forked from phoenix-oss/llama-stack-mirror
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
15
llama_toolchain/common/config_dirs.py
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15
llama_toolchain/common/config_dirs.py
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@ -0,0 +1,15 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import os
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from pathlib import Path
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LLAMA_STACK_CONFIG_DIR = Path(os.path.expanduser("~/.llama/"))
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DISTRIBS_BASE_DIR = LLAMA_STACK_CONFIG_DIR / "distributions"
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DEFAULT_CHECKPOINT_DIR = LLAMA_STACK_CONFIG_DIR / "checkpoints"
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@ -9,9 +9,9 @@ from typing import Dict, Optional
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from llama_models.llama3_1.api.datatypes import URL
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from pydantic import BaseModel
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from llama_models.schema_utils import json_schema_type
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from strong_typing.schema import json_schema_type
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from pydantic import BaseModel
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@json_schema_type
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105
llama_toolchain/common/exec.py
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105
llama_toolchain/common/exec.py
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@ -0,0 +1,105 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import errno
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import os
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import pty
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import select
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import signal
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import subprocess
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import sys
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import termios
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from termcolor import cprint
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# run a command in a pseudo-terminal, with interrupt handling,
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# useful when you want to run interactive things
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def run_with_pty(command):
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master, slave = pty.openpty()
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old_settings = termios.tcgetattr(sys.stdin)
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original_sigint = signal.getsignal(signal.SIGINT)
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ctrl_c_pressed = False
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def sigint_handler(signum, frame):
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nonlocal ctrl_c_pressed
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ctrl_c_pressed = True
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cprint("\nCtrl-C detected. Aborting...", "white", attrs=["bold"])
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try:
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# Set up the signal handler
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signal.signal(signal.SIGINT, sigint_handler)
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new_settings = termios.tcgetattr(sys.stdin)
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new_settings[3] = new_settings[3] & ~termios.ECHO # Disable echo
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new_settings[3] = new_settings[3] & ~termios.ICANON # Disable canonical mode
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termios.tcsetattr(sys.stdin, termios.TCSADRAIN, new_settings)
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process = subprocess.Popen(
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command,
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stdin=slave,
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stdout=slave,
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stderr=slave,
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universal_newlines=True,
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preexec_fn=os.setsid,
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)
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# Close the slave file descriptor as it's now owned by the subprocess
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os.close(slave)
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def handle_io():
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while not ctrl_c_pressed:
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try:
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rlist, _, _ = select.select([sys.stdin, master], [], [], 0.1)
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if sys.stdin in rlist:
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data = os.read(sys.stdin.fileno(), 1024)
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if not data:
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break
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os.write(master, data)
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if master in rlist:
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data = os.read(master, 1024)
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if not data:
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break
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sys.stdout.buffer.write(data)
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sys.stdout.flush()
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except KeyboardInterrupt:
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# This will be raised when Ctrl+C is pressed
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break
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if process.poll() is not None:
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break
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handle_io()
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except (EOFError, KeyboardInterrupt):
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pass
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except OSError as e:
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if e.errno != errno.EIO:
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raise
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finally:
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# Clean up
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termios.tcsetattr(sys.stdin, termios.TCSADRAIN, old_settings)
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signal.signal(signal.SIGINT, original_sigint)
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os.close(master)
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if process.poll() is None:
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process.terminate()
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process.wait()
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return process.returncode
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def run_command(command):
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process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
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output, error = process.communicate()
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if process.returncode != 0:
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print(f"Error: {error.decode('utf-8')}")
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sys.exit(1)
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return output.decode("utf-8")
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8
llama_toolchain/common/model_utils.py
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8
llama_toolchain/common/model_utils.py
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@ -0,0 +1,8 @@
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import os
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from llama_models.datatypes import Model
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from .config_dirs import DEFAULT_CHECKPOINT_DIR
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def model_local_dir(model: Model) -> str:
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return os.path.join(DEFAULT_CHECKPOINT_DIR, model.descriptor())
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256
llama_toolchain/common/prompt_for_config.py
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256
llama_toolchain/common/prompt_for_config.py
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@ -0,0 +1,256 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import inspect
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import json
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from enum import Enum
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from typing import Any, get_args, get_origin, List, Literal, Optional, Type, Union
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from pydantic import BaseModel
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from pydantic.fields import ModelField
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from typing_extensions import Annotated
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def is_list_of_primitives(field_type):
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"""Check if a field type is a List of primitive types."""
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origin = get_origin(field_type)
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if origin is List or origin is list:
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args = get_args(field_type)
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if len(args) == 1 and args[0] in (int, float, str, bool):
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return True
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return False
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def get_literal_values(field):
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"""Extract literal values from a field if it's a Literal type."""
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if get_origin(field.annotation) is Literal:
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return get_args(field.annotation)
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return None
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def is_optional(field_type):
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"""Check if a field type is Optional."""
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return get_origin(field_type) is Union and type(None) in get_args(field_type)
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def get_non_none_type(field_type):
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"""Get the non-None type from an Optional type."""
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return next(arg for arg in get_args(field_type) if arg is not type(None))
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def manually_validate_field(model: Type[BaseModel], field: ModelField, value: Any):
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validators = field.class_validators.values()
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for validator in validators:
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if validator.pre:
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value = validator.func(model, value)
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# Apply type coercion
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value = field.type_(value)
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for validator in validators:
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if not validator.pre:
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value = validator.func(model, value)
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return value
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# This is somewhat elaborate, but does not purport to be comprehensive in any way.
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# We should add handling for the most common cases to tide us over.
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#
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# doesn't support List[nested_class] yet or Dicts of any kind. needs a bunch of
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# unit tests for coverage.
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def prompt_for_config(
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config_type: type[BaseModel], existing_config: Optional[BaseModel] = None
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) -> BaseModel:
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"""
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Recursively prompt the user for configuration values based on a Pydantic BaseModel.
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Args:
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config_type: A Pydantic BaseModel class representing the configuration structure.
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Returns:
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An instance of the config_type with user-provided values.
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"""
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config_data = {}
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for field_name, field in config_type.__fields__.items():
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field_type = field.annotation
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existing_value = (
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getattr(existing_config, field_name) if existing_config else None
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)
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if existing_value:
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default_value = existing_value
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else:
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default_value = (
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field.default if not isinstance(field.default, type(Ellipsis)) else None
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)
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is_required = field.required
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# Skip fields with Literal type
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if get_origin(field_type) is Literal:
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continue
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if inspect.isclass(field_type) and issubclass(field_type, Enum):
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prompt = f"Choose {field_name} (options: {', '.join(e.name for e in field_type)}):"
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while True:
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# this branch does not handle existing and default values yet
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user_input = input(prompt + " ")
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try:
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value = field_type[user_input]
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validated_value = manually_validate_field(config_type, field, value)
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config_data[field_name] = validated_value
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break
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except KeyError:
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print(
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f"Invalid choice. Please choose from: {', '.join(e.name for e in field_type)}"
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)
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continue
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# Check if the field is a discriminated union
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if get_origin(field_type) is Annotated:
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inner_type = get_args(field_type)[0]
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if get_origin(inner_type) is Union:
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discriminator = field.field_info.discriminator
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if discriminator:
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union_types = get_args(inner_type)
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# Find the discriminator field in each union type
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type_map = {}
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for t in union_types:
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disc_field = t.__fields__[discriminator]
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literal_values = get_literal_values(disc_field)
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if literal_values:
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for value in literal_values:
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type_map[value] = t
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while True:
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discriminator_value = input(
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f"Enter the {discriminator} (options: {', '.join(type_map.keys())}): "
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)
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if discriminator_value in type_map:
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chosen_type = type_map[discriminator_value]
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print(f"\nConfiguring {chosen_type.__name__}:")
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if existing_value and (
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getattr(existing_value, discriminator)
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!= discriminator_value
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):
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existing_value = None
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sub_config = prompt_for_config(chosen_type, existing_value)
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config_data[field_name] = sub_config
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# Set the discriminator field in the sub-config
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setattr(sub_config, discriminator, discriminator_value)
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break
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else:
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print(f"Invalid {discriminator}. Please try again.")
