diff --git a/lmdeploy/pytorch/backends/dlinfer/ascend/op_backend.py b/lmdeploy/pytorch/backends/dlinfer/ascend/op_backend.py
index 273a6ca42c..94b1422e2a 100644
--- a/lmdeploy/pytorch/backends/dlinfer/ascend/op_backend.py
+++ b/lmdeploy/pytorch/backends/dlinfer/ascend/op_backend.py
@@ -212,7 +212,7 @@ def get_total_slots():
         elif is_unpaged_prefill:
             # prepare some params of unpaged_prefill attention stage.
             q_start_loc_cpu, kv_seqlens_cpu = None, None
-            q_seqlens_cpu = step_context.q_seqlens.cpu()
+            q_seqlens_cpu = step_context.q_seqlens.cpu().to(torch.int32)
             if SocVersion.is_Ascend910():
                 single_attention_mask = torch.logical_not(
                     torch.tril(
@@ -251,7 +251,7 @@ def get_total_slots():
                     step_context.block_offsets = step_context.block_offsets\
                         .repeat_interleave(step_context.q_seqlens, 0)
             dynamo.mark_dynamic(step_context.block_offsets, [0, 1])
-            kv_seqlens = step_context.kv_seqlens.to(torch.int32)
+            kv_seqlens = step_context.kv_seqlens.cpu().to(torch.int32)
             if not step_context.is_decoding:
                 if is_unpaged_prefill:
                     if SocVersion.is_Ascend910():
@@ -270,10 +270,10 @@ def get_total_slots():
                         raise ValueError(f"dlinfer doesn't support {SocVersion.device_name()} device currently.")
                     kv_seqlens = kv_seqlens.repeat_interleave(step_context.q_seqlens, 0)
             if not is_unpaged_prefill and AscendOpsBackend.enable_aclgraph():
-                kv_seqlens = kv_seqlens.cpu().tolist()
+                kv_seqlens = kv_seqlens.cpu().to(torch.int32)
         else:
             if step_context.is_decoding:
-                kv_seqlens_cpu = step_context.kv_seqlens.cpu()
+                kv_seqlens_cpu = step_context.kv_seqlens.cpu().to(torch.int32)
             elif is_unpaged_prefill:
                 pass
             else:
diff --git a/lmdeploy/pytorch/backends/dlinfer/rotary_embedding.py b/lmdeploy/pytorch/backends/dlinfer/rotary_embedding.py
index 16dfc99dea..87d4626a15 100644
--- a/lmdeploy/pytorch/backends/dlinfer/rotary_embedding.py
+++ b/lmdeploy/pytorch/backends/dlinfer/rotary_embedding.py
@@ -40,14 +40,13 @@ def _rotary_embedding_fwd(position_ids: torch.Tensor,
 class DlinferRotaryEmbeddingImpl(RotaryEmbeddingImpl, nn.Module):
     """Base rotary embedding."""
 
-    def __init__(self, dim: int, base: int = 10000, scaling_factor: float = 1.0):
+    def __init__(self, dim: int, base: float = 10000.0, scaling_factor: float = 1.0):
         super().__init__()
         self.scaling_factor = scaling_factor
         self.dim = dim
         self.base = base
         # yapf: disable
-        inv_freq = 1.0 / (self.base
-                          ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float() / self.dim)).float().cuda()
+        inv_freq = 1.0 / (self.base**(torch.arange(0, self.dim, 2, dtype=torch.float, device='cuda') / self.dim))
         # yapf: enable
         self.register_buffer('inv_freq', inv_freq, persistent=False)