!248 Add 5 random ops

Merge pull request !248 from peixu_ren/custom_aicpu

mindspore/ops/_op_impl/aicpu/__init__.py

@@ -26,7 +26,6 @@ from .squeeze import _squeeze_aicpu
 from .expand_dims import _expand_dims_aicpu
 from .random_choice_with_mask import _random_choice_with_mask_aicpu
 from .pack import _pack_aicpu
-from .normal import _normal_aicpu
 from .ctcloss import _ctcloss_aicpu
 from .reverse_sequence import _reverse_sequence_aicpu
 from .crop_and_resize import _crop_and_resize_aicpu
@@ -34,3 +33,8 @@ from .rnnt_loss import _rnnt_loss_aicpu
 from .random_categorical import _random_categorical_aicpu
 from .cast import _cast_aicpu
 from .mirror_pad import _mirror_pad_aicpu
+from .normal import _normal_aicpu
+from .gamma import _gamma_aicpu
+from .poisson import _poisson_aicpu
+from .uniform_int import _uniform_int_aicpu
+from .uniform_real import _uniform_real_aicpu

mindspore/ops/_op_impl/aicpu/gamma.py

@@ -0,0 +1,33 @@
+# Copyright 2020 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.
+# ============================================================================
+
+"""RandomGamma op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+
+gamma_op_info = AiCPURegOp("Gamma") \
+    .fusion_type("OPAQUE") \
+    .input(0, "shape", "required") \
+    .input(1, "alpha", "required") \
+    .input(2, "beta", "required") \
+    .output(0, "output", "required") \
+    .attr("seed", "int") \
+    .dtype_format(DataType.I32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.I32_NCHW, DataType.F32_NCHW, DataType.F32_NCHW, DataType.F32_NCHW) \
+    .get_op_info()
+
+@op_info_register(gamma_op_info)
+def _gamma_aicpu():
+    """RandomGamma AiCPU register"""
+    return

mindspore/ops/_op_impl/aicpu/normal.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ============================================================================
 
-"""Normal op"""
+"""RandomNormal op"""
 from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
 
 normal_op_info = AiCPURegOp("Normal") \
@@ -21,7 +21,7 @@ normal_op_info = AiCPURegOp("Normal") \
     .input(0, "shape", "required") \
     .input(1, "mean", "required") \
     .input(2, "stddev", "required") \
-    .output(0, "y", "required") \
+    .output(0, "output", "required") \
     .attr("seed", "int") \
     .dtype_format(DataType.I32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
     .dtype_format(DataType.I32_NCHW, DataType.F32_NCHW, DataType.F32_NCHW, DataType.F32_NCHW) \
@@ -29,5 +29,5 @@ normal_op_info = AiCPURegOp("Normal") \
 
 @op_info_register(normal_op_info)
 def _normal_aicpu():
-    """Normal AiCPU register"""
+    """RandomNormal AiCPU register"""
     return

mindspore/ops/_op_impl/aicpu/poisson.py

@@ -0,0 +1,32 @@
+# Copyright 2020 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.
+# ============================================================================
+
+"""RandomPoisson op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+
+poisson_op_info = AiCPURegOp("Poisson") \
+    .fusion_type("OPAQUE") \
+    .input(0, "shape", "required") \
+    .input(1, "mean", "required") \
+    .output(0, "output", "required") \
+    .attr("seed", "int") \
+    .dtype_format(DataType.I32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.I32_NCHW, DataType.F32_NCHW, DataType.I32_NCHW) \
+    .get_op_info()
+
+@op_info_register(poisson_op_info)
+def _poisson_aicpu():
+    """RandomPoisson AiCPU register"""
+    return

mindspore/ops/_op_impl/aicpu/uniform_int.py

@@ -0,0 +1,33 @@
+# Copyright 2020 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.
+# ============================================================================
+
+"""RandomUniformInt op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+
+uniform_int_op_info = AiCPURegOp("UniformInt") \
+    .fusion_type("OPAQUE") \
+    .input(0, "shape", "required") \
+    .input(1, "a", "required") \
+    .input(2, "b", "required") \
+    .output(0, "output", "required") \
+    .attr("seed", "int") \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.I32_NCHW, DataType.I32_NCHW, DataType.I32_NCHW, DataType.I32_NCHW) \
+    .get_op_info()
+
+@op_info_register(uniform_int_op_info)
+def _uniform_int_aicpu():
+    """RandomUniformInt AiCPU register"""
+    return

mindspore/ops/_op_impl/aicpu/uniform_real.py

@@ -0,0 +1,33 @@
+# Copyright 2020 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.
+# ============================================================================
+
+"""RandomUniformReal op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+
+uniform_real_op_info = AiCPURegOp("UniformReal") \
+    .fusion_type("OPAQUE") \
+    .input(0, "shape", "required") \
+    .input(1, "a", "required") \
+    .input(2, "b", "required") \
+    .output(0, "output", "required") \
+    .attr("seed", "int") \
+    .dtype_format(DataType.I32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.I32_NCHW, DataType.F32_NCHW, DataType.F32_NCHW, DataType.F32_NCHW) \
+    .get_op_info()
+
+@op_info_register(uniform_real_op_info)
+def _uniform_real_aicpu():
+    """RandomUniformReal AiCPU register"""
+    return

mindspore/ops/operations/__init__.py

@@ -54,7 +54,8 @@ from .math_ops import (Abs, ACos, Asin, Asinh, AddN, AccumulateNV2, AssignAdd, A
                        Sin, Sqrt, Rsqrt, BesselI0e, BesselI1e,
                        Square, Sub, TensorAdd, Sign, Round, SquareSumAll, Atan, Atanh, Cosh, Sinh, Eps)
 
-from .random_ops import (RandomChoiceWithMask, Normal, RandomCategorical)
+from .random_ops import (RandomChoiceWithMask, Normal, Gamma, Poisson, UniformInt, UniformReal,
+                         RandomCategorical)
 from .nn_ops import (LSTM, SGD, Adam, SparseApplyAdam, SparseApplyLazyAdam, ApplyMomentum, BatchNorm,
                      BiasAdd, Conv2D,
                      DepthwiseConv2dNative,
@@ -172,6 +173,10 @@ __all__ = [
     'Tanh',
     'RandomChoiceWithMask',
     'Normal',
+    'Gamma',
+    'Poisson',
+    'UniformInt',
+    'UniformReal',
     'RandomCategorical',
     'ResizeBilinear',
     'ScalarSummary',

