Quanto

The Quanto library is designed for the quantization of machine learning models, particularly focusing on techniques that improve inference efficiency on hardware with limited resources (such as mobile devices, embedded systems, or specialized AI accelerators). It helps to compress neural networks by reducing the precision of weights and activations, leading to smaller models, faster inference, and reduced energy consumption while attempting to maintain model accuracy.

Key Uses of the Quanto Library:

  1. Model Quantization: Quanto provides tools to apply various quantization techniques (such as 8-bit, 4-bit quantization) on neural networks, reducing the precision of weights and activations from higher precision (like FP32) to lower-bit representations.

  2. Post-Training Quantization: Similar to other quantization libraries, Quanto supports post-training quantization (PTQ), meaning the model can be quantized after it has been trained, without needing to modify the training process.

  3. Optimizing Inference: By quantizing the model, Quanto makes neural networks more efficient during inference, improving their performance on resource-constrained devices.

  4. Cross-Hardware Compatibility: Quanto aims to optimize models to run on a variety of hardware, from CPUs to specialized accelerators (like GPUs, TPUs, and other AI hardware), taking advantage of quantization-friendly operations on those devices.

Benefits of Using Quanto:

  • Faster inference: Quantized models use less computational power, allowing for quicker predictions.
  • Reduced memory usage: Lower-bit precision weights take up less storage, making models smaller.
  • Lower energy consumption: Quantized models are more efficient, consuming less power, especially important for mobile and edge devices.

Yes, Quanto (like most quantization libraries) creates a new version of the model that is optimized and quantized. The original full-precision model remains unchanged, and a new quantized model is generated. This quantized version has reduced precision in its weights and, depending on the type of quantization, possibly activations as well.

How Quanto (and similar tools) typically work:

  1. Load Original Model: The process starts with a full-precision model, usually trained with FP32 weights.
  2. Apply Quantization: Quanto applies quantization techniques (such as reducing precision to 8-bit or lower) to the model weights and possibly activations.
  3. Generate Quantized Model: A new model is created with quantized weights and activations. This model is smaller, more efficient, and ready for inference on hardware that supports quantized computations.
  4. Inference with Quantized Model: The new quantized model can be used for inference, which is typically faster and requires less memory than the original.

This quantized model is a new version of the model specifically optimized for efficiency at inference time, while the original full-precision model remains intact for possible further use (such as retraining, fine-tuning, or comparison).

Weights-only quantization

Quanto specializes in weight-only quantization, where only the model’s weights are quantized to lower precision (e.g., 8-bit or 4-bit), while activations remain unquantized or are handled dynamically during inference. This approach significantly reduces model size and memory bandwidth usage, especially useful for large models like transformers, where the weight matrices are the dominant storage component.

Quantized model is not serializable

Quanto performs quantization in-memory for inference optimization, but it doesn’t have the capability to serialize the quantized weights and model structure to a standard format (like a .pt file for PyTorch or .onnx for ONNX). The quantized model exists only temporarily in memory during the execution, and to reuse it, you must apply the quantization process again each time.