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Merge branch 'main' into drifkin/array-head-count-simple

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22
.github/workflows/release.yaml vendored
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@ -103,11 +103,6 @@ jobs:
arch: [amd64]
preset: ['CPU']
include:
- os: windows
arch: amd64
preset: 'CUDA 11'
install: https://developer.download.nvidia.com/compute/cuda/11.3.1/local_installers/cuda_11.3.1_465.89_win10.exe
cuda-version: '11.3'
- os: windows
arch: amd64
preset: 'CUDA 12'
@ -324,7 +319,6 @@ jobs:
case "$COMPONENT" in
bin/ollama) echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}.tar.in ;;
lib/ollama/*.so) echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}.tar.in ;;
lib/ollama/cuda_v11) echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}.tar.in ;;
lib/ollama/cuda_v12) echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}.tar.in ;;
lib/ollama/cuda_jetpack5) echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}-jetpack5.tar.in ;;
lib/ollama/cuda_jetpack6) echo $COMPONENT >>ollama-${{ matrix.os }}-${{ matrix.arch }}-jetpack6.tar.in ;;
@ -432,6 +426,22 @@ jobs:
docker buildx imagetools inspect ollama/ollama:${{ steps.metadata.outputs.version }}
working-directory: ${{ runner.temp }}
# Trigger downstream release process
trigger:
runs-on: ubuntu-latest
environment: release
needs: [darwin-build, windows-build, windows-depends]
steps:
- name: Trigger downstream release process
run: |
curl -L \
-X POST \
-H "Accept: application/vnd.github+json" \
-H "Authorization: Bearer ${{ secrets.RELEASE_TOKEN }}" \
-H "X-GitHub-Api-Version: 2022-11-28" \
https://api.github.com/repos/ollama/${{ vars.RELEASE_REPO }}/dispatches \
-d "{\"event_type\": \"trigger-workflow\", \"client_payload\": {\"run_id\": \"${GITHUB_RUN_ID}\", \"version\": \"${GITHUB_REF_NAME#v}\"}}"
# Aggregate all the assets and ship a release
release:
needs: [darwin-sign, windows-sign, linux-build]

6
.github/workflows/test.yaml vendored
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@ -46,7 +46,7 @@ jobs:
include:
- preset: CPU
- preset: CUDA
container: nvidia/cuda:11.8.0-devel-ubuntu22.04
container: nvidia/cuda:12.8.1-devel-ubuntu22.04
flags: '-DCMAKE_CUDA_ARCHITECTURES=87'
- preset: ROCm
container: rocm/dev-ubuntu-22.04:6.1.2
@ -78,7 +78,7 @@ jobs:
include:
- preset: CPU
- preset: CUDA
install: https://developer.download.nvidia.com/compute/cuda/11.3.1/local_installers/cuda_11.3.1_465.89_win10.exe
install: https://developer.download.nvidia.com/compute/cuda/12.8.0/local_installers/cuda_12.8.0_571.96_windows.exe
flags: '-DCMAKE_CUDA_ARCHITECTURES=80'
- preset: ROCm
install: https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q4-WinSvr2022-For-HIP.exe
@ -102,7 +102,7 @@ jobs:
$ErrorActionPreference = "Stop"
if ("${{ steps.cache-install.outputs.cache-hit }}" -ne 'true') {
Invoke-WebRequest -Uri "${{ matrix.install }}" -OutFile "install.exe"
Start-Process -FilePath .\install.exe -ArgumentList (@("-s", "cudart_11.3", "nvcc_11.3", "cublas_11.3", "cublas_dev_11.3")) -NoNewWindow -Wait
Start-Process -FilePath .\install.exe -ArgumentList (@("-s", "cudart_12.8", "nvcc_12.8", "cublas_12.8", "cublas_dev_12.8")) -NoNewWindow -Wait
}
$cudaPath = (Resolve-Path "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\*").path

2
.golangci.yaml
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@ -19,8 +19,8 @@ linters:
- nolintlint
- nosprintfhostport
- staticcheck
- tenv
- unconvert
- usetesting
- wastedassign
- whitespace
disable:

13
CMakePresets.json
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@ -17,14 +17,6 @@
"name": "CUDA",
"inherits": [ "Default" ]
},
{
"name": "CUDA 11",
"inherits": [ "CUDA" ],
"cacheVariables": {
"CMAKE_CUDA_ARCHITECTURES": "50;52;53;60;61;70;75;80;86",
"CMAKE_CUDA_FLAGS": "-Wno-deprecated-gpu-targets"
}
},
{
"name": "CUDA 12",
"inherits": [ "CUDA" ],
@ -78,11 +70,6 @@
"configurePreset": "CUDA",
"targets": [ "ggml-cuda" ]
},
{
"name": "CUDA 11",
"inherits": [ "CUDA" ],
"configurePreset": "CUDA 11"
},
{
"name": "CUDA 12",
"inherits": [ "CUDA" ],

17
Dockerfile
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@ -7,14 +7,10 @@ ARG JETPACK5VERSION=r35.4.1
ARG JETPACK6VERSION=r36.4.0
ARG CMAKEVERSION=3.31.2
# CUDA v11 requires gcc v10. v10.3 has regressions, so the rockylinux 8.5 AppStream has the latest compatible version
FROM --platform=linux/amd64 rocm/dev-almalinux-8:${ROCMVERSION}-complete AS base-amd64
RUN yum install -y yum-utils \
&& yum-config-manager --add-repo https://dl.rockylinux.org/vault/rocky/8.5/AppStream/\$basearch/os/ \
&& rpm --import https://dl.rockylinux.org/pub/rocky/RPM-GPG-KEY-Rocky-8 \
&& dnf install -y yum-utils ccache gcc-toolset-10-gcc-10.2.1-8.2.el8 gcc-toolset-10-gcc-c++-10.2.1-8.2.el8 gcc-toolset-10-binutils-2.35-11.el8 \
&& dnf install -y ccache \
&& yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel8/x86_64/cuda-rhel8.repo
ENV PATH=/opt/rh/gcc-toolset-10/root/usr/bin:$PATH
FROM --platform=linux/arm64 almalinux:8 AS base-arm64
# install epel-release for ccache
@ -38,15 +34,6 @@ RUN --mount=type=cache,target=/root/.ccache \
&& cmake --build --parallel --preset 'CPU' \
&& cmake --install build --component CPU --strip --parallel 8
FROM base AS cuda-11
ARG CUDA11VERSION=11.3
RUN dnf install -y cuda-toolkit-${CUDA11VERSION//./-}
ENV PATH=/usr/local/cuda-11/bin:$PATH
RUN --mount=type=cache,target=/root/.ccache \
cmake --preset 'CUDA 11' \
&& cmake --build --parallel --preset 'CUDA 11' \
&& cmake --install build --component CUDA --strip --parallel 8
FROM base AS cuda-12
ARG CUDA12VERSION=12.8
RUN dnf install -y cuda-toolkit-${CUDA12VERSION//./-}
@ -98,11 +85,9 @@ RUN --mount=type=cache,target=/root/.cache/go-build \
go build -trimpath -buildmode=pie -o /bin/ollama .
FROM --platform=linux/amd64 scratch AS amd64
COPY --from=cuda-11 dist/lib/ollama/cuda_v11 /lib/ollama/cuda_v11
COPY --from=cuda-12 dist/lib/ollama/cuda_v12 /lib/ollama/cuda_v12
FROM --platform=linux/arm64 scratch AS arm64
COPY --from=cuda-11 dist/lib/ollama/cuda_v11 /lib/ollama/cuda_v11
COPY --from=cuda-12 dist/lib/ollama/cuda_v12 /lib/ollama/cuda_v12
COPY --from=jetpack-5 dist/lib/ollama/cuda_v11 /lib/ollama/cuda_jetpack5
COPY --from=jetpack-6 dist/lib/ollama/cuda_v12 /lib/ollama/cuda_jetpack6

2
Makefile.sync
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@ -1,6 +1,6 @@
UPSTREAM=https://github.com/ggerganov/llama.cpp.git
WORKDIR=llama/vendor
FETCH_HEAD=2016f07bd106c73699ecbaace80f55db5ed95dac
FETCH_HEAD=e1e8e0991ffd9e99a445c6812bb519d5bac9f4b5
.PHONY: help
help:

43
README.md
View File

@ -61,6 +61,8 @@ Here are some example models that can be downloaded:
| QwQ | 32B | 20GB | `ollama run qwq` |
| DeepSeek-R1 | 7B | 4.7GB | `ollama run deepseek-r1` |
| DeepSeek-R1 | 671B | 404GB | `ollama run deepseek-r1:671b` |
| Llama 4 | 109B | 67GB | `ollama run llama4:scout` |
| Llama 4 | 400B | 245GB | `ollama run llama4:maverick` |
| Llama 3.3 | 70B | 43GB | `ollama run llama3.3` |
| Llama 3.2 | 3B | 2.0GB | `ollama run llama3.2` |
| Llama 3.2 | 1B | 1.3GB | `ollama run llama3.2:1b` |
@ -77,7 +79,7 @@ Here are some example models that can be downloaded:
| Code Llama | 7B | 3.8GB | `ollama run codellama` |
| Llama 2 Uncensored | 7B | 3.8GB | `ollama run llama2-uncensored` |
| LLaVA | 7B | 4.5GB | `ollama run llava` |
| Granite-3.2 | 8B | 4.9GB | `ollama run granite3.2` |
| Granite-3.3 | 8B | 4.9GB | `ollama run granite3.3` |
> [!NOTE]
> You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.
@ -285,7 +287,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [Bionic GPT](https://github.com/bionic-gpt/bionic-gpt)
- [HTML UI](https://github.com/rtcfirefly/ollama-ui)
- [Saddle](https://github.com/jikkuatwork/saddle)
- [TagSpaces](https://www.tagspaces.org) (A platform for file based apps, [utilizing Ollama](https://docs.tagspaces.org/ai/) for the generation of tags and descriptions)
- [TagSpaces](https://www.tagspaces.org) (A platform for file-based apps, [utilizing Ollama](https://docs.tagspaces.org/ai/) for the generation of tags and descriptions)
- [Chatbot UI](https://github.com/ivanfioravanti/chatbot-ollama)
- [Chatbot UI v2](https://github.com/mckaywrigley/chatbot-ui)
- [Typescript UI](https://github.com/ollama-interface/Ollama-Gui?tab=readme-ov-file)
@ -312,6 +314,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [Ollama Basic Chat: Uses HyperDiv Reactive UI](https://github.com/rapidarchitect/ollama_basic_chat)
- [Ollama-chats RPG](https://github.com/drazdra/ollama-chats)
- [IntelliBar](https://intellibar.app/) (AI-powered assistant for macOS)
- [Jirapt](https://github.com/AliAhmedNada/jirapt) (Jira Integration to generate issues, tasks, epics)
- [QA-Pilot](https://github.com/reid41/QA-Pilot) (Interactive chat tool that can leverage Ollama models for rapid understanding and navigation of GitHub code repositories)
- [ChatOllama](https://github.com/sugarforever/chat-ollama) (Open Source Chatbot based on Ollama with Knowledge Bases)
- [CRAG Ollama Chat](https://github.com/Nagi-ovo/CRAG-Ollama-Chat) (Simple Web Search with Corrective RAG)
@ -325,14 +328,14 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [RWKV-Runner](https://github.com/josStorer/RWKV-Runner) (RWKV offline LLM deployment tool, also usable as a client for ChatGPT and Ollama)
- [Ollama Grid Search](https://github.com/dezoito/ollama-grid-search) (app to evaluate and compare models)
- [Olpaka](https://github.com/Otacon/olpaka) (User-friendly Flutter Web App for Ollama)
- [Casibase](https://casibase.org) (An open source AI knowledge base and dialogue system combining the latest RAG, SSO, ollama support and multiple large language models.)
- [Casibase](https://casibase.org) (An open source AI knowledge base and dialogue system combining the latest RAG, SSO, ollama support, and multiple large language models.)
- [OllamaSpring](https://github.com/CrazyNeil/OllamaSpring) (Ollama Client for macOS)
- [LLocal.in](https://github.com/kartikm7/llocal) (Easy to use Electron Desktop Client for Ollama)
- [Shinkai Desktop](https://github.com/dcSpark/shinkai-apps) (Two click install Local AI using Ollama + Files + RAG)
- [AiLama](https://github.com/zeyoyt/ailama) (A Discord User App that allows you to interact with Ollama anywhere in discord )
- [AiLama](https://github.com/zeyoyt/ailama) (A Discord User App that allows you to interact with Ollama anywhere in Discord)
- [Ollama with Google Mesop](https://github.com/rapidarchitect/ollama_mesop/) (Mesop Chat Client implementation with Ollama)
- [R2R](https://github.com/SciPhi-AI/R2R) (Open-source RAG engine)
- [Ollama-Kis](https://github.com/elearningshow/ollama-kis) (A simple easy to use GUI with sample custom LLM for Drivers Education)
- [Ollama-Kis](https://github.com/elearningshow/ollama-kis) (A simple easy-to-use GUI with sample custom LLM for Drivers Education)
- [OpenGPA](https://opengpa.org) (Open-source offline-first Enterprise Agentic Application)
- [Painting Droid](https://github.com/mateuszmigas/painting-droid) (Painting app with AI integrations)
- [Kerlig AI](https://www.kerlig.com/) (AI writing assistant for macOS)
@ -341,16 +344,16 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [LLMStack](https://github.com/trypromptly/LLMStack) (No-code multi-agent framework to build LLM agents and workflows)
- [BoltAI for Mac](https://boltai.com) (AI Chat Client for Mac)
- [Harbor](https://github.com/av/harbor) (Containerized LLM Toolkit with Ollama as default backend)
- [PyGPT](https://github.com/szczyglis-dev/py-gpt) (AI desktop assistant for Linux, Windows and Mac)
- [Alpaca](https://github.com/Jeffser/Alpaca) (An Ollama client application for linux and macos made with GTK4 and Adwaita)
- [PyGPT](https://github.com/szczyglis-dev/py-gpt) (AI desktop assistant for Linux, Windows, and Mac)
- [Alpaca](https://github.com/Jeffser/Alpaca) (An Ollama client application for Linux and macOS made with GTK4 and Adwaita)
- [AutoGPT](https://github.com/Significant-Gravitas/AutoGPT/blob/master/docs/content/platform/ollama.md) (AutoGPT Ollama integration)
- [Go-CREW](https://www.jonathanhecl.com/go-crew/) (Powerful Offline RAG in Golang)
- [PartCAD](https://github.com/openvmp/partcad/) (CAD model generation with OpenSCAD and CadQuery)
- [Ollama4j Web UI](https://github.com/ollama4j/ollama4j-web-ui) - Java-based Web UI for Ollama built with Vaadin, Spring Boot and Ollama4j
- [Ollama4j Web UI](https://github.com/ollama4j/ollama4j-web-ui) - Java-based Web UI for Ollama built with Vaadin, Spring Boot, and Ollama4j
- [PyOllaMx](https://github.com/kspviswa/pyOllaMx) - macOS application capable of chatting with both Ollama and Apple MLX models.
- [Cline](https://github.com/cline/cline) - Formerly known as Claude Dev is a VSCode extension for multi-file/whole-repo coding
- [Cherry Studio](https://github.com/kangfenmao/cherry-studio) (Desktop client with Ollama support)
- [ConfiChat](https://github.com/1runeberg/confichat) (Lightweight, standalone, multi-platform, and privacy focused LLM chat interface with optional encryption)
- [ConfiChat](https://github.com/1runeberg/confichat) (Lightweight, standalone, multi-platform, and privacy-focused LLM chat interface with optional encryption)
- [Archyve](https://github.com/nickthecook/archyve) (RAG-enabling document library)
- [crewAI with Mesop](https://github.com/rapidarchitect/ollama-crew-mesop) (Mesop Web Interface to run crewAI with Ollama)
- [Tkinter-based client](https://github.com/chyok/ollama-gui) (Python tkinter-based Client for Ollama)
@ -368,7 +371,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [DualMind](https://github.com/tcsenpai/dualmind) (Experimental app allowing two models to talk to each other in the terminal or in a web interface)
- [ollamarama-matrix](https://github.com/h1ddenpr0cess20/ollamarama-matrix) (Ollama chatbot for the Matrix chat protocol)
- [ollama-chat-app](https://github.com/anan1213095357/ollama-chat-app) (Flutter-based chat app)
- [Perfect Memory AI](https://www.perfectmemory.ai/) (Productivity AI assists personalized by what you have seen on your screen, heard and said in the meetings)
- [Perfect Memory AI](https://www.perfectmemory.ai/) (Productivity AI assists personalized by what you have seen on your screen, heard, and said in the meetings)
- [Hexabot](https://github.com/hexastack/hexabot) (A conversational AI builder)
- [Reddit Rate](https://github.com/rapidarchitect/reddit_analyzer) (Search and Rate Reddit topics with a weighted summation)
- [OpenTalkGpt](https://github.com/adarshM84/OpenTalkGpt) (Chrome Extension to manage open-source models supported by Ollama, create custom models, and chat with models from a user-friendly UI)
@ -386,7 +389,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [ChibiChat](https://github.com/CosmicEventHorizon/ChibiChat) (Kotlin-based Android app to chat with Ollama and Koboldcpp API endpoints)
- [LocalLLM](https://github.com/qusaismael/localllm) (Minimal Web-App to run ollama models on it with a GUI)
- [Ollamazing](https://github.com/buiducnhat/ollamazing) (Web extension to run Ollama models)
- [OpenDeepResearcher-via-searxng](https://github.com/benhaotang/OpenDeepResearcher-via-searxng) (A Deep Research equivent endpoint with Ollama support for running locally)
- [OpenDeepResearcher-via-searxng](https://github.com/benhaotang/OpenDeepResearcher-via-searxng) (A Deep Research equivalent endpoint with Ollama support for running locally)
- [AntSK](https://github.com/AIDotNet/AntSK) (Out-of-the-box & Adaptable RAG Chatbot)
- [MaxKB](https://github.com/1Panel-dev/MaxKB/) (Ready-to-use & flexible RAG Chatbot)
- [yla](https://github.com/danielekp/yla) (Web interface to freely interact with your customized models)
@ -394,11 +397,13 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [1Panel](https://github.com/1Panel-dev/1Panel/) (Web-based Linux Server Management Tool)
- [AstrBot](https://github.com/Soulter/AstrBot/) (User-friendly LLM-based multi-platform chatbot with a WebUI, supporting RAG, LLM agents, and plugins integration)
- [Reins](https://github.com/ibrahimcetin/reins) (Easily tweak parameters, customize system prompts per chat, and enhance your AI experiments with reasoning model support.)
- [Flufy](https://github.com/Aharon-Bensadoun/Flufy) (A beautiful chat interface for interacting with Ollama's API. Built with React, TypeScript, and Material-UI.)
- [Ellama](https://github.com/zeozeozeo/ellama) (Friendly native app to chat with an Ollama instance)
- [screenpipe](https://github.com/mediar-ai/screenpipe) Build agents powered by your screen history
- [Ollamb](https://github.com/hengkysteen/ollamb) (Simple yet rich in features, cross-platform built with Flutter and designed for Ollama. Try the [web demo](https://hengkysteen.github.io/demo/ollamb/).)
- [Writeopia](https://github.com/Writeopia/Writeopia) (Text editor with integration with Ollama)
- [AppFlowy](https://github.com/AppFlowy-IO/AppFlowy) (AI collaborative workspace with Ollama, cross-platform and self-hostable)
- [Lumina](https://github.com/cushydigit/lumina.git) (A lightweight, minimal React.js frontend for interacting with Ollama servers)
### Cloud
@ -440,7 +445,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [PowershAI](https://github.com/rrg92/powershai) PowerShell module that brings AI to terminal on Windows, including support for Ollama
- [DeepShell](https://github.com/Abyss-c0re/deepshell) Your self-hosted AI assistant. Interactive Shell, Files and Folders analysis.
- [orbiton](https://github.com/xyproto/orbiton) Configuration-free text editor and IDE with support for tab completion with Ollama.
- [orca-cli](https://github.com/molbal/orca-cli) Ollama Registry CLI Application - Browse, pull and download models from Ollama Registry in your terminal.
- [orca-cli](https://github.com/molbal/orca-cli) Ollama Registry CLI Application - Browse, pull, and download models from Ollama Registry in your terminal.
- [GGUF-to-Ollama](https://github.com/jonathanhecl/gguf-to-ollama) - Importing GGUF to Ollama made easy (multiplatform)
### Apple Vision Pro
@ -468,7 +473,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
### Libraries
- [LangChain](https://python.langchain.com/docs/integrations/llms/ollama) and [LangChain.js](https://js.langchain.com/docs/integrations/chat/ollama/) with [example](https://js.langchain.com/docs/tutorials/local_rag/)
- [LangChain](https://python.langchain.com/docs/integrations/chat/ollama/) and [LangChain.js](https://js.langchain.com/docs/integrations/chat/ollama/) with [example](https://js.langchain.com/docs/tutorials/local_rag/)
- [Firebase Genkit](https://firebase.google.com/docs/genkit/plugins/ollama)
- [crewAI](https://github.com/crewAIInc/crewAI)
- [Yacana](https://remembersoftwares.github.io/yacana/) (User-friendly multi-agent framework for brainstorming and executing predetermined flows with built-in tool integration)
@ -515,7 +520,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [Swollama for Swift](https://github.com/marcusziade/Swollama) with [DocC](https://marcusziade.github.io/Swollama/documentation/swollama/)
- [GoLamify](https://github.com/prasad89/golamify)
- [Ollama for Haskell](https://github.com/tusharad/ollama-haskell)
- [multi-llm-ts](https://github.com/nbonamy/multi-llm-ts) (A Typescript/JavaScript library allowing access to different LLM in unified API)
- [multi-llm-ts](https://github.com/nbonamy/multi-llm-ts) (A Typescript/JavaScript library allowing access to different LLM in a unified API)
- [LlmTornado](https://github.com/lofcz/llmtornado) (C# library providing a unified interface for major FOSS & Commercial inference APIs)
- [Ollama for Zig](https://github.com/dravenk/ollama-zig)
- [Abso](https://github.com/lunary-ai/abso) (OpenAI-compatible TypeScript SDK for any LLM provider)
@ -524,11 +529,11 @@ See the [API documentation](./docs/api.md) for all endpoints.
### Mobile
- [SwiftChat](https://github.com/aws-samples/swift-chat) (Lightning-fast Cross-platform AI chat app with native UI for Android, iOS and iPad)
- [SwiftChat](https://github.com/aws-samples/swift-chat) (Lightning-fast Cross-platform AI chat app with native UI for Android, iOS, and iPad)
- [Enchanted](https://github.com/AugustDev/enchanted)
- [Maid](https://github.com/Mobile-Artificial-Intelligence/maid)
- [Ollama App](https://github.com/JHubi1/ollama-app) (Modern and easy-to-use multi-platform client for Ollama)
- [ConfiChat](https://github.com/1runeberg/confichat) (Lightweight, standalone, multi-platform, and privacy focused LLM chat interface with optional encryption)
- [ConfiChat](https://github.com/1runeberg/confichat) (Lightweight, standalone, multi-platform, and privacy-focused LLM chat interface with optional encryption)
- [Ollama Android Chat](https://github.com/sunshine0523/OllamaServer) (No need for Termux, start the Ollama service with one click on an Android device)
- [Reins](https://github.com/ibrahimcetin/reins) (Easily tweak parameters, customize system prompts per chat, and enhance your AI experiments with reasoning model support.)
@ -552,7 +557,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [Obsidian Local GPT plugin](https://github.com/pfrankov/obsidian-local-gpt)
- [Open Interpreter](https://docs.openinterpreter.com/language-model-setup/local-models/ollama)
- [Llama Coder](https://github.com/ex3ndr/llama-coder) (Copilot alternative using Ollama)
- [Ollama Copilot](https://github.com/bernardo-bruning/ollama-copilot) (Proxy that allows you to use ollama as a copilot like Github copilot)
- [Ollama Copilot](https://github.com/bernardo-bruning/ollama-copilot) (Proxy that allows you to use Ollama as a copilot like GitHub Copilot)
- [twinny](https://github.com/rjmacarthy/twinny) (Copilot and Copilot chat alternative using Ollama)
- [Wingman-AI](https://github.com/RussellCanfield/wingman-ai) (Copilot code and chat alternative using Ollama and Hugging Face)
- [Page Assist](https://github.com/n4ze3m/page-assist) (Chrome Extension)
@ -562,8 +567,8 @@ See the [API documentation](./docs/api.md) for all endpoints.
- [Discord-Ollama Chat Bot](https://github.com/kevinthedang/discord-ollama) (Generalized TypeScript Discord Bot w/ Tuning Documentation)
- [ChatGPTBox: All in one browser extension](https://github.com/josStorer/chatGPTBox) with [Integrating Tutorial](https://github.com/josStorer/chatGPTBox/issues/616#issuecomment-1975186467)
- [Discord AI chat/moderation bot](https://github.com/rapmd73/Companion) Chat/moderation bot written in python. Uses Ollama to create personalities.
- [Headless Ollama](https://github.com/nischalj10/headless-ollama) (Scripts to automatically install ollama client & models on any OS for apps that depends on ollama server)
- [Terraform AWS Ollama & Open WebUI](https://github.com/xuyangbocn/terraform-aws-self-host-llm) (A Terraform module to deploy on AWS a ready-to-use Ollama service, together with its front end Open WebUI service.)
- [Headless Ollama](https://github.com/nischalj10/headless-ollama) (Scripts to automatically install ollama client & models on any OS for apps that depend on ollama server)
- [Terraform AWS Ollama & Open WebUI](https://github.com/xuyangbocn/terraform-aws-self-host-llm) (A Terraform module to deploy on AWS a ready-to-use Ollama service, together with its front-end Open WebUI service.)
- [node-red-contrib-ollama](https://github.com/jakubburkiewicz/node-red-contrib-ollama)
- [Local AI Helper](https://github.com/ivostoykov/localAI) (Chrome and Firefox extensions that enable interactions with the active tab and customisable API endpoints. Includes secure storage for user prompts.)
- [vnc-lm](https://github.com/jake83741/vnc-lm) (Discord bot for messaging with LLMs through Ollama and LiteLLM. Seamlessly move between local and flagship models.)

