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get rid of autocasting in tts7

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This commit is contained in:
James Betker 2022-02-24 21:53:51 -07:00
parent f458f5d8f1
commit 34ee32a90e
1 changed files with 40 additions and 45 deletions
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85
codes/models/gpt_voice/unet_diffusion_tts7.py
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@ -431,56 +431,51 @@ class DiffusionTts(nn.Module):
unaligned_h = self.unaligned_embedder(unaligned_input).permute(0,2,1)
unaligned_h = self.unaligned_encoder(unaligned_h).permute(0,2,1)
with autocast(x.device.type):
orig_x_shape = x.shape[-1]
cm = ceil_multiple(x.shape[-1], 2048)
if cm != 0:
pc = (cm-x.shape[-1])/x.shape[-1]
x = F.pad(x, (0,cm-x.shape[-1]))
if tokens is not None:
tokens = F.pad(tokens, (0,int(pc*tokens.shape[-1])))
hs = []
time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
cond_emb = self.contextual_embedder(conditioning_input)
orig_x_shape = x.shape[-1]
cm = ceil_multiple(x.shape[-1], 2048)
if cm != 0:
pc = (cm-x.shape[-1])/x.shape[-1]
x = F.pad(x, (0,cm-x.shape[-1]))
if tokens is not None:
# Mask out guidance tokens for un-guided diffusion.
if self.training and self.nil_guidance_fwd_proportion > 0:
token_mask = clustered_mask(self.nil_guidance_fwd_proportion, tokens.shape, tokens.device, inverted=True)
tokens = torch.where(token_mask, self.mask_token_id, tokens)
code_emb = self.code_embedding(tokens).permute(0,2,1)
cond_emb = cond_emb.unsqueeze(-1).repeat(1,1,code_emb.shape[-1])
cond_time_emb = timestep_embedding(torch.zeros_like(timesteps), code_emb.shape[1]) # This was something I was doing (adding timesteps into this computation), but removed on second thought. TODO: completely remove.
cond_time_emb = cond_time_emb.unsqueeze(-1).repeat(1,1,code_emb.shape[-1])
code_emb = self.conditioning_conv(torch.cat([cond_emb, code_emb, cond_time_emb], dim=1))
else:
code_emb = cond_emb.unsqueeze(-1)
if self.enable_unaligned_inputs:
code_emb = self.conditioning_encoder(code_emb, context=unaligned_h)
else:
code_emb = self.conditioning_encoder(code_emb)
tokens = F.pad(tokens, (0,int(pc*tokens.shape[-1])))
first = True
time_emb = time_emb.float()
h = x
for k, module in enumerate(self.input_blocks):
if isinstance(module, nn.Conv1d):
h_tok = F.interpolate(module(code_emb), size=(h.shape[-1]), mode='nearest')
h = h + h_tok
else:
with autocast(x.device.type, enabled=not first):
# First block has autocast disabled to allow a high precision signal to be properly vectorized.
h = module(h, time_emb)
hs.append(h)
first = False
h = self.middle_block(h, time_emb)
for module in self.output_blocks:
h = torch.cat([h, hs.pop()], dim=1)
hs = []
time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
cond_emb = self.contextual_embedder(conditioning_input)
if tokens is not None:
# Mask out guidance tokens for un-guided diffusion.
if self.training and self.nil_guidance_fwd_proportion > 0:
token_mask = clustered_mask(self.nil_guidance_fwd_proportion, tokens.shape, tokens.device, inverted=True)
tokens = torch.where(token_mask, self.mask_token_id, tokens)
code_emb = self.code_embedding(tokens).permute(0,2,1)
cond_emb = cond_emb.unsqueeze(-1).repeat(1,1,code_emb.shape[-1])
cond_time_emb = timestep_embedding(torch.zeros_like(timesteps), code_emb.shape[1]) # This was something I was doing (adding timesteps into this computation), but removed on second thought. TODO: completely remove.
cond_time_emb = cond_time_emb.unsqueeze(-1).repeat(1,1,code_emb.shape[-1])
code_emb = self.conditioning_conv(torch.cat([cond_emb, code_emb, cond_time_emb], dim=1))
else:
code_emb = cond_emb.unsqueeze(-1)
if self.enable_unaligned_inputs:
code_emb = self.conditioning_encoder(code_emb, context=unaligned_h)
else:
code_emb = self.conditioning_encoder(code_emb)
time_emb = time_emb.float()
h = x
for k, module in enumerate(self.input_blocks):
if isinstance(module, nn.Conv1d):
h_tok = F.interpolate(module(code_emb), size=(h.shape[-1]), mode='nearest')
h = h + h_tok
else:
# First block has autocast disabled to allow a high precision signal to be properly vectorized.
h = module(h, time_emb)
hs.append(h)
h = self.middle_block(h, time_emb)
for module in self.output_blocks:
h = torch.cat([h, hs.pop()], dim=1)
h = module(h, time_emb)
# Last block also has autocast disabled for high-precision outputs.
h = h.float()
out = self.out(h)
return out[:, :, :orig_x_shape]