Feature/isaac 0.1 (#28367)

Signed-off-by: oscardev256 <42308241+oscardev256@users.noreply.github.com>
Signed-off-by: Oscar Gonzalez <ogonzal6@alumni.jh.edu>
Signed-off-by: Yang <lymailforjob@gmail.com>
Co-authored-by: Yang <lymailforjob@gmail.com>

docs/models/supported_models.md

@@ -688,6 +688,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `H2OVLChatModel` | H2OVL | T + I<sup>E+</sup> | `h2oai/h2ovl-mississippi-800m`, `h2oai/h2ovl-mississippi-2b`, etc. | | ✅︎ |
 | `HunYuanVLForConditionalGeneration` | HunyuanOCR | T + I<sup>E+</sup> | `tencent/HunyuanOCR`, etc. | ✅︎ | ✅︎ |
 | `Idefics3ForConditionalGeneration` | Idefics3 | T + I | `HuggingFaceM4/Idefics3-8B-Llama3`, etc. | ✅︎ | |
+| `IsaacForConditionalGeneration` | Isaac | T + I<sup>+</sup> | `PerceptronAI/Isaac-0.1` | ✅︎ | ✅︎ |
 | `InternS1ForConditionalGeneration` | Intern-S1 | T + I<sup>E+</sup> + V<sup>E+</sup> | `internlm/Intern-S1`, `internlm/Intern-S1-mini`, 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. | ✅︎ | ✅︎ |

requirements/test.in

@@ -56,3 +56,5 @@ pydantic>=2.12 # 2.11 leads to error on python 3.13
 decord==0.6.0
 terratorch @ git+https://github.com/IBM/terratorch.git@1.1.rc3 # required for PrithviMAE test
 gpt-oss >= 0.0.7; python_version > '3.11'
+
+perceptron # required for isaac test

requirements/test.txt

@@ -135,6 +135,7 @@ cloudpickle==3.1.1
     # via mlflow-skinny
 colorama==0.4.6
     # via
+    #   perceptron
     #   sacrebleu
     #   schemathesis
     #   tqdm-multiprocess
@@ -302,6 +303,8 @@ h11==0.14.0
     # via
     #   httpcore
     #   uvicorn
+h2==4.3.0
+    # via httpx
 h5py==3.13.0
     # via terratorch
 harfile==0.3.0
@@ -310,6 +313,8 @@ hf-xet==1.1.7
     # via huggingface-hub
 hiredis==3.0.0
     # via tensorizer
+hpack==4.1.0
+    # via h2
 html2text==2025.4.15
     # via gpt-oss
 httpcore==1.0.6
@@ -317,6 +322,7 @@ httpcore==1.0.6
 httpx==0.27.2
     # via
     #   -r requirements/test.in
+    #   perceptron
     #   schemathesis
 huggingface-hub==0.34.3
     # via
@@ -338,6 +344,8 @@ hydra-core==1.3.2
     # via
     #   lightly
     #   lightning
+hyperframe==6.1.0
+    # via h2
 hypothesis==6.131.0
     # via
     #   hypothesis-graphql
@@ -549,6 +557,7 @@ numpy==1.26.4
     #   pandas
     #   patsy
     #   peft
+    #   perceptron
     #   pycocotools
     #   pyogrio
     #   rasterio
@@ -702,6 +711,8 @@ peft==0.16.0
     # via
     #   -r requirements/test.in
     #   lm-eval
+perceptron==0.1.4
+    # via -r requirements/test.in
 pillow==10.4.0
     # via
     #   genai-perf
@@ -709,6 +720,7 @@ pillow==10.4.0
     #   lightly-utils
     #   matplotlib
     #   mistral-common
+    #   perceptron
     #   scikit-image
     #   segmentation-models-pytorch
     #   sentence-transformers
@@ -952,6 +964,7 @@ rich==13.9.4
     #   genai-perf
     #   lightning
     #   mteb
+    #   perceptron
     #   typer
 rioxarray==0.19.0
     # via terratorch
@@ -1024,7 +1037,9 @@ shapely==2.1.1
     #   geopandas
     #   torchgeo
 shellingham==1.5.4
-    # via typer
+    # via
+    #   perceptron
+    #   typer
 six==1.16.0
     # via
     #   junit-xml
@@ -1218,7 +1233,9 @@ typepy==1.3.2
     #   pytablewriter
     #   tabledata
 typer==0.15.2
-    # via fastsafetensors
+    # via
+    #   fastsafetensors
+    #   perceptron
 types-python-dateutil==2.9.0.20241206
     # via arrow
 typeshed-client==2.8.2

tests/models/multimodal/generation/test_common.py

@@ -529,6 +529,31 @@ VLM_TEST_SETTINGS = {
         use_tokenizer_eos=True,
         auto_cls=AutoModelForImageTextToText,
     ),
+    "isaac": VLMTestInfo(
+        models=["PerceptronAI/Isaac-0.1"],
+        test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
+        prompt_formatter=lambda img_prompt: (
+            f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n"
+        ),
+        img_idx_to_prompt=lambda idx: "<image>",
+        single_image_prompts=IMAGE_ASSETS.prompts(
+            {
+                "stop_sign": "<vlm_image>Please describe the image shortly.",
+                "cherry_blossom": "<vlm_image>Please infer the season with reason.",
+            }
+        ),
+        multi_image_prompt=(
+            "Picture 1: <vlm_image>\n"
+            "Picture 2: <vlm_image>\n"
+            "Describe these two images with one paragraph respectively."
+        ),
+        enforce_eager=False,
+        max_model_len=4096,
+        max_num_seqs=2,
+        hf_model_kwargs={"device_map": "auto"},
+        patch_hf_runner=model_utils.isaac_patch_hf_runner,
+        image_size_factors=[(0.25,), (0.25, 0.25, 0.25), (0.25, 0.2, 0.15)],
+    ),
     "kimi_vl": VLMTestInfo(
         models=["moonshotai/Kimi-VL-A3B-Instruct"],
         test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),

tests/models/multimodal/generation/vlm_utils/model_utils.py

@@ -522,6 +522,183 @@ def h2ovl_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
     return hf_model
 
 
+def isaac_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
+    """Patch HF runner for Isaac:
+    1) Move processor outputs to model device
+    2) Ensure IsaacModel.forward returns hidden_states
+    for compatibility with hidden_states_to_seq_logprobs()
+    """
+
+    from perceptron.tensorstream import TextType
+    from perceptron.tensorstream.ops import compute_mrope_pos_tensor, modality_mask
+    from transformers.modeling_outputs import BaseModelOutputWithPast
+
+    def compute_position_ids_input_ids(input_ids: torch.Tensor) -> torch.Tensor:
+        """
+        Create 3D positional indices for token input.
+        """
+        batch_size, seq_length = input_ids.shape
+        position_ids = torch.arange(seq_length, device=input_ids.device)
+        position_ids = position_ids.view(1, -1).expand(batch_size, -1)
+        position_ids = position_ids.unsqueeze(2).expand(-1, -1, 3)  # Add 3D for MRoPE
+        return position_ids
+
+    model_device = next(hf_model.model.parameters()).device
+
+    # ----------------------------
+    # 1) Patch processor: move BatchFeature input_ids and TensorStream to model device
+    # ----------------------------
+    original_processor = hf_model.processor
+
+    def patched_processor(*args, **kwargs):
+        result = original_processor(*args, **kwargs)
+        for k, v in result.data.items():
+            result[k] = v.to(model_device)
+        return result
+
+    hf_model.processor = patched_processor
+
+    tokenizer = AutoTokenizer.from_pretrained(
+        hf_model.model_name, trust_remote_code=True
+    )
+
+    original_generate = hf_model.model.generate
+
+    def patched_generate(*args, **kwargs):
+        kwargs["pad_token_id"] = tokenizer.eos_token_id
+        kwargs["eos_token_id"] = tokenizer.eos_token_id
+        return original_generate(*args, **kwargs)
+
+    hf_model.model.generate = patched_generate
+
+    # ----------------------------
+    # 2) Patch IsaacModel.forward: add hidden_states to the output
+    # ----------------------------
+    isaac_model = hf_model.model.model
+
+    def patched_forward(
+        self,
+        input_ids=None,
+        tensor_stream=None,
+        attention_mask=None,
+        position_ids=None,
+        modality_tensor=None,
+        past_key_values=None,
+        inputs_embeds=None,
+        use_cache=None,
+        output_hidden_states=None,
+        return_dict=None,
+        cache_position=None,
+        **kwargs,
+    ):
+        """
+        Forward pass with MRoPE position embeddings.
+        Computes position embeddings once and passes them through all layers.
+        """
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        # Get inputs
+        if tensor_stream is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both tensor_stream and inputs_embeds")
+        elif tensor_stream is not None:
+            # Embed TensorStream directly
+            inputs_embeds = self.embed_stream(tensor_stream)
+            # Create modality tensor if not provided
+            if modality_tensor is None:
+                modality_tensor = modality_mask(tensor_stream)
+        elif input_ids is not None and inputs_embeds is not None:
+            raise ValueError(
+                "You cannot specify both input_ids and inputs_embeds at the same time"
+            )
+        elif input_ids is not None:
+            inputs_embeds = self.embed_tokens(input_ids)
+            # Create text modality tensor if not provided
+            if modality_tensor is None:
+                batch_size, seq_length = input_ids.shape
+                modality_tensor = torch.full(
+                    (batch_size, seq_length),
+                    TextType.text.value,
+                    device=input_ids.device,
+                    dtype=torch.long,
+                )
+        elif inputs_embeds is None:
+            raise ValueError(
+                "You have to specify either tensor_stream, input_ids or inputs_embeds"
+            )
+
+        # Create default position_ids if not provided
+        if position_ids is None:
+            if tensor_stream is not None:
+                position_ids = compute_mrope_pos_tensor(tensor_stream)  # (B,L,3)
+            else:
+                position_ids = compute_position_ids_input_ids(input_ids)
+
+        # Compute MRoPE position embeddings if we have custom rotary_emb
+        cos, sin = self.rotary_emb(position_ids, modality_tensor)
+        cos = cos.to(inputs_embeds.dtype)
+        sin = sin.to(inputs_embeds.dtype)
+
+        # Prepare attention mask
+        if attention_mask is not None:
+            attention_mask = self._update_causal_mask(
+                attention_mask, inputs_embeds, cache_position, past_key_values, False
+            )
+
+        # Initialize and collect hidden states
+        hidden_states = inputs_embeds
+        hidden_states_list: list[torch.Tensor] = []
+
+        if output_hidden_states:
+            hidden_states_list.append(hidden_states)
+
+        for decoder_layer in self.layers:
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_values,
+                use_cache=use_cache,
+                cache_position=cache_position,
+                position_embeddings=(cos, sin),
+                **kwargs,
+            )
+
+            hidden_states = (
+                layer_outputs[0] if isinstance(layer_outputs, tuple) else layer_outputs
+            )
+
+            if output_hidden_states:
+                hidden_states_list.append(hidden_states)
+
+        # Final layer norm
+        hidden_states = self.norm(hidden_states)
+
+        if output_hidden_states:
+            hidden_states_list.append(hidden_states)
+
+        # Convert to tuple or None
+        all_hidden_states = tuple(hidden_states_list) if output_hidden_states else None
+
+        # Include hiden_states for compatibility with hidden_states_to_seq_logprobs()
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values,
+            hidden_states=all_hidden_states,
+        )
+
+    isaac_model.forward = types.MethodType(patched_forward, isaac_model)
+
+    return hf_model
+
+
 def skyworkr1v_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
     """Patches and returns an instance of the HfRunner to use for SkyworkR1V."""
 

tests/models/registry.py

@@ -662,6 +662,10 @@ _MULTIMODAL_EXAMPLE_MODELS = {
         "HuggingFaceM4/Idefics3-8B-Llama3",
         extras={"tiny": "HuggingFaceTB/SmolVLM-256M-Instruct"},
     ),
+    "IsaacForConditionalGeneration": _HfExamplesInfo(
+        "PerceptronAI/Isaac-0.1",
+        trust_remote_code=True,
+    ),
     "InternS1ForConditionalGeneration": _HfExamplesInfo(
         "internlm/Intern-S1", trust_remote_code=True
     ),

vllm/model_executor/models/isaac.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from __future__ import annotations
+
+import math
+from collections.abc import Iterable, Iterator, Mapping, Sequence
+from typing import Annotated, Any
+
+import numpy as np
+import PIL.Image
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from transformers.image_processing_utils import BatchFeature
+from transformers.tokenization_utils import TensorType
+from typing_extensions import TypedDict, Unpack
+
+from vllm.attention.layers.mm_encoder_attention import MMEncoderAttention
+from vllm.config import MultiModalConfig, VllmConfig
+from vllm.config.model import ModelConfig
+from vllm.distributed import parallel_state
+from vllm.distributed import utils as dist_utils
+from vllm.model_executor.layers.linear import (
+    ColumnParallelLinear,
+    QKVParallelLinear,
+    ReplicatedLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.model_loader.weight_utils import (
+    default_weight_loader,
+)
+from vllm.model_executor.models.interfaces import (
+    MultiModalEmbeddings,
+    SupportsLoRA,
+    SupportsMRoPE,
+    SupportsMultiModal,
+    SupportsPP,
+)
+from vllm.model_executor.models.module_mapping import MultiModelKeys
+from vllm.model_executor.models.siglip import SiglipMLP
+from vllm.model_executor.models.utils import (
+    AutoWeightsLoader,
+    WeightsMapper,
+    init_vllm_registered_model,
+    maybe_prefix,
+)
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (
+    MultiModalDataDict,
+    MultiModalFeatureSpec,
+    MultiModalFieldConfig,
+    MultiModalKwargsItems,
+)
+from vllm.multimodal.parse import ImageSize, MultiModalDataItems
+from vllm.multimodal.processing import (
+    BaseMultiModalProcessor,
+    BaseProcessingInfo,
+    PromptReplacement,
+    PromptUpdate,
+    PromptUpdateDetails,
+)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.sequence import IntermediateTensors
+from vllm.tokenizers import get_tokenizer
+from vllm.tokenizers.hf import get_cached_tokenizer
+from vllm.transformers_utils.configs import (
+    IsaacConfig,
+    PixelShuffleSiglip2VisionConfig,
+)
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
+
+
+def create_cumulative_seq_lengths(
+    seq_sizes: torch.Tensor, device: torch.device
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Create cumulative sequence lengths for variable-length attention."""
+    cu_seqlens = torch.zeros(len(seq_sizes) + 1, dtype=torch.int32, device=device)
+    cu_seqlens[1:] = seq_sizes.cumsum(0)
+    max_seqlen = (
+        seq_sizes.max()
+        if len(seq_sizes) > 0
+        else torch.tensor(0, dtype=torch.int32, device=device)
+    )
+    return cu_seqlens, max_seqlen
+
+
+class Siglip2VariableSequenceEmbeddings(nn.Module):
+    def __init__(self, config: PixelShuffleSiglip2VisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.patch_size = config.patch_size
+
+        self.patch_embedding = ReplicatedLinear(
+            input_size=config.num_channels * self.patch_size * self.patch_size,
+            output_size=self.embed_dim,
+            return_bias=False,
+        )
+
+        self.num_patches = config.num_patches
+        self.position_embedding_size = int(self.num_patches**0.5)
+        self.position_embedding = nn.Embedding(self.num_patches, self.embed_dim)
+
+    def positional_embeddings(
+        self, packed_seq_patches: tuple[torch.Tensor, torch.Tensor, torch.Tensor]
+    ) -> torch.Tensor:
+        # Prepare positional embeddings grid: (1, embed_dim, h, w)
+        positional_embeddings = (
+            self.position_embedding.weight.reshape(
+                self.position_embedding_size, self.position_embedding_size, -1
+            )
+            .permute(2, 0, 1)
+            .unsqueeze(0)
+        )
+
+        _seq_patches, _seq_sizes, spatial_shapes = packed_seq_patches
+        pos_embeds_list = []
+        mode = "bilinear"
+        align_corners = False
+        antialias = True
+        for spatial_shape in spatial_shapes:
+            height, width = int(spatial_shape[0]), int(spatial_shape[1])
+            # Guard to ensure height and width are positive for torch.compile
+            if height > 0 and width > 0:
+                resized_pos_embed = F.interpolate(
+                    positional_embeddings,
+                    size=(height, width),
+                    mode=mode,
+                    align_corners=align_corners,
+                    antialias=antialias,
+                )
+                # Reshape from (1, embed_dim, height, width) to
+                # (height*width, embed_dim)
+                resized_pos_embed = resized_pos_embed.reshape(
+                    self.embed_dim, height * width
+                ).transpose(0, 1)
+            else:
+                # Fallback - should never happen in practice
+                resized_pos_embed = positional_embeddings.reshape(
+                    self.embed_dim,
+                    self.position_embedding_size * self.position_embedding_size,
+                ).transpose(0, 1)[: height * width]
+            pos_embeds_list.append(resized_pos_embed)
+
+        # Concatenate all positional embeddings along the sequence dimension
+        pos_embeds = torch.cat(pos_embeds_list, dim=0)
+        return pos_embeds
+
+    def forward(
+        self, packed_seq_patches: tuple[torch.Tensor, torch.Tensor, torch.Tensor]
+    ):
+        seq_patches, _seq_sizes, _spatial_shapes = packed_seq_patches
+
+        target_weight = self.patch_embedding.weight
+        seq_patches = seq_patches.to(
+            device=target_weight.device, dtype=target_weight.dtype
+        )
+        patch_embeds = self.patch_embedding(seq_patches)
+        pos_embeds = self.positional_embeddings(packed_seq_patches)
+
+        # Flatten patch embeddings to match positional embeddings format
+        if patch_embeds.dim() == 3:
+            patch_embeds = patch_embeds.view(-1, patch_embeds.size(-1))
+
+        # Add positional embeddings to patch embeddings
+        embeddings = patch_embeds + pos_embeds
+        return embeddings
+
+
+def create_pixel_shuffle_index_map(
+    seq_sizes: torch.Tensor,
+    token_grids: torch.Tensor,
+    scale_factor: int = 1,
+    device: torch.device | None = None,
+) -> torch.Tensor:
+    """
+    Build a gather-index map that tells us, for every *output* token after
+    pixel-shuffle, which `scale_factor**2` *input* tokens are being merged.
+
+    Args
+    ----
+    seq_sizes     : (num_images,)  - #patches in each image (row-major order)
+    token_grids   : (num_images,2) - (height, width) for every image
+    scale_factor  : spatial down-scale factor (≥2)
+    device        : (optional) overrides `seq_sizes.device`
+
+    Returns
+    -------
+    gather_idx : (new_total_seq_len, scale_factor**2) int64 tensor.
+                 gather_idx[i, j] is the *flat* index into the *original*
+                 packed sequence for the j-th sub-patch that forms the
+                 i-th output token.
+    """
+    if device is None:
+        device = seq_sizes.device
+
+    r = int(scale_factor)
+    if r < 2:
+        raise ValueError("`scale_factor` must be ≥ 2")
+
+    # Safety: all spatial dims must be divisible by r
+    # Cannot run under torch compile fullgraph mode hence
+    if not torch.compiler.is_compiling() and not (
+        (token_grids[:, 0] % r == 0).all() and (token_grids[:, 1] % r == 0).all()
+    ):
+        raise AssertionError(
+            "Every (H,W) in `token_grids` must be divisible by "
+            f"scale_factor={r}, got {token_grids.tolist()}"
+        )
+
+    gather_chunks: list[torch.Tensor] = []
+    tok_offset = 0
+
+    for seq_len, (h, w) in zip(seq_sizes.tolist(), token_grids.tolist(), strict=False):
+        # Build the (H, W) grid of flat indices for this image
+        grid = torch.arange(seq_len, device=device, dtype=torch.int64) + tok_offset
+        grid = grid.view(h, w)  # (H, W)
+
+        # -------- identical ordering to your fixed-res routine --------
+        # Step 1: split width into blocks of r
+        grid = grid.view(h, w // r, r)  # (H, W/r, r)
+        # Step 2: now split height into blocks of r
+        grid = grid.view(h // r, r, w // r, r)  # (H/r, r, W/r, r)
+        # Step 3: final permutation to (H/r, W/r, r, r)
+        grid = grid.permute(0, 2, 1, 3).contiguous()  # (H/r, W/r, r, r)
+        # Step 4: each (r, r) block forms one output token
+        gather_chunks.append(grid.reshape(-1, r * r))  # (H*W / r², r²)
+
+        tok_offset += seq_len
+
+    # Concatenate over all images in the packed batch
+    gather_idx = torch.cat(gather_chunks, dim=0)  # (Σ_i HᵢWᵢ/r², r²)
+    return gather_idx
+
+
+def pixel_shuffle_varlen(
+    x: torch.Tensor,
+    token_grids: torch.Tensor,
+    scale_factor: int = 1,
+) -> torch.Tensor:
+    r"""Apply pixel shuffle to a packed vision sequence without unpacking per image.
+
+    Args:
+        x (`torch.Tensor`):
+            Concatenated vision embeddings. Accepts `(seq_len, hidden_size)` or
+            `(1, seq_len, hidden_size)` shapes produced by stacking image
+            patches.
+        token_grids (`torch.Tensor`):
+            Integer tensor of shape `(num_images, 2)` whose rows give the
+            `(height, width)` patch grid sizes corresponding to each image
+            segment inside `x`.
+        scale_factor (`int`, *optional*, defaults to 1):
+            Spatial down-sampling factor specific to pixel shuffle. Values
+            greater than one merge `scale_factor**2` neighboring patches into a
+            single embedding channel-group.
+
+    Returns:
+        `torch.Tensor`: Pixel-shuffled embeddings with shape matching the input
+        convention: `(seq_len, hidden_size * scale_factor**2)` when the input
+        was 2D, or `(1, seq_len, hidden_size * scale_factor**2)` if the
+        singleton batch dimension was present.
+
+    Raises:
+        ValueError: If more than one batch item is provided.
+    """
+    keep_batch_dim = x.dim() == 3
+    if keep_batch_dim:
+        if x.size(0) != 1:
+            raise AssertionError("Packed sequence is expected to have batch_size == 1")
+        x_ = x.squeeze(0)  # (seq, embed)
