vbatts/llama.cpp
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Remove unused variable/functions, add types to class variable and methods, delete blank liens

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wonjun Jang 2023-11-28 16:23:27 +09:00 • committed by GitHub
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commit 61edd1bc59
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1 changed files with 33 additions and 47 deletions
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convert.py
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@ -18,6 +18,7 @@ import sys
import time
import zipfile
from abc import ABCMeta, abstractmethod
from collections import OrderedDict
from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
from dataclasses import dataclass
from pathlib import Path
@ -313,30 +314,25 @@ class VocabLoader:
except:
self.tokenizer = AutoTokenizer.from_pretrained(str(fname_tokenizer), use_fast=False, trust_remote_code=True)
vocab_set = {encoded_tok for encoded_tok, id in self.tokenizer.get_vocab().items()}
self.added_tokens_list = []
self.added_tokens_dict = dict()
self.added_tokens_ids = set()
self.added_tokens_dict: OrderedDict[str, int] = OrderedDict()
for tok, tokidx in sorted(self.tokenizer.get_added_vocab().items(), key=lambda x: x[1]):
if tokidx >= params.n_vocab or tokidx < self.tokenizer.vocab_size:
continue
self.added_tokens_list.append(tok)
self.added_tokens_dict[tok] = tokidx
self.added_tokens_ids.add(tokidx)
self.unk_token_id = self.tokenizer.unk_token_id
self.specials = {
self.unk_token_id: int = self.tokenizer.unk_token_id
self.specials: dict[str, int] = {
tok: self.tokenizer.get_vocab()[tok]
for tok in self.tokenizer.all_special_tokens
}
print(self.specials)
self.special_ids = set(self.tokenizer.all_special_ids)
self.special_ids: set[int] = set(self.tokenizer.all_special_ids)
self.vocab_size_base: int = self.tokenizer.vocab_size
self.vocab_size: int = self.vocab_size_base + len(self.added_tokens_list)
self.fname_tokenizer = fname_tokenizer
self.vocab_size: int = self.vocab_size_base + len(self.added_tokens_dict)
self.fname_tokenizer: str = fname_tokenizer
vocab_file = "tokenizer.model"
path_candidate = find_vocab_file_path(self.fname_tokenizer, vocab_file)
if path_candidate is not None:
@ -348,18 +344,18 @@ class VocabLoader:
def hf_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
tokenizer = self.tokenizer
reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.get_vocab().items()}
special_ids = set(tokenizer.all_special_ids)
added_tokens_ids = set(self.added_tokens_dict.values())
for i in range(self.vocab_size_base):
if i in self.added_tokens_ids:
if i in added_tokens_ids:
continue
text = reverse_vocab[i].encode("utf-8")
yield text, self.get_token_score(i), self.get_token_type(i)
def get_token_type(self, token_id):
def get_token_type(self, token_id: int) -> gguf.TokenType:
toktype = gguf.TokenType.NORMAL
if self.spm is not None and token_id < self.spm.vocab_size():
if self.spm.is_unknown(token_id):
toktype = gguf.TokenType.UNKNOWN
@ -377,27 +373,27 @@ class VocabLoader:
return toktype
def get_token_score(self, token_id):
def get_token_score(self, token_id: int) -> float:
if self.spm is not None and token_id < self.spm.vocab_size():
return self.spm.get_score(token_id)
else:
return 0.0
def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
for text in self.added_tokens_list:
for text in self.added_tokens_dict:
if text in self.specials:
toktype = self.get_token_type(self.specials[text])
score = self.get_token_score(self.specials[text])
else:
toktype = gguf.TokenType.USER_DEFINED
score = -1000.0
yield text.encode("utf-8"), score, toktype
def has_newline_token(self):
def has_newline_token(self) -> bool:
return '<0x0A>' in self.tokenizer.vocab or '\n' in self.tokenizer.vocab
def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
@ -421,7 +417,7 @@ class VocabLoader:
vocab_file = "tokenizer.json"
path_candidate = find_vocab_file_path(self.fname_tokenizer, vocab_file)
if path_candidate:
if not self.has_newline_token():
if not self.has_newline_token():
return "gpt2"
else:
return "llama"
@ -432,7 +428,7 @@ class VocabLoader:
"if it's in another directory, pass the directory as --vocab-dir")
def __repr__(self) -> str:
return f"<VocabLoader with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
return f"<VocabLoader with {self.vocab_size_base} base tokens and {len(self.added_tokens_dict)} added tokens>"
Vocab: TypeAlias = 'VocabLoader'
@ -814,15 +810,15 @@ def check_vocab_size(params: Params, vocab: Vocab, pad_vocab: bool = False) -> N
if params.n_vocab != vocab.vocab_size:
if params.n_vocab == vocab.vocab_size:
print("Ignoring added_tokens.json since model matches vocab size without it.")
vocab.added_tokens_list = []
vocab.added_tokens_dict = OrderedDict()
vocab.vocab_size = vocab.vocab_size
return
if pad_vocab and params.n_vocab > vocab.vocab_size:
pad_count = params.n_vocab - vocab.vocab_size
print(f'Padding vocab with {pad_count} token(s) - <dummy00001> through <dummy{pad_count:05}>')
for i in range(1, (params.n_vocab - vocab.vocab_size) + 1):
vocab.added_tokens_list.append(f'<dummy{i:05}>')
vocab.added_tokens_dict[f'<dummy{i:05}>'] = -1
vocab.vocab_size = params.n_vocab
return
msg = f"Vocab size mismatch (model has {params.n_vocab}, but {vocab.fname_tokenizer}"
@ -1112,27 +1108,17 @@ def find_vocab_file_path(path: Path, vocab_file: str) -> Optional[Path]:
path2 = path / vocab_file
# Use `.parent` instead of /.. to handle the symlink case better.
path3 = path.parent / vocab_file
if path2.exists():
path = path2
elif path3.exists():
path = path3
else:
path = None
return path
def load_vocab(params: Params, path: Path) -> Vocab:
# Be extra-friendly and accept either a file or a directory. Also, if it's
# a directory, it might be the model directory, and tokenizer.model might
# be in the parent of that.
print(f"Loading vocab file '{path}'")
return VocabLoader(params, path)
def default_outfile(model_paths: list[Path], file_type: GGMLFileType) -> Path:
namestr = {
GGMLFileType.AllF32: "f32",
@ -1215,7 +1201,7 @@ def main(args_in: list[str] | None = None) -> None:
if not args.outfile:
raise ValueError("need --outfile if using --vocab-only")
# FIXME: Try to respect vocab_dir somehow?
vocab = load_vocab(params, args.vocab_dir or args.model)
vocab = VocabLoader(params, args.vocab_dir or args.model)
special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent,
load_merges = True,
n_vocab = vocab.vocab_size)
@ -1229,7 +1215,7 @@ def main(args_in: list[str] | None = None) -> None:
vocab = model_plus.vocab
else:
vocab_dir = args.vocab_dir if args.vocab_dir else model_plus.paths[0].parent
vocab = load_vocab(params, vocab_dir)
vocab = VocabLoader(params, vocab_dir)
# FIXME: Try to respect vocab_dir somehow?
print(f"Vocab info: {vocab}")