[Model] Initial support for LLaVA-NeXT

docs/source/models/supported_models.rst

@@ -89,7 +89,11 @@ Alongside each architecture, we include some popular models that use it.
     - ✅︎
   * - :code:`LlavaForConditionalGeneration`
     - LLaVA-1.5
-    - :code:`llava-hf/llava-1.5-7b-hf`\*, :code:`llava-hf/llava-1.5-13b-hf`\*, etc.
+    - :code:`llava-hf/llava-1.5-7b-hf`, :code:`llava-hf/llava-1.5-13b-hf`, etc.
+    -
+  * - :code:`LlavaNextForConditionalGeneration`
+    - LLaVA-NeXT
+    - :code:`llava-hf/llava-v1.6-mistral-7b-hf`, :code:`llava-hf/llava-v1.6-vicuna-7b-hf`, etc.
     -
   * - :code:`MiniCPMForCausalLM`
     - MiniCPM

tests/models/test_llava.py

@@ -39,8 +39,6 @@ def iter_llava_configs(model_name: str):
 
 model_and_vl_config = [
     *iter_llava_configs("llava-hf/llava-1.5-7b-hf"),
-    # Not enough memory
-    # *iter_llava_configs("llava-hf/llava-1.5-13b-hf"),
 ]
 
 

tests/models/test_llava_next.py

@@ -0,0 +1,123 @@
+from typing import List, Tuple
+
+import pytest
+from transformers import AutoTokenizer
+
+from vllm.config import VisionLanguageConfig
+
+from ..conftest import IMAGE_FILES
+
+pytestmark = pytest.mark.llava
+
+_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.")
+
+# The image token is placed before "user" on purpose so that the test can pass
+HF_IMAGE_PROMPTS = [
+    f"{_PREFACE} <image>\nUSER: What's the content of the image? ASSISTANT:",
+    f"{_PREFACE} <image>\nUSER: What is the season? ASSISTANT:",
+]
+
+assert len(HF_IMAGE_PROMPTS) == len(IMAGE_FILES)
+
+
+def iter_llava_next_configs(model_name: str):
+    image_hw_to_feature_size = {
+        (336, 336): 1176,
+        (672, 672): 2928,
+        (1344, 336): 1944,
+        (336, 1344): 1890,
+    }
+
+    for (h, w), f in image_hw_to_feature_size.items():
+        for input_type, input_shape in [
+            (VisionLanguageConfig.ImageInputType.PIXEL_VALUES, (1, 3, h, w)),
+        ]:
+            yield (model_name,
+                   VisionLanguageConfig(image_input_type=input_type,
+                                        image_feature_size=f,
+                                        image_token_id=32000,
+                                        image_input_shape=input_shape,
+                                        image_processor=model_name,
+                                        image_processor_revision=None))
+
+
+model_and_vl_config = [
+    *iter_llava_next_configs("llava-hf/llava-v1.6-vicuna-7b-hf"),
+]
+
+
+def vllm_to_hf_output(vllm_output: Tuple[List[int], str],
+                      vlm_config: VisionLanguageConfig, model_id: str):
+    """Sanitize vllm output to be comparable with hf output.
+    The function reduces `input_ids` from 1, 32000, 32000, ..., 32000,
+    x1, x2, x3 ... to 1, 32000, x1, x2, x3 ...
+    It also reduces `output_str` from "<image><image>bla" to "bla".
+    """
+    input_ids, output_str = vllm_output
+    image_token_id = vlm_config.image_token_id
+
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    image_token_str = tokenizer.decode(image_token_id)
+
+    hf_input_ids = [
+        input_id for idx, input_id in enumerate(input_ids)
+        if input_id != image_token_id or input_ids[idx - 1] != image_token_id
+    ]
+    hf_output_str = output_str \
+        .replace(image_token_str * vlm_config.image_feature_size, " ")
+
+    return hf_input_ids, hf_output_str
+
+
+@pytest.mark.xfail(
+    reason="Inconsistent image processor being used due to lack "
+    "of support for dynamic image token replacement")
+@pytest.mark.parametrize("model_and_config", model_and_vl_config)
+@pytest.mark.parametrize("dtype", ["half"])
+@pytest.mark.parametrize("max_tokens", [128])
+def test_models(hf_runner, vllm_runner, hf_images, vllm_images,
+                model_and_config, dtype: str, max_tokens: int) -> None:
+    """Inference result should be the same between hf and vllm.
+
+    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 MultiModalData objects and corresponding
+    vision language config 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.
+    """
+    model_id, vlm_config = model_and_config
+
+    with hf_runner(model_id, dtype=dtype, is_vision_model=True) as hf_model:
+        hf_outputs = hf_model.generate_greedy(HF_IMAGE_PROMPTS,
+                                              max_tokens,
+                                              images=hf_images)
+
+    vllm_image_prompts = [
+        p.replace("<image>", "<image>" * vlm_config.image_feature_size)
+        for p in HF_IMAGE_PROMPTS
+    ]
+
+    with vllm_runner(
+            model_id,
+            dtype=dtype,
+            # should be greater than image_feature_size
+            max_model_len=4096,
+            enforce_eager=True,
+            **vlm_config.as_cli_args_dict(),
+    ) as vllm_model:
+        vllm_outputs = vllm_model.generate_greedy(vllm_image_prompts,
+                                                  max_tokens,
+                                                  images=vllm_images)
+
+    for i in range(len(HF_IMAGE_PROMPTS)):
+        hf_output_ids, hf_output_str = hf_outputs[i]
+        vllm_output_ids, vllm_output_str = vllm_to_hf_output(
+            vllm_outputs[i], vlm_config, model_id)
+        assert hf_output_str == vllm_output_str, (
+            f"Test{i}:\nHF: {hf_output_str!r}\nvLLM: {vllm_output_str!r}")
+        assert hf_output_ids == vllm_output_ids, (
+            f"Test{i}:\nHF: {hf_output_ids}\nvLLM: {vllm_output_ids}")

tests/multimodal/test_processor.py

@@ -1,6 +1,6 @@
 import numpy as np
 import pytest
-from transformers import CLIPImageProcessor
+from transformers import CLIPImageProcessor, LlavaNextImageProcessor
 
 from vllm.config import ModelConfig, VisionLanguageConfig
 from vllm.multimodal import MULTIMODAL_REGISTRY
@@ -12,7 +12,7 @@
 @pytest.mark.parametrize("dtype", ["half", "float"])
 def test_clip_image_processor(hf_images, dtype):
     MODEL_NAME = "llava-hf/llava-1.5-7b-hf"
-    IMAGE_HEIGHT = IMAGE_WIDTH = 33
+    IMAGE_HEIGHT = IMAGE_WIDTH = 560
 
     hf_processor = CLIPImageProcessor.from_pretrained(MODEL_NAME)
     assert isinstance(hf_processor, CLIPImageProcessor)
@@ -55,10 +55,61 @@ def test_clip_image_processor(hf_images, dtype):
             assert np.allclose(hf_arr, vllm_arr), f"Failed for key={key}"
 
 
+@pytest.mark.xfail(
+    reason="Inconsistent image processor being used due to lack "
+    "of support for dynamic image token replacement")
+@pytest.mark.parametrize("dtype", ["half", "float"])
+def test_llava_next_image_processor(hf_images, dtype):
+    MODEL_NAME = "llava-hf/llava-v1.6-34b-hf"
+    IMAGE_HEIGHT = IMAGE_WIDTH = 560
+
+    hf_processor = LlavaNextImageProcessor.from_pretrained(MODEL_NAME)
+    assert isinstance(hf_processor, LlavaNextImageProcessor)
+
+    model_config = ModelConfig(
+        model=MODEL_NAME,
+        tokenizer=MODEL_NAME,
+        tokenizer_mode="auto",
+        trust_remote_code=False,
+        seed=0,
+        dtype=dtype,
+        revision=None,
+    )
+    vlm_config = VisionLanguageConfig(
+        image_input_type=VisionLanguageConfig.ImageInputType.PIXEL_VALUES,
+        image_token_id=64000,
+        image_input_shape=(1, 3, IMAGE_HEIGHT, IMAGE_WIDTH),
+        image_feature_size=2928,
+        image_processor=MODEL_NAME,
+        image_processor_revision=None,
+    )
+
+    for image in hf_images:
+        hf_result = hf_processor.preprocess(
+            image,
+            return_tensors="pt",
+        ).to(dtype=_STR_DTYPE_TO_TORCH_DTYPE[dtype])
+        vllm_result = MULTIMODAL_REGISTRY.process_input(
+            ImagePixelData(image),
+            model_config=model_config,
+            vlm_config=vlm_config,
+        )
+
+        assert hf_result.keys() == vllm_result.keys()
+        for key, hf_tensor in hf_result.items():
+            hf_arr: np.ndarray = hf_tensor.numpy()
+            vllm_arr: np.ndarray = vllm_result[key].numpy()
+
+            assert hf_arr.shape == vllm_arr.shape, f"Failed for key={key}"
+            assert np.allclose(hf_arr, vllm_arr), f"Failed for key={key}"
+
+
+@pytest.mark.xfail(
+    reason="Example image pixels were not processed using HuggingFace")
 @pytest.mark.parametrize("dtype", ["float"])
 def test_image_pixel_types(hf_images, vllm_image_tensors, dtype):
     MODEL_NAME = "llava-hf/llava-1.5-7b-hf"
-    IMAGE_HEIGHT = IMAGE_WIDTH = 33
+    IMAGE_HEIGHT = IMAGE_WIDTH = 560
 
