Custom Debugging Information

Overview

This section describes how to use the customized capabilities provided by MindSpore, such as callback, metrics, Print operators and log printing, to help you quickly debug the training network.

Introduction to Callback

Callback is a callback function, and callback is not a function but a class. You can use callback function to observe the internal status and related information of the network during training or perform specific actions in a specific period. For example, you can monitor the loss, save model parameters, dynamically adjust parameters, and terminate training tasks in advance.

Callback Capabilities of MindSpore

MindSpore provides the Callback capabilities to allow users to insert customized operations in a specific phase of training or inference, including:

  • Callback classes such as ModelCheckpoint, LossMonitor, and SummaryCollector provided by the MindSpore framework.

  • User-customized Callback supported by MindSpore .

Usage: Transfer the Callback object in the model.train method. It can be a Callback list, for example:

import mindspore as ms
from mindspore.train import ModelCheckpoint,LossMonitor

ckpt_cb = ModelCheckpoint()
loss_cb = LossMonitor()
summary_cb = ms.SummaryCollector(summary_dir='./summary_dir')
model.train(epoch, dataset, callbacks=[ckpt_cb, loss_cb, summary_cb])

ModelCheckpoint can save model parameters for retraining or inference. LossMonitor can output loss in the log for easily viewing, and it also monitors the changes in the loss value during training, terminates the training when the loss value is Nan or Inf.
SummaryCollector can save the training information to files for subsequent visualizations. During the training process, the Callback list will execute the Callback function in the defined order. Therefore, in the definition process, the dependency between Callback needs to be considered.

Custom Callback

You can customize Callback based on the callback base class as required.

The Callback base class is defined as follows:

class Callback():
    """Callback base class"""
    def begin(self, run_context):
        """Called once before the network executing."""
        pass

    def epoch_begin(self, run_context):
        """Called before each epoch beginning."""
        pass

    def epoch_end(self, run_context):
        """Called after each epoch finished."""
        pass

    def step_begin(self, run_context):
        """Called before each step beginning."""
        pass

    def step_end(self, run_context):
        """Called after each step finished."""
        pass

    def end(self, run_context):
        """Called once after network training."""
        pass

The Callback can record important information during training and transfer the information to the Callback object through a dictionary variable RunContext.original_args(), You can obtain related attributes from each custom Callback and perform customized operations. You can also customize other variables and transfer them to the RunContext.original_args() object.

The main attributes of RunContext.original_args() are as follows:

  • loss_fn: Loss function

  • optimizer: Optimizer

  • train_dataset: Training dataset

  • epoch_num: Number of training epochs

  • batch_num: Number of batches in an epoch

  • train_network: Training network

  • cur_epoch_num: Number of current epochs

  • cur_step_num: Number of current steps

  • parallel_mode: Parallel mode

  • list_callback: All callback functions

  • net_outputs: Network output results

  • …

You can inherit the Callback base class to customize a callback object.

Here are two examples to further explain the usage of custom Callback.

  • Terminate training within the specified time.

    import mindspore as ms

class StopAtTime(ms.train.Callback):
    def __init__(self, run_time):
        super(StopAtTime, self).__init__()
        self.run_time = run_time*60

    def begin(self, run_context):
        cb_params = run_context.original_args()
        cb_params.init_time = time.time()

    def step_end(self, run_context):
        cb_params = run_context.original_args()
        epoch_num = cb_params.cur_epoch_num
        step_num = cb_params.cur_step_num
        loss = cb_params.net_outputs
        cur_time = time.time()
        if (cur_time - cb_params.init_time) > self.run_time:
            print("epoch: ", epoch_num, " step: ", step_num, " loss: ", loss)
            run_context.request_stop()

    The implementation principle is: You can use the run_context.original_args method to obtain the cb_params dictionary, which contains the main attribute information described above. In addition, you can modify and add values in the dictionary. In the preceding example, an init_time object is defined in begin and transferred to the cb_params dictionary. A decision is made at each step_end. When the training time is longer than the configured time threshold, a training termination signal will be sent to the run_context to terminate the training in advance and the current values of epoch, step, and loss will be printed.

  • Save the checkpoint file with the highest accuracy during training.

    import mindspore as ms

class SaveCallback(ms.train.Callback):
    def __init__(self, eval_model, ds_eval):
        super(SaveCallback, self).__init__()
        self.model = eval_model
        self.ds_eval = ds_eval
        self.acc = 0

    def step_end(self, run_context):
        cb_params = run_context.original_args()
        result = self.model.eval(self.ds_eval)
        if result['accuracy'] > self.acc:
            self.acc = result['accuracy']
            file_name = str(self.acc) + ".ckpt"
            save_checkpoint(save_obj=cb_params.train_network, ckpt_file_name=file_name)
            print("Save the maximum accuracy checkpoint,the accuracy is", self.acc)

    The specific implementation principle is: define a Callback object, and initialize the object to receive the model object and the ds_eval (verification dataset). Verify the accuracy of the model in the step_end phase. When the accuracy is the current highest, automatically trigger the save checkpoint method to save the current parameters.

MindSpore Metrics Introduction

After the training is complete, you can use metrics to evaluate the training result.

MindSpore provides multiple metrics, such as accuracy, loss, precision, recall, and F1.

You can define a metrics dictionary object that contains multiple metrics and transfer them to the model object and use the model.eval function to verify the training result.

import mindspore as ms
import mindspore.nn as nn
from mindspore.train import Accuracy, F1, Model, Loss, Precision, Recall

metrics = {
    'accuracy': Accuracy(),
    'loss': Loss(),
    'precision': Precision(),
    'recall': Recall(),
    'f1_score': F1()
}
model = Model(network=net, loss_fn=net_loss, optimizer=net_opt, metrics=metrics)
result = model.eval(ds_eval)

The model.eval method returns a dictionary that contains the metrics and results transferred to the metrics.

