PyTorch
Understanding Triton Kernels from First Principles
A deep dive into how Triton kernels work, explained from absolute basics to complete understanding. …
Under the Hood: How PyTorch Chooses Attention Kernels and Why It Matters for Performance
A deep dive into PyTorch’s attention kernel selection and what each choice means for your …
From Theory to Practice: Quantization and Dequantization Made Simple
Quantization transforms floating-point values (‘float32’) into lower-precision formats, such as …
Breaking Down Vision Transformers: A Code-Driven Explanation
In this article, I’ll break down the layers of a ViT step by step with code snippets, and a …
The Simple Path to PyTorch Graphs: Dynamo and AOT Autograd Explained
Graph acquisition in PyTorch refers to the process of creating and managing the computational graph …
Profiling ResNet Models with PyTorch Profiler for Performance Optimization
In the realm of deep learning, model performance is paramount. Whether you’re working on image …
Warmup Wisdom: Accurate PyTorch Benchmarking Made Simple!
In the realm of PyTorch model benchmarking, achieving accurate results is paramount for gauging …