+    else:
+        x_ = x  # (seq, embed)
+
+    embed_dim = x_.size(-1)
+    r = int(scale_factor)
+
+    # Calculate seq_sizes from token_grids
+    seq_sizes = torch.prod(token_grids, dim=-1)
+
+    # Build index map and gather in one go
+    gather_idx = create_pixel_shuffle_index_map(
+        seq_sizes=seq_sizes,
+        token_grids=token_grids,
+        scale_factor=r,
+        device=x_.device,
+    )  # (new_seq, r²)
+
+    # Gather → (new_seq, r², embed_dim)
+    gathered = x_[gather_idx]  # fancy indexing keeps gradient
+
+    # Merge the r² group dimension into channels to finish the shuffle
+    out = gathered.reshape(gathered.size(0), embed_dim * r * r)
+
+    # Restore batch dimension if needed
+    if keep_batch_dim:
+        out = out.unsqueeze(0)
+    return out
+
+
+# ============================================================================
+# Configuration
+# ============================================================================
+
+MAX_PIXELS = 60_000_000  # 60-megapixel ceiling ≈ 8200 × 7300 px
+
+# Vision preprocessing constants
+VISION_MEAN = (0.5, 0.5, 0.5)
+VISION_STD = (0.5, 0.5, 0.5)
+VISION_SCALE = 1 / 255
+
+
+def _make_writeable(arr: np.ndarray) -> np.ndarray:
+    """Return *arr* itself if it is already writeable, otherwise try to flip the
+    write flag in-place and finally fall back to `arr.copy()`.
+    This guarantees the buffer handed to `torch.from_numpy()` is always
+    writeable, silencing the PyTorch warning about undefined behaviour.
+    """
+    if arr.flags.writeable:
+        return arr
+
+    # First, try the cheap path — in-place flag toggle (works for mmap'd arrays
+    # and some shared memory buffers):
+    try:
+        arr.setflags(write=True)
+        return arr  # success: no data copy
+    except ValueError:
+        # Buffer is inherently read-only (e.g. backed by PyAV / PIL): make copy
+        return arr.copy()
+
+
+def extract_image_pil(image: PIL.Image.Image) -> torch.Tensor | None:
+    if image.width * image.height > MAX_PIXELS:
+        raise ValueError(
+            f"Image (w={image.width}, h={image.height}) > MAX=`{MAX_PIXELS}`"
+        )
+    img = image if image.mode == "RGB" else image.convert("RGB")
+    arr = np.asarray(img)
+    arr = _make_writeable(arr)
+    return torch.from_numpy(arr)
+
+
+def get_image_size_for_max_num_patches(
+    image_height: int,
+    image_width: int,
+    patch_size: int,
+    max_num_patches: int,
+    min_num_patches: int | None = None,
+    eps: float = 1e-5,
+    pixel_shuffle_scale: int = 1,
+) -> tuple[int, int]:
+    r"""Compute a target resolution whose patch grid satisfies patching parametrization.
+
+    Args:
+        image_height (`int`):
+            Height in pixels of the source image prior to any resizing.
+        image_width (`int`):
+            Width in pixels of the source image prior to any resizing.
+        patch_size (`int`):
+            Size of the square patch used by the vision encoder.
+        max_num_patches (`int`):
+            Upper bound on `(height / patch_size) * (width / patch_size)` after
+            resizing.
+        min_num_patches (`int`, *optional*):
+            Lower bound on the number of patches. When provided the image will
+            be scaled up if necessary.
+        eps (`float`, *optional*, defaults to 1e-5):
+            Convergence tolerance for the internal binary search to determine
+            the target dimensions.
+        pixel_shuffle_scale (`int`, *optional*, defaults to 1):
+            Additional stride multiplier applied when pixel shuffle later
+            reduces spatial resolution.
+
+    Returns:
+        `tuple[int, int]`: Height and width (in pixels) that are multiples of
+        `patch_size * pixel_shuffle_scale` and respect both the maximum and
+        optional minimum patch-count constraints.
+    """
+
+    def get_scaled_image_size(scale, original_size, patch_size, pixel_shuffle_scale):
+        scaled_size = scale * original_size
+        divisor = patch_size * pixel_shuffle_scale
+        scaled_size = math.ceil(scaled_size / divisor) * divisor
+        scaled_size = max(divisor, scaled_size)
+        return int(scaled_size)
+
+    # Ensure divisibility
+    divisor = patch_size * pixel_shuffle_scale
+    adjusted_height = math.ceil(image_height / divisor) * divisor
+    adjusted_height = max(divisor, adjusted_height)
+    adjusted_width = math.ceil(image_width / divisor) * divisor
+    adjusted_width = max(divisor, adjusted_width)
+
+    num_patches = (adjusted_height / patch_size) * (adjusted_width / patch_size)
+
+    if min_num_patches is not None and num_patches < min_num_patches:
+        # Scale up
+        scale_min, scale_max = 1.0, 100.0
+        while (scale_max - scale_min) >= eps:
+            scale = (scale_min + scale_max) / 2
+            target_height = get_scaled_image_size(
+                scale, image_height, patch_size, pixel_shuffle_scale
+            )
+            target_width = get_scaled_image_size(
+                scale, image_width, patch_size, pixel_shuffle_scale
+            )
+            num_patches = (target_height / patch_size) * (target_width / patch_size)
+            if num_patches >= min_num_patches:
+                scale_max = scale
+            else:
+                scale_min = scale
+        scale = scale_max
+        target_height = get_scaled_image_size(
+            scale, image_height, patch_size, pixel_shuffle_scale
+        )
+        target_width = get_scaled_image_size(
+            scale, image_width, patch_size, pixel_shuffle_scale
+        )
+        return target_height, target_width
+    elif num_patches <= max_num_patches:
+        return adjusted_height, adjusted_width
+    else:
+        # Scale down
+        scale_min, scale_max = eps / 10, 1.0
+        while (scale_max - scale_min) >= eps:
+            scale = (scale_min + scale_max) / 2
+            target_height = get_scaled_image_size(
+                scale, image_height, patch_size, pixel_shuffle_scale
+            )
+            target_width = get_scaled_image_size(
+                scale, image_width, patch_size, pixel_shuffle_scale
+            )
+            num_patches = (target_height / patch_size) * (target_width / patch_size)
+            if num_patches <= max_num_patches:
+                scale_min = scale
+            else:
+                scale_max = scale
+        scale = scale_min
+        target_height = get_scaled_image_size(
+            scale, image_height, patch_size, pixel_shuffle_scale
+        )
+        target_width = get_scaled_image_size(
+            scale, image_width, patch_size, pixel_shuffle_scale
+        )
+        return target_height, target_width
+
+
+_MEAN_TENSOR = torch.tensor(VISION_MEAN, dtype=torch.float32).view(1, 1, 1, -1)
+_STD_TENSOR = torch.tensor(VISION_STD, dtype=torch.float32).view(1, 1, 1, -1)
+
+
+def _resolve_vision_token_id(model_config: ModelConfig, vision_token: str) -> int:
+    tokenizer_name = model_config.tokenizer or model_config.model
+    tokenizer = get_cached_tokenizer(
+        get_tokenizer(
+            tokenizer_name,
+            tokenizer_mode=model_config.tokenizer_mode,
+            trust_remote_code=model_config.trust_remote_code,
+            revision=model_config.tokenizer_revision or model_config.revision,
+        )
+    )
+    return tokenizer.encode(vision_token, add_special_tokens=False)[0]
+
+
+def prepare_image_tensor(
+    image: torch.Tensor,
+    scale: float = VISION_SCALE,
+) -> torch.Tensor:
+    r"""Standardize RGB images prior to patch extraction via rescaling and whitening.
+
+    Args:
+        image (`torch.Tensor`):
+            Tensor with shape `(..., height, width, 3)` containing RGB values.
+            The tensor is converted to floating point if needed.
+        scale (`float`, *optional*, defaults to `VISION_SCALE`):
+            Scalar multiplier applied before normalization.
+    Returns:
+        `torch.Tensor`: Normalized tensor with the same shape as the input and
+        dtype `torch.float32`.
+    """
+    if not torch.is_floating_point(image):
+        image = image.float()
+    rescaled = image * scale
+
+    # Use precomputed tensors and move to the correct device if needed
+    mean_tensor = _MEAN_TENSOR.to(image.device)
+    std_tensor = _STD_TENSOR.to(image.device)
+
+    normalized = (rescaled - mean_tensor) / std_tensor
+    return normalized
+
+
+def patchify_vision(image: torch.Tensor, patch_size: int) -> torch.Tensor:
+    r"""Convert normalized images into flattened ViT-style patches.
+
+    Args:
+        image (`torch.Tensor`):
+            Tensor of shape `(num_images, height, width, channels)`.
+        patch_size (`int`):
+            Edge length of the square patches
+
+    Returns:
+        `torch.Tensor`:
+            Patch tensor where each position stores the flattened pixels
+            belonging to that patch.
+
+    Raises:
+        ValueError: If `height` or `width` is not divisible by `patch_size`.
+    """
+    num_images, height, width, channels = image.shape
+    if height % patch_size or width % patch_size:
+        raise ValueError(
+            "Dimensions of images "
+            f"{image.shape} are not divisible by patch_size={patch_size}."
+        )
+    patches = image.reshape(
+        num_images,
+        height // patch_size,
+        patch_size,
+        width // patch_size,
+        patch_size,
+        channels,
+    )
+    patches = patches.permute(0, 1, 3, 2, 4, 5)
+    patches = patches.reshape(
+        num_images,
+        height // patch_size,
+        width // patch_size,
+        channels * patch_size * patch_size,
+    )
+    return patches
+
+
+def process_vision_for_patches(
+    images: torch.Tensor,
+    patch_size: int,
+    max_num_patches: int,
+    min_num_patches: int | None = None,
+    pixel_shuffle_scale: int = 1,
+) -> tuple[torch.Tensor, list[int]]:
+    r"""Resize, normalize, and patchify RGB images for the vision encoder.
+
+    Args:
+        images (`torch.Tensor`):
+            Either `(height, width, channels)` for a single image or
+            `(num_images, height, width, channels)` for a batch. Channels are
+            expected to be RGB.
+        patch_size (`int`):
+            Edge length of square patches; implictly controls resize grid granularity.
+        max_num_patches (`int`):
+            Maximum number of patches allowed after resizing.
+        min_num_patches (`int`, *optional*):
+            Minimum number of patches. If provided, the routine upsamples images
+            as needed to satisfy the lower bound.
+        pixel_shuffle_scale (`int`, *optional*, defaults to 1):
+            Pixel shuffle scale factor; influences the target grid that the
+            function produces.
+
+    Returns:
+        `tuple[torch.Tensor, list[int]]`: A pair `(patches, dims_virtual)`
+        where `patches` has shape `(num_images, target_h / patch_size, target_w
+        / patch_size, channels * patch_size**2)` and `dims_virtual` encodes
+        effective `(images, height, width)` dimensions after optional pixel
+        shuffling.
+    """
+    # Add batch dim if single image
+    if images.dim() == 3:
+        images = images.unsqueeze(0)
+
+    # Permute to channel first for resize
+    images = images.permute(0, 3, 1, 2)
+
+    # Get target dimensions
+    _, _, orig_height, orig_width = images.shape
+    target_height, target_width = get_image_size_for_max_num_patches(
+        orig_height,
+        orig_width,
+        patch_size,
+        max_num_patches,
+        min_num_patches=min_num_patches,
+        pixel_shuffle_scale=pixel_shuffle_scale,
+    )
+
+    # Resize
+    images = F.interpolate(
+        images,
+        size=(target_height, target_width),
+        mode="bilinear",
+        align_corners=False,
+    )
+
+    # Back to channel last
+    images = images.permute(0, 2, 3, 1)
+
+    # Normalize
+    images = prepare_image_tensor(images)
+
+    # Patchify
+    patches = patchify_vision(images, patch_size=patch_size)
+
+    # Calculate dimensions for the patches
+    n_images, h_patches, w_patches, _ = patches.shape
+    dims_virtual = (
+        [1, h_patches, w_patches]
+        if pixel_shuffle_scale == 1
+        else [1, h_patches // pixel_shuffle_scale, w_patches // pixel_shuffle_scale]
+    )
+
+    return patches, dims_virtual
+
+
+class IsaacImageProcessorKwargs(TypedDict, total=False):
+    patch_size: int
+    max_num_patches: int
+    min_num_patches: int
+    pixel_shuffle_scale: int
+
+
+class IsaacImageProcessor:
+    patch_size = 16
+    max_num_patches = 6144
+    min_num_patches = 256
+    pixel_shuffle_scale = 2
+
+    valid_kwargs = IsaacImageProcessorKwargs
+    model_input_names = ["pixel_values", "image_grid_thw"]
+
+    def __init__(self, kwargs):
+        self.patch_size = kwargs.pop("patch_size", self.patch_size)
+        self.vision_max_num_patches = kwargs.pop(
+            "vision_max_num_patches", self.max_num_patches
+        )
+        self.vision_min_num_patches = kwargs.pop(
+            "vision_min_num_patches", self.min_num_patches
+        )
+        self.pixel_shuffle_scale = kwargs.pop("pixel_shuffle_scale", 2)
+
+    def preprocess(
+        self,
+        images: list[torch.Tensor],
+        return_tensors: str | TensorType | None,
+        **kwargs: Unpack[IsaacImageProcessorKwargs],
+    ) -> BatchFeature:
+        """Preprocess images into format compatibile with vLLM input processing."""
+
+        all_pixel_values: list[torch.Tensor] = []
+        all_image_grids: list[torch.Tensor] = []
+
+        for image in images:
+            image_tensor = extract_image_pil(image)
+
+            patches, dims_virtual = process_vision_for_patches(
+                image_tensor,
+                patch_size=self.patch_size,
+                max_num_patches=self.vision_max_num_patches,
+                min_num_patches=self.vision_min_num_patches,
+                pixel_shuffle_scale=self.pixel_shuffle_scale,
+            )
+
+            # Isaac packs a dummy temporal dim for images
+            patches = patches.unsqueeze(1)  # [N, T=1, Hp, Wp, D]
+
+            hp, wp, dim = patches.shape[-3], patches.shape[-2], patches.shape[-1]
+            current_num_patches = hp * wp
+            pixel_values = patches.reshape(current_num_patches, dim)  # [N_tokens, D]
+
+            # Use real patch dimensions for image_grid_thw, not virtual dimensions
+            # This ensures the vision model receives correct grid info for pixel shuffle
+            dims_real = [1, hp, wp]  # Real patch dimensions
+            image_grid_thw = torch.tensor(dims_real).unsqueeze(0)
+
+            all_pixel_values.append(pixel_values)
+            all_image_grids.append(image_grid_thw)
+
+        if all_pixel_values:
+            final_pixel_values = torch.cat(all_pixel_values, dim=0)
+            final_image_grids = torch.cat(all_image_grids, dim=0)
+        else:
+            final_pixel_values = torch.empty(0, 0)
+            final_image_grids = torch.empty(0, 3)
+
+        return BatchFeature(
+            data={
+                "pixel_values": final_pixel_values,
+                "image_grid_thw": final_image_grids,
+            },
+            tensor_type=return_tensors,
+        )
+
+
+class IsaacProcessor:
+    """Processor wrapper (tokenizer + IsaacImageProcessor)."""
+
+    def __init__(self, image_processor=None, tokenizer=None, **kwargs):
+        self.image_token = kwargs.pop("image_token", "<image>")
+        self.image_processor = image_processor or IsaacImageProcessor(kwargs)
+        self.tokenizer = tokenizer
+
+    def __call__(self, text=None, images=None, **kwargs) -> BatchFeature:
+        result = {}
+
+        if images is not None:
+            image_inputs = self.image_processor.preprocess(images, **kwargs)
+            image_grid_thw = image_inputs["image_grid_thw"]
+            result.update(image_inputs)
+
+            if text is not None:
+                if not isinstance(text, list):
+                    text = [text]
+
+                text = text.copy()  # below lines change text in-place
+                merge_length = self.image_processor.pixel_shuffle_scale**2
+                index = 0
+                for i in range(len(text)):
+                    while self.image_token in text[i]:
+                        num_image_tokens = image_grid_thw[index].prod() // merge_length
+                        text[i] = text[i].replace(
+                            self.image_token, "<|placeholder|>" * num_image_tokens, 1
+                        )
+                        index += 1
+                    text[i] = text[i].replace("<|placeholder|>", "<|image_pad|>")
+
+        if text is not None:
+            result.update(self.tokenizer(text, **kwargs))
+
+        return BatchFeature(result)
+
+    def apply_chat_template(
+        self,
+        messages: list[dict[str, Any]],
+        tokenize: bool = False,
+        add_generation_prompt: bool = False,
+        **kwargs,
+    ) -> Any:
+        # Convert mixed content messages to simple text format
+        processed_messages = []
+
+        for message in messages:
+            if "content" in message and isinstance(message["content"], list):
+                # Handle mixed content (text + image)
+                text_parts = []
+                for content_item in message["content"]:
+                    if content_item.get("type") == "text":
+                        text_parts.append(content_item.get("text", ""))
+                    elif content_item.get("type") == "image":
+                        # Replace image with vision token
+                        text_parts.append(self.image_token)
+
+                processed_message = {
+                    "role": message.get("role", "user"),
+                    "content": "".join(text_parts),
+                }
+                processed_messages.append(processed_message)
+            else:
+                # Regular text message
+                processed_messages.append(message)
+
+        return self.tokenizer.apply_chat_template(
+            processed_messages,
+            tokenize=tokenize,
+            add_generation_prompt=add_generation_prompt,
+            **kwargs,
+        )
+
+
+class IsaacProcessingInfo(BaseProcessingInfo):
+    def get_hf_config(self) -> IsaacConfig:
+        if hasattr(self.ctx, "get_hf_config"):
+            original_config = self.ctx.get_hf_config()
+            # Map HF config parameters to our vLLM config parameters
+            return IsaacConfig(
+                # Vision parameters - map from HF names
+                vision_config=getattr(original_config, "vision_config", None),
+                vision_patch_size=getattr(original_config, "video_patch_size", 16),
+                vision_max_num_patches=getattr(
+                    original_config, "vision_max_num_patches", 256
+                ),
+                vision_min_num_patches=getattr(
+                    original_config, "vision_min_num_patches", None
+                ),
+                pixel_shuffle_scale=getattr(original_config, "pixel_shuffle_scale", 1),
+                max_sequence_length=getattr(
+                    original_config, "max_sequence_length", 16384
+                ),
+                vision_token=getattr(original_config, "vision_token", "<image>"),
+                vision_attn_implementation=getattr(
+                    original_config, "vision_attn_implementation", None
+                ),
+            )
+        return IsaacConfig()
+
+    def get_hf_processor(self, **kwargs) -> IsaacProcessor:
+        hf_config = self.get_hf_config()
+        processor_kwargs = {
+            "image_token": hf_config.vision_token,
+        }
+        processor_kwargs.update(kwargs)
+        return self.ctx.get_hf_processor(IsaacProcessor, **processor_kwargs)
+
+    def get_tokenizer(self):
+        return self.ctx.tokenizer
+
+    def get_image_size_with_most_features(self) -> ImageSize:
+        hf_config = self.get_hf_config()
+        # Get target dimensions
+        target_height, target_width = get_image_size_for_max_num_patches(
+            9999999,
+            9999999,
+            hf_config.video_patch_size,
+            hf_config.vision_max_num_patches,
+            min_num_patches=hf_config.vision_min_num_patches,
+            pixel_shuffle_scale=hf_config.pixel_shuffle_scale,
