This commit is contained in:
James Betker 2022-04-07 20:10:57 -06:00
parent 71b73db044
commit 573e5552b9
2 changed files with 174 additions and 4 deletions

147
codes/models/clip/clvp.py Normal file
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@ -0,0 +1,147 @@
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import einsum
from models.arch_util import AttentionBlock
from models.lucidrains.x_transformers import ContinuousTransformerWrapper, Encoder
from trainer.networks import register_model
from utils.util import opt_get, checkpoint
def exists(val):
return val is not None
def masked_mean(t, mask):
t = t.masked_fill(~mask, 0.)
return t.sum(dim = 1) / mask.sum(dim = 1)
class CollapsingTransformer(nn.Module):
def __init__(self, model_dim, output_dims, heads, dropout, depth, mask_percentage=0, **encoder_kwargs):
super().__init__()
self.transformer = ContinuousTransformerWrapper(
max_seq_len=-1,
use_pos_emb=False,
attn_layers=Encoder(
dim=model_dim,
depth=depth,
heads=heads,
ff_dropout=dropout,
ff_mult=1,
attn_dropout=dropout,
use_rmsnorm=True,
ff_glu=True,
rotary_pos_emb=True,
**encoder_kwargs,
))
self.pre_combiner = nn.Sequential(nn.Conv1d(model_dim, output_dims, 1),
AttentionBlock(output_dims, num_heads=heads, do_checkpoint=False),
nn.Conv1d(output_dims, output_dims, 1))
self.mask_percentage = mask_percentage
def forward(self, x, **transformer_kwargs):
h = self.transformer(x, **transformer_kwargs)
h = h.permute(0,2,1)
h = checkpoint(self.pre_combiner, h).permute(0,2,1)
if self.training:
mask = torch.rand_like(h.float()) > self.mask_percentage
else:
mask = torch.ones_like(h.float()).bool()
return masked_mean(h, mask)
class CLVP(nn.Module):
"""
Contrastic Language-Voice Pretraining model for generating embedding that can be used to associate text and
speech clips.
"""
def __init__(
self,
model_dim=512,
transformer_heads=8,
dropout=.1,
num_text_tokens=256,
text_enc_depth=6,
text_mask_percentage=0,
conditioning_enc_depth=4,
mel_channels=80,
speech_enc_depth=6,
speech_mask_percentage=0,
latent_multiplier=4,
):
super().__init__()
latent_dim = latent_multiplier*model_dim
self.temperature = nn.Parameter(torch.tensor(1.))
self.cond_emb = nn.Sequential(nn.Conv1d(mel_channels, model_dim//2, kernel_size=5, stride=2, padding=2),
nn.Conv1d(model_dim//2, model_dim, kernel_size=3, stride=2, padding=1))
self.conditioning_transformer = CollapsingTransformer(model_dim, model_dim*2, transformer_heads, dropout, conditioning_enc_depth, 0)
self.text_emb = nn.Embedding(num_text_tokens, model_dim)
self.text_transformer = CollapsingTransformer(model_dim, latent_dim, transformer_heads, dropout, text_enc_depth, text_mask_percentage, use_rms_scaleshift_norm=True)
self.to_text_latent = nn.Linear(latent_dim, latent_dim, bias=False)
self.speech_emb = nn.Conv1d(mel_channels, model_dim, kernel_size=5, padding=2)
self.speech_transformer = CollapsingTransformer(model_dim, latent_dim, transformer_heads, dropout, speech_enc_depth, speech_mask_percentage)
self.to_speech_latent = nn.Linear(latent_dim, latent_dim, bias=False)
def get_grad_norm_parameter_groups(self):
return {
'conditioning': list(self.conditioning_transformer.parameters()),
'text': list(self.text_transformer.parameters()),
'speech': list(self.speech_transformer.parameters()) + list(self.mel_head.parameters()),
}
def forward(
self,
text,
mel_input,
mel_cond,
return_loss=False
):
b, device = text.shape[0], text.device
text_emb = self.text_emb(text)
cond_emb = self.cond_emb(mel_cond).permute(0,2,1)
speech_emb = self.speech_emb(mel_input).permute(0,2,1)
enc_cond = self.conditioning_transformer(cond_emb)
enc_text = self.text_transformer(text_emb, norm_scale_shift_inp=enc_cond)
