Generation: stop at eos for assisted decoding (#31301)

* fix

* move changes to prompt lookup

* add test

* set eos in assistant model

* style

* fix flakiness

* changes for new `main`

* Update tests/generation/test_utils.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* Update tests/generation/test_utils.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* add comment to explain

---------

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

src/transformers/generation/candidate_generator.py

@@ -108,6 +108,9 @@ class AssistedCandidateGenerator(CandidateGenerator):
         self.assistant_model = assistant_model
         self.num_assistant_tokens = assistant_model.generation_config.num_assistant_tokens
 
+        # Set eos in assistant same as in target model
+        self.assistant_model.generation_config.eos_token_id = generation_config.eos_token_id
+
         # Prepare the kwargs for the assistant model
         assistant_kwargs = {}
         for key, value in model_kwargs.items():  # deepcopy crashes if we attempt to copy encoder outputs with grads
@@ -267,6 +270,7 @@ class PromptLookupCandidateGenerator(CandidateGenerator):
 
     def __init__(
         self,
+        eos_token_id: torch.Tensor = None,
         num_output_tokens: int = 10,
         max_matching_ngram_size: int = None,
         max_length: int = 20,
@@ -274,6 +278,7 @@ class PromptLookupCandidateGenerator(CandidateGenerator):
         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
+        self.eos_token_id = eos_token_id
 
         if self.max_matching_ngram_size <= 0 or self.num_output_tokens <= 0:
             raise ValueError("Invalid max_matching_ngram_size or num_output_tokens")
@@ -319,6 +324,15 @@ class PromptLookupCandidateGenerator(CandidateGenerator):
                 if start_idx < end_idx:
                     chosen_ids = input_ids[0, start_idx:end_idx]
                     match_found = True
+
+                    # remove remaining candidate ids if an "eos" token is found, otherwise the target model may
+                    # accept eos and the rest as valid, thus not stopping generation after "eos"
+                    # NOTE: below code is written based on the fact that assisted decoding supports only bs=1
+                    mask = torch.isin(chosen_ids, self.eos_token_id)
+                    match_indices_eos = torch.nonzero(mask)
+                    if match_indices_eos.numel() > 0:
+                        first_eos_index = match_indices_eos[0].item()
+                        chosen_ids = chosen_ids[:first_eos_index]
                     break
             if match_found:
                 break

src/transformers/generation/utils.py

@@ -725,6 +725,7 @@ class GenerationMixin:
         """
         if generation_config.prompt_lookup_num_tokens is not None:
             candidate_generator = PromptLookupCandidateGenerator(
+                eos_token_id=generation_config._eos_token_tensor,
                 num_output_tokens=generation_config.prompt_lookup_num_tokens,
                 max_matching_ngram_size=generation_config.max_matching_ngram_size,
                 max_length=generation_config.max_length,
@@ -3954,7 +3955,6 @@ class GenerationMixin:
 
             #  1. Fetch candidate sequences from a `CandidateGenerator`
             candidate_input_ids, candidate_logits = candidate_generator.get_candidates(input_ids)
-            candidate_input_ids = candidate_input_ids.to(self.device)
             if candidate_logits is not None:
                 candidate_logits = candidate_logits.to(self.device)
 

tests/generation/test_utils.py

@@ -77,6 +77,7 @@ if is_torch_available():
         MaxLengthCriteria,
         MinLengthLogitsProcessor,
         PhrasalConstraint,
+        PromptLookupCandidateGenerator,
         SampleDecoderOnlyOutput,
         SampleEncoderDecoderOutput,
         StoppingCriteria,
@@ -1372,6 +1373,34 @@ class GenerationTesterMixin:
 
             self._check_outputs(output_assisted, input_ids, model.config, use_cache=True)
 
+    def test_prompt_lookup_decoding_stops_at_eos(self):
+        # This test ensures that the prompt lookup generation stops at eos token and does not suggest more tokens
+        # (see https://github.com/huggingface/transformers/pull/31301)
+
+        # The main idea is to have an ngram (unigram in our case) that is repeated twice in the input ids.
+        # First time at the very end, so input ends with the unigrams, and second any arbitrary location.
+        # Also, we need an EOS token which will be injected just after the arbitrary located ngram.
+        # We verify that PLD will not copy and propose candidated that contain an EOS token, even if there are overlapping ngrams
+        # in input ids. Otherwise a proposed EOS along with the trailing (ngrams-1) tokens might be accepted by the target model.
+        # That seems as if the model "generated" and EOS but didn't stop from user's perspective
+
+        input_ids = torch.randint(1, 50, (1, 10), device=torch_device)  # generate inputs in range from 1-50
+        arbitrary_ngram = 51  # this is the arbitrary ngram, specifically chosen OOV to prevent flaky tests
+        input_ids[:, 3] = arbitrary_ngram  # set pre-eos to arbitrary_ngram which is for sure not present in inputs
+        input_ids[:, -1] = arbitrary_ngram  # put arbitrary_ngram in the end for the necessary match to happen
+
+        eos_token_id = torch.tensor([0], device=torch_device)
+        input_ids[:, 4] = eos_token_id  # inject eos-token-id in input ids so that it is located after arbitrary_ngram
+
+        # init cand geenerator with max_matching_ngram_size=1 to match per-token
+        candidate_generator = PromptLookupCandidateGenerator(
+            eos_token_id=eos_token_id, num_output_tokens=4, max_matching_ngram_size=1
+        )
+        output_prompt_lookup = candidate_generator.get_candidates(input_ids)[0]
+
+        # PLD shouldn't propose any new tokens based on eos-match
+        self.assertTrue(output_prompt_lookup.shape[-1] == 10)
+
     def test_generate_with_head_masking(self):
         """Test designed for encoder-decoder models to ensure the attention head masking is used."""
         attention_names = ["encoder_attentions", "decoder_attentions", "cross_attentions"]