From 5a958b4f4b09b78afc97cf8f4244920c4d72eb29 Mon Sep 17 00:00:00 2001 From: James Betker Date: Thu, 27 Jan 2022 23:19:29 -0700 Subject: [PATCH] Initial commit --- .gitignore | 3 + README.md | 43 +- data/tokenizer.json | 1 + do_tts.py | 168 ++++ models/arch_util.py | 319 +++++++ models/discrete_diffusion_vocoder.py | 468 ++++++++++ models/lucidrains_dvae.py | 390 ++++++++ models/text_voice_clip.py | 125 +++ models/transformer.py | 219 +++++ models/unified_voice.py | 530 +++++++++++ requirements.txt | 7 + utils/audio.py | 44 + utils/diffusion.py | 1232 ++++++++++++++++++++++++++ utils/tokenizer.py | 173 ++++ 14 files changed, 3720 insertions(+), 2 deletions(-) create mode 100644 data/tokenizer.json create mode 100644 do_tts.py create mode 100644 models/arch_util.py create mode 100644 models/discrete_diffusion_vocoder.py create mode 100644 models/lucidrains_dvae.py create mode 100644 models/text_voice_clip.py create mode 100644 models/transformer.py create mode 100644 models/unified_voice.py create mode 100644 requirements.txt create mode 100644 utils/audio.py create mode 100644 utils/diffusion.py create mode 100644 utils/tokenizer.py diff --git a/.gitignore b/.gitignore index b6e4761..fbc6b1c 100644 --- a/.gitignore +++ b/.gitignore @@ -127,3 +127,6 @@ dmypy.json # Pyre type checker .pyre/ + +.idea/* +.models/* diff --git a/README.md b/README.md index 59fc2b9..b9e7d87 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,41 @@ -# tortoise-tts -A multi-voice TTS system trained with an emphasis on quality +# Tortoise-TTS + +Tortoise TTS is an experimental text-to-speech program that uses recent machine learning techniques to generate +high-quality speech samples. + +This repo contains all the code needed to run Tortoise TTS in inference mode. + +## What's in a name? + +I'm naming my speech-related repos after Mojave desert flora and fauna. Tortoise is a bit tongue in cheek: this model +is insanely slow. It leverages both an autoregressive speech alignment model and a diffusion model, both of which +are known for their slow inference. It also performs CLIP sampling, which slows things down even further. You can +expect ~5 seconds of speech to take ~30 seconds to produce on the latest hardware. Still, the results are pretty cool. + +## What the heck is this? + +Tortoise TTS is inspired by OpenAI's DALLE, applied to speech data. It is made up of 4 separate models that work together: + +First, an autoregressive transformer stack predicts discrete speech "tokens" given a text prompt. This model is very +similar to the GPT model used by DALLE, except it operates on speech data. + +Next, a CLIP model judges a batch of outputs from the autoregressive transformer against the provided text and stack +ranks the outputs according to most probable. You could use greedy or beam-search decoding but in my experience CLIP +decoding creates considerably better results. + +Next, the speech "tokens" are decoded into a low-quality MEL spectrogram using a VQVAE. + +Finally, the output of the VQVAE is further decoded by a UNet diffusion model into raw audio, which can be placed in +a wav file. + +## How do I use this? + + + +## How do I train this? + +Frankly - you don't. Building this model has been a labor of love for me, consuming most of my 6 RTX3090s worth of +resources for the better part of 6 months. It uses a dataset I've gathered, refined and transcribed that consists of +a lot of audio data which I cannot distribute because of copywrite or no open licenses. + +With that said, I'm willing to help you out if you really want to give it a shot. DM me. \ No newline at end of file diff --git a/data/tokenizer.json b/data/tokenizer.json new file mode 100644 index 0000000..a128f27 --- /dev/null +++ b/data/tokenizer.json @@ -0,0 +1 @@ +{"version":"1.0","truncation":null,"padding":null,"added_tokens":[{"id":0,"special":true,"content":"[STOP]","single_word":false,"lstrip":false,"rstrip":false,"normalized":false},{"id":1,"special":true,"content":"[UNK]","single_word":false,"lstrip":false,"rstrip":false,"normalized":false},{"id":2,"special":true,"content":"[SPACE]","single_word":false,"lstrip":false,"rstrip":false,"normalized":false}],"normalizer":null,"pre_tokenizer":{"type":"Whitespace"},"post_processor":null,"decoder":null,"model":{"type":"BPE","dropout":null,"unk_token":"[UNK]","continuing_subword_prefix":null,"end_of_word_suffix":null,"fuse_unk":false,"vocab":{"[STOP]":0,"[UNK]":1,"[SPACE]":2,"!":3,"'":4,"(":5,")":6,",":7,"-":8,".":9,"/":10,":":11,";":12,"?":13,"a":14,"b":15,"c":16,"d":17,"e":18,"f":19,"g":20,"h":21,"i":22,"j":23,"k":24,"l":25,"m":26,"n":27,"o":28,"p":29,"q":30,"r":31,"s":32,"t":33,"u":34,"v":35,"w":36,"x":37,"y":38,"z":39,"th":40,"in":41,"the":42,"an":43,"er":44,"ou":45,"re":46,"on":47,"at":48,"ed":49,"en":50,"to":51,"ing":52,"and":53,"is":54,"as":55,"al":56,"or":57,"of":58,"ar":59,"it":60,"es":61,"he":62,"st":63,"le":64,"om":65,"se":66,"be":67,"ad":68,"ow":69,"ly":70,"ch":71,"wh":72,"that":73,"you":74,"li":75,"ve":76,"ac":77,"ti":78,"ld":79,"me":80,"was":81,"gh":82,"id":83,"ll":84,"wi":85,"ent":86,"for":87,"ay":88,"ro":89,"ver":90,"ic":91,"her":92,"ke":93,"his":94,"no":95,"ut":96,"un":97,"ir":98,"lo":99,"we":100,"ri":101,"ha":102,"with":103,"ght":104,"out":105,"im":106,"ion":107,"all":108,"ab":109,"one":110,"ne":111,"ge":112,"ould":113,"ter":114,"mo":115,"had":116,"ce":117,"she":118,"go":119,"sh":120,"ur":121,"am":122,"so":123,"pe":124,"my":125,"de":126,"are":127,"but":128,"ome":129,"fr":130,"ther":131,"fe":132,"su":133,"do":134,"con":135,"te":136,"ain":137,"ere":138,"po":139,"if":140,"they":141,"us":142,"ag":143,"tr":144,"now":145,"oun":146,"this":147,"have":148,"not":149,"sa":150,"il":151,"up":152,"thing":153,"from":154,"ap":155,"him":156,"ack":157,"ation":158,"ant":159,"our":160,"op":161,"like":162,"ust":163,"ess":164,"bo":165,"ok":166,"ul":167,"ind":168,"ex":169,"com":170,"some":171,"there":172,"ers":173,"co":174,"res":175,"man":176,"ard":177,"pl":178,"wor":179,"way":180,"tion":181,"fo":182,"ca":183,"were":184,"by":185,"ate":186,"pro":187,"ted":188,"ound":189,"own":190,"would":191,"ts":192,"what":193,"qu":194,"ally":195,"ight":196,"ck":197,"gr":198,"when":199,"ven":200,"can":201,"ough":202,"ine":203,"end":204,"per":205,"ous":206,"od":207,"ide":208,"know":209,"ty":210,"very":211,"si":212,"ak":213,"who":214,"about":215,"ill":216,"them":217,"est":218,"red":219,"ye":220,"could":221,"ong":222,"your":223,"their":224,"em":225,"just":226,"other":227,"into":228,"any":229,"whi":230,"um":231,"tw":232,"ast":233,"der":234,"did":235,"ie":236,"been":237,"ace":238,"ink":239,"ity":240,"back":241,"ting":242,"br":243,"more":244,"ake":245,"pp":246,"then":247,"sp":248,"el":249,"use":250,"bl":251,"said":252,"over":253,"get":254},"merges":["t 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import UnifiedVoice +from utils.audio import load_audio +from utils.diffusion import SpacedDiffusion, space_timesteps, get_named_beta_schedule +from utils.tokenizer import VoiceBpeTokenizer + + +def load_discrete_vocoder_diffuser(trained_diffusion_steps=4000, desired_diffusion_steps=200): + """ + Helper function to load a GaussianDiffusion instance configured for use as a vocoder. + """ + return SpacedDiffusion(use_timesteps=space_timesteps(trained_diffusion_steps, [desired_diffusion_steps]), model_mean_type='epsilon', + model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', trained_diffusion_steps)) + + +def do_spectrogram_diffusion(diffusion_model, dvae_model, diffuser, mel_codes, conditioning_input, spectrogram_compression_factor=128): + """ + Uses the specified diffusion model and DVAE model to convert the provided MEL & conditioning inputs into an audio clip. + """ + with torch.no_grad(): + mel = dvae_model.decode(mel_codes)[0] + + # Pad MEL to multiples of 2048//spectrogram_compression_factor + msl = mel.shape[-1] + dsl = 2048 // spectrogram_compression_factor + gap = dsl - (msl % dsl) + if gap > 0: + mel = torch.nn.functional.pad(mel, (0, gap)) + + output_shape = (mel.shape[0], 1, mel.shape[-1] * spectrogram_compression_factor) + return diffuser.p_sample_loop(diffusion_model, output_shape, model_kwargs={'spectrogram': mel, 'conditioning_input': conditioning_input}) + + +def load_conditioning(path, sample_rate=22050, cond_length=44100): + rel_clip = load_audio(path, sample_rate) + gap = rel_clip.shape[-1] - cond_length + if gap < 0: + rel_clip = F.pad(rel_clip, pad=(0, abs(gap))) + elif gap > 0: + rand_start = random.randint(0, gap) + rel_clip = rel_clip[:, rand_start:rand_start + cond_length] + mel_clip = TorchMelSpectrogram()(rel_clip.unsqueeze(0)).squeeze(0) + return mel_clip.unsqueeze(0).cuda(), rel_clip.unsqueeze(0).cuda() + + +def fix_autoregressive_output(codes, stop_token): + """ + This function performs some padding on coded audio that fixes a mismatch issue between what the diffusion model was + trained on and what the autoregressive code generator creates (which has no padding or end). + This is highly specific to the DVAE being used, so this particular coding will not necessarily work if used with + a different DVAE. This can be inferred by feeding a audio clip padded with lots of zeros on the end through the DVAE + and copying out the last few codes. + + Failing to do this padding will produce speech with a harsh end that sounds like "BLAH" or similar. + """ + # Strip off the autoregressive stop token and add padding. + stop_token_indices = (codes == stop_token).nonzero() + if len(stop_token_indices) == 0: + print("No stop tokens found, enjoy that output of yours!") + return + else: + codes[stop_token_indices] = 83 + stm = stop_token_indices.min().item() + codes[stm:] = 83 + if stm - 3 < codes.shape[0]: + codes[-3] = 45 + codes[-2] = 45 + codes[-1] = 248 + + return codes + + +if __name__ == '__main__': + preselected_cond_voices = { + 'simmons': ['Y:\\clips\\books1\\754_Dan Simmons - The Rise Of Endymion 356 of 450\\00026.wav'], + 'news_girl': ['Y:\\clips\\podcasts-0\\8288_20210113-Is More Violence Coming_\\00022.wav', 'Y:\\clips\\podcasts-0\\8288_20210113-Is More Violence Coming_\\00016.wav'], + 'dan_carlin': ['Y:\\clips\\books1\\5_dchha06 Shield of the West\\00476.wav', 'Y:\\clips\\books1\\15_dchha16 Nazi Tidbits\\00036.wav'], + 'libri_test': ['Y:\\libritts\\test-clean\\672\\122797\\672_122797_000057_000002.wav'], + } + + parser = argparse.ArgumentParser() + parser.add_argument('-autoregressive_model_path', type=str, help='Autoregressive model checkpoint to load.', default='.models/unified_voice.pth') + parser.add_argument('-clip_model_path', type=str, help='CLIP model checkpoint to load.', default='.models/clip.pth') + parser.add_argument('-diffusion_model_path', type=str, help='Diffusion model checkpoint to load.', default='./models/diffusion_vocoder.pth') + parser.add_argument('-dvae_model_path', type=str, help='DVAE model checkpoint to load.', default='./models/dvae.pth') + parser.add_argument('-text', type=str, help='Text to speak.', default="I am a language model that has learned to speak.") + parser.add_argument('-cond_preset', type=str, help='Use a preset conditioning voice (defined above). Overrides cond_path.', default='dan_carlin') + parser.add_argument('-num_samples', type=int, help='How many total outputs the autoregressive transformer should produce.', default=32) + parser.add_argument('-num_batches', type=int, help='How many batches those samples should be produced over.', default=2) + parser.add_argument('-num_outputs', type=int, help='Number of outputs to produce.', default=2) + parser.add_argument('-output_path', type=str, help='Where to store outputs.', default='results/') + args = parser.parse_args() + os.makedirs(args.output_path, exist_ok=True) + + print("Loading GPT TTS..") + autoregressive = UnifiedVoice(max_mel_tokens=300, max_text_tokens=200, max_conditioning_inputs=2, layers=30, model_dim=1024, heads=16, number_text_tokens=256, start_text_token=255, checkpointing=False, train_solo_embeddings=False).eval() + autoregressive.load_state_dict(torch.load(args.autoregressive_model_path)) + stop_mel_token = autoregressive.stop_mel_token + + print("Loading data..") + tokenizer = VoiceBpeTokenizer() + text = torch.IntTensor(tokenizer.encode(args.text)).unsqueeze(0).cuda() + text = F.pad(text, (0,1)) # This may not be necessary. + cond_paths = preselected_cond_voices[args.cond_preset] + conds = [] + for cond_path in cond_paths: + c, cond_wav = load_conditioning(cond_path, cond_length=132300) + conds.append(c) + conds = torch.stack(conds, dim=1) # And just use the last cond_wav for the diffusion model. + + with torch.no_grad(): + print("Performing GPT inference..") + samples = [] + for b in tqdm(range(args.num_batches)): + codes = autoregressive.inference_speech(conds, text, num_beams=1, repetition_penalty=1.0, do_sample=True, top_k=50, top_p=.95, + temperature=.9, num_return_sequences=args.num_samples//args.num_batches, length_penalty=1) + padding_needed = 250 - codes.shape[1] + codes = F.pad(codes, (0, padding_needed), value=stop_mel_token) + samples.append(codes) + samples = torch.cat(samples, dim=0) + del autoregressive + + print("Loading CLIP..") + clip = VoiceCLIP(dim_text=512, dim_speech=512, dim_latent=512, num_text_tokens=256, text_enc_depth=8, text_seq_len=120, text_heads=8, + num_speech_tokens=8192, speech_enc_depth=10, speech_heads=8, speech_seq_len=250).eval() + clip.load_state_dict(torch.load(args.clip_model_path)) + print("Performing CLIP filtering..") + for i in range(samples.shape[0]): + samples[i] = fix_autoregressive_output(samples[i], stop_mel_token) + clip_results = clip(text.repeat(samples.shape[0], 1), + torch.full((samples.shape[0],), fill_value=text.shape[1]-1, dtype=torch.long, device='cuda'), + samples, torch.full((samples.shape[0],), fill_value=samples.shape[1]*1024, dtype=torch.long, device='cuda'), + return_loss=False) + best_results = samples[torch.topk(clip_results, k=args.num_outputs).indices] + + # Delete the autoregressive and clip models to free up GPU memory + del samples, clip + + print("Loading DVAE..") + dvae = DiscreteVAE(positional_dims=1, channels=80, hidden_dim=512, num_resnet_blocks=3, codebook_dim=512, num_tokens=8192, num_layers=2, + record_codes=True, kernel_size=3, use_transposed_convs=False).eval() + dvae.load_state_dict(torch.load(args.dvae_model_path)) + print("Loading Diffusion Model..") + diffusion = DiscreteDiffusionVocoder(model_channels=128, dvae_dim=80, channel_mult=[1, 1, 1.5, 2, 3, 4, 6, 8, 8, 8, 8], num_res_blocks=[1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1], + spectrogram_conditioning_resolutions=[2,512], attention_resolutions=[512,1024], num_heads=4, kernel_size=3, scale_factor=2, + conditioning_inputs_provided=True, time_embed_dim_multiplier=4).eval() + diffusion.load_state_dict(torch.load(args.diffusion_model_path)) + diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=100) + + print("Performing vocoding..") + # Perform vocoding on each batch element separately: Vocoding is very memory (and compute!) intensive. + for b in range(best_results.shape[0]): + code = best_results[b].unsqueeze(0) + wav = do_spectrogram_diffusion(diffusion, dvae, diffuser, code, cond_wav, spectrogram_compression_factor=256) + torchaudio.save(os.path.join(args.output_path, f'gpt_tts_output_{b}.wav'), wav.squeeze(0).cpu(), 22050) diff --git a/models/arch_util.py b/models/arch_util.py new file mode 100644 index 0000000..ea2c214 --- /dev/null +++ b/models/arch_util.py @@ -0,0 +1,319 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchaudio + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +class GroupNorm32(nn.GroupNorm): + def forward(self, x): + return super().forward(x.float()).type(x.dtype) + + +def normalization(channels): + """ + Make a standard normalization layer. + + :param channels: number of input channels. + :return: an nn.Module for normalization. + """ + groups = 32 + if channels <= 16: + groups = 8 + elif channels <= 64: + groups = 16 + while channels % groups != 0: + groups = int(groups / 2) + assert groups > 2 + return GroupNorm32(groups, channels) + + +class QKVAttentionLegacy(nn.Module): + """ + A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping + """ + + def __init__(self, n_heads): + super().