Big rework of flat_diffusion
Back to the drawing board, boys. Time to waste some resources catching bugs....
This commit is contained in:
James Betker
parent
db38672dae
commit
57d6f6d366
|
|
@ -1,13 +1,17 @@
|
|||
import os
|
||||
import random
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torchvision
|
||||
from torch import autocast
|
||||
|
||||
from models.arch_util import ResBlock
|
||||
from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
|
||||
from models.diffusion.unet_diffusion import AttentionBlock, TimestepEmbedSequential, TimestepBlock
|
||||
from scripts.audio.gen.use_mel2vec_codes import collapse_codegroups
|
||||
from trainer.injectors.audio_injectors import normalize_mel
|
||||
from trainer.networks import register_model
|
||||
from utils.util import checkpoint
|
||||
|
||||
|
|
@ -107,6 +111,20 @@ class DiffusionLayer(TimestepBlock):
|
|||
return self.attn(y)
|
||||
|
||||
|
||||
class NonTimestepResidualAttentionNorm(nn.Module):
|
||||
def __init__(self, model_channels, dropout):
|
||||
super().__init__()
|
||||
self.resblk = ResBlock(dims=1, channels=model_channels, dropout=dropout)
|
||||
self.attn = AttentionBlock(model_channels, num_heads=model_channels//64, relative_pos_embeddings=True)
|
||||
self.norm = nn.GroupNorm(num_groups=8, num_channels=model_channels)
|
||||
|
||||
def forward(self, x):
|
||||
h = self.resblk(x)
|
||||
h = self.norm(h)
|
||||
h = self.attn(h)
|
||||
return h
|
||||
|
||||
|
||||
class FlatDiffusion(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
|
|
@ -123,7 +141,6 @@ class FlatDiffusion(nn.Module):
|
|||
# Parameters for regularization.
|
||||
layer_drop=.1,
|
||||
unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
|
||||
train_mel_head=False,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
|
|
@ -137,6 +154,7 @@ class FlatDiffusion(nn.Module):
|
|||
self.layer_drop = layer_drop
|
||||
|
||||
self.inp_block = conv_nd(1, in_channels, model_channels, 3, 1, 1)
|
||||
# TODO: I'd really like to see if this could be ablated. It seems useless to me: why can't the embedding just learn this mapping directly?
|
||||
self.time_embed = nn.Sequential(
|
||||
linear(model_channels, model_channels),
|
||||
nn.SiLU(),
|
||||
|
|
@ -150,32 +168,23 @@ class FlatDiffusion(nn.Module):
|
|||
self.embeddings = nn.ModuleList([nn.Embedding(in_vectors, model_channels//in_groups) for _ in range(in_groups)])
|
||||
self.latent_conditioner = nn.Sequential(
|
||||
nn.Conv1d(in_latent_channels, model_channels, 3, padding=1),
|
||||
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
|
||||
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
|
||||
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
|
||||
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
|
||||
NonTimestepResidualAttentionNorm(model_channels, dropout),
|
||||
NonTimestepResidualAttentionNorm(model_channels, dropout),
|
||||
nn.Conv1d(model_channels, model_channels, 3, padding=1),
|
||||
)
|
||||
self.latent_fade = nn.Parameter(torch.zeros(1,model_channels,1))
|
||||
self.code_converter = nn.Sequential(
|
||||
ResBlock(dims=1, channels=model_channels, dropout=dropout),
|
||||
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
|
||||
ResBlock(dims=1, channels=model_channels, dropout=dropout),
|
||||
AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
|
||||
ResBlock(dims=1, channels=model_channels, dropout=dropout),
|
||||
NonTimestepResidualAttentionNorm(model_channels, dropout),
|
||||
NonTimestepResidualAttentionNorm(model_channels, dropout),
|
||||
NonTimestepResidualAttentionNorm(model_channels, dropout),
|
||||
nn.Conv1d(model_channels, model_channels, 3, padding=1),
|
||||
)
|
||||
self.code_norm = normalization(model_channels)
|
||||
self.contextual_embedder = nn.Sequential(nn.Conv1d(in_channels,model_channels,3,padding=1,stride=2),
|
||||
nn.Conv1d(model_channels, model_channels*2,3,padding=1,stride=2),
|
||||
AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
|
||||
AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
|
||||
AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
|
||||
AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
|
||||
AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False))
|
||||
self.conditioning_embedder = nn.Sequential(nn.Conv1d(in_channels, model_channels // 2, 3, padding=1, stride=2),
|
||||
nn.Conv1d(model_channels//2, model_channels,3,padding=1,stride=2),
|
||||
NonTimestepResidualAttentionNorm(model_channels, dropout),
|
||||
NonTimestepResidualAttentionNorm(model_channels, dropout),
|
||||
NonTimestepResidualAttentionNorm(model_channels, dropout))
|
||||
self.unconditioned_embedding = nn.Parameter(torch.randn(1,model_channels,1))
|
||||
self.conditioning_timestep_integrator = TimestepEmbedSequential(
|
||||
DiffusionLayer(model_channels, dropout, num_heads),
|
||||
DiffusionLayer(model_channels, dropout, num_heads),
|
||||
DiffusionLayer(model_channels, dropout, num_heads),
|
||||
)
|
||||
self.integrating_conv = nn.Conv1d(model_channels*2, model_channels, kernel_size=1)
|
||||
self.mel_head = nn.Conv1d(model_channels, in_channels, kernel_size=3, padding=1)
|
||||
|
||||
|
|
@ -188,77 +197,78 @@ class FlatDiffusion(nn.Module):
|
|||
zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)),
|
||||
)
|
||||
|
||||
if train_mel_head:
|
||||
for m in [self.conditioning_timestep_integrator, self.integrating_conv, self.layers,
|
||||
self.out]:
|
||||
for p in m.parameters():
|
||||
p.requires_grad = False
|
||||
p.DO_NOT_TRAIN = True
|
||||
self.debug_codes = {}
|
||||
|
||||
def get_grad_norm_parameter_groups(self):
|
||||
groups = {
|
||||
'minicoder': list(self.contextual_embedder.parameters()),
|
||||
'layers': list(self.layers.parameters()),
|
||||
'contextual_embedder': list(self.conditioning_embedder.parameters()),
|
||||
'layers': list(self.layers.parameters()) + list(self.integrating_conv.parameters()) + list(self.inp_block.parameters()),
|
||||
'code_converters': list(self.embeddings.parameters()) + list(self.code_converter.parameters()) + list(self.latent_conditioner.parameters()),
|
||||
'timestep_integrator': list(self.conditioning_timestep_integrator.parameters()) + list(self.integrating_conv.parameters()),
|
||||
'time_embed': list(self.time_embed.parameters()),
|
||||
}
|
||||
return groups
|
||||
|
||||
def timestep_independent(self, aligned_conditioning, conditioning_input, expected_seq_len, return_code_pred):
|
||||
# Shuffle aligned_latent to BxCxS format
|
||||
if is_latent(aligned_conditioning):
|
||||
aligned_conditioning = aligned_conditioning.permute(0, 2, 1)
|
||||
def timestep_independent(self, codes, conditioning_input, expected_seq_len, prenet_latent=None, return_code_pred=False):
|
||||
cond_emb = self.conditioning_embedder(conditioning_input)[:, :, 0]
|
||||
|
||||
# Note: this block does not need to repeated on inference, since it is not timestep-dependent or x-dependent.
|
||||
speech_conditioning_input = conditioning_input.unsqueeze(1) if len(
|
||||
conditioning_input.shape) == 3 else conditioning_input
|
||||
conds = []
|
||||
for j in range(speech_conditioning_input.shape[1]):
|
||||
conds.append(self.contextual_embedder(speech_conditioning_input[:, j]))
|
||||
conds = torch.cat(conds, dim=-1)
|
||||
cond_emb = conds.mean(dim=-1)
|
||||
cond_scale, cond_shift = torch.chunk(cond_emb, 2, dim=1)
|
||||
if is_latent(aligned_conditioning):
|
||||
code_emb = self.latent_conditioner(aligned_conditioning)
|
||||
else:
|
||||
code_emb = [embedding(aligned_conditioning[:, :, i]) for i, embedding in enumerate(self.embeddings)]
|
||||
code_emb = torch.cat(code_emb, dim=-1).permute(0,2,1)
|
||||
# Shuffle prenet_latent to BxCxS format
|
||||
if prenet_latent is not None:
|
||||
prenet_latent = prenet_latent.permute(0, 2, 1)
|
||||
|
||||
code_emb = [embedding(codes[:, :, i]) for i, embedding in enumerate(self.embeddings)]
|
||||
code_emb = torch.cat(code_emb, dim=-1).permute(0,2,1)
|
||||
if prenet_latent is not None:
|
||||
latent_conditioning = self.latent_conditioner(prenet_latent)
|
||||
code_emb = code_emb + latent_conditioning * self.latent_fade
|
||||
|
||||
unconditioned_batches = torch.zeros((code_emb.shape[0], 1, 1), device=code_emb.device)
|
||||
# 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((code_emb.shape[0], 1, 1),
|
||||
device=code_emb.device) < self.unconditioned_percentage
|
||||
code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(aligned_conditioning.shape[0], 1, 1),
|
||||
code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(codes.shape[0], 1, 1),
|
||||
code_emb)
|
||||
code_emb = self.code_converter(code_emb)
|
||||
|
||||
expanded_code_emb = F.interpolate(code_emb, size=expected_seq_len, mode='nearest')
|
||||
expanded_code_emb = self.code_converter(expanded_code_emb)
|
||||
expanded_code_emb = self.code_norm(expanded_code_emb) * (1 + cond_scale.unsqueeze(-1)) + cond_shift.unsqueeze(-1)
|
||||
|
||||
if not return_code_pred:
|
||||
return expanded_code_emb
|
||||
return expanded_code_emb, cond_emb
|
||||
else:
|
||||
mel_pred = self.mel_head(expanded_code_emb)
|
||||
# Multiply mel_pred by !unconditioned_branches, which drops the gradient on unconditioned branches. This is because we don't want that gradient being used to train parameters through the codes_embedder as it unbalances contributions to that network from the MSE loss.
