File size: 23,528 Bytes
76de008 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 | from __future__ import annotations
import argparse
import json
import os
import re
import subprocess
import sys
import time
from dataclasses import asdict, dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional
CURRENT_DIR = Path(__file__).resolve().parent
PARENT_DIR = CURRENT_DIR.parent
if str(PARENT_DIR) not in sys.path:
sys.path.insert(0, str(PARENT_DIR))
from checkpoint_utils import final_checkpoint_root, normalize_to_final_checkpoint_root
DEFAULT_CHECKPOINT_ROOT = Path(final_checkpoint_root("small_model_20empty", "baseline"))
DEFAULT_CACHE_DIR = Path("/home/ubuntu/curriculum-CoT/.hf_cache")
DEFAULT_MODEL_NAME = "Qwen/Qwen2.5-0.5B-Instruct"
DEFAULT_WANDB_GROUP = "small_model_20empty_baseline_pipeline"
DEFAULT_SFT_PROJECT = "sudoku-small-20empty-baseline-sft"
DEFAULT_GRPO_PROJECT = "sudoku-small-20empty-baseline-grpo"
SFT_SCRIPT = PARENT_DIR / "multi_output_cell_policy" / "sft_multi_output_train.py"
GRPO_SCRIPT = PARENT_DIR / "multi_output_cell_policy" / "grpo_multi_output_train.py"
STAGE_COMPLETE_MARKER = "_stage_complete.json"
@dataclass
class Artifact:
path: str
stage: int
phase: str
step: int
mtime: float
source_dir: str
def parse_args() -> argparse.Namespace:
p = argparse.ArgumentParser()
p.add_argument("--python_executable", type=str, default=sys.executable)
p.add_argument("--checkpoint_root", type=str, default=str(DEFAULT_CHECKPOINT_ROOT))
p.add_argument("--output_root", type=str, default="")
p.add_argument("--run_tag", type=str, default="")
p.add_argument("--train_jsonl", type=str, default="")
p.add_argument("--cache_dir", type=str, default=str(DEFAULT_CACHE_DIR))
p.add_argument("--model_name", type=str, default=DEFAULT_MODEL_NAME)
p.add_argument("--seed", type=int, default=0)
p.add_argument("--total_empties_hint", type=int, default=20)
p.add_argument("--min_stage", type=int, default=1)
p.add_argument("--max_stage", type=int, default=4)
p.add_argument("--sft_gpu_id", type=int, default=0)
p.add_argument("--grpo_gpu_id", type=int, default=1)
p.add_argument("--stage1_init_adapter_dir", type=str, default="")
p.add_argument("--bootstrap_adapter_dir", type=str, default="")
p.add_argument("--distributed_gpu_ids", type=str, default="")
p.add_argument("--sft_num_processes", type=int, default=1)
p.add_argument("--grpo_num_processes", type=int, default=1)
p.add_argument("--wandb_mode", type=str, default="online")
p.add_argument("--use_wandb", action="store_true")
p.add_argument("--wandb_entity", type=str, default="")
p.add_argument("--wandb_group", type=str, default=DEFAULT_WANDB_GROUP)
p.add_argument("--wandb_sft_project", type=str, default=DEFAULT_SFT_PROJECT)
p.add_argument("--wandb_grpo_project", type=str, default=DEFAULT_GRPO_PROJECT)
p.add_argument("--sft_num_epochs", type=float, default=1.0)
p.add_argument("--sft_learning_rate_stage1", type=float, default=2e-4)
p.add_argument("--sft_learning_rate_later", type=float, default=5e-5)
p.add_argument("--sft_gradient_accumulation_steps", type=int, default=8)
p.add_argument("--sft_enable_gradient_checkpointing", action="store_true")
p.add_argument("--sft_logging_steps", type=int, default=10)
p.add_argument("--sft_eval_steps", type=int, default=100)
p.add_argument("--sft_save_steps", type=int, default=100)
p.add_argument("--sft_eval_rows", type=int, default=20)
p.add_argument("--sft_max_completion_length", type=int, default=24)
p.add_argument("--grpo_num_train_epochs", type=float, default=0.5)
p.add_argument("--grpo_learning_rate", type=float, default=1e-6)
p.add_argument("--grpo_per_device_train_batch_size", type=int, default=2)
p.add_argument("--grpo_gradient_accumulation_steps", type=int, default=4)
p.add_argument("--grpo_enable_gradient_checkpointing", action="store_true")
p.add_argument("--grpo_logging_steps", type=int, default=5)
p.add_argument("--grpo_eval_steps", type=int, default=25)
p.add_argument("--grpo_save_steps", type=int, default=25)
p.add_argument("--grpo_eval_rows", type=int, default=20)
p.add_argument("--grpo_num_generations", type=int, default=2)
p.add_argument("--grpo_max_prompt_length", type=int, default=1024)
p.add_argument("--grpo_max_completion_length", type=int, default=24)
p.add_argument("--grpo_beta", type=float, default=0.0)
p.add_argument("--phase_max_wall_clock_seconds", type=int, default=21600)
p.add_argument("--limit_train_rows", type=int, default=0)
p.add_argument("--sft_stage_max_steps", type=str, default="")
p.add_argument("--grpo_stage_max_steps", type=str, default="")
p.add_argument("--dry_run", action="store_true")
return p.parse_args()
def stage_dir_pattern(stage: int, phase: str, empties: int) -> str:
return f"stage{stage:02d}_{phase}_i{stage}_{empties}empty*"
def extract_numeric_suffix(name: str, prefix: str) -> Optional[int]:
match = re.fullmatch(rf"{re.escape(prefix)}(\d+)", name)
return int(match.group(1)) if match else None
def stage_complete_path(stage_dir: Path) -> Path:
return stage_dir / STAGE_COMPLETE_MARKER
def is_stage_complete(stage_dir: Path) -> bool:
return stage_complete_path(stage_dir).is_file()
def output_root_has_stage_artifacts(path: Path) -> bool:
if not path.exists():
return False
if (path / "pipeline_state.json").exists():
return True
return any(path.glob("stage[0-9][0-9]_*"))
def latest_sft_checkpoint(stage_dir: Path) -> Optional[Artifact]:
best: Optional[Artifact] = None
for child in stage_dir.iterdir():
if not child.is_dir():
continue
step = extract_numeric_suffix(child.name, "checkpoint-step-")
if step is None:
continue
artifact = Artifact(
path=str(child),
stage=-1,
phase="sft",
step=step,
mtime=child.stat().st_mtime,
source_dir=str(stage_dir),
)
if best is None or (artifact.mtime, artifact.step) > (best.mtime, best.step):
best = artifact
return best
def latest_grpo_artifact(stage_dir: Path) -> Optional[Artifact]:
best: Optional[Artifact] = None
root_adapter = stage_dir / "adapter_model.safetensors"
if root_adapter.exists():
best = Artifact(
path=str(stage_dir),
stage=-1,
phase="grpo",
step=10**9,
mtime=stage_dir.stat().st_mtime,
source_dir=str(stage_dir),
)
for child in stage_dir.iterdir():
if not child.is_dir():
continue
step = extract_numeric_suffix(child.name, "checkpoint-")
if step is None:
continue
adapter = child / "adapter_model.safetensors"
if not adapter.exists():
continue
artifact = Artifact(
path=str(child),
stage=-1,
phase="grpo",
step=step,
mtime=child.stat().st_mtime,
source_dir=str(stage_dir),
)
if best is None or (artifact.mtime, artifact.step) > (best.mtime, best.step):
best = artifact
return best
def discover_latest_artifact(
checkpoint_root: Path,
*,
stage: int,
phase: str,
empties: int,
require_complete: bool = True,
) -> Optional[Artifact]:
best: Optional[Artifact] = None
for stage_dir in checkpoint_root.rglob(stage_dir_pattern(stage, phase, empties)):
if not stage_dir.is_dir():
continue
if require_complete and not is_stage_complete(stage_dir):
continue
artifact = latest_sft_checkpoint(stage_dir) if phase == "sft" else latest_grpo_artifact(stage_dir)
if artifact is None:
continue
artifact.stage = stage
artifact.phase = phase
if best is None or (artifact.mtime, artifact.step) > (best.mtime, best.step):
best = artifact
return best
def choose_output_root(args: argparse.Namespace, checkpoint_root: Path) -> Path:
if str(args.output_root).strip():
requested_root = Path(
normalize_to_final_checkpoint_root(args.output_root, "small_model_20empty", "baseline")
).resolve()
if output_root_has_stage_artifacts(requested_root):
run_tag = str(args.run_tag).strip() or time.strftime("%Y%m%d_%H%M%S")
return requested_root / run_tag
return requested_root
run_tag = str(args.run_tag).strip() or time.strftime("%Y%m%d_%H%M%S")
return checkpoint_root / run_tag / f"baseline_pipeline_{args.total_empties_hint}empty_{args.max_stage}stage_small"
def default_train_jsonl(args: argparse.Namespace) -> Path:
if str(args.train_jsonl).strip():
return Path(args.train_jsonl).resolve()
return (PARENT_DIR / "data" / f"sudoku_t3_{int(args.total_empties_hint)}empty_value_qwen_text.jsonl").resolve()
def phase_output_dir(output_root: Path, *, stage: int, phase: str, empties: int) -> Path:
return output_root / f"stage{stage:02d}_{phase}_i{stage}_{empties}empty"
def run_command(command: List[str], *, env: Dict[str, str], dry_run: bool) -> None:
print("")
print("Running command:")
print(" ".join(subprocess.list2cmdline([part]) for part in command))
if dry_run:
print("Dry run enabled; command not executed.")
return
subprocess.run(command, env=env, check=True)
def parse_stage_int_map(raw: str) -> Dict[int, int]:
mapping: Dict[int, int] = {}
text = str(raw or "").strip()
if not text:
return mapping
for part in text.split(","):
item = part.strip()
if not item:
continue
stage_text, value_text = item.split(":", 1)
mapping[int(stage_text.strip())] = int(value_text.strip())
return mapping
def resolve_stage_value(mapping: Dict[int, int], stage: int) -> int:
return int(mapping.get(int(stage), 0))
def make_env(*, gpu_id: int, wandb_mode: str, gpu_ids: str, num_processes: int) -> Dict[str, str]:
env = os.environ.copy()
env["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
requested = [part.strip() for part in str(gpu_ids or "").split(",") if part.strip()]
if int(num_processes) > 1:
if requested:
env["CUDA_VISIBLE_DEVICES"] = ",".join(requested[: int(num_processes)])
else:
env["CUDA_VISIBLE_DEVICES"] = str(requested[0] if requested else int(gpu_id))
env["WANDB__SERVICE_WAIT"] = "300"
env["WANDB_MODE"] = str(wandb_mode)
return env
def build_sft_command(
args: argparse.Namespace,
*,
train_jsonl: Path,
output_dir: Path,
stage: int,
init_adapter_dir: Optional[str],
stage_max_steps: int,
) -> List[str]:
num_processes = max(1, int(args.sft_num_processes))
if num_processes > 1:
command = [
args.python_executable,
"-m",
"torch.distributed.run",
"--standalone",
"--nproc_per_node",
str(num_processes),
str(SFT_SCRIPT),
]
else:
command = [args.python_executable, "-u", str(SFT_SCRIPT)]
command.extend(
[
"--model_name",
args.model_name,
"--train_jsonl",
str(train_jsonl),
"--output_dir",
str(output_dir),
"--cache_dir",
args.cache_dir,
"--seed",
str(int(args.seed)),
"--gpu_id",
str(0 if num_processes > 1 else int(args.sft_gpu_id)),
"--stage_i",
str(int(stage)),
"--total_empties_hint",
str(int(args.total_empties_hint)),
"--num_epochs",
str(float(args.sft_num_epochs)),
"--learning_rate",
str(float(args.sft_learning_rate_stage1 if stage <= 1 else args.sft_learning_rate_later)),
"--gradient_accumulation_steps",
str(int(args.sft_gradient_accumulation_steps)),
"--enable_gradient_checkpointing" if args.sft_enable_gradient_checkpointing else "",
"--logging_steps",
str(int(args.sft_logging_steps)),
"--eval_steps",
str(int(args.sft_eval_steps)),
"--save_steps",
str(int(args.sft_save_steps)),
"--eval_rows",
str(int(args.sft_eval_rows)),
"--max_completion_length",
str(int(args.sft_max_completion_length)),
"--max_wall_clock_seconds",
str(int(args.phase_max_wall_clock_seconds)),
]
)
command = [part for part in command if part != ""]
if int(args.limit_train_rows) > 0:
command.extend(["--limit_train_rows", str(int(args.limit_train_rows))])
if int(stage_max_steps) > 0:
command.extend(["--max_steps", str(int(stage_max_steps))])
if args.use_wandb:
command.extend(["--use_wandb"])
if str(args.wandb_entity).strip():
command.extend(["--wandb_entity", args.wandb_entity])
command.extend(
[
"--wandb_project",
args.wandb_sft_project,
"--wandb_run_name",
f"small_baseline_stage{stage:02d}_sft_i{stage}_{args.total_empties_hint}empty",
"--wandb_mode",
args.wandb_mode,
]
)
if init_adapter_dir:
command.extend(["--init_adapter_dir", str(init_adapter_dir)])
return command
def build_grpo_command(
args: argparse.Namespace,
*,
train_jsonl: Path,
output_dir: Path,
stage: int,
init_adapter_dir: str,
stage_max_steps: int,
) -> List[str]:
num_processes = max(1, int(args.grpo_num_processes))
if num_processes > 1:
command = [
args.python_executable,
"-m",
"torch.distributed.run",
"--standalone",
"--nproc_per_node",
str(num_processes),
str(GRPO_SCRIPT),
]
else:
command = [args.python_executable, "-u", str(GRPO_SCRIPT)]
command.extend(
[
"--model_name",
args.model_name,
"--train_jsonl",
str(train_jsonl),
"--output_dir",
str(output_dir),
"--cache_dir",
args.cache_dir,
"--init_adapter_dir",
str(init_adapter_dir),
"--seed",
str(int(args.seed)),
"--gpu_id",
str(0 if num_processes > 1 else int(args.grpo_gpu_id)),
"--stage_i",
str(int(stage)),
"--total_empties_hint",
str(int(args.total_empties_hint)),
"--per_device_train_batch_size",
str(int(args.grpo_per_device_train_batch_size)),
"--gradient_accumulation_steps",
str(int(args.grpo_gradient_accumulation_steps)),
"--enable_gradient_checkpointing" if args.grpo_enable_gradient_checkpointing else "",
"--num_train_epochs",
str(float(args.grpo_num_train_epochs)),
"--learning_rate",
str(float(args.grpo_learning_rate)),
"--logging_steps",
str(int(args.grpo_logging_steps)),
"--save_steps",
str(int(args.grpo_save_steps)),
"--eval_steps",
str(int(args.grpo_eval_steps)),
"--eval_rows",
str(int(args.grpo_eval_rows)),
"--num_generations",
str(int(args.grpo_num_generations)),
"--max_prompt_length",
str(int(args.grpo_max_prompt_length)),
"--max_completion_length",
str(int(args.grpo_max_completion_length)),
"--beta",
str(float(args.grpo_beta)),
"--max_wall_clock_seconds",
str(int(args.phase_max_wall_clock_seconds)),
"--wandb_group",
args.wandb_group,
]
)
command = [part for part in command if part != ""]
if int(args.limit_train_rows) > 0:
command.extend(["--limit_train_rows", str(int(args.limit_train_rows))])
if int(stage_max_steps) > 0:
command.extend(["--max_steps", str(int(stage_max_steps))])
if args.use_wandb:
command.extend(["--use_wandb"])
if str(args.wandb_entity).strip():
command.extend(["--wandb_entity", args.wandb_entity])
command.extend(
[
"--wandb_project",
args.wandb_grpo_project,
"--wandb_run_name",
f"small_baseline_stage{stage:02d}_grpo_i{stage}_{args.total_empties_hint}empty",
"--wandb_mode",
args.wandb_mode,
]
)
return command
def write_state(path: Path, payload: Dict[str, Any]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
with open(path, "w", encoding="utf-8") as f:
json.dump(payload, f, indent=2, sort_keys=True)
def mark_stage_complete(stage_dir: Path, artifact: Artifact) -> None:
write_state(
stage_complete_path(stage_dir),
{
"completed_at": time.strftime("%Y-%m-%d %H:%M:%S"),
"artifact": asdict(artifact),
},
)
def main() -> None:
args = parse_args()
checkpoint_root = Path(
normalize_to_final_checkpoint_root(args.checkpoint_root, "small_model_20empty", "baseline")
).resolve()
output_root = choose_output_root(args, checkpoint_root)
train_jsonl = default_train_jsonl(args)
state_path = output_root / "pipeline_state.json"
sft_stage_max_steps = parse_stage_int_map(args.sft_stage_max_steps)
grpo_stage_max_steps = parse_stage_int_map(args.grpo_stage_max_steps)
output_root.mkdir(parents=True, exist_ok=True)
if not train_jsonl.exists():
raise FileNotFoundError(f"Missing train_jsonl: {train_jsonl}")
state: Dict[str, Any] = {
"updated_at": time.strftime("%Y-%m-%d %H:%M:%S"),
"train_jsonl": str(train_jsonl),
"checkpoint_root": str(checkpoint_root),
"output_root": str(output_root),
"min_stage": int(args.min_stage),
"max_stage": int(args.max_stage),
"total_empties_hint": int(args.total_empties_hint),
"model_name": str(args.model_name),
"stages": [],
}
previous_grpo: Optional[Artifact] = None
for stage in range(int(args.min_stage), int(args.max_stage) + 1):
stage_record: Dict[str, Any] = {"stage": stage}
existing_sft = discover_latest_artifact(
output_root, stage=stage, phase="sft", empties=int(args.total_empties_hint)
)
existing_grpo = discover_latest_artifact(
output_root, stage=stage, phase="grpo", empties=int(args.total_empties_hint)
)
if existing_grpo is not None:
previous_grpo = existing_grpo
stage_record["status"] = "using_existing_grpo"
stage_record["grpo_artifact"] = asdict(existing_grpo)
if existing_sft is not None:
stage_record["sft_artifact"] = asdict(existing_sft)
state["stages"].append(stage_record)
write_state(state_path, state)
print(f"Stage {stage}: using existing GRPO artifact {existing_grpo.path}")
continue
if existing_sft is None:
sft_output_dir = phase_output_dir(output_root, stage=stage, phase="sft", empties=int(args.total_empties_hint))
if stage == int(args.min_stage) and str(args.bootstrap_adapter_dir).strip():
init_adapter_dir = str(args.bootstrap_adapter_dir).strip()
elif stage == 1:
init_adapter_dir = str(args.stage1_init_adapter_dir).strip() or None
else:
if previous_grpo is None:
raise RuntimeError(f"Missing previous GRPO artifact needed to launch baseline stage {stage} SFT.")
init_adapter_dir = previous_grpo.path
print(f"Stage {stage}: launching SFT into {sft_output_dir}")
run_command(
build_sft_command(
args,
train_jsonl=train_jsonl,
output_dir=sft_output_dir,
stage=stage,
init_adapter_dir=init_adapter_dir,
stage_max_steps=resolve_stage_value(sft_stage_max_steps, stage),
),
env=make_env(
gpu_id=int(args.sft_gpu_id),
wandb_mode=args.wandb_mode,
gpu_ids=args.distributed_gpu_ids,
num_processes=int(args.sft_num_processes),
),
dry_run=bool(args.dry_run),
)
existing_sft = discover_latest_artifact(
output_root,
stage=stage,
phase="sft",
empties=int(args.total_empties_hint),
require_complete=False,
)
if existing_sft is None and not args.dry_run:
raise RuntimeError(f"Could not locate SFT checkpoint for stage {stage} after running SFT.")
if existing_sft is not None:
mark_stage_complete(Path(existing_sft.source_dir), existing_sft)
stage_record["sft_artifact"] = asdict(existing_sft)
else:
stage_record["sft_artifact"] = asdict(existing_sft)
print(f"Stage {stage}: using existing SFT artifact {existing_sft.path}")
if existing_sft is None:
stage_record["status"] = "dry_run_pending_grpo"
state["stages"].append(stage_record)
write_state(state_path, state)
break
grpo_output_dir = phase_output_dir(output_root, stage=stage, phase="grpo", empties=int(args.total_empties_hint))
print(f"Stage {stage}: launching GRPO into {grpo_output_dir}")
run_command(
build_grpo_command(
args,
train_jsonl=train_jsonl,
output_dir=grpo_output_dir,
stage=stage,
init_adapter_dir=existing_sft.path,
stage_max_steps=resolve_stage_value(grpo_stage_max_steps, stage),
),
env=make_env(
gpu_id=int(args.grpo_gpu_id),
wandb_mode=args.wandb_mode,
gpu_ids=args.distributed_gpu_ids,
num_processes=int(args.grpo_num_processes),
),
dry_run=bool(args.dry_run),
)
existing_grpo = discover_latest_artifact(
output_root,
stage=stage,
phase="grpo",
empties=int(args.total_empties_hint),
require_complete=False,
)
if existing_grpo is None and not args.dry_run:
raise RuntimeError(f"Could not locate GRPO artifact for stage {stage} after running GRPO.")
if existing_grpo is not None:
mark_stage_complete(Path(existing_grpo.source_dir), existing_grpo)
previous_grpo = existing_grpo
stage_record["grpo_artifact"] = asdict(existing_grpo)
stage_record["status"] = "launched"
state["stages"].append(stage_record)
write_state(state_path, state)
state["updated_at"] = time.strftime("%Y-%m-%d %H:%M:%S")
write_state(state_path, state)
print("")
print(f"Pipeline state written to: {state_path}")
if __name__ == "__main__":
main()
|