104 lines
3.5 KiB
C++
104 lines
3.5 KiB
C++
//*****************************************************************************
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// Copyright 2017-2020 Intel Corporation
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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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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#include "util/random.hpp"
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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(${BACKEND_NAME}, gelu_f32)
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{
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Shape shape{100000};
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auto A = make_shared<op::Parameter>(element::f32, shape);
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auto f = make_shared<Function>(make_shared<op::Gelu>(A), ParameterVector{A});
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auto backend = runtime::Backend::create("${BACKEND_NAME}");
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test::Uniform<float> rng(-100.0f, 100.0f);
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vector<vector<float>> args;
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for (shared_ptr<op::Parameter> param : f->get_parameters())
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{
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auto name = param->get_name();
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vector<float> tensor_val(shape_size(param->get_shape()));
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rng.initialize(tensor_val);
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args.push_back(tensor_val);
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}
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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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copy_data(a, args[0]);
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auto result = backend->create_tensor(element::f32, shape);
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std::transform(args[0].begin(), args[0].end(), args[0].begin(), [](float x) -> float {
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return 0.5f * x * (1.0f + erf(x / sqrt(2.0f)));
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});
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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(args[0], read_vector<float>(result), .007f, .007f));
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}
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NGRAPH_TEST(${BACKEND_NAME}, gelu_f64)
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{
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Shape shape{8};
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auto A = make_shared<op::Parameter>(element::f64, shape);
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auto f = make_shared<Function>(make_shared<op::Gelu>(A), ParameterVector{A});
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auto backend = runtime::Backend::create("${BACKEND_NAME}");
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// Create some tensors for input/output
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auto a = backend->create_tensor(element::f64, shape);
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vector<double> input{-4.0, -3.0, -2.0, -1.0, 0.0, 1.0, 2.0, 3.0};
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copy_data(a, input);
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auto result = backend->create_tensor(element::f64, shape);
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std::transform(input.begin(), input.end(), input.begin(), [](double x) -> double {
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return 0.5 * x * (1.0 + erf(x / sqrt(2.0)));
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});
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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(input, read_vector<double>(result)));
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}
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