rank table Startup

View Source On Gitee

Overview

rank table startup is a startup method unique to the Ascend hardware platform. This method does not rely on third-party libraries and runs as a single process on a single card, requiring the user to create a process in the script that matches the number of cards in use. This method is consistent across nodes in multiple machines and facilitates rapid batch deployment.

Related Configurations:

rank table mainly need to configure the rank_table file, taking the 2-card environment configuration file rank_table_2pcs.json as an example:

{
    "version": "1.0",
    "server_count": "1",
    "server_list": [
        {
            "server_id": "10.*.*.*",
            "device": [
                {"device_id": "0","device_ip": "192.1.*.6","rank_id": "0"},
                {"device_id": "1","device_ip": "192.2.*.6","rank_id": "1"}],
             "host_nic_ip": "reserve"
        }
    ],
    "status": "completed"
}

The parameter items that need to be modified according to the actual training environment are:

  • server_count represents the number of machines involved in training.

  • server_id represents the IP address of the current machine.

  • device_id represents the physical serial number of the card, i.e., the actual serial number in the machine where the card is located.

  • device_ip represents the IP address of the integrated NIC. You can execute the command cat /etc/hccn.conf on the current machine, and the key value of address_x is the IP address of the NIC.

  • rank_id represents the card logical serial number, fixed numbering from 0.

Operation Practice

You can download the full sample code here: startup_method.

The directory structure is as follows:

└─ sample_code
    ├─ startup_method
       ├── allgather_test.py
       ├── rank_table_8pcs.json
       ├── rank_table_16pcs.json
       ├── run_ran_table.sh
       ├── run_ran_table_cluster.sh
    ...

allgather_test.py defines the network structure, run_ran_table.sh, and run_ran_table_cluster.sh are executing the scripts. rank_table_8pcs.json, rank_table_16pcs.json are 8 cards and 16 cards rank_table config file.

1. Preparing Python Training Scripts

Here, as an example of data parallel, a recognition network is trained for the MNIST dataset.

First specify the operation mode, hardware device, etc. Unlike single card scripts, parallel scripts also need to specify configuration items such as parallel mode and initialize HCCL or NCCL communication via init. If you don't set device_target here, it will be automatically specified as the backend hardware device corresponding to the MindSpore package.

import os
import mindspore as ms
from mindspore.communication import init

device_id = int(os.getenv('DEVICE_ID'))
ms.set_context(device_id=device_id)
ms.set_context(mode=ms.GRAPH_MODE)
ms.set_auto_parallel_context(parallel_mode=ms.ParallelMode.DATA_PARALLEL, gradients_mean=True)
init()
ms.set_seed(1)

Then build the following network:

from mindspore import nn

class Network(nn.Cell):
    def __init__(self):
        super().__init__()
        self.flatten = nn.Flatten()
        self.fc = nn.Dense(28*28, 10, weight_init="normal", bias_init="zeros")
        self.relu = nn.ReLU()

    def construct(self, x):
        x = self.flatten(x)
        logits = self.relu(self.fc(x))
        return logits
net = Network()

Finally, the dataset is processed and the training process is defined:

import os
from mindspore import nn
import mindspore as ms
import mindspore.dataset as ds
from mindspore.communication import get_rank, get_group_size

def create_dataset(batch_size):
    dataset_path = os.getenv("DATA_PATH")
    rank_id = get_rank()
    rank_size = get_group_size()
    dataset = ds.MnistDataset(dataset_path, num_shards=rank_size, shard_id=rank_id)
    image_transforms = [
        ds.vision.Rescale(1.0 / 255.0, 0),
        ds.vision.Normalize(mean=(0.1307,), std=(0.3081,)),
        ds.vision.HWC2CHW()
    ]
    label_transform = ds.transforms.TypeCast(ms.int32)
    dataset = dataset.map(image_transforms, 'image')
    dataset = dataset.map(label_transform, 'label')
    dataset = dataset.batch(batch_size)
    return dataset

data_set = create_dataset(32)
loss_fn = nn.CrossEntropyLoss()
optimizer = nn.SGD(net.trainable_params(), 1e-2)

def forward_fn(data, label):
    logits = net(data)
    loss = loss_fn(logits, label)
    return loss, logits

grad_fn = ms.value_and_grad(forward_fn, None, net.trainable_params(), has_aux=True)
grad_reducer = nn.DistributedGradReducer(optimizer.parameters)

for epoch in range(10):
    i = 0
    for data, label in data_set:
        (loss, _), grads = grad_fn(data, label)
        grads = grad_reducer(grads)
        optimizer(grads)
        if i % 10 == 0:
            print("epoch: %s, step: %s, loss is %s" % (epoch, i, loss))
        i += 1

