94 lines
3.8 KiB
C++
94 lines
3.8 KiB
C++
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/*******************************************************************************
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* Copyright (c) 2015-2019 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 Paul Dubs
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//
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#ifndef LIBND4J_ATTENTIONHELPER_CPP
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#define LIBND4J_ATTENTIONHELPER_CPP
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#include <helpers/AttentionHelper.h>
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#include "../AttentionHelper.h"
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#include <ops/declarable/CustomOperations.h>
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namespace nd4j {
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nd4j::NDArray *
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AttentionHelper::multiHeadProject(const nd4j::NDArray *input, const nd4j::NDArray *projectionMatrix, nd4j::LaunchContext * context) {
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auto miniBatchSize = input->sizeAt(0);
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auto seqLength = input->sizeAt(2);
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auto numHeads = projectionMatrix->sizeAt(0);
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auto projectedSize = projectionMatrix->sizeAt(1);
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auto inputPerm = input->permute({1, 0, 2});
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auto inputPrep = inputPerm->reshape('c', {input->sizeAt(1), (miniBatchSize * seqLength)});
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auto projectionPrep = projectionMatrix->reshape('c', {numHeads * projectionMatrix->sizeAt(1), projectionMatrix->sizeAt(2)});
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NDArray* projected = new NDArray('c', {numHeads * projectionMatrix->sizeAt(1), (miniBatchSize * seqLength)}, input->dataType(), context);
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nd4j::ops::matmul mmul;
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mmul.execute({projectionPrep, inputPrep}, {projected}, {}, {}, {});
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projected->reshapei({numHeads, projectedSize, miniBatchSize, seqLength});
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projected->permutei({2, 0, 1, 3});
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delete inputPerm;
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delete inputPrep;
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delete projectionPrep;
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return projected;
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}
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void
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AttentionHelper::multiHeadProjectBp(const nd4j::NDArray *input, const nd4j::NDArray *projectionMatrix,
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const nd4j::NDArray *eps, nd4j::NDArray *dLdInput,
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nd4j::NDArray *dLdProjectionMatrix, nd4j::LaunchContext * context) {
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auto miniBatchSize = input->sizeAt(0);
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auto seqLength = input->sizeAt(2);
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auto numHeads = projectionMatrix->sizeAt(0);
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auto projectedSize = projectionMatrix->sizeAt(1);
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auto epsPerm = eps->permute({1, 2, 0, 3});
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auto epsReshaped = epsPerm->reshape('c', {numHeads * projectedSize, miniBatchSize * seqLength});
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auto inputPerm = input->permute({1, 0, 2});
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auto inputPrep = inputPerm->reshape('c', {input->sizeAt(1), (miniBatchSize * seqLength)});
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auto projectionPrep = projectionMatrix->reshape('c', {numHeads * projectionMatrix->sizeAt(1), projectionMatrix->sizeAt(2)});
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nd4j::ops::matmul_bp mmulBp;
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NDArray dLdProjectionPrep(projectionPrep->shapeInfo(), false, context);
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NDArray dLdInputPrep(inputPrep->shapeInfo(), false, context);
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mmulBp.execute({projectionPrep, inputPrep, epsReshaped}, {&dLdProjectionPrep, &dLdInputPrep}, {}, {}, {});
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dLdProjectionPrep.reshapei({numHeads, projectionMatrix->sizeAt(1), projectionMatrix->sizeAt(2)});
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dLdProjectionMatrix->assign(dLdProjectionPrep);
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dLdInputPrep.reshapei({input->sizeAt(1), miniBatchSize, seqLength});
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dLdInputPrep.permutei({1, 0, 2});
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dLdInput->assign(dLdInputPrep);
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delete inputPerm;
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delete inputPrep;
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delete epsPerm;
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delete epsReshaped;
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delete projectionPrep;
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}
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}
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#endif
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