mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-07-12 16:16:09 +00:00
impls -> inline, adapters -> remote (#381)
This commit is contained in:
Ashwin Bharambe
GitHub
parent
b10e9f46bb
commit
994732e2e0
169 changed files with 106 additions and 105 deletions
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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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from .config import LlamaGuardShieldConfig, SafetyConfig # noqa: F401
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async def get_provider_impl(config: SafetyConfig, deps):
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from .safety import MetaReferenceSafetyImpl
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assert isinstance(config, SafetyConfig), f"Unexpected config type: {type(config)}"
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impl = MetaReferenceSafetyImpl(config, deps)
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await impl.initialize()
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return impl
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57
llama_stack/providers/inline/meta_reference/safety/base.py
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57
llama_stack/providers/inline/meta_reference/safety/base.py
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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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from abc import ABC, abstractmethod
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from typing import List
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from llama_models.llama3.api.datatypes import interleaved_text_media_as_str, Message
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from pydantic import BaseModel
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from llama_stack.apis.safety import * # noqa: F403
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CANNED_RESPONSE_TEXT = "I can't answer that. Can I help with something else?"
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# TODO: clean this up; just remove this type completely
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class ShieldResponse(BaseModel):
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is_violation: bool
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violation_type: Optional[str] = None
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violation_return_message: Optional[str] = None
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# TODO: this is a caller / agent concern
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class OnViolationAction(Enum):
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IGNORE = 0
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WARN = 1
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RAISE = 2
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class ShieldBase(ABC):
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def __init__(
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self,
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on_violation_action: OnViolationAction = OnViolationAction.RAISE,
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):
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self.on_violation_action = on_violation_action
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@abstractmethod
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async def run(self, messages: List[Message]) -> ShieldResponse:
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raise NotImplementedError()
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def message_content_as_str(message: Message) -> str:
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return interleaved_text_media_as_str(message.content)
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class TextShield(ShieldBase):
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def convert_messages_to_text(self, messages: List[Message]) -> str:
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return "\n".join([message_content_as_str(m) for m in messages])
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async def run(self, messages: List[Message]) -> ShieldResponse:
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text = self.convert_messages_to_text(messages)
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return await self.run_impl(text)
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@abstractmethod
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async def run_impl(self, text: str) -> ShieldResponse:
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raise NotImplementedError()
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48
llama_stack/providers/inline/meta_reference/safety/config.py
Normal file
48
llama_stack/providers/inline/meta_reference/safety/config.py
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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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from enum import Enum
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from typing import List, Optional
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from llama_models.sku_list import CoreModelId, safety_models
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from pydantic import BaseModel, field_validator
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class PromptGuardType(Enum):
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injection = "injection"
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jailbreak = "jailbreak"
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class LlamaGuardShieldConfig(BaseModel):
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model: str = "Llama-Guard-3-1B"
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excluded_categories: List[str] = []
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@field_validator("model")
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@classmethod
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def validate_model(cls, model: str) -> str:
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permitted_models = [
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m.descriptor()
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for m in safety_models()
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if (
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m.core_model_id
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in {
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CoreModelId.llama_guard_3_8b,
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CoreModelId.llama_guard_3_1b,
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CoreModelId.llama_guard_3_11b_vision,
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}
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)
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]
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if model not in permitted_models:
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raise ValueError(
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f"Invalid model: {model}. Must be one of {permitted_models}"
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)
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return model
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class SafetyConfig(BaseModel):
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llama_guard_shield: Optional[LlamaGuardShieldConfig] = None
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enable_prompt_guard: Optional[bool] = False
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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 re
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from string import Template
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from typing import List, Optional
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from llama_models.llama3.api.datatypes import * # noqa: F403
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from llama_stack.apis.inference import * # noqa: F403
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from .base import CANNED_RESPONSE_TEXT, OnViolationAction, ShieldBase, ShieldResponse
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SAFE_RESPONSE = "safe"
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_INSTANCE = None
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CAT_VIOLENT_CRIMES = "Violent Crimes"
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CAT_NON_VIOLENT_CRIMES = "Non-Violent Crimes"
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CAT_SEX_CRIMES = "Sex Crimes"
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CAT_CHILD_EXPLOITATION = "Child Exploitation"
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CAT_DEFAMATION = "Defamation"
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CAT_SPECIALIZED_ADVICE = "Specialized Advice"
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CAT_PRIVACY = "Privacy"
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CAT_INTELLECTUAL_PROPERTY = "Intellectual Property"
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CAT_INDISCRIMINATE_WEAPONS = "Indiscriminate Weapons"
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CAT_HATE = "Hate"
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CAT_SELF_HARM = "Self-Harm"
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CAT_SEXUAL_CONTENT = "Sexual Content"
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CAT_ELECTIONS = "Elections"
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CAT_CODE_INTERPRETER_ABUSE = "Code Interpreter Abuse"
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SAFETY_CATEGORIES_TO_CODE_MAP = {
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CAT_VIOLENT_CRIMES: "S1",
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CAT_NON_VIOLENT_CRIMES: "S2",
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CAT_SEX_CRIMES: "S3",
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CAT_CHILD_EXPLOITATION: "S4",
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CAT_DEFAMATION: "S5",
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CAT_SPECIALIZED_ADVICE: "S6",
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CAT_PRIVACY: "S7",
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CAT_INTELLECTUAL_PROPERTY: "S8",
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CAT_INDISCRIMINATE_WEAPONS: "S9",
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CAT_HATE: "S10",
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CAT_SELF_HARM: "S11",
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CAT_SEXUAL_CONTENT: "S12",
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CAT_ELECTIONS: "S13",
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CAT_CODE_INTERPRETER_ABUSE: "S14",
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}
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DEFAULT_LG_V3_SAFETY_CATEGORIES = [
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CAT_VIOLENT_CRIMES,
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CAT_NON_VIOLENT_CRIMES,
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CAT_SEX_CRIMES,
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CAT_CHILD_EXPLOITATION,
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CAT_DEFAMATION,
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CAT_SPECIALIZED_ADVICE,
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CAT_PRIVACY,
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CAT_INTELLECTUAL_PROPERTY,
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CAT_INDISCRIMINATE_WEAPONS,
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CAT_HATE,
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CAT_SELF_HARM,
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CAT_SEXUAL_CONTENT,
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CAT_ELECTIONS,
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]
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MODEL_TO_SAFETY_CATEGORIES_MAP = {
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CoreModelId.llama_guard_3_8b.value: (
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DEFAULT_LG_V3_SAFETY_CATEGORIES + [CAT_CODE_INTERPRETER_ABUSE]
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),
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CoreModelId.llama_guard_3_1b.value: DEFAULT_LG_V3_SAFETY_CATEGORIES,
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CoreModelId.llama_guard_3_11b_vision.value: DEFAULT_LG_V3_SAFETY_CATEGORIES,
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}
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PROMPT_TASK = "Task: Check if there is unsafe content in '$agent_type' messages in conversations according our safety policy with the below categories."
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SAFETY_CATEGORIES = """
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<BEGIN UNSAFE CONTENT CATEGORIES>
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$categories
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<END UNSAFE CONTENT CATEGORIES>
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"""
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PROMPT_CONVERSATION = """
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<BEGIN CONVERSATION>
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$conversations
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<END CONVERSATION>
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"""
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PROMPT_INSTRUCTIONS = """
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Provide your safety assessment for ONLY THE LAST $agent_type message in the above conversation:
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- First line must read 'safe' or 'unsafe'.
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- If unsafe, a second line must include a comma-separated list of violated categories."""
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PROMPT_TEMPLATE = Template(
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f"{PROMPT_TASK}{SAFETY_CATEGORIES}{PROMPT_CONVERSATION}{PROMPT_INSTRUCTIONS}"
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)
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class LlamaGuardShield(ShieldBase):
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def __init__(
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self,
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model: str,
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inference_api: Inference,
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excluded_categories: List[str] = None,
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on_violation_action: OnViolationAction = OnViolationAction.RAISE,
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):
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super().__init__(on_violation_action)
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if excluded_categories is None:
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excluded_categories = []
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assert len(excluded_categories) == 0 or all(
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x in SAFETY_CATEGORIES_TO_CODE_MAP.values() for x in excluded_categories
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), "Invalid categories in excluded categories. Expected format is ['S1', 'S2', ..]"
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if model not in MODEL_TO_SAFETY_CATEGORIES_MAP:
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raise ValueError(f"Unsupported model: {model}")
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self.model = model
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self.inference_api = inference_api
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self.excluded_categories = excluded_categories
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def check_unsafe_response(self, response: str) -> Optional[str]:
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match = re.match(r"^unsafe\n(.*)$", response)
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if match:
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# extracts the unsafe code
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extracted = match.group(1)
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return extracted
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return None
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def get_safety_categories(self) -> List[str]:
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excluded_categories = self.excluded_categories
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if set(excluded_categories) == set(SAFETY_CATEGORIES_TO_CODE_MAP.values()):
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excluded_categories = []
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final_categories = []
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all_categories = MODEL_TO_SAFETY_CATEGORIES_MAP[self.model]
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for cat in all_categories:
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cat_code = SAFETY_CATEGORIES_TO_CODE_MAP[cat]
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if cat_code in excluded_categories:
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continue
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final_categories.append(f"{cat_code}: {cat}.")
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return final_categories
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def validate_messages(self, messages: List[Message]) -> None:
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if len(messages) == 0:
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raise ValueError("Messages must not be empty")
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if messages[0].role != Role.user.value:
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raise ValueError("Messages must start with user")
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if len(messages) >= 2 and (
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messages[0].role == Role.user.value and messages[1].role == Role.user.value
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):
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messages = messages[1:]
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for i in range(1, len(messages)):
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if messages[i].role == messages[i - 1].role:
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raise ValueError(
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f"Messages must alternate between user and assistant. Message {i} has the same role as message {i - 1}"
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)
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return messages
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async def run(self, messages: List[Message]) -> ShieldResponse:
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messages = self.validate_messages(messages)
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if self.model == CoreModelId.llama_guard_3_11b_vision.value:
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shield_input_message = self.build_vision_shield_input(messages)
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else:
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shield_input_message = self.build_text_shield_input(messages)
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# TODO: llama-stack inference protocol has issues with non-streaming inference code
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content = ""
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async for chunk in await self.inference_api.chat_completion(
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model=self.model,
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messages=[shield_input_message],
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stream=True,
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):
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event = chunk.event
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if event.event_type == ChatCompletionResponseEventType.progress:
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assert isinstance(event.delta, str)
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content += event.delta
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content = content.strip()
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shield_response = self.get_shield_response(content)
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return shield_response
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def build_text_shield_input(self, messages: List[Message]) -> UserMessage:
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return UserMessage(content=self.build_prompt(messages))
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def build_vision_shield_input(self, messages: List[Message]) -> UserMessage:
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conversation = []
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most_recent_img = None
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for m in messages[::-1]:
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if isinstance(m.content, str):
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conversation.append(m)
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elif isinstance(m.content, ImageMedia):
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if most_recent_img is None and m.role == Role.user.value:
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most_recent_img = m.content
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conversation.append(m)
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elif isinstance(m.content, list):
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content = []
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for c in m.content:
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if isinstance(c, str):
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content.append(c)
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elif isinstance(c, ImageMedia):
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if most_recent_img is None and m.role == Role.user.value:
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most_recent_img = c
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content.append(c)
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else:
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raise ValueError(f"Unknown content type: {c}")
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conversation.append(UserMessage(content=content))
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else:
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raise ValueError(f"Unknown content type: {m.content}")
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prompt = []
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if most_recent_img is not None:
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prompt.append(most_recent_img)
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prompt.append(self.build_prompt(conversation[::-1]))
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return UserMessage(content=prompt)
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def build_prompt(self, messages: List[Message]) -> str:
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categories = self.get_safety_categories()
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categories_str = "\n".join(categories)
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conversations_str = "\n\n".join(
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[
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f"{m.role.capitalize()}: {interleaved_text_media_as_str(m.content)}"
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for m in messages
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]
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)
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return PROMPT_TEMPLATE.substitute(
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agent_type=messages[-1].role.capitalize(),
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categories=categories_str,
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conversations=conversations_str,
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)
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def get_shield_response(self, response: str) -> ShieldResponse:
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response = response.strip()
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if response == SAFE_RESPONSE:
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return ShieldResponse(is_violation=False)
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unsafe_code = self.check_unsafe_response(response)
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if unsafe_code:
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unsafe_code_list = unsafe_code.split(",")
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if set(unsafe_code_list).issubset(set(self.excluded_categories)):
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return ShieldResponse(is_violation=False)
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return ShieldResponse(
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is_violation=True,
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violation_type=unsafe_code,
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violation_return_message=CANNED_RESPONSE_TEXT,
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)
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raise ValueError(f"Unexpected response: {response}")
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@ -0,0 +1,145 @@
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# 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.
|
||||
|
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from enum import auto, Enum
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from typing import List
|
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|
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import torch
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|
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from llama_models.llama3.api.datatypes import Message
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from termcolor import cprint
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from .base import message_content_as_str, OnViolationAction, ShieldResponse, TextShield
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class PromptGuardShield(TextShield):
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class Mode(Enum):
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INJECTION = auto()
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JAILBREAK = auto()
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_instances = {}
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_model_cache = None
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@staticmethod
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def instance(
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model_dir: str,
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threshold: float = 0.9,
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temperature: float = 1.0,
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mode: "PromptGuardShield.Mode" = Mode.JAILBREAK,
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on_violation_action=OnViolationAction.RAISE,
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) -> "PromptGuardShield":
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action_value = on_violation_action.value
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key = (model_dir, threshold, temperature, mode, action_value)
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if key not in PromptGuardShield._instances:
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PromptGuardShield._instances[key] = PromptGuardShield(
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model_dir=model_dir,
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threshold=threshold,
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temperature=temperature,
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mode=mode,
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on_violation_action=on_violation_action,
|
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)
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return PromptGuardShield._instances[key]
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def __init__(
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self,
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model_dir: str,
|
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threshold: float = 0.9,
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temperature: float = 1.0,
|
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mode: "PromptGuardShield.Mode" = Mode.JAILBREAK,
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on_violation_action: OnViolationAction = OnViolationAction.RAISE,
|
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):
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super().__init__(on_violation_action)
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assert (
|
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model_dir is not None
|
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), "Must provide a model directory for prompt injection shield"
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if temperature <= 0:
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raise ValueError("Temperature must be greater than 0")
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self.device = "cuda"
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if PromptGuardShield._model_cache is None:
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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# load model and tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_dir)
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model = AutoModelForSequenceClassification.from_pretrained(
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model_dir, device_map=self.device
|
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)
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PromptGuardShield._model_cache = (tokenizer, model)
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self.tokenizer, self.model = PromptGuardShield._model_cache
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self.temperature = temperature
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self.threshold = threshold
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self.mode = mode
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||||
|
||||
def convert_messages_to_text(self, messages: List[Message]) -> str:
|
||||
return message_content_as_str(messages[-1])
|
||||
|
||||
async def run_impl(self, text: str) -> ShieldResponse:
|
||||
# run model on messages and return response
|
||||
inputs = self.tokenizer(text, return_tensors="pt")
|
||||
inputs = {name: tensor.to(self.model.device) for name, tensor in inputs.items()}
|
||||
with torch.no_grad():
|
||||
outputs = self.model(**inputs)
|
||||
logits = outputs[0]
|
||||
probabilities = torch.softmax(logits / self.temperature, dim=-1)
|
||||
score_embedded = probabilities[0, 1].item()
|
||||
score_malicious = probabilities[0, 2].item()
|
||||
cprint(
|
||||
f"Ran PromptGuardShield and got Scores: Embedded: {score_embedded}, Malicious: {score_malicious}",
|
||||
color="magenta",
|
||||
)
|
||||
|
||||
if self.mode == self.Mode.INJECTION and (
|
||||
score_embedded + score_malicious > self.threshold
|
||||
):
|
||||
return ShieldResponse(
|
||||
is_violation=True,
|
||||
violation_type=f"prompt_injection:embedded={score_embedded},malicious={score_malicious}",
|
||||
violation_return_message="Sorry, I cannot do this.",
|
||||
)
|
||||
elif self.mode == self.Mode.JAILBREAK and score_malicious > self.threshold:
|
||||
return ShieldResponse(
|
||||
is_violation=True,
|
||||
violation_type=f"prompt_injection:malicious={score_malicious}",
|
||||
violation_return_message="Sorry, I cannot do this.",
|
||||
)
|
||||
|
||||
return ShieldResponse(
|
||||
is_violation=False,
|
||||
)
|
||||
|
||||
|
||||
class JailbreakShield(PromptGuardShield):
|
||||
def __init__(
|
||||
self,
|
||||
model_dir: str,
|
||||
threshold: float = 0.9,
|
||||
temperature: float = 1.0,
|
||||
on_violation_action: OnViolationAction = OnViolationAction.RAISE,
|
||||
):
|
||||
super().__init__(
|
||||
model_dir=model_dir,
|
||||
threshold=threshold,
|
||||
temperature=temperature,
|
||||
mode=PromptGuardShield.Mode.JAILBREAK,
|
||||
on_violation_action=on_violation_action,
|
||||
)
|
||||
|
||||
|
||||
class InjectionShield(PromptGuardShield):
|
||||
def __init__(
|
||||
self,
|
||||
model_dir: str,
|
||||
threshold: float = 0.9,
|
||||
temperature: float = 1.0,
|
||||
on_violation_action: OnViolationAction = OnViolationAction.RAISE,
|
||||
):
|
||||
super().__init__(
|
||||
model_dir=model_dir,
|
||||
threshold=threshold,
|
||||
temperature=temperature,
|
||||
mode=PromptGuardShield.Mode.INJECTION,
|
||||
on_violation_action=on_violation_action,
|
||||
)
|
||||
112
llama_stack/providers/inline/meta_reference/safety/safety.py
Normal file
112
llama_stack/providers/inline/meta_reference/safety/safety.py
Normal file
|
|
@ -0,0 +1,112 @@
|
|||
# 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, List
|
||||
|
||||
from llama_stack.distribution.utils.model_utils import model_local_dir
|
||||
from llama_stack.apis.inference import * # noqa: F403
|
||||
from llama_stack.apis.safety import * # noqa: F403
|
||||
from llama_models.llama3.api.datatypes import * # noqa: F403
|
||||
from llama_stack.distribution.datatypes import Api
|
||||
|
||||
from llama_stack.providers.datatypes import ShieldsProtocolPrivate
|
||||
|
||||
from .base import OnViolationAction, ShieldBase
|
||||
from .config import SafetyConfig
|
||||
from .llama_guard import LlamaGuardShield
|
||||
from .prompt_guard import InjectionShield, JailbreakShield, PromptGuardShield
|
||||
|
||||
|
||||
PROMPT_GUARD_MODEL = "Prompt-Guard-86M"
|
||||
|
||||
|
||||
class MetaReferenceSafetyImpl(Safety, ShieldsProtocolPrivate):
|
||||
def __init__(self, config: SafetyConfig, deps) -> None:
|
||||
self.config = config
|
||||
self.inference_api = deps[Api.inference]
|
||||
|
||||
self.available_shields = []
|
||||
if config.llama_guard_shield:
|
||||
self.available_shields.append(ShieldType.llama_guard.value)
|
||||
if config.enable_prompt_guard:
|
||||
self.available_shields.append(ShieldType.prompt_guard.value)
|
||||
|
||||
async def initialize(self) -> None:
|
||||
if self.config.enable_prompt_guard:
|
||||
model_dir = model_local_dir(PROMPT_GUARD_MODEL)
|
||||
_ = PromptGuardShield.instance(model_dir)
|
||||
|
||||
async def shutdown(self) -> None:
|
||||
pass
|
||||
|
||||
async def register_shield(self, shield: ShieldDef) -> None:
|
||||
raise ValueError("Registering dynamic shields is not supported")
|
||||
|
||||
async def list_shields(self) -> List[ShieldDef]:
|
||||
return [
|
||||
ShieldDef(
|
||||
identifier=shield_type,
|
||||
shield_type=shield_type,
|
||||
params={},
|
||||
)
|
||||
for shield_type in self.available_shields
|
||||
]
|
||||
|
||||
async def run_shield(
|
||||
self,
|
||||
shield_type: str,
|
||||
messages: List[Message],
|
||||
params: Dict[str, Any] = None,
|
||||
) -> RunShieldResponse:
|
||||
shield_def = await self.shield_store.get_shield(shield_type)
|
||||
if not shield_def:
|
||||
raise ValueError(f"Unknown shield {shield_type}")
|
||||
|
||||
shield = self.get_shield_impl(shield_def)
|
||||
|
||||
messages = messages.copy()
|
||||
# some shields like llama-guard require the first message to be a user message
|
||||
# since this might be a tool call, first role might not be user
|
||||
if len(messages) > 0 and messages[0].role != Role.user.value:
|
||||
messages[0] = UserMessage(content=messages[0].content)
|
||||
|
||||
# TODO: we can refactor ShieldBase, etc. to be inline with the API types
|
||||
res = await shield.run(messages)
|
||||
violation = None
|
||||
if res.is_violation and shield.on_violation_action != OnViolationAction.IGNORE:
|
||||
violation = SafetyViolation(
|
||||
violation_level=(
|
||||
ViolationLevel.ERROR
|
||||
if shield.on_violation_action == OnViolationAction.RAISE
|
||||
else ViolationLevel.WARN
|
||||
),
|
||||
user_message=res.violation_return_message,
|
||||
metadata={
|
||||
"violation_type": res.violation_type,
|
||||
},
|
||||
)
|
||||
|
||||
return RunShieldResponse(violation=violation)
|
||||
|
||||
def get_shield_impl(self, shield: ShieldDef) -> ShieldBase:
|
||||
if shield.shield_type == ShieldType.llama_guard.value:
|
||||
cfg = self.config.llama_guard_shield
|
||||
return LlamaGuardShield(
|
||||
model=cfg.model,
|
||||
inference_api=self.inference_api,
|
||||
excluded_categories=cfg.excluded_categories,
|
||||
)
|
||||
elif shield.shield_type == ShieldType.prompt_guard.value:
|
||||
model_dir = model_local_dir(PROMPT_GUARD_MODEL)
|
||||
subtype = shield.params.get("prompt_guard_type", "injection")
|
||||
if subtype == "injection":
|
||||
return InjectionShield.instance(model_dir)
|
||||
elif subtype == "jailbreak":
|
||||
return JailbreakShield.instance(model_dir)
|
||||
else:
|
||||
raise ValueError(f"Unknown prompt guard type: {subtype}")
|
||||
else:
|
||||
raise ValueError(f"Unknown shield type: {shield.shield_type}")
|
||||
Loading…
Add table
Add a link
Reference in a new issue