multimodalart's picture
Upload 80 files
0fd2f06 verified
raw
history blame
9.79 kB
import gc
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