[Model] Add multi-image input support for LLaVA-Next offline inference (#7230)

tests/conftest.py

@@ -41,6 +41,10 @@ _TEST_DIR = os.path.dirname(__file__)
 _TEST_PROMPTS = [os.path.join(_TEST_DIR, "prompts", "example.txt")]
 _LONG_PROMPTS = [os.path.join(_TEST_DIR, "prompts", "summary.txt")]
 
+PromptImageInput = Union[List[Image.Image], List[List[Image.Image]]]
+PromptAudioInput = Union[List[Tuple[np.ndarray, int]],
+                         List[List[Tuple[np.ndarray, int]]]]
+
 
 def _read_prompts(filename: str) -> List[str]:
     with open(filename, "r") as f:
@@ -161,7 +165,7 @@ def example_encoder_decoder_prompts(
     decoder prompt) tuple.
 
     Returns:
-    
+
     * Encoder prompt list
     * Decoder prompt list (reverse of encoder prompt list)
     '''
@@ -578,8 +582,7 @@ class VllmRunner:
         self,
         prompts: List[str],
         sampling_params: SamplingParams,
-        images: Optional[Union[List[Image.Image],
-                               List[List[Image.Image]]]] = None,
+        images: Optional[PromptImageInput] = None,
     ) -> List[Tuple[List[List[int]], List[str]]]:
         if images is not None:
             assert len(prompts) == len(images)
@@ -623,10 +626,8 @@ class VllmRunner:
         self,
         prompts: List[str],
         sampling_params: SamplingParams,
-        images: Optional[Union[List[Image.Image],
-                               List[List[Image.Image]]]] = None,
-        audios: Optional[Union[List[Tuple[np.ndarray, int]],
-                               List[List[Tuple[np.ndarray, int]]]]] = None
+        images: Optional[PromptImageInput] = None,
+        audios: Optional[PromptAudioInput] = None,
     ) -> List[Tuple[List[int], str, Optional[SampleLogprobs]]]:
         assert sampling_params.logprobs is not None
 
@@ -676,10 +677,8 @@ class VllmRunner:
         prompts: List[str],
         max_tokens: int,
         num_logprobs: int,
-        images: Optional[Union[List[Image.Image],
-                               List[List[Image.Image]]]] = None,
-        audios: Optional[Union[List[Tuple[np.ndarray, int]],
-                               List[List[Tuple[np.ndarray, int]]]]] = None,
+        images: Optional[PromptImageInput] = None,
+        audios: Optional[PromptAudioInput] = None,
         stop_token_ids: Optional[List[int]] = None,
     ) -> List[Tuple[List[int], str, Optional[SampleLogprobs]]]:
         greedy_logprobs_params = SamplingParams(temperature=0.0,

tests/models/test_llava_next.py

@@ -6,24 +6,22 @@ from transformers import AutoConfig, AutoModelForVision2Seq, AutoTokenizer
 from vllm.multimodal.utils import rescale_image_size
 from vllm.sequence import SampleLogprobs
 
-from ..conftest import IMAGE_ASSETS, HfRunner, VllmRunner, _ImageAssets
+from ..conftest import (IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner,
+                        _ImageAssets)
 from .utils import check_logprobs_close
 
 pytestmark = pytest.mark.vlm
 
-_PREFACE = (
-    "A chat between a curious human and an artificial intelligence assistant. "
-    "The assistant gives helpful, detailed, and polite answers to the human's "
-    "questions.")
+_LIMIT_IMAGE_PER_PROMPT = 4
 
 HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
     "stop_sign":
-    f"{_PREFACE} USER: <image>\nWhat's the content of the image? ASSISTANT:",
+    "[INST] <image>\nWhat's the content of the image? [/INST]",
     "cherry_blossom":
-    f"{_PREFACE} USER: <image>\nWhat is the season? ASSISTANT:",
+    "[INST] <image>\nWhat is the season? [/INST]",
 })
 
-models = ["llava-hf/llava-v1.6-vicuna-7b-hf"]
+models = ["llava-hf/llava-v1.6-mistral-7b-hf"]
 
 
 def vllm_to_hf_output(vllm_output: Tuple[List[int], str,
@@ -114,19 +112,43 @@ def run_test(
     else:
         raise ValueError("You must provide either `size_factors` or `sizes`")
 
+    _run_test(hf_runner,
+              vllm_runner,
+              inputs_per_image,
+              model,
+              dtype=dtype,
+              max_tokens=max_tokens,
+              num_logprobs=num_logprobs,
+              tensor_parallel_size=tensor_parallel_size,
+              distributed_executor_backend=distributed_executor_backend)
+
+
+def _run_test(
+    hf_runner: Type[HfRunner],
+    vllm_runner: Type[VllmRunner],
+    inputs: List[Tuple[List[str], PromptImageInput]],
+    model: str,
+    dtype: str,
+    max_tokens: int,
+    num_logprobs: int,
+    tensor_parallel_size: int,
+    distributed_executor_backend: Optional[str] = None,
+):
     # max_model_len should be greater than image_feature_size
     with vllm_runner(model,
                      dtype=dtype,
-                     max_model_len=4096,
+                     max_model_len=10240,
                      tensor_parallel_size=tensor_parallel_size,
                      distributed_executor_backend=distributed_executor_backend,
-                     enforce_eager=True) as vllm_model:
+                     enforce_eager=True,
+                     limit_mm_per_prompt={"image": _LIMIT_IMAGE_PER_PROMPT
+                                          }) as vllm_model:
         vllm_outputs_per_image = [
             vllm_model.generate_greedy_logprobs(prompts,
                                                 max_tokens,
                                                 num_logprobs=num_logprobs,
                                                 images=images)
-            for prompts, images in inputs_per_image
+            for prompts, images in inputs
         ]
 
     with hf_runner(model, dtype=dtype,
@@ -136,7 +158,7 @@ def run_test(
                                                     max_tokens,
                                                     num_logprobs=num_logprobs,
                                                     images=images)
-            for prompts, images in inputs_per_image
+            for prompts, images in inputs
         ]
 
     for hf_outputs, vllm_outputs in zip(hf_outputs_per_image,
@@ -177,7 +199,7 @@ def test_models(hf_runner, vllm_runner, image_assets, model, size_factors,
 
     All the image fixtures for the test is under tests/images.
     For huggingface runner, we provide the PIL images as input.
-    For vllm runner, we provide MultiModalDataDict objects 
+    For vllm runner, we provide MultiModalDataDict objects
     and corresponding MultiModalConfig as input.
     Note, the text input is also adjusted to abide by vllm contract.
     The text output is sanitized to be able to compare with hf.
@@ -216,3 +238,48 @@ def test_models_fixed_sizes(hf_runner, vllm_runner, image_assets, model, sizes,
         num_logprobs=num_logprobs,
         tensor_parallel_size=1,
     )
+
+
+@pytest.mark.parametrize("model", models)
+@pytest.mark.parametrize("dtype", ["half"])
+@pytest.mark.parametrize("max_tokens", [128])
+@pytest.mark.parametrize("num_logprobs", [5])
+def test_models_multiple_image_inputs(hf_runner, vllm_runner, image_assets,
+                                      model, dtype, max_tokens,
+                                      num_logprobs) -> None:
+    stop_sign = image_assets[0].pil_image
+    cherry_blossom = image_assets[1].pil_image
+
+    inputs = [(
+        [
+            "[INST] <image><image>\nDescribe 2 images. [/INST]",
+            "[INST] <image><image>\nDescribe 2 images. [/INST]",
+            "[INST] <image><image><image><image>\nDescribe 4 images. [/INST]",
+            "[INST] <image>\nWhat is the season? [/INST]"
+        ],
+        [
+            [stop_sign, cherry_blossom],
+            # Images with different sizes and aspect-ratios
+            [
+                rescale_image_size(stop_sign, 0.1),
+                stop_sign,
+            ],
+            [
+                stop_sign,
+                rescale_image_size(stop_sign, 0.25),
+                cherry_blossom.resize((183, 488)),
+                cherry_blossom.resize((488, 183))
+            ],
+            cherry_blossom,
+        ])]
+
+    _run_test(
+        hf_runner,
+        vllm_runner,
+        inputs,
+        model,
+        dtype=dtype,
+        max_tokens=max_tokens,
+        num_logprobs=num_logprobs,
+        tensor_parallel_size=1,
+    )

tests/multimodal/test_utils.py

@@ -6,8 +6,10 @@ from typing import Dict, Tuple
 import numpy as np
 import pytest
 from PIL import Image
+from transformers import AutoConfig, AutoTokenizer
 
-from vllm.multimodal.utils import async_fetch_image, fetch_image
+from vllm.multimodal.utils import (async_fetch_image, fetch_image,
+                                   repeat_and_pad_placeholder_tokens)
 
 # Test different image extensions (JPG/PNG) and formats (gray/RGB/RGBA)
 TEST_IMAGE_URLS = [
@@ -80,3 +82,34 @@ async def test_fetch_image_base64(url_images: Dict[str, Image.Image],
 
         data_image_async = await async_fetch_image(data_url)
         assert _image_equals(data_image_sync, data_image_async)
+
+
+@pytest.mark.parametrize("model", ["llava-hf/llava-v1.6-mistral-7b-hf"])
+def test_repeat_and_pad_placeholder_tokens(model):
+    config = AutoConfig.from_pretrained(model)
+    image_token_id = config.image_token_index
+
+    tokenizer = AutoTokenizer.from_pretrained(model)
+
+    test_cases = [
+        ("<image>", 2, "<image><image>", [32000, 32000]),
+        ("<image><image>", 2, "<image><image><image>", [32000, 32000, 32000]),
+        ("<image><image>", [3, 2], "<image><image><image><image><image>",
+         [32000, 32000, 32000, 32000, 32000]),
+        ("Image:<image>Image:<image>!", [3, 2],
+         "Image:<image><image><image>Image:<image><image>!",
+         [9833, 28747, 32000, 32000, 32000, 9833, 28747, 32000, 32000, 918]),
+        ("<image>", [3, 2], "<image><image><image>", [32000, 32000, 32000]),
+    ]
+
+    for prompt, repeat_count, expected_prompt, expected_token_ids in test_cases:
+        new_prompt, new_token_ids = repeat_and_pad_placeholder_tokens(
+            tokenizer=tokenizer,
+            prompt=prompt,
+            prompt_token_ids=tokenizer.encode(prompt,
+                                              add_special_tokens=False),
+            placeholder_token_id=image_token_id,
+            repeat_count=repeat_count,
+        )
+        assert new_prompt == expected_prompt
+        assert new_token_ids == expected_token_ids

vllm/model_executor/models/clip.py

-"""Minimal implementation of CLIPVisionModel intended to be only used 
+"""Minimal implementation of CLIPVisionModel intended to be only used
 within a vision language model."""
 from array import array
-from typing import Iterable, Optional, Tuple
+from typing import Iterable, List, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
@@ -84,7 +84,7 @@ def input_processor_for_clip(
     llm_inputs: LLMInputs,
     *,
     image_token_id: int,
-    image_feature_size_override: Optional[int] = None,
+    image_feature_size_override: Optional[Union[int, List[int]]] = None,
 ):
     multi_modal_data = llm_inputs.get("multi_modal_data")
     if multi_modal_data is None or "image" not in multi_modal_data:
@@ -217,7 +217,7 @@ class CLIPEncoderLayer(nn.Module):
 
 class CLIPEncoder(nn.Module):
     """
-    Transformer encoder consisting of `config.num_hidden_layers` self 
+    Transformer encoder consisting of `config.num_hidden_layers` self
     attention layers. Each layer is a [`CLIPEncoderLayer`].
 
     Args:

vllm/model_executor/models/llava_next.py

@@ -19,6 +19,7 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.sequence import IntermediateTensors, SamplerOutput
+from vllm.utils import is_list_of
 
 from .clip import (CLIPVisionModel, dummy_image_for_clip,
                    dummy_seq_data_for_clip, get_clip_image_feature_size,
@@ -223,6 +224,13 @@ def input_processor_for_llava_next(ctx: InputContext, llm_inputs: LLMInputs):
             input_height=height,
             input_width=width,
         )
+    elif is_list_of(image_data, Image.Image):
+        image_feature_size = [
+            get_llava_next_image_feature_size(hf_config,
+                                              input_height=img.height,
+                                              input_width=img.width)
+            for img in image_data
+        ]
     elif isinstance(image_data, torch.Tensor):
         image_feature_size = image_data.shape[0]
     else:
@@ -425,7 +433,10 @@ class LlavaNextForConditionalGeneration(nn.Module, SupportsMultiModal):
                     self.config.image_grid_pinpoints,
                     self.config.vision_config.image_size,
                 )
-                other_patch_embeds = other_patch_embeds \
+                num_patches = num_patch_height * num_patch_width
+
+                # Image patches might be padded for batch processing
+                other_patch_embeds = other_patch_embeds[:num_patches] \
                     .view(num_patch_height, num_patch_width, height, width, -1)
 
                 if "unpad" in strategy:
@@ -496,7 +507,6 @@ class LlavaNextForConditionalGeneration(nn.Module, SupportsMultiModal):
         self,
         image_input: LlavaNextImageInputs,
     ) -> Union[torch.Tensor, List[torch.Tensor]]:
-
         if image_input["type"] == "image_embeds":
             return [image_input["data"]]
 

vllm/model_executor/models/siglip.py

@@ -3,7 +3,7 @@ within a vision language model."""
 
 import math
 from array import array
-from typing import Iterable, Optional, Tuple
+from typing import Iterable, List, Optional, Tuple, Union
 
 import torch
 from PIL import Image
@@ -93,7 +93,7 @@ def input_processor_for_siglip(
     llm_inputs: LLMInputs,
     *,
     image_token_id: int,
-    image_feature_size_override: Optional[int] = None,
+    image_feature_size_override: Optional[Union[int, List[int]]] = None,
 ):
     multi_modal_data = llm_inputs.get("multi_modal_data")
     if multi_modal_data is None or "image" not in multi_modal_data:

vllm/multimodal/utils.py

@@ -189,10 +189,13 @@ def repeat_and_pad_placeholder_tokens(
     prompt_token_ids: List[int],
     *,
     placeholder_token_id: int,
-    repeat_count: int = 1,
+    repeat_count: Union[int, List[int]],
     pad_token_left: Optional[int] = None,
     pad_token_right: Optional[int] = None,
 ) -> Tuple[Optional[str], List[int]]:
+    if isinstance(repeat_count, int):
+        repeat_count = [repeat_count]
+
     if prompt is None:
         new_prompt = None
     else:
@@ -201,13 +204,6 @@ def repeat_and_pad_placeholder_tokens(
                               tokenizer.decode(pad_token_left))
         pad_token_str_right = (None if pad_token_right is None else
                                tokenizer.decode(pad_token_right))
-        replacement_str = "".join(
-            repeat_and_pad_token(
-                placeholder_token_str,
-                repeat_count=repeat_count,
-                pad_token_left=pad_token_str_left,
-                pad_token_right=pad_token_str_right,
-            ))
 
         placeholder_token_count = prompt.count(placeholder_token_str)
         # This is an arbitrary number to distinguish between the two cases
@@ -216,28 +212,45 @@ def repeat_and_pad_placeholder_tokens(
                 "Please follow the prompt format that is "
                 "documented on HuggingFace which does not involve "
                 "repeating %s tokens.", placeholder_token_str)
-        elif placeholder_token_count > 1:
-            logger.warning("Multiple multi-modal input is not supported yet, "
-                           "so any extra placeholder tokens will be treated "
-                           "as plain text.")
-
-        # The image tokens are removed to be consistent with HuggingFace
-        new_prompt = prompt.replace(placeholder_token_str, replacement_str, 1)
+        if placeholder_token_count < len(repeat_count):
+            logger.warning(
+                "The number of multi-modal placeholder tokens in the prompt "
+                "is less than the number of multi-modal inputs. Extra "
+                "placeholder tokens will be treated as plain text")
+            repeat_count = repeat_count[:placeholder_token_count]
+
+        prompt_parts = prompt.split(placeholder_token_str,
+                                    maxsplit=len(repeat_count))
+        new_prompt = ""
+        for i, repeat_count_item in enumerate(repeat_count):
+            replacement_str = "".join(
+                repeat_and_pad_token(
+                    placeholder_token_str,
+                    repeat_count=repeat_count_item,
+                    pad_token_left=pad_token_str_left,
+                    pad_token_right=pad_token_str_right,
+                ))
+            # The image tokens are removed to be consistent with HuggingFace
+            new_prompt += prompt_parts[i] + replacement_str
+        new_prompt += prompt_parts[-1]
 
     new_token_ids: List[int] = []
+    placeholder_token_idx = 0
     for i, token in enumerate(prompt_token_ids):
         if token == placeholder_token_id:
             replacement_ids = repeat_and_pad_token(
                 placeholder_token_id,
-                repeat_count=repeat_count,
+                repeat_count=repeat_count[placeholder_token_idx],
                 pad_token_left=pad_token_left,
                 pad_token_right=pad_token_right,
             )
             new_token_ids.extend(replacement_ids)
+            placeholder_token_idx += 1
 
-            # No need to further scan the list since we only replace once
-            new_token_ids.extend(prompt_token_ids[i + 1:])
-            break
+            # No need to further scan the list since we replaced all tokens
+            if placeholder_token_idx >= len(repeat_count):
+                new_token_ids.extend(prompt_token_ids[i + 1:])
+                break
         else:
             new_token_ids.append(token)