[Model] Nemotron Parse 1.1 Support (#30864)

Signed-off-by: amitz-nv <203509407+amitz-nv@users.noreply.github.com>
Signed-off-by: Michael Goin <mgoin64@gmail.com>
Co-authored-by: Michael Goin <mgoin64@gmail.com>

requirements/test.in

@@ -9,6 +9,7 @@ pytest-timeout
 pytest-cov
 
 # testing utils
+albumentations # required for Nemotron Parse in test_common.py
 backoff # required for phi4mm test
 blobfile # required for kimi-vl test
 einops # required for MPT, qwen-vl
@@ -31,7 +32,7 @@ transformers_stream_generator # required for qwen-vl test
 matplotlib # required for qwen-vl test
 mistral_common[image,audio] >= 1.8.8 # required for voxtral test
 num2words # required for smolvlm test
-open_clip_torch==2.32.0 # Required for nemotron_vl test
+open_clip_torch==2.32.0 # Required for nemotron_vl test, Nemotron Parse in test_common.py
 opencv-python-headless >= 4.11.0 # required for video test
 datamodel_code_generator # required for minicpm3 test
 lm-eval[api]>=0.4.9.2 # required for model evaluation test

requirements/test.txt

@@ -27,7 +27,9 @@ aiosignal==1.4.0
 albucore==0.0.16
     # via terratorch
 albumentations==1.4.6
-    # via terratorch
+    # via
+    #   -r requirements/test.in
+    #   terratorch
 alembic==1.16.4
     # via mlflow
 annotated-types==0.7.0

tests/conftest.py

@@ -685,6 +685,7 @@ class HfRunner:
         images: PromptImageInput | None = None,
         audios: PromptAudioInput | None = None,
         videos: PromptVideoInput | None = None,
+        use_cache: bool = True,
         **kwargs: Any,
     ) -> list[TokensTextLogprobs]:
         all_inputs = self.get_inputs(
@@ -698,7 +699,7 @@ class HfRunner:
         for inputs in all_inputs:
             output: "GenerateOutput" = self.model.generate(
                 **self.wrap_device(inputs),
-                use_cache=True,
+                use_cache=use_cache,
                 do_sample=False,
                 max_new_tokens=max_tokens,
                 output_hidden_states=True,

tests/models/multimodal/generation/test_nemotron_parse.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Sequence
+
+import pytest
+from transformers import AutoModel
+
+from tests.models.utils import check_logprobs_close
+from vllm.assets.image import ImageAsset
+
+from ....conftest import HfRunner, PromptImageInput, VllmRunner
+from ....utils import create_new_process_for_each_test
+
+IMAGE = ImageAsset("paper-11").pil_image_ext(ext="png").convert("RGB")
+PROMPT = "</s><s><predict_bbox><predict_classes><output_markdown>"
+
+
+def run_test(
+    hf_runner: type[HfRunner],
+    vllm_runner: type[VllmRunner],
+    inputs: Sequence[tuple[list[str], PromptImageInput]],
+    model: str,
+    *,
+    dtype: str,
+    max_tokens: int,
+    num_logprobs: int,
+) -> None:
+    """Verify that the inference result is the same between hf and vllm."""
+    with vllm_runner(
+        model,
+        dtype=dtype,
+        max_num_seqs=64,
+        limit_mm_per_prompt={"image": 1},
+        trust_remote_code=True,
+    ) as vllm_model:
+        vllm_outputs_per_case = [
+            vllm_model.generate_greedy_logprobs(
+                prompts,
+                max_tokens,
+                num_logprobs=num_logprobs,
+                images=images,
+            )
+            for prompts, images in inputs
+        ]
+
+    with hf_runner(model, dtype=dtype, auto_cls=AutoModel) as hf_model:
+        hf_outputs_per_case = [
+            hf_model.generate_greedy_logprobs_limit(
+                prompts,
+                max_tokens,
+                num_logprobs=num_logprobs,
+                images=images,
+                use_cache=False,  # HF Nemotron Parse crashes here without this
+            )
+            for prompts, images in inputs
+        ]
+
+    for hf_outputs, vllm_outputs in zip(hf_outputs_per_case, vllm_outputs_per_case):
+        check_logprobs_close(
+            outputs_0_lst=hf_outputs,
+            outputs_1_lst=vllm_outputs,
+            name_0="hf",
+            name_1="vllm",
+        )
+
+
+@pytest.mark.core_model
+@pytest.mark.parametrize("model", ["nvidia/NVIDIA-Nemotron-Parse-v1.1"])
+@pytest.mark.parametrize("dtype", ["bfloat16"])
+@pytest.mark.parametrize("num_logprobs", [5])
+@create_new_process_for_each_test("spawn")
+def test_models(
+    hf_runner, vllm_runner, model: str, dtype: str, num_logprobs: int
+) -> None:
+    run_test(
+        hf_runner,
+        vllm_runner,
+        inputs=[
+            (
+                [PROMPT] * 10,
+                [IMAGE] * 10,
+            ),
+        ],
+        model=model,
+        dtype=dtype,
+        max_tokens=100,
+        num_logprobs=num_logprobs,
+    )

tests/models/multimodal/pooling/test_radio.py

@@ -40,15 +40,15 @@ def run_radio_test(
         for image in images
     ]
 
-    config = AutoConfig.from_pretrained(model_id, trust_remote_code=True)
+    hf_config = AutoConfig.from_pretrained(model_id, trust_remote_code=True)
 
     # RADIO model on HF does not properly handle torch_dtype argument
     # And relies on args["dtype"] which we have to patch manually:
-    config.args["dtype"] = torch_dtype
+    hf_config.args["dtype"] = torch_dtype
 
     hf_model = AutoModel.from_pretrained(
         model_id,
-        config=config,
+        config=hf_config,
         dtype=torch_dtype,
         trust_remote_code=True,
     ).to("cuda")
@@ -62,13 +62,14 @@ def run_radio_test(
     hf_model.make_preprocessor_external()
 
     hf_outputs_per_image = [
-        hf_model(pixel_value.to("cuda")).features for pixel_value in pixel_values
+        hf_model(pixel_value.to("cuda")) for pixel_value in pixel_values
     ]
 
-    radio_config = RadioConfig(
-        model_name=config.args["model"], reg_tokens=config.args["register_multiple"]
+    vllm_config = RadioConfig(
+        model_name=hf_config.args["model"],
+        **hf_config.args,
     )
-    vllm_model = RadioModel(radio_config)
+    vllm_model = RadioModel(vllm_config)
     vllm_model.load_weights(hf_model.state_dict())
     vllm_model = vllm_model.to("cuda", torch_dtype)
 
@@ -80,7 +81,8 @@ def run_radio_test(
 
     cos_similar = nn.CosineSimilarity(dim=-1)
     for vllm_output, hf_output in zip(vllm_outputs_per_image, hf_outputs_per_image):
-        assert cos_similar(vllm_output, hf_output).mean() > 0.99
+        assert cos_similar(vllm_output[0], hf_output[0]).mean() > 0.99
+        assert cos_similar(vllm_output[1], hf_output[1]).mean() > 0.99
 
 
 @pytest.mark.parametrize(

tests/models/multimodal/processing/test_common.py

@@ -102,6 +102,7 @@ def glmasr_patch_mm_data(mm_data: MultiModalDataDict) -> MultiModalDataDict:
 # incorrect token ids. So we need use `add_special_tokens=False` here
 # to leave bos_token to be added by the processor.
 _ADD_SPECIAL_TOKENS_OVERRIDES = {
+    "nemotron_parse": False,
     "ovis": False,
     "ovis2_5": False,
     "paligemma": False,

tests/models/registry.py

@@ -907,6 +907,9 @@ _MULTIMODAL_EXAMPLE_MODELS = {
         is_available_online=False,
     ),
     # [Encoder-decoder]
+    "NemotronParseForConditionalGeneration": _HfExamplesInfo(
+        "nvidia/NVIDIA-Nemotron-Parse-v1.1", trust_remote_code=True
+    ),
     "WhisperForConditionalGeneration": _HfExamplesInfo(
         "openai/whisper-large-v3-turbo",
         extras={"v3": "openai/whisper-large-v3"},

vllm/assets/image.py

@@ -42,8 +42,11 @@ class ImageAsset:
         )
 
     @property
-    def pil_image(self, ext="jpg") -> Image.Image:
-        image_path = self.get_path(ext)
+    def pil_image(self) -> Image.Image:
+        return self.pil_image_ext(ext="jpg")
+
+    def pil_image_ext(self, ext: str) -> Image.Image:
+        image_path = self.get_path(ext=ext)
         return Image.open(image_path)
 
     @property

vllm/model_executor/models/nano_nemotron_vl.py

@@ -1220,7 +1220,7 @@ class NemotronH_Nano_VL_V2(
         n = pixel_values.shape[0]
         vit_embeds_list = []
         for i in range(0, n, micro_batch_size):
-            vit_embeds = self.vision_model(pixel_values[i : i + micro_batch_size])
+            _, vit_embeds = self.vision_model(pixel_values[i : i + micro_batch_size])
             vit_embeds = vit_embeds.to(dtype=torch.bfloat16)
             h = w = int(vit_embeds.shape[1] ** 0.5)
             vit_embeds = vit_embeds.reshape(vit_embeds.shape[0], h, w, -1)
@@ -1695,12 +1695,7 @@ class NemotronH_Nano_VL_V2(
             patch_size=patch_size,
             norm_mean=hf_config.norm_mean,
             norm_std=hf_config.norm_std,
-            reg_tokens=(
-                hf_config_vision.args.get("register_multiple")
-                if hasattr(hf_config_vision, "args")
-                and isinstance(hf_config_vision.args, dict)
-                else None
-            ),
+            **hf_config_vision.args,
         )
 
         return RadioModel(config=radio_config)

vllm/model_executor/models/nemotron_parse.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+#
+# Adapted from https://github.com/amalad/vllm/blob/nemotron_parse/vllm/model_executor/models/nemotron_parse.py
+# that's based on https://huggingface.co/nvidia/NVIDIA-Nemotron-Parse-v1.1/blob/main/hf_nemotron_parse_modeling.py
+#
+# Bart classes based on old vLLM codebase:
+# https://github.com/vllm-project/vllm/blob/v0.10.2/vllm/model_executor/models/bart.py
+
+import math
+from collections.abc import Iterable, Mapping, Sequence
+from typing import Annotated, Literal
+
+import cv2
+import numpy as np
+import torch
+import torch.nn as nn
+from einops import rearrange
+from PIL import Image
+from timm.data.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
+from torchvision import transforms as T
+from transformers import (
+    BartConfig,
+    BatchFeature,
+    PretrainedConfig,
+    TensorType,
+)
+
+from vllm.attention.backends.abstract import AttentionType
+from vllm.config import CacheConfig, VllmConfig
+from vllm.config.lora import LoRAConfig
+from vllm.config.multimodal import BaseDummyOptions
+from vllm.logger import init_logger
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.linear import ColumnParallelLinear, RowParallelLinear
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.interfaces import (
+    MultiModalEmbeddings,
+    SupportsMultiModal,
+)
+from vllm.model_executor.models.radio import RadioModel
+from vllm.model_executor.models.whisper import WhisperAttention, WhisperCrossAttention
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (
+    MultiModalDataDict,
+    MultiModalFieldConfig,
+    MultiModalKwargsItems,
+)
+from vllm.multimodal.parse import MultiModalDataItems
+from vllm.multimodal.processing import (
+    BaseProcessingInfo,
+    EncDecMultiModalProcessor,
+    PromptReplacement,
+    PromptUpdate,
+)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder
+from vllm.transformers_utils.configs.radio import RadioConfig
+from vllm.transformers_utils.tokenizer import AnyTokenizer
+from vllm.utils.tensor_schema import TensorSchema, TensorShape
+
+logger = init_logger(__name__)
+DEFAULT_FINAL_IMAGE_SIZE = (2048, 1648)
+
+
+class BartScaledWordEmbedding(VocabParallelEmbedding):
+    """
+    This module overrides VocabParallelEmbedding's
+    forward by multiplying with embeddings scale.
+    """
+
+    def __init__(
+        self, num_embeddings: int, embedding_dim: int, embed_scale: float = 1.0
+    ):
+        super().__init__(num_embeddings, embedding_dim)
+        self.embed_scale = embed_scale
+
+    def forward(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return super().forward(input_ids) * self.embed_scale
+
+
+class BartParallelLMHead(ParallelLMHead):
+    """
+    This module overrides ParallelLMHead's
+    forward by dividing by embeddings scale,
+    yielding effectively the inverse of
+    BartScaledWordEmbedding
+    """
+
+    def __init__(
+        self, num_embeddings: int, embedding_dim: int, embed_scale: float = 1.0
+    ):
+        super().__init__(num_embeddings, embedding_dim)
+        self.embed_scale = embed_scale
+
+    def forward(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return super().forward(input_ids) / self.embed_scale
+
+
+class BartDecoderLayer(nn.Module):
+    def __init__(
+        self,
+        config: BartConfig,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.embed_dim = config.d_model
+
+        self.self_attn = WhisperAttention(
+            embed_dim=self.embed_dim,
+            num_heads=config.decoder_attention_heads,
+            attn_type=AttentionType.DECODER,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.self_attn",
+        )
+        self.activation_fn = get_act_fn(config.activation_function)
+
+        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+        """
+        afeldman-nm: personally I would call this "cross-attention",
+        however I left the name as "encoder_attn" to maintain consistency
+        with the name of the pretrained weights.
+        """
+        self.encoder_attn = WhisperCrossAttention(
+            self.embed_dim,
+            config.decoder_attention_heads,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.encoder_attn",
+        )
+        self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+
+        ffn_hidden_size = self.embed_dim
+        ffn_intermediate_size = config.encoder_ffn_dim
+        ffn_has_bias = True
+        self.fc1 = ColumnParallelLinear(
+            ffn_hidden_size,
+            ffn_intermediate_size,
+            bias=ffn_has_bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.fc1",
+        )
+        self.fc2 = RowParallelLinear(
+            ffn_intermediate_size,
+            ffn_hidden_size,
+            bias=ffn_has_bias,
+            quant_config=quant_config,
+            prefix=f"{prefix}.fc2",
+        )
+
+        self.final_layer_norm = nn.LayerNorm(self.embed_dim)
+
+    def forward(
+        self,
+        decoder_hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        r"""
+        Args:
+            decoder_hidden_states: torch.Tensor of *decoder* input embeddings.
+            encoder_hidden_states: torch.Tensor of *encoder* input embeddings.
+        Returns:
+            Decoder layer output torch.Tensor
+        """
+        residual = decoder_hidden_states
+
+        # Self Attention
+        hidden_states = self.self_attn(hidden_states=decoder_hidden_states)
+
+        hidden_states = residual + hidden_states
+        hidden_states = self.self_attn_layer_norm(hidden_states)
+
+        # Cross-Attention Block
+
+        residual = hidden_states
+
+        hidden_states = self.encoder_attn(
+            hidden_states=hidden_states,
+            encoder_hidden_states=encoder_hidden_states,
+        )
+
+        hidden_states = residual + hidden_states
+        hidden_states = self.encoder_attn_layer_norm(hidden_states)
+
+        # Fully Connected
+        residual = hidden_states
+        fc1_out, _ = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(fc1_out)
+
+        hidden_states, _ = self.fc2(hidden_states)
+
+        hidden_states = residual + hidden_states
+        hidden_states = self.final_layer_norm(hidden_states)
+
+        return hidden_states
+
+
+class MBartDecoderLayer(BartDecoderLayer):
+    def forward(
+        self,
+        decoder_hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        residual = decoder_hidden_states
+        hidden_states = self.self_attn_layer_norm(decoder_hidden_states)
+
+        # Self Attention
+        hidden_states = self.self_attn(hidden_states=hidden_states)
+
+        hidden_states = residual + hidden_states
+
+        # Cross-Attention Block
+
+        residual = hidden_states
+        hidden_states = self.encoder_attn_layer_norm(hidden_states)
+
+        hidden_states = self.encoder_attn(
+            hidden_states=hidden_states,
+            encoder_hidden_states=encoder_hidden_states,
+        )
+
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.final_layer_norm(hidden_states)
+        fc1_out, _ = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(fc1_out)
+
+        hidden_states, _ = self.fc2(hidden_states)
+
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+
+class MBartDecoderNoPos(nn.Module):
+    """
+    Transformer decoder consisting of *config.decoder_layers* layers.
+    Each layer is a [`BartDecoderLayer`]
+    Args:
+        config: BartConfig
+        embed_tokens (nn.Embedding): output embedding
+    """
+
+    def __init__(
+        self,
+        config: BartConfig,
+        cache_config: CacheConfig | None = None,
+        quant_config: QuantizationConfig | None = None,
+        lora_config: LoRAConfig | None = None,
+        embed_tokens: nn.Embedding | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.cache_config = cache_config
+        self.quant_config = quant_config
+        self.lora_config = lora_config
+        embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
+
+        self.embed_tokens = BartScaledWordEmbedding(
+            config.vocab_size, config.d_model, embed_scale=embed_scale
+        )
+
+        if embed_tokens is not None:
+            self.embed_tokens.weight = embed_tokens.weight
+
+        self.layers = nn.ModuleList(
+            [
+                MBartDecoderLayer(
+                    config,
+                    cache_config,
+                    quant_config,
+                    prefix=f"{prefix}.layers.{layer_idx}",
+                )
+                for layer_idx in range(config.decoder_layers)
+            ]
+        )
+
+        self.layernorm_embedding = nn.LayerNorm(config.d_model)
+        self.layer_norm = nn.LayerNorm(config.d_model)
+
+    def forward(
+        self,
+        decoder_input_ids: torch.Tensor,
+        *,
+        encoder_hidden_states: torch.Tensor | None,
+        inputs_embeds: torch.Tensor | None = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        r"""
+        Args:
+            decoder_input_ids: Indices of *decoder* input sequence tokens in the
+                vocabulary. Padding will be ignored by default should you provide it.
+            encoder_hidden_states: Tensor of encoder output embeddings
+        Returns:
+            Decoder output torch.Tensor
+        """
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(decoder_input_ids)
+
+        hidden_states = self.layernorm_embedding(inputs_embeds)
+
+        # decoder layers
+
+        for decoder_layer in self.layers:
+            hidden_states = decoder_layer(
+                decoder_hidden_states=hidden_states,
+                encoder_hidden_states=encoder_hidden_states,
+            )
+
+        hidden_states = self.layer_norm(hidden_states)
+        return hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            (".self_attn.qkv_proj", ".self_attn.q_proj", "q"),
+            (".self_attn.qkv_proj", ".self_attn.k_proj", "k"),
+            (".self_attn.qkv_proj", ".self_attn.v_proj", "v"),
+            (".encoder_attn.kv_proj", ".encoder_attn.k_proj", "k"),
+            (".encoder_attn.kv_proj", ".encoder_attn.v_proj", "v"),
+        ]
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if name.startswith("embed_positions"):
+                continue
+
+            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)
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                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 NemotronParsePixelInputs(TensorSchema):
+    """
+    Dimensions:
+        - b: Batch size
+        - c: Number of channels (3)
+        - h: Height
+        - w: Width
+    """
+
+    type: Literal["pixel_values"]
+    data: Annotated[torch.Tensor, TensorShape("b", 3, "h", "w")]
+
+
+class NemotronParseImageProcessor:
+    """
+    NemotronParse Image Processor
+    """
+
+    def __init__(
+        self,
+        final_size: tuple = DEFAULT_FINAL_IMAGE_SIZE,
+        **kwargs,
+    ):
+        # Ensure final_size is properly formatted
+        if isinstance(final_size, (list, tuple)) and len(final_size) >= 2:
+            self.final_size = (int(final_size[0]), int(final_size[1]))
+        elif isinstance(final_size, (int, float)):
+            self.final_size = (int(final_size), int(final_size))
+        else:
+            self.final_size = DEFAULT_FINAL_IMAGE_SIZE  # Default fallback
+
+        self.norm_mean = torch.Tensor(OPENAI_CLIP_MEAN).reshape(1, 3, 1, 1)
+        self.norm_std = torch.Tensor(OPENAI_CLIP_STD).reshape(1, 3, 1, 1)
+
+        # Create transforms
+        self._create_transforms()
+
+    def _create_transforms(self):
+        """Create transform objects."""
+        try:
+            import albumentations as A
+        except ImportError as err:
+            raise ImportError(
+                "The package `albumentations` is required to use "
+                "NemotronParse model. Please install it with `pip install "
+                "albumentations`."
+            ) from err
+
+        # Ensure final_size is a tuple of integers
+        if isinstance(self.final_size, (list, tuple)):
+            self.target_height, self.target_width = (
+                int(self.final_size[0]),
+                int(self.final_size[1]),
+            )
+        else:
+            self.target_height = self.target_width = int(self.final_size)
+
+        self.transform = A.Compose(
+            [
+                A.PadIfNeeded(
+                    min_height=self.target_height,
+                    min_width=self.target_width,
+                    border_mode=cv2.BORDER_CONSTANT,
+                    fill=[255, 255, 255],
+                    p=1.0,
+                ),
+            ]
+        )
+
+        self.torch_transform = T.Compose(
+            [
+                T.ToTensor(),
+            ]
+        )
+
+    def _resize_with_aspect_ratio(self, image: np.ndarray) -> np.ndarray:
+        """Resize image maintaining aspect ratio (exact replica of original
+        LongestMaxSizeHW)."""
+        height, width = image.shape[:2]
+        max_size_height = self.target_height
+        max_size_width = self.target_width
+
+        # Original LongestMaxSizeHW algorithm from custom_augmentations.py
+        aspect_ratio = width / height
+        new_height = height
+        new_width = width
+
+        # If height too big then scale image down
+        if height > max_size_height:
+            new_height = max_size_height
+            new_width = int(new_height * aspect_ratio)
+
+        # If width too big, scale image down further
+        if new_width > max_size_width:
+            new_width = max_size_width
+            new_height = int(new_width / aspect_ratio)
+
+        # Use cv2.INTER_LINEAR like the original
+        return cv2.resize(
+            image, (new_width, new_height), interpolation=cv2.INTER_LINEAR
+        )
+
+    def _pad_to_size(self, image: np.ndarray) -> np.ndarray:
+        """Pad image to target size with white padding (matches A.PadIfNeeded
+        behavior)."""
+        h, w = image.shape[:2]
+        min_height, min_width = self.target_height, self.target_width
+
+        # Only pad if image is smaller than target (matches A.PadIfNeeded logic)
+        pad_h = max(0, min_height - h)
+        pad_w = max(0, min_width - w)
+
+        if pad_h == 0 and pad_w == 0:
+            return image
+
+        # A.PadIfNeeded pads to bottom-right with constant value
+        if len(image.shape) == 3:
+            # Color image - pad bottom and right with white (255, 255, 255)
+            padded = np.pad(
+                image,
+                ((0, pad_h), (0, pad_w), (0, 0)),
+                mode="constant",
+                constant_values=255,
+            )
+        else:
+            # Grayscale image - pad with white (255)
+            padded = np.pad(
+                image, ((0, pad_h), (0, pad_w)), mode="constant", constant_values=255
+            )
+
+        return padded
+
+    def preprocess(
+        self,
+        images: Image.Image | list[Image.Image],
+        **kwargs,
+    ) -> dict[str, torch.Tensor]:
+        """
+        Preprocess an image or batch of images for the NemotronParse model.
+
+        Args:
+            images: Input image(s)
+        """
+        # Ensure images is a list
+        if not isinstance(images, list):
+            images = [images]
+
+        # Convert PIL images to numpy arrays if needed
+        processed_images = []
+        for image in images:
+            if isinstance(image, Image.Image):
+                image = np.asarray(image)
+            processed_images.append(image)
+
+        # Apply NemotronParse-specific transforms
+        pixel_values = []
+        for image in processed_images:
+            # Manual resize with aspect ratio preservation
+            # (replaces LongestMaxSizeHW)
+            processed_image = self._resize_with_aspect_ratio(image)
+
+            # Apply remaining albumentations transforms if available
+            if self.transform is not None:
+                transformed = self.transform(image=processed_image)
+                processed_image = transformed["image"]
+            else:
+                # Fallback: just pad to target size
+                processed_image = self._pad_to_size(processed_image)
+
+            # Convert to tensor
+            pixel_values_tensor = self.torch_transform(processed_image)
+
+            # Handle grayscale images
+            if pixel_values_tensor.shape[0] == 1:
+                pixel_values_tensor = pixel_values_tensor.expand(3, -1, -1)
+
+            pixel_values.append(pixel_values_tensor)
+
+        # Stack into batch
+        pixel_values = torch.stack(pixel_values)
+
+        # Normalize pixel values
+        normalized_values = (pixel_values - self.norm_mean) / self.norm_std
+        return {"pixel_values": normalized_values}
+
+    def __call__(
+        self, images: Image.Image | list[Image.Image], **kwargs
+    ) -> dict[str, torch.Tensor]:
+        return self.preprocess(images, **kwargs)
+
+
+class NemotronParseProcessor:
+    """
+    NemotronParse Processor
+    """
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        tokenizer: AnyTokenizer,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+
+        self.config = config
+        self.tokenizer = tokenizer
+
+        self.image_processor = NemotronParseImageProcessor(final_size=config.image_size)
+
+    def _make_batch_input(self, input_item=None):
+        if input_item is None:
+            input_item = []
+        if not isinstance(input_item, list):
+            input_item = [input_item]
+        return input_item
+
+    def __call__(
+        self,
+        text: str | None = None,
+        images: Image.Image | list[Image.Image] | None = None,
+        return_tensors: str | TensorType | None = None,
+        **kwargs,
+    ) -> BatchFeature:
+        text, images = [self._make_batch_input(x) for x in (text, images)]
+        image_inputs = {} if len(images) == 0 else self.image_processor(images)
+
+        text_inputs = self.tokenizer(text, add_special_tokens=False, **kwargs)
+        combined_outputs = BatchFeature(
+            data={**text_inputs, **image_inputs},
+            tensor_type=return_tensors,
+        )
+        return combined_outputs
+
+
+class NemotronParseProcessingInfo(BaseProcessingInfo):
+    def get_hf_config(self):
+        return self.ctx.get_hf_config()
+
+    def get_hf_processor(self, **kwargs) -> NemotronParseProcessor:
+        return self.ctx.init_processor(
+            NemotronParseProcessor,
+            config=self.get_hf_config(),
+            tokenizer=self.get_tokenizer(),
+            **kwargs,
+        )
+
+    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
+        return {"image": 1}
+
+    def get_num_image_tokens(self) -> int:
+        config = self.get_hf_config()
+        final_size = config.image_size
+        patch_size = config.encoder.patch_size
+
+        return (final_size[0] // patch_size) * ((final_size[1] // patch_size) // 4) + 1
+
+    def get_mm_max_tokens_per_item(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> Mapping[str, int] | None:
+        image_tokens = self.get_num_image_tokens()
+        return {"image": image_tokens}
+
+
+class NemotronParseDummyInputsBuilder(
+    BaseDummyInputsBuilder[NemotronParseProcessingInfo]
+):
+    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_hf_config().image_size
+
+        return {
+            "image": self._get_dummy_images(
+                width=target_width, height=target_height, num_images=num_images
+            )
+        }
+
+
+class NemotronParseMultiModalProcessor(
+    EncDecMultiModalProcessor[NemotronParseProcessingInfo]
+):
+    def create_encoder_prompt(
+        self,
+        prompt: str | list[int],
+        mm_data: MultiModalDataDict,
+    ) -> str | list[int]:
+        return [0]
+
+    @property
+    def pad_dummy_encoder_prompt(self) -> bool:
+        return True
+
+    def _call_hf_processor(
+        self,
+        prompt: str,
+        mm_data: Mapping[str, object],
+        mm_kwargs: Mapping[str, object],
+        tok_kwargs: Mapping[str, object],
+    ) -> BatchFeature:
+        if mm_data:
+            processed_outputs = super()._call_hf_processor(
+                prompt, mm_data, mm_kwargs, tok_kwargs
+            )
+        else:
+            hf_processor = self.info.get_hf_processor()
+            tokenizer = hf_processor.tokenizer
+            processed_outputs = tokenizer(
+                prompt, add_special_tokens=False, return_tensors="pt"
+            )
+        return processed_outputs
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        return dict(pixel_values=MultiModalFieldConfig.batched("image"))
+
+    def _get_prompt_updates(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargsItems,
+    ) -> Sequence[PromptUpdate]:
+        num_image_tokens = self.info.get_num_image_tokens()
+
+        return [
+            PromptReplacement(
+                modality="image",
+                target=[0],
+                replacement=[0] * num_image_tokens,
+            )
+        ]
+
+
+class RadioWithNeck(nn.Module):
+    """Vision encoder using RADIO model with custom neck."""
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: QuantizationConfig | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.config = config.encoder
+
+        self.model_encoder = self.get_vit_model_from_radio_config(
+            config, quant_config=quant_config
+        )
+
+        # Neck components
+        last_hidden_state = 1024
+        self.conv1 = nn.Conv1d(1280, last_hidden_state, 1)
+        self.layer_norm1 = nn.LayerNorm(
+            last_hidden_state, eps=1e-06, elementwise_affine=True
+        )
+        self.conv2 = nn.Conv2d(
+            last_hidden_state,
+            last_hidden_state,
+            kernel_size=(1, 4),
+            stride=(1, 4),
+            padding=0,
+            bias=False,
+        )
+        self.layer_norm2 = nn.LayerNorm(
+            last_hidden_state, eps=1e-06, elementwise_affine=True
+        )
+        self.sum_proj = ColumnParallelLinear(
+            3840,
+            last_hidden_state,
+            quant_config=quant_config,
+            prefix=f"{prefix}.sum_proj",
+        )
+        self.layer_norm3 = nn.LayerNorm(
+            last_hidden_state, eps=1e-06, elementwise_affine=True
+        )
+
+    def get_vit_model_from_radio_config(
+        self,
+        hf_config: PretrainedConfig,
+        quant_config: QuantizationConfig | None = None,
+    ) -> RadioModel:
+        hf_config_vision = hf_config.encoder
+        model_name = hf_config_vision.args.get("model")
+        if model_name is None:
+            raise ValueError(f"Unsupported vit model type: {model_name}")
+
+        radio_config = RadioConfig(
+            model_name=model_name,
+            image_size=hf_config.image_size,
+            **hf_config_vision.args,
+        )
+
+        return RadioModel(config=radio_config, quant_config=quant_config)
+
+    def forward(self, pixel_values: torch.Tensor, **kwargs) -> torch.Tensor:
+        summary, feature = self.model_encoder(pixel_values)
+
+        output = self.conv1(feature.permute(0, 2, 1)).permute(0, 2, 1)
+        output = self.layer_norm1(output)
+
+        patch_size = self.config.patch_size
+        output = rearrange(
+            output,
+            "b (h w) d -> b d h w",
+            h=pixel_values.shape[-2] // patch_size,
+            w=pixel_values.shape[-1] // patch_size,
+        )
+
+        output = self.conv2(output)
+        output = rearrange(output, "b d h w -> b (h w) d")
+        output = self.layer_norm2(output)
+        summary = self.layer_norm3(self.sum_proj(summary)[0])
+        output = torch.cat((output, summary.unsqueeze(1)), dim=1)
+
+        return output
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        model_encoder_weights = []
+        adaptor_dict = {
+            name: param
+            for name, param in dict(self.named_parameters()).items()
+            if not name.startswith("model_encoder")
+        }
+        for name, w in weights:
+            if name.startswith("model_encoder"):
+                model_encoder_weights.append((".".join(name.split(".")[1:]), w))
+            else:
+                param = adaptor_dict[name]
+                with torch.no_grad():
+                    default_weight_loader(param, w)
+
+        self.model_encoder.load_weights(model_encoder_weights)
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    NemotronParseMultiModalProcessor,
+    info=NemotronParseProcessingInfo,
+    dummy_inputs=NemotronParseDummyInputsBuilder,
+)
+class NemotronParseForConditionalGeneration(nn.Module, SupportsMultiModal):
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.model_config.hf_config
+
+        self.config = config
+        self.vision_config = config.encoder
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+
+        self.encoder = RadioWithNeck(
+            config=config, quant_config=quant_config, prefix=f"{prefix}.encoder"
+        )
+
+        self.decoder = MBartDecoderNoPos(
+            config.decoder,
+            cache_config=cache_config,
+            quant_config=quant_config,
+            prefix=f"{prefix}.decoder",
+        )
+
+        self.vocab_size = config.decoder.vocab_size
+        self.lm_head = ParallelLMHead(
+            config.decoder.vocab_size, config.decoder.d_model, quant_config=quant_config
+        )
+        self.logits_processor = LogitsProcessor(
+            self.vocab_size, config.decoder.vocab_size
+        )
+
+    @classmethod
+    def get_placeholder_str(cls, modality: str, i: int) -> str | None:
+        if modality.startswith("image"):
+            return None
+
+        raise ValueError("Only image modality is supported")
+
+    def _parse_and_validate_image_input(
+        self, **kwargs: object
+    ) -> NemotronParsePixelInputs | None:
+        pixel_values = kwargs.pop("pixel_values", None)
+        image_embeds = kwargs.pop("image_embeds", None)
+
+        if pixel_values is None and image_embeds is None:
+            return None
+
+        if pixel_values is not None and image_embeds is not None:
+            raise ValueError("Both pixel values and image embeds are provided.")
+
+        if pixel_values is not None:
+            h, w = self.config.image_size
+            return NemotronParsePixelInputs(
+                type="pixel_values",
+                data=pixel_values,
+                resolve_bindings={
+                    "h": h,
+                    "w": w,
+                },
+            )
+
+        if image_embeds is not None:
+            raise NotImplementedError
+
+        raise AssertionError("This line should be unreachable.")
+
+    def _process_image_input(
+        self, image_input: NemotronParsePixelInputs
+    ) -> torch.Tensor:
+        assert image_input["type"] == "pixel_values"
+        pixel_values = image_input["data"]
+        dtype = next(self.encoder.parameters()).dtype
+        pixel_values = pixel_values.to(dtype)
+        return self.encoder(pixel_values)
+
+    def get_language_model(self) -> torch.nn.Module:
+        return self.decoder
+
+    def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings | None:
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return None
+        vision_embeddings = self._process_image_input(image_input)
+        return vision_embeddings
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        encoder_outputs: list[torch.Tensor] | None = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        r"""
+        Args:
+            input_ids: torch.Tensor of *decoder* input token ids.
+            positions: torch.Tensor of *decoder* position indices.
+            encoder_outputs: List of encoder output tensors (vision embeddings).
+                During profiling, this may be None or empty.
+        Returns:
+            Output torch.Tensor
+        """
+        inputs_embeds = None
+        if encoder_outputs:
+            inputs_embeds = torch.cat(encoder_outputs, dim=0)
+        hidden_states = self.decoder(
+            decoder_input_ids=input_ids, encoder_hidden_states=inputs_embeds
+        )
+        return hidden_states
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> torch.Tensor | None:
+        return self.logits_processor(self.lm_head, hidden_states)
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
+        lm_head_dict = dict(self.lm_head.named_parameters())
+
+        def is_encoder(name: str) -> bool:
+            return name.startswith("encoder")
+
+        def is_decoder(name: str) -> bool:
+            return name.startswith("decoder")
+
+        def is_lm_head(name: str):
+            return name.startswith("lm_head")
+
+        # Separate weights by component
+        encoder_weights = []
+        decoder_weights = []
+
+        for name, w in weights:
+            if is_encoder(name):
+                encoder_weights.append((".".join(name.split(".")[1:]), w))
+            elif is_decoder(name):
+                decoder_weights.append((".".join(name.split(".")[1:]), w))
+            elif is_lm_head(name):
+                trimmed_name = ".".join(name.split(".")[1:])
+                param = lm_head_dict[trimmed_name]
+                with torch.no_grad():
+                    default_weight_loader(param, w)
+            else:
+                logger.info("Found unexpected weight: %s", name)
+
+        # Load encoder weights
+        self.encoder.load_weights(encoder_weights)
+        # Load decoder weights
+        self.decoder.load_weights(decoder_weights)

vllm/model_executor/models/radio.py

@@ -427,15 +427,17 @@ class RadioInternVisionModel(nn.Module):
             to_2tuple(config.patch_size), config.image_size
         )
         max_img_size = int(
-            round(config.max_img_size / config.patch_size) * config.patch_size
+            round(config.cpe_max_size / config.patch_size) * config.patch_size
         )
+        unique_teachers = set(t["name"] for t in config.teachers)
         self.patch_generator = ViTPatchGenerator(
             config.patch_size,
             config.hidden_size,
             input_dims=self.img_size,
             max_input_dims=max_img_size,
             cls_token=True,
-            register_multiple=config.reg_tokens,
+            num_cls_tokens=len(unique_teachers) if config.cls_token_per_teacher else 1,
+            register_multiple=config.register_multiple,
         )
 
         self.encoder = InternVisionEncoder(
@@ -489,11 +491,20 @@ class RadioModel(nn.Module):
             prefix=prefix,
         )
 
+        summary_idxs = None
+        if config.teachers:
+            summary_idxs = torch.tensor(
+                [i for i, t in enumerate(config.teachers) if t.get("use_summary", True)]
+            )
+            if summary_idxs.numel() > 0:
+                self.register_buffer("summary_idxs", summary_idxs)
+        self.summary_idxs = summary_idxs
+
     def forward(
         self,
         pixel_values: torch.Tensor | None = None,
         pixel_embeds: torch.Tensor | None = None,
-    ) -> torch.FloatTensor:
+    ) -> tuple[torch.FloatTensor, torch.FloatTensor]:
         y = self.model(pixel_values)
         return self._extract_final(y)
 
@@ -546,10 +557,17 @@ class RadioModel(nn.Module):
 
         return loaded_params
 
-    def _extract_final(self, y: torch.Tensor):
+    def _extract_final(
+        self, y: torch.Tensor
+    ) -> tuple[torch.FloatTensor, torch.FloatTensor]:
         # Remove CLS + REGISTERS tokens
         patch_gen = getattr(self.model, "patch_generator", None)
         if patch_gen is not None:
+            all_summary = y[:, : patch_gen.num_cls_tokens]
+            if self.summary_idxs is not None:
+                bb_summary = all_summary[:, self.summary_idxs]
+            else:
+                bb_summary = all_summary
             all_feat = y[:, patch_gen.num_skip :]
 
-        return all_feat
+        return bb_summary.flatten(1), all_feat

vllm/model_executor/models/registry.py

@@ -428,6 +428,10 @@ _MULTIMODAL_MODELS = {
     "VoxtralForConditionalGeneration": ("voxtral", "VoxtralForConditionalGeneration"),  # noqa: E501
     "VoxtralStreamingGeneration": ("voxtral_streaming", "VoxtralStreamingGeneration"),  # noqa: E501
     # [Encoder-decoder]
+    "NemotronParseForConditionalGeneration": (
+        "nemotron_parse",
+        "NemotronParseForConditionalGeneration",
+    ),
     "WhisperForConditionalGeneration": ("whisper", "WhisperForConditionalGeneration"),  # noqa: E501
 }
 

vllm/transformers_utils/configs/radio.py

@@ -2,6 +2,8 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """Radio vision model configuration"""
 
+from typing import Any
+
 from transformers.configuration_utils import PretrainedConfig
 from transformers.utils import logging
 
@@ -36,12 +38,15 @@ class RadioConfig(PretrainedConfig):
         layer_norm_eps: The epsilon used by the layer normalization layers.
         initializer_factor: A factor for initializing all weight matrices.
         hidden_act: The non-linear activation function in the encoder.
-        max_img_size: Maximum image size for position embeddings.
+        cpe_max_size: Maximum image size for position embeddings.
         norm_mean: Mean values for image normalization (RGB channels).
             Defaults to (0.48145466, 0.4578275, 0.40821073)).
         norm_std: Standard deviation values for image normalization
             (RGB channels). Defaults to (0.26862954, 0.26130258, 0.27577711)).
-        reg_tokens: Number of register tokens to use.
+        register_multiple: Number of register tokens to use.
+        teachers: A list of teacher model configurations. Each teacher configuration is
+            a dict with keys like "name" and some may have "use_summary".
+        cls_token_per_teacher: Whether to use a separate CLS token for each teacher.
     """
 
     model_type = "radio"
@@ -57,10 +62,12 @@ class RadioConfig(PretrainedConfig):
         layer_norm_eps: float = 1e-6,
         initializer_factor: float = 1.0,
         hidden_act: str = "gelu",
-        max_img_size: int = 2048,
+        cpe_max_size: int = 2048,
         norm_mean: tuple[float, float, float] | list = OPENAI_CLIP_MEAN,
         norm_std: tuple[float, float, float] | list = OPENAI_CLIP_STD,
-        reg_tokens: int | None = None,
+        register_multiple: int | None = None,
+        teachers: list[dict[str, Any]] | None = None,
+        cls_token_per_teacher: bool = False,
         **kwargs,
     ):
         self.model_name = model_name
@@ -78,12 +85,14 @@ class RadioConfig(PretrainedConfig):
         self.layer_norm_eps = layer_norm_eps
         self.initializer_factor = initializer_factor
         self.hidden_act = hidden_act
-        self.max_img_size = max_img_size
+        self.cpe_max_size = cpe_max_size
         self.norm_mean = (
             list(norm_mean) if isinstance(norm_mean, (tuple, list)) else norm_mean
         )
         self.norm_std = (
             list(norm_std) if isinstance(norm_std, (tuple, list)) else norm_std
         )
-        self.reg_tokens = reg_tokens
+        self.register_multiple = register_multiple
+        self.teachers = teachers if teachers is not None else []
+        self.cls_token_per_teacher = cls_token_per_teacher
         super().__init__(**kwargs)