|
||||
# Perform the mel_head computation on the pre-exanded code embeddings, then interpolate it separately.
|
||||
mel_pred = self.mel_head(code_emb)
|
||||
mel_pred = F.interpolate(mel_pred, size=expected_seq_len, mode='nearest')
|
||||
# Multiply mel_pred by !unconditioned_branches, which drops the gradient on unconditioned branches.
|
||||
# This is because we don't want that gradient being used to train parameters through the codes_embedder as
|
||||
# it unbalances contributions to that network from the MSE loss.
|
||||
mel_pred = mel_pred * unconditioned_batches.logical_not()
|
||||
return expanded_code_emb, mel_pred
|
||||
return expanded_code_emb, cond_emb, mel_pred
|
||||
|
||||
|
||||
def forward(self, x, timesteps, aligned_conditioning=None, conditioning_input=None, precomputed_aligned_embeddings=None, conditioning_free=False, return_code_pred=False):
|
||||
def forward(self, x, timesteps,
|
||||
codes=None, conditioning_input=None, prenet_latent=None,
|
||||
precomputed_code_embeddings=None, precomputed_cond_embeddings=None,
|
||||
conditioning_free=False, return_code_pred=False):
|
||||
"""
|
||||
Apply the model to an input batch.
|
||||
|
||||
There are two ways to call this method:
|
||||
1) Specify codes, conditioning_input and optionally prenet_latent
|
||||
2) Specify precomputed_code_embeddings and precomputed_cond_embeddings, retrieved by calling timestep_independent yourself.
|
||||
|
||||
:param x: an [N x C x ...] Tensor of inputs.
|
||||
:param timesteps: a 1-D batch of timesteps.
|
||||
:param aligned_conditioning: an aligned latent or sequence of tokens providing useful data about the sample to be produced.
|
||||
:param codes: an aligned latent or sequence of tokens providing useful data about the sample to be produced.
|
||||
:param conditioning_input: a full-resolution audio clip that is used as a reference to the style you want decoded.
|
||||
:param precomputed_aligned_embeddings: Embeddings returned from self.timestep_independent()
|
||||
:param prenet_latent: optional latent vector aligned with codes derived from a prior network.
|
||||
:param precomputed_code_embeddings: Code embeddings returned from self.timestep_independent()
|
||||
:param precomputed_cond_embeddings: Conditional embeddings returned from self.timestep_independent()
|
||||
:param conditioning_free: When set, all conditioning inputs (including tokens and conditioning_input) will not be considered.
|
||||
:return: an [N x C x ...] Tensor of outputs.
|
||||
"""
|
||||
assert precomputed_aligned_embeddings is not None or (aligned_conditioning is not None and conditioning_input is not None)
|
||||
assert not (return_code_pred and precomputed_aligned_embeddings is not None) # These two are mutually exclusive.
|
||||
if precomputed_code_embeddings is not None:
|
||||
assert precomputed_cond_embeddings is not None, "Must specify both precomputed embeddings if one is specified"
|
||||
assert codes is None and conditioning_input is None and prenet_latent is None, "Do not provide precomputed embeddings and the other parameters. It is unclear what you want me to do here."
|
||||
assert not (return_code_pred and precomputed_code_embeddings is not None), "I cannot compute a code_pred output for you."
