File size: 7,384 Bytes
dee9fba | 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 | # 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`."
)
|