def __init__(self, z_shapes, l_bins, encoder, decoder, level,
downs_t, strides_t, labels, prior_kwargs, x_cond_kwargs, y_cond_kwargs,
prime_kwargs, copy_input, labels_v3=False,
merged_decoder=False, single_enc_dec=False):
super().__init__()
self.use_tokens = prime_kwargs.pop('use_tokens')
self.n_tokens = prime_kwargs.pop('n_tokens')
self.prime_loss_fraction = prime_kwargs.pop('prime_loss_fraction')
self.copy_input = copy_input
if self.copy_input:
prime_kwargs['bins'] = l_bins
self.z_shapes = z_shapes
self.levels = len(self.z_shapes)
self.z_shape = self.z_shapes[level]
self.level = level
assert level < self.levels, f"Total levels {self.levels}, got level {level}"
self.l_bins = l_bins
# Passing functions instead of the vqvae module to avoid getting params
self.encoder = encoder
self.decoder = decoder
# X conditioning
self.x_cond = (level != (self.levels - 1))
self.cond_level = level + 1
# Y conditioning
self.y_cond = labels
self.single_enc_dec = single_enc_dec
# X conditioning
if self.x_cond:
self.conditioner_blocks = nn.ModuleList()
conditioner_block = lambda _level: Conditioner(input_shape=z_shapes[_level],
bins=l_bins,
down_t=downs_t[_level],
stride_t=strides_t[_level],
**x_cond_kwargs)
if dist.get_rank() == 0: print(f"Conditioning on 1 above level(s)")
self.conditioner_blocks.append(conditioner_block(self.cond_level))
# Y conditioning
if self.y_cond:
self.n_time = self.z_shape[0] # Assuming STFT=TF order and raw=T1 order, so T is first dim
self.y_emb = LabelConditioner(n_time=self.n_time,include_time_signal=not self.x_cond,**y_cond_kwargs)
# Lyric conditioning
if single_enc_dec:
# Single encoder-decoder transformer
self.prior_shapes = [(self.n_tokens,), prior_kwargs.pop('input_shape')]
self.prior_bins = [prime_kwargs['bins'], prior_kwargs.pop('bins')]
self.prior_dims = [np.prod(shape) for shape in self.prior_shapes]
self.prior_bins_shift = np.cumsum([0, *self.prior_bins])[:-1]
self.prior_width = prior_kwargs['width']
print_once(f'Creating cond. autoregress with prior bins {self.prior_bins}, ')
print_once(f'dims {self.prior_dims}, ')
print_once(f'shift {self.prior_bins_shift}')
print_once(f'input shape {sum(self.prior_dims)}')
print_once(f'input bins {sum(self.prior_bins)}')
print_once(f'Self copy is {self.copy_input}')
self.prime_loss_dims, self.gen_loss_dims = self.prior_dims[0], self.prior_dims[1]
self.total_loss_dims = self.prime_loss_dims + self.gen_loss_dims
self.prior = ConditionalAutoregressive2D(input_shape=(sum(self.prior_dims),),
bins=sum(self.prior_bins),
x_cond=(self.x_cond or self.y_cond), y_cond=True,
prime_len=self.prime_loss_dims,
**prior_kwargs)
else:
# Separate encoder-decoder transformer
if self.n_tokens != 0 and self.use_tokens:
from jukebox.transformer.ops import Conv1D
prime_input_shape = (self.n_tokens,)
self.prime_loss_dims = np.prod(prime_input_shape)
self.prime_acts_width, self.prime_state_width = prime_kwargs['width'], prior_kwargs['width']
self.prime_prior = ConditionalAutoregressive2D(input_shape=prime_input_shape, x_cond=False, y_cond=False,
only_encode=True,
**prime_kwargs)
self.prime_state_proj = Conv1D(self.prime_acts_width, self.prime_state_width, init_scale=prime_kwargs['init_scale'])
self.prime_state_ln = LayerNorm(self.prime_state_width)
self.prime_bins = prime_kwargs['bins']
self.prime_x_out = nn.Linear(self.prime_state_width, self.prime_bins, bias=False)
