f844775f1e
tau2's current reward_info carries 'reward' (composite) + db_check / action_checks; the scorer read the old environment_reward / communication_reward split that no longer exists -- simulations scored 1.0 came out 0.0. Fallback to the old split kept for older engines. Co-Authored-By: Claude <noreply@anthropic.com>
323 lines
12 KiB
Python
323 lines
12 KiB
Python
"""Execution / agent benchmarks. Execution recipes build a runnable program
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(completion + tests + checker) via a harness closure and run it in a sandbox;
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agent recipes wait for the agent layer (env_reward slot)."""
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from ..recipe import EvalRecipe, register_eval
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def _humaneval_harness(sample, pred: str):
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test = sample.metadata.get('test', '')
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entry = sample.metadata.get('entry_point', 'f')
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base = (sample.metadata or {}).get('prompt') or sample.input
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prog = f'{base}{pred}\n\n{test}\n\ncheck({entry})\nprint("PASSED")\n'
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return {'main.py': prog}
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def _humaneval_extract(raw, sample):
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# es/official contract asks for 'ONLY the code' -> the model emits a bare
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# function with no markdown fence; fall back to the raw text then
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from ..extractor import make_extractor
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val, ok, note = make_extractor('code_any')(raw, sample)
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if ok:
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return val, ok, note
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body = (raw or '').strip()
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if body:
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return body, True, 'bare_code'
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return '', False, 'empty'
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@register_eval('humaneval')
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def humaneval():
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return EvalRecipe(
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name='humaneval',
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extract=_humaneval_extract,
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scorers={'pass': {'name': 'execution', 'harness': _humaneval_harness,
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'sandbox': 'docker', 'timeout_s': 30}},
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aggregators={'pass': 'pass_at_k'},
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exec_workers=8,
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description='HumanEval; completion + official tests in a sandbox, pass@k.',
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)
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def _bcb_harness(sample, pred: str):
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test = sample.metadata.get('test', '')
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entry = sample.metadata.get('entry_point', 'f')
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# BCB official semantics: completion is a standalone module; `test` is a
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# unittest.TestCase subclass -> run it with unittest (official runner uses
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# `unittest.main()` with a buffer; exit 0 == all tests pass)
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prog = f'{pred}\n\n{test}\n\nif __name__ == "__main__":\n import unittest\n unittest.main()\n'
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return {'main.py': prog}
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@register_eval('bigcodebench')
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def bigcodebench():
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return EvalRecipe(
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name='bigcodebench',
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extract='code_any',
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scorers={'pass': {'name': 'execution', 'harness': _bcb_harness,
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# official sandbox image (bundles every task's deps);
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# its ENTRYPOINT is the official evaluate CLI which
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# swallows our runner -> override with plain python3
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'image': 'bigcodebench/bigcodebench-evaluate:latest',
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'entrypoint': 'python3',
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'sandbox': 'docker', 'timeout_s': 120}},
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aggregators={'pass': 'pass_at_k'},
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exec_workers=12,
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description='BigCodeBench; official all-libs docker image, pass@k.',
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)
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_LCB_RUNNER = r'''
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import json, subprocess, sys
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cases = json.load(open('cases.json'))
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meta = json.load(open('meta.json')) if __import__('os').path.exists('meta.json') else {}
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fn_name = meta.get('fn_name')
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def as_lines(v):
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"""Normalize an expected output to a list of lines (no trailing empties)."""
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if not isinstance(v, list):
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v = [v]
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out = []
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for item in v:
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out.extend(str(item).rstrip('\n').split('\n'))
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return [l for l in out if l != '']
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failed = 0
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if fn_name:
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# function-call style (LeetCode / starter_code problems, es-official):
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# import the solution and call fn_name on each input, compare to output
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import importlib.util
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spec = importlib.util.spec_from_file_location('solution', 'solution.py')
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mod = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(mod)
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fn = getattr(mod, fn_name, None)
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if fn is None:
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# starter classes: instantiate and look for the method on the class
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for attr in vars(mod).values():
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if isinstance(attr, type) and hasattr(attr, fn_name):
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fn = getattr(attr(), fn_name)
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break
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if fn is None:
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print(f'fn_name {fn_name!r} not found in solution', file=sys.stderr)
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sys.exit(1)
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for i, case in enumerate(cases):
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try:
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raw_in, raw_out = case['input'], case['output']
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# lite packs fn-style args/results as JSON STRINGS
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args = json.loads(raw_in) if isinstance(raw_in, str) else raw_in
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expected = json.loads(raw_out) if isinstance(raw_out, str) else raw_out
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args = args if isinstance(args, list) else [args]
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got = fn(*args)
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except Exception as e:
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print(f'case {i}: raised {type(e).__name__}: {e}', file=sys.stderr)
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failed += 1
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continue
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expected = tuple(expected) if isinstance(expected, list) else expected
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got_t = tuple(got) if isinstance(got, list) else got
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if got_t != expected:
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print(f'case {i}: expected {expected!r} got {got_t!r}', file=sys.stderr)
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failed += 1
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else:
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for i, case in enumerate(cases):
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stdin = case.get('input', '')
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expected = as_lines(case.get('output', ''))
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r = subprocess.run([sys.executable, 'solution.py'], input=stdin,
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capture_output=True, text=True, timeout=20)
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got = [l for l in r.stdout.split('\n') if l != '']
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if got != expected:
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failed += 1
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print(f'case {i}: expected {expected!r} got {got!r}', file=sys.stderr)
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if failed:
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print(f'{failed}/{len(cases)} cases failed', file=sys.stderr)
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sys.exit(1)
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print('PASSED')
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'''
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def _lcb_decode_cases(raw):
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"""LCB test cases: official data packs private cases as base64+zlib+pickle."""
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import base64
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import io
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import json
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import pickle
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import zlib
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if raw is None:
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return []
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if not isinstance(raw, str):
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return raw if isinstance(raw, list) else []
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try:
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blob = zlib.decompress(base64.b64decode(raw))
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if blob[:2] in (b'\x80\x04', b'\x80\x05', b'\x80\x02'): # pickle protocol
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data = pickle.load(io.BytesIO(blob))
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else:
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data = json.loads(blob.decode())
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except Exception:
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data = None
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if data is None:
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try:
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data = json.loads(raw)
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except (ValueError, TypeError):
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return []
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# LCB double-packs: pickle list may hold a JSON STRING of the real list
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if isinstance(data, str):
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try:
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data = json.loads(data)
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except (ValueError, TypeError):
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return []
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if isinstance(data, dict): # {'input':..,'output':..} single case
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data = [data]
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return data if isinstance(data, list) else []
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def _lcb_harness(sample, pred: str, use_private: bool = True):
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import json
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starter = sample.metadata.get('starter_code') or ''
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# es-official case composition: PUBLIC + PRIVATE in full (use_private
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# toggles the private half; es load_utils.py always uses both)
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pub = _lcb_decode_cases(sample.metadata.get('public_test_cases'))
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priv = _lcb_decode_cases(sample.metadata.get('private_test_cases')) if use_private else []
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cases = pub + priv
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if not cases:
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cases = _lcb_decode_cases(sample.metadata.get('public_test_cases')) or []
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files = {
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'solution.py': f'{starter}\n{pred}\n',
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'cases.json': json.dumps(cases or []),
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'runner.py': _LCB_RUNNER,
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}
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fn_name = (sample.metadata.get('fn_name')
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or _lcb_fn_name_from_metadata(sample.metadata.get('raw_metadata')))
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if fn_name:
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files['meta.json'] = json.dumps({'fn_name': fn_name})
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return files
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def _lcb_fn_name_from_metadata(raw):
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"""Official lite packs fn_name inside the record's `metadata` JSON blob."""
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import json
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if not raw:
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return None
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try:
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md = json.loads(raw) if isinstance(raw, str) else raw
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return md.get('func_name')
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except Exception:
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return None
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@register_eval('live_code_bench')
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def live_code_bench():
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return EvalRecipe(
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name='live_code_bench',
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extract='code_any',
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scorers={'pass': {'name': 'execution', 'harness': _lcb_harness,
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'entry': 'runner.py', 'sandbox': 'local', 'timeout_s': 60}},
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aggregators={'pass': 'pass_at_k'},
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exec_workers=8,
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description='LiveCodeBench; stdin/stdout public-case runner in sandbox.',
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)
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import json as _json
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def _swe_harness(sample, pred: str):
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"""Apply the predicted patch in the official per-instance sweb image and
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run FAIL_TO_PASS (+PASS_TO_PASS) tests. Single-turn protocol: the model
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reads problem_statement and emits a unified diff."""
