[Bugfix] Fix vLLM UsageInfo and logprobs None AssertionError with empty token_ids (#9034)

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

tests/entrypoints/openai/test_chunked_prompt.py

+import openai  # use the official client for correctness check
+import pytest
+import pytest_asyncio
+
+from ...utils import RemoteOpenAIServer
+
+# any model with a chat template should work here
+MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
+
+
+@pytest.fixture(scope="module")
+def server():
+    args = [
+        # use half precision for speed and memory savings in CI environment
+        "--dtype",
+        "bfloat16",
+        "--max-model-len",
+        "8192",
+        "--enforce-eager",
+        # lora config below
+        "--max-num-seqs",
+        "128",
+        "--enable-chunked-prefill",
+        "--max-num-batched-tokens",
+        "1000",
+        # large prompts create a lot of output
+        "--disable-log-requests",
+    ]
+
+    with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
+        yield remote_server
+
+
+@pytest_asyncio.fixture
+async def client(server):
+    async with server.get_async_client() as async_client:
+        yield async_client
+
+
+@pytest.mark.asyncio
+async def test_completion_stream_options_and_logprobs_with_long_prompts(
+        client: openai.AsyncOpenAI):
+    # Test stream with long prompt
+    prompt = "What is the capital of France?" * 400
+
+    stream = await client.completions.create(
+        model=MODEL_NAME,
+        prompt=prompt,
+        max_tokens=5,
+        temperature=0.0,
+        stream=True,
+        stream_options={
+            "include_usage": True,
+            "continuous_usage_stats": True,
+        },
+        logprobs=5,
+    )
+
+    tokens_received = 0
+    finished = False
+    async for chunk in stream:
+        assert chunk.usage.prompt_tokens >= 0
+        assert chunk.usage.completion_tokens >= 0
+        assert chunk.usage.total_tokens == (chunk.usage.prompt_tokens +
+                                            chunk.usage.completion_tokens)
+        if not finished:
+            tokens_received += 1
+            assert chunk.choices[0].text
+
+            if chunk.choices[0].finish_reason is not None:
+                finished = True
+
+        if finished:
+            assert chunk.usage.completion_tokens == tokens_received
+
+
+@pytest.mark.asyncio
+async def test_chat_completion_stream_options_and_logprobs_with_long_prompts(
+        client: openai.AsyncOpenAI):
+    # Test stream with long prompt
+    messages = [{
+        "role": "system",
+        "content": "You are a helpful assistant."
+    }, {
+        "role": "user",
+        "content": "What is the capital of France?" * 400
+    }]
+    stream = await client.chat.completions.create(
+        model=MODEL_NAME,
+        messages=messages,
+        max_tokens=5,
+        temperature=0.0,
+        stream=True,
+        stream_options={
+            "include_usage": True,
+            "continuous_usage_stats": True,
+        },
+        logprobs=True,
+        top_logprobs=5,
+    )
+
+    tokens_received = 0
+    empty_chunks_received = 0
+    finished = False
+    async for chunk in stream:
+        assert chunk.usage.prompt_tokens >= 0
+        assert chunk.usage.completion_tokens >= 0
+        assert chunk.usage.total_tokens == (chunk.usage.prompt_tokens +
+                                            chunk.usage.completion_tokens)
+
+        if not finished:
+            if chunk.choices[0].delta.content == "":
+                # when there is no tokens generated
+                assert chunk.usage.completion_tokens == 0
+                assert chunk.choices[0].logprobs is None
+                empty_chunks_received += 1
+            else:
+                tokens_received += 1
+
+            if chunk.choices[0].finish_reason is not None:
+                finished = True
+
+        if finished:
+            assert chunk.usage.completion_tokens == tokens_received
+
+    assert empty_chunks_received <= 1

vllm/entrypoints/openai/serving_chat.py

@@ -435,6 +435,12 @@ class OpenAIServingChat(OpenAIServing):
                         logprobs = None
 
                     delta_text = output.text
+
+                    if not delta_text and not output.token_ids and \
+                        not previous_num_tokens[i]:
+                        # Chunked prefill case, don't return empty chunks
+                        continue
+
                     delta_message: Optional[DeltaMessage]
 
                     # handle streaming deltas for tools with named tool_choice

vllm/entrypoints/openai/serving_completion.py

@@ -274,8 +274,6 @@ class OpenAIServingCompletion(OpenAIServing):
 
                 for output in res.outputs:
                     i = output.index + prompt_idx * num_choices
-                    # TODO(simon): optimize the performance by avoiding full
-                    # text O(n^2) sending.
 
                     assert request.max_tokens is not None
                     if request.echo and request.max_tokens == 0:
@@ -307,6 +305,11 @@ class OpenAIServingCompletion(OpenAIServing):
                         delta_token_ids = output.token_ids
                         out_logprobs = output.logprobs
 
+                        if not delta_text and not delta_token_ids \
+                            and not previous_num_tokens[i]:
+                            # Chunked prefill case, don't return empty chunks
+                            continue
+
                     if request.logprobs is not None:
                         assert out_logprobs is not None, (
                             "Did not output logprobs")

vllm/sequence.py

@@ -532,6 +532,9 @@ class Sequence:
             # (which is what we have most of the time)
             return self.data._cached_all_token_ids[-1]
 
+        if num_new_tokens == 0:
+            return []
+
         return self.data._cached_all_token_ids[-num_new_tokens:]
 
     def hash_of_block(self, logical_idx: int) -> int: