255 lines
9.8 KiB
Python
255 lines
9.8 KiB
Python
"""Async generation runner: model + dataset -> predictions -> scored report.
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The async boundary is exactly "waiting on the model". Data loading and
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scoring stay synchronous (fast, CPU/disk bound); this coroutine fans out
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model calls with a semaphore, streams progress, then hands the collected
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raw strings to the sync evaluate().
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from evalharness.model import run_eval
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report = asyncio.run(run_eval(ds, 'mock', limit=50)) # offline smoke
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report = asyncio.run(run_eval(ds, 'openai/http://gpu03:8000/v1?qwen3-8b'))
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"""
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import asyncio
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import time
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from typing import Any, Dict, List, Optional, Union
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from ..data.dataset import Dataset
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from ..data.sample import ChatMessage, Sample
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from ..eval.recipe import EvalRecipe
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from ..eval.record import EvalReport
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from ..eval.runner import evaluate
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from .adapter import ModelAdapter, resolve_adapter
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from .output import Usage
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async def generate_predictions(
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adapter: ModelAdapter,
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samples: List[Sample],
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concurrency: int = 32,
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limit: Optional[int] = None,
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gen_kwargs: Optional[Dict[str, Any]] = None,
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progress: bool = True,
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env_factory=None,
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system: str = '',
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max_turns: int = 8,
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max_input_chars: int = 0,
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attach_context_keys: tuple = ('passage', 'context'),
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) -> tuple:
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"""Fan out model calls; returns (pred-dicts, total_usage).
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Without env_factory: single-turn generation (text or tool-call JSON).
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With env_factory(sample)->Environment: the agent message pump runs per
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sample and predictions carry trajectory/env_state/usage.
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max_input_chars: hard cap on the assembled input (anti-OOM for 128k
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contexts); 0 = no cap. Truncation keeps the head AND the question tail.
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attach_context_keys: metadata fields (passage/context) prepended to the
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question at generation time -- the data layer keeps them separate, the
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runner assembles the actual prompt.
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"""
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gen_kwargs = gen_kwargs or {}
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def assemble(sample: Sample) -> str:
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parts = []
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for key in attach_context_keys:
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ctx = (sample.metadata or {}).get(key)
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if ctx:
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parts.append(str(ctx))
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question = sample.input_text
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if sample.choices:
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# MCQ: options MUST be in the prompt; ask for the letter
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letters = 'ABCDEFGH'
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opts = '\n'.join(f'{letters[i]}. {c}' for i, c in enumerate(sample.choices)
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if i < len(letters))
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question = (f'{question}\n\n{opts}\n\n'
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'Answer with the letter of the correct option.')
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elif sample.task_type in ('qa',):
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# gentle output contract (matches official few-shot conventions)
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question = (f'{question}\n\n'
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'End your reply with the final answer on its own last line '
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'in the form "Answer: <answer>".')
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parts.append(question)
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text = '\n\n'.join(parts)
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if max_input_chars and len(text) > max_input_chars:
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keep = max_input_chars // 2
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head = text[:keep]
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tail = text[-keep:]
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text = f'{head}\n\n...[truncated {len(text) - 2 * keep} chars]...\n\n{tail}'
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return text
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sem = asyncio.Semaphore(concurrency)
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total_usage = Usage()
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done_count = 0
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t0 = time.time()
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usages: List[Dict[str, Any]] = []
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async def one(sample: Sample) -> Dict[str, Any]:
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nonlocal done_count, total_usage
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if env_factory is not None:
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from ..agent import drive, trajectory_to_prediction
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async with sem:
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traj = await drive(adapter, sample, env=env_factory(),
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max_turns=max_turns, system=system)
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total_usage = total_usage + traj.usage
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pred = trajectory_to_prediction(traj)
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pred['group_key'] = str(sample.metadata.get('test_category')
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or sample.metadata.get('category')
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or sample.metadata.get('id') or sample.id or '')
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done_count += 1
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_progress(progress, done_count, len(samples), t0, total_usage)
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return pred
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messages = ([ChatMessage(role='user', content=assemble(sample))]
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if isinstance(sample.input, str) else list(sample.input))
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tools = None
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if sample.tools:
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tools = [{'name': t.name, 'description': t.description or '',
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'parameters': t.parameters} for t in sample.tools]
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if getattr(adapter, 'name', '') == 'mock' \
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and adapter.extra.get('mode') in ('boxed', 'oracle', 'fc') \
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and sample.target not in ('', None):
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# oracle channel for mock verification so full pipelines run offline
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messages = messages + [ChatMessage(role='user',
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content=f'MOCKTARGET::{sample.target}')]
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async with sem:
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out = await adapter.generate(messages, tools=tools, **gen_kwargs)
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total_usage = total_usage + out.usage
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text = out.text
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if out.tool_calls: # fc tasks: serialize calls as the prediction
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import json
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text = (text + '\n' if text else '') + json.dumps(
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[c.to_openai()['function'] for c in out.tool_calls], ensure_ascii=False)
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done_count += 1
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_progress(progress, done_count, len(samples), t0, total_usage)
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return {'raw': text, 'usage': out.usage.model_dump()}
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work = samples[:limit] if limit else samples
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preds = list(await asyncio.gather(*(one(s) for s in work)))
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usages = [p.get('usage', {}) for p in preds]
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return preds, usages, total_usage
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def _progress(progress: bool, done: int, total: int, t0: float, usage: Usage) -> None:
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if progress and (done % 20 == 0 or done == total):
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rate = done / max(time.time() - t0, 1e-6)
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print(f' [{done}/{total}] {rate:.1f} samples/s tokens={usage.total_tokens}', flush=True)
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async def run_eval(
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dataset: Union[Dataset, List[Sample]],
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model_spec: str,
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recipe: Optional[EvalRecipe] = None,
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*,
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concurrency: int = 32,
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limit: Optional[int] = None,
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gen_kwargs: Optional[Dict[str, Any]] = None,
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judge_spec: Optional[str] = None,
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judge: Optional[Any] = None,
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progress: bool = True,
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env: str = '',
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system: str = '',
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max_turns: int = 8,
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max_input_chars: int = 0,
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) -> EvalReport:
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"""Generate + score in one call. Model spec examples:
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'mock', 'mock:boxed', 'openai/http://gpu03:8000/v1?qwen3-8b', 'deploy:vllm/qwen3-8b'.
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env: environment plugin name ('bfcl_mock') -> agent message pump per
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sample; omit for single-turn generation.
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"""
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adapter = _make_adapter(model_spec)
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spec = getattr(dataset, 'spec', None)
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name = spec.name if spec is not None else 'adhoc'
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if recipe is None:
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from ..eval.recipe import EvalRecipe, get_eval
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try:
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recipe = get_eval(name)
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except KeyError:
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if name != 'adhoc':
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raise
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recipe = EvalRecipe(name='adhoc', extract='identity',
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scorers={'acc': {'name': 'exact', 'mode': 'raw'}})
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samples = list(dataset)[:limit] if limit else list(dataset)
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if progress:
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mode = f'agent env={env}' if env else 'single-turn'
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print(f'generating: {adapter} on {len(samples)} samples '
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f'({mode}, concurrency={concurrency})', flush=True)
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env_factory = None
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if env:
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from ..agent.loop import ENV_REGISTRY, get_env
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if env not in ENV_REGISTRY:
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raise KeyError(f'unknown env {env!r}; available: {", ".join(ENV_REGISTRY.names())}')
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env_factory = lambda: get_env(env) # noqa: E731
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try:
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preds, _usages, usage = await generate_predictions(
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adapter, samples, concurrency, progress=progress,
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gen_kwargs=gen_kwargs, env_factory=env_factory,
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system=system, max_turns=max_turns, max_input_chars=max_input_chars)
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finally:
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await adapter.close()
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if judge is None and judge_spec:
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judge_adapter = _make_adapter(judge_spec)
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judge = _judge_callable(judge_adapter)
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report = evaluate(
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samples, preds, recipe,
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model=model_spec,
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judge=judge,
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extra_metadata={'gen_input_tokens': usage.input_tokens,
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'gen_output_tokens': usage.output_tokens,
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'gen_total_tokens': usage.total_tokens},
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)
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report.model = model_spec
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report.dataset = name
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return report
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def _make_adapter(spec: str) -> ModelAdapter:
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"""'mock:boxed' -> MockAdapter(mode='boxed'); else resolve_adapter().
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The colon-mode syntax exists ONLY for 'mock': adapter names contain no
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scheme/colon, so 'mock:xxx' is safe while URLs ('openai/http://...')
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must never be split on ':'.
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"""
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base, sep, mode = spec.partition(':')
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if sep and '/' not in base and base == 'mock':
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adapter = resolve_adapter('mock')
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adapter.extra['mode'] = mode or 'echo'
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return adapter
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return resolve_adapter(spec)
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def _judge_callable(judge_adapter: ModelAdapter):
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"""Sync judge bridge. Works inside a running event loop (evaluate() may be
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called from async run_eval): the coroutine runs on a private loop in a
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worker thread."""
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def ask(messages) -> str:
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import asyncio
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if isinstance(messages, list) and messages and isinstance(messages[0], dict):
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messages = [ChatMessage(role=m.get('role', 'user'), content=m.get('content', ''))
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for m in messages]
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async def go():
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out = await judge_adapter.generate(messages)
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return out.text
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try:
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asyncio.get_running_loop()
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except RuntimeError:
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return asyncio.run(go())
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import concurrent.futures
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with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool:
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return pool.submit(asyncio.run, go()).result()
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return ask
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