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import argparse
import json
import math
import os
import time
from typing import Optional
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs
from accelerate.utils import set_seed as accel_set_seed
try:
from transformers import AutoTokenizer, get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup
except Exception:
from transformers import AutoTokenizer
from transformers.optimization import get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup
from data import ALL_MAS_ROLES, build_dataloader
from model import INNER_ADAPTER_TYPES, INNER_ADAPTER_ALIASES, LatentReasoningModel, resolve_local_pretrained_path
try: # keep training output minimal: suppress datasets .map() progress bars
import datasets as _datasets
_datasets.disable_progress_bars()
except Exception:
pass
def resolve_dtype(dtype_str: str) -> Optional[torch.dtype]:
if dtype_str == "float32":
return torch.float32
if dtype_str == "float16":
return torch.float16
if dtype_str == "bfloat16":
return torch.bfloat16
return None
def build_optimizer(
model: torch.nn.Module,
weight_decay: float,
adapter_lr: float,
) -> torch.optim.Optimizer:
decay, no_decay = [], []
for name, param in model.adapter.named_parameters():
if not param.requires_grad:
continue
if param.ndim == 1 or name.endswith(".bias"):
no_decay.append(param)
else:
decay.append(param)
param_groups = []
if decay:
param_groups.append({"params": decay, "weight_decay": weight_decay, "lr": adapter_lr})
if no_decay:
param_groups.append({"params": no_decay, "weight_decay": 0.0, "lr": adapter_lr})
if not param_groups:
raise ValueError("No trainable adapter parameters found.")
return torch.optim.AdamW(param_groups, betas=(0.9, 0.95))
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--model_name_or_path", type=str, required=True)
parser.add_argument("--dataset_name", type=str, default="openai/gsm8k")
parser.add_argument("--dataset_split", type=str, default="train")
parser.add_argument("--text_field", type=str, default=None)
parser.add_argument("--mas_role", type=str, default=None, help="Role-specific supervision data: planner/refiner/solver.")
parser.add_argument("--dataset_json_field", type=str, default=None, help="Top-level field for local json dataset (e.g., data).")
parser.add_argument("--max_length", type=int, default=2048)
parser.add_argument("--batch_size", type=int, default=2)
parser.add_argument("--num_train_epochs", type=int, default=1)
parser.add_argument("--max_steps", type=int, default=20000)
parser.add_argument("--adapter_lr", type=float, default=5e-4)
parser.add_argument("--lr_scheduler_type", type=str, default="cosine", choices=["constant", "cosine"])
parser.add_argument("--weight_decay", type=float, default=0.0)
parser.add_argument("--warmup_steps", type=int, default=10)
parser.add_argument("--grad_accum_steps", type=int, default=1)
parser.add_argument("--max_grad_norm", type=float, default=1.0)
parser.add_argument("--log_every", type=int, default=10)
parser.add_argument("--save_dir", type=str, default=None)
parser.add_argument("--save_steps", type=int, default=0)
parser.add_argument("--load_dir", type=str, default=None)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--dtype", type=str, default="bfloat16", choices=["float32", "float16", "bfloat16"])
parser.add_argument("--adapter_dtype", type=str, default="auto")
parser.add_argument("--trust_remote_code", action="store_true", default=True)
parser.add_argument(
"--adapter_type",
type=str,
default="ln_res_adapter",
choices=sorted(INNER_ADAPTER_TYPES | set(INNER_ADAPTER_ALIASES)),
help="Inner latent adapter type. The release inference runner only loads "
"'ln_res_adapter' inner weights, so this is the default.",
)
parser.add_argument("--adapter_cos_weight", type=float, default=1.0)
parser.add_argument("--adapter_mse_weight", type=float, default=0.0)
parser.add_argument( "--enable_thinking", type=int, default=0, choices=[0, 1], help="0 is non-thinking mode, pass to tokenizer",)
parser.add_argument("--solver_pre_question", type=int, default=0, choices=[0, 1])
parser.add_argument("--mas_task", type=str, default="math", choices=["math", "code", "choice"])
parser.add_argument("--mas_design", type=str, default="sequential", choices=["sequential", "hie", "distill", "deliberation"])
return parser.parse_args()
def main() -> None:
args = parse_args()
if args.mas_role is not None and args.mas_role not in ALL_MAS_ROLES:
raise ValueError(f"--mas_role must be one of: {sorted(ALL_MAS_ROLES)}")
mixed_precision = "no"
if args.dtype == "float16":
mixed_precision = "fp16"
elif args.dtype == "bfloat16":
