Clean up
This commit is contained in:
James Betker
parent
8fb147e8ab
commit
5cc342de66
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@ -1,87 +0,0 @@
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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, XTransformer
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from models.gpt_voice.unet_diffusion_tts6 import CheckpointedLayer
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from trainer.networks import register_model
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from utils.util import opt_get
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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 = XTransformer(**xtransformer_kwargs)
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for xform in [self.transformer.encoder, self.transformer.decoder.net]:
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for i in range(len(xform.attn_layers.layers)):
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n, b, r = xform.attn_layers.layers[i]
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xform.attn_layers.layers[i] = nn.ModuleList([n, CheckpointedLayer(b), r])
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def forward(self, *args, **kwargs):
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return self.transformer(*args, **kwargs)
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class CtcCodeGenerator(nn.Module):
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def __init__(self, model_dim=512, layers=10, num_heads=8, dropout=.1, ctc_codes=36, max_pad=120, max_repeat=30):
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super().__init__()
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self.max_pad = max_pad
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self.max_repeat = max_repeat
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self.transformer = XTransformer(
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dim=model_dim,
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enc_depth=layers,
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dec_depth=layers,
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enc_heads=num_heads,
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dec_heads=num_heads,
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enc_num_tokens=ctc_codes,
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dec_num_tokens=(max_pad+1)*(max_repeat+1),
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enc_max_seq_len=-1,
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dec_max_seq_len=-1,
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enc_ff_dropout=dropout,
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enc_attn_dropout=dropout,
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enc_use_rmsnorm=True,
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enc_ff_glu=True,
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enc_rotary_pos_emb=True,
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dec_ff_dropout=dropout,
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dec_attn_dropout=dropout,
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dec_use_rmsnorm=True,
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dec_ff_glu=True,
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dec_rotary_pos_emb=True)
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def forward(self, codes, pads, repeats, unpadded_lengths=None):
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if unpadded_lengths is not None:
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max_len = unpadded_lengths.max()
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codes = codes[:, :max_len]
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pads = pads[:, :max_len]
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repeats = repeats[:, :max_len]
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if pads.max() > self.max_pad:
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print(f"Got unexpectedly long pads. Max: {pads.max()}, {pads}")
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pads = torch.clip(pads, 0, self.max_pad)
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if repeats.max() > self.max_repeat:
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print(f"Got unexpectedly long repeats. Max: {repeats.max()}, {repeats}")
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repeats = torch.clip(repeats, 0, self.max_repeat)
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assert codes.max() < 36, codes.max()
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labels = pads + repeats * self.max_pad
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loss = self.transformer(codes, labels)
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return loss
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@register_model
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def register_ctc_code_generator(opt_net, opt):
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return CtcCodeGenerator(**opt_get(opt_net, ['kwargs'], {}))
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if __name__ == '__main__':
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model = CtcCodeGenerator()
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inps = torch.randint(0,36, (4, 300))
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pads = torch.randint(0,100, (4,300))
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repeats = torch.randint(0,20, (4,300))
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loss = model(inps, pads, repeats)
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print(loss.shape)
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@ -1,347 +0,0 @@
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import operator
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from collections import OrderedDict
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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 AttentionPool2d, AttentionBlock, ResBlock, TimestepEmbedSequential, \
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Downsample, Upsample
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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 models.gpt_voice.mini_encoder import AudioMiniEncoder, EmbeddingCombiner
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from trainer.networks import register_model
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from utils.util import get_mask_from_lengths
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class DiffusionTts(nn.Module):
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"""
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The full UNet model with attention and timestep embedding.
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Customized to be conditioned on an aligned token prior.
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:param in_channels: channels in the input Tensor.
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:param num_tokens: number of tokens (e.g. characters) which can be provided.
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:param model_channels: base channel count for the model.
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:param out_channels: channels in the output Tensor.
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:param num_res_blocks: number of residual blocks per downsample.
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:param attention_resolutions: a collection of downsample rates at which
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attention will take place. May be a set, list, or tuple.
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For example, if this contains 4, then at 4x downsampling, attention
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will be used.
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:param dropout: the dropout probability.
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:param channel_mult: channel multiplier for each level of the UNet.
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:param conv_resample: if True, use learned convolutions for upsampling and
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downsampling.
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:param dims: determines if the signal is 1D, 2D, or 3D.
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:param num_heads: the number of attention heads in each attention layer.
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:param num_heads_channels: if specified, ignore num_heads and instead use
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a fixed channel width per attention head.
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:param num_heads_upsample: works with num_heads to set a different number
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of heads for upsampling. Deprecated.
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:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
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:param resblock_updown: use residual blocks for up/downsampling.
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:param use_new_attention_order: use a different attention pattern for potentially
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increased efficiency.
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"""
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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=30,
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out_channels=2, # mean and variancexs
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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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num_res_blocks=(1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2),
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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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attention_resolutions=(512,1024,2048),
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conv_resample=True,
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dims=1,
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use_fp16=False,
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num_heads=1,
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num_head_channels=-1,
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num_heads_upsample=-1,
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use_scale_shift_norm=False,
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resblock_updown=False,
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use_new_attention_order=False,
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kernel_size=3,
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scale_factor=2,
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conditioning_inputs_provided=True,
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time_embed_dim_multiplier=4,
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only_train_dvae_connection_layers=False,
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):
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super().__init__()
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if num_heads_upsample == -1:
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num_heads_upsample = num_heads
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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.channel_mult = channel_mult
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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.num_heads = num_heads
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self.num_head_channels = num_head_channels
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self.num_heads_upsample = num_heads_upsample
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self.dims = dims
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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.conditioning_enabled = conditioning_inputs_provided
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if conditioning_inputs_provided:
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self.contextual_embedder = AudioMiniEncoder(in_channels, time_embed_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.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, (mult, num_blocks) in enumerate(zip(channel_mult, num_res_blocks)):
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if ds in token_conditioning_resolutions:
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token_conditioning_block = nn.Embedding(num_tokens, ch)
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token_conditioning_block.weight.data.normal_(mean=0.0, std=.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=int(mult * model_channels),
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dims=dims,
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use_scale_shift_norm=use_scale_shift_norm,
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kernel_size=kernel_size,
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)
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]
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ch = int(mult * model_channels)
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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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num_head_channels=num_head_channels,
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use_new_attention_order=use_new_attention_order,
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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(channel_mult) - 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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ResBlock(
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ch,
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time_embed_dim,
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dropout,
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out_channels=out_ch,
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dims=dims,
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use_scale_shift_norm=use_scale_shift_norm,
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down=True,
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kernel_size=kernel_size,
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)
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if resblock_updown
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else Downsample(
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ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor
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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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use_scale_shift_norm=use_scale_shift_norm,
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kernel_size=kernel_size,
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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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num_head_channels=num_head_channels,
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use_new_attention_order=use_new_attention_order,
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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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use_scale_shift_norm=use_scale_shift_norm,
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kernel_size=kernel_size,
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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, (mult, num_blocks) in list(enumerate(zip(channel_mult, 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=int(model_channels * mult),
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dims=dims,
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use_scale_shift_norm=use_scale_shift_norm,
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kernel_size=kernel_size,
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)
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]
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ch = int(model_channels * mult)
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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_upsample,
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num_head_channels=num_head_channels,
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use_new_attention_order=use_new_attention_order,
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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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ResBlock(
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ch,
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time_embed_dim,
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dropout,
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out_channels=out_ch,
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dims=dims,
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use_scale_shift_norm=use_scale_shift_norm,
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up=True,
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kernel_size=kernel_size,
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)
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if resblock_updown
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else 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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if only_train_dvae_connection_layers:
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for p in self.parameters():
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p.DO_NOT_TRAIN = True
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p.requires_grad = False
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for sb in token_conditioning_blocks:
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for p in sb.parameters():
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del p.DO_NOT_TRAIN
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p.requires_grad = True
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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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assert x.shape[-1] % 4096 == 0 # This model operates at base//4096 at it's bottom levels, thus this requirement.
