[WIP] Few more pre-release fixes (#461)
* error code check in CudaMemoryManager Signed-off-by: raver119@gmail.com <raver119@gmail.com> * clear Signed-off-by: raver119@gmail.com <raver119@gmail.com> * clear model before exiting Signed-off-by: raver119@gmail.com <raver119@gmail.com> * MultiLayerNetwork/ComputationGraph.close() [WIP] (#460) * MultiLayerNetwork/ComputationGraph.close() Signed-off-by: Alex Black <blacka101@gmail.com> * Copyright header Signed-off-by: Alex Black <blacka101@gmail.com> * Fix Signed-off-by: Alex Black <blacka101@gmail.com> * - fix for handling release of nested DataBuffers - couple of additional tests for released DataBuffers Signed-off-by: raver119@gmail.com <raver119@gmail.com> * PW test: increase number of epochs slightly Signed-off-by: raver119@gmail.com <raver119@gmail.com> Co-authored-by: Alex Black <blacka101@gmail.com>master
raver119
GitHub
parent
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commit
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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.deeplearning4j.nn.misc;
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import org.deeplearning4j.BaseDL4JTest;
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import org.deeplearning4j.TestUtils;
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import org.deeplearning4j.nn.api.Updater;
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import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
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import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
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import org.deeplearning4j.nn.conf.inputs.InputType;
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import org.deeplearning4j.nn.conf.layers.*;
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import org.deeplearning4j.nn.graph.ComputationGraph;
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import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
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import org.junit.Test;
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import org.nd4j.linalg.activations.Activation;
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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.factory.Nd4j;
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import org.nd4j.linalg.learning.config.Adam;
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import static org.junit.Assert.assertTrue;
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public class CloseNetworkTests extends BaseDL4JTest {
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public static MultiLayerNetwork getTestNet() {
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MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
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.updater(new Adam(1e-3))
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.list()
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.layer(new ConvolutionLayer.Builder().nOut(5).kernelSize(3, 3).activation(Activation.TANH).build())
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.layer(new BatchNormalization.Builder().nOut(5).build())
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.layer(new SubsamplingLayer.Builder().build())
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.layer(new DenseLayer.Builder().nOut(10).activation(Activation.RELU).build())
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.layer(new OutputLayer.Builder().nOut(10).build())
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.setInputType(InputType.convolutional(28, 28, 1))
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.build();
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MultiLayerNetwork net = new MultiLayerNetwork(conf);
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net.init();
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return net;
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}
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@Test
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public void testCloseMLN() {
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for (boolean train : new boolean[]{false, true}) {
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for (boolean test : new boolean[]{false, true}) {
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MultiLayerNetwork net = getTestNet();
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INDArray f = Nd4j.rand(DataType.FLOAT, 16, 1, 28, 28);
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INDArray l = TestUtils.randomOneHot(16, 10);
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if (train) {
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for (int i = 0; i < 3; i++) {
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net.fit(f, l);
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}
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}
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if (test) {
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for (int i = 0; i < 3; i++) {
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net.output(f);
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}
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}
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net.close();
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assertTrue(net.params().wasClosed());
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if(train) {
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assertTrue(net.getGradientsViewArray().wasClosed());
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Updater u = net.getUpdater(false);
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assertTrue(u.getStateViewArray().wasClosed());
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}
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//Make sure we don't get crashes etc when trying to use after closing
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try {
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net.output(f);
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} catch (IllegalStateException e) {
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String msg = e.getMessage();
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assertTrue(msg, msg.contains("released"));
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}
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try {
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net.fit(f, l);
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} catch (IllegalStateException e) {
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String msg = e.getMessage();
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assertTrue(msg, msg.contains("released"));
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}
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}
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}
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}
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@Test
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public void testCloseCG() {
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for (boolean train : new boolean[]{false, true}) {
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for (boolean test : new boolean[]{false, true}) {
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ComputationGraph net = getTestNet().toComputationGraph();
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INDArray f = Nd4j.rand(DataType.FLOAT, 16, 1, 28, 28);
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INDArray l = TestUtils.randomOneHot(16, 10);
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if (train) {
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for (int i = 0; i < 3; i++) {
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net.fit(new INDArray[]{f}, new INDArray[]{l});
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}
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}
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if (test) {
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for (int i = 0; i < 3; i++) {
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net.output(f);
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}
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}
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net.close();
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assertTrue(net.params().wasClosed());
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if(train) {
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assertTrue(net.getGradientsViewArray().wasClosed());
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Updater u = net.getUpdater(false);
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assertTrue(u.getStateViewArray().wasClosed());
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}
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//Make sure we don't get crashes etc when trying to use after closing
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try {
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net.output(f);
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} catch (IllegalStateException e) {
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String msg = e.getMessage();
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assertTrue(msg, msg.contains("released"));
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}
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try {
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net.fit(new INDArray[]{f}, new INDArray[]{l});
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} catch (IllegalStateException e) {
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String msg = e.getMessage();
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assertTrue(msg, msg.contains("released"));
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}
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}
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}
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}
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}
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@ -1035,5 +1035,9 @@ public class TestOptimizers extends BaseDL4JTest {
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public boolean updaterDivideByMinibatch(String paramName) {
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return true;
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}
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@Override
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public void close(){
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}
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}
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}
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@ -1055,4 +1055,9 @@ public class BarnesHutTsne implements Model {
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}
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@Override
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public void close(){
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//No-op
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}
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}
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@ -0,0 +1,4 @@
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package org.deeplearning4j.nn.modelimport.keras;
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public class Temp {
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}
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@ -233,4 +233,7 @@ public interface Model {
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* Apply any constraints to the model
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*/
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void applyConstraints(int iteration, int epoch);
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void close();
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}
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@ -4824,4 +4824,28 @@ public class ComputationGraph implements Serializable, Model, NeuralNetwork {
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if (cg.getUpdater() != null && cg.getUpdater(false).getStateViewArray() != null)
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this.getUpdater(true).getStateViewArray().assign(cg.getUpdater(false).getStateViewArray());
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}
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/**
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* Close the network and deallocate all native memory, including: parameters, gradients, updater memory and workspaces
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* Note that the network should not be used again for any purpose after it has been closed
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*/
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@Override
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public void close(){
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//Close the INDArray and dealloc
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if(flattenedParams.closeable())
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flattenedParams.close();
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if(flattenedGradients != null && flattenedGradients.closeable())
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flattenedGradients.close();
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Updater u = getUpdater(false);
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if(u != null && u.getStateViewArray() != null) {
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INDArray state = u.getStateViewArray();
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if(state.closeable())
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state.close();
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}
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Nd4j.getWorkspaceManager().destroyAllWorkspacesForCurrentThread();
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System.gc();
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}
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}
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@ -428,4 +428,9 @@ public abstract class AbstractLayer<LayerConfT extends org.deeplearning4j.nn.con
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//Majority of params's gradients should be... Exception: batch norm mean/variance estimate
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return true;
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}
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@Override
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public void close(){
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//No-op for individual layers
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}
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}
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@ -599,4 +599,9 @@ public class BidirectionalLayer implements RecurrentLayer {
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return ret;
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}
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}
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@Override
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public void close(){
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//No-op for individual layers
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}
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}
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@ -1144,4 +1144,9 @@ public class VariationalAutoencoder implements Layer {
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}
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}
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}
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@Override
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public void close(){
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//No-op for individual layers
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}
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}
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@ -329,4 +329,9 @@ public abstract class BaseWrapperLayer implements Layer {
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public boolean updaterDivideByMinibatch(String paramName) {
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return underlying.updaterDivideByMinibatch(paramName);
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}
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@Override
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public void close(){
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//No-op for individual layers
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}
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}
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@ -4085,4 +4085,27 @@ public class MultiLayerNetwork implements Serializable, Classifier, Layer, Neura
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this.getUpdater(true).getStateViewArray().assign(mln.getUpdater(false).getStateViewArray());
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}
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/**
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* Close the network and deallocate all native memory, including: parameters, gradients, updater memory and workspaces
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* Note that the network should not be used again for any purpose after it has been closed
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*/
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@Override
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public void close(){
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//Close the INDArray and dealloc
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if(flattenedParams.closeable())
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flattenedParams.close();
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if(flattenedGradients != null && flattenedGradients.closeable())
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flattenedGradients.close();
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Updater u = getUpdater(false);
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if(u != null && u.getStateViewArray() != null) {
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INDArray state = u.getStateViewArray();
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if(state.closeable())
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state.close();
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}
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Nd4j.getWorkspaceManager().destroyAllWorkspacesForCurrentThread();
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System.gc();
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}
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}
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@ -450,6 +450,14 @@ public class DefaultTrainer extends Thread implements Trainer {
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} finally {
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log.debug("Terminating all workspaces for trainer_{}", threadId);
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Nd4j.getWorkspaceManager().destroyAllWorkspacesForCurrentThread();
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if (!onRootModel) {
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replicatedModel.close();
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}
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// let's try to enforce GC to actually clean all references now
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replicatedModel.clear();
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System.gc();
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isStopped.set(true);
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}
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}
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@ -58,7 +58,7 @@ public class ParallelWrapperTest extends BaseDL4JTest {
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// for GPU you usually want to have higher batchSize
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int batchSize = 128;
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int nEpochs = 2;
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int nEpochs = 5;
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int seed = 123;
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log.info("Load data....");
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@ -1957,6 +1957,9 @@ public abstract class BaseDataBuffer implements DataBuffer {
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@Override
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public boolean wasClosed() {
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if (wrappedDataBuffer != null && wrappedDataBuffer != this)
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return wrappedDataBuffer.wasClosed();
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return released;
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}
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@ -71,7 +71,13 @@ public class CudaMemoryManager extends BasicMemoryManager {
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return ptr;//allocator.getMemoryHandler().alloc(AllocationStatus.HOST, null, null, initialize).getHostPointer();
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} else if (kind == MemoryKind.DEVICE) {
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val ptr = NativeOpsHolder.getInstance().getDeviceNativeOps().mallocDevice(bytes, 0, 0);
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//log.info("Allocating {} bytes for device_{}", bytes, Nd4j.getAffinityManager().getDeviceForCurrentThread());
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log.trace("Allocating {} bytes for device_{}", bytes, Nd4j.getAffinityManager().getDeviceForCurrentThread());
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val ec = NativeOpsHolder.getInstance().getDeviceNativeOps().lastErrorCode();
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if (ec != 0) {
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val em = NativeOpsHolder.getInstance().getDeviceNativeOps().lastErrorMessage();
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throw new RuntimeException(em + "; Bytes: [" + bytes + "]; Error code [" + ec + "]; DEVICE [" + Nd4j.getAffinityManager().getDeviceForCurrentThread() + "]");
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}
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if (ptr == null)
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throw new RuntimeException("Failed to allocate " + bytes + " bytes from DEVICE [" + Nd4j.getAffinityManager().getDeviceForCurrentThread() + "] memory");
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@ -85,6 +85,9 @@ public abstract class BaseCudaDataBuffer extends BaseDataBuffer implements JCuda
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}
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public OpaqueDataBuffer getOpaqueDataBuffer() {
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if (released)
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throw new IllegalStateException("You can't use DataBuffer once it was released");
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return ptrDataBuffer;
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}
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@ -104,7 +107,8 @@ public abstract class BaseCudaDataBuffer extends BaseDataBuffer implements JCuda
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ptrDataBuffer = OpaqueDataBuffer.externalizedDataBuffer(length, this.type, pointer, specialPointer);
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this.allocationPoint = new AllocationPoint(ptrDataBuffer, this.type.width() * length);
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Nd4j.getDeallocatorService().pickObject(this);
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Nd4j.getDeallocatorService().pickObject(this);if (released)
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throw new IllegalStateException("You can't use DataBuffer once it was released");
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}
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/**
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@ -473,6 +477,9 @@ public abstract class BaseCudaDataBuffer extends BaseDataBuffer implements JCuda
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}
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public BaseCudaDataBuffer(@NonNull DataBuffer underlyingBuffer, long length, long offset) {
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if (underlyingBuffer.wasClosed())
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throw new IllegalStateException("You can't use DataBuffer once it was released");
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//this(length, underlyingBuffer.getElementSize(), offset);
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this.allocationMode = AllocationMode.MIXED_DATA_TYPES;
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initTypeAndSize();
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@ -198,4 +198,27 @@ public class BaseCudaDataBufferTest extends BaseND4JTest {
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// there shoul dbe no exceptions during execution
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assertEquals(Nd4j.getAffinityManager().getNumberOfDevices(), cnt.get());
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}
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@Test
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public void testClose_1() {
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val x = Nd4j.createFromArray(1, 2, 3);
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x.close();
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assertTrue(x.wasClosed());
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assertTrue(x.data().wasClosed());
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}
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@Test
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public void testClose_2() {
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val x = Nd4j.create(DataType.FLOAT, 5, 6);
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val row = x.getRow(1);
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x.close();
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assertTrue(x.wasClosed());
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assertTrue(x.data().wasClosed());
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assertTrue(row.wasClosed());
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assertTrue(row.data().wasClosed());
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}
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}
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@ -61,6 +61,9 @@ public abstract class BaseCpuDataBuffer extends BaseDataBuffer implements Deallo
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}
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public OpaqueDataBuffer getOpaqueDataBuffer() {
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if (released)
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throw new IllegalStateException("You can't use DataBuffer once it was released");
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return ptrDataBuffer;
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}
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Reference in New Issue