99 lines
3.4 KiB
Python
99 lines
3.4 KiB
Python
# coding=utf-8
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# Copyright 2023 Authors of "A Watermark for Large Language Models"
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# available at https://arxiv.org/abs/2301.10226
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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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import os
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import torch
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from transformers import LogitsProcessor
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from typing import List, Union
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GAMMA = os.getenv("WATERMARK_GAMMA", 0.5)
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DELTA = os.getenv("WATERMARK_DELTA", 2.0)
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class WatermarkLogitsProcessor(LogitsProcessor):
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def __init__(
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self,
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gamma: float = GAMMA,
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delta: float = DELTA,
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hash_key: int = 15485863, # just a large prime number to create a rng seed with sufficient bit width
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device: str = "cpu",
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):
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# watermarking parameters
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self.gamma = gamma
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self.delta = delta
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self.rng = torch.Generator(device=device)
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self.hash_key = hash_key
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def _seed_rng(self, input_ids: Union[List[int], torch.LongTensor]):
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if isinstance(input_ids, list):
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assert (
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len(input_ids) >= 1
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), "requires at least a 1 token prefix sequence to seed rng"
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prev_token = input_ids[-1]
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else:
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assert len(input_ids) == 1
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input_ids = input_ids[0]
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assert (
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input_ids.shape[-1] >= 1
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), "requires at least a 1 token prefix sequence to seed rng"
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prev_token = input_ids[-1].item()
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self.rng.manual_seed(self.hash_key * prev_token)
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def _get_greenlist_ids(
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self,
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input_ids: Union[List[int], torch.LongTensor],
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max_value: int,
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device: torch.device,
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) -> List[int]:
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# seed the rng using the previous tokens/prefix
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self._seed_rng(input_ids)
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greenlist_size = int(max_value * self.gamma)
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vocab_permutation = torch.randperm(max_value, device=device, generator=self.rng)
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greenlist_ids = vocab_permutation[:greenlist_size]
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return greenlist_ids
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@staticmethod
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def _calc_greenlist_mask(
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scores: torch.FloatTensor, greenlist_token_ids
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) -> torch.BoolTensor:
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green_tokens_mask = torch.zeros_like(scores)
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green_tokens_mask[-1, greenlist_token_ids] = 1
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final_mask = green_tokens_mask.bool()
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return final_mask
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@staticmethod
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def _bias_greenlist_logits(
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scores: torch.Tensor, greenlist_mask: torch.Tensor, greenlist_bias: float
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) -> torch.Tensor:
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scores[greenlist_mask] = scores[greenlist_mask] + greenlist_bias
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return scores
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def __call__(
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self, input_ids: Union[List[int], torch.LongTensor], scores: torch.FloatTensor
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) -> torch.FloatTensor:
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greenlist_ids = self._get_greenlist_ids(
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input_ids, scores.shape[-1], scores.device
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)
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green_tokens_mask = self._calc_greenlist_mask(
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scores=scores, greenlist_token_ids=greenlist_ids
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)
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scores = self._bias_greenlist_logits(
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scores=scores, greenlist_mask=green_tokens_mask, greenlist_bias=self.delta
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)
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return scores
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