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import pytorch_lightning as pl
import sys, gc
import random
import torch
import torchaudio
import typing as tp
import wandb
from aeiou.viz import pca_point_cloud, audio_spectrogram_image, tokens_spectrogram_image
from ema_pytorch import EMA
from einops import rearrange
from torch import optim
from torch.nn import functional as F
from pytorch_lightning.utilities.rank_zero import rank_zero_only
from audiocraft.models import MusicGen
from audiocraft.modules.conditioners import ClassifierFreeGuidanceDropout, ConditioningAttributes
from time import time
class Profiler:
def __init__(self):
self.ticks = [[time(), None]]
def tick(self, msg):
self.ticks.append([time(), msg])
def __repr__(self):
rep = 80 * "=" + "\n"
for i in range(1, len(self.ticks)):
msg = self.ticks[i][1]
ellapsed = self.ticks[i][0] - self.ticks[i - 1][0]
rep += msg + f": {ellapsed*1000:.2f}ms\n"
rep += 80 * "=" + "\n\n\n"
return rep
class MusicGenTrainingWrapper(pl.LightningModule):
def __init__(self, musicgen_model, lr = 1e-4, ema_copy=None):
super().__init__()
self.musicgen_model: MusicGen = musicgen_model
self.musicgen_model.compression_model.requires_grad_(False)
self.lm = self.musicgen_model.lm
self.lm.to(torch.float32).train().requires_grad_(True)
self.lm_ema = EMA(self.lm, ema_model=ema_copy, beta=0.99, update_every=10)
self.cfg_dropout = ClassifierFreeGuidanceDropout(0.1)
self.lr = lr
def configure_optimizers(self):
optimizer = optim.AdamW([*self.lm.parameters()], lr=self.lr, betas=(0.9, 0.95), weight_decay=0.1)
return optimizer
# Copied and modified from https://github.com/facebookresearch/audiocraft/blob/main/audiocraft/solvers/musicgen.py under MIT license
# License can be found in LICENSES/LICENSE_META.txt
def _compute_cross_entropy(
self, logits: torch.Tensor, targets: torch.Tensor, mask: torch.Tensor
) -> tp.Tuple[torch.Tensor, tp.List[torch.Tensor]]:
"""Compute cross entropy between multi-codebook targets and model's logits.
The cross entropy is computed per codebook to provide codebook-level cross entropy.
Valid timesteps for each of the codebook are pulled from the mask, where invalid
timesteps are set to 0.
Args:
logits (torch.Tensor): Model's logits of shape [B, K, T, card].
targets (torch.Tensor): Target codes, of shape [B, K, T].
mask (torch.Tensor): Mask for valid target codes, of shape [B, K, T].
Returns:
ce (torch.Tensor): Cross entropy averaged over the codebooks
ce_per_codebook (list of torch.Tensor): Cross entropy per codebook (detached).
"""
B, K, T = targets.shape
assert logits.shape[:-1] == targets.shape
assert mask.shape == targets.shape
ce = torch.zeros([], device=targets.device)
ce_per_codebook: tp.List[torch.Tensor] = []
for k in range(K):
logits_k = logits[:, k, ...].contiguous().view(-1, logits.size(-1)) # [B x T, card]
targets_k = targets[:, k, ...].contiguous().view(-1) # [B x T]
mask_k = mask[:, k, ...].contiguous().view(-1) # [B x T]
ce_targets = targets_k[mask_k]
ce_logits = logits_k[mask_k]
q_ce = F.cross_entropy(ce_logits, ce_targets)
ce += q_ce
ce_per_codebook.append(q_ce.detach())
# average cross entropy across codebooks
ce = ce / K
return ce, ce_per_codebook
def training_step(self, batch, batch_idx):
reals, metadata = batch
if reals.ndim == 4 and reals.shape[0] == 1:
reals = reals[0]
# Convert reals to mono if necessary
if self.musicgen_model.audio_channels == 1:
reals = reals.mean(dim=1, keepdim=True)
self.musicgen_model.compression_model.to(self.device).eval()
