audiocraft/conditioners.py

from collections import defaultdict
from dataclasses import dataclass, field
import logging
import random
import typing as tp
import warnings
from transformers import T5EncoderModel, T5Tokenizer  # type: ignore
import torch
from torch import nn
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 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, tp.Optional[str]] = 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


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 T5Conditioner(nn.Module):

    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,
                 device: str,
                 word_dropout: float = 0.,
                 normalize_text: bool = False,
                 finetune=False):
        print(f'{finetune=}')
        assert name in self.MODELS, f"Unrecognized t5 model name (should in {self.MODELS})"
        super().__init__()
        self.dim = self.MODELS_DIMS[name]
        self.output_dim = output_dim
        self.output_proj = nn.Linear(self.dim, output_dim)
        self.device = device
        self.name = name
        self.word_dropout = word_dropout

        # 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).eval()  #.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):
        mask = inputs['attention_mask']
        with torch.no_grad():
            embeds = self.t5(**inputs).last_hidden_state
        embeds = self.output_proj(embeds.to(self.output_proj.weight))
        embeds = (embeds * mask.unsqueeze(-1))
        
        # T5 torch.Size([2, 4, 1536]) dict_keys(['input_ids', 'attention_mask'])
        print(f'{embeds.dtype=}')  # inputs["input_ids"].shape=torch.Size([2, 4])
        return embeds, mask








class ConditioningProvider(nn.Module):
    
    def __init__(self,
                 conditioners):
        super().__init__()
        self.conditioners = nn.ModuleDict(conditioners)

    @property
    def text_conditions(self):
        return [k for k, v in self.conditioners.items() if isinstance(v, T5Conditioner)]



    def tokenize(self, inputs: tp.List[ConditioningAttributes]) -> tp.Dict[str, tp.Any]:
        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)
        print(f'COndProvToknz {output=}\n==')    
        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):
        """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