in Tacotron2 when modules are scripted in amp. Disable autocast in TorchScript by using `torch._C._jit_set_autocast_mode(False)` if you encounter this issue. PyTorch RN-08516-001_v23.07 | 120 Chapter the nvfuser backend for scripted models. Users can enable it using the context manager: with.torch.jit.fuser("fuser2"): PyTorch Release 21.04 PyTorch RN-08516-001_v23.07 | 200 ‣ Ubuntu 20.04 with executor at the beginning of your script might reduce this effect via: torch._C._jit_set_profiling_executor(False) torch._C._jit_set_profiling_mode(False) ‣ A workaround for the WaveGlow training regression

通过使用torch.jit.script函数来转换模型，我们就有能力编译和优化多层感知机中的计算，而模型的计算 结果保持不变。 506 12. 计算性能 net = torch.jit.script(net) net(x) tensor([[ 0.0722, -0.0190]], grad_fn=) 我们编写与之前相同的代码，再使用torch.jit.scrip net(x) net = torch.jit.script(net) with Benchmark('有torchscript'): for i in range(1000): net(x) 无torchscript: 0.1361 sec 有torchscript: 0.1204 sec 如以上结果所示，在nn.Sequential的实例被函数torch.jit.script脚本化后，通过使用符号式编程提高了计