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index 020cb7493..7a9e16c00 100644
--- a/tests/models/registry.py
+++ b/tests/models/registry.py
@@ -845,6 +845,10 @@ _MULTIMODAL_EXAMPLE_MODELS = {
# disable this temporarily until we support HF format
is_available_online=False,
),
+ "WhaleyeForConditionalGeneration": _HfExamplesInfo(
+ "umans-ai/Whaleye-V0",
+ is_available_online=False,
+ ),
# [Encoder-decoder]
"WhisperForConditionalGeneration": _HfExamplesInfo("openai/whisper-large-v3"),
# [Cross-encoder]
diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py
index a4a964bc7..fd40ff25c 100644
--- a/vllm/model_executor/models/registry.py
+++ b/vllm/model_executor/models/registry.py
@@ -411,6 +411,7 @@ _MULTIMODAL_MODELS = {
),
"UltravoxModel": ("ultravox", "UltravoxModel"),
"VoxtralForConditionalGeneration": ("voxtral", "VoxtralForConditionalGeneration"), # noqa: E501
+ "WhaleyeForConditionalGeneration": ("whaleye", "WhaleyeForConditionalGeneration"), # noqa: E501
# [Encoder-decoder]
"WhisperForConditionalGeneration": ("whisper", "WhisperForConditionalGeneration"), # noqa: E501
}
diff --git a/vllm/model_executor/models/whaleye.py b/vllm/model_executor/models/whaleye.py
new file mode 100644
index 000000000..60d8f8b22
--- /dev/null
+++ b/vllm/model_executor/models/whaleye.py
@@ -0,0 +1,598 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Whaleye: Pixtral Vision Encoder + DeepSeek V3.2 Language Model
+"""
+
+from collections.abc import Iterable, Mapping, Sequence
+from dataclasses import fields
+from functools import cached_property
+
+import torch
+from torch import nn
+from mistral_common.protocol.instruct.chunk import ImageChunk
+from mistral_common.tokens.tokenizers.image import (
+ ImageConfig,
+ ImageEncoder,
+ SpecialImageIDs,
+)
+from PIL import Image
+from transformers import TensorType
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.image_utils import ImageInput
+from transformers.tokenization_utils_base import TextInput
+
+from vllm.config import VllmConfig
+from vllm.config.multimodal import BaseDummyOptions
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargsItems
+from vllm.multimodal.inputs import (
+ MultiModalDataDict,
+ MultiModalFieldConfig,
+ MultiModalUUIDDict,
+ NestedTensors,
+)
+from vllm.multimodal.parse import ImageProcessorItems, ImageSize, MultiModalDataItems
+from vllm.multimodal.processing import (
+ BaseMultiModalProcessor,
+ BaseProcessingInfo,
+ MultiModalProcessingInfo,
+ PromptReplacement,
+ PromptUpdate,
+ PromptUpdateDetails,
+)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
+from vllm.sequence import IntermediateTensors
+from vllm.tokenizers import TokenizerLike, cached_tokenizer_from_config
+
+from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
+from .pixtral import (
+ PATCH_MERGE,
+ PatchMerger,
+ PixtralImagePixelInputs,
+ VisionEncoderArgs,
+ VisionLanguageAdapter,
+ VisionTransformer,
+)
+from .utils import init_vllm_registered_model, maybe_prefix
+
+# Re-use RMSNorm from layernorm module
+from vllm.model_executor.layers.layernorm import RMSNorm
+
+
+class WhaleyeProcessorAdapter:
+
+ def __init__(self, tokenizer: TokenizerLike, image_encoder: ImageEncoder) -> None:
+ super().__init__()
+ self._tokenizer = tokenizer
+ self._image_encoder = image_encoder
+
+ @property
+ def tokenizer(self) -> TokenizerLike:
+ return self._tokenizer
+
+ @property
+ def image_processor(self) -> ImageEncoder:
+ return self._image_encoder
+
+ @cached_property
+ def image_token_id(self) -> int:
+ return self.image_processor.special_ids.img
+
+ @cached_property
+ def image_break_id(self) -> int:
+ return self.image_processor.special_ids.img_break
+
+ @cached_property
+ def image_end_id(self) -> int:
+ return self.image_processor.special_ids.img_end
+
+ @cached_property
+ def image_size(self) -> int:
+ return self.image_processor.mm_config.max_image_size
+
+ @cached_property
+ def patch_size(self) -> int:
+ return self.image_processor.mm_config.image_patch_size
+
+ def __call__(
+ self,
+ text: TextInput | list[TextInput] | None = None,
+ images: ImageInput | list[ImageInput] | None = None,
+ return_tensors: str | TensorType | None = None,
+ **kwargs,
+ ) -> Mapping[str, NestedTensors]:
+ if text is None:
+ text_list: list[str] = []
+ elif isinstance(text, list):
+ text_list = list(text)
+ else:
+ text_list = [text]
+
+ if images is None:
+ images = []
+ if not isinstance(images, list):
+ images = [images]
+
+ if not images:
+ if not text_list:
+ return BatchFeature(dict(input_ids=torch.empty((0, 0), dtype=torch.long)))
+
+ encoded = [
+ self.tokenizer.encode(t, add_special_tokens=False)
+ for t in text_list
+ ]
+ max_len = max(len(ids) for ids in encoded) if encoded else 0
+ pad_id = getattr(self.tokenizer, "pad_token_id", 0) or 0
+ input_ids = torch.full((len(encoded), max_len), pad_id, dtype=torch.long)
+ for i, ids in enumerate(encoded):
+ if ids:
+ input_ids[i, :len(ids)] = torch.tensor(ids, dtype=torch.long)
+
+ return BatchFeature(dict(input_ids=input_ids))
+
+ pixel_values: list[torch.Tensor] = []
+ image_sizes: list[tuple[int, int]] = []
+
+ for image in images:
+ if hasattr(image, "media"):
+ image = image.media
+
+ image_inputs = self.image_processor(ImageChunk(image=image))
+ processed_image = torch.tensor(image_inputs.image)
+ pixel_values.append(processed_image)
+ image_sizes.append((processed_image.shape[1], processed_image.shape[2]))
+
+ input_ids = torch.empty((len(text_list) or 1, 0), dtype=torch.long)
+
+ return BatchFeature(
+ dict(
+ input_ids=input_ids,
+ pixel_values=pixel_values,
+ image_sizes=image_sizes,
+ )
+ )
+
+
+class WhaleyeProcessingInfo(BaseProcessingInfo):
+
+ def get_tokenizer(self) -> TokenizerLike:
+ return cached_tokenizer_from_config(self.ctx.model_config)
+
+ @cached_property
+ def _vision_config(self):
+ vision_cfg = self.ctx.model_config.hf_config.vision_config
+ # vision_config may be a dict or a config object depending on how it was loaded
+ if isinstance(vision_cfg, dict):
+ return vision_cfg
+ return vision_cfg.to_dict() if hasattr(vision_cfg, "to_dict") else vision_cfg
+
+ def _get_vision_value(self, key: str, default=None):
+ """Get a value from vision_config, handling both dict and object."""
+ vision_cfg = self._vision_config
+ if isinstance(vision_cfg, dict):
+ return vision_cfg.get(key, default)
+ return getattr(vision_cfg, key, default)
+
+ @cached_property
+ def _image_encoder(self) -> ImageEncoder:
+ hf_config = self.ctx.model_config.hf_config
+
+ # Get image_size from vision_config, with fallback to max_image_size
+ image_size = self._get_vision_value("max_image_size")
+ if image_size is None:
+ image_size = getattr(hf_config, "max_image_size", None)
+ if image_size is None:
+ image_size = self._get_vision_value("image_size")
+ image_size = int(image_size)
+
+ patch_size = int(self._get_vision_value("patch_size"))
+
+ spatial_merge_size = getattr(hf_config, "spatial_merge_size", None)
+ if spatial_merge_size is None:
+ spatial_merge_size = self._get_vision_value("spatial_merge_size", 1)
+ spatial_merge_size = int(spatial_merge_size)
+
+ image_config = ImageConfig(
+ image_patch_size=patch_size,
+ max_image_size=image_size,
+ spatial_merge_size=spatial_merge_size,
+ )
+
+ special_ids = SpecialImageIDs(
+ img=int(self._get_vision_value("image_token_id")),
+ img_break=int(self._get_vision_value("image_break_token_id")),
+ img_end=int(self._get_vision_value("image_end_token_id")),
+ )
+
+ return ImageEncoder(image_config=image_config, special_ids=special_ids)
+
+ def get_hf_processor(self, **kwargs: object) -> WhaleyeProcessorAdapter:
+ return WhaleyeProcessorAdapter(self.get_tokenizer(), self._image_encoder)
+
+ def get_supported_mm_limits(self) -> Mapping[str, int | None]:
+ return {"image": None}
+
+ def get_num_image_tokens(
+ self,
+ *,
+ image_width: int,
+ image_height: int,
+ processor: WhaleyeProcessorAdapter | None = None,
+ ) -> int:
+ if processor is None:
+ processor = self.get_hf_processor()
+
+ ncols, nrows = processor.image_processor._image_to_num_tokens(
+ Image.new("RGB", (image_width, image_height))
+ )
+ return ncols * nrows
+
+ def get_image_size_with_most_features(self) -> ImageSize:
+ cfg = self._image_encoder.image_config
+ return ImageSize(width=cfg.max_image_size, height=cfg.max_image_size)
+
+
+class WhaleyeDummyInputsBuilder(BaseDummyInputsBuilder[WhaleyeProcessingInfo]):
+
+ def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+ return ""
+
+ def get_dummy_mm_data(
+ self,
+ seq_len: int,
+ mm_counts: Mapping[str, int],
+ mm_options: Mapping[str, BaseDummyOptions] | None = None,
+ ) -> MultiModalDataDict:
+ num_images = mm_counts.get("image", 0)
+ target_width, target_height = self.info.get_image_size_with_most_features()
+ image_overrides = mm_options.get("image") if mm_options else None
+ return {
+ "image": self._get_dummy_images(
+ width=target_width,
+ height=target_height,
+ num_images=num_images,
+ overrides=image_overrides,
+ )
+ }
+
+ def get_dummy_processor_inputs(
+ self,
+ seq_len: int,
+ mm_counts: Mapping[str, int],
+ mm_options: Mapping[str, BaseDummyOptions] | None = None,
+ ) -> ProcessorInputs:
+ num_images = mm_counts.get("image", 0)
+ dummy_mm_data = self.get_dummy_mm_data(seq_len, mm_counts, mm_options)
+
+ processor = self.info.get_hf_processor()
+ image_token_id = processor.image_token_id
+
+ dummy_tokens = [image_token_id] * num_images
+
+ return ProcessorInputs(
+ prompt=dummy_tokens,
+ mm_data=dummy_mm_data,
+ tokenization_kwargs={"truncation": False},
+ )
+
+
+class WhaleyeMultiModalProcessor(BaseMultiModalProcessor[WhaleyeProcessingInfo]):
+
+ def _call_hf_processor(
+ self,
+ prompt: str,
+ mm_data: Mapping[str, object],
+ mm_kwargs: Mapping[str, object],
+ tok_kwargs: Mapping[str, object],
+ ) -> BatchFeature:
+ processed_outputs = super()._call_hf_processor(
+ prompt=prompt,
+ mm_data=mm_data,
+ mm_kwargs=mm_kwargs,
+ tok_kwargs=tok_kwargs,
+ )
+
+ pixel_values = processed_outputs.get("pixel_values")
+ if pixel_values is not None:
+ image_sizes = processed_outputs.get("image_sizes")
+ if isinstance(pixel_values, list) and image_sizes is not None:
+ assert len(pixel_values) == len(image_sizes)
+ processed_outputs["images"] = [
+ p[:, :h, :w] for p, (h, w) in zip(pixel_values, image_sizes)
+ ]
+ else:
+ processed_outputs["images"] = pixel_values
+ processed_outputs.pop("pixel_values", None)
+
+ return processed_outputs
+
+ def _get_mm_fields_config(
+ self,
+ hf_inputs: Mapping[str, NestedTensors],
+ hf_processor_mm_kwargs: Mapping[str, object],
+ ) -> Mapping[str, MultiModalFieldConfig]:
+ return dict(images=MultiModalFieldConfig.batched("image"))
+
+ def _get_prompt_updates(
+ self,
+ mm_items: MultiModalDataItems,
+ hf_processor_mm_kwargs: Mapping[str, object],
+ out_mm_kwargs: MultiModalKwargsItems,
+ ) -> Sequence[PromptUpdate]:
+ processor = self.info.get_hf_processor()
+ image_token_id = processor.image_token_id
+ image_break_id = processor.image_break_id
+ image_end_id = processor.image_end_id
+
+ def get_replacement(item_idx: int):
+ images = mm_items.get_items("image", ImageProcessorItems)
+ image_size = images.get_image_size(item_idx)
+
+ ncols, nrows = processor.image_processor._image_to_num_tokens(
+ Image.new("RGB", (image_size.width, image_size.height))
+ )
+
+ tokens = ([image_token_id] * ncols + [image_break_id]) * nrows
+ tokens[-1] = image_end_id
+
+ return PromptUpdateDetails.select_token_id(tokens, image_token_id)
+
+ return [
+ PromptReplacement(
+ modality="image",
+ target=[image_token_id],
+ replacement=get_replacement,
+ ),
+ ]
+
+ def _cached_apply_hf_processor(
+ self,
+ prompt: str | list[int],
+ mm_data_items: MultiModalDataItems,
+ hf_processor_mm_kwargs: Mapping[str, object],
+ tokenization_kwargs: Mapping[str, object],
+ mm_uuids: MultiModalUUIDDict | None = None,
+ ) -> tuple[list[int], MultiModalProcessingInfo, bool]:
+ prompt_ids, mm_info, _ = super()._cached_apply_hf_processor(
+ prompt=prompt,
+ mm_data_items=mm_data_items,
+ hf_processor_mm_kwargs=hf_processor_mm_kwargs,
+ tokenization_kwargs=tokenization_kwargs,
+ mm_uuids=mm_uuids,
+ )
+ return prompt_ids, mm_info, False
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+ WhaleyeMultiModalProcessor,
+ info=WhaleyeProcessingInfo,
+ dummy_inputs=WhaleyeDummyInputsBuilder,
+)
+class WhaleyeForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsPP):
+
+ @classmethod
+ def get_placeholder_str(cls, modality: str, i: int) -> str | None:
+ if modality.startswith("image"):
+ return "<｜img｜>"
+ raise ValueError("Only image modality is supported")
+
+ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+ super().__init__()
+ config = vllm_config.model_config.hf_config
+ multimodal_config = vllm_config.model_config.multimodal_config
+ self.config = config
+ self.multimodal_config = multimodal_config
+
+ # Build vision encoder args from vision_config
+ vision_config = config.vision_config
+ # vision_config may be a dict or a config object
+ if isinstance(vision_config, dict):
+ vision_config_dict = vision_config
+ else:
+ vision_config_dict = vision_config.to_dict()
+ dataclass_fields = {field.name for field in fields(VisionEncoderArgs)}
+ vision_args_dict = {
+ key: value
+ for key, value in vision_config_dict.items()
+ if key in dataclass_fields
+ }
+ self.vision_args = VisionEncoderArgs(**vision_args_dict)
+
+ # Initialize DeepSeek V3.2 language model
+ # Uses flat config (hf_config itself has all DeepSeek fields at top level)
+ self.language_model = init_vllm_registered_model(
+ vllm_config=vllm_config,
+ hf_config=config, # flat config with DeepSeek fields
+ architectures=["DeepseekV3ForCausalLM"],
+ prefix=maybe_prefix(prefix, "language_model"),
+ )
+
+ # Initialize vision components (from Pixtral)
+ if multimodal_config.get_limit_per_prompt("image"):
+ self.vision_encoder = VisionTransformer(self.vision_args)
+ self.pre_mm_projector_norm = (
+ RMSNorm(self.vision_args.hidden_size, eps=1e-5)
+ if self.vision_args.add_pre_mm_projector_layer_norm
+ else None
+ )
+ self.patch_merger = (
+ PatchMerger(
+ vision_encoder_dim=self.vision_args.hidden_size,
+ spatial_merge_size=self.vision_args.spatial_merge_size,
+ use_mlp_bias=False,
+ )
+ if self.vision_args.mm_projector_id == PATCH_MERGE
+ else None
+ )
+ # Use hidden_size from top-level config (DeepSeek LM hidden size)
+ self.vision_language_adapter = VisionLanguageAdapter(
+ self.vision_args, dim=config.hidden_size
+ )
+ else:
+ self.vision_encoder = None
+ self.pre_mm_projector_norm = None
+ self.patch_merger = None
+ self.vision_language_adapter = None
+
+ self.make_empty_intermediate_tensors = (
+ self.language_model.make_empty_intermediate_tensors
+ )
+
+ def _parse_and_validate_image_input(
+ self, **kwargs: object
+ ) -> PixtralImagePixelInputs | None:
+ images = kwargs.pop("images", None)
+ if images is None:
+ return None
+
+ return PixtralImagePixelInputs(
+ type="pixel_values",
+ images=images,
+ )
+
+ def _process_image_input(
+ self,
+ image_input: PixtralImagePixelInputs,
+ ) -> tuple[torch.Tensor, ...]:
+ assert (
+ self.vision_encoder is not None and self.vision_language_adapter is not None
+ )
+
+ images = image_input["images"]
+ image_features = self.vision_encoder(images)
+ feature_sizes = [image_feature.shape[0] for image_feature in image_features]
+ image_features = torch.cat(image_features)
+ if self.pre_mm_projector_norm is not None:
+ image_features = self.pre_mm_projector_norm(image_features)
+ if self.patch_merger is not None:
+ patch_size = self.vision_args.patch_size
+ spatial_merge_size_square = self.vision_args.spatial_merge_size**2
+ img_patch_dims = [
+ (img.shape[1] // patch_size, img.shape[2] // patch_size)
+ for img in images
+ ]
+ feature_sizes = [
+ feature_size // spatial_merge_size_square
+ for feature_size in feature_sizes
+ ]
+ image_features = self.patch_merger(
+ image_features, image_sizes=img_patch_dims
+ )
+ image_embeds = self.vision_language_adapter(image_features)
+ image_embeds = torch.split(image_embeds, feature_sizes)
+ return image_embeds
+
+ def get_language_model(self) -> nn.Module:
+ return self.language_model
+
+ def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings:
+ image_input = self._parse_and_validate_image_input(**kwargs)
+ if image_input is None:
+ return []
+
+ return self._process_image_input(image_input)
+
+ def forward(
+ self,
+ input_ids: torch.Tensor,
+ positions: torch.Tensor,
+ intermediate_tensors: IntermediateTensors | None = None,
+ inputs_embeds: torch.Tensor | None = None,
+ **kwargs: object,
+ ) -> torch.Tensor | IntermediateTensors:
+ """Run forward pass for Whaleye."""
+ if intermediate_tensors is not None:
+ inputs_embeds = None
+
+ hidden_states = self.language_model.model(
+ input_ids, positions, intermediate_tensors, inputs_embeds=inputs_embeds
+ )
+
+ return hidden_states
+
+ def compute_logits(
+ self,
+ hidden_states: torch.Tensor,
+ ) -> torch.Tensor | None:
+ return self.language_model.compute_logits(hidden_states)
+
+ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+ """Load weights for vision components and language model."""
+
+ def is_vision_encoder_weights(weight: tuple[str, torch.Tensor]):
+ return weight[0].startswith("vision_encoder")
+
+ def is_vision_lang_adapter_weights(weight: tuple[str, torch.Tensor]):
+ return weight[0].startswith("vision_language_adapter")
+
+ def is_patch_merger(weight: tuple[str, torch.Tensor]):
+ return weight[0].startswith("patch_merger")
+
+ def is_pre_mm_projector_norm(weight: tuple[str, torch.Tensor]):
+ return weight[0].startswith("pre_mm_projector_norm")
+
+ # Get references to parameters for direct loading
+ vision_encoder_dict = (
+ dict(self.vision_encoder.named_parameters())
+ if self.vision_encoder is not None
+ else {}
+ )
+ patch_merger_dict = (
+ dict(self.patch_merger.named_parameters())
+ if self.patch_merger is not None
+ else {}
+ )
+ pre_mm_projector_norm_dict = (
+ dict(self.pre_mm_projector_norm.named_parameters())
+ if self.pre_mm_projector_norm is not None
+ else {}
+ )
+ vision_lang_adapter_dict = (
+ dict(self.vision_language_adapter.named_parameters())
+ if self.vision_language_adapter is not None
+ else {}
+ )
+
+ def llm_weights_generator():
+ # Single pass over weights
+ for name, w in weights:
+ if is_vision_encoder_weights((name, w)):
+ if self.vision_encoder is None:
+ continue
+ # Load vision encoder weights directly
+ trimmed_name = ".".join(name.split(".")[1:])
+ param = vision_encoder_dict[trimmed_name]
+ with torch.no_grad():
+ default_weight_loader(param, w)
+ elif is_patch_merger((name, w)):
+ if self.patch_merger is None:
+ continue
+ # Load vision patch merger weights directly
+ trimmed_name = ".".join(name.split(".")[1:])
+ param = patch_merger_dict[trimmed_name]
+ with torch.no_grad():
+ default_weight_loader(param, w)
+ elif is_pre_mm_projector_norm((name, w)):
+ if self.pre_mm_projector_norm is None:
+ continue
+ # Load vision pre_mm_projector_norm weights directly
+ trimmed_name = ".".join(name.split(".")[1:])
+ param = pre_mm_projector_norm_dict[trimmed_name]
+ with torch.no_grad():
+ default_weight_loader(param, w)
+ elif is_vision_lang_adapter_weights((name, w)):
+ if self.vision_language_adapter is None:
+ continue
+ # Load vision-language adapter weights directly
+ trimmed_name = ".".join(name.split(".")[1:])
+ param = vision_lang_adapter_dict[trimmed_name]
+ with torch.no_grad():
+ default_weight_loader(param, w)
+ else:
+ # LLM weights: yield them to be loaded
+ # by language_model.load_weights
+ yield (name, w)
+
+ # Now we call the language model load with the generator
+ self.language_model.load_weights(llm_weights_generator())
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