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98 lines
3.3 KiB
Python
98 lines
3.3 KiB
Python
"""
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Evaluate the Chat model on HumanEval dataset.
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Btw this dataset is a misnomer and has nothing to do with humans.
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It is a coding benchmark.
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"""
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import re
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from datasets import load_dataset
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from nanochat.execution import execute_code
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from tasks.common import Task
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def extract_imports(prompt):
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"""Extract import statements from the beginning of a code block."""
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imports = []
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for line in prompt.split('\n'):
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stripped = line.strip()
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if stripped.startswith('import ') or stripped.startswith('from '):
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imports.append(stripped)
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elif stripped and not stripped.startswith('#'):
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# Stop at first non-import, non-comment line
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break
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return '\n'.join(imports)
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def extract_program(completion):
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"""
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Extract Python code from LLM completion.
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Handles various output formats:
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- Code wrapped in ```python ... ``` or ``` ... ``` blocks
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- Plain code without markdown blocks
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- Extra text before/after code blocks
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Returns the first code block if found, otherwise returns the whole completion.
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"""
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# Try to find markdown code blocks (```python or just ```)
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# Match ```python\n...\n``` or ```\n...\n```
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pattern = r'```(?:python)?\s*\n(.*?)\n```'
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matches = re.findall(pattern, completion, re.DOTALL)
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if matches:
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# Return the first code block found
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return matches[0].strip()
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# No code blocks found, return the whole completion
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return completion.strip()
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class HumanEval(Task):
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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self.ds = load_dataset("openai/openai_humaneval", split="test").shuffle(seed=42)
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@property
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def eval_type(self):
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return 'generative'
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def num_examples(self):
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return len(self.ds)
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def get_example(self, index):
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""" Get a single problem from the dataset. """
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row = self.ds[index]
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prompt = row['prompt'] # prompts in HumanEval are the beginning of the program
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solution = row['canonical_solution'] # the correct continuation of the program
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entry_point = row['entry_point'] # the function to check
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test = row['test'] # the test cases
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complete_solution = f"{prompt}\n{solution}"
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messages = [
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{"role": "user", "content": prompt},
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{"role": "assistant", "content": complete_solution},
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]
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conversation = {
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"messages": messages,
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"entry_point": entry_point, # needed during evaluation
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"test": test, # needed during evaluation
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}
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return conversation
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def evaluate(self, conversation, completion):
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""" Given (conversation, completion), return boolean success of the completion. """
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# the prompt will contain the imports and the function signature
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imports = extract_imports(conversation['messages'][0]['content'])
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# the completion will usually contain the whole function
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# but not always with the needed imports, so we manually append them
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completion_code = extract_program(completion)
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program = (
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imports
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+ "\n\n"
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+ completion_code
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+ "\n\n"
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+ conversation['test']
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+ "\n"
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+ f"check({conversation['entry_point']})"
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)
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result = execute_code(program)
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success = result.success
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return success
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