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use "session.runner.run " to predict with tensors,after many times, the memory grow higher and higher then oom #567

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hanfengatonline opened this issue Nov 12, 2024 · 14 comments

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@hanfengatonline
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hanfengatonline commented Nov 12, 2024

Please make sure that this is a bug. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:bug_template

System information

  • Have I written custom code (as opposed to using a stock example script provided in TensorFlow):
  • OS Platform and Distribution (e.g., Linux Ubuntu 16.04 x86_64):Linux x86_64)
  • TensorFlow installed from (source or binary):
  • TensorFlow version (use command below):
  • org.tensorflow tensorflow-core-api 0.4.2 org.tensorflow tensorflow-core-native 1.0.0-rc.2 linux-x86_64
  • Java version (i.e., the output of java -version): java 8
  • Java command line flags (e.g., GC parameters):
  • Python version (if transferring a model trained in Python):
  • Bazel version (if compiling from source):
  • GCC/Compiler version (if compiling from source):
  • CUDA/cuDNN version:
  • GPU model and memory:

Describe the current behavior
we use "session.runner.run " to predict with tensors online,After hours of requests, the instance's memory kept increasing and eventually resulted in an out-of-memory (OOM) (or core dumped )error,
tensorflow
tensorflow_mem

here is my behavior
At the start of the service, we load the session using the loadTensorflowModel method and persist it in memory. During runtime, a complete request process involves: 1. Feeding the Tensor using session.runner.feed, 2. Fetching the results using session.runner.fetch, 3. Obtaining the model's scoring results using the session.runner.run method, 4. Finally, closing both the input tensors and output tensors to complete the request and return the response.

Describe the expected behavior
I desire to keep the memory stable, maintaining it around 65%, rather than continuously increasing over time.

Code to reproduce the issue

package org.example
import org.tensorflow.SavedModelBundle
import org.tensorflow.ndarray.Shape
import org.tensorflow.ndarray.impl.buffer.raw.RawDataBufferFactory
import org.tensorflow.ndarray.buffer.FloatDataBuffer
import org.tensorflow.proto.framework.{ConfigProto, RunOptions}
import org.tensorflow.types.TFloat32

import scala.collection.JavaConversions.iterableAsScalaIterable
import scala.collection.JavaConverters.{asScalaBufferConverter, iterableAsScalaIterableConverter}
import scala.util.Random

object test {
  def main(args: Array[String]): Unit = {
    valpath = "/xxxxx/tensorflow_test"
    val kernel = loadTensorflowModel("/xxxxx/tensorflow_test")
    val json = 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    val data = JacksonUtil.decode[Map[String,Seq[String]]](json)
    val runner = kernel.session().runner()
    val runTimes = (0 to 1000000).toSeq
    runTimes.par.foreach(num =>{
      val inputTensorList: java.util.ArrayList[TFloat32] = new java.util.ArrayList[TFloat32]()
      //第一步构建输入tensor
      data.map{
        case (tensorName, featureId) => {
          //默认每个inputTensor是一个一维数组
          val seq = featureId.map(p => Random.nextFloat()).toSeq
          val data: FloatDataBuffer = RawDataBufferFactory.create(seq.toArray,true)
          val shape: Shape = Shape.of(1,seq.size)

          val tensor = TFloat32.tensorOf(shape,data)
          runner.feed(tensorName,tensor)
          inputTensorList.add(tensor)
        }
      }

      //构建输出tensor
      for(i <- 0 until 2 ){
        runner.fetch("StatefulPartitionedCall",i)
      }

      //开始预测
      val output = runner.run().asScala

      val scores:Array[Float] = output.map(ten => {
        ten.asRawTensor().data().asFloats().getFloat(0)
      }).toArray
      inputTensorList.asScala.foreach(_.close())
      output.foreach(_.close())
      print(scores)
    })
  }

  def loadTensorflowModel(path:String):Session= {
    val config = ConfigProto.newBuilder
    .putDeviceCount("CPU",  Runtime.getRuntime.availableProcessors)
    .setInterOpParallelismThreads(8) // 设置操作之间的线程数
    .setIntraOpParallelismThreads(8) // 设置操作内的线程数
    .setOperationTimeoutInMs(500)
    .build

    val options = RunOptions.newBuilder
      .setTimeoutInMs(500)
      .build // 设置超时时间为 5 秒

    val modelBundle = SavedModelBundle
      .loader(s"$path")
      .withTags("serve")
      .withConfigProto(config)
      .withRunOptions(options)
      .load
    modelBundle.session()
  }

}

Other info / logs
Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.

when i stop to request ,then the memory would gradually decrease until it returns to normal levels.

modelFile:after unzip, and load the path
test_model.zip

@Craigacp
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Are you mixing TF-Java versions? 1.0.0 doesn't run on Java 8, and you really shouldn't mix tensorflow-core-api 0.4.2 with tensorflow-core-native 1.0.0-rc.2.

@hanfengatonline
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Author

@Craigacp
tensorflow-core-native 1.0.0-rc.2 linux-x86_64, Is my package dependency usage incorrect?
native_tensorflow
sorry It seems that this package may not have been released with support for Java 8 version?

@Craigacp
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We dropped support for Java 8 with TF-Java 0.5.0. That version and 1.0.0-rc.2 require Java 11. You can probably compile the v1.0.0 tag with Java 8 by changing the pom file and removing the error-prone & spotless checks, but we're not supporting it anymore.

@hanfengatonline
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hanfengatonline commented Nov 13, 2024

@Craigacp hi, i have tried jdk 11, and tensorflow-core-api / native: 1.0.0-rc.2
image
it seems that the results have not changed.

jdk: openjdk version "11.0.23" 2024-04-16
linux: linux11,x86_64
my code:

import org.tensorflow.{SavedModelBundle, Session, Tensor}
import org.tensorflow.ndarray.Shape
import org.tensorflow.ndarray.buffer.FloatDataBuffer
import org.tensorflow.ndarray.impl.buffer.raw.RawDataBufferFactory
import org.tensorflow.proto.{ConfigProto, RunOptions}
import org.tensorflow.types.TFloat32

import scala.jdk.CollectionConverters.iterableAsScalaIterableConverter

object test {
  def main(args: Array[String]): Unit = {
    val path = "//Users/bytedance/Downloads/tensorflow_test"
    val kernel = loadTensorflowModel("/Users/bytedance/Downloads/tensorflow_test")
    val json = 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    val data = JacksonUtil.decode[Map[String,Seq[String]]](json)
    val runner = kernel.runner()
    val inputTensorList: java.util.ArrayList[Tensor] = new java.util.ArrayList[Tensor]()
    //第一步构建输入tensor
    data.map{
      case (tensorName, featureId) => {
        //默认每个inputTensor是一个一维数组
        val seq = featureId.map(p => p.toFloat).toSeq
        val data: FloatDataBuffer = RawDataBufferFactory.create(seq.toArray,true)
        val shape: Shape = Shape.of(1,seq.size)

        val tensor = TFloat32.tensorOf(shape,data)
        runner.feed(tensorName,tensor)
        inputTensorList.add(tensor)
      }
    }

    //构建输出tensor
    for(i <- 0 until 2 ){
      runner.fetch("StatefulPartitionedCall",i)
    }

    //开始预测
    val output = runner.run().asScala

    val scores:Array[Float] = output.map(ten => {
      ten.getValue.asRawTensor().data().asFloats().getFloat(0)
    }).toArray
    inputTensorList.asScala.foreach(_.close())
    output.foreach(_.getValue.close())
    print(scores)
  }

  def loadTensorflowModel(path:String):Session= {
    val config = ConfigProto.newBuilder
      .putDeviceCount("CPU",  Runtime.getRuntime.availableProcessors)
      .setInterOpParallelismThreads(8) // 设置操作之间的线程数
      .setIntraOpParallelismThreads(8) // 设置操作内的线程数
      .setOperationTimeoutInMs(500)
      .build

    val options = RunOptions.newBuilder
      .setTimeoutInMs(500)
      .build // 设置超时时间为 5 秒

    val modelBundle = SavedModelBundle
      .loader(s"$path")
      .withTags("serve")
      .withConfigProto(config)
      .withRunOptions(options)
      .load
    modelBundle.session()
  }
}

image

@Craigacp
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Is the Java heap expanding and triggering an OOM, or is the native heap expanding? How are the Java GC parameters set?

