mirror of https://github.com/hpcaitech/ColossalAI
Haofan Wang
GitHub
parent
b6a108cb91
commit
47ecb22387
|
|
@ -0,0 +1,691 @@
|
|||
import argparse
|
||||
import hashlib
|
||||
import math
|
||||
import os
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
import torch.utils.checkpoint
|
||||
from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
|
||||
from diffusers.loaders import AttnProcsLayers
|
||||
from diffusers.models.cross_attention import LoRACrossAttnProcessor
|
||||
from diffusers.optimization import get_scheduler
|
||||
from huggingface_hub import HfFolder, Repository, create_repo, whoami
|
||||
from PIL import Image
|
||||
from torch.utils.data import Dataset
|
||||
from torchvision import transforms
|
||||
from tqdm.auto import tqdm
|
||||
from transformers import AutoTokenizer, PretrainedConfig
|
||||
|
||||
import colossalai
|
||||
from colossalai.context.parallel_mode import ParallelMode
|
||||
from colossalai.core import global_context as gpc
|
||||
from colossalai.logging import disable_existing_loggers, get_dist_logger
|
||||
from colossalai.nn.optimizer.gemini_optimizer import GeminiAdamOptimizer
|
||||
from colossalai.nn.parallel.utils import get_static_torch_model
|
||||
from colossalai.utils import get_current_device
|
||||
from colossalai.utils.model.colo_init_context import ColoInitContext
|
||||
|
||||
disable_existing_loggers()
|
||||
logger = get_dist_logger()
|
||||
|
||||
|
||||
def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str):
|
||||
text_encoder_config = PretrainedConfig.from_pretrained(
|
||||
pretrained_model_name_or_path,
|
||||
subfolder="text_encoder",
|
||||
revision=args.revision,
|
||||
)
|
||||
model_class = text_encoder_config.architectures[0]
|
||||
|
||||
if model_class == "CLIPTextModel":
|
||||
from transformers import CLIPTextModel
|
||||
|
||||
return CLIPTextModel
|
||||
elif model_class == "RobertaSeriesModelWithTransformation":
|
||||
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation
|
||||
|
||||
return RobertaSeriesModelWithTransformation
|
||||
else:
|
||||
raise ValueError(f"{model_class} is not supported.")
|
||||
|
||||
|
||||
def parse_args(input_args=None):
|
||||
parser = argparse.ArgumentParser(description="Simple example of a training script.")
|
||||
parser.add_argument(
|
||||
"--pretrained_model_name_or_path",
|
||||
type=str,
|
||||
default=None,
|
||||
required=True,
|
||||
help="Path to pretrained model or model identifier from huggingface.co/models.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--revision",
|
||||
type=str,
|
||||
default=None,
|
||||
required=False,
|
||||
help="Revision of pretrained model identifier from huggingface.co/models.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--tokenizer_name",
|
||||
type=str,
|
||||
default=None,
|
||||
help="Pretrained tokenizer name or path if not the same as model_name",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--instance_data_dir",
|
||||
type=str,
|
||||
default=None,
|
||||
required=True,
|
||||
help="A folder containing the training data of instance images.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--class_data_dir",
|
||||
type=str,
|
||||
default=None,
|
||||
required=False,
|
||||
help="A folder containing the training data of class images.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--instance_prompt",
|
||||
type=str,
|
||||
default="a photo of sks dog",
|
||||
required=False,
|
||||
help="The prompt with identifier specifying the instance",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--class_prompt",
|
||||
type=str,
|
||||
default=None,
|
||||
help="The prompt to specify images in the same class as provided instance images.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--with_prior_preservation",
|
||||
default=False,
|
||||
action="store_true",
|
||||
help="Flag to add prior preservation loss.",
|
||||
)
|
||||
parser.add_argument("--prior_loss_weight", type=float, default=1.0, help="The weight of prior preservation loss.")
|
||||
parser.add_argument(
|
||||
"--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."),
|
||||
)
|
||||
parser.add_argument(
|
||||
"--output_dir",
|
||||
type=str,
|
||||
default="text-inversion-model",
|
||||
help="The output directory where the model predictions and checkpoints will be written.",
|
||||
)
|
||||
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
|
||||
parser.add_argument(
|
||||
"--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"),
|
||||
)
|
||||
parser.add_argument(
|
||||
"--placement",
|
||||
type=str,
|
||||
default="cpu",
|
||||
help="Placement Policy for Gemini. Valid when using colossalai as dist plan.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--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."),
|
||||
)
|
||||
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(
|
||||
"--max_train_steps",
|
||||
type=int,
|
||||
default=None,
|
||||
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
|
||||
)
|
||||
parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.")
|
||||
parser.add_argument(
|
||||
"--gradient_checkpointing",
|
||||
action="store_true",
|
||||
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--learning_rate",
|
||||
type=float,
|
||||
default=5e-6,
|
||||
help="Initial learning rate (after the potential warmup period) to use.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--scale_lr",
|
||||
action="store_true",
|
||||
default=False,
|
||||
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--lr_scheduler",
|
||||
type=str,
|
||||
default="constant",
|
||||
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("--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.")
|
||||
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(
|
||||
"--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(
|
||||
"--mixed_precision",
|
||||
type=str,
|
||||
default=None,
|
||||
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. 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."),
|
||||
)
|
||||
parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
|
||||
|
||||
if input_args is not None:
|
||||
args = parser.parse_args(input_args)
|
||||
else:
|
||||
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.with_prior_preservation:
|
||||
if args.class_data_dir is None:
|
||||
raise ValueError("You must specify a data directory for class images.")
|
||||
if args.class_prompt is None:
|
||||
raise ValueError("You must specify prompt for class images.")
|
||||
else:
|
||||
if args.class_data_dir is not None:
|
||||
logger.warning("You need not use --class_data_dir without --with_prior_preservation.")
|
||||
if args.class_prompt is not None:
|
||||
logger.warning("You need not use --class_prompt without --with_prior_preservation.")
|
||||
|
||||
return args
|
||||
|
||||
|
||||
class DreamBoothDataset(Dataset):
|
||||
"""
|
||||
A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
|
||||
It pre-processes the images and the tokenizes prompts.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
instance_data_root,
|
||||
instance_prompt,
|
||||
tokenizer,
|
||||
class_data_root=None,
|
||||
class_prompt=None,
|
||||
size=512,
|
||||
center_crop=False,
|
||||
):
|
||||
self.size = size
|
||||
self.center_crop = center_crop
|
||||
self.tokenizer = tokenizer
|
||||
|
||||
self.instance_data_root = Path(instance_data_root)
|
||||
if not self.instance_data_root.exists():
|
||||
raise ValueError("Instance images root doesn't exists.")
|
||||
|
||||
self.instance_images_path = list(Path(instance_data_root).iterdir())
|
||||
self.num_instance_images = len(self.instance_images_path)
|
||||
self.instance_prompt = instance_prompt
|
||||
self._length = self.num_instance_images
|
||||
|
||||
if class_data_root is not None:
|
||||
self.class_data_root = Path(class_data_root)
|
||||
self.class_data_root.mkdir(parents=True, exist_ok=True)
|
||||
self.class_images_path = list(self.class_data_root.iterdir())
|
||||
self.num_class_images = len(self.class_images_path)
|
||||
self._length = max(self.num_class_images, self.num_instance_images)
|
||||
self.class_prompt = class_prompt
|
||||
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]),
|
||||
])
|
||||
|
||||
def __len__(self):
|
||||
return self._length
|
||||
|
||||
def __getitem__(self, index):
|
||||
example = {}
|
||||
instance_image = Image.open(self.instance_images_path[index % self.num_instance_images])
|
||||
if not instance_image.mode == "RGB":
|
||||
instance_image = instance_image.convert("RGB")
|
||||
example["instance_images"] = self.image_transforms(instance_image)
|
||||
example["instance_prompt_ids"] = self.tokenizer(
|
||||
self.instance_prompt,
|
||||
padding="do_not_pad",
|
||||
truncation=True,
|
||||
max_length=self.tokenizer.model_max_length,
|
||||
).input_ids
|
||||
|
||||
if self.class_data_root:
|
||||
class_image = Image.open(self.class_images_path[index % self.num_class_images])
|
||||
if not class_image.mode == "RGB":
|
||||
class_image = class_image.convert("RGB")
|
||||
example["class_images"] = self.image_transforms(class_image)
|
||||
example["class_prompt_ids"] = self.tokenizer(
|
||||
self.class_prompt,
|
||||
padding="do_not_pad",
|
||||
truncation=True,
|
||||
max_length=self.tokenizer.model_max_length,
|
||||
).input_ids
|
||||
|
||||
return example
|
||||
|
||||
|
||||
class PromptDataset(Dataset):
|
||||
"A simple dataset to prepare the prompts to generate class images on multiple GPUs."
|
||||
|
||||
def __init__(self, prompt, num_samples):
|
||||
self.prompt = prompt
|
||||
self.num_samples = num_samples
|
||||
|
||||
def __len__(self):
|
||||
return self.num_samples
|
||||
|
||||
def __getitem__(self, index):
|
||||
example = {}
|
||||
example["prompt"] = self.prompt
|
||||
example["index"] = index
|
||||
return example
|
||||
|
||||
|
||||
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}"
|
||||
|
||||
|
||||
# Gemini + ZeRO DDP
|
||||
def gemini_zero_dpp(model: torch.nn.Module, placememt_policy: str = "auto"):
|
||||
from colossalai.nn.parallel import GeminiDDP
|
||||
|
||||
model = GeminiDDP(model,
|
||||
device=get_current_device(),
|
||||
placement_policy=placememt_policy,
|
||||
pin_memory=True,
|
||||
search_range_mb=64)
|
||||
return model
|
||||
|
||||
|
||||
def main(args):
|
||||
if args.seed is None:
|
||||
colossalai.launch_from_torch(config={})
|
||||
else:
|
||||
colossalai.launch_from_torch(config={}, seed=args.seed)
|
||||
|
||||
local_rank = gpc.get_local_rank(ParallelMode.DATA)
|
||||
world_size = gpc.get_world_size(ParallelMode.DATA)
|
||||
|
||||
if args.with_prior_preservation:
|
||||
class_images_dir = Path(args.class_data_dir)
|
||||
if not class_images_dir.exists():
|
||||
class_images_dir.mkdir(parents=True)
|
||||
cur_class_images = len(list(class_images_dir.iterdir()))
|
||||
|
||||
if cur_class_images < args.num_class_images:
|
||||
torch_dtype = torch.float16 if get_current_device() == "cuda" else torch.float32
|
||||
pipeline = DiffusionPipeline.from_pretrained(
|
||||
args.pretrained_model_name_or_path,
|
||||
torch_dtype=torch_dtype,
|
||||
safety_checker=None,
|
||||
revision=args.revision,
|
||||
)
|
||||
pipeline.set_progress_bar_config(disable=True)
|
||||
|
||||
num_new_images = args.num_class_images - cur_class_images
|
||||
logger.info(f"Number of class images to sample: {num_new_images}.")
