forked from mrq/DL-Art-School
Move stuff around
This commit is contained in:
James Betker
parent
9852599b34
commit
c46da0285c
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@ -1,261 +0,0 @@
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import os
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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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import torchvision
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from torch import autocast
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from models.arch_util import ResBlock
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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, Attention
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from scripts.audio.gen.use_mel2vec_codes import collapse_codegroups
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from trainer.injectors.audio_injectors import normalize_mel
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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_latent(t):
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return t.dtype == torch.float
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def is_sequence(t):
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return t.dtype == torch.long
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class MultiGroupEmbedding(nn.Module):
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def __init__(self, tokens, groups, dim):
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super().__init__()
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self.m = nn.ModuleList([nn.Embedding(tokens, dim // groups) for _ in range(groups)])
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def forward(self, x):
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h = [embedding(x[:, :, i]) for i, embedding in enumerate(self.m)]
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return torch.cat(h, dim=-1)
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class TransformerDiffusion(nn.Module):
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"""
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A diffusion model composed entirely of stacks of transformer layers. Why would you do it any other way?
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"""
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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=256,
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in_latent_channels=512,
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token_count=8,
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in_groups=None,
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out_channels=512, # mean and variance
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dropout=0,
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use_fp16=False,
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# Parameters for regularization.
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unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
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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.unconditioned_percentage = unconditioned_percentage
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self.enable_fp16 = use_fp16
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heads = model_channels//64
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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.conditioning_embedder = nn.Sequential(nn.Conv1d(in_channels, model_channels // 2, 3, padding=1, stride=2),
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nn.Conv1d(model_channels//2, model_channels,3,padding=1,stride=2))
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self.conditioning_encoder = Encoder(
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dim=model_channels,
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depth=4,
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heads=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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# Either code_converter or latent_converter is used, depending on what type of conditioning data is fed.
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# This model is meant to be able to be trained on both for efficiency purposes - it is far less computationally
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# complex to generate tokens, while generating latents will normally mean propagating through a deep autoregressive
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# transformer network.
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if in_groups is None:
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self.embeddings = nn.Embedding(token_count, model_channels)
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else:
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self.embeddings = MultiGroupEmbedding(token_count, in_groups, model_channels)
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self.latent_conditioner = nn.Sequential(
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nn.Conv1d(in_latent_channels, model_channels, 3, padding=1),
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Encoder(
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dim=model_channels,
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depth=2,
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heads=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.latent_fade = nn.Parameter(torch.zeros(1,1,model_channels))
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self.code_converter = Encoder(
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dim=model_channels,
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depth=3,
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heads=heads,
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ff_dropout=dropout,
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attn_dropout=dropout,
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use_rmsnorm=True,
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ff_glu=True,
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rotary_pos_emb=True,
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)
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self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,model_channels))
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self.integrator = nn.Linear(model_channels * 2, model_channels)
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self.mel_head = nn.Conv1d(model_channels, in_channels, kernel_size=3, padding=1)
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self.layers = Encoder(
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dim=model_channels,
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depth=num_layers,
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heads=heads,
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ff_dropout=dropout,
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attn_dropout=dropout,
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use_rms_scaleshift_norm=True,
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ff_glu=True,
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rotary_pos_emb=True,
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zero_init_branch_output=True,
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)
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self.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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self.debug_codes = {}
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def get_grad_norm_parameter_groups(self):
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groups = {
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'contextual_embedder': list(self.conditioning_embedder.parameters()),
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'layers': list(self.layers.parameters()) + list(self.integrator.parameters()) + list(self.inp_block.parameters()),
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'code_converters': list(self.embeddings.parameters()) + list(self.code_converter.parameters()) + list(self.latent_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 timestep_independent(self, codes, conditioning_input, expected_seq_len, prenet_latent=None, return_code_pred=False):
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cond_emb = self.conditioning_embedder(conditioning_input).permute(0,2,1)
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cond_emb = self.conditioning_encoder(cond_emb)[:, 0]
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code_emb = self.embeddings(codes)
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if prenet_latent is not None:
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latent_conditioning = self.latent_conditioner(prenet_latent)
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code_emb = code_emb + latent_conditioning * self.latent_fade
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unconditioned_batches = torch.zeros((code_emb.shape[0], 1, 1), device=code_emb.device)
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# Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
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if self.training and self.unconditioned_percentage > 0:
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unconditioned_batches = torch.rand((code_emb.shape[0], 1, 1),
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device=code_emb.device) < self.unconditioned_percentage
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code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(codes.shape[0], 1, 1),
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code_emb)
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code_emb = self.code_converter(code_emb)
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expanded_code_emb = F.interpolate(code_emb.permute(0,2,1), size=expected_seq_len, mode='nearest').permute(0,2,1)
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if not return_code_pred:
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return expanded_code_emb, cond_emb
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else:
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# Perform the mel_head computation on the pre-exanded code embeddings, then interpolate it separately.
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mel_pred = self.mel_head(code_emb.permute(0,2,1))
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mel_pred = F.interpolate(mel_pred, size=expected_seq_len, mode='nearest')
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# Multiply mel_pred by !unconditioned_branches, which drops the gradient on unconditioned branches.
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# This is because we don't want that gradient being used to train parameters through the codes_embedder as
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# it unbalances contributions to that network from the MSE loss.
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mel_pred = mel_pred * unconditioned_batches.logical_not()
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return expanded_code_emb, cond_emb, mel_pred
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def forward(self, x, timesteps,
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codes=None, conditioning_input=None, prenet_latent=None,
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precomputed_code_embeddings=None, precomputed_cond_embeddings=None,
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conditioning_free=False, return_code_pred=False):
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"""
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Apply the model to an input batch.
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There are two ways to call this method:
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1) Specify codes, conditioning_input and optionally prenet_latent
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2) Specify precomputed_code_embeddings and precomputed_cond_embeddings, retrieved by calling timestep_independent yourself.
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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 codes: an aligned latent or sequence of tokens providing useful data about the sample to be produced.
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:param conditioning_input: a full-resolution audio clip that is used as a reference to the style you want decoded.
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:param prenet_latent: optional latent vector aligned with codes derived from a prior network.
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:param precomputed_code_embeddings: Code embeddings returned from self.timestep_independent()
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:param precomputed_cond_embeddings: Conditional 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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if precomputed_code_embeddings is not None:
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assert precomputed_cond_embeddings is not None, "Must specify both precomputed embeddings if one is specified"
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assert codes is None and conditioning_input is None and prenet_latent is None, "Do not provide precomputed embeddings and the other parameters. It is unclear what you want me to do here."
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assert not (return_code_pred and precomputed_code_embeddings is not None), "I cannot compute a code_pred output for you."
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unused_params = []
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if not return_code_pred:
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unused_params.extend(list(self.mel_head.parameters()))
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if conditioning_free:
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code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, x.shape[-1])
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unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
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unused_params.extend(list(self.latent_conditioner.parameters()))
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else:
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if precomputed_code_embeddings is not None:
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code_emb = precomputed_code_embeddings
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cond_emb = precomputed_cond_embeddings
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else:
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code_emb, cond_emb, mel_pred = self.timestep_independent(codes, conditioning_input, x.shape[-1], prenet_latent, True)
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if prenet_latent is None:
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unused_params.extend(list(self.latent_conditioner.parameters()) + [self.latent_fade])
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unused_params.append(self.unconditioned_embedding)
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blk_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) + cond_emb
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x = self.inp_block(x).permute(0,2,1)
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x = torch.cat([x, code_emb], dim=2)
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x = self.integrator(x)
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x = self.layers(x, norm_scale_shift_inp=blk_emb)
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x = x.float().permute(0,2,1)
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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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if return_code_pred:
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return out, mel_pred
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return out
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@register_model
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def register_transformer_diffusion(opt_net, opt):
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return TransformerDiffusion(**opt_net['kwargs'])
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if __name__ == '__main__':
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clip = torch.randn(2, 256, 400)
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aligned_latent = torch.randn(2,100,512)
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aligned_sequence = torch.randint(0,8,(2,100,8))
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cond = torch.randn(2, 256, 400)
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ts = torch.LongTensor([600, 600])
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model = TransformerDiffusion(512, unconditioned_percentage=.5, in_groups=8)
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o = model(clip, ts, aligned_sequence, cond, return_code_pred=True)
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#o = model(clip, ts, aligned_sequence, cond, aligned_latent)
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@ -1,261 +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 models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
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from models.diffusion.unet_diffusion import TimestepEmbedSequential, TimestepBlock
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from models.lucidrains.x_transformers import Encoder, Attention, FeedForward, RMSScaleShiftNorm, RotaryEmbedding
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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_latent(t):
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return t.dtype == torch.float
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def is_sequence(t):
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return t.dtype == torch.long
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class MultiGroupEmbedding(nn.Module):
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def __init__(self, tokens, groups, dim):
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super().__init__()
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self.m = nn.ModuleList([nn.Embedding(tokens, dim // groups) for _ in range(groups)])
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def forward(self, x):
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h = [embedding(x[:, :, i]) for i, embedding in enumerate(self.m)]
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return torch.cat(h, dim=-1)
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class TimestepRotaryEmbedSequential(nn.Sequential, TimestepBlock):
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def forward(self, x, emb, rotary_emb):
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for layer in self:
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if isinstance(layer, TimestepBlock):
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x = layer(x, emb, rotary_emb)
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else:
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x = layer(x, rotary_emb)
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return x
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class AttentionBlock(TimestepBlock):
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def __init__(self, dim, heads, dropout):
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super().__init__()
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self.attn = Attention(dim, heads=heads, causal=False, dropout=dropout, zero_init_output=False)
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self.ff = FeedForward(dim, mult=2, dropout=dropout, zero_init_output=True)
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self.rms_scale_norm = RMSScaleShiftNorm(dim)
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def forward(self, x, timestep_emb, rotary_emb):
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h = self.rms_scale_norm(x, norm_scale_shift_inp=timestep_emb)
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h, _, _, _ = checkpoint(self.attn, h, None, None, None, None, None, rotary_emb)
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h = checkpoint(self.ff, h)
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return h + x
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class TransformerDiffusion(nn.Module):
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"""
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A diffusion model composed entirely of stacks of transformer layers. Why would you do it any other way?
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"""
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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=256,
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in_latent_channels=512,
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rotary_emb_dim=32,
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token_count=8,
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in_groups=None,
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out_channels=512, # mean and variance
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dropout=0,
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use_fp16=False,
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# Parameters for regularization.
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unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
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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.unconditioned_percentage = unconditioned_percentage
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self.enable_fp16 = use_fp16
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heads = model_channels//64
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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.conditioning_embedder = nn.Sequential(nn.Conv1d(in_channels, model_channels // 2, 3, padding=1, stride=2),
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nn.Conv1d(model_channels//2, model_channels,3,padding=1,stride=2))
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self.conditioning_encoder = Encoder(
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dim=model_channels,
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depth=4,
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heads=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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# Either code_converter or latent_converter is used, depending on what type of conditioning data is fed.
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# This model is meant to be able to be trained on both for efficiency purposes - it is far less computationally
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# complex to generate tokens, while generating latents will normally mean propagating through a deep autoregressive
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# transformer network.
