[Model]: support KeyeVL-1_5-8B (#23838)

Signed-off-by: wangruitao <wangruitao@kuaishou.com>
Co-authored-by: wangruitao <wangruitao@kuaishou.com>

docs/models/supported_models.md

@@ -634,7 +634,8 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `InternS1ForConditionalGeneration` | Intern-S1 | T + I<sup>E+</sup> + V<sup>E+</sup> | `internlm/Intern-S1`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `InternVLChatModel` | InternVL 3.5, InternVL 3.0, InternVideo 2.5, InternVL 2.5, Mono-InternVL, InternVL 2.0 | T + I<sup>E+</sup> + (V<sup>E+</sup>) | `OpenGVLab/InternVL3_5-14B`, `OpenGVLab/InternVL3-9B`, `OpenGVLab/InternVideo2_5_Chat_8B`, `OpenGVLab/InternVL2_5-4B`, `OpenGVLab/Mono-InternVL-2B`, `OpenGVLab/InternVL2-4B`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `InternVLForConditionalGeneration` | InternVL 3.0 (HF format) | T + I<sup>E+</sup> + V<sup>E+</sup> | `OpenGVLab/InternVL3-1B-hf`, etc. | ✅︎ | ✅︎ | ✅︎ |
-| `KeyeForConditionalGeneration` | Keye-VL-8B-Preview | T + I<sup>E+</sup> + V<sup>E+</sup> | `Kwai-Keye/Keye-VL-8B-Preview` | | | ✅︎ |
+| `KeyeForConditionalGeneration` | Keye-VL-8B-Preview | T + I<sup>E+</sup> + V<sup>E+</sup> | `Kwai-Keye/Keye-VL-8B-Preview` | ✅︎ | ✅︎ | ✅︎ |
+| `KeyeVL1_5ForConditionalGeneration` | Keye-VL-1_5-8B | T + I<sup>E+</sup> + V<sup>E+</sup> | `Kwai-Keye/Keye-VL-1_5-8B` | ✅︎ | ✅︎ | ✅︎ |
 | `KimiVLForConditionalGeneration` | Kimi-VL-A3B-Instruct, Kimi-VL-A3B-Thinking | T + I<sup>+</sup> | `moonshotai/Kimi-VL-A3B-Instruct`, `moonshotai/Kimi-VL-A3B-Thinking` | | ✅︎ | ✅︎ |
 | `Llama4ForConditionalGeneration` | Llama 4 | T + I<sup>+</sup> | `meta-llama/Llama-4-Scout-17B-16E-Instruct`, `meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8`, `meta-llama/Llama-4-Maverick-17B-128E-Instruct`, etc. | | ✅︎ | ✅︎ |
 | `Llama_Nemotron_Nano_VL` | Llama Nemotron Nano VL | T + I<sup>E+</sup> | `nvidia/Llama-3.1-Nemotron-Nano-VL-8B-V1` | ✅︎ | ✅︎ | ✅︎ |

examples/offline_inference/vision_language.py

@@ -683,6 +683,37 @@ def run_keye_vl(questions: list[str], modality: str) -> ModelRequestData:
     )
 
 
+# Keye-VL-1.5
+def run_keye_vl1_5(questions: list[str], modality: str) -> ModelRequestData:
+    model_name = "Kwai-Keye/Keye-VL-1.5-8B"
+
+    engine_args = EngineArgs(
+        model=model_name,
+        max_model_len=8192,
+        trust_remote_code=True,
+        limit_mm_per_prompt={modality: 1},
+    )
+
+    if modality == "image":
+        placeholder = "<|image_pad|>"
+    elif modality == "video":
+        placeholder = "<|video_pad|>"
+
+    prompts = [
+        (
+            f"<|im_start|>user\n<|vision_start|>{placeholder}<|vision_end|>"
+            f"{question}<|im_end|>\n"
+            "<|im_start|>assistant\n"
+        )
+        for question in questions
+    ]
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompts=prompts,
+    )
+
+
 # Kimi-VL
 def run_kimi_vl(questions: list[str], modality: str) -> ModelRequestData:
     assert modality == "image"
@@ -1648,6 +1679,7 @@ model_example_map = {
     "interns1": run_interns1,
     "internvl_chat": run_internvl,
     "keye_vl": run_keye_vl,
+    "keye_vl1_5": run_keye_vl1_5,
     "kimi_vl": run_kimi_vl,
     "llama4": run_llama4,
     "llava": run_llava,

examples/offline_inference/vision_language_multi_image.py

@@ -542,6 +542,43 @@ def load_keye_vl(question: str, image_urls: list[str]) -> ModelRequestData:
     )
 
 
+def load_keye_vl1_5(question: str, image_urls: list[str]) -> ModelRequestData:
+    model_name = "Kwai-Keye/Keye-VL-1_5-8B"
+
+    engine_args = EngineArgs(
+        model=model_name,
+        trust_remote_code=True,
+        max_model_len=8192,
+        max_num_seqs=5,
+        limit_mm_per_prompt={"image": len(image_urls)},
+    )
+
+    placeholders = [{"type": "image", "image": url} for url in image_urls]
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                *placeholders,
+                {"type": "text", "text": question},
+            ],
+        },
+    ]
+
+    processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
+
+    prompt = processor.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+
+    image_data = [fetch_image(url) for url in image_urls]
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompt=prompt,
+        image_data=image_data,
+    )
+
+
 def load_kimi_vl(question: str, image_urls: list[str]) -> ModelRequestData:
     model_name = "moonshotai/Kimi-VL-A3B-Instruct"
 
@@ -1209,6 +1246,7 @@ model_example_map = {
     "interns1": load_interns1,
     "internvl_chat": load_internvl,
     "keye_vl": load_keye_vl,
+    "keye_vl1_5": load_keye_vl1_5,
     "kimi_vl": load_kimi_vl,
     "llama4": load_llama4,
     "llava": load_llava,

tests/models/multimodal/processing/test_common.py

@@ -293,6 +293,7 @@ def _test_processing_correctness_one(
     "OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview",
     "OpenGVLab/InternVL3_5-30B-A3B",
     "Kwai-Keye/Keye-VL-8B-Preview",
+    "Kwai-Keye/Keye-VL-1_5-8B",
     "moonshotai/Kimi-VL-A3B-Instruct",
     "meta-llama/Llama-4-Scout-17B-16E-Instruct",
     "llava-hf/llava-1.5-7b-hf",

tests/models/registry.py

@@ -438,6 +438,8 @@ _MULTIMODAL_EXAMPLE_MODELS = {
     "InternVLForConditionalGeneration": _HfExamplesInfo("OpenGVLab/InternVL3-1B-hf"),    # noqa: E501
     "KeyeForConditionalGeneration": _HfExamplesInfo("Kwai-Keye/Keye-VL-8B-Preview", # noqa: E501
                                                     trust_remote_code=True),
+    "KeyeVL1_5ForConditionalGeneration": _HfExamplesInfo("Kwai-Keye/Keye-VL-1_5-8B", # noqa: E501
+                                                         trust_remote_code=True),
     "KimiVLForConditionalGeneration": _HfExamplesInfo("moonshotai/Kimi-VL-A3B-Instruct",  # noqa: E501
                                                       extras={"thinking": "moonshotai/Kimi-VL-A3B-Thinking"},  # noqa: E501
                                                       trust_remote_code=True),

vllm/model_executor/layers/rotary_embedding/mrope.py

@@ -402,6 +402,15 @@ class MRotaryEmbedding(RotaryEmbedding):
                 context_len=context_len,
                 seq_len=seq_len,
             )
+        elif "KeyeVL1_5" in hf_config.model_type:
+            return cls._keye_get_input_positions_tensor(
+                input_tokens=input_tokens,
+                hf_config=hf_config,
+                image_grid_thw=image_grid_thw,
+                video_grid_thw=video_grid_thw,
+                context_len=context_len,
+                seq_len=seq_len,
+            )
         else:
             return cls._vl_get_input_positions_tensor(
                 input_tokens=input_tokens,
@@ -636,6 +645,126 @@ class MRotaryEmbedding(RotaryEmbedding):
                                 len(input_tokens)).item()
         return llm_positions, mrope_position_delta
 
