Why use different variations of Softmax in training and validation for neural networks with Pytorch?

Specifically, I'm working on a modeling project, and I see someone else's code that looks like

def forward(self, x):
x = self.fc1(x)
x = self.activation1(x)
x = self.fc2(x)
x = self.activation2(x)
x = self.fc3(x)
x = self.activation3(x)
# use log softmax + NLLLoss in training; softmax to make predictions
if self.training:
x = self.log_softmax(x)
else:
x = self.softmax(x)
return x


For context, this is using PyTorch, and it is on a classification problem. The criterion is NLLLoss. What's the rationale behind using log_softmax for training but using softmax for actual predictions?