update rm to_tensor

x2paddle/optimizer/pytorch_code_optimizer/layer_code_generator.py

@@ -257,13 +257,6 @@ def gen_layer_code(graph, sub_layers, sub_layers_name, different_attrs=dict()):
             if is_set_item:
                 outputs.append(layer.outputs[0])
     no_output_count = 0
-    # remove to_tensor layer
-    invalid_list = list()
-    for layer_id, layer in sub_layers.items():
-        if layer.kernel == "paddle.to_tensor":
-            invalid_list.append(layer_id)
-    for layer_id in invalid_list:
-        sub_layers.pop(layer_id)
     for i, (layer_id, layer) in enumerate(sub_layers.items()):
         _update_attrs(layer, different_attrs)
         if ("paddle.nn" in layer.kernel and "functional" not in layer.kernel) or \
@@ -401,12 +394,16 @@ def gen_layer_code(graph, sub_layers, sub_layers_name, different_attrs=dict()):
                 forward_func.extend(gen_codes([line], indent=2))
             cur_outputs.extend(layer.outputs)
 
-    if inputs is not None:
-        inputs.sort()
     head, init_func_head, forward_func_head = gen_head(inputs, different_attrs)
     output_data_name = ", ".join(outputs)
+    # remove to_tensor op
+    forward_func_new = list()
+    for line in forward_func:
+        if "paddle.to_tensor" in line:
+            continue
+        forward_func_new.append(line)
     code_list = head + init_func_head + init_func + \
-                forward_func_head + forward_func + \
+                forward_func_head + forward_func_new + \
                 gen_codes(["return {}".format(output_data_name)], indent=2)
     code_str = "".join(code_list)
     return code_str