diff --git a/tests/st/ops/gpu/test_einsum_op.py b/tests/st/ops/gpu/test_einsum_op.py
index a08babf453e..3db962dfdd6 100644
--- a/tests/st/ops/gpu/test_einsum_op.py
+++ b/tests/st/ops/gpu/test_einsum_op.py
@@ -46,7 +46,7 @@ def einsum_test_cases(nptype, loss):
     test_cases = [["abcd->dacb", [[2, 3, 1, 1]]],
                   ["ijk->ik", [[1, 2, 3]]],
                   ["ij,ij->ij", [[2, 3], [2, 3]]],
-                  ["ij,kl->ijkl", [[1, 2], [3, 4]]],
+                  ["ij,kl->ijkl", [[3, 2], [2, 3]]],
                   ["ij,jk->ik", [[3, 2], [2, 3]]]
                  ]
     for cur_case in test_cases:
@@ -55,29 +55,17 @@ def einsum_test_cases(nptype, loss):
         ms_data = []
         np_data = []
         for cur_shape in shapes:
-            cur_data = np.random.randn(*cur_shape).astype(np.float64)
+            cur_data = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).reshape(cur_shape).astype(np.float64)
             ms_data.append(Tensor(cur_data.astype(nptype)))
             np_data.append(cur_data)
         net = Einsum(equation)
         ms_out = net(*ms_data)
         np_out = np.einsum(equation, *np_data)
         assert np.allclose(ms_out.asnumpy(), np_out.astype(nptype), loss, loss)
-        np_dout = np.random.randn(*np_out.shape).astype(nptype)
         grad_net = EinsumGrad(equation)
-        ms_dx = grad_net(*ms_data, Tensor(np_dout))
+        ms_dx = grad_net(*ms_data, Tensor(np_out.astype(nptype)))
         print(ms_dx)
 
-@pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
-@pytest.mark.env_onecard
-def test_einsum_graph_float32():
-    """
-    Feature: test transpose/ reduce_sum/dot/mul/transpose_with_ell/batchmatmul
-    Description: test the accuracy and precision of the preceding test cases in float32 types
-    Expectation: the diff between the result and the operator of np.einsum is within the loss range
-    """
-    einsum_test_cases(np.float32, 1e-4)
-
 @pytest.mark.level0
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
@@ -89,6 +77,17 @@ def test_einsum_graph_float16():
     """
     einsum_test_cases(np.float16, 1e-3)
 
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_einsum_graph_float32():
+    """
+    Feature: test transpose/ reduce_sum/dot/mul/transpose_with_ell/batchmatmul
+    Description: test the accuracy and precision of the preceding test cases in float32 types
+    Expectation: the diff between the result and the operator of np.einsum is within the loss range
+    """
+    einsum_test_cases(np.float32, 1e-4)
+
 @pytest.mark.level0
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard