From 419f4d37bd2391dfddbdf705856e659379f8276e Mon Sep 17 00:00:00 2001
From: James Betker <jbetker@gmail.com>
Date: Tue, 19 Apr 2022 23:38:37 -0600
Subject: [PATCH] gen2 music
---
.../music/unet_diffusion_waveform_gen2.py | 510 ++++++++++++++++++
codes/train.py | 2 +-
2 files changed, 511 insertions(+), 1 deletion(-)
create mode 100644 codes/models/audio/music/unet_diffusion_waveform_gen2.py
diff --git a/codes/models/audio/music/unet_diffusion_waveform_gen2.py b/codes/models/audio/music/unet_diffusion_waveform_gen2.py
new file mode 100644
index 00000000..4c250074
--- /dev/null
+++ b/codes/models/audio/music/unet_diffusion_waveform_gen2.py
@@ -0,0 +1,510 @@
+import random
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import autocast
+from x_transformers import Encoder
+
+from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
+from models.diffusion.unet_diffusion import AttentionBlock, TimestepEmbedSequential, \
+ Downsample, Upsample, TimestepBlock
+from models.audio.tts.mini_encoder import AudioMiniEncoder
+from models.audio.tts.unet_diffusion_tts7 import CheckpointedXTransformerEncoder
+from scripts.audio.gen.use_diffuse_tts import ceil_multiple
+from trainer.networks import register_model
+from utils.util import checkpoint
+
+def is_sequence(t):
+ return t.dtype == torch.long
+
+
+class ResBlock(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 ResBlockSimple(nn.Module):
+ def __init__(
+ self,
+ channels,
+ dropout,
+ out_channels=None,
+ dims=1,
+ kernel_size=3,
+ efficient_config=True,
+ ):
+ super().__init__()
+ self.channels = channels
+ self.dropout = dropout
+ self.out_channels = out_channels or channels
+ 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.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):
+ return checkpoint(
+ self._forward, x
+ )
+
+ def _forward(self, x):
+ h = self.in_layers(x)
+ return self.skip_connection(x) + h
+
+
+class StructuralProcessor(nn.Module):
+ def __init__(self, channels, dropout):
+ super().__init__()
+ # 256,128,64,32,16,8,4,2,1
+ level_resblocks = [3, 3, 2, 2, 2,1,1,1]
+ level_ch_div = [1, 1, 2, 4, 4,8,8,16]
+ levels = []
+ lastdiv = 1
+ for resblks, chdiv in zip(level_resblocks, level_ch_div):
+ levels.append(nn.Sequential(*([nn.Conv1d(channels//lastdiv, channels//chdiv, 1)] +
+ [ResBlockSimple(channels//chdiv, dropout) for _ in range(resblks)])))
+ lastdiv = chdiv
+ self.levels = nn.ModuleList(levels)
+
+ def forward(self, x):
+ h = x
+ for level in self.levels:
+ h = level(h)
+ h = F.interpolate(h, scale_factor=2, mode='linear')
+ return h
+
+
+class DiffusionTts(nn.Module):
+ """
+ The full UNet model with attention and timestep embedding.
+
+ Customized to be conditioned on an aligned prior derived from a autoregressive
+ GPT-style model.
+
+ :param in_channels: channels in the input Tensor.
+ :param in_latent_channels: channels from the input latent.
+ :param model_channels: base channel count for the model.
+ :param out_channels: channels in the output Tensor.
+ :param num_res_blocks: number of residual blocks per downsample.
+ :param attention_resolutions: a collection of downsample rates at which
+ attention will take place. May be a set, list, or tuple.
+ For example, if this contains 4, then at 4x downsampling, attention
+ will be used.
+ :param dropout: the dropout probability.
+ :param channel_mult: channel multiplier for each level of the UNet.
+ :param conv_resample: if True, use learned convolutions for upsampling and
+ downsampling.
+ :param dims: determines if the signal is 1D, 2D, or 3D.
+ :param num_heads: the number of attention heads in each attention layer.
+ :param num_heads_channels: if specified, ignore num_heads and instead use
+ a fixed channel width per attention head.
+ :param num_heads_upsample: works with num_heads to set a different number
+ of heads for upsampling. Deprecated.
+ :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
+ :param resblock_updown: use residual blocks for up/downsampling.
+ :param use_new_attention_order: use a different attention pattern for potentially
+ increased efficiency.
+ """
+
+ def __init__(
+ self,
+ model_channels,
+ in_channels=1,
+ in_mel_channels=120,
+ conditioning_dim_factor=8,
+ out_channels=2, # mean and variance
+ dropout=0,
+ # res 1, 2, 4, 8,16,32,64,128,256,512, 1K, 2K
+ channel_mult= (1,1.5,2, 3, 4, 6, 8, 12, 16, 24, 32, 48),
+ num_res_blocks=(1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2),
+ # spec_cond: 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0)
+ # attn: 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1
+ attention_resolutions=(512,1024,2048),
+ conv_resample=True,
+ dims=1,
+ use_fp16=False,
+ num_heads=1,
+ num_head_channels=-1,
+ num_heads_upsample=-1,
+ kernel_size=3,
+ scale_factor=2,
+ time_embed_dim_multiplier=4,
+ efficient_convs=True, # Uses kernels with width of 1 in several places rather than 3.
+ use_scale_shift_norm=True,
+ freeze_main=False,
+ # Parameters for regularization.
+ unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
+ ):
+ super().__init__()
+
+ if num_heads_upsample == -1:
+ num_heads_upsample = num_heads
+
+ self.in_channels = in_channels
+ self.model_channels = model_channels
+ self.out_channels = out_channels
+ self.attention_resolutions = attention_resolutions
+ self.dropout = dropout
+ self.channel_mult = channel_mult
+ self.conv_resample = conv_resample
+ self.num_heads = num_heads
+ self.num_head_channels = num_head_channels
+ self.num_heads_upsample = num_heads_upsample
+ self.dims = dims
+ self.unconditioned_percentage = unconditioned_percentage
+ self.enable_fp16 = use_fp16
+ self.alignment_size = 2 ** (len(channel_mult)+1)
+ self.in_mel_channels = in_mel_channels
+ padding = 1 if kernel_size == 3 else 2
+ down_kernel = 1 if efficient_convs else 3
+
+ time_embed_dim = model_channels * time_embed_dim_multiplier
+ self.time_embed = nn.Sequential(
+ linear(model_channels, time_embed_dim),
+ nn.SiLU(),
+ linear(time_embed_dim, time_embed_dim),
+ )
+
+ conditioning_dim = model_channels * conditioning_dim_factor
+ self.structural_cond_input = nn.Conv1d(in_mel_channels, conditioning_dim, 3, padding=1)
+ self.aligned_latent_padding_embedding = nn.Parameter(torch.randn(1,in_mel_channels,1))
+ self.unconditioned_embedding = nn.Parameter(torch.randn(1,conditioning_dim,1))
+ self.structural_processor = StructuralProcessor(conditioning_dim, dropout)
+ self.surrogate_head = nn.Conv1d(conditioning_dim//16, in_channels, 1)
+
+ self.input_block = conv_nd(dims, in_channels, model_channels//2, kernel_size, padding=padding)
+ self.input_blocks = nn.ModuleList(
+ [
+ TimestepEmbedSequential(
+ conv_nd(dims, model_channels, model_channels, kernel_size, padding=padding)
+ )
+ ]
+ )
+ self._feature_size = model_channels
+ input_block_chans = [model_channels]
+ ch = model_channels
+ ds = 1
+
+ for level, (mult, num_blocks) in enumerate(zip(channel_mult, num_res_blocks)):