mixed_precision = "bf16"
accelerator = Accelerator(
gradient_accumulation_steps=args.grad_accum_steps,
mixed_precision=mixed_precision,
kwargs_handlers=[DistributedDataParallelKwargs(find_unused_parameters=True)],
)
accel_set_seed(args.seed)
load_path = resolve_local_pretrained_path(args.load_dir or args.model_name_or_path)
tokenizer = AutoTokenizer.from_pretrained(
load_path,
trust_remote_code=args.trust_remote_code,
use_fast=True,
)
tokenizer.padding_side = "left"
if tokenizer.pad_token_id is None:
tokenizer.pad_token = tokenizer.eos_token
dataloader = build_dataloader(
tokenizer=tokenizer,
dataset_name=args.dataset_name,
dataset_split=args.dataset_split,
max_length=args.max_length,
batch_size=args.batch_size,
shuffle=True,
text_field=args.text_field,
enable_thinking=bool(args.enable_thinking),
mas_role=args.mas_role,
dataset_json_field=args.dataset_json_field,
solver_pre_question=args.solver_pre_question,
mas_task=args.mas_task,
mas_design=args.mas_design,
)
adapter_overrides = {}
if args.load_dir:
config_path = os.path.join(args.load_dir, "adapter_config.json")
if os.path.isfile(config_path):
with open(config_path, "r", encoding="utf-8") as f:
adapter_overrides = json.load(f)
model = LatentReasoningModel(
model_name_or_path=load_path,
adapter_type=adapter_overrides.get("adapter_type", args.adapter_type),
torch_dtype=resolve_dtype(args.dtype),
adapter_dtype=None if args.adapter_dtype == "auto" else resolve_dtype(args.adapter_dtype),
trust_remote_code=args.trust_remote_code,
)
if args.load_dir:
adapter_path = os.path.join(args.load_dir, "adapter.pt")
if os.path.isfile(adapter_path):
model.adapter.load_state_dict(torch.load(adapter_path, map_location="cpu"), strict=True)
optimizer = build_optimizer(
model=model,
weight_decay=args.weight_decay,
adapter_lr=args.adapter_lr,
)
model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)
num_update_steps_per_epoch = math.ceil(len(dataloader) / args.grad_accum_steps)
max_train_steps = args.max_steps if args.max_steps > 0 else args.num_train_epochs * num_update_steps_per_epoch
if args.lr_scheduler_type == "cosine":
scheduler = get_cosine_schedule_with_warmup(
optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=max_train_steps,
num_cycles=0.5,
)
else:
scheduler = get_constant_schedule_with_warmup(
optimizer,
num_warmup_steps=args.warmup_steps,
)
scheduler = accelerator.prepare(scheduler)
model.train()
def save_checkpoint(step: Optional[int] = None) -> None:
if args.save_dir is None:
return
output_dir = os.path.join(args.save_dir, f"checkpoint-{step}") if step else args.save_dir
accelerator.wait_for_everyone()
if accelerator.is_main_process:
os.makedirs(output_dir, exist_ok=True)
unwrapped = accelerator.unwrap_model(model)
unwrapped.model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
unwrapped.save_adapter(output_dir)
with open(os.path.join(output_dir, "train_args.json"), "w", encoding="utf-8") as f:
json.dump(vars(args), f, indent=2, sort_keys=True)
global_step = 0
start_time = time.time()
log_stats = torch.zeros(4, device=accelerator.device) # loss, cos_w, mse_w, samples
step_stats = torch.zeros(4, device=accelerator.device)
while global_step < max_train_steps:
for _, batch in enumerate(dataloader):
with accelerator.accumulate(model):
outputs = model(
input_ids=batch["input_ids"],
attention_mask=batch["attention_mask"],
loss_mask=batch.get("loss_mask"),
latent_mask=batch.get("latent_mask"),
adapter_cos_weight=args.adapter_cos_weight,
adapter_mse_weight=args.adapter_mse_weight,
)
bs = batch["input_ids"].size(0)
step_stats[0] += outputs["loss"].item() * bs
step_stats[1] += outputs["adapter_cosine_weighted"].item() * bs
step_stats[2] += outputs["adapter_mse_weighted"].item() * bs
step_stats[3] += bs
accelerator.backward(outputs["loss"] / args.grad_accum_steps)
if accelerator.sync_gradients:
if args.max_grad_norm > 0:
accelerator.clip_grad_norm_(model.parameters(), args.max_grad_norm)
optimizer.step()
scheduler.step()
optimizer.zero_grad(set_to_none=True)
global_step += 1
if step_stats[3] > 0:
log_stats += step_stats
step_stats.zero_()
if global_step % args.log_every == 0:
total = accelerator.reduce(log_stats, reduction="sum")
denom = max(total[3].item(), 1.0)
avg = total[:3] / denom
if accelerator.is_main_process:
print(f"step={global_step} loss={avg[0]:.4f}", flush=True)
log_stats.zero_()
if args.save_steps > 0 and global_step % args.save_steps == 0:
save_checkpoint(global_step)
if args.max_steps > 0 and global_step >= args.max_steps:
break
if args.max_steps > 0 and global_step >= args.max_steps:
break
save_checkpoint()
if __name__ == "__main__":
main()