import argparse import json import os import resource from contextlib import nullcontext import torch import torch.distributed as dist from coati.dataset import ( DataCollatorForPromptDataset, DataCollatorForSupervisedDataset, StatefulDistributedSampler, load_tokenized_dataset, setup_conversation_template, ) from coati.models import Critic, LoraConfig, RewardModel, convert_to_lora_module, disable_dropout, lora_manager from coati.trainer import PPOTrainer from coati.utils import load_checkpoint from transformers import AutoModelForCausalLM, AutoTokenizer import colossalai from colossalai.booster import Booster from colossalai.booster.plugin import GeminiPlugin, HybridParallelPlugin, LowLevelZeroPlugin from colossalai.cluster import DistCoordinator from colossalai.logging import get_dist_logger from colossalai.nn.lr_scheduler import CosineAnnealingWarmupLR from colossalai.nn.optimizer import HybridAdam from colossalai.shardformer.policies.auto_policy import get_autopolicy logger = get_dist_logger() def train(args): lora_config = None if args.lora_config is not None: lora_config = LoraConfig.from_file(args.lora_config) # check lora compatibility if "gemini" in args.plugin and lora_config is not None and lora_config.r > 0: raise ValueError("LoRA is not supported in GeminiPlugin. Please use other plugin") if args.plugin == "gemini_auto" and args.accumulation_steps > 1: raise ValueError("Gradient accumulation is not supported in GeminiPlugin. Please use other plugin") # ============================== # Initialize Distributed Training # ============================== colossalai.launch_from_torch() coordinator = DistCoordinator() # ====================================================== # Initialize Model, Objective, Optimizer and LR Scheduler # ====================================================== # Temp Fix: Disable lazy init due to version conflict # init_ctx = ( # LazyInitContext(default_device=get_current_device()) if isinstance(plugin, (GeminiPlugin,)) else nullcontext() # ) init_ctx = nullcontext() with init_ctx: if args.use_flash_attn: actor = AutoModelForCausalLM.from_pretrained( args.pretrain, torch_dtype=torch.bfloat16 if args.mixed_precision == "bf16" else torch.float16, use_flash_attention_2=True, local_files_only=True, ) ref_model = AutoModelForCausalLM.from_pretrained( args.pretrain, torch_dtype=torch.bfloat16 if args.mixed_precision == "bf16" else torch.float16, use_flash_attention_2=True, local_files_only=True, ) reward_model = RewardModel( args.rm_pretrain, torch_dtype=torch.bfloat16 if args.mixed_precision == "bf16" else torch.float16, use_flash_attention_2=True, ) critic = Critic( args.rm_pretrain, torch_dtype=torch.bfloat16 if args.mixed_precision == "bf16" else torch.float16, use_flash_attention_2=True, ) coordinator.print_on_master(msg="Flash-attention enabled successfully") else: actor = AutoModelForCausalLM.from_pretrained(args.pretrain, local_files_only=True) ref_model = AutoModelForCausalLM.from_pretrained(args.pretrain, local_files_only=True) reward_model = RewardModel(args.rm_pretrain) critic = Critic(args.rm_pretrain) if args.lora_config is not None: actor = convert_to_lora_module(actor, lora_config=lora_config) critic = convert_to_lora_module(critic, lora_config=lora_config) for name, module in actor.named_modules(): if "norm" in name or "gate" in name: module = module.to(torch.float32) for name, module in critic.named_modules(): if "norm" in name or "gate" in name: module = module.to(torch.float32) lora_manager.able_to_merge = False # Disable dropout disable_dropout(actor) disable_dropout(critic) if args.grad_checkpoint: actor.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": False}) critic.model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": False}) coordinator.print_on_master(msg="Gradient checkpointing enabled successfully") # configure tokenizer tokenizer_dir = args.tokenizer_dir if args.tokenizer_dir is not None else args.pretrain tokenizer = AutoTokenizer.from_pretrained(tokenizer_dir, use_fast=False, trust_remote_code=True) if os.path.exists(args.conversation_template_config): with open(args.conversation_template_config, "r", encoding="utf8") as f: conversation_template_config = json.load(f) dist.barrier() conversation_template = setup_conversation_template( tokenizer, chat_template_config=conversation_template_config, save_path=args.conversation_template_config ) stop_ids = conversation_template.stop_ids if len(conversation_template.stop_ids) > 0 else None else: raise ValueError("Conversation template config is not provided or incorrect") if hasattr(tokenizer, "pad_token") and hasattr(tokenizer, "eos_token") and tokenizer.eos_token is not None: try: # Some tokenizers doesn't allow to set pad_token mannually e.g., Qwen tokenizer.pad_token = tokenizer.eos_token except AttributeError as e: logger.warning(f"Unable to set pad token to eos token, {str(e)}") if not hasattr(tokenizer, "pad_token") or tokenizer.pad_token is None: logger.warning( "The tokenizer does not have a pad token which is required. May lead to unintended behavior in training, Please consider manually set them." ) tokenizer.add_bos_token = False tokenizer.add_eos_token = False tokenizer.padding_side = "left" # left padding for generation (online learning) # configure generation config actor.generation_config.update( pad_token_id=tokenizer.eos_token_id, bos_token_id=tokenizer.bos_token_id, eos_token_id=tokenizer.eos_token_id ) # configure optimizer coordinator.print_on_master(f"setting up optimizer for actor: lr={args.lr}, weight_decay={args.weight_decay}") actor_optim = HybridAdam( model_params=actor.parameters(), lr=args.lr, betas=(0.9, 0.95), weight_decay=args.weight_decay, adamw_mode=True, ) coordinator.print_on_master(f"setting up optimizer for critic: lr={args.lr}, weight_decay={args.weight_decay}") critic_optim = HybridAdam( model_params=critic.parameters(), lr=args.critic_lr, betas=(0.9, 0.95), weight_decay=args.weight_decay, adamw_mode=True, ) if args.warmup_steps is None: args.warmup_steps = int(0.025 * args.num_episodes) coordinator.print_on_master(f"Warmup steps is set to {args.warmup_steps}") actor_lr_scheduler = CosineAnnealingWarmupLR( optimizer=actor_optim, total_steps=args.num_episodes, warmup_steps=args.warmup_steps, eta_min=0.1 * args.lr, ) critic_lr_scheduler = CosineAnnealingWarmupLR( optimizer=critic_optim, total_steps=args.num_episodes, warmup_steps=args.warmup_steps, eta_min=0.1 * args.lr, ) # ============================== # Initialize Booster # ============================== if args.plugin == "ddp": """ Default torch ddp plugin without any acceleration, for debugging purpose acceleration, for debugging purpose """ plugin = TorchDDPPlugin(find_unused_parameters=True) elif args.plugin == "gemini": plugin = GeminiPlugin( precision=args.mixed_precision, placement_policy="static", initial_scale=2**16, max_norm=args.grad_clip, enable_gradient_accumulation=True, enable_flash_attention=args.use_flash_attn, ) elif args.plugin == "gemini_auto": plugin = GeminiPlugin( precision=args.mixed_precision, placement_policy="auto", initial_scale=2**16, max_norm=args.grad_clip, enable_flash_attention=args.use_flash_attn, ) elif args.plugin == "zero2": plugin = LowLevelZeroPlugin( stage=2, precision=args.mixed_precision, initial_scale=2**16, max_norm=args.grad_clip, ) elif args.plugin == "zero2_cpu": plugin = LowLevelZeroPlugin( stage=2, precision=args.mixed_precision, initial_scale=2**16, cpu_offload=True, max_norm=args.grad_clip, ) elif args.plugin == "3d": if args.use_flash_attn and (args.tp > 1 or args.pp > 1 or args.sp > 1 or args.enable_sequence_parallelism): logger.warning("Flash attention cannot be used with 3D parallelism for PPO training. Disabling it.") args.use_flash_attn = False plugin = HybridParallelPlugin( tp_size=args.tp, pp_size=args.pp, sp_size=args.sp, sequence_parallelism_mode=args.sp_mode, zero_stage=args.zero_stage, enable_flash_attention=args.use_flash_attn, enable_sequence_parallelism=args.enable_sequence_parallelism, cpu_offload=True if args.zero_stage >= 1 and args.zero_cpu_offload else False, parallel_output=False, max_norm=args.grad_clip, precision=args.mixed_precision, ) custom_plugin = HybridParallelPlugin( tp_size=args.tp, pp_size=args.pp, sp_size=args.sp, sequence_parallelism_mode=args.sp_mode, zero_stage=args.zero_stage, enable_flash_attention=args.use_flash_attn, enable_sequence_parallelism=args.enable_sequence_parallelism, cpu_offload=True if args.zero_stage >= 1 and args.zero_cpu_offload else False, parallel_output=False, max_norm=args.grad_clip, precision=args.mixed_precision, custom_policy=get_autopolicy(reward_model.model), ) else: raise ValueError(f"Unknown plugin {args.plugin}") if args.plugin != "3d": custom_plugin = plugin # configure dataset coordinator.print_on_master(f"Load dataset: {args.prompt_dataset}") mode_map = {"train": "train", "valid": "validation", "test": "test"} train_prompt_dataset = load_tokenized_dataset(dataset_paths=args.prompt_dataset, mode="train", mode_map=mode_map) data_collator = DataCollatorForPromptDataset(tokenizer=tokenizer, max_length=args.max_length - args.max_seq_len) train_prompt_dataloader = plugin.prepare_dataloader( dataset=train_prompt_dataset, batch_size=args.experience_batch_size, shuffle=True, drop_last=True, collate_fn=data_collator, distributed_sampler_cls=StatefulDistributedSampler, ) if len(args.ptx_dataset) > 0: train_ptx_dataset = load_tokenized_dataset(dataset_paths=args.ptx_dataset, mode="train", mode_map=mode_map) data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer, max_length=args.max_length) train_pretrain_dataloader = plugin.prepare_dataloader( dataset=train_ptx_dataset, batch_size=args.ptx_batch_size, shuffle=True, drop_last=True, collate_fn=data_collator, distributed_sampler_cls=StatefulDistributedSampler, ) else: train_pretrain_dataloader = None actor_booster = Booster(plugin=plugin) ref_booster = Booster(plugin=plugin) rm_booster = Booster(plugin=custom_plugin) critic_booster = Booster(plugin=custom_plugin) default_dtype = torch.float16 if args.mixed_precision == "fp16" else torch.bfloat16 torch.set_default_dtype(default_dtype) actor, actor_optim, _, train_prompt_dataloader, actor_lr_scheduler = actor_booster.boost( model=actor, optimizer=actor_optim, lr_scheduler=actor_lr_scheduler, dataloader=train_prompt_dataloader, ) critic, critic_optim, _, _, critic_lr_scheduler = critic_booster.boost( model=critic, optimizer=critic_optim, lr_scheduler=critic_lr_scheduler, dataloader=train_prompt_dataloader, ) reward_model, _, _, _, _ = rm_booster.boost(model=reward_model, dataloader=train_prompt_dataloader) ref_model, _, _, _, _ = ref_booster.boost(model=ref_model, dataloader=train_prompt_dataloader) torch.set_default_dtype(torch.float) coordinator.print_on_master(f"Booster init max CUDA memory: {torch.cuda.max_memory_allocated() / 1024 ** 2:.2f} MB") coordinator.print_on_master( f"Booster init max CPU memory: {resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024:.2f} MB" ) sampler_start_idx = 0 start_step = 0 if args.rm_checkpoint_path is not None: if "modeling" in args.rm_checkpoint_path: rm_booster.load_model(reward_model, args.rm_checkpoint_path) else: _, _, _ = load_checkpoint( load_dir=args.rm_checkpoint_path, booster=rm_booster, model=reward_model, optimizer=None, lr_scheduler=None, ) coordinator.print_on_master(f"Loaded reward model checkpoint {args.rm_checkpoint_path}") if args.checkpoint_path is not None: if "modeling" in args.checkpoint_path: actor_booster.load_model(actor, args.checkpoint_path) ref_booster.load_model(ref_model, args.checkpoint_path) coordinator.print_on_master(f"Loaded actor and reference model {args.checkpoint_path}") else: _, start_step, sampler_start_idx = load_checkpoint( load_dir=args.checkpoint_path, booster=actor_booster, model=actor, optimizer=actor_optim, lr_scheduler=actor_lr_scheduler, ) _, _, _ = load_checkpoint( load_dir=args.checkpoint_path, booster=ref_booster, model=ref_model, optimizer=critic_optim, lr_scheduler=critic_lr_scheduler, ) assert isinstance(train_prompt_dataloader.sampler, StatefulDistributedSampler) train_prompt_dataloader.sampler.set_start_index(start_index=sampler_start_idx) coordinator.print_on_master( f"Loaded actor and reference model checkpoint {args.checkpoint_path} at spisode {start_step}" ) coordinator.print_on_master(f"Loaded sample at index {sampler_start_idx}") coordinator.print_on_master( f"Checkpoint loaded max CUDA memory: {torch.cuda.max_memory_allocated() / 1024 ** 2:.2f} MB" ) coordinator.print_on_master( f"Checkpoint loaded CUDA memory: {torch.cuda.memory_allocated() / 1024 ** 2:.2f} MB" ) coordinator.print_on_master( f"Checkpoint loaded max CPU memory: {resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024:.2f} MB" ) if args.critic_checkpoint_path is not None: if "modeling" in args.critic_checkpoint_path: critic_booster.load_model(critic, args.critic_checkpoint_path) else: _, _, _ = load_checkpoint( load_dir=args.critic_checkpoint_path, booster=critic_booster, model=critic, optimizer=critic_optim, lr_scheduler=critic_lr_scheduler, ) coordinator.print_on_master(f"Loaded critic checkpoint {args.critic_checkpoint_path}") coordinator.print_on_master( f"Checkpoint loaded max CUDA memory: {torch.cuda.max_memory_allocated() / 1024 ** 2:.2f} MB" ) coordinator.print_on_master( f"Checkpoint loaded CUDA memory: {torch.cuda.memory_allocated() / 1024 ** 2:.2f} MB" ) coordinator.print_on_master( f"Checkpoint loaded max CPU memory: {resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024:.2f} MB" ) # configure trainer trainer = PPOTrainer( actor_booster, critic_booster, actor, critic, reward_model, ref_model, actor_optim, critic_optim, actor_lr_scheduler, critic_lr_scheduler, tokenizer=tokenizer, stop_token_ids=stop_ids, kl_coef=args.kl_coef, ptx_coef=args.ptx_coef, train_batch_size=args.train_batch_size, buffer_limit=args.num_collect_steps * args.experience_batch_size, max_length=args.max_length, max_new_tokens=args.max_seq_len, use_cache=True, do_sample=True, temperature=0.7, apply_loss_mask=not args.disable_loss_mask, accumulation_steps=args.accumulation_steps, save_dir=args.save_path, save_interval=args.save_interval, top_k=50, use_tp=args.tp > 1, offload_inference_models="gemini" not in args.plugin, coordinator=coordinator, ) trainer.fit( num_episodes=args.num_episodes, num_collect_steps=args.num_collect_steps, num_update_steps=args.num_update_steps, prompt_dataloader=train_prompt_dataloader, pretrain_dataloader=train_pretrain_dataloader, log_dir=args.log_dir, use_wandb=args.use_wandb, ) if lora_config is not None and lora_config.r > 0: # NOTE: set model to eval to merge LoRA weights lora_manager.able_to_merge = True actor.eval() critic.eval() # save model checkpoint after fitting on only rank0 coordinator.print_on_master("Start saving final actor model checkpoint") actor_booster.save_model(actor, os.path.join(trainer.actor_save_dir, "modeling"), shard=True) coordinator.print_on_master( f"Saved final actor model checkpoint at episodes {args.num_episodes} at folder {args.save_path}" ) coordinator.print_on_master("Start saving final critic model checkpoint") critic_booster.save_model(critic, os.path.join(trainer.critic_save_dir, "modeling"), shard=True) coordinator.print_on_master( f"Saved final critic model checkpoint at episodes {args.num_episodes} at folder {args.save_path}" ) coordinator.print_on_master(f"Max CUDA memory usage: {torch.cuda.max_memory_allocated()/1024**2:.2f} MB") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--prompt_dataset", nargs="+", default=[]) parser.add_argument("--ptx_dataset", nargs="+", default=[]) parser.add_argument( "--plugin", type=str, default="gemini", choices=["gemini", "gemini_auto", "zero2", "zero2_cpu", "3d"], help="Choose which plugin to use", ) parser.add_argument( "--conversation_template_config", type=str, default=None, help="Path \ to save conversation template config files.", ) parser.add_argument("--grad_clip", type=float, default=1.0, help="Gradient clipping value") parser.add_argument("--weight_decay", type=float, default=0.1, help="Weight decay") parser.add_argument("--warmup_steps", type=int, default=None, help="Warmup steps") parser.add_argument("--tokenizer_dir", type=str, default=None) parser.add_argument("--tp", type=int, default=1) parser.add_argument("--pp", type=int, default=1) parser.add_argument("--sp", type=int, default=1) parser.add_argument("--enable_sequence_parallelism", default=False, action="store_true") parser.add_argument("--zero_stage", type=int, default=0, help="Zero stage", choices=[0, 1, 2]) parser.add_argument("--zero_cpu_offload", default=False, action="store_true") parser.add_argument("--sp_mode", type=str, default="split_gather", choices=["split_gather", "ring", "all_to_all"]) parser.add_argument("--pretrain", type=str, default=None) parser.add_argument("--rm_pretrain", type=str, default=None) parser.add_argument("--checkpoint_path", type=str, default=None) parser.add_argument("--critic_checkpoint_path", type=str, default=None) parser.add_argument("--rm_checkpoint_path", type=str, help="Reward model checkpoint path") parser.add_argument("--save_path", type=str, default="actor_checkpoint_prompts") parser.add_argument("--num_episodes", type=int, default=1) parser.add_argument("--num_collect_steps", type=int, default=2) parser.add_argument("--num_update_steps", type=int, default=5) parser.add_argument("--save_interval", type=int, default=1000) parser.add_argument("--train_batch_size", type=int, default=16) parser.add_argument("--experience_batch_size", type=int, default=16) parser.add_argument("--ptx_batch_size", type=int, default=4) parser.add_argument("--lora_config", type=str, default=None, help="low-rank adaptation config file path") parser.add_argument("--mixed_precision", type=str, default="fp16", choices=["fp16", "bf16"], help="Mixed precision") parser.add_argument("--accumulation_steps", type=int, default=8) parser.add_argument("--lr", type=float, default=9e-6) parser.add_argument("--critic_lr", type=float, default=9e-6) parser.add_argument("--kl_coef", type=float, default=0.1) parser.add_argument("--ptx_coef", type=float, default=0.0) parser.add_argument("--disable_loss_mask", default=False, action="store_true") parser.add_argument("--max_length", type=int, default=2048) parser.add_argument("--max_seq_len", type=int, default=256) parser.add_argument("--log_dir", default=None, type=str) parser.add_argument("--use_wandb", default=False, action="store_true") parser.add_argument("--grad_checkpoint", default=False, action="store_true") parser.add_argument("--use_flash_attn", default=False, action="store_true") args = parser.parse_args() train(args)