predict.py

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()