Store `eos_token_id` in `Sequence` for easy access (#3166)

tests/test_cache_block_hashing.py

@@ -54,7 +54,8 @@ def test_auto_prefix_caching(model: str, block_size: int, max_num_seqs: int):
         for prompt in prompts:
             hashes[-1].append([])
             prompt_token_ids = tokenizer.encode(prompt)
-            seq = Sequence(seq_id, prompt, prompt_token_ids, block_size)
+            seq = Sequence(seq_id, prompt, prompt_token_ids, block_size,
+                           tokenizer.tokenizer.eos_token_id)
 
             num_blocks = len(prompt_token_ids) // block_size
             for idx in range(num_blocks):

vllm/core/scheduler.py

@@ -59,10 +59,9 @@ class SchedulerOutputs:
                 and not self.blocks_to_swap_out and not self.blocks_to_copy)
 
     def _sort_by_lora_ids(self) -> bool:
-        self.scheduled_seq_groups = sorted(
-            self.scheduled_seq_groups,
-            key=lambda g: (g.lora_request.lora_int_id
-                           if g.lora_request else 0, g.request_id))
+        self.scheduled_seq_groups = sorted(self.scheduled_seq_groups,
+                                           key=lambda g:
+                                           (g.lora_int_id, g.request_id))
 
     @property
     def lora_requests(self) -> Set[LoRARequest]:

vllm/engine/llm_engine.py

@@ -491,8 +491,10 @@ class LLMEngine:
         # Create the sequences.
         block_size = self.cache_config.block_size
         seq_id = next(self.seq_counter)
+        eos_token_id = self.tokenizer.get_lora_tokenizer(
+            lora_request).eos_token_id
         seq = Sequence(seq_id, prompt, prompt_token_ids, block_size,
-                       lora_request)
+                       eos_token_id, lora_request)
 
         # Defensive copy of SamplingParams, which are used by the sampler,
         # this doesn't deep-copy LogitsProcessor objects
@@ -548,15 +550,13 @@ class LLMEngine:
         if early_stopping is True:
             return True
 
-        current_worst_score = (current_worst_seq.get_beam_search_score(
+        current_worst_score = current_worst_seq.get_beam_search_score(
             length_penalty=length_penalty,
-            eos_token_id=self.get_tokenizer_for_seq(
-                current_worst_seq).eos_token_id))
+            eos_token_id=current_worst_seq.eos_token_id)
         if early_stopping is False:
-            highest_attainable_score = (best_running_seq.get_beam_search_score(
+            highest_attainable_score = best_running_seq.get_beam_search_score(
                 length_penalty=length_penalty,
-                eos_token_id=self.get_tokenizer_for_seq(
-                    best_running_seq).eos_token_id))
+                eos_token_id=best_running_seq.eos_token_id)
         else:
             assert early_stopping == "never"
             if length_penalty > 0.0:
@@ -570,8 +570,7 @@ class LLMEngine:
                 highest_attainable_score = (
                     best_running_seq.get_beam_search_score(
                         length_penalty=length_penalty,
-                        eos_token_id=self.get_tokenizer_for_seq(
-                            best_running_seq).eos_token_id,
+                        eos_token_id=best_running_seq.eos_token_id,
                         seq_len=max_possible_length))
             else:
                 # Otherwise, beam search will prefer shorter sequences. The
@@ -580,8 +579,7 @@ class LLMEngine:
                 highest_attainable_score = (
                     best_running_seq.get_beam_search_score(
                         length_penalty=length_penalty,
-                        eos_token_id=self.get_tokenizer_for_seq(
-                            best_running_seq).eos_token_id))
+                        eos_token_id=best_running_seq.eos_token_id))
         return current_worst_score >= highest_attainable_score
 
     def _process_sequence_group_outputs(self, seq_group: SequenceGroup,
@@ -679,8 +677,7 @@ class LLMEngine:
         all_finished_seqs = existing_finished_seqs + new_finished_seqs
         # Sort the finished sequences by their scores.
         all_finished_seqs.sort(key=lambda x: x[0].get_beam_search_score(
-            length_penalty=length_penalty,
-            eos_token_id=self.get_tokenizer_for_seq(x[0]).eos_token_id),
+            length_penalty=length_penalty, eos_token_id=x[0].eos_token_id),
                                reverse=True)
         for seq, parent, is_new in all_finished_seqs[:beam_width]:
             if is_new:
@@ -707,8 +704,7 @@ class LLMEngine:
                               if not seq.is_finished()]
         # Sort the running sequences by their scores.
         running_child_seqs.sort(key=lambda x: x[0].get_beam_search_score(
-            length_penalty=length_penalty,
-            eos_token_id=self.get_tokenizer_for_seq(x[0]).eos_token_id),
+            length_penalty=length_penalty, eos_token_id=x[0].eos_token_id),
                                 reverse=True)
 
         # Check if we can stop the beam search.
@@ -1014,8 +1010,8 @@ class LLMEngine:
             return
 
         # Check if the sequence has generated the EOS token.
-        if ((not sampling_params.ignore_eos) and seq.get_last_token_id()
-                == self.get_tokenizer_for_seq(seq).eos_token_id):
+        if ((not sampling_params.ignore_eos)
+                and seq.get_last_token_id() == seq.eos_token_id):
             seq.status = SequenceStatus.FINISHED_STOPPED
             return
 

vllm/model_executor/layers/sampler.py

@@ -516,7 +516,6 @@ def _get_logprobs(
         if (i < sampling_metadata.num_prompts
                 and sampling_params.prompt_logprobs is not None):
             num_logprobs = sampling_params.prompt_logprobs
-            prompt_len = sampling_metadata.prompt_lens[i]
             prompt_tokens = sampling_metadata.seq_data[
                 seq_ids[0]].prompt_token_ids
             group_prompt_logprobs: PromptLogprobs = [None]

vllm/outputs.py

@@ -90,6 +90,9 @@ class RequestOutput:
         # Get the top-n sequences.
         n = seq_group.sampling_params.n
         seqs = seq_group.get_seqs()
+        if n == 1:
+            top_n_seqs = seqs
+        else:
             if seq_group.sampling_params.use_beam_search:
                 sorting_key = lambda seq: seq.get_beam_search_score(
                     seq_group.sampling_params.length_penalty)
@@ -99,20 +102,18 @@ class RequestOutput:
             top_n_seqs = sorted_seqs[:n]
 
         # Create the outputs.
-        outputs: List[CompletionOutput] = []
-        for seq in top_n_seqs:
-            logprobs = seq.output_logprobs
-            if seq_group.sampling_params.logprobs is None:
-                # NOTE: We need to take care of this case because the sequence
+        # NOTE: We need omit logprobs here explicitly because the sequence
         # always has the logprobs of the sampled tokens even if the
         # logprobs are not requested.
-                logprobs = None
-            finshed_reason = SequenceStatus.get_finished_reason(seq.status)
-            output = CompletionOutput(seqs.index(seq), seq.output_text,
+        include_logprobs = seq_group.sampling_params.logprobs
+        outputs = [
+            CompletionOutput(seqs.index(seq), seq.output_text,
                              seq.get_output_token_ids(),
-                                      seq.get_cumulative_logprob(), logprobs,
-                                      finshed_reason)
-            outputs.append(output)
+                             seq.get_cumulative_logprob(),
+                             seq.output_logprobs if include_logprobs else None,
+                             SequenceStatus.get_finished_reason(seq.status))
+            for seq in top_n_seqs
+        ]
 
         # Every sequence in the sequence group should have the same prompt.
         prompt = seq_group.prompt

vllm/sequence.py

@@ -142,11 +142,13 @@ class Sequence:
         prompt: str,
         prompt_token_ids: List[int],
         block_size: int,
+        eos_token_id: int,
         lora_request: Optional[LoRARequest] = None,
     ) -> None:
         self.seq_id = seq_id
         self.prompt = prompt
         self.block_size = block_size
+        self.eos_token_id = eos_token_id
         self.lora_request = lora_request
 
         self.data = SequenceData(prompt_token_ids)
@@ -362,10 +364,7 @@ class SequenceGroup:
         self,
         status: Optional[SequenceStatus] = None,
     ) -> List[Sequence]:
-        if status is None:
-            return list(self.seqs_dict.values())
-        else:
-            return [
+        return list(self.seqs_dict.values()) if status is None else [
             seq for seq in self.seqs_dict.values() if seq.status == status
         ]