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from __future__ import annotations
from dataclasses import dataclass, field
from typing import Dict, List
from .models import (
HypothesisRecord,
PyTorchDebugAction,
PyTorchDebugObservation,
PyTorchDebugState,
)
from .reward import clamp_score, compute_step_reward
from .scenario_generator import ScenarioGenerator
from .graders import grade_easy, grade_medium, grade_hard
GRADER_MAP = {"easy": grade_easy, "medium": grade_medium, "hard": grade_hard}
LOSS_WINDOW_STEP = 25
GPU_WINDOW_STEP = 25
LOG_WINDOW_STEP = 10
@dataclass
class RuntimeState:
scenario: object | None = None
max_steps: int = 5
current_step: int = 0
revealed_files: List[str] = field(default_factory=list)
hypothesis_history: List[HypothesisRecord] = field(default_factory=list)
loss_curve_bonus: int = 0
gpu_profile_bonus: int = 0
log_tail_bonus: int = 0
diagnostic_revealed: bool = False
done: bool = False
final_score: float = 0.0
class PyTorchDebugEnv:
def __init__(self, generator: ScenarioGenerator, max_steps: int = 5):
"""Create a PyTorch debugging environment with a scenario generator."""
self.generator = generator
self.runtime = RuntimeState(max_steps=max_steps)
async def reset(self, task_id: str = "easy", seed: int | None = None):
"""Start a new episode and return the initial observation."""
scenario = self.generator.generate(task_id, seed=seed)
self.runtime = RuntimeState(
scenario=scenario,
max_steps=5 if task_id == "easy" else 6,
current_step=0,
revealed_files=["train.py", "config/training_config.yaml"],
hypothesis_history=[],
loss_curve_bonus=0,
gpu_profile_bonus=0,
log_tail_bonus=0,
diagnostic_revealed=False,
done=False,
final_score=0.0,
)
return self._build_observation(last_feedback="Episode reset.")
async def step(self, action: PyTorchDebugAction):
"""Advance the environment by one step using the provided action."""
if self.runtime.scenario is None:
raise RuntimeError("Call /reset before /step")
if self.runtime.done:
raise RuntimeError("Episode already completed")
self.runtime.current_step += 1
scenario = self.runtime.scenario
previous_quality = self.runtime.hypothesis_history[-1].quality if self.runtime.hypothesis_history else 0.0
investigation_target = None
if action.investigation_action:
action_type = action.investigation_action.action
if action_type == "reveal_file":
investigation_target = action.investigation_action.target
if (
investigation_target in scenario.repo_files
and investigation_target not in self.runtime.revealed_files
):
self.runtime.revealed_files.append(investigation_target)
elif action_type == "extend_loss_curve":
self.runtime.loss_curve_bonus += 1
elif action_type == "extend_gpu_profile":
self.runtime.gpu_profile_bonus += 1
elif action_type == "reveal_log_chunk":
self.runtime.log_tail_bonus += 1
elif action_type == "run_diagnostic":
self.runtime.diagnostic_revealed = True
committed = action.final_diagnosis.model_dump() if action.commit_diagnosis and action.final_diagnosis else None
reward, components = compute_step_reward(
previous_quality=previous_quality,
current_hypothesis=action.current_hypothesis.model_dump(),
ground_truth=scenario.ground_truth,
investigation_target=investigation_target,
committed_diagnosis=None, # Temporarily don't compute diagnosis reward here to use grader
step_num=self.runtime.current_step,
max_steps=self.runtime.max_steps,
)
reward = clamp_score(reward)
if committed:
grader = GRADER_MAP.get(scenario.task_id, grade_easy)
diagnosis_reward = grader(committed, scenario.ground_truth)
# Combine the diagnosis reward logic from `compute_step_reward` that applies on top
if diagnosis_reward > 0.7:
diagnosis_reward += max(0.0, 0.08 * (self.runtime.max_steps - self.runtime.current_step))
# Update the total reward incorporating diagnosis
components["diagnosis_reward"] = round(diagnosis_reward, 4)
delta = components["hypothesis_delta"]
inv_reward = components["investigation_reward"]
conf_bonus = components["confirmation_bonus"]
total = 0.60 * delta + 0.20 * inv_reward + 0.20 * diagnosis_reward + conf_bonus
reward = round(clamp_score(min(max(total, 0.0), 1.0)), 4)
self.runtime.hypothesis_history.append(
HypothesisRecord(
step=self.runtime.current_step,
hypothesis=action.current_hypothesis,
quality=components["hypothesis_quality"],
)
)
if action.commit_diagnosis or self.runtime.current_step >= self.runtime.max_steps:
self.runtime.done = True
self.runtime.final_score = reward
observation = self._build_observation(
last_feedback=self._feedback(action, scenario.ground_truth)
)
return {
"observation": observation,
"reward": reward,
"done": self.runtime.done,
"info": components,
}
async def state(self):
"""Return the current episode state, or None if not started."""
