mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-06-28 02:53:30 +00:00
Introduce Llama stack distributions (#22)
* Add distribution CLI scaffolding * More progress towards `llama distribution install` * getting closer to a distro definition, distro install + configure works * Distribution server now functioning * read existing configuration, save enums properly * Remove inference uvicorn server entrypoint and llama inference CLI command * updated dependency and client model name * Improved exception handling * local imports for faster cli * undo a typo, add a passthrough distribution * implement full-passthrough in the server * add safety adapters, configuration handling, server + clients * cleanup, moving stuff to common, nuke utils * Add a Path() wrapper at the earliest place * fixes * Bring agentic system api to toolchain Add adapter dependencies and resolve adapters using a topological sort * refactor to reduce size of `agentic_system` * move straggler files and fix some important existing bugs * ApiSurface -> Api * refactor a method out * Adapter -> Provider * Make each inference provider into its own subdirectory * installation fixes * Rename Distribution -> DistributionSpec, simplify RemoteProviders * dict key instead of attr * update inference config to take model and not model_dir * Fix passthrough streaming, send headers properly not part of body :facepalm * update safety to use model sku ids and not model dirs * Update cli_reference.md * minor fixes * add DistributionConfig, fix a bug in model download * Make install + start scripts do proper configuration automatically * Update CLI_reference * Nuke fp8_requirements, fold fbgemm into common requirements * Update README, add newline between API surface configurations * Refactor download functionality out of the Command so can be reused * Add `llama model download` alias for `llama download` * Show message about checksum file so users can check themselves * Simpler intro statements * get ollama working * Reduce a bunch of dependencies from toolchain Some improvements to the distribution install script * Avoid using `conda run` since it buffers everything * update dependencies and rely on LLAMA_TOOLCHAIN_DIR for dev purposes * add validation for configuration input * resort imports * make optional subclasses default to yes for configuration * Remove additional_pip_packages; move deps to providers * for inline make 8b model the default * Add scripts to MANIFEST * allow installing from test.pypi.org * Fix #2 to help with testing packages * Must install llama-models at that same version first * fix PIP_ARGS --------- Co-authored-by: Hardik Shah <hjshah@fb.com> Co-authored-by: Hardik Shah <hjshah@meta.com>
This commit is contained in:
Ashwin Bharambe
GitHub
parent
da4645a27a
commit
e830814399
115 changed files with 5839 additions and 1120 deletions
256
llama_toolchain/common/prompt_for_config.py
Normal file
256
llama_toolchain/common/prompt_for_config.py
Normal file
|
|
@ -0,0 +1,256 @@
|
|||
# Copyright (c) Meta Platforms, Inc. and affiliates.
|
||||
# All rights reserved.
|
||||
#
|
||||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
|
||||
|
||||
import inspect
|
||||
import json
|
||||
from enum import Enum
|
||||
|
||||
from typing import Any, get_args, get_origin, List, Literal, Optional, Type, Union
|
||||
|
||||
from pydantic import BaseModel
|
||||
from pydantic.fields import ModelField
|
||||
|
||||
from typing_extensions import Annotated
|
||||
|
||||
|
||||
def is_list_of_primitives(field_type):
|
||||
"""Check if a field type is a List of primitive types."""
|
||||
origin = get_origin(field_type)
|
||||
if origin is List or origin is list:
|
||||
args = get_args(field_type)
|
||||
if len(args) == 1 and args[0] in (int, float, str, bool):
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
def get_literal_values(field):
|
||||
"""Extract literal values from a field if it's a Literal type."""
|
||||
if get_origin(field.annotation) is Literal:
|
||||
return get_args(field.annotation)
|
||||
return None
|
||||
|
||||
|
||||
def is_optional(field_type):
|
||||
"""Check if a field type is Optional."""
|
||||
return get_origin(field_type) is Union and type(None) in get_args(field_type)
|
||||
|
||||
|
||||
def get_non_none_type(field_type):
|
||||
"""Get the non-None type from an Optional type."""
