Move eos_token_id to stopping criteria

src/transformers/generation/__init__.py

@@ -82,6 +82,7 @@
         "MaxNewTokensCriteria",
         "MaxLengthCriteria",
         "MaxTimeCriteria",
+        "EosTokenCriteria",
         "StoppingCriteria",
         "StoppingCriteriaList",
         "validate_stopping_criteria",
@@ -216,6 +217,7 @@
             WhisperTimeStampLogitsProcessor,
         )
         from .stopping_criteria import (
+            EosTokenCriteria,
             MaxLengthCriteria,
             MaxNewTokensCriteria,
             MaxTimeCriteria,

src/transformers/generation/candidate_generator.py

@@ -247,15 +247,20 @@ class PromptLookupCandidateGenerator(CandidateGenerator):
             The maximum ngram size to be considered for matching in the prompt
         num_output_tokens (`int`):
             The number of tokens to be output as candidate tokens.
+        max_length (`int`):
+            The number of total maximum tokens that can be generated. For decoder-only models that includes the prompt length.
+            Defaults to 20, which is the max length used as default in generation config.
     """
 
     def __init__(
         self,
         num_output_tokens: int = 10,
         max_matching_ngram_size: int = None,
+        max_length: int = 20,
     ):
         self.num_output_tokens = num_output_tokens
         self.max_matching_ngram_size = max_matching_ngram_size if max_matching_ngram_size else 2
+        self.max_length = max_length
 
         if self.max_matching_ngram_size <= 0 or self.num_output_tokens <= 0:
             raise ValueError("Invalid max_matching_ngram_size or num_output_tokens")
@@ -273,6 +278,10 @@ def get_candidates(self, input_ids: torch.LongTensor) -> Tuple[torch.LongTensor,
         """
         input_length = input_ids.size(1)
 
+        # Don't generate more than `max_length - 1` candidates since the target model generates one extra token.
+        if self.max_length == input_length + 1:
+            return input_ids, None
+
         chosen_ids = None
         match_found = False
         for ngram_size in range(min(self.max_matching_ngram_size, input_length - 1), 0, -1):
@@ -292,7 +301,7 @@ def get_candidates(self, input_ids: torch.LongTensor) -> Tuple[torch.LongTensor,
             for idx in match_indices:
                 start_idx = idx + ngram_size
                 end_idx = start_idx + self.num_output_tokens
-                end_idx = min(end_idx, input_length)
+                end_idx = min(end_idx, input_length, self.max_length)
 
                 if start_idx < end_idx:
                     chosen_ids = input_ids[0, start_idx:end_idx]

src/transformers/generation/stopping_criteria.py

@@ -2,7 +2,7 @@
 import warnings
 from abc import ABC
 from copy import deepcopy
-from typing import Optional
+from typing import List, Optional, Union
 
 import torch
 
@@ -129,6 +129,27 @@ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwa
         return torch.full((input_ids.shape[0],), is_done, device=input_ids.device, dtype=torch.bool)
 
 
+class EosTokenCriteria(StoppingCriteria):
+    """
+    This class can be used to stop generation whenever the "end-of-sequence" token is generated.
+    By default, it uses the `model.generation_config.eos_token_id`.
+
+    Args:
+        eos_token_id (`Union[int, List[int]]`):
+            The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
+    """
+
+    def __init__(self, eos_token_id: Union[int, List[int]]):
+        if isinstance(eos_token_id, int):
+            eos_token_id = [eos_token_id]
+        self.eos_token_id = torch.tensor(eos_token_id)
+
+    @add_start_docstrings(STOPPING_CRITERIA_INPUTS_DOCSTRING)
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> torch.BoolTensor:
+        is_done = torch.isin(input_ids[:, -1], self.eos_token_id.to(input_ids.device))
+        return is_done
+
+
 class StoppingCriteriaList(list):
     @add_start_docstrings(STOPPING_CRITERIA_INPUTS_DOCSTRING)
     def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> torch.BoolTensor: