[Test] Add test for MoveFlattenPastAffine

tests/transformation/test_move_flatten_past_affine.py

+# Copyright (c) 2020, Xilinx
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+#   list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+#   this list of conditions and the following disclaimer in the documentation
+#   and/or other materials provided with the distribution.
+#
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+#   contributors may be used to endorse or promote products derived from
+#   this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+import numpy as np
+from onnx import TensorProto, helper
+
+from finn.core.modelwrapper import ModelWrapper
+from finn.core.datatype import DataType
+from finn.util.basic import gen_finn_dt_tensor
+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 MoveFlattenPastAffine
+import finn.core.onnx_exec as oxe
+
+
+def test_move_flatten_past_affine():
+    inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, [1, 1, 1, 1024])
+    a0 = helper.make_tensor_value_info("a1", TensorProto.FLOAT, [1024, 1000])
+    a1 = helper.make_tensor_value_info("a2", TensorProto.FLOAT, [])
+    a2 = helper.make_tensor_value_info("a3", TensorProto.FLOAT, [1000])
+
+    outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, [1, 1000])
+    flatten_node = helper.make_node("Flatten", ["inp"], ["flatten_out"])
+    matmul_node = helper.make_node("MatMul", ["flatten_out", "a0"], ["matmul_out"])
+    mul_node = helper.make_node("Mul", ["matmul_out", "a1"], ["mul_out"])
+    add_node = helper.make_node("Add", ["mul_out", "a2"], ["outp"])
+
+    graph = helper.make_graph(
+        nodes=[flatten_node, matmul_node, mul_node, add_node],
+        name="move-reshape-graph",
+        inputs=[inp],
+        outputs=[outp],
+        value_info=[a0, a1, a2],
+    )
+
+    model = helper.make_model(graph, producer_name="move_reshape_model")
+    model = ModelWrapper(model)
+
+    # initialize values
+    a0_values = gen_finn_dt_tensor(DataType.TERNARY, [1024, 1000])
+    model.set_initializer("a0", a0_values)
+    a1_values = np.random.uniform(low=0.1, high=0.99, size=(1)).astype(np.float32)
+    model.set_initializer("a1", a1_values)
+    a2_values = np.random.uniform(low=-1, high=1, size=(1000)).astype(np.float32)
+    model.set_initializer("a2", a2_values)
+
+    model.set_tensor_datatype("inp", DataType.INT2)
+    model.set_tensor_datatype("flatten_out", DataType.INT2)
+    model = model.transform(InferShapes())
+    model = model.transform(InferDataTypes())
+    model = model.transform(GiveUniqueNodeNames())
+    model = model.transform(GiveReadableTensorNames())
+
+    # compare execution before and after transformation
+    inp_values = gen_finn_dt_tensor(DataType.INT2, [1, 1, 1, 1024])
+    idict = {"inp": inp_values}
+    model_transformed = model.transform(MoveFlattenPastAffine())
+    assert oxe.compare_execution(model, model_transformed, idict)
+
+    # check if nodes have new order in transformed graph
+    assert model.graph != model_transformed.graph
+    assert model_transformed.graph.node[-1].op_type == "Flatten"