[Bugfix] Set SamplingParams.max_tokens for OpenAI requests if not pro…

…vided by user (vllm-project#6954)

tests/entrypoints/openai/test_serving_chat.py

@@ -1,7 +1,12 @@
 import asyncio
+from contextlib import suppress
 from dataclasses import dataclass
+from unittest.mock import MagicMock
 
+from vllm.engine.async_llm_engine import AsyncLLMEngine
+from vllm.entrypoints.openai.protocol import ChatCompletionRequest
 from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
+from vllm.transformers_utils.tokenizer import get_tokenizer
 
 MODEL_NAME = "openai-community/gpt2"
 CHAT_TEMPLATE = "Dummy chat template for testing {}"
@@ -42,3 +47,37 @@ async def _async_serving_chat_init():
 def test_async_serving_chat_init():
     serving_completion = asyncio.run(_async_serving_chat_init())
     assert serving_completion.chat_template == CHAT_TEMPLATE
+
+
+def test_serving_chat_should_set_correct_max_tokens():
+    mock_engine = MagicMock(spec=AsyncLLMEngine)
+    mock_engine.get_tokenizer.return_value = get_tokenizer(MODEL_NAME)
+
+    serving_chat = OpenAIServingChat(mock_engine,
+                                     MockModelConfig(),
+                                     served_model_names=[MODEL_NAME],
+                                     response_role="assistant",
+                                     chat_template=CHAT_TEMPLATE,
+                                     lora_modules=None,
+                                     prompt_adapters=None,
+                                     request_logger=None)
+    req = ChatCompletionRequest(
+        model=MODEL_NAME,
+        messages=[{
+            "role": "user",
+            "content": "what is 1+1?"
+        }],
+        guided_decoding_backend="outlines",
+    )
+
+    with suppress(Exception):
+        asyncio.run(serving_chat.create_chat_completion(req))
+
+    # AsyncLLMEngine.generate(inputs, sampling_params, ...)
+    assert mock_engine.generate.call_args.args[1].max_tokens == 93
+
+    req.max_tokens = 10
+    with suppress(Exception):
+        asyncio.run(serving_chat.create_chat_completion(req))
+
+    assert mock_engine.generate.call_args.args[1].max_tokens == 10

vllm/entrypoints/openai/protocol.py

@@ -11,7 +11,7 @@
 from vllm.entrypoints.chat_utils import ChatCompletionMessageParam
 from vllm.entrypoints.openai.logits_processors import get_logits_processors
 from vllm.pooling_params import PoolingParams
-from vllm.sampling_params import SamplingParams
+from vllm.sampling_params import LogitsProcessor, SamplingParams
 from vllm.utils import random_uuid
 
 
@@ -215,15 +215,22 @@ class ChatCompletionRequest(OpenAIBaseModel):
 
     # doc: end-chat-completion-extra-params
 
-    def to_sampling_params(self,
-                           tokenizer: PreTrainedTokenizer) -> SamplingParams:
-        # We now allow logprobs being true without top_logrobs.
+    def to_sampling_params(
+            self, tokenizer: PreTrainedTokenizer,
+            guided_decode_logits_processor: Optional[LogitsProcessor],
+            default_max_tokens: int) -> SamplingParams:
+        max_tokens = self.max_tokens
+        if max_tokens is None:
+            max_tokens = default_max_tokens
 
+        # We now allow logprobs being true without top_logrobs.
         logits_processors = get_logits_processors(
             logit_bias=self.logit_bias,
             allowed_token_ids=None,
             tokenizer=tokenizer,
         )
+        if guided_decode_logits_processor:
+            logits_processors.append(guided_decode_logits_processor)
 
         return SamplingParams(
             n=self.n,
@@ -241,7 +248,7 @@ def to_sampling_params(self,
             logprobs=self.top_logprobs if self.logprobs else None,
             prompt_logprobs=self.top_logprobs if self.echo else None,
             ignore_eos=self.ignore_eos,
-            max_tokens=self.max_tokens,
+            max_tokens=max_tokens,
             min_tokens=self.min_tokens,
             use_beam_search=self.use_beam_search,
             early_stopping=self.early_stopping,
@@ -395,14 +402,23 @@ class CompletionRequest(OpenAIBaseModel):
 
     # doc: end-completion-extra-params
 
-    def to_sampling_params(self, tokenizer: PreTrainedTokenizer):
+    def to_sampling_params(
+            self, tokenizer: PreTrainedTokenizer,
+            guided_decode_logits_processor: Optional[LogitsProcessor],
+            default_max_tokens: int) -> SamplingParams:
+        max_tokens = self.max_tokens
+        if max_tokens is None:
+            max_tokens = default_max_tokens
+
         echo_without_generation = self.echo and self.max_tokens == 0
 
         logits_processors = get_logits_processors(
             logit_bias=self.logit_bias,
             allowed_token_ids=self.allowed_token_ids,
             tokenizer=tokenizer,
         )
+        if guided_decode_logits_processor:
+            logits_processors.append(guided_decode_logits_processor)
 
         return SamplingParams(
             n=self.n,
@@ -419,7 +435,7 @@ def to_sampling_params(self, tokenizer: PreTrainedTokenizer):
             stop_token_ids=self.stop_token_ids,
             logprobs=self.logprobs,
             ignore_eos=self.ignore_eos,
-            max_tokens=self.max_tokens if not echo_without_generation else 1,
+            max_tokens=max_tokens if not echo_without_generation else 1,
             min_tokens=self.min_tokens,
             use_beam_search=self.use_beam_search,
             early_stopping=self.early_stopping,

vllm/entrypoints/openai/serving_chat.py

@@ -25,8 +25,6 @@
                                                     PromptAdapterPath)
 from vllm.inputs import PromptInputs
 from vllm.logger import init_logger
-from vllm.model_executor.guided_decoding import (
-    get_guided_decoding_logits_processor)
 from vllm.multimodal import MultiModalDataDict
 from vllm.outputs import RequestOutput
 from vllm.sequence import Logprob
@@ -132,28 +130,23 @@ async def create_chat_completion(
 
         request_id = f"chat-{random_uuid()}"
         try:
-            sampling_params = request.to_sampling_params(tokenizer)
-            decoding_config = await self.engine.get_decoding_config()
-            guided_decoding_backend = request.guided_decoding_backend \
-                or decoding_config.guided_decoding_backend
             guided_decode_logits_processor = (
-                await
-                get_guided_decoding_logits_processor(guided_decoding_backend,
-                                                     request, tokenizer))
-            if guided_decode_logits_processor:
-                if sampling_params.logits_processors is None:
-                    sampling_params.logits_processors = []
-                sampling_params.logits_processors.append(
-                    guided_decode_logits_processor)
+                await self._guided_decode_logits_processor(request, tokenizer))
 
             prompt_inputs = self._tokenize_prompt_input(
                 request,
                 tokenizer,
                 prompt,
-                truncate_prompt_tokens=sampling_params.truncate_prompt_tokens,
+                truncate_prompt_tokens=request.truncate_prompt_tokens,
                 add_special_tokens=request.add_special_tokens,
             )
 
+            sampling_params = request.to_sampling_params(
+                tokenizer,
+                guided_decode_logits_processor,
+                default_max_tokens=self.max_model_len -
+                len(prompt_inputs["prompt_token_ids"]))
+
             self._log_inputs(request_id,
                              prompt_inputs,
                              params=sampling_params,

vllm/entrypoints/openai/serving_completion.py

@@ -24,8 +24,6 @@
                                                     OpenAIServing,
                                                     PromptAdapterPath)
 from vllm.logger import init_logger
-from vllm.model_executor.guided_decoding import (
-    get_guided_decoding_logits_processor)
 from vllm.outputs import RequestOutput
 from vllm.sequence import Logprob
 from vllm.tracing import (contains_trace_headers, extract_trace_headers,
@@ -93,31 +91,24 @@ async def create_completion(self, request: CompletionRequest,
 
             tokenizer = await self.engine.get_tokenizer(lora_request)
 
-            sampling_params = request.to_sampling_params(tokenizer)
-            decoding_config = await self.engine.get_decoding_config()
-            guided_decoding_backend = request.guided_decoding_backend \
-                or decoding_config.guided_decoding_backend
-            guided_decode_logit_processor = (
-                await
-                get_guided_decoding_logits_processor(guided_decoding_backend,
-                                                     request, tokenizer))
-            if guided_decode_logit_processor is not None:
-                if sampling_params.logits_processors is None:
-                    sampling_params.logits_processors = []
-                sampling_params.logits_processors.append(
-                    guided_decode_logit_processor)
-
+            guided_decode_logits_processor = (
+                await self._guided_decode_logits_processor(request, tokenizer))
             prompts = list(
                 self._tokenize_prompt_input_or_inputs(
                     request,
                     tokenizer,
                     request.prompt,
-                    truncate_prompt_tokens=sampling_params.
-                    truncate_prompt_tokens,
+                    truncate_prompt_tokens=request.truncate_prompt_tokens,
                     add_special_tokens=request.add_special_tokens,
                 ))
 
             for i, prompt_inputs in enumerate(prompts):
+                sampling_params = request.to_sampling_params(
+                    tokenizer,
+                    guided_decode_logits_processor,
+                    default_max_tokens=self.max_model_len -
+                    len(prompt_inputs["prompt_token_ids"]))
+
                 request_id_item = f"{request_id}-{i}"
 
                 self._log_inputs(request_id_item,

vllm/entrypoints/openai/serving_engine.py

@@ -26,9 +26,11 @@
 from vllm.inputs import parse_and_batch_prompt
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
+from vllm.model_executor.guided_decoding import (
+    get_guided_decoding_logits_processor)
 from vllm.pooling_params import PoolingParams
 from vllm.prompt_adapter.request import PromptAdapterRequest
-from vllm.sampling_params import SamplingParams
+from vllm.sampling_params import LogitsProcessor, SamplingParams
 from vllm.sequence import Logprob
 
 logger = init_logger(__name__)
@@ -150,6 +152,15 @@ def create_streaming_error_response(
         })
         return json_str
 
+    async def _guided_decode_logits_processor(
+            self, request: Union[ChatCompletionRequest, CompletionRequest],
+            tokenizer: AnyTokenizer) -> Optional[LogitsProcessor]:
+        decoding_config = await self.engine.get_decoding_config()
+        guided_decoding_backend = request.guided_decoding_backend \
+            or decoding_config.guided_decoding_backend
+        return await get_guided_decoding_logits_processor(
+            guided_decoding_backend, request, tokenizer)
+
     async def _check_model(
         self,
         request: AnyRequest,
@@ -254,9 +265,7 @@ def _validate_input(
                     f"{self.max_model_len} tokens. However, you requested "
                     f"{token_num} tokens in the messages, "
                     f"Please reduce the length of the messages.")
-            request.max_tokens = self.max_model_len - token_num
-
-        if token_num + request.max_tokens > self.max_model_len:
+        elif token_num + request.max_tokens > self.max_model_len:
             raise ValueError(
                 f"This model's maximum context length is "
                 f"{self.max_model_len} tokens. However, you requested "