|
||||
|
||||
unused_params = []
|
||||
if conditioning_free:
|
||||
|
|
@ -266,19 +276,17 @@ class FlatDiffusion(nn.Module):
|
|||
unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
|
||||
unused_params.extend(list(self.latent_conditioner.parameters()))
|
||||
else:
|
||||
if precomputed_aligned_embeddings is not None:
|
||||
code_emb = precomputed_aligned_embeddings
|
||||
if precomputed_code_embeddings is not None:
|
||||
code_emb = precomputed_code_embeddings
|
||||
cond_emb = precomputed_cond_embeddings
|
||||
else:
|
||||
code_emb, mel_pred = self.timestep_independent(aligned_conditioning, conditioning_input, x.shape[-1], True)
|
||||
if is_latent(aligned_conditioning):
|
||||
unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
|
||||
else:
|
||||
code_emb, cond_emb, mel_pred = self.timestep_independent(codes, conditioning_input, x.shape[-1], prenet_latent, True)
|
||||
if prenet_latent is None:
|
||||
unused_params.extend(list(self.latent_conditioner.parameters()))
|
||||
|
||||
unused_params.append(self.unconditioned_embedding)
|
||||
|
||||
time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
|
||||
code_emb = self.conditioning_timestep_integrator(code_emb, time_emb)
|
||||
blk_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) + cond_emb
|
||||
x = self.inp_block(x)
|
||||
x = torch.cat([x, code_emb], dim=1)
|
||||
x = self.integrating_conv(x)
|
||||
|
|
@ -289,7 +297,7 @@ class FlatDiffusion(nn.Module):
|
|||
else:
|
||||
# First and last blocks will have autocast disabled for improved precision.
|
||||
with autocast(x.device.type, enabled=self.enable_fp16 and i != 0):
|
||||
x = lyr(x, time_emb)
|
||||
x = lyr(x, blk_emb)
|
||||
|
||||
x = x.float()
|
||||
out = self.out(x)
|
||||
|
|
@ -309,7 +317,7 @@ class FlatDiffusion(nn.Module):
|
|||
conditioning_input.shape) == 3 else conditioning_input
|
||||
conds = []
|
||||
for j in range(speech_conditioning_input.shape[1]):
|
||||
conds.append(self.contextual_embedder(speech_conditioning_input[:, j]))
|
||||
conds.append(self.conditioning_embedder(speech_conditioning_input[:, j]))
|
||||
conds = torch.cat(conds, dim=-1)
|
||||
return conds.mean(dim=-1)
|
||||
|
||||
|
|
@ -320,13 +328,11 @@ def register_flat_diffusion(opt_net, opt):
|
|||
|
||||
if __name__ == '__main__':
|
||||
clip = torch.randn(2, 256, 400)
|
||||
aligned_latent = torch.randn(2,388,512)
|
||||
aligned_latent = torch.randn(2,100,512)
|
||||
aligned_sequence = torch.randint(0,8,(2,100,8))
|
||||
cond = torch.randn(2, 256, 400)
|
||||
ts = torch.LongTensor([600, 600])
|
||||
model = FlatDiffusion(512, layer_drop=.3, unconditioned_percentage=.5, train_mel_head=True)
|
||||
# Test with latent aligned conditioning
|
||||
#o = model(clip, ts, aligned_latent, cond)
|
||||
# Test with sequence aligned conditioning
|
||||
model = FlatDiffusion(512, layer_drop=.3, unconditioned_percentage=.5)
|
||||
o = model(clip, ts, aligned_sequence, cond, return_code_pred=True)
|
||||
o = model(clip, ts, aligned_sequence, cond, aligned_latent)
|
||||
|
||||
|
|
|
|||
|
|
@ -327,7 +327,7 @@ class Trainer:
|
|||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_music_gpt.yml')
|
||||
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_music_diffusion_flat.yml')
|
||||
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
|
||||
args = parser.parse_args()
|
||||
opt = option.parse(args.opt, is_train=True)
|
||||
|
|
|
|||
|
|
@ -167,10 +167,10 @@ class MusicDiffusionFid(evaluator.Evaluator):
|
|||
codegen = self.local_modules['codegen'].to(mel.device)
|
||||
codes = codegen.get_codes(mel)
|
||||
mel_norm = normalize_mel(mel)
|
||||
precomputed = self.model.timestep_independent(aligned_conditioning=codes, conditioning_input=mel[:,:,:112],
|
||||
precomputed_codes, precomputed_cond = self.model.timestep_independent(codes=codes, conditioning_input=mel_norm[:,:,:112],
|
||||
expected_seq_len=mel_norm.shape[-1], return_code_pred=False)
|
||||
gen_mel = self.diffuser.p_sample_loop(self.model, mel_norm.shape, noise=torch.zeros_like(mel_norm),
|
||||
model_kwargs={'precomputed_aligned_embeddings': precomputed})
|
||||
model_kwargs={'precomputed_code_embeddings': precomputed_codes, 'precomputed_cond_embeddings': precomputed_cond})
|
||||
|
||||
gen_mel_denorm = denormalize_mel(gen_mel)
|
||||
output_shape = (1,16,audio.shape[-1]//16)
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user