2. Preparing the Startup Script

Single-Machine Multi-Card

The rank table method uses a single-card single-process operation, i.e., 1 process runs on each card, with the same number of processes as the number of cards in use. Each process creates a directory to store log information and operator compilation information. Below is an example of how to run a distributed training script using 8 cards:

RANK_SIZE=8
EXEC_PATH=$(pwd)
if [ ! -d "${EXEC_PATH}/MNIST_Data" ]; then
    if [ ! -f "${EXEC_PATH}/MNIST_Data.zip" ]; then
        wget http://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/MNIST_Data.zip
    fi
    unzip MNIST_Data.zip
fi
export DATA_PATH=${EXEC_PATH}/MNIST_Data/train/

export RANK_TABLE_FILE=${EXEC_PATH}/rank_table_8pcs.json
export RANK_SIZE=$RANK_SIZE

for((i=1;i<${RANK_SIZE};i++))
do
    rm -rf device$i
    mkdir device$i
    cp ./net.py ./device$i
    cd ./device$i
    export DEVICE_ID=$i
    export RANK_ID=$i
    echo "start training for device $i"
    env > env$i.log
    python ./net.py > train$i.log 2>&1 &
    cd ../
done

Distributed-related environment variables are:

  • RANK_TABLE_FILE: Path to the networking information file.

  • DEVICE_ID: The actual serial number of the current card on the machine.

  • RANK_ID: The logical serial number of the current card.

After configuring rank_table_8pcs.json in the current path, execute the following command:

bash run_ran_table.sh

After running, the log files are saved in device0, device1 and other directories, env*.log records information about environment variables, and the output is saved in train*.log, as shown in the example below:

epoch: 0, step: 0, loss is 2.3391366
epoch: 0, step: 10, loss is 1.8047495
epoch: 0, step: 20, loss is 1.2186875
epoch: 0, step: 30, loss is 1.3065228
epoch: 0, step: 40, loss is 1.0825632
epoch: 0, step: 50, loss is 1.0281029
epoch: 0, step: 60, loss is 0.8405618
epoch: 0, step: 70, loss is 0.7346531
epoch: 0, step: 80, loss is 0.688364
epoch: 0, step: 90, loss is 0.51331174
epoch: 0, step: 100, loss is 0.53782797
...

Multi-Machine Multi-Card

In the Ascend environment, the communication of NPU units across machines is the same as the communication of individual NPU units within a single machine, still through the HCCL. The difference is that the NPU units within a single machine are naturally interoperable, while the cross-machine ones need to ensure that the networks of the two machines are interoperable. The method of confirmation is as follows:

Executing the following command on server 1 will configure each device with the device ip of the corresponding device on server 2. For example, configure the destination IP of card 0 on server 1 as the ip of card 0 on server 2. Configuration commands require the hccn_tool tool. The hccn_tool is an HCCL tool that comes with the CANN package.

hccn_tool -i 0 -netdetect -s address 192.*.92.131
hccn_tool -i 1 -netdetect -s address 192.*.93.131
hccn_tool -i 2 -netdetect -s address 192.*.94.131
hccn_tool -i 3 -netdetect -s address 192.*.95.131
hccn_tool -i 4 -netdetect -s address 192.*.92.141
hccn_tool -i 5 -netdetect -s address 192.*.93.141
hccn_tool -i 6 -netdetect -s address 192.*.94.141
hccn_tool -i 7 -netdetect -s address 192.*.95.141

-i 0 specifies the device ID. -netdetect specifies the network detection object IP attribute. -s address indicates that the attribute is set to an IP address. 192.*.92.131 indicates the ip address of device 0 on server 2. The interface command can be referenced here.

After executing the above command on server 1, start checking the network link status with the following command. Another function of hccn_tool is used here, the meaning of which can be found here.

hccn_tool -i 0 -net_health -g
hccn_tool -i 1 -net_health -g
hccn_tool -i 2 -net_health -g
hccn_tool -i 3 -net_health -g
hccn_tool -i 4 -net_health -g
hccn_tool -i 5 -net_health -g
hccn_tool -i 6 -net_health -g
hccn_tool -i 7 -net_health -g

If the connection is successful, the corresponding output is as follows:

net health status: Success

If the connection fails, the corresponding output is as follows:

net health status: Fault

After confirming that the network of the NPU units between the machines is smooth, configure the json configuration file of the multi-machine, this tutorial takes the configuration file of the 16 cards as an example. The detailed description of the configuration file can be referred to the introduction of single-machine multi-card part in this tutorial. It should be noted that in the configuration of the multi-machine json file, it is required that the order of rank_id is consistent with the dictionary order of server_id.

{
  "version": "1.0",
  "server_count": "2",
  "server_list": [
    {
      "server_id": "10.*.*.*",
      "device": [
        {"device_id": "0","device_ip": "192.1.*.6","rank_id": "0"},
        {"device_id": "1","device_ip": "192.2.*.6","rank_id": "1"},
        {"device_id": "2","device_ip": "192.3.*.6","rank_id": "2"},
        {"device_id": "3","device_ip": "192.4.*.6","rank_id": "3"},
        {"device_id": "4","device_ip": "192.1.*.7","rank_id": "4"},
        {"device_id": "5","device_ip": "192.2.*.7","rank_id": "5"},
        {"device_id": "6","device_ip": "192.3.*.7","rank_id": "6"},
        {"device_id": "7","device_ip": "192.4.*.7","rank_id": "7"}],
      "host_nic_ip": "reserve"
    },
    {
      "server_id": "10.*.*.*",
      "device": [
        {"device_id": "0","device_ip": "192.1.*.8","rank_id": "8"},
        {"device_id": "1","device_ip": "192.2.*.8","rank_id": "9"},
        {"device_id": "2","device_ip": "192.3.*.8","rank_id": "10"},
        {"device_id": "3","device_ip": "192.4.*.8","rank_id": "11"},
        {"device_id": "4","device_ip": "192.1.*.9","rank_id": "12"},
        {"device_id": "5","device_ip": "192.2.*.9","rank_id": "13"},
        {"device_id": "6","device_ip": "192.3.*.9","rank_id": "14"},
        {"device_id": "7","device_ip": "192.4.*.9","rank_id": "15"}],
      "host_nic_ip": "reserve"
    }
  ],
  "status": "completed"
}

After preparing the configuration file, you can carry out the organization of distributed multi-machine training scripts. In the case of 2-machine 16-card, the scripts on the two machines are similar to the scripts run on single-machine multi-card, the difference being the specification of different rank_id variables.

RANK_SIZE=16
EXEC_PATH=$(pwd)
if [ ! -d "${EXEC_PATH}/MNIST_Data" ]; then
    if [ ! -f "${EXEC_PATH}/MNIST_Data.zip" ]; then
        wget http://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/MNIST_Data.zip
    fi
    unzip MNIST_Data.zip
fi
export DATA_PATH=${EXEC_PATH}/MNIST_Data/train/

export RANK_TABLE_FILE=${EXEC_PATH}/rank_table_16pcs.json
export RANK_SIZE=$RANK_SIZE

RANK_START=$1
DEVICE_START=0

for((i=0;i<=7;i++));
do
  export RANK_ID=$[i+RANK_START]
  export DEVICE_ID=$[i+DEVICE_START]
  rm -rf ./device_$RANK_ID
  mkdir ./device_$RANK_ID
  cp ./allgather_test.py ./device_$RANK_ID
  cd ./device_$RANK_ID
  env > env$i.log
  python ./allgather_test.py >train$RANK_ID.log 2>&1 &
done

During execution, the following commands are executed on the two machines, where rank_table.json is configured according to the 16-card distributed json file reference shown in this section.

# server0
bash run_ran_table_cluster.sh 0
# server1
bash run_ran_table_cluster.sh 8

After running, the log files are saved in the directories device_0, device_1. The information about the environment variables is recorded in env*.log, and the output is saved in train*.log.