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import logging
from utils.dataset import ODERegressionLMDBDataset, cycle
from model import ODERegression
from collections import defaultdict
from utils.misc import set_seed
import torch.distributed as dist
from omegaconf import OmegaConf
import torch
import wandb
import time
import os
from utils.distributed import (
barrier,
fsdp_wrap,
fsdp_state_dict,
launch_distributed_job,
)
class Trainer:
def __init__(
self,
config,
):
self.config = config
self.step = 0
# 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
self.disable_wandb = config.disable_wandb
# 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 and not self.disable_wandb:
wandb.login(host=config.wandb_host, key=config.wandb_key)
wandb.init(
config=OmegaConf.to_container(config, resolve=True),
name=config.config_name,
mode="online",
entity=config.wandb_entity,
project=config.wandb_project,
dir=config.wandb_save_dir,
)
self.output_path = config.logdir
# Step 2: Initialize the model and optimizer
assert config.distribution_loss == "ode", "Only ODE loss is supported for ODE training"
self.model = ODERegression(config, device=self.device)
self.model.generator = fsdp_wrap(
self.model.generator,
sharding_strategy=config.sharding_strategy,
mixed_precision=config.mixed_precision,
wrap_strategy=config.generator_fsdp_wrap_strategy,
)
self.model.text_encoder = fsdp_wrap(
self.model.text_encoder,
sharding_strategy=config.sharding_strategy,
mixed_precision=config.mixed_precision,
wrap_strategy=config.text_encoder_fsdp_wrap_strategy,
cpu_offload=getattr(config, "text_encoder_cpu_offload", False),
)
if not config.no_visualize or config.load_raw_video:
self.model.vae = self.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.model.generator.parameters() if param.requires_grad],
lr=config.lr,
betas=(config.beta1, config.beta2),
weight_decay=config.weight_decay,
)
# Step 3: Initialize the dataloader
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,
)
total_batch_size = getattr(config, "total_batch_size", None)
if total_batch_size is not None:
assert (
total_batch_size == config.batch_size * self.world_size
), "Gradient accumulation is not supported for ODE training"
self.dataloader = cycle(dataloader)
self.step = 0
##############################################################################################################
# 7. (If resuming) Load the model and optimizer, lr_scheduler, ema's statedicts
if getattr(config, "generator_ckpt", False):
print(f"Loading pretrained generator from {config.generator_ckpt}")
state_dict = torch.load(config.generator_ckpt, map_location="cpu")["generator"]
self.model.generator.load_state_dict(state_dict, strict=True)
##############################################################################################################
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.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,
):
VISUALIZE = self.step % 100 == 0
self.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.model.text_encoder(text_prompts=text_prompts)
# Step 3: Train the generator
generator_loss, log_dict = self.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.model.generator.clip_grad_norm_(self.max_grad_norm)
self.generator_optimizer.step()
# Step 4: Visualization
if (
VISUALIZE
and not self.config.no_visualize
and not self.config.disable_wandb
and self.is_main_process
):
# Visualize the input, output, and ground truth
input = log_dict["input"]
output = log_dict["output"]
ground_truth = ode_latent[:, -1]
input_video = self.model.vae.decode_to_pixel(input)
output_video = self.model.vae.decode_to_pixel(output)
ground_truth_video = self.model.vae.decode_to_pixel(ground_truth)
input_video = 255.0 * (input_video.cpu().numpy() * 0.5 + 0.5)
output_video = 255.0 * (output_video.cpu().numpy() * 0.5 + 0.5)
ground_truth_video = 255.0 * (ground_truth_video.cpu().numpy() * 0.5 + 0.5)
# Visualize the input, output, and ground truth
wandb.log(
{
"input": wandb.Video(input_video, caption="Input", fps=16, format="mp4"),
"output": wandb.Video(output_video, caption="Output", fps=16, format="mp4"),
"ground_truth": wandb.Video(
ground_truth_video,
caption="Ground Truth",
fps=16,
format="mp4",
),
},
step=self.step,
)
# Step 5: Logging
if self.is_main_process and not self.disable_wandb:
wandb_loss_dict = {
"generator_loss": generator_loss.item(),
"generator_grad_norm": generator_grad_norm.item(),
**stats,
}
wandb.log(wandb_loss_dict, step=self.step)
if self.step % self.config.gc_interval == 0:
if dist.get_rank() == 0:
logging.info("DistGarbageCollector: Running GC.")
gc.collect()
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:
if not self.disable_wandb:
wandb.log(
{"per iteration time": current_time - self.previous_time},
step=self.step,
)
self.previous_time = current_time
self.step += 1
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