mirror of
https://github.com/bentoml/OpenLLM.git
synced 2026-01-22 22:39:47 -05:00
364 lines
11 KiB
Python
364 lines
11 KiB
Python
# Copyright 2023 BentoML Team. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from __future__ import annotations
|
|
import functools
|
|
import inspect
|
|
import linecache
|
|
import logging
|
|
import string
|
|
import types
|
|
import typing as t
|
|
from operator import itemgetter
|
|
from pathlib import Path
|
|
|
|
import orjson
|
|
|
|
|
|
if t.TYPE_CHECKING:
|
|
from fs.base import FS
|
|
|
|
import openllm
|
|
|
|
from .._types import AnyCallable
|
|
from .._types import DictStrAny
|
|
from .._types import ListStr
|
|
from .._types import P
|
|
|
|
PartialAny = functools.partial[t.Any]
|
|
else:
|
|
DictStrAny = dict
|
|
ListStr = list
|
|
PartialAny = functools.partial
|
|
|
|
_T = t.TypeVar("_T", bound=t.Callable[..., t.Any])
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
OPENLLM_MODEL_NAME = "# openllm: model name"
|
|
OPENLLM_MODEL_ID = "# openllm: model id"
|
|
OPENLLM_MODEL_ADAPTER_MAP = "# openllm: model adapter map"
|
|
|
|
|
|
class ModelNameFormatter(string.Formatter):
|
|
model_keyword: t.LiteralString = "__model_name__"
|
|
|
|
def __init__(self, model_name: str):
|
|
"""The formatter that extends model_name to be formatted the 'service.py'."""
|
|
super().__init__()
|
|
self.model_name = model_name
|
|
|
|
def vformat(self, format_string: str, *args: t.Any, **attrs: t.Any) -> t.LiteralString:
|
|
return t.cast("t.LiteralString", super().vformat(format_string, (), {self.model_keyword: self.model_name}))
|
|
|
|
def can_format(self, value: str) -> bool:
|
|
try:
|
|
self.parse(value)
|
|
return True
|
|
except ValueError:
|
|
return False
|
|
|
|
|
|
class ModelIdFormatter(ModelNameFormatter):
|
|
model_keyword: t.LiteralString = "__model_id__"
|
|
|
|
|
|
class ModelAdapterMapFormatter(ModelNameFormatter):
|
|
model_keyword: t.LiteralString = "__model_adapter_map__"
|
|
|
|
|
|
_service_file = Path(__file__).parent.parent / "_service.py"
|
|
|
|
|
|
def write_service(llm: openllm.LLM[t.Any, t.Any], adapter_map: dict[str, str | None] | None, llm_fs: FS):
|
|
from . import DEBUG
|
|
|
|
model_name = llm.config["model_name"]
|
|
|
|
logger.debug("Generating service for %s", model_name)
|
|
|
|
with open(_service_file.__fspath__(), "r") as f:
|
|
src_contents = f.readlines()
|
|
|
|
# modify with model name
|
|
for it in src_contents:
|
|
if OPENLLM_MODEL_NAME in it:
|
|
src_contents[src_contents.index(it)] = (
|
|
ModelNameFormatter(model_name).vformat(it)[: -(len(OPENLLM_MODEL_NAME) + 3)] + "\n"
|
|
)
|
|
elif OPENLLM_MODEL_ADAPTER_MAP in it:
|
|
src_contents[src_contents.index(it)] = (
|
|
ModelAdapterMapFormatter(orjson.dumps(adapter_map).decode()).vformat(it)[
|
|
: -(len(OPENLLM_MODEL_ADAPTER_MAP) + 3)
|
|
]
|
|
+ "\n"
|
|
)
|
|
|
|
script = f"# GENERATED BY 'openllm build {model_name}'. DO NOT EDIT\n\n" + "".join(src_contents)
|
|
|
|
if DEBUG:
|
|
logger.info("Generated script:\n%s", script)
|
|
|
|
llm_fs.writetext(llm.config["service_name"], script)
|
|
|
|
|
|
# NOTE: The following ins extracted from attrs internal APIs
|
|
|
|
# sentinel object for unequivocal object() getattr
|
|
_sentinel = object()
|
|
|
|
|
|
def has_own_attribute(cls: type[t.Any], attrib_name: t.Any):
|
|
"""Check whether *cls* defines *attrib_name* (and doesn't just inherit it)."""
|
|
attr = getattr(cls, attrib_name, _sentinel)
|
|
if attr is _sentinel:
|
|
return False
|
|
|
|
for base_cls in cls.__mro__[1:]:
|
|
a = getattr(base_cls, attrib_name, None)
|
|
if attr is a:
|
|
return False
|
|
|
|
return True
|
|
|
|
|
|
def get_annotations(cls: type[t.Any]) -> DictStrAny:
|
|
"""Get annotations for *cls*."""
|
|
if has_own_attribute(cls, "__annotations__"):
|
|
return cls.__annotations__
|
|
|
|
return DictStrAny()
|
|
|
|
|
|
_classvar_prefixes = (
|
|
"typing.ClassVar",
|
|
"t.ClassVar",
|
|
"ClassVar",
|
|
"typing_extensions.ClassVar",
|
|
)
|
|
|
|
|
|
def is_class_var(annot: str | t.Any) -> bool:
|
|
"""Check whether *annot* is a typing.ClassVar.
|
|
|
|
The string comparison hack is used to avoid evaluating all string
|
|
annotations which would put attrs-based classes at a performance
|
|
disadvantage compared to plain old classes.
|
|
"""
|
|
annot = str(annot)
|
|
|
|
# Annotation can be quoted.
|
|
if annot.startswith(("'", '"')) and annot.endswith(("'", '"')):
|
|
annot = annot[1:-1]
|
|
|
|
return annot.startswith(_classvar_prefixes)
|
|
|
|
|
|
def add_method_dunders(cls: type[t.Any], method_or_cls: _T, _overwrite_doc: str | None = None) -> _T:
|
|
"""Add __module__ and __qualname__ to a *method* if possible."""
|
|
try:
|
|
method_or_cls.__module__ = cls.__module__
|
|
except AttributeError:
|
|
pass
|
|
|
|
try:
|
|
method_or_cls.__qualname__ = f"{cls.__qualname__}.{method_or_cls.__name__}"
|
|
except AttributeError:
|
|
pass
|
|
|
|
try:
|
|
method_or_cls.__doc__ = (
|
|
_overwrite_doc or "Method or class generated by LLMConfig for class " f"{cls.__qualname__}."
|
|
)
|
|
except AttributeError:
|
|
pass
|
|
|
|
return method_or_cls
|
|
|
|
|
|
def _compile_and_eval(script: str, globs: DictStrAny, locs: t.Any = None, filename: str = ""):
|
|
"""Exec the script with the given global (globs) and local (locs) variables."""
|
|
bytecode = compile(script, filename, "exec")
|
|
eval(bytecode, globs, locs) # noqa: S307
|
|
|
|
|
|
# ported from attrs
|
|
def _make_method(name: str, script: str, filename: str, globs: DictStrAny) -> AnyCallable:
|
|
"""Create the method with the script given and return the method object."""
|
|
locs: DictStrAny = {}
|
|
|
|
# In order of debuggers like PDB being able to step through the code,
|
|
# we add a fake linecache entry.
|
|
count = 1
|
|
base_filename = filename
|
|
while True:
|
|
linecache_tuple = (
|
|
len(script),
|
|
None,
|
|
script.splitlines(True),
|
|
filename,
|
|
)
|
|
old_val = linecache.cache.setdefault(filename, linecache_tuple)
|
|
if old_val == linecache_tuple:
|
|
break
|
|
else:
|
|
filename = f"{base_filename[:-1]}-{count}>"
|
|
count += 1
|
|
|
|
_compile_and_eval(script, globs, locs, filename)
|
|
|
|
return locs[name]
|
|
|
|
|
|
def make_attr_tuple_class(cls_name: str, attr_names: t.Sequence[str]):
|
|
"""Create a tuple subclass to hold class attributes.
|
|
|
|
The subclass is a bare tuple with properties for names.
|
|
|
|
class MyClassAttributes(tuple):
|
|
__slots__ = ()
|
|
x = property(itemgetter(0))
|
|
"""
|
|
from . import SHOW_CODEGEN
|
|
|
|
attr_class_name = f"{cls_name}Attributes"
|
|
attr_class_template = [
|
|
f"class {attr_class_name}(tuple):",
|
|
" __slots__ = ()",
|
|
]
|
|
if attr_names:
|
|
for i, attr_name in enumerate(attr_names):
|
|
attr_class_template.append(f" {attr_name} = _attrs_property(_attrs_itemgetter({i}))")
|
|
else:
|
|
attr_class_template.append(" pass")
|
|
globs: DictStrAny = {"_attrs_itemgetter": itemgetter, "_attrs_property": property}
|
|
if SHOW_CODEGEN:
|
|
logger.info("Generated class for %s:\n\n%s", attr_class_name, "\n".join(attr_class_template))
|
|
|
|
_compile_and_eval("\n".join(attr_class_template), globs)
|
|
|
|
return globs[attr_class_name]
|
|
|
|
|
|
def generate_unique_filename(cls: type[t.Any], func_name: str):
|
|
return f"<{cls.__name__} generated {func_name} {cls.__module__}." f"{getattr(cls, '__qualname__', cls.__name__)}>"
|
|
|
|
|
|
def generate_function(
|
|
typ: type[t.Any],
|
|
func_name: str,
|
|
lines: list[str] | None,
|
|
args: tuple[str, ...] | None,
|
|
globs: dict[str, t.Any],
|
|
annotations: dict[str, t.Any] | None = None,
|
|
):
|
|
from . import SHOW_CODEGEN
|
|
|
|
script = "def %s(%s):\n %s\n" % (
|
|
func_name,
|
|
", ".join(args) if args is not None else "",
|
|
"\n ".join(lines) if lines else "pass",
|
|
)
|
|
meth = _make_method(func_name, script, generate_unique_filename(typ, func_name), globs)
|
|
if annotations:
|
|
meth.__annotations__ = annotations
|
|
|
|
if SHOW_CODEGEN:
|
|
logger.info("Generated script for %s:\n\n%s", typ, script)
|
|
|
|
return meth
|
|
|
|
|
|
def make_env_transformer(
|
|
cls: type[openllm.LLMConfig],
|
|
model_name: str,
|
|
suffix: t.LiteralString | None = None,
|
|
default_callback: t.Callable[[str, t.Any], t.Any] | None = None,
|
|
globs: DictStrAny | None = None,
|
|
):
|
|
from . import dantic
|
|
from . import field_env_key
|
|
|
|
def identity(_: str, x_value: t.Any) -> t.Any:
|
|
return x_value
|
|
|
|
default_callback = identity if default_callback is None else default_callback
|
|
|
|
globs = {} if globs is None else globs
|
|
globs.update(
|
|
{
|
|
"__populate_env": dantic.env_converter,
|
|
"__default_callback": default_callback,
|
|
"__field_env": field_env_key,
|
|
"__suffix": suffix or "",
|
|
"__model_name": model_name,
|
|
}
|
|
)
|
|
|
|
lines: ListStr = [
|
|
"__env = lambda field_name: __field_env(__model_name, field_name, __suffix)",
|
|
"return [",
|
|
" f.evolve(",
|
|
" default=__populate_env(__default_callback(f.name, f.default), __env(f.name)),",
|
|
" metadata={",
|
|
" 'env': f.metadata.get('env', __env(f.name)),",
|
|
" 'description': f.metadata.get('description', '(not provided)'),",
|
|
" },",
|
|
" )",
|
|
" for f in fields",
|
|
"]",
|
|
]
|
|
fields_ann = "list[attr.Attribute[t.Any]]"
|
|
|
|
return generate_function(
|
|
cls,
|
|
"__auto_env",
|
|
lines,
|
|
args=("_", "fields"),
|
|
globs=globs,
|
|
annotations={"_": "type[LLMConfig]", "fields": fields_ann, "return": fields_ann},
|
|
)
|
|
|
|
|
|
def gen_sdk(func: t.Callable[P, t.Any], name: str | None = None, **attrs: t.Any):
|
|
from .representation import ReprMixin
|
|
|
|
if name is None:
|
|
name = func.__name__.strip("_")
|
|
|
|
_signatures = inspect.signature(func).parameters
|
|
|
|
def _repr(self: ReprMixin) -> str:
|
|
return f"<generated function {name} {orjson.dumps(dict(self.__repr_args__()), option=orjson.OPT_NON_STR_KEYS | orjson.OPT_INDENT_2).decode()}>"
|
|
|
|
def _repr_args(self: ReprMixin) -> t.Iterator[t.Tuple[str, t.Any]]:
|
|
return ((k, _signatures[k].annotation) for k in self.__repr_keys__)
|
|
|
|
return functools.update_wrapper(
|
|
types.new_class(
|
|
name,
|
|
(PartialAny, ReprMixin),
|
|
exec_body=lambda ns: ns.update(
|
|
{
|
|
"__repr_keys__": property(lambda _: [i for i in _signatures.keys() if not i.startswith("_")]),
|
|
"__repr_args__": _repr_args,
|
|
"__repr__": _repr,
|
|
"__doc__": inspect.cleandoc(t.cast(str, func.__doc__)),
|
|
"__module__": "openllm",
|
|
}
|
|
),
|
|
)(func, **attrs),
|
|
func,
|
|
)
|