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if self.conditioning_enabled:
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assert conditioning_input is not None
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hs = []
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emb1 = self.time_embed(timestep_embedding(timesteps, self.model_channels))
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if self.conditioning_enabled:
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emb2 = self.contextual_embedder(conditioning_input)
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emb = emb1 + emb2
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else:
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emb = emb1
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h = x.type(self.dtype)
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for k, module in enumerate(self.input_blocks):
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if isinstance(module, nn.Embedding):
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h_tok = F.interpolate(module(tokens).permute(0,2,1), size=(h.shape[-1]), mode='nearest')
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h = h + h_tok
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else:
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h = module(h, emb)
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hs.append(h)
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h = self.middle_block(h, 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, emb)
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h = h.type(x.dtype)
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return self.out(h)
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def benchmark(self, x, timesteps, tokens, conditioning_input):
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profile = OrderedDict()
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hs = []
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emb1 = self.time_embed(timestep_embedding(timesteps, self.model_channels))
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from torchprofile import profile_macs
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profile['contextual_embedder'] = profile_macs(self.contextual_embedder, args=(conditioning_input,))
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emb2 = self.contextual_embedder(conditioning_input)
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emb = emb1 + emb2
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h = x.type(self.dtype)
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for k, module in enumerate(self.input_blocks):
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if isinstance(module, nn.Embedding):
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h_tok = F.interpolate(module(tokens).permute(0,2,1), size=(h.shape[-1]), mode='nearest')
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h = h + h_tok
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else:
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profile[f'in_{k}'] = profile_macs(module, args=(h,emb))
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h = module(h, emb)
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hs.append(h)
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profile['middle'] = profile_macs(self.middle_block, args=(h,emb))
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h = self.middle_block(h, emb)
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for k, module in enumerate(self.output_blocks):
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h = torch.cat([h, hs.pop()], dim=1)
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profile[f'out_{k}'] = profile_macs(module, args=(h,emb))
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h = module(h, emb)
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h = h.type(x.dtype)
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profile['out'] = profile_macs(self.out, args=(h,))
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return profile
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@register_model
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def register_diffusion_tts(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, 86016)
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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([555, 556])
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model = DiffusionTts(64, channel_mult=[1,1.5,2, 3, 4, 6, 8, 8, 8, 8 ], num_res_blocks=[2, 2, 2, 2, 2, 2, 2, 2, 1, 1 ],
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token_conditioning_resolutions=[1,4,16,64], attention_resolutions=[256,512], num_heads=4, kernel_size=3,
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scale_factor=2, conditioning_inputs_provided=True, time_embed_dim_multiplier=4)
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p = model.benchmark(clip, ts, tok, cond)
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p = {k: v / 1000000000 for k, v in p.items()}
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p = sorted(p.items(), key=operator.itemgetter(1))
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print(p)
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print(sum([j[1] for j in p]))
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@ -1,395 +0,0 @@
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import operator
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from collections import OrderedDict
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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 AttentionPool2d, AttentionBlock, TimestepEmbedSequential, \
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Downsample, Upsample, TimestepBlock
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from models.gpt_voice.mini_encoder import AudioMiniEncoder, EmbeddingCombiner
|
||||
from scripts.audio.gen.use_diffuse_tts import ceil_multiple
|
||||
from trainer.networks import register_model
|
||||
from utils.util import checkpoint
|
||||
|
||||
|
||||
class ResBlock(TimestepBlock):
|
||||
def __init__(
|
||||
self,
|
||||
channels,
|
||||
emb_channels,
|
||||
dropout,
|
||||
out_channels=None,
|
||||
dims=2,
|
||||
kernel_size=3,
|
||||
):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.emb_channels = emb_channels
|
||||
self.dropout = dropout
|
||||
self.out_channels = out_channels or channels
|
||||
padding = 1 if kernel_size == 3 else 2
|
||||
|
||||
self.in_layers = nn.Sequential(
|
||||
normalization(channels),
|
||||
nn.SiLU(),
|
||||
conv_nd(dims, channels, self.out_channels, 1, padding=0),
|
||||
)
|
||||
|
||||
self.emb_layers = nn.Sequential(
|
||||
nn.SiLU(),
|
||||
linear(
|
||||
emb_channels,
|
||||
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, 1)
|
||||
|
||||
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]
|
||||
h = h + emb_out
|
||||
h = self.out_layers(h)
|
||||
return self.skip_connection(x) + h
|
||||
|
||||
|
||||
class DiffusionTts(nn.Module):
|
||||
"""
|
||||
The full UNet model with attention and timestep embedding.
|
||||
|
||||
Customized to be conditioned on an aligned token prior.
|
||||
|
||||
:param in_channels: channels in the input Tensor.
|
||||
:param num_tokens: number of tokens (e.g. characters) which can be provided.
|
||||
:param model_channels: base channel count for the model.
|
||||
:param out_channels: channels in the output Tensor.
|
||||
:param num_res_blocks: number of residual blocks per downsample.
|
||||
:param attention_resolutions: a collection of downsample rates at which
|
||||
attention will take place. May be a set, list, or tuple.
|
||||
For example, if this contains 4, then at 4x downsampling, attention
|
||||
will be used.
|
||||
:param dropout: the dropout probability.
|
||||
:param channel_mult: channel multiplier for each level of the UNet.
|
||||
:param conv_resample: if True, use learned convolutions for upsampling and
|
||||
downsampling.
|
||||
:param dims: determines if the signal is 1D, 2D, or 3D.
|
||||
:param num_heads: the number of attention heads in each attention layer.
|
||||
:param num_heads_channels: if specified, ignore num_heads and instead use
|
||||
a fixed channel width per attention head.
|
||||
:param num_heads_upsample: works with num_heads to set a different number
|
||||
of heads for upsampling. Deprecated.
|
||||
:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
|
||||
:param resblock_updown: use residual blocks for up/downsampling.
|
||||
:param use_new_attention_order: use a different attention pattern for potentially
|
||||
increased efficiency.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model_channels,
|
||||
in_channels=1,
|
||||
num_tokens=32,
|
||||
out_channels=2, # mean and variance
|
||||
dropout=0,
|
||||
# res 1, 2, 4, 8,16,32,64,128,256,512, 1K, 2K
|
||||
channel_mult= (1,1.5,2, 3, 4, 6, 8, 12, 16, 24, 32, 48),
|
||||
num_res_blocks=(1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2),
|
||||
# spec_cond: 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0)
|
||||
# attn: 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1
|
||||
token_conditioning_resolutions=(1,16,),
|
||||
attention_resolutions=(512,1024,2048),
|
||||
conv_resample=True,
|
||||
dims=1,
|
||||
use_fp16=False,
|
||||
num_heads=1,
|
||||
num_head_channels=-1,
|
||||
num_heads_upsample=-1,
|
||||
kernel_size=3,
|
||||
scale_factor=2,
|
||||
conditioning_inputs_provided=True,
|
||||
time_embed_dim_multiplier=4,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
if num_heads_upsample == -1:
|
||||
num_heads_upsample = num_heads
|
||||
|
||||
self.in_channels = in_channels
|
||||
self.model_channels = model_channels
|
||||
self.out_channels = out_channels
|
||||
self.attention_resolutions = attention_resolutions
|
||||
self.dropout = dropout
|
||||
self.channel_mult = channel_mult
|
||||
self.conv_resample = conv_resample
|
||||
self.dtype = torch.float16 if use_fp16 else torch.float32
|
||||
self.num_heads = num_heads
|
||||
self.num_head_channels = num_head_channels
|
||||
self.num_heads_upsample = num_heads_upsample
|
||||
self.dims = dims
|
||||
|
||||
padding = 1 if kernel_size == 3 else 2
|
||||
|
||||
time_embed_dim = model_channels * time_embed_dim_multiplier
|
||||
self.time_embed = nn.Sequential(
|
||||
linear(model_channels, time_embed_dim),
|
||||
nn.SiLU(),
|
||||
linear(time_embed_dim, time_embed_dim),
|
||||
)
|
||||
|
||||
self.conditioning_enabled = conditioning_inputs_provided
|
||||
if conditioning_inputs_provided:
|
||||
self.contextual_embedder = AudioMiniEncoder(in_channels, time_embed_dim, base_channels=32, depth=6, resnet_blocks=1,
|
||||
attn_blocks=2, num_attn_heads=2, dropout=dropout, downsample_factor=4, kernel_size=5)
|
||||
|
||||
self.input_blocks = nn.ModuleList(
|
||||
[
|
||||
TimestepEmbedSequential(
|
||||
conv_nd(dims, in_channels, model_channels, kernel_size, padding=padding)
|
||||
)
|
||||
]
|
||||
)
|
||||
token_conditioning_blocks = []
|
||||
self._feature_size = model_channels
|
||||
input_block_chans = [model_channels]
|
||||
ch = model_channels
|
||||
ds = 1
|
||||
|
||||
for level, (mult, num_blocks) in enumerate(zip(channel_mult, num_res_blocks)):
|
||||
if ds in token_conditioning_resolutions:
|
||||
token_conditioning_block = nn.Embedding(num_tokens, ch)
|
||||
token_conditioning_block.weight.data.normal_(mean=0.0, std=.02)
|
||||
self.input_blocks.append(token_conditioning_block)
|
||||
token_conditioning_blocks.append(token_conditioning_block)
|
||||
|
||||
for _ in range(num_blocks):
|
||||
layers = [
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
out_channels=int(mult * model_channels),
|
||||
dims=dims,
|
||||
kernel_size=kernel_size,
|
||||
)
|
||||
]
|
||||
ch = int(mult * model_channels)
|
||||
if ds in attention_resolutions:
|
||||
layers.append(
|
||||
AttentionBlock(
|
||||
ch,
|
||||
num_heads=num_heads,
|
||||
num_head_channels=num_head_channels,
|
||||
)
|
||||
)
|
||||
self.input_blocks.append(TimestepEmbedSequential(*layers))
|
||||
self._feature_size += ch
|
||||
input_block_chans.append(ch)
|
||||
if level != len(channel_mult) - 1:
|
||||
out_ch = ch
|
||||
self.input_blocks.append(
|
||||
TimestepEmbedSequential(
|
||||
Downsample(
|
||||
ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor, ksize=1, pad=0
|
||||
)
|
||||
)
|
||||
)
|
||||
ch = out_ch
|
||||
input_block_chans.append(ch)
|
||||
ds *= 2
|
||||
self._feature_size += ch
|
||||
|
||||
self.middle_block = TimestepEmbedSequential(
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
dims=dims,
|
||||
kernel_size=kernel_size,
|
||||
),
|
||||
AttentionBlock(
|
||||
ch,
|
||||
num_heads=num_heads,
|
||||
num_head_channels=num_head_channels,
|
||||
),
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
dims=dims,
|
||||
kernel_size=kernel_size,
|
||||
),
|
||||
)
|
||||
self._feature_size += ch
|
||||
|
||||
self.output_blocks = nn.ModuleList([])
|
||||
for level, (mult, num_blocks) in list(enumerate(zip(channel_mult, num_res_blocks)))[::-1]:
|
||||
for i in range(num_blocks + 1):
|
||||
ich = input_block_chans.pop()
|
||||
layers = [
|
||||
ResBlock(
|
||||
ch + ich,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
out_channels=int(model_channels * mult),
|
||||
dims=dims,
|
||||
kernel_size=kernel_size,
|
||||
)
|
||||
]
|
||||
ch = int(model_channels * mult)
|
||||
if ds in attention_resolutions:
|
||||
layers.append(
|
||||
AttentionBlock(
|
||||
ch,
|
||||
num_heads=num_heads_upsample,
|
||||
num_head_channels=num_head_channels,
|
||||
)
|
||||
)
|
||||
if level and i == num_blocks:
|
||||
out_ch = ch
|
||||
layers.append(
|
||||
Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor)
|
||||
)
|
||||
ds //= 2
|
||||
self.output_blocks.append(TimestepEmbedSequential(*layers))
|
||||
self._feature_size += ch
|
||||
|
||||
self.out = nn.Sequential(
|
||||
normalization(ch),
|
||||
nn.SiLU(),
|
||||
zero_module(conv_nd(dims, model_channels, out_channels, kernel_size, padding=padding)),
|
||||
)
|
||||
|
||||
|
||||
def forward(self, x, timesteps, tokens, conditioning_input=None):
|
||||
"""
|
||||
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 tokens: an aligned text input.
|
||||
:return: an [N x C x ...] Tensor of outputs.
|
||||
"""
|
||||
orig_x_shape = x.shape[-1]
|
||||
cm = ceil_multiple(x.shape[-1], 2048)
|
||||
if cm != 0:
|
||||
pc = (cm-x.shape[-1])/x.shape[-1]
|
||||
x = F.pad(x, (0,cm-x.shape[-1]))
|
||||
tokens = F.pad(tokens, (0,int(pc*tokens.shape[-1])))
|
||||
if self.conditioning_enabled:
|
||||
assert conditioning_input is not None
|
||||
|
||||
hs = []
|
||||
emb1 = self.time_embed(timestep_embedding(timesteps, self.model_channels))
|
||||
if self.conditioning_enabled:
|
||||
actual_cond = self.contextual_embedder(conditioning_input)
|
||||
emb = emb1 + actual_cond
|
||||
else:
|
||||
emb = emb1
|
||||
|
||||
h = x.type(self.dtype)
|
||||
for k, module in enumerate(self.input_blocks):
|
||||
if isinstance(module, nn.Embedding):
|
||||
h_tok = F.interpolate(module(tokens).permute(0,2,1), size=(h.shape[-1]), mode='nearest')
|
||||
h = h + h_tok
|
||||
else:
|
||||
h = module(h, emb)
|
||||
hs.append(h)
|
||||
h = self.middle_block(h, emb)
|
||||
for module in self.output_blocks:
|
||||
h = torch.cat([h, hs.pop()], dim=1)
|
||||
h = module(h, emb)
|
||||
h = h.type(x.dtype)
|
||||
out = self.out(h)
|
||||
return out[:, :, :orig_x_shape]
|
||||
|
||||
def benchmark(self, x, timesteps, tokens, conditioning_input):
|
||||
profile = OrderedDict()
|
||||
params = OrderedDict()
|
||||
hs = []
|
||||
emb1 = self.time_embed(timestep_embedding(timesteps, self.model_channels))
|
||||
from torchprofile import profile_macs
|
||||
profile['contextual_embedder'] = profile_macs(self.contextual_embedder, args=(conditioning_input,))
|
||||
params['contextual_embedder'] = sum(p.numel() for p in self.contextual_embedder.parameters())
|
||||
emb2 = self.contextual_embedder(conditioning_input)
|
||||
emb = emb1 + emb2
|
||||
|
||||
h = x.type(self.dtype)
|
||||
for k, module in enumerate(self.input_blocks):
|
||||
if isinstance(module, nn.Embedding):
|
||||
h_tok = F.interpolate(module(tokens).permute(0,2,1), size=(h.shape[-1]), mode='nearest')
|
||||
h = h + h_tok
|
||||
else:
|
||||
profile[f'in_{k}'] = profile_macs(module, args=(h,emb))
|
||||
params[f'in_{k}'] = sum(p.numel() for p in module.parameters())
|
||||
h = module(h, emb)
|
||||
hs.append(h)
|
||||
profile['middle'] = profile_macs(self.middle_block, args=(h,emb))
|
||||
params['middle'] = sum(p.numel() for p in self.middle_block.parameters())
|
||||
h = self.middle_block(h, emb)
|
||||
for k, module in enumerate(self.output_blocks):
|
||||
h = torch.cat([h, hs.pop()], dim=1)
|
||||
profile[f'out_{k}'] = profile_macs(module, args=(h,emb))
|
||||
params[f'out_{k}'] = sum(p.numel() for p in module.parameters())
|
||||
h = module(h, emb)
|
||||
h = h.type(x.dtype)
|
||||
profile['out'] = profile_macs(self.out, args=(h,))
|
||||
params['out'] = sum(p.numel() for p in self.out.parameters())
|
||||
return profile, params
|
||||
|
||||
|
||||
@register_model
|
||||
def register_diffusion_tts2(opt_net, opt):
|
||||
return DiffusionTts(**opt_net['kwargs'])
|
||||
|
||||
|
||||
# Test for ~4 second audio clip at 22050Hz
|
||||
if __name__ == '__main__':
|
||||
clip = torch.randn(4, 1, 86016)
|
||||
tok = torch.randint(0,30, (4,388))
|
||||
cond = torch.randn(4, 1, 44000)
|
||||
ts = torch.LongTensor([555, 556, 600, 600])
|
||||
model = DiffusionTts(64, channel_mult=[1,1.5,2, 3, 4, 6, 8, 8, 8, 8], num_res_blocks=[2, 2, 2, 2, 2, 2, 2, 4, 4, 4],
|
||||
token_conditioning_resolutions=[1,4,16,64], attention_resolutions=[256,512], num_heads=4, kernel_size=3,
|
||||
scale_factor=2, conditioning_inputs_provided=True, time_embed_dim_multiplier=4)
|
||||
model(clip, ts, tok, cond)
|
||||
|
||||
p, r = model.benchmark(clip, ts, tok, cond)
|
||||
p = {k: v / 1000000000 for k, v in p.items()}
|
||||
p = sorted(p.items(), key=operator.itemgetter(1))
|
||||
print("Computational complexity:")
|
||||
print(p)
|
||||
print(sum([j[1] for j in p]))
|
||||
print()
|
||||
print("Memory complexity:")
|
||||
r = {k: v / 1000000 for k, v in r.items()}
|
||||
r = sorted(r.items(), key=operator.itemgetter(1))
|
||||
print(r)
|
||||
print(sum([j[1] for j in r]))
|
||||
|
||||
|
|
@ -1,429 +0,0 @@
|
|||
import functools
|
||||
from collections import OrderedDict
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from torch import autocast
|
||||
from x_transformers.x_transformers import AbsolutePositionalEmbedding, AttentionLayers
|
||||
|
||||
from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
|
||||
from models.diffusion.unet_diffusion import AttentionBlock, TimestepEmbedSequential, \
|
||||
Downsample, Upsample, TimestepBlock
|
||||
from models.gpt_voice.mini_encoder import AudioMiniEncoder
|
||||
from scripts.audio.gen.use_diffuse_tts import ceil_multiple
|
||||
from trainer.networks import register_model
|
||||
from utils.util import checkpoint
|
||||
from x_transformers import Encoder, ContinuousTransformerWrapper
|
||||
|
||||
|
||||
class CheckpointedLayer(nn.Module):
|
||||
"""
|
||||
Wraps a module. When forward() is called, passes kwargs that require_grad through torch.checkpoint() and bypasses
|
||||
checkpoint for all other args.
|
||||
"""
|
||||
def __init__(self, wrap):
|
||||
super().__init__()
|
||||
self.wrap = wrap
|
||||
|
||||
def forward(self, x, **kwargs):
|
||||
kw_requires_grad = {}
|
||||
kw_no_grad = {}
|
||||
for k, v in kwargs.items():
|
||||
if v is not None and isinstance(v, torch.Tensor) and v.requires_grad:
|
||||
kw_requires_grad[k] = v
|
||||
else:
|
||||
kw_no_grad[k] = v
|
||||
partial = functools.partial(self.wrap, **kw_no_grad)
|
||||
return torch.utils.checkpoint.checkpoint(partial, x, **kw_requires_grad)
|
||||
|
||||
|
||||
class ResBlock(TimestepBlock):
|
||||
def __init__(
|
||||
self,
|
||||
channels,
|
||||
emb_channels,
|
||||
dropout,
|
||||
out_channels=None,
|
||||
dims=2,
|
||||
kernel_size=3,
|
||||
):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.emb_channels = emb_channels
|
||||
self.dropout = dropout
|
||||
self.out_channels = out_channels or channels
|
||||
padding = 1 if kernel_size == 3 else 2
|
||||
|
||||
self.in_layers = nn.Sequential(
|
||||
normalization(channels),
|
||||
nn.SiLU(),
|
||||
conv_nd(dims, channels, self.out_channels, 1, padding=0),
|
||||
)
|
||||
|
||||
self.emb_layers = nn.Sequential(
|
||||
nn.SiLU(),
|
||||
linear(
|
||||
emb_channels,
|
||||
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, 1)
|
||||
|
||||
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]
|
||||
h = h + emb_out
|
||||
h = self.out_layers(h)
|
||||
return self.skip_connection(x) + h
|
||||
|
||||
|
||||
class DiffusionTts(nn.Module):
|
||||
"""
|
||||
The full UNet model with attention and timestep embedding.
|
||||
|
||||
Customized to be conditioned on an aligned token prior.
