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fix: cleanup typos
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@ -627,7 +627,7 @@ class HeterogeneousGrammarLogitProcessor(LogitsProcessor):
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class LogitBiasProcessor(LogitsProcessor):
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class LogitBiasProcessor(LogitsProcessor):
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"""
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"""
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`LogitsProcessor` creates a bias tensor from a dictionary of token IDs and their
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`LogitBiasProcessor` creates a bias tensor from a dictionary of token IDs and their
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corresponding bias values. Bias are applied to the logits during each forward pass.
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corresponding bias values. Bias are applied to the logits during each forward pass.
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Supports token IDs provided as strings (e.g., {"9707": -100}).
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Supports token IDs provided as strings (e.g., {"9707": -100}).
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@ -656,7 +656,7 @@ class LogitBiasProcessor(LogitsProcessor):
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def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor) -> torch.Tensor:
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def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor) -> torch.Tensor:
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# Apply bias tensor as a broadcasted addition
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# Apply bias tensor as a broadcasted addition
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if self.bias_tensor.shape[0] != scores.shape[1]:
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if self.bias_tensor.shape[0] != scores.shape[1]:
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# Fix if the bias tensor is smaller than the scores
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# Pad the bias matrix to match the scores if it's smaller
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self.bias_tensor = torch.nn.functional.pad(
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self.bias_tensor = torch.nn.functional.pad(
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self.bias_tensor, (0, scores.shape[1] - self.bias_tensor.shape[0])
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self.bias_tensor, (0, scores.shape[1] - self.bias_tensor.shape[0])
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)
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)
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@ -699,7 +699,7 @@ class HeterogeneousLogitBiasProcessor(LogitsProcessor):
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def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor) -> torch.Tensor:
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def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor) -> torch.Tensor:
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# Apply bias matrix as a broadcasted addition
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# Apply bias matrix as a broadcasted addition
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if self.bias_matrix.shape[1] != scores.shape[1]:
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if self.bias_matrix.shape[1] != scores.shape[1]:
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# Fix if the bias matrix is smaller than the scores
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# Pad the bias matrix to match the scores if it's smaller
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self.bias_matrix = torch.nn.functional.pad(
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self.bias_matrix = torch.nn.functional.pad(
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self.bias_matrix, (0, scores.shape[1] - self.bias_matrix.shape[1])
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self.bias_matrix, (0, scores.shape[1] - self.bias_matrix.shape[1])
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)
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)
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