# Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. from collections import defaultdict from copy import deepcopy from dataclasses import dataclass, field from itertools import chain import logging import math from pathlib import Path import random import re import typing as tp import warnings import soundfile import einops from num2words import num2words import spacy from transformers import T5EncoderModel, T5Tokenizer # type: ignore import torch from torch import nn import torch.nn.functional as F from torch.nn.utils.rnn import pad_sequence from .streaming import StreamingModule from .streaming import StreamingModule from .transformer import create_sin_embedding from .quantization import ResidualVectorQuantizer from .utils.autocast import TorchAutocast from .utils.cache import EmbeddingCache from .utils.utils import collate, hash_trick, length_to_mask, load_clap_state_dict, warn_once logger = logging.getLogger(__name__) TextCondition = tp.Optional[str] # a text condition can be a string or None (if doesn't exist) ConditionType = tp.Tuple[torch.Tensor, torch.Tensor] # condition, mask class WavCondition(tp.NamedTuple): wav: torch.Tensor length: torch.Tensor sample_rate: tp.List[int] path: tp.List[tp.Optional[str]] = [] seek_time: tp.List[tp.Optional[float]] = [] class JointEmbedCondition(tp.NamedTuple): wav: torch.Tensor text: tp.List[tp.Optional[str]] length: torch.Tensor sample_rate: tp.List[int] path: tp.List[tp.Optional[str]] = [] seek_time: tp.List[tp.Optional[float]] = [] @dataclass class ConditioningAttributes: text: tp.Dict[str, tp.Optional[str]] = field(default_factory=dict) wav: tp.Dict[str, WavCondition] = field(default_factory=dict) joint_embed: tp.Dict[str, JointEmbedCondition] = field(default_factory=dict) def __getitem__(self, item): return getattr(self, item) @property def text_attributes(self): return self.text.keys() @property def wav_attributes(self): return self.wav.keys() @property def joint_embed_attributes(self): return self.joint_embed.keys() @property def attributes(self): return { "text": self.text_attributes, "wav": self.wav_attributes, "joint_embed": self.joint_embed_attributes, } def to_flat_dict(self): return { **{f"text.{k}": v for k, v in self.text.items()}, **{f"wav.{k}": v for k, v in self.wav.items()}, **{f"joint_embed.{k}": v for k, v in self.joint_embed.items()} } @classmethod def from_flat_dict(cls, x): out = cls() for k, v in x.items(): kind, att = k.split(".") out[kind][att] = v return out def nullify_condition(condition: ConditionType, dim: int = 1): """Transform an input condition to a null condition. The way it is done by converting it to a single zero vector similarly to how it is done inside WhiteSpaceTokenizer and NoopTokenizer. Args: condition (ConditionType): A tuple of condition and mask (tuple[torch.Tensor, torch.Tensor]) dim (int): The dimension that will be truncated (should be the time dimension) WARNING!: dim should not be the batch dimension! Returns: ConditionType: A tuple of null condition and mask """ assert dim != 0, "dim cannot be the batch dimension!" assert isinstance(condition, tuple) and \ isinstance(condition[0], torch.Tensor) and \ isinstance(condition[1], torch.Tensor), "'nullify_condition' got an unexpected input type!" cond, mask = condition B = cond.shape[0] last_dim = cond.dim() - 1 out = cond.transpose(dim, last_dim) out = 0. * out[..., :1] out = out.transpose(dim, last_dim) mask = torch.zeros((B, 1), device=out.device).int() assert cond.dim() == out.dim() return out, mask def nullify_wav(cond: WavCondition) -> WavCondition: """Transform a WavCondition to a nullified WavCondition. It replaces the wav by a null tensor, forces its length to 0, and replaces metadata by dummy attributes. Args: cond (WavCondition): Wav condition with wav, tensor of shape [B, T]. Returns: WavCondition: Nullified wav condition. """ null_wav, _ = nullify_condition((cond.wav, torch.zeros_like(cond.wav)), dim=cond.wav.dim() - 1) return WavCondition( wav=null_wav, length=torch.tensor([0] * cond.wav.shape[0], device=cond.wav.device), sample_rate=cond.sample_rate, path=[None] * cond.wav.shape[0], seek_time=[None] * cond.wav.shape[0], ) def nullify_joint_embed(embed: JointEmbedCondition) -> JointEmbedCondition: """Nullify the joint embedding condition by replacing it by a null tensor, forcing its length to 0, and replacing metadata by dummy attributes. Args: cond (JointEmbedCondition): Joint embedding condition with wav and text, wav tensor of shape [B, C, T]. """ null_wav, _ = nullify_condition((embed.wav, torch.zeros_like(embed.wav)), dim=embed.wav.dim() - 1) return JointEmbedCondition( wav=null_wav, text=[None] * len(embed.text), length=torch.LongTensor([0]).to(embed.wav.device), sample_rate=embed.sample_rate, path=[None] * embed.wav.shape[0], seek_time=[0] * embed.wav.shape[0], ) class Tokenizer: """Base tokenizer implementation (in case we want to introduce more advances tokenizers in the future). """ def __call__(self, texts: tp.List[tp.Optional[str]]) -> tp.Tuple[torch.Tensor, torch.Tensor]: raise NotImplementedError() class WhiteSpaceTokenizer(Tokenizer): """This tokenizer should be used for natural language descriptions. For example: ["he didn't, know he's going home.", 'shorter sentence'] => [[78, 62, 31, 4, 78, 25, 19, 34], [59, 77, 0, 0, 0, 0, 0, 0]] """ PUNCTUATION = "?:!.,;" def __init__(self, n_bins: int, pad_idx: int = 0, language: str = "en_core_web_sm", lemma: bool = True, stopwords: bool = True) -> None: self.n_bins = n_bins self.pad_idx = pad_idx self.lemma = lemma self.stopwords = stopwords try: self.nlp = spacy.load(language) except IOError: spacy.cli.download(language) # type: ignore self.nlp = spacy.load(language) @tp.no_type_check def __call__(self, texts: tp.List[tp.Optional[str]], return_text: bool = False) -> tp.Tuple[torch.Tensor, torch.Tensor]: """Take a list of strings and convert them to a tensor of indices. Args: texts (list[str]): List of strings. return_text (bool, optional): Whether to return text as additional tuple item. Defaults to False. Returns: tuple[torch.Tensor, torch.Tensor]: - Indices of words in the LUT. - And a mask indicating where the padding tokens are """ output, lengths = [], [] texts = deepcopy(texts) for i, text in enumerate(texts): # if current sample doesn't have a certain attribute, replace with pad token if text is None: output.append(torch.Tensor([self.pad_idx])) lengths.append(0) continue # convert numbers to words text = re.sub(r"(\d+)", lambda x: num2words(int(x.group(0))), text) # type: ignore # normalize text text = self.nlp(text) # type: ignore # remove stopwords if self.stopwords: text = [w for w in text if not w.is_stop] # type: ignore # remove punctuation text = [w for w in text if w.text not in self.PUNCTUATION] # type: ignore # lemmatize if needed text = [getattr(t, "lemma_" if self.lemma else "text") for t in text] # type: ignore texts[i] = " ".join(text) lengths.append(len(text)) # convert to tensor tokens = torch.Tensor([hash_trick(w, self.n_bins) for w in text]) output.append(tokens) mask = length_to_mask(torch.IntTensor(lengths)).int() padded_output = pad_sequence(output, padding_value=self.pad_idx).int().t() if return_text: return padded_output, mask, texts # type: ignore return padded_output, mask class NoopTokenizer(Tokenizer): """This tokenizer should be used for global conditioners such as: artist, genre, key, etc. The difference between this and WhiteSpaceTokenizer is that NoopTokenizer does not split strings, so "Jeff Buckley" will get it's own index. Whereas WhiteSpaceTokenizer will split it to ["Jeff", "Buckley"] and return an index per word. For example: ["Queen", "ABBA", "Jeff Buckley"] => [43, 55, 101] ["Metal", "Rock", "Classical"] => [0, 223, 51] """ def __init__(self, n_bins: int, pad_idx: int = 0): self.n_bins = n_bins self.pad_idx = pad_idx def __call__(self, texts: tp.List[tp.Optional[str]]) -> tp.Tuple[torch.Tensor, torch.Tensor]: output, lengths = [], [] for text in texts: # if current sample doesn't have a certain attribute, replace with pad token if text is None: output.append(self.pad_idx) lengths.append(0) else: output.append(hash_trick(text, self.n_bins)) lengths.append(1) tokens = torch.LongTensor(output).unsqueeze(1) mask = length_to_mask(torch.IntTensor(lengths)).int() return tokens, mask class BaseConditioner(nn.Module): """Base model for all conditioner modules. We allow the output dim to be different than the hidden dim for two reasons: 1) keep our LUTs small when the vocab is large; 2) make all condition dims consistent. Args: dim (int): Hidden dim of the model. output_dim (int): Output dim of the conditioner. """ def __init__(self, dim: int, output_dim: int): super().__init__() self.dim = dim self.output_dim = output_dim self.output_proj = nn.Linear(dim, output_dim) def tokenize(self, *args, **kwargs) -> tp.Any: """Should be any part of the processing that will lead to a synchronization point, e.g. BPE tokenization with transfer to the GPU. The returned value will be saved and return later when calling forward(). """ raise NotImplementedError() def forward(self, inputs: tp.Any) -> ConditionType: """Gets input that should be used as conditioning (e.g, genre, description or a waveform). Outputs a ConditionType, after the input data was embedded as a dense vector. Returns: ConditionType: - A tensor of size [B, T, D] where B is the batch size, T is the length of the output embedding and D is the dimension of the embedding. - And a mask indicating where the padding tokens. """ raise NotImplementedError() class TextConditioner(BaseConditioner): ... class T5Conditioner(TextConditioner): """T5-based TextConditioner. Args: name (str): Name of the T5 model. output_dim (int): Output dim of the conditioner. finetune (bool): Whether to fine-tune T5 at train time. device (str): Device for T5 Conditioner. autocast_dtype (tp.Optional[str], optional): Autocast dtype. word_dropout (float, optional): Word dropout probability. normalize_text (bool, optional): Whether to apply text normalization. """ MODELS = ["t5-small", "t5-base", "t5-large", "t5-3b", "t5-11b", "google/flan-t5-small", "google/flan-t5-base", "google/flan-t5-large", "google/flan-t5-xl", "google/flan-t5-xxl"] MODELS_DIMS = { "t5-small": 512, "t5-base": 768, "t5-large": 1024, "t5-3b": 1024, "t5-11b": 1024, "google/flan-t5-small": 512, "google/flan-t5-base": 768, "google/flan-t5-large": 1024, "google/flan-t5-3b": 1024, "google/flan-t5-11b": 1024, } def __init__(self, name: str, output_dim: int, finetune: bool, device: str, autocast_dtype: tp.Optional[str] = 'float32', word_dropout: float = 0., normalize_text: bool = False): assert name in self.MODELS, f"Unrecognized t5 model name (should in {self.MODELS})" super().__init__(self.MODELS_DIMS[name], output_dim) self.device = device self.name = name self.finetune = finetune self.word_dropout = word_dropout if autocast_dtype is None or self.device == 'cpu': self.autocast = TorchAutocast(enabled=False) if self.device != 'cpu': logger.warning("T5 has no autocast, this might lead to NaN") else: dtype = getattr(torch, autocast_dtype) assert isinstance(dtype, torch.dtype) logger.info(f"T5 will be evaluated with autocast as {autocast_dtype}") self.autocast = TorchAutocast(enabled=True, device_type=self.device, dtype=dtype) # Let's disable logging temporarily because T5 will vomit some errors otherwise. # thanks https://gist.github.com/simon-weber/7853144 previous_level = logging.root.manager.disable logging.disable(logging.ERROR) with warnings.catch_warnings(): warnings.simplefilter("ignore") try: self.t5_tokenizer = T5Tokenizer.from_pretrained(name) t5 = T5EncoderModel.from_pretrained(name).train(mode=finetune) finally: logging.disable(previous_level) if finetune: self.t5 = t5 else: # this makes sure that the t5 models is not part # of the saved checkpoint self.