* correct logsoftmax looss (#2) * Small SameDiff listener fix (#4) * Various fixes (#6) * #7839 Fix for asXMatrix and tests * #7866 EmbeddingSequenceLayer dtype fix + test * #7856 SameDiff save/load stream methods * #7859 RegressionEvaluation rank 4 fix + tests + axis configuration * EvaluationBinary 3d/4d * More evaluation 3d/4d tests * #7847 Evaluation empty checks * Small test ifx * #7848 Fix median edge case * Improve DL4J samediff layer tests * [WIP] FastText wrapper implemented (#8) * FastText implemented * Some fixes * Fix shapes for wordsNearest * Validation of input vectors * Fixes * Fixed test * Thread tagged * Some tweaks * setContextClassLoader for DeallocatorServiceThread * Numpy format tests (#1) * Various fixes (#11) * #7852 SameDiff gather fix * #7892 SameDiff placeholder to constant conversion * #7890 validate input rank for MLN/CG init methods * Fix broken permute shape calculation * Permute and gather fixes * Tests * #7850 LogSumExp fix + test * Handful of test fixes * Empty arrays with non-scalar shapes (#10) * minor rearrangements for lambdas * empty tensors with non-scalar shapes * numpy empty tensors with non-scalar shapes * few more empty tweaks * Small fixes * conv3d signature update * micro fix in batchnorm mkldnn * Import fixes * Fix * MKL-DNN update * Small fill fix * fill with empty input + test * Fixes * Small error improvement * Fix * one special test * couple of fixes for lstm * Rewrite TFGraphMapper.getNDArrayFromTensor to be maintainable and less error prone * Fixes * FP16 * Unsigned * BFloat16 * Fill op - empty tweaks * - couple of fixes for empty arrays construction - stack updated * strided slice fix * one transform test * provide method for reducing shapeInfo in case of input array is empty * Fixed reduceAlongDimensions to use empty input properly. * couple of broadcast tests * couple of tests broadcast tests + tweak to make them pass * add check of non-empty to methods producing sub-arrays * Fixed reshapeC with zeros in shape. * complete empty check in reduce_... legacy ops * Concat and cumsum/prod * Tweak to empty shape inference on import * add empty check to the rest of reduce legacy ops * one more test * correct typo in evalReduceShapeInfoEmpty * Added tests for reduce_* ops to tests with zero shapes. * few more tests for empty reductions * Fixed strided_slice op with empty case and tests. * one more empty reduction test * Fixed strided_slice test. * add empty check to NDArray::reshapei * infOrMax * empty min/max with infinity tests * made unstack working correctly with empty arrays * few IndexReduce tests + tweaks for empty shapes * add test for empty concat * few tests fixed * Validation fix for reductions on empty shapes * Reverse fix * Reduction shape calc fixes * SameDiff.generateOutputVariable: don't use shape function to determine number of outputs * Range fix * - NDArray constructor updated for scalars/empty arrays - few tests fixed * More fixes * Empty creator fixes * concat fix * concat fix * TF import tests: allow 'both all NaN' and 'both all inf' to pass * Slice, zero fraction, and reshape fixes * transpose, gather * Zero fraction * scalar cast fix * Empty reduction axis support * few more tests fixed * Fixed input checks conforming with TF for concat op and tests. * few tests fixed * matmul scalar shape fix * Fixed checkout for data type and scalarity with concat to allow non-empty scalars with vector concats. * broadcast bool fix * few more tests * few more tests * correct evalReduceShapeInfoEmpty * argmax/argmin + tests * one more empty edge case + one more test * argmax/argmin/realdiv_bp tweaks * empty reshape test + fix * Helper fixes * Small fixes * Gather test fix * Gather test fix * Small fixes * reduce scalar zero values * scalar mean workaround * Remove debug code * along dim mean workaround * one more test * - equalsTo() tweak for empty arrays - one more test * broadcast tweaks
170 lines
5.4 KiB
C++
170 lines
5.4 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 Shyrma Yurii (iuriish@yahoo.com), created on 16.11.2017
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//
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#include <op_boilerplate.h>
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#if NOT_EXCLUDED(OP_gather)
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#include <ops/declarable/CustomOperations.h>
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#include<ops/declarable/helpers/gather.h>
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namespace nd4j {
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namespace ops {
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//////////////////////////////////////////////////////////////////////////
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CUSTOM_OP_IMPL(gather, 1, 1, false, 0, -2) {
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auto input = INPUT_VARIABLE(0);
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auto indices = block.width() > 1 ? INPUT_VARIABLE(1) : nullptr;
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auto output = OUTPUT_VARIABLE(0);
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//Edge case: empty indices -> empty output
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if(indices != nullptr && indices->isEmpty()){
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REQUIRE_TRUE(output->isEmpty(), 0, "Gather op: If indices are empty, output must also be empty");
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return Status::OK(); //No op
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}
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const int numOfIntArgs = block.numI();
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std::vector<int> intArgs;
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if (block.width() > 2) {
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intArgs = INPUT_VARIABLE(2)->template asVectorT<int>();
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}
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else {
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if (numOfIntArgs == 0)
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intArgs.emplace_back(0);
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else
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for (int i = 0; i < numOfIntArgs; ++i)
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intArgs.emplace_back(block.getIArguments()->at(i));
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}
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const int inputRank = input->rankOf();
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if(intArgs[0] < 0)
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intArgs[0] += inputRank;
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// input validation
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REQUIRE_TRUE(intArgs[0] < inputRank, 0, "GATHER op: input axis must be smaller than input array rank, but got %i and %i correspondingly!", intArgs[0], inputRank);
