Overview of PyTorch 2.0 Features and New APIs
The article provides a detailed overview of PyTorch 2.0, highlighting its stable and beta features such as torch.compile, accelerated transformers, MPS backend, new quantization support, and prototype parallelism tools, while emphasizing performance improvements for dynamic shapes, distributed training, and CPU/GPU inference.
PyTorch 2.0 Release Overview
At PyTorch Conference 2022 the team announced PyTorch 2.0, with the stable version released in March. The new release retains eager mode while fundamentally improving the compiler stack, offering faster performance for dynamic shapes and distributed execution.
Stable Features
torch.compile as the primary API for model compilation, fully backward‑compatible.
scaled_dot_product_attention added to torch.nn.functional.
Metal Performance Shaders (MPS) backend for GPU acceleration on macOS, covering over 300 operators.
torch.func module exposing the functorch API.
Beta / Prototype Features
Accelerated Transformers (formerly Better Transformers) with custom kernels for scaled dot‑product attention.
Improved CPU inference on AWS Graviton3 and oneDNN Graph for GNN workloads.
New dispatchable collectives API allowing seamless GPU/CPU backend selection.
Prototype support for DTensor, TensorParallel, 2D Parallel, and dynamic torch.compile.
Additional enhancements include default device context managers, an X86 quantization backend using FBGEMM and oneDNN, and broader support for torch.autograd.Function transformations.
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