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8f5a660 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 | # 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.
# 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.
from dataclasses import is_dataclass
from typing import Any, Optional
from omegaconf import DictConfig, ListConfig, OmegaConf
__all__ = ["omega_conf_to_dataclass", "validate_config"]
def omega_conf_to_dataclass(config: DictConfig | dict, dataclass_type: Optional[type[Any]] = None) -> Any:
"""
Convert an OmegaConf DictConfig to a dataclass.
Args:
config: The OmegaConf DictConfig or dict to convert.
dataclass_type: The dataclass type to convert to. When dataclass_type is None,
the DictConfig must contain _target_ to be instantiated via hydra.instantiate API.
Returns:
The dataclass instance.
"""
# Got an empty config
if not config:
return dataclass_type if dataclass_type is None else dataclass_type()
# Got an object
if not isinstance(config, DictConfig | ListConfig | dict | list):
return config
if dataclass_type is None:
assert "_target_" in config, (
"When dataclass_type is not provided, config must contain _target_. "
"See trainer/config/ppo_trainer.yaml algorithm section for an example. "
f"Got config: {config}"
)
from hydra.utils import instantiate
return instantiate(config, _convert_="partial")
if not is_dataclass(dataclass_type):
raise ValueError(f"{dataclass_type} must be a dataclass")
cfg = OmegaConf.create(config) # in case it's a dict
# pop _target_ to avoid hydra instantiate error, as most dataclass do not have _target_
# Updated (vermouth1992) We add _target_ to BaseConfig so that it is compatible.
# Otherwise, this code path can't support recursive instantiation.
# if "_target_" in cfg:
# cfg.pop("_target_")
cfg_from_dataclass = OmegaConf.structured(dataclass_type)
# let cfg override the existing vals in `cfg_from_dataclass`
cfg_merged = OmegaConf.merge(cfg_from_dataclass, cfg)
# now convert to `dataclass_type`
config_object = OmegaConf.to_object(cfg_merged)
return config_object
def update_dict_with_config(dictionary: dict, config: DictConfig):
for key in dictionary:
if hasattr(config, key):
dictionary[key] = getattr(config, key)
def validate_config(
config: DictConfig,
use_reference_policy: bool,
use_critic: bool,
) -> None:
"""Validate an OmegaConf DictConfig.
Args:
config (DictConfig): The OmegaConf DictConfig to validate.
use_reference_policy (bool): is ref policy needed
use_critic (bool): is critic needed
"""
# number of GPUs total
n_gpus = config.trainer.n_gpus_per_node * config.trainer.nnodes
if not config.actor_rollout_ref.actor.use_dynamic_bsz:
if config.actor_rollout_ref.actor.strategy == "megatron":
model_parallel_size = (
config.actor_rollout_ref.actor.megatron.tensor_model_parallel_size
* config.actor_rollout_ref.actor.megatron.pipeline_model_parallel_size
)
assert (
n_gpus % (model_parallel_size * config.actor_rollout_ref.actor.megatron.context_parallel_size) == 0
), (
f"n_gpus ({n_gpus}) must be divisible by model_parallel_size ({model_parallel_size}) times "
f"context_parallel_size ({config.actor_rollout_ref.actor.megatron.context_parallel_size})"
)
megatron_dp = n_gpus // (
model_parallel_size * config.actor_rollout_ref.actor.megatron.context_parallel_size
)
minimal_bsz = megatron_dp * config.actor_rollout_ref.actor.ppo_micro_batch_size_per_gpu
else:
minimal_bsz = n_gpus
# 1. Check total batch size for data correctness
real_train_batch_size = config.data.train_batch_size * config.actor_rollout_ref.rollout.n
assert real_train_batch_size % minimal_bsz == 0, (
f"real_train_batch_size ({real_train_batch_size}) must be divisible by minimal possible batch size "
f"({minimal_bsz})"
)
# A helper function to check "micro_batch_size" vs "micro_batch_size_per_gpu"
# We throw an error if the user sets both. The new convention is "..._micro_batch_size_per_gpu".
def check_mutually_exclusive(mbs, mbs_per_gpu, name: str):
"""Validate mutually exclusive micro batch size configuration options.
Ensures that users don't set both deprecated micro_batch_size and
the new micro_batch_size_per_gpu parameters simultaneously.
Args:
mbs: Deprecated micro batch size parameter value.
mbs_per_gpu: New micro batch size per GPU parameter value.
name (str): Configuration section name for error messages.
Raises:
ValueError: If both parameters are set or neither is set.
"""
settings = {
"reward_model": "micro_batch_size",
"actor_rollout_ref.ref": "log_prob_micro_batch_size",
"actor_rollout_ref.rollout": "log_prob_micro_batch_size",
}
if name in settings:
param = settings[name]
param_per_gpu = f"{param}_per_gpu"
if mbs is None and mbs_per_gpu is None:
raise ValueError(f"[{name}] Please set at least one of '{name}.{param}' or '{name}.{param_per_gpu}'.")
if mbs is not None and mbs_per_gpu is not None:
raise ValueError(
f"[{name}] You have set both '{name}.{param}' AND '{name}.{param_per_gpu}'. Please remove "
f"'{name}.{param}' because only '*_{param_per_gpu}' is supported (the former is deprecated)."
)
# Actor validation done in ActorConfig.__post_init__ and validate()
actor_config = omega_conf_to_dataclass(config.actor_rollout_ref.actor)
actor_config.validate(n_gpus, config.data.train_batch_size, config.actor_rollout_ref.model)
if not config.actor_rollout_ref.actor.use_dynamic_bsz:
if use_reference_policy:
# reference: log_prob_micro_batch_size vs. log_prob_micro_batch_size_per_gpu
check_mutually_exclusive(
config.actor_rollout_ref.ref.log_prob_micro_batch_size,
config.actor_rollout_ref.ref.log_prob_micro_batch_size_per_gpu,
"actor_rollout_ref.ref",
)
# The rollout section also has log_prob_micro_batch_size vs. log_prob_micro_batch_size_per_gpu
check_mutually_exclusive(
config.actor_rollout_ref.rollout.log_prob_micro_batch_size,
config.actor_rollout_ref.rollout.log_prob_micro_batch_size_per_gpu,
"actor_rollout_ref.rollout",
)
# Check for reward model micro-batch size conflicts
if config.reward_model.enable and not config.reward_model.use_dynamic_bsz:
check_mutually_exclusive(
config.reward_model.micro_batch_size, config.reward_model.micro_batch_size_per_gpu, "reward_model"
)
if config.algorithm.use_kl_in_reward and config.actor_rollout_ref.actor.use_kl_loss:
print("NOTICE: You have both enabled in-reward kl and kl loss.")
# critic
if use_critic:
critic_config = omega_conf_to_dataclass(config.critic)
critic_config.validate(n_gpus, config.data.train_batch_size)
if config.data.get("val_batch_size", None) is not None:
print(
"WARNING: val_batch_size is deprecated."
+ " Validation datasets are sent to inference engines as a whole batch,"
+ " which will schedule the memory themselves."
)
# check eval config
if config.actor_rollout_ref.rollout.val_kwargs.do_sample:
assert config.actor_rollout_ref.rollout.temperature > 0, (
"validation gen temperature should be greater than 0 when enabling do_sample"
)
# check LoRA rank in vLLM
if config.actor_rollout_ref.model.get("lora_rank", 0) > 0 and config.actor_rollout_ref.rollout.name == "vllm":
assert config.actor_rollout_ref.model.lora_rank <= 512, "LoRA rank in vLLM must be less than or equal to 512"
print("[validate_config] All configuration checks passed successfully!")
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