO_VERSION: "0.146.3"
steps:
- name: Checkout
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
fetch-depth: 0 # needed for enableGitInfo
submodules: true

View File

@@ -80,7 +80,7 @@ jobs:
steps:
- name: Checkout
uses: actions/checkout@v7
uses: actions/checkout@v6
- name: Configure apt mirror on runner
id: apt_mirror

View File

@@ -36,7 +36,7 @@ jobs:
# Sparse checkout: needed for .github/scripts/ (the keepalive cleanup
# script). Skips the rest of the source tree.
- name: Checkout (.github/scripts only)
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
sparse-checkout: |
.github/scripts

View File

@@ -20,7 +20,7 @@ jobs:
golangci-lint:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v7
- 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.

View File

@@ -10,7 +10,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
fetch-depth: 0
- name: Set up Go
@@ -24,35 +24,20 @@ jobs:
args: release --clean
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
MACOS_SIGN_P12: ${{ secrets.MACOS_CERTIFICATE }}
MACOS_SIGN_PASSWORD: ${{ secrets.MACOS_CERTIFICATE_PWD }}
MACOS_NOTARY_KEY: ${{ secrets.MACOS_NOTARY_KEY }}
MACOS_NOTARY_KEY_ID: ${{ secrets.MACOS_NOTARY_KEY_ID }}
MACOS_NOTARY_ISSUER_ID: ${{ secrets.MACOS_NOTARY_ISSUER_ID }}
launcher-build-darwin:
runs-on: macos-latest
steps:
- name: Checkout
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
fetch-depth: 0
- name: Set up Go
uses: actions/setup-go@v5
with:
go-version: 1.23
- name: Import signing certificate
env:
MACOS_CERTIFICATE: ${{ secrets.MACOS_CERTIFICATE }}
MACOS_CERTIFICATE_PWD: ${{ secrets.MACOS_CERTIFICATE_PWD }}
MACOS_CI_KEYCHAIN_PWD: ${{ secrets.MACOS_CI_KEYCHAIN_PWD }}
run: bash contrib/macos/sign-and-notarize.sh import-cert
- name: Build, sign and notarize the DMG
env:
MACOS_SIGN_IDENTITY: ${{ secrets.MACOS_SIGN_IDENTITY }}
MACOS_NOTARY_KEY: ${{ secrets.MACOS_NOTARY_KEY }}
MACOS_NOTARY_KEY_ID: ${{ secrets.MACOS_NOTARY_KEY_ID }}
MACOS_NOTARY_ISSUER_ID: ${{ secrets.MACOS_NOTARY_ISSUER_ID }}
run: make release-launcher-darwin
- name: Build launcher for macOS ARM64
run: |
make build-launcher-darwin
- name: Upload DMG to Release
uses: softprops/action-gh-release@v3
with:
@@ -61,7 +46,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
fetch-depth: 0
- name: Configure apt mirror on runner

View File

@@ -14,17 +14,14 @@ jobs:
GO111MODULE: on
steps:
- name: Checkout Source
uses: actions/checkout@v7
uses: actions/checkout@v6
if: ${{ github.actor != 'dependabot[bot]' }}
- name: Run Gosec Security Scanner
if: ${{ github.actor != 'dependabot[bot]' }}
uses: securego/gosec@v2.27.1
with:
# we let the report trigger content trigger a failure using the GitHub Security features.
# backend/go/supertonic is excluded: it vendors upstream supertone-inc/supertonic
# (helper.go), whose findings (G304 model-file loads, G404 math/rand for flow-matching
# noise, G104 unhandled errors) are inherent to that upstream code, not ours to rewrite.
args: '-no-fail -exclude-dir=backend/go/supertonic -fmt sarif -out results.sarif ./...'
args: '-no-fail -fmt sarif -out results.sarif ./...'
- name: Upload SARIF file
if: ${{ github.actor != 'dependabot[bot]' }}
uses: github/codeql-action/upload-sarif@v4

View File

@@ -38,7 +38,6 @@ jobs:
acestep-cpp: ${{ steps.detect.outputs.acestep-cpp }}
qwen3-tts-cpp: ${{ steps.detect.outputs.qwen3-tts-cpp }}
rfdetr-cpp: ${{ steps.detect.outputs.rfdetr-cpp }}
locate-anything-cpp: ${{ steps.detect.outputs.locate-anything-cpp }}
vibevoice-cpp: ${{ steps.detect.outputs.vibevoice-cpp }}
localvqe: ${{ steps.detect.outputs.localvqe }}
voxtral: ${{ steps.detect.outputs.voxtral }}
@@ -50,7 +49,7 @@ jobs:
parakeet-cpp: ${{ steps.detect.outputs.parakeet-cpp }}
steps:
- name: Checkout repository
uses: actions/checkout@v7
uses: actions/checkout@v6
- name: Setup Bun
uses: oven-sh/setup-bun@v2
- name: Install dependencies
@@ -67,7 +66,7 @@ jobs:
# runs-on: ubuntu-latest
# steps:
# - name: Clone
# uses: actions/checkout@v7
# uses: actions/checkout@v6
# with:
# submodules: true
# - name: Dependencies
@@ -90,7 +89,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
@@ -113,7 +112,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
@@ -137,7 +136,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
@@ -158,7 +157,7 @@ jobs:
# runs-on: ubuntu-latest
# steps:
# - name: Clone
# uses: actions/checkout@v7
# uses: actions/checkout@v6
# with:
# submodules: true
# - name: Dependencies
@@ -178,7 +177,7 @@ jobs:
# runs-on: ubuntu-latest
# steps:
# - name: Clone
# uses: actions/checkout@v7
# uses: actions/checkout@v6
# with:
# submodules: true
# - name: Dependencies
@@ -240,7 +239,7 @@ jobs:
# sudo rm -rf "$AGENT_TOOLSDIRECTORY" || true
# df -h
# - name: Clone
# uses: actions/checkout@v7
# uses: actions/checkout@v6
# with:
# submodules: true
# - name: Dependencies
@@ -265,7 +264,7 @@ jobs:
# runs-on: ubuntu-latest
# steps:
# - name: Clone
# uses: actions/checkout@v7
# uses: actions/checkout@v6
# with:
# submodules: true
# - name: Dependencies
@@ -288,7 +287,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
@@ -309,7 +308,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
@@ -330,7 +329,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
@@ -351,7 +350,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
@@ -373,7 +372,7 @@ jobs:
# timeout-minutes: 45
# steps:
# - name: Clone
# uses: actions/checkout@v7
# uses: actions/checkout@v6
# with:
# submodules: true
# - name: Dependencies
@@ -394,7 +393,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
@@ -415,7 +414,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
@@ -436,7 +435,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
@@ -462,7 +461,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
@@ -484,7 +483,7 @@ jobs:
timeout-minutes: 30
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
@@ -513,7 +512,7 @@ jobs:
timeout-minutes: 90
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Setup Go
@@ -530,7 +529,7 @@ jobs:
timeout-minutes: 90
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Setup Go
@@ -552,7 +551,7 @@ jobs:
timeout-minutes: 20
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Setup Go
@@ -564,7 +563,7 @@ jobs:
- 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,tokenize
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.
@@ -579,7 +578,7 @@ jobs:
timeout-minutes: 90
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Setup Go
@@ -604,7 +603,7 @@ jobs:
timeout-minutes: 90
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Setup Go
@@ -625,7 +624,7 @@ jobs:
timeout-minutes: 90
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Setup Go
@@ -645,7 +644,7 @@ jobs:
timeout-minutes: 90
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Setup Go
@@ -664,7 +663,7 @@ jobs:
timeout-minutes: 90
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Setup Go
@@ -681,7 +680,7 @@ jobs:
timeout-minutes: 90
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Setup Go
@@ -698,7 +697,7 @@ jobs:
timeout-minutes: 90
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Setup Go
@@ -741,7 +740,7 @@ jobs:
# timeout-minutes: 90
# steps:
# - name: Clone
# uses: actions/checkout@v7
# uses: actions/checkout@v6
# with:
# submodules: true
# - name: Dependencies
@@ -783,7 +782,7 @@ jobs:
# timeout-minutes: 90
# steps:
# - name: Clone
# uses: actions/checkout@v7
# uses: actions/checkout@v6
# with:
# submodules: true
# - name: Dependencies
@@ -808,7 +807,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
@@ -840,7 +839,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
@@ -876,7 +875,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
@@ -902,45 +901,6 @@ jobs:
- name: Test rfdetr-cpp
run: |
make --jobs=5 --output-sync=target -C backend/go/rfdetr-cpp test
# Per-backend e2e for locate-anything-cpp: builds the .so + Go binary and
# runs `make -C backend/go/locate-anything-cpp test`. test.sh fetches the
# locate-anything-q8_0 GGUF (~6.3 GB, NVIDIA LocateAnything-3B) from the
# published mudler/locate-anything.cpp-gguf HF repo + a COCO image, then the
# Go wire test loads the model and runs an open-vocabulary Detect, asserting
# at least one labeled box. Heavier than the other Go backends (it is a 3B),
# so it is gated to changes under backend/go/locate-anything-cpp/.
tests-locate-anything-cpp:
needs: detect-changes
if: needs.detect-changes.outputs.locate-anything-cpp == 'true' || needs.detect-changes.outputs.run-all == 'true'
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v7
with:
submodules: true
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install -y build-essential cmake curl libopenblas-dev
- name: Setup Go
uses: actions/setup-go@v5
- name: Display Go version
run: go version
- name: Proto Dependencies
run: |
# Install protoc
curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v26.1/protoc-26.1-linux-x86_64.zip -o protoc.zip && \
unzip -j -d /usr/local/bin protoc.zip bin/protoc && \
rm protoc.zip
go install google.golang.org/protobuf/cmd/protoc-gen-go@v1.34.2
go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@1958fcbe2ca8bd93af633f11e97d44e567e945af
PATH="$PATH:$HOME/go/bin" make protogen-go
- name: Build locate-anything-cpp
run: |
make --jobs=5 --output-sync=target -C backend/go/locate-anything-cpp
- name: Test locate-anything-cpp
run: |
make --jobs=5 --output-sync=target -C backend/go/locate-anything-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
@@ -952,7 +912,7 @@ jobs:
timeout-minutes: 90
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
@@ -987,7 +947,7 @@ jobs:
timeout-minutes: 90
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Setup Go
@@ -1008,16 +968,12 @@ jobs:
# image + working dir.
tests-vibevoice-cpp-grpc-transcription:
needs: detect-changes
# Skip on release tag pushes: the ASR Q4_K model is ~10 GB and cannot be
# pulled from HF within the inner `go test -timeout 30m` budget on a CI
# runner, so every tag build hung and timed out. Still runs on PRs/branch
# pushes that touch vibevoice-cpp so regressions are caught off the release path.
if: (needs.detect-changes.outputs.vibevoice-cpp == 'true' || needs.detect-changes.outputs.run-all == 'true') && !startsWith(github.ref, 'refs/tags/')
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@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
@@ -1046,7 +1002,7 @@ jobs:
timeout-minutes: 60
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Setup Go
@@ -1062,7 +1018,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
@@ -1095,7 +1051,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
@@ -1118,7 +1074,7 @@ jobs:
timeout-minutes: 90
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies
@@ -1144,7 +1100,7 @@ jobs:
timeout-minutes: 90
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies

View File

@@ -21,7 +21,7 @@ jobs:
go-version: ['1.26.x']
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Free disk space
@@ -84,7 +84,7 @@ jobs:
go-version: ['1.26.x']
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Setup Go ${{ matrix.go-version }}
@@ -121,19 +121,3 @@ jobs:
detached: true
connect-timeout-seconds: 180
limit-access-to-actor: true
# Fast standalone unit tests for the backends' pure C++ helpers - currently the
# llama-cpp message reconstruction (backend/cpp/llama-cpp/message_content.h),
# which guards the OpenAI chat content normalization (mudler/LocalAI#10524,
# #7324, #7528). The runner discovers every *_test.cpp under backend/cpp/, so
# new pure-C++ unit tests are picked up with no CI changes. These need only the
# C++ stdlib + nlohmann/json, so they run on every PR without the full
# llama.cpp + gRPC backend build. (The same suite is also wired as an opt-in
# CMake/ctest target, -DLLAMA_GRPC_BUILD_TESTS=ON, for in-backend-build runs.)
tests-backend-cpp:
runs-on: ubuntu-latest
steps:
- name: Clone
uses: actions/checkout@v7
- name: Run backend C++ unit tests
run: make test-backend-cpp

View File

@@ -62,7 +62,7 @@ jobs:
sudo rm -rfv build || true
df -h
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Dependencies

View File

@@ -21,7 +21,7 @@ jobs:
go-version: ['1.25.x']
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Configure apt mirror on runner

View File

@@ -1,97 +0,0 @@
---
name: 'PII NER tier E2E (live GGUF, CPU)'
# Runs the real privacy-filter GGUF NER tier end-to-end on CPU — the gap the
# hermetic tests/e2e suite cannot cover (it only exercises the in-process
# pattern tier). Heavy (builds the C++ backend image + downloads a ~2.7 GB
# GGUF), so it is path-filtered on PRs and otherwise runs nightly / on demand.
#
# This drives the container-level harness (tests/e2e-backends) via
# `make test-extra-backend-privacy-filter`: it builds the privacy-filter image,
# downloads the model, loads it on CPU, and asserts byte-correct, UTF-8-aligned
# TokenClassify spans. The complementary HTTP-path specs in tests/e2e
# (e2e_pii_ner_test.go) Skip unless PII_NER_MODEL_GGUF is wired.
on:
workflow_dispatch:
schedule:
- cron: '0 3 * * *'
push:
branches:
- master
paths:
- 'backend/cpp/privacy-filter/**'
- 'backend/Dockerfile.privacy-filter'
- 'core/services/routing/pii/**'
- 'core/services/routing/piidetector/**'
- 'core/backend/token_classify.go'
- 'core/http/endpoints/localai/pii.go'
- 'core/schema/pii.go'
- 'tests/e2e-backends/**'
- 'tests/e2e/e2e_pii_ner_test.go'
- 'tests/e2e/e2e_suite_test.go'
- '.github/workflows/tests-pii-ner-e2e.yml'
pull_request:
paths:
- 'backend/cpp/privacy-filter/**'
- 'backend/Dockerfile.privacy-filter'
- 'core/services/routing/pii/**'
- 'core/services/routing/piidetector/**'
- 'core/backend/token_classify.go'
- 'core/http/endpoints/localai/pii.go'
- 'core/schema/pii.go'
- 'tests/e2e-backends/**'
- 'tests/e2e/e2e_pii_ner_test.go'
- 'tests/e2e/e2e_suite_test.go'
- '.github/workflows/tests-pii-ner-e2e.yml'
concurrency:
group: ci-tests-pii-ner-e2e-${{ github.event.pull_request.number || github.sha }}-${{ github.repository }}
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
jobs:
tests-pii-ner-e2e:
runs-on: ubuntu-latest
strategy:
matrix:
go-version: ['1.25.x']
steps:
- name: Clone
uses: actions/checkout@v7
with:
submodules: true
- name: Free disk space
run: |
sudo rm -rf /usr/share/dotnet /usr/local/lib/android /opt/ghc /opt/hostedtoolcache/CodeQL || true
sudo docker image prune --all --force || true
df -h
- name: Configure apt mirror on runner
uses: ./.github/actions/configure-apt-mirror
- name: Setup Go ${{ matrix.go-version }}
uses: actions/setup-go@v5
with:
go-version: ${{ matrix.go-version }}
cache: false
- name: Proto Dependencies
run: |
curl -L -s https://github.com/protocolbuffers/protobuf/releases/download/v26.1/protoc-26.1-linux-x86_64.zip -o protoc.zip && \
unzip -j -d /usr/local/bin protoc.zip bin/protoc && \
rm protoc.zip
go install google.golang.org/protobuf/cmd/protoc-gen-go@v1.34.2
go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@1958fcbe2ca8bd93af633f11e97d44e567e945af
PATH="$PATH:$HOME/go/bin" make protogen-go
- name: Dependencies
run: |
sudo apt-get update
sudo apt-get install -y build-essential
# Builds local-ai-backend:privacy-filter, downloads the GGUF, loads it on
# CPU and runs the token_classify capability spec (byte-offset contract).
- name: Run live PII NER backend E2E
run: PATH="$PATH:$HOME/go/bin" make test-extra-backend-privacy-filter
- 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

@@ -23,7 +23,7 @@ jobs:
go-version: ['1.26.x']
steps:
- name: Clone
uses: actions/checkout@v7
uses: actions/checkout@v6
with:
submodules: true
- name: Configure apt mirror on runner

View File

@@ -10,7 +10,7 @@ jobs:
fail-fast: false
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v7
- uses: actions/checkout@v6
- name: Configure apt mirror on runner
uses: ./.github/actions/configure-apt-mirror
- uses: actions/setup-go@v5

6
.gitignore vendored
View File

@@ -91,9 +91,3 @@ core/http/react-ui/test-results/
# Local worktrees
.worktrees/
# SDD / brainstorm scratch (agent-driven development)
.superpowers/
# Local Apple signing material (never commit)
.certs/

View File

@@ -74,8 +74,6 @@ linters:
paths:
# Upstream whisper.cpp source tree fetched by the whisper backend Makefile.
- 'backend/go/whisper/sources'
# Vendored upstream supertonic pipeline (supertone-inc/supertonic go/helper.go).
- 'backend/go/supertonic/helper.go'
- 'docs/'
rules:
# CLI entry points: kong's `env:"..."` tag is the legitimate env→struct

View File

@@ -9,8 +9,7 @@ source:
enabled: true
name_template: '{{ .ProjectName }}-{{ .Tag }}-source'
builds:
- id: local-ai
main: ./cmd/local-ai
- main: ./cmd/local-ai
env:
- CGO_ENABLED=0
ldflags:
@@ -36,19 +35,3 @@ snapshot:
version_template: "{{ .Tag }}-next"
changelog:
use: github-native
# Sign + notarize the macOS server binary via the quill backend (runs on Linux,
# no macOS runner needed). Disabled automatically when MACOS_SIGN_P12 is unset
# (forks / PRs), so those builds stay unsigned and green.
notarize:
macos:
- enabled: '{{ isEnvSet "MACOS_SIGN_P12" }}'
ids:
- local-ai
sign:
certificate: "{{.Env.MACOS_SIGN_P12}}"
password: "{{.Env.MACOS_SIGN_PASSWORD}}"
notarize:
issuer_id: "{{.Env.MACOS_NOTARY_ISSUER_ID}}"
key_id: "{{.Env.MACOS_NOTARY_KEY_ID}}"
key: "{{.Env.MACOS_NOTARY_KEY}}"
wait: true

View File

@@ -43,5 +43,4 @@ LocalAI follows the Linux kernel project's [guidelines for AI coding assistants]
- **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
- **Backend OS coverage**: a new backend must target every OS it can build for, not just Linux. `.github/backend-matrix.yml` has two matrices — `include:` (Linux) and `includeDarwin:` (macOS / Apple Silicon). Most C/C++/GGML and many Python backends build on Darwin too — wire the `includeDarwin` entry + `backend/index.yaml` `metal:` entries, or say in the PR why an OS is unsupported. See the darwin checklist in [.agents/adding-backends.md](.agents/adding-backends.md).
- **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

@@ -108,7 +108,6 @@ RUN <<EOT bash
apt-get update && \
apt-get install -y --no-install-recommends \
cuda-nvcc-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
cuda-nvrtc-dev-${CUDA_MAJOR_VERSION}-${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} \

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/crispasr backends/parakeet-cpp backends/faster-whisper backends/silero-vad backends/local-store backends/huggingface backends/rfdetr backends/rfdetr-cpp 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/omnivoice-cpp backends/vibevoice-cpp backends/localvqe backends/tinygrad backends/sherpa-onnx backends/ds4 backends/ds4-darwin backends/liquid-audio backends/supertonic backends/depth-anything-cpp backends/privacy-filter backends/privacy-filter-darwin
.NOTPARALLEL: backends/diffusers backends/llama-cpp backends/turboquant backends/outetts backends/piper backends/stablediffusion-ggml backends/whisper backends/crispasr backends/parakeet-cpp backends/faster-whisper backends/silero-vad backends/local-store backends/huggingface backends/rfdetr backends/rfdetr-cpp backends/insightface backends/speaker-recognition backends/kitten-tts backends/kokoro backends/chatterbox backends/llama-cpp-darwin backends/neutts build-darwin-python-backend build-darwin-go-backend backends/mlx backends/diffuser-darwin backends/mlx-vlm backends/mlx-audio backends/mlx-distributed backends/stablediffusion-ggml-darwin backends/vllm backends/vllm-omni backends/sglang backends/moonshine backends/pocket-tts backends/qwen-tts backends/faster-qwen3-tts backends/qwen-asr backends/nemo backends/voxcpm backends/whisperx backends/ace-step backends/acestep-cpp backends/fish-speech backends/voxtral backends/opus backends/trl backends/llama-cpp-quantization backends/kokoros backends/sam3-cpp backends/qwen3-tts-cpp backends/vibevoice-cpp backends/localvqe backends/tinygrad backends/sherpa-onnx backends/ds4 backends/ds4-darwin backends/liquid-audio
GOCMD=go
GOTEST=$(GOCMD) test
@@ -103,7 +103,7 @@ COVERAGE_E2E_LABELS?=!real-models
COVERAGE_EXCLUDE_RE?=grpc/proto/.*[.]pb[.]go
.PHONY: all test test-coverage test-coverage-baseline test-coverage-check test-backend-cpp test-ui test-ui-coverage-baseline test-ui-coverage-check install-hooks build vendor lint lint-all
.PHONY: all test test-coverage test-coverage-baseline test-coverage-check test-ui test-ui-coverage-baseline test-ui-coverage-check install-hooks build vendor lint lint-all
all: help
@@ -180,7 +180,7 @@ osx-signed: build
## Run
run: ## run local-ai
CGO_LDFLAGS="$(CGO_LDFLAGS)" $(GOCMD) run ./cmd/local-ai
CGO_LDFLAGS="$(CGO_LDFLAGS)" $(GOCMD) run ./
prepare-test: protogen-go build-mock-backend
@@ -201,13 +201,6 @@ test: prepare-test
OPUS_SHIM_LIBRARY=$(abspath ./pkg/opus/shim/libopusshim.so) \
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --flake-attempts $(TEST_FLAKES) --fail-fast -v -r $(TEST_PATHS)
## Compiles and runs the standalone C++ unit tests for the backends (pure
## helpers that depend only on the stdlib + nlohmann/json, no full backend
## build). Discovers every *_test.cpp under backend/cpp/ - see
## backend/cpp/run-unit-tests.sh. Set NLOHMANN_INCLUDE to skip the header fetch.
test-backend-cpp:
bash backend/cpp/run-unit-tests.sh
## Runs the core suite ($(TEST_PATHS)) with statement-coverage instrumentation
## and writes a merged profile to $(COVERAGE_PROFILE). Deliberately omits
## --fail-fast so a single failure doesn't truncate the coverage number, and
@@ -316,20 +309,13 @@ 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
# Distributed architecture e2e (PostgreSQL + NATS via testcontainers).
# Includes NatsJWT specs (JWT-enabled NATS). Requires Docker.
# VLLMMultinode is excluded here; use test-e2e-vllm-multinode for that.
test-e2e-distributed: protogen-go
@echo 'Running distributed e2e tests (label Distributed, incl. NatsJWT)'
$(GOCMD) run github.com/onsi/ginkgo/v2/ginkgo --label-filter='Distributed && !VLLMMultinode' --flake-attempts $(TEST_FLAKES) -v -r ./tests/e2e/distributed
# 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
# test-e2e-distributed.
# 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 \
@@ -573,7 +559,6 @@ prepare-test-extra: protogen-python
$(MAKE) -C backend/python/speaker-recognition
$(MAKE) -C backend/rust/kokoros kokoros-grpc
$(MAKE) -C backend/go/rfdetr-cpp
$(MAKE) -C backend/go/locate-anything-cpp
test-extra: prepare-test-extra
$(MAKE) -C backend/python/transformers test
@@ -601,9 +586,6 @@ test-extra: prepare-test-extra
$(MAKE) -C backend/python/speaker-recognition test
$(MAKE) -C backend/rust/kokoros test
$(MAKE) -C backend/go/rfdetr-cpp test
$(MAKE) -C backend/go/locate-anything-cpp test
$(MAKE) -C backend/go/depth-anything-cpp test
$(MAKE) -C backend/go/supertonic test
##
## End-to-end gRPC tests that exercise a built backend container image.
@@ -697,16 +679,6 @@ test-extra-backend-llama-cpp-transcription: docker-build-llama-cpp
BACKEND_TEST_CTX_SIZE=2048 \
$(MAKE) test-extra-backend
## privacy-filter: the PII/NER token-classification backend. Exercises the
## TokenClassify RPC and asserts byte-correct, UTF-8-aligned span offsets
## against the openai-privacy-filter multilingual GGUF (CPU-runnable, ~50M
## active params). This is the live-backend coverage for the PII NER tier.
test-extra-backend-privacy-filter: docker-build-privacy-filter
BACKEND_IMAGE=local-ai-backend:privacy-filter \
BACKEND_TEST_MODEL_URL=https://huggingface.co/LocalAI-io/privacy-filter-multilingual-GGUF/resolve/main/privacy-filter-multilingual-f16.gguf \
BACKEND_TEST_CAPS=health,load,token_classify \
$(MAKE) test-extra-backend
## vllm is resolved from a HuggingFace model id (no file download) and
## exercises Predict + streaming + tool-call extraction via the hermes parser.
## Requires a host CPU with the SIMD instructions the prebuilt vllm CPU
@@ -1136,10 +1108,6 @@ backends/ds4-darwin: build
bash ./scripts/build/ds4-darwin.sh
./local-ai backends install "ocifile://$(abspath ./backend-images/ds4.tar)"
backends/privacy-filter-darwin: build
bash ./scripts/build/privacy-filter-darwin.sh
./local-ai backends install "ocifile://$(abspath ./backend-images/privacy-filter.tar)"
build-darwin-python-backend: build
bash ./scripts/build/python-darwin.sh
@@ -1185,10 +1153,6 @@ BACKEND_TURBOQUANT = turboquant|turboquant|.|false|false
# Single-model; hardware-only validation lives at tests/e2e-backends/
# (BACKEND_BINARY mode); see docs/superpowers/plans/2026-05-11-ds4-backend.md.
BACKEND_DS4 = ds4|ds4|.|false|false
# privacy-filter wraps the standalone privacy-filter.cpp GGML engine (the
# openai-privacy-filter PII/NER token classifier) — the TokenClassify RPC for
# the PII redactor tier, on stock ggml with no llama.cpp carry-patches.
BACKEND_PRIVACY_FILTER = privacy-filter|privacy-filter|.|false|false
# Golang backends
BACKEND_PIPER = piper|golang|.|false|true
@@ -1200,16 +1164,13 @@ BACKEND_STABLEDIFFUSION_GGML = stablediffusion-ggml|golang|.|--progress=plain|tr
BACKEND_WHISPER = whisper|golang|.|false|true
BACKEND_CRISPASR = crispasr|golang|.|false|true
BACKEND_PARAKEET_CPP = parakeet-cpp|golang|.|false|true
BACKEND_DEPTH_ANYTHING_CPP = depth-anything-cpp|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_OMNIVOICE_CPP = omnivoice-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
BACKEND_SUPERTONIC = supertonic|golang|.|false|true
# Python backends with root context
BACKEND_RERANKERS = rerankers|python|.|false|true
@@ -1283,7 +1244,6 @@ $(eval $(call generate-docker-build-target,$(BACKEND_LLAMA_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_IK_LLAMA_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_TURBOQUANT)))
$(eval $(call generate-docker-build-target,$(BACKEND_DS4)))
$(eval $(call generate-docker-build-target,$(BACKEND_PRIVACY_FILTER)))
$(eval $(call generate-docker-build-target,$(BACKEND_PIPER)))
$(eval $(call generate-docker-build-target,$(BACKEND_LOCAL_STORE)))
$(eval $(call generate-docker-build-target,$(BACKEND_CLOUD_PROXY)))
@@ -1293,7 +1253,6 @@ $(eval $(call generate-docker-build-target,$(BACKEND_STABLEDIFFUSION_GGML)))
$(eval $(call generate-docker-build-target,$(BACKEND_WHISPER)))
$(eval $(call generate-docker-build-target,$(BACKEND_CRISPASR)))
$(eval $(call generate-docker-build-target,$(BACKEND_PARAKEET_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_DEPTH_ANYTHING_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_VOXTRAL)))
$(eval $(call generate-docker-build-target,$(BACKEND_OPUS)))
$(eval $(call generate-docker-build-target,$(BACKEND_RERANKERS)))
@@ -1326,7 +1285,6 @@ $(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_OMNIVOICE_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)))
@@ -1339,13 +1297,12 @@ $(eval $(call generate-docker-build-target,$(BACKEND_KOKOROS)))
$(eval $(call generate-docker-build-target,$(BACKEND_SAM3_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_RFDETR_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_SHERPA_ONNX)))
$(eval $(call generate-docker-build-target,$(BACKEND_SUPERTONIC)))
# Pattern rule for docker-save targets
docker-save-%: backend-images
docker save local-ai-backend:$* -o backend-images/$*.tar
docker-build-backends: docker-build-llama-cpp docker-build-ik-llama-cpp docker-build-turboquant docker-build-ds4 docker-build-rerankers docker-build-vllm docker-build-vllm-omni docker-build-sglang docker-build-transformers docker-build-outetts docker-build-diffusers docker-build-kokoro docker-build-faster-whisper docker-build-crispasr docker-build-coqui docker-build-chatterbox docker-build-vibevoice docker-build-liquid-audio docker-build-moonshine docker-build-pocket-tts docker-build-qwen-tts docker-build-fish-speech docker-build-faster-qwen3-tts docker-build-qwen-asr docker-build-nemo docker-build-voxcpm docker-build-whisperx docker-build-ace-step docker-build-acestep-cpp docker-build-voxtral docker-build-mlx-distributed docker-build-trl docker-build-llama-cpp-quantization docker-build-tinygrad docker-build-kokoros docker-build-sam3-cpp docker-build-rfdetr-cpp docker-build-qwen3-tts-cpp docker-build-omnivoice-cpp docker-build-vibevoice-cpp docker-build-localvqe docker-build-insightface docker-build-speaker-recognition docker-build-sherpa-onnx docker-build-cloud-proxy docker-build-supertonic docker-build-depth-anything-cpp docker-build-privacy-filter
docker-build-backends: docker-build-llama-cpp docker-build-ik-llama-cpp docker-build-turboquant docker-build-ds4 docker-build-rerankers docker-build-vllm docker-build-vllm-omni docker-build-sglang docker-build-transformers docker-build-outetts docker-build-diffusers docker-build-kokoro docker-build-faster-whisper docker-build-crispasr docker-build-coqui docker-build-chatterbox docker-build-vibevoice docker-build-liquid-audio docker-build-moonshine docker-build-pocket-tts docker-build-qwen-tts docker-build-fish-speech docker-build-faster-qwen3-tts docker-build-qwen-asr docker-build-nemo docker-build-voxcpm docker-build-whisperx docker-build-ace-step docker-build-acestep-cpp docker-build-voxtral docker-build-mlx-distributed docker-build-trl docker-build-llama-cpp-quantization docker-build-tinygrad docker-build-kokoros docker-build-sam3-cpp docker-build-rfdetr-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-cloud-proxy
########################################################
### Mock Backend for E2E Tests
@@ -1460,32 +1417,13 @@ docs: docs/static/gallery.html
########################################################
## fyne cross-platform build
# Build LocalAI.app from the launcher via fyne (metadata read from cmd/launcher/FyneApp.toml).
# Signing happens via contrib/macos/sign-and-notarize.sh, which is a no-op when the signing
# secrets are unset, so unsigned local/fork builds keep working.
build-launcher-darwin:
rm -rf dist/LocalAI.app cmd/launcher/LocalAI.app
mkdir -p dist
cd cmd/launcher && go run fyne.io/tools/cmd/fyne@latest package -os darwin -icon ../../core/http/static/logo.png --executable $(LAUNCHER_BINARY_NAME)
mv cmd/launcher/LocalAI.app dist/LocalAI.app
bash contrib/macos/sign-and-notarize.sh sign dist/LocalAI.app
# Wrap the (signed) app into a drag-to-Applications DMG via hdiutil, then sign the DMG.
dmg-launcher-darwin: build-launcher-darwin
rm -rf dist/dmg dist/LocalAI.dmg
mkdir -p dist/dmg
cp -R dist/LocalAI.app dist/dmg/LocalAI.app
ln -s /Applications dist/dmg/Applications
hdiutil create -volname "LocalAI" -srcfolder dist/dmg -ov -format UDZO dist/LocalAI.dmg
bash contrib/macos/sign-and-notarize.sh sign dist/LocalAI.dmg
# Submit the DMG to Apple notarization and staple the ticket (no-op without notary secrets).
notarize-launcher-darwin: dmg-launcher-darwin
bash contrib/macos/sign-and-notarize.sh notarize dist/LocalAI.dmg
# Single entrypoint for CI: build -> sign app -> dmg -> sign dmg -> notarize -> staple.
release-launcher-darwin: notarize-launcher-darwin
@echo "dist/LocalAI.dmg is ready"
build-launcher-darwin: build-launcher
go run github.com/tiagomelo/macos-dmg-creator/cmd/createdmg@latest \
--appName "LocalAI" \
--appBinaryPath "$(LAUNCHER_BINARY_NAME)" \
--bundleIdentifier "com.localai.launcher" \
--iconPath "core/http/static/logo.png" \
--outputDir "dist/"
build-launcher-linux:
cd cmd/launcher && go run fyne.io/tools/cmd/fyne@latest package -os linux -icon ../../core/http/static/logo.png --executable $(LAUNCHER_BINARY_NAME)-linux && mv LocalAI.tar.xz ../../$(LAUNCHER_BINARY_NAME)-linux.tar.xz
cd cmd/launcher && go run fyne.io/tools/cmd/fyne@latest package -os linux -icon ../../core/http/static/logo.png --executable $(LAUNCHER_BINARY_NAME)-linux && mv launcher.tar.xz ../../$(LAUNCHER_BINARY_NAME)-linux.tar.xz

View File

@@ -29,18 +29,6 @@
<a href="https://trendshift.io/repositories/5539" target="_blank"><img src="https://trendshift.io/api/badge/repositories/5539" alt="mudler%2FLocalAI | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
</p>
<!-- Keep these links, translations synced daily. -->
<p align="center">
<a href="https://zdoc.app/de/mudler/LocalAI">Deutsch</a> |
<a href="https://zdoc.app/es/mudler/LocalAI">Español</a> |
<a href="https://zdoc.app/fr/mudler/LocalAI">français</a> |
<a href="https://zdoc.app/ja/mudler/LocalAI">日本語</a> |
<a href="https://zdoc.app/ko/mudler/LocalAI">한국어</a> |
<a href="https://zdoc.app/pt/mudler/LocalAI">Português</a> |
<a href="https://zdoc.app/ru/mudler/LocalAI">Русский</a> |
<a href="https://zdoc.app/zh/mudler/LocalAI">中文</a>
</p>
**LocalAI** is the open-source AI engine. Run any model - LLMs, vision, voice, image, video - on any hardware. No GPU required.
**A small core, not a bundle.** Each backend wraps a best-in-class engine (llama.cpp, vLLM, whisper.cpp, stable-diffusion, MLX...) in its own image, pulled only when a model needs it. You install nothing you don't use.
@@ -161,27 +149,12 @@ local-ai run https://gist.githubusercontent.com/.../phi-2.yaml
local-ai run oci://localai/phi-2:latest
```
To test a running LocalAI server from the terminal, open an interactive chat session from another shell. Inside the prompt, `/models` lists installed models and `/model <name>` switches between them.
```bash
# Terminal 1
local-ai run llama-3.2-1b-instruct:q4_k_m
# Terminal 2
local-ai chat --model llama-3.2-1b-instruct:q4_k_m
```
> **Automatic Backend Detection**: LocalAI automatically detects your GPU capabilities and downloads the appropriate backend. For advanced options, see [GPU Acceleration](https://localai.io/features/gpu-acceleration/).
For more details, see the [Getting Started guide](https://localai.io/basics/getting_started/).
## Latest News
- **June 2026**: New native biometric backends from the LocalAI team: [voice-detect.cpp](https://github.com/mudler/voice-detect.cpp) for speaker recognition and voice analysis (ECAPA-TDNN, WeSpeaker, ERes2Net, CAM++, wav2vec2 age/gender/emotion) and [face-detect.cpp](https://github.com/mudler/face-detect.cpp) for face detection, recognition, demographics and anti-spoofing (SCRFD/ArcFace, YuNet/SFace). Both are from-scratch C++/ggml engines with no Python or onnxruntime at inference, self-contained GGUF weights, bit-exact parity with the reference, and GPU cuDNN parity, replacing the heavier Python `insightface` and `speaker-recognition` backends ([PR #10441](https://github.com/mudler/LocalAI/pull/10441)).
- **June 2026**: New [realtime voice assistant demo](https://github.com/localai-org/localai-realtime-demo) (a tiny Go client for the Realtime API with a full talk-back voice loop and tool calling), plus [streaming of the realtime LLM / TTS / transcription pipeline stages](https://github.com/mudler/LocalAI/pull/10176) and [configurable WebRTC ICE candidates](https://github.com/mudler/LocalAI/pull/10231).
- **June 2026**: Big speech push: the [parakeet.cpp](https://github.com/mudler/parakeet.cpp) ASR engine gains [NeMo-faithful segment timestamps](https://github.com/mudler/LocalAI/pull/10207), a [multilingual streaming Nemotron-3.5 model](https://github.com/mudler/LocalAI/pull/10199), [dynamic batching for concurrent transcription](https://github.com/mudler/LocalAI/pull/10112) and [CUDA graphs](https://github.com/mudler/LocalAI/pull/10273); the new [CrispASR backend](https://github.com/mudler/LocalAI/pull/10099) adds multi-architecture ASR + TTS, and [60 Piper TTS voices across 42 languages](https://github.com/mudler/LocalAI/pull/10296) land in the gallery (plus [per-request TTS instructions and params](https://github.com/mudler/LocalAI/pull/10172)).
- **June 2026**: New backends and models: [locate-anything.cpp](https://github.com/mudler/LocalAI/pull/10264) for open-vocabulary object detection via ggml, [Ideogram4 image generation](https://github.com/mudler/LocalAI/pull/10201) in stablediffusion-ggml, [llama.cpp video input](https://github.com/mudler/LocalAI/pull/10216), and the [Gemma 4 QAT family with MTP speculative-decoding pairs](https://github.com/mudler/LocalAI/pull/10215). Plus an [interactive CLI chat mode](https://github.com/mudler/LocalAI/pull/10226) and [RAG source citations in agent responses](https://github.com/mudler/LocalAI/pull/10228).
- **June 2026**: Distributed mode hardening: [prefix-cache-aware routing](https://github.com/mudler/LocalAI/pull/10071), a [production-ready request router with auto-sized embedding/rerank batches](https://github.com/mudler/LocalAI/pull/10104), [ds4 layer-split distributed inference](https://github.com/mudler/LocalAI/pull/10098), [NATS JWT auth + TLS/mTLS](https://github.com/mudler/LocalAI/pull/10159), and [resumable file uploads](https://github.com/mudler/LocalAI/pull/10109).
- **May 2026**: **LocalAI 4.3.0** - `llama.cpp` [prompt cache on by default](https://github.com/mudler/LocalAI/pull/9925) (repeated system prompts collapse from minutes to seconds), [keyless cosign signing of backend OCI images](https://github.com/mudler/LocalAI/pull/9823), [per-API-key + per-user usage attribution](https://github.com/mudler/LocalAI/pull/9920), Distributed v3 with [per-request replica routing](https://github.com/mudler/LocalAI/pull/9968). [Release notes](https://github.com/mudler/LocalAI/releases/tag/v4.3.0)
- **May 2026**: **LocalAI 4.2.0** - LocalAI sees and hears: [voice recognition](https://github.com/mudler/LocalAI/pull/9500), [face recognition + antispoofing liveness](https://github.com/mudler/LocalAI/pull/9480), speaker diarization. Plus [drop-in Ollama API](https://github.com/mudler/LocalAI/pull/9284), [video generation](https://github.com/mudler/LocalAI/pull/9420), redesigned UI with i18n + admin-configurable branding, vLLM at feature parity with llama.cpp, and 11 new backends. [Release notes](https://github.com/mudler/LocalAI/releases/tag/v4.2.0)
- **April 2026**: **LocalAI 4.1.0** - LocalAI becomes a control tower: distributed cluster mode with VRAM-aware smart routing + autoscaling, multi-user platform with OIDC and API keys, per-user quotas with predictive analytics, in-UI fine-tuning with TRL (auto-export to GGUF), on-the-fly quantization backend, visual pipeline editor. [Release notes](https://github.com/mudler/LocalAI/releases/tag/v4.1.0)
@@ -221,29 +194,10 @@ For older news and full release notes, see [GitHub Releases](https://github.com/
## Supported Backends & Acceleration
LocalAI supports **60+ backends** including llama.cpp, vLLM, SGLang, transformers, whisper.cpp, diffusers, MLX, MLX-VLM, and many more. Hardware acceleration is available for **NVIDIA** (CUDA 12/13), **AMD** (ROCm), **Intel** (oneAPI/SYCL), **Apple Silicon** (Metal), **Vulkan**, and **NVIDIA Jetson** (L4T). All backends can be installed on-the-fly from the [Backend Gallery](https://localai.io/backends/).
LocalAI supports **36+ backends** including llama.cpp, vLLM, transformers, whisper.cpp, diffusers, MLX, MLX-VLM, and many more. Hardware acceleration is available for **NVIDIA** (CUDA 12/13), **AMD** (ROCm), **Intel** (oneAPI/SYCL), **Apple Silicon** (Metal), **Vulkan**, and **NVIDIA Jetson** (L4T). All backends can be installed on-the-fly from the [Backend Gallery](https://localai.io/backends/).
See the full [Backend & Model Compatibility Table](https://localai.io/model-compatibility/) and [GPU Acceleration guide](https://localai.io/features/gpu-acceleration/).
### Backends built by us
Most backends wrap a best-in-class upstream engine. A handful of them are native C/C++/GGML engines (no Python at inference) developed and maintained by the LocalAI project itself:
| Backend | What it does |
|---------|-------------|
| [parakeet.cpp](https://github.com/mudler/parakeet.cpp) | C++/GGML port of NVIDIA NeMo Parakeet ASR (tdt/ctc/rnnt/hybrid), with cache-aware streaming transcription |
| [ced.cpp](https://github.com/mudler/ced.cpp) | C++/GGML port of the CED audio-tagging models: sound-event classification (527-class AudioSet) over REST and the realtime API for live recognition |
| [voxtral.c](https://github.com/mudler/voxtral.c) | Voxtral Realtime 4B speech-to-text in pure C |
| [vibevoice.cpp](https://github.com/mudler/vibevoice.cpp) | Native port of Microsoft VibeVoice for TTS (voice cloning) and long-form ASR with speaker diarization |
| [rf-detr.cpp](https://github.com/mudler/rf-detr.cpp) | Native RF-DETR object detection and instance segmentation |
| [locate-anything.cpp](https://github.com/mudler/locate-anything.cpp) | Open-vocabulary object detection and visual grounding (LocateAnything-3B) |
| [depth-anything.cpp](https://github.com/mudler/depth-anything.cpp) | Depth Anything 3 monocular metric depth + camera pose estimation |
| [privacy-filter.cpp](https://github.com/localai-org/privacy-filter.cpp) | Standalone GGML PII/NER token-classification engine powering LocalAI's PII redaction tier |
| [LocalVQE](https://github.com/localai-org/LocalVQE) | Joint acoustic echo cancellation, noise suppression, and dereverberation |
| [local-store](https://github.com/mudler/LocalAI) | Local-first vector database for embeddings (shipped in-tree) |
We also maintain [apex-quant](https://github.com/localai-org/apex-quant), a per-tensor, per-layer quantization recipe for Mixture-of-Experts models that exploits their structural sparsity to produce GGUFs matching or beating Q8_0 quality - and they run out of the box on stock llama.cpp.
## Resources
- [Documentation](https://localai.io/)
@@ -253,7 +207,7 @@ We also maintain [apex-quant](https://github.com/localai-org/apex-quant), a per-
- [Integrations & community projects](https://localai.io/docs/integrations/)
- [Installation video walkthrough](https://www.youtube.com/watch?v=cMVNnlqwfw4)
- [Media & blog posts](https://localai.io/basics/news/#media-blogs-social)
- [Examples](https://github.com/mudler/LocalAI-examples) — including the [realtime voice assistant demo](https://github.com/localai-org/localai-realtime-demo) (Go client for the Realtime API with tool calling)
- [Examples](https://github.com/mudler/LocalAI-examples)
## Team

View File

@@ -65,12 +65,7 @@ RUN <<EOT bash
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 && \
apt-get install -y mesa-vulkan-drivers libdrm2
# Mesa Vulkan ICD drivers (ANV/RADV/lavapipe) + their manifests. The
# LunarG SDK below only provides the loader and shader tooling, not
# hardware drivers — without Mesa, package-gpu-libs.sh has no ICD to
# bundle and the packaged backend finds no GPU at runtime.
clang-format qtbase5-dev qt6-base-dev libxcb-glx0-dev sudo xz-utils
if [ "amd64" = "$TARGETARCH" ]; then
wget "https://sdk.lunarg.com/sdk/download/1.4.335.0/linux/vulkansdk-linux-x86_64-1.4.335.0.tar.xz" && \
tar -xf vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
@@ -137,7 +132,7 @@ RUN <<EOT bash
libcusolver-dev-${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} libcudnn9-dev-cuda-${CUDA_MAJOR_VERSION} cuda-cupti-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} libnvjitlink-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION}
libcufile-${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/*
@@ -211,16 +206,6 @@ RUN if [ "${BACKEND}" = "opus" ]; then \
apt-get clean && rm -rf /var/lib/apt/lists/*; \
fi
# CrispASR's piper TTS backend dlopens libespeak-ng at runtime to phonemize
# non-English text (the MIT-clean path; English uses a built-in G2P). Install
# the espeak-ng runtime + its libpcaudio/libsonic deps + voice data so
# package.sh can bundle them into the FROM scratch image.
RUN if [ "${BACKEND}" = "crispasr" ]; then \
apt-get update && apt-get install -y --no-install-recommends \
espeak-ng-data libespeak-ng1 libpcaudio0 libsonic0 && \
apt-get clean && rm -rf /var/lib/apt/lists/*; \
fi
COPY . /LocalAI
RUN git config --global --add safe.directory /LocalAI

View File

@@ -1,109 +0,0 @@
ARG BASE_IMAGE=ubuntu:24.04
# BUILDER_BASE_IMAGE defaults to BASE_IMAGE so the Dockerfile parses when no
# prebuilt base is supplied; the builder-prebuilt stage is only entered when
# BUILDER_TARGET=builder-prebuilt, so the fallback content is harmless
# (BuildKit prunes the unreferenced builder).
ARG BUILDER_BASE_IMAGE=${BASE_IMAGE}
# BUILDER_TARGET selects which builder stage the scratch image copies from.
# Declared before any FROM so it is usable in `FROM ${BUILDER_TARGET}`. The
# backend_build workflow sets it to builder-prebuilt when the matrix entry
# provides builder-base-image, else builder-fromsource (the local default).
ARG BUILDER_TARGET=builder-fromsource
ARG APT_MIRROR=""
ARG APT_PORTS_MIRROR=""
# privacy-filter: standalone GGML engine for the openai-privacy-filter PII/NER
# token classifier, wrapped as a LocalAI gRPC backend.
#
# Mirrors backend/Dockerfile.llama-cpp: the build toolchain (gRPC + cmake +
# protoc + conditional CUDA/Vulkan) comes from the shared
# .docker/install-base-deps.sh (from-source path) or a prebuilt
# quay.io/go-skynet/ci-cache:base-grpc-* image (CI path) — nothing GPU-specific
# is hand-rolled here. BUILD_TYPE selects the engine backend in the Makefile:
# "" = cpu, "cublas" -> -DPF_CUDA=ON, "vulkan" -> -DPF_VULKAN=ON.
# ============================================================================
# Stage: builder-fromsource — self-contained build. Runs the same install
# script backend/Dockerfile.base-grpc-builder runs, so this path is
# bit-equivalent to the prebuilt base. Used when BUILDER_TARGET=builder-fromsource
# (the default; local `make backends/privacy-filter`).
# ============================================================================
FROM ${BASE_IMAGE} AS builder-fromsource
ARG BUILD_TYPE
ARG CUDA_MAJOR_VERSION
ARG CUDA_MINOR_VERSION
ARG CMAKE_FROM_SOURCE=false
# CUDA Toolkit 13.x needs CMake 3.31.9+ for correct toolchain/arch detection.
ARG CMAKE_VERSION=3.31.10
ARG GRPC_VERSION=v1.65.0
ARG GRPC_MAKEFLAGS="-j4 -Otarget"
ARG SKIP_DRIVERS=false
ARG TARGETARCH
ARG UBUNTU_VERSION=2404
ARG APT_MIRROR
ARG APT_PORTS_MIRROR
ENV BUILD_TYPE=${BUILD_TYPE} \
CUDA_MAJOR_VERSION=${CUDA_MAJOR_VERSION} \
CUDA_MINOR_VERSION=${CUDA_MINOR_VERSION} \
CMAKE_FROM_SOURCE=${CMAKE_FROM_SOURCE} \
CMAKE_VERSION=${CMAKE_VERSION} \
GRPC_VERSION=${GRPC_VERSION} \
GRPC_MAKEFLAGS=${GRPC_MAKEFLAGS} \
SKIP_DRIVERS=${SKIP_DRIVERS} \
TARGETARCH=${TARGETARCH} \
UBUNTU_VERSION=${UBUNTU_VERSION} \
APT_MIRROR=${APT_MIRROR} \
APT_PORTS_MIRROR=${APT_PORTS_MIRROR} \
DEBIAN_FRONTEND=noninteractive
# CUDA on PATH (a no-op when CUDA is not installed, e.g. cpu/vulkan builds).
ENV PATH=/usr/local/cuda/bin:${PATH}
WORKDIR /build
# apt deps + cmake + protoc + gRPC + conditional CUDA/Vulkan, all from the
# shared script (the source of truth that base-grpc-builder also runs).
RUN --mount=type=bind,source=.docker/install-base-deps.sh,target=/usr/local/sbin/install-base-deps \
--mount=type=bind,source=.docker/apt-mirror.sh,target=/usr/local/sbin/apt-mirror \
bash /usr/local/sbin/install-base-deps
# install-base-deps installs gRPC under /opt/grpc; copy it to /usr/local so the
# backend's find_package(gRPC CONFIG) resolves it at the canonical prefix.
RUN cp -a /opt/grpc/. /usr/local/
COPY . /LocalAI
RUN --mount=type=cache,target=/root/.ccache,id=privacy-filter-ccache-${TARGETARCH}-${BUILD_TYPE},sharing=locked \
make -C /LocalAI/backend/cpp/privacy-filter BUILD_TYPE=${BUILD_TYPE} NATIVE=false grpc-server package
# ============================================================================
# Stage: builder-prebuilt — FROM a prebuilt
# quay.io/go-skynet/ci-cache:base-grpc-* image (gRPC at /opt/grpc + apt deps +
# CUDA/Vulkan already installed). Used in CI when the matrix entry sets
# builder-base-image.
# ============================================================================
FROM ${BUILDER_BASE_IMAGE} AS builder-prebuilt
ARG BUILD_TYPE
ARG TARGETARCH
ENV BUILD_TYPE=${BUILD_TYPE}
# CUDA on PATH (a no-op for the cpu/vulkan base images).
ENV PATH=/usr/local/cuda/bin:${PATH}
# Mirror builder-fromsource: the base-grpc image installs gRPC to /opt/grpc but
# does not copy it to /usr/local.
RUN cp -a /opt/grpc/. /usr/local/
COPY . /LocalAI
RUN --mount=type=cache,target=/root/.ccache,id=privacy-filter-ccache-${TARGETARCH}-${BUILD_TYPE},sharing=locked \
make -C /LocalAI/backend/cpp/privacy-filter BUILD_TYPE=${BUILD_TYPE} NATIVE=false grpc-server package
# ============================================================================
# Final stage — copy the package output from the selected builder. BuildKit
# does not expand variables in `COPY --from=`, so alias the chosen builder to a
# fixed stage name first.
# ============================================================================
FROM ${BUILDER_TARGET} AS builder
FROM scratch
COPY --from=builder /LocalAI/backend/cpp/privacy-filter/package/. ./

View File

@@ -66,12 +66,7 @@ RUN <<EOT bash
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 && \
apt-get install -y mesa-vulkan-drivers libdrm2
# Mesa Vulkan ICD drivers (ANV/RADV/lavapipe) + their manifests. The
# LunarG SDK below only provides the loader and shader tooling, not
# hardware drivers — without Mesa, package-gpu-libs.sh has no ICD to
# bundle and the packaged backend finds no GPU at runtime.
clang-format qtbase5-dev qt6-base-dev libxcb-glx0-dev sudo xz-utils
if [ "amd64" = "$TARGETARCH" ]; then
wget "https://sdk.lunarg.com/sdk/download/1.4.335.0/linux/vulkansdk-linux-x86_64-1.4.335.0.tar.xz" && \
tar -xf vulkansdk-linux-x86_64-1.4.335.0.tar.xz && \
@@ -131,7 +126,6 @@ RUN <<EOT bash
apt-get update && \
apt-get install -y --no-install-recommends \
cuda-nvcc-${CUDA_MAJOR_VERSION}-${CUDA_MINOR_VERSION} \
cuda-nvrtc-dev-${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} \

View File

@@ -24,10 +24,6 @@ service Backend {
rpc TokenizeString(PredictOptions) returns (TokenizationResponse) {}
rpc Status(HealthMessage) returns (StatusResponse) {}
rpc Detect(DetectOptions) returns (DetectResponse) {}
// SoundDetection runs an audio-tagging / sound-event-classification model
// (e.g. CED over the AudioSet ontology) on a clip and returns scored labels.
rpc SoundDetection(SoundDetectionRequest) returns (SoundDetectionResponse) {}
rpc Depth(DepthRequest) returns (DepthResponse) {}
rpc FaceVerify(FaceVerifyRequest) returns (FaceVerifyResponse) {}
rpc FaceAnalyze(FaceAnalyzeRequest) returns (FaceAnalyzeResponse) {}
rpc VoiceVerify(VoiceVerifyRequest) returns (VoiceVerifyResponse) {}
@@ -541,15 +537,6 @@ message TTSRequest {
string dst = 3;
string voice = 4;
optional string language = 5;
// instructions is a free-form, per-request style/voice description (maps to
// the OpenAI `instructions` field). Backends that support expressive synthesis
// (e.g. Qwen3-TTS CustomVoice/VoiceDesign) prefer this over the static YAML
// option when set; backends that don't simply ignore it.
optional string instructions = 6;
// params carries optional, backend-specific per-request generation parameters
// (e.g. Chatterbox exaggeration/cfg_weight/temperature). Values are strings and
// coerced by the backend; unset leaves the backend's configured defaults.
map<string, string> params = 7;
}
message VADRequest {
@@ -674,53 +661,6 @@ message DetectResponse {
repeated Detection Detections = 1;
}
// --- Sound-event classification / audio tagging messages (CED) ---
message SoundDetectionRequest {
string src = 1; // audio file path (LocalAI writes the upload to disk)
int32 top_k = 2; // number of top tags to return (0 = all classes)
float threshold = 3; // optional: drop tags scoring below this
}
message SoundClass {
string label = 1; // AudioSet class name, e.g. "Baby cry, infant cry"
float score = 2; // per-class probability (multi-label, independent)
int32 index = 3; // class index in the model ontology
}
message SoundDetectionResponse {
repeated SoundClass detections = 1; // score-descending
}
// --- Depth estimation messages (Depth Anything 3) ---
message DepthRequest {
string src = 1; // input image (filesystem path or base64-encoded payload)
string dst = 2; // optional output directory for exports (glb/colmap)
bool include_depth = 3; // return the per-pixel metric depth map
bool include_confidence = 4; // return the per-pixel confidence map (DualDPT)
bool include_pose = 5; // return camera extrinsics/intrinsics (DualDPT)
bool include_sky = 6; // return the per-pixel sky map (mono models)
bool include_points = 7; // back-project to a 3D point cloud (DualDPT)
float points_conf_thresh = 8; // keep points with confidence >= this threshold
repeated string exports = 9; // requested exports: "glb", "colmap"
}
message DepthResponse {
int32 width = 1; // processed depth-map width
int32 height = 2; // processed depth-map height
repeated float depth = 3; // width*height row-major metric depth
repeated float confidence = 4; // width*height row-major confidence (DualDPT)
repeated float sky = 5; // width*height row-major sky map (mono)
repeated float extrinsics = 6; // 12 floats, 3x4 row-major (world-to-camera)
repeated float intrinsics = 7; // 9 floats, 3x3 row-major
int32 num_points = 8; // number of 3D points
repeated float points = 9; // num_points*3 xyz, world space
bytes point_colors = 10; // num_points*3 uint8 rgb
repeated string export_paths = 11; // paths written for the requested exports
bool is_metric = 12; // depth is in metric units
}
// --- Face recognition messages ---
message FacialArea {

View File

@@ -9,22 +9,6 @@ option(DS4_NATIVE "Compile with -march=native / -mcpu=native" ON)
set(DS4_GPU "cpu" CACHE STRING "GPU backend: cpu, cuda, or metal")
set(DS4_DIR "${CMAKE_CURRENT_SOURCE_DIR}/ds4" CACHE PATH "Path to cloned ds4 source")
if(${CMAKE_SYSTEM_NAME} MATCHES "Darwin")
# Homebrew installs protobuf/grpc under a non-default prefix. The generated
# backend.pb.cc / backend.grpc.pb.cc pull in google/protobuf and grpcpp
# headers, but the hw_grpc_proto library links neither target, so on macOS
# the headers (e.g. google/protobuf/runtime_version.h) are never on the
# compiler's include path. Add the Homebrew prefix globally, matching the
# llama-cpp backend which builds on Darwin CI.
if(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "arm64")
set(HOMEBREW_DEFAULT_PREFIX "/opt/homebrew")
else()
set(HOMEBREW_DEFAULT_PREFIX "/usr/local")
endif()
link_directories("${HOMEBREW_DEFAULT_PREFIX}/lib")
include_directories("${HOMEBREW_DEFAULT_PREFIX}/include")
endif()
find_package(Threads REQUIRED)
find_package(Protobuf CONFIG QUIET)
if(NOT Protobuf_FOUND)
@@ -76,12 +60,10 @@ elseif(DS4_GPU STREQUAL "cpu")
set(DS4_OBJS "${DS4_DIR}/ds4_cpu.o")
endif()
# ds4.c now references ds4_distributed.c (distributed inference) and ds4_ssd.c
# (SSD expert-cache), each split into its own translation unit upstream. Both
# are GPU-agnostic objects shared by every GPU mode, so link them in regardless
# of DS4_GPU.
# ds4.c now references ds4_distributed.c (distributed inference was split into
# its own translation unit upstream). It is a single GPU-agnostic object shared
# by every GPU mode, so link it in regardless of DS4_GPU.
list(APPEND DS4_OBJS "${DS4_DIR}/ds4_distributed.o")
list(APPEND DS4_OBJS "${DS4_DIR}/ds4_ssd.o")
add_executable(${TARGET}
grpc-server.cpp

View File

@@ -1,10 +1,10 @@
# ds4 backend Makefile.
#
# Upstream pin lives below as DS4_VERSION?=80ebbc396aee40eedc1d829222f3362d10fa4c6c
# Upstream pin lives below as DS4_VERSION?=ba00a8a88c4c5810a3d1fed6b7b8fa2b44b82fdc
# (.github/bump_deps.sh) can find and update it - matches the
# llama-cpp / ik-llama-cpp / turboquant convention.
DS4_VERSION?=80ebbc396aee40eedc1d829222f3362d10fa4c6c
DS4_VERSION?=ba00a8a88c4c5810a3d1fed6b7b8fa2b44b82fdc
DS4_REPO?=https://github.com/antirez/ds4
CURRENT_MAKEFILE_DIR := $(dir $(abspath $(lastword $(MAKEFILE_LIST))))
@@ -18,20 +18,19 @@ UNAME_S := $(shell uname -s)
CMAKE_ARGS ?= -DCMAKE_BUILD_TYPE=Release
# ds4_distributed.o and ds4_ssd.o are GPU-agnostic translation units that
# ds4.c/ds4_cpu.o now reference (upstream split distributed inference and the
# SSD expert-cache into their own .c files). Both objects are shared by every
# GPU mode, so they are appended unconditionally below.
# ds4_distributed.o is a GPU-agnostic translation unit that ds4.c/ds4_cpu.o now
# reference (upstream split distributed inference into its own .c). The same
# object is shared by every GPU mode, so it is appended unconditionally below.
ifeq ($(BUILD_TYPE),cublas)
CMAKE_ARGS += -DDS4_GPU=cuda
DS4_OBJ_TARGET := ds4.o ds4_cuda.o ds4_distributed.o ds4_ssd.o
DS4_OBJ_TARGET := ds4.o ds4_cuda.o ds4_distributed.o
else ifeq ($(UNAME_S),Darwin)
CMAKE_ARGS += -DDS4_GPU=metal
DS4_OBJ_TARGET := ds4.o ds4_metal.o ds4_distributed.o ds4_ssd.o
DS4_OBJ_TARGET := ds4.o ds4_metal.o ds4_distributed.o
else
# CPU reference path (Linux only - macOS CPU path is broken by VM bug per ds4 README).
CMAKE_ARGS += -DDS4_GPU=cpu
DS4_OBJ_TARGET := ds4_cpu.o ds4_distributed.o ds4_ssd.o
DS4_OBJ_TARGET := ds4_cpu.o ds4_distributed.o
endif
ifneq ($(NATIVE),true)
@@ -56,11 +55,11 @@ ds4:
# the right per-platform compile flags (Objective-C/Metal on Darwin, nvcc on Linux+CUDA).
ds4/ds4.o: ds4
ifeq ($(BUILD_TYPE),cublas)
+$(MAKE) -C ds4 ds4.o ds4_cuda.o ds4_distributed.o ds4_ssd.o
+$(MAKE) -C ds4 ds4.o ds4_cuda.o ds4_distributed.o
else ifeq ($(UNAME_S),Darwin)
+$(MAKE) -C ds4 ds4.o ds4_metal.o ds4_distributed.o ds4_ssd.o
+$(MAKE) -C ds4 ds4.o ds4_metal.o ds4_distributed.o
else
+$(MAKE) -C ds4 ds4_cpu.o ds4_distributed.o ds4_ssd.o
+$(MAKE) -C ds4 ds4_cpu.o ds4_distributed.o
endif
grpc-server: ds4/ds4.o

View File

@@ -25,8 +25,6 @@ extern "C" {
#include <chrono>
#include <climits>
#include <csignal>
#include <cstddef>
#include <cstdint>
#include <cstdlib>
#include <cstring>
#include <ctime>
@@ -107,130 +105,6 @@ static bool parse_layers_spec(const std::string &spec, ds4_distributed_layers *o
return true;
}
// Parse a boolean LoadModel option. An empty value (a bare flag-style option
// like "ssd_streaming" with no colon) means true so model YAMLs can write
// options: ["ssd_streaming"] to enable a switch.
static bool parse_bool_option(const std::string &s, bool *out) {
if (s.empty() || s == "true" || s == "1" || s == "yes" || s == "on") { *out = true; return true; }
if (s == "false" || s == "0" || s == "no" || s == "off") { *out = false; return true; }
return false;
}
// Table-driven mapping from LoadModel option keys to ds4_engine_options fields.
// ds4_engine_options is a fixed C struct with no reflection, so the field set
// is enumerated once here; adding a future engine knob is a one-line table
// entry rather than a new branch in LoadModel. Two fields need ds4's own typed
// parsers (Gib, CacheExperts) so a plain string passthrough can't cover them.
enum class DsOptType { Bool, Int, Uint, Float, Str, Gib, CacheExperts };
struct DsOptSpec {
const char *key;
DsOptType type;
size_t off; // byte offset into ds4_engine_options
size_t off2; // second offset (CacheExperts writes experts + bytes)
bool is_path; // Str values: resolve a relative value against the model dir
};
static const DsOptSpec kEngineOptSpecs[] = {
{"mtp_path", DsOptType::Str, offsetof(ds4_engine_options, mtp_path), 0, true},
{"mtp_draft", DsOptType::Int, offsetof(ds4_engine_options, mtp_draft_tokens), 0},
{"mtp_margin", DsOptType::Float, offsetof(ds4_engine_options, mtp_margin), 0},
{"prefill_chunk", DsOptType::Uint, offsetof(ds4_engine_options, prefill_chunk), 0},
{"power_percent", DsOptType::Int, offsetof(ds4_engine_options, power_percent), 0},
{"warm_weights", DsOptType::Bool, offsetof(ds4_engine_options, warm_weights), 0},
{"quality", DsOptType::Bool, offsetof(ds4_engine_options, quality), 0},
{"ssd_streaming", DsOptType::Bool, offsetof(ds4_engine_options, ssd_streaming), 0},
{"ssd_streaming_cold", DsOptType::Bool, offsetof(ds4_engine_options, ssd_streaming_cold), 0},
{"ssd_streaming_preload_experts", DsOptType::Uint, offsetof(ds4_engine_options, ssd_streaming_preload_experts), 0},
{"ssd_streaming_cache_experts", DsOptType::CacheExperts, offsetof(ds4_engine_options, ssd_streaming_cache_experts),
offsetof(ds4_engine_options, ssd_streaming_cache_bytes)},
{"simulate_used_memory", DsOptType::Gib, offsetof(ds4_engine_options, simulate_used_memory_bytes), 0},
{"expert_profile_path", DsOptType::Str, offsetof(ds4_engine_options, expert_profile_path), 0, true},
{"directional_steering_file", DsOptType::Str, offsetof(ds4_engine_options, directional_steering_file), 0, true},
{"directional_steering_attn", DsOptType::Float, offsetof(ds4_engine_options, directional_steering_attn), 0},
{"directional_steering_ffn", DsOptType::Float, offsetof(ds4_engine_options, directional_steering_ffn), 0},
};
// Apply a single key:value LoadModel option to the engine options struct.
// Unknown keys are ignored (back-compat: callers pass mixed option sets).
// String values are copied into `storage`, whose elements the engine reads by
// pointer during ds4_engine_open; `storage` MUST have reserved capacity so
// push_back never reallocates and dangles an earlier c_str(). Returns false
// with `err` set when a recognized key has an invalid value.
static bool apply_engine_option(ds4_engine_options *opt, const std::string &key,
const std::string &val, const std::string &model_dir,
std::vector<std::string> &storage, std::string &err) {
const DsOptSpec *spec = nullptr;
for (const auto &s : kEngineOptSpecs) {
if (key == s.key) { spec = &s; break; }
}
if (!spec) return true; // unknown key: ignore
char *base = reinterpret_cast<char *>(opt);
switch (spec->type) {
case DsOptType::Bool: {
bool b = false;
if (!parse_bool_option(val, &b)) { err = key + " must be true/false"; return false; }
*reinterpret_cast<bool *>(base + spec->off) = b;
return true;
}
case DsOptType::Int: {
char *end = nullptr;
long v = std::strtol(val.c_str(), &end, 10);
if (val.empty() || !end || *end != '\0') { err = key + " must be an integer"; return false; }
*reinterpret_cast<int *>(base + spec->off) = static_cast<int>(v);
return true;
}
case DsOptType::Uint: {
char *end = nullptr;
long v = std::strtol(val.c_str(), &end, 10);
if (val.empty() || !end || *end != '\0' || v < 0 || v > static_cast<long>(UINT32_MAX)) {
err = key + " must be a non-negative integer"; return false;
}
*reinterpret_cast<uint32_t *>(base + spec->off) = static_cast<uint32_t>(v);
return true;
}
case DsOptType::Float: {
char *end = nullptr;
float f = std::strtof(val.c_str(), &end);
if (val.empty() || !end || *end != '\0') { err = key + " must be a number"; return false; }
*reinterpret_cast<float *>(base + spec->off) = f;
return true;
}
case DsOptType::Str: {
// Resolve a relative path option (e.g. mtp_path: a sibling GGUF the
// gallery downloaded next to the model) against the model directory, so
// YAMLs reference companion files by name. Absolute values pass through.
if (spec->is_path && !model_dir.empty() && !val.empty() && val.front() != '/') {
storage.push_back(model_dir + "/" + val);
} else {
storage.push_back(val);
}
*reinterpret_cast<const char **>(base + spec->off) = storage.back().c_str();
return true;
}
case DsOptType::Gib: {
uint64_t bytes = 0;
if (!ds4_parse_gib_arg(val.c_str(), &bytes)) {
err = key + " must be a GiB value, e.g. 64GB"; return false;
}
*reinterpret_cast<uint64_t *>(base + spec->off) = bytes;
return true;
}
case DsOptType::CacheExperts: {
uint32_t experts = 0;
uint64_t bytes = 0;
if (!ds4_parse_streaming_cache_experts_arg(val.c_str(), &experts, &bytes)) {
err = key + " must be a positive expert count or a <number>GB budget"; return false;
}
*reinterpret_cast<uint32_t *>(base + spec->off) = experts;
*reinterpret_cast<uint64_t *>(base + spec->off2) = bytes;
return true;
}
}
return true;
}
// When acting as a distributed coordinator, block until the worker route
// covers all layers (ds4_session_distributed_route_ready == 1) or the timeout
// elapses. Returns an empty string on success, or an error message to return
@@ -602,10 +476,39 @@ public:
return GStatus::OK;
}
std::string mtp_path;
int mtp_draft = 0;
float mtp_margin = 3.0f;
std::string ds4_role, ds4_layers, ds4_listen;
for (const auto &opt : request->options()) {
auto [k, v] = split_option(opt);
if (k == "mtp_path") mtp_path = v;
else if (k == "mtp_draft") mtp_draft = std::stoi(v);
else if (k == "mtp_margin") mtp_margin = std::stof(v);
else if (k == "kv_cache_dir") g_kv_cache_dir = v;
else if (k == "ds4_role") ds4_role = v;
else if (k == "ds4_layers") ds4_layers = v;
else if (k == "ds4_listen") ds4_listen = v;
else if (k == "ds4_route_timeout") {
if (!parse_positive_int(v, &g_route_timeout_sec)) {
result->set_success(false);
result->set_message("ds4: ds4_route_timeout must be a positive integer");
return GStatus::OK;
}
}
}
g_kv_cache.SetDir(g_kv_cache_dir);
ds4_engine_options opt = {};
opt.model_path = model_path.c_str();
opt.mtp_path = mtp_path.empty() ? nullptr : mtp_path.c_str();
opt.n_threads = request->threads() > 0 ? request->threads() : 0;
opt.mtp_margin = 3.0f; // ds4 default; overridable via the mtp_margin option
opt.mtp_draft_tokens = mtp_draft;
opt.mtp_margin = mtp_margin;
opt.directional_steering_file = nullptr;
opt.warm_weights = false;
opt.quality = false;
#if defined(DS4_NO_GPU)
opt.backend = DS4_BACKEND_CPU;
@@ -615,46 +518,6 @@ public:
opt.backend = DS4_BACKEND_CUDA;
#endif
// Stable storage for string-valued engine options. The engine reads
// these by pointer during ds4_engine_open, so the std::string backing
// store must outlive the call and not reallocate; reserve up front so
// push_back keeps every prior c_str() valid. Static + clear() reuses
// the buffer across LoadModel calls (the old engine is closed above).
static std::vector<std::string> s_opt_strings;
s_opt_strings.clear();
s_opt_strings.reserve(sizeof(kEngineOptSpecs) / sizeof(kEngineOptSpecs[0]));
// Directory of the main model, used to resolve relative path options.
std::string model_dir;
if (auto slash = model_path.find_last_of('/'); slash != std::string::npos) {
model_dir = model_path.substr(0, slash);
}
std::string ds4_role, ds4_layers, ds4_listen;
for (const auto &o : request->options()) {
auto [k, v] = split_option(o);
if (k == "kv_cache_dir") { g_kv_cache_dir = v; continue; }
else if (k == "ds4_role") { ds4_role = v; continue; }
else if (k == "ds4_layers") { ds4_layers = v; continue; }
else if (k == "ds4_listen") { ds4_listen = v; continue; }
else if (k == "ds4_route_timeout") {
if (!parse_positive_int(v, &g_route_timeout_sec)) {
result->set_success(false);
result->set_message("ds4: ds4_route_timeout must be a positive integer");
return GStatus::OK;
}
continue;
}
std::string err;
if (!apply_engine_option(&opt, k, v, model_dir, s_opt_strings, err)) {
result->set_success(false);
result->set_message("ds4: " + err);
return GStatus::OK;
}
}
g_kv_cache.SetDir(g_kv_cache_dir);
// Coordinator wiring. 'ds4_role:coordinator' enables layer-split
// distributed inference: this process listens on ds4_listen and owns
// the ds4_layers slice; workers dial in (see `local-ai worker

View File

@@ -1,6 +1,15 @@
## Multimodal support is provided by the in-tree `mtmd` library target
## (examples/mtmd/), which the grpc-server links and includes below. clip/llava
## were pruned upstream; the high-level mtmd_* / mtmd_helper_* API is used instead.
## Clip/LLaVA library for multimodal support — built locally from copied sources
set(TARGET myclip)
add_library(${TARGET} clip.cpp clip.h llava.cpp llava.h)
install(TARGETS ${TARGET} LIBRARY)
target_include_directories(myclip PUBLIC .)
target_include_directories(myclip PUBLIC ../..)
target_include_directories(myclip PUBLIC ../../common)
target_link_libraries(${TARGET} PRIVATE common ggml llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)
if (NOT MSVC)
target_compile_options(${TARGET} PRIVATE -Wno-cast-qual)
endif()
set(TARGET grpc-server)
set(CMAKE_CXX_STANDARD 17)
@@ -58,16 +67,12 @@ add_library(hw_grpc_proto
${hw_proto_hdrs} )
add_executable(${TARGET} grpc-server.cpp json.hpp)
# mtmd public headers (mtmd.h / mtmd-helper.h) live in examples/mtmd/.
# Linking the mtmd target also propagates this include dir, but we add it
# explicitly for clarity.
target_include_directories(${TARGET} PRIVATE ../mtmd)
target_link_libraries(${TARGET} PRIVATE common llama mtmd ${CMAKE_THREAD_LIBS_INIT} absl::flags hw_grpc_proto
target_link_libraries(${TARGET} PRIVATE common llama myclip ${CMAKE_THREAD_LIBS_INIT} absl::flags hw_grpc_proto
absl::flags_parse
gRPC::${_REFLECTION}
gRPC::${_GRPC_GRPCPP}
protobuf::${_PROTOBUF_LIBPROTOBUF})
target_compile_features(${TARGET} PRIVATE cxx_std_17)
target_compile_features(${TARGET} PRIVATE cxx_std_11)
if(TARGET BUILD_INFO)
add_dependencies(${TARGET} BUILD_INFO)
endif()

View File

@@ -1,5 +1,5 @@
IK_LLAMA_VERSION?=f96eaddba8bed6a9a5e628bbf6a566775c70b49c
IK_LLAMA_VERSION?=3f40e73c367ad9f0c1b1819f28c7348c26aa340d
LLAMA_REPO?=https://github.com/ikawrakow/ik_llama.cpp
CMAKE_ARGS?=

View File

@@ -11,8 +11,8 @@
#include <memory>
#include <string>
#include <getopt.h>
#include "mtmd.h"
#include "mtmd-helper.h"
#include "clip.h"
#include "llava.h"
#include "log.h"
#include "common.h"
#include "json.hpp"
@@ -45,9 +45,7 @@ using backend::HealthMessage;
///// LLAMA.CPP server code below
// Match mtmd.h and ik_llama's server/common headers, which all use
// nlohmann::ordered_json; a plain nlohmann::json alias collides at global scope.
using json = nlohmann::ordered_json;
using json = nlohmann::json;
struct server_params
{
@@ -221,11 +219,6 @@ struct llama_client_slot
// multimodal
std::vector<slot_image> images;
// Full prompt with mtmd media markers (mtmd_default_marker()) substituted in
// place of the legacy [img-N] tags, covering the text up to and including the
// last image. The text after the last image is kept in params.input_suffix and
// decoded through the normal token path so the sampling loop is unchanged.
std::string mtmd_prompt;
// stats
size_t sent_count = 0;
@@ -259,14 +252,14 @@ struct llama_client_slot
for (slot_image & img : images)
{
if (img.bitmap) {
mtmd_bitmap_free(img.bitmap);
img.bitmap = nullptr;
free(img.image_embedding);
if (img.img_data) {
clip_image_u8_free(img.img_data);
}
img.prefix_prompt = "";
}
images.clear();
mtmd_prompt = "";
}
bool has_budget(gpt_params &global_params) {
@@ -403,13 +396,46 @@ struct llama_metrics {
}
};
struct llava_embd_batch {
std::vector<llama_pos> pos;
std::vector<int32_t> n_seq_id;
std::vector<llama_seq_id> seq_id_0;
std::vector<llama_seq_id *> seq_ids;
std::vector<int8_t> logits;
llama_batch batch;
llava_embd_batch(float * embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) {
pos .resize(n_tokens);
n_seq_id.resize(n_tokens);
seq_ids .resize(n_tokens + 1);
logits .resize(n_tokens);
seq_id_0.resize(1);
seq_id_0[0] = seq_id;
seq_ids [n_tokens] = nullptr;
batch = {
/*n_tokens =*/ n_tokens,
/*tokens =*/ nullptr,
/*embd =*/ embd,
/*pos =*/ pos.data(),
/*n_seq_id =*/ n_seq_id.data(),
/*seq_id =*/ seq_ids.data(),
/*logits =*/ logits.data(),
};
for (int i = 0; i < n_tokens; i++) {
batch.pos [i] = pos_0 + i;
batch.n_seq_id[i] = 1;
batch.seq_id [i] = seq_id_0.data();
batch.logits [i] = false;
}
}
};
struct llama_server_context
{
llama_model *model = nullptr;
llama_context *ctx = nullptr;
const llama_vocab * vocab = nullptr;
mtmd_context *mctx = nullptr;
clip_ctx *clp_ctx = nullptr;
gpt_params params;
@@ -465,6 +491,11 @@ struct llama_server_context
if (!params.mmproj.path.empty()) {
multimodal = true;
LOG_INFO("Multi Modal Mode Enabled", {});
clp_ctx = clip_model_load(params.mmproj.path.c_str(), /*verbosity=*/ 1);
if(clp_ctx == nullptr) {
LOG_ERR("unable to load clip model: %s", params.mmproj.path.c_str());
return false;
}
if (params.n_ctx < 2048) { // request larger context for the image embedding
params.n_ctx = 2048;
@@ -481,24 +512,10 @@ struct llama_server_context
}
if (multimodal) {
// mtmd_init_from_file requires the already-loaded text model, so it must
// run AFTER llama_init_from_gpt_params. It validates the projector
// against the model internally and returns nullptr on dim mismatch, so
// the explicit clip_n_mmproj_embd check is no longer needed.
mtmd_context_params mparams = mtmd_context_params_default();
mparams.use_gpu = params.mmproj_use_gpu;
mparams.print_timings = false;
mparams.n_threads = params.n_threads_mtmd != -1 ? params.n_threads_mtmd
: params.n_threads_batch != -1 ? params.n_threads_batch
: params.n_threads;
mparams.verbosity = GGML_LOG_LEVEL_INFO;
mparams.flash_attn_type = params.flash_attn ? LLAMA_FLASH_ATTN_TYPE_ENABLED
: LLAMA_FLASH_ATTN_TYPE_DISABLED;
mparams.image_min_tokens = params.image_min_tokens;
mparams.image_max_tokens = params.image_max_tokens;
mctx = mtmd_init_from_file(params.mmproj.path.c_str(), model, mparams);
if (mctx == nullptr) {
LOG_ERR("unable to load multimodal projector: %s", params.mmproj.path.c_str());
const int n_embd_clip = clip_n_mmproj_embd(clp_ctx);
const int n_embd_llm = llama_model_n_embd(model);
if (n_embd_clip != n_embd_llm) {
LOG("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_embd_clip, n_embd_llm);
llama_free(ctx);
llama_free_model(model);
return false;
@@ -848,8 +865,8 @@ struct llama_server_context
slot_image img_sl;
img_sl.id = img.count("id") != 0 ? img["id"].get<int>() : slot->images.size();
img_sl.bitmap = mtmd_helper_bitmap_init_from_buf(mctx, image_buffer.data(), image_buffer.size());
if (img_sl.bitmap == nullptr)
img_sl.img_data = clip_image_u8_init();
if (!clip_image_load_from_bytes(image_buffer.data(), image_buffer.size(), img_sl.img_data))
{
LOG_ERR("%s: failed to load image, slot_id: %d, img_sl_id: %d",
__func__,
@@ -862,74 +879,50 @@ struct llama_server_context
{"slot_id", slot->id},
{"img_sl_id", img_sl.id}
});
img_sl.request_encode_image = true;
slot->images.push_back(img_sl);
}
// Translate the legacy [img-N] tags into mtmd media markers, in
// order, and collect the matching bitmaps in marker order so they
// line up with the markers passed to mtmd_tokenize(). The text after
// the last image stays in input_suffix and is decoded through the
// normal token path, so the sampling loop is unchanged.
// example: system prompt [img-102] user [img-103] describe [img-134]
// process prompt
// example: system prompt [img-102] user [img-103] describe [img-134] -> [{id: 102, prefix: 'system prompt '}, {id: 103, prefix: ' user '}, {id: 134, prefix: ' describe '}]}
if (slot->images.size() > 0 && !slot->prompt.is_array())
{
const std::string marker = mtmd_default_marker();
std::string prompt = slot->prompt.get<std::string>();
std::string built_prompt;
std::vector<slot_image> ordered;
size_t pos = 0, copy_from = 0;
size_t pos = 0, begin_prefix = 0;
std::string pattern = "[img-";
auto free_images = [&]() {
for (slot_image &img : slot->images) {
if (img.bitmap) {
mtmd_bitmap_free(img.bitmap);
img.bitmap = nullptr;
}
}
slot->images.clear();
};
while ((pos = prompt.find(pattern, pos)) != std::string::npos) {
size_t tag_begin = pos;
size_t end_prefix = pos;
pos += pattern.length();
size_t end_pos = prompt.find(']', pos);
if (end_pos == std::string::npos) {
break;
}
std::string image_id = prompt.substr(pos, end_pos - pos);
try
if (end_pos != std::string::npos)
{
int img_id = std::stoi(image_id);
bool found = false;
for (slot_image &img : slot->images)
std::string image_id = prompt.substr(pos, end_pos - pos);
try
{
if (img.id == img_id) {
found = true;
// text before this tag, then the media marker
built_prompt += prompt.substr(copy_from, tag_begin - copy_from);
built_prompt += marker;
copy_from = end_pos + 1;
ordered.push_back(img);
break;
int img_id = std::stoi(image_id);
bool found = false;
for (slot_image &img : slot->images)
{
if (img.id == img_id) {
found = true;
img.prefix_prompt = prompt.substr(begin_prefix, end_prefix - begin_prefix);
begin_prefix = end_pos + 1;
break;
}
}
}
if (!found) {
LOG("ERROR: Image with id: %i, not found.\n", img_id);
free_images();
if (!found) {
LOG("ERROR: Image with id: %i, not found.\n", img_id);
slot->images.clear();
return false;
}
} catch (const std::invalid_argument& e) {
LOG("Invalid image number id in prompt\n");
slot->images.clear();
return false;
}
} catch (const std::invalid_argument& e) {
LOG("Invalid image number id in prompt\n");
free_images();
return false;
}
pos = end_pos + 1;
}
// bitmaps are consumed in marker order by mtmd_tokenize()
slot->images = ordered;
slot->mtmd_prompt = built_prompt;
slot->prompt = "";
slot->params.input_suffix = prompt.substr(copy_from);
slot->params.input_suffix = prompt.substr(begin_prefix);
slot->params.cache_prompt = false; // multimodal doesn't support cache prompt
}
}
@@ -1183,10 +1176,21 @@ struct llama_server_context
bool process_images(llama_client_slot &slot) const
{
// With the mtmd pipeline, image encoding is no longer eager: the bitmaps
// are tokenized and encoded together with the surrounding text inside
// ingest_images() via mtmd_tokenize() + mtmd_helper_eval_chunks(). This
// just reports whether the slot carries any images to process.
for (slot_image &img : slot.images)
{
if (!img.request_encode_image)
{
continue;
}
if (!llava_image_embed_make_with_clip_img(clp_ctx, params.n_threads, img.img_data, &img.image_embedding, &img.image_tokens)) {
LOG("Error processing the given image");
return false;
}
img.request_encode_image = false;
}
return slot.images.size() > 0;
}
@@ -1431,70 +1435,69 @@ struct llama_server_context
}
}
// Tokenize the multimodal prompt (text interleaved with media markers) together
// with the slot's bitmaps, then decode the resulting chunks into the llama
// context via the high-level mtmd helper. The helper runs llama_decode() on the
// text chunks and mtmd_encode() + llama_decode() on the image chunks, handling
// batching and any pre/post decode setup (e.g. non-causal attention for gemma3).
// Advances slot.n_past by the number of positions consumed, then leaves the
// post-image suffix tokens in `batch` so the normal decode + sampling loop
// produces the first generated token.
// for multiple images processing
bool ingest_images(llama_client_slot &slot, int n_batch)
{
if (mctx == nullptr)
{
LOG("%s : multimodal context is not initialized\n", __func__);
return false;
}
int image_idx = 0;
// bitmaps stay owned by slot.images (freed on reset()); pass non-owning ptrs
std::vector<const mtmd_bitmap *> bitmaps;
bitmaps.reserve(slot.images.size());
for (const slot_image &img : slot.images)
while (image_idx < (int) slot.images.size())
{
bitmaps.push_back(img.bitmap);
}
slot_image &img = slot.images[image_idx];
mtmd_input_text inp_txt;
inp_txt.text = slot.mtmd_prompt.c_str();
inp_txt.add_special = add_bos_token;
inp_txt.parse_special = true;
// process prefix prompt
for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch)
{
const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
llama_batch batch_view = {
n_tokens,
batch.token + i,
nullptr,
batch.pos + i,
batch.n_seq_id + i,
batch.seq_id + i,
batch.logits + i,
};
if (llama_decode(ctx, batch_view))
{
LOG("%s : failed to eval\n", __func__);
return false;
}
}
mtmd::input_chunks chunks(mtmd_input_chunks_init());
int32_t res = mtmd_tokenize(mctx,
chunks.ptr.get(),
&inp_txt,
bitmaps.data(),
bitmaps.size());
if (res != 0)
{
LOG("%s : failed to tokenize multimodal prompt, res = %d\n", __func__, res);
return false;
}
// process image with llm
for (int i = 0; i < img.image_tokens; i += n_batch)
{
int n_eval = img.image_tokens - i;
if (n_eval > n_batch)
{
n_eval = n_batch;
}
const llama_pos start_pos = (llama_pos) system_tokens.size() + slot.n_past;
llama_pos new_n_past = start_pos;
if (mtmd_helper_eval_chunks(mctx,
ctx,
chunks.ptr.get(),
start_pos,
slot.id,
n_batch,
/*logits_last=*/ false,
&new_n_past) != 0)
{
LOG("%s : failed to eval multimodal chunks\n", __func__);
return false;
}
slot.n_past += (int32_t) (new_n_past - start_pos);
const int n_embd = llama_model_n_embd(model);
float * embd = img.image_embedding + i * n_embd;
llava_embd_batch llava_batch = llava_embd_batch(embd, n_eval, slot.n_past, 0);
if (llama_decode(ctx, llava_batch.batch))
{
LOG("%s : failed to eval image\n", __func__);
return false;
}
slot.n_past += n_eval;
}
image_idx++;
// queue the post-image suffix text for the normal decode + sampling path
common_batch_clear(batch);
std::vector<llama_token> suffix_tokens = tokenize(slot.params.input_suffix, false);
for (llama_token tok : suffix_tokens)
{
common_batch_add(batch, tok, system_tokens.size() + slot.n_past, { slot.id }, false);
slot.n_past += 1;
common_batch_clear(batch);
// append prefix of next image
const auto json_prompt = (image_idx >= (int) slot.images.size()) ?
slot.params.input_suffix : // no more images, then process suffix prompt
(json)(slot.images[image_idx].prefix_prompt);
std::vector<llama_token> append_tokens = tokenize(json_prompt, false); // has next image
for (int i = 0; i < (int) append_tokens.size(); ++i)
{
common_batch_add(batch, append_tokens[i], system_tokens.size() + slot.n_past, { slot.id }, true);
slot.n_past += 1;
}
}
return true;
@@ -1881,11 +1884,8 @@ struct llama_server_context
const bool has_images = process_images(slot);
// For the multimodal path the whole pre-image / inter-image text is
// tokenized and decoded inside ingest_images() via mtmd, so no prefix
// tokens are queued here; the post-image suffix is appended by
// ingest_images() for the normal decode + sampling loop.
std::vector<llama_token> prefix_tokens = has_images ? std::vector<llama_token>() : prompt_tokens;
// process the prefix of first image
std::vector<llama_token> prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, add_bos_token) : prompt_tokens;
int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past;

View File

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

View File

@@ -17,9 +17,28 @@ cp -r grpc-server.cpp llama.cpp/examples/grpc-server/
cp -r utils.hpp llama.cpp/examples/grpc-server/
cp -rfv llama.cpp/vendor/nlohmann/json.hpp llama.cpp/examples/grpc-server/
## Multimodal support is provided by the `mtmd` library target (examples/mtmd/),
## which the grpc-server links and includes directly. No source copy is needed:
## clip/llava were pruned upstream and the high-level mtmd_* API is used instead.
## Copy clip/llava files for multimodal support (built as myclip library)
cp -rfv llama.cpp/examples/llava/clip.h llama.cpp/examples/grpc-server/clip.h
cp -rfv llama.cpp/examples/llava/clip.cpp llama.cpp/examples/grpc-server/clip.cpp
cp -rfv llama.cpp/examples/llava/llava.cpp llama.cpp/examples/grpc-server/llava.cpp
# Prepend llama.h include to llava.h
echo '#include "llama.h"' > llama.cpp/examples/grpc-server/llava.h
cat llama.cpp/examples/llava/llava.h >> llama.cpp/examples/grpc-server/llava.h
# Copy clip-impl.h if it exists
if [ -f llama.cpp/examples/llava/clip-impl.h ]; then
cp -rfv llama.cpp/examples/llava/clip-impl.h llama.cpp/examples/grpc-server/clip-impl.h
fi
# Copy stb_image.h
if [ -f llama.cpp/vendor/stb/stb_image.h ]; then
cp -rfv llama.cpp/vendor/stb/stb_image.h llama.cpp/examples/grpc-server/stb_image.h
elif [ -f llama.cpp/common/stb_image.h ]; then
cp -rfv llama.cpp/common/stb_image.h llama.cpp/examples/grpc-server/stb_image.h
fi
## Fix API compatibility in llava.cpp (llama_n_embd -> llama_model_n_embd)
if [ -f llama.cpp/examples/grpc-server/llava.cpp ]; then
sed -i 's/llama_n_embd(/llama_model_n_embd(/g' llama.cpp/examples/grpc-server/llava.cpp
fi
set +e
if grep -q "grpc-server" llama.cpp/examples/CMakeLists.txt; then

View File

@@ -2,7 +2,7 @@
set -ex
# Get the absolute current dir where the script is located
CURDIR=$(dirname "$(realpath "$0")")
CURDIR=$(dirname "$(realpath $0)")
cd /
@@ -13,28 +13,28 @@ grep -e "flags" /proc/cpuinfo | head -1
# ik_llama.cpp requires AVX2 — default to avx2 binary
BINARY=ik-llama-cpp-avx2
if [ -e "$CURDIR"/ik-llama-cpp-fallback ] && ! grep -q -e "\savx2\s" /proc/cpuinfo ; then
if [ -e $CURDIR/ik-llama-cpp-fallback ] && ! grep -q -e "\savx2\s" /proc/cpuinfo ; then
echo "CPU: AVX2 NOT found, using fallback"
BINARY=ik-llama-cpp-fallback
fi
# Extend ld library path with the dir where this script is located/lib
if [ "$(uname)" == "Darwin" ]; then
export DYLD_LIBRARY_PATH="$CURDIR"/lib:$DYLD_LIBRARY_PATH
#export DYLD_FALLBACK_LIBRARY_PATH="$CURDIR"/lib:$DYLD_FALLBACK_LIBRARY_PATH
export DYLD_LIBRARY_PATH=$CURDIR/lib:$DYLD_LIBRARY_PATH
#export DYLD_FALLBACK_LIBRARY_PATH=$CURDIR/lib:$DYLD_FALLBACK_LIBRARY_PATH
else
export LD_LIBRARY_PATH="$CURDIR"/lib:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=$CURDIR/lib:$LD_LIBRARY_PATH
fi
# If there is a lib/ld.so, use it
if [ -f "$CURDIR"/lib/ld.so ]; then
if [ -f $CURDIR/lib/ld.so ]; then
echo "Using lib/ld.so"
echo "Using binary: $BINARY"
exec "$CURDIR"/lib/ld.so "$CURDIR"/$BINARY "$@"
exec $CURDIR/lib/ld.so $CURDIR/$BINARY "$@"
fi
echo "Using binary: $BINARY"
exec "$CURDIR"/$BINARY "$@"
exec $CURDIR/$BINARY "$@"
# We should never reach this point, however just in case we do, run fallback
exec "$CURDIR"/ik-llama-cpp-fallback "$@"
exec $CURDIR/ik-llama-cpp-fallback "$@"

View File

@@ -11,12 +11,9 @@
#include "json.hpp"
#include "mtmd.h"
#include "clip.h"
// mtmd.h and ik_llama's entire server/common stack (chat.h, server-common.h,
// server-task.h, ...) declare `using json = nlohmann::ordered_json`, so match it
// here: a plain `nlohmann::json` alias collides with mtmd.h's at global scope.
using json = nlohmann::ordered_json;
using json = nlohmann::json;
extern bool server_verbose;
@@ -114,12 +111,13 @@ struct slot_image
{
int32_t id;
// mtmd bitmap (image/audio) decoded from the request buffer. Owned by the
// slot; freed via mtmd_bitmap_free() on reset. The high-level mtmd pipeline
// (mtmd_tokenize + mtmd_helper_eval_chunks) consumes these directly, so the
// legacy eager-encode fields (embedding/tokens) and per-image prefix prompt
// are no longer needed.
mtmd_bitmap * bitmap = nullptr;
bool request_encode_image = false;
float * image_embedding = nullptr;
int32_t image_tokens = 0;
clip_image_u8 * img_data;
std::string prefix_prompt; // before of this image
};
// completion token output with probabilities

View File

@@ -50,13 +50,8 @@ add_custom_command(
"${hw_proto}"
DEPENDS "${hw_proto}")
# hw_grpc_proto: force STATIC. Under the CPU_ALL_VARIANTS build BUILD_SHARED_LIBS=ON
# (ggml/llama become shared), which would otherwise make this glue library a DSO. As a
# DSO it references the hidden-visibility symbols in the static libprotobuf.a, which the
# linker cannot satisfy ("hidden symbol ... in libprotobuf.a is referenced by DSO").
# Keeping it STATIC links protobuf/gRPC directly into the grpc-server executable while
# only ggml/llama stay shared. No effect on the static variants (already BUILD_SHARED_LIBS=OFF).
add_library(hw_grpc_proto STATIC
# hw_grpc_proto
add_library(hw_grpc_proto
${hw_grpc_srcs}
${hw_grpc_hdrs}
${hw_proto_srcs}
@@ -87,18 +82,3 @@ target_compile_features(${TARGET} PRIVATE cxx_std_11)
if(TARGET BUILD_INFO)
add_dependencies(${TARGET} BUILD_INFO)
endif()
# Unit test for the message-content normalization helper (message_content.h).
# Off by default so the normal backend build is untouched; enable with
# -DLLAMA_GRPC_BUILD_TESTS=ON and run via ctest. It reuses llama.cpp's vendored
# <nlohmann/json.hpp> (propagated by the common helpers library) so it has no
# extra dependency beyond what the backend already builds against.
option(LLAMA_GRPC_BUILD_TESTS "Build grpc-server unit tests" OFF)
if(LLAMA_GRPC_BUILD_TESTS)
enable_testing()
add_executable(message_content_test message_content_test.cpp message_content.h)
target_include_directories(message_content_test PRIVATE ${CMAKE_CURRENT_SOURCE_DIR})
target_link_libraries(message_content_test PRIVATE ${_LLAMA_COMMON_TARGET})
target_compile_features(message_content_test PRIVATE cxx_std_17)
add_test(NAME message_content_test COMMAND message_content_test)
endif()

View File

@@ -1,5 +1,5 @@
LLAMA_VERSION?=0ed235ea2c17a19fc8238668653946721ed136fd
LLAMA_VERSION?=5dcb71166686799f0d873eab7386234302d05ecf
LLAMA_REPO?=https://github.com/ggerganov/llama.cpp
CMAKE_ARGS?=
@@ -10,16 +10,8 @@ TARGET?=--target grpc-server
JOBS?=$(shell nproc 2>/dev/null || sysctl -n hw.ncpu 2>/dev/null || echo 1)
ARCH?=$(shell uname -m)
# Shared libs default to OFF: we link static gRPC and the avx/avx2/avx512/fallback
# variants are fully static. The CPU_ALL_VARIANTS build flips SHARED_LIBS=ON (ggml/llama
# become shared so the dynamic CPU backends work; gRPC stays static via its imported
# targets). SHARED_LIBS is a make variable, not an appended -D, so it survives the
# recursive sub-make into the VARIANT build dir (which re-parses this Makefile) instead
# of being re-clobbered by a second -DBUILD_SHARED_LIBS=OFF. EXTRA_CMAKE_ARGS is the hook
# the CPU_ALL_VARIANTS target uses to inject -DGGML_BACKEND_DL/-DGGML_CPU_ALL_VARIANTS.
SHARED_LIBS?=OFF
EXTRA_CMAKE_ARGS?=
CMAKE_ARGS+=-DBUILD_SHARED_LIBS=$(SHARED_LIBS) -DLLAMA_CURL=OFF $(EXTRA_CMAKE_ARGS)
# Disable Shared libs as we are linking on static gRPC and we can't mix shared and static
CMAKE_ARGS+=-DBUILD_SHARED_LIBS=OFF -DLLAMA_CURL=OFF
CURRENT_MAKEFILE_DIR := $(dir $(abspath $(lastword $(MAKEFILE_LIST))))
ifeq ($(NATIVE),false)
@@ -128,39 +120,15 @@ llama-cpp-fallback: llama.cpp
CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off" $(MAKE) VARIANT="llama-cpp-fallback-build" build-llama-cpp-grpc-server
cp -rfv $(CURRENT_MAKEFILE_DIR)/../llama-cpp-fallback-build/grpc-server llama-cpp-fallback
# Single-build CPU backend using ggml's CPU_ALL_VARIANTS. Produces ONE grpc-server
# plus a set of dlopen-able libggml-cpu-*.so (sandybridge/haswell/skylakex/...) that
# ggml's backend registry selects from at runtime by probing host CPU features.
# Replaces the avx/avx2/avx512/fallback multi-binary build on x86.
#
# CPU_ALL_VARIANTS requires GGML_BACKEND_DL, which requires BUILD_SHARED_LIBS=ON, so we
# pass SHARED_LIBS=ON and the DL flags as make variables (NOT pre-expanded into the
# CMAKE_ARGS env string): command-line make variables propagate through every recursive
# sub-make, so the deepest VARIANT-dir build computes BUILD_SHARED_LIBS=ON consistently.
# Only ggml/llama go shared - gRPC is found via its static imported targets, so the
# grpc-server binary keeps static gRPC and only dynamically links ggml.
#
# TARGET adds "ggml": the per-microarch backends are runtime-dlopened, not link deps of
# grpc-server, so they only build because each is an add_dependencies() of the ggml target.
llama-cpp-cpu-all: llama.cpp
cp -rf $(CURRENT_MAKEFILE_DIR)/../llama-cpp $(CURRENT_MAKEFILE_DIR)/../llama-cpp-cpu-all-build
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../llama-cpp-cpu-all-build purge
$(info ${GREEN}I llama-cpp build info:cpu-all-variants${RESET})
$(MAKE) SHARED_LIBS=ON EXTRA_CMAKE_ARGS="-DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON" TARGET="--target grpc-server --target ggml" VARIANT="llama-cpp-cpu-all-build" build-llama-cpp-grpc-server
cp -rfv $(CURRENT_MAKEFILE_DIR)/../llama-cpp-cpu-all-build/grpc-server llama-cpp-cpu-all
rm -rf ggml-shared-libs && mkdir -p ggml-shared-libs
find $(CURRENT_MAKEFILE_DIR)/../llama-cpp-cpu-all-build/llama.cpp/build \( -name '*.so*' -o -name '*.dylib' \) -exec cp -av {} ggml-shared-libs/ \;
@echo "Collected ggml shared backends:" && ls -la ggml-shared-libs/
llama-cpp-grpc: llama.cpp
cp -rf $(CURRENT_MAKEFILE_DIR)/../llama-cpp $(CURRENT_MAKEFILE_DIR)/../llama-cpp-grpc-build
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../llama-cpp-grpc-build purge
$(info ${GREEN}I llama-cpp build info:grpc${RESET})
CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_RPC=ON -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off" TARGET="--target grpc-server --target ggml-rpc-server" $(MAKE) VARIANT="llama-cpp-grpc-build" build-llama-cpp-grpc-server
CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_RPC=ON -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off" TARGET="--target grpc-server --target rpc-server" $(MAKE) VARIANT="llama-cpp-grpc-build" build-llama-cpp-grpc-server
cp -rfv $(CURRENT_MAKEFILE_DIR)/../llama-cpp-grpc-build/grpc-server llama-cpp-grpc
llama-cpp-rpc-server: llama-cpp-grpc
cp -rf $(CURRENT_MAKEFILE_DIR)/../llama-cpp-grpc-build/llama.cpp/build/bin/ggml-rpc-server llama-cpp-rpc-server
cp -rf $(CURRENT_MAKEFILE_DIR)/../llama-cpp-grpc-build/llama.cpp/build/bin/rpc-server llama-cpp-rpc-server
llama.cpp:
mkdir -p llama.cpp

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@@ -1,192 +0,0 @@
#pragma once
#include <string>
#include <vector>
#include <nlohmann/json.hpp>
namespace llama_grpc {
// Normalizes a proto message's content string into the JSON value used when
// reconstructing OpenAI-format messages for the tokenizer (jinja) template.
//
// Shared by the streaming (PredictStream) and non-streaming (Predict) message
// reconstruction paths so the two cannot drift.
//
// LocalAI's Go layer (schema.Messages.ToProto) always sends content as a plain
// text string; multimodal media travels in separate proto fields, never inside
// content. So user/system/developer content is *only ever* opaque text and must
// NOT be JSON-sniffed: a prompt that merely looks like JSON (e.g. an ingredient
// list ["1/4 cup sugar", ...]) would otherwise be reinterpreted as structured
// content parts and rejected by oaicompat_chat_params_parse with
// "unsupported content[].type" (https://github.com/mudler/LocalAI/issues/10524).
// (developer is OpenAI's modern system alias - same "human-authored text" nature.)
//
// For assistant/tool messages we still collapse a literal JSON null/object
// (tool-call bookkeeping) to a string, but we never turn a plain string into an
// array/scalar. The array defense is therefore role-independent (arrays/scalars
// fall through for every role); the role gate only governs the null/object case.
inline nlohmann::ordered_json normalize_message_content(const std::string& role,
const std::string& content) {
nlohmann::ordered_json content_val = content;
if (role != "user" && role != "system" && role != "developer") {
try {
nlohmann::ordered_json parsed = nlohmann::ordered_json::parse(content);
if (parsed.is_null()) {
content_val = "";
} else if (parsed.is_object()) {
content_val = parsed.dump();
}
// arrays / scalars: keep the original plain-text string as-is
} catch (const nlohmann::ordered_json::parse_error&) {
// Not JSON, already the plain string
}
}
return content_val;
}
// Final safety pass applied to each reconstructed OpenAI message right before it
// is handed to oaicompat_chat_params_parse (jinja templating). Jinja templates
// assume content is a string: a literal null breaks slicing such as
// message.content[:N] (#7324), and a tool message with array content is rejected
// (#7528). A multimodal user message legitimately carries a typed-part array
// ({type:text}, {type:image_url}, ...), which must be left intact. Shared by the
// streaming and non-streaming paths so this invariant cannot drift between them.
inline void normalize_template_message(nlohmann::ordered_json& msg) {
if (!msg.contains("content")) {
msg["content"] = ""; // templates expect the field to exist
return;
}
nlohmann::ordered_json& content = msg["content"];
const std::string role = (msg.contains("role") && msg["role"].is_string())
? msg["role"].get<std::string>()
: std::string();
if (content.is_null()) {
content = ""; // #7324: null would crash content[:N] slicing
} else if (role == "tool" && content.is_array()) {
content = content.dump(); // #7528: tool messages must have string content
} else if (!content.is_string() && !content.is_array()) {
if (content.is_object()) {
content = content.dump(); // tool-call bookkeeping object -> string
} else {
content = ""; // other scalar (number/bool) -> empty
}
}
// string, or a non-tool (multimodal) typed-part array: leave untouched
}
// One proto message's data, flattened to plain types so the reconstruction logic
// can be shared and unit-tested without protobuf. The streaming and non-streaming
// predict paths both populate this from proto::Message + the request's media.
struct ReconstructedMessageInput {
std::string role;
std::string content; // proto.Message.content (always a plain string)
std::string name;
std::string tool_call_id;
std::string reasoning_content;
std::string tool_calls; // tool_calls as a JSON string, or empty
bool is_last_user_msg = false; // attach request media to this message
std::vector<std::string> images; // base64 (jpeg)
std::vector<std::string> audios; // base64 (wav)
std::vector<std::string> videos; // base64
};
// Appends the request's media as OpenAI typed content parts. Imperative (not
// brace-init) to avoid nlohmann's object-vs-array initializer-list ambiguity.
inline void append_media_parts(nlohmann::ordered_json& content_array,
const std::vector<std::string>& images,
const std::vector<std::string>& audios,
const std::vector<std::string>& videos) {
for (const auto& img : images) {
nlohmann::ordered_json image_chunk;
image_chunk["type"] = "image_url";
nlohmann::ordered_json image_url;
image_url["url"] = "data:image/jpeg;base64," + img;
image_chunk["image_url"] = image_url;
content_array.push_back(image_chunk);
}
for (const auto& aud : audios) {
nlohmann::ordered_json audio_chunk;
audio_chunk["type"] = "input_audio";
nlohmann::ordered_json input_audio;
input_audio["data"] = aud;
input_audio["format"] = "wav"; // default; could be made configurable
audio_chunk["input_audio"] = input_audio;
content_array.push_back(audio_chunk);
}
for (const auto& vid : videos) {
nlohmann::ordered_json video_chunk;
video_chunk["type"] = "input_video";
nlohmann::ordered_json input_video;
input_video["data"] = vid;
video_chunk["input_video"] = input_video;
content_array.push_back(video_chunk);
}
}
// Reconstructs a single OpenAI-format message (the object fed to
// oaicompat_chat_params_parse) from a proto message. Shared by PredictStream and
// Predict so the content/multimodal/tool_calls handling cannot drift between the
// two stream modes (it previously lived as two ~150-line copies with a redundant
// Predict-only tool_calls->" " branch). Guarantees content is always a string or
// a typed-part array, never null/missing.
inline nlohmann::ordered_json build_reconstructed_message(const ReconstructedMessageInput& in) {
nlohmann::ordered_json msg_json;
msg_json["role"] = in.role;
const bool has_media = !in.images.empty() || !in.audios.empty() || !in.videos.empty();
if (!in.content.empty()) {
nlohmann::ordered_json content_val = normalize_message_content(in.role, in.content);
if (content_val.is_string() && in.is_last_user_msg && has_media) {
// Last user message + media: build a typed-part array (text first).
nlohmann::ordered_json content_array = nlohmann::ordered_json::array();
nlohmann::ordered_json text_part;
text_part["type"] = "text";
text_part["text"] = content_val.get<std::string>();
content_array.push_back(text_part);
append_media_parts(content_array, in.images, in.audios, in.videos);
msg_json["content"] = content_array;
} else if (content_val.is_null()) {
msg_json["content"] = "";
} else {
msg_json["content"] = content_val;
}
} else if (in.is_last_user_msg && has_media) {
// No text but media on the last user message: media-only typed array.
nlohmann::ordered_json content_array = nlohmann::ordered_json::array();
append_media_parts(content_array, in.images, in.audios, in.videos);
msg_json["content"] = content_array;
} else {
// Empty content (any role, incl. tool/assistant): templates need a string.
msg_json["content"] = "";
}
if (!in.name.empty()) {
msg_json["name"] = in.name;
}
if (!in.tool_call_id.empty()) {
msg_json["tool_call_id"] = in.tool_call_id;
}
if (!in.reasoning_content.empty()) {
msg_json["reasoning_content"] = in.reasoning_content;
}
if (!in.tool_calls.empty()) {
try {
nlohmann::ordered_json tool_calls = nlohmann::ordered_json::parse(in.tool_calls);
msg_json["tool_calls"] = tool_calls;
// tool_calls + empty/blank content: use " " not "", because llama.cpp's
// common_chat_msgs_to_json_oaicompat turns "" into null, which breaks
// templates that slice message.content[:tool_start_length] (#7324).
if (!msg_json.contains("content") ||
(msg_json["content"].is_string() && msg_json["content"].get<std::string>().empty())) {
msg_json["content"] = " ";
}
} catch (const nlohmann::ordered_json::parse_error&) {
// Malformed tool_calls JSON: leave content as-is (prior behavior).
}
}
return msg_json;
}
} // namespace llama_grpc

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@@ -1,234 +0,0 @@
// Unit tests for the shared message-reconstruction helpers (message_content.h).
//
// Build & run standalone (nlohmann/json single header on the include path):
// g++ -std=c++17 -I<dir-with-nlohmann> message_content_test.cpp -o t && ./t
// or via CMake: -DLLAMA_GRPC_BUILD_TESTS=ON then ctest.
//
// Regression coverage for:
// #10524 - a user/system prompt that is itself a JSON-array string must stay
// plain text, never be reinterpreted as OpenAI structured parts.
// #7324 - assistant/tool null content -> "" (templates slice content[:N]);
// assistant+tool_calls+empty content -> " " (not "", which becomes null).
// #7528 - tool message array content must reach the template as a string.
// multimodal - last user message text + media -> typed-part array, media kept.
#include <cassert>
#include <iostream>
#include <string>
#include "message_content.h"
using nlohmann::ordered_json;
using llama_grpc::normalize_message_content;
using llama_grpc::normalize_template_message;
using llama_grpc::build_reconstructed_message;
using llama_grpc::ReconstructedMessageInput;
static int failures = 0;
static void check(bool ok, const std::string& name, const std::string& detail = "") {
if (!ok) {
std::cerr << "FAIL " << name << (detail.empty() ? "" : ": " + detail) << "\n";
failures++;
}
}
// ---- normalize_message_content -------------------------------------------
static void expect_norm_string(const char* name, const std::string& role,
const std::string& content, const std::string& want) {
auto got = normalize_message_content(role, content);
if (!got.is_string()) {
check(false, name, "expected a JSON string, got " +
std::string(got.is_array() ? "array" : got.is_object() ? "object" : "other") +
" (" + got.dump() + ")");
return;
}
check(got.get<std::string>() == want, name, "expected \"" + want + "\", got \"" + got.get<std::string>() + "\"");
}
static void test_normalize() {
const std::string ingredients = R"(["1/4 cup brown sugar, packed","1 pound ground beef"])";
// #10524 - JSON-array text must stay a string. Role-INDEPENDENT array defense.
for (const char* role : {"user", "system", "developer", "function", "assistant", "tool"}) {
expect_norm_string((std::string("json_array_stays_text:") + role).c_str(), role, ingredients, ingredients);
}
// #10524 - user/system/developer JSON-object text stays verbatim (NOT re-dumped).
expect_norm_string("user_json_object_verbatim", "user", R"({"a":1})", R"({"a":1})");
expect_norm_string("system_json_object_verbatim", "system", R"({"a":1})", R"({"a":1})");
expect_norm_string("developer_json_object_verbatim", "developer", R"({"a":1})", R"({"a":1})");
// Plain text unchanged for all roles.
expect_norm_string("user_plain_text", "user", "hello world", "hello world");
expect_norm_string("assistant_non_json_text_kept", "assistant", "hi [unclosed", "hi [unclosed");
// #7324 boundary - user/system/developer literal "null" preserved (never parsed).
expect_norm_string("user_literal_null_stays", "user", "null", "null");
expect_norm_string("system_literal_null_stays", "system", "null", "null");
expect_norm_string("developer_literal_null_stays", "developer", "null", "null");
// #7324 - assistant/tool literal null collapses to empty string.
expect_norm_string("assistant_null_to_empty", "assistant", "null", "");
expect_norm_string("tool_null_to_empty", "tool", "null", "");
// #7324/#7528 - assistant/tool object bookkeeping stringified (stays a string).
check(normalize_message_content("assistant", R"({"tool":"x"})").is_string(), "assistant_object_stringified");
check(normalize_message_content("tool", R"({"error":"boom"})").is_string(), "tool_object_stringified");
// #10524-family - a bare scalar that parses as a JSON number stays the string.
expect_norm_string("assistant_scalar_number_stays_string", "assistant", "42", "42");
// baseline - empty content stays empty.
expect_norm_string("user_empty_stays_empty", "user", "", "");
}
// ---- normalize_template_message (BEFORE TEMPLATE sanitizer) ---------------
static void test_template_sanitizer() {
// #7528 - a tool message with an ACTUAL array becomes a string.
{
ordered_json msg = {{"role", "tool"}, {"content", ordered_json::array({{{"type", "text"}, {"text", "r"}}})}};
normalize_template_message(msg);
check(msg["content"].is_string(), "before_template_tool_array_to_string", "got " + msg["content"].dump());
}
// #7324 - null content -> "" for any role.
{
ordered_json msg = {{"role", "assistant"}, {"content", nullptr}};
normalize_template_message(msg);
check(msg["content"].is_string() && msg["content"] == "", "before_template_null_to_empty");
}
// object content -> dumped string (would otherwise throw at the template).
{
ordered_json msg = {{"role", "assistant"}, {"content", {{"x", 1}}}};
normalize_template_message(msg);
check(msg["content"].is_string(), "before_template_object_to_string", "got " + msg["content"].dump());
}
// missing content field -> "".
{
ordered_json msg = {{"role", "user"}};
normalize_template_message(msg);
check(msg.contains("content") && msg["content"] == "", "before_template_missing_to_empty");
}
// multimodal: a well-typed user array must be left UNTOUCHED (role!=tool).
{
ordered_json parts = ordered_json::array();
parts.push_back({{"type", "text"}, {"text", "x"}});
ordered_json img; img["type"] = "image_url"; img["image_url"] = {{"url", "data:..."}};
parts.push_back(img);
ordered_json msg = {{"role", "user"}, {"content", parts}};
normalize_template_message(msg);
check(msg["content"].is_array() && msg["content"].size() == 2, "before_template_user_typed_array_preserved",
"got " + msg["content"].dump());
}
// a plain string is left untouched.
{
ordered_json msg = {{"role", "user"}, {"content", "hello"}};
normalize_template_message(msg);
check(msg["content"] == "hello", "before_template_string_untouched");
}
}
// ---- build_reconstructed_message ----------------------------------------
static void test_reconstruction() {
const std::string ingredients = R"(["1/4 cup brown sugar","1 pound ground beef"])";
// #10524 end-state - user JSON-array text, no media -> string content.
{
ReconstructedMessageInput in;
in.role = "user"; in.content = ingredients;
auto m = build_reconstructed_message(in);
check(m["content"].is_string() && m["content"] == ingredients, "recon_user_json_array_string",
"got " + m["content"].dump());
}
// multimodal - user text + one image on last user msg -> typed array, image kept.
{
ReconstructedMessageInput in;
in.role = "user"; in.content = ingredients; in.is_last_user_msg = true;
in.images.push_back("BASE64IMG");
auto m = build_reconstructed_message(in);
check(m["content"].is_array() && m["content"].size() == 2, "recon_multimodal_text_plus_image",
"got " + m["content"].dump());
check(m["content"][0]["type"] == "text" && m["content"][0]["text"] == ingredients, "recon_multimodal_text_first");
check(m["content"][1]["type"] == "image_url", "recon_multimodal_image_kept");
}
// multimodal media-only - empty text + image on last user msg.
{
ReconstructedMessageInput in;
in.role = "user"; in.content = ""; in.is_last_user_msg = true;
in.images.push_back("BASE64IMG");
auto m = build_reconstructed_message(in);
check(m["content"].is_array() && m["content"].size() == 1 && m["content"][0]["type"] == "image_url",
"recon_media_only", "got " + m["content"].dump());
}
// #7528 - tool array-string content stays a string.
{
ReconstructedMessageInput in;
in.role = "tool"; in.content = R"(["a","b"])"; in.tool_call_id = "call_1";
auto m = build_reconstructed_message(in);
check(m["content"].is_string() && m["content"] == R"(["a","b"])", "recon_tool_array_string",
"got " + m["content"].dump());
check(m["tool_call_id"] == "call_1", "recon_tool_call_id_set");
}
// tool empty content -> "".
{
ReconstructedMessageInput in;
in.role = "tool"; in.content = "";
auto m = build_reconstructed_message(in);
check(m["content"].is_string() && m["content"] == "", "recon_tool_empty_to_string");
}
// #7324 - assistant + tool_calls + empty content -> " " (single space, not "").
{
ReconstructedMessageInput in;
in.role = "assistant"; in.content = "";
in.tool_calls = R"([{"id":"c1","type":"function","function":{"name":"f","arguments":"{}"}}])";
auto m = build_reconstructed_message(in);
check(m["content"].is_string() && m["content"] == " ", "recon_toolcalls_empty_content_space",
"got " + m["content"].dump());
check(m["tool_calls"].is_array() && m["tool_calls"].size() == 1, "recon_toolcalls_parsed");
}
// assistant + tool_calls + real content keeps the content.
{
ReconstructedMessageInput in;
in.role = "assistant"; in.content = "I'll call f";
in.tool_calls = R"([{"id":"c1","type":"function","function":{"name":"f","arguments":"{}"}}])";
auto m = build_reconstructed_message(in);
check(m["content"] == "I'll call f", "recon_toolcalls_with_content_kept");
}
// assistant null content -> "".
{
ReconstructedMessageInput in;
in.role = "assistant"; in.content = "null";
auto m = build_reconstructed_message(in);
check(m["content"] == "", "recon_assistant_null_to_empty");
}
// malformed tool_calls JSON must not throw; content preserved.
{
ReconstructedMessageInput in;
in.role = "assistant"; in.content = "hi"; in.tool_calls = "{not json";
auto m = build_reconstructed_message(in);
check(m["content"] == "hi" && !m.contains("tool_calls"), "recon_malformed_toolcalls_safe");
}
// optional fields: name + reasoning carried through.
{
ReconstructedMessageInput in;
in.role = "tool"; in.content = "result"; in.name = "get_weather"; in.reasoning_content = "thinking";
auto m = build_reconstructed_message(in);
check(m["name"] == "get_weather" && m["reasoning_content"] == "thinking", "recon_optional_fields");
}
}
int main() {
test_normalize();
test_template_sanitizer();
test_reconstruction();
if (failures == 0) {
std::cout << "OK: all message_content tests passed\n";
return 0;
}
std::cerr << failures << " test(s) failed\n";
return 1;
}

View File

@@ -14,22 +14,6 @@ mkdir -p $CURDIR/package/lib
cp -avrf $CURDIR/llama-cpp-* $CURDIR/package/
cp -rfv $CURDIR/run.sh $CURDIR/package/
# Bundle the ggml shared backends produced by the CPU_ALL_VARIANTS build (libggml-base.so,
# libggml.so, libllama.so and the per-microarch libggml-cpu-*.so), all into package/lib.
#
# Two distinct resolution mechanisms both land here:
# - NEEDED deps (libggml-base/libggml/libllama): resolved by the dynamic linker via the
# LD_LIBRARY_PATH=$CURDIR/lib that run.sh exports.
# - The per-microarch libggml-cpu-*.so are NOT linked; ggml *discovers* them at runtime by
# scanning the executable's own directory (readlink /proc/self/exe). run.sh launches via
# the bundled $CURDIR/lib/ld.so, so /proc/self/exe -> .../lib/ld.so and ggml scans lib/.
# That is why the variants must sit in lib/ (next to ld.so), not just on the link path.
# No-op on builds (arm64/darwin) that don't produce the all-variants set.
if [ -d "$CURDIR/ggml-shared-libs" ]; then
echo "Bundling ggml shared backends (CPU_ALL_VARIANTS)..."
cp -avf $CURDIR/ggml-shared-libs/*.so* $CURDIR/package/lib/
fi
# Detect architecture and copy appropriate libraries
if [ -f "/lib64/ld-linux-x86-64.so.2" ]; then
# x86_64 architecture

View File

@@ -18,10 +18,6 @@ done
cp -r CMakeLists.txt llama.cpp/tools/grpc-server/
cp -r grpc-server.cpp llama.cpp/tools/grpc-server/
# Shared message-reconstruction helpers (included by grpc-server.cpp) and their
# unit test (compiled only when -DLLAMA_GRPC_BUILD_TESTS=ON).
cp -r message_content.h llama.cpp/tools/grpc-server/
cp -r message_content_test.cpp llama.cpp/tools/grpc-server/
cp -rfv llama.cpp/vendor/nlohmann/json.hpp llama.cpp/tools/grpc-server/
cp -rfv llama.cpp/vendor/cpp-httplib/httplib.h llama.cpp/tools/grpc-server/

View File

@@ -2,7 +2,7 @@
set -ex
# Get the absolute current dir where the script is located
CURDIR=$(dirname "$(realpath "$0")")
CURDIR=$(dirname "$(realpath $0)")
cd /
@@ -12,41 +12,55 @@ grep -e "flags" /proc/cpuinfo | head -1
BINARY=llama-cpp-fallback
# CPU images (x86, arm64, darwin) ship a single llama-cpp-cpu-all built with ggml
# CPU_ALL_VARIANTS: ggml's backend registry dlopens the best libggml-cpu-*.so for this
# host, so no shell-side AVX probing. GPU images (cublas/sycl/vulkan/hipblas) ship only
# llama-cpp-fallback (the accelerator does the compute), so fall back to it when absent.
if [ -e "$CURDIR"/llama-cpp-cpu-all ]; then
BINARY=llama-cpp-cpu-all
if grep -q -e "\savx\s" /proc/cpuinfo ; then
echo "CPU: AVX found OK"
if [ -e $CURDIR/llama-cpp-avx ]; then
BINARY=llama-cpp-avx
fi
fi
if grep -q -e "\savx2\s" /proc/cpuinfo ; then
echo "CPU: AVX2 found OK"
if [ -e $CURDIR/llama-cpp-avx2 ]; then
BINARY=llama-cpp-avx2
fi
fi
# Check avx 512
if grep -q -e "\savx512f\s" /proc/cpuinfo ; then
echo "CPU: AVX512F found OK"
if [ -e $CURDIR/llama-cpp-avx512 ]; then
BINARY=llama-cpp-avx512
fi
fi
if [ -n "$LLAMACPP_GRPC_SERVERS" ]; then
if [ -e "$CURDIR"/llama-cpp-grpc ]; then
if [ -e $CURDIR/llama-cpp-grpc ]; then
BINARY=llama-cpp-grpc
fi
fi
# Extend ld library path with the dir where this script is located/lib
if [ "$(uname)" == "Darwin" ]; then
export DYLD_LIBRARY_PATH="$CURDIR"/lib:$DYLD_LIBRARY_PATH
#export DYLD_FALLBACK_LIBRARY_PATH="$CURDIR"/lib:$DYLD_FALLBACK_LIBRARY_PATH
export DYLD_LIBRARY_PATH=$CURDIR/lib:$DYLD_LIBRARY_PATH
#export DYLD_FALLBACK_LIBRARY_PATH=$CURDIR/lib:$DYLD_FALLBACK_LIBRARY_PATH
else
export LD_LIBRARY_PATH="$CURDIR"/lib:$LD_LIBRARY_PATH
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
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
if [ -f $CURDIR/lib/ld.so ]; then
echo "Using lib/ld.so"
echo "Using binary: $BINARY"
exec "$CURDIR"/lib/ld.so "$CURDIR"/$BINARY "$@"
exec $CURDIR/lib/ld.so $CURDIR/$BINARY "$@"
fi
echo "Using binary: $BINARY"
exec "$CURDIR"/$BINARY "$@"
exec $CURDIR/$BINARY "$@"
# We should never reach this point, however just in case we do, run fallback
exec "$CURDIR"/llama-cpp-fallback "$@"
exec $CURDIR/llama-cpp-fallback "$@"

View File

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

View File

@@ -1,77 +0,0 @@
cmake_minimum_required(VERSION 3.21)
project(privacy-filter-grpc-server LANGUAGES CXX C)
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
set(TARGET grpc-server)
# Path to the privacy-filter.cpp engine sources. The Makefile arranges for this
# to exist (clone of a pinned commit, or a symlink to PRIVACY_FILTER_SRC).
set(PRIVACY_FILTER_DIR "${CMAKE_CURRENT_SOURCE_DIR}/privacy-filter.cpp"
CACHE PATH "Path to the privacy-filter.cpp engine source tree")
find_package(Threads REQUIRED)
find_package(Protobuf CONFIG QUIET)
if(NOT Protobuf_FOUND)
find_package(Protobuf REQUIRED)
endif()
find_package(gRPC CONFIG QUIET)
if(NOT gRPC_FOUND)
# Ubuntu's apt-installed grpc++ does not ship a CMake config - fall back.
find_library(GRPCPP_LIB grpc++ REQUIRED)
find_library(GRPCPP_REFLECTION_LIB grpc++_reflection REQUIRED)
add_library(gRPC::grpc++ INTERFACE IMPORTED)
set_target_properties(gRPC::grpc++ PROPERTIES INTERFACE_LINK_LIBRARIES "${GRPCPP_LIB}")
add_library(gRPC::grpc++_reflection INTERFACE IMPORTED)
set_target_properties(gRPC::grpc++_reflection PROPERTIES INTERFACE_LINK_LIBRARIES "${GRPCPP_REFLECTION_LIB}")
endif()
find_program(_PROTOC NAMES protoc REQUIRED)
find_program(_GRPC_CPP_PLUGIN NAMES grpc_cpp_plugin REQUIRED)
get_filename_component(HW_PROTO "${CMAKE_CURRENT_SOURCE_DIR}/../../backend.proto" ABSOLUTE)
get_filename_component(HW_PROTO_PATH "${HW_PROTO}" PATH)
set(HW_PROTO_SRCS "${CMAKE_CURRENT_BINARY_DIR}/backend.pb.cc")
set(HW_PROTO_HDRS "${CMAKE_CURRENT_BINARY_DIR}/backend.pb.h")
set(HW_GRPC_SRCS "${CMAKE_CURRENT_BINARY_DIR}/backend.grpc.pb.cc")
set(HW_GRPC_HDRS "${CMAKE_CURRENT_BINARY_DIR}/backend.grpc.pb.h")
add_custom_command(
OUTPUT "${HW_PROTO_SRCS}" "${HW_PROTO_HDRS}" "${HW_GRPC_SRCS}" "${HW_GRPC_HDRS}"
COMMAND ${_PROTOC}
ARGS --grpc_out "${CMAKE_CURRENT_BINARY_DIR}"
--cpp_out "${CMAKE_CURRENT_BINARY_DIR}"
-I "${HW_PROTO_PATH}"
--plugin=protoc-gen-grpc="${_GRPC_CPP_PLUGIN}"
"${HW_PROTO}"
DEPENDS "${HW_PROTO}")
add_library(hw_grpc_proto STATIC
${HW_GRPC_SRCS} ${HW_GRPC_HDRS}
${HW_PROTO_SRCS} ${HW_PROTO_HDRS})
target_include_directories(hw_grpc_proto PUBLIC ${CMAKE_CURRENT_BINARY_DIR})
# The generated proto/grpc sources include protobuf and grpc++ headers, so this
# library must see their include dirs. Linking the imported targets propagates
# them. On Linux the apt headers live in /usr/include (default search path) so
# this was a no-op; on macOS the Homebrew headers are under /opt/homebrew and
# would otherwise be missed (runtime_version.h not found).
target_link_libraries(hw_grpc_proto PUBLIC
protobuf::libprotobuf
gRPC::grpc++)
# Build only the pf static lib (+ ggml) from the engine tree — no CLI/bench/tests.
# PF_VULKAN is honored when passed on the cmake command line (it lands in the
# shared cache the engine reads).
set(PF_BUILD_TOOLS OFF CACHE BOOL "" FORCE)
set(PF_BUILD_TESTS OFF CACHE BOOL "" FORCE)
add_subdirectory(${PRIVACY_FILTER_DIR} ${CMAKE_CURRENT_BINARY_DIR}/privacy-filter.cpp)
add_executable(${TARGET} grpc-server.cpp)
target_link_libraries(${TARGET} PRIVATE
pf
hw_grpc_proto
gRPC::grpc++
gRPC::grpc++_reflection
protobuf::libprotobuf
Threads::Threads)

View File

@@ -1,77 +0,0 @@
# privacy-filter backend Makefile.
#
# Wraps the standalone privacy-filter.cpp GGML engine (the openai-privacy-filter
# PII/NER token classifier) as a LocalAI gRPC backend. The engine source is
# fetched at the pin below — .github/workflows/bump_deps.yaml finds and updates
# PRIVACY_FILTER_VERSION, matching the llama-cpp / ds4 convention.
#
# Local development: point at a working checkout instead of cloning, e.g.
# make PRIVACY_FILTER_SRC=$HOME/c/privacy-filter.cpp grpc-server
PRIVACY_FILTER_VERSION?=98f52c5ef2250f207cc6b9a6aef05393a120cb7c
PRIVACY_FILTER_REPO?=https://github.com/localai-org/privacy-filter.cpp
PRIVACY_FILTER_SRC?=
CURRENT_MAKEFILE_DIR := $(dir $(abspath $(lastword $(MAKEFILE_LIST))))
BUILD_DIR := build
BUILD_TYPE ?=
NATIVE ?= false
JOBS ?= $(shell nproc 2>/dev/null || echo 4)
CMAKE_ARGS ?= -DCMAKE_BUILD_TYPE=Release
# GPU backends; the default (cpu) needs no extra flags. 'cublas' is LocalAI's
# name for the CUDA build (matches llama-cpp / ds4), mapping to the engine's
# GGML_CUDA path; 'vulkan' selects the ggml Vulkan backend.
ifeq ($(BUILD_TYPE),cublas)
CMAKE_ARGS += -DPF_CUDA=ON
endif
ifeq ($(BUILD_TYPE),vulkan)
CMAKE_ARGS += -DPF_VULKAN=ON
endif
# Portable binaries for distribution: disable -march=native unless asked.
ifneq ($(NATIVE),true)
CMAKE_ARGS += -DGGML_NATIVE=OFF
endif
.PHONY: grpc-server package clean purge test all
all: grpc-server
# Provide the engine sources at ./privacy-filter.cpp. With PRIVACY_FILTER_SRC
# set we symlink a local checkout (instant, no network); otherwise we clone the
# pinned commit and its ggml submodule. The directory/symlink is the target, so
# make only does this once — run 'make purge && make' to refetch after a bump.
privacy-filter.cpp:
ifneq ($(PRIVACY_FILTER_SRC),)
ln -sfn $(abspath $(PRIVACY_FILTER_SRC)) privacy-filter.cpp
else
mkdir -p privacy-filter.cpp
cd privacy-filter.cpp && \
git init -q && \
git remote add origin $(PRIVACY_FILTER_REPO) && \
git fetch --depth 1 origin $(PRIVACY_FILTER_VERSION) && \
git checkout FETCH_HEAD && \
git submodule update --init --recursive --depth 1
endif
grpc-server: privacy-filter.cpp
@echo "Building privacy-filter grpc-server ($(BUILD_TYPE)) with $(CMAKE_ARGS)"
mkdir -p $(BUILD_DIR)
cd $(BUILD_DIR) && cmake $(CMAKE_ARGS) $(CURRENT_MAKEFILE_DIR) && cmake --build . --config Release -j $(JOBS)
cp $(BUILD_DIR)/grpc-server grpc-server
package: grpc-server
bash package.sh
test:
@echo "privacy-filter backend: parity/regression coverage lives in the engine repo"
clean:
rm -rf $(BUILD_DIR) grpc-server package
# 'privacy-filter.cpp' may be a symlink (PRIVACY_FILTER_SRC) — rm without a
# trailing slash removes the link, never the linked-to checkout.
purge: clean
rm -rf privacy-filter.cpp

View File

@@ -1,210 +0,0 @@
// privacy-filter LocalAI gRPC backend.
//
// Thin shim over privacy-filter.cpp's flat C API (include/pf.h): a standalone
// GGML engine for the openai-privacy-filter token-classification model family
// (PII NER). It replaces the llama.cpp-patched TokenClassify path for this one
// model family — same GGUF files, no llama.cpp carry-patches.
//
// Only the RPCs the PII tier needs are implemented: LoadModel, TokenClassify,
// plus Health / Status / Free. Everything else inherits the generated base
// class default (UNIMPLEMENTED).
#include "backend.pb.h"
#include "backend.grpc.pb.h"
#include "pf.h"
#include <grpcpp/grpcpp.h>
#include <grpcpp/server.h>
#include <grpcpp/server_builder.h>
#include <grpcpp/ext/proto_server_reflection_plugin.h>
#include <atomic>
#include <chrono>
#include <csignal>
#include <iostream>
#include <memory>
#include <mutex>
#include <string>
using grpc::Server;
using grpc::ServerBuilder;
using grpc::ServerContext;
// NOTE: do NOT alias grpc::Status as Status — the Status RPC method below would
// shadow the type and break the other method signatures. Use GStatus instead.
using GStatus = ::grpc::Status;
using grpc::StatusCode;
namespace {
// The engine is single-model-per-process: LocalAI spawns one backend process
// per loaded model. g_mu guards (re)load against in-flight classification.
std::mutex g_mu;
pf_ctx * g_ctx = nullptr;
std::atomic<Server *> g_server{nullptr};
// Resolve the device string the engine expects ("cpu" / "gpu" / "cuda" /
// "vulkan", optionally ":N"). Priority: an explicit "device:..." in
// ModelOptions.Options, then a non-zero NGPULayers as a coarse "use the GPU"
// signal, else CPU. "gpu" lets the engine pick whichever GPU backend this
// binary was compiled with (CUDA or Vulkan), so the same config works on
// either build; pin "device:cuda"/"device:vulkan" to be explicit.
std::string resolve_device(const backend::ModelOptions * opts) {
for (const auto & o : opts->options()) {
const std::string prefix = "device:";
if (o.rfind(prefix, 0) == 0) {
return o.substr(prefix.size());
}
}
if (opts->ngpulayers() > 0) {
return "gpu";
}
return "cpu";
}
class PrivacyFilterBackend final : public backend::Backend::Service {
public:
GStatus Health(ServerContext *, const backend::HealthMessage *,
backend::Reply * reply) override {
reply->set_message("OK");
return GStatus::OK;
}
GStatus Status(ServerContext *, const backend::HealthMessage *,
backend::StatusResponse * response) override {
std::lock_guard<std::mutex> lock(g_mu);
response->set_state(g_ctx ? backend::StatusResponse::READY
: backend::StatusResponse::UNINITIALIZED);
return GStatus::OK;
}
GStatus LoadModel(ServerContext *, const backend::ModelOptions * request,
backend::Result * result) override {
std::lock_guard<std::mutex> lock(g_mu);
// ModelFile is the absolute path LocalAI resolves; Model is the bare
// name. Prefer the former, fall back to the latter.
const std::string path =
!request->modelfile().empty() ? request->modelfile() : request->model();
if (path.empty()) {
result->set_success(false);
result->set_message("no model path supplied");
return GStatus::OK;
}
const std::string device = resolve_device(request);
if (g_ctx) { pf_free(g_ctx); g_ctx = nullptr; }
pf_ctx * ctx = pf_load(path.c_str(), device.c_str(), request->threads());
const char * err = pf_last_error(ctx);
if (err) {
result->set_success(false);
result->set_message(std::string("privacy-filter load failed: ") + err);
pf_free(ctx);
return GStatus::OK;
}
// ContextSize, when set, becomes the per-forward window. The engine
// ignores values that are too small to window (<= 2*halo) and just
// runs a single forward, so passing it through is always safe.
if (request->contextsize() > 0) {
pf_set_window(ctx, request->contextsize());
}
g_ctx = ctx;
result->set_success(true);
result->set_message("privacy-filter loaded (" + device + ")");
return GStatus::OK;
}
GStatus TokenClassify(ServerContext *, const backend::TokenClassifyRequest * request,
backend::TokenClassifyResponse * response) override {
std::lock_guard<std::mutex> lock(g_mu);
if (!g_ctx) {
return GStatus(StatusCode::FAILED_PRECONDITION, "Model not loaded");
}
const std::string & text = request->text();
if (text.empty()) {
return GStatus::OK; // no text -> no entities
}
pf_entity * ents = nullptr;
size_t n = 0;
if (pf_classify(g_ctx, text.data(), text.size(), request->threshold(), &ents, &n) != 0) {
const char * err = pf_last_error(g_ctx);
return GStatus(StatusCode::INTERNAL,
std::string("TokenClassify failed: ") + (err ? err : "unknown"));
}
// Byte offsets are into the original UTF-8 text; the engine already
// applied the threshold and whitespace-trimmed span edges.
for (size_t i = 0; i < n; i++) {
backend::TokenClassifyEntity * ent = response->add_entities();
ent->set_entity_group(ents[i].label ? ents[i].label : "");
ent->set_start(ents[i].start);
ent->set_end(ents[i].end);
ent->set_score(ents[i].score);
ent->set_text(text.substr((size_t) ents[i].start,
(size_t) (ents[i].end - ents[i].start)));
}
pf_entities_free(ents, n);
return GStatus::OK;
}
GStatus Free(ServerContext *, const backend::HealthMessage *,
backend::Result * result) override {
std::lock_guard<std::mutex> lock(g_mu);
if (g_ctx) { pf_free(g_ctx); g_ctx = nullptr; }
result->set_success(true);
return GStatus::OK;
}
};
void RunServer(const std::string & addr) {
PrivacyFilterBackend service;
grpc::EnableDefaultHealthCheckService(true);
grpc::reflection::InitProtoReflectionServerBuilderPlugin();
ServerBuilder builder;
builder.AddListeningPort(addr, grpc::InsecureServerCredentials());
builder.RegisterService(&service);
builder.SetMaxReceiveMessageSize(64 * 1024 * 1024);
builder.SetMaxSendMessageSize(64 * 1024 * 1024);
std::unique_ptr<Server> server(builder.BuildAndStart());
if (!server) {
std::cerr << "privacy-filter grpc-server: failed to bind " << addr << "\n";
std::exit(1);
}
g_server = server.get();
std::cerr << "privacy-filter grpc-server listening on " << addr << "\n";
server->Wait();
}
void signal_handler(int) {
if (auto * srv = g_server.load()) {
srv->Shutdown(std::chrono::system_clock::now() + std::chrono::seconds(3));
}
}
} // namespace
int main(int argc, char * argv[]) {
std::string addr = "127.0.0.1:50051";
for (int i = 1; i < argc; ++i) {
std::string a = argv[i];
const std::string addr_flag = "--addr=";
if (a.rfind(addr_flag, 0) == 0) addr = a.substr(addr_flag.size());
else if (a == "--addr" && i + 1 < argc) addr = argv[++i];
else if (a == "--help" || a == "-h") {
std::cout << "Usage: grpc-server --addr=HOST:PORT\n";
return 0;
}
}
std::signal(SIGINT, signal_handler);
std::signal(SIGTERM, signal_handler);
RunServer(addr);
return 0;
}

View File

@@ -1,39 +0,0 @@
#!/bin/bash
# Assemble package/ for the from-scratch backend image: the grpc-server binary,
# run.sh, the dynamic loader, and every shared library the binary needs.
set -e
CURDIR=$(dirname "$(realpath "$0")")
REPO_ROOT="${CURDIR}/../../.."
mkdir -p "$CURDIR/package/lib"
cp -avf "$CURDIR/grpc-server" "$CURDIR/package/"
cp -rfv "$CURDIR/run.sh" "$CURDIR/package/"
# The dynamic loader, renamed to lib/ld.so so run.sh can invoke it explicitly
# (makes the image independent of the host's glibc layout).
if [ -f "/lib64/ld-linux-x86-64.so.2" ]; then
cp -arfLv /lib64/ld-linux-x86-64.so.2 "$CURDIR/package/lib/ld.so"
elif [ -f "/lib/ld-linux-aarch64.so.1" ]; then
cp -arfLv /lib/ld-linux-aarch64.so.1 "$CURDIR/package/lib/ld.so"
else
echo "package.sh: unknown architecture" >&2; exit 1
fi
# Bundle the binary's transitive shared deps (libstdc++, libgomp, and the apt
# grpc++/protobuf/absl stack) by walking ldd — robust to whichever of those are
# linked shared vs static. The loader line (no "=>") is skipped; ld.so above
# already covers it.
ldd "$CURDIR/grpc-server" | awk '$2 == "=>" && $3 ~ /^\// { print $3 }' | sort -u | \
while read -r so; do
[ -f "$so" ] && cp -arfLv "$so" "$CURDIR/package/lib/"
done
# Vulkan loader / GPU libs when building the GPU variant.
GPU_LIB_SCRIPT="${REPO_ROOT}/scripts/build/package-gpu-libs.sh"
if [ -f "$GPU_LIB_SCRIPT" ]; then
source "$GPU_LIB_SCRIPT" "$CURDIR/package/lib"
package_gpu_libs
fi
echo "privacy-filter package contents:"
ls -lah "$CURDIR/package/" "$CURDIR/package/lib/"

View File

@@ -1,15 +0,0 @@
#!/bin/bash
# Entry point for the privacy-filter backend image / BACKEND_BINARY mode.
set -e
CURDIR=$(dirname "$(realpath "$0")")
# macOS has no bundled ld.so; the darwin package ships only dylibs under lib/,
# resolved via DYLD_LIBRARY_PATH (the ld.so branch below is skipped there).
if [ "$(uname)" = "Darwin" ]; then
export DYLD_LIBRARY_PATH="$CURDIR/lib:$DYLD_LIBRARY_PATH"
else
export LD_LIBRARY_PATH="$CURDIR/lib:$LD_LIBRARY_PATH"
fi
if [ -f "$CURDIR/lib/ld.so" ]; then
exec "$CURDIR/lib/ld.so" "$CURDIR/grpc-server" "$@"
fi
exec "$CURDIR/grpc-server" "$@"

View File

@@ -1,71 +0,0 @@
#!/bin/bash
#
# Discovers and runs every standalone C++ unit test under backend/cpp/.
#
# A "standalone" unit test is a *_test.cpp that depends only on the C++ standard
# library and nlohmann/json (single header) - i.e. it exercises pure helpers and
# does not need the full llama.cpp + gRPC backend build. Tests that DO need the
# backend build use the CMake/ctest path (e.g. -DLLAMA_GRPC_BUILD_TESTS=ON)
# instead and are skipped here.
#
# This keeps CI generic: adding a new pure-C++ unit test file named *_test.cpp in
# an active backend source dir is picked up automatically, with no CI edits.
#
# Env:
# NLOHMANN_INCLUDE include dir that contains nlohmann/json.hpp. If unset, the
# nlohmann/json single header is fetched to a temp dir.
# CXX compiler (default: g++).
# JSON_VERSION nlohmann/json tag to fetch when NLOHMANN_INCLUDE is unset
# (default: v3.11.3).
set -uo pipefail
ROOT="$(cd "$(dirname "$0")" && pwd)"
CXX="${CXX:-g++}"
JSON_VERSION="${JSON_VERSION:-v3.11.3}"
JSON_INC="${NLOHMANN_INCLUDE:-}"
if [ -z "$JSON_INC" ]; then
JSON_INC="$(mktemp -d)"
mkdir -p "$JSON_INC/nlohmann"
echo "Fetching nlohmann/json ${JSON_VERSION} single header..."
if ! curl -L -sf \
"https://raw.githubusercontent.com/nlohmann/json/${JSON_VERSION}/single_include/nlohmann/json.hpp" \
-o "$JSON_INC/nlohmann/json.hpp"; then
echo "ERROR: failed to fetch nlohmann/json header" >&2
exit 1
fi
fi
# Active source dirs only - exclude per-variant build copies, dev snapshots and
# the vendored upstream llama.cpp tree.
mapfile -t tests < <(find "$ROOT" -name '*_test.cpp' \
-not -path '*/llama.cpp/*' \
-not -path '*-build/*' \
-not -path '*-dev/*' \
-not -path '*fallback*' | sort)
if [ "${#tests[@]}" -eq 0 ]; then
echo "No standalone C++ unit tests found under $ROOT"
exit 0
fi
fail=0
for test_src in "${tests[@]}"; do
name="$(basename "$test_src" .cpp)"
bin="$(mktemp -d)/$name"
echo "==> $test_src"
if ! "$CXX" -std=c++17 -Wall -Wextra \
-I"$JSON_INC" -I"$(dirname "$test_src")" \
"$test_src" -o "$bin"; then
echo "COMPILE FAILED: $test_src" >&2
fail=1
continue
fi
if ! "$bin"; then
echo "TEST FAILED: $test_src" >&2
fail=1
fi
done
echo "Ran ${#tests[@]} standalone C++ unit test file(s)"
exit "$fail"

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?=7d9715f1f071fa07c7b2ad3dbfd320b314139e65
TURBOQUANT_VERSION?=5aeb2fdbe26cd4c534c6fa15de73cb5749bd0403
LLAMA_REPO?=https://github.com/TheTom/llama-cpp-turboquant
CMAKE_ARGS?=
@@ -65,29 +65,6 @@ turboquant-avx:
turboquant-fallback:
$(call turboquant-build,fallback,-DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off,--target grpc-server)
# Single-build CPU backend via ggml CPU_ALL_VARIANTS (mirrors llama-cpp-cpu-all).
# turboquant reuses backend/cpp/llama-cpp's CMakeLists.txt (hw_grpc_proto STATIC) and
# Makefile (SHARED_LIBS make-var + EXTRA_CMAKE_ARGS), so this passes the same overrides
# through to the copied build: SHARED_LIBS=ON, the DL flags, and --target ggml (which
# pulls in the per-microarch libggml-cpu-*.so via ggml's add_dependencies). The .so set
# is collected for package.sh to bundle into package/lib.
turboquant-cpu-all:
rm -rf $(CURRENT_MAKEFILE_DIR)/../turboquant-cpu-all-build
cp -rf $(LLAMA_CPP_DIR) $(CURRENT_MAKEFILE_DIR)/../turboquant-cpu-all-build
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../turboquant-cpu-all-build purge
bash $(CURRENT_MAKEFILE_DIR)/patch-grpc-server.sh $(CURRENT_MAKEFILE_DIR)/../turboquant-cpu-all-build/grpc-server.cpp
$(info $(GREEN)I turboquant build info:cpu-all-variants$(RESET))
LLAMA_REPO=$(LLAMA_REPO) LLAMA_VERSION=$(TURBOQUANT_VERSION) \
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../turboquant-cpu-all-build llama.cpp
bash $(CURRENT_MAKEFILE_DIR)/apply-patches.sh $(CURRENT_MAKEFILE_DIR)/../turboquant-cpu-all-build/llama.cpp $(PATCHES_DIR)
SHARED_LIBS=ON EXTRA_CMAKE_ARGS="-DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON" TARGET="--target grpc-server --target ggml" \
LLAMA_REPO=$(LLAMA_REPO) LLAMA_VERSION=$(TURBOQUANT_VERSION) \
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../turboquant-cpu-all-build grpc-server
cp -rfv $(CURRENT_MAKEFILE_DIR)/../turboquant-cpu-all-build/grpc-server turboquant-cpu-all
rm -rf ggml-shared-libs && mkdir -p ggml-shared-libs
find $(CURRENT_MAKEFILE_DIR)/../turboquant-cpu-all-build/llama.cpp/build \( -name '*.so*' -o -name '*.dylib' \) -exec cp -av {} ggml-shared-libs/ \;
@echo "Collected ggml shared backends:" && ls -la ggml-shared-libs/
turboquant-grpc:
$(call turboquant-build,grpc,-DGGML_RPC=ON -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off,--target grpc-server --target rpc-server)

View File

@@ -14,15 +14,6 @@ mkdir -p $CURDIR/package/lib
cp -avrf $CURDIR/turboquant-* $CURDIR/package/
cp -rfv $CURDIR/run.sh $CURDIR/package/
# Bundle the ggml shared backends from the CPU_ALL_VARIANTS build into package/lib. ggml
# discovers the per-microarch libggml-cpu-*.so by scanning the executable directory, which
# (via the bundled lib/ld.so that run.sh launches through) resolves to lib/. See the
# matching comment in backend/cpp/llama-cpp/package.sh. No-op on the fallback/ROCm builds.
if [ -d "$CURDIR/ggml-shared-libs" ]; then
echo "Bundling ggml shared backends (CPU_ALL_VARIANTS)..."
cp -avf $CURDIR/ggml-shared-libs/*.so* $CURDIR/package/lib/
fi
# Detect architecture and copy appropriate libraries
if [ -f "/lib64/ld-linux-x86-64.so.2" ]; then
# x86_64 architecture

View File

@@ -4,19 +4,21 @@
#
# 1. Augment the kv_cache_types[] allow-list so `LoadModel` accepts the
# fork-specific `turbo2` / `turbo3` / `turbo4` cache types.
# 2. Define LOCALAI_TURBOQUANT_NO_CHECKPOINT_MIN_STEP at the top of the file
# so the grpc-server option parser skips the two references to
# common_params::checkpoint_min_step (the default and the option handler).
# That field does not exist in the fork yet; drop this once it does.
#
# The fork used to lag upstream on the whole common_params_speculative refactor
# (ggml-org/llama.cpp#22397/#22838/#22964), the model_tgt rename (#22838) and
# get_media_marker (#21962), which required a much larger compat shim here
# (flat-field sed renames + a coarse LOCALAI_LEGACY_LLAMA_CPP_SPEC define). The
# fork has since rebased past all of those, so the only remaining gap is
# checkpoint_min_step. If a future bump reintroduces a divergence, add a narrow
# guard in grpc-server.cpp keyed on a fork-specific macro and inject it here
# rather than resurrecting the coarse one.
# 2. Replace `get_media_marker()` (added upstream in ggml-org/llama.cpp#21962,
# server-side random per-instance marker) with the legacy "<__media__>"
# literal. The fork branched before that PR, so server-common.cpp has no
# get_media_marker symbol. The fork's mtmd_default_marker() still returns
# "<__media__>", and Go-side tooling falls back to that sentinel when the
# backend does not expose media_marker, so substituting the literal keeps
# behavior identical on the turboquant path.
# 3. Revert the `common_params_speculative` field references to the
# pre-refactor flat layout. Upstream ggml-org/llama.cpp#22397 split the
# struct into nested `draft` / `ngram_simple` / `ngram_mod` / etc. members;
# the turboquant fork branched before that PR and still exposes the flat
# `n_max`, `mparams_dft`, `ngram_size_n`, ... fields. The substitutions
# below map the new nested paths back to the legacy flat names so the
# shared grpc-server.cpp keeps compiling against the fork's common.h.
# Drop this block once the fork rebases past #22397.
#
# We patch the *copy* sitting in turboquant-<flavor>-build/, never the original
# under backend/cpp/llama-cpp/, so the stock llama-cpp build keeps compiling
@@ -70,20 +72,72 @@ else
echo "==> KV allow-list patch OK"
fi
# 2. Define LOCALAI_TURBOQUANT_NO_CHECKPOINT_MIN_STEP at the top of the file so
# the grpc-server option parser skips the two references to
# common_params::checkpoint_min_step (the default assignment and the option
# handler). That field does not exist in the fork yet. Drop this block once
# the fork rebases past the bump that added checkpoint_min_step.
if grep -q '^#define LOCALAI_TURBOQUANT_NO_CHECKPOINT_MIN_STEP' "$SRC"; then
echo "==> $SRC already defines LOCALAI_TURBOQUANT_NO_CHECKPOINT_MIN_STEP, skipping"
if grep -q 'get_media_marker()' "$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 "==> patching $SRC to define LOCALAI_TURBOQUANT_NO_CHECKPOINT_MIN_STEP at the top"
# Insert the define before the very first `#include` so it precedes the
# checkpoint_min_step references.
echo "==> $SRC has no get_media_marker() call, skipping media-marker patch"
fi
if grep -q 'params\.speculative\.draft\.\|params\.speculative\.ngram_simple\.' "$SRC"; then
echo "==> patching $SRC to revert common_params_speculative refs to pre-#22397 flat layout"
# Each substitution is the exact post-refactor path → legacy flat field.
# Order doesn't matter because the source paths are disjoint, but we keep
# the most-specific (mparams.path) first for readability.
sed -E \
-e 's/params\.speculative\.draft\.mparams\.path/params.speculative.mparams_dft.path/g' \
-e 's/params\.speculative\.draft\.n_max/params.speculative.n_max/g' \
-e 's/params\.speculative\.draft\.n_min/params.speculative.n_min/g' \
-e 's/params\.speculative\.draft\.p_min/params.speculative.p_min/g' \
-e 's/params\.speculative\.draft\.p_split/params.speculative.p_split/g' \
-e 's/params\.speculative\.draft\.n_gpu_layers/params.speculative.n_gpu_layers/g' \
-e 's/params\.speculative\.draft\.n_ctx/params.speculative.n_ctx/g' \
-e 's/params\.speculative\.ngram_simple\.size_n/params.speculative.ngram_size_n/g' \
-e 's/params\.speculative\.ngram_simple\.size_m/params.speculative.ngram_size_m/g' \
-e 's/params\.speculative\.ngram_simple\.min_hits/params.speculative.ngram_min_hits/g' \
"$SRC" > "$SRC.tmp"
mv "$SRC.tmp" "$SRC"
echo "==> speculative field rename OK"
else
echo "==> $SRC has no post-#22397 speculative field refs, skipping spec rename patch"
fi
# 4. Revert the `ctx_server.impl->model_tgt` rename introduced by upstream
# ggml-org/llama.cpp#22838 (parallel drafting). The turboquant fork still
# exposes the field as `model` on `server_context_impl`. The two call sites
# are in the Rerank and ModelMetadata RPC handlers.
if grep -q 'ctx_server\.impl->model_tgt' "$SRC"; then
echo "==> patching $SRC to revert ctx_server.impl->model_tgt -> ctx_server.impl->model"
sed -E 's/ctx_server\.impl->model_tgt/ctx_server.impl->model/g' "$SRC" > "$SRC.tmp"
mv "$SRC.tmp" "$SRC"
echo "==> model_tgt rename OK"
else
echo "==> $SRC has no ctx_server.impl->model_tgt refs, skipping model_tgt rename patch"
fi
# 5. Define LOCALAI_LEGACY_LLAMA_CPP_SPEC at the top of the file so the
# grpc-server option parser skips the new option-handler blocks (ngram_mod,
# ngram_map_k, ngram_map_k4v, ngram_cache, draft.cache_type_*, draft.cpuparams*,
# draft.tensor_buft_overrides) introduced for the post-#22838 layout, the
# draft.tensor_buft_overrides sentinel termination, and the
# common_params::checkpoint_min_step default/option (added with the
# 35c9b1f3 bump). Those blocks reference struct fields that simply do not
# exist in the fork.
if grep -q '^#define LOCALAI_LEGACY_LLAMA_CPP_SPEC' "$SRC"; then
echo "==> $SRC already defines LOCALAI_LEGACY_LLAMA_CPP_SPEC, skipping"
else
echo "==> patching $SRC to define LOCALAI_LEGACY_LLAMA_CPP_SPEC at the top"
# Insert the define before the very first `#include` so it precedes all the
# speculative-decoding code paths.
awk '
!done && /^#include/ {
print "#define LOCALAI_TURBOQUANT_NO_CHECKPOINT_MIN_STEP 1"
print "#define LOCALAI_LEGACY_LLAMA_CPP_SPEC 1"
print "// ^ injected by backend/cpp/turboquant/patch-grpc-server.sh"
print ""
done = 1
@@ -91,13 +145,13 @@ else
{ print }
END {
if (!done) {
print "patch-grpc-server.sh: no #include anchor found to insert LOCALAI_TURBOQUANT_NO_CHECKPOINT_MIN_STEP" > "/dev/stderr"
print "patch-grpc-server.sh: no #include anchor found to insert LOCALAI_LEGACY_LLAMA_CPP_SPEC" > "/dev/stderr"
exit 1
}
}
' "$SRC" > "$SRC.tmp"
mv "$SRC.tmp" "$SRC"
echo "==> LOCALAI_TURBOQUANT_NO_CHECKPOINT_MIN_STEP define OK"
echo "==> LOCALAI_LEGACY_LLAMA_CPP_SPEC define OK"
fi
echo "==> all patches applied"

View File

@@ -1,55 +0,0 @@
hip: port the turboquant CUDA additions that ggml's HIP shim doesn't cover
The turboquant fork adds/modifies a few ggml-cuda.cu spots with CUDA APIs
that ggml's HIP (and MUSA) compatibility layer does not provide, breaking
the -gpu-rocm-hipblas-turboquant build:
1. ggml_cuda_copy2d_across_devices() (host-staged cross-device copy for
split mul_mat output) uses the CUDA 3D-peer copy APIs
cudaMemcpy3DPeerParms / make_cudaPitchedPtr / make_cudaExtent /
cudaMemcpy3DPeerAsync. HIP genuinely does not support these (see the
fork's own comment "HIP does not support cudaMemcpy3DPeerAsync"), so
guard the peer fast path with #if !defined(GGML_USE_HIP) &&
!defined(GGML_USE_MUSA) -- matching how the fork already guards the
same API for the sibling 2D copy -- and fall through to the existing
cudaMemcpyAsync staging fallback below (functionally identical,
slightly slower on multi-GPU ROCm).
2. ggml_backend_cuda_device_event_new() creates its event with plain
cudaEventCreate, which ggml's HIP shim does not alias (it only aliases
cudaEventCreateWithFlags). Use cudaEventCreateWithFlags(...,
cudaEventDisableTiming) -- exactly what the rest of this file already
does (cf. lines ~1034, ~3461) and HIP-safe.
CUDA builds are unaffected. Drop the relevant hunk once the fork HIP-ports
these; apply-patches.sh fails fast if an anchor goes stale.
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 0427e6b..6352e6a 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -1933,6 +1933,7 @@ static cudaError_t ggml_cuda_copy2d_across_devices(
size_t width, size_t height, cudaStream_t dst_stream, cudaStream_t src_stream) {
const auto & info = ggml_cuda_info();
+#if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA) // 3D-peer copy types unmapped by ggml's HIP/MUSA shim; use staging fallback below
if (info.peer_access[src_device][dst_device]) {
cudaMemcpy3DPeerParms p = {};
p.dstDevice = dst_device;
@@ -1942,6 +1943,7 @@ static cudaError_t ggml_cuda_copy2d_across_devices(
p.extent = make_cudaExtent(width, height, 1);
return cudaMemcpy3DPeerAsync(&p, dst_stream);
}
+#endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
// Fallback: stage all rows through a single contiguous pinned buffer
int prev_device = ggml_cuda_get_device();
@@ -5714,7 +5716,7 @@ static ggml_backend_event_t ggml_backend_cuda_device_event_new(ggml_backend_dev_
ggml_cuda_set_device(dev_ctx->device);
cudaEvent_t event;
- CUDA_CHECK(cudaEventCreate(&event));
+ CUDA_CHECK(cudaEventCreateWithFlags(&event, cudaEventDisableTiming));
return new ggml_backend_event {
/* .device = */ dev,

View File

@@ -2,7 +2,7 @@
set -ex
# Get the absolute current dir where the script is located
CURDIR=$(dirname "$(realpath "$0")")
CURDIR=$(dirname "$(realpath $0)")
cd /
@@ -12,39 +12,54 @@ grep -e "flags" /proc/cpuinfo | head -1
BINARY=turboquant-fallback
# x86/arm64 ship a single turboquant-cpu-all built with ggml CPU_ALL_VARIANTS: ggml's
# backend registry dlopens the best libggml-cpu-*.so for this host, so no shell-side
# probing. ROCm ships only turboquant-fallback, so fall back to it when cpu-all is absent.
if [ -e "$CURDIR"/turboquant-cpu-all ]; then
BINARY=turboquant-cpu-all
if grep -q -e "\savx\s" /proc/cpuinfo ; then
echo "CPU: AVX found OK"
if [ -e $CURDIR/turboquant-avx ]; then
BINARY=turboquant-avx
fi
fi
if grep -q -e "\savx2\s" /proc/cpuinfo ; then
echo "CPU: AVX2 found OK"
if [ -e $CURDIR/turboquant-avx2 ]; then
BINARY=turboquant-avx2
fi
fi
# Check avx 512
if grep -q -e "\savx512f\s" /proc/cpuinfo ; then
echo "CPU: AVX512F found OK"
if [ -e $CURDIR/turboquant-avx512 ]; then
BINARY=turboquant-avx512
fi
fi
if [ -n "$LLAMACPP_GRPC_SERVERS" ]; then
if [ -e "$CURDIR"/turboquant-grpc ]; then
if [ -e $CURDIR/turboquant-grpc ]; then
BINARY=turboquant-grpc
fi
fi
# Extend ld library path with the dir where this script is located/lib
if [ "$(uname)" == "Darwin" ]; then
export DYLD_LIBRARY_PATH="$CURDIR"/lib:$DYLD_LIBRARY_PATH
export DYLD_LIBRARY_PATH=$CURDIR/lib:$DYLD_LIBRARY_PATH
else
export LD_LIBRARY_PATH="$CURDIR"/lib:$LD_LIBRARY_PATH
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
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
if [ -f $CURDIR/lib/ld.so ]; then
echo "Using lib/ld.so"
echo "Using binary: $BINARY"
exec "$CURDIR"/lib/ld.so "$CURDIR"/$BINARY "$@"
exec $CURDIR/lib/ld.so $CURDIR/$BINARY "$@"
fi
echo "Using binary: $BINARY"
exec "$CURDIR"/$BINARY "$@"
exec $CURDIR/$BINARY "$@"
# We should never reach this point, however just in case we do, run fallback
exec "$CURDIR"/turboquant-fallback "$@"
exec $CURDIR/turboquant-fallback "$@"

View File

@@ -117,8 +117,7 @@ libgoacestepcpp-custom: CMakeLists.txt cpp/goacestepcpp.cpp cpp/goacestepcpp.h
cmake .. $(CMAKE_ARGS) && \
cmake --build . --config Release -j$(JOBS) --target goacestepcpp && \
cd .. && \
(mv build-$(SO_TARGET)/libgoacestepcpp.so ./$(SO_TARGET) 2>/dev/null || \
mv build-$(SO_TARGET)/libgoacestepcpp.dylib ./$(SO_TARGET) 2>/dev/null)
mv build-$(SO_TARGET)/libgoacestepcpp.so ./$(SO_TARGET)
test: acestep-cpp
@echo "Running acestep-cpp tests..."

View File

@@ -4,7 +4,6 @@ package main
import (
"flag"
"os"
"runtime"
"github.com/ebitengine/purego"
grpc "github.com/mudler/LocalAI/pkg/grpc"
@@ -23,11 +22,7 @@ func main() {
// Get library name from environment variable, default to fallback
libName := os.Getenv("ACESTEP_LIBRARY")
if libName == "" {
if runtime.GOOS == "darwin" {
libName = "./libgoacestepcpp-fallback.dylib"
} else {
libName = "./libgoacestepcpp-fallback.so"
}
libName = "./libgoacestepcpp-fallback.so"
}
gosd, err := purego.Dlopen(libName, purego.RTLD_NOW|purego.RTLD_GLOBAL)

View File

@@ -13,7 +13,6 @@ mkdir -p $CURDIR/package/lib
cp -avf $CURDIR/acestep-cpp $CURDIR/package/
cp -fv $CURDIR/libgoacestepcpp-*.so $CURDIR/package/
cp -fv $CURDIR/libgoacestepcpp-*.dylib $CURDIR/package/ 2>/dev/null || true
cp -fv $CURDIR/run.sh $CURDIR/package/
# Detect architecture and copy appropriate libraries

View File

@@ -2,7 +2,7 @@
set -ex
# Get the absolute current dir where the script is located
CURDIR=$(dirname "$(realpath "$0")")
CURDIR=$(dirname "$(realpath $0)")
cd /
@@ -12,29 +12,19 @@ if [ "$(uname)" != "Darwin" ]; then
grep -e "flags" /proc/cpuinfo | head -1
fi
if [ "$(uname)" = "Darwin" ]; then
# macOS: single library variant (Metal or Accelerate). The goacestepcpp
# target is built as a CMake MODULE, which emits a .dylib for a SHARED
# build but a .so for a MODULE build on Apple, so prefer .dylib and fall
# back to .so.
LIBRARY="$CURDIR/libgoacestepcpp-fallback.dylib"
if [ ! -e "$LIBRARY" ]; then
LIBRARY="$CURDIR/libgoacestepcpp-fallback.so"
fi
export DYLD_LIBRARY_PATH="$CURDIR"/lib:$DYLD_LIBRARY_PATH
else
LIBRARY="$CURDIR/libgoacestepcpp-fallback.so"
LIBRARY="$CURDIR/libgoacestepcpp-fallback.so"
if [ "$(uname)" != "Darwin" ]; then
if grep -q -e "\savx\s" /proc/cpuinfo ; then
echo "CPU: AVX found OK"
if [ -e "$CURDIR"/libgoacestepcpp-avx.so ]; then
if [ -e $CURDIR/libgoacestepcpp-avx.so ]; then
LIBRARY="$CURDIR/libgoacestepcpp-avx.so"
fi
fi
if grep -q -e "\savx2\s" /proc/cpuinfo ; then
echo "CPU: AVX2 found OK"
if [ -e "$CURDIR"/libgoacestepcpp-avx2.so ]; then
if [ -e $CURDIR/libgoacestepcpp-avx2.so ]; then
LIBRARY="$CURDIR/libgoacestepcpp-avx2.so"
fi
fi
@@ -42,22 +32,21 @@ else
# Check avx 512
if grep -q -e "\savx512f\s" /proc/cpuinfo ; then
echo "CPU: AVX512F found OK"
if [ -e "$CURDIR"/libgoacestepcpp-avx512.so ]; then
if [ -e $CURDIR/libgoacestepcpp-avx512.so ]; then
LIBRARY="$CURDIR/libgoacestepcpp-avx512.so"
fi
fi
export LD_LIBRARY_PATH="$CURDIR"/lib:$LD_LIBRARY_PATH
fi
export LD_LIBRARY_PATH=$CURDIR/lib:$LD_LIBRARY_PATH
export ACESTEP_LIBRARY=$LIBRARY
# If there is a lib/ld.so, use it
if [ -f "$CURDIR"/lib/ld.so ]; then
if [ -f $CURDIR/lib/ld.so ]; then
echo "Using lib/ld.so"
echo "Using library: $LIBRARY"
exec "$CURDIR"/lib/ld.so "$CURDIR"/acestep-cpp "$@"
exec $CURDIR/lib/ld.so $CURDIR/acestep-cpp "$@"
fi
echo "Using library: $LIBRARY"
exec "$CURDIR"/acestep-cpp "$@"
exec $CURDIR/acestep-cpp "$@"

View File

@@ -1,11 +0,0 @@
.cache/
sources/
build/
package/
ced-grpc
# build artifacts staged in-tree by the Makefile (cp from sources/) or
# symlinked for local dev; the real sources live in ced.cpp upstream.
*.so
*.so.*
ced_capi.h
compile_commands.json

View File

@@ -1,78 +0,0 @@
# ced sound-classification backend Makefile.
#
# Upstream pin lives below as CED_VERSION?=<sha> so .github/bump_deps.sh can find
# and update it (matches the parakeet-cpp / whisper.cpp convention).
#
# Local dev shortcut: symlink an out-of-tree ced.cpp shared build + header and
# skip the clone/cmake steps entirely:
# ln -sf /path/to/ced.cpp/build-shared/libced.so .
# ln -sf /path/to/ced.cpp/include/ced_capi.h .
# go build -o ced-grpc .
CED_VERSION?=c04ac14b7992d00584d9e812c9bb6268598a6ce7
CED_REPO?=https://github.com/mudler/ced.cpp
GOCMD?=go
GO_TAGS?=
JOBS?=$(shell nproc 2>/dev/null || sysctl -n hw.ncpu 2>/dev/null || echo 4)
BUILD_TYPE?=
NATIVE?=false
# Static-link ggml into libced.so (PIC) so the shared lib is self-contained:
# dlopen needs no libggml*.so alongside it, only system libs the runtime image
# already provides.
CMAKE_ARGS?=-DCMAKE_BUILD_TYPE=Release -DCED_SHARED=ON -DCED_BUILD_CLI=OFF -DCED_BUILD_TESTS=OFF -DBUILD_SHARED_LIBS=OFF -DCMAKE_POSITION_INDEPENDENT_CODE=ON
ifeq ($(NATIVE),false)
CMAKE_ARGS+=-DGGML_NATIVE=OFF
endif
# ced.cpp gates its ggml backends behind CED_GGML_* options (set(... CACHE BOOL
# "" FORCE)), so forward those instead of a bare -DGGML_CUDA=ON.
ifeq ($(BUILD_TYPE),cublas)
CMAKE_ARGS+=-DCED_GGML_CUDA=ON -DGGML_CUDA_GRAPHS=ON
else ifeq ($(BUILD_TYPE),openblas)
CMAKE_ARGS+=-DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS
else ifeq ($(BUILD_TYPE),hipblas)
CMAKE_ARGS+=-DCED_GGML_HIP=ON
else ifeq ($(BUILD_TYPE),vulkan)
CMAKE_ARGS+=-DCED_GGML_VULKAN=ON
endif
.PHONY: ced-grpc package build clean purge test all
all: ced-grpc
sources/ced.cpp:
mkdir -p sources/ced.cpp
cd sources/ced.cpp && \
git init -q && \
git remote add origin $(CED_REPO) && \
git fetch --depth 1 origin $(CED_VERSION) && \
git checkout FETCH_HEAD && \
git submodule update --init --recursive --depth 1 --single-branch
libced.so: sources/ced.cpp
cmake -B sources/ced.cpp/build-shared -S sources/ced.cpp $(CMAKE_ARGS)
cmake --build sources/ced.cpp/build-shared --config Release -j$(JOBS)
cp -fv sources/ced.cpp/build-shared/libced.so* ./ 2>/dev/null || true
cp -fv sources/ced.cpp/build-shared/libced.dylib ./ 2>/dev/null || true
cp -fv sources/ced.cpp/include/ced_capi.h ./
ced-grpc: libced.so main.go goced.go
CGO_ENABLED=0 $(GOCMD) build -tags "$(GO_TAGS)" -o ced-grpc .
package: ced-grpc
bash package.sh
build: package
test:
LD_LIBRARY_PATH=$(CURDIR):$$LD_LIBRARY_PATH $(GOCMD) test ./... -count=1
clean: purge
rm -rf libced.so* ced_capi.h package ced-grpc
purge:
rm -rf sources/ced.cpp

View File

@@ -1,130 +0,0 @@
package main
// Go side of the ced backend: purego bindings over ced_capi.h plus the gRPC
// SoundDetection implementation.
//
// SKETCH: the pb.SoundDetection* types come from backend.proto (regenerate with
// `make protogen-go`). The C side is single-threaded per ctx, so we guard the
// engine with engineMu; LocalAI also serializes via base.SingleThread.
import (
"context"
"encoding/json"
"errors"
"fmt"
"sort"
"sync"
"unsafe"
"github.com/mudler/LocalAI/pkg/grpc/base"
pb "github.com/mudler/LocalAI/pkg/grpc/proto"
)
// purego-bound entry points from libced.so. Names match ced_capi.h exactly.
var (
CppAbiVersion func() int32
CppLoad func(ggufPath string) uintptr
CppFree func(ctx uintptr)
CppLastError func(ctx uintptr) string
CppNumClasses func(ctx uintptr) int32
CppSampleRate func(ctx uintptr) int32
CppClassifyPathJSON func(ctx uintptr, wavPath string, topK int32) uintptr
CppClassifyPcmJSON func(ctx uintptr, pcm []float32, nSamples int32, sampleRate int32, topK int32) uintptr
CppFreeString func(s uintptr)
)
// cstr copies a malloc'd C string (returned as uintptr) into a Go string and
// frees the original via ced_capi_free_string. Empty/0 -> "".
func cstr(p uintptr) string {
if p == 0 {
return ""
}
defer CppFreeString(p)
var b []byte
for i := 0; ; i++ {
ch := *(*byte)(unsafe.Pointer(p + uintptr(i))) //nolint:govet // #nosec G103 -- C-owned NUL-terminated string from libced (not Go-GC memory)
if ch == 0 {
break
}
b = append(b, ch)
}
return string(b)
}
// Ced is the gRPC backend. One loaded CED model per instance.
type Ced struct {
base.Base
ctxPtr uintptr
engineMu sync.Mutex
}
// Load resolves the GGUF and opens the C-API context.
func (c *Ced) Load(opts *pb.ModelOptions) error {
if opts.ModelFile == "" {
return errors.New("ced: ModelFile is required")
}
ctx := CppLoad(opts.ModelFile)
if ctx == 0 {
return fmt.Errorf("ced: ced_capi_load failed for %q: %s", opts.ModelFile, CppLastError(0))
}
c.ctxPtr = ctx
return nil
}
// jsonTag mirrors the ced_capi JSON tag objects.
type jsonTag struct {
Index int `json:"index"`
Score float32 `json:"score"`
Label string `json:"label"`
}
// SoundDetection classifies the clip at req.Src and returns scored AudioSet tags.
func (c *Ced) SoundDetection(ctx context.Context, req *pb.SoundDetectionRequest) (*pb.SoundDetectionResponse, error) {
if c.ctxPtr == 0 {
return nil, errors.New("ced: model not loaded")
}
if req.GetSrc() == "" {
return nil, errors.New("ced: SoundDetectionRequest.src (audio path) is required")
}
topK := req.GetTopK()
if topK <= 0 {
topK = 10 // sensible default for a tagging response
}
c.engineMu.Lock()
out := cstr(CppClassifyPathJSON(c.ctxPtr, req.GetSrc(), topK))
lastErr := CppLastError(c.ctxPtr)
c.engineMu.Unlock()
if out == "" {
return nil, fmt.Errorf("ced: classification failed: %s", lastErr)
}
var tags []jsonTag
if err := json.Unmarshal([]byte(out), &tags); err != nil {
return nil, fmt.Errorf("ced: bad classifier JSON: %w", err)
}
thr := req.GetThreshold()
resp := &pb.SoundDetectionResponse{}
for _, t := range tags {
if t.Score < thr {
continue
}
resp.Detections = append(resp.Detections, &pb.SoundClass{
Label: t.Label, Score: t.Score, Index: int32(t.Index),
})
}
sort.Slice(resp.Detections, func(i, j int) bool {
return resp.Detections[i].Score > resp.Detections[j].Score
})
return resp, nil
}
func (c *Ced) Free() error {
c.engineMu.Lock()
defer c.engineMu.Unlock()
if c.ctxPtr != 0 {
CppFree(c.ctxPtr)
c.ctxPtr = 0
}
return nil
}

View File

@@ -1,64 +0,0 @@
package main
// ced sound-classification backend. Started internally by LocalAI: one gRPC
// server per loaded model. Loads libced.so via purego and registers the flat
// C-API declared in ced_capi.h. The library name can be overridden with
// CED_LIBRARY (mirrors PARAKEET_LIBRARY / WHISPER_LIBRARY); the default looks
// for the .so next to this binary.
//
// SKETCH: requires `make protogen-go` after the backend.proto SoundDetection
// addition, and a built libced.so (see Makefile). See DESIGN.md.
import (
"flag"
"fmt"
"os"
"runtime"
"github.com/ebitengine/purego"
grpc "github.com/mudler/LocalAI/pkg/grpc"
)
var addr = flag.String("addr", "localhost:50051", "the address to connect to")
type libFunc struct {
ptr any
name string
}
func main() {
libName := os.Getenv("CED_LIBRARY")
if libName == "" {
if runtime.GOOS == "darwin" {
libName = "libced.dylib"
} else {
libName = "libced.so"
}
}
lib, err := purego.Dlopen(libName, purego.RTLD_NOW|purego.RTLD_GLOBAL)
if err != nil {
panic(fmt.Errorf("ced: dlopen %q: %w", libName, err))
}
// Bound 1:1 to ced_capi.h. char*-returning functions are declared uintptr
// so we can free the same pointer with ced_capi_free_string after copying
// (purego's string return would copy and leak the original).
for _, lf := range []libFunc{
{&CppAbiVersion, "ced_capi_abi_version"},
{&CppLoad, "ced_capi_load"},
{&CppFree, "ced_capi_free"},
{&CppLastError, "ced_capi_last_error"},
{&CppNumClasses, "ced_capi_num_classes"},
{&CppSampleRate, "ced_capi_sample_rate"},
{&CppClassifyPathJSON, "ced_capi_classify_path_json"},
{&CppClassifyPcmJSON, "ced_capi_classify_pcm_json"},
{&CppFreeString, "ced_capi_free_string"},
} {
purego.RegisterLibFunc(lf.ptr, lib, lf.name)
}
fmt.Fprintf(os.Stderr, "[ced] ABI=%d\n", CppAbiVersion())
flag.Parse()
if err := grpc.StartServer(*addr, &Ced{}); err != nil {
panic(err)
}
}

View File

@@ -1,62 +0,0 @@
#!/bin/bash
#
# Bundle the ced-grpc binary, libced.so, the core runtime libs (libc/libstdc++/
# libgomp + ld.so) and the GPU runtime for the active BUILD_TYPE so the package
# is self-contained. Mirrors backend/go/parakeet-cpp/package.sh; run.sh routes
# the (CGO_ENABLED=0) binary through lib/ld.so so the packaged libc is used.
set -e
CURDIR=$(dirname "$(realpath "$0")")
REPO_ROOT="${CURDIR}/../../.."
mkdir -p "$CURDIR/package/lib"
cp -avf "$CURDIR/ced-grpc" "$CURDIR/package/"
cp -avf "$CURDIR/run.sh" "$CURDIR/package/"
cp -avf "$CURDIR"/libced.so* "$CURDIR/package/lib/" 2>/dev/null || true
cp -avf "$CURDIR"/libced.dylib "$CURDIR/package/lib/" 2>/dev/null || true
if ! ls "$CURDIR"/package/lib/libced.* >/dev/null 2>&1; then
echo "ERROR: libced shared library not found in $CURDIR, run 'make' first" >&2
exit 1
fi
if [ -f "/lib64/ld-linux-x86-64.so.2" ]; then
echo "Detected x86_64 architecture, copying x86_64 libraries..."
cp -arfLv /lib64/ld-linux-x86-64.so.2 "$CURDIR/package/lib/ld.so"
cp -arfLv /lib/x86_64-linux-gnu/libc.so.6 "$CURDIR/package/lib/libc.so.6"
cp -arfLv /lib/x86_64-linux-gnu/libgcc_s.so.1 "$CURDIR/package/lib/libgcc_s.so.1"
cp -arfLv /lib/x86_64-linux-gnu/libstdc++.so.6 "$CURDIR/package/lib/libstdc++.so.6"
cp -arfLv /lib/x86_64-linux-gnu/libm.so.6 "$CURDIR/package/lib/libm.so.6"
cp -arfLv /lib/x86_64-linux-gnu/libgomp.so.1 "$CURDIR/package/lib/libgomp.so.1"
cp -arfLv /lib/x86_64-linux-gnu/libdl.so.2 "$CURDIR/package/lib/libdl.so.2"
cp -arfLv /lib/x86_64-linux-gnu/librt.so.1 "$CURDIR/package/lib/librt.so.1"
cp -arfLv /lib/x86_64-linux-gnu/libpthread.so.0 "$CURDIR/package/lib/libpthread.so.0"
elif [ -f "/lib/ld-linux-aarch64.so.1" ]; then
echo "Detected ARM64 architecture, copying ARM64 libraries..."
cp -arfLv /lib/ld-linux-aarch64.so.1 "$CURDIR/package/lib/ld.so"
cp -arfLv /lib/aarch64-linux-gnu/libc.so.6 "$CURDIR/package/lib/libc.so.6"
cp -arfLv /lib/aarch64-linux-gnu/libgcc_s.so.1 "$CURDIR/package/lib/libgcc_s.so.1"
cp -arfLv /lib/aarch64-linux-gnu/libstdc++.so.6 "$CURDIR/package/lib/libstdc++.so.6"
cp -arfLv /lib/aarch64-linux-gnu/libm.so.6 "$CURDIR/package/lib/libm.so.6"
cp -arfLv /lib/aarch64-linux-gnu/libgomp.so.1 "$CURDIR/package/lib/libgomp.so.1"
cp -arfLv /lib/aarch64-linux-gnu/libdl.so.2 "$CURDIR/package/lib/libdl.so.2"
cp -arfLv /lib/aarch64-linux-gnu/librt.so.1 "$CURDIR/package/lib/librt.so.1"
cp -arfLv /lib/aarch64-linux-gnu/libpthread.so.0 "$CURDIR/package/lib/libpthread.so.0"
elif [ "$(uname -s)" = "Darwin" ]; then
echo "Detected Darwin"
else
echo "Error: Could not detect architecture"
exit 1
fi
GPU_LIB_SCRIPT="${REPO_ROOT}/scripts/build/package-gpu-libs.sh"
if [ -f "$GPU_LIB_SCRIPT" ]; then
echo "Packaging GPU libraries for BUILD_TYPE=${BUILD_TYPE:-cpu}..."
source "$GPU_LIB_SCRIPT" "$CURDIR/package/lib"
package_gpu_libs
fi
echo "Packaging completed successfully"
ls -liah "$CURDIR/package/" "$CURDIR/package/lib/"

View File

@@ -1,20 +0,0 @@
#!/bin/bash
set -e
CURDIR=$(dirname "$(realpath "$0")")
if [ "$(uname)" = "Darwin" ]; then
export DYLD_LIBRARY_PATH="$CURDIR/lib:"$CURDIR":${DYLD_LIBRARY_PATH:-}"
export CED_LIBRARY="$CURDIR/lib/libced.dylib"
else
export LD_LIBRARY_PATH="$CURDIR/lib:"$CURDIR":${LD_LIBRARY_PATH:-}"
fi
# If a self-contained ld.so was packaged, route through it so the packaged
# libc / libstdc++ are used instead of the host's (matches the sibling backends).
if [ -f "$CURDIR/lib/ld.so" ]; then
echo "Using lib/ld.so"
exec "$CURDIR/lib/ld.so" "$CURDIR/ced-grpc" "$@"
fi
exec "$CURDIR/ced-grpc" "$@"

View File

@@ -14,7 +14,6 @@ import (
"github.com/mudler/xlog"
"github.com/mudler/LocalAI/pkg/grpc/base"
"github.com/mudler/LocalAI/pkg/grpc/grpcerrors"
pb "github.com/mudler/LocalAI/pkg/grpc/proto"
"github.com/mudler/LocalAI/pkg/httpclient"
)
@@ -146,7 +145,7 @@ func resolveAPIKey(envName, filePath string) (string, error) {
func (c *CloudProxy) PredictRich(opts *pb.PredictOptions) (reply *pb.Reply, err error) {
cfg := c.cfg.Load()
if cfg == nil {
return nil, grpcerrors.ModelNotLoaded("cloud-proxy")
return nil, errors.New("cloud-proxy: model not loaded")
}
if cfg.mode != modeTranslate {
return nil, fmt.Errorf("cloud-proxy: Predict only valid in translate mode (have %s)", cfg.mode)
@@ -176,7 +175,7 @@ func (c *CloudProxy) PredictRich(opts *pb.PredictOptions) (reply *pb.Reply, err
func (c *CloudProxy) PredictStreamRich(opts *pb.PredictOptions, results chan<- *pb.Reply) (err error) {
cfg := c.cfg.Load()
if cfg == nil {
return grpcerrors.ModelNotLoaded("cloud-proxy")
return errors.New("cloud-proxy: model not loaded")
}
if cfg.mode != modeTranslate {
return fmt.Errorf("cloud-proxy: PredictStream only valid in translate mode (have %s)", cfg.mode)
@@ -270,7 +269,7 @@ func (c *CloudProxy) Forward(ctx context.Context, in <-chan *pb.ForwardRequest,
cfg := c.cfg.Load()
if cfg == nil {
return grpcerrors.ModelNotLoaded("cloud-proxy")
return errors.New("cloud-proxy: model not loaded")
}
if cfg.mode != modePassthrough {
return fmt.Errorf("cloud-proxy: Forward only valid in passthrough mode (have %s)", cfg.mode)

View File

@@ -1,6 +1,6 @@
#!/bin/bash
set -ex
CURDIR=$(dirname "$(realpath "$0")")
CURDIR=$(dirname "$(realpath $0)")
exec "$CURDIR"/cloud-proxy "$@"
exec $CURDIR/cloud-proxy "$@"

View File

@@ -14,7 +14,7 @@ target_include_directories(gocrispasr PRIVATE
# whisper. crispasr is the referencer; the backend static libs supply the
# per-architecture symbols; ggml is the math/runtime base.
target_link_libraries(gocrispasr PRIVATE
crispasr-lib
crispasr
parakeet canary canary_ctc cohere granite_speech granite_nle
voxtral voxtral4b qwen3_asr qwen3_tts orpheus chatterbox indextts
kokoro voxcpm2_tts m2m100 t5_translate wav2vec2-ggml vibevoice

View File

@@ -8,7 +8,7 @@ JOBS?=$(shell nproc --ignore=1)
# CrispASR version (release tag)
CRISPASR_REPO?=https://github.com/CrispStrobe/CrispASR
CRISPASR_VERSION?=6514c9da00b03a2f0f1b49a43fae4f3a01a41844
CRISPASR_VERSION?=05e60432bcb5bc2113f8c395a41e86497c11504a
SO_TARGET?=libgocrispasr.so
CMAKE_ARGS+=-DBUILD_SHARED_LIBS=OFF
@@ -67,7 +67,7 @@ sources/CrispASR:
# it, so ${CMAKE_SOURCE_DIR} is THIS backend dir and the talk-llama sources
# aren't found. Rewrite to ${PROJECT_SOURCE_DIR} (the crispasr project root),
# which is correct both standalone and as a subproject. Idempotent.
sed -i.bak 's#\$${CMAKE_SOURCE_DIR}/examples/talk-llama#\$${PROJECT_SOURCE_DIR}/examples/talk-llama#' sources/CrispASR/src/CMakeLists.txt && rm -f sources/CrispASR/src/CMakeLists.txt.bak
sed -i 's#\$${CMAKE_SOURCE_DIR}/examples/talk-llama#\$${PROJECT_SOURCE_DIR}/examples/talk-llama#' sources/CrispASR/src/CMakeLists.txt
# Detect OS
UNAME_S := $(shell uname -s)
@@ -75,8 +75,7 @@ UNAME_S := $(shell uname -s)
ifeq ($(UNAME_S),Linux)
VARIANT_TARGETS = libgocrispasr-avx.so libgocrispasr-avx2.so libgocrispasr-avx512.so libgocrispasr-fallback.so
else
# On non-Linux (e.g., Darwin), build only fallback variant (as a dylib)
VARIANT_TARGETS = libgocrispasr-fallback.dylib
VARIANT_TARGETS = libgocrispasr-fallback.so
endif
crispasr: main.go gocrispasr.go $(VARIANT_TARGETS)
@@ -88,7 +87,7 @@ package: crispasr
build: package
clean: purge
rm -rf libgocrispasr*.so libgocrispasr*.dylib package sources/CrispASR crispasr
rm -rf libgocrispasr*.so package sources/CrispASR crispasr
purge:
rm -rf build*
@@ -119,21 +118,13 @@ libgocrispasr-fallback.so: sources/CrispASR
SO_TARGET=libgocrispasr-fallback.so CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off" $(MAKE) libgocrispasr-custom
rm -rfv build*
# Build fallback variant as a dylib (Darwin)
libgocrispasr-fallback.dylib: sources/CrispASR
$(MAKE) purge
$(info ${GREEN}I crispasr build info:fallback (dylib)${RESET})
SO_TARGET=libgocrispasr-fallback.dylib CMAKE_ARGS="$(CMAKE_ARGS) -DGGML_AVX=off -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off" $(MAKE) libgocrispasr-custom
rm -rfv build*
libgocrispasr-custom: CMakeLists.txt cpp/crispasr_shim.cpp cpp/crispasr_shim.h
mkdir -p build-$(SO_TARGET) && \
cd build-$(SO_TARGET) && \
cmake .. $(CMAKE_ARGS) && \
cmake --build . --config Release -j$(JOBS) && \
cd .. && \
(mv build-$(SO_TARGET)/libgocrispasr.so ./$(SO_TARGET) 2>/dev/null || \
mv build-$(SO_TARGET)/libgocrispasr.dylib ./$(SO_TARGET) 2>/dev/null)
mv build-$(SO_TARGET)/libgocrispasr.so ./$(SO_TARGET)
test: crispasr
CGO_ENABLED=0 $(GOCMD) test -v ./...

View File

@@ -47,74 +47,6 @@ extern "C" void set_abort(int v) {
g_abort.store(v, std::memory_order_relaxed);
}
// --- word-level timestamp accessors ---
extern "C" {
int crispasr_session_result_n_words(crispasr_session_result *r, int seg_i);
const char *crispasr_session_result_word_text(crispasr_session_result *r,
int seg_i, int word_i);
int64_t crispasr_session_result_word_t0(crispasr_session_result *r, int seg_i,
int word_i);
int64_t crispasr_session_result_word_t1(crispasr_session_result *r, int seg_i,
int word_i);
// Parakeet-specific word accessors
int crispasr_parakeet_result_n_words(void *r);
const char *crispasr_parakeet_result_word_text(void *r, int word_i);
int64_t crispasr_parakeet_result_word_t0(void *r, int word_i);
int64_t crispasr_parakeet_result_word_t1(void *r, int word_i);
}
void *get_result(void) { return g_result; }
int get_word_count(int seg_i) {
if (!g_result)
return 0;
return crispasr_session_result_n_words(g_result, seg_i);
}
const char *get_word_text(int seg_i, int word_i) {
if (!g_result)
return "";
return crispasr_session_result_word_text(g_result, seg_i, word_i);
}
int64_t get_word_t0(int seg_i, int word_i) {
if (!g_result)
return 0;
return crispasr_session_result_word_t0(g_result, seg_i, word_i);
}
int64_t get_word_t1(int seg_i, int word_i) {
if (!g_result)
return 0;
return crispasr_session_result_word_t1(g_result, seg_i, word_i);
}
// Parakeet-specific word accessors
int get_parakeet_word_count(void) {
if (!g_result)
return 0;
return crispasr_parakeet_result_n_words(g_result);
}
const char *get_parakeet_word_text(int word_i) {
if (!g_result)
return "";
return crispasr_parakeet_result_word_text(g_result, word_i);
}
int64_t get_parakeet_word_t0(int word_i) {
if (!g_result)
return 0;
return crispasr_parakeet_result_word_t0(g_result, word_i);
}
int64_t get_parakeet_word_t1(int word_i) {
if (!g_result)
return 0;
return crispasr_parakeet_result_word_t1(g_result, word_i);
}
static void ggml_log_cb(enum ggml_log_level level, const char *log,
void *data) {
const char *level_str;

View File

@@ -20,18 +20,4 @@ float *tts_synthesize(const char *text, int *out_n_samples); // 24kHz mono float
void tts_free(float *pcm);
int tts_set_voice(const char *name); // best-effort speaker selection; 0 ok
int tts_set_voice_file(const char *path, const char *ref_text); // load voice pack (.gguf) or zero-shot clone (.wav + ref_text)
// --- word-level timestamp accessors ---
// Session-based (works for whisper-like backends)
void *get_result(void);
int get_word_count(int seg_i);
const char *get_word_text(int seg_i, int word_i);
int64_t get_word_t0(int seg_i, int word_i);
int64_t get_word_t1(int seg_i, int word_i);
// Parakeet-specific (global word list, no segment index)
int get_parakeet_word_count(void);
const char *get_parakeet_word_text(int word_i);
int64_t get_parakeet_word_t0(int word_i);
int64_t get_parakeet_word_t1(int word_i);
}

View File

@@ -11,7 +11,6 @@ import (
"github.com/go-audio/audio"
"github.com/go-audio/wav"
gguf "github.com/gpustack/gguf-parser-go"
"github.com/mudler/LocalAI/pkg/grpc/base"
pb "github.com/mudler/LocalAI/pkg/grpc/proto"
"github.com/mudler/LocalAI/pkg/utils"
@@ -34,55 +33,10 @@ var (
CppTTSFree func(ptr uintptr)
CppTTSSetVoice func(name string) int
CppTTSSetVoiceFile func(path string, refText string) int
// Word-level timestamp accessors (session-based, per-segment)
CppGetWordCount func(segI int) int
CppGetWordText func(segI int, wordI int) string
CppGetWordT0 func(segI int, wordI int) int64
CppGetWordT1 func(segI int, wordI int) int64
// Parakeet-specific word accessors (global, no segment index)
CppGetParakeetWordCount func() int
CppGetParakeetWordText func(wordI int) string
CppGetParakeetWordT0 func(wordI int) int64
CppGetParakeetWordT1 func(wordI int) int64
)
type CrispASR struct {
base.SingleThread
// sampleRate is the output rate (Hz) of the loaded TTS engine's PCM, used to
// write a correct WAV header. Most CrispASR TTS backends emit 24 kHz, but
// piper returns its model's native rate (16 kHz for x_low/low voices,
// 22.05 kHz for medium/high), so it is read from the GGUF metadata at Load.
sampleRate int
}
// defaultTTSSampleRate is the output rate assumed for CrispASR TTS engines that
// don't advertise one in GGUF metadata (vibevoice/orpheus/chatterbox/qwen3-tts
// all emit 24 kHz). piper is the exception and carries piper.sample_rate.
const defaultTTSSampleRate = 24000
// piperSampleRate reads the piper.sample_rate metadata key from a GGUF model.
// CrispASR's piper backend returns PCM at the model's native rate without
// resampling, so the WAV header must match it. Returns ok=false for non-piper
// models (key absent) or an unreadable file, letting the caller fall back to
// defaultTTSSampleRate.
func piperSampleRate(modelPath string) (int, bool) {
// Only scalar architecture keys are read, so skip the large array metadata
// (phoneme map) and mmap the header - same rationale as pkg/vram's reader.
f, err := gguf.ParseGGUFFile(modelPath, gguf.UseMMap(), gguf.SkipLargeMetadata())
if err != nil {
return 0, false
}
kv, ok := f.Header.MetadataKV.Get("piper.sample_rate")
if !ok || kv.ValueType != gguf.GGUFMetadataValueTypeUint32 {
return 0, false
}
rate := int(kv.ValueUint32())
if rate <= 0 {
return 0, false
}
return rate, true
}
// splitOption splits a "prefix:value" model option into its key and value,
@@ -149,14 +103,6 @@ func (w *CrispASR) Load(opts *pb.ModelOptions) error {
return fmt.Errorf("Failed to load CrispASR transcription model")
}
// Determine the TTS output sample rate for the WAV header. piper voices
// carry their native rate in GGUF metadata and CrispASR does not resample;
// every other engine emits the 24 kHz default.
w.sampleRate = defaultTTSSampleRate
if rate, ok := piperSampleRate(opts.ModelFile); ok {
w.sampleRate = rate
}
// Load the companion file (codec/tokenizer/s3gen) after the session is open.
// rc==0 means success or "not applicable" for the active backend; only a
// negative code is fatal.
@@ -224,28 +170,6 @@ func (w *CrispASR) VAD(req *pb.VADRequest) (pb.VADResponse, error) {
}, nil
}
// isValidWord reports whether a TranscriptWord contains recognisable speech
// content. The parakeet-specific word accessors can return stale initialisation
// data (model name, binary blobs) when a segment has no real speech. A word is
// considered valid only when:
// - the text is non-empty after trimming,
// - it contains no U+FFFD replacement characters (from binary data scrubbing),
// - both timestamps are non-negative,
// - the word has positive duration (end > start).
func isValidWord(w *pb.TranscriptWord) bool {
txt := strings.TrimSpace(w.Text)
if txt == "" {
return false
}
if strings.ContainsRune(txt, '\uFFFD') {
return false
}
if w.Start < 0 || w.End < 0 || w.End <= w.Start {
return false
}
return true
}
func (w *CrispASR) AudioTranscription(ctx context.Context, opts *pb.TranscriptRequest) (pb.TranscriptResult, error) {
if err := ctx.Err(); err != nil {
return pb.TranscriptResult{}, status.Error(codes.Canceled, "transcription cancelled")
@@ -324,54 +248,15 @@ func (w *CrispASR) AudioTranscription(ctx context.Context, opts *pb.TranscriptRe
// IDs, so Tokens is left empty.
txt := strings.ToValidUTF8(strings.Clone(CppGetSegmentText(i)), "<22>")
// Populate word-level timestamps. Try session-based functions first
// (per-segment); fall back to parakeet-specific functions (global word
// list with no segment index — only populated on the first segment to
// avoid duplication).
words := []*pb.TranscriptWord{}
wordCount := CppGetWordCount(i)
if wordCount == 0 && i == 0 {
wordCount = CppGetParakeetWordCount()
for j := 0; j < wordCount; j++ {
w := &pb.TranscriptWord{
Start: CppGetParakeetWordT0(j) * (10000000),
End: CppGetParakeetWordT1(j) * (10000000),
Text: strings.ToValidUTF8(strings.Clone(CppGetParakeetWordText(j)), "<22>"),
}
if isValidWord(w) {
words = append(words, w)
}
}
} else {
for j := 0; j < wordCount; j++ {
w := &pb.TranscriptWord{
Start: CppGetWordT0(i, j) * (10000000),
End: CppGetWordT1(i, j) * (10000000),
Text: strings.ToValidUTF8(strings.Clone(CppGetWordText(i, j)), "<22>"),
}
if isValidWord(w) {
words = append(words, w)
}
}
}
// Skip empty segments with no recognisable content (e.g. trailing
// silence segments that parakeet emits with stale init data).
trimmed := strings.TrimSpace(txt)
if trimmed == "" && len(words) == 0 {
continue
}
segment := &pb.TranscriptSegment{
Id: int32(i),
Text: txt,
Start: s, End: t,
Words: words,
}
segments = append(segments, segment)
text += " " + trimmed
text += " " + strings.TrimSpace(txt)
}
return pb.TranscriptResult{
@@ -463,20 +348,13 @@ func (w *CrispASR) AudioTranscriptionStream(ctx context.Context, opts *pb.Transc
s := CppGetSegmentStart(i) * 10000000
t := CppGetSegmentEnd(i) * 10000000
txt := strings.ToValidUTF8(strings.Clone(CppGetSegmentText(i)), "<22>")
// Skip empty segments (e.g. trailing silence that parakeet emits
// with stale init data).
trimmed := strings.TrimSpace(txt)
if trimmed == "" && s == t {
continue
}
segments = append(segments, &pb.TranscriptSegment{
Id: int32(i),
Text: txt,
Start: s, End: t,
})
trimmed := strings.TrimSpace(txt)
if trimmed == "" {
continue
}
@@ -512,7 +390,7 @@ func (w *CrispASR) synthesize(text string) ([]float32, error) {
}
defer CppTTSFree(ptr)
src := unsafe.Slice((*float32)(unsafe.Pointer(ptr)), int(n)) //nolint:govet // ptr addresses C-allocated PCM returned across the purego boundary; copied out immediately below, before tts_free.
out := make([]float32, int(n)) // copy out of C memory before free
out := make([]float32, int(n)) // copy out of C memory before free
copy(out, src)
return out, nil
}
@@ -539,7 +417,7 @@ func (w *CrispASR) TTS(req *pb.TTSRequest) error {
if err != nil {
return err
}
return writeWAV(req.Dst, pcm, w.sampleRate)
return writeWAV24k(req.Dst, pcm)
}
// TTSStream is the streaming counterpart to TTS. CrispASR has no progressive
@@ -569,7 +447,7 @@ func (w *CrispASR) TTSStream(req *pb.TTSRequest, results chan []byte) error {
}
defer func() { _ = os.Remove(dst) }()
if err := writeWAV(dst, pcm, w.sampleRate); err != nil {
if err := writeWAV24k(dst, pcm); err != nil {
return err
}
@@ -581,14 +459,14 @@ func (w *CrispASR) TTSStream(req *pb.TTSRequest, results chan []byte) error {
return nil
}
// writeWAV writes pcm as a sampleRate Hz, mono, 16-bit PCM WAV at dst.
func writeWAV(dst string, pcm []float32, sampleRate int) error {
// writeWAV24k writes pcm as a 24000 Hz, mono, 16-bit PCM WAV at dst.
func writeWAV24k(dst string, pcm []float32) error {
f, err := os.Create(dst)
if err != nil {
return fmt.Errorf("crispasr: create %q: %w", dst, err)
}
enc := wav.NewEncoder(f, sampleRate, 16, 1, 1)
enc := wav.NewEncoder(f, 24000, 16, 1, 1)
ints := make([]int, len(pcm))
for i, s := range pcm {
if s > 1 {
@@ -599,7 +477,7 @@ func writeWAV(dst string, pcm []float32, sampleRate int) error {
ints[i] = int(s * 32767)
}
buf := &audio.IntBuffer{
Format: &audio.Format{NumChannels: 1, SampleRate: sampleRate},
Format: &audio.Format{NumChannels: 1, SampleRate: 24000},
Data: ints,
SourceBitDepth: 16,
}

View File

@@ -1,164 +0,0 @@
package main
import (
"bytes"
"encoding/binary"
"os"
"path/filepath"
"github.com/go-audio/wav"
pb "github.com/mudler/LocalAI/pkg/grpc/proto"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
// GGUF metadata value type tags (subset) from the GGUF spec.
const (
ggufTypeUint32 uint32 = 4
ggufTypeString uint32 = 8
)
type ggufKV struct {
key string
vtype uint32
val any
}
// writeMinimalGGUF emits a valid, tensor-less GGUF file carrying only the given
// metadata key-values. Enough for the header-only parse path piperSampleRate
// uses; avoids pulling a real multi-MB voice into the test.
func writeMinimalGGUF(path string, kvs []ggufKV) error {
var b bytes.Buffer
b.WriteString("GGUF") // magic
_ = binary.Write(&b, binary.LittleEndian, uint32(3)) // version
_ = binary.Write(&b, binary.LittleEndian, uint64(0)) // tensor count
_ = binary.Write(&b, binary.LittleEndian, uint64(len(kvs)))
for _, kv := range kvs {
_ = binary.Write(&b, binary.LittleEndian, uint64(len(kv.key)))
b.WriteString(kv.key)
_ = binary.Write(&b, binary.LittleEndian, kv.vtype)
switch v := kv.val.(type) {
case uint32:
_ = binary.Write(&b, binary.LittleEndian, v)
case string:
_ = binary.Write(&b, binary.LittleEndian, uint64(len(v)))
b.WriteString(v)
}
}
return os.WriteFile(path, b.Bytes(), 0o644)
}
// wavSampleRate decodes the WAV header at path and returns its sample rate.
func wavSampleRate(path string) (int, error) {
f, err := os.Open(path)
if err != nil {
return 0, err
}
defer func() { _ = f.Close() }()
dec := wav.NewDecoder(f)
dec.ReadInfo()
return int(dec.SampleRate), nil
}
var _ = Describe("piper sample rate", func() {
Context("piperSampleRate", func() {
It("reads piper.sample_rate from a piper GGUF (medium = 22050)", func() {
p := filepath.Join(GinkgoT().TempDir(), "voice.gguf")
Expect(writeMinimalGGUF(p, []ggufKV{
{key: "general.architecture", vtype: ggufTypeString, val: "piper"},
{key: "piper.sample_rate", vtype: ggufTypeUint32, val: uint32(22050)},
})).To(Succeed())
rate, ok := piperSampleRate(p)
Expect(ok).To(BeTrue(), "piper.sample_rate should be found")
Expect(rate).To(Equal(22050))
})
It("reads the low-quality rate (16000)", func() {
p := filepath.Join(GinkgoT().TempDir(), "voice.gguf")
Expect(writeMinimalGGUF(p, []ggufKV{
{key: "piper.sample_rate", vtype: ggufTypeUint32, val: uint32(16000)},
})).To(Succeed())
rate, ok := piperSampleRate(p)
Expect(ok).To(BeTrue())
Expect(rate).To(Equal(16000))
})
It("returns ok=false for a non-piper GGUF (no piper.sample_rate key)", func() {
p := filepath.Join(GinkgoT().TempDir(), "other.gguf")
Expect(writeMinimalGGUF(p, []ggufKV{
{key: "general.architecture", vtype: ggufTypeString, val: "vibevoice"},
})).To(Succeed())
_, ok := piperSampleRate(p)
Expect(ok).To(BeFalse())
})
It("returns ok=false for an unreadable/non-GGUF file", func() {
p := filepath.Join(GinkgoT().TempDir(), "garbage.gguf")
Expect(os.WriteFile(p, []byte("not a gguf"), 0o644)).To(Succeed())
_, ok := piperSampleRate(p)
Expect(ok).To(BeFalse())
})
})
// End-to-end through the built .so. Gated on CRISPASR_PIPER_MODEL_PATH (a
// real piper voice GGUF) like the other model-backed specs; never runs in
// default CI. Proves CrispASR's piper backend output rate flows into the
// WAV header instead of the hardcoded 24 kHz default.
Context("piper TTS end-to-end", func() {
It("writes the WAV at the model's native piper.sample_rate", func() {
model := os.Getenv("CRISPASR_PIPER_MODEL_PATH")
if model == "" {
Skip("set CRISPASR_PIPER_MODEL_PATH to run the piper e2e spec")
}
ensureLibLoaded()
expected, ok := piperSampleRate(model)
Expect(ok).To(BeTrue(), "model should carry piper.sample_rate metadata")
w := &CrispASR{}
Expect(w.Load(&pb.ModelOptions{
ModelFile: model,
Options: []string{"backend:piper"},
Threads: 4,
})).To(Succeed())
dst := filepath.Join(GinkgoT().TempDir(), "piper.wav")
Expect(w.TTS(&pb.TTSRequest{Text: "Hello from CrispASR piper.", Dst: dst})).To(Succeed())
info, err := os.Stat(dst)
Expect(err).ToNot(HaveOccurred())
Expect(info.Size()).To(BeNumerically(">", 1024), "expected a non-trivial WAV")
rate, err := wavSampleRate(dst)
Expect(err).ToNot(HaveOccurred())
Expect(rate).To(Equal(expected),
"WAV header rate must equal the model's native piper.sample_rate, not the 24k default")
})
})
Context("writeWAV", func() {
It("writes the WAV header at the given sample rate (22050 for piper, not the 24k default)", func() {
dst := filepath.Join(GinkgoT().TempDir(), "out.wav")
pcm := make([]float32, 220) // 10 ms of silence is enough for a header
Expect(writeWAV(dst, pcm, 22050)).To(Succeed())
rate, err := wavSampleRate(dst)
Expect(err).ToNot(HaveOccurred())
Expect(rate).To(Equal(22050))
})
It("writes a 16000 Hz header for low-quality piper voices", func() {
dst := filepath.Join(GinkgoT().TempDir(), "out.wav")
pcm := make([]float32, 160)
Expect(writeWAV(dst, pcm, 16000)).To(Succeed())
rate, err := wavSampleRate(dst)
Expect(err).ToNot(HaveOccurred())
Expect(rate).To(Equal(16000))
})
})
})

View File

@@ -4,7 +4,6 @@ package main
import (
"flag"
"os"
"runtime"
"github.com/ebitengine/purego"
grpc "github.com/mudler/LocalAI/pkg/grpc"
@@ -22,11 +21,7 @@ type LibFuncs struct {
func main() {
libName := os.Getenv("CRISPASR_LIBRARY")
if libName == "" {
if runtime.GOOS == "darwin" {
libName = "./libgocrispasr-fallback.dylib"
} else {
libName = "./libgocrispasr-fallback.so"
}
libName = "./libgocrispasr-fallback.so"
}
lib, err := purego.Dlopen(libName, purego.RTLD_NOW|purego.RTLD_GLOBAL)
@@ -49,14 +44,6 @@ func main() {
{&CppTTSFree, "tts_free"},
{&CppTTSSetVoice, "tts_set_voice"},
{&CppTTSSetVoiceFile, "tts_set_voice_file"},
{&CppGetWordCount, "get_word_count"},
{&CppGetWordText, "get_word_text"},
{&CppGetWordT0, "get_word_t0"},
{&CppGetWordT1, "get_word_t1"},
{&CppGetParakeetWordCount, "get_parakeet_word_count"},
{&CppGetParakeetWordText, "get_parakeet_word_text"},
{&CppGetParakeetWordT0, "get_parakeet_word_t0"},
{&CppGetParakeetWordT1, "get_parakeet_word_t1"},
}
for _, lf := range libFuncs {

View File

@@ -12,8 +12,7 @@ REPO_ROOT="${CURDIR}/../../.."
mkdir -p $CURDIR/package/lib
cp -avf $CURDIR/crispasr $CURDIR/package/
cp -fv $CURDIR/libgocrispasr-*.so $CURDIR/package/ 2>/dev/null || true
cp -fv $CURDIR/libgocrispasr-*.dylib $CURDIR/package/ 2>/dev/null || true
cp -fv $CURDIR/libgocrispasr-*.so $CURDIR/package/
cp -fv $CURDIR/run.sh $CURDIR/package/
# Detect architecture and copy appropriate libraries
@@ -52,32 +51,6 @@ else
exit 1
fi
# Bundle espeak-ng (+ its libpcaudio/libsonic runtime deps) and its voice data so
# the piper TTS backend can phonemize non-English text. CrispASR dlopens
# libespeak-ng.so.1 at runtime (the MIT-clean path); the dlopen succeeds loading
# libespeak-ng but FAILS if libpcaudio/libsonic are absent, so all three .so are
# required. run.sh points CRISPASR_ESPEAK_DATA_PATH at the bundled data dir.
# Best-effort: only copied when present, so a local dev build without espeak-ng
# installed still packages the rest (English voices keep working).
ESPEAK_LIBDIR=""
for d in /usr/lib/x86_64-linux-gnu /usr/lib/aarch64-linux-gnu; do
if [ -f "$d/libespeak-ng.so.1" ]; then
ESPEAK_LIBDIR="$d"
break
fi
done
if [ -n "$ESPEAK_LIBDIR" ]; then
echo "Bundling espeak-ng from $ESPEAK_LIBDIR ..."
cp -arfLv "$ESPEAK_LIBDIR/libespeak-ng.so.1" $CURDIR/package/lib/
cp -arfLv "$ESPEAK_LIBDIR/libpcaudio.so.0" $CURDIR/package/lib/
cp -arfLv "$ESPEAK_LIBDIR/libsonic.so.0" $CURDIR/package/lib/
if [ -d "$ESPEAK_LIBDIR/espeak-ng-data" ]; then
cp -arfLv "$ESPEAK_LIBDIR/espeak-ng-data" $CURDIR/package/
fi
else
echo "espeak-ng not found; non-English piper voices will not phonemize"
fi
# Package GPU libraries based on BUILD_TYPE
# The GPU library packaging script will detect BUILD_TYPE and copy appropriate GPU libraries
GPU_LIB_SCRIPT="${REPO_ROOT}/scripts/build/package-gpu-libs.sh"

View File

@@ -2,7 +2,7 @@
set -ex
# Get the absolute current dir where the script is located
CURDIR=$(dirname "$(realpath "$0")")
CURDIR=$(dirname "$(realpath $0)")
cd /
@@ -12,23 +12,19 @@ if [ "$(uname)" != "Darwin" ]; then
grep -e "flags" /proc/cpuinfo | head -1
fi
if [ "$(uname)" = "Darwin" ]; then
# macOS: single dylib variant (Metal or Accelerate)
LIBRARY="$CURDIR/libgocrispasr-fallback.dylib"
export DYLD_LIBRARY_PATH="$CURDIR"/lib:$DYLD_LIBRARY_PATH
else
LIBRARY="$CURDIR/libgocrispasr-fallback.so"
LIBRARY="$CURDIR/libgocrispasr-fallback.so"
if [ "$(uname)" != "Darwin" ]; then
if grep -q -e "\savx\s" /proc/cpuinfo ; then
echo "CPU: AVX found OK"
if [ -e "$CURDIR"/libgocrispasr-avx.so ]; then
if [ -e $CURDIR/libgocrispasr-avx.so ]; then
LIBRARY="$CURDIR/libgocrispasr-avx.so"
fi
fi
if grep -q -e "\savx2\s" /proc/cpuinfo ; then
echo "CPU: AVX2 found OK"
if [ -e "$CURDIR"/libgocrispasr-avx2.so ]; then
if [ -e $CURDIR/libgocrispasr-avx2.so ]; then
LIBRARY="$CURDIR/libgocrispasr-avx2.so"
fi
fi
@@ -36,27 +32,21 @@ else
# Check avx 512
if grep -q -e "\savx512f\s" /proc/cpuinfo ; then
echo "CPU: AVX512F found OK"
if [ -e "$CURDIR"/libgocrispasr-avx512.so ]; then
if [ -e $CURDIR/libgocrispasr-avx512.so ]; then
LIBRARY="$CURDIR/libgocrispasr-avx512.so"
fi
fi
export LD_LIBRARY_PATH="$CURDIR"/lib:$LD_LIBRARY_PATH
fi
export LD_LIBRARY_PATH=$CURDIR/lib:$LD_LIBRARY_PATH
export CRISPASR_LIBRARY=$LIBRARY
# Point piper's espeak-ng phonemizer at the bundled voice data. The variable
# names the directory CONTAINING espeak-ng-data (package.sh drops it next to
# this script). Harmless when espeak-ng wasn't bundled.
export CRISPASR_ESPEAK_DATA_PATH="$CURDIR"
# If there is a lib/ld.so, use it
if [ -f "$CURDIR"/lib/ld.so ]; then
if [ -f $CURDIR/lib/ld.so ]; then
echo "Using lib/ld.so"
echo "Using library: $LIBRARY"
exec "$CURDIR"/lib/ld.so "$CURDIR"/crispasr "$@"
exec $CURDIR/lib/ld.so $CURDIR/crispasr "$@"
fi
echo "Using library: $LIBRARY"
exec "$CURDIR"/crispasr "$@"
exec $CURDIR/crispasr "$@"

View File

@@ -1,7 +0,0 @@
sources/
build*/
package/
libdepthanythingcpp*.so
depth-anything-cpp
test-models/
test-data/

View File

@@ -1,28 +0,0 @@
cmake_minimum_required(VERSION 3.18)
project(libdepthanythingcpp LANGUAGES C CXX)
set(CMAKE_POSITION_INDEPENDENT_CODE ON)
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_STANDARD_REQUIRED ON)
# Static-link ggml into the depth-anything shared library so the resulting .so
# has no runtime dependency on an external libggml — only on
# libc/libstdc++/libgomp, which the LocalAI package step bundles into the
# docker image.
set(BUILD_SHARED_LIBS OFF CACHE BOOL "Build static libraries" FORCE)
# depth-anything.cpp build switches: skip CLI/tests, but build libdepthanything
# itself as a SHARED library (DA_SHARED) while ggml stays static
# (BUILD_SHARED_LIBS OFF above). The da_capi_* C ABI is compiled into
# src/da_capi.cpp and re-exported by that shared library, so no extra MODULE
# wrapper is needed (unlike locate-anything.cpp).
set(DA_BUILD_CLI OFF CACHE BOOL "Disable depth-anything CLI" FORCE)
set(DA_BUILD_TESTS OFF CACHE BOOL "Disable depth-anything tests" FORCE)
set(DA_SHARED ON CACHE BOOL "Build libdepthanything as a shared lib" FORCE)
add_subdirectory(./sources/depth-anything.cpp)
# Emit libdepthanything.so into the top-level build dir so the Makefile can
# rename it to the per-variant libdepthanythingcpp-<variant>.so.
set_target_properties(depthanything PROPERTIES
LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR})

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