# Copyright 2024 The HuggingFace Inc. 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 import warnings from dataclasses import dataclass, field from typing import Any, Dict, Literal, Optional, Tuple from transformers import is_bitsandbytes_available, is_torchvision_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. log_with (`Optional[Literal["wandb", "tensorboard"]]`, *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_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 (`Optional[str]`, *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 = os.path.basename(sys.argv[0])[: -len(".py")] run_name: str = "" seed: int = 0 log_with: Optional[Literal["wandb", "tensorboard"]] = None log_image_freq: int = 1 tracker_kwargs: Dict[str, Any] = field(default_factory=dict) accelerator_kwargs: Dict[str, Any] = field(default_factory=dict) project_kwargs: Dict[str, Any] = field(default_factory=dict) tracker_project_name: str = "trl" logdir: str = "logs" num_epochs: int = 100 save_freq: int = 1 num_checkpoint_limit: int = 5 mixed_precision: str = "fp16" allow_tf32: bool = True resume_from: str = "" sample_num_steps: int = 50 sample_eta: float = 1.0 sample_guidance_scale: float = 5.0 train_batch_size: int = 1 train_use_8bit_adam: bool = False train_learning_rate: float = 1e-3 train_adam_beta1: float = 0.9 train_adam_beta2: float = 0.999 train_adam_weight_decay: float = 1e-4 train_adam_epsilon: float = 1e-8 train_gradient_accumulation_steps: int = 1 train_max_grad_norm: float = 1.0 negative_prompts: Optional[str] = None truncated_backprop_rand: bool = True truncated_backprop_timestep: int = 49 truncated_rand_backprop_minmax: Tuple[int, int] = (0, 50) push_to_hub: bool = False 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.log_with not in ["wandb", "tensorboard"]: warnings.warn( "Accelerator tracking only supports image logging if `log_with` is set to 'wandb' or 'tensorboard'." ) if self.log_with == "wandb" and not is_torchvision_available(): warnings.warn("Wandb image logging requires torchvision to be installed") 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`." )