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- ollama -
+#
Ollama
-# Ollama +## 简介 -[![Discord](https://dcbadge.vercel.app/api/server/ollama?style=flat&compact=true)](https://discord.gg/ollama) +1 Ollama是以llama.cpp为后端的前端大模型推理框架，可快速部署主流模型。 -Get up and running with large language models. +## 安装 -### macOS +### 1、使用dockerfile方式安装 -[Download](https://ollama.com/download/Ollama-darwin.zip) +直接下载本仓库中的`v0.3.5`分支，然后执行`docker build xxxxx`相关命令（具体需自行查阅相关资料）。 -### Windows preview +如遇到卡数检测错误，请参考 https://developer.hpccube.com/codes/OpenDAS/ollama/-/issues/1 ，也可以提前进行修复。 -[Download](https://ollama.com/download/OllamaSetup.exe) +### 2、使用源码编译方式安装 -### Linux +#### 环境准备 -``` -curl -fsSL https://ollama.com/install.sh | sh -``` +##### Docker -[Manual install instructions](https://github.com/ollama/ollama/blob/main/docs/linux.md) + docker pull docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.1.0-py3.10-dtk24.04.3-ubuntu20.04 + + docker run -i -t -d --device=/dev/kfd --privileged --network=host --device=/dev/dri --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v 项目地址(绝对路径):/home -v /opt/hyhal:/opt/hyhal:ro -v --group-add video --shm-size 16G --name {容器名} {镜像ID} -### Docker -The official [Ollama Docker image](https://hub.docker.com/r/ollama/ollama) `ollama/ollama` is available on Docker Hub. +1、下载源码 -### Libraries + git clone -b v0.3.5 http://developer.sourcefind.cn/codes/OpenDAS/ollama.git --depth=1 + cd ollama -- [ollama-python](https://github.com/ollama/ollama-python) -- [ollama-js](https://github.com/ollama/ollama-js) +2、安装依赖包 -## Quickstart + cd llm/llama.cpp + pip install -r requirements.txt -To run and chat with [Llama 3.1](https://ollama.com/library/llama3.1): +#### 编译 -``` -ollama run llama3.1 -``` +##### 环境设置 + export AMDGPU_TARGETS=当前设备型号（如：gfx906，gfx928等） + export HSA_OVERRIDE_GFX_VERSION=设备型号（如: gfx906对应9.0.6；gfx928对应9.2.8） + export HIP_VISIBLE_DEVICES=所有设备号（0,1,2,3,4,5,6,...）/选择设备号 -## Model library +安装go -Ollama supports a list of models available on [ollama.com/library](https://ollama.com/library 'ollama model library') +https://golang.google.cn/dl/ -Here are some example models that can be downloaded: + # 请替换[]中下载的go文件 + cd ../.. + tar -C /usr/local -xzf [go-xxxx.tar.gz](ollama目录下) + export PATH=$PATH:/usr/local/go/bin + + # 修改go下载源，提升速度（按需设置） + go env -w GO111MODULE=on + go env -w GOPROXY=https://goproxy.cn,direct -| Model | Parameters | Size | Download | -| ------------------ | ---------- | ----- | ------------------------------ | -| Llama 3.1 | 8B | 4.7GB | `ollama run llama3.1` | -| Llama 3.1 | 70B | 40GB | `ollama run llama3.1:70b` | -| Llama 3.1 | 405B | 231GB | `ollama run llama3.1:405b` | -| Phi 3 Mini | 3.8B | 2.3GB | `ollama run phi3` | -| Phi 3 Medium | 14B | 7.9GB | `ollama run phi3:medium` | -| Gemma 2 | 2B | 1.6GB | `ollama run gemma2:2b` | -| Gemma 2 | 9B | 5.5GB | `ollama run gemma2` | -| Gemma 2 | 27B | 16GB | `ollama run gemma2:27b` | -| Mistral | 7B | 4.1GB | `ollama run mistral` | -| Moondream 2 | 1.4B | 829MB | `ollama run moondream` | -| Neural Chat | 7B | 4.1GB | `ollama run neural-chat` | -| Starling | 7B | 4.1GB | `ollama run starling-lm` | -| 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` | -| Solar | 10.7B | 6.1GB | `ollama run solar` | +##### 运行编译 -> [!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. + cd llm/generate && bash gen_linux.sh + cd ../.. && go build -## Customize a model +## 验证 -### Import from GGUF + ./ollama serve （选择可用设备，可通过上条命令输出结果查看） + ./ollama run llama3.1 -Ollama supports importing GGUF models in the Modelfile: +更多使用方式请参考[原项目](https://github.com/ollama/ollama)。 -1. Create a file named `Modelfile`, with a `FROM` instruction with the local filepath to the model you want to import. +注意：每次运行前请检查环境变量`HSA_OVERRIDE_GFX_VERSION`是否正确设置。 - ``` - FROM ./vicuna-33b.Q4_0.gguf - ``` - -2. Create the model in Ollama - - ``` - ollama create example -f Modelfile - ``` - -3. Run the model - - ``` - ollama run example - ``` - -### Import from PyTorch or Safetensors - -See the [guide](docs/import.md) on importing models for more information. - -### Customize a prompt - -Models from the Ollama library can be customized with a prompt. For example, to customize the `llama3.1` model: - -``` -ollama pull llama3.1 -``` - -Create a `Modelfile`: - -``` -FROM llama3.1 - -# set the temperature to 1 [higher is more creative, lower is more coherent] -PARAMETER temperature 1 - -# set the system message -SYSTEM """ -You are Mario from Super Mario Bros. Answer as Mario, the assistant, only. -""" -``` - -Next, create and run the model: - -``` -ollama create mario -f ./Modelfile -ollama run mario ->>> hi -Hello! It's your friend Mario. -``` - -For more examples, see the [examples](examples) directory. For more information on working with a Modelfile, see the [Modelfile](docs/modelfile.md) documentation. - -## CLI Reference - -### Create a model - -`ollama create` is used to create a model from a Modelfile. - -``` -ollama create mymodel -f ./Modelfile -``` - -### Pull a model - -``` -ollama pull llama3.1 -``` - -> This command can also be used to update a local model. Only the diff will be pulled. - -### Remove a model - -``` -ollama rm llama3.1 -``` - -### Copy a model - -``` -ollama cp llama3.1 my-model -``` - -### Multiline input - -For multiline input, you can wrap text with `"""`: - -``` ->>> """Hello, -... world! -... """ -I'm a basic program that