mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2026-07-28 11:46:09 +08:00
199 lines
7.4 KiB
Python
199 lines
7.4 KiB
Python
# Copyright 2025 the LlamaFactory team.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import os
|
|
from dataclasses import dataclass, field
|
|
from typing import Literal
|
|
from uuid import uuid4
|
|
|
|
from ..utils.logging import get_logger
|
|
from .arg_utils import BatchingStrategy, PluginConfig, get_plugin_config
|
|
|
|
|
|
logger = get_logger(__name__)
|
|
|
|
|
|
@dataclass
|
|
class TrainingArguments:
|
|
output_dir: str = field(
|
|
default=os.path.join("outputs", str(uuid4().hex)),
|
|
metadata={"help": "Path to the output directory."},
|
|
)
|
|
micro_batch_size: int = field(
|
|
default=1,
|
|
metadata={"help": "Micro batch size for training."},
|
|
)
|
|
global_batch_size: int | None = field(
|
|
default=None,
|
|
metadata={"help": "Global batch size for training, default to DP size * micro batch size."},
|
|
)
|
|
cutoff_len: int = field(
|
|
default=2048,
|
|
metadata={"help": "Maximum sequence length for training."},
|
|
)
|
|
learning_rate: float = field(
|
|
default=1e-4,
|
|
metadata={"help": "Learning rate for training."},
|
|
)
|
|
num_train_epochs: int = field(
|
|
default=3,
|
|
metadata={"help": "Number of training epochs."},
|
|
)
|
|
max_steps: int | None = field(
|
|
default=None,
|
|
metadata={"help": "Maximum number of training steps. If set, overrides num_train_epochs."},
|
|
)
|
|
max_grad_norm: float = field(
|
|
default=1.0,
|
|
metadata={"help": "Maximum gradient norm for training."},
|
|
)
|
|
bf16: bool = field(
|
|
default=True,
|
|
metadata={"help": "Use bf16 for training."},
|
|
)
|
|
batching_strategy: BatchingStrategy = field(
|
|
default=BatchingStrategy.NORMAL,
|
|
metadata={"help": "Batching strategy for training."},
|
|
)
|
|
batching_workers: int = field(
|
|
default=16,
|
|
metadata={"help": "Number of workers for batching."},
|
|
)
|
|
enable_activation_checkpointing: bool = field(
|
|
default=True,
|
|
metadata={"help": "Enable activation checkpointing for training."},
|
|
)
|
|
dist_config: PluginConfig | None = field(
|
|
default=None,
|
|
metadata={"help": "Distributed backend plugin configuration."},
|
|
)
|
|
dp_size: int | None = field(
|
|
default=None,
|
|
metadata={"help": "Data parallel size, default to world_size // cp_size."},
|
|
)
|
|
cp_size: int = field(
|
|
default=1,
|
|
metadata={"help": "Context parallel size."},
|
|
)
|
|
cp_mode: str = field(
|
|
default="ulysses",
|
|
metadata={"help": "Context parallel implementation."},
|
|
)
|
|
mp_replicate_size: int = field(
|
|
default=1,
|
|
metadata={"help": "Model parallel replicate size."},
|
|
)
|
|
mp_shard_size: int | None = field(
|
|
default=None,
|
|
metadata={"help": "Model parallel shard size, default to world_size // mp_replicate_size."},
|
|
)
|
|
dist_timeout: int = field(
|
|
default=18000,
|
|
metadata={"help": "Distributed process group initialization timeout in seconds."},
|
|
)
|
|
optim_config: PluginConfig | None = field(
|
|
default=None,
|
|
metadata={"help": "Optimizer configuration for training."},
|
|
)
|
|
lr_scheduler_config: PluginConfig | None = field(
|
|
default=None,
|
|
metadata={"help": "Learning rate scheduler configuration for training."},
|
|
)
|
|
seed: int = field(
|
|
default=42,
|
|
metadata={"help": "Random seed that will be set at the beginning of training."},
|
|
)
|
|
full_determinism: bool = field(
|
|
default=False,
|
|
metadata={"help": "Enable full deterministic mode for reproducible distributed training."},
|
|
)
|
|
resume_from_checkpoint: str | None = field(
|
|
default=None,
|
|
metadata={"help": "Path to a checkpoint directory to resume training from, or 'auto' to find the latest."},
|
|
)
|
|
save_steps: int | None = field(
|
|
default=None,
|
|
metadata={"help": "Save a training checkpoint every N global steps."},
|
|
)
|
|
save_epochs: float | None = field(
|
|
default=None,
|
|
metadata={"help": "Save a training checkpoint every N epochs."},
|
|
)
|
|
save_ckpt_as_hf: bool = field(
|
|
default=False,
|
|
metadata={
|
|
"help": "Save intermediate checkpoints in HuggingFace format instead of distributed format. Warning: doubles memory usage."
