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dnnl.cpp
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dnnl.cpp
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// Copyright (C) 2018-2024 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include <dnnl.hpp>
#include <iostream>
#include <vector>
#include "gtest/gtest.h"
static int tensor_volume(const dnnl::memory::dims& t) {
int x = 1;
for (const auto i : t)
x *= i;
return x;
}
void test() {
using namespace dnnl;
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();
}
TEST(dnnl, engine) {
EXPECT_NO_THROW(test());
}