Parameter Server Mode

Overview

A parameter server is a widely used architecture in distributed training. Compared with the synchronous AllReduce training method, a parameter server has better flexibility, scalability, and node failover capabilities. Specifically, the parameter server supports both synchronous and asynchronous SGD(Stochastic Gradient Descent) training algorithms. In terms of scalability, model computing and update are separately deployed in the worker and server processes, so that resources of the worker and server can be independently scaled out in horizontally (add or delete resources of the worker and server). In addition, in an environment of a large-scale data center, various failures often occur in a computing device, a network, and a storage device, and consequently some nodes are abnormal. However, in an architecture of a parameter server, such a failure can be relatively easily handled without affecting a training job.

Basic Principle

In the parameter server implementation of MindSpore, the self-developed communication framework (core) is used as the basic architecture. Based on the remote communication capability provided by the core and abstract Send/Broadcast primitives, the distributed training algorithm of the synchronous SGD is implemented. In addition, with the high-performance collective communication library in Ascend and GPU(HCCL and NCCL), MindSpore also provides the hybrid training mode of parameter server and AllReduce. Some weights can be stored and updated through the parameter server, and other weights are still trained through the AllReduce algorithm.

The ps-lite architecture consists of three independent components: server, worker, and scheduler. Their functions are as follows:

• Server: saves model weights and backward computation gradients, and updates the model using gradients pushed by workers.

• Worker: performs forward and backward computation on the network. The gradient value for backward computation is uploaded to a server through the Push API, and the model updated by the server is downloaded to the worker through the Pull API.

• Scheduler: establishes the communication relationship between the server and worker.

Preparations

The following describes how to use parameter server to train LeNet on Ascend 910:

Training Script Preparation

Learn how to train a LeNet using the MNIST dataset by referring to https://gitee.com/mindspore/models/tree/r1.9/official/cv/lenet.

Parameter Setting

1. First of all, use mindspore.set_ps_context(enable_ps=True) to enable Parameter Server training mode.

   • This method should be called before mindspore.communication.init().
   • If you don't call this method, the Environment Variable Setting below will not take effect.
   • Use mindspore.reset_ps_context() to disable Parameter Server training mode.

2. Secondly, call mindspore.communication.init() to initialize distributed training, including network building for Server, Worker and Scheduler nodes and initializing collective communication(HCCL, NCCL).

   • Launching Parameter Server training through mpirun is no longer supported in and after MindSpore-1.8.0 version. MindSpore uses built-in communication module to build the cluster and initialize collective communication, so data parallel/auto parallel modes are still available on Worker process side. Please refer to the link Training without Relying on OpenMPI for details.

3. In this training mode, you can use either of the following methods to control whether the training parameters are updated by the Parameter Server and whether the training parameters are initialized on Worker or Server:

   • Use mindspore.nn.Cell.set_param_ps() to set all weight recursions of nn.Cell.
   • Use mindspore.Parameter.set_param_ps() to set the weight.
   • The size of the weight which is updated by Parameter Server should not exceed INT_MAX(2^31 - 1) bytes.
   • The interface set_param_ps can receive a bool parameter:init_in_server, indicating whether this training parameter is initialized on the Server side. init_in_server defaults to False, indicating that this training parameter is initialized on Worker. Currently, only the training parameter embedding_table of the EmbeddingLookup operator is supported to be initialized on Server side to solve the problem of insufficient memory caused by the initialization of a large shape embedding_table on Worker. The EmbeddingLookup operator's target attribute needs to be set to 'CPU'. The training parameter initialized on the Server side will no longer be synchronized to Worker. If it involves multi-Server training and saves CheckPoint, each Server will save a CheckPoint after the training.

4. On the basis of the original training script, set all LeNet model weights to be trained on the Parameter Server:

   set_ps_context(enable_ps=True)
   init()
   network = LeNet5(cfg.num_classes)
   network.set_param_ps()
   

5. [optional configuration] For a large shape embedding_table, because the device can not store a full amount of embedding_table. You can configure the vocab_cache_size of EmbeddingLookup operator to enable the cache function of EmbeddingLookup in the Parameter Server training mode. The vocab_cache_size of embedding_table is trained on device, and a full amount of embedding_table is stored in the Server. The embedding_table of the next batch is swapped to the cache in advance, and the expired embedding_table is put back to the Server when the cache cannot be placed, to achieve the purpose of improving the training performance. Each Server could save a checkpoint containing the trained embedding_table after the training. The Embedding cache supports the sparse mode. User need to set the sparse parameter of all the EmbeddingLookup operators that enable cache to True. The sparse mode will deduplicate the input feature id of the operator to reduce the amount of calculation and communication. Detailed network training script can be referred to https://gitee.com/mindspore/models/tree/r1.9/official/recommend/wide_and_deep.

   set_auto_parallel_context(full_batch=True,
   parallel_mode=ParallelMode.AUTO_PARALLEL)
   network = Net()
   model = Model(network)
   model.train(epoch, train_dataset, dataset_sink_mode=True)
   

   In the information:

   • dataset_sink_mode: whether to enable the sink mode of dataset or not. When True, it indicates enabled, and pass the data through the dataset channel. It must be set to True in this scenario (The inference during training also needs to enable the sink mode of dataset).
   • full_batch: whether to load the dataset in full or not. When True, it indicates fully load, and data of each device is the same. It must be set to True in the multi-workers scenario.
   • parallel_mode:parallel mode, auto parallel mode must be enabled in the multi-workers scenario, please set parallel_mode=ParallelMode.AUTO_PARALLEL.

In Parameter Server mode, control flow is not supported. So we need to change model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()}, amp_level="O2") to model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()}) in train.py. This will unset amp_level and eliminate the impact of control flow.

Environment Variable Setting

MindSpore reads environment variables to control parameter server training. The environment variables include the following options (all scripts of MS_SCHED_HOST and MS_SCHED_PORT must be consistent):

export MS_SERVER_NUM=1                # Server number
export MS_WORKER_NUM=1                # Worker number
export MS_SCHED_HOST=XXX.XXX.XXX.XXX  # Scheduler IP address
export MS_SCHED_PORT=XXXX             # Scheduler port
export MS_ROLE=MS_SCHED               # The role of this process: MS_SCHED represents the scheduler, MS_WORKER represents the worker, MS_PSERVER represents the Server

Training

1. Shell scripts

   Provide the shell scripts corresponding to the worker, server, and scheduler roles to start training:

   Scheduler.sh:

   #!/bin/bash
   export MS_SERVER_NUM=8
   export MS_WORKER_NUM=8
   export MS_SCHED_HOST=XXX.XXX.XXX.XXX
   export MS_SCHED_PORT=XXXX
   export MS_ROLE=MS_SCHED
   python train.py --device_target=Ascend --data_path=path/to/dataset > scheduler.log 2>&1 &
   

   Server.sh:

   #!/bin/bash
   export MS_SERVER_NUM=8
   export MS_WORKER_NUM=8
   export MS_SCHED_HOST=XXX.XXX.XXX.XXX
   export MS_SCHED_PORT=XXXX
   export MS_ROLE=MS_PSERVER
   for((server_id=0;server_id<${MS_SERVER_NUM};server_id++))
   do
       python train.py --device_target=Ascend --data_path=path/to/dataset > server_${server_id}.log 2>&1 &
   done
   

   Worker.sh:

   #!/bin/bash
   export MS_SERVER_NUM=8
   export MS_WORKER_NUM=8
   export MS_SCHED_HOST=XXX.XXX.XXX.XXX
   export MS_SCHED_PORT=XXXX
   export MS_ROLE=MS_WORKER
   for((worker_id=0;worker_id<${MS_WORKER_NUM};worker_id++))
   do
       python train.py --device_target=Ascend --data_path=path/to/dataset > worker_${worker_id}.log 2>&1 &
   done
   

   Run the following commands separately:

   sh Scheduler.sh
   sh Server.sh
   sh Worker.sh
   

   Start training. MindSpore launches multiple-worker and multiple-server training through the above method and has no dependency on any third-party components.

2. Viewing result

   Run the following command to view the communication logs between the server and worker in the scheduler.log file:

   The server node id:b5d8a47c-46d7-49a5-aecf-d29d7f8b6124,node ip: 10.*.*.*,node port:46737 assign rank id:0
   The worker node id:55e86d4b-d717-4930-b414-ebd80082f541 assign rank id:1
   Start the scheduler node is successful!
   

   The preceding information indicates that the communication between the server, worker, and scheduler is established successfully.

   Check the training result in the worker.log file:

   epoch: 1 step: 1, loss is 2.302287
   epoch: 1 step: 2, loss is 2.304071
   epoch: 1 step: 3, loss is 2.308778
   epoch: 1 step: 4, loss is 2.301943
   ...