|
|
},
|
|
)
|
|
save_total_limit: int | None = field(
|
|
default=None,
|
|
metadata={"help": "Maximum number of checkpoints to keep. Oldest checkpoints are deleted."},
|
|
)
|
|
logging_steps: int = field(
|
|
default=1,
|
|
metadata={"help": "Log metrics every N optimizer steps."},
|
|
)
|
|
pref_loss: Literal["sigmoid", "orpo", "simpo"] = field(
|
|
default="sigmoid",
|
|
metadata={"help": "The type of DPO loss to use."},
|
|
)
|
|
pref_beta: float = field(
|
|
default=0.1,
|
|
metadata={"help": "The beta parameter in the preference loss."},
|
|
)
|
|
pref_ftx: float = field(
|
|
default=0.0,
|
|
metadata={"help": "The supervised fine-tuning loss coefficient in DPO training."},
|
|
)
|
|
simpo_gamma: float = field(
|
|
default=0.5,
|
|
metadata={"help": "The target reward margin term in SimPO loss."},
|
|
)
|
|
dpo_label_smoothing: float = field(
|
|
default=0.0,
|
|
metadata={"help": "The robust DPO label smoothing parameter in cDPO that should be between 0 and 0.5."},
|
|
)
|
|
ld_alpha: float | None = field(
|
|
default=None,
|
|
metadata={"help": "Alpha parameter from LD-DPO, controls weighting of verbose token log-probabilities."},
|
|
)
|
|
|
|
def __post_init__(self) -> None:
|
|
self.dist_config = get_plugin_config(self.dist_config)
|
|
self.optim_config = get_plugin_config(self.optim_config)
|
|
self.lr_scheduler_config = get_plugin_config(self.lr_scheduler_config)
|
|
try:
|
|
from ..plugins.model_plugins.deepspeed_utils import register_deepspeed_dist_config
|
|
|
|
register_deepspeed_dist_config(self.dist_config)
|
|
except ImportError:
|
|
pass
|
|
|
|
# The optimizer learning rate has a single source of truth: ``learning_rate``.
|
|
# Propagate it into ``optim_config["lr"]`` so optimizer plugins (e.g. Muon) pick it up
|
|
# via ``optim_config.get("lr")`` without each plugin needing a separate ``learning_rate`` arg.
|
|
if self.optim_config is not None:
|
|
if "lr" in self.optim_config:
|
|
logger.warning_rank0(
|
|
"`optim_config.lr` is overridden by `learning_rate`; set the learning rate via "
|
|
"`learning_rate` instead and remove `lr` from `optim_config`."
|
|
)
|
|
self.optim_config["lr"] = self.learning_rate
|
|
|
|
if str(self.batching_strategy) == str(BatchingStrategy.DYNAMIC_BATCHING):
|
|
if self.max_steps is None or self.max_steps <= 0:
|
|
raise ValueError("`dynamic_batching` requires `max_steps` because it is step-driven.")
|
|
if self.save_epochs is not None:
|
|
raise ValueError("`save_epochs` is not supported with `dynamic_batching`; use `save_steps` instead.")
|