NVIDIA/FasterTransformer
FasterTransformer
FasterTransformer is a high-performance inference optimization library and distributed runtime designed to accelerate the execution of transformer models. It provides a toolkit for reducing model precision and parallelizing execution across multiple GPUs to increase throughput and reduce latency for large language models.
The framework utilizes a C++ backend with custom CUDA kernels to replace generic operations with optimized GPU instructions. It implements tensor and pipeline parallelism to shard model weights and distribute compute operations across multiple devices.
The system includes capabilities for low-precision weight quantization and incremental key-value cache management. It also provides utilities for text output streaming and various sampling strategies to control generated output.
Features
- Distributed Transformer Runtimes - Provides a high-performance distributed runtime for parallelizing transformer execution across multiple GPUs.
- Framework-Agnostic Backends - Implements core execution logic in C++ to allow seamless integration into diverse deep learning runtime environments.
- Hardware Acceleration Toolkits - Provides a toolkit for accelerating BERT and GPT models through optimized tensor operations and memory management.
- Inference Performance Optimization - Implements high-performance kernels and optimizations to accelerate complex language models and reduce deployment latency.
- Model Parallelism - Distributes heavy workloads across multiple processors and nodes using tensor and pipeline splitting to increase throughput.
- Precision Quantization - Converts high-precision weights to lower bit-width formats to reduce memory bandwidth and accelerate hardware math.
- Tensor Parallelism - Splits large weight matrices across multiple GPU devices to distribute memory load and compute operations.
- Inference Kernels - Implements hand-optimized C++ and CUDA kernels to replace generic operations with high-performance GPU instructions.
- Transformer Inference Engines - Provides high-performance implementations of transformer kernels to reduce latency and increase throughput for LLMs.
- Pipeline Parallelism Strategies - Divides model layers across a sequence of processors to overlap computation and increase total throughput.
- KV Cache Management - Implements incremental key-value cache management to avoid redundant calculations during autoregressive token generation.
- Model Quantization Tools - Includes tools for reducing the numeric precision of model weights to lower memory requirements and speed up inference.
- Weight Quantization - Reduces the numeric precision of weights to lower memory usage and increase processing speed during inference.
- Text Generation Strategies - Provides various search and sampling strategies to control the randomness and accuracy of generated text.
- Inference and Serving - NVIDIA framework for accelerated LLM inference.
- Mixture of Experts - Optimizes MoE model execution for cloud-scale production.
- Model Quantization Tools - Optimized transformer implementation for cloud-scale production.
- Transformer Implementations - Optimized transformer implementation for high-performance inference.
Star history
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