267 lines
9.7 KiB
Python
267 lines
9.7 KiB
Python
import random
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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 torchaudio.transforms import TimeMasking, FrequencyMasking
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from models.audio.tts.unified_voice2 import ConditioningEncoder
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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, TimestepBlock
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from models.lucidrains.x_transformers import Encoder
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from trainer.networks import register_model
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from utils.util import checkpoint
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def is_sequence(t):
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return t.dtype == torch.long
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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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efficient_config=True,
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use_scale_shift_norm=False,
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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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self.use_scale_shift_norm = use_scale_shift_norm
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padding = {1: 0, 3: 1, 5: 2}[kernel_size]
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eff_kernel = 1 if efficient_config else 3
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eff_padding = 0 if efficient_config else 1
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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, eff_kernel, padding=eff_padding),
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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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2 * self.out_channels if use_scale_shift_norm else 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, eff_kernel, padding=eff_padding)
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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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if self.use_scale_shift_norm:
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out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
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scale, shift = torch.chunk(emb_out, 2, dim=1)
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h = out_norm(h) * (1 + scale) + shift
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h = out_rest(h)
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else:
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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 DiffusionLayer(TimestepBlock):
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def __init__(self, model_channels, dropout, num_heads):
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super().__init__()
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self.resblk = ResBlock(model_channels, model_channels, dropout, model_channels, dims=1, use_scale_shift_norm=True)
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self.attn = AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True)
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def forward(self, x, time_emb):
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y = self.resblk(x, time_emb)
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return self.attn(y)
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class ConditioningEncoder(nn.Module):
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def __init__(self,
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spec_dim,
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embedding_dim,
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attn_blocks=6):
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super().__init__()
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attn = []
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self.init = nn.Sequential(nn.Conv1d(spec_dim, embedding_dim//2, kernel_size=3, padding=1, stride=2),
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nn.Conv1d(embedding_dim//2, embedding_dim, kernel_size=3, padding=1, stride=2))
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self.attn = Encoder(dim=embedding_dim, depth=attn_blocks, use_scalenorm=True, rotary_pos_emb=True,
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heads=embedding_dim//64, ff_mult=1)
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self.dim = embedding_dim
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def forward(self, x):
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h = self.init(x)
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h = self.attn(h.permute(0,2,1))
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return h.mean(dim=1)
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class MusicGenerator(nn.Module):
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def __init__(
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self,
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model_channels=512,
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num_layers=8,
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in_channels=100,
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out_channels=200, # mean and variance
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dropout=0,
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use_fp16=False,
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num_heads=16,
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# Parameters for regularization.
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layer_drop=.1,
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unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
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# Masking parameters.
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frequency_mask_percent_max=0.2,
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time_mask_percent_max=0.2,
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):
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super().__init__()
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self.in_channels = in_channels
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self.model_channels = model_channels
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self.out_channels = out_channels
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self.dropout = dropout
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self.num_heads = num_heads
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self.unconditioned_percentage = unconditioned_percentage
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self.enable_fp16 = use_fp16
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self.layer_drop = layer_drop
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self.time_mask_percent_max = time_mask_percent_max
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self.frequency_mask_percent_mask = frequency_mask_percent_max
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self.inp_block = conv_nd(1, in_channels, model_channels, 3, 1, 1)
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self.time_embed = nn.Sequential(
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linear(model_channels, model_channels),
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nn.SiLU(),
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linear(model_channels, model_channels),
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)
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self.conditioner = ConditioningEncoder(in_channels, model_channels)
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self.unconditioned_embedding = nn.Parameter(torch.randn(1, model_channels))
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self.layers = nn.ModuleList([DiffusionLayer(model_channels, dropout, num_heads) for _ in range(num_layers)] +
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[ResBlock(model_channels, model_channels, dropout, dims=1, use_scale_shift_norm=True) for _ in range(3)])
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self.out = nn.Sequential(
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normalization(model_channels),
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nn.SiLU(),
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zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)),
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)
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def get_grad_norm_parameter_groups(self):
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groups = {
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'layers': list(self.layers.parameters()),
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'conditioner': list(self.conditioner.parameters()),
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'time_embed': list(self.time_embed.parameters()),
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}
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return groups
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def do_masking(self, truth):
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b, c, s = truth.shape
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# Frequency mask
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mask_freq = torch.ones_like(truth)
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cs = random.randint(0, c-10)
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ce = min(c-1, cs+random.randint(1, int(self.frequency_mask_percent_mask*c)))
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mask_freq[:, cs:ce] = 0
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# Time mask
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mask_time = torch.ones_like(truth)
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cs = random.randint(0, s-5)
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ce = min(s-1, cs+random.randint(1, int(self.frequency_mask_percent_mask*s)))
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mask_time[:, :, cs:ce] = 0
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return truth * mask_time * mask_freq
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def timestep_independent(self, truth):
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if self.training:
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truth = self.do_masking(truth)
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truth_emb = self.conditioner(truth)
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return truth_emb
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def forward(self, x, timesteps, truth=None, precomputed_aligned_embeddings=None, conditioning_free=False):
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"""
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Apply the model to an input batch.
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:param x: an [N x C x ...] Tensor of inputs.
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:param timesteps: a 1-D batch of timesteps.
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:param truth: Input value is either pre-masked (in inference), or unmasked (during training)
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:param precomputed_aligned_embeddings: Embeddings returned from self.timestep_independent()
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:param conditioning_free: When set, all conditioning inputs (including tokens and conditioning_input) will not be considered.
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:return: an [N x C x ...] Tensor of outputs.
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"""
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assert precomputed_aligned_embeddings is not None or truth is not None
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unused_params = []
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if conditioning_free:
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truth_emb = self.unconditioned_embedding
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unused_params.extend(list(self.conditioner.parameters()))
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else:
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if precomputed_aligned_embeddings is not None:
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truth_emb = precomputed_aligned_embeddings
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else:
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truth_emb = self.timestep_independent(truth)
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unused_params.append(self.unconditioned_embedding)
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time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) + truth_emb
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x = self.inp_block(x)
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for i, lyr in enumerate(self.layers):
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# Do layer drop where applicable. Do not drop first and last layers.
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if self.training and self.layer_drop > 0 and i != 0 and i != (len(self.layers)-1) and random.random() < self.layer_drop:
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unused_params.extend(list(lyr.parameters()))
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else:
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# First and last blocks will have autocast disabled for improved precision.
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with autocast(x.device.type, enabled=self.enable_fp16 and i != 0):
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x = lyr(x, time_emb)
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x = x.float()
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out = self.out(x)
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# Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
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extraneous_addition = 0
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for p in unused_params:
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extraneous_addition = extraneous_addition + p.mean()
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out = out + extraneous_addition * 0
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return out
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@register_model
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def register_music_gap_gen2(opt_net, opt):
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return MusicGenerator(**opt_net['kwargs'])
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if __name__ == '__main__':
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clip = torch.randn(2, 100, 400)
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aligned_latent = torch.randn(2,100,388)
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ts = torch.LongTensor([600, 600])
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model = MusicGenerator(512, layer_drop=.3, unconditioned_percentage=.5)
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o = model(clip, ts, aligned_latent)
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