+    @classmethod
+    def _keye_get_input_positions_tensor(
+        cls,
+        input_tokens: list[int],
+        hf_config: PretrainedConfig,
+        image_grid_thw: Union[list[list[int]], torch.Tensor],
+        video_grid_thw: Union[list[list[int]], torch.Tensor],
+        context_len: int = 0,
+        seq_len: Optional[int] = None,
+    ) -> tuple[torch.Tensor, int]:
+        if isinstance(video_grid_thw, list) and len(video_grid_thw) > 0:
+            video_grid_thw = video_grid_thw[0]
+        """Get mrope input positions and delta value (Keye series)."""
+
+        def split_thw(
+                grid_thw: Union[torch.Tensor, list[int]]) -> list[list[int]]:
+            """
+            Split grid_thw along the t dimension.
+
+            Args:
+                grid_thw: shape [N, 3] tensor or nested list of [t, h, w].
+
+            Returns:
+                List of [1, h, w] rows, repeated t times for each original row.
+            """
+
+            if isinstance(grid_thw, list):
+                grid_thw = torch.tensor(grid_thw, dtype=torch.long)
+
+            if grid_thw.numel() == 0:
+                return []
+
+            t, hw = grid_thw[:, 0], grid_thw[:, 1:]
+            ones = torch.ones_like(hw[:, :1])  # [N,1]
+            out = torch.cat([ones, hw], dim=1).repeat_interleave(t, dim=0)
+            return out.tolist()
+
+        video_grid_thw = split_thw(video_grid_thw)
+
+        image_token_id = hf_config.image_token_id
+        video_token_id = hf_config.video_token_id
+        spatial_merge_size = hf_config.vision_config.spatial_merge_size
+
+        image_nums = len(image_grid_thw)
+        frame_nums = len(video_grid_thw)
+        llm_pos_ids_list: list = []
+
+        st = 0
+        remain_images, remain_frames = image_nums, frame_nums
+
+        image_index, video_index = 0, 0
+        for _ in range(image_nums + frame_nums):
+            if remain_images > 0:
+                try:
+                    ed_image = input_tokens.index(image_token_id, st)
+                except ValueError:
+                    ed_image = len(input_tokens) + 1
+            else:
+                ed_image = len(input_tokens) + 1
+            if remain_frames > 0:
+                try:
+                    ed_video = input_tokens.index(video_token_id, st)
+                except ValueError:
+                    ed_video = len(input_tokens) + 1
+            else:
+                ed_video = len(input_tokens) + 1
+
+            if ed_image < ed_video:
+                t, h, w = (
+                    image_grid_thw[image_index][0],
+                    image_grid_thw[image_index][1],
+                    image_grid_thw[image_index][2],
+                )
+                image_index += 1
+                remain_images -= 1
+                ed = ed_image
+            else:
+                t, h, w = (
+                    video_grid_thw[video_index][0],
+                    video_grid_thw[video_index][1],
+                    video_grid_thw[video_index][2],
+                )
+                video_index += 1
+                remain_frames -= 1
+                ed = ed_video
+
+            llm_grid_t, llm_grid_h, llm_grid_w = \
+                t, h // spatial_merge_size, w // spatial_merge_size
+            text_len = ed - st
+
+            st_idx = llm_pos_ids_list[-1].max() + 1 if len(
+                llm_pos_ids_list) > 0 else 0
+            llm_pos_ids_list.append(
+                torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+            t_index = (torch.arange(llm_grid_t).view(-1, 1).expand(
+                -1, llm_grid_h * llm_grid_w)).long().flatten()
+
+            h_index = torch.arange(llm_grid_h).view(1, -1, 1).expand(
+                llm_grid_t, -1, llm_grid_w).flatten()
+            w_index = torch.arange(llm_grid_w).view(1, 1, -1).expand(
+                llm_grid_t, llm_grid_h, -1).flatten()
+            llm_pos_ids_list.append(
+                torch.stack([t_index, h_index, w_index]) + text_len + st_idx)
+            st = ed + llm_grid_t * llm_grid_h * llm_grid_w
+
+        if st < len(input_tokens):
+            st_idx = llm_pos_ids_list[-1].max() + 1 if len(
+                llm_pos_ids_list) > 0 else 0
+            text_len = len(input_tokens) - st
+            llm_pos_ids_list.append(
+                torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+        llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
+        mrope_position_delta = (llm_positions.max() + 1 -
+                                len(input_tokens)).item()
+        llm_positions = llm_positions[:, context_len:seq_len]
+
+        return llm_positions, mrope_position_delta
+
     @classmethod
     def _vl_get_input_positions_tensor(
         cls,

vllm/model_executor/models/keye.py

@@ -1,9 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import math
+from abc import abstractmethod
 from collections.abc import Iterable, Mapping, Sequence
 from functools import partial
-from typing import Annotated, Any, Literal, Optional, Union
+from typing import Annotated, Any, Literal, Optional, TypeVar, Union
 
 import numpy as np
 import torch
@@ -57,16 +58,13 @@ from .vision import get_vit_attn_backend
 
 logger = init_logger(__name__)
 
-_MAX_FRAMES_PER_VIDEO = 16
-_MAX_IMAGE_SIZE = 9999999
-
 
 def smart_resize(
     height: int,
     width: int,
-    factor: int = 28,
+    factor: int,
-    min_pixels: int = 28 * 28 * 130,
+    min_pixels: int,
-    max_pixels: int = 28 * 28 * 1280,
+    max_pixels: int,
 ):
     if height < factor:
         logger.warning(
@@ -887,9 +885,9 @@ class Projector(nn.Module):
 
     def forward(
         self,
-        image_features: torch.Tensor,
+        image_features: Union[torch.Tensor, list[torch.Tensor]],
         image_grid_thw: list[tuple[int, int, int]],
-    ) -> torch.Tensor:
+    ) -> Union[torch.Tensor, list[torch.Tensor]]:
         m1, m2 = self.merge_kernel_size
         if isinstance(image_features, (list, tuple)):
             processed_features = list()
@@ -986,6 +984,12 @@ class KeyeMultiModalDataParser(MultiModalDataParser):
 
 class KeyeProcessingInfo(BaseProcessingInfo):
 
+    def get_max_image_size(self) -> int:
+        return 9999999  #_MAX_IMAGE_SIZE
+
+    def get_max_frame_per_video(self) -> int:
+        return 16  #_MAX_FRAMES_PER_VIDEO
+
     def get_image_processor(self, **kwargs: object):
         return self.get_hf_processor(**kwargs).image_processor
 
@@ -1077,8 +1081,8 @@ class KeyeProcessingInfo(BaseProcessingInfo):
 
     def get_image_size_with_most_features(self, ) -> ImageSize:
         max_image_size, _ = self._get_vision_info(
-            image_width=_MAX_IMAGE_SIZE,
+            image_width=self.get_max_image_size(),
-            image_height=_MAX_IMAGE_SIZE,
+            image_height=self.get_max_image_size(),
             image_processor=None,
         )
         return max_image_size
@@ -1123,7 +1127,7 @@ class KeyeProcessingInfo(BaseProcessingInfo):
                                                       max_image_tokens)
         max_frames_per_video = min(
             max_total_frames // max(max_videos, 1),
-            _MAX_FRAMES_PER_VIDEO,
+            self.get_max_frame_per_video(),
         )
 
         return max(max_frames_per_video, 1)
@@ -1139,7 +1143,10 @@ class KeyeProcessingInfo(BaseProcessingInfo):
         )
 
 
-class KeyeDummyInputsBuilder(BaseDummyInputsBuilder[KeyeProcessingInfo]):
+_I = TypeVar("_I", bound=KeyeProcessingInfo)
+
+
+class KeyeBaseDummyInputsBuilder(BaseDummyInputsBuilder[_I]):
 
     def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
         num_images = mm_counts.get("image", 0)
@@ -1183,6 +1190,10 @@ class KeyeDummyInputsBuilder(BaseDummyInputsBuilder[KeyeProcessingInfo]):
         return mm_data
 
 
+class KeyeDummyInputsBuilder(KeyeBaseDummyInputsBuilder[KeyeProcessingInfo]):
+    ...
+
+
 class KeyeMultiModalProcessor(BaseMultiModalProcessor[KeyeProcessingInfo]):
 
     def _get_data_parser(self) -> MultiModalDataParser:
@@ -1231,13 +1242,7 @@ class KeyeMultiModalProcessor(BaseMultiModalProcessor[KeyeProcessingInfo]):
         return _keye_field_config(hf_inputs)
 
 
-@MULTIMODAL_REGISTRY.register_processor(
+class BaseKeyeModule(nn.Module):
-    KeyeMultiModalProcessor,
-    info=KeyeProcessingInfo,
-    dummy_inputs=KeyeDummyInputsBuilder,
-)
-class KeyeForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsLoRA,
-                                   SupportsPP):
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",
@@ -1264,6 +1269,11 @@ class KeyeForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsLoRA,
 
         raise ValueError("Only image or video modality is supported")
 
+    def _maybe_ignore_quant_config(self, quant_config: QuantizationConfig):
+        if isinstance(quant_config, (GPTQConfig, GPTQMarlinConfig)):
+            return None
+        return quant_config
+
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config: PretrainedConfig = vllm_config.model_config.hf_config
@@ -1278,7 +1288,8 @@ class KeyeForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsLoRA,
             quant_config=self._maybe_ignore_quant_config(quant_config),
             prefix=maybe_prefix(prefix, "visual"),
         )
-        self.mlp_AR = Projector(
+
+        self.mlp_AR = self._build_projector(
             config,
             config.vision_config,
             quant_config=self._maybe_ignore_quant_config(quant_config),
@@ -1294,13 +1305,287 @@ class KeyeForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsLoRA,
         self.make_empty_intermediate_tensors = (
             self.language_model.make_empty_intermediate_tensors)
 
-    def _maybe_ignore_quant_config(self, quant_config: QuantizationConfig):
+    @abstractmethod
-        if isinstance(quant_config, (GPTQConfig, GPTQMarlinConfig)):
+    def _build_projector(self,
-            return None
+                         text_config: PretrainedConfig,
-        return quant_config
+                         vision_config: PretrainedConfig,
+                         quant_config: Optional[QuantizationConfig] = None,
+                         prefix: str = "") -> nn.Module:
+        raise ValueError("Need projector")
 
