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Unverified Commit fbcc3833 authored by Anton Lozhkov's avatar Anton Lozhkov Committed by GitHub
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Deprecate `init_git_repo`, refactor `train_unconditional.py` (#1022) 

Deprecate `init_git_repo` and `push_to_hub`, refactor `train_unconditional.py`
parent 90f91adb
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Showing with 186 additions and 55 deletions
examples/unconditional_image_generation/train_unconditional.py
View file @ fbcc3833
import argparse
import math
import os
from pathlib import Path
from typing import Optional
import torch
import torch.nn.functional as F
......@@ -9,9 +11,9 @@ from accelerate import Accelerator
from accelerate.logging import get_logger
from datasets import load_dataset
from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel
from diffusers.hub_utils import init_git_repo
from diffusers.optimization import get_scheduler
from diffusers.training_utils import EMAModel
from huggingface_hub import HfFolder, Repository, whoami
from torchvision.transforms import (
CenterCrop,
Compose,
......@@ -27,6 +29,160 @@ from tqdm.auto import tqdm
logger = get_logger(__name__)
def parse_args():
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument(
"--dataset_name",
type=str,
default=None,
help=(
"The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
" dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
" or to a folder containing files that HF Datasets can understand."
),
)
parser.add_argument(
"--dataset_config_name",
type=str,
default=None,
help="The config of the Dataset, leave as None if there's only one config.",
)
parser.add_argument(
"--train_data_dir",
type=str,
default=None,
help=(
"A folder containing the training data. Folder contents must follow the structure described in"
" https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
" must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
),
)
parser.add_argument(
"--output_dir",
type=str,
default="ddpm-model-64",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument("--overwrite_output_dir", action="store_true")
parser.add_argument(
"--cache_dir",
type=str,
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument(
"--resolution",
type=int,
default=64,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
)
parser.add_argument(
"--eval_batch_size", type=int, default=16, help="Batch size (per device) for the eval dataloader."
)
parser.add_argument("--num_epochs", type=int, default=100)
parser.add_argument("--save_images_epochs", type=int, default=10, help="How often to save images during training.")
parser.add_argument(
"--save_model_epochs", type=int, default=10, help="How often to save the model during training."
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="cosine",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
parser.add_argument(
"--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
)
parser.add_argument("--adam_beta1", type=float, default=0.95, help="The beta1 parameter for the Adam optimizer.")
parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
parser.add_argument(
"--adam_weight_decay", type=float, default=1e-6, help="Weight decay magnitude for the Adam optimizer."
)
parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer.")
parser.add_argument(
"--use_ema",
action="store_true",
default=True,
help="Whether to use Exponential Moving Average for the final model weights.",
)
parser.add_argument("--ema_inv_gamma", type=float, default=1.0, help="The inverse gamma value for the EMA decay.")
parser.add_argument("--ema_power", type=float, default=3 / 4, help="The power value for the EMA decay.")
parser.add_argument("--ema_max_decay", type=float, default=0.9999, help="The maximum decay magnitude for EMA.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
parser.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument(
"--hub_private_repo", action="store_true", help="Whether or not to create a private repository."
)
parser.add_argument(
"--logging_dir",
type=str,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
parser.add_argument(
"--mixed_precision",
type=str,
default="no",
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU."
),
)
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
if args.dataset_name is None and args.train_data_dir is None:
raise ValueError("You must specify either a dataset name from the hub or a train data directory.")
return args
def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
if token is None:
token = HfFolder.get_token()
if organization is None:
username = whoami(token)["name"]
return f"{username}/{model_id}"
else:
return f"{organization}/{model_id}"
def main(args):
logging_dir = os.path.join(args.output_dir, args.logging_dir)
accelerator = Accelerator(
......@@ -110,8 +266,22 @@ def main(args):
ema_model = EMAModel(model, inv_gamma=args.ema_inv_gamma, power=args.ema_power, max_value=args.ema_max_decay)
if args.push_to_hub:
repo = init_git_repo(args, at_init=True)
# Handle the repository creation
if accelerator.is_main_process:
if args.push_to_hub:
if args.hub_model_id is None:
repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
else:
repo_name = args.hub_model_id
repo = Repository(args.output_dir, clone_from=repo_name)
with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:
gitignore.write("step_*\n")
if "epoch_*" not in gitignore:
gitignore.write("epoch_*\n")
elif args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=True)
if accelerator.is_main_process:
run = os.path.split(__file__)[-1].split(".")[0]
......@@ -193,55 +363,5 @@ def main(args):
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Simple example of a training script.")
parser.add_argument("--local_rank", type=int, default=-1)
parser.add_argument("--dataset_name", type=str, default=None)
parser.add_argument("--dataset_config_name", type=str, default=None)
parser.add_argument("--train_data_dir", type=str, default=None, help="A folder containing the training data.")
parser.add_argument("--output_dir", type=str, default="ddpm-model-64")
parser.add_argument("--overwrite_output_dir", action="store_true")
parser.add_argument("--cache_dir", type=str, default=None)
parser.add_argument("--resolution", type=int, default=64)
parser.add_argument("--train_batch_size", type=int, default=16)
parser.add_argument("--eval_batch_size", type=int, default=16)
parser.add_argument("--num_epochs", type=int, default=100)
parser.add_argument("--save_images_epochs", type=int, default=10)
parser.add_argument("--save_model_epochs", type=int, default=10)
parser.add_argument("--gradient_accumulation_steps", type=int, default=1)
parser.add_argument("--learning_rate", type=float, default=1e-4)
parser.add_argument("--lr_scheduler", type=str, default="cosine")
parser.add_argument("--lr_warmup_steps", type=int, default=500)
parser.add_argument("--adam_beta1", type=float, default=0.95)
parser.add_argument("--adam_beta2", type=float, default=0.999)
parser.add_argument("--adam_weight_decay", type=float, default=1e-6)
parser.add_argument("--adam_epsilon", type=float, default=1e-08)
parser.add_argument("--use_ema", action="store_true", default=True)
parser.add_argument("--ema_inv_gamma", type=float, default=1.0)
parser.add_argument("--ema_power", type=float, default=3 / 4)
parser.add_argument("--ema_max_decay", type=float, default=0.9999)
parser.add_argument("--push_to_hub", action="store_true")
parser.add_argument("--hub_token", type=str, default=None)
parser.add_argument("--hub_model_id", type=str, default=None)
parser.add_argument("--hub_private_repo", action="store_true")
parser.add_argument("--logging_dir", type=str, default="logs")
parser.add_argument(
"--mixed_precision",
type=str,
default="no",
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU."
),
)
args = parser.parse_args()
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
if args.dataset_name is None and args.train_data_dir is None:
raise ValueError("You must specify either a dataset name from the hub or a train data directory.")
args = parse_args()
main(args)
src/diffusers/hub_utils.py
View file @ fbcc3833
......@@ -22,7 +22,7 @@ from typing import Optional
from huggingface_hub import HfFolder, Repository, whoami
from .pipeline_utils import DiffusionPipeline
from .utils import is_modelcards_available, logging
from .utils import deprecate, is_modelcards_available, logging
if is_modelcards_available():
......@@ -53,6 +53,12 @@ def init_git_repo(args, at_init: bool = False):
Whether this function is called before any training or not. If `self.args.overwrite_output_dir` is `True`
and `at_init` is `True`, the path to the repo (which is `self.args.output_dir`) might be wiped out.
"""
deprecation_message = (
"Please use `huggingface_hub.Repository`. "
"See `examples/unconditional_image_generation/train_unconditional.py` for an example."
)
deprecate("init_git_repo()", "0.10.0", deprecation_message)
if hasattr(args, "local_rank") and args.local_rank not in [-1, 0]:
return
hub_token = args.hub_token if hasattr(args, "hub_token") else None
......@@ -114,6 +120,11 @@ def push_to_hub(
The url of the commit of your model in the given repository if `blocking=False`, a tuple with the url of the
commit and an object to track the progress of the commit if `blocking=True`
"""
deprecation_message = (
"Please use `huggingface_hub.Repository` and `Repository.push_to_hub()`. "
"See `examples/unconditional_image_generation/train_unconditional.py` for an example."
)
deprecate("push_to_hub()", "0.10.0", deprecation_message)
if not hasattr(args, "hub_model_id") or args.hub_model_id is None:
model_name = Path(args.output_dir).name
......
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