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chore(qol): update CLI options and performance upgrade for build cache (#997)

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* chore(qol): update CLI options and performance upgrade for build cache

Signed-off-by: paperspace <29749331+aarnphm@users.noreply.github.com>

* chore: update default python version for dev

Signed-off-by: paperspace <29749331+aarnphm@users.noreply.github.com>

* fix: install custom tar.gz models

Signed-off-by: paperspace <29749331+aarnphm@users.noreply.github.com>

---------

Signed-off-by: paperspace <29749331+aarnphm@users.noreply.github.com>
This commit is contained in:
Aaron Pham
2024-05-26 04:17:23 -04:00
committed by GitHub
parent bc0be036d5
commit 3f048d8a5b
14 changed files with 273 additions and 202 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
.python-version-default
View File

@@ -1 +1 @@
3.9
3.11

42
openllm-core/src/openllm_core/_schemas.py
View File

@@ -1,6 +1,6 @@
from __future__ import annotations
import pydantic, inflection, orjson, typing as t
import pydantic, orjson, typing as t
from ._configuration import LLMConfig
from .utils import gen_random_uuid
from ._typing_compat import Required, TypedDict, LiteralString
@@ -47,14 +47,10 @@ class GenerationInput(pydantic.BaseModel):
request_id: t.Optional[str] = pydantic.Field(default=None)
adapter_name: t.Optional[str] = pydantic.Field(default=None)
_class_ref: t.ClassVar[type[LLMConfig]] = pydantic.PrivateAttr()
@pydantic.field_validator('llm_config')
@classmethod
def llm_config_validator(cls, v: LLMConfig | dict[str, t.Any]) -> LLMConfig:
if isinstance(v, dict):
return cls._class_ref.model_construct_env(**v)
return v
def __init__(self, *, _internal=False, **data: t.Any):
if not _internal:
raise RuntimeError('This class is not meant to be used directly. Use "from_config" instead')
super().__init__(**data)
@pydantic.field_validator('stop')
@classmethod
@@ -81,35 +77,9 @@ class GenerationInput(pydantic.BaseModel):
flattened['stop_token_ids'] = self.stop_token_ids
return flattened
def __init__(self, /, *, _internal: bool = False, **data: t.Any) -> None:
if not _internal:
raise RuntimeError(
f'Cannot instantiate GenerationInput directly. Use "{self.__class__.__qualname__}.from_dict" instead.'
)
super().__init__(**data)
@classmethod
def from_dict(cls, structured: GenerationInputDict) -> GenerationInput:
if not hasattr(cls, '_class_ref'):
raise ValueError(
'Cannot use "from_dict" from a raw GenerationInput class. Currently only supports class created from "from_config".'
)
filtered: dict[str, t.Any] = {k: v for k, v in structured.items() if v is not None}
llm_config: dict[str, t.Any] | None = filtered.pop('llm_config', None)
if llm_config is not None:
filtered['llm_config'] = cls._class_ref.model_construct_env(**llm_config)
return cls(_internal=True, **filtered)
@classmethod
def from_config(cls, llm_config: LLMConfig) -> type[GenerationInput]:
klass = pydantic.create_model(
inflection.camelize(llm_config['start_name']) + 'GenerationInput',
__base__=cls,
llm_config=(type(llm_config), llm_config),
_class_ref=(llm_config.__class__, pydantic.PrivateAttr(default=llm_config.__class__)),
)
return klass
return cls(_internal=True, llm_config=llm_config)
# NOTE: parameters from vllm.RequestOutput and vllm.CompletionOutput since vllm is not available on CPU.

13
openllm-core/src/openllm_core/config/configuration_auto.py
View File

@@ -218,3 +218,16 @@ class AutoConfig:
raise ValueError(
f"Failed to determine config class for '{bentomodel.name}'. Make sure {bentomodel.name} is saved with openllm."
)
@classmethod
def from_id(cls, model_id: str, *, trust_remote_code: bool = False, **attrs: t.Any) -> openllm_core.LLMConfig:
import transformers
config = transformers.AutoConfig.from_pretrained(model_id, trust_remote_code=trust_remote_code)
for arch in config.architectures:
if arch in cls._architecture_mappings:
return cls.for_model(cls._architecture_mappings[arch]).model_construct_env(**attrs)
else:
raise RuntimeError(
f'Failed to determine config class for {model_id}. Got {config.architectures}, which is not yet supported (Supported: {list(cls._architecture_mappings.keys())})'
)

1
openllm-core/src/openllm_core/utils/__init__.py
View File

@@ -14,6 +14,7 @@ from ._constants import (
DEBUG as DEBUG,
SHOW_CODEGEN as SHOW_CODEGEN,
MYPY as MYPY,
OPENLLM_DEV_BUILD as OPENLLM_DEV_BUILD,
)
if t.TYPE_CHECKING:

1
openllm-core/src/openllm_core/utils/__init__.pyi
View File

@@ -44,6 +44,7 @@ DEBUG_ENV_VAR: str = ...
QUIET_ENV_VAR: str = ...
DEV_DEBUG_VAR: str = ...
WARNING_ENV_VAR: str = ...
OPENLLM_DEV_BUILD: str = ...
_T = TypeVar('_T')
R = TypeVar('R')

