From e58dab14c347370807417fc1bd9c4fcf94bc5f98 Mon Sep 17 00:00:00 2001 From: James Betker Date: Sat, 29 Jan 2022 11:01:01 -0700 Subject: [PATCH] new diffusion updates from testing --- codes/models/gpt_voice/unet_diffusion_tts3.py | 135 +++-- codes/models/gpt_voice/unet_diffusion_tts4.py | 406 ++++++++++++++++ codes/models/gpt_voice/unet_diffusion_tts5.py | 460 ++++++++++++++++++ codes/models/lucidrains/dalle/transformer.py | 5 +- codes/train.py | 2 +- 5 files changed, 965 insertions(+), 43 deletions(-) create mode 100644 codes/models/gpt_voice/unet_diffusion_tts4.py create mode 100644 codes/models/gpt_voice/unet_diffusion_tts5.py diff --git a/codes/models/gpt_voice/unet_diffusion_tts3.py b/codes/models/gpt_voice/unet_diffusion_tts3.py index 071cbaa4..f1958d83 100644 --- a/codes/models/gpt_voice/unet_diffusion_tts3.py +++ b/codes/models/gpt_voice/unet_diffusion_tts3.py @@ -1,17 +1,41 @@ -import operator +import functools from collections import OrderedDict -from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear -from models.diffusion.unet_diffusion import AttentionPool2d, AttentionBlock, TimestepEmbedSequential, \ - Downsample, Upsample, TimestepBlock import torch import torch.nn as nn import torch.nn.functional as F +from torch import autocast +from x_transformers.x_transformers import AbsolutePositionalEmbedding, AttentionLayers -from models.gpt_voice.mini_encoder import AudioMiniEncoder, EmbeddingCombiner +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.gpt_voice.mini_encoder import AudioMiniEncoder from scripts.audio.gen.use_diffuse_tts import ceil_multiple from trainer.networks import register_model from utils.util import checkpoint +from x_transformers import Encoder, ContinuousTransformerWrapper + + +class CheckpointedLayer(nn.Module): + """ + Wraps a module. When forward() is called, passes kwargs that require_grad through torch.checkpoint() and bypasses + checkpoint for all other args. + """ + def __init__(self, wrap): + super().__init__() + self.wrap = wrap + + def forward(self, x, **kwargs): + kw_requires_grad = {} + kw_no_grad = {} + for k, v in kwargs.items(): + if v is not None and isinstance(v, torch.Tensor) and v.requires_grad: + kw_requires_grad[k] = v + else: + kw_no_grad[k] = v + partial = functools.partial(self.wrap, **kw_no_grad) + return torch.utils.checkpoint.checkpoint(partial, x, **kw_requires_grad) class ResBlock(TimestepBlock): @@ -186,6 +210,12 @@ class DiffusionTts(nn.Module): ch = model_channels ds = 1 + class Permute(nn.Module): + def __init__(self): + super().__init__() + def forward(self, x): + return x.permute(0,2,1) + for level, (mult, num_blocks) in enumerate(zip(channel_mult, num_res_blocks)): if ds in token_conditioning_resolutions: token_conditioning_block = nn.Conv1d(embedding_dim, ch, 1) @@ -230,6 +260,26 @@ class DiffusionTts(nn.Module): ds *= 2 self._feature_size += ch + mid_transformer = ContinuousTransformerWrapper( + max_seq_len=-1, # Should be unused + use_pos_emb=False, + attn_layers=Encoder( + dim=ch, + depth=8, + heads=num_heads, + ff_dropout=dropout, + attn_dropout=dropout, + use_rmsnorm=True, + ff_glu=True, + rotary_pos_emb=True, + ) + ) + + for i in range(len(mid_transformer.attn_layers.layers)): + n, b, r = mid_transformer.attn_layers.layers[i] + mid_transformer.attn_layers.layers[i] = nn.ModuleList([n, CheckpointedLayer(b), r]) + + self.middle_block = TimestepEmbedSequential( ResBlock( ch, @@ -238,11 +288,9 @@ class DiffusionTts(nn.Module): dims=dims, kernel_size=kernel_size, ), - AttentionBlock( - ch, - num_heads=num_heads, - num_head_channels=num_head_channels, - ), + Permute(), + mid_transformer, + Permute(), ResBlock( ch, time_embed_dim, @@ -318,41 +366,48 @@ class DiffusionTts(nn.Module): :param tokens: an aligned text input. :return: an [N x C x ...] Tensor of outputs. """ - orig_x_shape = x.shape[-1] - cm = ceil_multiple(x.shape[-1], 2048) - if cm != 0: - pc = (cm-x.shape[-1])/x.shape[-1] - x = F.pad(x, (0,cm-x.shape[-1])) - tokens = F.pad(tokens, (0,int(pc*tokens.shape[-1]))) - if self.conditioning_enabled: - assert conditioning_input is not None + with autocast(x.device.type): + orig_x_shape = x.shape[-1] + cm = ceil_multiple(x.shape[-1], 2048) + if cm != 0: + pc = (cm-x.shape[-1])/x.shape[-1] + x = F.pad(x, (0,cm-x.shape[-1])) + tokens = F.pad(tokens, (0,int(pc*tokens.shape[-1]))) + if self.conditioning_enabled: + assert conditioning_input is not None - hs = [] - time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) + hs = [] + time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) - # Mask out guidance tokens for un-guided diffusion. - if self.training and self.nil_guidance_fwd_proportion > 0: - token_mask = torch.rand(tokens.shape, device=tokens.device) < self.nil_guidance_fwd_proportion - tokens = torch.where(token_mask, self.mask_token_id, tokens) - code_emb = self.code_embedding(tokens).permute(0,2,1) - if self.conditioning_enabled: - cond_emb = self.contextual_embedder(conditioning_input) - code_emb = cond_emb.unsqueeze(-1) * code_emb + # Mask out guidance tokens for un-guided diffusion. + if self.training and self.nil_guidance_fwd_proportion > 0: + token_mask = torch.rand(tokens.shape, device=tokens.device) < self.nil_guidance_fwd_proportion + tokens = torch.where(token_mask, self.mask_token_id, tokens) + code_emb = self.code_embedding(tokens).permute(0,2,1) + if self.conditioning_enabled: + cond_emb = self.contextual_embedder(conditioning_input) + code_emb = cond_emb.unsqueeze(-1) * code_emb - h = x.type(self.dtype) + first = False # First block has autocast disabled. + time_emb = time_emb.float() + h = x for k, module in enumerate(self.input_blocks): - if isinstance(module, nn.Conv1d): - h_tok = F.interpolate(module(code_emb), size=(h.shape[-1]), mode='nearest') - h = h + h_tok - else: + with autocast(x.device.type, enabled=not first): + if isinstance(module, nn.Conv1d): + h_tok = F.interpolate(module(code_emb), size=(h.shape[-1]), mode='nearest') + h = h + h_tok + else: + h = module(h, time_emb) + hs.append(h) + first = True + with autocast(x.device.type): + h = self.middle_block(h, time_emb) + for module in self.output_blocks: + h = torch.cat([h, hs.pop()], dim=1) h = module(h, time_emb) - hs.append(h) - h = self.middle_block(h, time_emb) - for module in self.output_blocks: - h = torch.cat([h, hs.pop()], dim=1) - h = module(h, time_emb) - h = h.type(x.dtype) - out = self.out(h) + h = h.type(x.dtype) + h = h.float() + out = self.out(h) # Last block also has autocast disabled. return out[:, :, :orig_x_shape] diff --git a/codes/models/gpt_voice/unet_diffusion_tts4.py b/codes/models/gpt_voice/unet_diffusion_tts4.py new file mode 100644 index 00000000..3046e83c --- /dev/null +++ b/codes/models/gpt_voice/unet_diffusion_tts4.py @@ -0,0 +1,406 @@ +import functools +from collections import OrderedDict + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import autocast +from x_transformers.x_transformers import AbsolutePositionalEmbedding, AttentionLayers + +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.gpt_voice.mini_encoder import AudioMiniEncoder +from scripts.audio.gen.use_diffuse_tts import ceil_multiple +from trainer.networks import register_model +from utils.util import checkpoint +from x_transformers import Encoder, ContinuousTransformerWrapper + + +class CheckpointedLayer(nn.Module): + """ + Wraps a module. When forward() is called, passes kwargs that require_grad through torch.checkpoint() and bypasses + checkpoint for all other args. + """ + def __init__(self, wrap): + super().