Fixing Stan Model Benchmark Failures: A Comprehensive Guide

by Alex Johnson 60 views

Introduction

When working with the Stan probabilistic programming language, benchmarking your models is crucial for ensuring performance and stability. However, changes to the interface between R and Stan, such as modifications in data variable names or parameter names, can lead to benchmark failures. This article delves into the causes of these failures and explores effective solutions to address them. Understanding these issues is vital for maintaining a robust and reliable Stan model development workflow. We'll cover common problems, propose solutions, and recommend best practices to keep your benchmarks running smoothly, even during significant refactoring efforts. This comprehensive guide aims to equip you with the knowledge to tackle these challenges head-on, ensuring your models perform optimally.

Understanding the Problem: Interface Changes and Benchmark Failures

The core issue arises when changes in the Stan model's interface, particularly in data variable names or parameter names, are not synchronized with the R code used for benchmarking. The current benchmarking approach often involves using the R code from a pull request (PR) branch to run against both the PR's Stan model and the main branch's Stan model. This method falters when the new R code generates data tailored for the updated Stan model, while the older Stan model from the main branch expects a different set of variable names. This discrepancy leads to errors and benchmark failures, rendering the benchmark unavailable precisely when it is most critical – during substantial Stan model refactoring. An example of this failure, such as the one encountered in #1180, highlights the problem of missing input data for variables like gt_id, trunc_id, and delay_id. This situation underscores the need for a robust solution that can handle interface changes gracefully.

Why Benchmarking Matters During Refactoring

Benchmarking is especially vital during refactoring because it provides insights into how changes affect model performance. If the benchmark fails during this critical period, identifying performance regressions becomes significantly more challenging. The benchmark's value lies in its ability to provide granular timing information, such as the time spent in specific operations like "infections," "delays," or "gp lp." This level of detail, which is not available from simple end-to-end timing, is invaluable for pinpointing performance bottlenecks. Therefore, maintaining a functional benchmark during interface-changing PRs is crucial for ensuring the efficiency and stability of Stan models.

Current Benchmarking Value: Granular Timing Information

The existing benchmark leverages Stan's internal profile() blocks to offer detailed timing information. This feature allows developers to track the time spent in various operations within the model, such as "infections," "delays," and "gp lp." This level of granularity is crucial because it goes beyond simple end-to-end timing, providing insights into specific operations that might be causing performance bottlenecks. By identifying these bottlenecks, developers can optimize their models more effectively. The granular data from profile() blocks allows for targeted improvements, ensuring that the Stan model runs efficiently. This level of detail is especially important when refactoring or making significant changes to the model structure.

Importance of Detailed Profiling

Detailed profiling is essential for diagnosing performance regressions in specific operations. For example, if the time spent in the "infections" operation increases significantly after a change, it indicates a potential issue within that part of the model. Without this granular information, it would be much harder to pinpoint the cause of the slowdown. The ability to isolate and analyze specific operations makes the benchmark a powerful tool for maintaining Stan model performance. Therefore, any proposed solution to the benchmark failure problem must preserve this granular profiling data.

Proposed Solutions to Benchmark Failures

To address the issue of benchmark failures due to interface changes, several solutions have been proposed. Each option has its own set of advantages and disadvantages, which need careful consideration.

Option A: Run Each Version End-to-End Independently (Recommended)

This approach involves checking out and installing both the R code and the Stan model for each version separately. This ensures that each version runs with its own compatible R code and Stan model.

Pros:

  • Handles Interface Changes Gracefully: This is the most significant advantage. By running each version independently, the benchmark can seamlessly handle changes in the interface between R and Stan.
  • Preserves Granular Stan Profiling Data: This option maintains the detailed timing information provided by Stan's profile() blocks, which is crucial for identifying performance bottlenecks.
  • If Main Fails, PR Can Still Be Benchmarked: If the main branch fails for unrelated reasons, the PR branch can still be benchmarked independently, ensuring continuous evaluation.

Cons:

  • Doubles the CI Time: Running each version separately doubles the continuous integration (CI) time, as it requires two package installations and two benchmark runs.
  • Package Installation is Slow: Package installation can be time-consuming, and the overall benchmarking process adds to the execution time.

This option is the most robust because it ensures that the benchmark works reliably regardless of interface changes. Although it increases CI time, the benefits of accurate and detailed benchmarking often outweigh this drawback. This approach provides a solid foundation for maintaining Stan model performance.

Option B: Store Baseline in Repository

This solution proposes storing benchmark results in the repository and comparing PRs against a stored baseline. The main branch would contain a benchmark-baseline.csv file, and PRs would only benchmark the PR branch, comparing its performance against the stored baseline. When a PR is merged, the baseline would be updated.

Pros:

  • Halves CI Time: This approach reduces CI time by only requiring one package install and benchmark run.
  • Enables Historical Performance Tracking: Storing baselines allows for tracking performance changes over time, providing valuable insights into long-term trends.

Cons:

  • Hardware Variability: GitHub Actions runners are not identical, so a PR might appear slower simply because it was run on a different runner. This variability can lead to false positives and inaccurate comparisons.
  • Baseline Staleness: If the main branch hasn't been benchmarked recently, the baseline might become stale, leading to comparisons against outdated performance metrics.
  • Merge Conflicts: Multiple PRs touching Stan code can result in merge conflicts when updating the baseline, adding complexity to the workflow.
  • Requires Wider Thresholds: To account for hardware variability, wider thresholds (e.g., >20% change) would be needed, potentially masking smaller but significant performance regressions.

Possible Mitigations for Hardware Variability:

  • Use wider thresholds (only flag changes >20%).
  • Store operation ratios rather than absolute times.
  • Re-run the baseline periodically via a scheduled job.
  • Include runner metadata to flag hardware differences.

While this option reduces CI time, the challenges related to hardware variability and baseline management need careful consideration. The potential for inaccurate comparisons and the complexity of managing baselines make this a less robust solution compared to Option A. Accurate Stan model benchmarking is paramount, even if it requires more resources.

Option C: Skip Comparison When Interface Changes

This approach maintains the current benchmarking method but adds a mechanism to detect incompatibility and skip the comparison gracefully. A tryCatch block would be used to catch errors related to missing input data, indicating an interface change. If such an error is detected, the comparison would be skipped with a message.

Pros:

  • Minimal Changes: This option requires the least amount of code changes, making it a quick and easy solution to implement.
  • Still Works When Interface is Stable: When the interface between R and Stan remains stable, the benchmark functions as expected.

Cons:

  • No Comparison During Interface-Changing PRs: This is the most significant drawback. The benchmark becomes unavailable precisely when it is most needed – during interface-changing PRs.
  • Doesn't Solve the Problem: This option only hides the problem rather than solving it, as it avoids benchmarking during critical periods.

While this option is simple to implement, it fails to provide the necessary benchmarking data during significant refactoring efforts. The lack of comparison during interface changes makes this option less desirable for ensuring Stan model performance.

Recommendation: Option A - End-to-End Isolation

After evaluating the proposed solutions, Option A (running each version end-to-end independently) emerges as the most robust and reliable approach. This method ensures that each version of the Stan model and its corresponding R code are benchmarked in isolation, effectively handling interface changes. While the increased CI time is a drawback, the benefits of accurate and detailed benchmarking outweigh this concern. Option A provides the necessary data to identify and address performance regressions, ensuring the long-term health and efficiency of Stan models.

Why Option A is the Best Choice

Option A's ability to handle interface changes gracefully and preserve granular profiling data makes it the ideal solution. By running each version independently, the benchmark remains functional even during significant refactoring efforts. The detailed timing information provided by Stan's profile() blocks allows for targeted optimization, ensuring that performance bottlenecks are quickly identified and addressed. Although the increased CI time is a valid concern, the accuracy and reliability of the benchmark are paramount. Option A provides a solid foundation for maintaining high-performing Stan models.

Conclusion

Addressing benchmark failures due to interface changes is crucial for maintaining a robust Stan model development workflow. Among the proposed solutions, running each version end-to-end independently (Option A) stands out as the most reliable approach. This method ensures that benchmarks remain functional even during significant refactoring efforts, providing the necessary data to identify and address performance regressions. While other options offer potential benefits, such as reduced CI time, they often come with significant drawbacks, such as hardware variability or the inability to benchmark during critical periods. Therefore, prioritizing accuracy and reliability through Option A is the best strategy for ensuring the long-term health and efficiency of Stan models. Embracing this approach will lead to more stable and performant models, ultimately enhancing the quality of your work.

For further information on Stan and best practices in statistical modeling, consider visiting the Stan Modeling Language website.