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This commit is contained in:
James Betker 2022-06-10 14:34:05 -06:00
parent 89bd40d39f
commit 2158383fa4
2 changed files with 31 additions and 54 deletions
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28
codes/models/audio/music/music_quantizer2.py
View File

@ -45,7 +45,7 @@ class Upsample(nn.Module):
class ResBlock(nn.Module): class ResBlock(nn.Module):
def __init__(self, chan, checkpoint=True): def __init__(self, chan):
super().__init__() super().__init__()
self.net = nn.Sequential( self.net = nn.Sequential(
nn.Conv1d(chan, chan, 3, padding = 1), nn.Conv1d(chan, chan, 3, padding = 1),
@ -56,13 +56,9 @@ class ResBlock(nn.Module):
nn.SiLU(), nn.SiLU(),
zero_module(nn.Conv1d(chan, chan, 3, padding = 1)), zero_module(nn.Conv1d(chan, chan, 3, padding = 1)),
) )
self.checkpoint = checkpoint
def forward(self, x): def forward(self, x):
if self.checkpoint:
return checkpoint(self._forward, x) + x return checkpoint(self._forward, x) + x
else:
return self._forward(x) + x
def _forward(self, x): def _forward(self, x):
return self.net(x) return self.net(x)
@ -169,7 +165,7 @@ class Wav2Vec2GumbelVectorQuantizer(nn.Module):
class MusicQuantizer2(nn.Module): class MusicQuantizer2(nn.Module):
def __init__(self, inp_channels=256, inner_dim=1024, codevector_dim=1024, down_steps=2, def __init__(self, inp_channels=256, inner_dim=1024, codevector_dim=1024, down_steps=2,
max_gumbel_temperature=2.0, min_gumbel_temperature=.5, gumbel_temperature_decay=.999995, max_gumbel_temperature=2.0, min_gumbel_temperature=.5, gumbel_temperature_decay=.999995,
codebook_size=16, codebook_groups=4, checkpoint=True, codebook_size=16, codebook_groups=4,
# Downsample args: # Downsample args:
expressive_downsamples=False): expressive_downsamples=False):
super().__init__() super().__init__()
@ -195,14 +191,14 @@ class MusicQuantizer2(nn.Module):
self.up = nn.Sequential(*[Upsample(inner_dim[i], inner_dim[i+1]) for i in range(len(inner_dim)-1)] + self.up = nn.Sequential(*[Upsample(inner_dim[i], inner_dim[i+1]) for i in range(len(inner_dim)-1)] +
[nn.Conv1d(inner_dim[-1], inp_channels, kernel_size=3, padding=1)]) [nn.Conv1d(inner_dim[-1], inp_channels, kernel_size=3, padding=1)])
self.encoder = nn.Sequential(ResBlock(inner_dim[0], checkpoint=checkpoint), self.encoder = nn.Sequential(ResBlock(inner_dim[0]),
ResBlock(inner_dim[0], checkpoint=checkpoint), ResBlock(inner_dim[0]),
ResBlock(inner_dim[0], checkpoint=checkpoint)) ResBlock(inner_dim[0]))
self.enc_norm = nn.LayerNorm(inner_dim[0], eps=1e-5) self.enc_norm = nn.LayerNorm(inner_dim[0], eps=1e-5)
self.decoder = nn.Sequential(nn.Conv1d(codevector_dim, inner_dim[0], kernel_size=3, padding=1), self.decoder = nn.Sequential(nn.Conv1d(codevector_dim, inner_dim[0], kernel_size=3, padding=1),
ResBlock(inner_dim[0], checkpoint=checkpoint), ResBlock(inner_dim[0]),
ResBlock(inner_dim[0], checkpoint=checkpoint), ResBlock(inner_dim[0]),
ResBlock(inner_dim[0], checkpoint=checkpoint)) ResBlock(inner_dim[0]))
self.codes = torch.zeros((3000000,), dtype=torch.long) self.codes = torch.zeros((3000000,), dtype=torch.long)
self.internal_step = 0 self.internal_step = 0
@ -228,17 +224,13 @@ class MusicQuantizer2(nn.Module):
diversity = (self.quantizer.num_codevectors - perplexity) / self.quantizer.num_codevectors diversity = (self.quantizer.num_codevectors - perplexity) / self.quantizer.num_codevectors
self.log_codes(codes) self.log_codes(codes)
h = self.decoder(codevectors.permute(0,2,1)) h = self.decoder(codevectors.permute(0,2,1))
if return_decoder_latent:
if not hasattr(self, 'up') and return_decoder_latent: return h, diversity
return None, diversity, h
reconstructed = self.up(h.float()) reconstructed = self.up(h.float())
reconstructed = reconstructed[:, :, :orig_mel.shape[-1]] reconstructed = reconstructed[:, :, :orig_mel.shape[-1]]
mse = F.mse_loss(reconstructed, orig_mel) mse = F.mse_loss(reconstructed, orig_mel)
if return_decoder_latent:
return mse, diversity, h
else:
return mse, diversity return mse, diversity
def log_codes(self, codes): def log_codes(self, codes):

47
codes/models/audio/music/transformer_diffusion8.py
View File

@ -196,18 +196,15 @@ class TransformerDiffusion(nn.Module):
class TransformerDiffusionWithQuantizer(nn.Module): class TransformerDiffusionWithQuantizer(nn.Module):
def __init__(self, freeze_quantizer_until=20000, quantizer_dims=[1024], no_reconstruction=True, **kwargs): def __init__(self, freeze_quantizer_until=20000, **kwargs):
super().__init__() super().__init__()
self.internal_step = 0 self.internal_step = 0
self.freeze_quantizer_until = freeze_quantizer_until self.freeze_quantizer_until = freeze_quantizer_until
self.diff = TransformerDiffusion(**kwargs) self.diff = TransformerDiffusion(**kwargs)
self.quantizer = MusicQuantizer2(inp_channels=kwargs['in_channels'], inner_dim=quantizer_dims, self.quantizer = MusicQuantizer2(inp_channels=256, inner_dim=[1024], codevector_dim=1024, codebook_size=256,
codevector_dim=quantizer_dims[0], checkpoint=False, codebook_groups=2, max_gumbel_temperature=4, min_gumbel_temperature=.5)
codebook_size=256, codebook_groups=2,
max_gumbel_temperature=4, min_gumbel_temperature=.5)
self.quantizer.quantizer.temperature = self.quantizer.min_gumbel_temperature self.quantizer.quantizer.temperature = self.quantizer.min_gumbel_temperature
if no_reconstruction:
del self.quantizer.up del self.quantizer.up
def update_for_step(self, step, *args): def update_for_step(self, step, *args):
@ -219,30 +216,27 @@ class TransformerDiffusionWithQuantizer(nn.Module):
) )
def forward(self, x, timesteps, truth_mel, conditioning_input=None, disable_diversity=False, conditioning_free=False): def forward(self, x, timesteps, truth_mel, conditioning_input=None, disable_diversity=False, conditioning_free=False):
mse, diversity_loss, proj = self.quantizer(truth_mel, return_decoder_latent=True) quant_grad_enabled = self.internal_step > self.freeze_quantizer_until
with torch.set_grad_enabled(quant_grad_enabled):
proj, diversity_loss = self.quantizer(truth_mel, return_decoder_latent=True)
proj = proj.permute(0,2,1) proj = proj.permute(0,2,1)
quant_grad_enabled = self.internal_step > self.freeze_quantizer_until
if not quant_grad_enabled:
proj = proj.detach()
# Make sure this does not cause issues in DDP by explicitly using the parameters for nothing. # Make sure this does not cause issues in DDP by explicitly using the parameters for nothing.
if not quant_grad_enabled:
unused = 0 unused = 0
for p in self.quantizer.parameters(): for p in self.quantizer.parameters():
unused = unused + p.mean() * 0 unused = unused + p.mean() * 0
proj = proj + unused proj = proj + unused
diversity_loss = diversity_loss * 0
diff = self.diff(x, timesteps, codes=proj, conditioning_input=conditioning_input, diff = self.diff(x, timesteps, codes=proj, conditioning_input=conditioning_input, conditioning_free=conditioning_free)
conditioning_free=conditioning_free)
if disable_diversity: if disable_diversity:
return diff return diff
if mse is None:
return diff, diversity_loss return diff, diversity_loss
return diff, diversity_loss, mse
def get_debug_values(self, step, __): def get_debug_values(self, step, __):
if self.quantizer.total_codes > 0: if self.quantizer.total_codes > 0:
return {'histogram_quant_codes': self.quantizer.codes[:self.quantizer.total_codes], return {'histogram_codes': self.quantizer.codes[:self.quantizer.total_codes],
'gumbel_temperature': self.quantizer.quantizer.temperature} 'gumbel_temperature': self.quantizer.quantizer.temperature}
else: else:
return {} return {}
@ -320,26 +314,18 @@ def register_transformer_diffusion8_with_ar_prior(opt_net, opt):
def test_quant_model(): def test_quant_model():
clip = torch.randn(2, 100, 401) clip = torch.randn(2, 256, 400)
ts = torch.LongTensor([600, 600]) ts = torch.LongTensor([600, 600])
model = TransformerDiffusionWithQuantizer(in_channels=100, out_channels=200, quantizer_dims=[1024,768,512,384], model = TransformerDiffusionWithQuantizer(model_channels=2048, block_channels=1024, prenet_channels=1024,
model_channels=2048, block_channels=1024, prenet_channels=1024, input_vec_dim=1024, num_layers=16, prenet_layers=6)
input_vec_dim=1024, num_layers=16, prenet_layers=6, model.get_grad_norm_parameter_groups()
no_reconstruction=False)
#model.get_grad_norm_parameter_groups()
#quant_weights = torch.load('D:\\dlas\\experiments\\train_music_quant_r4\\models\\5000_generator.pth')
#diff_weights = torch.load('X:\\dlas\\experiments\\train_music_diffusion_tfd5\\models\\48000_generator_ema.pth')
#model.quantizer.load_state_dict(quant_weights, strict=False)
#model.diff.load_state_dict(diff_weights)
#torch.save(model.state_dict(), 'sample.pth')
print_network(model) print_network(model)
o = model(clip, ts, clip) o = model(clip, ts, clip)
def test_ar_model(): def test_ar_model():
clip = torch.randn(2, 256, 401) clip = torch.randn(2, 256, 400)
cond = torch.randn(2, 256, 400) cond = torch.randn(2, 256, 400)
ts = torch.LongTensor([600, 600]) ts = torch.LongTensor([600, 600])
model = TransformerDiffusionWithARPrior(model_channels=2048, block_channels=1024, prenet_channels=1024, model = TransformerDiffusionWithARPrior(model_channels=2048, block_channels=1024, prenet_channels=1024,
@ -357,8 +343,7 @@ def test_ar_model():
model.diff.load_state_dict(pruned_diff_weights, strict=False) model.diff.load_state_dict(pruned_diff_weights, strict=False)
torch.save(model.state_dict(), 'sample.pth') torch.save(model.state_dict(), 'sample.pth')
model(clip, ts, cond, conditioning_input=cond) model(clip, ts, cond)
if __name__ == '__main__': if __name__ == '__main__':