mkldnn.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 <iostream>
#include <mkldnn.hpp>
#include <vector>

#include "gtest/gtest.h"

static int tensor_volume(const mkldnn::memory::dims& t)
{
    int x = 1;
    for (const auto i : t)
        x *= i;
    return x;
}

TEST(mkldnn, engine)
{
    using namespace mkldnn;

#pragma GCC diagnostic ignored "-Wgnu-statement-expression"

    EXPECT_NO_THROW(({
        auto cpu_engine = engine(engine::cpu, 0);

        const int mb = 2;
        const int groups = 2;
        memory::dims input_tz = {mb, 256, 13, 13};
        memory::dims weights_tz = {groups, 384 / groups, 256 / groups, 3, 3};
        memory::dims bias_tz = {384};
        memory::dims strides = {1, 1};
        memory::dims padding = {0, 0};
        memory::dims output_tz = {
            mb,
            384,
            (input_tz[2] + 2 * padding[0] - weights_tz[3]) / strides[0] + 1,
            (input_tz[3] + 2 * padding[1] - weights_tz[4]) / strides[1] + 1,
        };

        std::vector<float> input(tensor_volume(input_tz), .0f);
        std::vector<float> weights(tensor_volume(weights_tz), .0f);
        std::vector<float> bias(tensor_volume(bias_tz), .0f);
        std::vector<float> output(tensor_volume(output_tz), .0f);

        auto c3_src_desc = memory::desc({input_tz}, memory::data_type::f32, memory::format::nchw);
        auto c3_weights_desc =
            memory::desc({weights_tz}, memory::data_type::f32, memory::format::goihw);
        auto c3_bias_desc = memory::desc({bias_tz}, memory::data_type::f32, memory::format::x);
        auto c3_dst_desc = memory::desc({output_tz}, memory::data_type::f32, memory::format::nchw);

        auto c3_src = memory({c3_src_desc, cpu_engine}, input.data());
        auto c3_weights = memory({c3_weights_desc, cpu_engine}, weights.data());
        auto c3_bias = memory({c3_bias_desc, cpu_engine}, bias.data());
        auto c3_dst = memory({c3_dst_desc, cpu_engine}, output.data());

        auto c3 = convolution_forward(convolution_forward::primitive_desc(
                                          convolution_forward::desc(prop_kind::forward,
                                                                    algorithm::convolution_direct,
                                                                    c3_src_desc,
                                                                    c3_weights_desc,
                                                                    c3_bias_desc,
                                                                    c3_dst_desc,
                                                                    strides,
                                                                    padding,
                                                                    padding,
                                                                    padding_kind::zero),
                                          cpu_engine),
                                      c3_src,
                                      c3_weights,
                                      c3_bias,
                                      c3_dst);

        stream(stream::kind::eager).submit({c3}).wait();
    }));
}