cavis/libnd4j/include/helpers/impl/AttentionHelper.cpp

82 lines
3.5 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 Paul Dubs
//
#ifndef LIBND4J_ATTENTIONHELPER_CPP
#define LIBND4J_ATTENTIONHELPER_CPP
#include <helpers/AttentionHelper.h>
#include "../AttentionHelper.h"
#include <ops/declarable/CustomOperations.h>
namespace nd4j {
nd4j::NDArray AttentionHelper::multiHeadProject(const nd4j::NDArray *input, const nd4j::NDArray *projectionMatrix, nd4j::LaunchContext * context) {
auto miniBatchSize = input->sizeAt(0);
auto seqLength = input->sizeAt(2);
auto numHeads = projectionMatrix->sizeAt(0);
auto projectedSize = projectionMatrix->sizeAt(1);
auto inputPerm = input->permute({1, 0, 2});
auto inputPrep = inputPerm.reshape('c', {input->sizeAt(1), (miniBatchSize * seqLength)});
auto projectionPrep = projectionMatrix->reshape('c', {numHeads * projectionMatrix->sizeAt(1), projectionMatrix->sizeAt(2)});
NDArray projected('c', {numHeads * projectionMatrix->sizeAt(1), (miniBatchSize * seqLength)}, input->dataType(), context);
nd4j::ops::matmul mmul;
mmul.execute({&projectionPrep, &inputPrep}, {&projected}, {}, {}, {});
projected.reshapei({numHeads, projectedSize, miniBatchSize, seqLength});
projected.permutei({2, 0, 1, 3});
return projected;
}
void AttentionHelper::multiHeadProjectBp(const nd4j::NDArray *input, const nd4j::NDArray *projectionMatrix,
const nd4j::NDArray *eps, nd4j::NDArray *dLdInput,
nd4j::NDArray *dLdProjectionMatrix, nd4j::LaunchContext * context) {
auto miniBatchSize = input->sizeAt(0);
auto seqLength = input->sizeAt(2);
auto numHeads = projectionMatrix->sizeAt(0);
auto projectedSize = projectionMatrix->sizeAt(1);
auto epsPerm = eps->permute({1, 2, 0, 3});
auto epsReshaped = epsPerm.reshape('c', {numHeads * projectedSize, miniBatchSize * seqLength});
auto inputPerm = input->permute({1, 0, 2});
auto inputPrep = inputPerm.reshape('c', {input->sizeAt(1), (miniBatchSize * seqLength)});
auto projectionPrep = projectionMatrix->reshape('c', {numHeads * projectionMatrix->sizeAt(1), projectionMatrix->sizeAt(2)});
nd4j::ops::matmul_bp mmulBp;
NDArray dLdProjectionPrep(projectionPrep.shapeInfo(), false, context);
NDArray dLdInputPrep(inputPrep.shapeInfo(), false, context);
mmulBp.execute({&projectionPrep, &inputPrep, &epsReshaped}, {&dLdProjectionPrep, &dLdInputPrep}, {}, {}, {});
dLdProjectionPrep.reshapei({numHeads, projectionMatrix->sizeAt(1), projectionMatrix->sizeAt(2)});
dLdProjectionMatrix->assign(dLdProjectionPrep);
dLdInputPrep.reshapei({input->sizeAt(1), miniBatchSize, seqLength});
dLdInputPrep.permutei({1, 0, 2});
dLdInput->assign(dLdInputPrep);
}
}
#endif