src/vocalrender/model/utils.py

from typing import List
import torch
from transformers import PreTrainedTokenizer


def next_and_close(gen):
    try:
        return next(gen)
    finally:
        gen.close()


def build_prefill_and_persistent_kpm(
    text_mask: torch.Tensor,
    feat_mask: torch.Tensor,
    max_cache_len: int,
):
    """Pad-safe key-padding masks for batched prefill + AR steps.

    Left-padded positions (neither text nor audio) must be excluded from
    attention so zero-embedding pad keys do not leak into real queries,
    which otherwise contaminates reference-audio signal and degrades voice
    cloning vs single-sample inference. AR-step positions (T, T+1, ...) are
    always real, so ``persistent_kpm`` only needs updating for the prefill
    span.
    """
    prefill_kpm = ~((text_mask.bool() | feat_mask.bool()))  # [B, T], True=pad
    B, T = prefill_kpm.shape
    persistent_kpm = torch.zeros(
        B, max_cache_len, dtype=torch.bool, device=prefill_kpm.device,
    )
    persistent_kpm[:, :T] = prefill_kpm
    return prefill_kpm, persistent_kpm


def _audio_rate_owner(obj):
    return getattr(obj, "audio_vae", obj)


def get_in_sample_rate(obj) -> int:
    owner = _audio_rate_owner(obj)
    return int(getattr(obj, "in_sample_rate", getattr(owner, "in_sample_rate")))


def get_out_sample_rate(obj) -> int:
    owner = _audio_rate_owner(obj)
    return int(getattr(obj, "out_sample_rate", getattr(owner, "out_sample_rate")))


def mask_multichar_chinese_tokens(tokenizer: PreTrainedTokenizer):
    """Create a tokenizer wrapper that converts multi-character Chinese tokens to single characters.
    
    This function creates a wrapper around the provided tokenizer that automatically
    splits multi-character Chinese tokens into individual characters. This is useful
    for ensuring consistent tokenization of Chinese text.
    
    Args:
        tokenizer: The base tokenizer to wrap
        
    Returns:
        A CharTokenizerWrapper instance that handles multi-character Chinese tokens
        
    Example:
        >>> from transformers import LlamaTokenizerFast
        >>> tokenizer = LlamaTokenizerFast.from_pretrained("path/to/tokenizer")
        >>> wrapped_tokenizer = mask_multichar_chinese_tokens(tokenizer)
        >>> tokens = wrapped_tokenizer("你好世界")
    """
    # Pre-compute multi-character tokens (length >= 2, pure Chinese characters)
    multichar_tokens = {
        token for token in tokenizer.vocab.keys() 
        if len(token) >= 2 and all("\u4e00" <= c <= "\u9fff" for c in token)
    }

    class CharTokenizerWrapper:
        """Wrapper class for tokenizers that handles multi-character Chinese tokens.
        
        This wrapper automatically splits multi-character Chinese tokens into
        individual characters while preserving the original tokenizer's interface.
        """
        
        def __init__(self, base_tokenizer: PreTrainedTokenizer) -> None:
            """Initialize the wrapper with a base tokenizer.
            
            Args:
                base_tokenizer: The tokenizer to wrap
            """
            self.tokenizer = base_tokenizer
            self.multichar_tokens = multichar_tokens

        def tokenize(self, text: str, **kwargs) -> List[str]:
            """Tokenize text and split multi-character Chinese tokens into single characters.
            
            Args:
                text: Input text to tokenize
                **kwargs: Additional arguments passed to the base tokenizer
                
            Returns:
                List of processed tokens with multi-character Chinese tokens split
                
            Example:
                >>> wrapper = CharTokenizerWrapper(tokenizer)
                >>> tokens = wrapper.tokenize("你好世界")
                >>> # Returns ["你", "好", "世", "界"] instead of ["你好", "世界"]
            """
            if not isinstance(text, str):
                raise TypeError(f"Expected string input, got {type(text)}")
                
            tokens = self.tokenizer.tokenize(text, **kwargs)
            processed = []
            
            for token in tokens:
                # Remove possible subword prefix
                clean_token = token.replace("▁", "")

                if clean_token in self.multichar_tokens:
                    # Split multi-character token into single characters
                    chars = list(clean_token)
                    processed.extend(chars)
                else:
                    processed.append(token)
                    
            return processed

        def __call__(self, text: str, **kwargs) -> List[int]:
            """Call the tokenizer and return token IDs.
            
            This method provides the same interface as the original tokenizer
            but with multi-character Chinese token handling.
            
            Args:
                text: Input text to tokenize
                **kwargs: Additional arguments passed to the base tokenizer
                
            Returns:
                List of token IDs
                
            Raises:
                TypeError: If input is not a string
                ValueError: If tokenization fails
            """
            try:
                tokens = self.tokenize(text, **kwargs)
                result = self.tokenizer.convert_tokens_to_ids(tokens)
                return result
            except Exception as e:
                raise ValueError(f"Tokenization failed: {str(e)}") from e

    return CharTokenizerWrapper(tokenizer)


def get_dtype(dtype: str):
    if dtype == "bfloat16":
        return torch.bfloat16
    elif dtype == "bf16":
        return torch.bfloat16
    elif dtype == "float16":
        return torch.float16
    elif dtype == "fp16":
        return torch.float16
    elif dtype == "float32":
        return torch.float32
    elif dtype == "fp32":
        return torch.float32
    else:
        raise ValueError(f"Unsupported dtype: {dtype}")