Abstract
本文:描述automatic differentiation module of PyTorch
包括:Lua Torch, Chainer, HIPS Autograd
Task: Provides a high-performance environment on different devices(both CPUs and GPUs)
方法:不用symbolic differentiation, 而是使用differentiation on purely imperative programs
特点:focus on extensibility and low overhead
1. Background
2. Interface
3. Implementation
3.1 Supporting in-place operations
- differentiation CDeepFuzz Automatic Reading PyTorchdifferentiation cdeepfuzz automatic reading semantic-aware cdeepfuzz automatic libraries combinatorial cdeepfuzz learning reading pre-trained cdeepfuzz natural reading state-of-the-art cdeepfuzz the reading cdeepfuzz networks reading testing introduction-to-pytorch-reading-n pytorchstreamreader pytorch archive reading introduction-to-pytorch-reading-n introduction automatic