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if in_groups is None:
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self.embeddings = nn.Embedding(token_count, model_channels)
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else:
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self.embeddings = MultiGroupEmbedding(token_count, in_groups, model_channels)
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self.latent_conditioner = nn.Sequential(
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nn.Conv1d(in_latent_channels, model_channels, 3, padding=1),
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Encoder(
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dim=model_channels,
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depth=2,
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heads=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.latent_fade = nn.Parameter(torch.zeros(1,1,model_channels))
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self.code_converter = Encoder(
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dim=model_channels,
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depth=3,
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heads=heads,
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ff_dropout=dropout,
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attn_dropout=dropout,
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use_rmsnorm=True,
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ff_glu=True,
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rotary_pos_emb=True,
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)
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self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,model_channels))
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self.mel_head = nn.Conv1d(model_channels, in_channels, kernel_size=3, padding=1)
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self.rotary_embeddings = RotaryEmbedding(rotary_emb_dim)
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self.top_layers = TimestepRotaryEmbedSequential(*[AttentionBlock(model_channels, model_channels//64, dropout) for _ in range(num_layers//4)])
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self.mid_intg = nn.Linear(model_channels*2, model_channels, bias=False)
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self.mid_layers = TimestepRotaryEmbedSequential(*[AttentionBlock(model_channels, model_channels//64, dropout) for _ in range(num_layers//2)])
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self.final_intg = nn.Linear(model_channels*2, model_channels, bias=False)
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self.final_layers = TimestepRotaryEmbedSequential(*[AttentionBlock(model_channels, model_channels//64, dropout) for _ in range(num_layers//4)])
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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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self.debug_codes = {}
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def get_grad_norm_parameter_groups(self):
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groups = {
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'contextual_embedder': list(self.conditioning_embedder.parameters()),
|
||||
'top_layers': list(self.top_layers.parameters()) + list(self.inp_block.parameters()),
|
||||
'mid_layers': list(self.mid_layers.parameters()),
|
||||
'final_layers': list(self.final_layers.parameters()),
|
||||
'code_converters': list(self.embeddings.parameters()) + list(self.code_converter.parameters()) + list(self.latent_conditioner.parameters()),
|
||||
'time_embed': list(self.time_embed.parameters()),
|
||||
}
|
||||
return groups
|
||||
|
||||
def timestep_independent(self, codes, conditioning_input, expected_seq_len, prenet_latent=None, return_code_pred=False):
|
||||
cond_emb = self.conditioning_embedder(conditioning_input).permute(0,2,1)
|
||||
cond_emb = self.conditioning_encoder(cond_emb)[:, 0]
|
||||
|
||||
code_emb = self.embeddings(codes)
|
||||
if prenet_latent is not None:
|
||||
latent_conditioning = self.latent_conditioner(prenet_latent)
|
||||
code_emb = code_emb + latent_conditioning * self.latent_fade
|
||||
|
||||
unconditioned_batches = torch.zeros((code_emb.shape[0], 1, 1), device=code_emb.device)
|
||||
# Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
|
||||
if self.training and self.unconditioned_percentage > 0:
|
||||
unconditioned_batches = torch.rand((code_emb.shape[0], 1, 1),
|
||||
device=code_emb.device) < self.unconditioned_percentage
|
||||
code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(codes.shape[0], 1, 1),
|
||||
code_emb)
|
||||
code_emb = self.code_converter(code_emb)
|
||||
|
||||
expanded_code_emb = F.interpolate(code_emb.permute(0,2,1), size=expected_seq_len, mode='nearest').permute(0,2,1)
|
||||
if not return_code_pred:
|
||||
return expanded_code_emb, cond_emb
|
||||
else:
|
||||
# Perform the mel_head computation on the pre-exanded code embeddings, then interpolate it separately.
|
||||
mel_pred = self.mel_head(code_emb.permute(0,2,1))
|
||||
mel_pred = F.interpolate(mel_pred, size=expected_seq_len, mode='nearest')
|
||||
# Multiply mel_pred by !unconditioned_branches, which drops the gradient on unconditioned branches.
|
||||
# This is because we don't want that gradient being used to train parameters through the codes_embedder as
|
||||
# it unbalances contributions to that network from the MSE loss.
|
||||
mel_pred = mel_pred * unconditioned_batches.logical_not()
|
||||
return expanded_code_emb, cond_emb, mel_pred
|
||||
|
||||
|
||||
def forward(self, x, timesteps, codes=None, conditioning_input=None, prenet_latent=None, precomputed_code_embeddings=None,
|
||||
precomputed_cond_embeddings=None, conditioning_free=False, return_code_pred=False):
|
||||
if precomputed_code_embeddings is not None:
|
||||
assert precomputed_cond_embeddings is not None, "Must specify both precomputed embeddings if one is specified"
|
||||
assert codes is None and conditioning_input is None and prenet_latent is None, "Do not provide precomputed embeddings and the other parameters. It is unclear what you want me to do here."
