[VLM] Report multi_modal_placeholders in output

tests/models/decoder_only/vision_language/test_pixtral.py

@@ -8,13 +8,17 @@
 from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
 
 import pytest
+from mistral_common.multimodal import download_image
 from mistral_common.protocol.instruct.messages import ImageURLChunk
 from mistral_common.protocol.instruct.request import ChatCompletionRequest
 from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
 from mistral_common.tokens.tokenizers.multimodal import image_from_chunk
+from transformers import AutoProcessor
 
-from vllm import EngineArgs, LLMEngine, SamplingParams, TokensPrompt
+from vllm import (EngineArgs, LLMEngine, RequestOutput, SamplingParams,
+                  TextPrompt, TokensPrompt)
 from vllm.multimodal import MultiModalDataBuiltins
+from vllm.multimodal.inputs import PlaceholderRange
 from vllm.sequence import Logprob, SampleLogprobs
 
 from ....utils import VLLM_PATH, large_gpu_test
@@ -49,6 +53,20 @@ def _create_msg_format(urls: List[str]) -> List[Dict[str, Any]]:
     }]
 
 
+def _create_msg_format_hf(urls: List[str]) -> List[Dict[str, Any]]:
+    return [{
+        "role":
+        "user",
+        "content": [{
+            "type": "text",
+            "content": PROMPT,
+        }, *({
+            "type": "image",
+            "image": download_image(url)
+        } for url in urls)],
+    }]
+
+
 def _create_engine_inputs(urls: List[str]) -> TokensPrompt:
     msg = _create_msg_format(urls)
 
@@ -70,6 +88,23 @@ def _create_engine_inputs(urls: List[str]) -> TokensPrompt:
     return engine_inputs
 
 
+def _create_engine_inputs_hf(urls: List[str]) -> TextPrompt:
+    msg = _create_msg_format_hf(urls)
+
+    tokenizer = AutoProcessor.from_pretrained("mistral-community/pixtral-12b")
+    prompt = tokenizer.apply_chat_template(msg)
+
+    images = []
+    for chunk in msg[0]["content"]:
+        if chunk["type"] == "image":
+            images.append(chunk["image"])
+
+    mm_data = MultiModalDataBuiltins(image=images)
+    engine_inputs = TextPrompt(prompt=prompt, multi_modal_data=mm_data)
+
+    return engine_inputs
+
+
 MSGS = [
     _create_msg_format(IMG_URLS[:1]),
     _create_msg_format(IMG_URLS[:2]),
@@ -191,3 +226,45 @@ def test_model_engine(vllm_runner, model: str, dtype: str) -> None:
                          outputs_1_lst=logprobs,
                          name_0="h100_ref",
                          name_1="output")
+
+
+@large_gpu_test(min_gb=24)
+@pytest.mark.parametrize(
+    "prompt,expected_ranges",
+    [(_create_engine_inputs_hf(IMG_URLS[:1]), [{
+        "offset": 10,
+        "length": 494
+    }]),
+     (_create_engine_inputs_hf(IMG_URLS[1:4]), [{
+         "offset": 10,
+         "length": 266
+     }, {
+         "offset": 276,
+         "length": 1056
+     }, {
+         "offset": 1332,
+         "length": 418
+     }])])
+def test_multi_modal_placeholders(
+        vllm_runner, prompt, expected_ranges: list[PlaceholderRange]) -> None:
+    with vllm_runner(
+            "mistral-community/pixtral-12b",
+            max_model_len=8192,
+            limit_mm_per_prompt=LIMIT_MM_PER_PROMPT,
+    ) as vllm_model:
+        outputs = vllm_model.model.generate(prompt)
+
+        assert len(outputs) == 1, f"{len(outputs)=}"
+        output: RequestOutput = outputs[0]
+        assert hasattr(output,
+                       "multi_modal_placeholders"), f"{output.__dict__=}"
+        assert "image" in output.multi_modal_placeholders, \
+            f"{output.multi_modal_placeholders.keys()=}"
+        image_placeholder_ranges: list[
+            PlaceholderRange] = output.multi_modal_placeholders["image"]
+        assert len(image_placeholder_ranges) == len(
+            expected_ranges), f"{image_placeholder_ranges=}"
+        for real_range, expected_range in zip(image_placeholder_ranges,
+                                              expected_ranges):
+            assert real_range == expected_range, \
+                f"{real_range=} {expected_range=}"

vllm/model_executor/models/pixtral.py

@@ -30,6 +30,7 @@
 from vllm.model_executor.models.utils import merge_multimodal_embeddings
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargs
+from vllm.multimodal.inputs import PlaceholderRange
 from vllm.multimodal.utils import (cached_get_tokenizer,
                                    consecutive_placeholder_ranges)
 from vllm.sequence import IntermediateTensors, SequenceData
@@ -773,15 +774,28 @@ def input_processor_for_pixtral_hf(
         replace_tokens[-1] = image_end_id
         replace_tokens_list.append(replace_tokens)
 
