diff --git a/colossalai/fx/_meta_registrations.py b/colossalai/fx/_meta_registrations.py
index d614219db..8c0201c71 100644
--- a/colossalai/fx/_meta_registrations.py
+++ b/colossalai/fx/_meta_registrations.py
@@ -200,19 +200,56 @@ def meta_adaptive_avg_pool2d_backward(
 # https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cudnn/RNN.cpp
 @register_meta(aten._cudnn_rnn.default)
 def meta_cuda_rnn(
-    input: torch.Tensor,
-    weight: torch.Tensor,
-    weight_stride0: int,
-    weight_buf: torch.Tensor,
-    hx: torch.Tensor,
-    cx: Optional[torch.Tensor] = None,
-    *args,
-    **kwargs,
+    input,
+    weight,
+    weight_stride0,
+    weight_buf,
+    hx,
+    cx,
+    mode,
+    hidden_size,
+    proj_size,
+    num_layers,
+    batch_first,
+    dropout,
+    train,
+    bidirectional,
+    batch_sizes,
+    dropout_state,
 ):
-    if cx is not None:
-        return torch.empty_like(input), torch.empty_like(hx), torch.empty_like(cx)
+
+    is_input_packed = len(batch_sizes) != 0
+    if is_input_packed:
+        seq_length = len(batch_sizes)
+        mini_batch = batch_sizes[0]
+        batch_sizes_sum = input.shape[0]
     else:
-        return torch.empty_like(input), torch.empty_like(hx), torch.empty((), device='meta')
+        seq_length = input.shape[1] if batch_first else input.shape[0]
+        mini_batch = input.shape[0] if batch_first else input.shape[1]
+        batch_sizes_sum = -1
+
+    num_directions = 2 if bidirectional else 1
+    out_size = proj_size if proj_size != 0 else hidden_size
+    if is_input_packed:
+        out_shape = [batch_sizes_sum, out_size * num_directions]
+    else:
+        out_shape = (
+            [mini_batch, seq_length, out_size * num_directions]
+            if batch_first
+            else [seq_length, mini_batch, out_size * num_directions]
+        )
+    output = input.new_empty(out_shape)
+
+    cell_shape = [num_layers * num_directions, mini_batch, hidden_size]
+    cy = torch.empty(0) if cx is None else cx.new_empty(cell_shape)
+
+    hy = hx.new_empty([num_layers * num_directions, mini_batch, out_size])
+
+    # TODO: Query cudnnGetRNNTrainingReserveSize (expose to python)
+    reserve_shape = 0 if train else 0
+    reserve = input.new_empty(reserve_shape, dtype=torch.uint8)
+
+    return output, hy, cy, reserve, weight_buf
 
 
 # https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/cudnn/RNN.cpp