[Core] [Frontend] Make detokenization optional (#3749)

Co-authored-by: Nick Hill <nickhill@us.ibm.com>

tests/engine/test_detokenization.py

+import pytest
+
+from vllm.entrypoints.llm import LLM
+from vllm.sampling_params import SamplingParams
+
+
+@pytest.mark.parametrize("model", ["facebook/opt-125m"])
+def test_computed_prefix_blocks(model: str):
+    # This test checks if the engine generates completions both with and
+    # without optional detokenization, that detokenization includes text
+    # and no-detokenization doesn't, and that both completions have the same
+    # token_ids.
+    prompt = (
+        "You are a helpful assistant. How do I build a car from cardboard and "
+        "paper clips? Is there an easy to follow video tutorial available "
+        "online for free?")
+
+    llm = LLM(model=model)
+    sampling_params = SamplingParams(max_tokens=10,
+                                     temperature=0.0,
+                                     detokenize=False)
+
+    outputs_no_detokenization = llm.generate(prompt,
+                                             sampling_params)[0].outputs[0]
+    sampling_params.detokenize = True
+    outputs_with_detokenization = llm.generate(prompt,
+                                               sampling_params)[0].outputs[0]
+
+    assert outputs_no_detokenization.text == ''
+    assert outputs_with_detokenization.text != ''
+    assert outputs_no_detokenization.token_ids == \
+        outputs_with_detokenization.token_ids

vllm/engine/llm_engine.py

@@ -432,7 +432,7 @@ class LLMEngine:
 
         # Process prompt logprobs
         prompt_logprobs = outputs.prompt_logprobs
-        if prompt_logprobs is not None:
+        if prompt_logprobs is not None and seq_group.sampling_params.detokenize:
             self.detokenizer.decode_prompt_logprobs_inplace(
                 seq_group, prompt_logprobs)
             seq_group.prompt_logprobs = prompt_logprobs
@@ -478,8 +478,9 @@ class LLMEngine:
             child_seqs.append((parent, parent))
 
         for seq, _ in child_seqs:
-            self.detokenizer.decode_sequence_inplace(seq,
-                                                     seq_group.sampling_params)
+            if seq_group.sampling_params.detokenize:
+                self.detokenizer.decode_sequence_inplace(
+                    seq, seq_group.sampling_params)
             self._check_stop(seq, seq_group.sampling_params)
 
         # Non-beam search case
@@ -791,12 +792,13 @@ class LLMEngine:
         if seq.get_output_len() < sampling_params.min_tokens:
             return
 
-        for stop_str in sampling_params.stop:
-            if seq.output_text.endswith(stop_str):
-                self._finalize_sequence(seq, sampling_params, stop_str)
-                seq.status = SequenceStatus.FINISHED_STOPPED
-                seq.stop_reason = stop_str
-                return
+        if sampling_params.detokenize:
+            for stop_str in sampling_params.stop:
+                if seq.output_text.endswith(stop_str):
+                    self._finalize_sequence(seq, sampling_params, stop_str)
+                    seq.status = SequenceStatus.FINISHED_STOPPED
+                    seq.stop_reason = stop_str
+                    return
         last_token_id = seq.get_last_token_id()
         if last_token_id in sampling_params.stop_token_ids:
             stop_str = self.get_tokenizer_for_seq(seq).convert_ids_to_tokens(

vllm/sampling_params.py

@@ -88,6 +88,7 @@ class SamplingParams:
             log probability of the sampled token, so there  may be up to
             `logprobs+1` elements in the response.
         prompt_logprobs: Number of log probabilities to return per prompt token.
+        detokenize: Whether to detokenize the output. Defaults to True.
         skip_special_tokens: Whether to skip special tokens in the output.
         spaces_between_special_tokens: Whether to add spaces between special
             tokens in the output.  Defaults to True.
@@ -118,6 +119,7 @@ class SamplingParams:
         min_tokens: int = 0,
         logprobs: Optional[int] = None,
         prompt_logprobs: Optional[int] = None,
+        detokenize: bool = True,
         skip_special_tokens: bool = True,
         spaces_between_special_tokens: bool = True,
         logits_processors: Optional[List[LogitsProcessor]] = None,
@@ -150,6 +152,10 @@ class SamplingParams:
         self.min_tokens = min_tokens
         self.logprobs = logprobs
         self.prompt_logprobs = prompt_logprobs
+        # NOTE: This parameter is only exposed at the engine level for now.
+        # It is not exposed in the OpenAI API server, as the OpenAI API does
+        # not support returning only a list of token IDs.
+        self.detokenize = detokenize
         self.skip_special_tokens = skip_special_tokens
         self.spaces_between_special_tokens = spaces_between_special_tokens
         self.logits_processors = logits_processors
@@ -210,6 +216,10 @@ class SamplingParams:
         if self.prompt_logprobs is not None and self.prompt_logprobs < 0:
             raise ValueError(f"prompt_logprobs must be non-negative, got "
                              f"{self.prompt_logprobs}.")
+        if self.stop and not self.detokenize:
+            raise ValueError(
+                "stop strings are only supported when detokenize is True. "
+                "Set detokenize=True to use stop.")
 
     def _verify_beam_search(self) -> None:
         if self.best_of == 1: