chore: Fixup integration of accuracy runs (part of #4)

[arekay-nv]

What does this PR do?

Addresses comments in previous PR on integration of accuracy datasets in benchmark command.
#4

Type of change

• Bug fix
• New feature
• Documentation update
• Refactor/cleanup

Related issues

Testing

• Tests added/updated
• All tests pass locally
• Manual testing completed

Checklist

• Code follows project style
• Pre-commit hooks pass
• Documentation updated (if needed)

[github-actions]

MLCommons CLA bot All contributors have signed the MLCommons CLA ✍️ ✅

[gemini-code-assist]

Summary of Changes

Hello @arekay-nv, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly enhances the flexibility and configurability of accuracy evaluations within the benchmark command. By introducing a dedicated AccuracyConfiguration dataclass and a dynamic Scorer registration system, it allows for easier integration of various scoring methods and provides granular control over dataset repetition during accuracy runs. This makes the benchmarking framework more robust and adaptable to different evaluation requirements.

Highlights

• Refactored Accuracy Evaluation: Introduced a new AccuracyConfiguration dataclass and a Scorer registry for more flexible and extensible accuracy evaluation.
• Dynamic Scorer Selection: Enabled dynamic selection of scoring methods (e.g., PassAt1Scorer) based on configuration, replacing hardcoded usage.
• Configurable Dataset Repeats: Added num_repeats to accuracy dataset configurations, allowing users to specify how many times a dataset should be repeated for evaluation.
• YAML Configuration Updates: Updated example YAML files to include the new num_repeats parameter and a modified report directory name.

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[Copilot]

Pull request overview

This PR addresses feedback from a previous pull request by refactoring the integration of accuracy datasets in the benchmark command. The changes introduce a registration system for scorers, add support for configurable dataset repeats, and improve code organization through the use of a dataclass.

Changes:

• Added a scorer registration system using __init_subclass__ to enable dynamic scorer lookup by name
• Introduced num_repeats configuration parameter for accuracy evaluation datasets
• Refactored accuracy evaluation configuration using a dedicated AccuracyConfiguration dataclass

Reviewed changes

Copilot reviewed 5 out of 5 changed files in this pull request and generated 3 comments.

Show a summary per file

File	Description
src/inference_endpoint/evaluation/scoring.py	Added scorer registration system with PREDEFINED class variable and lookup methods, updated PassAt1Scorer to use registration
src/inference_endpoint/dataset_manager/factory.py	Added **kwargs support to pass through additional parameters like num_repeats to data loaders
src/inference_endpoint/config/schema.py	Added num_repeats field to AccuracyConfig with default value of 1
src/inference_endpoint/commands/benchmark.py	Replaced tuple-based accuracy configuration with AccuracyConfiguration dataclass, improved variable naming from dataset_id to dataset_name
examples/04_GPTOSS120B_Example/sglang_gptoss_120b_example.yaml	Added num_repeats values to example accuracy configurations and updated report directory path

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[gemini-code-assist]

Code Review

This pull request refactors the integration of accuracy datasets in the benchmark command, addressing comments from a previous PR. The changes are a significant improvement, introducing a dataclass for accuracy configuration and a factory pattern for scorers, which makes the code more readable, modular, and extensible. The logic for handling multiple accuracy datasets is now much cleaner. I've identified a couple of issues in src/inference_endpoint/dataset_manager/factory.py. One is a bug where num_repeats is ignored for non-predefined datasets, and the other is a confusing type hint that affects maintainability. My detailed comments are below.

[Copilot]

Pull request overview

Copilot reviewed 8 out of 8 changed files in this pull request and generated 5 comments.

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[Copilot]

Pull request overview

Copilot reviewed 12 out of 12 changed files in this pull request and generated 3 comments.

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[Copilot]

Pull request overview

Copilot reviewed 13 out of 13 changed files in this pull request and generated 3 comments.

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[Copilot]

Pull request overview

Copilot reviewed 14 out of 14 changed files in this pull request and generated 1 comment.

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[Copilot]

Pull request overview

Copilot reviewed 14 out of 14 changed files in this pull request and generated 1 comment.

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[Copilot]

Pull request overview

Copilot reviewed 14 out of 14 changed files in this pull request and generated 1 comment.

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