sizecheck 0.2.0

Runtime shape validation for size-annotated Python code

Runtime Shape Validation for Size-Annotated Python Code

The sizecheck package provides a decorator that automatically adds runtime shape checking to Python functions based on size-annotated variable names using AST transformation.

For previous versions of the documentation, substitute latest in the documentation URL above for the desired version.

Overview

When writing PyTorch or NumPy code, it's common to use naming conventions that indicate tensor shapes, as in this Medium post. For example, if a tensor weights has shape N × K, you might name the variable weights_NK. This library automatically validates that tensors match their annotated shapes at runtime by analyzing and modifying your function's Abstract Syntax Tree (AST).

Key Features

• AST-based transformation: Automatically injects shape checks into function arguments and variable assignments
• Intuitive naming convention: Use underscores to indicate tensor shapes
• Framework agnostic: Works with PyTorch, NumPy, Jax, and any other libraries that use .shape to indicate tensor shapes.
• Comprehensive checking: Validates both function parameters and intermediate assignments
• Clear error messages: Provides detailed information when shapes don't match

Quick Start

import torch
from sizecheck import sizecheck

@sizecheck
def matrix_multiply(a_NK, b_KM):
    """Matrix multiplication with automatic shape checking."""
    result_NM = torch.matmul(a_NK, b_KM)
    return result_NM

# This works fine
a_NK = torch.randn(3, 4)  # N=3, K=4
b_KM = torch.randn(4, 5)  # K=4, M=5
result = matrix_multiply(a_NK, b_KM)  # Shape: (3, 5)

# This raises an AssertionError
a_NK = torch.randn(3, 4)
b_KM = torch.randn(5, 6)  # Wrong! K dimensions don't match
result = matrix_multiply(a_NK, b_KM)  # AssertionError!

Shape Annotation Format

Each character in the dimensions suffix represents one dimension:

• tensor_NK: 2D tensor with dimensions N × K
• data_BCHW: 4D tensor with dimensions B × C × H × W

Dimension annotations can contain:

• Variable dimensions (uppercase letters): N, K, M - stored in variables of the same name
• Constant dimensions (single digits): 3, 4, 2 - checked for exact size

What Gets Checked

The decorator automatically adds shape validation for:

• Function arguments with underscores in their names
• Variable assignments to names containing underscores

Dimension Scope

The dimensions are scoped to the function they are defined in. For example, if you define a function foo with a parameter x_NK, the dimension N is only valid within the scope of foo. If you define another function bar with a parameter y_NL, this dimension N can differ from the one in foo, but it is only valid within the scope of bar.

Julia version

If you're looking for the Julia version of this library, check out SizeCheck.jl.