nn.init.normal_(self.prime_x_out.weight, std=0.02 * prior_kwargs['init_scale'])
else:
self.prime_loss_dims = 0
self.gen_loss_dims = np.prod(self.z_shape)
self.total_loss_dims = self.prime_loss_dims + self.gen_loss_dims
self.prior = ConditionalAutoregressive2D(x_cond=(self.x_cond or self.y_cond), y_cond=self.y_cond,
encoder_dims = self.prime_loss_dims, merged_decoder=merged_decoder,
**prior_kwargs)
self.n_ctx = self.gen_loss_dims
self.downsamples = calculate_strides(strides_t, downs_t)
self.cond_downsample = self.downsamples[level+1] if level != self.levels - 1 else None
self.raw_to_tokens = np.prod(self.downsamples[:level+1])
self.sample_length = self.n_ctx*self.raw_to_tokens
if labels:
self.labels_v3 = labels_v3
self.labeller = Labeller(self.y_emb.max_bow_genre_size, self.n_tokens, self.sample_length, v3=self.labels_v3)
else:
self.labeller = EmptyLabeller()
print(f"Level:{level}, Cond downsample:{self.cond_downsample}, Raw to tokens:{self.raw_to_tokens}, Sample length:{self.sample_length}")
def _sample_single_window(zs, labels, sampling_kwargs, level, prior, start,
hps):
n_samples = hps.n_samples
n_ctx = prior.n_ctx
end = start + n_ctx
zs = [z.cpu() for z in zs
] # force tokens onto ram incase it was created externally
# get z already sampled at current level
z = zs[level][:, start:end].cuda()
if 'sample_tokens' in sampling_kwargs:
# Support sampling a window shorter than n_ctx
sample_tokens = sampling_kwargs['sample_tokens']
else:
sample_tokens = (end - start)
conditioning_tokens, new_tokens = z.shape[1], sample_tokens - z.shape[1]
print_once(
f"Sampling {sample_tokens} tokens for [{start},{start+sample_tokens}]. Conditioning on {conditioning_tokens} tokens"
)
if new_tokens <= 0:
# Nothing new to sample
return zs
# get z_conds from level above
z_conds = prior.get_z_conds(zs, start, end)
if z_conds != None:
for k in range(len(z_conds)):
z_conds[k] = z_conds[k].cuda()
# set y offset, sample_length and lyrics tokens
y = prior.get_y(labels, start)
empty_cache()
max_batch_size = sampling_kwargs['max_batch_size']
del sampling_kwargs['max_batch_size']
z_list = split_batch(z, n_samples, max_batch_size)
z_conds_list = split_batch(z_conds, n_samples, max_batch_size)
y_list = split_batch(y, n_samples, max_batch_size)
z_samples = []
for z_i, z_conds_i, y_i in zip(z_list, z_conds_list, y_list):
z_samples_i = prior.sample(n_samples=z_i.shape[0],
z=z_i,
z_conds=z_conds_i,
y=y_i,
**sampling_kwargs)
z_samples.append(z_samples_i)
z = t.cat(z_samples, dim=0)
sampling_kwargs['max_batch_size'] = max_batch_size
# Update z with new sample
z_new = z[:, -new_tokens:].cpu()
del z
del y
zs[level] = t.cat([zs[level], z_new], dim=1)
return zs
def run(hps="teeny", port=29500, **kwargs):
from jukebox.utils.dist_utils import setup_dist_from_mpi
rank, local_rank, device = setup_dist_from_mpi(port=port)
wandb_utils.setup_wandb()
hps = setup_hparams(hps, kwargs)
hps.ngpus = dist.get_world_size()
hps.argv = " ".join(sys.argv)
hps.bs_sample = hps.nworkers = hps.bs
# Setup dataset
data_processor = DataProcessor(hps)
# Setup models
vqvae = make_vqvae(hps, device)
print_once(f"Parameters VQVAE:{count_parameters(vqvae)}")
if hps.prior:
prior = make_prior(hps, vqvae, device)
print_once(f"Parameters Prior:{count_parameters(prior)}")
model = prior
else:
model = vqvae
wandb_utils.watch_model(model)
wandb_utils.log_hyperparams(hps)
# Setup opt, ema and distributed_model.
opt, shd, scalar = get_optimizer(model, hps)
ema = get_ema(model, hps)
distributed_model = get_ddp(model, hps)
logger, metrics = init_logging(hps, local_rank, rank)
logger.iters = model.step
# Run training, eval, sample
for epoch in range(hps.curr_epoch, hps.epochs):
metrics.reset()
data_processor.set_epoch(epoch)
if hps.train:
train_metrics = train(distributed_model, model, opt, shd, scalar,
ema, logger, metrics, data_processor, hps)
train_metrics['epoch'] = epoch
if rank == 0:
print(
'Train', ' '.join([
f'{key}: {val:0.4f}'
for key, val in train_metrics.items()
]))
dist.barrier()
if hps.test:
if ema: ema.swap()
test_metrics = evaluate(distributed_model, model, logger, metrics,
data_processor, hps)
test_metrics['epoch'] = epoch
if rank == 0:
print(
'Ema', ' '.join([
f'{key}: {val:0.4f}'
for key, val in test_metrics.items()
]))
dist.barrier()
if ema: ema.swap()
dist.barrier()
def run(hps="teeny", port=29500, **kwargs):
from jukebox.utils.dist_utils import setup_dist_from_mpi
# t.cuda.set_limit_lms(2000000000)
# print('Limit Settings:')
# print('Enabled', t.cuda.get_enabled_lms())
# print('Limit', t.cuda.get_limit_lms())
# print('Size', t.cuda.get_size_lms())
audio_database = kwargs['audio_database']
del kwargs['audio_database']
rank, local_rank, device = setup_dist_from_mpi(port=port)
print('device:', device)
hps = setup_hparams(hps, kwargs)
hps.ngpus = dist.get_world_size()
hps.argv = " ".join(sys.argv)
hps.bs_sample, hps.nworkers, hps.bs = 1, 0, 1
print('hps.nworkers', hps.nworkers)
# Setup dataset
data_processor = DataProcessor(hps, audio_database)
# Setup models
vqvae = make_vqvae(hps, device)
print_once(f"Parameters VQVAE:{count_parameters(vqvae)}")
if hps.prior:
prior = make_prior(hps, vqvae, device)
print_once(f"Parameters Prior:{count_parameters(prior)}")
model = prior
else:
model = vqvae
# Setup opt, ema and distributed_model.
opt, shd, scalar = get_optimizer(model, hps)
ema = get_ema(model, hps)
#distributed_model = get_ddp(model, hps)
logger, metrics = init_logging(hps, local_rank, rank)
logger.iters = model.step
# Run training, eval, sample
for epoch in range(hps.curr_epoch, hps.epochs):
metrics.reset()
data_processor.set_epoch(epoch)
if hps.train:
train_metrics = train(model, model, opt, shd, scalar, ema, logger, metrics, data_processor, hps)
train_metrics['epoch'] = epoch
if rank == 0:
print('Train',' '.join([f'{key}: {val:0.4f}' for key,val in train_metrics.items()]))
dist.barrier()
if hps.test:
if ema: ema.swap()
test_metrics = evaluate(model, model, logger, metrics, data_processor, hps)
test_metrics['epoch'] = epoch
if rank == 0:
print('Ema',' '.join([f'{key}: {val:0.4f}' for key,val in test_metrics.items()]))
dist.barrier()
if ema: ema.swap()
dist.barrier()
def sample_single_window(zs,
labels_1,
labels_2,
sampling_kwargs,
level,
prior,
start,
hps,
total_length=1):
n_samples = hps.n_samples
n_ctx = prior.n_ctx
end = start + n_ctx
# get z already sampled at current level
z = zs[level][:, start:end]
if 'sample_tokens' in sampling_kwargs:
# Support sampling a window shorter than n_ctx
sample_tokens = sampling_kwargs['sample_tokens']
else:
sample_tokens = (end - start)
conditioning_tokens, new_tokens = z.shape[1], sample_tokens - z.shape[1]
print_once(
f"Sampling {sample_tokens} tokens for [{start},{start+sample_tokens}]. Conditioning on {conditioning_tokens} tokens"
)
print_once(
f"{round( (start+sample_tokens)/total_length*100.0 )}%-ish, level {level}"
)
if new_tokens <= 0:
# Nothing new to sample
return zs
# get z_conds from level above
z_conds = prior.get_z_conds(zs, start, end)
# set y offset, sample_length and lyrics tokens
y1 = prior.get_y(labels_1, start)
y2 = prior.get_y(labels_2, start)
empty_cache()
max_batch_size = sampling_kwargs['max_batch_size']
del sampling_kwargs['max_batch_size']
z_list = split_batch(z, n_samples, max_batch_size)
z_conds_list = split_batch(z_conds, n_samples, max_batch_size)
y1_list = split_batch(y1, n_samples, max_batch_size)
y2_list = split_batch(y2, n_samples, max_batch_size)
z_samples = []
for z_i, z_conds_i, y1_i, y2_i in zip(z_list, z_conds_list, y1_list,
y2_list):
z_samples_i = prior.sample(n_samples=z_i.shape[0],
z=z_i,
z_conds=z_conds_i,
y1=y1_i,
y2=y2_i,
**sampling_kwargs)
z_samples.append(z_samples_i)
z = t.cat(z_samples, dim=0)
sampling_kwargs['max_batch_size'] = max_batch_size
# Update z with new sample
z_new = z[:, -new_tokens:]
zs[level] = t.cat([zs[level], z_new], dim=1)
return zs