Remove flat0 and move it into flat
This commit is contained in:
James Betker
parent
81c952a00a
commit
19ca5b26c1
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@ -1,13 +1,14 @@
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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 x_transformers import Encoder
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from torch import autocast
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from models.audio.tts.diffusion_encoder import TimestepEmbeddingAttentionLayers
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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 models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
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from models.diffusion.unet_diffusion import AttentionBlock, TimestepEmbedSequential, TimestepBlock
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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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@ -17,19 +18,107 @@ 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(TimestepBlock):
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def __init__(self, model_channels, dropout, num_heads):
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super().__init__()
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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, relative_pos_embeddings=True)
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def forward(self, x, time_emb):
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y = self.resblk(x, time_emb)
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return self.attn(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=16,
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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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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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freeze_everything_except_autoregressive_inputs=False,
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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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@ -45,100 +134,43 @@ class DiffusionTtsFlat(nn.Module):
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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 = nn.Conv1d(in_channels, model_channels, kernel_size=3, padding=1)
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time_embed_dim = model_channels
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self.inp_block = conv_nd(1, in_channels, model_channels, 3, 1, 1)
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self.time_embed = nn.Sequential(
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linear(model_channels, time_embed_dim),
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linear(model_channels, model_channels),
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nn.SiLU(),
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linear(time_embed_dim, time_embed_dim),
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linear(model_channels, model_channels),
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)
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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_embedding = nn.Embedding(in_tokens, model_channels)
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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_pos_emb=True,
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)
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)
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AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
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AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
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AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
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)
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self.code_norm = normalization(model_channels)
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self.latent_converter = nn.Conv1d(in_latent_channels, model_channels, 1)
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# The contextual embedder processes a sample MEL that the output should be "like".
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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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checkpoint=False, # This is repeatedly executed for many conditioning signals, which is incompatible with checkpointing & DDP.
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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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ff_mult=2,
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rotary_pos_emb=True,
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)
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))
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self.conditioning_conv = nn.Conv1d(model_channels*2, model_channels, 1)
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nn.Conv1d(model_channels, model_channels*2,3,padding=1,stride=2),
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AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
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AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
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AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
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AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
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AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False))
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self.unconditioned_embedding = nn.Parameter(torch.randn(1,model_channels,1))
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# This is a further encoder extension that integrates a timestep signal into the conditioning signal.
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self.conditioning_timestep_integrator = 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=TimestepEmbeddingAttentionLayers(
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dim=model_channels,
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timestep_dim=time_embed_dim,
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depth=2,
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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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ff_mult=2,
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rotary_pos_emb=True,
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layerdrop_percent=0,
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)
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)
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self.integrate_conditioning = nn.Conv1d(model_channels*2, model_channels, 1)
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self.conditioning_timestep_integrator = TimestepEmbedSequential(
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DiffusionLayer(model_channels, dropout, num_heads),
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DiffusionLayer(model_channels, dropout, num_heads),
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DiffusionLayer(model_channels, dropout, num_heads),
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)
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self.integrating_conv = nn.Conv1d(model_channels*2, model_channels, kernel_size=1)
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self.mel_head = nn.Conv1d(model_channels, in_channels, kernel_size=3, padding=1)
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# This is the main processing module.
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self.layers = 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=TimestepEmbeddingAttentionLayers(
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dim=model_channels,
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timestep_dim=time_embed_dim,
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depth=num_layers,
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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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ff_mult=2,
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rotary_pos_emb=True,
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layerdrop_percent=layer_drop,
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zero_init_branch_output=True,
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)
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)
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self.layers.transformer.norm = nn.Identity() # We don't want the final norm for the main encoder.
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self.layers = nn.ModuleList([DiffusionLayer(model_channels, dropout, num_heads) for _ in range(num_layers)] +
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[ResBlock(model_channels, model_channels, dropout, dims=1, use_scale_shift_norm=True) for _ in range(3)])
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self.out = nn.Sequential(
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normalization(model_channels),
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@ -146,54 +178,64 @@ class DiffusionTtsFlat(nn.Module):
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zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)),
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)
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if freeze_everything_except_autoregressive_inputs:
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for ap in list(self.latent_converter.parameters()):
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ap.ALLOWED_IN_FLAT = True
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for p in self.parameters():
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if not hasattr(p, 'ALLOWED_IN_FLAT'):
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p.requires_grad = False
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p.DO_NOT_TRAIN = True
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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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'conditioning_timestep_integrator': list(self.conditioning_timestep_integrator.parameters()),
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'layers': list(self.layers.parameters()),
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'code_converters': list(self.code_embedding.parameters()) + list(self.code_converter.parameters()) + list(self.latent_converter.parameters()) + list(self.latent_converter.parameters()),
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'timestep_integrator': list(self.conditioning_timestep_integrator.parameters()) + list(self.integrating_conv.parameters()),
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'time_embed': list(self.time_embed.parameters()),
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}
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return groups
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def get_conditioning_encodings(self, aligned_conditioning, conditioning_input, conditioning_free, return_unused=False):
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def timestep_independent(self, aligned_conditioning, conditioning_input, expected_seq_len, return_code_pred):
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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(conditioning_input.shape[0], 1, 1)
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speech_conditioning_input = conditioning_input.unsqueeze(1) if len(
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conditioning_input.shape) == 3 else conditioning_input
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conds = []
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for j in range(speech_conditioning_input.shape[1]):
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conds.append(self.contextual_embedder(speech_conditioning_input[:, j]))
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conds = torch.cat(conds, dim=-1)
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cond_emb = conds.mean(dim=-1)
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cond_scale, cond_shift = torch.chunk(cond_emb, 2, dim=1)
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if is_latent(aligned_conditioning):
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code_emb = self.latent_converter(aligned_conditioning)
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else:
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unused_params.append(self.unconditioned_embedding)
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speech_conditioning_input = conditioning_input.unsqueeze(1) if len(conditioning_input.shape) == 3 else conditioning_input
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conds = []
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for j in range(speech_conditioning_input.shape[1]):
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conds.append(self.contextual_embedder(speech_conditioning_input[:, j]))
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conds = torch.cat(conds, dim=-1)
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cond_emb = conds.mean(dim=-1).unsqueeze(-1)
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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.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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code_emb = self.code_embedding(aligned_conditioning).permute(0, 2, 1)
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code_emb = self.code_converter(code_emb)
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code_emb = self.code_norm(code_emb) * (1 + cond_scale.unsqueeze(-1)) + cond_shift.unsqueeze(-1)
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unconditioned_batches = torch.zeros((code_emb.shape[0], 1, 1), device=code_emb.device)
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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(conditioning_input.shape[0], 1, 1),
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code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(aligned_conditioning.shape[0], 1, 1),
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code_emb)
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expanded_code_emb = F.interpolate(code_emb, size=expected_seq_len, mode='nearest')
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if return_unused:
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return code_emb, unused_params
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return code_emb
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if not return_code_pred:
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return expanded_code_emb
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else:
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mel_pred = self.mel_head(expanded_code_emb)
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# 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.
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mel_pred = mel_pred * unconditioned_batches.logical_not()
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return expanded_code_emb, mel_pred
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def forward(self, x, timesteps, aligned_conditioning, conditioning_input, conditioning_free=False):
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def forward(self, x, timesteps, aligned_conditioning=None, conditioning_input=None, precomputed_aligned_embeddings=None, conditioning_free=False, return_code_pred=False):
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"""
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Apply the model to an input batch.
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@ -201,17 +243,44 @@ class DiffusionTtsFlat(nn.Module):
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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 precomputed_aligned_embeddings: Embeddings returned from self.timestep_independent()
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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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code_emb, unused_params = self.get_conditioning_encodings(aligned_conditioning, conditioning_input, conditioning_free, return_unused=True)
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# Everything after this comment is timestep-dependent.
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assert precomputed_aligned_embeddings is not None or (aligned_conditioning is not None and conditioning_input is not None)
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assert not (return_code_pred and precomputed_aligned_embeddings is not None) # These two are mutually exclusive.
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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, x.shape[-1])
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unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
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unused_params.extend(list(self.latent_converter.parameters()))
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else:
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if precomputed_aligned_embeddings is not None:
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code_emb = precomputed_aligned_embeddings
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else:
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code_emb, mel_pred = self.timestep_independent(aligned_conditioning, conditioning_input, x.shape[-1], True)
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if is_latent(aligned_conditioning):
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unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
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else:
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unused_params.extend(list(self.latent_converter.parameters()))
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unused_params.append(self.unconditioned_embedding)
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time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
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code_emb = self.conditioning_timestep_integrator(code_emb, time_emb=time_emb)
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code_emb = self.conditioning_timestep_integrator(code_emb, time_emb)
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x = self.inp_block(x)
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x = self.integrate_conditioning(torch.cat([x, F.interpolate(code_emb, size=x.shape[-1], mode='nearest')], dim=1))
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with torch.autocast(x.device.type, enabled=self.enable_fp16):
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x = self.layers(x, time_emb=time_emb)
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x = torch.cat([x, code_emb], dim=1)
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x = self.integrating_conv(x)
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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.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, time_emb)
|
||||
|
||||
x = x.float()
|
||||
out = self.out(x)
|
||||
|
||||
|
|
@ -221,6 +290,8 @@ class DiffusionTtsFlat(nn.Module):
|
|||
extraneous_addition = extraneous_addition + p.mean()
|
||||
out = out + extraneous_addition * 0
|
||||
|
||||
if return_code_pred:
|
||||
return out, mel_pred
|
||||
return out
|
||||
|
||||
|
||||
|
|
@ -232,12 +303,12 @@ def register_diffusion_tts_flat(opt_net, opt):
|
|||
if __name__ == '__main__':
|
||||
clip = torch.randn(2, 100, 400)
|
||||
aligned_latent = torch.randn(2,388,512)
|
||||
aligned_sequence = torch.randint(0,8192,(2,388))
|
||||
cond = torch.randn(2, 2, 100, 400)
|
||||
aligned_sequence = torch.randint(0,8192,(2,100))
|
||||
cond = torch.randn(2, 100, 400)
|
||||
ts = torch.LongTensor([600, 600])
|
||||
model = DiffusionTtsFlat(512, layer_drop=.3)
|
||||
model = DiffusionTtsFlat(512, layer_drop=.3, unconditioned_percentage=.5, freeze_everything_except_autoregressive_inputs=True)
|
||||
# Test with latent aligned conditioning
|
||||
o = model(clip, ts, aligned_latent, cond)
|
||||
#o = model(clip, ts, aligned_latent, cond)
|
||||
# Test with sequence aligned conditioning
|
||||
o = model(clip, ts, aligned_sequence, cond)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,314 +0,0 @@
|
|||
import random
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from torch import autocast
|
||||
|
||||
from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
|
||||
from models.diffusion.unet_diffusion import AttentionBlock, TimestepEmbedSequential, TimestepBlock
|
||||
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 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 DiffusionLayer(TimestepBlock):
|
||||
def __init__(self, model_channels, dropout, num_heads):
|
||||
super().__init__()
|
||||
self.resblk = ResBlock(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 DiffusionTtsFlat(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
model_channels=512,
|
||||
num_layers=8,
|
||||
in_channels=100,
|
||||
in_latent_channels=512,
|
||||
in_tokens=8193,
|
||||
out_channels=200, # mean and variance
|
||||
dropout=0,
|
||||
use_fp16=False,
|
||||
num_heads=16,
|
||||
freeze_everything_except_autoregressive_inputs=False,
|
||||
# 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)
|
||||
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.code_embedding = nn.Embedding(in_tokens, model_channels)
|
||||
self.code_converter = nn.Sequential(
|
||||
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),
|
||||
)
|
||||
self.code_norm = normalization(model_channels)
|
||||
self.latent_converter = nn.Conv1d(in_latent_channels, model_channels, 1)
|
||||
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.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)
|
||||
|
||||
self.layers = nn.ModuleList([DiffusionLayer(model_channels, dropout, num_heads) for _ in range(num_layers)] +
|
||||
[ResBlock(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)),
|
||||
)
|
||||
|
||||
if freeze_everything_except_autoregressive_inputs:
|
||||
for ap in list(self.latent_converter.parameters()):
|
||||
ap.ALLOWED_IN_FLAT = True
|
||||
for p in self.parameters():
|
||||
if not hasattr(p, 'ALLOWED_IN_FLAT'):
|
||||
p.requires_grad = False
|
||||
p.DO_NOT_TRAIN = True
|
||||
|
||||
def get_grad_norm_parameter_groups(self):
|
||||
groups = {
|
||||
'minicoder': list(self.contextual_embedder.parameters()),
|
||||
'layers': list(self.layers.parameters()),
|
||||
'code_converters': list(self.code_embedding.parameters()) + list(self.code_converter.parameters()) + list(self.latent_converter.parameters()) + list(self.latent_converter.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)
|
||||
|
||||
# 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_converter(aligned_conditioning)
|
||||
else:
|
||||
code_emb = self.code_embedding(aligned_conditioning).permute(0, 2, 1)
|
||||
code_emb = self.code_converter(code_emb)
|
||||
code_emb = self.code_norm(code_emb) * (1 + cond_scale.unsqueeze(-1)) + cond_shift.unsqueeze(-1)
|
||||
|
||||
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)
|
||||
expanded_code_emb = F.interpolate(code_emb, size=expected_seq_len, mode='nearest')
|
||||
|
||||
if not return_code_pred:
|
||||
return expanded_code_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.
|
||||
mel_pred = mel_pred * unconditioned_batches.logical_not()
|
||||
return expanded_code_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):
|
||||
"""
|
||||
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_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 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.
|
||||
|
||||
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_converter.parameters()))
|
||||
else:
|
||||
if precomputed_aligned_embeddings is not None:
|
||||
code_emb = precomputed_aligned_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:
|
||||
unused_params.extend(list(self.latent_converter.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)
|
||||
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, time_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
|
||||
|
||||
|
||||
@register_model
|
||||
def register_diffusion_tts_flat0(opt_net, opt):
|
||||
return DiffusionTtsFlat(**opt_net['kwargs'])
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
clip = torch.randn(2, 100, 400)
|
||||
aligned_latent = torch.randn(2,388,512)
|
||||
aligned_sequence = torch.randint(0,8192,(2,100))
|
||||
cond = torch.randn(2, 100, 400)
|
||||
ts = torch.LongTensor([600, 600])
|
||||
model = DiffusionTtsFlat(512, layer_drop=.3, unconditioned_percentage=.5, freeze_everything_except_autoregressive_inputs=True)
|
||||
# Test with latent aligned conditioning
|
||||
#o = model(clip, ts, aligned_latent, cond)
|
||||
# Test with sequence aligned conditioning
|
||||
o = model(clip, ts, aligned_sequence, cond)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user