|
||||
return next(arg for arg in get_args(field_type) if arg is not type(None))
|
||||
|
||||
|
||||
def manually_validate_field(model: Type[BaseModel], field: ModelField, value: Any):
|
||||
validators = field.class_validators.values()
|
||||
|
||||
for validator in validators:
|
||||
if validator.pre:
|
||||
value = validator.func(model, value)
|
||||
|
||||
# Apply type coercion
|
||||
value = field.type_(value)
|
||||
|
||||
for validator in validators:
|
||||
if not validator.pre:
|
||||
value = validator.func(model, value)
|
||||
|
||||
return value
|
||||
|
||||
|
||||
# This is somewhat elaborate, but does not purport to be comprehensive in any way.
|
||||
# We should add handling for the most common cases to tide us over.
|
||||
#
|
||||
# doesn't support List[nested_class] yet or Dicts of any kind. needs a bunch of
|
||||
# unit tests for coverage.
|
||||
def prompt_for_config(
|
||||
config_type: type[BaseModel], existing_config: Optional[BaseModel] = None
|
||||
) -> BaseModel:
|
||||
"""
|
||||
Recursively prompt the user for configuration values based on a Pydantic BaseModel.
|
||||
|
||||
Args:
|
||||
config_type: A Pydantic BaseModel class representing the configuration structure.
|
||||
|
||||
Returns:
|
||||
An instance of the config_type with user-provided values.
|
||||
"""
|
||||
config_data = {}
|
||||
|
||||
for field_name, field in config_type.__fields__.items():
|
||||
field_type = field.annotation
|
||||
|
||||
existing_value = (
|
||||
getattr(existing_config, field_name) if existing_config else None
|
||||
)
|
||||
if existing_value:
|
||||
default_value = existing_value
|
||||
else:
|
||||
default_value = (
|
||||
field.default if not isinstance(field.default, type(Ellipsis)) else None
|
||||
)
|
||||
is_required = field.required
|
||||
|
||||
# Skip fields with Literal type
|
||||
if get_origin(field_type) is Literal:
|
||||
continue
|
||||
|
||||
if inspect.isclass(field_type) and issubclass(field_type, Enum):
|
||||
prompt = f"Choose {field_name} (options: {', '.join(e.name for e in field_type)}):"
|
||||
while True:
|
||||
# this branch does not handle existing and default values yet
|
||||
user_input = input(prompt + " ")
|
||||
try:
|
||||
value = field_type[user_input]
|
||||
validated_value = manually_validate_field(config_type, field, value)
|
||||
config_data[field_name] = validated_value
|
||||
break
|
||||
except KeyError:
|
||||
print(
|
||||
f"Invalid choice. Please choose from: {', '.join(e.name for e in field_type)}"
|
||||
)
|
||||
continue
|
||||
|
||||
# Check if the field is a discriminated union
|
||||
if get_origin(field_type) is Annotated:
|
||||
inner_type = get_args(field_type)[0]
|
||||
if get_origin(inner_type) is Union:
|
||||
discriminator = field.field_info.discriminator
|
||||
if discriminator:
|
||||
union_types = get_args(inner_type)
|
||||
# Find the discriminator field in each union type
|
||||
type_map = {}
|
||||
for t in union_types:
|
||||
disc_field = t.__fields__[discriminator]
|
||||
literal_values = get_literal_values(disc_field)
|
||||
if literal_values:
|
||||
for value in literal_values:
|
||||
type_map[value] = t
|
||||
|
||||
while True:
|
||||
discriminator_value = input(
|
||||
f"Enter the {discriminator} (options: {', '.join(type_map.keys())}): "
|
||||
)
|
||||
if discriminator_value in type_map:
|
||||
chosen_type = type_map[discriminator_value]
|
||||
print(f"\nConfiguring {chosen_type.__name__}:")
|
||||
|
||||
if existing_value and (
|
||||
getattr(existing_value, discriminator)
|
||||
!= discriminator_value
|
||||
):
|
||||
existing_value = None
|
||||
|
||||
sub_config = prompt_for_config(chosen_type, existing_value)
|
||||
config_data[field_name] = sub_config
|
||||
# Set the discriminator field in the sub-config
|
||||
setattr(sub_config, discriminator, discriminator_value)
|
||||
break
|
||||
else:
|
||||
print(f"Invalid {discriminator}. Please try again.")
|
||||
continue
|
||||
|
||||
if (
|
||||
is_optional(field_type)
|
||||
and inspect.isclass(get_non_none_type(field_type))
|
||||
and issubclass(get_non_none_type(field_type), BaseModel)
|
||||
):
|
||||
prompt = f"Do you want to configure {field_name}? (y/n): "
|
||||
if input(prompt).lower() == "n":
|
||||
config_data[field_name] = None
|
||||
continue
|
||||
nested_type = get_non_none_type(field_type)
|
||||
print(f"Entering sub-configuration for {field_name}:")
|
||||
config_data[field_name] = prompt_for_config(nested_type, existing_value)
|
||||
elif (
|
||||
inspect.isclass(field_type)
|
||||
and issubclass(field_type, BaseModel)
|
||||
and len(field_type.__fields__) > 0
|
||||
):
|
||||
print(f"\nEntering sub-configuration for {field_name}:")
|
||||
config_data[field_name] = prompt_for_config(
|
||||
field_type,
|
||||
existing_value,
|
||||
)
|
||||
else:
|
||||
prompt = f"Enter value for {field_name}"
|
||||
if existing_value is not None:
|
||||
prompt += f" (existing: {existing_value})"
|
||||
elif default_value is not None:
|
||||
prompt += f" (default: {default_value})"
|
||||
if is_optional(field_type):
|
||||
prompt += " (optional)"
|
||||
elif is_required:
|
||||
prompt += " (required)"
|
||||
prompt += ": "
|
||||
|
||||
while True:
|
||||
user_input = input(prompt)
|
||||
if user_input == "":
|
||||
if default_value is not None:
|
||||
config_data[field_name] = default_value
|
||||
break
|
||||
elif is_optional(field_type) or not is_required:
|
||||
config_data[field_name] = None
|
||||
break
|
||||
else:
|
||||
print("This field is required. Please provide a value.")
|
||||
continue
|
||||
else:
|
||||
try:
|
||||
# Handle Optional types
|
||||
if is_optional(field_type):
|
||||
if user_input.lower() == "none":
|
||||
value = None
|
||||
else:
|
||||
field_type = get_non_none_type(field_type)
|
||||
value = user_input
|
||||
|
||||
# Handle List of primitives
|
||||
elif is_list_of_primitives(field_type):
|
||||
try:
|
||||
value = json.loads(user_input)
|
||||
if not isinstance(value, list):
|
||||
raise ValueError(
|
||||
"Input must be a JSON-encoded list"
|
||||
)
|
||||
element_type = get_args(field_type)[0]
|
||||
value = [element_type(item) for item in value]
|
||||
|
||||
except json.JSONDecodeError:
|
||||
print(
|
||||
"Invalid JSON. Please enter a valid JSON-encoded list."
|
||||
)
|
||||
continue
|
||||
except ValueError as e:
|
||||
print(f"{str(e)}")
|
||||
continue
|
||||
|
||||
# Convert the input to the correct type
|
||||
elif inspect.isclass(field_type) and issubclass(
|
||||
field_type, BaseModel
|
||||
):
|
||||
# For nested BaseModels, we assume a dictionary-like string input
|
||||
import ast
|
||||
|
||||
value = field_type(**ast.literal_eval(user_input))
|
||||
else:
|
||||
value = field_type(user_input)
|
||||
|
||||
except ValueError:
|
||||
print(
|
||||
f"Invalid input. Expected type: {getattr(field_type, '__name__', str(field_type))}"
|
||||
)
|
||||
continue
|
||||
|
||||
try:
|
||||
# Validate the field using our manual validation function
|
||||
validated_value = manually_validate_field(config_type, field, value)
|
||||
config_data[field_name] = validated_value
|
||||
break
|
||||
except ValueError as e:
|
||||
print(f"Validation error: {str(e)}")
|
||||
|
||||
return config_type(**config_data)
|
||||
Loading…
Add table
Add a link
Reference in a new issue