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test_cudnn_softmax.cc
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test_cudnn_softmax.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 "singa/singa_config.h"
#ifdef USE_CUDNN
#include <cudnn.h>
#include <math.h> // exp
#include "../src/model/layer/cudnn_softmax.h"
#include "gtest/gtest.h"
// TODO(wangwei) add test for matrix input
using singa::CudnnSoftmax;
using singa::Shape;
TEST(CudnnSoftmax, Setup) {
CudnnSoftmax sft;
// EXPECT_EQ("CudnnSoftmax", sft.layer_type());
singa::LayerConf conf;
singa::SoftmaxConf* softmaxconf = conf.mutable_softmax_conf();
softmaxconf->set_algorithm("fast");
sft.Setup(Shape{1}, conf);
EXPECT_EQ(CUDNN_SOFTMAX_FAST, sft.Algorithm());
}
TEST(CudnnSoftmax, Forward1D) {
const float x[] = {1.f, 2.f, 0.f, -2.f, -3.f, -1.f};
size_t n = sizeof(x) / sizeof(float);
auto cuda = std::make_shared<singa::CudaGPU>();
singa::Shape shape = {n};
singa::Tensor in(shape, cuda);
in.CopyDataFromHostPtr<float>(x, n);
CudnnSoftmax sft;
singa::LayerConf conf;
singa::SoftmaxConf* softmaxconf = conf.mutable_softmax_conf();
softmaxconf->set_algorithm("accurate");
sft.Setup(Shape{1}, conf);
singa::Tensor out = sft.Forward(singa::kTrain, in);
out.ToHost();
const float* yptr = out.data<float>();
EXPECT_EQ(n, out.Size());
float* y = new float[n];
float sigma = 0.f;
for (size_t i = 0; i < n; i++) sigma += exp(x[i]);
for (size_t i = 0; i < n; i++) y[i] = exp(x[i]) / sigma;
for (size_t i = 0; i < n; i++) EXPECT_FLOAT_EQ(y[i], yptr[i]);
delete[] y;
}
TEST(CudnnSoftmax, Backward1D) {
const float x[] = {1.f, 2.f, 3.f, -2.f, -3.f, -1.f};
size_t n = sizeof(x) / sizeof(float);
singa::Shape shape = {n};
auto cuda = std::make_shared<singa::CudaGPU>();
singa::Tensor in(shape, cuda);
in.CopyDataFromHostPtr<float>(x, n);
CudnnSoftmax sft;
singa::LayerConf conf;
singa::SoftmaxConf* softmaxconf = conf.mutable_softmax_conf();
softmaxconf->set_algorithm("accurate");
sft.Setup(Shape{1}, conf);
singa::Tensor out = sft.Forward(singa::kTrain, in);
out.ToHost();
const float* yptr = out.data<float>();
const float grad[] = {2.f, -3.f, 1.f, 3.f, -1.f, -2.f};
singa::Tensor out_diff(shape, cuda);
out_diff.CopyDataFromHostPtr<float>(grad, n);
const auto ret = sft.Backward(singa::kTrain, out_diff);
singa::Tensor in_diff = ret.first;
in_diff.ToHost();
const float* xptr = in_diff.data<float>();
float* dx = new float[n];
float sigma = 0.f;
for (size_t i = 0; i < n; i++) sigma += grad[i] * yptr[i];
for (size_t i = 0; i < n; i++) dx[i] = (grad[i] - sigma) * yptr[i];
for (size_t i = 0; i < n; i++) EXPECT_FLOAT_EQ(dx[i], xptr[i]);
delete[] dx;
}
TEST(CudnnSoftmax, Forward2D) {
const float x[] = {1.f, 2.f, 0.f, -2.f, -3.f, -1.f};
size_t n = sizeof(x) / sizeof(float);
size_t batch = 2, c = 3;
singa::Shape shape = {batch, c};
auto cuda = std::make_shared<singa::CudaGPU>();
singa::Tensor in(shape, cuda);
in.CopyDataFromHostPtr<float>(x, n);
CudnnSoftmax sft;
singa::LayerConf conf;
singa::SoftmaxConf* softmaxconf = conf.mutable_softmax_conf();
softmaxconf->set_algorithm("accurate");
sft.Setup(Shape{c}, conf);
singa::Tensor out = sft.Forward(singa::kTrain, in);
out.ToHost();
const float* yptr = out.data<float>();
EXPECT_EQ(n, out.Size());
float* y = new float[n];
float* sigma = new float[batch];
for (size_t i = 0; i < batch; i++) sigma[i] = 0.f;
for (size_t i = 0; i < n; i++) sigma[i / c] += exp(x[i]);
for (size_t i = 0; i < n; i++) y[i] = exp(x[i]) / sigma[i / c];
for (size_t i = 0; i < n; i++) EXPECT_FLOAT_EQ(y[i], yptr[i]);
delete[] y;
delete[] sigma;
}
TEST(CudnnSoftmax, Backward2D) {
const float x[] = {1.f, 2.f, 3.f, -2.f, -3.f, -1.f};
size_t n = sizeof(x) / sizeof(float);
size_t batch = 2, c = 3;
auto cuda = std::make_shared<singa::CudaGPU>();
singa::Shape shape = {batch, c};
singa::Tensor in(shape, cuda);
in.CopyDataFromHostPtr<float>(x, n);
CudnnSoftmax sft;
singa::LayerConf conf;
singa::SoftmaxConf* softmaxconf = conf.mutable_softmax_conf();
softmaxconf->set_algorithm("accurate");
sft.Setup(Shape{c}, conf);
singa::Tensor out = sft.Forward(singa::kTrain, in);
out.ToHost();
const float* yptr = out.data<float>();
const float grad[] = {2.f, -3.f, 1.f, 3.f, -1.f, -2.f};
singa::Tensor out_diff(shape, cuda);
out_diff.CopyDataFromHostPtr<float>(grad, n);
const auto ret = sft.Backward(singa::kTrain, out_diff);
singa::Tensor in_diff = ret.first;
in_diff.ToHost();
const float* xptr = in_diff.data<float>();
float* dx = new float[n];
float* sigma = new float[batch];
for (size_t i = 0; i < batch; i++) sigma[i] = 0.f;
for (size_t i = 0; i < n; i++) sigma[i / c] += grad[i] * yptr[i];
for (size_t i = 0; i < n; i++) dx[i] = (grad[i] - sigma[i / c]) * yptr[i];
for (size_t i = 0; i < n; i++) EXPECT_FLOAT_EQ(dx[i], xptr[i]);
delete[] dx;
delete[] sigma;
}
#endif // USE_CUDNN