The Callback function can also be used in the eval process, and the user can call the related API or customize the Callback method to achieve the desired function.

You can also define your own metrics class by inheriting the Metric base class and rewriting the clear, update, and eval methods.

The Accuracy operator is used as an example to describe the internal implementation principle.

The Accuracy inherits the EvaluationBase base class and rewrites the preceding three methods.

  • The clear method initializes related calculation parameters in the class.

  • The update method accepts the predicted value and tag value and updates the internal variables of Accuracy.

  • The eval method calculates related indicators and returns the calculation result.

By invoking the eval method of Accuracy, you will obtain the calculation result.

You can understand how Accuracy runs by using the following code:

import mindspore as ms
from mindspore.train import Accuracy
import numpy as np

x = ms.Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
y = ms.Tensor(np.array([1, 0, 1]))
metric = Accuracy()
metric.clear()
metric.update(x, y)
accuracy = metric.eval()
print('Accuracy is ', accuracy)

The output is as follows:

Accuracy is 0.6667

MindSpore Print Operator Introduction

MindSpore-developed Print operator is used to print the tensors or character strings input by users. Multiple strings, multiple tensors, and a combination of tensors and strings are supported, which are separated by comma (,). The Print operator is supported in Ascend and GPU environment. The method of using the MindSpore Print operator is the same as that of other operators. You need to declare the operator in the __init__ in the network and call it inconstruct, and the specific usage examples and output results are as follows:

import numpy as np
import mindspore as ms
import mindspore.ops as ops
import mindspore.nn as nn

ms.set_context(mode=ms.GRAPH_MODE)

class PrintDemo(nn.Cell):
    def __init__(self):
        super(PrintDemo, self).__init__()
        self.print = ops.Print()

    def construct(self, x, y):
        self.print('print Tensor x and Tensor y:', x, y)
        return x

x = ms.Tensor(np.ones([2, 1]).astype(np.int32))
y = ms.Tensor(np.ones([2, 2]).astype(np.int32))
net = PrintDemo()
output = net(x, y)

The output is as follows:

print Tensor x and Tensor y:
Tensor(shape=[2, 1], dtype=Int32, value=
[[1]
 [1]])
Tensor(shape=[2, 2], dtype=Int32, value=
[[1 1]
 [1 1]])

Running Data Recorder

Running Data Recorder(RDR) is the feature MindSpore provides to record data while training program is running. If a running exception occurs in MindSpore, the pre-recorded data in MindSpore is automatically exported to assist in locating the cause of the running exception. Different exceptions will export different data, for instance, the occurrence of Run task error exception, the computational graph, execution sequence of the graph, memory allocation and other information will be exported to assist in locating the cause of the exception.

Not all run exceptions export data, and only partial exception exports are currently supported.

Only supports the data collection of CPU/Ascend/GPU in the training scenario with the graph mode.

Usage

Set RDR by Configuration File

  1. Create the configuration file mindspore_config.json.

    {
    "rdr": {
        "enable": true,
        "mode": 1,
        "path": "/path/to/rdr/dir"
    }
}

    enable: Controls whether the RDR is enabled.

    mode: Controls RDR data exporting mode. When mode is set to 1, RDR exports data only in the exceptional scenario. When mode is set to 2, RDR exports data in exceptional or normal scenario.

    path: Set the path to which RDR stores data. Only absolute path is supported.

  2. Configure RDR via context.

    import mindspore as ms
ms.set_context(env_config_path="./mindspore_config.json")

Set RDR by Environment Variables

Set export MS_RDR_ENABLE=1 to enable RDR, and set export MS_RDR_MODE=1 or export MS_RDR_MODE=2 to control exporting mode for RDR data, and set the root directory by export MS_RDR_PATH=/path/to/root/dir for recording data. The final directory for recording data is /path/to/root/dir/rank_{RANK_ID}/rdr/. RANK_ID is the unique ID for multi-cards training, the single card scenario defaults to RANK_ID=0.

The configuration file set by the user takes precedence over the environment variables.

Exception Handling

If MindSpore is used for training on Ascend 910, there is an exception Run task error in training.

When we go to the directory for recording data, we can see several files appear in this directory, each file represents a kind of data. For example, hwopt_d_before_graph_0.ir is a computational graph file. You can use a text tool to open this file to view the calculational graph and analyze whether the calculational graph meets your expectations.

Diagnosis Handling

When RDR is enabled and environment variable export MS_RDR_MODE=2 is set, it is diagnostic mode. After the graph compilation is complete, we can also see the saved file which is the same as those that are exception handled in the export directory of the RDR file.

Memory Reuse

The memory reuse is to let different Tensors share the same part of the memory to reduce memory overhead and support a larger network. After shutting down, each Tensor has its own independent memory space, and tensors have no shared memory.

The MindSpore memory multiplexing function is turned on by default, and the function can be manually controlled to turn off and on in the following ways.

Usage

  1. Construct configuration file mindspore_config.json.

    {
    "sys": {
        "mem_reuse": true
    }
}

mem_reuse: controls whether the memory multiplexing function is turned on. When it is set to true, the control memory multiplexing function is turned on, and when false, the memory multiplexing function is turned off.

  1. Configure the memory multiplexing function through context.

    import mindspore as ms
ms.set_context(env_config_path="./mindspore_config.json")