!31642 Add custom aicpu kernel st.

Merge pull request !31642 from linqingke/fix_custom_aicpu

mindspore/ccsrc/plugin/device/ascend/kernel/aicpu/aicpu_kernel_load.cc

@@ -105,7 +105,7 @@ bool AicpuOpKernelLoad::GetSoNeedLoadPath(const std::string &so_name, std::strin
     MS_LOG(ERROR) << "Current path [" << cust_kernel_so_path << "] is invalid.";
     return false;
   }
-  auto real_cust_kernel_so_path = cust_kernel_so_path.substr(0, pos) + "/lib/";
+  auto real_cust_kernel_so_path = cust_kernel_so_path.substr(0, pos) + "/";
 
   if (real_cust_kernel_so_path.size() > PATH_MAX) {
     MS_LOG(ERROR) << "Current path [" << real_cust_kernel_so_path << "] is too long.";

tests/st/ops/ascend/test_aicpu_ops/test_random_choice_with_mask.py

@@ -0,0 +1,73 @@
+# Copyright 2022 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import numpy as np
+import pytest
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops import operations as P
+
+
+class RandomChoiceWithMaskNet(nn.Cell):
+    def __init__(self):
+        super(RandomChoiceWithMaskNet, self).__init__()
+        self.random_choice_with_mask = P.RandomChoiceWithMask(count=4, seed=1)
+        self.random_choice_with_mask.add_prim_attr("cust_aicpu", "mindspore_aicpu_kernels")
+
+    def construct(self, x):
+        return self.random_choice_with_mask(x)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_random_choice_with_mask_graph():
+    """
+    Feature: Custom aicpu feature.
+    Description: Test random_choice_with_mask kernel in graph mode.
+    Expectation: No exception.
+    """
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    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 = (4, 2)
+    expect2 = (4,)
+    net = RandomChoiceWithMaskNet()
+    output1, output2 = net(input_tensor)
+    assert output1.shape == expect1
+    assert output2.shape == expect2
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_random_choice_with_mask_pynative():
+    """
+    Feature: Custom aicpu feature.
+    Description: Test random_choice_with_mask kernel in pynative mode.
+    Expectation: No exception.
+    """
+    context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
+    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 = (4, 2)
+    expect2 = (4,)
+    net = RandomChoiceWithMaskNet()
+    output1, output2 = net(input_tensor)
+    assert output1.shape == expect1
+    assert output2.shape == expect2