Source code for trinity.trainer.verl.fsdp_workers

# Copyright 2024 Bytedance Ltd. 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.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""
The main entry point to run the PPO algorithm.
Modified from https://github.com/volcengine/verl/blob/0758489422e8d41a89e6c36d4c477714520f0dcc/verl/workers/fsdp_workers.py
"""

import json
import logging
import os
import warnings
from dataclasses import asdict
from datetime import timedelta

import psutil
import torch
import torch.distributed
import torch.distributed as dist
import vllm  # noqa: F401 ; import vllm to set NCCL_CUMEM_ENABLE automatically.
from codetiming import Timer
from omegaconf import DictConfig, open_dict
from peft import LoraConfig, TaskType, get_peft_model
from safetensors.torch import save_file
from torch.distributed.device_mesh import init_device_mesh
from torch.distributed.fsdp import FlatParameter
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp.fully_sharded_data_parallel import FSDP_PREFIX
from verl import DataProto
from verl.models.transformers.monkey_patch import apply_monkey_patch
from verl.single_controller.base import Worker
from verl.single_controller.base.decorator import Dispatch, register
from verl.utils import hf_processor, hf_tokenizer
from verl.utils.activation_offload import enable_activation_offloading
from verl.utils.checkpoint.fsdp_checkpoint_manager import FSDPCheckpointManager
from verl.utils.debug import log_gpu_memory_usage
from verl.utils.device import get_torch_device, is_cuda_available
from verl.utils.flops_counter import FlopsCounter
from verl.utils.fs import copy_to_local
from verl.utils.fsdp_utils import (
    CPUOffloadPolicy,
    MixedPrecisionPolicy,
    apply_fsdp2,
    fsdp2_load_full_state_dict,
    fsdp_version,
    get_fsdp_wrap_policy,
    get_init_weight_context_manager,
    init_fn,
    layered_summon_lora_params,
    load_fsdp_model_to_gpu,
    load_fsdp_optimizer,
    offload_fsdp_model_to_cpu,
    offload_fsdp_optimizer,
)
from verl.utils.import_utils import import_external_libs
from verl.utils.py_functional import convert_to_regular_types
from verl.workers.fsdp_workers import (
    create_device_mesh,
    device_name,
    get_sharding_strategy,
)
from verl.workers.sharding_manager.fsdp_ulysses import FSDPUlyssesShardingManager

from trinity.common.config import AlgorithmConfig
from trinity.common.constants import ROLLOUT_WEIGHT_SYNC_GROUP_NAME, SyncMethod
from trinity.utils.distributed import init_process_group, is_ipv6_address

logger = logging.getLogger(__file__)
logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN"))


[docs] class ActorRolloutRefWorker(Worker): """ This worker can be instantiated as a standalone actor or a standalone rollout or a standalone reference policy or a hybrid engine based on the config.rollout """
[docs] def __init__(self, config: DictConfig, role: str): super().__init__() self.config = config import torch.distributed if not torch.distributed.is_initialized(): rank = int(os.environ.get("RANK", 0)) world_size = int(os.environ.get("WORLD_SIZE", 1)) torch.distributed.init_process_group( backend="cpu:gloo,cuda:nccl" if is_cuda_available else "cpu:gloo,npu:hccl", rank=rank, world_size=world_size, timeout=timedelta(seconds=self.config.synchronizer.sync_timeout), ) # build device mesh for FSDP world_size = torch.distributed.get_world_size() # TODO(sgm): support FSDP hybrid shard for larger model self.device_mesh = create_device_mesh( world_size=world_size, fsdp_size=self.config.actor.fsdp_config.fsdp_size ) # build device mesh for Ulysses Sequence Parallel self.ulysses_device_mesh = None self.ulysses_sequence_parallel_size = self.config.actor.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( device_name, mesh_shape=(dp, self.ulysses_sequence_parallel_size), mesh_dim_names=["dp", "sp"], ) self.ulysses_sharding_manager = FSDPUlyssesShardingManager(self.ulysses_device_mesh) self._lora_rank = self.config.model.get("lora_rank", 0) self._is_lora = self._lora_rank > 0 self.role = role assert self.role in ["actor", "rollout", "ref", "actor_rollout", "actor_rollout_ref"] self._is_actor = self.role in ["actor", "actor_rollout", "actor_rollout_ref"] self._is_ref = self.role in ["ref", "actor_rollout_ref"] self._is_offload_param = False self._is_offload_optimizer = False if self._is_actor: self._is_offload_param = self.config.actor.fsdp_config.get("param_offload", False) self._is_offload_optimizer = self.config.actor.fsdp_config.get( "optimizer_offload", False ) elif self._is_ref: # TODO: it seems that manual offload is slowly than FSDP offload self._is_offload_param = self.config.ref.fsdp_config.get("param_offload", False) # normalize config if self._is_actor: self.config.actor.ppo_mini_batch_size *= self.config.rollout.n self.config.actor.ppo_mini_batch_size //= ( self.device_mesh.size() // self.ulysses_sequence_parallel_size ) assert ( self.config.actor.ppo_mini_batch_size > 0 ), f"ppo_mini_batch_size {self.config.actor.ppo_mini_batch_size} should be larger than 0 after normalization" # micro bsz if self.config.actor.ppo_micro_batch_size is not None: self.config.actor.ppo_micro_batch_size //= ( self.device_mesh.size() // self.ulysses_sequence_parallel_size ) self.config.actor.ppo_micro_batch_size_per_gpu = ( self.config.actor.ppo_micro_batch_size ) if self.config.actor.ppo_micro_batch_size_per_gpu is not None: assert ( self.config.actor.ppo_mini_batch_size % self.config.actor.ppo_micro_batch_size_per_gpu == 0 ), f"normalized ppo_mini_batch_size {self.config.actor.ppo_mini_batch_size} should be divisible by ppo_micro_batch_size_per_gpu {self.config.actor.ppo_micro_batch_size_per_gpu}" assert ( self.config.actor.ppo_mini_batch_size // self.config.actor.ppo_micro_batch_size_per_gpu > 0 ), f"normalized ppo_mini_batch_size {self.config.actor.ppo_mini_batch_size} should be larger than ppo_micro_batch_size_per_gpu {self.config.actor.ppo_micro_batch_size_per_gpu}" # normalize ref config if self._is_ref and self.config.ref.log_prob_micro_batch_size is not None: self.config.ref.log_prob_micro_batch_size //= ( self.device_mesh.size() // self.ulysses_sequence_parallel_size ) self.config.ref.log_prob_micro_batch_size_per_gpu = ( self.config.ref.log_prob_micro_batch_size )
def _build_model_optimizer( # noqa: C901 self, model_path, fsdp_config, optim_config, override_model_config, use_remove_padding=False, use_fused_kernels=False, enable_gradient_checkpointing=False, trust_remote_code=False, use_liger=False, role="actor", enable_activation_offload=False, ): from torch import optim from torch.distributed.fsdp import CPUOffload, MixedPrecision from transformers import ( AutoConfig, AutoModelForCausalLM, AutoModelForVision2Seq, ) from verl.utils.model import ( get_generation_config, print_model_size, update_model_config, ) from verl.utils.torch_dtypes import PrecisionType assert role in ["actor", "ref"] log_gpu_memory_usage(f"Before init {role} from HF AutoModel", logger=logger) local_path = model_path # note that we have to create model in fp32. Otherwise, the optimizer is in bf16, which is incorrect # TODO(zhangchi.usc1992): 1. support create from random initialized model. 2. Support init with FSDP directly self.tokenizer = hf_tokenizer(local_path, trust_remote_code=trust_remote_code) self.processor = hf_processor(local_path, trust_remote_code=trust_remote_code) torch_dtype = fsdp_config.get("model_dtype", None) if torch_dtype is None: torch_dtype = torch.float32 if self._is_actor else torch.bfloat16 else: torch_dtype = PrecisionType.to_dtype(torch_dtype) # override model kwargs actor_model_config = AutoConfig.from_pretrained( local_path, trust_remote_code=trust_remote_code, attn_implementation="flash_attention_2" ) # patch for kimi-vl if getattr(actor_model_config, "model_type", None) == "kimi_vl": actor_model_config.text_config.topk_method = "greedy" self.generation_config = get_generation_config( local_path, trust_remote_code=trust_remote_code ) 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(actor_model_config, override_config_kwargs=override_config_kwargs) if self.rank == 0: print(f"Model config after override: {actor_model_config}") # NOTE(fix me): tie_word_embedding causes meta_tensor init to hang init_context = get_init_weight_context_manager( use_meta_tensor=not actor_model_config.tie_word_embeddings, mesh=self.device_mesh ) with init_context(), warnings.catch_warnings(): warnings.simplefilter("ignore") if type(actor_model_config) in AutoModelForVision2Seq._model_mapping.keys(): actor_module_class = AutoModelForVision2Seq else: actor_module_class = AutoModelForCausalLM actor_module = actor_module_class.from_pretrained( pretrained_model_name_or_path=local_path, torch_dtype=torch_dtype, config=actor_model_config, trust_remote_code=trust_remote_code, ) # Apply Liger kernel to the model if use_liger is set to True if use_liger: from liger_kernel.transformers.monkey_patch import ( _apply_liger_kernel_to_instance, ) _apply_liger_kernel_to_instance(model=actor_module) apply_monkey_patch( model=actor_module, use_remove_padding=use_remove_padding, ulysses_sp_size=self.ulysses_sequence_parallel_size, use_fused_kernels=use_fused_kernels, ) # some parameters may not in torch_dtype. TODO(zhangchi.usc1992) remove this after we switch to fsdp2 actor_module.to(torch_dtype) if enable_gradient_checkpointing: actor_module.gradient_checkpointing_enable( gradient_checkpointing_kwargs={"use_reentrant": False} ) if self._is_lora: print("Applying LoRA to actor module") actor_module.enable_input_require_grads() # Convert config to regular Python types before creating PEFT model lora_config = { "task_type": TaskType.CAUSAL_LM, "r": self.config.model.lora_rank, "lora_alpha": self.config.model.lora_alpha, "target_modules": convert_to_regular_types(self.config.model.target_modules), "bias": "none", } actor_module = get_peft_model(actor_module, LoraConfig(**lora_config)) torch.distributed.barrier() if self.rank == 0: print_model_size(actor_module) log_gpu_memory_usage(f"After init {role} from HF AutoModel", logger=logger) # We wrap FSDP for rollout as well 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=actor_module, config=fsdp_config.get("wrap_policy", None), is_lora=self.config.model.get("lora_rank", 0) > 0, ) if self.rank == 0: print(f"wrap_policy: {auto_wrap_policy}") fsdp_mesh = self.device_mesh sharding_strategy = get_sharding_strategy(fsdp_mesh) # TODO: add transformer policy # We force reference policy to use CPUOffload to save memory. # We force turn off CPUOffload for actor because it causes incorrect results when using grad accumulation cpu_offload = None if role == "actor" else CPUOffload(offload_params=True) fsdp_strategy = self.config.actor.strategy if fsdp_strategy == "fsdp": actor_module_fsdp = FSDP( actor_module, cpu_offload=cpu_offload, param_init_fn=init_fn, use_orig_params=False, auto_wrap_policy=auto_wrap_policy, device_id=get_torch_device().current_device(), sharding_strategy=sharding_strategy, # zero3 mixed_precision=mixed_precision, sync_module_states=True, device_mesh=self.device_mesh, forward_prefetch=False, ) elif fsdp_strategy == "fsdp2": assert ( CPUOffloadPolicy is not None ), "PyTorch version >= 2.4 is required for using fully_shard API (FSDP2)" mp_policy = MixedPrecisionPolicy( param_dtype=param_dtype, reduce_dtype=reduce_dtype, cast_forward_inputs=True ) if role == "actor" and fsdp_config.offload_policy: cpu_offload = CPUOffloadPolicy(pin_memory=True) self._is_offload_param = False self._is_offload_optimizer = False else: cpu_offload = None if role == "actor" else CPUOffloadPolicy(pin_memory=True) fsdp_kwargs = { "mesh": fsdp_mesh, "mp_policy": mp_policy, "offload_policy": cpu_offload, "reshard_after_forward": fsdp_config.reshard_after_forward, } full_state = actor_module.state_dict() apply_fsdp2(actor_module, fsdp_kwargs, fsdp_config) fsdp2_load_full_state_dict(actor_module, full_state, fsdp_mesh, cpu_offload) actor_module_fsdp = actor_module else: raise NotImplementedError(f"not implement {fsdp_strategy}") if enable_activation_offload: enable_activation_offloading( actor_module_fsdp, fsdp_strategy, enable_gradient_checkpointing ) log_gpu_memory_usage(f"After {role} FSDP init", logger=logger) # TODO: add more optimizer args into config if role == "actor" and optim_config is not None: from verl.utils.torch_functional import ( get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup, ) actor_optimizer = optim.AdamW( actor_module_fsdp.parameters(), lr=optim_config.lr, betas=optim_config.get("betas", (0.9, 0.999)), weight_decay=optim_config.get("weight_decay", 1e-2), ) total_steps = optim_config.get("total_training_steps", 0) num_warmup_steps = int(optim_config.get("lr_warmup_steps", -1)) warmup_style = optim_config.get("warmup_style", "constant") min_lr_ratio = optim_config.get("min_lr_ratio", 0.0) num_cycles = optim_config.get("num_cycles", 0.5) if num_warmup_steps < 0: num_warmup_steps_ratio = optim_config.get("lr_warmup_steps_ratio", 0.0) num_warmup_steps = int(num_warmup_steps_ratio * total_steps) if self.rank == 0: print(f"Total steps: {total_steps}, num_warmup_steps: {num_warmup_steps}") if warmup_style == "constant": actor_lr_scheduler = get_constant_schedule_with_warmup( optimizer=actor_optimizer, num_warmup_steps=num_warmup_steps ) elif warmup_style == "cosine": actor_lr_scheduler = get_cosine_schedule_with_warmup( optimizer=actor_optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=total_steps, min_lr_ratio=min_lr_ratio, num_cycles=num_cycles, ) else: raise NotImplementedError(f"Warmup style {warmup_style} is not supported") log_gpu_memory_usage(f"After {role} optimizer init", logger=logger) else: actor_optimizer = None actor_lr_scheduler = None return actor_module_fsdp, actor_optimizer, actor_lr_scheduler, actor_model_config
[docs] @register(dispatch_mode=Dispatch.ONE_TO_ALL) def init_model(self): from trinity.trainer.verl.dp_actor import DataParallelPPOActor # This is used to import external_lib into the huggingface systems import_external_libs(self.config.model.get("external_lib", None)) from omegaconf import OmegaConf override_model_config = OmegaConf.to_container( self.config.model.get("override_config", OmegaConf.create()) ) use_remove_padding = self.config.model.get("use_remove_padding", False) use_shm = self.config.model.get("use_shm", False) use_fused_kernels = self.config.model.get("use_fused_kernels", False) if self._is_actor: # we need the model for actor and rollout optim_config = self.config.actor.optim fsdp_config = self.config.actor.fsdp_config