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# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
#
# 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
import sys
from dataclasses import dataclass, field
from typing import Any, Optional
from transformers import is_bitsandbytes_available
from ..core import flatten_dict
@dataclass
class AlignPropConfig:
r"""
Configuration class for the [`AlignPropTrainer`].
Using [`~transformers.HfArgumentParser`] we can turn this class into
[argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
command line.
Parameters:
exp_name (`str`, *optional*, defaults to `os.path.basename(sys.argv[0])[: -len(".py")]`):
Name of this experiment (defaults to the file name without the extension).
run_name (`str`, *optional*, defaults to `""`):
Name of this run.
seed (`int`, *optional*, defaults to `0`):
Random seed for reproducibility.
log_with (`str` or `None`, *optional*, defaults to `None`):
Log with either `"wandb"` or `"tensorboard"`. Check
[tracking](https://huggingface.co/docs/accelerate/usage_guides/tracking) for more details.
log_image_freq (`int`, *optional*, defaults to `1`):
Frequency for logging images.
tracker_kwargs (`dict[str, Any]`, *optional*, defaults to `{}`):
Keyword arguments for the tracker (e.g., `wandb_project`).
accelerator_kwargs (`dict[str, Any]`, *optional*, defaults to `{}`):
Keyword arguments for the accelerator.
project_kwargs (`dict[str, Any]`, *optional*, defaults to `{}`):
Keyword arguments for the accelerator project config (e.g., `logging_dir`).
tracker_project_name (`str`, *optional*, defaults to `"trl"`):
Name of project to use for tracking.
logdir (`str`, *optional*, defaults to `"logs"`):
Top-level logging directory for checkpoint saving.
num_epochs (`int`, *optional*, defaults to `100`):
Number of epochs to train.
save_freq (`int`, *optional*, defaults to `1`):
Number of epochs between saving model checkpoints.
num_checkpoint_limit (`int`, *optional*, defaults to `5`):
Number of checkpoints to keep before overwriting old ones.
mixed_precision (`str`, *optional*, defaults to `"fp16"`):
Mixed precision training.
allow_tf32 (`bool`, *optional*, defaults to `True`):
Allow `tf32` on Ampere GPUs.
resume_from (`str`, *optional*, defaults to `""`):
Path to resume training from a checkpoint.
sample_num_steps (`int`, *optional*, defaults to `50`):
Number of sampler inference steps.
sample_eta (`float`, *optional*, defaults to `1.0`):
Eta parameter for the DDIM sampler.
sample_guidance_scale (`float`, *optional*, defaults to `5.0`):
Classifier-free guidance weight.
train_batch_size (`int`, *optional*, defaults to `1`):
Batch size for training.
train_use_8bit_adam (`bool`, *optional*, defaults to `False`):
Whether to use the 8bit Adam optimizer from `bitsandbytes`.
train_learning_rate (`float`, *optional*, defaults to `1e-3`):
Learning rate.
train_adam_beta1 (`float`, *optional*, defaults to `0.9`):
Beta1 for Adam optimizer.
train_adam_beta2 (`float`, *optional*, defaults to `0.999`):
Beta2 for Adam optimizer.
train_adam_weight_decay (`float`, *optional*, defaults to `1e-4`):
Weight decay for Adam optimizer.
train_adam_epsilon (`float`, *optional*, defaults to `1e-8`):
Epsilon value for Adam optimizer.
train_gradient_accumulation_steps (`int`, *optional*, defaults to `1`):
Number of gradient accumulation steps.
train_max_grad_norm (`float`, *optional*, defaults to `1.0`):
Maximum gradient norm for gradient clipping.
negative_prompts (`str` or `None`, *optional*, defaults to `None`):
Comma-separated list of prompts to use as negative examples.
truncated_backprop_rand (`bool`, *optional*, defaults to `True`):
If `True`, randomized truncation to different diffusion timesteps is used.
truncated_backprop_timestep (`int`, *optional*, defaults to `49`):
Absolute timestep to which the gradients are backpropagated. Used only if `truncated_backprop_rand=False`.
truncated_rand_backprop_minmax (`tuple[int, int]`, *optional*, defaults to `(0, 50)`):
Range of diffusion timesteps for randomized truncated backpropagation.
push_to_hub (`bool`, *optional*, defaults to `False`):
Whether to push the final model to the Hub.