|
||||
|
||||
:param in_channels: channels in the input Tensor.
|
||||
:param num_tokens: number of tokens (e.g. characters) which can be provided.
|
||||
:param model_channels: base channel count for the model.
|
||||
:param out_channels: channels in the output Tensor.
|
||||
:param num_res_blocks: number of residual blocks per downsample.
|
||||
:param attention_resolutions: a collection of downsample rates at which
|
||||
attention will take place. May be a set, list, or tuple.
|
||||
For example, if this contains 4, then at 4x downsampling, attention
|
||||
will be used.
|
||||
:param dropout: the dropout probability.
|
||||
:param channel_mult: channel multiplier for each level of the UNet.
|
||||
:param conv_resample: if True, use learned convolutions for upsampling and
|
||||
downsampling.
|
||||
:param dims: determines if the signal is 1D, 2D, or 3D.
|
||||
:param num_heads: the number of attention heads in each attention layer.
|
||||
:param num_heads_channels: if specified, ignore num_heads and instead use
|
||||
a fixed channel width per attention head.
|
||||
:param num_heads_upsample: works with num_heads to set a different number
|
||||
of heads for upsampling. Deprecated.
|
||||
:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
|
||||
:param resblock_updown: use residual blocks for up/downsampling.
|
||||
:param use_new_attention_order: use a different attention pattern for potentially
|
||||
increased efficiency.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model_channels,
|
||||
in_channels=1,
|
||||
num_tokens=32,
|
||||
out_channels=2, # mean and variance
|
||||
dropout=0,
|
||||
# res 1, 2, 4, 8,16,32,64,128,256,512, 1K, 2K
|
||||
channel_mult= (1,1.5,2, 3, 4, 6, 8, 12, 16, 24, 32, 48),
|
||||
num_res_blocks=(1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2),
|
||||
# spec_cond: 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0)
|
||||
# attn: 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1
|
||||
token_conditioning_resolutions=(1,16,),
|
||||
attention_resolutions=(512,1024,2048),
|
||||
conv_resample=True,
|
||||
dims=1,
|
||||
use_fp16=False,
|
||||
num_heads=1,
|
||||
num_head_channels=-1,
|
||||
num_heads_upsample=-1,
|
||||
kernel_size=3,
|
||||
scale_factor=2,
|
||||
conditioning_inputs_provided=True,
|
||||
time_embed_dim_multiplier=4,
|
||||
nil_guidance_fwd_proportion=.3,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
if num_heads_upsample == -1:
|
||||
num_heads_upsample = num_heads
|
||||
|
||||
self.in_channels = in_channels
|
||||
self.model_channels = model_channels
|
||||
self.out_channels = out_channels
|
||||
self.attention_resolutions = attention_resolutions
|
||||
self.dropout = dropout
|
||||
self.channel_mult = channel_mult
|
||||
self.conv_resample = conv_resample
|
||||
self.dtype = torch.float16 if use_fp16 else torch.float32
|
||||
self.num_heads = num_heads
|
||||
self.num_head_channels = num_head_channels
|
||||
self.num_heads_upsample = num_heads_upsample
|
||||
self.dims = dims
|
||||
self.nil_guidance_fwd_proportion = nil_guidance_fwd_proportion
|
||||
self.mask_token_id = num_tokens
|
||||
|
||||
padding = 1 if kernel_size == 3 else 2
|
||||
|
||||
time_embed_dim = model_channels * time_embed_dim_multiplier
|
||||
self.time_embed = nn.Sequential(
|
||||
linear(model_channels, time_embed_dim),
|
||||
nn.SiLU(),
|
||||
linear(time_embed_dim, time_embed_dim),
|
||||
)
|
||||
|
||||
embedding_dim = model_channels * 4
|
||||
self.code_embedding = nn.Embedding(num_tokens+1, embedding_dim)
|
||||
self.conditioning_enabled = conditioning_inputs_provided
|
||||
if conditioning_inputs_provided:
|
||||
self.contextual_embedder = AudioMiniEncoder(in_channels, embedding_dim, base_channels=32, depth=6, resnet_blocks=1,
|
||||
attn_blocks=2, num_attn_heads=2, dropout=dropout, downsample_factor=4, kernel_size=5)
|
||||
|
||||
self.input_blocks = nn.ModuleList(
|
||||
[
|
||||
TimestepEmbedSequential(
|
||||
conv_nd(dims, in_channels, model_channels, kernel_size, padding=padding)
|
||||
)
|
||||
]
|
||||
)
|
||||
token_conditioning_blocks = []
|
||||
self._feature_size = model_channels
|
||||
input_block_chans = [model_channels]
|
||||
ch = model_channels
|
||||
ds = 1
|
||||
|
||||
class Permute(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
def forward(self, x):
|
||||
return x.permute(0,2,1)
|
||||
|
||||
for level, (mult, num_blocks) in enumerate(zip(channel_mult, num_res_blocks)):
|
||||
if ds in token_conditioning_resolutions:
|
||||
token_conditioning_block = nn.Conv1d(embedding_dim, ch, 1)
|
||||
token_conditioning_block.weight.data *= .02
|
||||
self.input_blocks.append(token_conditioning_block)
|
||||
token_conditioning_blocks.append(token_conditioning_block)
|
||||
|
||||
for _ in range(num_blocks):
|
||||
layers = [
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
out_channels=int(mult * model_channels),
|
||||
dims=dims,
|
||||
kernel_size=kernel_size,
|
||||
)
|
||||
]
|
||||
ch = int(mult * model_channels)
|
||||
if ds in attention_resolutions:
|
||||
layers.append(
|
||||
AttentionBlock(
|
||||
ch,
|
||||
num_heads=num_heads,
|
||||
num_head_channels=num_head_channels,
|
||||
)
|
||||
)
|
||||
self.input_blocks.append(TimestepEmbedSequential(*layers))
|
||||
self._feature_size += ch
|
||||
input_block_chans.append(ch)
|
||||
if level != len(channel_mult) - 1:
|
||||
out_ch = ch
|
||||
self.input_blocks.append(
|
||||
TimestepEmbedSequential(
|
||||
Downsample(
|
||||
ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor, ksize=1, pad=0
|
||||
)
|
||||
)
|
||||
)
|
||||
ch = out_ch
|
||||
input_block_chans.append(ch)
|
||||
ds *= 2
|
||||
self._feature_size += ch
|
||||
|
||||
mid_transformer = ContinuousTransformerWrapper(
|
||||
max_seq_len=-1, # Should be unused
|
||||
use_pos_emb=False,
|
||||
attn_layers=Encoder(
|
||||
dim=ch,
|
||||
depth=8,
|
||||
heads=num_heads,
|
||||
ff_dropout=dropout,
|
||||
attn_dropout=dropout,
|
||||
use_rmsnorm=True,
|
||||
ff_glu=True,
|
||||
rotary_pos_emb=True,
|
||||
)
|
||||
)
|
||||
|
||||
for i in range(len(mid_transformer.attn_layers.layers)):
|
||||
n, b, r = mid_transformer.attn_layers.layers[i]
|
||||
mid_transformer.attn_layers.layers[i] = nn.ModuleList([n, CheckpointedLayer(b), r])
|
||||
|
||||
|
||||
self.middle_block = TimestepEmbedSequential(
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
dims=dims,
|
||||
kernel_size=kernel_size,
|
||||
),
|
||||
Permute(),
|
||||
mid_transformer,
|
||||
Permute(),
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
dims=dims,
|
||||
kernel_size=kernel_size,
|
||||
),
|
||||
)
|
||||
self._feature_size += ch
|
||||
|
||||
self.output_blocks = nn.ModuleList([])
|
||||
for level, (mult, num_blocks) in list(enumerate(zip(channel_mult, num_res_blocks)))[::-1]:
|
||||
for i in range(num_blocks + 1):
|
||||
ich = input_block_chans.pop()
|
||||
layers = [
|
||||
ResBlock(
|
||||
ch + ich,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
out_channels=int(model_channels * mult),
|
||||
dims=dims,
|
||||
kernel_size=kernel_size,
|
||||
)
|
||||
]
|
||||
ch = int(model_channels * mult)
|
||||
if ds in attention_resolutions:
|
||||
layers.append(
|
||||
AttentionBlock(
|
||||
ch,
|
||||
num_heads=num_heads_upsample,
|
||||
num_head_channels=num_head_channels,
|
||||
)
|
||||
)
|
||||
if level and i == num_blocks:
|
||||
out_ch = ch
|
||||
layers.append(
|
||||
Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor)
|
||||
)
|
||||
ds //= 2
|
||||
self.output_blocks.append(TimestepEmbedSequential(*layers))
|
||||
self._feature_size += ch
|
||||
|
||||
self.out = nn.Sequential(
|
||||
normalization(ch),
|
||||
nn.SiLU(),
|
||||
zero_module(conv_nd(dims, model_channels, out_channels, kernel_size, padding=padding)),
|
||||
)
|
||||
|
||||
def load_state_dict(self, state_dict: 'OrderedDict[str, Tensor]',
|
||||
strict: bool = True):
|
||||
# Temporary hack to allow the addition of nil-guidance token embeddings to the existing guidance embeddings.
|
||||
lsd = self.state_dict()
|
||||
revised = 0
|
||||
for i, blk in enumerate(self.input_blocks):
|
||||
if isinstance(blk, nn.Embedding):
|
||||
key = f'input_blocks.{i}.weight'
|
||||
if state_dict[key].shape[0] != lsd[key].shape[0]:
|
||||
t = torch.randn_like(lsd[key]) * .02
|
||||
t[:state_dict[key].shape[0]] = state_dict[key]
|
||||
state_dict[key] = t
|
||||
revised += 1
|
||||
print(f"Loaded experimental unet_diffusion_net with {revised} modifications.")
|
||||
return super().load_state_dict(state_dict, strict)
|
||||
|
||||
|
||||
|
||||
def forward(self, x, timesteps, tokens, conditioning_input=None):
|
||||
"""
|
||||
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 tokens: an aligned text input.
|
||||
:return: an [N x C x ...] Tensor of outputs.
|
||||
"""
|
||||
with autocast(x.device.type):
|
||||
orig_x_shape = x.shape[-1]
|
||||
cm = ceil_multiple(x.shape[-1], 2048)
|
||||
if cm != 0:
|
||||
pc = (cm-x.shape[-1])/x.shape[-1]
|
||||
x = F.pad(x, (0,cm-x.shape[-1]))
|
||||
tokens = F.pad(tokens, (0,int(pc*tokens.shape[-1])))
|
||||
if self.conditioning_enabled:
|
||||
assert conditioning_input is not None
|
||||
|
||||
hs = []
|
||||
time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
|
||||
|
||||
# Mask out guidance tokens for un-guided diffusion.
|
||||
if self.training and self.nil_guidance_fwd_proportion > 0:
|
||||
token_mask = torch.rand(tokens.shape, device=tokens.device) < self.nil_guidance_fwd_proportion
|
||||
tokens = torch.where(token_mask, self.mask_token_id, tokens)
|
||||
code_emb = self.code_embedding(tokens).permute(0,2,1)
|
||||
if self.conditioning_enabled:
|
||||
cond_emb = self.contextual_embedder(conditioning_input)
|
||||
code_emb = cond_emb.unsqueeze(-1) * code_emb
|
||||
|
||||
first = False # First block has autocast disabled.
|
||||
time_emb = time_emb.float()
|
||||
h = x
|
||||
for k, module in enumerate(self.input_blocks):
|
||||
with autocast(x.device.type, enabled=not first):
|
||||
if isinstance(module, nn.Conv1d):
|
||||
h_tok = F.interpolate(module(code_emb), size=(h.shape[-1]), mode='nearest')
|
||||
h = h + h_tok
|
||||
else:
|
||||
h = module(h, time_emb)
|
||||
hs.append(h)
|
||||
first = True
|
||||
with autocast(x.device.type):
|
||||
h = self.middle_block(h, time_emb)
|
||||
for module in self.output_blocks:
|
||||
h = torch.cat([h, hs.pop()], dim=1)
|
||||
h = module(h, time_emb)
|
||||
h = h.type(x.dtype)
|
||||
h = h.float()
|
||||
out = self.out(h) # Last block also has autocast disabled.
|
||||
return out[:, :, :orig_x_shape]
|
||||
|
||||
|
||||
@register_model
|
||||
def register_diffusion_tts3(opt_net, opt):
|
||||
return DiffusionTts(**opt_net['kwargs'])
|
||||
|
||||
|
||||
# Test for ~4 second audio clip at 22050Hz
|
||||
if __name__ == '__main__':
|
||||
clip = torch.randn(4, 1, 86016)
|
||||
tok = torch.randint(0,30, (4,388))
|
||||
cond = torch.randn(4, 1, 44000)
|
||||
ts = torch.LongTensor([555, 556, 600, 600])
|
||||
model = DiffusionTts(64, channel_mult=[1,1.5,2, 3, 4, 6, 8, 8, 8, 8], num_res_blocks=[2, 2, 2, 2, 2, 2, 2, 4, 4, 4],
|
||||
token_conditioning_resolutions=[1,4,16,64], attention_resolutions=[256,512], num_heads=4, kernel_size=3,
|
||||
scale_factor=2, conditioning_inputs_provided=True, time_embed_dim_multiplier=4)
|
||||
model(clip, ts, tok, cond)
|
||||
|
||||
|
|
@ -1,406 +0,0 @@
|
|||
import functools
|
||||
from collections import OrderedDict
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from torch import autocast
|
||||
from x_transformers.x_transformers import AbsolutePositionalEmbedding, AttentionLayers
|
||||
|
||||
from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
|
||||
from models.diffusion.unet_diffusion import AttentionBlock, TimestepEmbedSequential, \
|
||||
Downsample, Upsample, TimestepBlock
|
||||
from models.gpt_voice.mini_encoder import AudioMiniEncoder
|
||||
from scripts.audio.gen.use_diffuse_tts import ceil_multiple
|
||||
from trainer.networks import register_model
|
||||
from utils.util import checkpoint
|
||||
from x_transformers import Encoder, ContinuousTransformerWrapper
|
||||
|
||||
|
||||
class CheckpointedLayer(nn.Module):
|
||||
"""
|
||||
Wraps a module. When forward() is called, passes kwargs that require_grad through torch.checkpoint() and bypasses
|
||||
checkpoint for all other args.
|
||||
"""
|
||||
def __init__(self, wrap):
|
||||
super().__init__()
|
||||
self.wrap = wrap
|
||||
|
||||
def forward(self, x, **kwargs):
|
||||
kw_requires_grad = {}
|
||||
kw_no_grad = {}
|
||||
for k, v in kwargs.items():
|
||||
if v is not None and isinstance(v, torch.Tensor) and v.requires_grad:
|
||||
kw_requires_grad[k] = v
|
||||
else:
|
||||
kw_no_grad[k] = v
|
||||
partial = functools.partial(self.wrap, **kw_no_grad)
|
||||
return torch.utils.checkpoint.checkpoint(partial, x, **kw_requires_grad)
|
||||
|
||||
|
||||
class ResBlock(TimestepBlock):
|
||||
def __init__(
|
||||
self,
|
||||
channels,
|
||||
emb_channels,
|
||||
dropout,
|
||||
out_channels=None,
|
||||
dims=2,
|
||||
kernel_size=3,
|
||||
):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.emb_channels = emb_channels
|
||||
self.dropout = dropout
|
||||
self.out_channels = out_channels or channels
|
||||
padding = 1 if kernel_size == 3 else 2
|
||||
|
||||
self.in_layers = nn.Sequential(
|
||||
normalization(channels),
|
||||
nn.SiLU(),
|
||||
conv_nd(dims, channels, self.out_channels, 1, padding=0),
|
||||
)
|
||||
|
||||
self.emb_layers = nn.Sequential(
|
||||
nn.SiLU(),
|
||||
linear(
|
||||
emb_channels,
|
||||
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, 1)
|
||||
|
||||
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]
|
||||
h = h + emb_out
|
||||
h = self.out_layers(h)
|
||||
return self.skip_connection(x) + h
|
||||
|
||||
|
||||
class DiffusionTts(nn.Module):
|
||||
"""
|
||||
The full UNet model with attention and timestep embedding.
|
||||
|
||||
Customized to be conditioned on an aligned token prior.
|
||||
|
||||
:param in_channels: channels in the input Tensor.
|
||||
:param num_tokens: number of tokens (e.g. characters) which can be provided.
|
||||
:param model_channels: base channel count for the model.
|
||||
:param out_channels: channels in the output Tensor.
|
||||
:param num_res_blocks: number of residual blocks per downsample.
|
||||
:param attention_resolutions: a collection of downsample rates at which
|
||||
attention will take place. May be a set, list, or tuple.
|
||||
For example, if this contains 4, then at 4x downsampling, attention
|
||||
will be used.
|
||||
:param dropout: the dropout probability.
|
||||
:param channel_mult: channel multiplier for each level of the UNet.
|
||||
:param conv_resample: if True, use learned convolutions for upsampling and
|
||||
downsampling.
|
||||
:param dims: determines if the signal is 1D, 2D, or 3D.
|
||||
:param num_heads: the number of attention heads in each attention layer.
|
||||
:param num_heads_channels: if specified, ignore num_heads and instead use
|
||||
a fixed channel width per attention head.
|
||||
:param num_heads_upsample: works with num_heads to set a different number
|
||||
of heads for upsampling. Deprecated.
|
||||
:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
|
||||
:param resblock_updown: use residual blocks for up/downsampling.
|
||||
:param use_new_attention_order: use a different attention pattern for potentially
|
||||
increased efficiency.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model_channels,
|
||||
in_channels=1,
|
||||
num_tokens=32,
|
||||
out_channels=2, # mean and variance
|
||||
dropout=0,
|
||||
# res 1, 2, 4, 8,16,32,64,128,256,512, 1K, 2K
|
||||
channel_mult= (1,1.5,2, 3, 4, 6, 8, 12, 16, 24, 32, 48),
|
||||
num_res_blocks=(1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2),
|
||||
# spec_cond: 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0)
|
||||
# attn: 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1
|
||||
token_conditioning_resolutions=(1,16,),
|
||||
attention_resolutions=(512,1024,2048),
|
||||
conv_resample=True,
|
||||
dims=1,
|
||||
use_fp16=False,
|
||||
num_heads=1,
|
||||
num_head_channels=-1,
|
||||
num_heads_upsample=-1,
|
||||
kernel_size=3,
|
||||
scale_factor=2,
|
||||
conditioning_inputs_provided=True,
|
||||
time_embed_dim_multiplier=4,
|
||||
nil_guidance_fwd_proportion=.3,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
if num_heads_upsample == -1:
|
||||
num_heads_upsample = num_heads
|
||||
|
||||
self.in_channels = in_channels
|
||||
self.model_channels = model_channels
|
||||
self.out_channels = out_channels
|
||||
self.attention_resolutions = attention_resolutions
|
||||
self.dropout = dropout
|
||||
self.channel_mult = channel_mult
|
||||
self.conv_resample = conv_resample
|
||||
self.dtype = torch.float16 if use_fp16 else torch.float32
|
||||
self.num_heads = num_heads
|
||||
self.num_head_channels = num_head_channels
|
||||
self.num_heads_upsample = num_heads_upsample
|
||||
self.dims = dims
|
||||
self.nil_guidance_fwd_proportion = nil_guidance_fwd_proportion
|
||||
self.mask_token_id = num_tokens
|
||||
|
||||
padding = 1 if kernel_size == 3 else 2
|
||||
|
||||
time_embed_dim = model_channels * time_embed_dim_multiplier
|
||||
self.time_embed = nn.Sequential(
|
||||
linear(model_channels, time_embed_dim),
|
||||
nn.SiLU(),
|
||||
linear(time_embed_dim, time_embed_dim),
|
||||
)
|
||||
|
||||
embedding_dim = model_channels * 4
|
||||
self.code_embedding = nn.Embedding(num_tokens+1, embedding_dim)
|
||||
self.conditioning_enabled = conditioning_inputs_provided
|
||||
if conditioning_inputs_provided:
|
||||
self.contextual_embedder = AudioMiniEncoder(in_channels, embedding_dim, base_channels=32, depth=6, resnet_blocks=1,
|
||||
attn_blocks=2, num_attn_heads=2, dropout=dropout, downsample_factor=4, kernel_size=5)
|
||||
|
||||
self.input_blocks = nn.ModuleList(
|
||||
[
|
||||
TimestepEmbedSequential(
|
||||
conv_nd(dims, in_channels, model_channels, kernel_size, padding=padding)
|
||||
)
|
||||
]
|
||||
)
|
||||
token_conditioning_blocks = []
|
||||
self._feature_size = model_channels
|
||||
input_block_chans = [model_channels]
|
||||
ch = model_channels
|
||||
ds = 1
|
||||
|
||||
class Permute(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
def forward(self, x):
|
||||
return x.permute(0,2,1)
|
||||
|
||||
for level, (mult, num_blocks) in enumerate(zip(channel_mult, num_res_blocks)):
|
||||
if ds in token_conditioning_resolutions:
|
||||
token_conditioning_block = nn.Conv1d(embedding_dim, ch, 1)
