File size: 6,877 Bytes
aec0295 | 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 | """
PettingZoo-compatible GRPO training pipeline for Qwen 2.5.
Uses MultiAgentTradingEnv-derived scenarios where the Risk Manager and
Portfolio Manager send governance messages that become part of the Trader
prompt. The Trader is then trained with Unsloth + TRL GRPOTrainer.
"""
from __future__ import annotations
import argparse
import inspect
import json
import os
import random
import sys
from pathlib import Path
import numpy as np
from datasets import Dataset
os.environ.setdefault("OPENBLAS_NUM_THREADS", "1")
os.environ.setdefault("OMP_NUM_THREADS", "1")
ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
sys.path.insert(0, str(ROOT))
from env.reward import (
alignment_reward_func,
format_reward_func,
profit_reward_func,
)
from training.grpo_verifiers_multiagent import (
governance_reward_func_multiagent,
risk_reward_func_multiagent,
)
from training.prompt_utils import (
SYSTEM_PROMPT,
build_prompt_multiagent,
generate_pz_scenarios,
)
DEFAULT_MODEL_NAME = "unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit"
DEFAULT_OUTPUT_DIR = "models/local_policy_grpo_multiagent"
def require_cuda():
import torch
if not torch.cuda.is_available():
raise SystemExit("GRPO training requires CUDA.")
return torch
def load_model(model_name: str, max_seq_length: int):
from unsloth import FastLanguageModel, PatchFastRL
PatchFastRL("GRPO", "unsloth")
model, tokenizer = FastLanguageModel.from_pretrained(
model_name=model_name,
max_seq_length=max_seq_length,
dtype=None,
load_in_4bit=True,
)
model = FastLanguageModel.get_peft_model(
model,
r=16,
target_modules=[
"q_proj",
"k_proj",
"v_proj",
"o_proj",
"gate_proj",
"up_proj",
"down_proj",
],
lora_alpha=16,
lora_dropout=0,
bias="none",
use_gradient_checkpointing="unsloth",
random_state=3407,
use_rslora=False,
)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
return model, tokenizer
def make_trainer(model, tokenizer, dataset, args, torch_module):
from trl.trainer.grpo_config import GRPOConfig
from trl.trainer.grpo_trainer import GRPOTrainer
training_args = GRPOConfig(
output_dir=args.output_dir,
learning_rate=args.learning_rate,
per_device_train_batch_size=args.per_device_batch_size,
gradient_accumulation_steps=args.gradient_accumulation_steps,
num_train_epochs=1,
max_steps=args.max_steps,
save_steps=args.save_steps,
logging_steps=args.logging_steps,
bf16=torch_module.cuda.is_bf16_supported(),
fp16=not torch_module.cuda.is_bf16_supported(),
max_prompt_length=args.max_prompt_length,
max_completion_length=args.max_completion_length,
num_generations=args.num_generations,
report_to="none",
)
reward_funcs = [
format_reward_func,
alignment_reward_func,
risk_reward_func_multiagent,
profit_reward_func,
governance_reward_func_multiagent,
]
trainer_kwargs = {
"model": model,
"reward_funcs": reward_funcs,
"args": training_args,
"train_dataset": dataset,
}
trainer_signature = inspect.signature(GRPOTrainer.__init__)
if "processing_class" in trainer_signature.parameters:
trainer_kwargs["processing_class"] = tokenizer
elif "tokenizer" in trainer_signature.parameters:
trainer_kwargs["tokenizer"] = tokenizer
return GRPOTrainer(**trainer_kwargs)
def save_model(model, tokenizer, output_dir: str) -> None:
os.makedirs(output_dir, exist_ok=True)
if hasattr(model, "save_pretrained_merged"):
model.save_pretrained_merged(output_dir, tokenizer, save_method="merged_16bit")
else:
model.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
def parse_args():
parser = argparse.ArgumentParser(description="Multi-agent GRPO training for Trader (Qwen 2.5)")
parser.add_argument("--model-name", default=DEFAULT_MODEL_NAME)
parser.add_argument("--output-dir", default=DEFAULT_OUTPUT_DIR)
parser.add_argument("--difficulty", choices=["easy", "medium", "hard"], default="easy")
parser.add_argument("--num-scenarios", type=int, default=500)
parser.add_argument("--max-seq-length", type=int, default=1024)
parser.add_argument("--max-prompt-length", type=int, default=768)
parser.add_argument("--max-completion-length", type=int, default=200)
parser.add_argument("--max-steps", type=int, default=250)
parser.add_argument("--save-steps", type=int, default=50)
parser.add_argument("--logging-steps", type=int, default=1)
parser.add_argument("--per-device-batch-size", type=int, default=4)
parser.add_argument("--gradient-accumulation-steps", type=int, default=2)
parser.add_argument("--num-generations", type=int, default=4)
parser.add_argument("--learning-rate", type=float, default=5e-5)
parser.add_argument("--seed", type=int, default=3407)
return parser.parse_args()
def main():
args = parse_args()
random.seed(args.seed)
np.random.seed(args.seed)
print(
f"Generating {args.num_scenarios} scenarios from MultiAgentTradingEnv "
f"(difficulty={args.difficulty})..."
)
scenarios = generate_pz_scenarios(n=args.num_scenarios, difficulty=args.difficulty)
print(f" Generated {len(scenarios)} scenarios.")
prompts = [{"prompt": build_prompt_multiagent(sc)} for sc in scenarios]
dataset = Dataset.from_list(prompts)
torch_module = require_cuda()
model, tokenizer = load_model(args.model_name, args.max_seq_length)
trainer = make_trainer(model, tokenizer, dataset, args, torch_module)
print(f"Starting multi-agent GRPO training on {len(dataset)} prompts...")
trainer.train()
history = trainer.state.log_history
rewards = [x["reward"] for x in history if "reward" in x]
losses = [x["loss"] for x in history if "loss" in x]
try:
from utils.plotting import plot_training_results
plot_training_results(rewards, losses)
except Exception as exc:
print(f" Warning: could not generate plots: {exc}")
print(f"Saving GRPO policy to {args.output_dir}...")
save_model(model, tokenizer, args.output_dir)
metrics_path = Path(args.output_dir) / "training_metrics.json"
with open(metrics_path, "w", encoding="utf-8") as handle:
json.dump({"rewards": rewards, "losses": losses}, handle, indent=2)
print("Multi-agent GRPO training complete.")
print(f" Model saved to: {args.output_dir}")
print(f" Metrics saved to: {metrics_path}")
if __name__ == "__main__":
main()
|