330 lines
14 KiB
Python
330 lines
14 KiB
Python
import argparse
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import logging
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import sys
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import os
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import json
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import datetime
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from pathlib import Path
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import yaml
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from pathlib import PurePath # base for PosixPath / WindowsPath
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# Register once – covers Path, PosixPath, WindowsPath …
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yaml.SafeDumper.add_multi_representer(
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PurePath,
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lambda dumper, value: dumper.represent_scalar(
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"tag:yaml.org,2002:str", str(value))
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)
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# ── Resolve project root and expose it on sys.path ─────────────────────────────
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ROOT_DIR = Path(__file__).resolve().parent
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sys.path.insert(0, str(ROOT_DIR)) # so "utils" is on sys.path
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# ── Hard fail early if required submodules are missing ─────────────────────────
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def _ensure_required_submodules():
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required = ("slop-forensics", "antislop-vllm")
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missing = [name for name in required if not (ROOT_DIR / name).is_dir()]
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if missing:
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msg = (
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"Required git submodules are missing: "
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+ ", ".join(missing)
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+ "\n\nClone the repo with submodules:\n"
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" git clone --recurse-submodules <repo-url>\n\n"
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"If you already cloned without submodules, run:\n"
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" git submodule update --init --recursive\n"
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)
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print("WARNING: " + msg, file=sys.stderr)
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sys.exit(2)
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_ensure_required_submodules()
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# ── guarantee NLTK data is present *before* any other project import ───────────
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from utils.fs_helpers import ensure_core_nltk_resources
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ensure_core_nltk_resources() # downloads punkt, punkt_tab, stopwords
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# --- Add project directories to sys.path --------------------------------------
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# This allows importing from core, utils, and submodules
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sys.path.insert(0, str(ROOT_DIR / "slop-forensics"))
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# antislop-vllm is called as a script, its path for direct import is not strictly
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# needed unless some of its utils were to be imported by auto-antislop.
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from utils.config_loader import load_pipeline_config, merge_config_with_cli_args
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from utils.fs_helpers import (
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create_experiment_dir,
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ensure_antislop_vllm_config_exists
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)
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from utils.vllm_manager import start_vllm_server, stop_vllm_server, is_vllm_server_alive
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from core.orchestration import orchestrate_pipeline
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from core.finetuning import run_dpo_finetune
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# --- Basic Logging Setup -------------------------------------------------------
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logging.basicConfig( # root stays at WARNING
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level=logging.WARNING,
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format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
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)
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logger = logging.getLogger("auto_antislop_main")
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def str2bool(v):
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if v is None:
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return None
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if isinstance(v, bool):
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return v
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v = str(v).lower()
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if v in ("yes", "true", "t", "1", "y"):
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return True
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if v in ("no", "false", "f", "0", "n"):
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return False
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raise argparse.ArgumentTypeError("Boolean value expected.")
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# ── QUICK CHECK: are *all* generation files already complete? ──────────────────
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def _all_generations_done(cfg: dict, resume_dir: Path | None) -> bool:
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if not resume_dir or not resume_dir.is_dir():
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return False
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need = cfg.get("generation_max_prompts", 0)
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if need <= 0:
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return False
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def _ids(path: Path) -> int:
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if not path.is_file():
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return 0
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seen = set()
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for ln in path.read_text(encoding="utf-8").splitlines():
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try:
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seen.add(int(json.loads(ln).get("prompt_id", -1)))
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except Exception:
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pass
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return len(seen)
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for i in range(cfg["num_iterations"]):
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p = resume_dir / f"iter_{i}_creative_writing_generations.jsonl"
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if _ids(p) < need:
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return False
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return True
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def main():
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parser = argparse.ArgumentParser(description="Auto-Antislop: Iterative dataset generation and DPO finetuning.")
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# --- General Arguments ---
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parser.add_argument(
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"-c", "--config-file", type=Path, default=Path("auto_antislop_config.yaml"),
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help="Path to the main YAML configuration file."
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)
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parser.add_argument(
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"-r", "--resume-from-dir", type=Path, default=None,
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help="Path to an existing experiment run directory to resume."
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)
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parser.add_argument(
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"--log-level", choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"],
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default=None, help="Set the logging level for the auto-antislop script."
