Saving and Loading Models in Hybrid Parallel Mode

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Overview

Background

In the MindSpore model parallel scenario, each instance process stores only the parameter data on the current node. The parameter data of a model parallel Cell on each node is a slice of the complete parameter data. For example, the complete parameter data shape is [8, 8], and the parameter data on each node is a part of the data, for example, shape [2, 8].

In the auto parallel scenario, MindSpore automatically generates the dividing strategy. The MindSpore checkpoint module supports automatic integrating, saving, and loading.

In the hybrid parallel scenario, the dividing strategy is implemented by users. MindSpore saves the slice strategy of model, which is the same on each node, and the data corresponding to each node is stored respectively. Users need to integrate, save, and load the checkpoint files by themselves. This tutorial describes how to integrate, save, and load checkpoint files in the hybrid parallel scenario.

Application Scenario

If you encounter the following scenarios, refer to this tutorial to integrate, save, and load checkpoint files:

Scenario 1: multi-device training and single-device inference

The following describes the overall process of training on 64 devices and inference on a single device:

  1. Execute the training to automatically generate the checkpoint files and the slice strategy files.

  2. Integrate the saved checkpoint files.

    Integrate the divided model parameters based on the specific dividing strategy to generate a new checkpoint file.

  3. Load the new checkpoint file in the single-GPU environment and call the export API to export the model for inference as required.

If the number of GPUs in a cluster in the checkpoint saving environment is the same as that in the loading environment, for example, if the checkpoint files are saved and loaded in the same training environment or training and inference is performed on a single device, you do not need to perform integration, saving and loading.

Scenario 2: The training is divided into multiple stages, and the cluster size in each stage is different.

For example, in the training stage 1, the training environment with 64 devices is used, and in the training stage 2, the training environment with 56 devices is used. The overall operation process is as follows:

  1. Execute the training in stage 1 to automatically generate the checkpoint files and the slice strategy files.

  2. Integrate the saved checkpoint files.

    Integrate the divided model parameters based on the specific dividing strategy to generate a new checkpoint file.

  3. Load the checkpoint file that is integrated and saved in the stage 2 cluster.

    During the loading, you need to redivide the parameter data in the checkpoint file based on the new training environment configuration.

  4. Perform stage 2 training.

Integrating the Saved Checkpoint Files

Overall Process

Import the checkpoint files to be integrated to the network in rank id order and obtain the list of all parameters through the API provided by MindSpore, and then obtain the slice strategy of model. See steps 1 and 2 in the following figure.

Then, update the parameter list and integrate the model parallel parameters. See step 3 in the following figure.

Finally, save the updated parameter list to a file through the API provided by MindSpore to generate a new checkpoint file. See step 4 in the following figure.

img

Preparations

Importing the Checkpoint Files in rank id order

Define the network, call the load_checkpoint and load_param_into_net APIs to import the checkpoint files to the network in rank id order, and then call parameters_and_names API to obtain all parameters in this network.

net = Net()
opt = Momentum(learning_rate=0.01, momentum=0.9, params=net.get_parameters())
net = TrainOneStepCell(net, opt)
param_dicts = []
for i in range(rank_size):
    file_name = os.path.join("./node"+str(i), "CKP_1-4_32.ckpt")  # checkpoint file name of current node
    param_dict = load_checkpoint(file_name)  
    load_param_into_net(net, param_dict)
    param_dict = {}
    for _, param in net.parameters_and_names():
        param_dict[param.name] = param
    param_dicts.append(param_dict)

In the preceding information:

  • rank_size: number of nodes in previous distributed training.

  • load_checkpoint: loads the checkpoint model parameter file and returns a parameter dictionary.

  • load_param_into_net: loads model parameter data to the network.

Obtaining a List of All Parameters on the Network

Call the build_searched_strategy API to obtain the slice strategy of model.

strategy = build_searched_strategy("./strategy_train.ckpt")

In the preceding information:

  • strategy_train.ckpt: name of model slice strategy, set by users calling set_auto_parallel_context API and customizing strategy_ckpt_save_file parameter before training network.

Integrate the Model Parallel Parameters

The following uses a model parameter as an example to describe a specific integration process.

The parameter name is weight and the dividing strategy is to perform dividing in a 4-device scenario.

  1. Obtain the data value on all nodes for model parallel parameters.

     sliced_parameters = []
 for i in range(4):
     parameter = param_dicts[i].get("weight")
     sliced_parameters.append(parameter)

    To ensure that the parameter update speed remains unchanged, you need to integrate the parameters saved in the optimizer, for example, moments.weight.

  2. Call the merge_sliced_parameter API to merge the sliced parameters.

    merged_parameter = merge_sliced_parameter(sliced_parameters, strategy)

If there are multiple model parallel parameters, repeat steps 1 to 2 to process them one by one.

Saving the Data and Generating a New Checkpoint File

  1. Convert param_dict to param_list.

    param_list = []
for (key, value) in param_dict.items():
    each_param = {}
    each_param["name"] = key
    if isinstance(value.data, Tensor):
        param_data = value.data
    else:
        param_data = Tensor(value.data)
    each_param["data"] = param_data
    param_list.append(each_param)

  2. Call the save_checkpoint API to write the parameter data to a file and generate a new checkpoint file.

    save_checkpoint(param_list, "./CKP-Integrated_1-4_32.ckpt")

    In the preceding information:

    • save_checkpoint: saves network model parameters to a file.

    • CKP-Integrated_1-4_32.ckpt: name of the generated checkpoint model parameter file.

Loading the Integrated and Saved Checkpoint File

Overall Process

If you need to load the integrated and saved checkpoint file to multi-device training or inference, divide the parallel parameter data based on the new strategy before loading the model parameters to the network. The following steps are implemented in the pre-training script. Steps 1 and 3 are the same as the strategy of checkpoint loading in a single-node system. Step 2 is added to divide model parallel parameters. In the single-device training/inference scenario, data dividing is not involved. In this case, step 2 can be skipped.

Step 1: Loading the Checkpoint File

Call the load_checkpoint API to load model parameter data from the checkpoint file.

param_dict = load_checkpoint("./CKP-Integrated_1-4_32.ckpt")

  • load_checkpoint: loads the checkpoint model parameter file and returns a parameter dictionary.

  • CKP-Integrated_1-4_32.ckpt: name of the checkpoint model parameter file to be loaded.

Step 2: Dividing a Model Parallel Parameter

The following uses a specific model parameter as an example. The parameter name is weight, the data value is Tensor [[1, 2, 3, 4], [5, 6, 7, 8]], and the dividing strategy is to perform dividing in the two-device scenario based on [2, 1]. Data distribution after dividing is as follows:

Device0

Device1

Value [1, 2, 3, 4]

Value [5, 6, 7, 8]

  1. Divide the model parameter data.

    In the following code example, data is divided into two slices in dimension 0.

    new_param = parameter_dict["weight"]
slice_list = np.split(new_param.data.asnumpy(), 2, axis=0)
new_param_moments = parameter_dict["moments.weight"]
slice_moments_list = np.split(new_param_moments.data.asnumpy(), 2, axis=0)

    Data after dividing:

    slice_list[0]  --- [1, 2, 3, 4]    Corresponding to device0
slice_list[1]  --- [5, 6, 7, 8]    Corresponding to device1

    Similar to slice_list, slice_moments_list is divided into two tensors with the shape of [1, 4].

  2. Load the corresponding data slice on each node.

    Obtain rank_id of the current node and load data based on rank_id.

    rank = get_rank()
tensor_slice = Tensor(slice_list[rank])
tensor_slice_moments = Tensor(slice_moments_list[rank])

    • get_rank: obtains the ID of the current device in the cluster.

  3. Modify values of model parameters.

    new_param.set_data(tensor_slice, True)
new_param_moments.set_data(tensor_slice_moments, True)

    • set_data: sets the value of a model parameter. The API parameter type is Tensor or number.

Step 3: Loading the Modified Parameter Data to the Network

Call the load_param_into_net API to load the model parameter data to the network.

net = Net()
opt = Momentum(learning_rate=0.01, momentum=0.9, params=parallel_net.get_parameters())
load_param_into_net(net, param_dict)
load_param_into_net(opt, param_dict)

Example

Scenario Description

Overall scenario: The training is divided into two stages. The cluster scales in the two stages are different. The MatMul operator at the FC layer is simulated to run in parallel.

User process:

  1. Execute stage 1 training. There are four devices in stage 1 training environment. The weight shape of the MatMul operator on each device is [2, 8]. Checkpoint files are automatically exported during the training.

