cavis/libnd4j/include/ops/declarable/generic/transforms/cumprod.cpp

158 lines
6.0 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
******************************************************************************/
//
// @author raver119@gmail.com
//
#include <op_boilerplate.h>
#if NOT_EXCLUDED(OP_cumprod)
#include <ops/declarable/helpers/prefix.h>
#include <ops/declarable/CustomOperations.h>
namespace nd4j {
namespace ops {
CONFIGURABLE_OP_IMPL(cumprod, 1, 1, true, 0, 2) {
auto input = INPUT_VARIABLE(0);
auto output = OUTPUT_VARIABLE(0);
REQUIRE_TRUE(input->dataType() == output->dataType(), 0, "CumSum: input and output data types must be equal");
if(input->isEmpty()){
//No-op
return Status::OK();
}
const bool exclusive = INT_ARG(0) == 1;
const bool reverse = INT_ARG(1) == 1;
if (block.getIArguments()->size() == 2 && block.width() == 1) {
// all at once case
nd4j::ops::helpers::prefix(block.launchContext(), scalar::Multiply, input, output, exclusive, reverse);
} else {
std::vector<int> dims(block.numI() - 2);
if (block.width() == 1) {
for (int e = 0; e < block.numI() - 2; e++)
dims[e] = INT_ARG(e + 2);
} else {
auto ax = INPUT_VARIABLE(1);
dims = ax->template asVectorT<int>();
}
for (int e = 0; e < dims.size(); e++)
if (dims[e] < 0)
dims[e] += input->rankOf();
nd4j::ops::helpers::prefix(block.launchContext(), scalar::Multiply, input, output, dims, exclusive, reverse);
}
return Status::OK();
}
DECLARE_TYPES(cumprod) {
getOpDescriptor()
->setAllowedInputTypes(0, nd4j::DataType::ANY)
->setAllowedInputTypes(1, {ALL_INTS})
->setAllowedOutputTypes({ALL_FLOATS})
->setSameMode(true);
}
DECLARE_TYPES(cumprod_bp) {
getOpDescriptor()
->setAllowedInputTypes(0, nd4j::DataType::ANY)
->setAllowedInputTypes(1, {ALL_INTS, ALL_FLOATS}) // there is a case when axes given as IArgs
->setAllowedInputTypes(2, {ALL_FLOATS})
->setAllowedOutputTypes({ALL_FLOATS})
->setSameMode(true);
}
CUSTOM_OP_IMPL(cumprod_bp, 2, 1, false, 0, 2) {
auto input = INPUT_VARIABLE(0);
auto axis = block.width() == 3 ? INPUT_VARIABLE(1) : nullptr;
auto gradOut = block.width() == 3 ? INPUT_VARIABLE(2) : INPUT_VARIABLE(1);
auto output = OUTPUT_VARIABLE(0);
const bool exclusive = INT_ARG(0) == 1;
const bool reverse = INT_ARG(1) == 1;
std::vector<int> dims;
if (block.width() > 2) {
dims = axis->template asVectorT<int>();
OUTPUT_VARIABLE(1)->assign(1.0f);
} else if (int newSize = (block.numI() - 2)) {
dims.resize(newSize);
for (int e = 0; e < newSize; e++)
dims[e] = INT_ARG(e + 2);
}
nd4j::ops::helpers::prefix(block.launchContext(), scalar::Multiply, input, output, dims, exclusive, reverse);
NDArray val = NDArray(output->dup());
gradOut->applyPairwiseTransform(pairwise::Multiply, *output, val);
val.applyPairwiseTransform(pairwise::Divide, *input, val);
if (!exclusive && !reverse) {
if (dims.size())
nd4j::ops::helpers::prefix(block.launchContext(), scalar::Add, &val, output, dims, true, false);
else
nd4j::ops::helpers::prefix(block.launchContext(), scalar::Add, &val, output, false, true);
}
else if (!exclusive && reverse){
if (dims.size())
nd4j::ops::helpers::prefix(block.launchContext(), scalar::Add, &val, output, dims, false, false);
else
nd4j::ops::helpers::prefix(block.launchContext(), scalar::Add, &val, output, false, false);
}
else if (exclusive && !reverse) {
if (dims.size())
nd4j::ops::helpers::prefix(block.launchContext(), scalar::Add, &val, output, dims, true, true);
else
nd4j::ops::helpers::prefix(block.launchContext(), scalar::Add, &val, output, true, true);
}
else {
if (dims.size())
nd4j::ops::helpers::prefix(block.launchContext(), scalar::Add, &val, output, dims, true, false);
else
nd4j::ops::helpers::prefix(block.launchContext(), scalar::Add, &val, output, true, false);
}
return Status::OK();
}
DECLARE_SHAPE_FN(cumprod_bp) {
auto inp = inputShape->at(0);
Nd4jLong *newShapeX = nullptr;
COPY_SHAPE(inp, newShapeX);
if (block.width() == 2) {
return SHAPELIST(CONSTANT(newShapeX));
} else {
Nd4jLong *newShapeA = nullptr;
COPY_SHAPE(inputShape->at(1), newShapeA);
return SHAPELIST(CONSTANT(newShapeX), CONSTANT(newShapeA));
}
}
}
}
#endif