!4297 sync code incubator to master

Merge pull request !4297 from guozhijian/code_sync_incubator_f3c32baf_to_master_fcfc75a3_0811

mindspore/ops/_op_impl/aicpu/__init__.py

@@ -15,6 +15,7 @@
 """aicpu ops"""
 from .init_data_set_queue import _init_data_set_queue_aicpu
 from .embedding_lookup import _embedding_lookup_aicpu
+from .padding import _padding_aicpu
 from .dropout_genmask import _dropout_genmask_aicpu
 from .get_next import _get_next_aicpu
 from .print_tensor import _print_aicpu
@@ -43,3 +44,7 @@ from .laplace import _laplace_aicpu
 from .strided_slice import _strided_slice_aicpu
 from .strided_slice_grad import _strided_slice_grad_aicpu
 from .end_of_sequence import _end_of_sequence_aicpu
+from .fused_sparse_adam import _fused_sparse_adam_aicpu
+from .fused_sparse_lazy_adam import _fused_sparse_lazy_adam_aicpu
+from .fused_sparse_ftrl import _fused_sparse_ftrl_aicpu
+from .fused_sparse_proximal_adagrad import _fused_sparse_proximal_adagrad_aicpu

mindspore/ops/_op_impl/aicpu/fused_sparse_adam.py

@@ -0,0 +1,46 @@
+# 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.
+# ============================================================================
+
+"""FusedSparseAdam op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+
+fused_sparse_adam_op_info = AiCPURegOp("FusedSparseAdam") \
+    .fusion_type("OPAQUE") \
+    .attr("use_locking", "bool") \
+    .attr("use_nesterov", "bool") \
+    .input(0, "var", "required") \
+    .input(1, "m", "required") \
+    .input(2, "v", "required") \
+    .input(3, "beta1_power", "required") \
+    .input(4, "beta2_power", "required") \
+    .input(5, "lr", "required") \
+    .input(6, "beta1", "required") \
+    .input(7, "beta2", "required") \
+    .input(8, "epsilon", "required") \
+    .input(9, "grad", "required") \
+    .input(10, "indices", "required") \
+    .output(0, "var", "required") \
+    .output(1, "m", "required") \
+    .output(2, "v", "required") \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.I32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
+
+@op_info_register(fused_sparse_adam_op_info)
+def _fused_sparse_adam_aicpu():
+    """FusedSparseAdam aicpu register"""
+    return

mindspore/ops/_op_impl/aicpu/fused_sparse_ftrl.py

@@ -0,0 +1,41 @@
+# 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.
+# ============================================================================
+
+"""FusedSparseFtrl op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+
+fused_sparse_ftrl_op_info = AiCPURegOp("FusedSparseFtrl") \
+    .fusion_type("OPAQUE") \
+    .attr("lr", "float") \
+    .attr("l1", "float") \
+    .attr("l2", "float") \
+    .attr("lr_power", "float") \
+    .attr("use_locking", "bool") \
+    .input(0, "var", "required") \
+    .input(1, "accum", "required") \
+    .input(2, "linear", "required") \
+    .input(3, "grad", "required") \
+    .input(4, "indices", "required") \
+    .output(0, "var", "required") \
+    .output(1, "accum", "required") \
+    .output(2, "linear", "required") \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.I32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
+
+@op_info_register(fused_sparse_ftrl_op_info)
+def _fused_sparse_ftrl_aicpu():
+    """FusedSparseFtrl aicpu register"""
+    return

mindspore/ops/_op_impl/aicpu/fused_sparse_lazy_adam.py

@@ -0,0 +1,46 @@
+# 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.
+# ============================================================================
+
+"""FusedSparseLazyAdam op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+
+fused_sparse_lazy_adam_op_info = AiCPURegOp("FusedSparseLazyAdam") \
+    .fusion_type("OPAQUE") \
+    .attr("use_locking", "bool") \
+    .attr("use_nesterov", "bool") \
+    .input(0, "var", "required") \
+    .input(1, "m", "required") \
+    .input(2, "v", "required") \
+    .input(3, "beta1_power", "required") \
+    .input(4, "beta2_power", "required") \
+    .input(5, "lr", "required") \
+    .input(6, "beta1", "required") \
+    .input(7, "beta2", "required") \
+    .input(8, "epsilon", "required") \
+    .input(9, "grad", "required") \
+    .input(10, "indices", "required") \
+    .output(0, "var", "required") \
+    .output(1, "m", "required") \
+    .output(2, "v", "required") \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.I32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default) \
+    .get_op_info()
+
+@op_info_register(fused_sparse_lazy_adam_op_info)
+def _fused_sparse_lazy_adam_aicpu():
+    """FusedSparseLazyAdam aicpu register"""
+    return

mindspore/ops/_op_impl/aicpu/fused_sparse_proximal_adagrad.py

@@ -0,0 +1,39 @@
+# 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.
+# ============================================================================
+
+"""FusedSparseProximalAdagrad op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+
+fused_sparse_proximal_adagrad_op_info = AiCPURegOp("FusedSparseProximalAdagrad") \
+    .fusion_type("OPAQUE") \
+    .attr("use_locking", "bool") \
+    .input(0, "var", "required") \
+    .input(1, "accum", "required") \
+    .input(2, "lr", "required") \
+    .input(3, "l1", "required") \
+    .input(4, "l2", "required") \
+    .input(5, "grad", "required") \
+    .input(6, "indices", "required") \
+    .output(0, "var", "required") \
+    .output(1, "accum", "required") \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.F32_Default, DataType.F32_Default,
+                  DataType.F32_Default, DataType.F32_Default, DataType.I32_Default, DataType.F32_Default,
+                  DataType.F32_Default) \
+    .get_op_info()
+
+@op_info_register(fused_sparse_proximal_adagrad_op_info)
+def _fused_sparse_proximal_adagrad_aicpu():
+    """FusedSparseProximalAdagrad aicpu register"""
+    return

mindspore/ops/_op_impl/aicpu/gamma.py

@@ -23,6 +23,7 @@ gamma_op_info = AiCPURegOp("Gamma") \
     .input(2, "beta", "required") \
     .output(0, "output", "required") \
     .attr("seed", "int") \
+    .attr("seed2", "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()

mindspore/ops/_op_impl/aicpu/padding.py

@@ -0,0 +1,41 @@
+# 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.
+# ============================================================================
+
+"""Padding op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+
+padding_op_info = AiCPURegOp("Padding") \
+    .fusion_type("OPAQUE") \
+    .input(0, "x", "required") \
+    .output(0, "y", "required") \
+    .attr("pad_dim_size", "int") \
+    .dtype_format(DataType.I8_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I16_Default, DataType.I16_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I64_Default, DataType.I64_Default) \
+    .dtype_format(DataType.U8_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U16_Default, DataType.U16_Default) \
+    .dtype_format(DataType.U32_Default, DataType.U32_Default) \
+    .dtype_format(DataType.U64_Default, DataType.U64_Default) \
+    .dtype_format(DataType.F16_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F64_Default, DataType.F64_Default) \
+    .dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
+    .get_op_info()
+
+@op_info_register(padding_op_info)
+def _padding_aicpu():
+    """Padding AiCPU register"""
+    return

mindspore/ops/_op_impl/aicpu/poisson.py

@@ -22,6 +22,7 @@ poisson_op_info = AiCPURegOp("Poisson") \
     .input(1, "mean", "required") \
     .output(0, "output", "required") \
     .attr("seed", "int") \
+    .attr("seed2", "int") \
     .dtype_format(DataType.I32_Default, DataType.F32_Default, DataType.I32_Default) \
     .dtype_format(DataType.I32_NCHW, DataType.F32_NCHW, DataType.I32_NCHW) \
     .get_op_info()

mindspore/ops/_op_impl/aicpu/uniform_int.py

@@ -23,6 +23,7 @@ uniform_int_op_info = AiCPURegOp("UniformInt") \
     .input(2, "b", "required") \
     .output(0, "output", "required") \
     .attr("seed", "int") \
+    .attr("seed2", "int") \
     .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
     .dtype_format(DataType.I32_NCHW, DataType.I32_NCHW, DataType.I32_NCHW, DataType.I32_NCHW) \
     .get_op_info()

mindspore/ops/_op_impl/aicpu/uniform_real.py

@@ -19,12 +19,11 @@ from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataTyp
 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) \
+    .attr("seed2", "int") \
+    .dtype_format(DataType.I32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.I32_NCHW, DataType.F32_NCHW) \
     .get_op_info()
 
 @op_info_register(uniform_real_op_info)

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
+from .random_ops import set_seed, normal, uniform, gamma, poisson, multinomial
 
 
 __all__ = [
@@ -50,5 +50,8 @@ __all__ = [
     'zip_operation',
     'set_seed',
     'normal',
+    'uniform',
+    'gamma',
+    'poisson',
     'multinomial',
     'clip_by_value',]

mindspore/ops/composite/random_ops.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ============================================================================
 
-"""Operations for random number generatos."""
+"""Operations for random number generators."""
 
 from .. import operations as P
 from .. import functional as F
@@ -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.
@@ -137,10 +136,108 @@ def multinomial(inputs, num_sample=None, replacement=True, seed=0):
             n_dist = shape(inputs)[-2]
         a = Tensor(0.0, mstype.float32)
         b = Tensor(1.0, mstype.float32)
-        uniform = P.UniformReal(seed=seed)((n_dist * num_sample,), a, b)
+        random_uniform = P.UniformReal(seed=seed)((n_dist * num_sample,), a, b)
         if n_dist != 1:
-            uniform = reshape(uniform, (n_dist, num_sample))
-        vals = P.RealDiv()(P.Log()(uniform), inputs + 1e-6)
+            random_uniform = reshape(random_uniform, (n_dist, num_sample))
+        vals = P.RealDiv()(P.Log()(random_uniform), inputs + 1e-6)
         _, indices = P.TopK()(vals, num_sample)
         return indices
     return P.Multinomial(seed=seed)(inputs, num_sample)
+
+def uniform(shape, a, b, seed=0, dtype=mstype.float32):
+    """
+    Generates random numbers according to the Uniform random number distribution.
+
+    Args:
+        shape (tuple): The shape of random tensor to be generated.
+        a (Tensor): The a distribution parameter.
+          It defines the minimum possibly generated value. With int32 or float32 data type.
+          If dtype is int32, only one number is allowed.
+        b (Tensor): The b distribution parameter.
+          It defines the maximum possibly generated value. With int32 or float32 data type.
+          If dtype is int32, only one number is allowed.
+        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 a and b.
+        The dtype is float32.
+
+    Examples:
+        >>> shape = (4, 16)
+        >>> a = Tensor(1.0, mstype.float32)
+        >>> b = Tensor(1.0, mstype.float32)
+        >>> output = C.uniform(shape, a, b, seed=5)
+    """
+    a_dtype = F.dtype(a)
+    b_dtype = F.dtype(b)
+    const_utils.check_tensors_dtype_same(a_dtype, dtype, "uniform")
+    const_utils.check_tensors_dtype_same(b_dtype, dtype, "uniform")
+    seed1 = get_seed()
+    seed2 = seed
+    if const_utils.is_same_type(dtype, mstype.int32):
+        rnd = P.UniformInt(seed1, seed2)
+        value = rnd(shape, a, b)
+    else:
+        uniform_real = P.UniformReal(seed1, seed2)
+        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
+    random_gamma = P.Gamma(seed1, seed2)
+    value = random_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
+    random_poisson = P.Poisson(seed1, seed2)
+    value = random_poisson(shape, mean)
+    return value

mindspore/ops/operations/__init__.py

@@ -27,7 +27,7 @@ from .array_ops import (Argmax, Argmin, Cast, Concat, Pack, Unpack,
                         Rank, Reshape, ResizeNearestNeighbor, ArgMinWithValue,
                         SameTypeShape, ScatterAdd, ScatterSub, ScatterMul, ScatterDiv, ScatterMax, ScatterMin,
                         ScatterUpdate, ScalarToArray, ScalarToTensor, ScatterNd, ScatterNdUpdate, Select,
-                        Shape, Size, Slice, Split, TransShape, ParallelConcat,
+                        Shape, Size, Slice, Split, TransShape, ParallelConcat, Padding,
                         ScatterNdAdd, ScatterNdSub, ScatterNonAliasingAdd, ReverseV2, Rint,
                         Squeeze, StridedSlice, Tile, TensorScatterUpdate,
                         Transpose, TruncatedNormal, TupleToArray, UnsortedSegmentMin, UnsortedSegmentProd,
@@ -147,6 +147,7 @@ __all__ = [
     'GatherV2',
     'SparseGatherV2',
     'EmbeddingLookup',
+    'Padding',
     'Concat',
     'Pack',
     'Unpack',

mindspore/ops/operations/array_ops.py

@@ -645,6 +645,46 @@ class SparseGatherV2(GatherV2):
     """
 
 
+class Padding(PrimitiveWithInfer):
+    """
+    Extend the last dimension of input tensor from 1 to pad_dim_size, fill with 0.
+
+    Args:
+        pad_dim_size (int): The extend value of last dimension of x, must be positive.
+
+    Inputs:
+        - **x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`. The rank of x should be at least 2.
+        The last dimension of x should be 1.
+
+    Outputs:
+        Tensor, the shape of tensor is :math:`(z_1, z_2, ..., z_N)`.
+
+    Examples:
+        >>> x = Tensor(np.array([[8], [10]]), mindspore.float32)
+        >>> pad_dim_size = 4
+        >>> out = P.Padding(pad_dim_size)(x)
+        [[8, 0, 0, 0], [10, 0, 0, 0]]
+    """
+    @prim_attr_register
+    def __init__(self, pad_dim_size=8):
+        """init padding"""
+        validator.check_value_type("pad_dim_size", pad_dim_size, [int], self.name)
+        validator.check_integer("pad_dim_size", pad_dim_size, 0, Rel.GT, self.name)
+        self.pad_dim_size = pad_dim_size
+
+    def __infer__(self, x):
+        validator.check_subclass("x", x['dtype'], mstype.tensor, self.name)
+        x_shape = list(x['shape'])
+        validator.check_integer("rank of x", len(x_shape), 1, Rel.GT, self.name)
+        validator.check_integer("last dim of x", x_shape[-1], 1, Rel.EQ, self.name)
+        out_shape = x_shape
+        out_shape[-1] = self.pad_dim_size
+        out = {'shape': out_shape,
+               'dtype': x['dtype'],
+               'value': None}
+        return out
+
+
 class Split(PrimitiveWithInfer):
     """
     Splits input tensor into output_num of tensors along the given axis and output numbers.

mindspore/ops/operations/random_ops.py

@@ -34,8 +34,7 @@ class StandardNormal(PrimitiveWithInfer):
         - **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed.
 
     Outputs:
-        Tensor. The shape should be the broadcasted shape of Input "shape" and shapes of mean and stddev.
-        The dtype is float32.
+        Tensor. The shape that the input 'shape' denotes. The dtype is float32.
 
     Examples:
         >>> shape = (4, 16)
@@ -126,8 +125,8 @@ class Gamma(PrimitiveWithInfer):
         \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.
+        seed (int): Random seed. Default: 0.
+        seed2 (int): Random seed2. Default: 0.
 
     Inputs:
         - **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed.
@@ -149,10 +148,11 @@ class Gamma(PrimitiveWithInfer):
     """
 
     @prim_attr_register
-    def __init__(self, seed=0):
+    def __init__(self, seed=0, seed2=0):
         """Init Gamma"""
         self.init_prim_io_names(inputs=['shape', 'alpha', 'beta'], outputs=['output'])
         validator.check_value_type('seed', seed, [int], self.name)
+        validator.check_value_type('seed2', seed2, [int], self.name)
 
     def __infer__(self, shape, alpha, beta):
         shape_v = shape["value"]
@@ -180,8 +180,8 @@ class Poisson(PrimitiveWithInfer):
         \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.
+        seed (int): Random seed. Default: 0.
+        seed2 (int): Random seed2. Default: 0.
 
     Inputs:
         - **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed.
@@ -200,10 +200,11 @@ class Poisson(PrimitiveWithInfer):
     """
 
     @prim_attr_register
-    def __init__(self, seed=0):
+    def __init__(self, seed=0, seed2=0):
         """Init Poisson"""
         self.init_prim_io_names(inputs=['shape', 'mean'], outputs=['output'])
         validator.check_value_type('seed', seed, [int], self.name)
+        validator.check_value_type('seed2', seed2, [int], self.name)
 
     def __infer__(self, shape, mean):
         shape_v = shape["value"]
@@ -223,7 +224,7 @@ class Poisson(PrimitiveWithInfer):
 
 class UniformInt(PrimitiveWithInfer):
     r"""
-    Produces random integer values i, uniformly distributed on the closed interval [a, b], that is,
+    Produces random integer values i, uniformly distributed on the closed interval [a, b), that is,
     distributed according to the discrete probability function:
 
     .. math::
@@ -233,19 +234,18 @@ class UniformInt(PrimitiveWithInfer):
         The number in tensor a should be strictly less than b at any position after broadcasting.
 
