GRU and GRUBp (#410)
* GRU and GRUBp ops added and tested Signed-off-by: Andrii Tuzhykov <andrewtuzhykov@gmail.com> * minor polishing Signed-off-by: Andrii Tuzhykov <andrewtuzhykov@gmail.com> * few requested changes Signed-off-by: Andrii Tuzhykov <andrewtuzhykov@gmail.com> * regenerated namespace + small fix in RnnOpValidation Signed-off-by: Andrii Tuzhykov <andrewtuzhykov@gmail.com> * Fix bad character in RnnOpValidation Signed-off-by: Alex Black <blacka101@gmail.com> Co-authored-by: Alex Black <blacka101@gmail.com>master
Andrii T
GitHub
parent
5e779574cb
commit
bd376ca993
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@ -35,6 +35,48 @@ public class SDRNN extends SDOps {
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super(sameDiff);
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}
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/**
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* The GRU operation. Gated Recurrent Unit - Cho et al. 2014.<br>
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*
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* @param x input [time, bS, nIn] (NUMERIC type)
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* @param hLast initial cell output (at time step = 0) [bS, nOut] (NUMERIC type)
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* @param Wx input-to-hidden weights, [nIn, 3*nOut] (NUMERIC type)
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* @param Wh hidden-to-hidden weights, [nOut, 3*nOut] (NUMERIC type)
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* @param biases biases, [3*nOut] (NUMERIC type)
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* @return h cell outputs [time, bS, nOut], that is per each time step (NUMERIC type)
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*/
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public SDVariable gru(SDVariable x, SDVariable hLast, SDVariable Wx, SDVariable Wh,
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SDVariable biases) {
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SDValidation.validateNumerical("gru", "x", x);
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SDValidation.validateNumerical("gru", "hLast", hLast);
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SDValidation.validateNumerical("gru", "Wx", Wx);
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SDValidation.validateNumerical("gru", "Wh", Wh);
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SDValidation.validateNumerical("gru", "biases", biases);
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return new org.nd4j.linalg.api.ops.impl.layers.recurrent.GRU(sd,x, hLast, Wx, Wh, biases).outputVariable();
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}
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/**
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* The GRU operation. Gated Recurrent Unit - Cho et al. 2014.<br>
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*
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* @param name name May be null. Name for the output variable
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* @param x input [time, bS, nIn] (NUMERIC type)
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* @param hLast initial cell output (at time step = 0) [bS, nOut] (NUMERIC type)
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* @param Wx input-to-hidden weights, [nIn, 3*nOut] (NUMERIC type)
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* @param Wh hidden-to-hidden weights, [nOut, 3*nOut] (NUMERIC type)
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* @param biases biases, [3*nOut] (NUMERIC type)
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* @return h cell outputs [time, bS, nOut], that is per each time step (NUMERIC type)
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*/
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public SDVariable gru(String name, SDVariable x, SDVariable hLast, SDVariable Wx, SDVariable Wh,
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SDVariable biases) {
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SDValidation.validateNumerical("gru", "x", x);
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SDValidation.validateNumerical("gru", "hLast", hLast);
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SDValidation.validateNumerical("gru", "Wx", Wx);
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SDValidation.validateNumerical("gru", "Wh", Wh);
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SDValidation.validateNumerical("gru", "biases", biases);
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SDVariable out = new org.nd4j.linalg.api.ops.impl.layers.recurrent.GRU(sd,x, hLast, Wx, Wh, biases).outputVariable();
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return sd.updateVariableNameAndReference(out, name);
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}
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/**
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* The GRU cell. Does a single time step operation<br>
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*
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@ -42,9 +84,9 @@ public class SDRNN extends SDOps {
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* @param hLast Output of the previous cell/time step, with shape [batchSize, numUnits] (NUMERIC type)
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* @param GRUWeights Configuration Object
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*/
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public SDVariable[] gru(SDVariable x, SDVariable hLast, GRUWeights GRUWeights) {
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SDValidation.validateNumerical("gru", "x", x);
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SDValidation.validateNumerical("gru", "hLast", hLast);
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public SDVariable[] gruCell(SDVariable x, SDVariable hLast, GRUWeights GRUWeights) {
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SDValidation.validateNumerical("gruCell", "x", x);
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SDValidation.validateNumerical("gruCell", "hLast", hLast);
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return new org.nd4j.linalg.api.ops.impl.layers.recurrent.GRUCell(sd,x, hLast, GRUWeights).outputVariables();
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}
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@ -56,9 +98,10 @@ public class SDRNN extends SDOps {
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* @param hLast Output of the previous cell/time step, with shape [batchSize, numUnits] (NUMERIC type)
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* @param GRUWeights Configuration Object
