import argparse import ray import torch from coati.distributed.launch import launch_distributed if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("-m", "--model", type=str, default="Qwen/Qwen2.5-7B") parser.add_argument("-rm", "--reward_model", type=str, default="Qwen/Qwen2.5-7B") parser.add_argument("-d", "--dataset", type=str, default="data.jsonl") parser.add_argument("-t", "--num-trainers", type=int, default=2) parser.add_argument("-i", "--num-inferencer", type=int, default=2) parser.add_argument("-ibs", "--inference-batch-size", type=int, default=64) parser.add_argument("-imbs", "--inference-microbatch-size", type=int, default=16) parser.add_argument("-tbs", "--train-batch-size", type=int, default=16) parser.add_argument("-tmbs", "--train-microbatch-size", type=int, default=2) parser.add_argument("-b", "--backend", type=str, default="transformers") parser.add_argument("-a", "--algo", type=str, default="GRPO", choices=["Simple", "GPRO", "PPO"]) args = parser.parse_args() ray.init(address="local", namespace="ray-example") inference_model_config = dict(path=args.model) train_model_config = dict(path=args.model) generate_config = dict( top_k=50, top_p=0.8, ) if args.backend == "transformers": inference_model_config.update( dict( use_flash_attention_2=True, torch_dtype=torch.bfloat16, ) ) train_model_config.update( dict( use_flash_attention_2=True, torch_dtype=torch.bfloat16, use_cache=False, ) ) generate_config.update( dict(max_length=768, do_sample=True, max_new_tokens=None, early_stopping=False, stop_strings=[""]) ) elif args.backend == "vllm": inference_model_config.update( dict( gpu_memory_utilization=0.7, ) ) generate_config.update( dict( max_tokens=512, ignore_eos=True, include_stop_str_in_output=True, stop=[""], temperature=0.5, top_p=0.95, n=1, ) ) else: inference_model_config.update( dict( mem_fraction_static=0.6, ) ) generate_config.update( dict( max_new_tokens=256, ignore_eos=True, ) ) launch_distributed( num_producers=args.num_inferencer, num_proc_per_producer=1, num_consumer_procs=args.num_trainers, num_episodes=1, inference_batch_size=args.inference_batch_size, inference_microbatch_size=args.inference_microbatch_size, train_batch_size=args.train_batch_size, train_microbatch_size=args.train_microbatch_size, dataset_config={"path": args.dataset, "max_length": 256}, dataloaders_config={}, inference_model_config=inference_model_config, generate_config=generate_config, train_model_config=train_model_config, plugin_config={}, inference_backend=args.backend, master_addr="localhost", master_port=29504, core_algo=args.algo, )