Ascend
GPU
Distributed Parallel
Whole Process
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 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 and in horizontally. 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.
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.
The following describes how to use parameter server to train LeNet on Ascend 910:
Learn how to train a LeNet using the MNIST dataset by referring to https://gitee.com/mindspore/models/tree/r1.5/official/cv/lenet.
First of all, use mindspore.context.set_ps_context(enable_ps=True)
to enable Parameter Server training mode.
mindspore.communication.init()
.mindspore.context.reset_ps_context()
to disable Parameter Server training mode.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:
mindspore.nn.Cell.set_param_ps()
to set all weight recursions of nn.Cell
.mindspore.Parameter.set_param_ps()
to set the weight.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.On the basis of the original training script, set all LeNet model weights to be trained on the parameter server:
context.set_ps_context(enable_ps=True)
network = LeNet5(cfg.num_classes)
network.set_param_ps()
[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. Detailed network training script can be referred to https://gitee.com/mindspore/models/tree/r1.5/official/recommend/wide_and_deep.
context.set_auto_parallel_context(full_batch=True,
parallel_mode=ParallelMode.AUTO_PARALLEL)
context.set_context(enable_sparse=True)
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
.enable_sparse
: whether to enable sparse training, default: False
. enable_sparse
=True
indicates enabling sparse training. The parameter sparse
of all EmbeddingLookup
kernels which enable cache must be equal to the value of enable_sparse
in the parameter server mode.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
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=1
export MS_WORKER_NUM=1
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
Server.sh
:
#!/bin/bash
export MS_SERVER_NUM=1
export MS_WORKER_NUM=1
export MS_SCHED_HOST=XXX.XXX.XXX.XXX
export MS_SCHED_PORT=XXXX
export MS_ROLE=MS_PSERVER
python train.py --device_target=Ascend --data_path=path/to/dataset
Worker.sh
:
#!/bin/bash
export MS_SERVER_NUM=1
export MS_WORKER_NUM=1
export MS_SCHED_HOST=XXX.XXX.XXX.XXX
export MS_SCHED_PORT=XXXX
export MS_ROLE=MS_WORKER
python train.py --device_target=Ascend --data_path=path/to/dataset
Run the following commands separately:
sh Scheduler.sh > scheduler.log 2>&1 &
sh Server.sh > server.log 2>&1 &
sh Worker.sh > worker.log 2>&1 &
Start training.
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
...
此处可能存在不合适展示的内容,页面不予展示。您可通过相关编辑功能自查并修改。
如您确认内容无涉及 不当用语 / 纯广告导流 / 暴力 / 低俗色情 / 侵权 / 盗版 / 虚假 / 无价值内容或违法国家有关法律法规的内容,可点击提交进行申诉,我们将尽快为您处理。