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openvino/ngraph/test/backend/gelu.in.cpp
2020-07-10 13:49:43 +03:00

104 lines
3.5 KiB
C++

//*****************************************************************************
// Copyright 2017-2020 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 <cinttypes>
#include <cmath>
#include <cstdlib>
#include <random>
#include <string>
#include "util/random.hpp"
// clang-format off
#ifdef ${BACKEND_NAME}_FLOAT_TOLERANCE_BITS
#define DEFAULT_FLOAT_TOLERANCE_BITS ${BACKEND_NAME}_FLOAT_TOLERANCE_BITS
#endif
#ifdef ${BACKEND_NAME}_DOUBLE_TOLERANCE_BITS
#define DEFAULT_DOUBLE_TOLERANCE_BITS ${BACKEND_NAME}_DOUBLE_TOLERANCE_BITS
#endif
// clang-format on
#include "gtest/gtest.h"
#include "runtime/backend.hpp"
#include "ngraph/runtime/tensor.hpp"
#include "ngraph/ngraph.hpp"
#include "util/all_close.hpp"
#include "util/all_close_f.hpp"
#include "util/ndarray.hpp"
#include "util/test_control.hpp"
#include "util/test_tools.hpp"
using namespace std;
using namespace ngraph;
static string s_manifest = "${MANIFEST}";
NGRAPH_TEST(${BACKEND_NAME}, gelu_f32)
{
Shape shape{100000};
auto A = make_shared<op::Parameter>(element::f32, shape);
auto f = make_shared<Function>(make_shared<op::Gelu>(A), ParameterVector{A});
auto backend = runtime::Backend::create("${BACKEND_NAME}");
test::Uniform<float> rng(-100.0f, 100.0f);
vector<vector<float>> args;
for (shared_ptr<op::Parameter> param : f->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);
}
// Create some tensors for input/output
auto a = backend->create_tensor(element::f32, shape);
copy_data(a, args[0]);
auto result = backend->create_tensor(element::f32, shape);
std::transform(args[0].begin(), args[0].end(), args[0].begin(), [](float x) -> float {
return 0.5f * x * (1.0f + erf(x / sqrt(2.0f)));
});
auto handle = backend->compile(f);
handle->call_with_validate({result}, {a});
EXPECT_TRUE(test::all_close(args[0], read_vector<float>(result), .007f, .007f));
}
NGRAPH_TEST(${BACKEND_NAME}, gelu_f64)
{
Shape shape{8};
auto A = make_shared<op::Parameter>(element::f64, shape);
auto f = make_shared<Function>(make_shared<op::Gelu>(A), ParameterVector{A});
auto backend = runtime::Backend::create("${BACKEND_NAME}");
// Create some tensors for input/output
auto a = backend->create_tensor(element::f64, shape);
vector<double> input{-4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0};
copy_data(a, input);
auto result = backend->create_tensor(element::f64, shape);
std::transform(input.begin(), input.end(), input.begin(), [](double x) -> double {
return 0.5 * x * (1.0 + erf(x / sqrt(2.0)));
});
auto handle = backend->compile(f);
handle->call_with_validate({result}, {a});
EXPECT_TRUE(test::all_close_f(input, read_vector<double>(result)));
}