__init__() + self.n_heads = n_heads + + def forward(self, qkv, mask=None): + """ + Apply QKV attention. + + :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs. + :return: an [N x (H * C) x T] tensor after attention. + """ + bs, width, length = qkv.shape + assert width % (3 * self.n_heads) == 0 + ch = width // (3 * self.n_heads) + q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1) + scale = 1 / math.sqrt(math.sqrt(ch)) + weight = torch.einsum( + "bct,bcs->bts", q * scale, k * scale + ) # More stable with f16 than dividing afterwards + weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) + if mask is not None: + # The proper way to do this is to mask before the softmax using -inf, but that doesn't work properly on CPUs. + mask = mask.repeat(self.n_heads, 1).unsqueeze(1) + weight = weight * mask + a = torch.einsum("bts,bcs->bct", weight, v) + + return a.reshape(bs, -1, length) + + +class AttentionBlock(nn.Module): + """ + An attention block that allows spatial positions to attend to each other. + + Originally ported from here, but adapted to the N-d case. + https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66. + """ + + def __init__( + self, + channels, + num_heads=1, + num_head_channels=-1, + ): + super().__init__() + self.channels = channels + if num_head_channels == -1: + self.num_heads = num_heads + else: + assert ( + channels % num_head_channels == 0 + ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}" + self.num_heads = channels // num_head_channels + self.norm = normalization(channels) + self.qkv = nn.Conv1d(channels, channels * 3, 1) + self.attention = QKVAttentionLegacy(self.num_heads) + + self.proj_out = zero_module(nn.Conv1d(channels, channels, 1)) + + def forward(self, x, mask=None): + if mask is not None: + return self._forward(x, mask) + else: + return self._forward(x) + + def _forward(self, x, mask=None): + b, c, *spatial = x.shape + x = x.reshape(b, c, -1) + qkv = self.qkv(self.norm(x)) + h = self.attention(qkv, mask) + h = self.proj_out(h) + return (x + h).reshape(b, c, *spatial) + + +class Upsample(nn.Module): + """ + An upsampling layer with an optional convolution. + + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + """ + + def __init__(self, channels, use_conv, out_channels=None, factor=4): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.factor = factor + if use_conv: + ksize = 5 + pad = 2 + self.conv = nn.Conv1d(self.channels, self.out_channels, ksize, padding=pad) + + def forward(self, x): + assert x.shape[1] == self.channels + x = F.interpolate(x, scale_factor=self.factor, mode="nearest") + if self.use_conv: + x = self.conv(x) + return x + + +class Downsample(nn.Module): + """ + A downsampling layer with an optional convolution. + + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + """ + + def __init__(self, channels, use_conv, out_channels=None, factor=4, ksize=5, pad=2): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + + stride = factor + if use_conv: + self.op = nn.Conv1d( + self.channels, self.out_channels, ksize, stride=stride, padding=pad + ) + else: + assert self.channels == self.out_channels + self.op = nn.AvgPool1d(kernel_size=stride, stride=stride) + + def forward(self, x): + assert x.shape[1] == self.channels + return self.op(x) + + +class ResBlock(nn.Module): + def __init__( + self, + channels, + dropout, + out_channels=None, + use_conv=False, + use_scale_shift_norm=False, + up=False, + down=False, + kernel_size=3, + ): + super().__init__() + self.channels = channels + self.dropout = dropout + 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 + + self.in_layers = nn.Sequential( + normalization(channels), + nn.SiLU(), + nn.Conv1d(channels, self.out_channels, kernel_size, padding=padding), + ) + + self.updown = up or down + + if up: + self.h_upd = Upsample(channels, False) + self.x_upd = Upsample(channels, False) + elif down: + self.h_upd = Downsample(channels, False) + self.x_upd = Downsample(channels, False) + else: + self.h_upd = self.x_upd = nn.Identity() + + self.out_layers = nn.Sequential( + normalization(self.out_channels), + nn.SiLU(), + nn.Dropout(p=dropout), + zero_module( + nn.Conv1d(self.out_channels, self.out_channels, kernel_size, padding=padding) + ), + ) + + if self.out_channels == channels: + self.skip_connection = nn.Identity() + elif use_conv: + self.skip_connection = nn.Conv1d( + channels, self.out_channels, kernel_size, padding=padding + ) + else: + self.skip_connection = nn.Conv1d(channels, self.out_channels, 1) + + def forward(self, x): + if self.updown: + in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] + h = in_rest(x) + h = self.h_upd(h) + x = self.x_upd(x) + h = in_conv(h) + else: + h = self.in_layers(x) + h = self.out_layers(h) + return self.skip_connection(x) + h + + +class AudioMiniEncoder(nn.Module): + def __init__(self, + spec_dim, + embedding_dim, + base_channels=128, + depth=2, + resnet_blocks=2, + attn_blocks=4, + num_attn_heads=4, + dropout=0, + downsample_factor=2, + kernel_size=3): + super().__init__() + self.init = nn.Sequential( + nn.Conv1d(spec_dim, base_channels, 3, padding=1) + ) + ch = base_channels + res = [] + for l in range(depth): + for r in range(resnet_blocks): + res.append(ResBlock(ch, dropout, kernel_size=kernel_size)) + res.append(Downsample(ch, use_conv=True, out_channels=ch*2, factor=downsample_factor)) + ch *= 2 + self.res = nn.Sequential(*res) + self.final = nn.Sequential( + normalization(ch), + nn.SiLU(), + nn.Conv1d(ch, embedding_dim, 1) + ) + attn = [] + for a in range(attn_blocks): + attn.append(AttentionBlock(embedding_dim, num_attn_heads,)) + self.attn = nn.Sequential(*attn) + self.dim = embedding_dim + + def forward(self, x): + h = self.init(x) + h = self.res(h) + h = self.final(h) + h = self.attn(h) + return h[:, :, 0] + + +class TorchMelSpectrogram(nn.Module): + def __init__(self, filter_length=1024, hop_length=256, win_length=1024, n_mel_channels=80, mel_fmin=0, mel_fmax=8000, + sampling_rate=22050, normalize=False, mel_norm_file='data/mel_norms.pth'): + super().__init__() + # These are the default tacotron values for the MEL spectrogram. + self.filter_length = filter_length + self.hop_length = hop_length + self.win_length = win_length + self.n_mel_channels = n_mel_channels + self.mel_fmin = mel_fmin + self.mel_fmax = mel_fmax + self.sampling_rate = sampling_rate + self.mel_stft = torchaudio.transforms.MelSpectrogram(n_fft=self.filter_length, hop_length=self.hop_length, + win_length=self.win_length, power=2, normalized=normalize, + sample_rate=self.sampling_rate, f_min=self.mel_fmin, + f_max=self.mel_fmax, n_mels=self.n_mel_channels, + norm="slaney") + self.mel_norm_file = mel_norm_file + if self.mel_norm_file is not None: + self.mel_norms = torch.load(self.mel_norm_file) + else: + self.mel_norms = None + + def forward(self, inp): + if len(inp.shape) == 3: # Automatically squeeze out the channels dimension if it is present (assuming mono-audio) + inp = inp.squeeze(1) + assert len(inp.shape) == 2 + self.mel_stft = self.mel_stft.to(inp.device) + mel = self.mel_stft(inp) + # Perform dynamic range compression + mel = torch.log(torch.clamp(mel, min=1e-5)) + if self.mel_norms is not None: + self.mel_norms = self.mel_norms.to(mel.device) + mel = mel / self.mel_norms.unsqueeze(0).unsqueeze(-1) + return mel \ No newline at end of file diff --git a/models/discrete_diffusion_vocoder.py b/models/discrete_diffusion_vocoder.py new file mode 100644 index 0000000..6fe6053 --- /dev/null +++ b/models/discrete_diffusion_vocoder.py @@ -0,0 +1,468 @@ +""" +This model is based on OpenAI's UNet from improved diffusion, with modifications to support a MEL conditioning signal +and an audio conditioning input. It has also been simplified somewhat. +Credit: https://github.com/openai/improved-diffusion +""" + + +import math +from abc import abstractmethod + +import torch +import torch.nn as nn + +from models.arch_util import normalization, zero_module, Downsample, Upsample, AudioMiniEncoder, AttentionBlock + + +def timestep_embedding(timesteps, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings. + + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an [N x dim] Tensor of positional embeddings. + """ + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half + ).to(device=timesteps.device) + args = timesteps[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + return embedding + + +class TimestepBlock(nn.Module): + """ + Any module where forward() takes timestep embeddings as a second argument. + """ + + @abstractmethod + def forward(self, x, emb): + """ + Apply the module to `x` given `emb` timestep embeddings. + """ + + +class TimestepEmbedSequential(nn.Sequential, TimestepBlock): + """ + A sequential module that passes timestep embeddings to the children that + support it as an extra input. + """ + + def forward(self, x, emb): + for layer in self: + if isinstance(layer, TimestepBlock): + x = layer(x, emb) + else: + x = layer(x) + return x + + +class TimestepResBlock(TimestepBlock): + """ + A residual block that can optionally change the number of channels. + + :param channels: the number of input channels. + :param emb_channels: the number of timestep embedding channels. + :param dropout: the rate of dropout. + :param out_channels: if specified, the number of out channels. + :param use_conv: if True and out_channels is specified, use a spatial + convolution instead of a smaller 1x1 convolution to change the + channels in the skip connection. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param up: if True, use this block for upsampling. + :param down: if True, use this block for downsampling. + """ + + def __init__( + self, + channels, + emb_channels, + dropout, + out_channels=None, + use_conv=False, + use_scale_shift_norm=False, + up=False, + down=False, + kernel_size=3, + ): + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.dropout = dropout + 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 if kernel_size == 5 else 0) + + self.in_layers = nn.Sequential( + normalization(channels), + nn.SiLU(), + nn.Conv1d(channels, self.out_channels, kernel_size, padding=padding), + ) + + self.updown = up or down + + if up: + self.h_upd = Upsample(channels, False, dims) + self.x_upd = Upsample(channels, False, dims) + elif down: + self.h_upd = Downsample(channels, False, dims) + self.x_upd = Downsample(channels, False, dims) + else: + self.h_upd = self.x_upd = nn.Identity() + + self.emb_layers = nn.Sequential( + nn.SiLU(), + nn.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( + nn.Conv1d(self.out_channels, self.out_channels, kernel_size, padding=padding) + ), + ) + + if self.out_channels == channels: + self.skip_connection = nn.Identity() + elif use_conv: + self.skip_connection = nn.Conv1d( + channels, self.out_channels, kernel_size, padding=padding + ) + else: + self.skip_connection = nn.Conv1d(channels, self.out_channels, 1) + + def forward(self, x, emb): + if self.updown: + in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] + h = in_rest(x) + h = self.h_upd(h) + x = self.x_upd(x) + h = in_conv(h) + else: + 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 DiscreteSpectrogramConditioningBlock(nn.Module): + def __init__(self, dvae_channels, channels, level): + super().__init__() + self.intg = nn.Sequential(nn.Conv1d(dvae_channels, channels, kernel_size=1), + normalization(channels), + nn.SiLU(), + nn.Conv1d(channels, channels, kernel_size=3)) + self.level = level + + """ + Embeds the given codes and concatenates them onto x. Return shape is the same as x.shape. + + :param x: bxcxS waveform latent + :param codes: bxN discrete codes, N <= S + """ + def forward(self, x, dvae_in): + b, c, S = x.shape + _, q, N = dvae_in.shape + emb = self.intg(dvae_in) + emb = nn.functional.interpolate(emb, size=(S,), mode='nearest') + return torch.cat([x, emb], dim=1) + + +class DiscreteDiffusionVocoder(nn.Module): + """ + The full UNet model with attention and timestep embedding. + + Customized to be conditioned on a spectrogram prior. + + :param in_channels: channels in the input Tensor. + :param spectrogram_channels: channels in the conditioning spectrogram. + :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, + out_channels=2, # mean and variance + dvae_dim=512, + 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 + spectrogram_conditioning_resolutions=(512,), + attention_resolutions=(512,1024,2048), + conv_resample=True, + dims=1, + use_fp16=False, + num_heads=1, + num_head_channels=-1, + num_heads_upsample=-1, + use_scale_shift_norm=False, + resblock_updown=False, + 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( + nn.Linear(model_channels, time_embed_dim), + nn.SiLU(), + nn.