When working with recurrent neural networks (RNNs) in PyTorch, it's not uncommon to encounter the error message: RuntimeError: cudnn RNN backward can only be called in training mode. This error can be quite frustrating, especially for those new to neural network challenges. Fortunately, understanding why this error occurs and how to fix it is fairly straightforward, once you grasp a few key concepts related to PyTorch's handling of RNNs, CUDNN backends, and training dynamics.
Understanding the Error
The error arises when PyTorch attempts to compute gradients for an RNN during the backward pass, using the highly efficient CUDNN library, but isn't in training mode. In this mode, specific computations for gradient updates are not prepared as they would be in forward pass only scenarios (evaluation).
PyTorch Modes: Training vs. Evaluation
In PyTorch, models often exhibit different behaviors during training and evaluation. The method call model.train() switches all relevant layers to training mode, which for many units includes allowing dropout layers to drop activations, and normalization layers to update their statistics. Conversely, the call model.eval() switches these to evaluation mode.
The Core Problem
Running the backward pass in evaluation mode is unsupported when using cudnn enabled RNNs due to optimization choices made in the RNN layer for inferencing (not expecting to compute gradients).
How to Fix the Error
Ensure Training Mode During Backpropagation
The simplest way to eliminate this error is to ensure your model is in training mode by calling model.train() before any backward passes if you're seeing this in unexpected scenarios.
import torch.nn as nn
# Sample Model
class SimpleRNN(nn.Module):
def __init__(self, input_size, hidden_size):
super(SimpleRNN, self).__init__()
self.rnn = nn.RNN(input_size, hidden_size)
self.fc = nn.Linear(hidden_size, 1)
def forward(self, x):
out, _ = self.rnn(x)
out = self.fc(out[-1])
return out
model = SimpleRNN(input_size=10, hidden_size=5)
model.train() # Ensure the model is in training mode
Adjust RNN Call in the Training Loop
In a training loop, when the model is in evaluation mode by mistake, switching to the training mode ensures the backward pass handles cudnn RNNs correctly:
for data in data_loader:
inputs, target = data
optimizer.zero_grad()
model.train() # Make sure we're training
output = model(inputs)
loss = criterion(output, target)
loss.backward()
optimizer.step()
Encapsulation for Flexibility
It can be useful to explicitly encapsulate training logic so as to avoid similar issues:
def train_model(train_loader, model, criterion, optimizer):
model.train()
for data in train_loader:
inputs, target = data
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, target)
loss.backward()
optimizer.step()
train_model(train_loader, model, criterion, optimizer)
Conclusion
Handling errors efficiently is key in machine learning workflows. By understanding PyTorch's training and evaluation modes and correctly transitioning between them, combining the best of CUDNN's optimized performances is possible while maintaining accurate backpropagation. The key takeaway is to ensure that any operation involving backward computation occurs when the model is explicitly in training mode.
Debugging RNN issues often comes down to these mode challenges and knowing the state of your model before starting important computation can save time and computing resources.
