Fixing Std::bad_alloc Errors In MinerU On Huawei 910B NPU

Troubleshooting MinerU Application Crashes on Huawei 910B with std::bad_alloc Error

Hey guys! Having trouble with your MinerU application crashing on a Huawei 910B NPU? Seeing that dreaded std::bad_alloc error? Don't worry, you're not alone, and we're going to dive deep into what might be causing this and how to fix it. This guide is tailored for those using MinerU on the Huawei 910B NPU, especially with the configurations you've mentioned. We'll break down the error, the potential causes, and some steps you can take to resolve it. So, let's get started! This will include a comprehensive guide to help you resolve the 'std::bad_alloc' error, which typically surfaces when memory allocation fails, often due to insufficient available memory or fragmentation. It is a common issue in resource-intensive applications such as those dealing with PDF parsing and document processing. Understanding the root causes and applying the suggested solutions can significantly improve the stability of your application, especially when handling complex documents or large datasets. The 'std::bad_alloc' error is a critical sign that your program is struggling with memory management and needs immediate attention. This guide will provide insights into how to identify the problem, diagnose the underlying reasons, and implement strategies to efficiently resolve the issue. Whether you are a seasoned developer or just starting out with MinerU and NPU, these techniques will help you understand the problem and effectively manage your resources. We will also be looking at how to optimize your code for better memory utilization. This will include exploring strategies to reduce memory footprint, such as using efficient data structures, optimizing data loading, and employing techniques like object pooling to reuse memory blocks. This will help you manage resources more effectively and reduce the risk of encountering 'std::bad_alloc' errors. In addition, we will delve into the best practices for monitoring memory usage and setting appropriate resource limits to prevent crashes and ensure optimal performance. By following these guidelines, you can not only prevent the error but also improve the overall performance of your document processing application. We'll cover everything from basic checks to advanced debugging techniques, ensuring you can get your MinerU application running smoothly on your Huawei 910B NPU. By the end of this, you'll be equipped to troubleshoot and solve this issue effectively. So, let's get started on this journey together! The main goal is to reduce the frequency of this error. This usually involves tweaking the code to improve the memory footprint. This also includes checking the environment in which the program is running. These are the most common solutions to address the 'std::bad_alloc' error.

Understanding the Error: std::bad_alloc

First things first, what exactly is std::bad_alloc? In simple terms, it's a C++ exception that's thrown when the new operator fails to allocate memory. This can happen for a few reasons: The system simply doesn't have enough memory available, or memory fragmentation has made it impossible to find a contiguous block large enough to satisfy the allocation request. The std::bad_alloc error is a clear sign that the program cannot secure memory to continue operations, usually happening when the application requests more memory than available or when memory allocation fails. The application often crashes abruptly, leading to data loss or incomplete processing. It becomes even more critical in resource-intensive applications like document processing, where large files and complex formats require significant memory. When std::bad_alloc occurs, the operating system is not able to allocate the requested memory, usually indicating that the system has run out of memory, or that there isn't enough available contiguous memory to fulfill the request. This may happen because the program is requesting too much memory at once, or due to memory fragmentation, where small blocks of memory are scattered throughout the system, preventing large allocations. The exception std::bad_alloc is usually caught by an exception handler in the program, preventing it from crashing. In most cases, the application will terminate immediately. The application will often exit, resulting in data loss or incomplete processing. Diagnosing std::bad_alloc can be challenging, but knowing the context and the code can help you pinpoint the issue. The error message itself is usually not helpful, but it does tell you where to start looking for the problem. To diagnose the issue effectively, it is crucial to understand the factors that lead to memory allocation failures, which include insufficient system resources, memory fragmentation, and inefficient memory management within the application. Also, ensure to check for any memory leaks that may gradually deplete available resources. Memory leaks and resource exhaustion are critical factors that may lead to this error. Ensure your application has efficient memory management strategies in place to allocate and deallocate memory efficiently, reducing the chances of the 'std::bad_alloc' error. Now that you know what it is, let's figure out why it's happening in your MinerU application on the Huawei 910B.

Potential Causes in Your Setup

Based on your setup (Huawei 910B, specific container, MinerU 1.3.10, torch_npu 2.3.1, and torch 2.3.1), here are some potential culprits:

• Memory Exhaustion: The most likely reason. Your application might be trying to allocate more memory than is available on the NPU or the system running the container. This could be due to large PDF documents, complex layouts, or inefficient memory management within MinerU.
• Memory Fragmentation: Over time, as memory is allocated and deallocated, it can become fragmented. This means there might be enough total memory available, but not in a single contiguous block, preventing larger allocations.
• Concurrency Issues: When using multiple threads or processes (as you mention with multi-threading), contention for memory can arise, potentially leading to allocation failures, especially if the application is not designed to handle concurrent memory requests effectively.
• Resource Limits: The container might have resource limits (like memory) set that are too restrictive for the operations your application is trying to perform.
• Bug in MinerU or Dependencies: Although less likely, there might be a memory leak or an inefficient allocation pattern within MinerU itself or one of its dependencies (like the torch_npu libraries) that's causing the issue.

Steps to Reproduce the Bug and Your Environment

You've provided the following steps to reproduce the bug:

1. Run multi-threading to call apply function to process PDF documents.
2. The error appears occasionally.

Your environment includes:

• Server: Huawei 910B.
• Base Image: swr.cn-central-221.ovaijisuan.com/mindformers/mindformers1.2_mindspore2.3:20240722 (packaged container).
• MinerU Version: 1.3.10.
• torch_npu Version: 2.3.1.
• torch Version: 2.3.1.
• Operating System: Linux, running within the container.

