Qualcomm AI Engine Direct - Cat Fix (pytorch#14325)

### Summary
Fix op cat to retrieve the right node.

### Test plan
CI pass

Author: winskuo-quic

backends/qualcomm/builders/op_cat.py

@@ -29,22 +29,23 @@ def define_node(
         node: torch.fx.Node,
         nodes_to_wrappers: Dict[torch.fx.Node, PyQnnWrapper.TensorWrapper],
     ) -> PyQnnWrapper.PyQnnOpWrapper:
-        list_of_tensors = cast(List[torch.fx.Node], node.args[0])
-        list_of_tensor_wrappers = []
+        input_nodes = cast(List[torch.fx.Node], node.args[0])
+        input_tensor_wrappers = []
 
-        for tensor_input in list_of_tensors:
-            input_tensor = self.get_tensor(self.get_node(tensor_input), node)
-            list_of_tensor_wrappers.append(
+        for input_node in input_nodes:
+            source_input_node = self.get_node(input_node)
+            input_tensor = self.get_tensor(source_input_node, node)
+            input_tensor_wrappers.append(
                 self.define_tensor(
-                    tensor_input,
+                    source_input_node,
                     node,
                     input_tensor,
                     PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_NATIVE,
                     nodes_to_wrappers,
                 )
             )
 
-        if len(list_of_tensors) != len(list_of_tensor_wrappers):
+        if len(input_nodes) != len(input_tensor_wrappers):
             warnings.warn(
                 "[QNN Delegate Op Builder]: The number or input tensors is not equal to the number of input tensor wrappers.",
                 stacklevel=1,
@@ -76,7 +77,7 @@ def define_node(
             QNN_OP_PACKAGE_NAME_QTI_AISW,
             OpConcat.op_name,
         )
-        concat_op.AddInputTensors(list_of_tensor_wrappers)
+        concat_op.AddInputTensors(input_tensor_wrappers)
         concat_op.AddOutputTensors([output_tensor_wrapper])
 
         concat_op.AddScalarParam(

backends/qualcomm/tests/models.py

@@ -274,6 +274,15 @@ def forward(self, x, y):
         return torch.cat((y, y, x, x), axis=2)
 
 
+class Cat5(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.const_tensor = torch.randn(1, 1, 2, 2)
+
+    def forward(self, x, y):
+        return torch.cat((x, y, self.const_tensor), axis=2)
+
+
 class CausalMask(torch.nn.Module):
     def __init__(self):
         super().__init__()

backends/qualcomm/tests/test_qnn_delegate.py

@@ -232,7 +232,7 @@ def test_qnn_backend_cast(self):
                 self.lower_module_and_test_output(module, sample_input)
 
     def test_qnn_backend_cat(self):
-        modules = [Cat2(), Cat3(), Cat4()]  # noqa: F405
+        modules = [Cat2(), Cat3(), Cat4(), Cat5()]  # noqa: F405
         sample_input = (torch.randn(1, 1, 2, 2), torch.randn(1, 1, 4, 2))
         for i, module in enumerate(modules):
             with self.subTest(i=i):
@@ -1699,7 +1699,7 @@ def test_qnn_backend_cast(self):
         self.lower_module_and_test_output(module, sample_input)
 
     def test_qnn_backend_cat(self):
-        modules = [Cat2(), Cat3(), Cat4()]  # noqa: F405
+        modules = [Cat2(), Cat3(), Cat4(), Cat5()]  # noqa: F405
         sample_input = (torch.randn(1, 1, 2, 2), torch.randn(1, 1, 4, 2))
         for i, module in enumerate(modules):
             with self.subTest(i=i):