__dict__['t5'] = t5.to(device) self.normalize_text = normalize_text if normalize_text: self.text_normalizer = WhiteSpaceTokenizer(1, lemma=True, stopwords=True) def tokenize(self, x: tp.List[tp.Optional[str]]) -> tp.Dict[str, torch.Tensor]: # if current sample doesn't have a certain attribute, replace with empty string entries: tp.List[str] = [xi if xi is not None else "" for xi in x] if self.normalize_text: _, _, entries = self.text_normalizer(entries, return_text=True) if self.word_dropout > 0. and self.training: new_entries = [] for entry in entries: words = [word for word in entry.split(" ") if random.random() >= self.word_dropout] new_entries.append(" ".join(words)) entries = new_entries empty_idx = torch.LongTensor([i for i, xi in enumerate(entries) if xi == ""]) inputs = self.t5_tokenizer(entries, return_tensors='pt', padding=True).to(self.device) mask = inputs['attention_mask'] mask[empty_idx, :] = 0 # zero-out index where the input is non-existant return inputs def forward(self, inputs: tp.Dict[str, torch.Tensor]) -> ConditionType: mask = inputs['attention_mask'] with torch.set_grad_enabled(self.finetune), self.autocast: embeds = self.t5(**inputs).last_hidden_state embeds = self.output_proj(embeds.to(self.output_proj.weight)) embeds = (embeds * mask.unsqueeze(-1)) return embeds, mask def dropout_condition(sample: ConditioningAttributes, condition_type: str, condition: str) -> ConditioningAttributes: """Utility function for nullifying an attribute inside an ConditioningAttributes object. If the condition is of type "wav", then nullify it using `nullify_condition` function. If the condition is of any other type, set its value to None. Works in-place. """ if condition_type not in ['text', 'wav', 'joint_embed']: raise ValueError( "dropout_condition got an unexpected condition type!" f" expected 'text', 'wav' or 'joint_embed' but got '{condition_type}'" ) if condition not in getattr(sample, condition_type): raise ValueError( "dropout_condition received an unexpected condition!" f" expected wav={sample.wav.keys()} and text={sample.text.keys()}" f" but got '{condition}' of type '{condition_type}'!" ) if condition_type == 'wav': wav_cond = sample.wav[condition] sample.wav[condition] = nullify_wav(wav_cond) elif condition_type == 'joint_embed': embed = sample.joint_embed[condition] sample.joint_embed[condition] = nullify_joint_embed(embed) else: sample.text[condition] = None return sample class DropoutModule(nn.Module): """Base module for all dropout modules.""" def __init__(self, seed: int = 1234): super().__init__() self.rng = torch.Generator() self.rng.manual_seed(seed) class AttributeDropout(DropoutModule): """Dropout with a given probability per attribute. This is different from the behavior of ClassifierFreeGuidanceDropout as this allows for attributes to be dropped out separately. For example, "artist" can be dropped while "genre" remains. This is in contrast to ClassifierFreeGuidanceDropout where if "artist" is dropped "genre" must also be dropped. Args: p (tp.Dict[str, float]): A dict mapping between attributes and dropout probability. For example: ... "genre": 0.1, "artist": 0.5, "wav": 0.25, ... active_on_eval (bool, optional): Whether the dropout is active at eval. Default to False. seed (int, optional): Random seed. """ def __init__(self, p: tp.Dict[str, tp.Dict[str, float]], active_on_eval: bool = False, seed: int = 1234): super().__init__(seed=seed) self.active_on_eval = active_on_eval # construct dict that return the values from p otherwise 0 self.p = {} for condition_type, probs in p.items(): self.p[condition_type] = defaultdict(lambda: 0, probs) def forward(self, samples: tp.List[ConditioningAttributes]) -> tp.List[ConditioningAttributes]: """ Args: samples (list[ConditioningAttributes]): List of conditions. Returns: list[ConditioningAttributes]: List of conditions after certain attributes were set to None. """ if not self.training and not self.active_on_eval: return samples samples = deepcopy(samples) for condition_type, ps in self.p.items(): # for condition types [text, wav] for condition, p in ps.items(): # for attributes of each type (e.g., [artist, genre]) if torch.rand(1, generator=self.rng).item() < p: for sample in samples: dropout_condition(sample, condition_type, condition) return samples def __repr__(self): return f"AttributeDropout({dict(self.p)})" class ClassifierFreeGuidanceDropout(DropoutModule): """Classifier Free Guidance dropout. All attributes are dropped with the same probability. Args: p (float): Probability to apply condition dropout during training. seed (int): Random seed. """ def __init__(self, p: float, seed: int = 1234): super().