diff --git a/test/nn/test_linear_functions.py b/test/nn/test_linear_functions.py
index 8fd8be8a4ce49bdb43b4addaf3d3cbaa7d77712f..ec7795483a5ad30d4f8f7c575e818ff77647f887 100644
--- a/test/nn/test_linear_functions.py
+++ b/test/nn/test_linear_functions.py
@@ -4,36 +4,54 @@ import unittest
 import torch
 import torch.nn.functional as F
 import torch_npu
+import numpy as np
 
 from torch_npu.testing.testcase import TestCase, run_tests
+from torch_npu.testing.common_utils import create_common_tensor
 
+DEVICE_NAME = torch_npu.npu.get_device_name(0)[:10]
 
 class TestLinearFunctions(TestCase):
-    @unittest.skip("skip test_linear now")
     def test_linear(self):
-        input1 = torch.randn(2, 3, 4)
-        weight = torch.randn(3, 4)
-        npu_input = copy.deepcopy(input1).npu()
-        npu_weight = copy.deepcopy(weight).npu()
+        cpu_input, npu_input = create_common_tensor([np.float16, 2, [2, 3, 4]], 0, 1)
+        cpu_weight, npu_weight = create_common_tensor([np.float16, 2, [3, 4]], 0, 1)
 
-        cpu_output = F.linear(input1, weight)
+        cpu_output = F.linear(cpu_input, cpu_weight)
+        npu_output = F.linear(npu_input, npu_weight)
+
+        self.assertRtolEqual(cpu_output.numpy(), npu_output.cpu().numpy())
+
+    @unittest.skipIf(DEVICE_NAME == 'Ascend910A',
+        "fp32 is not supported on 910A, skip this ut for this device type!")
+    def test_linear_32(self):
+        cpu_input, npu_input = create_common_tensor([np.float32, 2, [2, 3, 4]], 0, 1)
+        cpu_weight, npu_weight = create_common_tensor([np.float32, 2, [3, 4]], 0, 1)
+
+        cpu_output = F.linear(cpu_input, cpu_weight)
         npu_output = F.linear(npu_input, npu_weight)
 
         self.assertRtolEqual(cpu_output.numpy(), npu_output.cpu().numpy())
     
-    @unittest.skip("skip test_bilinear now")
     def test_bilinear(self):
-        input1 = torch.randn(10, 30)
-        input2 = torch.randn(10, 40)
-        weight = torch.randn(5, 30, 40)
-        bias = torch.randn(5)
+        cpu_input1, npu_input1 = create_common_tensor([np.float16, 2, [10, 30]], 0, 1)
+        cpu_input2, npu_input2 = create_common_tensor([np.float16, 2, [10, 40]], 0, 1)
+        cpu_weight, npu_weight = create_common_tensor([np.float16, 2, [5, 30, 40]], 0, 1)
+        cpu_bias, npu_bias = create_common_tensor([np.float16, 2, [5]], 0, 1)
+
+        cpu_output = F.bilinear(cpu_input1, cpu_input2, cpu_weight, cpu_bias)
+        npu_output = F.bilinear(npu_input1, npu_input2, npu_weight, npu_bias)
+
+        self.assertRtolEqual(cpu_output.numpy(), npu_output.cpu().numpy())
 
-        npu_input1 = copy.deepcopy(input1).npu()
-        npu_input2 = copy.deepcopy(input2).npu()
-        npu_weight = copy.deepcopy(weight).npu()
-        npu_bias = copy.deepcopy(bias).npu()
+    @unittest.skipIf(DEVICE_NAME == 'Ascend910A',
+        "fp32 is not supported on 910A, skip this ut for this device type!")
+    def test_bilinear_32(self):
+        cpu_input1, npu_input1 = create_common_tensor([np.float32, 2, [10, 30]], 0, 1)
+        cpu_input2, npu_input2 = create_common_tensor([np.float32, 2, [10, 40]], 0, 1)
+        cpu_weight, npu_weight = create_common_tensor([np.float32, 2, [5, 30, 40]], 0, 1)
+        cpu_bias, npu_bias = create_common_tensor([np.float32, 2, [5]], 0, 1)
 
-        cpu_output = F.bilinear(input1, input2, weight, bias)
+        cpu_output = F.bilinear(cpu_input1, cpu_input2, cpu_weight, cpu_bias)
         npu_output = F.bilinear(npu_input1, npu_input2, npu_weight, npu_bias)
 
         self.assertRtolEqual(cpu_output.numpy(), npu_output.cpu().numpy())