     Args:
-        seed (int): Seed data is used as entropy source for Random number engines generating pseudo-random numbers.
-          Default: 0.
+        seed (int): Random seed. Default: 0.
+        seed2 (int): Random seed2. 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.
+          It defines the minimum possibly generated value. With int32 data type. Only one number is supported.
         - **b** (Tensor) - The b distribution parameter.
-          It defines the maximum possibly generated value. With int32 data type.
+          It defines the maximum possibly generated value. With int32 data type. Only one number is supported.
 
     Outputs:
-        Tensor. The shape should be the broadcasted shape of Input "shape" and shapes of a and b.
-        The dtype is int32.
+        Tensor. The shape that the input 'shape' denotes. The dtype is int32.
 
     Examples:
         >>> shape = (4, 16)
@@ -256,10 +256,11 @@ class UniformInt(PrimitiveWithInfer):
     """
 
     @prim_attr_register
-    def __init__(self, seed=0):
+    def __init__(self, seed=0, seed2=0):
         """Init UniformInt"""
         self.init_prim_io_names(inputs=['shape', 'a', 'b'], outputs=['output'])
         validator.check_value_type('seed', seed, [int], self.name)
+        validator.check_value_type('seed2', seed2, [int], self.name)
 
     def __infer__(self, shape, a, b):
         shape_v = shape["value"]
@@ -270,10 +271,12 @@ class UniformInt(PrimitiveWithInfer):
             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)
+        a_shape = a['shape']
+        b_shape = b['shape']
+        validator.check("dim of a", len(a_shape), '0(scalar)', 0, Rel.EQ, self.name)
+        validator.check("dim of b", len(b_shape), '0(scalar)', 0, Rel.EQ, self.name)
         out = {
-            'shape': broadcast_shape,
+            'shape': shape_v,
             'dtype': mstype.int32,
             'value': None}
         return out
@@ -281,54 +284,40 @@ class UniformInt(PrimitiveWithInfer):
 
 class UniformReal(PrimitiveWithInfer):
     r"""
-    Produces random floating-point values i, uniformly distributed on the interval [min(a, b), max(a, b)), that is,\
-    distributed according to the probability density function:
-
-    .. math::
-        \text{P}(i|a,b) = \frac{1}{b-a},
+    Produces random floating-point values i, uniformly distributed on the interval [0, 1).
 
     Args:
-        seed (int): Seed data is used as entropy source for Random number engines generating pseudo-random numbers.
-          Default: 0.
+        seed (int): Random seed. Default: 0.
+        seed2 (int): Random seed2. 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. The shape should be the broadcasted shape of Input "shape" and shapes of a and b.
-        The dtype is float32.
+        Tensor. The shape that the input 'shape' denotes. The dtype is float32.
 
     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)
+        >>> uniformreal = P.UniformReal(seed=2)
+        >>> output = uniformreal(shape)
     """
 
     @prim_attr_register
-    def __init__(self, seed=0):
+    def __init__(self, seed=0, seed2=0):
         """Init UniformReal"""
-        self.init_prim_io_names(inputs=['shape', 'a', 'b'], outputs=['output'])
+        self.init_prim_io_names(inputs=['shape'], outputs=['output'])
         validator.check_value_type('seed', seed, [int], self.name)
+        validator.check_value_type('seed2', seed2, [int], self.name)
 
