# Copyright 2024 PRIME team and/or its affiliates # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import copy import logging import os import warnings import torch import torch.distributed from torch.distributed.device_mesh import init_device_mesh import verl.utils.torch_functional as verl_F from omegaconf import DictConfig, open_dict from verl import DataProto from verl.single_controller.base import Worker from verl.single_controller.base.decorator import register, Dispatch from verl.utils import hf_tokenizer from verl.utils.debug import log_gpu_memory_usage from verl.utils.fs import copy_local_path_from_hdfs from verl.utils.fsdp_utils import get_fsdp_wrap_policy, init_fn, get_init_weight_context_manager from verl.utils.fsdp_utils import offload_fsdp_optimizer, offload_fsdp_model_to_cpu, load_fsdp_optimizer, \ load_fsdp_model_to_gpu from verl.utils.import_utils import import_external_libs from verl.utils.model import compute_position_id_with_mask from verl.utils.flops_counter import FlopsCounter from verl.utils.checkpoint.fsdp_checkpoint_manager import FSDPCheckpointManager from verl.workers.sharding_manager.fsdp_ulysses import FSDPUlyssesShardingManager from codetiming import Timer from verl.workers.fsdp_workers import create_device_mesh, get_sharding_strategy from .prime_core_algos import compute_dpo_accuracy, compute_dpo_abs_accuracy logger = logging.getLogger(__file__) logger.setLevel(os.getenv('VERL_PPO_LOGGING_LEVEL', 'WARN')) class PRIMERewardModelWorker(Worker): def __init__(self, config): super().__init__() import torch.distributed if not torch.distributed.is_initialized(): torch.distributed.init_process_group(backend="nccl") self.config = config # build device mesh for Ulysses Sequence Parallel world_size = torch.distributed.get_world_size() from torch.distributed.device_mesh import init_device_mesh fsdp_size = self.config.model.fsdp_config.fsdp_size self.device_mesh = create_device_mesh(world_size=world_size, fsdp_size=fsdp_size) self.ulysses_device_mesh = None self.ulysses_sequence_parallel_size = self.config.get('ulysses_sequence_parallel_size', 1) dp = world_size // self.ulysses_sequence_parallel_size if self.ulysses_sequence_parallel_size > 1: self.ulysses_device_mesh = init_device_mesh('cuda', mesh_shape=(dp, self.ulysses_sequence_parallel_size), mesh_dim_names=['dp', 'sp']) self.ulysses_sharding_manager = FSDPUlyssesShardingManager(self.ulysses_device_mesh) # set FSDP offload params self._is_offload_param = self.config.model.fsdp_config.param_offload self._is_offload_optimizer = self.config.model.fsdp_config.optimizer_offload # normalize config self.config.mini_batch_size //= (torch.distributed.get_world_size() // self.ulysses_sequence_parallel_size) if self.config.micro_batch_size is not None: self.config.micro_batch_size //= (torch.distributed.get_world_size() // self.ulysses_sequence_parallel_size) self.config.micro_batch_size_per_gpu = self.config.micro_batch_size assert self.config.mini_batch_size % self.config.micro_batch_size_per_gpu == 0 def _build_reward_ref_model_optimizer(self, config): # the following line is necessary from verl.utils.model import LambdaLayer, print_model_size, squeeze from verl.utils.torch_dtypes import PrecisionType from torch.distributed.fsdp import FullyShardedDataParallel as FSDP, ShardingStrategy, MixedPrecision from torch import optim local_path = copy_local_path_from_hdfs(config.model.path) tokenizer_path = copy_local_path_from_hdfs(config.model.tokenizer_path) self.tokenizer = hf_tokenizer(tokenizer_path, trust_remote_code=config.model.get('trust_remote_code', False)) from omegaconf import OmegaConf override_config = OmegaConf.to_container(self.config.model.get('override_config', OmegaConf.create())) override_config_kwargs = { 'bos_token_id': self.tokenizer.bos_token_id, 'eos_token_id': self.tokenizer.eos_token_id, 'pad_token_id': self.tokenizer.pad_token_id, } override_config_kwargs.update(override_config) if self.rank == 0: print(f'Reward model overriding config {override_config_kwargs}') torch_dtype = self.config.model.fsdp_config.get('model_dtype', 'fp32') torch_dtype = PrecisionType.to_dtype(torch_dtype) from transformers import AutoConfig, AutoModelForCausalLM from torch import nn trust_remote_code = False reward_model_config = AutoConfig.from_pretrained(local_path, trust_remote_code=trust_remote_code) reward_model_config.num_labels = 1 init_context = get_init_weight_context_manager(use_meta_tensor=not reward_model_config.tie_word_embeddings) with init_context(), warnings.catch_warnings(): warnings.simplefilter("ignore") setattr(reward_model_config, 'classifier_dropout', 0.) setattr(reward_model_config, 'hidden_dropout', '0') reward_module = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path=local_path, torch_dtype=torch_dtype, config=reward_model_config, attn_implementation='flash_attention_2', trust_remote_code=trust_remote_code) if config.model.get('use_remove_padding', False) or self.ulysses_sequence_parallel_size > 1: from verl.models.transformers.monkey_patch import apply_monkey_patch apply_monkey_patch(model=reward_module, ulysses_sp_size=self.ulysses_sequence_parallel_size) # some parameters may not in torch_dtype reward_module.to(torch_dtype) if config.model.get('enable_gradient_checkpointing', False): reward_module.gradient_checkpointing_enable(gradient_checkpointing_kwargs={'use_reentrant': False}) if self.rank == 0: print_model_size(reward_module) self.reward_model_config = reward_model_config fsdp_config = self.config.model.fsdp_config mixed_precision_config = fsdp_config.get('mixed_precision', None) if mixed_precision_config is not None: param_dtype = PrecisionType.to_dtype(mixed_precision_config.get('param_dtype', 'bf16')) reduce_dtype = PrecisionType.to_dtype(mixed_precision_config.get('reduce_dtype', 'fp32')) buffer_dtype = PrecisionType.to_dtype(mixed_precision_config.get('buffer_dtype', 'fp32')) else: param_dtype = torch.bfloat16 reduce_dtype = torch.float32 buffer_dtype = torch.float32 mixed_precision = MixedPrecision(param_dtype=param_dtype, reduce_dtype=reduce_dtype, buffer_dtype=buffer_dtype) auto_wrap_policy = get_fsdp_wrap_policy(module=reward_module, config=self.config.model.fsdp_config.wrap_policy) log_gpu_memory_usage('Before reward model FSDP', logger=None) fsdp_mesh = self.device_mesh sharding_strategy = get_sharding_strategy(fsdp_mesh) with init_context(), warnings.catch_warnings(): warnings.simplefilter("ignore") setattr(reward_model_config, 'classifier_dropout', 0.) setattr(reward_model_config, 'hidden_dropout', '0') ref_module = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path=copy_local_path_from_hdfs( config.model.ref_path), torch_dtype=torch_dtype, config=reward_model_config, attn_implementation='flash_attention_2', trust_remote_code=trust_remote_code) # some parameters may not in torch_dtype ref_module.to(torch_dtype) reward_module = FSDP(reward_module, param_init_fn=init_fn, use_orig_params=False, auto_wrap_policy=auto_wrap_policy, device_id=torch.cuda.current_device(), sharding_strategy=sharding_strategy, mixed_precision=mixed_precision, sync_module_states=True, forward_prefetch=False, device_mesh=self.device_mesh, cpu_offload=None) log_gpu_memory_usage('After reward FSDP', logger=None) ref_module = FSDP(ref_module, param_init_fn=init_fn, use_orig_params=False, auto_wrap_policy=auto_wrap_policy, device_id=torch.cuda.current_device(), sharding_strategy=sharding_strategy, mixed_precision=mixed_precision, sync_module_states=True, forward_prefetch=False, device_mesh=self.device_mesh, cpu_offload=None) reward_optimizer = optim.AdamW(reward_module.parameters(), lr=config.model.optim.lr, betas=config.model.optim.get('betas', (0.9, 0.999)), weight_decay=config.model.optim.get('weight_decay', 1e-2)) total_steps = config.model.optim.get('total_training_steps', 0) num_warmup_steps = int(config.model.optim.get('lr_warmup_steps', -1)) if num_warmup_steps < 0: num_warmup_steps_ratio = config.model.optim.get('lr_warmup_steps_ratio', 0.) num_warmup_steps = int(num_warmup_steps_ratio * total_steps) print(f'Total steps: {total_steps}, num_warmup_steps: {num_warmup_steps}') from verl.utils.torch_functional import get_constant_schedule_with_warmup reward_lr_scheduler = get_constant_schedule_with_warmup(optimizer=reward_optimizer, num_warmup_steps=num_warmup_steps) return reward_module, ref_module, reward_optimizer, reward_lr_scheduler @register(dispatch_mode=Dispatch.ONE_TO_ALL) def init_model(self): # This is used to import external_lib into the huggingface systems import_external_libs(self.config.model.get('external_lib', None)) from .prime_dp_rm import DataParallelPRIMERewardModel self.reward_module, self.ref_module, self.reward_optimizer, self.reward_lr_scheduler = self._build_reward_ref_model_optimizer( config=self.config) if self._is_offload_param: offload_fsdp_model_to_cpu(self.reward_module) offload_fsdp_model_to_cpu(self.ref_module) if self._is_offload_optimizer: offload_fsdp_optimizer(optimizer=self.reward_optimizer) self.rm = DataParallelPRIMERewardModel(config=self.config, reward_module=self.reward_module, ref_module=self.ref_module, reward_optimizer=self.reward_optimizer) self.flops_counter = FlopsCounter(self.reward_model_config) self.checkpoint_manager = FSDPCheckpointManager(model=self.reward_module, optimizer=self.reward_optimizer, lr_scheduler=self.reward_lr_scheduler, tokenizer=self.tokenizer) @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def compute_rm_score(self, data: DataProto): data = data.to('cuda') if self._is_offload_param: load_fsdp_model_to_gpu(self.reward_module) load_fsdp_model_to_gpu(self.ref_module) micro_batch_size = self.config.micro_batch_size_per_gpu data.meta_info['micro_batch_size'] = micro_batch_size data.meta_info['max_token_len'] = self.config.forward_max_token_len_per_gpu data.meta_info['use_dynamic_bsz'] = self.config.use_dynamic_bsz # perform forward computation with self.ulysses_sharding_manager: data = self.ulysses_sharding_manager.preprocess_data(data=data) rm_scores, q, metrics = self.rm.compute_rm_score(data=data) prompt_length = data.batch['prompts'].shape[-1] response_mask = data.batch['attention_mask'][:, prompt_length:] acc = data.batch['acc'] dpo_acc = compute_dpo_accuracy(rm_scores, acc, response_mask=response_mask, n_samples=data.meta_info['n']) dpo_acc_abs = compute_dpo_abs_accuracy(rm_scores, acc, response_mask, n_samples=data.meta_info['n']) metrics['reward_model/dpo_acc'] = dpo_acc.detach().item() metrics['reward_model/dpo_acc_abs'] = dpo_acc_abs.detach().item() output = DataProto.from_dict(tensors={'rm_scores': rm_scores, 'q': q}, meta_info={'metrics': metrics}) output = self.ulysses_sharding_manager.postprocess_data(data=output) output = output.to('cpu') if self._is_offload_param: offload_fsdp_model_to_cpu(self.reward_module) offload_fsdp_model_to_cpu(self.ref_module) return output @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def update_rm(self, data: DataProto): data = data.to('cuda') if self._is_offload_param: load_fsdp_model_to_gpu(self.ref_module) load_fsdp_model_to_gpu(self.reward_module) if self._is_offload_optimizer: load_fsdp_optimizer(optimizer=self.reward_optimizer, device_id=torch.cuda.current_device()) # perform forward computation with self.ulysses_sharding_manager: data = self.ulysses_sharding_manager.preprocess_data(data=data) rm_scores, metrics = self.rm.update_rm(data=data) self.reward_lr_scheduler.step() lr = self.reward_lr_scheduler.get_last_lr()[0] metrics['rm/lr'] = lr prompt_length = data.batch['prompts'].shape[-1] response_mask = data.batch['attention_mask'][:, prompt_length:] acc = data.batch['acc'] dpo_acc_before = compute_dpo_accuracy(rm_scores, acc, response_mask=response_mask, n_samples=data.meta_info['n']) dpo_acc_abs = compute_dpo_abs_accuracy(rm_scores, acc, response_mask, n_samples=data.meta_info['n']) metrics['reward_model/dpo_acc_before'] = dpo_acc_before.detach().item() metrics['reward_model/dpo_acc_abs_before'] = dpo_acc_abs.detach().item() output = DataProto.from_dict(tensors={'rm_scores': rm_scores}, meta_info={'metrics': metrics}) output = self.ulysses_sharding_manager.postprocess_data(data=output) if self._is_offload_param: offload_fsdp_model_to_cpu(self.reward_module) offload_fsdp_model_to_cpu(self.ref_module) if self._is_offload_optimizer: offload_fsdp_optimizer(optimizer=self.reward_optimizer) output = output.to('cpu') return output @register(dispatch_mode=Dispatch.ONE_TO_ALL) def save_checkpoint(self, local_path, hdfs_path=None, global_step=0, max_ckpt_to_keep=None): import torch if self._is_offload_param: load_fsdp_model_to_gpu(self.reward_module) self.checkpoint_manager.save_checkpoint(local_path=local_path, hdfs_path=hdfs_path, global_step=global_step, max_ckpt_to_keep=max_ckpt_to_keep) torch.distributed.barrier() if self._is_offload_param: offload_fsdp_model_to_cpu(self.reward_module) @register(dispatch_mode=Dispatch.ONE_TO_ALL) def load_checkpoint(self, local_path, del_local_after_load=True): import torch if self._is_offload_param: load_fsdp_model_to_gpu(self.reward_module) self.checkpoint_manager.load_checkpoint(local_path=local_path, del_local_after_load=del_local_after_load) torch.distributed.barrier() if self._is_offload_param: offload_fsdp_model_to_cpu(self.reward_module)