5
api/client_test.go
View File

@ -1,7 +1,6 @@
package api
import (
"context"
"encoding/json"
"fmt"
"net/http"
@ -137,7 +136,7 @@ func TestClientStream(t *testing.T) {
client := NewClient(&url.URL{Scheme: "http", Host: ts.Listener.Addr().String()}, http.DefaultClient)
var receivedChunks []ChatResponse
err := client.stream(context.Background(), http.MethodPost, "/v1/chat", nil, func(chunk []byte) error {
err := client.stream(t.Context(), http.MethodPost, "/v1/chat", nil, func(chunk []byte) error {
var resp ChatResponse
if err := json.Unmarshal(chunk, &resp); err != nil {
return fmt.Errorf("failed to unmarshal chunk: %w", err)
@ -223,7 +222,7 @@ func TestClientDo(t *testing.T) {
ID string `json:"id"`
Success bool `json:"success"`
}
err := client.do(context.Background(), http.MethodPost, "/v1/messages", nil, &resp)
err := client.do(t.Context(), http.MethodPost, "/v1/messages", nil, &resp)
if tc.wantErr != "" {
if err == nil {

20
api/types.go
View File

@ -271,9 +271,6 @@ type Options struct {
RepeatPenalty float32 `json:"repeat_penalty,omitempty"`
PresencePenalty float32 `json:"presence_penalty,omitempty"`
FrequencyPenalty float32 `json:"frequency_penalty,omitempty"`
Mirostat int `json:"mirostat,omitempty"`
MirostatTau float32 `json:"mirostat_tau,omitempty"`
MirostatEta float32 `json:"mirostat_eta,omitempty"`
Stop []string `json:"stop,omitempty"`
}
@ -283,12 +280,7 @@ type Runner struct {
NumBatch int `json:"num_batch,omitempty"`
NumGPU int `json:"num_gpu,omitempty"`
MainGPU int `json:"main_gpu,omitempty"`
LowVRAM bool `json:"low_vram,omitempty"`
F16KV bool `json:"f16_kv,omitempty"` // Deprecated: This option is ignored
LogitsAll bool `json:"logits_all,omitempty"`
VocabOnly bool `json:"vocab_only,omitempty"`
UseMMap *bool `json:"use_mmap,omitempty"`
UseMLock bool `json:"use_mlock,omitempty"`
NumThread int `json:"num_thread,omitempty"`
}
@ -471,13 +463,6 @@ type ProcessModelResponse struct {
SizeVRAM int64 `json:"size_vram"`
}
type RetrieveModelResponse struct {
Id string `json:"id"`
Object string `json:"object"`
Created int64 `json:"created"`
OwnedBy string `json:"owned_by"`
}
type TokenResponse struct {
Token string `json:"token"`
}
@ -660,9 +645,6 @@ func DefaultOptions() Options {
RepeatPenalty: 1.1,
PresencePenalty: 0.0,
FrequencyPenalty: 0.0,
Mirostat: 0,
MirostatTau: 5.0,
MirostatEta: 0.1,
Seed: -1,
Runner: Runner{
@ -671,8 +653,6 @@ func DefaultOptions() Options {
NumBatch: 512,
NumGPU: -1, // -1 here indicates that NumGPU should be set dynamically
NumThread: 0, // let the runtime decide
LowVRAM: false,
UseMLock: false,
UseMMap: nil,
},
}

6
benchmark/server_benchmark_test.go
View File

@ -78,7 +78,7 @@ func BenchmarkColdStart(b *testing.B) {
for _, tt := range tests {
b.Run(fmt.Sprintf("%s/cold/%s", m, tt.name), func(b *testing.B) {
ctx := context.Background()
ctx := b.Context()
// Set number of tokens as our throughput metric
b.SetBytes(int64(tt.maxTokens))
@ -113,7 +113,7 @@ func BenchmarkWarmStart(b *testing.B) {
for _, tt := range tests {
b.Run(fmt.Sprintf("%s/warm/%s", m, tt.name), func(b *testing.B) {
ctx := context.Background()
ctx := b.Context()
// Pre-warm the model
warmup(client, m, tt.prompt, b)
@ -140,7 +140,7 @@ func setup(b *testing.B) *api.Client {
if err != nil {
b.Fatal(err)
}
if _, err := client.Show(context.Background(), &api.ShowRequest{Model: modelName(b)}); err != nil {
if _, err := client.Show(b.Context(), &api.ShowRequest{Model: modelName(b)}); err != nil {
b.Fatalf("Model unavailable: %v", err)
}

53
cmd/cmd.go
View File

@ -31,6 +31,7 @@ import (
"github.com/olekukonko/tablewriter"
"github.com/spf13/cobra"
"golang.org/x/crypto/ssh"
"golang.org/x/sync/errgroup"
"golang.org/x/term"
"github.com/ollama/ollama/api"
@ -41,6 +42,7 @@ import (
"github.com/ollama/ollama/runner"
"github.com/ollama/ollama/server"
"github.com/ollama/ollama/types/model"
"github.com/ollama/ollama/types/syncmap"
"github.com/ollama/ollama/version"
)
@ -106,7 +108,7 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
}
spinner.Stop()
req.Name = args[0]
req.Model = args[0]
quantize, _ := cmd.Flags().GetString("quantize")
if quantize != "" {
req.Quantize = quantize
@ -117,34 +119,54 @@ func CreateHandler(cmd *cobra.Command, args []string) error {
return err
}
if len(req.Files) > 0 {
fileMap := map[string]string{}
for f, digest := range req.Files {
var g errgroup.Group
g.SetLimit(max(runtime.GOMAXPROCS(0)-1, 1))
files := syncmap.NewSyncMap[string, string]()
for f, digest := range req.Files {
g.Go(func() error {
if _, err := createBlob(cmd, client, f, digest, p); err != nil {
return err
}
fileMap[filepath.Base(f)] = digest
}
req.Files = fileMap
// TODO: this is incorrect since the file might be in a subdirectory
// instead this should take the path relative to the model directory
// but the current implementation does not allow this
files.Store(filepath.Base(f), digest)
return nil
})
}
if len(req.Adapters) > 0 {
fileMap := map[string]string{}
for f, digest := range req.Adapters {
adapters := syncmap.NewSyncMap[string, string]()
for f, digest := range req.Adapters {
g.Go(func() error {
if _, err := createBlob(cmd, client, f, digest, p); err != nil {
return err
}
fileMap[filepath.Base(f)] = digest
}
req.Adapters = fileMap
// TODO: same here
adapters.Store(filepath.Base(f), digest)
return nil
})
}
if err := g.Wait(); err != nil {
return err
}
req.Files = files.Items()
req.Adapters = adapters.Items()
bars := make(map[string]*progress.Bar)
fn := func(resp api.ProgressResponse) error {
if resp.Digest != "" {
bar, ok := bars[resp.Digest]
if !ok {
bar = progress.NewBar(fmt.Sprintf("pulling %s...", resp.Digest[7:19]), resp.Total, resp.Completed)
msg := resp.Status
if msg == "" {
msg = fmt.Sprintf("pulling %s...", resp.Digest[7:19])
}
bar = progress.NewBar(msg, resp.Total, resp.Completed)
bars[resp.Digest] = bar
p.Add(resp.Digest, bar)
}
@ -213,7 +235,7 @@ func createBlob(cmd *cobra.Command, client *api.Client, path string, digest stri
}
}()
if err = client.CreateBlob(cmd.Context(), digest, io.TeeReader(bin, &pw)); err != nil {
if err := client.CreateBlob(cmd.Context(), digest, io.TeeReader(bin, &pw)); err != nil {
return "", err
}
return digest, nil
@ -1407,7 +1429,6 @@ func NewCLI() *cobra.Command {
envVars["OLLAMA_LLM_LIBRARY"],
envVars["OLLAMA_GPU_OVERHEAD"],
envVars["OLLAMA_LOAD_TIMEOUT"],
envVars["OLLAMA_CONTEXT_LENGTH"],
})
default:
appendEnvDocs(cmd, envs)

20
cmd/cmd_test.go
View File

@ -2,7 +2,6 @@ package cmd
import (
"bytes"
"context"
"encoding/json"
"io"
"net/http"
@ -337,7 +336,7 @@ func TestDeleteHandler(t *testing.T) {
t.Cleanup(mockServer.Close)
cmd := &cobra.Command{}
cmd.SetContext(context.TODO())
cmd.SetContext(t.Context())
if err := DeleteHandler(cmd, []string{"test-model"}); err != nil {
t.Fatalf("DeleteHandler failed: %v", err)
}
@ -399,11 +398,6 @@ func TestGetModelfileName(t *testing.T) {
var expectedFilename string
if tt.fileExists {
tempDir, err := os.MkdirTemp("", "modelfiledir")
defer os.RemoveAll(tempDir)
if err != nil {
t.Fatalf("temp modelfile dir creation failed: %v", err)
}
var fn string
if tt.modelfileName != "" {
fn = tt.modelfileName
@ -411,7 +405,7 @@ func TestGetModelfileName(t *testing.T) {
fn = "Modelfile"
}
tempFile, err := os.CreateTemp(tempDir, fn)
tempFile, err := os.CreateTemp(t.TempDir(), fn)
if err != nil {
t.Fatalf("temp modelfile creation failed: %v", err)
}
@ -530,7 +524,7 @@ func TestPushHandler(t *testing.T) {
cmd := &cobra.Command{}
cmd.Flags().Bool("insecure", false, "")
cmd.SetContext(context.TODO())
cmd.SetContext(t.Context())
// Redirect stderr to capture progress output
oldStderr := os.Stderr
@ -635,7 +629,7 @@ func TestListHandler(t *testing.T) {
t.Setenv("OLLAMA_HOST", mockServer.URL)
cmd := &cobra.Command{}
cmd.SetContext(context.TODO())
cmd.SetContext(t.Context())
// Capture stdout
oldStdout := os.Stdout
@ -690,7 +684,7 @@ func TestCreateHandler(t *testing.T) {
return
}
if req.Name != "test-model" {
if req.Model != "test-model" {
t.Errorf("expected model name 'test-model', got %s", req.Name)
}
@ -730,7 +724,7 @@ func TestCreateHandler(t *testing.T) {
}))
t.Setenv("OLLAMA_HOST", mockServer.URL)
t.Cleanup(mockServer.Close)
tempFile, err := os.CreateTemp("", "modelfile")
tempFile, err := os.CreateTemp(t.TempDir(), "modelfile")
if err != nil {
t.Fatal(err)
}
@ -750,7 +744,7 @@ func TestCreateHandler(t *testing.T) {
}
cmd.Flags().Bool("insecure", false, "")
cmd.SetContext(context.TODO())
cmd.SetContext(t.Context())
// Redirect stderr to capture progress output
oldStderr := os.Stderr

18
convert/convert.go
View File

@ -4,9 +4,9 @@ import (
"encoding/json"
"errors"
"fmt"
"io"
"io/fs"
"log/slog"
"os"
"slices"
"strings"
@ -89,7 +89,7 @@ type ModelConverter interface {
// KV maps parameters to LLM key-values
KV(*Tokenizer) ggml.KV
// Tensors maps input tensors to LLM tensors. Model specific modifications can be done here.
Tensors([]Tensor) []ggml.Tensor
Tensors([]Tensor) []*ggml.Tensor
// Replacements returns a list of string pairs to replace in tensor names.
// See [strings.Replacer](https://pkg.go.dev/strings#Replacer) for details
Replacements() []string
@ -106,13 +106,13 @@ type AdapterConverter interface {
// KV maps parameters to LLM key-values
KV(ggml.KV) ggml.KV
// Tensors maps input tensors to LLM tensors. Adapter specific modifications can be done here.
Tensors([]Tensor) []ggml.Tensor
Tensors([]Tensor) []*ggml.Tensor
// Replacements returns a list of string pairs to replace in tensor names.
// See [strings.Replacer](https://pkg.go.dev/strings#Replacer) for details
Replacements() []string
}
func ConvertAdapter(fsys fs.FS, ws io.WriteSeeker, baseKV ggml.KV) error {
func ConvertAdapter(fsys fs.FS, f *os.File, baseKV ggml.KV) error {
bts, err := fs.ReadFile(fsys, "adapter_config.json")
if err != nil {
return err
@ -147,14 +147,14 @@ func ConvertAdapter(fsys fs.FS, ws io.WriteSeeker, baseKV ggml.KV) error {
return err
}
return writeFile(ws, conv.KV(baseKV), conv.Tensors(ts))
return writeFile(f, conv.KV(baseKV), conv.Tensors(ts))
}
// Convert writes an Ollama compatible model to the provided io.WriteSeeker based on configurations
// and files it finds in the input path.
// Supported input model formats include safetensors.
// Supported input tokenizers files include tokenizer.json (preferred) and tokenizer.model.
func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
func ConvertModel(fsys fs.FS, f *os.File) error {
bts, err := fs.ReadFile(fsys, "config.json")
if err != nil {
return err
@ -239,13 +239,13 @@ func ConvertModel(fsys fs.FS, ws io.WriteSeeker) error {
return err
}
return writeFile(ws, conv.KV(t), conv.Tensors(ts))
return writeFile(f, conv.KV(t), conv.Tensors(ts))
}
func writeFile(ws io.WriteSeeker, kv ggml.KV, ts []ggml.Tensor) error {
func writeFile(f *os.File, kv ggml.KV, ts []*ggml.Tensor) error {
for i := range ts {
ts[i].Shape = slices.Clone(ts[i].Shape)
slices.Reverse(ts[i].Shape)
}
return ggml.WriteGGUF(ws, kv, ts)
return ggml.WriteGGUF(f, kv, ts)
}

6
convert/convert_bert.go
View File

@ -132,8 +132,8 @@ func (p *bertModel) KV(t *Tokenizer) ggml.KV {
return kv
}
func (p *bertModel) Tensors(ts []Tensor) []ggml.Tensor {
var out []ggml.Tensor
func (p *bertModel) Tensors(ts []Tensor) []*ggml.Tensor {
var out []*ggml.Tensor
for _, t := range ts {
if slices.Contains([]string{
"embeddings.position_ids",
@ -143,7 +143,7 @@ func (p *bertModel) Tensors(ts []Tensor) []ggml.Tensor {
continue
}
out = append(out, ggml.Tensor{
out = append(out, &ggml.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: t.Shape(),

6
convert/convert_commandr.go
View File

@ -43,10 +43,10 @@ func (p *commandrModel) KV(t *Tokenizer) ggml.KV {
return kv
}
func (p *commandrModel) Tensors(ts []Tensor) []ggml.Tensor {
var out []ggml.Tensor
func (p *commandrModel) Tensors(ts []Tensor) []*ggml.Tensor {
var out []*ggml.Tensor
for _, t := range ts {
out = append(out, ggml.Tensor{
out = append(out, &ggml.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: t.Shape(),

6
convert/convert_gemma.go
View File

@ -42,14 +42,14 @@ func (p *gemmaModel) KV(t *Tokenizer) ggml.KV {
return kv
}
func (p *gemmaModel) Tensors(ts []Tensor) []ggml.Tensor {
var out []ggml.Tensor
func (p *gemmaModel) Tensors(ts []Tensor) []*ggml.Tensor {
var out []*ggml.Tensor
for _, t := range ts {
if !strings.HasPrefix(t.Name(), "v.") && strings.HasSuffix(t.Name(), "_norm.weight") {
t.SetRepacker(p.addOne)
}
out = append(out, ggml.Tensor{
out = append(out, &ggml.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: t.Shape(),

6
convert/convert_gemma2_adapter.go
View File

@ -21,8 +21,8 @@ func (p *gemma2Adapter) KV(baseKV ggml.KV) ggml.KV {
return kv
}
func (p *gemma2Adapter) Tensors(ts []Tensor) []ggml.Tensor {
var out []ggml.Tensor
func (p *gemma2Adapter) Tensors(ts []Tensor) []*ggml.Tensor {
var out []*ggml.Tensor
for _, t := range ts {
shape := t.Shape()
if (strings.HasSuffix(t.Name(), "weight.lora_a") && shape[0] > shape[1]) ||
@ -31,7 +31,7 @@ func (p *gemma2Adapter) Tensors(ts []Tensor) []ggml.Tensor {
t.SetRepacker(p.repack)
}
out = append(out, ggml.Tensor{
out = append(out, &ggml.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: t.Shape(),

8
convert/convert_llama.go
View File

@ -126,11 +126,11 @@ func (p *llamaModel) KV(t *Tokenizer) ggml.KV {
return kv
}
func (p *llamaModel) Tensors(ts []Tensor) []ggml.Tensor {
var out []ggml.Tensor
func (p *llamaModel) Tensors(ts []Tensor) []*ggml.Tensor {
var out []*ggml.Tensor
if p.RopeScaling.factors != nil {
out = append(out, ggml.Tensor{
out = append(out, &ggml.Tensor{
Name: "rope_freqs.weight",
Kind: 0,
Shape: []uint64{uint64(len(p.RopeScaling.factors))},
@ -145,7 +145,7 @@ func (p *llamaModel) Tensors(ts []Tensor) []ggml.Tensor {
}
}
out = append(out, ggml.Tensor{
out = append(out, &ggml.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: t.Shape(),

10
convert/convert_llama4.go
View File

@ -88,13 +88,13 @@ func (p *llama4Model) Replacements() []string {
}
// Tensors implements ModelConverter.
func (p *llama4Model) Tensors(ts []Tensor) []ggml.Tensor {
var out []ggml.Tensor
func (p *llama4Model) Tensors(ts []Tensor) []*ggml.Tensor {
var out []*ggml.Tensor
var textTensors []Tensor
for _, t := range ts {
if strings.HasPrefix(t.Name(), "v.") || strings.HasPrefix(t.Name(), "mm.") {
out = append(out, ggml.Tensor{
out = append(out, &ggml.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: t.Shape(),
@ -112,7 +112,7 @@ func (p *llama4Model) Tensors(ts []Tensor) []ggml.Tensor {
// clone tensor since we need separate repackers
tt := t.Clone()
tt.SetRepacker(p.repack(nil, nil, tensor.S(i*halfDim, (i+1)*halfDim)))
out = append(out, ggml.Tensor{
out = append(out, &ggml.Tensor{
Name: strings.ReplaceAll(tt.Name(), "ffn_gate_up_exps", name),
Kind: tt.Kind(),
Shape: newShape,
@ -125,7 +125,7 @@ func (p *llama4Model) Tensors(ts []Tensor) []ggml.Tensor {
t.SetRepacker(p.repack())
newShape := slices.Clone(t.Shape())
newShape[1], newShape[2] = newShape[2], newShape[1]
out = append(out, ggml.Tensor{
out = append(out, &ggml.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: newShape,

6
convert/convert_llama_adapter.go
View File

@ -29,8 +29,8 @@ func (p *llamaAdapter) KV(baseKV ggml.KV) ggml.KV {
return kv
}
func (p *llamaAdapter) Tensors(ts []Tensor) []ggml.Tensor {
var out []ggml.Tensor
func (p *llamaAdapter) Tensors(ts []Tensor) []*ggml.Tensor {
var out []*ggml.Tensor
for _, t := range ts {
shape := t.Shape()
if (strings.HasSuffix(t.Name(), "weight.lora_a") && shape[0] > shape[1]) ||
@ -41,7 +41,7 @@ func (p *llamaAdapter) Tensors(ts []Tensor) []ggml.Tensor {
t.SetRepacker(p.repack)
}
out = append(out, ggml.Tensor{
out = append(out, &ggml.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: shape,

6
convert/convert_mistral.go
View File

@ -89,8 +89,8 @@ func (p *mistral3Model) KV(t *Tokenizer) ggml.KV {
return kv
}
func (p *mistral3Model) Tensors(ts []Tensor) []ggml.Tensor {
var out []ggml.Tensor
func (p *mistral3Model) Tensors(ts []Tensor) []*ggml.Tensor {
var out []*ggml.Tensor
for _, t := range ts {
if !strings.HasPrefix(t.Name(), "v.") {
@ -100,7 +100,7 @@ func (p *mistral3Model) Tensors(ts []Tensor) []ggml.Tensor {
}
}
out = append(out, ggml.Tensor{
out = append(out, &ggml.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: t.Shape(),

6
convert/convert_mixtral.go
View File

@ -29,7 +29,7 @@ func (p *mixtralModel) KV(t *Tokenizer) ggml.KV {
return kv
}
func (p *mixtralModel) Tensors(ts []Tensor) []ggml.Tensor {
func (p *mixtralModel) Tensors(ts []Tensor) []*ggml.Tensor {
oldnew := []string{
"model.layers", "blk",
"w1", "ffn_gate_exps",
@ -56,10 +56,10 @@ func (p *mixtralModel) Tensors(ts []Tensor) []ggml.Tensor {
return true
})
var out []ggml.Tensor
var out []*ggml.Tensor
for n, e := range experts {
// TODO(mxyng): sanity check experts
out = append(out, ggml.Tensor{
out = append(out, &ggml.Tensor{
Name: n,
Kind: e[0].Kind(),
Shape: append([]uint64{uint64(len(e))}, e[0].Shape()...),

10
convert/convert_phi3.go
View File

@ -68,19 +68,19 @@ func (p *phi3Model) KV(t *Tokenizer) ggml.KV {
return kv
}
func (p *phi3Model) Tensors(ts []Tensor) []ggml.Tensor {
func (p *phi3Model) Tensors(ts []Tensor) []*ggml.Tensor {
var addRopeFactors sync.Once
out := make([]ggml.Tensor, 0, len(ts)+2)
out := make([]*ggml.Tensor, 0, len(ts)+2)
for _, t := range ts {
if strings.HasPrefix(t.Name(), "blk.0.") {
addRopeFactors.Do(func() {
out = append(out, ggml.Tensor{
out = append(out, &ggml.Tensor{
Name: "rope_factors_long.weight",
Kind: 0,
Shape: []uint64{uint64(len(p.RopeScaling.LongFactor))},
WriterTo: p.RopeScaling.LongFactor,
}, ggml.Tensor{
}, &ggml.Tensor{
Name: "rope_factors_short.weight",
Kind: 0,
Shape: []uint64{uint64(len(p.RopeScaling.ShortFactor))},
@ -89,7 +89,7 @@ func (p *phi3Model) Tensors(ts []Tensor) []ggml.Tensor {
})
}
out = append(out, ggml.Tensor{
out = append(out, &ggml.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: t.Shape(),

6
convert/convert_qwen2.go
View File

@ -45,10 +45,10 @@ func (q *qwen2Model) KV(t *Tokenizer) ggml.KV {
return kv
}
func (q *qwen2Model) Tensors(ts []Tensor) []ggml.Tensor {
var out []ggml.Tensor
func (q *qwen2Model) Tensors(ts []Tensor) []*ggml.Tensor {
var out []*ggml.Tensor
for _, t := range ts {
out = append(out, ggml.Tensor{
out = append(out, &ggml.Tensor{
Name: t.Name(),
Kind: t.Kind(),
Shape: t.Shape(),

1
convert/convert_test.go
View File

@ -130,6 +130,7 @@ func TestConvertModel(t *testing.T) {
if err != nil {
t.Fatal(err)
}
defer expectFile.Close()
var expect map[string]string
if err := json.NewDecoder(expectFile).Decode(&expect); err != nil {

58
convert/fs.go
View File

@ -1,58 +0,0 @@
package convert
import (
"archive/zip"
"errors"
"io"
"io/fs"
"os"
"path/filepath"
)
type ZipReader struct {
r *zip.Reader
p string
// limit is the maximum size of a file that can be read directly
// from the zip archive. Files larger than this size will be extracted
limit int64
}
func NewZipReader(r *zip.Reader, p string, limit int64) fs.FS {
return &ZipReader{r, p, limit}
}
func (z *ZipReader) Open(name string) (fs.File, error) {
r, err := z.r.Open(name)
if err != nil {
return nil, err
}
defer r.Close()
if fi, err := r.Stat(); err != nil {
return nil, err
} else if fi.Size() < z.limit {
return r, nil
}
if !filepath.IsLocal(name) {
return nil, zip.ErrInsecurePath
}
n := filepath.Join(z.p, name)
if _, err := os.Stat(n); errors.Is(err, os.ErrNotExist) {
w, err := os.Create(n)
if err != nil {
return nil, err
}
defer w.Close()
if _, err := io.Copy(w, r); err != nil {
return nil, err
}
} else if err != nil {
return nil, err
}
return os.Open(n)
}

3
discover/cuda_common.go
View File

@ -3,6 +3,7 @@
package discover
import (
"fmt"
"log/slog"
"os"
"regexp"
@ -59,6 +60,8 @@ func cudaVariant(gpuInfo CudaGPUInfo) string {
// driver 12.0 has problems with the cuda v12 library, so run v11 on those older drivers
if gpuInfo.DriverMajor < 12 || (gpuInfo.DriverMajor == 12 && gpuInfo.DriverMinor == 0) {
// The detected driver is older than Feb 2023
slog.Warn("old CUDA driver detected - please upgrade to a newer driver", "version", fmt.Sprintf("%d.%d", gpuInfo.DriverMajor, gpuInfo.DriverMinor))
return "v11"
}
return "v12"

2
discover/gpu_info.h
View File

@ -27,12 +27,14 @@
#endif
#ifndef LOG
#define LOG(verbose, ...) \
do { \
if (verbose) { \
fprintf(stderr, __VA_ARGS__); \
} \
} while (0)
#endif
#ifdef __cplusplus
extern "C" {

11
discover/gpu_info_cudart.c
View File

@ -1,6 +1,7 @@
#ifndef __APPLE__ // TODO - maybe consider nvidia support on intel macs?
#include <string.h>
#include <inttypes.h>
#include "gpu_info_cudart.h"
void cudart_init(char *cudart_lib_path, cudart_init_resp_t *resp) {
@ -58,7 +59,7 @@ void cudart_init(char *cudart_lib_path, cudart_init_resp_t *resp) {
LOG(resp->ch.verbose, "cudaSetDevice err: %d\n", ret);
UNLOAD_LIBRARY(resp->ch.handle);
resp->ch.handle = NULL;
if (ret == CUDA_ERROR_INSUFFICIENT_DRIVER) {
if (ret == CUDART_ERROR_INSUFFICIENT_DRIVER) {
resp->err = strdup("your nvidia driver is too old or missing. If you have a CUDA GPU please upgrade to run ollama");
return;
}
@ -168,9 +169,9 @@ void cudart_bootstrap(cudart_handle_t h, int i, mem_info_t *resp) {
resp->free = memInfo.free;
resp->used = memInfo.used;
LOG(h.verbose, "[%s] CUDA totalMem %lu\n", resp->gpu_id, resp->total);
LOG(h.verbose, "[%s] CUDA freeMem %lu\n", resp->gpu_id, resp->free);
LOG(h.verbose, "[%s] CUDA usedMem %lu\n", resp->gpu_id, resp->used);
LOG(h.verbose, "[%s] CUDA totalMem %" PRId64 "\n", resp->gpu_id, resp->total);
LOG(h.verbose, "[%s] CUDA freeMem %" PRId64 "\n", resp->gpu_id, resp->free);
LOG(h.verbose, "[%s] CUDA usedMem %" PRId64 "\n", resp->gpu_id, resp->used);
LOG(h.verbose, "[%s] Compute Capability %d.%d\n", resp->gpu_id, resp->major, resp->minor);
}
@ -180,4 +181,4 @@ void cudart_release(cudart_handle_t h) {
h.handle = NULL;
}
#endif // __APPLE__
#endif // __APPLE__

7
discover/gpu_info_nvcuda.c
View File

@ -1,6 +1,7 @@
#ifndef __APPLE__ // TODO - maybe consider nvidia support on intel macs?
#include <string.h>
#include <inttypes.h>
#include "gpu_info_nvcuda.h"
void nvcuda_init(char *nvcuda_lib_path, nvcuda_init_resp_t *resp) {
@ -193,8 +194,8 @@ void nvcuda_bootstrap(nvcuda_handle_t h, int i, mem_info_t *resp) {
resp->total = memInfo.total;
resp->free = memInfo.free;
LOG(h.verbose, "[%s] CUDA totalMem %lu mb\n", resp->gpu_id, resp->total / 1024 / 1024);
LOG(h.verbose, "[%s] CUDA freeMem %lu mb\n", resp->gpu_id, resp->free / 1024 / 1024);
LOG(h.verbose, "[%s] CUDA totalMem %" PRId64 "mb\n", resp->gpu_id, resp->total / 1024 / 1024);
LOG(h.verbose, "[%s] CUDA freeMem %" PRId64 "mb\n", resp->gpu_id, resp->free / 1024 / 1024);
LOG(h.verbose, "[%s] Compute Capability %d.%d\n", resp->gpu_id, resp->major, resp->minor);
@ -247,4 +248,4 @@ void nvcuda_release(nvcuda_handle_t h) {
h.handle = NULL;
}
#endif // __APPLE__
#endif // __APPLE__

2
discover/path.go
View File

@ -12,7 +12,7 @@ import (
// '../lib/ollama' on Linux and the executable's directory on macOS
// note: distribution builds, additional GPU-specific libraries are
// found in subdirectories of the returned path, such as
// 'cuda_v11', 'cuda_v12', 'rocm', etc.
// 'cuda_v12', 'rocm', etc.
var LibOllamaPath string = func() string {
exe, err := os.Executable()
if err != nil {

6
docs/api.md
View File

@ -394,9 +394,6 @@ curl http://localhost:11434/api/generate -d '{
"repeat_penalty": 1.2,
"presence_penalty": 1.5,
"frequency_penalty": 1.0,
"mirostat": 1,
"mirostat_tau": 0.8,
"mirostat_eta": 0.6,
"penalize_newline": true,
"stop": ["\n", "user:"],
"numa": false,
@ -404,10 +401,7 @@ curl http://localhost:11434/api/generate -d '{
"num_batch": 2,
"num_gpu": 1,
"main_gpu": 0,
"low_vram": false,
"vocab_only": false,
"use_mmap": true,
"use_mlock": false,
"num_thread": 8
}
}'

6
docs/faq.md
View File

@ -20,7 +20,7 @@ Please refer to the [GPU docs](./gpu.md).
## How can I specify the context window size?
By default, Ollama uses a context window size of 4096 tokens, unless you have a single GPU with <= 4 GB of VRAM, in which case it will default to 2048 tokens.
By default, Ollama uses a context window size of 4096 tokens.
This can be overridden with the `OLLAMA_CONTEXT_LENGTH` environment variable. For example, to set the default context window to 8K, use:
@ -31,7 +31,7 @@ OLLAMA_CONTEXT_LENGTH=8192 ollama serve
To change this when using `ollama run`, use `/set parameter`:
```shell
/set parameter num_ctx 8192
/set parameter num_ctx 4096
```
When using the API, specify the `num_ctx` parameter:
@ -41,7 +41,7 @@ curl http://localhost:11434/api/generate -d '{
"model": "llama3.2",
"prompt": "Why is the sky blue?",
"options": {
"num_ctx": 8192
"num_ctx": 4096
}
}'
```

2
docs/gpu.md
View File

@ -1,6 +1,6 @@
# GPU
## Nvidia
Ollama supports Nvidia GPUs with compute capability 5.0+.
Ollama supports Nvidia GPUs with compute capability 5.0+ and driver version 531 and newer.
Check your compute compatibility to see if your card is supported:
[https://developer.nvidia.com/cuda-gpus](https://developer.nvidia.com/cuda-gpus)

3
docs/modelfile.md
View File

@ -150,9 +150,6 @@ PARAMETER <parameter> <parametervalue>
| Parameter | Description | Value Type | Example Usage |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------- | -------------------- |
| mirostat | Enable Mirostat sampling for controlling perplexity. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0) | int | mirostat 0 |
| mirostat_eta | Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. (Default: 0.1) | float | mirostat_eta 0.1 |
| mirostat_tau | Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. (Default: 5.0) | float | mirostat_tau 5.0 |
| num_ctx | Sets the size of the context window used to generate the next token. (Default: 2048) | int | num_ctx 4096 |
| repeat_last_n | Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx) | int | repeat_last_n 64 |
| repeat_penalty | Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1) | float | repeat_penalty 1.1 |

2
docs/troubleshooting.md
View File

@ -43,7 +43,7 @@ Ollama includes multiple LLM libraries compiled for different GPUs and CPU vecto
In the server log, you will see a message that looks something like this (varies from release to release):
```
Dynamic LLM libraries [rocm_v6 cpu cpu_avx cpu_avx2 cuda_v11 rocm_v5]
Dynamic LLM libraries [rocm_v6 cpu cpu_avx cpu_avx2 cuda_v12 rocm_v5]
```
**Experimental LLM Library Override**

18
envconfig/config.go
View File

@ -169,7 +169,7 @@ var (
// Enable the new Ollama engine
NewEngine = Bool("OLLAMA_NEW_ENGINE")
// ContextLength sets the default context length
ContextLength = Int64("OLLAMA_CONTEXT_LENGTH", -1)
ContextLength = Uint("OLLAMA_CONTEXT_LENGTH", 4096)
)
func String(s string) func() string {
@ -227,20 +227,6 @@ func Uint64(key string, defaultValue uint64) func() uint64 {
}
}
func Int64(key string, defaultValue int64) func() int64 {
return func() int64 {
if s := Var(key); s != "" {
if n, err := strconv.ParseInt(s, 10, 64); err != nil {
slog.Warn("invalid environment variable, using default", "key", key, "value", s, "default", defaultValue)
} else {
return n
}
}
return defaultValue
}
}
// Set aside VRAM per GPU
var GpuOverhead = Uint64("OLLAMA_GPU_OVERHEAD", 0)
@ -269,7 +255,7 @@ func AsMap() map[string]EnvVar {
"OLLAMA_ORIGINS": {"OLLAMA_ORIGINS", AllowedOrigins(), "A comma separated list of allowed origins"},
"OLLAMA_SCHED_SPREAD": {"OLLAMA_SCHED_SPREAD", SchedSpread(), "Always schedule model across all GPUs"},
"OLLAMA_MULTIUSER_CACHE": {"OLLAMA_MULTIUSER_CACHE", MultiUserCache(), "Optimize prompt caching for multi-user scenarios"},
"OLLAMA_CONTEXT_LENGTH": {"OLLAMA_CONTEXT_LENGTH", ContextLength(), "Context length to use unless otherwise specified (default 4096 or 2048 with low VRAM)"},
"OLLAMA_CONTEXT_LENGTH": {"OLLAMA_CONTEXT_LENGTH", ContextLength(), "Context length to use unless otherwise specified (default: 4096)"},
"OLLAMA_NEW_ENGINE": {"OLLAMA_NEW_ENGINE", NewEngine(), "Enable the new Ollama engine"},
// Informational

6
envconfig/config_test.go
View File

@ -278,9 +278,9 @@ func TestVar(t *testing.T) {
}
func TestContextLength(t *testing.T) {
cases := map[string]int64{
"": -1,
"4096": 4096,
cases := map[string]uint{
"": 4096,
"2048": 2048,
}
for k, v := range cases {

88
fs/ggml/ggml.go
View File

@ -37,12 +37,12 @@ func (kv KV) ParameterCount() uint64 {
return val
}
func (kv KV) FileType() fileType {
func (kv KV) FileType() FileType {
if t := kv.Uint("general.file_type"); t > 0 {
return fileType(t)
return FileType(t)
}
return fileTypeUnknown
return FileTypeUnknown
}
func (kv KV) BlockCount() uint64 {
@ -194,7 +194,7 @@ func keyValue[T valueTypes | arrayValueTypes](kv KV, key string, defaultValue ..
return val, true
}
slog.Warn("key with type not found", "key", key, "default", defaultValue[0])
slog.Debug("key with type not found", "key", key, "default", defaultValue[0])
return defaultValue[0], false
}
@ -271,7 +271,11 @@ func (t Tensor) block() (n int) {
}
func (t Tensor) blockSize() uint64 {
switch t.Kind {
return (TensorType)(t.Kind).BlockSize()
}
func (t TensorType) BlockSize() uint64 {
switch t {
case
0, // F32
1, // F16
@ -297,73 +301,77 @@ func (t Tensor) blockSize() uint64 {
}
func (t Tensor) typeSize() uint64 {
blockSize := t.blockSize()
return TensorType(t.Kind).TypeSize()
}
switch t.Kind {
case 0: // FP32
func (t TensorType) TypeSize() uint64 {
blockSize := t.BlockSize()
switch t {
case TensorTypeF32:
return 4
case 1: // FP16
case TensorTypeF16:
return 2
case 2: // Q4_0
case TensorTypeQ4_0:
return 2 + blockSize/2
case 3: // Q4_1
case TensorTypeQ4_1:
return 2 + 2 + blockSize/2
case 6: // Q5_0
case TensorTypeQ5_0:
return 2 + 4 + blockSize/2
case 7: // Q5_1
case TensorTypeQ5_1:
return 2 + 2 + 4 + blockSize/2
case 8: // Q8_0
case TensorTypeQ8_0:
return 2 + blockSize
case 9: // Q8_1
case TensorTypeQ8_1:
return 2 + 2 + blockSize
case 10: // Q2_K
case TensorTypeQ2_K:
return blockSize/16 + blockSize/4 + 2 + 2
case 11: // Q3_K
case TensorTypeQ3_K:
return blockSize/8 + blockSize/4 + 12 + 2
case 12: // Q4_K
case TensorTypeQ4_K:
return 2 + 2 + 12 + blockSize/2
case 13: // Q5_K
case TensorTypeQ5_K:
return 2 + 2 + 12 + blockSize/8 + blockSize/2
case 14: // Q6_K
case TensorTypeQ6_K:
return blockSize/2 + blockSize/4 + blockSize/16 + 2
case 15: // Q8_K
case TensorTypeQ8_K:
return 4 + blockSize + 2*blockSize/16
case 16: // IQ2_XXS
case tensorTypeIQ2_XXS:
return 2 + 2*blockSize/8
case 17: // IQ2_XS
case tensorTypeIQ2_XS:
return 2 + 2*blockSize/8 + blockSize/32
case 18: // IQ3_XXS
case tensorTypeIQ3_XXS:
return 2 + blockSize/4 + blockSize/8
case 19: // IQ1_S
case tensorTypeIQ1_S:
return 2 + blockSize/8 + blockSize/16
case 20: // IQ4_NL
case tensorTypeIQ4_NL:
return 2 + blockSize/2
case 21: // IQ3_S
case tensorTypeIQ3_S:
return 2 + blockSize/4 + blockSize/8 + blockSize/32 + 4
case 22: // IQ2_S
case tensorTypeIQ2_S:
return 2 + blockSize/4 + blockSize/16
case 23: // IQ4_XS
case tensorTypeIQ4_XS:
return 2 + 2 + blockSize/2 + blockSize/64
case 24: // I8
case TensorTypeI8:
return 1
case 25: // I16
case TensorTypeI16:
return 2
case 26: // I32
case TensorTypeI32:
return 4
case 27: // I64
case TensorTypeI64:
return 8
case 28: // F64
case TensorTypeF64:
return 8
case 29: // IQ1_M
case tensorTypeIQ1_M:
return blockSize/8 + blockSize/16 + blockSize/32
case 30: // BF16
case TensorTypeBF16:
return 2
default:
return 0
}
}
func (t Tensor) parameters() uint64 {
func (t Tensor) Elements() uint64 {
var count uint64 = 1
for _, n := range t.Shape {
count *= n
@ -372,11 +380,11 @@ func (t Tensor) parameters() uint64 {
}
func (t Tensor) Size() uint64 {
return t.parameters() * t.typeSize() / t.blockSize()
return t.Elements() * t.typeSize() / t.blockSize()
}
func (t Tensor) Type() string {
return fileType(t.Kind).String()
return TensorType(t.Kind).String()
}
type container interface {
@ -525,7 +533,7 @@ func (f GGML) GraphSize(context, batch uint64, numParallel int, kvCacheType stri
var ropeFreqsCount uint64
if ropeFreqs, ok := f.Tensors().GroupLayers()["rope_freqs"]; ok {
if ropeFreqsWeights, ok := ropeFreqs["weights"]; ok {
ropeFreqsCount = ropeFreqsWeights.parameters()
ropeFreqsCount = ropeFreqsWeights.Elements()
}
}

110
fs/ggml/gguf.go
View File

@ -9,8 +9,12 @@ import (
"io"
"log/slog"
"maps"
"os"
"runtime"
"slices"
"strings"
"golang.org/x/sync/errgroup"
)
type containerGGUF struct {
@ -225,7 +229,7 @@ func (llm *gguf) Decode(rs io.ReadSeeker) error {
}
llm.tensors = append(llm.tensors, &tensor)
llm.parameters += tensor.parameters()
llm.parameters += tensor.Elements()
}
// patch KV with parameter count
@ -488,25 +492,38 @@ func writeGGUFArray[S ~[]E, E any](w io.Writer, t uint32, s S) error {
return err
}
if t == ggufTypeString {
for _, e := range any(s).([]string) {
if err := binary.Write(w, binary.LittleEndian, uint64(len(e))); err != nil {
return err
}
if err := binary.Write(w, binary.LittleEndian, []byte(e)); err != nil {
return err
}
}
return nil
}
return binary.Write(w, binary.LittleEndian, s)
}
func WriteGGUF(ws io.WriteSeeker, kv KV, ts []Tensor) error {
func WriteGGUF(f *os.File, kv KV, ts []*Tensor) error {
alignment := kv.Uint("general.alignment", 32)
if err := binary.Write(ws, binary.LittleEndian, []byte("GGUF")); err != nil {
if err := binary.Write(f, binary.LittleEndian, []byte("GGUF")); err != nil {
return err
}
if err := binary.Write(ws, binary.LittleEndian, uint32(3)); err != nil {
if err := binary.Write(f, binary.LittleEndian, uint32(3)); err != nil {
return err
}
if err := binary.Write(ws, binary.LittleEndian, uint64(len(ts))); err != nil {
if err := binary.Write(f, binary.LittleEndian, uint64(len(ts))); err != nil {
return err
}
if err := binary.Write(ws, binary.LittleEndian, uint64(len(kv))); err != nil {
if err := binary.Write(f, binary.LittleEndian, uint64(len(kv))); err != nil {
return err
}
@ -514,12 +531,12 @@ func WriteGGUF(ws io.WriteSeeker, kv KV, ts []Tensor) error {
slices.Sort(keys)
for _, key := range keys {
if err := ggufWriteKV(ws, key, kv[key]); err != nil {
if err := ggufWriteKV(f, key, kv[key]); err != nil {
return err
}
}
slices.SortStableFunc(ts, func(a, b Tensor) int {
slices.SortStableFunc(ts, func(a, b *Tensor) int {
if i, j := a.block(), b.block(); i < 0 && j > 0 {
return 1
} else if i > 0 && j < 0 {
@ -530,21 +547,34 @@ func WriteGGUF(ws io.WriteSeeker, kv KV, ts []Tensor) error {
})
var s uint64
for _, t := range ts {
t.Offset = s + uint64(ggufPadding(int64(s), int64(alignment)))
if err := ggufWriteTensorInfo(ws, t); err != nil {
for i := range ts {
ts[i].Offset = s
if err := ggufWriteTensorInfo(f, ts[i]); err != nil {
return err
}
s += t.Size()
s += ts[i].Size()
s += uint64(ggufPadding(int64(s), int64(alignment)))
}
offset, err := f.Seek(0, io.SeekCurrent)
if err != nil {
return err
}
offset += ggufPadding(offset, int64(alignment))
var g errgroup.Group
g.SetLimit(runtime.GOMAXPROCS(0))
// TODO consider reducing if tensors size * gomaxprocs is larger than free memory
for _, t := range ts {
if err := ggufWriteTensor(ws, t, int64(alignment)); err != nil {
t := t
w := io.NewOffsetWriter(f, offset+int64(t.Offset))
g.Go(func() error {
_, err := t.WriteTo(w)
return err
}
})
}
return nil
return g.Wait()
}
func ggufWriteKV(ws io.WriteSeeker, k string, v any) error {
@ -559,8 +589,10 @@ func ggufWriteKV(ws io.WriteSeeker, k string, v any) error {
var err error
switch v := v.(type) {
case uint32:
case uint32, FileType:
err = writeGGUF(ws, ggufTypeUint32, v)
case uint64:
err = writeGGUF(ws, ggufTypeUint64, v)
case float32:
err = writeGGUF(ws, ggufTypeFloat32, v)
case bool:
@ -569,32 +601,20 @@ func ggufWriteKV(ws io.WriteSeeker, k string, v any) error {
err = writeGGUFString(ws, v)
case []int32:
err = writeGGUFArray(ws, ggufTypeInt32, v)
case *array[int32]:
err = writeGGUFArray(ws, ggufTypeInt32, v.values)
case []uint32:
err = writeGGUFArray(ws, ggufTypeUint32, v)
case *array[uint32]:
err = writeGGUFArray(ws, ggufTypeUint32, v.values)
case []float32:
err = writeGGUFArray(ws, ggufTypeFloat32, v)
case *array[float32]:
err = writeGGUFArray(ws, ggufTypeFloat32, v.values)
case []string:
if err := binary.Write(ws, binary.LittleEndian, ggufTypeArray); err != nil {
return err
}
if err := binary.Write(ws, binary.LittleEndian, ggufTypeString); err != nil {
return err
}
if err := binary.Write(ws, binary.LittleEndian, uint64(len(v))); err != nil {
return err
}
for _, e := range v {
if err := binary.Write(ws, binary.LittleEndian, uint64(len(e))); err != nil {
return err
}
if err := binary.Write(ws, binary.LittleEndian, []byte(e)); err != nil {
return err
}
}
err = writeGGUFArray(ws, ggufTypeString, v)
case *array[string]:
err = writeGGUFArray(ws, ggufTypeString, v.values)
default:
return fmt.Errorf("improper type for '%s'", k)
}
@ -602,7 +622,7 @@ func ggufWriteKV(ws io.WriteSeeker, k string, v any) error {
return err
}
func ggufWriteTensorInfo(ws io.WriteSeeker, t Tensor) error {
func ggufWriteTensorInfo(ws io.WriteSeeker, t *Tensor) error {
slog.Debug(t.Name, "kind", t.Kind, "shape", t.Shape, "offset", t.Offset)
if err := binary.Write(ws, binary.LittleEndian, uint64(len(t.Name))); err != nil {
return err
@ -629,20 +649,6 @@ func ggufWriteTensorInfo(ws io.WriteSeeker, t Tensor) error {
return binary.Write(ws, binary.LittleEndian, t.Offset)
}
func ggufWriteTensor(ws io.WriteSeeker, t Tensor, alignment int64) error {
offset, err := ws.Seek(0, io.SeekCurrent)
if err != nil {
return err
}
if err := binary.Write(ws, binary.LittleEndian, bytes.Repeat([]byte{0}, int(ggufPadding(offset, alignment)))); err != nil {
return err
}
_, err = t.WriteTo(ws)
return err
}
func ggufPadding(offset, align int64) int64 {
return (align - offset%align) % align
}

63
fs/ggml/gguf_test.go Normal file
View File

@ -0,0 +1,63 @@
package ggml
import (
"bytes"
"os"
"slices"
"testing"
"github.com/google/go-cmp/cmp"
)
func TestWriteGGUF(t *testing.T) {
w, err := os.CreateTemp(t.TempDir(), "*.bin")
if err != nil {
t.Fatal(err)
}
defer w.Close()
if err := WriteGGUF(w, KV{
"general.alignment": uint32(16),
}, []*Tensor{
{Name: "test.0", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(slices.Repeat([]byte{0}, 2*3*4))},
{Name: "test.1", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(slices.Repeat([]byte{0}, 2*3*4))},
{Name: "test.2", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(slices.Repeat([]byte{0}, 2*3*4))},
{Name: "test.3", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(slices.Repeat([]byte{0}, 2*3*4))},
{Name: "test.4", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(slices.Repeat([]byte{0}, 2*3*4))},
{Name: "test.5", Shape: []uint64{2, 3}, WriterTo: bytes.NewBuffer(slices.Repeat([]byte{0}, 2*3*4))},
}); err != nil {
t.Fatal(err)
}
r, err := os.Open(w.Name())
if err != nil {
t.Fatal(err)
}
defer r.Close()
ff, _, err := Decode(r, 0)
if err != nil {
t.Fatal(err)
}
if diff := cmp.Diff(ff.KV(), KV{
"general.alignment": uint32(16),
"general.parameter_count": uint64(36),
}); diff != "" {
t.Errorf("Mismatch (-want +got):\n%s", diff)
}
if diff := cmp.Diff(ff.Tensors(), Tensors{
Offset: 336,
items: []*Tensor{
{Name: "test.0", Offset: 0, Shape: []uint64{2, 3}},
{Name: "test.1", Offset: 32, Shape: []uint64{2, 3}},
{Name: "test.2", Offset: 64, Shape: []uint64{2, 3}},
{Name: "test.3", Offset: 96, Shape: []uint64{2, 3}},
{Name: "test.4", Offset: 128, Shape: []uint64{2, 3}},
{Name: "test.5", Offset: 160, Shape: []uint64{2, 3}},
},
}, cmp.AllowUnexported(Tensors{})); diff != "" {
t.Errorf("Mismatch (-want +got):\n%s", diff)
}
}

424
fs/ggml/type.go
View File

@ -1,185 +1,341 @@
package ggml
import "fmt"
type fileType uint32
const (
fileTypeF32 fileType = iota
fileTypeF16
fileTypeQ4_0
fileTypeQ4_1
fileTypeQ4_1_F16
fileTypeQ4_2 // unused
fileTypeQ4_3 // unused
fileTypeQ8_0
fileTypeQ5_0
fileTypeQ5_1
fileTypeQ2_K
fileTypeQ3_K_S
fileTypeQ3_K_M
fileTypeQ3_K_L
fileTypeQ4_K_S
fileTypeQ4_K_M
fileTypeQ5_K_S
fileTypeQ5_K_M
fileTypeQ6_K
fileTypeIQ2_XXS
fileTypeIQ2_XS
fileTypeQ2_K_S
fileTypeIQ3_XS
fileTypeIQ3_XXS
fileTypeIQ1_S
fileTypeIQ4_NL
fileTypeIQ3_S
fileTypeIQ3_M
fileTypeIQ2_S
fileTypeIQ2_M
fileTypeIQ4_XS
fileTypeIQ1_M
fileTypeBF16
fileTypeUnknown
import (
"fmt"
"log/slog"
"strings"
)
func ParseFileType(s string) (fileType, error) {
// FileType is the Go equivalent to llama_ftype used for gguf file typing
type FileType uint32
const (
FileTypeF32 FileType = iota
FileTypeF16
FileTypeQ4_0
FileTypeQ4_1
fileTypeQ4_1_F16 // unused by GGML
fileTypeQ4_2 // unused by GGML
fileTypeQ4_3 // unused by GGML
FileTypeQ8_0
FileTypeQ5_0
FileTypeQ5_1
FileTypeQ2_K
FileTypeQ3_K_S
FileTypeQ3_K_M
FileTypeQ3_K_L
FileTypeQ4_K_S
FileTypeQ4_K_M
FileTypeQ5_K_S
FileTypeQ5_K_M
FileTypeQ6_K
fileTypeIQ2_XXS // not supported by ollama
fileTypeIQ2_XS // not supported by ollama
FileTypeQ2_K_S
fileTypeIQ3_XS // not supported by ollama
fileTypeIQ3_XXS // not supported by ollama
fileTypeIQ1_S // not supported by ollama
fileTypeIQ4_NL // not supported by ollama
fileTypeIQ3_S // not supported by ollama
fileTypeIQ3_M // not supported by ollama
fileTypeIQ2_S // not supported by ollama
fileTypeIQ2_M // not supported by ollama
fileTypeIQ4_XS // not supported by ollama
fileTypeIQ1_M // not supported by ollama
FileTypeBF16
fileTypeQ4_0_4_4 // unused by GGML
fileTypeQ4_0_4_8 // unused by GGML
fileTypeQ4_0_8_8 // unused by GGML
fileTypeTQ1_0 // not supported by ollama
fileTypeTQ2_0 // not supported by ollama
FileTypeUnknown = 1024
)
// ParseFileType parses the provided GGUF file type
// Only Ollama supported types are considered valid
func ParseFileType(s string) (FileType, error) {
switch s {
case "F32":
return fileTypeF32, nil
return FileTypeF32, nil
case "F16":
return fileTypeF16, nil
return FileTypeF16, nil
case "Q4_0":
return fileTypeQ4_0, nil
return FileTypeQ4_0, nil
case "Q4_1":
return fileTypeQ4_1, nil
case "Q4_1_F16":
return fileTypeQ4_1_F16, nil
return FileTypeQ4_1, nil
case "Q8_0":
return fileTypeQ8_0, nil
return FileTypeQ8_0, nil
case "Q5_0":
return fileTypeQ5_0, nil
return FileTypeQ5_0, nil
case "Q5_1":
return fileTypeQ5_1, nil
return FileTypeQ5_1, nil
case "Q2_K":
return fileTypeQ2_K, nil
return FileTypeQ2_K, nil
case "Q3_K_S":
return fileTypeQ3_K_S, nil
return FileTypeQ3_K_S, nil
case "Q3_K_M":
return fileTypeQ3_K_M, nil
return FileTypeQ3_K_M, nil
case "Q3_K_L":
return fileTypeQ3_K_L, nil
return FileTypeQ3_K_L, nil
case "Q4_K_S":
return fileTypeQ4_K_S, nil
case "Q4_K_M":
return fileTypeQ4_K_M, nil
return FileTypeQ4_K_S, nil
case "Q4_K_M", "Q4_K":
return FileTypeQ4_K_M, nil
case "Q5_K_S":
return fileTypeQ5_K_S, nil
case "Q5_K_M":
return fileTypeQ5_K_M, nil
return FileTypeQ5_K_S, nil
case "Q5_K_M", "Q5_K":
return FileTypeQ5_K_M, nil
case "Q6_K":
return fileTypeQ6_K, nil
case "IQ2_XXS":
return fileTypeIQ2_XXS, nil
case "IQ2_XS":
return fileTypeIQ2_XS, nil
return FileTypeQ6_K, nil
case "Q2_K_S":
return fileTypeQ2_K_S, nil
case "IQ3_XS":
return fileTypeIQ3_XS, nil
case "IQ3_XXS":
return fileTypeIQ3_XXS, nil
case "IQ1_S":
return fileTypeIQ1_S, nil
case "IQ4_NL":
return fileTypeIQ4_NL, nil
case "IQ3_S":
return fileTypeIQ3_S, nil
case "IQ3_M":
return fileTypeIQ3_M, nil
case "IQ2_S":
return fileTypeIQ2_S, nil
case "IQ2_M":
return fileTypeIQ2_M, nil
case "IQ4_XS":
return fileTypeIQ4_XS, nil
case "IQ1_M":
return fileTypeIQ1_M, nil
return FileTypeQ2_K_S, nil
case "BF16":
return fileTypeBF16, nil
return FileTypeBF16, nil
default:
return fileTypeUnknown, fmt.Errorf("unknown fileType: %s", s)
supportedFileTypes := []FileType{
FileTypeF32,
FileTypeF16,
FileTypeQ4_K_S,
FileTypeQ4_K_M,
FileTypeQ8_0,
// fsggml.FileTypeBF16, // TODO
}
strs := make([]string, len(supportedFileTypes))
for i := range supportedFileTypes {
strs[i] = supportedFileTypes[i].String()
}
return FileTypeUnknown, fmt.Errorf("unsupported quantization type %s - supported types are %s", s, strings.Join(strs, ", "))
}
}
func (t fileType) String() string {
func (t FileType) String() string {
switch t {
case fileTypeF32:
case FileTypeF32:
return "F32"
case fileTypeF16:
case FileTypeF16:
return "F16"
case fileTypeQ4_0:
case FileTypeQ4_0:
return "Q4_0"
case fileTypeQ4_1:
case FileTypeQ4_1:
return "Q4_1"
case fileTypeQ4_1_F16:
return "Q4_1_F16"
case fileTypeQ8_0:
case FileTypeQ8_0:
return "Q8_0"
case fileTypeQ5_0:
case FileTypeQ5_0:
return "Q5_0"
case fileTypeQ5_1:
case FileTypeQ5_1:
return "Q5_1"
case fileTypeQ2_K:
case FileTypeQ2_K:
return "Q2_K"
case fileTypeQ3_K_S:
case FileTypeQ3_K_S:
return "Q3_K_S"
case fileTypeQ3_K_M:
case FileTypeQ3_K_M:
return "Q3_K_M"
case fileTypeQ3_K_L:
case FileTypeQ3_K_L:
return "Q3_K_L"
case fileTypeQ4_K_S:
case FileTypeQ4_K_S:
return "Q4_K_S"
case fileTypeQ4_K_M:
case FileTypeQ4_K_M:
return "Q4_K_M"
case fileTypeQ5_K_S:
case FileTypeQ5_K_S:
return "Q5_K_S"
case fileTypeQ5_K_M:
case FileTypeQ5_K_M:
return "Q5_K_M"
case fileTypeQ6_K:
case FileTypeQ6_K:
return "Q6_K"
case fileTypeIQ2_XXS:
return "IQ2_XXS"
case fileTypeIQ2_XS:
return "IQ2_XS"
case fileTypeQ2_K_S:
case FileTypeQ2_K_S:
return "Q2_K_S"
case fileTypeIQ3_XS:
return "IQ3_XS"
case fileTypeIQ3_XXS:
return "IQ3_XXS"
case fileTypeIQ1_S:
return "IQ1_S"
case fileTypeIQ4_NL:
return "IQ4_NL"
case fileTypeIQ3_S:
return "IQ3_S"
case fileTypeIQ3_M:
return "IQ3_M"
case fileTypeIQ2_S:
return "IQ2_S"
case fileTypeIQ4_XS:
return "IQ4_XS"
case fileTypeIQ2_M:
return "IQ2_M"
case fileTypeIQ1_M:
return "IQ1_M"
case fileTypeBF16:
case FileTypeBF16:
return "BF16"
default:
return "unknown"
}
}
func (t fileType) Value() uint32 {
func (t FileType) Value() uint32 {
return uint32(t)
}
func (ftype FileType) ToTensorType() TensorType {
switch ftype {
case FileTypeF32:
return TensorTypeF32
case FileTypeF16:
return TensorTypeF16
case FileTypeQ4_0:
return TensorTypeQ4_0
case FileTypeQ4_1:
return TensorTypeQ4_1
case FileTypeQ8_0:
return TensorTypeQ8_0
case FileTypeQ5_0:
return TensorTypeQ5_0
case FileTypeQ5_1:
return TensorTypeQ5_1
case FileTypeQ2_K:
return TensorTypeQ2_K
case FileTypeQ3_K_S:
return TensorTypeQ3_K
case FileTypeQ3_K_M:
return TensorTypeQ3_K
case FileTypeQ3_K_L:
return TensorTypeQ3_K
case FileTypeQ4_K_S:
return TensorTypeQ4_K
case FileTypeQ4_K_M:
return TensorTypeQ4_K
case FileTypeQ5_K_S:
return TensorTypeQ5_K
case FileTypeQ5_K_M:
return TensorTypeQ5_K
case FileTypeQ6_K:
return TensorTypeQ6_K
case FileTypeQ2_K_S:
return TensorTypeQ2_K
case FileTypeBF16:
return TensorTypeBF16
default:
slog.Warn("unsupported file type", "type", ftype)
return 0 // F32
}
}
// TensorType is equivalent to ggml_type for individual tensor types
// Note: these are not the same as FileType
type TensorType uint32
const (
TensorTypeF32 TensorType = iota
TensorTypeF16
TensorTypeQ4_0
TensorTypeQ4_1
tensorTypeQ4_2 // unused by GGML
tensorTypeQ4_3 // unused by GGML
TensorTypeQ5_0
TensorTypeQ5_1
TensorTypeQ8_0
TensorTypeQ8_1
TensorTypeQ2_K
TensorTypeQ3_K
TensorTypeQ4_K
TensorTypeQ5_K
TensorTypeQ6_K
TensorTypeQ8_K
tensorTypeIQ2_XXS // not supported by ollama
tensorTypeIQ2_XS // not supported by ollama
tensorTypeIQ3_XXS // not supported by ollama
tensorTypeIQ1_S // not supported by ollama
tensorTypeIQ4_NL // not supported by ollama
tensorTypeIQ3_S // not supported by ollama
tensorTypeIQ2_S // not supported by ollama
tensorTypeIQ4_XS // not supported by ollama
TensorTypeI8
TensorTypeI16
TensorTypeI32
TensorTypeI64
TensorTypeF64
tensorTypeIQ1_M // not supported by ollama
TensorTypeBF16
tensorTypeQ4_0_4_4 // unused by GGML
tensorTypeQ4_0_4_8 // unused by GGML
tensorTypeQ4_0_8_8 // unused by GGML
tensorTypeTQ1_0 // not supported by ollama
tensorTypeTQ2_0 // not supported by ollama
tensorTypeIQ4_NL_4_4 // unused by GGML
tensorTypeIQ4_NL_4_8 // unused by GGML
tensorTypeIQ4_NL_8_8 // unused by GGML
)
// ParseFileType parses the provided GGUF file type
// Only Ollama supported types are considered valid
func ParseTensorType(s string) (TensorType, error) {
switch s {
case "F32":
return TensorTypeF32, nil
case "F16":
return TensorTypeF16, nil
case "Q4_0":
return TensorTypeQ4_0, nil
case "Q4_1":
return TensorTypeQ4_1, nil
case "Q5_0":
return TensorTypeQ5_0, nil
case "Q5_1":
return TensorTypeQ5_1, nil
case "Q8_0":
return TensorTypeQ8_0, nil
case "Q8_1":
return TensorTypeQ8_1, nil
case "Q2_K":
return TensorTypeQ2_K, nil
case "Q3_K":
return TensorTypeQ3_K, nil
case "Q4_K":
return TensorTypeQ4_K, nil
case "Q5_K":
return TensorTypeQ5_K, nil
case "Q6_K":
return TensorTypeQ6_K, nil
case "Q8_K":
return TensorTypeQ8_K, nil
case "F64":
return TensorTypeF64, nil
case "BF16":
return TensorTypeBF16, nil
default:
return 0, fmt.Errorf("unsupported quantization type %s", s)
}
}
func (t TensorType) IsQuantized() bool {
switch t {
case TensorTypeF32, TensorTypeF16, TensorTypeBF16:
return false
default:
return true
}
}
func (t TensorType) RowSize(ne uint64) uint64 {
return t.TypeSize() * ne / t.BlockSize()
}
func (t TensorType) String() string {
switch t {
case TensorTypeF32:
return "F32"
case TensorTypeF16:
return "F16"
case TensorTypeQ4_0:
return "Q4_0"
case TensorTypeQ4_1:
return "Q4_1"
case TensorTypeQ5_0:
return "Q5_0"
case TensorTypeQ5_1:
return "Q5_1"
case TensorTypeQ8_0:
return "Q8_0"
case TensorTypeQ8_1:
return "Q8_1"
case TensorTypeQ2_K:
return "Q2_K"
case TensorTypeQ3_K:
return "Q3_K"
case TensorTypeQ4_K:
return "Q4_K"
case TensorTypeQ5_K:
return "Q5_K"
case TensorTypeQ6_K:
return "Q6_K"
case TensorTypeQ8_K:
return "Q8_K"
case TensorTypeF64:
return "F64"
case TensorTypeBF16:
return "BF16"
default:
return "unknown"
}
}

12
go.mod
View File

@ -11,7 +11,7 @@ require (
github.com/spf13/cobra v1.7.0
github.com/stretchr/testify v1.9.0
github.com/x448/float16 v0.8.4
golang.org/x/sync v0.11.0
golang.org/x/sync v0.12.0
)
require (
@ -70,12 +70,12 @@ require (
github.com/twitchyliquid64/golang-asm v0.15.1 // indirect
github.com/ugorji/go/codec v1.2.12 // indirect
golang.org/x/arch v0.8.0 // indirect
golang.org/x/crypto v0.33.0
golang.org/x/crypto v0.36.0
golang.org/x/exp v0.0.0-20250218142911-aa4b98e5adaa
golang.org/x/net v0.35.0 // indirect
golang.org/x/sys v0.30.0
golang.org/x/term v0.29.0
golang.org/x/text v0.22.0
golang.org/x/net v0.38.0 // indirect
golang.org/x/sys v0.31.0
golang.org/x/term v0.30.0
golang.org/x/text v0.23.0
google.golang.org/protobuf v1.34.1
gopkg.in/yaml.v3 v3.0.1 // indirect
)

24
go.sum
View File

@ -214,8 +214,8 @@ golang.org/x/crypto v0.0.0-20190308221718-c2843e01d9a2/go.mod h1:djNgcEr1/C05ACk
golang.org/x/crypto v0.0.0-20190510104115-cbcb75029529/go.mod h1:yigFU9vqHzYiE8UmvKecakEJjdnWj3jj499lnFckfCI=
golang.org/x/crypto v0.0.0-20191011191535-87dc89f01550/go.mod h1:yigFU9vqHzYiE8UmvKecakEJjdnWj3jj499lnFckfCI=
golang.org/x/crypto v0.0.0-20200622213623-75b288015ac9/go.mod h1:LzIPMQfyMNhhGPhUkYOs5KpL4U8rLKemX1yGLhDgUto=
golang.org/x/crypto v0.33.0 h1:IOBPskki6Lysi0lo9qQvbxiQ+FvsCC/YWOecCHAixus=
golang.org/x/crypto v0.33.0/go.mod h1:bVdXmD7IV/4GdElGPozy6U7lWdRXA4qyRVGJV57uQ5M=
golang.org/x/crypto v0.36.0 h1:AnAEvhDddvBdpY+uR+MyHmuZzzNqXSe/GvuDeob5L34=
golang.org/x/crypto v0.36.0/go.mod h1:Y4J0ReaxCR1IMaabaSMugxJES1EpwhBHhv2bDHklZvc=
golang.org/x/exp v0.0.0-20180321215751-8460e604b9de/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
golang.org/x/exp v0.0.0-20180807140117-3d87b88a115f/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
golang.org/x/exp v0.0.0-20190121172915-509febef88a4/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
@ -257,8 +257,8 @@ golang.org/x/net v0.0.0-20200822124328-c89045814202/go.mod h1:/O7V0waA8r7cgGh81R
golang.org/x/net v0.0.0-20201021035429-f5854403a974/go.mod h1:sp8m0HH+o8qH0wwXwYZr8TS3Oi6o0r6Gce1SSxlDquU=
golang.org/x/net v0.0.0-20210405180319-a5a99cb37ef4/go.mod h1:p54w0d4576C0XHj96bSt6lcn1PtDYWL6XObtHCRCNQM=
golang.org/x/net v0.0.0-20210614182718-04defd469f4e/go.mod h1:9nx3DQGgdP8bBQD5qxJ1jj9UTztislL4KSBs9R2vV5Y=
golang.org/x/net v0.35.0 h1:T5GQRQb2y08kTAByq9L4/bz8cipCdA8FbRTXewonqY8=
golang.org/x/net v0.35.0/go.mod h1:EglIi67kWsHKlRzzVMUD93VMSWGFOMSZgxFjparz1Qk=
golang.org/x/net v0.38.0 h1:vRMAPTMaeGqVhG5QyLJHqNDwecKTomGeqbnfZyKlBI8=
golang.org/x/net v0.38.0/go.mod h1:ivrbrMbzFq5J41QOQh0siUuly180yBYtLp+CKbEaFx8=
golang.org/x/oauth2 v0.0.0-20180821212333-d2e6202438be/go.mod h1:N/0e6XlmueqKjAGxoOufVs8QHGRruUQn6yWY3a++T0U=
golang.org/x/oauth2 v0.0.0-20200107190931-bf48bf16ab8d/go.mod h1:gOpvHmFTYa4IltrdGE7lF6nIHvwfUNPOp7c8zoXwtLw=
golang.org/x/sync v0.0.0-20180314180146-1d60e4601c6f/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
@ -268,8 +268,8 @@ golang.org/x/sync v0.0.0-20190423024810-112230192c58/go.mod h1:RxMgew5VJxzue5/jJ
golang.org/x/sync v0.0.0-20190911185100-cd5d95a43a6e/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
golang.org/x/sync v0.0.0-20201020160332-67f06af15bc9/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
golang.org/x/sync v0.0.0-20210220032951-036812b2e83c/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM=
golang.org/x/sync v0.11.0 h1:GGz8+XQP4FvTTrjZPzNKTMFtSXH80RAzG+5ghFPgK9w=
golang.org/x/sync v0.11.0/go.mod h1:Czt+wKu1gCyEFDUtn0jG5QVvpJ6rzVqr5aXyt9drQfk=
golang.org/x/sync v0.12.0 h1:MHc5BpPuC30uJk597Ri8TV3CNZcTLu6B6z4lJy+g6Jw=
golang.org/x/sync v0.12.0/go.mod h1:1dzgHSNfp02xaA81J2MS99Qcpr2w7fw1gpm99rleRqA=
golang.org/x/sys v0.0.0-20180830151530-49385e6e1522/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
golang.org/x/sys v0.0.0-20190215142949-d0b11bdaac8a/go.mod h1:STP8DvDyc/dI5b8T5hshtkjS+E42TnysNCUPdjciGhY=
golang.org/x/sys v0.0.0-20190312061237-fead79001313/go.mod h1:h1NjWce9XRLGQEsW7wpKNCjG9DtNlClVuFLEZdDNbEs=
@ -285,17 +285,17 @@ golang.org/x/sys v0.0.0-20210510120138-977fb7262007/go.mod h1:oPkhp1MJrh7nUepCBc
golang.org/x/sys v0.0.0-20210630005230-0f9fa26af87c/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
golang.org/x/sys v0.5.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
golang.org/x/sys v0.6.0/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
golang.org/x/sys v0.30.0 h1:QjkSwP/36a20jFYWkSue1YwXzLmsV5Gfq7Eiy72C1uc=
golang.org/x/sys v0.30.0/go.mod h1:/VUhepiaJMQUp4+oa/7Zr1D23ma6VTLIYjOOTFZPUcA=
golang.org/x/sys v0.31.0 h1:ioabZlmFYtWhL+TRYpcnNlLwhyxaM9kWTDEmfnprqik=
golang.org/x/sys v0.31.0/go.mod h1:BJP2sWEmIv4KK5OTEluFJCKSidICx8ciO85XgH3Ak8k=
golang.org/x/term v0.0.0-20201126162022-7de9c90e9dd1/go.mod h1:bj7SfCRtBDWHUb9snDiAeCFNEtKQo2Wmx5Cou7ajbmo=
golang.org/x/term v0.29.0 h1:L6pJp37ocefwRRtYPKSWOWzOtWSxVajvz2ldH/xi3iU=
golang.org/x/term v0.29.0/go.mod h1:6bl4lRlvVuDgSf3179VpIxBF0o10JUpXWOnI7nErv7s=
golang.org/x/term v0.30.0 h1:PQ39fJZ+mfadBm0y5WlL4vlM7Sx1Hgf13sMIY2+QS9Y=
golang.org/x/term v0.30.0/go.mod h1:NYYFdzHoI5wRh/h5tDMdMqCqPJZEuNqVR5xJLd/n67g=
golang.org/x/text v0.3.0/go.mod h1:NqM8EUOU14njkJ3fqMW+pc6Ldnwhi/IjpwHt7yyuwOQ=
golang.org/x/text v0.3.3/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
golang.org/x/text v0.3.5/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
golang.org/x/text v0.3.6/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
golang.org/x/text v0.22.0 h1:bofq7m3/HAFvbF51jz3Q9wLg3jkvSPuiZu/pD1XwgtM=
golang.org/x/text v0.22.0/go.mod h1:YRoo4H8PVmsu+E3Ou7cqLVH8oXWIHVoX0jqUWALQhfY=
golang.org/x/text v0.23.0 h1:D71I7dUrlY+VX0gQShAThNGHFxZ13dGLBHQLVl1mJlY=
golang.org/x/text v0.23.0/go.mod h1:/BLNzu4aZCJ1+kcD0DNRotWKage4q2rGVAg4o22unh4=
golang.org/x/tools v0.0.0-20180525024113-a5b4c53f6e8b/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
golang.org/x/tools v0.0.0-20180917221912-90fa682c2a6e/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=
golang.org/x/tools v0.0.0-20190114222345-bf090417da8b/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=

26
integration/embed_test.go
View File

@ -34,13 +34,15 @@ func cosineSimilarity[V float32 | float64](v1, v2 []V) V {
func TestAllMiniLMEmbeddings(t *testing.T) {
ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
defer cancel()
client, _, cleanup := InitServerConnection(ctx, t)
defer cleanup()
req := api.EmbeddingRequest{
Model: "all-minilm",
Prompt: "why is the sky blue?",
}
res, err := embeddingTestHelper(ctx, t, req)
res, err := embeddingTestHelper(ctx, client, t, req)
if err != nil {
t.Fatalf("error: %v", err)
@ -62,13 +64,15 @@ func TestAllMiniLMEmbeddings(t *testing.T) {
func TestAllMiniLMEmbed(t *testing.T) {
ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
defer cancel()
client, _, cleanup := InitServerConnection(ctx, t)
defer cleanup()
req := api.EmbedRequest{
Model: "all-minilm",
Input: "why is the sky blue?",
}
res, err := embedTestHelper(ctx, t, req)
res, err := embedTestHelper(ctx, client, t, req)
if err != nil {
t.Fatalf("error: %v", err)
@ -98,13 +102,15 @@ func TestAllMiniLMEmbed(t *testing.T) {
func TestAllMiniLMBatchEmbed(t *testing.T) {
ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
defer cancel()
client, _, cleanup := InitServerConnection(ctx, t)
defer cleanup()
req := api.EmbedRequest{
Model: "all-minilm",
Input: []string{"why is the sky blue?", "why is the grass green?"},
}
res, err := embedTestHelper(ctx, t, req)
res, err := embedTestHelper(ctx, client, t, req)
if err != nil {
t.Fatalf("error: %v", err)
@ -144,6 +150,8 @@ func TestAllMiniLMBatchEmbed(t *testing.T) {
func TestAllMiniLMEmbedTruncate(t *testing.T) {
ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
defer cancel()
client, _, cleanup := InitServerConnection(ctx, t)
defer cleanup()
truncTrue, truncFalse := true, false
@ -182,7 +190,7 @@ func TestAllMiniLMEmbedTruncate(t *testing.T) {
res := make(map[string]*api.EmbedResponse)
for _, req := range reqs {
response, err := embedTestHelper(ctx, t, req.Request)
response, err := embedTestHelper(ctx, client, t, req.Request)
if err != nil {
t.Fatalf("error: %v", err)
}
@ -198,7 +206,7 @@ func TestAllMiniLMEmbedTruncate(t *testing.T) {
}
// check that truncate set to false returns an error if context length is exceeded
_, err := embedTestHelper(ctx, t, api.EmbedRequest{
_, err := embedTestHelper(ctx, client, t, api.EmbedRequest{
Model: "all-minilm",
Input: "why is the sky blue?",
Truncate: &truncFalse,
@ -210,9 +218,7 @@ func TestAllMiniLMEmbedTruncate(t *testing.T) {
}
}
func embeddingTestHelper(ctx context.Context, t *testing.T, req api.EmbeddingRequest) (*api.EmbeddingResponse, error) {
client, _, cleanup := InitServerConnection(ctx, t)
defer cleanup()
func embeddingTestHelper(ctx context.Context, client *api.Client, t *testing.T, req api.EmbeddingRequest) (*api.EmbeddingResponse, error) {
if err := PullIfMissing(ctx, client, req.Model); err != nil {
t.Fatalf("failed to pull model %s: %v", req.Model, err)
}
@ -226,9 +232,7 @@ func embeddingTestHelper(ctx context.Context, t *testing.T, req api.EmbeddingReq
return response, nil
}
func embedTestHelper(ctx context.Context, t *testing.T, req api.EmbedRequest) (*api.EmbedResponse, error) {
client, _, cleanup := InitServerConnection(ctx, t)
defer cleanup()
func embedTestHelper(ctx context.Context, client *api.Client, t *testing.T, req api.EmbedRequest) (*api.EmbedResponse, error) {
if err := PullIfMissing(ctx, client, req.Model); err != nil {
t.Fatalf("failed to pull model %s: %v", req.Model, err)
}

11
integration/model_arch_test.go
View File

@ -48,17 +48,6 @@ var (
}
)
func getTimeouts(t *testing.T) (soft time.Duration, hard time.Duration) {
deadline, hasDeadline := t.Deadline()
if !hasDeadline {
return 8 * time.Minute, 10 * time.Minute
} else if deadline.Compare(time.Now().Add(2*time.Minute)) <= 0 {
t.Skip("too little time")
return time.Duration(0), time.Duration(0)
}
return -time.Since(deadline.Add(-2 * time.Minute)), -time.Since(deadline.Add(-20 * time.Second))
}
func TestModelsGenerate(t *testing.T) {
softTimeout, hardTimeout := getTimeouts(t)
slog.Info("Setting timeouts", "soft", softTimeout, "hard", hardTimeout)

130
integration/quantization_test.go Normal file
View File

@ -0,0 +1,130 @@
//go:build integration && models
package integration
import (
"bytes"
"context"
"fmt"
"log/slog"
"strings"
"testing"
"time"
"github.com/ollama/ollama/api"
)
func TestQuantization(t *testing.T) {
sourceModels := []string{
"qwen2.5:0.5b-instruct-fp16",
}
quantizations := []string{
"Q8_0",
"Q4_K_S",
"Q4_K_M",
"Q4_K",
}
softTimeout, hardTimeout := getTimeouts(t)
started := time.Now()
slog.Info("Setting timeouts", "soft", softTimeout, "hard", hardTimeout)
ctx, cancel := context.WithTimeout(context.Background(), hardTimeout)
defer cancel()
client, _, cleanup := InitServerConnection(ctx, t)
defer cleanup()
for _, base := range sourceModels {
if err := PullIfMissing(ctx, client, base); err != nil {
t.Fatalf("pull failed %s", err)
}
for _, quant := range quantizations {
newName := fmt.Sprintf("%s__%s", base, quant)
t.Run(newName, func(t *testing.T) {
if time.Now().Sub(started) > softTimeout {
t.Skip("skipping remaining tests to avoid excessive runtime")
}
req := &api.CreateRequest{
Model: newName,
Quantization: quant,
From: base,
}
fn := func(resp api.ProgressResponse) error {
// fmt.Print(".")
return nil
}
t.Logf("quantizing: %s -> %s", base, quant)
if err := client.Create(ctx, req, fn); err != nil {
t.Fatalf("create failed %s", err)
}
defer func() {
req := &api.DeleteRequest{
Model: newName,
}
t.Logf("deleting: %s -> %s", base, quant)
if err := client.Delete(ctx, req); err != nil {
t.Logf("failed to clean up %s: %s", req.Model, err)
}
}()
// Check metadata on the model
resp, err := client.Show(ctx, &api.ShowRequest{Name: newName})
if err != nil {
t.Fatalf("unable to show model: %s", err)
}
if !strings.Contains(resp.Details.QuantizationLevel, quant) {
t.Fatalf("unexpected quantization for %s:\ngot: %s", newName, resp.Details.QuantizationLevel)
}
stream := true
genReq := api.GenerateRequest{
Model: newName,
Prompt: "why is the sky blue?",
KeepAlive: &api.Duration{Duration: 3 * time.Second},
Options: map[string]any{
"seed": 42,
"temperature": 0.0,
},
Stream: &stream,
}
t.Logf("verifying: %s -> %s", base, quant)
// Some smaller quantizations can cause models to have poor quality
// or get stuck in repetition loops, so we stop as soon as we have any matches
anyResp := []string{"rayleigh", "scattering", "day", "sun", "moon", "color", "nitrogen", "oxygen"}
reqCtx, reqCancel := context.WithCancel(ctx)
atLeastOne := false
var buf bytes.Buffer
genfn := func(response api.GenerateResponse) error {
buf.Write([]byte(response.Response))
fullResp := strings.ToLower(buf.String())
for _, resp := range anyResp {
if strings.Contains(fullResp, resp) {
atLeastOne = true
t.Log(fullResp)
reqCancel()
break
}
}
return nil
}
done := make(chan int)
var genErr error
go func() {
genErr = client.Generate(reqCtx, &genReq, genfn)
done <- 0
}()
select {
case <-done:
if genErr != nil && !atLeastOne {
t.Fatalf("failed with %s request prompt %s ", genReq.Model, genReq.Prompt)
}
case <-ctx.Done():
t.Error("outer test context done while waiting for generate")
}
t.Logf("passed")
})
}
}
}

12
integration/utils_test.go
View File

@ -217,6 +217,7 @@ func InitServerConnection(ctx context.Context, t *testing.T) (*api.Client, strin
slog.Error("failed to open server log", "logfile", lifecycle.ServerLogFile, "error", err)
return
}
defer fp.Close()
data, err := io.ReadAll(fp)
if err != nil {
slog.Error("failed to read server log", "logfile", lifecycle.ServerLogFile, "error", err)
@ -358,3 +359,14 @@ func skipUnderMinVRAM(t *testing.T, gb uint64) {
}
}
}
func getTimeouts(t *testing.T) (soft time.Duration, hard time.Duration) {
deadline, hasDeadline := t.Deadline()
if !hasDeadline {
return 8 * time.Minute, 10 * time.Minute
} else if deadline.Compare(time.Now().Add(2*time.Minute)) <= 0 {
t.Skip("too little time")
return time.Duration(0), time.Duration(0)
}
return -time.Since(deadline.Add(-2 * time.Minute)), -time.Since(deadline.Add(-20 * time.Second))
}

2
kvcache/causal.go
View File

@ -239,7 +239,7 @@ func (c *Causal) findStartLoc() (int, error) {
}
}
return 0, fmt.Errorf("%w (length: %v)", ErrKvCacheFull, len(c.cells))
return 0, fmt.Errorf("%w (cache: %v batch: %v)", ErrKvCacheFull, len(c.cells), c.curBatchSize)
}
func (c *Causal) updateSlidingWindow() {

2
llama/build-info.cpp generated vendored
View File

@ -1,4 +1,4 @@
int LLAMA_BUILD_NUMBER = 0;
char const *LLAMA_COMMIT = "2016f07bd106c73699ecbaace80f55db5ed95dac";
char const *LLAMA_COMMIT = "e1e8e0991ffd9e99a445c6812bb519d5bac9f4b5";
char const *LLAMA_COMPILER = "";
char const *LLAMA_BUILD_TARGET = "";

2
llama/llama.cpp/common/common.h vendored
View File

@ -342,6 +342,8 @@ struct common_params {
// multimodal models (see examples/llava)
struct common_params_model mmproj;
bool mmproj_use_gpu = true; // use GPU for multimodal model
bool no_mmproj = false; // explicitly disable multimodal model
std::vector<std::string> image; // path to image file(s)
// embedding

3
llama/llama.cpp/common/json-schema-to-grammar.cpp vendored
View File

@ -16,6 +16,9 @@ using json = nlohmann::ordered_json;
static std::string build_repetition(const std::string & item_rule, int min_items, int max_items, const std::string & separator_rule = "") {
auto has_max = max_items != std::numeric_limits<int>::max();
if (max_items == 0) {
return "";
}
if (min_items == 0 && max_items == 1) {
return item_rule + "?";
}

50
llama/llama.cpp/examples/llava/clip-impl.h vendored
View File

@ -2,8 +2,6 @@
#include "gguf.h"
#include "clip.h"
#include "clip.h"
#include <climits>
#include <cstdarg>
#include <string>
@ -17,33 +15,31 @@
#define KEY_FTYPE "general.file_type"
#define KEY_NAME "general.name"
#define KEY_DESCRIPTION "general.description"
#define KEY_HAS_TEXT_ENC "clip.has_text_encoder"
#define KEY_HAS_VIS_ENC "clip.has_vision_encoder"
#define KEY_HAS_LLAVA_PROJ "clip.has_llava_projector"
#define KEY_HAS_MINICPMV_PROJ "clip.has_minicpmv_projector"
#define KEY_HAS_GLM_PROJ "clip.has_glm_projector"
#define KEY_MINICPMV_VERSION "clip.minicpmv_version"
#define KEY_HAS_QWEN2VL_MERGER "clip.has_qwen2vl_merger"
#define KEY_USE_GELU "clip.use_gelu"
#define KEY_USE_SILU "clip.use_silu"
#define KEY_N_EMBD "clip.%s.embedding_length"
#define KEY_N_FF "clip.%s.feed_forward_length"
#define KEY_N_BLOCK "clip.%s.block_count"
#define KEY_N_HEAD "clip.%s.attention.head_count"
#define KEY_LAYER_NORM_EPS "clip.%s.attention.layer_norm_epsilon"
#define KEY_PROJ_DIM "clip.%s.projection_dim"
#define KEY_TOKENS "tokenizer.ggml.tokens"
#define KEY_N_POSITIONS "clip.text.context_length"
#define KEY_N_EMBD "clip.vision.embedding_length"
#define KEY_N_FF "clip.vision.feed_forward_length"
#define KEY_N_BLOCK "clip.vision.block_count"
#define KEY_N_HEAD "clip.vision.attention.head_count"
#define KEY_LAYER_NORM_EPS "clip.vision.attention.layer_norm_epsilon"
#define KEY_PROJ_DIM "clip.vision.projection_dim"
#define KEY_IMAGE_SIZE "clip.vision.image_size"
#define KEY_PATCH_SIZE "clip.vision.patch_size"
#define KEY_IMAGE_MEAN "clip.vision.image_mean"
#define KEY_IMAGE_STD "clip.vision.image_std"
#define KEY_PROJ_TYPE "clip.projector_type"
#define KEY_FEATURE_LAYER "clip.vision.feature_layer"
#define KEY_PROJ_SCALE_FACTOR "clip.vision.projector.scale_factor"
#define KEY_PROJ_TYPE "clip.projector_type"
#define KEY_USE_GLU_MLP "clip.use_glu_mlp" // for qwen2.5vl
#define KEY_USE_RMS_NORM "clip.use_rms_norm" // for qwen2.5vl
#define KEY_MM_PATCH_MERGE_TYPE "clip.vision.mm_patch_merge_type"
#define KEY_IMAGE_GRID_PINPOINTS "clip.vision.image_grid_pinpoints"
#define KEY_IMAGE_CROP_RESOLUTION "clip.vision.image_crop_resolution"
#define KEY_WIN_ATTN_PATTERN "clip.vision.n_wa_pattern"
#define KEY_ATTN_WINDOW_SIZE "clip.vision.window_size"
//
@ -60,7 +56,9 @@
#define TN_ATTN_V "%s.blk.%d.attn_v.%s"
#define TN_ATTN_OUTPUT "%s.blk.%d.attn_out.%s"
#define TN_FFN_DOWN "%s.blk.%d.ffn_down.%s"
#define TN_FFN_GATE "%s.blk.%d.ffn_gate.%s"
#define TN_FFN_UP "%s.blk.%d.ffn_up.%s"
#define TN_FFN_GATE "%s.blk.%d.ffn_gate.%s"
#define TN_LN_1 "%s.blk.%d.ln1.%s"
#define TN_LN_2 "%s.blk.%d.ln2.%s"
#define TN_LN_PRE "%s.pre_ln.%s"
@ -72,6 +70,8 @@
#define TN_IMAGE_NEWLINE "model.image_newline"
#define TN_MM_INP_PROJ "mm.input_projection.weight" // gemma3
#define TN_MM_SOFT_EMB_N "mm.soft_emb_norm.weight" // gemma3
#define TN_MM_PROJECTOR "mm.model.fc.weight" // idefics3
#define TN_TOK_IMG_BREAK "v.token_embd.img_break" // pixtral
// mimicpmv
#define TN_MINICPMV_POS_EMBD_K "resampler.pos_embed_k"
@ -87,18 +87,19 @@
#define TN_GLM_ADAPTER_D_H_2_4H "adapter.linear.dense_h_to_4h.%s"
#define TN_GLM_ADAPTER_GATE "adapter.linear.gate.%s"
#define TN_GLM_ADAPTER_D_4H_2_H "adapter.linear.dense_4h_to_h.%s"
#define TN_GLM_BOI_W "adapter.boi"
#define TN_GLM_EOI_W "adapter.eoi"
enum projector_type {
PROJECTOR_TYPE_MLP,
PROJECTOR_TYPE_MLP_NORM,
PROJECTOR_TYPE_LDP,
PROJECTOR_TYPE_LDPV2,
PROJECTOR_TYPE_RESAMPLER,
PROJECTOR_TYPE_MINICPMV,
PROJECTOR_TYPE_GLM_EDGE,
PROJECTOR_TYPE_MERGER,
PROJECTOR_TYPE_QWEN2VL,
PROJECTOR_TYPE_GEMMA3,
PROJECTOR_TYPE_IDEFICS3,
PROJECTOR_TYPE_PIXTRAL,
PROJECTOR_TYPE_QWEN25VL,
PROJECTOR_TYPE_UNKNOWN,
};
@ -106,10 +107,13 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
{ PROJECTOR_TYPE_MLP, "mlp" },
{ PROJECTOR_TYPE_LDP, "ldp" },
{ PROJECTOR_TYPE_LDPV2, "ldpv2"},
{ PROJECTOR_TYPE_RESAMPLER, "resampler"},
{ PROJECTOR_TYPE_MINICPMV, "resampler"},
{ PROJECTOR_TYPE_GLM_EDGE, "adapter"},
{ PROJECTOR_TYPE_MERGER, "qwen2vl_merger"},
{ PROJECTOR_TYPE_QWEN2VL, "qwen2vl_merger"},
{ PROJECTOR_TYPE_QWEN25VL, "qwen2.5vl_merger"},
{ PROJECTOR_TYPE_GEMMA3, "gemma3"},
{ PROJECTOR_TYPE_IDEFICS3, "idefics3"},
{ PROJECTOR_TYPE_PIXTRAL, "pixtral"},
};
static projector_type clip_projector_type_from_string(const std::string & str) {

1535
llama/llama.cpp/examples/llava/clip.cpp vendored
View File

File diff suppressed because it is too large Load Diff

21
llama/llama.cpp/examples/llava/clip.h vendored
View File

@ -47,7 +47,7 @@ CLIP_API struct clip_ctx * clip_init(const char * fname, struct clip_context_par
CLIP_API void clip_free(struct clip_ctx * ctx);
CLIP_API size_t clip_embd_nbytes(const struct clip_ctx * ctx);
CLIP_API size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_h, int img_w);
CLIP_API size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_w, int img_h);
CLIP_API int32_t clip_get_image_size (const struct clip_ctx * ctx);
CLIP_API int32_t clip_get_patch_size (const struct clip_ctx * ctx);
@ -59,9 +59,20 @@ CLIP_API const char * clip_patch_merge_type(const struct clip_ctx * ctx);
CLIP_API const int32_t * clip_image_grid(const struct clip_ctx * ctx);
CLIP_API size_t get_clip_image_grid_size(const struct clip_ctx * ctx);
CLIP_API int clip_n_patches (const struct clip_ctx * ctx);
CLIP_API int clip_n_patches_by_img (const struct clip_ctx * ctx, struct clip_image_f32 * img);
CLIP_API int clip_n_mmproj_embd (const struct clip_ctx * ctx);
GGML_DEPRECATED(CLIP_API int clip_n_patches(const struct clip_ctx * ctx),
"use clip_n_output_tokens instead");
GGML_DEPRECATED(CLIP_API int clip_n_patches_by_img(const struct clip_ctx * ctx, struct clip_image_f32 * img),
"use clip_n_output_tokens instead");
CLIP_API int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * img);
// for M-RoPE, this will be the number of token positions in X and Y directions
// for other models, X will be the total number of tokens and Y will be 1
CLIP_API int clip_n_output_tokens_x(const struct clip_ctx * ctx, struct clip_image_f32 * img);
CLIP_API int clip_n_output_tokens_y(const struct clip_ctx * ctx, struct clip_image_f32 * img);
// this should be equal to the embedding dimension of the text model
CLIP_API int clip_n_mmproj_embd(const struct clip_ctx * ctx);
CLIP_API int clip_uhd_num_image_embeds_col(struct clip_ctx * ctx_clip);
CLIP_API void clip_add_load_image_size(struct clip_ctx * ctx_clip, struct clip_image_size * load_image_size);
@ -114,8 +125,6 @@ CLIP_API bool clip_is_qwen2vl(const struct clip_ctx * ctx);
CLIP_API bool clip_is_llava(const struct clip_ctx * ctx);
CLIP_API bool clip_is_gemma3(const struct clip_ctx * ctx);
CLIP_API int get_deepest_feature_layer(const struct clip_ctx * ctx);
CLIP_API bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec);

15
llama/llama.cpp/examples/llava/llava.cpp vendored
View File

@ -112,7 +112,7 @@ static struct clip_image_grid_shape get_anyres_image_grid_shape(const std::pair<
}
