85 lines
3.1 KiB
C++
85 lines
3.1 KiB
C++
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/*******************************************************************************
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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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// @author raver119@gmail.com
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//
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#include <op_boilerplate.h>
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#if NOT_EXCLUDED(OP_tensormmul)
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#include <helpers/ShapeUtils.h>
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#include <ops/declarable/CustomOperations.h>
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#include <MmulHelper.h>
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namespace nd4j {
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namespace ops {
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CUSTOM_OP_IMPL(tensormmul, 2, 1, false, 0, -1) {
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auto a = INPUT_VARIABLE(0);
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auto b = INPUT_VARIABLE(1);
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auto c = OUTPUT_VARIABLE(0); //
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// building axes
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int axe0_size = INT_ARG(0);
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int axe1_size = INT_ARG(axe0_size+1);
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std::vector<int> axes_0(axe0_size), axes_1(axe1_size);
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for (int e = 0; e < axe0_size; e++)
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axes_0[e] = (int) INT_ARG(e+1);
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for (int e = 0; e < axe1_size; e++)
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axes_1[e] = (int) INT_ARG(e + axe0_size + 2);
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nd4j_verbose("axe0: %i; axe1: %i;\n", axes_0.size(), axes_1.size());
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MmulHelper::tensorDot(a, b, c, axes_0, axes_1);
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return Status::OK();
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}
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DECLARE_SYN(tensordot, tensormmul);
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DECLARE_SHAPE_FN(tensormmul) {
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auto aShapeInfo = inputShape->at(0);
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auto bShapeInfo = inputShape->at(1);
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// building axes
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int axe0_size = INT_ARG(0);
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int axe1_size = INT_ARG(axe0_size+1);
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std::vector<int> axes_0(axe0_size), axes_1(axe1_size);
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for (int e = 0; e < axe0_size; e++)
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axes_0[e] = (int) INT_ARG(e+1);
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for (int e = 0; e < axe1_size; e++)
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axes_1[e] = (int) INT_ARG(e + axe0_size + 2);
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// evaluate shapes
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std::vector<int> permutAt, permutBt;
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std::vector<Nd4jLong> shapeAt, shapeBt;
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auto outShape = nd4j::ShapeUtils::evalShapeForTensorDot(aShapeInfo, bShapeInfo, axes_0, axes_1, permutAt, permutBt, shapeAt, shapeBt);
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return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(block.dataType(), 'c', outShape)));
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}
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DECLARE_TYPES(tensormmul) {
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getOpDescriptor()
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->setAllowedInputTypes(0, {DataType::FLOAT32, DataType ::DOUBLE, DataType::HALF})
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->setAllowedInputTypes(1, {DataType::FLOAT32, DataType ::DOUBLE, DataType::HALF})
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->setAllowedOutputTypes(0, {DataType::FLOAT32, DataType ::DOUBLE, DataType::HALF});
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}
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}
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}
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#endif
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