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