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py : type-check all Python scripts with Pyright (#8341)

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* py : type-check all Python scripts with Pyright

* server-tests : use trailing slash in openai base_url

* server-tests : add more type annotations

* server-tests : strip "chat" from base_url in oai_chat_completions

* server-tests : model metadata is a dict

* ci : disable pip cache in type-check workflow

The cache is not shared between branches, and it's 250MB in size,
so it would become quite a big part of the 10GB cache limit of the repo.

* py : fix new type errors from master branch

* tests : fix test-tokenizer-random.py

Apparently, gcc applies optimisations even when pre-processing,
which confuses pycparser.

* ci : only show warnings and errors in python type-check

The "information" level otherwise has entries
from 'examples/pydantic_models_to_grammar.py',
which could be confusing for someone trying to figure out what failed,
considering that these messages can safely be ignored
even though they look like errors.
This commit is contained in:
compilade 2024-07-07 15:04:39 -04:00 • committed by GitHub
parent a8db2a9ce6
commit 3fd62a6b1c
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33 changed files with 297 additions and 173 deletions
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44
tests/test-tokenizer-random.py
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@ -6,6 +6,8 @@
# python3 tests/test-tokenizer-random.py ./models/ggml-vocab-llama-bpe.gguf ./models/tokenizers/llama-bpe
#
from __future__ import annotations
import time
import logging
import argparse
@ -13,7 +15,9 @@ import subprocess
import random
import unicodedata
from typing import Iterator
from pathlib import Path
from typing import Any, Iterator, cast
from typing_extensions import Buffer
import cffi
from transformers import AutoTokenizer
@ -28,15 +32,15 @@ class LibLlama:
DEFAULT_PATH_INCLUDES = ["./ggml/include/", "./include/"]
DEFAULT_PATH_LIBLLAMA = "./build/src/libllama.so" # CMakeLists.txt: BUILD_SHARED_LIBS ON
def __init__(self, path_llama_h: str = None, path_includes: list[str] = [], path_libllama: str = None):
def __init__(self, path_llama_h: str | None = None, path_includes: list[str] = [], path_libllama: str | None = None):
path_llama_h = path_llama_h or self.DEFAULT_PATH_LLAMA_H
path_includes = path_includes or self.DEFAULT_PATH_INCLUDES
path_libllama = path_libllama or self.DEFAULT_PATH_LIBLLAMA
(self.ffi, self.lib) = self._load_libllama_cffi(path_llama_h, path_includes, path_libllama)
self.lib.llama_backend_init()
def _load_libllama_cffi(self, path_llama_h: str, path_includes: list[str], path_libllama: str):
cmd = ["gcc", "-E", "-P", "-D__restrict=", "-D__attribute__(x)=", "-D__asm__(x)="]
def _load_libllama_cffi(self, path_llama_h: str, path_includes: list[str], path_libllama: str) -> tuple[cffi.FFI, Any]:
cmd = ["gcc", "-O0", "-E", "-P", "-D__restrict=", "-D__attribute__(x)=", "-D__asm__(x)="]
cmd += ["-I" + path for path in path_includes] + [path_llama_h]
res = subprocess.run(cmd, stdout=subprocess.PIPE)
assert (res.returncode == 0)
@ -68,7 +72,7 @@ class LibLlama:
class LibLlamaModel:
def __init__(self, libllama: LibLlama, path_model: str, mparams={}, cparams={}):
self.lib = libllama.lib
self.lib: Any = libllama.lib
self.ffi = libllama.ffi
if isinstance(mparams, dict):
mparams = libllama.model_default_params(**mparams)
@ -94,11 +98,11 @@ class LibLlamaModel:
self.lib = None
def tokenize(self, text: str, add_special: bool = False, parse_special: bool = False) -> list[int]:
text = text.encode("utf-8")
num = self.lib.llama_tokenize(self.model, text, len(text), self.token_ids, len(self.token_ids), add_special, parse_special)
encoded_text: bytes = text.encode("utf-8")
num = self.lib.llama_tokenize(self.model, encoded_text, len(encoded_text), self.token_ids, len(self.token_ids), add_special, parse_special)
while num < 0 and len(self.token_ids) < (16 << 20):
self.token_ids = self.ffi.new("llama_token[]", -2 * num)
num = self.lib.llama_tokenize(self.model, text, len(text), self.token_ids, len(self.token_ids), add_special, parse_special)
num = self.lib.llama_tokenize(self.model, encoded_text, len(encoded_text), self.token_ids, len(self.token_ids), add_special, parse_special)
return list(self.token_ids[0:num])
def detokenize(self, ids: list[int], remove_special: bool = False, unparse_special: bool = False) -> str:
@ -110,7 +114,7 @@ class LibLlamaModel:
while num < 0 and len(self.text_buff) < (16 << 20):
self.text_buff = self.ffi.new("uint8_t[]", -2 * num)
num = self.lib.llama_detokenize(self.model, self.token_ids, len(ids), self.text_buff, len(self.text_buff), remove_special, unparse_special)
return str(self.ffi.buffer(self.text_buff, num), encoding="utf-8", errors="replace") # replace errors with '\uFFFD'
return str(cast(Buffer, self.ffi.buffer(self.text_buff, num)), encoding="utf-8", errors="replace") # replace errors with '\uFFFD'
class Tokenizer:
@ -152,7 +156,7 @@ class TokenizerGroundtruth (Tokenizer):
class TokenizerLlamaCpp (Tokenizer):
libllama: LibLlama = None
libllama: LibLlama | None = None
def __init__(self, vocab_file: str):
if not self.libllama:
@ -404,7 +408,7 @@ def generator_random_vocab_words(tokenizer: TokenizerGroundtruth, iterations=100
def compare_tokenizers(tokenizer1: TokenizerGroundtruth, tokenizer2: TokenizerLlamaCpp, generator: Iterator[str]):
def find_first_mismatch(ids1: list[int], ids2: list[int]):
def find_first_mismatch(ids1: list[int] | str, ids2: list[int] | str):
for i, (a, b) in enumerate(zip(ids1, ids2)):
if a != b:
return i
@ -433,7 +437,7 @@ def compare_tokenizers(tokenizer1: TokenizerGroundtruth, tokenizer2: TokenizerLl
decode_errors = 0
MAX_ERRORS = 10
logger.info("%s: %s" % (generator.__name__, "ini"))
logger.info("%s: %s" % (generator.__qualname__, "ini"))
for text in generator:
# print(repr(text), text.encode())
# print(repr(text), hex(ord(text[0])), text.encode())
@ -472,13 +476,13 @@ def compare_tokenizers(tokenizer1: TokenizerGroundtruth, tokenizer2: TokenizerLl
break
t_total = time.perf_counter() - t_start
logger.info(f"{generator.__name__}: end, {t_encode1=:.3f} {t_encode2=:.3f} {t_decode1=:.3f} {t_decode2=:.3f} {t_total=:.3f}")
logger.info(f"{generator.__qualname__}: end, {t_encode1=:.3f} {t_encode2=:.3f} {t_decode1=:.3f} {t_decode2=:.3f} {t_total=:.3f}")
def main(argv: list[str] = None):
def main(argv: list[str] | None = None):
parser = argparse.ArgumentParser()
parser.add_argument("vocab_file", help="path to vocab 'gguf' file")
parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file")
parser.add_argument("vocab_file", type=str, help="path to vocab 'gguf' file")
parser.add_argument("dir_tokenizer", type=str, help="directory containing 'tokenizer.model' file")
parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
args = parser.parse_args(argv)
@ -520,7 +524,7 @@ if __name__ == "__main__":
format = "%(levelname)s %(message)s",
)
path_tokenizers = "./models/tokenizers/"
path_tokenizers = Path("./models/tokenizers/")
path_vocab_format = "./models/ggml-vocab-%s.gguf"
tokenizers = [
@ -556,6 +560,6 @@ if __name__ == "__main__":
for tokenizer in tokenizers:
logger.info("-" * 50)
logger.info(f"TOKENIZER: '{tokenizer}'")
vocab_file = path_vocab_format % tokenizer
dir_tokenizer = path_tokenizers + "/" + tokenizer
main([vocab_file, dir_tokenizer, "--verbose"])
vocab_file = Path(path_vocab_format % tokenizer)
dir_tokenizer = path_tokenizers / tokenizer
main([str(vocab_file), str(dir_tokenizer), "--verbose"])