flat diffusion network
This commit is contained in:
James Betker
parent
bf08519d71
commit
428911cd4d
304
codes/models/audio/tts/unet_diffusion_tts_flat.py
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304
codes/models/audio/tts/unet_diffusion_tts_flat.py
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@ -0,0 +1,304 @@
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import random
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch import autocast
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from x_transformers import Encoder
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from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
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from models.diffusion.unet_diffusion import AttentionBlock, TimestepEmbedSequential, \
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Downsample, Upsample, TimestepBlock
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from models.audio.tts.mini_encoder import AudioMiniEncoder
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from models.audio.tts.unet_diffusion_tts7 import CheckpointedXTransformerEncoder
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from scripts.audio.gen.use_diffuse_tts import ceil_multiple
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from trainer.networks import register_model
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from utils.util import checkpoint
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def is_latent(t):
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return t.dtype == torch.float
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def is_sequence(t):
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return t.dtype == torch.long
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class ResBlock(TimestepBlock):
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def __init__(
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self,
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channels,
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emb_channels,
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dropout,
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out_channels=None,
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dims=2,
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kernel_size=3,
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efficient_config=True,
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use_scale_shift_norm=False,
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):
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super().__init__()
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self.channels = channels
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self.emb_channels = emb_channels
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self.dropout = dropout
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self.out_channels = out_channels or channels
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self.use_scale_shift_norm = use_scale_shift_norm
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padding = {1: 0, 3: 1, 5: 2}[kernel_size]
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eff_kernel = 1 if efficient_config else 3
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eff_padding = 0 if efficient_config else 1
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self.in_layers = nn.Sequential(
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normalization(channels),
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nn.SiLU(),
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conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding),
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)
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self.emb_layers = nn.Sequential(
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nn.SiLU(),
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linear(
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emb_channels,
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2 * self.out_channels if use_scale_shift_norm else self.out_channels,
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),
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)
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self.out_layers = nn.Sequential(
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normalization(self.out_channels),
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nn.SiLU(),
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nn.Dropout(p=dropout),
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zero_module(
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conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding)
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),
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)
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if self.out_channels == channels:
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self.skip_connection = nn.Identity()
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else:
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self.skip_connection = conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding)
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def forward(self, x, emb):
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"""
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Apply the block to a Tensor, conditioned on a timestep embedding.
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:param x: an [N x C x ...] Tensor of features.
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:param emb: an [N x emb_channels] Tensor of timestep embeddings.
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:return: an [N x C x ...] Tensor of outputs.
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"""
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return checkpoint(
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self._forward, x, emb
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)
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def _forward(self, x, emb):
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h = self.in_layers(x)
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emb_out = self.emb_layers(emb).type(h.dtype)
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while len(emb_out.shape) < len(h.shape):
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emb_out = emb_out[..., None]
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if self.use_scale_shift_norm:
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out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
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scale, shift = torch.chunk(emb_out, 2, dim=1)
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h = out_norm(h) * (1 + scale) + shift
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h = out_rest(h)
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else:
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h = h + emb_out
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h = self.out_layers(h)
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return self.skip_connection(x) + h
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class DiffusionLayer(nn.Module):
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def __init__(self, model_channels, aligned_channels, cond_channels, dropout, num_heads):
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super().__init__()
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self.aligned_mutation = zero_module(conv_nd(1, aligned_channels, model_channels, 1))
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self.cond_mutation = zero_module(conv_nd(1, cond_channels, model_channels, 1))
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self.inp_mutation = conv_nd(1, model_channels, model_channels, 1)
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self.resblk = ResBlock(model_channels, model_channels, dropout, model_channels, dims=1, use_scale_shift_norm=True)
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self.attn = AttentionBlock(model_channels, num_heads)
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def forward(self, x, aligned, pointwise, time_emb):
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a = self.aligned_mutation(aligned)
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c = self.cond_mutation(pointwise.unsqueeze(-1))
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f = self.inp_mutation(x)
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y = self.resblk(f + c.repeat(1,1,f.shape[-1]) + F.interpolate(a, size=f.shape[-1], mode='nearest'), time_emb)
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y = self.attn(y)
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return y
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class DiffusionTtsFlat(nn.Module):
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def __init__(
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self,
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model_channels=512,
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num_layers=8,
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in_channels=100,
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in_latent_channels=512,
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in_tokens=8193,
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max_timesteps=4000,
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max_positions=4000,
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out_channels=200, # mean and variance
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dropout=0,
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use_fp16=False,
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num_heads=16,
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# Parameters for regularization.