-    def __infer__(self, shape, a, b):
+    def __infer__(self, shape):
         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,
+            'shape': shape_v,
             'dtype': mstype.float32,
             'value': None}
         return out

tests/st/ops/ascend/test_aicpu_ops/test_fused_sparse_adam.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.nn as nn
+import mindspore.common.dtype as mstype
+import mindspore.context as context
+from mindspore import Tensor
+from mindspore.ops import operations as P
+from mindspore.common.parameter import Parameter
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+beta1_power = 0.9
+beta2_power = 0.999
+lr = 0.001
+beta1 = 0.9
+beta2 = 0.999
+epsilon = 1e-8
+
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.fused_sparse_adam = P.FusedSparseAdam()
+        self.var = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="var")
+        self.m = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="m")
+        self.v = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="v")
+
+    def construct(self, grad, indices):
+        return self.fused_sparse_adam(self.var, self.m, self.v, beta1_power, beta2_power, lr, beta1, beta2, epsilon,
+                                      grad, indices)
+
+def test_net():
+    gradient = Tensor(np.array([0.22948648, 0.14569908, 0.92861906, 0.66870148])
+                      .reshape([2, 1, 2]).astype(np.float32))
+    indices = Tensor([0, 1], mstype.int32)
+    net = Net()
+    output = net(gradient, indices)
+    print(output)
+    print(net.var.default_input)
+    print(net.m.default_input)
+    print(net.v.default_input)

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

@@ -0,0 +1,50 @@
+# 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.common.dtype as mstype
+import mindspore.nn as nn
+import mindspore.context as context
+from mindspore import Tensor
+from mindspore.ops import operations as P
+from mindspore.common.parameter import Parameter
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+lr = 0.01
+l1 = 0.0
+l2 = 0.0
+lr_power = -0.5
+
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.fused_sparse_ftrl = P.FusedSparseFtrl(lr=0.1, l1=0.0, l2=0.0, lr_power=-0.5)
+        self.var = Parameter(Tensor(np.ones([3, 3]).astype(np.float32)), name="var")
+        self.accum = Parameter(Tensor(np.ones([3, 3]).astype(np.float32)), name="accum")
+        self.linear = Parameter(Tensor(np.ones([3, 3]).astype(np.float32)), name="linear")
+
+    def construct(self, grad, indices):
+        return self.fused_sparse_ftrl(self.var, self.accum, self.linear, grad, indices)
+
+def test_net():
+    gradient = Tensor(np.array([-3, 2, 3, 0, 0, 0, -4, -1, -2])
+                      .reshape([3, 3]).astype(np.float32))
+    indices = Tensor(np.ones([3]), mstype.int32)
+    net = Net()
+    output = net(gradient, indices)
+    print(output)
+    print(net.var.default_input)
+    print(net.accum.default_input)
+    print(net.linear.default_input)

tests/st/ops/ascend/test_aicpu_ops/test_fused_sparse_lazy_adam.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.common.dtype as mstype
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops import operations as P
+from mindspore.common.parameter import Parameter
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+beta1_power = 0.9
+beta2_power = 0.999
+lr = 0.001
+beta1 = 0.9
+beta2 = 0.999
+epsilon = 1e-8
+
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.fused_sparse_lazy_adam = P.FusedSparseLazyAdam()
+        self.var = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="var")
+        self.m = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="m")
+        self.v = Parameter(Tensor(np.ones([3, 1, 2]).astype(np.float32)), name="v")
+
+    def construct(self, grad, indices):
+        return self.fused_sparse_lazy_adam(self.var, self.m, self.v, beta1_power, beta2_power,
+                                           lr, beta1, beta2, epsilon, grad, indices)
+
+def test_net():
+    gradient = Tensor(np.array([0.22948648, 0.14569908, 0.92861906, 0.66870148])
+                      .reshape([2, 1, 2]).astype(np.float32))
+    indices = Tensor([0, 1], mstype.int32)
+    net = Net()
+    output = net(gradient, indices)
+    print(output)
+    print(net.var.default_input)
+    print(net.m.default_input)
+    print(net.v.default_input)

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

@@ -0,0 +1,47 @@
+# 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.nn as nn
+import mindspore.context as context
+import mindspore.common.dtype as mstype
+from mindspore import Tensor
+from mindspore.ops import operations as P
+from mindspore.common.parameter import Parameter
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.fused_sparse_proximal_adagrad = P.FusedSparseProximalAdagrad()
+        self.var = Parameter(Tensor(np.ones([3, 3]).astype(np.float32)), name="var")
+        self.accum = Parameter(Tensor(np.ones([3, 3]).astype(np.float32)), name="accum")
+        self.lr = 0.01
+        self.l1 = 0.0
+        self.l2 = 0.0
+
+    def construct(self, grad, indices):
+        return self.fused_sparse_proximal_adagrad(self.var, self.accum, self.lr, self.l1, self.l2,
+                                                  grad, indices)
+
+def test_net():
+    gradient = Tensor(np.array([-3, 2, 3, 0, 0, 0, -4, -1, -2])
+                      .reshape([3, 3]).astype(np.float32))
+    indices = Tensor(np.ones([3]), mstype.int32)
+    net = Net()
+    output = net(gradient, indices)
+    print(output)
+    print(net.var.default_input)
+    print(net.accum.default_input)

