Torch.bmm Sparse Tensor Error: A CUDA Bug

Understanding the Problem: global Write Errors with Sparse Tensors

PyTorch users often encounter the need to perform matrix multiplications, and torch.bmm is a crucial function for batch matrix-matrix products. However, when working with sparse tensors in PyTorch, a specific issue can arise, leading to a CUDA __global__ memory write out-of-bounds error. This error indicates a problem within the CUDA kernels, specifically during memory access, and it can disrupt the correct execution of the code. The problem occurs when the program attempts to write to a memory location that is outside the allocated space. This can lead to unpredictable behavior, including incorrect results or crashes.

To understand this, let's break down the scenario. When torch.bmm is used with sparse tensors, the underlying CUDA kernels are designed to handle the efficient multiplication of these specialized data structures. Sparse tensors store only the non-zero elements, saving memory and computation. The error arises when there's a discrepancy in how these non-zero elements are accessed and written to during the batch matrix multiplication. This is commonly due to an out-of-bounds write, which often occurs due to memory alignment issues or incorrect indexing within the CUDA kernels.

Specifically, the error message Invalid __global__ write of size 16 bytes points to the cusparse::vector_scalar_multiply_kernel function within the libcusparse.so.12 library. This is a clear indicator that the issue lies within the cuSparse library, which is responsible for handling sparse matrix operations. The error is further compounded by memory misalignment, which can lead to unpredictable behavior and can result in the program crashing. The specific memory address that causes the error can provide clues about the source of the problem. This can be inside the allocation of the sparse tensor's data. To debug this, you'll need to look at the memory layout and the indexing logic within the kernel.

import torch

m1 = torch.randn(2, 291105, 1).to_sparse().cuda()
m2 = torch.randn(2, 1, 1).cuda()
print([m1.size(), m2.size()])

torch.bmm(m1, m2)

The Python code provided in the original bug report showcases the issue. The key elements are the creation of a sparse tensor m1 and a regular tensor m2. The code then attempts to compute the batch matrix product using torch.bmm(m1, m2). When this code runs, compute-sanitizer detects the __global__ write error, which confirms the issue. The use of compute-sanitizer is crucial for debugging CUDA code. It helps in identifying memory access errors, and it can point to the specific lines of code where the errors originate. This tool can be used to monitor memory usage and identify potential problems before the application crashes. This example is very useful because it's concise, reproducible, and provides all the necessary information to replicate the error. This kind of easy-to-use example is extremely helpful for developers to quickly test and verify their understanding of the problem.

Steps to Reproduce the Error and the Observed Results

Reproducing the error involves a simple process: save the provided Python code, and then run it using compute-sanitizer. The compute-sanitizer is a tool for detecting memory access errors within CUDA applications. This allows us to observe the specific error message.

1. Save the Code: Start by saving the provided Python code as a file named poc.py. This ensures that you have a runnable script to test the issue. This allows you to have a reproducible test case. This is a very important step because it ensures that you can run the code and see the same results. Reproducibility is crucial in debugging and helps the developers to better pinpoint the issue.
2. Run with compute-sanitizer: Execute the script using the command compute-sanitizer python poc.py. This command runs the Python script while simultaneously monitoring CUDA memory accesses. The compute-sanitizer will analyze the program's CUDA operations and report any illegal memory accesses or other issues. The output of the command will include a detailed error report, with the specifics of the __global__ write error.

The error report will provide details such as the size of the write (16 bytes), the memory address, and the function where the error occurred. The backtrace provides a sequence of function calls that led to the error, assisting in pinpointing the exact code segment causing the problem. The specific error message, Invalid __global__ write of size 16 bytes, shows the nature and location of the error, making it easier to diagnose. The inclusion of the host backtrace, along with the involved libraries and functions, further refines the understanding of the execution flow and the origins of the bug. The backtrace is particularly useful for finding the origin of the error. Knowing the sequence of function calls that lead to the error can assist in debugging the program.

This reproducible process simplifies the diagnosis and confirmation of the error. The results of this process, including the error messages and the backtrace, are essential for developers working on the PyTorch framework to find and resolve the issue. These details help in creating a complete picture of the problem, from the code level to the system level.

Analyzing the Error and Potential Solutions

When encountering an Invalid __global__ write error in CUDA, the problem is often related to how the CUDA kernels are accessing and writing to memory. The error message indicates that a write operation is attempting to access a memory location outside of the allocated bounds, or that there's a memory alignment problem. In the context of torch.bmm with sparse tensors, several factors might be contributing to this issue.

One potential cause is an error in the indexing logic within the CUDA kernels used for sparse matrix multiplication. Sparse tensors only store non-zero elements, and the kernels must correctly calculate the memory offsets to access these elements during the multiplication. If the indexing calculations are incorrect, the kernel may attempt to write to an invalid memory address.

Another possible cause could be related to memory alignment. CUDA requires data to be aligned in memory. If data is not correctly aligned, it can lead to memory access errors. The error message often includes information about the misaligned address, providing a clue to diagnose this type of problem.

Data corruption can be a side effect. Since the error happens during the bmm operation, it can mean that the result tensor is being corrupted. This could result in incorrect results. This makes this problem particularly dangerous, as the application may continue to run but produce wrong results.

To find a solution, the following steps can be useful:

1. Examine the CUDA Kernels: Examine the CUDA kernels used for sparse matrix multiplication. Check the indexing calculations, memory access patterns, and alignment requirements. Review the code to confirm that it correctly handles the sparse tensor format and avoids out-of-bounds memory accesses.
2. Verify Memory Alignment: Ensure that all memory accesses are properly aligned. Use CUDA tools to examine the memory layout. The CUDA driver can issue an error if the memory is misaligned, this is very important for CUDA programming and must always be considered.
3. Check for Race Conditions: In cases of concurrent operations, check for potential race conditions that might cause memory corruption. Using synchronization primitives in CUDA can help prevent race conditions. If multiple threads are attempting to write to the same memory location, this can cause errors. If the access is not properly synchronized, data corruption may occur.
4. Update PyTorch: Ensure that you're using the latest version of PyTorch. Bugs are often fixed in new releases. New versions can include bug fixes, performance improvements, and other enhancements. Sometimes, these releases include fixes to issues that are related to the memory access problems that are happening.
5. Report the Issue: Report the issue on the PyTorch GitHub repository, providing the code and the error report. This will help the developers fix the problem. Providing the necessary details helps the development team to understand the bug, reproduce it, and develop a solution.

By carefully examining these areas, developers can locate and solve the underlying causes of the __global__ write errors. The main goal is to prevent invalid memory writes and ensure data integrity.

Version Details and System Configuration

When reporting an issue, providing accurate version information is crucial for developers to reproduce and diagnose the problem. The provided report includes details about the software and hardware configuration. Understanding this information helps in pinpointing the issue and determining its root cause. The version of PyTorch, CUDA, and other related libraries can significantly affect the behavior of the code, so knowing these details is important.

The user’s environment details are:

• PyTorch version: 2.3.1+cu121. This indicates the specific version of PyTorch that was in use when the error occurred. The PyTorch version is very important when debugging the program, because PyTorch is updated frequently and different versions can have different bugs.
• CUDA version: 12.1. CUDA is NVIDIA's parallel computing platform, and the version of CUDA that is used can be very important because it interacts directly with the GPU. This indicates the version of CUDA used to build PyTorch. CUDA is very sensitive to the driver versions of the GPU. A version mismatch between the CUDA toolkit and the NVIDIA drivers may cause different problems.
• Operating System: Ubuntu 24.04.2 LTS. The operating system and its version provide context about the system's environment. The operating system can have an impact on CUDA operations. The same code may behave differently on different versions of the operating system.
• GPU: NVIDIA H100 PCIe. Understanding the GPU model is important, since the GPU is used by CUDA to perform the calculations. This specifies the GPU used for the computations. Different GPUs have different hardware characteristics and potential issues.
• Driver Version: NVIDIA driver version 580.82.07. This helps to determine whether there is any compatibility issue between the driver version and the installed CUDA version.
• cuDNN version: Likely one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.9.10.0 or other related versions. The cuDNN version is important because it provides optimized implementations of deep neural network primitives. The cuDNN version should align with the CUDA version for optimal compatibility. The versions of cuDNN are crucial for performance. Incorrect cuDNN versions can cause problems.

This information helps the developers to determine if the issue is a bug or a compatibility issue. The system configuration details help identify whether the problem is specific to a particular setup. This information will help others reproduce the error and fix it.

Conclusion

The Invalid __global__ write error when using torch.bmm with sparse tensors in PyTorch is a complex issue. The error is a serious concern, as it can corrupt the data and lead to unpredictable program behavior. The CUDA kernels used for sparse matrix multiplication may have memory access problems. The provided code example is helpful because it's concise, reproducible, and provides all the necessary information to replicate the error. When dealing with similar errors, debugging tools such as compute-sanitizer are extremely helpful for diagnosing the problems. It's essential to report the issue to the PyTorch community, including the system configuration and the error report. By reporting these details, developers can work together to address this problem.

For more information and potential solutions, consider reviewing the official PyTorch documentation and community forums.

Further Reading:

• PyTorch Documentation: https://pytorch.org/docs/stable/ - This is the official PyTorch documentation and is a valuable resource for learning how to use PyTorch effectively.
• CUDA Documentation: https://docs.nvidia.com/cuda/ - Understanding the underlying CUDA can provide some insights when debugging memory access issues.