local_path = copy_to_local(self.config.model.path, use_shm=use_shm) ( self.actor_module_fsdp, self.actor_optimizer, self.actor_lr_scheduler, self.actor_model_config, ) = self._build_model_optimizer( model_path=local_path, fsdp_config=fsdp_config, optim_config=optim_config, override_model_config=override_model_config, use_remove_padding=use_remove_padding, use_fused_kernels=use_fused_kernels, enable_gradient_checkpointing=self.config.model.get( "enable_gradient_checkpointing", False ), trust_remote_code=self.config.model.get("trust_remote_code", False), use_liger=self.config.model.get("use_liger", False), role="actor", enable_activation_offload=self.config.model.get("enable_activation_offload", False), ) # get the original unwrapped module if fsdp_version(self.actor_module_fsdp) == 1: self.actor_module = self.actor_module_fsdp._fsdp_wrapped_module if self._is_offload_param: offload_fsdp_model_to_cpu(self.actor_module_fsdp) log_gpu_memory_usage("After offload actor model during init", logger=logger) if self._is_offload_optimizer: offload_fsdp_optimizer(optimizer=self.actor_optimizer) log_gpu_memory_usage("After offload actor optimizer during init", logger=logger) # load from checkpoint if self._is_actor: OmegaConf.set_struct(self.config.actor, True) with open_dict(self.config.actor): self.config.actor.use_remove_padding = use_remove_padding self.config.actor.use_fused_kernels = use_fused_kernels self.actor = DataParallelPPOActor( config=self.config.actor, actor_module=self.actor_module_fsdp, actor_optimizer=self.actor_optimizer, ) if self._is_ref: local_path = copy_to_local(self.config.model.path, use_shm=use_shm) self.ref_module_fsdp = self._build_model_optimizer( model_path=local_path, fsdp_config=self.config.ref.fsdp_config, optim_config=None, override_model_config=override_model_config, use_remove_padding=use_remove_padding, use_fused_kernels=use_fused_kernels, trust_remote_code=self.config.model.get("trust_remote_code", False), use_liger=self.config.model.get("use_liger", False), role="ref", )[0] OmegaConf.set_struct(self.config.ref, True) with open_dict(self.config.ref): self.config.ref.use_remove_padding = use_remove_padding self.config.ref.use_fused_kernels = use_fused_kernels self.ref_policy = DataParallelPPOActor( config=self.config.ref, actor_module=self.ref_module_fsdp ) self.checkpoint_manager = FSDPCheckpointManager( model=self.ref_module_fsdp, optimizer=None, lr_scheduler=None, processing_class=self.processor if self.processor is not None else self.tokenizer, ) if self._is_actor: self.flops_counter = FlopsCounter(self.actor_model_config) self.checkpoint_manager = FSDPCheckpointManager( model=self.actor_module_fsdp, optimizer=self.actor.actor_optimizer, lr_scheduler=self.actor_lr_scheduler, processing_class=self.processor if self.processor is not None else self.tokenizer, checkpoint_contents=self.config.actor.checkpoint.contents, )
[docs] @register(dispatch_mode=Dispatch.ONE_TO_ALL) def setup_weight_sync_group(self): if ( hasattr(self.config, "synchronizer") and getattr(self.config.synchronizer, "sync_method", None) == SyncMethod.NCCL ): model = self.actor_module_fsdp self.named_modules = [] self.state_dict_meta = [] for name, module in model.named_modules(): if isinstance(module, FSDP): self.named_modules.append((name, module)) for name_prefix, module in self.named_modules: with FSDP.summon_full_params(module, recurse=False): for name, param in module.named_parameters(): if isinstance(param, FlatParameter): continue realname = ( name_prefix[len(FSDP_PREFIX) :] + "." + name if name_prefix else name ) self.state_dict_meta.append( (realname, str(param.dtype), tuple(param.shape)) ) param = None torch.cuda.empty_cache() if torch.distributed.get_rank() == 0: import ray master_address, master_port = self.get_availale_master_addr_port() world_size = self.config.synchronizer.explorer_world_size + 1 print(f"Trainer init_process_group {master_address}:{master_port} ({world_size}).") explorer = ray.get_actor(self.config.explorer_name) setup_ref = explorer.setup_weight_sync_group.remote( master_address, master_port, self.state_dict_meta ) if is_ipv6_address(master_address): # using tcp://ipv6:port will lead to ValueError init_method = f"tcp://[{master_address}]:{master_port}" else: init_method = f"tcp://{master_address}:{master_port}" timeout = self.config.synchronizer.sync_timeout self._model_update_group = init_process_group( backend="nccl", init_method=init_method, timeout=timeout, world_size=world_size, rank=0, group_name=ROLLOUT_WEIGHT_SYNC_GROUP_NAME, ) ray.get(setup_ref) torch.distributed.barrier()
[docs] @register(dispatch_mode=Dispatch.ONE_TO_ALL) def sync_weight(self): for name_prefix, module in self.named_modules: with FSDP.summon_full_params(module, recurse=False): if torch.distributed.get_rank() == 0: for name, param in module.named_parameters(): if isinstance(param, FlatParameter): continue torch.distributed.broadcast(param, 0, group=self._model_update_group) param = None torch.distributed.barrier() torch.cuda.synchronize() torch.cuda.empty_cache()
[docs] @register(dispatch_mode=Dispatch.ONE_TO_ALL) def set_algorithm(self, algo_config: AlgorithmConfig): self.actor.set_algorithm(algo_config)
[docs] @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def update_actor(self, data: DataProto): # Support all hardwares data = data.to(get_torch_device().current_device()) assert self._is_actor if self._is_offload_param: load_fsdp_model_to_gpu(self.actor_module_fsdp) if self._is_offload_optimizer: load_fsdp_optimizer( optimizer=self.actor_optimizer, device_id=get_torch_device().current_device() ) with self.ulysses_sharding_manager: data = self.ulysses_sharding_manager.preprocess_data(data=data) # perform training with Timer(name="update_policy", logger=None) as timer: metrics = self.actor.update_policy(data=data) delta_time = timer.last global_num_tokens = data.meta_info["global_token_num"] estimated_flops, promised_flops = self.flops_counter.estimate_flops( global_num_tokens, delta_time ) metrics["perf/mfu/actor"] = ( estimated_flops * self.config.actor.ppo_epochs / promised_flops / self.world_size ) metrics["perf/max_memory_allocated_gb"] = get_torch_device().max_memory_allocated() / ( 1024**3 ) metrics["perf/max_memory_reserved_gb"] = get_torch_device().max_memory_reserved() / ( 1024**3 ) metrics["perf/cpu_memory_used_gb"] = psutil.virtual_memory().used / (1024**3) lr = self.actor_lr_scheduler.get_last_lr()[0] metrics["actor/lr"] = lr self.actor_lr_scheduler.step() # TODO: here, we should return all metrics output = DataProto(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.actor_module_fsdp) log_gpu_memory_usage("After offload actor model during update_actor", logger=logger) if self._is_offload_optimizer: offload_fsdp_optimizer(optimizer=self.actor_optimizer) log_gpu_memory_usage("After offload actor optimizer during update_actor", logger=logger) return output
[docs] @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def compute_log_prob(self, data: DataProto): # when is_lora is True, we use the actor without lora applied to calculate the log_prob # which is mostly used for ref log_prob calculation assert self._is_actor if self._is_offload_param: load_fsdp_model_to_gpu(self.actor_module_fsdp) # Support all hardwares from contextlib import nullcontext is_lora = data.meta_info.pop("is_lora", False) adapter_ctx = self.actor.actor_module.disable_adapter() if is_lora else nullcontext() data = data.to(get_torch_device().current_device()) # we should always recompute old_log_probs when it is HybridEngine data.meta_info["micro_batch_size"] = self.config.rollout.log_prob_micro_batch_size_per_gpu data.meta_info["max_token_len"] = self.config.rollout.log_prob_max_token_len_per_gpu data.meta_info["use_dynamic_bsz"] = self.config.rollout.log_prob_use_dynamic_bsz data.meta_info["temperature"] = self.config.rollout.temperature # perform recompute log_prob with self.ulysses_sharding_manager: data = self.ulysses_sharding_manager.preprocess_data(data) with adapter_ctx: output, entropys = self.actor.compute_log_prob(data=data, calculate_entropy=True) output = DataProto.from_dict( tensors={"old_log_probs": output, "entropys": entropys}, meta_info={"temperature": self.config.rollout.temperature}, ) output = self.ulysses_sharding_manager.postprocess_data(output) output = output.to("cpu") # https://pytorch.org/docs/stable/notes/fsdp.html#fsdp-notes # unshard the root FSDP module if self.world_size > 1 and fsdp_version(self.actor.actor_module) == 1: self.actor.actor_module._handle.reshard(True) if self._is_offload_param: offload_fsdp_model_to_cpu(self.actor_module_fsdp) log_gpu_memory_usage("After offload actor model during compute_log_prob", logger=logger) return output
[docs] @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def compute_ref_log_prob(self, data: DataProto): if self._is_lora: # if _is_lora, actor without lora applied is the ref data.meta_info["is_lora"] = True data = self.compute_log_prob(data) # this old_log_probs is in fact ref_log_prob data = DataProto.from_dict(tensors={"ref_log_prob": data.batch["old_log_probs"]}) return data assert self._is_ref # else: # otherwise, the class have a standalone ref model # Support all hardwares data = data.to(get_torch_device().current_device()) micro_batch_size = self.config.ref.log_prob_micro_batch_size_per_gpu data.meta_info["micro_batch_size"] = micro_batch_size data.meta_info["temperature"] = self.config.rollout.temperature data.meta_info["max_token_len"] = self.config.ref.log_prob_max_token_len_per_gpu data.meta_info["use_dynamic_bsz"] = self.config.ref.log_prob_use_dynamic_bsz with self.ulysses_sharding_manager: data = self.ulysses_sharding_manager.preprocess_data(data) output, _ = self.ref_policy.compute_log_prob(data=data, calculate_entropy=False) output = DataProto.from_dict(tensors={"ref_log_prob": output}) output = self.ulysses_sharding_manager.postprocess_data(output) output = output.to("cpu") # https://pytorch.org/docs/stable/notes/fsdp.html#fsdp-notes # unshard the root FSDP module if self.world_size > 1 and fsdp_version(self.ref_policy.actor_module) == 1: self.ref_policy.actor_module._handle.reshard(True) return output
[docs] @register(dispatch_mode=Dispatch.ONE_TO_ALL) def save_checkpoint(self, local_path, hdfs_path=None, global_step=0, max_ckpt_to_keep=None): # only support save and load ckpt for actor assert self._is_actor if self._is_offload_param: load_fsdp_model_to_gpu(self.actor_module_fsdp) 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, ) dist.barrier() if self._is_lora and hasattr( getattr(self, "actor_module", self.actor_module_fsdp), "peft_config" ): lora_save_path = os.path.join(local_path, "lora_adapter") peft_model = getattr(self, "actor_module", self.actor_module_fsdp) peft_config = {} if dist.get_rank() == 0: os.makedirs(lora_save_path, exist_ok=True) peft_config = asdict(peft_model.peft_config.get("default", {})) peft_config["task_type"] = peft_config["task_type"].value peft_config["peft_type"] = peft_config["peft_type"].value peft_config["target_modules"] = list(peft_config["target_modules"]) try: if fsdp_version(self.actor_module_fsdp) > 0: self.actor_module_fsdp = self.actor_module_fsdp.cuda() lora_params = layered_summon_lora_params(self.actor_module_fsdp) if dist.get_rank() == 0: save_file( lora_params, os.path.join(lora_save_path, "adapter_model.safetensors") ) with open( os.path.join(lora_save_path, "adapter_config.json"), "w", encoding="utf-8", ) as f: json.dump(peft_config, f, ensure_ascii=False, indent=4) except Exception as e: if dist.get_rank() == 0: print(f"[rank-{self.rank}]: Save LoRA Adapter Error ({e})") dist.barrier() if dist.get_rank() == 0: print(f"[rank-{self.rank}]: Saved LoRA adapter to: {lora_save_path}") if self._is_offload_param: offload_fsdp_model_to_cpu(self.actor_module_fsdp)
[docs] @register(dispatch_mode=Dispatch.ONE_TO_ALL) def load_checkpoint(self, local_path, hdfs_path=None, del_local_after_load=False): if self._is_actor and self._is_offload_param: load_fsdp_model_to_gpu(self.actor_module_fsdp) self.checkpoint_manager.load_checkpoint( local_path=local_path, hdfs_path=hdfs_path, del_local_after_load=del_local_after_load ) if self._is_actor and self._is_offload_param: offload_fsdp_model_to_cpu(self.actor_module_fsdp) if self._is_actor and self._is_offload_optimizer: offload_fsdp_optimizer(self.actor_optimizer)
[docs] @register(dispatch_mode=Dispatch.ONE_TO_ALL) def clear_optimizer_state(self): print("Clear actor optimizer state") if self._is_offload_optimizer: load_fsdp_optimizer( optimizer=self.actor_optimizer, device_id=torch.cuda.current_device() ) self.actor_optimizer.state.clear() self.actor_optimizer.zero_grad() if self._is_offload_optimizer: offload_fsdp_optimizer(self.actor_optimizer)
[docs] class CriticWorker(Worker):
[docs] def __init__(self, config): super().__init__() import torch.distributed if not torch.distributed.is_initialized(): torch.distributed.init_process_group( backend="nccl" if is_cuda_available else "hccl", timeout=timedelta(seconds=self.config.synchronizer.sync_timeout), ) 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( device_name, 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.ppo_mini_batch_size *= self.config.rollout_n self.config.ppo_mini_batch_size //= ( torch.distributed.get_world_size() // self.ulysses_sequence_parallel_size ) if self.config.ppo_micro_batch_size is not None: self.config.ppo_micro_batch_size //= ( torch.distributed.get_world_size() // self.ulysses_sequence_parallel_size ) self.config.forward_micro_batch_size //= ( torch.distributed.get_world_size() // self.ulysses_sequence_parallel_size ) self.config.ppo_micro_batch_size_per_gpu = self.config.ppo_micro_batch_size self.config.forward_micro_batch_size_per_gpu = self.config.forward_micro_batch_size if self.config.ppo_micro_batch_size_per_gpu is not None: assert ( self.config.ppo_mini_batch_size % self.config.ppo_micro_batch_size_per_gpu == 0 ), f"normalized ppo_mini_batch_size {self.config.ppo_mini_batch_size} should be divisible by ppo_micro_batch_size_per_gpu {self.config.ppo_micro_batch_size_per_gpu}" assert ( self.config.ppo_mini_batch_size // self.config.ppo_micro_batch_size_per_gpu > 0 ), f"normalized ppo_mini_batch_size {self.config.ppo_mini_batch_size} should be larger than ppo_micro_batch_size_per_gpu {self.config.ppo_micro_batch_size_per_gpu}" self._is_lora = self.config.model.get("lora_rank", 0) > 0
def _build_critic_model_optimizer(self, config): # the following line is necessary from torch import optim from torch.distributed.fsdp import MixedPrecision from verl.utils.model import print_model_size from verl.utils.torch_dtypes import PrecisionType use_shm = config.model.get("use_shm", False) local_path = copy_to_local(config.model.path, use_shm=use_shm) # note that the tokenizer between actor and critic may be different. So override tokenizer info with actor info # using random initialized model from any architecture. May not be the same as Actor. tokenizer_path = copy_to_local(config.model.tokenizer_path, use_shm=use_shm) self.tokenizer = hf_tokenizer( tokenizer_path, trust_remote_code=config.model.get("trust_remote_code", False) ) self.processor = hf_processor( 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"Critic 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, AutoModelForTokenClassification critic_model_config = AutoConfig.from_pretrained( local_path, attn_implementation="flash_attention_2", trust_remote_code=config.model.get("trust_remote_code", False), ) critic_model_config.num_labels = 1 # patch for kimi-vl if getattr(critic_model_config, "model_type", None) == "kimi_vl": critic_model_config.text_config.topk_method = "greedy" init_context = get_init_weight_context_manager( use_meta_tensor=not critic_model_config.tie_word_embeddings, mesh=self.device_mesh ) with init_context(), warnings.catch_warnings(): warnings.simplefilter("ignore") critic_model_config.classifier_dropout = 0.0 critic_model_config.hidden_dropout = "0" critic_module = AutoModelForTokenClassification.from_pretrained( pretrained_model_name_or_path=local_path, torch_dtype=torch_dtype, config=critic_model_config, trust_remote_code=config.model.get("trust_remote_code", False), ) use_remove_padding = config.model.get("use_remove_padding", False) apply_monkey_patch( model=critic_module, use_remove_padding=use_remove_padding, ulysses_sp_size=self.ulysses_sequence_parallel_size, ) # some parameters may not in torch_dtype critic_module.to(torch_dtype) if config.model.get("enable_gradient_checkpointing", False): critic_module.gradient_checkpointing_enable( gradient_checkpointing_kwargs={"use_reentrant": False} ) if self._is_lora: print("Applying LoRA to critic module") critic_module.enable_input_require_grads() # Convert config to regular Python types before creating PEFT model lora_config = { "task_type": TaskType.CAUSAL_LM, "r": self.config.model.lora_rank, "lora_alpha": self.config.model.lora_alpha, "target_modules": convert_to_regular_types(self.config.model.target_modules), "bias": "none", } critic_module = get_peft_model(critic_module, LoraConfig(**lora_config)) if self.rank == 0: print_model_size(critic_module) self.critic_model_config = critic_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=critic_module, config=self.config.model.fsdp_config.wrap_policy, is_lora=self.config.model.get("lora_rank", 0) > 0, ) log_gpu_memory_usage("Before critic FSDP", logger=None) fsdp_mesh = self.device_mesh sharding_strategy = get_sharding_strategy(fsdp_mesh) # Note: We force turn off CPUOffload for critic because it causes incorrect results when using grad accumulation if config.strategy == "fsdp": critic_module = FSDP( critic_module, param_init_fn=init_fn, use_orig_params=False, auto_wrap_policy=auto_wrap_policy, device_id=get_torch_device().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, ) elif config.strategy == "fsdp2": assert ( CPUOffloadPolicy is not None ), "PyTorch version >= 2.4 is required for using fully_shard API (FSDP2)" mp_policy = MixedPrecisionPolicy( param_dtype=param_dtype, reduce_dtype=reduce_dtype, cast_forward_inputs=True ) offload_policy = None if fsdp_config.offload_policy: self._is_offload_param = False self._is_offload_optimizer = False offload_policy = CPUOffloadPolicy(pin_memory=True) fsdp_kwargs = { "mesh": fsdp_mesh, "mp_policy": mp_policy, "offload_policy": offload_policy, "reshard_after_forward": fsdp_config.reshard_after_forward, } full_state = critic_module.state_dict() apply_fsdp2(critic_module, fsdp_kwargs, fsdp_config) fsdp2_load_full_state_dict(critic_module, full_state, fsdp_mesh, offload_policy) else: raise NotImplementedError(f"Unknown strategy {config.strategy}") if config.model.get("enable_activation_offload", False): enable_gradient_checkpointing = config.model.get("enable_gradient_checkpointing", False) enable_activation_offloading( critic_module, config.strategy, enable_gradient_checkpointing ) log_gpu_memory_usage("After critic FSDP", logger=None) critic_optimizer = optim.AdamW( critic_module.parameters(), lr=config.optim.lr, betas=config.optim.get("betas", (0.9, 0.999)), weight_decay=config.optim.get("weight_decay", 1e-2), ) total_steps = config.optim.get("total_training_steps", 0) num_warmup_steps = int(config.optim.get("lr_warmup_steps", -1)) warmup_style = config.optim.get("warmup_style", "constant") if num_warmup_steps < 0: num_warmup_steps_ratio = config.optim.get("lr_warmup_steps_ratio", 0.0) num_warmup_steps = int(num_warmup_steps_ratio * total_steps) if self.rank == 0: print(f"Total steps: {total_steps}, num_warmup_steps: {num_warmup_steps}") from verl.utils.torch_functional import ( get_constant_schedule_with_warmup, get_cosine_schedule_with_warmup, ) if warmup_style == "constant": critic_lr_scheduler = get_constant_schedule_with_warmup( optimizer=critic_optimizer, num_warmup_steps=num_warmup_steps ) elif warmup_style == "cosine": critic_lr_scheduler = get_cosine_schedule_with_warmup( optimizer=critic_optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=total_steps, ) else: raise NotImplementedError(f"Warmup style {warmup_style} is not supported") return critic_module, critic_optimizer, critic_lr_scheduler
[docs] @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 verl.workers.critic import DataParallelPPOCritic ( self.critic_module, self.critic_optimizer, self.critic_lr_scheduler, ) = self._build_critic_model_optimizer(self.config) if self._is_offload_param: offload_fsdp_model_to_cpu(self.critic_module) log_gpu_memory_usage("After offload critic model during init", logger=logger) if self._is_offload_optimizer: offload_fsdp_optimizer(optimizer=self.critic_optimizer) log_gpu_memory_usage("After offload critic optimizer during init", logger=logger) self.critic = DataParallelPPOCritic( config=self.config, critic_module=self.critic_module, critic_optimizer=self.critic_optimizer, ) self.flops_counter = FlopsCounter(self.critic_model_config) self.checkpoint_manager = FSDPCheckpointManager( model=self.critic_module, optimizer=self.critic_optimizer, lr_scheduler=self.critic_lr_scheduler, processing_class=self.processor if self.processor is not None else self.tokenizer, checkpoint_contents=self.config.checkpoint.contents, )
[docs] @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def compute_values(self, data: DataProto): # Support all hardwares data = data.to(get_torch_device().current_device()) if self._is_offload_param: load_fsdp_model_to_gpu(self.critic_module) micro_batch_size = self.config.forward_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) values = self.critic.compute_values(data=data) output = DataProto.from_dict(tensors={"values": values}) output = self.ulysses_sharding_manager.postprocess_data(data=output) output = output.to("cpu") if self._is_offload_param: offload_fsdp_model_to_cpu(self.critic_module) return output
[docs] @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO) def update_critic(self, data: DataProto): # Support all hardwares data = data.to(get_torch_device().current_device()) if self._is_offload_param: load_fsdp_model_to_gpu(self.critic_module) if self._is_offload_optimizer: load_fsdp_optimizer( optimizer=self.critic_optimizer, device_id=get_torch_device().current_device() ) # perform forward computation with self.ulysses_sharding_manager: data = self.ulysses_sharding_manager.preprocess_data(data=data) with Timer(name="update_critic", logger=None) as timer: metrics = self.critic.update_critic(data=data) delta_time = timer.last global_num_tokens = data.meta_info["global_token_num"] estimated_flops, promised_flops = self.flops_counter.estimate_flops( global_num_tokens, delta_time ) metrics["perf/mfu/critic"] = ( estimated_flops * self.config.ppo_epochs / promised_flops / self.world_size ) self.critic_lr_scheduler.step() lr = self.critic_lr_scheduler.get_last_lr()[0] metrics["critic/lr"] = lr output = DataProto(batch=None, meta_info={"metrics": metrics}) output = self.ulysses_sharding_manager.postprocess_data(data=output) if self._is_offload_param: offload_fsdp_model_to_cpu(self.critic_module) if self._is_offload_optimizer: offload_fsdp_optimizer(optimizer=self.critic_optimizer) output = output.to("cpu") return output
[docs] @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.critic_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.critic_module)
[docs] @register(dispatch_mode=Dispatch.ONE_TO_ALL) def load_checkpoint(self, local_path, hdfs_path=None, del_local_after_load=True): import torch if self._is_offload_param: load_fsdp_model_to_gpu(self.critic_module) self.checkpoint_manager.load_checkpoint( local_path=local_path, hdfs_path=hdfs_path, del_local_after_load=del_local_after_load ) torch.distributed.barrier() if self._is_offload_param: offload_fsdp_model_to_cpu(self.critic_module) if self._is_offload_optimizer: offload_fsdp_optimizer(self.critic_optimizer)
[docs] @register(dispatch_mode=Dispatch.ONE_TO_ALL) def clear_optimizer_state(self): print("Clear critic optimizer state") if self._is_offload_optimizer: load_fsdp_optimizer( optimizer=self.critic_optimizer, device_id=torch.cuda.current_device() ) self.critic_optimizer.state.clear() self.critic_optimizer.zero_grad() if self._is_offload_optimizer: offload_fsdp_optimizer(self.critic_optimizer)