1
openllm-core/src/openllm_core/utils/_constants.py
View File

@@ -9,6 +9,7 @@ WARNING_ENV_VAR = 'OPENLLM_DISABLE_WARNING'
DEV_DEBUG_VAR = 'DEBUG'
ENV_VARS_TRUE_VALUES = {'1', 'ON', 'YES', 'TRUE'}
OPENLLM_DEV_BUILD = 'OPENLLM_DEV_BUILD'
def check_bool_env(env: str, default: bool = True):

4
openllm-python/pyproject.toml
View File

@@ -38,7 +38,7 @@ classifiers = [
"Programming Language :: Python :: Implementation :: PyPy",
]
dependencies = [
"bentoml[io]>=1.2",
"bentoml[io]>=1.2.16",
"transformers[torch,tokenizers]>=4.36.0",
"openllm-client>=0.5.0-alpha.14",
"openllm-core>=0.5.0-alpha.14",
@@ -112,7 +112,7 @@ gemma = ["xformers"]
ggml = ["ctransformers"]
gpt-neox = ["xformers"]
gptq = ["auto-gptq[triton]>=0.4.2"]
grpc = ["bentoml[grpc]>=1.2"]
grpc = ["bentoml[grpc]>=1.2.16"]
llama = ["xformers"]
mistral = ["xformers"]
mixtral = ["xformers"]

370
openllm-python/src/_openllm_tiny/_entrypoint.py
View File

@@ -1,16 +1,17 @@
from __future__ import annotations
import os, logging, traceback, pathlib, sys, fs, click, enum, inflection, bentoml, orjson, openllm, openllm_core, platform, typing as t
import os, traceback, io, pathlib, sys, fs, click, enum, importlib, importlib.metadata, inflection, bentoml, orjson, openllm, openllm_core as core, platform, tarfile, typing as t
from ._helpers import recommended_instance_type
from openllm_core.utils import (
DEBUG,
DEBUG_ENV_VAR,
QUIET_ENV_VAR,
OPENLLM_DEV_BUILD,
SHOW_CODEGEN,
check_bool_env,
compose,
first_not_none,
dantic,
pkg,
gen_random_uuid,
get_debug_mode,
get_quiet_mode,
@@ -25,7 +26,10 @@ from openllm_core._typing_compat import (
)
from openllm_cli import termui
logger = logging.getLogger(__name__)
if sys.version_info >= (3, 11):
import tomllib
else:
import tomli as tomllib
OPENLLM_FIGLET = """
██████╗ ██████╗ ███████╗███╗ ██╗██╗ ██╗ ███╗ ███╗
@@ -38,20 +42,21 @@ OPENLLM_FIGLET = """
_PACKAGE_NAME = 'openllm'
_TINY_PATH = pathlib.Path(os.path.abspath(__file__)).parent
_SERVICE_FILE = _TINY_PATH / '_service.py'
_SERVICE_VARS = '''\
_SERVICE_README = _TINY_PATH / 'service.md'
_SERVICE_VARS = """\
# fmt: off
# GENERATED BY 'openllm build {__model_id__}'. DO NOT EDIT
# GENERATED BY '{__command__}'. DO NOT EDIT
import orjson,openllm_core.utils as coreutils
model_id='{__model_id__}'
model_name='{__model_name__}'
revision=orjson.loads(coreutils.getenv('revision',default={__model_revision__}))
quantise=coreutils.getenv('quantize',default='{__model_quantise__}',var=['QUANTISE'])
serialisation=coreutils.getenv('serialization',default='{__model_serialization__}',var=['SERIALISATION'])
dtype=coreutils.getenv('dtype', default='{__model_dtype__}', var=['TORCH_DTYPE'])
dtype=coreutils.getenv('dtype', default='{__model_dtype__}',var=['TORCH_DTYPE'])
trust_remote_code=coreutils.check_bool_env("TRUST_REMOTE_CODE",{__model_trust_remote_code__})
max_model_len=orjson.loads(coreutils.getenv('max_model_len', default=orjson.dumps({__max_model_len__})))
gpu_memory_utilization=orjson.loads(coreutils.getenv('gpu_memory_utilization', default=orjson.dumps({__gpu_memory_utilization__}), var=['GPU_MEMORY_UTILISATION']))
services_config=orjson.loads(coreutils.getenv('services_config',"""{__services_config__}"""))
'''
max_model_len=orjson.loads(coreutils.getenv('max_model_len',default=orjson.dumps({__max_model_len__})))
gpu_memory_utilization=orjson.loads(coreutils.getenv('gpu_memory_utilization',default=orjson.dumps({__gpu_memory_utilization__}),var=['GPU_MEMORY_UTILISATION']))
services_config=orjson.loads(coreutils.getenv('services_config',default={__services_config__}))
"""
HF_HUB_DISABLE_PROGRESS_BARS = 'HF_HUB_DISABLE_PROGRESS_BARS'
@@ -62,20 +67,6 @@ class ItemState(enum.Enum):
OVERWRITE = 'OVERWRITE'
def parse_device_callback(
_: click.Context, param: click.Parameter, value: tuple[tuple[str], ...] | None
) -> t.Tuple[str, ...] | None:
if value is None:
return value
el: t.Tuple[str, ...] = tuple(i for k in value for i in k)
# NOTE: --device all is a special case
if len(el) == 1 and el[0] == 'all':
return tuple(map(str, openllm.utils.available_devices()))
if len(el) == 1 and el[0] == 'gpu':
return ('0',)
return el
@click.group(context_settings=termui.CONTEXT_SETTINGS, name='openllm')
@click.version_option(
None,
@@ -94,53 +85,47 @@ def cli() -> None:
╚═════╝ ╚═╝ ╚══════╝╚═╝ ╚═══╝╚══════╝╚══════╝╚═╝ ╚═╝.
\b
An open platform for operating large language models in production.
Fine-tune, serve, deploy, and monitor any LLMs with ease.
Self-Hosting LLMs Made Easy
"""
def optimization_decorator(fn):
# NOTE: return device, quantize, serialisation, dtype, max_model_len, gpu_memory_utilization
def optimization_decorator(fn: t.Callable[..., t.Any]):
optimization = [
click.option(
'--device',
type=dantic.CUDA,
multiple=True,
envvar='CUDA_VISIBLE_DEVICES',
callback=parse_device_callback,
help='Assign GPU devices (if available)',
'--concurrency',
type=int,
envvar='CONCURRENCY',
help='See https://docs.bentoml.com/en/latest/guides/concurrency.html#concurrency for more information.',
show_envvar=True,
default=None,
),
click.option('--timeout', type=int, default=360000, help='Timeout for the model executor in seconds'),
click.option(
'--dtype',
type=str,
envvar='DTYPE',
default='auto',
help="Optional dtype for casting tensors for running inference ['float16', 'float32', 'bfloat16', 'int8', 'int16']",
help="Optional dtype for casting tensors for running inference ['float16', 'float32', 'bfloat16']. Default to auto for infering dtype based on available accelerator.",
),
click.option(
'--quantise',
'--quantize',
'quantize',
'quantise',
type=str,
default=None,
envvar='QUANTIZE',
show_envvar=True,
help="""Dynamic quantization for running this LLM.
help="""Quantisation options for this LLM.
The following quantization strategies are supported:
The following quantisation strategies are supported:
- ``int8``: ``LLM.int8`` for [8-bit](https://arxiv.org/abs/2208.07339) quantization.
- ``gptq``: ``GPTQ`` [quantisation](https://arxiv.org/abs/2210.17323)
