cavis/libnd4j/tests_cpu/layers_tests/CuDnnTests.cu

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cuDNN integration (#150) * initial commit Signed-off-by: raver119 <raver119@gmail.com> * one file Signed-off-by: raver119 <raver119@gmail.com> * few more includes Signed-off-by: raver119 <raver119@gmail.com> * m? Signed-off-by: raver119 <raver119@gmail.com> * const Signed-off-by: raver119 <raver119@gmail.com> * cudnn linkage in tests Signed-off-by: raver119 <raver119@gmail.com> * culibos Signed-off-by: raver119 <raver119@gmail.com> * static reminder Signed-off-by: raver119 <raver119@gmail.com> * platform engine tag Signed-off-by: raver119 <raver119@gmail.com> * HAVE_CUDNN moved to config.h.in Signed-off-by: raver119 <raver119@gmail.com> * include Signed-off-by: raver119 <raver119@gmail.com> * include Signed-off-by: raver119 <raver119@gmail.com> * skip cudnn handle creation if there's not cudnn Signed-off-by: raver119 <raver119@gmail.com> * meh Signed-off-by: raver119 <raver119@gmail.com> * target device in context Signed-off-by: raver119 <raver119@gmail.com> * platform engines Signed-off-by: raver119 <raver119@gmail.com> * platform engines Signed-off-by: raver119 <raver119@gmail.com> * allow multiple -h args Signed-off-by: raver119 <raver119@gmail.com> * allow multiple -h args Signed-off-by: raver119 <raver119@gmail.com> * move mkldnn out of CPU block Signed-off-by: raver119 <raver119@gmail.com> * link to mkldnn on cuda Signed-off-by: raver119 <raver119@gmail.com> * less prints Signed-off-by: raver119 <raver119@gmail.com> * minor tweaks Signed-off-by: raver119 <raver119@gmail.com> * next step Signed-off-by: raver119 <raver119@gmail.com> * conv2d NCHW draft Signed-off-by: raver119 <raver119@gmail.com> * conv2d biasAdd Signed-off-by: raver119 <raver119@gmail.com> * test for MKL/CUDNN combined use Signed-off-by: raver119 <raver119@gmail.com> * - provide additional code for conv2d ff based on cudnn api, not tested yet Signed-off-by: Yurii <iuriish@yahoo.com> * - further work on conv2d helper based on using cudnn api Signed-off-by: Yurii <iuriish@yahoo.com> * - fixing several cuda bugs which appeared after cudnn lib had been started to use Signed-off-by: Yurii <iuriish@yahoo.com> * - implementation of conv2d backprop op based on cudnn api Signed-off-by: Yurii <iuriish@yahoo.com> * - implementaion of conv3d and conv3d_bp ops based on cudnn api Signed-off-by: Yurii <iuriish@yahoo.com> * - bugs fixing in conv3d/conv3d_bp ops (cudnn in use) Signed-off-by: Yurii <iuriish@yahoo.com> * - implementation of depthwiseConv2d (ff/bp) op based on cudnn api Signed-off-by: Yurii <iuriish@yahoo.com> * - implementation of batchnorm ff op based on cudnn api Signed-off-by: Yurii <iuriish@yahoo.com> * - disable cudnn batchnorm temporary Signed-off-by: Yurii <iuriish@yahoo.com> * - add minor change in cmake Signed-off-by: Yurii <iuriish@yahoo.com> * engine for depthwise mkldnn Signed-off-by: raver119 <raver119@gmail.com> * couple of includes Signed-off-by: raver119 <raver119@gmail.com> * - provide permutation to cudnn batchnorm ff when format is NHWC Signed-off-by: Yurii <iuriish@yahoo.com> * lgamma fix Signed-off-by: raver119 <raver119@gmail.com> * - eliminate memory leak in two tests Signed-off-by: Yurii <iuriish@yahoo.com> Co-authored-by: Yurii Shyrma <iuriish@yahoo.com>
