cavis/libnd4j/tests_cpu/layers_tests/AtomicTests.cu

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/*******************************************************************************
* Copyright (c) 2019 Konduit K.K.
*
* 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.
*
* 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 <ops/declarable/CustomOperations.h>
#include <array/NDArray.h>
#include <ops/ops.h>
#include <helpers/GradCheck.h>
#include <helpers/RandomLauncher.h>
#include <exceptions/cuda_exception.h>
using namespace sd;
class AtomicTests : public testing::Test {
public:
AtomicTests() {
//
}
};
template <typename T>
static _CUDA_G void multiplyKernel(void *vbuffer, uint64_t length, void *vresult) {
auto buffer = reinterpret_cast<T*>(vbuffer);
auto result = reinterpret_cast<T*>(vresult);
auto tid = blockIdx.x * blockDim.x + threadIdx.x;
for (auto e = tid; e < length; e += gridDim.x * blockDim.x) {
auto rem = e % 4;
auto i = (e - rem) / 4;
sd::math::atomics::nd4j_atomicMul<T>(&result[i], buffer[e]);
}
}
template <typename T>
static void multiplyLauncher(void *vbuffer, uint64_t length, void *vresult) {
multiplyKernel<T><<<256, 256, 1024, *sd::LaunchContext::defaultContext()->getCudaStream()>>>(vbuffer, length, vresult);
auto err = cudaStreamSynchronize(*sd::LaunchContext::defaultContext()->getCudaStream());
if (err != 0)
sd::cuda_exception::build("multiply failed", err);
}
template <typename T>
static _CUDA_G void sumKernel(void *vbuffer, uint64_t length, void *vresult) {
auto buffer = reinterpret_cast<T*>(vbuffer);
auto result = reinterpret_cast<T*>(vresult);
auto tid = blockIdx.x * blockDim.x + threadIdx.x;
for (auto e = tid; e < length; e += gridDim.x * blockDim.x) {
auto rem = e % 4;
auto i = (e - rem) / 4;
sd::math::atomics::nd4j_atomicAdd<T>(&result[i], buffer[e]);
}
}
template <typename T>
static void sumLauncher(void *vbuffer, uint64_t length, void *vresult) {
sumKernel<T><<<256, 256, 1024, *sd::LaunchContext::defaultContext()->getCudaStream()>>>(vbuffer, length, vresult);
auto err = cudaStreamSynchronize(*sd::LaunchContext::defaultContext()->getCudaStream());
if (err != 0)
sd::cuda_exception::build("sum failed", err);
}
template <typename T>
static _CUDA_G void subKernel(void *vbuffer, uint64_t length, void *vresult) {
auto buffer = reinterpret_cast<T*>(vbuffer);
auto result = reinterpret_cast<T*>(vresult);
auto tid = blockIdx.x * blockDim.x + threadIdx.x;
for (auto e = tid; e < length; e += gridDim.x * blockDim.x) {
auto rem = e % 4;
auto i = (e - rem) / 4;
sd::math::atomics::nd4j_atomicSub<T>(&result[i], buffer[e]);
}
}
template <typename T>
static void subLauncher(void *vbuffer, uint64_t length, void *vresult) {
subKernel<T><<<256, 256, 1024, *sd::LaunchContext::defaultContext()->getCudaStream()>>>(vbuffer, length, vresult);
auto err = cudaStreamSynchronize(*sd::LaunchContext::defaultContext()->getCudaStream());
if (err != 0)
sd::cuda_exception::build("sub failed", err);
}
template <typename T>
static _CUDA_G void divKernel(void *vbuffer, uint64_t length, void *vresult) {
auto buffer = reinterpret_cast<T*>(vbuffer);
auto result = reinterpret_cast<T*>(vresult);
auto tid = blockIdx.x * blockDim.x + threadIdx.x;
for (auto e = tid; e < length; e += gridDim.x * blockDim.x) {
auto rem = e % 4;
auto i = (e - rem) / 4;
sd::math::atomics::nd4j_atomicDiv<T>(&result[i], buffer[e]);
}
}
template <typename T>
static void divLauncher(void *vbuffer, uint64_t length, void *vresult) {
divKernel<T><<<256, 256, 1024, *sd::LaunchContext::defaultContext()->getCudaStream()>>>(vbuffer, length, vresult);
auto err = cudaStreamSynchronize(*sd::LaunchContext::defaultContext()->getCudaStream());
if (err != 0)
sd::cuda_exception::build("div failed", err);
}
static void multiplyHost(NDArray &input, NDArray &output) {
BUILD_SINGLE_SELECTOR(input.dataType(), multiplyLauncher, (input.specialBuffer(), input.lengthOf(), output.specialBuffer()), NUMERIC_TYPES);
}
static void sumHost(NDArray &input, NDArray &output) {
BUILD_SINGLE_SELECTOR(input.dataType(), sumLauncher, (input.specialBuffer(), input.lengthOf(), output.specialBuffer()), NUMERIC_TYPES);
}
static void subHost(NDArray &input, NDArray &output) {
BUILD_SINGLE_SELECTOR(input.dataType(), subLauncher, (input.specialBuffer(), input.lengthOf(), output.specialBuffer()), FLOAT_TYPES);
}
static void divHost(NDArray &input, NDArray &output) {
BUILD_SINGLE_SELECTOR(input.dataType(), divLauncher, (input.specialBuffer(), input.lengthOf(), output.specialBuffer()), FLOAT_TYPES);
}
TEST_F(AtomicTests, test_multiply) {
std::vector<sd::DataType> dtypes = {sd::DataType::FLOAT32, sd::DataType::DOUBLE, sd::DataType::INT16, sd::DataType::HALF};
for (auto t:dtypes) {
nd4j_printf("Trying data type [%s]\n", DataTypeUtils::asString(t).c_str());
NDArray input('c', {4, 25}, t);
NDArray output('c', {input.lengthOf() / 4}, t);
NDArray exp = output.ulike();
input.assign(2);
output.assign(2);
exp.assign(32);
multiplyHost(input, output);
ASSERT_EQ(exp, output);
}
}
TEST_F(AtomicTests, test_multiply_2) {
std::vector<sd::DataType> dtypes = {sd::DataType::FLOAT32, sd::DataType::DOUBLE, sd::DataType::HALF, sd::DataType::BFLOAT16};
for (auto t:dtypes) {
nd4j_printf("Trying data type [%s]\n", DataTypeUtils::asString(t).c_str());
NDArray input('c', {4, 25}, t);
NDArray output('c', {input.lengthOf() / 4}, t);
NDArray exp = output.ulike();
input.assign(1.5);
output.assign(2);
exp.assign(10.125);
multiplyHost(input, output);
// output.printBuffer("multiply 2");
ASSERT_EQ(exp, output);
}
}
TEST_F(AtomicTests, test_sum) {
std::vector<sd::DataType> dtypes = {sd::DataType::FLOAT32, sd::DataType::DOUBLE, sd::DataType::BFLOAT16, sd::DataType::HALF, sd::DataType::INT16};
for (auto t:dtypes) {
nd4j_printf("Trying data type [%s]\n", DataTypeUtils::asString(t).c_str());
NDArray input('c', {4, 25}, t);
NDArray output('c', {input.lengthOf() / 4}, t);
NDArray exp = output.ulike();
input.assign(1);
output.assign(1);
exp.assign(5);
sumHost(input, output);
// output.printIndexedBuffer("Sum");
ASSERT_EQ(exp, output);
}
}
TEST_F(AtomicTests, test_sub) {
std::vector<sd::DataType> dtypes = {sd::DataType::FLOAT32, sd::DataType::DOUBLE, sd::DataType::HALF};
for (auto t:dtypes) {
nd4j_printf("Trying data type [%s]\n", DataTypeUtils::asString(t).c_str());
NDArray input('c', {4, 25}, t);
NDArray output('c', {input.lengthOf() / 4}, t);
NDArray exp = output.ulike();
input.assign(1);
output.assign(5);
exp.assign(1);
subHost(input, output);
// output.printBuffer("Sub");
ASSERT_EQ(exp, output);
}
}
TEST_F(AtomicTests, test_div) {
std::vector<sd::DataType> dtypes = {sd::DataType::FLOAT32, sd::DataType::DOUBLE, sd::DataType::BFLOAT16, sd::DataType::HALF};
for (auto t:dtypes) {
nd4j_printf("Trying data type [%s]\n", DataTypeUtils::asString(t).c_str());
NDArray input('c', {4, 25}, t);
NDArray output('c', {input.lengthOf() / 4}, t);
NDArray exp = output.ulike();
input.assign(2);
output.assign(32);
exp.assign(2);
divHost(input, output);
// output.printBuffer("Div");
ASSERT_EQ(exp, output);
}
}