forked from mrq/DL-Art-School
461 lines
18 KiB
Python
461 lines
18 KiB
Python
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import functools
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from collections import OrderedDict
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch import autocast
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from x_transformers.x_transformers import AbsolutePositionalEmbedding, AttentionLayers
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from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
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from models.diffusion.unet_diffusion import AttentionBlock, TimestepEmbedSequential, \
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Downsample, Upsample, TimestepBlock
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from models.gpt_voice.mini_encoder import AudioMiniEncoder
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from scripts.audio.gen.use_diffuse_tts import ceil_multiple
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from trainer.networks import register_model
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from utils.util import checkpoint
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from x_transformers import Encoder, ContinuousTransformerWrapper
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class CheckpointedLayer(nn.Module):
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"""
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Wraps a module. When forward() is called, passes kwargs that require_grad through torch.checkpoint() and bypasses
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checkpoint for all other args.
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"""
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def __init__(self, wrap):
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super().__init__()
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self.wrap = wrap
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def forward(self, x, **kwargs):
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kw_requires_grad = {}
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kw_no_grad = {}
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for k, v in kwargs.items():
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if v is not None and isinstance(v, torch.Tensor) and v.requires_grad:
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kw_requires_grad[k] = v
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else:
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kw_no_grad[k] = v
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partial = functools.partial(self.wrap, **kw_no_grad)
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return torch.utils.checkpoint.checkpoint(partial, x, **kw_requires_grad)
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class CheckpointedXTransformerEncoder(nn.Module):
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"""
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Wraps a ContinuousTransformerWrapper and applies CheckpointedLayer to each layer and permutes from channels-mid
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to channels-last that XTransformer expects.
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"""
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def __init__(self, **xtransformer_kwargs):
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super().__init__()
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self.transformer = ContinuousTransformerWrapper(**xtransformer_kwargs)
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for i in range(len(self.transformer.attn_layers.layers)):
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n, b, r = self.transformer.attn_layers.layers[i]
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self.transformer.attn_layers.layers[i] = nn.ModuleList([n, CheckpointedLayer(b), r])
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def forward(self, x):
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x = x.permute(0,2,1)
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h = self.transformer(x)
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return h.permute(0,2,1)
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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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"""
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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=32,
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out_channels=2, # mean and variance
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dropout=0,
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# res 1, 2, 4, 8,16,32,64,128,256,512, 1K, 2K
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channel_mult= (1,1.5,2, 3, 4, 6, 8, 12, 16, 24, 32, 48),
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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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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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transformer_depths=8,
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nil_guidance_fwd_proportion=.3,
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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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self.nil_guidance_fwd_proportion = nil_guidance_fwd_proportion
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self.mask_token_id = num_tokens
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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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embedding_dim = model_channels * 8
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self.code_embedding = nn.Embedding(num_tokens+1, embedding_dim)
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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, embedding_dim, base_channels=32, depth=6, resnet_blocks=1,
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attn_blocks=2, num_attn_heads=2, dropout=dropout, downsample_factor=4, kernel_size=5)
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self.conditioning_conv = nn.Conv1d(embedding_dim*2, embedding_dim, 1)
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self.conditioning_encoder = CheckpointedXTransformerEncoder(
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max_seq_len=-1, # Should be unused
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use_pos_emb=False,
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attn_layers=Encoder(
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dim=embedding_dim,
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depth=transformer_depths,
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heads=num_heads,
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ff_dropout=dropout,
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attn_dropout=dropout,
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use_rmsnorm=True,
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ff_glu=True,
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rotary_pos_emb=True,
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)
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)
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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.Conv1d(embedding_dim, ch, 1)
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token_conditioning_block.weight.data *= .02
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self.input_blocks.append(token_conditioning_block)
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token_conditioning_blocks.append(token_conditioning_block)
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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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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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)
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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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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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mid_transformer = CheckpointedXTransformerEncoder(
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max_seq_len=-1, # Should be unused
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use_pos_emb=False,
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attn_layers=Encoder(
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dim=ch,
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depth=transformer_depths,
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heads=num_heads,
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ff_dropout=dropout,
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attn_dropout=dropout,
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use_rmsnorm=True,
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ff_glu=True,
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rotary_pos_emb=True,
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)
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)
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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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kernel_size=kernel_size,
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),
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mid_transformer,
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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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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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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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)
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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 load_state_dict(self, state_dict: 'OrderedDict[str, Tensor]',
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strict: bool = True):
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# Temporary hack to allow the addition of nil-guidance token embeddings to the existing guidance embeddings.
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lsd = self.state_dict()
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revised = 0
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for i, blk in enumerate(self.input_blocks):
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if isinstance(blk, nn.Embedding):
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key = f'input_blocks.{i}.weight'
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if state_dict[key].shape[0] != lsd[key].shape[0]:
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t = torch.randn_like(lsd[key]) * .02
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t[:state_dict[key].shape[0]] = state_dict[key]
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|
state_dict[key] = t
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revised += 1
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|
print(f"Loaded experimental unet_diffusion_net with {revised} modifications.")
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|
return super().load_state_dict(state_dict, strict)
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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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||
|
|
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||
|
|
:param x: an [N x C x ...] Tensor of inputs.
|
||
|
|
:param timesteps: a 1-D batch of timesteps.
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||
|
|
: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 = self.conditioning_conv(torch.cat([cond_emb.unsqueeze(-1).repeat(1,1,code_emb.shape[-1]), code_emb], dim=1))
|
||
|
|
code_emb = self.conditioning_encoder(code_emb)
|
||
|
|
|
||
|
|
first = True
|
||
|
|
time_emb = time_emb.float()
|
||
|
|
h = x
|
||
|
|
for k, module in enumerate(self.input_blocks):
|
||
|
|
if isinstance(module, nn.Conv1d):
|
||
|
|
h_tok = F.interpolate(module(code_emb), size=(h.shape[-1]), mode='nearest')
|
||
|
|
h = h + h_tok
|
||
|
|
else:
|
||
|
|
with autocast(x.device.type, enabled=not first):
|
||
|
|
# First block has autocast disabled to allow a high precision signal to be properly vectorized.
|
||
|
|
h = module(h, time_emb)
|
||
|
|
hs.append(h)
|
||
|
|
first = False
|
||
|
|
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)
|
||
|
|
|
||
|
|
# Last block also has autocast disabled for high-precision outputs.
|
||
|
|
h = h.float()
|
||
|
|
out = self.out(h)
|
||
|
|
return out[:, :, :orig_x_shape]
|
||
|
|
|
||
|
|
|
||
|
|
@register_model
|
||
|
|
def register_diffusion_tts6(opt_net, opt):
|
||
|
|
return DiffusionTts(**opt_net['kwargs'])
|
||
|
|
|
||
|
|
|
||
|
|
# Test for ~4 second audio clip at 22050Hz
|
||
|
|
if __name__ == '__main__':
|
||
|
|
clip = torch.randn(2, 1, 32768)
|
||
|
|
tok = torch.randint(0,30, (2,388))
|
||
|
|
cond = torch.randn(2, 1, 44000)
|
||
|
|
ts = torch.LongTensor([600, 600])
|
||
|
|
model = DiffusionTts(128,
|
||
|
|
channel_mult=[1,1.5,2, 3, 4, 6, 8],
|
||
|
|
num_res_blocks=[2, 2, 2, 2, 2, 2, 1],
|
||
|
|
token_conditioning_resolutions=[1,4,16,64],
|
||
|
|
attention_resolutions=[],
|
||
|
|
num_heads=8,
|
||
|
|
kernel_size=3,
|
||
|
|
scale_factor=2,
|
||
|
|
conditioning_inputs_provided=True,
|
||
|
|
time_embed_dim_multiplier=4)
|
||
|
|
model(clip, ts, tok, cond)
|
||
|
|
torch.save(model.state_dict(), 'test_out.pth')
|
||
|
|
|