-    def _validate_and_reshape_mm_tensor(self, mm_input: NestedTensors,
+    def _process_image_input(self,
-                                        name: str) -> torch.Tensor:
+                             image_input: Any) -> tuple[torch.Tensor, ...]:
+        siglip_position_ids = list()
+        image_grid_hws = list()
+        sample_indices = list()
+        cu_seqlens = [0]
+
+        image_grid_thw = image_input["image_grid_thw"]
+        assert image_grid_thw.ndim == 2
+
+        for idx, thaw in enumerate(image_grid_thw):
+            thw_tuple = tuple(thaw.detach().cpu().numpy().tolist())
+            numel = np.prod(thw_tuple)
+            image_grid_hws.append(thw_tuple)
+            image_position_ids = torch.arange(numel) % np.prod(thw_tuple[1:])
+            siglip_position_ids.append(image_position_ids)
+            sample_indices.append(torch.full((numel, ), idx,
+                                             dtype=torch.int64))
+            cu_seqlens.append(cu_seqlens[-1] + numel)
+
+        if image_input["type"] == "image_embeds":
+            raise ValueError(
+                "Image embeddings are not supported for this processing path.")
+        else:
+            pixel_values = image_input["pixel_values"].type(self.visual.dtype)
+            siglip_position_ids = torch.concat(siglip_position_ids,
+                                               dim=0).to(pixel_values.device)
+            cu_seqlens = torch.tensor(cu_seqlens, dtype=torch.int32).to(
+                pixel_values.device)
+            sample_indices = torch.concat(sample_indices,
+                                          dim=0).to(pixel_values.device)
+
+            image_embeds = self.visual(
+                pixel_values=pixel_values,
+                image_grid_thw=image_grid_hws,
+                position_ids=siglip_position_ids,
+                vision_return_embed_list=False,
+                interpolate_pos_encoding=True,
+                sample_indices=sample_indices,
+                cu_seqlens=cu_seqlens,
+                use_rope=True,
+                window_size=-1,
+            )
+            image_embeds = tuple(self.mlp_AR(image_embeds, image_grid_thw))
+            return image_embeds
+
+    def _process_video_embeds(
+        self,
+        video_type: Literal["video_embeds", "pixel_values_videos"],
+        video_grid_thw: list[torch.Tensor],
+        pixel_values_videos: Optional[torch.Tensor] = None
+    ) -> Union[torch.Tensor, list[torch.Tensor]]:
+        siglip_position_ids = list()
+        video_grid_hws = list()
+        sample_indices = list()
+        cu_seqlens = [0]
+
+        assert video_grid_thw.ndim == 2
+        for idx, sub_thw in enumerate(video_grid_thw):
+            thw_tuple = tuple(sub_thw.detach().cpu().numpy().tolist())
+            numel = np.prod(thw_tuple)
+
+            video_grid_hws.append(thw_tuple)
+            video_position_ids = torch.arange(numel) % np.prod(thw_tuple[1:])
+            siglip_position_ids.append(video_position_ids)
+            sample_indices.append(torch.full((numel, ), idx,
+                                             dtype=torch.int64))
+            cu_seqlens.append(cu_seqlens[-1] + numel)
+
+        if video_type == "video_embeds":
+            raise ValueError(
+                "Video embeddings are not supported for this processing path.")
+        else:
+            pixel_values_videos = pixel_values_videos.type(self.visual.dtype)
+            siglip_position_ids = torch.concat(siglip_position_ids, dim=0).to(
+                pixel_values_videos.device)
+            cu_seqlens = torch.tensor(cu_seqlens, dtype=torch.int32).to(
+                pixel_values_videos.device)
+            sample_indices = torch.concat(sample_indices,
+                                          dim=0).to(pixel_values_videos.device)
+
+            video_embeds = self.visual(
+                pixel_values=pixel_values_videos,
+                image_grid_thw=video_grid_hws,
+                position_ids=siglip_position_ids,
+                vision_return_embed_list=True,
+                interpolate_pos_encoding=True,
+                sample_indices=sample_indices,
+                cu_seqlens=cu_seqlens,
+                use_rope=True,
+                window_size=-1,
+            )
+            video_embeds = self.mlp_AR(video_embeds, video_grid_thw)
+            return video_embeds
+
+    def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
+        modalities = {}
+
+        for input_key in kwargs:
+            if (input_key in ("pixel_values", "image_embeds")
+                    and "images" not in modalities):
+                modalities["images"] = self._parse_and_validate_image_input(
+                    **kwargs)
+            if (input_key in ("pixel_values_videos", "video_embeds")
+                    and "videos" not in modalities):
+                modalities["videos"] = self._parse_and_validate_video_input(
+                    **kwargs)
+
+        return modalities
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    def get_multimodal_embeddings(
+            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
+
+        modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
+        if not modalities:
+            return None
+
+        multimodal_embeddings: tuple[torch.Tensor, ...] = ()
+
+        for modality in modalities:
+            if modality == "images":
+                image_input = modalities["images"]
+                vision_embeddings = self._process_image_input(image_input)
+                multimodal_embeddings += vision_embeddings
+            if modality == "videos":
+                video_input = modalities["videos"]
+                video_embeddings = self._process_video_input(video_input)
+                multimodal_embeddings += video_embeddings
+        return multimodal_embeddings
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if multimodal_embeddings is not None:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                multimodal_embeddings,
+                [
+                    self.config.image_token_id,
+                    self.config.video_token_id,
+                ],
+            )
+        return inputs_embeds
+
+    def get_input_embeddings_v0(
+        self,
+        input_ids: torch.Tensor,
+        image_input: Optional[Any] = None,
+        video_input: Optional[Any] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.get_input_embeddings(input_ids)
+        if image_input is not None:
+            image_embeds = self._process_image_input(image_input)
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                image_embeds,
+                placeholder_token_id=self.config.image_token_id,
+            )
+
+        if video_input is not None:
+            video_embeds = self._process_video_input(video_input)
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids,
+                inputs_embeds,
+                video_embeds,
+                placeholder_token_id=self.config.video_token_id,
+            )
+        return inputs_embeds
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        """Run forward pass for Keye-VL.
+
+        Args:
+            input_ids: Flattened (concatenated) input_ids corresponding to a
+                batch.
+            positions: Flattened (concatenated) position ids corresponding to a
+                batch.
+                **NOTE**: If mrope is enabled (default setting for Qwen2-VL
+                opensource models), the shape will be `(3, seq_len)`,
+                otherwise it will be `(seq_len,).
+            pixel_values: Pixel values to be fed to a model.
+                `None` if no images are passed.
+            image_grid_thw: Tensor `(n_images, 3)` of image 3D grid in LLM.
+                `None` if no images are passed.
+            pixel_values_videos: Pixel values of videos to be fed to a model.
+                `None` if no videos are passed.
+            video_grid_thw: Tensor `(n_videos, 3)` of video 3D grid in LLM.
+                `None` if no videos are passed.
+        """
+        if intermediate_tensors is not None:
+            inputs_embeds = None
+
+        elif inputs_embeds is None:
+            image_input = self._parse_and_validate_image_input(**kwargs)
+            video_input = self._parse_and_validate_video_input(**kwargs)
+            if image_input is None and video_input is None:
+                inputs_embeds = None
+            else:
+                if uses_mrope(self.config):
+                    assert positions.ndim == 2 and positions.size(0) == 3, (
+                        "multimodal section rotary embedding requires "
+                        f"(3, seq_len) positions, but got {positions.size()}")
+                inputs_embeds = self.get_input_embeddings_v0(
+                    input_ids,
+                    image_input=image_input,
+                    video_input=video_input,
+                )
+                input_ids = None
+
+        hidden_states = self.language_model.model(
+            input_ids=input_ids,
+            positions=positions,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+        )
+
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        return self.language_model.compute_logits(hidden_states,
+                                                  sampling_metadata)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
+
+    def get_mm_mapping(self) -> MultiModelKeys:
+        """Get the module prefix in multimodal models."""
+        return MultiModelKeys.from_string_field(
+            language_model="language_model",
+            connector="mlp_AR.",
+            tower_model="visual.",
+        )
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    KeyeMultiModalProcessor,
+    info=KeyeProcessingInfo,
+    dummy_inputs=KeyeDummyInputsBuilder,
+)
+class KeyeForConditionalGeneration(BaseKeyeModule, SupportsMultiModal,
+                                   SupportsLoRA, SupportsPP):
+
+    def _build_projector(self,
+                         text_config: PretrainedConfig,
+                         vision_config: PretrainedConfig,
+                         quant_config: Optional[QuantizationConfig] = None,
+                         prefix: str = "") -> nn.Module:
+        return Projector(text_config, vision_config, quant_config, prefix)
+
+    def _validate_and_reshape_mm_tensor(
+            self, mm_input: NestedTensors,
+            name: str) -> Union[torch.Tensor, list[torch.Tensor]]:
         if not isinstance(mm_input, (torch.Tensor, list)):
             raise ValueError(f"Incorrect type of {name}. "
                              f"Got type: {type(mm_input)}")
@@ -1388,257 +1673,12 @@ class KeyeForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsLoRA,
                 video_grid_thw=video_grid_thw,
             )
 
-    def _process_image_input(
-            self, image_input: KeyeImageInputs) -> tuple[torch.Tensor, ...]:
-        siglip_position_ids = list()
-        image_grid_hws = list()
-        sample_indices = list()
-        cu_seqlens = [0]
-
-        image_grid_thw = image_input["image_grid_thw"]
-        assert image_grid_thw.ndim == 2
-
-        for idx, thaw in enumerate(image_grid_thw):
-            thw_tuple = tuple(thaw.detach().cpu().numpy().tolist())
-            numel = np.prod(thw_tuple)
-            image_grid_hws.append(thw_tuple)
-            image_position_ids = torch.arange(numel) % np.prod(thw_tuple[1:])
-            siglip_position_ids.append(image_position_ids)
-            sample_indices.append(torch.full((numel, ), idx,
-                                             dtype=torch.int64))
-            cu_seqlens.append(cu_seqlens[-1] + numel)
-
-        if image_input["type"] == "image_embeds":
-            raise ValueError(
-                "Image embeddings are not supported for this processing path.")
-        else:
-            pixel_values = image_input["pixel_values"].type(self.visual.dtype)
-            siglip_position_ids = torch.concat(siglip_position_ids,
-                                               dim=0).to(pixel_values.device)
-            cu_seqlens = torch.tensor(cu_seqlens, dtype=torch.int32).to(
-                pixel_values.device)
-            sample_indices = torch.concat(sample_indices,
-                                          dim=0).to(pixel_values.device)
-
-            image_embeds = self.visual(
-                pixel_values=pixel_values,
-                image_grid_thw=image_grid_hws,
-                position_ids=siglip_position_ids,
-                vision_return_embed_list=False,
-                interpolate_pos_encoding=True,
-                sample_indices=sample_indices,
-                cu_seqlens=cu_seqlens,
-                use_rope=True,
-                window_size=-1,
-            )
-            image_embeds = tuple(self.mlp_AR(image_embeds, image_grid_thw))
-            return image_embeds
-
     def _process_video_input(
             self, video_input: KeyeVideoInputs) -> tuple[torch.Tensor, ...]:
-        siglip_position_ids = list()
+        video_type = video_input["type"]
-        video_grid_hws = list()
-        sample_indices = list()
-        cu_seqlens = [0]
-
         video_grid_thw = video_input["video_grid_thw"]
-        assert video_grid_thw.ndim == 2
+        pixel_values_videos = video_input.get("pixel_values_videos", None)
 
