[Misc] Include matched stop string/token in responses (vllm-project#2976)

Co-authored-by: Sahil Suneja <[email protected]>

Author: 2 people

tests/samplers/test_stop_reason.py

@@ -0,0 +1,59 @@
+"""Test the different finish_reason="stop" situations during generation:
+    1. One of the provided stop strings
+    2. One of the provided stop tokens
+    3. The EOS token
+
+Run `pytest tests/samplers/test_stop_reason.py`.
+"""
+
+import pytest
+import transformers
+
+from vllm import SamplingParams
+
+MODEL = "facebook/opt-350m"
+STOP_STR = "."
+SEED = 42
+MAX_TOKENS = 1024
+
+
+@pytest.fixture
+def vllm_model(vllm_runner):
+    vllm_model = vllm_runner(MODEL)
+    yield vllm_model
+    del vllm_model
+
+
+def test_stop_reason(vllm_model, example_prompts):
+    tokenizer = transformers.AutoTokenizer.from_pretrained(MODEL)
+    stop_token_id = tokenizer.convert_tokens_to_ids(STOP_STR)
+    llm = vllm_model.model
+
+    # test stop token
+    outputs = llm.generate(example_prompts,
+                           sampling_params=SamplingParams(
+                               seed=SEED,
+                               max_tokens=MAX_TOKENS,
+                               stop_token_ids=[stop_token_id]))
+    for output in outputs:
+        output = output.outputs[0]
+        assert output.finish_reason == "stop"
+        assert output.stop_reason == stop_token_id
+
+    # test stop string
+    outputs = llm.generate(example_prompts,
+                           sampling_params=SamplingParams(
+                               seed=SEED, max_tokens=MAX_TOKENS, stop="."))
+    for output in outputs:
+        output = output.outputs[0]
+        assert output.finish_reason == "stop"
+        assert output.stop_reason == STOP_STR
+
+    # test EOS token
+    outputs = llm.generate(example_prompts,
+                           sampling_params=SamplingParams(
+                               seed=SEED, max_tokens=MAX_TOKENS))
+    for output in outputs:
+        output = output.outputs[0]
+        assert output.finish_reason == "length" or (
+            output.finish_reason == "stop" and output.stop_reason is None)

vllm/engine/llm_engine.py

@@ -740,12 +740,15 @@ def _check_stop(self, seq: Sequence,
             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 seq.get_last_token_id() in sampling_params.stop_token_ids:
+        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(
-                seq.get_last_token_id())
+                last_token_id)
             self._finalize_sequence(seq, sampling_params, stop_str)
             seq.status = SequenceStatus.FINISHED_STOPPED
+            seq.stop_reason = last_token_id
             return
 
         # Check if the sequence has generated the EOS token.

vllm/entrypoints/openai/protocol.py

@@ -338,6 +338,13 @@ class CompletionResponseChoice(BaseModel):
     text: str
     logprobs: Optional[LogProbs] = None
     finish_reason: Optional[Literal["stop", "length"]] = None
+    stop_reason: Union[None, int, str] = Field(
+        default=None,
+        description=(
+            "The stop string or token id that caused the completion "
+            "to stop, None if the completion finished for some other reason "
+            "including encountering the EOS token"),
+    )
 
 
 class CompletionResponse(BaseModel):
@@ -354,6 +361,13 @@ class CompletionResponseStreamChoice(BaseModel):
     text: str
     logprobs: Optional[LogProbs] = None
     finish_reason: Optional[Literal["stop", "length"]] = None
+    stop_reason: Union[None, int, str] = Field(
+        default=None,
+        description=(
+            "The stop string or token id that caused the completion "
+            "to stop, None if the completion finished for some other reason "
+            "including encountering the EOS token"),
+    )
 
 
 class CompletionStreamResponse(BaseModel):
@@ -375,6 +389,7 @@ class ChatCompletionResponseChoice(BaseModel):
     message: ChatMessage
     logprobs: Optional[LogProbs] = None
     finish_reason: Optional[Literal["stop", "length"]] = None
+    stop_reason: Union[None, int, str] = None
 
 
 class ChatCompletionResponse(BaseModel):
@@ -396,6 +411,7 @@ class ChatCompletionResponseStreamChoice(BaseModel):
     delta: DeltaMessage
     logprobs: Optional[LogProbs] = None
     finish_reason: Optional[Literal["stop", "length"]] = None
+    stop_reason: Union[None, int, str] = None
 
 
 class ChatCompletionStreamResponse(BaseModel):

vllm/entrypoints/openai/serving_chat.py

@@ -220,7 +220,8 @@ async def chat_completion_stream_generator(
                             index=i,
                             delta=DeltaMessage(content=delta_text),
                             logprobs=logprobs,
-                            finish_reason=output.finish_reason)
+                            finish_reason=output.finish_reason,
+                            stop_reason=output.stop_reason)
                         chunk = ChatCompletionStreamResponse(
                             id=request_id,
                             object=chunk_object_type,
@@ -278,6 +279,7 @@ async def chat_completion_full_generator(
                 message=ChatMessage(role=role, content=output.text),
                 logprobs=logprobs,
                 finish_reason=output.finish_reason,
+                stop_reason=output.stop_reason,
             )
             choices.append(choice_data)
 

vllm/entrypoints/openai/serving_completion.py

@@ -266,6 +266,7 @@ async def completion_stream_generator(
                     previous_texts[i] = output.text
                     previous_num_tokens[i] = len(output.token_ids)
                     finish_reason = output.finish_reason
+                    stop_reason = output.stop_reason
                     if output.finish_reason is not None:  # return final usage
                         prompt_tokens = len(res.prompt_token_ids)
                         completion_tokens = len(output.token_ids)
@@ -286,6 +287,7 @@ async def completion_stream_generator(
                                 text=delta_text,
                                 logprobs=logprobs,
                                 finish_reason=finish_reason,
+                                stop_reason=stop_reason,
                             )
                         ],
                         usage=final_usage,
@@ -342,6 +344,7 @@ def request_output_to_completion_response(
                     text=output_text,
                     logprobs=logprobs,
                     finish_reason=output.finish_reason,
+                    stop_reason=output.stop_reason,
                 )
                 choices.append(choice_data)
 

vllm/outputs.py

@@ -1,5 +1,5 @@
 import time
-from typing import List, Optional
+from typing import List, Optional, Union
 
 from vllm.lora.request import LoRARequest
 from vllm.sequence import (PromptLogprobs, RequestMetrics, SampleLogprobs,
@@ -18,6 +18,9 @@ class CompletionOutput:
         logprobs: The log probabilities of the top probability words at each
             position if the logprobs are requested.
         finish_reason: The reason why the sequence is finished.
+        stop_reason: The stop string or token id that caused the completion
+            to stop, None if the completion finished for some other reason
+            including encountering the EOS token.
         lora_request: The LoRA request that was used to generate the output.
     """
 
@@ -29,6 +32,7 @@ def __init__(
         cumulative_logprob: float,
         logprobs: Optional[SampleLogprobs],
         finish_reason: Optional[str] = None,
+        stop_reason: Union[int, str, None] = None,
         lora_request: Optional[LoRARequest] = None,
     ) -> None:
         self.index = index
@@ -37,6 +41,7 @@ def __init__(
         self.cumulative_logprob = cumulative_logprob
         self.logprobs = logprobs
         self.finish_reason = finish_reason
+        self.stop_reason = stop_reason
         self.lora_request = lora_request
 
     def finished(self) -> bool:
@@ -48,7 +53,8 @@ def __repr__(self) -> str:
                 f"token_ids={self.token_ids}, "
                 f"cumulative_logprob={self.cumulative_logprob}, "
                 f"logprobs={self.logprobs}, "
-                f"finish_reason={self.finish_reason})")
+                f"finish_reason={self.finish_reason}, "
+                f"stop_reason={self.stop_reason})")
 
 
 class RequestOutput:
@@ -111,8 +117,8 @@ def from_seq_group(cls, seq_group: SequenceGroup) -> "RequestOutput":
                              seq.get_output_token_ids(),
                              seq.get_cumulative_logprob(),
                              seq.output_logprobs if include_logprobs else None,
-                             SequenceStatus.get_finished_reason(seq.status))
-            for seq in top_n_seqs
+                             SequenceStatus.get_finished_reason(seq.status),
+                             seq.stop_reason) for seq in top_n_seqs
         ]
 
         # Every sequence in the sequence group should have the same prompt.

vllm/sequence.py

@@ -183,6 +183,7 @@ def __init__(
         # Initialize the logical token blocks with the prompt token ids.
         self._append_tokens_to_blocks(prompt_token_ids)
         self.status = SequenceStatus.WAITING
+        self.stop_reason: Union[int, str, None] = None
 
         # Used for incremental detokenization
         self.prefix_offset = 0