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OpenLLM/openllm-python/src/openllm/cli/_factory.py
Aaron Pham cd872ef631 refactor: monorepo (#203)
2023-08-15 02:11:14 -04:00

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from __future__ import annotations
import functools, importlib.util, os, typing as t
import click, click_option_group as cog, inflection, orjson, bentoml, openllm
from bentoml_cli.utils import BentoMLCommandGroup
from click.shell_completion import CompletionItem
from bentoml._internal.configuration.containers import BentoMLContainer
from openllm._typing_compat import LiteralString, DictStrAny, ParamSpec, Concatenate
from . import termui
if t.TYPE_CHECKING:
import subprocess
from openllm._configuration import LLMConfig
P = ParamSpec("P")
LiteralOutput = t.Literal["json", "pretty", "porcelain"]
_AnyCallable = t.Callable[..., t.Any]
FC = t.TypeVar("FC", bound=t.Union[_AnyCallable, click.Command])
def parse_config_options(config: LLMConfig, server_timeout: int, workers_per_resource: float, device: t.Tuple[str, ...] | None, environ: DictStrAny,) -> DictStrAny:
# TODO: Support amd.com/gpu on k8s
_bentoml_config_options_env = environ.pop("BENTOML_CONFIG_OPTIONS", "")
_bentoml_config_options_opts = ["tracing.sample_rate=1.0", f"api_server.traffic.timeout={server_timeout}", f'runners."llm-{config["start_name"]}-runner".traffic.timeout={config["timeout"]}', f'runners."llm-{config["start_name"]}-runner".workers_per_resource={workers_per_resource}']
if device:
if len(device) > 1: _bentoml_config_options_opts.extend([f'runners."llm-{config["start_name"]}-runner".resources."nvidia.com/gpu"[{idx}]={dev}' for idx, dev in enumerate(device)])
else: _bentoml_config_options_opts.append(f'runners."llm-{config["start_name"]}-runner".resources."nvidia.com/gpu"=[{device[0]}]')
_bentoml_config_options_env += " " if _bentoml_config_options_env else "" + " ".join(_bentoml_config_options_opts)
environ["BENTOML_CONFIG_OPTIONS"] = _bentoml_config_options_env
return environ
_adapter_mapping_key = "adapter_map"
def _id_callback(ctx: click.Context, _: click.Parameter, value: t.Tuple[str, ...] | None) -> None:
if not value: return None
if _adapter_mapping_key not in ctx.params: ctx.params[_adapter_mapping_key] = {}
for v in value:
adapter_id, *adapter_name = v.rsplit(":", maxsplit=1)
# try to resolve the full path if users pass in relative,
# currently only support one level of resolve path with current directory
try: adapter_id = openllm.utils.resolve_user_filepath(adapter_id, os.getcwd())
except FileNotFoundError: pass
ctx.params[_adapter_mapping_key][adapter_id] = adapter_name[0] if len(adapter_name) > 0 else None
return None
def start_command_factory(group: click.Group, model: str, _context_settings: DictStrAny | None = None, _serve_grpc: bool = False) -> click.Command:
"""Generate a 'click.Command' for any given LLM.
Args:
group: the target ``click.Group`` to save this LLM cli under
model: The name of the model or the ``bentoml.Bento`` instance.
Returns:
The click.Command for starting the model server
Note that the internal commands will return the llm_config and a boolean determine
whether the server is run with GPU or not.
"""
llm_config = openllm.AutoConfig.for_model(model)
command_attrs: DictStrAny = dict(
name=llm_config["model_name"], context_settings=_context_settings or termui.CONTEXT_SETTINGS, short_help=f"Start a LLMServer for '{model}'", aliases=[llm_config["start_name"]] if llm_config["name_type"] == "dasherize" else None, help=f"""\
{llm_config['env'].start_docstring}
\b
Note: ``{llm_config['start_name']}`` can also be run with any other models available on HuggingFace
or fine-tuned variants as long as it belongs to the architecture generation ``{llm_config['architecture']}`` (trust_remote_code={llm_config['trust_remote_code']}).
\b
For example: One can start [Fastchat-T5](https://huggingface.co/lmsys/fastchat-t5-3b-v1.0) with ``openllm start flan-t5``:
\b
$ openllm start flan-t5 --model-id lmsys/fastchat-t5-3b-v1.0
\b
Available official model_id(s): [default: {llm_config['default_id']}]
\b
{orjson.dumps(llm_config['model_ids'], option=orjson.OPT_INDENT_2).decode()}
""",
)
if llm_config["requires_gpu"] and openllm.utils.device_count() < 1:
# NOTE: The model requires GPU, therefore we will return a dummy command
command_attrs.update({"short_help": "(Disabled because there is no GPU available)", "help": f"""{model} is currently not available to run on your local machine because it requires GPU for inference."""})
return noop_command(group, llm_config, _serve_grpc, **command_attrs)
@group.command(**command_attrs)
@start_decorator(llm_config, serve_grpc=_serve_grpc)
@click.pass_context
def start_cmd(
ctx: click.Context, /, server_timeout: int, model_id: str | None, model_version: str | None, workers_per_resource: t.Literal["conserved", "round_robin"] | LiteralString, device: t.Tuple[str, ...], quantize: t.Literal["int8", "int4", "gptq"] | None, bettertransformer: bool | None, runtime: t.Literal["ggml", "transformers"], fast: bool,
serialisation_format: t.Literal["safetensors", "legacy"], adapter_id: str | None, return_process: bool, **attrs: t.Any,
) -> LLMConfig | subprocess.Popen[bytes]:
fast = str(fast).upper() in openllm.utils.ENV_VARS_TRUE_VALUES
if serialisation_format == "safetensors" and quantize is not None and os.environ.get("OPENLLM_SERIALIZATION_WARNING", str(True)).upper() in openllm.utils.ENV_VARS_TRUE_VALUES:
termui.echo(f"'--quantize={quantize}' might not work with 'safetensors' serialisation format. Use with caution!. To silence this warning, set \"OPENLLM_SERIALIZATION_WARNING=False\"\nNote: You can always fallback to '--serialisation legacy' when running quantisation.", fg="yellow")
adapter_map: dict[str, str | None] | None = attrs.pop(_adapter_mapping_key, None)
config, server_attrs = llm_config.model_validate_click(**attrs)
server_timeout = openllm.utils.first_not_none(server_timeout, default=config["timeout"])
server_attrs.update({"working_dir": os.path.dirname(os.path.dirname(__file__)), "timeout": server_timeout})
if _serve_grpc: server_attrs["grpc_protocol_version"] = "v1"
# NOTE: currently, theres no development args in bentoml.Server. To be fixed upstream.
development = server_attrs.pop("development")
server_attrs.setdefault("production", not development)
wpr = openllm.utils.first_not_none(workers_per_resource, default=config["workers_per_resource"])
if isinstance(wpr, str):
if wpr == "round_robin": wpr = 1.0
elif wpr == "conserved":
if device and openllm.utils.device_count() == 0:
termui.echo("--device will have no effect as there is no GPUs available", fg="yellow")
wpr = 1.0
else:
available_gpu = len(device) if device else openllm.utils.device_count()
wpr = 1.0 if available_gpu == 0 else float(1 / available_gpu)
else:
wpr = float(wpr)
elif isinstance(wpr, int):
wpr = float(wpr)
# Create a new model env to work with the envvar during CLI invocation
env = openllm.utils.EnvVarMixin(config["model_name"], config.default_implementation(), model_id=model_id or config["default_id"], bettertransformer=bettertransformer, quantize=quantize, runtime=runtime)
prerequisite_check(ctx, config, quantize, adapter_map, int(1 / wpr))
# NOTE: This is to set current configuration
start_env = os.environ.copy()
start_env = parse_config_options(config, server_timeout, wpr, device, start_env)
if fast: termui.echo(f"Fast mode is enabled. Make sure the model is available in local store before 'start': 'openllm import {model}{' --model-id ' + model_id if model_id else ''}'", fg="yellow")
start_env.update({"OPENLLM_MODEL": model, "BENTOML_DEBUG": str(openllm.utils.get_debug_mode()), "BENTOML_HOME": os.environ.get("BENTOML_HOME", BentoMLContainer.bentoml_home.get()), "OPENLLM_ADAPTER_MAP": orjson.dumps(adapter_map).decode(), "OPENLLM_SERIALIZATION": serialisation_format, env.runtime: env["runtime_value"], env.framework: env["framework_value"]})
if env["model_id_value"]: start_env[env.model_id] = str(env["model_id_value"])
# NOTE: quantize and bettertransformer value is already assigned within env
if bettertransformer is not None: start_env[env.bettertransformer] = str(env["bettertransformer_value"])
if quantize is not None: start_env[env.quantize] = str(t.cast(str, env["quantize_value"]))
llm = openllm.utils.infer_auto_class(env["framework_value"]).for_model(model, model_id=start_env[env.model_id], model_version=model_version, llm_config=config, ensure_available=not fast, adapter_map=adapter_map, serialisation=serialisation_format)
start_env.update({env.config: llm.config.model_dump_json().decode()})
server = bentoml.GrpcServer("_service:svc", **server_attrs) if _serve_grpc else bentoml.HTTPServer("_service:svc", **server_attrs)
openllm.utils.analytics.track_start_init(llm.config)
def next_step(model_name: str, adapter_map: DictStrAny | None) -> None:
cmd_name = f"openllm build {model_name}"
if adapter_map is not None: cmd_name += " " + " ".join([f"--adapter-id {s}" for s in [f"{p}:{name}" if name not in (None, "default") else p for p, name in adapter_map.items()]])
if not openllm.utils.get_quiet_mode(): termui.echo(f"\n🚀 Next step: run '{cmd_name}' to create a Bento for {model_name}", fg="blue")
if return_process:
server.start(env=start_env, text=True)
if server.process is None: raise click.ClickException("Failed to start the server.")
return server.process
else:
try:
server.start(env=start_env, text=True, blocking=True)
except KeyboardInterrupt:
next_step(model, adapter_map)
except Exception as err:
termui.echo(f"Error caught while running LLM Server:\n{err}", fg="red")
else:
next_step(model, adapter_map)
# NOTE: Return the configuration for telemetry purposes.
return config
return start_cmd
def noop_command(group: click.Group, llm_config: LLMConfig, _serve_grpc: bool, **command_attrs: t.Any) -> click.Command:
context_settings = command_attrs.pop("context_settings", {})
context_settings.update({"ignore_unknown_options": True, "allow_extra_args": True})
command_attrs["context_settings"] = context_settings
# NOTE: The model requires GPU, therefore we will return a dummy command
@group.command(**command_attrs)
def noop(**_: t.Any) -> LLMConfig:
termui.echo("No GPU available, therefore this command is disabled", fg="red")
openllm.utils.analytics.track_start_init(llm_config)
return llm_config
return noop
def prerequisite_check(ctx: click.Context, llm_config: LLMConfig, quantize: LiteralString | None, adapter_map: dict[str, str | None] | None, num_workers: int) -> None:
if adapter_map and not openllm.utils.is_peft_available(): ctx.fail("Using adapter requires 'peft' to be available. Make sure to install with 'pip install \"openllm[fine-tune]\"'")
if quantize and llm_config.default_implementation() == "vllm": ctx.fail(f"Quantization is not yet supported with vLLM. Set '{llm_config['env']['framework']}=\"pt\"' to run with quantization.")
requirements = llm_config["requirements"]
if requirements is not None and len(requirements) > 0:
missing_requirements = [i for i in requirements if importlib.util.find_spec(inflection.underscore(i)) is None]
if len(missing_requirements) > 0: termui.echo(f"Make sure to have the following dependencies available: {missing_requirements}", fg="yellow")
def start_decorator(llm_config: LLMConfig, serve_grpc: bool = False) -> t.Callable[[FC], t.Callable[[FC], FC]]:
def wrapper(fn: FC) -> t.Callable[[FC], FC]:
composed = openllm.utils.compose(
llm_config.to_click_options, _http_server_args if not serve_grpc else _grpc_server_args,
cog.optgroup.group("General LLM Options", help=f"The following options are related to running '{llm_config['start_name']}' LLM Server."),
model_id_option(factory=cog.optgroup, model_env=llm_config["env"]),
model_version_option(factory=cog.optgroup),
cog.optgroup.option("--server-timeout", type=int, default=None, help="Server timeout in seconds"),
workers_per_resource_option(factory=cog.optgroup),
fast_option(factory=cog.optgroup),
cog.optgroup.group(
"LLM Optimization Options", help="""Optimization related options.
OpenLLM supports running model with [BetterTransformer](https://pytorch.org/blog/a-better-transformer-for-fast-transformer-encoder-inference/),
k-bit quantization (8-bit, 4-bit), GPTQ quantization, PagedAttention via vLLM.
The following are either in our roadmap or currently being worked on:
- DeepSpeed Inference: [link](https://www.deepspeed.ai/inference/)
- GGML: Fast inference on [bare metal](https://github.com/ggerganov/ggml)
""",
),
cog.optgroup.option("--device", type=openllm.utils.dantic.CUDA, multiple=True, envvar="CUDA_VISIBLE_DEVICES", callback=parse_device_callback, help=f"Assign GPU devices (if available) for {llm_config['model_name']}.", show_envvar=True),
cog.optgroup.option("--runtime", type=click.Choice(["ggml", "transformers"]), default="transformers", help="The runtime to use for the given model. Default is transformers."),
quantize_option(factory=cog.optgroup, model_env=llm_config["env"]),
bettertransformer_option(factory=cog.optgroup, model_env=llm_config["env"]),
serialisation_option(factory=cog.optgroup),
cog.optgroup.group(
"Fine-tuning related options", help="""\
Note that the argument `--adapter-id` can accept the following format:
- `--adapter-id /path/to/adapter` (local adapter)
- `--adapter-id remote/adapter` (remote adapter from HuggingFace Hub)
- `--adapter-id remote/adapter:eng_lora` (two previous adapter options with the given adapter_name)
```bash
$ openllm start opt --adapter-id /path/to/adapter_dir --adapter-id remote/adapter:eng_lora
```
""",
),
cog.optgroup.option("--adapter-id", default=None, help="Optional name or path for given LoRA adapter" + f" to wrap '{llm_config['model_name']}'", multiple=True, callback=_id_callback, metavar="[PATH | [remote/][adapter_name:]adapter_id][, ...]"),
click.option("--return-process", is_flag=True, default=False, help="Internal use only.", hidden=True),
)
return composed(fn)
return wrapper
def parse_device_callback(ctx: click.Context, param: click.Parameter, value: tuple[tuple[str], ...] | None) -> t.Tuple[str, ...] | None:
if value is None: return value
if not isinstance(value, tuple): ctx.fail(f"{param} only accept multiple values, not {type(value)} (value: {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()))
return el
# NOTE: A list of bentoml option that is not needed for parsing.
# NOTE: User shouldn't set '--working-dir', as OpenLLM will setup this.
# NOTE: production is also deprecated
_IGNORED_OPTIONS = {"working_dir", "production", "protocol_version"}
def parse_serve_args(serve_grpc: bool) -> t.Callable[[t.Callable[..., LLMConfig]], t.Callable[[FC], FC]]:
"""Parsing `bentoml serve|serve-grpc` click.Option to be parsed via `openllm start`."""
from bentoml_cli.cli import cli
command = "serve" if not serve_grpc else "serve-grpc"
group = cog.optgroup.group(f"Start a {'HTTP' if not serve_grpc else 'gRPC'} server options", help=f"Related to serving the model [synonymous to `bentoml {'serve-http' if not serve_grpc else command }`]",)
def decorator(f: t.Callable[Concatenate[int, t.Optional[str], P], LLMConfig]) -> t.Callable[[FC], FC]:
serve_command = cli.commands[command]
# The first variable is the argument bento
# The last five is from BentoMLCommandGroup.NUMBER_OF_COMMON_PARAMS
serve_options = [p for p in serve_command.params[1:-BentoMLCommandGroup.NUMBER_OF_COMMON_PARAMS] if p.name not in _IGNORED_OPTIONS]
for options in reversed(serve_options):
attrs = options.to_info_dict()
# we don't need param_type_name, since it should all be options
attrs.pop("param_type_name")
# name is not a valid args
attrs.pop("name")
# type can be determine from default value
attrs.pop("type")
param_decls = (*attrs.pop("opts"), *attrs.pop("secondary_opts"))
f = cog.optgroup.option(*param_decls, **attrs)(f)
return group(f)
return decorator
_http_server_args, _grpc_server_args = parse_serve_args(False), parse_serve_args(True)
def _click_factory_type(*param_decls: t.Any, **attrs: t.Any) -> t.Callable[[FC | None], FC]:
"""General ``@click`` decorator with some sauce.
This decorator extends the default ``@click.option`` plus a factory option and factory attr to
provide type-safe click.option or click.argument wrapper for all compatible factory.
"""
factory = attrs.pop("factory", click)
factory_attr = attrs.pop("attr", "option")
if factory_attr != "argument": attrs.setdefault("help", "General option for OpenLLM CLI.")
def decorator(f: FC | None) -> FC:
callback = getattr(factory, factory_attr, None)
if callback is None: raise ValueError(f"Factory {factory} has no attribute {factory_attr}.")
return t.cast(FC, callback(*param_decls, **attrs)(f) if f is not None else callback(*param_decls, **attrs))
return decorator
cli_option = functools.partial(_click_factory_type, attr="option")
cli_argument = functools.partial(_click_factory_type, attr="argument")
def output_option(f: _AnyCallable | None = None, *, default_value: LiteralOutput = "pretty", **attrs: t.Any) -> t.Callable[[FC], FC]:
output = ["json", "pretty", "porcelain"]
def complete_output_var(ctx: click.Context, param: click.Parameter, incomplete: str) -> list[CompletionItem]: return [CompletionItem(it) for it in output]
return cli_option("-o", "--output", "output", type=click.Choice(output), default=default_value, help="Showing output type.", show_default=True, envvar="OPENLLM_OUTPUT", show_envvar=True, shell_complete=complete_output_var, **attrs)(f)
def fast_option(f: _AnyCallable | None = None, **attrs: t.Any) -> t.Callable[[FC], FC]:
return cli_option(
"--fast/--no-fast", show_default=True, default=False, envvar="OPENLLM_USE_LOCAL_LATEST", show_envvar=True, help="""Whether to skip checking if models is already in store.
This is useful if you already downloaded or setup the model beforehand.
""", **attrs
)(f)
def machine_option(f: _AnyCallable | None = None, **attrs: t.Any) -> t.Callable[[FC], FC]: return cli_option("--machine", is_flag=True, default=False, hidden=True, **attrs)(f)
def model_id_option(f: _AnyCallable | None = None, *, model_env: openllm.utils.EnvVarMixin | None = None, **attrs: t.Any) -> t.Callable[[FC], FC]: return cli_option("--model-id", type=click.STRING, default=None, envvar=model_env.model_id if model_env is not None else None, show_envvar=model_env is not None, help="Optional model_id name or path for (fine-tune) weight.", **attrs)(f)
def model_version_option(f: _AnyCallable | None = None, **attrs: t.Any) -> t.Callable[[FC], FC]: return cli_option("--model-version", type=click.STRING, default=None, help="Optional model version to save for this model. It will be inferred automatically from model-id.", **attrs)(f)
def model_name_argument(f: _AnyCallable | None = None, required: bool = True) -> t.Callable[[FC], FC]: return cli_argument("model_name", type=click.Choice([inflection.dasherize(name) for name in openllm.CONFIG_MAPPING]), required=required)(f)
def quantize_option(f: _AnyCallable | None = None, *, build: bool = False, model_env: openllm.utils.EnvVarMixin | None = None, **attrs: t.Any) -> t.Callable[[FC], FC]:
return cli_option(
"--quantise", "--quantize", "quantize", type=click.Choice(["int8", "int4", "gptq"]), default=None, envvar=model_env.quantize if model_env is not None else None, show_envvar=model_env is not None, help="""Dynamic quantization for running this LLM.
The following quantization strategies are supported:
- ``int8``: ``LLM.int8`` for [8-bit](https://arxiv.org/abs/2208.07339) quantization.
- ``int4``: ``SpQR`` for [4-bit](https://arxiv.org/abs/2306.03078) quantization.
- ``gptq``: ``GPTQ`` [quantization](https://arxiv.org/abs/2210.17323)
> [!NOTE] that the model can also be served with quantized weights.
""" + (
"""
> [!NOTE] that this will set the mode for serving within deployment.""" if build else ""
) + """
> [!NOTE] that quantization are currently only available in *PyTorch* models.""", **attrs
)(f)
def workers_per_resource_option(f: _AnyCallable | None = None, *, build: bool = False, **attrs: t.Any) -> t.Callable[[FC], FC]:
return cli_option(
"--workers-per-resource", default=None, callback=workers_per_resource_callback, type=str, required=False, help="""Number of workers per resource assigned.
See https://docs.bentoml.org/en/latest/guides/scheduling.html#resource-scheduling-strategy
for more information. By default, this is set to 1.
> [!NOTE] ``--workers-per-resource`` will also accept the following strategies:
- ``round_robin``: Similar behaviour when setting ``--workers-per-resource 1``. This is useful for smaller models.
- ``conserved``: This will determine the number of available GPU resources, and only assign one worker for the LLMRunner. For example, if ther are 4 GPUs available, then ``conserved`` is equivalent to ``--workers-per-resource 0.25``.
""" + (
"""\n
> [!NOTE] The workers value passed into 'build' will determine how the LLM can
> be provisioned in Kubernetes as well as in standalone container. This will
> ensure it has the same effect with 'openllm start --workers ...'""" if build else ""
), **attrs
)(f)
def bettertransformer_option(f: _AnyCallable | None = None, *, build: bool = False, model_env: openllm.utils.EnvVarMixin | None = None, **attrs: t.Any) -> t.Callable[[FC], FC]:
return cli_option(
"--bettertransformer", is_flag=True, default=None, envvar=model_env.bettertransformer if model_env is not None else None, show_envvar=model_env is not None, help="Apply FasterTransformer wrapper to serve model. This will applies during serving time." if not build else "Set default environment variable whether to serve this model with FasterTransformer in build time.", **attrs
)(f)
def serialisation_option(f: _AnyCallable | None = None, **attrs: t.Any) -> t.Callable[[FC], FC]:
return cli_option(
"--serialisation", "--serialization", "serialisation_format", type=click.Choice(["safetensors", "legacy"]), default="safetensors", show_default=True, show_envvar=True, envvar="OPENLLM_SERIALIZATION", help="""Serialisation format for save/load LLM.
Currently the following strategies are supported:
- ``safetensors``: This will use safetensors format, which is synonymous to
\b
``safe_serialization=True``.
\b
> [!NOTE] that this format might not work for every cases, 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.
> [!NOTE] that GGML format is working in progress.
""", **attrs
)(f)
def container_registry_option(f: _AnyCallable | None = None, **attrs: t.Any) -> t.Callable[[FC], FC]:
return cli_option(
"--container-registry", "container_registry", type=str, default="ecr", show_default=True, show_envvar=True, envvar="OPENLLM_CONTAINER_REGISTRY", callback=container_registry_callback, help="""The default container registry to get the base image for building BentoLLM.
Currently, it supports 'ecr', 'ghcr.io', 'docker.io'
\b
> [!NOTE] that in order to build the base image, you will need a GPUs to compile custom kernel. See ``openllm ext build-base-container`` for more information.
"""
)(f)
_wpr_strategies = {"round_robin", "conserved"}
def workers_per_resource_callback(ctx: click.Context, param: click.Parameter, value: str | None) -> str | None:
if value is None: return value
value = inflection.underscore(value)
if value in _wpr_strategies: return value
else:
try: float(value) # type: ignore[arg-type]
except ValueError: raise click.BadParameter(f"'workers_per_resource' only accept '{_wpr_strategies}' as possible strategies, otherwise pass in float.", ctx, param) from None
else:
return value
def container_registry_callback(ctx: click.Context, param: click.Parameter, value: str | None) -> str | None:
if value is None: return value
if value not in openllm.bundle.supported_registries: raise click.BadParameter(f"Value must be one of {openllm.bundle.supported_registries}", ctx, param)
return value
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