From 428911cd4d464b61a2e6431df12f80778248973f Mon Sep 17 00:00:00 2001 From: James Betker Date: Thu, 17 Mar 2022 10:53:56 -0600 Subject: [PATCH] flat diffusion network --- .../audio/tts/unet_diffusion_tts_flat.py | 304 ++++++++++++++++++ codes/trainer/ExtensibleTrainer.py | 8 +- 2 files changed, 311 insertions(+), 1 deletion(-) create mode 100644 codes/models/audio/tts/unet_diffusion_tts_flat.py diff --git a/codes/models/audio/tts/unet_diffusion_tts_flat.py b/codes/models/audio/tts/unet_diffusion_tts_flat.py new file mode 100644 index 00000000..a7c96527 --- /dev/null +++ b/codes/models/audio/tts/unet_diffusion_tts_flat.py @@ -0,0 +1,304 @@ +import random + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import autocast +from x_transformers import Encoder + +from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear +from models.diffusion.unet_diffusion import AttentionBlock, TimestepEmbedSequential, \ + Downsample, Upsample, TimestepBlock +from models.audio.tts.mini_encoder import AudioMiniEncoder +from models.audio.tts.unet_diffusion_tts7 import CheckpointedXTransformerEncoder +from scripts.audio.gen.use_diffuse_tts import ceil_multiple +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 ResBlock(TimestepBlock): + def __init__( + self, + channels, + emb_channels, + dropout, + out_channels=None, + dims=2, + kernel_size=3, + efficient_config=True, + use_scale_shift_norm=False, + ): + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.dropout = dropout + self.out_channels = out_channels or channels + self.use_scale_shift_norm = use_scale_shift_norm + padding = {1: 0, 3: 1, 5: 2}[kernel_size] + eff_kernel = 1 if efficient_config else 3 + eff_padding = 0 if efficient_config else 1 + + self.in_layers = nn.Sequential( + normalization(channels), + nn.SiLU(), + conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding), + ) + + self.emb_layers = nn.Sequential( + nn.SiLU(), + linear( + emb_channels, + 2 * self.out_channels if use_scale_shift_norm else self.out_channels, + ), + ) + self.out_layers = nn.Sequential( + normalization(self.out_channels), + nn.SiLU(), + nn.Dropout(p=dropout), + zero_module( + conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding) + ), + ) + + if self.out_channels == channels: + self.skip_connection = nn.Identity() + else: + self.skip_connection = conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding) + + def forward(self, x, emb): + """ + Apply the block to a Tensor, conditioned on a timestep embedding. + + :param x: an [N x C x ...] Tensor of features. + :param emb: an [N x emb_channels] Tensor of timestep embeddings. + :return: an [N x C x ...] Tensor of outputs. + """ + return checkpoint( + self._forward, x, emb + ) + + def _forward(self, x, emb): + h = self.in_layers(x) + emb_out = self.emb_layers(emb).type(h.dtype) + while len(emb_out.shape) < len(h.shape): + emb_out = emb_out[..., None] + if self.use_scale_shift_norm: + out_norm, out_rest = self.out_layers[0], self.out_layers[1:] + scale, shift = torch.chunk(emb_out, 2, dim=1) + h = out_norm(h) * (1 + scale) + shift + h = out_rest(h) + else: + h = h + emb_out + h = self.out_layers(h) + return self.skip_connection(x) + h + + +class DiffusionLayer(nn.Module): + def __init__(self, model_channels, aligned_channels, cond_channels, dropout, num_heads): + super().