Main signal processing function that applies the selected algorithm. Args: input_signal: Input time series data window_size:

Remove the monkey patch and restore original attention

Infers the task name from environment variables or the file path.

Forward pass: snaps values to the nearest half-integer.

Run a coroutine in the pool Args: coro: Coroutine function to run *args: Arguments to pass to the coroutine

Run comprehensive comparison between standard and optimized attention. Uses the full benchmark suite for thorough analysis.

Run comparison test between standard and optimized attention

Runs batch_size instances in parallel (max_workers concurrently) On any failure: cancels the rest, kills children, re-raises the first error

Run multiple independent trials of random search.

Run OpenEvolve on a single AlgoTune task. Args: task_name: Name of the task task_dir: Path to the task directory

Sets up and runs the full optimization process.

Sets up and runs the full optimization process.

Sets up and runs the full optimization process.

Run benchmark with the optimized attention from best_program.py.

Run the circle packing constructor for n=26

Run the circle packing constructor for n=26

Run the circle packing constructor for n=26

Run the pipeline configuration (entry point for evaluator).

Runs python program as a subprocess, returns stdout/stderr + elapsed time Kills the whole process group on timeout (POSIX)

Run random search until solution found or max_samples reached. Returns: dict with results

Run all configs in parallel On first failure raises (timeout, exit or internal) Evaluator's try/except will handle it

Run the signal processing algorithm on a test signal. Returns: Dictionary containing results and metrics

Runs one full optimization from a near-good starting point with small noise.

Run a single OpenEvolve trial.

Run multiple trials and collect statistics.

Run multiple trials and collect statistics.

Run a function with a timeout using subprocess. Args: func: Function to run args: Arguments to pass to the function

Convert a value safely for evaluation

Convert a value safely for evaluation

Convert a value safely for evaluation

Convert a value safely for evaluation

Convert a value safely for evaluation

Convert a value safely for evaluation

Convert a value safely for evaluation

Convert a value safely for evaluation

Sample a program and inspirations from a specific island without modifying current_island This method is thread-safe and doesn't mod

Save detailed comparison results to files.

Save benchmark results to files

Score the configuration against the target. Returns: tuple: (correct_count, module_results dict)

Score a configuration (number of correct modules).

Extract API documentation from HTML content. Args: html_content: Raw HTML content of a documentation page Returns: List

A simple random search algorithm that often gets stuck in local minima. Args: iterations: Number of iterations to run bounds

Set up test environment

Setup Python import paths for AlgoTune modules.

Setup Python import paths for AlgoTune modules.

Setup Python import paths for AlgoTune modules.

Setup Python import paths for AlgoTune modules.

Setup Python import paths for AlgoTune modules.

Setup Python import paths for AlgoTune modules.

Setup Python import paths for AlgoTune modules.

Setup Python import paths for AlgoTune modules.

Simulate MLIR compilation success

Solve the fft_cmplx_scipy_fftpack problem. Args: problem: Dictionary containing problem data specific to fft_cmp

Solve the eigenvectors_complex problem. Args: problem: Dictionary containing problem data specific to eigenvecto

Solve the convolve2d_full_fill problem. Args: problem: Dictionary containing problem data specific to convolve2d

Solve the affine_transform_2d problem. Args: problem: Dictionary containing problem data specific to affine_tran

Solve the polynomial_real problem. Args: problem: Dictionary containing problem data specific to polynomial_real

Solve the fft_convolution problem. Args: problem: Dictionary containing problem data specific to fft_convolution

Solve the lu_factorization problem. Args: problem: Dictionary containing problem data specific to lu_factorizati

Start optillm server for testing Returns tuple of (process_handle, actual_port_used)

Stop the test server

Make a submission. The url_opener argument is an OpenerDirector object to use (as returned by the login() function) Returns the requests

Suggest ways to enable real execution

Clean up test environment

Test with your actual MLIR file

Test the bulletproof Metal kernel evaluator

Test actual controller behavior with iteration counting - requires optillm server

Test that all OpenAI regional endpoints are detected correctly

Test that all OpenAI regional endpoints are detected correctly

Test different lowering pass combinations

Test that Metal kernel implementation produces correct results.

Test that Metal kernel implementation produces correct results.

Test the corrected MLIR baseline syntax

Test MLIR to LLVM translation

Test a specific sequence of passes

Test the linalg tiling pass syntax

Convert trace to dictionary format

Calculates the area of a triangle given its vertices p1, p2, and p3.

Calculates the area of a triangle given its vertices p1, p2, and p3.

Validate that the configuration has valid values.