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*/
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public SDVariable[] gru(String[] names, SDVariable x, SDVariable hLast, GRUWeights GRUWeights) {
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SDValidation.validateNumerical("gru", "x", x);
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SDValidation.validateNumerical("gru", "hLast", hLast);
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public SDVariable[] gruCell(String[] names, SDVariable x, SDVariable hLast,
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GRUWeights GRUWeights) {
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SDValidation.validateNumerical("gruCell", "x", x);
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SDValidation.validateNumerical("gruCell", "hLast", hLast);
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SDVariable[] out = new org.nd4j.linalg.api.ops.impl.layers.recurrent.GRUCell(sd,x, hLast, GRUWeights).outputVariables();
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return sd.updateVariableNamesAndReferences(out, names);
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}
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@ -144,6 +144,8 @@ public class ImportClassMapping {
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org.nd4j.linalg.api.ops.impl.layers.convolution.SpaceToDepth.class,
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org.nd4j.linalg.api.ops.impl.layers.convolution.Upsampling2d.class,
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org.nd4j.linalg.api.ops.impl.layers.convolution.Upsampling2dDerivative.class,
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org.nd4j.linalg.api.ops.impl.layers.recurrent.GRU.class,
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org.nd4j.linalg.api.ops.impl.layers.recurrent.GRUBp.class,
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org.nd4j.linalg.api.ops.impl.layers.recurrent.GRUCell.class,
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org.nd4j.linalg.api.ops.impl.layers.recurrent.LSTMBlockCell.class,
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org.nd4j.linalg.api.ops.impl.layers.recurrent.LSTMCell.class,
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@ -0,0 +1,67 @@
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/* ******************************************************************************
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* Copyright (c) 2020 Konduit K.K.
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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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package org.nd4j.linalg.api.ops.impl.layers.recurrent;
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import lombok.NoArgsConstructor;
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import lombok.NonNull;
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import org.nd4j.autodiff.samediff.SDVariable;
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import org.nd4j.autodiff.samediff.SameDiff;
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import org.nd4j.base.Preconditions;
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import org.nd4j.linalg.api.buffer.DataType;
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import org.nd4j.linalg.api.ndarray.INDArray;
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import org.nd4j.linalg.api.ops.DynamicCustomOp;
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import java.util.Arrays;
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import java.util.Collections;
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import java.util.List;
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@NoArgsConstructor
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public class GRU extends DynamicCustomOp {
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public GRU(@NonNull SameDiff sameDiff, @NonNull SDVariable x, @NonNull SDVariable hI, @NonNull SDVariable Wx, @NonNull SDVariable Wh, @NonNull SDVariable biases) {
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super(null, sameDiff, new SDVariable[]{x, hI, Wx, Wh, biases});
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}
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public GRU(@NonNull INDArray x, @NonNull INDArray hI, @NonNull INDArray Wx, @NonNull INDArray Wh, @NonNull INDArray biases) {
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super(new INDArray[]{x, hI, Wx, Wh, biases}, null);
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}
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@Override
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public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes) {
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Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 5, "Expected 5 inputs to GRU: initial cell output, input-to-hidden weights, hidden-to-hidden weights and biases got %s", inputDataTypes);
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DataType dt = inputDataTypes.get(1);
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for (int i = 0; i < inputDataTypes.size(); i++) {
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Preconditions.checkState(inputDataTypes.get(i).isFPType(), "All input types must be a floating point type, got %s", dt);
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}
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Preconditions.checkState(dt.isFPType(), "Input type 1 must be a floating point type, got %s", dt);
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return Collections.singletonList(dt);
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}
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@Override
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public List<SDVariable> doDiff(List<SDVariable> grads) {
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return Arrays.asList(new GRUBp(sameDiff, arg(0), arg(1), arg(2), arg(3),
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arg(4), grads.get(0)).outputVariables());
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}
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@Override
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public String opName() {
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return "gru";
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}
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}
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@ -0,0 +1,56 @@
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/* ******************************************************************************
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* Copyright (c) 2020 Konduit K.K.