-        for idx, thaw in enumerate(video_grid_thw):
+        return tuple(
-            thw_tuple = tuple(thaw.detach().cpu().numpy().tolist())
+            self._process_video_embeds(video_type, video_grid_thw,
-            numel = np.prod(thw_tuple)
+                                       pixel_values_videos))
-
-            video_grid_hws.append(thw_tuple)
-            video_position_ids = torch.arange(numel) % np.prod(thw_tuple[1:])
-            siglip_position_ids.append(video_position_ids)
-            sample_indices.append(torch.full((numel, ), idx,
-                                             dtype=torch.int64))
-            cu_seqlens.append(cu_seqlens[-1] + numel)
-
-        if video_input["type"] == "video_embeds":
-            raise ValueError(
-                "Video embeddings are not supported for this processing path.")
-        else:
-            pixel_values_videos = video_input["pixel_values_videos"].type(
-                self.visual.dtype)
-            siglip_position_ids = torch.concat(siglip_position_ids, dim=0).to(
-                pixel_values_videos.device)
-            cu_seqlens = torch.tensor(cu_seqlens, dtype=torch.int32).to(
-                pixel_values_videos.device)
-            sample_indices = torch.concat(sample_indices,
-                                          dim=0).to(pixel_values_videos.device)
-
-            video_embeds = self.visual(
-                pixel_values=pixel_values_videos,
-                image_grid_thw=video_grid_hws,
-                position_ids=siglip_position_ids,
-                vision_return_embed_list=True,
-                interpolate_pos_encoding=True,
-                sample_indices=sample_indices,
-                cu_seqlens=cu_seqlens,
-                use_rope=True,
-                window_size=-1,
-            )
-            video_embeds = tuple(self.mlp_AR(video_embeds, video_grid_thw))
-            return video_embeds
-
-    def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
-        modalities = {}
-
-        for input_key in kwargs:
-            if (input_key in ("pixel_values", "image_embeds")
-                    and "images" not in modalities):
-                modalities["images"] = self._parse_and_validate_image_input(
-                    **kwargs)
-            if (input_key in ("pixel_values_videos", "video_embeds")
-                    and "videos" not in modalities):
-                modalities["videos"] = self._parse_and_validate_video_input(
-                    **kwargs)
-
-        return modalities
-
-    def get_language_model(self) -> torch.nn.Module:
-        return self.language_model
-
-    def get_multimodal_embeddings(
-            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
-
-        modalities = self._parse_and_validate_multimodal_inputs(**kwargs)
-        if not modalities:
-            return None
-
-        multimodal_embeddings: tuple[torch.Tensor, ...] = ()
-
-        for modality in modalities:
-            if modality == "images":
-                image_input = modalities["images"]
-                vision_embeddings = self._process_image_input(image_input)
-                multimodal_embeddings += vision_embeddings
-            if modality == "videos":
-                video_input = modalities["videos"]
-                video_embeddings = self._process_video_input(video_input)
-                multimodal_embeddings += video_embeddings
-        return multimodal_embeddings
-
-    def get_input_embeddings(
-        self,
-        input_ids: torch.Tensor,
-        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
-    ) -> torch.Tensor:
-        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
-        if multimodal_embeddings is not None:
-            inputs_embeds = merge_multimodal_embeddings(
-                input_ids,
-                inputs_embeds,
-                multimodal_embeddings,
-                [
-                    self.config.image_token_id,
-                    self.config.video_token_id,
-                ],
-            )
-        return inputs_embeds
-
-    def get_input_embeddings_v0(
-        self,
-        input_ids: torch.Tensor,
-        image_input: Optional[KeyeImagePixelInputs] = None,
-        video_input: Optional[KeyeVideoPixelInputs] = None,
-    ) -> torch.Tensor:
-        inputs_embeds = self.get_input_embeddings(input_ids)
-        if image_input is not None:
-            image_embeds = self._process_image_input(image_input)
-            inputs_embeds = merge_multimodal_embeddings(
-                input_ids,
-                inputs_embeds,
-                image_embeds,
-                placeholder_token_id=self.config.image_token_id,
-            )
-
-        if video_input is not None:
-            video_embeds = self._process_video_input(video_input)
-            inputs_embeds = merge_multimodal_embeddings(
-                input_ids,
-                inputs_embeds,
-                video_embeds,
-                placeholder_token_id=self.config.video_token_id,
-            )
-        return inputs_embeds
-
-    def forward(
-        self,
-        input_ids: torch.Tensor,
-        positions: torch.Tensor,
-        intermediate_tensors: Optional[IntermediateTensors] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        **kwargs: object,
-    ) -> Union[torch.Tensor, IntermediateTensors]:
-        """Run forward pass for Qwen2-VL.
-
-        Args:
-            input_ids: Flattened (concatenated) input_ids corresponding to a
-                batch.
-            positions: Flattened (concatenated) position ids corresponding to a
-                batch.
-                **NOTE**: If mrope is enabled (default setting for Qwen2-VL
-                opensource models), the shape will be `(3, seq_len)`,
-                otherwise it will be `(seq_len,).
-            pixel_values: Pixel values to be fed to a model.
-                `None` if no images are passed.
-            image_grid_thw: Tensor `(n_images, 3)` of image 3D grid in LLM.
-                `None` if no images are passed.
-            pixel_values_videos: Pixel values of videos to be fed to a model.
-                `None` if no videos are passed.
-            video_grid_thw: Tensor `(n_videos, 3)` of video 3D grid in LLM.
-                `None` if no videos are passed.
-        """
-
-        if intermediate_tensors is not None:
-            inputs_embeds = None
-
-        elif inputs_embeds is None:
-            image_input = self._parse_and_validate_image_input(**kwargs)
-            video_input = self._parse_and_validate_video_input(**kwargs)
-
-            if image_input is None and video_input is None:
-                inputs_embeds = None
-            else:
-                if uses_mrope(self.config):
-                    assert positions.ndim == 2 and positions.size(0) == 3, (
-                        "multimodal section rotary embedding requires "
-                        f"(3, seq_len) positions, but got {positions.size()}")
-                inputs_embeds = self.get_input_embeddings_v0(
-                    input_ids,
-                    image_input=image_input,
-                    video_input=video_input,
-                )
-                input_ids = None
-
-        hidden_states = self.language_model.model(
-            input_ids=input_ids,
-            positions=positions,
-            intermediate_tensors=intermediate_tensors,
-            inputs_embeds=inputs_embeds,
-        )
-        return hidden_states
-
-    def compute_logits(
-        self,
-        hidden_states: torch.Tensor,
-        sampling_metadata: SamplingMetadata,
-    ) -> Optional[torch.Tensor]:
-        return self.language_model.compute_logits(hidden_states,
-                                                  sampling_metadata)
-
-    def load_weights(self, weights: Iterable[tuple[str,
-                                                   torch.Tensor]]) -> set[str]:
-
-        loader = AutoWeightsLoader(self)
-        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
-
-    def get_mm_mapping(self) -> MultiModelKeys:
-        """Get the module prefix in multimodal models."""
-        return MultiModelKeys.from_string_field(
-            language_model="language_model",
-            connector="visual.",
-            tower_model="mlp_AR.",
-        )

vllm/model_executor/models/keye_vl1_5.py

@@ -0,0 +1,601 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import itertools
+from collections.abc import Mapping, Sequence
+from functools import partial
+from typing import Annotated, Any, Literal, Optional, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+from einops import rearrange
+from transformers import PretrainedConfig
+from transformers.activations import GELUActivation
+from transformers.feature_extraction_utils import BatchFeature
+
+from vllm.config import VllmConfig
+from vllm.logger import init_logger
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.multimodal import MULTIMODAL_REGISTRY, NestedTensors
+from vllm.multimodal.inputs import (ImageItem, ModalityData,
+                                    MultiModalFieldConfig,
+                                    MultiModalKwargsItems, VideoItem)
+from vllm.multimodal.parse import (DictEmbeddingItems, ModalityDataItems,
+                                   MultiModalDataItems, MultiModalDataParser)
+from vllm.multimodal.processing import (PromptReplacement, PromptUpdate,
+                                        PromptUpdateDetails)
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
+
+from .interfaces import SupportsLoRA, SupportsMultiModal, SupportsPP
+from .keye import (BaseKeyeModule, BaseMultiModalProcessor,
+                   KeyeBaseDummyInputsBuilder, KeyeProcessingInfo)
+
+logger = init_logger(__name__)
+
+
+def split_thw(grid_thw: torch.Tensor) -> torch.Tensor:
+    """
+    Split grid_thw in t dimension.
+
+    Args:
+        grid_thw: [N, 3] tensor of [t, h, w]
+
+    Returns:
+        [Σt, 3] tensor where each row is [1, h, w]
+
+    Example:
+    >>> grid_thw = torch.tensor([[2, 3, 4], [1, 5, 6]])
+    >>> split_thw(grid_thw)
+    tensor([[1, 3, 4],
+           [1, 3, 4],
+           [1, 5, 6]])
+    """
+    t = grid_thw[:, 0]
+    h_w = grid_thw[:, 1:]
+    ones = torch.ones_like(h_w[:, :1])
+    return torch.cat([ones, h_w], dim=1).repeat_interleave(t, dim=0)
+
+
+def get_num_patches(grid_thw: torch.Tensor, num_frames: Union[list[int],
+                                                              torch.Tensor]):
+    """
+    Return num_patches per video.
+
+    Args:
+        t: tensor with shape [N, ...] where each item is a list/tensor
+        cu_seqlens: list indicating the boundaries of groups
+
+    Returns:
+        list of ints representing the sum of products for each group
+
+    Examples:
+        >>> # Suppose there are 2 videos with a total of 3 grids
+        >>> grid_thw = torch.tensor([[2, 2, 2],  # grid 0: 2*2*2=8 patches
+        ...                          [2, 2, 2],  # grid 1: 2*2*2=8 patches
+        ...                          [1, 1, 1]]) # grid 2: 1*1*1=1 patches
+        >>> num_frames = [2, 1]  # The first video contains 2 grids,
+                                   the second contains 1 grid.
+        >>> get_num_patches(grid_thw, num_frames)
+        tensor([16, 1])  # Total patches for first video: 8+8=16,
+                           second video: 1.
+    """
+
+    assert len(grid_thw.shape) == 2
+    if isinstance(num_frames, torch.Tensor):
+        num_frames = num_frames.clone().tolist()
+
+    num_grids_per_frame = grid_thw.prod(dim=1)
+    start_idx_per_video = [0, *itertools.accumulate(num_frames)]
+    num_patches = [
+        num_grids_per_frame[start_idx_per_video[i]:start_idx_per_video[i + 1]].
+        sum() for i in range(len(num_frames))
+    ]
+    return torch.stack(num_patches) if num_patches else torch.zeros(
+        0, dtype=grid_thw.dtype, device=grid_thw.device)
+
+
+class KeyeVL1_5ImagePixelInputs(TensorSchema):
+    """
+    Dimensions:
+        - b: Batch size
+        - np: Number of patches
+        - c: Number of channels
+        - ps: Patch size
+        - ni: Number of images
+        - g: Grid dimensions (3 for t, h, w)
+    """
+    type: Literal["pixel_values"]
+
+    pixel_values: Annotated[
+        torch.Tensor,
+        TensorShape("np", 3, "ps", "ps", dynamic_dims={"np"})]
+
+    image_grid_thw: Annotated[torch.Tensor, TensorShape("ni", 3)]
+
+
+class KeyeVL1_5ImageEmbeddingInputs(TensorSchema):
+    """
+    Dimensions:
+        - nf: Number of image features
+        - hs: Hidden size (must match the hidden size of language model
+          backbone)
+        - ni: Number of images
+        - g: Grid dimensions (3 for t, h, w)
+    """
+    type: Literal["image_embeds"]
+    image_embeds: Annotated[torch.Tensor, TensorShape("nf", "hs")]
+    image_grid_thw: Annotated[torch.Tensor, TensorShape("ni", 3)]
+
+
+KeyeVL1_5ImageInputs = Union[KeyeVL1_5ImagePixelInputs,
+                             KeyeVL1_5ImageEmbeddingInputs]
+
+
+class KeyeVL1_5VideoPixelInputs(TensorSchema):
+    """
+    Dimensions:
+        - b: Batch size
+        - np: Number of patches
+        - c: Number of channels
+        - ps: Patch size
+        - ni: Number of images
+        - g: Grid dimensions (3 for t, h, w)
+    """
+    type: Literal["pixel_values_videos"]
+    pixel_values_videos: Annotated[
+        torch.Tensor,
+        TensorShape("np", 3, "ps", "ps", dynamic_dims={"np"})]
+    video_grid_thw: Annotated[torch.Tensor, TensorShape("nv", 3)]
+
+    num_frames: torch.Tensor
+
+
+class KeyeVL1_5VideoEmbeddingInputs(TensorSchema):
+    """
+    Dimensions:
+        - nf: Number of video features
+        - hs: Hidden size (must match the hidden size of language model
+          backbone)
+        - nv: Number of videos
+        - g: Grid dimensions (3 for t, h, w)
+    """
+    type: Literal["video_embeds"]
+    video_embeds: Annotated[torch.Tensor, TensorShape("nf", "hs")]
+    video_grid_thw: Annotated[torch.Tensor, TensorShape("nv", 3)]
+    num_frames: torch.Tensor
+
+
+KeyeVL1_5VideoInputs = Union[KeyeVL1_5VideoPixelInputs,
+                             KeyeVL1_5VideoEmbeddingInputs]
+
+
+class KeyeVL1_5Projector(nn.Module):
+
+    def __init__(
+        self,
+        text_config: PretrainedConfig,
+        vision_config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.text_config = text_config
+        self.vision_config = vision_config
+        self.merge_kernel_size = (2, 2)
+
+        self.hidden_size = (self.vision_config.hidden_size *
+                            self.merge_kernel_size[0] *
+                            self.merge_kernel_size[1])
+
+        self.pre_norm = torch.nn.LayerNorm(self.hidden_size, eps=1e-05)
+        self.act = GELUActivation()
+
+        self.linear_1 = ColumnParallelLinear(
+            self.hidden_size,
+            self.hidden_size,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.linear_1",
+        )
+        self.linear_2 = RowParallelLinear(
+            self.hidden_size,
+            self.text_config.hidden_size,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.linear_2",
+        )
+
+    def forward(
+        self,
+        image_features: Union[torch.Tensor, tuple[torch.Tensor],
+                              list[torch.Tensor]],
+        image_grid_thw: list[tuple[int, int, int]],
+    ) -> Union[torch.Tensor, list[torch.Tensor]]:
+        m1, m2 = self.merge_kernel_size
+        if isinstance(image_features, (list, tuple)):
+            processed_features = list()
+            for image_feature, image_grid in zip(image_features,
+                                                 image_grid_thw):
+                t, h, w = image_grid
+                image_feature = rearrange(
+                    image_feature,
+                    "(t h p1 w p2) d -> (t h w) (p1 p2 d)",
+                    t=t,
+                    h=h // m1,
+                    p1=m1,
+                    w=w // m2,
+                    p2=m2,
+                )
+                image_feature = self.pre_norm(image_feature)
+                hidden_states, _ = self.linear_1(image_feature)
+                hidden_states = self.act(hidden_states)
