Troubleshooting "Very simple torch.tensor().to("cuda") gives CUDA error" in PyTorch

Error Context:

This error arises when you attempt to transfer a PyTorch tensor to a CUDA device (GPU) using tensor.to("cuda"), but there's an issue preventing the transfer. It typically indicates a problem on the GPU side, hence the term "device-side assert triggered."

Common Causes:

1. Unavailable CUDA Device:

   • The most likely cause is that your system might not have a compatible NVIDIA GPU or doesn't have CUDA enabled.
   • To verify, use torch.cuda.is_available(). If False, your system lacks a suitable GPU.

2. Incorrect Device String:

   • Double-check that you're using the correct device string: "cuda" for the first available CUDA device.
   • If you have multiple GPUs or want to specify a particular one, use "cuda:X", where X is the device index.

3. Tensor Data Type Issues:

   • In rare cases, the tensor's data type (e.g., float32, int64) might not be fully supported on your GPU.
   • Try creating the tensor on the desired device explicitly: tensor = torch.tensor(data, dtype=torch.float32, device="cuda").

4. PyTorch or CUDA Version Mismatch:

   • Outdated PyTorch or CUDA versions can lead to compatibility problems.
   • Ensure you're using compatible versions. Consider updating if necessary.

Debugging Steps:

1. Check for CUDA Availability:

   if torch.cuda.is_available():
       # Proceed with CUDA operations
   else:
       print("CUDA not available. Using CPU...")
   

2. Handle Multiple GPUs (Optional):

   device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
   tensor = torch.tensor(data, device=device)
   

3. Consider Data Type Compatibility (if needed):

   tensor = torch.tensor(data, dtype=torch.float32, device="cuda")
   

4. Print Error Details (Advanced):

By following these steps and understanding the potential causes, you should be able to effectively resolve the "device-side assert triggered" error and successfully transfer your PyTorch tensors to the GPU for computations.

import torch

# Attempt to create a tensor on the GPU (may fail)
try:
  tensor = torch.tensor([1, 2, 3]).to("cuda")
  print(tensor)  # This line might not execute if GPU is unavailable
except RuntimeError as e:
  if "CUDA not available" in str(e):
    print("CUDA unavailable. Using CPU...")
    tensor = torch.tensor([1, 2, 3])  # Create on CPU
  else:
    raise e  # Re-raise other errors

print(tensor)

This code first tries to create a tensor on the GPU using to("cuda"). If a CUDA device is unavailable, a RuntimeError will occur with a message indicating that CUDA is not found. The code then handles this error by creating the tensor on the CPU using torch.tensor([1, 2, 3]) and prints it.

import torch

# Assuming you have multiple GPUs
device = "cuda:1"  # Change this to the correct device index if needed

# Attempt to create a tensor on an incorrect device (may fail)
try:
  tensor = torch.tensor([1, 2, 3]).to(device)
  print(tensor)
except RuntimeError as e:
  if "invalid device" in str(e):
    print("Invalid device specified. Using first available CUDA device...")
    device = "cuda:0"  # Change to the correct available device
    tensor = torch.tensor([1, 2, 3]).to(device)
  else:
    raise e  # Re-raise other errors

print(tensor)

This code assumes you have multiple GPUs. It attempts to create a tensor on a specific device (cuda:1). If the specified device is not available, you'll get a RuntimeError with a message indicating an invalid device. The code then handles this error by using the first available CUDA device (cuda:0) and creates the tensor on that device.

Remember:

• Replace "cuda:1" with the actual index of your desired GPU if you have multiple.
• Adapt these examples to your specific needs and error messages.
• Consider using torch.cuda.device_count() to check for the number of available CUDA devices.

Explicit Device Creation:

• Create the tensor directly on the desired device using torch.tensor(data, device="cuda"). This approach explicitly specifies the device during tensor creation.

import torch

data = [1, 2, 3]

# Create tensor on the first available CUDA device
tensor_cuda = torch.tensor(data, device="cuda")

# Create tensor on a specific device (replace 0 with desired index)
device = torch.device("cuda:0")
tensor_specific_cuda = torch.tensor(data, device=device)

torch.device for Flexibility:

• Use torch.device to create a device object representing CPU or GPU, then pass it to tensor creation or model operations. This provides flexibility for switching between CPU and GPU.

import torch

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Create tensor based on device availability
tensor = torch.tensor(data, device=device)

# Move model to the device
model.to(device)

with torch.cuda.device(device): Context Manager:

• Use the with torch.cuda.device(device): context manager to temporarily set the current device for all PyTorch operations within the block. This is useful for code sections that involve multiple tensors on the same device.

import torch

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

with torch.cuda.device(device):
  # All operations within this block will use the specified device
  tensor1 = torch.randn(2, 3)
  tensor2 = torch.randn(2, 3)
  # ... perform operations on tensors

Choosing the Right Method:

• If you consistently work on the GPU and want a concise approach, explicit device creation is suitable.
• If you need to switch between CPU and GPU or have dynamic device allocation, torch.device offers flexibility.
• The context manager is useful for code sections involving multiple tensors on the same device.

By understanding these alternate methods, you can effectively transfer PyTorch tensors to the GPU and streamline your deep learning workflows.