adv-optm 2.5.4

A family of highly efficient, lightweight yet powerful optimizers.

Advanced Optimizers (AIO)

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

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📦 Installation

pip install adv_optm

Requires PyTorch 2.3+ for torch.compile support.

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What's New

🌟 Version 2.5.x: The Massive Refactor

This major update introduces a complete architectural refactor of the library:

🆕 New Optimizers & Scaling

• SinkSGD_adv: Added a powerful new optimizer to the lineup.
• Spectral Scaling: Now available across all optimizers, achieving width/rank invariant updates for highly stable training.

💾 Memory & State Precision Control

• Granular State Precision (state_precision): Drastically reduce memory overhead with new optimizer state modes:
  • factored (Rank-2 factored mode)
  • fp32 (Full precision)
  • bf16_sr & int8_sr (BF16/Int8 with Stochastic Rounding)
• Factored Second Moment (factored_2nd): Available for all Adam variants. Works seamlessly alongside any state_precision setting to further slash memory usage.

⚙️ Advanced Dynamics & Momentum

• Variance Normalized Momentum (normed_momentum): Applies optimizer normalization before momentum (Normalization then Momentum/NtM). Available for AdamW_adv, SignSGD_adv, and SinkSGD_adv.
• Universal Nesterov Momentum: Replaced the hard-to-tune Simplified_AdEMAMix with Nesterov momentum (nesterov) and a dedicated coefficient (nesterov_coef) across all optimizers.
• Preconditioning & Signs:
  • Added Variance/Confidence Preconditioning (snr_cond) for SignSGD_adv and SinkSGD_adv (requires normed_momentum). Read the technical reports: AASS & sink-v.
  • Added Adaptive Stochastic Sign with $L_\infty$ preconditioning (stochastic_sign) for SignSGD_Adv and Lion_adv.
• Improved CANS (accelerated_ns): Enhanced for Muon variants by integrating a dynamic lower bound.
• New OrthoGrad modes (orthogonal_gradient): Standard OrthoGrad flattened and a new matrix-wise mode iterative.

⚓ Weight Decay Innovations

• Centered Weight Decay (centered_wd): Pulls weights toward their pre-train state (anchor). To save memory, anchor precision (centered_wd_mode) can be set to full, float8, int8, or int4.
• Fisher Weight Decay (fisher_wd): Now available for Adam variants based on the FAdam paper.
• Geometric Weight Decay: Added specifically for SinkSGD_adv and SignSGD_adv.

(Note: Lion_Prodigy_adv, Simplified_AdEMAMix, and heuristic cautious/grams modes have been deprecated in favor of these superior, theoretically-grounded features).

Click to see older release notes (v1.2.x - v2.1.x)

Version 2.1.x

• New Optimizer: Added Signum (SignSGD with momentum) to the SignSGD_adv family.

Version 2.0.x

• ⚡ torch.compile Support: Fully implemented for all advanced optimizers. Enable via compiled_optimizer=True to heavily fuse and optimize the optimizer step path.
• 📉 1-Bit Factored Mode: Vastly improved implementation via nnmf_factor=True.
• 🛠️ Broad performance and stability improvements across all optimizers.

Version 1.2.x

• Advanced Muon Variants: Brought the groundbreaking Muon optimizer into the fold, enriched with features from recent literature.

Optimizer	Description
Muon_adv	Advanced Muon implementation featuring CANS, NorMuon, Low-Rank Orthogonalization, and more.
AdaMuon_adv	Combines Muon's geometry with Adam-like adaptive scaling and sign-based orthogonalization.

• Prodigy Speedup: Prodigy variants are now 50% faster by eliminating unnecessary CUDA syncs (Shoutout to @dxqb!).
• Stochastic Rounding for BF16: Parameter updates and weight decay now accumulate in float32 and round once at the end.
• Cautious Weight Decay: Implemented for all advanced optimizers (Paper).
• Fused Operations: Transitioned to fused and in-place operations wherever possible.

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💡 Core Innovations

(Documentation expanding on the theory and usage of these features is coming soon!)