From 0d3b831cf9e965ca970cd60a5cce3730604f851b Mon Sep 17 00:00:00 2001 From: James Betker Date: Sat, 28 May 2022 10:55:43 -0600 Subject: [PATCH] big fatty --- .../audio/music/transformer_diffusion3.py | 8 +- .../audio/music/transformer_diffusion4.py | 225 ++++++++++++++++++ 2 files changed, 230 insertions(+), 3 deletions(-) create mode 100644 codes/models/audio/music/transformer_diffusion4.py diff --git a/codes/models/audio/music/transformer_diffusion3.py b/codes/models/audio/music/transformer_diffusion3.py index a1cd7f9d..a2f8c371 100644 --- a/codes/models/audio/music/transformer_diffusion3.py +++ b/codes/models/audio/music/transformer_diffusion3.py @@ -1,12 +1,13 @@ import torch import torch.nn as nn import torch.nn.functional as F +import torchsummary 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 +from utils.util import checkpoint, print_network def is_latent(t): @@ -250,7 +251,8 @@ if __name__ == '__main__': 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) + model = TransformerDiffusion(model_channels=2048, num_layers=8) + print_network(model) + #torchsummary.torchsummary.summary(model, clip, ts, aligned_sequence, cond, return_code_pred=True) #o = model(clip, ts, aligned_sequence, cond, aligned_latent) diff --git a/codes/models/audio/music/transformer_diffusion4.py b/codes/models/audio/music/transformer_diffusion4.py new file mode 100644 index 00000000..8fec716e --- /dev/null +++ b/codes/models/audio/music/transformer_diffusion4.py @@ -0,0 +1,225 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchsummary + +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, print_network + + +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 DietAttentionBlock(TimestepBlock): + def __init__(self, in_dim, dim, heads, dropout): + super().__init__() + self.rms_scale_norm = RMSScaleShiftNorm(in_dim) + self.proj = nn.Linear(in_dim, dim) + self.attn = Attention(dim, heads=heads, causal=False, dropout=dropout) + self.ff = FeedForward(dim, in_dim, mult=1, dropout=dropout, zero_init_output=True) + + def forward(self, x, timestep_emb, rotary_emb): + h = self.rms_scale_norm(x, norm_scale_shift_inp=timestep_emb) + h = self.proj(h) + 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, + prenet_channels=256, + model_channels=512, + block_channels=256, + num_layers=8, + in_channels=256, + 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.prenet_channels = prenet_channels + self.out_channels = out_channels + self.dropout = dropout + self.unconditioned_percentage = unconditioned_percentage + self.enable_fp16 = use_fp16 + + self.inp_block = conv_nd(1, in_channels, prenet_channels, 3, 1, 1) + + self.time_embed = nn.Sequential( + linear(prenet_channels, prenet_channels), + nn.SiLU(), + linear(prenet_channels, prenet_channels), + ) + prenet_heads = prenet_channels//64 + self.conditioning_embedder = nn.Sequential(nn.Conv1d(in_channels, prenet_channels // 2, 3, padding=1, stride=2), + nn.Conv1d(prenet_channels//2, prenet_channels,3,padding=1,stride=2)) + self.conditioning_encoder = Encoder( + dim=prenet_channels, + depth=4, + heads=prenet_heads, + ff_dropout=dropout, + attn_dropout=dropout, + use_rmsnorm=True, + ff_glu=True, + rotary_pos_emb=True, + zero_init_branch_output=True, + ff_mult=1, + ) + + # 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, prenet_channels) + else: + self.embeddings = MultiGroupEmbedding(token_count, in_groups, prenet_channels) + self.code_converter = Encoder( + dim=prenet_channels, + depth=3, + heads=prenet_heads, + ff_dropout=dropout, + attn_dropout=dropout, + use_rmsnorm=True, + ff_glu=True, + rotary_pos_emb=True, + zero_init_branch_output=True, + ff_mult=1, + ) + + self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,prenet_channels)) + self.rotary_embeddings = RotaryEmbedding(rotary_emb_dim) + self.cond_intg = nn.Linear(prenet_channels*2, model_channels) + self.intg = nn.Linear(prenet_channels*2, model_channels) + self.layers = TimestepRotaryEmbedSequential(*[DietAttentionBlock(model_channels, block_channels, block_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()), + 'time_embed': list(self.time_embed.parameters()), + } + return groups + + def timestep_independent(self, codes, conditioning_input, expected_seq_len): + cond_emb = self.conditioning_embedder(conditioning_input).permute(0,2,1) + cond_emb = self.conditioning_encoder(cond_emb)[:, 0] + + code_emb = self.embeddings(codes) + + 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) + return expanded_code_emb, cond_emb + + + def forward(self, x, timesteps, codes=None, conditioning_input=None, precomputed_code_embeddings=None, + precomputed_cond_embeddings=None, conditioning_free=False): + if precomputed_code_embeddings is not None: + assert codes is None and conditioning_input is None, "Do not provide precomputed embeddings and the other parameters. It is unclear what you want me to do here." + + unused_params = [] + 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())) + else: + if precomputed_code_embeddings is not None: + code_emb = precomputed_code_embeddings + cond_emb = precomputed_cond_embeddings + else: + code_emb, cond_emb = self.timestep_independent(codes, conditioning_input, x.shape[-1]) + unused_params.append(self.unconditioned_embedding) + + blk_emb = torch.cat([self.time_embed(timestep_embedding(timesteps, self.prenet_channels)), cond_emb], dim=-1) + blk_emb = self.cond_intg(blk_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)) + for layer in self.layers: + x = checkpoint(layer, 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 + + 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_sequence = torch.randint(0,8,(2,100,8)) + cond = torch.randn(2, 256, 400) + ts = torch.LongTensor([600, 600]) + model = TransformerDiffusion(model_channels=3072, block_channels=1536, prenet_channels=1536, num_layers=16, in_groups=8) + torch.save(model, 'sample.pth') + print_network(model) + #torchsummary.torchsummary.summary(model, clip, ts, aligned_sequence, cond, return_code_pred=True) + o = model(clip, ts, aligned_sequence, cond) +