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continue
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if (
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is_optional(field_type)
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and inspect.isclass(get_non_none_type(field_type))
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and issubclass(get_non_none_type(field_type), BaseModel)
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):
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prompt = f"Do you want to configure {field_name}? (y/n): "
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if input(prompt).lower() == "n":
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config_data[field_name] = None
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continue
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nested_type = get_non_none_type(field_type)
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print(f"Entering sub-configuration for {field_name}:")
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config_data[field_name] = prompt_for_config(nested_type, existing_value)
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elif (
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inspect.isclass(field_type)
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and issubclass(field_type, BaseModel)
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and len(field_type.__fields__) > 0
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):
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print(f"\nEntering sub-configuration for {field_name}:")
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config_data[field_name] = prompt_for_config(
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field_type,
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existing_value,
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)
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else:
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prompt = f"Enter value for {field_name}"
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if existing_value is not None:
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prompt += f" (existing: {existing_value})"
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elif default_value is not None:
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prompt += f" (default: {default_value})"
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if is_optional(field_type):
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prompt += " (optional)"
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elif is_required:
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prompt += " (required)"
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prompt += ": "
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while True:
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user_input = input(prompt)
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if user_input == "":
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if default_value is not None:
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config_data[field_name] = default_value
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break
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elif is_optional(field_type) or not is_required:
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config_data[field_name] = None
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break
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else:
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print("This field is required. Please provide a value.")
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continue
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else:
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try:
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# Handle Optional types
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if is_optional(field_type):
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if user_input.lower() == "none":
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value = None
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else:
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field_type = get_non_none_type(field_type)
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value = user_input
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# Handle List of primitives
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elif is_list_of_primitives(field_type):
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try:
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value = json.loads(user_input)
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if not isinstance(value, list):
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raise ValueError(
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"Input must be a JSON-encoded list"
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)
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element_type = get_args(field_type)[0]
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value = [element_type(item) for item in value]
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except json.JSONDecodeError:
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print(
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"Invalid JSON. Please enter a valid JSON-encoded list."
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)
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continue
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except ValueError as e:
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print(f"{str(e)}")
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continue
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# Convert the input to the correct type
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elif inspect.isclass(field_type) and issubclass(
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field_type, BaseModel
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):
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# For nested BaseModels, we assume a dictionary-like string input
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import ast
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value = field_type(**ast.literal_eval(user_input))
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else:
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value = field_type(user_input)
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except ValueError:
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print(
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f"Invalid input. Expected type: {getattr(field_type, '__name__', str(field_type))}"
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)
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continue
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try:
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# Validate the field using our manual validation function
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validated_value = manually_validate_field(config_type, field, value)
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config_data[field_name] = validated_value
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break
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except ValueError as e:
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print(f"Validation error: {str(e)}")
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return config_type(**config_data)
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15
llama_toolchain/common/serialize.py
Normal file
15
llama_toolchain/common/serialize.py
Normal file
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@ -0,0 +1,15 @@
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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||||
# All rights reserved.
|
||||
#
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||||
# This source code is licensed under the terms described in the LICENSE file in
|
||||
# the root directory of this source tree.
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import json
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from enum import Enum
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class EnumEncoder(json.JSONEncoder):
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def default(self, obj):
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if isinstance(obj, Enum):
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return obj.value
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return super().default(obj)
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|
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@ -5,8 +5,8 @@
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# the root directory of this source tree.
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from llama_models.llama3_1.api.datatypes import URL
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from llama_models.schema_utils import json_schema_type
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from pydantic import BaseModel
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from strong_typing.schema import json_schema_type
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|
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|
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@json_schema_type(schema={"description": "Checkpoint created during training runs"})
|
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|
|
|
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Loading…
Add table
Add a link
Reference in a new issue