cavis/libnd4j/include/ops/declarable/generic/blas/tensormmul.cpp

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2019-06-06 14:21:15 +02:00
/*******************************************************************************
* 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_tensormmul)
#include <helpers/ShapeUtils.h>
#include <ops/declarable/CustomOperations.h>
#include <MmulHelper.h>
namespace nd4j {
namespace ops {
CUSTOM_OP_IMPL(tensormmul, 2, 1, false, 0, -1) {
auto a = INPUT_VARIABLE(0);
auto b = INPUT_VARIABLE(1);
auto c = OUTPUT_VARIABLE(0); //
// building axes
int axe0_size = INT_ARG(0);
int axe1_size = INT_ARG(axe0_size+1);
std::vector<int> axes_0(axe0_size), axes_1(axe1_size);
for (int e = 0; e < axe0_size; e++)
axes_0[e] = (int) INT_ARG(e+1);
for (int e = 0; e < axe1_size; e++)
axes_1[e] = (int) INT_ARG(e + axe0_size + 2);
nd4j_verbose("axe0: %i; axe1: %i;\n", axes_0.size(), axes_1.size());
MmulHelper::tensorDot(a, b, c, axes_0, axes_1);
return Status::OK();
}
DECLARE_SYN(tensordot, tensormmul);
DECLARE_SHAPE_FN(tensormmul) {
auto aShapeInfo = inputShape->at(0);
auto bShapeInfo = inputShape->at(1);
// building axes
int axe0_size = INT_ARG(0);
int axe1_size = INT_ARG(axe0_size+1);
std::vector<int> axes_0(axe0_size), axes_1(axe1_size);
for (int e = 0; e < axe0_size; e++)
axes_0[e] = (int) INT_ARG(e+1);
for (int e = 0; e < axe1_size; e++)
axes_1[e] = (int) INT_ARG(e + axe0_size + 2);
// evaluate shapes
std::vector<int> permutAt, permutBt;
std::vector<Nd4jLong> shapeAt, shapeBt;
auto outShape = nd4j::ShapeUtils::evalShapeForTensorDot(aShapeInfo, bShapeInfo, axes_0, axes_1, permutAt, permutBt, shapeAt, shapeBt);
return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(block.dataType(), 'c', outShape)));
}
DECLARE_TYPES(tensormmul) {
getOpDescriptor()
->setAllowedInputTypes(0, {DataType::FLOAT32, DataType ::DOUBLE, DataType::HALF})
->setAllowedInputTypes(1, {DataType::FLOAT32, DataType ::DOUBLE, DataType::HALF})
->setAllowedOutputTypes(0, {DataType::FLOAT32, DataType ::DOUBLE, DataType::HALF});
}
}
}
#endif