diff --git a/backends/cadence/aot/quantizer/fusion_pass.py b/backends/cadence/aot/quantizer/fusion_pass.py
index 5be90a4f52e..349659d1e7f 100644
--- a/backends/cadence/aot/quantizer/fusion_pass.py
+++ b/backends/cadence/aot/quantizer/fusion_pass.py
@@ -160,20 +160,15 @@ def get_args_and_kwargs_layer_norm(
             ),
             {"dtype": torch.float32},
         )
-        if len(inputs_inputs) > 0:
-            if "val" in inputs_inputs[0].meta:
-                fake_mode = inputs_inputs[0].meta["val"].fake_mode
-                if fake_mode is not None:
-                    with fake_mode:
-                        fake_weight = torch.full(
-                            other_inputs[0], 1, dtype=torch.float32
-                        )
-                    weight.meta["val"] = fake_weight
-                else:
-                    weight.meta["val"] = torch.full(
-                        other_inputs[0], 1, dtype=torch.float32
-                    )
-            copy_node_metadata(weight, inputs_inputs[0])
+        assert (
+            len(inputs_inputs) == 1
+        ), f"Expected 1 input for layer norm weight, got {len(inputs_inputs)}"
+        assert "val" in inputs_inputs[0].meta, "Missing val metadata on input node"
+        fake_mode = inputs_inputs[0].meta["val"].fake_mode
+        assert fake_mode is not None, "fake_mode is None on input node"
+        with fake_mode:
+            weight.meta["val"] = torch.full(other_inputs[0], 1, dtype=torch.float32)
+        copy_node_metadata(weight, inputs_inputs[0])
 
     bias = other_inputs[2] if len(other_inputs) > 2 else None
 
@@ -186,18 +181,15 @@ def get_args_and_kwargs_layer_norm(
             ),
             {"dtype": torch.float32},
         )
-        if len(inputs_inputs) > 0:
-            if "val" in inputs_inputs[0].meta:
-                fake_mode = inputs_inputs[0].meta["val"].fake_mode
-                if fake_mode is not None:
-                    with fake_mode:
-                        fake_bias = torch.full(other_inputs[0], 0, dtype=torch.float32)
-                    bias.meta["val"] = fake_bias
-                else:
-                    bias.meta["val"] = torch.full(
-                        other_inputs[0], 0, dtype=torch.float32
-                    )
-            copy_node_metadata(bias, inputs_inputs[0])
+        assert (
+            len(inputs_inputs) == 1
+        ), f"Expected 1 input for layer norm bias, got {len(inputs_inputs)}"
+        assert "val" in inputs_inputs[0].meta, "Missing val metadata on input node"
+        fake_mode = inputs_inputs[0].meta["val"].fake_mode
+        assert fake_mode is not None, "fake_mode is None on input node"
+        with fake_mode:
+            bias.meta["val"] = torch.full(other_inputs[0], 0, dtype=torch.float32)
+        copy_node_metadata(bias, inputs_inputs[0])
 
     # Make the args and kwargs for the replacement op
     args = tuple(inputs_inputs + [scale, zero_point])
@@ -373,16 +365,15 @@ def get_args_and_kwargs_softmax(
         ),
         {"dtype": torch.int32},
     )
-    if len(inputs_inputs) > 0:
-        if "val" in inputs_inputs[0].meta:
-            fake_mode = inputs_inputs[0].meta["val"].fake_mode
-            if fake_mode is not None:
-                with fake_mode:
-                    fake_mask = torch.full(mask_shape, 0.0, dtype=torch.int32)
-                mask_tensor.meta["val"] = fake_mask
-            else:
-                mask_tensor.meta["val"] = torch.full(mask_shape, 0.0, dtype=torch.int32)
-        copy_node_metadata(mask_tensor, inputs_inputs[0])
+    assert (
+        len(inputs_inputs) == 1
+    ), f"Expected 1 input for softmax, got {len(inputs_inputs)}"
+    assert "val" in inputs_inputs[0].meta, "Missing val metadata on input node"
+    fake_mode = inputs_inputs[0].meta["val"].fake_mode
+    assert fake_mode is not None, "fake_mode is None on input node"
+    with fake_mode:
+        mask_tensor.meta["val"] = torch.full(mask_shape, 0.0, dtype=torch.int32)
+    copy_node_metadata(mask_tensor, inputs_inputs[0])
     # Make the scale and zero_point tensors
     in_scale = dequants_inputs[0].args[1]
     in_zero_point = dequants_inputs[0].args[2]
@@ -636,25 +627,18 @@ def call(self, graph_module: fx.GraphModule) -> PassResult:  # noqa: C901
                             torch.ops.aten.transpose.int,
                             (weights_inputs[0], 0, 1),
                         )
-                        if "val" in weights_inputs[0].meta:
-                            original_val = weights_inputs[0].meta["val"]
-                            fake_mode = original_val.fake_mode
-                            if fake_mode is not None:
-                                with fake_mode:
-                                    transposed_val = torch.ops.aten.transpose.int(
-                                        original_val, 0, 1
-                                    )
-                                transposed_weights.meta["val"] = transposed_val
-                            else:
-                                transposed_shape = list(original_val.shape)
-                                transposed_shape[0], transposed_shape[1] = (
-                                    transposed_shape[1],
-                                    transposed_shape[0],
-                                )
-                                transposed_weights.meta["val"] = torch.zeros(
-                                    transposed_shape, dtype=original_val.dtype
-                                )
-                            copy_node_metadata(transposed_weights, weights_inputs[0])
+                        assert (
+                            "val" in weights_inputs[0].meta
+                        ), "Missing val metadata on weight node"
+                        original_val = weights_inputs[0].meta["val"]
+                        assert (
+                            original_val.fake_mode is not None
+                        ), "fake_mode is None on weight node"
+                        with original_val.fake_mode:
+                            transposed_weights.meta["val"] = (
+                                torch.ops.aten.transpose.int(original_val, 0, 1)
+                            )
+                        copy_node_metadata(transposed_weights, weights_inputs[0])
 
                         # Call linear with transposed weight
                         args, kwargs = get_args_and_kwargs_linear(