|
||||
token_conditioning_block.weight.data *= .02
|
||||
self.input_blocks.append(token_conditioning_block)
|
||||
token_conditioning_blocks.append(token_conditioning_block)
|
||||
|
||||
for _ in range(num_blocks):
|
||||
layers = [
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
out_channels=int(mult * model_channels),
|
||||
dims=dims,
|
||||
kernel_size=kernel_size,
|
||||
)
|
||||
]
|
||||
ch = int(mult * model_channels)
|
||||
if ds in attention_resolutions:
|
||||
layers.append(
|
||||
AttentionBlock(
|
||||
ch,
|
||||
num_heads=num_heads,
|
||||
num_head_channels=num_head_channels,
|
||||
)
|
||||
)
|
||||
self.input_blocks.append(TimestepEmbedSequential(*layers))
|
||||
self._feature_size += ch
|
||||
input_block_chans.append(ch)
|
||||
if level != len(channel_mult) - 1:
|
||||
out_ch = ch
|
||||
self.input_blocks.append(
|
||||
TimestepEmbedSequential(
|
||||
Downsample(
|
||||
ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor, ksize=1, pad=0
|
||||
)
|
||||
)
|
||||
)
|
||||
ch = out_ch
|
||||
input_block_chans.append(ch)
|
||||
ds *= 2
|
||||
self._feature_size += ch
|
||||
|
||||
self.middle_block = TimestepEmbedSequential(
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
dims=dims,
|
||||
kernel_size=kernel_size,
|
||||
),
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
dims=dims,
|
||||
kernel_size=kernel_size,
|
||||
),
|
||||
)
|
||||
self._feature_size += ch
|
||||
|
||||
self.output_blocks = nn.ModuleList([])
|
||||
for level, (mult, num_blocks) in list(enumerate(zip(channel_mult, num_res_blocks)))[::-1]:
|
||||
for i in range(num_blocks + 1):
|
||||
ich = input_block_chans.pop()
|
||||
layers = [
|
||||
ResBlock(
|
||||
ch + ich,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
out_channels=int(model_channels * mult),
|
||||
dims=dims,
|
||||
kernel_size=kernel_size,
|
||||
)
|
||||
]
|
||||
ch = int(model_channels * mult)
|
||||
if ds in attention_resolutions:
|
||||
layers.append(
|
||||
AttentionBlock(
|
||||
ch,
|
||||
num_heads=num_heads_upsample,
|
||||
num_head_channels=num_head_channels,
|
||||
)
|
||||
)
|
||||
if level and i == num_blocks:
|
||||
out_ch = ch
|
||||
layers.append(
|
||||
Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor)
|
||||
)
|
||||
ds //= 2
|
||||
self.output_blocks.append(TimestepEmbedSequential(*layers))
|
||||
self._feature_size += ch
|
||||
|
||||
self.out = nn.Sequential(
|
||||
normalization(ch),
|
||||
nn.SiLU(),
|
||||
zero_module(conv_nd(dims, model_channels, out_channels, kernel_size, padding=padding)),
|
||||
)
|
||||
|
||||
def load_state_dict(self, state_dict: 'OrderedDict[str, Tensor]',
|
||||
strict: bool = True):
|
||||
# Temporary hack to allow the addition of nil-guidance token embeddings to the existing guidance embeddings.
|
||||
lsd = self.state_dict()
|
||||
revised = 0
|
||||
for i, blk in enumerate(self.input_blocks):
|
||||
if isinstance(blk, nn.Embedding):
|
||||
key = f'input_blocks.{i}.weight'
|
||||
if state_dict[key].shape[0] != lsd[key].shape[0]:
|
||||
t = torch.randn_like(lsd[key]) * .02
|
||||
t[:state_dict[key].shape[0]] = state_dict[key]
|
||||
state_dict[key] = t
|
||||
revised += 1
|
||||
print(f"Loaded experimental unet_diffusion_net with {revised} modifications.")
|
||||
return super().load_state_dict(state_dict, strict)
|
||||
|
||||
|
||||
|
||||
def forward(self, x, timesteps, tokens, conditioning_input=None):
|
||||
"""
|
||||
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 tokens: an aligned text input.
|
||||
:return: an [N x C x ...] Tensor of outputs.
|
||||
"""
|
||||
with autocast(x.device.type):
|
||||
orig_x_shape = x.shape[-1]
|
||||
cm = ceil_multiple(x.shape[-1], 2048)
|
||||
if cm != 0:
|
||||
pc = (cm-x.shape[-1])/x.shape[-1]
|
||||
x = F.pad(x, (0,cm-x.shape[-1]))
|
||||
tokens = F.pad(tokens, (0,int(pc*tokens.shape[-1])))
|
||||
if self.conditioning_enabled:
|
||||
assert conditioning_input is not None
|
||||
|
||||
hs = []
|
||||
time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
|
||||
|
||||
# Mask out guidance tokens for un-guided diffusion.
|
||||
if self.training and self.nil_guidance_fwd_proportion > 0:
|
||||
token_mask = torch.rand(tokens.shape, device=tokens.device) < self.nil_guidance_fwd_proportion
|
||||
tokens = torch.where(token_mask, self.mask_token_id, tokens)
|
||||
code_emb = self.code_embedding(tokens).permute(0,2,1)
|
||||
if self.conditioning_enabled:
|
||||
cond_emb = self.contextual_embedder(conditioning_input)
|
||||
code_emb = cond_emb.unsqueeze(-1) * code_emb
|
||||
|
||||
first = False # First block has autocast disabled.
|
||||
time_emb = time_emb.float()
|
||||
h = x
|
||||
for k, module in enumerate(self.input_blocks):
|
||||
with autocast(x.device.type, enabled=not first):
|
||||
if isinstance(module, nn.Conv1d):
|
||||
h_tok = F.interpolate(module(code_emb), size=(h.shape[-1]), mode='nearest')
|
||||
h = h + h_tok
|
||||
else:
|
||||
h = module(h, time_emb)
|
||||
hs.append(h)
|
||||
first = True
|
||||
with autocast(x.device.type):
|
||||
h = self.middle_block(h, time_emb)
|
||||
for module in self.output_blocks:
|
||||
h = torch.cat([h, hs.pop()], dim=1)
|
||||
h = module(h, time_emb)
|
||||
h = h.type(x.dtype)
|
||||
h = h.float()
|
||||
out = self.out(h) # Last block also has autocast disabled.
|
||||
return out[:, :, :orig_x_shape]
|
||||
|
||||
|
||||
@register_model
|
||||
def register_diffusion_tts4(opt_net, opt):
|
||||
return DiffusionTts(**opt_net['kwargs'])
|
||||
|
||||
|
||||
# Test for ~4 second audio clip at 22050Hz
|
||||
if __name__ == '__main__':
|
||||
clip = torch.randn(4, 1, 86016)
|
||||
tok = torch.randint(0,30, (4,388))
|
||||
cond = torch.randn(4, 1, 44000)
|
||||
ts = torch.LongTensor([555, 556, 600, 600])
|
||||
model = DiffusionTts(64, channel_mult=[1,1.5,2, 3, 4, 6, 8, 8, 8, 8], num_res_blocks=[2, 2, 2, 2, 2, 2, 2, 4, 4, 4],
|
||||
token_conditioning_resolutions=[1,4,16,64], attention_resolutions=[256,512], num_heads=4, kernel_size=3,
|
||||
scale_factor=2, conditioning_inputs_provided=True, time_embed_dim_multiplier=4)
|
||||
model(clip, ts, tok, cond)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user