[Core][Bugfix] Fix Offline MM Beam Search (vllm-project#16390)

Signed-off-by: Alex-Brooks <[email protected]>
Co-authored-by: Cyrus Leung <[email protected]>

Author: 2 people

tests/conftest.py

@@ -29,12 +29,11 @@
                               init_distributed_environment,
                               initialize_model_parallel)
 from vllm.inputs import (ExplicitEncoderDecoderPrompt, TextPrompt,
-                         TokensPrompt, to_enc_dec_tuple_list,
-                         zip_enc_dec_prompts)
+                         to_enc_dec_tuple_list, zip_enc_dec_prompts)
 from vllm.logger import init_logger
 from vllm.outputs import RequestOutput
 from vllm.sampling_params import BeamSearchParams
-from vllm.utils import cuda_device_count_stateless, is_list_of
+from vllm.utils import cuda_device_count_stateless
 
 logger = init_logger(__name__)
 
@@ -469,12 +468,19 @@ def generate_beam_search(
         prompts: list[str],
         beam_width: int,
         max_tokens: int,
+        images: Optional[PromptImageInput] = None,
+        videos: Optional[PromptVideoInput] = None,
+        audios: Optional[PromptAudioInput] = None,
     ) -> list[tuple[list[list[int]], list[str]]]:
         outputs = self.generate(prompts,
                                 do_sample=False,
                                 max_new_tokens=max_tokens,
                                 num_beams=beam_width,
-                                num_return_sequences=beam_width)
+                                num_return_sequences=beam_width,
+                                images=images,
+                                videos=videos,
+                                audios=audios)
+
         for i in range(len(outputs)):
             output_ids, output_str = outputs[i]
             for j in range(len(output_ids)):
@@ -936,18 +942,20 @@ def generate_encoder_decoder_greedy_logprobs(
 
     def generate_beam_search(
         self,
-        prompts: Union[list[str], list[list[int]]],
+        prompts: list[str],
         beam_width: int,
         max_tokens: int,
+        images: Optional[PromptImageInput] = None,
+        videos: Optional[PromptVideoInput] = None,
+        audios: Optional[PromptAudioInput] = None,
     ) -> list[tuple[list[list[int]], list[str]]]:
-        if is_list_of(prompts, str, check="all"):
-            prompts = [TextPrompt(prompt=prompt) for prompt in prompts]
-        else:
-            prompts = [
-                TokensPrompt(prompt_token_ids=tokens) for tokens in prompts
-            ]
+        inputs = self.get_inputs(prompts,
+                                 images=images,
+                                 videos=videos,
+                                 audios=audios)
+
         outputs = self.model.beam_search(
-            prompts,
+            inputs,
             BeamSearchParams(beam_width=beam_width, max_tokens=max_tokens))
         returned_outputs = []
         for output in outputs:

tests/samplers/test_beam_search.py

@@ -5,6 +5,9 @@
 """
 
 import pytest
+from transformers import AutoModelForSeq2SeqLM
+
+from vllm.assets.audio import AudioAsset
 
 
 @pytest.fixture(autouse=True)
@@ -19,6 +22,7 @@ def v1(run_with_both_engines):
 #   3. Use the model "huggyllama/llama-7b".
 MAX_TOKENS = [64]
 BEAM_WIDTHS = [4]
+MM_BEAM_WIDTHS = [2]
 MODELS = ["TinyLlama/TinyLlama-1.1B-Chat-v1.0"]
 
