diff --git a/vllm/forward_context.py b/vllm/forward_context.py
index 353105a8be7a..0ad43a6ba499 100644
--- a/vllm/forward_context.py
+++ b/vllm/forward_context.py
@@ -156,6 +156,7 @@ def set_forward_context(attn_metadata: Any,
             dist.all_reduce(num_tokens_tensor, group=get_dp_group().cpu_group)
         cu_tokens_across_dp_cpu = torch.cumsum(num_tokens_tensor, dim=0)
 
+        assert current_platform is not None, "current_platform is None"  # noqa
         if current_platform.is_hpu():  # noqa
             num_experts_per_tok = 0
             num_experts_per_tok = getattr(
diff --git a/vllm/model_executor/models/mllama.py b/vllm/model_executor/models/mllama.py
index 1f7c43db5d6e..0291a7d1c965 100644
--- a/vllm/model_executor/models/mllama.py
+++ b/vllm/model_executor/models/mllama.py
@@ -1064,22 +1064,19 @@ def _attention_with_mask_hpu(
         # Skip writing kv-cache for the initial profiling run.
         if kv_cache is not None and isinstance(kv_cache, tuple):
             assert self.attn.backend == _Backend.HPU_ATTN
-            # During cross-attention decode, key & value will be None,
-            # we don't need to cache them.
-            if (k is not None) and (v is not None):
-                from vllm.attention.ops.hpu_paged_attn import HPUPagedAttention
-                key_cache, value_cache = HPUPagedAttention.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])
-                cached_v = torch.cat([v[s:e] for s, e in kv_range_for_decode])
-                slot_mapping = torch.cat([
-                    attn_metadata.cross_slot_mapping[s:e]
-                    for s, e in kv_range_for_decode
-                ])
-                key_cache = self.attn.impl.k_cache(cached_k, key_cache,
-                                                   slot_mapping)
-                value_cache = self.attn.impl.v_cache(cached_v, value_cache,
-                                                     slot_mapping)
+            from vllm.attention.ops.hpu_paged_attn import HPUPagedAttention
+            key_cache, value_cache = HPUPagedAttention.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])
+            cached_v = torch.cat([v[s:e] for s, e in kv_range_for_decode])
+            slot_mapping = torch.cat([
+                attn_metadata.cross_slot_mapping[s:e]
+                for s, e in kv_range_for_decode
+            ])
+            key_cache = self.attn.impl.k_cache(cached_k, key_cache,
+                                               slot_mapping)
+            value_cache = self.attn.impl.v_cache(cached_v, value_cache,
+                                                 slot_mapping)
 
         q_len = q.shape[0]
         kv_len = k.shape[0]
diff --git a/vllm/worker/hpu_model_runner.py b/vllm/worker/hpu_model_runner.py
index 9fdfbe0c7b96..4c4f6c0d4e15 100755
--- a/vllm/worker/hpu_model_runner.py
+++ b/vllm/worker/hpu_model_runner.py
@@ -2747,7 +2747,7 @@ def create_dummy_seq_group_metadata(self,
             sampling_params = None
         else:
             sampling_params = SamplingParams(temperature=temperature)
-            num_blocks = math.ceil(seq_len / self.block_size)
+        num_blocks = math.ceil(seq_len / self.block_size)
         seq_len = max(seq_len, 1)
         computed_block_nums = None
         if is_prompt: