diff --git a/vllm/attention/backends/blocksparse_attn.py b/vllm/attention/backends/blocksparse_attn.py
index 94002e36db2b..9e54c3b40c54 100644
--- a/vllm/attention/backends/blocksparse_attn.py
+++ b/vllm/attention/backends/blocksparse_attn.py
@@ -87,6 +87,11 @@ class BlocksparseParams:
 
 class BlocksparseFlashAttentionBackend(AttentionBackend):
 
+    @staticmethod
+    def get_name() -> str:
+        # For attention layer compatibility
+        return "FLASH_ATTN"
+
     @staticmethod
     def get_impl_cls() -> Type["BlocksparseFlashAttentionImpl"]:
         return BlocksparseFlashAttentionImpl
diff --git a/vllm/attention/layer.py b/vllm/attention/layer.py
index cb4dedf481c7..1bb335909484 100644
--- a/vllm/attention/layer.py
+++ b/vllm/attention/layer.py
@@ -6,7 +6,7 @@ import torch.nn as nn
 
 import vllm.envs as envs
 from vllm.attention import AttentionMetadata, AttentionType
-from vllm.attention.selector import get_attn_backend
+from vllm.attention.selector import backend_name_to_enum, get_attn_backend
 from vllm.config import CacheConfig
 from vllm.forward_context import ForwardContext, get_forward_context
 from vllm.model_executor.layers.quantization.base_config import (
@@ -98,6 +98,7 @@ class Attention(nn.Module):
         self.impl = impl_cls(num_heads, head_size, scale, num_kv_heads,
                              alibi_slopes, sliding_window, kv_cache_dtype,
                              blocksparse_params, logits_soft_cap)
+        self.backend = backend_name_to_enum(attn_backend.get_name())
 
         # For cuda-alike (CUDA and ROCM) and cpu platforms, we control how
         # torch.compile works by registering the attention as one giant
diff --git a/vllm/model_executor/models/mllama.py b/vllm/model_executor/models/mllama.py
index 41f62b37f3bd..9e6634a9a757 100644
--- a/vllm/model_executor/models/mllama.py
+++ b/vllm/model_executor/models/mllama.py
@@ -32,9 +32,8 @@ from transformers.models.mllama.processing_mllama import (
 
 import vllm.distributed.parallel_state as ps
 from vllm.attention import Attention, AttentionMetadata, AttentionType
-from vllm.attention.backends.flash_attn import FlashAttentionMetadata
-from vllm.attention.backends.xformers import XFormersMetadata
 from vllm.attention.ops.paged_attn import PagedAttention
+from vllm.attention.selector import _Backend
 from vllm.config import VllmConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
 from vllm.inputs import (INPUT_REGISTRY, DummyData, EncoderDecoderInputs,
@@ -828,7 +827,8 @@ class MllamaTextCrossAttention(nn.Module):
     ) -> torch.Tensor:
         # Skip writing kv-cache for the initial profiling run.
         if len(kv_cache.shape) > 1:
-            if isinstance(attn_metadata, FlashAttentionMetadata):
+            if self.attn.backend in (_Backend.FLASH_ATTN,
+                                     _Backend.FLASH_ATTN_VLLM_V1):
                 cached_k = torch.cat([k[s:e] for s, e in kv_range_for_decode])
                 cached_v = torch.cat([v[s:e] for s, e in kv_range_for_decode])
                 torch.ops._C_cache_ops.reshape_and_cache_flash(
@@ -842,7 +842,7 @@ class MllamaTextCrossAttention(nn.Module):
                     1.0,
                     1.0,
                 )
-            elif isinstance(attn_metadata, XFormersMetadata):
+            elif self.attn.backend in (_Backend.XFORMERS, _Backend.TORCH_SDPA):
                 key_cache, value_cache = PagedAttention.split_kv_cache(
                     kv_cache, self.num_local_key_value_heads, self.head_dim)
                 cached_k = torch.cat([k[s:e] for s, e in kv_range_for_decode])
@@ -852,9 +852,9 @@ class MllamaTextCrossAttention(nn.Module):
                     attn_metadata.cross_slot_mapping, "auto", 1.0, 1.0)
             else:
                 raise ValueError(
-                    f"Unsupported AttentionMetadata {type(attn_metadata)} "
-                    f"class found. Expected the AttentionMetadata to "
-                    f"be either XFormersMetadata or FlashAttentionMetadata.")
+                    f"Unsupported Attention backend {self.attn.backend} "
+                    "enum found. Expected the Attention backend to be "
+                    "FLASH_ATTN, FLASH_ATTN_VLLM_V1, XFORMERS or TORCH_SDPA.")
 
         # We have to call torch.sdpa for prefill when using a
         # custom cross-attention mask. Because the mask is not a
diff --git a/vllm/platforms/openvino.py b/vllm/platforms/openvino.py
index 91e615481ff8..ea5ec7b40b95 100644
--- a/vllm/platforms/openvino.py
+++ b/vllm/platforms/openvino.py
@@ -1,7 +1,5 @@
 from typing import TYPE_CHECKING
 
-import openvino as ov
-import openvino.properties.hint as hints
 import torch
 
 import vllm.envs as envs
@@ -16,6 +14,12 @@ else:
 
 logger = init_logger(__name__)
 
+try:
+    import openvino as ov
+    import openvino.properties.hint as hints
+except ImportError as e:
+    logger.warning("Failed to import OpenVINO with %r", e)
+
 
 class OpenVinoPlatform(Platform):
     _enum = PlatformEnum.OPENVINO
diff --git a/vllm/v1/attention/backends/flash_attn.py b/vllm/v1/attention/backends/flash_attn.py
index d98bb5a716e9..5f8535eaa303 100644
--- a/vllm/v1/attention/backends/flash_attn.py
+++ b/vllm/v1/attention/backends/flash_attn.py
@@ -19,7 +19,7 @@ class FlashAttentionBackend(AttentionBackend):
 
     @staticmethod
     def get_name() -> str:
-        return "flash-attn-vllm-v1"
+        return "FLASH_ATTN_VLLM_V1"
 
     @staticmethod
     def get_impl_cls() -> Type["FlashAttentionImpl"]: