# Optimizations Guide

A guide to the performance and memory optimizations available in Axolotl.

Axolotl includes numerous optimizations to speed up training, reduce memory usage, and handle large models.

This guide provides a high-level overview and directs you to the detailed documentation for each feature.

## Speed Optimizations

These optimizations focus on increasing training throughput and reducing total training time.

### Sample Packing

Improves GPU utilization by combining multiple short sequences into a single packed sequence for training. This requires enabling one of the attention implementations below.

- Config: `sample_packing: true`
- Learn more: Sample Packing

### Attention Implementations

Using an optimized attention implementation is critical for training speed.

- Flash Attention 2: `flash_attention: true`. (Recommended) The industry standard for fast attention on modern GPUs. Requires Ampere or higher. For AMD, check AMD Support.
- Flex Attention: `flex_attention: true`.
- SDP Attention: `sdp_attention: true`. PyTorch’s native implementation.
- Xformers: `xformers_attention: true`. Works with FP16.

Note: You should only enable one attention backend.

### LoRA Optimizations

Leverages optimized kernels to accelerate LoRA training and reduce memory usage.

- Learn more: LoRA Optimizations Documentation

## Memory Optimizations

These techniques help you fit larger models or use bigger batch sizes on your existing hardware.

### Parameter Efficient Finetuning (LoRA & QLoRA)

Drastically reduces memory by training a small set of “adapter” parameters instead of the full model. This is the most common and effective memory-saving technique.

- Examples: Find configs with `lora` or `qlora` in the examples directory.
- Config Reference: See `adapter`, `load_in_4bit`, and `load_in_8bit` in the Configuration Reference.

### Gradient Checkpointing & Activation Offloading

These techniques save VRAM by changing how activations are handled.

- Gradient Checkpointing: re-computes activations during the backward pass, trading compute time for VRAM.
- Activation Offloading: moves activations to CPU RAM or disk, trading I/O overhead for VRAM.
- Learn more: Gradient Checkpointing and Offloading Docs

### Cut Cross Entropy (CCE)

Reduces VRAM usage by using an optimized cross-entropy loss calculation.

- Learn more: Custom Integrations - CCE

### Liger Kernels

Provides efficient Triton kernels to improve training speed and reduce memory usage.

- Learn more: Custom Integrations - Liger Kernels

## Long Context Models

Techniques to train models on sequences longer than their original context window.

### RoPE Scaling

Extends a model’s context window by interpolating its Rotary Position Embeddings.

- Config: Pass the `rope_scaling` config under the `overrides_of_model_config:`. To learn how to set RoPE, check the respective model config.

### Sequence Parallelism

Splits long sequences across multiple GPUs, enabling training with sequence lengths that would not fit on a single device.

- Learn more: Sequence Parallelism Documentation

### Artic Long Sequence Training (ALST)

ALST is a recipe that combines several techniques to train long-context models efficiently. It typically involves:

- TiledMLP to reduce memory usage in MLP layers.

- Tiled Loss functions (like CCE.

- Activation Offloading to CPU.

- Example: ALST Example Configuration

## Large Models (Distributed Training)

To train models that don’t fit on a single GPU, you’ll need to use a distributed training strategy like FSDP or DeepSpeed. These frameworks shard the model weights, gradients, and optimizer states across multiple GPUs and nodes.

- Learn more: Multi-GPU Guide
- Learn more: Multi-Node Guide

### N-D Parallelism (Beta)

For advanced scaling, Axolotl allows you to compose different parallelism techniques (e.g., Data, Tensor, Sequence Parallelism). This is a powerful approach to train an extremely large model by overcoming multiple bottlenecks at once.

- Learn more: N-D Parallelism Guide

## Quantization

Techniques to reduce the precision of model weights for memory savings.

### 4-bit Training (QLoRA)

The recommended approach for quantization-based training. It loads the base model in 4-bit using `bitsandbytes` and then trains QLoRA adapters. See Adapter Finetuning for details.

### FP8 Training

Enables training with 8-bit floating point precision on supported hardware (e.g., NVIDIA Hopper series GPUs) for significant speed and memory gains.

- Example: Llama 3 FP8 FSDP Example

### Quantization Aware Training (QAT)

Simulates quantization effects during training, helping the model adapt and potentially improving the final accuracy of the quantized model.

- Learn more: QAT Documentation

### GPTQ

Allows you to finetune LoRA adapters on top of a model that has already been quantized using the GPTQ method.

- Example: GPTQ LoRA Example