[Model][OpenVINO] Fix regressions from #8346

.buildkite/run-openvino-test.sh

@@ -11,4 +11,4 @@ trap remove_docker_container EXIT
 remove_docker_container
 
 # Run the image and launch offline inference
-docker run --network host --env VLLM_OPENVINO_KVCACHE_SPACE=1 --name openvino-test openvino-test python3 /workspace/vllm/examples/offline_inference.py
+docker run --network host --env VLLM_OPENVINO_KVCACHE_SPACE=1 --name openvino-test openvino-test python3 /workspace/examples/offline_inference.py

vllm/attention/backends/openvino.py

@@ -1,12 +1,13 @@
 from dataclasses import dataclass
-from typing import List, Tuple, Type
+from typing import Dict, List, Optional, Tuple, Type
 
 import openvino as ov
 import torch
 
 from vllm.attention.backends.abstract import (AttentionBackend,
                                               AttentionMetadata)
 from vllm.attention.backends.utils import CommonAttentionState
+from vllm.multimodal import MultiModalPlaceholderMap
 
 
 def copy_cache_block(src_tensor: ov.Tensor, dst_tensor: ov.Tensor,
@@ -128,3 +129,12 @@ class OpenVINOAttentionMetadata:
     # Shape: scalar
     # Type: i32
     max_context_len: torch.Tensor
+
+    # The index maps that relate multi-modal embeddings to the corresponding
+    # placeholders.
+    #
+    # N.B. These aren't really related to attention and don't belong on this
+    # type -- this is just a temporary solution to make them available to
+    # `model_executable`.
+    multi_modal_placeholder_index_maps: Optional[Dict[
+        str, MultiModalPlaceholderMap.IndexMap]]

vllm/model_executor/models/molmo.py

@@ -21,8 +21,8 @@
                               get_tensor_model_parallel_world_size,
                               split_tensor_along_last_dim,
                               tensor_model_parallel_all_gather)
-from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, InputContext,
-                         token_inputs)
+from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs, DummyData,
+                         InputContext, token_inputs)
 from vllm.model_executor import SamplingMetadata
 from vllm.model_executor.layers.activation import QuickGELU, SiluAndMul
 from vllm.model_executor.layers.layernorm import RMSNorm
@@ -913,7 +913,7 @@ def dummy_data_for_molmo(ctx: InputContext, seq_len: int,
     if "image_masks" in out:
         dummy_imgdata["image_masks"] = out["image_masks"]
     dummy_imgdata["seq_len"] = torch.tensor(seq_len, dtype=torch.long)
-    return dummy_seqdata, {"image": dummy_imgdata}
+    return DummyData(dummy_seqdata, {"image": dummy_imgdata})
 
 
 def pad_images(