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...
April 15, 2023
Experience the power of PyTorch 2.0 on AMD Solutions
PyTorch 2.0 represents a significant step forward for the PyTorch machine learning framework. The stable release of PyTorch 2.0 brings new features that unlock even higher performance, while remaining backward compatible with prior releases and retaining the Pythonic focus which has helped to make PyTorch so enthusiastically adopted by the AI/ML community. AMD has long been a strong proponent of PyTorch, and we are delighted that the PyTorch 2.0 stable release includes support for AMD Instin...
April 14, 2023
Accelerated Generative Diffusion Models with PyTorch 2
TL;DR: PyTorch 2.0 nightly offers out-of-the-box performance improvement for Generative Diffusion models by using the new torch.compile() compiler and optimized implementations of Multihead Attention integrated with PyTorch 2.
April 07, 2023
Straggler Mitigation On PyTorch DDP By Hierarchical SGD
PyTorch DDP has been widely adopted across the industry for distributed training, which by default runs synchronous SGD to synchronize gradients across model replicas at every step. The performance of this technique is critical for fast iteration during model exploration as well as resource and cost saving. The performance is critical for fast iteration and cost saving of model development and exploration. To resolve a ubiquitous performance bottleneck introduced by slow nodes in large-scale ...
