torchquery 2.1.2

High-performance SDC detection and neural healing for billion-scale tensors.

TorchQuery 🛡️

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TorchQuery is a high-performance reliability engine for PyTorch. It provides a "Neural Shield" against Silent Data Corruption (SDC), hardware bit-flips, and numerical instability in massive Deep Learning models.

🚀 Key Features

• Billion-Scale Protection: Optimized streaming logic designed to handle tensors with $10^9$ elements without crashing.
• Neural Healing: Automatically detects and repairs corrupted weights or neurons using statistical outlier detection ($\sigma$-clamping).
• Distributed SyncBatch: Cluster-aware protection using All-Reduce to ensure safety across multi-GPU and multi-server environments.
• Zero-Invasive: Simply wrap your existing tensors or model parameters; no architecture changes required.

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📦 Installation

pip install torchquery

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  <img src="https://raw.githubusercontent.com/powerofaisinstudy-debug/torchquery/main/chl.png" width="600">
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### Visualizing Silent Data Corruption (SDC)

Hardware glitches—like cosmic rays or VRAM overclocks—can cause random bit-flips. These create massive statistical outliers or `NaNs` in your tensor data.

[Image Link to Image_5.png]

**TorchQuery** acts as a `Neural Shield` that sweeps your multidimensional arrays. It identifies values that can lead to exploding gradients (`3e38`) or numerical instability (`NaN`), "healing" them before they propagate.

**Pre-Sweep State:**
* `NaN` (Not a Number): Corrupts entire model during backpropagation.
* `3e38`: Causes exploding gradients, destroying training stability.

**Post-Sweep State:**
* Invalid data is removed, leaving behind validated tensor values.