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layer_drop=.1,
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unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
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):
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super().__init__()
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self.in_channels = in_channels
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self.model_channels = model_channels
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self.out_channels = out_channels
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self.dropout = dropout
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self.num_heads = num_heads
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self.unconditioned_percentage = unconditioned_percentage
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self.enable_fp16 = use_fp16
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self.layer_drop = layer_drop
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self.inp_block = conv_nd(1, in_channels, model_channels, 3, 1, 1)
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self.position_embed = nn.Embedding(max_positions, model_channels)
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self.time_embed = nn.Embedding(max_timesteps, model_channels)
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# Either code_converter or latent_converter is used, depending on what type of conditioning data is fed.
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# This model is meant to be able to be trained on both for efficiency purposes - it is far less computationally
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# complex to generate tokens, while generating latents will normally mean propagating through a deep autoregressive
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# transformer network.
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self.code_converter = nn.Sequential(
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nn.Embedding(in_tokens, model_channels),
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CheckpointedXTransformerEncoder(
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needs_permute=False,
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max_seq_len=-1,
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use_pos_emb=False,
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attn_layers=Encoder(
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dim=model_channels,
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depth=3,
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heads=num_heads,
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ff_dropout=dropout,
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attn_dropout=dropout,
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use_rmsnorm=True,
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ff_glu=True,
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rotary_emb_dim=True,
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)
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))
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self.latent_converter = nn.Conv1d(in_latent_channels, model_channels, 1)
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if in_channels > 60: # It's a spectrogram.
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self.contextual_embedder = nn.Sequential(nn.Conv1d(in_channels,model_channels,3,padding=1,stride=2),
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CheckpointedXTransformerEncoder(
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needs_permute=True,
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max_seq_len=-1,
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use_pos_emb=False,
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attn_layers=Encoder(
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dim=model_channels,
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depth=4,
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heads=num_heads,
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ff_dropout=dropout,
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attn_dropout=dropout,
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use_rmsnorm=True,
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ff_glu=True,
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rotary_emb_dim=True,
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)
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))
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else:
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self.contextual_embedder = AudioMiniEncoder(1, model_channels, base_channels=32, depth=6, resnet_blocks=1,
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attn_blocks=3, num_attn_heads=8, dropout=dropout, downsample_factor=4, kernel_size=5)
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self.conditioning_conv = nn.Conv1d(model_channels*2, model_channels, 1)
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self.unconditioned_embedding = nn.Parameter(torch.randn(1,model_channels,1))
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self.conditioning_timestep_integrator = TimestepEmbedSequential(
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ResBlock(model_channels, model_channels, dropout, out_channels=model_channels, dims=1, kernel_size=1, use_scale_shift_norm=True),
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AttentionBlock(model_channels, num_heads=num_heads),
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ResBlock(model_channels, model_channels, dropout, out_channels=model_channels, dims=1, kernel_size=1, use_scale_shift_norm=True),
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AttentionBlock(model_channels, num_heads=num_heads),
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ResBlock(model_channels, model_channels, dropout, out_channels=model_channels, dims=1, kernel_size=1, use_scale_shift_norm=True),
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)
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self.layers = nn.ModuleList([DiffusionLayer(model_channels, model_channels, model_channels, dropout, num_heads) for _ in range(num_layers)])
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self.out = nn.Sequential(
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normalization(model_channels),
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nn.SiLU(),
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zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)),
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)
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def get_grad_norm_parameter_groups(self):
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groups = {
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'minicoder': list(self.contextual_embedder.parameters()),
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'layers': list(self.layers),
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}
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return groups
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def forward(self, x, timesteps, aligned_conditioning, conditioning_input, lr_input=None, conditioning_free=False):
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"""
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Apply the model to an input batch.
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:param x: an [N x C x ...] Tensor of inputs.
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:param timesteps: a 1-D batch of timesteps.
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:param aligned_conditioning: an aligned latent or sequence of tokens providing useful data about the sample to be produced.
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:param conditioning_input: a full-resolution audio clip that is used as a reference to the style you want decoded.
