diff --git a/src/operator/subgraph/mkldnn/mkldnn_fc-inl.h b/src/operator/subgraph/mkldnn/mkldnn_fc-inl.h
index de8a2569261d..d0881086ac97 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_fc-inl.h
+++ b/src/operator/subgraph/mkldnn/mkldnn_fc-inl.h
@@ -33,9 +33,13 @@ namespace op {
 static inline bool SupportMKLDNNFCEltwiseFusion(const std::string op_name) {
   if (op_name == "Activation" ||
       op_name == "square" ||
+      op_name == "_npi_square" ||
       op_name == "sqrt" ||
+      op_name == "_npi_sqrt" ||
       op_name == "exp" ||
+      op_name == "_npi_exp" ||
       op_name == "abs" ||
+      op_name == "_npi_absolute" ||
       op_name == "clip" ||
       op_name == "LeakyReLU") {
     return true;
@@ -45,13 +49,13 @@ static inline bool SupportMKLDNNFCEltwiseFusion(const std::string op_name) {
 }
 
 static inline mkldnn::algorithm GetMKLDNNEltwiseAlgo(const std::string op_name) {
-  if (op_name == "square")
+  if (op_name == "square" || op_name == "_npi_square")
     return mkldnn::algorithm::eltwise_square;
-  else if (op_name == "sqrt")
+  else if (op_name == "sqrt" || op_name == "_npi_sqrt")
     return mkldnn::algorithm::eltwise_sqrt;
-  else if (op_name == "exp")
+  else if (op_name == "exp" || op_name == "_npi_exp")
     return mkldnn::algorithm::eltwise_exp;
-  else if (op_name == "abs")
+  else if (op_name == "abs" || op_name == "_npi_absolute")
     return mkldnn::algorithm::eltwise_abs;
   else
     LOG(FATAL) << "Unsupported eltwise fusion op: " << op_name;
diff --git a/src/operator/subgraph/mkldnn/mkldnn_fc_property.h b/src/operator/subgraph/mkldnn/mkldnn_fc_property.h
index 9a0c7770a22d..a96c71153869 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_fc_property.h
+++ b/src/operator/subgraph/mkldnn/mkldnn_fc_property.h
@@ -113,13 +113,17 @@ class SgMKLDNNFCSelector : public SubgraphSelector {
           }
         }
         if (!quantized_ && (new_node.op() == Op::Get("square") ||
-            new_node.op() == Op::Get("sqrt") ||
-            new_node.op() == Op::Get("exp"))) {
+                            new_node.op() == Op::Get("_npi_square") ||
+                            new_node.op() == Op::Get("sqrt") ||
+                            new_node.op() == Op::Get("_npi_sqrt") ||
+                            new_node.op() == Op::Get("exp") ||
+                            new_node.op() == Op::Get("_npi_exp"))) {
           matched_list_.push_back(&new_node);
           status_ = kSuccess;
           return true;
         }
-        if (new_node.op() == Op::Get("abs")) {
+        if (new_node.op() == Op::Get("abs") ||
+            new_node.op() == Op::Get("_npi_absolute")) {
           matched_list_.push_back(&new_node);
           status_ = kSuccess;
           return true;
diff --git a/tests/python/mkl/subgraphs/subgraph_common.py b/tests/python/mkl/subgraphs/subgraph_common.py
index 9f518414ac91..4467443166cb 100644
--- a/tests/python/mkl/subgraphs/subgraph_common.py
+++ b/tests/python/mkl/subgraphs/subgraph_common.py
@@ -71,13 +71,16 @@ class CustomNormalInit(mx.init.Initializer):
     """Initializes weights with random values sampled from a normal distribution
     with a custom mean and standard deviation of `sigma`.
     """
-    def __init__(self, mean=0, sigma=0.01):
-        super(CustomNormalInit, self).__init__(mean=mean, sigma=sigma)
+    def __init__(self, mean=0, sigma=0.01, bounded=False):
+        super(CustomNormalInit, self).__init__(mean=mean, sigma=sigma, bounded=bounded)
         self.mean = mean
         self.sigma = sigma
+        self.bounded = bounded
 
     def _init_weight(self, _, arr):
         mx.np.random.normal(self.mean, self.sigma, arr.shape, dtype=arr.dtype, out=arr)
+        if self.bounded:
+            mx.np.abs(arr, out=arr)
 
 
 def check_qsym_calibrated(qsym, out_type, name='conv'):
diff --git a/tests/python/mkl/subgraphs/test_fc_subgraph.py b/tests/python/mkl/subgraphs/test_fc_subgraph.py
index 1bcd332e3b8c..5c0aee7500c1 100644
--- a/tests/python/mkl/subgraphs/test_fc_subgraph.py
+++ b/tests/python/mkl/subgraphs/test_fc_subgraph.py
@@ -59,18 +59,19 @@ def forward(self, x):
 @pytest.mark.parametrize('use_bias', [True, False])
 @pytest.mark.parametrize('flatten', [True, False])
 @pytest.mark.parametrize('alg', fc_post_ops_list)
-@pytest.mark.skip("Operator square, square_root, abs, exp cannot be found in numpy mode")
 def test_fc_eltwise(data_shape, use_bias, flatten, alg):
   # fc + eltwise fusion case
   class FCEltwise(nn.HybridBlock):
     def __init__(self, use_bias, flatten, alg, **kwargs):
       super(FCEltwise, self).__init__(**kwargs)
       self.fc = nn.Dense(units=64, use_bias=use_bias, flatten=flatten,
-                         weight_initializer=CustomNormalInit(mean=0.5, sigma=0.1) if alg == 'square_root' else None)
+                         weight_initializer=CustomNormalInit(mean=0.5, sigma=0.1, bounded=True) if alg == 'square_root' else None)
                                             #avoid calculating square root of negative values
       self.alg = alg
 
     def forward(self, x):
+      if self.alg == 'square_root':
+        x = abs(x)
       fc_out = self.fc(x)
       if self.alg in ['relu', 'sigmoid', 'log_sigmoid', 'mish', 'tanh', 'softrelu']:
         out = mx.npx.activation(fc_out, act_type=self.alg)