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https://github.com/meta-llama/llama-stack.git
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0a3999a9a4
We should use Inference APIs to execute Llama Guard instead of directly needing to use HuggingFace modeling related code. The actual inference consideration is handled by Inference.
114 lines
4.4 KiB
Python
114 lines
4.4 KiB
Python
# 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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from llama_models.sku_list import resolve_model
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from llama_stack.distribution.utils.model_utils import model_local_dir
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from llama_stack.apis.inference import * # noqa: F403
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from llama_stack.apis.safety import * # noqa: F403
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from llama_models.llama3.api.datatypes import * # noqa: F403
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from llama_stack.distribution.datatypes import Api
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from llama_stack.providers.impls.meta_reference.safety.shields.base import (
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OnViolationAction,
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)
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from .config import MetaReferenceShieldType, SafetyConfig
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from .shields import (
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CodeScannerShield,
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InjectionShield,
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JailbreakShield,
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LlamaGuardShield,
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PromptGuardShield,
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ShieldBase,
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)
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def resolve_and_get_path(model_name: str) -> str:
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model = resolve_model(model_name)
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assert model is not None, f"Could not resolve model {model_name}"
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model_dir = model_local_dir(model.descriptor())
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return model_dir
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class MetaReferenceSafetyImpl(Safety):
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def __init__(self, config: SafetyConfig, deps) -> None:
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self.config = config
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self.inference_api = deps[Api.inference]
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async def initialize(self) -> None:
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shield_cfg = self.config.prompt_guard_shield
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if shield_cfg is not None:
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model_dir = resolve_and_get_path(shield_cfg.model)
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_ = PromptGuardShield.instance(model_dir)
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async def run_shield(
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self,
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shield_type: str,
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messages: List[Message],
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params: Dict[str, Any] = None,
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) -> RunShieldResponse:
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available_shields = [v.value for v in MetaReferenceShieldType]
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assert shield_type in available_shields, f"Unknown shield {shield_type}"
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shield = self.get_shield_impl(MetaReferenceShieldType(shield_type))
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messages = messages.copy()
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# some shields like llama-guard require the first message to be a user message
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# since this might be a tool call, first role might not be user
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if len(messages) > 0 and messages[0].role != Role.user.value:
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messages[0] = UserMessage(content=messages[0].content)
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# TODO: we can refactor ShieldBase, etc. to be inline with the API types
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res = await shield.run(messages)
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violation = None
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if res.is_violation and shield.on_violation_action != OnViolationAction.IGNORE:
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violation = SafetyViolation(
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violation_level=(
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ViolationLevel.ERROR
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if shield.on_violation_action == OnViolationAction.RAISE
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else ViolationLevel.WARN
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),
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user_message=res.violation_return_message,
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metadata={
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"violation_type": res.violation_type,
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},
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)
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return RunShieldResponse(violation=violation)
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def get_shield_impl(self, typ: MetaReferenceShieldType) -> ShieldBase:
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cfg = self.config
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if typ == MetaReferenceShieldType.llama_guard:
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cfg = cfg.llama_guard_shield
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assert (
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cfg is not None
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), "Cannot use LlamaGuardShield since not present in config"
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return LlamaGuardShield(
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model=cfg.model,
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inference_api=self.inference_api,
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excluded_categories=cfg.excluded_categories,
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disable_input_check=cfg.disable_input_check,
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disable_output_check=cfg.disable_output_check,
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)
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elif typ == MetaReferenceShieldType.jailbreak_shield:
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assert (
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cfg.prompt_guard_shield is not None
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), "Cannot use Jailbreak Shield since Prompt Guard not present in config"
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model_dir = resolve_and_get_path(cfg.prompt_guard_shield.model)
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return JailbreakShield.instance(model_dir)
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elif typ == MetaReferenceShieldType.injection_shield:
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assert (
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cfg.prompt_guard_shield is not None
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), "Cannot use PromptGuardShield since not present in config"
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model_dir = resolve_and_get_path(cfg.prompt_guard_shield.model)
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return InjectionShield.instance(model_dir)
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elif typ == MetaReferenceShieldType.code_scanner_guard:
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return CodeScannerShield.instance()
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else:
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raise ValueError(f"Unknown shield type: {typ}")
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