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f2p = _json.loads(sample.metadata.get('FAIL_TO_PASS') or '[]')
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p2p = _json.loads(sample.metadata.get('PASS_TO_PASS') or '[]')
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tests = f2p + p2p[:20] # guard: cap regression tests for runtime
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script = f'''set -e
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cd /testbed
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git apply --whitespace=fix /work/patch.diff || {{ echo PATCH_FAILED; exit 2; }}
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FAIL=0
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while IFS= read -r t; do
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[ -z "$t" ] && continue
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if ! (conda run -n testbed python -m pytest -x -q "$t" > /dev/null 2>&1); then
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echo "TEST_FAILED $t"; FAIL=1
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fi
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done <<'EOF'
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{chr(10).join(tests)}
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EOF
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[ "$FAIL" = 0 ] && echo RESOLVED
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exit $FAIL
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'''
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return {'patch.diff': pred or '', 'run.sh': script}
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@register_eval('swe_bench_verified')
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def swe_bench_verified():
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return EvalRecipe(
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name='swe_bench_verified',
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extract='identity', # a patch, not an answer
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scorers={'resolved': {'name': 'execution', 'harness': _swe_harness,
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'entry': 'run.sh', 'sandbox': 'docker',
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'timeout_s': 900}},
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description='SWE-bench Verified single-turn: model emits a unified diff; '
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'applied in the official sweb.eval.* image, FAIL_TO_PASS(+P2P) '
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'must pass. Prefetch: evalharness sandbox prefetch swe_bench_verified',
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)
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def _tau2_reward(pred, target, sample, ctx):
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"""Score from the official engine's reward_info (env_state).
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Current tau2 reward_info carries the COMPOSITE 'reward' plus detail
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fields (db_check / action_checks / communicate_checks); the old
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environment_reward/communication_reward split no longer exists."""
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env_state = ctx.params.get('env_state') or {}
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rewards = env_state.get('tau2_rewards') or {}
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r = rewards.get('reward')
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if not isinstance(r, (int, float)):
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vals = [v for v in (rewards.get('environment_reward'),
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rewards.get('communication_reward'))
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if isinstance(v, (int, float))]
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r = sum(vals) / len(vals) if vals else 0.0
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return ({'acc': float(r)}, {'acc': {'mode': 'official_tau2',
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'reward': r,
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'db_check': rewards.get('db_check'),
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'note': str((rewards.get('info') or {}).get('note', ''))[:120]}})
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@register_eval('tau2_bench')
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def tau2_bench():
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return EvalRecipe(
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name='tau2_bench',
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extract='identity',
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scorers={'acc': _tau2_reward},
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aggregators={'acc': 'grouped_avg'},
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description='tau2-bench via OFFICIAL engine (user simulator + env + reward); '
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"run with env='tau2_official'",
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)
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@register_eval('bfcl_v3')
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def bfcl_v3():
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return EvalRecipe(
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name='bfcl_v3',
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extract='identity',
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scorers={'acc': 'env_reward'}, # call-sequence vs ground truth (bfcl_mock env)
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aggregators={'acc': 'weighted_group_avg'}, # group_key = test_category
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description='BFCL v3; run with env=bfcl_mock (agent pump), official call-sequence scoring.',
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)
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@register_eval('general_fc')
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def general_fc():
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from ..recipe import EvalRecipe, register_eval as _re # noqa: F401 (keep import local)
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def _gfc_extract(raw, sample):
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# prediction = did the model call any tool? serialized tool_calls in raw
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called = '"name"' in (raw or '') and ('tool_call' in (raw or '').lower()
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or raw.strip().startswith('[{"name"'))
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return ('True' if called else 'False'), True, 'tool_called_bool'
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return EvalRecipe(
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name='general_fc',
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extract=_gfc_extract,
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scorers={'acc': {'name': 'exact', 'mode': 'raw'}},
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description='General function calling; predicts should-call-tool (True/False) vs target.',
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)
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