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xinference/thirdparty/fish_speech/fish_speech/conversation.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,2 +1,256 @@ | ||
| from dataclasses import dataclass, field | ||
| from typing import Literal | ||
|
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| import torch | ||
| from transformers import AutoTokenizer, PretrainedConfig, PreTrainedTokenizerFast | ||
|
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| IM_START_TOKEN = "<|im_start|>" | ||
| IM_END_TOKEN = "<|im_end|>" | ||
| SEMANTIC_TOKEN = "<|semantic|>" | ||
| MEL_TOKEN = "<|mel|>" | ||
| PHONEME_START_TOKEN = "<|phoneme_start|>" | ||
| PHONEME_END_TOKEN = "<|phoneme_end|>" | ||
| ALL_SPECIAL_TOKENS = [ | ||
| IM_START_TOKEN, | ||
| IM_END_TOKEN, | ||
| SEMANTIC_TOKEN, | ||
| MEL_TOKEN, | ||
| PHONEME_START_TOKEN, | ||
| PHONEME_END_TOKEN, | ||
| ] | ||
|
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| CODEBOOK_PAD_TOKEN_ID = 0 | ||
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| class FishTokenizerConfig(PretrainedConfig): | ||
| share_codebook_embeddings: bool = True | ||
| codebook_size: int = 1024 | ||
| num_codebooks: int = 8 | ||
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|
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| class FishTokenizerFast(PreTrainedTokenizerFast): | ||
| def __init__(self, *args, **kwargs): | ||
| super().__init__(*args, **kwargs) | ||
| self.share_codebook_embeddings = kwargs.pop("share_codebook_embeddings", True) | ||
| self.codebook_size = kwargs.pop("codebook_size", 1024) | ||
| self.num_codebooks = kwargs.pop("num_codebooks", 8) | ||
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| AutoTokenizer.register(FishTokenizerConfig, fast_tokenizer_class=FishTokenizerFast) | ||
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| @dataclass(kw_only=True) | ||
| class BasePart: | ||
| pass | ||
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| @dataclass(kw_only=True) | ||
| class VQPart(BasePart): | ||
| codes: torch.Tensor | ||
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| @dataclass(kw_only=True) | ||
| class TextPart(BasePart): | ||
| text: str | ||
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| @dataclass(kw_only=True) | ||
| class MelPart(BasePart): | ||
| mels: torch.Tensor | ||
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| @dataclass(kw_only=True) | ||
| class EncodedMessage: | ||
| tokens: torch.Tensor | ||
| labels: torch.Tensor | ||
| vq_parts: list[torch.Tensor] | ||
| mel_parts: list[torch.Tensor] | ||
| vq_require_losses: torch.Tensor | None = None | ||
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| @dataclass(kw_only=True) | ||
| class Message: | ||
| role: Literal["system", "user", "assistant"] | ||
| parts: list[VQPart | TextPart | MelPart] = field(default_factory=list) | ||
| add_im_start: bool = True | ||
| add_im_end: bool = True | ||
| cal_loss: bool = False | ||
|
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| # By default, ignore the loss of the auto-generated im_start token | ||
| ignore_im_start_loss: bool = True | ||
|
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| def encode( | ||
| self: "Message", | ||
| tokenizer: AutoTokenizer, | ||
| ) -> EncodedMessage: | ||
| all_tokens = [] | ||
| all_labels = [] | ||
|
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| # Multi-modal tokens | ||
| vq_parts = [] | ||
| mel_parts = [] | ||
|
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| semantic_id, mel_id = tokenizer.convert_tokens_to_ids( | ||
| [SEMANTIC_TOKEN, MEL_TOKEN] | ||
| ) | ||
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| parts = self.parts.copy() | ||
| if self.add_im_start: | ||
| parts.insert(0, TextPart(text=f"<|im_start|>{self.role}\n")) | ||
|
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| if self.add_im_end: | ||
| parts.append(TextPart(text="<|im_end|>")) | ||
|
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| for part in parts: | ||
| if isinstance(part, TextPart): | ||
| tokens = tokenizer.encode( | ||
| part.text, | ||
| add_special_tokens=False, | ||
| truncation=False, | ||
| return_tensors="pt", | ||
| ).int()[0] | ||
| elif isinstance(part, VQPart): | ||
| tokens = torch.zeros(part.codes.shape[1], dtype=torch.int) + semantic_id | ||
| codes = part.codes.clone() + 1 | ||
|
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| if getattr(tokenizer, "share_codebook_embeddings", True) is False: | ||
| for i in range(len(codes)): | ||
| codes[i] += tokenizer.codebook_size * i | ||
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| vq_parts.append(codes) | ||
| elif isinstance(part, MelPart): | ||
| tokens = torch.zeros(part.mels.shape[1], dtype=torch.int) + mel_id | ||
| mel_parts.append(part.mels) | ||
| else: | ||
| raise ValueError(f"Unsupported part type: {type(part)}") | ||
|
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| all_tokens.append(tokens) | ||
| if self.cal_loss: | ||
| all_labels.append(tokens.clone()) | ||
| else: | ||
| all_labels.append(torch.full_like(tokens, -100)) | ||
|
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| tokens = torch.cat(all_tokens, dim=0) | ||
| labels = torch.cat(all_labels, dim=0) | ||
| assert tokens.shape == labels.shape | ||
|
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| if self.ignore_im_start_loss and self.add_im_start: | ||
| labels[: len(all_tokens[0])] = -100 | ||
|
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| return EncodedMessage( | ||
| tokens=tokens, | ||
| labels=labels, | ||
| vq_parts=vq_parts, | ||
| mel_parts=mel_parts, | ||
| ) | ||
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| @dataclass | ||
| class Conversation: | ||
| messages: list[Message] | ||
|
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| def encode( | ||
| self: "Conversation", | ||
| tokenizer: AutoTokenizer, | ||
| add_shift: bool = True, | ||
| ) -> EncodedMessage: | ||
| # Build the input_ids and labels | ||
| tokens = [] | ||
| labels = [] | ||
| vq_parts = [] | ||
| mel_parts = [] | ||
| vq_require_losses = [] | ||
|
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| for message in self.messages: | ||
| encoded = message.encode( | ||
| tokenizer, | ||
| ) | ||
| tokens.append(encoded.tokens) | ||
| labels.append(encoded.labels) | ||
| vq_parts.extend(encoded.vq_parts) | ||
| mel_parts.extend(encoded.mel_parts) | ||
| vq_require_losses.extend([message.cal_loss] * len(encoded.vq_parts)) | ||
|
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| tokens = torch.cat(tokens, dim=0) | ||
| labels = torch.cat(labels, dim=0) | ||
| vq_require_losses = torch.tensor(vq_require_losses, dtype=torch.bool) | ||
|
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| if add_shift: | ||
| tokens = tokens[:-1] | ||
| labels = labels[1:] | ||
|
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| assert tokens.dtype in [ | ||
| torch.int, | ||
| torch.long, | ||
| ], f"Invalid dtype: {tokens.dtype}, conv: {conversation}" | ||
|
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| return EncodedMessage( | ||
| tokens=tokens, | ||
| labels=labels, | ||
| vq_parts=vq_parts, | ||
| mel_parts=mel_parts, | ||
| vq_require_losses=vq_require_losses, | ||
| ) | ||
|
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| def encode_for_inference( | ||
| self: "Conversation", | ||
| tokenizer: AutoTokenizer, | ||
| num_codebooks: int, | ||
| ) -> EncodedMessage: | ||
| encoded = self.encode(tokenizer, add_shift=False) | ||
| tokens = encoded.tokens | ||
| values = torch.zeros((num_codebooks + 1, len(tokens)), dtype=torch.int) | ||
| values[0] = tokens | ||
|
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| if encoded.vq_parts is None or len(encoded.vq_parts) == 0: | ||
| return values | ||
|
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| semantic_id, mel_id = tokenizer.convert_tokens_to_ids( | ||
| [SEMANTIC_TOKEN, MEL_TOKEN] | ||
| ) | ||
| vq_parts = encoded.vq_parts | ||
| vq_parts = torch.cat(vq_parts, dim=1) | ||
| values[1:, tokens == semantic_id] = vq_parts | ||
| return values | ||
|
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| def visualize(self: "Conversation", tokenizer: AutoTokenizer): | ||
| encoded = self.encode(tokenizer, add_shift=False) | ||
|
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| print_in_blue = lambda x: print("\033[94m" + x + "\033[0m", end="") | ||
| print_in_green = lambda x: print("\033[92m" + x + "\033[0m", end="") | ||
|
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| for tok, lab in zip(encoded.tokens, encoded.labels): | ||
| val = tokenizer.decode(tok, skip_special_tokens=False) | ||
| if val == "\n": | ||
| val = "\\n\n" | ||
|
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| if lab == -100: | ||
| print_in_green(val) | ||
| else: | ||
| print_in_blue(val) | ||
|
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| print() | ||
|
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|
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| if __name__ == "__main__": | ||
| message0 = Message( | ||
| role="user", | ||
| parts=[ | ||
| TextPart(text="Hello, how are you?"), | ||
| VQPart(codes=torch.zeros((4, 10))), | ||
| ], | ||
| cal_loss=False, | ||
| ) | ||
|
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| message1 = Message( | ||
| role="assistant", | ||
| parts=[TextPart(text="I'm fine, thank you.")], | ||
| cal_loss=True, | ||
| ) | ||
| conversation = Conversation([message0, message1]) | ||
| tokenizer = AutoTokenizer.from_pretrained("checkpoints/Qwen2-1.5B-Instruct") | ||
| conversation.visualize(tokenizer) | ||
|
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| encoded = conversation.encode(tokenizer) | ||
| print(encoded) | ||
| print(tokenizer.batch_decode(encoded.tokens)) |
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