llama-stack-mirror/llama_toolchain/safety/meta_reference/shields/prompt_guard.py
Ashwin Bharambe 7bc7785b0d
API Updates: fleshing out RAG APIs, introduce "llama stack" CLI command (#51)
* add tools to chat completion request

* use templates for generating system prompts

* Moved ToolPromptFormat and jinja templates to llama_models.llama3.api

* <WIP> memory changes

- inlined AgenticSystemInstanceConfig so API feels more ergonomic
- renamed it to AgentConfig, AgentInstance -> Agent
- added a MemoryConfig and `memory` parameter
- added `attachments` to input and `output_attachments` to the response

- some naming changes

* InterleavedTextAttachment -> InterleavedTextMedia, introduce memory tool

* flesh out memory banks API

* agentic loop has a RAG implementation

* faiss provider implementation

* memory client works

* re-work tool definitions, fix FastAPI issues, fix tool regressions

* fix agentic_system utils

* basic RAG seems to work

* small bug fixes for inline attachments

* Refactor custom tool execution utilities

* Bug fix, show memory retrieval steps in EventLogger

* No need for api_key for Remote providers

* add special unicode character ↵ to showcase newlines in model prompt templates

* remove api.endpoints imports

* combine datatypes.py and endpoints.py into api.py

* Attachment / add TTL api

* split batch_inference from inference

* minor import fixes

* use a single impl for ChatFormat.decode_assistant_mesage

* use interleaved_text_media_as_str() utilityt

* Fix api.datatypes imports

* Add blobfile for tiktoken

* Add ToolPromptFormat to ChatFormat.encode_message so that tools are encoded properly

* templates take optional --format={json,function_tag}

* Rag Updates

* Add `api build` subcommand -- WIP

* fix

* build + run image seems to work

* <WIP> adapters

* bunch more work to make adapters work

* api build works for conda now

* ollama remote adapter works

* Several smaller fixes to make adapters work

Also, reorganized the pattern of __init__ inside providers so
configuration can stay lightweight

* llama distribution -> llama stack + containers (WIP)

* All the new CLI for api + stack work

* Make Fireworks and Together into the Adapter format

* Some quick fixes to the CLI behavior to make it consistent

* Updated README phew

* Update cli_reference.md

* llama_toolchain/distribution -> llama_toolchain/core

* Add termcolor

* update paths

* Add a log just for consistency

* chmod +x scripts

* Fix api dependencies not getting added to configuration

* missing import lol

* Delete utils.py; move to agentic system

* Support downloading of URLs for attachments for code interpreter

* Simplify and generalize `llama api build` yay

* Update `llama stack configure` to be very simple also

* Fix stack start

* Allow building an "adhoc" distribution

* Remote `llama api []` subcommands

* Fixes to llama stack commands and update docs

* Update documentation again and add error messages to llama stack start

* llama stack start -> llama stack run

* Change name of build for less confusion

* Add pyopenapi fork to the repository, update RFC assets

* Remove conflicting annotation

* Added a "--raw" option for model template printing

---------

Co-authored-by: Hardik Shah <hjshah@fb.com>
Co-authored-by: Ashwin Bharambe <ashwin@meta.com>
Co-authored-by: Dalton Flanagan <6599399+dltn@users.noreply.github.com>
2024-09-03 22:39:39 -07:00

155 lines
5.4 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 enum import auto, Enum
from typing import List
import torch
from llama_models.llama3.api.datatypes import Message
from termcolor import cprint
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from .base import message_content_as_str, OnViolationAction, ShieldResponse, TextShield
from llama_toolchain.safety.api import * # noqa: F403
class PromptGuardShield(TextShield):
class Mode(Enum):
INJECTION = auto()
JAILBREAK = auto()
_instances = {}
_model_cache = None
@staticmethod
def instance(
model_dir: str,
threshold: float = 0.9,
temperature: float = 1.0,
mode: "PromptGuardShield.Mode" = Mode.JAILBREAK,
on_violation_action=OnViolationAction.RAISE,
) -> "PromptGuardShield":
action_value = on_violation_action.value
key = (model_dir, threshold, temperature, mode, action_value)
if key not in PromptGuardShield._instances:
PromptGuardShield._instances[key] = PromptGuardShield(
model_dir=model_dir,
threshold=threshold,
temperature=temperature,
mode=mode,
on_violation_action=on_violation_action,
)
return PromptGuardShield._instances[key]
def __init__(
self,
model_dir: str,
threshold: float = 0.9,
temperature: float = 1.0,
mode: "PromptGuardShield.Mode" = Mode.JAILBREAK,
on_violation_action: OnViolationAction = OnViolationAction.RAISE,
):
super().__init__(on_violation_action)
assert (
model_dir is not None
), "Must provide a model directory for prompt injection shield"
if temperature <= 0:
raise ValueError("Temperature must be greater than 0")
self.device = "cuda"
if PromptGuardShield._model_cache is None:
# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_dir)
model = AutoModelForSequenceClassification.from_pretrained(
model_dir, device_map=self.device
)
PromptGuardShield._model_cache = (tokenizer, model)
self.tokenizer, self.model = PromptGuardShield._model_cache
self.temperature = temperature
self.threshold = threshold
self.mode = mode
def get_shield_type(self) -> ShieldType:
return (
BuiltinShield.jailbreak_shield
if self.mode == self.Mode.JAILBREAK
else BuiltinShield.injection_shield
)
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(
shield_type=self.get_shield_type(),
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(
shield_type=self.get_shield_type(),
is_violation=True,
violation_type=f"prompt_injection:malicious={score_malicious}",
violation_return_message="Sorry, I cannot do this.",
)
return ShieldResponse(
shield_type=self.get_shield_type(),
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,
)