// Take the image segments in a grid configuration and return the embeddings and the number of embeddings into preallocated memory (image_embd_out)
static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *> & image_embd_v, struct clip_image_grid_shape grid_shape, float * image_embd_out, int * n_img_pos_out) {
static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *> & image_embd_v, struct clip_image_grid_shape grid_shape, float * image_embd_out, int * n_img_pos_out, clip_image_f32 * img_input) {
struct {
struct ggml_context * ctx;
} model;
@ -175,7 +175,7 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *>
model.ctx = ggml_init(params);
struct ggml_tensor * image_features = ggml_new_tensor_3d(model.ctx, GGML_TYPE_F32, clip_n_mmproj_embd(ctx_clip), clip_n_patches(ctx_clip), num_images - 1); // example: 4096 x 576 x 4
struct ggml_tensor * image_features = ggml_new_tensor_3d(model.ctx, GGML_TYPE_F32, clip_n_mmproj_embd(ctx_clip), clip_n_output_tokens(ctx_clip, img_input), num_images - 1); // example: 4096 x 576 x 4
// ggml_tensor_printf(image_features,"image_features",__LINE__,false,false);
// fill it with the image embeddings, ignoring the base
for (size_t i = 1; i < num_images; i++) {
@ -214,8 +214,8 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector<float *>
memcpy(image_embd_out, image_embd_v[0], clip_embd_nbytes(ctx_clip)); // main image as global context
// append without newline tokens (default behavior in llava_arch when not using unpad ):
memcpy(image_embd_out + clip_n_patches(ctx_clip) * clip_n_mmproj_embd(ctx_clip), (float*)result->data, clip_embd_nbytes(ctx_clip) * (num_images-1)); // grid patches
*n_img_pos_out = static_cast<int>(result->ne[1]+clip_n_patches(ctx_clip));
memcpy(image_embd_out + clip_n_output_tokens(ctx_clip, img_input) * clip_n_mmproj_embd(ctx_clip), (float*)result->data, clip_embd_nbytes(ctx_clip) * (num_images-1)); // grid patches
*n_img_pos_out = static_cast<int>(result->ne[1]+clip_n_output_tokens(ctx_clip, img_input));
// Debug: Test single segments
// Current findings: sending base image, sending a segment embedding all works similar to python
@ -313,7 +313,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
image_embd + n_img_pos_out * clip_n_mmproj_embd(ctx_clip),
image_embd_v[i],
clip_embd_nbytes_by_img(ctx_clip, nx, ny));
n_img_pos_out += clip_n_patches_by_img(ctx_clip, img_res);
n_img_pos_out += clip_n_output_tokens(ctx_clip, img_res);
}
*n_img_pos = n_img_pos_out;
for (size_t i = 0; i < image_embd_v.size(); i++) {
@ -342,8 +342,8 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
}
else if (strcmp(mm_patch_merge_type, "spatial_unpad") != 0) {
// flat / default llava-1.5 type embedding
*n_img_pos = clip_n_patches(ctx_clip);
clip_image_f32 * img_res = clip_image_f32_get_img(img_res_v.get(), 0);
*n_img_pos = clip_n_output_tokens(ctx_clip, img_res);
bool encoded = clip_image_encode(ctx_clip, n_threads, img_res, image_embd); // image_embd shape is 576 x 4096
if (!encoded) {
LOG_ERR("Unable to encode image\n");
@ -381,7 +381,8 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
struct clip_image_grid_shape grid_shape = get_anyres_image_grid_shape({img->nx,img->ny}, grid_pinpoints, image_size);
int n_img_pos_out;
clip_llava_handle_patches(ctx_clip, image_embd_v, grid_shape, image_embd, &n_img_pos_out);
clip_image_f32 * img_input = clip_image_f32_get_img(img_res_v.get(), 0);
clip_llava_handle_patches(ctx_clip, image_embd_v, grid_shape, image_embd, &n_img_pos_out, img_input);
*n_img_pos = n_img_pos_out;
for (size_t i = 0; i < image_embd_v.size(); i++) {

2
llama/llama.cpp/include/llama.h vendored
View File

@ -111,6 +111,7 @@ extern "C" {
LLAMA_VOCAB_PRE_TYPE_TRILLION = 31,
LLAMA_VOCAB_PRE_TYPE_BAILINGMOE = 32,
LLAMA_VOCAB_PRE_TYPE_LLAMA4 = 33,
LLAMA_VOCAB_PRE_TYPE_PIXTRAL = 34,
};
enum llama_rope_type {
@ -1237,6 +1238,7 @@ extern "C" {
"will be removed in the future (see https://github.com/ggml-org/llama.cpp/pull/9896#discussion_r1800920915)");
/// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
/// Setting k <= 0 makes this a noop
LLAMA_API struct llama_sampler * llama_sampler_init_top_k (int32_t k);
/// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751

37
llama/llama.cpp/src/llama-arch.cpp vendored
View File

@ -20,6 +20,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_REFACT, "refact" },
{ LLM_ARCH_BERT, "bert" },
{ LLM_ARCH_NOMIC_BERT, "nomic-bert" },
{ LLM_ARCH_NOMIC_BERT_MOE, "nomic-bert-moe" },
{ LLM_ARCH_JINA_BERT_V2, "jina-bert-v2" },
{ LLM_ARCH_BLOOM, "bloom" },
{ LLM_ARCH_STABLELM, "stablelm" },
@ -73,7 +74,6 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_WAVTOKENIZER_DEC, "wavtokenizer-dec" },
{ LLM_ARCH_PLM, "plm" },
{ LLM_ARCH_BAILINGMOE, "bailingmoe" },
{ LLM_ARCH_MISTRAL3, "mistral3" },
{ LLM_ARCH_UNKNOWN, "(unknown)" },
};
@ -109,6 +109,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_EXPERT_WEIGHTS_SCALE, "%s.expert_weights_scale" },
{ LLM_KV_EXPERT_WEIGHTS_NORM, "%s.expert_weights_norm" },
{ LLM_KV_EXPERT_GATING_FUNC, "%s.expert_gating_func" },
{ LLM_KV_MOE_EVERY_N_LAYERS, "%s.moe_every_n_layers" },
{ LLM_KV_POOLING_TYPE, "%s.pooling_type" },
{ LLM_KV_LOGIT_SCALE, "%s.logit_scale" },
{ LLM_KV_DECODER_START_TOKEN_ID, "%s.decoder_start_token_id" },
@ -511,6 +512,24 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_NOMIC_BERT_MOE,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
{ LLM_TENSOR_TOKEN_TYPES, "token_types" },
{ LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" },
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" },
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
{ LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
},
},
{
LLM_ARCH_JINA_BERT_V2,
{
@ -1587,22 +1606,6 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" },
},
},
{
LLM_ARCH_MISTRAL3,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
}
},
{
LLM_ARCH_UNKNOWN,
{

3
llama/llama.cpp/src/llama-arch.h vendored
View File

@ -24,6 +24,7 @@ enum llm_arch {
LLM_ARCH_REFACT,
LLM_ARCH_BERT,
LLM_ARCH_NOMIC_BERT,
LLM_ARCH_NOMIC_BERT_MOE,
LLM_ARCH_JINA_BERT_V2,
LLM_ARCH_BLOOM,
LLM_ARCH_STABLELM,
@ -75,7 +76,6 @@ enum llm_arch {
LLM_ARCH_CHAMELEON,
LLM_ARCH_SOLAR,
LLM_ARCH_WAVTOKENIZER_DEC,
LLM_ARCH_MISTRAL3,
LLM_ARCH_PLM,
LLM_ARCH_BAILINGMOE,
LLM_ARCH_UNKNOWN,
@ -113,6 +113,7 @@ enum llm_kv {
LLM_KV_EXPERT_WEIGHTS_SCALE,
LLM_KV_EXPERT_WEIGHTS_NORM,
LLM_KV_EXPERT_GATING_FUNC,
LLM_KV_MOE_EVERY_N_LAYERS,
LLM_KV_POOLING_TYPE,
LLM_KV_LOGIT_SCALE,
LLM_KV_DECODER_START_TOKEN_ID,

47
llama/llama.cpp/src/llama-chat.cpp vendored
View File

@ -50,8 +50,8 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
{ "deepseek3", LLM_CHAT_TEMPLATE_DEEPSEEK_3 },
{ "command-r", LLM_CHAT_TEMPLATE_COMMAND_R },
{ "llama3", LLM_CHAT_TEMPLATE_LLAMA_3 },
{ "chatglm3", LLM_CHAT_TEMPLATE_CHATGML_3 },
{ "chatglm4", LLM_CHAT_TEMPLATE_CHATGML_4 },
{ "chatglm3", LLM_CHAT_TEMPLATE_CHATGLM_3 },
{ "chatglm4", LLM_CHAT_TEMPLATE_CHATGLM_4 },
{ "glmedge", LLM_CHAT_TEMPLATE_GLMEDGE },
{ "minicpm", LLM_CHAT_TEMPLATE_MINICPM },
{ "exaone3", LLM_CHAT_TEMPLATE_EXAONE_3 },
@ -62,6 +62,7 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
{ "yandex", LLM_CHAT_TEMPLATE_YANDEX },
{ "bailing", LLM_CHAT_TEMPLATE_BAILING },
{ "llama4", LLM_CHAT_TEMPLATE_LLAMA4 },
{ "smolvlm", LLM_CHAT_TEMPLATE_SMOLVLM },
};
llm_chat_template llm_chat_template_from_str(const std::string & name) {
@ -81,7 +82,9 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) {
if (tmpl_contains("<|im_start|>")) {
return tmpl_contains("<|im_sep|>")
? LLM_CHAT_TEMPLATE_PHI_4
: LLM_CHAT_TEMPLATE_CHATML;
: tmpl_contains("<end_of_utterance>")
? LLM_CHAT_TEMPLATE_SMOLVLM // SmolVLM uses <|im_start|> as BOS, but it is NOT chatml
: LLM_CHAT_TEMPLATE_CHATML;
} else if (tmpl.find("mistral") == 0 || tmpl_contains("[INST]")) {
if (tmpl_contains("[SYSTEM_PROMPT]")) {
return LLM_CHAT_TEMPLATE_MISTRAL_V7;
@ -119,8 +122,12 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) {
}
} else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|end|>")) {
return LLM_CHAT_TEMPLATE_PHI_3;
} else if (tmpl_contains("[gMASK]<sop>")) {
return LLM_CHAT_TEMPLATE_CHATGLM_4;
} else if (tmpl_contains("<|assistant|>") && tmpl_contains("<|user|>")) {
return tmpl_contains("</s>") ? LLM_CHAT_TEMPLATE_FALCON_3 : LLM_CHAT_TEMPLATE_GLMEDGE;
} else if (tmpl_contains("<|{{ item['role'] }}|>") && tmpl_contains("<|begin_of_image|>")) {
return LLM_CHAT_TEMPLATE_GLMEDGE;
} else if (tmpl_contains("<|user|>") && tmpl_contains("<|endoftext|>")) {
return LLM_CHAT_TEMPLATE_ZEPHYR;
} else if (tmpl_contains("bos_token + message['role']")) {
@ -149,9 +156,7 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) {
return LLM_CHAT_TEMPLATE_LLAMA_3;
} else if (tmpl_contains("[gMASK]sop")) {
// chatglm3-6b
return LLM_CHAT_TEMPLATE_CHATGML_3;
} else if (tmpl_contains("[gMASK]<sop>")) {
return LLM_CHAT_TEMPLATE_CHATGML_4;
return LLM_CHAT_TEMPLATE_CHATGLM_3;
} else if (tmpl_contains(LU8("<用户>"))) {
// MiniCPM-3B-OpenHermes-2.5-v2-GGUF
return LLM_CHAT_TEMPLATE_MINICPM;
@ -432,7 +437,7 @@ int32_t llm_chat_apply_template(
if (add_ass) {
ss << "<|start_header_id|>assistant<|end_header_id|>\n\n";
}
} else if (tmpl == LLM_CHAT_TEMPLATE_CHATGML_3) {
} else if (tmpl == LLM_CHAT_TEMPLATE_CHATGLM_3) {
// chatglm3-6b
ss << "[gMASK]" << "sop";
for (auto message : chat) {
@ -442,7 +447,7 @@ int32_t llm_chat_apply_template(
if (add_ass) {
ss << "<|assistant|>";
}
} else if (tmpl == LLM_CHAT_TEMPLATE_CHATGML_4) {
} else if (tmpl == LLM_CHAT_TEMPLATE_CHATGLM_4 || tmpl == LLM_CHAT_TEMPLATE_GLMEDGE) {
ss << "[gMASK]" << "<sop>";
for (auto message : chat) {
std::string role(message->role);
@ -451,14 +456,6 @@ int32_t llm_chat_apply_template(
if (add_ass) {
ss << "<|assistant|>";
}
} else if (tmpl == LLM_CHAT_TEMPLATE_GLMEDGE) {
for (auto message : chat) {
std::string role(message->role);
ss << "<|" << role << "|>" << "\n" << message->content;
}
if (add_ass) {
ss << "<|assistant|>";
}
} else if (tmpl == LLM_CHAT_TEMPLATE_MINICPM) {
// MiniCPM-3B-OpenHermes-2.5-v2-GGUF
for (auto message : chat) {
@ -620,7 +617,23 @@ int32_t llm_chat_apply_template(
if (add_ass) {
ss << "<|header_start|>assistant<|header_end|>\n\n";
}
} else {
} else if (tmpl == LLM_CHAT_TEMPLATE_SMOLVLM) {
// SmolVLM
ss << "<|im_start|>"; // uses <|im_start|> as BOS, but the actual content is NOT chatml
for (auto message : chat) {
std::string role(message->role);
if (role == "system") {
ss << message->content << "\n\n";
} else if (role == "user") {
ss << "User: " << message->content << "<end_of_utterance>\n";
} else {
ss << "Assistant: " << message->content << "<end_of_utterance>\n";
}
}
if (add_ass) {
ss << "Assistant:";
}
} else {
// template not supported
return -1;
}

5
llama/llama.cpp/src/llama-chat.h vendored
View File

@ -29,8 +29,8 @@ enum llm_chat_template {
LLM_CHAT_TEMPLATE_DEEPSEEK_3,
LLM_CHAT_TEMPLATE_COMMAND_R,
LLM_CHAT_TEMPLATE_LLAMA_3,
LLM_CHAT_TEMPLATE_CHATGML_3,
LLM_CHAT_TEMPLATE_CHATGML_4,
LLM_CHAT_TEMPLATE_CHATGLM_3,
LLM_CHAT_TEMPLATE_CHATGLM_4,
LLM_CHAT_TEMPLATE_GLMEDGE,
LLM_CHAT_TEMPLATE_MINICPM,
LLM_CHAT_TEMPLATE_EXAONE_3,
@ -41,6 +41,7 @@ enum llm_chat_template {
LLM_CHAT_TEMPLATE_YANDEX,
LLM_CHAT_TEMPLATE_BAILING,
LLM_CHAT_TEMPLATE_LLAMA4,
LLM_CHAT_TEMPLATE_SMOLVLM,
LLM_CHAT_TEMPLATE_UNKNOWN,
};

21
llama/llama.cpp/src/llama-context.cpp vendored
View File

@ -114,7 +114,7 @@ llama_context::llama_context(
}
if (n_ctx_per_seq > hparams.n_ctx_train) {
LLAMA_LOG_WARN("%s: n_ctx_pre_seq (%u) > n_ctx_train (%u) -- possible training context overflow\n",
LLAMA_LOG_WARN("%s: n_ctx_per_seq (%u) > n_ctx_train (%u) -- possible training context overflow\n",
__func__, n_ctx_per_seq, hparams.n_ctx_train);
}
@ -469,8 +469,7 @@ ggml_tensor * llama_context::build_rope_shift(
ggml_tensor * shift,
ggml_tensor * factors,
float freq_base,
float freq_scale,
ggml_backend_buffer * bbuf) const {
float freq_scale) const {
const auto & n_ctx_orig = cparams.n_ctx_orig_yarn;
const auto & yarn_ext_factor = cparams.yarn_ext_factor;
@ -492,17 +491,7 @@ ggml_tensor * llama_context::build_rope_shift(
// dequantize to f32 -> RoPE -> quantize back
tmp = ggml_cast(ctx0, cur, GGML_TYPE_F32);
if (bbuf) {
for (const auto & backend : backends) {
// Figure out which backend KV cache belongs to
if (ggml_backend_supports_buft(backend.get(), ggml_backend_buffer_get_type(bbuf))) {
ggml_backend_sched_set_tensor_backend(sched.get(), tmp, backend.get());
break;
}
}
}
tmp = ggml_rope_ext_inplace(ctx0, tmp,
tmp = ggml_rope_ext(ctx0, tmp,
shift, factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
yarn_ext_factor, yarn_attn_factor, yarn_beta_fast, yarn_beta_slow);
@ -582,7 +571,7 @@ llm_graph_result_ptr llama_context::build_kv_self_shift(
ggml_row_size(kv_self->k_l[il]->type, n_embd_k_gqa),
0);
ggml_tensor * cur = build_rope_shift(ctx0, k, inp->k_shift, rope_factors, freq_base_l, freq_scale_l, kv_self->k_l[il]->buffer);
ggml_tensor * cur = build_rope_shift(ctx0, k, inp->k_shift, rope_factors, freq_base_l, freq_scale_l);
ggml_build_forward_expand(gf, cur);
}
@ -1510,8 +1499,6 @@ int32_t llama_context::output_reserve(int32_t n_outputs) {
// set all ids as invalid (negative)
std::fill(output_ids.begin(), output_ids.end(), -1);
ggml_backend_buffer_clear(buf_output.get(), 0);
this->n_outputs = 0;
this->n_outputs_max = n_outputs_max;

3
llama/llama.cpp/src/llama-context.h vendored
View File

@ -172,8 +172,7 @@ private:
ggml_tensor * shift,
ggml_tensor * factors,
float freq_base,
float freq_scale,
ggml_backend_buffer * bbuf) const;
float freq_scale) const;
llm_graph_result_ptr build_kv_self_shift(
ggml_context * ctx0,

58
llama/llama.cpp/src/llama-graph.cpp vendored
View File

@ -55,7 +55,21 @@ void llm_graph_input_pos::set_input(const llama_ubatch * ubatch) {
if (ubatch->pos && pos) {
const int64_t n_tokens = ubatch->n_tokens;
ggml_backend_tensor_set(pos, ubatch->pos, 0, n_tokens*n_pos_per_token*ggml_element_size(pos));
if (ubatch->token && n_pos_per_embd == 4) {
// in case we're using M-RoPE with text tokens, convert the 1D positions to 4D
// the 3 first dims are the same, and 4th dim is all 0
std::vector<llama_pos> pos_data(n_tokens*n_pos_per_embd);
// copy the first dimension
for (int i = 0; i < n_tokens; ++i) {
pos_data[ i] = ubatch->pos[i];
pos_data[ n_tokens + i] = ubatch->pos[i];
pos_data[2 * n_tokens + i] = ubatch->pos[i];
pos_data[3 * n_tokens + i] = 0; // 4th dim is 0
}
ggml_backend_tensor_set(pos, pos_data.data(), 0, pos_data.size()*ggml_element_size(pos));
} else {
ggml_backend_tensor_set(pos, ubatch->pos, 0, n_tokens*n_pos_per_embd*ggml_element_size(pos));
}
}
}
@ -71,7 +85,7 @@ void llm_graph_input_attn_temp::set_input(const llama_ubatch * ubatch) {
) * f_attn_temp_scale + 1.0;
}
ggml_backend_tensor_set(attn_scale, attn_scale_data.data(), 0, n_tokens*n_pos_per_token*ggml_element_size(attn_scale));
ggml_backend_tensor_set(attn_scale, attn_scale_data.data(), 0, n_tokens*ggml_element_size(attn_scale));
}
}
@ -598,7 +612,7 @@ llm_graph_context::llm_graph_context(const llm_graph_params & params) :
res (std::make_unique<llm_graph_result>()) {
}
int64_t llm_graph_context::n_pos_per_token() const {
int64_t llm_graph_context::n_pos_per_embd() const {
return arch == LLM_ARCH_QWEN2VL ? 4 : 1;
}
@ -809,6 +823,10 @@ ggml_tensor * llm_graph_context::build_ffn(
if (down) {
cur = build_lora_mm(down, cur);
if (arch == LLM_ARCH_GLM4) {
// GLM4 seems to have numerical issues with half-precision accumulators
ggml_mul_mat_set_prec(cur, GGML_PREC_F32);
}
}
if (down_b) {
@ -916,28 +934,35 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
ggml_tensor * up = build_lora_mm_id(up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
cb(up, "ffn_moe_up", il);
ggml_tensor * gate = build_lora_mm_id(gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
cb(gate, "ffn_moe_gate", il);
ggml_tensor * experts = nullptr;
if (gate_exps) {
cur = build_lora_mm_id(gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
cb(cur, "ffn_moe_gate", il);
} else {
cur = up;
}
switch (type_op) {
case LLM_FFN_SILU:
{
gate = ggml_silu(ctx0, gate);
cb(gate, "ffn_moe_silu", il);
cur = ggml_silu(ctx0, cur);
cb(cur, "ffn_moe_silu", il);
} break;
case LLM_FFN_GELU:
{
gate = ggml_gelu(ctx0, gate);
cb(gate, "ffn_moe_gelu", il);
cur = ggml_gelu(ctx0, cur);
cb(cur, "ffn_moe_gelu", il);
} break;
default:
GGML_ABORT("fatal error");
}
ggml_tensor * par = ggml_mul(ctx0, up, gate); // [n_ff, n_expert_used, n_tokens]
cb(par, "ffn_moe_gate_par", il);
if (gate_exps) {
cur = ggml_mul(ctx0, cur, up); // [n_ff, n_expert_used, n_tokens]
cb(cur, "ffn_moe_gate_par", il);
}
ggml_tensor * experts = build_lora_mm_id(down_exps, par, selected_experts); // [n_embd, n_expert_used, n_tokens]
experts = build_lora_mm_id(down_exps, cur, selected_experts); // [n_embd, n_expert_used, n_tokens]
cb(experts, "ffn_moe_down", il);
if (!weight_before_ffn) {
@ -1020,11 +1045,11 @@ ggml_tensor * llm_graph_context::build_inp_embd(ggml_tensor * tok_embd) const {
}
ggml_tensor * llm_graph_context::build_inp_pos() const {
auto inp = std::make_unique<llm_graph_input_pos>(n_pos_per_token());
auto inp = std::make_unique<llm_graph_input_pos>(n_pos_per_embd());
auto & cur = inp->pos;
cur = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens*n_pos_per_token());
cur = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens*n_pos_per_embd());
ggml_set_input(cur);
res->add_input(std::move(inp));
@ -1033,11 +1058,12 @@ ggml_tensor * llm_graph_context::build_inp_pos() const {
}
ggml_tensor * llm_graph_context::build_inp_attn_scale() const {
auto inp = std::make_unique<llm_graph_input_attn_temp>(n_pos_per_token(), hparams.n_attn_temp_floor_scale, hparams.f_attn_temp_scale);
auto inp = std::make_unique<llm_graph_input_attn_temp>(hparams.n_attn_temp_floor_scale, hparams.f_attn_temp_scale);
auto & cur = inp->attn_scale;
cur = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 1, 1, n_tokens*n_pos_per_token());
// this need to be 1x1xN for broadcasting
cur = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 1, 1, n_tokens);
ggml_set_input(cur);
res->add_input(std::move(inp));

12
llama/llama.cpp/src/llama-graph.h vendored
View File

@ -91,29 +91,27 @@ public:
class llm_graph_input_pos : public llm_graph_input_i {
public:
llm_graph_input_pos(int64_t n_pos_per_token) : n_pos_per_token(n_pos_per_token) {}
llm_graph_input_pos(int64_t n_pos_per_embd) : n_pos_per_embd(n_pos_per_embd) {}
virtual ~llm_graph_input_pos() = default;
void set_input(const llama_ubatch * ubatch) override;
ggml_tensor * pos = nullptr; // I32 [n_batch]
const int64_t n_pos_per_token = 1;
const int64_t n_pos_per_embd = 1;
};
// temperature tuning, used by llama4
class llm_graph_input_attn_temp : public llm_graph_input_i {
public:
llm_graph_input_attn_temp(int64_t n_pos_per_token, uint32_t n_attn_temp_floor_scale, float f_attn_temp_scale)
: n_pos_per_token(n_pos_per_token), n_attn_temp_floor_scale(n_attn_temp_floor_scale), f_attn_temp_scale(f_attn_temp_scale) {}
llm_graph_input_attn_temp(uint32_t n_attn_temp_floor_scale, float f_attn_temp_scale)
: n_attn_temp_floor_scale(n_attn_temp_floor_scale), f_attn_temp_scale(f_attn_temp_scale) {}
virtual ~llm_graph_input_attn_temp() = default;
void set_input(const llama_ubatch * ubatch) override;
ggml_tensor * attn_scale = nullptr; // F32 [n_batch]
const int64_t n_pos_per_token = 1;
const uint32_t n_attn_temp_floor_scale;
const float f_attn_temp_scale;
};
@ -430,7 +428,7 @@ struct llm_graph_context {
llm_graph_context(const llm_graph_params & params);
int64_t n_pos_per_token() const;
int64_t n_pos_per_embd() const;
void cb(ggml_tensor * cur, const char * name, int il) const;

1
llama/llama.cpp/src/llama-hparams.h vendored
View File

@ -72,6 +72,7 @@ struct llama_hparams {
float expert_weights_scale = 0.0;
bool expert_weights_norm = false;
uint32_t expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_NONE;
uint32_t moe_every_n_layers = 0;
float f_norm_eps;
float f_norm_rms_eps;

72
llama/llama.cpp/src/llama-model.cpp vendored
View File

@ -43,11 +43,13 @@ const char * llm_type_name(llm_type type) {
case LLM_TYPE_770M: return "770M";
case LLM_TYPE_780M: return "780M";
case LLM_TYPE_0_5B: return "0.5B";
case LLM_TYPE_0_6B: return "0.6B";
case LLM_TYPE_1B: return "1B";
case LLM_TYPE_1_3B: return "1.3B";
case LLM_TYPE_1_4B: return "1.4B";
case LLM_TYPE_1_5B: return "1.5B";
case LLM_TYPE_1_6B: return "1.6B";
case LLM_TYPE_1_7B: return "1.7B";
case LLM_TYPE_1_8B: return "1.8B";
case LLM_TYPE_2B: return "2B";
case LLM_TYPE_2_8B: return "2.8B";
@ -66,6 +68,7 @@ const char * llm_type_name(llm_type type) {
case LLM_TYPE_15B: return "15B";
case LLM_TYPE_16B: return "16B";
case LLM_TYPE_20B: return "20B";
case LLM_TYPE_27B: return "27B";
case LLM_TYPE_30B: return "30B";
case LLM_TYPE_32B: return "32B";
case LLM_TYPE_34B: return "34B";
@ -74,6 +77,7 @@ const char * llm_type_name(llm_type type) {
case LLM_TYPE_65B: return "65B";
case LLM_TYPE_70B: return "70B";
case LLM_TYPE_236B: return "236B";
case LLM_TYPE_290B: return "290B";
case LLM_TYPE_314B: return "314B";
case LLM_TYPE_671B: return "671B";
case LLM_TYPE_SMALL: return "0.1B";
@ -88,10 +92,10 @@ const char * llm_type_name(llm_type type) {
case LLM_TYPE_16x3_8B: return "16x3.8B";
case LLM_TYPE_10B_128x3_66B: return "10B+128x3.66B";
case LLM_TYPE_57B_A14B: return "57B.A14B";
case LLM_TYPE_27B: return "27B";
case LLM_TYPE_290B: return "290B";
case LLM_TYPE_17B_16E: return "17Bx16E (Scout)";
case LLM_TYPE_17B_128E: return "17Bx128E (Maverick)";
case LLM_TYPE_30B_A3B: return "30B.A3B";
case LLM_TYPE_235B_A22B: return "235B.A22B";
default: return "?B";
}
}
@ -709,10 +713,12 @@ void llama_model::load_hparams(llama_model_loader & ml) {
}
} break;
case LLM_ARCH_NOMIC_BERT:
case LLM_ARCH_NOMIC_BERT_MOE:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type);
ml.get_key(LLM_KV_MOE_EVERY_N_LAYERS, hparams.moe_every_n_layers, 0);
if (hparams.n_layer == 12 && hparams.n_embd == 768) {
type = LLM_TYPE_137M;
@ -805,6 +811,10 @@ void llama_model::load_hparams(llama_model_loader & ml) {
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
switch (hparams.n_layer) {
case 28: type = hparams.n_embd == 1024 ? LLM_TYPE_0_6B : LLM_TYPE_1_7B; break;
case 36: type = hparams.n_embd == 2560 ? LLM_TYPE_4B : LLM_TYPE_8B; break;
case 40: type = LLM_TYPE_14B; break;
case 64: type = LLM_TYPE_32B; break;
default: type = LLM_TYPE_UNKNOWN;
}
} break;
@ -814,6 +824,8 @@ void llama_model::load_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
switch (hparams.n_layer) {
case 48: type = LLM_TYPE_30B_A3B; break;
case 94: type = LLM_TYPE_235B_A22B; break;
default: type = LLM_TYPE_UNKNOWN;
}
} break;
@ -1425,7 +1437,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
default: type = LLM_TYPE_UNKNOWN;
}
} break;
case LLM_ARCH_MISTRAL3: break;
default: throw std::runtime_error("unsupported model architecture");
}
@ -2133,6 +2144,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
} break;
case LLM_ARCH_BERT:
case LLM_ARCH_NOMIC_BERT:
case LLM_ARCH_NOMIC_BERT_MOE:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
type_embd = create_tensor(tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_token_types}, 0);
@ -2166,20 +2178,31 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, 0);
}
if (arch == LLM_ARCH_NOMIC_BERT_MOE) {
layer.bqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, 0);
}
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
layer.attn_out_norm = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}, 0);
layer.attn_out_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
if (arch == LLM_ARCH_BERT) {
if (hparams.moe_every_n_layers > 0 && i % hparams.moe_every_n_layers == 1) {
layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0);
layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff, n_expert}, 0);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}, 0);
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
} else {
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
if (arch == LLM_ARCH_BERT || arch == LLM_ARCH_NOMIC_BERT_MOE) {
layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0);
layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0);
} else {
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
}
}
layer.layer_out_norm = create_tensor(tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}, 0);
@ -6074,6 +6097,11 @@ struct llm_build_bert : public llm_graph_context {
cur = build_lora_mm(model.layers[il].wqkv, cur);
cb(cur, "wqkv", il);
if (model.arch == LLM_ARCH_NOMIC_BERT_MOE) {
cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
cb(cur, "bqkv", il);
}
Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
@ -6126,13 +6154,29 @@ struct llm_build_bert : public llm_graph_context {
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
if (model.arch == LLM_ARCH_BERT) {
if (hparams.moe_every_n_layers > 0 && il % hparams.moe_every_n_layers == 1) {
// MoE branch
cur = build_moe_ffn(cur,
model.layers[il].ffn_gate_inp,
model.layers[il].ffn_up_exps,
nullptr,
model.layers[il].ffn_down_exps,
nullptr,
hparams.n_expert,
hparams.n_expert_used,
LLM_FFN_GELU,
false, false,
0.0f,
LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il);
cb(cur, "ffn_moe_out", il);
} else if (model.arch == LLM_ARCH_BERT || model.arch == LLM_ARCH_NOMIC_BERT_MOE) {
cur = build_ffn(cur,
model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
NULL, NULL, NULL,
model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
NULL,
LLM_FFN_GELU, LLM_FFN_SEQ, il);
cb(cur, "ffn_out", il);
} else if (model.arch == LLM_ARCH_JINA_BERT_V2) {
cur = build_ffn(cur,
model.layers[il].ffn_up, NULL, NULL,
@ -6140,6 +6184,7 @@ struct llm_build_bert : public llm_graph_context {
model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
NULL,
LLM_FFN_GELU, LLM_FFN_PAR, il);
cb(cur, "ffn_out", il);
} else {
cur = build_ffn(cur,
model.layers[il].ffn_up, NULL, NULL,
@ -6147,8 +6192,8 @@ struct llm_build_bert : public llm_graph_context {
model.layers[il].ffn_down, NULL, NULL,
NULL,
LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(cur, "ffn_out", il);
}
cb(cur, "ffn_out", il);
// attentions bypass the intermediate layer
cur = ggml_add(ctx0, cur, ffn_inp);
@ -13349,6 +13394,7 @@ llm_graph_result_ptr llama_model::build_graph(
case LLM_ARCH_BERT:
case LLM_ARCH_JINA_BERT_V2:
case LLM_ARCH_NOMIC_BERT:
case LLM_ARCH_NOMIC_BERT_MOE:
{
llm = std::make_unique<llm_build_bert>(*this, params, gf);
} break;
@ -13705,7 +13751,6 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_CHAMELEON:
case LLM_ARCH_SOLAR:
case LLM_ARCH_BAILINGMOE:
case LLM_ARCH_MISTRAL3:
return LLAMA_ROPE_TYPE_NORM;
// the pairs of head values are offset by n_rot/2
@ -13714,6 +13759,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_DBRX:
case LLM_ARCH_BERT:
case LLM_ARCH_NOMIC_BERT:
case LLM_ARCH_NOMIC_BERT_MOE:
case LLM_ARCH_STABLELM:
case LLM_ARCH_BITNET:
case LLM_ARCH_QWEN:

8
llama/llama.cpp/src/llama-model.h vendored
View File

@ -40,11 +40,13 @@ enum llm_type {
LLM_TYPE_770M,
LLM_TYPE_780M,
LLM_TYPE_0_5B,
LLM_TYPE_0_6B,
LLM_TYPE_1B,
LLM_TYPE_1_3B,
LLM_TYPE_1_4B,
LLM_TYPE_1_5B,
LLM_TYPE_1_6B,
LLM_TYPE_1_7B,
LLM_TYPE_1_8B,
LLM_TYPE_2B,
LLM_TYPE_2_8B,
@ -64,6 +66,7 @@ enum llm_type {
LLM_TYPE_16B,
LLM_TYPE_20B,
LLM_TYPE_22B,
LLM_TYPE_27B,
LLM_TYPE_30B,
LLM_TYPE_32B,
LLM_TYPE_34B,
@ -73,6 +76,7 @@ enum llm_type {
LLM_TYPE_70B,
LLM_TYPE_90B,
LLM_TYPE_236B,
LLM_TYPE_290B,
LLM_TYPE_314B,
LLM_TYPE_671B,
LLM_TYPE_SMALL,
@ -87,10 +91,10 @@ enum llm_type {
LLM_TYPE_16x3_8B,
LLM_TYPE_10B_128x3_66B,
LLM_TYPE_57B_A14B,
LLM_TYPE_27B,
LLM_TYPE_290B,
LLM_TYPE_17B_16E, // llama4 Scout
LLM_TYPE_17B_128E, // llama4 Maverick
LLM_TYPE_30B_A3B,
LLM_TYPE_235B_A22B,
};
struct llama_layer_posnet {

4
llama/llama.cpp/src/llama-quant.cpp vendored
View File

@ -744,10 +744,6 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
// This used to be a regex, but <regex> has an extreme cost to compile times.
bool quantize = name.rfind("weight") == name.size() - 6; // ends with 'weight'?
// don't quantize vision stuff
quantize &= name.find("v.") == std::string::npos;
quantize &= name.find("mm.") == std::string::npos;
// quantize only 2D and 3D tensors (experts)
quantize &= (ggml_n_dims(tensor) >= 2);

3
llama/llama.cpp/src/llama-sampling.cpp vendored
View File

@ -232,7 +232,7 @@ static void llama_sampler_top_k_impl(llama_token_data_array * cur_p, int32_t k)
// }
if (k <= 0) {
k = cur_p->size;
return;
}
k = std::min(k, (int) cur_p->size);
@ -298,6 +298,7 @@ static void llama_sampler_top_k_impl(llama_token_data_array * cur_p, int32_t k)
}
cur_p->sorted = true;
}
cur_p->size = k;
}

3
llama/llama.cpp/src/llama-vocab.cpp vendored
View File

@ -1497,7 +1497,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
tokenizer_pre == "llama3" ||
tokenizer_pre == "llama-v3" ||
tokenizer_pre == "llama-bpe"||
tokenizer_pre == "falcon3") {
tokenizer_pre == "falcon3" ||
tokenizer_pre == "pixtral") {
pre_type = LLAMA_VOCAB_PRE_TYPE_LLAMA3;
ignore_merges = true;
add_bos = true;

27
llama/llama.go
View File

@ -2,6 +2,7 @@ package llama
/*
#cgo CFLAGS: -std=c11
#cgo windows CFLAGS: -Wno-dll-attribute-on-redeclaration
#cgo CXXFLAGS: -std=c++17
#cgo CPPFLAGS: -I${SRCDIR}/llama.cpp/include
#cgo CPPFLAGS: -I${SRCDIR}/llama.cpp/common
@ -198,7 +199,6 @@ type ModelParams struct {
NumGpuLayers int
MainGpu int
UseMmap bool
UseMlock bool
TensorSplit []float32
Progress func(float32)
VocabOnly bool
@ -217,7 +217,6 @@ func LoadModelFromFile(modelPath string, params ModelParams) (*Model, error) {
cparams.n_gpu_layers = C.int(params.NumGpuLayers)
cparams.main_gpu = C.int32_t(params.MainGpu)
cparams.use_mmap = C.bool(params.UseMmap)
cparams.use_mlock = C.bool(params.UseMlock)
cparams.vocab_only = C.bool(params.VocabOnly)
if len(params.TensorSplit) > 0 {
@ -461,24 +460,6 @@ func (m *Model) NEmbd() int {
return int(C.llama_model_n_embd(m.c))
}
func Quantize(infile, outfile string, ftype uint32) error {
cinfile := C.CString(infile)
defer C.free(unsafe.Pointer(cinfile))
coutfile := C.CString(outfile)
defer C.free(unsafe.Pointer(coutfile))
params := C.llama_model_quantize_default_params()
params.nthread = -1
params.ftype = ftype
if rc := C.llama_model_quantize(cinfile, coutfile, &params); rc != 0 {
return fmt.Errorf("llama_model_quantize: %d", rc)
}
return nil
}
// vision processing
type ClipContext struct {
c *C.struct_clip_ctx
@ -606,9 +587,6 @@ type SamplingParams struct {
PenaltyRepeat float32
PenaltyFreq float32
PenaltyPresent float32
Mirostat int
MirostatTau float32
MirostatEta float32
PenalizeNl bool
Seed uint32
Grammar string
@ -625,9 +603,6 @@ func NewSamplingContext(model *Model, params SamplingParams) (*SamplingContext,
cparams.penalty_repeat = C.float(params.PenaltyRepeat)
cparams.penalty_freq = C.float(params.PenaltyFreq)
cparams.penalty_present = C.float(params.PenaltyFreq)
cparams.mirostat = C.int32_t(params.Mirostat)
cparams.mirostat_tau = C.float(params.MirostatTau)
cparams.mirostat_eta = C.float(params.MirostatEta)
cparams.seed = C.uint32_t(params.Seed)
grammar := C.CString(params.Grammar)

24
llama/patches/0001-ggml-backend-malloc-and-free-using-the-same-compiler.patch
View File

@ -85,7 +85,7 @@ index e2617b06..242e50a7 100644
/**
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index a7febef7..31750b6f 100644
index 9fb2134f..04ce764e 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -534,6 +534,7 @@ struct ggml_backend_cuda_buffer_context {
@ -125,10 +125,10 @@ index 50579227..2799a0a5 100644
static void * ggml_backend_kompute_buffer_get_base(ggml_backend_buffer_t buffer) {
diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m
index 266d8af4..12886cd3 100644
index d92392ed..425524d0 100644
--- a/ggml/src/ggml-metal/ggml-metal.m
+++ b/ggml/src/ggml-metal/ggml-metal.m
@@ -4759,6 +4759,7 @@ static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer)
@@ -5077,6 +5077,7 @@ static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_t buffer)
}
free(ctx);
@ -149,10 +149,10 @@ index 05a2f4e6..392cc18d 100644
static void * ggml_backend_opencl_buffer_get_base(ggml_backend_buffer_t buffer) {
diff --git a/ggml/src/ggml-rpc/ggml-rpc.cpp b/ggml/src/ggml-rpc/ggml-rpc.cpp
index a0667b7d..bd83adc5 100644
index 140a775f..e33c4ba0 100644
--- a/ggml/src/ggml-rpc/ggml-rpc.cpp
+++ b/ggml/src/ggml-rpc/ggml-rpc.cpp
@@ -468,6 +468,7 @@ static void ggml_backend_rpc_buffer_free_buffer(ggml_backend_buffer_t buffer) {
@@ -477,6 +477,7 @@ static void ggml_backend_rpc_buffer_free_buffer(ggml_backend_buffer_t buffer) {
bool status = send_rpc_cmd(ctx->sock, RPC_CMD_FREE_BUFFER, &request, sizeof(request), nullptr, 0);
GGML_ASSERT(status);
delete ctx;
@ -161,10 +161,10 @@ index a0667b7d..bd83adc5 100644
static void * ggml_backend_rpc_buffer_get_base(ggml_backend_buffer_t buffer) {
diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index 1de34c96..4600f61e 100644
index 66b6f2cc..e3e6deae 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -316,6 +316,7 @@ ggml_backend_sycl_buffer_free_buffer(ggml_backend_buffer_t buffer) try {
@@ -317,6 +317,7 @@ ggml_backend_sycl_buffer_free_buffer(ggml_backend_buffer_t buffer) try {
ggml_sycl_set_device(ctx->device);
delete ctx;
@ -172,7 +172,7 @@ index 1de34c96..4600f61e 100644
}
catch (sycl::exception const &exc) {
std::cerr << exc.what() << "Exception caught at file:" << __FILE__
@@ -761,6 +762,7 @@ struct ggml_backend_sycl_split_buffer_context {
@@ -762,6 +763,7 @@ struct ggml_backend_sycl_split_buffer_context {
static void ggml_backend_sycl_split_buffer_free_buffer(ggml_backend_buffer_t buffer) {
ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context;
delete ctx;
@ -180,7 +180,7 @@ index 1de34c96..4600f61e 100644
}
static void * ggml_backend_sycl_split_buffer_get_base(ggml_backend_buffer_t buffer) {
@@ -1095,6 +1097,7 @@ static const char * ggml_backend_sycl_host_buffer_type_name(ggml_backend_buffer_
@@ -1096,6 +1098,7 @@ static const char * ggml_backend_sycl_host_buffer_type_name(ggml_backend_buffer_
static void ggml_backend_sycl_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
ggml_sycl_host_free(buffer->context);
@ -189,10 +189,10 @@ index 1de34c96..4600f61e 100644
static ggml_backend_buffer_t ggml_backend_sycl_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 39f3cd34..c569a8a5 100644
index c0bdb9e1..03d03064 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -8653,6 +8653,7 @@ static void ggml_backend_vk_buffer_free_buffer(ggml_backend_buffer_t buffer) {
@@ -8660,6 +8660,7 @@ static void ggml_backend_vk_buffer_free_buffer(ggml_backend_buffer_t buffer) {
ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context;
ggml_vk_destroy_buffer(ctx->dev_buffer);
delete ctx;
@ -200,7 +200,7 @@ index 39f3cd34..c569a8a5 100644
}
static void * ggml_backend_vk_buffer_get_base(ggml_backend_buffer_t buffer) {
@@ -8796,6 +8797,7 @@ static const char * ggml_backend_vk_host_buffer_name(ggml_backend_buffer_t buffe
@@ -8803,6 +8804,7 @@ static const char * ggml_backend_vk_host_buffer_name(ggml_backend_buffer_t buffe
static void ggml_backend_vk_host_buffer_free_buffer(ggml_backend_buffer_t buffer) {
VK_LOG_MEMORY("ggml_backend_vk_host_buffer_free_buffer()");
ggml_vk_host_free(vk_instance.devices[0], buffer->context);

4
llama/patches/0002-pretokenizer.patch
View File

@ -10,7 +10,7 @@ logs instead of throwing an error
1 file changed, 3 insertions(+), 11 deletions(-)
diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index 48060517..a35b498c 100644
index 50ded286..a9ee9f03 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -1491,16 +1491,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
@ -31,7 +31,7 @@ index 48060517..a35b498c 100644
pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
} else if (
tokenizer_pre == "llama3" ||
@@ -1634,7 +1625,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
@@ -1635,7 +1626,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
pre_type = LLAMA_VOCAB_PRE_TYPE_BAILINGMOE;
clean_spaces = false;
} else {

8
llama/patches/0003-embeddings.patch
View File

@ -11,10 +11,10 @@ instead of forcing one or the error
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 983385f8..32f59819 100644
index 5a2eef9b..9c1fe93f 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -1236,7 +1236,7 @@ int llama_context::decode(llama_batch & inp_batch) {
@@ -1225,7 +1225,7 @@ int llama_context::decode(llama_batch & inp_batch) {
int64_t n_outputs_all = 0;
// count outputs
@ -23,7 +23,7 @@ index 983385f8..32f59819 100644
for (uint32_t i = 0; i < n_tokens_all; ++i) {
n_outputs_all += batch.logits[i] != 0;
}
@@ -1348,7 +1348,7 @@ int llama_context::decode(llama_batch & inp_batch) {
@@ -1337,7 +1337,7 @@ int llama_context::decode(llama_batch & inp_batch) {
// ggml_graph_dump_dot(gf, NULL, "llama.dot");
//}
@ -32,7 +32,7 @@ index 983385f8..32f59819 100644
auto * t_embd = cparams.embeddings ? res->get_embd() : nullptr;
if (t_embd && res->get_embd_pooled()) {
@@ -1492,7 +1492,7 @@ int32_t llama_context::output_reserve(int32_t n_outputs) {
@@ -1481,7 +1481,7 @@ int32_t llama_context::output_reserve(int32_t n_outputs) {
const auto n_embd = hparams.n_embd;
// TODO: use a per-batch flag for logits presence instead

10
llama/patches/0004-clip-unicode.patch
View File

@ -10,12 +10,12 @@ filesystems for paths that include wide characters
1 file changed, 39 insertions(+)
diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp
index 75970615..d57b4bd6 100644
index ad3e7df1..b3218c78 100644
--- a/examples/llava/clip.cpp
+++ b/examples/llava/clip.cpp
@@ -29,6 +29,19 @@
#include <limits>
@@ -30,6 +30,19 @@
#include <array>
#include <numeric>
+#if defined(_WIN32)
+#define WIN32_LEAN_AND_MEAN
@ -33,7 +33,7 @@ index 75970615..d57b4bd6 100644
struct clip_logger_state g_logger_state = {GGML_LOG_LEVEL_CONT, clip_log_callback_default, NULL};
//#define CLIP_DEBUG_FUNCTIONS
@@ -1430,7 +1443,29 @@ struct clip_model_loader {
@@ -1971,7 +1984,29 @@ struct clip_model_loader {
{
std::vector<uint8_t> read_buf;
@ -63,7 +63,7 @@ index 75970615..d57b4bd6 100644
if (!fin) {
throw std::runtime_error(string_format("%s: failed to open %s\n", __func__, fname.c_str()));
}
@@ -1457,7 +1492,11 @@ struct clip_model_loader {
@@ -1998,7 +2033,11 @@ struct clip_model_loader {
ggml_backend_tensor_set(cur, read_buf.data(), 0, num_bytes);
}
}

42
llama/patches/0005-solar-pro.patch
View File

@ -15,10 +15,10 @@ adds support for the Solar Pro architecture
7 files changed, 248 insertions(+)
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index 62e1480b..f754bc8f 100644
index f2bc8ca7..5ab3f572 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -68,6 +68,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
@@ -69,6 +69,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_GRANITE, "granite" },
{ LLM_ARCH_GRANITE_MOE, "granitemoe" },
{ LLM_ARCH_CHAMELEON, "chameleon" },
@ -26,7 +26,7 @@ index 62e1480b..f754bc8f 100644
{ LLM_ARCH_WAVTOKENIZER_DEC, "wavtokenizer-dec" },
{ LLM_ARCH_PLM, "plm" },
{ LLM_ARCH_BAILINGMOE, "bailingmoe" },
@@ -140,6 +141,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
@@ -142,6 +143,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, "%s.attention.relative_buckets_count" },
{ LLM_KV_ATTENTION_SLIDING_WINDOW, "%s.attention.sliding_window" },
{ LLM_KV_ATTENTION_SCALE, "%s.attention.scale" },
@ -34,7 +34,7 @@ index 62e1480b..f754bc8f 100644
{ LLM_KV_ATTENTION_KEY_LENGTH_MLA, "%s.attention.key_length_mla" },
{ LLM_KV_ATTENTION_VALUE_LENGTH_MLA, "%s.attention.value_length_mla" },
@@ -1482,6 +1484,24 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
@@ -1502,6 +1504,24 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
},
},
@ -59,7 +59,7 @@ index 62e1480b..f754bc8f 100644
{
LLM_ARCH_WAVTOKENIZER_DEC,
{
@@ -1660,6 +1680,7 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
@@ -1680,6 +1700,7 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
{LLM_TENSOR_FFN_EXP_PROBS_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ADD}},
// this tensor is loaded for T5, but never used
{LLM_TENSOR_DEC_CROSS_ATTN_REL_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_NONE}},
@ -68,10 +68,10 @@ index 62e1480b..f754bc8f 100644
{LLM_TENSOR_POS_NET_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
{LLM_TENSOR_POS_NET_NORM1, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
diff --git a/src/llama-arch.h b/src/llama-arch.h
index 98ca00a1..439aaeab 100644
index 41a023da..525c1b7d 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -72,6 +72,7 @@ enum llm_arch {
@@ -73,6 +73,7 @@ enum llm_arch {
LLM_ARCH_GRANITE,
LLM_ARCH_GRANITE_MOE,
LLM_ARCH_CHAMELEON,
@ -79,7 +79,7 @@ index 98ca00a1..439aaeab 100644
LLM_ARCH_WAVTOKENIZER_DEC,
LLM_ARCH_PLM,
LLM_ARCH_BAILINGMOE,
@@ -144,6 +145,7 @@ enum llm_kv {
@@ -146,6 +147,7 @@ enum llm_kv {
LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,
LLM_KV_ATTENTION_SLIDING_WINDOW,
LLM_KV_ATTENTION_SCALE,
@ -87,7 +87,7 @@ index 98ca00a1..439aaeab 100644
LLM_KV_ATTENTION_KEY_LENGTH_MLA,
LLM_KV_ATTENTION_VALUE_LENGTH_MLA,
@@ -344,6 +346,7 @@ enum llm_tensor {
@@ -346,6 +348,7 @@ enum llm_tensor {
LLM_TENSOR_ENC_OUTPUT_NORM,
LLM_TENSOR_CLS,
LLM_TENSOR_CLS_OUT,
@ -115,7 +115,7 @@ index 90dfe7a7..8a667960 100644
if (il < n_layer) {
return n_swa > 0 && n_swa_pattern > 0 && il % n_swa_pattern < (n_swa_pattern - 1);
diff --git a/src/llama-hparams.h b/src/llama-hparams.h
index 80fcd65d..6e278945 100644
index 7ee6a5b7..48dce407 100644
--- a/src/llama-hparams.h
+++ b/src/llama-hparams.h
@@ -55,6 +55,8 @@ struct llama_hparams {
@ -127,7 +127,7 @@ index 80fcd65d..6e278945 100644
uint32_t n_layer_dense_lead = 0;
uint32_t n_lora_q = 0;
uint32_t n_lora_kv = 0;
@@ -153,6 +155,9 @@ struct llama_hparams {
@@ -154,6 +156,9 @@ struct llama_hparams {
// dimension of the recurrent state embeddings
uint32_t n_embd_v_s() const;
@ -150,10 +150,10 @@ index ea73a8a7..a012aeae 100644
llama_model_loader::llama_model_loader(
const std::string & fname,
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 6b7bfecf..aba42819 100644
index 822e2bb2..572378c9 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -1374,6 +1374,21 @@ void llama_model::load_hparams(llama_model_loader & ml) {
@@ -1386,6 +1386,21 @@ void llama_model::load_hparams(llama_model_loader & ml) {
default: type = LLM_TYPE_UNKNOWN;
}
} break;
@ -175,7 +175,7 @@ index 6b7bfecf..aba42819 100644
case LLM_ARCH_WAVTOKENIZER_DEC:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
@@ -3717,6 +3732,34 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
@@ -3741,6 +3756,34 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
@ -210,7 +210,7 @@ index 6b7bfecf..aba42819 100644
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
@@ -12296,6 +12339,165 @@ struct llm_build_chameleon : public llm_graph_context {
@@ -12342,6 +12385,165 @@ struct llm_build_chameleon : public llm_graph_context {
}
};
@ -376,7 +376,7 @@ index 6b7bfecf..aba42819 100644
struct llm_build_wavtokenizer_dec : public llm_graph_context {
llm_build_wavtokenizer_dec(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) {
ggml_tensor * cur;
@@ -13045,6 +13247,10 @@ llm_graph_result_ptr llama_model::build_graph(
@@ -13092,6 +13294,10 @@ llm_graph_result_ptr llama_model::build_graph(
{
llm = std::make_unique<llm_build_chameleon>(*this, params, gf);
} break;
@ -387,7 +387,7 @@ index 6b7bfecf..aba42819 100644
case LLM_ARCH_WAVTOKENIZER_DEC:
{
llm = std::make_unique<llm_build_wavtokenizer_dec>(*this, params, gf);
@@ -13191,6 +13397,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
@@ -13238,6 +13444,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_GRANITE:
case LLM_ARCH_GRANITE_MOE:
case LLM_ARCH_CHAMELEON:
@ -396,18 +396,18 @@ index 6b7bfecf..aba42819 100644
return LLAMA_ROPE_TYPE_NORM;
diff --git a/src/llama-model.h b/src/llama-model.h
index fd82d106..5865d5e9 100644
index 95eca002..856e6042 100644
--- a/src/llama-model.h
+++ b/src/llama-model.h
@@ -62,6 +62,7 @@ enum llm_type {
@@ -64,6 +64,7 @@ enum llm_type {
LLM_TYPE_15B,
LLM_TYPE_16B,
LLM_TYPE_20B,
+ LLM_TYPE_22B,
LLM_TYPE_27B,
LLM_TYPE_30B,
LLM_TYPE_32B,
LLM_TYPE_34B,
@@ -307,6 +308,8 @@ struct llama_layer {
@@ -311,6 +312,8 @@ struct llama_layer {
struct ggml_tensor * ffn_up_scale = nullptr;
struct ggml_tensor * ffn_down_scale = nullptr;

152
llama/patches/0006-add-mllama-support.patch
View File

@ -5,7 +5,6 @@ Subject: [PATCH] add mllama support
adds support for the llama 3.2 vision architecture
---
examples/llava/gemma3-cli.cpp | 3 +-
examples/llava/llava.cpp | 5 +-
examples/llava/mtmd.cpp | 6 +-
ggml/src/ggml-backend-reg.cpp | 6 +-
@ -25,34 +24,13 @@ adds support for the llama 3.2 vision architecture
src/llama-model.cpp | 309 +++++++++++++++++++++++++++++++++-
src/llama-model.h | 12 ++
src/llama-quant.cpp | 4 +-
20 files changed, 475 insertions(+), 22 deletions(-)
19 files changed, 473 insertions(+), 21 deletions(-)
diff --git a/examples/llava/gemma3-cli.cpp b/examples/llava/gemma3-cli.cpp
index 3d566475..654d1358 100644
--- a/examples/llava/gemma3-cli.cpp
+++ b/examples/llava/gemma3-cli.cpp
@@ -106,7 +106,7 @@ struct decode_embd_batch {
std::vector<llama_seq_id *> seq_ids;
std::vector<int8_t> logits;
llama_batch batch;
- decode_embd_batch(float * embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) {
+ decode_embd_batch(float * embd, int32_t n_embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) {
pos .resize(n_tokens);
n_seq_id.resize(n_tokens);
seq_ids .resize(n_tokens + 1);
@@ -118,6 +118,7 @@ struct decode_embd_batch {
/*n_tokens =*/ n_tokens,
/*tokens =*/ nullptr,
/*embd =*/ embd,
+ /*n_embd =*/ n_embd,
/*pos =*/ pos.data(),
/*n_seq_id =*/ n_seq_id.data(),
/*seq_id =*/ seq_ids.data(),
diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp
index 03a22cbb..5eb40bcd 100644
index c00d16ae..bab027b5 100644
--- a/examples/llava/llava.cpp
+++ b/examples/llava/llava.cpp
@@ -456,7 +456,7 @@ struct llava_embd_batch {
@@ -457,7 +457,7 @@ struct llava_embd_batch {
std::vector<llama_seq_id *> seq_ids;
std::vector<int8_t> logits;
llama_batch batch;
@ -61,7 +39,7 @@ index 03a22cbb..5eb40bcd 100644
pos .resize(n_tokens);
n_seq_id.resize(n_tokens);
seq_ids .resize(n_tokens + 1);
@@ -468,6 +468,7 @@ struct llava_embd_batch {
@@ -469,6 +469,7 @@ struct llava_embd_batch {
/*n_tokens =*/ n_tokens,
/*tokens =*/ nullptr,
/*embd =*/ embd,
@ -69,7 +47,7 @@ index 03a22cbb..5eb40bcd 100644
/*pos =*/ pos.data(),
/*n_seq_id =*/ n_seq_id.data(),
/*seq_id =*/ seq_ids.data(),
@@ -491,7 +492,7 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_
@@ -492,7 +493,7 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_
n_eval = n_batch;
}
float * embd = image_embed->embed+i*n_embd;
@ -79,19 +57,19 @@ index 03a22cbb..5eb40bcd 100644
LOG_ERR("%s : failed to eval\n", __func__);
return false;
diff --git a/examples/llava/mtmd.cpp b/examples/llava/mtmd.cpp
index 3fd5bebc..f0cec596 100644
index 7081fd73..c14ac501 100644
--- a/examples/llava/mtmd.cpp
+++ b/examples/llava/mtmd.cpp
@@ -233,7 +233,7 @@ struct decode_embd_batch {
@@ -476,7 +476,7 @@ struct decode_embd_batch {
std::vector<llama_seq_id *> seq_ids;
std::vector<int8_t> logits;
llama_batch batch;
- decode_embd_batch(float * embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) {
+ decode_embd_batch(float * embd, int32_t n_embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) {
pos .resize(n_tokens);
- decode_embd_batch(float * embd, int32_t n_tokens, int n_pos_per_embd, int n_mmproj_embd) : n_pos_per_embd(n_pos_per_embd), n_mmproj_embd(n_mmproj_embd) {
+ decode_embd_batch(float * embd, int32_t n_embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) : n_pos_per_embd(n_pos_per_embd), n_mmproj_embd(n_mmproj_embd) {
pos .resize(n_tokens * n_pos_per_embd);
n_seq_id.resize(n_tokens);
seq_ids .resize(n_tokens + 1);
@@ -245,6 +245,7 @@ struct decode_embd_batch {
@@ -487,6 +487,7 @@ struct decode_embd_batch {
/*n_tokens =*/ n_tokens,
/*tokens =*/ nullptr,
/*embd =*/ embd,
@ -99,16 +77,16 @@ index 3fd5bebc..f0cec596 100644
/*pos =*/ pos.data(),
/*n_seq_id =*/ n_seq_id.data(),
/*seq_id =*/ seq_ids.data(),
@@ -311,7 +312,8 @@ int32_t mtmd_helper_eval(mtmd_context * ctx,
int32_t n_tokens = mtmd_image_tokens_get_n_tokens(chunk.tokens_image.get());
