Quantization for GenAI Models

Explore why quantization is necessary for generative AI models and learn its benefits and implementation, grounded in transformer-based architectures with self-attention, large parameters, and embeddings.

Explore model optimization techniques like distillation, quantization, and pruning to make large generative models, such as GPT-3 with 175 billion parameters, efficient for deployment on devices.

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Learn how pruning trims neural networks by removing unimportant weights, making models smaller and faster. Use unstructured or structured pruning, then magnitude or vendor pruning and finish with fine-tuning.

Explore knowledge distillation, transferring knowledge from a large teacher to a smaller student via soft labels, including logit distillation and feature based distillation, for efficient, high-performance models.

Explore how quantization reduces neural network size and compute by lowering precision of weights and activations, including post-training quantization and quantization aware training, with trade-offs in accuracy and hardware compatibility.

Explore data types used to represent numbers in quantized models, including int8 and floating point formats such as fp32, fp16, and bfloat16, and learn how precision affects performance.

Explore how integer data types are represented and computed in quantization, covering four-bit and eight-bit unsigned and signed integers, two's complement, signed magnitude, and value ranges.

Explore integer data types in torch, query min and max values with torch.info for 8-bit and 16-bit variants, and preview quantization workflows using PyTorch, transformers, and a fashion mnist cnn.

Explore eight bit fixed point numbers with four integer and fractional bits. Include unsigned and signed representations, explain binary fractions and powers of two, and show how -2.5 is represented.

Explore floating point numbers, including sign, mantissa, exponent, and bias, and learn how fp32, fp16, and bf16 balance precision and range for deep learning, subnormal numbers, zero, infinity, and NaN.

Explore fp16 and bf16 floating-point formats, compare their exponent and mantissa structures, ranges, and trade-offs between memory and computational cost for efficient deep-learning inference and training.

Explore floating point types in PyTorch using torch.finfo to compare float32, float16, bf16 and their min, max, and resolution; compare mantissa accuracy and effects of downcasting.

Explore fp8 formats for genai models, such as e4m3 and e5m2, balancing precision, range, and efficiency for training and inference. Review four-bit integers and Php4 for lightweight inferences.

Explore downcasting model parameters from fp32 to bf16 to halve storage from 280 GB to 140 GB, with a dynamic range similar to fp32 and fewer issues than fp16.

Experiment with downcasting fp32 tensors to bf16 in PyTorch and observe precision loss in values. Compare dot products to quantify the accuracy impact, illustrating consequences for model efficiency.

Downcast a PyTorch CNN from fp32 to bf16 and compare accuracy and model size on Fashion-MNIST. Observe negligible accuracy loss and about half the model size.

Explain how downcasting from fp32 to int8 shrinks model size, speeds up inference, and maps the range with linear scaling using min and max parameters, including symmetric and asymmetric quantization.

Map fp32 values to a symmetric int8 range around zero, covering -128 to 127 via a scaling factor. Quantize and dequantize to reveal quantization error and its impact on accuracy.

Explore asymmetric quantization, mapping fp32 to int8 using a scale and zero point. Compare with symmetric quantization, highlighting full-range utilization, accuracy gains, and extra parameter storage.

Explore GPT Neo 125 million parameter variant for causal language modeling and demonstrate quantizing fp32 weights to int8 for efficient deployment.

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