prints the famous "Hello, world!" message to the console. -``` - -### Multimodal models - -``` -ollama run llava "What's in this image? /Users/jmorgan/Desktop/smile.png" -The image features a yellow smiley face, which is likely the central focus of the picture. -``` - -### Pass the prompt as an argument - -``` -$ ollama run llama3.1 "Summarize this file: $(cat README.md)" - Ollama is a lightweight, extensible framework for building and running language models on the local machine. It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications. -``` - -### Show model information - -``` -ollama show llama3.1 -``` - -### List models on your computer - -``` -ollama list -``` - -### Start Ollama - -`ollama serve` is used when you want to start ollama without running the desktop application. - -## Building - -See the [developer guide](https://github.com/ollama/ollama/blob/main/docs/development.md) - -### Running local builds - -Next, start the server: - -``` -./ollama serve -``` - -Finally, in a separate shell, run a model: - -``` -./ollama run llama3.1 -``` - -## REST API - -Ollama has a REST API for running and managing models. - -### Generate a response - -``` -curl http://localhost:11434/api/generate -d '{ - "model": "llama3.1", - "prompt":"Why is the sky blue?" -}' -``` - -### Chat with a model - -``` -curl http://localhost:11434/api/chat -d '{ - "model": "llama3.1", - "messages": [ - { "role": "user", "content": "why is the sky blue?" } - ] -}' -``` - -See the [API documentation](./docs/api.md) for all endpoints. - -## Community Integrations - -### Web & Desktop - -- [Open WebUI](https://github.com/open-webui/open-webui) -- [Enchanted (macOS native)](https://github.com/AugustDev/enchanted) -- [Hollama](https://github.com/fmaclen/hollama) -- [Lollms-Webui](https://github.com/ParisNeo/lollms-webui) -- [LibreChat](https://github.com/danny-avila/LibreChat) -- [Bionic GPT](https://github.com/bionic-gpt/bionic-gpt) -- [HTML UI](https://github.com/rtcfirefly/ollama-ui) -- [Saddle](https://github.com/jikkuatwork/saddle) -- [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) -- [Minimalistic React UI for Ollama Models](https://github.com/richawo/minimal-llm-ui) -- [Ollamac](https://github.com/kevinhermawan/Ollamac) -- [big-AGI](https://github.com/enricoros/big-AGI/blob/main/docs/config-local-ollama.md) -- [Cheshire Cat assistant framework](https://github.com/cheshire-cat-ai/core) -- [Amica](https://github.com/semperai/amica) -- [chatd](https://github.com/BruceMacD/chatd) -- [Ollama-SwiftUI](https://github.com/kghandour/Ollama-SwiftUI) -- [Dify.AI](https://github.com/langgenius/dify) -- [MindMac](https://mindmac.app) -- [NextJS Web Interface for Ollama](https://github.com/jakobhoeg/nextjs-ollama-llm-ui) -- [Msty](https://msty.app) -- [Chatbox](https://github.com/Bin-Huang/Chatbox) -- [WinForm Ollama Copilot](https://github.com/tgraupmann/WinForm_Ollama_Copilot) -- [NextChat](https://github.com/ChatGPTNextWeb/ChatGPT-Next-Web) with [Get Started Doc](https://docs.nextchat.dev/models/ollama) -- [Alpaca WebUI](https://github.com/mmo80/alpaca-webui) -- [OllamaGUI](https://github.com/enoch1118/ollamaGUI) -- [OpenAOE](https://github.com/InternLM/OpenAOE) -- [Odin Runes](https://github.com/leonid20000/OdinRunes) -- [LLM-X](https://github.com/mrdjohnson/llm-x) (Progressive Web App) -- [AnythingLLM (Docker + MacOs/Windows/Linux native app)](https://github.com/Mintplex-Labs/anything-llm) -- [Ollama Basic Chat: Uses HyperDiv Reactive UI](https://github.com/rapidarchitect/ollama_basic_chat) -- [Ollama-chats RPG](https://github.com/drazdra/ollama-chats) -- [QA-Pilot](https://github.com/reid41/QA-Pilot) (Chat with Code Repository) -- [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) -- [RAGFlow](https://github.com/infiniflow/ragflow) (Open-source Retrieval-Augmented Generation engine based on deep document understanding) -- [StreamDeploy](https://github.com/StreamDeploy-DevRel/streamdeploy-llm-app-scaffold) (LLM Application Scaffold) -- [chat](https://github.com/swuecho/chat) (chat web app for teams) -- [Lobe Chat](https://github.com/lobehub/lobe-chat) with [Integrating Doc](https://lobehub.com/docs/self-hosting/examples/ollama) -- [Ollama RAG Chatbot](https://github.com/datvodinh/rag-chatbot.git) (Local Chat with multiple PDFs using Ollama and RAG) -- [BrainSoup](https://www.nurgo-software.com/products/brainsoup) (Flexible native client with RAG & multi-agent automation) -- [macai](https://github.com/Renset/macai) (macOS client for Ollama, ChatGPT, and other compatible API back-ends) -- [Olpaka](https://github.com/Otacon/olpaka) (User-friendly Flutter Web App for Ollama) -- [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) -- [Ollama with Google Mesop](https://github.com/rapidarchitect/ollama_mesop/) (Mesop Chat Client implementation with Ollama) -- [Kerlig AI](https://www.kerlig.com/) (AI writing assistant for macOS) -- [AI Studio](https://github.com/MindWorkAI/AI-Studio) -- [Sidellama](https://github.com/gyopak/sidellama) (browser-based LLM client) -- [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) - -### Terminal - -- [oterm](https://github.com/ggozad/oterm) -- [Ellama Emacs client](https://github.com/s-kostyaev/ellama) -- [Emacs client](https://github.com/zweifisch/ollama) -- [gen.nvim](https://github.com/David-Kunz/gen.nvim) -- [ollama.nvim](https://github.com/nomnivore/ollama.nvim) -- [ollero.nvim](https://github.com/marco-souza/ollero.nvim) -- [ollama-chat.nvim](https://github.com/gerazov/ollama-chat.nvim) -- [ogpt.nvim](https://github.com/huynle/ogpt.nvim) -- [gptel Emacs client](https://github.com/karthink/gptel) -- [Oatmeal](https://github.com/dustinblackman/oatmeal) -- [cmdh](https://github.com/pgibler/cmdh) -- [ooo](https://github.com/npahlfer/ooo) -- [shell-pilot](https://github.com/reid41/shell-pilot) -- [tenere](https://github.com/pythops/tenere) -- [llm-ollama](https://github.com/taketwo/llm-ollama) for [Datasette's LLM CLI](https://llm.datasette.io/en/stable/). -- [typechat-cli](https://github.com/anaisbetts/typechat-cli) -- [ShellOracle](https://github.com/djcopley/ShellOracle) -- [tlm](https://github.com/yusufcanb/tlm) -- [podman-ollama](https://github.com/ericcurtin/podman-ollama) -- [gollama](https://github.com/sammcj/gollama) -- [Ollama eBook Summary](https://github.com/cognitivetech/ollama-ebook-summary/) - -### Database - -- [MindsDB](https://github.com/mindsdb/mindsdb/blob/staging/mindsdb/integrations/handlers/ollama_handler/README.md) (Connects Ollama models with nearly 200 data platforms and apps) -- [chromem-go](https://github.com/philippgille/chromem-go/blob/v0.5.0/embed_ollama.go) with [example](https://github.com/philippgille/chromem-go/tree/v0.5.0/examples/rag-wikipedia-ollama) - -### Package managers - -- [Pacman](https://archlinux.org/packages/extra/x86_64/ollama/) -- [Helm Chart](https://artifacthub.io/packages/helm/ollama-helm/ollama) -- [Guix channel](https://codeberg.org/tusharhero/ollama-guix) - -### Libraries - -- [LangChain](https://python.langchain.com/docs/integrations/llms/ollama) and [LangChain.js](https://js.langchain.com/docs/modules/model_io/models/llms/integrations/ollama) with [example](https://js.langchain.com/docs/use_cases/question_answering/local_retrieval_qa) -- [Firebase Genkit](https://firebase.google.com/docs/genkit/plugins/ollama) -- [LangChainGo](https://github.com/tmc/langchaingo/) with [example](https://github.com/tmc/langchaingo/tree/main/examples/ollama-completion-example) -- [LangChain4j](https://github.com/langchain4j/langchain4j) with [example](https://github.com/langchain4j/langchain4j-examples/tree/main/ollama-examples/src/main/java) -- [LangChainRust](https://github.com/Abraxas-365/langchain-rust) with [example](https://github.com/Abraxas-365/langchain-rust/blob/main/examples/llm_ollama.rs) -- [LlamaIndex](https://gpt-index.readthedocs.io/en/stable/examples/llm/ollama.html) -- [LiteLLM](https://github.com/BerriAI/litellm) -- [OllamaSharp for .NET](https://github.com/awaescher/OllamaSharp) -- [Ollama for Ruby](https://github.com/gbaptista/ollama-ai) -- [Ollama-rs for Rust](https://github.com/pepperoni21/ollama-rs) -- [Ollama-hpp for C++](https://github.com/jmont-dev/ollama-hpp) -- [Ollama4j for Java](https://github.com/amithkoujalgi/ollama4j) -- [ModelFusion Typescript Library](https://modelfusion.dev/integration/model-provider/ollama) -- [OllamaKit for Swift](https://github.com/kevinhermawan/OllamaKit) -- [Ollama for Dart](https://github.com/breitburg/dart-ollama) -- [Ollama for Laravel](https://github.com/cloudstudio/ollama-laravel) -- [LangChainDart](https://github.com/davidmigloz/langchain_dart) -- [Semantic Kernel - Python](https://github.com/microsoft/semantic-kernel/tree/main/python/semantic_kernel/connectors/ai/ollama) -- [Haystack](https://github.com/deepset-ai/haystack-integrations/blob/main/integrations/ollama.md) -- [Elixir LangChain](https://github.com/brainlid/langchain) -- [Ollama for R - rollama](https://github.com/JBGruber/rollama) -- [Ollama for R - ollama-r](https://github.com/hauselin/ollama-r) -- [Ollama-ex for Elixir](https://github.com/lebrunel/ollama-ex) -- [Ollama Connector for SAP ABAP](https://github.com/b-tocs/abap_btocs_ollama) -- [Testcontainers](https://testcontainers.com/modules/ollama/) -- [Portkey](https://portkey.ai/docs/welcome/integration-guides/ollama) -- [PromptingTools.jl](https://github.com/svilupp/PromptingTools.jl) with an [example](https://svilupp.github.io/PromptingTools.jl/dev/examples/working_with_ollama) -- [LlamaScript](https://github.com/Project-Llama/llamascript) - -### Mobile - -- [Enchanted](https://github.com/AugustDev/enchanted) -- [Maid](https://github.com/Mobile-Artificial-Intelligence/maid) - -### Extensions & Plugins - -- [Raycast extension](https://github.com/MassimilianoPasquini97/raycast_ollama) -- [Discollama](https://github.com/mxyng/discollama) (Discord bot inside the Ollama discord channel) -- [Continue](https://github.com/continuedev/continue) -- [Obsidian Ollama plugin](https://github.com/hinterdupfinger/obsidian-ollama) -- [Logseq Ollama plugin](https://github.com/omagdy7/ollama-logseq) -- [NotesOllama](https://github.com/andersrex/notesollama) (Apple Notes Ollama plugin) -- [Dagger Chatbot](https://github.com/samalba/dagger-chatbot) -- [Discord AI Bot](https://github.com/mekb-turtle/discord-ai-bot) -- [Ollama Telegram Bot](https://github.com/ruecat/ollama-telegram) -- [Hass Ollama Conversation](https://github.com/ej52/hass-ollama-conversation) -- [Rivet plugin](https://github.com/abrenneke/rivet-plugin-ollama) -- [Obsidian BMO Chatbot plugin](https://github.com/longy2k/obsidian-bmo-chatbot) -- [Cliobot](https://github.com/herval/cliobot) (Telegram bot with Ollama support) -- [Copilot for Obsidian