__init__() + self.aligned_mutation = zero_module(conv_nd(1, aligned_channels, model_channels, 1)) + self.cond_mutation = zero_module(conv_nd(1, cond_channels, model_channels, 1)) + self.inp_mutation = conv_nd(1, model_channels, model_channels, 1) + self.resblk = ResBlock(model_channels, model_channels, dropout, model_channels, dims=1, use_scale_shift_norm=True) + self.attn = AttentionBlock(model_channels, num_heads) + + def forward(self, x, aligned, pointwise, time_emb): + a = self.aligned_mutation(aligned) + c = self.cond_mutation(pointwise.unsqueeze(-1)) + f = self.inp_mutation(x) + y = self.resblk(f + c.repeat(1,1,f.shape[-1]) + F.interpolate(a, size=f.shape[-1], mode='nearest'), time_emb) + y = self.attn(y) + return y + + +class DiffusionTtsFlat(nn.Module): + def __init__( + self, + model_channels=512, + num_layers=8, + in_channels=100, + in_latent_channels=512, + in_tokens=8193, + max_timesteps=4000, + max_positions=4000, + out_channels=200, # mean and variance + dropout=0, + use_fp16=False, + num_heads=16, + # Parameters for regularization. + layer_drop=.1, + 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.num_heads = num_heads + self.unconditioned_percentage = unconditioned_percentage + self.enable_fp16 = use_fp16 + self.layer_drop = layer_drop + + self.inp_block = conv_nd(1, in_channels, model_channels, 3, 1, 1) + self.position_embed = nn.Embedding(max_positions, model_channels) + self.time_embed = nn.Embedding(max_timesteps, model_channels) + + # 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. + self.code_converter = nn.Sequential( + nn.Embedding(in_tokens, model_channels), + CheckpointedXTransformerEncoder( + needs_permute=False, + max_seq_len=-1, + use_pos_emb=False, + attn_layers=Encoder( + dim=model_channels, + depth=3, + heads=num_heads, + ff_dropout=dropout, + attn_dropout=dropout, + use_rmsnorm=True, + ff_glu=True, + rotary_emb_dim=True, + ) + )) + self.latent_converter = nn.Conv1d(in_latent_channels, model_channels, 1) + if in_channels > 60: # It's a spectrogram. + self.contextual_embedder = nn.Sequential(nn.Conv1d(in_channels,model_channels,3,padding=1,stride=2), + CheckpointedXTransformerEncoder( + needs_permute=True, + max_seq_len=-1, + use_pos_emb=False, + attn_layers=Encoder( + dim=model_channels, + depth=4, + heads=num_heads, + ff_dropout=dropout, + attn_dropout=dropout, + use_rmsnorm=True, + ff_glu=True, + rotary_emb_dim=True, + ) + )) + else: + self.contextual_embedder = AudioMiniEncoder(1, model_channels, base_channels=32, depth=6, resnet_blocks=1, + attn_blocks=3, num_attn_heads=8, dropout=dropout, downsample_factor=4, kernel_size=5) + self.conditioning_conv = nn.Conv1d(model_channels*2, model_channels, 1) + self.unconditioned_embedding = nn.Parameter(torch.randn(1,model_channels,1)) + self.conditioning_timestep_integrator = TimestepEmbedSequential( + ResBlock(model_channels, model_channels, dropout, out_channels=model_channels, dims=1, kernel_size=1, use_scale_shift_norm=True), + AttentionBlock(model_channels, num_heads=num_heads), + ResBlock(model_channels, model_channels, dropout, out_channels=model_channels, dims=1, kernel_size=1, use_scale_shift_norm=True), + AttentionBlock(model_channels, num_heads=num_heads), + ResBlock(model_channels, model_channels, dropout, out_channels=model_channels, dims=1, kernel_size=1, use_scale_shift_norm=True), + ) + + self.layers = nn.ModuleList([DiffusionLayer(model_channels, model_channels, model_channels, dropout, num_heads) 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)), + ) + + def get_grad_norm_parameter_groups(self): + groups = { + 'minicoder': list(self.contextual_embedder.parameters()), + 'layers': list(self.layers), + } + return groups + + def forward(self, x, timesteps, aligned_conditioning, conditioning_input, lr_input=None, conditioning_free=False): + """ + Apply the model to an input batch. + + :param x: an [N x C x ...] Tensor of inputs. + :param timesteps: a 1-D batch of timesteps. + :param aligned_conditioning: an aligned latent or sequence of tokens providing useful data about the sample to be produced. + :param conditioning_input: a full-resolution audio clip that is used as a reference