+ for _ in range(num_blocks):
+ layers = [
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ out_channels=int(mult * model_channels),
+ dims=dims,
+ kernel_size=kernel_size,
+ efficient_config=efficient_convs,
+ use_scale_shift_norm=use_scale_shift_norm,
+ )
+ ]
+ ch = int(mult * model_channels)
+ if ds in attention_resolutions:
+ layers.append(
+ AttentionBlock(
+ ch,
+ num_heads=num_heads,
+ num_head_channels=num_head_channels,
+ )
+ )
+ self.input_blocks.append(TimestepEmbedSequential(*layers))
+ self._feature_size += ch
+ input_block_chans.append(ch)
+ if level != len(channel_mult) - 1:
+ out_ch = ch
+ self.input_blocks.append(
+ TimestepEmbedSequential(
+ Downsample(
+ ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor, ksize=down_kernel, pad=0 if down_kernel == 1 else 1
+ )
+ )
+ )
+ ch = out_ch
+ input_block_chans.append(ch)
+ ds *= 2
+ self._feature_size += ch
+
+ self.middle_block = TimestepEmbedSequential(
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ dims=dims,
+ kernel_size=kernel_size,
+ efficient_config=efficient_convs,
+ use_scale_shift_norm=use_scale_shift_norm,
+ ),
+ AttentionBlock(
+ ch,
+ num_heads=num_heads,
+ num_head_channels=num_head_channels,
+ ),
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ dims=dims,
+ kernel_size=kernel_size,
+ efficient_config=efficient_convs,
+ use_scale_shift_norm=use_scale_shift_norm,
+ ),
+ )
+ self._feature_size += ch
+
+ self.output_blocks = nn.ModuleList([])
+ for level, (mult, num_blocks) in list(enumerate(zip(channel_mult, num_res_blocks)))[::-1]:
+ for i in range(num_blocks + 1):
+ ich = input_block_chans.pop()
+ layers = [
+ ResBlock(
+ ch + ich,
+ time_embed_dim,
+ dropout,
+ out_channels=int(model_channels * mult),
+ dims=dims,
+ kernel_size=kernel_size,
+ efficient_config=efficient_convs,
+ use_scale_shift_norm=use_scale_shift_norm,
+ )
+ ]
+ ch = int(model_channels * mult)
+ if ds in attention_resolutions:
+ layers.append(
+ AttentionBlock(
+ ch,
+ num_heads=num_heads_upsample,
+ num_head_channels=num_head_channels,
+ )
+ )
+ if level and i == num_blocks:
+ out_ch = ch
+ layers.append(
+ Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor)
+ )
+ ds //= 2
+ self.output_blocks.append(TimestepEmbedSequential(*layers))
+ self._feature_size += ch
+
+ self.out = nn.Sequential(
+ normalization(ch),
+ nn.SiLU(),
+ zero_module(conv_nd(dims, model_channels, out_channels, kernel_size, padding=padding)),
+ )
+
+ if freeze_main:
+ for p in self.parameters():
+ p.DO_NOT_TRAIN = True
+ p.requires_grad = False
+ for m in [self.structural_processor, self.structural_cond_input, self.surrogate_head]:
+ for p in m.parameters():
+ del p.DO_NOT_TRAIN
+ p.requires_grad = True
+
+
+ def get_grad_norm_parameter_groups(self):
+ if self.freeze_main_net:
+ return {}
+ groups = {
+ 'input_blocks': list(self.input_blocks.parameters()),
+ 'output_blocks': list(self.output_blocks.parameters()),
+ 'middle_transformer': list(self.middle_block.parameters()),
+ 'structural_processor': list(self.structural_processor.parameters()),
+ }
+ return groups
+
+ def is_latent(self, t):
+ return t.shape[1] != self.in_mel_channels
+
+ def fix_alignment(self, x, aligned_conditioning):
+ """
+ The UNet requires that the input <x> is a certain multiple of 2, defined by the UNet depth. Enforce this by
+ padding both <x> and <aligned_conditioning> before forward propagation and removing the padding before returning.