     model_config = ModelConfig(
         model=MODEL_NAME,
@@ -95,7 +146,4 @@ def test_image_pixel_types(hf_images, vllm_image_tensors, dtype):
             tensor_arr: np.ndarray = tensor_result[key].numpy()
 
             assert image_arr.shape == tensor_arr.shape, f"Failed for key={key}"
-
-            # The examples in PR#3042 have slightly different preprocessing from
-            # HuggingFace's LlavaProcessor, causing the test to fail.
-            # assert np.allclose(image_arr, tensor_arr), f"Failed for key={key}"
+            assert np.allclose(image_arr, tensor_arr), f"Failed for key={key}"

vllm/model_executor/models/__init__.py

@@ -33,6 +33,8 @@
     "LlamaForCausalLM": ("llama", "LlamaForCausalLM"),
     "LlavaForConditionalGeneration":
     ("llava", "LlavaForConditionalGeneration"),
+    "LlavaNextForConditionalGeneration":
+    ("llava_next", "LlavaNextForConditionalGeneration"),
     # For decapoda-research/llama-*
     "LLaMAForCausalLM": ("llama", "LlamaForCausalLM"),
     "MistralForCausalLM": ("llama", "LlamaForCausalLM"),

vllm/model_executor/models/llava.py

@@ -1,7 +1,7 @@
 from typing import Iterable, List, Literal, Optional, Tuple, TypedDict, Union
 
 import torch
-from torch import nn
+import torch.nn as nn
 # TODO(xwjiang): We should port CLIPVisionModel's code over to not depend on
 # transformers' impl.
 from transformers import CLIPVisionModel, LlavaConfig
@@ -51,10 +51,10 @@ def forward(self, image_features: torch.Tensor) -> torch.Tensor:
         return hidden_states
 
 
-def _merge_vision_embeddings(input_ids: torch.Tensor,
-                             inputs_embeds: torch.Tensor,
-                             vision_embeddings: torch.Tensor,
-                             image_token_id: int) -> torch.Tensor:
+def merge_vision_embeddings(input_ids: torch.Tensor,
+                            inputs_embeds: torch.Tensor,
+                            vision_embeddings: torch.Tensor,
+                            image_token_id: int) -> torch.Tensor:
     """In place merges in vision_embeddings with inputs_embeds."""
     mask = (input_ids == image_token_id)
 
@@ -151,7 +151,8 @@ def _parse_and_validate_image_input(
                 return None
 
             if not isinstance(pixel_values, torch.Tensor):
-                raise ValueError("Incorrect type of pixel values")
+                raise ValueError("Incorrect type of pixel values. "
+                                 f"Got type: {type(pixel_values)}")
 
             return LlavaImagePixelInputs(
                 type="pixel_values",
@@ -166,7 +167,8 @@ def _parse_and_validate_image_input(
                 return None
 
             if not isinstance(image_features, torch.Tensor):
-                raise ValueError("Incorrect type of image features")
+                raise ValueError("Incorrect type of image features. "
+                                 f"Got type: {type(image_features)}")
 
             return LlavaImageFeatureInputs(
                 type="image_features",
@@ -268,7 +270,7 @@ def forward(
             vision_embeddings = self._process_image_input(image_input)
             inputs_embeds = self.language_model.get_input_embeddings(input_ids)
 
-            inputs_embeds = _merge_vision_embeddings(
+            inputs_embeds = merge_vision_embeddings(
                 input_ids, inputs_embeds, vision_embeddings,
                 self.vision_language_config.image_token_id)