[Test] add some tests for move_add_past_mul

tests/test_move_add_past_mul.py

+import numpy as np
+import onnx.helper as oh
+from onnx import TensorProto
+
+import finn.core.onnx_exec as ox
+import finn.transformation.infer_shapes as si
+import finn.transformation.streamline as tx
+from finn.core.modelwrapper import ModelWrapper
+
+
+def test_move_add_past_mul_single():
+    top_in = oh.make_tensor_value_info("top_in", TensorProto.FLOAT, [2])
+    add_param = oh.make_tensor_value_info("add_param", TensorProto.FLOAT, [2])
+    mul_param = oh.make_tensor_value_info("mul_param", TensorProto.FLOAT, [2])
+    top_out = oh.make_tensor_value_info("top_out", TensorProto.FLOAT, [2])
+    modelproto = oh.make_model(
+        oh.make_graph(
+            name="test",
+            inputs=[top_in],
+            outputs=[top_out],
+            value_info=[add_param, mul_param],
+            nodes=[
+                oh.make_node("Add", ["top_in", "add_param"], ["middle"]),
+                oh.make_node("Mul", ["middle", "mul_param"], ["top_out"]),
+            ],
+        )
+    )
+    model = ModelWrapper(modelproto)
+    model = model.transform_single(si.infer_shapes)
+    model.set_initializer("add_param", np.asarray([1, 3], dtype=np.float32))
+    model.set_initializer("mul_param", np.asarray([2, 4], dtype=np.float32))
+    new_model = model.transform_repeated(tx.move_add_past_mul)
+    inp_dict = {"top_in": np.asarray([-1.0, 1.0], dtype=np.float32)}
+    out_orig = ox.execute_onnx(model, inp_dict)["top_out"]
+    out_transformed = ox.execute_onnx(new_model, inp_dict)["top_out"]
+    assert np.isclose(out_orig, out_transformed).all()
+
+
+def test_move_add_past_mul_multi():
+    top_in = oh.make_tensor_value_info("top_in", TensorProto.FLOAT, [2])
+    add_param_0 = oh.make_tensor_value_info("add_param_0", TensorProto.FLOAT, [2])
+    mul_param_0 = oh.make_tensor_value_info("mul_param_0", TensorProto.FLOAT, [2])
+    add_param_1 = oh.make_tensor_value_info("add_param_1", TensorProto.FLOAT, [2])
+    mul_param_1 = oh.make_tensor_value_info("mul_param_1", TensorProto.FLOAT, [2])
+    top_out = oh.make_tensor_value_info("top_out", TensorProto.FLOAT, [2])
+    modelproto = oh.make_model(
+        oh.make_graph(
+            name="test",
+            inputs=[top_in],
+            outputs=[top_out],
+            value_info=[add_param_0, mul_param_0, add_param_1, mul_param_1],
+            nodes=[
+                oh.make_node("Add", ["top_in", "add_param_0"], ["middle_0"]),
+                oh.make_node("Mul", ["middle_0", "mul_param_0"], ["middle_1"]),
+                oh.make_node("Add", ["middle_1", "add_param_1"], ["middle_2"]),
+                oh.make_node("Mul", ["middle_2", "mul_param_1"], ["top_out"]),
+            ],
+        )
+    )
+    model = ModelWrapper(modelproto)
+    model = model.transform_single(si.infer_shapes)
+    model.set_initializer("add_param_0", np.asarray([1, 3], dtype=np.float32))
+    model.set_initializer("mul_param_0", np.asarray([2, 4], dtype=np.float32))
+    model.set_initializer("add_param_1", np.asarray([-1, 3], dtype=np.float32))
+    model.set_initializer("mul_param_1", np.asarray([2, -4], dtype=np.float32))
+    new_model = model.transform_repeated(tx.move_add_past_mul)
+    inp_dict = {"top_in": np.asarray([-1.0, 1.0], dtype=np.float32)}
+    out_orig = ox.execute_onnx(model, inp_dict)["top_out"]
+    out_transformed = ox.execute_onnx(new_model, inp_dict)["top_out"]
+    assert np.isclose(out_orig, out_transformed).all()