mindspore/ops/operations/random_ops.py

@@ -19,6 +19,269 @@ from ..._checkparam import Validator as validator
 from ..._checkparam import Rel
 from ...common import dtype as mstype
 from ..primitive import PrimitiveWithInfer, prim_attr_register
+from .._utils import get_broadcast_shape
+
+
+class Normal(PrimitiveWithInfer):
+    r"""
+    Generates random numbers according to the Normal (or Gaussian) random number distribution.
+    It is defined as:
+
+    .. math::
+        \text{f}(x;μ,σ) = \frac{1}{σ\sqrt{2π}}\exp(-\frac{1}{2}(\frac{x-μ}{σ})^2),
+
+    Args:
+        seed (int): Seed data is used as entropy source for Random number engines generating pseudo-random numbers.
+          Default: 0.
+
+    Inputs:
+        - **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed.
+        - **mean** (Tensor) - The mean μ distribution parameter, The mean specifies the location of the peak.
+            With float32 data type.
+        - **stddev** (Tensor) - the deviation σ distribution parameter. With float32 data type.
+
+    Outputs:
+        Tensor, has the shape 'shape' input and dtype as float32.
+
+    Examples:
+        >>> shape = (4, 16)
+        >>> mean = Tensor(1.0, mstype.float32)
+        >>> stddev = Tensor(1.0, mstype.float32)
+        >>> normal = P.Normal(seed=2)
+        >>> output = normal(shape, mean, stddev)
+    """
+
+    @prim_attr_register
+    def __init__(self, seed=0):
+        """Init Normal"""
+        self.init_prim_io_names(inputs=['shape', 'mean', 'stddev'], outputs=['output'])
+        validator.check_value_type('seed', seed, [int], self.name)
+
+    def __infer__(self, shape, mean, stddev):
+        shape_v = shape["value"]
+        if shape_v is None:
+            raise ValueError(f"For {self.name}, shape must be const.")
+        validator.check_value_type("shape", shape_v, [tuple], self.name)
+        for i, shape_i in enumerate(shape_v):
+            validator.check_integer("shape[%d]" % i, shape_i, 0, Rel.GT, self.name)
+        validator.check_tensor_type_same({"mean": mean["dtype"]}, [mstype.float32], self.name)
+        validator.check_tensor_type_same({"stddev": stddev["dtype"]}, [mstype.float32], self.name)
+        broadcast_shape = get_broadcast_shape(mean['shape'], stddev['shape'], self.name)
+        broadcast_shape = get_broadcast_shape(broadcast_shape, shape_v, self.name)
+        out = {
+            'shape': broadcast_shape,
+            'dtype': mstype.float32,
+            'value': None}
+        return out
+
+
+class Gamma(PrimitiveWithInfer):
+    r"""
+    Produces random positive floating-point values x, distributed according to probability density function:
+
+    .. math::
+        \text{P}(x|α,β) = \frac{\exp(-x/β)}{{β^α}\cdot{\Gamma(α)}}\cdot{x^{α-1}},
+
+    Args:
+        seed (int): Seed data is used as entropy source for Random number engines generating pseudo-random numbers.
+          Default: 0.
+
+    Inputs:
+        - **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed.
+        - **alpha** (Tensor) - The α distribution parameter.
+          It is also known as the shape parameter. With float32 data type.
+        - **beta** (Tensor) - The β distribution parameter.
+          It is also known as the scale parameter. With float32 data type.
+
+    Outputs:
+        Tensor, has the shape 'shape' input and dtype as float32.
+
+    Examples:
+        >>> shape = (4, 16)
+        >>> alpha = Tensor(1.0, mstype.float32)
+        >>> beta = Tensor(1.0, mstype.float32)
+        >>> gamma = P.Gamma(seed=3)
+        >>> output = normal(shape, alpha, beta)
+    """
+
+    @prim_attr_register
+    def __init__(self, seed=0):
+        """Init Gamma"""
+        self.init_prim_io_names(inputs=['shape', 'alpha', 'beta'], outputs=['output'])
+        validator.check_value_type('seed', seed, [int], self.name)
+
+    def __infer__(self, shape, alpha, beta):
+        shape_v = shape["value"]
+        if shape_v is None:
+            raise ValueError(f"For {self.name}, shape must be const.")
+        validator.check_value_type("shape", shape_v, [tuple], self.name)
+        for i, shape_i in enumerate(shape_v):
+            validator.check_integer("shape[%d]" % i, shape_i, 0, Rel.GT, self.name)
+        validator.check_tensor_type_same({"alpha": alpha["dtype"]}, [mstype.float32], self.name)
+        validator.check_tensor_type_same({"beta": beta["dtype"]}, [mstype.float32], self.name)
+        broadcast_shape = get_broadcast_shape(alpha['shape'], beta['shape'], self.name)
+        broadcast_shape = get_broadcast_shape(broadcast_shape, shape_v, self.name)
+        out = {
+            'shape': broadcast_shape,
+            'dtype': mstype.float32,
+            'value': None}
+        return out
+
+
+class Poisson(PrimitiveWithInfer):
+    r"""
+    Produces random non-negative integer values i, distributed according to discrete probability function:
+
+    .. math::
+        \text{P}(i|μ) = \frac{\exp(-μ)μ^{i}}{i!},
+
+    Args:
+        seed (int): Seed data is used as entropy source for Random number engines generating pseudo-random numbers.
+          Default: 0.
+
+    Inputs:
+        - **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed.
+        - **mean** (Tensor) - μ parameter the distribution was constructed with.
+          The parameter defines mean number of occurrences of the event. With float32 data type.
+
+    Outputs:
+        Tensor, has the shape 'shape' input and dtype as int32.
+
+    Examples:
+        >>> shape = (4, 16)
+        >>> mean = Tensor(5.0, mstype.float32)
+        >>> poisson = P.Poisson(seed=5)
+        >>> output = poisson(shape, mean)
+    """
+
+    @prim_attr_register
+    def __init__(self, seed=0):
+        """Init Poisson"""
+        self.init_prim_io_names(inputs=['shape', 'mean'], outputs=['output'])
+        validator.check_value_type('seed', seed, [int], self.name)
+
+    def __infer__(self, shape, mean):
+        shape_v = shape["value"]
+        if shape_v is None:
+            raise ValueError(f"For {self.name}, shape must be const.")
+        validator.check_value_type("shape", shape_v, [tuple], self.name)
+        for i, shape_i in enumerate(shape_v):
+            validator.check_integer("shape[%d]" % i, shape_i, 0, Rel.GT, self.name)
+        validator.check_tensor_type_same({"mean": mean["dtype"]}, [mstype.float32], self.name)
+        broadcast_shape = get_broadcast_shape(mean['shape'], shape_v, self.name)
+        out = {
+            'shape': broadcast_shape,
+            'dtype': mstype.int32,
+            'value': None}
+        return out
+
+
+class UniformInt(PrimitiveWithInfer):
+    r"""
+    Produces random integer values i, uniformly distributed on the closed interval [a, b], that is,
+    distributed according to the discrete probability function:
+
+    .. math::
+        \text{P}(i|a,b) = \frac{1}{b-a+1},
+
+    Args:
+        seed (int): Seed data is used as entropy source for Random number engines generating pseudo-random numbers.
+          Default: 0.
+
+    Inputs:
+        - **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed.
+        - **a** (Tensor) - The a distribution parameter.
+          It defines the minimum possibly generated value. With int32 data type.
+        - **b** (Tensor) - The b distribution parameter.
+          It defines the maximum possibly generated value. With int32 data type.
+
+    Outputs:
+        Tensor, has the shape 'shape' input and dtype as int32.
+
+    Examples:
+        >>> shape = (4, 16)
+        >>> a = Tensor(1, mstype.int32)
+        >>> b = Tensor(5, mstype.int32)
+        >>> uniform_int = P.UniformInt(seed=10)
+        >>> output = uniform_int(shape, a, b)
+    """
+
+    @prim_attr_register
+    def __init__(self, seed=0):
+        """Init UniformInt"""
+        self.init_prim_io_names(inputs=['shape', 'a', 'b'], outputs=['output'])
+        validator.check_value_type('seed', seed, [int], self.name)
+
+    def __infer__(self, shape, a, b):
+        shape_v = shape["value"]
+        if shape_v is None:
+            raise ValueError(f"For {self.name}, shape must be const.")
+        validator.check_value_type("shape", shape_v, [tuple], self.name)
+        for i, shape_i in enumerate(shape_v):
+            validator.check_integer("shape[%d]" % i, shape_i, 0, Rel.GT, self.name)
+        validator.check_tensor_type_same({"a": a["dtype"]}, [mstype.int32], self.name)
+        validator.check_tensor_type_same({"b": b["dtype"]}, [mstype.int32], self.name)
+        broadcast_shape = get_broadcast_shape(a['shape'], b['shape'], self.name)
+        broadcast_shape = get_broadcast_shape(broadcast_shape, shape_v, self.name)
+        out = {
+            'shape': broadcast_shape,
+            'dtype': mstype.int32,
+            'value': None}
+        return out
+
+
+class UniformReal(PrimitiveWithInfer):
+    r"""
+    Produces random floating-point values i, uniformly distributed on the interval [a, b), that is,\
+    distributed according to the probability density function:
+
+    .. math::
+        \text{P}(i|a,b) = \frac{1}{b-a},
+
+    Args:
+        seed (int): Seed data is used as entropy source for Random number engines generating pseudo-random numbers.
+          Default: 0.
+
+    Inputs:
+        - **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed.
+        - **a** (Tensor) - The a distribution parameter.
+          It defines the minimum possibly generated value. With float32 data type.
+        - **b** (Tensor) - The b distribution parameter.
+          It defines the maximum possibly generated value. With float32 data type.
+
+    Outputs:
+        Tensor, has the shape 'shape' input and dtype as int32.
+
+    Examples:
+        >>> shape = (4, 16)
+        >>> a = Tensor(1.0, mstype.float32)
+        >>> b = Tensor(5.0, mstype.float32)
+        >>> uniform_real = P.UniformReal(seed=10)
+        >>> output = uniform_real(shape, a, b)
+    """
+
+    @prim_attr_register
+    def __init__(self, seed=0):
+        """Init UniformReal"""
+        self.init_prim_io_names(inputs=['shape', 'a', 'b'], outputs=['output'])
+        validator.check_value_type('seed', seed, [int], self.name)
+
+    def __infer__(self, shape, a, b):
+        shape_v = shape["value"]
+        if shape_v is None:
+            raise ValueError(f"For {self.name}, shape must be const.")
+        validator.check_value_type("shape", shape_v, [tuple], self.name)
+        for i, shape_i in enumerate(shape_v):
+            validator.check_integer("shape[%d]" % i, shape_i, 0, Rel.GT, self.name)
+        validator.check_tensor_type_same({"a": a["dtype"]}, [mstype.float32], self.name)
+        validator.check_tensor_type_same({"b": b["dtype"]}, [mstype.float32], self.name)
+        broadcast_shape = get_broadcast_shape(a['shape'], b['shape'], self.name)
+        broadcast_shape = get_broadcast_shape(broadcast_shape, shape_v, self.name)
+        out = {
+            'shape': broadcast_shape,
+            'dtype': mstype.float32,
+            'value': None}
+        return out
 
 
 class RandomChoiceWithMask(PrimitiveWithInfer):
@@ -66,49 +329,6 @@ class RandomChoiceWithMask(PrimitiveWithInfer):
         return (mstype.int32, mstype.bool_)
 
 
-class Normal(PrimitiveWithInfer):
-    """
-    Generates random samples from a normal(Gaussian) distribution.
-
-    Args:
-        seed (int): Random seed. Default: 0.
-
-    Inputs:
-        - **shape** (tuple[int]) - The shape of output tensor. Only constant value is allowed.
-        - **mean** (Tensor) - The mean of the distribution, with float32 data type.
-        - **stddev** (Tensor) - The standard deviation of the distribution, with float32 data type.
-
-    Outputs:
-        Tensor, with the given shape from the specific distribution and float32 data type.
-
-    Examples:
-        >>> normal = P.Normal()
-        >>> mean = Tensor(0., mstype.float32)
-        >>> stddev = Tensor(1., mstype.float32)
-        >>> out = normal((32, 3, 3), mean, stddev)
-    """
-
-    @prim_attr_register
-    def __init__(self, seed=0):
-        """Init Normal"""
-        validator.check_value_type("seed", seed, [int], self.name)
-
-    def __infer__(self, shape, mean, stddev):
-        shape_value = shape["value"]
-        if shape_value is None:
-            raise ValueError(f"For {self.name}, shape must be const.")
-        validator.check_value_type("shape", shape_value, [tuple], self.name)
-        for i, shape_i in enumerate(shape_value):
-            validator.check_integer("shape[%d]" % i, shape_i, 0, Rel.GE, self.name)
-
-        validator.check_tensor_type_same({"mean": mean["dtype"]}, [mstype.float32], self.name)
-        validator.check_tensor_type_same({"stddev": stddev["dtype"]}, [mstype.float32], self.name)
-
-        out = {"shape": shape_value,
-               "dtype": mstype.float32,
-               "value": None}
-        return out
-
 class RandomCategorical(PrimitiveWithInfer):
     """
     Generates random samples from a given categorical distribution tensor.