|
||||
|
||||
sample_dataset = PromptDataset(args.class_prompt, num_new_images)
|
||||
sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size)
|
||||
|
||||
pipeline.to(get_current_device())
|
||||
|
||||
for example in tqdm(
|
||||
sample_dataloader,
|
||||
desc="Generating class images",
|
||||
disable=not local_rank == 0,
|
||||
):
|
||||
images = pipeline(example["prompt"]).images
|
||||
|
||||
for i, image in enumerate(images):
|
||||
hash_image = hashlib.sha1(image.tobytes()).hexdigest()
|
||||
image_filename = class_images_dir / f"{example['index'][i] + cur_class_images}-{hash_image}.jpg"
|
||||
image.save(image_filename)
|
||||
|
||||
del pipeline
|
||||
|
||||
# Handle the repository creation
|
||||
if local_rank == 0:
|
||||
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
|
||||
create_repo(repo_name, exist_ok=True, token=args.hub_token)
|
||||
repo = Repository(args.output_dir, clone_from=repo_name, token=args.hub_token)
|
||||
|
||||
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)
|
||||
|
||||
# Load the tokenizer
|
||||
if args.tokenizer_name:
|
||||
logger.info(f"Loading tokenizer from {args.tokenizer_name}", ranks=[0])
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
args.tokenizer_name,
|
||||
revision=args.revision,
|
||||
use_fast=False,
|
||||
)
|
||||
elif args.pretrained_model_name_or_path:
|
||||
logger.info("Loading tokenizer from pretrained model", ranks=[0])
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
args.pretrained_model_name_or_path,
|
||||
subfolder="tokenizer",
|
||||
revision=args.revision,
|
||||
use_fast=False,
|
||||
)
|
||||
# import correct text encoder class
|
||||
text_encoder_cls = import_model_class_from_model_name_or_path(args.pretrained_model_name_or_path)
|
||||
|
||||
# Load models and create wrapper for stable diffusion
|
||||
|
||||
logger.info(f"Loading text_encoder from {args.pretrained_model_name_or_path}", ranks=[0])
|
||||
|
||||
text_encoder = text_encoder_cls.from_pretrained(
|
||||
args.pretrained_model_name_or_path,
|
||||
subfolder="text_encoder",
|
||||
revision=args.revision,
|
||||
)
|
||||
|
||||
logger.info(f"Loading AutoencoderKL from {args.pretrained_model_name_or_path}", ranks=[0])
|
||||
vae = AutoencoderKL.from_pretrained(
|
||||
args.pretrained_model_name_or_path,
|
||||
subfolder="vae",
|
||||
revision=args.revision,
|
||||
)
|
||||
|
||||
logger.info(f"Loading UNet2DConditionModel from {args.pretrained_model_name_or_path}", ranks=[0])
|
||||
with ColoInitContext(device=get_current_device()):
|
||||
unet = UNet2DConditionModel.from_pretrained(args.pretrained_model_name_or_path,
|
||||
subfolder="unet",
|
||||
revision=args.revision,
|
||||
low_cpu_mem_usage=False)
|
||||
unet.requires_grad_(False)
|
||||
|
||||
# Set correct lora layers
|
||||
lora_attn_procs = {}
|
||||
for name in unet.attn_processors.keys():
|
||||
cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
|
||||
if name.startswith("mid_block"):
|
||||
hidden_size = unet.config.block_out_channels[-1]
|
||||
elif name.startswith("up_blocks"):
|
||||
block_id = int(name[len("up_blocks.")])
|
||||
hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
|
||||
elif name.startswith("down_blocks"):
|
||||
block_id = int(name[len("down_blocks.")])
|
||||
hidden_size = unet.config.block_out_channels[block_id]
|
||||
|
||||
lora_attn_procs[name] = LoRACrossAttnProcessor(hidden_size=hidden_size,
|
||||
cross_attention_dim=cross_attention_dim)
|
||||
|
||||
unet.set_attn_processor(lora_attn_procs)
|
||||
lora_layers = AttnProcsLayers(unet.attn_processors)
|
||||
|
||||
vae.requires_grad_(False)
|
||||
text_encoder.requires_grad_(False)
|
||||
|
||||
if args.gradient_checkpointing:
|
||||
unet.enable_gradient_checkpointing()
|
||||
|
||||
if args.scale_lr:
|
||||
args.learning_rate = args.learning_rate * args.train_batch_size * world_size
|
||||
|
||||
unet = gemini_zero_dpp(unet, args.placement)
|
||||
|
||||
# config optimizer for colossalai zero
|
||||
optimizer = GeminiAdamOptimizer(unet, 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,
|
||||
)
|
||||
|
||||
def collate_fn(examples):
|
||||
input_ids = [example["instance_prompt_ids"] for example in examples]
|
||||
pixel_values = [example["instance_images"] for example in examples]
|
||||
|
||||
# Concat class and instance examples for prior preservation.
|
||||
# We do this to avoid doing two forward passes.
|
||||
if args.with_prior_preservation:
|
||||
input_ids += [example["class_prompt_ids"] for example in examples]
|
||||
pixel_values += [example["class_images"] for example in examples]
|
||||
|
||||
pixel_values = torch.stack(pixel_values)
|
||||
pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float()
|
||||
|
||||
input_ids = tokenizer.pad(
|
||||
{
|
||||
"input_ids": input_ids
|
||||
},
|
||||
padding="max_length",
|
||||
max_length=tokenizer.model_max_length,
|
||||
return_tensors="pt",
|
||||
).input_ids
|
||||
|
||||
batch = {
|
||||
"input_ids": input_ids,
|
||||
"pixel_values": pixel_values,
|
||||
}
|
||||
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)
|
||||
|
||||
# Scheduler and math around the number of training steps.
|
||||
overrode_max_train_steps = False
|
||||
num_update_steps_per_epoch = math.ceil(len(train_dataloader))
|
||||
if args.max_train_steps is None:
|
||||
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
|
||||
overrode_max_train_steps = True
|
||||
|
||||
lr_scheduler = get_scheduler(
|
||||
args.lr_scheduler,
|
||||
optimizer=optimizer,
|
||||
num_warmup_steps=args.lr_warmup_steps,
|
||||
num_training_steps=args.max_train_steps,
|
||||
)
|
||||
weight_dtype = torch.float32
|
||||
if args.mixed_precision == "fp16":
|
||||
weight_dtype = torch.float16
|
||||
elif args.mixed_precision == "bf16":
|
||||
weight_dtype = torch.bfloat16
|
||||
|
||||
# Move text_encode and vae to gpu.
|
||||
# For mixed precision training we cast the text_encoder and vae weights to half-precision
|
||||
# as these models are only used for inference, keeping weights in full precision is not required.
|
||||
vae.to(get_current_device(), dtype=weight_dtype)
|
||||
text_encoder.to(get_current_device(), dtype=weight_dtype)
|
||||
|
||||
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
|
||||
num_update_steps_per_epoch = math.ceil(len(train_dataloader))
|
||||
if overrode_max_train_steps:
|
||||
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
|
||||
# Afterwards we recalculate our number of training epochs
|
||||
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
|
||||
|
||||
# Train!
|
||||
total_batch_size = args.train_batch_size * world_size
|
||||
|
||||
logger.info("***** Running training *****", ranks=[0])
|
||||
logger.info(f" Num examples = {len(train_dataset)}", ranks=[0])
|
||||
logger.info(f" Num batches each epoch = {len(train_dataloader)}", ranks=[0])
|
||||
logger.info(f" Num Epochs = {args.num_train_epochs}", ranks=[0])
|
||||
logger.info(f" Instantaneous batch size per device = {args.train_batch_size}", ranks=[0])
|
||||
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}", ranks=[0])
|
||||
logger.info(f" Total optimization steps = {args.max_train_steps}", ranks=[0])
|
||||
|
||||
# Only show the progress bar once on each machine.
|
||||
progress_bar = tqdm(range(args.max_train_steps), disable=not local_rank == 0)
|
||||
progress_bar.set_description("Steps")
|
||||
global_step = 0
|
||||
|
||||
torch.cuda.synchronize()
|
||||
for epoch in range(args.num_train_epochs):
|
||||
unet.train()
|
||||
for step, batch in enumerate(train_dataloader):
|
||||
torch.cuda.reset_peak_memory_stats()
|
||||
# Move batch to gpu
|
||||
for key, value in batch.items():
|
||||
batch[key] = value.to(get_current_device(), non_blocking=True)
|
||||
|
||||
# Convert images to latent space
|
||||
optimizer.zero_grad()
|
||||
|
||||
latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample()
|
||||
latents = latents * 0.18215
|
||||
|
||||
# Sample noise that we'll add to the latents
|
||||
noise = torch.randn_like(latents)
|
||||
bsz = latents.shape[0]
|
||||
# Sample a random timestep for each image
|
||||
timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
|
||||
timesteps = timesteps.long()
|
||||
|
||||
# Add noise to the latents according to the noise magnitude at each timestep
|
||||
# (this is the forward diffusion process)
|
||||
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
|
||||
|
||||
# Get the text embedding for conditioning
|
||||
encoder_hidden_states = text_encoder(batch["input_ids"])[0]
|
||||
|
||||
# Predict the noise residual
|
||||
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
|
||||
|
||||
# Get the target for loss depending on the prediction type
|
||||
if noise_scheduler.config.prediction_type == "epsilon":
|
||||
target = noise
|
||||
elif noise_scheduler.config.prediction_type == "v_prediction":
|
||||
target = noise_scheduler.get_velocity(latents, noise, timesteps)
|
||||
else:
|
||||
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
|
||||
|
||||
if args.with_prior_preservation:
|
||||
# Chunk the noise and model_pred into two parts and compute the loss on each part separately.
|
||||
model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
|
||||
target, target_prior = torch.chunk(target, 2, dim=0)
|
||||
|
||||
# Compute instance loss
|
||||
loss = F.mse_loss(model_pred.float(), target.float(), reduction="none").mean([1, 2, 3]).mean()
|
||||
|
||||
# Compute prior loss
|
||||
prior_loss = F.mse_loss(model_pred_prior.float(), target_prior.float(), reduction="mean")
|
||||
|
||||
# Add the prior loss to the instance loss.
|
||||
loss = loss + args.prior_loss_weight * prior_loss
|
||||
else:
|
||||
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
|
||||
|
||||
optimizer.backward(loss)
|
||||
|
||||
optimizer.step()
|
||||
lr_scheduler.step()
|
||||
logger.info(f"max GPU_mem cost is {torch.cuda.max_memory_allocated()/2**20} MB", ranks=[0])
|
||||
# Checks if the accelerator has performed an optimization step behind the scenes
|
||||
progress_bar.update(1)
|
||||
global_step += 1
|
||||
logs = {
|
||||
"loss": loss.detach().item(),
|
||||
"lr": optimizer.param_groups[0]["lr"],
|
||||
} # lr_scheduler.get_last_lr()[0]}
|
||||
progress_bar.set_postfix(**logs)
|
||||
|
||||
if global_step % args.save_steps == 0:
|
||||
torch.cuda.synchronize()
|
||||
torch_unet = get_static_torch_model(unet)
|
||||
if local_rank == 0:
|
||||
save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
|
||||
torch_unet = torch_unet.to(torch.float32)
|
||||
torch_unet.save_attn_procs(save_path)
|
||||
logger.info(f"Saving model checkpoint to {save_path}", ranks=[0])
|
||||
if global_step >= args.max_train_steps:
|
||||
break
|
||||
|
||||
torch.cuda.synchronize()
|
||||
torch_unet = get_static_torch_model(unet)
|
||||
|
||||
if local_rank == 0:
|
||||
torch_unet = torch_unet.to(torch.float32)
|
||||
torch_unet.save_attn_procs(save_path)
|
||||
logger.info(f"Saving model checkpoint to {args.output_dir}", ranks=[0])
|
||||
|
||||
if args.push_to_hub:
|
||||
repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
args = parse_args()
|
||||
main(args)
|
||||
Loading…
Reference in New Issue