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180 lines
7.9 KiB
Python
180 lines
7.9 KiB
Python
'''
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This file is AGPL-licensed.
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Some of the code in this file is from Clover Edition:
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https://github.com/cloveranon/Clover-Edition/blob/master/aidungeon/gpt2generator.py
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The license for Clover Edition is shown below:
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Copyright (c) 2019 Nick Walton
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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'''
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import torch
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from transformers import LogitsWarper
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class AdvancedRepetitionPenaltyLogitsProcessor(LogitsWarper):
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def __init__(self, *args, **kwargs):
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pass
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def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
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self.penalty_range = int(self.penalty_range)
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clipped_penalty_range = min(input_ids.shape[-1], self.penalty_range)
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if self.penalty != 1.0:
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if self.penalty_range > 0:
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if clipped_penalty_range < input_ids.shape[1]:
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input_ids = input_ids[..., -clipped_penalty_range:]
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if self.penalty_slope != 0:
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_penalty = (torch.arange(self.penalty_range, dtype=scores.dtype, device=scores.device)/(self.penalty_range - 1)) * 2. - 1
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_penalty = (self.penalty_slope * _penalty) / (1 + torch.abs(_penalty) * (self.penalty_slope - 1))
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_penalty = 1 + ((_penalty + 1) / 2).unsqueeze(0) * (self.penalty - 1)
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self.penalty = _penalty[..., -clipped_penalty_range:]
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score = torch.gather(scores, 1, input_ids)
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score = torch.where(score <= 0, score * self.penalty, score / self.penalty)
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scores.scatter_(1, input_ids, score)
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return scores
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class TailFreeLogitsWarper(LogitsWarper):
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def __init__(self, tfs: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
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tfs = float(tfs)
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if tfs < 0 or tfs > 1.0:
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raise ValueError(f"`tfs` has to be a float >= 0 and <= 1, but is {tfs}")
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self.tfs = tfs
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self.filter_value = filter_value
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self.min_tokens_to_keep = min_tokens_to_keep
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def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
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if self.filter_value >= 1.0:
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return scores
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sorted_logits, sorted_indices = torch.sort(scores, descending=True)
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probs = sorted_logits.softmax(dim=-1)
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# Compute second derivative normalized CDF
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d2 = probs.diff().diff().abs()
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normalized_d2 = d2 / d2.sum(dim=-1, keepdim=True)
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normalized_d2_cdf = normalized_d2.cumsum(dim=-1)
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# Remove tokens with CDF value above the threshold (token with 0 are kept)
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sorted_indices_to_remove = normalized_d2_cdf > self.tfs
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# Centre the distribution around the cutoff as in the original implementation of the algorithm
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sorted_indices_to_remove = torch.cat(
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(
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torch.zeros(scores.shape[0], 1, dtype=torch.bool, device=scores.device),
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sorted_indices_to_remove,
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torch.ones(scores.shape[0], 1, dtype=torch.bool, device=scores.device),
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),
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dim=-1,
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)
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if self.min_tokens_to_keep > 1:
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# Keep at least min_tokens_to_keep
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sorted_indices_to_remove[..., : self.min_tokens_to_keep] = 0
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indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
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scores = scores.masked_fill(indices_to_remove, self.filter_value)
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return scores
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class TypicalLogitsWarper(LogitsWarper):
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'''
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Typical sampling, described in https://arxiv.org/pdf/2202.00666.pdf
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'''
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def __init__(self, typical: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
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typical = float(typical)
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if typical < 0 or typical > 1.0:
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raise ValueError(f"`typical` has to be a float >= 0 and <= 1, but is {typical}")
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self.typical = typical
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self.filter_value = filter_value
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self.min_tokens_to_keep = min_tokens_to_keep
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def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
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if self.filter_value >= 1.0:
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return scores
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# Compute softmax probabilities and the natural logarithms of them
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probs = scores.softmax(dim=-1)
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log_probs = probs.log()
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# Compute the negative of entropy, which is the sum of p*ln(p) for all p
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# in the set of softmax probabilities of the logits
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neg_entropy = (probs * log_probs).nansum(dim=-1, keepdim=True)
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# Determine absolute difference between the negative entropy and the
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# log probabilities
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entropy_deviation = (neg_entropy - log_probs).abs()
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# Keep certain tokens such that the sum of the entropy_deviation of the
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# kept tokens is the smallest possible value such that the sum of the
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# softmax probabilities of the kept tokens is at least the threshold
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# value (by sorting the tokens in ascending order of entropy_deviation
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# and then keeping the smallest possible number of tokens from the
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# beginning such that sum of softmax probabilities is at or above the
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# threshold)
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_, sorted_indices = torch.sort(entropy_deviation)
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sorted_logits = probs.gather(-1, sorted_indices)
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sorted_indices_to_remove = sorted_logits.cumsum(dim=-1) >= self.typical
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sorted_indices_to_remove = sorted_indices_to_remove.roll(1, dims=-1)
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min_tokens_to_keep = max(self.min_tokens_to_keep, 1)
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# Keep at least min_tokens_to_keep
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sorted_indices_to_remove[..., : min_tokens_to_keep] = 0
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indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
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scores = scores.masked_fill(indices_to_remove, self.filter_value)
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return scores
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class TopALogitsWarper(LogitsWarper):
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def __init__(self, top_a: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
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top_a = float(top_a)
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if top_a < 0 or top_a > 1.0:
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raise ValueError(f"`top_a` has to be a float >= 0 and <= 1, but is {top_a}")
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self.top_a = top_a
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self.filter_value = filter_value
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self.min_tokens_to_keep = min_tokens_to_keep
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def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
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if self.filter_value >= 1.0:
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return scores
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sorted_logits, sorted_indices = torch.sort(scores, descending=True)
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probs = sorted_logits.softmax(dim=-1)
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# Remove tokens with probability less than top_a*(max(probs))^2 (token with 0 are kept)
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probs_max = probs[..., 0, None]
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sorted_indices_to_remove = probs < probs_max * probs_max * self.top_a
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if self.min_tokens_to_keep > 1:
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# Keep at least min_tokens_to_keep
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sorted_indices_to_remove[..., : self.min_tokens_to_keep] = 0
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indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
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scores = scores.masked_fill(indices_to_remove, self.filter_value)
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return scores
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