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from dataclasses import dataclass, field
from typing import Literal
import torch
from transformers import AutoTokenizer, PretrainedConfig, PreTrainedTokenizerFast
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,
]
CODEBOOK_PAD_TOKEN_ID = 0
class FishTokenizerConfig(PretrainedConfig):
share_codebook_embeddings: bool = True
codebook_size: int = 1024
num_codebooks: int = 8
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)
AutoTokenizer.register(FishTokenizerConfig, fast_tokenizer_class=FishTokenizerFast)
@dataclass(kw_only=True)
class BasePart:
pass
@dataclass(kw_only=True)
class VQPart(BasePart):
codes: torch.Tensor
@dataclass(kw_only=True)
class TextPart(BasePart):
text: str
@dataclass(kw_only=True)
class MelPart(BasePart):
mels: torch.Tensor
@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
@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
# By default, ignore the loss of the auto-generated im_start token
ignore_im_start_loss: bool = True
def encode(
self: "Message",
tokenizer: AutoTokenizer,
) -> EncodedMessage:
all_tokens = []
all_labels = []
# Multi-modal tokens
vq_parts = []
mel_parts = []
semantic_id, mel_id = tokenizer.convert_tokens_to_ids(
[SEMANTIC_TOKEN, MEL_TOKEN]
)
parts = self.parts.copy()
if self.add_im_start:
parts.insert(0, TextPart(text=f"<|im_start|>{self.role}\n"))
if self.add_im_end:
parts.append(TextPart(text="<|im_end|>"))
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
if getattr(tokenizer, "share_codebook_embeddings", True) is False:
for i in range(len(codes)):
codes[i] += tokenizer.codebook_size * i
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)}")
all_tokens.append(tokens)
if self.cal_loss:
all_labels.append(tokens.clone())
else:
all_labels.append(torch.full_like(tokens, -100))
tokens = torch.cat(all_tokens, dim=0)
labels = torch.cat(all_labels, dim=0)
assert tokens.shape == labels.shape
if self.ignore_im_start_loss and self.add_im_start:
labels[: len(all_tokens[0])] = -100
return EncodedMessage(
tokens=tokens,
labels=labels,
vq_parts=vq_parts,
mel_parts=mel_parts,
)
@dataclass
class Conversation:
messages: list[Message]
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 = []
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))
tokens = torch.cat(tokens, dim=0)
labels = torch.cat(labels, dim=0)
vq_require_losses = torch.tensor(vq_require_losses, dtype=torch.bool)
if add_shift:
tokens = tokens[:-1]
labels = labels[1:]
assert tokens.dtype in [
torch.int,
torch.long,
], f"Invalid dtype: {tokens.dtype}, conv: {conversation}"
return EncodedMessage(
tokens=tokens,
labels=labels,
vq_parts=vq_parts,
mel_parts=mel_parts,
vq_require_losses=vq_require_losses,
)
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
if encoded.vq_parts is None or len(encoded.vq_parts) == 0:
return values
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
def visualize(self: "Conversation", tokenizer: AutoTokenizer):
encoded = self.encode(tokenizer, add_shift=False)
print_in_blue = lambda x: print("\033[94m" + x + "\033[0m", end="")
print_in_green = lambda x: print("\033[92m" + x + "\033[0m", end="")
for tok, lab in zip(encoded.tokens, encoded.labels):
val = tokenizer.decode(tok, skip_special_tokens=False)
if val == "\n":
val = "\\n\n"
if lab == -100:
print_in_green(val)
else:
print_in_blue(val)
print()
if __name__ == "__main__":
message0 = Message(
role="user",
parts=[
TextPart(text="Hello, how are you?"),
VQPart(codes=torch.zeros((4, 10))),
],
cal_loss=False,
)
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)
encoded = conversation.encode(tokenizer)
print(encoded)
print(tokenizer.batch_decode(encoded.tokens))
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