MLorc-optim 0.1.8

Unofficial implementation of Momentum Low-Rank Compression (MLorc) for memory-efficient LLM fine-tuning

MLorc - Momentum Low-Rank Compression for Memory-Efficient LLM Fine-tuning

Unofficial implementation of "MLorc: Momentum Low-rank Compression for Large Language Model Adaptation"

This repository introduces MLorc (Momentum Low-rank Compression), a novel and highly memory-efficient paradigm designed to significantly reduce the memory footprint of full-parameter fine-tuning for large language models. Based on the paper "MLorc: Momentum Low-rank Compression for Large Language Model Adaptation" this method offers a compelling alternative to existing memory-efficient techniques.

---

Install

pip install MLorc-optim

---

How MLorc Works

MLorc's core innovation lies in its approach to momentum compression and reconstruction:

• Direct Momentum Compression: It directly compresses and reconstructs both first and second-order momentum using Randomized SVD (RSVD) at each optimization step.
• Adaptive Second-Order Momentum Handling: To ensure stability, especially for non-negative second-order momentum, MLorc adaptively adds a small constant to zero values introduced by ReLU during reconstruction.

---

Key Advantages of MLorc

MLorc is broadly applicable to any momentum-based optimizer (e.g., Adam, Lion) and delivers superior performance:

• State-of-the-Art Performance: Empirically, MLorc consistently outperforms other memory-efficient methods like LoRA and GaLore in terms of validation accuracy. It can even match or exceed the performance of full fine-tuning with a small rank (e.g., rank=4).
• Memory and Time Efficiency: It maintains comparable memory efficiency to LoRA while demonstrating improved time efficiency compared to GaLore.
• Theoretical Guarantees: MLorc offers a theoretical guarantee for convergence, matching the convergence rate of the original Lion optimizer under reasonable assumptions.

---

Included MLorc-Integrated Optimizers

This repository integrates MLorc into six momentum-based optimizers, each with additional enhancements for improved performance and stability:

1. MLorc_AdamW: AdamW with MLorc compression, featuring:

   • Fused Backward Pass
   • Gradient Descent with Adaptive Momentum Scaling (Grams): For better performance and faster convergence.
   • atan2 smoothing & scaling: A robust replacement for eps (no tuning required), which also incorporates gradient clipping. (If enabled, eps is ignored.)
   • OrthoGrad: Prevents "naïve loss minimization" (NLM) that can lead to overfitting by removing the gradient component parallel to the weight, thus improving generalization

2. MLorc_Prodigy:

   • Same Features as MLorc_AdamW
   • Incorporates MLorc with the Prodigy adaptive method and its associated features.

3. MLorc_Lion: Lion with MLorc compression, featuring:

   • Fused Backward Pass
   • OrthoGrad
   • use_cautious: use the cautious varaint of Lion.
   • clip_threshold: whether to clip the gradients norm per-parameter as proposed in the paper Lions and Muons: Optimization via Stochastic Frank-Wolfe to make Lion more stable (default: 5.0, from the paper).

4. MLorc_DAdapt_Lion:

   • Same Features as MLorc_Lion
   • Integrates MLorc with the DAdaptation adaptive method for LION, and includes the slice_p feature (from Prodigy).

5. MLorc_Adopt:

   • Same Features as MLorc_AdamW
   • Implements the method of ADOPT: Modified Adam Can Converge with Any β_2 with the Optimal Rate.

6. MLorc_CAME:

   • Same Features as MLorc_AdamW
   • The first moment (momentum) is compressed using the low-rank factorization from MLorc, while the adaptive pre-conditioning and confidence-guided updates are from CAME: Confidence-guided Adaptive Memory Efficient Optimization.