|
||||
assert not (return_code_pred and precomputed_code_embeddings is not None), "I cannot compute a code_pred output for you."
|
||||
|
||||
unused_params = []
|
||||
if not return_code_pred:
|
||||
unused_params.extend(list(self.mel_head.parameters()))
|
||||
if conditioning_free:
|
||||
code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, x.shape[-1])
|
||||
unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
|
||||
unused_params.extend(list(self.latent_conditioner.parameters()))
|
||||
else:
|
||||
if precomputed_code_embeddings is not None:
|
||||
code_emb = precomputed_code_embeddings
|
||||
cond_emb = precomputed_cond_embeddings
|
||||
else:
|
||||
code_emb, cond_emb, mel_pred = self.timestep_independent(codes, conditioning_input, x.shape[-1], prenet_latent, True)
|
||||
if prenet_latent is None:
|
||||
unused_params.extend(list(self.latent_conditioner.parameters()) + [self.latent_fade])
|
||||
unused_params.append(self.unconditioned_embedding)
|
||||
|
||||
blk_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) + cond_emb
|
||||
x = self.inp_block(x).permute(0,2,1)
|
||||
|
||||
rotary_pos_emb = self.rotary_embeddings(x.shape[1], x.device)
|
||||
xt = self.top_layers(x, blk_emb, rotary_pos_emb)
|
||||
xm = torch.cat([xt, code_emb], dim=2)
|
||||
xm = self.mid_intg(xm)
|
||||
xm = self.mid_layers(xm, blk_emb, rotary_pos_emb)
|
||||
xb = torch.cat([xt, xm], dim=2)
|
||||
xb = self.final_intg(xb)
|
||||
x = self.final_layers(xb, blk_emb, rotary_pos_emb)
|
||||
|
||||
x = x.float().permute(0,2,1)
|
||||
out = self.out(x)
|
||||
|
||||
# Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
|
||||
extraneous_addition = 0
|
||||
for p in unused_params:
|
||||
extraneous_addition = extraneous_addition + p.mean()
|
||||
out = out + extraneous_addition * 0
|
||||
|
||||
if return_code_pred:
|
||||
return out, mel_pred
|
||||
return out
|
||||
|
||||
|
||||
@register_model
|
||||
def register_transformer_diffusion2(opt_net, opt):
|
||||
return TransformerDiffusion(**opt_net['kwargs'])
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
clip = torch.randn(2, 256, 400)
|
||||
aligned_latent = torch.randn(2,100,512)
|
||||
aligned_sequence = torch.randint(0,8,(2,100,8))
|
||||
cond = torch.randn(2, 256, 400)
|
||||
ts = torch.LongTensor([600, 600])
|
||||
model = TransformerDiffusion(512, unconditioned_percentage=.5, in_groups=8)
|
||||
o = model(clip, ts, aligned_sequence, cond, return_code_pred=True)
|
||||
#o = model(clip, ts, aligned_sequence, cond, aligned_latent)
|
||||
|
||||
|
|
@ -1,250 +0,0 @@
|
|||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
|
||||
from models.diffusion.unet_diffusion import TimestepEmbedSequential, TimestepBlock
|
||||
from models.lucidrains.x_transformers import Encoder, Attention, FeedForward, RMSScaleShiftNorm, RotaryEmbedding
|
||||
from trainer.networks import register_model
|
||||
from utils.util import checkpoint
|
||||
|
||||
|
||||
def is_latent(t):
|
||||
return t.dtype == torch.float
|
||||
|
||||
def is_sequence(t):
|
||||
return t.dtype == torch.long
|
||||
|
||||
|
||||
class MultiGroupEmbedding(nn.Module):
|
||||
def __init__(self, tokens, groups, dim):
|
||||
super().__init__()
|
||||
self.m = nn.ModuleList([nn.Embedding(tokens, dim // groups) for _ in range(groups)])
|
||||
|
||||
def forward(self, x):
|
||||
h = [embedding(x[:, :, i]) for i, embedding in enumerate(self.m)]
|
||||
return torch.cat(h, dim=-1)
|
||||
|
||||
|
||||
class SpecialSequential(nn.Sequential, TimestepBlock):
|
||||
def forward(self, x, aligned_emb, point_emb, rotary_emb):
|
||||
for layer in self:
|
||||
if isinstance(layer, TimestepBlock):
|
||||
x = layer(x, aligned_emb, point_emb, rotary_emb)
|
||||
else:
|
||||
x = layer(x, aligned_emb, rotary_emb)
|
||||
return x
|
||||
|
||||
|
||||
class AttentionBlock(TimestepBlock):
|
||||
def __init__(self, dim, heads, dropout):
|
||||
super().__init__()
|
||||
self.intg = nn.Linear(dim*2, dim)
|
||||
self.attn = Attention(dim, heads=heads, causal=False, dropout=dropout, zero_init_output=False)
|
||||
self.ff = FeedForward(dim, mult=2, dropout=dropout, zero_init_output=True)
|
||||
self.rms_scale_norm = RMSScaleShiftNorm(dim)
|
||||
|
||||
def forward(self, x, aligned_emb, timestep_emb, rotary_emb):
|
||||
h = self.intg(torch.cat([x, aligned_emb], dim=-1))
|
||||
h = self.rms_scale_norm(h, norm_scale_shift_inp=timestep_emb)
|
||||
h, _, _, _ = checkpoint(self.attn, h, None, None, None, None, None, rotary_emb)
|
||||
h = checkpoint(self.ff, h)
|
||||
return h + x
|
||||
|
||||
|
||||
class TransformerDiffusion(nn.Module):
|
||||
"""
|
||||
A diffusion model composed entirely of stacks of transformer layers. Why would you do it any other way?
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model_channels=512,
|
||||
num_layers=8,
|
||||
in_channels=256,
|
||||
in_latent_channels=512,
|
||||
rotary_emb_dim=32,
|
||||
token_count=8,
|
||||
in_groups=None,
|
||||
out_channels=512, # mean and variance
|
||||
dropout=0,
|
||||
use_fp16=False,
|
||||
# Parameters for regularization.
|
||||
unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.in_channels = in_channels
|
||||
self.model_channels = model_channels
|
||||
self.out_channels = out_channels
|
||||
self.dropout = dropout
|
||||
self.unconditioned_percentage = unconditioned_percentage
|
||||
self.enable_fp16 = use_fp16
|
||||
heads = model_channels//64
|
||||
|
||||
self.inp_block = conv_nd(1, in_channels, model_channels, 3, 1, 1)
|
||||
|
||||
self.time_embed = nn.Sequential(
|
||||
linear(model_channels, model_channels),
|
||||
nn.SiLU(),
|
||||
linear(model_channels, model_channels),
|
||||
)
|
||||
self.conditioning_embedder = nn.Sequential(nn.Conv1d(in_channels, model_channels // 2, 3, padding=1, stride=2),
|
||||
nn.Conv1d(model_channels//2, model_channels,3,padding=1,stride=2))
|
||||
self.conditioning_encoder = Encoder(
|
||||
dim=model_channels,
|
||||
depth=4,
|
||||
heads=heads,
|
||||
ff_dropout=dropout,
|
||||
attn_dropout=dropout,
|
||||
use_rmsnorm=True,
|
||||
ff_glu=True,
|
||||
rotary_pos_emb=True,
|
||||
)
|
||||
|
||||
# Either code_converter or latent_converter is used, depending on what type of conditioning data is fed.
|
||||
# This model is meant to be able to be trained on both for efficiency purposes - it is far less computationally
|
||||
# complex to generate tokens, while generating latents will normally mean propagating through a deep autoregressive
|
||||
# transformer network.
|
||||
if in_groups is None:
|
||||
self.embeddings = nn.Embedding(token_count, model_channels)
|
||||
else:
|
||||
self.embeddings = MultiGroupEmbedding(token_count, in_groups, model_channels)
|
||||
self.latent_conditioner = nn.Sequential(
|
||||
nn.Conv1d(in_latent_channels, model_channels, 3, padding=1),
|
||||
Encoder(
|
||||
dim=model_channels,
|
||||
depth=2,
|
||||
heads=heads,
|
||||
ff_dropout=dropout,
|
||||
attn_dropout=dropout,
|
||||
use_rmsnorm=True,
|
||||
ff_glu=True,
|
||||
rotary_pos_emb=True,
|
||||
)
|
||||
)
|
||||
self.latent_fade = nn.Parameter(torch.zeros(1,1,model_channels))
|
||||
self.code_converter = Encoder(
|
||||
dim=model_channels,
|
||||
depth=3,
|
||||
heads=heads,
|
||||
ff_dropout=dropout,
|
||||
attn_dropout=dropout,
|
||||
use_rmsnorm=True,
|
||||
ff_glu=True,
|
||||
rotary_pos_emb=True,
|
||||
)
|
||||
|
||||
self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,model_channels))
|
||||
self.mel_head = nn.Conv1d(model_channels, in_channels, kernel_size=3, padding=1)
|
||||
|
||||
self.rotary_embeddings = None if rotary_emb_dim is None else RotaryEmbedding(rotary_emb_dim)
|
||||
self.layers = SpecialSequential(*[AttentionBlock(model_channels, model_channels // 64, dropout) for _ in range(num_layers)])
|
||||
|
||||
self.out = nn.Sequential(
|
||||
normalization(model_channels),
|
||||
nn.SiLU(),
|
||||
zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)),
|
||||
)
|
||||
|
||||
self.debug_codes = {}
|
||||
|
||||
def get_grad_norm_parameter_groups(self):
|
||||
groups = {
|
||||
'contextual_embedder': list(self.conditioning_embedder.parameters()),
|
||||
'layers': list(self.layers.parameters()) + list(self.inp_block.parameters()),
|
||||
'code_converters': list(self.embeddings.parameters()) + list(self.code_converter.parameters()) + list(self.latent_conditioner.parameters()),
|
||||
'time_embed': list(self.time_embed.parameters()),
|
||||
}
|
||||
return groups
|
||||
|
||||
def timestep_independent(self, codes, conditioning_input, expected_seq_len, prenet_latent=None, return_code_pred=False):
|
||||
cond_emb = self.conditioning_embedder(conditioning_input).permute(0,2,1)
|
||||
cond_emb = self.conditioning_encoder(cond_emb)[:, 0]
|
||||
|
||||
code_emb = self.embeddings(codes)
|
||||
if prenet_latent is not None:
|
||||
latent_conditioning = self.latent_conditioner(prenet_latent)
|
||||
code_emb = code_emb + latent_conditioning * self.latent_fade
|
||||
|
||||
unconditioned_batches = torch.zeros((code_emb.shape[0], 1, 1), device=code_emb.device)
|
||||
# Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
|
||||
if self.training and self.unconditioned_percentage > 0:
|
||||
unconditioned_batches = torch.rand((code_emb.shape[0], 1, 1),
|
||||
device=code_emb.device) < self.unconditioned_percentage
|
||||
code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(codes.shape[0], 1, 1),
|
||||
code_emb)
|
||||
code_emb = self.code_converter(code_emb)
|
||||
|
||||
expanded_code_emb = F.interpolate(code_emb.permute(0,2,1), size=expected_seq_len, mode='nearest').permute(0,2,1)
|
||||
if not return_code_pred:
|
||||
return expanded_code_emb, cond_emb
|
||||
else:
