Quantization

As explained in Deep Learning with PyTorch, Second Edition

A model optimization technique that reduces numerical precision of parameters and operations to lower memory use and often speed up inference.

How Deep Learning with PyTorch, Second Edition covers it

How deep it goes

Defines 8-bit conversion and demonstrates both PyTorch API usage and custom linear-layer implementation.

Examples

Uses serialized model-size measurement, custom int8 linear layers, and PyTorch dynamic quantization.

What it assumes

Assumes familiarity with PyTorch models, parameters, operations, and layer types such as torch.nn.Linear.

What you can do after

Apply it via PyTorch’s dynamic quantization API or a custom int8 implementation.

How it connects

Contrasts with distillation and mixed-precision training, and connects to quantized tensors and linear layers.

liveBookmanning.com

Read Deep Learning with PyTorch, Second Edition on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  LLMs in ProductionQuantization is the conversion of model parameters or computations from higher-precision numerical representations to lower-precision representations.
• 

  liveBookmanning.com
  Domain-Specific Small Language ModelsMethods that reduce numeric precision or representational size of model values to improve efficiency, especially for inference; here the absmax method is a specific quantization procedure.
• 

  liveBookmanning.com
  AI-Powered SearchQuantization is the transformation of numerical values into a smaller set of discrete values to reduce representation precision and resource requirements.
• 

  liveBookmanning.com
  Deep Learning with Python, Third EditionThe technique of reducing numeric precision of model weights or activations to speed up computation while preserving output behavior as much as possible.
• 

  liveBookmanning.com
  Deep Learning with R, Third EditionModel quantization is the conversion of model computations or parameters to lower-precision numerical representations to reduce resource requirements.
• 

  liveBookmanning.com
  Math and Architectures of Deep LearningThe conversion of floating-point region boundaries or ratios into integer-aligned indices for feature extraction.
• 

  liveBookmanning.com
  Generative AI in ActionQuantization is the reduction of numerical precision used to represent model parameters, typically to reduce memory use and computational cost.
• 

  liveBookmanning.com
  Transformers in ActionQuantization is the transformation of numerical model parameters or activations into lower-precision representations to reduce memory consumption or computational cost.
• 

  liveBookmanning.com
  The Complete Obsolete Guide to Generative AIA quantized model is a machine-learning model whose numerical parameters or activations are represented at reduced precision to lower resource requirements.
• 

  liveBookmanning.com
  Time Series Forecasting Using Foundation ModelsQuantization is the conversion of continuous numerical values into a finite set of discrete levels or tokens.
• 

  liveBookmanning.com
  Natural Language Processing in Action, Second EditionQuantization is the transformation of numerical values into lower-precision discrete representations, often to reduce memory use and accelerate computation.
• 

  liveBookmanning.com
  Building Reliable AI SystemsQuantization reduces the numerical precision used to represent model values, often lowering storage or computation requirements.

Compare how 13 books explain quantization →

Related concepts

• QLoRAused by · QLoRA is a parameter-efficient fine-tuning method that quantizes a pretrained model’s weights while training low-rank adapters to reduce memory requirements.
• scalar quantizationincludes · Scalar quantization maps numerical scalar values to a finite set of discrete levels, typically reducing their precision or range.
• QA-LoRAused by · QA-LoRA is a low-rank adaptation method that integrates quantization-aware operations and group-wise processing to support efficient low-bit adaptation.
• post-training quantizationincludes · Post-training quantization is the conversion of a trained model’s parameters or computations to lower-precision representations without additional training.
• quantization-aware trainingincludes · Quantization-aware training optimizes a model while simulating or incorporating reduced-precision effects so it adapts to quantized representations.
• PyTorchuses · PyTorch is an open-source machine-learning framework for Python that provides tensor computation, automatic differentiation, and tools for building and training neural networks.

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with PyTorch, Second Edition. The section links point to the parts of the book it draws on.