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Zero
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from prefigure.prefigure import get_all_args, push_wandb_config
import json
import os
import re
import torch
import torchaudio
# import pytorch_lightning as pl
import lightning as L
from lightning.pytorch.callbacks import Timer, ModelCheckpoint, BasePredictionWriter
from lightning.pytorch.callbacks import Callback
from lightning.pytorch.tuner import Tuner
from lightning.pytorch import seed_everything
import random
from datetime import datetime
from ThinkSound.data.datamodule import DataModule
from ThinkSound.models import create_model_from_config
from ThinkSound.models.utils import load_ckpt_state_dict, remove_weight_norm_from_model
from ThinkSound.training import create_training_wrapper_from_config, create_demo_callback_from_config
from ThinkSound.training.utils import copy_state_dict
from huggingface_hub import hf_hub_download
class ExceptionCallback(Callback):
def on_exception(self, trainer, module, err):
print(f'{type(err).__name__}: {err}')
class ModelConfigEmbedderCallback(Callback):
def __init__(self, model_config):
self.model_config = model_config
def on_save_checkpoint(self, trainer, pl_module, checkpoint):
checkpoint["model_config"] = self.model_config
class CustomWriter(BasePredictionWriter):
def __init__(self, output_dir, write_interval='batch', batch_size=32):
super().__init__(write_interval)
self.output_dir = output_dir
self.batch_size = batch_size
def write_on_batch_end(self, trainer, pl_module, predictions, batch_indices, batch, batch_idx, dataloader_idx):
audios = predictions
ids = [item['id'] for item in batch[1]]
current_date = datetime.now()
formatted_date = current_date.strftime('%m%d')
os.makedirs(os.path.join(self.output_dir, f'{formatted_date}_batch_size{self.batch_size}'),exist_ok=True)
for audio, id in zip(audios, ids):
save_path = os.path.join(self.output_dir, f'{formatted_date}_batch_size{self.batch_size}', f'{id}.wav')
torchaudio.save(save_path, audio, 44100)
def main():
args = get_all_args()
# args.pretransform_ckpt_path = hf_hub_download(
# repo_id="liuhuadai/ThinkSound",
# filename="vae.ckpt"
# )
args.pretransform_ckpt_path = "./ckpts/vae.ckpt"
seed = 10086
# Set a different seed for each process if using SLURM
if os.environ.get("SLURM_PROCID") is not None:
seed += int(os.environ.get("SLURM_PROCID"))
# random.seed(seed)
# torch.manual_seed(seed)
seed_everything(seed, workers=True)
#Get JSON config from args.model_config
with open(args.model_config) as f:
model_config = json.load(f)
with open(args.dataset_config) as f:
dataset_config = json.load(f)
for td in dataset_config["test_datasets"]:
td["path"] = args.results_dir
# train_dl = create_dataloader_from_config(
# dataset_config,
# batch_size=args.batch_size,
# num_workers=args.num_workers,
# sample_rate=model_config["sample_rate"],
# sample_size=model_config["sample_size"],
# audio_channels=model_config.get("audio_channels", 2),
# )
duration=(float)(args.duration_sec)
dm = DataModule(
dataset_config,
batch_size=args.batch_size,
test_batch_size=args.test_batch_size,
num_workers=args.num_workers,
sample_rate=model_config["sample_rate"],
sample_size=(float)(args.duration_sec) * model_config["sample_rate"],
audio_channels=model_config.get("audio_channels", 2),
latent_length=round(44100/64/32*duration),
)
model_config["sample_size"] = duration * model_config["sample_rate"]
model_config["model"]["diffusion"]["config"]["sync_seq_len"] = 24*int(duration)
model_config["model"]["diffusion"]["config"]["clip_seq_len"] = 8*int(duration)
model_config["model"]["diffusion"]["config"]["latent_seq_len"] = round(44100/64/32*duration)
model = create_model_from_config(model_config)
## speed by torch.compile
if args.compile:
model = torch.compile(model)
if args.pretrained_ckpt_path:
copy_state_dict(model, load_ckpt_state_dict(args.pretrained_ckpt_path,prefix='diffusion.')) # autoencoder. diffusion.
if args.remove_pretransform_weight_norm == "pre_load":
remove_weight_norm_from_model(model.pretransform)
# import ipdb
# ipdb.set_trace()
if args.pretransform_ckpt_path:
load_vae_state = load_ckpt_state_dict(args.pretransform_ckpt_path, prefix='autoencoder.')
