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cleanup, moving stuff to common, nuke utils

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This commit is contained in:
Ashwin Bharambe 2024-08-03 20:32:57 -07:00
parent fe582a739d
commit 803976df26
13 changed files with 263 additions and 396 deletions
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226
llama_toolchain/cli/distribution/configure.py
View file

@ -5,22 +5,16 @@
# the root directory of this source tree.
import argparse
import importlib
import inspect
import json
import shlex
from enum import Enum
from pathlib import Path
from typing import get_args, get_origin, List, Literal, Optional, Union
import yaml
from pydantic import BaseModel
from termcolor import cprint
from typing_extensions import Annotated
from llama_toolchain.cli.subcommand import Subcommand
from llama_toolchain.utils import DISTRIBS_BASE_DIR, EnumEncoder
from llama_toolchain.common.config_dirs import DISTRIBS_BASE_DIR
class DistributionConfigure(Subcommand):
@ -66,9 +60,11 @@ class DistributionConfigure(Subcommand):
def configure_llama_distribution(dist: "Distribution", conda_env: str):
from llama_toolchain.common.exec import run_command
from llama_toolchain.common.prompt_for_config import prompt_for_config
from llama_toolchain.common.serialize import EnumEncoder
from llama_toolchain.distribution.datatypes import PassthroughApiAdapter
from .utils import run_command
from llama_toolchain.distribution.dynamic import instantiate_class_type
python_exe = run_command(shlex.split("which python"))
# simple check
@ -121,215 +117,3 @@ def configure_llama_distribution(dist: "Distribution", conda_env: str):
fp.write(yaml.dump(dist_config, sort_keys=False))
print(f"YAML configuration has been written to {config_path}")
def instantiate_class_type(fully_qualified_name):
module_name, class_name = fully_qualified_name.rsplit(".", 1)
module = importlib.import_module(module_name)
return getattr(module, class_name)
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))
# TODO: maybe support List values (for simple types, it should be comma-separated and for complex ones,
# it should prompt iteratively if the user wants to add more values)
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:
config_data[field_name] = field_type[user_input]
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() != "y":
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):
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
try:
# Handle Optional types
if is_optional(field_type):
if user_input.lower() == "none":
config_data[field_name] = None
break
field_type = get_non_none_type(field_type)
# Handle List of primitives
if 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]
config_data[field_name] = [
element_type(item) for item in value
]
break
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
if inspect.isclass(field_type) and issubclass(
field_type, BaseModel
):
# For nested BaseModels, we assume a dictionary-like string input
import ast
config_data[field_name] = field_type(
**ast.literal_eval(user_input)
)
else:
config_data[field_name] = field_type(user_input)
break
except ValueError:
print(
f"Invalid input. Expected type: {getattr(field_type, '__name__', str(field_type))}"
)
return config_type(**config_data)

4
llama_toolchain/cli/distribution/install.py
View file

@ -11,7 +11,7 @@ import shlex
import pkg_resources
from llama_toolchain.cli.subcommand import Subcommand
from llama_toolchain.utils import DISTRIBS_BASE_DIR
from llama_toolchain.common.config_dirs import DISTRIBS_BASE_DIR
class DistributionInstall(Subcommand):
@ -46,9 +46,9 @@ class DistributionInstall(Subcommand):
)
def _run_distribution_install_cmd(self, args: argparse.Namespace) -> None:
from llama_toolchain.common.exec import run_command, run_with_pty
from llama_toolchain.distribution.distribution import distribution_dependencies
from llama_toolchain.distribution.registry import resolve_distribution
from .utils import run_command, run_with_pty
os.makedirs(DISTRIBS_BASE_DIR, exist_ok=True)
script = pkg_resources.resource_filename(

10
llama_toolchain/cli/distribution/start.py
View file

@ -11,7 +11,7 @@ from pathlib import Path
import yaml
from llama_toolchain.cli.subcommand import Subcommand
from llama_toolchain.utils import DISTRIBS_BASE_DIR
from llama_toolchain.common.config_dirs import DISTRIBS_BASE_DIR
class DistributionStart(Subcommand):
@ -48,9 +48,9 @@ class DistributionStart(Subcommand):
)
def _run_distribution_start_cmd(self, args: argparse.Namespace) -> None:
from llama_toolchain.common.exec import run_command
from llama_toolchain.distribution.registry import resolve_distribution
from llama_toolchain.distribution.server import main as distribution_server_init
from .utils import run_command
dist = resolve_distribution(args.name)
if dist is None:
@ -67,6 +67,7 @@ class DistributionStart(Subcommand):
config = yaml.safe_load(fp)
conda_env = config["conda_env"]
python_exe = run_command(shlex.split("which python"))
# simple check, unfortunate
if conda_env not in python_exe:
@ -80,8 +81,3 @@ class DistributionStart(Subcommand):
args.port,
disable_ipv6=args.disable_ipv6,
)
# run_with_pty(
# shlex.split(
# f"conda run -n {conda_env} python -m llama_toolchain.distribution.server {dist.name} {config_yaml} --port 5000"
# )
# )

5
llama_toolchain/cli/download.py
View file

@ -16,10 +16,7 @@ import httpx
from termcolor import cprint
from llama_toolchain.cli.subcommand import Subcommand
from llama_toolchain.utils import LLAMA_STACK_CONFIG_DIR
DEFAULT_CHECKPOINT_DIR = os.path.join(LLAMA_STACK_CONFIG_DIR, "checkpoints")
from llama_toolchain.common.config_dirs import DEFAULT_CHECKPOINT_DIR
class Download(Subcommand):

91
llama_toolchain/cli/inference/configure.py
View file

@ -1,91 +0,0 @@
# 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 argparse
import os
import textwrap
from pathlib import Path
import pkg_resources
from llama_toolchain.cli.subcommand import Subcommand
from llama_toolchain.utils import LLAMA_STACK_CONFIG_DIR
CONFIGS_BASE_DIR = os.path.join(LLAMA_STACK_CONFIG_DIR, "configs")
class InferenceConfigure(Subcommand):
"""Llama cli for configuring llama toolchain configs"""
def __init__(self, subparsers: argparse._SubParsersAction):
super().__init__()
self.parser = subparsers.add_parser(
"configure",
prog="llama inference configure",
description="Configure llama toolchain inference configs",
epilog=textwrap.dedent(
"""
Example:
llama inference configure
"""
),
formatter_class=argparse.RawTextHelpFormatter,
)
self._add_arguments()
self.parser.set_defaults(func=self._run_inference_configure_cmd)
def _add_arguments(self):
pass
def read_user_inputs(self):
checkpoint_dir = input(
"Enter the checkpoint directory for the model (e.g., ~/.llama/checkpoints/Meta-Llama-3-8B/): "
)
model_parallel_size = input(
"Enter model parallel size (e.g., 1 for 8B / 8 for 70B and 405B): "
)
assert model_parallel_size.isdigit() and int(model_parallel_size) in {
1,
8,
}, "model parallel size must be 1 or 8"
return checkpoint_dir, model_parallel_size
def write_output_yaml(self, checkpoint_dir, model_parallel_size, yaml_output_path):
default_conf_path = pkg_resources.resource_filename(
"llama_toolchain", "data/default_inference_config.yaml"
)
with open(default_conf_path, "r") as f:
yaml_content = f.read()
yaml_content = yaml_content.format(
checkpoint_dir=checkpoint_dir,
model_parallel_size=model_parallel_size,
)
with open(yaml_output_path, "w") as yaml_file:
yaml_file.write(yaml_content.strip())
print(f"YAML configuration has been written to {yaml_output_path}")
def _run_inference_configure_cmd(self, args: argparse.Namespace) -> None:
checkpoint_dir, model_parallel_size = self.read_user_inputs()
checkpoint_dir = os.path.expanduser(checkpoint_dir)
assert (
Path(checkpoint_dir).exists() and Path(checkpoint_dir).is_dir()
), f"{checkpoint_dir} does not exist or it not a directory"
os.makedirs(CONFIGS_BASE_DIR, exist_ok=True)
yaml_output_path = Path(CONFIGS_BASE_DIR) / "inference.yaml"
self.write_output_yaml(
checkpoint_dir,
model_parallel_size,
yaml_output_path,
)

34
llama_toolchain/cli/inference/inference.py
View file

@ -1,34 +0,0 @@
# 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 argparse
import textwrap
from llama_toolchain.cli.inference.configure import InferenceConfigure
from llama_toolchain.cli.subcommand import Subcommand
class InferenceParser(Subcommand):
"""Llama cli for inference apis"""
def __init__(self, subparsers: argparse._SubParsersAction):
super().__init__()
self.parser = subparsers.add_parser(
"inference",
prog="llama inference",
description="Run inference on a llama model",
epilog=textwrap.dedent(
"""
Example:
llama inference start <options>
"""
),
)
subparsers = self.parser.add_subparsers(title="inference_subcommands")
# Add sub-commands
InferenceConfigure.create(subparsers)

2
llama_toolchain/cli/model/describe.py
View file

@ -13,7 +13,7 @@ from termcolor import colored
from llama_toolchain.cli.subcommand import Subcommand
from llama_toolchain.cli.table import print_table
from llama_toolchain.utils import EnumEncoder
from llama_toolchain.common.serialize import EnumEncoder
class ModelDescribe(Subcommand):

15
llama_toolchain/common/config_dirs.py Normal file
View file

@ -0,0 +1,15 @@
# 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 os
from pathlib import Path
LLAMA_STACK_CONFIG_DIR = os.path.expanduser("~/.llama/")
DISTRIBS_BASE_DIR = Path(LLAMA_STACK_CONFIG_DIR) / "distributions"
DEFAULT_CHECKPOINT_DIR = Path(LLAMA_STACK_CONFIG_DIR) / "checkpoints"

2
llama_toolchain/cli/distribution/utils.py → llama_toolchain/common/exec.py
View file

@ -16,6 +16,8 @@ import termios
from termcolor import cprint
# run a command in a pseudo-terminal, with interrupt handling,
# useful when you want to run interactive things
def run_with_pty(command):
master, slave = pty.openpty()

224
llama_toolchain/common/prompt_for_config.py Normal file
View file

@ -0,0 +1,224 @@
# 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 get_args, get_origin, List, Literal, Optional, Union
from pydantic import BaseModel
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))
# 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:
config_data[field_name] = field_type[user_input]
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() != "y":
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):
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
try:
# Handle Optional types
if is_optional(field_type):
if user_input.lower() == "none":
config_data[field_name] = None
break
field_type = get_non_none_type(field_type)
# Handle List of primitives
if 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]
config_data[field_name] = [
element_type(item) for item in value
]
break
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
if inspect.isclass(field_type) and issubclass(
field_type, BaseModel
):
# For nested BaseModels, we assume a dictionary-like string input
import ast
config_data[field_name] = field_type(
**ast.literal_eval(user_input)
)
else:
config_data[field_name] = field_type(user_input)
break
except ValueError:
print(
f"Invalid input. Expected type: {getattr(field_type, '__name__', str(field_type))}"
)
return config_type(**config_data)

10
llama_toolchain/cli/inference/__init__.py → llama_toolchain/common/serialize.py
View file

@ -4,4 +4,12 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from .inference import InferenceParser # noqa
import json
from enum import Enum
class EnumEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, Enum):
return obj.value
return super().default(obj)

2
llama_toolchain/inference/inference.py
View file

@ -54,7 +54,7 @@ class MetaReferenceInferenceImpl(Inference):
async def initialize(self) -> None:
self.generator = LlamaModelParallelGenerator(self.config)
# self.generator.start()
self.generator.start()
async def shutdown(self) -> None:
self.generator.stop()

34
llama_toolchain/utils.py
View file

@ -1,34 +0,0 @@
# 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 json
import os
from enum import Enum
from pathlib import Path
LLAMA_STACK_CONFIG_DIR = os.path.expanduser("~/.llama/")
DISTRIBS_BASE_DIR = Path(LLAMA_STACK_CONFIG_DIR) / "distributions"
def get_root_directory():
current_dir = os.path.dirname(os.path.abspath(__file__))
while os.path.isfile(os.path.join(current_dir, "__init__.py")):
current_dir = os.path.dirname(current_dir)
return current_dir
def get_default_config_dir():
return os.path.join(LLAMA_STACK_CONFIG_DIR, "configs")
class EnumEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, Enum):
return obj.value
return super().default(obj)