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chore(style): cleanup bytes

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Signed-off-by: Aaron <29749331+aarnphm@users.noreply.github.com>
This commit is contained in:
Aaron
2023-11-28 01:27:27 -05:00
parent d859cb096d
commit ce6efc2a9e
7 changed files with 65 additions and 182 deletions
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46
openllm-python/src/openllm/serialisation/__init__.py
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@@ -1,32 +1,18 @@
from __future__ import annotations
import importlib
import typing as t
from openllm_core._typing_compat import M, ParamSpec, T, TypeGuard
import importlib, typing as t
from openllm_core._typing_compat import M, ParamSpec, T, TypeGuard, Concatenate
from openllm_core.exceptions import OpenLLMException
if t.TYPE_CHECKING:
from bentoml import Model
from .._llm import LLM
if t.TYPE_CHECKING: from bentoml import Model; from .._llm import LLM
P = ParamSpec('P')
def load_tokenizer(llm: LLM[M, T], **tokenizer_attrs: t.Any) -> TypeGuard[T]:
'''Load the tokenizer from BentoML store.
By default, it will try to find the bentomodel whether it is in store..
If model is not found, it will raises a ``bentoml.exceptions.NotFound``.
'''
import cloudpickle
import fs
from transformers import AutoTokenizer
import cloudpickle, fs, transformers
from bentoml._internal.models.model import CUSTOM_OBJECTS_FILENAME
from .transformers._helpers import process_config
tokenizer_attrs = {**llm.llm_parameters[-1], **tokenizer_attrs}
from bentoml._internal.models.model import CUSTOM_OBJECTS_FILENAME
from .transformers._helpers import process_config
config, *_ = process_config(llm.bentomodel.path, llm.trust_remote_code)
@@ -41,9 +27,7 @@ def load_tokenizer(llm: LLM[M, T], **tokenizer_attrs: t.Any) -> TypeGuard[T]:
'For example: "bentoml.transformers.save_model(..., custom_objects={\'tokenizer\': tokenizer})"'
) from None
else:
tokenizer = AutoTokenizer.from_pretrained(
bentomodel_fs.getsyspath('/'), trust_remote_code=llm.trust_remote_code, **tokenizer_attrs
)
tokenizer = transformers.AutoTokenizer.from_pretrained(bentomodel_fs.getsyspath('/'), trust_remote_code=llm.trust_remote_code, **tokenizer_attrs)
if tokenizer.pad_token_id is None:
if config.pad_token_id is not None:
@@ -56,17 +40,16 @@ def load_tokenizer(llm: LLM[M, T], **tokenizer_attrs: t.Any) -> TypeGuard[T]:
tokenizer.add_special_tokens({'pad_token': '[PAD]'})
return tokenizer
def _make_dispatch_function(fn):
def _make_dispatch_function(fn: str) -> t.Callable[Concatenate[LLM[M, T], P], TypeGuard[M | T | Model]]:
def caller(llm: LLM[M, T], *args: P.args, **kwargs: P.kwargs) -> TypeGuard[M | T | Model]:
"""Generic function dispatch to correct serialisation submodules based on LLM runtime.
'''Generic function dispatch to correct serialisation submodules based on LLM runtime.
> [!NOTE] See 'openllm.serialisation.transformers' if 'llm.__llm_backend__ in ("pt", "vllm")'
> [!NOTE] See 'openllm.serialisation.ggml' if 'llm.__llm_backend__="ggml"'
> [!NOTE] See 'openllm.serialisation.ctranslate' if 'llm.__llm_backend__="ctranslate"'
"""
'''
if llm.__llm_backend__ == 'ggml':
serde = 'ggml'
elif llm.__llm_backend__ == 'ctranslate':
@@ -76,19 +59,12 @@ def _make_dispatch_function(fn):
else:
raise OpenLLMException(f'Not supported backend {llm.__llm_backend__}')
return getattr(importlib.import_module(f'.{serde}', 'openllm.serialisation'), fn)(llm, *args, **kwargs)
return caller
_extras = ['get', 'import_model', 'load_model']
_import_structure = {'ggml', 'transformers', 'ctranslate', 'constants'}
__all__ = ['load_tokenizer', *_extras, *_import_structure]
def __dir__() -> t.Sequence[str]:
return sorted(__all__)
def __dir__() -> t.Sequence[str]: return sorted(__all__)
def __getattr__(name: str) -> t.Any:
if name == 'load_tokenizer':
return load_tokenizer