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Remove MinPLogitsWarper (it's now a transformers built-in)
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@ -125,36 +125,6 @@ class QuadraticSamplingLogitsWarper(LogitsWarper):
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return transformed_logits
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class MinPLogitsWarper(LogitsWarper):
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def __init__(self, min_p: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
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if min_p < 0 or min_p > 1.0:
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raise ValueError(f"`min_p` has to be a float >= 0 and <= 1, but is {min_p}")
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self.min_p = min_p
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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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# Convert logits to probabilities
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probs = torch.softmax(scores, dim=-1)
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# Get the probability of the top token for each sequence in the batch
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top_probs, _ = probs.max(dim=-1, keepdim=True)
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# Calculate the actual min_p threshold by scaling min_p with the top token's probability
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scaled_min_p = self.min_p * top_probs
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# Create a mask for tokens that have a probability less than the scaled min_p
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tokens_to_remove = probs < scaled_min_p
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sorted_indices = torch.argsort(scores, descending=True, dim=-1)
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sorted_indices_to_remove = torch.gather(tokens_to_remove, dim=-1, index=sorted_indices)
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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] = False
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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 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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@ -421,14 +391,6 @@ def get_logits_warper_patch(self, generation_config):
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)
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)
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if generation_config.min_p is not None and 0.0 < generation_config.min_p <= 1.0:
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warpers_to_add.append(
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MinPLogitsWarper(
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min_p=generation_config.min_p,
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min_tokens_to_keep=min_tokens_to_keep
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)
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)
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if generation_config.dynamic_temperature:
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warpers_to_add.append(
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DynamicTemperatureLogitsWarper(
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