From aa653115f1b78d8a0e16ace421caf33a236bb0ed Mon Sep 17 00:00:00 2001 From: James Betker Date: Thu, 26 May 2022 10:09:11 -0600 Subject: [PATCH] tfd3 --- .../audio/music/transformer_diffusion3.py | 253 ++++++++++++++++++ 1 file changed, 253 insertions(+) create mode 100644 codes/models/audio/music/transformer_diffusion3.py diff --git a/codes/models/audio/music/transformer_diffusion3.py b/codes/models/audio/music/transformer_diffusion3.py new file mode 100644 index 00000000..6183e1ba --- /dev/null +++ b/codes/models/audio/music/transformer_diffusion3.py @@ -0,0 +1,253 @@ +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 TimestepRotaryEmbedSequential(nn.Sequential, TimestepBlock): + def forward(self, x, emb, rotary_emb): + for layer in self: + if isinstance(layer, TimestepBlock): + x = layer(x, emb, rotary_emb) + else: + x = layer(x, rotary_emb) + return x + + +class AttentionBlock(TimestepBlock): + def __init__(self, dim, heads, dropout): + super().__init__() + 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, timestep_emb, rotary_emb): + h = self.rms_scale_norm(x, 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 = RotaryEmbedding(rotary_emb_dim) + self.intg = nn.Linear(model_channels*2, model_channels) + self.layers = TimestepRotaryEmbedSequential(*[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()), + '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) + x = self.intg(torch.cat([x, code_emb], dim=-1)) + x = self.layers(x, 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_diffusion3(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) +