forked from phoenix-oss/llama-stack-mirror
694c142b89
* Add provider deprecation support; change directory structure * fix a couple dangling imports * move the meta_reference safety dir also
93 lines
3.1 KiB
Python
93 lines
3.1 KiB
Python
# 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 copy import deepcopy
|
|
from functools import partial
|
|
from typing import Any, Generator
|
|
|
|
from llama_models.llama3.api.chat_format import ChatFormat
|
|
from llama_models.llama3.api.tokenizer import Tokenizer
|
|
from llama_models.sku_list import resolve_model
|
|
|
|
from llama_stack.apis.inference import ChatCompletionRequest, CompletionRequest
|
|
|
|
from .config import MetaReferenceInferenceConfig
|
|
from .generation import Llama, model_checkpoint_dir
|
|
from .parallel_utils import ModelParallelProcessGroup
|
|
|
|
|
|
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, req: Any):
|
|
if isinstance(req, ChatCompletionRequest):
|
|
return self.llama.chat_completion(req)
|
|
elif isinstance(req, CompletionRequest):
|
|
return self.llama.completion(req)
|
|
else:
|
|
raise ValueError(f"Unexpected task type {type(req)}")
|
|
|
|
|
|
def init_model_cb(config: MetaReferenceInferenceConfig):
|
|
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: MetaReferenceInferenceConfig):
|
|
self.config = config
|
|
self.model = resolve_model(self.config.model)
|
|
# 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_dir = model_checkpoint_dir(self.model)
|
|
tokenizer_path = os.path.join(checkpoint_dir, "tokenizer.model")
|
|
self.formatter = ChatFormat(Tokenizer(tokenizer_path))
|
|
|
|
def start(self):
|
|
self.__enter__()
|
|
|
|
def stop(self):
|
|
self.__exit__(None, None, None)
|
|
|
|
def __enter__(self):
|
|
self.group = ModelParallelProcessGroup(
|
|
self.config.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 completion(
|
|
self,
|
|
request: CompletionRequest,
|
|
) -> Generator:
|
|
req_obj = deepcopy(request)
|
|
gen = self.group.run_inference(req_obj)
|
|
yield from gen
|
|
|
|
def chat_completion(
|
|
self,
|
|
request: ChatCompletionRequest,
|
|
) -> Generator:
|
|
req_obj = deepcopy(request)
|
|
gen = self.group.run_inference(req_obj)
|
|
yield from gen
|