  2. Execute the script to integrate checkpoint files. Based on the specific dividing strategy, integrate the divided model parameters to generate the integrated checkpoint file.

  3. Execute stage 2 training: There are two devices in stage 2 training environment. The weight shape of the MatMul operator on each device is [4, 8]. Load the initialized model parameter data from the integrated checkpoint file and then perform training.

For details about the distributed environment configuration and training code, see Distributed Training.

This document provides the example code for integrating checkpoint files and loading checkpoint files before distributed training. The code is for reference only.

Example Code

  1. Run the following script to integrate the checkpoint files:

    python  ./integrate_checkpoint.py "Name of the checkpoint file to be integrated" "Path and name of the checkpoint file generated after integration" "Path and name of the strategy file" "Number of nodes"

    integrate_checkpoint.py:

     import numpy as np
 import os
 import mindspore.nn as nn
 from mindspore import Tensor, Parameter
 import mindspore.ops as ops
 from mindspore import save_checkpoint, load_checkpoint, build_searched_strategy, merge_sliced_parameter

 class Net(nn.Cell):
     def __init__(self,weight_init):
         super(Net, self).__init__()
         self.weight = Parameter(Tensor(weight_init), layerwise_parallel=True)
         self.fc = ops.MatMul(transpose_b=True)

     def construct(self, x):
         x = self.fc(x, self.weight)
         return x

 def integrate_ckpt_file(old_ckpt_file, new_ckpt_file, strategy_file, rank_size):
     weight = np.ones([2, 8]).astype(np.float32)
     net = Net(weight)
     opt = Momentum(learning_rate=0.01, momentum=0.9, params=net.get_parameters())
     net = TrainOneStepCell(net, opt)

     # load CheckPoint into net in rank id order
     param_dicts = []
     for i in range(rank_size):
         file_name = os.path.join("./node"+str(i), old_ckpt_file)
         param_dict = load_checkpoint(file_name)  
         load_param_into_net(net, param_dict)
         param_dict = {}
         for _, param in net.parameters_and_names():
             param_dict[param.name] = param
             param_dicts.append(param_dict)

     strategy = build_searched_strategy(strategy_file)
     param_dict = {}

     for paramname in ["weight", "moments.weight"]:
         # get layer wise model parallel parameter
         sliced_parameters = []
         for i in range(rank_size):
             parameter = param_dicts[i].get(paramname)
             sliced_parameters.append(parameter)

         # merge the parallel parameters of the model
         merged_parameter = merge_sliced_parameter(sliced_parameters, strategy)
         param_dict[paramname] = merged_parameter

     # convert param_dict to list type data
     param_list = []
     for (key, value) in param_dict.items():
         each_param = {}
         each_param["name"] = key
         if isinstance(value.data, Tensor):
             param_data = value.data
         else:
             param_data = Tensor(value.data)
         each_param["data"] = param_data
         param_list.append(each_param)

     # call the API to generate a new CheckPoint file
     save_checkpoint(param_list, new_ckpt_file)

     return

 if __name__ == "__main__":
     try:
         old_ckpt_file = sys.argv[1]
         new_ckpt_file = sys.argv[2]
         strategy_file = sys.argv[3]
         rank_size = int(sys.argv[4])
         integrate_ckpt_file(old_ckpt_file, new_ckpt_file, strategy_file, rank_size)
     except:
         print("Fail to integrate checkpoint file")
         sys.exit(-1)

    The command output is as follows.

    Before the script is executed, the parameter values in the checkpoint files are as follows:

    device0:
name is weight
value is
[[0.87537426 1.0448935 0.86736983 0.8836905 0.77354026 0.69588304 0.9183654 0.7792076]
 [0.87224025 0.8726848 0.771446 0.81967723 0.88974726 0.7988162 0.72919345 0.7677011]]
name is learning_rate
value is [0.01]
name is momentum
value is [0.9]
name is moments.weight
value is
[[0.2567724 -0.07485991 0.282002 0.2456022 0.454939 0.619168 0.18964815 0.45714882]
 [0.25946522 0.24344791 0.45677605 0.3611395 0.23378398 0.41439137 0.5312468 0.4696194]]

