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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from models.base.base_trainer import BaseTrainer
from diffusers import DDPMScheduler
from models.tta.ldm.audioldm_dataset import AudioLDMDataset, AudioLDMCollator
from models.tta.autoencoder.autoencoder import AutoencoderKL
from models.tta.ldm.audioldm import AudioLDM, UNetModel
import torch
import torch.nn as nn
from torch.nn import MSELoss, L1Loss
import torch.nn.functional as F
from torch.utils.data import ConcatDataset, DataLoader
from transformers import T5EncoderModel
from diffusers import DDPMScheduler
class AudioLDMTrainer(BaseTrainer):
def __init__(self, args, cfg):
BaseTrainer.__init__(self, args, cfg)
self.cfg = cfg
self.build_autoencoderkl()
self.build_textencoder()
self.nosie_scheduler = self.build_noise_scheduler()
self.save_config_file()
def build_autoencoderkl(self):
self.autoencoderkl = AutoencoderKL(self.cfg.model.autoencoderkl)
self.autoencoder_path = self.cfg.model.autoencoder_path
checkpoint = torch.load(self.autoencoder_path, map_location="cpu")
self.autoencoderkl.load_state_dict(checkpoint["model"])
self.autoencoderkl.cuda(self.args.local_rank)
self.autoencoderkl.requires_grad_(requires_grad=False)
self.autoencoderkl.eval()
def build_textencoder(self):
self.text_encoder = T5EncoderModel.from_pretrained("t5-base")
self.text_encoder.cuda(self.args.local_rank)
self.text_encoder.requires_grad_(requires_grad=False)
self.text_encoder.eval()
def build_noise_scheduler(self):
nosie_scheduler = DDPMScheduler(
num_train_timesteps=self.cfg.model.noise_scheduler.num_train_timesteps,
beta_start=self.cfg.model.noise_scheduler.beta_start,
beta_end=self.cfg.model.noise_scheduler.beta_end,
beta_schedule=self.cfg.model.noise_scheduler.beta_schedule,
clip_sample=self.cfg.model.noise_scheduler.clip_sample,
# steps_offset=self.cfg.model.noise_scheduler.steps_offset,
# set_alpha_to_one=self.cfg.model.noise_scheduler.set_alpha_to_one,
# skip_prk_steps=self.cfg.model.noise_scheduler.skip_prk_steps,
prediction_type=self.cfg.model.noise_scheduler.prediction_type,
)
return nosie_scheduler
def build_dataset(self):
return AudioLDMDataset, AudioLDMCollator
def build_data_loader(self):
Dataset, Collator = self.build_dataset()
# build dataset instance for each dataset and combine them by ConcatDataset
datasets_list = []
for dataset in self.cfg.dataset:
subdataset = Dataset(self.cfg, dataset, is_valid=False)
datasets_list.append(subdataset)
train_dataset = ConcatDataset(datasets_list)
train_collate = Collator(self.cfg)
# use batch_sampler argument instead of (sampler, shuffle, drop_last, batch_size)
train_loader = DataLoader(
train_dataset,
collate_fn=train_collate,
num_workers=self.args.num_workers,
batch_size=self.cfg.train.batch_size,
pin_memory=False,
)
if not self.cfg.train.ddp or self.args.local_rank == 0:
datasets_list = []
for dataset in self.cfg.dataset:
subdataset = Dataset(self.cfg, dataset, is_valid=True)
datasets_list.append(subdataset)
valid_dataset = ConcatDataset(datasets_list)
valid_collate = Collator(self.cfg)
valid_loader = DataLoader(
valid_dataset,
collate_fn=valid_collate,
num_workers=1,
batch_size=self.cfg.train.batch_size,
)
else:
raise NotImplementedError("DDP is not supported yet.")
# valid_loader = None
data_loader = {"train": train_loader, "valid": valid_loader}
return data_loader
def build_optimizer(self):
optimizer = torch.optim.AdamW(self.model.parameters(), **self.cfg.train.adam)
return optimizer
# TODO: check it...
def build_scheduler(self):
return None
# return ReduceLROnPlateau(self.optimizer["opt_ae"], **self.cfg.train.lronPlateau)
def write_summary(self, losses, stats):
for key, value in losses.items():
self.sw.add_scalar(key, value, self.step)
def write_valid_summary(self, losses, stats):
for key, value in losses.items():
self.sw.add_scalar(key, value, self.step)
def build_criterion(self):
criterion = nn.MSELoss(reduction="mean")
return criterion
def get_state_dict(self):
if self.scheduler != None:
state_dict = {
"model": self.model.state_dict(),
"optimizer": self.optimizer.state_dict(),
"scheduler": self.scheduler.state_dict(),
"step": self.step,
"epoch": self.epoch,
"batch_size": self.cfg.train.batch_size,
}
else:
state_dict = {
"model": self.model.state_dict(),
"optimizer": self.optimizer.state_dict(),
"step": self.step,
"epoch": self.epoch,
"batch_size": self.cfg.train.batch_size,
}
return state_dict
def load_model(self, checkpoint):
self.step = checkpoint["step"]
self.epoch = checkpoint["epoch"]
self.model.load_state_dict(checkpoint["model"])
self.optimizer.load_state_dict(checkpoint["optimizer"])
if self.scheduler != None:
self.scheduler.load_state_dict(checkpoint["scheduler"])
def build_model(self):
self.model = AudioLDM(self.cfg.model.audioldm)
return self.model
@torch.no_grad()
def mel_to_latent(self, melspec):
posterior = self.autoencoderkl.encode(melspec)
latent = posterior.sample() # (B, 4, 5, 78)
return latent
@torch.no_grad()
def get_text_embedding(self, text_input_ids, text_attention_mask):
text_embedding = self.text_encoder(
input_ids=text_input_ids, attention_mask=text_attention_mask
).last_hidden_state
return text_embedding # (B, T, 768)
def train_step(self, data):
train_losses = {}
total_loss = 0
train_stats = {}
melspec = data["melspec"].unsqueeze(1) # (B, 80, T) -> (B, 1, 80, T)
latents = self.mel_to_latent(melspec)
text_embedding = self.get_text_embedding(
data["text_input_ids"], data["text_attention_mask"]
)
noise = torch.randn_like(latents).float()
bsz = latents.shape[0]
timesteps = torch.randint(
0,
self.cfg.model.noise_scheduler.num_train_timesteps,
(bsz,),
device=latents.device,
)
timesteps = timesteps.long()
with torch.no_grad():
noisy_latents = self.nosie_scheduler.add_noise(latents, noise, timesteps)
model_pred = self.model(
noisy_latents, timesteps=timesteps, context=text_embedding
)
loss = self.criterion(model_pred, noise)
train_losses["loss"] = loss
total_loss += loss
self.optimizer.zero_grad()
total_loss.backward()
self.optimizer.step()
for item in train_losses:
train_losses[item] = train_losses[item].item()
return train_losses, train_stats, total_loss.item()
# TODO: eval step
@torch.no_grad()
def eval_step(self, data, index):
valid_loss = {}
total_valid_loss = 0
valid_stats = {}
melspec = data["melspec"].unsqueeze(1) # (B, 80, T) -> (B, 1, 80, T)
latents = self.mel_to_latent(melspec)
text_embedding = self.get_text_embedding(
data["text_input_ids"], data["text_attention_mask"]
)
noise = torch.randn_like(latents).float()
bsz = latents.shape[0]
timesteps = torch.randint(
0,
self.cfg.model.noise_scheduler.num_train_timesteps,
(bsz,),
device=latents.device,
)
timesteps = timesteps.long()
noisy_latents = self.nosie_scheduler.add_noise(latents, noise, timesteps)
model_pred = self.model(noisy_latents, timesteps, text_embedding)
loss = self.criterion(model_pred, noise)
valid_loss["loss"] = loss
total_valid_loss += loss
for item in valid_loss:
valid_loss[item] = valid_loss[item].item()
return valid_loss, valid_stats, total_valid_loss.item()
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