Best AI tools for< Mlir Developer >

This repository contains an MLIR-based toolchain for AI Engine-enabled devices, such as AMD Ryzen™ AI and Versal™. This repository can be used to generate low-level configurations for the AI Engine portion of these devices. AI Engines are organized as a spatial array of tiles, where each tile contains AI Engine cores and/or memories. The spatial array is connected by stream switches that can be configured to route data between AI Engine tiles scheduled by their programmable Data Movement Accelerators (DMAs). This repository contains MLIR representations, with multiple levels of abstraction, to target AI Engine devices. This enables compilers and developers to program AI Engine cores, as well as describe data movements and array connectivity. A Python API is made available as a convenient interface for generating MLIR design descriptions. Backend code generation is also included, targeting the aie-rt library. This toolchain uses the AI Engine compiler tool which is part of the AMD Vitis™ software installation: these tools require a free license for use from the Product Licensing Site.

Basalt is a lightweight and flexible CSS framework designed to help developers quickly build responsive and modern websites. It provides a set of pre-designed components and utilities that can be easily customized to create unique and visually appealing web interfaces. With Basalt, developers can save time and effort by leveraging its modular structure and responsive design principles to create professional-looking websites with ease.

This repository contains an early-phase IREE compiler and runtime plugin for interfacing the AMD AIE accelerator to IREE. It provides architectural overview, developer setup instructions, building guidelines, and runtime driver setup details. The repository focuses on enabling the integration of the AMD AIE accelerator with IREE, offering developers the tools and resources needed to build and run applications leveraging this technology.

This repository documents how to optimize common algorithms based on CUDA. It includes subdirectories with code implementations for specific optimizations. The optimizations cover topics such as compiling PyTorch from source, NVIDIA's reduce optimization, OneFlow's elementwise template, fast atomic add for half data types, upsample nearest2d optimization in OneFlow, optimized indexing in PyTorch, OneFlow's softmax kernel, linear attention optimization, and more. The repository also includes learning resources related to deep learning frameworks, compilers, and optimization techniques.

ZML is a high-performance AI inference stack built for production, using Zig language, MLIR, and Bazel. It allows users to create exciting AI projects, run pre-packaged models like MNIST, TinyLlama, OpenLLama, and Meta Llama, and compile models for accelerator runtimes. Users can also run tests, explore examples, and contribute to the project. ZML is licensed under the Apache 2.0 license.

LLM-TPU project aims to deploy various open-source generative AI models on the BM1684X chip, with a focus on LLM. Models are converted to bmodel using TPU-MLIR compiler and deployed to PCIe or SoC environments using C++ code. The project has deployed various open-source models such as Baichuan2-7B, ChatGLM3-6B, CodeFuse-7B, DeepSeek-6.7B, Falcon-40B, Phi-3-mini-4k, Qwen-7B, Qwen-14B, Qwen-72B, Qwen1.5-0.5B, Qwen1.5-1.8B, Llama2-7B, Llama2-13B, LWM-Text-Chat, Mistral-7B-Instruct, Stable Diffusion, Stable Diffusion XL, WizardCoder-15B, Yi-6B-chat, Yi-34B-chat. Detailed model deployment information can be found in the 'models' subdirectory of the project. For demonstrations, users can follow the 'Quick Start' section. For inquiries about the chip, users can contact SOPHGO via the official website.

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llm-resource is a comprehensive collection of high-quality resources for Large Language Models (LLM). It covers various aspects of LLM including algorithms, training, fine-tuning, alignment, inference, data engineering, compression, evaluation, prompt engineering, AI frameworks, AI basics, AI infrastructure, AI compilers, LLM application development, LLM operations, AI systems, and practical implementations. The repository aims to gather and share valuable resources related to LLM for the community to benefit from.

Prajna is an open-source programming language specifically developed for building more modular, automated, and intelligent artificial intelligence infrastructure. It aims to cater to various stages of AI research, training, and deployment by providing easy access to CPU, GPU, and various TPUs for AI computing. Prajna features just-in-time compilation, GPU/heterogeneous programming support, tensor computing, syntax improvements, and user-friendly interactions through main functions, Repl, and Jupyter, making it suitable for algorithm development and deployment in various scenarios.

The ByteIR Project is a ByteDance model compilation solution. ByteIR includes compiler, runtime, and frontends, and provides an end-to-end model compilation solution. Although all ByteIR components (compiler/runtime/frontends) are together to provide an end-to-end solution, and all under the same umbrella of this repository, each component technically can perform independently. The name, ByteIR, comes from a legacy purpose internally. The ByteIR project is NOT an IR spec definition project. Instead, in most scenarios, ByteIR directly uses several upstream MLIR dialects and Google Mhlo. Most of ByteIR compiler passes are compatible with the selected upstream MLIR dialects and Google Mhlo.

This repository provides a comprehensive guide to large language models (LLMs), covering various aspects such as training, fine-tuning, compression, and applications. It includes detailed tutorials, code examples, and explanations of key concepts and techniques. The repository is maintained by Liguo Dong, an AI researcher and engineer with expertise in LLM research and development.

This repository contains tools and libraries for building AIR platforms, runtimes and compilers.