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:param lr_input: for super-sampling models, a guidance audio clip at a lower sampling rate.
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:param conditioning_free: When set, all conditioning inputs (including tokens and conditioning_input) will not be considered.
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:return: an [N x C x ...] Tensor of outputs.
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"""
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# Shuffle aligned_latent to BxCxS format
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if is_latent(aligned_conditioning):
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aligned_conditioning = aligned_conditioning.permute(0, 2, 1)
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# Note: this block does not need to repeated on inference, since it is not timestep-dependent or x-dependent.
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unused_params = []
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if conditioning_free:
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code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, 1)
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else:
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unused_params.append(self.unconditioned_embedding)
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cond_emb = self.contextual_embedder(conditioning_input)
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if len(cond_emb.shape) == 3: # Just take the first element.
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cond_emb = cond_emb[:, :, 0]
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if is_latent(aligned_conditioning):
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code_emb = self.latent_converter(aligned_conditioning)
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unused_params.extend(list(self.code_converter.parameters()))
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else:
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code_emb = self.code_converter(aligned_conditioning)
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unused_params.extend(list(self.latent_converter.parameters()))
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cond_emb_spread = cond_emb.unsqueeze(-1).repeat(1, 1, code_emb.shape[-1])
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code_emb = self.conditioning_conv(torch.cat([cond_emb_spread, code_emb], dim=1))
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# Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
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if self.training and self.unconditioned_percentage > 0:
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unconditioned_batches = torch.rand((code_emb.shape[0], 1, 1),
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device=code_emb.device) < self.unconditioned_percentage
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code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(x.shape[0], 1, 1),
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code_emb)
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# Everything after this comment is timestep dependent.
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x = self.inp_block(x)
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pos_emb = self.position_embed(torch.arange(0, x.shape[-1], device=x.device)).unsqueeze(0).repeat(x.shape[0],1,1).permute(0,2,1)
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x = x + pos_emb
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time_emb = self.time_embed(timesteps)
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code_emb = self.conditioning_timestep_integrator(code_emb, time_emb)
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for i, lyr in enumerate(self.layers):
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# Do layer drop where applicable. Do not drop first and last layers.
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if self.layer_drop > 0 and i != 0 and i != (len(self.layers)-1) and random.random() < self.layer_drop:
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unused_params.extend(list(lyr.parameters()))
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else:
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# First and last blocks will have autocast disabled for improved precision.
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with autocast(x.device.type, enabled=self.enable_fp16 and i != 0):
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x = lyr(x, code_emb, cond_emb, time_emb)
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x = x.float()
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out = self.out(x)
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# Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
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extraneous_addition = 0
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for p in unused_params:
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extraneous_addition = extraneous_addition + p.mean()
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out = out + extraneous_addition * 0
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return out
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@register_model
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def register_diffusion_tts_flat(opt_net, opt):
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return DiffusionTtsFlat(**opt_net['kwargs'])
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if __name__ == '__main__':
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clip = torch.randn(2, 100, 400)
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aligned_latent = torch.randn(2,388,512)
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aligned_sequence = torch.randint(0,8192,(2,388))
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cond = torch.randn(2, 100, 400)
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ts = torch.LongTensor([600, 600])
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model = DiffusionTtsFlat(512, layer_drop=.3)
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# Test with latent aligned conditioning
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o = model(clip, ts, aligned_latent, cond)
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# Test with sequence aligned conditioning
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o = model(clip, ts, aligned_sequence, cond)
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@ -327,7 +327,13 @@ class ExtensibleTrainer(BaseModel):
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else:
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pgroups = {f'{name}_all_parameters': list(model.parameters())}
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for name in pgroups.keys():
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grad_norms[name] = torch.norm(torch.stack([torch.norm(p.grad.detach(), 2) for p in pgroups[name]]), 2)
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stacked_grads = []
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for p in pgroups[name]:
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if hasattr(p, 'grad') and p.grad is not None:
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stacked_grads.append(torch.norm(p.grad.detach(), 2))
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if not stacked_grads:
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continue
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grad_norms[name] = torch.norm(torch.stack(stacked_grads), 2)
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if distributed.is_available() and distributed.is_initialized():
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# Gather the metric from all devices if in a distributed setting.
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distributed.all_reduce(grad_norms[name], op=distributed.ReduceOp.SUM)
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