mirror of
https://github.com/bentoml/OpenLLM.git
synced 2026-01-23 06:52:42 -05:00
271 lines
7.5 KiB
Python
271 lines
7.5 KiB
Python
# Copyright 2023 BentoML Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import logging
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import os
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import string
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import typing as t
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from pathlib import Path
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import orjson
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if t.TYPE_CHECKING:
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from fs.base import FS
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import openllm
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DictStrAny = dict[str, t.Any]
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ListStr = list[str]
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from attr import _make_method
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else:
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# NOTE: Using internal API from attr here, since we are actually
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# allowing subclass of openllm.LLMConfig to become 'attrs'-ish
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from attr._make import _make_method
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DictStrAny = dict
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ListStr = list
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_T = t.TypeVar("_T", bound=t.Callable[..., t.Any])
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logger = logging.getLogger(__name__)
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OPENLLM_MODEL_NAME = "# openllm: model name"
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OPENLLM_MODEL_ID = "# openllm: model id"
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OPENLLM_MODEL_ADAPTER_MAP = "# openllm: model adapter map"
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class ModelNameFormatter(string.Formatter):
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model_keyword: t.LiteralString = "__model_name__"
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def __init__(self, model_name: str):
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super().__init__()
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self.model_name = model_name
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def vformat(self, format_string: str) -> str:
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return super().vformat(format_string, (), {self.model_keyword: self.model_name})
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def can_format(self, value: str) -> bool:
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try:
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self.parse(value)
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return True
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except ValueError:
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return False
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class ModelIdFormatter(ModelNameFormatter):
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model_keyword: t.LiteralString = "__model_id__"
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class ModelAdapterMapFormatter(ModelNameFormatter):
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model_keyword: t.LiteralString = "__model_adapter_map__"
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_service_file = Path(__file__).parent.parent / "_service.py"
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def write_service(llm: openllm.LLM[t.Any, t.Any], adapter_map: dict[str, str | None] | None, llm_fs: FS):
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from . import DEBUG
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model_name = llm.config["model_name"]
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logger.debug("Generating service for %s", model_name)
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with open(_service_file.__fspath__(), "r") as f:
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src_contents = f.readlines()
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# modify with model name
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for it in src_contents:
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if OPENLLM_MODEL_NAME in it:
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src_contents[src_contents.index(it)] = (
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ModelNameFormatter(model_name).vformat(it)[: -(len(OPENLLM_MODEL_NAME) + 3)] + "\n"
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)
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elif OPENLLM_MODEL_ADAPTER_MAP in it:
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src_contents[src_contents.index(it)] = (
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ModelAdapterMapFormatter(orjson.dumps(adapter_map).decode()).vformat(it)[
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: -(len(OPENLLM_MODEL_ADAPTER_MAP) + 3)
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]
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+ "\n"
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)
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script = f"# GENERATED BY 'openllm build {model_name}'. DO NOT EDIT\n\n" + "".join(src_contents)
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if DEBUG:
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logger.info("Generated script:\n%s", script)
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llm_fs.writetext(llm.config["service_name"], script)
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# NOTE: The following ins extracted from attrs internal APIs
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# sentinel object for unequivocal object() getattr
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_sentinel = object()
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def has_own_attribute(cls: type[t.Any], attrib_name: t.Any):
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"""
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Check whether *cls* defines *attrib_name* (and doesn't just inherit it).
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"""
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attr = getattr(cls, attrib_name, _sentinel)
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if attr is _sentinel:
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return False
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for base_cls in cls.__mro__[1:]:
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a = getattr(base_cls, attrib_name, None)
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if attr is a:
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return False
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return True
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def get_annotations(cls: type[t.Any]) -> DictStrAny:
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"""
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Get annotations for *cls*.
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"""
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if has_own_attribute(cls, "__annotations__"):
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return cls.__annotations__
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return DictStrAny()
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_classvar_prefixes = (
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"typing.ClassVar",
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"t.ClassVar",
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"ClassVar",
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"typing_extensions.ClassVar",
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)
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def is_class_var(annot: str | t.Any) -> bool:
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"""
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Check whether *annot* is a typing.ClassVar.
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The string comparison hack is used to avoid evaluating all string
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annotations which would put attrs-based classes at a performance
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disadvantage compared to plain old classes.
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"""
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annot = str(annot)
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# Annotation can be quoted.
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if annot.startswith(("'", '"')) and annot.endswith(("'", '"')):
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annot = annot[1:-1]
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return annot.startswith(_classvar_prefixes)
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def add_method_dunders(cls: type[t.Any], method_or_cls: _T, _overwrite_doc: str | None = None) -> _T:
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"""
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Add __module__ and __qualname__ to a *method* if possible.
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"""
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try:
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method_or_cls.__module__ = cls.__module__
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except AttributeError:
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pass
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try:
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method_or_cls.__qualname__ = ".".join((cls.__qualname__, method_or_cls.__name__))
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except AttributeError:
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pass
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try:
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method_or_cls.__doc__ = (
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_overwrite_doc or "Method or class generated by LLMConfig for class " f"{cls.__qualname__}."
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)
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except AttributeError:
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pass
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return method_or_cls
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def generate_unique_filename(cls: type[t.Any], func_name: str):
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return f"<{cls.__name__} generated {func_name} {cls.__module__}." f"{getattr(cls, '__qualname__', cls.__name__)}>"
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def generate_function(
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typ: type[t.Any],
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func_name: str,
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lines: list[str] | None,
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args: tuple[str, ...] | None,
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globs: dict[str, t.Any],
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annotations: dict[str, t.Any] | None = None,
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):
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from . import DEBUG
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script = "def %s(%s):\n %s\n" % (
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func_name,
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", ".join(args) if args is not None else "",
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"\n ".join(lines) if lines else "pass",
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)
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meth = _make_method(func_name, script, generate_unique_filename(typ, func_name), globs)
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if annotations:
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meth.__annotations__ = annotations
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if DEBUG and int(os.environ.get("OPENLLMDEVDEBUG", str(0))) > 3:
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logger.info("Generated script for %s:\n\n%s", typ, script)
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return meth
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def make_env_transformer(
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cls: type[openllm.LLMConfig],
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model_name: str,
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suffix: t.LiteralString | None = None,
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default_callback: t.Callable[[str, t.Any], t.Any] | None = None,
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globs: DictStrAny | None = None,
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):
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from . import dantic, field_env_key
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def identity(_: str, x_value: t.Any) -> t.Any:
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return x_value
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default_callback = identity if default_callback is None else default_callback
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globs = {} if globs is None else globs
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globs.update(
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{
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"__populate_env": dantic.env_converter,
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"__default_callback": default_callback,
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"__field_env": field_env_key,
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"__suffix": suffix or "",
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"__model_name": model_name,
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}
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)
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lines: ListStr = [
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"__env = lambda field_name: __field_env(__model_name, field_name, __suffix)",
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"return [",
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" f.evolve(",
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" default=__populate_env(__default_callback(f.name, f.default), __env(f.name)),",
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" metadata={",
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" 'env': f.metadata.get('env', __env(f.name)),",
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" 'description': f.metadata.get('description', '(not provided)'),",
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" },",
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" )",
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" for f in fields",
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"]",
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]
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fields_ann = "list[attr.Attribute[t.Any]]"
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return generate_function(
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cls,
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"__auto_env",
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lines,
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args=("_", "fields"),
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globs=globs,
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annotations={"_": "type[LLMConfig]", "fields": fields_ann, "return": fields_ann},
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)
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