fms-acceleration-foak 0.2

FMS Acceleration using Fused Operations and Kernels

FMS Acceleration for Fused Operations and Kernels

This library contains fused operations and custom kernels, to be expanded over time. Currently it contains the following:

1. Fused operations and kernels extracted from unsloth.
   • Low-Rank Adapter Fused Operations
   • Fast RoPE Triton Kernels
   • Fast RMS LayerNorm Triton Kernels
   • Fast Cross Entropy Triton Kernels

Plugins

Plugin	Description	Depends	Loading	Augmentation	Callbacks
fast_quantized_peft	LoRA fused ops, fast cross-entropy, fast rms, fast RoPE	Contains extracted code		✅

Code Extracted from Unsloth

Notes on the extraction of code from unsloth:

• While unsloth is released under Apache 2.0, there are comments indicating some exceptions strewn throughout the code base, see an example here.

  it would require a commercial license if used to run on more than 4 GPUs ...
  

• These exceptions appear to be located around the trainer improvements, see another example here.
• These exceptions appear around Feb 2024 Release; any code that appears in any file where such exceptions occur is not extracted.
• Instead in its place, we have adopted a different approach; we adopt the approach of model patching, as opposed unsloths' approach to rewrite the model. Our approach is novel and completely rewritten from scratch.
• We have also enabled dropout on the lora fused operations.
• All extracted code appears before the Feb 2024 Release.
• In the table below we record what was extracted, and the exact commit from which it was taken.

Path	Description	Extracted From	Modifications	Date
fused_ops/unsloth_lora	QLoRA fast dequant, activation kernels	unsloth/main @ 1ecc0185		28 Jan 2024
fused_ops/unsloth_lora/bnb	BNB fast lora	unsloth/main @ 1ecc0185	fast_lora.py	28 Jan 2024
fused_ops/unsloth_lora/gptq	GPTQ fast dequant (triton_v2)	jeromeku/main @ 2839d39	fast_lora.py triton/layers.py	6 Feb 2024
kernels/unsloth	Fast RMS, RoPE, CrossEnt kernels	unsloth/main @ 1ecc0185	cross_entropy_loss.py rms_layernorm.py	28 Jan 2024

Known Issues

• MixedPrecision --fp16 should be used fast_lora. Also consider loading the model in torch.float16.
• fast_lora has issues with FSDP with the peft style of FSDP wrapping.
  • This is because the adapter's forward functions are bypassed in the fused ops.
  • For AutoGPTQ this is addressed by distributing the adapters using DDP so they will be unsharded in time for the fused ops.
  • However for QLoRA this is not yet done https://github.com/foundation-model-stack/fms-acceleration/issues/3.
• fast_rope_embeddings does not work with position_ids. Currently position_ids are ignored and could give wrong results.