adv-optm 2.5

A family of highly efficient, lightweight yet powerful optimizers.

Advanced Optimizers (AIO)

A comprehensive, all-in-one collection of optimization algorithms for deep learning, designed for maximum efficiency, minimal memory footprint, and superior performance across diverse model architectures and training scenarios.

🔥 What's New

In 2.4.x:

This update introduces a whole refactor of the library with many new features and changes:

• New optimizers state mode option (state_precision) with many precision settings for the optimizer states: rank-2 factored mode (factored), full FP32 (fp32), BF16 with Stochastic Rounding (bf16_sr), int8/uint8 with Stochastic Rounding (int8_sr), FP16 (fp16)
• Added new powerful optimizer: SinkSGD_adv.
• Added spectral scaling option to all optimizers, achieving width/rank invariant updates.
• Added Nesterov momentum (nesterov) and its coef (nesterov_coef) to all optimizers.
• Added centered weight decay (centered_wd), to pull the weights toward their pre-train state (anchor)
  • anchor precision can be changed to save memory (centered_wd_mode): full, float8, int8, int4
• Added Fisher Weight Decay option for Adam variants (fisher_wd).
  • Paper: FAdam...
• Added Factored Second Moment option for Adam variants (factored_2nd). This works alongside any state_precision setting.
• Added Geometric Weight Decay for SinkSGD_adv and SignSGD_adv.
• Added new powerful mode: variance normalized momentum (normed_momentum). Which applies the optimizer normalization before the momentum (also called as Normalization then momentum NtM)
  • For: AdamW_adv, SignSGD_adv, SinkSGD_adv.
• Added Variance/Confidence Preconditioning (snr_cond) for SignSGD_adv, SinkSGD_adv.
  • Only works with normed_momentum.
  • Technical reports: AASS, and sink-v.
• Added Adaptive Stochastic Sign with L_inf preconditioning (stochastic_sign) for SignSGD_Adv and Lion_adv.
• Improved CANS (accelerated_ns) for Muon variants, by integrating dynamic lower bound.
• Removed Simplified_AdEMAMix optimizer and its settings in other optimizers, they are now replaced by Nesterov momentum and its coef. Which is better and less hard to tune.
• Removed cautious and grams modes, as they were heuristic and not working well.
• Removed optimizers: Lion_Prodigy_adv, and Simplified_AdEMAMix.

in 2.1.x

• Added Signum (SignSGD with momentum): A new optimizer in the family (SignSGD_adv)
• More info coming soon.

in 2.0.x

• Implemented torch.compile for all advanced optimizers. Enabled via (compiled_optimizer=True) to fuse and optimize the optimizer step path.
• Better and improved 1-bit factored mode via (nnmf_factor=True).
• Various improvements across the optimizers.

in 1.2.x

• Added advanced variants of Muon optimizer with features and settings from recent papers.

Optimizer	Description
Muon_adv	Advanced Muon implementation with CANS, NorMuon, Low-Rank ortho, etc. features.
AdaMuon_adv	Advanced AdaMuon implementation, which combines Muon's geometry with Adam-like adaptive scaling and sign-based orthogonalization.

Documentation coming soon.

• Implemented Cautious Weight Decay for all advanced optimizers.

• Improved parameter update and weight decay for BF16 with stochastic rounding. The updates are now accumulated in float32 and rounded once at the end.

• Use fused and in-place operations whenever possible for all advanced optimizers.

• Prodigy variants are now 50% faster by avoiding CUDA syncs. Thanks to @dxqb!

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📦 Installation

pip install adv_optm

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🧠 Core Innovations

This library integrates multiple state-of-the-art optimization techniques validated through extensive research and practical training.

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