[Frontend] re-enable multi-modality input in the new beam search implementation (#9427)

Signed-off-by: Qishuai Ferdinandzhong@gmail.com

tests/entrypoints/openai/test_vision.py

@@ -107,6 +107,42 @@ async def test_single_chat_session_image(client: openai.AsyncOpenAI,
     assert message.content is not None and len(message.content) >= 0
 
 
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+@pytest.mark.parametrize("image_url", TEST_IMAGE_URLS)
+async def test_single_chat_session_image_beamsearch(client: openai.AsyncOpenAI,
+                                                    model_name: str,
+                                                    image_url: str):
+    messages = [{
+        "role":
+        "user",
+        "content": [
+            {
+                "type": "image_url",
+                "image_url": {
+                    "url": image_url
+                }
+            },
+            {
+                "type": "text",
+                "text": "What's in this image?"
+            },
+        ],
+    }]
+
+    chat_completion = await client.chat.completions.create(
+        model=model_name,
+        messages=messages,
+        n=2,
+        max_tokens=10,
+        logprobs=True,
+        top_logprobs=5,
+        extra_body=dict(use_beam_search=True))
+    assert len(chat_completion.choices) == 2
+    assert chat_completion.choices[
+        0].message.content != chat_completion.choices[1].message.content
+
+
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
 @pytest.mark.parametrize("image_url", TEST_IMAGE_URLS)
@@ -162,6 +198,41 @@ async def test_single_chat_session_image_base64encoded(
     assert message.content is not None and len(message.content) >= 0
 
 
+@pytest.mark.asyncio
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+@pytest.mark.parametrize("image_url", TEST_IMAGE_URLS)
+async def test_single_chat_session_image_base64encoded_beamsearch(
+        client: openai.AsyncOpenAI, model_name: str, image_url: str,
+        base64_encoded_image: Dict[str, str]):
+
+    messages = [{
+        "role":
+        "user",
+        "content": [
+            {
+                "type": "image_url",
+                "image_url": {
+                    "url":
+                    f"data:image/jpeg;base64,{base64_encoded_image[image_url]}"
+                }
+            },
+            {
+                "type": "text",
+                "text": "What's in this image?"
+            },
+        ],
+    }]
+    chat_completion = await client.chat.completions.create(
+        model=model_name,
+        messages=messages,
+        n=2,
+        max_tokens=10,
+        extra_body=dict(use_beam_search=True))
+    assert len(chat_completion.choices) == 2
+    assert chat_completion.choices[
+        0].message.content != chat_completion.choices[1].message.content
+
+
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
 @pytest.mark.parametrize("image_url", TEST_IMAGE_URLS)

vllm/beam_search.py

 from dataclasses import dataclass
-from typing import Dict, List, Optional
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
 
 from vllm.sequence import Logprob
 
+if TYPE_CHECKING:
+    from vllm.multimodal import MultiModalDataDict
+
 
 @dataclass
 class BeamSearchSequence:
@@ -16,6 +19,10 @@ class BeamSearchSequence:
     logprobs: List[Dict[int, Logprob]]
     cum_logprob: float = 0.0
     text: Optional[str] = None
+    finish_reason: Optional[str] = None
+    stop_reason: Union[int, str, None] = None
+    multi_modal_data: Optional["MultiModalDataDict"] = None
+    mm_processor_kwargs: Optional[Dict[str, Any]] = None
 
 
 @dataclass

vllm/engine/protocol.py

@@ -6,6 +6,7 @@ from vllm.beam_search import BeamSearchSequence, create_sort_beams_key_function
 from vllm.config import DecodingConfig, ModelConfig
 from vllm.core.scheduler import SchedulerOutputs
 from vllm.inputs.data import PromptType, TokensPrompt
+from vllm.inputs.preprocess import InputPreprocessor
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
 from vllm.model_executor.layers.sampler import SamplerOutput
@@ -59,7 +60,8 @@ class EngineClient(ABC):
 
     async def beam_search(
         self,
-        prompt: Union[str, List[int]],
+        prompt: Union[PromptType, List[int]],
+        model_config: ModelConfig,
         request_id: str,
         params: BeamSearchParams,
     ) -> AsyncGenerator[RequestOutput, None]:
@@ -69,32 +71,40 @@ class EngineClient(ABC):
         ignore_eos = params.ignore_eos
         temperature = params.temperature
         length_penalty = params.length_penalty
+        include_stop_str_in_output = params.include_stop_str_in_output
 
-        tokenizer = await self.get_tokenizer(lora_request=None)
-        if isinstance(prompt, str):
-            tokenized_prompt = tokenizer.encode(prompt)
-            prompt_text = prompt
-        else:
-            tokenized_prompt = prompt
-            prompt_text = None
-        tokenized_length = len(tokenized_prompt)
+        tokenizer = await self.get_tokenizer()
+        input_preprocessor = InputPreprocessor(model_config, tokenizer)
+
+        (prompt_text, prompt_token_ids, multi_modal_data,
+         mm_processor_kwargs) = input_preprocessor._extract_prompt_components(
+             prompt,
+             request_id=request_id,
+         )
+        tokenized_length = len(prompt_token_ids)
 
         sort_beams_key = create_sort_beams_key_function(
             tokenizer.eos_token_id, length_penalty)
 
