Tests_Scripts.md

TorchDevice Demo Scripts

Test Scripts (Demos)

Below are several test scripts using NumPy arrays and PyTorch tensors that demonstrate how to use TorchDevice. You can include these as demos in your project.

Demo 1: Basic Tensor Computation

# demo_basic_tensor.py

import TorchDevice
import torch
import numpy as np

def main():
    # Select the default device
    device = torch.device('cuda')

    # Create NumPy arrays
    np_array1 = np.array([1, 2, 3], dtype=np.float32)
    np_array2 = np.array([4, 5, 6], dtype=np.float32)

    # Convert to PyTorch tensors and move to device
    tensor1 = torch.from_numpy(np_array1).to(device)
    tensor2 = torch.from_numpy(np_array2).to(device)

    # Perform tensor operations
    result = tensor1 + tensor2

    # Move result back to CPU and convert to NumPy
    result_np = result.cpu().numpy()

    print(f"Result: {result_np}")

if __name__ == '__main__':
    main()

Usage:

python demo_basic_tensor.py

---

Demo 2: Matrix Multiplication

# demo_matrix_multiplication.py

import TorchDevice
import torch
import numpy as np

def main():
    device = torch.device('cuda')

    # Create random matrices
    np_matrix1 = np.random.rand(3, 3).astype(np.float32)
    np_matrix2 = np.random.rand(3, 3).astype(np.float32)

    # Convert to tensors
    tensor_matrix1 = torch.from_numpy(np_matrix1).to(device)
    tensor_matrix2 = torch.from_numpy(np_matrix2).to(device)

    # Matrix multiplication
    result = torch.matmul(tensor_matrix1, tensor_matrix2)

    # Move result back to CPU
    result_cpu = result.cpu()

    print(f"Matrix 1:\n{np_matrix1}")
    print(f"Matrix 2:\n{np_matrix2}")
    print(f"Result:\n{result_cpu.numpy()}")

if __name__ == '__main__':
    main()

Usage:

python demo_matrix_multiplication.py

---

Demo 3: Neural Network Training Loop

# demo_neural_network.py

import TorchDevice
import torch
import torch.nn as nn
import torch.optim as optim

def main():
    device = torch.device('cuda')

    # Simple dataset
    x = torch.linspace(-1, 1, 100).unsqueeze(1).to(device)
    y = x.pow(3) + 0.3 * torch.rand(x.size()).to(device)

    # Define a simple neural network
    model = nn.Sequential(
        nn.Linear(1, 20),
        nn.ReLU(),
        nn.Linear(20, 1)
    ).to(device)

    criterion = nn.MSELoss()
    optimizer = optim.SGD(model.parameters(), lr=0.01)

    # Training loop
    for epoch in range(200):
        model.train()
        optimizer.zero_grad()
        outputs = model(x)
        loss = criterion(outputs, y)
        loss.backward()
        optimizer.step()

        if (epoch + 1) % 50 == 0:
            print(f'Epoch [{epoch + 1}/200], Loss: {loss.item():.4f}')

    # Test the model
    model.eval()
    test_input = torch.tensor([[0.5]]).to(device)
    prediction = model(test_input)
    print(f'Prediction for input 0.5: {prediction.item():.4f}')

if __name__ == '__main__':
    main()

Usage:

python demo_neural_network.py

---

Demo 4: Handling Unsupported Functions

# demo_unsupported_functions.py

import TorchDevice
import torch

def main():
    device = torch.device('cuda')

    # Try to use an unsupported CUDA function
    torch.cuda.ipc_collect()

    # Proceed with other operations
    tensor = torch.tensor([1, 2, 3], device=device)
    print(f"Tensor on {device.type}: {tensor}")

if __name__ == '__main__':
    main()

Usage:

python demo_unsupported_functions.py

Note:

• This demo shows how the module handles unsupported functions by logging a warning but allowing the code to continue execution.

---

Demo 5: Device Information

# demo_device_info.py

import TorchDevice
import torch

def main():
    is_available = torch.cuda.is_available()
    device_count = torch.cuda.device_count()
    current_device = torch.cuda.current_device()
    device_name = torch.cuda.get_device_name(current_device)
    capability = torch.cuda.get_device_capability(current_device)

    print(f"CUDA Available: {is_available}")
    print(f"Device Count: {device_count}")
    print(f"Current Device Index: {current_device}")
    print(f"Device Name: {device_name}")
    print(f"Device Capability: {capability}")

if __name__ == '__main__':
    main()

Usage:

python demo_device_info.py