This information is extremely helpful! It gives us a clear picture of what's going on. Also, note that the specific version of torch and related libraries are important.

Troubleshooting and Solutions

Alright, let's get into fixing this. Here's a breakdown of how to troubleshoot and solve the std::bad_alloc error:

1. Monitor Memory Usage

• Within the Container: Use tools like top, htop, or free -m inside your container to monitor memory usage while your application is running. Check how much memory is being used and when the error occurs. This helps determine if you're running out of memory.
• NPU Memory: Check the NPU's memory usage specifically. You might need to use Huawei-specific tools or monitoring interfaces to see this. Make sure you're not hitting the NPU's memory limits.

2. Code Optimization

• Memory Profiling: Use a memory profiler (like memory_profiler in Python) to analyze your code and identify memory-intensive parts. This helps you pinpoint where allocations are happening.
• Optimize Data Structures: Are you using the most efficient data structures? For example, use numpy arrays instead of large lists when possible. Choosing the right data structures is crucial for optimal performance. This will result in less memory and improve the performance of the application.
• Reduce Data Copies: Minimize unnecessary data copies. Data copying can be a huge memory consumer. Try to work with data in place whenever possible.
• Batch Processing: Instead of processing documents one by one, try batching them. This can reduce the overhead of frequent allocation and deallocation.
• Lazy Loading: If possible, load data lazily (on-demand) instead of loading everything upfront. This way, you only use the memory you need at any given time.
• Object Pooling: Consider object pooling for frequently used objects. This can reduce the number of allocations and deallocations, potentially improving performance.

3. Resource Limits and Configuration

• Container Memory Limits: Check the container's memory limits. You might need to increase these limits to provide your application with more resources. Review your container's configuration (e.g., Docker, Singularity, etc.). Make sure the memory limits are not too restrictive.
• NPU Configuration: Verify the NPU configuration. Ensure that the NPU is configured correctly and has sufficient memory available to the application.
• CPU Threads: Reduce the number of CPU threads if it helps. Using too many threads can lead to memory contention, especially if the application isn't designed to handle it.

4. Concurrency Considerations

• Synchronization: If you're using multi-threading, ensure that your code is properly synchronized to prevent multiple threads from trying to allocate memory at the same time. Synchronization primitives such as mutexes, semaphores, and locks are critical for managing access to shared memory. They prevent race conditions and ensure data consistency. Ensure that memory accesses are properly synchronized to prevent conflicts. Improper synchronization can lead to memory corruption and allocation failures.
• Thread Pool: Use a thread pool to manage the number of threads and reuse threads. This can help reduce the overhead of creating and destroying threads. Thread pools can help to limit the number of threads. This helps prevent memory overload.

5. Upgrade and Update

• Update Dependencies: Make sure that you have the latest versions of MinerU, torch_npu, and torch. Sometimes, bug fixes and performance improvements are included in newer releases. Consider updating the dependencies if possible. The newer versions might include fixes and improvements related to memory management.
• Check for MinerU Updates: Check for updates to MinerU. Newer versions may have bug fixes or memory optimization improvements.

6. Deep Dive Debugging

• Logging: Add extensive logging to your code. Log memory allocations and deallocations, the size of data being processed, and the frequency of these operations. This can give you a more detailed view of memory behavior.
• Custom Allocators (Advanced): If you're comfortable, you might consider using a custom allocator. This can provide more control over memory allocation and potentially improve performance.
• Reproducible Test Case: Try to create a minimal, reproducible test case that triggers the error. This will help you isolate the problem and make it easier to debug. The test case must trigger the bug so that you can debug it easily.

7. Debugging Tools

• GDB or LLDB: If you're comfortable with debugging tools like GDB or LLDB, you can attach them to your process to examine the state of your application when the error occurs. These tools will allow you to inspect the memory, threads, and the state of the variables.

Specific Recommendations for Your Setup

Given your setup, here's what I'd prioritize:

• Monitor Memory Usage: Use top or htop inside your container to closely monitor memory usage, especially while running your multi-threaded application. Watch for sudden spikes or gradual increases leading up to the error.
• Profile Your Code: Use a memory profiler to find out which parts of your code are using the most memory. Focus on the apply function and any data loading or processing steps.
• Optimize Data Structures: Ensure you're using efficient data structures (e.g., numpy arrays) where appropriate.
• Batch Processing: Experiment with batching PDF documents to reduce the frequency of apply calls and potential memory churn.
• Check Container Limits: Review the memory limits set for your container. Increase them if necessary, but be mindful of the resources available on the host system.
• Review Concurrency: Carefully examine how your multi-threaded code is allocating memory. Make sure threads aren't competing for memory and causing contention.

Example Code Snippets (Python)

Here's a small example of how you might monitor memory usage within your Python code:

import psutil
import time

def get_memory_usage():
    memory_info = psutil.virtual_memory()
    return memory_info.percent

# ... your code ...

while True:
    memory_percent = get_memory_usage()
    print(f"Memory Usage: {memory_percent}%")
    time.sleep(5) # Check every 5 seconds

    # ... your code ...

To help diagnose the issue, include memory usage checks and logging statements. This code will print the current memory usage. This will help you track your memory usage.

Conclusion

Debugging std::bad_alloc errors can be tricky, but by following these steps, you should be able to identify the root cause of the problem and find a solution. Start with monitoring memory usage, profile your code, and optimize your resource usage. Also, check your concurrency, and make sure that the configuration is appropriate. By following these steps, you will be able to fix the error effectively. Remember to be patient and methodical in your debugging process. Good luck, and happy coding!