diff --git a/mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/random_choice_with_mask_impl.cu b/mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/random_choice_with_mask_impl.cu
index 6ce1fda22b..436f962131 100644
--- a/mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/random_choice_with_mask_impl.cu
+++ b/mindspore/ccsrc/backend/kernel_compiler/gpu/cuda_impl/random_choice_with_mask_impl.cu
@@ -134,7 +134,7 @@ template <typename T>
 __global__ void Sort(const int ceil_power2, T *rank_buff) {
   for (size_t i = 2; i <= ceil_power2; i <<= 1) {
     for (size_t j = (i >> 1); j > 0; j >>= 1) {
-      for (size_t tid = blockIdx.x * blockDim.x + threadIdx.x; tid < ceil_power2; tid += blockDim.x * gridDim.x) {
+      for (size_t tid = threadIdx.x; tid < ceil_power2; tid += blockDim.x) {
         size_t tid_comp = tid ^ j;
         if (tid_comp > tid) {
           if ((tid & i) == 0) {
@@ -165,7 +165,7 @@ __global__ void Shuffle(const int ceil_power2, curandState *globalState, T *rank
   int value;
   for (size_t i = 2; i <= ceil_power2; i <<= 1) {
     for (size_t j = (i >> 1); j > 0; j >>= 1) {
-      for (size_t tid = blockIdx.x * blockDim.x + threadIdx.x; tid < ceil_power2; tid += blockDim.x * gridDim.x) {
+      for (size_t tid = threadIdx.x; tid < ceil_power2; tid += blockDim.x) {
         size_t tid_comp = tid ^ j;
         if (tid_comp > tid) {
           value = static_cast<int>(curand(&globalState[tid]));
@@ -249,10 +249,10 @@ void CalRandomChoiceWithMask(const int &input_size, const int &input_shape_size,
   Reshape2Index<<<GET_BLOCKS(input_size), GET_THREADS, 0, stream>>>(input_size, input_shape_size, d1, d2, d3, d4, d5,
                                                                     input, index_buff);
 
-  Sort<<<GET_BLOCKS(ceil_power2), GET_THREADS, 0, stream>>>(ceil_power2, rank_buff);
+  Sort<<<1, GET_THREADS, 0, stream>>>(ceil_power2, rank_buff);
 
   SrandInit<<<GET_BLOCKS(ceil_power2), GET_THREADS, 0, stream>>>(ceil_power2, globalState, seedc);
-  Shuffle<<<GET_BLOCKS(ceil_power2), GET_THREADS, 0, stream>>>(ceil_power2, globalState, rank_buff);
+  Shuffle<<<1, GET_THREADS, 0, stream>>>(ceil_power2, globalState, rank_buff);
 
   MoveToOutput<<<GET_BLOCKS(count), GET_THREADS, 0, stream>>>(input_shape_size, count, input, output_index, output_mask,
                                                               index_buff, rank_buff, Tnum_buff);
diff --git a/tests/st/ops/gpu/test_random_choice_with_mask.py b/tests/st/ops/gpu/test_random_choice_with_mask.py
index 3ca12e7dd6..07bbd26e4e 100644
--- a/tests/st/ops/gpu/test_random_choice_with_mask.py
+++ b/tests/st/ops/gpu/test_random_choice_with_mask.py
@@ -49,38 +49,37 @@ class RCWM_3D(nn.Cell):
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_RCWM_3D():
-    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
     input_tensor = Tensor(np.ones([3, 4, 5]).astype(np.bool))
-    expect1 = [[0, 1, 1], [0, 2, 1], [0, 2, 2], [1, 0, 1], [0, 1, 3], [0, 3, 0], [1, 3, 2], \
-    [0, 0, 0], [1, 1, 2], [1, 3, 4]]
-    expect2 = [True, True, True, True, True, True, True, True, True, True]
+    expect1 = (10, 3)
+    expect2 = (10,)
     rcwm = RCWM_3D()
     output1, output2 = rcwm(input_tensor)
-    assert np.all(output1.asnumpy() == np.array(expect1)), "output: {}, expect: {}".format(output1, expect1)
-    assert np.all(output2.asnumpy() == np.array(expect2)), "output: {}, expect: {}".format(output2, expect2)
+    assert output1.shape == expect1
+    assert output2.shape == expect2
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_RCWM_count_out():
-    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
     input_tensor = Tensor(np.array([[1, 0, 1, 0], [0, 0, 0, 1], [1, 1, 1, 1], [0, 0, 0, 1]]).astype(np.bool))
-    expect1 = [[0, 2], [2, 2], [2, 1], [2, 0], [0, 0], [3, 3], [2, 3], [1, 3], [0, 0], [0, 0]]
-    expect2 = [True, True, True, True, True, True, True, True, False, False]
+    expect1 = (10, 2)
+    expect2 = (10,)
     rcwm = RCWM_count_out()
     output1, output2 = rcwm(input_tensor)
-    assert np.all(output1.asnumpy() == np.array(expect1)), "output: {}, expect: {}".format(output1, expect1)
-    assert np.all(output2.asnumpy() == np.array(expect2)), "output: {}, expect: {}".format(output2, expect2)
+    assert output1.shape == expect1
+    assert output2.shape == expect2
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard
 def test_RCWM_count_in():
-    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
+    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
     input_tensor = Tensor(np.array([[1, 0, 1, 0], [0, 0, 0, 1], [1, 1, 1, 1], [0, 0, 0, 1]]).astype(np.bool))
-    expect1 = [[0, 2], [2, 2], [2, 1], [2, 0]]
-    expect2 = [True, True, True, True]
+    expect1 = (4, 2)
+    expect2 = (4,)
     rcwm = RCWM_count_in()
     output1, output2 = rcwm(input_tensor)
-    assert np.all(output1.asnumpy() == np.array(expect1)), "output: {}, expect: {}".format(output1, expect1)
-    assert np.all(output2.asnumpy() == np.array(expect2)), "output: {}, expect: {}".format(output2, expect2)
+    assert output1.shape == expect1
+    assert output2.shape == expect2