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tfdpc5

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James Betker 2022-06-26 19:46:57 -06:00
parent d0f2560396
commit 69b614e08a
4 changed files with 369 additions and 533 deletions
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268
codes/models/audio/music/tfdpc_v1.py
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import itertools
from time import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.arch_util import ResBlock, AttentionBlock
from models.audio.music.gpt_music2 import UpperEncoder, GptMusicLower
from models.audio.music.music_quantizer2 import MusicQuantizer2
from models.audio.tts.lucidrains_dvae import DiscreteVAE
from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
from models.diffusion.unet_diffusion import TimestepBlock
from models.lucidrains.x_transformers import Encoder, Attention, RMSScaleShiftNorm, RotaryEmbedding, \
FeedForward
from trainer.networks import register_model
from utils.util import checkpoint, print_network
def is_latent(t):
return t.dtype == torch.float
def is_sequence(t):
return t.dtype == torch.long
class MultiGroupEmbedding(nn.Module):
def __init__(self, tokens, groups, dim):
super().__init__()
self.m = nn.ModuleList([nn.Embedding(tokens, dim // groups) for _ in range(groups)])
def forward(self, x):
h = [embedding(x[:, :, i]) for i, embedding in enumerate(self.m)]
return torch.cat(h, dim=-1)
class TimestepRotaryEmbedSequential(nn.Sequential, TimestepBlock):
def forward(self, x, emb, rotary_emb):
for layer in self:
if isinstance(layer, TimestepBlock):
x = layer(x, emb, rotary_emb)
else:
x = layer(x, rotary_emb)
return x
class SubBlock(nn.Module):
def __init__(self, inp_dim, contraction_dim, heads, dropout):
super().__init__()
self.attn = Attention(inp_dim, out_dim=contraction_dim, heads=heads, dim_head=contraction_dim//heads, causal=False, dropout=dropout)
self.attnorm = nn.LayerNorm(contraction_dim)
self.ff = FeedForward(inp_dim+contraction_dim, dim_out=contraction_dim, mult=2, dropout=dropout)
self.ffnorm = nn.LayerNorm(contraction_dim)
def forward(self, x, rotary_emb):
ah, _, _, _ = checkpoint(self.attn, x, None, None, None, None, None, rotary_emb)
ah = F.gelu(self.attnorm(ah))
h = torch.cat([ah, x], dim=-1)
hf = checkpoint(self.ff, h)
hf = F.gelu(self.ffnorm(hf))
h = torch.cat([h, hf], dim=-1)
return h
class ConcatAttentionBlock(TimestepBlock):
def __init__(self, trunk_dim, contraction_dim, time_embed_dim, heads, dropout):
super().__init__()
self.prenorm = RMSScaleShiftNorm(trunk_dim, embed_dim=time_embed_dim, bias=False)
self.block1 = SubBlock(trunk_dim, contraction_dim, heads, dropout)
self.block2 = SubBlock(trunk_dim+contraction_dim*2, contraction_dim, heads, dropout)
self.out = nn.Linear(contraction_dim*4, trunk_dim, bias=False)
self.out.weight.data.zero_()
def forward(self, x, conditioning, timestep_emb, rotary_emb):
h = self.prenorm(x, norm_scale_shift_inp=timestep_emb)
h = torch.cat([conditioning, h], dim=1)
h = self.block1(h, rotary_emb)
h = self.block2(h, rotary_emb)
h = self.out(h[:,:,x.shape[-1]:])
return h[:, 1:] + x
class TransformerDiffusionWithPointConditioning(nn.Module):
"""
A diffusion model composed entirely of stacks of transformer layers. Why would you do it any other way?
"""
def __init__(
self,
in_channels=256,
out_channels=512, # mean and variance
model_channels=1024,
contraction_dim=256,
time_embed_dim=256,
num_layers=8,
rotary_emb_dim=32,
input_cond_dim=1024,
num_heads=8,
dropout=0,
use_fp16=False,
# Parameters for regularization.
unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
):
super().__init__()
self.in_channels = in_channels
self.model_channels = model_channels
self.time_embed_dim = time_embed_dim
self.out_channels = out_channels
self.dropout = dropout
self.unconditioned_percentage = unconditioned_percentage
self.enable_fp16 = use_fp16
self.inp_block = conv_nd(1, in_channels, model_channels, 3, 1, 1)
self.time_embed = nn.Sequential(
linear(time_embed_dim, time_embed_dim),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim),
)
self.conditioner = nn.Linear(input_cond_dim, model_channels) if input_cond_dim != model_channels else nn.Identity()
self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,model_channels))
self.rotary_embeddings = RotaryEmbedding(rotary_emb_dim)
self.layers = TimestepRotaryEmbedSequential(*[ConcatAttentionBlock(model_channels, contraction_dim, time_embed_dim, num_heads, dropout) for _ in range(num_layers)])
self.out = nn.Sequential(
normalization(model_channels),
nn.SiLU(),
zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)),
)
self.debug_codes = {}
def get_grad_norm_parameter_groups(self):
attn1 = list(itertools.chain.from_iterable([lyr.block1.attn.parameters() for lyr in self.layers]))
attn2 = list(itertools.chain.from_iterable([lyr.block2.attn.parameters() for lyr in self.layers]))
ff1 = list(itertools.chain.from_iterable([lyr.block1.ff.parameters() for lyr in self.layers]))
ff2 = list(itertools.chain.from_iterable([lyr.block2.ff.parameters() for lyr in self.layers]))
blkout_layers = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.layers]))
groups = {
'prenorms': list(itertools.chain.from_iterable([lyr.prenorm.parameters() for lyr in self.layers])),
'blk1_attention_layers': attn1,
'blk2_attention_layers': attn2,
'attention_layers': attn1 + attn2,
'blk1_ff_layers': ff1,
'blk2_ff_layers': ff2,
'ff_layers': ff1 + ff2,
'block_out_layers': blkout_layers,
'rotary_embeddings': list(self.rotary_embeddings.parameters()),
'out': list(self.out.parameters()),
'x_proj': list(self.inp_block.parameters()),
'layers': list(self.layers.parameters()),
'time_embed': list(self.time_embed.parameters()),
}
return groups
def forward(self, x, timesteps, conditioning_input, conditioning_free=False):
unused_params = []
if conditioning_free:
cond = self.unconditioned_embedding.repeat(x.shape[0], x.shape[-1], 1)
else:
cond = self.conditioner(conditioning_input)
# Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
if self.training and self.unconditioned_percentage > 0:
unconditioned_batches = torch.rand((cond.shape[0], 1, 1),
device=cond.device) < self.unconditioned_percentage
cond = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(cond.shape[0], 1, 1),
cond)
unused_params.append(self.unconditioned_embedding)
with torch.autocast(x.device.type, enabled=self.enable_fp16):
blk_emb = self.time_embed(timestep_embedding(timesteps, self.time_embed_dim))
x = self.inp_block(x).permute(0,2,1)
rotary_pos_emb = self.rotary_embeddings(x.shape[1]+1, x.device)
for layer in self.layers:
x = checkpoint(layer, x, cond, blk_emb, rotary_pos_emb)
x = x.float().permute(0,2,1)
out = self.out(x)
# Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
extraneous_addition = 0
for p in unused_params:
extraneous_addition = extraneous_addition + p.mean()
out = out + extraneous_addition * 0
return out
class ConditioningEncoder(nn.Module):
def __init__(self,
cond_dim,
embedding_dim,
attn_blocks=6,
num_attn_heads=8,
do_checkpointing=False):
super().__init__()
attn = []
self.init = nn.Conv1d(cond_dim, embedding_dim, kernel_size=1)
for a in range(attn_blocks):
attn.append(AttentionBlock(embedding_dim, num_attn_heads, do_checkpoint=do_checkpointing))
self.attn = nn.Sequential(*attn)
self.dim = embedding_dim
self.do_checkpointing = do_checkpointing
def forward(self, x):
h = self.init(x)
h = self.attn(h)
return h.mean(dim=2).unsqueeze(1)
class TransformerDiffusionWithConditioningEncoder(nn.Module):
def __init__(self, **kwargs):
super().__init__()
self.internal_step = 0
self.diff = TransformerDiffusionWithPointConditioning(**kwargs)
self.conditioning_encoder = ConditioningEncoder(256, kwargs['model_channels'])
def forward(self, x, timesteps, true_cheater, conditioning_input=None, disable_diversity=False, conditioning_free=False):
cond = self.conditioning_encoder(true_cheater)
diff = self.diff(x, timesteps, conditioning_input=cond, conditioning_free=conditioning_free)
return diff
def get_debug_values(self, step, __):
self.internal_step = step
return {}
def get_grad_norm_parameter_groups(self):
groups = self.diff.get_grad_norm_parameter_groups()
groups['conditioning_encoder'] = list(self.conditioning_encoder.parameters())
return groups
def before_step(self, step):
scaled_grad_parameters = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.diff.layers])) + \
list(itertools.chain.from_iterable([lyr.prenorm.parameters() for lyr in self.diff.layers]))
# Scale back the gradients of the blkout and prenorm layers by a constant factor. These get two orders of magnitudes
# higher gradients. Ideally we would use parameter groups, but ZeroRedundancyOptimizer makes this trickier than
# directly fiddling with the gradients.
for p in scaled_grad_parameters:
if hasattr(p, 'grad') and p.grad is not None:
p.grad *= .2
@register_model
def register_tfdpc(opt_net, opt):
return TransformerDiffusionWithPointConditioning(**opt_net['kwargs'])
@register_model
def register_tfdpc_with_conditioning_encoder(opt_net, opt):
return TransformerDiffusionWithConditioningEncoder(**opt_net['kwargs'])
def test_cheater_model():
clip = torch.randn(2, 256, 400)
cl = torch.randn(2, 1, 400)
ts = torch.LongTensor([600, 600])
# For music:
model = TransformerDiffusionWithConditioningEncoder(model_channels=1024)
print_network(model)
o = model(clip, ts, cl)
pg = model.get_grad_norm_parameter_groups()
if __name__ == '__main__':
test_cheater_model()

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codes/models/audio/music/tfdpc_v2.py
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import itertools
from time import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.arch_util import ResBlock, AttentionBlock
from models.audio.music.gpt_music2 import UpperEncoder, GptMusicLower
from models.audio.music.music_quantizer2 import MusicQuantizer2
from models.audio.tts.lucidrains_dvae import DiscreteVAE
from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
from models.diffusion.unet_diffusion import TimestepBlock
from models.lucidrains.x_transformers import Encoder, Attention, RMSScaleShiftNorm, RotaryEmbedding, \
FeedForward
from trainer.networks import register_model
from utils.util import checkpoint, print_network
class TimestepRotaryEmbedSequential(nn.Sequential, TimestepBlock):
def forward(self, x, emb, rotary_emb):
for layer in self:
if isinstance(layer, TimestepBlock):
x = layer(x, emb, rotary_emb)
else:
x = layer(x, rotary_emb)
return x
class SubBlock(nn.Module):
def __init__(self, inp_dim, contraction_dim, heads, dropout):
super().__init__()
self.attn = Attention(inp_dim, out_dim=contraction_dim, heads=heads, dim_head=contraction_dim//heads, causal=False, dropout=dropout)
self.attnorm = nn.LayerNorm(contraction_dim)
self.ff = FeedForward(inp_dim+contraction_dim, dim_out=contraction_dim, mult=2, dropout=dropout)
self.ffnorm = nn.LayerNorm(contraction_dim)
def forward(self, x, rotary_emb):
ah, _, _, _ = checkpoint(self.attn, x, None, None, None, None, None, rotary_emb)
ah = F.gelu(self.attnorm(ah))
h = torch.cat([ah, x], dim=-1)
hf = checkpoint(self.ff, h)
hf = F.gelu(self.ffnorm(hf))
h = torch.cat([h, hf], dim=-1)
return h
class ConcatAttentionBlock(TimestepBlock):
def __init__(self, trunk_dim, contraction_dim, time_embed_dim, heads, dropout):
super().__init__()
self.prenorm = RMSScaleShiftNorm(trunk_dim, embed_dim=time_embed_dim, bias=False)
self.block1 = SubBlock(trunk_dim, contraction_dim, heads, dropout)
self.block2 = SubBlock(trunk_dim+contraction_dim*2, contraction_dim, heads, dropout)
self.out = nn.Linear(contraction_dim*4, trunk_dim, bias=False)
self.out.weight.data.zero_()
def forward(self, x, timestep_emb, rotary_emb):
h = self.prenorm(x, norm_scale_shift_inp=timestep_emb)
h = self.block1(h, rotary_emb)
h = self.block2(h, rotary_emb)
h = self.out(h[:,:,x.shape[-1]:])
return h + x
class TransformerDiffusionWithPointConditioning(nn.Module):
"""
A diffusion model composed entirely of stacks of transformer layers. Why would you do it any other way?
"""
def __init__(
self,
in_channels=256,
out_channels=512, # mean and variance
model_channels=1024,
contraction_dim=256,
time_embed_dim=256,
num_layers=8,
rotary_emb_dim=32,
input_cond_dim=1024,
num_heads=8,
dropout=0,
use_fp16=False,
# Parameters for regularization.
unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
):
super().__init__()
self.in_channels = in_channels
self.model_channels = model_channels
self.time_embed_dim = time_embed_dim
self.out_channels = out_channels
self.dropout = dropout
self.unconditioned_percentage = unconditioned_percentage
self.enable_fp16 = use_fp16
self.inp_block = conv_nd(1, in_channels, model_channels//2, 3, 1, 1)
self.time_embed = nn.Sequential(
linear(time_embed_dim, time_embed_dim),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim),
)
self.conditioner = nn.Linear(input_cond_dim, model_channels//2)
self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,model_channels//2))
self.rotary_embeddings = RotaryEmbedding(rotary_emb_dim)
self.layers = TimestepRotaryEmbedSequential(*[ConcatAttentionBlock(model_channels, contraction_dim, time_embed_dim, num_heads, dropout) for _ in range(num_layers)])
self.out = nn.Sequential(
normalization(model_channels),
nn.SiLU(),
zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)),
)
self.debug_codes = {}
def get_grad_norm_parameter_groups(self):
attn1 = list(itertools.chain.from_iterable([lyr.block1.attn.parameters() for lyr in self.layers]))
attn2 = list(itertools.chain.from_iterable([lyr.block2.attn.parameters() for lyr in self.layers]))
ff1 = list(itertools.chain.from_iterable([lyr.block1.ff.parameters() for lyr in self.layers]))
ff2 = list(itertools.chain.from_iterable([lyr.block2.ff.parameters() for lyr in self.layers]))
blkout_layers = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.layers]))
groups = {
'prenorms': list(itertools.chain.from_iterable([lyr.prenorm.parameters() for lyr in self.layers])),
'blk1_attention_layers': attn1,
'blk2_attention_layers': attn2,
'attention_layers': attn1 + attn2,
'blk1_ff_layers': ff1,
'blk2_ff_layers': ff2,
'ff_layers': ff1 + ff2,
'block_out_layers': blkout_layers,
'rotary_embeddings': list(self.rotary_embeddings.parameters()),
'out': list(self.out.parameters()),
'x_proj': list(self.inp_block.parameters()),
'layers': list(self.layers.parameters()),
'time_embed': list(self.time_embed.parameters()),
}
return groups
def forward(self, x, timesteps, conditioning_input, conditioning_free=False):
unused_params = []
if conditioning_free:
cond = self.unconditioned_embedding.repeat(x.shape[0], x.shape[-1], 1)
else:
cond = self.conditioner(conditioning_input)
# Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
if self.training and self.unconditioned_percentage > 0:
unconditioned_batches = torch.rand((cond.shape[0], 1, 1),
device=cond.device) < self.unconditioned_percentage
cond = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(cond.shape[0], 1, 1), cond)
unused_params.append(self.unconditioned_embedding)
with torch.autocast(x.device.type, enabled=self.enable_fp16):
blk_emb = self.time_embed(timestep_embedding(timesteps, self.time_embed_dim))
x = self.inp_block(x).permute(0,2,1)
x = torch.cat([x, cond.repeat(1,x.shape[1],1)], dim=-1)
rotary_pos_emb = self.rotary_embeddings(x.shape[1]+1, x.device)
for layer in self.layers:
x = checkpoint(layer, x, blk_emb, rotary_pos_emb)
x = x.float().permute(0,2,1)
out = self.out(x)
# Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
extraneous_addition = 0
for p in unused_params:
extraneous_addition = extraneous_addition + p.mean()
out = out + extraneous_addition * 0
return out
class ConditioningEncoder(nn.Module):
def __init__(self,
cond_dim,
embedding_dim,
attn_blocks=6,
num_attn_heads=8,
dropout=.1,
do_checkpointing=False):
super().__init__()
attn = []
self.init = nn.Conv1d(cond_dim, embedding_dim, kernel_size=1)
self.attn = Encoder(
dim=embedding_dim,
depth=attn_blocks,
heads=num_attn_heads,
ff_dropout=dropout,
attn_dropout=dropout,
use_rmsnorm=True,
ff_glu=True,
rotary_pos_emb=True,
zero_init_branch_output=True,
ff_mult=2,
)
self.dim = embedding_dim
self.do_checkpointing = do_checkpointing
def forward(self, x):
h = self.init(x).permute(0,2,1)
h = self.attn(h).permute(0,2,1)
return h.mean(dim=2).unsqueeze(1)
class TransformerDiffusionWithConditioningEncoder(nn.Module):
def __init__(self, **kwargs):
super().__init__()
self.internal_step = 0
self.diff = TransformerDiffusionWithPointConditioning(**kwargs)
self.conditioning_encoder = ConditioningEncoder(256, kwargs['model_channels'])
def forward(self, x, timesteps, true_cheater, conditioning_input=None, disable_diversity=False, conditioning_free=False):
cond = self.conditioning_encoder(true_cheater)
diff = self.diff(x, timesteps, conditioning_input=cond, conditioning_free=conditioning_free)
return diff
def get_debug_values(self, step, __):
self.internal_step = step
return {}
def get_grad_norm_parameter_groups(self):
groups = self.diff.get_grad_norm_parameter_groups()
groups['conditioning_encoder'] = list(self.conditioning_encoder.parameters())
return groups
def before_step(self, step):
scaled_grad_parameters = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.diff.layers])) + \
list(itertools.chain.from_iterable([lyr.prenorm.parameters() for lyr in self.diff.layers]))
# Scale back the gradients of the blkout and prenorm layers by a constant factor. These get two orders of magnitudes
# higher gradients. Ideally we would use parameter groups, but ZeroRedundancyOptimizer makes this trickier than
# directly fiddling with the gradients.
for p in scaled_grad_parameters:
if hasattr(p, 'grad') and p.grad is not None:
p.grad *= .2
@register_model
def register_tfdpc2(opt_net, opt):
return TransformerDiffusionWithPointConditioning(**opt_net['kwargs'])
@register_model
def register_tfdpc2_with_conditioning_encoder(opt_net, opt):
return TransformerDiffusionWithConditioningEncoder(**opt_net['kwargs'])
def test_cheater_model():
clip = torch.randn(2, 256, 400)
cl = torch.randn(2, 256, 400)
ts = torch.LongTensor([600, 600])
# For music:
model = TransformerDiffusionWithConditioningEncoder(model_channels=1024)
print_network(model)
o = model(clip, ts, cl)
pg = model.get_grad_norm_parameter_groups()
if __name__ == '__main__':
test_cheater_model()

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codes/models/audio/music/tfdpc_v5.py Normal file
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import itertools
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio
import torchvision
from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
from models.diffusion.unet_diffusion import TimestepBlock
from models.lucidrains.x_transformers import Encoder, Attention, RMSScaleShiftNorm, RotaryEmbedding, \
FeedForward
from trainer.networks import register_model
from utils.util import checkpoint, print_network, load_audio
class TimestepRotaryEmbedSequential(nn.Sequential, TimestepBlock):
def forward(self, x, emb, rotary_emb):
for layer in self:
if isinstance(layer, TimestepBlock):
x = layer(x, emb, rotary_emb)
else:
x = layer(x, rotary_emb)
return x
class SubBlock(nn.Module):
def __init__(self, inp_dim, contraction_dim, heads, dropout, use_conv):
super().__init__()
self.attn = Attention(inp_dim, out_dim=contraction_dim, heads=heads, dim_head=contraction_dim//heads, causal=False, dropout=dropout)
self.attnorm = nn.LayerNorm(contraction_dim)
self.use_conv = use_conv
if use_conv:
self.ff = nn.Conv1d(inp_dim+contraction_dim, contraction_dim, kernel_size=3, padding=1)
else:
self.ff = FeedForward(inp_dim+contraction_dim, dim_out=contraction_dim, mult=2, dropout=dropout)
self.ffnorm = nn.LayerNorm(contraction_dim)
def forward(self, x, rotary_emb):
ah, _, _, _ = checkpoint(self.attn, x, None, None, None, None, None, rotary_emb)
ah = F.gelu(self.attnorm(ah))
h = torch.cat([ah, x], dim=-1)
hf = checkpoint(self.ff, h.permute(0,2,1) if self.use_conv else h)
hf = F.gelu(self.ffnorm(hf.permute(0,2,1) if self.use_conv else hf))
h = torch.cat([h, hf], dim=-1)
return h
class ConcatAttentionBlock(TimestepBlock):
def __init__(self, trunk_dim, contraction_dim, time_embed_dim, cond_dim_in, cond_dim_hidden, heads, dropout, cond_projection=True, use_conv=False):
super().__init__()
self.prenorm = RMSScaleShiftNorm(trunk_dim, embed_dim=time_embed_dim, bias=False)
if cond_projection:
self.tdim = trunk_dim+cond_dim_hidden
self.cond_project = nn.Linear(cond_dim_in, cond_dim_hidden)
else:
self.tdim = trunk_dim
self.block1 = SubBlock(self.tdim, contraction_dim, heads, dropout, use_conv)
self.block2 = SubBlock(self.tdim+contraction_dim*2, contraction_dim, heads, dropout, use_conv)
self.out = nn.Linear(contraction_dim*4, trunk_dim, bias=False)
self.out.weight.data.zero_()
def forward(self, x, cond, timestep_emb, rotary_emb):
h = self.prenorm(x, norm_scale_shift_inp=timestep_emb)
if hasattr(self, 'cond_project'):
cond = self.cond_project(cond)
h = torch.cat([h, cond], dim=-1)
h = self.block1(h, rotary_emb)
h = self.block2(h, rotary_emb)
h = self.out(h[:,:,self.tdim:])
return h + x
class ConditioningEncoder(nn.Module):
def __init__(self,
cond_dim,
embedding_dim,
time_embed_dim,
attn_blocks=6,
num_attn_heads=8,
dropout=.1,
do_checkpointing=False):
super().__init__()
attn = []
self.init = nn.Conv1d(cond_dim, embedding_dim, kernel_size=1)
self.time_proj = nn.Linear(time_embed_dim, embedding_dim)
self.attn = Encoder(
dim=embedding_dim,
depth=attn_blocks,
heads=num_attn_heads,
ff_dropout=dropout,
attn_dropout=dropout,
use_rmsnorm=True,
ff_glu=True,
rotary_pos_emb=True,
zero_init_branch_output=True,
ff_mult=2,
)
self.dim = embedding_dim
self.do_checkpointing = do_checkpointing
def forward(self, x, time_emb):
h = self.init(x).permute(0,2,1)
time_enc = self.time_proj(time_emb)
h = torch.cat([time_enc.unsqueeze(1), h], dim=1)
h = self.attn(h).permute(0,2,1)
return h
class TransformerDiffusionWithPointConditioning(nn.Module):
"""
A diffusion model composed entirely of stacks of transformer layers. Why would you do it any other way?