@hanfengatonline
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hanfengatonline commented Nov 13, 2024

@Craigacp yes, there has oom,
my bootstrap.sh :
java -Xms18G -Xmx18G
-XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=${root_dir}/dist/java_headdump.hprof
-XX:ActiveProcessorCount=${MY_CPU_LIMIT:-2}
-XX:+UseG1GC
-XX:MaxGCPauseMillis=200
--add-opens java.base/java.lang=ALL-UNNAMED
--add-opens java.base/java.lang.invoke=ALL-UNNAMED
--add-opens java.base/java.lang.reflect=ALL-UNNAMED
--add-opens java.base/java.io=ALL-UNNAMED
--add-opens java.base/java.nio=ALL-UNNAMED
--add-opens java.base/java.net=ALL-UNNAMED
--add-opens java.base/java.util=ALL-UNNAMED
--add-opens java.base/java.util.jar=ALL-UNNAMED
--add-opens java.base/java.util.concurrent=ALL-UNNAMED
--add-opens java.base/java.util.concurrent.atomic=ALL-UNNAMED
--add-opens java.base/java.security=ALL-UNNAMED
--add-opens java.base/jdk.internal=ALL-UNNAMED
--add-opens java.base/jdk.internal.access=ALL-UNNAMED
--add-opens java.base/jdk.internal.misc=ALL-UNNAMED
--add-opens java.base/jdk.internal.vm=ALL-UNNAMED
--add-opens java.base/sun.security.action=ALL-UNNAMED
--add-opens java.base/sun.net=ALL-UNNAMED
--add-opens java.base/sun.net.util=ALL-UNNAMED
--add-opens jdk.unsupported/sun.misc=ALL-UNNAMED
--add-opens java.base/sun.nio.ch=ALL-UNNAMED
--add-opens java.base/sun.nio.cs=ALL-UNNAMED
--add-opens jdk.naming.rmi/com.sun.jndi.rmi.registry=ALL-UNNAMED
--add-opens java.base/sun.util.calendar=ALL-UNNAMED
-Xloggc:/opt/tiger/toutiao/log/app/gc.log
-XX:+ExplicitGCInvokesConcurrent
-javaagent:/opt/tiger/toutiao/caijing/app/quasar-core-0.8.0.jar
-Dspring.profiles.active=${tce_host_env}
-Djava.net.preferIPv6Addresses=true
-Dlogback.configurationFile=file:${root_dir}/dist/logback.xml
-Dspring.properties.active=/opt/tiger/toutiao/caijing/app/environment.properties
-jar ${root_dir}/mkt-server-predict-${tce_host_env}-1.0-SNAPSHOT.jar \

@hanfengatonline
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@Craigacp java heap is ok, only 3GB, but total cost 25.1G by tensorflow graph and runtime tensorflow netive memory

@hanfengatonline
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@Craigacp after shell pmap -x "mypid"
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@hanfengatonline
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hanfengatonline commented Nov 13, 2024

@Craigacp we find the memory cost by [ anon ],
Address Kbytes RSS Dirty Mode Mapping
0000000380000000 18862080 12397204 12397204 rw--- [ anon ]

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@Craigacp
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Ok, I'll see if I can replicate it and run it down.

@hanfengatonline
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hanfengatonline commented Nov 13, 2024

@Craigacp thanks so much , we will not close session and graph, so i will session.runner.feed/fetch many times,even though I have closed the tensor generated each time, I am unsure if there is a cache in the session.runner, causing memory to gradually be consumed as the number of requests increases.

import org.tensorflow.{SavedModelBundle, Session, Tensor}
import org.tensorflow.ndarray.Shape
import org.tensorflow.ndarray.buffer.FloatDataBuffer
import org.tensorflow.ndarray.impl.buffer.raw.RawDataBufferFactory
import org.tensorflow.proto.{ConfigProto, RunOptions}
import org.tensorflow.types.TFloat32

import scala.jdk.CollectionConverters.iterableAsScalaIterableConverter

object test {
  def main(args: Array[String]): Unit = {
    val path = "//Users/bytedance/Downloads/tensorflow_test"
    val kernel = loadTensorflowModel("/Users/bytedance/Downloads/tensorflow_test")
    val json = 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    val data = JacksonUtil.decode[Map[String,Seq[String]]](json)
    val runner = kernel.runner()
    val inputTensorList: java.util.ArrayList[Tensor] = new java.util.ArrayList[Tensor]()
   
    // 100 1000 10000 100000 Please try it yourself
    for( i<- 0 to 100){
      data.map{
        case (tensorName, featureId) => {
          val seq = featureId.map(p => p.toFloat).toSeq
          val data: FloatDataBuffer = RawDataBufferFactory.create(seq.toArray,true)
          val shape: Shape = Shape.of(1,seq.size)

          val tensor = TFloat32.tensorOf(shape,data)
          runner.feed(tensorName,tensor)
          inputTensorList.add(tensor)
        }
      }

      for(i <- 0 until 2 ){
        runner.fetch("StatefulPartitionedCall",i)
      }

      val output = runner.run().asScala

      val scores:Array[Float] = output.map(ten => {
        ten.getValue.asRawTensor().data().asFloats().getFloat(0)
      }).toArray
      inputTensorList.asScala.foreach(_.close())
      output.foreach(_.getValue.close())
      print(scores)
    }
    
