diff --git a/autoparallel/collective_runtime_estimation.py b/autoparallel/collective_runtime_estimation.py
index cbffcd1..ec3f01e 100644
--- a/autoparallel/collective_runtime_estimation.py
+++ b/autoparallel/collective_runtime_estimation.py
@@ -12,7 +12,6 @@
     allgather_cost,
     allreduce_cost,
     reduce_scatter_cost,
-    spec_to_bytes,
 )
 from torch.distributed.tensor.placement_types import Partial, Shard
 
@@ -63,29 +62,50 @@ def redistribute_cost(
 
     mesh_topo = MeshTopoInfo.build_from_mesh(current_spec.mesh)
     cost = 0.0
-    comm_bytes_gb = (
-        spec_to_bytes(current_spec) / current_spec.num_shards / 1024 / 1024 / 1024
+    import math
+
+    from torch.distributed._functional_collectives import _are_we_tracing
+    from torch.distributed.tensor._redistribute import (
+        _gen_transform_infos,
+        _gen_transform_infos_non_cached,
     )
-    # Transformation that considered for redistribute cost:
-    # 1. allgather 2. alltoall
-    # 3. allreduce 4. reduce_scatter
-    curr_placements = [current_spec.placements[i] for i in order]
-    tgt_placements = [target_spec.placements[i] for i in order]
+
+    if _are_we_tracing():
+        transform_infos = _gen_transform_infos_non_cached(current_spec, target_spec)
+    else:
+        transform_infos = _gen_transform_infos(current_spec, target_spec)
+
+        # Transformation that considered for redistribute cost:
+        # 1. allgather 2. alltoall
+        # 3. allreduce 4. reduce_scatter
+        # curr_placements = [current_spec.placements[i] for i in order]
+        # tgt_placements = [target_spec.placements[i] for i in order]
     is_contiguous: bool = check_contiguous_sizes_strides(
         current_spec.shape, current_spec.stride
     )
-    for i, current, target in zip(order, curr_placements, tgt_placements):
-        if current == target:
-            continue
-        num_devices_on_mesh_dim = mesh_topo.mesh_dim_devices[i]
+    for transform_info in transform_infos:
+        assert (
+            current_spec.tensor_meta is not None
+        ), "spec should have tensor meta defined!"
+        comm_bytes_gb = (
+            current_spec.tensor_meta.dtype.itemsize
+            * math.prod(transform_info.logical_shape)
+            / 1024
+            / 1024
+            / 1024
+        )
         if not is_contiguous:
             cost += compute_read_write_time(comm_bytes_gb * 2 * 1024**3)
+        current = transform_info.src_dst_placements[0]
+        target = transform_info.src_dst_placements[1]
+        if current == target:
+            continue
+        mesh_dim = transform_info.mesh_dim
+        num_devices_on_mesh_dim = mesh_topo.mesh_dim_devices[mesh_dim]
         if current.is_shard() and target.is_replicate():
             current = cast(Shard, current)
-            # allgather gives larger comm bytes
-            comm_bytes_gb *= num_devices_on_mesh_dim
             # add up allgather comm cost
-            cost += allgather_cost(comm_bytes_gb, mesh_topo, i)
+            cost += allgather_cost(comm_bytes_gb, mesh_topo, mesh_dim)
             if current.dim != 0:
                 # penalize cases like  S(1) -> R as there are additional compute cost
                 # which corresponds to reshuffling the whole output tensor
@@ -98,7 +118,7 @@ def redistribute_cost(
             target = cast(Shard, target)
             # should be alltoall comm, since we haven't implement it yet, add penalty
             # to favor allgather instead
-            cost += all_to_all_cost(comm_bytes_gb, mesh_topo, i)  # us
+            cost += all_to_all_cost(comm_bytes_gb, mesh_topo, mesh_dim)  # us
 
             num_copies = 0
             if current.dim != 0:
@@ -112,11 +132,11 @@ def redistribute_cost(
 
         elif current.is_partial() and target.is_replicate():
             # add up allreduce comm cost
-            cost += allreduce_cost(comm_bytes_gb, mesh_topo, i)
+            cost += allreduce_cost(comm_bytes_gb, mesh_topo, mesh_dim)
         elif current.is_partial() and target.is_shard():
             target = cast(Shard, target)
             # add up reduce_scatter comm cost
-            cost += reduce_scatter_cost(comm_bytes_gb, mesh_topo, i)
+            cost += reduce_scatter_cost(comm_bytes_gb, mesh_topo, mesh_dim)
             if target.dim != 0:
                 # penalize cases like  P -> S(1) as there are additional compute cost
                 # which corresponds to reshuffling the whole input tensor