dynamic.in.cpp

//*****************************************************************************
// Copyright 2017-2019 Intel Corporation
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//*****************************************************************************

#include "gtest/gtest.h"
#include "ngraph/ngraph.hpp"
#include "util/all_close_f.hpp"
#include "util/test_control.hpp"
#include "util/test_tools.hpp"

using namespace std;
using namespace ngraph;

static string s_manifest = "${MANIFEST}";

NGRAPH_TEST(dynamic_${BACKEND_NAME}, create)
{
    auto backend = runtime::Backend::create("${BACKEND_NAME}", true);
    ASSERT_NE(backend, nullptr);
    ASSERT_TRUE(backend->supports_dynamic_tensors());
}

NGRAPH_TEST(dynamic_${BACKEND_NAME}, create_no_dynamic)
{
    auto backend = runtime::Backend::create("${BACKEND_NAME}");
    ASSERT_NE(backend, nullptr);
    ASSERT_FALSE(backend->supports_dynamic_tensors());
}

NGRAPH_TEST(dynamic_${BACKEND_NAME}, create_dynamic_tensor)
{
    auto backend = runtime::Backend::create("${BACKEND_NAME}", true);
    auto t = backend->create_dynamic_tensor(element::f32, PartialShape{2, Dimension::dynamic(), 3});
    ASSERT_TRUE(t->get_partial_shape().same_scheme(PartialShape{2, Dimension::dynamic(), 3}));
}

NGRAPH_TEST(dynamic_${BACKEND_NAME}, abc)
{
    //
    // Create a graph for f(a,b,c) = (a+b)*c, where a, b, c all have shape {2,?,3}.
    //
    auto a = make_shared<op::Parameter>(element::f32, PartialShape{2, Dimension::dynamic(), 3});
    auto b = make_shared<op::Parameter>(element::f32, PartialShape{2, Dimension::dynamic(), 3});
    auto c = make_shared<op::Parameter>(element::f32, PartialShape{2, Dimension::dynamic(), 3});

    auto a_plus_b_times_c = (a + b) * c;

    auto f = make_shared<Function>(NodeVector{a_plus_b_times_c}, ParameterVector{a, b, c});

    //
    // Get a backend with dynamic support, and compile f.
    //
    auto backend = runtime::Backend::create("${BACKEND_NAME}", true);

    auto ex = backend->compile(f);

    //
    // Create a dynamic output tensor with shape {2,?,3}.
    //
    auto t_r =
        backend->create_dynamic_tensor(element::f32, PartialShape{2, Dimension::dynamic(), 3});

    //
    // For each of n=[0,...,5), run the compiled executable against a test vector of shape
    // {2,n,3}, and check the results.
    //
    for (size_t middle_dim = 0; middle_dim < 5; middle_dim++)
    {
        // Fill in some test input values, which we'll use for a, b, and c.
        vector<float> inputs(2 * middle_dim * 3);
        for (size_t i = 0; i < 2 * middle_dim * 3; i++)
        {
            inputs[i] = i;
        }

        // Create static tensors for the inputs and copy data.
        auto t_a = backend->create_tensor(element::f32, Shape{2, middle_dim, 3});
        auto t_b = backend->create_tensor(element::f32, Shape{2, middle_dim, 3});
        auto t_c = backend->create_tensor(element::f32, Shape{2, middle_dim, 3});

        copy_data(t_a, inputs);
        copy_data(t_b, inputs);
        copy_data(t_c, inputs);

        // Call ex, writing result into t_r (note we're using the same t_r from outside the loop.)
        ex->call_with_validate({t_r}, {t_a, t_b, t_c});

        // After call, t_r should have a shape of {2,n,3}.
        ASSERT_EQ(t_r->get_shape(), (Shape{2, middle_dim, 3}));

        // Read out the results, and compare them against expected values.
        auto results = read_vector<float>(t_r);

        vector<float> expected_values(2 * middle_dim * 3);
        for (size_t i = 0; i < 2 * middle_dim * 3; i++)
        {
            expected_values[i] = (i + i) * i;
        }

        EXPECT_TRUE(test::all_close_f(results, expected_values));
    }
}

NGRAPH_TEST(dynamic_${BACKEND_NAME}, transpose)
{
    //
    // Create a graph for f(x,perm) = Transpose(x,Convert<i64>(perm)). We'll do the permutation in
    // i32 and cast it to i64, just for fun (and to mirror the TensorFlow test I am porting here).
    //
    auto x = make_shared<op::Parameter>(element::f32, PartialShape::dynamic());
    auto perm = make_shared<op::Parameter>(element::i32, PartialShape{Dimension::dynamic()});
    auto perm_i64 = make_shared<op::Convert>(perm, element::i64);

    auto x_transpose = make_shared<op::Transpose>(x, perm_i64);

    auto f = make_shared<Function>(NodeVector{x_transpose}, ParameterVector{x, perm});

    auto backend = runtime::Backend::create("${BACKEND_NAME}", true);

    auto ex = backend->compile(f);

    auto t_r = backend->create_dynamic_tensor(element::f32, PartialShape::dynamic());

    std::vector<Shape> x_shapes{Shape{2, 3}, Shape{2, 3}, Shape{2, 2, 3}};
    std::vector<std::vector<int32_t>> perms{{0, 1}, {1, 0}, {2, 1, 0}};
    std::vector<std::vector<float>> inputs{
        {1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6}, {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}};
    std::vector<Shape> expected_result_shapes{Shape{2, 3}, Shape{3, 2}, {3, 2, 2}};
    // Generated with numpy, so don't worry. :)
    std::vector<std::vector<float>> expected_results{
        {1, 2, 3, 4, 5, 6}, {1, 4, 2, 5, 3, 6}, {1, 7, 4, 10, 2, 8, 5, 11, 3, 9, 6, 12}};

    for (size_t i = 0; i < x_shapes.size(); i++)
    {
        auto t_x = backend->create_tensor(element::f32, x_shapes[i]);
        auto t_perm = backend->create_tensor(element::i32, Shape{perms[i].size()});

        copy_data(t_x, inputs[i]);
        copy_data(t_perm, perms[i]);

        ex->call_with_validate({t_r}, {t_x, t_perm});

        ASSERT_EQ(t_r->get_shape(), expected_result_shapes[i]);

        auto results = read_vector<float>(t_r);

        ASSERT_TRUE(test::all_close_f(results, expected_results[i], MIN_FLOAT_TOLERANCE_BITS));
    }
}