  }

  def loadTensorflowModel(path:String):Session= {
    val config = ConfigProto.newBuilder
      .putDeviceCount("CPU",  Runtime.getRuntime.availableProcessors)
      .setInterOpParallelismThreads(8) 
      .setIntraOpParallelismThreads(8) 
      .setOperationTimeoutInMs(500)
      .build

    val options = RunOptions.newBuilder
      .setTimeoutInMs(500)
      .build

    val modelBundle = SavedModelBundle
      .loader(s"$path")
      .withTags("serve")
      .withConfigProto(config)
      .withRunOptions(options)
      .load
    modelBundle.session()
  }

}

@hanfengatonline
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Author

@Craigacp hi, Any progress?

@pluppens
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pluppens commented Nov 26, 2024

@hanfengatonline Could you try with 1.0.0-rc1 and see if the problem persists?

Edit: apologies, I was fearing a native memory leak, but that doesn't seem to be the case (see #588 for more context).

@Craigacp
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Collaborator

Craigacp commented Dec 6, 2024

I ported your code over to Java so I could test it and I couldn't see any leak when running it.

My memory usage was pretty solid at 2.7GB on a 2023 M3 Max MBP, using Java 17.

I had to move the runner creation inside the loop so it didn't crash, runners should be short lived objects for a single execution of the TF graph.

import com.fasterxml.jackson.core.JsonProcessingException;
import com.fasterxml.jackson.core.type.TypeReference;
import com.fasterxml.jackson.databind.ObjectMapper;
import org.tensorflow.SavedModelBundle;
import org.tensorflow.Session;
import org.tensorflow.Tensor;
import org.tensorflow.ndarray.Shape;
import org.tensorflow.ndarray.impl.buffer.raw.RawDataBufferFactory;
import org.tensorflow.proto.ConfigProto;
import org.tensorflow.proto.RunOptions;
import org.tensorflow.types.TFloat32;

import java.util.ArrayList;
import java.util.Arrays;
import java.util.HashMap;
import java.util.List;


public class MemLeakTest {
    public static void main(String[] args) throws JsonProcessingException {
        var kernel = loadTensorflowModel("~/Downloads/test_model");
        var json = 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        ObjectMapper mapper = new ObjectMapper();
        TypeReference<HashMap<String, List<String>>> typeRef
                = new TypeReference<>() {};

        HashMap<String,List<String>> data = mapper.readValue(json, typeRef);
        System.out.println("Got " + data);


        // 100 1000 10000 100000 Please try it yourself
        for (int i = 0; i < 100000; i++) {
            var runner = kernel.runner();
            var inputTensorList = new ArrayList<Tensor>();
            data.forEach((var tensorName, var featureId) -> {
                float[] seq = new float[featureId.size()];
                for (int j = 0; j < featureId.size(); j++) {
                    seq[j] = Float.parseFloat(featureId.get(j));
                }
                var innerData = RawDataBufferFactory.create(seq, true);
                var shape = Shape.of(1, seq.length);

                var tensor = TFloat32.tensorOf(shape, innerData);
                runner.feed(tensorName, tensor);
                inputTensorList.add(tensor);
            });

            for (int j = 0; j < 2; j++) {
                runner.fetch("StatefulPartitionedCall", j);
            }

            var output = runner.run();

            var scores = new float[output.size()];
            for (int j = 0; j < output.size(); j++) {
                scores[j] = output.get(j).asRawTensor().data().asFloats().getFloat(0);
            }
            inputTensorList.forEach(Tensor::close);
            output.close();
            System.out.println(Arrays.toString(scores));

            if (i % 1000==0) {
                System.out.println("counter" + i);
            }
        }
    }

    public static Session loadTensorflowModel(String path) {
        var config = ConfigProto.newBuilder()
                .putDeviceCount("CPU", Runtime.getRuntime().availableProcessors())
                .setInterOpParallelismThreads(8)
                .setIntraOpParallelismThreads(8)
                .setOperationTimeoutInMs(500)
                .build();

        var options = RunOptions.newBuilder()
                .setTimeoutInMs(500)
                .build();

        var modelBundle = SavedModelBundle
                .loader(path)
                .withTags("serve")
                .withConfigProto(config)
                .withRunOptions(options)
                .load();
        return modelBundle.session();
    }

}

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