+ """
+ cm = ceil_multiple(x.shape[-1], self.alignment_size)
+ if cm != 0:
+ pc = (cm-x.shape[-1])/x.shape[-1]
+ x = F.pad(x, (0,cm-x.shape[-1]))
+ # Also fix aligned_latent, which is aligned to x.
+ if self.is_latent(aligned_conditioning):
+ aligned_conditioning = torch.cat([aligned_conditioning,
+ self.aligned_latent_padding_embedding.repeat(x.shape[0], 1, int(pc * aligned_conditioning.shape[-1]))], dim=-1)
+ else:
+ aligned_conditioning = F.pad(aligned_conditioning, (0,int(pc*aligned_conditioning.shape[-1])))
+ return x, aligned_conditioning
+
+ def forward(self, x, timesteps, aligned_conditioning, conditioning_free=False):
+ """
+ Apply the model to an input batch.
+
+ :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 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.
+ """
+
+ # Shuffle aligned_latent to BxCxS format
+ if self.is_latent(aligned_conditioning):
+ aligned_conditioning = aligned_conditioning.permute(0, 2, 1)
+
+ # Fix input size to the proper multiple of 2 so we don't get alignment errors going down and back up the U-net.
+ orig_x_shape = x.shape[-1]
+ x, aligned_conditioning = self.fix_alignment(x, aligned_conditioning)
+
+ with autocast(x.device.type, enabled=self.enable_fp16):
+ hs = []
+ time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
+
+ # Note: this block does not need to repeated on inference, since it is not timestep-dependent.
+ if conditioning_free:
+ code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, 1)
+ surrogate = torch.zeros_like(x)
+ else:
+ code_emb = self.structural_cond_input(aligned_conditioning)
+ code_emb = self.structural_processor(code_emb)
+ code_emb = F.interpolate(code_emb, size=(x.shape[-1],), mode='linear')
+ surrogate = self.surrogate_head(code_emb)
+
+ # Everything after this comment is timestep dependent.
+ x = self.input_block(x)
+ x = torch.cat([x, code_emb], dim=1)
+
+ time_emb = time_emb.float()
+ h = x
+ for k, module in enumerate(self.input_blocks):
+ with autocast(x.device.type, enabled=self.enable_fp16 and 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)
+ 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)
+
+ # Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
+ extraneous_addition = 0
+ params = [self.aligned_latent_padding_embedding, self.unconditioned_embedding]
+ for p in params:
+ extraneous_addition = extraneous_addition + p.mean()
+ out = out + extraneous_addition * 0
+
+ return out[:, :, :orig_x_shape], surrogate[:, :, :orig_x_shape]
+
+
+@register_model
+def register_unet_diffusion_waveform_gen2(opt_net, opt):
+ return DiffusionTts(**opt_net['kwargs'])
+
+
+if __name__ == '__main__':
+ clip = torch.randn(2, 1, 32868)
+ aligned_sequence = torch.randn(2,120,128)
+ ts = torch.LongTensor([600, 600])
+ model = DiffusionTts(128,
+ channel_mult=[1,1.5,2, 3, 4, 6, 8],
+ num_res_blocks=[2, 2, 2, 2, 2, 2, 1],
+ attention_resolutions=[],
+ num_heads=8,
+ kernel_size=3,
+ scale_factor=2,
+ time_embed_dim_multiplier=4,
+ efficient_convs=False)
+ # Test with sequence aligned conditioning
+ o = model(clip, ts, aligned_sequence)
+
diff --git a/codes/train.py b/codes/train.py
index e47aad76..70578721 100644
--- a/codes/train.py
+++ b/codes/train.py
@@ -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_cvvp_codes.yml')
+ parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_clip_text_to_voice.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)