Error Reporting Analysis

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This section is used to introduce the various functional debugging capabilities provided by MindSpore for neural network developers and framework developers. Functional debugging refers to the debugging capabilities of developers in the process of developing neural network or framework functions, which is different from the debugging and tuning of performance and accuracy after the functions are implemented. The functional debugging is divided into network development debugging and framework development debugging from different purposes of use. Network development debugging is used to meet the debugging requirements of network developers (also known as users) for error debugging, control and observation of network execution during neural network development, and framework development debugging is used to meet the debugging requirements of framework developers.

  • network development debugging: The functional debugging capabilities for neural network developers can be divided into network error debugging and network execution debugging.

    • network error debugging: Provide error diagnosis and debugging capabilities when network errors are reported, e.g., error description, debugging with PyNative.

    • network execution debugging: Provide the observation and execution control capabilities during normal network execution, e.g. callback, hook.

  • Framework development debugging: Provide functional debugging capabilities for MindSpore framework developers, such as logging and RDR (run data saving).

Network development debugging and framework development debugging are only distinguished from a more applicable perspective, not a strict functional division. Network developers can also use the framework development debugging function to debug problems, and vice versa.

Network Error Debugging

Network error debugging is to solve the problem of error reporting during network training or inference. By understanding the meaning of the error message, hypothesize the cause of the problem, and use debugging methods to verify the hypothesis. Network error debugging is usually a process of multiple hypothesis and verification cycles, which includes two parts: error analysis and debug positioning. Error analysis is the process of obtaining the content of reported errors, understanding the description and analyzing their causes, mainly including information summarization, error analysis and error retrieval. Debug positioning is the process of selecting a suitable debugging strategy for the problem scenario and verifying the assumptions of the reported error, which mainly includes strategy selection, fault recurrence, and debug verification.

Error Analysis

Error analysis is the process of obtaining the content of error reports, understanding their description and analyzing their causes.

Information Summarization

The first step in network error debugging is to summarize information, where information summarization refers to categorizing the information obtained and understanding its meaning to provide a basis for error analysis. Generally, several types of information needs to be obtained when an error is reproted:

  1. Information about the environment in which the error is reported, including: OS type and version, MindSpore version, execution mode (dynamic graph mode OR static graph mode), device information (x86 or ARM, Ascend or GPU)

  2. Error description information, including: error type, error description, error stacks, etc.

  3. If suspect a frame problem, you need to get the log information printed by the frame.

Understanding the meaning of error description information plays an important role in problem analysis, and the following will introduce how to read and understand MindSpore error messages.

MindSpore error messages are processed by using Python Traceback processing, including Python stack information, error types and error descriptions, error messages related to networkdevelopers, and error messages related to framework developers. As shown in the following figure:
  • Python stack information:

    As shown in figure ①, you can see the Python stack calling relationship from top to bottom.

  • Error type and error description:

    As shown in figure ②, the error type is TypeError, that is, parameter type error. The error description is the cause of the error. The number of parameters in the function is not correct, and the number of input parameters is required to be 2, but the actual number of parameters provided is 3.

  • Error messages related to networkdevelopers

    As shown in figure ③, it contains The Traceback of Net Construct Codeand other error messages related to network developers. The Traceback of Net Construct Codeis the C++ back-end compilation error location mapped to the Python front-end code call stack, printed in reverse order, corresponding to the constructfunction or @ms_functiondecorator-modified function in the neural network.

  • Error messages related to framework developers

    As shown in figure ④, it contains C++ stack information and other error reporting information related to framework developers, marked by \ For framework developers. You can set the environment variable export MS_EXCEPTION_DISPLAY_LEVEL=1to hide the error message related to the framework developer. The default value of this environment variable is 0, indicating that error messages related to the framework developer are displayed by default.

Error Analysis

Performing error analysis is an important step in network error debugging. Error analysis refers to the analysis of error causes based on various information obtained from the network and framework (e.g., error messages, network codes, and other information) to infer the possible causes of errors.

