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Author SHA1 Message Date
Ettore Di Giacinto 6fb6419213 fix(cachyllama): link SSD cache implementation
CachyLLaMA splits persistent prompt-cache support into separate source files. Include those implementations in the monolithic LocalAI gRPC adapter when the fork provides them so the ARM64 fallback build resolves the page-manager symbols.

Assisted-by: Codex:gpt-5 [Codex]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-08-09 22:03:11 +00:00
Ettore Di Giacinto d0d9f1efd9 fix(cachyllama): use portable ARM build
Avoid the CPU_ALL_VARIANTS SME matrix on ARM, where the Linux and Darwin backend toolchains fail to compile CachyLLaMA. Keep the existing variant build for x86 and use the fully linked fallback build for ARM.

Assisted-by: Codex:gpt-5 [systematic-debugging]
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-08-09 22:03:11 +00:00
Ettore Di Giacinto 02f5194c33 feat(backends): add CachyLLaMA support
Add CachyLLaMA as a GGUF-compatible llama.cpp fork backend with CPU and Vulkan builds on Linux and Metal on Apple silicon. Expose it through model import, document persistent SSD cache options, and wire CI path filtering and dependency updates.

Assisted-by: Codex:gpt-5
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
2026-08-09 22:03:11 +00:00
498 changed files with 6198 additions and 31078 deletions

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+41
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@@ -0,0 +1,41 @@
#!/usr/bin/env bash
# Shared compile logic for backend/Dockerfile.cachyllama.
# Sourced (via bind mount) from both builder-fromsource and builder-prebuilt stages.
set -euxo pipefail
export CCACHE_DIR=/root/.ccache
ccache --max-size=5G || true
ccache -z || true
export CMAKE_ARGS="${CMAKE_ARGS:-} -DCMAKE_C_COMPILER_LAUNCHER=ccache -DCMAKE_CXX_COMPILER_LAUNCHER=ccache -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache"
if [[ -n "${CUDA_DOCKER_ARCH:-}" ]]; then
CUDA_ARCH_ESC="${CUDA_DOCKER_ARCH//;/\\;}"
export CMAKE_ARGS="${CMAKE_ARGS} -DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCH_ESC}"
echo "CMAKE_ARGS(env) = ${CMAKE_ARGS}"
rm -rf /LocalAI/backend/cpp/cachyllama-*-build
fi
cd /LocalAI/backend/cpp/cachyllama
if [ -z "${BUILD_TYPE:-}" ]; then
# Keep arm64 on the portable, fully linked build. CachyLLaMA's ARM
# CPU_ALL_VARIANTS build includes SME variants that do not build reliably
# across the Linux and Darwin toolchains used by backend CI.
if [ "${TARGETARCH}" = "arm64" ]; then
make cachyllama-fallback
else
# One ggml CPU_ALL_VARIANTS build replaces the per-microarch x86 binaries.
make cachyllama-cpu-all
fi
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.
make cachyllama-fallback
fi
make cachyllama-grpc
make cachyllama-rpc-server
ccache -s || true
-2
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@@ -59,9 +59,7 @@ backend/rust/*/target
backend-images
local-backends
local-ai
.claude
.crush
.tools
protoc
tests
+69 -18
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@@ -2584,7 +2584,7 @@ include:
tag-latest: 'auto'
tag-suffix: '-gpu-intel-vllm'
runs-on: 'ubuntu-latest'
base-image: "intel/oneapi-basekit:2025.3.2-0-devel-ubuntu24.04"
base-image: "intel/oneapi-basekit:2025.3.0-0-devel-ubuntu24.04"
skip-drivers: 'false'
backend: "vllm"
dockerfile: "./backend/Dockerfile.python"
@@ -3208,10 +3208,9 @@ include:
# consumed. Same reason CUDA needs its toolkit in base-image rather than in a
# builder image: this is the ds4 shape, not the llama-cpp one.
#
# ROCm uses upstream's HIP backend and the project-wide ROCm 7.2.1 base. No
# CUDA arm64 or L4T entry: upstream documents and validates CUDA on x86 only.
# Darwin/Metal is in the includeDarwin matrix below, built by
# scripts/build/audio-cpp-darwin.sh.
# No ROCm entry: upstream has no HIP configuration. No CUDA arm64 or L4T
# entry: upstream documents and validates CUDA on x86 only. Darwin/Metal is in
# the includeDarwin matrix below, built by scripts/build/audio-cpp-darwin.sh.
#
# No vulkan entry either, though Dockerfile.audio-cpp and the backend Makefile
# both handle BUILD_TYPE=vulkan for local builds. Every other vulkan backend
@@ -3279,19 +3278,6 @@ include:
dockerfile: "./backend/Dockerfile.audio-cpp"
context: "./"
ubuntu-version: '2404'
- build-type: 'hipblas'
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-rocm-hipblas-audio-cpp'
runs-on: 'ubuntu-latest'
base-image: "rocm/dev-ubuntu-24.04:7.2.1"
skip-drivers: 'false'
backend: "audio-cpp"
dockerfile: "./backend/Dockerfile.audio-cpp"
context: "./"
ubuntu-version: '2404'
- build-type: ''
cuda-major-version: ""
cuda-minor-version: ""
@@ -6314,6 +6300,68 @@ include:
context: "./"
ubuntu-version: '2404'
# CachyLLaMA targets the lower-spec/APU use case with CPU and Vulkan builds.
- build-type: ''
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64'
platform-tag: 'amd64'
tag-latest: 'auto'
tag-suffix: '-cpu-cachyllama'
builder-base-image: 'quay.io/go-skynet/ci-cache:base-grpc-amd64'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "cachyllama"
dockerfile: "./backend/Dockerfile.cachyllama"
context: "./"
ubuntu-version: '2404'
- build-type: ''
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/arm64'
platform-tag: 'arm64'
tag-latest: 'auto'
tag-suffix: '-cpu-cachyllama'
builder-base-image: 'quay.io/go-skynet/ci-cache:base-grpc-arm64'
runs-on: 'ubuntu-24.04-arm'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "cachyllama"
dockerfile: "./backend/Dockerfile.cachyllama"
context: "./"
ubuntu-version: '2404'
- build-type: 'vulkan'
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/amd64'
platform-tag: 'amd64'
tag-latest: 'auto'
tag-suffix: '-gpu-vulkan-cachyllama'
builder-base-image: 'quay.io/go-skynet/ci-cache:base-grpc-vulkan-amd64'
runs-on: 'ubuntu-latest'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "cachyllama"
dockerfile: "./backend/Dockerfile.cachyllama"
context: "./"
ubuntu-version: '2404'
- build-type: 'vulkan'
cuda-major-version: ""
cuda-minor-version: ""
platforms: 'linux/arm64'
platform-tag: 'arm64'
tag-latest: 'auto'
tag-suffix: '-gpu-vulkan-cachyllama'
builder-base-image: 'quay.io/go-skynet/ci-cache:base-grpc-vulkan-arm64'
runs-on: 'ubuntu-24.04-arm'
base-image: "ubuntu:24.04"
skip-drivers: 'false'
backend: "cachyllama"
dockerfile: "./backend/Dockerfile.cachyllama"
context: "./"
ubuntu-version: '2404'
# Darwin matrix (consumed by backend-jobs-darwin).
includeDarwin:
- backend: "diffusers"
@@ -6340,6 +6388,9 @@ includeDarwin:
- backend: "llama-cpp"
tag-suffix: "-metal-darwin-arm64-llama-cpp"
lang: "go"
- backend: "cachyllama"
tag-suffix: "-metal-darwin-arm64-cachyllama"
lang: "go"
- backend: "stablediffusion-ggml"
tag-suffix: "-metal-darwin-arm64-stablediffusion-ggml"
build-type: "metal"
+3 -3
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@@ -3,7 +3,7 @@
# 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 at the pinned
# 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.
#
@@ -32,10 +32,10 @@ LATEST_TAG=$(gh_curl -H "Accept: application/vnd.github+json" \
# The coupled vLLM source version lives in vllm-metal's installer at that tag.
NEW_VLLM_VERSION=$(gh_curl \
"https://raw.githubusercontent.com/$REPO/$LATEST_TAG/install.sh" \
| "$(dirname "${BASH_SOURCE[0]}")/../scripts/lib/extract-vllm-metal-version.sh")
| grep -oE 'vllm_v="[0-9]+\.[0-9]+\.[0-9]+"' | head -1 | cut -d'"' -f2)
if [ -z "$LATEST_TAG" ] || [ -z "$NEW_VLLM_VERSION" ]; then
echo "Could not resolve vllm-metal tag ($LATEST_TAG) or its vLLM version ($NEW_VLLM_VERSION)." >&2
echo "Could not resolve vllm-metal tag ($LATEST_TAG) or its vllm_v ($NEW_VLLM_VERSION)." >&2
exit 1
fi
-44
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@@ -1,44 +0,0 @@
#!/usr/bin/env bash
set -euo pipefail
python3 - <<'PY'
from pathlib import Path
home = Path("website/layouts/index.html").read_text()
css = Path("website/static/css/site.css").read_text()
install = Path("docs/content/getting-started/install.md").read_text()
containers = Path("docs/content/getting-started/containers.md").read_text()
def require(condition, message):
if not condition:
raise SystemExit(f"FAIL: {message}")
require("Drop-in replacement for most upstream APIs." in home,
"homepage must use the requested drop-in API heading")
require("Everything else plugs into LocalAI." not in home,
"old runtime heading must be removed")
require("When the engine we need" not in home,
"hero must describe user outcomes instead of team implementation")
require('href="mailto:contact@localai.io"' in home and "business" in home.lower(),
"homepage must provide a direct business contact action")
require(home.index('id="localai"') < home.index('id="proof-quotes"') < home.index('id="mission"'),
"headline testimonials must directly follow the runtime section")
require(home.count('id="proof-quotes"') == 1,
"headline testimonials must appear exactly once")
require('id="engines"' not in home and "Engines we build" not in home,
"homepage engine showcase must be removed")
require('href="/docs/installation/index.html"' in home,
"installation guide action must use the direct installation URL")
require('<iframe' in install and "youtube.com/embed/cMVNnlqwfw4" in install,
"installation page must embed the walkthrough video")
require("## Quick Start" not in install,
"installation landing page must not duplicate Quick Start")
for text in ("CUDA 12", "CUDA 13", "ROCm", "Intel", "Jetson", "Vulkan", "fallback"):
require(text.lower() in containers.lower(), f"GPU chooser must explain {text}")
require('class="sn__e"><a href="https://github.com/mudler/parakeet.cpp">parakeet.cpp</a>' in home,
"capability engine names must link to their repositories")
require(".pane{min-height:" in css.replace(" ", ""),
"all installation panes must have a fixed minimum height")
print("website review 143 source checks passed")
PY
+7 -1
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@@ -230,6 +230,12 @@ jobs:
make protogen-go
make backends/llama-cpp-darwin
- name: Build CachyLLaMA backend (Darwin Metal)
if: inputs.backend == 'cachyllama'
run: |
make protogen-go
make backends/cachyllama-darwin
- name: Build ds4 backend (Darwin Metal)
if: inputs.backend == 'ds4'
run: |
@@ -256,7 +262,7 @@ jobs:
make backends/audio-cpp-darwin
- name: Build ${{ inputs.backend }}-darwin
if: inputs.backend != 'llama-cpp' && inputs.backend != 'ds4' && inputs.backend != 'privacy-filter' && inputs.backend != 'audio-cpp'
if: inputs.backend != 'llama-cpp' && inputs.backend != 'cachyllama' && inputs.backend != 'ds4' && inputs.backend != 'privacy-filter' && inputs.backend != 'audio-cpp'
run: |
make protogen-go
BACKEND=${{ inputs.backend }} BUILD_TYPE=${{ inputs.build-type }} USE_PIP=${{ inputs.use-pip }} make build-darwin-${{ inputs.lang }}-backend
+4
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@@ -22,6 +22,10 @@ jobs:
variable: "TURBOQUANT_VERSION"
branch: "feature/turboquant-kv-cache"
file: "backend/cpp/turboquant/Makefile"
- repository: "fewtarius/CachyLLama"
variable: "CACHYLLAMA_VERSION"
branch: "master"
file: "backend/cpp/cachyllama/Makefile"
- repository: "PrismML-Eng/llama.cpp"
variable: "BONSAI_VERSION"
branch: "prism"
+9 -20
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@@ -14,7 +14,6 @@ on:
permissions:
contents: write
pull-requests: write
concurrency:
group: refresh-site-counters
@@ -31,25 +30,15 @@ jobs:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
run: ./.github/ci/refresh-site-counters.sh
- name: Show changes
- name: Commit only if something moved
run: |
if git diff --quiet -- website/data/stats.yaml; then
echo "counters unchanged"
else
git diff --unified=0 -- website/data/stats.yaml
echo "counters unchanged, nothing to commit"
exit 0
fi
- name: Create pull request when counters moved
uses: peter-evans/create-pull-request@v8
with:
token: ${{ secrets.UPDATE_BOT_TOKEN }}
push-to-fork: ci-forks/LocalAI
commit-message: "chore(website): refresh the counters"
title: "chore(website): refresh the counters"
body: |
Weekly refresh of the landing-page counters from the GitHub API.
This PR was created automatically by the `refresh-site-counters` workflow.
branch: update/site-counters
delete-branch: true
labels: automated
git diff --unified=0 -- website/data/stats.yaml
git config user.name "github-actions[bot]"
git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
git add website/data/stats.yaml
git commit -m "chore(website): refresh the counters"
git push
-1
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@@ -526,7 +526,6 @@ jobs:
- name: Build llama-cpp backend image and run gRPC e2e tests
run: |
make test-extra-backend-llama-cpp
make test-extra-backend-llama-cpp-embeddings
tests-llama-cpp-grpc-transcription:
needs: detect-changes
if: needs.detect-changes.outputs.llama-cpp == 'true' || needs.detect-changes.outputs.run-all == 'true'
-6
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@@ -65,12 +65,6 @@ jobs:
- name: Test (with coverage gate)
run: |
PATH="$PATH:/root/go/bin" make --jobs 5 --output-sync=target test-coverage-check
# tests/integration is outside the coverage roots because its store specs
# need a live backend. test-stores builds and installs local-store before
# running the complete suite, so new local-store specs are collected
# automatically without adding another workflow entry.
- name: Test local-store integration
run: PATH="$PATH:$HOME/go/bin" make test-stores
- name: Upload coverage report
if: ${{ always() }}
uses: actions/upload-artifact@v4
-2
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@@ -52,8 +52,6 @@ jobs:
run: |
sudo apt-get update
sudo apt-get install -y build-essential libopus-dev
- name: Run stale chunk recovery tests
run: PATH="$PATH:$HOME/go/bin" make test-ui-stale-chunk
# Builds an instrumented UI bundle, runs the Playwright specs, and fails
# if line coverage regressed beyond the jitter tolerance (the gate is
# in `make test-ui-coverage-check`). PLAYWRIGHT_CHROMIUM_PATH is unset
-21
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@@ -1,21 +0,0 @@
## Design Context
### Users
LocalAI serves both single-host users who want to install and try models quickly and experienced developers, ML engineers, system administrators, and DevOps operators who manage production hosts or distributed clusters. The interface must support first-time discovery without hiding the runtime state, configuration, and control that returning operators need.
### Brand Personality
Capable, easy to use, and trustworthy. The interface should make sophisticated local-AI infrastructure feel understandable and under control. It should be direct and calm rather than playful, ornamental, or intimidating.
### Aesthetic Direction
Use LocalAI's established technical, editorial design language: Geist typography, compact information density, sharp geometry, deep blue-black surfaces, action blue, mint for healthy/local/live state, and amber only for decisions requiring attention. Support both dark and light themes. Avoid generic card dashboards, decorative gradients, glass effects, and visual noise.
### Design Principles
1. Use progressive disclosure to serve newcomers and operators in the same workflow: make the common path obvious, then reveal operational depth in context.
2. Organize navigation around user intent and lifecycle state, not implementation concepts or nested containers.
3. Give each resource one canonical home; expose discovery, installed state, and runtime state as clear views of that resource instead of duplicating management surfaces.
4. Keep operational status visible and trustworthy through precise labels, explicit scope, and actionable state—not decoration.
5. Preserve information density for expert use while flattening navigation and reducing repeated summaries, tabs, rails, and panels.
-1
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@@ -27,7 +27,6 @@ To be removed, open a pull request deleting your row, or email
| Organisation | What they use it for | Status |
|---|---|---|
| [walcz.de](https://walcz.de) | Self-hosted appliance for a German B2B consultancy: local-only inference on AMD Strix Halo (gfx1151/ROCm), agents with MCP tools, RAG over an internal knowledge base, and a document/bookkeeping pipeline. | Production |
| _Your organisation here_ | | |
## What this list is not
-1
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@@ -33,7 +33,6 @@ LocalAI follows the Linux kernel project's [guidelines for AI coding assistants]
| [.agents/localai-assistant-mcp.md](.agents/localai-assistant-mcp.md) | LocalAI Assistant chat modality — adding admin tools to the in-process MCP server, editing skill prompts, keeping REST + MCP + skills in sync |
| [.agents/backend-signing.md](.agents/backend-signing.md) | Backend OCI image signing (keyless cosign + sigstore-go) — producer-side CI setup, consumer-side gallery `verification:` block, strict mode (`LOCALAI_REQUIRE_BACKEND_INTEGRITY`), revocation via `not_before` |
| [.agents/preparing-a-release.md](.agents/preparing-a-release.md) | Cutting a release: PR labels, `RELEASE_NOTES_vX.Y.Z.md`, the blog post under `website/content/blog/`, and the demo clips under `website/static/media/` |
| [.impeccable.md](.impeccable.md) | Design context for UI/UX work — users, brand personality, aesthetic direction, and design principles |
## Quick Reference
+11 -23
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@@ -1,5 +1,5 @@
# Disable parallel execution for backend builds
.NOTPARALLEL: backends/diffusers backends/llama-cpp backends/turboquant backends/bonsai backends/outetts backends/piper backends/stablediffusion-ggml backends/trellis2cpp backends/trellis2cpp-darwin backends/whisper backends/crispasr backends/parakeet-cpp backends/moss-transcribe-cpp backends/nemo-speech-cpp backends/faster-whisper backends/silero-vad backends/local-store backends/valkey-store backends/cloud-proxy 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/longcat-video 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/moss-tts-cpp backends/magpie-tts-cpp backends/vllm-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 backends/audio-cpp backends/audio-cpp-darwin
.NOTPARALLEL: backends/diffusers backends/llama-cpp backends/turboquant backends/cachyllama backends/cachyllama-darwin backends/bonsai backends/outetts backends/piper backends/stablediffusion-ggml backends/trellis2cpp backends/trellis2cpp-darwin backends/whisper backends/crispasr backends/parakeet-cpp backends/moss-transcribe-cpp backends/nemo-speech-cpp backends/faster-whisper backends/silero-vad backends/local-store backends/valkey-store backends/cloud-proxy 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/longcat-video 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/moss-tts-cpp backends/magpie-tts-cpp backends/vllm-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 backends/audio-cpp backends/audio-cpp-darwin
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-build-scripts test-ui test-ui-stale-chunk test-ui-coverage-baseline test-ui-coverage-check build vendor lint lint-all
.PHONY: all test test-coverage test-coverage-baseline test-coverage-check test-backend-cpp test-build-scripts test-ui test-ui-coverage-baseline test-ui-coverage-check build vendor lint lint-all
all: help
@@ -676,7 +676,6 @@ test-extra: prepare-test-extra
## BACKEND_TEST_PROMPT Override the prompt used in predict/stream specs.
## BACKEND_TEST_OPTIONS Comma-separated Options[] entries forwarded to LoadModel,
## e.g. "tool_parser:hermes,reasoning_parser:qwen3".
## BACKEND_TEST_EMBEDDING_LAYOUT Expected EmbeddingResult layout: "final" or "per_token".
##
## Direct usage (image already built, no docker-build-* dependency):
##
@@ -706,7 +705,6 @@ test-extra-backend: protogen-go
BACKEND_TEST_CAPS="$$BACKEND_TEST_CAPS" \
BACKEND_TEST_PROMPT="$$BACKEND_TEST_PROMPT" \
BACKEND_TEST_OPTIONS="$$BACKEND_TEST_OPTIONS" \
BACKEND_TEST_EMBEDDING_LAYOUT="$$BACKEND_TEST_EMBEDDING_LAYOUT" \
BACKEND_TEST_TOOL_PROMPT="$$BACKEND_TEST_TOOL_PROMPT" \
BACKEND_TEST_TOOL_NAME="$$BACKEND_TEST_TOOL_NAME" \
BACKEND_TEST_CACHE_TYPE_K="$$BACKEND_TEST_CACHE_TYPE_K" \
@@ -726,15 +724,6 @@ test-extra-backend-llama-cpp: docker-build-llama-cpp
BACKEND_TEST_CAPS=health,load,predict,stream,logprobs,logit_bias \
$(MAKE) test-extra-backend
## Raw llama.cpp embeddings are required by Go-side pooling. This exercises the
## real C++ backend and verifies that it marks the flattened matrix per-token.
test-extra-backend-llama-cpp-embeddings: docker-build-llama-cpp
BACKEND_IMAGE=local-ai-backend:llama-cpp \
BACKEND_TEST_CAPS=health,load,embeddings \
BACKEND_TEST_OPTIONS=pooling:none \
BACKEND_TEST_EMBEDDING_LAYOUT=per_token \
$(MAKE) test-extra-backend
test-extra-backend-ik-llama-cpp: docker-build-ik-llama-cpp
BACKEND_IMAGE=local-ai-backend:ik-llama-cpp $(MAKE) test-extra-backend
@@ -824,7 +813,6 @@ test-extra-backend-tinygrad-embeddings: docker-build-tinygrad
BACKEND_IMAGE=local-ai-backend:tinygrad \
BACKEND_TEST_MODEL_NAME=Qwen/Qwen3-0.6B \
BACKEND_TEST_CAPS=health,load,embeddings \
BACKEND_TEST_EMBEDDING_LAYOUT=final \
$(MAKE) test-extra-backend
## tinygrad — Stable Diffusion 1.5. The original CompVis/runwayml repos have
@@ -1223,6 +1211,10 @@ backends/llama-cpp-darwin: build
bash ./scripts/build/llama-cpp-darwin.sh
./local-ai backends install "ocifile://$(abspath ./backend-images/llama-cpp.tar)"
backends/cachyllama-darwin: build
bash ./scripts/build/cachyllama-darwin.sh
./local-ai backends install "ocifile://$(abspath ./backend-images/cachyllama.tar)"
backends/ds4-darwin: build
bash ./scripts/build/ds4-darwin.sh
./local-ai backends install "ocifile://$(abspath ./backend-images/ds4.tar)"
@@ -1280,6 +1272,8 @@ BACKEND_IK_LLAMA_CPP = ik-llama-cpp|ik-llama-cpp|.|false|false
# turboquant is a llama.cpp fork with TurboQuant KV-cache quantization.
# Reuses backend/cpp/llama-cpp grpc-server sources via a thin wrapper Makefile.
BACKEND_TURBOQUANT = turboquant|turboquant|.|false|false
# CachyLLaMA is a llama.cpp fork with persistent SSD prompt caching and APU tuning.
BACKEND_CACHYLLAMA = cachyllama|cachyllama|.|false|false
# bonsai is a llama.cpp fork (PrismML) adding the Q1_0 (1-bit) and Q2_0 (ternary)
# weight-quant kernels the Bonsai / Ternary-Bonsai models ship in. Reuses
# backend/cpp/llama-cpp grpc-server sources via a thin wrapper Makefile.
@@ -1398,6 +1392,7 @@ endef
$(eval $(call generate-docker-build-target,$(BACKEND_LLAMA_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_IK_LLAMA_CPP)))
$(eval $(call generate-docker-build-target,$(BACKEND_TURBOQUANT)))
$(eval $(call generate-docker-build-target,$(BACKEND_CACHYLLAMA)))
$(eval $(call generate-docker-build-target,$(BACKEND_BONSAI)))
$(eval $(call generate-docker-build-target,$(BACKEND_DS4)))
$(eval $(call generate-docker-build-target,$(BACKEND_PRIVACY_FILTER)))
@@ -1471,7 +1466,7 @@ $(eval $(call generate-docker-build-target,$(BACKEND_SUPERTONIC)))
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-bonsai docker-build-ds4 docker-build-rerankers docker-build-vllm docker-build-vllm-omni docker-build-longcat-video 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-moss-tts-cpp docker-build-magpie-tts-cpp docker-build-vllm-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-moss-transcribe-cpp docker-build-nemo-speech-cpp docker-build-privacy-filter docker-build-trellis2cpp docker-build-valkey-store docker-build-audio-cpp
docker-build-backends: docker-build-llama-cpp docker-build-ik-llama-cpp docker-build-turboquant docker-build-cachyllama docker-build-bonsai docker-build-ds4 docker-build-rerankers docker-build-vllm docker-build-vllm-omni docker-build-longcat-video 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-moss-tts-cpp docker-build-magpie-tts-cpp docker-build-vllm-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-moss-transcribe-cpp docker-build-nemo-speech-cpp docker-build-privacy-filter docker-build-trellis2cpp docker-build-valkey-store docker-build-audio-cpp
########################################################
### Mock Backend for E2E Tests
@@ -1517,13 +1512,6 @@ test-ui: build-mock-backend protogen-go
$(GOCMD) build -o tests/e2e-ui/ui-test-server ./tests/e2e-ui
cd core/http/react-ui && sh $(CURDIR)/scripts/ensure-playwright-browser.sh && bunx playwright test $(PLAYWRIGHT_WORKERS_FLAG)
## The stale-chunk specs need the production code-split bundle. The V8 coverage
## bundle below inlines dynamic imports to keep every page in its denominator.
test-ui-stale-chunk: build-mock-backend protogen-go
cd core/http/react-ui && bun install && bun run build
$(GOCMD) build -o tests/e2e-ui/ui-test-server ./tests/e2e-ui
cd core/http/react-ui && sh $(CURDIR)/scripts/ensure-playwright-browser.sh && bunx playwright test --grep @production-chunks --workers=1
## React UI code coverage from the Playwright e2e suite. Builds a
## NON-instrumented bundle with source maps (COVERAGE_V8=true), re-embeds it
## into the ui-test-server (the dist is //go:embed'ed at compile time), runs the
@@ -1539,7 +1527,7 @@ test-ui-coverage: build-mock-backend protogen-go
$(GOCMD) build -o tests/e2e-ui/ui-test-server ./tests/e2e-ui && \
( cd core/http/react-ui && rm -rf .nyc_output coverage && \
sh $(CURDIR)/scripts/ensure-playwright-browser.sh && \
PW_V8_COVERAGE=1 bunx playwright test --grep-invert @production-chunks $(PLAYWRIGHT_WORKERS_FLAG) && bun run coverage:report )
PW_V8_COVERAGE=1 bunx playwright test $(PLAYWRIGHT_WORKERS_FLAG) && bun run coverage:report )
## UI coverage baseline (committed) and the strict gate that compares against
## it — the React mirror of test-coverage-baseline / test-coverage-check.
+7
View File
@@ -5,6 +5,9 @@
</h1>
<p align="center">
<a href="https://github.com/go-skynet/LocalAI/stargazers" target="blank">
<img src="https://img.shields.io/github/stars/go-skynet/LocalAI?style=for-the-badge" alt="LocalAI stars"/>
</a>
<a href='https://github.com/go-skynet/LocalAI/releases'>
<img src='https://img.shields.io/github/release/go-skynet/LocalAI?&label=Latest&style=for-the-badge'>
</a>
@@ -315,6 +318,10 @@ Past sponsors
A special thanks to individual sponsors, a full list is on [GitHub](https://github.com/sponsors/mudler) and [buymeacoffee](https://buymeacoffee.com/mudler). Special shout out to [drikster80](https://github.com/drikster80) for being generous. Thank you everyone!
## Star history
[![LocalAI Star history Chart](https://api.star-history.com/svg?repos=go-skynet/LocalAI&type=Date)](https://star-history.com/#go-skynet/LocalAI&Date)
## License
LocalAI is a community-driven project created by [Ettore Di Giacinto](https://github.com/mudler/) and maintained by the [LocalAI team](#team).