to the style you want decoded. + :param lr_input: for super-sampling models, a guidance audio clip at a lower sampling rate. + :param conditioning_free: When set, all conditioning inputs (including tokens and conditioning_input) will not be considered. + :return: an [N x C x ...] Tensor of outputs. + """ + # Shuffle aligned_latent to BxCxS format + if is_latent(aligned_conditioning): + aligned_conditioning = aligned_conditioning.permute(0, 2, 1) + + # Note: this block does not need to repeated on inference, since it is not timestep-dependent or x-dependent. + unused_params = [] + if conditioning_free: + code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, 1) + else: + unused_params.append(self.unconditioned_embedding) + cond_emb = self.contextual_embedder(conditioning_input) + if len(cond_emb.shape) == 3: # Just take the first element. + cond_emb = cond_emb[:, :, 0] + if is_latent(aligned_conditioning): + code_emb = self.latent_converter(aligned_conditioning) + unused_params.extend(list(self.code_converter.parameters())) + else: + code_emb = self.code_converter(aligned_conditioning) + unused_params.extend(list(self.latent_converter.parameters())) + cond_emb_spread = cond_emb.unsqueeze(-1).repeat(1, 1, code_emb.shape[-1]) + code_emb = self.conditioning_conv(torch.cat([cond_emb_spread, code_emb], dim=1)) + # 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(x.shape[0], 1, 1), + code_emb) + + # Everything after this comment is timestep dependent. + x = self.inp_block(x) + pos_emb = self.position_embed(torch.arange(0, x.shape[-1], device=x.device)).unsqueeze(0).repeat(x.shape[0],1,1).permute(0,2,1) + x = x + pos_emb + time_emb = self.time_embed(timesteps) + code_emb = self.conditioning_timestep_integrator(code_emb, time_emb) + for i, lyr in enumerate(self.layers): + # Do layer drop where applicable. Do not drop first and last layers. + if self.layer_drop > 0 and i != 0 and i != (len(self.layers)-1) and random.random() < self.layer_drop: + unused_params.extend(list(lyr.parameters())) + else: + # First and last blocks will have autocast disabled for improved precision. + with autocast(x.device.type, enabled=self.enable_fp16 and i != 0): + x = lyr(x, code_emb, cond_emb, time_emb) + + x = x.float() + 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_diffusion_tts_flat(opt_net, opt): + return DiffusionTtsFlat(**opt_net['kwargs']) + + +if __name__ == '__main__': + clip = torch.randn(2, 100, 400) + aligned_latent = torch.randn(2,388,512) + aligned_sequence = torch.randint(0,8192,(2,388)) + cond = torch.randn(2, 100, 400) + ts = torch.LongTensor([600, 600]) + model = DiffusionTtsFlat(512, layer_drop=.3) + # Test with latent aligned conditioning + o = model(clip, ts, aligned_latent, cond) + # Test with sequence aligned conditioning + o = model(clip, ts, aligned_sequence, cond) + diff --git a/codes/trainer/ExtensibleTrainer.py b/codes/trainer/ExtensibleTrainer.py index 706559e8..0a470e0f 100644 --- a/codes/trainer/ExtensibleTrainer.py +++ b/codes/trainer/ExtensibleTrainer.py @@ -327,7 +327,13 @@ class ExtensibleTrainer(BaseModel): else: pgroups = {f'{name}_all_parameters': list(model.parameters())} for name in pgroups.keys(): - grad_norms[name] = torch.norm(torch.stack([torch.norm(p.grad.detach(), 2) for p in pgroups[name]]), 2) + stacked_grads = [] + for p in pgroups[name]: + if hasattr(p, 'grad') and p.grad is not None: + stacked_grads.append(torch.norm(p.grad.detach(), 2)) + if not stacked_grads: + continue + grad_norms[name] = torch.norm(torch.stack(stacked_grads), 2) if distributed.is_available() and distributed.is_initialized(): # Gather the metric from all devices if in a distributed setting. distributed.all_reduce(grad_norms[name], op=distributed.ReduceOp.SUM)