wehub-resource-sync
253 lines
8.5 KiB
C++
253 lines
8.5 KiB
C++
/* ******************************************************************************
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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 raver119@gmail.com
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//
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#include <system/op_boilerplate.h>
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#if NOT_EXCLUDED(OP_cbow)
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#include <ops/declarable/CustomOperations.h>
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#include <ops/declarable/helpers/sg_cb.h>
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namespace sd {
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namespace ops {
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CONFIGURABLE_OP_IMPL(cbow_inference, 6, 6, true, -2, -2) {
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//construct codes and indices from the IARGS
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//we do this to avoid serialization overhead from the JVM for frequently created small arrays
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auto numCodes = I_ARG(0);
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auto numIndices = I_ARG(1);
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auto numContext = I_ARG(2);
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auto numLockedWords = I_ARG(3);
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//2 for the codes, indices, context, locked words, 4 for the mandatory args such as target
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auto numMin = numIndices + numCodes + numCodes + numLockedWords + 4 + 4;
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std::vector<sd::LongType> *codes = new std::vector<sd::LongType>();
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std::vector<sd::LongType> *indices = new std::vector<sd::LongType>();
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std::vector<sd::LongType> *context = new std::vector<sd::LongType>();
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std::vector<sd::LongType> *lockedWords = new std::vector<sd::LongType>();
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int currIdx = 4;
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for(int i = 0; i < numCodes; i++) {
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codes->push_back(I_ARG(currIdx));
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currIdx++;
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}
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for(int i = 0; i < numIndices; i++) {
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indices->push_back(I_ARG(currIdx));
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currIdx++;
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}
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for(int i = 0; i < numContext; i++) {
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context->push_back(I_ARG(currIdx));
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currIdx++;
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}
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for(int i = 0; i < numLockedWords; i++) {
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lockedWords->push_back(I_ARG(currIdx));
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currIdx++;
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}
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const std::vector<sd::LongType> *indicesVec = indices;
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const std::vector<sd::LongType> *codesVec = codes;
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const std::vector<sd::LongType> *contextVec = context;
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const std::vector<sd::LongType> *lockedWordsVec = lockedWords;
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std::vector<sd::LongType> *indicesSize = new std::vector<sd::LongType>();
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indicesSize->push_back(indices->size());
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const std::vector<sd::LongType> *indicesShape = indicesSize;
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std::vector<sd::LongType> *codesSize = new std::vector<sd::LongType>();
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codesSize->push_back(codes->size());
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const std::vector<sd::LongType> *codesShape = codesSize;
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std::vector<sd::LongType> *contextSize = new std::vector<sd::LongType>();
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contextSize->push_back(context->size());
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const std::vector<sd::LongType> *contextShape = contextSize;
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std::vector<sd::LongType> *lockedWordsSize = new std::vector<sd::LongType>();
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lockedWordsSize->push_back(lockedWords->size());
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const std::vector<sd::LongType> *lockedWordsShape = lockedWordsSize;
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auto indicesArrOne = indicesVec->size() > 0 ? NDArrayFactory::create_<sd::LongType>('c',*indicesShape,*indicesVec,LaunchContext::defaultContext()) : NDArrayFactory::empty<sd::LongType>();
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auto indicesArr = indicesArrOne;
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auto codesArrOne = codesVec->size() > 0 ? NDArrayFactory::create_<sd::LongType>('c',*codesShape,*codesVec,LaunchContext::defaultContext()) : NDArrayFactory::empty<sd::LongType>();
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auto codesArr = codesArrOne;
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auto contextArrOne = context->size() > 0 ? NDArrayFactory::create_<sd::LongType>('c',*contextShape,*contextVec,LaunchContext::defaultContext()) : NDArrayFactory::empty<sd::LongType>();
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auto contextArr =contextArrOne;
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auto lockedWordsOne = lockedWordsVec->size() > 0 ? NDArrayFactory::create_<sd::LongType>('c',*lockedWordsShape,*lockedWordsVec,LaunchContext::defaultContext()) : NDArrayFactory::empty<sd::LongType>();
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auto lockedWordsArr = lockedWordsOne;
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auto target = I_ARG(currIdx++);
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auto ngStarter = I_ARG(currIdx++);
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auto numLabels = I_ARG(currIdx++);
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auto randomValue = I_ARG(currIdx++);
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auto iterations = I_ARG(currIdx++);
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auto numWorkers = block.numI() > 0 ? INT_ARG(5) : omp_get_max_threads();
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auto nsRounds = block.numI() > 1 ? INT_ARG(6) : 0;
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auto alpha = T_ARG(0);
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auto minLearningRate = block.numT() > 1 ? T_ARG(1) : 1e-3;
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auto syn0 = INPUT_VARIABLE(0);
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auto syn1 = INPUT_VARIABLE(1);
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auto syn1neg = INPUT_VARIABLE(2);
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auto expTable = INPUT_VARIABLE(3);
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auto negTable = INPUT_VARIABLE(4);
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auto inferenceVector = INPUT_VARIABLE(5);
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auto trainWords = block.numB() > 0 ? B_ARG(0) : true;
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auto isInference = block.numB() > 1 ? B_ARG(1) : false;
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REQUIRE_TRUE(block.isInplace(), 0, "CBOW: this operation requires inplace execution only");
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REQUIRE_TRUE(syn0->dataType() == syn1->dataType() && syn0->dataType() == syn1neg->dataType(), 0,
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"CBOW: all syn tables must have the same data type");
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REQUIRE_TRUE(syn0->dataType() == expTable->dataType(), 0,
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"CBOW: expTable must have the same data type as syn0 table");
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sd::ops::helpers::cbowInference(
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*syn0,
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*syn1,
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*syn1neg,
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*expTable,
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*negTable,
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target,
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ngStarter,
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nsRounds,
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*contextArr,
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*lockedWordsArr,
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*indicesArr,
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*codesArr,
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alpha,
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randomValue,
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numLabels,
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*inferenceVector,
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trainWords,
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numWorkers,iterations,minLearningRate);
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return sd::Status::OK;
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}
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DECLARE_TYPES(cbow_inference) {
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getOpDescriptor()
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->setAllowedInputTypes(0, {ALL_FLOATS})
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->setAllowedInputTypes(1, {ALL_FLOATS})
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->setAllowedInputTypes(2, {ALL_FLOATS})
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->setAllowedInputTypes(3, {ALL_FLOATS})
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->setAllowedInputTypes(4, {ALL_FLOATS})
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->setAllowedInputTypes(5, {ALL_FLOATS})
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->setAllowedOutputTypes(sd::DataType::ANY);
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}
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CONFIGURABLE_OP_IMPL(cbow, 15, 15, true, 0, 0) {
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auto target = INPUT_VARIABLE(0);
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auto ngStarter = INPUT_VARIABLE(1);
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// required part
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auto context = INPUT_VARIABLE(2);
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auto indices = INPUT_VARIABLE(3);
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auto codes = INPUT_VARIABLE(4);
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auto syn0 = INPUT_VARIABLE(5);
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auto syn1 = INPUT_VARIABLE(6);
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auto syn1neg = INPUT_VARIABLE(7);
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auto expTable = INPUT_VARIABLE(8);
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auto negTable = INPUT_VARIABLE(9);
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auto alpha = INPUT_VARIABLE(10);
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auto randomValue = INPUT_VARIABLE(11);
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auto numLabels = INPUT_VARIABLE(12);
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auto lockedWords = INPUT_VARIABLE(13);
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auto inferenceVector = INPUT_VARIABLE(14);
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auto numWorkers = block.numI() > 0 ? INT_ARG(0) : omp_get_max_threads();
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auto nsRounds = block.numI() > 1 ? INT_ARG(1) : 0;
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auto iterations = block.numI() > 2 ? INT_ARG(2) : 1;
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auto trainWords = block.numB() > 0 ? B_ARG(0) : true;
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auto isInference = block.numB() > 1 ? B_ARG(1) : false;
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auto minLearningRate = block.numT() > 0 ? T_ARG(0) : 1e-3;
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REQUIRE_TRUE(block.isInplace(), 0, "CBOW: this operation requires inplace execution only");
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REQUIRE_TRUE(syn0->dataType() == syn1->dataType() && syn0->dataType() == syn1neg->dataType(), 0,
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"CBOW: all syn tables must have the same data type");
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REQUIRE_TRUE(syn0->dataType() == expTable->dataType(), 0,
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"CBOW: expTable must have the same data type as syn0 table");
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sd::ops::helpers::cbow(*syn0, *syn1, *syn1neg, *expTable, *negTable, *target, *ngStarter, nsRounds, *context,
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*lockedWords, *indices, *codes, *alpha, *randomValue, *numLabels, *inferenceVector, trainWords,
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numWorkers,minLearningRate,iterations);
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return sd::Status::OK;
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}
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DECLARE_TYPES(cbow) {
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getOpDescriptor()
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->setAllowedInputTypes(0, sd::DataType::INT32)
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->setAllowedInputTypes(1, sd::DataType::INT32)
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->setAllowedInputTypes(2, sd::DataType::INT32)
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->setAllowedInputTypes(3, sd::DataType::INT32)
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->setAllowedInputTypes(4, {ALL_INTS})
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->setAllowedInputTypes(5, {ALL_FLOATS})
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->setAllowedInputTypes(6, {ALL_FLOATS})
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->setAllowedInputTypes(7, {ALL_FLOATS})
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->setAllowedInputTypes(8, {ALL_FLOATS})
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->setAllowedInputTypes(9, {ALL_FLOATS})
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->setAllowedInputTypes(10, {ALL_FLOATS})
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->setAllowedInputTypes(11, sd::DataType::INT64)
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->setAllowedInputTypes(12, sd::DataType::INT32)
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->setAllowedInputTypes(13, sd::DataType::INT32)
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->setAllowedInputTypes(14, {ALL_FLOATS})
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->setAllowedOutputTypes(sd::DataType::ANY);
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}
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} // namespace ops
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} // namespace sd
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#endif
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