plugin](https://github.com/logancyang/obsidian-copilot) -- [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) -- [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) -- [AI Telegram Bot](https://github.com/tusharhero/aitelegrambot) (Telegram bot using Ollama in backend) -- [AI ST Completion](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (Sublime Text 4 AI assistant plugin with Ollama support) -- [Discord-Ollama Chat Bot](https://github.com/kevinthedang/discord-ollama) (Generalized TypeScript Discord Bot w/ Tuning Documentation) -- [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) - -### Supported backends - -- [llama.cpp](https://github.com/ggerganov/llama.cpp) project founded by Georgi Gerganov. +## 参考资料 +* https://github.com/ollama/ollama +* https://github.com/ggerganov/llama.cpp diff --git a/SECURITY.md b/SECURITY.md old mode 100644 new mode 100755 diff --git a/api/client.go b/api/client.go old mode 100644 new mode 100755 diff --git a/api/client_test.go b/api/client_test.go old mode 100644 new mode 100755 diff --git a/api/types.go b/api/types.go old mode 100644 new mode 100755 diff --git a/api/types_test.go b/api/types_test.go old mode 100644 new mode 100755 diff --git a/app/.gitignore b/app/.gitignore old mode 100644 new mode 100755 diff --git a/app/README.md b/app/README.md old mode 100644 new mode 100755 diff --git a/app/assets/app.ico 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a/llm/llama.cpp/common/build-info.cpp.in b/llm/llama.cpp/common/build-info.cpp.in old mode 100644 new mode 100755 diff --git a/llm/llama.cpp/common/cmake/build-info-gen-cpp.cmake b/llm/llama.cpp/common/cmake/build-info-gen-cpp.cmake old mode 100644 new mode 100755 diff --git a/llm/llama.cpp/common/common.cpp b/llm/llama.cpp/common/common.cpp old mode 100644 new mode 100755 index 56cad1af975f066fcb77af652a04e896eb3361a9..2e8374d50cafad99cb9f59eccb4ac5c22ed6b32b --- a/llm/llama.cpp/common/common.cpp +++ b/llm/llama.cpp/common/common.cpp @@ -2110,21 +2110,9 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) { loaded_la.adapter = llama_lora_adapter_init(model, la.path.c_str()); if (loaded_la.adapter == nullptr) { fprintf(stderr, "%s: error: failed to apply lora adapter '%s'\n", __func__, la.path.c_str()); - - // if that fails, try loading as ggla for compatibility - int err = llama_model_apply_lora_from_file(model, - la.path.c_str(), - la.scale, - nullptr, - params.n_threads); - if (err != 0) { - fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__); - llama_free(lctx); - llama_free_model(model); - return iparams; - } else { - break; - } + llama_free(lctx); + llama_free_model(model); + return iparams; } iparams.lora_adapters.push_back(loaded_la); // copy to list of loaded adapters } @@ -2190,8 +2178,6 @@ struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & mparams.use_mmap = params.use_mmap; mparams.use_mlock = params.use_mlock; mparams.check_tensors = params.check_tensors; - mparams.progress_callback = params.progress_callback; - mparams.progress_callback_user_data = params.progress_callback_user_data; if (params.kv_overrides.empty()) { mparams.kv_overrides = NULL; } else { diff --git a/llm/llama.cpp/common/common.h b/llm/llama.cpp/common/common.h old mode 100644 new mode 100755 index c238cd7417ddd28b965af154ee86a9d88ec0294b..d88966ece20aa73da262d047e6c0137e7a0e972f --- a/llm/llama.cpp/common/common.h +++ b/llm/llama.cpp/common/common.h @@ -194,13 +194,6 @@ struct gpt_params { std::string mmproj = ""; // path to multimodal projector std::vector image; // path to image file(s) - // Called with a progress value between 0.0 and 1.0. 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a/llm/llama.cpp/examples/llava/android/build_64.sh b/llm/llama.cpp/examples/llava/android/build_64.sh old mode 100644 new mode 100755 diff --git a/llm/llama.cpp/examples/llava/clip.cpp b/llm/llama.cpp/examples/llava/clip.cpp old mode 100644 new mode 100755 index b86c7a58ea6f766418c1694669d62b5c7b3a1b52..7cda5f10c4b05dce52334ec6636c0d0fa7bcc1b6 --- a/llm/llama.cpp/examples/llava/clip.cpp +++ b/llm/llama.cpp/examples/llava/clip.cpp @@ -3,7 +3,6 @@ // I'll gradually clean and extend it // Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch #include "clip.h" -#include "common.h" #include "log.h" #include "ggml.h" #include "ggml-alloc.h" @@ -37,14 +36,6 @@ #include #include -#if defined(_WIN32) -#define WIN32_LEAN_AND_MEAN -#ifndef NOMINMAX - #define NOMINMAX -#endif -#include -#endif - //#define CLIP_DEBUG_FUNCTIONS // RGB uint8 image @@ -1073,22 +1064,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { return nullptr; } -#ifdef _WIN32 - int wlen = MultiByteToWideChar(CP_UTF8, 0, fname, -1, NULL, 0); - if (!wlen) { - return NULL; - } - wchar_t * wbuf = (wchar_t *) malloc(wlen * sizeof(wchar_t)); - wlen = MultiByteToWideChar(CP_UTF8, 0, fname, -1, wbuf, wlen); - if (!wlen) { - free(wbuf); - return NULL; - } - auto fin = std::ifstream(wbuf, std::ios::binary); - free(wbuf); -#else auto fin = std::ifstream(fname, std::ios::binary); -#endif if (!fin) { LOG_TEE("cannot open model file for loading tensors\n"); clip_free(new_clip); diff --git a/llm/llama.cpp/examples/llava/clip.h b/llm/llama.cpp/examples/llava/clip.h old mode 100644 new mode 100755 diff --git a/llm/llama.cpp/examples/llava/convert_image_encoder_to_gguf.py b/llm/llama.cpp/examples/llava/convert_image_encoder_to_gguf.py old mode 100644 new mode 100755 diff --git a/llm/llama.cpp/examples/llava/llava-cli.cpp b/llm/llama.cpp/examples/llava/llava-cli.cpp old mode 100644 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a/llm/llama.cpp/ggml/src/ggml-cuda/im2col.cuh b/llm/llama.cpp/ggml/src/ggml-cuda/im2col.cuh old mode 100644 new mode 100755 diff --git a/llm/llama.cpp/ggml/src/ggml-cuda/mma.cuh b/llm/llama.cpp/ggml/src/ggml-cuda/mma.cuh old mode 100644 new mode 100755 diff --git a/llm/llama.cpp/ggml/src/ggml-cuda/mmq.cu b/llm/llama.cpp/ggml/src/ggml-cuda/mmq.cu old mode 100644 new mode 100755 diff --git a/llm/llama.cpp/ggml/src/ggml-cuda/mmq.cuh b/llm/llama.cpp/ggml/src/ggml-cuda/mmq.cuh old mode 100644 new mode 100755 index 2306db89f2f72884a54d16bf03bd96c4ceee25e7..703c620c70f21636e67a434896346c0017258f97 --- a/llm/llama.cpp/ggml/src/ggml-cuda/mmq.cuh +++ b/llm/llama.cpp/ggml/src/ggml-cuda/mmq.cuh @@ -2580,7 +2580,7 @@ template __launch_bounds__(WARP_SIZE*nwarps, 2) #endif // __CUDA_ARCH__ >= CC_VOLTA #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -static __global__ __launch_bounds__(256) void mul_mat_q( +static __global__ __launch_bounds__(1024) void mul_mat_q( const char * __restrict__ x, const char * __restrict__ yc, float * __restrict__ dst, float * __restrict__ tmp_fixup, const int ne00, const int ne01, const int stride01, const int ne10, const int ne11, const int stride11, const int ne0) { diff --git a/llm/llama.cpp/ggml/src/ggml-cuda/mmvq.cu b/llm/llama.cpp/ggml/src/ggml-cuda/mmvq.cu old mode 100644 new mode 100755 diff --git a/llm/llama.cpp/ggml/src/ggml-cuda/mmvq.cuh b/llm/llama.cpp/ggml/src/ggml-cuda/mmvq.cuh old mode 100644 new mode 100755 diff --git a/llm/llama.cpp/ggml/src/ggml-cuda/norm.cu b/llm/llama.cpp/ggml/src/ggml-cuda/norm.cu old mode 100644 new mode 100755 diff --git a/llm/llama.cpp/ggml/src/ggml-cuda/norm.cuh b/llm/llama.cpp/ggml/src/ggml-cuda/norm.cuh old mode 100644 new mode 100755 diff --git a/llm/llama.cpp/ggml/src/ggml-cuda/pad.cu b/llm/llama.cpp/ggml/src/ggml-cuda/pad.cu old mode 100644 new mode 100755 diff --git a/llm/llama.cpp/ggml/src/ggml-cuda/pad.cuh b/llm/llama.cpp/ggml/src/ggml-cuda/pad.cuh old mode 100644 new mode 100755 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8740a16c733298386dbd8001604e5a30e108a1ff..b512eb0be132e7dfd5dac5d7e4dcee0f1fce5fe9 --- a/llm/llama.cpp/ggml/src/ggml-metal.m +++ b/llm/llama.cpp/ggml/src/ggml-metal.m @@ -1545,23 +1545,27 @@ static enum ggml_status ggml_metal_graph_compute( // to the matrix-vector kernel int ne11_mm_min = 1; +#if 0 // the numbers below are measured on M2 Ultra for 7B and 13B models // these numbers do not translate to other devices or model sizes // TODO: need to find a better approach - switch (src0t) { - case GGML_TYPE_F16: ne11_mm_min = 2; break; - case GGML_TYPE_Q8_0: ne11_mm_min = 7; break; - case GGML_TYPE_Q2_K: ne11_mm_min = 15; break; - case GGML_TYPE_Q3_K: ne11_mm_min = 7; break; - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: ne11_mm_min = 15; break; - case GGML_TYPE_Q4_K: ne11_mm_min = 11; break; - case GGML_TYPE_Q5_0: // not tested yet - case GGML_TYPE_Q5_1: ne11_mm_min = 13; break; // not tested yet - case GGML_TYPE_Q5_K: ne11_mm_min = 7; break; - case GGML_TYPE_Q6_K: ne11_mm_min = 7; break; - default: ne11_mm_min = 1; break; + if ([ctx->device.name isEqualToString:@"Apple M2 Ultra"]) { + switch (src0t) { + case GGML_TYPE_F16: ne11_mm_min = 2; break; + case GGML_TYPE_Q8_0: ne11_mm_min = 7; break; + case GGML_TYPE_Q2_K: ne11_mm_min = 15; break; + case GGML_TYPE_Q3_K: ne11_mm_min = 7; break; + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: ne11_mm_min = 15; break; + case GGML_TYPE_Q4_K: ne11_mm_min = 11; break; + case GGML_TYPE_Q5_0: // not tested yet + case GGML_TYPE_Q5_1: ne11_mm_min = 13; break; // not tested yet + case GGML_TYPE_Q5_K: ne11_mm_min = 7; break; + case GGML_TYPE_Q6_K: ne11_mm_min = 7; break; + default: ne11_mm_min = 1; break; + } } +#endif // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel diff --git a/llm/llama.cpp/ggml/src/ggml-quants.c b/llm/llama.cpp/ggml/src/ggml-quants.c old mode 100644 new mode 100755 diff --git a/llm/llama.cpp/ggml/src/ggml-quants.h 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Can be NULL to use the current loaded model. - // The model needs to be reloaded before applying a new adapter, otherwise the adapter - // will be applied on top of the previous one - // Returns 0 on success - LLAMA_API int32_t llama_model_apply_lora_from_file( - const struct llama_model * model, - const char * path_lora, - float scale, - const char * path_base_model, - int32_t n_threads); - - #ifdef __cplusplus } #endif diff --git a/llm/llama.cpp/media/llama-leader.jpeg b/llm/llama.cpp/media/llama-leader.jpeg old mode 100644 new mode 100755 diff --git a/llm/llama.cpp/media/llama0-banner.png b/llm/llama.cpp/media/llama0-banner.png old mode 100644 new mode 100755 diff --git a/llm/llama.cpp/media/llama0-logo.png b/llm/llama.cpp/media/llama0-logo.png old mode 100644 new mode 100755 diff --git a/llm/llama.cpp/media/llama1-banner.png b/llm/llama.cpp/media/llama1-banner.png old mode 100644 new mode 100755 diff --git a/llm/llama.cpp/media/llama1-logo.png b/llm/llama.cpp/media/llama1-logo.png 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mode 100755 diff --git a/llm/llama.cpp/src/llama-sampling.cpp b/llm/llama.cpp/src/llama-sampling.cpp old mode 100644 new mode 100755 diff --git a/llm/llama.cpp/src/llama-sampling.h