DL-Art-School/codes/models/audio/music/flat_diffusion.py
James Betker 57d6f6d366 Big rework of flat_diffusion
Back to the drawing board, boys. Time to waste some resources catching bugs....
2022-05-22 08:09:33 -06:00

339 lines
16 KiB
Python

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
def is_latent(t):
return t.dtype == torch.float
def is_sequence(t):
return t.dtype == torch.long
class TimestepResBlock(TimestepBlock):
def __init__(
self,
channels,
emb_channels,
dropout,
out_channels=None,
dims=2,
kernel_size=3,
efficient_config=True,
use_scale_shift_norm=False,
):
super().__init__()
self.channels = channels
self.emb_channels = emb_channels
self.dropout = dropout
self.out_channels = out_channels or channels
self.use_scale_shift_norm = use_scale_shift_norm
padding = {1: 0, 3: 1, 5: 2}[kernel_size]
eff_kernel = 1 if efficient_config else 3
eff_padding = 0 if efficient_config else 1
self.in_layers = nn.Sequential(
normalization(channels),
nn.SiLU(),
conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding),
)
self.emb_layers = nn.Sequential(
nn.SiLU(),
linear(
emb_channels,
2 * self.out_channels if use_scale_shift_norm else self.out_channels,
),
)
self.out_layers = nn.Sequential(
normalization(self.out_channels),
nn.SiLU(),
nn.Dropout(p=dropout),
zero_module(
conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding)
),
)
if self.out_channels == channels:
self.skip_connection = nn.Identity()
else:
self.skip_connection = conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding)
def forward(self, x, emb):
"""
Apply the block to a Tensor, conditioned on a timestep embedding.
:param x: an [N x C x ...] Tensor of features.
:param emb: an [N x emb_channels] Tensor of timestep embeddings.
:return: an [N x C x ...] Tensor of outputs.
"""
return checkpoint(
self._forward, x, emb
)
def _forward(self, x, emb):
h = self.in_layers(x)
emb_out = self.emb_layers(emb).type(h.dtype)
while len(emb_out.shape) < len(h.shape):
emb_out = emb_out[..., None]
if self.use_scale_shift_norm:
out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
scale, shift = torch.chunk(emb_out, 2, dim=1)
h = out_norm(h) * (1 + scale) + shift
h = out_rest(h)
else:
h = h + emb_out
h = self.out_layers(h)
return self.skip_connection(x) + h
class DiffusionLayer(TimestepBlock):
def __init__(self, model_channels, dropout, num_heads):
super().__init__()
self.resblk = TimestepResBlock(model_channels, model_channels, dropout, model_channels, dims=1, use_scale_shift_norm=True)
self.attn = AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True)
def forward(self, x, time_emb):
y = self.resblk(x, time_emb)
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,
model_channels=512,
num_layers=8,
in_channels=256,
in_latent_channels=512,
in_vectors=8,
in_groups=8,
out_channels=512, # mean and variance
dropout=0,
use_fp16=False,
num_heads=8,
# Parameters for regularization.
layer_drop=.1,
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.out_channels = out_channels
self.dropout = dropout
self.num_heads = num_heads
self.unconditioned_percentage = unconditioned_percentage
self.enable_fp16 = use_fp16
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(),
linear(model_channels, model_channels),
)
# Either code_converter or latent_converter is used, depending on what type of conditioning data is fed.
# This model is meant to be able to be trained on both for efficiency purposes - it is far less computationally
# complex to generate tokens, while generating latents will normally mean propagating through a deep autoregressive
# transformer network.
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),
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(
NonTimestepResidualAttentionNorm(model_channels, dropout),
NonTimestepResidualAttentionNorm(model_channels, dropout),
NonTimestepResidualAttentionNorm(model_channels, dropout),
nn.Conv1d(model_channels, model_channels, 3, padding=1),
)
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.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)
self.layers = nn.ModuleList([DiffusionLayer(model_channels, dropout, num_heads) for _ in range(num_layers)] +
[TimestepResBlock(model_channels, model_channels, dropout, dims=1, use_scale_shift_norm=True) for _ in range(3)])
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):
groups = {
'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()),
'time_embed': list(self.time_embed.parameters()),
}
return groups
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]
# 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(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')
if not return_code_pred:
return expanded_code_emb, cond_emb
else:
# 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, cond_emb, mel_pred
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 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 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.
"""
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:
code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, x.shape[-1])
unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
unused_params.extend(list(self.latent_conditioner.parameters()))
else:
if precomputed_code_embeddings is not None:
code_emb = precomputed_code_embeddings
cond_emb = precomputed_cond_embeddings
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)
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)
for i, lyr in enumerate(self.layers):
# Do layer drop where applicable. Do not drop first and last layers.
if self.training and self.layer_drop > 0 and i != 0 and i != (len(self.layers)-1) and random.random() < self.layer_drop:
unused_params.extend(list(lyr.parameters()))
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, blk_emb)
x = x.float()
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
if return_code_pred:
return out, mel_pred
return out
def get_conditioning_latent(self, conditioning_input):
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.conditioning_embedder(speech_conditioning_input[:, j]))
conds = torch.cat(conds, dim=-1)
return conds.mean(dim=-1)
@register_model
def register_flat_diffusion(opt_net, opt):
return FlatDiffusion(**opt_net['kwargs'])
if __name__ == '__main__':
clip = torch.randn(2, 256, 400)
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)
o = model(clip, ts, aligned_sequence, cond, return_code_pred=True)
o = model(clip, ts, aligned_sequence, cond, aligned_latent)