244 lines
11 KiB
Python
244 lines
11 KiB
Python
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 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 trainer.networks import register_model
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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 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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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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# 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 = 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.time_embed = nn.Sequential(
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linear(model_channels, time_embed_dim),
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nn.SiLU(),
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linear(time_embed_dim, time_embed_dim),
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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_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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)
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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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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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# 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.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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'conditioning_timestep_integrator': list(self.conditioning_timestep_integrator.parameters()),
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'layers': list(self.layers.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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# 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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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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# 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)
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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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def forward(self, x, timesteps, aligned_conditioning, conditioning_input, 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 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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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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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 = 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, 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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