b/llm/llama.cpp/src/llama-sampling.h old mode 100644 new mode 100755 diff --git a/llm/llama.cpp/src/llama-vocab.cpp b/llm/llama.cpp/src/llama-vocab.cpp old mode 100644 new mode 100755 diff --git a/llm/llama.cpp/src/llama-vocab.h b/llm/llama.cpp/src/llama-vocab.h old mode 100644 new mode 100755 diff --git a/llm/llama.cpp/src/llama.cpp b/llm/llama.cpp/src/llama.cpp old mode 100644 new mode 100755 index 6479e121ed5648c6fa60e3f854948682dbd3f4cd..a7b1c9ebd9e37d1e9017bdec315b86ecd51a738b --- a/llm/llama.cpp/src/llama.cpp +++ b/llm/llama.cpp/src/llama.cpp @@ -4892,7 +4892,7 @@ static void llm_load_hparams( } break; case LLM_ARCH_PHI3: { - ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false); + ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa); ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); switch (hparams.n_layer) { @@ -5347,7 +5347,16 @@ static void llm_load_vocab( if (vocab.type == LLAMA_VOCAB_TYPE_BPE) { vocab.tokenizer_add_space_prefix = false; vocab.tokenizer_clean_spaces = true; - if (tokenizer_pre == "default") { + if (tokenizer_pre.empty()) { + LLAMA_LOG_WARN("%s: missing pre-tokenizer type, using: 'default'\n", __func__); + LLAMA_LOG_WARN("%s: \n", __func__); + LLAMA_LOG_WARN("%s: ************************************ \n", __func__); + LLAMA_LOG_WARN("%s: GENERATION QUALITY WILL BE DEGRADED! \n", __func__); + LLAMA_LOG_WARN("%s: CONSIDER REGENERATING THE MODEL \n", __func__); + LLAMA_LOG_WARN("%s: ************************************ \n", __func__); + LLAMA_LOG_WARN("%s: \n", __func__); + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT; + } else if (tokenizer_pre == "default") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT; } else if ( tokenizer_pre == "llama3" || @@ -5434,8 +5443,7 @@ static void llm_load_vocab( tokenizer_pre == "codeshell") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_CODESHELL; } else { - LLAMA_LOG_WARN("%s: missing or unrecognized pre-tokenizer type, using: 'default'\n", __func__); - vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT; + throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str())); } } else if (vocab.type == LLAMA_VOCAB_TYPE_SPM) { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT; @@ -7700,7 +7708,7 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam } } catch (const std::exception & err) { LLAMA_LOG_ERROR("%s: error loading model: %s\n", __func__, err.what()); - throw; + return -1; } return 0; @@ -8584,14 +8592,14 @@ struct llm_build_context { } struct ggml_tensor * build_inp_mean() { - lctx.inp_mean = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, cparams.n_seq_max); + lctx.inp_mean = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens); cb(lctx.inp_mean, "inp_mean", -1); ggml_set_input(lctx.inp_mean); return lctx.inp_mean; } struct ggml_tensor * build_inp_cls() { - lctx.inp_cls = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, cparams.n_seq_max); + lctx.inp_cls = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); cb(lctx.inp_cls, "inp_cls", -1); ggml_set_input(lctx.inp_cls); return lctx.inp_cls; @@ -10766,7 +10774,7 @@ struct llm_build_context { struct ggml_tensor * inp_pos = build_inp_pos(); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = hparams.n_swa > 0 ? build_inp_KQ_mask_swa() : build_inp_KQ_mask(); + struct ggml_tensor * KQ_mask_swa = build_inp_KQ_mask_swa(); for (int il = 0; il < n_layer; ++il) { auto residual = inpL; @@ -10824,7 +10832,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, lctx, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il); + Kcur, Vcur, Qcur, KQ_mask_swa, n_tokens, kv_head, n_kv, 1.0f, cb, il); } if (il == n_layer - 1) { @@ -14146,16 +14154,19 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer)); float * data = (float *) lctx.inp_mean->data; - memset(lctx.inp_mean->data, 0, n_tokens * cparams.n_seq_max * ggml_element_size(lctx.inp_mean)); + memset(lctx.inp_mean->data, 0, n_tokens * n_tokens * ggml_element_size(lctx.inp_mean)); std::vector sum(n_tokens, 0); for (int i = 0; i < n_tokens; ++i) { const llama_seq_id seq_id = batch.seq_id[i][0]; + + GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == MEAN"); + sum[seq_id] += 1; } - std::vector div(cparams.n_seq_max, 0.0f); - for (uint32_t i = 0; i < cparams.n_seq_max; ++i) { + std::vector div(n_tokens, 0.0f); + for (int i = 0; i < n_tokens; ++i) { const uint64_t s = sum[i]; if (s > 0) { div[i] = 1.0f/float(s); @@ -14175,11 +14186,14 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer)); uint32_t * data = (uint32_t *) lctx.inp_cls->data; - memset(lctx.inp_cls->data, 0, cparams.n_seq_max * ggml_element_size(lctx.inp_cls)); + memset(lctx.inp_cls->data, 0, n_tokens * ggml_element_size(lctx.inp_cls)); for (int i = 0; i < n_tokens; ++i) { const llama_seq_id seq_id = batch.seq_id[i][0]; const llama_pos pos = batch.pos[i]; + + GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS"); + if (pos == 0) { data[seq_id] = i; } @@ -14342,7 +14356,7 @@ static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) { const auto n_embd = hparams.n_embd; // TODO: use a per-batch flag for logits presence instead - const bool has_logits = cparams.causal_attn; + const bool has_logits = !cparams.embeddings; const bool has_embd = lctx.is_encoding || (cparams.embeddings && (cparams.pooling_type == LLAMA_POOLING_TYPE_NONE)); const size_t logits_size = has_logits ? n_vocab*n_outputs_max : 0; @@ -14613,25 +14627,17 @@ static int llama_decode_internal( // no output res = nullptr; embd = nullptr; - } - - if (cparams.embeddings) { - for (int i = gf->n_nodes - 1; i >= 0; --i) { - embd = gf->nodes[i]; - if (strcmp(embd->name, "result_embd_pooled") == 0) { - break; - } + } else if (cparams.embeddings) { + res = nullptr; // do not extract logits for embedding case + embd = gf->nodes[gf->n_nodes - 1]; + if (strcmp(embd->name, "result_embd_pooled") != 0) { + embd = gf->nodes[gf->n_nodes - 2]; } GGML_ASSERT(strcmp(embd->name, "result_embd_pooled") == 0 && "missing embeddings tensor"); - } else { + } else { embd = nullptr; // do not extract embeddings when not needed GGML_ASSERT(strcmp(res->name, "result_output") == 0 && "missing result_output tensor"); } - - if (!cparams.causal_attn) { - res = nullptr; // do not extract logits when not needed - } - // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs); ggml_backend_sched_alloc_graph(lctx.sched, gf); @@ -16423,23 +16429,16 @@ struct llama_model * llama_load_model_from_file( } model->rpc_servers.push_back(servers); } - - try { - int status = llama_model_load(path_model, *model, params); - GGML_ASSERT(status <= 0); - if (status < 0) { - if (status == -1) { - LLAMA_LOG_ERROR("%s: failed to load model\n", __func__); - } else if (status == -2) { - LLAMA_LOG_INFO("%s: cancelled model load\n", __func__); - } - delete model; - return nullptr; + int status = llama_model_load(path_model, *model, params); + GGML_ASSERT(status <= 0); + if (status < 0) { + if (status == -1) { + LLAMA_LOG_ERROR("%s: failed to load model\n", __func__); + } else if (status == -2) { + LLAMA_LOG_INFO("%s: cancelled model load\n", __func__); } - } catch (...) { - LLAMA_LOG_ERROR("%s: exception loading model\n", __func__); delete model; - throw; + return nullptr; } return model; @@ -19172,290 +19171,3 @@ void llama_log_callback_default(ggml_log_level level, const char * text, void * fputs(text, stderr); fflush(stderr); } - -static int llama_apply_lora_from_file_internal( - const struct llama_model & model, const char * path_lora, float scale, const char * path_base_model, int n_threads -) { - LLAMA_LOG_INFO("%s: applying lora adapter from '%s' - please wait ...\n", __func__, path_lora); - - const int64_t t_start_lora_us = ggml_time_us(); - - llama_file fin(path_lora, "rb"); - - // verify magic and version - { - uint32_t magic = fin.read_u32(); - if (magic != LLAMA_FILE_MAGIC_GGLA) { - LLAMA_LOG_ERROR("%s: bad file magic\n", __func__); - return 1; - } - - uint32_t format_version = fin.read_u32(); - if (format_version != 1) { - LLAMA_LOG_ERROR("%s: unsupported file version\n", __func__ ); - return 1; - } - } - - int32_t lora_r = fin.read_u32(); - int32_t lora_alpha = fin.read_u32(); - float scaling = scale * (float)lora_alpha / (float)lora_r; - - LLAMA_LOG_INFO("%s: r = %d, alpha = %d, scaling = %.2f\n", __func__, lora_r, lora_alpha, scaling); - - // load base model - std::unique_ptr ml; - if (path_base_model) { - LLAMA_LOG_INFO("%s: loading base model from '%s'\n", __func__, path_base_model); - ml.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*check_tensors*/ false, /*kv_overrides*/ nullptr)); - ml->init_mappings(/*prefetch*/ false); // no prefetching - } - - struct tensor_meta { - std::string name; - ggml_type type; - int32_t ne[2]; - size_t offset; - }; - std::map tensor_meta_map; - - // load all tensor meta - while (true) { - if (fin.tell() == fin.size) { - // eof - break; - } - - int32_t n_dims; - int32_t name_len; - int32_t ftype; - - fin.read_raw(&n_dims, sizeof(n_dims)); - fin.read_raw(&name_len, sizeof(name_len)); - fin.read_raw(&ftype, sizeof(ftype)); - - if (n_dims != 1 && n_dims != 2) { - LLAMA_LOG_ERROR("%s: unsupported tensor dimension %d\n", __func__, n_dims); - return 1; - } - - int32_t ne[2] = { 1, 1 }; - for (int i = 0; i < n_dims; ++i) { - fin.read_raw(&ne[i], sizeof(ne[i])); - } - - std::string name; - { - GGML_ASSERT(name_len < GGML_MAX_NAME); - char buf[GGML_MAX_NAME]; - fin.read_raw(buf, name_len); - name = std::string(buf, name_len); - } - - // check for lora suffix - std::string lora_suffix; - if (name.length() > 6) { - lora_suffix = name.substr(name.length() - 6); - } - if (lora_suffix != ".loraA" && lora_suffix != ".loraB") { - LLAMA_LOG_ERROR("%s: error: '%s' is not a lora tensor\n", __func__, name.c_str()); - return 1; - } - - // tensor type - ggml_type wtype; - switch (ftype) { - case 0: wtype = GGML_TYPE_F32; break; - case 1: wtype = GGML_TYPE_F16; break; - default: - { - LLAMA_LOG_ERROR("%s: invalid tensor data type '%d'\n", - __func__, ftype); - return 1; - } - } - - // data offset - size_t offset = fin.tell(); - offset = (offset + 31) & -32; - - // skip tensor data - fin.seek(offset + ggml_row_size(wtype, ne[0]) * ne[1], SEEK_SET); - - tensor_meta_map.emplace(name, tensor_meta{ name, wtype, { ne[0], ne[1] }, offset }); - } - - bool warned = false; - int n_tensors = 0; - - // apply - ggml_backend_t backend_cpu = ggml_backend_cpu_init(); - if (backend_cpu == nullptr) { - LLAMA_LOG_ERROR("%s: error: failed to initialize cpu backend\n", __func__); - return 1; - } - ggml_backend_cpu_set_n_threads(backend_cpu, n_threads); - - std::vector> read_buf; - for (const auto & it : model.tensors_by_name) { - const std::string & base_name = it.first; - ggml_tensor * model_t = it.second; - - if (tensor_meta_map.find(base_name + ".loraA") == tensor_meta_map.end() || - tensor_meta_map.find(base_name + ".loraB") == tensor_meta_map.end()) { - continue; - } - - tensor_meta & metaA = tensor_meta_map.at(base_name + ".loraA"); - tensor_meta & metaB = tensor_meta_map.at(base_name + ".loraB"); - - ggml_init_params lora_init_params = { - /* .mem_size */ ggml_tensor_overhead()*128 + ggml_graph_overhead(), - /* .mem_buffer */ nullptr, - /* .no_alloc */ true, - }; - ggml_context * lora_ctx = ggml_init(lora_init_params); - if (lora_ctx == nullptr) { - LLAMA_LOG_ERROR("%s: error: failed to initialize lora context\n", __func__); - ggml_backend_free(backend_cpu); - return 1; - } - - // create tensors - ggml_tensor * loraA = ggml_new_tensor_2d(lora_ctx, metaA.type, metaA.ne[0], metaA.ne[1]); - ggml_tensor * loraB = ggml_new_tensor_2d(lora_ctx, metaB.type, metaB.ne[0], metaB.ne[1]); - ggml_set_name(loraA, metaA.name.c_str()); - ggml_set_name(loraB, metaB.name.c_str()); - - ggml_tensor * base_t; - if (ml) { - if (!ml->get_tensor_meta(base_name.c_str())) { - LLAMA_LOG_ERROR("%s: error: tensor '%s' not found in base model\n", __func__, base_name.c_str()); - return 1; - } - base_t = ggml_dup_tensor(lora_ctx, ml->get_tensor_meta(base_name.c_str())); - } else { - base_t = ggml_dup_tensor(lora_ctx, model_t); - } - ggml_set_name(base_t, base_name.c_str()); - - // allocate in backend buffer - ggml_backend_buffer_t lora_buf = ggml_backend_alloc_ctx_tensors_from_buft(lora_ctx, ggml_backend_cpu_buffer_type()); - if (lora_buf == nullptr) { - LLAMA_LOG_ERROR("%s: error: failed to allocate lora tensors\n", __func__); - return 1; - } - - // load tensor data - auto load_tensor = [&read_buf, &fin](const tensor_meta & tensor_meta, ggml_tensor * tensor) { - read_buf.resize(ggml_nbytes(tensor)); - fin.seek(tensor_meta.offset, SEEK_SET); - fin.read_raw(read_buf.data(), ggml_nbytes(tensor)); - ggml_backend_tensor_set(tensor, read_buf.data(), 0, read_buf.size()); - }; - load_tensor(metaA, loraA); - load_tensor(metaB, loraB); - - // load base model tensor data - if (ml) { - ml->load_data_for(base_t); - } else { - ggml_backend_tensor_copy(model_t, base_t); - } - - if (ggml_is_quantized(base_t->type) && !warned) { - LLAMA_LOG_WARN("%s: warning: using a lora adapter with a quantized model may result in poor quality, " - "use a f16 or f32 base model with --lora-base\n", __func__); - warned = true; - } - - if (base_t->ne[0] != loraA->ne[1] || base_t->ne[1] != loraB->ne[1]) { - LLAMA_LOG_ERROR("%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");" - " are you sure that this adapter is for this model?\n", __func__, base_t->ne[0], loraA->ne[1]); - ggml_free(lora_ctx); - ggml_backend_buffer_free(lora_buf); - ggml_backend_free(backend_cpu); - return 1; - } - - auto build_lora_graph = [&]() { - // w = w + BA*s - ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB); - ggml_set_name(BA, "BA"); - - if (scaling != 1.0f) { - BA = ggml_scale(lora_ctx, BA, scaling); - ggml_set_name(BA, "BA_scaled"); - } - - ggml_tensor * r; - r = ggml_add_inplace(lora_ctx, base_t, BA); - ggml_set_name(r, "r_add"); - - if (base_t->type != model_t->type) { - // convert the result to the model type - r = ggml_cast(lora_ctx, r, model_t->type); - ggml_set_name(r, "r_cast"); - } - - return r; - }; - - ggml_cgraph * gf = ggml_new_graph(lora_ctx); - ggml_tensor * r = build_lora_graph(); - ggml_build_forward_expand(gf, r); - - ggml_backend_buffer_t graph_buf = ggml_backend_alloc_ctx_tensors_from_buft(lora_ctx, ggml_backend_cpu_buffer_type()); - if (graph_buf == nullptr) { - LLAMA_LOG_ERROR("%s: error: failed to allocate graph tensors\n", __func__); - ggml_free(lora_ctx); - ggml_backend_buffer_free(lora_buf); - ggml_backend_free(backend_cpu); - return 1; - } - - ggml_backend_graph_compute(backend_cpu, gf); - - ggml_backend_tensor_set(model_t, r->data, 0, ggml_nbytes(r)); - -#if 0 - // TODO: use scheduler with fallback to CPU for less copies between CPU and GPU - //ggml_backend_sched_t sched = ggml_backend_sched_new(backends.data(), backends.size(), GGML_DEFAULT_GRAPH_SIZE); - - // sched compute - ggml_build_forward_expand(gf, build_graph()); - ggml_backend_sched_init_measure(sched, gf); - - // create the graph again, since the previous one was destroyed by the measure - ggml_graph_clear(gf); - ggml_build_forward_expand(gf, build_graph()); - ggml_backend_sched_graph_compute(sched, gf); - ggml_backend_sched_free(sched); -#endif - - ggml_backend_buffer_free(lora_buf); - ggml_backend_buffer_free(graph_buf); - ggml_free(lora_ctx); - - n_tensors++; - if (n_tensors % 4 == 0) { - LLAMA_LOG_INFO("."); - } - } - - ggml_backend_free(backend_cpu); - - const int64_t t_lora_us = ggml_time_us() - t_start_lora_us; - LLAMA_LOG_INFO(" done (%.2f ms)\n", t_lora_us / 1000.0); - - return 0; -} - -int32_t llama_model_apply_lora_from_file(const struct llama_model * model, const char * path_lora, float scale, const char * path_base_model, int32_t n_threads) { - try { - return llama_apply_lora_from_file_internal(*model, path_lora, scale, path_base_model, n_threads); - } catch (const std::exception & err) { - LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what()); - return 1; - } -} \ No newline at end of file diff --git a/llm/llama.cpp/src/unicode-data.cpp b/llm/llama.cpp/src/unicode-data.cpp old mode 100644 new mode 100755 diff --git a/llm/llama.cpp/src/unicode-data.h b/llm/llama.cpp/src/unicode-data.h old 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