+        )
+        return ImageSize(width=target_width, height=target_height)
+
+    def get_image_processor(self, **kwargs) -> IsaacImageProcessor:
+        return self.get_hf_processor(**kwargs).image_processor
+
+    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
+        return {"image": None}
+
+    def get_mm_max_tokens_per_item(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> Mapping[str, int]:
+        hf_config = self.get_hf_config()
+        num_vision_tokens = hf_config.vision_max_num_patches // (
+            hf_config.pixel_shuffle_scale**2
+        )
+        return {"image": num_vision_tokens}
+
+
+class IsaacDummyInputsBuilder(BaseDummyInputsBuilder[IsaacProcessingInfo]):
+    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
+        num_images = mm_counts.get("image", 0)
+
+        hf_processor = self.info.get_hf_processor()
+        image_token: str = hf_processor.image_token
+
+        return image_token * num_images
+
+    def get_dummy_mm_data(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+        mm_options: Mapping[str] | 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,
+            ),
+        }
+
+
+class IsaacImagePixelInputs(TensorSchema):
+    """
+    Schema for validating Isaac image inputs.
+
+    Dimensions:
+        - np: Number of patches
+        - d: Patch dimension
+        - ni: Number of images
+
+    The schema enforces:
+        - pixel_values must be 2D: (num_patches, patch_dim)
+        - image_grid_thw must be 2D: (num_images, 3)
+          where 3 represents [T, H, W]
+    """
+
+    pixel_values: Annotated[
+        torch.Tensor,
+        TensorShape("np", "d"),
+    ]
+
+    image_grid_thw: Annotated[
+        torch.Tensor,
+        TensorShape("ni", 3),
+    ]
+
+
+class IsaacMultiModalProcessor(BaseMultiModalProcessor):
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        # Configure multimodal fields for Isaac model
+        image_grid_thw = hf_inputs.get("image_grid_thw", torch.empty((0, 3)))
+        image_grid_sizes = image_grid_thw.prod(-1)
+
+        return {
+            "pixel_values": MultiModalFieldConfig.flat_from_sizes(
+                "image", image_grid_sizes
+            ),
+            "image_grid_thw": MultiModalFieldConfig.batched("image"),
+        }
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, Any],
+        out_mm_kwargs: MultiModalKwargsItems,
+    ) -> Sequence[PromptUpdate]:
+        image_processor = self.info.get_image_processor(**hf_processor_mm_kwargs)
+
+        pixel_shuffle_scale = getattr(image_processor, "pixel_shuffle_scale", 2)
+        merge_length = pixel_shuffle_scale**2
+
+        def get_replacement_isaac(item_idx: int):
+            out_item = out_mm_kwargs["image"][item_idx]
+            grid_thw = out_item["image_grid_thw"].data
+            assert isinstance(grid_thw, torch.Tensor)
+
+            feature_size = int(grid_thw.prod()) // merge_length
+            repl_full = "<|image_pad|>" * feature_size
+            return PromptUpdateDetails.select_text(repl_full, "<|image_pad|>")
+
+        return [
+            PromptReplacement(
+                modality="image",
+                target="<image>",
+                replacement=get_replacement_isaac,
+            )
+        ]
+
+
+class Siglip2VisionAttention(nn.Module):
+    def __init__(
+        self,
+        config: PixelShuffleSiglip2VisionConfig,
+        quant_config: QuantizationConfig | None = None,
+        *,
+        prefix: str = "",
+        multimodal_config: MultiModalConfig | None = None,
+    ) -> None:
+        super().__init__()
+
+        use_data_parallel = (
+            multimodal_config.mm_encoder_tp_mode == "data"
+            if multimodal_config
+            else False
+        )
+        self.tp_size = (
+            1
+            if use_data_parallel
+            else parallel_state.get_tensor_model_parallel_world_size()
+        )
+        self.tp_rank = parallel_state.get_tensor_model_parallel_rank()
+        self.hidden_size_per_attention_head = dist_utils.divide(
+            config.hidden_size, config.num_attention_heads
+        )
+        self.num_attention_heads_per_partition = dist_utils.divide(
+            config.num_attention_heads, self.tp_size
+        )
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size=config.hidden_size,
+            head_size=self.hidden_size_per_attention_head,
+            total_num_heads=config.num_attention_heads,
+            total_num_kv_heads=config.num_attention_heads,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+            disable_tp=use_data_parallel,
+        )
+        self.out_proj = RowParallelLinear(
+            input_size=config.hidden_size,
+            output_size=config.hidden_size,
+            quant_config=quant_config,
+            prefix=f"{prefix}.out_proj",
+            disable_tp=use_data_parallel,
+        )
+
+        self.attn = MMEncoderAttention(
+            num_heads=self.num_attention_heads_per_partition,
+            head_size=self.hidden_size_per_attention_head,
+            prefix=f"{prefix}.attn",
+            multimodal_config=multimodal_config,
+        )
+
+    def split_qkv(self, qkv: torch.Tensor) -> tuple[torch.Tensor, ...]:
+        seq_len, bs, _ = qkv.shape
+        q, k, v = qkv.chunk(3, dim=2)
+        new_shape = (
+            seq_len,
+            bs,
+            self.num_attention_heads_per_partition,
+            self.hidden_size_per_attention_head,
+        )
+        q, k, v = (x.view(*new_shape) for x in (q, k, v))
+        return q, k, v
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        *,
+        cu_seqlens: torch.Tensor,
+        max_seqlen: torch.Tensor | None,
+    ) -> torch.Tensor:
+        batch_size, _, _ = hidden_states.shape
+        if batch_size != 1:
+            raise ValueError("packed variable-length attention expects batch_size=1")
+        x = rearrange(hidden_states, "b s d -> s b d")
+        x, _ = self.qkv_proj(x)
+        q, k, v = self.split_qkv(x)
+        q, k, v = (rearrange(t, "s b h d -> b s h d") for t in (q, k, v))
+
+        context_layer = self.attn(
+            query=q,
+            key=k,
+            value=v,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+        )
+        context_layer = rearrange(context_layer, "b s h d -> s b (h d)").contiguous()
+
+        output, _ = self.out_proj(context_layer)
+        output = rearrange(output, "s b d -> b s d")
+        return output
+
+
+class Siglip2EncoderLayer(nn.Module):
+    def __init__(
+        self,
+        config: PixelShuffleSiglip2VisionConfig,
+        quant_config: QuantizationConfig | None = None,
+        *,
+        prefix: str = "",
+        multimodal_config: MultiModalConfig | None = None,
+    ) -> None:
+        super().__init__()
+        self.embed_dim = config.hidden_size
+        self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+        self.self_attn = Siglip2VisionAttention(
+            config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+            multimodal_config=multimodal_config,
+        )
+        self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+        self.mlp = SiglipMLP(
+            config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.mlp",
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        *,
+        cu_seqlens: torch.Tensor,
+        max_seqlen: torch.Tensor | None,
+    ) -> torch.Tensor:
+        residual = hidden_states
+
+        hidden_states = self.layer_norm1(hidden_states)
+        hidden_states = self.self_attn(
+            hidden_states=hidden_states,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+        )
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.layer_norm2(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+
+class Siglip2Encoder(nn.Module):
+    def __init__(
+        self,
+        config: PixelShuffleSiglip2VisionConfig,
+        quant_config: QuantizationConfig | None = None,
+        *,
+        prefix: str = "",
+        multimodal_config: MultiModalConfig | None = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.layers = nn.ModuleList(
+            [
+                Siglip2EncoderLayer(
+                    config,
+                    quant_config=quant_config,
+                    prefix=f"{prefix}.layers.{layer_idx}",
+                    multimodal_config=multimodal_config,
+                )
+                for layer_idx in range(config.num_hidden_layers)
+            ]
+        )
+
+    def forward(
+        self,
+        inputs_embeds: torch.Tensor,
+        *,
+        cu_seqlens: torch.Tensor | None = None,
+        max_seqlen: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        hidden_states = inputs_embeds
+        for encoder_layer in self.layers:
+            hidden_states = encoder_layer(
+                hidden_states,
+                cu_seqlens=cu_seqlens,
+                max_seqlen=max_seqlen,
+            )
+        return hidden_states
+
+
+class Siglip2VisionTransformer(nn.Module):
+    def __init__(
+        self,
+        config: PixelShuffleSiglip2VisionConfig,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+        multimodal_config: MultiModalConfig | None = None,
+    ):
+        super().__init__()
+        self.config = config
+        self.quant_config = quant_config
+        embed_dim = config.hidden_size
+
+        self.embeddings = Siglip2VariableSequenceEmbeddings(config)
+        self.pixel_shuffle_scale_factor = config.pixel_shuffle_scale_factor
+        self.encoder = Siglip2Encoder(
+            config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.encoder",
+            multimodal_config=multimodal_config,
+        )
+        self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        packed_seq_patches: tuple[torch.Tensor, torch.Tensor],
+    ) -> torch.Tensor:
+        r"""
+        spatial_shapes (`torch.LongTensor` of shape `(batch_size, 2)`):
+            Tensor containing the spatial dimensions (height, width)
+            of the input images.
+        """
+
+        seq_patches, token_grids = packed_seq_patches
+        seq_sizes = torch.prod(token_grids, dim=-1)
+