2020-01-20 19:32:46 +01:00
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* 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 <initializer_list>
#include <NDArrayFactory.h>
#include <ops/declarable/PlatformHelper.h>
#include <ops/declarable/CustomOperations.h>
#include <execution/Engine.h>
#ifdef HAVE_CUDNN
#include <ops/declarable/platform/cudnn/cudnnUtils.h>
#endif
using namespace nd4j;
class CuDnnTests : public testing::Test {
public:
};
static void printer(std::initializer_list<nd4j::ops::platforms::PlatformHelper*> helpers) {
for (auto v:helpers) {
nd4j_printf("Initialized [%s]\n", v->name().c_str());
}
}
TEST_F(CuDnnTests, helpers_includer) {
// we need this block, to make sure all helpers are still available within binary, and not optimized out by linker
#ifdef HAVE_CUDNN
nd4j::ops::platforms::PLATFORM_conv2d_ENGINE_CUDA conv2d;
nd4j::ops::platforms::PLATFORM_conv2d_bp_ENGINE_CUDA conv2d_bp;
nd4j::ops::platforms::PLATFORM_conv3dnew_ENGINE_CUDA conv3dnew;
nd4j::ops::platforms::PLATFORM_conv3dnew_bp_ENGINE_CUDA conv3dnew_bp;
nd4j::ops::platforms::PLATFORM_depthwise_conv2d_ENGINE_CUDA depthwise_conv2d;
nd4j::ops::platforms::PLATFORM_depthwise_conv2d_bp_ENGINE_CUDA depthwise_conv2d_bp;
nd4j::ops::platforms::PLATFORM_batchnorm_ENGINE_CUDA batchnorm;
printer({&conv2d});
printer({&conv2d_bp});
printer({&conv3dnew});
printer({&conv3dnew_bp});
printer({&depthwise_conv2d});
printer({&depthwise_conv2d_bp});
printer({&batchnorm});
#endif
}
TEST_F(CuDnnTests, mixed_helpers_test_1) {
#if defined(HAVE_CUDNN) && defined (HAVE_MKLDNN)
nd4j_printf("Mixed platforms test\n", "");
int bS=2, iH=4,iW=3, iC=4,oC=3, kH=3,kW=2, sH=1,sW=1, pH=0,pW=0, dH=1,dW=1;
int oH=2,oW=2;
int paddingMode = 0; // 1-SAME, 0-VALID;
int dataFormat = 0; // 1-NHWC, 0-NCHW
auto input = NDArrayFactory::create<float>('c', {bS, iC, iH, iW});
auto weights = NDArrayFactory::create<float>('c', {oC, iC, kH, kW});
auto bias = NDArrayFactory::create<float>('c', {oC}, {1,2,3});
auto expOutput = NDArrayFactory::create<float>('c', {bS, oC, oH, oW}, {61.f, 61.f, 61.f, 61.f, 177.2f, 177.2f, 177.2f, 177.2f, 293.4f, 293.4f, 293.4f, 293.4f, 61.f, 61.f, 61.f, 61.f, 177.2f, 177.2f, 177.2f, 177.2f, 293.4f, 293.4f, 293.4f, 293.4f});
auto zCUDA = expOutput.like();
auto zMKL = expOutput.like();
input = 2.;
weights.linspace(0.1, 0.1);
weights.permutei({2,3,1,0});
input.syncToHost();
weights.syncToHost();
bias.syncToHost();
nd4j::ops::conv2d op;
// cuDNN part
Context cuda(1);
cuda.setTargetEngine(samediff::Engine::ENGINE_CUDA);
cuda.setInputArray(0, &input);
cuda.setInputArray(1, &weights);
cuda.setInputArray(2, &bias);
cuda.setOutputArray(0, &zCUDA);
cuda.setIArguments({kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat});
auto statusCUDA = op.execute(&cuda);
ASSERT_EQ(Status::OK(), statusCUDA);
ASSERT_EQ(expOutput, zCUDA);
// MKL-DNN part
Context mkl(1);
mkl.setTargetEngine(samediff::Engine::ENGINE_CPU);
mkl.setInputArray(0, &input);
mkl.setInputArray(1, &weights);
mkl.setInputArray(2, &bias);
mkl.setOutputArray(0, &zMKL);
mkl.setIArguments({kH,kW, sH,sW, pH,pW, dH,dW, paddingMode, dataFormat});
auto statusMKL = op.execute(&mkl);
zMKL.tickWriteHost();
ASSERT_EQ(Status::OK(), statusMKL);
ASSERT_EQ(expOutput, zMKL);
#endif
}