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)
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# --- vLLM Management ---
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vllm_group = parser.add_argument_group('vLLM Server Management')
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vllm_group.add_argument(
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"--manage-vllm",
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type=str2bool,
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nargs="?",
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const=True, # `--manage-vllm` ⇒ True
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default=None, # fall back to config
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help="true/false to let this script start/stop a local vLLM server "
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"(default comes from config)."
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)
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vllm_group.add_argument("--vllm-port", type=int, default=None, help="Port for vLLM server. Overrides config.")
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vllm_group.add_argument("--vllm-model-id", type=str, default=None, help="Model ID for vLLM server. Overrides config.")
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vllm_group.add_argument(
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"--generation-api-base-url", type=str,
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default=None,
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help="API base URL for generation requests (passed to antislop-vllm). E.g., http://host:port/v1. Overrides config."
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)
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# --- Pipeline Control ---
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pipeline_group = parser.add_argument_group('Pipeline Control')
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pipeline_group.add_argument("--num-iterations", type=int, default=None, help="Number of anti-slop iterations. Overrides config.")
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pipeline_group.add_argument("--generation-max-prompts", type=int, default=None, help="Max prompts for antislop-vllm. Overrides config.")
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pipeline_group.add_argument(
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"--generation-step-enabled",
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type=str2bool,
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nargs="?",
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const=True,
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default=None,
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help="true/false to execute the generation step. (default from config)."
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)
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# --- Finetuning Control ---
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finetune_group = parser.add_argument_group('DPO Finetuning Control')
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finetune_group.add_argument(
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"--run-finetune",
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type=str2bool,
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nargs="?",
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const=True,
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default=None,
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help="true/false to run DPO finetuning after the pipeline (default from config)."
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)
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finetune_group.add_argument("--finetune-base-model-id", type=str, default=None, help="Base model for DPO. Overrides config.")
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finetune_group.add_argument("--finetune-num-epochs", type=int, default=None, help="Number of epochs for DPO. Overrides config.")
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finetune_group.add_argument(
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"--finetune-mode",
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choices=["dpo", "ftpo"],
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default=None,
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help="dpo = vanilla DPO on full continuations (default); "
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"ftpo = masked Tokenwise-DPO on partial generation pairs, only computing loss for the completion token."
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)
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finetune_group.add_argument(
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"--finetune-ftpo-dataset",
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type=Path,
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default=None,
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help="(Optional) explicit path to a ftpo/last-token JSONL file. "
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"If omitted and --finetune-mode is ftpo, the script will "
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"pick the highest iter_*_ftpo_pairs.jsonl in the experiment dir."
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)
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finetune_group.add_argument(
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"--finetune-cuda-visible-devices",
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type=str,
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default=None,
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help='Comma-separated GPU ids for the finetune stage only (e.g. "1,3").'
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)
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args = parser.parse_args()
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# --- Load and Merge Configuration ---
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config = load_pipeline_config(args.config_file)
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config = merge_config_with_cli_args(config, args)
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# refine levels once CLI/YAML are merged
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numeric_log_level = getattr(logging, config['log_level'].upper(), logging.INFO)
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# raise only *our* loggers, keep external libs at WARNING
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for name in logging.root.manager.loggerDict:
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if name.startswith(("auto_antislop", "core", "utils")):
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l = logging.getLogger(name)
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l.setLevel(numeric_log_level)
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for h in l.handlers:
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h.setLevel(min(numeric_log_level, h.level))
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# keep root at WARNING so torch / dynamo INFO spam is hidden
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logging.getLogger().setLevel(logging.WARNING)
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logger.info(f"Logging level for project set to: {config['log_level'].upper()}")
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# --- Ensure NLTK resources ---
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logger.info("Verifying / downloading required NLTK data …")
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ensure_core_nltk_resources()
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# --- Ensure antislop-vllm config-example is copied (user convenience) ---
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antislop_vllm_dir = ROOT_DIR / "antislop-vllm"
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if antislop_vllm_dir.is_dir():
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ensure_antislop_vllm_config_exists(antislop_vllm_dir)
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else:
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logger.warning(f"antislop-vllm submodule directory not found at {antislop_vllm_dir}. Generation will likely fail.")
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# --- vLLM Server Management -------------------------------------------------
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vllm_server_proc = None
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should_manage_vllm = config.get('manage_vllm', True)
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# Fast-path: if every generation file is already finished, don’t even start vLLM
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if should_manage_vllm and _all_generations_done(config, args.resume_from_dir):
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logger.info("All generation files complete – skipping vLLM startup altogether.")
