From 19ca5b26c10ebf3d2c7f17338d336e76b15408b8 Mon Sep 17 00:00:00 2001 From: James Betker Date: Sun, 10 Apr 2022 21:01:59 -0600 Subject: [PATCH] Remove flat0 and move it into flat --- .../audio/tts/unet_diffusion_tts_flat.py | 317 +++++++++++------- .../audio/tts/unet_diffusion_tts_flat0.py | 314 ----------------- 2 files changed, 194 insertions(+), 437 deletions(-) delete mode 100644 codes/models/audio/tts/unet_diffusion_tts_flat0.py diff --git a/codes/models/audio/tts/unet_diffusion_tts_flat.py b/codes/models/audio/tts/unet_diffusion_tts_flat.py index 35410212..379ca3b7 100644 --- a/codes/models/audio/tts/unet_diffusion_tts_flat.py +++ b/codes/models/audio/tts/unet_diffusion_tts_flat.py @@ -1,13 +1,14 @@ +import random + import torch import torch.nn as nn import torch.nn.functional as F -from x_transformers import Encoder +from torch import autocast -from models.audio.tts.diffusion_encoder import TimestepEmbeddingAttentionLayers -from models.audio.tts.mini_encoder import AudioMiniEncoder -from models.audio.tts.unet_diffusion_tts7 import CheckpointedXTransformerEncoder from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear +from models.diffusion.unet_diffusion import AttentionBlock, TimestepEmbedSequential, TimestepBlock from trainer.networks import register_model +from utils.util import checkpoint def is_latent(t): @@ -17,19 +18,107 @@ 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(TimestepBlock): + def __init__(self, model_channels, dropout, num_heads): + super().__init__() + self.resblk = ResBlock(model_channels, model_channels, dropout, model_channels, dims=1, use_scale_shift_norm=True) + self.attn = AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True) + + def forward(self, x, time_emb): + y = self.resblk(x, time_emb) + return self.attn(y) + + class DiffusionTtsFlat(nn.Module): def __init__( self, model_channels=512, - num_layers=16, + num_layers=8, in_channels=100, in_latent_channels=512, in_tokens=8193, - max_timesteps=4000, out_channels=200, # mean and variance dropout=0, use_fp16=False, num_heads=16, + freeze_everything_except_autoregressive_inputs=False, # Parameters for regularization. layer_drop=.1, unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training. @@ -45,100 +134,43 @@ class DiffusionTtsFlat(nn.Module): self.enable_fp16 = use_fp16 self.layer_drop = layer_drop - self.inp_block = nn.Conv1d(in_channels, model_channels, kernel_size=3, padding=1) - time_embed_dim = model_channels + self.inp_block = conv_nd(1, in_channels, model_channels, 3, 1, 1) self.time_embed = nn.Sequential( - linear(model_channels, time_embed_dim), + linear(model_channels, model_channels), nn.SiLU(), - linear(time_embed_dim, time_embed_dim), + linear(model_channels, 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_embedding = nn.Embedding(in_tokens, model_channels) 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_pos_emb=True, - ) - ) + AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True), + AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True), + AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True), ) + self.code_norm = normalization(model_channels) self.latent_converter = nn.Conv1d(in_latent_channels, model_channels, 1) - # The contextual embedder processes a sample MEL that the output should be "like". self.contextual_embedder = nn.Sequential(nn.Conv1d(in_channels,model_channels,3,padding=1,stride=2), - CheckpointedXTransformerEncoder( - needs_permute=True, - checkpoint=False, # This is repeatedly executed for many conditioning signals, which is incompatible with checkpointing & DDP. - 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, - ff_mult=2, - rotary_pos_emb=True, - ) - )) - self.conditioning_conv = nn.Conv1d(model_channels*2, model_channels, 1) + nn.Conv1d(model_channels, model_channels*2,3,padding=1,stride=2), + AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False), + AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False), + AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False), + AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False), + AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False)) self.unconditioned_embedding = nn.Parameter(torch.randn(1,model_channels,1)) - # This is a further encoder extension that integrates a timestep signal into the conditioning signal. - self.conditioning_timestep_integrator = CheckpointedXTransformerEncoder( - needs_permute=True, - max_seq_len=-1, - use_pos_emb=False, - attn_layers=TimestepEmbeddingAttentionLayers( - dim=model_channels, - timestep_dim=time_embed_dim, - depth=2, - heads=num_heads, - ff_dropout=dropout, - attn_dropout=dropout, - use_rmsnorm=True, - ff_glu=True, - ff_mult=2, - rotary_pos_emb=True, - layerdrop_percent=0, - ) - ) - self.integrate_conditioning = nn.Conv1d(model_channels*2, model_channels, 1) + self.conditioning_timestep_integrator = TimestepEmbedSequential( + DiffusionLayer(model_channels, dropout, num_heads), + DiffusionLayer(model_channels, dropout, num_heads), + DiffusionLayer(model_channels, dropout, num_heads), + ) + self.integrating_conv = nn.Conv1d(model_channels*2, model_channels, kernel_size=1) + self.mel_head = nn.Conv1d(model_channels, in_channels, kernel_size=3, padding=1) - # This is the main processing module. - self.layers = CheckpointedXTransformerEncoder( - needs_permute=True, - max_seq_len=-1, - use_pos_emb=False, - attn_layers=TimestepEmbeddingAttentionLayers( - dim=model_channels, - timestep_dim=time_embed_dim, - depth=num_layers, - heads=num_heads, - ff_dropout=dropout, - attn_dropout=dropout, - use_rmsnorm=True, - ff_glu=True, - ff_mult=2, - rotary_pos_emb=True, - layerdrop_percent=layer_drop, - zero_init_branch_output=True, - ) - ) - self.layers.transformer.norm = nn.Identity() # We don't want the final norm for the main encoder. + self.layers = nn.ModuleList([DiffusionLayer(model_channels, dropout, num_heads) for _ in range(num_layers)] + + [ResBlock(model_channels, model_channels, dropout, dims=1, use_scale_shift_norm=True) for _ in range(3)]) self.out = nn.Sequential( normalization(model_channels), @@ -146,54 +178,64 @@ class DiffusionTtsFlat(nn.Module): zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)), ) + if freeze_everything_except_autoregressive_inputs: + for ap in list(self.latent_converter.parameters()): + ap.ALLOWED_IN_FLAT = True + for p in self.parameters(): + if not hasattr(p, 'ALLOWED_IN_FLAT'): + p.requires_grad = False + p.DO_NOT_TRAIN = True + def get_grad_norm_parameter_groups(self): groups = { 'minicoder': list(self.contextual_embedder.parameters()), - 'conditioning_timestep_integrator': list(self.conditioning_timestep_integrator.parameters()), 'layers': list(self.layers.parameters()), + 'code_converters': list(self.code_embedding.parameters()) + list(self.code_converter.parameters()) + list(self.latent_converter.parameters()) + list(self.latent_converter.parameters()), + 'timestep_integrator': list(self.conditioning_timestep_integrator.parameters()) + list(self.integrating_conv.parameters()), + 'time_embed': list(self.time_embed.parameters()), } return groups - def get_conditioning_encodings(self, aligned_conditioning, conditioning_input, conditioning_free, return_unused=False): + def timestep_independent(self, aligned_conditioning, conditioning_input, expected_seq_len, return_code_pred): # 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(conditioning_input.shape[0], 1, 1) + speech_conditioning_input = conditioning_input.unsqueeze(1) if len( + conditioning_input.shape) == 3 else conditioning_input + conds = [] + for j in range(speech_conditioning_input.shape[1]): + conds.append(self.contextual_embedder(speech_conditioning_input[:, j])) + conds = torch.cat(conds, dim=-1) + cond_emb = conds.mean(dim=-1) + cond_scale, cond_shift = torch.chunk(cond_emb, 2, dim=1) + if is_latent(aligned_conditioning): + code_emb = self.latent_converter(aligned_conditioning) else: - unused_params.append(self.unconditioned_embedding) - - speech_conditioning_input = conditioning_input.unsqueeze(1) if len(conditioning_input.shape) == 3 else conditioning_input - conds = [] - for j in range(speech_conditioning_input.shape[1]): - conds.append(self.contextual_embedder(speech_conditioning_input[:, j])) - conds = torch.cat(conds, dim=-1) - cond_emb = conds.mean(dim=-1).unsqueeze(-1) - - 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.repeat(1, 1, code_emb.shape[-1]) - code_emb = self.conditioning_conv(torch.cat([cond_emb_spread, code_emb], dim=1)) + code_emb = self.code_embedding(aligned_conditioning).permute(0, 2, 1) + code_emb = self.code_converter(code_emb) + code_emb = self.code_norm(code_emb) * (1 + cond_scale.unsqueeze(-1)) + cond_shift.unsqueeze(-1) + 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(conditioning_input.shape[0], 1, 1), + code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(aligned_conditioning.shape[0], 1, 1), code_emb) + expanded_code_emb = F.interpolate(code_emb, size=expected_seq_len, mode='nearest') - if return_unused: - return code_emb, unused_params - return code_emb + if not return_code_pred: + return expanded_code_emb + else: + mel_pred = self.mel_head(expanded_code_emb) + # 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, mel_pred - def forward(self, x, timesteps, aligned_conditioning, conditioning_input, conditioning_free=False): + + def forward(self, x, timesteps, aligned_conditioning=None, conditioning_input=None, precomputed_aligned_embeddings=None, conditioning_free=False, return_code_pred=False): """ Apply the model to an input batch. @@ -201,17 +243,44 @@ class DiffusionTtsFlat(nn.Module): :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 precomputed_aligned_embeddings: Embeddings returned from self.timestep_independent() :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. """ - code_emb, unused_params = self.get_conditioning_encodings(aligned_conditioning, conditioning_input, conditioning_free, return_unused=True) - # Everything