diff --git a/tests/st/probability/distribution/test_categorical_gpu.py b/tests/st/probability/distribution/test_categorical_gpu.py
new file mode 100644
index 0000000000..c306e7f633
--- /dev/null
+++ b/tests/st/probability/distribution/test_categorical_gpu.py
@@ -0,0 +1,63 @@
+# Copyright 2021 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.
+# ============================================================================
+"""test cases for categorical distribution"""
+
+import numpy as np
+import mindspore.context as context
+import mindspore.nn as nn
+import mindspore.nn.probability.distribution as msd
+from mindspore import Tensor
+from mindspore import dtype as ms
+import pytest
+
+context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
+
+def generate_probs(seed, shape):
+    np.random.seed(seed)
+    probs = np.random.dirichlet(np.ones(shape[3]), size=1)
+    for _ in range(shape[0] - 1):
+        for _ in range(shape[1] - 1):
+            for _ in range(shape[2] - 1):
+                probs = np.vstack(((np.random.dirichlet(np.ones(shape[3]), size=1)), probs))
+            probs = np.array([probs, probs])
+        probs = np.array([probs, probs])
+    return probs
+
+
+class CategoricalProb(nn.Cell):
+    def __init__(self, probs, seed=10, dtype=ms.int32, name='Categorical'):
+        super().__init__()
+        self.b = msd.Categorical(probs, seed, dtype, name)
+
+    def construct(self, value, probs=None):
+        out1 = self.b.prob(value, probs)
+        out2 = self.b.log_prob(value, probs)
+        out3 = self.b.cdf(value, probs)
+        out4 = self.b.log_cdf(value, probs)
+        out5 = self.b.survival_function(value, probs)
+        out6 = self.b.log_survival(value, probs)
+        return out1, out2, out3, out4, out5, out6
+
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_probability_categorical_prob_cdf_probs_none():
+    probs = None
+    probs1 = generate_probs(3, shape=(2, 2, 1, 64))
+    value = np.random.randint(0, 63, size=(64)).astype(np.float32)
+    net = CategoricalProb(probs)
+    net(Tensor(value), Tensor(probs1))
diff --git a/tests/st/probability/distribution/test_cauchy_pynative.py b/tests/st/probability/distribution/test_cauchy_pynative.py
new file mode 100644
index 0000000000..13082d7cd8
--- /dev/null
+++ b/tests/st/probability/distribution/test_cauchy_pynative.py
@@ -0,0 +1,72 @@
+# Copyright 2021 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.
+# ============================================================================
+"""test cases for cauchy distribution"""
+
+import numpy as np
+import mindspore.context as context
+import mindspore.nn as nn
+import mindspore.nn.probability.distribution as msd
+from mindspore import Tensor
+from mindspore import dtype as ms
+import pytest
+
+context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
+
+
+class CauchyMean(nn.Cell):
+    def __init__(self, loc, scale, seed=10, dtype=ms.float32, name='Cauchy'):
+        super().__init__()
+        self.b = msd.Cauchy(loc, scale, seed, dtype, name)
+
+    def construct(self):
+        out4 = self.b.entropy()
+        return out4
+
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.env_onecard
+def test_probability_cauchy_mean_loc_scale_rand_2_ndarray():
+    loc = np.random.randn(1024, 512, 7, 7).astype(np.float32)
+    scale = np.random.uniform(0.0001, 100, size=(1024, 512, 7, 7)).astype(np.float32)
+    net = CauchyMean(loc, scale)
+    net()
+
+
+class CauchyProb(nn.Cell):
+    def __init__(self, loc, scale, seed=10, dtype=ms.float32, name='Cauchy'):
+        super().__init__()
+        self.b = msd.Cauchy(loc, scale, seed, dtype, name)
+
+    def construct(self, value):
+        out1 = self.b.prob(value)
+        out2 = self.b.log_prob(value)
+        out3 = self.b.cdf(value)
+        out4 = self.b.log_cdf(value)
+        out5 = self.b.survival_function(value)
+        out6 = self.b.log_survival(value)
+        return out1, out2, out3, out4, out5, out6
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.env_onecard
+def test_probability_cauchy_prob_cdf_loc_scale_rand_4_ndarray():
+    loc = np.random.randn(1024, 512, 7, 7).astype(np.float32)
+    scale = np.random.uniform(0.0001, 100, size=(1024, 512, 7, 7)).astype(np.float32)
+    value = np.random.randn(1024, 512, 7, 7).astype(np.float32)
+    net = CauchyProb(loc, scale)
+    net(Tensor(value))
diff --git a/tests/st/probability/distribution/test_gamma_pynative.py b/tests/st/probability/distribution/test_gamma_pynative.py
new file mode 100644
index 0000000000..fc80249dfe
--- /dev/null
+++ b/tests/st/probability/distribution/test_gamma_pynative.py
@@ -0,0 +1,49 @@
+# Copyright 2021 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.
+# ============================================================================
+"""test cases for gamma distribution"""
+
+import numpy as np
+import mindspore.context as context
+import mindspore.nn as nn
+import mindspore.nn.probability.distribution as msd
+from mindspore import dtype as ms
+import pytest
+
+context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
+
+
+class GammaMean(nn.Cell):
+    def __init__(self, concentration, rate, seed=10, dtype=ms.float32, name='Gamma'):
+        super().__init__()
+        self.b = msd.Gamma(concentration, rate, seed, dtype, name)
+
+    def construct(self):
+        out1 = self.b.mean()
+        out2 = self.b.mode()
+        out3 = self.b.var()
+        out4 = self.b.entropy()
+        out5 = self.b.sd()
+        return out1, out2, out3, out4, out5
+
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.env_onecard
+def test_probability_gamma_mean_cdoncentration_rate_rand_2_ndarray():
+    concentration = np.random.uniform(0.0001, 100, size=(1024, 512, 7, 7)).astype(np.float32)
+    rate = np.random.uniform(0.0001, 100, size=(1024, 512, 7, 7)).astype(np.float32)
+    net = GammaMean(concentration, rate)
+    net()