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from causvid.data import ODERegressionDataset, ODERegressionLMDBDataset
from causvid.ode_regression import ODERegression
from causvid.models import get_block_class
from collections import defaultdict
from causvid.util import (
launch_distributed_job,
set_seed, init_logging_folder,
fsdp_wrap, cycle,
fsdp_state_dict,
barrier
)
import torch.distributed as dist
from omegaconf import OmegaConf
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.world_size = dist.get_world_size()
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)
# Step 2: Initialize the model and optimizer
if config.distillation_loss == "ode":
self.distillation_model = ODERegression(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.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
)
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)
)
# Step 3: Initialize the dataloader
# dataset = ODERegressionDataset(config.data_path)
dataset = ODERegressionLMDBDataset(
config.data_path, max_pair=getattr(config, "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, num_workers=8)
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)
state_dict = {
"generator": generator_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)
# Step 1: Get the next batch of text prompts
batch = next(self.dataloader)
text_prompts = batch["prompts"]
ode_latent = batch["ode_latent"].to(
device=self.device, dtype=self.dtype)
# Step 2: Extract the conditional infos
with torch.no_grad():
conditional_dict = self.distillation_model.text_encoder(
text_prompts=text_prompts)
# Step 3: Train the generator
generator_loss, log_dict = self.distillation_model.generator_loss(
ode_latent=ode_latent,
conditional_dict=conditional_dict
)
unnormalized_loss = log_dict["unnormalized_loss"]
timestep = log_dict["timestep"]
if self.world_size > 1:
gathered_unnormalized_loss = torch.zeros(
[self.world_size, *unnormalized_loss.shape],
dtype=unnormalized_loss.dtype, device=self.device)
gathered_timestep = torch.zeros(
[self.world_size, *timestep.shape],
dtype=timestep.dtype, device=self.device)
dist.all_gather_into_tensor(
gathered_unnormalized_loss, unnormalized_loss)
dist.all_gather_into_tensor(gathered_timestep, timestep)
else:
gathered_unnormalized_loss = unnormalized_loss
gathered_timestep = timestep
loss_breakdown = defaultdict(list)
stats = {}
for index, t in enumerate(timestep):
loss_breakdown[str(int(t.item()) // 250 * 250)].append(
unnormalized_loss[index].item())
for key_t in loss_breakdown.keys():
stats["loss_at_time_" + key_t] = sum(loss_breakdown[key_t]) / \
len(loss_breakdown[key_t])
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()
# Step 4: Logging
# if self.is_main_process:
# wandb_loss_dict = {
# "generator_loss": generator_loss.item(),
# "generator_grad_norm": generator_grad_norm.item(),
# **stats
# }
# wandb.log(wandb_loss_dict, step=self.step)
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")
args = parser.parse_args()
config = OmegaConf.load(args.config_path)
config.no_save = args.no_save
trainer = Trainer(config)
trainer.train()
# wandb.finish()
if __name__ == "__main__":
main()
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