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