diff --git a/vllm_ascend/ops/triton/mamba/causal_conv1d.py b/vllm_ascend/ops/triton/mamba/causal_conv1d.py
index 9fb9465b..84c330b5 100644
--- a/vllm_ascend/ops/triton/mamba/causal_conv1d.py
+++ b/vllm_ascend/ops/triton/mamba/causal_conv1d.py
@@ -595,6 +595,8 @@ def causal_conv1d_update_npu(
             indices 0 and 3
     out: (batch, dim) or (batch, dim, seqlen) or (num_tokens, dim), same shape as `x`
     """
+    weight = weight.transpose(0, 1).contiguous()
+    conv_state = conv_state.transpose(1, 2).contiguous()
     if validate_data:
         assert pad_slot_id is not None
         assert x.stride(1) == 1
@@ -608,40 +610,33 @@ def causal_conv1d_update_npu(
     unsqueeze = query_start_loc is None and x.dim() == 2
     if unsqueeze:
         # make it (batch, dim, seqlen) with seqlen == 1
-        x = x.unsqueeze(-1)
+        x = x.unsqueeze(1)
 
     if query_start_loc is None:
-        batch, dim, seqlen = x.shape
+        batch,  seqlen, dim = x.shape
     else:
         assert conv_state_indices is not None
         batch = conv_state_indices.size(0)
         dim = x.size(1)
         seqlen = max_query_len
 
-    _, width = weight.shape
-    num_cache_lines, _, state_len_total = conv_state.size()
-
-    if validate_data:
-        assert dim == weight.size(0)
-        assert conv_state.stride(-2) == 1
-        assert state_len_total >= width - 1
-        assert num_cache_lines >= batch
-        assert weight.stride(1) == 1
+    width, _ = weight.shape
+    num_cache_lines, state_len_total,_ = conv_state.size()
 
     # overwrite-on-x strategy same as original
     out = x
 
-    stride_w_dim, stride_w_width = weight.stride()
+    stride_w_width, stride_w_dim = weight.stride()
     if query_start_loc is None:
-        stride_x_seq, stride_x_dim, stride_x_token = x.stride()
-        stride_o_seq, stride_o_dim, stride_o_token = out.stride()
+        stride_x_seq, stride_x_token,stride_x_dim = x.stride()
+        stride_o_seq, stride_o_token, stride_o_dim = out.stride()
     else:
         stride_x_token, stride_x_dim = x.stride()
         stride_x_seq = 0
         stride_o_token, stride_o_dim = out.stride()
         stride_o_seq = 0
 
-    stride_istate_seq, stride_istate_dim, stride_istate_token = conv_state.stride(
+    stride_istate_seq, stride_istate_token, stride_istate_dim = conv_state.stride(
     )
     stride_state_indices = conv_state_indices.stride(
         0) if conv_state_indices is not None else 0
@@ -657,7 +652,7 @@ def causal_conv1d_update_npu(
     #keep program count around ~[80..160]
     # vector core 40
     # TODO: use driver to get the vector core num
-    CORE_HINT = 40
+    CORE_HINT = 40 
     # channel tile: 512 when dim large (reduce tasks), else 256
     block_n = 512 if dim >= 512 else 256
     g = triton.cdiv(dim, block_n)
@@ -674,14 +669,13 @@ def causal_conv1d_update_npu(
         b_tile = 8
 
     # token chunk based on block_n (32KB UB idea); conservative
-    t_chunk = 20 if block_n == 512 else 48
+    t_chunk = 1 if block_n == 512 else 48
 
     def grid(META):
         return (
             triton.cdiv(batch, META["B_TILE"]),
             triton.cdiv(dim, META["BLOCK_N"]),
         )
-
     _causal_conv1d_update_kernel_npu_tiled[grid](
         x,
         weight,
@@ -725,5 +719,5 @@ def causal_conv1d_update_npu(
     )
 
     if unsqueeze:
-        out = out.squeeze(-1)
+        out = out.squeeze(1)
     return out.to(original_x_dtype)