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) + + seqlyr = TimestepEmbedSequential( + nn.Conv1d(in_channels, model_channels, kernel_size, padding=padding) + ) + seqlyr.level = 0 + self.input_blocks = nn.ModuleList([seqlyr]) + spectrogram_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 spectrogram_conditioning_resolutions: + spec_cond_block = DiscreteSpectrogramConditioningBlock(dvae_dim, ch, 2 ** level) + self.input_blocks.append(spec_cond_block) + spectrogram_blocks.append(spec_cond_block) + ch *= 2 + + for _ in range(num_blocks): + layers = [ + TimestepResBlock( + ch, + time_embed_dim, + dropout, + out_channels=int(mult * model_channels), + use_scale_shift_norm=use_scale_shift_norm, + 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, + ) + ) + layer = TimestepEmbedSequential(*layers) + layer.level = 2 ** level + self.input_blocks.append(layer) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + upblk = TimestepEmbedSequential( + TimestepResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + use_scale_shift_norm=use_scale_shift_norm, + down=True, + kernel_size=kernel_size, + ) + if resblock_updown + else Downsample( + ch, conv_resample, out_channels=out_ch, factor=scale_factor + ) + ) + upblk.level = 2 ** level + self.input_blocks.append(upblk) + ch = out_ch + input_block_chans.append(ch) + ds *= 2 + self._feature_size += ch + + self.middle_block = TimestepEmbedSequential( + TimestepResBlock( + ch, + time_embed_dim, + dropout, + use_scale_shift_norm=use_scale_shift_norm, + kernel_size=kernel_size, + ), + AttentionBlock( + ch, + num_heads=num_heads, + num_head_channels=num_head_channels, + ), + TimestepResBlock( + ch, + time_embed_dim, + dropout, + use_scale_shift_norm=use_scale_shift_norm, + 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 = [ + TimestepResBlock( + ch + ich, + time_embed_dim, + dropout, + out_channels=int(model_channels * mult), + use_scale_shift_norm=use_scale_shift_norm, + 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( + TimestepResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + use_scale_shift_norm=use_scale_shift_norm, + up=True, + kernel_size=kernel_size, + ) + if resblock_updown + else Upsample(ch, conv_resample, out_channels=out_ch, factor=scale_factor) + ) + ds //= 2 + layer = TimestepEmbedSequential(*layers) + layer.level = 2 ** level + self.output_blocks.append(layer) + self._feature_size += ch + + self.out = nn.Sequential( + normalization(ch), + nn.SiLU(), + zero_module(nn.Conv1d(model_channels, out_channels, kernel_size, padding=padding)), + ) + + def forward(self, x, timesteps, spectrogram, 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 y: an [N] Tensor of labels, if class-conditional. + :return: an [N x C x ...] Tensor of outputs. + """ + assert x.shape[-1] % 2048 == 0 # This model operates at base//2048 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, DiscreteSpectrogramConditioningBlock): + h = module(h, spectrogram) + 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) + + +# Test for ~4 second audio clip at 22050Hz +if __name__ == '__main__': + clip = torch.randn(2, 1, 40960) + spec = torch.randn(2,80,160) + cond = torch.randn(2, 1, 40960) + ts = torch.LongTensor([555, 556]) + model = DiscreteDiffusionVocoder(model_channels=128, channel_mult=[1, 1, 1.5, 2, 3, 4, 6, 8, 8, 8, 8], + num_res_blocks=[1,2, 2, 2, 2, 2, 2, 2, 2, 1, 1 ], spectrogram_conditioning_resolutions=[2,512], + dropout=.05, attention_resolutions=[512,1024], num_heads=4, kernel_size=3, scale_factor=2, + conditioning_inputs_provided=True, conditioning_input_dim=80, time_embed_dim_multiplier=4, + dvae_dim=80) + + print(model(clip, ts, spec, cond).shape) diff --git a/models/lucidrains_dvae.py b/models/lucidrains_dvae.py new file mode 100644 index 0000000..3465ba6 --- /dev/null +++ b/models/lucidrains_dvae.py @@ -0,0 +1,390 @@ +import functools +from math import sqrt + +import torch +import torch.distributed as distributed +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange + + +def default(val, d): + return val if val is not None else d + + +def eval_decorator(fn): + def inner(model, *args, **kwargs): + was_training = model.training + model.eval() + out = fn(model, *args, **kwargs) + model.train(was_training) + return out + return inner + + +# Quantizer implemented by the rosinality vqvae repo. +# Credit: https://github.com/rosinality/vq-vae-2-pytorch +class Quantize(nn.Module): + def __init__(self, dim, n_embed, decay=0.99, eps=1e-5, balancing_heuristic=False, new_return_order=False): + super().__init__() + + self.dim = dim + self.n_embed = n_embed + self.decay = decay + self.eps = eps + + self.balancing_heuristic = balancing_heuristic + self.codes = None + self.max_codes = 64000 + self.codes_full = False + self.new_return_order = new_return_order + + embed = torch.randn(dim, n_embed) + self.register_buffer("embed", embed) + self.register_buffer("cluster_size", torch.zeros(n_embed)) + self.register_buffer("embed_avg", embed.clone()) + + def forward(self, input, return_soft_codes=False): + if self.balancing_heuristic and self.codes_full: + h = torch.histc(self.codes, bins=self.n_embed, min=0, max=self.n_embed) / len(self.codes) + mask = torch.logical_or(h > .9, h < .01).unsqueeze(1) + ep = self.embed.permute(1,0) + ea = self.embed_avg.permute(1,0) + rand_embed = torch.randn_like(ep) * mask + self.embed = (ep * ~mask + rand_embed).permute(1,0) + self.embed_avg = (ea * ~mask + rand_embed).permute(1,0) + self.cluster_size = self.cluster_size * ~mask.squeeze() + if torch.any(mask): + print(f"Reset {torch.sum(mask)} embedding codes.") + self.codes = None + self.codes_full = False + + flatten = input.reshape(-1, self.dim) + dist = ( + flatten.pow(2).sum(1, keepdim=True) + - 2 * flatten @ self.embed + + self.embed.pow(2).sum(0, keepdim=True) + ) + soft_codes = -dist + _, embed_ind = soft_codes.max(1) + embed_onehot = F.one_hot(embed_ind, self.n_embed).type(flatten.dtype) + embed_ind = embed_ind.view(*input.shape[:-1]) + quantize = self.embed_code(embed_ind) + + if self.balancing_heuristic: + if self.codes is None: + self.codes = embed_ind.flatten() + else: + self.codes = torch.cat([self.codes, embed_ind.flatten()]) + if len(self.codes) > self.max_codes: + self.codes = self.codes[-self.max_codes:] + self.codes_full = True + + if self.training: + embed_onehot_sum = embed_onehot.sum(0) + embed_sum = flatten.transpose(0, 1) @ embed_onehot + + if distributed.is_initialized() and distributed.get_world_size() > 1: + distributed.all_reduce(embed_onehot_sum) + distributed.all_reduce(embed_sum) + + self.cluster_size.data.mul_(self.decay).add_( + embed_onehot_sum, alpha=1 - self.decay + ) + self.embed_avg.data.mul_(self.decay).add_(embed_sum, alpha=1 - self.decay) + n = self.cluster_size.sum() + cluster_size = ( + (self.cluster_size + self.eps) / (n + self.n_embed * self.eps) * n + ) + embed_normalized = self.embed_avg / cluster_size.unsqueeze(0) + self.embed.data.copy_(embed_normalized) + + diff = (quantize.detach() - input).pow(2).mean() + quantize = input + (quantize - input).detach() + + if return_soft_codes: + return quantize, diff, embed_ind, soft_codes.view(input.shape[:-1] + (-1,)) + elif self.new_return_order: + return quantize, embed_ind, diff + else: + return quantize, diff, embed_ind + + def embed_code(self, embed_id): + return F.embedding(embed_id, self.embed.transpose(0, 1)) + + +# Fits a soft-discretized input to a normal-PDF across the specified dimension. +# In other words, attempts to force the discretization function to have a mean equal utilization across all discrete +# values with the specified expected variance. +class DiscretizationLoss(nn.Module): + def __init__(self, discrete_bins, dim, expected_variance, store_past=0): + super().__init__() + self.discrete_bins = discrete_bins + self.dim = dim + self.dist = torch.distributions.Normal(0, scale=expected_variance) + if store_past > 0: + self.record_past = True + self.register_buffer("accumulator_index", torch.zeros(1, dtype=torch.long, device='cpu')) + self.register_buffer("accumulator_filled", torch.zeros(1, dtype=torch.long, device='cpu')) + self.register_buffer("accumulator", torch.zeros(store_past, discrete_bins)) + else: + self.record_past = False + + def forward(self, x): + other_dims = set(range(len(x.shape)))-set([self.dim]) + averaged = x.sum(dim=tuple(other_dims)) / x.sum() + averaged = averaged - averaged.mean() + + if self.record_past: + acc_count = self.accumulator.shape[0] + avg = averaged.detach().clone() + if self.accumulator_filled > 0: + averaged = torch.mean(self.accumulator, dim=0) * (acc_count-1) / acc_count + \ + averaged / acc_count + + # Also push averaged into the accumulator. + self.accumulator[self.accumulator_index] = avg + self.accumulator_index += 1 + if self.accumulator_index >= acc_count: + self.accumulator_index *= 0 + if self.accumulator_filled <= 0: + self.accumulator_filled += 1 + + return torch.sum(-self.dist.log_prob(averaged)) + + +class ResBlock(nn.Module): + def __init__(self, chan, conv, activation): + super().__init__() + self.net = nn.Sequential( + conv(chan, chan, 3, padding = 1), + activation(), + conv(chan, chan, 3, padding = 1), + activation(), + conv(chan, chan, 1) + ) + + def forward(self, x): + return self.net(x) + x + + +class UpsampledConv(nn.Module): + def __init__(self, conv, *args, **kwargs): + super().__init__() + assert 'stride' in kwargs.keys() + self.stride = kwargs['stride'] + del kwargs['stride'] + self.conv = conv(*args, **kwargs) + + def forward(self, x): + up = nn.functional.interpolate(x, scale_factor=self.stride, mode='nearest') + return self.conv(up) + + +# DiscreteVAE partially derived from lucidrains DALLE implementation +# Credit: https://github.com/lucidrains/DALLE-pytorch +class DiscreteVAE(nn.Module): + def __init__( + self, + positional_dims=2, + num_tokens = 512, + codebook_dim = 512, + num_layers = 3, + num_resnet_blocks = 0, + hidden_dim = 64, + channels = 3, + stride = 2, + kernel_size = 4, + use_transposed_convs = True, + encoder_norm = False, + activation = 'relu', + smooth_l1_loss = False, + straight_through = False, + normalization = None, # ((0.5,) * 3, (0.5,) * 3), + record_codes = False, + discretization_loss_averaging_steps = 100, + lr_quantizer_args = {}, + ): + super().__init__() + has_resblocks = num_resnet_blocks > 0 + + self.num_tokens = num_tokens + self.num_layers = num_layers + self.straight_through = straight_through + self.positional_dims = positional_dims + self.discrete_loss = DiscretizationLoss(num_tokens, 2, 1 / (num_tokens*2), discretization_loss_averaging_steps) + + assert positional_dims > 0 and positional_dims < 3 # This VAE only supports 1d and 2d inputs for now. + if positional_dims == 2: + conv = nn.Conv2d + conv_transpose = nn.ConvTranspose2d + else: + conv = nn.Conv1d + conv_transpose = nn.ConvTranspose1d + if not use_transposed_convs: + conv_transpose = functools.partial(UpsampledConv, conv) + + if activation == 'relu': + act = nn.ReLU + elif activation == 'silu': + act = nn.SiLU + else: + assert NotImplementedError() + + + enc_layers = [] + dec_layers = [] + + if num_layers > 0: + enc_chans = [hidden_dim * 2 ** i for i in range(num_layers)] + dec_chans = list(reversed(enc_chans)) + + enc_chans = [channels, *enc_chans] + + dec_init_chan = codebook_dim if not has_resblocks else dec_chans[0] + dec_chans = [dec_init_chan, *dec_chans] + + enc_chans_io, dec_chans_io = map(lambda t: list(zip(t[:-1], t[1:])), (enc_chans, dec_chans)) + + pad = (kernel_size - 1) // 2 + for (enc_in, enc_out), (dec_in, dec_out) in zip(enc_chans_io, dec_chans_io): + enc_layers.append(nn.Sequential(conv(enc_in, enc_out, kernel_size, stride = stride, padding = pad), act())) + if encoder_norm: + enc_layers.append(nn.GroupNorm(8, enc_out)) + dec_layers.append(nn.Sequential(conv_transpose(dec_in, dec_out, kernel_size, stride = stride, padding = pad), act())) + dec_out_chans = dec_chans[-1] + innermost_dim = dec_chans[0] + else: + enc_layers.append(nn.Sequential(conv(channels, hidden_dim, 1), act())) + dec_out_chans = hidden_dim + innermost_dim = hidden_dim + + for _ in range(num_resnet_blocks): + dec_layers.insert(0, ResBlock(innermost_dim, conv, act)) + enc_layers.append(ResBlock(innermost_dim, conv, act)) + + if num_resnet_blocks > 0: + dec_layers.insert(0, conv(codebook_dim, innermost_dim, 1)) + + + enc_layers.append(conv(innermost_dim, codebook_dim, 1)) + dec_layers.append(conv(dec_out_chans, channels, 1)) + + self.encoder = nn.Sequential(*enc_layers) + self.decoder = nn.Sequential(*dec_layers) + + self.loss_fn = F.smooth_l1_loss if smooth_l1_loss else F.mse_loss + self.codebook = Quantize(codebook_dim, num_tokens, new_return_order=True) + + # take care of normalization within class + self.normalization = normalization + self.record_codes = record_codes + if record_codes: + self.codes = torch.zeros((1228800,), dtype=torch.long) + self.code_ind = 0 + self.total_codes = 0 + self.internal_step = 0 + + def norm(self, images): + if not self.normalization is not None: + return images + + means, stds = map(lambda t: torch.as_tensor(t).to(images), self.normalization) + arrange = 'c -> () c () ()' if self.positional_dims == 2 else 'c -> () c ()' + means, stds = map(lambda t: rearrange(t, arrange), (means, stds)) + images = images.clone() + images.sub_(means).div_(stds) + return images + + def get_debug_values(self, step, __): + if self.record_codes and self.total_codes > 0: + # Report annealing schedule + return {'histogram_codes': self.codes[:self.total_codes]} + else: + return {} + + @torch.no_grad() + @eval_decorator + def get_codebook_indices(self, images): + img = self.norm(images) + logits = self.encoder(img).permute((0,2,3,1) if len(img.shape) == 4 else (0,2,1)) + sampled, codes, _ = self.codebook(logits) + self.log_codes(codes) + return codes + + def decode( + self, + img_seq + ): + self.log_codes(img_seq) + if hasattr(self.codebook, 'embed_code'): + image_embeds = self.codebook.embed_code(img_seq) + else: + image_embeds = F.embedding(img_seq, self.codebook.codebook) + b, n, d = image_embeds.shape + + kwargs = {} + if self.positional_dims == 1: + arrange = 'b n d -> b d n' + else: + h = w = int(sqrt(n)) + arrange = 'b (h w) d -> b d h w' + kwargs = {'h': h, 'w': w} + image_embeds = rearrange(image_embeds, arrange, **kwargs) + images = [image_embeds] + for layer in self.decoder: + images.append(layer(images[-1])) + return images[-1], images[-2] + + def infer(self, img): + img = self.norm(img) + logits = self.encoder(img).permute((0,2,3,1) if len(img.shape) == 4 else (0,2,1)) + sampled, codes, commitment_loss = self.codebook(logits) + return self.decode(codes) + + # Note: This module is not meant to be run in forward() except while training. It has special logic which performs + # evaluation using quantized values when it detects that it is being run in eval() mode, which will be substantially + # more lossy (but useful for determining network performance). + def forward( + self, + img + ): + img = self.norm(img) + logits = self.encoder(img).permute((0,2,3,1) if len(img.shape) == 4 else (0,2,1)) + sampled, codes, commitment_loss = self.codebook(logits) + sampled = sampled.permute((0,3,1,2) if len(img.shape) == 4 else (0,2,1)) + + if self.training: + out = sampled + for d in self.decoder: + out = d(out) + self.log_codes(codes) + else: + # This is non-differentiable, but gives a better idea of how the network is actually performing. + out, _ = self.decode(codes) + + # reconstruction loss + recon_loss = self.loss_fn(img, out, reduction='none') + + return recon_loss, commitment_loss, out + + def log_codes(self, codes): + # This is so we can debug the distribution of codes being learned. + if self.record_codes and self.internal_step % 10 == 0: + codes = codes.flatten() + l = codes.shape[0] + i = self.code_ind if (self.codes.shape[0] - self.code_ind) > l else self.codes.shape[0] - l + self.codes[i:i+l] = codes.cpu() + self.code_ind = self.code_ind + l + if self.code_ind >= self.codes.shape[0]: + self.code_ind = 0 + self.total_codes += 1 + self.internal_step += 1 + + +if __name__ == '__main__': + v = DiscreteVAE(channels=80, normalization=None, positional_dims=1, num_tokens=8192, codebook_dim=2048, + hidden_dim=512, num_resnet_blocks=3, kernel_size=3, num_layers=1, use_transposed_convs=False) + r,l,o=v(torch.randn(1,80,256)) + v.decode(torch.randint(0,8192,(1,256))) + print(o.shape, l.shape) diff --git a/models/text_voice_clip.py b/models/text_voice_clip.py new file mode 100644 index 0000000..31194ae --- /dev/null +++ b/models/text_voice_clip.py @@ -0,0 +1,125 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import einsum +from models.transformer import Transformer + + +def exists(val): + return val is not None + + +def masked_mean(t, mask, dim = 1): + t = t.masked_fill(~mask[:, :, None], 0.) + return t.sum(dim = 1) / mask.sum(dim = 1)[..., None] + + +class VoiceCLIP(nn.Module): + """ + CLIP model retrofitted for performing contrastive evaluation between tokenized audio data and the corresponding + transcribed text. + + Originally from https://github.com/lucidrains/DALLE-pytorch/blob/main/dalle_pytorch/dalle_pytorch.py + """ + + def __init__( + self, + *, + dim_text=512, + dim_speech=512, + dim_latent=512, + num_text_tokens=256, + text_enc_depth=6, + text_seq_len=120, + text_heads=8, + num_speech_tokens=8192, + speech_enc_depth=6, + speech_heads=8, + speech_seq_len=250, + text_mask_percentage=0, + voice_mask_percentage=0, + wav_token_compression=1024, + ): + super().__init__() + self.text_emb = nn.Embedding(num_text_tokens, dim_text) + self.text_pos_emb = nn.Embedding(text_seq_len, dim_text) + self.text_transformer = Transformer(causal=False, seq_len=text_seq_len, dim=dim_text, depth=text_enc_depth, + heads=text_heads) + self.to_text_latent = nn.Linear(dim_text, dim_latent, bias=False) + + self.speech_emb = nn.Embedding(num_speech_tokens, dim_speech) + self.speech_pos_emb = nn.Embedding(num_speech_tokens, dim_speech) + self.speech_transformer = Transformer(causal=False, seq_len=speech_seq_len, dim=dim_speech, + depth=speech_enc_depth, heads=speech_heads) + self.to_speech_latent = nn.Linear(dim_speech, dim_latent, bias=False) + + self.temperature = nn.Parameter(torch.tensor(1.)) + self.text_mask_percentage = text_mask_percentage + self.voice_mask_percentage = voice_mask_percentage + self.wav_token_compression = wav_token_compression + + def forward( + self, + text, + text_lengths, + speech_tokens, + wav_lengths, + return_loss=False + ): + # This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by + # chopping the inputs by the maximum actual length. + max_text_len = text_lengths.max() + text = text[:, :max_text_len] + max_mel_len = wav_lengths.max() // self.wav_token_compression + speech_tokens = speech_tokens[:, :max_mel_len] + + b, device = text.shape[0], text.device + if self.training: + text_mask = torch.rand_like(text.float()) > self.text_mask_percentage + voice_mask = torch.rand_like(speech_tokens.float()) > self.voice_mask_percentage + else: + text_mask = torch.ones_like(text.float()).bool() + voice_mask = torch.ones_like(speech_tokens.float()).bool() + + text_emb = self.text_emb(text) + text_emb += self.text_pos_emb(torch.arange(text.shape[1], device=device)) + + speech_emb = self.speech_emb(speech_tokens) + speech_emb += self.speech_pos_emb(torch.arange(speech_emb.shape[1], device=device)) + + enc_text = self.text_transformer(text_emb, mask=text_mask) + enc_speech = self.speech_transformer(speech_emb, mask=voice_mask) + + text_latents = masked_mean(enc_text, text_mask, dim=1) + speech_latents = masked_mean(enc_speech, voice_mask, dim=1) + + text_latents = self.to_text_latent(text_latents) + speech_latents = self.to_speech_latent(speech_latents) + + text_latents, speech_latents = map(lambda t: F.normalize(t, p=2, dim=-1), (text_latents, speech_latents)) + + temp = self.temperature.exp() + + if not return_loss: + sim = einsum('n d, n d -> n', text_latents, speech_latents) * temp + return sim + + sim = einsum('i d, j d -> i j', text_latents, speech_latents) * temp + labels = torch.arange(b, device=device) + loss = (F.cross_entropy(sim, labels) + F.cross_entropy(sim.t(), labels)) / 2 + return loss + + +if __name__ == '__main__': + clip = VoiceCLIP(text_mask_percentage=.2, voice_mask_percentage=.2) + clip(torch.randint(0,256,(2,120)), + torch.tensor([50,100]), + torch.randint(0,8192,(2,250)), + torch.tensor([101,102]), + return_loss=True) + nonloss = clip(torch.randint(0,256,(2,120)), + torch.tensor([50,100]), + torch.randint(0,8192,(2,250)), + torch.tensor([101,102]), + return_loss=False) + print(nonloss.shape) \ No newline at end of file diff --git a/models/transformer.py b/models/transformer.py new file mode 100644 index 0000000..aa59b46 --- /dev/null +++ b/models/transformer.py @@ -0,0 +1,219 @@ +from functools import partial + +import torch +import torch.nn.functional as F +from einops import rearrange +from rotary_embedding_torch import RotaryEmbedding, broadcat +from torch import nn + + +# helpers + + +def exists(val): + return val is not None + + +def default(val, d): + return val if exists(val) else d + + +def cast_tuple(val, depth = 1): + if isinstance(val, list): + val = tuple(val) + return val if isinstance(val, tuple) else (val,) * depth + + +def max_neg_value(t): + return -torch.finfo(t.dtype).max + + +def stable_softmax(t, dim = -1, alpha = 32 ** 2): + t = t / alpha + t = t - torch.amax(t, dim = dim, keepdim = True).detach() + return (t * alpha).softmax(dim = dim) + + +def route_args(router, args, depth): + routed_args = [(dict(), dict()) for _ in range(depth)] + matched_keys = [key for key in args.keys() if key in router] + + for key in matched_keys: + val = args[key] + for depth, ((f_args, g_args), routes) in enumerate(zip(routed_args, router[key])): + new_f_args, new_g_args = map(lambda route: ({key: val} if route else {}), routes) + routed_args[depth] = ({**f_args, **new_f_args}, {**g_args, **new_g_args}) + return routed_args + + +# classes +class SequentialSequence(nn.Module): + def __init__(self, layers, args_route = {}, layer_dropout = 0.): + super().__init__() + assert all(len(route) == len(layers) for route in args_route.values()), 'each argument route map must have the same depth as the number of sequential layers' + self.layers = layers + self.args_route = args_route + self.layer_dropout = layer_dropout + + def forward(self, x, **kwargs): + args = route_args(self.args_route, kwargs, len(self.layers)) + layers_and_args = list(zip(self.layers, args)) + + for (f, g), (f_args, g_args) in layers_and_args: + x = x + f(x, **f_args) + x = x + g(x, **g_args) + return x + + +class DivideMax(nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + + def forward(self, x): + maxes = x.amax(dim = self.dim, keepdim = True).detach() + return x / maxes + + +# https://arxiv.org/abs/2103.17239 +class LayerScale(nn.Module): + def __init__(self, dim, depth, fn): + super().__init__() + if depth <= 18: + init_eps = 0.1 + elif depth > 18 and depth <= 24: + init_eps = 1e-5 + else: + init_eps = 1e-6 + + scale = torch.zeros(1, 1, dim).fill_(init_eps) + self.scale = nn.Parameter(scale) + self.fn = fn + def forward(self, x, **kwargs): + return self.fn(x, **kwargs) * self.scale + +# layer norm + + +class PreNorm(nn.Module): + def __init__(self, dim, fn, sandwich = False): + super().__init__() + self.norm = nn.LayerNorm(dim) + self.norm_out = nn.LayerNorm(dim) if sandwich else nn.Identity() + self.fn = fn + + def forward(self, x, **kwargs): + x = self.norm(x) + x = self.fn(x, **kwargs) + return self.norm_out(x) + +# feed forward + + +class GEGLU(nn.Module): + def forward(self, x): + x, gates = x.chunk(2, dim = -1) + return x * F.gelu(gates) + + +class FeedForward(nn.Module): + def __init__(self, dim, dropout = 0., mult = 4.): + super().__init__() + self.net = nn.Sequential( + nn.Linear(dim, dim * mult * 2), + GEGLU(), + nn.Dropout(dropout), + nn.Linear(dim * mult, dim) + ) + + def forward(self, x): + return self.net(x) + +# Attention + + +class Attention(nn.Module): + def __init__(self, dim, seq_len, causal = True, heads = 8, dim_head = 64, dropout = 0.): + super().__init__() + inner_dim = dim_head * heads + self.heads = heads + self.seq_len = seq_len + self.scale = dim_head ** -0.5 + + self.causal = causal + + self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False) + self.to_out = nn.Sequential( + nn.Linear(inner_dim, dim), + nn.Dropout(dropout) + ) + + def forward(self, x, mask = None): + b, n, _, h, device = *x.shape, self.heads, x.device + softmax = torch.softmax + + qkv = self.to_qkv(x).chunk(3, dim = -1) + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv) + + q = q * self.scale + + dots = torch.einsum('b h i d, b h j d -> b h i j', q, k) + mask_value = max_neg_value(dots) + + if exists(mask): + mask = rearrange(mask, 'b j -> b () () j') + dots.masked_fill_(~mask, mask_value) + del mask + + if self.causal: + i, j = dots.shape[-2:] + mask = torch.ones(i, j, device = device).triu_(j - i + 1).bool() + dots.masked_fill_(mask, mask_value) + + attn = softmax(dots, dim=-1) + + out = torch.einsum('b h i j, b h j d -> b h i d', attn, v) + out = rearrange(out, 'b h n d -> b n (h d)') + out = self.to_out(out) + return out + + +# main transformer class +class Transformer(nn.Module): + def __init__( + self, + *, + dim, + depth, + seq_len, + causal = True, + heads = 8, + dim_head = 64, + ff_mult = 4, + attn_dropout = 0., + ff_dropout = 0., + sparse_attn = False, + sandwich_norm = False, + ): + super().__init__() + layers = nn.ModuleList([]) + sparse_layer = cast_tuple(sparse_attn, depth) + + for ind, sparse_attn in zip(range(depth), sparse_layer): + attn = Attention(dim, causal = causal, seq_len = seq_len, heads = heads, dim_head = dim_head, dropout = attn_dropout) + + ff = FeedForward(dim, mult = ff_mult, dropout = ff_dropout) + + layers.append(nn.ModuleList([ + LayerScale(dim, ind + 1, PreNorm(dim, attn, sandwich = sandwich_norm)), + LayerScale(dim, ind + 1, PreNorm(dim, ff, sandwich = sandwich_norm)) + ])) + + execute_type = SequentialSequence + route_attn = ((True, False),) * depth + attn_route_map = {'mask': route_attn} + + self.layers = execute_type(layers, args_route = attn_route_map) + + def forward(self, x, **kwargs): + return self.layers(x, **kwargs) \ No newline at end of file diff --git a/models/unified_voice.py b/models/unified_voice.py new file mode 100644 index 0000000..b513246 --- /dev/null +++ b/models/unified_voice.py @@ -0,0 +1,530 @@ +import functools + +import torch +import torch.nn as nn +import torch.nn.functional as F +from transformers import GPT2Config, GPT2PreTrainedModel +from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions +from transformers.utils.model_parallel_utils import get_device_map, assert_device_map +from models.arch_util import AttentionBlock + + + +def null_position_embeddings(range, dim): + return torch.zeros((range.shape[0], range.shape[1], dim), device=range.device) + + +class ResBlock(nn.Module): + """ + Basic residual convolutional block that uses GroupNorm. + """ + def __init__(self, chan): + super().__init__() + self.net = nn.Sequential( + nn.Conv1d(chan, chan, kernel_size=3, padding=1), + nn.GroupNorm(chan//8, chan), + nn.ReLU(), + nn.Conv1d(chan, chan, kernel_size=3, padding=1), + nn.GroupNorm(chan//8, chan) + ) + + def forward(self, x): + return F.relu(self.net(x) + x) + + +class GPT2InferenceModel(GPT2PreTrainedModel): + def __init__(self, config, gpt, text_pos_emb, embeddings, norm, linear): + super().__init__(config) + self.transformer = gpt + self.text_pos_embedding = text_pos_emb + self.embeddings = embeddings + self.lm_head = nn.Sequential(norm, linear) + + # Model parallel + self.model_parallel = False + self.device_map = None + self.cached_mel_emb = None + + def parallelize(self, device_map=None): + self.device_map = ( + get_device_map(len(self.transformer.h), range(torch.cuda.device_count())) + if device_map is None + else device_map + ) + assert_device_map(self.device_map, len(self.transformer.h)) + self.transformer.parallelize(self.device_map) + self.lm_head = self.lm_head.to(self.transformer.first_device) + self.model_parallel = True + + def deparallelize(self): + self.transformer.deparallelize() + self.transformer = self.transformer.to("cpu") + self.lm_head = self.lm_head.to("cpu") + self.model_parallel = False + torch.cuda.empty_cache() + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def store_mel_emb(self, mel_emb): + self.cached_mel_emb = mel_emb + + def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs): + + token_type_ids = kwargs.get("token_type_ids", None) + # only last token for inputs_ids if past is defined in kwargs + if past: + input_ids = input_ids[:, -1].unsqueeze(-1) + if token_type_ids is not None: + token_type_ids = token_type_ids[:, -1].unsqueeze(-1) + + attention_mask = kwargs.get("attention_mask", None) + position_ids = kwargs.get("position_ids", None) + + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past: + position_ids = position_ids[:, -1].unsqueeze(-1) + else: + position_ids = None + return { + "input_ids": input_ids, + "past_key_values": past, + "use_cache": kwargs.get("use_cache"), + "position_ids": position_ids, + "attention_mask": attention_mask, + "token_type_ids": token_type_ids, + } + + def forward( + self, + input_ids=None, + past_key_values=None, + attention_mask=None, + token_type_ids=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + labels=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + ): + assert self.cached_mel_emb is not None + assert inputs_embeds is None # Not supported by this inference model. + assert labels is None # Training not supported by this inference model. + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # Create embedding + mel_len = self.cached_mel_emb.shape[1] + if input_ids.shape[1] != 1: + text_inputs = input_ids[:, mel_len:] + text_emb = self.embeddings(text_inputs) + text_emb = text_emb + self.text_pos_embedding(text_emb) + if self.cached_mel_emb.shape[0] != text_emb.shape[0]: + mel_emb = self.cached_mel_emb.repeat_interleave(text_emb.shape[0]//self.cached_mel_emb.shape[0], 0) + else: + mel_emb = self.cached_mel_emb + emb = torch.cat([mel_emb, text_emb], dim=1) + else: + emb = self.embeddings(input_ids) + emb = emb + self.text_pos_embedding.get_fixed_embedding(attention_mask.shape[1]-mel_len, attention_mask.device) + + transformer_outputs = self.transformer( + inputs_embeds=emb, + past_key_values=past_key_values, + attention_mask=attention_mask, + token_type_ids=token_type_ids, + position_ids=position_ids, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + + # Set device for model parallelism + if self.model_parallel: + torch.cuda.set_device(self.transformer.first_device) + hidden_states = hidden_states.to(self.lm_head.weight.device) + + lm_logits = self.lm_head(hidden_states) + + if not return_dict: + return (lm_logits,) + transformer_outputs[1:] + + return CausalLMOutputWithCrossAttentions( + loss=None, + logits=lm_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + cross_attentions=transformer_outputs.cross_attentions, + ) + + @staticmethod + def _reorder_cache(past, beam_idx): + """ + This function is used to re-order the :obj:`past_key_values` cache if + :meth:`~transformers.PreTrainedModel.beam_search` or :meth:`~transformers.PreTrainedModel.beam_sample` is + called. This is required to match :obj:`past_key_values` with the correct beam_idx at every generation step. + """ + return tuple( + tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past) + for layer_past in past + ) + + +class ConditioningEncoder(nn.Module): + def __init__(self, + spec_dim, + embedding_dim, + attn_blocks=6, + num_attn_heads=4, + do_checkpointing=False): + super().__init__() + attn = [] + self.init = nn.Conv1d(spec_dim, embedding_dim, kernel_size=1) + for a in range(attn_blocks): + attn.append(AttentionBlock(embedding_dim, num_attn_heads)) + self.attn = nn.Sequential(*attn) + self.dim = embedding_dim + self.do_checkpointing = do_checkpointing + + def forward(self, x): + h = self.init(x) + h = self.attn(h) + return h[:, :, 0] + + +class LearnedPositionEmbeddings(nn.Module): + def __init__(self, seq_len, model_dim, init=.02): + super().__init__() + self.emb = nn.Embedding(seq_len, model_dim) + # Initializing this way is standard for GPT-2 + self.emb.weight.data.normal_(mean=0.0, std=init) + + def forward(self, x): + sl = x.shape[1] + return self.emb(torch.arange(0, sl, device=x.device)) + + def get_fixed_embedding(self, ind, dev): + return self.emb(torch.tensor([ind], device=dev)).unsqueeze(0) + + +def build_hf_gpt_transformer(layers, model_dim, heads, max_mel_seq_len, max_text_seq_len, checkpointing): + """ + GPT-2 implemented by the HuggingFace library. + """ + from transformers import GPT2Config, GPT2Model + gpt_config = GPT2Config(vocab_size=256, # Unused. + n_positions=max_mel_seq_len+max_text_seq_len, + n_ctx=max_mel_seq_len+max_text_seq_len, + n_embd=model_dim, + n_layer=layers, + n_head=heads, + gradient_checkpointing=checkpointing, + use_cache=not checkpointing) + gpt = GPT2Model(gpt_config) + # Override the built in positional embeddings + del gpt.wpe + gpt.wpe = functools.partial(null_position_embeddings, dim=model_dim) + # Built-in token embeddings are unused. + del gpt.wte + return gpt, LearnedPositionEmbeddings(max_mel_seq_len, model_dim), LearnedPositionEmbeddings(max_text_seq_len, model_dim),\ + None, None + + +class MelEncoder(nn.Module): + def __init__(self, channels, mel_channels=80, resblocks_per_reduction=2): + super().__init__() + self.channels = channels + self.encoder = nn.Sequential(nn.Conv1d(mel_channels, channels//4, kernel_size=3, padding=1), + nn.Sequential(*[ResBlock(channels//4) for _ in range(resblocks_per_reduction)]), + nn.Conv1d(channels//4, channels//2, kernel_size=3, stride=2, padding=1), + nn.GroupNorm(channels//16, channels//2), + nn.ReLU(), + nn.Sequential(*[ResBlock(channels//2) for _ in range(resblocks_per_reduction)]), + nn.Conv1d(channels//2, channels, kernel_size=3, stride=2, padding=1), + nn.GroupNorm(channels//8, channels), + nn.ReLU(), + nn.Sequential(*[ResBlock(channels) for _ in range(resblocks_per_reduction)]), + ) + self.reduction = 4 + + + def forward(self, x): + for e in self.encoder: + x = e(x) + return x.permute(0,2,1) + + +class UnifiedVoice(nn.Module): + def __init__(self, layers=8, model_dim=512, heads=8, max_text_tokens=120, max_mel_tokens=250, max_conditioning_inputs=1, + mel_length_compression=1024, number_text_tokens=256, + start_text_token=255, stop_text_token=0, number_mel_codes=8194, start_mel_token=8192, + stop_mel_token=8193, train_solo_embeddings=False, use_mel_codes_as_input=True, + checkpointing=True): + """ + Args: + layers: Number of layers in transformer stack. + model_dim: Operating dimensions of the transformer + heads: Number of transformer heads. Must be divisible by model_dim. Recommend model_dim//64 + max_text_tokens: Maximum number of text tokens that will be encountered by model. + max_mel_tokens: Maximum number of MEL tokens that will be encountered by model. + max_conditioning_inputs: Maximum number of conditioning inputs provided to the model. If (1), conditioning input can be of format (b,80,s), otherwise (b,n,80,s). + mel_length_compression: The factor between and . Used to compute MEL code padding given wav input length. + number_text_tokens: + start_text_token: + stop_text_token: + number_mel_codes: + start_mel_token: + stop_mel_token: + train_solo_embeddings: + use_mel_codes_as_input: + checkpointing: + """ + super().__init__() + + self.number_text_tokens = number_text_tokens + self.start_text_token = start_text_token + self.stop_text_token = stop_text_token + self.number_mel_codes = number_mel_codes + self.start_mel_token = start_mel_token + self.stop_mel_token = stop_mel_token + self.layers = layers + self.heads = heads + self.max_mel_tokens = max_mel_tokens + self.max_text_tokens = max_text_tokens + self.model_dim = model_dim + self.max_conditioning_inputs = max_conditioning_inputs + self.mel_length_compression = mel_length_compression + self.conditioning_encoder = ConditioningEncoder(80, model_dim, num_attn_heads=heads) + self.text_embedding = nn.Embedding(self.number_text_tokens, model_dim) + if use_mel_codes_as_input: + self.mel_embedding = nn.Embedding(self.number_mel_codes, model_dim) + else: + self.mel_embedding = MelEncoder(model_dim, resblocks_per_reduction=1) + self.gpt, self.mel_pos_embedding, self.text_pos_embedding, self.mel_layer_pos_embedding, self.text_layer_pos_embedding = \ + build_hf_gpt_transformer(layers, model_dim, heads, self.max_mel_tokens+2+self.max_conditioning_inputs, self.max_text_tokens+2, checkpointing) + if train_solo_embeddings: + self.mel_solo_embedding = nn.Parameter(torch.randn(1, 1, model_dim) * .02, requires_grad=True) + self.text_solo_embedding = nn.Parameter(torch.randn(1, 1, model_dim) * .02, requires_grad=True) + else: + self.mel_solo_embedding = 0 + self.text_solo_embedding = 0 + + self.final_norm = nn.LayerNorm(model_dim) + self.text_head = nn.Linear(model_dim, self.number_text_tokens) + self.mel_head = nn.Linear(model_dim, self.number_mel_codes) + + # Initialize the embeddings per the GPT-2 scheme + embeddings = [self.text_embedding] + if use_mel_codes_as_input: + embeddings.append(self.mel_embedding) + for module in embeddings: + module.weight.data.normal_(mean=0.0, std=.02) + + def build_aligned_inputs_and_targets(self, input, start_token, stop_token): + inp = F.pad(input, (1,0), value=start_token) + tar = F.pad(input, (0,1), value=stop_token) + return inp, tar + + def set_mel_padding(self, mel_input_tokens, wav_lengths): + """ + Given mel tokens that are derived from a padded audio clip and the actual lengths of each batch element in + that audio clip, reformats the tokens with STOP_MEL_TOKEN in place of the zero padding. This is required + preformatting to create a working TTS model. + """ + # Set padding areas within MEL (currently it is coded with the MEL code for ). + mel_lengths = wav_lengths // self.mel_length_compression + for b in range(len(mel_lengths)): + actual_end = mel_lengths[b] + 1 # Due to the convolutional nature of how these tokens are generated, it would be best if the model predicts a token past the actual last token. + if actual_end < mel_input_tokens.shape[-1]: + mel_input_tokens[b, actual_end:] = self.stop_mel_token + return mel_input_tokens + + def get_logits(self, speech_conditioning_inputs, first_inputs, first_head, second_inputs=None, second_head=None, get_attns=False): + if second_inputs is not None: + emb = torch.cat([speech_conditioning_inputs, first_inputs, second_inputs], dim=1) + else: + emb = torch.cat([speech_conditioning_inputs, first_inputs], dim=1) + + gpt_out = self.gpt(inputs_embeds=emb, return_dict=True, output_attentions=get_attns) + if get_attns: + return gpt_out.attentions + + enc = gpt_out.last_hidden_state[:, 1:] # The first logit is tied to the speech_conditioning_input + enc = self.final_norm(enc) + first_logits = enc[:, :first_inputs.shape[1]] + first_logits = first_head(first_logits) + first_logits = first_logits.permute(0,2,1) + if second_inputs is not None: + second_logits = enc[:, -second_inputs.shape[1]:] + second_logits = second_head(second_logits) + second_logits = second_logits.permute(0,2,1) + return first_logits, second_logits + else: + return first_logits + + def forward(self, speech_conditioning_input, text_inputs, text_lengths, mel_codes, wav_lengths, text_first=True, raw_mels=None, return_attentions=False): + """ + Forward pass that uses both text and voice in either text conditioning mode or voice conditioning mode + (actuated by `text_first`). + + speech_conditioning_input: MEL float tensor, (b,80,s) + text_inputs: long tensor, (b,t) + text_lengths: long tensor, (b,) + mel_inputs: long tensor, (b,m) + wav_lengths: long tensor, (b,) + raw_mels: MEL float tensor (b,80,s) + """ + assert self.max_mel_tokens >= mel_codes.shape[1], f'{mel_codes.shape[1]}' + assert self.max_text_tokens >= text_inputs.shape[1], f'{text_inputs.shape[1]}' + + # This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by + # chopping the inputs by the maximum actual length. + max_text_len = text_lengths.max() + text_inputs = F.pad(text_inputs[:, :max_text_len], (0,1), value=self.stop_text_token) + max_mel_len = wav_lengths.max() // self.mel_length_compression + mel_codes = F.pad(mel_codes[:, :max_mel_len], (0,1), value=self.stop_mel_token) + if raw_mels is not None: + raw_mels = raw_mels[:, :, :max_mel_len*4] + mel_codes = self.set_mel_padding(mel_codes, wav_lengths) + + speech_conditioning_input = speech_conditioning_input.unsqueeze(1) if len(speech_conditioning_input.shape) == 3 else speech_conditioning_input + conds = [] + for j in range(speech_conditioning_input.shape[1]): + conds.append(self.conditioning_encoder(speech_conditioning_input[:, j])) + conds = torch.stack(conds, dim=1) + + text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token) + text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(text_inputs) + mel_codes, mel_targets = self.build_aligned_inputs_and_targets(mel_codes, self.start_mel_token, self.stop_mel_token) + if raw_mels is not None: + mel_inp = F.pad(raw_mels, (0, 8)) + else: + mel_inp = mel_codes + mel_emb = self.mel_embedding(mel_inp) + mel_emb = mel_emb + self.mel_pos_embedding(mel_codes) + if text_first: + text_logits, mel_logits = self.get_logits(conds, text_emb, self.text_head, mel_emb, self.mel_head, get_attns=return_attentions) + else: + mel_logits, text_logits = self.get_logits(conds, mel_emb, self.mel_head, text_emb, self.text_head, get_attns=return_attentions) + + if return_attentions: + return mel_logits + loss_text = F.cross_entropy(text_logits, text_targets.long()) + loss_mel = F.cross_entropy(mel_logits, mel_targets.long()) + return loss_text.mean(), loss_mel.mean(), mel_logits + + def text_forward(self, speech_conditioning_input, text_inputs, text_lengths): + """ + Performs autoregressive modeling on only text. Still requires a speech_conditioning_input due to the way the + model inputs are formatted. Just provide any audio clip (arguably, zeros could be provided). + """ + assert self.max_text_tokens >= text_inputs.shape[1], f'{text_inputs.shape[1]}' + + # This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by + # chopping the inputs by the maximum actual length. + max_text_len = text_lengths.max() + text_inputs = F.pad(text_inputs[:, :max_text_len], (0,1), value=self.stop_text_token) + + speech_conditioning_input = speech_conditioning_input.unsqueeze(1) if len(speech_conditioning_input.shape) == 3 else speech_conditioning_input + conds = [] + for j in range(speech_conditioning_input.shape[1]): + conds.append(self.conditioning_encoder(speech_conditioning_input[:, j])) + conds = torch.stack(conds, dim=1) + + text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token) + text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(text_inputs) + self.text_solo_embedding + text_logits = self.get_logits(conds, text_emb, self.text_head) + loss_text = F.cross_entropy(text_logits, text_targets.long()) + return loss_text.mean() + + def speech_forward(self, speech_conditioning_input, mel_codes, wav_lengths, raw_mels=None): + """ + Performs autoregressive modeling on only speech data. + """ + assert self.max_mel_tokens >= mel_codes.shape[1], f'{mel_codes.shape[1]}' + + # This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by + # chopping the inputs by the maximum actual length. + max_mel_len = wav_lengths.max() // self.mel_length_compression + mel_codes = F.pad(mel_codes[:, :max_mel_len], (0,1), value=self.stop_mel_token) + mel_codes = self.set_mel_padding(mel_codes, wav_lengths) + if raw_mels is not None: + raw_mels = raw_mels[:, :, :max_mel_len*4] + + speech_conditioning_input = speech_conditioning_input.unsqueeze(1) if len(speech_conditioning_input.shape) == 3 else speech_conditioning_input + conds = [] + for j in range(speech_conditioning_input.shape[1]): + conds.append(self.conditioning_encoder(speech_conditioning_input[:, j])) + conds = torch.stack(conds, dim=1) + + mel_codes, mel_targets = self.build_aligned_inputs_and_targets(mel_codes, self.start_mel_token, self.stop_mel_token) + if raw_mels is not None: + mel_inp = F.pad(raw_mels, (0, 4)) + else: + mel_inp = mel_codes + mel_emb = self.mel_embedding(mel_inp) + mel_emb = mel_emb + self.mel_pos_embedding(mel_codes) + self.mel_solo_embedding + mel_logits = self.get_logits(conds, mel_emb, self.mel_head) + loss_mel = F.cross_entropy(mel_logits, mel_targets.long()) + return loss_mel.mean() + + def inference_speech(self, speech_conditioning_input, text_inputs, **hf_generate_kwargs): + seq_length = self.max_mel_tokens + self.max_text_tokens + 2 + if not hasattr(self, 'inference_model'): + # TODO: Decouple gpt_config from this inference model. + gpt_config = GPT2Config(vocab_size=self.max_mel_tokens, + n_positions=seq_length, + n_ctx=seq_length, + n_embd=self.model_dim, + n_layer=self.layers, + n_head=self.heads, + gradient_checkpointing=False, + use_cache=True) + self.inference_model = GPT2InferenceModel(gpt_config, self.gpt, self.mel_pos_embedding, self.mel_embedding, self.final_norm, self.mel_head) + self.gpt.wte = self.mel_embedding + + text_inputs = F.pad(text_inputs, (0, 1), value=self.stop_text_token) + text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token) + text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(text_inputs) + + speech_conditioning_input = speech_conditioning_input.unsqueeze(1) if len(speech_conditioning_input.shape) == 3 else speech_conditioning_input + conds = [] + for j in range(speech_conditioning_input.shape[1]): + conds.append(self.conditioning_encoder(speech_conditioning_input[:, j])) + conds = torch.stack(conds, dim=1) + + emb = torch.cat([conds, text_emb], dim=1) + self.inference_model.store_mel_emb(emb) + + fake_inputs = torch.full((emb.shape[0], conds.shape[1]+emb.shape[1],), fill_value=1, dtype=torch.long, device=text_inputs.device) + fake_inputs[:,-1] = self.start_mel_token + + gen = self.inference_model.generate(fake_inputs, bos_token_id=self.start_mel_token, pad_token_id=self.stop_mel_token, eos_token_id=self.stop_mel_token, + max_length=seq_length, **hf_generate_kwargs) + return gen[:, fake_inputs.shape[1]:] + + +if __name__ == '__main__': + gpt = UnifiedVoice(model_dim=256, heads=4, train_solo_embeddings=True, use_mel_codes_as_input=True, max_conditioning_inputs=4) + l = gpt(torch.randn(2, 3, 80, 800), + torch.randint(high=120, size=(2,120)), + torch.tensor([32, 120]), + torch.randint(high=8192, size=(2,250)), + torch.tensor([250*256,195*256])) + gpt.text_forward(torch.randn(2,80,800), torch.randint(high=50, size=(2,80)), torch.tensor([32, 80])) diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..29c8e82 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,7 @@ +torch +torchaudio +rotary_embedding_torch +transformers +tokenizers +pyfastmp3decoder +inflect \ No newline at end of file diff --git a/utils/audio.py b/utils/audio.py new file mode 100644 index 0000000..5a61b25 --- /dev/null +++ b/utils/audio.py @@ -0,0 +1,44 @@ +import torch +import torchaudio + + +def load_wav_to_torch(full_path): + sampling_rate, data = read(full_path) + if data.dtype == np.int32: + norm_fix = 2 ** 31 + elif data.dtype == np.int16: + norm_fix = 2 ** 15 + elif data.dtype == np.float16 or data.dtype == np.float32: + norm_fix = 1. + else: + raise NotImplemented(f"Provided data dtype not supported: {data.dtype}") + return (torch.FloatTensor(data.astype(np.float32)) / norm_fix, sampling_rate) + + +def load_audio(audiopath, sampling_rate): + if audiopath[-4:] == '.wav': + audio, lsr = load_wav_to_torch(audiopath) + elif audiopath[-4:] == '.mp3': + # https://github.com/neonbjb/pyfastmp3decoder - Definitely worth it. + from pyfastmp3decoder.mp3decoder import load_mp3 + audio, lsr = load_mp3(audiopath, sampling_rate) + audio = torch.FloatTensor(audio) + + # Remove any channel data. + if len(audio.shape) > 1: + if audio.shape[0] < 5: + audio = audio[0] + else: + assert audio.shape[1] < 5 + audio = audio[:, 0] + + if lsr != sampling_rate: + audio = torchaudio.functional.resample(audio, lsr, sampling_rate) + + # Check some assumptions about audio range. This should be automatically fixed in load_wav_to_torch, but might not be in some edge cases, where we should squawk. + # '2' is arbitrarily chosen since it seems like audio will often "overdrive" the [-1,1] bounds. + if torch.any(audio > 2) or not torch.any(audio < 0): + print(f"Error with {audiopath}. Max={audio.max()} min={audio.min()}") + audio.clip_(-1, 1) + + return audio.unsqueeze(0) \ No newline at end of file diff --git a/utils/diffusion.py b/utils/diffusion.py new file mode 100644 index 0000000..acd5633 --- /dev/null +++ b/utils/diffusion.py @@ -0,0 +1,1232 @@ +""" +This is an almost carbon copy of gaussian_diffusion.py from OpenAI's ImprovedDiffusion repo, which itself: + +This code started out as a PyTorch port of Ho et al's diffusion models: +https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py + +Docstrings have been added, as well as DDIM sampling and a new collection of beta schedules. +""" + +import enum +import math + +import numpy as np +import torch +import torch as th +from tqdm import tqdm + + +def normal_kl(mean1, logvar1, mean2, logvar2): + """ + Compute the KL divergence between two gaussians. + + Shapes are automatically broadcasted, so batches can be compared to + scalars, among other use cases. + """ + tensor = None + for obj in (mean1, logvar1, mean2, logvar2): + if isinstance(obj, th.Tensor): + tensor = obj + break + assert tensor is not None, "at least one argument must be a Tensor" + + # Force variances to be Tensors. Broadcasting helps convert scalars to + # Tensors, but it does not work for th.exp(). + logvar1, logvar2 = [ + x if isinstance(x, th.Tensor) else th.tensor(x).to(tensor) + for x in (logvar1, logvar2) + ] + + return 0.5 * ( + -1.0 + + logvar2 + - logvar1 + + th.exp(logvar1 - logvar2) + + ((mean1 - mean2) ** 2) * th.exp(-logvar2) + ) + + +def approx_standard_normal_cdf(x): + """ + A fast approximation of the cumulative distribution function of the + standard normal. + """ + return 0.5 * (1.0 + th.tanh(np.sqrt(2.0 / np.pi) * (x + 0.044715 * th.pow(x, 3)))) + + +def discretized_gaussian_log_likelihood(x, *, means, log_scales): + """ + Compute the log-likelihood of a Gaussian distribution discretizing to a + given image. + + :param x: the target images. It is assumed that this was uint8 values, + rescaled to the range [-1, 1]. + :param means: the Gaussian mean Tensor. + :param log_scales: the Gaussian log stddev Tensor. + :return: a tensor like x of log probabilities (in nats). + """ + assert x.shape == means.shape == log_scales.shape + centered_x = x - means + inv_stdv = th.exp(-log_scales) + plus_in = inv_stdv * (centered_x + 1.0 / 255.0) + cdf_plus = approx_standard_normal_cdf(plus_in) + min_in = inv_stdv * (centered_x - 1.0 / 255.0) + cdf_min = approx_standard_normal_cdf(min_in) + log_cdf_plus = th.log(cdf_plus.clamp(min=1e-12)) + log_one_minus_cdf_min = th.log((1.0 - cdf_min).clamp(min=1e-12)) + cdf_delta = cdf_plus - cdf_min + log_probs = th.where( + x < -0.999, + log_cdf_plus, + th.where(x > 0.999, log_one_minus_cdf_min, th.log(cdf_delta.clamp(min=1e-12))), + ) + assert log_probs.shape == x.shape + return log_probs + + +def mean_flat(tensor): + """ + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + + +def get_named_beta_schedule(schedule_name, num_diffusion_timesteps): + """ + Get a pre-defined beta schedule for the given name. + + The beta schedule library consists of beta schedules which remain similar + in the limit of num_diffusion_timesteps. + Beta schedules may be added, but should not be removed or changed once + they are committed to maintain backwards compatibility. + """ + if schedule_name == "linear": + # Linear schedule from Ho et al, extended to work for any number of + # diffusion steps. + scale = 1000 / num_diffusion_timesteps + beta_start = scale * 0.0001 + beta_end = scale * 0.02 + return np.linspace( + beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64 + ) + elif schedule_name == "cosine": + return betas_for_alpha_bar( + num_diffusion_timesteps, + lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2, + ) + else: + raise NotImplementedError(f"unknown beta schedule: {schedule_name}") + + +def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): + """ + Create a beta schedule that discretizes the given alpha_t_bar function, + which defines the cumulative product of (1-beta) over time from t = [0,1]. + + :param num_diffusion_timesteps: the number of betas to produce. + :param alpha_bar: a lambda that takes an argument t from 0 to 1 and + produces the cumulative product of (1-beta) up to that + part of the diffusion process. + :param max_beta: the maximum beta to use; use values lower than 1 to + prevent singularities. + """ + betas = [] + for i in range(num_diffusion_timesteps): + t1 = i / num_diffusion_timesteps + t2 = (i + 1) / num_diffusion_timesteps + betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) + return np.array(betas) + + +class ModelMeanType(enum.Enum): + """ + Which type of output the model predicts. + """ + + PREVIOUS_X = 'previous_x' # the model predicts x_{t-1} + START_X = 'start_x' # the model predicts x_0 + EPSILON = 'epsilon' # the model predicts epsilon + + +class ModelVarType(enum.Enum): + """ + What is used as the model's output variance. + + The LEARNED_RANGE option has been added to allow the model to predict + values between FIXED_SMALL and FIXED_LARGE, making its job easier. + """ + + LEARNED = 'learned' + FIXED_SMALL = 'fixed_small' + FIXED_LARGE = 'fixed_large' + LEARNED_RANGE = 'learned_range' + + +class LossType(enum.Enum): + MSE = 'mse' # use raw MSE loss (and KL when learning variances) + RESCALED_MSE = 'rescaled_mse' # use raw MSE loss (with RESCALED_KL when learning variances) + KL = 'kl' # use the variational lower-bound + RESCALED_KL = 'rescaled_kl' # like KL, but rescale to estimate the full VLB + + def is_vb(self): + return self == LossType.KL or self == LossType.RESCALED_KL + + +class GaussianDiffusion: + """ + Utilities for training and sampling diffusion models. + + Ported directly from here, and then adapted over time to further experimentation. + https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py#L42 + + :param betas: a 1-D numpy array of betas for each diffusion timestep, + starting at T and going to 1. + :param model_mean_type: a ModelMeanType determining what the model outputs. + :param model_var_type: a ModelVarType determining how variance is output. + :param loss_type: a LossType determining the loss function to use. + :param rescale_timesteps: if True, pass floating point timesteps into the + model so that they are always scaled like in the + original paper (0 to 1000). + """ + + def __init__( + self, + *, + betas, + model_mean_type, + model_var_type, + loss_type, + rescale_timesteps=False, + ): + self.model_mean_type = ModelMeanType(model_mean_type) + self.model_var_type = ModelVarType(model_var_type) + self.loss_type = LossType(loss_type) + self.rescale_timesteps = rescale_timesteps + + # Use float64 for accuracy. + betas = np.array(betas, dtype=np.float64) + self.betas = betas + assert len(betas.shape) == 1, "betas must be 1-D" + assert (betas > 0).all() and (betas <= 1).all() + + self.num_timesteps = int(betas.shape[0]) + + alphas = 1.0 - betas + self.alphas_cumprod = np.cumprod(alphas, axis=0) + self.alphas_cumprod_prev = np.append(1.0, self.alphas_cumprod[:-1]) + self.alphas_cumprod_next = np.append(self.alphas_cumprod[1:], 0.0) + assert self.alphas_cumprod_prev.shape == (self.num_timesteps,) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.sqrt_alphas_cumprod = np.sqrt(self.alphas_cumprod) + self.sqrt_one_minus_alphas_cumprod = np.sqrt(1.0 - self.alphas_cumprod) + self.log_one_minus_alphas_cumprod = np.log(1.0 - self.alphas_cumprod) + self.sqrt_recip_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod) + self.sqrt_recipm1_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod - 1) + + # calculations for posterior q(x_{t-1} | x_t, x_0) + self.posterior_variance = ( + betas * (1.0 - self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod) + ) + # log calculation clipped because the posterior variance is 0 at the + # beginning of the diffusion chain. + self.posterior_log_variance_clipped = np.log( + np.append(self.posterior_variance[1], self.posterior_variance[1:]) + ) + self.posterior_mean_coef1 = ( + betas * np.sqrt(self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod) + ) + self.posterior_mean_coef2 = ( + (1.0 - self.alphas_cumprod_prev) + * np.sqrt(alphas) + / (1.0 - self.alphas_cumprod) + ) + + def q_mean_variance(self, x_start, t): + """ + Get the distribution q(x_t | x_0). + + :param x_start: the [N x C x ...] tensor of noiseless inputs. + :param t: the number of diffusion steps (minus 1). Here, 0 means one step. + :return: A tuple (mean, variance, log_variance), all of x_start's shape. + """ + mean = ( + _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + ) + variance = _extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape) + log_variance = _extract_into_tensor( + self.log_one_minus_alphas_cumprod, t, x_start.shape + ) + return mean, variance, log_variance + + def q_sample(self, x_start, t, noise=None): + """ + Diffuse the data for a given number of diffusion steps. + + In other words, sample from q(x_t | x_0). + + :param x_start: the initial data batch. + :param t: the number of diffusion steps (minus 1). Here, 0 means one step. + :param noise: if specified, the split-out normal noise. + :return: A noisy version of x_start. + """ + if noise is None: + noise = th.randn_like(x_start) + assert noise.shape == x_start.shape + return ( + _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start + + _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) + * noise + ) + + def q_posterior_mean_variance(self, x_start, x_t, t): + """ + Compute the mean and variance of the diffusion posterior: + + q(x_{t-1} | x_t, x_0) + + """ + assert x_start.shape == x_t.shape + posterior_mean = ( + _extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start + + _extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t + ) + posterior_variance = _extract_into_tensor(self.posterior_variance, t, x_t.shape) + posterior_log_variance_clipped = _extract_into_tensor( + self.posterior_log_variance_clipped, t, x_t.shape + ) + assert ( + posterior_mean.shape[0] + == posterior_variance.shape[0] + == posterior_log_variance_clipped.shape[0] + == x_start.shape[0] + ) + return posterior_mean, posterior_variance, posterior_log_variance_clipped + + def p_mean_variance( + self, model, x, t, clip_denoised=True, denoised_fn=None, model_kwargs=None + ): + """ + Apply the model to get p(x_{t-1} | x_t), as well as a prediction of + the initial x, x_0. + + :param model: the model, which takes a signal and a batch of timesteps + as input. + :param x: the [N x C x ...] tensor at time t. + :param t: a 1-D Tensor of timesteps. + :param clip_denoised: if True, clip the denoised signal into [-1, 1]. + :param denoised_fn: if not None, a function which applies to the + x_start prediction before it is used to sample. Applies before + clip_denoised. + :param model_kwargs: if not None, a dict of extra keyword arguments to + pass to the model. This can be used for conditioning. + :return: a dict with the following keys: + - 'mean': the model mean output. + - 'variance': the model variance output. + - 'log_variance': the log of 'variance'. + - 'pred_xstart': the prediction for x_0. + """ + if model_kwargs is None: + model_kwargs = {} + + B, C = x.shape[:2] + assert t.shape == (B,) + model_output = model(x, self._scale_timesteps(t), **model_kwargs) + + if self.model_var_type in [ModelVarType.LEARNED, ModelVarType.LEARNED_RANGE]: + assert model_output.shape == (B, C * 2, *x.shape[2:]) + model_output, model_var_values = th.split(model_output, C, dim=1) + if self.model_var_type == ModelVarType.LEARNED: + model_log_variance = model_var_values + model_variance = th.exp(model_log_variance) + else: + min_log = _extract_into_tensor( + self.posterior_log_variance_clipped, t, x.shape + ) + max_log = _extract_into_tensor(np.log(self.betas), t, x.shape) + # The model_var_values is [-1, 1] for [min_var, max_var]. + frac = (model_var_values + 1) / 2 + model_log_variance = frac * max_log + (1 - frac) * min_log + model_variance = th.exp(model_log_variance) + else: + model_variance, model_log_variance = { + # for fixedlarge, we set the initial (log-)variance like so + # to get a better decoder log likelihood. + ModelVarType.FIXED_LARGE: ( + np.append(self.posterior_variance[1], self.betas[1:]), + np.log(np.append(self.posterior_variance[1], self.betas[1:])), + ), + ModelVarType.FIXED_SMALL: ( + self.posterior_variance, + self.posterior_log_variance_clipped, + ), + }[self.model_var_type] + model_variance = _extract_into_tensor(model_variance, t, x.shape) + model_log_variance = _extract_into_tensor(model_log_variance, t, x.shape) + + def process_xstart(x): + if denoised_fn is not None: + x = denoised_fn(x) + if clip_denoised: + return x.clamp(-1, 1) + return x + + if self.model_mean_type == ModelMeanType.PREVIOUS_X: + pred_xstart = process_xstart( + self._predict_xstart_from_xprev(x_t=x, t=t, xprev=model_output) + ) + model_mean = model_output + elif self.model_mean_type in [ModelMeanType.START_X, ModelMeanType.EPSILON]: + if self.model_mean_type == ModelMeanType.START_X: + pred_xstart = process_xstart(model_output) + else: + pred_xstart = process_xstart( + self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output) + ) + model_mean, _, _ = self.q_posterior_mean_variance( + x_start=pred_xstart, x_t=x, t=t + ) + else: + raise NotImplementedError(self.model_mean_type) + + assert ( + model_mean.shape == model_log_variance.shape == pred_xstart.shape == x.shape + ) + return { + "mean": model_mean, + "variance": model_variance, + "log_variance": model_log_variance, + "pred_xstart": pred_xstart, + } + + def _predict_xstart_from_eps(self, x_t, t, eps): + assert x_t.shape == eps.shape + return ( + _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t + - _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * eps + ) + + def _predict_xstart_from_xprev(self, x_t, t, xprev): + assert x_t.shape == xprev.shape + return ( # (xprev - coef2*x_t) / coef1 + _extract_into_tensor(1.0 / self.posterior_mean_coef1, t, x_t.shape) * xprev + - _extract_into_tensor( + self.posterior_mean_coef2 / self.posterior_mean_coef1, t, x_t.shape + ) + * x_t + ) + + def _predict_eps_from_xstart(self, x_t, t, pred_xstart): + return ( + _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t + - pred_xstart + ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) + + def _scale_timesteps(self, t): + if self.rescale_timesteps: + return t.float() * (1000.0 / self.num_timesteps) + return t + + def condition_mean(self, cond_fn, p_mean_var, x, t, model_kwargs=None): + """ + Compute the mean for the previous step, given a function cond_fn that + computes the gradient of a conditional log probability with respect to + x. In particular, cond_fn computes grad(log(p(y|x))), and we want to + condition on y. + + This uses the conditioning strategy from Sohl-Dickstein et al. (2015). + """ + gradient = cond_fn(x, self._scale_timesteps(t), **model_kwargs) + new_mean = ( + p_mean_var["mean"].float() + p_mean_var["variance"] * gradient.float() + ) + return new_mean + + def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None): + """ + Compute what the p_mean_variance output would have been, should the + model's score function be conditioned by cond_fn. + + See condition_mean() for details on cond_fn. + + Unlike condition_mean(), this instead uses the conditioning strategy + from Song et al (2020). + """ + alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape) + + eps = self._predict_eps_from_xstart(x, t, p_mean_var["pred_xstart"]) + eps = eps - (1 - alpha_bar).sqrt() * cond_fn( + x, self._scale_timesteps(t), **model_kwargs + ) + + out = p_mean_var.copy() + out["pred_xstart"] = self._predict_xstart_from_eps(x, t, eps) + out["mean"], _, _ = self.q_posterior_mean_variance( + x_start=out["pred_xstart"], x_t=x, t=t + ) + return out + + def p_sample( + self, + model, + x, + t, + clip_denoised=True, + denoised_fn=None, + cond_fn=None, + model_kwargs=None, + ): + """ + Sample x_{t-1} from the model at the given timestep. + + :param model: the model to sample from. + :param x: the current tensor at x_{t-1}. + :param t: the value of t, starting at 0 for the first diffusion step. + :param clip_denoised: if True, clip the x_start prediction to [-1, 1]. + :param denoised_fn: if not None, a function which applies to the + x_start prediction before it is used to sample. + :param cond_fn: if not None, this is a gradient function that acts + similarly to the model. + :param model_kwargs: if not None, a dict of extra keyword arguments to + pass to the model. This can be used for conditioning. + :return: a dict containing the following keys: + - 'sample': a random sample from the model. + - 'pred_xstart': a prediction of x_0. + """ + out = self.p_mean_variance( + model, + x, + t, + clip_denoised=clip_denoised, + denoised_fn=denoised_fn, + model_kwargs=model_kwargs, + ) + noise = th.randn_like(x) + nonzero_mask = ( + (t != 0).float().view(-1, *([1] * (len(x.shape) - 1))) + ) # no noise when t == 0 + if cond_fn is not None: + out["mean"] = self.condition_mean( + cond_fn, out, x, t, model_kwargs=model_kwargs + ) + sample = out["mean"] + nonzero_mask * th.exp(0.5 * out["log_variance"]) * noise + return {"sample": sample, "pred_xstart": out["pred_xstart"]} + + def p_sample_loop( + self, + model, + shape, + noise=None, + clip_denoised=True, + denoised_fn=None, + cond_fn=None, + model_kwargs=None, + device=None, + progress=False, + ): + """ + Generate samples from the model. + + :param model: the model module. + :param shape: the shape of the samples, (N, C, H, W). + :param noise: if specified, the noise from the encoder to sample. + Should be of the same shape as `shape`. + :param clip_denoised: if True, clip x_start predictions to [-1, 1]. + :param denoised_fn: if not None, a function which applies to the + x_start prediction before it is used to sample. + :param cond_fn: if not None, this is a gradient function that acts + similarly to the model. + :param model_kwargs: if not None, a dict of extra keyword arguments to + pass to the model. This can be used for conditioning. + :param device: if specified, the device to create the samples on. + If not specified, use a model parameter's device. + :param progress: if True, show a tqdm progress bar. + :return: a non-differentiable batch of samples. + """ + final = None + for sample in self.p_sample_loop_progressive( + model, + shape, + noise=noise, + clip_denoised=clip_denoised, + denoised_fn=denoised_fn, + cond_fn=cond_fn, + model_kwargs=model_kwargs, + device=device, + progress=progress, + ): + final = sample + return final["sample"] + + def p_sample_loop_progressive( + self, + model, + shape, + noise=None, + clip_denoised=True, + denoised_fn=None, + cond_fn=None, + model_kwargs=None, + device=None, + progress=False, + ): + """ + Generate samples from the model and yield intermediate samples from + each timestep of diffusion. + + Arguments are the same as p_sample_loop(). + Returns a generator over dicts, where each dict is the return value of + p_sample(). + """ + if device is None: + device = next(model.parameters()).device + assert isinstance(shape, (tuple, list)) + if noise is not None: + img = noise + else: + img = th.randn(*shape, device=device) + indices = list(range(self.num_timesteps))[::-1] + + for i in tqdm(indices): + t = th.tensor([i] * shape[0], device=device) + with th.no_grad(): + out = self.p_sample( + model, + img, + t, + clip_denoised=clip_denoised, + denoised_fn=denoised_fn, + cond_fn=cond_fn, + model_kwargs=model_kwargs, + ) + yield out + img = out["sample"] + + def ddim_sample( + self, + model, + x, + t, + clip_denoised=True, + denoised_fn=None, + cond_fn=None, + model_kwargs=None, + eta=0.0, + ): + """ + Sample x_{t-1} from the model using DDIM. + + Same usage as p_sample(). + """ + out = self.p_mean_variance( + model, + x, + t, + clip_denoised=clip_denoised, + denoised_fn=denoised_fn, + model_kwargs=model_kwargs, + ) + if cond_fn is not None: + out = self.condition_score(cond_fn, out, x, t, model_kwargs=model_kwargs) + + # Usually our model outputs epsilon, but we re-derive it + # in case we used x_start or x_prev prediction. + eps = self._predict_eps_from_xstart(x, t, out["pred_xstart"]) + + alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape) + alpha_bar_prev = _extract_into_tensor(self.alphas_cumprod_prev, t, x.shape) + sigma = ( + eta + * th.sqrt((1 - alpha_bar_prev) / (1 - alpha_bar)) + * th.sqrt(1 - alpha_bar / alpha_bar_prev) + ) + # Equation 12. + noise = th.randn_like(x) + mean_pred = ( + out["pred_xstart"] * th.sqrt(alpha_bar_prev) + + th.sqrt(1 - alpha_bar_prev - sigma ** 2) * eps + ) + nonzero_mask = ( + (t != 0).float().view(-1, *([1] * (len(x.shape) - 1))) + ) # no noise when t == 0 + sample = mean_pred + nonzero_mask * sigma * noise + return {"sample": sample, "pred_xstart": out["pred_xstart"]} + + def ddim_reverse_sample( + self, + model, + x, + t, + clip_denoised=True, + denoised_fn=None, + model_kwargs=None, + eta=0.0, + ): + """ + Sample x_{t+1} from the model using DDIM reverse ODE. + """ + assert eta == 0.0, "Reverse ODE only for deterministic path" + out = self.p_mean_variance( + model, + x, + t, + clip_denoised=clip_denoised, + denoised_fn=denoised_fn, + model_kwargs=model_kwargs, + ) + # Usually our model outputs epsilon, but we re-derive it + # in case we used x_start or x_prev prediction. + eps = ( + _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x.shape) * x + - out["pred_xstart"] + ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x.shape) + alpha_bar_next = _extract_into_tensor(self.alphas_cumprod_next, t, x.shape) + + # Equation 12. reversed + mean_pred = ( + out["pred_xstart"] * th.sqrt(alpha_bar_next) + + th.sqrt(1 - alpha_bar_next) * eps + ) + + return {"sample": mean_pred, "pred_xstart": out["pred_xstart"]} + + def ddim_sample_loop( + self, + model, + shape, + noise=None, + clip_denoised=True, + denoised_fn=None, + cond_fn=None, + model_kwargs=None, + device=None, + progress=False, + eta=0.0, + ): + """ + Generate samples from the model using DDIM. + + Same usage as p_sample_loop(). + """ + final = None + for sample in self.ddim_sample_loop_progressive( + model, + shape, + noise=noise, + clip_denoised=clip_denoised, + denoised_fn=denoised_fn, + cond_fn=cond_fn, + model_kwargs=model_kwargs, + device=device, + progress=progress, + eta=eta, + ): + final = sample + return final["sample"] + + def ddim_sample_loop_progressive( + self, + model, + shape, + noise=None, + clip_denoised=True, + denoised_fn=None, + cond_fn=None, + model_kwargs=None, + device=None, + progress=False, + eta=0.0, + ): + """ + Use DDIM to sample from the model and yield intermediate samples from + each timestep of DDIM. + + Same usage as p_sample_loop_progressive(). + """ + if device is None: + device = next(model.parameters()).device + assert isinstance(shape, (tuple, list)) + if noise is not None: + img = noise + else: + img = th.randn(*shape, device=device) + indices = list(range(self.num_timesteps))[::-1] + + if progress: + # Lazy import so that we don't depend on tqdm. + from tqdm.auto import tqdm + + indices = tqdm(indices) + + for i in indices: + t = th.tensor([i] * shape[0], device=device) + with th.no_grad(): + out = self.ddim_sample( + model, + img, + t, + clip_denoised=clip_denoised, + denoised_fn=denoised_fn, + cond_fn=cond_fn, + model_kwargs=model_kwargs, + eta=eta, + ) + yield out + img = out["sample"] + + def _vb_terms_bpd( + self, model, x_start, x_t, t, clip_denoised=True, model_kwargs=None + ): + """ + Get a term for the variational lower-bound. + + The resulting units are bits (rather than nats, as one might expect). + This allows for comparison to other papers. + + :return: a dict with the following keys: + - 'output': a shape [N] tensor of NLLs or KLs. + - 'pred_xstart': the x_0 predictions. + """ + true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance( + x_start=x_start, x_t=x_t, t=t + ) + out = self.p_mean_variance( + model, x_t, t, clip_denoised=clip_denoised, model_kwargs=model_kwargs + ) + kl = normal_kl( + true_mean, true_log_variance_clipped, out["mean"], out["log_variance"] + ) + kl = mean_flat(kl) / np.log(2.0) + + decoder_nll = -discretized_gaussian_log_likelihood( + x_start, means=out["mean"], log_scales=0.5 * out["log_variance"] + ) + assert decoder_nll.shape == x_start.shape + decoder_nll = mean_flat(decoder_nll) / np.log(2.0) + + # At the first timestep return the decoder NLL, + # otherwise return KL(q(x_{t-1}|x_t,x_0) || p(x_{t-1}|x_t)) + output = th.where((t == 0), decoder_nll, kl) + return {"output": output, "pred_xstart": out["pred_xstart"]} + + def training_losses(self, model, x_start, t, model_kwargs=None, noise=None): + """ + Compute training losses for a single timestep. + + :param model: the model to evaluate loss on. + :param x_start: the [N x C x ...] tensor of inputs. + :param t: a batch of timestep indices. + :param model_kwargs: if not None, a dict of extra keyword arguments to + pass to the model. This can be used for conditioning. + :param noise: if specified, the specific Gaussian noise to try to remove. + :return: a dict with the key "loss" containing a tensor of shape [N]. + Some mean or variance settings may also have other keys. + """ + if model_kwargs is None: + model_kwargs = {} + if noise is None: + noise = th.randn_like(x_start) + x_t = self.q_sample(x_start, t, noise=noise) + + terms = {} + + if self.loss_type == LossType.KL or self.loss_type == LossType.RESCALED_KL: + # TODO: support multiple model outputs for this mode. + terms["loss"] = self._vb_terms_bpd( + model=model, + x_start=x_start, + x_t=x_t, + t=t, + clip_denoised=False, + model_kwargs=model_kwargs, + )["output"] + if self.loss_type == LossType.RESCALED_KL: + terms["loss"] *= self.num_timesteps + elif self.loss_type == LossType.MSE or self.loss_type == LossType.RESCALED_MSE: + model_outputs = model(x_t, self._scale_timesteps(t), **model_kwargs) + if isinstance(model_outputs, tuple): + model_output = model_outputs[0] + terms['extra_outputs'] = model_outputs[1:] + else: + model_output = model_outputs + + if self.model_var_type in [ + ModelVarType.LEARNED, + ModelVarType.LEARNED_RANGE, + ]: + B, C = x_t.shape[:2] + assert model_output.shape == (B, C * 2, *x_t.shape[2:]) + model_output, model_var_values = th.split(model_output, C, dim=1) + # Learn the variance using the variational bound, but don't let + # it affect our mean prediction. + frozen_out = th.cat([model_output.detach(), model_var_values], dim=1) + terms["vb"] = self._vb_terms_bpd( + model=lambda *args, r=frozen_out: r, + x_start=x_start, + x_t=x_t, + t=t, + clip_denoised=False, + )["output"] + if self.loss_type == LossType.RESCALED_MSE: + # Divide by 1000 for equivalence with initial implementation. + # Without a factor of 1/1000, the VB term hurts the MSE term. + terms["vb"] *= self.num_timesteps / 1000.0 + + if self.model_mean_type == ModelMeanType.PREVIOUS_X: + target = self.q_posterior_mean_variance( + x_start=x_start, x_t=x_t, t=t + )[0] + x_start_pred = torch.zeros(x_start) # Not supported. + elif self.model_mean_type == ModelMeanType.START_X: + target = x_start + x_start_pred = model_output + elif self.model_mean_type == ModelMeanType.EPSILON: + target = noise + x_start_pred = self._predict_xstart_from_eps(x_t, t, model_output) + else: + raise NotImplementedError(self.model_mean_type) + assert model_output.shape == target.shape == x_start.shape + terms["mse"] = mean_flat((target - model_output) ** 2) + terms["x_start_predicted"] = x_start_pred + if "vb" in terms: + terms["loss"] = terms["mse"] + terms["vb"] + else: + terms["loss"] = terms["mse"] + else: + raise NotImplementedError(self.loss_type) + + return terms + + def autoregressive_training_losses(self, model, x_start, t, model_output_keys, gd_out_key, model_kwargs=None, noise=None): + """ + Compute training losses for a single timestep. + + :param model: the model to evaluate loss on. + :param x_start: the [N x C x ...] tensor of inputs. + :param t: a batch of timestep indices. + :param model_kwargs: if not None, a dict of extra keyword arguments to + pass to the model. This can be used for conditioning. + :param noise: if specified, the specific Gaussian noise to try to remove. + :return: a dict with the key "loss" containing a tensor of shape [N]. + Some mean or variance settings may also have other keys. + """ + if model_kwargs is None: + model_kwargs = {} + if noise is None: + noise = th.randn_like(x_start) + x_t = self.q_sample(x_start, t, noise=noise) + terms = {} + if self.loss_type == LossType.KL or self.loss_type == LossType.RESCALED_KL: + assert False # not currently supported for this type of diffusion. + elif self.loss_type == LossType.MSE or self.loss_type == LossType.RESCALED_MSE: + model_outputs = model(x_t, x_start, self._scale_timesteps(t), **model_kwargs) + terms.update({k: o for k, o in zip(model_output_keys, model_outputs)}) + model_output = terms[gd_out_key] + if self.model_var_type in [ + ModelVarType.LEARNED, + ModelVarType.LEARNED_RANGE, + ]: + B, C = x_t.shape[:2] + assert model_output.shape == (B, C, 2, *x_t.shape[2:]) + model_output, model_var_values = model_output[:, :, 0], model_output[:, :, 1] + # Learn the variance using the variational bound, but don't let + # it affect our mean prediction. + frozen_out = th.cat([model_output.detach(), model_var_values], dim=1) + terms["vb"] = self._vb_terms_bpd( + model=lambda *args, r=frozen_out: r, + x_start=x_start, + x_t=x_t, + t=t, + clip_denoised=False, + )["output"] + if self.loss_type == LossType.RESCALED_MSE: + # Divide by 1000 for equivalence with initial implementation. + # Without a factor of 1/1000, the VB term hurts the MSE term. + terms["vb"] *= self.num_timesteps / 1000.0 + + if self.model_mean_type == ModelMeanType.PREVIOUS_X: + target = self.q_posterior_mean_variance( + x_start=x_start, x_t=x_t, t=t + )[0] + x_start_pred = torch.zeros(x_start) # Not supported. + elif self.model_mean_type == ModelMeanType.START_X: + target = x_start + x_start_pred = model_output + elif self.model_mean_type == ModelMeanType.EPSILON: + target = noise + x_start_pred = self._predict_xstart_from_eps(x_t, t, model_output) + else: + raise NotImplementedError(self.model_mean_type) + assert model_output.shape == target.shape == x_start.shape + terms["mse"] = mean_flat((target - model_output) ** 2) + terms["x_start_predicted"] = x_start_pred + if "vb" in terms: + terms["loss"] = terms["mse"] + terms["vb"] + else: + terms["loss"] = terms["mse"] + else: + raise NotImplementedError(self.loss_type) + + return terms + + def _prior_bpd(self, x_start): + """ + Get the prior KL term for the variational lower-bound, measured in + bits-per-dim. + + This term can't be optimized, as it only depends on the encoder. + + :param x_start: the [N x C x ...] tensor of inputs. + :return: a batch of [N] KL values (in bits), one per batch element. + """ + batch_size = x_start.shape[0] + t = th.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device) + qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t) + kl_prior = normal_kl( + mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0 + ) + return mean_flat(kl_prior) / np.log(2.0) + + def calc_bpd_loop(self, model, x_start, clip_denoised=True, model_kwargs=None): + """ + Compute the entire variational lower-bound, measured in bits-per-dim, + as well as other related quantities. + + :param model: the model to evaluate loss on. + :param x_start: the [N x C x ...] tensor of inputs. + :param clip_denoised: if True, clip denoised samples. + :param model_kwargs: if not None, a dict of extra keyword arguments to + pass to the model. This can be used for conditioning. + + :return: a dict containing the following keys: + - total_bpd: the total variational lower-bound, per batch element. + - prior_bpd: the prior term in the lower-bound. + - vb: an [N x T] tensor of terms in the lower-bound. + - xstart_mse: an [N x T] tensor of x_0 MSEs for each timestep. + - mse: an [N x T] tensor of epsilon MSEs for each timestep. + """ + device = x_start.device + batch_size = x_start.shape[0] + + vb = [] + xstart_mse = [] + mse = [] + for t in list(range(self.num_timesteps))[::-1]: + t_batch = th.tensor([t] * batch_size, device=device) + noise = th.randn_like(x_start) + x_t = self.q_sample(x_start=x_start, t=t_batch, noise=noise) + # Calculate VLB term at the current timestep + with th.no_grad(): + out = self._vb_terms_bpd( + model, + x_start=x_start, + x_t=x_t, + t=t_batch, + clip_denoised=clip_denoised, + model_kwargs=model_kwargs, + ) + vb.append(out["output"]) + xstart_mse.append(mean_flat((out["pred_xstart"] - x_start) ** 2)) + eps = self._predict_eps_from_xstart(x_t, t_batch, out["pred_xstart"]) + mse.append(mean_flat((eps - noise) ** 2)) + + vb = th.stack(vb, dim=1) + xstart_mse = th.stack(xstart_mse, dim=1) + mse = th.stack(mse, dim=1) + + prior_bpd = self._prior_bpd(x_start) + total_bpd = vb.sum(dim=1) + prior_bpd + return { + "total_bpd": total_bpd, + "prior_bpd": prior_bpd, + "vb": vb, + "xstart_mse": xstart_mse, + "mse": mse, + } + + +def get_named_beta_schedule(schedule_name, num_diffusion_timesteps): + """ + Get a pre-defined beta schedule for the given name. + + The beta schedule library consists of beta schedules which remain similar + in the limit of num_diffusion_timesteps. + Beta schedules may be added, but should not be removed or changed once + they are committed to maintain backwards compatibility. + """ + if schedule_name == "linear": + # Linear schedule from Ho et al, extended to work for any number of + # diffusion steps. + scale = 1000 / num_diffusion_timesteps + beta_start = scale * 0.0001 + beta_end = scale * 0.02 + return np.linspace( + beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64 + ) + elif schedule_name == "cosine": + return betas_for_alpha_bar( + num_diffusion_timesteps, + lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2, + ) + else: + raise NotImplementedError(f"unknown beta schedule: {schedule_name}") + + +class SpacedDiffusion(GaussianDiffusion): + """ + A diffusion process which can skip steps in a base diffusion process. + + :param use_timesteps: a collection (sequence or set) of timesteps from the + original diffusion process to retain. + :param kwargs: the kwargs to create the base diffusion process. + """ + + def __init__(self, use_timesteps, **kwargs): + self.use_timesteps = set(use_timesteps) + self.timestep_map = [] + self.original_num_steps = len(kwargs["betas"]) + + base_diffusion = GaussianDiffusion(**kwargs) # pylint: disable=missing-kwoa + last_alpha_cumprod = 1.0 + new_betas = [] + for i, alpha_cumprod in enumerate(base_diffusion.alphas_cumprod): + if i in self.use_timesteps: + new_betas.append(1 - alpha_cumprod / last_alpha_cumprod) + last_alpha_cumprod = alpha_cumprod + self.timestep_map.append(i) + kwargs["betas"] = np.array(new_betas) + super().__init__(**kwargs) + + def p_mean_variance( + self, model, *args, **kwargs + ): # pylint: disable=signature-differs + return super().p_mean_variance(self._wrap_model(model), *args, **kwargs) + + def training_losses( + self, model, *args, **kwargs + ): # pylint: disable=signature-differs + return super().training_losses(self._wrap_model(model), *args, **kwargs) + + def autoregressive_training_losses( + self, model, *args, **kwargs + ): # pylint: disable=signature-differs + return super().autoregressive_training_losses(self._wrap_model(model, True), *args, **kwargs) + + def condition_mean(self, cond_fn, *args, **kwargs): + return super().condition_mean(self._wrap_model(cond_fn), *args, **kwargs) + + def condition_score(self, cond_fn, *args, **kwargs): + return super().condition_score(self._wrap_model(cond_fn), *args, **kwargs) + + def _wrap_model(self, model, autoregressive=False): + if isinstance(model, _WrappedModel) or isinstance(model, _WrappedAutoregressiveModel): + return model + mod = _WrappedAutoregressiveModel if autoregressive else _WrappedModel + return mod( + model, self.timestep_map, self.rescale_timesteps, self.original_num_steps + ) + + def _scale_timesteps(self, t): + # Scaling is done by the wrapped model. + return t + + +def space_timesteps(num_timesteps, section_counts): + """ + Create a list of timesteps to use from an original diffusion process, + given the number of timesteps we want to take from equally-sized portions + of the original process. + + For example, if there's 300 timesteps and the section counts are [10,15,20] + then the first 100 timesteps are strided to be 10 timesteps, the second 100 + are strided to be 15 timesteps, and the final 100 are strided to be 20. + + If the stride is a string starting with "ddim", then the fixed striding + from the DDIM paper is used, and only one section is allowed. + + :param num_timesteps: the number of diffusion steps in the original + process to divide up. + :param section_counts: either a list of numbers, or a string containing + comma-separated numbers, indicating the step count + per section. As a special case, use "ddimN" where N + is a number of steps to use the striding from the + DDIM paper. + :return: a set of diffusion steps from the original process to use. + """ + if isinstance(section_counts, str): + if section_counts.startswith("ddim"): + desired_count = int(section_counts[len("ddim") :]) + for i in range(1, num_timesteps): + if len(range(0, num_timesteps, i)) == desired_count: + return set(range(0, num_timesteps, i)) + raise ValueError( + f"cannot create exactly {num_timesteps} steps with an integer stride" + ) + section_counts = [int(x) for x in section_counts.split(",")] + size_per = num_timesteps // len(section_counts) + extra = num_timesteps % len(section_counts) + start_idx = 0 + all_steps = [] + for i, section_count in enumerate(section_counts): + size = size_per + (1 if i < extra else 0) + if size < section_count: + raise ValueError( + f"cannot divide section of {size} steps into {section_count}" + ) + if section_count <= 1: + frac_stride = 1 + else: + frac_stride = (size - 1) / (section_count - 1) + cur_idx = 0.0 + taken_steps = [] + for _ in range(section_count): + taken_steps.append(start_idx + round(cur_idx)) + cur_idx += frac_stride + all_steps += taken_steps + start_idx += size + return set(all_steps) + + +class _WrappedModel: + def __init__(self, model, timestep_map, rescale_timesteps, original_num_steps): + self.model = model + self.timestep_map = timestep_map + self.rescale_timesteps = rescale_timesteps + self.original_num_steps = original_num_steps + + def __call__(self, x, ts, **kwargs): + map_tensor = th.tensor(self.timestep_map, device=ts.device, dtype=ts.dtype) + new_ts = map_tensor[ts] + if self.rescale_timesteps: + new_ts = new_ts.float() * (1000.0 / self.original_num_steps) + return self.model(x, new_ts, **kwargs) + + +class _WrappedAutoregressiveModel: + def __init__(self, model, timestep_map, rescale_timesteps, original_num_steps): + self.model = model + self.timestep_map = timestep_map + self.rescale_timesteps = rescale_timesteps + self.original_num_steps = original_num_steps + + def __call__(self, x, x0, ts, **kwargs): + map_tensor = th.tensor(self.timestep_map, device=ts.device, dtype=ts.dtype) + new_ts = map_tensor[ts] + if self.rescale_timesteps: + new_ts = new_ts.float() * (1000.0 / self.original_num_steps) + return self.model(x, x0, new_ts, **kwargs) + +def _extract_into_tensor(arr, timesteps, broadcast_shape): + """ + Extract values from a 1-D numpy array for a batch of indices. + + :param arr: the 1-D numpy array. + :param timesteps: a tensor of indices into the array to extract. + :param broadcast_shape: a larger shape of K dimensions with the batch + dimension equal to the length of timesteps. + :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims. + """ + res = th.from_numpy(arr).to(device=timesteps.device)[timesteps].float() + while len(res.shape) < len(broadcast_shape): + res = res[..., None] + return res.expand(broadcast_shape) \ No newline at end of file diff --git a/utils/tokenizer.py b/utils/tokenizer.py new file mode 100644 index 0000000..53cd2de --- /dev/null +++ b/utils/tokenizer.py @@ -0,0 +1,173 @@ +import re + +import inflect +import torch +from tokenizers import Tokenizer + + +# Regular expression matching whitespace: +from unidecode import unidecode + +_whitespace_re = re.compile(r'\s+') + + +# List of (regular expression, replacement) pairs for abbreviations: +_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [ + ('mrs', 'misess'), + ('mr', 'mister'), + ('dr', 'doctor'), + ('st', 'saint'), + ('co', 'company'), + ('jr', 'junior'), + ('maj', 'major'), + ('gen', 'general'), + ('drs', 'doctors'), + ('rev', 'reverend'), + ('lt', 'lieutenant'), + ('hon', 'honorable'), + ('sgt', 'sergeant'), + ('capt', 'captain'), + ('esq', 'esquire'), + ('ltd', 'limited'), + ('col', 'colonel'), + ('ft', 'fort'), +]] + + +def expand_abbreviations(text): + for regex, replacement in _abbreviations: + text = re.sub(regex, replacement, text) + return text + + +_inflect = inflect.engine() +_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])') +_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)') +_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)') +_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)') +_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)') +_number_re = re.compile(r'[0-9]+') + + +def _remove_commas(m): + return m.group(1).replace(',', '') + + +def _expand_decimal_point(m): + return m.group(1).replace('.', ' point ') + + +def _expand_dollars(m): + match = m.group(1) + parts = match.split('.') + if len(parts) > 2: + return match + ' dollars' # Unexpected format + dollars = int(parts[0]) if parts[0] else 0 + cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0 + if dollars and cents: + dollar_unit = 'dollar' if dollars == 1 else 'dollars' + cent_unit = 'cent' if cents == 1 else 'cents' + return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit) + elif dollars: + dollar_unit = 'dollar' if dollars == 1 else 'dollars' + return '%s %s' % (dollars, dollar_unit) + elif cents: + cent_unit = 'cent' if cents == 1 else 'cents' + return '%s %s' % (cents, cent_unit) + else: + return 'zero dollars' + + +def _expand_ordinal(m): + return _inflect.number_to_words(m.group(0)) + + +def _expand_number(m): + num = int(m.group(0)) + if num > 1000 and num < 3000: + if num == 2000: + return 'two thousand' + elif num > 2000 and num < 2010: + return 'two thousand ' + _inflect.number_to_words(num % 100) + elif num % 100 == 0: + return _inflect.number_to_words(num // 100) + ' hundred' + else: + return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ') + else: + return _inflect.number_to_words(num, andword='') + + +def normalize_numbers(text): + text = re.sub(_comma_number_re, _remove_commas, text) + text = re.sub(_pounds_re, r'\1 pounds', text) + text = re.sub(_dollars_re, _expand_dollars, text) + text = re.sub(_decimal_number_re, _expand_decimal_point, text) + text = re.sub(_ordinal_re, _expand_ordinal, text) + text = re.sub(_number_re, _expand_number, text) + return text + + +def expand_numbers(text): + return normalize_numbers(text) + + +def lowercase(text): + return text.lower() + + +def collapse_whitespace(text): + return re.sub(_whitespace_re, ' ', text) + + +def convert_to_ascii(text): + return unidecode(text) + + +def basic_cleaners(text): + '''Basic pipeline that lowercases and collapses whitespace without transliteration.''' + text = lowercase(text) + text = collapse_whitespace(text) + return text + + +def transliteration_cleaners(text): + '''Pipeline for non-English text that transliterates to ASCII.''' + text = convert_to_ascii(text) + text = lowercase(text) + text = collapse_whitespace(text) + return text + + +def english_cleaners(text): + '''Pipeline for English text, including number and abbreviation expansion.''' + text = convert_to_ascii(text) + text = lowercase(text) + text = expand_numbers(text) + text = expand_abbreviations(text) + text = collapse_whitespace(text) + text = text.replace('"', '') + return text + + +class VoiceBpeTokenizer: + def __init__(self, vocab_file='data/tokenizer.json'): + if vocab_file is not None: + self.tokenizer = Tokenizer.from_file(vocab_file) + + def preprocess_text(self, txt): + txt = english_cleaners(txt) + return txt + + def encode(self, txt): + txt = self.preprocess_text(txt) + txt = txt.replace(' ', '[SPACE]') + return self.tokenizer.encode(txt).ids + + def decode(self, seq): + if isinstance(seq, torch.Tensor): + seq = seq.cpu().numpy() + txt = self.tokenizer.decode(seq, skip_special_tokens=False).replace(' ', '') + txt = txt.replace('[SPACE]', ' ') + txt = txt.replace('[STOP]', '') + txt = txt.replace('[UNK]', '') + return txt \ No newline at end of file