scenario = self.runtime.scenario
if not scenario:
return None
return PyTorchDebugState(
scenario_id=scenario.scenario_id,
task_id=scenario.task_id,
max_steps=self.runtime.max_steps,
current_step=self.runtime.current_step,
revealed_files=self.runtime.revealed_files,
remaining_files=[
f for f in scenario.repo_files.keys() if f not in self.runtime.revealed_files
],
diagnostic_revealed=self.runtime.diagnostic_revealed,
done=self.runtime.done,
final_score=self.runtime.final_score,
)
def _build_observation(self, last_feedback: str) -> PyTorchDebugObservation:
scenario = self.runtime.scenario
revealed = {k: v for k, v in scenario.repo_files.items() if k in self.runtime.revealed_files}
available = [k for k in scenario.repo_files.keys() if k not in self.runtime.revealed_files]
loss_window_size = min(
len(scenario.loss_curve),
LOSS_WINDOW_STEP * (self.runtime.current_step + 1 + self.runtime.loss_curve_bonus),
)
gpu_window_size = min(
len(scenario.gpu_profile),
GPU_WINDOW_STEP * (self.runtime.current_step + 1 + self.runtime.gpu_profile_bonus),
)
log_lines = scenario.training_log.splitlines()
log_window = LOG_WINDOW_STEP * (self.runtime.current_step + 1 + self.runtime.log_tail_bonus)
visible_log = "\n".join(log_lines[-min(len(log_lines), log_window):])
diagnostic_report = scenario.diagnostic_report if self.runtime.diagnostic_revealed else None
return PyTorchDebugObservation(
scenario_id=scenario.scenario_id,
task_id=scenario.task_id,
revealed_files=revealed,
available_files=available,
loss_curve_window=scenario.loss_curve[:loss_window_size],
gpu_profile_window=scenario.gpu_profile[:gpu_window_size],
training_log_tail=visible_log,
diagnostic_report=diagnostic_report,
step_num=self.runtime.current_step,
steps_remaining=max(0, self.runtime.max_steps - self.runtime.current_step),
investigation_budget=max(0, self.runtime.max_steps - self.runtime.current_step),
hypothesis_history=self.runtime.hypothesis_history,
last_feedback=last_feedback,
)
def _feedback(self, action: PyTorchDebugAction, gt: Dict) -> str:
suspected_file = action.current_hypothesis.affected_file
suspected_bug = action.current_hypothesis.bug_type
if suspected_file == gt.get("red_herring_file"):
return "That file contains a plausible symptom, but not the root cause. Investigate upstream causes."
if suspected_file == gt["primary_bug_file"] and suspected_bug != gt["bug_type"]:
return "Correct region, wrong failure mode. Re-check the training artifacts more carefully."
if suspected_bug == gt["bug_type"] and suspected_file != gt["primary_bug_file"]:
return "The bug class looks right, but the faulty implementation is in another file."
return "Continue refining the hypothesis using newly revealed evidence."
|