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Evaluator implementations for code generation metrics.
Each evaluator exposes a single method:
evaluate(model, tokenizer, dataset) -> float
Scores are always in [0, 1].
"""
from __future__ import annotations
import ast
import multiprocessing
import textwrap
from abc import ABC, abstractmethod
from concurrent.futures import ProcessPoolExecutor, TimeoutError as FuturesTimeoutError
from typing import Any
import numpy as np
import torch
from datasets import Dataset
from sacrebleu.metrics import BLEU
from transformers import PreTrainedModel, PreTrainedTokenizerBase
# ---------------------------------------------------------------------------
# Base
# ---------------------------------------------------------------------------
class BaseEvaluator(ABC):
@abstractmethod
def evaluate(
self,
model: PreTrainedModel,
tokenizer: PreTrainedTokenizerBase,
dataset: Dataset,
) -> float:
...
def _generate_batch(
self,
model: PreTrainedModel,
tokenizer: PreTrainedTokenizerBase,
prompts: list[str],
max_new_tokens: int = 256,
num_return_sequences: int = 1,
temperature: float = 0.2,
) -> list[list[str]]:
"""Generate completions for a list of prompts. Returns list-of-lists."""
results: list[list[str]] = []
device = next(model.parameters()).device
for prompt in prompts:
inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
inputs = {k: v.to(device) for k, v in inputs.items()}
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=max_new_tokens,
num_return_sequences=num_return_sequences,
do_sample=temperature > 0,
temperature=temperature if temperature > 0 else 1.0,
top_p=0.95,
pad_token_id=tokenizer.eos_token_id,
)
prompt_len = inputs["input_ids"].shape[1]
completions = [
tokenizer.decode(out[prompt_len:], skip_special_tokens=True)
for out in outputs
]
results.append(completions)
return results
# ---------------------------------------------------------------------------
# Pass@k
# ---------------------------------------------------------------------------
class PassAtKEvaluator(BaseEvaluator):
"""
Unbiased pass@k estimator from Chen et al. (2021):
pass@k = 1 - C(n-c, k) / C(n, k)
where n = total samples, c = correct samples.
"""
def __init__(self, k: int = 1, n: int = 10) -> None:
self.k = k
self.n = n
def evaluate(
self,
model: PreTrainedModel,
tokenizer: PreTrainedTokenizerBase,
dataset: Dataset,
num_problems: int = 50,
) -> float:
problems = dataset.select(range(min(num_problems, len(dataset))))
prompts = [str(ex.get("prompt", ex.get("content", ""))) for ex in problems]
references = [str(ex.get("canonical_solution", ex.get("content", ""))) for ex in problems]
all_completions = self._generate_batch(
model, tokenizer, prompts,
num_return_sequences=self.n,
temperature=0.8, # diversity for pass@k
)
scores: list[float] = []
for completions, reference in zip(all_completions, references):
correct = sum(
1 for c in completions
if self._is_correct(c, reference)
)
scores.append(self._pass_at_k(n=self.n, c=correct, k=self.k))
return float(np.mean(scores))
@staticmethod
def _pass_at_k(n: int, c: int, k: int) -> float:
if n - c < k:
return 1.0
return 1.0 - float(np.prod([(n - c - i) / (n - i) for i in range(k)]))
@staticmethod
def _is_correct(completion: str, reference: str) -> bool:
# Basic syntactic check — override with execution check for HumanEval-style
try:
ast.parse(completion)
return completion.strip() == reference.strip()
except SyntaxError:
return False
# ---------------------------------------------------------------------------
# BLEU
# ---------------------------------------------------------------------------
class BleuEvaluator(BaseEvaluator):
def __init__(self, max_new_tokens: int = 256) -> None:
self._max_new_tokens = max_new_tokens
self._bleu = BLEU(effective_order=True)
def evaluate(
self,
model: PreTrainedModel,
tokenizer: PreTrainedTokenizerBase,
dataset: Dataset,
num_samples: int = 100,
) -> float:
subset = dataset.select(range(min(num_samples, len(dataset))))
prompts = [str(ex.get("prompt", ex.get("content", ""))) for ex in subset]
references = [str(ex.get("canonical_solution", ex.get("content", ""))) for ex in subset]
completions_batch = self._generate_batch(
model, tokenizer, prompts, max_new_tokens=self._max_new_tokens
)
hypotheses = [batch[0] for batch in completions_batch]
result = self._bleu.corpus_score(hypotheses, [references])
# sacrebleu returns score in [0, 100]; normalise to [0, 1]
return result.score / 100.0
# ---------------------------------------------------------------------------
# Execution accuracy
# ---------------------------------------------------------------------------
def _run_code_safe(code: str, timeout: int) -> bool:
"""Run in a subprocess to enforce timeout and isolate crashes."""
try:
exec(compile(code, "<string>", "exec"), {}) # noqa: S102
return True
except Exception:
return False
class ExecutionAccuracyEvaluator(BaseEvaluator):
"""Fraction of generated code snippets that execute without error."""
def __init__(self, timeout: int = 10, max_new_tokens: int = 256) -> None:
self._timeout = timeout
self._max_new_tokens = max_new_tokens
def evaluate(
self,
model: PreTrainedModel,
tokenizer: PreTrainedTokenizerBase,
dataset: Dataset,
num_samples: int = 50,
) -> float:
subset = dataset.select(range(min(num_samples, len(dataset))))
prompts = [str(ex.get("prompt", ex.get("content", ""))) for ex in subset]
completions_batch = self._generate_batch(
model, tokenizer, prompts, max_new_tokens=self._max_new_tokens
)
codes = [batch[0] for batch in completions_batch]
passed = 0
with ProcessPoolExecutor(max_workers=4) as executor:
futures = {executor.submit(_run_code_safe, code, self._timeout): code for code in codes}
for future in futures:
try:
if future.result(timeout=self._timeout + 1):
passed += 1
except (FuturesTimeoutError, Exception):
pass
return passed / len(codes) if codes else 0.0
# ---------------------------------------------------------------------------
# Exact match
# ---------------------------------------------------------------------------
class ExactMatchEvaluator(BaseEvaluator):
def evaluate(
self,
model: PreTrainedModel,
tokenizer: PreTrainedTokenizerBase,
dataset: Dataset,
num_samples: int = 100,
) -> float:
subset = dataset.select(range(min(num_samples, len(dataset))))
prompts = [str(ex.get("prompt", ex.get("content", ""))) for ex in subset]
references = [str(ex.get("canonical_solution", ex.get("content", ""))) for ex in subset]
completions_batch = self._generate_batch(model, tokenizer, prompts)
hypotheses = [batch[0].strip() for batch in completions_batch]
matches = sum(h == r.strip() for h, r in zip(hypotheses, references))
return matches / len(references) if references else 0.0
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