__init__(seed=seed) self.p = p def forward(self, samples: tp.List[ConditioningAttributes]) -> tp.List[ConditioningAttributes]: """ Args: samples (list[ConditioningAttributes]): List of conditions. Returns: list[ConditioningAttributes]: List of conditions after all attributes were set to None. """ if not self.training: return samples # decide on which attributes to drop in a batched fashion drop = torch.rand(1, generator=self.rng).item() < self.p if not drop: return samples # nullify conditions of all attributes samples = deepcopy(samples) for condition_type in ["wav", "text"]: for sample in samples: for condition in sample.attributes[condition_type]: dropout_condition(sample, condition_type, condition) return samples def __repr__(self): return f"ClassifierFreeGuidanceDropout(p={self.p})" class ConditioningProvider(nn.Module): """Prepare and provide conditions given all the supported conditioners. Args: conditioners (dict): Dictionary of conditioners. device (torch.device or str, optional): Device for conditioners and output condition types. """ def __init__(self, conditioners: tp.Dict[str, BaseConditioner], device: tp.Union[torch.device, str] = "cpu"): super().__init__() self.device = device self.conditioners = nn.ModuleDict(conditioners) # @property # def joint_embed_conditions(self): # return [m.attribute for m in self.conditioners.values() if isinstance(m, JointEmbeddingConditioner)] # @property # def has_joint_embed_conditions(self): # return len(self.joint_embed_conditions) > 0 @property def text_conditions(self): return [k for k, v in self.conditioners.items() if isinstance(v, TextConditioner)] def tokenize(self, inputs: tp.List[ConditioningAttributes]) -> tp.Dict[str, tp.Any]: """Match attributes/wavs with existing conditioners in self, and compute tokenize them accordingly. This should be called before starting any real GPU work to avoid synchronization points. This will return a dict matching conditioner names to their arbitrary tokenized representations. Args: inputs (list[ConditioningAttributes]): List of ConditioningAttributes objects containing text and wav conditions. """ assert all([isinstance(x, ConditioningAttributes) for x in inputs]), ( "Got unexpected types input for conditioner! should be tp.List[ConditioningAttributes]", f" but types were {set([type(x) for x in inputs])}" ) output = {} text = self._collate_text(inputs) # wavs = self._collate_wavs(inputs) # joint_embeds = self._collate_joint_embeds(inputs) # assert set(text.keys() | wavs.keys() | joint_embeds.keys()).issubset(set(self.conditioners.keys())), ( # f"Got an unexpected attribute! Expected {self.conditioners.keys()}, ", # f"got {text.keys(), wavs.keys(), joint_embeds.keys()}" # ) for attribute, batch in text.items(): #, joint_embeds.items()): output[attribute] = self.conditioners[attribute].tokenize(batch) return output def forward(self, tokenized: tp.Dict[str, tp.Any]) -> tp.Dict[str, ConditionType]: """Compute pairs of `(embedding, mask)` using the configured conditioners and the tokenized representations. The output is for example: { "genre": (torch.Tensor([B, 1, D_genre]), torch.Tensor([B, 1])), "description": (torch.Tensor([B, T_desc, D_desc]), torch.Tensor([B, T_desc])), ... } Args: tokenized (dict): Dict of tokenized representations as returned by `tokenize()`. """ output = {} for attribute, inputs in tokenized.items(): condition, mask = self.conditioners[attribute](inputs) output[attribute] = (condition, mask) return output def _collate_text(self, samples: tp.List[ConditioningAttributes]) -> tp.Dict[str, tp.List[tp.Optional[str]]]: """Given a list of ConditioningAttributes objects, compile a dictionary where the keys are the attributes and the values are the aggregated input per attribute. For example: Input: [ ConditioningAttributes(text={"genre": "Rock", "description": "A rock song with a guitar solo"}, wav=...), ConditioningAttributes(text={"genre": "Hip-hop", "description": "A hip-hop verse"}, wav=...), ] Output: { "genre": ["Rock", "Hip-hop"], "description": ["A rock song with a guitar solo", "A hip-hop verse"] } Args: samples (list of ConditioningAttributes): List of ConditioningAttributes samples. Returns: dict[str, list[str, optional]]: A dictionary mapping an attribute name to text batch. """ out: tp.Dict[str, tp.List[tp.Optional[str]]] = defaultdict(list) texts = [x.text for x in samples] for text in texts: for condition in self.text_conditions: out[condition].append(text[condition]) return out class ConditionFuser(StreamingModule): """Condition fuser handles the logic to combine the different conditions to the actual model input. Args: fuse2cond (tp.Dict[str, str]): A dictionary that says how to fuse each condition. For example: { "prepend": ["description"], "sum": ["genre", "bpm"], "cross": ["description"], } cross_attention_pos_emb (bool, optional): Use positional embeddings in cross attention. cross_attention_pos_emb_scale (int): Scale for positional embeddings in cross attention if used. """ FUSING_METHODS = ["sum", "prepend", "cross", "input_interpolate"] def __init__(self, fuse2cond: tp.Dict[str, tp.List[str]], cross_attention_pos_emb: bool = False, cross_attention_pos_emb_scale: float = 1.0): super().__init__() assert all( [k in self.FUSING_METHODS for k in fuse2cond.keys()] ), f"Got invalid fuse method, allowed methods: {self.FUSING_METHODS}" self.cross_attention_pos_emb = cross_attention_pos_emb self.cross_attention_pos_emb_scale = cross_attention_pos_emb_scale self.fuse2cond: tp.Dict[str, tp.List[str]] = fuse2cond self.cond2fuse: tp.Dict[str, str] = {} for fuse_method, conditions in fuse2cond.items(): for condition in conditions: self.cond2fuse[condition] = fuse_method def forward( self, input: torch.Tensor, conditions: tp.Dict[str, ConditionType] ) -> tp.Tuple[torch.Tensor, tp.Optional[torch.Tensor]]: """Fuse the conditions to the provided model input. Args: input (torch.Tensor): Transformer input. conditions (dict[str, ConditionType]): Dict of conditions. Returns: tuple[torch.Tensor, torch.Tensor]: The first tensor is the transformer input after the conditions have been fused. The second output tensor is the tensor used for cross-attention or None if no cross attention inputs exist. """ B, T, _ = input.shape if 'offsets' in self._streaming_state: first_step = False offsets = self._streaming_state['offsets'] else: first_step = True offsets = torch.zeros(input.shape[0], dtype=torch.long, device=input.device) assert set(conditions.keys()).issubset(set(self.cond2fuse.keys())), \ f"given conditions contain unknown attributes for fuser, " \ f"expected {self.cond2fuse.keys()}, got {conditions.keys()}" cross_attention_output = None for cond_type, (cond, cond_mask) in conditions.items(): op = self.cond2fuse[cond_type] if op == 'sum': input += cond elif op == 'input_interpolate': cond = einops.rearrange(cond, "b t d -> b d t") cond = F.interpolate(cond, size=input.shape[1]) input += einops.rearrange(cond, "b d t -> b t d") elif op == 'prepend': if first_step: input = torch.cat([cond, input], dim=1) elif op == 'cross': if cross_attention_output is not None: cross_attention_output = torch.cat([cross_attention_output, cond], dim=1) else: cross_attention_output = cond else: raise ValueError(f"unknown op ({op})") if self.cross_attention_pos_emb and cross_attention_output is not None: positions = torch.arange( cross_attention_output.shape[1], device=cross_attention_output.device ).view(1, -1, 1) pos_emb = create_sin_embedding(positions, cross_attention_output.shape[-1]) cross_attention_output = cross_attention_output + self.cross_attention_pos_emb_scale * pos_emb if self._is_streaming: self._streaming_state['offsets'] = offsets + T return input, cross_attention_output