human-eval-rust 1.0.0

Evaluation harness for the HumanEval Rust problem solving dataset

HumanEval Rust: Evaluation Harness for SigilDERG Ecosystem

A specialized evaluation harness for assessing Rust code generation capabilities of language models, designed as a core component of the SigilDERG ecosystem for Rust-focused AI development.

About the SigilDERG Ecosystem

This evaluation harness is part of an integrated pipeline for training and evaluating Rust code generation models:

1. SigilDERG-Data_Production: Generates high-quality, instruction-style Rust code datasets from real-world crates using static analysis and quality filters
2. SigilDERG-Finetuner: Fine-tunes language models (like Llama-3.1-8B-Instruct) on Rust code using QLoRA and multi-phase training strategies
3. HumanEval Rust (this project): Evaluates model performance on standardized Rust programming problems using the HumanEval benchmark format
4. sigil-mmf-codex-priv: Additional components for the ecosystem

Target Model

This evaluator is designed to work with fine-tuned Rust code generation models, particularly:

• Llama-3.1-8B-Instruct-Rust-QLora: A Phase 1 fine-tuned model produced using the SigilDERG Finetuner

Installation

Prerequisites

This package requires Python 3.12.10 or later. We recommend using a virtual environment:

# Using venv (recommended)
python3.12 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Or using uv (fast alternative)
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

Install a Rust toolchain via rustup and ensure a modern compiler with Edition 2021 support (Rust 1.56+; we recommend the latest stable toolchain):

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
rustup default stable
rustc --version

Install from PyPI

pip install human-eval-rust

Install from source

git clone https://github.com/Superuser666-Sigil/human-eval-Rust.git
cd human-eval-Rust
pip install -e .

Usage

⚠️ Security Warning: This program exists to run untrusted model-generated Rust code. Users are strongly encouraged not to do so outside of a robust security sandbox. Rust completions are compiled and executed via rust_execution.py; you should sandbox the Rust evaluator, because it builds binaries from untrusted code and runs their tests locally.

Basic Evaluation Workflow

1. Generate completions from your model using the HumanEval Rust prompts
2. Save samples in JSONL format with task_id and completion fields
3. Run evaluation to get pass@k metrics and detailed results

Example: Evaluating a Fine-Tuned Model

from human_eval.data import read_problems, write_jsonl, get_human_eval_dataset
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load your fine-tuned model (e.g., from HuggingFace)
model_name = "Superuser666-Sigil/Llama-3.1-8B-Instruct-Rust-QLora"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Load HumanEval Rust problems
rust_problems = read_problems(get_human_eval_dataset())

# Generate completions
samples = []
for task_id, problem in rust_problems.items():
    prompt = problem["prompt"]
    
    # Generate completion (adjust parameters as needed)
    inputs = tokenizer(prompt, return_tensors="pt")
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=512,
            temperature=0.2,
            do_sample=True,
        )
    completion = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
    
    samples.append(dict(task_id=task_id, completion=completion))

# Save samples
write_jsonl("rust_samples.jsonl", samples)

# Evaluate
# Run: evaluate_functional_correctness rust_samples.jsonl

Command-Line Evaluation

$ evaluate_functional_correctness rust_samples.jsonl
Reading samples...
164it [00:01, 1959.50it/s]
Running test suites...
100%|...| 164/164 [00:45<00:00,  3.62it/s]
Writing results to rust_samples.jsonl_results.jsonl...
100%|...| 164/164 [00:00<00:00, 42876.84it/s]
{'pass@1': 0.42, 'pass@10': 0.68, 'pass@100': 0.85}

The evaluator provides detailed results in <input>_results.jsonl with per-sample pass/fail status and execution results ("passed", "timed out", or "failed").

Integration with SigilDERG Finetuner

The evaluation workflow integrates seamlessly with the SigilDERG Finetuner evaluation system:

1. After training: Use the finetuner's evaluation scripts to generate samples
2. Run this evaluator: Process the generated samples to get HumanEval metrics
3. Compare metrics: Track improvements across training phases

Example integration:

# After Phase 1 training, evaluate checkpoint
python scripts/generate_samples.py \
  --checkpoint out/llama8b-rust-qlora-phase1/checkpoint-1000 \
  --output eval_samples.jsonl

# Evaluate with HumanEval Rust
evaluate_functional_correctness eval_samples.jsonl \
  --problem_file=data/HumanEval_rust.jsonl

Quick Sanity Check

The example samples should yield 0.5 pass@1:

$ evaluate_functional_correctness data/example_rust_samples.jsonl --problem_file=data/example_rust_problem.jsonl
Reading samples...
4it [00:00, 1959.50it/s]
Running test suites...
100%|...| 4/4 [00:03<00:00,  1.13it/s]
Writing results to data/example_rust_samples.jsonl_results.jsonl...
100%|...| 4/4 [00:00<00:00, 1536.38it/s]
{'pass@1': 0.5}

Advanced Options

# Custom pass@k values
evaluate_functional_correctness samples.jsonl --k=1,5,10,20

# Adjust parallelism
evaluate_functional_correctness samples.jsonl --n_workers=8

# Custom timeout
evaluate_functional_correctness samples.jsonl --timeout=5.0

# See all options
evaluate_functional_correctness --help

Dataset Format

The HumanEval Rust dataset (data/HumanEval_rust.jsonl) contains 164 Rust programming problems. Each problem includes:

• task_id: Unique identifier (e.g., "HumanEval/0")
• prompt: Function signature and docstring
• canonical_solution: Reference implementation
• test: Rust test cases using #[cfg(test)]
• entry_point: Function name

Sample format:

{"task_id": "HumanEval/0", "prompt": "fn has_close_elements(...) -> bool{", "canonical_solution": "...", "test": "#[cfg(test)]\nmod tests {...}", "entry_point": "has_close_elements"}

Integration with SigilDERG Pipeline

Complete Workflow

1. Data Production → Generate training data with SigilDERG-Data_Production
2. Model Fine-Tuning → Train on Rust code with SigilDERG-Finetuner
3. Evaluation → Assess performance with this HumanEval Rust harness
4. Iteration → Use results to guide further training and data collection

Metrics and Benchmarking

This evaluator provides standardized pass@k metrics that complement the comprehensive evaluation metrics from the SigilDERG Finetuner:

• Compilation metrics: Success rates, clippy warnings
• Code quality: Documentation, idiomatic patterns
• Functional correctness: HumanEval pass@k scores (this project)

Together, these metrics provide a complete picture of model performance for Rust code generation.

Known Issues

While evaluation uses very little memory, you might see the following error message when the system is running out of RAM. Since this may cause some correct programs to fail, we recommend that you free some memory and try again.

malloc: can't allocate region

Citation

This evaluation harness is based on the HumanEval benchmark format described in the original Codex paper. Please cite:

@article{chen2021codex,
  title={Evaluating Large Language Models Trained on Code},
  author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser and Mohammad Bavarian and Clemens Winter and Philippe Tillet and Felipe Petroski Such and Dave Cummings and Matthias Plappert and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain and William Saunders and Christopher Hesse and Andrew N. Carr and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},
  year={2021},
  eprint={2107.03374},
  archivePrefix={arXiv},
  primaryClass={cs.LG}
}

License

MIT License