-        beam_search_params = SamplingParams(logprobs=2 * beam_width,
-                                            max_tokens=1,
-                                            temperature=temperature)
+        beam_search_params = SamplingParams(
+            logprobs=2 * beam_width,
+            max_tokens=1,
+            temperature=temperature,
+        )
         all_beams = [
-            BeamSearchSequence(tokens=tokenized_prompt,
+            BeamSearchSequence(tokens=prompt_token_ids,
+                               cum_logprob=0,
                                logprobs=[],
-                               cum_logprob=0)
+                               multi_modal_data=multi_modal_data,
+                               mm_processor_kwargs=mm_processor_kwargs)
         ]
         completed = []
 
         for _ in range(max_tokens):
             prompts_batch = [
-                TokensPrompt(prompt_token_ids=beam.tokens)
+                TokensPrompt(prompt_token_ids=beam.tokens,
+                             multi_modal_data=beam.multi_modal_data,
+                             mm_processor_kwargs=beam.mm_processor_kwargs)
                 for beam in all_beams
             ]
 
@@ -120,17 +130,31 @@ class EngineClient(ABC):
                 if result.outputs[0].logprobs is not None:
                     logprobs = result.outputs[0].logprobs[0]
                     for token_id, logprob_obj in logprobs.items():
-                        new_beam = BeamSearchSequence(
-                            tokens=current_beam.tokens + [token_id],
-                            logprobs=current_beam.logprobs + [logprobs],
-                            cum_logprob=current_beam.cum_logprob +
-                            logprob_obj.logprob)
-
                         if token_id == tokenizer.eos_token_id and \
                             not ignore_eos:
-                            completed.append(new_beam)
+                            completed.append(
+                                BeamSearchSequence(
+                                    tokens=current_beam.tokens +
+                                    [token_id] if include_stop_str_in_output
+                                    else current_beam.tokens,
+                                    logprobs=current_beam.logprobs +
+                                    [logprobs],
+                                    cum_logprob=current_beam.cum_logprob +
+                                    logprob_obj.logprob,
+                                    finish_reason="stop",
+                                    stop_reason=tokenizer.eos_token_id))
                         else:
-                            new_beams.append(new_beam)
+                            new_beams.append(
+                                BeamSearchSequence(
+                                    tokens=current_beam.tokens + [token_id],
+                                    logprobs=current_beam.logprobs +
+                                    [logprobs],
+                                    cum_logprob=current_beam.cum_logprob +
+                                    logprob_obj.logprob,
+                                    multi_modal_data=current_beam.
+                                    multi_modal_data,
+                                    mm_processor_kwargs=current_beam.
+                                    mm_processor_kwargs))
 
             sorted_beams = sorted(new_beams, key=sort_beams_key, reverse=True)
             all_beams = sorted_beams[:beam_width]
@@ -151,16 +175,18 @@ class EngineClient(ABC):
             request_id=request_id,
             prompt=prompt_text,
             outputs=[
-                CompletionOutput(
-                    text=beam.text,
-                    cumulative_logprob=beam.cum_logprob,
-                    token_ids=beam.tokens[tokenized_length:],
-                    index=i,
-                    logprobs=beam.logprobs,
-                ) for (i, beam) in enumerate(best_beams)
+                CompletionOutput(text=beam.text,
+                                 cumulative_logprob=beam.cum_logprob,
+                                 token_ids=beam.tokens[tokenized_length:],
+                                 index=i,
+                                 logprobs=beam.logprobs,
+                                 finish_reason=beam.finish_reason if
+                                 beam.finish_reason is not None else "length",
+                                 stop_reason=beam.stop_reason)
+                for (i, beam) in enumerate(best_beams)
             ],
             finished=True,
-            prompt_token_ids=tokenized_prompt,
+            prompt_token_ids=prompt_token_ids,
             prompt_logprobs=None)
 
         yield beam_search_output

vllm/entrypoints/openai/protocol.py

@@ -308,7 +308,7 @@ class ChatCompletionRequest(OpenAIBaseModel):
             ignore_eos=self.ignore_eos,
             temperature=temperature,
             length_penalty=self.length_penalty,
-        )
+            include_stop_str_in_output=self.include_stop_str_in_output)
 
     def to_sampling_params(self, default_max_tokens: int) -> SamplingParams:
         max_tokens = self.max_tokens
@@ -606,7 +606,7 @@ class CompletionRequest(OpenAIBaseModel):
             ignore_eos=self.ignore_eos,
             temperature=temperature,
             length_penalty=self.length_penalty,
-        )
+            include_stop_str_in_output=self.include_stop_str_in_output)
 
     def to_sampling_params(self, default_max_tokens: int) -> SamplingParams:
         max_tokens = self.max_tokens

vllm/entrypoints/openai/serving_chat.py

@@ -236,9 +236,10 @@ class OpenAIServingChat(OpenAIServing):
 
             if isinstance(sampling_params, BeamSearchParams):
                 result_generator = self.engine_client.beam_search(
-                    engine_inputs['prompt_token_ids'],
-                    request_id,
-                    sampling_params,
+                    prompt=engine_inputs,
+                    model_config=self.model_config,
+                    request_id=request_id,
+                    params=sampling_params,
                 )
             else:
                 result_generator = self.engine_client.generate(

vllm/entrypoints/openai/serving_completion.py

@@ -150,9 +150,13 @@ class OpenAIServingCompletion(OpenAIServing):
 
                 if isinstance(sampling_params, BeamSearchParams):
                     generator = self.engine_client.beam_search(
-                        prompt_inputs["prompt_token_ids"],
-                        request_id_item,
-                        sampling_params,
+                        prompt={
+                            "prompt_token_ids":
+                            prompt_inputs["prompt_token_ids"]
+                        },
+                        model_config=self.model_config,
+                        request_id=request_id,
+                        params=sampling_params,
                     )
                 else:
                     generator = self.engine_client.generate(

vllm/sampling_params.py

@@ -500,3 +500,4 @@ class BeamSearchParams(
     ignore_eos: bool = False
     temperature: float = 0.0
     length_penalty: float = 1.0
+    include_stop_str_in_output: bool = False