736 lines
36 KiB
Python
736 lines
36 KiB
Python
# Copyright 2024 Bytedance Ltd. and/or its affiliates
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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The main entry point to run the PPO algorithm
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"""
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import os
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import logging
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import ray
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import torch
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import torch.distributed
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import torch.nn as nn
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from omegaconf import DictConfig
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from verl.single_controller.base.megatron.worker import MegatronWorker
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from verl.workers.actor.megatron_actor import MegatronPPOActor
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from verl.workers.critic.megatron_critic import MegatronPPOCritic
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from verl.workers.sharding_manager import AllGatherPPModel
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from verl.workers.reward_model.megatron.reward_model import MegatronRewardModel
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from verl.single_controller.base.decorator import register, Dispatch
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from verl import DataProto
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from verl.utils.fs import copy_local_path_from_hdfs
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from verl.utils.debug import log_gpu_memory_usage
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from verl.utils.model import load_megatron_model_weights
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from verl.utils.megatron_utils import init_model_parallel_config
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from verl.utils.megatron_utils import offload_megatron_param_and_grad, load_megatron_param_and_grad
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from verl.utils import hf_tokenizer
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from megatron.core import parallel_state as mpu
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from megatron.core import ModelParallelConfig
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logger = logging.getLogger(__file__)
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logger.setLevel(os.getenv('VERL_PPO_LOGGING_LEVEL', 'WARN'))
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def set_random_seed(seed):
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import torch
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import numpy as np
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import random
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torch.manual_seed(seed)
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np.random.seed(seed)
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random.seed(seed)
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if torch.cuda.device_count() > 0:
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from megatron.core import tensor_parallel
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tensor_parallel.model_parallel_cuda_manual_seed(seed)
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# FIXME: torch cumsum not support deterministic (used in vllm sampler),
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# https://github.com/pytorch/pytorch/issues/89492
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# torch.use_deterministic_algorithms(True, warn_only=True)
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# os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8'
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class ActorRolloutRefWorker(MegatronWorker):
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"""
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This worker can be instantiated as a standalone actor or a standalone rollout or a standalone reference policy
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or a hybrid engine based on the config.rollout
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"""
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def __init__(self, config: DictConfig, role: str):
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super().__init__()
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self.config = config
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# NOTE(sgm): We utilize colocate WorkerGroup by default.
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# As a result, Workers for different model share the same process.
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# Therefore, we only require one distribute initialization.
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# To utilize different parallel startegy in different models:
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# 1, users should disable WorkerDict; 2.assign different ResourcePool to different models,
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# 3. and apply the following patch in ray==2.10, https://github.com/ray-project/ray/pull/44385
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if not torch.distributed.is_initialized():
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rank = int(os.environ['LOCAL_RANK'])
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torch.distributed.init_process_group(backend="nccl")
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torch.cuda.set_device(rank)
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if self.config.actor.megatron.sequence_parallel:
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os.environ['CUDA_DEVICE_MAX_CONNECTIONS'] = '1'
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mpu.initialize_model_parallel(
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tensor_model_parallel_size=self.config.actor.megatron.tensor_model_parallel_size,
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pipeline_model_parallel_size=self.config.actor.megatron.pipeline_model_parallel_size,
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virtual_pipeline_model_parallel_size=None,
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pipeline_model_parallel_split_rank=None,
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use_sharp=False,
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context_parallel_size=1,
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expert_model_parallel_size=1,
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nccl_communicator_config_path=None,
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)
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set_random_seed(seed=self.config.actor.megatron.seed)
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self.role = role
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assert self.role in ['actor', 'rollout', 'ref', 'actor_rollout', 'actor_rollout_ref']
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self._is_actor = self.role in ['actor', 'actor_rollout', 'actor_rollout_ref']
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self._is_rollout = self.role in ['rollout', 'actor_rollout', 'actor_rollout_ref']
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self._is_ref = self.role in ['ref', 'actor_rollout_ref']
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# TODO(sgm): Currently, we only support reference model param offload
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# will support other offload later
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self._is_offload_param = False
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self._is_offload_grad = False
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self._is_offload_optimizer = False
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# normalize config
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if self._is_actor and self._is_rollout:
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self.config.actor.ppo_mini_batch_size //= mpu.get_data_parallel_world_size()
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self.config.actor.ppo_micro_batch_size //= mpu.get_data_parallel_world_size()
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self.config.rollout.log_prob_micro_batch_size //= mpu.get_data_parallel_world_size()
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self._is_offload_param = self.config.actor.get('param_offload', False)
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self._is_offload_grad = self.config.actor.get('grad_offload', False)
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self._is_offload_optimizer = self.config.actor.get('optimizer_offload', False)
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elif self._is_ref:
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self.config.ref.log_prob_micro_batch_size //= mpu.get_data_parallel_world_size()
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self._is_offload_param = self.config.ref.get('param_offload', False)
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def _build_model_optimizer(self,
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model_path,
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megatron_config: ModelParallelConfig,
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optim_config,
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override_model_config,
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enable_gradient_checkpointing=False):
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from verl.utils.megatron.optimizer import get_megatron_optimizer
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from megatron.core.models.gpt.gpt_model import ModelType
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from verl.utils.model import print_model_size, update_model_config
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from verl.utils.megatron_utils import get_model, init_megatron_optim_config
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from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
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# Step 1: initialize the tokenizer
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local_path = copy_local_path_from_hdfs(model_path)
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self.tokenizer = hf_tokenizer(local_path)
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# Step 2: get the actor_model_config
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actor_model_config = AutoConfig.from_pretrained(local_path)
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override_config_kwargs = {
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'bos_token_id': self.tokenizer.bos_token_id,
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'eos_token_id': self.tokenizer.eos_token_id,
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'pad_token_id': self.tokenizer.pad_token_id,
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}
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override_config_kwargs.update(override_model_config)
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update_model_config(actor_model_config, override_config_kwargs=override_config_kwargs)
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if self.rank == 0:
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print(f'Model config after override: {actor_model_config}')
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def megatron_actor_model_provider(pre_process, post_process):
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from verl.utils.model import get_parallel_model_from_config
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# vpp is not supported yet because it will hang for some reason. Need debugging
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vpp_rank = mpu.get_virtual_pipeline_model_parallel_rank() # this will be set inside get_model
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# this_megatron_config = copy.deepcopy(megatron_config)
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# this_megatron_config.virtual_pipeline_model_parallel_rank = vpp_rank
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parallel_model = get_parallel_model_from_config(config=actor_model_config,
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megatron_config=megatron_config,
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pre_process=pre_process,
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post_process=post_process,
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value=False)
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parallel_model.cuda()
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return parallel_model
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# Step 3: initialize the megatron model
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if self._is_actor and self._is_rollout:
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# Initialize the 3D HybridEngine
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hybrid_engine = AllGatherPPModel(model_provider=megatron_actor_model_provider)
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# Fetch the model at current rank
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actor_module = hybrid_engine.this_rank_models
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if isinstance(actor_module, nn.ModuleList):
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actor_module = [actor_module[0]]
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if self.config.actor.load_weight:
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load_megatron_model_weights(self.config,
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actor_model_config,
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actor_module,
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params_dtype=megatron_config.params_dtype,
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is_value_model=False)
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if self.rank == 0:
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print_model_size(actor_module[0])
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log_gpu_memory_usage('After AllGatherPPModel init', logger=logger)
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elif self._is_ref:
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print(f'self.config.ref.load_weight: {self.config.ref.load_weight}')
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ref_module = get_model(model_provider_func=megatron_actor_model_provider,
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model_type=ModelType.encoder_or_decoder,
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wrap_with_ddp=False)
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# ref_module = nn.ModuleList(ref_module)
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if self.config.ref.load_weight: # should align with the actor:
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assert self.config.actor.load_weight == self.config.ref.load_weight
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print(f'load ref weight start')
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load_megatron_model_weights(self.config,
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actor_model_config,
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ref_module,
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params_dtype=megatron_config.params_dtype,
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is_value_model=False)
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log_gpu_memory_usage('After ref module init', logger=logger)
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return ref_module, actor_model_config
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# TODO: add more optimizer args into config
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if self._is_actor:
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optim_config = init_megatron_optim_config(optim_config)
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actor_optimizer = get_megatron_optimizer(model=actor_module, config=optim_config)
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else:
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optim_config = None
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actor_optimizer = None
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log_gpu_memory_usage('After actor optimizer init', logger=logger)
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return actor_module, hybrid_engine, actor_optimizer, actor_model_config, optim_config
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def _build_rollout(self):
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if self.config.rollout.name == 'vllm':
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from verl.workers.rollout.vllm_rollout import vLLMRollout
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from verl.workers.sharding_manager import MegatronVLLMShardingManager
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from verl.utils.model import normalize_pp_vpp_params
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# NOTE(sgm): If the QKV and gate_up projection layer are concate together in actor,
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# we will reorganize their weight format when resharding from actor to rollout.
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layer_name_mapping = {
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"qkv_layer_name":
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self.config.rollout.layer_name_map.get("qkv_layer_name", "qkv"),
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"gate_proj_layer_name":
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self.config.rollout.layer_name_map.get("gate_proj_layer_name", "linear_fc1.weight"),
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}
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# reshard the weight partition from actor to rollout to initialize the rollout class
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# create a new cuda space for parameters not in this pp rank
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self.hybrid_engine.load_params_to_cuda()
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# broadcast the parameters from pp rank to other ranks
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self.hybrid_engine.allgather_params()
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# obtain name to parameters in pp/vpp
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params = self.hybrid_engine.get_all_params()
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# update the param name for the
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params = normalize_pp_vpp_params(params=params,
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num_hidden_layers=self.actor_model_config.num_hidden_layers,
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layer_name='layers')
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rollout = vLLMRollout(actor_module=params,
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config=self.config.rollout,
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tokenizer=self.tokenizer,
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model_hf_config=self.actor_model_config,
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train_tp=mpu.get_tensor_model_parallel_world_size())
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log_gpu_memory_usage('After building vllm rollout', logger=logger)
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# perform weight resharding between actor and rollout
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sharding_manager = MegatronVLLMShardingManager(module=self.hybrid_engine,
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inference_engine=rollout.inference_engine,
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model_config=self.actor_model_config,
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layer_name_mapping=layer_name_mapping)
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log_gpu_memory_usage('After building sharding manager', logger=logger)
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else:
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NotImplementedError('Only vllmRollout is supported with Megatron now')
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return rollout, sharding_manager
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@register(dispatch_mode=Dispatch.ONE_TO_ALL)
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def init_model(self):
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if self.config.model.get('external_lib', None) is not None:
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# This is used to import external_lib into the huggingface systems
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import importlib
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importlib.import_module(self.config.model.external_lib)
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from omegaconf import OmegaConf
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from verl.utils.torch_dtypes import PrecisionType
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override_model_config = OmegaConf.to_container(self.config.model.get('override_config', OmegaConf.create()))
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torch_dtype = torch.bfloat16
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megatron_config = OmegaConf.create({
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'sequence_parallel': self.config.actor.megatron.get('sequence_parallel', True),
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'param_dtype': PrecisionType.to_str(torch_dtype),
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'tensor_model_parallel_size': mpu.get_tensor_model_parallel_world_size(),
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'pipeline_model_parallel_rank': mpu.get_pipeline_model_parallel_rank(),
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'pipeline_model_parallel_size': mpu.get_pipeline_model_parallel_world_size(),
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'virtual_pipeline_model_parallel_rank': mpu.get_virtual_pipeline_model_parallel_rank(),
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'virtual_pipeline_model_parallel_size': mpu.get_virtual_pipeline_model_parallel_world_size()
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})
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megatron_config = init_model_parallel_config(megatron_config)
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if self._is_actor or self._is_rollout:
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# we need the model for actor and rollout
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if self._is_actor:
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optim_config = self.config.actor.optim
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else:
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optim_config = None
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self.actor_module, self.hybrid_engine, self.actor_optimizer, \
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self.actor_model_config, self.actor_optim_config = self._build_model_optimizer(
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model_path=self.config.model.path,
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megatron_config=megatron_config,
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optim_config=optim_config,
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override_model_config=override_model_config,
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)
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if self._is_actor:
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self.actor = MegatronPPOActor(config=self.config.actor,
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model_config=self.actor_model_config,
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megatron_config=megatron_config,
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actor_module=self.actor_module,
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actor_optimizer=self.actor_optimizer,
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actor_optimizer_config=self.actor_optim_config)
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if self._is_rollout:
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self.rollout, self.sharding_manager = self._build_rollout()
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if self._is_ref:
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self.ref_module, self.ref_model_config = self._build_model_optimizer(
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model_path=self.config.model.path,
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megatron_config=megatron_config,
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optim_config=None,
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override_model_config=override_model_config,
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)
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self.ref_policy = MegatronPPOActor(config=self.config.ref,
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model_config=self.ref_model_config,
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megatron_config=megatron_config,
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actor_module=self.ref_module,
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actor_optimizer=None,
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actor_optimizer_config=None)
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torch.cuda.empty_cache()
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@register(dispatch_mode=Dispatch.MEGATRON_COMPUTE_PROTO)
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def update_actor(self, data: DataProto):
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assert self._is_actor
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data.batch = data.batch.cuda()
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log_gpu_memory_usage('Before update policy', logger=logger)
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dataloader = self.actor.make_minibatch_iterator(data=data)
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metrics = self.actor.update_policy(dataloader=dataloader)
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log_gpu_memory_usage('After update policy', logger=logger)
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# TODO: here, we should return all metrics
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output = DataProto(meta_info={'metrics': metrics})
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output = output.to('cpu')
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torch.cuda.empty_cache()
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return output
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# @register(dispatch_mode=Dispatch.MEGATRON_PP_AS_DP_PROTO)
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# def compute_log_prob(self, data: DataProto) -> DataProto:
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# assert self._is_rollout
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# output = self.actor.compute_log_prob(data=data)
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# output = DataProto.from_dict(tensors={'old_log_probs': output})
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# torch.cuda.empty_cache()
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# return output
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@register(dispatch_mode=Dispatch.MEGATRON_PP_AS_DP_PROTO)
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def generate_sequences(self, prompts: DataProto):
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assert self._is_rollout
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prompts.batch = prompts.batch.cuda()
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meta_info = {'eos_token_id': self.tokenizer.eos_token_id, 'pad_token_id': self.tokenizer.pad_token_id}
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prompts.meta_info.update(meta_info)
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with self.sharding_manager:
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log_gpu_memory_usage('After entering sharding manager', logger=logger)
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prompts = self.sharding_manager.preprocess_data(prompts)
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output = self.rollout.generate_sequences(prompts=prompts)
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log_gpu_memory_usage('After rollout generation', logger=logger)
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output = self.sharding_manager.postprocess_data(output)
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validate = prompts.meta_info.get('validate', False)
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if self._is_actor and not validate:
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# we should always recompute old_log_probs when it is HybridEngine
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output.meta_info['micro_batch_size'] = self.config.rollout.log_prob_micro_batch_size
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output.meta_info['temperature'] = self.config.rollout.temperature
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old_log_probs = self.actor.compute_log_prob(data=output)
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output.batch['old_log_probs'] = old_log_probs
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output = output.to('cpu')
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# clear kv cache
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torch.cuda.empty_cache()
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log_gpu_memory_usage('After recompute log prob', logger=logger)
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return output
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@register(dispatch_mode=Dispatch.MEGATRON_COMPUTE_PROTO)
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def compute_ref_log_prob(self, data: DataProto):
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data = data.to('cuda')
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assert self._is_ref
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if self._is_offload_param:
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load_megatron_param_and_grad(self.ref_module, torch.cuda.current_device(), self._is_offload_grad)
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micro_batch_size = self.config.rollout.log_prob_micro_batch_size
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data.meta_info['micro_batch_size'] = micro_batch_size
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data.meta_info['temperature'] = self.config.rollout.temperature
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output = self.ref_policy.compute_log_prob(data=data)
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output = DataProto.from_dict(tensors={'ref_log_prob': output})
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output = output.to('cpu')
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if self._is_offload_param:
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offload_megatron_param_and_grad(self.ref_module, self._is_offload_grad)
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torch.cuda.empty_cache()
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return output
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@register(dispatch_mode=Dispatch.ONE_TO_ALL)
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def load_checkpoint(self, checkpoint_path):
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pass
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@register(dispatch_mode=Dispatch.ONE_TO_ALL)
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def load_pretrained_model(self, checkpoint_path):
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pass
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@register(dispatch_mode=Dispatch.ONE_TO_ALL)
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def save_checkpoint(self, checkpoint_path):
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assert self._is_actor
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pass
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class CriticWorker(MegatronWorker):
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def __init__(self, config):
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super().__init__()
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self.config = config
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|
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# NOTE(sgm): We utilize colocate WorkerGroup by default.
