Add testing hpu file

whisper/hpu_model_tests.py

@@ -0,0 +1,54 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class SimpleCNN(nn.Module):
+    def __init__(self, num_classes=10):
+        super(SimpleCNN, self).__init__()
+
+        # Define layers
+        self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=1, padding=1)
+        self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1)
+        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
+        self.fc1 = nn.Linear(32 * 56 * 56, 128)
+        self.fc2 = nn.Linear(128, num_classes)
+
+    def forward(self, x):
+        # Forward pass through the network
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = x.view(-1, 32 * 56 * 56)  # Flatten the tensor
+        x = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+
+# Example usage
+if __name__ == "__main__":
+    # Load Habana module for HPU support
+    from habana_frameworks.torch.utils.library_loader import load_habana_module
+    import habana_frameworks.torch.hpu as hthpu
+
+    load_habana_module()
+
+    device = None
+    # Set device to HPU
+    if hthpu.is_available():
+        device = torch.device("hpu")
+        print("Using HPU")
+
+    if not device:
+        print("HPU is not available")
+        exit(1)
+
+    # Create model instance and move it to the HPU
+    model = SimpleCNN(num_classes=10).to(device)
+
+    # Create a dummy input tensor and move it to the HPU
+    input_tensor = torch.rand((64, 3, 224, 224), device=device)  # Batch size of 64
+
+    # Forward pass through the model on HPU
+    output = model(input_tensor)
+
+    print("Output shape:", output.shape)  # Should be [64, num_classes]