[FAW] move coloparam setting in test code.
diff --git a/colossalai/nn/_ops/cache_embedding/parallel_freq_aware_embedding.py b/colossalai/nn/_ops/cache_embedding/parallel_freq_aware_embedding.py
index 4400d6fc2034..083076532c91 100644
--- a/colossalai/nn/_ops/cache_embedding/parallel_freq_aware_embedding.py
+++ b/colossalai/nn/_ops/cache_embedding/parallel_freq_aware_embedding.py
@@ -67,9 +67,6 @@ def __init__(self,
             self.init_parameters()
         else:
             assert isinstance(_weight, ColoParameter), "initialized weight must in type of ColoParameter"
-            _weight.process_group = ProcessGroup(tp_degree=self.world_size)
-            _weight.set_tensor_spec(ShardSpec(dims=[-1], num_partitions=[self.world_size]),
-                                    ComputeSpec(ComputePattern.TP1D))
             self._weight = _weight
 
     @property
diff --git a/tests/test_tensor/ops/test_cache_embedding.py b/tests/test_tensor/ops/test_cache_embedding.py
index ac5b3bc40853..8471975dfc9e 100644
--- a/tests/test_tensor/ops/test_cache_embedding.py
+++ b/tests/test_tensor/ops/test_cache_embedding.py
@@ -8,11 +8,9 @@
 import colossalai
 from colossalai.utils import free_port
 from colossalai.testing import rerun_if_address_is_in_use
-from colossalai.tensor import ColoParameter
+from colossalai.tensor import ColoParameter, ProcessGroup, ShardSpec, ComputePattern, ComputeSpec
 from colossalai.nn._ops.cache_embedding import CachedParamMgr, FreqAwareEmbeddingBag, ParallelFreqAwareEmbeddingBag
 
-from colossalai.nn._ops.cache_embedding import CachedParamMgr, FreqAwareEmbeddingBag
-
 NUM_EMBED, EMBED_DIM = 10, 8
 BATCH_SIZE = 8
 
@@ -161,6 +159,11 @@ def run_parallel_freq_aware_embed(rank, world_size):
     weight = torch.rand(num_embed, embed_dim)
     coloweight = ColoParameter(weight.clone().detach().cpu(), requires_grad=False)
 
+    # initialize the tensor spec for the embedding weight parameter,
+    # which is an ColoParameter.
+    coloweight.process_group = ProcessGroup(tp_degree=world_size)
+    coloweight.set_tensor_spec(ShardSpec(dims=[-1], num_partitions=[world_size]), ComputeSpec(ComputePattern.TP1D))
+
     model = ParallelFreqAwareEmbeddingBag.from_pretrained(coloweight,
                                                           include_last_offset=True,
                                                           freeze=False,
