Python2与Python3中einsum的已知差异及代码运行报错咨询

Yes, there are known differences between numpy.einsum behavior in Python 2 and Python 3—though most trace back to NumPy version differences (since Python 2.7 typically uses older NumPy releases, while Python 3.6+ uses newer ones) rather than the Python language itself. The error you're hitting is a direct result of these changes.

Why You're Seeing This Error #

Your error:

ValueError: ("Size of label '%s' for operand %d does not match previous terms.", 'h', 1)

This occurs because newer NumPy versions (common in Python 3.6+) enforce stricter checks for dimension consistency across operands in einsum. When you reuse a label like h across multiple tensors in an einsum call, NumPy now requires all dimensions with that label to have the exact same size.

In older NumPy versions (often paired with Python 2.7), this kind of dimension mismatch might have been silently ignored (leading to incorrect, hard-to-debug results) or allowed under looser implicit rules. The newer version correctly flags this as an error to prevent invalid computations.

How to Fix It #

Follow these steps to resolve the issue:

• Check your NumPy versions: Run import numpy; print(numpy.__version__) in both environments. You’ll probably see an older version (e.g., 1.16.x or earlier) in Python 2.7, and a newer one (e.g., 1.18.x+) in Python 3.6.
• Locate the problematic einsum call: Find where GrouPy’s code uses einsum with the h label. Inspect the shapes of the tensors being passed—one of them has a different size for the h dimension than the others. This is likely a bug that went unnoticed in older NumPy.
• Fix the dimension mismatch: Adjust the code to ensure all tensors share the same size for the h dimension. For example, if a tensor’s h dimension was accidentally squeezed or expanded incorrectly, correct that before passing it to einsum.
• Temporary workaround (not ideal long-term): If you need to run the code quickly without modifications, downgrade NumPy in your Python 3.6 environment to match the version from Python 2.7 (e.g., pip install numpy==1.16.6). Note this is a band-aid—fixing the dimension inconsistency is the proper solution.

内容的提问来源于stack exchange，提问作者girl-meets-world