80 lines
2.4 KiB
C++
80 lines
2.4 KiB
C++
// Copyright (C) 2018-2021 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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#include <algorithm>
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#include <cinttypes>
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#include <cmath>
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#include <cstdlib>
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#include <random>
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#include <string>
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// clang-format off
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#ifdef ${BACKEND_NAME}_FLOAT_TOLERANCE_BITS
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#define DEFAULT_FLOAT_TOLERANCE_BITS ${BACKEND_NAME}_FLOAT_TOLERANCE_BITS
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#endif
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#ifdef ${BACKEND_NAME}_DOUBLE_TOLERANCE_BITS
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#define DEFAULT_DOUBLE_TOLERANCE_BITS ${BACKEND_NAME}_DOUBLE_TOLERANCE_BITS
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#endif
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// clang-format on
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#include "gtest/gtest.h"
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#include "runtime/backend.hpp"
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#include "ngraph/runtime/tensor.hpp"
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#include "ngraph/ngraph.hpp"
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#include "util/all_close.hpp"
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#include "util/all_close_f.hpp"
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#include "util/ndarray.hpp"
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#include "util/test_control.hpp"
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#include "util/test_tools.hpp"
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using namespace std;
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using namespace ngraph;
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static string s_manifest = "${MANIFEST}";
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NGRAPH_TEST(${BACKEND_NAME}, tanh_f32)
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{
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Shape shape{8};
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auto A = make_shared<op::Parameter>(element::f32, shape);
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auto f = make_shared<Function>(make_shared<op::Tanh>(A), ParameterVector{A});
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auto backend = runtime::Backend::create("${BACKEND_NAME}");
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// Create some tensors for input/output
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auto a = backend->create_tensor(element::f32, shape);
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const vector<float> input{2.f, 1.f, 0.5f, 0.f, -0.f, -0.5f, -1.f, -2.f};
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const auto expected = [&]{
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vector<float> e(input.size(),0);
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std::transform(begin(input), end(input), begin(e), [](float v){return std::tanh(v);});
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return e;
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}();
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copy_data(a, input);
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auto result = backend->create_tensor(element::f32, shape);
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auto handle = backend->compile(f);
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handle->call_with_validate({result}, {a});
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EXPECT_TRUE(test::all_close_f(expected, read_vector<float>(result)));
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}
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NGRAPH_TEST(${BACKEND_NAME}, tanh_int32)
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{
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Shape shape{5};
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auto A = make_shared<op::Parameter>(element::i32, shape);
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auto f = make_shared<Function>(make_shared<op::Tanh>(A), ParameterVector{A});
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auto backend = runtime::Backend::create("${BACKEND_NAME}");
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// Create some tensors for input/output
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auto a = backend->create_tensor(element::i32, shape);
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const vector<int32_t> input{2, 1, 0, -1, -2};
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const vector<int32_t> expected{1, 1, 0, -1, -1};
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copy_data(a, input);
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auto result = backend->create_tensor(element::i32, shape);
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auto handle = backend->compile(f);
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handle->call_with_validate({result}, {a});
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EXPECT_EQ(expected, read_vector<int32_t>(result));
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}
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