+        # Get embeddings from packed sequence
+        hidden_states = self.embeddings((seq_patches, seq_sizes, token_grids))
+
+        # Add a pseudo batch dimension for the encoder
+        hidden_states = hidden_states.unsqueeze(0)
+
+        cu_seqlens, max_seqlen = create_cumulative_seq_lengths(
+            seq_sizes, hidden_states.device
+        )
+
+        hidden_states = self.encoder(
+            inputs_embeds=hidden_states,
+            cu_seqlens=cu_seqlens,
+            max_seqlen=max_seqlen,
+        )
+        hidden_states = self.post_layernorm(hidden_states)
+
+        if self.pixel_shuffle_scale_factor > 1:
+            hidden_states = pixel_shuffle_varlen(
+                x=hidden_states,
+                token_grids=token_grids,
+                scale_factor=self.pixel_shuffle_scale_factor,
+            )
+        # Remove the pseudo batch dimension we added earlier
+        hidden_states = hidden_states.squeeze(0)
+
+        # return last_hidden_state
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+
+        for name, loaded_weight in weights:
+            for param_name, weight_name, shard_id in stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class IsaacVisionEmbedding(nn.Module):
+    def __init__(
+        self,
+        vision_cfg: PixelShuffleSiglip2VisionConfig,
+        hidden_dim: int,
+        output_dim: int,
+        quant_config: QuantizationConfig | None = None,
+        multimodal_config: MultiModalConfig | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.transformer = Siglip2VisionTransformer(
+            vision_cfg,
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "0"),
+            multimodal_config=multimodal_config,
+        )
+        self.linear_fc1 = ColumnParallelLinear(
+            hidden_dim,
+            4 * hidden_dim,
+            bias=False,
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "1"),
+            return_bias=False,
+        )
+        self.act = nn.SiLU()
+        self.linear_fc2 = RowParallelLinear(
+            4 * hidden_dim,
+            output_dim,
+            bias=False,
+            quant_config=quant_config,
+            prefix=maybe_prefix(prefix, "3"),
+            return_bias=False,
+        )
+
+    def forward(
+        self, packed_seq_patches: tuple[torch.Tensor, torch.Tensor]
+    ) -> torch.Tensor:
+        hidden_states = self.transformer(packed_seq_patches)
+        hidden_states = self.linear_fc1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.linear_fc2(hidden_states)
+        return hidden_states
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    IsaacMultiModalProcessor,
+    info=IsaacProcessingInfo,
+    dummy_inputs=IsaacDummyInputsBuilder,
+)
+class IsaacForConditionalGeneration(
+    nn.Module, SupportsMultiModal, SupportsLoRA, SupportsPP, SupportsMRoPE
+):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    supports_encoder_tp_data = True
+
+    # To ensure correct weight loading and mapping.
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_prefix={
+            "lm_head.": "language_model.lm_head.",
+            "model.vision_embedding.0": "vision_embedding.transformer",
+            "model.vision_embedding.1": "vision_embedding.linear_fc1",
+            "model.vision_embedding.2": "vision_embedding.act",
+            "model.vision_embedding.3": "vision_embedding.linear_fc2",
+            "model.vision_embedding.": "vision_embedding.",
+            "model.": "language_model.model.",
+        }
+    )
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
+        if modality.startswith("image"):
+            return "<image>"
+
+        raise ValueError("Only image modality is supported")
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = "model"):
+        super().__init__()
+        config: IsaacConfig = vllm_config.model_config.hf_config
+        quant_config = vllm_config.quant_config
+        self.config = config
+        self.multimodal_config = vllm_config.model_config.multimodal_config
+
+        head_dim = config.head_dim
+        calculated_mrope_section = [
+            head_dim // 4,  # 2x more for temporal dim
+            head_dim // 8,
+            head_dim // 8,
+        ]
+
+        self.vision_token_id = _resolve_vision_token_id(
+            vllm_config.model_config, config.vision_token
+        )
+        config.image_token_id = self.vision_token_id
+
+        config.rope_scaling["mrope_section"] = calculated_mrope_section
+        self.language_model = init_vllm_registered_model(
+            vllm_config=vllm_config,
+            architectures=["Qwen3ForCausalLM"],
+            prefix=maybe_prefix(prefix, "language_model"),
+        )
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors
+        )
+
+        vision_cfg = config.vision_config
+        if vision_cfg is None:
+            raise ValueError("IsaacConfig should always have vision_config")
+        attn_impl = (
+            config.vision_attn_implementation
+            if config.vision_attn_implementation is not None
+            else getattr(config, "_attn_implementation", None)
+        )
+        if attn_impl is not None:
+            vision_cfg._attn_implementation = attn_impl
+
+        hidden_dim = vision_cfg.hidden_size * (vision_cfg.pixel_shuffle_scale_factor**2)
+        self.vision_embedding = IsaacVisionEmbedding(
+            vision_cfg=vision_cfg,
+            hidden_dim=hidden_dim,
+            output_dim=config.hidden_size,
+            quant_config=quant_config,
+            multimodal_config=self.multimodal_config,
+            prefix=maybe_prefix(prefix, "vision_embedding"),
+        )
+
+    def iter_mm_grid_hw(
+        self, input_tokens: list[int], mm_features: list[MultiModalFeatureSpec]
+    ) -> Iterator[tuple[int, int, int]]:
+        spatial_merge_size = self.config.vision_config.pixel_shuffle_scale_factor
+        for mm_feature in sorted(mm_features, key=lambda f: f.mm_position.offset):
+            offset = mm_feature.mm_position.offset
+            if mm_feature.modality == "image":
+                t, h, w = mm_feature.data["image_grid_thw"].data.tolist()
+                assert t == 1, f"Image must have 1 frame, got {t}"
+                yield offset, h // spatial_merge_size, w // spatial_merge_size
+            else:
+                raise ValueError(f"Unsupported modality: {mm_feature.modality}")
+
+    def get_mrope_input_positions(
+        self,
+        input_tokens: list[int],
+        mm_features: list[MultiModalFeatureSpec],
+    ) -> tuple[torch.Tensor, int]:
+        llm_pos_ids_list = []
+        st = 0
+        for offset, llm_grid_h, llm_grid_w in self.iter_mm_grid_hw(
+            input_tokens, mm_features
+        ):
+            text_len = offset - st
+            st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+            llm_pos_ids_list.append(
+                np.broadcast_to(np.arange(text_len), (3, text_len)) + st_idx
+            )
+
+            grid_indices = np.indices((1, llm_grid_h, llm_grid_w)).reshape(3, -1)
+            grid_indices[0, :] = grid_indices[0, :] + text_len + st_idx
+            llm_pos_ids_list.append(grid_indices)
+            st = offset + llm_grid_h * llm_grid_w
+
+        if st < len(input_tokens):
+            st_idx = llm_pos_ids_list[-1][0, -1] + 1 if len(llm_pos_ids_list) > 0 else 0
+            text_len = len(input_tokens) - st
+            llm_pos_ids_list.append(
+                np.broadcast_to(np.arange(text_len), (3, text_len)) + st_idx
+            )
+
+        llm_positions = np.concatenate(llm_pos_ids_list, axis=1).reshape(3, -1)
+        mrope_position_delta = (llm_positions.max() + 1 - len(input_tokens)).item()
+
+        return torch.from_numpy(llm_positions), mrope_position_delta
+
+    def _parse_and_validate_image_input(
+        self, **kwargs: object
+    ) -> IsaacImagePixelInputs | None:
+        pixel_values = kwargs.get("pixel_values")
+        image_grid_thw = kwargs.get("image_grid_thw")
+        if pixel_values is None or image_grid_thw is None:
+            return None
+
+        # TensorSchema will automatically validate shapes on initialization
+        return IsaacImagePixelInputs(
+            pixel_values=pixel_values,
+            image_grid_thw=image_grid_thw,
+        )
+
+    def _process_image_input(
+        self,
+        image_input: IsaacImagePixelInputs,
+    ) -> tuple[torch.Tensor, ...]:
+        pixel_values = image_input["pixel_values"]
+        image_grid_thw = image_input["image_grid_thw"]
+        if pixel_values.numel() == 0:
+            return ()
+
+        device = next(self.language_model.parameters()).device
+        dtype = self.vision_embedding.linear_fc1.weight.dtype
+        pixel_values = pixel_values.to(device=device, dtype=dtype)
+        spatial_grids = image_grid_thw[:, 1:3].to(device, dtype=torch.int32)
+
+        vision_embeddings = self.vision_embedding((pixel_values, spatial_grids))
+        merge_size = self.config.vision_config.pixel_shuffle_scale_factor
+        sizes = spatial_grids.prod(-1) // (merge_size * merge_size)
+        return tuple(vision_embeddings.split(sizes.tolist()))
+
+    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings | None:
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return ()
+        return self._process_image_input(image_input)
+
+    def get_multimodal_embeddings(
+        self, **kwargs: object
+    ) -> MultiModalEmbeddings | None:
+        # Backward compatibility for older runners.
+        embeddings = self.embed_multimodal(**kwargs)
+        if not embeddings:
+            return []
+        return embeddings
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.language_model
+
+    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:
+        return self.language_model(
+            input_ids=input_ids,
+            positions=positions,
+            intermediate_tensors=intermediate_tensors,
+            inputs_embeds=inputs_embeds,
+            **kwargs,
+        )
+
+    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]]) -> 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="vision_embedding.linear_fc2",  # The final linear layer
+            tower_model="vision_embedding",
+        )