@@ -610,7 +611,8 @@ int32_t mtmd_helper_eval(mtmd_context * ctx,
int32_t i_batch = 0;
int32_t n_img_batches = GGML_PAD(n_tokens, n_batch) / n_batch;
float * embd = mtmd_get_output_embd(ctx);
- decode_embd_batch batch_img(embd, n_tokens, n_past, 0);
- decode_embd_batch batch_embd(embd, n_tokens, n_pos_per_embd, n_mmproj_embd);
+ int n_embd = llama_model_n_embd(llama_get_model(lctx));
+ decode_embd_batch batch_img(embd, n_embd, n_tokens, n_past, 0);
int64_t t1 = ggml_time_ms();
ret = llama_decode(lctx, batch_img.batch);
if (ret != 0) {
+ decode_embd_batch batch_embd(embd, n_embd, n_tokens, n_past, 0);
const int nx = mtmd_image_tokens_get_nx(chunk.tokens_image.get());
const int ny = mtmd_image_tokens_get_ny(chunk.tokens_image.get());
diff --git a/ggml/src/ggml-backend-reg.cpp b/ggml/src/ggml-backend-reg.cpp
index 405d8e31..82ae1b5b 100644
--- a/ggml/src/ggml-backend-reg.cpp
@ -127,10 +105,10 @@ index 405d8e31..82ae1b5b 100644
register_backend(ggml_backend_rpc_reg());
#endif
diff --git a/include/llama.h b/include/llama.h
index 5657fbf0..f91896e4 100644
index 06c56395..f1628e88 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -255,6 +255,7 @@ extern "C" {
@@ -256,6 +256,7 @@ extern "C" {
llama_token * token;
float * embd;
@ -138,7 +116,7 @@ index 5657fbf0..f91896e4 100644
llama_pos * pos;
int32_t * n_seq_id;
llama_seq_id ** seq_id;
@@ -357,6 +358,7 @@ extern "C" {
@@ -358,6 +359,7 @@ extern "C" {
bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
bool flash_attn; // whether to use flash attention [EXPERIMENTAL]
bool no_perf; // whether to measure performance timings
@ -146,7 +124,7 @@ index 5657fbf0..f91896e4 100644
// Abort callback
// if it returns true, execution of llama_decode() will be aborted
@@ -458,6 +460,10 @@ extern "C" {
@@ -459,6 +461,10 @@ extern "C" {
struct llama_context_params params),
"use llama_init_from_model instead");
@ -158,7 +136,7 @@ index 5657fbf0..f91896e4 100644
LLAMA_API void llama_free(struct llama_context * ctx);
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index f754bc8f..0568565f 100644
index 5ab3f572..eb7b5325 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -6,6 +6,7 @@
@ -169,7 +147,7 @@ index f754bc8f..0568565f 100644
{ LLM_ARCH_LLAMA4, "llama4" },
{ LLM_ARCH_DECI, "deci" },
{ LLM_ARCH_FALCON, "falcon" },
@@ -142,6 +143,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
@@ -144,6 +145,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_ATTENTION_SLIDING_WINDOW, "%s.attention.sliding_window" },
{ LLM_KV_ATTENTION_SCALE, "%s.attention.scale" },
{ LLM_KV_ATTENTION_BLOCK_SKIP_CONNECTION, "%s.attention.block_skip_connection" },
@ -177,7 +155,7 @@ index f754bc8f..0568565f 100644
{ LLM_KV_ATTENTION_KEY_LENGTH_MLA, "%s.attention.key_length_mla" },
{ LLM_KV_ATTENTION_VALUE_LENGTH_MLA, "%s.attention.value_length_mla" },
@@ -271,6 +273,40 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
@@ -273,6 +275,40 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" },
},
},
@ -218,7 +196,7 @@ index f754bc8f..0568565f 100644
{
LLM_ARCH_DECI,
{
@@ -1681,6 +1717,14 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
@@ -1701,6 +1737,14 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
// this tensor is loaded for T5, but never used
{LLM_TENSOR_DEC_CROSS_ATTN_REL_B, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_NONE}},
{LLM_TENSOR_BSKCN_TV, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
@ -234,7 +212,7 @@ index f754bc8f..0568565f 100644
{LLM_TENSOR_POS_NET_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
{LLM_TENSOR_POS_NET_NORM1, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
diff --git a/src/llama-arch.h b/src/llama-arch.h
index 439aaeab..6a989034 100644
index 525c1b7d..bc8a4f0b 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -11,6 +11,7 @@
@ -245,7 +223,7 @@ index 439aaeab..6a989034 100644
LLM_ARCH_DECI,
LLM_ARCH_FALCON,
LLM_ARCH_BAICHUAN,
@@ -146,6 +147,7 @@ enum llm_kv {
@@ -148,6 +149,7 @@ enum llm_kv {
LLM_KV_ATTENTION_SLIDING_WINDOW,
LLM_KV_ATTENTION_SCALE,
LLM_KV_ATTENTION_BLOCK_SKIP_CONNECTION,
@ -253,7 +231,7 @@ index 439aaeab..6a989034 100644
LLM_KV_ATTENTION_KEY_LENGTH_MLA,
LLM_KV_ATTENTION_VALUE_LENGTH_MLA,
@@ -347,6 +349,14 @@ enum llm_tensor {
@@ -349,6 +351,14 @@ enum llm_tensor {
LLM_TENSOR_CLS,
LLM_TENSOR_CLS_OUT,
LLM_TENSOR_BSKCN_TV,
@ -297,10 +275,10 @@ index 01d5ca57..8682b0e6 100644
batch.token = (llama_token *) malloc(sizeof(llama_token) * n_tokens_alloc);
}
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 32f59819..0343ba8a 100644
index 9c1fe93f..cd06ad91 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -862,7 +862,7 @@ float * llama_context::get_logits_ith(int32_t i) {
@@ -851,7 +851,7 @@ float * llama_context::get_logits_ith(int32_t i) {
throw std::runtime_error(format("corrupt output buffer (j=%d, n_outputs=%d)", j, n_outputs));
}
@ -309,7 +287,7 @@ index 32f59819..0343ba8a 100644
} catch (const std::exception & err) {
LLAMA_LOG_ERROR("%s: invalid logits id %d, reason: %s\n", __func__, i, err.what());
#ifndef NDEBUG
@@ -983,6 +983,10 @@ void llama_context::set_warmup(bool value) {
@@ -972,6 +972,10 @@ void llama_context::set_warmup(bool value) {
cparams.warmup = value;
}
@ -320,7 +298,7 @@ index 32f59819..0343ba8a 100644
void llama_context::set_adapter_lora(
llama_adapter_lora * adapter,
float scale) {
@@ -1058,7 +1062,7 @@ int llama_context::encode(llama_batch & inp_batch) {
@@ -1047,7 +1051,7 @@ int llama_context::encode(llama_batch & inp_batch) {
const int64_t n_embd = hparams.n_embd;
@ -329,7 +307,7 @@ index 32f59819..0343ba8a 100644
const llama_ubatch ubatch = sbatch.split_simple(n_tokens);
@@ -1198,10 +1202,9 @@ int llama_context::decode(llama_batch & inp_batch) {
@@ -1187,10 +1191,9 @@ int llama_context::decode(llama_batch & inp_batch) {
const llama_batch & batch = batch_allocr.batch;
@ -341,7 +319,7 @@ index 32f59819..0343ba8a 100644
const int64_t n_tokens_all = batch.n_tokens;
const int64_t n_embd = hparams.n_embd;
@@ -1249,7 +1252,7 @@ int llama_context::decode(llama_batch & inp_batch) {
@@ -1238,7 +1241,7 @@ int llama_context::decode(llama_batch & inp_batch) {
const bool logits_all = n_outputs_all == n_tokens_all;
@ -350,7 +328,7 @@ index 32f59819..0343ba8a 100644
/* simple_split */ !kv_self->recurrent,
/* logits_all */ logits_all);
@@ -1483,12 +1486,11 @@ int llama_context::decode(llama_batch & inp_batch) {
@@ -1472,12 +1475,11 @@ int llama_context::decode(llama_batch & inp_batch) {
int32_t llama_context::output_reserve(int32_t n_outputs) {
const auto & hparams = model.hparams;
@ -364,7 +342,7 @@ index 32f59819..0343ba8a 100644
const auto n_embd = hparams.n_embd;
// TODO: use a per-batch flag for logits presence instead
@@ -1558,7 +1560,7 @@ int32_t llama_context::output_reserve(int32_t n_outputs) {
@@ -1545,7 +1547,7 @@ int32_t llama_context::output_reserve(int32_t n_outputs) {
void llama_context::output_reorder() {
auto & out_ids = sbatch.out_ids;
if (!out_ids.empty()) {
@ -373,7 +351,7 @@ index 32f59819..0343ba8a 100644
const uint32_t n_embd = model.hparams.n_embd;
GGML_ASSERT((size_t) n_outputs == out_ids.size());
@@ -2065,7 +2067,7 @@ size_t llama_context::state_write_data(llama_io_write_i & io) {
@@ -2052,7 +2054,7 @@ size_t llama_context::state_write_data(llama_io_write_i & io) {
{
LLAMA_LOG_DEBUG("%s: - writing logits\n", __func__);
@ -382,7 +360,7 @@ index 32f59819..0343ba8a 100644
io.write(&logits_size, sizeof(logits_size));
@@ -2248,6 +2250,7 @@ llama_context_params llama_context_default_params() {
@@ -2235,6 +2237,7 @@ llama_context_params llama_context_default_params() {
/*.offload_kqv =*/ true,
/*.flash_attn =*/ false,
/*.no_perf =*/ true,
@ -390,7 +368,7 @@ index 32f59819..0343ba8a 100644
/*.abort_callback =*/ nullptr,
/*.abort_callback_data =*/ nullptr,
};
@@ -2375,6 +2378,10 @@ void llama_set_warmup(llama_context * ctx, bool warmup) {
@@ -2362,6 +2365,10 @@ void llama_set_warmup(llama_context * ctx, bool warmup) {
ctx->set_warmup(warmup);
}
@ -402,7 +380,7 @@ index 32f59819..0343ba8a 100644
ctx->synchronize();
}
diff --git a/src/llama-context.h b/src/llama-context.h
index 04facb54..baa03276 100644
index 5457f077..a50c4afa 100644
--- a/src/llama-context.h
+++ b/src/llama-context.h
@@ -65,6 +65,7 @@ struct llama_context {
@ -426,10 +404,10 @@ index 30e550f0..85ad91b9 100644
enum llama_pooling_type pooling_type;
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index a85e9728..d740c120 100644
index fabb9ca2..b67216a4 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -546,6 +546,12 @@ void llm_graph_input_attn_cross::set_input(const llama_ubatch * ubatch) {
@@ -560,6 +560,12 @@ void llm_graph_input_attn_cross::set_input(const llama_ubatch * ubatch) {
}
}
@ -442,7 +420,7 @@ index a85e9728..d740c120 100644
//
// llm_graph_context
//
@@ -1506,6 +1512,25 @@ llm_graph_input_attn_cross * llm_graph_context::build_attn_inp_cross() const {
@@ -1532,6 +1538,25 @@ llm_graph_input_attn_cross * llm_graph_context::build_attn_inp_cross() const {
return (llm_graph_input_attn_cross *) res->add_input(std::move(inp));
}
@ -469,7 +447,7 @@ index a85e9728..d740c120 100644
llm_graph_input_attn_cross * inp,
ggml_cgraph * gf,
diff --git a/src/llama-graph.h b/src/llama-graph.h
index d192dc14..260a2af2 100644
index d0c8d321..0fe18150 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -86,6 +86,7 @@ public:
@ -480,7 +458,7 @@ index d192dc14..260a2af2 100644
};
class llm_graph_input_pos : public llm_graph_input_i {
@@ -285,6 +286,16 @@ public:
@@ -283,6 +284,16 @@ public:
const llama_cross * cross = nullptr;
};
@ -497,7 +475,7 @@ index d192dc14..260a2af2 100644
//
// llm_graph_result
//
@@ -493,6 +504,7 @@ struct llm_graph_context {
@@ -491,6 +502,7 @@ struct llm_graph_context {
ggml_tensor * build_inp_cls() const;
ggml_tensor * build_inp_s_copy() const;
ggml_tensor * build_inp_s_mask() const;
@ -518,7 +496,7 @@ index 8a667960..6a02de03 100644
+ return std::find(cross_attn_layers.begin(), cross_attn_layers.end(), il) != cross_attn_layers.end();
+}
diff --git a/src/llama-hparams.h b/src/llama-hparams.h
index 6e278945..c8a34d52 100644
index 48dce407..b6fc7e6d 100644
--- a/src/llama-hparams.h
+++ b/src/llama-hparams.h
@@ -2,6 +2,8 @@
@ -546,7 +524,7 @@ index 6e278945..c8a34d52 100644
uint32_t n_layer_dense_lead = 0;
uint32_t n_lora_q = 0;
@@ -158,6 +162,9 @@ struct llama_hparams {
@@ -159,6 +163,9 @@ struct llama_hparams {
// Block skip connection
bool n_bskcn(uint32_t n, uint32_t il) const;
@ -593,10 +571,10 @@ index a012aeae..2e11507d 100644
bool llama_model_loader::get_arr(const std::string & key, std::array<T, N_MAX> & result, bool required) {
const int kid = gguf_find_key(meta.get(), key.c_str());
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index aba42819..d051696c 100644
index 572378c9..9d099f11 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -419,6 +419,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
@@ -423,6 +423,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
// get general kv
ml.get_key(LLM_KV_GENERAL_NAME, name, false);
@ -604,7 +582,7 @@ index aba42819..d051696c 100644
// everything past this point is not vocab-related
if (hparams.vocab_only) {
@@ -430,6 +431,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
@@ -434,6 +435,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_BLOCK_COUNT, hparams.n_layer);
ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert, false);
ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false);
@ -612,7 +590,7 @@ index aba42819..d051696c 100644
if (arch == LLM_ARCH_WAVTOKENIZER_DEC) {
ml.get_key(LLM_KV_FEATURES_LENGTH, hparams.n_embd_features);
@@ -453,9 +455,11 @@ void llama_model::load_hparams(llama_model_loader & ml) {
@@ -457,9 +459,11 @@ void llama_model::load_hparams(llama_model_loader & ml) {
std::fill(hparams.n_head_arr.begin(), hparams.n_head_arr.end(), 0);
std::fill(hparams.n_head_kv_arr.begin(), hparams.n_head_kv_arr.end(), 0);
std::fill(hparams.n_ff_arr.begin(), hparams.n_ff_arr.end(), 0);
@ -624,7 +602,7 @@ index aba42819..d051696c 100644
// n_head_kv is optional, default to n_head
hparams.n_head_kv_arr = hparams.n_head_arr;
@@ -508,7 +512,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
@@ -512,7 +516,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false);
@ -633,7 +611,7 @@ index aba42819..d051696c 100644
if (hparams.n_rot != hparams.n_embd_head_k) {
throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd_head_k));
}
@@ -571,6 +575,16 @@ void llama_model::load_hparams(llama_model_loader & ml) {
@@ -575,6 +579,16 @@ void llama_model::load_hparams(llama_model_loader & ml) {
hparams.use_kq_norm = false;
}
} break;
@ -650,7 +628,7 @@ index aba42819..d051696c 100644
case LLM_ARCH_DECI:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@@ -1550,7 +1564,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
@@ -1562,7 +1576,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
const int64_t n_embd_head_v = hparams.n_embd_head_v;
const int64_t n_ff = hparams.n_ff();
const int64_t n_embd_gqa = n_embd_v_gqa;
@ -659,7 +637,7 @@ index aba42819..d051696c 100644
const int64_t n_token_types = vocab.n_token_types();
const int64_t n_rot = hparams.n_rot;
const int64_t n_expert = hparams.n_expert;
@@ -1803,6 +1817,52 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
@@ -1815,6 +1829,52 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
}
}
} break;
@ -712,7 +690,7 @@ index aba42819..d051696c 100644
case LLM_ARCH_DECI:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@@ -4683,6 +4743,246 @@ struct llm_build_llama : public llm_graph_context {
@@ -4707,6 +4767,246 @@ struct llm_build_llama : public llm_graph_context {
}
};
@ -959,7 +937,7 @@ index aba42819..d051696c 100644
struct llm_build_deci : public llm_graph_context {
llm_build_deci(const llama_model & model, const llm_graph_params & params, ggml_cgraph * gf) : llm_graph_context(params) {
const int64_t n_embd_head = hparams.n_embd_head_v;
@@ -13017,6 +13317,10 @@ llm_graph_result_ptr llama_model::build_graph(
@@ -13063,6 +13363,10 @@ llm_graph_result_ptr llama_model::build_graph(
{
llm = std::make_unique<llm_build_llama>(*this, params, gf);
} break;
@ -970,7 +948,7 @@ index aba42819..d051696c 100644
case LLM_ARCH_DECI:
{
llm = std::make_unique<llm_build_deci>(*this, params, gf);
@@ -13377,6 +13681,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
@@ -13424,6 +13728,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
// use what we call a normal RoPE, operating on pairs of consecutive head values
case LLM_ARCH_LLAMA:
case LLM_ARCH_LLAMA4:
@ -979,7 +957,7 @@ index aba42819..d051696c 100644
case LLM_ARCH_BAICHUAN:
case LLM_ARCH_STARCODER:
diff --git a/src/llama-model.h b/src/llama-model.h
index 5865d5e9..72bab5be 100644
index 856e6042..6be91282 100644
--- a/src/llama-model.h
+++ b/src/llama-model.h
@@ -11,6 +11,7 @@
@ -990,15 +968,15 @@ index 5865d5e9..72bab5be 100644
struct llama_cparams;
struct llama_ubatch;
@@ -70,6 +71,7 @@ enum llm_type {
@@ -73,6 +74,7 @@ enum llm_type {
LLM_TYPE_40B,
LLM_TYPE_65B,
LLM_TYPE_70B,
+ LLM_TYPE_90B,
LLM_TYPE_236B,
LLM_TYPE_290B,
LLM_TYPE_314B,
LLM_TYPE_671B,
@@ -310,6 +312,16 @@ struct llama_layer {
@@ -314,6 +316,16 @@ struct llama_layer {
struct ggml_tensor * bskcn_tv = nullptr;

56
llama/patches/0007-add-unpad-operator.patch
View File

@ -18,10 +18,10 @@ adds the unpad operator to GGML
10 files changed, 223 insertions(+), 2 deletions(-)
diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index 8fcc16df..d19fc167 100644
index 1b8603e7..53ef31b2 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -488,6 +488,7 @@ extern "C" {
@@ -489,6 +489,7 @@ extern "C" {
GGML_OP_UPSCALE, // nearest interpolate
GGML_OP_PAD,
GGML_OP_PAD_REFLECT_1D,
@ -29,7 +29,7 @@ index 8fcc16df..d19fc167 100644
GGML_OP_ARANGE,
GGML_OP_TIMESTEP_EMBEDDING,
GGML_OP_ARGSORT,
@@ -1757,6 +1758,15 @@ extern "C" {
@@ -1777,6 +1778,15 @@ extern "C" {
int p0,
int p1);
@ -46,10 +46,10 @@ index 8fcc16df..d19fc167 100644
// timesteps: [N,]
// return: [N, dim]
diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index 50400328..432942bf 100644
index 64405449..34624cca 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -1960,6 +1960,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
@@ -1964,6 +1964,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
{
ggml_compute_forward_pad_reflect_1d(params, tensor);
} break;
@ -60,7 +60,7 @@ index 50400328..432942bf 100644
case GGML_OP_ARANGE:
{
ggml_compute_forward_arange(params, tensor);
@@ -2282,6 +2286,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
@@ -2287,6 +2291,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
case GGML_OP_UPSCALE:
case GGML_OP_PAD:
case GGML_OP_PAD_REFLECT_1D:
@ -69,10 +69,10 @@ index 50400328..432942bf 100644
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_ARGSORT:
diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp
index 6050147b..66b8da68 100644
index 7413192b..becdae07 100644
--- a/ggml/src/ggml-cpu/ops.cpp
+++ b/ggml/src/ggml-cpu/ops.cpp
@@ -6531,6 +6531,61 @@ void ggml_compute_forward_pad_reflect_1d(
@@ -6703,6 +6703,61 @@ void ggml_compute_forward_pad_reflect_1d(
}
}
@ -135,10 +135,10 @@ index 6050147b..66b8da68 100644
static void ggml_compute_forward_arange_f32(
diff --git a/ggml/src/ggml-cpu/ops.h b/ggml/src/ggml-cpu/ops.h
index 410a3720..3eca1cf8 100644
index dc081b9e..a7125555 100644
--- a/ggml/src/ggml-cpu/ops.h
+++ b/ggml/src/ggml-cpu/ops.h
@@ -71,6 +71,7 @@ void ggml_compute_forward_pool_2d_back(const struct ggml_compute_params * params
@@ -72,6 +72,7 @@ void ggml_compute_forward_pool_2d_back(const struct ggml_compute_params * params
void ggml_compute_forward_upscale(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_pad(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_pad_reflect_1d(const struct ggml_compute_params * params, struct ggml_tensor * dst);
@ -147,10 +147,10 @@ index 410a3720..3eca1cf8 100644
void ggml_compute_forward_timestep_embedding(const struct ggml_compute_params * params, struct ggml_tensor * dst);
void ggml_compute_forward_argsort(const struct ggml_compute_params * params, struct ggml_tensor * dst);
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 31750b6f..0fef9522 100644
index 04ce764e..491acccb 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -2246,6 +2246,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
@@ -2223,6 +2223,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
case GGML_OP_PAD:
ggml_cuda_op_pad(ctx, dst);
break;
@ -160,7 +160,7 @@ index 31750b6f..0fef9522 100644
case GGML_OP_ARANGE:
ggml_cuda_op_arange(ctx, dst);
break;
@@ -3222,6 +3225,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
@@ -3197,6 +3200,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
case GGML_OP_UPSCALE:
return op->src[0]->type == GGML_TYPE_F32 && op->op_params[0] == GGML_SCALE_MODE_NEAREST;
case GGML_OP_PAD:
@ -233,7 +233,7 @@ index 8fd386b0..e2ededc3 100644
void ggml_cuda_op_pad(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
+void ggml_cuda_op_unpad(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml/src/ggml-metal/ggml-metal.m b/ggml/src/ggml-metal/ggml-metal.m
index 12886cd3..b2e95a66 100644
index 425524d0..112abef6 100644
--- a/ggml/src/ggml-metal/ggml-metal.m
+++ b/ggml/src/ggml-metal/ggml-metal.m
@@ -341,6 +341,7 @@ static void ggml_backend_metal_device_rel(struct ggml_backend_metal_device_conte
@ -244,7 +244,7 @@ index 12886cd3..b2e95a66 100644
GGML_METAL_KERNEL_TYPE_ARANGE_F32,
GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32,
GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC,
@@ -1020,6 +1021,7 @@ @implementation GGMLMetalClass
@@ -1277,6 +1278,7 @@ @implementation GGMLMetalClass
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32, upscale_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32, pad_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_REFLECT_1D_F32, pad_reflect_1d_f32, true);
@ -252,7 +252,7 @@ index 12886cd3..b2e95a66 100644
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32, timestep_embedding_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARANGE_F32, arange_f32, true);
GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, argsort_f32_i32_asc, true);
@@ -1384,6 +1386,7 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
@@ -1647,6 +1649,7 @@ static bool ggml_metal_supports_op(const struct ggml_backend_metal_device_contex
case GGML_OP_POOL_2D:
case GGML_OP_PAD:
case GGML_OP_PAD_REFLECT_1D:
@ -260,7 +260,7 @@ index 12886cd3..b2e95a66 100644
case GGML_OP_TIMESTEP_EMBEDDING:
case GGML_OP_ARGSORT:
case GGML_OP_LEAKY_RELU:
@@ -3731,6 +3734,36 @@ static void ggml_metal_encode_node(
@@ -4047,6 +4050,36 @@ static bool ggml_metal_encode_node(
const int nth = MIN(1024, ne0);
@ -298,7 +298,7 @@ index 12886cd3..b2e95a66 100644
} break;
case GGML_OP_ARANGE:
diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
index 8d6e99e6..71f0f97f 100644
index 9f4147e9..6ceb3cef 100644
--- a/ggml/src/ggml-metal/ggml-metal.metal
+++ b/ggml/src/ggml-metal/ggml-metal.metal
@@ -2975,6 +2975,51 @@ kernel void kernel_pad_reflect_1d_f32(
@ -354,10 +354,10 @@ index 8d6e99e6..71f0f97f 100644
device char * dst,
constant ggml_metal_kargs_arange & args,
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 950772c7..2276b631 100644
index 7654ae17..3c57aff8 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -963,6 +963,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
@@ -923,6 +923,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"UPSCALE",
"PAD",
"PAD_REFLECT_1D",
@ -365,16 +365,16 @@ index 950772c7..2276b631 100644
"ARANGE",
"TIMESTEP_EMBEDDING",
"ARGSORT",
@@ -993,7 +994,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
@@ -953,7 +954,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"OPT_STEP_ADAMW",
};
-static_assert(GGML_OP_COUNT == 81, "GGML_OP_COUNT != 81");
+static_assert(GGML_OP_COUNT == 82, "GGML_OP_COUNT != 82");
-static_assert(GGML_OP_COUNT == 82, "GGML_OP_COUNT != 82");
+static_assert(GGML_OP_COUNT == 83, "GGML_OP_COUNT != 83");
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
@@ -1057,6 +1058,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
@@ -1018,6 +1019,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"upscale(x)",
"pad(x)",
"pad_reflect_1d(x)",
@ -382,16 +382,16 @@ index 950772c7..2276b631 100644
"arange(start, stop, step)",
"timestep_embedding(timesteps, dim, max_period)",
"argsort(x)",
@@ -1087,7 +1089,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
@@ -1048,7 +1050,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"adamw(x)",
};
-static_assert(GGML_OP_COUNT == 81, "GGML_OP_COUNT != 81");
+static_assert(GGML_OP_COUNT == 82, "GGML_OP_COUNT != 82");
-static_assert(GGML_OP_COUNT == 82, "GGML_OP_COUNT != 82");
+static_assert(GGML_OP_COUNT == 83, "GGML_OP_COUNT != 83");
static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
@@ -4262,6 +4264,25 @@ struct ggml_tensor * ggml_pad_reflect_1d(
@@ -4270,6 +4272,25 @@ struct ggml_tensor * ggml_pad_reflect_1d(
return result;
}

2
llama/patches/0008-fix-deepseek-deseret-regex.patch
View File

@ -12,7 +12,7 @@ regex
2 files changed, 22 insertions(+), 1 deletion(-)
diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index a35b498c..032019c9 100644
index a9ee9f03..1306864e 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -296,7 +296,7 @@ struct llm_tokenizer_bpe : llm_tokenizer {

4
llama/patches/0009-maintain-ordering-for-rules-for-grammar.patch
View File

@ -8,10 +8,10 @@ Subject: [PATCH] maintain ordering for rules for grammar
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/common/json-schema-to-grammar.cpp b/common/json-schema-to-grammar.cpp
index 90679822..56043678 100644
index 5b3059c2..656b3eca 100644
--- a/common/json-schema-to-grammar.cpp
+++ b/common/json-schema-to-grammar.cpp
@@ -346,7 +346,7 @@ private:
@@ -349,7 +349,7 @@ private:
friend std::string build_grammar(const std::function<void(const common_grammar_builder &)> & cb, const common_grammar_options & options);
std::function<json(const std::string &)> _fetch_json;
bool _dotall;

20
llama/patches/0010-ensure-KV-cache-is-fully-defragmented.patch
View File

@ -22,10 +22,10 @@ multiple batches of processing until everything is complete.
4 files changed, 51 insertions(+), 106 deletions(-)
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 0343ba8a..4b3e6a83 100644
index cd06ad91..77177c5e 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -594,13 +594,12 @@ llm_graph_result_ptr llama_context::build_kv_self_shift(
@@ -583,13 +583,12 @@ llm_graph_result_ptr llama_context::build_kv_self_shift(
llm_graph_result_ptr llama_context::build_kv_self_defrag(
ggml_context * ctx0,
@ -41,7 +41,7 @@ index 0343ba8a..4b3e6a83 100644
#if 0
// CPU defrag
//
@@ -672,32 +671,20 @@ llm_graph_result_ptr llama_context::build_kv_self_defrag(
@@ -661,32 +660,20 @@ llm_graph_result_ptr llama_context::build_kv_self_defrag(
ggml_backend_tensor_set(v_l[il], buf_v.data(), 0, buf_v.size());
}
#else
@ -79,7 +79,7 @@ index 0343ba8a..4b3e6a83 100644
ggml_tensor * view_v_src;
ggml_tensor * view_v_dst;
@@ -705,34 +692,30 @@ llm_graph_result_ptr llama_context::build_kv_self_defrag(
@@ -694,34 +681,30 @@ llm_graph_result_ptr llama_context::build_kv_self_defrag(
if (cparams.flash_attn) {
// NOTE: the V cache is not transposed when using flash attention
view_v_src = ggml_view_2d(ctx0, kv_self->v_l[il],
@ -122,7 +122,7 @@ index 0343ba8a..4b3e6a83 100644
#endif
return res;
@@ -741,8 +724,6 @@ llm_graph_result_ptr llama_context::build_kv_self_defrag(
@@ -730,8 +713,6 @@ llm_graph_result_ptr llama_context::build_kv_self_defrag(
void llama_context::kv_self_update() {
auto & kv = kv_self;
@ -131,7 +131,7 @@ index 0343ba8a..4b3e6a83 100644
if (kv->has_shift) {
if (!kv->get_can_shift()) {
GGML_ABORT("The current context does not support K-shift");
@@ -763,8 +744,6 @@ void llama_context::kv_self_update() {
@@ -752,8 +733,6 @@ void llama_context::kv_self_update() {
res->set_inputs(nullptr);
graph_compute(gf, false);
@ -140,7 +140,7 @@ index 0343ba8a..4b3e6a83 100644
}
{
@@ -779,49 +758,28 @@ void llama_context::kv_self_update() {
@@ -768,49 +747,28 @@ void llama_context::kv_self_update() {
// defragment the KV cache if needed
if (kv->do_defrag) {
LLAMA_LOG_DEBUG("%s: defragmenting KV cache\n", __func__);
@ -202,7 +202,7 @@ index 0343ba8a..4b3e6a83 100644
}
enum llama_pooling_type llama_context::pooling_type() const {
@@ -1305,9 +1263,12 @@ int llama_context::decode(llama_batch & inp_batch) {
@@ -1294,9 +1252,12 @@ int llama_context::decode(llama_batch & inp_batch) {
// find KV slot
{
if (!kv_self->find_slot(ubatch)) {
@ -219,7 +219,7 @@ index 0343ba8a..4b3e6a83 100644
if (!kv_self->recurrent) {
diff --git a/src/llama-context.h b/src/llama-context.h
index baa03276..a59ff8fd 100644
index a50c4afa..30f84bfd 100644
--- a/src/llama-context.h
+++ b/src/llama-context.h
@@ -5,6 +5,7 @@
@ -230,7 +230,7 @@ index baa03276..a59ff8fd 100644
#include "ggml-cpp.h"
@@ -180,7 +181,8 @@ private:
@@ -179,7 +180,8 @@ private:
llm_graph_result_ptr build_kv_self_defrag(
ggml_context * ctx0,

12
llama/patches/0012-add-phony-target-ggml-cpu-for-all-cpu-variants.patch
View File

@ -8,10 +8,10 @@ Subject: [PATCH] add phony target ggml-cpu for all cpu variants
1 file changed, 2 insertions(+)
diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt
index f00700da..91d6a7d5 100644
index 43d9fc4f..4c0d3824 100644
--- a/ggml/src/CMakeLists.txt
+++ b/ggml/src/CMakeLists.txt
@@ -278,6 +278,7 @@ function(ggml_add_cpu_backend_variant tag_name)
@@ -279,6 +279,7 @@ function(ggml_add_cpu_backend_variant tag_name)
endforeach()
ggml_add_cpu_backend_variant_impl(${tag_name})
@ -19,11 +19,11 @@ index f00700da..91d6a7d5 100644
endfunction()
ggml_add_backend(CPU)
@@ -286,6 +287,7 @@ if (GGML_CPU_ALL_VARIANTS)
@@ -287,6 +288,7 @@ if (GGML_CPU_ALL_VARIANTS)
if (NOT GGML_BACKEND_DL)
message(FATAL_ERROR "GGML_CPU_ALL_VARIANTS requires GGML_BACKEND_DL")
endif()
+ add_custom_target(ggml-cpu)
ggml_add_cpu_backend_variant(sandybridge AVX)
ggml_add_cpu_backend_variant(haswell AVX F16C AVX2 BMI2 FMA)
ggml_add_cpu_backend_variant(skylakex AVX F16C AVX2 BMI2 FMA AVX512)
ggml_add_cpu_backend_variant(x64)
ggml_add_cpu_backend_variant(sse42 SSE42)
ggml_add_cpu_backend_variant(sandybridge SSE42 AVX)

14
llama/patches/0013-remove-amx.patch
View File

@ -1,6 +1,6 @@
From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
From: jmorganca <jmorganca@gmail.com>
Date: Tue, 8 Apr 2025 20:33:01 -0700
Date: Thu, 1 May 2025 15:05:08 -0700
Subject: [PATCH] remove amx
disable amx as it reduces performance on some systems
@ -9,16 +9,16 @@ disable amx as it reduces performance on some systems
1 file changed, 4 deletions(-)
diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt
index 91d6a7d5..d6b393a2 100644
index 4c0d3824..79c26312 100644
--- a/ggml/src/CMakeLists.txt
+++ b/ggml/src/CMakeLists.txt
@@ -293,10 +293,6 @@ if (GGML_CPU_ALL_VARIANTS)
ggml_add_cpu_backend_variant(skylakex AVX F16C AVX2 BMI2 FMA AVX512)
ggml_add_cpu_backend_variant(icelake AVX F16C AVX2 BMI2 FMA AVX512 AVX512_VBMI AVX512_VNNI)
ggml_add_cpu_backend_variant(alderlake AVX F16C AVX2 BMI2 FMA AVX_VNNI)
@@ -296,10 +296,6 @@ if (GGML_CPU_ALL_VARIANTS)
ggml_add_cpu_backend_variant(skylakex SSE42 AVX F16C AVX2 BMI2 FMA AVX512)
ggml_add_cpu_backend_variant(icelake SSE42 AVX F16C AVX2 BMI2 FMA AVX512 AVX512_VBMI AVX512_VNNI)
ggml_add_cpu_backend_variant(alderlake SSE42 AVX F16C AVX2 BMI2 FMA AVX_VNNI)
- if (NOT MSVC)
- # MSVC doesn't support AMX
- ggml_add_cpu_backend_variant(sapphirerapids AVX F16C AVX2 BMI2 FMA AVX512 AVX512_VBMI AVX512_VNNI AVX512_BF16 AMX_TILE AMX_INT8)
- ggml_add_cpu_backend_variant(sapphirerapids SSE42 AVX F16C AVX2 BMI2 FMA AVX512 AVX512_VBMI AVX512_VNNI AVX512_BF16 AMX_TILE AMX_INT8)
- endif()
elseif (GGML_CPU)
ggml_add_cpu_backend_variant_impl("")

2
llama/patches/0014-fix-string-arr-kv-loading.patch
View File

@ -53,7 +53,7 @@ index 381a9c7d..e45b453d 100644
}
diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index 032019c9..ba37df35 100644
index 1306864e..d6515ff6 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -1459,7 +1459,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {

4
llama/patches/0015-ollama-debug-tensor.patch
View File

@ -8,7 +8,7 @@ Subject: [PATCH] ollama debug tensor
1 file changed, 6 insertions(+)
diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index 432942bf..6d4abe4c 100644
index 34624cca..59bd3c62 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -15,6 +15,8 @@
@ -20,7 +20,7 @@ index 432942bf..6d4abe4c 100644
#if defined(_MSC_VER) || defined(__MINGW32__)
#include <malloc.h> // using malloc.h with MSC/MINGW
#elif !defined(__FreeBSD__) && !defined(__NetBSD__) && !defined(__OpenBSD__)
@@ -2854,6 +2856,10 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
@@ -2859,6 +2861,10 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
ggml_compute_forward(&params, node);

96
llama/patches/0016-add-model-quantizations.patch
View File

@ -1,96 +0,0 @@
From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
From: jmorganca <jmorganca@gmail.com>
Date: Tue, 8 Apr 2025 20:39:32 -0700
Subject: [PATCH] add model quantizations
a temporary patch to add model quantization for
models not supported in llama.cpp
---
src/llama-arch.cpp | 17 +++++++++++++++++
src/llama-arch.h | 1 +
src/llama-model.cpp | 2 ++
src/llama-quant.cpp | 4 ++++
4 files changed, 24 insertions(+)
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index 0568565f..dd01df60 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -73,6 +73,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_WAVTOKENIZER_DEC, "wavtokenizer-dec" },
{ LLM_ARCH_PLM, "plm" },
{ LLM_ARCH_BAILINGMOE, "bailingmoe" },
+ { LLM_ARCH_MISTRAL3, "mistral3" },
{ LLM_ARCH_UNKNOWN, "(unknown)" },
};
@@ -1586,6 +1587,22 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" },
},
},
+ {
+ LLM_ARCH_MISTRAL3,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
+ { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
+ { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
+ { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
+ { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
+ { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ }
+ },
{
LLM_ARCH_UNKNOWN,
{
diff --git a/src/llama-arch.h b/src/llama-arch.h
index 6a989034..b6227eeb 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -75,6 +75,7 @@ enum llm_arch {
LLM_ARCH_CHAMELEON,
LLM_ARCH_SOLAR,
LLM_ARCH_WAVTOKENIZER_DEC,
+ LLM_ARCH_MISTRAL3,
LLM_ARCH_PLM,
LLM_ARCH_BAILINGMOE,
LLM_ARCH_UNKNOWN,
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index d051696c..c8374159 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -1425,6 +1425,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
default: type = LLM_TYPE_UNKNOWN;
}
} break;
+ case LLM_ARCH_MISTRAL3: break;
default: throw std::runtime_error("unsupported model architecture");
}
@@ -13704,6 +13705,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_CHAMELEON:
case LLM_ARCH_SOLAR:
case LLM_ARCH_BAILINGMOE:
+ case LLM_ARCH_MISTRAL3:
return LLAMA_ROPE_TYPE_NORM;
// the pairs of head values are offset by n_rot/2
diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp
index 223e1f3f..8ae6dde8 100644
--- a/src/llama-quant.cpp
+++ b/src/llama-quant.cpp
@@ -744,6 +744,10 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
// This used to be a regex, but <regex> has an extreme cost to compile times.
bool quantize = name.rfind("weight") == name.size() - 6; // ends with 'weight'?
+ // don't quantize vision stuff
+ quantize &= name.find("v.") == std::string::npos;
+ quantize &= name.find("mm.") == std::string::npos;
+
// quantize only 2D and 3D tensors (experts)
quantize &= (ggml_n_dims(tensor) >= 2);

6
llama/patches/0021-add-ollama-vocab-for-grammar-support.patch → llama/patches/0016-add-ollama-vocab-for-grammar-support.patch
View File

@ -184,10 +184,10 @@ index f8c291de..2a3a62db 100644
const char * grammar_root,
bool lazy,
diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
index d1497985..b1a9dca3 100644
index c0a5f934..75731053 100644
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampling.cpp
@@ -1465,7 +1465,7 @@ static void llama_sampler_grammar_reset(struct llama_sampler * smpl) {
@@ -1466,7 +1466,7 @@ static void llama_sampler_grammar_reset(struct llama_sampler * smpl) {
trigger_patterns_c.push_back(trigger_pattern.pattern.c_str());
}
@ -196,7 +196,7 @@ index d1497985..b1a9dca3 100644
ctx->grammar->lazy, trigger_patterns_c.data(), trigger_patterns_c.size(),
ctx->grammar->trigger_tokens.data(), ctx->grammar->trigger_tokens.size());
@@ -1547,7 +1547,7 @@ static struct llama_sampler * llama_sampler_init_grammar_impl(
@@ -1548,7 +1548,7 @@ static struct llama_sampler * llama_sampler_init_grammar_impl(
/* .vocab = */ vocab,
/* .grammar_str = */ grammar_str,
/* .grammar_root = */ grammar_root,

38
llama/patches/0017-ggml-Don-t-assert-fail-when-tensor-data-changes-1322.patch Normal file
View File

@ -0,0 +1,38 @@
From 0000000000000000000000000000000000000000 Mon Sep 17 00:00:00 2001
From: Jesse Gross <jesse@kernel.org>
Date: Thu, 1 May 2025 13:46:10 -0700
Subject: [PATCH] ggml: Don't assert fail when tensor data changes (#13222)
The following scenario will cause an assertion failure in the graph
allocator:
- Build and allocate a graph containing a tensor with a non-NULL data
pointer
- Build and allocate a new graph where that data is NULL
Result:
ggml-alloc.c:819: GGML_ASSERT(talloc->buffer_id >= 0) failed
This happens during revalidation because we think that memory should
have been previously allocated based on the current graph but in
reality the previous graph was different. In this situation, we
should do a full reallocation pass.
---
ggml/src/ggml-alloc.c | 5 ++++-
1 file changed, 4 insertions(+), 1 deletion(-)
diff --git a/ggml/src/ggml-alloc.c b/ggml/src/ggml-alloc.c
index a3d3f690..5fd379f6 100644
--- a/ggml/src/ggml-alloc.c
+++ b/ggml/src/ggml-alloc.c
@@ -816,7 +816,10 @@ static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor *
static bool ggml_gallocr_node_needs_realloc(ggml_gallocr_t galloc, struct ggml_tensor * node, struct tensor_alloc * talloc) {
size_t node_size = 0;
if (!node->data && !node->view_src) {
- GGML_ASSERT(talloc->buffer_id >= 0); // prevent segfault when misusing the API
+ // If we previously had data but don't now then reallocate
+ if (talloc->buffer_id < 0) {
+ return false;
+ }
node_size = ggml_backend_buft_get_alloc_size(galloc->bufts[talloc->buffer_id], node);
}
return talloc->size_max >= node_size;

3
llama/sampling_ext.cpp vendored
View File

@ -19,9 +19,6 @@ struct common_sampler *common_sampler_cinit(const struct llama_model *model, str
sparams.penalty_repeat = params->penalty_repeat;
sparams.penalty_freq = params->penalty_freq;
sparams.penalty_present = params->penalty_present;
sparams.mirostat = params->mirostat;
sparams.mirostat_tau = params->mirostat_tau;
sparams.mirostat_eta = params->mirostat_eta;
sparams.seed = params->seed;
sparams.grammar = params->grammar;
sparams.xtc_probability = 0.0;

3
llama/sampling_ext.h vendored
View File

@ -20,9 +20,6 @@ extern "C"
float penalty_repeat;
float penalty_freq;
float penalty_present;
int32_t mirostat;
float mirostat_tau;
float mirostat_eta;
uint32_t seed;
char *grammar;
};

3
llm/llm_windows.go
View File

@ -7,6 +7,7 @@ import (
const (
CREATE_DEFAULT_ERROR_MODE = 0x04000000
ABOVE_NORMAL_PRIORITY_CLASS = 0x00008000
CREATE_NO_WINDOW = 0x08000000
)
var LlamaServerSysProcAttr = &syscall.SysProcAttr{
@ -18,5 +19,5 @@ var LlamaServerSysProcAttr = &syscall.SysProcAttr{
//
// Setting Above Normal priority class ensures when running as a "background service"
// with "programs" given best priority, we aren't starved of cpu cycles
CreationFlags: CREATE_DEFAULT_ERROR_MODE | ABOVE_NORMAL_PRIORITY_CLASS,
CreationFlags: CREATE_DEFAULT_ERROR_MODE | ABOVE_NORMAL_PRIORITY_CLASS | CREATE_NO_WINDOW,
}

2
llm/memory_test.go
View File

@ -25,7 +25,7 @@ func TestEstimateGPULayers(t *testing.T) {
defer f.Close()
inputLayerCount := 5
tensors := []ggml.Tensor{
tensors := []*ggml.Tensor{
{Name: "blk.0.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
{Name: "blk.1.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
{Name: "blk.2.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},

38
llm/server.go
View File

@ -44,6 +44,7 @@ type LlamaServer interface {
EstimatedVRAM() uint64 // Total VRAM across all GPUs
EstimatedTotal() uint64
EstimatedVRAMByGPU(gpuID string) uint64
Pid() int
}
// llmServer is an instance of the llama.cpp server
@ -216,10 +217,6 @@ func NewLlamaServer(gpus discover.GpuInfoList, modelPath string, f *ggml.GGML, a
params = append(params, "--no-mmap")
}
if opts.UseMLock {
params = append(params, "--mlock")
}
// TODO - NUMA support currently doesn't work properly
params = append(params, "--parallel", strconv.Itoa(numParallel))
@ -289,7 +286,7 @@ func NewLlamaServer(gpus discover.GpuInfoList, modelPath string, f *ggml.GGML, a
params = append(params, "--mmproj", projectors[0])
}
// iterate through compatible GPU libraries such as 'cuda_v12', 'cuda_v11', 'rocm', etc.
// iterate through compatible GPU libraries such as 'cuda_v12', 'rocm', etc.
// adding each library's respective path to the LD_LIBRARY_PATH, until finally running
// without any LD_LIBRARY_PATH flags
for {
@ -324,21 +321,23 @@ func NewLlamaServer(gpus discover.GpuInfoList, modelPath string, f *ggml.GGML, a
pathEnv = "LD_LIBRARY_PATH"
}
var libraryPaths []string
// Note: we always put our dependency paths first
// since these are the exact version we compiled/linked against
libraryPaths := []string{discover.LibOllamaPath}
if libraryPath, ok := os.LookupEnv(pathEnv); ok {
libraryPaths = append(libraryPaths, filepath.SplitList(libraryPath)...)
}
ggmlPaths := []string{discover.LibOllamaPath}
if len(compatible) > 0 {
c := compatible[0]
if libpath, ok := libs[c]; ok {
slog.Debug("adding gpu library", "path", libpath)
libraryPaths = append(libraryPaths, libpath)
libraryPaths = append([]string{libpath}, libraryPaths...)
ggmlPaths = append(ggmlPaths, libpath)
}
}
// Note: we always put the dependency path first
// since this was the exact version we compiled/linked against
if gpus[0].DependencyPath != nil {
slog.Debug("adding gpu dependency paths", "paths", gpus[0].DependencyPath)
// assume gpus from the same library have the same dependency path
@ -369,6 +368,8 @@ func NewLlamaServer(gpus discover.GpuInfoList, modelPath string, f *ggml.GGML, a
s.cmd.Stderr = s.status
s.cmd.SysProcAttr = LlamaServerSysProcAttr
s.cmd.Env = append(s.cmd.Env, "OLLAMA_LIBRARY_PATH="+strings.Join(ggmlPaths, string(filepath.ListSeparator)))
envWorkarounds := [][2]string{}
for _, gpu := range gpus {
envWorkarounds = append(envWorkarounds, gpu.EnvWorkarounds...)
@ -406,7 +407,8 @@ func NewLlamaServer(gpus discover.GpuInfoList, modelPath string, f *ggml.GGML, a
if envconfig.Debug() {
filteredEnv := []string{}
for _, ev := range s.cmd.Env {
if strings.HasPrefix(ev, "CUDA_") ||
if strings.HasPrefix(ev, "OLLAMA_") ||
strings.HasPrefix(ev, "CUDA_") ||
strings.HasPrefix(ev, "ROCR_") ||
strings.HasPrefix(ev, "ROCM_") ||
strings.HasPrefix(ev, "HIP_") ||
@ -515,6 +517,9 @@ func (s *llmServer) getServerStatus(ctx context.Context) (ServerStatus, error) {
if errors.Is(err, context.DeadlineExceeded) {
return ServerStatusNotResponding, errors.New("server not responding")
}
if strings.Contains(err.Error(), "connection refused") {
return ServerStatusNotResponding, errors.New("connection refused")
}
return ServerStatusError, fmt.Errorf("health resp: %w", err)
}
defer resp.Body.Close()
@ -635,6 +640,13 @@ func (s *llmServer) WaitUntilRunning(ctx context.Context) error {
}
}
func (s *llmServer) Pid() int {
if s.cmd != nil && s.cmd.Process != nil {
return s.cmd.Process.Pid
}
return -1
}
var grammarJSON = `
root ::= object
value ::= object | array | string | number | ("true" | "false" | "null") ws
@ -998,17 +1010,17 @@ func (s *llmServer) Close() error {
s.llamaModelLock.Unlock()
if s.cmd != nil {
slog.Debug("stopping llama server")
slog.Debug("stopping llama server", "pid", s.Pid())
if err := s.cmd.Process.Kill(); err != nil {
return err
}
// if ProcessState is already populated, Wait already completed, no need to wait again
if s.cmd.ProcessState == nil {
slog.Debug("waiting for llama server to exit")
slog.Debug("waiting for llama server to exit", "pid", s.Pid())
<-s.done
}
slog.Debug("llama server stopped")
slog.Debug("llama server stopped", "pid", s.Pid())
}
return nil

2
llm/server_test.go
View File

@ -16,7 +16,7 @@ func TestLLMServerCompletionFormat(t *testing.T) {
// of a mess, and but it's good enough, until we can refactoring the
// Completion method to be more testable.
ctx, cancel := context.WithCancel(context.Background())
ctx, cancel := context.WithCancel(t.Context())
s := &llmServer{
sem: semaphore.NewWeighted(1), // required to prevent nil panic
}

14
ml/backend/ggml/ggml.go
View File

@ -312,6 +312,7 @@ func New(ctx context.Context, r *os.File, params ml.BackendParams) (ml.Backend,
g, ctx := errgroup.WithContext(ctx)
g.SetLimit(runtime.GOMAXPROCS(0))
for _, t := range meta.Tensors().Items() {
t := t
g.Go(func() error {
tts := make([]*C.struct_ggml_tensor, max(1, len(targets[t.Name])))
for i := range tts {
@ -341,6 +342,11 @@ func New(ctx context.Context, r *os.File, params ml.BackendParams) (ml.Backend,
var s uint64
for s < t.Size() {
// Stop if either the parent context has been canceled or if any of the other tensors returned an error
if err := ctx.Err(); err != nil {
return err
}
n, err := io.ReadFull(sr, bts[:min(len(bts), int(t.Size()-s))])
if err != nil {
slog.Warn("file read error", "file", r.Name(), "error", err)
@ -363,14 +369,6 @@ func New(ctx context.Context, r *os.File, params ml.BackendParams) (ml.Backend,
})
}
// start a goroutine to cancel the errgroup if the parent context is done
go func() {
<-ctx.Done()
g.Go(func() error {
return ctx.Err()
})
}()
if err := g.Wait(); err != nil {
return nil, err
}

5
ml/backend/ggml/ggml/include/ggml-cpu.h vendored
View File

@ -133,6 +133,11 @@ extern "C" {
GGML_BACKEND_API ggml_backend_reg_t ggml_backend_cpu_reg(void);
GGML_BACKEND_API void ggml_cpu_fp32_to_fp16(const float *, ggml_fp16_t *, int64_t);
GGML_BACKEND_API void ggml_cpu_fp16_to_fp32(const ggml_fp16_t *, float *, int64_t);
GGML_BACKEND_API void ggml_cpu_fp32_to_bf16(const float *, ggml_bf16_t *, int64_t);
GGML_BACKEND_API void ggml_cpu_bf16_to_fp32(const ggml_bf16_t *, float *, int64_t);
#ifdef __cplusplus
}
#endif

2
ml/backend/ggml/ggml/include/ggml-rpc.h vendored
View File

@ -7,7 +7,7 @@
extern "C" {
#endif
#define RPC_PROTO_MAJOR_VERSION 1
#define RPC_PROTO_MAJOR_VERSION 2
#define RPC_PROTO_MINOR_VERSION 0
#define RPC_PROTO_PATCH_VERSION 0
#define GGML_RPC_MAX_SERVERS 16

26
ml/backend/ggml/ggml/include/ggml.h vendored
View File

@ -393,8 +393,8 @@ extern "C" {
// precision
enum ggml_prec {
GGML_PREC_DEFAULT,
GGML_PREC_F32,
GGML_PREC_DEFAULT = 0, // stored as ggml_tensor.op_params, 0 by default
GGML_PREC_F32 = 10,
};
// model file types
@ -481,6 +481,7 @@ extern "C" {
GGML_OP_CONV_TRANSPOSE_1D,
GGML_OP_IM2COL,
GGML_OP_IM2COL_BACK,
GGML_OP_CONV_2D_DW,
GGML_OP_CONV_TRANSPOSE_2D,
GGML_OP_POOL_1D,
GGML_OP_POOL_2D,
@ -678,6 +679,9 @@ extern "C" {
GGML_API bool ggml_is_contiguous_1(const struct ggml_tensor * tensor); // contiguous for dims >= 1
GGML_API bool ggml_is_contiguous_2(const struct ggml_tensor * tensor); // contiguous for dims >= 2
// true for tensor that is stored in memory as CxWxHxN and has been permuted to WxHxCxN
GGML_API bool ggml_is_contiguous_channels(const struct ggml_tensor * tensor);
GGML_API bool ggml_are_same_shape (const struct ggml_tensor * t0, const struct ggml_tensor * t1);
GGML_API bool ggml_are_same_stride(const struct ggml_tensor * t0, const struct ggml_tensor * t1);
@ -1661,7 +1665,7 @@ extern "C" {
struct ggml_tensor * a,
struct ggml_tensor * b);
// depthwise
// depthwise (via im2col and mul_mat)
GGML_API struct ggml_tensor * ggml_conv_2d_dw(
struct ggml_context * ctx,
struct ggml_tensor * a, // convolution kernel
@ -1673,6 +1677,22 @@ extern "C" {
int d0, // dilation dimension 0
int d1); // dilation dimension 1
// Depthwise 2D convolution
// may be faster than ggml_conv_2d_dw, but not available in all backends
// a: KW KH 1 C convolution kernel
// b: W H C N input data
// res: W_out H_out C N
GGML_API struct ggml_tensor * ggml_conv_2d_dw_direct(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
int stride0,
int stride1,
int pad0,
int pad1,
int dilation0,
int dilation1);
GGML_API struct ggml_tensor * ggml_conv_transpose_2d_p0(
struct ggml_context * ctx,
struct ggml_tensor * a,

13
ml/backend/ggml/ggml/src/CMakeLists.txt vendored
View File

@ -267,6 +267,7 @@ function(ggml_add_cpu_backend_variant tag_name)
set(GGML_CPU_TAG_NAME ${tag_name})
# other: OPENMP LLAMAFILE CPU_HBM
foreach (feat NATIVE
SSE42
AVX AVX2 BMI2 AVX_VNNI FMA F16C
AVX512 AVX512_VBMI AVX512_VNNI AVX512_BF16
AMX_TILE AMX_INT8 AMX_BF16)
@ -288,11 +289,13 @@ if (GGML_CPU_ALL_VARIANTS)
message(FATAL_ERROR "GGML_CPU_ALL_VARIANTS requires GGML_BACKEND_DL")
endif()
add_custom_target(ggml-cpu)
ggml_add_cpu_backend_variant(sandybridge AVX)
ggml_add_cpu_backend_variant(haswell AVX F16C AVX2 BMI2 FMA)
ggml_add_cpu_backend_variant(skylakex AVX F16C AVX2 BMI2 FMA AVX512)
ggml_add_cpu_backend_variant(icelake AVX F16C AVX2 BMI2 FMA AVX512 AVX512_VBMI AVX512_VNNI)
ggml_add_cpu_backend_variant(alderlake AVX F16C AVX2 BMI2 FMA AVX_VNNI)
ggml_add_cpu_backend_variant(x64)
ggml_add_cpu_backend_variant(sse42 SSE42)
ggml_add_cpu_backend_variant(sandybridge SSE42 AVX)
ggml_add_cpu_backend_variant(haswell SSE42 AVX F16C AVX2 BMI2 FMA)
ggml_add_cpu_backend_variant(skylakex SSE42 AVX F16C AVX2 BMI2 FMA AVX512)
ggml_add_cpu_backend_variant(icelake SSE42 AVX F16C AVX2 BMI2 FMA AVX512 AVX512_VBMI AVX512_VNNI)
ggml_add_cpu_backend_variant(alderlake SSE42 AVX F16C AVX2 BMI2 FMA AVX_VNNI)
elseif (GGML_CPU)
ggml_add_cpu_backend_variant_impl("")
endif()

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