diff --git a/optimum/habana/transformers/models/deepseek_v2/modeling_deepseek_v2.py b/optimum/habana/transformers/models/deepseek_v2/modeling_deepseek_v2.py
index 4d7e3320be..5d794c9810 100644
--- a/optimum/habana/transformers/models/deepseek_v2/modeling_deepseek_v2.py
+++ b/optimum/habana/transformers/models/deepseek_v2/modeling_deepseek_v2.py
@@ -1203,7 +1203,16 @@ def prefill_forward(
                     "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
                     "with a layer index."
                 )
-            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+            if token_idx is None:
+                if hasattr(past_key_value, "get_usable_length"):
+                    kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+                else:
+                    kv_seq_len += past_key_value[0].shape[-2]
+            else:
+                if reuse_cache:
+                    kv_seq_len = past_key_value[0][-2]
+                else:
+                    kv_seq_len = past_key_value[0].shape[-2]
         cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
         q_pe, k_pe = apply_customized_rope(q_pe, k_pe, cos, sin, position_ids)
         # update & get all compressed_kv, k_pe
@@ -1247,7 +1256,7 @@ def prefill_forward(
                 f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
                 f" {attn_weights.size()}"
             )
-
+        assert attention_mask is not None
         if attention_mask is not None:
             if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
                 raise ValueError(
@@ -1360,14 +1369,14 @@ def decode_forward(
             torch.matmul(q_pe, k_pe.mT) + torch.matmul(q_nope, compressed_kv.unsqueeze(-3).mT)
         ) * self.softmax_scale
 
+        assert attention_mask is not None
         if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
             raise ValueError(
                 f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
                 f" {attn_weights.size()}"
             )
-        assert attention_mask is not None
-        if attention_mask is not None:
-            attn_weights = attn_weights + attention_mask
+
+        attn_weights = attn_weights + attention_mask
 
         # upcast attention to fp32
         attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(q_nope.dtype)