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should_manage_vllm = False
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config['manage_vllm'] = False # keep downstream logic consistent
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if should_manage_vllm:
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if not is_vllm_server_alive(config['vllm_port']):
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logger.info("Attempting to start and manage vLLM server.")
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vllm_server_proc = start_vllm_server(
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model_id=config['vllm_model_id'],
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port=config['vllm_port'],
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hf_token=config.get('vllm_hf_token'),
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cuda_visible_devices=config['vllm_cuda_visible_devices'],
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gpu_memory_utilization=config['vllm_gpu_memory_utilization'],
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max_model_len=config['vllm_max_model_len'],
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dtype=config['vllm_dtype'],
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vllm_extra_args=config.get('vllm_extra_args'),
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extra_env=config.get('vllm_env'),
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uvicorn_log_level="error", # cut vllm chatter
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quiet_stdout=True, # discard server stream
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)
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if vllm_server_proc is None: # Failed to start
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logger.error("Failed to start managed vLLM server. Exiting.")
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sys.exit(1)
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else:
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logger.info(f"vLLM server already running on port {config['vllm_port']}. Script will not manage it.")
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should_manage_vllm = False # Don't try to stop it later
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else:
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logger.info("vLLM server management is disabled by config/CLI.")
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# --- Main Pipeline ---
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pipeline_start_time = datetime.datetime.now()
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experiment_run_dir = None
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try:
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base_dir = Path(config['experiment_base_dir'])
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resume_dir_path = Path(config['resume_from_dir']) if config.get('resume_from_dir', None) else None
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experiment_run_dir = create_experiment_dir(base_dir, resume_dir_path)
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# Pass the actual experiment_run_dir to orchestrate_pipeline
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config['current_experiment_run_dir'] = str(experiment_run_dir)
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# ---------- persist the exact config used for this run ----------
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timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
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cfg_path = experiment_run_dir / f"run_config_{timestamp}.yaml"
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cfg_path.write_text(
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yaml.safe_dump(config, sort_keys=False, allow_unicode=True),
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encoding="utf-8"
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)
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logger.info(f"Run configuration written → {cfg_path}")
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orchestrate_pipeline(config, experiment_run_dir, resume_mode=(resume_dir_path is not None))
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except FileNotFoundError as e:
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logger.error(f"A required file was not found: {e}. Halting pipeline.")
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sys.exit(1)
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except Exception as e:
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logger.error(f"An unexpected error occurred during the anti-slop pipeline: {e}", exc_info=True)
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sys.exit(1)
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finally:
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pipeline_duration = datetime.datetime.now() - pipeline_start_time
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logger.info(f"Total anti-slop pipeline duration: {pipeline_duration}")
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# --- Finetuning (Optional) ---
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should_run_finetune = config.get('finetune_enabled', False)
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if should_run_finetune:
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if experiment_run_dir:
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# NEW: shut down vLLM so the GPU is free for training
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if should_manage_vllm and vllm_server_proc:
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logger.info("Stopping managed vLLM server before finetuning.")
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stop_vllm_server(vllm_server_proc)
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vllm_server_proc = None # prevent a second stop later
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logger.info("Proceeding to finetuning.")
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finetune_start_time = datetime.datetime.now()
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try:
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finetune_output_dir = experiment_run_dir / f"finetuned_model{config['finetune_output_dir_suffix']}"
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if finetune_output_dir.exists():
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reply = input(
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f"⚠️ Finetune dir '{finetune_output_dir}' already exists. "
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"Delete & re-run finetune? [y/N]: "
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).strip().lower()
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if reply != "y":
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logger.info("Finetune stage skipped by user request.")
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return
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import shutil
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shutil.rmtree(finetune_output_dir, ignore_errors=True)
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logger.info("Old finetune directory removed.")
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run_dpo_finetune(config, experiment_run_dir)
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except Exception as e:
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logger.error("An error occurred during finetuning: %s", e, exc_info=True)
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finally:
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finetune_duration = datetime.datetime.now() - finetune_start_time
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logger.info("Total finetuning duration: %s", finetune_duration)
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else:
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logger.warning("Skipping finetuning as the main pipeline did not complete successfully or experiment directory is not set.")
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else:
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logger.info("Finetuning is disabled by config/CLI or due to pipeline issues.")
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if __name__ == "__main__":
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main()
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