CuPy Error with Python 3.13 | Generated by AI

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Problem Description

You’re encountering an AttributeError when running a Python script that imports the CuPy library for GPU computations. The error occurs during the import cupy as cp statement, preventing the script from executing. The traceback points to an issue within CuPy’s internal code, specifically in cupy/cuda/device.pyx at line 105, where a memoize decorator attempts to modify the __name__ attribute of a built-in function or method. The error message is:

AttributeError: attribute '__name__' of 'builtin_function_or_method' objects is not writable

This issue appears to be related to your use of Python 3.13, which may introduce compatibility problems with the version of CuPy you have installed.

Cause of the Error

The error arises because:

• CuPy’s memoize Decorator: CuPy uses a memoize decorator to cache function results for performance optimization. This decorator relies on Python’s functools.update_wrapper to copy attributes (like __name__) from the original function to the wrapper function.
• Built-in Functions: In Python, built-in functions (implemented in C) have a read-only __name__ attribute. When update_wrapper tries to set this attribute, it fails with an AttributeError.
• Python 3.13 Compatibility: The specific function being memoized in CuPy’s device.pyx is likely a built-in function, and Python 3.13 may enforce stricter rules or handle built-ins differently than previous versions, exposing this issue.

Since the error occurs during the import of CuPy, it’s a systemic issue tied to the library’s initialization rather than your script’s logic.

Recommended Solution

The simplest and most practical fix is to run your script with an earlier version of Python where CuPy is known to be compatible, such as Python 3.11 or 3.12. This avoids the compatibility issue with Python 3.13 without requiring modifications to CuPy’s source code or complex workarounds.

Why This Works

• Compatibility: CuPy versions up to the latest releases (e.g., v11) are tested and supported on Python versions like 3.11 and 3.12, where this specific error does not occur.
• No Code Changes: Your script, which benchmarks GPU sorting with CuPy, can run as-is in an earlier Python environment, ensuring the benchmark’s integrity.
• Ease of Implementation: Switching Python versions is straightforward using virtual environments or tools like conda.

Step-by-Step Fix

Here’s how to resolve the issue:

Option 1: Using virtualenv

1. Install Python 3.11 or 3.12
   • Ensure Python 3.11 or 3.12 is installed on your system (e.g., via your package manager or official Python website).
2. Create a Virtual Environment

   virtualenv -p python3.11 venv
   

   This creates a virtual environment named venv using Python 3.11. Replace python3.11 with python3.12 if preferred.

3. Activate the Virtual Environment

   source venv/bin/activate
   

4. Install CuPy

   pip install cupy
   

   This installs the latest compatible version of CuPy for Python 3.11.

5. Run Your Script

   python scripts/benchmark/cudamark.py
   

   Your script should now execute without the AttributeError.

6. Deactivate (When Done)

   deactivate
   

Option 2: Using conda

1. Create a Conda Environment

   conda create -n cudabench python=3.11
   

   This creates an environment named cudabench with Python 3.11. Use python=3.12 if you prefer Python 3.12.

2. Activate the Environment

   conda activate cudabench
   

3. Install CuPy

   pip install cupy
   

4. Run Your Script

   python scripts/benchmark/cudamark.py
   

5. Deactivate (When Done)

   conda deactivate
   

Verifying the Fix

After setting up the environment and running the script, you should see output similar to:

CPU cores: <your_cpu_cores>
Threads: 1, Time taken: X.XXXX seconds
Threads: 2, Time taken: X.XXXX seconds
...
ThreadCount,TimeTakenSeconds
1,X.XXXXXX
2,X.XXXXXX
...

If the script runs without the AttributeError, the fix is successful.

Additional Notes

• Thread Counts in Your Script: Your script iterates over thread counts (1, 2, 4, 8, 16, 32, 64, 128), but the parallel_sort_gpu function uses CuPy’s GPU-based sorting (cp.sort), which doesn’t directly utilize CPU threads. The thread count loop may not affect GPU sorting performance as expected. If this is intentional for future modifications (e.g., adding CPU threading), it’s fine; otherwise, you might simplify the benchmark to focus solely on GPU performance.
• CuPy Version: If you suspect an outdated CuPy version, ensure you’re installing the latest by running pip install cupy --upgrade. However, the primary issue here is Python 3.13 compatibility, not the CuPy version alone.
• Long-Term Solution: If you must use Python 3.13, consider checking CuPy’s GitHub repository or issue tracker for updates supporting Python 3.13 (as of June 2025, this might be available). Alternatively, you could fork CuPy and patch the memoize decorator to handle built-in functions, but this is more complex and unnecessary for a benchmark.

By using Python 3.11 or 3.12, you can reliably run your GPU benchmark without encountering this error.

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