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|
# As a result, Workers for different model share the same process.
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|
# Therefore, we only require one distribute initialization.
|
|
# To utilize different parallel startegy in different models:
|
|
# 1, users should disable WorkerDict; 2.assign different ResourcePool to different models,
|
|
# 3. and apply the following patch in ray==2.10, https://github.com/ray-project/ray/pull/44385
|
|
if not torch.distributed.is_initialized():
|
|
rank = int(os.environ['LOCAL_RANK'])
|
|
torch.distributed.init_process_group(backend="nccl")
|
|
torch.cuda.set_device(rank)
|
|
|
|
if self.config.megatron.sequence_parallel:
|
|
os.environ['CUDA_DEVICE_MAX_CONNECTIONS'] = '1'
|
|
mpu.initialize_model_parallel(
|
|
tensor_model_parallel_size=self.config.megatron.tensor_model_parallel_size,
|
|
pipeline_model_parallel_size=self.config.megatron.pipeline_model_parallel_size,
|
|
virtual_pipeline_model_parallel_size=None,
|
|
pipeline_model_parallel_split_rank=None,
|
|
use_sharp=False,
|
|
context_parallel_size=1,
|
|
expert_model_parallel_size=1,
|
|
nccl_communicator_config_path=None,
|
|
)
|
|
|
|
set_random_seed(seed=self.config.megatron.seed)
|
|
|
|
# normalize config
|
|
self.config.ppo_mini_batch_size //= mpu.get_data_parallel_world_size()
|
|
self.config.ppo_micro_batch_size //= mpu.get_data_parallel_world_size()
|
|
|
|
# TODO(sgm): support critic model offload
|
|
|
|
def _build_critic_model_optimizer(self,
|
|
model_path,
|
|
megatron_config: ModelParallelConfig,
|
|
optim_config,
|
|
override_model_config,
|
|
enable_gradient_checkpointing=False):
|
|
from megatron.core.models.gpt.gpt_model import ModelType
|
|
from verl.utils.model import print_model_size, update_model_config
|
|
from verl.utils.megatron.optimizer import get_megatron_optimizer
|
|
from verl.utils.megatron_utils import get_model, init_megatron_optim_config, init_model_parallel_config
|
|
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
|
|
|
|
# Step 1: initialize the tokenizer
|
|
local_path = copy_local_path_from_hdfs(model_path)
|
|
self.tokenizer = hf_tokenizer(local_path)
|
|
|
|
# Step 2: get the actor_model_config
|
|
critic_model_config = AutoConfig.from_pretrained(local_path)
|
|
|
|
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_model_config)
|
|
update_model_config(critic_model_config, override_config_kwargs=override_config_kwargs)
|
|
|
|
if self.rank == 0:
|
|
print(f'Model config after override: {critic_model_config}')
|
|
|
|
def megatron_critic_model_provider(pre_process, post_process):
|
|
from verl.utils.model import get_parallel_model_from_config
|
|
# TODO: support vpp here
|
|
# vpp_rank = mpu.get_virtual_pipeline_model_parallel_rank() # this will be set inside get_model
|
|
# this_megatron_config = copy.deepcopy(megatron_config)
|
|
# this_megatron_config.virtual_pipeline_model_parallel_rank = vpp_rank
|
|
parallel_model = get_parallel_model_from_config(config=critic_model_config,
|
|
megatron_config=megatron_config,
|
|
pre_process=pre_process,
|
|
post_process=post_process,
|
|
value=True)
|
|
parallel_model.cuda()
|
|
return parallel_model
|
|
|
|
# Step 3: initialize the megatron model
|
|
critic_module = get_model(model_provider_func=megatron_critic_model_provider,
|
|
model_type=ModelType.encoder_or_decoder,
|
|
wrap_with_ddp=True)
|
|
# note that here critic_module will be a list to be compatible with the construction of interleaved pp (vpp).
|
|
# but here, we do not use pp (vpp) yet. For simplicity, we remove the list
|
|
# critic_module = nn.ModuleList(critic_module)
|
|
|
|
if self.config.load_weight:
|
|
load_megatron_model_weights(self.config,
|
|
critic_model_config,
|
|
critic_module,
|
|
params_dtype=megatron_config.params_dtype,
|
|
is_value_model=True)
|
|
if self.rank == 0:
|
|
print_model_size(critic_module[0])
|
|
|
|
# TODO: add more optimizer args into config
|
|
optim_config = init_megatron_optim_config(optim_config)
|
|
critic_optimizer = get_megatron_optimizer(model=critic_module, config=optim_config)
|
|
torch.cuda.empty_cache()
|
|
return critic_module, critic_optimizer, critic_model_config, optim_config
|
|
|
|
@register(dispatch_mode=Dispatch.ONE_TO_ALL)
|
|
def init_model(self):
|
|
# create critic
|
|
from omegaconf import OmegaConf
|
|
from verl.utils.torch_dtypes import PrecisionType
|
|
|
|
if self.config.model.get('external_lib', None) is not None:
|
|
# This is used to import external_lib into the huggingface systems
|
|
import importlib
|
|
importlib.import_module(self.config.model.external_lib)
|
|
override_model_config = OmegaConf.to_container(self.config.model.get('override_config', OmegaConf.create()))
|
|
torch_dtype = torch.bfloat16
|
|
|
|
megatron_config = OmegaConf.create({
|
|
'sequence_parallel': self.config.megatron.get('sequence_parallel', True),
|
|
'param_dtype': PrecisionType.to_str(torch_dtype),
|
|
'tensor_model_parallel_size': mpu.get_tensor_model_parallel_world_size(),
|
|
'pipeline_model_parallel_rank': mpu.get_pipeline_model_parallel_rank(),
|
|
'pipeline_model_parallel_size': mpu.get_pipeline_model_parallel_world_size(),
|
|
'virtual_pipeline_model_parallel_rank': mpu.get_virtual_pipeline_model_parallel_rank(),
|
|
'virtual_pipeline_model_parallel_size': mpu.get_virtual_pipeline_model_parallel_world_size()
|
|
})
|
|
|
|
megatron_config = init_model_parallel_config(megatron_config)
|
|
|
|
critic_module, critic_optimizer, critic_model_config, critic_optimizer_config = self._build_critic_model_optimizer(
|
|
model_path=self.config.model.path,
|
|
megatron_config=megatron_config,
|
|
optim_config=self.config.optim,
|
|
override_model_config=override_model_config)
|
|
self.critic = MegatronPPOCritic(config=self.config,
|
|
model_config=critic_model_config,
|
|
megatron_config=megatron_config,
|
|
critic_module=critic_module,
|
|
critic_optimizer=critic_optimizer,
|
|
critic_optimizer_config=critic_optimizer_config)
|
|
|
|
@register(dispatch_mode=Dispatch.MEGATRON_COMPUTE_PROTO)
|
|
def compute_values(self, data: DataProto):
|
|
data = data.to('cuda')
|
|
values = self.critic.compute_values(data=data)
|
|
output = DataProto.from_dict(tensors={'values': values})
|
|
output = output.to('cpu')
|
|
return output
|
|
|
|
@register(dispatch_mode=Dispatch.MEGATRON_COMPUTE_PROTO)
|
|
def update_critic(self, data: DataProto):
|
|
data = data.to('cuda')
|
|
dataloader = self.critic.make_minibatch_iterator(data)
|
|
metrics = self.critic.update_critic(dataloader=dataloader)
|
|
output = DataProto(batch=None, meta_info={'metrics': metrics})
|
|
output = output.to('cpu')
|
|
return output
|
|
|
|
@register(dispatch_mode=Dispatch.ONE_TO_ALL)
|
|
def load_checkpoint(self, checkpoint_path):
|
|
pass
|
|
|
|
@register(dispatch_mode=Dispatch.ONE_TO_ALL)
|
|
def save_checkpoint(self, checkpoint_path):
|
|
pass
|
|
|
|
|
|
class RewardModelWorker(MegatronWorker):
|
|
"""
|
|
Note that we only implement the reward model that is subclass of AutoModelForSequenceClassification.
|
|
"""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
self.config = config
|
|
|
|
# NOTE(sgm): We utilize colocate WorkerGroup by default.
|
|
# As a result, Workers for different model share the same process.
|
|
# Therefore, we only require one distribute initialization.
|
|
# To utilize different parallel startegy in different models:
|
|
# 1, users should disable WorkerDict; 2.assign different ResourcePool to different models,
|
|
# 3. and apply the following patch in ray==2.10, https://github.com/ray-project/ray/pull/44385
|
|
if not torch.distributed.is_initialized():
|
|
rank = int(os.environ['LOCAL_RANK'])
|
|
torch.distributed.init_process_group(backend="nccl")
|
|
torch.cuda.set_device(rank)
|
|
|
|
if self.config.megatron.sequence_parallel:
|
|
os.environ['CUDA_DEVICE_MAX_CONNECTIONS'] = '1'
|
|
mpu.initialize_model_parallel(
|
|
tensor_model_parallel_size=self.config.megatron.tensor_model_parallel_size,
|
|
pipeline_model_parallel_size=self.config.megatron.pipeline_model_parallel_size,
|
|
virtual_pipeline_model_parallel_size=None,
|
|
pipeline_model_parallel_split_rank=None,
|
|
use_sharp=False,
|
|
context_parallel_size=1,
|
|
expert_model_parallel_size=1,
|
|
nccl_communicator_config_path=None,
|
|
)
|
|
|
|
set_random_seed(seed=self.config.megatron.seed)
|
|
|
|
# normalize config
|
|
self.config.micro_batch_size //= mpu.get_data_parallel_world_size()
|
|
|
|
def _build_rm_model(self, model_path, megatron_config: ModelParallelConfig, override_model_config):
|
|
from megatron.core.models.gpt.gpt_model import ModelType
|
|
from verl.utils.model import print_model_size, update_model_config
|
|
from verl.utils.megatron_utils import get_model
|
|
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
|
|
|
|
# Step 1: initialize the tokenizer
|
|
local_path = copy_local_path_from_hdfs(model_path)
|
|
self.tokenizer = hf_tokenizer(local_path)
|
|
|
|
# Step 2: get the actor_model_config
|
|
rm_model_config = AutoConfig.from_pretrained(local_path)
|
|
|
|
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_model_config)
|
|
update_model_config(rm_model_config, override_config_kwargs=override_config_kwargs)
|
|
|
|
if self.rank == 0:
|
|
print(f'Model config after override: {rm_model_config}')
|
|
|
|
def megatron_rm_model_provider(pre_process, post_process):
|
|
from verl.utils.model import get_parallel_model_from_config
|
|
# vpp is not supported yet because it will hang for some reason. Need debugging
|
|
vpp_rank = mpu.get_virtual_pipeline_model_parallel_rank() # this will be set inside get_model
|
|
# this_megatron_config = copy.deepcopy(megatron_config)
|
|
# this_megatron_config.virtual_pipeline_model_parallel_rank = vpp_rank
|
|
parallel_model = get_parallel_model_from_config(config=rm_model_config,
|
|
megatron_config=megatron_config,
|
|
pre_process=pre_process,
|
|
post_process=post_process,
|
|
value=True)
|
|
parallel_model.cuda()
|
|
return parallel_model
|
|
|
|
# Step 3: initialize the megatron model
|
|
reward_model = get_model(model_provider_func=megatron_rm_model_provider,
|
|
model_type=ModelType.encoder_or_decoder,
|
|
wrap_with_ddp=False)
|
|
# note that here critic_module will be a list to be compatible with the construction of interleaved pp (vpp).
|
|
# but here, we do not use pp (vpp) yet. For simplicity, we remove the list
|
|
# reward_model = nn.ModuleList(reward_model)
|
|
|
|
if self.config.load_weight:
|
|
load_megatron_model_weights(self.config,
|
|
rm_model_config,
|
|
reward_model,
|
|
params_dtype=megatron_config.params_dtype,
|
|
is_value_model=True)
|
|
|
|
# TODO: add more optimizer args into config
|
|
torch.cuda.empty_cache()
|
|
return reward_model, rm_model_config
|
|
|
|
@register(dispatch_mode=Dispatch.ONE_TO_ALL)
|
|
def init_model(self):
|
|
# create critic
|
|
from omegaconf import OmegaConf
|
|
from verl.utils.torch_dtypes import PrecisionType
|
|
from transformers import AutoTokenizer
|
|
|
|
if self.config.model.get('external_lib', None) is not None:
|
|
# This is used to import external_lib into the huggingface systems
|
|
import importlib
|
|
importlib.import_module(self.config.model.external_lib)
|
|
override_model_config = OmegaConf.to_container(self.config.model.get('override_config', OmegaConf.create()))
|
|
|
|
sft_tokenizer_local_path = copy_local_path_from_hdfs(self.config.model.input_tokenizer)
|
|
sft_tokenizer = hf_tokenizer(sft_tokenizer_local_path)
|
|
rm_tokenizer_path = self.config.model.get('rm_tokenizer', None)
|
|
rm_tokenizer = None
|
|
if rm_tokenizer_path is not None:
|
|
rm_tokenizer_local_path = copy_local_path_from_hdfs(rm_tokenizer_path)
|
|
rm_tokenizer = hf_tokenizer(rm_tokenizer_local_path)
|
|
|
|
torch_dtype = torch.bfloat16
|
|
|
|
megatron_config = OmegaConf.create({
|
|
'sequence_parallel': self.config.megatron.get('sequence_parallel', True),
|
|
'param_dtype': PrecisionType.to_str(torch_dtype),
|
|
'tensor_model_parallel_size': mpu.get_tensor_model_parallel_world_size(),
|
|
'pipeline_model_parallel_rank': mpu.get_pipeline_model_parallel_rank(),
|
|
'pipeline_model_parallel_size': mpu.get_pipeline_model_parallel_world_size(),
|
|
'virtual_pipeline_model_parallel_rank': mpu.get_virtual_pipeline_model_parallel_rank(),
|
|
'virtual_pipeline_model_parallel_size': mpu.get_virtual_pipeline_model_parallel_world_size()
|
|
})
|
|
|
|
megatron_config = init_model_parallel_config(megatron_config)
|
|
|
|
reward_model_module, reward_model_config = self._build_rm_model(
|
|
model_path=self.config.model.path,
|
|
megatron_config=megatron_config,
|
|
override_model_config=override_model_config,
|
|
)
|
|
# FIXME(sgm): reward model param offload is implemented in MegatronRewardModel
|
|
# should be implemented in workers
|
|
self.rm = MegatronRewardModel(config=self.config,
|
|
reward_model_module=reward_model_module,
|
|
model_config=reward_model_config,
|
|
megatron_config=megatron_config,
|
|
sft_tokenizer=sft_tokenizer,
|
|
rm_tokenizer=rm_tokenizer)
|
|
|
|
# TODO: reward model use itself tokenizer instead of sft tokenizer
|
|
# the input_ids, responses, attention_mask and position_ids may be different!
|
|
@register(dispatch_mode=Dispatch.MEGATRON_COMPUTE_PROTO)
|
|
def compute_rm_score(self, data: DataProto):
|
|
data.batch = data.batch.cuda()
|
|
output = self.rm.compute_reward(data)
|
|
output = output.to('cpu')
|
|
return output
|