152 lines
6.2 KiB
C++
152 lines
6.2 KiB
C++
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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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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* 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), created on 24.07.2018
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//
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#include <op_boilerplate.h>
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#if NOT_EXCLUDED(OP_prelu)
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#include <ops/declarable/CustomOperations.h>
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#include <ops/declarable/helpers/activations.h>
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#include <numeric>
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namespace nd4j {
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namespace ops {
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////////////////////////////////////////////////////////////////////////
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CONFIGURABLE_OP_IMPL(prelu, 2, 1, true, 0, 0) {
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auto input = INPUT_VARIABLE(0);
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auto alpha = INPUT_VARIABLE(1);
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auto output = OUTPUT_VARIABLE(0);
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std::vector<int> sharedAxes = *block.getIArguments();
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const int inputRank = input->rankOf();
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const int numSharedAxes = sharedAxes.size(); // can be zero as well
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const Nd4jLong inputLen = input->lengthOf();
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const Nd4jLong alphaLen = alpha->lengthOf();
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const std::vector<Nd4jLong> inputShape = input->getShapeAsVector();
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const std::vector<Nd4jLong> alphaShape = alpha->getShapeAsVector();
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//***** input validation *****//
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std::vector<Nd4jLong> expectedAlphaShape(&inputShape[1], &inputShape[inputRank]);
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REQUIRE_TRUE(inputRank > 1, 0, "PRELU OP: wrong rank of input array, expected rank should be > 1, but got %i instead !", inputRank);
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for(int i = 0; i < numSharedAxes; ++i) {
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if(sharedAxes[i] <= 0)
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sharedAxes[i] += inputRank - 1;
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REQUIRE_TRUE(1 <= sharedAxes[i] && sharedAxes[i] <= inputRank - 1, 0, "PRELU OP: wrong axis value %i in sharedAxes at position %i, axis value must be within range [1, input_rank-1] !", sharedAxes[i], i);
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expectedAlphaShape[sharedAxes[i] - 1] = 1;
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}
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Nd4jLong product = 1;
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for(const auto& item : expectedAlphaShape)
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product *= item;
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REQUIRE_TRUE(product == alphaLen, 0, "PRELU OP: wrong shape of alpha array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedAlphaShape).c_str(), ShapeUtils::shapeAsString(alphaShape).c_str());
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// ***** end of validation ***** //
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helpers::prelu(block.launchContext(), *input, alphaShape != expectedAlphaShape ? alpha->reshape(alpha->ordering(), expectedAlphaShape) : *alpha, *output);
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return Status::OK();
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}
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DECLARE_TYPES(prelu) {
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getOpDescriptor()
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->setAllowedInputTypes(0, DataType::ANY)
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->setAllowedInputTypes(1, {ALL_FLOATS})
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->setAllowedOutputTypes(0, {ALL_FLOATS});
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}
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////////////////////////////////////////////////////////////////////////
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CONFIGURABLE_OP_IMPL(prelu_bp, 3, 2, true, 0, 0) {
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auto input = INPUT_VARIABLE(0);
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auto alpha = INPUT_VARIABLE(1);
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auto dLdO = INPUT_VARIABLE(2);
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auto dLdI = OUTPUT_VARIABLE(0);
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auto dLdA = OUTPUT_VARIABLE(1);
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std::vector<int> sharedAxes = *block.getIArguments();
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const int inputRank = input->rankOf();
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const int numSharedAxes = sharedAxes.size(); // can be zero as well
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const Nd4jLong inputLen = input->lengthOf();
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const Nd4jLong alphaLen = alpha->lengthOf();
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const std::vector<Nd4jLong> inputShape = input->getShapeAsVector();
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const std::vector<Nd4jLong> alphaShape = alpha->getShapeAsVector();
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//***** input validation *****//
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// temporary limitation imposed by Yurii
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REQUIRE_TRUE(inputRank <= MAX_RANK/2, 0, "rank of input array should be <= MAX_RANK/2, but got %i instead!", inputRank);
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REQUIRE_TRUE(input->lengthOf() / alpha->lengthOf() <= MAX_RANK*2, 0, "the length of input array should be no more than MAX_RANK*2 times the alpha array length, but got %lld and %lld correspondingly!", input->lengthOf(), alpha->lengthOf());
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std::vector<Nd4jLong> expectedAlphaShape(&inputShape[1], &inputShape[inputRank]);
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REQUIRE_TRUE(inputRank > 1, 0, "PRELU_BP OP: wrong rank of input array, expected rank should be > 1, but got %i instead !", inputRank);
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for(int i = 0; i < numSharedAxes; ++i) {
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if(sharedAxes[i] <= 0)
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sharedAxes[i] += inputRank - 1;
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REQUIRE_TRUE(1 <= sharedAxes[i] && sharedAxes[i] <= inputRank - 1, 0, "PRELU_BP OP: wrong axis value %i in sharedAxes at position %i, axis value must be within range [1, input_rank-1] !", sharedAxes[i], i);
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expectedAlphaShape[sharedAxes[i] - 1] = 1;
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}
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Nd4jLong product = 1;
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for(const auto& item : expectedAlphaShape)
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product *= item;
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REQUIRE_TRUE(product == alphaLen, 0, "PRELU_BP OP: wrong shape of alpha array, expected is %s, but got %s instead !", ShapeUtils::shapeAsString(expectedAlphaShape).c_str(), ShapeUtils::shapeAsString(alphaShape).c_str());
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// ***** end of validation ***** //
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if(alphaShape != expectedAlphaShape) {
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alpha = new NDArray(alpha->reshape(alpha->ordering(), expectedAlphaShape));
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dLdA = new NDArray(dLdA->reshape(dLdA->ordering(), expectedAlphaShape));
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}
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helpers::preluBP(block.launchContext(), *input, *alpha, *dLdO, *dLdI, *dLdA);
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if(alphaShape != expectedAlphaShape) {
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delete alpha;
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delete dLdA;
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}
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return Status::OK();
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}
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DECLARE_TYPES(prelu_bp) {
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getOpDescriptor()
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->setAllowedInputTypes(0, DataType::ANY)
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->setAllowedInputTypes(1, {DataType::FLOAT32, DataType ::DOUBLE, DataType::HALF})
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->setAllowedInputTypes(2, {DataType::FLOAT32, DataType ::DOUBLE, DataType::HALF})
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->setAllowedOutputTypes(0, {DataType::FLOAT32, DataType ::DOUBLE, DataType::HALF})
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->setAllowedOutputTypes(1, {DataType::FLOAT32, DataType ::DOUBLE, DataType::HALF});
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}
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}
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}
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#endif |