RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cpu and cuda:0! When predicting with my model error occurs when there are at least two tensors in the computation graph that are allocated on different devices (one on the CPU and the other on the GPU).
To fix this error, use the “input.to(dev)” function to move the input tensor to the GPU device.
Here’s an example of how to move a tensor to the GPU device:
# Move the input tensor to the GPU device
input_tensor = input_tensor.to('cuda')
Another way is to move the output tensor to the CPU device as follows:
# Move the output tensor to the CPU device
output_tensor = output_tensor.to('cpu')
It’s essential to ensure that all tensors are on the same device before executing any calculations to avoid this error.
Here is the step-by-step guide to fix it:
Step 1: Load your model and move it to the desired device (GPU in this case)
import torch
from transformers import AutoModel
model = AutoModel.from_pretrained("model_name")
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
Step 2: Move your input tokens to the same device
tokens = tokenizer("Your input text", return_tensors="pt")
tokens = tokens.to(device)
Step 3: Perform the prediction with both the model and input tokens on the same device
with torch.no_grad():
output = model(**tokens)
By ensuring that both the model and input tokens are on the same device, you should no longer encounter the RuntimeError.
Krunal Lathiya is a seasoned Computer Science expert with over eight years in the tech industry. He boasts deep knowledge in Data Science and Machine Learning. Versed in Python, JavaScript, PHP, R, and Golang. Skilled in frameworks like Angular and React and platforms such as Node.js. His expertise spans both front-end and back-end development. His proficiency in the Python language stands as a testament to his versatility and commitment to the craft.