+                hidden_states, _ = self.linear_2(hidden_states)
+                processed_features.append(hidden_states)
+
+            return processed_features
+
+        dims = image_features.shape[:-1]
+        dim = image_features.shape[-1]
+        image_features = image_features.view(np.prod(dims), dim)
+        hidden_states = self.pre_norm(image_features.view(
+            -1, self.hidden_size))
+        hidden_states = self.linear_1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.linear_2(hidden_states)
+
+        return hidden_states.view(*dims, -1)
+
+
+class KeyeVL1_5ProcessingInfo(KeyeProcessingInfo):
+
+    def get_max_frame_per_video(self) -> int:
+        return 2048
+
+    def get_supported_mm_limits(self, ) -> Mapping[str, Optional[int]]:
+        return {"image": None, "video": 1}
+
+
+def _keye_field_config(hf_inputs: Mapping[str, torch.Tensor], ):
+    image_grid_thw = hf_inputs.get("image_grid_thw",
+                                   torch.empty((0, 3), dtype=torch.int64))
+    image_grid_sizes = image_grid_thw.prod(-1)
+
+    video_grid_thw = hf_inputs.get("video_grid_thw",
+                                   torch.empty((0, 3), dtype=torch.int64))
+    video_grid_thw = split_thw(video_grid_thw)
+    num_frames = hf_inputs.get("num_frames",
+                               video_grid_thw[:, 0]).clone().tolist()
+
+    video_num_patches = get_num_patches(video_grid_thw, num_frames)
+
+    video_num_grids = []
+    if len(num_frames) > 0:
+        i = 0
+        j = 1
+        cur_frames = num_frames[i]
+        for t, _, _ in video_grid_thw.tolist():
+            cur_frames -= t
+            if cur_frames == 0:
+                video_num_grids.append(j)
+                i += 1
+                if i < len(num_frames):
+                    cur_frames = num_frames[i]
+                j = 1
+            else:
+                j += 1
+    video_num_grids = torch.tensor(video_num_grids)
+    return dict(pixel_values=MultiModalFieldConfig.flat_from_sizes(
+        "image", image_grid_sizes),
+                image_embeds=MultiModalFieldConfig.flat_from_sizes(
+                    "image", image_grid_sizes),
+                image_grid_thw=MultiModalFieldConfig.batched("image"),
+                pixel_values_videos=MultiModalFieldConfig.flat_from_sizes(
+                    "video", video_num_patches),
+                video_embeds=MultiModalFieldConfig.flat_from_sizes(
+                    "video", video_num_patches),
+                video_grid_thw=MultiModalFieldConfig.flat_from_sizes(
+                    "video", video_num_grids),
+                num_frames=MultiModalFieldConfig.batched("video"))
+
+
+class KeyeVL1_5MultiModalDataParser(MultiModalDataParser):
+
+    def _parse_image_data(
+        self,
+        data: Union[dict[str, torch.Tensor], ModalityData[ImageItem]],
+    ) -> ModalityDataItems[Any, Any]:
+        if isinstance(data, dict):
+            return DictEmbeddingItems(
+                data,
+                modality="image",
+                required_fields={
+                    "image_embeds",
+                    "image_grid_thw",
+                },
+                fields_factory=_keye_field_config,
+            )
+
+        return super()._parse_image_data(data)
+
+    def _parse_video_data(
+        self,
+        data: Union[dict[str, torch.Tensor], ModalityData[VideoItem]],
+    ) -> ModalityDataItems[Any, Any]:
+        if isinstance(data, dict):
+            return DictEmbeddingItems(
+                data,
+                modality="video",
+                required_fields={
+                    "video_embeds",
+                    "video_grid_thw",
+                },
+                fields_factory=_keye_field_config,
+            )
+
+        return super()._parse_video_data(data)
+
+
+class KeyeVL1_5MultiModalProcessor(
+        BaseMultiModalProcessor[KeyeVL1_5ProcessingInfo]):
+
+    def _get_data_parser(self) -> MultiModalDataParser:
+        return KeyeVL1_5MultiModalDataParser()
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, Any],
+        out_mm_kwargs: MultiModalKwargsItems,
+    ) -> Sequence[PromptUpdate]:
+        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)
+        image_processor = self.info.get_image_processor(
+            **hf_processor_mm_kwargs)
+        tokenizer = self.info.get_tokenizer()
+        vocab = tokenizer.get_vocab()
+        image_token_id = vocab[hf_processor.image_token]
+        video_token_id = vocab[hf_processor.video_token]
+        placeholder = {"image": image_token_id, "video": video_token_id}
+        merge_length = image_processor.merge_size**2
+
+        out_mm_kwargs_data = out_mm_kwargs.get_data()
+        frame_types: list[torch.Tensor] = \
+            hf_processor_mm_kwargs.get("frame_types", None)
+        timestamps: list[torch.Tensor] = \
+            hf_processor_mm_kwargs.get("timestamps", None)
+        num_videos = mm_items.get_count("video", strict=False)
+
+        if frame_types is None:
+            frame_types = [None] * num_videos
+        assert len(frame_types) == num_videos, \
+            f"Number of frame_types={len(frame_types)} " \
+            f"doesn't equal to number of videos={num_videos}"
+        if timestamps is None:
+            timestamps = [None] * num_videos
+        assert len(timestamps) == num_videos, \
+            f"Number of timestamps={len(timestamps)} " \
+            f"doesn't equal to number of videos={num_videos}"
+
+        video_grid_thw = out_mm_kwargs_data.get(
+            'video_grid_thw', torch.empty((0, 3), dtype=torch.int64))
+        num_frames = out_mm_kwargs_data.get(
+            'num_frames', torch.tensor([], dtype=torch.int64))
+
+        assert len(num_frames) == num_videos, \
+            f"Size of num_frames={len(num_frames)} " \
+            f"doesn't equal to number of videos={num_videos}"
+
+        video_grid_hws = split_thw(video_grid_thw)
+        assert int(num_frames.sum().tolist()) == video_grid_hws.shape[0], (
+            f"The first dimension of `video_grid_hws`={video_grid_hws.shape[0]}"
+            f"doesn't equal to num of frames.")
+
+        cu_seqlens = torch.cumsum(torch.tensor([0] + num_frames.tolist()),
+                                  dim=-1)
+
+        def get_replacement_keye(item_idx: int, modality: str):
+            """
+            Args:
+                item_idx(int): The item index of modality to replace 
+                modality(str): The modality
+            """
+            if modality == "image":
+                out_item = out_mm_kwargs[modality][item_idx]
+                grid_thw = out_item[f"{modality}_grid_thw"].data
+                assert isinstance(grid_thw, torch.Tensor)
+
+                num_tokens = int(grid_thw.prod()) // merge_length
+                return [image_token_id] * num_tokens
+            elif modality == "video":
+                placeholders = []
+                video_timestamps = timestamps[item_idx]
+                video_frame_types = frame_types[item_idx]
+                grid_thw = video_grid_hws[
+                    cu_seqlens[item_idx]:cu_seqlens[item_idx + 1]]
+
+                nframes = grid_thw.shape[0]
+
+                if video_timestamps is None:
+                    video_timestamps = [""] * nframes
+                else:
+                    video_timestamps = [
+                        format(ts, ".1f") for ts in video_timestamps
+                    ]
+
+                if video_frame_types is None:
+                    video_frame_types = [0] * nframes
+                for i, sub_thw in enumerate(grid_thw):
+                    s = f"{hf_processor.frame_token}{video_timestamps[i]}"
+                    if video_frame_types[i] == 1:
+                        s += hf_processor.fast_start
+                    placeholders.extend(tokenizer.encode(s))
+                    num_frame_tokens = int(sub_thw.prod()) // merge_length