# new_state_dict = {k.replace("autoencoder.", ""): v for k, v in load_vae_state.items() if k.startswith("autoencoder.")}
model.pretransform.load_state_dict(load_vae_state)
# Remove weight_norm from the pretransform if specified
if args.remove_pretransform_weight_norm == "post_load":
remove_weight_norm_from_model(model.pretransform)
training_wrapper = create_training_wrapper_from_config(model_config, model)
# wandb_logger = L.pytorch.loggers.WandbLogger(project=args.name)
# wandb_logger.watch(training_wrapper)
exc_callback = ExceptionCallback()
# if args.save_dir and isinstance(wandb_logger.experiment.id, str):
# checkpoint_dir = os.path.join(args.save_dir, wandb_logger.experiment.project, wandb_logger.experiment.id, "checkpoints")
# else:
# checkpoint_dir = None
# ckpt_callback = ModelCheckpoint(every_n_train_steps=args.checkpoint_every, dirpath=checkpoint_dir, monitor='val_loss', mode='min', save_top_k=10)
save_model_config_callback = ModelConfigEmbedderCallback(model_config)
audio_dir = args.results_dir
pred_writer = CustomWriter(output_dir=audio_dir, write_interval="batch", batch_size=args.test_batch_size)
timer = Timer(duration="00:15:00:00")
demo_callback = create_demo_callback_from_config(model_config, demo_dl=dm)
#Combine args and config dicts
args_dict = vars(args)
args_dict.update({"model_config": model_config})
args_dict.update({"dataset_config": dataset_config})
# push_wandb_config(wandb_logger, args_dict)
#Set multi-GPU strategy if specified
if args.strategy:
if args.strategy == "deepspeed":
from pytorch_lightning.strategies import DeepSpeedStrategy
strategy = DeepSpeedStrategy(stage=2,
contiguous_gradients=True,
overlap_comm=True,
reduce_scatter=True,
reduce_bucket_size=5e8,
allgather_bucket_size=5e8,
load_full_weights=True
)
else:
strategy = args.strategy
else:
strategy = 'ddp_find_unused_parameters_true' if args.num_gpus > 1 else "auto"
trainer = L.Trainer(
devices=args.num_gpus,
accelerator="gpu",
num_nodes = args.num_nodes,
strategy=strategy,
precision=args.precision,
accumulate_grad_batches=args.accum_batches,
callbacks=[demo_callback, exc_callback, save_model_config_callback, timer, pred_writer],
log_every_n_steps=1,
max_epochs=1000,
default_root_dir=args.save_dir,
gradient_clip_val=args.gradient_clip_val,
reload_dataloaders_every_n_epochs = 0,
check_val_every_n_epoch=2,
)
# ckpt_path = hf_hub_download(
# repo_id="liuhuadai/ThinkSound",
# filename="thinksound.ckpt"
# )
ckpt_path = 'ckpts/thinksound.ckpt'
current_date = datetime.now()
formatted_date = current_date.strftime('%m%d')
audio_dir = f'{formatted_date}_step68k_batch_size'+str(args.test_batch_size)
metrics_path = os.path.join(args.ckpt_dir, 'audios',audio_dir,'cache',"output_metrics.json")
# if os.path.exists(metrics_path): continue
trainer.predict(training_wrapper, dm, return_predictions=False,ckpt_path=ckpt_path)
if __name__ == '__main__':
main() |