 
@@ -48,15 +52,90 @@ def test_beam_search_single_input(
     for i in range(len(example_prompts)):
         hf_output_ids, hf_output_texts = hf_outputs[i]
         vllm_output_ids, vllm_output_texts = vllm_outputs[i]
-        for i, (hf_text,
+        for j, (hf_text,
                 vllm_text) in enumerate(zip(hf_output_texts,
                                             vllm_output_texts)):
-            print(f">>>{i}-th hf output:")
+            print(f">>>{j}-th hf output:")
             print(hf_text)
-            print(f">>>{i}-th vllm output:")
+            print(f">>>{j}-th vllm output:")
             print(vllm_text)
         assert len(hf_output_ids) == len(vllm_output_ids)
         for j in range(len(hf_output_ids)):
             assert hf_output_ids[j] == vllm_output_ids[j], (
                 f"Test{i} output{j}:\nHF: {hf_output_ids}\n"
                 f"vLLM: {vllm_output_ids}")
+
+
+@pytest.mark.parametrize("dtype", ["half"])
+@pytest.mark.parametrize("max_tokens", MAX_TOKENS)
+@pytest.mark.parametrize("beam_width", MM_BEAM_WIDTHS)
+def test_beam_search_passes_multimodal_data(
+    hf_runner,
+    vllm_runner,
+    dtype: str,
+    max_tokens: int,
+    beam_width: int,
+) -> None:
+    """Ensure that beam search passes multimodal data through correctly."""
+    # NOTE - this test is primarily to check that mm data is passed to beams
+    # correctly. As such, we just need to check one extra modality to make
+    # sure things pass through properly.
+    audios = [AudioAsset("mary_had_lamb").audio_and_sample_rate]
+    model = "Qwen/Qwen2-Audio-7B-Instruct"
+    audio_seq = "<|audio_bos|><|AUDIO|><|audio_eos|>"
+    prompts = [
+        f"<|im_start|>user\n{audio_seq}Can you transcribe this?<|im_end|>\n<|im_start|>assistant\n"  #noqa: E501
+    ]
+
+    with hf_runner(model, dtype=dtype,
+                   auto_cls=AutoModelForSeq2SeqLM) as hf_model:
+        audio_token_id = hf_model.config.audio_token_index
+        eos_token_id = hf_model.tokenizer.eos_token_id  # <|im_end|>
+        hf_outputs = hf_model.generate_beam_search(
+            prompts,
+            beam_width=beam_width,
+            max_tokens=max_tokens,
+            audios=audios,
+        )
+
+    with vllm_runner(model, dtype=dtype) as vllm_model:
+        vllm_outputs = vllm_model.generate_beam_search(
+            prompts,
+            beam_width=beam_width,
+            max_tokens=max_tokens,
+            audios=audios,
+        )
+
+    seq_with_no_audio_toks = lambda seq: [
+        tok for tok in seq if tok != audio_token_id
+    ]
+
+    for i in range(len(prompts)):
+        hf_output_ids, hf_output_texts = hf_outputs[i]
+        vllm_output_ids, vllm_output_texts = vllm_outputs[i]
+
+        for j, (hf_text,
+                vllm_text) in enumerate(zip(hf_output_texts,
+                                            vllm_output_texts)):
+            print(f">>>{j}-th hf output [NOTE: special tokens are filtered]:")
+            print(hf_text)
+            print(f">>>{j}-th vllm output:")
+            print(vllm_text)
+        assert len(hf_output_ids) == len(vllm_output_ids)
+
+        for j in range(len(hf_output_ids)):
+            # Compare everything except for the audio tokens; we do this since
+            # the IDs returned from the transformers helper expands the audio
+            # token to match features, while the vLLM helper maintains the
+            # single audio token in the input text
+            filtered_hf_output_ids = seq_with_no_audio_toks(hf_output_ids[j])
+            filtered_vllm_output_ids = seq_with_no_audio_toks(
+                vllm_output_ids[j])
+
+            # HF output IDs may contain the end of sequence
+            if len(filtered_hf_output_ids
+                   ) == len(filtered_vllm_output_ids) + 1:
+                assert filtered_hf_output_ids[-1] == eos_token_id
+                filtered_hf_output_ids = filtered_hf_output_ids[:-1]
+
+            assert filtered_hf_output_ids == filtered_vllm_output_ids

vllm/beam_search.py

@@ -38,9 +38,18 @@ class BeamSearchOutput:
 
 class BeamSearchInstance:
 
