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674 lines (595 loc) · 29.7 KB
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import argparse
import json
import math
import os
import time
from typing import List, Optional
import torch
from torch.utils.data import DataLoader
from transformers import get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup
from mas_prompt import (
FEEDBACK_SLOT,
PLANNER_SLOT,
REFINED_SLOT,
build_code_planner_prompt_with_feedback_slot,
build_code_refiner_prompt_with_slot,
build_code_solver_prompt_with_slots,
build_math_planner_prompt_with_feedback_slot,
build_math_refiner_prompt_with_slot,
build_math_solver_prompt_with_slots,
)
from model import CrossModelAdapter
from .common import (
build_planner_teacher_forced_inputs,
build_stage_with_slot,
compute_solver_ce_loss,
load_inner_adapter,
load_model_and_tokenizer,
load_outer_training_dataset,
resolve_dtype,
run_inner_adapter_preserve_input_grad,
run_outer_adapter,
trim_latent,
write_outerlink_manifest,
)
ALLOWED_OUTER_TYPES = {
"outer_linear_adapter",
"outer_linear_res_adapter",
"outer_adapter",
"outer_res_adapter",
"outer_ln_adapter",
"outer_ln_res_adapter",
}
def parse_args(argv=None) -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--agent1_model_name_or_path", type=str, required=True)
parser.add_argument("--agent2_model_name_or_path", type=str, required=True)
parser.add_argument("--agent3_model_name_or_path", type=str, required=True)
parser.add_argument("--agent1_inner_aligner_path", type=str, required=True)
parser.add_argument("--agent2_inner_aligner_path", type=str, required=True)
parser.add_argument("--agent3_inner_aligner_path", type=str, required=True)
parser.add_argument(
"--inner_adapter_type_fallback",
type=str,
default="res_adapter",
choices=["1layer", "1layer_res", "2layer", "2layer_res", "2layer_ln_res", "adapter", "linear_adapter", "linear_res_adapter", "ln_res_adapter", "res_adapter"],
)
parser.add_argument("--dataset_name", type=str, required=True)
parser.add_argument("--dataset_split", type=str, default="train")
parser.add_argument("--dataset_json_field", type=str, default="data")
parser.add_argument("--num_samples", type=int, default=-1)
parser.add_argument("--shuffle", action="store_true")
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--mas_shape", type=str, default="chain", choices=["chain"], help=argparse.SUPPRESS)
parser.add_argument("--mas_task", type=str, default="math", choices=["math", "code"])
parser.add_argument("--solver_pre_question", type=int, default=0)
parser.add_argument("--enable_thinking", type=int, default=0, choices=[0, 1])
parser.add_argument("--gradient_checkpointing", type=int, default=1, choices=[0, 1])
parser.add_argument("--max_length", type=int, default=4096)
parser.add_argument("--max_latent_tokens", type=int, default=80)
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("--outer_lr", type=float, default=5e-4)
parser.add_argument("--lr_scheduler_type", type=str, default="cosine", choices=["constant", "cosine"])
parser.add_argument("--warmup_steps", type=int, default=10)
parser.add_argument("--weight_decay", type=float, default=0.0)
parser.add_argument("--max_grad_norm", type=float, default=1.0)
parser.add_argument("--log_every", type=int, default=10)
parser.add_argument(
"--num_recursive_rounds",
type=int,
default=3,
help="Number of recursive rounds (planner->refiner->solver cycles).",
)
parser.add_argument(
"--supervise_final_only",
type=int,
default=1,
choices=[0, 1],
help="If 1, optimize only the final-round CE loss.",
)
parser.add_argument(
"--non_last_loss_weight",
type=float,
default=0.1,
help="When supervise_final_only=0, add this weight * mean(non-last round losses).",
)
parser.add_argument(
"--outer_adapter_type",
type=str,
default="outer_ln_res_adapter",
choices=sorted(ALLOWED_OUTER_TYPES),
)
parser.add_argument("--outer_12_type", type=str, default=None)
parser.add_argument("--outer_23_type", type=str, default=None)
parser.add_argument("--outer_31_type", type=str, default=None)
parser.add_argument("--dtype", type=str, default="bfloat16", choices=["float32", "float16", "bfloat16"])
parser.add_argument("--outer_dtype", type=str, default="bfloat16", choices=["float32", "float16", "bfloat16"])
parser.add_argument("--trust_remote_code", action="store_true", default=True)
parser.add_argument("--device", type=str, default=None)
parser.add_argument("--save_dir", type=str, required=True)
parser.add_argument("--save_steps", type=int, default=0)
return parser.parse_args(argv)
def normalize_outer_type(name: Optional[str], fallback: str) -> str:
out = fallback if name is None else name
if out not in ALLOWED_OUTER_TYPES:
raise ValueError(f"Unsupported outer adapter type: {out}")
return out
def activate_gc_runtime(model: torch.nn.Module, model_name: str) -> None:
# In many HF models, gradient-checkpointing branches are gated by `self.training`.
# Keep params frozen, switch to train-mode only to activate checkpointing,
# and disable dropout for near-deterministic behavior.
model.train()
if hasattr(model, "config") and hasattr(model.config, "use_cache"):
model.config.use_cache = False
dropout_count = 0
for module in model.modules():
if isinstance(module, torch.nn.Dropout):
module.p = 0.0
dropout_count += 1
def build_optional_token_type_ids(
model: torch.nn.Module,
attention_mask: Optional[torch.Tensor] = None,
input_ids: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
) -> Optional[torch.Tensor]:
config = getattr(model, "config", None)
model_type = str(getattr(config, "model_type", "")).lower() if config is not None else ""
if model_type != "gemma3" or not model.training:
return None
if input_ids is not None:
base = input_ids
elif attention_mask is not None:
base = attention_mask
elif inputs_embeds is not None:
base = inputs_embeds[..., 0]
else:
return None
return torch.zeros_like(base, dtype=torch.long)
def save_recursive_outer_checkpoint(
save_dir: str,
step: Optional[int],
outer_12: CrossModelAdapter,
outer_23: CrossModelAdapter,
outer_31: CrossModelAdapter,
args: argparse.Namespace,
) -> None:
output_dir = os.path.join(save_dir, f"checkpoint-{step}") if step is not None else save_dir
os.makedirs(output_dir, exist_ok=True)
torch.save(outer_12.state_dict(), os.path.join(output_dir, "outer_12.pt"))
torch.save(outer_23.state_dict(), os.path.join(output_dir, "outer_23.pt"))
torch.save(outer_31.state_dict(), os.path.join(output_dir, "outer_31.pt"))
cfg = {
"outer_12_type": outer_12.adapter_type,
"outer_23_type": outer_23.adapter_type,
"outer_31_type": outer_31.adapter_type,
"outer_12_in_dim": outer_12.in_dim,
"outer_12_out_dim": outer_12.out_dim,
"outer_23_in_dim": outer_23.in_dim,
"outer_23_out_dim": outer_23.out_dim,
"outer_31_in_dim": outer_31.in_dim,
"outer_31_out_dim": outer_31.out_dim,
"mas_shape": args.mas_shape,
"agent1_model_name_or_path": args.agent1_model_name_or_path,
"agent2_model_name_or_path": args.agent2_model_name_or_path,
"agent3_model_name_or_path": args.agent3_model_name_or_path,
"agent1_inner_aligner_path": args.agent1_inner_aligner_path,
"agent2_inner_aligner_path": args.agent2_inner_aligner_path,
"agent3_inner_aligner_path": args.agent3_inner_aligner_path,
"enable_thinking": args.enable_thinking,
"mas_task": args.mas_task,
"num_recursive_rounds": args.num_recursive_rounds,
"supervise_final_only": args.supervise_final_only,
"non_last_loss_weight": args.non_last_loss_weight,
}
with open(os.path.join(output_dir, "outer_adapter_config.json"), "w", encoding="utf-8") as f:
json.dump(cfg, f, indent=2, sort_keys=True)
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)
write_outerlink_manifest(output_dir, "sequential", [
{"legacy_key": "outer_12", "filename": "outer_12.pt", "adapter_type": outer_12.adapter_type, "in_dim": outer_12.in_dim, "out_dim": outer_12.out_dim},
{"legacy_key": "outer_23", "filename": "outer_23.pt", "adapter_type": outer_23.adapter_type, "in_dim": outer_23.in_dim, "out_dim": outer_23.out_dim},
{"legacy_key": "outer_31", "filename": "outer_31.pt", "adapter_type": outer_31.adapter_type, "in_dim": outer_31.in_dim, "out_dim": outer_31.out_dim},
])
def main(argv=None) -> None:
args = parse_args(argv)
if args.mas_shape != "chain":
raise ValueError("Only mas_shape=chain is supported.")
if args.num_recursive_rounds <= 0:
raise ValueError("--num_recursive_rounds must be positive.")
device = torch.device(args.device or ("cuda" if torch.cuda.is_available() else "cpu"))
model_dtype = resolve_dtype(args.dtype)
outer_dtype = resolve_dtype(args.outer_dtype)
if model_dtype is None or outer_dtype is None:
raise ValueError("Unsupported dtype configuration.")
if device.type == "cpu" and model_dtype in {torch.float16, torch.bfloat16}:
print("[warn] CPU + fp16/bf16 is unstable. Falling back model dtype to float32.")
model_dtype = torch.float32
if device.type == "cpu" and outer_dtype in {torch.float16, torch.bfloat16}:
print("[warn] CPU + fp16/bf16 is unstable. Falling back outer dtype to float32.")
outer_dtype = torch.float32
torch.manual_seed(args.seed)
enable_thinking = bool(args.enable_thinking)
solver_args = argparse.Namespace(solver_pre_question=args.solver_pre_question)
planner_model, planner_tok = load_model_and_tokenizer(
args.agent1_model_name_or_path,
device=device,
dtype=model_dtype,
trust_remote_code=args.trust_remote_code,
agent_name="planner",
gradient_checkpointing=bool(args.gradient_checkpointing),
)
refiner_model, refiner_tok = load_model_and_tokenizer(
args.agent2_model_name_or_path,
device=device,
dtype=model_dtype,
trust_remote_code=args.trust_remote_code,
agent_name="refiner",
gradient_checkpointing=bool(args.gradient_checkpointing),
)
solver_model, solver_tok = load_model_and_tokenizer(
args.agent3_model_name_or_path,
device=device,
dtype=model_dtype,
trust_remote_code=args.trust_remote_code,
agent_name="solver",
gradient_checkpointing=bool(args.gradient_checkpointing),
)
if bool(args.gradient_checkpointing):
activate_gc_runtime(planner_model, "planner")
activate_gc_runtime(refiner_model, "refiner")
activate_gc_runtime(solver_model, "solver")
planner_embed = planner_model.get_input_embeddings()
refiner_embed = refiner_model.get_input_embeddings()
solver_embed = solver_model.get_input_embeddings()
planner_hidden = planner_embed.weight.size(-1)
refiner_hidden = refiner_embed.weight.size(-1)
solver_hidden = solver_embed.weight.size(-1)
inner_1 = load_inner_adapter(
args.agent1_inner_aligner_path,
hidden_size=planner_hidden,
device=device,
dtype=model_dtype,
fallback_adapter_type=args.inner_adapter_type_fallback,
)
inner_2 = load_inner_adapter(
args.agent2_inner_aligner_path,
hidden_size=refiner_hidden,
device=device,
dtype=model_dtype,
fallback_adapter_type=args.inner_adapter_type_fallback,
)
inner_3 = load_inner_adapter(
args.agent3_inner_aligner_path,
hidden_size=solver_hidden,
device=device,
dtype=model_dtype,
fallback_adapter_type=args.inner_adapter_type_fallback,
)
outer_12_type = normalize_outer_type(args.outer_12_type, args.outer_adapter_type)
outer_23_type = normalize_outer_type(args.outer_23_type, args.outer_adapter_type)
outer_31_type = normalize_outer_type(args.outer_31_type, args.outer_adapter_type)
outer_12 = CrossModelAdapter(planner_hidden, refiner_hidden, outer_12_type).to(device=device, dtype=outer_dtype)
outer_23 = CrossModelAdapter(refiner_hidden, solver_hidden, outer_23_type).to(device=device, dtype=outer_dtype)
outer_31 = CrossModelAdapter(solver_hidden, planner_hidden, outer_31_type).to(device=device, dtype=outer_dtype)
outer_12.train()
outer_23.train()
outer_31.train()
params = list(outer_12.parameters()) + list(outer_23.parameters()) + list(outer_31.parameters())
optimizer = torch.optim.AdamW(params, lr=args.outer_lr, weight_decay=args.weight_decay, betas=(0.9, 0.95))
dataset = load_outer_training_dataset(args.dataset_name, args.dataset_split, args.dataset_json_field)
needed_cols = {"question", "plan", "refined_plan", "answer"}
missing = needed_cols.difference(set(dataset.column_names))
if missing:
raise ValueError(f"Dataset missing required fields: {sorted(missing)}")
if args.shuffle:
dataset = dataset.shuffle(seed=args.seed)
if args.num_samples > 0:
dataset = dataset.select(range(min(args.num_samples, len(dataset))))
if len(dataset) == 0:
raise ValueError("Dataset is empty.")
rows = [
{
"question": sample.get("question", ""),
"plan": sample.get("plan", ""),
"refined_plan": sample.get("refined_plan", ""),
"answer": sample.get("answer", ""),
"type": sample.get("type", "complete"),
"fn_name": sample.get("fn_name", None),
}
for sample in dataset
]
dataloader = DataLoader(rows, batch_size=args.batch_size, shuffle=True, drop_last=True, collate_fn=lambda x: x)
if len(dataloader) == 0:
raise ValueError("Dataloader is empty. Increase dataset size or reduce batch_size.")
steps_per_epoch = len(dataloader)
max_train_steps = args.max_steps if args.max_steps > 0 else args.num_train_epochs * 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)
os.makedirs(args.save_dir, exist_ok=True)
global_step = 0
start_time = time.time()
skipped_count = 0
log_loss = 0.0
log_r_first = 0.0
log_r_last = 0.0
log_count = 0
while global_step < max_train_steps:
for batch in dataloader:
if global_step >= max_train_steps:
break
sample_r_first_losses: List[float] = []
sample_r_last_losses: List[float] = []
valid_count = 0
batch_loss_sum = 0.0
optimizer.zero_grad(set_to_none=True)
for sample in batch:
q = str(sample["question"]).strip()
p = str(sample["plan"]).strip()
rp = str(sample["refined_plan"]).strip()
ans = str(sample["answer"]).strip()
task_type = str(sample.get("type", "complete")).strip().lower() or "complete"
fn_name = sample.get("fn_name", None)
if not q or not p or not rp or not ans:
skipped_count += 1
continue
try:
feedback_to_planner = None
round_losses: List[torch.Tensor] = []
for round_idx in range(args.num_recursive_rounds):
# Planner
if round_idx == 0:
planner_input_ids, planner_attention_mask, planner_assist_mask = (
build_planner_teacher_forced_inputs(
planner_tok,
question=q,
plan=p,
enable_thinking=enable_thinking,
device=device,
max_length=args.max_length,
mas_task=args.mas_task,
task_type=task_type,
fn_name=fn_name,
)
)
with torch.no_grad():
planner_token_type_ids = build_optional_token_type_ids(
planner_model,
attention_mask=planner_attention_mask,
input_ids=planner_input_ids,
)
planner_kwargs = {}
if planner_token_type_ids is not None:
planner_kwargs["token_type_ids"] = planner_token_type_ids
planner_out = planner_model(
input_ids=planner_input_ids,
attention_mask=planner_attention_mask,
output_hidden_states=True,
use_cache=False,
return_dict=True,
**planner_kwargs,
)
planner_hidden = planner_out.hidden_states[-1][0][planner_assist_mask]
else:
if feedback_to_planner is None or feedback_to_planner.size(0) == 0:
round_losses = []
break
if args.mas_task == "code":
planner_user_with_slot = build_code_planner_prompt_with_feedback_slot(
q,
task_type=task_type,
fn_name=fn_name,
)
else:
planner_user_with_slot = build_math_planner_prompt_with_feedback_slot(q)
planner_pack = build_stage_with_slot(
tokenizer=planner_tok,
embedding_layer=planner_embed,
user_prompt_with_slot=planner_user_with_slot,
assistant_text=p,
slot_text=FEEDBACK_SLOT,
slot_embeds=feedback_to_planner,
enable_thinking=enable_thinking,
device=device,
embed_dtype=planner_embed.weight.dtype,
max_length=args.max_length,
)
planner_token_type_ids = build_optional_token_type_ids(
planner_model,
attention_mask=planner_pack.attention_mask,
inputs_embeds=planner_pack.inputs_embeds,
)
planner_kwargs = {}
if planner_token_type_ids is not None:
planner_kwargs["token_type_ids"] = planner_token_type_ids
planner_out = planner_model(
inputs_embeds=planner_pack.inputs_embeds,
attention_mask=planner_pack.attention_mask,
output_hidden_states=True,
use_cache=False,
return_dict=True,
**planner_kwargs,
)
planner_hidden = planner_out.hidden_states[-1][0][planner_pack.assistant_mask]
if planner_hidden.size(0) == 0:
round_losses = []
break
planner_inner = run_inner_adapter_preserve_input_grad(inner_1, planner_hidden, out_dtype=model_dtype)
planner_to_refiner = run_outer_adapter(
outer_12, planner_inner, out_dtype=refiner_embed.weight.dtype
)
planner_to_refiner = trim_latent(planner_to_refiner, args.max_latent_tokens)
# Refiner
if args.mas_task == "code":
refiner_user_with_slot = build_code_refiner_prompt_with_slot(
q,
task_type=task_type,
fn_name=fn_name,
)
else:
refiner_user_with_slot = build_math_refiner_prompt_with_slot(q)
refiner_pack = build_stage_with_slot(
tokenizer=refiner_tok,
embedding_layer=refiner_embed,
user_prompt_with_slot=refiner_user_with_slot,
assistant_text=rp,
slot_text=PLANNER_SLOT,
slot_embeds=planner_to_refiner,
enable_thinking=enable_thinking,
device=device,
embed_dtype=refiner_embed.weight.dtype,
max_length=args.max_length,
)
refiner_token_type_ids = build_optional_token_type_ids(
refiner_model,
attention_mask=refiner_pack.attention_mask,
inputs_embeds=refiner_pack.inputs_embeds,
)
refiner_kwargs = {}
if refiner_token_type_ids is not None:
refiner_kwargs["token_type_ids"] = refiner_token_type_ids
refiner_out = refiner_model(
inputs_embeds=refiner_pack.inputs_embeds,
attention_mask=refiner_pack.attention_mask,
output_hidden_states=True,
use_cache=False,
return_dict=True,
**refiner_kwargs,
)
refiner_hidden = refiner_out.hidden_states[-1][0][refiner_pack.assistant_mask]
if refiner_hidden.size(0) == 0:
round_losses = []
break
refiner_inner = run_inner_adapter_preserve_input_grad(inner_2, refiner_hidden, out_dtype=model_dtype)
refiner_to_solver = run_outer_adapter(
outer_23, refiner_inner, out_dtype=solver_embed.weight.dtype
)
refiner_to_solver = trim_latent(refiner_to_solver, args.max_latent_tokens)
# Solver
if args.mas_task == "code":
solver_user_with_slot = build_code_solver_prompt_with_slots(
q,
task_type=task_type,
args=solver_args,
mas_shape=args.mas_shape,
fn_name=fn_name,
)
else:
solver_user_with_slot = build_math_solver_prompt_with_slots(
q,
args=solver_args,
mas_shape=args.mas_shape,
)
solver_pack = build_stage_with_slot(
tokenizer=solver_tok,
embedding_layer=solver_embed,
user_prompt_with_slot=solver_user_with_slot,
assistant_text=ans,
slot_text=REFINED_SLOT,
slot_embeds=refiner_to_solver,
enable_thinking=enable_thinking,
device=device,
embed_dtype=solver_embed.weight.dtype,
max_length=args.max_length,
)
need_feedback = round_idx < args.num_recursive_rounds - 1
solver_token_type_ids = build_optional_token_type_ids(
solver_model,
attention_mask=solver_pack.attention_mask,
inputs_embeds=solver_pack.inputs_embeds,
)
solver_kwargs = {}
if solver_token_type_ids is not None:
solver_kwargs["token_type_ids"] = solver_token_type_ids
solver_out = solver_model(
inputs_embeds=solver_pack.inputs_embeds,
attention_mask=solver_pack.attention_mask,
output_hidden_states=need_feedback,
use_cache=False,
return_dict=True,
**solver_kwargs,
)
loss_round = compute_solver_ce_loss(solver_out.logits, solver_pack.labels)
if torch.isnan(loss_round) or torch.isinf(loss_round):
round_losses = []
break
round_losses.append(loss_round)
# Feedback for next round (solver -> planner)
if need_feedback:
solver_hidden = solver_out.hidden_states[-1][0][solver_pack.assistant_mask]
if solver_hidden.size(0) == 0:
round_losses = []
break
solver_inner = run_inner_adapter_preserve_input_grad(inner_3, solver_hidden, out_dtype=model_dtype)
feedback_to_planner = run_outer_adapter(
outer_31, solver_inner, out_dtype=planner_embed.weight.dtype
)
feedback_to_planner = trim_latent(feedback_to_planner, args.max_latent_tokens)
if not round_losses:
skipped_count += 1
continue
if args.supervise_final_only:
loss = round_losses[-1]
else:
if len(round_losses) > 1:
non_last_mean = torch.stack(round_losses[:-1]).mean()
loss = round_losses[-1] + args.non_last_loss_weight * non_last_mean
else:
loss = round_losses[-1]
(loss / max(args.batch_size, 1)).backward()
valid_count += 1
batch_loss_sum += float(loss.item())
sample_r_first_losses.append(float(round_losses[0].item()))
sample_r_last_losses.append(float(round_losses[-1].item()))
except RuntimeError as exc:
exc_msg = str(exc).lower()
if "sequence_too_long" in exc_msg:
skipped_count += 1
continue
raise
if valid_count == 0:
continue
if valid_count != args.batch_size:
grad_scale = args.batch_size / valid_count
for param in params:
if param.grad is not None:
param.grad.mul_(grad_scale)
if args.max_grad_norm > 0:
torch.nn.utils.clip_grad_norm_(params, args.max_grad_norm)
optimizer.step()
scheduler.step()
global_step += 1
loss_batch = batch_loss_sum / valid_count
log_loss += float(loss_batch)
log_r_first += sum(sample_r_first_losses) / max(len(sample_r_first_losses), 1)
log_r_last += sum(sample_r_last_losses) / max(len(sample_r_last_losses), 1)
log_count += 1
if global_step % args.log_every == 0:
avg_loss = log_loss / max(log_count, 1)
avg_r_first = log_r_first / max(log_count, 1)
avg_r_last = log_r_last / max(log_count, 1)
lr = scheduler.get_last_lr()[0]
elapsed = time.time() - start_time
print(f"step={global_step} loss={avg_loss:.4f}", flush=True)
log_loss = 0.0
log_r_first = 0.0
log_r_last = 0.0
log_count = 0
if args.save_steps > 0 and global_step % args.save_steps == 0:
save_recursive_outer_checkpoint(args.save_dir, global_step, outer_12, outer_23, outer_31, args)
if global_step >= max_train_steps:
break
if global_step >= max_train_steps:
break
save_recursive_outer_checkpoint(args.save_dir, None, outer_12, outer_23, outer_31, args)
elapsed = time.time() - start_time
if __name__ == "__main__":
main()