* 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
209 lines
8.4 KiB
C++
209 lines
8.4 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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// Created by raver119 on 02.11.2017.
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//
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#include <op_boilerplate.h>
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//#if NOT_EXCLUDED(OP_slice)
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#include <ops/declarable/CustomOperations.h>
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#include <helpers/ShapeUtils.h>
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namespace nd4j {
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namespace ops {
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CUSTOM_OP_IMPL(slice, 1, 1, false, 0, -2) {
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auto input = INPUT_VARIABLE(0);
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auto output = OUTPUT_VARIABLE(0);
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int x_rank = input->rankOf();
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std::vector<int> begin;
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std::vector<int> sz;
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if (block.width() == 3) {
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auto b = INPUT_VARIABLE(1);
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auto e = INPUT_VARIABLE(2);
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begin = b->template asVectorT<int>();
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sz = e->template asVectorT<int>();
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} else {
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REQUIRE_TRUE(block.numI() >= x_rank * 2, 0, "Number of IArgs should be equal to [%i] but got [%i] instead", x_rank * 2, block.numI());
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ShapeUtils::copyVectorPart(begin, *(block.getIArguments()), x_rank, 0);
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ShapeUtils::copyVectorPart(sz, *(block.getIArguments()), x_rank, x_rank);
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}
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REQUIRE_TRUE(begin.size() == x_rank, 0, "begin array should have length of [%i] but got [%i] instead", x_rank, begin.size());
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REQUIRE_TRUE(sz.size() == x_rank, 0, "size array should have length of [%i] but got [%i] instead", x_rank, sz.size());
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std::vector<Nd4jLong> indices(2 * x_rank);
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auto empty = false;
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for (int e = 0; e < x_rank; e++) {
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int size = sz[e];
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int start = begin[e];
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REQUIRE_TRUE(start >= 0, 0, "Slice: start index should not be negative");
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REQUIRE_TRUE(start <= input->sizeAt(e), 0, "Index %i is invalid for dimension %i with size %i.", start, e, input->shapeInfo()[e + 1]);
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if (size == -1){
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size = input->sizeAt(e) - start;
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}
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REQUIRE_TRUE(size >= 0, 0, "Slice: interval for dimension %i is less then 1");
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REQUIRE_TRUE(start + size <= input->sizeAt(e), 0, "Slice: interval [%i, %i] is out of bounds for dimension %i with size %i", start, start + size, e, input->sizeAt(e));
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if(start == input->sizeAt(e) || size == 0 ){
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empty = true;
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//Don't break to perform input validation on other dims
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}
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indices[2*e] = start;
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indices[2*e+1] = start + size;
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}
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if(empty){
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REQUIRE_TRUE(output->isEmpty(), 0, "Slice: empty array indices requested, but output array is not empty");
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return Status::OK();
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}
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auto sub = (*input)(indices, true);
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output->assign(sub);
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STORE_RESULT(output);
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return Status::OK();
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}
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DECLARE_TYPES(slice) {
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getOpDescriptor()
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->setAllowedInputTypes(nd4j::DataType::ANY)
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->setSameMode(true);
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}
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DECLARE_SHAPE_FN(slice) {
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auto inShape = inputShape->at(0);
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auto x_rank = shape::rank(inShape);
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std::vector<int> begin;
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std::vector<int> sz;
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if (block.width() == 3) {
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auto b = INPUT_VARIABLE(1);
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auto e = INPUT_VARIABLE(2);
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begin = b->template asVectorT<int>();
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sz = e->template asVectorT<int>();
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} else {
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REQUIRE_TRUE(block.numI() >= x_rank * 2, 0, "Number of IArgs should be equal to [%i] but got [%i] instead", x_rank * 2, block.numI());
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ShapeUtils::copyVectorPart(begin, *(block.getIArguments()), x_rank, 0);
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ShapeUtils::copyVectorPart(sz, *(block.getIArguments()), x_rank, x_rank);
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}
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REQUIRE_TRUE(begin.size() == x_rank, 0, "Begin array should have length of [%i] but got [%i] instead", x_rank, begin.size());
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REQUIRE_TRUE(sz.size() == x_rank, 0, "Size array should have length of [%i] but got [%i] instead", x_rank, sz.size());
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std::vector<Nd4jLong> shape;
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auto empty = false;
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for (int e = 0; e < x_rank; e++) {
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auto size = sz[e];
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auto start = begin[e];
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if(size == -1){
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size = inShape[e+1] - start;
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}
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//Bounds checking. Note that begin[i] == size[i] means empty array
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REQUIRE_TRUE(start >= 0 && start <= inShape[e+1], 0, "Invalid begin[%i] value: Begin must satisfy 0 <= begin <= size[i], got begin=%i for dimension size %i", e, start, inShape[e+1]);
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REQUIRE_TRUE(size == -1 || size >= 0, 0, "Invalid size[%i] value: must be positive (or -1 for 'all remaining'), got %i", e, size, inShape[e+1]);
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REQUIRE_TRUE(start >= 0 && start <= inShape[e+1], 0, "Invalid begin[%i] value: Begin must satisfy 0 <= begin <= size[i], got begin=%i for dimension size %i", e, start, inShape[e+1]);
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REQUIRE_TRUE(start + size <= inShape[e+1], 0, "Slice: interval [%i, %i] is out of bounds for dimension %i with size %i", start, start + size, e, inShape[e+1]);
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if(start == inShape[e+1] ){
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size = 0;
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}
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shape.emplace_back(size);
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}
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auto newShape = ConstantShapeHelper::getInstance()->createShapeInfo(ArrayOptions::dataType(inShape), 'c', shape);
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return SHAPELIST(newShape);
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}
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DECLARE_TYPES(slice_bp) {
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getOpDescriptor()
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->setAllowedInputTypes(nd4j::DataType::ANY)
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->setAllowedOutputTypes({ALL_FLOATS});
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}
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CUSTOM_OP_IMPL(slice_bp, 2, 1, false, 0, -2) {
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auto input = INPUT_VARIABLE(0);
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auto epsNext = block.width() == 4 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(1);
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auto output = OUTPUT_VARIABLE(0);
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output->assign(0.);
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int x_rank = input->rankOf();
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std::vector<int> begin;
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std::vector<int> end;
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if (block.width() == 4) {
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auto b = INPUT_VARIABLE(1);
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auto e = INPUT_VARIABLE(2);
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begin = b->template asVectorT<int>();
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end = e->template asVectorT<int>();
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} else {
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REQUIRE_TRUE(block.numI() >= x_rank * 2, 0, "Number of IArgs should be equal to [%i] but got [%i] instead", x_rank * 2, block.numI());
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ShapeUtils::copyVectorPart(begin, *(block.getIArguments()), x_rank, 0);
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ShapeUtils::copyVectorPart(end, *(block.getIArguments()), x_rank, x_rank);
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}
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REQUIRE_TRUE(begin.size() == x_rank, 0, "begin array should have length of [%i] but got [%i] instead", x_rank, begin.size());
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REQUIRE_TRUE(end.size() == x_rank, 0, "end array should have length of [%i] but got [%i] instead", x_rank, end.size());
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std::vector<Nd4jLong> indices(2 * x_rank);
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for (int e = 0; e < x_rank; e++) {
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int size = end[e];
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int start = begin[e];
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if (size == -1){ //-1 means all remaining values
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size = input->sizeAt(e) - start;
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}
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REQUIRE_TRUE(size > 0, 0, "Slice: interval for dimension %i is less then 1", e);
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indices[2*e] = start;
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indices[2*e + 1] = start + size;
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}
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auto sub = (*output)(indices, true);
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sub.assign(epsNext);
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return Status::OK();
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}
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DECLARE_SHAPE_FN(slice_bp) {
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auto inShape = inputShape->at(0);
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Nd4jLong *newShape;
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COPY_SHAPE(inShape, newShape);
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return SHAPELIST(CONSTANT(newShape));
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}
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}
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}
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//#endif
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