self.lm.to(self.device).train()
self.lm.condition_provider.to(self.device).eval()
self.lm.condition_provider.conditioners["description"].device = self.device
self.lm.condition_provider.conditioners["description"].t5.to(self.device).eval()
with torch.cuda.amp.autocast():
codes, _ = self.musicgen_model.compression_model.encode(reals) # [b, k, t]
attributes = [ConditioningAttributes(text={'description': md["prompt"][0][:512]}) for md in metadata]
attributes = self.lm.cfg_dropout(attributes)
attributes = self.lm.att_dropout(attributes)
tokenized = self.lm.condition_provider.tokenize(attributes)
with torch.cuda.amp.autocast(enabled=False):
condition_tensors = self.lm.condition_provider(tokenized)
lm_output = self.lm.compute_predictions(
codes=codes,
conditions = [],
condition_tensors = condition_tensors,
)
logits = lm_output.logits # [b, k, t, c]
logits_mask = lm_output.mask # [b, k, t]
cross_entropy, cross_entropy_per_codebook = self._compute_cross_entropy(logits, codes, logits_mask)
loss = cross_entropy
log_dict = {
'train/loss': loss.detach(),
'train/cross_entropy': cross_entropy.detach(),
'train/perplexity': torch.exp(cross_entropy).detach(),
}
for k, ce_q in enumerate(cross_entropy_per_codebook):
log_dict[f'cross_entropy_q{k + 1}'] = ce_q
log_dict[f'perplexity_q{k + 1}'] = torch.exp(ce_q)
self.log_dict(log_dict, prog_bar=True, on_step=True)
return loss
def on_before_zero_grad(self, *args, **kwargs):
self.lm_ema.update()
def export_model(self, path):
self.musicgen_model.lm = self.lm_ema.ema_model
export_state_dict = {"state_dict": self.musicgen_model.state_dict()}
torch.save(export_state_dict, path)
class MusicGenDemoCallback(pl.Callback):
def __init__(self,
demo_every=2000,
num_demos=8,
sample_size=65536,
sample_rate=48000,
demo_conditioning: tp.Optional[tp.Dict[str, tp.Any]] = None,
demo_cfg_scales: tp.Optional[tp.List[int]] = [3, 5, 7],
**kwargs
):
super().__init__()
self.demo_every = demo_every
self.num_demos = num_demos
self.demo_samples = sample_size
self.sample_rate = sample_rate
self.last_demo_step = -1
self.demo_conditioning = demo_conditioning
self.demo_cfg_scales = demo_cfg_scales
@rank_zero_only
@torch.no_grad()
def on_train_batch_end(self, trainer, module: MusicGenTrainingWrapper, outputs, batch, batch_idx):
if (trainer.global_step - 1) % self.demo_every != 0 or self.last_demo_step == trainer.global_step:
return
module.eval()
print(f"Generating demo")
self.last_demo_step = trainer.global_step
demo_length_sec = self.demo_samples // self.sample_rate
try:
print("Getting conditioning")
prompts = [md["prompt"][:512] for md in self.demo_conditioning]
for cfg_scale in self.demo_cfg_scales:
module.musicgen_model.set_generation_params(duration=demo_length_sec, cfg_coef=cfg_scale)
with torch.cuda.amp.autocast():
print(f"Generating demo for cfg scale {cfg_scale}")
fakes = module.musicgen_model.generate(prompts, progress=True)
# Put the demos together
fakes = rearrange(fakes, 'b d n -> d (b n)')
log_dict = {}
filename = f'demo_cfg_{cfg_scale}_{trainer.global_step:08}.wav'
fakes = fakes.to(torch.float32).clamp(-1, 1).mul(32767).to(torch.int16).cpu()
torchaudio.save(filename, fakes, self.sample_rate)
log_dict[f'demo_cfg_{cfg_scale}'] = wandb.Audio(filename,
sample_rate=self.sample_rate,
caption=f'Reconstructed')
log_dict[f'demo_melspec_left_cfg_{cfg_scale}'] = wandb.Image(audio_spectrogram_image(fakes))
trainer.logger.experiment.log(log_dict)
except Exception as e:
raise e
finally:
gc.collect()
torch.cuda.empty_cache()
module.train() |