__init__() + self.wrap = wrap + + def forward(self, x, **kwargs): + kw_requires_grad = {} + kw_no_grad = {} + for k, v in kwargs.items(): + if v is not None and isinstance(v, torch.Tensor) and v.requires_grad: + kw_requires_grad[k] = v + else: + kw_no_grad[k] = v + partial = functools.partial(self.wrap, **kw_no_grad) + return torch.utils.checkpoint.checkpoint(partial, x, **kw_requires_grad) + + +class ResBlock(TimestepBlock): + def __init__( + self, + channels, + emb_channels, + dropout, + out_channels=None, + dims=2, + kernel_size=3, + ): + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.dropout = dropout + self.out_channels = out_channels or channels + padding = 1 if kernel_size == 3 else 2 + + self.in_layers = nn.Sequential( + normalization(channels), + nn.SiLU(), + conv_nd(dims, channels, self.out_channels, 1, padding=0), + ) + + self.emb_layers = nn.Sequential( + nn.SiLU(), + linear( + emb_channels, + 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, 1) + + 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] + h = h + emb_out + h = self.out_layers(h) + return self.skip_connection(x) + h + + +class DiffusionTts(nn.Module): + """ + The full UNet model with attention and timestep embedding. + + Customized to be conditioned on an aligned token prior. + + :param in_channels: channels in the input Tensor. + :param num_tokens: number of tokens (e.g. characters) which can be provided. + :param model_channels: base channel count for the model. + :param out_channels: channels in the output Tensor. + :param num_res_blocks: number of residual blocks per downsample. + :param attention_resolutions: a collection of downsample rates at which + attention will take place. May be a set, list, or tuple. + For example, if this contains 4, then at 4x downsampling, attention + will be used. + :param dropout: the dropout probability. + :param channel_mult: channel multiplier for each level of the UNet. + :param conv_resample: if True, use learned convolutions for upsampling and + downsampling. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param num_heads: the number of attention heads in each attention layer. + :param num_heads_channels: if specified, ignore num_heads and instead use + a fixed channel width per attention head. + :param num_heads_upsample: works with num_heads to set a different number + of heads for upsampling. Deprecated. + :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. + :param resblock_updown: use residual blocks for up/downsampling. + :param use_new_attention_order: use a different attention pattern for potentially + increased efficiency. + """ + + def __init__( + self, + model_channels, + in_channels=1, + num_tokens=32, + out_channels=2, # mean and variance + dropout=0, + # res 1, 2, 4, 8,16,32,64,128,256,512, 1K, 2K + channel_mult= (1,1.5,2, 3, 4, 6, 8, 12, 16, 24, 32, 48), + num_res_blocks=(1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2), + # spec_cond: 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0) + # attn: 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1 + token_conditioning_resolutions=(1,16,), + attention_resolutions=(512,1024,2048), + conv_resample=True, + dims=1, + use_fp16=False, + num_heads=1, + num_head_channels=-1, + num_heads_upsample=-1, + kernel_size=3, + scale_factor=2, + conditioning_inputs_provided=True, + time_embed_dim_multiplier=4, + nil_guidance_fwd_proportion=.3, + ): + super().__init__() + + if num_heads_upsample == -1: + num_heads_upsample = num_heads + + self.in_channels = in_channels + self.model_channels = model_channels + self.out_channels = out_channels + self.attention_resolutions = attention_resolutions + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.dtype = torch.float16 if use_fp16 else torch.float32 + self.num_heads = num_heads + self.num_head_channels = num_head_channels + self.num_heads_upsample = num_heads_upsample + self.dims = dims + self.nil_guidance_fwd_proportion = nil_guidance_fwd_proportion + self.mask_token_id = num_tokens + + padding = 1 if kernel_size == 3 else 2 + + time_embed_dim = model_channels * time_embed_dim_multiplier + self.time_embed = nn.Sequential( + linear(model_channels, time_embed_dim), + nn.SiLU(), + linear(time_embed_dim, time_embed_dim), + ) + + embedding_dim = model_channels * 4 + self.code_embedding = nn.Embedding(num_tokens+1, embedding_dim) + self.conditioning_enabled = conditioning_inputs_provided + if conditioning_inputs_provided: + self.contextual_embedder = AudioMiniEncoder(in_channels, embedding_dim, base_channels=32, depth=6, resnet_blocks=1, + attn_blocks=2, num_attn_heads=2, dropout=dropout, downsample_factor=4, kernel_size=5) + + self.input_blocks = nn.ModuleList( + [ + TimestepEmbedSequential( + conv_nd(dims, in_channels, model_channels, kernel_size, padding=padding) + ) + ] + ) + token_conditioning_blocks = [] + self._feature_size = model_channels + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + + class Permute(nn.Module): + def __init__(self): + super().__init__() + def forward(self, x): + return x.permute(0,2,1) + + for level, (mult, num_blocks) in enumerate(zip(channel_mult, num_res_blocks)): + if ds in token_conditioning_resolutions: + token_conditioning_block = nn.Conv1d(embedding_dim, ch, 1) + token_conditioning_block.weight.data *= .02 + self.input_blocks.append(token_conditioning_block) + token_conditioning_blocks.append(token_conditioning_block) + + for _ in range(num_blocks): + layers = [ + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=int(mult * model_channels), + dims=dims, + kernel_size=kernel_size, + ) + ] + ch = int(mult * model_channels) + if ds in attention_resolutions: + layers.append( + AttentionBlock( + ch, + num_heads=num_heads, + num_head_channels=num_head_channels, + ) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + TimestepEmbedSequential( + Downsample( + ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor, ksize=1, pad=0 + ) + ) + ) + ch = out_ch + input_block_chans.append(ch) + ds *= 2 + self._feature_size += ch + + self.middle_block = TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + kernel_size=kernel_size, + ), + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + kernel_size=kernel_size, + ), + ) + self._feature_size += ch + + self.output_blocks = nn.ModuleList([]) + for level, (mult, num_blocks) in list(enumerate(zip(channel_mult, num_res_blocks)))[::-1]: + for i in range(num_blocks + 1): + ich = input_block_chans.pop() + layers = [ + ResBlock( + ch + ich, + time_embed_dim, + dropout, + out_channels=int(model_channels * mult), + dims=dims, + kernel_size=kernel_size, + ) + ] + ch = int(model_channels * mult) + if ds in attention_resolutions: + layers.append( + AttentionBlock( + ch, + num_heads=num_heads_upsample, + num_head_channels=num_head_channels, + ) + ) + if level and i == num_blocks: + out_ch = ch + layers.append( + Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor) + ) + ds //= 2 + self.output_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + + self.out = nn.Sequential( + normalization(ch), + nn.SiLU(), + zero_module(conv_nd(dims, model_channels, out_channels, kernel_size, padding=padding)), + ) + + def load_state_dict(self, state_dict: 'OrderedDict[str, Tensor]', + strict: bool = True): + # Temporary hack to allow the addition of nil-guidance token embeddings to the existing guidance embeddings. + lsd = self.state_dict() + revised = 0 + for i, blk in enumerate(self.input_blocks): + if isinstance(blk, nn.Embedding): + key = f'input_blocks.{i}.weight' + if state_dict[key].shape[0] != lsd[key].shape[0]: + t = torch.randn_like(lsd[key]) * .02 + t[:state_dict[key].shape[0]] = state_dict[key] + state_dict[key] = t + revised += 1 + print(f"Loaded experimental unet_diffusion_net with {revised} modifications.") + return super().load_state_dict(state_dict, strict) + + + + def forward(self, x, timesteps, tokens, conditioning_input=None): + """ + 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 tokens: an aligned text input. + :return: an [N x C x ...] Tensor of outputs. + """ + with autocast(x.device.type): + orig_x_shape = x.shape[-1] + cm = ceil_multiple(x.shape[-1], 2048) + if cm != 0: + pc = (cm-x.shape[-1])/x.shape[-1] + x = F.pad(x, (0,cm-x.shape[-1])) + tokens = F.pad(tokens, (0,int(pc*tokens.shape[-1]))) + if self.conditioning_enabled: + assert conditioning_input is not None + + hs = [] + time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) + + # Mask out guidance tokens for un-guided diffusion. + if self.training and self.nil_guidance_fwd_proportion > 0: + token_mask = torch.rand(tokens.shape, device=tokens.device) < self.nil_guidance_fwd_proportion + tokens = torch.where(token_mask, self.mask_token_id, tokens) + code_emb = self.code_embedding(tokens).permute(0,2,1) + if self.conditioning_enabled: + cond_emb = self.contextual_embedder(conditioning_input) + code_emb = cond_emb.unsqueeze(-1) * code_emb + + first = False # First block has autocast disabled. + time_emb = time_emb.float() + h = x + for k, module in enumerate(self.input_blocks): + with autocast(x.device.type, enabled=not first): + if isinstance(module, nn.Conv1d): + h_tok = F.interpolate(module(code_emb), size=(h.shape[-1]), mode='nearest') + h = h + h_tok + else: + h = module(h, time_emb) + hs.append(h) + first = True + with autocast(x.device.type): + h = self.middle_block(h, time_emb) + for module in self.output_blocks: + h = torch.cat([h, hs.pop()], dim=1) + h = module(h, time_emb) + h = h.type(x.dtype) + h = h.float() + out = self.out(h) # Last block also has autocast disabled. + return out[:, :, :orig_x_shape] + + +@register_model +def register_diffusion_tts4(opt_net, opt): + return DiffusionTts(**opt_net['kwargs']) + + +# Test for ~4 second audio clip at 22050Hz +if __name__ == '__main__': + clip = torch.randn(4, 1, 86016) + tok = torch.randint(0,30, (4,388)) + cond = torch.randn(4, 1, 44000) + ts = torch.LongTensor([555, 556, 600, 600]) + model = DiffusionTts(64, channel_mult=[1,1.5,2, 3, 4, 6, 8, 8, 8, 8], num_res_blocks=[2, 2, 2, 2, 2, 2, 2, 4, 4, 4], + token_conditioning_resolutions=[1,4,16,64], attention_resolutions=[256,512], num_heads=4, kernel_size=3, + scale_factor=2, conditioning_inputs_provided=True, time_embed_dim_multiplier=4) + model(clip, ts, tok, cond) + diff --git a/codes/models/gpt_voice/unet_diffusion_tts5.py b/codes/models/gpt_voice/unet_diffusion_tts5.py new file mode 100644 index 00000000..66654ff1 --- /dev/null +++ b/codes/models/gpt_voice/unet_diffusion_tts5.py @@ -0,0 +1,460 @@ +import functools +from collections import OrderedDict + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import autocast +from x_transformers.x_transformers import AbsolutePositionalEmbedding, AttentionLayers + +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.gpt_voice.mini_encoder import AudioMiniEncoder +from scripts.audio.gen.use_diffuse_tts import ceil_multiple +from trainer.networks import register_model +from utils.util import checkpoint +from x_transformers import Encoder, ContinuousTransformerWrapper + + +class CheckpointedLayer(nn.Module): + """ + Wraps a module. When forward() is called, passes kwargs that require_grad through torch.checkpoint() and bypasses + checkpoint for all other args. + """ + def __init__(self, wrap): + super().__init__() + self.wrap = wrap + + def forward(self, x, **kwargs): + kw_requires_grad = {} + kw_no_grad = {} + for k, v in kwargs.items(): + if v is not None and isinstance(v, torch.Tensor) and v.requires_grad: + kw_requires_grad[k] = v + else: + kw_no_grad[k] = v + partial = functools.partial(self.wrap, **kw_no_grad) + return torch.utils.checkpoint.checkpoint(partial, x, **kw_requires_grad) + + +class CheckpointedXTransformerEncoder(nn.Module): + """ + Wraps a ContinuousTransformerWrapper and applies CheckpointedLayer to each layer and permutes from channels-mid + to channels-last that XTransformer expects. + """ + def __init__(self, **xtransformer_kwargs): + super().__init__() + self.transformer = ContinuousTransformerWrapper(**xtransformer_kwargs) + + for i in range(len(self.transformer.attn_layers.layers)): + n, b, r = self.transformer.attn_layers.layers[i] + self.transformer.attn_layers.layers[i] = nn.ModuleList([n, CheckpointedLayer(b), r]) + + def forward(self, x): + x = x.permute(0,2,1) + h = self.transformer(x) + return h.permute(0,2,1) + + +class ResBlock(TimestepBlock): + def __init__( + self, + channels, + emb_channels, + dropout, + out_channels=None, + dims=2, + kernel_size=3, + ): + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.dropout = dropout + self.out_channels = out_channels or channels + padding = 1 if kernel_size == 3 else 2 + + self.in_layers = nn.Sequential( + normalization(channels), + nn.SiLU(), + conv_nd(dims, channels, self.out_channels, 1, padding=0), + ) + + self.emb_layers = nn.Sequential( + nn.SiLU(), + linear( + emb_channels, + 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, 1) + + 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] + h = h + emb_out + h = self.out_layers(h) + return self.skip_connection(x) + h + + +class DiffusionTts(nn.Module): + """ + The full UNet model with attention and timestep embedding. + + Customized to be conditioned on an aligned token prior. + + :param in_channels: channels in the input Tensor. + :param num_tokens: number of tokens (e.g. characters) which can be provided. + :param model_channels: base channel count for the model. + :param out_channels: channels in the output Tensor. + :param num_res_blocks: number of residual blocks per downsample. + :param attention_resolutions: a collection of downsample rates at which + attention will take place. May be a set, list, or tuple. + For example, if this contains 4, then at 4x downsampling, attention + will be used. + :param dropout: the dropout probability. + :param channel_mult: channel multiplier for each level of the UNet. + :param conv_resample: if True, use learned convolutions for upsampling and + downsampling. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param num_heads: the number of attention heads in each attention layer. + :param num_heads_channels: if specified, ignore num_heads and instead use + a fixed channel width per attention head. + :param num_heads_upsample: works with num_heads to set a different number + of heads for upsampling. Deprecated. + :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. + :param resblock_updown: use residual blocks for up/downsampling. + :param use_new_attention_order: use a different attention pattern for potentially + increased efficiency. + """ + + def __init__( + self, + model_channels, + in_channels=1, + num_tokens=32, + out_channels=2, # mean and variance + dropout=0, + # res 1, 2, 4, 8,16,32,64,128,256,512, 1K, 2K + channel_mult= (1,1.5,2, 3, 4, 6, 8, 12, 16, 24, 32, 48), + num_res_blocks=(1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2), + # spec_cond: 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0) + # attn: 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1 + token_conditioning_resolutions=(1,16,), + attention_resolutions=(512,1024,2048), + conv_resample=True, + dims=1, + use_fp16=False, + num_heads=1, + num_head_channels=-1, + num_heads_upsample=-1, + kernel_size=3, + scale_factor=2, + conditioning_inputs_provided=True, + time_embed_dim_multiplier=4, + nil_guidance_fwd_proportion=.3, + ): + super().__init__() + + if num_heads_upsample == -1: + num_heads_upsample = num_heads + + self.in_channels = in_channels + self.model_channels = model_channels + self.out_channels = out_channels + self.attention_resolutions = attention_resolutions + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.dtype = torch.float16 if use_fp16 else torch.float32 + self.num_heads = num_heads + self.num_head_channels = num_head_channels + self.num_heads_upsample = num_heads_upsample + self.dims = dims + self.nil_guidance_fwd_proportion = nil_guidance_fwd_proportion + self.mask_token_id = num_tokens + + padding = 1 if kernel_size == 3 else 2 + + time_embed_dim = model_channels * time_embed_dim_multiplier + self.time_embed = nn.Sequential( + linear(model_channels, time_embed_dim), + nn.SiLU(), + linear(time_embed_dim, time_embed_dim), + ) + + embedding_dim = model_channels * 8 + self.code_embedding = nn.Embedding(num_tokens+1, embedding_dim) + self.conditioning_enabled = conditioning_inputs_provided + if conditioning_inputs_provided: + self.contextual_embedder = AudioMiniEncoder(in_channels, embedding_dim, base_channels=32, depth=6, resnet_blocks=1, + attn_blocks=2, num_attn_heads=2, dropout=dropout, downsample_factor=4, kernel_size=5) + self.conditioning_encoder = CheckpointedXTransformerEncoder( + max_seq_len=-1, # Should be unused + use_pos_emb=False, + attn_layers=Encoder( + dim=embedding_dim, + depth=8, + heads=num_heads, + ff_dropout=dropout, + attn_dropout=dropout, + use_rmsnorm=True, + ff_glu=True, + rotary_pos_emb=True, + ) + ) + + self.input_blocks = nn.ModuleList( + [ + TimestepEmbedSequential( + conv_nd(dims, in_channels, model_channels, kernel_size, padding=padding) + ) + ] + ) + token_conditioning_blocks = [] + self._feature_size = model_channels + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + + for level, (mult, num_blocks) in enumerate(zip(channel_mult, num_res_blocks)): + if ds in token_conditioning_resolutions: + token_conditioning_block = nn.Conv1d(embedding_dim, ch, 1) + token_conditioning_block.weight.data *= .02 + self.input_blocks.append(token_conditioning_block) + token_conditioning_blocks.append(token_conditioning_block) + + for _ in range(num_blocks): + layers = [ + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=int(mult * model_channels), + dims=dims, + kernel_size=kernel_size, + ) + ] + ch = int(mult * model_channels) + if ds in attention_resolutions: + layers.append( + AttentionBlock( + ch, + num_heads=num_heads, + num_head_channels=num_head_channels, + ) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + TimestepEmbedSequential( + Downsample( + ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor, ksize=1, pad=0 + ) + ) + ) + ch = out_ch + input_block_chans.append(ch) + ds *= 2 + self._feature_size += ch + + mid_transformer = CheckpointedXTransformerEncoder( + max_seq_len=-1, # Should be unused + use_pos_emb=False, + attn_layers=Encoder( + dim=ch, + depth=8, + heads=num_heads, + ff_dropout=dropout, + attn_dropout=dropout, + use_rmsnorm=True, + ff_glu=True, + rotary_pos_emb=True, + ) + ) + + + self.middle_block = TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + kernel_size=kernel_size, + ), + mid_transformer, + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + kernel_size=kernel_size, + ), + ) + self._feature_size += ch + + self.output_blocks = nn.ModuleList([]) + for level, (mult, num_blocks) in list(enumerate(zip(channel_mult, num_res_blocks)))[::-1]: + for i in range(num_blocks + 1): + ich = input_block_chans.pop() + layers = [ + ResBlock( + ch + ich, + time_embed_dim, + dropout, + out_channels=int(model_channels * mult), + dims=dims, + kernel_size=kernel_size, + ) + ] + ch = int(model_channels * mult) + if ds in attention_resolutions: + layers.append( + AttentionBlock( + ch, + num_heads=num_heads_upsample, + num_head_channels=num_head_channels, + ) + ) + if level and i == num_blocks: + out_ch = ch + layers.append( + Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor) + ) + ds //= 2 + self.output_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + + self.out = nn.Sequential( + normalization(ch), + nn.SiLU(), + zero_module(conv_nd(dims, model_channels, out_channels, kernel_size, padding=padding)), + ) + + def load_state_dict(self, state_dict: 'OrderedDict[str, Tensor]', + strict: bool = True): + # Temporary hack to allow the addition of nil-guidance token embeddings to the existing guidance embeddings. + lsd = self.state_dict() + revised = 0 + for i, blk in enumerate(self.input_blocks): + if isinstance(blk, nn.Embedding): + key = f'input_blocks.{i}.weight' + if state_dict[key].shape[0] != lsd[key].shape[0]: + t = torch.randn_like(lsd[key]) * .02 + t[:state_dict[key].shape[0]] = state_dict[key] + state_dict[key] = t + revised += 1 + print(f"Loaded experimental unet_diffusion_net with {revised} modifications.") + return super().load_state_dict(state_dict, strict) + + + + def forward(self, x, timesteps, tokens, conditioning_input=None): + """ + 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 tokens: an aligned text input. + :return: an [N x C x ...] Tensor of outputs. + """ + with autocast(x.device.type): + orig_x_shape = x.shape[-1] + cm = ceil_multiple(x.shape[-1], 2048) + if cm != 0: + pc = (cm-x.shape[-1])/x.shape[-1] + x = F.pad(x, (0,cm-x.shape[-1])) + tokens = F.pad(tokens, (0,int(pc*tokens.shape[-1]))) + if self.conditioning_enabled: + assert conditioning_input is not None + + hs = [] + time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) + + # Mask out guidance tokens for un-guided diffusion. + if self.training and self.nil_guidance_fwd_proportion > 0: + token_mask = torch.rand(tokens.shape, device=tokens.device) < self.nil_guidance_fwd_proportion + tokens = torch.where(token_mask, self.mask_token_id, tokens) + code_emb = self.code_embedding(tokens).permute(0,2,1) + if self.conditioning_enabled: + cond_emb = self.contextual_embedder(conditioning_input) + code_emb = cond_emb.unsqueeze(-1) * code_emb + code_emb = self.conditioning_encoder(code_emb) + + first = True + time_emb = time_emb.float() + h = x + for k, module in enumerate(self.input_blocks): + if isinstance(module, nn.Conv1d): + h_tok = F.interpolate(module(code_emb), size=(h.shape[-1]), mode='nearest') + h = h + h_tok + else: + with autocast(x.device.type, enabled=not first): + # First block has autocast disabled to allow a high precision signal to be properly vectorized. + h = module(h, time_emb) + hs.append(h) + first = False + h = self.middle_block(h, time_emb) + for module in self.output_blocks: + h = torch.cat([h, hs.pop()], dim=1) + h = module(h, time_emb) + + # Last block also has autocast disabled for high-precision outputs. + h = h.float() + out = self.out(h) + return out[:, :, :orig_x_shape] + + +@register_model +def register_diffusion_tts5(opt_net, opt): + return DiffusionTts(**opt_net['kwargs']) + + +# Test for ~4 second audio clip at 22050Hz +if __name__ == '__main__': + clip = torch.randn(2, 1, 32768) + tok = torch.randint(0,30, (2,388)) + cond = torch.randn(2, 1, 44000) + ts = torch.LongTensor([600, 600]) + model = DiffusionTts(128, + channel_mult=[1,1.5,2, 3, 4, 6, 8], + num_res_blocks=[2, 2, 2, 2, 2, 2, 1], + token_conditioning_resolutions=[1,4,16,64], + attention_resolutions=[], + num_heads=8, + kernel_size=3, + scale_factor=2, + conditioning_inputs_provided=True, + time_embed_dim_multiplier=4) + model(clip, ts, tok, cond) + torch.save(model.state_dict(), 'test_out.pth') + diff --git a/codes/models/lucidrains/dalle/transformer.py b/codes/models/lucidrains/dalle/transformer.py index 27f5f2bd..389e6849 100644 --- a/codes/models/lucidrains/dalle/transformer.py +++ b/codes/models/lucidrains/dalle/transformer.py @@ -146,6 +146,7 @@ class Transformer(nn.Module): ff_dropout = 0., attn_types = None, image_fmap_size = None, + oned_fmap_size = None, sparse_attn = False, stable = False, sandwich_norm = False, @@ -204,7 +205,7 @@ class Transformer(nn.Module): assert 'mlp' not in attn_types, 'you cannot use gMLPs if rotary embedding is turned on' rot_dim = dim_head // 3 - img_seq_len = (image_fmap_size ** 2) + img_seq_len = (image_fmap_size ** 2) if image_fmap_size is not None else oned_fmap_size text_len = seq_len - img_seq_len + 1 text_pos_emb = RotaryEmbedding(dim = rot_dim) @@ -214,7 +215,7 @@ class Transformer(nn.Module): img_to_text_freqs = text_pos_emb(torch.full((img_seq_len,), 8192)) # image is given a position far away from text text_freqs = torch.cat((text_freqs, img_to_text_freqs), dim = 0) - img_freqs_axial = img_axial_pos_emb(torch.linspace(-1, 1, steps = image_fmap_size)) + img_freqs_axial = img_axial_pos_emb(torch.linspace(-1, 1, steps = image_fmap_size if image_fmap_size is not None else oned_fmap_size)) img_freqs = broadcat((rearrange(img_freqs_axial, 'i d -> i () d'), rearrange(img_freqs_axial, 'j d -> () j d')), dim = -1) img_freqs = rearrange(img_freqs, 'h w d -> (h w) d') diff --git a/codes/train.py b/codes/train.py index a3c4edd2..0a159119 100644 --- a/codes/train.py +++ b/codes/train.py @@ -299,7 +299,7 @@ class Trainer: if __name__ == '__main__': parser = argparse.ArgumentParser() - parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../experiments/train_diffusion_tts_experimental_fp16/train_diffusion_tts.yml') + parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_diffusion_tts5_medium.yml') parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher') parser.add_argument('--local_rank', type=int, default=0) args = parser.parse_args()