FMInference/FlexGenArchived
FlexGen
FlexGen is an inference engine for large language models designed for high-throughput execution on single or multiple GPUs. It functions as a framework for managing model execution through a combination of memory offloading, weight compression, and pipeline orchestration.
The system enables the execution of models that exceed available GPU memory by moving tensors and caches between GPU memory, system RAM, and disk storage. It utilizes 4-bit weight quantization to reduce the memory footprint of model parameters, allowing for increased batch processing capacity.
The project covers distributed execution through pipeline parallelism across multiple machines, as well as memory optimization techniques such as asynchronous tensor loading and attention cache compression.
Features
- Efficient Inference Engines - A high-throughput inference engine for large language models optimizing for memory efficiency and speed.
- High Throughput Inference - Maximizes inference requests per second through optimized data and weight flow and batching.
- Large Language Model Optimization - Optimizes large language model inference using memory-efficient offloading and data compression.
- Parallelism Orchestration - Coordinates the splitting of model execution across multiple GPUs on separate machines.
- Weight Offloading - Moves model parameters between GPU memory, system RAM, and disk to run models exceeding VRAM limits.
- Weight Quantization - Implements 4-bit weight quantization to reduce the memory footprint of model parameters.
- Quantized Model Runners - Provides an execution environment using 4-bit precision to compress weights and increase batch processing capacity.
- Model Layer Offloading - Enables execution of oversized models by moving tensors and caches to system RAM or disk.
- Pipeline Parallelism Strategies - Distributes model layers across multiple GPUs on different machines to scale inference for massive models.
- Memory Offloading Frameworks - Implements a mechanism to move model tensors between GPU memory, system RAM, and disk.
- Asynchronous Tensor Loading - Overlaps weight transfers from host memory to GPU with the computation of current model layers.
- Single-GPU Scaling - Deploys massive generative models on limited hardware using weight compression and efficient memory management.
- Sparsity-Aware Weight Compression - Reduces memory usage of weights and caches using 4-bit precision to increase throughput.
- KV Cache Management - Reduces memory overhead of the KV cache to support larger batches and token counts.
- Hardware Optimized Inference - High-throughput generative inference optimized for single GPU environments.
- Inference Frameworks - High-throughput generative inference on single-GPU systems.
- Model Serving Engines - Throughput-oriented inference engine for running models on single GPUs.
