[FIX_FOR_VLLM_CUSTOM=14acf429ac08b6d538ca6feb3e06b6d13895804d] Fix CPUOffloadingSpec import path and remove obsolete roberta patch

vllm_gaudi/__init__.py

@@ -112,6 +112,5 @@ def register_models():
     import vllm_gaudi.models.utils  # noqa: F401
     import vllm_gaudi.models.interfaces  # noqa: F401
     import vllm_gaudi.models.bert  # noqa: F401
-    import vllm_gaudi.models.roberta  # noqa: F401
     from .models import register_model
     register_model()

vllm_gaudi/attention/backends/hpu_attn.py

@@ -649,6 +649,9 @@ def forward(
                         dtype=self.alibi_slopes.dtype,
                     )
 
+            if key_cache is None:
+                return torch.zeros(*output_shape, dtype=query.dtype, device=query.device)
+
             output = HPUPagedAttention.forward_decode(query=query,
                                                       block_mapping=block_mapping,
                                                       block_bias=attn_bias,

vllm_gaudi/attention/oot_mla.py

@@ -55,7 +55,7 @@ def forward(
             attn_metadata = forward_context.attn_metadata
             if isinstance(attn_metadata, dict):
                 attn_metadata = attn_metadata[self.layer_name]
-            self_kv_cache = self.kv_cache[0]
+            self_kv_cache = self.kv_cache
             #slot_mapping = forward_context.slot_mapping
 
             #assert isinstance(slot_mapping, dict), (

vllm_gaudi/models/qwen3_5.py

@@ -70,9 +70,8 @@ def _extract_metadata(self, num_tokens):
         is_prompt = bool(getattr(attn_metadata, "is_prompt", False))
         state_indices = self._resolve_state_indices(attn_metadata)
 
-        self_kv_cache = self.kv_cache[0]
-        conv_state = self_kv_cache[0]
-        ssm_state = self_kv_cache[1]
+        conv_state = self.kv_cache[0]
+        ssm_state = self.kv_cache[1]
 
         query_start_loc = attn_metadata.query_start_loc_p
         has_initial_state = getattr(attn_metadata, "has_initial_states_p", None)

vllm_gaudi/ops/hpu_attention.py

@@ -53,7 +53,7 @@ def patched_attention_forward(
     attn_metadata = forward_context.attn_metadata
     if isinstance(attn_metadata, dict):
         attn_metadata = attn_metadata[self.layer_name]
-    self_kv_cache = self.kv_cache[0]
+    self_kv_cache = self.kv_cache
     return self.impl.forward(self, query, key, value, self_kv_cache, attn_metadata)
 
 

vllm_gaudi/ops/hpu_grouped_topk_router.py

@@ -5,8 +5,6 @@
 import vllm
 
 from vllm import envs as envs
-from vllm.model_executor.layers.batch_invariant import (
-    vllm_is_batch_invariant, )
 from vllm.model_executor.utils import maybe_disable_graph_partition
 from vllm.platforms import current_platform
 from vllm.model_executor.layers.fused_moe.router.grouped_topk_router import (GroupedTopk, fused_grouped_topk)
@@ -55,7 +53,7 @@ def grouped_topk(
         raise ValueError(f"Unsupported scoring function: {scoring_func}")
 
     # For batch invariance, use sorted=True to ensure deterministic expert selection
-    use_sorted = vllm_is_batch_invariant()
+    use_sorted = envs.VLLM_BATCH_INVARIANT
 
     num_token = scores.size(0)
 
@@ -130,7 +128,7 @@ def forward_oot(
             raise ValueError(f"Unsupported scoring function: {self.scoring_func}")
 
         # For batch invariance, use sorted=True to ensure deterministic expert selection
-        use_sorted = vllm_is_batch_invariant()
+        use_sorted = envs.VLLM_BATCH_INVARIANT
 
         num_token = scores.size(0)
         if e_score_correction_bias is not None:

vllm_gaudi/v1/kv_offload/worker/cpu_hpu.py

@@ -18,7 +18,7 @@
     OffloadingHandler,
     TransferSpec,
 )
-from vllm.v1.kv_offload.cpu import CPUOffloadingSpec
+from vllm.v1.kv_offload.cpu.spec import CPUOffloadingSpec
 from vllm.v1.kv_offload.abstract import LoadStoreSpec
 from vllm.v1.kv_offload.mediums import CPULoadStoreSpec, GPULoadStoreSpec
 from vllm.v1.kv_offload.worker.cpu_gpu import (SingleDirectionOffloadingHandler, CpuGpuOffloadingHandlers)