RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same

RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same

This error message occurs in PyTorch when you attempt to perform an operation involving a tensor that is on the CPU (torch.FloatTensor) and a tensor that is on the GPU (torch.cuda.FloatTensor). PyTorch requires that the data types of both the input and weights match.

Understanding the Error

Why it occurs:

• Data placement: PyTorch allows you to run computations on both the CPU and GPU. This error arises when you try to process data located on different devices without explicitly moving them to the same device.
• Tensor types: When you work with tensors, you need to ensure they reside on the same device (CPU or GPU). The ‘torch.FloatTensor’ type signifies a tensor residing on the CPU, while ‘torch.cuda.FloatTensor’ indicates a tensor on the GPU.

Example:

import torch
import torch.nn as nn

# Define a simple neural network
class SimpleNet(nn.Module):
  def __init__(self):
    super(SimpleNet, self).__init__()
    self.fc = nn.Linear(10, 5) # Weight initialized on CPU

  def forward(self, x):
    return self.fc(x)

# Create an instance of the model
model = SimpleNet()

# Sample input data
input_data = torch.randn(1, 10)

# Move input to GPU 
input_data = input_data.cuda() 

# Attempt to process input
output = model(input_data) # Error: RuntimeError: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same

Troubleshooting

• Check your tensors’ locations: Use `input_data.device` and `model.fc.weight.device` to check the locations of your input data and model weights, respectively.
• Move your data to the GPU: If your input data is on the CPU and your model is on the GPU (or vice versa), use the `.to(‘cuda’)` method to move them to the same device:

  
      model.to('cuda') # Move model to GPU
      input_data = input_data.to('cuda') # Move data to GPU
      

• Use `.cuda()` in the model definition: For new models, create your layers on the GPU:

  
      class SimpleNet(nn.Module):
        def __init__(self):
          super(SimpleNet, self).__init__()
          self.fc = nn.Linear(10, 5).cuda() # Initialize on GPU 
      

Code Example (Corrected):

import torch
import torch.nn as nn

class SimpleNet(nn.Module):
  def __init__(self):
    super(SimpleNet, self).__init__()
    self.fc = nn.Linear(10, 5).cuda() # Initialize on GPU

  def forward(self, x):
    return self.fc(x)

# Create an instance of the model
model = SimpleNet()

# Sample input data
input_data = torch.randn(1, 10).cuda() # Move input to GPU

# Attempt to process input
output = model(input_data) # This now works correctly

Summary

Ensuring that your input data and model parameters reside on the same device (either CPU or GPU) is crucial in PyTorch. By consistently moving your tensors to the appropriate device, you can avoid the “RuntimeError: Input type (torch.FloatTensor) and weight type (torch.cuda.FloatTensor) should be the same” error and ensure seamless computations within your PyTorch models.