330 lines
12 KiB
Python
330 lines
12 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 torch import autocast
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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.lucidrains.x_transformers import Encoder
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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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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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):
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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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padding = 1 if kernel_size == 3 else 2
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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, 1, padding=0),
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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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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, 1)
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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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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 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=100,
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num_tokens=256,
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out_channels=200, # mean and variance
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dropout=0,
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# m 1, 2, 4, 8
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block_channels= (512,640, 768,1024),
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num_res_blocks= (3, 3, 3, 3),
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token_conditioning_resolutions=(2,4,8),
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attention_resolutions=(2,4,8),
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conv_resample=True,
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dims=1,
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use_fp16=False,
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kernel_size=3,
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scale_factor=2,
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time_embed_dim_multiplier=4,
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nil_guidance_fwd_proportion=.15,
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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.attention_resolutions = attention_resolutions
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self.dropout = dropout
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self.conv_resample = conv_resample
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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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num_heads = model_channels // 64
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padding = 1 if kernel_size == 3 else 2
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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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self.code_embedding = nn.Embedding(num_tokens+1, model_channels)
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self.conditioning_embedder = nn.Sequential(nn.Conv1d(in_channels, model_channels // 2, 3, padding=1, stride=2),
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nn.Conv1d(model_channels//2, model_channels,3,padding=1,stride=2))
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self.conditioning_encoder = Encoder(
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dim=model_channels,
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depth=4,
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heads=num_heads,
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ff_dropout=dropout,
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attn_dropout=dropout,
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use_rmsnorm=True,
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ff_glu=True,
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rotary_pos_emb=True,
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)
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self.codes_encoder = Encoder(
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dim=model_channels,
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depth=8,
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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_rms_scaleshift_norm=True,
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ff_glu=True,
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rotary_pos_emb=True,
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zero_init_branch_output=True,
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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, kernel_size, padding=padding)
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)
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]
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)
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token_conditioning_blocks = []
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self._feature_size = model_channels
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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, (blk_chan, num_blocks) in enumerate(zip(block_channels, num_res_blocks)):
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if ds in token_conditioning_resolutions:
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token_conditioning_block = nn.Conv1d(model_channels, 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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for _ in range(num_blocks):
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layers = [
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ResBlock(
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ch,
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time_embed_dim,
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dropout,
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out_channels=blk_chan,
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dims=dims,
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kernel_size=kernel_size,
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)
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]
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ch = blk_chan
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if ds in attention_resolutions:
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layers.append(
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AttentionBlock(
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ch,
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num_heads=num_heads,
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)
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)
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self.input_blocks.append(TimestepEmbedSequential(*layers))
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self._feature_size += ch
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input_block_chans.append(ch)
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if level != len(block_channels) - 1:
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out_ch = ch
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self.input_blocks.append(
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TimestepEmbedSequential(
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Downsample(
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ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor, ksize=1, pad=0
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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._feature_size += ch
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self.middle_block = TimestepEmbedSequential(
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ResBlock(
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ch,
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time_embed_dim,
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dropout,
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dims=dims,
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),
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AttentionBlock(
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ch,
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num_heads=num_heads,
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),
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ResBlock(
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ch,
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time_embed_dim,
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dropout,
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dims=dims,
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),
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)
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self._feature_size += ch
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self.output_blocks = nn.ModuleList([])
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for level, (blk_chan, num_blocks) in list(enumerate(zip(block_channels, num_res_blocks)))[::-1]:
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for i in range(num_blocks + 1):
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ich = input_block_chans.pop()
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layers = [
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ResBlock(
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ch + ich,
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time_embed_dim,
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dropout,
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out_channels=blk_chan,
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dims=dims,
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kernel_size=kernel_size,
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)
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]
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ch = blk_chan
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if ds in attention_resolutions:
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layers.append(
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AttentionBlock(
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ch,
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)
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)
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if level and i == num_blocks:
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out_ch = ch
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layers.append(
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Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor)
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)
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ds //= 2
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self.output_blocks.append(TimestepEmbedSequential(*layers))
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self._feature_size += ch
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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, kernel_size, padding=padding)),
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)
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def forward(self, x, timesteps, tokens, conditioning_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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:return: an [N x C x ...] Tensor of outputs.
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"""
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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], 16)
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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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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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# 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 = self.conditioning_embedder(conditioning_input).permute(0,2,1)
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cond_emb = self.conditioning_encoder(cond_emb)[:, 0]
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code_emb = self.codes_encoder(code_emb.permute(0,2,1), norm_scale_shift_inp=cond_emb).permute(0,2,1)
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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, time_emb)
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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_tts10(opt_net, opt):
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return DiffusionTts(**opt_net['kwargs'])
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if __name__ == '__main__':
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clip = torch.randn(2, 100, 500).cuda()
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tok = torch.randint(0,256, (2,230)).cuda()
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cond = torch.randn(2, 100, 300).cuda()
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ts = torch.LongTensor([600, 600]).cuda()
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model = DiffusionTts(512).cuda()
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print(sum(p.numel() for p in model.parameters()) / 1000000)
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model(clip, ts, tok, cond)
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