device1:
name is weight
value is
[[0.9210751 0.9050457 0.9827775 0.920396 0.9240526 0.9750359 1.0275179 1.0819869]
 [0.73605865 0.84631145 0.9746683 0.9386582 0.82902765 0.83565056 0.9702136 1.0514659]]
name is learning_rate
value is [0.01]
name is momentum
value is [0.9]
name is moments.weight
value is
[[0.2417504 0.28193963 0.06713893 0.21510397 0.23380603 0.11424308 0.0218009 -0.11969765]
 [0.45955992 0.22664294 0.01990281 0.0731914 0.27125207 0.27298513 -0.01716102 -0.15327111]]

device2:
name is weight
value is
[[1.0108461 0.8689414  0.91719437 0.8805056 0.7994629 0.8999671 0.7585804 1.0287056 ]
 [0.90653455 0.60146594 0.7206475 0.8306303 0.8364681 0.89625114 0.7354735 0.8447268]]
name is learning_rate
value is [0.01]
name is momentum
value is [0.9]
name is moments.weight
value is
[[0.03440702 0.41419312 0.24817684 0.30765256 0.48516113 0.24904746 0.57791173 0.00955463]
 [0.13458519 0.6690533 0.49259356 0.28319967 0.25951773 0.16777472 0.45696738 0.24933104]]

device3:
name is weight
value is
[[0.7147005 0.9168278 0.80178416 0.6258351 0.8413766 0.5909515 0.696347 0.71359116]
 [0.20506378 0.03691584 0.2454556 0.12978578 0.19065076 0.23904312 0.27509746 0.34614682]]
name is learning_rate
value is [0.01]
name is momentum
value is [0.9]
name is moments.weight
value is
[[0.14152306 0.5040985 0.24455397 0.10907605 0.11319532 0.19538902 0.01208619 0.40430856]
[-0.7773164 -0.47611716 -0.6041424 -0.6144473 -0.2651842 -0.31909415 -0.4510405 -0.12860501]]

    After the script is executed, the parameter values in the checkpoint files are as follows:

    name is weight
value is
[[1.1138763 1.0962057 1.3516843 1.0812817 1.1579804 1.1078343 1.0906502 1.3207073]
 [0.916671 1.0781671 1.0368758 0.9680898 1.1735439 1.0628364 0.9960786 1.0135143]
 [0.8828271 0.7963984 0.90675324 0.9830291 0.89010954 0.897052 0.7890109 0.89784735]
 [1.0011744 1.0840297 1.0201758 1.0882459 0.94232416 1.0775206 1.0195118 1.0528734]
 [1.0053468 0.98402303 0.99762845 0.97587246 1.0259694 1.0055295 0.99420834 0.9496847]
 [1.0851002 1.0295962 1.0999886 1.0958165 0.9765328 1.146529 1.0970603 1.1388365]
 [0.7147005 0.9168278 0.80178416 0.6258351 0.8413766 0.5909515 0.696347 0.71359116]
 [0.20506378 0.03691584 0.2454556 0.12978578 0.19065076 0.23904312 0.27509746 0.34614682]]
name is learning_rate
value is [0.01]
name is momentum
value is [0.9]
name is moments.weight
value is
[[0.2567724 -0.07485991 0.282002 0.2456022 0.454939 0.619168 0.18964815 0.45714882]
 [0.25946522 0.24344791 0.45677605 0.3611395 0.23378398 0.41439137 0.5312468 0.4696194 ]
 [0.2417504 0.28193963 0.06713893 0.21510397 0.23380603 0.11424308 0.0218009 -0.11969765]
 [0.45955992 0.22664294 0.01990281 0.0731914 0.27125207 0.27298513 -0.01716102 -0.15327111]
 [0.03440702 0.41419312 0.24817684 0.30765256 0.48516113 0.24904746 0.57791173 0.00955463]
 [0.13458519 0.6690533 0.49259356 0.28319967 0.25951773 0.16777472 0.45696738  0.24933104]
 [0.14152306 0.5040985 0.24455397 0.10907605 0.11319532 0.19538902 0.01208619  0.40430856]
 [-0.7773164 -0.47611716 -0.6041424 -0.6144473 -0.2651842 -0.31909415 -0.4510405
  -0.12860501]]

  2. Execute stage 2 training and load the checkpoint file before training. The training code needs to be supplemented based on the site requirements.

