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fix: cleanup typos

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drbh 2025-04-28 13:59:23 +00:00
parent 9eeccbf9a5
commit b3ead6e959
1 changed files with 3 additions and 3 deletions
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server/text_generation_server/utils/logits_process.py
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@ -627,7 +627,7 @@ class HeterogeneousGrammarLogitProcessor(LogitsProcessor):
class LogitBiasProcessor(LogitsProcessor): class LogitBiasProcessor(LogitsProcessor):
""" """
`LogitsProcessor` creates a bias tensor from a dictionary of token IDs and their `LogitBiasProcessor` creates a bias tensor from a dictionary of token IDs and their
corresponding bias values. Bias are applied to the logits during each forward pass. corresponding bias values. Bias are applied to the logits during each forward pass.
Supports token IDs provided as strings (e.g., {"9707": -100}). Supports token IDs provided as strings (e.g., {"9707": -100}).
@ -656,7 +656,7 @@ class LogitBiasProcessor(LogitsProcessor):
def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor) -> torch.Tensor: def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor) -> torch.Tensor:
# Apply bias tensor as a broadcasted addition # Apply bias tensor as a broadcasted addition
if self.bias_tensor.shape[0] != scores.shape[1]: if self.bias_tensor.shape[0] != scores.shape[1]:
# Fix if the bias tensor is smaller than the scores # Pad the bias matrix to match the scores if it's smaller
self.bias_tensor = torch.nn.functional.pad( self.bias_tensor = torch.nn.functional.pad(
self.bias_tensor, (0, scores.shape[1] - self.bias_tensor.shape[0]) self.bias_tensor, (0, scores.shape[1] - self.bias_tensor.shape[0])
) )
@ -699,7 +699,7 @@ class HeterogeneousLogitBiasProcessor(LogitsProcessor):
def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor) -> torch.Tensor: def __call__(self, input_ids: torch.Tensor, scores: torch.Tensor) -> torch.Tensor:
# Apply bias matrix as a broadcasted addition # Apply bias matrix as a broadcasted addition
if self.bias_matrix.shape[1] != scores.shape[1]: if self.bias_matrix.shape[1] != scores.shape[1]:
# Fix if the bias matrix is smaller than the scores # Pad the bias matrix to match the scores if it's smaller
self.bias_matrix = torch.nn.functional.pad( self.bias_matrix = torch.nn.functional.pad(
self.bias_matrix, (0, scores.shape[1] - self.bias_matrix.shape[1]) self.bias_matrix, (0, scores.shape[1] - self.bias_matrix.shape[1])
) )