|
||||
# Perform the mel_head computation on the pre-exanded code embeddings, then interpolate it separately.
|
||||
mel_pred = self.mel_head(code_emb.permute(0,2,1))
|
||||
mel_pred = F.interpolate(mel_pred, size=expected_seq_len, mode='nearest')
|
||||
# Multiply mel_pred by !unconditioned_branches, which drops the gradient on unconditioned branches.
|
||||
# This is because we don't want that gradient being used to train parameters through the codes_embedder as
|
||||
# it unbalances contributions to that network from the MSE loss.
|
||||
mel_pred = mel_pred * unconditioned_batches.logical_not()
|
||||
return expanded_code_emb, cond_emb, mel_pred
|
||||
|
||||
|
||||
def forward(self, x, timesteps, codes=None, conditioning_input=None, prenet_latent=None, precomputed_code_embeddings=None,
|
||||
precomputed_cond_embeddings=None, conditioning_free=False, return_code_pred=False):
|
||||
if precomputed_code_embeddings is not None:
|
||||
assert precomputed_cond_embeddings is not None, "Must specify both precomputed embeddings if one is specified"
|
||||
assert codes is None and conditioning_input is None and prenet_latent is None, "Do not provide precomputed embeddings and the other parameters. It is unclear what you want me to do here."
|
||||
assert not (return_code_pred and precomputed_code_embeddings is not None), "I cannot compute a code_pred output for you."
|
||||
|
||||
unused_params = []
|
||||
if not return_code_pred:
|
||||
unused_params.extend(list(self.mel_head.parameters()))
|
||||
if conditioning_free:
|
||||
code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, x.shape[-1])
|
||||
unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
|
||||
unused_params.extend(list(self.latent_conditioner.parameters()))
|
||||
else:
|
||||
if precomputed_code_embeddings is not None:
|
||||
code_emb = precomputed_code_embeddings
|
||||
cond_emb = precomputed_cond_embeddings
|
||||
else:
|
||||
code_emb, cond_emb, mel_pred = self.timestep_independent(codes, conditioning_input, x.shape[-1], prenet_latent, True)
|
||||
if prenet_latent is None:
|
||||
unused_params.extend(list(self.latent_conditioner.parameters()) + [self.latent_fade])
|
||||
unused_params.append(self.unconditioned_embedding)
|
||||
|
||||
blk_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) + cond_emb
|
||||
x = self.inp_block(x).permute(0,2,1)
|
||||
|
||||
rotary_pos_emb = None if self.rotary_embeddings is None else self.rotary_embeddings(x.shape[1], x.device)
|
||||
x = self.layers(x, code_emb, blk_emb, rotary_pos_emb)
|
||||
|
||||
x = x.float().permute(0,2,1)
|
||||
out = self.out(x)
|
||||
# Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
|
||||
extraneous_addition = 0
|
||||
for p in unused_params:
|
||||
extraneous_addition = extraneous_addition + p.mean()
|
||||
out = out + extraneous_addition * 0
|
||||
|
||||
if return_code_pred:
|
||||
return out, mel_pred
|
||||
return out
|
||||
|
||||
|
||||
@register_model
|
||||
def register_transformer_diffusion4(opt_net, opt):
|
||||
return TransformerDiffusion(**opt_net['kwargs'])
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
clip = torch.randn(2, 256, 400)
|
||||
aligned_latent = torch.randn(2,100,512)
|
||||
aligned_sequence = torch.randint(0,8,(2,100,8))
|
||||
cond = torch.randn(2, 256, 400)
|
||||
ts = torch.LongTensor([600, 600])
|
||||
model = TransformerDiffusion(512, unconditioned_percentage=.5, in_groups=8)
|
||||
o = model(clip, ts, aligned_sequence, cond, return_code_pred=True)
|
||||
#o = model(clip, ts, aligned_sequence, cond, aligned_latent)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user