"""
def __init__(
self,
in_channels=256,
out_channels=512, # mean and variance
model_channels=1024,
contraction_dim=256,
time_embed_dim=256,
num_layers=8,
rotary_emb_dim=32,
input_cond_dim=1024,
num_heads=8,
dropout=0,
use_fp16=False,
# Parameters for regularization.
unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
):
super().__init__()
self.in_channels = in_channels
self.model_channels = model_channels
self.time_embed_dim = time_embed_dim
self.out_channels = out_channels
self.dropout = dropout
self.unconditioned_percentage = unconditioned_percentage
self.enable_fp16 = use_fp16
self.inp_block = conv_nd(1, in_channels, model_channels, 3, 1, 1)
self.conditioning_encoder = ConditioningEncoder(256, model_channels, time_embed_dim)
self.time_embed = nn.Sequential(
linear(time_embed_dim, time_embed_dim),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim),
)
self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,model_channels))
self.rotary_embeddings = RotaryEmbedding(rotary_emb_dim)
self.layers = TimestepRotaryEmbedSequential(*[ConcatAttentionBlock(model_channels,
contraction_dim,
time_embed_dim,
cond_dim_in=input_cond_dim,
cond_dim_hidden=input_cond_dim//2,
heads=num_heads,
dropout=dropout,
cond_projection=(k % 3 == 0),
use_conv=(k % 3 != 0),
) for k in range(num_layers)])
self.out = nn.Sequential(
normalization(model_channels),
nn.SiLU(),
zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)),
)
self.debug_codes = {}
def get_grad_norm_parameter_groups(self):
attn1 = list(itertools.chain.from_iterable([lyr.block1.attn.parameters() for lyr in self.layers]))
attn2 = list(itertools.chain.from_iterable([lyr.block2.attn.parameters() for lyr in self.layers]))
ff1 = list(itertools.chain.from_iterable([lyr.block1.ff.parameters() for lyr in self.layers]))
ff2 = list(itertools.chain.from_iterable([lyr.block2.ff.parameters() for lyr in self.layers]))
blkout_layers = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.layers]))
groups = {
'prenorms': list(itertools.chain.from_iterable([lyr.prenorm.parameters() for lyr in self.layers])),
'blk1_attention_layers': attn1,
'blk2_attention_layers': attn2,
'attention_layers': attn1 + attn2,
'blk1_ff_layers': ff1,
'blk2_ff_layers': ff2,
'ff_layers': ff1 + ff2,
'block_out_layers': blkout_layers,
'rotary_embeddings': list(self.rotary_embeddings.parameters()),
'out': list(self.out.parameters()),
'x_proj': list(self.inp_block.parameters()),
'layers': list(self.layers.parameters()),
'time_embed': list(self.time_embed.parameters()),
'conditioning_encoder': list(self.conditioning_encoder.parameters()),
}
return groups
def forward(self, x, timesteps, conditioning_input, conditioning_free=False, cond_start=0):
unused_params = []
time_emb = self.time_embed(timestep_embedding(timesteps, self.time_embed_dim))
cond_enc = self.conditioning_encoder(conditioning_input, time_emb)
cs = cond_enc[:,:,cond_start]
ce = cond_enc[:,:,x.shape[-1]+cond_start]
cond_enc = torch.cat([cs.unsqueeze(-1), ce.unsqueeze(-1)], dim=-1)
cond_enc = F.interpolate(cond_enc, size=(x.shape[-1],), mode='linear').permute(0,2,1)
if conditioning_free:
cond = self.unconditioned_embedding
else:
cond = cond_enc
# Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
if self.training and self.unconditioned_percentage > 0:
unconditioned_batches = torch.rand((cond.shape[0], 1, 1),
device=cond.device) < self.unconditioned_percentage
cond = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(cond.shape[0], 1, 1), cond)
unused_params.append(self.unconditioned_embedding)
with torch.autocast(x.device.type, enabled=self.enable_fp16):
x = self.inp_block(x).permute(0,2,1)
rotary_pos_emb = self.rotary_embeddings(x.shape[1]+1, x.device)
for layer in self.layers:
x = checkpoint(layer, x, cond, time_emb, rotary_pos_emb)
x = x.float().permute(0,2,1)
out = self.out(x)
# Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
extraneous_addition = 0
for p in unused_params:
extraneous_addition = extraneous_addition + p.mean()
out = out + extraneous_addition * 0
return out
def before_step(self, step):
scaled_grad_parameters = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.layers])) + \
list(itertools.chain.from_iterable([lyr.prenorm.parameters() for lyr in self.layers]))
# Scale back the gradients of the blkout and prenorm layers by a constant factor. These get two orders of magnitudes
# higher gradients. Ideally we would use parameter groups, but ZeroRedundancyOptimizer makes this trickier than
# directly fiddling with the gradients.
for p in scaled_grad_parameters:
if hasattr(p, 'grad') and p.grad is not None:
p.grad *= .2
@register_model
def register_tfdpc5(opt_net, opt):
return TransformerDiffusionWithPointConditioning(**opt_net['kwargs'])
def test_cheater_model():
clip = torch.randn(2, 256, 400)
cl = torch.randn(2, 256, 400)
ts = torch.LongTensor([600, 600])
# For music:
model = TransformerDiffusionWithPointConditioning(in_channels=256, out_channels=512, model_channels=1024,
contraction_dim=512, num_heads=8, num_layers=15, dropout=0,
unconditioned_percentage=.4)
print_network(model)
o = model(clip, ts, cl)
pg = model.get_grad_norm_parameter_groups()
def prmsz(lp):
sz = 0
for p in lp:
q = 1
for s in p.shape:
q *= s
sz += q
return sz
for k, v in pg.items():
print(f'{k}: {prmsz(v)/1000000}')
def inference_tfdpc5_with_cheater():
with torch.no_grad():
os.makedirs('results/tfdpc_v3', exist_ok=True)
#length = 40 * 22050 // 256 // 16
samples = {'electronica1': load_audio('Y:\\split\\yt-music-eval\\00001.wav', 22050),
'electronica2': load_audio('Y:\\split\\yt-music-eval\\00272.wav', 22050),
'e_guitar': load_audio('Y:\\split\\yt-music-eval\\00227.wav', 22050),
'creep': load_audio('Y:\\separated\\bt-music-3\\[2007] MTV Unplugged (Live) (Japan Edition)\\05 - Creep [Cover On Radiohead]\\00001\\no_vocals.wav', 22050),
'rock1': load_audio('Y:\\separated\\bt-music-3\\2016 - Heal My Soul\\01 - Daze Of The Night\\00000\\no_vocals.wav', 22050),
'kiss': load_audio('Y:\\separated\\bt-music-3\\KISS (2001) Box Set CD1\\02 Deuce (Demo Version)\\00000\\no_vocals.wav', 22050),
'purp': load_audio('Y:\\separated\\bt-music-3\\Shades of Deep Purple\\11 Help (Alternate Take)\\00001\\no_vocals.wav', 22050),
'western_stars': load_audio('Y:\\separated\\bt-music-3\\Western Stars\\01 Hitch Hikin\'\\00000\\no_vocals.wav', 22050),
'silk': load_audio('Y:\\separated\\silk\\MonstercatSilkShowcase\\890\\00007\\no_vocals.wav', 22050),
'long_electronica': load_audio('C:\\Users\\James\\Music\\longer_sample.wav', 22050),}
for k, sample in samples.items():
sample = sample.cuda()
length = sample.shape[0]//256//16
model = TransformerDiffusionWithPointConditioning(in_channels=256, out_channels=512, model_channels=1024,
contraction_dim=512, num_heads=8, num_layers=12, dropout=0,
use_fp16=False, unconditioned_percentage=0).eval().cuda()
model.load_state_dict(torch.load('x:/dlas/experiments/train_music_cheater_gen_v3/models/59000_generator_ema.pth'))
from trainer.injectors.audio_injectors import TorchMelSpectrogramInjector
spec_fn = TorchMelSpectrogramInjector({'n_mel_channels': 256, 'mel_fmax': 11000, 'filter_length': 16000, 'true_normalization': True,
'normalize': True, 'in': 'in', 'out': 'out'}, {}).cuda()
ref_mel = spec_fn({'in': sample.unsqueeze(0)})['out']
from trainer.injectors.audio_injectors import MusicCheaterLatentInjector
cheater_encoder = MusicCheaterLatentInjector({'in': 'in', 'out': 'out'}, {}).cuda()
ref_cheater = cheater_encoder({'in': ref_mel})['out']
from models.diffusion.respace import SpacedDiffusion
from models.diffusion.respace import space_timesteps
from models.diffusion.gaussian_diffusion import get_named_beta_schedule
diffuser = SpacedDiffusion(use_timesteps=space_timesteps(4000, [128]), model_mean_type='epsilon',
model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', 4000),
conditioning_free=True, conditioning_free_k=1)
# Conventional decoding method:
gen_cheater = diffuser.ddim_sample_loop(model, (1,256,length), progress=True, model_kwargs={'true_cheater': ref_cheater})
# Guidance decoding method:
#mask = torch.ones_like(ref_cheater)
#mask[:,:,15:-15] = 0
#gen_cheater = diffuser.p_sample_loop_with_guidance(model, ref_cheater, mask, model_kwargs={'true_cheater': ref_cheater})
