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from causvid.data import ODERegressionLMDBDataset
from causvid.models import get_block_class
from causvid.data import TextDataset
from causvid.util import (
launch_distributed_job,
prepare_for_saving,
set_seed, init_logging_folder,
fsdp_wrap, cycle,
fsdp_state_dict,
barrier
)
import torch.distributed as dist
from omegaconf import OmegaConf
from causvid.dmd import DMD
import argparse
import torch
import wandb
import time
import os
class Trainer:
def __init__(self, config):
self.config = config
# Step 1: Initialize the distributed training environment (rank, seed, dtype, logging etc.)
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
launch_distributed_job()
global_rank = dist.get_rank()
self.dtype = torch.bfloat16 if config.mixed_precision else torch.float32
self.device = torch.cuda.current_device()
self.is_main_process = global_rank == 0
# use a random seed for the training
if config.seed == 0:
random_seed = torch.randint(0, 10000000, (1,), device=self.device)
dist.broadcast(random_seed, src=0)
config.seed = random_seed.item()
set_seed(config.seed + global_rank)
if self.is_main_process:
# self.output_path, self.wandb_folder = init_logging_folder(config)
self.output_path, self.wandb_folder = "output", "output_wandb"
# Step 2: Initialize the model and optimizer
if config.distillation_loss == "dmd":
self.distillation_model = DMD(config, device=self.device)
else:
raise ValueError("Invalid distillation loss type")
self.distillation_model.generator = fsdp_wrap(
self.distillation_model.generator,
sharding_strategy=config.sharding_strategy,
mixed_precision=config.mixed_precision,
wrap_strategy=config.generator_fsdp_wrap_strategy,
transformer_module=(get_block_class(config.generator_fsdp_transformer_module),
) if config.generator_fsdp_wrap_strategy == "transformer" else None
)
self.distillation_model.real_score = fsdp_wrap(
self.distillation_model.real_score,
sharding_strategy=config.sharding_strategy,
mixed_precision=config.mixed_precision,
wrap_strategy=config.real_score_fsdp_wrap_strategy,
transformer_module=(get_block_class(config.real_score_fsdp_transformer_module),
) if config.real_score_fsdp_wrap_strategy == "transformer" else None
)
self.distillation_model.fake_score = fsdp_wrap(
self.distillation_model.fake_score,
sharding_strategy=config.sharding_strategy,
mixed_precision=config.mixed_precision,
wrap_strategy=config.fake_score_fsdp_wrap_strategy,
transformer_module=(get_block_class(config.fake_score_fsdp_transformer_module),
) if config.fake_score_fsdp_wrap_strategy == "transformer" else None
)
self.distillation_model.text_encoder = fsdp_wrap(
self.distillation_model.text_encoder,
sharding_strategy=config.sharding_strategy,
mixed_precision=config.mixed_precision,
wrap_strategy=config.text_encoder_fsdp_wrap_strategy,
transformer_module=(get_block_class(config.text_encoder_fsdp_transformer_module),
) if config.text_encoder_fsdp_wrap_strategy == "transformer" else None
)
if not config.no_visualize:
self.distillation_model.vae = self.distillation_model.vae.to(
device=self.device, dtype=torch.bfloat16 if config.mixed_precision else torch.float32)
self.generator_optimizer = torch.optim.AdamW(
[param for param in self.distillation_model.generator.parameters()
if param.requires_grad],
lr=config.lr,
betas=(config.beta1, config.beta2)
)
self.critic_optimizer = torch.optim.AdamW(
[param for param in self.distillation_model.fake_score.parameters()
if param.requires_grad],
lr=config.lr,
betas=(config.beta1, config.beta2)
)
# Step 3: Initialize the dataloader
self.backward_simulation = getattr(config, "backward_simulation", True)
if self.backward_simulation:
dataset = TextDataset(config.data_path)
else:
dataset = ODERegressionLMDBDataset(
config.data_path, max_pair=int(1e8))
sampler = torch.utils.data.distributed.DistributedSampler(
dataset, shuffle=True, drop_last=True)
dataloader = torch.utils.data.DataLoader(
dataset, batch_size=config.batch_size, sampler=sampler)
self.dataloader = cycle(dataloader)
self.step = 0
self.max_grad_norm = 10.0
self.previous_time = None
def save(self):
print("Start gathering distributed model states...")
generator_state_dict = fsdp_state_dict(
self.distillation_model.generator)
critic_state_dict = fsdp_state_dict(
self.distillation_model.fake_score)
state_dict = {
"generator": generator_state_dict,
"critic": critic_state_dict
}
if self.is_main_process:
os.makedirs(os.path.join(self.output_path,
f"checkpoint_model_{self.step:06d}"), exist_ok=True)
torch.save(state_dict, os.path.join(self.output_path,
f"checkpoint_model_{self.step:06d}", "model.pt"))
print("Model saved to", os.path.join(self.output_path,
f"checkpoint_model_{self.step:06d}", "model.pt"))
def train_one_step(self):
self.distillation_model.eval() # prevent any randomness (e.g. dropout)
TRAIN_GENERATOR = self.step % self.config.dfake_gen_update_ratio == 0
VISUALIZE = self.step % self.config.log_iters == 0 and not self.config.no_visualize
if self.step % 20 == 0:
torch.cuda.empty_cache()
# Step 1: Get the next batch of text prompts
if not self.backward_simulation:
batch = next(self.dataloader)
text_prompts = batch["prompts"]
clean_latent = batch["ode_latent"][:, -1].to(
device=self.device, dtype=self.dtype)
else:
text_prompts = next(self.dataloader)
clean_latent = None
batch_size = len(text_prompts)
image_or_video_shape = list(self.config.image_or_video_shape)
image_or_video_shape[0] = batch_size
# Step 2: Extract the conditional infos
with torch.no_grad():
conditional_dict = self.distillation_model.text_encoder(
text_prompts=text_prompts)
if not getattr(self, "unconditional_dict", None):
unconditional_dict = self.distillation_model.text_encoder(
text_prompts=[self.config.negative_prompt] * batch_size)
unconditional_dict = {k: v.detach()
for k, v in unconditional_dict.items()}
self.unconditional_dict = unconditional_dict # cache the unconditional_dict
else:
unconditional_dict = self.unconditional_dict
# Step 3: Train the generator
if TRAIN_GENERATOR:
generator_loss, generator_log_dict = self.distillation_model.generator_loss(
image_or_video_shape=image_or_video_shape,
conditional_dict=conditional_dict,
unconditional_dict=unconditional_dict,
clean_latent=clean_latent
)
self.generator_optimizer.zero_grad()
generator_loss.backward()
generator_grad_norm = self.distillation_model.generator.clip_grad_norm_(
self.max_grad_norm)
self.generator_optimizer.step()
else:
generator_log_dict = {}
# Step 4: Train the critic
critic_loss, critic_log_dict = self.distillation_model.critic_loss(
image_or_video_shape=image_or_video_shape,
conditional_dict=conditional_dict,
unconditional_dict=unconditional_dict,
clean_latent=clean_latent
)
self.critic_optimizer.zero_grad()
critic_loss.backward()
critic_grad_norm = self.distillation_model.fake_score.clip_grad_norm_(
self.max_grad_norm)
self.critic_optimizer.step()
# Step 5: Logging
# if self.is_main_process:
# wandb_loss_dict = {
# "critic_loss": critic_loss.item(),
# "critic_grad_norm": critic_grad_norm.item()
# }
# if TRAIN_GENERATOR:
# wandb_loss_dict.update(
# {
# "generator_loss": generator_loss.item(),
# "generator_grad_norm": generator_grad_norm.item(),
# "dmdtrain_gradient_norm": generator_log_dict["dmdtrain_gradient_norm"].item()
# }
# )
# if VISUALIZE:
# self.add_visualization(generator_log_dict, critic_log_dict, wandb_loss_dict)
# wandb.log(wandb_loss_dict, step=self.step)
# def add_visualization(self, generator_log_dict, critic_log_dict, wandb_loss_dict):
# critictrain_latent, critictrain_noisy_latent, critictrain_pred_image = map(
# lambda x: self.distillation_model.vae.decode_to_pixel(
# x).squeeze(1),
# [critic_log_dict['critictrain_latent'], critic_log_dict['critictrain_noisy_latent'],
# critic_log_dict['critictrain_pred_image']]
# )
# wandb_loss_dict.update({
# "critictrain_latent": prepare_for_saving(critictrain_latent),
# "critictrain_noisy_latent": prepare_for_saving(critictrain_noisy_latent),
# "critictrain_pred_image": prepare_for_saving(critictrain_pred_image)
# })
# if "dmdtrain_clean_latent" in generator_log_dict:
# (dmdtrain_clean_latent, dmdtrain_noisy_latent, dmdtrain_pred_real_image, dmdtrain_pred_fake_image) = map(
# lambda x: self.distillation_model.vae.decode_to_pixel(
# x).squeeze(1),
# [generator_log_dict['dmdtrain_clean_latent'], generator_log_dict['dmdtrain_noisy_latent'],
# generator_log_dict['dmdtrain_pred_real_image'], generator_log_dict['dmdtrain_pred_fake_image']]
# )
# wandb_loss_dict.update(
# {
# "dmdtrain_clean_latent": prepare_for_saving(dmdtrain_clean_latent),
# "dmdtrain_noisy_latent": prepare_for_saving(dmdtrain_noisy_latent),
# "dmdtrain_pred_real_image": prepare_for_saving(dmdtrain_pred_real_image),
# "dmdtrain_pred_fake_image": prepare_for_saving(dmdtrain_pred_fake_image)
# }
# )
def train(self):
while True:
self.train_one_step()
if (not self.config.no_save) and self.step % self.config.log_iters == 0:
self.save()
torch.cuda.empty_cache()
barrier()
if self.is_main_process:
current_time = time.time()
if self.previous_time is None:
self.previous_time = current_time
else:
# wandb.log({"per iteration time": current_time -
# self.previous_time}, step=self.step)
self.previous_time = current_time
self.step += 1
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--config_path", type=str, required=True)
parser.add_argument("--local_rank", type=int, default=-1)
parser.add_argument("--no_save", action="store_true")
parser.add_argument("--no_visualize", action="store_true")
args = parser.parse_args()
config = OmegaConf.load(args.config_path)
config.no_save = args.no_save
config.no_visualize = args.no_visualize
trainer = Trainer(config)
trainer.train()
# wandb.finish()
if __name__ == "__main__":
main()
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