from __future__ import annotations
import re
from typing import Iterable, TYPE_CHECKING
if TYPE_CHECKING:
from torch import Tensor
from .base import ModelBase, TextModel, gguf
@ModelBase.register("XverseForCausalLM")
@ModelBase.example("xverse/XVERSE-7B")
class XverseModel(TextModel):
model_arch = gguf.MODEL_ARCH.XVERSE
def set_vocab(self):
assert (self.dir_model / "tokenizer.json").is_file()
dir_model = self.dir_model
hparams = self.hparams
tokens: list[bytes] = []
toktypes: list[int] = []
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(dir_model)
vocab_size = hparams.get("vocab_size", len(tokenizer.vocab)) # ty: ignore[unresolved-attribute]
# Since we are checking the maximum index, we need to ensure it's strictly less than vocab_size,
# because vocab_size is the count of items, and indexes start at 0.
max_vocab_index = max(tokenizer.get_vocab().values()) # ty: ignore[unresolved-attribute]
if max_vocab_index >= vocab_size:
raise ValueError("Vocabulary size exceeds expected maximum size.")
reverse_vocab: dict[int, str] = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()} # ty: ignore[unresolved-attribute]
added_vocab = tokenizer.get_added_vocab() # ty: ignore[unresolved-attribute]
for token_id in range(vocab_size):
token_text = reverse_vocab[token_id].encode('utf-8')
# replace "\x00" to string with length > 0
if token_text == b"\x00":
toktype = gguf.TokenType.BYTE # special
token_text = f"<{token_text}>".encode('utf-8')
elif re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text):
toktype = gguf.TokenType.BYTE # special
elif reverse_vocab[token_id] in added_vocab:
if tokenizer.added_tokens_decoder[token_id].special: # ty: ignore[unresolved-attribute]
toktype = gguf.TokenType.CONTROL
else:
toktype = gguf.TokenType.USER_DEFINED
else:
toktype = gguf.TokenType.NORMAL
tokens.append(token_text)
toktypes.append(toktype)
self.gguf_writer.add_tokenizer_model("llama")
self.gguf_writer.add_tokenizer_pre("default")
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(dir_model, n_vocab=len(tokens))
special_vocab.add_to_gguf(self.gguf_writer)
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
head_count = self.hparams["num_attention_heads"]
head_count_kv = self.hparams.get("num_key_value_heads", head_count)
# HF models permute some of the tensors, so we need to undo that
if name.endswith("q_proj.weight"):
data_torch = self._reverse_hf_permute(data_torch, head_count, head_count)
if name.endswith("k_proj.weight"):
data_torch = self._reverse_hf_permute(data_torch, head_count, head_count_kv)
yield from super().modify_tensors(data_torch, name, bid)
def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor:
if n_kv_head is not None and n_head != n_kv_head:
n_head //= n_kv_head
return (
weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
.swapaxes(1, 2)
.reshape(weights.shape)
)