"""
exp_name: str = field(
default=os.path.basename(sys.argv[0])[: -len(".py")],
metadata={"help": "Name of this experiment (defaults to the file name without the extension)."},
)
run_name: str = field(default="", metadata={"help": "Name of this run."})
seed: int = field(default=0, metadata={"help": "Random seed for reproducibility."})
log_with: Optional[str] = field(
default=None,
metadata={"help": "Log with either 'wandb' or 'tensorboard'.", "choices": ["wandb", "tensorboard"]},
)
log_image_freq: int = field(default=1, metadata={"help": "Frequency for logging images."})
tracker_kwargs: dict[str, Any] = field(
default_factory=dict,
metadata={"help": "Keyword arguments for the tracker (e.g., `wandb_project`)."},
)
accelerator_kwargs: dict[str, Any] = field(
default_factory=dict, metadata={"help": "Keyword arguments for the accelerator."}
)
project_kwargs: dict[str, Any] = field(
default_factory=dict,
metadata={"help": "Keyword arguments for the accelerator project config (e.g., `logging_dir`)."},
)
tracker_project_name: str = field(default="trl", metadata={"help": "Name of project to use for tracking."})
logdir: str = field(default="logs", metadata={"help": "Top-level logging directory for checkpoint saving."})
num_epochs: int = field(default=100, metadata={"help": "Number of epochs to train."})
save_freq: int = field(default=1, metadata={"help": "Number of epochs between saving model checkpoints."})
num_checkpoint_limit: int = field(
default=5, metadata={"help": "Number of checkpoints to keep before overwriting old ones."}
)
mixed_precision: str = field(
default="fp16",
metadata={
"help": "Mixed precision training. Possible values are 'fp16', 'bf16', 'none'.",
"choices": ["fp16", "bf16", "none"],
},
)
allow_tf32: bool = field(default=True, metadata={"help": "Allow `tf32` on Ampere GPUs."})
resume_from: str = field(default="", metadata={"help": "Path to resume training from a checkpoint."})
sample_num_steps: int = field(default=50, metadata={"help": "Number of sampler inference steps."})
sample_eta: float = field(default=1.0, metadata={"help": "Eta parameter for the DDIM sampler."})
sample_guidance_scale: float = field(default=5.0, metadata={"help": "Classifier-free guidance weight."})
train_batch_size: int = field(default=1, metadata={"help": "Batch size for training."})
train_use_8bit_adam: bool = field(
default=False, metadata={"help": "Whether to use the 8bit Adam optimizer from `bitsandbytes`."}
)
train_learning_rate: float = field(default=1e-3, metadata={"help": "Learning rate."})
train_adam_beta1: float = field(default=0.9, metadata={"help": "Beta1 for Adam optimizer."})
train_adam_beta2: float = field(default=0.999, metadata={"help": "Beta2 for Adam optimizer."})
train_adam_weight_decay: float = field(default=1e-4, metadata={"help": "Weight decay for Adam optimizer."})
train_adam_epsilon: float = field(default=1e-8, metadata={"help": "Epsilon value for Adam optimizer."})
train_gradient_accumulation_steps: int = field(
default=1, metadata={"help": "Number of gradient accumulation steps."}
)
train_max_grad_norm: float = field(default=1.0, metadata={"help": "Maximum gradient norm for gradient clipping."})
negative_prompts: Optional[str] = field(
default=None,
metadata={"help": "Comma-separated list of prompts to use as negative examples."},
)
truncated_backprop_rand: bool = field(
default=True,
metadata={"help": "If `True`, randomized truncation to different diffusion timesteps is used."},
)
truncated_backprop_timestep: int = field(
default=49,
metadata={
"help": "Absolute timestep to which the gradients are backpropagated. Used only if "
"`truncated_backprop_rand=False`."
},
)
truncated_rand_backprop_minmax: tuple[int, int] = field(
default=(0, 50),
metadata={
"help": "Range of diffusion timesteps for randomized truncated backpropagation.",
},
)
push_to_hub: bool = field(default=False, metadata={"help": "Whether to push the final model to the Hub."})
def to_dict(self):
output_dict = {}
for key, value in self.__dict__.items():
output_dict[key] = value
return flatten_dict(output_dict)
def __post_init__(self):
if self.train_use_8bit_adam and not is_bitsandbytes_available():
raise ImportError(
"You need to install bitsandbytes to use 8bit Adam. "
"You can install it with `pip install bitsandbytes`."
)
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