scripts/run_eval.py

"""
StepProbe: End-to-End Evaluation Pipeline

Orchestrates the full pipeline:
    inference -> segment -> diagnose -> metrics -> restore -> re-evaluate

Usage:
    python scripts/run_eval.py --config configs/default.yaml
    python scripts/run_eval.py --model deepseek-ai/DeepSeek-R1-Distill-Qwen-7B --benchmark gsm8k --quick
"""

import argparse
import json
import os
import sys
import time
from datetime import datetime

import yaml

sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from stepprobe.segment import segment_cot
from stepprobe.align import align_steps, alignment_summary
from stepprobe.diagnose import diagnose_batch, LLMJudge, RuleBasedJudge
from stepprobe.metrics import aggregate_metrics, format_results_table
from stepprobe.utils import load_jsonl, save_jsonl, save_json, set_seed, check_answer, extract_gsm8k_answer, extract_number


def run_inference_phase(model_name, quant, bits, benchmark, output_dir, max_samples=None, max_tokens=4096):
    """Run inference and return path to output file."""
    from scripts.run_inference import load_model, load_benchmark, generate_cot
    from tqdm import tqdm

    os.makedirs(output_dir, exist_ok=True)
    tag = f"{quant}_w{bits}" if quant != "fp16" else "fp16"
    out_file = os.path.join(output_dir, f"{benchmark}_{tag}.jsonl")

    if os.path.exists(out_file):
        print(f"  [SKIP] {out_file} already exists")
        return out_file

    print(f"  Loading model: {model_name} ({tag})")
    model, tokenizer = load_model(model_name, quant, bits)

    print(f"  Loading benchmark: {benchmark}")
    problems = load_benchmark(benchmark, max_samples=max_samples)

    records = []
    for prob in tqdm(problems, desc=f"Inference ({tag})"):
        result = generate_cot(model, tokenizer, prob["question"], max_tokens)
        record = {
            "problem_id": prob["id"],
            "question": prob["question"],
            "gold_answer": prob["answer"],
            "model": model_name,
            "quantization": tag,
            **result,
        }
        records.append(record)

    save_jsonl(records, out_file)
    print(f"  Saved {len(records)} results -> {out_file}")

    # Free GPU memory
    del model
    import torch
    if torch.cuda.is_available():
        torch.cuda.empty_cache()

    return out_file


def run_segment_phase(inference_file, output_dir, model_name="", quant="fp16"):
    """Segment CoT traces into steps."""
    os.makedirs(output_dir, exist_ok=True)
    basename = os.path.basename(inference_file)
    out_file = os.path.join(output_dir, basename)

    if os.path.exists(out_file):
        print(f"  [SKIP] {out_file} already exists")
        return out_file

    records = load_jsonl(inference_file)
    segmented = []

    for rec in records:
        seg = segment_cot(
            problem_id=rec["problem_id"],
            raw_output=rec.get("output", ""),
            model=model_name,
            quantization=quant,
        )
        seg_dict = seg.to_dict()
        # Carry over metadata
        seg_dict["question"] = rec.get("question", "")
        seg_dict["gold_answer"] = rec.get("gold_answer", "")
        seg_dict["n_tokens"] = rec.get("n_tokens", 0)
        segmented.append(seg_dict)

    save_jsonl(segmented, out_file)
    print(f"  Segmented {len(segmented)} traces -> {out_file}")
    return out_file


def run_diagnose_phase(ref_file, hyp_file, output_dir, use_llm_judge=False, judge_provider="openai"):
    """Diagnose step-level errors."""
    os.makedirs(output_dir, exist_ok=True)
    basename = os.path.basename(hyp_file)
    out_file = os.path.join(output_dir, basename)

    if os.path.exists(out_file):
        print(f"  [SKIP] {out_file} already exists")
        return out_file

    ref_traces = load_jsonl(ref_file)
    hyp_traces = load_jsonl(hyp_file)

    # Reconstruct problems
    problems = []
    for t in ref_traces:
        problems.append({
            "problem_id": t["problem_id"],
            "question": t.get("question", ""),
            "gold_answer": t.get("gold_answer", ""),
        })

    judge = None
    if use_llm_judge:
        judge = LLMJudge(provider=judge_provider)

    diagnosed = diagnose_batch(
        ref_traces=ref_traces,
        hyp_traces=hyp_traces,
        problems=problems,
        judge=judge,
    )

    save_jsonl(diagnosed, out_file)
    print(f"  Diagnosed {len(diagnosed)} traces -> {out_file}")
    return out_file


def run_metrics_phase(diagnosis_file, output_dir, fp16_acc=None, fp16_ffs=None):
    """Compute StepProbe metrics."""
    os.makedirs(output_dir, exist_ok=True)
    basename = os.path.splitext(os.path.basename(diagnosis_file))[0]
    out_file = os.path.join(output_dir, f"{basename}_metrics.json")

    traces = load_jsonl(diagnosis_file)
    if not traces:
        print(f"  [WARN] No traces in {diagnosis_file}")
        return None

    result = aggregate_metrics(traces, fp16_accuracy=fp16_acc, fp16_avg_ffs=fp16_ffs)

    save_json({
        "model": result.model,
        "quantization": result.quantization,
        "n_problems": result.n_problems,
        "accuracy": result.accuracy,
        "accuracy_delta": result.accuracy_delta,
        "avg_ffs": result.avg_ffs,
        "median_ffs": result.median_ffs,
        "ffs_std": result.ffs_std,
        "ecr": result.ecr,
        "ssr_curve": result.ssr_curve,
        "error_type_dist": result.error_type_dist,
        "avg_token_count": result.avg_token_count,
    }, out_file)

    print(f"  Metrics: acc={result.accuracy:.1%}, FFS={result.avg_ffs:.1f}, ECR={result.ecr:.1%}")
    return result


def main():
    parser = argparse.ArgumentParser(description="End-to-end StepProbe evaluation")
    parser.add_argument("--config", default=None, help="YAML config file")
    parser.add_argument("--model", default="deepseek-ai/DeepSeek-R1-Distill-Qwen-7B")
    parser.add_argument("--benchmark", default="gsm8k", choices=["gsm8k", "math500", "gpqa"])
    parser.add_argument("--quant-methods", nargs="*", default=["bnb_nf4"],
                        help="Quantization methods to test")
    parser.add_argument("--bits", nargs="*", type=int, default=[4])
    parser.add_argument("--output", default="results/")
    parser.add_argument("--max-samples", type=int, default=None, help="Limit samples (for testing)")
    parser.add_argument("--quick", action="store_true", help="Quick test with 20 samples")
    parser.add_argument("--use-llm-judge", action="store_true")
    parser.add_argument("--skip-restore", action="store_true")
    parser.add_argument("--seed", type=int, default=42)
    args = parser.parse_args()

    set_seed(args.seed)

    if args.quick:
        args.max_samples = 20

    base_dir = args.output
    os.makedirs(base_dir, exist_ok=True)

    # Log
    log = {
        "start_time": datetime.now().isoformat(),
        "model": args.model,
        "benchmark": args.benchmark,
        "quant_methods": args.quant_methods,
        "bits": args.bits,
        "max_samples": args.max_samples,
    }

    print("=" * 70)
    print("StepProbe: End-to-End Evaluation")
    print("=" * 70)
    print(f"Model:      {args.model}")
    print(f"Benchmark:  {args.benchmark}")
    print(f"Quant:      {args.quant_methods} x {args.bits}-bit")
    print(f"Samples:    {args.max_samples or 'all'}")
    print()

    # ========== Phase 1: FP16 Baseline ==========
    print("[Phase 1] FP16 Baseline Inference")
    fp16_inf = run_inference_phase(
        args.model, "fp16", 16, args.benchmark,
        os.path.join(base_dir, "inference", "fp16"),
        max_samples=args.max_samples,
    )

    print("\n[Phase 1b] Segmenting FP16 traces")
    fp16_seg = run_segment_phase(
        fp16_inf, os.path.join(base_dir, "segmented", "fp16"),
        model_name=args.model, quant="fp16",
    )

    # Compute FP16 accuracy
    fp16_traces = load_jsonl(fp16_inf)
    fp16_correct = 0
    for t in fp16_traces:
        gold = t.get("gold_answer", "")
        if "####" in gold:
            gold = extract_gsm8k_answer(gold)
        pred = extract_number(t.get("output", ""))
        if pred and check_answer(pred, gold):
            fp16_correct += 1
    fp16_acc = fp16_correct / len(fp16_traces) if fp16_traces else 0
    print(f"  FP16 accuracy: {fp16_acc:.1%} ({fp16_correct}/{len(fp16_traces)})")

    # ========== Phase 2: Quantized Inference ==========
    all_results = []

    for quant_method in args.quant_methods:
        for bits in args.bits:
            tag = f"{quant_method}_w{bits}"
            print(f"\n[Phase 2] Quantized Inference: {tag}")

            quant_inf = run_inference_phase(
                args.model, quant_method, bits, args.benchmark,
                os.path.join(base_dir, "inference", tag),
                max_samples=args.max_samples,
            )

            # ========== Phase 3: Segment ==========
            print(f"[Phase 3] Segmenting {tag} traces")
            quant_seg = run_segment_phase(
                quant_inf, os.path.join(base_dir, "segmented", tag),
                model_name=args.model, quant=tag,
            )

            # ========== Phase 4: Diagnose ==========
            print(f"[Phase 4] Diagnosing {tag}")
            diag_file = run_diagnose_phase(
                fp16_seg, quant_seg,
                os.path.join(base_dir, "diagnosis", tag),
                use_llm_judge=args.use_llm_judge,
            )

            # ========== Phase 5: Metrics ==========
            print(f"[Phase 5] Computing metrics for {tag}")
            result = run_metrics_phase(
                diag_file,
                os.path.join(base_dir, "metrics"),
                fp16_acc=fp16_acc,
            )
            if result:
                all_results.append(result)

    # ========== Summary ==========
    print("\n" + "=" * 70)
    print("RESULTS SUMMARY")
    print("=" * 70)
    if all_results:
        print(format_results_table(all_results))

    log["end_time"] = datetime.now().isoformat()
    log["fp16_accuracy"] = fp16_acc
    log["results"] = [
        {"quant": r.quantization, "accuracy": r.accuracy, "avg_ffs": r.avg_ffs, "ecr": r.ecr}
        for r in all_results
    ]
    save_json(log, os.path.join(base_dir, "eval_log.json"))

    print(f"\nAll results saved to {base_dir}")
    print("Done!")


if __name__ == "__main__":
    main()