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>

llama_toolchain/inference/model_parallel.py

@@ -1,104 +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.
-
-from copy import deepcopy
-from dataclasses import dataclass
-from functools import partial
-from typing import Generator, List, Optional
-
-from llama_models.llama3_1.api.chat_format import ChatFormat
-from llama_models.llama3_1.api.datatypes import Message
-from llama_models.llama3_1.api.tokenizer import Tokenizer
-
-from .api.config import InlineImplConfig
-from .generation import Llama
-from .parallel_utils import ModelParallelProcessGroup
-
-
-@dataclass
-class InferenceArgs:
-    messages: List[Message]
-    temperature: float
-    top_p: float
-    max_gen_len: int
-    logprobs: bool
-
-
-class ModelRunner:
-    def __init__(self, llama):
-        self.llama = llama
-
-    # the `task` object is the same that is sent to `ModelParallelProcessGroup.run_inference()`
-    def __call__(self, task: InferenceArgs):
-        return self.llama.chat_completion(
-            task.messages,
-            task.temperature,
-            task.top_p,
-            task.max_gen_len,
-            task.logprobs,
-        )
-
-
-def init_model_cb(config: InlineImplConfig):
-    llama = Llama.build(config)
-    return ModelRunner(llama)
-
-
-class LlamaModelParallelGenerator:
-    """
-    This abstraction exists so
-     - we can run model parallel code without needing to run the CLIs via torchrun
-     - this also enables use model parallel code within a notebook context.
-
-    A Context Manager is used to ensure that the model parallel process is started and stopped
-    correctly. This does make the ergonomics a little awkward, because it isn't immediately
-    clear at the callsite why we need to use a context manager.
-    """
-
-    def __init__(self, config: InlineImplConfig):
-        self.config = config
-
-        # this is a hack because Agent's loop uses this to tokenize and check if input is too long
-        # while the tool-use loop is going
-        checkpoint = self.config.checkpoint_config.checkpoint
-        self.formatter = ChatFormat(Tokenizer(checkpoint.tokenizer_path))
-
-    def start(self):
-        self.__enter__()
-
-    def stop(self):
-        self.__exit__(None, None, None)
-
-    def __enter__(self):
-        checkpoint = self.config.checkpoint_config.checkpoint
-        self.group = ModelParallelProcessGroup(
-            checkpoint.model_parallel_size,
-            init_model_cb=partial(init_model_cb, self.config),
-        )
-        self.group.start()
-        return self
-
-    def __exit__(self, exc_type, exc_value, exc_traceback):
-        self.group.stop()
-
-    def chat_completion(
-        self,
-        messages: List[Message],
-        temperature: float = 0.6,
-        top_p: float = 0.9,
-        max_gen_len: Optional[int] = None,
-        logprobs: bool = False,
-    ) -> Generator:
-        req_obj = InferenceArgs(
-            messages=deepcopy(messages),
-            temperature=temperature,
-            top_p=top_p,
-            max_gen_len=max_gen_len,
-            logprobs=logprobs,
-        )
-
-        gen = self.group.run_inference(req_obj)
-        yield from gen