forked from phoenix-oss/llama-stack-mirror
268 lines
8.9 KiB
Python
268 lines
8.9 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 re
|
|
|
|
from string import Template
|
|
from typing import List, Optional
|
|
|
|
from llama_models.llama3.api.datatypes import * # noqa: F403
|
|
from llama_stack.apis.inference import * # noqa: F403
|
|
|
|
from .base import CANNED_RESPONSE_TEXT, OnViolationAction, ShieldBase, ShieldResponse
|
|
|
|
|
|
SAFE_RESPONSE = "safe"
|
|
_INSTANCE = None
|
|
|
|
CAT_VIOLENT_CRIMES = "Violent Crimes"
|
|
CAT_NON_VIOLENT_CRIMES = "Non-Violent Crimes"
|
|
CAT_SEX_CRIMES = "Sex Crimes"
|
|
CAT_CHILD_EXPLOITATION = "Child Exploitation"
|
|
CAT_DEFAMATION = "Defamation"
|
|
CAT_SPECIALIZED_ADVICE = "Specialized Advice"
|
|
CAT_PRIVACY = "Privacy"
|
|
CAT_INTELLECTUAL_PROPERTY = "Intellectual Property"
|
|
CAT_INDISCRIMINATE_WEAPONS = "Indiscriminate Weapons"
|
|
CAT_HATE = "Hate"
|
|
CAT_SELF_HARM = "Self-Harm"
|
|
CAT_SEXUAL_CONTENT = "Sexual Content"
|
|
CAT_ELECTIONS = "Elections"
|
|
CAT_CODE_INTERPRETER_ABUSE = "Code Interpreter Abuse"
|
|
|
|
|
|
SAFETY_CATEGORIES_TO_CODE_MAP = {
|
|
CAT_VIOLENT_CRIMES: "S1",
|
|
CAT_NON_VIOLENT_CRIMES: "S2",
|
|
CAT_SEX_CRIMES: "S3",
|
|
CAT_CHILD_EXPLOITATION: "S4",
|
|
CAT_DEFAMATION: "S5",
|
|
CAT_SPECIALIZED_ADVICE: "S6",
|
|
CAT_PRIVACY: "S7",
|
|
CAT_INTELLECTUAL_PROPERTY: "S8",
|
|
CAT_INDISCRIMINATE_WEAPONS: "S9",
|
|
CAT_HATE: "S10",
|
|
CAT_SELF_HARM: "S11",
|
|
CAT_SEXUAL_CONTENT: "S12",
|
|
CAT_ELECTIONS: "S13",
|
|
CAT_CODE_INTERPRETER_ABUSE: "S14",
|
|
}
|
|
|
|
|
|
DEFAULT_LG_V3_SAFETY_CATEGORIES = [
|
|
CAT_VIOLENT_CRIMES,
|
|
CAT_NON_VIOLENT_CRIMES,
|
|
CAT_SEX_CRIMES,
|
|
CAT_CHILD_EXPLOITATION,
|
|
CAT_DEFAMATION,
|
|
CAT_SPECIALIZED_ADVICE,
|
|
CAT_PRIVACY,
|
|
CAT_INTELLECTUAL_PROPERTY,
|
|
CAT_INDISCRIMINATE_WEAPONS,
|
|
CAT_HATE,
|
|
CAT_SELF_HARM,
|
|
CAT_SEXUAL_CONTENT,
|
|
CAT_ELECTIONS,
|
|
]
|
|
|
|
|
|
MODEL_TO_SAFETY_CATEGORIES_MAP = {
|
|
CoreModelId.llama_guard_3_8b.value: (
|
|
DEFAULT_LG_V3_SAFETY_CATEGORIES + [CAT_CODE_INTERPRETER_ABUSE]
|
|
),
|
|
CoreModelId.llama_guard_3_1b.value: DEFAULT_LG_V3_SAFETY_CATEGORIES,
|
|
CoreModelId.llama_guard_3_11b_vision.value: DEFAULT_LG_V3_SAFETY_CATEGORIES,
|
|
}
|
|
|
|
|
|
PROMPT_TASK = "Task: Check if there is unsafe content in '$agent_type' messages in conversations according our safety policy with the below categories."
|
|
|
|
SAFETY_CATEGORIES = """
|
|
|
|
<BEGIN UNSAFE CONTENT CATEGORIES>
|
|
$categories
|
|
<END UNSAFE CONTENT CATEGORIES>
|
|
"""
|
|
|
|
PROMPT_CONVERSATION = """
|
|
<BEGIN CONVERSATION>
|
|
|
|
$conversations
|
|
|
|
<END CONVERSATION>
|
|
"""
|
|
|
|
|
|
PROMPT_INSTRUCTIONS = """
|
|
Provide your safety assessment for ONLY THE LAST $agent_type message in the above conversation:
|
|
- First line must read 'safe' or 'unsafe'.
|
|
- If unsafe, a second line must include a comma-separated list of violated categories."""
|
|
|
|
|
|
PROMPT_TEMPLATE = Template(
|
|
f"{PROMPT_TASK}{SAFETY_CATEGORIES}{PROMPT_CONVERSATION}{PROMPT_INSTRUCTIONS}"
|
|
)
|
|
|
|
|
|
class LlamaGuardShield(ShieldBase):
|
|
def __init__(
|
|
self,
|
|
model: str,
|
|
inference_api: Inference,
|
|
excluded_categories: List[str] = None,
|
|
on_violation_action: OnViolationAction = OnViolationAction.RAISE,
|
|
):
|
|
super().__init__(on_violation_action)
|
|
|
|
if excluded_categories is None:
|
|
excluded_categories = []
|
|
|
|
assert len(excluded_categories) == 0 or all(
|
|
x in SAFETY_CATEGORIES_TO_CODE_MAP.values() for x in excluded_categories
|
|
), "Invalid categories in excluded categories. Expected format is ['S1', 'S2', ..]"
|
|
|
|
if model not in MODEL_TO_SAFETY_CATEGORIES_MAP:
|
|
raise ValueError(f"Unsupported model: {model}")
|
|
|
|
self.model = model
|
|
self.inference_api = inference_api
|
|
self.excluded_categories = excluded_categories
|
|
|
|
def check_unsafe_response(self, response: str) -> Optional[str]:
|
|
match = re.match(r"^unsafe\n(.*)$", response)
|
|
if match:
|
|
# extracts the unsafe code
|
|
extracted = match.group(1)
|
|
return extracted
|
|
|
|
return None
|
|
|
|
def get_safety_categories(self) -> List[str]:
|
|
excluded_categories = self.excluded_categories
|
|
if set(excluded_categories) == set(SAFETY_CATEGORIES_TO_CODE_MAP.values()):
|
|
excluded_categories = []
|
|
|
|
final_categories = []
|
|
|
|
all_categories = MODEL_TO_SAFETY_CATEGORIES_MAP[self.model]
|
|
for cat in all_categories:
|
|
cat_code = SAFETY_CATEGORIES_TO_CODE_MAP[cat]
|
|
if cat_code in excluded_categories:
|
|
continue
|
|
final_categories.append(f"{cat_code}: {cat}.")
|
|
|
|
return final_categories
|
|
|
|
def validate_messages(self, messages: List[Message]) -> None:
|
|
if len(messages) == 0:
|
|
raise ValueError("Messages must not be empty")
|
|
if messages[0].role != Role.user.value:
|
|
raise ValueError("Messages must start with user")
|
|
|
|
if len(messages) >= 2 and (
|
|
messages[0].role == Role.user.value and messages[1].role == Role.user.value
|
|
):
|
|
messages = messages[1:]
|
|
|
|
for i in range(1, len(messages)):
|
|
if messages[i].role == messages[i - 1].role:
|
|
raise ValueError(
|
|
f"Messages must alternate between user and assistant. Message {i} has the same role as message {i - 1}"
|
|
)
|
|
return messages
|
|
|
|
async def run(self, messages: List[Message]) -> ShieldResponse:
|
|
messages = self.validate_messages(messages)
|
|
|
|
if self.model == CoreModelId.llama_guard_3_11b_vision.value:
|
|
shield_input_message = self.build_vision_shield_input(messages)
|
|
else:
|
|
shield_input_message = self.build_text_shield_input(messages)
|
|
|
|
# TODO: llama-stack inference protocol has issues with non-streaming inference code
|
|
content = ""
|
|
async for chunk in self.inference_api.chat_completion(
|
|
model=self.model,
|
|
messages=[shield_input_message],
|
|
stream=True,
|
|
):
|
|
event = chunk.event
|
|
if event.event_type == ChatCompletionResponseEventType.progress:
|
|
assert isinstance(event.delta, str)
|
|
content += event.delta
|
|
|
|
content = content.strip()
|
|
shield_response = self.get_shield_response(content)
|
|
return shield_response
|
|
|
|
def build_text_shield_input(self, messages: List[Message]) -> UserMessage:
|
|
return UserMessage(content=self.build_prompt(messages))
|
|
|
|
def build_vision_shield_input(self, messages: List[Message]) -> UserMessage:
|
|
conversation = []
|
|
most_recent_img = None
|
|
|
|
for m in messages[::-1]:
|
|
if isinstance(m.content, str):
|
|
conversation.append(m)
|
|
elif isinstance(m.content, ImageMedia):
|
|
if most_recent_img is None and m.role == Role.user.value:
|
|
most_recent_img = m.content
|
|
conversation.append(m)
|
|
elif isinstance(m.content, list):
|
|
content = []
|
|
for c in m.content:
|
|
if isinstance(c, str):
|
|
content.append(c)
|
|
elif isinstance(c, ImageMedia):
|
|
if most_recent_img is None and m.role == Role.user.value:
|
|
most_recent_img = c
|
|
content.append(c)
|
|
else:
|
|
raise ValueError(f"Unknown content type: {c}")
|
|
|
|
conversation.append(UserMessage(content=content))
|
|
else:
|
|
raise ValueError(f"Unknown content type: {m.content}")
|
|
|
|
prompt = []
|
|
if most_recent_img is not None:
|
|
prompt.append(most_recent_img)
|
|
prompt.append(self.build_prompt(conversation[::-1]))
|
|
|
|
return UserMessage(content=prompt)
|
|
|
|
def build_prompt(self, messages: List[Message]) -> str:
|
|
categories = self.get_safety_categories()
|
|
categories_str = "\n".join(categories)
|
|
conversations_str = "\n\n".join(
|
|
[
|
|
f"{m.role.capitalize()}: {interleaved_text_media_as_str(m.content)}"
|
|
for m in messages
|
|
]
|
|
)
|
|
return PROMPT_TEMPLATE.substitute(
|
|
agent_type=messages[-1].role.capitalize(),
|
|
categories=categories_str,
|
|
conversations=conversations_str,
|
|
)
|
|
|
|
def get_shield_response(self, response: str) -> ShieldResponse:
|
|
response = response.strip()
|
|
if response == SAFE_RESPONSE:
|
|
return ShieldResponse(is_violation=False)
|
|
unsafe_code = self.check_unsafe_response(response)
|
|
if unsafe_code:
|
|
unsafe_code_list = unsafe_code.split(",")
|
|
if set(unsafe_code_list).issubset(set(self.excluded_categories)):
|
|
return ShieldResponse(is_violation=False)
|
|
return ShieldResponse(
|
|
is_violation=True,
|
|
violation_type=unsafe_code,
|
|
violation_return_message=CANNED_RESPONSE_TEXT,
|
|
)
|
|
|
|
raise ValueError(f"Unexpected response: {response}")
|