[Model] IBM/NASA Prithvi Geospatial model (#12830)

examples/offline_inference/prithvi_geospatial_mae.py

+# SPDX-License-Identifier: Apache-2.0
+"""
+This is a demo script showing how to use the
+PrithviGeospatialMAE model with vLLM
+This script is based on: https://huggingface.co/ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL-Sen1Floods11/blob/main/inference.py # noqa
+
+Target model weights: https://huggingface.co/ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL-Sen1Floods11/resolve/main/Prithvi-EO-V2-300M-TL-Sen1Floods11.pt # noqa
+
+The requirements for running this script are:
+- Installing [terratorch, albumentations, rasterio] in your python environment
+- downloading the model weights in a 'model' folder local to the script
+  (temporary measure until the proper config.json file is uploaded to HF)
+- download an input example image (India_900498_S2Hand.tif) and place it in
+  the same folder with the script (or specify with the --data_file argument)
+
+Run the example:
+python prithvi_geospatial_mae.py
+
+""" # noqa: E501
+import argparse
+import datetime
+import os
+import re
+from typing import List, Union
+
+import albumentations
+import numpy as np
+import rasterio
+import torch
+from einops import rearrange
+from terratorch.datamodules import Sen1Floods11NonGeoDataModule
+
+from vllm import LLM
+
+NO_DATA = -9999
+NO_DATA_FLOAT = 0.0001
+OFFSET = 0
+PERCENTILE = 99
+
+model_config = """{
+  "architectures": ["PrithviGeoSpatialMAE"],
+  "num_classes": 0,
+  "pretrained_cfg": {
+    "task_args": {
+      "task": "SemanticSegmentationTask",
+      "model_factory": "EncoderDecoderFactory",
+      "loss": "ce",
+      "ignore_index": -1,
+      "lr": 0.001,
+      "freeze_backbone": false,
+      "freeze_decoder": false,
+      "plot_on_val": 10,
+      "optimizer": "AdamW",
+      "scheduler": "CosineAnnealingLR"
+    },
+    "model_args": {
+      "backbone_pretrained": false,
+      "backbone": "prithvi_eo_v2_300_tl",
+      "decoder": "UperNetDecoder",
+      "decoder_channels": 256,
+      "decoder_scale_modules": true,
+      "num_classes": 2,
+      "rescale": true,
+      "backbone_bands": [
+        "BLUE",
+        "GREEN",
+        "RED",
+        "NIR_NARROW",
+        "SWIR_1",
+        "SWIR_2"
+      ],
+      "head_dropout": 0.1,
+      "necks": [
+        {
+          "name": "SelectIndices",
+          "indices": [
+            5,
+            11,
+            17,
+            23
+          ]
+        },
+        {
+          "name": "ReshapeTokensToImage"
+        }
+      ]
+    },
+    "optimizer_params" : {
+      "lr": 5.0e-05,
+      "betas": [0.9, 0.999],
+      "eps": [1.0e-08],
+      "weight_decay": 0.05,
+      "amsgrad": false,
+      "maximize": false,
+      "capturable": false,
+      "differentiable": false
+    },
+    "scheduler_params" : {
+        "T_max": 50,
+        "eta_min": 0,
+        "last_epoch": -1,
+        "verbose": "deprecated"
+    }
+  },
+
+
+  "torch_dtype": "float32"
+}
+"""
+
+# Temporarily creating the "config.json" for the model.
+# This is going to disappear once the correct config.json is available on HF
+with open(os.path.join(os.path.dirname(__file__), "./model/config.json"),
+          'w') as config_file:
+    config_file.write(model_config)
+
+datamodule_config = {
+    'bands': ['BLUE', 'GREEN', 'RED', 'NIR_NARROW', 'SWIR_1', 'SWIR_2'],
+    'batch_size':
+    16,
+    'constant_scale':
+    0.0001,
+    'data_root':
+    '/dccstor/geofm-finetuning/datasets/sen1floods11',
+    'drop_last':
+    True,
+    'no_data_replace':
+    0.0,
+    'no_label_replace':
+    -1,
+    'num_workers':
+    8,
+    'test_transform': [
+        albumentations.Resize(always_apply=False,
+                              height=448,
+                              interpolation=1,
+                              p=1,
+                              width=448),
+        albumentations.pytorch.ToTensorV2(transpose_mask=False,
+                                          always_apply=True,
+                                          p=1.0)
+    ],
+}
+
+
+class PrithviMAE:
+
+    def __init__(self):
+        print("Initializing PrithviMAE model")
+        self.model = LLM(model=os.path.join(os.path.dirname(__file__),
+                                            "./model"),
+                         skip_tokenizer_init=True,
+                         dtype="float32")
+
+    def run(self, input_data, location_coords):
+        print("################ Running inference on vLLM ##############")
+        # merge the inputs into one data structure
+        mm_data = {
+            "pixel_values":
+            torch.empty(0) if input_data is None else input_data,
+            "location_coords":
+            torch.empty(0) if location_coords is None else location_coords
+        }
+
+        prompt = {"prompt_token_ids": [1], "multi_modal_data": mm_data}
+
+        outputs = self.model.encode(prompt, use_tqdm=False)
+        print(
+            "################ Inference done (it took seconds)  ##############"
+        )
+
+        return outputs[0].outputs.data
+
+
+def generate_datamodule():
+    datamodule = Sen1Floods11NonGeoDataModule(
+        data_root=datamodule_config['data_root'],
+        batch_size=datamodule_config["batch_size"],
+        num_workers=datamodule_config["num_workers"],
+        bands=datamodule_config["bands"],
+        drop_last=datamodule_config["drop_last"],
+        test_transform=datamodule_config["test_transform"
+                                         ""])
+
+    return datamodule
+
+
+def process_channel_group(orig_img, channels):
+    """
+    Args:
+        orig_img: torch.Tensor representing original image (reference)
+                  with shape = (bands, H, W).
+        channels: list of indices representing RGB channels.
+
+    Returns:
+        torch.Tensor with shape (num_channels, height, width) for original image
+    """
+
+    orig_img = orig_img[channels, ...]
+    valid_mask = torch.ones_like(orig_img, dtype=torch.bool)
+    valid_mask[orig_img == NO_DATA_FLOAT] = False
+
+    # Rescale (enhancing contrast)
+    max_value = max(3000, np.percentile(orig_img[valid_mask], PERCENTILE))
+    min_value = OFFSET
+
+    orig_img = torch.clamp((orig_img - min_value) / (max_value - min_value), 0,
+                           1)
+
+    # No data as zeros
+    orig_img[~valid_mask] = 0
+
+    return orig_img
+
+
+def read_geotiff(file_path: str):
+    """Read all bands from *file_path* and return image + meta info.
+
+    Args:
+        file_path: path to image file.
+
+    Returns:
+        np.ndarray with shape (bands, height, width)
+        meta info dict
+    """
+
+    with rasterio.open(file_path) as src:
+        img = src.read()
+        meta = src.meta
+        try:
+            coords = src.lnglat()
+        except Exception:
+            # Cannot read coords
+            coords = None
+
+    return img, meta, coords
+
+
+def save_geotiff(image, output_path: str, meta: dict):
+    """Save multi-band image in Geotiff file.
+
+    Args:
+        image: np.ndarray with shape (bands, height, width)
+        output_path: path where to save the image
+        meta: dict with meta info.
+    """
+
+    with rasterio.open(output_path, "w", **meta) as dest:
+        for i in range(image.shape[0]):
+            dest.write(image[i, :, :], i + 1)
+
+    return
+
+
+def _convert_np_uint8(float_image: torch.Tensor):
+    image = float_image.numpy() * 255.0
+    image = image.astype(dtype=np.uint8)
+
+    return image
+
+
+def load_example(
+    file_paths: List[str],
+    mean: List[float] = None,
+    std: List[float] = None,
+    indices: Union[list[int], None] = None,
+):
+    """Build an input example by loading images in *file_paths*.
+
+    Args:
+        file_paths: list of file paths .
+        mean: list containing mean values for each band in the images
+              in *file_paths*.
+        std: list containing std values for each band in the images
+             in *file_paths*.
+
+    Returns:
+        np.array containing created example
+        list of meta info for each image in *file_paths*
+    """
+
+    imgs = []
+    metas = []
+    temporal_coords = []
+    location_coords = []
+
+    for file in file_paths:
+        img, meta, coords = read_geotiff(file)
+
+        # Rescaling (don't normalize on nodata)
+        img = np.moveaxis(img, 0, -1)  # channels last for rescaling
+        if indices is not None:
+            img = img[..., indices]
+        if mean is not None and std is not None:
+            img = np.where(img == NO_DATA, NO_DATA_FLOAT, (img - mean) / std)
+
+        imgs.append(img)
+        metas.append(meta)
+        if coords is not None:
+            location_coords.append(coords)
+
+        try:
+            match = re.search(r'(\d{7,8}T\d{6})', file)
+            if match:
+                year = int(match.group(1)[:4])
+                julian_day = match.group(1).split('T')[0][4:]
+                if len(julian_day) == 3:
+                    julian_day = int(julian_day)
+                else:
+                    julian_day = datetime.datetime.strptime(
+                        julian_day, '%m%d').timetuple().tm_yday
+                temporal_coords.append([year, julian_day])
+        except Exception as e:
+            print(f'Could not extract timestamp for {file} ({e})')
+
+    imgs = np.stack(imgs, axis=0)  # num_frames, H, W, C
+    imgs = np.moveaxis(imgs, -1, 0).astype("float32")
+    imgs = np.expand_dims(imgs, axis=0)  # add batch di
+
+    return imgs, temporal_coords, location_coords, metas
+
+
+def run_model(input_data,
+              temporal_coords,
+              location_coords,
+              model,
+              datamodule,
+              img_size,
+              lightning_model=None):
+    # Reflect pad if not divisible by img_size
+    original_h, original_w = input_data.shape[-2:]
+    pad_h = (img_size - (original_h % img_size)) % img_size
+    pad_w = (img_size - (original_w % img_size)) % img_size
+    input_data = np.pad(input_data,
+                        ((0, 0), (0, 0), (0, 0), (0, pad_h), (0, pad_w)),
+                        mode="reflect")
+
+    # Build sliding window
+    batch_size = 1
+    batch = torch.tensor(input_data, device="cpu")
+    windows = (batch.unfold(3, img_size,
+                            img_size).unfold(4, img_size, img_size))
+    h1, w1 = windows.shape[3:5]
+    windows = rearrange(windows,
+                        "b c t h1 w1 h w -> (b h1 w1) c t h w",
+                        h=img_size,
+                        w=img_size)
+
+    # Split into batches if number of windows > batch_size
+    num_batches = windows.shape[0] // batch_size if windows.shape[
+        0] > batch_size else 1
+    windows = torch.tensor_split(windows, num_batches, dim=0)
+
+    if torch.cuda.is_available():
+        device = torch.device('cuda')
+    else:
+        device = torch.device('cpu')
+
+    if temporal_coords:
+        temporal_coords = torch.tensor(temporal_coords,
+                                       device=device).unsqueeze(0)
+    else:
+        temporal_coords = None
+    if location_coords:
+        location_coords = torch.tensor(location_coords[0],
+                                       device=device).unsqueeze(0)
+    else:
+        location_coords = None
+
+    # Run model
+    pred_imgs = []
+    for x in windows:
+        # Apply standardization
+        x = datamodule.test_transform(
+            image=x.squeeze().numpy().transpose(1, 2, 0))
+        x = datamodule.aug(x)['image']
+
+        with torch.no_grad():
+            x = x.to(device)
+            pred = model.run(x, location_coords=location_coords)
+            if lightning_model:
+                pred_lightning = lightning_model(
+                    x,
+                    temporal_coords=temporal_coords,
+                    location_coords=location_coords)
+                pred_lightning = pred_lightning.output.detach().cpu()
+                if not torch.equal(pred, pred_lightning):
+                    print("Inference output is not equal")
+        y_hat = pred.argmax(dim=1)
+
+        y_hat = torch.nn.functional.interpolate(y_hat.unsqueeze(1).float(),
+                                                size=img_size,
+                                                mode="nearest")
+
+        pred_imgs.append(y_hat)
+
+    pred_imgs = torch.concat(pred_imgs, dim=0)
+
+    # Build images from patches
+    pred_imgs = rearrange(
+        pred_imgs,
+        "(b h1 w1) c h w -> b c (h1 h) (w1 w)",
+        h=img_size,
+        w=img_size,
+        b=1,
+        c=1,
+        h1=h1,
+        w1=w1,
+    )
+
+    # Cut padded area back to original size
+    pred_imgs = pred_imgs[..., :original_h, :original_w]
+
+    # Squeeze (batch size 1)
+    pred_imgs = pred_imgs[0]
+
+    return pred_imgs
+
+
+def main(
+    data_file: str,
+    output_dir: str,
+    rgb_outputs: bool,
+    input_indices: list[int] = None,
+):
+    os.makedirs(output_dir, exist_ok=True)
+
+    # Load model ---------------------------------------------------------------
+
+    model_obj = PrithviMAE()
+    datamodule = generate_datamodule()
+    img_size = 256  # Size of Sen1Floods11
+
+    # Loading data -------------------------------------------------------------
+
+    input_data, temporal_coords, location_coords, meta_data = load_example(
+        file_paths=[data_file],
+        indices=input_indices,
+    )
+
+    meta_data = meta_data[0]  # only one image
+
+    if input_data.mean() > 1:
+        input_data = input_data / 10000  # Convert to range 0-1
+
+    # Running model ------------------------------------------------------------
+
+    channels = [
+        datamodule_config['bands'].index(b) for b in ["RED", "GREEN", "BLUE"]
+    ]  # BGR -> RGB
+
+    pred = run_model(input_data, temporal_coords, location_coords, model_obj,
+                     datamodule, img_size)
+
+    # Save pred
+    meta_data.update(count=1, dtype="uint8", compress="lzw", nodata=0)
+    pred_file = os.path.join(
+        output_dir,
+        f"pred_{os.path.splitext(os.path.basename(data_file))[0]}.tiff")
+    save_geotiff(_convert_np_uint8(pred), pred_file, meta_data)
+
+    # Save image + pred
+    meta_data.update(count=3, dtype="uint8", compress="lzw", nodata=0)
+
+    if input_data.mean() < 1:
+        input_data = input_data * 10000  # Scale to 0-10000
+
+    rgb_orig = process_channel_group(
+        orig_img=torch.Tensor(input_data[0, :, 0, ...]),
+        channels=channels,
+    )
+
+    pred[pred == 0.] = np.nan
+    img_pred = rgb_orig * 0.7 + pred * 0.3
+    img_pred[img_pred.isnan()] = rgb_orig[img_pred.isnan()]
+
+    img_pred_file = os.path.join(
+        output_dir,
+        f"rgb_pred_{os.path.splitext(os.path.basename(data_file))[0]}.tiff")
+    save_geotiff(
+        image=_convert_np_uint8(img_pred),
+        output_path=img_pred_file,
+        meta=meta_data,
+    )
+
+    # Save image rgb
+    if rgb_outputs:
+        rgb_file = os.path.join(
+            output_dir, "original_rgb_"
+            f"{os.path.splitext(os.path.basename(data_file))[0]}.tiff")
+        save_geotiff(
+            image=_convert_np_uint8(rgb_orig),
+            output_path=rgb_file,
+            meta=meta_data,
+        )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser("MAE run inference", add_help=False)
+
+    parser.add_argument(
+        "--data_file",
+        type=str,
+        default="./India_900498_S2Hand.tif",
+        help="Path to the file.",
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="output",
+        help="Path to the directory where to save outputs.",
+    )
+    parser.add_argument(
+        "--input_indices",
+        default=[1, 2, 3, 8, 11, 12],
+        type=int,
+        nargs="+",
+        help=
+        "0-based indices of the six Prithvi channels to be selected from the  "
+        "input. By default selects [1,2,3,8,11,12] for S2L1C data.",
+    )
+    parser.add_argument(
+        "--rgb_outputs",
+        action="store_true",
+        help="If present, output files will only contain RGB channels. "
+        "Otherwise, all bands will be saved.",
+    )
+    args = parser.parse_args()
+
+    main(**vars(args))

tests/models/registry.py

@@ -214,6 +214,10 @@ _EMBEDDING_EXAMPLE_MODELS = {
     "Phi3VForCausalLM": _HfExamplesInfo("TIGER-Lab/VLM2Vec-Full",
                                          trust_remote_code=True),
     "Qwen2VLForConditionalGeneration": _HfExamplesInfo("MrLight/dse-qwen2-2b-mrl-v1"), # noqa: E501
+    # The model on Huggingface is currently being updated,
+    # hence I temporarily mark it as not available online
+    "PrithviGeoSpatialMAE": _HfExamplesInfo("ibm-nasa-geospatial/Prithvi-EO-2.0-300M-TL-Sen1Floods11",  # noqa: E501
+                                            is_available_online=False),
 }
 
 _CROSS_ENCODER_EXAMPLE_MODELS = {

vllm/attention/backends/placeholder_attn.py

@@ -320,9 +320,14 @@ class PlaceholderAttentionMetadataBuilder(
                                  -1 if cuda graph is not used.
             batch_size: The maybe padded batch size.
         """
-        for inter_data in self.input_builder.inter_data_list:
-            self._add_seq_group(inter_data,
-                                self.input_builder.chunked_prefill_enabled)
+
+        # Some input builders such as ModelInputForCPUBuilder do not have the
+        # "inter_data_list" attribute.
+        # Let's check inter_data_list exists before we reference it.
+        if hasattr(self.input_builder, "inter_data_list"):
+            for inter_data in self.input_builder.inter_data_list:
+                self._add_seq_group(inter_data,
+                                    self.input_builder.chunked_prefill_enabled)
 
         device = self.runner.device
         use_captured_graph = cuda_graph_pad_size != -1

vllm/inputs/preprocess.py

@@ -254,8 +254,14 @@ class InputPreprocessor:
         Apply the model's multi-modal processor to a multi-modal prompt,
         returning the corresponding token IDs and metadata.
         """
-        tokenizer_group = self.get_tokenizer_group()
-        tokenizer = tokenizer_group.get_lora_tokenizer(lora_request)
+        # At the moment on model (PrithviGeoSpatialMAE) requires to be
+        # initialized without a tokenizer while using also multi-modal
+        # input.
+        if not self.tokenizer:
+            tokenizer = None
+        else:
+            tokenizer_group = self.get_tokenizer_group()
+            tokenizer = tokenizer_group.get_lora_tokenizer(lora_request)
 
         mm_processor = self.mm_registry.create_processor(
             self.model_config, tokenizer)
@@ -273,9 +279,15 @@ class InputPreprocessor:
         lora_request: Optional[LoRARequest],
     ) -> MultiModalInputs:
         """Async version of :meth:`_process_multimodal`."""
-        tokenizer_group = self.get_tokenizer_group()
-        tokenizer = await tokenizer_group.get_lora_tokenizer_async(lora_request
-                                                                   )
+        # At the moment on model (PrithviGeoSpatialMAE) requires to be
+        # initialized without a tokenizer while using also multi-modal
+        # input.
+        if not self.tokenizer:
+            tokenizer = None
+        else:
+            tokenizer_group = self.get_tokenizer_group()
+            tokenizer = await tokenizer_group.get_lora_tokenizer_async(
+                lora_request)
 
         mm_processor = self.mm_registry.create_processor(
             self.model_config, tokenizer)

vllm/model_executor/models/prithvi_geospatial_mae.py

+# SPDX-License-Identifier: Apache-2.0
+
+# Copyright 2025 The vLLM team.
+# Copyright 2025 IBM.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only IBM/NASA Prithvi Geospatial model."""
+from typing import Iterable, List, Mapping, Optional, Set, Tuple, Union
+
+import torch
+import torch.nn as nn
+from transformers import BatchFeature
+
+from vllm.attention import AttentionMetadata
+from vllm.config import VllmConfig
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.interfaces import (IsAttentionFree,
+                                                   SupportsMultiModal)
+from vllm.model_executor.models.utils import AutoWeightsLoader
+from vllm.model_executor.pooling_metadata import PoolingMetadata
+from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
+                                    MultiModalInputs, MultiModalKwargs)
+from vllm.multimodal.parse import MultiModalDataItems
+from vllm.multimodal.processing import (BaseMultiModalProcessor,
+                                        BaseProcessingInfo, PromptReplacement)
+from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
+from vllm.sequence import (IntermediateTensors, PoolerOutput,
+                           PoolingSequenceGroupOutput)
+
+
+class PrithviGeoSpatialMAEProcessingInfo(BaseProcessingInfo):
+
+    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
+        return {"image": None}
+
+    def get_mm_max_tokens_per_item(self, seq_len: int) -> Mapping[str, int]:
+        pass
+
+
+class PrithviGeoSpatialMAEInputBuilder(
+        BaseDummyInputsBuilder[PrithviGeoSpatialMAEProcessingInfo]):
+
+    def get_dummy_processor_inputs(
+        self,
+        seq_len: int,
+        mm_counts: Mapping[str, int],
+    ) -> ProcessorInputs:
+        return ProcessorInputs(
+            prompt_text="",
+            # This model input is fixed and is in the form of a torch Tensor.
+            # The size of pixel_values might change in the cases where we resize
+            # the input but never exceeds the dimensions below.
+            mm_data={
+                "pixel_values": torch.full((1, 6, 512, 512), 1.0),
+                "location_coords": torch.full((1, 2), 1.0)
+            })
+
+
+class PrithviGeoSpatialMAEMultiModalProcessor(BaseMultiModalProcessor):
+
+    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"),
+            location_coords=MultiModalFieldConfig.batched("image"),
+        )
+
+    def _get_prompt_replacements(
+        self,
+        mm_items: MultiModalDataItems,
+        hf_processor_mm_kwargs: Mapping[str, object],
+        out_mm_kwargs: MultiModalKwargs,
+    ) -> list[PromptReplacement]:
+        pass
+
+    def _get_mm_fields_config(
+        self,
+        hf_inputs: BatchFeature,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> Mapping[str, MultiModalFieldConfig]:
+        pass
+
+    def apply(
+        self,
+        prompt: Union[str, list[int]],
+        mm_data: MultiModalDataDict,
+        hf_processor_mm_kwargs: Mapping[str, object],
+    ) -> MultiModalInputs:
+        mm_kwargs = {}
+
+        for k, v in mm_data.items():
+            mm_kwargs[k] = v
+
+        return MultiModalInputs(
+            type="multimodal",
+            prompt=prompt,
+            prompt_token_ids=[1],
+            mm_kwargs=MultiModalKwargs(mm_kwargs),
+            mm_placeholders={},
+        )
+
+
+@MULTIMODAL_REGISTRY.register_processor(
+    PrithviGeoSpatialMAEMultiModalProcessor,
+    info=PrithviGeoSpatialMAEProcessingInfo,
+    dummy_inputs=PrithviGeoSpatialMAEInputBuilder)
+class PrithviGeoSpatialMAE(nn.Module, IsAttentionFree, SupportsMultiModal):
+    """ Prithvi Masked Autoencoder"""
+
+    def _instantiate_model(self, config: dict) -> nn.Module | None:
+
+        # We might be able/need to support different tasks with this same model
+        if config["task_args"]["task"] == "SemanticSegmentationTask":
+            from terratorch.cli_tools import SemanticSegmentationTask
+            task = SemanticSegmentationTask(
+                config["model_args"],
+                config["task_args"]["model_factory"],
+                loss=config["task_args"]["loss"],
+                lr=config["task_args"]["lr"],
+                ignore_index=config["task_args"]["ignore_index"],
+                optimizer=config["task_args"]["optimizer"],
+                optimizer_hparams=config["optimizer_params"],
+                scheduler=config["task_args"]["scheduler"],
+                scheduler_hparams=config["scheduler_params"],
+                plot_on_val=config["task_args"]["plot_on_val"],
+                freeze_decoder=config["task_args"]["freeze_decoder"],
+                freeze_backbone=config["task_args"]["freeze_backbone"])
+
+            return task.model
+        else:
+            return None
+
+    def __init__(self, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+
+        # the actual model is dynamically instantiated using terratorch
+        # allowing us to perform changes to the model architecture
+        # at startup time (e.g., change the model decoder class.)
+        self.model = self._instantiate_model(
+            vllm_config.model_config.hf_config.to_dict()["pretrained_cfg"])
+        if self.model is None:
+            raise ValueError(
+                "Unsupported task."
+                "Only SemanticSegmentationTask is supported for now"
+                "by PrithviGeospatialMAE.")
+
+    def _parse_and_validate_multimodal_data(
+            self, **kwargs) -> Tuple[torch.Tensor, torch.Tensor | None]:
+
+        pixel_values = kwargs.pop("pixel_values", None)
+        if not isinstance(pixel_values, torch.Tensor):
+            raise ValueError(f"Incorrect type of pixel_values. "
+                             f"Got type: {type(pixel_values)}")
+        pixel_values = torch.unbind(pixel_values, dim=0)[0]
+
+        location_coords = kwargs.pop("location_coords", None)
+        if not isinstance(location_coords, torch.Tensor):
+            raise ValueError(f"Incorrect type of location_coords. "
+                             f"Got type: {type(location_coords)}")
+        location_coords = torch.unbind(location_coords, dim=0)[0]
+        if location_coords.shape == torch.Size([0]):
+            location_coords = None
+
+        return pixel_values, location_coords
+
+    def forward(
+        self,
+        input_ids: Optional[torch.Tensor],
+        positions: torch.Tensor,
+        kv_caches: List[torch.Tensor],
+        attn_metadata: AttentionMetadata,
+        intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs: object,
+    ):
+
+        pixel_values, location_coords = (
+            self._parse_and_validate_multimodal_data(**kwargs))
+        model_output = self.model(pixel_values,
+                                  location_coords=location_coords)
+
+        return model_output.output
+
+    def pooler(
+        self,
+        hidden_states: torch.Tensor,
+        pooling_metadata: PoolingMetadata,
+    ) -> Optional[PoolerOutput]:
+        return PoolerOutput([PoolingSequenceGroupOutput(hidden_states)])
+
+    def load_weights(self, weights: Iterable[Tuple[str,
+                                                   torch.Tensor]]) -> Set[str]:
+        params_list = []
+        model_buffers = dict(self.named_buffers())
+        loaded_buffers = []
+        for key, value in weights:
+            if key == "state_dict":
+                weights_to_parse = value
+                for name, weight in weights_to_parse.items():
+                    if "pos_embed" in name:
+                        continue
+
+                    if "_timm_module." in name:
+                        name = name.replace("_timm_module.", "")
+
+                    # this model requires a couple of buffers to be loaded
+                    # that are not loadable with the AutoWeightsLoader
+                    if name in model_buffers:
+                        if "_timm_module." in name:
+                            name = name.replace("_timm_module.", "")
+                        buffer = model_buffers[name]
+                        weight_loader = getattr(buffer, "weight_loader",
+                                                default_weight_loader)
+                        weight_loader(buffer, weight)
+                        loaded_buffers.append(name)
+                    else:
+                        params_list.append((name, weight))
+                break
+
+        # Load the remaining model parameters
+        loader = AutoWeightsLoader(self)
+        autoloaded_weights = loader.load_weights(params_list)
+
+        return autoloaded_weights.union(set(loaded_buffers))

vllm/model_executor/models/registry.py

@@ -137,6 +137,10 @@ _EMBEDDING_MODELS = {
     "Qwen2VLForConditionalGeneration": ("qwen2_vl", "Qwen2VLForConditionalGeneration"),  # noqa: E501
     # [Auto-converted (see adapters.py)]
     "Qwen2ForSequenceClassification": ("qwen2", "Qwen2ForCausalLM"),
+    # Technically PrithviGeoSpatialMAE is a model that works on images, both in
+    # input and output. I am adding it here because it piggy-backs on embedding
+    # models for the time being.
+    "PrithviGeoSpatialMAE": ("prithvi_geospatial_mae", "PrithviGeoSpatialMAE"),
 }
 
 _CROSS_ENCODER_MODELS = {

vllm/worker/pooling_model_runner.py

@@ -74,7 +74,16 @@ class PoolingModelRunner(
         prefill_meta = model_input.attn_metadata.prefill_metadata
         decode_meta = model_input.attn_metadata.decode_metadata
         virtual_engine = model_input.virtual_engine
-        if prefill_meta is None and decode_meta.use_cuda_graph:
+        # Pooling models are (ab-)used also to integrate non text models that
+        # are not autoregressive (PrithviGeosaptialMAE).
+        # These model might not use attention and do not really have a prefill
+        # and decode phase. The model input is processed in one shot and both
+        # decode_metadata and prefill_metadata would be None for such models.
+        # See the PlaceholderAttentionMetadata class.
+        # TODO: Figure out if cuda_graph is of any use for these models and
+        #  explore how to leverage it.
+        if (prefill_meta is None and decode_meta is not None
+                and decode_meta.use_cuda_graph):
             assert model_input.input_tokens is not None
             graph_batch_size = model_input.input_tokens.shape[0]
             model_executable = self.graph_runners[virtual_engine][