raver119 320924278d
Legacy API changes (#441)
* initial commit

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* another initial commit

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* another initial commit

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* one more initial commit

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* next step

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* next step

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* Refactored buffer() and shapeInfo() methods usage with NDArray class.

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* Adopt Graph class methods to use const shapes.

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* Adopt choose op to use constant shapes.

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* Adopt where op shape method to use constant shapes.

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* Adopt lstsq op to use constant empty shapes.

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* Adopt matrix_diag_part op shape routine to use constant shapes.

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* Adopt determinant ops to use constant shapes.

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* Adopt mean_pairwssqerr_loss ops to use constant shapes.

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* Adopt ops shape methods.

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* Adopt shape methods for loss ops.

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* Adopt log_loss op shape method.

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* Adopt shape methods for ops.

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* Adopt dilation2d ops shape methods.

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* Adopted deconv2d ops shape methods.

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* Adopted dynamicRNN op shape method.

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* Adopted shape methods for ops.

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* Adopted shape methods for lstm layer ops.

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* few updates

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* first cuda tweak

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* Adopt constant shapes for sconv2d ops.

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* Adopt constant shapes for gru ops.

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* Adopt constant shapes with shape methods for segment ops and so on.

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* Adopted constant shapes with unsorted_segment_* ops.

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* Adopted constant shapes with gamma op shape method.

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* Adopted shape methods of reduce_stddev ops.

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* Adopted shape methods for reduce_* ops.

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* Adopt shape method for squeeze op.

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* Adopt strided_slice shape method.

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* Refactored concat op shape method to adopt constant shapes.

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* Adopted shape method for mirror_pad op.

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* Adopted split op shape method.

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* Adopted tile ops shape methods.

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* Added const cast for mkldnn routines handles.

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* Refactored logSoftMaxForVector_ routine to conform with proper data and shape pointer casts.

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* Cosmetic changes to proper usage of constant pointers.

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* Refactored a couple shape comparators for strides and addBias helpers to proper use data pointers with inplace option.

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* Refactored depthToSpace helpers.

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* Refactored histogram helpers.

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* Refactored im2col helpers.

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* Refactored gather and gatherND helpers.

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* Fixed buffer usage on percentile helper.

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* Fixed gather shape with helpers and range buffer usage.

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* Fixed buffer usage with space to depth helpers.

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* Fixed buffer usage and constant shapes.

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* Fixed buffer usage with LUP decomposition>

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* Refactored onehot_ helper.

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* Refactored pad and prefix to use constant shapes.

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* Refactoed softmax helpers.

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* Fixed space to batch helpers to use buffers properly.

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* Fixed stack and split helpers.

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* Fixed buffer usage with sparse to dense helpers.

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* Fixed buffer usage with mindistance_ helpers.

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* Fixed buffer usage with tile helper.

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* Fixed constant shape usage.

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* Fixed constant shape usage with legacy pairwise bool ops.

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* Refactored a couple of methods to adopt constant shape usage.

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* Fixed broadcasting with constant shape."

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* Fixed const usage with inplace reverse and constant shapes with legacy reduction.

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* Refactored legacy ops with const shapes.

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* Refactored sort to adopt constant shapes.

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* Corrected sort for constant shape usage.

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* Fixed constant shape usage with special methods.

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* Refactored Context to conform with constant shape usage.

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* CUDA broadcasting headers

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* pairwise/indexreduce/random headers

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* Refactored native ops to adopt constant shapes.

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* legacy reduce3/scalar headers

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* Corrected pullRow signature and tests.

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* Corrected routines to proper use of constant shapes.

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* Refactored tests to use constant shapes properly.

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* Refactored legacy ops tests to use constant shapes properly.

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* Refactored buffer usage with NDArray tests.

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* Fixed native ops tests.

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* Fixed special concat routine.

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* Fixed buffer usage with test.

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* Fixed buffer usage with a test.

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* Refactored TAD.h and tests.

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* Refactored calcStrides* routines to use constant shapes.

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* Fixed miscelaneous errors with constant shapes.

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* NativeOps const changes

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* Corrected definitions for declared functions.

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* NativeOps const changes

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* few more const changes

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* Fixed const shapes with shape routines.

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* few more const changes

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* Fixed shape method for broadcastable case.

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* few more const changes

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* xw_plus_b BP shape fn restored

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* Fixed signatures with broadcasting.

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* Repaired backprops shape methods for a set of operations.

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* Refactored broadcast bool for cuda.

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* Refactored methods for 3 args with const qualifier.

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* Fixed a couple of kernel signatures for broadcasting.

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* Fixed kernels signatures for const buffers and shapes.

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* Refactored pairwise methods to persistent buffers and shapes usage.

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* Adopt const to buffers and shapes with kernels.

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* Adopt const to buffers and shapes with scalar kernels.

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* Refactored indexreduce kernels signatures to use const buffers and shapes.

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* Refactored pairwise kernels to adopt cons shapes and buffers.

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* Refactored pairwise bool kernels to adopt cons shapes and buffers.

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* Refactored random special ops to conform with const shapes and buffers.

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* Refactored native ops to conform with const shapes and buffers under cuda platform.

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* Cosmetical changes only.

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* Fixed const shapes and buffers error.

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* Corrected start pos routine.

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* Refactored methods to conform with const shapes and buffers.

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* Refactored helpers to use proper methods instead.

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* bunch of changes

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* next bunch of changes

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* next bunch of changes

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* Fixed execScalar declaration.

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* Fixed execScalar declaration.

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* Corrected const shape cases with sort and so on.

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* Fixed const shapes for sort.

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* Refactored kernel declarations to adopt const shapes.

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* Fixed kernels declarations to adopt const shapes.

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* Corrected kernel declarations to adopt const shapes and buffers.

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* Fixed kernels declarations to adopt const shapes.

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* Fixed segment helpers kernels declarations and so on to adopt const shapes.

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* Fixed const shape usage with segment and solve helpers.

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* Fixed kernel declaration with adjustWeight helper.

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* Fixed cuda implementations for constant shape helpers.

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* Adopted const shape usage with kernels.

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* Adopted top_k kernels to use const shapes and buffers.

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* Corrected kernels declarations to adopt const shapes with helpers.

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* Refactored NDArray definitions to adopt const shapes and buffers.

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* Fixed const shapes with image suppression helpers.

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* Slight improvement with buffers.

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* Refactored buffer usage.

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* Refactored buffer usage with tests.

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* Fixed const shape usage with definitions.

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* minor updates on cpu side

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* Refactored const shape usage with ConstantDescritor and native ops with cuda platform.

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* Refactored tear and tile kernels to adopt with const shapes.

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* softmax_loop fix

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* update missing signature

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* softmax again

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* few more missing consts

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* new methods updated

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Co-authored-by: shugeo <sgazeos@gmail.com>
2020-05-09 08:06:14 +03:00

230 lines
9.6 KiB
C++

/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// Created by raver119 on 02.11.2017.
//
#include <system/op_boilerplate.h>
//#if NOT_EXCLUDED(OP_slice)
#include <ops/declarable/CustomOperations.h>
#include <helpers/ShapeUtils.h>
namespace sd {
namespace ops {
CUSTOM_OP_IMPL(slice, 1, 1, false, 0, -2) {
auto input = INPUT_VARIABLE(0);
auto output = OUTPUT_VARIABLE(0);
int x_rank = input->rankOf();
std::vector<int> begin;
std::vector<int> sz;
if (block.width() == 3) {
auto b = INPUT_VARIABLE(1);
auto e = INPUT_VARIABLE(2);
begin = b->template asVectorT<int>();
sz = e->template asVectorT<int>();
} else {
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());
ShapeUtils::copyVectorPart(begin, *(block.getIArguments()), x_rank, 0);
ShapeUtils::copyVectorPart(sz, *(block.getIArguments()), x_rank, x_rank);
}
REQUIRE_TRUE(begin.size() == x_rank, 0, "begin array should have length of [%i] but got [%i] instead", x_rank, begin.size());
REQUIRE_TRUE(sz.size() == x_rank, 0, "size array should have length of [%i] but got [%i] instead", x_rank, sz.size());
std::vector<Nd4jLong> indices(2 * x_rank);
auto empty = false;
for (int e = 0; e < x_rank; e++) {
int size = sz[e];
int start = begin[e];
REQUIRE_TRUE(start >= 0, 0, "Slice: start index should not be negative");
REQUIRE_TRUE(start <= input->sizeAt(e), 0, "Index %i is invalid for dimension %i with size %i.", start, e, input->shapeInfo()[e + 1]);
if (size == -1){
size = input->sizeAt(e) - start;
}
REQUIRE_TRUE(size >= 0, 0, "Slice: interval for dimension %i is less then 1");
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));
if(start == input->sizeAt(e) || size == 0 ){
empty = true;
//Don't break to perform input validation on other dims
}
indices[2*e] = start;
indices[2*e+1] = start + size;
}
if(empty){
REQUIRE_TRUE(output->isEmpty(), 0, "Slice: empty array indices requested, but output array is not empty");
return Status::OK();
}
Nd4jLong* subArrShapeInfo = nullptr;
ALLOCATE(subArrShapeInfo, block.getWorkspace(), shape::shapeInfoLength(input->rankOf()), Nd4jLong);
Nd4jLong offset;
shape::calcSubArrShapeInfoAndOffset(indices.data(), input->shapeInfo(), subArrShapeInfo, offset, true);
auto subArrShapeInfoPack = ConstantShapeHelper::getInstance()->bufferForShapeInfo(subArrShapeInfo);
NDArray::prepareSpecialUse({output}, {input});
NativeOpExecutioner::execTransformAny(block.launchContext(), sd::transform::Assign,
input->bufferWithOffset(offset), reinterpret_cast<Nd4jLong *>(subArrShapeInfoPack.primary()),
input->specialBufferWithOffset(offset), reinterpret_cast<Nd4jLong *>(subArrShapeInfoPack.special()),
output->buffer(), output->shapeInfo(), output->specialBuffer(), output->specialShapeInfo(),
nullptr, nullptr, nullptr, true);
NDArray::registerSpecialUse({output}, {input});
RELEASE(subArrShapeInfo, block.getWorkspace());
// auto sub = (*input)(indices, true);
// output->assign(sub);
STORE_RESULT(output);
return Status::OK();
}
DECLARE_TYPES(slice) {
getOpDescriptor()
->setAllowedInputTypes(sd::DataType::ANY)
->setSameMode(true);
}
DECLARE_SHAPE_FN(slice) {
auto inShape = inputShape->at(0);
auto x_rank = shape::rank(inShape);
std::vector<int> begin;
std::vector<int> sz;
if (block.width() == 3) {
auto b = INPUT_VARIABLE(1);
auto e = INPUT_VARIABLE(2);
begin = b->template asVectorT<int>();
sz = e->template asVectorT<int>();
} else {
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());
ShapeUtils::copyVectorPart(begin, *(block.getIArguments()), x_rank, 0);
ShapeUtils::copyVectorPart(sz, *(block.getIArguments()), x_rank, x_rank);
}
REQUIRE_TRUE(begin.size() == x_rank, 0, "Begin array should have length of [%i] but got [%i] instead", x_rank, begin.size());
REQUIRE_TRUE(sz.size() == x_rank, 0, "Size array should have length of [%i] but got [%i] instead", x_rank, sz.size());
std::vector<Nd4jLong> shape;
auto empty = false;
for (int e = 0; e < x_rank; e++) {
auto size = sz[e];
auto start = begin[e];
if(size == -1){
size = inShape[e+1] - start;
}
//Bounds checking. Note that begin[i] == size[i] means empty array
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]);
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]);
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]);
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]);
if(start == inShape[e+1] ){
size = 0;
}
shape.emplace_back(size);
}
auto newShape = ConstantShapeHelper::getInstance()->createShapeInfo(ArrayOptions::dataType(inShape), 'c', shape);
return SHAPELIST(newShape);
}
DECLARE_TYPES(slice_bp) {
getOpDescriptor()
->setAllowedInputTypes(sd::DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS});
}
CUSTOM_OP_IMPL(slice_bp, 2, 1, false, 0, -2) {
auto input = INPUT_VARIABLE(0);
auto epsNext = block.width() == 4 ? INPUT_VARIABLE(3) : INPUT_VARIABLE(1);
auto output = OUTPUT_VARIABLE(0);
output->assign(0.);
int x_rank = input->rankOf();
std::vector<int> begin;
std::vector<int> end;
if (block.width() == 4) {
auto b = INPUT_VARIABLE(1);
auto e = INPUT_VARIABLE(2);
begin = b->template asVectorT<int>();
end = e->template asVectorT<int>();
} else {
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());
ShapeUtils::copyVectorPart(begin, *(block.getIArguments()), x_rank, 0);
ShapeUtils::copyVectorPart(end, *(block.getIArguments()), x_rank, x_rank);
}
REQUIRE_TRUE(begin.size() == x_rank, 0, "begin array should have length of [%i] but got [%i] instead", x_rank, begin.size());
REQUIRE_TRUE(end.size() == x_rank, 0, "end array should have length of [%i] but got [%i] instead", x_rank, end.size());
std::vector<Nd4jLong> indices(2 * x_rank);
for (int e = 0; e < x_rank; e++) {
int size = end[e];
int start = begin[e];
if (size == -1){ //-1 means all remaining values
size = input->sizeAt(e) - start;
}
REQUIRE_TRUE(size > 0, 0, "Slice: interval for dimension %i is less then 1", e);
indices[2*e] = start;
indices[2*e + 1] = start + size;
}
auto sub = (*output)(indices, true);
sub.assign(epsNext);
return Status::OK();
}
DECLARE_SHAPE_FN(slice_bp) {
auto inShape = inputShape->at(0);
Nd4jLong *newShape;
COPY_SHAPE(inShape, newShape);
return SHAPELIST(CONSTANT(newShape));
}
}
}
//#endif