-    def __init__(self, prompt_tokens: list[int]):
+    def __init__(
+        self,
+        prompt_tokens: list[int],
+        logprobs: Optional[list[dict[int, Logprob]]] = None,
+        **kwargs,
+    ):
         self.beams: list[BeamSearchSequence] = [
-            BeamSearchSequence(tokens=prompt_tokens, logprobs=[])
+            BeamSearchSequence(
+                tokens=prompt_tokens,
+                logprobs=[] if logprobs is None else list(logprobs),
+                **kwargs,
+            )
         ]
         self.completed: list[BeamSearchSequence] = []
 

vllm/entrypoints/llm.py

@@ -536,15 +536,18 @@ def sort_beams_key(x: BeamSearchSequence) -> float:
                                          tokenizer.eos_token_id,
                                          length_penalty)
 
-        # TODO - fix handling of multimodal data for beam search; we pass it
-        # through in the async version on the abstract EngineClient, but not
-        # here.
-        if any("multi_modal_data" in prompt
-               and prompt["multi_modal_data"] is not None
-               for prompt in prompts):
-            logger.warning(
-                "Multimodal data appears to have been provided, but is not"
-                " currently being passed through in LLM.beam_search()!")
+        def create_tokens_prompt_from_beam(
+                beam: BeamSearchSequence) -> TokensPrompt:
+            token_prompt_kwargs: TokensPrompt = {
+                "prompt_token_ids": beam.tokens
+            }
+            if beam.multi_modal_data is not None:
+                token_prompt_kwargs["multi_modal_data"] = beam.multi_modal_data
+
+            if beam.mm_processor_kwargs is not None:
+                token_prompt_kwargs[
+                    "mm_processor_kwargs"] = beam.mm_processor_kwargs
+            return TokensPrompt(**token_prompt_kwargs)
 
         tokenizer = self.get_tokenizer()
         # generate 2 * beam_width candidates at each step
@@ -556,11 +559,20 @@ def sort_beams_key(x: BeamSearchSequence) -> float:
         instances: list[BeamSearchInstance] = []
 
         for prompt in prompts:
+            # Add multimodal processor kwargs & data
+            mm_kwargs = {}
+            if "multi_modal_data" in prompt:
+                mm_kwargs["multi_modal_data"] = prompt["multi_modal_data"]
+            if "mm_processor_kwargs" in prompt:
+                mm_kwargs["mm_processor_kwargs"] = prompt[
+                    "mm_processor_kwargs"]
+
             if is_token_prompt(prompt):
                 prompt_tokens = prompt["prompt_token_ids"]
             else:
                 prompt_tokens = tokenizer.encode(prompt["prompt"])
-            instances.append(BeamSearchInstance(prompt_tokens))
+            instances.append(
+                BeamSearchInstance(prompt_tokens, logprobs=None, **mm_kwargs))
 
         for _ in range(max_tokens):
             all_beams: list[BeamSearchSequence] = list(
@@ -575,8 +587,7 @@ def sort_beams_key(x: BeamSearchSequence) -> float:
                 break
 
             prompts_batch = [
-                TokensPrompt(prompt_token_ids=beam.tokens)
-                for beam in all_beams
+                create_tokens_prompt_from_beam(beam) for beam in all_beams
             ]
 
             # only runs for one step
@@ -602,7 +613,10 @@ def sort_beams_key(x: BeamSearchSequence) -> float:
                                 tokens=current_beam.tokens + [token_id],
                                 logprobs=current_beam.logprobs + [logprobs],
                                 cum_logprob=current_beam.cum_logprob +
-                                logprob_obj.logprob)
+                                logprob_obj.logprob,
+                                multi_modal_data=current_beam.multi_modal_data,
+                                mm_processor_kwargs=current_beam.
+                                mm_processor_kwargs)
 
                             if token_id == tokenizer.eos_token_id and \
                                 not ignore_eos: