test_absorb_mul_into_topk.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.
#
# * Neither the name of FINN nor the names of its
#   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
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# 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 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.insert_topk import InsertTopK
from finn.transformation.streamline.absorb import AbsorbScalarMulIntoTopK
import finn.core.onnx_exec as oxe

# parameter to indicate if mul parameter is negative or positive
@pytest.mark.parametrize("mul_positive", [True, False])
# parameter to indicate if mul parameter is scalar or not
@pytest.mark.parametrize("scalar", [True, False])
def test_absorb_mul_into_topk(mul_positive, scalar):
    if scalar is True:
        shape = [1]
    else:
        shape = [1, 1, 1, 1000]
    inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, [1, 1, 1, 1000])
    a0 = helper.make_tensor_value_info("a0", TensorProto.FLOAT, shape)
    outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, [1, 1, 1, 1000])

    mul_node = helper.make_node("Mul", ["inp", "a0"], ["outp"])
    mul_graph = helper.make_graph(
        nodes=[mul_node],
        name="mul-graph",
        inputs=[inp],
        outputs=[outp],
        value_info=[a0],
    )

    model = helper.make_model(mul_graph, producer_name="mul_model")
    model = ModelWrapper(model)
    # initialize values
    if mul_positive is True:
        a0_values = np.random.uniform(low=0.1, high=1, size=tuple(shape)).astype(
            np.float32
        )
    else:
        a0_values = np.random.uniform(low=-1, high=-0.1, size=tuple(shape)).astype(
            np.float32
        )
    model.set_initializer("a0", a0_values)
    model = model.transform(InsertTopK())
    model = model.transform(InferShapes())
    model = model.transform(InferDataTypes())
    model = model.transform(GiveUniqueNodeNames())
    model = model.transform(GiveReadableTensorNames())
    model_transformed = model.transform(AbsorbScalarMulIntoTopK())

    # compare execution results
    inp_values = np.random.uniform(low=-10, high=10, size=(1, 1, 1, 1000)).astype(
        np.float32
    )
    idict = {"global_in": inp_values}
    odict = oxe.execute_onnx(model, idict, True)
    y_indices = odict["global_out"]
    y_values = odict["TopK_0_out0"]
    odict = oxe.execute_onnx(model_transformed, idict, True)
    y_tr_indices = odict["global_out"]
    y_tr_values = odict["TopK_0_out0"]

    # the indices stay the same, if the model is transformed or not
    assert (y_indices == y_tr_indices).all()

    if scalar is True and mul_positive is True:
        # the values change if the model was transformed
        assert (y_values != y_tr_values).all()

        # check for new order
        assert model.graph != model_transformed.graph
        assert len(model.graph.node) - 1 == len(model_transformed.graph.node)
        assert model_transformed.graph.node[0].op_type == "TopK"

    else:
        assert (y_values == y_tr_values).all()
        assert model.graph == model_transformed.graph