wehub-resource-sync
130 lines
4.5 KiB
Python
130 lines
4.5 KiB
Python
"""LiveMathematicianBench environment adapter for ReflACT."""
|
|
from __future__ import annotations
|
|
|
|
import json
|
|
|
|
from skillopt.datasets.base import BatchSpec
|
|
from skillopt.envs.base import EnvAdapter
|
|
from skillopt.envs.livemathematicianbench.dataloader import LiveMathematicianBenchDataLoader
|
|
from skillopt.envs.livemathematicianbench.rollout import run_batch
|
|
from skillopt.model import get_target_backend
|
|
|
|
|
|
class LiveMathematicianBenchAdapter(EnvAdapter):
|
|
"""LiveMathematicianBench adapter."""
|
|
|
|
def build_reference_text(self, item: dict) -> str:
|
|
parts: list[str] = []
|
|
theorem = str(item.get("theorem") or "").strip()
|
|
sketch = str(item.get("sketch") or "").strip()
|
|
if theorem:
|
|
parts.append(f"## Reference Theorem\n{theorem}")
|
|
if sketch:
|
|
parts.append(f"## Reference Sketch\n{sketch}")
|
|
return "\n\n".join(parts)
|
|
|
|
def get_reference_metadata(self, item: dict) -> dict:
|
|
fields: list[str] = []
|
|
previews: list[str] = []
|
|
theorem = str(item.get("theorem") or "").strip()
|
|
sketch = str(item.get("sketch") or "").strip()
|
|
if theorem:
|
|
fields.append("theorem")
|
|
previews.append(f"[theorem]\n{theorem[:220]}")
|
|
if sketch:
|
|
fields.append("sketch")
|
|
previews.append(f"[sketch]\n{sketch[:220]}")
|
|
return {
|
|
"fields": fields,
|
|
"preview": "\n\n".join(previews)[:500],
|
|
}
|
|
|
|
def __init__(
|
|
self,
|
|
split_dir: str = "",
|
|
data_path: str = "",
|
|
split_mode: str = "ratio",
|
|
split_ratio: str = "2:1:7",
|
|
split_seed: int = 42,
|
|
split_output_dir: str = "",
|
|
max_turns: int = 1,
|
|
exec_timeout: int = 600,
|
|
workers: int = 64,
|
|
analyst_workers: int = 16,
|
|
failure_only: bool = False,
|
|
minibatch_size: int = 8,
|
|
edit_budget: int = 4,
|
|
seed: int = 42,
|
|
limit: int = 0,
|
|
shuffle_choices: bool = True,
|
|
use_theorem: bool = False,
|
|
use_sketch: bool = False,
|
|
max_completion_tokens: int = 16384,
|
|
) -> None:
|
|
self.max_turns = max_turns
|
|
self.exec_timeout = exec_timeout
|
|
self.workers = workers
|
|
self.max_completion_tokens = int(max_completion_tokens)
|
|
self.analyst_workers = analyst_workers
|
|
self.failure_only = failure_only
|
|
self.minibatch_size = minibatch_size
|
|
self.edit_budget = edit_budget
|
|
self.use_theorem = use_theorem
|
|
self.use_sketch = use_sketch
|
|
self.dataloader = LiveMathematicianBenchDataLoader(
|
|
split_dir=split_dir,
|
|
data_path=data_path,
|
|
split_mode=split_mode,
|
|
split_ratio=split_ratio,
|
|
split_seed=split_seed,
|
|
split_output_dir=split_output_dir,
|
|
seed=seed,
|
|
limit=limit,
|
|
shuffle_choices=shuffle_choices,
|
|
)
|
|
|
|
def setup(self, cfg: dict) -> None:
|
|
super().setup(cfg)
|
|
self.dataloader.setup(cfg)
|
|
|
|
def get_dataloader(self):
|
|
return self.dataloader
|
|
|
|
def build_env_from_batch(self, batch: BatchSpec, **kwargs):
|
|
return list(batch.payload or [])
|
|
|
|
def build_train_env(self, batch_size: int, seed: int, **kwargs):
|
|
batch = self.dataloader.build_train_batch(batch_size=batch_size, seed=seed, **kwargs)
|
|
return self.build_env_from_batch(batch, **kwargs)
|
|
|
|
def build_eval_env(self, env_num: int, split: str, seed: int, **kwargs):
|
|
batch = self.dataloader.build_eval_batch(env_num=env_num, split=split, seed=seed, **kwargs)
|
|
return self.build_env_from_batch(batch, **kwargs)
|
|
|
|
def rollout(
|
|
self,
|
|
env_manager,
|
|
skill_content: str,
|
|
out_dir: str,
|
|
**kwargs,
|
|
) -> list[dict]:
|
|
items: list[dict] = env_manager
|
|
return run_batch(
|
|
items=items,
|
|
out_root=out_dir,
|
|
skill_content=skill_content,
|
|
max_turns=self.max_turns,
|
|
exec_timeout=self.exec_timeout,
|
|
workers=self.workers,
|
|
max_completion_tokens=self.max_completion_tokens,
|
|
use_theorem=self.use_theorem,
|
|
use_sketch=self.use_sketch,
|
|
diagnostic_mode=kwargs.get("diagnostic_mode", False),
|
|
diagnostic_instruction=kwargs.get("diagnostic_instruction", ""),
|
|
diagnostic_trace_context_by_id=kwargs.get("diagnostic_trace_context_by_id"),
|
|
task_timeout=self.exec_timeout,
|
|
)
|
|
|
|
def get_task_types(self) -> list[str]:
|
|
return self.dataloader.get_task_types()
|