# Just decode the ref.
#gen_cheater = ref_cheater
from models.audio.music.transformer_diffusion12 import TransformerDiffusionWithCheaterLatent
diffuser = SpacedDiffusion(use_timesteps=space_timesteps(4000, [32]), model_mean_type='epsilon',
model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', 4000),
conditioning_free=True, conditioning_free_k=1)
wrap = TransformerDiffusionWithCheaterLatent(in_channels=256, out_channels=512, model_channels=1024,
contraction_dim=512, prenet_channels=1024, input_vec_dim=256,
prenet_layers=6, num_heads=8, num_layers=16, new_code_expansion=True,
dropout=0, unconditioned_percentage=0).eval().cuda()
wrap.load_state_dict(torch.load('x:/dlas/experiments/train_music_diffusion_tfd_cheater_from_scratch/models/56500_generator_ema.pth'))
cheater_to_mel = wrap.diff
gen_mel = diffuser.ddim_sample_loop(cheater_to_mel, (1,256,gen_cheater.shape[-1]*16), progress=True,
model_kwargs={'codes': gen_cheater.permute(0,2,1)})
torchvision.utils.save_image((gen_mel + 1)/2, f'results/tfdpc_v3/{k}.png')
from utils.music_utils import get_mel2wav_v3_model
m2w = get_mel2wav_v3_model().cuda()
spectral_diffuser = SpacedDiffusion(use_timesteps=space_timesteps(4000, [32]), model_mean_type='epsilon',
model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', 4000),
conditioning_free=True, conditioning_free_k=1)
from trainer.injectors.audio_injectors import denormalize_mel
gen_mel_denorm = denormalize_mel(gen_mel)
output_shape = (1,16,gen_mel_denorm.shape[-1]*256//16)
gen_wav = spectral_diffuser.ddim_sample_loop(m2w, output_shape, model_kwargs={'codes': gen_mel_denorm})
from trainer.injectors.audio_injectors import pixel_shuffle_1d
gen_wav = pixel_shuffle_1d(gen_wav, 16)
torchaudio.save(f'results/tfdpc_v3/{k}.wav', gen_wav.squeeze(1).cpu(), 22050)
torchaudio.save(f'results/tfdpc_v3/{k}_ref.wav', sample.unsqueeze(0).cpu(), 22050)
if __name__ == '__main__':
test_cheater_model()
#inference_tfdpc5_with_cheater()

19
codes/trainer/injectors/audio_injectors.py
View File

@ -96,17 +96,26 @@ class RandomAudioCropInjector(Injector):
self.min_crop_sz = opt['min_crop_size']
self.max_crop_sz = opt['max_crop_size']
self.lengths_key = opt['lengths_key']
self.crop_start_key = opt['crop_start_key']
def forward(self, state):
crop_sz = random.randint(self.min_crop_sz, self.max_crop_sz)
inp = state[self.input]
lens = state[self.lengths_key]
len = torch.min(lens)
if self.lengths_key is not None:
lens = state[self.lengths_key]
len = torch.min(lens)
else:
len = inp.shape[-1]
margin = len - crop_sz
if margin < 0:
return {self.output: inp}
start = random.randint(0, margin)
return {self.output: inp[:, :, start:start+crop_sz]}
res = {self.output: inp}
else:
start = random.randint(0, margin)
res = {self.output: inp[:, :, start:start+crop_sz]}
if self.crop_start_key is not None:
res[self.crop_start_key] = start
return res
class AudioClipInjector(Injector):