[Test] Add test for mv transpose node past scal mul

tests/transformation/test_move_transpose_past_scalar_mul.py

+import pytest
+
+import numpy as np
+from onnx import TensorProto, helper
+
+from finn.core.modelwrapper import ModelWrapper
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.infer_datatypes import InferDataTypes
+from finn.transformation.general import GiveUniqueNodeNames, GiveReadableTensorNames
+from finn.transformation.streamline.reorder import MoveTransposePastScalarMul
+import finn.core.onnx_exec as oxe
+
+# permutation of transpose node
+@pytest.mark.parametrize("perm", [[0, 2, 3, 1], [0, 1, 3, 2], [3, 2, 0, 1]])
+# scalar mul
+@pytest.mark.parametrize("scalar", [True, False])
+def test_move_transpose_past_scalar_mul(perm, scalar):
+    inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, [1, 2, 3, 4])
+    # to determine out_size we need to calculate with "perm" for this test case
+    dummy_in = np.random.uniform(low=0, high=1, size=(1, 2, 3, 4)).astype(np.float32)
+    out_size = dummy_in.transpose(tuple(perm)).shape
+
+    if scalar is True:
+        a0_size = []
+    else:
+        a0_size = out_size
+    a0 = helper.make_tensor_value_info("a0", TensorProto.FLOAT, a0_size)
+    outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, out_size)
+    transp_node = helper.make_node("Transpose", ["inp"], ["transp_out"], perm=perm)
+    mul_node = helper.make_node("Mul", ["transp_out", "a0"], ["outp"])
+
+    graph = helper.make_graph(
+        nodes=[transp_node, mul_node],
+        name="mv-transpose-graph",
+        inputs=[inp],
+        outputs=[outp],
+        value_info=[a0],
+    )
+
+    model = helper.make_model(graph, producer_name="mv_transpose_model")
+    model = ModelWrapper(model)
+
+    # initialize values
+    a0_values = np.random.uniform(low=0, high=1, size=tuple(a0_size)).astype(np.float32)
+    model.set_initializer("a0", a0_values)
+
+    model = model.transform(InferShapes())
+    model = model.transform(InferDataTypes())
+    model = model.transform(GiveUniqueNodeNames())
+    model = model.transform(GiveReadableTensorNames())
+
+    # compare execution before and after transformation
+    inp_values = np.random.uniform(low=0, high=1, size=(1, 2, 3, 4)).astype(np.float32)
+    idict = {"inp": inp_values}
+    model_transformed = model.transform(MoveTransposePastScalarMul())
+    assert oxe.compare_execution(model, model_transformed, idict)
+
+    # check if order changed
+    if scalar is True:
+        assert model_transformed.graph.node[0] != model.graph.node[0]
+        assert model_transformed.graph.node[1] != model.graph.node[1]
+        assert model_transformed.graph.node[0].op_type == "Mul"
+        assert model_transformed.graph.node[1].op_type == "Transpose"
+    else:
+        assert model_transformed.graph.node[0] == model.graph.node[0]
+        assert model_transformed.graph.node[1] == model.graph.node[1]