test_env.py

"""
Quick sanity test for the Compiler Pass Ordering environment.
Run with: python test_env.py
Server must be running: uvicorn server.app:app --reload --host 0.0.0.0 --port 8000
"""

from compiler_opt_env import CompilerOptAction, CompilerOptEnv
from compiler_opt_env.models import PASS_NAMES, TASK_EASY, TASK_MEDIUM, TASK_HARD
from compiler_opt_env.server.compiler_opt_env_environment import BASE_PASS_EFFECTS


def run_greedy(env, task_id: int, label: str):
    """Greedy agent: always pick the available pass with highest base effect."""
    print(f"\n{'─'*60}")
    print(f"  {label} (greedy agent)")
    print(f"{'─'*60}")

    obs = env.reset().observation if hasattr(env.reset(), 'observation') else env.reset()
    # reset() returns StepResult in sync mode
    result = env.reset()
    obs = result.observation

    print(f"  Program type:   {obs.program_type}")
    print(f"  Baseline cost:  {obs.baseline_cost:.1f}")
    print()

    while not obs.done:
        available = obs.passes_available
        best = max(available, key=lambda p: BASE_PASS_EFFECTS[p])
        step_result = env.step(CompilerOptAction(pass_id=best, task_id=task_id))
        obs = step_result.observation
        print(f"  Step {obs.step_count:2d}: {PASS_NAMES[best]:35s} "
              f"cost={obs.estimated_cost:7.1f}  "
              f"improvement={obs.improvement_pct:5.1f}%  "
              f"reward={step_result.reward:+.4f}")

    print(f"\n  Greedy improvement: {obs.improvement_pct:.1f}%")
    print(f"  Grader score:       {obs.grader_score:.3f}")
    return obs.improvement_pct, obs.grader_score


def run_optimal_task1(env):
    """Hand-crafted optimal sequence for Task 1: alias → DCE → vectorization chain."""
    print(f"\n{'─'*60}")
    print(f"  Task 1 — Optimal sequence (alias → DCE → vectorization)")
    print(f"{'─'*60}")

    result = env.reset()
    obs = result.observation
    print(f"  Program type:  {obs.program_type}")
    print(f"  Baseline cost: {obs.baseline_cost:.1f}\n")

    # FIX: Padded the sequence to 10 steps to ensure the episode finishes (done=True)
    optimal_sequence = [13, 0, 4, 5, 2, 7, 1, 10, 8, 9]

    for pass_id in optimal_sequence:
        if obs.done:
            break
        step_result = env.step(CompilerOptAction(pass_id=pass_id, task_id=TASK_EASY))
        obs = step_result.observation
        print(f"  Step {obs.step_count:2d}: {PASS_NAMES[pass_id]:35s} "
              f"cost={obs.estimated_cost:7.1f}  "
              f"improvement={obs.improvement_pct:5.1f}%  "
              f"reward={step_result.reward:+.4f}")

    print(f"\n  Optimal improvement: {obs.improvement_pct:.1f}%")
    print(f"  Grader score:        {obs.grader_score:.3f}")
    return obs.improvement_pct, obs.grader_score


if __name__ == "__main__":
    print("Compiler Pass Ordering — Environment Sanity Test")
    print("=" * 60)

    with CompilerOptEnv(base_url="http://localhost:8000").sync() as env:

        # Task 1: greedy vs optimal
        greedy_improv_1, greedy_score_1 = run_greedy(env, TASK_EASY,   "Task 1 (Easy)")
        opt_improv_1,    opt_score_1    = run_optimal_task1(env)

        # Task 2: greedy
        greedy_improv_2, greedy_score_2 = run_greedy(env, TASK_MEDIUM, "Task 2 (Medium)")

        # Task 3: greedy
        greedy_improv_3, greedy_score_3 = run_greedy(env, TASK_HARD,   "Task 3 (Hard)")

    print(f"\n{'='*60}")
    print("  SUMMARY")
    print(f"{'='*60}")
    print(f"  Task 1 greedy:  {greedy_improv_1:.1f}% improvement  score={greedy_score_1:.3f}")
    print(f"  Task 1 optimal: {opt_improv_1:.1f}% improvement  score={opt_score_1:.3f}")
    print(f"  Task 2 greedy:  {greedy_improv_2:.1f}% improvement  score={greedy_score_2:.3f}")
    print(f"  Task 3 greedy:  {greedy_improv_3:.1f}% improvement  score={greedy_score_3:.3f}")
    print()
    print("  Expected: greedy ~19-24% | optimal Task 1 ~40-50%")
    print("  If greedy << optimal: ✓ environment requires RL")
    print(f"{'='*60}")