//*****************************************************************************
// Copyright 2017-2019 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>
// 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 ${BACKEND_NAME}_DOUBLE_TOLERANCE_BITS
#endif
// clang-format on
#include "gtest/gtest.h"
#include "ngraph/ngraph.hpp"
#include "util/all_close.hpp"
#include "util/all_close_f.hpp"
#include "util/autodiff/numeric_compare.hpp"
#include "util/ndarray.hpp"
#include "util/test_control.hpp"
#include "util/test_tools.hpp"
using namespace std;
using namespace ngraph;
static string s_manifest = "${MANIFEST}";
NGRAPH_TEST(${BACKEND_NAME}, layer_norm_affine_stats)
{
auto p_data = make_shared<op::Parameter>(element::f32, Shape{2, 4});
auto p_scale = make_shared<op::Parameter>(element::f32, Shape{4});
auto p_bias = make_shared<op::Parameter>(element::f32, Shape{4});
auto ln = make_shared<op::LayerNorm>(p_data, p_scale, p_bias);
auto f = make_shared<Function>(ln->outputs(), ParameterVector{p_data, p_scale, p_bias});
auto backend = runtime::Backend::create("${BACKEND_NAME}");
// Create tensors for input
auto data = backend->create_tensor(element::f32, Shape{2, 4});
auto scale = backend->create_tensor(element::f32, Shape{4});
auto bias = backend->create_tensor(element::f32, Shape{4});
// Fill in input tensors
vector<float> d_input{-4.0f, -3.0f, -2.0f, -1.0f, 0.0f, 1.0f, 2.0f, 3.0f};
copy_data(data, d_input);
vector<float> s_input{-1.0f, 1.0f, 2.0f, 3.0f};
copy_data(scale, s_input);
vector<float> b_input{-4.0f, -3.0f, -2.0f, -1.0f};
copy_data(bias, b_input);
// Create tensors for output
auto norm = backend->create_tensor(element::f32, Shape{2, 4});
auto mean = backend->create_tensor(element::f32, Shape{2});
auto var = backend->create_tensor(element::f32, Shape{2});
// Expected results (Manually computed)
vector<float> exp_norm{-2.658364534378051758f,
-3.447211742401123047f,
-1.105576276779174805f,
3.024906158447265625f,
-2.658364534378051758f,
-3.447211742401123047f,
-1.105576276779174805f,
3.024906158447265625f};
vector<float> exp_mean{-2.5f, 1.5f};
vector<float> exp_var{1.25f, 1.25f};
auto handle = backend->compile(f);
handle->call_with_validate({norm, mean, var}, {data, scale, bias});
EXPECT_TRUE(test::all_close_f(exp_norm, read_vector<float>(norm)));
EXPECT_TRUE(test::all_close_f(exp_mean, read_vector<float>(mean)));
EXPECT_TRUE(test::all_close_f(exp_var, read_vector<float>(var)));
}
NGRAPH_TEST(${BACKEND_NAME}, layer_norm_bprop_affine_stats)
{
auto p_data = make_shared<op::Parameter>(element::f32, Shape{2, 4});
auto p_delta = make_shared<op::Parameter>(element::f32, Shape{2, 4});
auto p_mean = make_shared<op::Parameter>(element::f32, Shape{2});
auto p_var = make_shared<op::Parameter>(element::f32, Shape{2});
auto p_scale = make_shared<op::Parameter>(element::f32, Shape{4});
auto lnb = make_shared<op::LayerNormBackprop>(p_data, p_delta, p_mean, p_var, p_scale);
auto f = make_shared<Function>(lnb->outputs(),
ParameterVector{p_data, p_delta, p_mean, p_var, p_scale});
auto backend = runtime::Backend::create("${BACKEND_NAME}");
// Create tensors for input
auto data = backend->create_tensor(element::f32, Shape{2, 4});
auto delta = backend->create_tensor(element::f32, Shape{2, 4});
auto mean = backend->create_tensor(element::f32, Shape{2});
auto var = backend->create_tensor(element::f32, Shape{2});
auto scale = backend->create_tensor(element::f32, Shape{4});
// Fill in input tensors
vector<float> d_input{-4.0f, -3.0f, -2.0f, -1.0f, 0.0f, 1.0f, 2.0f, 3.0f};
copy_data(data, d_input);
vector<float> dt_input{0.1f, -0.1f, 0.2f, -0.2f, 0.1f, -0.1f, 0.2f, -0.2f};
copy_data(delta, dt_input);
vector<float> s_input{-1.0f, 1.0f, 2.0f, 3.0f};
copy_data(scale, s_input);
vector<float> m_input{-2.5f, 1.5f};
copy_data(mean, m_input);
vector<float> v_input{1.25f, 1.25f};
copy_data(var, v_input);
// Create tensors for output
auto d_data = backend->create_tensor(element::f32, Shape{2, 4});
auto d_scale = backend->create_tensor(element::f32, Shape{4});
auto d_bias = backend->create_tensor(element::f32, Shape{4});
// Expected results (Manually compute)
vector<float> exp_d_data{-0.1341624855995178223f,
-0.04472083225846290588f,
0.4919326305389404297f,
-0.31304931640625f,
-0.1341624855995178223f,
-0.04472083225846290588f,
0.4919326305389404297f,
-0.31304931640625f};
vector<float> exp_d_scale{-0.2683270871639251709f,
0.08944236487150192261f,
0.1788847297430038452f,
-0.5366541743278503418f};
vector<float> exp_d_bias{0.2f, -0.2f, 0.4f, -0.4f};
auto handle = backend->compile(f);
handle->call_with_validate({d_data, d_scale, d_bias}, {data, delta, mean, var, scale});
EXPECT_TRUE(test::all_close_f(exp_d_data, read_vector<float>(d_data)));
EXPECT_TRUE(test::all_close_f(exp_d_scale, read_vector<float>(d_scale)));
EXPECT_TRUE(test::all_close_f(exp_d_bias, read_vector<float>(d_bias)));
}
NGRAPH_TEST(${BACKEND_NAME}, layer_norm_bprop_affine)
{
auto p_data = make_shared<op::Parameter>(element::f32, Shape{2, 4});
auto p_delta = make_shared<op::Parameter>(element::f32, Shape{2, 4});
auto p_scale = make_shared<op::Parameter>(element::f32, Shape{4});
auto lnb = make_shared<op::LayerNormBackprop>(p_data, p_delta, p_scale);
auto f = make_shared<Function>(lnb->outputs(), ParameterVector{p_data, p_delta, p_scale});
auto backend = runtime::Backend::create("${BACKEND_NAME}");
// Create tensors for input
auto data = backend->create_tensor(element::f32, Shape{2, 4});
auto delta = backend->create_tensor(element::f32, Shape{2, 4});
auto scale = backend->create_tensor(element::f32, Shape{4});
// Fill in input tensors
vector<float> d_input{-4.0f, -3.0f, -2.0f, -1.0f, 0.0f, 1.0f, 2.0f, 3.0f};
copy_data(data, d_input);
vector<float> dt_input{0.1f, -0.1f, 0.2f, -0.2f, 0.1f, -0.1f, 0.2f, -0.2f};
copy_data(delta, dt_input);
vector<float> s_input{-1.0f, 1.0f, 2.0f, 3.0f};
copy_data(scale, s_input);
// Create tensors for output
auto d_data = backend->create_tensor(element::f32, Shape{2, 4});
auto d_scale = backend->create_tensor(element::f32, Shape{4});
auto d_bias = backend->create_tensor(element::f32, Shape{4});
// Expected results (Manually computed)
vector<float> exp_d_data{-0.1341624855995178223f,
-0.04472083225846290588f,
0.4919326305389404297f,
-0.31304931640625f,
-0.1341624855995178223f,
-0.04472083225846290588f,
0.4919326305389404297f,
-0.31304931640625f};
vector<float> exp_d_scale{-0.2683270871639251709f,
0.08944236487150192261f,
0.1788847297430038452f,
-0.5366541743278503418f};
vector<float> exp_d_bias{0.2f, -0.2f, 0.4f, -0.4f};
auto handle = backend->compile(f);
handle->call_with_validate({d_data, d_scale, d_bias}, {data, delta, scale});
EXPECT_TRUE(test::all_close_f(exp_d_data, read_vector<float>(d_data)));
EXPECT_TRUE(test::all_close_f(exp_d_scale, read_vector<float>(d_scale)));
EXPECT_TRUE(test::all_close_f(exp_d_bias, read_vector<float>(d_bias)));
}