[Test] add test_move_scalar_mul_past_matmul

tests/test_move_scalar_mul_past_matmul.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_scalar_mul_past_matmul():
+    top_in = oh.make_tensor_value_info("top_in", TensorProto.FLOAT, [1, 2])
+    mul_param = oh.make_tensor_value_info("mul_param", TensorProto.FLOAT, [1, 1])
+    matmul_param = oh.make_tensor_value_info("matmul_param", TensorProto.FLOAT, [2, 2])
+    top_out = oh.make_tensor_value_info("top_out", TensorProto.FLOAT, [1, 2])
+    modelproto = oh.make_model(
+        oh.make_graph(
+            name="test",
+            inputs=[top_in],
+            outputs=[top_out],
+            value_info=[mul_param, matmul_param],
+            nodes=[
+                oh.make_node("Mul", ["top_in", "mul_param"], ["middle"]),
+                oh.make_node("MatMul", ["middle", "matmul_param"], ["top_out"]),
+            ],
+        )
+    )
+    model = ModelWrapper(modelproto)
+    model = model.transform_single(si.infer_shapes)
+    model.set_initializer("mul_param", np.asarray([[3]], dtype=np.float32))
+    model.set_initializer(
+        "matmul_param", np.asarray([[2, 4], [-1, 1]], dtype=np.float32)
+    )
+    new_model = model.transform_repeated(tx.move_scalar_mul_past_matmul)
+    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()
+    assert new_model.graph.node[0].op_type == "MatMul"
+    assert new_model.graph.node[1].op_type == "Mul"
+    assert new_model.graph.node[0].output[0] == new_model.graph.node[1].input[0]