82 lines
3.7 KiB
C++
82 lines
3.7 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 sd {
|
|
|
|
sd::NDArray AttentionHelper::multiHeadProject(const sd::NDArray *input, const sd::NDArray *projectionMatrix, sd::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}); //[batch, nIn, timeSteps] -> [nIn, batch, timeSteps]
|
|
auto inputPrep = inputPerm.reshape('c', {input->sizeAt(1), (miniBatchSize * seqLength)}); //[nIn, batch*timeSteps]
|
|
auto projectionPrep = projectionMatrix->reshape('c', {numHeads * projectionMatrix->sizeAt(1), projectionMatrix->sizeAt(2)}); //[nHeads, hS, nIn] -> [nHeads*hS, nIn]
|
|
|
|
NDArray projected('c', {numHeads * projectionMatrix->sizeAt(1), (miniBatchSize * seqLength)}, input->dataType(), context); //[nHeads*hS, batch*timeSteps]
|
|
sd::ops::matmul mmul;
|
|
mmul.execute({&projectionPrep, &inputPrep}, {&projected});
|
|
|
|
projected.reshapei({numHeads, projectedSize, miniBatchSize, seqLength});
|
|
projected.permutei({2, 0, 1, 3}); //[minibatch, numHeads, projectedSize, seqLength]
|
|
|
|
return projected;
|
|
}
|
|
|
|
void AttentionHelper::multiHeadProjectBp(const sd::NDArray *input, const sd::NDArray *projectionMatrix,
|
|
const sd::NDArray *eps, sd::NDArray *dLdInput,
|
|
sd::NDArray *dLdProjectionMatrix, sd::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)});
|
|
|
|
sd::ops::matmul_bp mmulBp;
|
|
NDArray dLdProjectionPrep(projectionPrep.shapeInfo(), false, context);
|
|
NDArray dLdInputPrep(inputPrep.shapeInfo(), false, context);
|
|
mmulBp.execute({&projectionPrep, &inputPrep, &epsReshaped}, std::vector<NDArray*>{&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
|