513 lines
21 KiB
Python
513 lines
21 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 os
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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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max_input_tokens: int = 0,
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tokenizer_path: str = '',
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attach_context_keys: tuple = ('passage', 'context'),
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dataset_spec=None,
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limit_per_task: Optional[int] = None,
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checkpoint: Union[bool, str] = False,
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dataset_name: str = 'adhoc',
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few_shot_num: int = 0,
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few_shot_samples: Optional[List[Sample]] = None,
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few_shot_text: Optional[str] = None,
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prompt_style: str = 'strict_letter',
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) -> tuple:
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"""Fan out model calls; returns (pred-dicts, total_usage).
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MCQ samples are generated with the strict-letter contract ('ANSWER: X',
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evalscope parity). few_shot: official exemplar text (few_shot_text) or
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dev/train-split samples (few_shot_samples) are prepended.
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"""
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gen_kwargs = gen_kwargs or {}
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def _default_max_tokens() -> int:
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return 4096
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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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def assemble(sample: Sample) -> str:
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parts = []
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if few_shot_text:
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parts.append(few_shot_text.strip()) # official exemplars, verbatim
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elif few_shot_num and few_shot_samples:
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letters_fs = 'ABCDEFGHIJ'
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for fs in few_shot_samples[:few_shot_num]:
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line = f'Question: {fs.input_text}'
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if fs.choices:
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line += '\n' + '\n'.join(f'{letters_fs[j]}. {c}' for j, c in enumerate(fs.choices))
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ans = fs.target if not isinstance(fs.target, list) else fs.target[0]
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line += f'\nAnswer: {ans}'
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parts.append(line)
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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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if prompt_style in ('strict_letter', 'auto'):
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# evalscope/OpenAI-style contract: reply ONLY 'ANSWER: X'
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letters = 'ABCDEFGHIJ'
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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'Answer the following multiple choice question. The entire '
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f'content of your response should be of the following format: '
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f"'ANSWER: [LETTER]' (without quotes) where [LETTER] is one of "
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f'{letters[:len(sample.choices)]}.\n\n{question}\n\n{opts}')
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else:
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letters = 'ABCDEFGHIJ'
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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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# hle OFFICIAL protocol: answer_type-specific system contract
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at = (sample.metadata or {}).get('answer_type')
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if at == 'exactMatch':
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question = (
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'Your response should be in the following format:\n'
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'Explanation: {your explanation for your final answer}\n'
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'Exact Answer: {your succinct, final answer}\n'
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'Confidence: {your confidence score between 0% and 100% for your answer}\n\n'
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f'{question}')
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elif at == 'multipleChoice':
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question = (
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'Your response should be in the following format:\n'
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'Explanation: {your explanation for your answer choice}\n'
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'Answer: {your chosen answer}\n'
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'Confidence: {your confidence score between 0% and 100% for your answer}\n\n'
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f'{question}')
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else:
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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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ds_spec = dataset_spec
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if ds_spec is not None and getattr(ds_spec, 'prompt_suffix', ''):
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question = question + ds_spec.prompt_suffix
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parts.append(question)
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text = '\n\n'.join(parts)
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if max_input_tokens:
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# reserve room for the OUTPUT budget + safety margin, else the
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# server rejects input+max_tokens > context_limit by 1 token
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budget = max(1024, max_input_tokens
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- int(gen_kwargs.get('max_tokens') or 4096) - 2048)
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try:
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from .truncation import truncate_middle_tokens, default_tokenizer_path
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text = truncate_middle_tokens(text, budget,
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tokenizer_path or default_tokenizer_path())
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except Exception as e:
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# no tokenizer/transformers: degrade to a CHARS budget that
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# approximates the token cap (never send the raw 2M-token input)
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approx_chars = budget * 3
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if len(text) > approx_chars:
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keep = approx_chars // 2
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text = f'{text[:keep]}\n\n...[truncated {len(text) - 2 * keep} chars]...\n\n{text[-keep:]}'
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print(f'truncation degraded to chars ({type(e).__name__})', flush=True)
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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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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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from ..agent.loop import Environment, Usage as _U # noqa: F401
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async with sem:
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env = env_factory()
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if type(env).run_task is not Environment.run_task:
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# self-running env (official engine bundles: tau2/swe)
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pred = await env.run_task(adapter, sample,
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max_turns=max_turns, system=system)
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if pred is None:
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traj = await drive(adapter, sample, env=env,
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max_turns=max_turns, system=system)
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pred = trajectory_to_prediction(traj)
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else:
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traj = await drive(adapter, sample, env=env,
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max_turns=max_turns, system=system)
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pred = trajectory_to_prediction(traj)
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if not pred.get('usage'):
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pred['usage'] = traj.usage.model_dump() if 'traj' in dir() else {}
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pred.setdefault('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('domain')
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or sample.metadata.get('id') or sample.id or ''))
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u = pred.get('usage') or {}
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total_usage = total_usage + Usage(
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input_tokens=int(u.get('input_tokens', 0) or 0),
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output_tokens=int(u.get('output_tokens', 0) or 0),
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total_tokens=int(u.get('total_tokens', 0) or 0),
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latency_s=float(u.get('latency_s', 0) or 0))
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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 = _apply_limits(samples, limit, limit_per_task)
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# checkpointing: restore completed samples, generate only the rest
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ckpt_store = None
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if checkpoint:
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from ..eval.checkpoint import CheckpointStore, checkpoint_path
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if isinstance(checkpoint, str):
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ckpt = checkpoint
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else:
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ckpt = checkpoint_path(os.path.expanduser('~/.cache/evalharness'),
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dataset_name, adapter.model or str(adapter))
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ckpt_store = CheckpointStore(ckpt, model=adapter.model or str(adapter))
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restored = ckpt_store.load()
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else:
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restored = {}
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keys = []
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pending = []
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preds_by_key: Dict[str, Dict[str, Any]] = {}
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for i, s in enumerate(work):
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k = CheckpointStore.key_for(s, i) if ckpt_store else str(i)
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keys.append(k)
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if k in restored:
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preds_by_key[k] = restored[k]
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else:
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pending.append((i, s))
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if ckpt_store is not None and restored:
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print(f'checkpoint: restored {len(restored)} predictions '
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f'({len(pending)} to generate) -> {ckpt_store.path}', flush=True)
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async def run_one(i_s):
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i, s = i_s
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pred = await one(s)
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if ckpt_store is not None:
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ckpt_store.append(keys[i], pred)
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return i, pred
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fresh = await asyncio.gather(*(run_one((i, s)) for i, s in pending))
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for i, pred in fresh:
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preds_by_key[keys[i]] = pred
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preds = [preds_by_key[k] for k in keys]
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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 _apply_limits(samples: List[Sample], total: Optional[int],
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per_task: Optional[int], dataset=None,
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shuffle: bool = True, seed: int = 42) -> List[Sample]:
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"""total: cap the WHOLE run (ours semantics). per_task: cap each subset/
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category (evalscope's --limit semantics) -- first N per group_key.
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shuffle+seed mirror evalscope run.py: dataset_args.shuffle=True with
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--seed 42 -> random.Random(seed).shuffle BEFORE limiting, so both
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frameworks evaluate the IDENTICAL sample subset."""
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if shuffle:
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import random
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random.Random(seed).shuffle(samples)
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if per_task:
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seen: Dict[str, int] = {}
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out = []
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for s in samples:
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key = str((s.metadata or {}).get('category')
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or (s.metadata or {}).get('subject')
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or (s.metadata or {}).get('test_category')
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or getattr(getattr(dataset, 'spec', None), 'subset', 'default'))
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if seen.get(key, 0) < per_task:
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seen[key] = seen.get(key, 0) + 1
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out.append(s)
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samples = out
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if total:
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samples = samples[:total]
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return samples
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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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max_input_tokens: int = 0,
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limit_per_task: Optional[int] = None,
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checkpoint: Union[bool, str] = False,
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dataset_name: str = 'adhoc',
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few_shot_num: int = -1,
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prompt_style: str = 'strict_letter',
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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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'auto' = logprob when the adapter supports it.
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few_shot_num: -1 = the dataset's declared paper default (mmlu 5, bbh 3,
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gsm8k 4, ...); 0 = zero-shot; N = explicit override.
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prompt_style: 'strict_letter' (default, evalscope-style 'ANSWER: X')
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| 'cot' (reasoning-friendly).
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"""
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spec = getattr(dataset, 'spec', None)
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if few_shot_num < 0:
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few_shot_num = (spec.few_shot_num if spec is not None else 0)
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adapter = _make_adapter(model_spec)
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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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samples = _apply_limits(samples, limit, limit_per_task)
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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 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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probe = get_env(env)
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if getattr(probe, 'needs_adapter', False):
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env_factory = lambda: get_env(env, adapter=adapter) # noqa: E731
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else:
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env_factory = lambda: get_env(env) # noqa: E731
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# paper-faithful few-shot exemplars: official hand-written hooks first
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# (bbh CoT), else the dataset's own dev/train split
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few_shot_samples = None
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few_shot_text = None
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if few_shot_num:
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from ..data.registry import get_dataset_provider
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prov = get_dataset_provider(name)
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hook = getattr(prov, 'few_shot_hook', None)
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if hook is not None:
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few_shot_text = hook('', spec.subset if spec else 'default', few_shot_num)
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if few_shot_text is None:
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fs_split = (spec.few_shot_split if spec is not None else None) or 'dev'
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try:
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import dataclasses
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fs_spec = dataclasses.replace(spec, split=fs_split) if spec is not None else None
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if fs_spec is not None:
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from ..data.loader import load_raw_records
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fn = prov.resolve_record_fn()
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fs_raw = load_raw_records(fs_spec)
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few_shot_samples = [fn(r) for r in fs_raw[:few_shot_num]]
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except Exception as e:
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print(f'few-shot: could not load {fs_split} split ({type(e).__name__}: '
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f'{str(e)[:80]}); continuing 0-shot', flush=True)
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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={**(getattr(spec, 'gen_config', {}) or {}), **(gen_kwargs or {})},
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env_factory=env_factory,
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system=system, max_turns=max_turns, max_input_chars=max_input_chars,
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max_input_tokens=max_input_tokens,
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dataset_spec=spec,
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limit_per_task=limit_per_task,
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checkpoint=checkpoint,
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dataset_name=name,
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few_shot_num=few_shot_num,
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few_shot_samples=few_shot_samples, few_shot_text=few_shot_text,
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prompt_style=prompt_style)
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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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# performance profile: pool success rate + latency/ttft percentiles
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try:
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from .aggregator import get_aggregator
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perf = get_aggregator('perf_stats')(report.samples, 'acc')
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if hasattr(adapter, 'stats'):
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perf.update({f'pool_{k}': round(v, 3) if isinstance(v, float) else v
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for k, v in adapter.request_stats().items()})
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report.metric_groups['perf'] = perf
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except Exception:
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pass
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return report
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def _make_adapter(spec: str) -> ModelAdapter:
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"""Model spec forms:
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- 'mock[:mode]' offline adapter
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- 'openai-pool/<base-url-template>?model' with {port} placeholder:
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e.g. 'openai-pool/http://127.0.0.1:{8123..8130}/v1?Qwen3-8B' -> N ports
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- else resolve_adapter(spec) single endpoint
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Pooled specs are CACHED per spec: all benches share one pool so the
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round-robin counter stays global (independent pools would each restart
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at the first backend and starve the rest).
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"""
|
|
from .adapter import _ADAPTER_CACHE as _CACHE
|
|
|
|
cache_key = spec
|
|
if cache_key in _CACHE:
|
|
return _CACHE[cache_key]
|
|
opts = {}
|
|
while True:
|
|
for f in ('!nothink', '!textools', '!perf'):
|
|
if spec.endswith(f):
|
|
spec = spec[:-len(f)]
|
|
opts[f] = True
|
|
break
|
|
else:
|
|
break
|
|
if spec.startswith('openai-pool/'):
|
|
from .pool import pooled
|
|
|
|
rest = spec[len('openai-pool/'):]
|
|
m = __import__('re').search(r'\{(\d+)\.\.(\d+)\}', rest)
|
|
if not m:
|
|
raise ValueError("openai-pool needs a {start..end} port range")
|
|
lo, hi = int(m.group(1)), int(m.group(2))
|
|
base_url, _, model = rest.partition('?')
|
|
specs = []
|
|
for port in range(lo, hi + 1):
|
|
specs.append(f'openai/{base_url.replace(m.group(0), str(port))}?{model}')
|
|
adapter = pooled(specs)
|
|
elif spec.partition(':')[0] == 'mock' and ':' in spec and '/' not in spec.partition(':')[0]:
|
|
adapter = resolve_adapter('mock')
|
|
adapter.extra['mode'] = spec.partition(':')[2] or 'echo'
|
|
return adapter
|
|
else:
|
|
adapter = resolve_adapter(spec)
|
|
members = adapter.adapters if hasattr(adapter, 'adapters') else [adapter]
|
|
for a in members:
|
|
if opts.get('!nothink'):
|
|
a.extra['no_think'] = True
|
|
if opts.get('!textools'):
|
|
a.extra['tools_mode'] = 'text'
|
|
if opts.get('!perf'):
|
|
a.extra['collect_perf'] = True
|
|
_CACHE[cache_key] = adapter
|
|
return adapter
|
|
|
|
|
|
def _judge_callable(judge_adapter: ModelAdapter):
|
|
"""Sync judge bridge. Works inside a running event loop (evaluate() may be
|
|
called from async run_eval): the coroutine runs on a private loop in a
|
|
worker thread."""
|
|
|
|
def ask(messages) -> str:
|
|
import asyncio
|
|
|
|
if isinstance(messages, list) and messages and isinstance(messages[0], dict):
|
|
messages = [ChatMessage(role=m.get('role', 'user'), content=m.get('content', ''))
|
|
for m in messages]
|
|
|
|
async def go():
|
|
out = await judge_adapter.generate(messages)
|
|
return out.text
|
|
|
|
try:
|
|
asyncio.get_running_loop()
|
|
except RuntimeError:
|
|
return asyncio.run(go())
|
|
import concurrent.futures
|
|
|
|
with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool:
|
|
return pool.submit(asyncio.run, go()).result()
|
|
|
|
return ask
|