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Legacy API changes (#441)
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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

123 lines
5.3 KiB
C++

/*******************************************************************************
* Copyright (c) 2015-2019 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
******************************************************************************/
//
// @author raver119@gmail.com
//
#include <ops/declarable/helpers/sparse_to_dense.h>
#include <helpers/StringUtils.h>
#include <helpers/ShapeUtils.h>
namespace sd {
namespace ops {
namespace helpers {
template <typename X, typename I>
static void fill_(const void *vvalues, const void *vindices, void *voutput, const Nd4jLong *zShapeInfo, uint8_t rank, uint64_t length) {
auto values = reinterpret_cast<const X*>(vvalues);
auto indices = reinterpret_cast<const I*>(vindices);
auto output = reinterpret_cast<X*>(voutput);
int coords[MAX_RANK];
uint64_t pos = 0;
for (uint64_t e = 0L; e < length; e++) {
// indices come in blocks
for (uint8_t p = 0; p < rank; p++) {
coords[p] = indices[pos++];
}
// fill output at given coords with sparse value
output[shape::getOffset(zShapeInfo, coords)] = values[e];
}
}
void compat_sparse_to_dense(const NDArray &values, const NDArray &indices, NDArray *def, NDArray &output) {
// make sure host buffer is updated
values.syncToHost();
indices.syncToHost();
auto rank = output.rankOf();
if (output.isS()) {
// string case is not so trivial, since elements might, and probably will, have different sizes
auto numValues = values.lengthOf();
auto numElements = output.lengthOf();
// first of all we calculate final buffer sizes and offsets
auto defaultLength = def == nullptr ? 0 : StringUtils::byteLength(*def);
auto valuesLength = StringUtils::byteLength(values);
auto bufferLength = defaultLength * (output.lengthOf() - numValues) + valuesLength;
auto headerLength = ShapeUtils::stringBufferHeaderRequirements(numElements);
// now we make sure our output buffer can hold results
output.dataBuffer()->expand( bufferLength + headerLength);
std::vector<Nd4jLong> outputCoords(rank);
std::vector<Nd4jLong> valueCoords(rank);
auto offsetsBuffer = output.bufferAsT<Nd4jLong>();
auto dataBuffer = reinterpret_cast<uint8_t*>(offsetsBuffer + output.lengthOf());
offsetsBuffer[0] = 0;
// getting initial value coords
for (int e = 0; e < rank; e++)
valueCoords[e] = indices.e<Nd4jLong>(e);
// write results individually
for (Nd4jLong e = 0; e < numElements; e++) {
auto vIndex = shape::coords2index(output.shapeInfo(), valueCoords.data());
auto cLength = 0L;
std::string str;
if (vIndex == e) {
// we're writing down sparse value here
str = values.e<std::string>(e);
} else {
// we're writing down default value if it exists
if (def != nullptr)
str = def->e<std::string>(0);
else
str = "";
}
// TODO: make it unicode compliant
memcpy(&dataBuffer[offsetsBuffer[e]], str.c_str(), str.length());
// writing down offset
offsetsBuffer[e+1] = cLength;
}
} else {
// numeric case is trivial, since all elements have equal sizes
// write out default values, if they are present
if (def != nullptr) {
output.assign(def);
// make sure output is synced back
output.syncToHost();
}
// write out values
BUILD_DOUBLE_SELECTOR(values.dataType(), indices.dataType(), fill_, (values.buffer(), indices.buffer(), output.buffer(), output.shapeInfo(), rank, values.lengthOf()), LIBND4J_TYPES, INDEXING_TYPES);
}
// copy back to device, if there's any
output.syncToDevice();
}
}
}
}