- ``int4``: ``SpQR`` for [4-bit](https://arxiv.org/abs/2306.03078) quantization.
- ``awq``: ``AWQ`` [AWQ: Activation-aware Weight Quantisation](https://arxiv.org/abs/2306.00978)
- ``gptq``: ``GPTQ`` [quantization](https://arxiv.org/abs/2210.17323)
- ``squeezellm``: ``SqueezeLLM`` [SqueezeLLM: Dense-and-Sparse Quantisation](https://arxiv.org/abs/2306.07629)
- ``awq``: ``AWQ`` [AWQ: Activation-aware Weight Quantization](https://arxiv.org/abs/2306.00978)
- ``squeezellm``: ``SqueezeLLM`` [SqueezeLLM: Dense-and-Sparse Quantization](https://arxiv.org/abs/2306.07629)
> [!NOTE] that the model can also be served with quantized weights.
> [!NOTE] that the model must be pre-quantised to ensure correct loading, as all aforementioned quantization scheme are post-training quantization.
""",
),
click.option(
@@ -151,14 +136,14 @@ def optimization_decorator(fn):
default=None,
show_default=True,
show_envvar=True,
envvar='OPENLLM_SERIALIZATION',
help="""Serialisation format for save/load LLM.
envvar='SERIALIZATION',
help="""Serialisation format for loading LLM. Make sure to check HF repository for the correct format.
Currently the following strategies are supported:
- ``safetensors``: This will use safetensors format, which is synonymous to ``safe_serialization=True``.
> [!NOTE] Safetensors might not work for every cases, and you can always fallback to ``legacy`` if needed.
> [!NOTE] Safetensors might not work for older models, and you can always fallback to ``legacy`` if needed.
- ``legacy``: This will use PyTorch serialisation format, often as ``.bin`` files. This should be used if the model doesn't yet support safetensors.
""",
@@ -178,14 +163,28 @@ def optimization_decorator(fn):
default=0.9,
help='The percentage of GPU memory to be used for the model executor',
),
click.option(
'--trust-remote-code',
'--trust_remote_code',
'trust_remote_code',
type=bool,
is_flag=True,
default=False,
show_envvar=True,
envvar='TRUST_REMOTE_CODE',
help='If model from HuggingFace requires custom code, pass this to enable remote code execution. If the model is a private model, make sure to also pass this argument such that OpenLLM can determine model architecture to load.',
),
]
return compose(*optimization)(fn)
def shared_decorator(fn):
def shared_decorator(fn: t.Callable[..., t.Any]):
shared = [
click.argument(
'model_id', type=click.STRING, metavar='[REMOTE_REPO/MODEL_ID | /path/to/local/model]', required=True
'model_id',
type=click.STRING,
metavar='[REMOTE_REPO/MODEL_ID | /path/to/local/model | bentomodel_tag]',
required=True,
),
click.option(
'--revision',
@@ -194,15 +193,17 @@ def shared_decorator(fn):
'model_version',
type=click.STRING,
default=None,
help='Optional model revision to save for this model. It will be inferred automatically from model-id.',
help='Optional model revision to for this LLM. If this is a private model, specify this alongside model_id will be used as a bentomodel tag. If using in conjunction with a HF model id, this will be a specific revision, code branch, or a commit id on HF repo.',
),
click.option(
'--model-tag',
'--bentomodel-tag',
'model_tag',
type=click.STRING,
default=None,
help='Optional bentomodel tag to save for this model. It will be generated automatically based on model_id and model_version if not specified.',
'--debug',
'--verbose',
type=bool,
default=False,
show_envvar=True,
is_flag=True,
envvar='DEBUG',
help='whether to enable verbose logging (For more fine-grained control, set DEBUG to number instead of this flag.).',
),
]
return compose(*shared)(fn)
@@ -210,76 +211,77 @@ def shared_decorator(fn):
@cli.command(name='start')
@shared_decorator
@click.option('--timeout', type=int, default=360000, help='Timeout for the model executor in seconds')
@click.option('--port', type=int, default=3000, help='Port to serve the LLM. Default to 3000.')
@optimization_decorator
def start_command(
model_id: str,
model_version: str | None,
model_tag: str | None,
timeout: int,
concurrency: int | None,
port: int,
device: t.Tuple[str, ...],
quantize: LiteralQuantise | None,
quantise: LiteralQuantise | None,
serialisation: LiteralSerialisation | None,
dtype: LiteralDtype | t.Literal['auto', 'float'],
max_model_len: int | None,
gpu_memory_utilization: float,
trust_remote_code: bool,
debug: bool,
):
"""Start any LLM as a REST server.
\b
```bash
$ openllm <start|start-http> <model_id> --<options> ...
$ openllm start microsoft/Phi-3-mini-4k-instruct --trust-remote-code
```
"""
import transformers
from _bentoml_impl.server import serve_http
from bentoml._internal.service.loader import load
from bentoml._internal.log import configure_server_logging
configure_server_logging()
trust_remote_code = check_bool_env('TRUST_REMOTE_CODE', False)
try:
# if given model_id is a private model, then we can use it directly
# NOTE: if given model_id is a private model (assuming this is packaged into a bentomodel), then we can use it directly
bentomodel = bentoml.models.get(model_id.lower())
model_id = bentomodel.path
if not trust_remote_code:
trust_remote_code = True
except (ValueError, bentoml.exceptions.NotFound):
bentomodel = None
config = transformers.AutoConfig.from_pretrained(model_id, trust_remote_code=trust_remote_code)
for arch in config.architectures:
if arch in openllm_core.AutoConfig._architecture_mappings:
model_name = openllm_core.AutoConfig._architecture_mappings[arch]
break
else:
raise RuntimeError(f'Failed to determine config class for {model_id}')
llm_config = openllm_core.AutoConfig.for_model(model_name).model_construct_env()
llm_config = core.AutoConfig.from_id(model_id, trust_remote_code=trust_remote_code)
if serialisation is None:
termui.warning(
f"Serialisation format is not specified. Defaulting to '{llm_config['serialisation']}'. Your model might not work with this format. Make sure to explicitly specify the serialisation format."
)
serialisation = llm_config['serialisation']
# TODO: support LoRA adapters
os.environ.update({
QUIET_ENV_VAR: str(openllm.utils.get_quiet_mode()),
DEBUG_ENV_VAR: str(openllm.utils.get_debug_mode()),
DEBUG_ENV_VAR: str(debug) or str(openllm.utils.get_debug_mode()),
HF_HUB_DISABLE_PROGRESS_BARS: str(not openllm.utils.get_debug_mode()),
'MODEL_ID': model_id,
'MODEL_NAME': model_name,
# handling custom revision if users specify --revision alongside with model_id
# this should work only if bentomodel is None
'REVISION': orjson.dumps(first_not_none(model_version, default=None)).decode(),
'SERIALIZATION': serialisation,
'OPENLLM_CONFIG': llm_config.model_dump_json(),
'DTYPE': dtype,
'TRUST_REMOTE_CODE': str(trust_remote_code),
'GPU_MEMORY_UTILIZATION': orjson.dumps(gpu_memory_utilization).decode(),
'SERVICES_CONFIG': orjson.dumps(
dict(resources={'gpu' if device else 'cpu': len(device) if device else '1'}, traffic=dict(timeout=timeout))
# XXX: right now we just enable GPU by default. will revisit this if we decide to support TPU later.
dict(
resources={'gpu': len(openllm.utils.available_devices())},
traffic=dict(timeout=timeout, concurrency=concurrency),
)
).decode(),
})
if max_model_len is not None:
os.environ['MAX_MODEL_LEN'] = orjson.dumps(max_model_len)
if quantize:
os.environ['QUANTIZE'] = str(quantize)
os.environ['MAX_MODEL_LEN'] = orjson.dumps(max_model_len).decode()
if quantise:
os.environ['QUANTIZE'] = str(quantise)
working_dir = os.path.abspath(os.path.dirname(__file__))
if sys.path[0] != working_dir:
@@ -290,22 +292,69 @@ def start_command(
)
def construct_python_options(llm_config, llm_fs):
from bentoml._internal.bento.build_config import PythonOptions
from openllm.bundle._package import build_editable
def get_package_version(_package: str) -> str:
try:
return importlib.import_module('._version', _package).__version__
except ModuleNotFoundError:
return importlib.metadata.version(_package)
# TODO: Add this line back once 0.5 is out, for now depends on OPENLLM_DEV_BUILD
# packages = ['scipy', 'bentoml[tracing]>=1.2.8', 'openllm[vllm]>0.4', 'vllm>=0.3']
packages = ['scipy', 'bentoml[tracing]>=1.2.8', 'openllm']
if llm_config['requirements'] is not None:
packages.extend(llm_config['requirements'])
built_wheels = [build_editable(llm_fs.getsyspath('/'), p) for p in ('openllm_core', 'openllm_client', 'openllm')]
return PythonOptions(
packages=packages,
wheels=[llm_fs.getsyspath(f"/{i.split('/')[-1]}") for i in built_wheels] if all(i for i in built_wheels) else None,
lock_packages=False,
def build_sdist(target_path: str, package: t.Literal['openllm', 'openllm-core', 'openllm-client']):
import tomli_w
if not check_bool_env(OPENLLM_DEV_BUILD, default=False):
return None
module_location = pkg.source_locations(inflection.underscore(package))
if not module_location:
raise RuntimeError(
f'Could not find the source location of {package}. Make sure to set "{OPENLLM_DEV_BUILD}=False" if you are not using development build.'
)
package_path = pathlib.Path(module_location)
package_version = get_package_version(package)
project_path = package_path.parent.parent
if not (project_path / 'pyproject.toml').exists():
termui.warning(
f'Custom "{package}" is detected. For a Bento to use the same build at serving time, at your custom "{package}" build to pip packages list under your "bentofile.yaml". i.e: "packages=[\'git+https://github.com/bentoml/openllm.git@bc0be03\']"'
)
return
termui.debug(
f'"{package}" is installed in "editable" mode; building "{package}" distribution with local code base. the built tar will be included in generated bento.'
)
def exclude_pycache(tarinfo: tarfile.TarInfo):
if '__pycache__' in tarinfo.name or tarinfo.name.endswith(('.pyc', '.pyo')):
return None
return tarinfo
with open(project_path / 'pyproject.toml', 'rb') as f:
pyproject_toml = tomllib.load(f)
pyproject_toml['project']['version'] = package_version
if 'dynamic' in pyproject_toml['project'] and 'version' in pyproject_toml['project']['dynamic']:
pyproject_toml['project']['dynamic'].remove('version')
pyproject_io = io.BytesIO()
tomli_w.dump(pyproject_toml, pyproject_io)
# make a tarball of this package-version
base_name = f'{package}-{package_version}'
sdist_filename = f'{base_name}.tar.gz'
files_to_include = ['src', 'LICENSE.md', 'README.md']
if not pathlib.Path(target_path).exists():
pathlib.Path(target_path).mkdir(parents=True, exist_ok=True)
with tarfile.open(pathlib.Path(target_path, sdist_filename), 'w:gz') as tar:
for file in files_to_include:
tar.add(project_path / file, arcname=f'{base_name}/{file}', filter=exclude_pycache)
if package == 'openllm':
tar.add(project_path / 'CHANGELOG.md', arcname=f'{base_name}/CHANGELOG.md', filter=exclude_pycache)
tarinfo = tar.gettarinfo(project_path / 'pyproject.toml', arcname=f'{base_name}/pyproject.toml')
tarinfo.size = pyproject_io.tell()
pyproject_io.seek(0)
tar.addfile(tarinfo, pyproject_io)
return sdist_filename
class EnvironmentEntry(TypedDict):
name: str
@@ -327,8 +376,6 @@ class EnvironmentEntry(TypedDict):
help='Optional bento version for this BentoLLM. Default is the the model revision.',
)
@click.option('--overwrite', is_flag=True, help='Overwrite existing Bento for given LLM if it already exists.')
@click.option('--nightly', is_flag=True, default=False, help='Package with OpenLLM nightly version.')
@click.option('--timeout', type=int, default=360000, help='Timeout for the model executor in seconds')
@optimization_decorator
@click.option(
'-o',
@@ -338,31 +385,28 @@ class EnvironmentEntry(TypedDict):
show_default=True,
help="Output log format. '-o tag' to display only bento tag.",
)
@click.pass_context
def build_command(
ctx: click.Context,
/,
model_id: str,
model_version: str | None,
model_tag: str | None,
bento_version: str | None,
bento_tag: str | None,
overwrite: bool,
device: t.Tuple[str, ...],
nightly: bool,
timeout: int,
quantize: LiteralQuantise | None,
concurrency: int | None,
quantise: LiteralQuantise | None,
serialisation: LiteralSerialisation | None,
dtype: LiteralDtype | t.Literal['auto', 'float'],
max_model_len: int | None,
gpu_memory_utilization: float,
output: t.Literal['default', 'tag'],
trust_remote_code: bool,
debug: bool,
):
"""Package a given models into a BentoLLM.
"""Package a given LLM into a servable artefacts.
\b
```bash
$ openllm build google/flan-t5-large
$ openllm build microsoft/Phi-3-mini-4k-instruct --trust-remote-code
```
\b
@@ -371,43 +415,42 @@ def build_command(
> to have https://github.com/NVIDIA/nvidia-container-toolkit install locally.
\b
> [!IMPORTANT]
> To build the bento with compiled OpenLLM, make sure to prepend HATCH_BUILD_HOOKS_ENABLE=1. Make sure that the deployment
> target also use the same Python version and architecture as build machine.
> [!NOTE]
> For private model, make sure to save it to the bentomodel store first. See https://docs.bentoml.com/en/latest/guides/model-store.html#model-store for more information
"""
import transformers
from bentoml._internal.configuration.containers import BentoMLContainer
from bentoml._internal.configuration import set_quiet_mode
from bentoml._internal.log import configure_logging
from bentoml._internal.bento.build_config import BentoBuildConfig
from bentoml._internal.bento.build_config import DockerOptions
from bentoml._internal.bento.build_config import ModelSpec
from bentoml._internal.configuration import set_quiet_mode
from bentoml._internal.configuration.containers import BentoMLContainer
from bentoml._internal.bento.build_config import BentoBuildConfig, DockerOptions, ModelSpec, PythonOptions
if output == 'tag':
set_quiet_mode(True)
configure_logging()
trust_remote_code = check_bool_env('TRUST_REMOTE_CODE', False)
try:
# if given model_id is a private model, then we can use it directly
# NOTE: if given model_id is a private model (assuming the model is packaged into a bentomodel), then we can use it directly
bentomodel = bentoml.models.get(model_id.lower())
model_id = bentomodel.path
_revision = bentomodel.tag.version
if not trust_remote_code:
trust_remote_code = True
except (ValueError, bentoml.exceptions.NotFound):
bentomodel = None
_revision = None
bentomodel, _revision = None, None
config = transformers.AutoConfig.from_pretrained(model_id, trust_remote_code=trust_remote_code)
for arch in config.architectures:
if arch in openllm_core.AutoConfig._architecture_mappings:
model_name = openllm_core.AutoConfig._architecture_mappings[arch]
break
else:
raise RuntimeError(f'Failed to determine config class for {model_id}')
llm_config = core.AutoConfig.from_id(model_id, trust_remote_code=trust_remote_code)
transformers_config = transformers.AutoConfig.from_pretrained(model_id, trust_remote_code=trust_remote_code)
commit_hash = getattr(transformers_config, '_commit_hash', None)
llm_config: openllm_core.LLMConfig = openllm_core.AutoConfig.for_model(model_name).model_construct_env()
# in case that user specify the revision here,
generated_uuid = gen_random_uuid()
_revision = first_not_none(_revision, model_version, commit_hash, default=generated_uuid)
model_revision = None
if bentomodel is None and model_version is not None:
# this is when --revision|--model-version is specified alongside with HF model-id, then we set it in the generated service_vars.py, then we let users manage this themselves
model_revision = model_version
_revision = first_not_none(_revision, getattr(config, '_commit_hash', None), default=gen_random_uuid())
if serialisation is None:
termui.warning(
f"Serialisation format is not specified. Defaulting to '{llm_config['serialisation']}'. Your model might not work with this format. Make sure to explicitly specify the serialisation format."
@@ -416,74 +459,93 @@ def build_command(
if bento_tag is None:
_bento_version = first_not_none(bento_version, default=_revision)
bento_tag = bentoml.Tag.from_taglike(f'{normalise_model_name(model_id)}-service:{_bento_version}'.lower().strip())
generated_tag = bentoml.Tag.from_taglike(
f'{normalise_model_name(model_id)}-service:{_bento_version}'.lower().strip()
)
else:
bento_tag = bentoml.Tag.from_taglike(bento_tag)
generated_tag = bentoml.Tag.from_taglike(bento_tag)
state = ItemState.NOT_FOUND
try:
bento = bentoml.get(bento_tag)
bento = bentoml.get(generated_tag)
if overwrite:
bentoml.delete(bento_tag)
bentoml.delete(generated_tag)
state = ItemState.OVERWRITE
raise bentoml.exceptions.NotFound(f'Rebuilding existing Bento {bento_tag}') from None
raise bentoml.exceptions.NotFound(f'Rebuilding existing Bento {generated_tag}') from None
state = ItemState.EXISTS
except bentoml.exceptions.NotFound:
if state != ItemState.OVERWRITE:
state = ItemState.ADDED
labels = {'library': 'vllm'}
labels = {'runtime': 'vllm'}
# XXX: right now we just enable GPU by default. will revisit this if we decide to support TPU later.
service_config = dict(
resources={
'gpu' if device else 'cpu': len(device) if device else '1',
'gpu_type': recommended_instance_type(model_id, bentomodel, serialisation),
},
traffic=dict(timeout=timeout),
resources=dict(
gpu=len(openllm.utils.available_devices()),
gpu_type=recommended_instance_type(model_id, bentomodel, serialisation),
),
traffic=dict(timeout=timeout, concurrency=concurrency),
)
with fs.open_fs(f'temp://llm_{gen_random_uuid()}') as llm_fs:
logger.debug('Generating service vars %s (dir=%s)', model_id, llm_fs.getsyspath('/'))
with fs.open_fs(f'temp://{gen_random_uuid()}') as llm_fs, fs.open_fs(
f'temp://wheels_{gen_random_uuid()}'
) as wheel_fs:
termui.debug(f'Generating service vars {model_id} (dir={llm_fs.getsyspath("/")})')
script = _SERVICE_VARS.format(
__command__=' '.join(['openllm', *sys.argv[1:]]),
__model_id__=model_id,
__model_name__=model_name,
__model_quantise__=quantize,
__model_revision__=orjson.dumps(model_revision),
__model_quantise__=quantise,
__model_dtype__=dtype,
__model_serialization__=serialisation,
__model_trust_remote_code__=trust_remote_code,
__max_model_len__=max_model_len,
__gpu_memory_utilization__=gpu_memory_utilization,
__services_config__=orjson.dumps(service_config).decode(),
__services_config__=orjson.dumps(service_config),
)
models = []
if bentomodel is not None:
models.append(ModelSpec.from_item({'tag': str(bentomodel.tag), 'alias': bentomodel.tag.name}))
if SHOW_CODEGEN:
logger.info('Generated _service_vars.py:\n%s', script)
termui.info(f'\n{"=" * 27}\nGenerated _service_vars.py:\n\n{script}\n{"=" * 27}\n')
llm_fs.writetext('_service_vars.py', script)
with _SERVICE_README.open('r') as f:
service_readme = f.read()
service_readme = service_readme.format(model_id=model_id)
with _SERVICE_FILE.open('r') as f:
service_src = f.read()
llm_fs.writetext(llm_config['service_name'], service_src)
built_wheels = [build_sdist(wheel_fs.getsyspath('/'), p) for p in ('openllm-core', 'openllm-client', 'openllm')]
bento = bentoml.Bento.create(
version=bento_tag.version,
version=generated_tag.version,
build_ctx=llm_fs.getsyspath('/'),
build_config=BentoBuildConfig(
service=f"{llm_config['service_name']}:LLMService",
name=bento_tag.name,
name=generated_tag.name,
labels=labels,
models=models,
models=[ModelSpec.from_item({'tag': str(bentomodel.tag), 'alias': bentomodel.tag.name})]
if bentomodel is not None
else [],
envs=[
EnvironmentEntry(name='NVIDIA_DRIVER_CAPABILITIES', value='compute,utility'),
EnvironmentEntry(name='VLLM_VERSION', value='0.4.2'),
EnvironmentEntry(name=HF_HUB_DISABLE_PROGRESS_BARS, value='TRUE'),
],
description=f"OpenLLM service for {llm_config['start_name']}",
description=service_readme,
include=list(llm_fs.walk.files()),
exclude=['/venv', '/.venv', '__pycache__/', '*.py[cod]', '*$py.class'],
python=construct_python_options(llm_config, llm_fs),
python=PythonOptions(
packages=['scipy', 'bentoml[tracing]>=1.2.16', 'openllm[vllm]'],
pip_args='--no-color --progress-bar off',
wheels=[wheel_fs.getsyspath(f"/{i.split('/')[-1]}") for i in built_wheels]
if all(i for i in built_wheels)
else None,
lock_packages=False,
),
docker=DockerOptions(
python_version='3.11',
setup_script=str(_TINY_PATH / 'setup.sh') if nightly else None,
setup_script=str(_TINY_PATH / 'setup.sh'),
dockerfile_template=str(_TINY_PATH / 'Dockerfile.j2'),
system_packages=['git'],
),
),
).save(bento_store=BentoMLContainer.bento_store.get(), model_store=BentoMLContainer.model_store.get())
@@ -500,7 +562,7 @@ def build_command(
termui.info(f"Successfully built Bento '{bento.tag}'.\n")
elif not overwrite:
termui.warning(f"Bento for '{model_id}' already exists [{bento}]. To overwrite it pass '--overwrite'.\n")
if not get_debug_mode():
if not (debug or get_debug_mode()):
termui.echo(OPENLLM_FIGLET)
termui.echo('📖 Next steps:\n', nl=False)
termui.echo(f'☁️ Deploy to BentoCloud:\n $ bentoml deploy {bento.tag} -n ${{DEPLOYMENT_NAME}}\n', nl=False)

8
openllm-python/src/_openllm_tiny/_service.py
View File

@@ -24,14 +24,14 @@ try:
except Exception:
bentomodel = None
model_id = svars.model_id
LLMConfig = core.AutoConfig.for_model(svars.model_name)
LLMConfig = core.AutoConfig.from_id(model_id, trust_remote_code=svars.trust_remote_code)
GenerationInput = core.GenerationInput.from_config(LLMConfig)
ChatMessages = [
MessageParam(
role='system',
content='You are acting as Ernest Hemmingway. All of your response will follow his and his writing style ONLY.',
),
MessageParam(role='user', content='Write an essay about Nietzsche and absurdism.'),
MessageParam(role='user', content='Write an essay on absurdism and its impact in the 20th century.'),
]
app_v1 = FastAPI(
@@ -42,6 +42,9 @@ app_v1 = FastAPI(
contact={'name': 'BentoML Team', 'email': 'contact@bentoml.com'},
)
if core.utils.get_debug_mode():
logger.info('service_config: %s', svars.services_config)
@bentoml.mount_asgi_app(app_v1, path='/v1')
@bentoml.service(name=f"llm-{LLMConfig['start_name']}-service", **svars.services_config)
@@ -53,6 +56,7 @@ class LLMService:
model_id,
dtype=svars.dtype,
bentomodel=bentomodel,
revision=svars.revision,
serialisation=svars.serialisation,
quantise=svars.quantise,
llm_config=LLMConfig,

4
openllm-python/src/_openllm_tiny/_service_vars.py
View File

@@ -4,7 +4,7 @@ from _openllm_tiny._llm import Dtype
(
model_id,
model_name,
revision,
quantise,
serialisation,
dtype,
@@ -14,7 +14,7 @@ from _openllm_tiny._llm import Dtype
services_config,
) = (
coreutils.getenv('model_id', var=['MODEL_ID'], return_type=str),
coreutils.getenv('model_name', return_type=str),
orjson.loads(coreutils.getenv('revision', return_type=str)),
coreutils.getenv('quantize', var=['QUANTISE'], return_type=LiteralQuantise),
coreutils.getenv('serialization', default='safetensors', var=['SERIALISATION'], return_type=LiteralSerialisation),
coreutils.getenv('dtype', default='auto', var=['TORCH_DTYPE'], return_type=Dtype),

10
openllm-python/src/_openllm_tiny/service.md Normal file
View File

@@ -0,0 +1,10 @@
## OpenLLM generated services for {model_id}
Available endpoints:
- `GET /v1/models`: compatible with OpenAI's models list.
- `POST /v1/chat/completions`: compatible with OpenAI's chat completions client.
- `POST /v1/generate_stream`: low-level SSE-formatted streams. Users are responsible for correct prompt format strings.
- `POST /v1/generate`: low-level one-shot generation. Users are responsible for correct prompt format strings.
- `POST /v1/helpers/messages`: helpers endpoints to return fully formatted chat messages should the model is a chat model.
- `POST /v1/metadata (deprecated)`: returns compatible metadata for OpenLLM server.

13
openllm-python/src/_openllm_tiny/setup.sh
View File

@@ -1,5 +1,14 @@
#!/usr/bin/env bash
set -Eexuo pipefail
pip3 install --no-color --progress-bar off --pre openllm openllm-core openllm-client || true
FLASH_ATTENTION_SKIP_CUDA_BUILD=TRUE pip3 install --no-build-isolation --no-color --progress-bar off flash-attn==2.5.7 || true
BASEDIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]:-$0}")" &>/dev/null && pwd 2>/dev/null)"
PARENT_DIR="$(dirname -- "$BASEDIR")"
WHEELS_DIR="${PARENT_DIR}/python/wheels"
pushd "${PARENT_DIR}/python" &>/dev/null
shopt -s nullglob
targzs=($WHEELS_DIR/*.tar.gz)
if [ ${#targzs[@]} -gt 0 ]; then
echo "Installing tar.gz packaged in Bento.."
pip3 install --no-color --progress-bar off "${targzs[@]}"
fi
popd &>/dev/null

4
openllm-python/src/_service_vars.pyi
View File

@@ -1,9 +1,9 @@
from typing import Dict, Optional, Any
from openllm_core._typing_compat import LiteralSerialisation, LiteralQuantise, LiteralString
from openllm_core._typing_compat import LiteralSerialisation, LiteralQuantise
from _openllm_tiny._llm import Dtype
model_id: str = ...
model_name: LiteralString = ...
revision: str = ...
model_tag: Optional[str] = ...
model_version: Optional[str] = ...
quantise: LiteralQuantise = ...

2
tools/dependencies.py
View File

@@ -141,7 +141,7 @@ class Dependencies:
return cls(*decls)
_LOWER_BENTOML_CONSTRAINT = '1.2'
_LOWER_BENTOML_CONSTRAINT = '1.2.16'
_BENTOML_EXT = ['io']
_TRANSFORMERS_EXT = ['torch', 'tokenizers']
_TRANSFORMERS_CONSTRAINTS = '4.36.0'