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[misc] update pre-commit and run all files (#4752)

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* [misc] update pre-commit

* [misc] run pre-commit

* [misc] remove useless configuration files

* [misc] ignore cuda for clang-format
This commit is contained in:
Hongxin Liu
2023-09-19 14:20:26 +08:00
committed by GitHub
parent 3c6b831c26
commit 079bf3cb26
1268 changed files with 50037 additions and 38444 deletions
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149
examples/images/dreambooth/train_dreambooth_colossalai.py
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@@ -117,8 +117,10 @@ def parse_args(input_args=None):
"--num_class_images",
type=int,
default=100,
help=("Minimal class images for prior preservation loss. If there are not enough images already present in"
" class_data_dir, additional images will be sampled with class_prompt."),
help=(
"Minimal class images for prior preservation loss. If there are not enough images already present in"
" class_data_dir, additional images will be sampled with class_prompt."
),
)
parser.add_argument(
"--output_dir",
@@ -131,8 +133,10 @@ def parse_args(input_args=None):
"--resolution",
type=int,
default=512,
help=("The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"),
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
parser.add_argument(
"--offload_optim_frac",
@@ -144,13 +148,14 @@ def parse_args(input_args=None):
"--center_crop",
default=False,
action="store_true",
help=("Whether to center crop the input images to the resolution. If not set, the images will be randomly"
" cropped. The images will be resized to the resolution first before cropping."),
help=(
"Whether to center crop the input images to the resolution. If not set, the images will be randomly"
" cropped. The images will be resized to the resolution first before cropping."
),
)
parser.add_argument(
"--train_batch_size", type=int, default=4, help="Batch size (per device) for the training dataloader."
)
parser.add_argument("--train_batch_size",
type=int,
default=4,
help="Batch size (per device) for the training dataloader.")
parser.add_argument("--sample_batch_size", type=int, default=4, help="Batch size (per device) for sampling images.")
parser.add_argument("--num_train_epochs", type=int, default=1)
parser.add_argument(
@@ -181,16 +186,17 @@ def parse_args(input_args=None):
"--lr_scheduler",
type=str,
default="constant",
help=('The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'),
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(
"--use_8bit_adam", action="store_true", help="Whether or not to use 8-bit Adam from bitsandbytes."
)
parser.add_argument("--lr_warmup_steps",
type=int,
default=500,
help="Number of steps for the warmup in the lr scheduler.")
parser.add_argument("--use_8bit_adam",
action="store_true",
help="Whether or not to use 8-bit Adam from bitsandbytes.")
parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.")
parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
@@ -202,18 +208,22 @@ def parse_args(input_args=None):
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
parser.add_argument('-p',
'--plugin',
type=str,
default='torch_ddp',
choices=['torch_ddp', 'torch_ddp_fp16', 'gemini', 'low_level_zero'],
help="plugin to use")
parser.add_argument(
"-p",
"--plugin",
type=str,
default="torch_ddp",
choices=["torch_ddp", "torch_ddp_fp16", "gemini", "low_level_zero"],
help="plugin to use",
)
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***."),
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
parser.add_argument(
"--mixed_precision",
@@ -223,7 +233,8 @@ def parse_args(input_args=None):
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."),
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
@@ -292,12 +303,14 @@ class DreamBoothDataset(Dataset):
else:
self.class_data_root = None
self.image_transforms = transforms.Compose([
transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
])
self.image_transforms = transforms.Compose(
[
transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR),
transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
def __len__(self):
return self._length
@@ -391,9 +404,9 @@ def main(args):
pipeline.to(get_current_device())
for example in tqdm(
sample_dataloader,
desc="Generating class images",
disable=not local_rank == 0,
sample_dataloader,
desc="Generating class images",
disable=not local_rank == 0,
):
images = pipeline(example["prompt"]).images
@@ -460,15 +473,14 @@ def main(args):
if args.externel_unet_path is None:
logger.info(f"Loading UNet2DConditionModel from {args.pretrained_model_name_or_path}", ranks=[0])
unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path,
subfolder="unet",
revision=args.revision,
low_cpu_mem_usage=False)
unet = UNet2DConditionModel.from_pretrained(
args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision, low_cpu_mem_usage=False
)
else:
logger.info(f"Loading UNet2DConditionModel from {args.externel_unet_path}", ranks=[0])
unet = UNet2DConditionModel.from_pretrained(args.externel_unet_path,
revision=args.revision,
low_cpu_mem_usage=False)
unet = UNet2DConditionModel.from_pretrained(
args.externel_unet_path, revision=args.revision, low_cpu_mem_usage=False
)
vae.requires_grad_(False)
text_encoder.requires_grad_(False)
@@ -482,36 +494,37 @@ def main(args):
# Use Booster API to use Gemini/Zero with ColossalAI
booster_kwargs = {}
if args.plugin == 'torch_ddp_fp16':
booster_kwargs['mixed_precision'] = 'fp16'
if args.plugin.startswith('torch_ddp'):
if args.plugin == "torch_ddp_fp16":
booster_kwargs["mixed_precision"] = "fp16"
if args.plugin.startswith("torch_ddp"):
plugin = TorchDDPPlugin()
elif args.plugin == 'gemini':
elif args.plugin == "gemini":
plugin = GeminiPlugin(offload_optim_frac=args.offload_optim_frac, strict_ddp_mode=True, initial_scale=2**5)
elif args.plugin == 'low_level_zero':
elif args.plugin == "low_level_zero":
plugin = LowLevelZeroPlugin(initial_scale=2**5)
booster = Booster(plugin=plugin, **booster_kwargs)
# config optimizer for colossalai zero
optimizer = HybridAdam(unet.parameters(),
lr=args.learning_rate,
initial_scale=2**5,
clipping_norm=args.max_grad_norm)
optimizer = HybridAdam(
unet.parameters(), lr=args.learning_rate, initial_scale=2**5, clipping_norm=args.max_grad_norm
)
# load noise_scheduler
noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
# prepare dataset
logger.info(f"Prepare dataset from {args.instance_data_dir}", ranks=[0])
train_dataset = DreamBoothDataset(instance_data_root=args.instance_data_dir,
instance_prompt=args.instance_prompt,
class_data_root=args.class_data_dir if args.with_prior_preservation else None,
class_prompt=args.class_prompt,
tokenizer=tokenizer,
size=args.resolution,
center_crop=args.center_crop,
test=args.test_run)
train_dataset = DreamBoothDataset(
instance_data_root=args.instance_data_dir,
instance_prompt=args.instance_prompt,
class_data_root=args.class_data_dir if args.with_prior_preservation else None,
class_prompt=args.class_prompt,
tokenizer=tokenizer,
size=args.resolution,
center_crop=args.center_crop,
test=args.test_run,
)
def collate_fn(examples):
input_ids = [example["instance_prompt_ids"] for example in examples]
@@ -527,9 +540,7 @@ def main(args):
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
input_ids = tokenizer.pad(
{
"input_ids": input_ids
},
{"input_ids": input_ids},
padding="max_length",
max_length=tokenizer.model_max_length,
return_tensors="pt",
@@ -541,11 +552,9 @@ def main(args):
}
return batch
train_dataloader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.train_batch_size,
shuffle=True,
collate_fn=collate_fn,
num_workers=1)
train_dataloader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.train_batch_size, shuffle=True, collate_fn=collate_fn, num_workers=1
)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
@@ -664,7 +673,7 @@ def main(args):
logs = {
"loss": loss.detach().item(),
"lr": optimizer.param_groups[0]["lr"],
} # lr_scheduler.get_last_lr()[0]}
} # lr_scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
if global_step % args.save_steps == 0: