diff --git a/codes/models/diffusion/unet_diffusion.py b/codes/models/diffusion/unet_diffusion.py index a2cd1744..940fc875 100644 --- a/codes/models/diffusion/unet_diffusion.py +++ b/codes/models/diffusion/unet_diffusion.py @@ -91,7 +91,7 @@ class Upsample(nn.Module): upsampling occurs in the inner-two dimensions. """ - def __init__(self, channels, use_conv, dims=2, out_channels=None, factor=None): + def __init__(self, channels, use_conv, dims=2, out_channels=None, factor=None, ksize=3, pad=1): super().__init__() self.channels = channels self.out_channels = out_channels or channels @@ -105,8 +105,6 @@ class Upsample(nn.Module): else: self.factor = factor if use_conv: - ksize = 3 - pad = 1 if dims == 1: ksize = 5 pad = 2 @@ -134,18 +132,22 @@ class Downsample(nn.Module): downsampling occurs in the inner-two dimensions. """ - def __init__(self, channels, use_conv, dims=2, out_channels=None, factor=None): + def __init__(self, channels, use_conv, dims=2, out_channels=None, factor=None, ksize=None, pad=None): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.dims = dims - ksize = 3 - pad = 1 + + if ksize is None: + ksize = 3 + pad = 1 + if dims == 1: + ksize = 5 + pad = 2 + if dims == 1: stride = 4 - ksize = 5 - pad = 2 elif dims == 2: stride = 2 else: @@ -201,7 +203,7 @@ class ResBlock(TimestepBlock): self.out_channels = out_channels or channels self.use_conv = use_conv self.use_scale_shift_norm = use_scale_shift_norm - padding = 1 if kernel_size == 3 else 2 + padding = 1 if kernel_size == 3 else (2 if kernel_size == 5 else 0) self.in_layers = nn.Sequential( normalization(channels), diff --git a/codes/models/gpt_voice/unet_diffusion_tts.py b/codes/models/gpt_voice/unet_diffusion_tts.py index 5f2d3cc6..331752ba 100644 --- a/codes/models/gpt_voice/unet_diffusion_tts.py +++ b/codes/models/gpt_voice/unet_diffusion_tts.py @@ -1,3 +1,6 @@ +import operator +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, ResBlock, TimestepEmbedSequential, \ Downsample, Upsample @@ -294,6 +297,34 @@ class DiffusionTts(nn.Module): h = h.type(x.dtype) return self.out(h) + def benchmark(self, x, timesteps, tokens, conditioning_input): + profile = OrderedDict() + hs = [] + emb1 = self.time_embed(timestep_embedding(timesteps, self.model_channels)) + from torchprofile import profile_macs + profile['contextual_embedder'] = profile_macs(self.contextual_embedder, args=(conditioning_input,)) + emb2 = self.contextual_embedder(conditioning_input) + emb = emb1 + emb2 + + h = x.type(self.dtype) + for k, module in enumerate(self.input_blocks): + if isinstance(module, nn.Embedding): + h_tok = F.interpolate(module(tokens).permute(0,2,1), size=(h.shape[-1]), mode='nearest') + h = h + h_tok + else: + profile[f'in_{k}'] = profile_macs(module, args=(h,emb)) + h = module(h, emb) + hs.append(h) + profile['middle'] = profile_macs(self.middle_block, args=(h,emb)) + h = self.middle_block(h, emb) + for k, module in enumerate(self.output_blocks): + h = torch.cat([h, hs.pop()], dim=1) + profile[f'out_{k}'] = profile_macs(module, args=(h,emb)) + h = module(h, emb) + h = h.type(x.dtype) + profile['out'] = profile_macs(self.out, args=(h,)) + return profile + @register_model def register_diffusion_tts(opt_net, opt): @@ -302,9 +333,15 @@ def register_diffusion_tts(opt_net, opt): # Test for ~4 second audio clip at 22050Hz if __name__ == '__main__': - clip = torch.randn(2, 1, 40960) - tok = torch.randint(0,30, (2,200)) - cond = torch.randn(2, 1, 40960) + clip = torch.randn(2, 1, 86016) + tok = torch.randint(0,30, (2,388)) + cond = torch.randn(2, 1, 44000) ts = torch.LongTensor([555, 556]) - model = DiffusionTts(32, conditioning_inputs_provided=True, time_embed_dim_multiplier=8) - print(model(clip, ts, tok, cond).shape) + 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, 2, 1, 1 ], + 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) + p = model.benchmark(clip, ts, tok, cond) + p = {k: v / 1000000000 for k, v in p.items()} + p = sorted(p.items(), key=operator.itemgetter(1)) + print(p) + print(sum([j[1] for j in p])) diff --git a/codes/models/gpt_voice/unet_diffusion_tts_experimental.py b/codes/models/gpt_voice/unet_diffusion_tts_experimental.py new file mode 100644 index 00000000..91d88986 --- /dev/null +++ b/codes/models/gpt_voice/unet_diffusion_tts_experimental.py @@ -0,0 +1,386 @@ +import operator +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 models.gpt_voice.mini_encoder import AudioMiniEncoder, EmbeddingCombiner +from trainer.networks import register_model +from utils.util import get_mask_from_lengths +from utils.util import checkpoint + + +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=30, + 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, + ): + 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 + + 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), + ) + + self.conditioning_enabled = conditioning_inputs_provided + if conditioning_inputs_provided: + self.contextual_embedder = AudioMiniEncoder(in_channels, time_embed_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 + + for level, (mult, num_blocks) in enumerate(zip(channel_mult, num_res_blocks)): + if ds in token_conditioning_resolutions: + token_conditioning_block = nn.Embedding(num_tokens, ch) + token_conditioning_block.weight.data.normal_(mean=0.0, std=.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, + ), + AttentionBlock( + ch, + num_heads=num_heads, + num_head_channels=num_head_channels, + ), + 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 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. + """ + assert x.shape[-1] % 4096 == 0 # This model operates at base//4096 at it's bottom levels, thus this requirement. + if self.conditioning_enabled: + assert conditioning_input is not None + + hs = [] + emb1 = self.time_embed(timestep_embedding(timesteps, self.model_channels)) + if self.conditioning_enabled: + emb2 = self.contextual_embedder(conditioning_input) + emb = emb1 + emb2 + else: + emb = emb1 + + h = x.type(self.dtype) + for k, module in enumerate(self.input_blocks): + if isinstance(module, nn.Embedding): + h_tok = F.interpolate(module(tokens).permute(0,2,1), size=(h.shape[-1]), mode='nearest') + h = h + h_tok + else: + h = module(h, emb) + hs.append(h) + h = self.middle_block(h, emb) + for module in self.output_blocks: + h = torch.cat([h, hs.pop()], dim=1) + h = module(h, emb) + h = h.type(x.dtype) + return self.out(h) + + def benchmark(self, x, timesteps, tokens, conditioning_input): + profile = OrderedDict() + params = OrderedDict() + hs = [] + emb1 = self.time_embed(timestep_embedding(timesteps, self.model_channels)) + from torchprofile import profile_macs + profile['contextual_embedder'] = profile_macs(self.contextual_embedder, args=(conditioning_input,)) + params['contextual_embedder'] = sum(p.numel() for p in self.contextual_embedder.parameters()) + emb2 = self.contextual_embedder(conditioning_input) + emb = emb1 + emb2 + + h = x.type(self.dtype) + for k, module in enumerate(self.input_blocks): + if isinstance(module, nn.Embedding): + h_tok = F.interpolate(module(tokens).permute(0,2,1), size=(h.shape[-1]), mode='nearest') + h = h + h_tok + else: + profile[f'in_{k}'] = profile_macs(module, args=(h,emb)) + params[f'in_{k}'] = sum(p.numel() for p in module.parameters()) + h = module(h, emb) + hs.append(h) + profile['middle'] = profile_macs(self.middle_block, args=(h,emb)) + params['middle'] = sum(p.numel() for p in self.middle_block.parameters()) + h = self.middle_block(h, emb) + for k, module in enumerate(self.output_blocks): + h = torch.cat([h, hs.pop()], dim=1) + profile[f'out_{k}'] = profile_macs(module, args=(h,emb)) + params[f'out_{k}'] = sum(p.numel() for p in module.parameters()) + h = module(h, emb) + h = h.type(x.dtype) + profile['out'] = profile_macs(self.out, args=(h,)) + params['out'] = sum(p.numel() for p in self.out.parameters()) + return profile, params + + +@register_model +def register_diffusion_tts_experimental(opt_net, opt): + return DiffusionTts(**opt_net['kwargs']) + + +# Test for ~4 second audio clip at 22050Hz +if __name__ == '__main__': + clip = torch.randn(2, 1, 86016) + tok = torch.randint(0,30, (2,388)) + cond = torch.randn(2, 1, 44000) + ts = torch.LongTensor([555, 556]) + 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) + p, r = model.benchmark(clip, ts, tok, cond) + p = {k: v / 1000000000 for k, v in p.items()} + p = sorted(p.items(), key=operator.itemgetter(1)) + print("Computational complexity:") + print(p) + print(sum([j[1] for j in p])) + print() + print("Memory complexity:") + r = {k: v / 1000000 for k, v in r.items()} + r = sorted(r.items(), key=operator.itemgetter(1)) + print(r) + print(sum([j[1] for j in r])) + diff --git a/codes/scripts/audio/gen/use_diffuse_tts.py b/codes/scripts/audio/gen/use_diffuse_tts.py new file mode 100644 index 00000000..e69de29b