316 lines
13 KiB
Python
316 lines
13 KiB
Python
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import functools
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import random
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from collections import OrderedDict
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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.x_transformers import AbsolutePositionalEmbedding, AttentionLayers, CrossAttender
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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.gpt_voice.mini_encoder import AudioMiniEncoder
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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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from x_transformers import Encoder, ContinuousTransformerWrapper
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class CheckpointedLayer(nn.Module):
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"""
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Wraps a module. When forward() is called, passes kwargs that require_grad through torch.checkpoint() and bypasses
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checkpoint for all other args.
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"""
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def __init__(self, wrap):
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super().__init__()
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self.wrap = wrap
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def forward(self, x, *args, **kwargs):
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for k, v in kwargs.items():
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assert not (isinstance(v, torch.Tensor) and v.requires_grad) # This would screw up checkpointing.
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partial = functools.partial(self.wrap, **kwargs)
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return torch.utils.checkpoint.checkpoint(partial, x, *args)
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class CheckpointedXTransformerEncoder(nn.Module):
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"""
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Wraps a ContinuousTransformerWrapper and applies CheckpointedLayer to each layer and permutes from channels-mid
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to channels-last that XTransformer expects.
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"""
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def __init__(self, **xtransformer_kwargs):
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super().__init__()
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self.transformer = ContinuousTransformerWrapper(**xtransformer_kwargs)
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for i in range(len(self.transformer.attn_layers.layers)):
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n, b, r = self.transformer.attn_layers.layers[i]
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self.transformer.attn_layers.layers[i] = nn.ModuleList([n, CheckpointedLayer(b), r])
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def forward(self, x, **kwargs):
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x = x.permute(0,2,1)
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h = self.transformer(x, **kwargs)
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return h.permute(0,2,1)
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class DiffusionTts(nn.Module):
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def __init__(
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self,
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model_channels,
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in_channels=1,
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num_tokens=32,
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out_channels=2, # mean and variance
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dropout=0,
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# res 1, 2, 4, 8,16,32,64,128,256,512, 1K, 2K
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channel_mult= (1,1.5,2, 3, 4, 6, 8, 12, 16, 24, 32, 48),
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# spec_cond: 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0)
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# attn: 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1
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token_conditioning_resolutions=(1,16,),
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dims=1,
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use_fp16=False,
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time_embed_dim_multiplier=4,
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cond_transformer_depth=8,
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mid_transformer_depth=8,
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nil_guidance_fwd_proportion=.3,
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# Parameters for super-sampling.
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super_sampling=False,
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super_sampling_max_noising_factor=.1,
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# Parameters for unaligned inputs.
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enabled_unaligned_inputs=False,
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num_unaligned_tokens=164,
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unaligned_encoder_depth=8,
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):
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super().__init__()
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if super_sampling:
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in_channels *= 2 # In super-sampling mode, the LR input is concatenated directly onto the input.
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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.channel_mult = channel_mult
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self.dtype = torch.float16 if use_fp16 else torch.float32
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self.dims = dims
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self.nil_guidance_fwd_proportion = nil_guidance_fwd_proportion
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self.mask_token_id = num_tokens
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self.super_sampling_enabled = super_sampling
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self.super_sampling_max_noising_factor = super_sampling_max_noising_factor
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time_embed_dim = model_channels * time_embed_dim_multiplier
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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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embedding_dim = model_channels * 8
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self.code_embedding = nn.Embedding(num_tokens+1, embedding_dim)
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self.contextual_embedder = AudioMiniEncoder(1, embedding_dim, base_channels=32, depth=6, resnet_blocks=1,
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attn_blocks=2, num_attn_heads=2, dropout=dropout, downsample_factor=4, kernel_size=5)
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self.conditioning_conv = nn.Conv1d(embedding_dim*3, embedding_dim, 1)
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self.enable_unaligned_inputs = enabled_unaligned_inputs
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if enabled_unaligned_inputs:
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self.unaligned_embedder = nn.Embedding(num_unaligned_tokens, embedding_dim)
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self.unaligned_encoder = CheckpointedXTransformerEncoder(
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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=embedding_dim,
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depth=unaligned_encoder_depth,
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heads=embedding_dim//128,
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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.conditioning_encoder = CheckpointedXTransformerEncoder(
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max_seq_len=-1, # Should be unused
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use_pos_emb=False,
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attn_layers=Encoder(
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dim=embedding_dim,
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depth=cond_transformer_depth,
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heads=embedding_dim//128,
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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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cross_attend=self.enable_unaligned_inputs,
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)
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)
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self.input_blocks = nn.ModuleList(
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[
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TimestepEmbedSequential(
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conv_nd(dims, in_channels, model_channels, 3, padding=1)
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)
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]
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)
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token_conditioning_blocks = []
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input_block_chans = [model_channels]
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ch = model_channels
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ds = 1
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for level, mult in enumerate(channel_mult):
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if ds in token_conditioning_resolutions:
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token_conditioning_block = nn.Conv1d(embedding_dim, ch, 1)
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token_conditioning_block.weight.data *= .02
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self.input_blocks.append(token_conditioning_block)
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token_conditioning_blocks.append(token_conditioning_block)
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out_ch = int(mult * model_channels)
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if level != len(channel_mult) - 1:
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self.input_blocks.append(
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TimestepEmbedSequential(
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Downsample(
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ch, use_conv=True, dims=dims, out_channels=out_ch, factor=2, ksize=3, pad=1
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)
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)
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)
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ch = out_ch
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input_block_chans.append(ch)
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ds *= 2
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self.middle_block = CheckpointedXTransformerEncoder(
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max_seq_len=-1, # Should be unused
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use_pos_emb=False,
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attn_layers=Encoder(
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dim=ch,
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depth=mid_transformer_depth,
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heads=ch//128,
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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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self.output_blocks = nn.ModuleList([])
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for level, mult in list(enumerate(channel_mult))[::-1]:
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ich = ch + input_block_chans.pop()
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out_ch = int(model_channels * mult)
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if level != 0:
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self.output_blocks.append(
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TimestepEmbedSequential(Upsample(ich, use_conv=True, dims=dims, out_channels=out_ch, factor=2))
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)
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else:
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self.output_blocks.append(
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TimestepEmbedSequential(conv_nd(dims, ich, out_ch, 3, padding=1))
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)
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ch = out_ch
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ds //= 2
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self.out = nn.Sequential(
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normalization(ch),
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nn.SiLU(),
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zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
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)
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def forward(self, x, timesteps, tokens=None, conditioning_input=None, lr_input=None, unaligned_input=None):
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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 tokens: an aligned text input.
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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 unaligned_input: A structural input that is not properly aligned with the output of the diffusion model.
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Can be combined with a conditioning input to produce more robust conditioning.
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:return: an [N x C x ...] Tensor of outputs.
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"""
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assert conditioning_input is not None
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if self.super_sampling_enabled:
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assert lr_input is not None
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if self.training and self.super_sampling_max_noising_factor > 0:
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noising_factor = random.uniform(0,self.super_sampling_max_noising_factor)
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lr_input = torch.randn_like(lr_input) * noising_factor + lr_input
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lr_input = F.interpolate(lr_input, size=(x.shape[-1],), mode='nearest')
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x = torch.cat([x, lr_input], dim=1)
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if self.enable_unaligned_inputs:
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assert unaligned_input is not None
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unaligned_h = self.unaligned_embedder(unaligned_input).permute(0,2,1)
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unaligned_h = self.unaligned_encoder(unaligned_h).permute(0,2,1)
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with autocast(x.device.type):
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orig_x_shape = x.shape[-1]
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cm = ceil_multiple(x.shape[-1], 2048)
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if cm != 0:
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pc = (cm-x.shape[-1])/x.shape[-1]
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x = F.pad(x, (0,cm-x.shape[-1]))
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if tokens is not None:
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tokens = F.pad(tokens, (0,int(pc*tokens.shape[-1])))
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hs = []
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time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
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cond_emb = self.contextual_embedder(conditioning_input)
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if tokens is not None:
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# Mask out guidance tokens for un-guided diffusion.
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if self.training and self.nil_guidance_fwd_proportion > 0:
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token_mask = torch.rand(tokens.shape, device=tokens.device) < self.nil_guidance_fwd_proportion
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tokens = torch.where(token_mask, self.mask_token_id, tokens)
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code_emb = self.code_embedding(tokens).permute(0,2,1)
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cond_emb = cond_emb.unsqueeze(-1).repeat(1,1,code_emb.shape[-1])
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cond_time_emb = timestep_embedding(torch.zeros_like(timesteps), code_emb.shape[1]) # This was something I was doing (adding timesteps into this computation), but removed on second thought. TODO: completely remove.
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cond_time_emb = cond_time_emb.unsqueeze(-1).repeat(1,1,code_emb.shape[-1])
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code_emb = self.conditioning_conv(torch.cat([cond_emb, code_emb, cond_time_emb], dim=1))
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else:
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code_emb = cond_emb.unsqueeze(-1)
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if self.enable_unaligned_inputs:
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code_emb = self.conditioning_encoder(code_emb, context=unaligned_h)
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else:
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code_emb = self.conditioning_encoder(code_emb)
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first = True
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time_emb = time_emb.float()
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h = x
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for k, module in enumerate(self.input_blocks):
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if isinstance(module, nn.Conv1d):
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h_tok = F.interpolate(module(code_emb), size=(h.shape[-1]), mode='nearest')
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h = h + h_tok
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else:
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with autocast(x.device.type, enabled=not first):
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# First block has autocast disabled to allow a high precision signal to be properly vectorized.
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h = module(h, time_emb)
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hs.append(h)
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first = False
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h = self.middle_block(h)
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for module in self.output_blocks:
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h = torch.cat([h, hs.pop()], dim=1)
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h = module(h, time_emb)
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# Last block also has autocast disabled for high-precision outputs.
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h = h.float()
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out = self.out(h)
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return out[:, :, :orig_x_shape]
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@register_model
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def register_diffusion_tts8(opt_net, opt):
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return DiffusionTts(**opt_net['kwargs'])
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# Test for ~4 second audio clip at 22050Hz
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if __name__ == '__main__':
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clip = torch.randn(2, 1, 32768)
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tok = torch.randint(0,30, (2,388))
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cond = torch.randn(2, 1, 44000)
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ts = torch.LongTensor([600, 600])
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lr = torch.randn(2,1,10000)
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un = torch.randint(0,120, (2,100))
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model = DiffusionTts(128,
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channel_mult=[1,1.5,2, 3, 4, 6, 8],
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token_conditioning_resolutions=[1,4,16,64],
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time_embed_dim_multiplier=4, super_sampling=False,
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enabled_unaligned_inputs=True)
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model(clip, ts, tok, cond, lr, un)
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