+9 -13
View File
@@ -6,14 +6,13 @@ ARG APT_PORTS_MIRROR=""
# ASR, VAD, diarization, source separation and music generation, wrapped as a
# LocalAI gRPC backend.
#
# BASE_IMAGE is ubuntu:24.04 for cpu and vulkan builds,
# nvidia/cuda:<ver>-devel-ubuntu24.04 for cublas builds, or
# rocm/dev-ubuntu-24.04:<ver> for hipblas builds. All ship apt and Ubuntu Noble
# packages; the GPU bases also provide their toolkits. BUILD_TYPE selects the
# engine backend in the Makefile: "" = portable CPU with all ggml CPU variants,
# "cublas" -> -DENGINE_ENABLE_CUDA=ON, "hipblas" -> -DENGINE_ENABLE_HIP=ON,
# and "vulkan" -> -DENGINE_ENABLE_VULKAN=ON. Darwin (Metal) builds bypass this
# Dockerfile entirely.
# BASE_IMAGE is ubuntu:24.04 for cpu and vulkan builds, or
# nvidia/cuda:<ver>-devel-ubuntu24.04 for cublas builds; both ship apt and
# Ubuntu Noble packages, and the CUDA base additionally provides
# /usr/local/cuda. BUILD_TYPE selects the engine backend in the Makefile:
# "" = portable CPU with all ggml CPU variants, "cublas" ->
# -DENGINE_ENABLE_CUDA=ON, "vulkan" -> -DENGINE_ENABLE_VULKAN=ON. Darwin
# (Metal) builds bypass this Dockerfile entirely.
#
# Upstream needs GCC 13 or newer, which ubuntu:24.04 and the CUDA 12/13
# devel-ubuntu24.04 images all provide.
@@ -63,7 +62,7 @@ ENV BUILD_TYPE=${BUILD_TYPE} \
APT_MIRROR=${APT_MIRROR} \
APT_PORTS_MIRROR=${APT_PORTS_MIRROR} \
DEBIAN_FRONTEND=noninteractive \
PATH=/opt/rocm/bin:/usr/local/cuda/bin:${PATH}
PATH=/usr/local/cuda/bin:${PATH}
WORKDIR /build
@@ -74,7 +73,7 @@ WORKDIR /build
# fallback of its own.
#
# BUILD_TYPE=vulkan additionally needs the loader headers and glslc; both are in
# Noble. The CUDA and ROCm toolkits come from their matching BASE_IMAGE.
# Noble. The CUDA toolkit for BUILD_TYPE=cublas comes from BASE_IMAGE.
RUN --mount=type=bind,source=.docker/apt-mirror.sh,target=/usr/local/sbin/apt-mirror \
sh /usr/local/sbin/apt-mirror && \
apt-get update && \
@@ -84,9 +83,6 @@ RUN --mount=type=bind,source=.docker/apt-mirror.sh,target=/usr/local/sbin/apt-mi
if [ "${BUILD_TYPE}" = "vulkan" ]; then \
apt-get install -y --no-install-recommends libvulkan-dev glslc; \
fi && \
if [ "${BUILD_TYPE}" = "hipblas" ]; then \
apt-get install -y --no-install-recommends hipblas-dev rocblas-dev; \
fi && \
if [ "${TARGETARCH}" = "arm64" ]; then \
apt-get install -y --no-install-recommends gcc-14 g++-14; \
fi && \
+160
View File
@@ -0,0 +1,160 @@
ARG BASE_IMAGE=ubuntu:24.04
# BUILDER_BASE_IMAGE defaults to BASE_IMAGE so the Dockerfile parses even
# when no prebuilt base is supplied. The builder-prebuilt stage is only
# entered when BUILDER_TARGET=builder-prebuilt, so a "wrong" fallback
# content here is harmless — BuildKit prunes the unreferenced builder.
ARG BUILDER_BASE_IMAGE=${BASE_IMAGE}
# BUILDER_TARGET selects which builder stage the final scratch image copies
# package output from. Declared at global scope (before any FROM) so it's
# usable in `FROM ${BUILDER_TARGET}` below. Default keeps local
# `make backends/cachyllama` on the from-source path.
ARG BUILDER_TARGET=builder-fromsource
ARG APT_MIRROR=""
ARG APT_PORTS_MIRROR=""
# ============================================================================
# Stage: builder-fromsource — self-contained build path.
# Runs .docker/install-base-deps.sh (apt deps + cmake + protoc + gRPC +
# conditional CUDA/ROCm/Vulkan), copies /opt/grpc to /usr/local, then
# compiles the variant. Used when BUILDER_TARGET=builder-fromsource (the
# default; local `make backends/cachyllama`).
#
# The install script is the same one that backend/Dockerfile.base-grpc-builder
# runs, so the result is bit-equivalent to the prebuilt-base path
# (builder-prebuilt below).
# ============================================================================
FROM ${BASE_IMAGE} AS builder-fromsource
ARG BUILD_TYPE
ARG CUDA_MAJOR_VERSION
ARG CUDA_MINOR_VERSION
ARG CMAKE_FROM_SOURCE=false
# CUDA Toolkit 13.x compatibility: CMake 3.31.9+ fixes toolchain detection/arch table issues
ARG CMAKE_VERSION=3.31.10
ARG GRPC_VERSION=v1.65.0
ARG GRPC_MAKEFLAGS="-j4 -Otarget"
ARG SKIP_DRIVERS=false
ARG TARGETARCH
ARG TARGETVARIANT
ARG GO_VERSION=1.25.4
ARG UBUNTU_VERSION=2404
ARG APT_MIRROR
ARG APT_PORTS_MIRROR
ARG AMDGPU_TARGETS=""
ARG BACKEND=rerankers
# CUDA target archs, e.g. --build-arg CUDA_DOCKER_ARCH='75;86;89;120'
ARG CUDA_DOCKER_ARCH
ARG CMAKE_ARGS
ENV BUILD_TYPE=${BUILD_TYPE} \
CUDA_MAJOR_VERSION=${CUDA_MAJOR_VERSION} \
CUDA_MINOR_VERSION=${CUDA_MINOR_VERSION} \
CMAKE_FROM_SOURCE=${CMAKE_FROM_SOURCE} \
CMAKE_VERSION=${CMAKE_VERSION} \
GRPC_VERSION=${GRPC_VERSION} \
GRPC_MAKEFLAGS=${GRPC_MAKEFLAGS} \
SKIP_DRIVERS=${SKIP_DRIVERS} \
TARGETARCH=${TARGETARCH} \
UBUNTU_VERSION=${UBUNTU_VERSION} \
APT_MIRROR=${APT_MIRROR} \
APT_PORTS_MIRROR=${APT_PORTS_MIRROR} \
AMDGPU_TARGETS=${AMDGPU_TARGETS} \
CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH} \
CMAKE_ARGS=${CMAKE_ARGS} \
DEBIAN_FRONTEND=noninteractive
# CUDA on PATH (no-op when CUDA isn't installed)
ENV PATH=/usr/local/cuda/bin:${PATH}
# HipBLAS / ROCm on PATH (no-op when ROCm isn't installed)
ENV PATH=/opt/rocm/bin:${PATH}
WORKDIR /build
# Install everything via the shared script — the same one that
# backend/Dockerfile.base-grpc-builder runs, so the prebuilt CI base and
# this from-source path are bit-equivalent.
RUN --mount=type=bind,source=.docker/install-base-deps.sh,target=/usr/local/sbin/install-base-deps \
--mount=type=bind,source=.docker/apt-mirror.sh,target=/usr/local/sbin/apt-mirror \
bash /usr/local/sbin/install-base-deps
# Mirror builder-prebuilt: copy gRPC from /opt/grpc to /usr/local so
# CMake's find_package finds it at the canonical prefix the Makefile expects.
RUN cp -a /opt/grpc/. /usr/local/
COPY . /LocalAI
# BuildKit cache mount for ccache. See Dockerfile.llama-cpp (commit 9228e5b4)
# for rationale. cachyllama is a llama.cpp fork that reuses
# backend/cpp/llama-cpp source via a thin wrapper Makefile, so MOST TUs
# are content-identical to the upstream llama-cpp build. Sharing a cache
# id with llama-cpp could give cross-fork hits — but for now keep them
# separate so a regression in one doesn't poison the other. Revisit
# sharing after measuring the actual hit rate.
#
# The compile body is shared with builder-prebuilt via .docker/cachyllama-compile.sh.
RUN --mount=type=bind,source=.docker/cachyllama-compile.sh,target=/usr/local/sbin/compile.sh \
--mount=type=cache,target=/root/.ccache,id=cachyllama-ccache-${TARGETARCH}-${BUILD_TYPE},sharing=locked \
bash /usr/local/sbin/compile.sh
# Copy libraries using a script to handle architecture differences
RUN make -BC /LocalAI/backend/cpp/cachyllama package
# ============================================================================
# Stage: builder-prebuilt — uses the pre-built base from
# quay.io/go-skynet/ci-cache:base-grpc-* (built by .github/workflows/base-images.yml).
# That image already has gRPC at /opt/grpc + apt deps + CUDA/ROCm/Vulkan
# pre-installed, so we just copy gRPC to /usr/local and compile. Used when
# BUILDER_TARGET=builder-prebuilt (CI when the matrix entry sets
# builder-base-image).
# ============================================================================
FROM ${BUILDER_BASE_IMAGE} AS builder-prebuilt
ARG BUILD_TYPE
ENV BUILD_TYPE=${BUILD_TYPE}
ARG CUDA_DOCKER_ARCH
ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
ARG CMAKE_ARGS
ENV CMAKE_ARGS=${CMAKE_ARGS}
# AMDGPU_TARGETS must be forwarded into the env here too — backend/cpp/llama-cpp/Makefile
# (which the cachyllama Makefile reuses via a sibling build dir) errors out when the var
# is empty on a hipblas build, and the prebuilt path is what CI exercises most of the
# time. The builder-fromsource stage above already does this; mirror it here.
ARG AMDGPU_TARGETS
ENV AMDGPU_TARGETS=${AMDGPU_TARGETS}
ARG TARGETARCH
ARG TARGETVARIANT
# The base-grpc-* image installs gRPC to /opt/grpc but doesn't copy it to
# /usr/local. Mirror what the from-source path does so the compile step
# can find gRPC at the canonical prefix the Makefile expects.
RUN cp -a /opt/grpc/. /usr/local/
COPY . /LocalAI
RUN --mount=type=bind,source=.docker/cachyllama-compile.sh,target=/usr/local/sbin/compile.sh \
--mount=type=cache,target=/root/.ccache,id=cachyllama-ccache-${TARGETARCH}-${BUILD_TYPE},sharing=locked \
bash /usr/local/sbin/compile.sh
RUN make -BC /LocalAI/backend/cpp/cachyllama package
# ============================================================================
# Final stage — copies package output from one of the two builders.
# BUILDER_TARGET selects which one. BuildKit prunes the unreferenced builder.
#
# BuildKit doesn't support variable expansion in `COPY --from=` directly,
# so we resolve the ARG by aliasing the chosen builder to a fixed stage
# name via `FROM ${BUILDER_TARGET} AS builder` and then COPY --from=builder.
# BUILDER_TARGET itself is declared as a global ARG at the top of this
# file (required for use in FROM), so we just re-import it into this
# stage's scope before the FROM directive.
# ============================================================================
FROM ${BUILDER_TARGET} AS builder
FROM scratch
# Copy all available binaries (the build process only creates the appropriate ones for the target architecture)
COPY --from=builder /LocalAI/backend/cpp/cachyllama/package/. ./
-20
View File
@@ -536,28 +536,8 @@ message Result {
bool success = 2;
}
// EmbeddingLayout describes whether embeddings contains one final vector or
// a matrix of per-token vectors. Go-side pooling must never infer this from
// tokens/dim alone: a one-token raw matrix and a final vector have the same
// shape.
enum EmbeddingLayout {
EMBEDDING_LAYOUT_UNSPECIFIED = 0;
EMBEDDING_LAYOUT_FINAL = 1;
EMBEDDING_LAYOUT_PER_TOKEN = 2;
}
message EmbeddingResult {
repeated float embeddings = 1;
// Shape of the payload above: dim is the embedding width, tokens is the
// number of vectors packed into `embeddings` (1 when the backend pooled
// server-side, N with pooling:none; total across prompts if a request
// carried several). tokens=0/dim=0 means the backend predates shape
// reporting. prompt_tokens is the number of prompt tokens evaluated, for
// usage accounting.
int32 tokens = 2;
int32 dim = 3;
int32 prompt_tokens = 4;
EmbeddingLayout layout = 5;
}
message TranscriptRequest {
+1 -11
View File
@@ -9,7 +9,7 @@
# recipe is a make target (not a prepare.sh) so 'make purge && make' is a clean
# rebuild and so the bump bot can see the pin.
AUDIO_CPP_VERSION?=43001a7e0f452d80f4588e613f13332940dd4d3a
AUDIO_CPP_VERSION?=7efbb58def443722ea540d931dd3debee3e4d5e8
AUDIO_CPP_REPO?=https://github.com/0xShug0/audio.cpp
CURRENT_MAKEFILE_DIR := $(dir $(abspath $(lastword $(MAKEFILE_LIST))))
@@ -77,16 +77,6 @@ endif
ifeq ($(BUILD_TYPE),cublas)
CMAKE_ARGS += -DENGINE_ENABLE_CUDA=ON "-DCMAKE_CUDA_ARCHITECTURES=$(CUDA_ARCHITECTURES)"
else ifeq ($(BUILD_TYPE),hipblas)
ROCM_HOME ?= /opt/rocm
ROCM_PATH ?= /opt/rocm
export CXX=$(ROCM_HOME)/llvm/bin/clang++
export CC=$(ROCM_HOME)/llvm/bin/clang
AMDGPU_TARGETS ?= gfx908,gfx90a,gfx942,gfx950,gfx1030,gfx1100,gfx1101,gfx1102,gfx1151,gfx1200,gfx1201
# audio.cpp forwards GPU_TARGETS to CMake's semicolon-delimited HIP list.
comma := ,
HIP_TARGETS := $(subst $(comma),;,$(AMDGPU_TARGETS))
CMAKE_ARGS += -DENGINE_ENABLE_HIP=ON "-DGPU_TARGETS=$(HIP_TARGETS)"
else ifeq ($(BUILD_TYPE),vulkan)
CMAKE_ARGS += -DENGINE_ENABLE_VULKAN=ON
else ifeq ($(UNAME_S),Darwin)
@@ -29,7 +29,6 @@ const NamedTask kTaskNames[] = {
{Task::VoiceDesign, "vdes"},
{Task::SpeakerRecognition, "spk"},
{Task::Svc, "svc"},
{Task::Midi, "midi"},
};
// Accepted on input but never emitted. "spkrec" was this backend's own earlier
@@ -25,7 +25,6 @@ enum class Task {
VoiceDesign,
SpeakerRecognition,
Svc,
Midi,
};
// Mirrors engine::runtime::RunMode.
@@ -361,7 +361,7 @@ static void test_names_round_trip() {
Task::SourceSeparation, Task::AudioGeneration, Task::Tts,
Task::VoiceCloning, Task::VoiceConversion,
Task::SpeechToSpeech, Task::Alignment, Task::VoiceDesign,
Task::SpeakerRecognition, Task::Svc, Task::Midi};
Task::SpeakerRecognition, Task::Svc};
for (const Task t : all) {
Task parsed = Task::Vad;
const bool ok = parse_task_name(task_name(t), parsed);
+2 -9
View File
@@ -69,8 +69,7 @@ static_assert(kEngine(engine::runtime::VoiceTaskKind::VoiceDesign) == 10, "Voice
static_assert(kEngine(engine::runtime::VoiceTaskKind::SpeakerRecognition) == 11, "VoiceTaskKind drifted");
// The last member. Pinning it pins the member count too, as long as the
// enumerators stay contiguous and unassigned, which upstream's declaration is.
static_assert(kEngine(engine::runtime::VoiceTaskKind::Svc) == 12, "VoiceTaskKind drifted");
static_assert(kEngine(engine::runtime::VoiceTaskKind::Midi) == 13,
static_assert(kEngine(engine::runtime::VoiceTaskKind::Svc) == 12,
"engine::runtime::VoiceTaskKind gained, lost or reordered a member. "
"audiocpp_backend::Task mirrors it positionally: update capability_routing.h, "
"to_engine_task and from_engine_task together, then move this pin.");
@@ -88,7 +87,6 @@ static_assert(kMirror(Task::Alignment) == 9, "Task drifted from VoiceTaskKind");
static_assert(kMirror(Task::VoiceDesign) == 10, "Task drifted from VoiceTaskKind");
static_assert(kMirror(Task::SpeakerRecognition) == 11, "Task drifted from VoiceTaskKind");
static_assert(kMirror(Task::Svc) == 12, "Task drifted from VoiceTaskKind");
static_assert(kMirror(Task::Midi) == 13, "Task drifted from VoiceTaskKind");
static_assert(static_cast<int>(engine::runtime::RunMode::Offline) == 0, "RunMode drifted");
static_assert(static_cast<int>(engine::runtime::RunMode::Streaming) == 1,
@@ -103,9 +101,6 @@ engine::core::BackendType parse_backend_type(const std::string &value) {
if (value == "cuda") {
return engine::core::BackendType::Cuda;
}
if (value == "hip" || value == "rocm") {
return engine::core::BackendType::Hip;
}
if (value == "vulkan") {
return engine::core::BackendType::Vulkan;
}
@@ -119,7 +114,7 @@ engine::core::BackendType parse_backend_type(const std::string &value) {
return engine::core::BackendType::Cpu;
}
throw ConfigError("audio-cpp: unknown backend option '" + value +
"'. Known backends: cpu, cuda, hip, rocm, vulkan, metal, best");
"'. Known backends: cpu, cuda, vulkan, metal, best");
}
std::filesystem::path executable_directory() {
@@ -246,7 +241,6 @@ engine::runtime::VoiceTaskKind to_engine_task(Task task) {
case Task::VoiceDesign: return K::VoiceDesign;
case Task::SpeakerRecognition: return K::SpeakerRecognition;
case Task::Svc: return K::Svc;
case Task::Midi: return K::Midi;
}
// Unreachable for any valid enumerator. No `default:` label, so -Wswitch
// still reports a member this switch stops covering.
@@ -269,7 +263,6 @@ Task from_engine_task(engine::runtime::VoiceTaskKind kind) {
case K::VoiceDesign: return Task::VoiceDesign;
case K::SpeakerRecognition: return Task::SpeakerRecognition;
case K::Svc: return Task::Svc;
case K::Midi: return Task::Midi;
}
return Task::Vad;
}
-12
View File
@@ -59,12 +59,6 @@ bool starts_with(const std::string &value, const std::string &prefix) {
value.compare(0, prefix.size(), prefix) == 0;
}
bool is_known_backend(const std::string &value) {
return value == "cpu" || value == "cuda" || value == "hip" ||
value == "rocm" || value == "vulkan" || value == "metal" ||
value == "best";
}
} // namespace
ParsedOptions parse_model_options(const std::vector<std::string> &entries) {
@@ -113,12 +107,6 @@ ParsedOptions parse_model_options(const std::vector<std::string> &entries) {
} else if (key == "task") {
parsed.options.task = value;
} else if (key == "backend") {
if (!is_known_backend(value)) {
parsed.error = "audio-cpp: unknown backend option '" + value +
"'. Known backends: cpu, cuda, hip, rocm, "
"vulkan, metal, best";
return parsed;
}
parsed.options.backend = value;
} else if (key == "model_spec_override") {
parsed.options.model_spec_override = value;
+1 -1
View File
@@ -17,7 +17,7 @@ struct ModelOptions {
std::string family;
// Pins the audio.cpp task, overriding RPC-based routing. Empty means route.
std::string task;
// ggml backend: cpu, cuda, hip (or rocm), vulkan, metal, best.
// ggml backend: cpu, cuda, vulkan, metal, best.
std::string backend = "cpu";
int device = 0;
// True once a `device:` entry has been seen. 0 is both the default and a
@@ -75,11 +75,6 @@ static void test_scalar_options() {
check(parse_model_options({"live_idle_timeout_ms:0"}).options.live_idle_timeout_ms == 0,
"an explicit 0 turns the live idle limit off rather than reverting to "
"the default");
check(parse_model_options({"backend:hip"}).error.empty(),
"HIP backend option is accepted");
check(parse_model_options({"backend:rocm"}).error.empty(),
"ROCm backend alias is accepted");
}
// Values containing colons must survive: split on the FIRST colon only.
@@ -129,8 +124,6 @@ static void test_errors() {
"negative device is rejected");
check(!parse_model_options({"threads:x"}).error.empty(),
"non-numeric threads is rejected");
check(!parse_model_options({"backend:unknown"}).error.empty(),
"unknown compute backend is rejected before model loading");
// Values too large for int must be rejected, not silently wrapped into a
// negative device index that then reaches the ggml backend selector.
-2
View File
@@ -42,7 +42,6 @@ define bonsai-build
rm -rf $(CURRENT_MAKEFILE_DIR)/../bonsai-$(1)-build/patches
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../bonsai-$(1)-build purge
bash $(LLAMA_CPP_DIR)/disable-score-task.sh $(CURRENT_MAKEFILE_DIR)/../bonsai-$(1)-build/grpc-server.cpp
bash $(LLAMA_CPP_DIR)/disable-tts-task.sh $(CURRENT_MAKEFILE_DIR)/../bonsai-$(1)-build/grpc-server.cpp
$(info $(GREEN)I bonsai build info:$(1)$(RESET))
LLAMA_REPO=$(LLAMA_REPO) LLAMA_VERSION=$(BONSAI_VERSION) \
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../bonsai-$(1)-build llama.cpp
@@ -80,7 +79,6 @@ bonsai-cpu-all:
rm -rf $(CURRENT_MAKEFILE_DIR)/../bonsai-cpu-all-build/patches
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../bonsai-cpu-all-build purge
bash $(LLAMA_CPP_DIR)/disable-score-task.sh $(CURRENT_MAKEFILE_DIR)/../bonsai-cpu-all-build/grpc-server.cpp
bash $(LLAMA_CPP_DIR)/disable-tts-task.sh $(CURRENT_MAKEFILE_DIR)/../bonsai-cpu-all-build/grpc-server.cpp
$(info $(GREEN)I bonsai build info:cpu-all-variants$(RESET))
LLAMA_REPO=$(LLAMA_REPO) LLAMA_VERSION=$(BONSAI_VERSION) \
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../bonsai-cpu-all-build llama.cpp
+99
View File
@@ -0,0 +1,99 @@
# Pinned to the HEAD of master on https://github.com/fewtarius/CachyLLama.
# Auto-bumped nightly by .github/workflows/bump_deps.yaml.
CACHYLLAMA_VERSION?=cea45f4222e1eea5c2c68388d7e75904a65b6778
LLAMA_REPO?=https://github.com/fewtarius/CachyLLama
CMAKE_ARGS?=
BUILD_TYPE?=
NATIVE?=false
ONEAPI_VARS?=/opt/intel/oneapi/setvars.sh
TARGET?=--target grpc-server
JOBS?=$(shell nproc 2>/dev/null || sysctl -n hw.ncpu 2>/dev/null || echo 1)
ARCH?=$(shell uname -m)
CURRENT_MAKEFILE_DIR := $(dir $(abspath $(lastword $(MAKEFILE_LIST))))
LLAMA_CPP_DIR := $(CURRENT_MAKEFILE_DIR)/../llama-cpp
GREEN := \033[0;32m
RESET := \033[0m
# cachyllama is a llama.cpp fork. Rather than duplicating grpc-server.cpp / CMakeLists.txt /
# prepare.sh we reuse the ones in backend/cpp/llama-cpp, and only swap which repo+sha the
# fetch step pulls. Each flavor target copies ../llama-cpp into a sibling ../cachyllama-<flavor>-build
# directory, then invokes llama-cpp's own build-llama-cpp-grpc-server with LLAMA_REPO/LLAMA_VERSION
# overridden to point at the fork.
# Each flavor target:
# 1. copies backend/cpp/llama-cpp/ (grpc-server.cpp + prepare.sh + CMakeLists.txt + Makefile)
# into a sibling cachyllama-<flavor>-build directory;
# 2. clones the cachyllama fork into cachyllama-<flavor>-build/llama.cpp via the copy's
# own `llama.cpp` target, overriding LLAMA_REPO/LLAMA_VERSION;
# 3. runs the copy's `grpc-server` target, which produces the binary we copy up as
# cachyllama-<flavor>.
define cachyllama-build
rm -rf $(CURRENT_MAKEFILE_DIR)/../cachyllama-$(1)-build
cp -rf $(LLAMA_CPP_DIR) $(CURRENT_MAKEFILE_DIR)/../cachyllama-$(1)-build
# Drop patches vendored for upstream llama.cpp; CachyLLaMA tracks upstream
# closely but must not accidentally receive patches pinned to another SHA.
rm -rf $(CURRENT_MAKEFILE_DIR)/../cachyllama-$(1)-build/patches
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../cachyllama-$(1)-build purge
bash $(LLAMA_CPP_DIR)/disable-score-task.sh $(CURRENT_MAKEFILE_DIR)/../cachyllama-$(1)-build/grpc-server.cpp
$(info $(GREEN)I cachyllama build info:$(1)$(RESET))
LLAMA_REPO=$(LLAMA_REPO) LLAMA_VERSION=$(CACHYLLAMA_VERSION) \
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../cachyllama-$(1)-build llama.cpp
CMAKE_ARGS="$(CMAKE_ARGS) $(2)" TARGET="$(3)" \
LLAMA_REPO=$(LLAMA_REPO) LLAMA_VERSION=$(CACHYLLAMA_VERSION) \
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../cachyllama-$(1)-build grpc-server
cp -rfv $(CURRENT_MAKEFILE_DIR)/../cachyllama-$(1)-build/grpc-server cachyllama-$(1)
endef
cachyllama-avx2:
$(call cachyllama-build,avx2,-DGGML_AVX=on -DGGML_AVX2=on -DGGML_AVX512=off -DGGML_FMA=on -DGGML_F16C=on,--target grpc-server)
cachyllama-avx512:
$(call cachyllama-build,avx512,-DGGML_AVX=on -DGGML_AVX2=off -DGGML_AVX512=on -DGGML_FMA=on -DGGML_F16C=on,--target grpc-server)
cachyllama-avx:
$(call cachyllama-build,avx,-DGGML_AVX=on -DGGML_AVX2=off -DGGML_AVX512=off -DGGML_FMA=off -DGGML_F16C=off -DGGML_BMI2=off,--target grpc-server)
cachyllama-fallback:
$(call cachyllama-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).
# cachyllama 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.
cachyllama-cpu-all:
rm -rf $(CURRENT_MAKEFILE_DIR)/../cachyllama-cpu-all-build
cp -rf $(LLAMA_CPP_DIR) $(CURRENT_MAKEFILE_DIR)/../cachyllama-cpu-all-build
# Drop patches vendored for upstream llama.cpp; they are SHA-specific.
rm -rf $(CURRENT_MAKEFILE_DIR)/../cachyllama-cpu-all-build/patches
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../cachyllama-cpu-all-build purge
bash $(LLAMA_CPP_DIR)/disable-score-task.sh $(CURRENT_MAKEFILE_DIR)/../cachyllama-cpu-all-build/grpc-server.cpp
$(info $(GREEN)I cachyllama build info:cpu-all-variants$(RESET))
LLAMA_REPO=$(LLAMA_REPO) LLAMA_VERSION=$(CACHYLLAMA_VERSION) \
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../cachyllama-cpu-all-build llama.cpp
SHARED_LIBS=ON EXTRA_CMAKE_ARGS="-DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON" TARGET="--target grpc-server --target ggml" \
LLAMA_REPO=$(LLAMA_REPO) LLAMA_VERSION=$(CACHYLLAMA_VERSION) \
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../cachyllama-cpu-all-build grpc-server
cp -rfv $(CURRENT_MAKEFILE_DIR)/../cachyllama-cpu-all-build/grpc-server cachyllama-cpu-all
rm -rf ggml-shared-libs && mkdir -p ggml-shared-libs
find $(CURRENT_MAKEFILE_DIR)/../cachyllama-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/
cachyllama-grpc:
$(call cachyllama-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 ggml-rpc-server)
cachyllama-rpc-server: cachyllama-grpc
cp -rf $(CURRENT_MAKEFILE_DIR)/../cachyllama-grpc-build/llama.cpp/build/bin/ggml-rpc-server cachyllama-rpc-server
package:
bash package.sh
purge:
rm -rf $(CURRENT_MAKEFILE_DIR)/../cachyllama-*-build
rm -rf cachyllama-* package
clean: purge
+66
View File
@@ -0,0 +1,66 @@
#!/bin/bash
# Script to copy the appropriate libraries based on architecture
# This script is used in the final stage of the Dockerfile
set -e
CURDIR=$(dirname "$(realpath $0)")
REPO_ROOT="${CURDIR}/../../.."
