Dynamic fusion is a cutting-edge optimization technique in deep learning frameworks like PyTorch, aimed at enhancing efficiency by combining multiple operations into a single, optimized computational unit (kernel). This approach reduces memory overhead, accelerates execution, and is particularly powerful when paired with Just-In-Time (JIT) compilation, TorchScript, and quantization. In this comprehensive article, we explore dynamic … Continue reading Dynamic Fusion in PyTorch: The Future of Accelerated Deep Learning with JIT, TorchScript, and Quantization