257 lines
11 KiB
Python
257 lines
11 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from 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=1, 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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zero_init_branch_output=True,
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ff_mult=1,
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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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zero_init_branch_output=True,
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ff_mult=1,
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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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zero_init_branch_output=True,
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ff_mult=1,
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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.intg = nn.Linear(model_channels*2, model_channels)
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self.layers = TimestepRotaryEmbedSequential(*[AttentionBlock(model_channels, model_channels//64, dropout) for _ in range(num_layers)])
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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.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, codes=None, conditioning_input=None, prenet_latent=None, precomputed_code_embeddings=None,
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precomputed_cond_embeddings=None, conditioning_free=False, return_code_pred=False):
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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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rotary_pos_emb = self.rotary_embeddings(x.shape[1], x.device)
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x = self.intg(torch.cat([x, code_emb], dim=-1))
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x = self.layers(x, blk_emb, rotary_pos_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_diffusion3(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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