[BUGFIX] [FRONTEND] Correct chat logprobs (vllm-project#5029)

Co-authored-by: Breno Faria <[email protected]>

Author: br3no

tests/async_engine/test_openapi_server_ray.py

@@ -94,8 +94,10 @@ async def test_single_chat_session(server, client: openai.AsyncOpenAI):
         chat_completion.choices) == 1
     assert chat_completion.choices[0].message is not None
     assert chat_completion.choices[0].logprobs is not None
-    assert chat_completion.choices[0].logprobs.top_logprobs is not None
-    assert len(chat_completion.choices[0].logprobs.top_logprobs[0]) == 5
+    assert chat_completion.choices[0].logprobs.content[
+        0].top_logprobs is not None
+    assert len(
+        chat_completion.choices[0].logprobs.content[0].top_logprobs) == 5
     message = chat_completion.choices[0].message
     assert message.content is not None and len(message.content) >= 10
     assert message.role == "assistant"

tests/entrypoints/test_openai_server.py

@@ -183,6 +183,26 @@ async def test_single_completion(server, client: openai.AsyncOpenAI,
         completion.choices[0].text) >= 5
 
 
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    # first test base model, then test loras
+    "model_name",
+    [MODEL_NAME, "zephyr-lora", "zephyr-lora2"],
+)
+async def test_no_logprobs(server, client: openai.AsyncOpenAI,
+                           model_name: str):
+    # test using token IDs
+    completion = await client.completions.create(
+        model=MODEL_NAME,
+        prompt=[0, 0, 0, 0, 0],
+        max_tokens=5,
+        temperature=0.0,
+        logprobs=None,
+    )
+    choice = completion.choices[0]
+    assert choice.logprobs is None
+
+
 @pytest.mark.asyncio
 @pytest.mark.parametrize(
     # first test base model, then test loras
@@ -202,7 +222,72 @@ async def test_zero_logprobs(server, client: openai.AsyncOpenAI,
     choice = completion.choices[0]
     assert choice.logprobs is not None
     assert choice.logprobs.token_logprobs is not None
-    assert choice.logprobs.top_logprobs is None
+    assert choice.logprobs.top_logprobs is not None
+    assert len(choice.logprobs.top_logprobs[0]) <= 1
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "model_name",
+    [MODEL_NAME, "zephyr-lora"],
+)
+async def test_some_logprobs(server, client: openai.AsyncOpenAI,
+                             model_name: str):
+    # test using token IDs
+    completion = await client.completions.create(
+        model=MODEL_NAME,
+        prompt=[0, 0, 0, 0, 0],
+        max_tokens=5,
+        temperature=0.0,
+        logprobs=5,
+    )
+    choice = completion.choices[0]
+    assert choice.logprobs is not None
+    assert choice.logprobs.token_logprobs is not None
+    assert choice.logprobs.top_logprobs is not None
+    assert len(choice.logprobs.top_logprobs[0]) <= 6
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "model_name",
+    [MODEL_NAME, "zephyr-lora"],
+)
+async def test_too_many_completion_logprobs(server, client: openai.AsyncOpenAI,
+                                            model_name: str):
+
+    with pytest.raises(
+        (openai.BadRequestError, openai.APIError)):  # test using token IDs
+        await client.completions.create(
+            model=MODEL_NAME,
+            prompt=[0, 0, 0, 0, 0],
+            max_tokens=5,
+            temperature=0.0,
+            logprobs=6,
+        )
+        ...
+    with pytest.raises(
+        (openai.BadRequestError, openai.APIError)):  # test using token IDs
+        stream = await client.completions.create(
+            model=MODEL_NAME,
+            prompt=[0, 0, 0, 0, 0],
+            max_tokens=5,
+            temperature=0.0,
+            logprobs=6,
+            stream=True,
+        )
+        async for chunk in stream:
+            ...
+
+    # the server should still work afterwards
+    completion = await client.completions.create(
+        model=model_name,
+        prompt=[0, 0, 0, 0, 0],
+        max_tokens=5,
+        temperature=0.0,
+    )
+    completion = completion.choices[0].text
+    assert completion is not None and len(completion) >= 0
 
 
 @pytest.mark.asyncio
@@ -232,8 +317,10 @@ async def test_single_chat_session(server, client: openai.AsyncOpenAI,
         chat_completion.choices) == 1
     assert chat_completion.choices[0].message is not None
     assert chat_completion.choices[0].logprobs is not None
-    assert chat_completion.choices[0].logprobs.top_logprobs is not None
-    assert len(chat_completion.choices[0].logprobs.top_logprobs[0]) == 5
+    assert chat_completion.choices[0].logprobs.content[
+        0].top_logprobs is not None
+    assert len(
+        chat_completion.choices[0].logprobs.content[0].top_logprobs) == 5
     message = chat_completion.choices[0].message
     assert message.content is not None and len(message.content) >= 10
     assert message.role == "assistant"
@@ -250,10 +337,93 @@ async def test_single_chat_session(server, client: openai.AsyncOpenAI,
     assert message.content is not None and len(message.content) >= 0
 
 
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    # first test base model, then test loras
+    "model_name",
+    [MODEL_NAME, "zephyr-lora", "zephyr-lora2"],
+)
+async def test_no_logprobs_chat(server, client: openai.AsyncOpenAI,
+                                model_name: str):
+    messages = [{
+        "role": "system",
+        "content": "you are a helpful assistant"
+    }, {
+        "role": "user",
+        "content": "what is 1+1?"
+    }]
+
+    chat_completion = await client.chat.completions.create(model=model_name,
+                                                           messages=messages,
+                                                           max_tokens=5,
+                                                           temperature=0.0,
+                                                           logprobs=False)
+
+    choice = chat_completion.choices[0]
+    assert choice.logprobs is None
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    # just test 1 lora hereafter
+    "model_name",
+    [MODEL_NAME, "zephyr-lora"],
+)
+async def test_zero_logprobs_chat(server, client: openai.AsyncOpenAI,
+                                  model_name: str):
+    messages = [{
+        "role": "system",
+        "content": "you are a helpful assistant"
+    }, {
+        "role": "user",
+        "content": "what is 1+1?"
+    }]
+
+    chat_completion = await client.chat.completions.create(model=model_name,
+                                                           messages=messages,
+                                                           max_tokens=5,
+                                                           temperature=0.0,
+                                                           logprobs=True,
+                                                           top_logprobs=0)
+
+    choice = chat_completion.choices[0]
+    assert choice.logprobs is not None
+    assert choice.logprobs.content is not None
+    assert len(choice.logprobs.content[0].top_logprobs) <= 1
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "model_name",
+    [MODEL_NAME, "zephyr-lora"],
+)
+async def test_some_logprobs_chat(server, client: openai.AsyncOpenAI,
+                                  model_name: str):
+    messages = [{
+        "role": "system",
+        "content": "you are a helpful assistant"
+    }, {
+        "role": "user",
+        "content": "what is 1+1?"
+    }]
+
+    chat_completion = await client.chat.completions.create(model=model_name,
+                                                           messages=messages,
+                                                           max_tokens=5,
+                                                           temperature=0.0,
+                                                           logprobs=True,
+                                                           top_logprobs=5)
+
+    choice = chat_completion.choices[0]
+    assert choice.logprobs is not None
+    assert choice.logprobs.content is not None
+    assert len(choice.logprobs.content[0].top_logprobs) <= 6
+
+
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
-async def test_too_many_logprobs(server, client: openai.AsyncOpenAI,
-                                 model_name: str):
+async def test_too_many_chat_logprobs(server, client: openai.AsyncOpenAI,
+                                      model_name: str):
     messages = [{
         "role": "system",
         "content": "you are a helpful assistant"
@@ -262,13 +432,13 @@ async def test_too_many_logprobs(server, client: openai.AsyncOpenAI,
         "content": "what is 1+1?"
     }]
 
-    # Default max_logprobs is 5, so this should raise an error
+    # Default max_logprobs is 20, so this should raise an error
     with pytest.raises((openai.BadRequestError, openai.APIError)):
         stream = await client.chat.completions.create(model=model_name,
                                                       messages=messages,
                                                       max_tokens=10,
                                                       logprobs=True,
-                                                      top_logprobs=10,
+                                                      top_logprobs=21,
                                                       stream=True)
         async for chunk in stream:
             ...
@@ -278,25 +448,9 @@ async def test_too_many_logprobs(server, client: openai.AsyncOpenAI,
                                              messages=messages,
                                              max_tokens=10,
                                              logprobs=True,
-                                             top_logprobs=10,
+                                             top_logprobs=30,
                                              stream=False)
 
-    with pytest.raises((openai.BadRequestError, openai.APIError)):
-        stream = await client.completions.create(model=model_name,
-                                                 prompt="Test",
-                                                 max_tokens=10,
-                                                 logprobs=10,
-                                                 stream=True)
-        async for chunk in stream:
-            ...
-
-    with pytest.raises(openai.BadRequestError):
-        await client.completions.create(model=model_name,
-                                        prompt="Test",
-                                        max_tokens=10,
-                                        logprobs=10,
-                                        stream=False)
-
     # the server should still work afterwards
     chat_completion = await client.chat.completions.create(model=model_name,
                                                            messages=messages,
@@ -743,13 +897,12 @@ async def test_guided_choice_chat_logprobs(server, client: openai.AsyncOpenAI,
         top_logprobs=5,
         extra_body=dict(guided_choice=TEST_CHOICE,
                         guided_decoding_backend=guided_decoding_backend))
-    top_logprobs = chat_completion.choices[0].logprobs.top_logprobs
+    top_logprobs = chat_completion.choices[0].logprobs.content[0].top_logprobs
 
     # -9999.0 is the minimum logprob returned by OpenAI
     assert all(
-        isinstance(logprob, float) and logprob >= -9999.0
-        for token_dict in top_logprobs
-        for token, logprob in token_dict.items())
+        isinstance(token.logprob, float) and token.logprob >= -9999.0
+        for token in top_logprobs)
 
 
 @pytest.mark.asyncio

vllm/entrypoints/openai/protocol.py

@@ -250,6 +250,19 @@ def check_guided_decoding_count(cls, data):
                 "('guided_json', 'guided_regex' or 'guided_choice').")
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_logprobs(cls, data):
+        if "top_logprobs" in data and data["top_logprobs"] is not None:
+            if "logprobs" not in data or data["logprobs"] is False:
+                raise ValueError(
+                    "when using `top_logprobs`, `logprobs` must be set to true."
+                )
+            elif not 0 <= data["top_logprobs"] <= 20:
+                raise ValueError(
+                    "`top_logprobs` must be a value in the interval [0, 20].")
+        return data
+
 
 class CompletionRequest(OpenAIBaseModel):
     # Ordered by official OpenAI API documentation
@@ -396,6 +409,15 @@ def check_guided_decoding_count(cls, data):
                 "('guided_json', 'guided_regex' or 'guided_choice').")
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_logprobs(cls, data):
+        if "logprobs" in data and data[
+                "logprobs"] is not None and not 0 <= data["logprobs"] <= 5:
+            raise ValueError(("if passed, `logprobs` must be a value",
+                              " in the interval [0, 5]."))
+        return data
+
 
 class EmbeddingRequest(BaseModel):
     # Ordered by official OpenAI API documentation
@@ -415,7 +437,7 @@ def to_pooling_params(self):
         return PoolingParams(additional_data=self.additional_data)
 
 
-class LogProbs(OpenAIBaseModel):
+class CompletionLogProbs(OpenAIBaseModel):
     text_offset: List[int] = Field(default_factory=list)
     token_logprobs: List[Optional[float]] = Field(default_factory=list)
     tokens: List[str] = Field(default_factory=list)
@@ -425,7 +447,7 @@ class LogProbs(OpenAIBaseModel):
 class CompletionResponseChoice(OpenAIBaseModel):
     index: int
     text: str
-    logprobs: Optional[LogProbs] = None
+    logprobs: Optional[CompletionLogProbs] = None
     finish_reason: Optional[str] = None
     stop_reason: Optional[Union[int, str]] = Field(
         default=None,
@@ -448,7 +470,7 @@ class CompletionResponse(OpenAIBaseModel):
 class CompletionResponseStreamChoice(OpenAIBaseModel):
     index: int
     text: str
-    logprobs: Optional[LogProbs] = None
+    logprobs: Optional[CompletionLogProbs] = None
     finish_reason: Optional[str] = None
     stop_reason: Optional[Union[int, str]] = Field(
         default=None,
@@ -488,11 +510,25 @@ class ChatMessage(OpenAIBaseModel):
     content: str
 
 
+class ChatCompletionLogProb(OpenAIBaseModel):
+    token: str
+    logprob: float = -9999.0
+    bytes: Optional[List[int]] = None
+
+
+class ChatCompletionLogProbsContent(ChatCompletionLogProb):
+    top_logprobs: List[ChatCompletionLogProb] = Field(default_factory=list)
+
+
+class ChatCompletionLogProbs(OpenAIBaseModel):
+    content: Optional[List[ChatCompletionLogProbsContent]] = None
+
+
 class ChatCompletionResponseChoice(OpenAIBaseModel):
     index: int
     message: ChatMessage
-    logprobs: Optional[LogProbs] = None
-    finish_reason: Optional[str] = None
+    logprobs: Optional[ChatCompletionLogProbs] = None
+    finish_reason: Optional[Literal["stop", "length", "tool_calls"]] = None
     stop_reason: Optional[Union[int, str]] = None
 
 
@@ -513,8 +549,8 @@ class DeltaMessage(OpenAIBaseModel):
 class ChatCompletionResponseStreamChoice(OpenAIBaseModel):
     index: int
     delta: DeltaMessage
-    logprobs: Optional[LogProbs] = None
-    finish_reason: Optional[str] = None
+    logprobs: Optional[ChatCompletionLogProbs] = None
+    finish_reason: Optional[Literal["stop", "length", "tool_calls"]] = None
     stop_reason: Optional[Union[int, str]] = None