meta-pytorch/gpt-fast
Gpt Fast
gpt-fast is a PyTorch transformer inference engine designed for text generation using a native tensor library implementation. It provides a runtime for executing large language models without the need for external C++ extensions.
The project implements speculative decoding to accelerate generation by using a small draft model for token prediction and a larger model for verification. It further optimizes performance through a compiled prefill stage and a multi-GPU tensor parallelism library that shards linear layers across multiple graphics processing units.
Memory efficiency is managed through a quantized runtime supporting int8 and int4 weight and grouped tensor quantization. The system also includes tools for architecture parameterization, text tokenization, and model accuracy evaluation using standardized harnesses.
Features
- Model Inference Runtimes - Provides a lightweight native PyTorch runtime for executing transformer-based text generation.
- Generative Text Inference - Implements a native tensor library for producing text outputs from language models.
- Speculative Decoding Strategies - Implements speculative decoding using a small draft model to accelerate token generation.
- PyTorch Tensor Operations - Performs transformer inference using native PyTorch tensor operations without requiring external C++ extensions.
- Weight Quantization - Provides a runtime that supports int8 and int4 weight quantization to decrease GPU memory consumption.
- Transformer Inference Engines - Implements a high-performance inference engine optimized specifically for transformer models.
- Multi-GPU Parallelism Strategies - Distributes large model weights across multiple GPUs using tensor parallelism to increase throughput.
- Compiled Prefill Implementations - Offers a high-performance implementation that optimizes the prefill stage through model compilation.
- Compiled Prefill Stages - Uses just-in-time compilation to optimize the initial prompt processing phase for faster generation.
- Compiled Prefills - Implements a compiled prefill stage to accelerate the initial processing of prompt tokens.
- Tensor-Parallel Inference Distributions - Provides a toolkit for splitting model weights across multiple GPUs using tensor parallelism.
- Model Quantization Frameworks - Provides a framework for converting high-precision model weights into lower-precision formats like int4.
- Quantized Inference Runtimes - Provides an execution environment designed to run compressed int8 and int4 quantized models.
- Grouped Quantization - Supports grouped tensor quantization to maintain precision while reducing memory usage.
- Linear Layer Sharding - Splits linear layer weights across multiple GPUs using row or column sharding to manage memory.
- Tensor Parallelism - Implements strategies for partitioning model weights across multiple GPUs to handle larger models.
- Weight Quantization - Reduces GPU memory consumption by compressing model weights into int8 and int4 formats.
- Speculative Decoding - Ships a framework that uses smaller draft models to accelerate token generation via verification.
- Language Model Development - Efficient PyTorch-native transformer text generation.
Star history
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