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

@@ -0,0 +1,58 @@
+# Copyright 2020 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 mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops import operations as P
+from mindspore.common import dtype as mstype
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+
+class Net(nn.Cell):
+    def __init__(self, shape, seed=0):
+        super(Net, self).__init__()
+        self.gamma = P.Gamma(seed=seed)
+        self.shape = shape
+
+    def construct(self, alpha, beta):
+        return self.gamma(self.shape, alpha, beta)
+
+
+def test_net_1D():
+    seed = 10
+    shape = (3, 2, 4)
+    alpha = 1.0
+    beta = 1.0
+    net = Net(shape, seed)
+    talpha, tbeta = Tensor(alpha, mstype.float32), Tensor(beta, mstype.float32)
+    output = net(talpha, tbeta)
+    print(output.asnumpy())
+    assert output.shape == (3, 2, 4)
+
+
+def test_net_ND():
+    seed = 10
+    shape = (3, 1, 2)
+    alpha = np.array([[[1], [2]], [[3], [4]], [[5], [6]]]).astype(np.float32)
+    beta = np.array([1.0]).astype(np.float32)
+    net = Net(shape, seed)
+    talpha, tbeta = Tensor(alpha), Tensor(beta)
+    output = net(talpha, tbeta)
+    print(output.asnumpy())
+    assert output.shape == (3, 2, 2)
+

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

@@ -12,32 +12,46 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
+import numpy as np
+
 import mindspore.context as context
 import mindspore.nn as nn
+from mindspore import Tensor
 from mindspore.ops import operations as P
-from mindspore.common import Tensor
 from mindspore.common import dtype as mstype
 
-
-context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
 
 class Net(nn.Cell):
-    def __init__(self, shape=None, mean=0.0, stddev=1.0, seed=0):
+    def __init__(self, shape, seed=0):
         super(Net, self).__init__()
-        self._mean = Tensor(mean, mstype.float32)
-        self._stddev = Tensor(stddev, mstype.float32)
-        self._normal = P.Normal(seed=seed)
-        self._shape = shape
+        self.normal = P.Normal(seed=seed)
+        self.shape = shape
 
-    def construct(self):
-        return self._normal(self._shape, self._mean, self._stddev)
+    def construct(self, mean, stddev):
+        return self.normal(self.shape, mean, stddev)
 
 
-def test_net_3x2x4():
-    mean = 0.0
+def test_net_1D():
+    seed = 10
+    shape = (3, 2, 4)
+    mean = 1.0
     stddev = 1.0
-    seed = 0
-    net = Net((3, 2, 4), mean, stddev, seed)
-    out = net()
-    assert out.shape == (3, 2, 4)
+    net = Net(shape, seed)
+    tmean, tstddev = Tensor(mean, mstype.float32), Tensor(stddev, mstype.float32)
+    output = net(tmean, tstddev)
+    print(output.asnumpy())
+    assert output.shape == (3, 2, 4)
+
+
+def test_net_ND():
+    seed = 10
+    shape = (3, 1, 2)
+    mean = np.array([[[1], [2]], [[3], [4]], [[5], [6]]]).astype(np.float32)
+    stddev = np.array([1.0]).astype(np.float32)
+    net = Net(shape, seed)
+    tmean, tstddev = Tensor(mean), Tensor(stddev)
+    output = net(tmean, tstddev)
+    print(output.asnumpy())
+    assert output.shape == (3, 2, 2)

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

@@ -0,0 +1,53 @@
+# Copyright 2020 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 mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops import operations as P
+from mindspore.common import dtype as mstype
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+
+class Net(nn.Cell):
+    def __init__(self, shape):
+        super(Net, self).__init__()
+        self.poisson = P.Poisson()
+        self.shape = shape
+
+    def construct(self, mean):
+        return self.poisson(self.shape, mean)
+
+
+def test_net_1():
+    shape = (2, 16)
+    mean = np.array([5.0]).astype(np.float32)
+    net = Net(shape)
+    tmean = Tensor(mean)
+    output = net(tmean)
+    print(output.asnumpy())
+    assert output.shape == (2, 16)
+
+
+def test_net_2():
+    shape = (4, 1)
+    mean = np.array([5.0, 10.0]).astype(np.float32)
+    net = Net(shape)
+    tmean = Tensor(mean)
+    output = net(tmean)
+    print(output.asnumpy())
+    assert output.shape == (4, 2)

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

@@ -0,0 +1,57 @@
+# Copyright 2020 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 mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops import operations as P
+from mindspore.common import dtype as mstype
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+
+class Net(nn.Cell):
+    def __init__(self, shape, seed=0):
+        super(Net, self).__init__()
+        self.uniformint = P.UniformInt(seed=seed)
+        self.shape = shape
+
+    def construct(self, a, b):
+        return self.uniformint(self.shape, a, b)
+
+
+def test_net_1D():
+    seed = 10
+    shape = (3, 2, 4)
+    a = 1
+    b = 5
+    net = Net(shape, seed)
+    ta, tb = Tensor(a, mstype.int32), Tensor(b, mstype.int32)
+    output = net(ta, tb)
+    print(output.asnumpy())
+    assert output.shape == (3, 2, 4)
+
+
+def test_net_ND():
+    seed = 10
+    shape = (3, 2, 1)
+    a = np.array([[[1, 2]], [[3, 4]], [[5, 6]]]).astype(np.int32)
+    b = np.array([10]).astype(np.int32)
+    net = Net(shape, seed)
+    ta, tb = Tensor(a), Tensor(b)
+    output = net(ta, tb)
+    print(output.asnumpy())
+    assert output.shape == (3, 2, 2)

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

@@ -0,0 +1,57 @@
+# Copyright 2020 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 mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops import operations as P
+from mindspore.common import dtype as mstype
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+
+class Net(nn.Cell):
+    def __init__(self, shape, seed=0):
+        super(Net, self).__init__()
+        self.uniformreal = P.UniformReal(seed=seed)
+        self.shape = shape
+
+    def construct(self, a, b):
+        return self.uniformreal(self.shape, a, b)
+
+
+def test_net_1D():
+    seed = 10
+    shape = (3, 2, 4)
+    a = 1.0
+    b = 5.0
+    net = Net(shape, seed)
+    ta, tb = Tensor(a, mstype.float32), Tensor(b, mstype.float32)
+    output = net(ta, tb)
+    print(output.asnumpy())
+    assert output.shape == (3, 2, 4)
+
+
+def test_net_ND():
+    seed = 10
+    shape = (3, 2, 1)
+    a = np.array([[[1, 2]], [[3, 4]], [[5, 6]]]).astype(np.float32)
+    b = np.array([10]).astype(np.float32)
+    net = Net(shape, seed)
+    ta, tb = Tensor(a), Tensor(b)
+    output = net(ta, tb)
+    print(output.asnumpy())
+    assert output.shape == (3, 2, 2)

tests/ut/python/ops/test_ops.py

@@ -400,15 +400,57 @@ class InplaceSubNet(nn.Cell):
 
 
 class NormalNet(nn.Cell):
-    def __init__(self, shape=None, mean=0.0, stddev=1.0, seed=0):
+    def __init__(self, shape=None, seed=0):
         super(NormalNet, self).__init__()
         self.normal = P.Normal(seed=seed)
         self.shape = shape
-        self.mean = Tensor(mean, mstype.float32)
-        self.stddev = Tensor(stddev, mstype.float32)
 
-    def construct(self):
-        out = self.normal(self.shape, self.mean, self.stddev)
+    def construct(self, mean, stddev):
+        out = self.normal(self.shape, mean, stddev)
+        return out
+
+
+class GammaNet(nn.Cell):
+    def __init__(self, shape=None, seed=0):
+        super(GammaNet, self).__init__()
+        self.gamma = P.Gamma(seed=seed)
+        self.shape = shape
+
+    def construct(self, alpha, beta):
+        out = self.gamma(self.shape, alpha, beta)
+        return out
+
+
+class PoissonNet(nn.Cell):
+    def __init__(self, shape=None, seed=0):
+        super(PoissonNet, self).__init__()
+        self.poisson = P.Poisson(seed=seed)
+        self.shape = shape
+
+    def construct(self, mean):
+        out = self.poisson(self.shape, mean)
+        return out
+
+
+class UniformIntNet(nn.Cell):
+    def __init__(self, shape=None, seed=0):
+        super(UniformIntNet, self).__init__()
+        self.uniformint = P.UniformInt(seed=seed)
+        self.shape = shape
+
+    def construct(self, a, b):
+        out = self.uniformint(self.shape, a, b)
+        return out
+
+
+class UniformRealNet(nn.Cell):
+    def __init__(self, shape=None, seed=0):
+        super(UniformRealNet, self).__init__()
+        self.uniformreal = P.UniformReal(seed=seed)
+        self.shape = shape
+
+    def construct(self, a, b):
+        out = self.uniformreal(self.shape, a, b)
         return out
 
 
@@ -620,6 +662,26 @@ test_case_math_ops = [
                        (1, 1, 1)],
         'desc_inputs': [[64, 128, 1024]],
         'skip': ['backward']}),
+    ('Normal', {
+        'block': NormalNet((3, 2, 4), 0),
+        'desc_inputs': [Tensor(1.0, mstype.float32), Tensor(1.0, mstype.float32)],
+        'skip': ['backward']}),
+    ('Gamma', {
+        'block': GammaNet((3, 2, 4), 0),
+        'desc_inputs': [Tensor(1.0, mstype.float32), Tensor(1.0, mstype.float32)],
+        'skip': ['backward']}),
+    ('Poisson', {
+        'block': PoissonNet((3, 2, 4), 0),
+        'desc_inputs': [Tensor(2.0, mstype.float32)],
+        'skip': ['backward']}),
+    ('UniformInt', {
+        'block': UniformIntNet((3, 2, 4), 0),
+        'desc_inputs': [Tensor(1, mstype.int32), Tensor(15, mstype.int32)],
+        'skip': ['backward']}),
+    ('UniformReal', {
+        'block': UniformRealNet((3, 2, 4), 0),
+        'desc_inputs': [Tensor(1.0, mstype.float32), Tensor(5.0, mstype.float32)],
+        'skip': ['backward']}),
     ('RandomChoiceWithMask', {
         'block': P.RandomChoiceWithMask(256),
         'desc_inputs': [Tensor(np.random.rand(24000, 4).astype(np.bool_))],
@@ -908,10 +970,6 @@ test_case_math_ops = [
         'desc_inputs': [Tensor([-1.0, 0.0, 1.5, 2.0, 5.0, 15], mstype.float16), Tensor([0.0, 5.0], mstype.float16)],
         'desc_bprop': [],
         'skip': ['backward']}),
-    ('Normal', {
-        'block': NormalNet((3, 2, 4), 0.0, 1.0, 0),
-        'desc_inputs': [],
-        'skip': ['backward']}),
 ]
 
 test_case_nn_ops = [