backend_performance.cpp

/*******************************************************************************
* Copyright 2017-2018 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 <sstream>
#include <string>
#include <vector>

#include "gtest/gtest.h"

#include "ngraph/codegen/compiler.hpp"
#include "ngraph/codegen/execution_engine.hpp"
#include "ngraph/file_util.hpp"
#include "ngraph/log.hpp"
#include "ngraph/op/concat.hpp"
#include "ngraph/runtime/backend.hpp"
#include "ngraph/serializer.hpp"
#include "ngraph/util.hpp"
#include "util/benchmark.hpp"
#include "util/random.hpp"
#include "util/test_tools.hpp"

using namespace std;
using namespace ngraph;

TEST(benchmark, mxnet_mnist_mlp_forward)
{
    const string json_path = file_util::path_join(SERIALIZED_ZOO, "mxnet/mnist_mlp_forward.json");
    run_benchmark(json_path, "CPU", 1000);
}

TEST(benchmark, gpu_mxnet_mnist_mlp_forward)
{
    const string json_path = file_util::path_join(SERIALIZED_ZOO, "mxnet/mnist_mlp_forward.json");
    run_benchmark(json_path, "GPU", 1000);
}

TEST(benchmark, mxnet_10_bucket_lstm)
{
    const string json_path = file_util::path_join(SERIALIZED_ZOO, "mxnet/10_bucket_LSTM.json");
    run_benchmark(json_path, "CPU", 10);
}

TEST(benchmark, mxnet_lstm_backward)
{
    const string json_path = file_util::path_join(SERIALIZED_ZOO, "mxnet/LSTM_backward.json");
    run_benchmark(json_path, "CPU", 10);
}

TEST(benchmark, mxnet_lstm_forward)
{
    const string json_path = file_util::path_join(SERIALIZED_ZOO, "mxnet/LSTM_forward.json");
    run_benchmark(json_path, "CPU", 10);
}

TEST(benchmark, mxnet_seq2seq_forward)
{
    const string json_path = file_util::path_join(SERIALIZED_ZOO, "mxnet/Seq2Seq_forward.json");
    run_benchmark(json_path, "CPU", 10);
}

TEST(benchmark, mxnet_seq2seq_backward)
{
    const string json_path = file_util::path_join(SERIALIZED_ZOO, "mxnet/Seq2Seq_backward.json");
    run_benchmark(json_path, "CPU", 10);
}

TEST(benchmark, mxnet_sockeye_seq2seq_forward)
{
    const string json_path =
        file_util::path_join(SERIALIZED_ZOO, "mxnet/Sockeye_Seq2Seq_forward.json");
    run_benchmark(json_path, "CPU", 10);
}

TEST(benchmark, mxnet_sockeye_seq2seq_backward)
{
    const string json_path =
        file_util::path_join(SERIALIZED_ZOO, "mxnet/Sockeye_Seq2Seq_backward.json");
    run_benchmark(json_path, "CPU", 10);
}

//
// Benchmarks a graph that concatenates six 32x1x200 arrays along the middle axis.
//
TEST(benchmark, concat_32x1x200_axis1_6)
{
    const size_t n_arrays = 6;
    Shape shape_of_each_array = Shape{32, 1, 200};
    size_t concatenation_axis = 1;

    Shape result_shape;
    result_shape = shape_of_each_array;
    result_shape[concatenation_axis] *= n_arrays;

    size_t elements_per_array = 1;
    for (size_t d : shape_of_each_array)
    {
        elements_per_array *= d;
    }

    vector<vector<float>> data_arrays(n_arrays);
    for (size_t i = 0; i < n_arrays; i++)
    {
        data_arrays[i] = vector<float>(elements_per_array);
        for (size_t j = 0; j < elements_per_array; j++)
        {
            data_arrays[i][j] = float(j + 1);
        }
    }

    bool using_ref_kernels = (std::getenv("NGRAPH_CPU_USE_REF_KERNELS") != nullptr);

    vector<std::string> backend_names{"INTERPRETER", "CPU"};
    vector<int> n_runs{200, 200, using_ref_kernels ? 200 : 200000}; // one for each backend
    vector<std::function<void()>> test_callbacks;                   // one for each backend
    vector<std::shared_ptr<runtime::TensorView>> result_tvs;        // one for each backend

    for (std::string backend_name : backend_names)
    {
        vector<std::shared_ptr<op::Parameter>> params(n_arrays);
        vector<std::shared_ptr<Node>> params_as_nodes(n_arrays);
        for (size_t i = 0; i < n_arrays; i++)
        {
            auto param = make_shared<op::Parameter>(element::f32, shape_of_each_array);
            params[i] = param;
            params_as_nodes[i] = param;
        }

        auto concat = make_shared<op::Concat>(params_as_nodes, concatenation_axis);
        auto f = make_shared<Function>(concat, params);

        auto backend = runtime::Backend::create(backend_name);

        vector<shared_ptr<runtime::TensorView>> input_vals;

        for (size_t i = 0; i < n_arrays; i++)
        {
            auto tv = backend->create_tensor(element::f32, shape_of_each_array);
            copy_data(tv, data_arrays[i]);
            input_vals.push_back(tv);
        }

        auto result_tv = backend->create_tensor(element::f32, result_shape);
        result_tvs.push_back(result_tv);

        std::function<void()> cb = [&]() { backend->call(f, {result_tv}, input_vals); };

        test_callbacks.push_back(cb);
    }

    for (size_t i = 0; i < backend_names.size(); i++)
    {
        std::cout << backend_names[i] << ": " << n_runs[i] << " tests in " << std::flush;

        stopwatch sw;
        std::function<void()> cb = test_callbacks[i];

        sw.start();
        for (int j = 0; j < n_runs[i]; j++)
        {
            cb();
        }
        sw.stop();

        std::cout << sw.get_milliseconds() << "ms (" << (sw.get_microseconds() / n_runs[i])
                  << " us/test)" << std::endl;
    }

    for (size_t i = 1; i < backend_names.size(); i++)
    {
        std::cout << "Verifying " << backend_names[i] << " result against " << backend_names[0]
                  << "..." << std::flush;

        if (read_vector<float>(result_tvs[i]) == read_vector<float>(result_tvs[0]))
        {
            std::cout << " OK" << std::endl;
        }
        else
        {
            std::cout << " FAILED" << std::endl;
            ADD_FAILURE();
        }
    }
}