forked from phoenix-oss/llama-stack-mirror
983d6ce2df
# What does this PR do?
This PR kills the notion of "ShieldType". The impetus for this is the
realization:
> Why is keyword llama-guard appearing so many times everywhere,
sometimes with hyphens, sometimes with underscores?
Now that we have a notion of "provider specific resource identifiers"
and "user specific aliases" for those and the fact that this works with
models ("Llama3.1-8B-Instruct" <> "fireworks/llama-3pv1-..."), we can
follow the same rules for Shields.
So each Safety provider can make up a notion of identifiers it has
registered. This already happens with Bedrock correctly. We just
generalize it for Llama Guard, Prompt Guard, etc.
For Llama Guard, we further simplify by just adopting the underlying
model name itself as the identifier! No confusion necessary.
While doing this, I noticed a bug in our DistributionRegistry where we
weren't scoping identifiers by type. Fixed.
## Feature/Issue validation/testing/test plan
Ran (inference, safety, memory, agents) tests with ollama and fireworks
providers.
99 lines
3.4 KiB
Python
99 lines
3.4 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.
|
|
|
|
from typing import Any, Dict
|
|
|
|
from termcolor import colored
|
|
|
|
from llama_models.llama3.api.datatypes import * # noqa: F403
|
|
from llama_stack.apis.agents import * # noqa: F403
|
|
from llama_stack.apis.datasets import * # noqa: F403
|
|
from llama_stack.apis.datasetio import * # noqa: F403
|
|
from llama_stack.apis.scoring import * # noqa: F403
|
|
from llama_stack.apis.scoring_functions import * # noqa: F403
|
|
from llama_stack.apis.eval import * # noqa: F403
|
|
from llama_stack.apis.inference import * # noqa: F403
|
|
from llama_stack.apis.batch_inference import * # noqa: F403
|
|
from llama_stack.apis.memory import * # noqa: F403
|
|
from llama_stack.apis.telemetry import * # noqa: F403
|
|
from llama_stack.apis.post_training import * # noqa: F403
|
|
from llama_stack.apis.synthetic_data_generation import * # noqa: F403
|
|
from llama_stack.apis.safety import * # noqa: F403
|
|
from llama_stack.apis.models import * # noqa: F403
|
|
from llama_stack.apis.memory_banks import * # noqa: F403
|
|
from llama_stack.apis.shields import * # noqa: F403
|
|
from llama_stack.apis.inspect import * # noqa: F403
|
|
from llama_stack.apis.eval_tasks import * # noqa: F403
|
|
|
|
from llama_stack.distribution.datatypes import StackRunConfig
|
|
from llama_stack.distribution.distribution import get_provider_registry
|
|
from llama_stack.distribution.resolver import resolve_impls
|
|
from llama_stack.distribution.store.registry import create_dist_registry
|
|
from llama_stack.providers.datatypes import Api
|
|
|
|
|
|
class LlamaStack(
|
|
MemoryBanks,
|
|
Inference,
|
|
BatchInference,
|
|
Agents,
|
|
Safety,
|
|
SyntheticDataGeneration,
|
|
Datasets,
|
|
Telemetry,
|
|
PostTraining,
|
|
Memory,
|
|
Eval,
|
|
EvalTasks,
|
|
Scoring,
|
|
ScoringFunctions,
|
|
DatasetIO,
|
|
Models,
|
|
Shields,
|
|
Inspect,
|
|
):
|
|
pass
|
|
|
|
|
|
# Produces a stack of providers for the given run config. Not all APIs may be
|
|
# asked for in the run config.
|
|
async def construct_stack(run_config: StackRunConfig) -> Dict[Api, Any]:
|
|
dist_registry, _ = await create_dist_registry(
|
|
run_config.metadata_store, run_config.image_name
|
|
)
|
|
|
|
impls = await resolve_impls(run_config, get_provider_registry(), dist_registry)
|
|
|
|
resources = [
|
|
("models", Api.models, "register_model", "list_models"),
|
|
("shields", Api.shields, "register_shield", "list_shields"),
|
|
("memory_banks", Api.memory_banks, "register_memory_bank", "list_memory_banks"),
|
|
("datasets", Api.datasets, "register_dataset", "list_datasets"),
|
|
(
|
|
"scoring_fns",
|
|
Api.scoring_functions,
|
|
"register_scoring_function",
|
|
"list_scoring_functions",
|
|
),
|
|
("eval_tasks", Api.eval_tasks, "register_eval_task", "list_eval_tasks"),
|
|
]
|
|
for rsrc, api, register_method, list_method in resources:
|
|
objects = getattr(run_config, rsrc)
|
|
if api not in impls:
|
|
continue
|
|
|
|
method = getattr(impls[api], register_method)
|
|
for obj in objects:
|
|
await method(**obj.model_dump())
|
|
|
|
method = getattr(impls[api], list_method)
|
|
for obj in await method():
|
|
print(
|
|
f"{rsrc.capitalize()}: {colored(obj.identifier, 'white', attrs=['bold'])} served by {colored(obj.provider_id, 'white', attrs=['bold'])}",
|
|
)
|
|
|
|
print("")
|
|
return impls
|