+                    placeholders.extend([video_token_id] * num_frame_tokens)
+                    if video_frame_types[i] == 1:
+                        placeholders.append(vocab[hf_processor.fast_end])
+
+                return PromptUpdateDetails.select_token_id(
+                    placeholders, embed_token_id=video_token_id)
+            else:
+                raise ValueError(f"Unsupported modality {modality}")
+
+        return [
+            PromptReplacement(
+                modality=modality,
+                target=[placeholder[modality]],
+                replacement=partial(get_replacement_keye, modality=modality),
+            ) for modality in ("image", "video")
+        ]
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        return _keye_field_config(hf_inputs)
+
+
+class KeyeVL1_5DummyInputsBuilder(
+        KeyeBaseDummyInputsBuilder[KeyeVL1_5ProcessingInfo]):
+    ...
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    KeyeVL1_5MultiModalProcessor,
+    info=KeyeVL1_5ProcessingInfo,
+    dummy_inputs=KeyeVL1_5DummyInputsBuilder,
+)
+class KeyeVL1_5ForConditionalGeneration(BaseKeyeModule, SupportsMultiModal,
+                                        SupportsLoRA, SupportsPP):
+
+    def _build_projector(self,
+                         text_config: PretrainedConfig,
+                         vision_config: PretrainedConfig,
+                         quant_config: Optional[QuantizationConfig] = None,
+                         prefix: str = "") -> nn.Module:
+        return KeyeVL1_5Projector(text_config, vision_config, quant_config,
+                                  prefix)
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        config: PretrainedConfig = vllm_config.model_config.hf_config
+        self.merge_size = config.vision_config.spatial_merge_size
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+
+    def _validate_and_reshape_mm_tensor(self, mm_input: NestedTensors,
+                                        expected_dim: int, name: str):
+        if not isinstance(mm_input, (torch.Tensor, list)):
+            raise ValueError(f"Incorrect type of {name}. "
+                             f"Got type: {type(mm_input)}")
+        if isinstance(mm_input, torch.Tensor):
+            if mm_input.ndim == expected_dim:
+                return mm_input
+            elif mm_input.ndim == expected_dim + 1:
+                return torch.concat(list(mm_input))
+            else:
+                raise ValueError(
+                    f"{name} should be {expected_dim}D or "
+                    f"batched {expected_dim}D tensor."
+                    f"Got ndim: {mm_input.ndim} (shape={mm_input.shape})")
+        else:
+            return torch.concat(list(mm_input))
+
+    def _parse_and_validate_image_input(
+            self, **kwargs: object) -> Optional[KeyeVL1_5ImageInputs]:
+        pixel_values = kwargs.pop("pixel_values", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+        image_grid_thw = kwargs.pop("image_grid_thw", None)
+
+        if pixel_values is None and image_embeds is None:
+            return None
+
+        if pixel_values is not None:
+            pixel_values = self._validate_and_reshape_mm_tensor(
+                pixel_values, expected_dim=4, name="image pixel values")
+            image_grid_thw = self._validate_and_reshape_mm_tensor(
+                image_grid_thw, expected_dim=2, name="image grid_thw")
+
+            return KeyeVL1_5ImagePixelInputs(
+                type="pixel_values",
+                pixel_values=pixel_values,
+                image_grid_thw=image_grid_thw,
+            )
+
+        if image_embeds is not None:
+            image_embeds = self._validate_and_reshape_mm_tensor(
+                image_embeds, expected_dim=2, name="image embeds")
+            image_grid_thw = self._validate_and_reshape_mm_tensor(
+                image_grid_thw, expected_dim=2, name="image grid_thw")
+
+            return KeyeVL1_5ImageEmbeddingInputs(
+                type="image_embeds",
+                image_embeds=image_embeds,
+                image_grid_thw=image_grid_thw,
+            )
+
+    def _parse_and_validate_video_input(
+            self, **kwargs: object) -> Optional[KeyeVL1_5VideoInputs]:
+        pixel_values_videos = kwargs.pop("pixel_values_videos", None)
+        video_embeds = kwargs.pop("video_embeds", None)
+        video_grid_thw = kwargs.pop("video_grid_thw", None)
+        num_frames = kwargs.pop("num_frames", None)
+
+        if pixel_values_videos is None and video_embeds is None:
+            return None
+
+        if pixel_values_videos is not None:
+            pixel_values_videos = self._validate_and_reshape_mm_tensor(
+                pixel_values_videos,
+                expected_dim=4,
+                name="video pixel values",
+            )
+            video_grid_thw = self._validate_and_reshape_mm_tensor(
+                video_grid_thw, expected_dim=2, name="video grid_thw")
+
+            num_frames = self._validate_and_reshape_mm_tensor(
+                num_frames, expected_dim=1, name="video num frames")
+
+            return KeyeVL1_5VideoPixelInputs(
+                type="pixel_values_videos",
+                pixel_values_videos=pixel_values_videos,
+                video_grid_thw=video_grid_thw,
+                num_frames=num_frames)
+
+        if video_embeds is not None:
+            video_embeds = self._validate_and_reshape_mm_tensor(
+                video_embeds, expected_dim=2, name="video embeds")
+            video_grid_thw = self._validate_and_reshape_mm_tensor(
+                video_grid_thw, expected_dim=2, name="video grid_thw")
+
+            return KeyeVL1_5VideoEmbeddingInputs(type="video_embeds",
+                                                 video_embeds=video_embeds,
+                                                 video_grid_thw=video_grid_thw,
+                                                 num_frames=num_frames)
+
+    def _process_video_input(
+            self,
+            video_input: KeyeVL1_5VideoInputs) -> tuple[torch.Tensor, ...]:
+        video_type = video_input["type"]
+        video_grid_thw = split_thw(video_input["video_grid_thw"])
+        pixel_values_videos = video_input.get("pixel_values_videos", None)
+
+        video_embeds = self._process_video_embeds(video_type, video_grid_thw,
+                                                  pixel_values_videos)
+        video_embeds = torch.concat(video_embeds, dim=0)
+
+        num_frames = video_input["num_frames"].clone().tolist()
+
+        num_patches = get_num_patches(video_grid_thw, num_frames).tolist()
+
+        patch_cu_seqlens = torch.cumsum(
+            torch.tensor([0] + num_patches).detach().clone(), dim=-1)
+        patch_cu_seqlens = torch.div(patch_cu_seqlens,
+                                     self.merge_size**2,
+                                     rounding_mode="floor")
+
+        new_video_embeds = []
+        for idx in range(patch_cu_seqlens.shape[0] - 1):
+            start = patch_cu_seqlens[idx]
+            end = patch_cu_seqlens[idx + 1]
+            new_video_embeds.append(video_embeds[start:end])
+        return tuple(new_video_embeds)

vllm/model_executor/models/registry.py

@@ -227,6 +227,7 @@ _MULTIMODAL_MODELS = {
     "Idefics3ForConditionalGeneration":("idefics3","Idefics3ForConditionalGeneration"),
     "SmolVLMForConditionalGeneration": ("smolvlm","SmolVLMForConditionalGeneration"),  # noqa: E501
     "KeyeForConditionalGeneration": ("keye", "KeyeForConditionalGeneration"),
+    "KeyeVL1_5ForConditionalGeneration": ("keye_vl1_5", "KeyeVL1_5ForConditionalGeneration"), # noqa: E501
     "RForConditionalGeneration": ("rvl", "RForConditionalGeneration"),
     "KimiVLForConditionalGeneration": ("kimi_vl", "KimiVLForConditionalGeneration"),  # noqa: E501
     "Llama_Nemotron_Nano_VL": ("nemotron_vl", "LlamaNemotronVLChatModel"),