May 22, 2023
Out of the box acceleration and memory savings of 🤗 decoder models with PyTorch 2.0
As part of PyTorch 2.0 release, an accelerated implementation of the attention mechanism as part of the “Better Transformer” project (and known in PyTorch as Accelerated Transformers) has been added natively into PyTorch as torch.nn.functional.scaled_dot_product_attention. This implementation leverages fused kernels from FlashAttention and Memory-efficient attention, and supports both training and inference.
May 12, 2023
Language Identification: Building an End-to-End AI Solution using PyTorch
Language Identification is the process of identifying the primary language from multiple audio input samples. In natural language processing (NLP), language identification is an important problem and a challenging issue. There are many language-related tasks such as entering text on your phone, finding news articles you enjoy, or discovering answers to questions that you may have. All these tasks are powered by NLP models. To decide which model to invoke at a particular point in time, we must...
May 02, 2023
Accelerated Image Segmentation using PyTorch
Using Intel® Extension for PyTorch to Boost Image Processing Performance
April 27, 2023
Introducing Hidet: A Deep Learning Compiler for Efficient Model Serving
Hidet is a powerful deep learning compiler that simplifies the process of implementing high-performing deep learning operators on modern accelerators (e.g., NVIDIA GPUs). With the new feature of torch.compile(...) in PyTorch 2.0, integrating a novel compiler into PyTorch is easier than ever - Hidet now can be used as a torch.compile(...) backend to accelerate PyTorch models, making it an attractive option for PyTorch users who want to improve the inference performance of their models, especia...
April 19, 2023
Accelerating Large Language Models with Accelerated Transformers
TL;DR. We show how to use Accelerated PyTorch 2.0 Transformers and the newly introduced torch.compile() method to accelerate Large Language Models on the example of nanoGPT, a compact open-source implementation of the GPT model from Andrej Karpathy. Using the new scaled dot product attention operator introduced with Accelerated PT2 Transformers, we select the flash_attention custom kernel and achieve faster training time per batch (measured with Nvidia A100 GPUs), going from a ~143ms/batch ba...
