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test_cudnn_batchnorm.cc
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test_cudnn_batchnorm.cc
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/*********************************************************
*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you 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 "../src/model/layer/cudnn_batchnorm.h"
#ifdef USE_CUDNN
#include "gtest/gtest.h"
using singa::CudnnBatchNorm;
using singa::Shape;
TEST(CudnnBatchNorm, Setup) {
CudnnBatchNorm batchnorm;
// EXPECT_EQ("CudnnBatchNorm", batchnorm.layer_type());
singa::LayerConf conf;
singa::BatchNormConf *batchnorm_conf = conf.mutable_batchnorm_conf();
batchnorm_conf->set_factor(0.01);
batchnorm.Setup(Shape{2, 4, 4}, conf);
EXPECT_FLOAT_EQ(0.01, batchnorm.factor());
EXPECT_EQ(2u, batchnorm.channels());
EXPECT_EQ(4u, batchnorm.height());
EXPECT_EQ(4u, batchnorm.width());
}
TEST(CudnnBatchNorm, Forward) {
CudnnBatchNorm batchnorm;
const float x[] = {0.0736655, 0.0459045, 0.0779517, 0.0771059, 0.0586862,
0.0561263, 0.0708457, 0.0977273, 0.0405025, -0.170897,
0.0208982, 0.136865, -0.0367905, -0.0618205, -0.0103908,
-0.0522777, -0.122161, -0.025427, -0.0718576, -0.185941,
0.0166533, 0.178679, -0.0576606, -0.137817, 0.150676,
0.153442, -0.0929899, -0.148675, -0.112459, -0.106284,
-0.103074, -0.0668811};
auto cuda = std::make_shared<singa::CudaGPU>();
singa::Tensor in(singa::Shape{1, 2, 4, 4}, cuda);
in.CopyDataFromHostPtr(x, 1 * 2 * 4 * 4);
const float alpha_[] = {1, 1};
singa::Tensor alpha(singa::Shape{1, 2, 1, 1}, cuda);
alpha.CopyDataFromHostPtr(alpha_, 1 * 2 * 1 * 1);
const float beta_[] = {0, 0};
singa::Tensor beta(singa::Shape{1, 2, 1, 1}, cuda);
beta.CopyDataFromHostPtr(beta_, 1 * 2 * 1 * 1);
singa::LayerConf conf;
singa::BatchNormConf *batchnorm_conf = conf.mutable_batchnorm_conf();
batchnorm_conf->set_factor(0.9);
batchnorm.Setup(Shape{2, 4, 4}, conf);
batchnorm.ToDevice(cuda);
batchnorm.set_bnScale(alpha);
batchnorm.set_bnBias(beta);
batchnorm.set_runningMean(beta);
batchnorm.set_runningVariance(beta);
singa::Tensor out = batchnorm.Forward(singa::kTrain, in);
out.ToHost();
const float *outptr = out.data<float>();
const auto &shape = out.shape();
EXPECT_EQ(4u, shape.size());
EXPECT_EQ(1u, shape[0]);
EXPECT_EQ(2u, shape[1]);
EXPECT_EQ(4u, shape[2]);
EXPECT_EQ(4u, shape[3]);
EXPECT_NEAR(0.637092, outptr[0], 1e-4f);
EXPECT_NEAR(0.262057, outptr[1], 1e-4f);
EXPECT_NEAR(0.694995, outptr[2], 1e-4f);
EXPECT_NEAR(0.683569, outptr[3], 1e-4f);
EXPECT_NEAR(0.43473, outptr[4], 1e-4f);
EXPECT_NEAR(0.400147, outptr[5], 1e-4f);
EXPECT_NEAR(0.598998, outptr[6], 1e-4f);
EXPECT_NEAR(0.962152, outptr[7], 1e-4f);
EXPECT_NEAR(0.189079, outptr[8], 1e-4f);
EXPECT_NEAR(-2.6668, outptr[9], 1e-4f);
EXPECT_NEAR(-0.0757632, outptr[10], 1e-4f);
EXPECT_NEAR(1.49088, outptr[11], 1e-4f);
EXPECT_NEAR(-0.855104, outptr[12], 1e-4f);
EXPECT_NEAR(-1.19324, outptr[13], 1e-4f);
EXPECT_NEAR(-0.498459, outptr[14], 1e-4f);
EXPECT_NEAR(-1.06433, outptr[15], 1e-4f);
EXPECT_NEAR(-0.696646, outptr[16], 1e-4f);
EXPECT_NEAR(0.185125, outptr[17], 1e-4f);
EXPECT_NEAR(-0.238109, outptr[18], 1e-4f);
EXPECT_NEAR(-1.27803, outptr[19], 1e-4f);
EXPECT_NEAR(0.568704, outptr[20], 1e-4f);
EXPECT_NEAR(2.04564, outptr[21], 1e-4f);
EXPECT_NEAR(-0.108697, outptr[22], 1e-4f);
EXPECT_NEAR(-0.839356, outptr[23], 1e-4f);
EXPECT_NEAR(1.79038, outptr[24], 1e-4f);
EXPECT_NEAR(1.81559, outptr[25], 1e-4f);
EXPECT_NEAR(-0.430738, outptr[26], 1e-4f);
EXPECT_NEAR(-0.938335, outptr[27], 1e-4f);
EXPECT_NEAR(-0.608203, outptr[28], 1e-4f);
EXPECT_NEAR(-0.551921, outptr[29], 1e-4f);
EXPECT_NEAR(-0.522658, outptr[30], 1e-4f);
EXPECT_NEAR(-0.192746, outptr[31], 1e-4f);
}
TEST(CudnnBatchNorm, Backward) {
CudnnBatchNorm batchnorm;
const float x[] = {0.0736655, 0.0459045, 0.0779517, 0.0771059, 0.0586862,
0.0561263, 0.0708457, 0.0977273, 0.0405025, -0.170897,
0.0208982, 0.136865, -0.0367905, -0.0618205, -0.0103908,
-0.0522777, -0.122161, -0.025427, -0.0718576, -0.185941,
0.0166533, 0.178679, -0.0576606, -0.137817, 0.150676,
0.153442, -0.0929899, -0.148675, -0.112459, -0.106284,
-0.103074, -0.0668811};
auto cuda = std::make_shared<singa::CudaGPU>();
singa::Tensor x_tensor(singa::Shape{1, 2, 4, 4}, cuda);
x_tensor.CopyDataFromHostPtr(x, 1 * 2 * 4 * 4);
singa::LayerConf conf;
singa::BatchNormConf *batchnorm_conf = conf.mutable_batchnorm_conf();
batchnorm_conf->set_factor(1);
batchnorm.Setup(Shape{2, 4, 4}, conf);
const float dy[] = {
-0.0064714, 0, 0, 0, 0, -0.00297655,
-0.0195729, 0, 0, 0, 0, 0,
0, 0, 0, -0.0032594, 0, 0,
0, 0, 0, 0, 0.0125562, 0,
0.00041933, 0.000386108, -0.0074611, 0.0015929, 0.00468428, 0.00735506,
-0.00682525, 0.00342023};
singa::Tensor dy_tensor(singa::Shape{1, 2, 4, 4}, cuda);
dy_tensor.CopyDataFromHostPtr(dy, 1 * 2 * 4 * 4);
const float alpha_[] = {1, 1};
singa::Tensor alpha(singa::Shape{2}, cuda);
alpha.CopyDataFromHostPtr(alpha_, 1 * 2 * 1 * 1);
const float beta_[] = {0, 0};
singa::Tensor beta(singa::Shape{2}, cuda);
beta.CopyDataFromHostPtr(beta_, 1 * 2 * 1 * 1);
const float mean_[] = {0.0123405, -0.0622333};
singa::Tensor mean(singa::Shape{2}, cuda);
mean.CopyDataFromHostPtr(mean_, 1 * 2 * 1 * 1);
const float var_[] = {15.9948, 8.68198};
singa::Tensor var(singa::Shape{2}, cuda);
var.CopyDataFromHostPtr(var_, 1 * 2 * 1 * 1);
batchnorm.ToDevice(cuda);
batchnorm.set_bnScale(alpha);
batchnorm.set_bnBias(beta);
batchnorm.set_runningMean(beta);
batchnorm.set_runningVariance(beta);
batchnorm.Forward(singa::kTrain, x_tensor);
const auto ret = batchnorm.Backward(singa::kTrain, dy_tensor);
singa::Tensor dx = ret.first;
dx.ToHost();
const float *dxptr = dx.data<float>();
const auto &shape = dx.shape();
EXPECT_EQ(4u, shape.size());
EXPECT_EQ(1u, shape[0]);
EXPECT_EQ(2u, shape[1]);
EXPECT_EQ(4u, shape[2]);
EXPECT_EQ(4u, shape[3]);
EXPECT_NEAR(-0.0528703, dxptr[0], 1e-4f);
EXPECT_NEAR(0.0302578, dxptr[1], 1e-4f);
EXPECT_NEAR(0.0352178, dxptr[2], 1e-4f);
EXPECT_NEAR(0.0350869, dxptr[3], 1e-4f);
EXPECT_NEAR(0.032236, dxptr[4], 1e-4f);
EXPECT_NEAR(-0.00837157, dxptr[5], 1e-4f);
EXPECT_NEAR(-0.2303, dxptr[6], 1e-4f);
EXPECT_NEAR(0.0382786, dxptr[7], 1e-4f);
EXPECT_NEAR(0.0294217, dxptr[8], 1e-4f);
EXPECT_NEAR(-0.00329757, dxptr[9], 1e-4f);
EXPECT_NEAR(0.0263874, dxptr[10], 1e-4f);
EXPECT_NEAR(0.0443361, dxptr[11], 1e-4f);
EXPECT_NEAR(0.0174587, dxptr[12], 1e-4f);
EXPECT_NEAR(0.0135847, dxptr[13], 1e-4f);
EXPECT_NEAR(0.0215447, dxptr[14], 1e-4f);
EXPECT_NEAR(-0.0289709, dxptr[15], 1e-4f);
EXPECT_NEAR(-0.0100591, dxptr[16], 1e-4f);
EXPECT_NEAR(-0.00895677, dxptr[17], 1e-4f);
EXPECT_NEAR(-0.00948587, dxptr[18], 1e-4f);
EXPECT_NEAR(-0.0107859, dxptr[19], 1e-4f);
EXPECT_NEAR(-0.00847725, dxptr[20], 1e-4f);
EXPECT_NEAR(-0.0066309, dxptr[21], 1e-4f);
EXPECT_NEAR(0.105131, dxptr[22], 1e-4f);
EXPECT_NEAR(-0.0102375, dxptr[23], 1e-4f);
EXPECT_NEAR(-0.00312763, dxptr[24], 1e-4f);
EXPECT_NEAR(-0.00339895, dxptr[25], 1e-4f);
EXPECT_NEAR(-0.0777377, dxptr[26], 1e-4f);
EXPECT_NEAR(0.00415871, dxptr[27], 1e-4f);
EXPECT_NEAR(0.0327506, dxptr[28], 1e-4f);
EXPECT_NEAR(0.0571663, dxptr[29], 1e-4f);
EXPECT_NEAR(-0.0720566, dxptr[30], 1e-4f);
EXPECT_NEAR(0.0217477, dxptr[31], 1e-4f);
singa::Tensor dbnScale = ret.second.at(0);
dbnScale.ToHost();
const float *dbnScaleptr = dbnScale.data<float>();
const auto &dbnScaleShape = dbnScale.shape();
EXPECT_EQ(1u, dbnScaleShape.size());
EXPECT_EQ(2u, dbnScaleShape[0]);
EXPECT_NEAR(-0.013569f, dbnScaleptr[0], 1e-4f);
EXPECT_NEAR(-0.00219431f, dbnScaleptr[1], 1e-4f);
singa::Tensor dbnBias = ret.second.at(1);
dbnBias.ToHost();
const float *dbnBiasptr = dbnBias.data<float>();
const auto &dbnBiasShape = dbnBias.shape();
EXPECT_EQ(1u, dbnBiasShape.size());
EXPECT_EQ(2u, dbnBiasShape[0]);
EXPECT_NEAR(-0.0322803f, dbnBiasptr[0], 1e-4f);
EXPECT_NEAR(0.0161278f, dbnBiasptr[1], 1e-4f);
}
#endif // USE_CUDNN