The general process of MindSpore network training is data loading and processing, network construction and training. In distributed parallel scenario, it also includes distributed parallel mode configuration. The error analysis of network error reporting usually includes the following steps:

  1. Based on the error message, identify which problem scenario it is, such as data loading and processing problem scenario, network construction and training problem scenario, or distributed parallelism problem scenario. Usually, a distinction can be made by using the error message related to the network developer.

  2. Analyze the problem scenarios and further identify which type of problem it is under that problem scenario. For example, the data loading and processing problem scenario includes three types: data preparation problems, data loading problems and data enhancement problems. Usually, the distinction needs to be made based on the type of error reported and the description of the error reported.

  3. Analyze the location where the error is reported based on the Python call stack and the error information. In dynamic graph mode, it is easier to determine the location of the code error. In the static graph mode, you need to analyze the location of the error report according to the error message “The Traceback of Net Construct Code” part of the error message.

  4. Based on possible error problem scenarios and types, hypothesize the possible causes of the error problem.

Please refer to error analysis for details on how to perform error analysis based on different scenarios.

Debug Positioning

Strategy Selection

  • Static to Dynamic Debugging Strategy

    Dynamic graph mode is a better debugging execution mode. Set the dynamic graph mode way: set_context(mode=mindspore.PYNATIVE_MODE). The program in dynamic graph mode is executed line by line according to the order in which the code is written, avoiding the back-end and front-end compilation optimization in static graph mode and ensuring uniform user code and execution logic. Dynamic diagrams are executed line by line to avoid the black box execution of the whole diagram sinking in diagram mode, which is more convenient to print the execution results and track the execution process.

  • Asynchronous-to-synchronous Debugging Strategy

    Dynamic diagram mode uses asynchronous execution by default in order to improve the efficiency of dynamic diagram execution, and error information are displayed at the last stage of execution. In Figure 3, you can see that the asynchronous execution method of error reporting will have alarm messages that interfere with the error reporting analysis.

    MindSpore provides a way to switch synchronous execution by setting set_context(mode=mindspore.PYNATIVE_MODE, pynative_synchronize=True) to switch to synchronous execution. If the operator execution error occurs, the task terminates directly and displays the current error message. For details, see PyNative Synchronous Execution.

  • Dichotomy Strategy

    Simplifying the problem scenario is an effective way to improve debugging efficiency. Based on the error message, it is usually a reliable way to confirm the scope of the error reporting problem and eliminate unnecessary influencing factors. In the case where the scope of error reporting cannot be accurately determined, a dichotomous approach can be attempted. For example, if the network calculation process contains nan values, the dichotomy method can be used to debug the data processing module and the network calculation module separately to verify whether the data input to the network calculation contains nan values and to confirm whether the outliers are introduced by the data or generated during the calculation process.

  • Deductive Inference Strategy

    Deductive inference is the process of deducing the cause of a problem and further verifying the conclusion. MindSpore error debugging is a step-by-step reverse inference based on the causal chain of problem propagation to locate the root cause of the problem. For example, the execution of MindSpore operator reports an error problem, which is directly caused by the fact that the input data of the operator contains illegal values, and the illegal values are derived from the computation of the previous operator, so we need to analyze whether the input data and computation process of the previous operator are correct. If there is a problem with the computation process of the previous operator, i.e., the scope of the problem is confirmed, if there are also illegal values in the input data of the previous operator, it is necessary to continue analyzing the previous operator until the root cause of the problem is found.

Problem Recurrence

Stable problem recurrence is a prerequisite for network debugging and a condition to verify whether the problem is completely solved. The network training process introduces randomness due to random initialization of network parameters, and different input data, which can easily cause inconsistent running results or error reporting locations.

Debugging Verification

  • Dynamic graph debugging

    Due to the line-by-line code execution, single-step debugging, breakpoint debugging and process tracing can be performed by using the debugging tool pdb. Debugging Steps:

    1. insert import pdb; pdb.set_trace() before the code you want to debug to enable pdb debugging.

    2. run the .py file normally. The following similar result will appear in the terminal, debug by entering the corresponding pdb command after the (Pdb).

    3. Enter commands such as l and p in pdb interactive mode to view the corresponding code and variables, and then troubleshoot the related problems.

  • Static graph debugging

    1. ops.print_

      In static graph mode, MindSpore provides ops.print_ interface to print Tensor information or string information in the computational graph. The outputs are printed to screen by default, and it can also be saved in a file.

    2. Debugger

      The Debugger can be used for reporting errors during the execution phase of the computational graph. Using the debugger, the following can be implemented.

      1. View the output of the graph node in the debugger interface in conjunction with the computational graph.

      2. Set monitoring points to monitor for training anomalies (such as check tensor overflows) and to track the cause of errors when they occur.

      3. View changes in parameters such as weights.

      4. Check the correspondence between diagram nodes and source code.

      For details, refer to Visual Debugger.

Network Execution Debugging

Network execution debugging is the corresponding debugging capability provided by MindSpore to meet the demands of network developers for network execution process observation and execution control, which can be divided into network execution observation and network execution control.

  • Network execution observation: Obtain the internal state or data of the network to observe the network execution information during the network execution, for example, visualization of training process, intermediate file (i.e. IR) saving function.

  • Network execution control: Perform specific actions during specific periods of network execution, for example, monitor loss, save model parameters, terminate training tasks early.

Function classification

Main debugging functions

Description of use

Detailed introduction

Execution observation

Visualization of the training process

The scalar, image, computation graph, training optimization process and model superparameter information during the training process are recorded in a file and made available for users to view through the MindSpore Insight visualization interface, including: scalar visualization, parameter distribution visualization, computational graph visualization, data graph visualization, image visualization, tensor visualization and optimization process visualization.

Collect Summary data

Training traceability and comparison

The model traceability, data traceability, and comparison dashboard allow users to observe different scalar trend graphs to identify problems and then use the traceability function to locate the cause of the problem, providing users with the ability to efficiently tune the data augmentation and deep neural networks.

Viewing Lineage and Scalars Comparison


When the training is finished, metrics can be used to evaluate the training results. A variety of metrics are provided for evaluation, such as: accuracy, loss, preci sion, recall, F1.

MindSpore metrics function introduction

print_ interface

The print_ interface prints out the Tensor or string information entered by the user.

print_ interface introduction

Intermediate file saving

Used to save the intermediate files generated during the diagram compilation process, which we call IR files, to support the diagnosis of problems related to diagram structure and diagram information.

Reading IR

Data Dump

When training the network, if the training result deviates from the expectation, the operator input and output data are saved for debugging by the Du mp function.

Dump function debugging

Execution control


Users can use callback functions to perform specific actions at specific times or to observe network information during training, e.g., save model parameters, monitor loss, dynamically adjust parameters, terminate training tasks early.

Callback mechanism


The Hook function in pynative mode captures the input and output data and the backward gradient of the middle layer operator. Four forms of Hook functions are available: HookBackward operator and register_forward_pre_hook, register_forward_hook, and register_backward_hook functions registered on the Cell object.

Hook function

Synchronous execution

In dynamic graph mode, operators are executed asynchronously on the device to improve performance, so operator execution errors may be displayed at the end of program execution. In this case, MindSpore provides a synchronous execution setting to control whether the arithmetic is executed asynchronously on the device.

Synchronized execution of dynamic graph

Framework Development Debugging

MindSpore provides framework developers with rich debugging tools. Debugging features cover the framework’s execution process, the framework’s execution data, the framework’s special control, for example: the log can record the framework’s execution process, and the RDR can record the framework’s key state information, memory reuse control.

Function classification

Main debugging functions

Description of use

Detailed introduction

Process records


used to record information at each stage of the framework implementation to provide information for understanding the framework implementation process or for problem diagnosis.

Log-related environment variables and configurations

Data records


Running Data Recorder (RDR) provides the ability to record framework execution status data while the training program is running. It can also save key frame state data, such as IR, graph execution order,

Running Data Recorder

Specialized control

Memory reuse

Configure memory reuse on and off for troubleshooting or debugging suspected problems related to memory reuse.

Memory Reuse