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import os
import torch
import torch.distributed as dist
import torchvision.transforms as transforms
from accelerate import Accelerator
from helios.dataset.dataloader_mp4_dist import BucketedFeatureDataset, BucketedSampler, collate_fn
from helios.utils.utils_base import encode_prompt
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer, UMT5EncoderModel
from diffusers import AutoencoderKLWan
from diffusers.training_utils import free_memory
def setup_distributed_env():
dist.init_process_group(backend="nccl")
torch.cuda.set_device(int(os.environ["LOCAL_RANK"]))
def cleanup_distributed_env():
dist.destroy_process_group()
def main(
rank,
world_size,
global_rank,
stride,
batch_size,
dataloader_num_workers,
json_file,
video_folder,
output_latent_folder,
pretrained_model_name_or_path,
resolution=640,
):
weight_dtype = torch.bfloat16
device = rank
seed = 42
# Load the tokenizers
tokenizer = AutoTokenizer.from_pretrained(
pretrained_model_name_or_path,
subfolder="tokenizer",
)
text_encoder = UMT5EncoderModel.from_pretrained(
pretrained_model_name_or_path,
subfolder="text_encoder",
torch_dtype=weight_dtype,
)
vae = AutoencoderKLWan.from_pretrained(
pretrained_model_name_or_path,
subfolder="vae",
torch_dtype=torch.float32,
)
latents_mean = torch.tensor(vae.config.latents_mean).view(1, vae.config.z_dim, 1, 1, 1).to(device, weight_dtype)
latents_std = 1.0 / torch.tensor(vae.config.latents_std).view(1, vae.config.z_dim, 1, 1, 1).to(
device, weight_dtype
)
vae.eval()
vae.requires_grad_(False)
text_encoder.eval()
text_encoder.requires_grad_(False)
vae = vae.to(device)
text_encoder = text_encoder.to(device)
# dist.barrier()
dataset = BucketedFeatureDataset(
json_files=json_file,
video_folders=video_folder,
stride=stride,
force_rebuild=False,
resolution=resolution,
single_res=True,
single_height=384,
single_width=640,
single_length=True,
single_num_frame=81,
)
sampler = BucketedSampler(dataset, batch_size=batch_size, drop_last=False, shuffle=True, seed=seed)
dataloader = DataLoader(
dataset,
batch_sampler=sampler,
collate_fn=collate_fn,
num_workers=dataloader_num_workers,
pin_memory=True,
prefetch_factor=2 if dataloader_num_workers != 0 else None,
# persistent_workers=True if dataloader_num_workers > 0 else False,
)
print(len(dataset), len(dataloader))
accelerator = Accelerator()
dataloader = accelerator.prepare(dataloader)
print(f"Dataset size: {len(dataset)}, Dataloader batches: {len(dataloader)}")
print(f"Process index: {accelerator.process_index}, World size: {accelerator.num_processes}")
sampler.set_epoch(0)
if rank == 0:
pbar = tqdm(total=len(dataloader), desc="Processing")
# dist.barrier()
for idx, batch in enumerate(dataloader):
if batch is None or batch["videos"] is None:
print("None batch, continuing")
continue
free_memory()
valid_indices = []
valid_uttids = []
valid_num_frames = []
valid_heights = []
valid_widths = []
valid_videos = []
valid_prompts = []
valid_first_frames_images = []
if batch["uttid"] is None:
print("None batch, contiuning")
continue
for i, (uttid, num_frame, height, width) in enumerate(
zip(
batch["uttid"],
batch["video_metadata"]["num_frames"],
batch["video_metadata"]["height"],
batch["video_metadata"]["width"],
)
):
os.makedirs(output_latent_folder, exist_ok=True)
output_path = os.path.join(output_latent_folder, f"{uttid}_{num_frame}_{height}_{width}.pt")
if not os.path.exists(output_path):
valid_indices.append(i)
valid_uttids.append(uttid)
valid_num_frames.append(num_frame)
valid_heights.append(height)
valid_widths.append(width)
valid_videos.append(batch["videos"][i])
valid_prompts.append(batch["prompts"][i])
valid_first_frames_images.append(batch["first_frames_images"][i])
else:
print(f"skipping {uttid}")
if not valid_indices:
print("skipping entire batch!")
if rank == 0:
pbar.update(1)
pbar.set_postfix({"batch": idx})
continue
batch = None
del batch
free_memory()
batch = {
"uttid": valid_uttids,
"video_metadata": {"num_frames": valid_num_frames, "height": valid_heights, "width": valid_widths},
"videos": torch.stack(valid_videos),
"prompts": valid_prompts,
"first_frames_images": torch.stack(valid_first_frames_images),
}
if len(batch["uttid"]) == 0:
print("All samples in this batch are already processed, skipping!")
continue
with torch.no_grad():
# Get Vae feature
pixel_values = batch["videos"].permute(0, 2, 1, 3, 4).to(dtype=vae.dtype, device=device)
vae_latents = vae.encode(pixel_values).latent_dist.sample()
vae_latents = (vae_latents - latents_mean) * latents_std
# Encode prompts
prompts = batch["prompts"]
prompt_embeds, prompt_attention_mask = encode_prompt(
tokenizer=tokenizer,
text_encoder=text_encoder,
prompt=prompts,
device=device,
)
image_tensor = batch["first_frames_images"]
images = [transforms.ToPILImage()(x.to(torch.uint8)) for x in image_tensor]
for (
uttid,
num_frame,
height,
width,
cur_vae_latent,
cur_prompt_embed,
cur_prompt_attention_mask,
cur_first_frames_image,
cur_prompt,
) in zip(
batch["uttid"],
batch["video_metadata"]["num_frames"],
batch["video_metadata"]["height"],
batch["video_metadata"]["width"],
vae_latents,
prompt_embeds,
prompt_attention_mask,
images,
prompts,
):
output_path = os.path.join(output_latent_folder, f"{uttid}_{num_frame}_{height}_{width}.pt")
temp_to_save = {
"vae_latent": cur_vae_latent.cpu().detach(),
"prompt_embed": cur_prompt_embed.cpu().detach(),
# "prompt_attention_mask": cur_prompt_attention_mask.cpu().detach(),
"first_frames_image": cur_first_frames_image,
"prompt_raw": cur_prompt,
}
try:
torch.save(temp_to_save, output_path)
except Exception:
continue
print(f"save latent to: {output_path}")
if rank == 0:
pbar.update(1)
pbar.set_postfix({"batch": idx})
pixel_values = None
prompts = None
image_tensor = None
images = None
vae_latents = None
vae_latents_2 = None
image_embeds = None
prompt_embeds = None
batch = None
valid_indices = None
valid_uttids = None
valid_num_frames = None
valid_heights = None
valid_widths = None
valid_videos = None
valid_prompts = None
valid_first_frames_images = None
temp_to_save = None
del pixel_values
del prompts
del image_tensor
del images
del vae_latents
del vae_latents_2
del image_embeds
del batch
del valid_indices
del valid_uttids
del valid_num_frames
del valid_heights
del valid_widths
del valid_videos
del valid_prompts
del valid_first_frames_images
del temp_to_save
free_memory()
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Script for running model training and data processing.")
parser.add_argument("--dataloader_num_workers", type=int, default=8, help="Number of workers for data loading")
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default="./checkpoints/Helios-Base",
help="Pretrained model path",
)
args = parser.parse_args()
setup_distributed_env()
global_rank = dist.get_rank()
local_rank = int(os.environ["LOCAL_RANK"])
device = torch.cuda.current_device()
world_size = dist.get_world_size()
base_video_path = "example"
video_paths = [
"toy_data",
]
base_output_latent_path = "example/toy_data/latents_long"
output_latent_paths = [
"toy_data",
]
base_csv_paths = [
"example",
]
csv_paths = [
"toy_data/toy_filter.json",
]
resolutions = [640]
strides = [1]
batch_sizes = [4]
for stride, batch_size, base_csv_path, csv_path, video_path, output_latent_path, cur_resolution in zip(
strides, batch_sizes, base_csv_paths, csv_paths, video_paths, output_latent_paths, resolutions
):
json_file = os.path.join(base_csv_path, csv_path)
video_folder = os.path.join(base_video_path, video_path)
output_latent_folder = os.path.join(base_output_latent_path, output_latent_path)
main(
rank=device,
world_size=world_size,
global_rank=global_rank,
stride=stride,
batch_size=batch_size,
dataloader_num_workers=args.dataloader_num_workers,
json_file=json_file,
video_folder=video_folder,
output_latent_folder=output_latent_folder,
pretrained_model_name_or_path=args.pretrained_model_name_or_path,
resolution=cur_resolution,
)
dist.barrier()
dist.destroy_process_group()
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