# Create lib directory
mkdir -p $CURDIR/package/lib
cp -avrf $CURDIR/cachyllama-* $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
echo "Detected x86_64 architecture, copying x86_64 libraries..."
cp -arfLv /lib64/ld-linux-x86-64.so.2 $CURDIR/package/lib/ld.so
cp -arfLv /lib/x86_64-linux-gnu/libc.so.6 $CURDIR/package/lib/libc.so.6
cp -arfLv /lib/x86_64-linux-gnu/libgcc_s.so.1 $CURDIR/package/lib/libgcc_s.so.1
cp -arfLv /lib/x86_64-linux-gnu/libstdc++.so.6 $CURDIR/package/lib/libstdc++.so.6
cp -arfLv /lib/x86_64-linux-gnu/libm.so.6 $CURDIR/package/lib/libm.so.6
cp -arfLv /lib/x86_64-linux-gnu/libgomp.so.1 $CURDIR/package/lib/libgomp.so.1
cp -arfLv /lib/x86_64-linux-gnu/libdl.so.2 $CURDIR/package/lib/libdl.so.2
cp -arfLv /lib/x86_64-linux-gnu/librt.so.1 $CURDIR/package/lib/librt.so.1
cp -arfLv /lib/x86_64-linux-gnu/libpthread.so.0 $CURDIR/package/lib/libpthread.so.0
elif [ -f "/lib/ld-linux-aarch64.so.1" ]; then
# ARM64 architecture
echo "Detected ARM64 architecture, copying ARM64 libraries..."
cp -arfLv /lib/ld-linux-aarch64.so.1 $CURDIR/package/lib/ld.so
cp -arfLv /lib/aarch64-linux-gnu/libc.so.6 $CURDIR/package/lib/libc.so.6
cp -arfLv /lib/aarch64-linux-gnu/libgcc_s.so.1 $CURDIR/package/lib/libgcc_s.so.1
cp -arfLv /lib/aarch64-linux-gnu/libstdc++.so.6 $CURDIR/package/lib/libstdc++.so.6
cp -arfLv /lib/aarch64-linux-gnu/libm.so.6 $CURDIR/package/lib/libm.so.6
cp -arfLv /lib/aarch64-linux-gnu/libgomp.so.1 $CURDIR/package/lib/libgomp.so.1
cp -arfLv /lib/aarch64-linux-gnu/libdl.so.2 $CURDIR/package/lib/libdl.so.2
cp -arfLv /lib/aarch64-linux-gnu/librt.so.1 $CURDIR/package/lib/librt.so.1
cp -arfLv /lib/aarch64-linux-gnu/libpthread.so.0 $CURDIR/package/lib/libpthread.so.0
else
echo "Error: Could not detect architecture"
exit 1
fi
# Package GPU libraries based on BUILD_TYPE
GPU_LIB_SCRIPT="${REPO_ROOT}/scripts/build/package-gpu-libs.sh"
if [ -f "$GPU_LIB_SCRIPT" ]; then
echo "Packaging GPU libraries for BUILD_TYPE=${BUILD_TYPE:-cpu}..."
source "$GPU_LIB_SCRIPT" "$CURDIR/package/lib"
package_gpu_libs
fi
echo "Packaging completed successfully"
ls -liah $CURDIR/package/
ls -liah $CURDIR/package/lib/
+56
View File
@@ -0,0 +1,56 @@
#!/bin/bash
set -ex
# Get the absolute current dir where the script is located
CURDIR=$(dirname "$(realpath "$0")")
cd /
echo "CPU info:"
grep -e "model\sname" /proc/cpuinfo | head -1
grep -e "flags" /proc/cpuinfo | head -1
BINARY=cachyllama-fallback
# x86/arm64 ship a single cachyllama-cpu-all built with ggml CPU_ALL_VARIANTS: ggml's
# backend registry dlopens the best libggml-cpu-*.so for this host, so no shell-side
# probing. ROCm ships only cachyllama-fallback, so fall back to it when cpu-all is absent.
if [ -e "$CURDIR"/cachyllama-cpu-all ]; then
BINARY=cachyllama-cpu-all
fi
if [ -n "$LLAMACPP_GRPC_SERVERS" ]; then
if [ -e "$CURDIR"/cachyllama-grpc ]; then
BINARY=cachyllama-grpc
fi
fi
# Extend ld library path with the dir where this script is located/lib
if [ "$(uname)" == "Darwin" ]; then
export DYLD_LIBRARY_PATH="$CURDIR"/lib:$DYLD_LIBRARY_PATH
else
export LD_LIBRARY_PATH="$CURDIR"/lib:$LD_LIBRARY_PATH
# Tell rocBLAS where to find TensileLibrary data (GPU kernel tuning files)
if [ -d "$CURDIR/lib/rocblas/library" ]; then
export ROCBLAS_TENSILE_LIBPATH="$CURDIR"/lib/rocblas/library
fi
# Same for hipBLASLt (rocblaslt): the bundled libhipblaslt.so resolves its
# TensileLibrary_lazy_gfx*.dat kernel data relative to itself, so point it at
# the bundled data or it falls back to slow generic kernels (issue #10660).
if [ -d "$CURDIR/lib/hipblaslt/library" ]; then
export HIPBLASLT_TENSILE_LIBPATH="$CURDIR"/lib/hipblaslt/library
fi
fi
# If there is a lib/ld.so, use it
if [ -f "$CURDIR"/lib/ld.so ]; then
echo "Using lib/ld.so"
echo "Using binary: $BINARY"
exec "$CURDIR"/lib/ld.so "$CURDIR"/$BINARY "$@"
fi
echo "Using binary: $BINARY"
exec "$CURDIR"/$BINARY "$@"
# We should never reach this point, however just in case we do, run fallback
exec "$CURDIR"/cachyllama-fallback "$@"
+1 -1
View File
@@ -1,5 +1,5 @@
IK_LLAMA_VERSION?=8337e4cd3861406fc04e0854b1409cd1b027fbc9
IK_LLAMA_VERSION?=a7c81affa48c6800d63111bdb33469a01d062daa
LLAMA_REPO?=https://github.com/ikawrakow/ik_llama.cpp
CMAKE_ARGS?=
-1
View File
@@ -2565,7 +2565,6 @@ public:
grpc::Status Embedding(ServerContext* context, const backend::PredictOptions* request, backend::EmbeddingResult* embeddingResult) {
auto identity = checkModelIdentity(request);
if (!identity.ok()) return identity;
embeddingResult->set_layout(backend::EMBEDDING_LAYOUT_FINAL);
json data = parse_options(false, request, llama);
const int task_id = llama.queue_tasks.get_new_id();
llama.queue_results.add_waiting_task_id(task_id);
-10
View File
@@ -115,14 +115,4 @@ if(LLAMA_GRPC_BUILD_TESTS)
target_include_directories(passthrough_options_test PRIVATE ${CMAKE_CURRENT_SOURCE_DIR})
target_compile_features(passthrough_options_test PRIVATE cxx_std_17)
add_test(NAME passthrough_options_test COMMAND passthrough_options_test)
add_executable(tts_request_options_test tts_request_options_test.cpp tts_request_options.h)
target_include_directories(tts_request_options_test PRIVATE ${CMAKE_CURRENT_SOURCE_DIR})
target_compile_features(tts_request_options_test PRIVATE cxx_std_17)
add_test(NAME tts_request_options_test COMMAND tts_request_options_test)
add_executable(thread_params_test thread_params_test.cpp thread_params.h)
target_include_directories(thread_params_test PRIVATE ${CMAKE_CURRENT_SOURCE_DIR})
target_compile_features(thread_params_test PRIVATE cxx_std_17)
add_test(NAME thread_params_test COMMAND thread_params_test)
endif()
+1 -1
View File
@@ -1,5 +1,5 @@
LLAMA_VERSION?=d59d455fd8ea09e5a2e87ce2a9d668267ffb5ccd
LLAMA_VERSION?=221f0f6356efe2260023208365705ec5d5a7c8f5
LLAMA_REPO?=https://github.com/ggerganov/llama.cpp
CMAKE_ARGS?=
-43
View File
@@ -1,43 +0,0 @@
#!/bin/bash
# Mark a copied gRPC server as targeting a llama.cpp fork that does not carry
# LocalAI's SERVER_TASK_TYPE_TTS patch. The RPCs remain present in the shared
# protobuf service, but respond with UNIMPLEMENTED instead of referencing
# server task types and mtmd gen-audio APIs absent from those forks.
set -euo pipefail
if [[ $# -ne 1 ]]; then
echo "usage: $0 <grpc-server.cpp>" >&2
exit 2
fi
SRC=$1
if [[ ! -f "$SRC" ]]; then
echo "grpc-server.cpp not found at $SRC" >&2
exit 2
fi
if grep -q '^#define LOCALAI_LLAMA_CPP_NO_TTS_TASK' "$SRC"; then
echo "==> $SRC already disables the LocalAI TTS task, skipping"
exit 0
fi
awk '
!done && /^#include/ {
print "#define LOCALAI_LLAMA_CPP_NO_TTS_TASK 1"
print "// ^ injected by disable-tts-task.sh for an unpatched llama.cpp fork"
print ""
done = 1
}
{ print }
END {
if (!done) {
print "disable-tts-task.sh: no #include anchor found" > "/dev/stderr"
exit 1
}
}
' "$SRC" > "$SRC.tmp"
mv "$SRC.tmp" "$SRC"
echo "==> LocalAI TTS task disabled in $SRC"
+42 -313
View File
@@ -43,6 +43,15 @@
#if __has_include("server-stream.cpp")
#include "server-stream.cpp"
#endif
// CachyLLaMA splits its persistent prompt-cache implementation out of
// server-context.cpp. Pull both implementation files into this monolithic
// adapter TU when present; older llama.cpp pins do not ship them.
#if __has_include("server-context-ssd-cache.cpp")
#include "server-context-ssd-cache.cpp"
#endif
#if __has_include("server-context-page-manager.cpp")
#include "server-context-page-manager.cpp"
#endif
#include "server-context.cpp"
// LocalAI
@@ -53,10 +62,8 @@
#include "arg.h"
#include "chat-auto-parser.h"
#include "llama_compat.h" // fork-skew switches, generated by prepare.sh
#include "thread_params.h"
#include "message_content.h"
#include "passthrough_options.h"
#include "tts_request_options.h"
#include <getopt.h>
#include <grpcpp/ext/proto_server_reflection_plugin.h>
#include <grpcpp/grpcpp.h>
@@ -67,7 +74,6 @@
#include <atomic>
#include <cmath>
#include <cstdlib>
#include <cstring>
#include <fstream>
#include <iterator>
#include <list>
@@ -236,15 +242,7 @@ json parse_options(bool streaming, const backend::PredictOptions* predict, const
data["typical_p"] = predict->typicalp();
data["temperature"] = predict->temperature();
data["repeat_last_n"] = predict->repeat();
// PredictOptions.Penalty is a bare proto float, so a caller that names no
// repetition penalty sends 0 rather than omitting the field. Since
// llama.cpp 9de0fcf2b, common_sampler_init() rejects a non-positive
// penalty_repeat outright (it would divide logits by zero), which turned
// every such request into "Failed to initialize samplers". Treat 0 as
// "unset" and leave llama.cpp's own neutral default in place.
if (predict->penalty() > 0.0f) {
data["repeat_penalty"] = predict->penalty();
}
data["repeat_penalty"] = predict->penalty();
data["frequency_penalty"] = predict->frequencypenalty();
data["presence_penalty"] = predict->presencepenalty();
data["mirostat"] = predict->mirostat();
@@ -1413,12 +1411,6 @@ static void params_parse(server_context& /*ctx_server*/, const backend::ModelOpt
passthrough_draft_gpu_layers);
}
// The library initializer now creates both threadpools before the server
// can apply llama_context's fallback for the -1 batch-thread sentinel.
params.cpuparams_batch.n_threads = llama_grpc::resolve_batch_threads(
params.cpuparams_batch.n_threads,
params.cpuparams.n_threads);
#ifndef LOCALAI_LLAMA_CPP_NO_SCORE_TASK
// Score-task suffix forking: reserve seq ids (and recurrent-state cells)
// beyond the slots so one scoring call decodes all candidate tails in a
@@ -1462,26 +1454,6 @@ static void params_parse(server_context& /*ctx_server*/, const backend::ModelOpt
}
#ifndef LOCALAI_LLAMA_CPP_NO_TTS_TASK
// MTMD_HELPER_GEN_AUDIO_OUTTYPE_PCM hands back raw float32 samples, but the
// WAV header core/backend/tts.go builds around the streamed chunks announces
// 16-bit samples, so the wire has to carry s16 or the client decodes floats as
// integers and hears noise. The scaling matches write_wav16() in
// tools/mtmd/mtmd-helper-gen.cpp, which is what the non-streaming path writes.
static std::string tts_pcm_f32_to_s16(const std::string & samples) {
const size_t n = samples.size() / sizeof(float);
std::string out;
out.resize(n * sizeof(int16_t));
for (size_t i = 0; i < n; i++) {
float v = 0.0f;
std::memcpy(&v, samples.data() + i * sizeof(float), sizeof(float));
const int16_t s = (int16_t) (std::max(-1.0f, std::min(1.0f, v)) * 32767.0f);
std::memcpy(&out[i * sizeof(int16_t)], &s, sizeof(int16_t));
}
return out;
}
#endif
// GRPC Server start
class BackendServiceImpl final : public backend::Backend::Service {
private:
@@ -2126,23 +2098,15 @@ public:
task.tokens = std::move(inputs[i]);
#ifdef LOCALAI_HAS_SERVER_SCHEMA
// The schema evaluator no longer takes the per-slot n_ctx: upstream
// dropped the parameter and server-schema stopped consulting n_ctx at
// all, leaving the context bound to the slot. Forks that predate the
// server-schema split still expect it, so only this branch loses it.
task.params = server_schema::eval_llama_cmpl_schema(
ctx_server.impl->vocab,
params_base,
ctx_server.get_meta().logit_bias_eog,
data);
#else
task.params = server_task::params_from_json_cmpl(
#endif
ctx_server.impl->vocab,
params_base,
ctx_server.get_meta().slot_n_ctx,
ctx_server.get_meta().logit_bias_eog,
data);
#endif
task.id_slot = json_value(data, "id_slot", -1);
// OAI-compat: enable autoparser (PEG-based chat parsing) so that
@@ -2704,23 +2668,15 @@ public:
task.tokens = std::move(inputs[i]);
#ifdef LOCALAI_HAS_SERVER_SCHEMA
// The schema evaluator no longer takes the per-slot n_ctx: upstream
// dropped the parameter and server-schema stopped consulting n_ctx at
// all, leaving the context bound to the slot. Forks that predate the
// server-schema split still expect it, so only this branch loses it.
task.params = server_schema::eval_llama_cmpl_schema(
ctx_server.impl->vocab,
params_base,
ctx_server.get_meta().logit_bias_eog,
data);
#else
task.params = server_task::params_from_json_cmpl(
#endif
ctx_server.impl->vocab,
params_base,
ctx_server.get_meta().slot_n_ctx,
ctx_server.get_meta().logit_bias_eog,
data);
#endif
task.id_slot = json_value(data, "id_slot", -1);
// OAI-compat: enable autoparser (PEG-based chat parsing) so that
@@ -2918,40 +2874,42 @@ public:
return grpc::Status(grpc::StatusCode::INTERNAL, all_results.error->to_json().value("message", "Error in receiving results"));
}
// Extract the embeddings typed, straight from the task results (no
// JSON round-trip), and report the payload shape alongside the same
// flat float array as before: dim is the embedding width, tokens the
// number of vectors packed into `embeddings` (1 per prompt when the
// server pooled, one per token with pooling:none; summed across
// prompts if the request carried several), prompt_tokens the prompt
// tokens evaluated, for usage accounting. Consumers seeing 0/0 know
// the backend predates shape reporting.
int32_t n_vectors = 0;
int32_t dim = 0;
int32_t prompt_tokens = 0;
// Collect responses
json responses = json::array();
for (auto & res : all_results.results) {
auto * embd_res = dynamic_cast<server_task_result_embd*>(res.get());
GGML_ASSERT(embd_res != nullptr);
prompt_tokens += embd_res->n_tokens;
for (const auto & vec : embd_res->embedding) {
for (const float value : vec) {
embeddingResult->add_embeddings(value);
GGML_ASSERT(dynamic_cast<server_task_result_embd*>(res.get()) != nullptr);
responses.push_back(res->to_json());
}
std::cout << "[DEBUG] Responses size: " << responses.size() << std::endl;
// Process the responses and extract embeddings
for (const auto & response_elem : responses) {
// Check if the response has an "embedding" field
if (response_elem.contains("embedding")) {
json embedding_data = json_value(response_elem, "embedding", json::array());
if (embedding_data.is_array() && !embedding_data.empty()) {
for (const auto & embedding_vector : embedding_data) {
if (embedding_vector.is_array()) {
for (const auto & embedding_value : embedding_vector) {
embeddingResult->add_embeddings(embedding_value.get<float>());
}
}
}
}
if (!vec.empty()) {
n_vectors++;
dim = (int32_t) vec.size();
} else {
// Check if the response itself contains the embedding data directly
if (response_elem.is_array()) {
for (const auto & embedding_value : response_elem) {
embeddingResult->add_embeddings(embedding_value.get<float>());
}
}
}
}
embeddingResult->set_tokens(n_vectors);
embeddingResult->set_dim(dim);
embeddingResult->set_prompt_tokens(prompt_tokens);
embeddingResult->set_layout(
llama_pooling_type(ctx_server.get_llama_context()) == LLAMA_POOLING_TYPE_NONE
? backend::EMBEDDING_LAYOUT_PER_TOKEN
: backend::EMBEDDING_LAYOUT_FINAL);
std::cout << "[DEBUG] Embedding vectors: " << n_vectors << " x " << dim << std::endl;
return grpc::Status::OK;
}
@@ -3045,229 +3003,6 @@ public:
return grpc::Status::OK;
}
#ifndef LOCALAI_LLAMA_CPP_NO_TTS_TASK
// Builds the shared TTS task from a request. Returns a non-OK status and
// leaves `task` untouched when the request is malformed or the loaded model
// cannot synthesise audio.
grpc::Status prepareTTSTask(const backend::TTSRequest* request, bool stream, server_task & task) {
if (!ctx_server.get_meta().has_cap_tts) {
return grpc::Status(grpc::StatusCode::UNIMPLEMENTED,
"the loaded model does not support audio generation (no gen-audio mmproj)");
}
std::map<std::string, std::string> params(request->params().begin(), request->params().end());
const auto opts = llama_grpc::parse_tts_request_options(
request->text(),
request->voice(),
request->has_language() ? request->language() : std::string(),
params);
if (!opts.ok) {
return grpc::Status(grpc::StatusCode::INVALID_ARGUMENT, opts.error);
}
auto wrapper = mtmd_helper_bitmap_init_from_file(ctx_server.impl->mctx, opts.voice_path.c_str(), false);
if (!wrapper.bitmap) {
return grpc::Status(grpc::StatusCode::INVALID_ARGUMENT,
"failed to read speaker reference audio: " + opts.voice_path);
}
task.tts_inp.set_prompt(opts.text);
// core/backend/tts.go always sets TTSRequest.language, so has_language()
// is true even when the caller named no language and the string is empty.
// gen_audio::inp::get() already maps a stored blank to nullptr, so this
// guard is behavior-preserving rather than behavior-fixing. It is kept
// so the "unset" intent is visible at the call site instead of resting
// on a detail of the helper.
if (!opts.language.empty()) {
task.tts_inp.set_lang(opts.language);
}
task.tts_inp.set_speaker_ref(mtmd::bitmap_ptr(wrapper.bitmap));
task.tts_inp.data.top_k = opts.top_k;
task.tts_inp.data.top_p = opts.top_p;
task.tts_inp.data.stream = stream;
task.tts_inp.data.out_type = stream
? MTMD_HELPER_GEN_AUDIO_OUTTYPE_PCM // Go prepends its own WAV header, see core/backend/tts.go
: MTMD_HELPER_GEN_AUDIO_OUTTYPE_WAV;
task.params.stream = stream;
// -1 keeps upstream's 512-frame default. The model does not always emit
// its codec EOS, so a short input can otherwise generate the full cap.
task.params.n_predict = opts.max_frames > 0 ? opts.max_frames : -1;
task.params.sampling = params_base.sampling;
// Both values mirror upstream's draft POST /tts handler. Note that the
// pair is INERT at this pin: llama_sampler_init_penalties() clamps
// penalty_last_n with std::max(penalty_last_n, 0), so -1 means "off",
// not "the whole generation", and the penalty sampler is then built
// disabled. No repetition penalty is actually applied.
//
// That is deliberate. Dropping the second line lets the sampling
// default of 64 apply and genuinely engages the 1.05 penalty, which was
// measured here against the model's habit of never emitting its codec
// EOS and running to the frame cap: 0 of 15 short requests ran away
// with the penalty inert, 1 of 15 with it active over the last 64
// tokens. It does not fix the runaway, so the line stays for parity
// with the draft. Use max_frames to bound the output instead.
task.params.sampling.penalty_repeat = 1.05f;
task.params.sampling.penalty_last_n = -1;
if (opts.top_k > 0) {
task.params.sampling.top_k = opts.top_k;
}
if (opts.top_p > 0) {
task.params.sampling.top_p = opts.top_p;
}
return grpc::Status::OK;
}
grpc::Status TTS(ServerContext* context, const backend::TTSRequest* request, backend::Result* result) override {
auto auth = checkAuth(context);
if (!auth.ok()) return auth;
auto identity = checkModelIdentity(request);
if (!identity.ok()) return identity;
if (params_base.model.path.empty()) {
return grpc::Status(grpc::StatusCode::FAILED_PRECONDITION, "Model not loaded");
}
if (request->dst().empty()) {
return grpc::Status(grpc::StatusCode::INVALID_ARGUMENT, "dst must name an output file path");
}
server_task task(SERVER_TASK_TYPE_TTS);
auto prepared = prepareTTSTask(request, /* stream= */ false, task);
if (!prepared.ok()) return prepared;
auto rd = ctx_server.get_response_reader();
task.id = rd.get_new_id();
rd.post_task(std::move(task));
auto should_stop = [context]() { return context->IsCancelled(); };
std::string audio;
while (true) {
auto res = rd.next(should_stop);
if (!res) {
return grpc::Status(grpc::StatusCode::CANCELLED, "TTS request cancelled");
}
if (res->is_error()) {
return grpc::Status(grpc::StatusCode::INTERNAL, res->to_json().dump());
}
auto * tts_res = dynamic_cast<server_task_result_tts *>(res.get());
if (tts_res == nullptr) {
return grpc::Status(grpc::StatusCode::INTERNAL, "unexpected result type for a TTS task");
}
audio.append(tts_res->audio);
if (tts_res->final) {
break;
}
}
std::ofstream out(request->dst(), std::ios::binary | std::ios::trunc);
if (!out) {
return grpc::Status(grpc::StatusCode::INTERNAL, "failed to open output file: " + request->dst());
}
out.write(audio.data(), (std::streamsize) audio.size());
if (!out) {
return grpc::Status(grpc::StatusCode::INTERNAL, "failed to write output file: " + request->dst());
}
// Buffered data is flushed here, so a full disk or a failing device can
// surface for the first time on close. Reporting success then would
// leave a truncated file behind under the name the caller will read.
out.close();
if (!out) {
return grpc::Status(grpc::StatusCode::INTERNAL, "failed to close output file: " + request->dst());
}
result->set_success(true);
result->set_message("TTS audio generated");
return grpc::Status::OK;
}
grpc::Status TTSStream(ServerContext* context, const backend::TTSRequest* request, grpc::ServerWriter<backend::Reply>* writer) override {
auto auth = checkAuth(context);
if (!auth.ok()) return auth;
auto identity = checkModelIdentity(request);
if (!identity.ok()) return identity;
if (params_base.model.path.empty()) {
return grpc::Status(grpc::StatusCode::FAILED_PRECONDITION, "Model not loaded");
}
server_task task(SERVER_TASK_TYPE_TTS);
auto prepared = prepareTTSTask(request, /* stream= */ true, task);
if (!prepared.ok()) return prepared;
auto rd = ctx_server.get_response_reader();
task.id = rd.get_new_id();
rd.post_task(std::move(task));
auto should_stop = [context]() { return context->IsCancelled(); };
// core/backend/tts.go:ModelTTSStream builds the WAV header itself from
// the sample rate in the first reply's Message, then concatenates every
// Reply.Audio verbatim. So the rate goes out once, up front, and the
// chunks stay raw PCM.
//
// Send it before draining rather than off the first audio result: a
// chunk needs a whole 72-frame window, about 5.8 s of audio and far
// longer in wall time on CPU, and the Go side cannot emit the WAV
// header until this reply lands. Waiting would hold the client at zero
// bytes for that entire stretch. The rate is a property of the loaded
// model, available synchronously, so there is nothing to wait for.
{
backend::Reply header;
const json info = { {"sample_rate", mtmd_gen_audio_get_info(ctx_server.impl->mctx).sample_rate} };
header.set_message(info.dump());
if (!writer->Write(header)) {
return grpc::Status(grpc::StatusCode::CANCELLED, "client closed the TTS stream");
}
}
while (true) {
auto res = rd.next(should_stop);
if (!res) {
return grpc::Status(grpc::StatusCode::CANCELLED, "TTS request cancelled");
}
if (res->is_error()) {
return grpc::Status(grpc::StatusCode::INTERNAL, res->to_json().dump());
}
auto * tts_res = dynamic_cast<server_task_result_tts *>(res.get());
if (tts_res == nullptr) {
return grpc::Status(grpc::StatusCode::INTERNAL, "unexpected result type for a TTS task");
}
if (!tts_res->audio.empty()) {
backend::Reply chunk;
chunk.set_audio(tts_pcm_f32_to_s16(tts_res->audio));
if (!writer->Write(chunk)) {
return grpc::Status(grpc::StatusCode::CANCELLED, "client closed the TTS stream");
}
}
if (tts_res->final) {
break;
}
}
return grpc::Status::OK;
}
#else
grpc::Status TTS(ServerContext* context, const backend::TTSRequest* request, backend::Result* result) override {
auto auth = checkAuth(context);
if (!auth.ok()) return auth;
(void) request;
(void) result;
return grpc::Status(grpc::StatusCode::UNIMPLEMENTED,
"TTS is unavailable in this llama.cpp fork backend");
}
grpc::Status TTSStream(ServerContext* context, const backend::TTSRequest* request, grpc::ServerWriter<backend::Reply>* writer) override {
auto auth = checkAuth(context);
if (!auth.ok()) return auth;
(void) request;
(void) writer;
return grpc::Status(grpc::StatusCode::UNIMPLEMENTED,
"TTSStream is unavailable in this llama.cpp fork backend");
}
#endif
// Score returns the model's joint log-probability of each candidate
// continuation given a shared prompt.
//
@@ -3602,15 +3337,9 @@ public:
// Populate the response with metrics
response->set_slot_id(0);
response->set_prompt_json_for_slot("");
#if LOCALAI_HAS_SERVER_METRICS
response->set_tokens_per_second(res_metrics->metrics.prompt_bucket.n_per_second());
response->set_tokens_generated(res_metrics->metrics.predict.count);
response->set_prompt_tokens_processed(res_metrics->metrics.prompt.count);
#else
response->set_tokens_per_second(res_metrics->n_prompt_tokens_processed ? 1.e3 / res_metrics->t_prompt_processing * res_metrics->n_prompt_tokens_processed : 0.);
response->set_tokens_generated(res_metrics->n_tokens_predicted_total);
response->set_prompt_tokens_processed(res_metrics->n_prompt_tokens_processed_total);
#endif
return grpc::Status::OK;
@@ -1,21 +1,8 @@
From 75220a0d74892e3315f4042274b1efa6195868d8 Mon Sep 17 00:00:00 2001
From: Codex <codex@local>
Date: Mon, 10 Aug 2026 23:05:52 +0000
Subject: [PATCH 1/2] score-patch
---
common/common.cpp | 6 +-
common/common.h | 3 +
tools/CMakeLists.txt | 1 +
tools/server/server-context.cpp | 358 +++++++++++++++++++++++++++++++-
tools/server/server-task.h | 47 +++++
5 files changed, 406 insertions(+), 9 deletions(-)
diff --git a/common/common.cpp b/common/common.cpp
index 2e3f14c..0cec0dc 100644
index 8f13217..fc584e1 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -1636,8 +1636,10 @@ struct llama_context_params common_context_params_to_llama(const common_params &
@@ -1591,8 +1591,10 @@ struct llama_context_params common_context_params_to_llama(const common_params &
auto cparams = llama_context_default_params();
cparams.n_ctx = params.n_ctx;
@@ -26,13 +13,13 @@ index 2e3f14c..0cec0dc 100644
+ cparams.n_seq_max = params.n_parallel + params.n_seq_score_forks;
+ cparams.n_rs_seq = std::max(params.speculative.need_n_rs_seq(), (uint32_t) std::max(0, params.n_rs_seq));
cparams.n_outputs_max = std::max(params.n_outputs_max, 0);
cparams.n_outputs_max_per_seq = std::max(params.n_outputs_max_per_seq, 0);
cparams.n_batch = params.n_batch;
cparams.n_ubatch = params.n_ubatch;
diff --git a/common/common.h b/common/common.h
index 878534d..4001df2 100644
index bffc176..e313bd6 100644
--- a/common/common.h
+++ b/common/common.h
@@ -445,6 +445,9 @@ struct common_params {
@@ -455,6 +455,9 @@ struct common_params {
int32_t n_keep = 0; // number of tokens to keep from initial prompt
int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited)
int32_t n_parallel = 1; // number of parallel sequences to decode
@@ -41,7 +28,7 @@ index 878534d..4001df2 100644
+ bool score_enabled = false; // reserve server resources for the Score task type
int32_t n_sequences = 1; // number of sequences to decode
int32_t n_outputs_max = 0; // max outputs in a batch (0 = n_batch)
int32_t n_outputs_max_per_seq = 1; // max outputs per sequence
int32_t grp_attn_n = 1; // group-attention factor
diff --git a/tools/CMakeLists.txt b/tools/CMakeLists.txt
index 780df32..1d2fe8f 100644
--- a/tools/CMakeLists.txt
@@ -52,24 +39,28 @@ index 780df32..1d2fe8f 100644
endif()
+add_subdirectory(grpc-server)
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index 3b5f6a1..d0e18e6 100644
index 715477e..de5bed8 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -48,6 +48,13 @@ static common_speculative_output_limits server_output_limits(const common_params
auto result = common_speculative_get_output_limits(
params.n_batch, params.n_parallel, common_speculative_n_max(&params.speculative));
@@ -49,7 +49,16 @@ static uint32_t server_n_outputs_max(const common_params & params) {
+ // Score tasks output logits for every candidate token, so reserve room
+ // for a bounded candidate tail per parallel slot.
+ if (params.score_enabled) {
+ result.per_seq = std::max<int32_t>(result.per_seq, 1 + SERVER_SCORE_MAX_CAND_TOKENS);
+ result.total = std::min<int32_t>(params.n_batch, params.n_parallel * result.per_seq);
const uint32_t n_outputs_per_seq = 1 + common_speculative_n_max(&params.speculative);
- const uint64_t n_outputs = (uint64_t) params.n_parallel * n_outputs_per_seq;
+ // score tasks (SERVER_TASK_TYPE_SCORE) output logits for every candidate
+ // token, so reserve room for a bounded candidate tail per parallel slot
+ if (!params.score_enabled) {
+ return std::max<uint32_t>(1, std::min<uint64_t>(n_batch,
+ (uint64_t) params.n_parallel * n_outputs_per_seq));
+ }
+
result.total = std::max<int32_t>(1, result.total);
result.per_seq = std::max<int32_t>(1, result.per_seq);
return result;
@@ -239,6 +246,26 @@ struct server_slot {
+ const uint32_t n_outputs_score_seq = 1 + SERVER_SCORE_MAX_CAND_TOKENS;
+
+ const uint64_t n_outputs = (uint64_t) params.n_parallel * std::max(n_outputs_per_seq, n_outputs_score_seq);
return std::max<uint32_t>(1, std::min<uint64_t>(n_batch, n_outputs));
}
@@ -202,6 +211,26 @@ struct server_slot {
std::vector<completion_token_output> generated_token_probs;
@@ -96,7 +87,7 @@ index 3b5f6a1..d0e18e6 100644
bool has_next_token = true;
bool has_new_line = false;
bool truncated = false;
@@ -341,6 +368,10 @@ struct server_slot {
@@ -311,6 +340,10 @@ struct server_slot {
}
generated_tokens.clear();
generated_token_probs.clear();
@@ -106,8 +97,8 @@ index 3b5f6a1..d0e18e6 100644
+ score_divergence = -1;
json_schema = json();
task_prev = std::move(task);
@@ -2271,6 +2302,229 @@ private:
// clear speculative decoding stats
@@ -2205,6 +2238,229 @@ private:
queue_results.send(std::move(res));
}
@@ -337,7 +328,7 @@ index 3b5f6a1..d0e18e6 100644
//
// Functions to process the task
//
@@ -2407,6 +2661,7 @@ private:
@@ -2341,6 +2597,7 @@ private:
case SERVER_TASK_TYPE_INFILL:
case SERVER_TASK_TYPE_EMBEDDING:
case SERVER_TASK_TYPE_RERANK:
@@ -345,7 +336,7 @@ index 3b5f6a1..d0e18e6 100644
{
// special case: if input is provided via CLI, tokenize it first
// otherwise, no need to tokenize as it's already done inside the HTTP thread
@@ -2903,6 +3158,13 @@ private:
@@ -2832,6 +3089,13 @@ private:
break; // stop any further processing
}
}
@@ -359,7 +350,7 @@ index 3b5f6a1..d0e18e6 100644
}
void pre_decode() {
@@ -3222,6 +3484,16 @@ private:
@@ -3154,6 +3418,16 @@ private:
n_past = std::min(n_past, slot.alora_invocation_start - 1);
}
@@ -376,7 +367,7 @@ index 3b5f6a1..d0e18e6 100644
const auto n_cache_reuse = slot.task->params.n_cache_reuse;
const bool can_cache_reuse =
@@ -3455,8 +3727,12 @@ private:
@@ -3395,8 +3669,12 @@ private:
bool do_checkpoint = params_base.n_ctx_checkpoints > 0;
@@ -391,7 +382,7 @@ index 3b5f6a1..d0e18e6 100644
// make a checkpoint of the parts of the memory that cannot be rolled back.
// checkpoints are created only if:
@@ -3444,9 +3720,16 @@ private:
@@ -3463,10 +3741,17 @@ private:
// embedding requires all tokens in the batch to be output;
// MTP also wants logits at every prompt position so the
// streaming hook can mirror t_h_nextn into ctx_dft.
@@ -404,12 +395,16 @@ index 3b5f6a1..d0e18e6 100644
+ slot.prompt.n_tokens() + 1 < slot.task->n_tokens();
add_ok &= batch.add(slot.id,
cur_tok,
/* pos = */ slot.prompt.tokens.pos_next(),
- /* output = */ slot.need_embd(),
+ /* output = */ slot.need_embd() || need_score_logit,
/* is_prompt = */ true);
slot.prompt.tokens.pos_next(),
- slot.need_embd());
+ slot.need_embd() || need_score_logit);
slot.prompt.tokens.push_back(cur_tok);
@@ -3454,2 +3737,28 @@ private:
slot.n_prompt_tokens_processed++;
@@ -3481,6 +3766,32 @@ private:
}
}
+ // score tasks: break at the shared-prompt boundary so the checkpoint
+ // below lands exactly there — the other candidates of the same
+ // scoring call re-process only their own tokens. Also break at the
@@ -436,9 +431,10 @@ index 3b5f6a1..d0e18e6 100644
+ }
+ }
+
// break at the last user message, or at user messages at least min step past the last checkpoint
if (do_checkpoint && spans.is_user_start(slot.prompt.n_tokens())) {
@@ -3573,6 +3882,15 @@ private:
// process the last few tokens of the prompt separately in order to allow for a checkpoint to be created.
// create checkpoints that many tokens before the end of the prompt:
// - 4 + n_ubatch
@@ -3513,6 +3824,15 @@ private:
const bool is_user_start = spans.is_user_start(n_tokens_start);
const bool is_last_user_message = n_tokens_start == last_user_pos;
@@ -454,7 +450,7 @@ index 3b5f6a1..d0e18e6 100644
// entire prompt has been processed
if (slot.prompt.n_tokens() == slot.task->n_tokens()) {
slot.state = SLOT_STATE_DONE_PROMPT;
@@ -3588,8 +3906,8 @@ private:
@@ -3528,8 +3848,8 @@ private:
slot.init_sampler();
} else {
// skip ordinary mid-prompt checkpoints, unless the batch starts a user
@@ -465,7 +461,7 @@ index 3b5f6a1..d0e18e6 100644
do_checkpoint = false;
}
}
@@ -3606,10 +3924,10 @@ private:
@@ -3546,10 +3866,10 @@ private:
// do not checkpoint after mtmd chunks
do_checkpoint = do_checkpoint && !has_mtmd;
@@ -478,7 +474,7 @@ index 3b5f6a1..d0e18e6 100644
n_tokens_start > slot.prompt.checkpoints.back().n_tokens + params_base.checkpoint_min_step);
SLT_DBG(slot, "main/do_checkpoint = %s, pos_min = %d, pos_max = %d\n", do_checkpoint ? "yes" : "no", pos_min, pos_max);
@@ -3772,6 +4090,13 @@ private:
@@ -3703,6 +4023,13 @@ private:
}
}
@@ -492,7 +488,7 @@ index 3b5f6a1..d0e18e6 100644
if (!is_inside_view(slot.i_batch)) {
// the required token not in this sub-batch, skip
return;
@@ -3793,6 +4118,25 @@ private:
@@ -3724,6 +4051,25 @@ private:
return;
}
@@ -519,7 +515,7 @@ index 3b5f6a1..d0e18e6 100644
// prompt evaluated for next-token prediction
diff --git a/tools/server/server-task.h b/tools/server/server-task.h
index 6275ec7..5bedf19 100644
index c3eea2e..fb3c178 100644
--- a/tools/server/server-task.h
+++ b/tools/server/server-task.h
@@ -13,10 +13,25 @@
@@ -601,5 +597,3 @@ index 6275ec7..5bedf19 100644
struct server_task_result_error : server_task_result {
error_type err_type = ERROR_TYPE_SERVER;
std::string err_msg;
--
2.39.5
@@ -1,845 +0,0 @@
diff --git a/tools/mtmd/mtmd-helper-gen.cpp b/tools/mtmd/mtmd-helper-gen.cpp
index 1c58d3ae1..196cbd433 100644
--- a/tools/mtmd/mtmd-helper-gen.cpp
+++ b/tools/mtmd/mtmd-helper-gen.cpp
@@ -50,29 +50,38 @@ static llama_token find_special_token(const llama_vocab * vocab, const std::stri
return LLAMA_TOKEN_NULL;
}
+static void put_bytes(std::vector<char> & buf, const void * p, size_t n) {
+ const char * c = (const char *) p;
+ buf.insert(buf.end(), c, c + n);
+}
+
+// data_sz == UINT32_MAX writes the "unknown length" sentinel (streaming), same as ffmpeg does on a pipe
+static void write_wav16_header(std::vector<char> & buf, uint32_t data_sz, int32_t rate) {
+ const uint32_t riff_sz = data_sz == UINT32_MAX ? UINT32_MAX : 36 + data_sz;
+ const uint32_t fmt_sz = 16, byte_rate = (uint32_t) rate * 2;
+ const uint16_t fmt = 1, ch = 1, align = 2, bits = 16;
+ const uint32_t rate32 = (uint32_t) rate;
+ put_bytes(buf, "RIFF", 4); put_bytes(buf, &riff_sz, 4); put_bytes(buf, "WAVE", 4);
+ put_bytes(buf, "fmt ", 4); put_bytes(buf, &fmt_sz, 4);
+ put_bytes(buf, &fmt, 2); put_bytes(buf, &ch, 2); put_bytes(buf, &rate32, 4);
+ put_bytes(buf, &byte_rate, 4); put_bytes(buf, &align, 2); put_bytes(buf, &bits, 2);
+ put_bytes(buf, "data", 4); put_bytes(buf, &data_sz, 4);
+}
+
+static void append_wav16_pcm(std::vector<char> & buf, const float * pcm, size_t n) {
+ for (size_t i = 0; i < n; i++) {
+ int16_t s = (int16_t) (std::max(-1.0f, std::min(1.0f, pcm[i])) * 32767.0f);
+ put_bytes(buf, &s, 2);
+ }
+}
+
static bool write_wav16(std::vector<char> & buf, const std::vector<float> & pcm, int32_t rate) {
// RIFF chunk sizes are 32-bit; refuse to emit a file with a truncated header
if (pcm.size() > ((size_t) UINT32_MAX - 36) / 2) {
return false;
}
- const uint32_t data_sz = (uint32_t) (pcm.size() * 2);
- const uint32_t riff_sz = 36 + data_sz;
- const uint32_t fmt_sz = 16, byte_rate = (uint32_t) rate * 2;
- const uint16_t fmt = 1, ch = 1, align = 2, bits = 16;
- const uint32_t rate32 = (uint32_t) rate;
- auto put = [&](const void * p, size_t n) {
- const char * c = (const char *) p;
- buf.insert(buf.end(), c, c + n);
- };
- put("RIFF", 4); put(&riff_sz, 4); put("WAVE", 4);
- put("fmt ", 4); put(&fmt_sz, 4);
- put(&fmt, 2); put(&ch, 2); put(&rate32, 4);
- put(&byte_rate, 4); put(&align, 2); put(&bits, 2);
- put("data", 4); put(&data_sz, 4);
- for (float v : pcm) {
- int16_t s = (int16_t) (std::max(-1.0f, std::min(1.0f, v)) * 32767.0f);
- put(&s, 2);
- }
+ write_wav16_header(buf, (uint32_t) (pcm.size() * 2), rate);
+ append_wav16_pcm(buf, pcm.data(), pcm.size());
return true;
}
@@ -92,6 +101,8 @@ public:
// set out_stop on end-of-speech, h_state_out must be null if no frame is generated
virtual int32_t step_gen(llama_token sampled, const float * h_state_in, const float ** h_state_out, bool * out_stop) = 0;
virtual int32_t get_output(int32_t * out_sample_rate, const char ** out_data, size_t * out_data_len, int64_t * out_n_samples) = 0;
+ // forces any buffered codes through code2wav now, regardless of window_frames
+ virtual int32_t flush() { return 0; }
protected:
llama_context * lctx;
@@ -121,6 +132,9 @@ public:
prompt_batch.reset();
n_prompt = 0;
prompt_pos = 0;
+ stream = false;
+ pcm_sent = 0;
+ wav_header_sent = false;
}
int32_t set_input(const mtmd_helper_gen_audio_inp * inp) override {
@@ -208,6 +222,7 @@ public:
top_p = inp->top_p > 0 ? inp->top_p : def.top_p;
seed = inp->seed;
out_type = inp->out_type;
+ stream = inp->stream;
// the prompt above holds the whole text stream up to tts_eos, so every generated
// frame adds tts_pad on top of the codes embedding
@@ -302,31 +317,60 @@ public:
}
int32_t get_output(int32_t * out_sample_rate, const char ** out_data, size_t * out_data_len, int64_t * out_n_samples) override {
- if (!flush_gen_wav()) {
- return 1;
+ *out_sample_rate = info.sample_rate;
+
+ if (!stream) {
+ // one-shot call: force out whatever's left, regardless of window_frames
+ if (!flush_gen_wav()) {
+ return 1;
+ }
+ if (out_n_samples) {
+ *out_n_samples = (int64_t) audio_pcm.size();
+ }
+ if (out_type == MTMD_HELPER_GEN_AUDIO_OUTTYPE_PCM) {
+ *out_data = (const char *) audio_pcm.data();
+ *out_data_len = audio_pcm.size() * sizeof(float);
+ return 0;
+ }
+ out_buf.clear();
+ if (!write_wav16(out_buf, audio_pcm, info.sample_rate)) {
+ LOG_ERR("mtmd_helper_gen_audio: output too large for WAV\n");
+ return 1;
+ }
+ *out_data = out_buf.data();
+ *out_data_len = out_buf.size();
+ return 0;
}
- *out_sample_rate = info.sample_rate;
+ // streaming: only return audio produced since the previous call
+ const size_t n_new = audio_pcm.size() - pcm_sent;
if (out_n_samples) {
- *out_n_samples = (int64_t) audio_pcm.size();
+ *out_n_samples = (int64_t) n_new;
}
if (out_type == MTMD_HELPER_GEN_AUDIO_OUTTYPE_PCM) {
- *out_data = (const char *) audio_pcm.data();
- *out_data_len = audio_pcm.size() * sizeof(float);
+ *out_data = (const char *) (audio_pcm.data() + pcm_sent);
+ *out_data_len = n_new * sizeof(float);
+ pcm_sent = audio_pcm.size();
return 0;
}
out_buf.clear();
- if (!write_wav16(out_buf, audio_pcm, info.sample_rate)) {
- LOG_ERR("mtmd_helper_gen_audio: output too large for WAV\n");
- return 1;
+ if (!wav_header_sent) {
+ write_wav16_header(out_buf, UINT32_MAX, info.sample_rate);
+ wav_header_sent = true;
}
+ append_wav16_pcm(out_buf, audio_pcm.data() + pcm_sent, n_new);
+ pcm_sent = audio_pcm.size();
*out_data = out_buf.data();
*out_data_len = out_buf.size();
return 0;
}
+ int32_t flush() override {
+ return flush_gen_wav() ? 0 : 1;
+ }
+
private:
bool ensure_cache() {
if (specials_ok) {
@@ -370,7 +414,7 @@ private:
LOG_ERR("mtmd_helper_gen_audio: mmproj has no speaker/audio encoder\n");
return false;
}
- const std::string marker = mtmd_default_marker();
+ const std::string marker = mtmd_get_marker(mctx);
mtmd_input_text text{ marker.c_str(), marker.size(), false, true };
mtmd_input_chunks * chunks = mtmd_input_chunks_init();
const mtmd_bitmap * bptr = bitmap;
@@ -456,6 +500,9 @@ private:
std::vector<float> h_state_buf;
mtmd_helper_gen_audio_outtype out_type = MTMD_HELPER_GEN_AUDIO_OUTTYPE_WAV;
std::vector<char> out_buf;
+ bool stream = false;
+ size_t pcm_sent = 0; // samples already returned by get_output()
+ bool wav_header_sent = false;
};
// settings that only live in the reference's per-pack yaml, not in the checkpoint
@@ -1024,6 +1071,14 @@ void mtmd_helper_gen_audio_reset(mtmd_helper_gen_audio * ctx) {
}
}
+struct mtmd_helper_gen_audio_inp mtmd_helper_gen_audio_inp_default(void) {
+ mtmd_helper_gen_audio_inp inp{};
+ inp.top_k = 50;
+ inp.top_p = 1.0f;
+ inp.out_type = MTMD_HELPER_GEN_AUDIO_OUTTYPE_WAV;
+ return inp;
+}
+
int32_t mtmd_helper_gen_audio_set_input(mtmd_helper_gen_audio * ctx, const mtmd_helper_gen_audio_inp * inp) {
if (!ctx->pipeline) {
LOG_ERR("mtmd_helper_gen_audio: unsupported or missing gen-audio pipeline\n");
@@ -1060,3 +1115,10 @@ int32_t mtmd_helper_gen_audio_get_output(mtmd_helper_gen_audio * ctx, int32_t *
}
return ctx->pipeline->get_output(out_sample_rate, out_data, out_data_len, out_n_samples);
}
+
+int32_t mtmd_helper_gen_audio_flush(mtmd_helper_gen_audio * ctx) {
+ if (!ctx->pipeline) {
+ return 1;
+ }
+ return ctx->pipeline->flush();
+}
diff --git a/tools/mtmd/mtmd-helper.h b/tools/mtmd/mtmd-helper.h
index 832f7171a..3eaa01aab 100644
--- a/tools/mtmd/mtmd-helper.h
+++ b/tools/mtmd/mtmd-helper.h
@@ -175,6 +175,7 @@ enum mtmd_helper_gen_audio_outtype {
MTMD_HELPER_GEN_AUDIO_OUTTYPE_WAV, // WAV PCM 16-bit LE, mono
};
struct mtmd_helper_gen_audio_inp {
+ bool stream; // if true, output() must be called after each step_gen()
llama_seq_id seq_id;
const char * prompt;
@@ -190,6 +191,8 @@ struct mtmd_helper_gen_audio_inp {
enum mtmd_helper_gen_audio_outtype out_type;
};
+MTMD_API struct mtmd_helper_gen_audio_inp mtmd_helper_gen_audio_inp_default(void);
+
MTMD_API mtmd_helper_gen_audio * mtmd_helper_gen_audio_init(
struct llama_context * lctx,
struct mtmd_context * mctx);
@@ -221,6 +224,8 @@ MTMD_API int32_t mtmd_helper_gen_audio_step_gen(
// out_data valid until next get_output() or reset() call
// out_n_samples (optional, can be NULL) receives the number of generated PCM samples
+// if inp->stream is true: returns only audio produced since the previous call, and
+// *out_data_len == 0 whenever a full window_frames batch hasn't accumulated yet
MTMD_API int32_t mtmd_helper_gen_audio_get_output(
mtmd_helper_gen_audio * ctx,
int32_t * out_sample_rate,
@@ -228,6 +233,10 @@ MTMD_API int32_t mtmd_helper_gen_audio_get_output(
size_t * out_data_len,
int64_t * out_n_samples);
+// forces any buffered codes through code2wav now, regardless of window_frames;
+// call once when generation has ended, before the last get_output() in stream mode
+MTMD_API int32_t mtmd_helper_gen_audio_flush(mtmd_helper_gen_audio * ctx);
+
#ifdef __cplusplus
} // extern "C"
#endif
@@ -254,8 +263,41 @@ struct mtmd_helper_gen_audio_deleter {
};
using gen_audio_ptr = std::unique_ptr<mtmd_helper_gen_audio, mtmd_helper_gen_audio_deleter>;
struct gen_audio {
+
+ // sub-struct, RAII wrapper for mtmd_helper_gen_audio_inp
+ struct inp {
+ mtmd_helper_gen_audio_inp data = mtmd_helper_gen_audio_inp_default();
+ std::string prompt_str;
+ std::string lang_str;
+ mtmd::bitmap_ptr speaker_ref_ptr;
+
+ inp() = default;
+ inp(inp &&) = default;
+ inp & operator=(inp &&) = default;
+ inp(const inp &) = delete;
+ inp & operator=(const inp &) = delete;
+
+ void set_prompt (std::string p) { prompt_str = std::move(p); }
+ void set_lang (std::string l) { lang_str = std::move(l); }
+ void set_speaker_ref(mtmd::bitmap_ptr bmp) { speaker_ref_ptr = std::move(bmp); }
+
+ // pointers are only valid as long as *this is alive
+ const mtmd_helper_gen_audio_inp * get() {
+ data.prompt = prompt_str.c_str();
+ data.prompt_len = prompt_str.size();
+ data.lang = lang_str.empty() ? nullptr : lang_str.c_str();
+ data.speaker_ref = speaker_ref_ptr.get();
+ return &data;
+ }
+ };
+
gen_audio_ptr ctx;
- gen_audio(struct llama_context * lctx, struct mtmd_context * mctx) : ctx(mtmd_helper_gen_audio_init(lctx, mctx)) {}
+ void init(struct llama_context * lctx, struct mtmd_context * mctx) {
+ ctx.reset(mtmd_helper_gen_audio_init(lctx, mctx));
+ }
+ bool valid() const {
+ return ctx.get() != nullptr;
+ }
void reset() {
mtmd_helper_gen_audio_reset(ctx.get());
}
@@ -271,6 +313,9 @@ struct gen_audio {
int32_t get_output(int32_t * out_sample_rate, const char ** out_data, size_t * out_data_len, int64_t * out_n_samples = nullptr) {
return mtmd_helper_gen_audio_get_output(ctx.get(), out_sample_rate, out_data, out_data_len, out_n_samples);
}
+ int32_t flush() {
+ return mtmd_helper_gen_audio_flush(ctx.get());
+ }
};
} // namespace mtmd_helper
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index 9069463fe..b7fa1e534 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -16,6 +16,7 @@
#include "speculative.h"
#include "mtmd.h"
#include "mtmd-helper.h"
+#include "base64.hpp"
#include <algorithm>
#include <cstddef>
@@ -41,8 +42,9 @@ constexpr int HTTP_POLLING_SECONDS = 1;
static common_speculative_output_limits server_output_limits(const common_params & params) {
if (params.embedding ||
- (params.pooling_type != LLAMA_POOLING_TYPE_UNSPECIFIED && params.pooling_type != LLAMA_POOLING_TYPE_NONE)) {
- return { params.n_batch, 1 };
+ (params.pooling_type != LLAMA_POOLING_TYPE_UNSPECIFIED && params.pooling_type != LLAMA_POOLING_TYPE_NONE) ||
+ !params.mmproj.path.empty()) { // gen-audio (TTS) capability isn't known until the mmproj loads, size generously
+ return { params.n_batch, params.n_batch };
}
auto result = common_speculative_get_output_limits(
@@ -212,6 +214,30 @@ struct server_slot {
mtmd_context * mctx = nullptr;
mtmd::batch_ptr mbatch = nullptr;
+ struct tts_ctx {
+ mtmd_helper::gen_audio ctx;
+ const float * h_state;
+ llama_token sampled;
+ int32_t n_decoded;
+ bool is_supported() const {
+ return ctx.valid();
+ }
+ void reset() {
+ // mtmd_helper_gen_audio_reset() dereferences its argument before it
+ // null-checks the pipeline, and the pipeline is only allocated for
+ // models that actually carry a gen-audio mmproj. server_slot::reset()
+ // runs for every slot of every model, so without this guard any
+ // non-TTS model segfaults during slot initialization.
+ if (is_supported()) {
+ ctx.reset();
+ }
+ h_state = nullptr;
+ sampled = LLAMA_TOKEN_NULL;
+ n_decoded = 0;
+ }
+ };
+ tts_ctx tts;
+
// speculative decoding
common_speculative * spec;
@@ -391,6 +417,8 @@ struct server_slot {
// clear multimodal state
mbatch.reset();
+
+ tts.reset();
}
void init_sampler() const {
@@ -829,6 +857,14 @@ public:
mtmd_context * mctx = nullptr;
const llama_vocab * vocab = nullptr;
+ bool has_cap_tts() const {
+ return mctx != nullptr && mtmd_gen_audio_get_info(mctx).type != MTMD_GEN_AUDIO_TYPE_NONE;
+ }
+
+ bool has_cap_chat() const {
+ return mctx == nullptr || mtmd_helper_model_can_chat(ctx_tgt, mctx);
+ }
+
server_queue queue_tasks;
server_response queue_results;
@@ -1288,6 +1324,10 @@ private:
slot.mctx = mctx;
slot.prompt.tokens.has_mtmd = mctx != nullptr;
+ if (has_cap_tts()) {
+ slot.tts.ctx.init(ctx_tgt, mctx);
+ }
+
SLT_TRC(slot, "new slot, n_ctx = %d\n", slot.n_ctx);
slot.callback_on_release = [this](int id_slot) {
@@ -1748,6 +1788,28 @@ private:
SLT_DBG(slot, "launching slot : %s\n", safe_json_to_str(slot.to_json()).c_str());
+ if (task.type == SERVER_TASK_TYPE_TTS) {
+ GGML_ASSERT(has_cap_tts()); // should already checked in route handler
+ if (!slot.tts.is_supported()) {
+ slot.tts.ctx.init(ctx_tgt, slot.mctx);
+ }
+
+ // TTS slots never enter the shared batch: pre_decode() returns early for
+ // them and process_tts_slots() drives them instead, so they skip the
+ // prompt-cache bookkeeping that clears this sequence between requests.
+ // The gen-audio pipeline always decodes from position 0, and its own
+ // reset() only clears host-side buffers, so without this the second and
+ // later tasks on a slot decode over the previous request's tokens and
+ // step_prompt() fails immediately.
+ slot.prompt_clear();
+
+ task.tts_inp.data.seq_id = slot.id;
+ if (slot.tts.ctx.set_input(task.tts_inp.get()) != 0) {
+ send_error(task, "failed to process TTS prompt", ERROR_TYPE_SERVER);
+ return false;
+ }
+ }
+
// initialize samplers
if (task.need_sampling()) {
try {
@@ -1765,6 +1827,9 @@ private:
// TODO: getting pre sampling logits is not yet supported with backend sampling
use_backend_sampling &= !need_pre_sample_logits;
+ // TODO: check verify if this actually works with TTS
+ use_backend_sampling &= task.type != SERVER_TASK_TYPE_TTS;
+
// TODO: tmp until backend sampling is fully implemented
if (use_backend_sampling) {
llama_set_sampler(ctx_tgt, slot.id, common_sampler_get(slot.smpl.get()));
@@ -1783,9 +1848,13 @@ private:
slot.task = std::make_unique<const server_task>(std::move(task));
- slot.state = slot.task->is_child()
- ? SLOT_STATE_WAIT_OTHER // wait for the parent to process prompt
- : SLOT_STATE_STARTED;
+ if (slot.task->type == SERVER_TASK_TYPE_TTS) {
+ slot.state = SLOT_STATE_PROCESSING_PROMPT;
+ } else {
+ slot.state = slot.task->is_child()
+ ? SLOT_STATE_WAIT_OTHER // wait for the parent to process prompt
+ : SLOT_STATE_STARTED;
+ }
// reset server kill-switch counter
n_empty_consecutive = 0;
@@ -2050,6 +2119,18 @@ private:
queue_results.send(std::move(res));
}
+ void send_tts_result(server_slot & slot, int32_t sample_rate, const char * data, size_t data_len, bool final) {
+ auto res = std::make_unique<server_task_result_tts>();
+
+ res->id = slot.task->id;
+ res->index = slot.task->index;
+ res->sample_rate = sample_rate;
+ res->audio.assign(data, data_len);
+ res->final = final;
+
+ queue_results.send(std::move(res));
+ }
+
void send_final_response(server_slot & slot) {
auto res = std::make_unique<server_task_result_cmpl_final>();
@@ -2556,6 +2637,7 @@ private:
case SERVER_TASK_TYPE_EMBEDDING:
case SERVER_TASK_TYPE_RERANK:
case SERVER_TASK_TYPE_SCORE:
+ case SERVER_TASK_TYPE_TTS:
{
// special case: if input is provided via CLI, tokenize it first
// otherwise, no need to tokenize as it's already done inside the HTTP thread
@@ -3007,1 +3089,9 @@ private:
+ // note: TTS slots bypass the shared batch entirely
+ try {
+ process_tts_slots();
+ } catch (const std::exception & e) {
+ SRV_ERR("process_tts_slots() failed: %s\n", e.what());
+ abort_all_slots("process_tts_slots() failed: " + std::string(e.what()));
+ }
+
GGML_ASSERT(batch.slot_batched || batch.size() == 0);
@@ -3074,10 +3164,77 @@ private:
}
}
+ void process_tts_slots() {
+ iterate(slots, [&](server_slot & slot) {
+ if (!slot.is_processing() || slot.task->type != SERVER_TASK_TYPE_TTS) {
+ return;
+ }
+
+ llama_set_embeddings(ctx_tgt, true);
+
+ if (slot.state == SLOT_STATE_PROCESSING_PROMPT) {
+ const int32_t ret = slot.tts.ctx.step_prompt(llama_n_batch(ctx_tgt));
+ if (ret < 0) {
+ send_error(slot, "TTS prompt processing failed", ERROR_TYPE_SERVER);
+ slot.release();
+ } else if (ret == 0) {
+ slot.tts.sampled = common_sampler_sample(slot.smpl.get(), ctx_tgt, -1);
+ common_sampler_accept(slot.smpl.get(), slot.tts.sampled, true);
+ slot.tts.h_state = llama_get_embeddings_ith(ctx_tgt, -1);
+ slot.state = SLOT_STATE_GENERATING;
+ }
+ return;
+ }
+
+ const int32_t n_predict = slot.task->params.n_predict > 0 ? slot.task->params.n_predict : 512;
+ if (slot.tts.n_decoded >= n_predict || llama_vocab_is_eog(vocab, slot.tts.sampled)) {
+ int32_t sample_rate = 0;
+ const char * data = nullptr;
+ size_t data_len = 0;
+ // generation truly ends here: force out any sub-window remainder still buffered
+ if (slot.tts.ctx.flush() != 0 || slot.tts.ctx.get_output(&sample_rate, &data, &data_len) != 0) {
+ send_error(slot, "failed to finalize TTS output", ERROR_TYPE_SERVER);
+ } else {
+ send_tts_result(slot, sample_rate, data, data_len, true);
+ }
+ slot.release();
+ return;
+ }
+
+ const float * h_state_next = nullptr;
+ if (slot.tts.ctx.step_gen(slot.tts.sampled, slot.tts.h_state, &h_state_next) != 0) {
+ send_error(slot, "TTS generation failed", ERROR_TYPE_SERVER);
+ slot.release();
+ return;
+ }
+ slot.tts.h_state = h_state_next;
+ slot.tts.n_decoded++;
+
+ slot.tts.sampled = common_sampler_sample(slot.smpl.get(), ctx_tgt, -1);
+ common_sampler_accept(slot.smpl.get(), slot.tts.sampled, true);
+
+ if (slot.task->params.stream) {
+ int32_t sample_rate = 0;
+ const char * data = nullptr;
+ size_t data_len = 0;
+ if (slot.tts.ctx.get_output(&sample_rate, &data, &data_len) != 0) {
+ send_error(slot, "TTS streaming output failed", ERROR_TYPE_SERVER);
+ slot.release();
+ } else if (data_len > 0) {
+ send_tts_result(slot, sample_rate, data, data_len, false);
+ }
+ }
+ });
+ }
+
void pre_decode() {
// apply context-shift if needed
// TODO: simplify and improve
iterate(slots, [&](server_slot & slot) {
+ if (slot.task && slot.task->type == SERVER_TASK_TYPE_TTS) {
+ // TTS slots drive their own decode loop in process_tts_slots(), never enter the shared batch
+ return;
+ }
if (slot.state == SLOT_STATE_GENERATING && slot.prompt.n_tokens() + 1 >= slot.n_ctx) {
if (!params_base.ctx_shift) {
// this check is redundant (for good)
@@ -3150,7 +3307,7 @@ private:
// determine which slots are generating and drafting
iterate(slots, [&](server_slot & slot) {
- if (slot.state != SLOT_STATE_GENERATING) {
+ if (slot.state != SLOT_STATE_GENERATING || slot.task->type == SERVER_TASK_TYPE_TTS) {
return;
}
@@ -3284,7 +3441,7 @@ private:
return; // batch is full, skip remaining slots
}
- if (!slot.is_processing()) {
+ if (!slot.is_processing() || slot.task->type == SERVER_TASK_TYPE_TTS) {
return;
}
@@ -4433,6 +4590,8 @@ server_context_meta server_context::get_meta() const {
/* has_inp_image */ impl->chat_params.allow_image,
/* has_inp_audio */ impl->chat_params.allow_audio,
/* has_inp_video */ impl->chat_params.allow_video,
+ /* has_cap_chat */ impl->has_cap_chat(),
+ /* has_cap_tts */ impl->has_cap_tts(),
/* json_ui_settings */ impl->json_ui_settings,
/* slot_n_ctx */ impl->get_slot_n_ctx(),
/* pooling_type */ llama_pooling_type(impl->ctx_tgt),
@@ -4512,6 +4671,11 @@ std::unique_ptr<server_res_generator> server_routes::handle_completions_impl(
res->set_req(&req); // will also set spipe if needed
+ if (!ctx_server.has_cap_chat()) {
+ res->error(format_error_response("this server does not support chat/completions", ERROR_TYPE_NOT_SUPPORTED));
+ return res;
+ }
+
int32_t sse_ping_interval = params.sse_ping_interval;
try {
@@ -5399,6 +5563,150 @@ void server_routes::init_routes() {
return res;
};
+ this->post_tts = [this](const server_http_req & req) {
+ auto res = create_response();
+ res->set_req(&req); // will also set spipe if needed
+
+ if (!ctx_server.has_cap_tts()) {
+ res->error(format_error_response("this server does not support audio generation", ERROR_TYPE_NOT_SUPPORTED));
+ return res;
+ }
+
+ const json body = json::parse(req.body);
+
+ std::string prompt = json_value(body, "input", json_value(body, "prompt", std::string()));
+ if (prompt.empty()) {
+ res->error(format_error_response("\"input\" must be a non-empty string", ERROR_TYPE_INVALID_REQUEST));
+ return res;
+ }
+
+ const std::string response_format = json_value(body, "response_format", std::string("wav"));
+ const bool stream = json_value(body, "stream", false);
+
+ server_task task(SERVER_TASK_TYPE_TTS);
+ task.tts_inp.set_prompt(prompt);
+ task.tts_inp.set_lang(json_value(body, "lang", std::string()));
+ task.tts_inp.data.top_k = json_value(body, "top_k", 0);
+ task.tts_inp.data.top_p = json_value(body, "top_p", 0.0f);
+ task.tts_inp.data.stream = stream;
+ task.tts_inp.data.out_type = response_format == "pcm"
+ ? MTMD_HELPER_GEN_AUDIO_OUTTYPE_PCM
+ : MTMD_HELPER_GEN_AUDIO_OUTTYPE_WAV;
+ task.params.stream = stream;
+ task.params.n_predict = json_value(body, "n_predict", -1);
+ task.params.sampling = params.sampling; // baseline defaults, then apply overrides below
+ task.params.sampling.penalty_repeat = json_value(body, "repeat_penalty", 1.05f);
+ task.params.sampling.penalty_last_n = -1;
+ if (task.tts_inp.data.top_k > 0) {
+ task.params.sampling.top_k = task.tts_inp.data.top_k;
+ }
+ if (task.tts_inp.data.top_p > 0) {
+ task.params.sampling.top_p = task.tts_inp.data.top_p;
+ }
+
+ // speaker reference: either an uploaded form file ("speaker_ref") or a base64 JSON field ("speaker_ref_b64")
+ const unsigned char * speaker_ref_data = nullptr;
+ size_t speaker_ref_len = 0;
+ std::string speaker_ref_b64_decoded;
+
+ auto speaker_ref_file = req.files.find("speaker_ref");
+ if (speaker_ref_file != req.files.end()) {
+ speaker_ref_data = speaker_ref_file->second.data.data();
+ speaker_ref_len = speaker_ref_file->second.data.size();
+ } else {
+ std::string speaker_ref_b64 = json_value(body, "speaker_ref_b64", std::string());
+ if (!speaker_ref_b64.empty()) {
+ speaker_ref_b64_decoded = base64::decode(speaker_ref_b64);
+ speaker_ref_data = (const unsigned char *) speaker_ref_b64_decoded.data();
+ speaker_ref_len = speaker_ref_b64_decoded.size();
+ }
+ }
+
+ if (speaker_ref_len > 0) {
+ auto wrapper = mtmd_helper_bitmap_init_from_buf(ctx_server.mctx, speaker_ref_data, speaker_ref_len, false);
+ if (!wrapper.bitmap) {
+ res->error(format_error_response("failed to decode \"speaker_ref\"", ERROR_TYPE_INVALID_REQUEST));
+ return res;
+ }
+ task.tts_inp.set_speaker_ref(mtmd::bitmap_ptr(wrapper.bitmap));
+ } else {
+ // SRV_WRN expands __VA_ARGS__ without the GNU comma-elision extension,
+ // so a bare format string leaves a trailing comma and will not compile
+ SRV_WRN("%s", "no speaker reference provided, the model may behave randomly\n");
+ }
+
+ auto & rd = res->rd;
+ task.id = rd.get_new_id();
+ rd.post_task(std::move(task));
+
+ const std::string content_type = response_format == "pcm" ? "audio/L16" : "audio/wav";
+
+ if (!stream) {
+ auto result = rd.next(req.should_stop);
+ if (!result) {
+ GGML_ASSERT(req.should_stop());
+ return res; // connection is closed
+ }
+ if (result->is_error()) {
+ res->error(result->to_json());
+ return res;
+ }
+ auto * tts_res = dynamic_cast<server_task_result_tts *>(result.get());
+ GGML_ASSERT(tts_res != nullptr);
+ res->status = 200;
+ res->content_type = content_type;
+ res->data = std::move(tts_res->audio);
+ return res;
+ } else {
+ auto first_result = rd.next(req.should_stop);
+ if (!first_result) {
+ GGML_ASSERT(req.should_stop());
+ return res; // connection is closed
+ }
+ if (first_result->is_error()) {
+ res->error(first_result->to_json());
+ return res;
+ }
+ auto * first_tts_res = dynamic_cast<server_task_result_tts *>(first_result.get());
+ GGML_ASSERT(first_tts_res != nullptr);
+
+ res->status = 200;
+ res->content_type = content_type;
+ res->data = std::move(first_tts_res->audio);
+ bool is_done = first_tts_res->final;
+
+ res->set_next([res_this = res.get(), is_done](std::string & output) mutable -> bool {
+ if (is_done) {
+ return false;
+ }
+ if (res_this->should_stop()) {
+ return false;
+ }
+ if (!res_this->data.empty()) {
+ output = std::move(res_this->data);
+ res_this->data.clear();
+ return true;
+ }
+
+ server_response_reader & rd = res_this->rd;
+ if (!rd.has_next()) {
+ return false;
+ }
+ auto result = rd.next([&res_this]() { return res_this->should_stop(); });
+ if (!result || result->is_error()) {
+ return false;
+ }
+ auto * tts_res = dynamic_cast<server_task_result_tts *>(result.get());
+ GGML_ASSERT(tts_res != nullptr);
+ output = std::move(tts_res->audio);
+ is_done = tts_res->final;
+ return true;
+ });
+ }
+
+ return res;
+ };
+
this->get_lora_adapters = [this](const server_http_req & req) {
auto res = create_response();
diff --git a/tools/server/server-context.h b/tools/server/server-context.h
index f9ab1132b..610512678 100644
--- a/tools/server/server-context.h
+++ b/tools/server/server-context.h
@@ -22,6 +22,8 @@ struct server_context_meta {
bool has_inp_image;
bool has_inp_audio;
bool has_inp_video;
+ bool has_cap_chat;
+ bool has_cap_tts;
json json_ui_settings;
int slot_n_ctx;
enum llama_pooling_type pooling_type;
@@ -151,6 +153,7 @@ struct server_routes {
server_http_context::handler_t post_embeddings;
server_http_context::handler_t post_embeddings_oai;
server_http_context::handler_t post_rerank;
+ server_http_context::handler_t post_tts;
server_http_context::handler_t get_lora_adapters;
server_http_context::handler_t post_lora_adapters;
diff --git a/tools/server/server-task.cpp b/tools/server/server-task.cpp
index 1ee677553..939630b8b 100644
--- a/tools/server/server-task.cpp
+++ b/tools/server/server-task.cpp
@@ -1497,6 +1497,17 @@ json server_task_result_rerank::to_json() {
};
}
+//
+// server_task_result_tts
+//
+json server_task_result_tts::to_json() {
+ return json {
+ {"sample_rate", sample_rate},
+ {"n_bytes", audio.size()},
+ {"final", final},
+ };
+}
+
//
// server_task_result_error
//
diff --git a/tools/server/server-task.h b/tools/server/server-task.h
index 5bedf1987..e6ca67a65 100644
--- a/tools/server/server-task.h
+++ b/tools/server/server-task.h
@@ -10,6 +10,7 @@
// TODO: prevent including the whole server-common.h as we only use server_tokens
#include "server-common.h"
+#include "mtmd-helper.h"
using json = nlohmann::ordered_json;
@@ -42,6 +43,7 @@ enum server_task_type {
SERVER_TASK_TYPE_SLOT_ERASE,
SERVER_TASK_TYPE_GET_LORA,
SERVER_TASK_TYPE_SET_LORA,
+ SERVER_TASK_TYPE_TTS,
};
// TODO: change this to more generic "response_format" to replace the "format_response_*" in server-common
@@ -202,6 +204,9 @@ struct server_task {
// used by SERVER_TASK_TYPE_SET_LORA
std::map<int, float> set_lora; // mapping adapter ID -> scale
+ // used by SERVER_TASK_TYPE_TTS
+ mtmd_helper::gen_audio::inp tts_inp;
+
server_task() = default;
server_task(server_task_type type) : type(type) {}
@@ -235,6 +240,7 @@ struct server_task {
switch (type) {
case SERVER_TASK_TYPE_COMPLETION:
case SERVER_TASK_TYPE_INFILL:
+ case SERVER_TASK_TYPE_TTS:
return true;
default:
return false;
@@ -494,5 +500,15 @@ struct server_task_result_embd : server_task_result {
json to_json_oaicompat();
};
+struct server_task_result_tts : server_task_result {
+ std::string audio; // raw bytes for this chunk (WAV or PCM, per request's out_type)
+ int32_t sample_rate = 0;
+ bool final = false; // true for the last chunk of a request
+
+ virtual bool is_stop() override { return final; }
+
+ virtual json to_json() override;
+};
+
struct server_task_result_rerank : server_task_result {
float score = -1e6;
-13
View File
@@ -28,13 +28,6 @@ cp -r message_content_test.cpp llama.cpp/tools/grpc-server/
# Generic passthrough parser staging and its standalone regression test.
cp -r passthrough_options.h llama.cpp/tools/grpc-server/
cp -r passthrough_options_test.cpp llama.cpp/tools/grpc-server/
# TTS request validation (included by grpc-server.cpp) and its standalone
# regression test.
cp -r tts_request_options.h llama.cpp/tools/grpc-server/
cp -r tts_request_options_test.cpp llama.cpp/tools/grpc-server/
# Thread-count default normalization and its standalone regression test.
cp -r thread_params.h llama.cpp/tools/grpc-server/
cp -r thread_params_test.cpp llama.cpp/tools/grpc-server/
# Parent-death watcher (included by grpc-server.cpp) and its standalone unit
# test (run via backend/cpp/run-unit-tests.sh; also buildable under ctest).
cp -r parent_watch.h llama.cpp/tools/grpc-server/
@@ -56,16 +49,10 @@ else
echo "==> llama.cpp predates the load-mode enum, using the legacy mmap/mlock/direct-io booleans"
LEGACY_LOAD_MODE=1
fi
if grep -q "server_metrics metrics;" llama.cpp/tools/server/server-task.h; then
HAS_SERVER_METRICS=1
else
HAS_SERVER_METRICS=0
fi
cat > llama.cpp/tools/grpc-server/llama_compat.h <<EOF
// Generated by backend/cpp/llama-cpp/prepare.sh. Do not edit.
#pragma once
#define LOCALAI_LEGACY_LOAD_MODE ${LEGACY_LOAD_MODE}
#define LOCALAI_HAS_SERVER_METRICS ${HAS_SERVER_METRICS}
EOF
set +e
-11
View File
@@ -1,11 +0,0 @@
#pragma once
#include <cstdint>
namespace llama_grpc {
inline int32_t resolve_batch_threads(int32_t batch_threads, int32_t inference_threads) {
return batch_threads < 0 ? inference_threads : batch_threads;
}
} // namespace llama_grpc
@@ -1,15 +0,0 @@
#include "thread_params.h"
#include <cstdio>
int main() {
if (llama_grpc::resolve_batch_threads(-1, 4) != 4) {
std::fprintf(stderr, "default batch threads did not inherit inference threads\n");
return 1;
}
if (llama_grpc::resolve_batch_threads(2, 4) != 2) {
std::fprintf(stderr, "explicit batch threads were overwritten\n");
return 1;
}
return 0;
}
-149
View File
@@ -1,149 +0,0 @@
// SPDX-License-Identifier: MIT
#pragma once
#include <cstdint>
#include <exception>
#include <map>
#include <string>
namespace llama_grpc {
// Validated, parsed form of a backend::TTSRequest, kept free of llama.cpp,
// mtmd and gRPC headers so backend/cpp/run-unit-tests.sh can compile it as a
// standalone translation unit. grpc-server.cpp turns this into a
// mtmd_helper::gen_audio::inp.
struct tts_request_options {
bool ok = false;
std::string error;
std::string text;
std::string voice_path;
std::string language;
// 0 / 0.0f mean "unset": upstream only overrides the sampler defaults when
// the value is strictly positive.
int32_t top_k = 0;
float top_p = 0.0f;
// Upper bound on generated audio frames, exposed because the model does not
// always emit its codec EOS and will otherwise run to the 512-frame default,
// which is roughly 41 s at the 12.5 Hz frame rate. 0 means unset, leaving
// that default in place.
int32_t max_frames = 0;
};
namespace detail {
// Strict whole-string numeric parsing. std::stoi/stof accept trailing garbage
// ("40abc" -> 40), which would silently honour a typo'd request.
inline bool parse_whole_int32(const std::string & value, int32_t & out) {
if (value.empty()) {
return false;
}
try {
size_t consumed = 0;
const long parsed = std::stol(value, &consumed);
if (consumed != value.size()) {
return false;
}
if (parsed < INT32_MIN || parsed > INT32_MAX) {
return false;
}
out = static_cast<int32_t>(parsed);
return true;
} catch (const std::exception &) {
return false;
}
}
inline bool parse_whole_float(const std::string & value, float & out) {
if (value.empty()) {
return false;
}
try {
size_t consumed = 0;
const float parsed = std::stof(value, &consumed);
if (consumed != value.size()) {
return false;
}
out = parsed;
return true;
} catch (const std::exception &) {
return false;
}
}
inline tts_request_options reject(const std::string & message) {
tts_request_options opts;
opts.ok = false;
opts.error = message;
return opts;
}
} // namespace detail
inline tts_request_options parse_tts_request_options(
const std::string & text,
const std::string & voice,
const std::string & language,
const std::map<std::string, std::string> & params) {
if (text.empty()) {
return detail::reject("text must be a non-empty string");
}
// The Qwen3-TTS Base checkpoints have no built-in speaker. Without a
// reference clip the model picks an arbitrary voice, so an unset voice is
// a request error rather than a defaulted one.
if (voice.empty()) {
return detail::reject("voice must name a speaker reference audio file");
}
tts_request_options opts;
opts.text = text;
opts.voice_path = voice;
opts.language = language;
// Both values are range-checked here rather than left to the caller: the
// consumer copies them straight into mtmd_helper::gen_audio::inp, and only
// its separate sampler assignment is guarded by "> 0". An out-of-range or
// non-finite value would slip past that guard and reach llama.cpp.
const auto top_k_it = params.find("top_k");
if (top_k_it != params.end()) {
if (!detail::parse_whole_int32(top_k_it->second, opts.top_k)) {
return detail::reject("top_k must be an integer, got \"" + top_k_it->second + "\"");
}
if (opts.top_k < 0) {
return detail::reject("top_k must be >= 0, got \"" + top_k_it->second + "\"");
}
}
const auto top_p_it = params.find("top_p");
if (top_p_it != params.end()) {
if (!detail::parse_whole_float(top_p_it->second, opts.top_p)) {
return detail::reject("top_p must be a number, got \"" + top_p_it->second + "\"");
}
// Phrased as a negated in-range test, not "p < 0.0f || p > 1.0f",
// because every comparison against NaN is false: the obvious form
// would accept NaN, and NaN then defeats the consumer's "> 0" guard
// too, since that comparison is false as well.
if (!(opts.top_p >= 0.0f && opts.top_p <= 1.0f)) {
return detail::reject("top_p must be between 0.0 and 1.0, got \"" + top_p_it->second + "\"");
}
}
const auto max_frames_it = params.find("max_frames");
if (max_frames_it != params.end()) {
if (!detail::parse_whole_int32(max_frames_it->second, opts.max_frames)) {
return detail::reject("max_frames must be an integer, got \"" + max_frames_it->second + "\"");
}
if (opts.max_frames < 0) {
return detail::reject("max_frames must be >= 0, got \"" + max_frames_it->second + "\"");
}
}
opts.ok = true;
return opts;
}
} // namespace llama_grpc
@@ -1,209 +0,0 @@
// SPDX-License-Identifier: MIT
#include <cstdio>
#include <map>
#include <string>
#include "tts_request_options.h"
static int failures = 0;
static void check(bool ok, const char * name) {
if (!ok) {
++failures;
std::fprintf(stderr, "FAIL: %s\n", name);
}
}
static void test_accepts_a_minimal_valid_request() {
const auto opts = llama_grpc::parse_tts_request_options(
"Hello world", "/models/voices/ref.wav", "en", {});
check(opts.ok, "minimal request is accepted");
check(opts.error.empty(), "minimal request has no error");
check(opts.text == "Hello world", "text passes through");
check(opts.voice_path == "/models/voices/ref.wav", "voice path passes through");
check(opts.language == "en", "language passes through");
check(opts.top_k == 0, "top_k defaults to the unset sentinel");
check(opts.top_p == 0.0f, "top_p defaults to the unset sentinel");
check(opts.max_frames == 0, "max_frames defaults to the unset sentinel");
}
static void test_rejects_empty_text() {
const auto opts = llama_grpc::parse_tts_request_options(
"", "/models/voices/ref.wav", "en", {});
check(!opts.ok, "empty text is rejected");
check(opts.error.find("text") != std::string::npos, "empty-text error names the field");
}
static void test_rejects_missing_speaker_reference() {
// Qwen3-TTS Base has no built-in speaker; without a reference it produces
// an arbitrary voice, so this must be a hard error rather than a surprise.
const auto opts = llama_grpc::parse_tts_request_options(
"Hello world", "", "en", {});
check(!opts.ok, "missing voice is rejected");
check(opts.error.find("voice") != std::string::npos, "missing-voice error names the field");
}
static void test_parses_sampling_params() {
const std::map<std::string, std::string> params{
{"top_k", "40"},
{"top_p", "0.85"},
};
const auto opts = llama_grpc::parse_tts_request_options(
"Hello world", "/models/voices/ref.wav", "", params);
check(opts.ok, "sampling params are accepted");
check(opts.top_k == 40, "top_k is parsed");
check(opts.top_p > 0.849f && opts.top_p < 0.851f, "top_p is parsed");
check(opts.language.empty(), "absent language stays empty");
}
static void test_rejects_malformed_sampling_params() {
const auto bad_top_k = llama_grpc::parse_tts_request_options(
"Hello world", "/models/voices/ref.wav", "", {{"top_k", "forty"}});
check(!bad_top_k.ok, "non-numeric top_k is rejected");
check(bad_top_k.error.find("top_k") != std::string::npos, "top_k error names the field");
const auto bad_top_p = llama_grpc::parse_tts_request_options(
"Hello world", "/models/voices/ref.wav", "", {{"top_p", ""}});
check(!bad_top_p.ok, "empty top_p is rejected");
const auto trailing = llama_grpc::parse_tts_request_options(
"Hello world", "/models/voices/ref.wav", "", {{"top_k", "40abc"}});
check(!trailing.ok, "top_k with trailing garbage is rejected");
const auto trailing_float = llama_grpc::parse_tts_request_options(
"Hello world", "/models/voices/ref.wav", "", {{"top_p", "0.8abc"}});
check(!trailing_float.ok, "top_p with trailing garbage is rejected");
// std::stol returns a long, which is wider than int32_t on 64-bit hosts, so
// an in-range-for-long value still has to be caught before the narrowing.
const auto overflow_top_k = llama_grpc::parse_tts_request_options(
"Hello world", "/models/voices/ref.wav", "", {{"top_k", "99999999999"}});
check(!overflow_top_k.ok, "top_k beyond int32 range is rejected");
check(overflow_top_k.error.find("top_k") != std::string::npos,
"top_k overflow error names the field");
}
static void test_rejects_out_of_range_sampling_params() {
// These reach mtmd_helper::gen_audio::inp unconditionally downstream, where
// the "> 0" sampler guard does not screen them, so they must die here.
const auto negative_top_k = llama_grpc::parse_tts_request_options(
"Hello world", "/models/voices/ref.wav", "", {{"top_k", "-5"}});
check(!negative_top_k.ok, "negative top_k is rejected");
check(negative_top_k.error.find("top_k") != std::string::npos,
"negative top_k error names the field");
const auto negative_top_p = llama_grpc::parse_tts_request_options(
"Hello world", "/models/voices/ref.wav", "", {{"top_p", "-0.1"}});
check(!negative_top_p.ok, "negative top_p is rejected");
check(negative_top_p.error.find("top_p") != std::string::npos,
"negative top_p error names the field");
const auto large_top_p = llama_grpc::parse_tts_request_options(
"Hello world", "/models/voices/ref.wav", "", {{"top_p", "1.5"}});
check(!large_top_p.ok, "top_p above 1.0 is rejected");
// NaN survives a naive "p < 0.0f || p > 1.0f" range test because every
// comparison against NaN is false. This case pins the correct form.
const auto nan_top_p = llama_grpc::parse_tts_request_options(
"Hello world", "/models/voices/ref.wav", "", {{"top_p", "nan"}});
check(!nan_top_p.ok, "NaN top_p is rejected");
const auto inf_top_p = llama_grpc::parse_tts_request_options(
"Hello world", "/models/voices/ref.wav", "", {{"top_p", "inf"}});
check(!inf_top_p.ok, "infinite top_p is rejected");
}
static void test_accepts_sampling_param_boundaries() {
const auto zero_top_p = llama_grpc::parse_tts_request_options(
"Hello world", "/models/voices/ref.wav", "", {{"top_p", "0.0"}});
check(zero_top_p.ok, "top_p of 0.0 is accepted");
check(zero_top_p.top_p == 0.0f, "top_p of 0.0 round-trips");
const auto one_top_p = llama_grpc::parse_tts_request_options(
"Hello world", "/models/voices/ref.wav", "", {{"top_p", "1.0"}});
check(one_top_p.ok, "top_p of 1.0 is accepted");
check(one_top_p.top_p == 1.0f, "top_p of 1.0 round-trips");
const auto zero_top_k = llama_grpc::parse_tts_request_options(
"Hello world", "/models/voices/ref.wav", "", {{"top_k", "0"}});
check(zero_top_k.ok, "top_k of 0 is accepted");
}
static void test_parses_max_frames() {
// The consumer maps a positive value onto n_predict and leaves upstream's
// 512-frame default in place when it is unset, so the sentinel matters as
// much as the parsed value.
const auto opts = llama_grpc::parse_tts_request_options(
"Hello world", "/models/voices/ref.wav", "", {{"max_frames", "120"}});
check(opts.ok, "max_frames is accepted");
check(opts.max_frames == 120, "max_frames is parsed");
const auto absent = llama_grpc::parse_tts_request_options(
"Hello world", "/models/voices/ref.wav", "", {{"top_k", "40"}});
check(absent.ok, "a request without max_frames is accepted");
check(absent.max_frames == 0, "absent max_frames leaves the unset sentinel");
const auto zero = llama_grpc::parse_tts_request_options(
"Hello world", "/models/voices/ref.wav", "", {{"max_frames", "0"}});
check(zero.ok, "max_frames of 0 is accepted");
check(zero.max_frames == 0, "max_frames of 0 means unset");
}
static void test_rejects_malformed_max_frames() {
const auto negative = llama_grpc::parse_tts_request_options(
"Hello world", "/models/voices/ref.wav", "", {{"max_frames", "-1"}});
check(!negative.ok, "negative max_frames is rejected");
check(negative.error.find("max_frames") != std::string::npos,
"negative max_frames error names the field");
const auto non_numeric = llama_grpc::parse_tts_request_options(
"Hello world", "/models/voices/ref.wav", "", {{"max_frames", "many"}});
check(!non_numeric.ok, "non-numeric max_frames is rejected");
check(non_numeric.error.find("max_frames") != std::string::npos,
"non-numeric max_frames error names the field");
const auto trailing = llama_grpc::parse_tts_request_options(
"Hello world", "/models/voices/ref.wav", "", {{"max_frames", "120abc"}});
check(!trailing.ok, "max_frames with trailing garbage is rejected");
const auto empty = llama_grpc::parse_tts_request_options(
"Hello world", "/models/voices/ref.wav", "", {{"max_frames", ""}});
check(!empty.ok, "empty max_frames is rejected");
const auto overflow = llama_grpc::parse_tts_request_options(
"Hello world", "/models/voices/ref.wav", "", {{"max_frames", "99999999999"}});
check(!overflow.ok, "max_frames beyond int32 range is rejected");
}
static void test_ignores_unknown_params() {
// Unknown keys are backend-specific knobs meant for other TTS engines. A
// request routed here must not fail just because it carries them.
const auto opts = llama_grpc::parse_tts_request_options(
"Hello world", "/models/voices/ref.wav", "", {{"exaggeration", "0.7"}});
check(opts.ok, "unknown params are ignored, not rejected");
}
int main() {
test_accepts_a_minimal_valid_request();
test_rejects_empty_text();
test_rejects_missing_speaker_reference();
test_parses_sampling_params();
test_rejects_malformed_sampling_params();
test_rejects_out_of_range_sampling_params();
test_accepts_sampling_param_boundaries();
test_parses_max_frames();
test_rejects_malformed_max_frames();
test_ignores_unknown_params();
if (failures == 0) {
std::printf("tts_request_options_test: all checks passed\n");
}
return failures;
}
-2
View File
@@ -48,7 +48,6 @@ define turboquant-build
# stays compiling against vanilla upstream.
bash $(CURRENT_MAKEFILE_DIR)/patch-grpc-server.sh $(CURRENT_MAKEFILE_DIR)/../turboquant-$(1)-build/grpc-server.cpp
bash $(LLAMA_CPP_DIR)/disable-score-task.sh $(CURRENT_MAKEFILE_DIR)/../turboquant-$(1)-build/grpc-server.cpp
bash $(LLAMA_CPP_DIR)/disable-tts-task.sh $(CURRENT_MAKEFILE_DIR)/../turboquant-$(1)-build/grpc-server.cpp
$(info $(GREEN)I turboquant build info:$(1)$(RESET))
LLAMA_REPO=$(LLAMA_REPO) LLAMA_VERSION=$(TURBOQUANT_VERSION) \
$(MAKE) -C $(CURRENT_MAKEFILE_DIR)/../turboquant-$(1)-build llama.cpp
@@ -87,7 +86,6 @@ turboquant-cpu-all:
$(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
bash $(LLAMA_CPP_DIR)/disable-score-task.sh $(CURRENT_MAKEFILE_DIR)/../turboquant-cpu-all-build/grpc-server.cpp
bash $(LLAMA_CPP_DIR)/disable-tts-task.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 $(CURRENT_MAKEFILE_DIR)/../turboquant-cpu-all-build llama.cpp
+1 -1
View File
@@ -8,7 +8,7 @@ JOBS?=$(shell nproc --ignore=1)
# CrispASR version (release tag)
CRISPASR_REPO?=https://github.com/CrispStrobe/CrispASR
CRISPASR_VERSION?=a153b09b37c90cd55cd9336fccbdf3ba7a289596
CRISPASR_VERSION?=17a6cc99422bfafadf7161e96dd7294c89da9c36
SO_TARGET?=libgocrispasr.so
CMAKE_ARGS+=-DBUILD_SHARED_LIBS=OFF
+1 -1
View File
@@ -14,7 +14,7 @@ JOBS?=$(shell nproc --ignore=1)
# It is kept alive by the upstream tag da2-support (survives a squash-merge);
# repoint to the master merge commit once mudler/depth-anything.cpp PR #1 lands.
DEPTHANYTHING_REPO?=https://github.com/mudler/depth-anything.cpp.git
DEPTHANYTHING_VERSION?=54abd5c0abfd1f394e01cb3c38f2e3af4daedf85
DEPTHANYTHING_VERSION?=2028b47ac75a8659c6a9aa617baf09be193eb55f
ifeq ($(NATIVE),false)
CMAKE_ARGS+=-DGGML_NATIVE=OFF
+2 -7
View File
@@ -38,9 +38,8 @@ type Store struct {
// keysAreNormalized stays true until any non-unit-magnitude key
// is added; once false, the magnitude-aware fallback path is
// used by Find. Re-evaluated only at Set time, never again on
// its own — a partial deletion of the offending key does NOT flip
// it back to true (the bookkeeping cost would dominate the gain).
// An empty store returns to its initial state.
// its own — a deletion of the offending key does NOT flip it
// back to true (the bookkeeping cost would dominate the gain).
keysAreNormalized bool
// keyLen is the dimension of every stored key. -1 means "no
@@ -143,10 +142,6 @@ func (s *Store) StoresDelete(opts *pb.StoresDeleteOptions) error {
mergedV = append(mergedV, tailV...)
s.keys = mergedK
s.values = mergedV
if len(s.keys) == 0 {
s.keyLen = -1
s.keysAreNormalized = true
}
assert(slices.IsSortedFunc(s.keys, slices.Compare[[]float32]), "Delete: s.keys not sorted post-merge")
assert(len(s.keys) == len(s.values), "Delete: keys/values length skew")
return nil
-40
View File
@@ -105,46 +105,6 @@ var _ = Describe("StoresDelete", func() {
})).To(Succeed(), "delete of missing key should succeed")
Expect(s.keys).To(HaveLen(1))
})
It("reopens the dimension after deleting every key", func() {
s := NewStore()
oldKey := []float32{2, 0, 0}
mustSet(s, [][]float32{oldKey}, [][]byte{[]byte("3d")})
Expect(s.keysAreNormalized).To(BeFalse())
Expect(s.StoresDelete(&pb.StoresDeleteOptions{
Keys: wrapKeys([][]float32{oldKey}),
})).To(Succeed())
Expect(s.keys).To(BeEmpty())
Expect(s.keyLen).To(Equal(-1))
Expect(s.keysAreNormalized).To(BeTrue())
newKey := normalizeVec([]float32{1, 1})
mustSet(s, [][]float32{newKey}, [][]byte{[]byte("2d")})
res, err := s.StoresFind(&pb.StoresFindOptions{
Key: &pb.StoresKey{Floats: newKey},
TopK: 1,
})
Expect(err).NotTo(HaveOccurred())
Expect(res.Values).To(HaveLen(1))
Expect(string(res.Values[0].Bytes)).To(Equal("2d"))
})
It("retains the dimension after a partial delete", func() {
s := NewStore()
mustSet(s,
[][]float32{{1, 0, 0}, {0, 1, 0}},
[][]byte{[]byte("x"), []byte("y")},
)
Expect(s.StoresDelete(&pb.StoresDeleteOptions{
Keys: wrapKeys([][]float32{{1, 0, 0}}),
})).To(Succeed())
Expect(s.keyLen).To(Equal(3))
Expect(s.StoresSet(&pb.StoresSetOptions{
Keys: wrapKeys([][]float32{{1, 0}}),
Values: wrapValues([][]byte{[]byte("2d")}),
})).NotTo(Succeed())
})
})
var _ = Describe("StoresFind", func() {
+3 -32
View File
@@ -12,7 +12,7 @@
# runs 'make -C backend/go/$(BACKEND) build' and then copies package/), so it
# has to produce the binary and the package, not just the shared libraries.
NEMO_SPEECH_VERSION?=4f9676226f667d14608487df744f375db87127f8
NEMO_SPEECH_VERSION?=2e12e2def8a98ed06666f7ee3ca94e7193e04be4
NEMO_SPEECH_REPO?=https://github.com/NVIDIA/NeMo-Speech.cpp
GOCMD?=go
@@ -88,18 +88,6 @@ ITN_LIB_DIR=$(ITN_PREFIX)/lib
ITN_MARKER=$(ITN_LIB_DIR)/libsparrowhawk.so
ITN_FST_HEADER=$(ITN_PREFIX)/include/fst/fst.h
# SentencePiece became a core ASR dependency in 5be7bfb: RNNT context biasing
# uses it even when Flashlight and text normalization are disabled. Build the
# pinned static archive provided by upstream so every platform gets the same
# dependency instead of relying on an undeclared system package.
SENTENCEPIECE_PREFIX=sources/NeMo-Speech.cpp/.deps/sentencepiece
SENTENCEPIECE_MARKER=$(SENTENCEPIECE_PREFIX)/lib/libsentencepiece.a
# Linux's ASR CMake block looks in NEMO_SPEECH_DEPENDENCY_PREFIX directly, but
# the Apple branch uses generic find_library()/find_path(). Put the same private
# prefix on CMake's search path so Darwin consumes the archive built above too.
CMAKE_ARGS+=-DCMAKE_PREFIX_PATH=$(abspath $(SENTENCEPIECE_PREFIX))
ITN_CC?=gcc-12
ITN_CXX?=g++-12
@@ -164,7 +152,7 @@ else
endif
CMAKE_ARGS+=-DNEMO_SPEECH_GGML_PATCHED=$(GGML_PATCHED)
.PHONY: nemo-speech-cpp-grpc package build clean purge test all stage-libs patch-ggml engine itn sentencepiece patch-itn-headers
.PHONY: nemo-speech-cpp-grpc package build clean purge test all stage-libs patch-ggml engine itn patch-itn-headers
all: nemo-speech-cpp-grpc package
@@ -278,28 +266,11 @@ patch-itn-headers:
itn: $(ITN_MARKER)
$(SENTENCEPIECE_MARKER): | sources/NeMo-Speech.cpp
# Upstream's license copies use GNU install's -D flag, which BSD install
# does not support. Homebrew CMake 4 also rejects SentencePiece's old policy
# floor. Patch both incompatibilities before running the helper on Darwin.
@if [ "$(shell uname -s)" = Darwin ]; then \
cd sources/NeMo-Speech.cpp && \
mkdir -p .deps/sentencepiece/share/licenses/nemo-speech/third_party/sentencepiece && \
perl -pi \
-e 's/install -Dm0644/install -m 0644/g;' \
-e 's/-DCMAKE_BUILD_TYPE=Release /-DCMAKE_BUILD_TYPE=Release -DCMAKE_POLICY_VERSION_MINIMUM=3.5 /;' \
scripts/build_sentencepiece_static.sh; \
fi
cd sources/NeMo-Speech.cpp && JOBS=$(JOBS) scripts/build_sentencepiece_static.sh
sentencepiece: $(SENTENCEPIECE_MARKER)
# Only a WITH_NORM=ON build needs the ITN stack, and it must exist before cmake
# configures, since the WITH_NORM cmake block find_library()s into the prefix
# with REQUIRED.
NEMO_RUNTIME_PREREQS=$(SENTENCEPIECE_MARKER)
ifeq ($(WITH_NORM),ON)
NEMO_RUNTIME_PREREQS+=$(ITN_MARKER)
NEMO_RUNTIME_PREREQS=$(ITN_MARKER)
endif
# Upstream sets CMAKE_LIBRARY_OUTPUT_DIRECTORY to ${CMAKE_BINARY_DIR}/bin, so the
+2 -4
View File
@@ -1,6 +1,6 @@
# parakeet-cpp backend Makefile.
#
# Upstream pin lives below as PARAKEET_VERSION?=e75de9b6b9b688fd293aa22f7e27aa724ea286f8
# Upstream pin lives below as PARAKEET_VERSION?=1bfbebfaaf493866f49597cd3b7901959d395c60
# (.github/bump_deps.sh) can find and update it - matches the
# whisper.cpp / ds4 / vibevoice-cpp convention.
#
@@ -15,7 +15,7 @@
# That's what the L0 smoke test uses. The default target below does the
# proper clone-at-pin + cmake build so CI doesn't need a side-checkout.
PARAKEET_VERSION?=e75de9b6b9b688fd293aa22f7e27aa724ea286f8
PARAKEET_VERSION?=1bfbebfaaf493866f49597cd3b7901959d395c60
PARAKEET_REPO?=https://github.com/mudler/parakeet.cpp
GOCMD?=go
@@ -49,8 +49,6 @@ else ifeq ($(BUILD_TYPE),hipblas)
CMAKE_ARGS+=-DPARAKEET_GGML_HIP=ON
else ifeq ($(BUILD_TYPE),vulkan)
CMAKE_ARGS+=-DPARAKEET_GGML_VULKAN=ON
else ifeq ($(BUILD_TYPE),metal)
CMAKE_ARGS+=-DPARAKEET_GGML_METAL=ON
endif
.PHONY: parakeet-cpp-grpc package build clean purge test all
+10 -5
View File
@@ -8,7 +8,7 @@ JOBS?=$(shell nproc --ignore=1)
# stablediffusion.cpp (ggml)
STABLEDIFFUSION_GGML_REPO?=https://github.com/leejet/stable-diffusion.cpp
STABLEDIFFUSION_GGML_VERSION?=97d2990807fe6d558e395f8764198d7c7e7b411c
STABLEDIFFUSION_GGML_VERSION?=c6beeef35526c6dc94b74a7fb69f9d2e6a2a7a12
CMAKE_ARGS+=-DGGML_MAX_NAME=128
@@ -42,9 +42,13 @@ else ifeq ($(BUILD_TYPE),hipblas)
CMAKE_ARGS+=-DSD_HIPBLAS=ON -DGGML_HIPBLAS=ON -DAMDGPU_TARGETS=$(AMDGPU_TARGETS)
else ifeq ($(BUILD_TYPE),vulkan)
CMAKE_ARGS+=-DSD_VULKAN=ON -DGGML_VULKAN=ON
else ifeq ($(BUILD_TYPE),metal)
CMAKE_ARGS+=-DSD_METAL=ON -DGGML_METAL=ON
CMAKE_ARGS+=-DGGML_METAL_EMBED_LIBRARY=ON
else ifeq ($(OS),Darwin)
ifneq ($(BUILD_TYPE),metal)
CMAKE_ARGS+=-DSD_METAL=OFF -DGGML_METAL=OFF
else
CMAKE_ARGS+=-DSD_METAL=ON -DGGML_METAL=ON
CMAKE_ARGS+=-DGGML_METAL_EMBED_LIBRARY=ON
endif
endif
ifeq ($(BUILD_TYPE),sycl_f16)
@@ -68,6 +72,7 @@ sources/stablediffusion-ggml.cpp:
git checkout $(STABLEDIFFUSION_GGML_VERSION) && \
git submodule update --init --recursive --depth 1 --single-branch
# Detect OS
UNAME_S := $(shell uname -s)
# Only build CPU variants on Linux
@@ -129,4 +134,4 @@ libgosd-custom: CMakeLists.txt cpp/gosd.cpp cpp/gosd.h
(mv build-$(SO_TARGET)/libgosd.so ./$(SO_TARGET) 2>/dev/null || \
mv build-$(SO_TARGET)/libgosd.dylib ./$(SO_TARGET) 2>/dev/null)
all: stablediffusion-ggml package
all: stablediffusion-ggml package
+19 -28
View File
@@ -11,7 +11,7 @@ JOBS?=$(shell nproc --ignore=1 2>/dev/null || sysctl -n hw.ncpu 2>/dev/null || e
# vllm.cpp version
VLLM_CPP_REPO?=https://github.com/mudler/vllm.cpp
VLLM_CPP_VERSION?=438305e1577768ec0f75729456a4c8b9f425e2ee
VLLM_CPP_VERSION?=2b08dd246e04b3f0a4bf1f276170fd28004ced01
# MLX GEMM provider (darwin/metal only; see the metal branch below for why).
# Consumed as the prebuilt pip wheel: building MLX from source needs `xcrun
@@ -47,35 +47,26 @@ CMAKE_ARGS+=-DCMAKE_BUILD_TYPE=Release
UNAME_M := $(shell uname -m)
ifeq ($(BUILD_TYPE),cublas)
# Every CUDA architecture upstream builds that the platform can actually
# host, split by where the silicon exists: Jetson (87 Orin, 110 Thor) is
# arm64-only, desktop 120a is amd64-only, and 90a/100a appear on both
# because of the SBSA parts (GH200, GB200).
#
# This deliberately matches vllm.cpp's own release archive rather than
# narrowing to the boxes we benchmark on. A narrower list does not degrade
# on an unlisted card, it dies at the first request with "no kernel image
# is available for execution on the device", long after `backends install`
# reported success -- so an arch we merely lack numbers for still belongs
# in the binary.
#
# Triton-AOT stays ON for both. A fat build is supported on the BUILDER
# path: it embeds every vendored cubin tree (sm_80/86/89/90a/100a/121a) and
# selects by exact SM at runtime, so the arches with no tree (87, 103a,
# 110, 120a) take the portable CUDA kernels and can never load a
# neighbouring cubin. Only maintainer REGEN needs a single pinned arch.
# See vllm.cpp cmake/TritonAOT.cmake `_triton_aot_arch_names`.
#
# CUDA builds REQUIRE the CUDA 13 toolchain: 12.x nvcc lacks compute_121a
# (GB10) and its ptxas rejects the sm_120a NVFP4 MMA kernels ("Vector type
# too large"), so no cuda-12 variant is shipped.
ifeq ($(CUDA_MAJOR_VERSION),12)
$(error vllm.cpp needs the CUDA 13 toolchain: CUDA 12.x cannot compile the Blackwell fp4 kernels)
endif
# Blackwell-family targets only: other CUDA arches are build-supported
# upstream but have no runtime-proven fast path. amd64 gets the consumer
# (120a) + GB10 (121a) fat binary; arm64 CUDA (l4t-style images, DGX
# Spark) is GB10 only. Triton-AOT GDN cubins are vendored per-arch, no
# Python needed to consume them.
ifeq ($(UNAME_M),x86_64)
CMAKE_ARGS+=-DVLLM_CPP_CUDA=ON "-DVLLM_CPP_CUDA_ARCHITECTURES=80;86;89;90a;100a;103a;120a;121a" -DVLLM_CPP_TRITON=ON
# NO -DVLLM_CPP_TRITON on fat builds: the vendored Triton-AOT cubin
# trees are per-arch and the engine refuses a multi-arch build unless
# pinned to one tree (unsound for the other arch). The non-AOT GDN
# path serves the fat binary; single-arch builds keep the cubins.
#
# CUDA builds REQUIRE the CUDA 13 toolchain: 12.x nvcc lacks
# compute_121a (GB10) and its ptxas rejects the sm_120a NVFP4 MMA
# kernels ("Vector type too large"), so no cuda-12 variant is shipped.
ifeq ($(CUDA_MAJOR_VERSION),12)
$(error vllm.cpp needs the CUDA 13 toolchain: CUDA 12.x cannot compile the Blackwell fp4 kernels)
endif
CMAKE_ARGS+=-DVLLM_CPP_CUDA=ON "-DVLLM_CPP_CUDA_ARCHITECTURES=120a;121a"
else
CMAKE_ARGS+=-DVLLM_CPP_CUDA=ON "-DVLLM_CPP_CUDA_ARCHITECTURES=87;90a;100a;110;121a" -DVLLM_CPP_TRITON=ON
CMAKE_ARGS+=-DVLLM_CPP_CUDA=ON -DVLLM_CPP_CUDA_ARCHITECTURES=121a -DVLLM_CPP_TRITON=ON
endif
else ifeq ($(BUILD_TYPE),vulkan)
CMAKE_ARGS+=-DVLLM_CPP_VULKAN=ON -DVLLM_CPP_CUDA=OFF
+1 -1
View File
@@ -9,7 +9,7 @@ It serves two things: text generation, and MiniMax-H3 joint video+audio
generation.
The backend dlopens the engine's stable C ABI (`libvllm`, `include/vllm.h`,
ABI v20) through purego:
ABI v16) through purego:
- `Load` -> `vllm_engine_load`: accepts a `.gguf` file or a HF-style model
directory (`config.json` + safetensors). `context_size` maps to
+18 -31
View File
@@ -1,6 +1,6 @@
package main
// purego bindings for the vllm.cpp stable C ABI (include/vllm.h, ABI v21).
// purego bindings for the vllm.cpp stable C ABI (include/vllm.h, ABI v16).
//
// The structs below are hand-mirrored PODs of the C declarations, with
// explicit padding so the Go layout matches the C layout on linux/darwin
@@ -21,7 +21,7 @@ import (
// the header of the VLLM_CPP_VERSION pinned in the Makefile: the build checks
// the two against each other, because a mismatch is only caught at runtime by
// registerLib, where it takes the backend down on every load (issue #11379).
const abiVersion = 21
const abiVersion = 17
// The ABI's tri-state toggles (enable_prefix_caching ABI v7,
// enable_jump_forward ABI v10) share one encoding: 0 is NOT "off", it is
@@ -69,7 +69,6 @@ type cModelParams struct {
MaxNumBatchedTokens int32 // <= 0 = per-arch default (ABI v9)
SchedulingPolicy uintptr // const char*; NULL = "fcfs" (ABI v9)
KVTransferConfig uintptr // const char* JSON; NULL = no connector (ABI v9)
OffloadConfig uintptr // const char* JSON; NULL = no weight offload
EnableJumpForward int32 // tri-state 0/1/2 (ABI v10)
// v14/v16 tail. LocalAI sets none of these (0 is "auto" for the device and
// "unset" for both sizing knobs, i.e. the pre-v14 engine byte for byte), but
@@ -80,9 +79,6 @@ type cModelParams struct {
Device int32 // 0 auto, 1 cpu, 2 cuda (ABI v14)
GPUMemoryUtil float64 // 0 => 0.92 (ABI v16)
KVCacheMemoryBytes int64 // 0 => unset (ABI v16)
LanguageModelOnly int32 // 0 = multimodal inputs enabled (ABI v19)
_ [4]byte
LimitMMPerPrompt uintptr // const char* JSON; NULL = default limits (ABI v19)
}
// cSamplingParams mirrors vllm_sampling_params (structured fields included).
@@ -151,11 +147,6 @@ type cVideoModelParams struct {
Device int32 // 0 cpu, 1 cuda
DequantBf16 int32 // 0 keep-quant, 1 dequant/stream bf16
Fp4Resident int32 // NVFP4+cuda: keep FP4 packed, Marlin W4A16
_ [4]byte
Family uintptr // const char*; NULL = detect (ABI v18)
ExtraKeys uintptr // const char* const* (ABI v18)
ExtraValues uintptr // const char* const* (ABI v18)
NExtras int32 // 0 = none (ABI v18)
_ [4]byte // trailing pad to the struct's 8-byte alignment
}
@@ -163,26 +154,22 @@ type cVideoModelParams struct {
// `steps` pair up into 8-byte slots; the uint64 seed forces the alignment after
// them, and the float noise_aug leaves a pad before output_dir.
type cVideoParams struct {
Prompt uintptr // const char*
Width int32
Height int32
NumFrames int32 // <= 1 => per-task default (124 for t2va/fl2va)
Steps int32 // <= 0 => the H3 default (50)
Seed uint64
HasSeed int32
_ [4]byte
FirstFrame uintptr // const char*; fl2va keyframe, binary PPM (P6)
LastFrame uintptr // const char*
RefImage uintptr // const char*; ref2va only
RefVideo uintptr // const char*; ref2va only, a frame_%06d.ppm DIRECTORY
RefAudio uintptr // const char*; ref2va only, 16-bit PCM WAV
NoiseAug float32 // <= 0 => 1.0
_ [4]byte
OutputDir uintptr // const char*; REQUIRED
ExtraKeys uintptr // const char* const* (ABI v18)
ExtraValues uintptr // const char* const* (ABI v18)
NExtras int32 // 0 = none (ABI v18)
_ [4]byte
Prompt uintptr // const char*
Width int32
Height int32
NumFrames int32 // <= 1 => per-task default (124 for t2va/fl2va)
Steps int32 // <= 0 => the H3 default (50)
Seed uint64
HasSeed int32
_ [4]byte
FirstFrame uintptr // const char*; fl2va keyframe, binary PPM (P6)
LastFrame uintptr // const char*
RefImage uintptr // const char*; ref2va only
RefVideo uintptr // const char*; ref2va only, a frame_%06d.ppm DIRECTORY
RefAudio uintptr // const char*; ref2va only, 16-bit PCM WAV
NoiseAug float32 // <= 0 => 1.0
_ [4]byte
OutputDir uintptr // const char*; REQUIRED
}
// cVideoResult mirrors vllm_video_result. Every member is library-allocated and
+2 -9
View File
@@ -28,11 +28,7 @@ var _ = Describe("C ABI video struct mirrors", func() {
Expect(unsafe.Offsetof(p.Device)).To(Equal(uintptr(72)))
Expect(unsafe.Offsetof(p.DequantBf16)).To(Equal(uintptr(76)))
Expect(unsafe.Offsetof(p.Fp4Resident)).To(Equal(uintptr(80)))
Expect(unsafe.Offsetof(p.Family)).To(Equal(uintptr(88)))
Expect(unsafe.Offsetof(p.ExtraKeys)).To(Equal(uintptr(96)))
Expect(unsafe.Offsetof(p.ExtraValues)).To(Equal(uintptr(104)))
Expect(unsafe.Offsetof(p.NExtras)).To(Equal(uintptr(112)))
Expect(unsafe.Sizeof(p)).To(Equal(uintptr(120)))
Expect(unsafe.Sizeof(p)).To(Equal(uintptr(88)))
})
It("cVideoParams matches vllm_video_params", func() {
@@ -51,10 +47,7 @@ var _ = Describe("C ABI video struct mirrors", func() {
Expect(unsafe.Offsetof(p.RefAudio)).To(Equal(uintptr(72)))
Expect(unsafe.Offsetof(p.NoiseAug)).To(Equal(uintptr(80)))
Expect(unsafe.Offsetof(p.OutputDir)).To(Equal(uintptr(88)))
Expect(unsafe.Offsetof(p.ExtraKeys)).To(Equal(uintptr(96)))
Expect(unsafe.Offsetof(p.ExtraValues)).To(Equal(uintptr(104)))
Expect(unsafe.Offsetof(p.NExtras)).To(Equal(uintptr(112)))
Expect(unsafe.Sizeof(p)).To(Equal(uintptr(120)))
Expect(unsafe.Sizeof(p)).To(Equal(uintptr(96)))
})
It("cVideoResult matches vllm_video_result", func() {
+8 -11
View File
@@ -16,7 +16,7 @@ func TestVllmCpp(t *testing.T) {
RunSpecs(t, "vllm-cpp suite")
}
// The Go POD mirrors must match the C struct layout of vllm.h (ABI v21)
// The Go POD mirrors must match the C struct layout of vllm.h (ABI v16)
// byte-for-byte: these offsets are the C offsets on LP64 (linux/darwin
// amd64+arm64). A failure here means govllmcpp.go drifted from vllm.h.
var _ = Describe("C ABI struct mirrors", func() {
@@ -24,7 +24,7 @@ var _ = Describe("C ABI struct mirrors", func() {
// VLLM_ABI_VERSION in the vllm.h of VLLM_CPP_VERSION (Makefile).
// Moving the pin past this without growing the mirrors below ships a
// backend that refuses every load at startup (issue #11379).
Expect(abiVersion).To(Equal(21))
Expect(abiVersion).To(Equal(16))
})
It("cModelParams matches vllm_model_params", func() {
@@ -42,16 +42,13 @@ var _ = Describe("C ABI struct mirrors", func() {
Expect(unsafe.Offsetof(p.MaxNumBatchedTokens)).To(Equal(uintptr(60)))
Expect(unsafe.Offsetof(p.SchedulingPolicy)).To(Equal(uintptr(64)))
Expect(unsafe.Offsetof(p.KVTransferConfig)).To(Equal(uintptr(72)))
Expect(unsafe.Offsetof(p.OffloadConfig)).To(Equal(uintptr(80)))
Expect(unsafe.Offsetof(p.EnableJumpForward)).To(Equal(uintptr(88)))
Expect(unsafe.Offsetof(p.Device)).To(Equal(uintptr(92)))
// 96: gpu_memory_utilization is a double, so it takes the next
Expect(unsafe.Offsetof(p.EnableJumpForward)).To(Equal(uintptr(80)))
Expect(unsafe.Offsetof(p.Device)).To(Equal(uintptr(84)))
// 88, not 92: gpu_memory_utilization is a double, so it takes the next
// 8-aligned slot after the int32 pair. Go pads identically.
Expect(unsafe.Offsetof(p.GPUMemoryUtil)).To(Equal(uintptr(96)))
Expect(unsafe.Offsetof(p.KVCacheMemoryBytes)).To(Equal(uintptr(104)))
Expect(unsafe.Offsetof(p.LanguageModelOnly)).To(Equal(uintptr(112)))
Expect(unsafe.Offsetof(p.LimitMMPerPrompt)).To(Equal(uintptr(120)))
Expect(unsafe.Sizeof(p)).To(Equal(uintptr(128)))
Expect(unsafe.Offsetof(p.GPUMemoryUtil)).To(Equal(uintptr(88)))
Expect(unsafe.Offsetof(p.KVCacheMemoryBytes)).To(Equal(uintptr(96)))
Expect(unsafe.Sizeof(p)).To(Equal(uintptr(104)))
})
It("cSamplingParams matches vllm_sampling_params (ABI v8)", func() {
+1 -1
View File
@@ -8,7 +8,7 @@ JOBS?=$(shell nproc --ignore=1)
# whisper.cpp version
WHISPER_REPO?=https://github.com/ggml-org/whisper.cpp
WHISPER_CPP_VERSION?=4834a2327d008ace3ec5a9ed00f51454bcabbc1c
WHISPER_CPP_VERSION?=592feef04a1802b18cbeffd0fd0eb5d02570c2ec
SO_TARGET?=libgowhisper.so
CMAKE_ARGS+=-DBUILD_SHARED_LIBS=OFF
+58 -15
View File
@@ -11,8 +11,6 @@
- https://github.com/ggerganov/llama.cpp
tags:
- text-to-text
- text-to-speech
- TTS
- LLM
- CPU
- GPU
@@ -74,6 +72,27 @@
nvidia-cuda-12: "cuda12-turboquant"
nvidia-l4t-cuda-12: "nvidia-l4t-arm64-turboquant"
nvidia-l4t-cuda-13: "cuda13-nvidia-l4t-arm64-turboquant"
- &cachyllama
name: "cachyllama"
alias: "cachyllama"
license: mit
description: |
llama.cpp fork for lower-spec and shared-memory systems, with persistent
SSD-backed prompt caching and Vulkan APU tuning.
urls:
- https://github.com/fewtarius/CachyLLama
tags:
- text-to-text
- LLM
- CPU
- GPU
- Vulkan
- Metal
- kv-cache
capabilities:
default: "cpu-cachyllama"
vulkan: "vulkan-cachyllama"
metal: "metal-cachyllama"
- &bonsai
name: "bonsai"
alias: "bonsai"
@@ -150,7 +169,6 @@
- audio-transcription
- CPU
- CUDA
- HIP
- Metal
# No vulkan key: the vulkan image would carry a Vulkan loader with no Mesa ICD
# (see the audio-cpp block in .github/backend-matrix.yml). Pointing a
@@ -162,7 +180,6 @@
nvidia: "cuda12-audio-cpp"
nvidia-cuda-12: "cuda12-audio-cpp"
nvidia-cuda-13: "cuda13-audio-cpp"
amd: "rocm-audio-cpp"
metal: "metal-audio-cpp"
metal-darwin-arm64: "metal-audio-cpp"
- &whispercpp
@@ -2074,6 +2091,12 @@
nvidia-cuda-12: "cuda12-turboquant-development"
nvidia-l4t-cuda-12: "nvidia-l4t-arm64-turboquant-development"
nvidia-l4t-cuda-13: "cuda13-nvidia-l4t-arm64-turboquant-development"
- !!merge <<: *cachyllama
name: "cachyllama-development"
capabilities:
default: "cpu-cachyllama-development"
vulkan: "vulkan-cachyllama-development"
metal: "metal-cachyllama-development"
- !!merge <<: *bonsai
name: "bonsai-development"
capabilities:
@@ -2103,7 +2126,6 @@
nvidia: "cuda12-audio-cpp-development"
nvidia-cuda-12: "cuda12-audio-cpp-development"
nvidia-cuda-13: "cuda13-audio-cpp-development"
amd: "rocm-audio-cpp-development"
metal: "metal-audio-cpp-development"
metal-darwin-arm64: "metal-audio-cpp-development"
- !!merge <<: *stablediffusionggml
@@ -2831,6 +2853,37 @@
uri: "quay.io/go-skynet/local-ai-backends:master-nvidia-l4t-cuda-13-arm64-turboquant"
mirrors:
- localai/localai-backends:master-nvidia-l4t-cuda-13-arm64-turboquant
## cachyllama
- !!merge <<: *cachyllama
name: "cpu-cachyllama"
uri: "quay.io/go-skynet/local-ai-backends:latest-cpu-cachyllama"
mirrors:
- localai/localai-backends:latest-cpu-cachyllama
- !!merge <<: *cachyllama
name: "cpu-cachyllama-development"
uri: "quay.io/go-skynet/local-ai-backends:master-cpu-cachyllama"
mirrors:
- localai/localai-backends:master-cpu-cachyllama
- !!merge <<: *cachyllama
name: "vulkan-cachyllama"
uri: "quay.io/go-skynet/local-ai-backends:latest-gpu-vulkan-cachyllama"
mirrors:
- localai/localai-backends:latest-gpu-vulkan-cachyllama
- !!merge <<: *cachyllama
name: "vulkan-cachyllama-development"
uri: "quay.io/go-skynet/local-ai-backends:master-gpu-vulkan-cachyllama"
mirrors:
- localai/localai-backends:master-gpu-vulkan-cachyllama
- !!merge <<: *cachyllama
name: "metal-cachyllama"
uri: "quay.io/go-skynet/local-ai-backends:latest-metal-darwin-arm64-cachyllama"
mirrors:
- localai/localai-backends:latest-metal-darwin-arm64-cachyllama
- !!merge <<: *cachyllama
name: "metal-cachyllama-development"
uri: "quay.io/go-skynet/local-ai-backends:master-metal-darwin-arm64-cachyllama"
mirrors:
- localai/localai-backends:master-metal-darwin-arm64-cachyllama
## bonsai
- !!merge <<: *bonsai
name: "cpu-bonsai"
@@ -7047,16 +7100,6 @@
uri: "quay.io/go-skynet/local-ai-backends:master-gpu-nvidia-cuda-13-audio-cpp"
mirrors:
- localai/localai-backends:master-gpu-nvidia-cuda-13-audio-cpp
- !!merge <<: *audiocpp
name: "rocm-audio-cpp"
uri: "quay.io/go-skynet/local-ai-backends:latest-gpu-rocm-hipblas-audio-cpp"
mirrors:
- localai/localai-backends:latest-gpu-rocm-hipblas-audio-cpp
- !!merge <<: *audiocpp
name: "rocm-audio-cpp-development"
uri: "quay.io/go-skynet/local-ai-backends:master-gpu-rocm-hipblas-audio-cpp"
mirrors:
- localai/localai-backends:master-gpu-rocm-hipblas-audio-cpp
- !!merge <<: *audiocpp
name: "metal-audio-cpp"
uri: "quay.io/go-skynet/local-ai-backends:latest-metal-darwin-arm64-audio-cpp"
+1 -1
View File
@@ -1,4 +1,4 @@
grpcio==1.83.0
protobuf
certifi
packaging==26.3
packaging==26.2
+2 -5
View File
@@ -1,10 +1,7 @@
.PHONY: fish-speech test-source-preparation
fish-speech: test-source-preparation
.PHONY: fish-speech
fish-speech:
bash install.sh
test-source-preparation:
bash prepare-source_test.sh
.PHONY: run
run: fish-speech
@echo "Running fish-speech..."
+4 -4
View File
@@ -39,10 +39,10 @@ else
cd "${FISH_SPEECH_DIR}" && git pull && cd -
fi
# Keep the platform-specific PyTorch installed above. Upstream pins the generic
# PyPI torch wheel, which replaces ROCm builds with a CUDA wheel during the
# editable install. pyaudio is only used by the upstream playback client.
bash "${backend_dir}/prepare-source.sh" "${BUILD_TYPE:-}" "${FISH_SPEECH_DIR}/pyproject.toml"
# Remove pyaudio from fish-speech deps — it's only used by the upstream client tool
# (tools/api_client.py) for speaker playback, not by our gRPC backend server.
# It requires native portaudio libs which aren't available on all build environments.
sed -i.bak '/"pyaudio"/d' "${FISH_SPEECH_DIR}/pyproject.toml"
# Install fish-speech deps from source (without the package itself since we use PYTHONPATH)
ensureVenv
@@ -1,18 +0,0 @@
#!/bin/bash
set -euo pipefail
build_type=${1:-}
pyproject=${2:?usage: prepare-source.sh BUILD_TYPE PYPROJECT}
prepared=$(mktemp "${pyproject}.XXXXXX")
trap 'rm -f "$prepared"' EXIT
awk -v build_type="$build_type" '
/^dependencies = \[$/ { in_project_dependencies = 1 }
build_type == "hipblas" && in_project_dependencies && /^[[:space:]]*"(torch|torchaudio)[^"]*",?[[:space:]]*$/ { next }
in_project_dependencies && /^[[:space:]]*"pyaudio",?[[:space:]]*$/ { next }
{ print }
in_project_dependencies && /^\]$/ { in_project_dependencies = 0 }
' "$pyproject" > "$prepared"
mv "$prepared" "$pyproject"
trap - EXIT
@@ -1,66 +0,0 @@
#!/bin/bash
set -euo pipefail
SCRIPT_DIR=$(dirname "$(realpath "$0")")
WORK_DIR=$(mktemp -d)
trap 'rm -rf "$WORK_DIR"' EXIT
write_fixture() {
cat > "$1" <<'EOF'
[project]
dependencies = [
"numpy",
"torch==2.8.0",
"torchaudio==2.8.0",
"pyaudio",
]
[project.optional-dependencies]
stable = [
"torch==2.8.0",
"torchaudio",
]
EOF
}
write_fixture "$WORK_DIR/rocm.toml"
write_fixture "$WORK_DIR/cuda.toml"
write_fixture "$WORK_DIR/cpu.toml"
bash "$SCRIPT_DIR/prepare-source.sh" hipblas "$WORK_DIR/rocm.toml"
bash "$SCRIPT_DIR/prepare-source.sh" cublas "$WORK_DIR/cuda.toml"
bash "$SCRIPT_DIR/prepare-source.sh" "" "$WORK_DIR/cpu.toml"
cat > "$WORK_DIR/expected-rocm.toml" <<'EOF'
[project]
dependencies = [
"numpy",
]
[project.optional-dependencies]
stable = [
"torch==2.8.0",
"torchaudio",
]
EOF
cat > "$WORK_DIR/expected-default.toml" <<'EOF'
[project]
dependencies = [
"numpy",
"torch==2.8.0",
"torchaudio==2.8.0",
]
[project.optional-dependencies]
stable = [
"torch==2.8.0",
"torchaudio",
]
EOF
diff -u "$WORK_DIR/expected-rocm.toml" "$WORK_DIR/rocm.toml"
diff -u "$WORK_DIR/expected-default.toml" "$WORK_DIR/cuda.toml"
diff -u "$WORK_DIR/expected-default.toml" "$WORK_DIR/cpu.toml"
echo "PASS: source preparation preserves each platform's PyTorch dependencies"
-1
View File
@@ -8,5 +8,4 @@ else
source $backend_dir/../common/libbackend.sh
fi
bash "${backend_dir}/prepare-source_test.sh"
runUnittests
+1 -4
View File
@@ -127,10 +127,7 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
context.set_code(grpc.StatusCode.NOT_FOUND)
context.set_details("no face detected")
return backend_pb2.EmbeddingResult()
return backend_pb2.EmbeddingResult(
embeddings=[float(x) for x in vec],
layout=backend_pb2.EMBEDDING_LAYOUT_FINAL,
)
return backend_pb2.EmbeddingResult(embeddings=[float(x) for x in vec])
def Detect(self, request, context):
if self.engine is None:
+1 -4
View File
@@ -638,10 +638,7 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
normalized = (pooled / (norm + 1e-12))
vec = normalized.cast(dtypes.float32).tolist()
return backend_pb2.EmbeddingResult(
embeddings=[float(x) for x in vec],
layout=backend_pb2.EMBEDDING_LAYOUT_FINAL,
)
return backend_pb2.EmbeddingResult(embeddings=[float(x) for x in vec])
except Exception as exc:
import traceback
traceback.print_exc()
+1 -4
View File
@@ -375,10 +375,7 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
# Pool to get sentence embeddings; i.e. generate one 1024 vector for the entire sentence
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
embeds = sentence_embeddings[0]
return backend_pb2.EmbeddingResult(
embeddings=embeds,
layout=backend_pb2.EMBEDDING_LAYOUT_FINAL,
)
return backend_pb2.EmbeddingResult(embeddings=embeds)
async def _predict(self, request, context, streaming=False):
set_seed(request.Seed)
@@ -2,9 +2,9 @@ torch==2.7.1
llvmlite==0.43.0
numba==0.60.0
accelerate
transformers>=5.15.0
transformers>=5.14.1
bitsandbytes
sentence-transformers==5.7.0
sentence-transformers==5.6.1
diffusers
soundfile
protobuf==7.35.0
@@ -2,9 +2,9 @@ torch==2.7.1
accelerate
llvmlite==0.43.0
numba==0.60.0
transformers>=5.15.0
transformers>=5.14.1
bitsandbytes
sentence-transformers==5.7.0
sentence-transformers==5.6.1
diffusers
soundfile
protobuf==7.35.0
@@ -2,9 +2,9 @@
torch==2.9.0
llvmlite==0.43.0
numba==0.60.0
transformers>=5.15.0
transformers>=5.14.1
bitsandbytes
sentence-transformers==5.7.0
sentence-transformers==5.6.1
diffusers
soundfile
protobuf==7.35.0
@@ -1,11 +1,11 @@
--extra-index-url https://download.pytorch.org/whl/rocm7.0
torch==2.10.0+rocm7.0
accelerate
transformers>=5.15.0
transformers>=5.14.1
llvmlite==0.43.0
numba==0.60.0
bitsandbytes
sentence-transformers==5.7.0
sentence-transformers==5.6.1
diffusers
soundfile
protobuf==7.35.0
@@ -3,9 +3,9 @@ torch
optimum[openvino]
llvmlite==0.43.0
numba==0.60.0
transformers>=5.15.0
transformers>=5.14.1
bitsandbytes
sentence-transformers==5.7.0
sentence-transformers==5.6.1
diffusers
soundfile
protobuf==7.35.0
@@ -2,9 +2,9 @@ torch==2.7.1
llvmlite==0.43.0
numba==0.60.0
accelerate
transformers>=5.15.0
transformers>=5.14.1
bitsandbytes
sentence-transformers==5.7.0
sentence-transformers==5.6.1
diffusers
soundfile
protobuf==7.35.0
+1 -4
View File
@@ -336,10 +336,7 @@ class BackendServicer(backend_pb2_grpc.BackendServicer):
context.set_code(grpc.StatusCode.INVALID_ARGUMENT)
context.set_details("No embeddings were calculated.")
return backend_pb2.EmbeddingResult()
return backend_pb2.EmbeddingResult(
embeddings=outputs[0].outputs.embedding,
layout=backend_pb2.EMBEDDING_LAYOUT_FINAL,
)
return backend_pb2.EmbeddingResult(embeddings=outputs[0].outputs.embedding)
async def PredictStream(self, request, context):
"""
+7 -12
View File
@@ -119,14 +119,14 @@ if [ "$(uname -s)" = "Darwin" ]; then
# can rewrite it. Darwin therefore follows vllm-metal and can lag the Linux
# vllm pin (requirements-cublas13-after.txt, bumped independently against
# vllm/vllm) until vllm-metal supports a newer vLLM.
VLLM_METAL_VERSION="v0.3.0.dev20260818075955"
VLLM_METAL_VERSION="v0.3.0.dev20260726174827"
# The coupled vLLM source version is whatever this vllm-metal release builds
# against. Derive it from
# against -- it declares it in its own installer as `vllm_v=`. Derive it from
# the PINNED tag rather than hardcoding a second value that could drift. The
# tag is immutable, so this stays reproducible across rebuilds.
VLLM_VERSION=$(curl -fsSL "https://raw.githubusercontent.com/vllm-project/vllm-metal/${VLLM_METAL_VERSION}/install.sh" \
| "$backend_dir/../../../scripts/lib/extract-vllm-metal-version.sh")
| grep -oE 'vllm_v="[0-9]+\.[0-9]+\.[0-9]+"' | head -n1 | cut -d'"' -f2)
if [ -z "${VLLM_VERSION}" ]; then
echo "ERROR: could not derive the vLLM version from vllm-metal ${VLLM_METAL_VERSION}" >&2
exit 1
@@ -168,7 +168,7 @@ if [ "$(uname -s)" = "Darwin" ]; then
# Intel XPU has no upstream-published vllm wheels, so we always build vllm
# from source against torch-xpu and replace the default triton with
# triton-xpu. Mirrors the upstream procedure:
# triton-xpu (matching torch 2.11). Mirrors the upstream procedure:
# https://github.com/vllm-project/vllm/blob/main/docs/getting_started/installation/gpu.xpu.inc.md
elif [ "x${BUILD_TYPE}" == "xintel" ]; then
# Hide requirements-intel-after.txt so installRequirements doesn't
@@ -194,23 +194,18 @@ elif [ "x${BUILD_TYPE}" == "xintel" ]; then
_vllm_src=$(mktemp -d)
trap 'rm -rf "${_vllm_src}"' EXIT
# Keep the source build aligned with the version shipped by the other
# accelerator profiles. Building the moving main branch can silently pull
# a newer torch/XPU runtime than the selected oneAPI base image supports.
VLLM_VERSION="0.26.0"
git clone --depth 1 --branch "v${VLLM_VERSION}" \
https://github.com/vllm-project/vllm "${_vllm_src}/vllm"
git clone --depth 1 https://github.com/vllm-project/vllm "${_vllm_src}/vllm"
pushd "${_vllm_src}/vllm"
# Install vllm's own runtime deps (torch-xpu, vllm_xpu_kernels,
# pydantic, fastapi, …) from upstream's requirements/xpu.txt — the
# canonical source of truth. Avoids re-pinning everything ourselves.
uv pip install ${EXTRA_PIP_INSTALL_FLAGS:-} -r requirements/xpu.txt
# Stock triton (NVIDIA-only) may have come in transitively; replace
# with the version vLLM 0.26.0 specifies for torch 2.12.
# with triton-xpu==3.7.0 which matches torch 2.11.
uv pip uninstall triton triton-xpu 2>/dev/null || true
uv pip install ${EXTRA_PIP_INSTALL_FLAGS:-} \
--extra-index-url https://download.pytorch.org/whl/xpu \
triton-xpu==3.7.1
triton-xpu==3.7.0
export CMAKE_PREFIX_PATH="$(python -c 'import site; print(site.getsitepackages()[0])'):${CMAKE_PREFIX_PATH:-}"
VLLM_TARGET_DEVICE=xpu uv pip install ${EXTRA_PIP_INSTALL_FLAGS:-} --no-deps .
popd
@@ -3,8 +3,8 @@
# on a cu130 host. Pull the cu130-flavoured wheel from vLLM's per-tag index
# instead — the cublas13 case in install.sh adds --index-strategy=unsafe-best-match
# so uv consults this index alongside PyPI.
--extra-index-url https://wheels.vllm.ai/0.27.1/cu130
--extra-index-url https://wheels.vllm.ai/0.26.0/cu130
# VERSION COUPLING: darwin/Apple-Silicon builds use vllm-metal (see install.sh),
# which pins this exact vLLM version. Bumping vllm here means coordinating with a
# vllm-metal release that supports the new version, or macOS/Metal builds break.
vllm==0.27.1
vllm==0.26.0
@@ -9,4 +9,4 @@
# memory architecture crash deterministically with an empty "Engine core init
# failed" set (mudler/LocalAI#10722). Leaving this unpinned let the L4T image
# drift onto whatever wheel was latest at build time.
vllm==0.26.0
vllm==0.25.1
+1 -1
View File
@@ -3,5 +3,5 @@ protobuf
certifi
setuptools
pillow
charset-normalizer>=3.5.1
charset-normalizer>=3.4.9
chardet
+1 -19
View File
@@ -1,10 +1,8 @@
package application
import (
"cmp"
"context"
"math/rand/v2"
"path/filepath"
"sync"
"sync/atomic"
"time"
@@ -21,14 +19,12 @@ import (
"github.com/mudler/LocalAI/core/services/nodes"
"github.com/mudler/LocalAI/core/services/routing/admission"
"github.com/mudler/LocalAI/core/services/routing/billing"
"github.com/mudler/LocalAI/core/services/routing/corpus"
"github.com/mudler/LocalAI/core/services/routing/pii"
"github.com/mudler/LocalAI/core/services/routing/piidetector"
"github.com/mudler/LocalAI/core/services/routing/router"
"github.com/mudler/LocalAI/core/services/voiceprofile"
"github.com/mudler/LocalAI/core/services/voicerecognition"
"github.com/mudler/LocalAI/core/templates"
"github.com/mudler/LocalAI/core/trace"
pkggrpc "github.com/mudler/LocalAI/pkg/grpc"
localaitools "github.com/mudler/LocalAI/pkg/mcp/localaitools"
localaiInproc "github.com/mudler/LocalAI/pkg/mcp/localaitools/inproc"
@@ -80,7 +76,6 @@ type Application struct {
mitmHostConflicts atomic.Pointer[map[string][]string]
routerDecisions router.DecisionStore
routerRegistry *router.Registry
routerCorpus *corpus.Manager
admissionLimiter *admission.Limiter
watchdogMutex sync.Mutex
watchdogStop chan bool
@@ -124,8 +119,6 @@ func (a *Application) Ready() bool { return a.startupComplete.Load() }
func (a *Application) markStartupComplete() { a.startupComplete.Store(true) }
func newApplication(appConfig *config.ApplicationConfig) *Application {
corebackend.ConfigureGlobalBackendAdmission(appConfig.MaxConcurrentBackendRequests)
trace.ConfigureBackendTraceMaxInFlight(appConfig.MaxConcurrentBackendRequests)
ml := model.NewModelLoader(appConfig.SystemState)
// Apply the per-model load-failure cooldown (0 disables). Set here rather
@@ -141,7 +134,7 @@ func newApplication(appConfig *config.ApplicationConfig) *Application {
// Record a model_load backend trace for every real backend load, so the
// Traces UI shows which backend runtime served each model and how long
// the load took. Load failures are traced by the modality wrappers.
ml.SetLoadLifecycleObserver(corebackend.ModelLoadTraceObserver(appConfig))
ml.SetLoadObserver(corebackend.ModelLoadTraceObserver(appConfig))
app := &Application{
backendLoader: config.NewModelConfigLoader(
@@ -153,10 +146,6 @@ func newApplication(appConfig *config.ApplicationConfig) *Application {
applicationConfig: appConfig,
templatesEvaluator: templates.NewEvaluator(appConfig.SystemState.Model.ModelsPath),
voiceProfileStore: voiceprofile.NewStore(appConfig.DataPath),
// KNN corpus files live under <state dir>/router-corpus (same
// DataPath → DynamicConfigsDir precedence the agent pool uses).
routerCorpus: corpus.NewManager(filepath.Join(
cmp.Or(appConfig.DataPath, appConfig.DynamicConfigsDir, "."), "router-corpus")),
}
// Face-recognition registry backed by LocalAI's built-in vector store.
@@ -586,13 +575,6 @@ func (a *Application) start() error {
assistantClient.PIIRedactor = a.piiRedactor
assistantClient.PIIEvents = a.piiEvents
assistantClient.RouterDecisions = a.routerDecisions
// Router corpus tools — same factories the RouteModel middleware
// uses, so the assistant and the request path agree on store
// namespaces and model resolution.
assistantClient.RouterCorpus = a.RouterCorpus()
assistantClient.RouterEmbedder = a.Embedder
assistantClient.RouterEmbedderFingerprint = a.EmbedderFingerprint
assistantClient.RouterVectorStore = a.VectorStore
if err := holder.Initialize(a.applicationConfig.Context, assistantClient, localaitools.Options{}); err != nil {
// Why log+continue instead of fail: the assistant is an optional
// feature; a failure here must not take down the whole server.
-7
View File
@@ -41,7 +41,6 @@ type DistributedServices struct {
FileStager nodes.FileStager
ModelAdapter *nodes.ModelRouterAdapter
Unloader *nodes.RemoteUnloaderAdapter
ModelCleanup *nodes.ModelCleanupService
shutdownOnce sync.Once
}
@@ -347,10 +346,8 @@ func initDistributed(cfg *config.ApplicationConfig, authDB *gorm.DB, configLoade
if configLoader != nil {
conflictResolver = configLoader
}
modelCleanup := nodes.NewModelCleanupService(registry, remoteUnloader)
router := nodes.NewSmartRouter(registry, nodes.SmartRouterOptions{
Unloader: remoteUnloader,
ModelCleanup: modelCleanup,
FileStager: fileStager,
GalleriesJSON: routerGalleriesJSON,
AuthToken: routerAuthToken,
@@ -381,9 +378,6 @@ func initDistributed(cfg *config.ApplicationConfig, authDB *gorm.DB, configLoade
cfg.Distributed.BackendInstallTimeoutOrDefault(),
cfg.Distributed.ModelLoadTimeoutOrDefault(),
),
// Bounds the REQUEST, not the load: a caller out of budget gets 503 with
// live staging progress while the job keeps running underneath.
ModelLoadWait: cfg.Distributed.ModelLoadWait,
})
// Wire staging-progress broadcasting so file-staging shows up on every
@@ -440,7 +434,6 @@ func initDistributed(cfg *config.ApplicationConfig, authDB *gorm.DB, configLoade
FileStager: fileStager,
ModelAdapter: modelAdapter,
Unloader: remoteUnloader,
ModelCleanup: modelCleanup,
}, nil
}
-67
View File
@@ -2,18 +2,10 @@ package application
import (
"context"
"crypto/sha256"
"encoding/hex"
"fmt"
"os"
"path/filepath"
"sort"
"strings"
"github.com/mudler/LocalAI/core/backend"
"github.com/mudler/LocalAI/core/config"
"github.com/mudler/LocalAI/core/services/routing/corpus"
"gopkg.in/yaml.v3"
)
// adapterConfig resolves a model name to its runtime ModelConfig, or nil when
@@ -108,59 +100,6 @@ func (a *Application) Embedder(modelName string) backend.Embedder {
return &lazyEmbedder{app: a, modelName: modelName}
}
// EmbedderFingerprint returns a stable identity for the embedding space a
// named model currently produces. The effective config covers backend and
// embedding-affecting options; declared download checksums are part of that
// config, while local artifact stat data detects the common in-place file
// replacement case. Remote services without a stable artifact identity can
// use router.knn.embedding_revision to force invalidation explicitly.
func (a *Application) EmbedderFingerprint(modelName string) (string, error) {
cfg := a.adapterConfig(modelName)
if cfg == nil {
return "", fmt.Errorf("embedding model %q not available", modelName)
}
raw, err := yaml.Marshal(cfg)
if err != nil {
return "", fmt.Errorf("fingerprint embedding model %q config: %w", modelName, err)
}
h := sha256.New()
_, _ = h.Write(raw)
paths := map[string]struct{}{}
for _, name := range append([]string{cfg.Model, cfg.MMProj}, downloadFileNames(cfg)...) {
if name == "" || strings.Contains(name, "://") {
continue
}
if !filepath.IsAbs(name) {
name = filepath.Join(a.applicationConfig.SystemState.Model.ModelsPath, name)
}
paths[filepath.Clean(name)] = struct{}{}
}
ordered := make([]string, 0, len(paths))
for path := range paths {
ordered = append(ordered, path)
}
sort.Strings(ordered)
for _, path := range ordered {
_, _ = h.Write([]byte("\x00" + path))
fi, statErr := os.Stat(path)
if statErr != nil {
_, _ = h.Write([]byte("\x00missing"))
continue
}
_, _ = fmt.Fprintf(h, "\x00%d\x00%d\x00%s", fi.Size(), fi.ModTime().UnixNano(), fi.Mode().String())
}
return hex.EncodeToString(h.Sum(nil)), nil
}
func downloadFileNames(cfg *config.ModelConfig) []string {
out := make([]string, 0, len(cfg.DownloadFiles))
for _, f := range cfg.DownloadFiles {
out = append(out, f.Filename)
}
return out
}
type lazyEmbedder struct {
app *Application
modelName string
@@ -179,9 +118,3 @@ func (l *lazyEmbedder) Embed(ctx context.Context, text string) ([]float32, error
func (a *Application) VectorStore(storeName string) backend.VectorStore {
return backend.NewVectorStore(a.modelLoader, a.applicationConfig, a.backendLoader, storeName)
}
// RouterCorpus returns the process-wide KNN corpus manager, built in
// newApplication.
func (a *Application) RouterCorpus() *corpus.Manager {
return a.routerCorpus
}
-31
View File
@@ -94,37 +94,6 @@ var _ = Describe("router_factories lazy config resolution", func() {
})
})
Context("EmbedderFingerprint", func() {
It("changes when the effective config or local artifact changes", func() {
writeCfg("emb-test", "llama-cpp")
artifact := filepath.Join(tmpDir, "emb-test.bin")
Expect(os.WriteFile(artifact, []byte("first"), 0o644)).To(Succeed())
first, err := app.EmbedderFingerprint("emb-test")
Expect(err).NotTo(HaveOccurred())
again, err := app.EmbedderFingerprint("emb-test")
Expect(err).NotTo(HaveOccurred())
Expect(again).To(Equal(first))
Expect(os.WriteFile(artifact, []byte("replacement-with-different-size"), 0o644)).To(Succeed())
replaced, err := app.EmbedderFingerprint("emb-test")
Expect(err).NotTo(HaveOccurred())
Expect(replaced).NotTo(Equal(first))
app.backendLoader.UpdateModelConfig("emb-test", func(c *config.ModelConfig) {
c.Backend = "rerankers"
})
updated, err := app.EmbedderFingerprint("emb-test")
Expect(err).NotTo(HaveOccurred())
Expect(updated).NotTo(Equal(replaced))
})
It("rejects an unknown model", func() {
_, err := app.EmbedderFingerprint("missing")
Expect(err).To(HaveOccurred())
})
})
Context("Scorer", func() {
It("returns nil at construction for an unknown model", func() {
Expect(app.Scorer("missing")).To(BeNil())
+1 -4
View File
@@ -298,7 +298,6 @@ func New(opts ...config.AppOption) (*Application, error) {
if distSvc.Reconciler != nil {
go distSvc.Reconciler.Run(options.Context)
}
go distSvc.ModelCleanup.Run(options.Context)
// In distributed mode, MCP CI jobs are executed by agent workers (not the frontend)
// because the frontend can't create MCP sessions (e.g., stdio servers using docker).
// The dispatcher still subscribes to jobs.new for persistence (result/progress subs)
@@ -371,15 +370,13 @@ func New(opts ...config.AppOption) (*Application, error) {
gs := application.galleryService
sys := options.SystemState
cfgLoaderOpts := options.ToConfigLoaderOptions()
modelRevisionLifecycle := modeladmin.NewDistributedModelRevisionLifecycle(distSvc.Registry, distSvc.ModelCleanup)
gs.SetModelRevisionLifecycle(modelRevisionLifecycle)
gs.OnModelsChanged = func(evt messaging.CacheInvalidateEvent) {
// ApplyRemoteChange honors the op: a "delete" prunes the element
// (a reload-from-path is additive and cannot drop it), anything
// else reloads from disk; a named element's running instance is
// shut down so the new config takes effect. The originating
// replica reloads inline and never depends on this path.
if err := modeladmin.ApplyRemoteChange(options.Context, application.ModelConfigLoader(), sys.Model.ModelsPath, evt, modelRevisionLifecycle, cfgLoaderOpts...); err != nil {
if err := modeladmin.ApplyRemoteChange(application.ModelConfigLoader(), application.modelLoader, sys.Model.ModelsPath, evt, cfgLoaderOpts...); err != nil {
xlog.Warn("Failed to apply peer model config change", "error", err)
}
}
+1 -20
View File
@@ -91,20 +91,12 @@ func ModelAudioTransform(
return AudioTransformOutputs{}, nil, fmt.Errorf("persist reference: %w", err)
}
}
release, err := AcquireGlobalBackendSlot()
if err != nil {
return AudioTransformOutputs{}, nil, err
}
defer release()
var startTime time.Time
var traceID string
if appConfig.EnableTracing {
trace.InitBackendTracingIfEnabled(appConfig.TracingMaxItems, appConfig.TracingMaxBodyBytes)
startTime = time.Now()
traceID = trace.BeginBackendTrace(trace.BackendTrace{Timestamp: startTime, Type: trace.BackendTraceAudioTransform, ModelName: modelConfig.Name, Backend: modelConfig.Backend, Summary: trace.TruncateString(filepath.Base(audioPath), 200)})
}
defer trace.CancelBackendTrace(traceID)
res, err := transformModel.AudioTransform(ctx, &proto.AudioTransformRequest{
ModelIdentity: modelConfig.Model,
@@ -134,7 +126,6 @@ func ModelAudioTransform(
}
}
trace.RecordBackendTrace(trace.BackendTrace{
ID: traceID,
Timestamp: startTime,
Duration: time.Since(startTime),
Type: trace.BackendTraceAudioTransform,
@@ -207,17 +198,7 @@ func ModelAudioTransformStream(
if transformModel == nil {
return nil, fmt.Errorf("could not load audio-transform model %q", modelConfig.Model)
}
release, err := AcquireGlobalBackendSlot()
if err != nil {
return nil, err
}
stream, err := transformModel.AudioTransformStream(ctx)
if err != nil {
release()
return nil, err
}
stream.AddCleanup(release)
return stream, nil
return transformModel.AudioTransformStream(ctx)
}
// persistAudioInput copies a transient input file (typically a multipart
+2 -2
View File
@@ -24,8 +24,8 @@ import (
"github.com/mudler/LocalAI/core/backend"
"github.com/mudler/LocalAI/core/config"
"github.com/mudler/LocalAI/core/schema"
"github.com/mudler/LocalAI/pkg/distributedhdr"
pbproto "github.com/mudler/LocalAI/pkg/grpc/proto"
"github.com/mudler/LocalAI/pkg/distributedhdr"
"github.com/mudler/LocalAI/pkg/model"
"github.com/mudler/LocalAI/pkg/system"
@@ -41,7 +41,7 @@ import (
func newCapturingLoader() (*model.ModelLoader, *atomic.Value, func() context.Context) {
loader := model.NewModelLoader(&system.SystemState{})
var captured atomic.Value
loader.SetModelRouter(func(ctx context.Context, _ string, _, _, _, _ string, _ *pbproto.ModelOptions, _ bool) (*model.Model, error) {
loader.SetModelRouter(func(ctx context.Context, _ string, _, _, _ string, _ *pbproto.ModelOptions, _ bool) (*model.Model, error) {
captured.Store(ctx)
// Return an error so the backend short-circuits before trying to
// dial gRPC. We only care about the context-arrival contract.
-9
View File
@@ -33,20 +33,12 @@ func Depth(
if depthModel == nil {
return nil, fmt.Errorf("could not load depth model")
}
release, err := AcquireGlobalBackendSlot()
if err != nil {
return nil, err
}
defer release()
var startTime time.Time
var traceID string
if appConfig.EnableTracing {
trace.InitBackendTracingIfEnabled(appConfig.TracingMaxItems, appConfig.TracingMaxBodyBytes)
startTime = time.Now()
traceID = trace.BeginBackendTrace(trace.BackendTrace{Timestamp: startTime, Type: trace.BackendTraceDepth, ModelName: modelConfig.Name, Backend: modelConfig.Backend, Summary: trace.TruncateString(in.GetSrc(), 200)})
}
defer trace.CancelBackendTrace(traceID)
// Stamped here for the same reason as in rerank.go: the caller builds the
// request without a ModelConfig, this function has the one that loaded.
@@ -61,7 +53,6 @@ func Depth(
}
trace.RecordBackendTrace(trace.BackendTrace{
ID: traceID,
Timestamp: startTime,
Duration: time.Since(startTime),
Type: trace.BackendTraceDepth,
-9
View File
@@ -33,19 +33,11 @@ func Detection(
return nil, fmt.Errorf("could not load detection model")
}
release, err := AcquireGlobalBackendSlot()
if err != nil {
return nil, err
}
defer release()
var startTime time.Time
var traceID string
if appConfig.EnableTracing {
trace.InitBackendTracingIfEnabled(appConfig.TracingMaxItems, appConfig.TracingMaxBodyBytes)
startTime = time.Now()
traceID = trace.BeginBackendTrace(trace.BackendTrace{Timestamp: startTime, Type: trace.BackendTraceDetection, ModelName: modelConfig.Name, Backend: modelConfig.Backend, Summary: trace.TruncateString(sourceFile, 200)})
}
defer trace.CancelBackendTrace(traceID)
res, err := detectionModel.Detect(ctx, &proto.DetectOptions{
ModelIdentity: modelConfig.Model,
@@ -63,7 +55,6 @@ func Detection(
}
trace.RecordBackendTrace(trace.BackendTrace{
ID: traceID,
Timestamp: startTime,
Duration: time.Since(startTime),
Type: trace.BackendTraceDetection,
-9
View File
@@ -23,19 +23,11 @@ func ModelDetokenize(tokens []int32, loader *model.ModelLoader, modelConfig conf
return schema.DetokenizeResponse{}, err
}
release, err := AcquireGlobalBackendSlot()
if err != nil {
return schema.DetokenizeResponse{}, err
}
defer release()
var startTime time.Time
var traceID string
if appConfig.EnableTracing {
trace.InitBackendTracingIfEnabled(appConfig.TracingMaxItems, appConfig.TracingMaxBodyBytes)
startTime = time.Now()
traceID = trace.BeginBackendTrace(trace.BackendTrace{Timestamp: startTime, Type: trace.BackendTraceTokenize, ModelName: modelConfig.Name, Backend: modelConfig.Backend, Summary: "detokenize"})
}
defer trace.CancelBackendTrace(traceID)
resp, err := inferenceModel.Detokenize(appConfig.Context, &pb.DetokenizeRequest{Tokens: tokens})
@@ -51,7 +43,6 @@ func ModelDetokenize(tokens []int32, loader *model.ModelLoader, modelConfig conf
}
trace.RecordBackendTrace(trace.BackendTrace{
ID: traceID,
Timestamp: startTime,
Duration: time.Since(startTime),
Type: trace.BackendTraceTokenize,
+10 -93
View File
@@ -2,7 +2,6 @@ package backend
import (
"context"
"errors"
"fmt"
"time"
@@ -10,80 +9,9 @@ import (
"github.com/mudler/LocalAI/core/trace"
"github.com/mudler/LocalAI/pkg/grpc"
"github.com/mudler/LocalAI/pkg/grpc/proto"
model "github.com/mudler/LocalAI/pkg/model"
"google.golang.org/grpc/codes"
"google.golang.org/grpc/status"
)
type embeddingPoolingCompatibilityError struct {
message string
}
func (e *embeddingPoolingCompatibilityError) Error() string {
return e.message
}
func poolingCompatibilityErrorf(format string, args ...any) error {
return &embeddingPoolingCompatibilityError{message: fmt.Sprintf(format, args...)}
}
// IsEmbeddingPoolingCompatibilityError reports errors caused by a requested
// pooling scheme disagreeing with the layout declared by the loaded backend.
// HTTP callers map these client-selectable incompatibilities to status 400.
func IsEmbeddingPoolingCompatibilityError(err error) bool {
var target *embeddingPoolingCompatibilityError
return errors.As(err, &target)
}
// finishEmbeddingResult applies the model's Go-side pooling scheme only when
// the backend declares that it returned per-token vectors. Shape alone is not
// sufficient: one raw token and one final vector are both reported as 1 x dim.
// Legacy backends remain compatible with backend pooling, but cannot opt in to
// Go-side pooling until they declare their result layout.
func finishEmbeddingResult(res *proto.EmbeddingResult, modelConfig config.ModelConfig) ([]float32, error) {
scheme := modelConfig.Pooling
if scheme == "" || scheme == PoolingBackend {
switch res.GetLayout() {
case proto.EmbeddingLayout_EMBEDDING_LAYOUT_UNSPECIFIED, proto.EmbeddingLayout_EMBEDDING_LAYOUT_FINAL:
return res.Embeddings, nil
case proto.EmbeddingLayout_EMBEDDING_LAYOUT_PER_TOKEN:
return nil, poolingCompatibilityErrorf(
"pooling %q cannot pass through per-token embeddings: choose %q, %q or %q, or load the backend with pooling enabled",
PoolingBackend, PoolingMean, PoolingLast, PoolingDecayedMean)
default:
return nil, poolingCompatibilityErrorf("pooling %q cannot use unknown embedding layout %d", PoolingBackend, res.GetLayout())
}
}
switch res.GetLayout() {
case proto.EmbeddingLayout_EMBEDDING_LAYOUT_PER_TOKEN:
// Pool below after validating the reported matrix shape.
case proto.EmbeddingLayout_EMBEDDING_LAYOUT_FINAL:
return nil, poolingCompatibilityErrorf(
"pooling %q needs per-token embeddings but this backend returned a final vector; configure raw per-token output if the backend supports it (llama.cpp: options [\"pooling:none\"])",
scheme)
case proto.EmbeddingLayout_EMBEDDING_LAYOUT_UNSPECIFIED:
return nil, poolingCompatibilityErrorf(
"pooling %q needs per-token embeddings but the backend did not declare its embedding layout: rebuild/update the backend to report EmbeddingResult.layout",
scheme)
default:
return nil, poolingCompatibilityErrorf("pooling %q cannot use unknown embedding layout %d", scheme, res.GetLayout())
}
return PoolEmbeddingResult(res, scheme,
float64(modelConfig.PoolingHalfLifeTokens),
embdNormalizeFromOptions(modelConfig.Options))
}
// mapEmbeddingGRPCError turns a gRPC ResourceExhausted — the per-token
// payload of a very long conversation exceeding the 50MB message cap —
// into an actionable message; everything else passes through unchanged.
func mapEmbeddingGRPCError(err error) error {
if status.Code(err) == codes.ResourceExhausted {
return fmt.Errorf("conversation too long for per-token embeddings (gRPC message limit exceeded): %w", err)
}
return err
}
// Embedder produces a fixed-dimension vector from a prompt. The
// router's L2 embedding cache uses it to look up semantically-similar
// past decisions.
@@ -138,19 +66,19 @@ func ModelEmbedding(ctx context.Context, s string, tokens []int, loader *model.M
res, err := model.Embeddings(appConfig.Context, predictOptions)
if err != nil {
return nil, mapEmbeddingGRPCError(err)
return nil, err
}
return finishEmbeddingResult(res, modelConfig)
return res.Embeddings, nil
}
predictOptions.Embeddings = s
res, err := model.Embeddings(appConfig.Context, predictOptions)
if err != nil {
return nil, mapEmbeddingGRPCError(err)
return nil, err
}
return finishEmbeddingResult(res, modelConfig)
return res.Embeddings, nil
}
default:
fn = func() ([]float32, error) {
@@ -181,15 +109,9 @@ func ModelEmbedding(ctx context.Context, s string, tokens []int, loader *model.M
traceData["input_tokens_count"] = len(tokens)
}
summary := trace.TruncateString(s, 200)
if summary == "" {
summary = fmt.Sprintf("tokens[%d]", len(tokens))
}
startTime := time.Now()
originalFn := wrappedFn
wrappedFn = func() ([]float32, error) {
startTime := time.Now()
traceID := trace.BeginBackendTrace(trace.BackendTrace{Timestamp: startTime, Type: trace.BackendTraceEmbedding, ModelName: modelConfig.Name, Backend: modelConfig.Backend, Summary: summary})
defer trace.CancelBackendTrace(traceID)
result, err := originalFn()
duration := time.Since(startTime)
@@ -200,8 +122,12 @@ func ModelEmbedding(ctx context.Context, s string, tokens []int, loader *model.M
errStr = err.Error()
}
summary := trace.TruncateString(s, 200)
if summary == "" {
summary = fmt.Sprintf("tokens[%d]", len(tokens))
}
trace.RecordBackendTrace(trace.BackendTrace{
ID: traceID,
Timestamp: startTime,
Duration: duration,
Type: trace.BackendTraceEmbedding,
@@ -215,15 +141,6 @@ func ModelEmbedding(ctx context.Context, s string, tokens []int, loader *model.M
return result, err
}
}
originalFn := wrappedFn
wrappedFn = func() ([]float32, error) {
release, err := AcquireGlobalBackendSlot()
if err != nil {
return nil, err
}
defer release()
return originalFn()
}
return wrappedFn, nil
}
-9
View File
@@ -30,19 +30,11 @@ func FaceAnalyze(
return nil, fmt.Errorf("could not load face recognition model")
}
release, err := AcquireGlobalBackendSlot()
if err != nil {
return nil, err
}
defer release()
var startTime time.Time
var traceID string
if appConfig.EnableTracing {
trace.InitBackendTracingIfEnabled(appConfig.TracingMaxItems, appConfig.TracingMaxBodyBytes)
startTime = time.Now()
traceID = trace.BeginBackendTrace(trace.BackendTrace{Timestamp: startTime, Type: trace.BackendTraceFaceAnalyze, ModelName: modelConfig.Name, Backend: modelConfig.Backend, Summary: "face analysis"})
}
defer trace.CancelBackendTrace(traceID)
res, err := faceModel.FaceAnalyze(ctx, &proto.FaceAnalyzeRequest{
ModelIdentity: modelConfig.Model,
@@ -57,7 +49,6 @@ func FaceAnalyze(
errStr = err.Error()
}
trace.RecordBackendTrace(trace.BackendTrace{
ID: traceID,
Timestamp: startTime,
Duration: time.Since(startTime),
Type: trace.BackendTraceFaceAnalyze,
-5
View File
@@ -32,11 +32,6 @@ func FaceEmbed(
predictOpts := gRPCPredictOpts(modelConfig, loader.ModelPath)
predictOpts.Images = []string{imgBase64}
release, err := AcquireGlobalBackendSlot()
if err != nil {
return nil, err
}
defer release()
res, err := faceModel.Embeddings(ctx, predictOpts)
if err != nil {
-9
View File
@@ -30,19 +30,11 @@ func FaceVerify(
return nil, fmt.Errorf("could not load face recognition model")
}
release, err := AcquireGlobalBackendSlot()
if err != nil {
return nil, err
}
defer release()
var startTime time.Time
var traceID string
if appConfig.EnableTracing {
trace.InitBackendTracingIfEnabled(appConfig.TracingMaxItems, appConfig.TracingMaxBodyBytes)
startTime = time.Now()
traceID = trace.BeginBackendTrace(trace.BackendTrace{Timestamp: startTime, Type: trace.BackendTraceFaceVerify, ModelName: modelConfig.Name, Backend: modelConfig.Backend, Summary: "face verification"})
}
defer trace.CancelBackendTrace(traceID)
res, err := faceModel.FaceVerify(ctx, &proto.FaceVerifyRequest{
ModelIdentity: modelConfig.Model,
@@ -58,7 +50,6 @@ func FaceVerify(
errStr = err.Error()
}
trace.RecordBackendTrace(trace.BackendTrace{
ID: traceID,
Timestamp: startTime,
Duration: time.Since(startTime),
Type: trace.BackendTraceFaceVerify,
-72
View File
@@ -1,72 +0,0 @@
// SPDX-License-Identifier: MIT
package backend
import (
"fmt"
"sync"
"time"
"github.com/mudler/LocalAI/core/config"
)
// BackendAdmissionError reports that the process-wide backend execution
// ceiling is full. HTTP callers map it to 503; internal callers receive the
// same typed error instead of silently queueing and growing in-flight state.
type BackendAdmissionError struct {
Limit int
RetryAfter time.Duration
}
func (e *BackendAdmissionError) Error() string {
return fmt.Sprintf("backend inference capacity reached (max_concurrent=%d); retry after %s", e.Limit, e.RetryAfter)
}
var backendAdmission = struct {
sync.RWMutex
limit int
slots chan struct{}
}{}
// ConfigureGlobalBackendAdmission sets the process-wide ceiling. It is called
// during application construction, before backend work can begin.
func ConfigureGlobalBackendAdmission(limit int) {
if limit <= 0 {
limit = config.DefaultMaxConcurrentBackendRequests
}
backendAdmission.Lock()
backendAdmission.limit = limit
backendAdmission.slots = make(chan struct{}, limit)
backendAdmission.Unlock()
}
// AcquireGlobalBackendSlot admits one backend operation without queueing.
// Callers must invoke release on every completion path.
func AcquireGlobalBackendSlot() (release func(), err error) {
backendAdmission.RLock()
limit, slots := backendAdmission.limit, backendAdmission.slots
backendAdmission.RUnlock()
if slots == nil {
backendAdmission.Lock()
if backendAdmission.slots == nil {
backendAdmission.limit = config.DefaultMaxConcurrentBackendRequests
backendAdmission.slots = make(chan struct{}, backendAdmission.limit)
}
limit, slots = backendAdmission.limit, backendAdmission.slots
backendAdmission.Unlock()
}
select {
case slots <- struct{}{}:
var once sync.Once
return func() { once.Do(func() { <-slots }) }, nil
default:
return nil, &BackendAdmissionError{Limit: limit, RetryAfter: time.Second}
}
}
// GlobalBackendInFlight is the current number of admitted backend operations.
func GlobalBackendInFlight() int {
backendAdmission.RLock()
defer backendAdmission.RUnlock()
return len(backendAdmission.slots)
}
-39
View File
@@ -1,39 +0,0 @@
// SPDX-License-Identifier: MIT
package backend_test
import (
"errors"
"github.com/mudler/LocalAI/core/backend"
. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"
)
var _ = Describe("global backend admission", func() {
BeforeEach(func() {
backend.ConfigureGlobalBackendAdmission(1)
})
It("rejects excess backend work without queueing", func() {
release, err := backend.AcquireGlobalBackendSlot()
Expect(err).NotTo(HaveOccurred())
Expect(backend.GlobalBackendInFlight()).To(Equal(1))
_, err = backend.AcquireGlobalBackendSlot()
var capacityErr *backend.BackendAdmissionError
Expect(errors.As(err, &capacityErr)).To(BeTrue())
Expect(capacityErr.Limit).To(Equal(1))
release()
Expect(backend.GlobalBackendInFlight()).To(BeZero())
})
It("makes release idempotent", func() {
release, err := backend.AcquireGlobalBackendSlot()
Expect(err).NotTo(HaveOccurred())
release()
release()
Expect(backend.GlobalBackendInFlight()).To(BeZero())
})
})
+1 -13
View File
@@ -63,11 +63,9 @@ func ImageGeneration(ctx context.Context, height, width, step, seed int, positiv
"destination": dst,
}
startTime := time.Now()
originalFn := fn
fn = func() error {
startTime := time.Now()
traceID := trace.BeginBackendTrace(trace.BackendTrace{Timestamp: startTime, Type: trace.BackendTraceImageGeneration, ModelName: modelConfig.Name, Backend: modelConfig.Backend, Summary: trace.TruncateString(positive_prompt, 200)})
defer trace.CancelBackendTrace(traceID)
err := originalFn()
duration := time.Since(startTime)
@@ -77,7 +75,6 @@ func ImageGeneration(ctx context.Context, height, width, step, seed int, positiv
}
trace.RecordBackendTrace(trace.BackendTrace{
ID: traceID,
Timestamp: startTime,
Duration: duration,
Type: trace.BackendTraceImageGeneration,
@@ -91,15 +88,6 @@ func ImageGeneration(ctx context.Context, height, width, step, seed int, positiv
return err
}
}
originalFn := fn
fn = func() error {
release, err := AcquireGlobalBackendSlot()
if err != nil {
return err
}
defer release()
return originalFn()
}
return fn, nil
}
+1 -19
View File
@@ -378,17 +378,9 @@ func ModelInference(ctx context.Context, s string, messages schema.Messages, ima
"xml_format_preset": c.FunctionsConfig.XMLFormatPreset,
}
startTime := time.Now()
originalFn := fn
fn = func() (LLMResponse, error) {
startTime := time.Now()
traceID := trace.BeginBackendTrace(trace.BackendTrace{
Timestamp: startTime,
Type: trace.BackendTraceLLM,
ModelName: c.Name,
Backend: c.Backend,
Summary: trace.GenerateLLMSummary(messages, s),
})
defer trace.CancelBackendTrace(traceID)
resp, err := originalFn()
duration := time.Since(startTime)
@@ -440,7 +432,6 @@ func ModelInference(ctx context.Context, s string, messages schema.Messages, ima
}
trace.RecordBackendTrace(trace.BackendTrace{
ID: traceID,
Timestamp: startTime,
Duration: duration,
Type: trace.BackendTraceLLM,
@@ -454,15 +445,6 @@ func ModelInference(ctx context.Context, s string, messages schema.Messages, ima
return resp, err
}
}
originalFn := fn
fn = func() (LLMResponse, error) {
release, err := AcquireGlobalBackendSlot()
if err != nil {
return LLMResponse{}, err
}
defer release()
return originalFn()
}
return fn, nil
}
+1 -13
View File
@@ -74,11 +74,9 @@ func Model3DGeneration(options Model3DGenerationOptions, loader *model.ModelLoad
}
}
startTime := time.Now()
originalFn := fn
fn = func() error {
startTime := time.Now()
traceID := trace.BeginBackendTrace(trace.BackendTrace{Timestamp: startTime, Type: traceType, ModelName: modelConfig.Name, Backend: modelConfig.Backend, Summary: trace.TruncateString(traceSummary, 200)})
defer trace.CancelBackendTrace(traceID)
err := originalFn()
duration := time.Since(startTime)
@@ -88,7 +86,6 @@ func Model3DGeneration(options Model3DGenerationOptions, loader *model.ModelLoad
}
trace.RecordBackendTrace(trace.BackendTrace{
ID: traceID,
Timestamp: startTime,
Duration: duration,
Type: traceType,
@@ -102,15 +99,6 @@ func Model3DGeneration(options Model3DGenerationOptions, loader *model.ModelLoad
return err
}
}
originalFn := fn
fn = func() error {
release, err := AcquireGlobalBackendSlot()
if err != nil {
return err
}
defer release()
return originalFn()
}
return fn, nil
}
@@ -163,7 +163,7 @@ func (r *recordingBackend) VoiceEmbed(_ context.Context, in *pb.VoiceEmbedReques
// backend, so every helper below reaches it through the real Load path.
func newRecordingLoader(rec *recordingBackend) *model.ModelLoader {
loader := model.NewModelLoader(&system.SystemState{})
loader.SetModelRouter(func(_ context.Context, id string, _, _, _, _ string, _ *pb.ModelOptions, _ bool) (*model.Model, error) {
loader.SetModelRouter(func(_ context.Context, id string, _, _, _ string, _ *pb.ModelOptions, _ bool) (*model.Model, error) {
return model.NewModelWithClient(id, "test://recording", rec), nil
})
return loader
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