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Fix the sampling monkey patch (and add more options to sampler_priority) (#6411)
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@ -44,7 +44,7 @@ def default_preset():
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'dry_base': 1.75,
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'dry_allowed_length': 2,
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'dry_sequence_breakers': '"\\n", ":", "\\"", "*"',
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'sampler_priority': 'temperature\ndynamic_temperature\nquadratic_sampling\ntop_k\ntop_p\ntypical_p\nepsilon_cutoff\neta_cutoff\ntfs\ntop_a\nmin_p\nmirostat'
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'sampler_priority': 'repetition_penalty\npresence_penalty\nfrequency_penalty\ntemperature\ndynamic_temperature\nquadratic_sampling\ntop_k\ntop_p\ntypical_p\nepsilon_cutoff\neta_cutoff\ntfs\ntop_a\nmin_p\nmirostat\nencoder_repetition_penalty\nno_repeat_ngram'
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}
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@ -323,62 +323,141 @@ class SpyLogitsWarper(LogitsWarper):
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class RepetitionPenaltyLogitsProcessorWithRange(LogitsProcessor):
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'''
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Copied from the transformers library
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'''
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def __init__(self, penalty: float, presence_penalty: float, frequency_penalty: float, _range: int):
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def __init__(self, penalty: float, _range: int):
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if not (penalty > 0):
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raise ValueError(f"`penalty` has to be strictly positive, but is {penalty}")
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self.penalty = penalty
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self.presence_penalty = presence_penalty
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self.frequency_penalty = frequency_penalty
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self._range = _range
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def apply_repetition_penalty(self, input_ids_row, scores_row):
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unique_ids = torch.unique(input_ids_row)
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score = torch.gather(scores_row, 0, unique_ids)
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# Apply multiplicative repetition penalty
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score = torch.where(score < 0, score * self.penalty, score / self.penalty)
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scores_row.scatter_(0, unique_ids, score)
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return scores_row
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def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
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input_ids = input_ids[:, -self._range:]
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# We loop here because torch.unique() needs to process each row separately in the
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# case that batch_size > 1.
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for input_ids_row, scores_row in zip(input_ids, scores):
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unique_ids, counts = torch.unique(input_ids_row, return_counts=True)
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score = torch.gather(scores_row, 0, unique_ids)
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# multiplicative repetition penalty
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# if score < 0 then repetition penalty has to be multiplied to reduce the previous token probability
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score = torch.where(score < 0, score * self.penalty, score / self.penalty)
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scores_row.scatter_(0, unique_ids, score)
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# presence_penalty and frequency_penalty
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raw_presence_penalty = (counts > 0).to(scores.dtype)
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raw_frequency_penalty = counts.to(scores.dtype)
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additive_penalty = raw_presence_penalty * self.presence_penalty + raw_frequency_penalty * self.frequency_penalty
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scores_row.scatter_add_(0, unique_ids, -additive_penalty)
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scores_row = self.apply_repetition_penalty(input_ids_row, scores_row)
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return scores
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def get_logits_warper_patch(self, generation_config, **kwargs):
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class PresencePenaltyLogitsProcessor(LogitsProcessor):
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def __init__(self, presence_penalty: float, _range: int):
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self.presence_penalty = presence_penalty
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self._range = _range
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def apply_presence_penalty(self, input_ids_row, scores_row):
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unique_ids, counts = torch.unique(input_ids_row, return_counts=True)
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# Apply presence penalty
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raw_presence_penalty = (counts > 0).to(scores_row.dtype)
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presence_penalty = raw_presence_penalty * self.presence_penalty
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scores_row.scatter_add_(0, unique_ids, -presence_penalty)
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return scores_row
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def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
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input_ids = input_ids[:, -self._range:]
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for input_ids_row, scores_row in zip(input_ids, scores):
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scores_row = self.apply_presence_penalty(input_ids_row, scores_row)
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return scores
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class FrequencyPenaltyLogitsProcessor(LogitsProcessor):
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def __init__(self, frequency_penalty: float, _range: int):
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self.frequency_penalty = frequency_penalty
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self._range = _range
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def apply_frequency_penalty(self, input_ids_row, scores_row):
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unique_ids, counts = torch.unique(input_ids_row, return_counts=True)
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# Apply frequency penalty
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raw_frequency_penalty = counts.to(scores_row.dtype)
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frequency_penalty = raw_frequency_penalty * self.frequency_penalty
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scores_row.scatter_add_(0, unique_ids, -frequency_penalty)
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return scores_row
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def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
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input_ids = input_ids[:, -self._range:]
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for input_ids_row, scores_row in zip(input_ids, scores):
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scores_row = self.apply_frequency_penalty(input_ids_row, scores_row)
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return scores
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def get_logits_processor_patch(self, **kwargs):
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generation_config = kwargs['generation_config']
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# Parameter sanitization
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if isinstance(generation_config.temperature, int):
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generation_config.temperature = float(generation_config.temperature) # Must be float
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# Get the original warpers
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warpers = self._get_logits_warper_old(generation_config, **kwargs)
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warpers = self._get_logits_processor_old(**kwargs)
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# Replace temperature with our modified class.
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# Currently, it behaves identically to the original.
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for i in range(len(warpers)):
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for i in range(len(warpers) - 1, -1, -1):
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# Replace temperature with our modified class.
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if warpers[i].__class__.__name__ == 'TemperatureLogitsWarper':
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warpers[i] = TemperatureLogitsWarperCustom(
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generation_config.temperature,
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)
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# Stuff we don't need
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elif warpers[i].__class__.__name__ in ['SuppressTokensLogitsProcessor', 'RepetitionPenaltyLogitsProcessor']:
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del warpers[i]
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# Add custom warpers
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warpers_to_add = LogitsProcessorList()
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min_tokens_to_keep = 2 if generation_config.num_beams > 1 else 1
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if generation_config.repetition_penalty is not None and generation_config.repetition_penalty != 1.0:
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warpers_to_add.append(
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RepetitionPenaltyLogitsProcessorWithRange(
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penalty=generation_config.repetition_penalty,
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_range=generation_config.repetition_penalty_range
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)
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)
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if generation_config.presence_penalty is not None and generation_config.presence_penalty != 0.0:
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warpers_to_add.append(
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PresencePenaltyLogitsProcessor(
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presence_penalty=generation_config.presence_penalty,
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_range=generation_config.repetition_penalty_range
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)
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)
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if generation_config.frequency_penalty is not None and generation_config.frequency_penalty != 0.0:
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warpers_to_add.append(
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FrequencyPenaltyLogitsProcessor(
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frequency_penalty=generation_config.frequency_penalty,
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_range=generation_config.repetition_penalty_range
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)
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)
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if generation_config.dry_multiplier is not None and generation_config.dry_multiplier > 0.0:
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dry_sequence_breakers = generation_config.dry_sequence_breakers
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# Support both JSON array notation and comma-separated strings.
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if not dry_sequence_breakers.startswith("["):
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dry_sequence_breakers = "[" + dry_sequence_breakers + "]"
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sequence_breaker_strings = json.loads(dry_sequence_breakers)
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# Prefix with 'a' to get the correct encoding of the token at the end of a text.
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sequence_breakers = {shared.tokenizer.encode(f'a{s}')[-1] for s in sequence_breaker_strings}
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warpers.append(
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DRYLogitsProcessor(
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multiplier=generation_config.dry_multiplier,
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base=generation_config.dry_base,
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allowed_length=generation_config.dry_allowed_length,
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sequence_breakers=sequence_breakers,
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_range=generation_config.repetition_penalty_range,
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)
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)
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if generation_config.tfs is not None and 0.0 <= generation_config.tfs < 1.0:
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warpers_to_add.append(
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TailFreeLogitsWarper(
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@ -454,7 +533,12 @@ def get_logits_warper_patch(self, generation_config, **kwargs):
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'TopALogitsWarper': 'top_a',
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'TopKLogitsWarper': 'top_k',
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'TopPLogitsWarper': 'top_p',
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'TypicalLogitsWarper': 'typical_p'
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'TypicalLogitsWarper': 'typical_p',
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'RepetitionPenaltyLogitsProcessorWithRange': 'repetition_penalty',
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'PresencePenaltyLogitsProcessor': 'presence_penalty',
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'FrequencyPenaltyLogitsProcessor': 'frequency_penalty',
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'EncoderRepetitionPenaltyLogitsProcessor': 'encoder_repetition_penalty',
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'NoRepeatNGramLogitsProcessor': 'no_repeat_ngram',
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}
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def custom_sort_key(obj):
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@ -482,49 +566,6 @@ def get_logits_warper_patch(self, generation_config, **kwargs):
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return warpers
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def get_logits_processor_patch(self, **kwargs):
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generation_config = kwargs['generation_config']
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do_rep_pen_hijack = (generation_config.repetition_penalty > 1) or (generation_config.presence_penalty != 0) or (generation_config.frequency_penalty != 0)
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if do_rep_pen_hijack:
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generation_config.repetition_penalty = 1.1 # Set to value > 1 to ensure RepetitionPenaltyLogitsProcessor is created
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result = self._get_logits_processor_old(**kwargs)
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if do_rep_pen_hijack:
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for i in range(len(result)):
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if result[i].__class__.__name__ == 'RepetitionPenaltyLogitsProcessor':
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result[i] = RepetitionPenaltyLogitsProcessorWithRange(
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generation_config.repetition_penalty,
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generation_config.presence_penalty,
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generation_config.frequency_penalty,
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generation_config.repetition_penalty_range
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)
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if generation_config.dry_multiplier is not None and generation_config.dry_multiplier > 0.0:
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dry_sequence_breakers = generation_config.dry_sequence_breakers
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# Support both JSON array notation and comma-separated strings.
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if not dry_sequence_breakers.startswith("["):
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dry_sequence_breakers = "[" + dry_sequence_breakers + "]"
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sequence_breaker_strings = json.loads(dry_sequence_breakers)
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# Prefix with 'a' to get the correct encoding of the token at the end of a text.
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sequence_breakers = {shared.tokenizer.encode(f'a{s}')[-1] for s in sequence_breaker_strings}
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result.append(
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DRYLogitsProcessor(
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multiplier=generation_config.dry_multiplier,
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base=generation_config.dry_base,
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allowed_length=generation_config.dry_allowed_length,
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sequence_breakers=sequence_breakers,
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_range=generation_config.repetition_penalty_range,
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)
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)
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return result
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def generation_config_init_patch(self, **kwargs):
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self.__init___old(**kwargs)
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self.min_p = kwargs.pop("min_p", 0.0)
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@ -547,13 +588,10 @@ def generation_config_init_patch(self, **kwargs):
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self.dry_allowed_length = kwargs.pop("dry_allowed_length", 2)
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self.dry_sequence_breakers = kwargs.pop("dry_sequence_breakers", '"\\n", ":", "\\"", "*"')
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self.temperature_last = kwargs.pop("temperature_last", False)
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self.sampler_priority = kwargs.pop("sampler_priority", ['temperature', 'dynamic_temperature', 'quadratic_sampling', 'top_k', 'top_p', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'tfs', 'top_a', 'min_p', 'mirostat'])
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self.sampler_priority = kwargs.pop("sampler_priority", ['repetition_penalty', 'presence_penalty', 'frequency_penalty', 'temperature', 'dynamic_temperature', 'quadratic_sampling', 'top_k', 'top_p', 'typical_p', 'epsilon_cutoff', 'eta_cutoff', 'tfs', 'top_a', 'min_p', 'mirostat', 'encoder_repetition_penalty', 'no_repeat_ngram'])
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def hijack_samplers():
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transformers.GenerationMixin._get_logits_warper_old = transformers.GenerationMixin._get_logits_warper
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transformers.GenerationMixin._get_logits_warper = get_logits_warper_patch
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transformers.GenerationMixin._get_logits_processor_old = transformers.GenerationMixin._get_logits_processor
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transformers.GenerationMixin._get_logits_processor = get_logits_processor_patch
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