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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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package org.nd4j.linalg.api.ops.impl.layers.recurrent;
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import lombok.NoArgsConstructor;
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import lombok.NonNull;
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import org.nd4j.autodiff.samediff.SDVariable;
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import org.nd4j.autodiff.samediff.SameDiff;
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import org.nd4j.base.Preconditions;
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import org.nd4j.linalg.api.buffer.DataType;
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import org.nd4j.linalg.api.ops.DynamicCustomOp;
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import java.util.ArrayList;
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import java.util.Collections;
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import java.util.List;
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@NoArgsConstructor
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public class GRUBp extends DynamicCustomOp {
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public GRUBp(@NonNull SameDiff sameDiff, @NonNull SDVariable x, @NonNull SDVariable hI, @NonNull SDVariable Wx, @NonNull SDVariable Wh, @NonNull SDVariable biases, @NonNull SDVariable dLdh) {
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super(null, sameDiff, new SDVariable[]{x, hI, Wx, Wh, biases, dLdh});
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}
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@Override
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public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes) {
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DataType dt = inputDataTypes.get(1);
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List<DataType> list = new ArrayList<DataType>();
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list.add(dt);
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list.add(dt);
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list.add(dt);
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list.add(dt);
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list.add(dt);
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return list;
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}
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@Override
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public String opName() {
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return "gru_bp";
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}
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}
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@ -34,6 +34,25 @@ public class NDRNN {
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public NDRNN() {
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}
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/**
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* The GRU operation. Gated Recurrent Unit - Cho et al. 2014.<br>
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*
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* @param x input [time, bS, nIn] (NUMERIC type)
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* @param hLast initial cell output (at time step = 0) [bS, nOut] (NUMERIC type)
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* @param Wx input-to-hidden weights, [nIn, 3*nOut] (NUMERIC type)
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* @param Wh hidden-to-hidden weights, [nOut, 3*nOut] (NUMERIC type)
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* @param biases biases, [3*nOut] (NUMERIC type)
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* @return h cell outputs [time, bS, nOut], that is per each time step (NUMERIC type)
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*/
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public INDArray gru(INDArray x, INDArray hLast, INDArray Wx, INDArray Wh, INDArray biases) {
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NDValidation.validateNumerical("gru", "x", x);
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NDValidation.validateNumerical("gru", "hLast", hLast);
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NDValidation.validateNumerical("gru", "Wx", Wx);
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NDValidation.validateNumerical("gru", "Wh", Wh);
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NDValidation.validateNumerical("gru", "biases", biases);
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return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.recurrent.GRU(x, hLast, Wx, Wh, biases))[0];
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}
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/**
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* The GRU cell. Does a single time step operation<br>
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*
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@ -41,9 +60,9 @@ public class NDRNN {
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* @param hLast Output of the previous cell/time step, with shape [batchSize, numUnits] (NUMERIC type)
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* @param GRUWeights Configuration Object
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*/
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public INDArray[] gru(INDArray x, INDArray hLast, GRUWeights GRUWeights) {
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NDValidation.validateNumerical("gru", "x", x);
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NDValidation.validateNumerical("gru", "hLast", hLast);
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public INDArray[] gruCell(INDArray x, INDArray hLast, GRUWeights GRUWeights) {
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NDValidation.validateNumerical("gruCell", "x", x);
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NDValidation.validateNumerical("gruCell", "hLast", hLast);
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return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.recurrent.GRUCell(x, hLast, GRUWeights));
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}
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@ -38,6 +38,7 @@ import org.nd4j.linalg.api.ops.impl.layers.convolution.DepthwiseConv2D;
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import org.nd4j.linalg.api.ops.impl.layers.convolution.Pooling2D;
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import org.nd4j.linalg.api.ops.impl.layers.convolution.Pooling2DDerivative;
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import org.nd4j.linalg.api.ops.impl.layers.convolution.config.*;
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import org.nd4j.linalg.api.ops.impl.layers.recurrent.GRU;
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import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.LSTMActivations;
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import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.LSTMDataFormat;
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import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.LSTMDirectionMode;
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@ -1701,6 +1702,35 @@ public class LayerOpValidation extends BaseOpValidation {
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assertNull(err);
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}
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@Test
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public void GRUTestCase() {
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int bS = 5;
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int nIn = 4;
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int nOut = 6;
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int time = 2;
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SameDiff sd = SameDiff.create();
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SDVariable in = sd.var("in", Nd4j.randn(DataType.DOUBLE, time, bS, nIn).muli(10));
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SDVariable hLast = sd.var("cLast", Nd4j.zeros(DataType.DOUBLE, bS, nOut));
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SDVariable Wx = sd.var("Wx", Nd4j.randn(DataType.DOUBLE, nIn, 3*nOut));
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SDVariable Wh = sd.var("Wh", Nd4j.randn(DataType.DOUBLE, nOut, 3*nOut));
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SDVariable biases = sd.var("bias", Nd4j.randn(DataType.DOUBLE, 3*nOut));
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SDVariable out = new GRU(sd, in, hLast, Wx, Wh,biases).outputVariable();
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long[] outShapes = new long[]{time,bS, nOut};
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assertArrayEquals(new long[]{time,bS, nOut}, out.eval().shape());
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sd.setLossVariables(out.std(true));
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String err = OpValidation.validate(new TestCase(sd)
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.gradientCheck(true)
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);
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assertNull(err);
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}
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@ -227,7 +227,7 @@ public class RnnOpValidation extends BaseOpValidation {
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.cBias(bc)
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.build();
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SDVariable[] v = sd.rnn().gru(x, hLast, weights);
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SDVariable[] v = sd.rnn().gruCell(x, hLast, weights);
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List<String> toExec = new ArrayList<>();
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for(SDVariable sdv : v){
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toExec.add(sdv.name());
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