+    reverse_offsets: List[int] = []
     # Backward iteration for replacement without affecting known indices
     for placeholder_idx, replace_tokens in zip(reversed(placeholder_indices),
                                                reversed(replace_tokens_list)):
+        reverse_offsets.append(
+            len(new_token_ids) - placeholder_idx + len(replace_tokens))
         new_token_ids[placeholder_idx:placeholder_idx + 1] = replace_tokens
 
+    placeholder_ranges: List[PlaceholderRange] = []
+    for reverse_offset, replace_tokens in zip(reversed(reverse_offsets),
+                                              replace_tokens_list):
+        placeholder_ranges.append(
+            PlaceholderRange(
+                offset=len(new_token_ids) - reverse_offset,
+                length=len(replace_tokens),
+            ))
+
     # NOTE: Create a defensive copy of the original inputs
     return token_inputs(prompt_token_ids=new_token_ids,
                         prompt=new_prompt,
-                        multi_modal_data=multi_modal_data)
+                        multi_modal_data=multi_modal_data,
+                        multi_modal_placeholders={"image": placeholder_ranges})
 
 
 class PixtralHFMLP(nn.Module):

vllm/outputs.py

@@ -5,6 +5,7 @@
 from typing import Union
 
 from vllm.lora.request import LoRARequest
+from vllm.multimodal.inputs import MultiModalPlaceholderDict
 from vllm.sampling_params import RequestOutputKind
 from vllm.sequence import (PromptLogprobs, RequestMetrics, SampleLogprobs,
                            SequenceGroup, SequenceGroupBase, SequenceStatus)
@@ -103,10 +104,13 @@ def __init__(
         encoder_prompt: Optional[str] = None,
         encoder_prompt_token_ids: Optional[List[int]] = None,
         num_cached_tokens: Optional[int] = None,
+        *,
+        multi_modal_placeholders: Optional[MultiModalPlaceholderDict] = None,
     ) -> None:
         self.request_id = request_id
         self.prompt = prompt
         self.prompt_token_ids = prompt_token_ids
+        self.multi_modal_placeholders = multi_modal_placeholders or {}
         self.prompt_logprobs = prompt_logprobs
         self.outputs = outputs
         self.finished = finished
@@ -275,17 +279,26 @@ def from_seq_group(
         finished_time = time.time() if finished else None
         seq_group.set_finished_time(finished_time)
 
-        init_args = (seq_group.request_id, prompt, prompt_token_ids,
-                     prompt_logprobs, outputs, finished, seq_group.metrics,
-                     seq_group.lora_request, encoder_prompt,
-                     encoder_prompt_token_ids, num_cached_tokens)
+        init_kwargs = {
+            "request_id": seq_group.request_id,
+            "prompt": prompt,
+            "prompt_token_ids": prompt_token_ids,
+            "prompt_logprobs": prompt_logprobs,
+            "outputs": outputs,
+            "finished": finished,
+            "metrics": seq_group.metrics,
+            "lora_request": seq_group.lora_request,
+            "encoder_prompt": encoder_prompt,
+            "encoder_prompt_token_ids": encoder_prompt_token_ids,
+            "num_cached_tokens": num_cached_tokens,
+            "multi_modal_placeholders": seq_group.multi_modal_placeholders
+        }
 
         if use_cache:
             request_output = seq_group.cached_request_output
-            request_output.__init__(*init_args)  # type: ignore
-
+            request_output.__init__(**init_kwargs)  # type: ignore
         else:
-            request_output = cls(*init_args)
+            request_output = cls(**init_kwargs)  # type: ignore
 
         return request_output
 
@@ -300,7 +313,8 @@ def __repr__(self) -> str:
                 f"finished={self.finished}, "
                 f"metrics={self.metrics}, "
                 f"lora_request={self.lora_request}, "
-                f"num_cached_tokens={self.num_cached_tokens})")
+                f"num_cached_tokens={self.num_cached_tokens}, "
+                f"multi_modal_placeholders={self.multi_modal_placeholders})")
 
 
 class EmbeddingRequestOutput: