Refactor Gamma and Poisson ops

mindspore/ops/composite/__init__.py

@@ -27,7 +27,7 @@ from .clip_ops import clip_by_value
 from .multitype_ops.add_impl import hyper_add
 from .multitype_ops.ones_like_impl import ones_like
 from .multitype_ops.zeros_like_impl import zeros_like
-from .random_ops import set_seed, normal, multinomial, uniform
+from .random_ops import set_seed, normal, uniform, gamma, poisson, multinomial
 
 
 __all__ = [
@@ -49,7 +49,9 @@ __all__ = [
     'ones_like',
     'zip_operation',
     'set_seed',
-    'uniform',
     'normal',
+    'uniform',
+    'gamma',
+    'poisson',
     'multinomial',
     'clip_by_value',]

mindspore/ops/composite/random_ops.py

@@ -66,7 +66,6 @@ def get_seed():
 def normal(shape, mean, stddev, seed=0):
     """
     Generates random numbers according to the Normal (or Gaussian) random number distribution.
-    It is defined as:
 
     Args:
         shape (tuple): The shape of random tensor to be generated.
@@ -84,7 +83,6 @@ def normal(shape, mean, stddev, seed=0):
         >>> shape = (4, 16)
         >>> mean = Tensor(1.0, mstype.float32)
         >>> stddev = Tensor(1.0, mstype.float32)
-        >>> C.set_seed(10)
         >>> output = C.normal(shape, mean, stddev, seed=5)
     """
     mean_dtype = F.dtype(mean)
@@ -148,8 +146,7 @@ def multinomial(inputs, num_sample=None, replacement=True, seed=0):
 
 def uniform(shape, a, b, seed=0, dtype=mstype.float32):
     """
-    Generates random numbers according to the Uniform (or Gaussian) random number distribution.
-    It is defined as:
+    Generates random numbers according to the Uniform random number distribution.
 
     Args:
         shape (tuple): The shape of random tensor to be generated.
@@ -170,7 +167,6 @@ def uniform(shape, a, b, seed=0, dtype=mstype.float32):
         >>> shape = (4, 16)
         >>> a = Tensor(1.0, mstype.float32)
         >>> b = Tensor(1.0, mstype.float32)
-        >>> C.set_seed(10)
         >>> output = C.uniform(shape, a, b, seed=5)
     """
     a_dtype = F.dtype(a)
@@ -187,3 +183,61 @@ def uniform(shape, a, b, seed=0, dtype=mstype.float32):
         rnd = uniform_real(shape)
         value = rnd * (b - a) + a
     return value
+
+def gamma(shape, alpha, beta, seed=0):
+    """
+    Generates random numbers according to the Gamma random number distribution.
+
+    Args:
+        shape (tuple): The shape of random tensor to be generated.
+        alpha (Tensor): The alpha α distribution parameter. With float32 data type.
+        beta (Tensor): The beta β distribution parameter. With float32 data type.
+        seed (int): Seed is used as entropy source for Random number engines generating pseudo-random numbers.
+          Default: 0.
+
+    Returns:
+        Tensor. The shape should be the broadcasted shape of Input "shape" and shapes of alpha and beta.
+        The dtype is float32.
+
+    Examples:
+        >>> shape = (4, 16)
+        >>> alpha = Tensor(1.0, mstype.float32)
+        >>> beta = Tensor(1.0, mstype.float32)
+        >>> output = C.gamma(shape, alpha, beta, seed=5)
+    """
+    alpha_dtype = F.dtype(alpha)
+    beta_dtype = F.dtype(beta)
+    const_utils.check_tensors_dtype_same(alpha_dtype, mstype.float32, "gamma")
+    const_utils.check_tensors_dtype_same(beta_dtype, mstype.float32, "gamma")
+    seed1 = get_seed()
+    seed2 = seed
+    gamma = P.Gamma(seed1, seed2)
+    value = gamma(shape, alpha, beta)
+    return value
+
+def poisson(shape, mean, seed=0):
+    """
+    Generates random numbers according to the Poisson random number distribution.
+
+    Args:
+        shape (tuple): The shape of random tensor to be generated.
+        mean (Tensor): The mean μ distribution parameter. With float32 data type.
+        seed (int): Seed is used as entropy source for Random number engines generating pseudo-random numbers.
+          Default: 0.
+
+    Returns:
+        Tensor. The shape should be the broadcasted shape of Input "shape" and shapes of mean.
+        The dtype is float32.
+
+    Examples:
+        >>> shape = (4, 16)
+        >>> mean = Tensor(1.0, mstype.float32)
+        >>> output = C.poisson(shape, mean, seed=5)
+    """
+    mean_dtype = F.dtype(mean)
+    const_utils.check_tensors_dtype_same(mean_dtype, mstype.float32, "poisson")
+    seed1 = get_seed()
+    seed2 = seed
+    poisson = P.Poisson(seed1, seed2)
+    value = poisson(shape, mean)
+    return value

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

@@ -0,0 +1,56 @@
+# 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.common import dtype as mstype
+from mindspore.ops import composite as C
+
+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.shape = shape
+        self.seed = seed
+
+    def construct(self, alpha, beta):
+        C.set_seed(20)
+        return C.gamma(self.shape, alpha, beta, self.seed)
+
+
+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)
+    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, mstype.float32), Tensor(beta, mstype.float32)
+    output = net(talpha, tbeta)
+    assert output.shape == (3, 2, 2)

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

@@ -12,9 +12,7 @@
 # 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
@@ -56,4 +54,3 @@ def test_net_ND():
     tmean, tstddev = Tensor(mean, mstype.float32), Tensor(stddev, mstype.float32)
     output = net(tmean, tstddev)
     assert output.shape == (3, 2, 2)
-

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

@@ -0,0 +1,54 @@
+# 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.common import dtype as mstype
+from mindspore.ops import composite as C
+
+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.shape = shape
+        self.seed = seed
+
+    def construct(self, mean):
+        C.set_seed(20)
+        return C.poisson(self.shape, mean, self.seed)
+
+
+def test_net_1D():
+    seed = 10
+    shape = (3, 2, 4)
+    mean = 1.0
+    net = Net(shape, seed)
+    tmean = Tensor(mean, mstype.float32)
+    output = net(tmean)
+    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)
+    net = Net(shape, seed)
+    tmean = Tensor(mean, mstype.float32)
+    output = net(tmean)
+    assert output.shape == (3, 2, 2)

tests/st/ops/ascend/test_aicpu_ops/test_uniform.py → tests/st/ops/ascend/test_compoite_random_ops/test_uniform.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-
 import numpy as np
 
 import mindspore.context as context

tests/ut/python/ops/test_ops.py

@@ -592,22 +592,22 @@ class LaplaceNet(nn.Cell):
 class GammaNet(nn.Cell):
     def __init__(self, shape=None, seed=0):
         super(GammaNet, self).__init__()
-        self.gamma = P.Gamma(seed=seed)
         self.shape = shape
+        self.seed = seed
 
     def construct(self, alpha, beta):
-        out = self.gamma(self.shape, alpha, beta)
+        out = C.gamma(self.shape, alpha, beta, self.seed)
         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
+        self.seed = seed
 
     def construct(self, mean):
-        out = self.poisson(self.shape, mean)
+        out = C.poisson(self.shape, mean, self.seed)
         return out