vllm/model_executor/models/registry.py

@@ -333,6 +333,7 @@ _MULTIMODAL_MODELS = {
         "idefics3",
         "Idefics3ForConditionalGeneration",
     ),
+    "IsaacForConditionalGeneration": ("isaac", "IsaacForConditionalGeneration"),
     "SmolVLMForConditionalGeneration": ("smolvlm", "SmolVLMForConditionalGeneration"),  # noqa: E501
     "KeyeForConditionalGeneration": ("keye", "KeyeForConditionalGeneration"),
     "KeyeVL1_5ForConditionalGeneration": (

vllm/transformers_utils/config.py

@@ -81,6 +81,7 @@ _CONFIG_REGISTRY: dict[str, type[PretrainedConfig]] = LazyConfigDict(
     deepseek_v32="DeepseekV3Config",
     flex_olmo="FlexOlmoConfig",
     hunyuan_vl="HunYuanVLConfig",
+    isaac="IsaacConfig",
     kimi_linear="KimiLinearConfig",
     kimi_vl="KimiVLConfig",
     RefinedWeb="RWConfig",  # For tiiuae/falcon-40b(-instruct)

vllm/transformers_utils/configs/__init__.py

@@ -25,6 +25,7 @@ _CLASS_TO_MODULE: dict[str, str] = {
     "HunYuanVLConfig": "vllm.transformers_utils.configs.hunyuan_vl",
     "HunYuanVLTextConfig": "vllm.transformers_utils.configs.hunyuan_vl",
     "HunYuanVLVisionConfig": "vllm.transformers_utils.configs.hunyuan_vl",
+    "IsaacConfig": "vllm.transformers_utils.configs.isaac",
     # RWConfig is for the original tiiuae/falcon-40b(-instruct) and
     # tiiuae/falcon-7b(-instruct) models. Newer Falcon models will use the
     # `FalconConfig` class from the official HuggingFace transformers library.
@@ -41,6 +42,7 @@ _CLASS_TO_MODULE: dict[str, str] = {
     "NemotronHConfig": "vllm.transformers_utils.configs.nemotron_h",
     "Olmo3Config": "vllm.transformers_utils.configs.olmo3",
     "OvisConfig": "vllm.transformers_utils.configs.ovis",
+    "PixelShuffleSiglip2VisionConfig": "vllm.transformers_utils.configs.isaac",
     "RadioConfig": "vllm.transformers_utils.configs.radio",
     "SpeculatorsConfig": "vllm.transformers_utils.configs.speculators.base",
     "UltravoxConfig": "vllm.transformers_utils.configs.ultravox",
@@ -65,6 +67,7 @@ __all__ = [
     "HunYuanVLConfig",
     "HunYuanVLTextConfig",
     "HunYuanVLVisionConfig",
+    "IsaacConfig",
     "RWConfig",
     "JAISConfig",
     "Lfm2MoeConfig",
@@ -78,6 +81,7 @@ __all__ = [
     "NemotronHConfig",
     "Olmo3Config",
     "OvisConfig",
+    "PixelShuffleSiglip2VisionConfig",
     "RadioConfig",
     "SpeculatorsConfig",
     "UltravoxConfig",

vllm/transformers_utils/configs/isaac.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from __future__ import annotations
+
+from transformers import Qwen3Config
+from transformers.models.siglip2.configuration_siglip2 import Siglip2VisionConfig
+
+
+class PixelShuffleSiglip2VisionConfig(Siglip2VisionConfig):
+    """Vision configuration for Isaac with Pixel Shuffle support.
+
+    Extends Siglip2VisionConfig with additional fields for pixel shuffle.
+    """
+
+    model_type = "pixel_shuffle_siglip2"
+    base_config_key = "vision_config"
+
+    def __init__(
+        self,
+        pixel_shuffle_scale_factor: int = 1,
+        num_patches: int = 256,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        # Add our custom fields
+        self.pixel_shuffle_scale_factor = pixel_shuffle_scale_factor
+        self.num_patches = num_patches
+
+
+class IsaacConfig(Qwen3Config):
+    """Configuration class for Isaac multimodal model."""
+
+    model_type = "isaac"
+    sub_configs = {"vision_config": PixelShuffleSiglip2VisionConfig}
+
+    def __init__(
+        self,
+        vision_config=None,
+        vision_patch_size: int = 16,
+        vision_max_num_patches: int = 256,
+        vision_min_num_patches: int | None = None,
+        pixel_shuffle_scale: int = 1,
+        max_sequence_length: int = 16384,
+        vision_token: str = "<image>",
+        vision_attn_implementation: str | None = None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        # EventStreamProcessor parameters (for backward compatibility)
+        self.video_patch_size = vision_patch_size
+        self.vision_max_num_patches = vision_max_num_patches
+        self.vision_min_num_patches = vision_min_num_patches
+        self.pixel_shuffle_scale = pixel_shuffle_scale
+
+        # Processing parameters
+        self.max_sequence_length = max_sequence_length
+        self.vision_token = vision_token
+
+        # Handle vision config - PixelShuffleSiglip2VisionConfig instance
+        if isinstance(vision_config, dict):
+            self.vision_config = PixelShuffleSiglip2VisionConfig(**vision_config)
+        elif vision_config is None:
+            self.vision_config = PixelShuffleSiglip2VisionConfig()
+        else:
+            self.vision_config = vision_config
+
+        # Ensure compatibility with pretrained checkpoints
+        self.vision_config.pixel_shuffle_scale_factor = getattr(
+            self.vision_config,
+            "pixel_shuffle_scale_factor",
+            pixel_shuffle_scale,
+        )
+        self.vision_config.num_patches = getattr(
+            self.vision_config,
+            "num_patches",
+            vision_max_num_patches,
+        )
+        self.vision_attn_implementation = vision_attn_implementation
+
+
+__all__ = [
+    "IsaacConfig",
+    "PixelShuffleSiglip2VisionConfig",
+]