fms-acceleration-ilab 0.1.0

FMS Acceleration Plugin for Functionalities Used in Instruct Lab Training

FMS Acceleration for Instruct Lab

This library contains plugins to accelerate finetuning with the following optimizations:

1. Padding-Free Flash Attention Computation

Plugins

Plugin	Description	Depends	Loading	Augmentation	Callbacks
padding_free	Padding-Free Flash Attention Computation	flash_attn		✅	✅

Native Transformers Support from v4.44.0

Transformers natively supports padding-free from v4.44.0 see here. The padding-free plugin will use the transformers library if compatible, otherwise if transformers < v4.44.0 the plugin will use an internal implementation instead.

Known Issues

Currently Only Supports Pre-Tokenized Dataset

The padding-free plugin currently only works with pre-tokenized datasets, this is because it is currently designed to replace the data collator from SFTTrainer with a custom data collator to manipulate the input to the modified flash attention forward.

There are some cases, the data collator for SFTTrainer will handle the formatting and tokenization from raw text datasets. The plugin is currently unable to both handle the original data collation and apply its custom data collator over it as the same time. This issue will be addressed in a future commit to support this case.

In the meantime, the plugin expects the user to provide a pretokenized dataset that

• is formatted with a template for instruct-tuning cases
• is tokenized
• has template labels that are masked to exclude from loss computation
• has eos token appended