after this comment is timestep-dependent. + assert precomputed_aligned_embeddings is not None or (aligned_conditioning is not None and conditioning_input is not None) + assert not (return_code_pred and precomputed_aligned_embeddings is not None) # These two are mutually exclusive. + + 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())) + unused_params.extend(list(self.latent_converter.parameters())) + else: + if precomputed_aligned_embeddings is not None: + code_emb = precomputed_aligned_embeddings + else: + code_emb, mel_pred = self.timestep_independent(aligned_conditioning, conditioning_input, x.shape[-1], True) + if is_latent(aligned_conditioning): + unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters())) + else: + unused_params.extend(list(self.latent_converter.parameters())) + + unused_params.append(self.unconditioned_embedding) + time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) - code_emb = self.conditioning_timestep_integrator(code_emb, time_emb=time_emb) + code_emb = self.conditioning_timestep_integrator(code_emb, time_emb) x = self.inp_block(x) - x = self.integrate_conditioning(torch.cat([x, F.interpolate(code_emb, size=x.shape[-1], mode='nearest')], dim=1)) - with torch.autocast(x.device.type, enabled=self.enable_fp16): - x = self.layers(x, time_emb=time_emb) + x = torch.cat([x, code_emb], dim=1) + x = self.integrating_conv(x) + for i, lyr in enumerate(self.layers): + # Do layer drop where applicable. Do not drop first and last layers. + if self.training and 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, time_emb) + x = x.float() out = self.out(x) @@ -221,6 +290,8 @@ class DiffusionTtsFlat(nn.Module): extraneous_addition = extraneous_addition + p.mean() out = out + extraneous_addition * 0 + if return_code_pred: + return out, mel_pred return out @@ -232,12 +303,12 @@ def register_diffusion_tts_flat(opt_net, opt): 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, 2, 100, 400) + aligned_sequence = torch.randint(0,8192,(2,100)) + cond = torch.randn(2, 100, 400) ts = torch.LongTensor([600, 600]) - model = DiffusionTtsFlat(512, layer_drop=.3) + model = DiffusionTtsFlat(512, layer_drop=.3, unconditioned_percentage=.5, freeze_everything_except_autoregressive_inputs=True) # Test with latent aligned conditioning - o = model(clip, ts, aligned_latent, cond) + #o = model(clip, ts, aligned_latent, cond) # Test with sequence aligned conditioning o = model(clip, ts, aligned_sequence, cond) diff --git a/codes/models/audio/tts/unet_diffusion_tts_flat0.py b/codes/models/audio/tts/unet_diffusion_tts_flat0.py deleted file mode 100644 index c984dcdf..00000000 --- a/codes/models/audio/tts/unet_diffusion_tts_flat0.py +++ /dev/null @@ -1,314 +0,0 @@ -import random - -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch import autocast - -from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear -from models.diffusion.unet_diffusion import AttentionBlock, TimestepEmbedSequential, TimestepBlock -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(TimestepBlock): - def __init__(self, model_channels, dropout, num_heads): - super().__init__() - self.resblk = ResBlock(model_channels, model_channels, dropout, model_channels, dims=1, use_scale_shift_norm=True) - self.attn = AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True) - - def forward(self, x, time_emb): - y = self.resblk(x, time_emb) - return self.attn(y) - - -class DiffusionTtsFlat(nn.Module): - def __init__( - self, - model_channels=512, - num_layers=8, - in_channels=100, - in_latent_channels=512, - in_tokens=8193, - out_channels=200, # mean and variance - dropout=0, - use_fp16=False, - num_heads=16, - freeze_everything_except_autoregressive_inputs=False, - # 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.time_embed = nn.Sequential( - linear(model_channels, model_channels), - nn.SiLU(), - linear(model_channels, 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_embedding = nn.Embedding(in_tokens, model_channels) - self.code_converter = nn.Sequential( - AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True), - AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True), - AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True), - ) - self.code_norm = normalization(model_channels) - self.latent_converter = nn.Conv1d(in_latent_channels, model_channels, 1) - self.contextual_embedder = nn.Sequential(nn.Conv1d(in_channels,model_channels,3,padding=1,stride=2), - nn.Conv1d(model_channels, model_channels*2,3,padding=1,stride=2), - AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False), - AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False), - AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False), - AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False), - AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False)) - self.unconditioned_embedding = nn.Parameter(torch.randn(1,model_channels,1)) - self.conditioning_timestep_integrator = TimestepEmbedSequential( - DiffusionLayer(model_channels, dropout, num_heads), - DiffusionLayer(model_channels, dropout, num_heads), - DiffusionLayer(model_channels, dropout, num_heads), - ) - self.integrating_conv = nn.Conv1d(model_channels*2, model_channels, kernel_size=1) - self.mel_head = nn.Conv1d(model_channels, in_channels, kernel_size=3, padding=1) - - self.layers = nn.ModuleList([DiffusionLayer(model_channels, dropout, num_heads) for _ in range(num_layers)] + - [ResBlock(model_channels, model_channels, dropout, dims=1, use_scale_shift_norm=True) for _ in range(3)]) - - self.out = nn.Sequential( - normalization(model_channels), - nn.SiLU(), - zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)), - ) - - if freeze_everything_except_autoregressive_inputs: - for ap in list(self.latent_converter.parameters()): - ap.ALLOWED_IN_FLAT = True - for p in self.parameters(): - if not hasattr(p, 'ALLOWED_IN_FLAT'): - p.requires_grad = False - p.DO_NOT_TRAIN = True - - def get_grad_norm_parameter_groups(self): - groups = { - 'minicoder': list(self.contextual_embedder.parameters()), - 'layers': list(self.layers.parameters()), - 'code_converters': list(self.code_embedding.parameters()) + list(self.code_converter.parameters()) + list(self.latent_converter.parameters()) + list(self.latent_converter.parameters()), - 'timestep_integrator': list(self.conditioning_timestep_integrator.parameters()) + list(self.integrating_conv.parameters()), - 'time_embed': list(self.time_embed.parameters()), - } - return groups - - def timestep_independent(self, aligned_conditioning, conditioning_input, expected_seq_len, return_code_pred): - # 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. - speech_conditioning_input = conditioning_input.unsqueeze(1) if len( - conditioning_input.shape) == 3 else conditioning_input - conds = [] - for j in range(speech_conditioning_input.shape[1]): - conds.append(self.contextual_embedder(speech_conditioning_input[:, j])) - conds = torch.cat(conds, dim=-1) - cond_emb = conds.mean(dim=-1) - cond_scale, cond_shift = torch.chunk(cond_emb, 2, dim=1) - if is_latent(aligned_conditioning): - code_emb = self.latent_converter(aligned_conditioning) - else: - code_emb = self.code_embedding(aligned_conditioning).permute(0, 2, 1) - code_emb = self.code_converter(code_emb) - code_emb = self.code_norm(code_emb) * (1 + cond_scale.unsqueeze(-1)) + cond_shift.unsqueeze(-1) - - 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(aligned_conditioning.shape[0], 1, 1), - code_emb) - expanded_code_emb = F.interpolate(code_emb, size=expected_seq_len, mode='nearest') - - if not return_code_pred: - return expanded_code_emb - else: - mel_pred = self.mel_head(expanded_code_emb) - # 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, mel_pred - - - def forward(self, x, timesteps, aligned_conditioning=None, conditioning_input=None, precomputed_aligned_embeddings=None, conditioning_free=False, return_code_pred=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 precomputed_aligned_embeddings: Embeddings returned from self.timestep_independent() - :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. - """ - assert precomputed_aligned_embeddings is not None or (aligned_conditioning is not None and conditioning_input is not None) - assert not (return_code_pred and precomputed_aligned_embeddings is not None) # These two are mutually exclusive. - - 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())) - unused_params.extend(list(self.latent_converter.parameters())) - else: - if precomputed_aligned_embeddings is not None: - code_emb = precomputed_aligned_embeddings - else: - code_emb, mel_pred = self.timestep_independent(aligned_conditioning, conditioning_input, x.shape[-1], True) - if is_latent(aligned_conditioning): - unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters())) - else: - unused_params.extend(list(self.latent_converter.parameters())) - - unused_params.append(self.unconditioned_embedding) - - time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) - code_emb = self.conditioning_timestep_integrator(code_emb, time_emb) - x = self.inp_block(x) - x = torch.cat([x, code_emb], dim=1) - x = self.integrating_conv(x) - for i, lyr in enumerate(self.layers): - # Do layer drop where applicable. Do not drop first and last layers. - if self.training and 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, 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 - - if return_code_pred: - return out, mel_pred - return out - - -@register_model -def register_diffusion_tts_flat0(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,(2,100)) - cond = torch.randn(2, 100, 400) - ts = torch.LongTensor([600, 600]) - model = DiffusionTtsFlat(512, layer_drop=.3, unconditioned_percentage=.5, freeze_everything_except_autoregressive_inputs=True) - # Test with latent aligned conditioning - #o = model(clip, ts, aligned_latent, cond) - # Test with sequence aligned conditioning - o = model(clip, ts, aligned_sequence, cond) -