Publishing 2019 R1 content

[platform/upstream/dldt.git] / inference-engine / thirdparty / clDNN / tests / test_cases / gemm_gpu_test.cpp

// Copyright (c) 2018 Intel Corporation
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//      http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

///////////////////////////////////////////////////////////////////////////////////////////////////
#include <gtest/gtest.h>

#include <api/CPP/engine.hpp>
#include <api/CPP/input_layout.hpp>
#include <api/CPP/memory.hpp>
#include <api/CPP/gemm.hpp>
#include <api/CPP/topology.hpp>
#include <api/CPP/network.hpp>

#include "test_utils/test_utils.h"
#include "test_utils/uniform_quantized_real_distribution.hpp"

#include <cstddef>


using namespace cldnn;
using namespace ::tests;

TEST(gemm_gpu, basic_bfyx_t1) {
    const auto& engine = get_test_engine();
    auto input = memory::allocate(engine, { data_types::f32, format::bfyx,{ 1, 1, 3, 4 } });
    auto input2 = memory::allocate(engine, { data_types::f32, format::bfyx,{ 1, 1, 1, 4 } });

    std::vector<float> input_data = {
        1.f, -2.f,  3.f,  
        -4.f, 5.f,  6.f, 
        1.f, 2.f, 3.f, 
        3.f, 2.f, -1.f,

    };

    std::vector<float> input_data2 = {
        2.f,
        5.f,
        -4.f,
        -7.f,
    };
    set_values(input, input_data);
    set_values(input2, input_data2);

    std::vector<float> out_data = {
        -43.f, -1.f, 31.f
    };

    topology topology;
    topology.add(
        input_layout("input", input.get_layout())
    );
    topology.add(
        input_layout("input2", input2.get_layout())
    );
    topology.add(
        gemm("output", "input", "input2",true,false)
    );

    network network(engine, topology);
    network.set_input_data("input", input);
    network.set_input_data("input2", input2);
    auto outputs = network.execute();

    auto output = outputs.at("output").get_memory();
    auto output_ptr = output.pointer<float>();


    EXPECT_EQ(output_ptr.size(), (uint32_t)3);
    for (uint32_t i = 0; i < out_data.size(); ++i) {
        EXPECT_FLOAT_EQ(output_ptr[i], out_data[i]);
    }
}
TEST(gemm_gpu, basic_bfyx_t2) {
    const auto& engine = get_test_engine();
    auto input = memory::allocate(engine, { data_types::f32, format::bfyx,{ 1, 1, 4, 3 } });
    auto input2 = memory::allocate(engine, { data_types::f32, format::bfyx,{ 1, 1, 4, 1 } });

    std::vector<float> input_data = {
        1.f, -2.f,  3.f, -4.f, 
        5.f,  6.f, 1.f, 2.f, 
        3.f, 3.f, 2.f, -1.f,

    };

    std::vector<float> input_data2 = {
        2.f, 5.f, -4.f, -7.f,
    };
    set_values(input, input_data);
    set_values(input2, input_data2);

    std::vector<float> out_data = {
        8.f, 22.f, 20.f
    };

    topology topology;
    topology.add(
        input_layout("input", input.get_layout())
    );
    topology.add(
        input_layout("input2", input2.get_layout())
    );
    topology.add(
        gemm("output", "input", "input2", false, true)
    );

    network network(engine, topology);
    network.set_input_data("input", input);
    network.set_input_data("input2", input2);
    auto outputs = network.execute();

    auto output = outputs.at("output").get_memory();
    auto output_ptr = output.pointer<float>();


    EXPECT_EQ(output_ptr.size(), (uint32_t)3);
    for (uint32_t i = 0; i < out_data.size(); ++i) {
        EXPECT_FLOAT_EQ(output_ptr[i], out_data[i]);
    }
}

TEST(gemm_gpu, basic_bfyx_t1t2) {
    const auto& engine = get_test_engine();
    auto input = memory::allocate(engine, { data_types::f32, format::bfyx,{ 2, 1, 3, 4 } });
    auto input2 = memory::allocate(engine, { data_types::f32, format::bfyx,{ 2, 1, 4, 1 } });

    std::vector<float> input_data = {
        1.f, -2.f,  3.f, 
        -4.f, 5.f,  6.f, 
        1.f, 2.f, 3.f, 
        3.f, 2.f, -1.f,

        1.f, -2.f,  3.f,
        -4.f, 5.f,  6.f,
        1.f, 2.f, 3.f,
        3.f, 2.f, -1.f,

    };

    std::vector<float> input_data2 = {
        2.f, 5.f, -4.f, -7.f,

        2.f, 5.f, -4.f, -7.f,
    };
    set_values(input, input_data);
    set_values(input2, input_data2);

    std::vector<float> out_data = {
        -43.f, -1.f, 31.f,
        -43.f, -1.f, 31.f
    };

    topology topology;
    topology.add(
        input_layout("input", input.get_layout())
    );
    topology.add(
        input_layout("input2", input2.get_layout())
    );
    topology.add(
        gemm("output", "input", "input2", true, true)
    );

    network network(engine, topology);
    network.set_input_data("input", input);
    network.set_input_data("input2", input2);
    auto outputs = network.execute();

    auto output = outputs.at("output").get_memory();
    auto output_ptr = output.pointer<float>();


    EXPECT_EQ(output_ptr.size(), (uint32_t)6);
    for (uint32_t i = 0; i < out_data.size(); ++i) {
        EXPECT_FLOAT_EQ(output_ptr[i], out_data[i]);
    }
}

TEST(gemm_gpu, basic_input3) {
    const auto& engine = get_test_engine();
    auto input = memory::allocate(engine, { data_types::f32, format::bfyx,{ 1, 1, 3, 2 } });
    auto input2 = memory::allocate(engine, { data_types::f32, format::bfyx,{ 1, 1, 2, 3 } });
    auto input3 = memory::allocate(engine, { data_types::f32, format::bfyx,{ 1, 1, 2, 2 } });
    float alpha = 2.f;
    float beta = 10.f;


    std::vector<float> input_data = { 
        1.0f, 2.0f, 3.0f,
        1.0f, 0.0f, 1.0f
    };

    std::vector<float> input_data2 = { 
        3.0f, 3.0f,
        1.0f, 2.0f,
        1.0f, 2.0f,
    };

    std::vector<float> input3_data = {
        1.0f, 0.0f,
        2.0f, 0.0f,
    };

    set_values(input, input_data);
    set_values(input2, input_data2);
    set_values(input3, input3_data);

    std::vector<float> out_data = {
        26.0f, 26.0f,
        28.0f, 10.0f
    };

    topology topology;
    topology.add(
        input_layout("input", input.get_layout())
    );
    topology.add(
        input_layout("input2", input2.get_layout())
    );
    topology.add(
        input_layout("input3", input3.get_layout())
    );
    topology.add(
        gemm("output", "input", "input2", "input3",  false, false, alpha, beta)
    );

    network network(engine, topology);
    network.set_input_data("input", input);
    network.set_input_data("input2", input2);
    network.set_input_data("input3", input3);
    auto outputs = network.execute();

    auto output = outputs.at("output").get_memory();
    auto output_ptr = output.pointer<float>();

    EXPECT_EQ(output_ptr.size(), (uint32_t)4);

    for (uint32_t i = 0; i < out_data.size(); ++i) {         
        EXPECT_FLOAT_EQ(output_ptr[i], out_data[i]);
    }
}

TEST(gemm_gpu, basic_input3_t1t2) {
    const auto& engine = get_test_engine();
    auto input = memory::allocate(engine, { data_types::f32, format::bfyx,{ 1, 1, 4, 3 } });
    auto input2 = memory::allocate(engine, { data_types::f32, format::bfyx,{ 1, 1, 3, 2 } });
    auto input3 = memory::allocate(engine, { data_types::f32, format::bfyx,{ 1, 1, 2, 4 } });
    float alpha = 2.f;
    float beta = 3.f;


    std::vector<float> input_data = {
        1.0f, 2.0f, 3.0f, 4.0f,
        1.0f, 0.0f, 1.0f, 0.0f,
        0.0f, 0.0f, 0.0f, 0.0f
    };

    std::vector<float> input_data2 = {
        3.0f, 3.0f, 1.0f, 
        2.0f, 1.0f, 2.0f,
    };

    std::vector<float> input3_data = {
        1.0f, 0.0f,
        1.0f, 0.0f,
        2.0f, 2.0f,
        1.0f, 1.0f,
    };

    set_values(input, input_data);
    set_values(input2, input_data2);
    set_values(input3, input3_data);

    std::vector<float> out_data = {
        15.0f, 6.0f,
        15.0f, 8.0f,
        30.0f, 20.0f,
        27.0f, 19.0f
    };

    topology topology;
    topology.add(
        input_layout("input", input.get_layout())
    );
    topology.add(
        input_layout("input2", input2.get_layout())
    );
    topology.add(
        input_layout("input3", input3.get_layout())
    );
    topology.add(
        gemm("output", "input", "input2", "input3", true, true, alpha, beta)
    );

    network network(engine, topology);
    network.set_input_data("input", input);
    network.set_input_data("input2", input2);
    network.set_input_data("input3", input3);
    auto outputs = network.execute();

    auto output = outputs.at("output").get_memory();
    auto output_ptr = output.pointer<float>();

    EXPECT_EQ(output_ptr.size(), (uint32_t)8);

    for (uint32_t i = 0; i < out_data.size(); ++i) {
        EXPECT_FLOAT_EQ(output_ptr[i], out_data[i]);
    }
}
TEST(gemm_gpu, basic_input3_1) {
    const auto& engine = get_test_engine();
    auto input = memory::allocate(engine, { data_types::f32, format::bfyx,{ 1, 1, 3, 4 } });
    auto input2 = memory::allocate(engine, { data_types::f32, format::bfyx,{ 1, 1, 2, 3 } });
    auto input3 = memory::allocate(engine, { data_types::f32, format::bfyx,{ 1, 1, 2, 4 } });
    float alpha = 2.f;
    float beta = 3.f;

    std::vector<float> input_data = {
        1.0f, 1.0f, 0.0f,
        2.0f, 0.0f, 0.0f,
        3.0f, 1.0f, 0.0f,
        4.0f, 0.0f, 0.0f
    };

    std::vector<float> input_data2 = {
        3.0f, 2.0f,
        3.0f, 1.0f,
        1.0f, 2.0f,
    };

    std::vector<float> input3_data = {
        1.0f, 0.0f,
        1.0f, 0.0f,
        2.0f, 2.0f,
        1.0f, 1.0f,
    };

    set_values(input, input_data);
    set_values(input2, input_data2);
    set_values(input3, input3_data);

    std::vector<float> out_data = {
        15.0f, 6.0f,
        15.0f, 8.0f,
        30.0f, 20.0f,
        27.0f, 19.0f
    };

    topology topology;
    topology.add(
        input_layout("input", input.get_layout())
    );
    topology.add(
        input_layout("input2", input2.get_layout())
    );
    topology.add(
        input_layout("input3", input3.get_layout())
    );
    topology.add(
        gemm("output", "input", "input2", "input3", false, false, alpha, beta)

    );

    network network(engine, topology);
    network.set_input_data("input", input);
    network.set_input_data("input2", input2);
    network.set_input_data("input3", input3);
    auto outputs = network.execute();

    auto output = outputs.at("output").get_memory();
    auto output_ptr = output.pointer<float>();

    EXPECT_EQ(output_ptr.size(), (uint32_t)8);

    for (uint32_t i = 0; i < out_data.size(); ++i) {
        EXPECT_FLOAT_EQ(output_ptr[i], out_data[i]);
    }
}

TEST(gemm_gpu, basic_input3_t2) {
    const auto& engine = get_test_engine();
    auto input = memory::allocate(engine, { data_types::f32, format::bfyx,{ 1, 1, 3, 4 } });
    auto input2 = memory::allocate(engine, { data_types::f32, format::bfyx,{ 1, 1, 3, 2 } });
    auto input3 = memory::allocate(engine, { data_types::f32, format::bfyx,{ 1, 1, 2, 4 } });
    float alpha = 2.f;
    float beta = 3.f;


    std::vector<float> input_data = {
        1.0f, 1.0f, 0.0f,
        2.0f, 0.0f, 0.0f,
        3.0f, 1.0f, 0.0f,
        4.0f, 0.0f, 0.0f
    };


    std::vector<float> input_data2 = {
        3.0f, 3.0f, 1.0f,
        2.0f, 1.0f, 2.0f,
    };

    std::vector<float> input3_data = {
        1.0f, 0.0f,
        1.0f, 0.0f,
        2.0f, 2.0f,
        1.0f, 1.0f,
    };

    set_values(input, input_data);
    set_values(input2, input_data2);
    set_values(input3, input3_data);

    std::vector<float> out_data = {
        15.0f, 6.0f,
        15.0f, 8.0f,
        30.0f, 20.0f,
        27.0f, 19.0f,
    };

    topology topology;
    topology.add(
        input_layout("input", input.get_layout())
    );
    topology.add(
        input_layout("input2", input2.get_layout())
    );
    topology.add(
        input_layout("input3", input3.get_layout())
    );
    topology.add(
        gemm("output", "input", "input2", "input3", false, true, alpha, beta)
    );

    network network(engine, topology);
    network.set_input_data("input", input);
    network.set_input_data("input2", input2);
    network.set_input_data("input3", input3);
    auto outputs = network.execute();

    auto output = outputs.at("output").get_memory();
    auto output_ptr = output.pointer<float>();

    EXPECT_EQ(output_ptr.size(), (uint32_t)8);

    for (uint32_t i = 0; i < out_data.size(); ++i) {
        EXPECT_FLOAT_EQ(output_ptr[i], out_data[i]);
    }
}

TEST(gemm_gpu, basic_input3_t1) {
    const auto& engine = get_test_engine();
    auto input = memory::allocate(engine, { data_types::f32, format::bfyx,{ 1, 1, 4, 3 } });
    auto input2 = memory::allocate(engine, { data_types::f32, format::bfyx,{ 1, 1, 2, 3 } });
    auto input3 = memory::allocate(engine, { data_types::f32, format::bfyx,{ 1, 1, 2, 4 } });
    float alpha = 2.f;
    float beta = 3.f;


    std::vector<float> input_data = {
        1.0f, 2.0f, 3.0f, 4.0f,
        1.0f, 0.0f, 1.0f, 0.0f,
        0.0f, 0.0f, 0.0f, 0.0f
    };

    std::vector<float> input_data2 = {
        3.0f, 2.0f,
        3.0f, 1.0f,
        1.0f, 2.0f
    };

    std::vector<float> input3_data = {
        1.0f, 0.0f,
        1.0f, 0.0f,
        2.0f, 2.0f,
        1.0f, 1.0f,
    };

    set_values(input, input_data);
    set_values(input2, input_data2);
    set_values(input3, input3_data);

    std::vector<float> out_data = {
        15.0f, 6.0f,
        15.0f, 8.0f,
        30.0f, 20.0f,
        27.0f, 19.0f,
    };

    topology topology;
    topology.add(
        input_layout("input", input.get_layout())
    );
    topology.add(
        input_layout("input2", input2.get_layout())
    );
    topology.add(
        input_layout("input3", input3.get_layout())
    );
    topology.add(
        gemm("output", "input", "input2", "input3", true, false, alpha, beta)
    );

    network network(engine, topology);
    network.set_input_data("input", input);
    network.set_input_data("input2", input2);
    network.set_input_data("input3", input3);
    auto outputs = network.execute();

    auto output = outputs.at("output").get_memory();
    auto output_ptr = output.pointer<float>();

    EXPECT_EQ(output_ptr.size(), (uint32_t)8);

    for (uint32_t i = 0; i < out_data.size(); ++i) {
        EXPECT_FLOAT_EQ(output_ptr[i], out_data[i]);
    }
}

TEST(gemm_gpu, basic_bfyx) {
    const auto& engine = get_test_engine();
    auto input = memory::allocate(engine, { data_types::f32, format::bfyx,{ 2, 1, 4, 3 } });
    auto input2 = memory::allocate(engine, { data_types::f32, format::bfyx,{ 2, 1, 1, 4 } });

    std::vector<float> input_data = {
        1.f, -2.f,  3.f,  -4.f, 
        5.f,  6.f,  7.f,   8.f, 
        -10.f, 12.f, 13.f, -13.f,

        1.f, -2.f,  3.f,  -4.f,
        5.f,  6.f,  7.f,   8.f,
        -10.f, 12.f, 13.f, -13.f,
    };

    std::vector<float> input_data2 = {
        2.f, 
        5.f, 
        -4.f,  
        -7.f,
        2.f,
        5.f,
        -4.f,
        -7.f,
    };
    set_values(input, input_data);
    set_values(input2, input_data2);

    std::vector<float> out_data = {
        8.f, -44.f, 79.f, 8.f, -44.f, 79.f,
    };

    topology topology;
    topology.add(
        input_layout("input", input.get_layout())
    );
    topology.add(
        input_layout("input2", input2.get_layout())
    );
    topology.add(
        gemm("output", "input", "input2")
    );

    network network(engine, topology);
    network.set_input_data("input", input);
    network.set_input_data("input2", input2);
    auto outputs = network.execute();

    auto output = outputs.at("output").get_memory();
    auto output_ptr = output.pointer<float>();

    
    EXPECT_EQ(output_ptr.size(), (uint32_t)6);
    for (uint32_t i = 0; i < out_data.size(); ++i) {
            EXPECT_FLOAT_EQ(output_ptr[i], out_data[i]);
    }
}

TEST(gemm_gpu, basic3_bfyx) {
    const auto& engine = get_test_engine();
    auto input = memory::allocate(engine, { data_types::f32, format::bfyx,{ 5, 1, 500, 9 } });
    auto input2 = memory::allocate(engine, { data_types::f32, format::bfyx,{ 5, 1, 1, 500 } });

    std::vector<float> input_data = {
        -0.000449777f, -0.000137405f, -0.0762075f, 0.000949166f, 0.0346124f, -0.0111424f, 0.0108357f, 0.0121679f, 0.0242749f, 0.052692f, -0.0017713f, 0.0053728f, 0.0128862f, -0.0162366f, 0.0125041f, -0.00602398f, 0.0107778f, -0.00323086f, -0.00914208f, -0.013884f, 0.00755173f, -0.0175622f, 0.00473339f, -0.015003f, -0.0238219f, 0.004502f, 0.00187154f, 0.0041163f, -9.36184e-05f, 0.00873372f, 0.0121869f, -0.020973f, -0.006006f, -0.0038208f, 0.00210471f, 0.00255549f, -0.0251856f, -0.0626372f, -0.0059258f, -0.0058662f, -0.0946306f, 0.00197436f, 0.00105865f, -0.0033595f, 0.0158977f, -0.0036025f, -0.00568902f, -0.0202577f, -0.000251319f, -0.0117895f, -0.0144239f, -0.0144024f, -0.0150431f, -0.0354826f, -0.0135123f, -0.000422157f, 0.0286438f, -0.000884989f, -0.00675718f, 0.013241f, -0.0118388f, 0.0321394f, -0.000803071f, 0.11408f, -0.00806301f, -0.00831608f, 0.0165189f, 0.016094f, -0.000449332f, -0.00695901f, 0.0437514f, -0.00172117f, 0.00180391f, -0.000859933f, -0.0144826f, 0.0262613f, -0.00194352f, -1.98829e-05f, -0.00902827f, -0.00400867f, -0.00600827f, 0.0120846f, -0.0162493f, 0.0418596f, 0.00131911f, -0.00631566f, 0.00270484f, -0.0950513f, 0.00726431f, -0.0169798f, -0.000554365f, -0.00256903f, -0.00885843f, 0.0104025f, 0.00590779f, -0.00175832f, 0.0168603f, 0.00964353f, -0.0180614f, 0.0213157f, 0.0209548f, -0.0231143f, -0.00121617f, -0.0129815f, -0.0199287f, 0.00863336f, -0.00464991f, 0.0162288f, -0.340115f, -0.011018f, -0.0593997f, 0.00644821f, 0.0416332f, 0.0394596f, 0.0172296f, 0.00494231f, 0.0143805f, -0.00819845f, 0.00196982f, 0.00393258f, 0.0246168f, -0.0235927f, 0.0131416f, -0.0190432f, -0.0237865f, -0.0155627f, 0.0265165f, 0.0162884f, 0.00321098f, 0.0136674f, -0.000966112f, -0.0100813f, -0.00604589f, 0.00889466f, 0.0113945f, 0.0264707f, 0.00371883f, -0.00843358f, 0.0145675f, 0.0048638f, 0.00110399f, -0.00130233f, 0.00740726f, -0.00393368f, -0.0242178f, 0.00341681f, 0.00115369f, -0.00297881f, -0.0844071f, 0.0537151f, -0.00209399f, 0.0310295f, 0.0383914f, 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        -0.0730483f,
        0.60233f,
        -0.0485716f,
        0.445732f,
        -0.652175f,
        0.176092f,
        0.0424668f,
        -0.464009f,
        0.0419261f,
        0.437008f,
        -0.0949722f,
        0.322407f,
        0.00229612f,
        -0.0698273f,
        0.279531f,
        0.0615138f,
        0.029985f,
        0.0397836f,
        -0.0450066f,
        -0.153192f,
        -0.00434645f,
        0.235137f,
        -0.430068f,
        0.0479958f,
        0.0538638f,
        0.0335953f,
        0.059768f,
        -0.00594587f,
        0.313076f,
        0.173102f,
        -0.285881f,
        0.0860142f,
        -0.0656319f,
        0.0903421f,
        0.285596f,
        -0.00867567f,
        0.140828f,
        0.0679629f,
        -0.0386886f,
        -0.0432885f,
        -0.0523634f,
        -0.202516f,
        -0.393785f,
        0.0862689f,
        0.196867f,
        -1.25594f,
        0.407078f,
        0.0883489f,
        -0.145128f,
        -0.0270446f,
        -0.436027f,
        0.124345f,
        0.0216037f,
        -0.0341296f,
        -0.0550939f,
        -0.0143608f,
        0.269727f,
        -0.316092f,
        0.24142f,
        -0.0602843f,
        0.677077f,
        0.015578f,
        -0.0866818f,
        -0.0114968f,
        0.233375f,
        0.0768293f,
        -0.305731f,
        -0.101196f,
        0.116959f,
        -0.705778f,
        -0.274866f,
        0.0159985f,
        -0.0331097f,
        -0.165272f,
        0.00676643f,
        0.194435f,
        -0.644084f,
        -0.0719713f,
        -0.38506f,
        -0.0766179f,
        -0.232665f,
        -0.184962f,
        -0.0873918f,
        0.123643f,
        -0.10653f,
        -0.0842152f,
        0.173415f,
        0.0549799f,
        -0.246653f,
        -0.753266f,
        -0.17178f,
        -0.315154f,
        -0.284002f,
        -0.0944792f,
        0.513692f,
        -0.555574f,
        0.207956f,
        0.108376f,
        -0.322661f,
        -0.181751f,
        0.0175992f,
        -0.067274f,
        -0.0244339f,
        0.264443f,
        -0.307399f,
        0.435229f,
        -0.0912658f,
        0.41608f,
        0.203329f,
        0.0611729f,
        -0.11855f,
        -0.0901752f,
        0.219847f,
        -0.00417572f,
        2.35094f,
        0.0649107f,
        -0.0693629f,
        0.118256f,
        0.0929001f,
        -0.295607f,
        0.102728f,
        0.0786511f,
        -0.164747f,
        0.0778936f,
        0.269988f,
        -0.0515926f,
        0.147636f,
        -0.121939f,
        -0.0303203f,
        -0.702988f,
        -0.220738f,
        -0.104178f,
        0.102754f,
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        -0.202057f,
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        -0.0329241f,
        0.124936f,
        0.0593826f,
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        0.336101f,
        0.360896f,
        0.118351f,
        -0.169321f,
        -0.00791483f,
        -0.584241f,
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        0.17687f,
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        -0.046226f,
        0.143653f,
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        0.0206962f,
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        0.179695f,
        0.1175f,
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        -0.385674f,
        -0.983871f,
        -0.42516f,
        0.0679211f,
        -0.199898f,
        0.192904f,
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        0.198246f,
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        0.161858f,
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        0.520186f,
        -0.0842066f,
        0.236264f,
        0.22663f,
        0.313905f,
        -0.26988f,
        0.188891f,
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        -0.339143f,
        0.251947f,
        -0.800452f,
        0.0130017f,
        0.0306637f,
        -0.488503f,
        0.368267f,
        -0.0427116f,
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        -0.16417f,
        -0.0864108f,
        0.153452f,
        0.37565f,
        0.431599f,
        0.145022f,
        0.0238288f,
        -0.344559f,
        0.170844f,
        0.220166f,
        0.0584108f,
        -0.0242578f,
        0.459242f,
        -0.309433f,
        -0.183178f,
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        0.131252f,
        0.0709812f,
        0.158865f,
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        0.179904f,
        -0.325517f,
        0.0144405f,
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        0.0659974f,
        0.384588f,
        -0.12348f,
        0.0427167f,
        0.0150143f,
        -0.354596f,
        -0.276234f,
        0.0335361f,
        0.207341f,
        -0.456912f,
        -0.439884f,
        0.0628522f,
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        -0.367666f,
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        0.15368f,
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        -0.0735893f,
        0.0660757f,
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        0.0614654f,
        -0.159744f,
        -0.245473f,
        -0.207488f,
        0.253759f,
        -0.0102803f,
        0.116993f,
        -0.0493576f,
        0.0167945f,
        0.138382f,
        -0.00102203f,
        -0.144797f,
        -0.222619f,
        0.320866f,
        0.0507977f,
        -0.0458033f,
        1.10501f,
        -0.116079f,
        -0.393548f,
        -0.320955f,
        -0.386687f,
        0.277903f,
        0.0759589f,
        -0.278561f,
        -0.162686f,
        -0.022386f,
        -0.244537f,
        0.0274179f,
        -0.080937f,
        -0.222133f,
        0.0633341f,
        0.155947f,
        -0.0298876f,
        -0.18921f,
        0.190984f,
        -0.35663f,
        -0.137884f,
        0.550159f,
        0.096353f,
        0.273687f,
        0.288882f,
        0.0811719f,
        0.279464f,
        0.168219f,
        -0.231556f,
        0.338719f,
        0.170307f,
        -0.04944f,
        1.94646f,
        -0.151242f,
        0.192923f,
        0.197884f,
        -0.193047f,
        -0.148041f,
        -0.0801752f,
        0.00753225f,
        -0.0705527f,
        0.10537f,
        -0.212792f,
        0.0661733f,
        0.00248664f,
        0.12624f,
        -0.622626f,
        0.106069f,
        0.0252939f,
        -0.0474646f,
        -0.0415958f,
        -0.207594f,
        -0.0870699f,
        0.0662641f,
        -0.172468f,
        0.152725f,
        0.118974f,
        0.471971f,
        -0.187808f,
        0.0202502f,
        -0.271737f,
        0.395173f,
        0.172049f,
        -0.115912f,
        0.293842f,
        -0.0161143f,
        0.268388f,
        -0.488364f,
        0.405653f,
        -0.318354f,
        0.226183f,
        -0.371718f,
        -0.124455f,
        0.114567f,
        -0.0197764f,
        -0.255166f,
        -0.151114f,
        -0.189752f,
        -0.674399f,
        -0.532053f,

    };
    set_values(input, input_data);
    set_values(input2, input_data2);

    std::vector<float> out_data = {
        1.4538f,  0.1023f,  0.9402f,  0.1373f, -0.5619f, -0.6068f, -1.4220f,
        -0.8573f,  0.9406f,

        1.3968f, -0.0450f,  0.7518f,  0.4434f, -0.2937f, -0.4165f, -0.8629f,
        -0.5942f,  0.4324f,

        1.3968f, -0.0450f,  0.7518f,  0.4434f, -0.2937f, -0.4165f, -0.8629f,
        -0.5942f,  0.4324f,

        1.3968f, -0.0450f,  0.7518f,  0.4434f, -0.2937f, -0.4165f, -0.8629f,
        -0.5942f,  0.4324f,

        1.3968f, -0.0450f,  0.7518f,  0.4434f, -0.2937f, -0.4165f, -0.8629f,
        -0.5942f,  0.4324f

    };

    topology topology;
    topology.add(
        input_layout("input", input.get_layout())
    );
    topology.add(
        input_layout("input2", input2.get_layout())
    );
    topology.add(
        gemm("output", "input", "input2")
    );

    network network(engine, topology);
    network.set_input_data("input", input);
    network.set_input_data("input2", input2);
    auto outputs = network.execute();

    auto output = outputs.at("output").get_memory();
    auto output_ptr = output.pointer<float>();


    EXPECT_EQ(output_ptr.size(), (uint32_t)45);
    for (uint32_t  i = 0; i < out_data.size(); ++i) {
        EXPECT_NEAR(output_ptr[i], out_data[i], 0.0001);
    }



}

TEST(gemm_gpu, basic_smarcink2) {
    const auto& engine = get_test_engine();
    auto input = memory::allocate(engine, { data_types::f32, format::bfyx,{ 2, 1, 3, 2 } });
    auto input2 = memory::allocate(engine, { data_types::f32, format::bfyx,{ 2, 1, 2, 3 } });

    std::vector<float> input_data = {
        1.0f, 2.0f, 3.0f,
        4.0f, 5.0f, 6.0f,

        1.0f, 1.0f, 1.0f,
        2.0f, 3.0f, 4.0f
    };

    std::vector<float> input_data2 = {
        7.0f, 8.0f,
        9.0f, 10.0f,
        11.0f, 12.0f,

        1.0f, 1.0f,
        1.0f, 1.0f,
        3.0f, 2.0f
    };
    set_values(input, input_data);
    set_values(input2, input_data2);

    std::vector<float> out_data = {
        58.0f, 64.0f,
        139.0f, 154.0f,

        5.0f, 4.0f,
        17.0f, 13.0f
    };

    topology topology;
    topology.add(
        input_layout("input", input.get_layout())
    );
    topology.add(
        input_layout("input2", input2.get_layout())
    );
    topology.add(
        gemm("output", "input", "input2")
    );

    network network(engine, topology);
    network.set_input_data("input", input);
    network.set_input_data("input2", input2);
    auto outputs = network.execute();

    auto output = outputs.at("output").get_memory();
    auto output_ptr = output.pointer<float>();


    EXPECT_EQ(output_ptr.size(), (uint32_t)8);
    for (uint32_t i = 0; i < out_data.size(); ++i) {         
        EXPECT_FLOAT_EQ(output_ptr[i], out_data[i]);
    }
}