fix torch.compile with no cl.

.gitignore

@@ -160,3 +160,5 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 .idea/
+
+debug.py

tests/module_pool/simple_nn.py

@@ -0,0 +1,14 @@
+import torch
+import torch.nn as nn
+
+
+class SimpleNN(nn.Module):
+    def __init__(self):
+        super(SimpleNN, self).__init__()
+        self.fc1 = nn.Linear(10, 5)
+        self.fc2 = nn.Linear(5, 1)
+
+    def forward(self, x):
+        x = torch.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x

tests/test_torch.py

@@ -2,17 +2,7 @@
 import torch.nn as nn
 import torch.optim as optim
 
-
-class SimpleNN(nn.Module):
-    def __init__(self):
-        super(SimpleNN, self).__init__()
-        self.fc1 = nn.Linear(10, 5)
-        self.fc2 = nn.Linear(5, 1)
-
-    def forward(self, x):
-        x = torch.relu(self.fc1(x))
-        x = self.fc2(x)
-        return x
+from tests.module_pool.simple_nn import SimpleNN
 
 
 def test_forward_pass():

tests/test_torch_compile.py

@@ -0,0 +1,36 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+from tests.module_pool.simple_nn import SimpleNN
+
+
+def test_torch_compile_forward():
+    model = SimpleNN()
+    compiled_model = torch.compile(model, backend="aot_eager")
+
+    input_data = torch.randn(1, 10)
+    expected_output = model(input_data)
+    actual_output = compiled_model(input_data)
+    assert torch.allclose(expected_output, actual_output), "Output mismatch between compiled and original model"
+
+
+def test_torch_compile_backward():
+    model = SimpleNN()
+    compiled_model = torch.compile(model, backend="aot_eager")
+
+    criterion = nn.MSELoss()
+    optimizer = optim.SGD(compiled_model.parameters(), lr=0.01)
+
+    input_data = torch.randn(1, 10)
+    target = torch.tensor([1.0])
+
+    output = compiled_model(input_data)
+    loss = criterion(output, target)
+
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+
+    for param in compiled_model.parameters():
+        assert param.grad is not None, "Gradient not computed for parameter in compiled model"