nvlabs--sana
wehub-resource-sync
70 行
2.2 KiB
Python
70 行
2.2 KiB
Python
# Copyright 2024 NVIDIA CORPORATION & AFFILIATES
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# SPDX-License-Identifier: Apache-2.0
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# This file is copy from https://github.com/mit-han-lab/hart/blob/main/hart/utils/safety_check.py
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import torch
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import transformers
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from torch.nn.functional import softmax
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from transformers import AutoModelForCausalLM, AutoTokenizer
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__all__ = ["is_dangerous"]
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__rank, __local_rank, __world_size, __device = (
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0,
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0,
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1,
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"cuda" if torch.cuda.is_available() else "cpu",
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)
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safety_policy = """
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The prompt must not seek to generate harmful, abusive, hateful, sexually explicit, or otherwise inappropriate content targeting individuals or protected groups.
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"""
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def is_dangerous(tokenizer, model, user_prompt, threshold=0.5):
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prompt = f"""You are a policy expert trying to help determine whether a user
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prompt is in violation of the defined safety policies.
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<start_of_turn>
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Human Question: {user_prompt.strip()}
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<end_of_turn>
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Our safety principle is defined in the below:
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{safety_policy.strip()}
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Does the human question violate the above principle? Your answer must start
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with 'Yes' or 'No'. And then walk through step by step to be sure we answer
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correctly.
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"""
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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with torch.no_grad():
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logits = model(**inputs).logits
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# Extract the logits for the Yes and No tokens
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vocab = tokenizer.get_vocab()
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selected_logits = logits[0, -1, [vocab["Yes"], vocab["No"]]]
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# Convert these logits to a probability with softmax
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probabilities = softmax(selected_logits, dim=0)
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# Return probability of 'Yes'
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score = probabilities[0].item()
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return score > threshold
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