    import numpy as np
import os
import mindspore.nn as nn
from mindspore import context
from mindspore.communication import init
from mindspore import Tensor, Parameter
import mindspore.ops as ops
from mindspore import load_checkpoint, load_param_into_net

from mindspore.communication import init
devid = int(os.getenv('DEVICE_ID'))
context.set_context(mode=context.GRAPH_MODE,device_target='Ascend',save_graphs=True, device_id=devid)
init()

class Net(nn.Cell):
    def __init__(self,weight_init):
        super(Net, self).__init__()
        self.weight = Parameter(Tensor(weight_init), layerwise_parallel=True)
        self.fc = ops.MatMul(transpose_b=True)

    def construct(self, x):
        x = self.fc(x, self.weight)
        return x
def train_mindspore_impl_fc(input, label, ckpt_file):
    param_dict = load_checkpoint(ckpt_file)

    for paramname in ["weight", "moments.weight"]:
        # get layer wise model parallel parameter
        new_param = parameter_dict[paramname]
        # split the model parameter data
        slice_list = np.split(new_param.data.asnumpy(), 2, axis=0)
        # Load the corresponding data slice
        rank = get_rank()
        tensor_slice = Tensor(slice_list[rank])
        # modify model parameter data values
        new_param.set_data(tensor_slice, True)

        # load the modified parameter data into the network
        weight = np.ones([4, 8]).astype(np.float32)
        net = Net(weight)
        load_param_into_net(net, param_dict)
        opt = Momentum(learning_rate=0.01, momentum=0.9, params=parallel_net.get_parameters())
        load_param_into_net(opt, param_dict)
        # train code
        ...

    if __name__ == "__main__":
        input = np.random.random((4, 8)).astype(np.float32)
        print("mean = ", np.mean(input,axis=1, keepdims=True))
        label = np.random.random((4, 4)).astype(np.float32)
        ckpt_file = sys.argv[1]
        train_mindspore_impl_fc(input, label, ckpt_file)

    In the preceding information:

    • mode=context.GRAPH_MODE: sets the running mode to graph mode for distributed training. (The PyNative mode does not support parallel running.)

    • device_id: physical sequence number of a device, that is, the actual sequence number of the device on a computer where the device is located.

    • init: completes the distributed training initialization.

    Parameter values after loading:

    device0:
name is weight
value is
[[0.87537426 1.0448935 0.86736983 0.8836905 0.77354026 0.69588304 0.9183654 0.7792076]
[0.87224025 0.8726848 0.771446 0.81967723 0.88974726 0.7988162 0.72919345 0.7677011]
[0.8828271 0.7963984 0.90675324 0.9830291 0.89010954 0.897052 0.7890109 0.89784735]
[1.0011744 1.0840297 1.0201758 1.0882459 0.94232416 1.0775206 1.0195118 1.0528734]]
name is learning_rate
value is [0.01]
name is momentum
value is [0.9]
name is moments.weight
value is
[[0.2567724 -0.07485991 0.282002 0.2456022 0.454939 0.619168 0.18964815 0.45714882]
[0.25946522 0.24344791 0.45677605 0.3611395 0.23378398 0.41439137 0.5312468 0.4696194]
[0.2417504 0.28193963 0.06713893 0.21510397 0.23380603 0.11424308 0.0218009 -0.11969765]
[0.45955992 0.22664294 0.01990281 0.0731914 0.27125207 0.27298513 -0.01716102  -0.15327111]]

device1:
name is weight
value is
[[1.0053468 0.98402303 0.99762845 0.97587246 1.0259694 1.0055295 0.99420834 0.9496847]
[1.0851002 1.0295962 1.0999886 1.0958165 0.9765328 1.146529 1.0970603 1.1388365]
[0.7147005 0.9168278 0.80178416 0.6258351 0.8413766 0.5909515 0.696347 0.71359116]
[0.20506378 0.03691584 0.2454556 0.12978578 0.19065076 0.23904312 0.27509746 0.34614682]]
name is learning_rate
value is [0.01]
name is momentum
value is [0.9]
name is moments.weight
value is
[[0.03440702 0.41419312 0.24817684 0.30765256 0.48516113 0.24904746 0.57791173 0.00955463]
[0.13458519 0.6690533 0.49259356 0.28319967 0.25951773 0.16777472 0.45696738  0.24933104]
[0.14152306 0.5040985 0.24455397 0.10907605 0.11319532 0.19538902 0.01208619  0.40430856]
[-0.7773164 -0.47611716 -0.6041424 -0.6144473 -0.2651842 -0.31909415 -0.4510405 -0.12860501]]