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REQUIRE_TRUE(indices != nullptr || numOfIntArgs > 1, 0, "GATHER op: indices should be provided either as additional input array or as IntArguments !");
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if (indices != nullptr) {
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for(int i = 0; i < indices->lengthOf(); ++i)
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REQUIRE_TRUE(indices->e<Nd4jLong>(i) < input->sizeAt(intArgs[0]), 0, "GATHER op: indices array contains wrong elements, each element must be smaller than corresponding dimension of input array !");
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}
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else {
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for(int i = 1; i < numOfIntArgs; ++i)
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REQUIRE_TRUE(intArgs[i] < input->sizeAt(intArgs[0]), 0, "GATHER op: some of indexes is larger than corresponding shape of input array !");
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}
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helpers::gather(block.launchContext(), input, indices, output, intArgs);
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return Status::OK();
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}
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DECLARE_TYPES(gather) {
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getOpDescriptor()->setAllowedInputTypes(0, {ALL_INTS, ALL_FLOATS});
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getOpDescriptor()->setAllowedInputTypes(1, {ALL_INTS});
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getOpDescriptor()->setAllowedOutputTypes(0, {ALL_INTS, ALL_FLOATS});
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}
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DECLARE_SHAPE_FN(gather) {
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// check shape of paddings
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auto inputShapeInfo = inputShape->at(0);
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Nd4jLong* outputShapeInfo = nullptr;
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int axis = 0;
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if (block.width() > 2) {
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axis = INPUT_VARIABLE(2)->e<int>(0);
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} else
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axis = block.numI() > 0 ? block.getIArguments()->at(0) : 0;
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int inputRank = shape::rank(inputShapeInfo);
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if(axis < 0)
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axis += inputRank;
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REQUIRE_TRUE(axis < inputRank, 0, "GATHER op: input axis must be smaller than input array rank, but got %i and %i correspondingly!", axis, inputRank);
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bool isEmpty = false;
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if (block.width() > 1) {
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auto indicesShapeInfo = inputShape->at(1);
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int indicesRank = shape::rank(indicesShapeInfo);
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int outputRank = inputRank + indicesRank - 1;
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ALLOCATE(outputShapeInfo, block.getWorkspace(), shape::shapeInfoLength(outputRank), Nd4jLong);
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// fill output shapeInfo
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outputShapeInfo[0] = outputRank;
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int shapeIdx = 1;
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for(int i = 0; i < axis; ++i)
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outputShapeInfo[shapeIdx++] = inputShapeInfo[i+1];
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for(int i = 0; i < indicesRank; ++i)
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outputShapeInfo[shapeIdx++] = indicesShapeInfo[i+1];
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for(int i = axis+1; i < inputRank; ++i)
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outputShapeInfo[shapeIdx++] = inputShapeInfo[i+1];
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}
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else if (block.numI() > 1) {
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int indicesRank = block.numI() == 2 ? 0 : 1;
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int outputRank = inputRank + indicesRank - 1;
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ALLOCATE(outputShapeInfo, block.getWorkspace(), shape::shapeInfoLength(outputRank), Nd4jLong);
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// building shape manually
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outputShapeInfo[0] = outputRank;
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int shapeIdx = 1;
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for(int i = 0; i < axis; ++i)
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outputShapeInfo[shapeIdx++] = inputShapeInfo[i+1];
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if (block.numI() > 2)
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outputShapeInfo[shapeIdx++] = block.numI() - 1;
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for(int i = axis+1; i < inputRank; ++i)
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outputShapeInfo[shapeIdx++] = inputShapeInfo[i+1];
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}
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else
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REQUIRE_TRUE(false, 0, "GATHER op: indices should be provided either as additional input array or as IntArguments !");
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ShapeUtils::updateStridesAndType(outputShapeInfo, inputShapeInfo, shape::order(inputShapeInfo));
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if(isEmpty){
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ArrayOptions::setPropertyBit(outputShapeInfo, ARRAY_EMPTY);
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}
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auto result = ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(outputShapeInfo));
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RELEASE(outputShapeInfo, block.getWorkspace());
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return SHAPELIST(result);
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}
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}
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}
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#endif
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