20121130 — Slideshows for you (20). Working With Big Data by Seth Familian, has 78 slides with 307072 views.

After installation, verify that the rustup --version command is accessible by running it from the terminal. If the command isn't recognized, try opening a new terminal session.

In particular, Wasm modules cannot access the filesystem, network, or any other resources. They also cannot spin threads or access any timers (this is relevant for Spectre/Meltdown attacks).

You can use an existing controller module. We provide PyCtrl and JsCtrl that let you script controllers using server-side Python and JavaScript, respectively. The pyaici package contains aici command line tool that lets you upload and run scripts with any controller (we also provide REST API definition for the curious).

AICI currently integrates with llama.cpp, HuggingFace Transformers, and rLLM (custom tch-based LLM inference engine), with vLLM in the works.

The purpose of AICI is to make it easy to build and experiment with both existing and entirely new Controller strategies for improving LLM generations. By abstracting away implementation details of the underlying LLM inference and serving engine, AICI aims to simplify the development of Controllers, make it easier to write fast Controllers, and ease compatibility across LLM inference and serving engines.

You can pass other model names as argument (run ./server.sh without arguments to see available models). You can also use a HuggingFace URL to .gguf file or a local path to a .gguf file. (For rllm-cuda use HuggingFace model id or path to folder).

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Thus, each step of generation takes on the order of 20-50ms. With careful engineering, this is more than enough to compute the set of allowed tokens in Rust compiled to Wasm. These can be combined either natively in Rust, or via Python or JavaScript interpreters we provide.

The above numbers are for a single sequence, however each sequence is processed in separate process, and thus if there is more cores than sequences (which is typical), they do not change. They also include overhead of calling into Python interpreter implemented in Wasm, and then back into Rust-generated Wasm code for the constraint itself. They are all well within the 20-50ms budget, so do not affect the generation time at all.

AICI is designed for both local and cloud execution, including (eventually) multi-tenant LLM deployments. Controllers are implemented as light-weight WebAssembly (Wasm) modules which run on the same machine as the LLM inference engine, utilizing the CPU while the GPU is busy with token generation. AICI is one layer in the inference stack, and is designed to allow control libraries such as Guidance, LMQL, and others to run on top of it and gain both efficiency and performance improvements, as well as portability across LLM inference and serving engines.

We anticipate libraries will be built on top of controllers. We provide an example in promptlib - a client-side Python library that generates interacts with DeclCtrl via the pyaici package.

Using the system package manager, install the necessary tools for building code in the repository, including git, cmake and ccache.

If you find the AI Controller Interface and its ideas for defining a new layer in the LLM inference stack useful, please cite the package using the following reference:

AICI allows hosting custom logic, called Controllers, that initiate, terminate, and interact with LLMs token generation. Controllers take input arguments, process them, and return a result with logs, LLM tokens, and variables.

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The rllm-cuda backend only works with NVidia GPUs with compute capability 8.0 or later (A100 and later; RTX 30x0 and later) and requires a fiddly setup of libtorch -- it's strongly recommended to use the included devcontainer. While this guide focuses on the rllm-llamacpp backend, the build steps are the same for rllm-cuda, modulo the folder name.

In general, controllers require building and deployment, while scripts (Python or JavaScript) are sent with each request.

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This repository provides a Rust library that makes it easy to implement controllers in Rust, and provides efficient implementations of specific constraints (regular expressions, yacc grammars, substrings). We also provide Python and JavaScript interpreters that allow to glue these constraints together. All of these can be easily extended.

To add AICI support to a new LLM inference engine, you will need to implement LLM-side of the protocol that talks to AICI runtime.

The pyaici package makes it easier to integrate AICI with Python-based LLM inference engines. Take a look at integration with HuggingFace Transformers, though note that it doesn't support forking (generation of multiple sequences in parallel). The vLLM REST server is currently out of date. Please use the rLLM-cuda or rLLM-llama.cpp for now.

WebAssembly is designed to have minimal overhead, compared to native code. In our experience, highly optimized Rust code is less than 2x slower when run in Wasmtime than native. This is 10-100x better than JavaScript or Python.

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In this example we'll utilize pyctrl to manage token generation using a simple Python script. If you want, you can build and upload pyctrl, however by default the server will automatically download the latest release of pyctrl from GitHub.

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When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

CUDA: the CUDA build relies on specific libtorch installation. It's highly recommended you use the included devcontainer.

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The rLLM server has two backends, one based on libtorch and CUDA (rllm-cuda), and the other based on llama.cpp (rllm-llamacpp).

Last, to work with Python controllers and scripts (like this tutorial), run this command to install the required packages:

To compile AICI components, you need to set up your development environment for Rust. For this quickstart you also need Python 3.11 or later to create a controller.

AICI abstracts LLM inference engine from the controller and vice-versa, as in the picture below. The rounded nodes are aspirational. Additional layers can be built on top - we provide promptlib, but we strongly believe that Guidance, LMQL, SGLang, Outlines, jsonformer, LMFE, etc. can also run on top of AICI (either with custom controllers or utilizing PyCtrl or JsCtrl).

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The prompt would also vary depending on the model in use, given that each model tends to add explanations and understands instructions in different ways.

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MacOS users: please make sure you have XCode command line tools installed by running xcode-select -p and, if not installed, run xcode-select --install.

The controllers can be run in a cloud or local AICI-enabled LLM inference engine. You can run the provided reference engine (rLLM) locally with either libtorch+CUDA or llama.cpp backend.

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Most of computation in AICI Controllers occurs on the CPU, in parallel with the logit generation on the GPU. The generation occurs in steps, where logits are generated in parallel for a new token for each sequence in a batch (typically between 1 and 50). This involves reading the whole model and KV caches for sequences in the batch from the GPU memory. For optimal batch throughput, the model and KV caches should utilize a major fraction of the GPU memory, and reading the whole memory takes about 40ms on A100 GPU (80GB).

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The rLLM server provides a HTTP interface, utilized for configuration tasks and processing requests. You can also use this interface to promptly verify its status. For instance, if you open http://127.0.0.1:4242/v1/models, you should see:

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The Artificial Intelligence Controller Interface (AICI) lets you build Controllers that constrain and direct output of a Large Language Model (LLM) in real time. Controllers are flexible programs capable of implementing constrained decoding, dynamic editing of prompts and generated text, and coordinating execution across multiple, parallel generations. Controllers incorporate custom logic during the token-by-token decoding and maintain state during an LLM request. This allows diverse Controller strategies, from programmatic or query-based decoding to multi-agent conversations to execute efficiently in tight integration with the LLM itself.

There is also some overhead in the critical path of sampling. It comes down to about 0.3ms per generation step when executing 10 sequences in parallel (this is irrespective of the constraint used). The overhead goes up to around 0.7ms for 40 sequences (though it has not been fully optimized yet).

To develop a new controller, use a Rust starter project that shows usage of aici_abi library, which simplifies implementing the low-level AICI interface.

Running the script is not too different from sending a prompt. In this case, we're sending control logic and instructions all together.