93 lines
3.6 KiB
C++
93 lines
3.6 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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// 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(${BACKEND_NAME}, layer_norm_affine_stats)
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{
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auto p_data = make_shared<op::Parameter>(element::f32, Shape{2, 4});
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auto p_scale = make_shared<op::Parameter>(element::f32, Shape{4});
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auto p_bias = make_shared<op::Parameter>(element::f32, Shape{4});
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auto ln = make_shared<op::LayerNorm>(p_data, p_scale, p_bias);
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auto f = make_shared<Function>(ln->outputs(), ParameterVector{p_data, p_scale, p_bias});
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auto backend = runtime::Backend::create("${BACKEND_NAME}");
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// Create tensors for input
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auto data = backend->create_tensor(element::f32, Shape{2, 4});
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auto scale = backend->create_tensor(element::f32, Shape{4});
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auto bias = backend->create_tensor(element::f32, Shape{4});
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// Fill in input tensors
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vector<float> d_input{-4.0f, -3.0f, -2.0f, -1.0f, 0.0f, 1.0f, 2.0f, 3.0f};
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copy_data(data, d_input);
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vector<float> s_input{-1.0f, 1.0f, 2.0f, 3.0f};
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copy_data(scale, s_input);
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vector<float> b_input{-4.0f, -3.0f, -2.0f, -1.0f};
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copy_data(bias, b_input);
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// Create tensors for output
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auto norm = backend->create_tensor(element::f32, Shape{2, 4});
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auto mean = backend->create_tensor(element::f32, Shape{2});
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auto var = backend->create_tensor(element::f32, Shape{2});
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// Expected results (Manually computed)
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vector<float> exp_norm{-2.658364534378051758f,
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-3.447211742401123047f,
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-1.105576276779174805f,
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3.024906158447265625f,
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-2.658364534378051758f,
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-3.447211742401123047f,
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-1.105576276779174805f,
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3.024906158447265625f};
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vector<float> exp_mean{-2.5f, 1.5f};
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vector<float> exp_var{1.25f, 1.25f};
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auto handle = backend->compile(f);
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handle->call_with_validate({norm, mean, var}, {data, scale, bias});
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EXPECT_TRUE(test::all_close_f(exp_norm, read_vector<float>(norm)));
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EXPECT_TRUE(test::all_close_f(exp_mean, read_vector<float>(mean)));
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EXPECT_TRUE(test::all_close_f(exp_var, read_vector<float>(var)));
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}
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