enc_speech = self.speech_transformer(speech_emb)
text_latents = self.to_text_latent(enc_text)
speech_latents = self.to_speech_latent(enc_speech)
text_latents, speech_latents = map(lambda t: F.normalize(t, p=2, dim=-1), (text_latents, speech_latents))
temp = self.temperature.exp()
if not return_loss:
sim = einsum('n d, n d -> n', text_latents, speech_latents) * temp
return sim
sim = einsum('i d, j d -> i j', text_latents, speech_latents) * temp
labels = torch.arange(b, device=device)
loss = (F.cross_entropy(sim, labels) + F.cross_entropy(sim.t(), labels)) / 2
return loss
@register_model
def register_clvp(opt_net, opt):
return CLVP(**opt_get(opt_net, ['kwargs'], {}))
if __name__ == '__main__':
clip = CLVP()
clip(torch.randint(0,256,(2,120)),
torch.randn(2,80,100),
torch.randn(2,80,95),
return_loss=True)
nonloss = clip(torch.randint(0,256,(2,120)),
torch.randn(2,80,100),
torch.randn(2,80,95),
return_loss=False)
print(nonloss.shape)

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@ -319,6 +319,23 @@ class RMSNorm(nn.Module):
norm = torch.norm(x, dim = -1, keepdim = True) * self.scale
return x / norm.clamp(min = self.eps) * self.g
class RMSScaleShiftNorm(nn.Module):
def __init__(self, dim, eps = 1e-8):
super().__init__()
self.scale = dim ** -0.5
self.eps = eps
self.g = nn.Parameter(torch.ones(dim))
self.scale_shift_process = nn.Linear(dim*2, dim*2)
def forward(self, x, norm_scale_shift_inp):
norm = torch.norm(x, dim = -1, keepdim = True) * self.scale
norm = x / norm.clamp(min = self.eps) * self.g
ss_emb = self.scale_shift_process(norm_scale_shift_inp)
scale, shift = torch.chunk(ss_emb, 2, dim=1)
h = norm * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
return h
# residual and residual gates
class Residual(nn.Module):
@ -677,6 +694,7 @@ class AttentionLayers(nn.Module):
cross_attend = False,
only_cross = False,
use_scalenorm = False,
use_rms_scaleshift_norm = False,
use_rmsnorm = False,
use_rezero = False,
alibi_pos_bias = False,
@ -738,6 +756,7 @@ class AttentionLayers(nn.Module):
norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm
norm_class = RMSNorm if use_rmsnorm else norm_class
norm_class = RMSScaleShiftNorm if use_rms_scaleshift_norm else norm_class
norm_fn = partial(norm_class, dim)
norm_fn = nn.Identity if use_rezero else norm_fn
@ -846,7 +865,8 @@ class AttentionLayers(nn.Module):
context_mask = None,
attn_mask = None,
mems = None,
return_hiddens = False
return_hiddens = False,
norm_scale_shift_inp = None,
):
assert not (self.cross_attend ^ (exists(context) or exists(full_context))), 'context must be passed in if cross_attend is set to True'
@ -858,6 +878,9 @@ class AttentionLayers(nn.Module):
prev_cross_attn = None
mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers
norm_args = {}
if exists(norm_scale_shift_inp):
norm_args['norm_scale_shift_inp'] = norm_scale_shift_inp
rotary_pos_emb = None
if exists(self.rotary_pos_emb):
@ -874,7 +897,7 @@ class AttentionLayers(nn.Module):
pre_branch_norm, post_branch_norm, post_main_norm = norm
if exists(pre_branch_norm):
x = pre_branch_norm(x)
x = pre_branch_norm(x, **norm_args)
if layer_type == 'a':
out, inter = checkpoint(block, x, None, mask, None, attn_mask, self.pia_pos_emb, rotary_pos_emb, prev_attn, layer_mem)
@ -887,7 +910,7 @@ class AttentionLayers(nn.Module):
out = checkpoint(block, x)
if exists(post_branch_norm):
out = post_branch_norm(out)
out = post_branch_norm(out, **norm_args)
x = residual_fn(out, residual)
@ -900,7 +923,7 @@ class AttentionLayers(nn.Module):
prev_cross_attn = inter.pre_softmax_attn
if exists(post_main_norm):
x = post_main_norm(x)
x = post_main_norm(x, **norm_args)
if layer_type == 'c':
cross_attn_count += 1