[Model] Add multi-image input support for LLaVA-Next offline inference

tests/conftest.py

@@ -41,6 +41,8 @@
 _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]]]
+
 
 def _read_prompts(filename: str) -> List[str]:
     with open(filename, "r") as f:
@@ -161,7 +163,7 @@ def example_encoder_decoder_prompts(
     decoder prompt) tuple.
 
     Returns:
-    
+
     * Encoder prompt list
     * Decoder prompt list (reverse of encoder prompt list)
     '''
@@ -580,8 +582,7 @@ def generate(
         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)
@@ -625,8 +626,7 @@ def generate_w_logprobs(
         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[int], str, Optional[SampleLogprobs]]]:
         assert sampling_params.logprobs is not None
 
@@ -672,8 +672,7 @@ def generate_greedy_logprobs(
         prompts: List[str],
         max_tokens: int,
         num_logprobs: int,
-        images: Optional[Union[List[Image.Image],
-                               List[List[Image.Image]]]] = None,
+        images: Optional[PromptImageInput] = 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

@@ -6,11 +6,14 @@
 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
 
+_LIMIT_IMAGE_PER_PROMPT = 4
+
 _PREFACE = (
     "A chat between a curious human and an artificial intelligence assistant. "
     "The assistant gives helpful, detailed, and polite answers to the human's "
@@ -23,7 +26,7 @@
     f"{_PREFACE} USER: <image>\nWhat is the season? ASSISTANT:",
 })
 
-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 +117,43 @@ 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=32768,
                      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, is_vision_model=True) as hf_model:
@@ -135,7 +162,7 @@ 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,
@@ -176,7 +203,7 @@ 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.
@@ -215,3 +242,48 @@ 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 = [(
+        [
+            f"{_PREFACE} USER: <image><image>\nDescribe the 2 images. ASSISTANT:",  # noqa: E501
+            f"{_PREFACE} USER: <image><image>\nDescribe the 2 images. ASSISTANT:",  # noqa: E501
+            f"{_PREFACE} USER: <image><image><image><image>\nDescribe the 4 images. ASSISTANT:",  # noqa: E501
+            f"{_PREFACE} USER: <image>\nWhat is the season? ASSISTANT:"
+        ],
+        [
+            [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_image.py

@@ -0,0 +1,35 @@
+import pytest
+from transformers import AutoConfig, AutoTokenizer
+
+from vllm.multimodal.image import repeat_and_pad_image_tokens
+
+
+@pytest.mark.parametrize("model", ["llava-hf/llava-v1.6-mistral-7b-hf"])
+def test_repeat_and_pad_image_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_image_tokens(
+            tokenizer=tokenizer,
+            prompt=prompt,
+            prompt_token_ids=tokenizer.encode(prompt,
+                                              add_special_tokens=False),
+            image_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

@@ -1,7 +1,7 @@
-"""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 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
 
 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.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 @@ def _merge_image_patch_embeddings(self, image_size: torch.Tensor,
                     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 @@ def _process_image_input(
         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 @@
 
 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: