forked from phoenix-oss/llama-stack-mirror
34ab7a3b6c
Lint check in main branch is failing. This fixes the lint check after we moved to ruff in https://github.com/meta-llama/llama-stack/pull/921. We need to move to a `ruff.toml` file as well as fixing and ignoring some additional checks. Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
121 lines
4.2 KiB
Python
121 lines
4.2 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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import logging
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from typing import Any, Dict, List
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import torch
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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from llama_stack.apis.inference import Message
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from llama_stack.apis.safety import (
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RunShieldResponse,
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Safety,
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SafetyViolation,
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ViolationLevel,
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)
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from llama_stack.apis.shields import Shield
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from llama_stack.distribution.utils.model_utils import model_local_dir
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from llama_stack.providers.datatypes import ShieldsProtocolPrivate
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from llama_stack.providers.utils.inference.prompt_adapter import (
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interleaved_content_as_str,
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)
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from .config import PromptGuardConfig, PromptGuardType
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log = logging.getLogger(__name__)
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PROMPT_GUARD_MODEL = "Prompt-Guard-86M"
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class PromptGuardSafetyImpl(Safety, ShieldsProtocolPrivate):
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def __init__(self, config: PromptGuardConfig, _deps) -> None:
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self.config = config
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async def initialize(self) -> None:
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model_dir = model_local_dir(PROMPT_GUARD_MODEL)
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self.shield = PromptGuardShield(model_dir, self.config)
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async def shutdown(self) -> None:
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pass
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async def register_shield(self, shield: Shield) -> None:
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if shield.provider_resource_id != PROMPT_GUARD_MODEL:
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raise ValueError(f"Only {PROMPT_GUARD_MODEL} is supported for Prompt Guard. ")
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async def run_shield(
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self,
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shield_id: 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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shield = await self.shield_store.get_shield(shield_id)
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if not shield:
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raise ValueError(f"Unknown shield {shield_id}")
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return await self.shield.run(messages)
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class PromptGuardShield:
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def __init__(
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self,
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model_dir: str,
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config: PromptGuardConfig,
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threshold: float = 0.9,
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temperature: float = 1.0,
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):
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assert model_dir is not None, "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.config = config
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self.temperature = temperature
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self.threshold = threshold
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self.device = "cuda"
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# load model and tokenizer
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self.tokenizer = AutoTokenizer.from_pretrained(model_dir)
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self.model = AutoModelForSequenceClassification.from_pretrained(model_dir, device_map=self.device)
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async def run(self, messages: List[Message]) -> RunShieldResponse:
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message = messages[-1]
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text = interleaved_content_as_str(message.content)
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# run model on messages and return response
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inputs = self.tokenizer(text, return_tensors="pt")
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inputs = {name: tensor.to(self.model.device) for name, tensor in inputs.items()}
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with torch.no_grad():
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outputs = self.model(**inputs)
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logits = outputs[0]
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probabilities = torch.softmax(logits / self.temperature, dim=-1)
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score_embedded = probabilities[0, 1].item()
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score_malicious = probabilities[0, 2].item()
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log.info(
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f"Ran PromptGuardShield and got Scores: Embedded: {score_embedded}, Malicious: {score_malicious}",
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)
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violation = None
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if self.config.guard_type == PromptGuardType.injection.value and (
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score_embedded + score_malicious > self.threshold
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):
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violation = SafetyViolation(
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violation_level=ViolationLevel.ERROR,
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user_message="Sorry, I cannot do this.",
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metadata={
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"violation_type": f"prompt_injection:embedded={score_embedded},malicious={score_malicious}",
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},
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)
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elif self.config.guard_type == PromptGuardType.jailbreak.value and score_malicious > self.threshold:
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violation = SafetyViolation(
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violation_level=ViolationLevel.ERROR,
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violation_type=f"prompt_injection:malicious={score_malicious}",
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violation_return_message="Sorry, I cannot do this.",
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)
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return RunShieldResponse(violation=violation)
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