cpu_fusion.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 <algorithm>
#include <cstdio>
#include <iostream>
#include <list>
#include <memory>

#include "gtest/gtest.h"
#include "misc.hpp"
#include "ngraph/autodiff/adjoints.hpp"
#include "ngraph/file_util.hpp"
#include "ngraph/graph_util.hpp"
#include "ngraph/log.hpp"
#include "ngraph/ngraph.hpp"
#include "ngraph/op/batch_norm.hpp"
#include "ngraph/op/concat.hpp"
#include "ngraph/op/dequantize.hpp"
#include "ngraph/op/experimental/generate_mask.hpp"
#include "ngraph/op/experimental/quantized_concat.hpp"
#include "ngraph/op/experimental/quantized_conv_bias.hpp"
#include "ngraph/op/fused/conv_fused.hpp"
#include "ngraph/op/fused/group_conv.hpp"
#include "ngraph/op/get_output_element.hpp"
#include "ngraph/op/max_pool.hpp"
#include "ngraph/op/negative.hpp"
#include "ngraph/op/parameter.hpp"
#include "ngraph/op/quantize.hpp"
#include "ngraph/op/quantized_convolution.hpp"
#include "ngraph/op/relu.hpp"
#include "ngraph/op/result.hpp"
#include "ngraph/op/reverse_sequence.hpp"
#include "ngraph/op/sigmoid.hpp"
#include "ngraph/op/sum.hpp"
#include "ngraph/op/tanh.hpp"
#include "ngraph/pass/algebraic_simplification.hpp"
#include "ngraph/pass/batch_fusion.hpp"
#include "ngraph/pass/core_fusion.hpp"
#include "ngraph/pass/graph_rewrite.hpp"
#include "ngraph/pass/manager.hpp"
#include "ngraph/pass/reshape_elimination.hpp"
#include "ngraph/pass/visualize_tree.hpp"
#include "ngraph/pattern/matcher.hpp"
#include "ngraph/pattern/op/label.hpp"
#include "ngraph/pattern/op/skip.hpp"
#include "ngraph/runtime/cpu/cpu_layout_descriptor.hpp"
#include "ngraph/runtime/cpu/cpu_tensor_view.hpp"
#include "ngraph/runtime/cpu/op/batch_mat_mul_transpose.hpp"
#include "ngraph/runtime/cpu/op/batch_norm_relu.hpp"
#include "ngraph/runtime/cpu/op/bounded_relu.hpp"
#include "ngraph/runtime/cpu/op/conv_add.hpp"
#include "ngraph/runtime/cpu/op/conv_relu.hpp"
#include "ngraph/runtime/cpu/op/convert_layout.hpp"
#include "ngraph/runtime/cpu/op/deconv.hpp"
#include "ngraph/runtime/cpu/op/dropout.hpp"
#include "ngraph/runtime/cpu/op/group_conv_bias.hpp"
#include "ngraph/runtime/cpu/op/leaky_relu.hpp"
#include "ngraph/runtime/cpu/op/lstm.hpp"
#include "ngraph/runtime/cpu/op/matmul_bias.hpp"
#include "ngraph/runtime/cpu/op/rnn.hpp"
#include "ngraph/runtime/cpu/op/rnn_utils.hpp"
#include "ngraph/runtime/cpu/op/sigmoid_mul.hpp"
#include "ngraph/runtime/cpu/op/update_slice.hpp"
#include "ngraph/runtime/cpu/pass/cpu_fusion.hpp"
#include "ngraph/runtime/cpu/pass/cpu_mat_fusion.hpp"
#include "ngraph/runtime/cpu/pass/cpu_post_layout_optimizations.hpp"
#include "ngraph/runtime/cpu/pass/cpu_rnn_fusion.hpp"
#include "ngraph/runtime/cpu/pass/cpu_workspace_insertion.hpp"
#include "ngraph/serializer.hpp"
#include "ngraph/util.hpp"
#include "util/all_close.hpp"
#include "util/all_close_f.hpp"
#include "util/autodiff/backprop_function.hpp"
#include "util/autodiff/numeric_compare.hpp"
#include "util/matcher.hpp"
#include "util/random.hpp"
#include "util/random.hpp"
#include "util/test_tools.hpp"

using namespace ngraph;
using namespace std;

TEST(cpu_fusion, gemm_pattern)
{
    Shape shape_w{2, 4};
    Shape shape_x{4, 1};
    Shape shape_b{1};
    auto A = make_shared<op::Parameter>(element::f32, shape_w);
    auto B = make_shared<op::Parameter>(element::f32, shape_x);
    auto C = make_shared<op::Parameter>(element::f32, shape_b);

    auto dot = make_shared<op::Dot>(A, B);
    auto broadcast = make_shared<op::Broadcast>(C, dot->get_shape(), AxisSet{0});
    auto add = dot + broadcast;

    auto W = std::make_shared<pattern::op::Label>(A);
    auto x = std::make_shared<pattern::op::Label>(B);

    auto reshape_pred = [](std::shared_ptr<Node> n) {
        return static_cast<bool>(std::dynamic_pointer_cast<op::Reshape>(n));
    };

    auto skip_w = std::make_shared<pattern::op::Skip>(W, reshape_pred);
    auto skip_x = std::make_shared<pattern::op::Skip>(x, reshape_pred);

    auto pdot = make_shared<op::Dot>(skip_w, skip_x);
    auto b = std::make_shared<pattern::op::Label>(C);
    auto pbroadcast = make_shared<op::Broadcast>(b, dot->get_shape(), AxisSet{0});
    auto padd = pdot + pbroadcast;

    TestMatcher n(nullptr);
    ASSERT_TRUE(n.match(padd, add));
    ASSERT_EQ(n.get_pattern_map()[W], A);
    ASSERT_EQ(n.get_pattern_map()[x], B);
    ASSERT_EQ(n.get_pattern_map()[b], C);

    auto reshape_w = make_shared<op::Reshape>(A, AxisVector{1, 0}, W->get_shape());
    auto reshape_x = make_shared<op::Reshape>(B, AxisVector{1, 0}, x->get_shape());
    auto re_dot = make_shared<op::Dot>(reshape_w, reshape_x);
    auto re_add = re_dot + broadcast;
    ASSERT_TRUE(n.match(padd, re_add));
    ASSERT_EQ(n.get_pattern_map()[W], A);
    ASSERT_EQ(n.get_pattern_map()[x], B);
    ASSERT_EQ(n.get_pattern_map()[b], C);

    auto cg = make_shared<op::MatmulBias>(
        W, x, C, W->get_shape(), x->get_shape(), false, false, AxisSet{0});
}

TEST(cpu_fusion, gemm_cpu_broadcast_row)
{
    Shape shapeA{3, 2};
    Shape shapeB{2, 3};
    Shape shapeC{2, 2};
    auto A = make_shared<op::Parameter>(element::f32, shapeA);
    auto B = make_shared<op::Parameter>(element::f32, shapeB);

    auto bias = op::Constant::create<float>(element::f32, Shape{2}, std::vector<float>{2.0f, 3.0f});

    auto cg = make_shared<op::MatmulBias>(
        A, B, bias, A->get_shape(), B->get_shape(), true, true, AxisSet{0});

    auto f = make_shared<Function>(cg, ParameterVector{A, B});

    auto backend = runtime::Backend::create("CPU");

    shared_ptr<runtime::Tensor> a = backend->create_tensor(element::f32, shapeA);
    shared_ptr<runtime::Tensor> b = backend->create_tensor(element::f32, shapeB);
    shared_ptr<runtime::Tensor> result = backend->create_tensor(element::f32, shapeC);

    vector<float> dataA{1.0f, 4.0f, 1.0f, 4.0f, 1.0f, 4.0f};
    vector<float> dataB{3.0f, 3.0f, 3.0f, 9.0f, 9.0f, 9.0f};
    copy_data(a, dataA);
    copy_data(b, dataB);

    auto handle = backend->compile(f);
    handle->call_with_validate({result}, {a, b});
    vector<float> expected{11, 30, 38, 111};
    EXPECT_TRUE(test::all_close_f(read_vector<float>(result), expected, MIN_FLOAT_TOLERANCE_BITS));
}

TEST(cpu_fusion, gemm_cpu_broadcast_column)
{
    Shape shapeA{3, 2};
    Shape shapeB{2, 3};
    Shape shapeC{2, 2};
    auto A = make_shared<op::Parameter>(element::f32, shapeA);
    auto B = make_shared<op::Parameter>(element::f32, shapeB);

    auto bias = op::Constant::create<float>(element::f32, Shape{2}, std::vector<float>{2.0f, 3.0f});

    auto cg = make_shared<op::MatmulBias>(
        A, B, bias, A->get_shape(), B->get_shape(), true, true, AxisSet{1});

    auto f = make_shared<Function>(cg, ParameterVector{A, B});

    auto backend = runtime::Backend::create("CPU");

    shared_ptr<runtime::Tensor> a = backend->create_tensor(element::f32, shapeA);
    shared_ptr<runtime::Tensor> b = backend->create_tensor(element::f32, shapeB);
    shared_ptr<runtime::Tensor> result = backend->create_tensor(element::f32, shapeC);

    vector<float> dataA{1.0f, 4.0f, 1.0f, 4.0f, 1.0f, 4.0f};
    vector<float> dataB{3.0f, 3.0f, 3.0f, 9.0f, 9.0f, 9.0f};
    copy_data(a, dataA);
    copy_data(b, dataB);

    auto handle = backend->compile(f);
    handle->call_with_validate({result}, {a, b});
    vector<float> expected{11, 29, 39, 111};
    EXPECT_TRUE(test::all_close_f(read_vector<float>(result), expected, MIN_FLOAT_TOLERANCE_BITS));
}

TEST(cpu_fusion, gemm_cpu_broadcast_matrix)
{
    Shape shapeA{3, 2};
    Shape shapeB{2, 3};
    Shape shapeC{2, 2};
    auto A = make_shared<op::Parameter>(element::f32, shapeA);
    auto B = make_shared<op::Parameter>(element::f32, shapeB);

    auto reshape_w = make_shared<op::Reshape>(A, AxisVector{1, 0}, Shape{2, 3});
    auto reshape_x = make_shared<op::Reshape>(B, AxisVector{1, 0}, Shape{3, 2});

    auto one = op::Constant::create<float>(element::f32, Shape{}, std::vector<float>{1.0f});

    auto broadcast = make_shared<op::Broadcast>(one, shapeC, AxisSet{0, 1});
    auto cg = make_shared<op::MatmulBias>(
        A, B, one, A->get_shape(), B->get_shape(), true, true, AxisSet{0, 1});

    auto f = make_shared<Function>(cg, ParameterVector{A, B});

    auto backend = runtime::Backend::create("CPU");

    shared_ptr<runtime::Tensor> a = backend->create_tensor(element::f32, shapeA);
    shared_ptr<runtime::Tensor> b = backend->create_tensor(element::f32, shapeB);
    shared_ptr<runtime::Tensor> result = backend->create_tensor(element::f32, shapeC);

    vector<float> dataA{1.0f, 4.0f, 1.0f, 4.0f, 1.0f, 4.0f};
    vector<float> dataB{3.0f, 3.0f, 3.0f, 9.0f, 9.0f, 9.0f};
    copy_data(a, dataA);
    copy_data(b, dataB);

    auto handle = backend->compile(f);
    handle->call_with_validate({result}, {a, b});
    vector<float> expected{10, 28, 37, 109};
    EXPECT_TRUE(test::all_close_f(read_vector<float>(result), expected, MIN_FLOAT_TOLERANCE_BITS));
}

TEST(cpu_fusion, gemm_cpu_no_bias)
{
    auto shapeA = Shape{3, 2};
    auto shapeB = Shape{2, 3};
    auto shapeC = Shape{2, 2};
    auto A = make_shared<op::Parameter>(element::f32, shapeA);
    auto B = make_shared<op::Parameter>(element::f32, shapeB);

    auto reshape_w = make_shared<op::Reshape>(A, AxisVector{1, 0}, Shape{2, 3});
    auto reshape_x = make_shared<op::Reshape>(B, AxisVector{1, 0}, Shape{3, 2});

    auto cg = make_shared<op::MatmulBias>(
        A, B, Output<Node>(), A->get_shape(), B->get_shape(), true, true);

    auto f = make_shared<Function>(cg, ParameterVector{A, B});

    auto backend = runtime::Backend::create("CPU");

    shared_ptr<runtime::Tensor> a = backend->create_tensor(element::f32, shapeA);
    shared_ptr<runtime::Tensor> b = backend->create_tensor(element::f32, shapeB);
    shared_ptr<runtime::Tensor> result = backend->create_tensor(element::f32, shapeC);

    vector<float> dataA{1.0f, 4.0f, 1.0f, 4.0f, 1.0f, 4.0f};
    vector<float> dataB{3.0f, 3.0f, 3.0f, 9.0f, 9.0f, 9.0f};
    copy_data(a, dataA);
    copy_data(b, dataB);

    auto handle = backend->compile(f);
    handle->call_with_validate({result}, {a, b});
    vector<float> expected{9, 27, 36, 108};
    EXPECT_TRUE(test::all_close_f(read_vector<float>(result), expected, MIN_FLOAT_TOLERANCE_BITS));
}

TEST(cpu_fusion, cpu_fusion_pass_basic)
{
    Shape shape{};
    Shape shape_w{2, 4};
    Shape shape_x{4, 1};
    Shape shape_b{1};
    auto A = make_shared<op::Parameter>(element::f32, shape_w);
    auto B = make_shared<op::Parameter>(element::f32, shape_x);
    auto C = make_shared<op::Parameter>(element::f32, shape_b);

    auto dot = make_shared<op::Dot>(A, B);
    auto broadcast = make_shared<op::Broadcast>(C, dot->get_shape(), AxisSet{0});
    auto add = dot + broadcast;
    auto graph = make_shared<op::Abs>(add);
    pass::Manager pass_manager;
    pass_manager.register_pass<runtime::cpu::pass::CPUFusion>(pass::FusionType::REGULAR_FUSIONS);
    auto func = make_shared<Function>(graph, ParameterVector{A, B, C});
    pass_manager.run_passes(func);
    ASSERT_NE(std::dynamic_pointer_cast<op::MatmulBias>(graph->get_argument(0)), nullptr);
}

TEST(cpu_fusion, commutative_matmul_bias)
{
    Shape shape{};
    Shape shape_w{2, 4};
    Shape shape_x{4, 1};
    Shape shape_b{1};
    auto A = make_shared<op::Parameter>(element::f32, shape_w);
    auto B = make_shared<op::Parameter>(element::f32, shape_x);
    auto C = make_shared<op::Parameter>(element::f32, shape_b);

    auto dot = make_shared<op::Dot>(A, B);
    auto broadcast = make_shared<op::Broadcast>(C, dot->get_shape(), AxisSet{0});
    auto add = broadcast + dot;
    auto graph = make_shared<op::Abs>(add);
    pass::Manager pass_manager;
    pass_manager.register_pass<runtime::cpu::pass::CPUFusion>(pass::FusionType::REGULAR_FUSIONS);
    auto func = make_shared<Function>(graph, ParameterVector{A, B, C});
    pass_manager.run_passes(func);
    ASSERT_NE(std::dynamic_pointer_cast<op::MatmulBias>(graph->get_argument(0)), nullptr);
}

TEST(cpu_fusion, cpu_fusion_pass_matmul_bias)
{
    Shape shape_w{2, 4};
    Shape shape_x{4, 1};
    Shape shape_b{1};
    auto W = make_shared<op::Parameter>(element::f32, shape_w);
    auto x = make_shared<op::Parameter>(element::f32, shape_x);
    auto b = make_shared<op::Parameter>(element::f32, shape_b);

    auto mmb = std::make_shared<op::MatmulBias>(
        W, x, Output<Node>(), W->get_shape(), x->get_shape(), false, false);
    auto broadcast = std::make_shared<op::Broadcast>(b, mmb->get_shape(), AxisSet{0});
    auto add = mmb + broadcast;

    auto graph = make_shared<op::Abs>(add);
    pass::Manager pass_manager;
    pass_manager.register_pass<runtime::cpu::pass::CPUFusion>(pass::FusionType::REGULAR_FUSIONS);
    auto func = make_shared<Function>(graph, ParameterVector{W, x, b});
    pass_manager.run_passes(func);
    auto gmm = graph->get_argument(0);
    ASSERT_TRUE(std::dynamic_pointer_cast<op::MatmulBias>(gmm));
    ASSERT_EQ(gmm->get_argument(2), b);
}

TEST(cpu_fusion, cpu_fusion_pass_matmul_no_bias)
{
    Shape shape_w{4, 2};
    Shape shape_x{1, 4};
    auto W = make_shared<op::Parameter>(element::f32, shape_w);
    auto x = make_shared<op::Parameter>(element::f32, shape_x);

    auto reshape_w = std::make_shared<op::Reshape>(W, AxisVector{1, 0}, Shape{2, 4});
    auto reshape_x = std::make_shared<op::Reshape>(x, AxisVector{1, 0}, Shape{4, 1});
    auto re_dot = make_shared<op::Dot>(reshape_w, reshape_x);
    auto graph = make_shared<op::Abs>(re_dot);

    pass::Manager pass_manager;
    pass_manager.register_pass<runtime::cpu::pass::CPUFusion>(pass::FusionType::REGULAR_FUSIONS);
    auto func = make_shared<Function>(graph, ParameterVector{W, x});
    pass_manager.run_passes(func);
    size_t mmb = count_ops_of_type<op::MatmulBias>(func);
    ASSERT_EQ(mmb, 1);
}

struct ConvolutionBiasTestData
{
    size_t n{0};
    size_t c{0};
    size_t filter{0};
    size_t kernel_size{0};
    size_t w{0};
    size_t h{0};
    shared_ptr<runtime::Tensor> data_val;
    shared_ptr<runtime::Tensor> weights_val;
    shared_ptr<runtime::Tensor> bias_val;
    shared_ptr<runtime::Tensor> result_val;
    shared_ptr<runtime::Tensor> delta_val;
    shared_ptr<runtime::Tensor> d_data_val;
    shared_ptr<runtime::Tensor> d_weights_val;
    shared_ptr<runtime::Tensor> d_bias_val;
    vector<float> expected_result_val;
    vector<float> expected_d_data_val;
    vector<float> expected_d_weights_val;
    vector<float> expected_d_bias_val;

    Shape data_shape;
    Shape weights_shape;
    Shape bias_shape;
    Shape result_shape;
    shared_ptr<op::Parameter> data;
    shared_ptr<op::Parameter> weights;
    shared_ptr<op::Parameter> bias;
    shared_ptr<op::Parameter> delta;

    void n1c1h3w3(runtime::Backend* backend)
    {
        n = 1;
        c = 1;
        filter = 1;
        kernel_size = 3;
        w = 3;
        h = w;

        data_shape = Shape{n, c, h, w};
        data = make_shared<op::Parameter>(element::f32, data_shape);
        weights_shape = Shape{filter, c, kernel_size, kernel_size};
        weights = make_shared<op::Parameter>(element::f32, weights_shape);
        bias_shape = Shape{filter};
        bias = make_shared<op::Parameter>(element::f32, bias_shape);
        result_shape = Shape{n, filter, 1, 1};

        data_val = backend->create_tensor(element::f32, data_shape);
        copy_data(data_val,
                  vector<float>{-0.67765152f,
                                0.10073948f,
                                0.57595438f,
                                -0.3469252f,
                                -0.22134334f,
                                -1.80471897f,
                                -0.80642909f,
                                1.22033095f,
                                2.23235631f});
        weights_val = backend->create_tensor(element::f32, weights_shape);
        copy_data(weights_val,
                  vector<float>{0.20070229f,
                                -0.54968649f,
                                -0.19819015f,
                                -0.38577855f,
                                1.37109005f,
                                -0.23789984f,
                                0.14867957f,
                                -0.49851316f,
                                -0.84815776f});
        bias_val = backend->create_tensor(element::f32, bias_shape);
        copy_data(bias_val, vector<float>{0.07811152f});

        result_val = backend->create_tensor(element::f32, result_shape);
        copy_data(result_val, vector<float>{0});

        delta = make_shared<op::Parameter>(element::f32, result_shape);
        delta_val = backend->create_tensor(element::f32, result_shape);
        copy_data(delta_val, vector<float>{-2.58936238f});

        d_data_val = backend->create_tensor(element::f32, data_shape);
        copy_data(d_data_val, vector<float>{0, 0, 0, 0, 0, 0, 0, 0, 0});

        d_weights_val = backend->create_tensor(element::f32, weights_shape);
        copy_data(d_weights_val, vector<float>{0, 0, 0, 0, 0, 0, 0, 0, 0});

        d_bias_val = backend->create_tensor(element::f32, bias_shape);
        copy_data(d_bias_val, vector<float>{0});

        expected_result_val = vector<float>{-2.58936238f};
        expected_d_data_val = vector<float>{-0.51969099f,
                                            1.42333758f,
                                            0.5131861f,
                                            0.99892044f,
                                            -3.5502491f,
                                            0.61600888f,
                                            -0.3849853f,
                                            1.29083121f,
                                            2.19618773f};
        expected_d_weights_val = vector<float>{1.7546854f,
                                               -0.26085103f,
                                               -1.49135458f,
                                               0.89831507f,
                                               0.57313812f,
                                               4.67307138f,
                                               2.08813715f,
                                               -3.15987897f,
                                               -5.7803793f};
        expected_d_bias_val = vector<float>{-2.58936238f};
    }
};

TEST(cpu_fusion, conv_bias_fprop_n1c1h3w3)
{
    auto backend = runtime::Backend::create("CPU");

    ConvolutionBiasTestData conv_test;
    conv_test.n1c1h3w3(backend.get());

    auto convolution = make_shared<op::Convolution>(conv_test.data, conv_test.weights);
    auto convolution_bias = make_shared<op::ConvolutionBias>(convolution, conv_test.bias);

    auto f = make_shared<Function>(
        convolution_bias, ParameterVector{conv_test.data, conv_test.weights, conv_test.bias});

    auto handle = backend->compile(f);
    handle->call_with_validate({conv_test.result_val},
                               {conv_test.data_val, conv_test.weights_val, conv_test.bias_val});
    auto result_vec = read_vector<float>(conv_test.result_val);

    EXPECT_TRUE(
        test::all_close(conv_test.expected_result_val, read_vector<float>(conv_test.result_val)));
}

TEST(cpu_fusion, conv_bias_bprop_n1c1h3w3)
{
    auto backend = runtime::Backend::create("CPU");

    ConvolutionBiasTestData conv_test;
    conv_test.n1c1h3w3(backend.get());

    auto convolution = make_shared<op::Convolution>(conv_test.data, conv_test.weights);
    auto convolution_bias = make_shared<op::ConvolutionBias>(convolution, conv_test.bias);

    auto f = make_shared<Function>(
        convolution_bias, ParameterVector{conv_test.data, conv_test.weights, conv_test.bias});

    ngraph::autodiff::Adjoints adjoints(OutputVector{convolution_bias},
                                        OutputVector{conv_test.delta});

    auto d_data = adjoints.backprop_node(conv_test.data);
    auto d_weights = adjoints.backprop_node(conv_test.weights);
    auto d_bias = adjoints.backprop_node(conv_test.bias);

    auto df = make_shared<Function>(
        NodeVector{d_data, d_weights, d_bias},
        ParameterVector{conv_test.data, conv_test.weights, conv_test.bias, conv_test.delta});
    auto handle = backend->compile(df);
    handle->call_with_validate(

        {conv_test.d_data_val, conv_test.d_weights_val, conv_test.d_bias_val},
        {conv_test.data_val, conv_test.weights_val, conv_test.bias_val, conv_test.delta_val});

    EXPECT_TRUE(
        test::all_close(conv_test.expected_d_data_val, read_vector<float>(conv_test.d_data_val)));
    EXPECT_TRUE(test::all_close(conv_test.expected_d_weights_val,
                                read_vector<float>(conv_test.d_weights_val)));
    EXPECT_TRUE(
        test::all_close(conv_test.expected_d_bias_val, read_vector<float>(conv_test.d_bias_val)));
}

TEST(cpu_fusion, conv_bias_bprop)
{
    Shape shape{2, 2, 1, 1};
    auto data_batch = std::make_shared<op::Parameter>(element::f32, shape);
    auto filters = std::make_shared<op::Parameter>(element::f32, shape);
    auto delta = std::make_shared<op::Parameter>(element::f32, shape);
    auto bias = make_shared<op::Parameter>(element::f32, Shape{shape[0]});
    auto pbroadcast = std::make_shared<op::Broadcast>(bias, shape, AxisSet{1, 2, 3});
    auto conv = std::make_shared<op::Convolution>(data_batch, filters);
    auto conv_bias = std::make_shared<op::Add>(conv, pbroadcast);

    pass::Manager pass_manager;
    pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
    pass_manager.register_pass<pass::VisualizeTree>("conv_bias_bprop_fusion.png");
    auto f = make_shared<Function>(conv_bias, ParameterVector{data_batch, filters, bias});

    ngraph::autodiff::Adjoints adjoints(OutputVector{conv_bias}, OutputVector{delta});

    auto d_data = adjoints.backprop_node(data_batch);
    auto d_weights = adjoints.backprop_node(filters);
    auto d_bias = adjoints.backprop_node(bias);

    auto df = make_shared<Function>(NodeVector{d_data, d_weights, d_bias},
                                    ParameterVector{data_batch, filters, bias, delta});

    pass_manager.run_passes(df);
    size_t ccg = count_ops_of_type<op::ConvolutionBiasBackpropFiltersBias>(df);
    ASSERT_EQ(ccg, 1);
}

static void test_batchnorm_multiply_add_relu(Shape input_shape)
{
    auto make_bn_relu_function = [&]() {
        auto c_axis = input_shape[1];
        auto input = make_shared<op::Parameter>(element::f32, input_shape);
        auto mean_shape = Shape{c_axis};
        auto mean = std::make_shared<op::Parameter>(element::f32, mean_shape);
        auto var_shape = Shape{c_axis};
        auto var = std::make_shared<op::Parameter>(element::f32, var_shape);
        auto gamma_shape = Shape{c_axis};
        auto gamma = make_shared<op::Parameter>(element::f32, gamma_shape);
        auto beta_shape = Shape{c_axis};
        auto beta = make_shared<op::Parameter>(element::f32, beta_shape);
        double eps = 0.001;
        auto bn =
            std::make_shared<ngraph::op::BatchNormInference>(eps, gamma, beta, input, mean, var);

        std::vector<size_t> vec{0};
        for (auto i = 2; i < input_shape.size(); i++)
        {
            vec.push_back(i);
        }
        auto broadcast1_input = std::make_shared<op::Parameter>(element::f32, gamma_shape);
        auto broadcast1 =
            std::make_shared<ngraph::op::Broadcast>(broadcast1_input, input_shape, AxisSet(vec));
        auto multiply = std::make_shared<ngraph::op::Multiply>(bn, broadcast1);

        auto broadcast2_input = std::make_shared<op::Parameter>(element::f32, gamma_shape);
        auto broadcast2 =
            std::make_shared<ngraph::op::Broadcast>(broadcast2_input, input_shape, AxisSet(vec));

        auto add = std::make_shared<ngraph::op::Add>(multiply, broadcast2);
        auto relu = std::make_shared<ngraph::op::Relu>(add);
        auto f = make_shared<Function>(
            relu,
            ParameterVector{gamma, beta, input, mean, var, broadcast1_input, broadcast2_input});
        return f;
    };

    auto cpu_f = make_bn_relu_function();
    auto int_f = make_bn_relu_function();
    test::Uniform<float> rng(1.0f, 10.0f);
    vector<vector<float>> args;

    for (shared_ptr<op::Parameter> param : int_f->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }
    auto int_results = execute(int_f, args, "INTERPRETER");
    auto cpu_results = execute(cpu_f, args, "CPU");
    for (size_t i = 0; i < cpu_results.size(); i++)
    {
        EXPECT_TRUE(test::all_close(cpu_results.at(i), int_results.at(i), 1.0e-4f, 1.0e-4f));
    }

    size_t bn_relu = count_ops_of_type<op::BatchNormInferenceRelu>(cpu_f);
    ASSERT_EQ(bn_relu, 1);
}

TEST(cpu_fusion, batchnorm_multiply_add_relu)
{
    test_batchnorm_multiply_add_relu(Shape{1, 3, 2, 2});
    test_batchnorm_multiply_add_relu(Shape{1, 2, 2, 2, 2});
    test_batchnorm_multiply_add_relu(Shape{2, 2, 2, 4, 4});
}

TEST(cpu_fusion, batchnorm_multiply_add_relu_no_fusion)
{
    auto input_shape = Shape{3, 3, 2, 2};
    auto make_bn_relu_function = [&]() {
        auto c_axis = input_shape[1];
        auto input = make_shared<op::Parameter>(element::f32, input_shape);
        auto mean_shape = Shape{c_axis};
        auto mean = std::make_shared<op::Parameter>(element::f32, mean_shape);
        auto var_shape = Shape{c_axis};
        auto var = std::make_shared<op::Parameter>(element::f32, var_shape);
        auto gamma_shape = Shape{c_axis};
        auto gamma = make_shared<op::Parameter>(element::f32, gamma_shape);
        auto beta_shape = Shape{c_axis};
        auto beta = make_shared<op::Parameter>(element::f32, beta_shape);
        double eps = 0.001;
        auto bn =
            std::make_shared<ngraph::op::BatchNormInference>(eps, gamma, beta, input, mean, var);

        std::vector<size_t> vec;
        for (auto i = 1; i < input_shape.size(); i++)
        {
            vec.push_back(i);
        }
        auto broadcast1_input = std::make_shared<op::Parameter>(element::f32, Shape{3});
        auto broadcast1 =
            std::make_shared<ngraph::op::Broadcast>(broadcast1_input, input_shape, AxisSet(vec));
        auto multiply = std::make_shared<ngraph::op::Multiply>(bn, broadcast1);

        auto broadcast2_input = std::make_shared<op::Parameter>(element::f32, Shape{3});
        auto broadcast2 =
            std::make_shared<ngraph::op::Broadcast>(broadcast2_input, input_shape, AxisSet(vec));

        auto add = std::make_shared<ngraph::op::Add>(multiply, broadcast2);
        auto relu = std::make_shared<ngraph::op::Relu>(add);
        auto f = make_shared<Function>(
            relu,
            ParameterVector{gamma, beta, input, mean, var, broadcast1_input, broadcast2_input});
        return f;
    };

    auto cpu_f = make_bn_relu_function();
    auto int_f = make_bn_relu_function();
    test::Uniform<float> rng(1.0f, 10.0f);
    vector<vector<float>> args;

    for (shared_ptr<op::Parameter> param : int_f->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }
    auto int_results = execute(int_f, args, "INTERPRETER");
    auto cpu_results = execute(cpu_f, args, "CPU");
    for (size_t i = 0; i < cpu_results.size(); i++)
    {
        EXPECT_TRUE(test::all_close(cpu_results.at(i), int_results.at(i), 1.0e-4f, 1.0e-4f));
    }

    size_t bn_relu = count_ops_of_type<op::BatchNormInferenceRelu>(cpu_f);
    ASSERT_EQ(bn_relu, 0);
}

TEST(cpu_fusion, batchnorm_fprop_relu_b1c2h2w2)
{
    auto input_shape = Shape{1, 2, 2, 2};
    auto input = make_shared<op::Parameter>(element::f32, input_shape);
    auto mean_shape = Shape{2};
    auto var_shape = Shape{2};
    auto gamma_shape = Shape{2};
    auto gamma = make_shared<op::Parameter>(element::f32, gamma_shape);
    auto beta_shape = Shape{2};
    auto beta = make_shared<op::Parameter>(element::f32, beta_shape);
    double eps = 0.001;
    auto shape_r = Shape{1, 2, 2, 2};
    auto bn = make_shared<op::BatchNormTraining>(input, gamma, beta, eps);

    auto output_rt = std::make_shared<op::GetOutputElement>(bn, 0);
    // Note, op::Splice is used to break Relu(BatchNorm) fusion
    // otherwise we will be comparing two BatchNormRelus
    // Unfortunately, we can't use INTERPRETER for
    // verifying the results as it doesn't implement
    // BatchNorm op.
    auto slice =
        std::make_shared<op::Slice>(output_rt, Coordinate{0, 0, 0, 0}, Coordinate{1, 2, 2, 2});
    auto output_relu = std::make_shared<op::Relu>(slice);
    auto mean_rt = std::make_shared<op::GetOutputElement>(bn, 1);
    auto variance_rt = std::make_shared<op::GetOutputElement>(bn, 2);

    auto bn_relu = make_shared<op::BatchNormTrainingRelu>(eps, gamma, beta, input);
    auto output_rt_bnr = std::make_shared<op::GetOutputElement>(bn_relu, 0);
    auto mean_rt_bnr = std::make_shared<op::GetOutputElement>(bn_relu, 1);
    auto variance_rt_bnr = std::make_shared<op::GetOutputElement>(bn_relu, 2);

    auto f = make_shared<Function>(
        NodeVector{output_relu, mean_rt, variance_rt, output_rt_bnr, mean_rt_bnr, variance_rt_bnr},
        ParameterVector{input, gamma, beta});
    auto backend = runtime::Backend::create("CPU");

    // Create some tensors for input/output
    auto input_t = backend->create_tensor(element::f32, Shape{1, 2, 2, 2});

    copy_data(input_t,
              vector<float>{0.54881352f,
                            0.71518934f,
                            0.60276335f,
                            0.54488319f,
                            0.42365479f,
                            0.64589411f,
                            0.4375872f,
                            0.89177299f});
    auto gamma_t = backend->create_tensor(element::f32, gamma_shape);
    copy_data(gamma_t, vector<float>{1.0f, 1.0f});
    auto beta_t = backend->create_tensor(element::f32, beta_shape);
    copy_data(beta_t, vector<float>{0.0f, 0.0f});
    auto bn_output = backend->create_tensor(element::f32, shape_r);
    auto result_mean = backend->create_tensor(element::f32, mean_shape);
    auto result_variance = backend->create_tensor(element::f32, var_shape);

    auto bn_output_bnr = backend->create_tensor(element::f32, shape_r);
    auto result_mean_bnr = backend->create_tensor(element::f32, mean_shape);
    auto result_variance_bnr = backend->create_tensor(element::f32, var_shape);

    auto handle = backend->compile(f);
    handle->call_with_validate({bn_output,
                                result_mean,
                                result_variance,
                                bn_output_bnr,
                                result_mean_bnr,
                                result_variance_bnr},
                               {input_t, gamma_t, beta_t});

    EXPECT_TRUE(test::all_close(read_vector<float>(bn_output), read_vector<float>(bn_output_bnr)));
    EXPECT_TRUE(
        test::all_close(read_vector<float>(result_mean), read_vector<float>(result_mean_bnr)));
    EXPECT_TRUE(test::all_close(read_vector<float>(result_variance),
                                read_vector<float>(result_variance_bnr)));
}

static void test_batchnorm_fprop_relu(Shape input_shape)
{
    auto make_bn_relu_function = [&]() {
        auto c_axis = input_shape[1];
        auto input = make_shared<op::Parameter>(element::f32, input_shape);
        auto mean_shape = Shape{c_axis};
        auto var_shape = Shape{c_axis};
        auto gamma_shape = Shape{c_axis};
        auto gamma = make_shared<op::Parameter>(element::f32, gamma_shape);
        auto beta_shape = Shape{c_axis};
        auto beta = make_shared<op::Parameter>(element::f32, beta_shape);
        double eps = 0.001;
        auto shape_r = input_shape;
        auto bn = make_shared<op::BatchNormTraining>(eps, gamma, beta, input);
        auto output_rt = std::make_shared<op::GetOutputElement>(bn, 0);

        auto output_relu = std::make_shared<op::Relu>(output_rt);
        auto mean_rt = std::make_shared<op::GetOutputElement>(bn, 1);
        auto variance_rt = std::make_shared<op::GetOutputElement>(bn, 2);

        auto f = make_shared<Function>(NodeVector{output_relu, mean_rt, variance_rt},
                                       ParameterVector{input, gamma, beta});
        return f;
    };
    auto cpu_f = make_bn_relu_function();
    auto int_f = make_bn_relu_function();
    test::Uniform<float> rng(-10.0f, 10.0f);
    vector<vector<float>> args;

    for (shared_ptr<op::Parameter> param : int_f->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }
    auto int_results = execute(int_f, args, "INTERPRETER");
    auto cpu_results = execute(cpu_f, args, "CPU");
    for (size_t i = 0; i < cpu_results.size(); i++)
    {
        EXPECT_TRUE(test::all_close(cpu_results.at(i), int_results.at(i), 1.0e-4f, 1.0e-4f));
    }
}

TEST(cpu_fusion, batchnorm_fprop_relu)
{
    test_batchnorm_fprop_relu(Shape{1, 2, 2, 2});
    test_batchnorm_fprop_relu(Shape{1, 2, 2, 2, 2});
    test_batchnorm_fprop_relu(Shape{2, 2, 2, 4, 4});
}

TEST(cpu_fusion, fuse_conv_relu)
{
    auto A = std::make_shared<op::Parameter>(element::f32, Shape{2, 1, 2, 2});
    auto weights = std::make_shared<op::Parameter>(element::f32, Shape{1, 1, 2, 2});
    auto convolution = std::make_shared<op::Convolution>(A, weights, Strides{1, 1}, Strides{1, 1});
    auto relu = std::make_shared<op::Relu>(convolution);
    auto abs_node =
        std::make_shared<op::Abs>(std::make_shared<op::Abs>(std::make_shared<op::Abs>(relu)));
    auto func = make_shared<Function>(abs_node, ParameterVector{A, weights});

    pass::Manager pass_manager;
    pass_manager.register_pass<runtime::cpu::pass::CPUFusion>(pass::FusionType::REGULAR_FUSIONS);
    pass_manager.run_passes(func);
    size_t cb = count_ops_of_type<op::ConvolutionRelu>(func);
    ASSERT_GT(cb, 0);
}

TEST(cpu_fusion, conv_relu_n2c1h2w2_2)
{
    Shape shape_a{2, 1, 6, 6};
    Shape shape_weights{1, 1, 2, 2};

    auto make_int_function = [shape_a, shape_weights]() {
        auto A = std::make_shared<op::Parameter>(element::f32, shape_a);
        auto weights = std::make_shared<op::Parameter>(element::f32, shape_weights);
        auto conv = std::make_shared<op::Convolution>(A, weights, Strides{2, 2}, Strides{1, 1});
        auto relu = std::make_shared<op::Relu>(conv);
        auto f = make_shared<Function>(NodeVector{relu}, ParameterVector{A, weights});
        return f;
    };

    auto int_f = make_int_function();

    auto make_cpu_function = [shape_a, shape_weights]() {
        auto A = std::make_shared<op::Parameter>(element::f32, shape_a);
        auto weights = std::make_shared<op::Parameter>(element::f32, shape_weights);
        auto conv = std::make_shared<op::Convolution>(A, weights, Strides{2, 2}, Strides{1, 1});
        auto conv_relu = std::make_shared<op::ConvolutionRelu>(conv);
        auto f = make_shared<Function>(NodeVector{conv_relu}, ParameterVector{A, weights});
        return f;
    };

    auto cpu_f = make_cpu_function();

    vector<vector<float>> args{
        {1.25f,  2.25f, 5.25f, 6.25f,  -1.25f, -1.25f, 3.25f, -4.25f, 7.25f,  8.25f,  -1.25f,
         -1.25f, 1.25f, 2.25f, -3.25f, 2.25f,  4.25f,  4.25f, 1.25f,  2.25f,  -4.25f, 2.25f,
         4.25f,  4.25f, 0.f,   0.f,    -1.f,   0.f,    2.f,   2.f,    0.f,    0.f,    0.f,
         0.f,    2.f,   2.f,   1.25f,  2.25f,  5.25f,  6.25f, 1.25f,  1.25f,  3.25f,  4.25f,
         -7.25f, 8.25f, 1.25f, -1.25f, -1.25f, 2.25f,  3.25f, 2.25f,  -4.25f, -4.25f, -1.25f,
         -2.25f, 4.25f, 2.25f, 4.25f,  4.25f,  0.f,    0.f,   1.f,    0.f,    -2.f,   2.f,
         0.f,    0.f,   0.f,   0.f,    -2.f,   -2.f},
        {2., 2., 2., 2.}};

    auto int_results = execute(int_f, args, "INTERPRETER");
    auto cpu_results = execute(cpu_f, args, "CPU");
    EXPECT_TRUE(test::all_close(cpu_results.at(0), int_results.at(0)));
}

TEST(cpu_fusion, conv_bias_relu_n2c1h2w2_2)
{
    Shape shape_a{2, 1, 6, 6};
    Shape shape_weights{1, 1, 2, 2};
    Shape shape_bias{1};

    auto make_int_function = [shape_a, shape_weights, shape_bias]() {
        auto A = std::make_shared<op::Parameter>(element::f32, shape_a);
        auto weights = std::make_shared<op::Parameter>(element::f32, shape_weights);
        auto conv = std::make_shared<op::Convolution>(A, weights, Strides{2, 2}, Strides{1, 1});
        auto bias = std::make_shared<op::Parameter>(element::f32, shape_bias);
        auto conv_bias =
            conv + std::make_shared<op::Broadcast>(bias, conv->get_shape(), AxisSet{0, 2, 3});
        auto relu = std::make_shared<op::Relu>(conv_bias);
        auto f = make_shared<Function>(NodeVector{relu}, ParameterVector{A, weights, bias});
        return f;
    };

    auto int_f = make_int_function();

    auto make_cpu_function = [shape_a, shape_weights, shape_bias]() {
        auto A = std::make_shared<op::Parameter>(element::f32, shape_a);
        auto weights = std::make_shared<op::Parameter>(element::f32, shape_weights);
        auto bias = std::make_shared<op::Parameter>(element::f32, shape_bias);
        auto conv = std::make_shared<op::Convolution>(A, weights, Strides{2, 2}, Strides{1, 1});
        auto conv_bias_relu = std::make_shared<op::ConvolutionBias>(conv, bias, true);
        auto f =
            make_shared<Function>(NodeVector{conv_bias_relu}, ParameterVector{A, weights, bias});
        return f;
    };

    auto cpu_f = make_cpu_function();

    vector<vector<float>> args{
        {1.25f,  2.25f, 5.25f, 6.25f,  -1.25f, -1.25f, 3.25f, -4.25f, 7.25f,  8.25f,  -1.25f,
         -1.25f, 1.25f, 2.25f, -3.25f, 2.25f,  4.25f,  4.25f, 1.25f,  2.25f,  -4.25f, 2.25f,
         4.25f,  4.25f, 0.f,   0.f,    -1.f,   0.f,    2.f,   2.f,    0.f,    0.f,    0.f,
         0.f,    2.f,   2.f,   1.25f,  2.25f,  5.25f,  6.25f, 1.25f,  1.25f,  3.25f,  4.25f,
         -7.25f, 8.25f, 1.25f, -1.25f, -1.25f, 2.25f,  3.25f, 2.25f,  -4.25f, -4.25f, -1.25f,
         -2.25f, 4.25f, 2.25f, 4.25f,  4.25f,  0.f,    0.f,   1.f,    0.f,    -2.f,   2.f,
         0.f,    0.f,   0.f,   0.f,    -2.f,   -2.f},
        {2., 2., 2., 2.},
        {0.1f}};

    auto int_results = execute(int_f, args, "INTERPRETER");
    auto cpu_results = execute(cpu_f, args, "CPU");
    EXPECT_TRUE(test::all_close(cpu_results.at(0), int_results.at(0)));
}

TEST(cpu_fusion, conv_horizontal_fusion)
{
    Shape shape_a{2, 1, 6, 6};
    Shape shape_weights{1, 1, 2, 2};
    Shape shape_bias{1};

    auto make_function = [shape_a, shape_weights, shape_bias]() {
        auto A = std::make_shared<op::Parameter>(element::f32, shape_a);
        auto weights1 = std::make_shared<op::Parameter>(element::f32, shape_weights);
        auto conv1 = std::make_shared<op::Convolution>(A, weights1, Strides{2, 2}, Strides{1, 1});
        auto bias1 = std::make_shared<op::Parameter>(element::f32, shape_bias);
        auto conv_bias1 =
            conv1 + std::make_shared<op::Broadcast>(bias1, conv1->get_shape(), AxisSet{0, 2, 3});
        auto relu1 = std::make_shared<op::Relu>(conv_bias1);

        auto weights2 = std::make_shared<op::Parameter>(element::f32, shape_weights);
        auto conv2 = std::make_shared<op::Convolution>(A, weights2, Strides{2, 2}, Strides{1, 1});
        auto bias2 = std::make_shared<op::Parameter>(element::f32, shape_bias);
        auto conv_bias2 =
            conv2 + std::make_shared<op::Broadcast>(bias2, conv2->get_shape(), AxisSet{0, 2, 3});
        auto relu2 = std::make_shared<op::Relu>(conv_bias2);

        auto concat = std::make_shared<op::Concat>(NodeVector{relu1, relu2}, 1);
        auto f = make_shared<Function>(NodeVector{concat},
                                       ParameterVector{A, weights1, bias1, weights2, bias2});
        return f;
    };
    auto int_f = make_function();
    auto cpu_f = make_function();

    vector<vector<float>> args{
        {1.25f,  2.25f, 5.25f, 6.25f,  -1.25f, -1.25f, 3.25f, -4.25f, 7.25f,  8.25f,  -1.25f,
         -1.25f, 1.25f, 2.25f, -3.25f, 2.25f,  4.25f,  4.25f, 1.25f,  2.25f,  -4.25f, 2.25f,
         4.25f,  4.25f, 0.f,   0.f,    -1.f,   0.f,    2.f,   2.f,    0.f,    0.f,    0.f,
         0.f,    2.f,   2.f,   1.25f,  2.25f,  5.25f,  6.25f, 1.25f,  1.25f,  3.25f,  4.25f,
         -7.25f, 8.25f, 1.25f, -1.25f, -1.25f, 2.25f,  3.25f, 2.25f,  -4.25f, -4.25f, -1.25f,
         -2.25f, 4.25f, 2.25f, 4.25f,  4.25f,  0.f,    0.f,   1.f,    0.f,    -2.f,   2.f,
         0.f,    0.f,   0.f,   0.f,    -2.f,   -2.f},
        {2., 2., 2., 2.},
        {0.1f},
        {3., 3., 3., 3.},
        {0.2f}};

    auto int_results = execute(int_f, args, "INTERPRETER");
    auto cpu_results = execute(cpu_f, args, "CPU");
    EXPECT_TRUE(test::all_close(cpu_results.at(0), int_results.at(0)));

    size_t cpu_cb = count_ops_of_type<op::ConvolutionBias>(cpu_f);
    ASSERT_EQ(cpu_cb, 1);
}

// ConvolutionBiasAdd relies on an in-place fused MKLDNN kernel.
// Need to ensure that it is fused only when in-place buffer allocation is feasible
shared_ptr<Function> gen_conv_bias_add(bool param_input, bool result_output)
{
    auto A = make_shared<op::Parameter>(element::f32, Shape{2, 1, 2, 2});
    auto weights = make_shared<op::Parameter>(element::f32, Shape{1, 1, 1, 1});
    auto bias = make_shared<op::Parameter>(element::f32, Shape{1});
    auto conv = make_shared<op::Convolution>(A, weights, Strides{1, 1}, Strides{1, 1});
    auto bias_broadcast = make_shared<op::Broadcast>(bias, conv->get_shape(), AxisSet{0, 2, 3});
    auto convbias = conv + bias_broadcast;
    auto B = make_shared<op::Parameter>(element::f32, Shape{2, 1, 2, 2});
    auto abs_B = make_shared<op::Abs>(B);
    auto add =
        param_input ? make_shared<op::Add>(convbias, B) : make_shared<op::Add>(convbias, abs_B);
    auto abs = make_shared<op::Abs>(add);

    return result_output ? make_shared<Function>(add, ParameterVector{A, weights, bias, B})
                         : make_shared<Function>(abs, ParameterVector{A, weights, bias, B});
}

TEST(cpu_fusion, fuse_conv_bias_add)
{
    auto func_fuse = gen_conv_bias_add(false, false);
    auto func_nofuse1 = gen_conv_bias_add(true, false);
    auto func_nofuse2 = gen_conv_bias_add(false, true);

    pass::Manager pass_manager;
    pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
    pass_manager.run_passes(func_fuse);
    ASSERT_EQ(count_ops_of_type<op::ConvolutionBiasAdd>(func_fuse), 1);

    pass_manager.run_passes(func_nofuse1);
    ASSERT_EQ(count_ops_of_type<op::ConvolutionBiasAdd>(func_nofuse1), 0);

    pass_manager.run_passes(func_nofuse2);
    ASSERT_EQ(count_ops_of_type<op::ConvolutionBiasAdd>(func_nofuse2), 1);
}

TEST(cpu_fusion, conv_bias_add)
{
    auto int_f = gen_conv_bias_add(false, false);
    auto cpu_f = gen_conv_bias_add(false, false);

    vector<vector<float>> args{{1.25f, 2.25f, 5.25f, 6.25f, -1.25f, -1.25f, 3.25f, -4.25f},
                               {-1.25f},
                               {2.25f},
                               {1.25f, 2.25f, -3.25f, 2.25f, 4.25f, 4.25f, 1.25f, 2.25f}};

    auto int_results = execute(int_f, args, "INTERPRETER");
    auto cpu_results = execute(cpu_f, args, "CPU");
    EXPECT_TRUE(test::all_close(cpu_results.at(0), int_results.at(0)));
}

// ConvolutionAdd relies on an in-place fused MKLDNN kernel.
// Need to ensure that it is fused only when in-place buffer allocation is feasible
shared_ptr<Function> gen_conv_add(bool param_input, bool result_output)
{
    auto A = make_shared<op::Parameter>(element::f32, Shape{2, 1, 2, 2});
    auto weights = make_shared<op::Parameter>(element::f32, Shape{1, 1, 1, 1});
    auto conv = make_shared<op::Convolution>(A, weights, Strides{1, 1}, Strides{1, 1});
    auto B = make_shared<op::Parameter>(element::f32, Shape{2, 1, 2, 2});
    auto abs_B = make_shared<op::Abs>(B);
    auto add = param_input ? make_shared<op::Add>(conv, B) : make_shared<op::Add>(conv, abs_B);
    auto abs = make_shared<op::Abs>(add);

    return result_output ? make_shared<Function>(add, ParameterVector{A, weights, B})
                         : make_shared<Function>(abs, ParameterVector{A, weights, B});
}

TEST(cpu_fusion, fuse_conv_add)
{
    auto func_fuse = gen_conv_add(false, false);
    auto func_nofuse1 = gen_conv_add(true, false);
    auto func_nofuse2 = gen_conv_add(false, true);

    pass::Manager pass_manager;
    pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
    pass_manager.run_passes(func_fuse);
    ASSERT_EQ(count_ops_of_type<op::ConvolutionAdd>(func_fuse), 1);

    pass_manager.run_passes(func_nofuse1);
    ASSERT_EQ(count_ops_of_type<op::ConvolutionAdd>(func_nofuse1), 0);

    pass_manager.run_passes(func_nofuse2);
    ASSERT_EQ(count_ops_of_type<op::ConvolutionAdd>(func_nofuse2), 1);
}

TEST(cpu_fusion, conv_add)
{
    auto int_f = gen_conv_add(false, false);
    auto cpu_f = gen_conv_add(false, false);

    vector<vector<float>> args{{1.25f, 2.25f, 5.25f, 6.25f, -1.25f, -1.25f, 3.25f, -4.25f},
                               {-1.25f},
                               {1.25f, 2.25f, -3.25f, 2.25f, 4.25f, 4.25f, 1.25f, 2.25f}};

    auto int_results = execute(int_f, args, "INTERPRETER");
    auto cpu_results = execute(cpu_f, args, "CPU");
    EXPECT_TRUE(test::all_close(cpu_results.at(0), int_results.at(0)));

    int_f = gen_conv_add(false, true);
    cpu_f = gen_conv_add(false, true);

    int_results = execute(int_f, args, "INTERPRETER");
    cpu_results = execute(cpu_f, args, "CPU");
    EXPECT_TRUE(test::all_close(cpu_results.at(0), int_results.at(0)));
}

shared_ptr<Function> gen_deconv(const bool add_goe)
{
    Shape conv_out_shape{100, 64, 1, 1};
    auto out_delta = std::make_shared<op::Parameter>(element::f32, conv_out_shape);

    Shape filters_shape{64, 512, 4, 4};
    Shape bias_shape{512};
    Shape data_batch_shape{100, 512, 4, 4};

    auto data_label = std::make_shared<pattern::op::Label>(element::f32, data_batch_shape);
    auto filters = std::make_shared<op::Parameter>(element::f32, filters_shape);

    auto conv = std::make_shared<op::ConvolutionBackpropData>(data_label->get_shape(),
                                                              filters,
                                                              out_delta,
                                                              Strides{1, 1},
                                                              Strides{1, 1},
                                                              CoordinateDiff{0, 0},
                                                              CoordinateDiff{0, 0},
                                                              Strides{1, 1});
    auto conv_label = std::make_shared<pattern::op::Label>(conv, nullptr, NodeVector{conv});

    auto mean = std::make_shared<op::Parameter>(element::f32, bias_shape);
    auto var = std::make_shared<op::Parameter>(element::f32, bias_shape);
    auto gamma = std::make_shared<op::Parameter>(element::f32, bias_shape);
    auto beta = std::make_shared<op::Parameter>(element::f32, bias_shape);
    double eps = 0.001;

    auto goe_bn = std::make_shared<op::GetOutputElement>(conv, 0);

    // Adding a goe will stop fusion since the patterns wont expect to see this op
    auto bn = add_goe
                  ? std::make_shared<op::BatchNormInference>(goe_bn, gamma, beta, mean, var, eps)
                  : std::make_shared<op::BatchNormInference>(conv, gamma, beta, mean, var, eps);

    return make_shared<Function>(NodeVector{bn},
                                 ParameterVector{filters, out_delta, gamma, beta, mean, var});
}

TEST(cpu_fusion, fuse_deconv)
{
    bool use_deconv_fuse = (getenv("NGRAPH_DECONV_FUSE") != nullptr);
    if (!use_deconv_fuse)
    {
        set_environment("NGRAPH_DECONV_FUSE", "1", 1);
    }

    auto fuse_func = gen_deconv(false);
    auto nofuse_func = gen_deconv(true);

    {
        pass::Manager pass_manager;
        pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
        pass_manager.run_passes(fuse_func);
        ASSERT_EQ(count_ops_of_type<op::DeconvolutionBias>(fuse_func), 1);
    }

    {
        pass::Manager pass_manager;
        pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
        pass_manager.run_passes(nofuse_func);
        ASSERT_EQ(count_ops_of_type<op::DeconvolutionBias>(nofuse_func), 0);
        ASSERT_EQ(count_ops_of_type<op::Relu>(nofuse_func), 0);
    }

    // Test values
    {
        test::Uniform<float> rng(1.0f, 100.0f);
        vector<vector<float>> args;
        for (shared_ptr<op::Parameter> param : fuse_func->get_parameters())
        {
            auto name = param->get_name();
            vector<float> tensor_val(shape_size(param->get_shape()));
            rng.initialize(tensor_val);
            args.push_back(tensor_val);
        }
        auto nofuse_results = execute(nofuse_func, args, "CPU");
        auto fuse_results = execute(fuse_func, args, "CPU");

        EXPECT_TRUE(test::all_close(fuse_results.at(0), nofuse_results.at(0)));
    }

    if (!use_deconv_fuse)
    {
        unset_environment("NGRAPH_DECONV_FUSE");
    }
}

shared_ptr<Function> gen_groupconv_batchnorm(const bool add_goe,
                                             const bool with_relu,
                                             const Shape shape_in,
                                             const Shape shape_weights,
                                             const Shape shape_out,
                                             const size_t groups)
{
    auto input = make_shared<op::Parameter>(element::f32, shape_in);
    auto weights = make_shared<op::Parameter>(element::f32, shape_weights);

    unsigned long OC = shape_out.at(1);
    Shape shape_bn{OC};
    auto group_conv = make_shared<op::GroupConvolution>(input,
                                                        weights,
                                                        Strides{1, 1},
                                                        Strides{1, 1},
                                                        CoordinateDiff{0, 0},
                                                        CoordinateDiff{0, 0},
                                                        Strides{1, 1},
                                                        groups);

    double eps = 0.001;
    auto gamma = std::make_shared<op::Parameter>(element::f32, shape_bn);
    auto beta = std::make_shared<op::Parameter>(element::f32, shape_bn);
    auto mean = std::make_shared<op::Parameter>(element::f32, shape_bn);
    auto var = std::make_shared<op::Parameter>(element::f32, shape_bn);

    auto goe_bn = std::make_shared<op::GetOutputElement>(group_conv, 0);

    // Adding a goe will stop fusion since the patterns wont expect to see this op
    auto bn =
        add_goe ? std::make_shared<op::BatchNormInference>(goe_bn, gamma, beta, mean, var, eps)
                : std::make_shared<op::BatchNormInference>(group_conv, gamma, beta, mean, var, eps);
    if (with_relu)
    {
        auto prelu = std::make_shared<op::Relu>(bn);
        auto f = make_shared<Function>(NodeVector{prelu},
                                       ParameterVector{input, weights, gamma, beta, mean, var});
        return f;
    }
    else
    {
        auto f = make_shared<Function>(NodeVector{bn},
                                       ParameterVector{input, weights, gamma, beta, mean, var});
        return f;
    }
}

void fuse_groupconv_batchnorm_helper(Shape shape_in,
                                     Shape shape_weights,
                                     Shape shape_r,
                                     size_t groups)
{
    auto func_fuse =
        gen_groupconv_batchnorm(false, false, shape_in, shape_weights, shape_r, groups);
    auto func_fuse2 =
        gen_groupconv_batchnorm(false, true, shape_in, shape_weights, shape_r, groups);

    {
        pass::Manager pass_manager;
        pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
        pass_manager.run_passes(func_fuse);
        ASSERT_EQ(count_ops_of_type<op::GroupConvolutionBias>(func_fuse), 1);
    }

    {
        // test groupconv + batchnorm + relu fusion
        pass::Manager pass_manager;
        pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
        pass_manager.run_passes(func_fuse2);
        ASSERT_EQ(count_ops_of_type<op::GroupConvolutionBias>(func_fuse2), 1);
        ASSERT_EQ(count_ops_of_type<op::Relu>(func_fuse2), 0);
    }
}

void groupconv_batchnorm_test_val_helper(
    const bool with_relu, Shape shape_in, Shape shape_weights, Shape shape_r, size_t groups)
{
    shared_ptr<Function> fuse_func =
        gen_groupconv_batchnorm(false, with_relu, shape_in, shape_weights, shape_r, groups);
    shared_ptr<Function> nofuse_func =
        gen_groupconv_batchnorm(true, with_relu, shape_in, shape_weights, shape_r, groups);

    test::Uniform<float> rng(1.0f, 100.0f);
    vector<vector<float>> args;
    for (shared_ptr<op::Parameter> param : fuse_func->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }

    auto fuse_results = execute(fuse_func, args, "CPU");
    auto nofuse_results = execute(nofuse_func, args, "CPU");

    EXPECT_TRUE(test::all_close(fuse_results.at(0), nofuse_results.at(0)));
}

TEST(cpu_fusion, fuse_groupconv_batchnorm1)
{
    Shape shape_in{1, 20, 5, 5};
    Shape shape_weights{8, 10, 3, 3};
    Shape shape_r{1, 8, 3, 3};
    fuse_groupconv_batchnorm_helper(shape_in, shape_weights, shape_r, 2);
    groupconv_batchnorm_test_val_helper(false, shape_in, shape_weights, shape_r, 2);
    groupconv_batchnorm_test_val_helper(true, shape_in, shape_weights, shape_r, 2);
}

TEST(cpu_fusion, fuse_groupconv_batchnorm2)
{
    Shape shape_in{1, 20, 5, 5};
    Shape shape_weights{5, 4, 3, 3};
    Shape shape_r{1, 5, 3, 3};
    fuse_groupconv_batchnorm_helper(shape_in, shape_weights, shape_r, 5);
    groupconv_batchnorm_test_val_helper(false, shape_in, shape_weights, shape_r, 5);
    groupconv_batchnorm_test_val_helper(true, shape_in, shape_weights, shape_r, 5);
}

TEST(cpu_fusion, fuse_groupconv_batchnorm3)
{
    Shape shape_in{1, 20, 5, 5};
    Shape shape_weights{20, 1, 3, 3};
    Shape shape_r{1, 20, 3, 3};
    fuse_groupconv_batchnorm_helper(shape_in, shape_weights, shape_r, 20);
    groupconv_batchnorm_test_val_helper(false, shape_in, shape_weights, shape_r, 20);
    groupconv_batchnorm_test_val_helper(true, shape_in, shape_weights, shape_r, 20);
}

TEST(cpu_fusion, fuse_groupconv_batchnorm4)
{
    Shape shape_in{1, 20, 4, 4};
    Shape shape_weights{5, 20, 1, 1};
    Shape shape_r{1, 5, 4, 4};
    fuse_groupconv_batchnorm_helper(shape_in, shape_weights, shape_r, 1);
    groupconv_batchnorm_test_val_helper(false, shape_in, shape_weights, shape_r, 1);
    groupconv_batchnorm_test_val_helper(true, shape_in, shape_weights, shape_r, 1);
}

std::vector<shared_ptr<runtime::Tensor>> rnn_matrix_fusion_eval(const size_t time_steps,
                                                                const Shape& data_shape,
                                                                const Shape& weights_shape,
                                                                const Shape& bias_shape,
                                                                const vector<float>& data_val,
                                                                const vector<float>& weights_val,
                                                                const vector<float>& bias_val,
                                                                const bool enable_pass)
{
    auto data = make_shared<op::Parameter>(element::f32, data_shape);
    auto weights = make_shared<op::Parameter>(element::f32, weights_shape);
    auto bias = make_shared<op::Parameter>(element::f32, bias_shape);

    // results from each time step
    NodeVector results;
    for (size_t t = 0; t < time_steps; ++t)
    {
        auto data_slice = make_shared<op::Slice>(
            data, Coordinate{0, t, 0}, Coordinate{data_shape[0], t + 1, data_shape[2]});
        auto data_reshape = make_shared<op::Reshape>(
            data_slice, AxisVector{0, 1, 2}, Shape{data_shape[0], data_shape[2]});
        auto weights_reshape = make_shared<op::Reshape>(
            weights, AxisVector{1, 0}, Shape{weights_shape[1], weights_shape[0]});
        auto dot = make_shared<op::Dot>(data_reshape, weights_reshape);
        auto bias_broadcast = make_shared<op::Broadcast>(bias, dot->get_shape(), AxisSet{0});
        auto add = make_shared<op::Add>(dot, bias_broadcast);
        results.push_back(add);
    }
    auto func = make_shared<Function>(results, ParameterVector{data, weights, bias});
    if (enable_pass)
    {
        pass::Manager pass_manager;
        pass_manager.register_pass<runtime::cpu::pass::CPURnnMatFusion>();
        pass_manager.register_pass<runtime::cpu::pass::CPUFusion>(
            pass::FusionType::REGULAR_FUSIONS);
        pass_manager.run_passes(func);
        // check all of our dot/add are converted to a single MatmulBias op.
        size_t count = count_ops_of_type<op::MatmulBias>(func);
        EXPECT_EQ(count, 1);
    }

    auto backend = runtime::Backend::create("CPU");

    shared_ptr<runtime::Tensor> data_tensor =
        backend->create_tensor(element::f32, data->get_shape());
    shared_ptr<runtime::Tensor> weights_tensor =
        backend->create_tensor(element::f32, weights->get_shape());
    shared_ptr<runtime::Tensor> bias_tensor =
        backend->create_tensor(element::f32, bias->get_shape());

    std::vector<shared_ptr<runtime::Tensor>> result_tensors;
    for (auto r : results)
    {
        result_tensors.push_back(backend->create_tensor(element::f32, r->get_shape()));
    }

    copy_data(data_tensor, data_val);
    copy_data(weights_tensor, weights_val);
    copy_data(bias_tensor, bias_val);
    auto handle = backend->compile(func);
    handle->call_with_validate(result_tensors, {data_tensor, weights_tensor, bias_tensor});
    return result_tensors;
}

TEST(cpu_fusion, rnn_matrix_fusion_eval_pass)
{
    const size_t time_steps = 4;
    Shape data_shape{3, time_steps, 5};
    Shape weights_shape{6, data_shape[2]};
    Shape bias_shape{6};

    test::Uniform<float> rng{0, 1, 0};
    vector<float> data_val(shape_size(data_shape));
    vector<float> weights_val(shape_size(weights_shape));
    vector<float> bias_val(shape_size(bias_shape));
    rng.initialize(data_val);
    rng.initialize(weights_val);
    rng.initialize(bias_val);

    std::vector<shared_ptr<runtime::Tensor>> result_expected = rnn_matrix_fusion_eval(
        time_steps, data_shape, weights_shape, bias_shape, data_val, weights_val, bias_val, false);
    std::vector<shared_ptr<runtime::Tensor>> result_fused = rnn_matrix_fusion_eval(
        time_steps, data_shape, weights_shape, bias_shape, data_val, weights_val, bias_val, true);
    for (size_t i = 0; i < result_expected.size(); ++i)
    {
        EXPECT_TRUE(test::all_close<float>(result_expected[i], result_fused[i]));
    }
}

TEST(cpu_fusion, weight_fusion)
{
    auto param = std::make_shared<op::Parameter>(element::f32, Shape{64});
    auto reshape_conv =
        std::make_shared<ngraph::op::Reshape>(param, AxisVector{0}, Shape{16, 4, 1, 1});
    auto data_conv = std::make_shared<op::Parameter>(element::f32, Shape{16, 4, 7, 7});
    auto tvt = &reshape_conv->output(0).get_tensor();
    auto lt_desc = std::make_shared<runtime::cpu::LayoutDescriptor>(*tvt);
    auto cvt_lt_conv = std::make_shared<runtime::cpu::op::ConvertLayout>(reshape_conv, lt_desc);
    auto conv = std::make_shared<ngraph::op::Convolution>(
        data_conv, cvt_lt_conv, Strides{1, 1}, Strides{1, 1});

    auto reshape_conv_bprop =
        std::make_shared<op::Reshape>(param, AxisVector{0}, Shape{16, 4, 1, 1});
    auto dummy_arg_conv_bprop = std::make_shared<op::Parameter>(element::f32, Shape{1, 16, 7, 7});
    auto tvt_bprop = &reshape_conv_bprop->output(0).get_tensor();
    auto lt_desc_bprop = std::make_shared<runtime::cpu::LayoutDescriptor>(*tvt_bprop);
    auto cvt_lt_conv_bprop =
        std::make_shared<runtime::cpu::op::ConvertLayout>(reshape_conv_bprop, lt_desc_bprop);
    auto conv_bprop = std::make_shared<op::ConvolutionBackpropData>(Shape{1, 4, 7, 7},
                                                                    cvt_lt_conv_bprop,
                                                                    dummy_arg_conv_bprop,
                                                                    Strides{1, 1},
                                                                    Strides{1, 1},
                                                                    CoordinateDiff{0, 0},
                                                                    CoordinateDiff{0, 0},
                                                                    Strides{1, 1});

    auto conv_relu = std::make_shared<op::Relu>(conv);
    auto conv_bprop_abs = std::make_shared<op::Abs>(conv_bprop);

    auto f = make_shared<Function>(NodeVector{conv_relu, conv_bprop_abs},
                                   ParameterVector{param, data_conv, dummy_arg_conv_bprop});

    pass::Manager pass_manager;
    pass_manager.register_pass<runtime::cpu::pass::CPUPostLayoutOptimizations>();
    pass_manager.run_passes(f);

    auto new_conv_bprop_data = conv_bprop_abs->get_argument(0);
    auto new_convert_layout = new_conv_bprop_data->get_argument(0);

    ASSERT_EQ(std::dynamic_pointer_cast<runtime::cpu::op::ConvertLayout>(
                  new_convert_layout->get_argument(0)),
              cvt_lt_conv);
}

TEST(cpu_fusion, max_pool_with_indices)
{
    Shape shape_a{10, 3, 28, 28};
    auto input = std::make_shared<op::Parameter>(element::f32, shape_a);
    Shape window_shape{2, 2};
    auto max_pool = std::make_shared<op::MaxPool>(input, window_shape);
    auto C = std::make_shared<op::Parameter>(element::f32, max_pool->get_shape());

    ngraph::autodiff::Adjoints adjoints(ngraph::OutputVector{max_pool}, ngraph::OutputVector{C});

    auto dinput = adjoints.backprop_node(input);

    auto df = std::make_shared<Function>(NodeVector{dinput}, ParameterVector{input, C});

    auto f = std::make_shared<Function>(NodeVector{max_pool}, ParameterVector{input});

    {
        pass::Manager pass_manager;
        pass_manager.register_pass<pass::VisualizeTree>("max_pool_fprop_before.png");
        pass_manager.run_passes(f);
    }

    {
        NodeVector nv_cwi;
        pass::Manager pass_manager;
        pass_manager.register_pass<pass::VisualizeTree>("max_pool_bprop_before.png");
        pass_manager.register_pass<runtime::cpu::pass::CPUWorkspaceInsertion>(nv_cwi);
        pass_manager.register_pass<pass::VisualizeTree>("max_pool_bprop_after.png");
        pass_manager.run_passes(df);
    }

    {
        pass::Manager pass_manager;
        pass_manager.register_pass<pass::VisualizeTree>("max_pool_fprop_after.png");
        pass_manager.run_passes(f);
    }

    auto maxpool_goe_output =
        std::dynamic_pointer_cast<op::GetOutputElement>(f->get_results().at(0)->get_argument(0));
    ASSERT_TRUE(maxpool_goe_output);
    ASSERT_EQ(maxpool_goe_output->get_n(), 0);
    auto maxpool_with_indices = df->get_results().at(0)->get_argument(0);
    auto maxpool_goe_indices =
        std::dynamic_pointer_cast<op::GetOutputElement>(maxpool_with_indices->get_argument(2));
    ASSERT_TRUE(maxpool_goe_indices);
    ASSERT_EQ(maxpool_goe_indices->get_n(), 1);
}

TEST(cpu_fusion, backwards_maxpool_with_indices_n4_c1_hw4_2x2_max)
{
    Shape shape_a{1, 4, 4, 4};
    Shape maxpool_shape{1, 4, 3, 3};
    auto A = std::make_shared<op::Parameter>(element::f32, shape_a);
    Shape window_shape{2, 2};
    auto window_movement_strides = Strides{1, 1};
    auto maxpool = std::make_shared<op::MaxPool>(A, window_shape, window_movement_strides);
    auto f = std::make_shared<Function>(maxpool, ParameterVector{A});

    auto backend = runtime::Backend::create("CPU");
    shared_ptr<runtime::Tensor> ep = backend->create_tensor(element::f32, maxpool_shape);
    vector<float> dataEp(shape_size(maxpool_shape), 4);

    shared_ptr<runtime::Tensor> input = backend->create_tensor(element::f32, shape_a);
    shared_ptr<runtime::Tensor> output = backend->create_tensor(element::f32, shape_a);

    vector<float> dataInput{11.f, 31.f, 40.f, 47.f, 13.f, 61.f, 48.f, 59.f, 17.f, 39.f, 64.f,
                            62.f, 45.f, 55.f, 36.f, 19.f, 65.f, 33.f, 49.f, 30.f, 56.f, 41.f,
                            53.f, 58.f, 22.f, 35.f, 52.f, 50.f, 63.f, 54.f, 12.f, 26.f, 44.f,
                            21.f, 69.f, 24.f, 46.f, 25.f, 51.f, 29.f, 72.f, 15.f, 73.f, 10.f,
                            16.f, 37.f, 70.f, 32.f, 28.f, 66.f, 57.f, 27.f, 60.f, 42.f, 43.f,
                            71.f, 18.f, 38.f, 67.f, 68.f, 14.f, 20.f, 34.f, 23.f};

    vector<float> expected{0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 12.0f, 0.0f, 4.0f, 0.0f, 0.0f,  16.0f,
                           0.0f, 0.0f, 4.0f, 0.0f, 0.0f, 4.0f,  0.0f, 0.0f, 0.0f, 4.0f,  0.0f,
                           8.0f, 8.0f, 0.0f, 0.0f, 4.0f, 0.0f,  4.0f, 4.0f, 0.0f, 0.0f,  0.0f,
                           0.0f, 8.0f, 0.0f, 4.0f, 0.0f, 0.0f,  0.0f, 8.0f, 0.0f, 16.0f, 0.0f,
                           0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 8.0f,  0.0f, 0.0f, 4.0f, 0.0f,  0.0f,
                           8.0f, 0.0f, 4.0f, 8.0f, 4.0f, 0.0f,  0.0f, 0.0f, 0.0f};

    copy_data(ep, dataEp);
    copy_data(input, dataInput);

    auto C = std::make_shared<op::Parameter>(element::f32, maxpool_shape);
    auto df = autodiff::backprop_function(f);

    {
        NodeVector nv_cwi;
        pass::Manager pass_manager;
        pass_manager.register_pass<pass::VisualizeTree>("max_pool_bprop_before2.png");
        pass_manager.register_pass<runtime::cpu::pass::CPUWorkspaceInsertion>(nv_cwi);
        pass_manager.register_pass<pass::VisualizeTree>("max_pool_bprop_after2.png");
        pass_manager.run_passes(df);
    }

    auto handle = backend->compile(df);
    handle->call_with_validate({output}, {input, ep});
    EXPECT_TRUE(test::all_close_f(read_vector<float>(output), expected, MIN_FLOAT_TOLERANCE_BITS));
}

static std::shared_ptr<ngraph::Function> make_forward_function()
{
    Shape shape_a{10, 3, 28, 28};
    auto input = std::make_shared<op::Parameter>(element::f32, shape_a);
    Shape window_shape{2, 2};
    auto max_pool = std::make_shared<op::MaxPool>(input, window_shape);
    auto neg = std::make_shared<op::Negative>(max_pool);
    auto absn = std::make_shared<op::Abs>(max_pool);
    return std::make_shared<Function>(NodeVector{max_pool, neg, absn}, ParameterVector{input});
}

static std::pair<std::shared_ptr<ngraph::Function>, OutputVector>
    make_backward_function(std::shared_ptr<ngraph::Function> f)
{
    // get parameters
    std::vector<std::shared_ptr<ngraph::op::Parameter>> back_parameters = f->get_parameters();

    ngraph::OutputVector adjoints;
    ngraph::OutputVector outputs;
    for (auto Y : f->get_results())
    {
        // Get the output
        // Create the Adjoint
        auto C = std::make_shared<ngraph::op::Parameter>(Y->get_element_type(), Y->get_shape());
        outputs.push_back(Y);
        adjoints.push_back(C);
    }

    ngraph::autodiff::Adjoints adjoint{outputs, adjoints};

    // Perform autodiff
    OutputVector dYdXs(back_parameters.size());
    transform(back_parameters.begin(),
              back_parameters.end(),
              dYdXs.begin(),
              [&adjoint](const std::shared_ptr<Node>& X) { return adjoint.backprop_node(X); });

    // create the backward function
    std::vector<std::shared_ptr<ngraph::op::Parameter>> param_adjoints;
    for (auto n : adjoints)
        param_adjoints.push_back(
            std::dynamic_pointer_cast<ngraph::op::Parameter>(n.get_node_shared_ptr()));
    back_parameters.insert(back_parameters.begin(), param_adjoints.begin(), param_adjoints.end());

    return {std::make_shared<ngraph::Function>(dYdXs, back_parameters), adjoints};
}

void optimize_graph(std::shared_ptr<ngraph::Function>& f, std::shared_ptr<ngraph::Function> bf)
{
    // start by removing excess reshapes
    NodeVector nv_cwi;
    ngraph::pass::Manager pass_manager;
    pass_manager.register_pass<ngraph::pass::ReshapeElimination>();
    pass_manager.register_pass<ngraph::pass::ReshapeElimination>();
    pass_manager.register_pass<runtime::cpu::pass::CPUWorkspaceInsertion>(nv_cwi);
    pass_manager.register_pass<pass::VisualizeTree>("before.fprop_cache.png");

    pass_manager.run_passes(f);
    pass_manager.run_passes(bf);
    if (nv_cwi.size() > 0)
    {
        NodeVector new_outputs;
        for (auto r : f->get_results())
        {
            new_outputs.push_back(r->get_argument(0));
        }

        new_outputs.insert(new_outputs.end(), nv_cwi.begin(), nv_cwi.end());
        f = std::make_shared<ngraph::Function>(new_outputs, f->get_parameters());
    }

    ngraph::NodeVector dYdXs;
    for (size_t i = 0; i < bf->get_output_size(); ++i)
    {
        dYdXs.push_back(bf->get_output_op(i)->get_argument(0));
    }

    ngraph::NodeVector combined_outputs;
    for (auto r : f->get_results())
    {
        combined_outputs.push_back(r->get_argument(0));
    }

    combined_outputs.insert(combined_outputs.end(), dYdXs.begin(), dYdXs.end());

    std::vector<std::shared_ptr<ngraph::op::Parameter>> combined_parameters = f->get_parameters();
    std::vector<std::shared_ptr<ngraph::op::Parameter>> back_parameters = bf->get_parameters();

    combined_parameters.insert(
        combined_parameters.end(), back_parameters.begin(), back_parameters.end());
    auto combinedf = std::make_shared<ngraph::Function>(combined_outputs, combined_parameters);
    // rerun Reshape elimination to help simplify the graph again, run CPUFusion
    // this replaces nodes in both f and bf due to shared-ptr - ness
    ngraph::pass::Manager pass_manager_comb;
    pass_manager_comb.register_pass<ngraph::pass::ReshapeElimination>();
    pass_manager_comb.register_pass<ngraph::runtime::cpu::pass::CPUFusion>();
    pass_manager_comb.run_passes(combinedf);
}

TEST(cpu_fusion, maxpool_with_indices_in_mxnet)
{
    auto f = make_forward_function();
    auto bfa = make_backward_function(f);
    auto maybe_bf = bfa.first;
    auto adjoints = bfa.second;
    optimize_graph(f, maybe_bf);
    auto fprop_cache = ngraph::cache_fprop(f, maybe_bf);

    auto mpwi_bprop = fprop_cache.bprop->get_results().at(0)->get_argument(0);
    ASSERT_TRUE(std::dynamic_pointer_cast<op::Parameter>(mpwi_bprop->get_argument(0)));
    ASSERT_TRUE(std::dynamic_pointer_cast<op::Parameter>(mpwi_bprop->get_argument(2)));
}

TEST(cpu_fusion, conv_batch_norm_folding)
{
    Shape shape_input{1, 8, 3, 3};
    Shape shape_weights{2, 8, 1, 1};
    Shape shape_norm{2};

    auto make_function = [shape_input, shape_weights, shape_norm]() {
        auto input = std::make_shared<op::Parameter>(element::f32, shape_input);
        auto weights = std::make_shared<op::Parameter>(element::f32, shape_weights);
        double eps = 0.001;
        auto gamma = std::make_shared<op::Parameter>(element::f32, shape_norm);
        auto beta = std::make_shared<op::Parameter>(element::f32, shape_norm);
        auto mean = std::make_shared<op::Parameter>(element::f32, shape_norm);
        auto var = std::make_shared<op::Parameter>(element::f32, shape_norm);
        auto conv = std::make_shared<op::Convolution>(input, weights, Strides{1, 1}, Strides{1, 1});
        auto bn = std::make_shared<op::BatchNormInference>(conv, gamma, beta, mean, var, eps);
        auto f = make_shared<Function>(NodeVector{bn},
                                       ParameterVector{input, weights, gamma, beta, mean, var});
        return f;
    };

    auto int_f = make_function();
    auto cpu_f = make_function();

    vector<vector<float>> args{
        {1.25f,  2.25f, 5.25f, 6.25f,  -1.25f, -1.25f, 3.25f, -4.25f, 7.25f,  8.25f,  -1.25f,
         -1.25f, 1.25f, 2.25f, -3.25f, 2.25f,  4.25f,  4.25f, 1.25f,  2.25f,  -4.25f, 2.25f,
         4.25f,  4.25f, 0.f,   0.f,    -1.f,   0.f,    2.f,   2.f,    0.f,    0.f,    0.f,
         0.f,    2.f,   2.f,   1.25f,  2.25f,  5.25f,  6.25f, 1.25f,  1.25f,  3.25f,  4.25f,
         -7.25f, 8.25f, 1.25f, -1.25f, -1.25f, 2.25f,  3.25f, 2.25f,  -4.25f, -4.25f, -1.25f,
         -2.25f, 4.25f, 2.25f, 4.25f,  4.25f,  0.f,    0.f,   1.f,    0.f,    -2.f,   2.f,
         0.f,    0.f,   0.f,   0.f,    -2.f,   -2.f},
        {1.25f,
         2.25f,
         5.25f,
         6.25f,
         -1.25f,
         -1.25f,
         3.25f,
         -4.25f,
         7.25f,
         8.25f,
         -1.25f,
         0.f,
         0.f,
         0.f,
         0.f,
         -2.f},
        {-0.9384f, 0.01875f},
        {11.0f, 1.3f},
        {0.12f, 0.31f},
        {0.01f, 0.11f},
    };

    auto int_results = execute(int_f, args, "INTERPRETER");
    auto cpu_results = execute(cpu_f, args, "CPU");
    EXPECT_TRUE(test::all_close(cpu_results.at(0), int_results.at(0)));
}

TEST(cpu_fusion, convbias_batch_norm_folding)
{
    Shape shape_input{2, 8, 5, 5};
    Shape shape_weights{2, 8, 2, 2};
    Shape shape_norm{2};

    auto make_function = [shape_input, shape_weights, shape_norm]() {
        auto input = std::make_shared<op::Parameter>(element::f32, shape_input);
        auto weights = std::make_shared<op::Parameter>(element::f32, shape_weights);
        auto bias = std::make_shared<op::Parameter>(element::f32, Shape{2});
        double eps = 1.01;
        auto gamma = std::make_shared<op::Parameter>(element::f32, shape_norm);
        auto beta = std::make_shared<op::Parameter>(element::f32, shape_norm);
        auto mean = std::make_shared<op::Parameter>(element::f32, shape_norm);
        auto var = std::make_shared<op::Parameter>(element::f32, shape_norm);
        auto conv = std::make_shared<op::Convolution>(input, weights, Strides{1, 1}, Strides{1, 1});
        auto convbias =
            conv + std::make_shared<op::Broadcast>(bias, conv->get_shape(), AxisSet{0, 2, 3});
        auto bn = std::make_shared<op::BatchNormInference>(convbias, gamma, beta, mean, var, eps);
        auto f = make_shared<Function>(
            NodeVector{bn}, ParameterVector{input, weights, bias, gamma, beta, mean, var});
        return f;
    };

    auto int_f = make_function();
    auto cpu_f = make_function();

    test::Uniform<float> rng(1.0f, 100.0f);
    vector<vector<float>> args;
    for (shared_ptr<op::Parameter> param : cpu_f->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }

    auto int_results = execute(int_f, args, "INTERPRETER");
    auto cpu_results = execute(cpu_f, args, "CPU");
    EXPECT_TRUE(test::all_close(cpu_results.at(0), int_results.at(0)));
}

TEST(cpu_fusion, conv_affine_folding)
{
    Shape shape_input{1, 8, 3, 3};
    Shape shape_weights{2, 8, 1, 1};
    Shape shape_norm{2};

    auto make_function = [shape_input, shape_weights, shape_norm]() {
        auto input = std::make_shared<op::Parameter>(element::f32, shape_input);
        auto weights = std::make_shared<op::Parameter>(element::f32, shape_weights);

        auto a = std::make_shared<op::Parameter>(element::f32, shape_norm);
        auto b = std::make_shared<op::Parameter>(element::f32, shape_norm);
        auto conv = std::make_shared<op::Convolution>(input, weights, Strides{1, 1}, Strides{1, 1});
        auto out = std::make_shared<op::Add>(
            std::make_shared<op::Multiply>(
                conv, std::make_shared<op::Broadcast>(a, conv->get_shape(), AxisSet{0, 2, 3})),
            std::make_shared<op::Broadcast>(b, conv->get_shape(), AxisSet{0, 2, 3}));
        auto f = make_shared<Function>(NodeVector{out}, ParameterVector{input, weights, a, b});
        return f;
    };

    auto int_f = make_function();
    auto cpu_f = make_function();

    vector<vector<float>> args{
        {1.25f,  2.25f, 5.25f, 6.25f,  -1.25f, -1.25f, 3.25f, -4.25f, 7.25f,  8.25f,  -1.25f,
         -1.25f, 1.25f, 2.25f, -3.25f, 2.25f,  4.25f,  4.25f, 1.25f,  2.25f,  -4.25f, 2.25f,
         4.25f,  4.25f, 0.f,   0.f,    -1.f,   0.f,    2.f,   2.f,    0.f,    0.f,    0.f,
         0.f,    2.f,   2.f,   1.25f,  2.25f,  5.25f,  6.25f, 1.25f,  1.25f,  3.25f,  4.25f,
         -7.25f, 8.25f, 1.25f, -1.25f, -1.25f, 2.25f,  3.25f, 2.25f,  -4.25f, -4.25f, -1.25f,
         -2.25f, 4.25f, 2.25f, 4.25f,  4.25f,  0.f,    0.f,   1.f,    0.f,    -2.f,   2.f,
         0.f,    0.f,   0.f,   0.f,    -2.f,   -2.f},
        {1.25f,
         2.25f,
         5.25f,
         6.25f,
         -1.25f,
         -1.25f,
         3.25f,
         -4.25f,
         7.25f,
         8.25f,
         -1.25f,
         0.f,
         0.f,
         0.f,
         0.f,
         -2.f},
        {-0.9384f, 0.01875f},
        {11.0f, 1.3f},
    };

    auto int_results = execute(int_f, args, "INTERPRETER");
    auto cpu_results = execute(cpu_f, args, "CPU");
    EXPECT_TRUE(test::all_close(cpu_results.at(0), int_results.at(0)));
}

TEST(cpu_fusion, convbias_affine_folding1)
{
    Shape shape_input{1, 6, 3, 3};
    Shape shape_weights{3, 6, 1, 1};
    Shape shape_norm{3};

    auto make_function = [shape_input, shape_weights, shape_norm]() {
        auto input = std::make_shared<op::Parameter>(element::f32, shape_input);
        auto weights = std::make_shared<op::Parameter>(element::f32, shape_weights);
        auto bias = std::make_shared<op::Parameter>(element::f32, Shape{3});

        auto a = std::make_shared<op::Parameter>(element::f32, shape_norm);
        auto b = std::make_shared<op::Parameter>(element::f32, shape_norm);
        auto conv = std::make_shared<op::Convolution>(input, weights, Strides{1, 1}, Strides{1, 1});
        auto convbias =
            conv + std::make_shared<op::Broadcast>(bias, conv->get_shape(), AxisSet{0, 2, 3});
        auto out = std::make_shared<op::Add>(
            std::make_shared<op::Multiply>(
                convbias, std::make_shared<op::Broadcast>(a, conv->get_shape(), AxisSet{0, 2, 3})),
            std::make_shared<op::Broadcast>(b, conv->get_shape(), AxisSet{0, 2, 3}));
        auto f =
            make_shared<Function>(NodeVector{out}, ParameterVector{input, weights, bias, a, b});
        return f;
    };

    pass::Manager pass_manager;
    pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
    auto func = make_function();
    pass_manager.run_passes(func);
    ASSERT_EQ(count_ops_of_type<op::ConvolutionBiasAdd>(func), 1);

    auto int_f = make_function();
    auto cpu_f = make_function();

    test::Uniform<float> rng(20.0f, 300.0f);
    vector<vector<float>> args;
    for (shared_ptr<op::Parameter> param : cpu_f->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }

    auto int_results = execute(int_f, args, "INTERPRETER");
    auto cpu_results = execute(cpu_f, args, "CPU");
    EXPECT_TRUE(test::all_close(cpu_results.at(0), int_results.at(0)));
}

TEST(cpu_fusion, convbias_affine_folding2)
{
    Shape shape_input{1, 6, 3, 3};
    Shape shape_weights{3, 6, 1, 1};
    Shape shape_norm{1};

    auto make_function = [shape_input, shape_weights, shape_norm]() {
        auto input = std::make_shared<op::Parameter>(element::f32, shape_input);
        auto weights = std::make_shared<op::Parameter>(element::f32, shape_weights);
        auto bias = std::make_shared<op::Parameter>(element::f32, Shape{3});

        auto a = std::make_shared<op::Parameter>(element::f32, shape_norm);
        auto b = std::make_shared<op::Parameter>(element::f32, shape_norm);
        auto conv = std::make_shared<op::Convolution>(input, weights, Strides{1, 1}, Strides{1, 1});
        auto convbias =
            conv + std::make_shared<op::Broadcast>(bias, conv->get_shape(), AxisSet{0, 2, 3});
        auto out = std::make_shared<op::Add>(
            std::make_shared<op::Multiply>(
                convbias, std::make_shared<op::Broadcast>(a, conv->get_shape(), AxisSet{1, 2, 3})),
            std::make_shared<op::Broadcast>(b, conv->get_shape(), AxisSet{1, 2, 3}));
        auto f =
            make_shared<Function>(NodeVector{out}, ParameterVector{input, weights, bias, a, b});
        return f;
    };

    pass::Manager pass_manager;
    pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
    auto func = make_function();
    pass_manager.run_passes(func);
    ASSERT_EQ(count_ops_of_type<op::ConvolutionBiasAdd>(func), 1);

    auto int_f = make_function();
    auto cpu_f = make_function();

    test::Uniform<float> rng(20.0f, 300.0f);
    vector<vector<float>> args;
    for (shared_ptr<op::Parameter> param : cpu_f->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }

    auto int_results = execute(int_f, args, "INTERPRETER");
    auto cpu_results = execute(cpu_f, args, "CPU");
    EXPECT_TRUE(test::all_close(cpu_results.at(0), int_results.at(0)));
}

TEST(batch_fusion, group_convolution)
{
    auto backend = runtime::Backend::create("CPU");
    test::Uniform<float> rng(2.0f, 10.0f);

    const size_t GROUPS = 2;
    Shape shape_a{1, 32, 2, 2};
    auto A = make_shared<op::Parameter>(element::f32, shape_a);
    Shape shape_b{2, 16, 1, 1};
    auto B = make_shared<op::Parameter>(element::f32, shape_b);
    Shape shape_r{1, 2, 2, 2};
    auto group_conv = make_shared<op::GroupConvolution>(A,
                                                        B,
                                                        Strides{1, 1},
                                                        Strides{1, 1},
                                                        CoordinateDiff{0, 0},
                                                        CoordinateDiff{0, 0},
                                                        Strides{1, 1},
                                                        GROUPS);

    Shape shape_c{1, 16, 2, 2};
    auto C = make_shared<op::Parameter>(element::f32, shape_c);
    Shape shape_d{1, 16, 1, 1};
    auto D = make_shared<op::Parameter>(element::f32, shape_d);
    auto conv_lower = make_shared<op::Convolution>(C,
                                                   D,
                                                   Strides{1, 1},
                                                   Strides{1, 1},
                                                   CoordinateDiff{0, 0},
                                                   CoordinateDiff{0, 0},
                                                   Strides{1, 1});

    auto E = make_shared<op::Parameter>(element::f32, shape_c);
    auto F = make_shared<op::Parameter>(element::f32, shape_d);
    auto conv_upper = make_shared<op::Convolution>(E,
                                                   F,
                                                   Strides{1, 1},
                                                   Strides{1, 1},
                                                   CoordinateDiff{0, 0},
                                                   CoordinateDiff{0, 0},
                                                   Strides{1, 1});

    auto f = make_shared<Function>(NodeVector{group_conv, conv_lower, conv_upper},
                                   ParameterVector{A, B, C, D, E, F});

    auto a_ = rng.initialize(backend->create_tensor(element::f32, shape_a));
    auto b_ = rng.initialize(backend->create_tensor(element::f32, shape_b));

    vector<float> rv(shape_size(shape_r), 0);
    auto group_result = std::dynamic_pointer_cast<ngraph::runtime::cpu::CPUTensorView>(
        backend->create_tensor(element::f32, shape_r, rv.data()));

    auto av = read_vector<float>(a_);
    auto bv = read_vector<float>(b_);
    auto c_ = backend->create_tensor(element::f32, shape_c, av.data()); // lower data
    auto d_ = backend->create_tensor(element::f32, shape_d, bv.data()); // upper data

    auto e_ =
        backend->create_tensor(element::f32, shape_c, av.data() + av.size() / 2); // lower weights
    auto f_ =
        backend->create_tensor(element::f32, shape_d, bv.data() + bv.size() / 2); // upper weights

    Shape shape_ur{1, 1, 2, 2};
    // allocate a contigious storage for both lower and upper halves.
    vector<float> erv(shape_size(shape_r), 0);
    auto lower_result = std::dynamic_pointer_cast<ngraph::runtime::cpu::CPUTensorView>(
        backend->create_tensor(element::f32, shape_ur, erv.data()));
    auto upper_result = std::dynamic_pointer_cast<ngraph::runtime::cpu::CPUTensorView>(
        backend->create_tensor(element::f32, shape_ur, erv.data() + erv.size() / 2));
    auto handle = backend->compile(f);
    handle->call_with_validate({group_result, lower_result, upper_result},
                               {a_, b_, c_, d_, e_, f_});
    EXPECT_TRUE(test::all_close_f(rv, erv));
}

TEST(cpu_fusion, rnn_fprop_1_lstm_cell)
{
    auto src_layer = make_shared<op::Parameter>(element::f32, Shape{10, 100});
    auto src_iter = make_shared<op::Parameter>(element::f32, Shape{20, 100});
    auto weights_layer = make_shared<op::Parameter>(element::f32, Shape{100, 400});
    auto weights_iter = make_shared<op::Parameter>(element::f32, Shape{100, 400});
    auto biases = make_shared<op::Parameter>(element::f32, Shape{400});
    const int number_of_timesteps = 1;
    const int number_of_gates_per_cell = 4;
    const int src_seq_length = 1;
    const int num_rnn_cell_states = 2;
    const int rnn_direction = 1;
    const int num_of_rnn_fused_layer = 1;
    ngraph::runtime::cpu::rnn_utils::rnntype rnn_type =
        ngraph::runtime::cpu::rnn_utils::rnntype::vanilla_lstm;

    auto rnn_node = make_shared<op::Rnn>(src_layer,
                                         src_iter,
                                         weights_layer,
                                         weights_iter,
                                         biases,
                                         number_of_timesteps,
                                         number_of_gates_per_cell,
                                         src_seq_length,
                                         num_rnn_cell_states,
                                         rnn_direction,
                                         num_of_rnn_fused_layer,
                                         rnn_type);

    auto rnn_ht_output = make_shared<op::GetOutputElement>(rnn_node, 0);
    auto rnn_ct_output = make_shared<op::GetOutputElement>(rnn_node, 1);

    auto func = make_shared<Function>(
        NodeVector{rnn_ht_output, rnn_ct_output},
        ParameterVector{src_layer, src_iter, weights_layer, weights_iter, biases});
    auto backend = runtime::Backend::create("CPU");

    shared_ptr<runtime::Tensor> src_layer_t =
        backend->create_tensor(element::f32, src_layer->get_shape());
    shared_ptr<runtime::Tensor> src_iter_t =
        backend->create_tensor(element::f32, src_iter->get_shape());
    shared_ptr<runtime::Tensor> weights_layer_t =
        backend->create_tensor(element::f32, weights_layer->get_shape());
    shared_ptr<runtime::Tensor> weights_iter_t =
        backend->create_tensor(element::f32, weights_iter->get_shape());
    shared_ptr<runtime::Tensor> biases_t =
        backend->create_tensor(element::f32, biases->get_shape());
    shared_ptr<runtime::Tensor> result_ht = backend->create_tensor(element::f32, {10, 100});
    shared_ptr<runtime::Tensor> result_ct = backend->create_tensor(element::f32, Shape{20, 100});

    copy_data(src_layer_t, vector<float>(1000, 1));
    copy_data(src_iter_t, vector<float>(2000, 1));
    copy_data(weights_layer_t, vector<float>(400 * 100, 1));
    copy_data(weights_iter_t, vector<float>(400 * 100, 1));
    copy_data(biases_t, vector<float>(400, 1));

    auto handle = backend->compile(func);
    handle->call_with_validate(
        {result_ht, result_ct},
        {src_layer_t, src_iter_t, weights_layer_t, weights_iter_t, biases_t});
    vector<float> expected_ht(10 * 100, 0.964028f);
    vector<float> expected_ct;
    for (size_t i = 0; i < 20 * 100; i++)
    {
        if (i < 1000)
        {
            expected_ct.push_back(0.964028f);
        }
        else
        {
            expected_ct.push_back(2.0f);
        }
    }

    EXPECT_TRUE(test::all_close(expected_ht, read_vector<float>(result_ht)));
    EXPECT_TRUE(test::all_close(expected_ct, read_vector<float>(result_ct)));
}

void sigmoid_multiply_fusion_forward_compute(runtime::Backend* backend,
                                             const ParameterVector& input_params,
                                             const vector<vector<float>>& input_data,
                                             const vector<Shape>& input_shapes,
                                             const Shape& result_shape,
                                             shared_ptr<Node> input_0_node,
                                             shared_ptr<Node> input_1_node,
                                             const vector<float>& expected)
{
    shared_ptr<runtime::Tensor> result_tensor = backend->create_tensor(element::f32, result_shape);

    vector<shared_ptr<runtime::Tensor>> input_tensors;
    for (int i = 0; i < input_params.size(); ++i)
    {
        input_tensors.push_back(backend->create_tensor(element::f32, input_shapes[i]));
        copy_data(input_tensors[i], input_data[i]);
    }

    auto mul_node = input_0_node * input_1_node;
    auto func = make_shared<Function>(mul_node, input_params);
    auto handle = backend->compile(func);
    handle->call_with_validate({result_tensor}, input_tensors);
    EXPECT_TRUE(test::all_close(read_vector<float>(result_tensor), expected));
}

TEST(cpu_fusion, sigmoid_multiply_fusion_forward)
{
    auto backend = runtime::Backend::create("CPU");

    Shape data_shape{1, 1, 2, 2};
    Shape const_shape{1};

    vector<float> input_0_data{1.f, 2.f, 3.f, 4.f};
    vector<float> input_1_data{1.2f, 2.3f, 3.5f, 4.7f};
    vector<float> const_data{1.2f};
    {
        auto input_0_param = make_shared<op::Parameter>(element::f32, data_shape);
        auto input_1_param = make_shared<op::Parameter>(element::f32, data_shape);
        auto input_2_param = make_shared<op::Parameter>(element::f32, data_shape);
        auto sigmoid_0 = make_shared<op::Sigmoid>(input_0_param);
        auto sigmoid_1 = make_shared<op::Add>(input_1_param, input_2_param);
        vector<float> expected{1.60833f, 3.78743f, 6.19173f, 8.54352f};
        ParameterVector input_params{input_0_param, input_1_param, input_2_param};
        vector<vector<float>> input_data{input_0_data, input_0_data, input_1_data};
        vector<Shape> input_shapes{data_shape, data_shape, data_shape};
        sigmoid_multiply_fusion_forward_compute(backend.get(),
                                                input_params,
                                                input_data,
                                                input_shapes,
                                                data_shape,
                                                sigmoid_0,
                                                sigmoid_1,
                                                expected);
    }
    {
        auto input_0_param = make_shared<op::Parameter>(element::f32, data_shape);
        auto input_1_param = make_shared<op::Parameter>(element::f32, const_shape);
        auto sigmoid_0 = make_shared<op::Broadcast>(input_1_param, data_shape, AxisSet{1, 2, 3});
        auto sigmoid_1 = make_shared<op::Sigmoid>(input_0_param);
        vector<float> expected{0.87727f, 1.05696f, 1.14309f, 1.17842f};
        ParameterVector input_params{input_0_param, input_1_param};
        vector<vector<float>> input_data{input_0_data, const_data};
        vector<Shape> input_shapes{data_shape, const_shape};
        sigmoid_multiply_fusion_forward_compute(backend.get(),
                                                input_params,
                                                input_data,
                                                input_shapes,
                                                data_shape,
                                                sigmoid_0,
                                                sigmoid_1,
                                                expected);
    }
    {
        auto input_0_param = make_shared<op::Parameter>(element::f32, data_shape);
        auto input_1_param = make_shared<op::Parameter>(element::f32, const_shape);
        auto sigmoid_0 = make_shared<op::Sigmoid>(input_0_param);
        auto sigmoid_1 = make_shared<op::Broadcast>(input_1_param, data_shape, AxisSet{1, 2, 3});
        vector<float> expected{0.87727f, 1.05696f, 1.14309f, 1.17842f};
        ParameterVector input_params{input_0_param, input_1_param};
        vector<vector<float>> input_data{input_0_data, const_data};
        vector<Shape> input_shapes{data_shape, const_shape};
        sigmoid_multiply_fusion_forward_compute(backend.get(),
                                                input_params,
                                                input_data,
                                                input_shapes,
                                                data_shape,
                                                sigmoid_0,
                                                sigmoid_1,
                                                expected);
    }
    {
        auto input_0_param = make_shared<op::Parameter>(element::f32, data_shape);
        auto input_1_param = make_shared<op::Parameter>(element::f32, data_shape);
        auto sigmoid_0 = make_shared<op::Sigmoid>(input_0_param);
        auto sigmoid_1 = make_shared<op::Sigmoid>(input_1_param);
        vector<float> expected{0.561837f, 0.800536f, 0.924652f, 0.973163f};
        ParameterVector input_params{input_0_param, input_1_param};
        vector<vector<float>> input_data{input_0_data, input_1_data};
        vector<Shape> input_shapes{data_shape, data_shape};
        sigmoid_multiply_fusion_forward_compute(backend.get(),
                                                input_params,
                                                input_data,
                                                input_shapes,
                                                data_shape,
                                                sigmoid_0,
                                                sigmoid_1,
                                                expected);
    }
    {
        auto input_0_param = make_shared<op::Parameter>(element::f32, data_shape);
        auto input_1_param = make_shared<op::Parameter>(element::f32, data_shape);
        auto sigmoid_0 = make_shared<op::Sigmoid>(input_0_param);
        auto sigmoid_1 = make_shared<op::Tanh>(input_1_param);
        vector<float> expected{0.60945f, 0.863266f, 0.950838f, 0.981851f};
        ParameterVector input_params{input_0_param, input_1_param};
        vector<vector<float>> input_data{input_0_data, input_1_data};
        vector<Shape> input_shapes{data_shape, data_shape};
        sigmoid_multiply_fusion_forward_compute(backend.get(),
                                                input_params,
                                                input_data,
                                                input_shapes,
                                                data_shape,
                                                sigmoid_0,
                                                sigmoid_1,
                                                expected);
    }
    {
        auto input_0_param = make_shared<op::Parameter>(element::f32, data_shape);
        auto input_1_param = make_shared<op::Parameter>(element::f32, data_shape);
        auto sigmoid_0 = make_shared<op::Tanh>(input_0_param);
        auto sigmoid_1 = make_shared<op::Sigmoid>(input_1_param);
        vector<float> expected{0.585304f, 0.876182f, 0.965887f, 0.990322f};
        ParameterVector input_params{input_0_param, input_1_param};
        vector<vector<float>> input_data{input_0_data, input_1_data};
        vector<Shape> input_shapes{data_shape, data_shape};
        sigmoid_multiply_fusion_forward_compute(backend.get(),
                                                input_params,
                                                input_data,
                                                input_shapes,
                                                data_shape,
                                                sigmoid_0,
                                                sigmoid_1,
                                                expected);
    }
    {
        auto input_0_param = make_shared<op::Parameter>(element::f32, data_shape);
        auto input_1_param = make_shared<op::Parameter>(element::f32, data_shape);
        auto sigmoid_0 = make_shared<op::Tanh>(input_0_param);
        auto sigmoid_1 = make_shared<op::Tanh>(input_1_param);
        vector<float> expected{0.634907f, 0.94484f, 0.993242f, 0.999164f};
        ParameterVector input_params{input_0_param, input_1_param};
        vector<vector<float>> input_data{input_0_data, input_1_data};
        vector<Shape> input_shapes{data_shape, data_shape};
        sigmoid_multiply_fusion_forward_compute(backend.get(),
                                                input_params,
                                                input_data,
                                                input_shapes,
                                                data_shape,
                                                sigmoid_0,
                                                sigmoid_1,
                                                expected);
    }
}

void sigmoid_multiply_fusion_backward_compute(runtime::Backend* backend,
                                              const ParameterVector& input_params,
                                              const vector<vector<float>>& input_data,
                                              const vector<Shape>& input_shapes,
                                              const vector<float> delta_data,
                                              const Shape& delta_shape,
                                              const Shape& d_input_0_shape,
                                              const Shape& d_input_1_shape,
                                              shared_ptr<Node> input_0_node,
                                              shared_ptr<Node> input_1_node,
                                              shared_ptr<Node> input_0_adjoint,
                                              shared_ptr<Node> input_1_adjoint,
                                              const vector<float>& expected_0,
                                              const vector<float>& expected_1)
{
    vector<shared_ptr<runtime::Tensor>> input_tensors;
    for (int i = 0; i < input_params.size(); ++i)
    {
        input_tensors.push_back(backend->create_tensor(element::f32, input_shapes[i]));
        copy_data(input_tensors[i], input_data[i]);
    }

    auto delta_param = make_shared<op::Parameter>(element::f32, delta_shape);
    shared_ptr<runtime::Tensor> delta_tensor = backend->create_tensor(element::f32, delta_shape);
    copy_data(delta_tensor, delta_data);

    ParameterVector back_params(input_params);
    back_params.push_back(delta_param);
    input_tensors.push_back(delta_tensor);

    shared_ptr<runtime::Tensor> d_input_0_tensor =
        backend->create_tensor(element::f32, d_input_0_shape);
    shared_ptr<runtime::Tensor> d_input_1_tensor =
        backend->create_tensor(element::f32, d_input_1_shape);

    using FunctionType = op::SigmoidMultiply::FunctionType;
    auto input_0_type = op::SigmoidMultiply::identify_node_type(input_0_node);
    auto input_1_type = op::SigmoidMultiply::identify_node_type(input_1_node);
    // for Identity functions, we use the node itself, otherwise use its input
    // where we will apply the function of input node
    auto input_0_alt =
        (input_0_type == FunctionType::Identity) ? input_0_node : input_0_node->get_argument(0);
    auto input_1_alt =
        (input_1_type == FunctionType::Identity) ? input_1_node : input_1_node->get_argument(0);
    auto sigmoid_mul =
        make_shared<op::SigmoidMultiply>(input_0_alt, input_1_alt, input_0_type, input_1_type);

    ngraph::autodiff::Adjoints adjoints(OutputVector{sigmoid_mul}, OutputVector{delta_param});
    auto d_input_0 = adjoints.backprop_node(input_0_adjoint);
    auto d_input_1 = adjoints.backprop_node(input_1_adjoint);
    auto df = make_shared<Function>(NodeVector{d_input_0, d_input_1}, back_params);
    auto handle = backend->compile(df);
    handle->call_with_validate({d_input_0_tensor, d_input_1_tensor}, input_tensors);
    EXPECT_TRUE(test::all_close(read_vector<float>(d_input_0_tensor), expected_0));
    EXPECT_TRUE(test::all_close(read_vector<float>(d_input_1_tensor), expected_1));
}

TEST(cpu_fusion, sigmoid_multiply_fusion_backward)
{
    auto backend = runtime::Backend::create("CPU");

    Shape data_shape{1, 1, 2, 2};
    Shape const_shape{1};

    vector<float> input_0_data{1.f, 2.f, 3.f, 4.f};
    vector<float> input_1_data{1.2f, 2.2f, 3.2f, 4.2f};
    vector<float> const_data{1.2f};
    vector<float> delta_data(shape_size(data_shape), 20.0f);

    {
        auto input_0_param = make_shared<op::Parameter>(element::f32, data_shape);
        auto input_1_param = make_shared<op::Parameter>(element::f32, data_shape);
        auto input_2_param = make_shared<op::Parameter>(element::f32, data_shape);
        auto sigmoid_0 = make_shared<op::Sigmoid>(input_0_param);
        auto sigmoid_1 = make_shared<op::Add>(input_1_param, input_2_param);
        vector<float> expected_0{8.65093f, 8.81946f, 5.60191f, 2.89668f};
        vector<float> expected_1{14.6212f, 17.6159f, 19.0515f, 19.6403f};
        ParameterVector input_params{input_0_param, input_1_param, input_2_param};
        vector<vector<float>> input_data{input_0_data, input_0_data, input_1_data};
        vector<Shape> input_shapes{data_shape, data_shape, data_shape};
        sigmoid_multiply_fusion_backward_compute(backend.get(),
                                                 input_params,
                                                 input_data,
                                                 input_shapes,
                                                 delta_data,
                                                 data_shape,
                                                 data_shape,
                                                 data_shape,
                                                 sigmoid_0,
                                                 sigmoid_1,
                                                 input_0_param,
                                                 sigmoid_1,
                                                 expected_0,
                                                 expected_1);
    }
    {
        auto input_0_param = make_shared<op::Parameter>(element::f32, data_shape);
        auto input_1_param = make_shared<op::Parameter>(element::f32, const_shape);
        auto sigmoid_0 = make_shared<op::Broadcast>(input_1_param, data_shape, AxisSet{1, 2, 3});
        auto sigmoid_1 = make_shared<op::Tanh>(input_0_param);
        vector<float> expected_0{15.2319f, 19.2806f, 19.9011f, 19.9866f};
        vector<float> expected_1{10.0794f, 1.69562f, 0.236785f, 0.0321828f};
        ParameterVector input_params{input_0_param, input_1_param};
        vector<vector<float>> input_data{input_0_data, const_data};
        vector<Shape> input_shapes{data_shape, const_shape};
        sigmoid_multiply_fusion_backward_compute(backend.get(),
                                                 input_params,
                                                 input_data,
                                                 input_shapes,
                                                 delta_data,
                                                 data_shape,
                                                 data_shape,
                                                 data_shape,
                                                 sigmoid_0,
                                                 sigmoid_1,
                                                 sigmoid_0,
                                                 input_0_param,
                                                 expected_0,
                                                 expected_1);
    }
    {
        auto input_0_param = make_shared<op::Parameter>(element::f32, data_shape);
        auto input_1_param = make_shared<op::Parameter>(element::f32, const_shape);
        auto sigmoid_0 = make_shared<op::Tanh>(input_0_param);
        auto sigmoid_1 = make_shared<op::Broadcast>(input_1_param, data_shape, AxisSet{1, 2, 3});
        vector<float> expected_0{10.0794f, 1.69562f, 0.236785f, 0.0321828f};
        vector<float> expected_1{15.2319f, 19.2806f, 19.9011f, 19.9866f};
        ParameterVector input_params{input_0_param, input_1_param};
        vector<vector<float>> input_data{input_0_data, const_data};
        vector<Shape> input_shapes{data_shape, const_shape};
        sigmoid_multiply_fusion_backward_compute(backend.get(),
                                                 input_params,
                                                 input_data,
                                                 input_shapes,
                                                 delta_data,
                                                 data_shape,
                                                 data_shape,
                                                 data_shape,
                                                 sigmoid_0,
                                                 sigmoid_1,
                                                 input_0_param,
                                                 sigmoid_1,
                                                 expected_0,
                                                 expected_1);
    }
    {
        auto input_0_param = make_shared<op::Parameter>(element::f32, data_shape);
        auto input_1_param = make_shared<op::Parameter>(element::f32, data_shape);
        auto sigmoid_0 = make_shared<op::Sigmoid>(input_0_param);
        auto sigmoid_1 = make_shared<op::Sigmoid>(input_1_param);
        vector<float> expected_0{3.02202f, 1.89041f, 0.868146f, 0.348035f};
        vector<float> expected_1{2.60102f, 1.58192f, 0.716941f, 0.285879f};
        ParameterVector input_params{input_0_param, input_1_param};
        vector<vector<float>> input_data{input_0_data, input_1_data};
        vector<Shape> input_shapes{data_shape, data_shape};
        sigmoid_multiply_fusion_backward_compute(backend.get(),
                                                 input_params,
                                                 input_data,
                                                 input_shapes,
                                                 delta_data,
                                                 data_shape,
                                                 data_shape,
                                                 data_shape,
                                                 sigmoid_0,
                                                 sigmoid_1,
                                                 input_0_param,
                                                 input_1_param,
                                                 expected_0,
                                                 expected_1);
    }
    {
        auto input_0_param = make_shared<op::Parameter>(element::f32, data_shape);
        auto input_1_param = make_shared<op::Parameter>(element::f32, data_shape);
        auto sigmoid_0 = make_shared<op::Sigmoid>(input_0_param);
        auto sigmoid_1 = make_shared<op::Tanh>(input_1_param);
        vector<float> expected_0{3.27813f, 2.04894f, 0.900536f, 0.353095f};
        vector<float> expected_1{4.45975f, 0.84425f, 0.126201f, 0.0176579f};
        ParameterVector input_params{input_0_param, input_1_param};
        vector<vector<float>> input_data{input_0_data, input_1_data};
        vector<Shape> input_shapes{data_shape, data_shape};
        sigmoid_multiply_fusion_backward_compute(backend.get(),
                                                 input_params,
                                                 input_data,
                                                 input_shapes,
                                                 delta_data,
                                                 data_shape,
                                                 data_shape,
                                                 data_shape,
                                                 sigmoid_0,
                                                 sigmoid_1,
                                                 input_0_param,
                                                 input_1_param,
                                                 expected_0,
                                                 expected_1);
    }
    {
        auto input_0_param = make_shared<op::Parameter>(element::f32, data_shape);
        auto input_1_param = make_shared<op::Parameter>(element::f32, data_shape);
        auto sigmoid_0 = make_shared<op::Tanh>(input_0_param);
        auto sigmoid_1 = make_shared<op::Sigmoid>(input_1_param);
        vector<float> expected_0{6.45521f, 1.27207f, 0.189593f, 0.0264228f};
        vector<float> expected_1{2.70967f, 1.7314f, 0.748913f, 0.29092f};
        ParameterVector input_params{input_0_param, input_1_param};
        vector<vector<float>> input_data{input_0_data, input_1_data};
        vector<Shape> input_shapes{data_shape, data_shape};
        sigmoid_multiply_fusion_backward_compute(backend.get(),
                                                 input_params,
                                                 input_data,
                                                 input_shapes,
                                                 delta_data,
                                                 data_shape,
                                                 data_shape,
                                                 data_shape,
                                                 sigmoid_0,
                                                 sigmoid_1,
                                                 input_0_param,
                                                 input_1_param,
                                                 expected_0,
                                                 expected_1);
    }
    {
        auto input_0_param = make_shared<op::Parameter>(element::f32, data_shape);
        auto input_1_param = make_shared<op::Parameter>(element::f32, data_shape);
        auto sigmoid_0 = make_shared<op::Tanh>(input_0_param);
        auto sigmoid_1 = make_shared<op::Tanh>(input_1_param);
        vector<float> expected_0{7.00227f, 1.37874f, 0.196666f, 0.026807f};
        vector<float> expected_1{4.64603f, 0.924027f, 0.131829f, 0.0179692f};
        ParameterVector input_params{input_0_param, input_1_param};
        vector<vector<float>> input_data{input_0_data, input_1_data};
        vector<Shape> input_shapes{data_shape, data_shape};
        sigmoid_multiply_fusion_backward_compute(backend.get(),
                                                 input_params,
                                                 input_data,
                                                 input_shapes,
                                                 delta_data,
                                                 data_shape,
                                                 data_shape,
                                                 data_shape,
                                                 sigmoid_0,
                                                 sigmoid_1,
                                                 input_0_param,
                                                 input_1_param,
                                                 expected_0,
                                                 expected_1);
    }
}

static void check_bounded_relu(Shape param_shape, float constant_val)
{
    auto make_function = [](Shape input_shape, float alpha_val) {
        auto relu_input = std::make_shared<op::Parameter>(element::f32, input_shape);
        auto relu = std::make_shared<op::Relu>(relu_input);
        auto alpha = op::Constant::create<float>(
            element::f32, input_shape, std::vector<float>(1.0f, alpha_val));
        auto min = std::make_shared<op::Minimum>(relu, alpha);
        auto f = make_shared<Function>(NodeVector{min}, ParameterVector{relu_input});
        return f;
    };

    auto cpu_f = make_function(param_shape, constant_val);
    auto int_f = make_function(param_shape, constant_val);
    test::Uniform<float> rng(-10.0f, 10.0f);
    vector<vector<float>> args;

    for (shared_ptr<op::Parameter> param : int_f->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }
    auto int_results = execute(int_f, args, "INTERPRETER");
    auto cpu_results = execute(cpu_f, args, "CPU");

    EXPECT_EQ(1, count_ops_of_type<op::BoundedRelu>(cpu_f));
    EXPECT_TRUE(test::all_close(cpu_results.at(0), int_results.at(0), 1.0e-4f, 1.0e-4f));
}

TEST(cpu_fusion, fuse_bounded_relu_inter_vs_cpu)
{
    check_bounded_relu(Shape{4, 3, 2, 2}, 6.0f);
    check_bounded_relu(Shape{4, 3}, 4.0f);
    check_bounded_relu(Shape{4, 3, 2}, 2.0f);
}

TEST(cpu_fusion, fuse_dropout)
{
    auto make_function = [](Shape input_shape,
                            const uint32_t seed_val,
                            double one_minus_prob,
                            bool fuse,
                            bool use_seed) {
        auto input = std::make_shared<op::Parameter>(element::f32, input_shape);
        auto value = op::Constant::create(element::f32, input_shape, {one_minus_prob});
        auto const1 = op::Constant::create(input->get_element_type(), Shape{}, {1});

        auto gen_mask = std::make_shared<op::GenerateMask>(const1,
                                                           input->get_shape(),
                                                           input->get_element_type(),
                                                           seed_val,
                                                           one_minus_prob,
                                                           use_seed);

        auto mult = std::make_shared<op::Multiply>(gen_mask, input);

        auto goe = std::make_shared<op::GetOutputElement>(mult, 0);

        auto pdivide = fuse ? std::make_shared<op::Divide>(mult, value)
                            : std::make_shared<op::Divide>(goe, value);

        auto f = make_shared<Function>(NodeVector{pdivide, gen_mask}, ParameterVector{input});

        return f;

    };

    uint32_t seed = rand();
    auto fuse_func = make_function(Shape{2, 2, 256, 256}, seed, 0.9, true, true);
    auto fuse_func2 = make_function(Shape{2, 2, 256, 256}, seed, 0.9, true, true);
    auto nofuse_func = make_function(Shape{2, 2, 256, 256}, 1, 0.9, false, false);
    {
        pass::Manager pass_manager;
        pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
        pass_manager.run_passes(fuse_func);
        pass_manager.run_passes(nofuse_func);
        ASSERT_EQ(count_ops_of_type<op::Dropout>(fuse_func), 1);
        ASSERT_EQ(count_ops_of_type<op::GenerateMask>(fuse_func), 0);
        ASSERT_EQ(count_ops_of_type<op::Dropout>(nofuse_func), 0);
    }

    auto fuse_func3 = make_function(Shape{2, 2, 256, 256}, seed, 0.9, true, false);
    auto fuse_func4 = make_function(Shape{2, 2, 256, 256}, seed, 0.9, true, false);
    {
        test::Uniform<float> rng(1.0f, 100.0f);
        vector<vector<float>> args;
        for (shared_ptr<op::Parameter> param : fuse_func->get_parameters())
        {
            auto name = param->get_name();
            vector<float> tensor_val(shape_size(param->get_shape()));
            rng.initialize(tensor_val);
            args.push_back(tensor_val);
        }

        auto fuse_results = execute(fuse_func, args, "CPU");
        auto fuse_results2 = execute(fuse_func2, args, "CPU");
        EXPECT_TRUE(test::all_close(fuse_results.at(0), fuse_results2.at(0)));
        EXPECT_TRUE(test::all_close(fuse_results.at(1), fuse_results2.at(1)));

        auto fuse_results3 = execute(fuse_func3, args, "CPU");
        auto fuse_results4 = execute(fuse_func4, args, "CPU");
        EXPECT_FALSE(test::all_close(fuse_results3.at(0), fuse_results4.at(0)));
        EXPECT_FALSE(test::all_close(fuse_results3.at(1), fuse_results4.at(1)));

        // Note: Since the RNG used in Dropout kernel is different than RNG used in GenerateMask
        // kernel, we can't compare fuse_results and nofuse_results
    }
}

TEST(cpu_fusion, fuse_leaky_relu)
{
    auto make_function = [](Shape input_shape, vector<float> alpha_val) {
        auto input = std::make_shared<op::Parameter>(element::f32, input_shape);
        auto alpha = op::Constant::create<float>(element::f32, input_shape, alpha_val);
        auto out =
            std::make_shared<op::Maximum>(input, std::make_shared<op::Multiply>(input, alpha));
        auto f = make_shared<Function>(NodeVector{out}, ParameterVector{input});
        return f;
    };

    auto no_fuse1 = make_function(Shape{1, 2, 3}, std::vector<float>(6, -1.0f));
    auto no_fuse2 = make_function(Shape{1, 3}, std::vector<float>{1.4f, 1.2f, 1.4f});

    pass::Manager pass_manager;
    pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
    pass_manager.run_passes(no_fuse1);
    pass_manager.run_passes(no_fuse2);
    EXPECT_EQ(0, count_ops_of_type<op::CPULeakyRelu>(no_fuse1));
    EXPECT_EQ(0, count_ops_of_type<op::CPULeakyRelu>(no_fuse2));

    // non-mkldnn kernel
    auto cpu_f1 = make_function(Shape{1, 2, 3}, std::vector<float>(6, 0.1f));
    // mkldnn kernel
    auto cpu_f2 = make_function(Shape{2, 3}, std::vector<float>(6, 0.1f));

    vector<vector<float>> args;
    args.push_back(std::vector<float>{-1, -2, 0, 1, 2, 3});
    std::vector<float> expected_result{-0.1f, -0.2f, 0.0f, 1.0f, 2.0f, 3.0f};

    auto cpu1_results = execute(cpu_f1, args, "CPU");
    EXPECT_EQ(1, count_ops_of_type<op::CPULeakyRelu>(cpu_f1));
    EXPECT_TRUE(test::all_close(cpu1_results.at(0), expected_result));

    auto cpu2_results = execute(cpu_f2, args, "CPU");
    EXPECT_EQ(1, count_ops_of_type<op::CPULeakyRelu>(cpu_f2));
    EXPECT_TRUE(test::all_close(cpu2_results.at(0), expected_result));
}

TEST(cpu_fusion, fuse_update_slice)
{
    auto make_function = [](bool fuse = true) {
        auto input = std::make_shared<op::Parameter>(element::f32, Shape{4, 32, 16});
        Shape lower_bounds{1, 0, 0};
        Shape upper_bounds{2, 32, 16};
        auto slice = std::make_shared<op::Slice>(
            input, fuse ? lower_bounds : Shape{3, 0, 0}, fuse ? upper_bounds : Shape{4, 32, 16});
        auto update = std::make_shared<op::Parameter>(element::f32, Shape{1, 32, 16});
        auto add = std::make_shared<op::Add>(slice, update);
        auto out = std::make_shared<op::ReplaceSlice>(input, add, lower_bounds, upper_bounds);
        auto f = make_shared<Function>(NodeVector{out}, ParameterVector{input, update});
        return f;
    };

    auto fuse = make_function(true);
    auto no_fuse = make_function(false);

    pass::Manager pass_manager;
    pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
    pass_manager.run_passes(fuse);
    pass_manager.run_passes(no_fuse);
    EXPECT_EQ(1, count_ops_of_type<op::UpdateSlice>(fuse));
    EXPECT_EQ(0, count_ops_of_type<op::UpdateSlice>(no_fuse));

    auto int_f = make_function();
    auto cpu_f = make_function();

    test::Uniform<float> rng(0.0f, 1.0f);
    vector<vector<float>> args;
    for (shared_ptr<op::Parameter> param : int_f->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }
    auto int_results = execute(int_f, args, "INTERPRETER");
    auto cpu_results = execute(cpu_f, args, "CPU");
    for (size_t i = 0; i < cpu_results.size(); i++)
    {
        EXPECT_TRUE(test::all_close(cpu_results.at(i), int_results.at(i)));
    }
}

TEST(cpu_fusion, fuse_update_slice_inplace)
{
    auto make_function = [](bool fuse = true) {
        auto input = std::make_shared<op::Parameter>(element::f32, Shape{4, 32, 16});
        auto abs = std::make_shared<op::Abs>(input);
        Shape lower_bounds{1, 0, 0};
        Shape upper_bounds{2, 32, 16};
        auto slice = std::make_shared<op::Slice>(abs, lower_bounds, upper_bounds);
        auto update = std::make_shared<op::Parameter>(element::f32, Shape{1, 32, 16});
        auto add = std::make_shared<op::Add>(slice, update);
        auto rs = std::make_shared<op::ReplaceSlice>(abs, add, lower_bounds, upper_bounds);
        auto out = std::make_shared<op::Abs>(rs);
        if (fuse)
        {
            return make_shared<Function>(NodeVector{out}, ParameterVector{input, update});
        }
        else
        {
            return make_shared<Function>(NodeVector{out, add}, ParameterVector{input, update});
        }
    };

    auto fuse = make_function(true);
    auto no_fuse = make_function(false);

    pass::Manager pass_manager;
    pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
    pass_manager.run_passes(fuse);
    pass_manager.run_passes(no_fuse);
    EXPECT_EQ(1, count_ops_of_type<op::UpdateSlice>(fuse));
    EXPECT_EQ(0, count_ops_of_type<op::UpdateSlice>(no_fuse));

    auto int_f = make_function();
    auto cpu_f = make_function();

    test::Uniform<float> rng(0.0f, 1.0f);
    vector<vector<float>> args;
    for (shared_ptr<op::Parameter> param : int_f->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }
    auto int_results = execute(int_f, args, "INTERPRETER");
    auto cpu_results = execute(cpu_f, args, "CPU");
    for (size_t i = 0; i < cpu_results.size(); i++)
    {
        EXPECT_TRUE(test::all_close(cpu_results.at(i), int_results.at(i)));
    }
}

TEST(cpu_fusion, fuse_update_slice_strided)
{
    auto make_function = [](bool fuse = true) {
        auto input = std::make_shared<op::Parameter>(element::f32, Shape{4, 32, 16});
        Shape lower_bounds{1, 0, 0};
        Shape upper_bounds{2, 32, 16};
        Strides strides{1, 2, 2};
        auto slice = std::make_shared<op::Slice>(input,
                                                 fuse ? lower_bounds : Shape{3, 0, 0},
                                                 fuse ? upper_bounds : Shape{4, 32, 16},
                                                 strides);
        auto update = std::make_shared<op::Parameter>(element::f32, Shape{1, 16, 8});
        auto add = std::make_shared<op::Add>(slice, update);
        auto out =
            std::make_shared<op::ReplaceSlice>(input, add, lower_bounds, upper_bounds, strides);
        auto f = make_shared<Function>(NodeVector{out}, ParameterVector{input, update});
        return f;
    };

    auto fuse = make_function(true);
    auto no_fuse = make_function(false);

    pass::Manager pass_manager;
    pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
    pass_manager.run_passes(fuse);
    pass_manager.run_passes(no_fuse);
    EXPECT_EQ(1, count_ops_of_type<op::UpdateSlice>(fuse));
    EXPECT_EQ(0, count_ops_of_type<op::UpdateSlice>(no_fuse));

    auto int_f = make_function();
    auto cpu_f = make_function();

    test::Uniform<float> rng(0.0f, 1.0f);
    vector<vector<float>> args;
    for (shared_ptr<op::Parameter> param : int_f->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }
    auto int_results = execute(int_f, args, "INTERPRETER");
    auto cpu_results = execute(cpu_f, args, "CPU");
    for (size_t i = 0; i < cpu_results.size(); i++)
    {
        EXPECT_TRUE(test::all_close(cpu_results.at(i), int_results.at(i)));
    }
}

TEST(cpu_fusion, fuse_update_slice_strided_inplace)
{
    auto make_function = [](bool fuse = true) {
        auto input = std::make_shared<op::Parameter>(element::f32, Shape{4, 32, 16});
        auto abs = std::make_shared<op::Abs>(input);
        Shape lower_bounds{1, 0, 0};
        Shape upper_bounds{2, 32, 16};
        Strides strides{1, 4, 2};
        auto slice = std::make_shared<op::Slice>(abs, lower_bounds, upper_bounds, strides);
        auto update = std::make_shared<op::Parameter>(element::f32, Shape{1, 8, 8});
        auto add = std::make_shared<op::Add>(slice, update);
        auto rs = std::make_shared<op::ReplaceSlice>(abs, add, lower_bounds, upper_bounds, strides);
        auto out = std::make_shared<op::Abs>(rs);
        if (fuse)
        {
            return make_shared<Function>(NodeVector{out}, ParameterVector{input, update});
        }
        else
        {
            return make_shared<Function>(NodeVector{out, add}, ParameterVector{input, update});
        }
    };

    auto fuse = make_function(true);
    auto no_fuse = make_function(false);

    pass::Manager pass_manager;
    pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
    pass_manager.run_passes(fuse);
    pass_manager.run_passes(no_fuse);
    EXPECT_EQ(1, count_ops_of_type<op::UpdateSlice>(fuse));
    EXPECT_EQ(0, count_ops_of_type<op::UpdateSlice>(no_fuse));

    auto int_f = make_function();
    auto cpu_f = make_function();

    test::Uniform<float> rng(0.0f, 1.0f);
    vector<vector<float>> args;
    for (shared_ptr<op::Parameter> param : int_f->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }
    auto int_results = execute(int_f, args, "INTERPRETER");
    auto cpu_results = execute(cpu_f, args, "CPU");
    for (size_t i = 0; i < cpu_results.size(); i++)
    {
        EXPECT_TRUE(test::all_close(cpu_results.at(i), int_results.at(i)));
    }
}

TEST(cpu_fusion, dot_batch_forward)
{
    const Shape shape_a{2, 3, 2};
    const Shape shape_b{2, 3};

    auto generate_func = [&shape_a, &shape_b]() -> shared_ptr<Function> {
        auto a = make_shared<op::Parameter>(element::f32, shape_a);
        auto b = make_shared<op::Parameter>(element::f32, shape_b);
        auto dot = make_shared<op::Dot>(a, b);
        return make_shared<Function>(dot, ParameterVector{a, b});
    };
    shared_ptr<Function> cpu_func = generate_func();
    shared_ptr<Function> int_func = generate_func();

    test::Uniform<float> rng(0.0f, 1.0f);
    vector<vector<float>> args;
    for (shared_ptr<op::Parameter> param : int_func->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }

    auto int_results = execute(int_func, args, "INTERPRETER");
    auto cpu_results = execute(cpu_func, args, "CPU");
    for (size_t i = 0; i < cpu_results.size(); i++)
    {
        EXPECT_TRUE(test::all_close(cpu_results.at(i), int_results.at(i), 1.0e-4f, 1.0e-4f));
    }
}
static std::shared_ptr<Function>
    create_rnn_input_linear_transformation_function(size_t num_timesteps, bool data_is_4d = false)
{
    auto W = std::make_shared<op::Parameter>(element::f32, Shape{400, 50});
    auto bias = std::make_shared<op::Parameter>(element::f32, Shape{400});
    ParameterVector params{W, bias};
    auto create_graph = [&]() -> std::shared_ptr<Node> {

        auto data_param = (data_is_4d)
                              ? std::make_shared<op::Parameter>(element::f32, Shape{2, 5, 1, 50})
                              : std::make_shared<op::Parameter>(element::f32, Shape{10, 1, 50});
        params.push_back(data_param);
        auto reshape_axis_order = data_is_4d ? AxisVector{0, 1, 2, 3} : AxisVector{0, 1, 2};
        auto data_param_reshape =
            std::make_shared<op::Reshape>(data_param, reshape_axis_order, Shape{10, 50});
        auto W_reshape = std::make_shared<op::Reshape>(W, AxisVector{1, 0}, Shape{50, 400});
        auto dot = std::make_shared<op::Dot>(data_param_reshape, W_reshape);
        auto bias_broadcast = make_shared<op::Broadcast>(bias, dot->get_shape(), AxisSet{0});
        auto add_bias = std::make_shared<op::Add>(dot, bias_broadcast);
        return move(add_bias);

    };

    NodeVector graph_nodes;
    for (size_t i = 0; i < num_timesteps; i++)
    {
        graph_nodes.push_back(create_graph());
    }
    auto concat = std::make_shared<op::Concat>(graph_nodes, 0);
    return make_shared<Function>(NodeVector{concat}, params);
}

TEST(cpu_fusion, fuse_rnn_input_across_time_steps)
{
    auto func = create_rnn_input_linear_transformation_function(10);
    pass::Manager pass_manager;
    pass_manager.register_pass<runtime::cpu::pass::CPURnnMatFusion>();
    pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
    pass_manager.run_passes(func);
    size_t ref_matmulbias_count = 1;
    auto matmulbias_count = count_ops_of_type<op::MatmulBias>(func);
    EXPECT_EQ(ref_matmulbias_count, matmulbias_count);
}

TEST(cpu_fusion, fuse_rnn_input_across_time_steps_4d_data)
{
    auto func = create_rnn_input_linear_transformation_function(10, true);
    pass::Manager pass_manager;
    pass_manager.register_pass<runtime::cpu::pass::CPURnnMatFusion>();
    pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
    pass_manager.run_passes(func);
    size_t ref_matmulbias_count = 10; // no CPURnnMatFusion transformations
    auto matmulbias_count = count_ops_of_type<op::MatmulBias>(func);
    EXPECT_EQ(ref_matmulbias_count, matmulbias_count);
}

TEST(cpu_fusion, rnn_input_fusion_inter_vs_cpu)
{
    shared_ptr<Function> cpu_func = create_rnn_input_linear_transformation_function(10);
    shared_ptr<Function> int_func = create_rnn_input_linear_transformation_function(10);

    test::Uniform<float> rng(-10.0f, 10.0f);
    vector<vector<float>> args;
    for (shared_ptr<op::Parameter> param : int_func->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }

    auto int_results = execute(int_func, args, "INTERPRETER");
    auto cpu_results = execute(cpu_func, args, "CPU");
    for (size_t i = 0; i < cpu_results.size(); i++)
    {
        EXPECT_TRUE(test::all_close(cpu_results.at(i), int_results.at(i), 1.0e-4f, 1.0e-4f));
    }
}

TEST(cpu_quant_fusion, qconv_relu)
{
    auto make_function = []() {
        Shape shape_input{1, 2, 2, 2};
        Shape shape_weights{1, 2, 1, 1};
        auto input = std::make_shared<op::Parameter>(element::f32, shape_input);
        auto weights = std::make_shared<op::Parameter>(element::f32, shape_weights);
        auto input_scale = op::Constant::create(element::f32, Shape{}, {2.0f});
        auto weights_scale = op::Constant::create(element::f32, Shape{}, {2.0f});
        auto output_scale = op::Constant::create(element::f32, Shape{}, {4.0f});
        auto int8_zero = op::Constant::create(element::i8, Shape{}, {0});
        auto uint8_zero = op::Constant::create(element::u8, Shape{}, {0});

        op::Quantize::RoundMode round_mode = op::Quantize::RoundMode::ROUND_NEAREST_TOWARD_EVEN;
        auto q_input = std::make_shared<op::Quantize>(
            input, input_scale, uint8_zero, element::u8, AxisSet{}, round_mode);
        auto q_weights = std::make_shared<op::Quantize>(
            weights, weights_scale, int8_zero, element::i8, AxisSet{}, round_mode);
        auto conv = std::make_shared<op::QuantizedConvolution>(q_input,
                                                               q_weights,
                                                               Strides{1, 1},
                                                               Strides{1, 1},
                                                               CoordinateDiff{0, 0},
                                                               CoordinateDiff{0, 0},
                                                               Strides{1, 1},
                                                               input_scale,
                                                               uint8_zero,
                                                               weights_scale,
                                                               int8_zero,
                                                               output_scale,
                                                               int8_zero,
                                                               element::i8,
                                                               AxisSet{},
                                                               AxisSet{},
                                                               AxisSet{});
        auto dq = std::make_shared<op::Dequantize>(
            conv, output_scale, int8_zero, element::f32, AxisSet{});
        auto relu = std::make_shared<op::Relu>(dq);
        auto q = std::make_shared<op::Quantize>(
            relu, output_scale, uint8_zero, element::u8, AxisSet{}, round_mode);
        auto q_f =
            std::make_shared<op::Dequantize>(q, output_scale, uint8_zero, element::f32, AxisSet{});
        return make_shared<Function>(NodeVector{q_f}, ParameterVector{input, weights});
    };

    auto cpu_f1 = make_function();
    auto cpu_f2 = make_function();

    test::Uniform<float> rng(2.0f, 2.0f);
    vector<vector<float>> args;
    for (shared_ptr<op::Parameter> param : cpu_f1->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }

    set_environment("NGRAPH_PASS_ENABLES", "CPUQuantFusion:0", 1);
    auto cpu1_results = execute(cpu_f1, args, "CPU");
    set_environment("NGRAPH_PASS_ENABLES", "CPUQuantFusion:1", 1);
    auto cpu2_results = execute(cpu_f2, args, "CPU");
    // Expected output - [2, 2, ...]
    EXPECT_TRUE(test::all_close(cpu1_results.at(0), cpu2_results.at(0)));
}

TEST(cpu_quant_fusion, qconvb_relu)
{
    auto make_function = []() {
        Shape shape_input{1, 2, 2, 2};
        Shape shape_weights{1, 2, 1, 1};
        auto input = std::make_shared<op::Parameter>(element::f32, shape_input);
        auto weights = std::make_shared<op::Parameter>(element::f32, shape_weights);
        auto bias = std::make_shared<op::Parameter>(element::f32, Shape{shape_weights[0]});
        auto input_scale = op::Constant::create(element::f32, Shape{}, {2.0f});
        auto weights_scale = op::Constant::create(element::f32, Shape{}, {2.0f});
        auto output_scale = op::Constant::create(element::f32, Shape{}, {4.0f});
        auto int8_zero = op::Constant::create(element::i8, Shape{}, {0});
        auto int32_zero = op::Constant::create(element::i32, Shape{}, {0});
        auto uint8_zero = op::Constant::create(element::u8, Shape{}, {0});

        op::Quantize::RoundMode round_mode = op::Quantize::RoundMode::ROUND_NEAREST_TOWARD_EVEN;
        auto q_input = std::make_shared<op::Quantize>(
            input, input_scale, uint8_zero, element::u8, AxisSet{}, round_mode);
        auto q_weights = std::make_shared<op::Quantize>(
            weights, weights_scale, int8_zero, element::i8, AxisSet{}, round_mode);
        auto q_bias = std::make_shared<op::Quantize>(
            bias, input_scale * weights_scale, int32_zero, element::i32, AxisSet{}, round_mode);
        auto requant_scale = (input_scale * weights_scale) / output_scale;
        auto conv = std::make_shared<op::QuantizedConvolutionBias>(q_input,
                                                                   q_weights,
                                                                   bias,
                                                                   Strides{1, 1},
                                                                   Strides{1, 1},
                                                                   CoordinateDiff{0, 0},
                                                                   CoordinateDiff{0, 0},
                                                                   Strides{1, 1},
                                                                   requant_scale);
        auto dq = std::make_shared<op::Dequantize>(
            conv, output_scale, int8_zero, element::f32, AxisSet{});
        auto relu = std::make_shared<op::Relu>(dq);
        auto q = std::make_shared<op::Quantize>(
            relu, output_scale, uint8_zero, element::u8, AxisSet{}, round_mode);
        auto q_f =
            std::make_shared<op::Dequantize>(q, output_scale, uint8_zero, element::f32, AxisSet{});
        return make_shared<Function>(NodeVector{q_f}, ParameterVector{input, weights, bias});
    };

    auto cpu_f1 = make_function();
    auto cpu_f2 = make_function();

    test::Uniform<float> rng(2.0f, 2.0f);
    vector<vector<float>> args;
    for (shared_ptr<op::Parameter> param : cpu_f1->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }
    set_environment("NGRAPH_PASS_ENABLES", "CPUQuantFusion:0", 1);
    auto cpu1_results = execute(cpu_f1, args, "CPU");
    set_environment("NGRAPH_PASS_ENABLES", "CPUQuantFusion:1", 1);
    auto cpu2_results = execute(cpu_f2, args, "CPU");
    EXPECT_TRUE(test::all_close(cpu1_results.at(0), cpu2_results.at(0)));
}

TEST(cpu_quant_fusion, qavg_pool)
{
    auto make_function = []() {
        Shape shape_input{1, 2, 4, 4};
        auto input = std::make_shared<op::Parameter>(element::f32, shape_input);
        auto input_scale = op::Constant::create(element::f32, Shape{}, {2.0f});
        auto weights_scale = op::Constant::create(element::f32, Shape{}, {2.0f});
        auto int8_zero = op::Constant::create(element::i8, Shape{}, {0});
        auto uint8_zero = op::Constant::create(element::u8, Shape{}, {0});

        op::Quantize::RoundMode round_mode = op::Quantize::RoundMode::ROUND_NEAREST_TOWARD_EVEN;
        auto q_input = std::make_shared<op::Quantize>(
            input, input_scale, uint8_zero, element::u8, AxisSet{}, round_mode);
        auto dq = std::make_shared<op::Dequantize>(
            q_input, input_scale, uint8_zero, element::f32, AxisSet{});
        auto avg_pool = std::make_shared<op::AvgPool>(dq, Shape{2, 2});
        return make_shared<Function>(NodeVector{avg_pool}, ParameterVector{input});
    };

    auto cpu_f1 = make_function();
    auto cpu_f2 = make_function();

    test::Uniform<float> rng(4.0f, 4.0f);
    vector<vector<float>> args;
    for (shared_ptr<op::Parameter> param : cpu_f1->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }

    set_environment("NGRAPH_PASS_ENABLES", "CPUQuantFusion:0", 1);
    auto cpu1_results = execute(cpu_f1, args, "CPU");
    set_environment("NGRAPH_PASS_ENABLES", "CPUQuantFusion:1", 1);
    auto cpu2_results = execute(cpu_f2, args, "CPU");
    EXPECT_TRUE(test::all_close(cpu1_results.at(0), cpu2_results.at(0)));
}

TEST(cpu_quant_fusion, qmax_pool)
{
    auto make_function = []() {
        Shape shape_input{1, 2, 4, 4};
        auto input = std::make_shared<op::Parameter>(element::f32, shape_input);
        auto input_scale = op::Constant::create(element::f32, Shape{}, {2.0f});
        auto weights_scale = op::Constant::create(element::f32, Shape{}, {2.0f});
        auto int8_zero = op::Constant::create(element::i8, Shape{}, {0});
        auto uint8_zero = op::Constant::create(element::u8, Shape{}, {0});

        op::Quantize::RoundMode round_mode = op::Quantize::RoundMode::ROUND_NEAREST_TOWARD_EVEN;
        auto q_input = std::make_shared<op::Quantize>(
            input, input_scale, uint8_zero, element::u8, AxisSet{}, round_mode);
        auto dq = std::make_shared<op::Dequantize>(
            q_input, input_scale, uint8_zero, element::f32, AxisSet{});
        auto maxpool = std::make_shared<op::MaxPool>(dq, Shape{2, 2});
        return make_shared<Function>(NodeVector{maxpool}, ParameterVector{input});
    };

    auto cpu_f1 = make_function();
    auto cpu_f2 = make_function();

    test::Uniform<float> rng(1.0f, 10.0f);
    vector<vector<float>> args;
    for (shared_ptr<op::Parameter> param : cpu_f1->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }

    set_environment("NGRAPH_PASS_ENABLES", "CPUQuantFusion:0", 1);
    auto cpu1_results = execute(cpu_f1, args, "CPU");
    set_environment("NGRAPH_PASS_ENABLES", "CPUQuantFusion:1", 1);
    auto cpu2_results = execute(cpu_f2, args, "CPU");
    EXPECT_TRUE(test::all_close(cpu1_results.at(0), cpu2_results.at(0)));
}

TEST(cpu_quant_fusion, qconcat)
{
    auto make_function = []() {
        auto get_input_slice = [](std::shared_ptr<op::Parameter>& input) {
            auto input_scale = op::Constant::create(element::f32, Shape{}, {2.0f});
            auto int8_zero = op::Constant::create(element::i8, Shape{}, {0});
            auto uint8_zero = op::Constant::create(element::u8, Shape{}, {0});

            op::Quantize::RoundMode round_mode = op::Quantize::RoundMode::ROUND_NEAREST_TOWARD_EVEN;
            auto q_input = std::make_shared<op::Quantize>(
                input, input_scale, uint8_zero, element::u8, AxisSet{}, round_mode);
            auto dq = std::make_shared<op::Dequantize>(
                q_input, input_scale, uint8_zero, element::f32, AxisSet{});
            return dq;
        };

        NodeVector concat_inputs, concats;
        ParameterVector inputs;
        Shape shape_input{1, 2, 4, 4};
        inputs.push_back(std::make_shared<op::Parameter>(element::f32, shape_input));
        concat_inputs.push_back(get_input_slice(inputs.back()));
        // Concat2  -- Concat7
        for (size_t i = 0; i < 6; i++)
        {
            inputs.push_back(std::make_shared<op::Parameter>(element::f32, shape_input));
            concat_inputs.push_back(get_input_slice(inputs.back()));
            concats.push_back(std::make_shared<op::Concat>(concat_inputs, 0));
        }
        return make_shared<Function>(concats, inputs);
    };

    auto cpu_f1 = make_function();
    auto cpu_f2 = make_function();

    test::Uniform<float> rng(2.0f, 2.0f);
    vector<vector<float>> args;
    for (shared_ptr<op::Parameter> param : cpu_f1->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }

    set_environment("NGRAPH_PASS_ENABLES", "CPUQuantFusion:0", 1);
    auto cpu1_results = execute(cpu_f1, args, "CPU");
    set_environment("NGRAPH_PASS_ENABLES", "CPUQuantFusion:1", 1);
    auto cpu2_results = execute(cpu_f2, args, "CPU");
    // Expect Concat2 -- Concat6 to be fused and not Concat7
    ASSERT_EQ(count_ops_of_type<op::QuantizedConcat>(cpu_f2), 5);
    EXPECT_TRUE(test::all_close(cpu1_results.at(0), cpu2_results.at(0)));
}

TEST(cpu_quant_fusion, dq_q)
{
    auto make_function = [](bool match_scales = true, bool match_et = true) {
        Shape shape_input{1, 2, 2};
        auto input = std::make_shared<op::Parameter>(element::i8, shape_input);
        auto dq_scale = op::Constant::create(element::f32, Shape{}, {2.0f});
        auto int8_zero = op::Constant::create(element::i8, Shape{}, {0});
        auto dq =
            std::make_shared<op::Dequantize>(input, dq_scale, int8_zero, element::f32, AxisSet{});
        float q_scalev = 2.0f;
        if (!match_scales)
        {
            q_scalev = 1.0f;
        }
        auto q_scale = op::Constant::create(element::f32, Shape{}, {q_scalev});
        op::Quantize::RoundMode round_mode = op::Quantize::RoundMode::ROUND_NEAREST_TOWARD_EVEN;
        if (match_et)
        {
            auto q = std::make_shared<op::Quantize>(
                dq, q_scale, int8_zero, element::i8, AxisSet{}, round_mode);
            return make_shared<Function>(NodeVector{q}, ParameterVector{input});
        }
        else
        {
            auto uint8_zero = op::Constant::create(element::u8, Shape{}, {0});
            auto q = std::make_shared<op::Quantize>(
                dq, q_scale, uint8_zero, element::u8, AxisSet{}, round_mode);
            return make_shared<Function>(NodeVector{q}, ParameterVector{input});
        }
    };

    auto cpu_f1 = make_function();
    auto cpu_f2 = make_function();

    vector<vector<int8_t>> args;
    args.push_back({-1, 2, 3, 4});

    set_environment("NGRAPH_PASS_ENABLES", "CPUQuantFusion:0", 1);
    auto cpu1_results = execute(cpu_f1, args, "CPU");
    set_environment("NGRAPH_PASS_ENABLES", "CPUQuantFusion:1", 1);
    auto cpu2_results = execute(cpu_f2, args, "CPU");
    EXPECT_TRUE(test::all_close(cpu1_results.at(0), cpu2_results.at(0)));

    auto backend = runtime::Backend::create("CPU");
    auto fuse = make_function(true, true);
    auto no_fuse1 = make_function(false, true);
    auto no_fuse2 = make_function(true, false);
    backend->compile(fuse);
    backend->compile(no_fuse1);
    backend->compile(no_fuse2);
    ASSERT_EQ(count_ops_of_type<op::Quantize>(fuse), 0);
    ASSERT_EQ(count_ops_of_type<op::Quantize>(no_fuse1), 1);
    ASSERT_EQ(count_ops_of_type<op::Quantize>(no_fuse2), 1);
}

TEST(cpu_quant_fusion, qconvbsa)
{
    auto make_function = []() {
        Shape shape_input{1, 2, 2, 2};
        Shape shape_weights{1, 2, 1, 1};
        Shape shape_summand{1, 1, 2, 2};
        auto input = std::make_shared<op::Parameter>(element::f32, shape_input);
        auto weights = std::make_shared<op::Parameter>(element::f32, shape_weights);
        auto bias = std::make_shared<op::Parameter>(element::f32, Shape{shape_weights[0]});
        auto summand = std::make_shared<op::Parameter>(element::f32, shape_summand);

        auto input_scale = op::Constant::create(element::f32, Shape{}, {2.0f});
        auto weights_scale = op::Constant::create(element::f32, Shape{}, {2.0f});
        auto output_scale = op::Constant::create(element::f32, Shape{}, {4.0f});
        auto summand_scale = op::Constant::create(element::f32, Shape{}, {2.0f});

        auto int8_zero = op::Constant::create(element::i8, Shape{}, {0});
        auto int32_zero = op::Constant::create(element::i32, Shape{}, {0});
        auto uint8_zero = op::Constant::create(element::u8, Shape{}, {0});

        op::Quantize::RoundMode round_mode = op::Quantize::RoundMode::ROUND_NEAREST_TOWARD_EVEN;
        auto q_input = std::make_shared<op::Quantize>(
            input, input_scale, uint8_zero, element::u8, AxisSet{}, round_mode);
        auto q_weights = std::make_shared<op::Quantize>(
            weights, weights_scale, int8_zero, element::i8, AxisSet{}, round_mode);
        auto q_bias = std::make_shared<op::Quantize>(
            bias, input_scale * weights_scale, int32_zero, element::i32, AxisSet{}, round_mode);
        auto q_summand = std::make_shared<op::Quantize>(
            summand, summand_scale, int8_zero, element::i8, AxisSet{}, round_mode);

        // Left Graph
        auto requant_scale = (input_scale * weights_scale) / output_scale;
        auto conv = std::make_shared<op::QuantizedConvolutionBias>(q_input,
                                                                   q_weights,
                                                                   bias,
                                                                   Strides{1, 1},
                                                                   Strides{1, 1},
                                                                   CoordinateDiff{0, 0},
                                                                   CoordinateDiff{0, 0},
                                                                   Strides{1, 1},
                                                                   requant_scale);
        auto dq_l = std::make_shared<op::Dequantize>(
            conv, output_scale, int8_zero, element::f32, AxisSet{});
        auto r_l = std::make_shared<op::Reshape>(dq_l, AxisVector{0, 1, 2, 3}, Shape{1, 2, 2});
        auto b_l = std::make_shared<op::Broadcast>(r_l, Shape{1, 1, 2, 2}, AxisSet{0});

        // Right Graph
        auto dq_r = std::make_shared<op::Dequantize>(
            q_summand, summand_scale, int8_zero, element::f32, AxisSet{});
        auto r_r = std::make_shared<op::Reshape>(dq_r, AxisVector{0, 1, 2, 3}, Shape{1, 2, 2});
        auto b_r = std::make_shared<op::Broadcast>(r_r, Shape{1, 1, 2, 2}, AxisSet{0});
        auto add = b_l + b_r;
        auto relu = std::make_shared<op::Relu>(add);
        return make_shared<Function>(NodeVector{relu},
                                     ParameterVector{input, weights, bias, summand});
    };

    auto cpu_f1 = make_function();
    auto cpu_f2 = make_function();

    test::Uniform<float> rng(4.0f, 4.0f);
    vector<vector<float>> args;
    for (shared_ptr<op::Parameter> param : cpu_f1->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }

    // Disable CPUQuantFusion
    set_environment("NGRAPH_PASS_ENABLES", "CPUQuantFusion:0", 1);
    auto cpu1_results = execute(cpu_f1, args, "CPU");
    // Enable CPUQuantFusion
    set_environment("NGRAPH_PASS_ENABLES", "CPUQuantFusion:1", 1);
    auto cpu2_results = execute(cpu_f2, args, "CPU");
    EXPECT_TRUE(test::all_close(cpu1_results.at(0), cpu2_results.at(0)));
}

TEST(cpu_quant_fusion, qconvba)
{
    auto make_function = []() {
        Shape shape_input{1, 2, 2, 2};
        Shape shape_weights{1, 2, 1, 1};
        Shape shape_summand{1, 1, 2, 2};
        auto input = std::make_shared<op::Parameter>(element::f32, shape_input);
        auto weights = std::make_shared<op::Parameter>(element::f32, shape_weights);
        auto bias = std::make_shared<op::Parameter>(element::f32, Shape{shape_weights[0]});
        auto summand = std::make_shared<op::Parameter>(element::f32, shape_summand);

        auto input_scale = op::Constant::create(element::f32, Shape{}, {2.0f});
        auto weights_scale = op::Constant::create(element::f32, Shape{}, {2.0f});
        auto output_scale = op::Constant::create(element::f32, Shape{}, {4.0f});
        auto summand_scale = op::Constant::create(element::f32, Shape{}, {4.0f});

        auto int8_zero = op::Constant::create(element::i8, Shape{}, {0});
        auto int32_zero = op::Constant::create(element::i32, Shape{}, {0});
        auto uint8_zero = op::Constant::create(element::u8, Shape{}, {0});

        op::Quantize::RoundMode round_mode = op::Quantize::RoundMode::ROUND_NEAREST_TOWARD_EVEN;
        auto q_input = std::make_shared<op::Quantize>(
            input, input_scale, uint8_zero, element::u8, AxisSet{}, round_mode);
        auto q_weights = std::make_shared<op::Quantize>(
            weights, weights_scale, int8_zero, element::i8, AxisSet{}, round_mode);
        auto q_bias = std::make_shared<op::Quantize>(
            bias, input_scale * weights_scale, int32_zero, element::i32, AxisSet{}, round_mode);
        auto q_summand = std::make_shared<op::Quantize>(
            summand, summand_scale, uint8_zero, element::u8, AxisSet{}, round_mode);

        // Left Graph
        auto requant_scale = (input_scale * weights_scale) / output_scale;
        auto conv = std::make_shared<op::QuantizedConvolutionBias>(q_input,
                                                                   q_weights,
                                                                   bias,
                                                                   Strides{1, 1},
                                                                   Strides{1, 1},
                                                                   CoordinateDiff{0, 0},
                                                                   CoordinateDiff{0, 0},
                                                                   Strides{1, 1},
                                                                   requant_scale);
        auto dq_l = std::make_shared<op::Dequantize>(
            conv, output_scale, int8_zero, element::f32, AxisSet{});
        auto r_l = std::make_shared<op::Reshape>(dq_l, AxisVector{0, 1, 2, 3}, Shape{1, 2, 2});
        auto b_l = std::make_shared<op::Broadcast>(r_l, Shape{1, 1, 2, 2}, AxisSet{0});

        // Right Graph
        auto dq_r = std::make_shared<op::Dequantize>(
            q_summand, summand_scale, uint8_zero, element::f32, AxisSet{});
        auto r_r = std::make_shared<op::Reshape>(dq_r, AxisVector{0, 1, 2, 3}, Shape{1, 2, 2});
        auto b_r = std::make_shared<op::Broadcast>(r_r, Shape{1, 1, 2, 2}, AxisSet{0});
        auto add = b_l + b_r;
        auto relu = std::make_shared<op::Relu>(add);
        return make_shared<Function>(NodeVector{relu},
                                     ParameterVector{input, weights, bias, summand});
    };

    auto cpu_f1 = make_function();
    auto cpu_f2 = make_function();

    test::Uniform<float> rng(2.0f, 2.0f);
    vector<vector<float>> args;
    for (shared_ptr<op::Parameter> param : cpu_f1->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }

    // Disable CPUQuantFusion
    set_environment("NGRAPH_PASS_ENABLES", "CPUQuantFusion:0", 1);
    auto cpu1_results = execute(cpu_f1, args, "CPU");
    // Enable CPUQuantFusion
    set_environment("NGRAPH_PASS_ENABLES", "CPUQuantFusion:1", 1);
    auto cpu2_results = execute(cpu_f2, args, "CPU");
    EXPECT_TRUE(test::all_close(cpu1_results.at(0), cpu2_results.at(0)));
}

TEST(cpu_quant_fusion, qconvba_q)
{
    auto make_function = []() {
        Shape shape_input{1, 2, 2, 2};
        Shape shape_weights{1, 2, 1, 1};
        Shape shape_summand{1, 1, 2, 2};
        auto input_l = std::make_shared<op::Parameter>(element::f32, shape_input);
        auto weights_l = std::make_shared<op::Parameter>(element::f32, shape_weights);
        auto bias_l = std::make_shared<op::Parameter>(element::f32, Shape{shape_weights[0]});
        auto input_r = std::make_shared<op::Parameter>(element::f32, shape_input);
        auto weights_r = std::make_shared<op::Parameter>(element::f32, shape_weights);
        auto bias_r = std::make_shared<op::Parameter>(element::f32, Shape{shape_weights[0]});

        auto input_scale_l = op::Constant::create(element::f32, Shape{}, {2.0f});
        auto weights_scale_l = op::Constant::create(element::f32, Shape{}, {2.0f});
        auto output_scale_l = op::Constant::create(element::f32, Shape{}, {4.0f});
        auto input_scale_r = op::Constant::create(element::f32, Shape{}, {5.0f});
        auto weights_scale_r = op::Constant::create(element::f32, Shape{}, {5.0f});
        auto output_scale_r = op::Constant::create(element::f32, Shape{}, {20.0f});

        auto int8_zero = op::Constant::create(element::i8, Shape{}, {0});
        auto int32_zero = op::Constant::create(element::i32, Shape{}, {0});
        auto uint8_zero = op::Constant::create(element::u8, Shape{}, {0});

        op::Quantize::RoundMode round_mode = op::Quantize::RoundMode::ROUND_NEAREST_TOWARD_EVEN;
        auto q_input_l = std::make_shared<op::Quantize>(
            input_l, input_scale_l, uint8_zero, element::u8, AxisSet{}, round_mode);
        auto q_weights_l = std::make_shared<op::Quantize>(
            weights_l, weights_scale_l, int8_zero, element::i8, AxisSet{}, round_mode);
        auto q_bias_l = std::make_shared<op::Quantize>(bias_l,
                                                       input_scale_l * weights_scale_l,
                                                       int32_zero,
                                                       element::i32,
                                                       AxisSet{},
                                                       round_mode);
        auto q_input_r = std::make_shared<op::Quantize>(
            input_r, input_scale_r, uint8_zero, element::u8, AxisSet{}, round_mode);
        auto q_weights_r = std::make_shared<op::Quantize>(
            weights_r, weights_scale_r, int8_zero, element::i8, AxisSet{}, round_mode);
        auto q_bias_r = std::make_shared<op::Quantize>(bias_r,
                                                       input_scale_r * weights_scale_r,
                                                       int32_zero,
                                                       element::i32,
                                                       AxisSet{},
                                                       round_mode);

        // Left Graph
        auto requant_scale_l = (input_scale_l * weights_scale_l) / output_scale_l;
        auto conv_l = std::make_shared<op::QuantizedConvolutionBias>(q_input_l,
                                                                     q_weights_l,
                                                                     q_bias_l,
                                                                     Strides{1, 1},
                                                                     Strides{1, 1},
                                                                     CoordinateDiff{0, 0},
                                                                     CoordinateDiff{0, 0},
                                                                     Strides{1, 1},
                                                                     requant_scale_l);
        auto dq_l = std::make_shared<op::Dequantize>(
            conv_l, output_scale_l, int8_zero, element::f32, AxisSet{});
        auto r_l = std::make_shared<op::Reshape>(dq_l, AxisVector{0, 1, 2, 3}, Shape{1, 2, 2});
        auto b_l = std::make_shared<op::Broadcast>(r_l, Shape{1, 1, 2, 2}, AxisSet{0});

        // Right Graph
        auto requant_scale_r = (input_scale_r * weights_scale_r) / output_scale_r;
        auto conv_r = std::make_shared<op::QuantizedConvolutionBias>(q_input_r,
                                                                     q_weights_r,
                                                                     q_bias_r,
                                                                     Strides{1, 1},
                                                                     Strides{1, 1},
                                                                     CoordinateDiff{0, 0},
                                                                     CoordinateDiff{0, 0},
                                                                     Strides{1, 1},
                                                                     requant_scale_r);
        auto dq_r = std::make_shared<op::Dequantize>(
            conv_r, output_scale_r, int8_zero, element::f32, AxisSet{});
        auto r_r = std::make_shared<op::Reshape>(dq_r, AxisVector{0, 1, 2, 3}, Shape{1, 2, 2});
        auto b_r = std::make_shared<op::Broadcast>(r_r, Shape{1, 1, 2, 2}, AxisSet{0});
        auto add = b_l + b_r;
        auto relu = std::make_shared<op::Relu>(add);
        auto q = std::make_shared<op::Quantize>(
            relu, output_scale_r, uint8_zero, element::u8, AxisSet{}, round_mode);
        auto dq = std::make_shared<op::Dequantize>(
            q, output_scale_r, uint8_zero, element::f32, AxisSet{});
        return make_shared<Function>(
            NodeVector{dq},
            ParameterVector{input_l, weights_l, bias_l, input_r, weights_r, bias_r});
    };

    auto cpu_f1 = make_function();
    auto cpu_f2 = make_function();

    test::Uniform<float> rng(2.0f, 2.0f);
    vector<vector<float>> args;
    for (shared_ptr<op::Parameter> param : cpu_f1->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }

    // Disable CPUQuantFusion
    set_environment("NGRAPH_PASS_ENABLES", "CPUQuantFusion:0", 1);
    auto cpu1_results = execute(cpu_f1, args, "CPU");
    // Enable CPUQuantFusion
    set_environment("NGRAPH_PASS_ENABLES", "CPUQuantFusion:1", 1);
    auto cpu2_results = execute(cpu_f2, args, "CPU");
    EXPECT_TRUE(test::all_close(cpu1_results.at(0), cpu2_results.at(0)));

    auto backend = runtime::Backend::create("CPU");
    auto fuse = make_function();
    backend->compile(fuse);
    ASSERT_EQ(count_ops_of_type<op::Quantize>(fuse), 6);
}

#ifndef NGRAPH_JSON_DISABLE
// Tests that rely on deserializing json files
TEST(cpu_fusion, fuse_conv_bias)
{
    pass::Manager pass_manager;
    pass_manager.register_pass<ngraph::pass::ReshapeElimination>();
    pass_manager.register_pass<ngraph::runtime::cpu::pass::CPUFusion>();
    const string json_path = file_util::path_join(SERIALIZED_ZOO, "conv_bias.json");
    const string json_string = file_util::read_file_to_string(json_path);
    stringstream ss(json_string);
    shared_ptr<Function> func = ngraph::deserialize(ss);
    pass_manager.run_passes(func);
    size_t cb = count_ops_of_type<op::ConvolutionBias>(func);
    ASSERT_GT(cb, 0);
}

TEST(cpu_fusion, gemm_mlp)
{
    const string json_path = file_util::path_join(SERIALIZED_ZOO, "mxnet/mnist_mlp_forward.json");
    const string json_string = file_util::read_file_to_string(json_path);
    stringstream ss(json_string);
    shared_ptr<Function> func = ngraph::deserialize(ss);
    pass::Manager pass_manager;
    pass_manager.register_pass<runtime::cpu::pass::CPUFusion>(pass::FusionType::REGULAR_FUSIONS);
    pass_manager.run_passes(func);
    auto mmbs = count_ops_of_type<op::MatmulBias>(func);
    ASSERT_EQ(mmbs, 3);
}

TEST(cpu_fusion, fuse_fprop_bn)
{
    pass::Manager pass_manager;
    pass_manager.register_pass<pass::VisualizeTree>("bn_fprop_before_fusion.png");
    pass_manager.register_pass<ngraph::pass::ReshapeElimination>();
    pass_manager.register_pass<runtime::cpu::pass::CPUFusion>(pass::FusionType::REGULAR_FUSIONS);
    pass_manager.register_pass<pass::VisualizeTree>("bn_fprop_after_fusion.png");
    const string json_path = file_util::path_join(SERIALIZED_ZOO, "mxnet/bn_fprop_b2c3h2w2.json");
    const string json_string = file_util::read_file_to_string(json_path);
    stringstream ss(json_string);
    shared_ptr<Function> func = ngraph::deserialize(ss);
    pass_manager.run_passes(func);
    size_t ccg = count_ops_of_type<op::BatchNormTraining>(func);
    ASSERT_EQ(ccg, 1);
}

TEST(cpu_fusion, sigmoid_multiply_fusion)
{
    pass::Manager pass_manager;
    pass_manager.register_pass<pass::CoreFusion>();
    pass_manager.register_pass<runtime::cpu::pass::CPUFusion>();
    const string json_path = file_util::path_join(SERIALIZED_ZOO, "mxnet/3_lstm_cell_forward.json");
    const string json_string = file_util::read_file_to_string(json_path);
    stringstream ss(json_string);
    shared_ptr<Function> func = ngraph::deserialize(ss);
    pass_manager.run_passes(func);
    size_t ccg = count_ops_of_type<op::SigmoidMultiply>(func);
    ASSERT_EQ(ccg, 18);
}

TEST(cpu_fusion, fuse_batch_mat_mul_transpose)
{
    pass::Manager pass_manager;
    pass_manager.register_pass<runtime::cpu::pass::CPUBatchFusion>();
    const string json_path = file_util::path_join(SERIALIZED_ZOO, "mxnet/batch_dot_3.json");
    const string json_string = file_util::read_file_to_string(json_path);
    stringstream ss(json_string);
    shared_ptr<Function> func = ngraph::deserialize(ss);
    pass_manager.run_passes(func);
    size_t ccg = count_ops_of_type<op::BatchMatMulTranspose>(func);
    ASSERT_EQ(ccg, 1);
}

TEST(cpu_fusion, fuse_batch_mat_mul_transpose_forward)
{
    pass::Manager pass_manager;
    pass_manager.register_pass<runtime::cpu::pass::CPUBatchFusion>();

    const std::string file_name("mxnet/batch_dot_3.json");
    auto cpu_f = make_function_from_file(file_name);
    auto int_f = make_function_from_file(file_name);
    pass_manager.run_passes(cpu_f);
    test::Uniform<float> rng(0.0f, 1.0f);
    vector<vector<float>> args;

    for (shared_ptr<op::Parameter> param : int_f->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }
    auto int_results = execute(int_f, args, "INTERPRETER");
    auto cpu_results = execute(cpu_f, args, "CPU");
    for (size_t i = 0; i < int_results.size(); i++)
    {
        EXPECT_TRUE(test::all_close(cpu_results.at(i), int_results.at(i), 1.0e-4f, 1.0e-4f));
    }
}

TEST(cpu_fusion, fuse_batch_dot_backward)
{
    const std::string file_name("mxnet/batch_dot_3.json");
    auto cpu_f = make_function_from_file(file_name);
    auto int_f = make_function_from_file(file_name);

    pass::Manager pass_manager;
    pass_manager.register_pass<runtime::cpu::pass::CPUBatchFusion>();
    pass_manager.run_passes(cpu_f);

    auto int_df = autodiff::backprop_function(int_f);
    auto cpu_df = autodiff::backprop_function(cpu_f);

    test::Uniform<float> rng(-1.0f, 1.0f);
    vector<vector<float>> args;
    for (shared_ptr<op::Parameter> param : cpu_df->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }

    auto int_results = execute(int_df, args, "INTERPRETER");
    auto cpu_results = execute(cpu_df, args, "CPU");

    for (size_t i = 0; i < cpu_results.size(); i++)
    {
        EXPECT_TRUE(test::all_close(cpu_results.at(i), int_results.at(i), 1.0e-4f, 1.0e-4f));
    }
}

TEST(cpu_fusion, fuse_rnn_across_layer_2layer_3timestep)
{
    const std::string file_name("mxnet/2layer_3timestep_ic100oc100.json");
    auto cpu_f = make_function_from_file(file_name);
    auto int_f = make_function_from_file(file_name);
    test::Uniform<float> rng(-1.0f, 1.0f);
    vector<vector<float>> args;

    for (shared_ptr<op::Parameter> param : int_f->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }
    auto int_results = execute(int_f, args, "INTERPRETER");
    auto cpu_results = execute(cpu_f, args, "CPU");

    EXPECT_EQ(1, count_ops_of_type<op::Rnn>(cpu_f));
    for (size_t i = 0; i < cpu_results.size(); i++)
    {
        EXPECT_TRUE(test::all_close(cpu_results.at(i), int_results.at(i), 1.0e-4f, 1.0e-4f));
    }
}

TEST(cpu_fusion, fuse_bi_directional_rnn)
{
    pass::Manager pass_manager;
    pass_manager.register_pass<runtime::cpu::pass::LSTMFusion>();
    pass_manager.register_pass<runtime::cpu::pass::RNNFusion>();
    pass_manager.register_pass<ngraph::pass::AlgebraicSimplification>();
    pass_manager.register_pass<runtime::cpu::pass::MultiLayerRNNFusion>();
    pass_manager.register_pass<runtime::cpu::pass::BiDirectionalRnn>();
    const string json_path = file_util::path_join(SERIALIZED_ZOO, "mxnet/lstm_bi_directional.json");
    const string json_string = file_util::read_file_to_string(json_path);
    stringstream ss(json_string);
    shared_ptr<Function> func = ngraph::deserialize(ss);
    pass_manager.run_passes(func);
    // Bidirectional graph pass will folds the reverse seq
    auto rev_seq_ops = get_ops_of_type<op::Reverse>(func);
    auto rnn_ops = get_ops_of_type<op::Rnn>(func);
    EXPECT_EQ(rev_seq_ops.size(), 0);
    // fuse two bi-directional rnn layers in to one MKLDNN Op
    EXPECT_EQ(rnn_ops.size(), 1);
}

TEST(cpu_fusion, bi_rnn_interpreter_vs_cpu)
{
    const std::string file_name("mxnet/lstm_bi_directional.json");
    auto cpu_f = make_function_from_file(file_name);
    auto int_f = make_function_from_file(file_name);
    test::Uniform<float> rng(0.0f, 1.0f);
    vector<vector<float>> args;

    for (shared_ptr<op::Parameter> param : int_f->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }
    auto int_results = execute(int_f, args, "INTERPRETER");
    auto cpu_results = execute(cpu_f, args, "CPU");
    for (size_t i = 0; i < int_results.size(); i++)
    {
        EXPECT_TRUE(test::all_close(cpu_results.at(i), int_results.at(i), 1.0e-4f, 1.0e-4f));
    }
}

TEST(cpu_fusion, rnn_fusion_from_json_model)
{
    pass::Manager pass_manager;
    pass_manager.register_pass<runtime::cpu::pass::CPURnnMatFusion>();
    pass_manager.register_pass<runtime::cpu::pass::CPUFusion>(pass::FusionType::REGULAR_FUSIONS);
    const string json_path =
        file_util::path_join(SERIALIZED_ZOO, "mxnet/rnn-10-step-fusion-test.json");
    const string json_string = file_util::read_file_to_string(json_path);
    stringstream ss(json_string);
    shared_ptr<Function> func = ngraph::deserialize(ss);
    pass_manager.run_passes(func);
    const size_t NUM_STEPS = 10;
    auto mmb_predicate = [=](std::shared_ptr<Node> node) {
        auto users = node->get_users();
        return (users.size() == NUM_STEPS) &&
               std::all_of(begin(users), end(users), [](std::shared_ptr<Node> n) {
                   return std::dynamic_pointer_cast<op::Slice>(n) != nullptr;
               });
    };

    auto mmbs = get_ops_of_type<op::MatmulBias>(func);
    ASSERT_TRUE(std::any_of(begin(mmbs), end(mmbs), mmb_predicate));
}

TEST(cpu_fusion, fuse_lstm_cells)
{
    pass::Manager pass_manager;
    pass_manager.register_pass<runtime::cpu::pass::LSTMFusion>();
    const string json_path =
        file_util::path_join(SERIALIZED_ZOO, "mxnet/2rnn_layer_3lstm_cell.json");
    const string json_string = file_util::read_file_to_string(json_path);
    stringstream ss(json_string);
    shared_ptr<Function> func = ngraph::deserialize(ss);
    pass_manager.run_passes(func);
    auto lstm_ops = get_ops_of_type<op::Lstm>(func);
    EXPECT_EQ(lstm_ops.size(), 6);
}

TEST(cpu_fusion, fuse_2_layer_rnn)
{
    pass::Manager pass_manager;
    pass_manager.register_pass<runtime::cpu::pass::LSTMFusion>();
    pass_manager.register_pass<runtime::cpu::pass::RNNFusion>();
    const string json_path =
        file_util::path_join(SERIALIZED_ZOO, "mxnet/2rnn_layer_3lstm_cell.json");
    const string json_string = file_util::read_file_to_string(json_path);
    stringstream ss(json_string);
    shared_ptr<Function> func = ngraph::deserialize(ss);
    pass_manager.run_passes(func);
    size_t count = count_ops_of_type<op::Rnn>(func);
    auto rnn_ops = get_ops_of_type<op::Rnn>(func);
    EXPECT_EQ(rnn_ops.size(), count);
    for (auto& node : rnn_ops)
    {
        EXPECT_EQ(node->get_num_timesteps(), node->get_src_sequence_length());
        EXPECT_EQ(node->get_num_cell_states(), node->get_argument(1)->get_arguments().size());
    }
}

TEST(cpu_fusion, fuse_1_layer_rnn)
{
    pass::Manager pass_manager;
    pass_manager.register_pass<runtime::cpu::pass::LSTMFusion>();
    pass_manager.register_pass<runtime::cpu::pass::RNNFusion>();
    const string json_path =
        file_util::path_join(SERIALIZED_ZOO, "mxnet/1rnn_layer_3lstm_cell.json");
    const string json_string = file_util::read_file_to_string(json_path);
    stringstream ss(json_string);
    shared_ptr<Function> func = ngraph::deserialize(ss);
    pass_manager.run_passes(func);
    size_t count = count_ops_of_type<op::Rnn>(func);
    auto rnn_ops = get_ops_of_type<op::Rnn>(func);
    EXPECT_EQ(rnn_ops.size(), 1);
    EXPECT_EQ(rnn_ops.size(), count);
    for (auto& node : rnn_ops)
    {
        EXPECT_EQ(node->get_num_timesteps(), node->get_src_sequence_length());
        EXPECT_EQ(node->get_num_cell_states(), node->get_argument(1)->get_arguments().size());
    }
}

TEST(cpu_fusion, rnn_fusion_1lstm_cell)
{
    const std::string file_name("mxnet/1_lstm_cell_forward.json");
    auto cpu_f = make_function_from_file(file_name);
    auto int_f = make_function_from_file(file_name);
    test::Uniform<float> rng(-1.0f, 1.0f);
    vector<vector<float>> args;

    for (shared_ptr<op::Parameter> param : int_f->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }
    auto int_results = execute(int_f, args, "INTERPRETER");
    auto cpu_results = execute(cpu_f, args, "CPU");
    for (size_t i = 0; i < cpu_results.size(); i++)
    {
        EXPECT_TRUE(test::all_close(cpu_results.at(i), int_results.at(i), 1.0e-4f, 1.0e-4f));
    }
}

TEST(cpu_fusion, rnn_fusion_1rnn_layer_3lstm_cell)
{
    const std::string file_name("mxnet/1rnn_layer_3lstm_cell.json");
    auto cpu_f = make_function_from_file(file_name);
    auto int_f = make_function_from_file(file_name);
    test::Uniform<float> rng(-1.0f, 1.0f);
    vector<vector<float>> args;

    for (shared_ptr<op::Parameter> param : int_f->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }
    auto int_results = execute(int_f, args, "INTERPRETER");
    auto cpu_results = execute(cpu_f, args, "CPU");
    for (size_t i = 0; i < cpu_results.size(); i++)
    {
        EXPECT_TRUE(test::all_close(cpu_results.at(i), int_results.at(i), 1.0e-4f, 1.0e-4f));
    }
}

TEST(cpu_fusion, rnn_fusion_2rnn_layer_3lstm_cell)
{
    const std::string file_name("mxnet/2rnn_layer_3lstm_cell.json");
    auto cpu_f = make_function_from_file(file_name);
    auto int_f = make_function_from_file(file_name);
    test::Uniform<float> rng(-1.0f, 1.0f);
    vector<vector<float>> args;

    for (shared_ptr<op::Parameter> param : int_f->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }
    auto int_results = execute(int_f, args, "INTERPRETER");
    auto cpu_results = execute(cpu_f, args, "CPU");
    for (size_t i = 0; i < cpu_results.size(); i++)
    {
        EXPECT_TRUE(test::all_close(cpu_results.at(i), int_results.at(i), 1.0e-4f, 1.0e-4f));
    }
}

TEST(cpu_fusion, validate_fuse_gru_inputs)
{
    const std::string file_name("mxnet/gru_debug.json");
    auto cpu_func = make_function_from_file(file_name);
    auto int_func = make_function_from_file(file_name);

    test::Uniform<float> rng(-10.0f, 10.0f);
    vector<vector<float>> args;
    for (shared_ptr<op::Parameter> param : int_func->get_parameters())
    {
        vector<float> tensor_val(shape_size(param->get_shape()));
        rng.initialize(tensor_val);
        args.push_back(tensor_val);
    }

    auto int_results = execute(int_func, args, "INTERPRETER");
    auto cpu_results = execute(cpu_func, args, "CPU");
    for (size_t i = 0; i < cpu_results.size(); i++)
    {
        EXPECT_TRUE(test::all_close(cpu_results.at(i), int_results.at(i), 1.0e-4f, 1.0e-4f));
    }
}

#if defined(AUTODIFF_BACKEND_CPU) && !defined(NGRAPH_JSON_DISABLE)
NGRAPH_TEST(cpu_fusion, backwards_batchmatmultranspose_tensor2_tensor2)
{
    auto backend = runtime::Backend::create("CPU");

    const std::string file_name("mxnet/batch_dot_3.json");
    auto f = make_function_from_file(file_name);

    test::Uniform<float> rng(-1.0f, 1.0f);
    std::vector<std::shared_ptr<ngraph::runtime::Tensor>> args;
    for (shared_ptr<op::Parameter> param : f->get_parameters())
    {
        args.push_back(rng.initialize(backend->create_tensor<float>(param->get_shape())));
    }

    auto g = make_function_from_file(file_name);
    pass::Manager pass_manager;
    pass_manager.register_pass<runtime::cpu::pass::CPUBatchFusion>();
    pass_manager.run_passes(g);
    EXPECT_TRUE(autodiff_numeric_compare<float>(backend.get(), f, g, args, .01f, .01f));
}
#endif

#endif