libnd4j/include/ops/declarable/platform/cudnn/maxpool2d.cu
/* ******************************************************************************
*
*
* 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 Yurii Shyrma (iuriish@yahoo.com)
//
#include <ops/declarable/helpers/convolutions.h>
#include "cudnnUtils.h"
namespace sd {
namespace ops {
namespace platforms {
//////////////////////////////////////////////////////////////////////////
PLATFORM_IMPL(maxpool2d, ENGINE_CUDA) {
auto input = INPUT_VARIABLE(0);
auto output = OUTPUT_VARIABLE(0);
// 0,1 - kernel Height/Width; 2,3 - stride Height/Width; 4,5 - pad Height/Width; 6,7 - dilation Height/Width; 8 -
// paddingModee;
const sd::LongType kH = INT_ARG(0);
const sd::LongType kW = INT_ARG(1);
const sd::LongType sH = INT_ARG(2);
const sd::LongType sW = INT_ARG(3);
sd::LongType pH = INT_ARG(4);
sd::LongType pW = INT_ARG(5);
const sd::LongType dH = INT_ARG(6);
const sd::LongType dW = INT_ARG(7);
const auto paddingMode = static_cast<bool>(INT_ARG(8));
const int isNCHW = block.getIArguments()->size() > 10 ? !INT_ARG(10) : 1; // INT_ARG(10): 0-NCHW, 1-NHWC
REQUIRE_TRUE(input->rankOf() == 4, 0, "MAXPOOL2D CUDNN op: input should have rank of 4, but got %i instead",
input->rankOf());
REQUIRE_TRUE(dH != 0 && dW != 0, 0, "MAXPOOL2D CUDNN op: dilation must not be zero, but got instead {%i, %i}", dH,
dW);
sd::LongType oH = 0;
sd::LongType oW = 0;
const sd::LongType iH = static_cast<sd::LongType>(isNCHW ? input->sizeAt(2) : input->sizeAt(1));
const sd::LongType iW = static_cast<sd::LongType>(isNCHW ? input->sizeAt(3) : input->sizeAt(2));
ConvolutionUtils::calcOutSizePool2D(oH, oW, kH, kW, sH, sW, pH, pW, dH, dW, iH, iW, paddingMode);
if (paddingMode) ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);
pooling2dCUDNN(block.launchContext(), input, output, kH, kW, sH, sW, pH, pW, dH, dW, isNCHW, CUDNN_POOLING_MAX);
return sd::Status::OK;
}
//////////////////////////////////////////////////////////////////////////
PLATFORM_CHECK(maxpool2d, ENGINE_CUDA) {
auto input = INPUT_VARIABLE(0);
auto output = OUTPUT_VARIABLE(0);
Requirements req("CUDNN MAXPOOL2d OP");
req.expectEq(makeInfoVariable(input->dataType(), TYPE_MSG_INPUT),
makeInfoVariable(output->dataType(), TYPE_MSG_OUTPUT)) &&
req.expectIn(makeInfoVariable(input->dataType(), TYPE_MSG_INPUT),
{DataType::INT32, DataType::HALF, DataType::FLOAT32, DataType::DOUBLE});
req.logTheSuccess();
return req;
}
//////////////////////////////////////////////////////////////////////////
PLATFORM_IMPL(maxpool2d_bp, ENGINE_CUDA) {
auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
auto gradO = INPUT_VARIABLE(1); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
const sd::LongType kH = INT_ARG(0); // filter(kernel) height
const sd::LongType kW = INT_ARG(1); // filter(kernel) width
const sd::LongType sH = INT_ARG(2); // strides height
const sd::LongType sW = INT_ARG(3); // strides width
sd::LongType pH = INT_ARG(4); // paddings height
sd::LongType pW = INT_ARG(5); // paddings width
const sd::LongType dH = INT_ARG(6); // dilations height
const sd::LongType dW = INT_ARG(7); // dilations width
const auto paddingMode = INT_ARG(8); // 0-VALID, 1-SAME
const auto isNCHW = block.getIArguments()->size() > 10 ? !INT_ARG(10) : 1; // INT_ARG(10): 0-NCHW, 1-NHWC
REQUIRE_TRUE(input->rankOf() == 4, 0, "MAXPOOL2D_BP CUDNN op: input should have rank of 4, but got %i instead",
input->rankOf());
REQUIRE_TRUE(dH != 0 && dW != 0, 0, "MAXPOOL2D_BP CUDNN op: dilation must not be zero, but got instead {%i, %i}", dH,
dW);
sd::LongType bS, iC, iH, iW, oC, oH,
oW; // batch size, input channels, input height/width, output channels, output height/width;
sd::LongType indIOioC, indIiH, indWoC, indWiC, indWkH, indOoH; // corresponding indexes
ConvolutionUtils::getSizesAndIndexesConv2d(isNCHW, 0, *input, *gradO, bS, iC, iH, iW, oC, oH, oW, indIOioC, indIiH,
indWiC, indWoC, indWkH, indOoH);
std::vector<sd::LongType> expectedGradOShape =
ShapeUtils::composeShapeUsingDimsAndIdx({bS, iC, oH, oW, 0, indIOioC, indIiH, indIiH + 1});
std::vector<sd::LongType> expectedGradIShape =
ShapeUtils::composeShapeUsingDimsAndIdx({bS, iC, iH, iW, 0, indIOioC, indIiH, indIiH + 1});
REQUIRE_TRUE(gradO->isSameShape(expectedGradOShape), 0,
"MAXPOOL2D_BP CUDNN op: wrong shape of output's gradients array (next epsilon), expected is %s, but got "
"%s instead !",
ShapeUtils::shapeAsString(expectedGradOShape).c_str(), ShapeUtils::shapeAsString(gradO).c_str());
REQUIRE_TRUE(
gradI->isSameShape(expectedGradIShape), 0,
"MAXPOOL2D_BP CUDNN op: wrong shape of input's gradients array (epsilon), expected is %s, but got %s instead !",
ShapeUtils::shapeAsString(expectedGradIShape).c_str(), ShapeUtils::shapeAsString(gradI).c_str());
if (paddingMode) // SAME
ConvolutionUtils::calcPadding2D(pH, pW, oH, oW, iH, iW, kH, kW, sH, sW, dH, dW);
pooling2dBpCUDNN(block.launchContext(), input, gradO, gradI, kH, kW, sH, sW, pH, pW, dH, dW, isNCHW,
CUDNN_POOLING_MAX);
return sd::Status::OK;
}
PLATFORM_CHECK(maxpool2d_bp, ENGINE_CUDA) {
auto input = INPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW)
auto gradO = INPUT_VARIABLE(1); // [bS, oH, oW, oC] (NHWC) or [bS, oC, oH, oW] (NCHW), epsilon_next
auto gradI = OUTPUT_VARIABLE(0); // [bS, iH, iW, iC] (NHWC) or [bS, iC, iH, iW] (NCHW), epsilon
Requirements req("CUDNN MAXPOOL2d_BP OP");
req.expectEq(makeInfoVariable(input->ordering(), ORDERING_MSG_INPUT), 'c') &&
req.expectEq(makeInfoVariable(input->ews(), EWS_MSG_INPUT), 1) &&
req.expectEq(makeInfoVariable(input->dataType(), TYPE_MSG_INPUT0),
makeInfoVariable(gradO->dataType(), TYPE_MSG_INPUT1)) &&
req.expectEq(makeInfoVariable(input->dataType(), TYPE_MSG_INPUT),
makeInfoVariable(gradI->dataType(), TYPE_MSG_OUTPUT)) &&
req.expectIn(makeInfoVariable(input->dataType(), TYPE_MSG_INPUT),
{DataType::INT32, DataType::HALF, DataType::FLOAT32, DataType::DOUBLE}) &&
req.expect(
makeShapeInfoVariable(input, SHAPE_MSG_INPUT0), makeShapeInfoVariable(gradI, SHAPE_MSG_OUTPUT),
[](const decltype(input)& l, const decltype(gradI)& r) {
return shape::haveSameShapeAndStrides(l->shapeInfo(), r->shapeInfo());
},
EXPECTED_EQ_MSG);
req.logTheSuccess();
return req;
}
} // namespace platforms
} // namespace ops
} // namespace sd