/* ******************************************************************************
*
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* See the NOTICE file distributed with this work for additional
* information regarding copyright ownership.
* 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.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// @author raver119@gmail.com
//
#include "testlayers.h"
#include <array/ExtraArguments.h>
#include <array>
#include <cuda.h>
#include <cuda_runtime.h>
using namespace sd;
class LambdaTests : public testing::Test {
public:
LambdaTests() {
printf("\n");
fflush(stdout);
}
};
template <typename Lambda>
__global__ void runLambda(double *input, double *output, Nd4jLong length, Lambda lambda) {
auto tid = blockIdx.x * blockDim.x + threadIdx.x;
for (Nd4jLong e = tid; e < length; e += gridDim.x * blockDim.x) {
output[e] = lambda(input[e]);
}
}
void launcher(cudaStream_t *stream, double *input, double *output, Nd4jLong length) {
//auto f = [] __host__ __device__ (double x) -> double {
// return x + 1.;
//};
auto f = LAMBDA_D(x) {
return x+1.;
};
runLambda<<<128, 128, 128, *stream>>>(input, output, length, f);
}
// TEST_F(LambdaTests, test_basic_1) {
// auto x = NDArrayFactory::create<double>('c', {5});
// auto e = NDArrayFactory::create<double>('c', {5}, {1., 1., 1., 1., 1.});
// //x.applyLambda<double>(f, nullptr);
// launcher(LaunchContext::defaultContext()->getCudaStream(), (double *)x.specialBuffer(), (double *)x.specialBuffer(), x.lengthOf());
// auto res = cudaStreamSynchronize(*LaunchContext::defaultContext()->getCudaStream());
// ASSERT_EQ(0, res);
// ASSERT_EQ(e, x);
// }
// void test(NDArray &x) {
// auto f = LAMBDA_D(x) {
// return x+1.;
// };
// x.applyLambda(f, x);
// }
// template <typename T>
// void test2(NDArray &x) {
// auto f = LAMBDA_T(x) {
// return x+1.;
// };
// x.applyLambda(f, x);
// }
// void testPairwise(NDArray &x, NDArray &y) {
// auto f = LAMBDA_DD(x, y) {
// return x + y +1.;
// };
// x.applyPairwiseLambda(y, f, x);
// }
// void testTriplewise(NDArray &i, NDArray &j, NDArray &k) {
// auto f = LAMBDA_DDD(i, j, k) {
// return i + j + k + 2.;
// };
// i.applyTriplewiseLambda(j, k, f, i);
// }
// void testIndexed(NDArray &x) {
// auto f = ILAMBDA_D(x) {
// return _idx + 1.;
// };
// x.applyIndexedLambda(f, x);
// }
// void testIndexedPairwise(NDArray &x, NDArray &y) {
// auto f = ILAMBDA_DD(x, y) {
// return _idx + x + y +1.;
// };
// x.applyIndexedPairwiseLambda(y, f, x);
// }
// TEST_F(LambdaTests, test_basic_2) {
// auto x = NDArrayFactory::create<double>('c', {5});
// auto e = NDArrayFactory::create<double>('c', {5}, {1., 1., 1., 1., 1.});
// test(x);
// ASSERT_EQ(e, x);
// }
// TEST_F(LambdaTests, test_basic_3) {
// auto x = NDArrayFactory::create<float>('c', {5});
// auto e = NDArrayFactory::create<float>('c', {5}, {1.f, 1.f, 1.f, 1.f, 1.f});
// test(x);
// ASSERT_EQ(e, x);
// }
// TEST_F(LambdaTests, test_basic_4) {
// auto x = NDArrayFactory::create<float>('c', {5});
// auto e = NDArrayFactory::create<float>('c', {5}, {1.f, 1.f, 1.f, 1.f, 1.f});
// test2<float>(x);
// ASSERT_EQ(e, x);
// }
// TEST_F(LambdaTests, test_basic_5) {
// auto x = NDArrayFactory::create<double>('c', {5}, {1., 1., 1., 1., 1.});
// auto y = NDArrayFactory::create<double>('c', {5}, {2., 2., 2., 2., 2.});
// auto e = NDArrayFactory::create<double>('c', {5}, {4., 4., 4., 4., 4.});
// testPairwise(x, y);
// ASSERT_EQ(e, x);
// }
// TEST_F(LambdaTests, test_basic_6) {
// auto x = NDArrayFactory::create<double>('c', {5});
// auto e = NDArrayFactory::create<double>('c', {5}, {1., 2., 3., 4., 5.});
// testIndexed(x);
// ASSERT_EQ(e, x);
// }
// TEST_F(LambdaTests, test_basic_7) {
// auto w = NDArrayFactory::create<double>('c', {5}, {0., 0., 0., 0., 0.});
// auto x = NDArrayFactory::create<double>('c', {5}, {1., 1., 1., 1., 1.});
// auto y = NDArrayFactory::create<double>('c', {5}, {2., 2., 2., 2., 2.});
// auto e = NDArrayFactory::create<double>('c', {5}, {5., 5., 5., 5., 5.});
// testTriplewise(w, x, y);
// ASSERT_EQ(e, w);
// }
// TEST_F(LambdaTests, test_basic_8) {
// auto x = NDArrayFactory::create<double>('c', {5}, {1., 1., 1., 1., 1.});
// auto y = NDArrayFactory::create<double>('c', {5}, {2., 2., 2., 2., 2.});
// auto e = NDArrayFactory::create<double>('c', {5}, {4., 5., 6., 7., 8.});
// testIndexedPairwise(x, y);
// ASSERT_EQ(e, x);
// }
// template <typename T>
// void testPairwiseMy(NDArray &x, NDArray &y, NDArray &z) {
// auto f = LAMBDA_TT(x, y){
// return sd::math::nd4j_max<T>(x, (T)0.f)
// - x * y
// + sd::math::nd4j_log<T,T>((T)1.f
// + sd::math::nd4j_exp<T,T>(-sd::math::nd4j_abs(x)));
// };
// x.applyPairwiseLambda(y, f, z);
// }
// ///////////////////////////////////////////////////////////////////
// TEST_F(LambdaTests, test_basic_9) {
// NDArray labels('c', {2,3,4},{0,1,1,0,1,0,1,0,1,0,1,0,1,0,1,0,1,0,0,1,1,0,1,0});
// NDArray logits('c', {2,3,4}, sd::DataType::DOUBLE);
// NDArray output('c', {2,3,4}, sd::DataType::DOUBLE);
// NDArray expected('c', {2,3,4}, {0.744397, 0.598139, 0.554355, 0.913015, 0.474077, 1.037488, 0.403186, 1.171101, 0.341154, 1.313262, 0.287335, 1.463282, 0.241008, 1.620417, 0.201413, 1.783901, 0.167786, 1.952978, 2.039387, 0.126928, 0.115520, 2.305083, 0.095545, 2.486836});
// logits.linspace(0.1, 0.1);
// NDArray::prepareSpecialUse({&output}, {&logits, &labels});
// testPairwiseMy<double>(logits, labels, output);
// NDArray::registerSpecialUse({&output}, {&logits, &labels});
// // output.printBuffer(nullptr, -1, true);
// ASSERT_TRUE(expected.equalsTo(output));
// }