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

@@ -24,9 +24,9 @@ context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
 
 class Net(nn.Cell):
-    def __init__(self, shape, seed=0):
+    def __init__(self, shape, seed=0, seed2=0):
         super(Net, self).__init__()
-        self.gamma = P.Gamma(seed=seed)
+        self.gamma = P.Gamma(seed=seed, seed2=seed2)
         self.shape = shape
 
     def construct(self, alpha, beta):
@@ -38,10 +38,9 @@ def test_net_1D():
     shape = (3, 2, 4)
     alpha = 1.0
     beta = 1.0
-    net = Net(shape, seed)
+    net = Net(shape=shape, seed=seed)
     talpha, tbeta = Tensor(alpha, mstype.float32), Tensor(beta, mstype.float32)
     output = net(talpha, tbeta)
-    print(output.asnumpy())
     assert output.shape == (3, 2, 4)
 
 
@@ -50,8 +49,7 @@ def test_net_ND():
     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)
+    net = Net(shape=shape, seed=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_poisson.py

@@ -24,7 +24,7 @@ context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
 
 class Net(nn.Cell):
-    def __init__(self, shape):
+    def __init__(self, shape, seed=0, seed2=0):
         super(Net, self).__init__()
         self.poisson = P.Poisson()
         self.shape = shape
@@ -36,17 +36,16 @@ class Net(nn.Cell):
 def test_net_1():
     shape = (2, 16)
     mean = np.array([5.0]).astype(np.float32)
-    net = Net(shape)
+    net = Net(shape=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)
+    net = Net(shape=shape)
     tmean = Tensor(mean)
     output = net(tmean)
     print(output.asnumpy())

tests/st/ops/ascend/test_aicpu_ops/test_uniform_int.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
 import mindspore.nn as nn
@@ -24,7 +23,7 @@ context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
 
 
 class Net(nn.Cell):
-    def __init__(self, shape, seed=0):
+    def __init__(self, shape, seed=0, seed2=0):
         super(Net, self).__init__()
         self.uniformint = P.UniformInt(seed=seed)
         self.shape = shape
@@ -38,10 +37,9 @@ def test_net_1D():
     shape = (3, 2, 4)
     a = 1
     b = 5
-    net = Net(shape, seed)
+    net = Net(shape, seed=seed)
     ta, tb = Tensor(a, mstype.int32), Tensor(b, mstype.int32)
     output = net(ta, tb)
-    print(output.asnumpy())
     assert output.shape == (3, 2, 4)
 
 

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

@@ -12,36 +12,29 @@
 # 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):
+    def __init__(self, shape, seed=0, seed2=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 construct(self):
+        return self.uniformreal(self.shape)
 
 
-def test_net_1D():
+def test_net():
     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())
+    net = Net(shape, seed=seed)
+    output = net()
     assert output.shape == (3, 2, 4)
 
 

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
@@ -32,6 +30,7 @@ class Net(nn.Cell):
         self.seed = seed
 
     def construct(self, mean, stddev):
+        C.set_seed(20)
         return C.normal(self.shape, mean, stddev, self.seed)
 
 

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_compoite_random_ops/test_uniform.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, a, b):
+        C.set_seed(20)
+        return C.uniform(self.shape, a, b, self.seed)
+
+
+def test_net_1D():
+    seed = 10
+    shape = (3, 2, 4)
+    a = 1.0
+    b = 6.0
+    net = Net(shape, seed)
+    ta, tb = Tensor(a, mstype.float32), Tensor(b, mstype.float32)
+    output = net(ta, tb)
+    assert output.shape == (3, 2, 4)
+
+
+def test_net_ND():
+    seed = 10
+    shape = (3, 1, 2)
+    a = np.array([[[1], [2]], [[3], [4]], [[5], [6]]]).astype(np.float32)
+    b = np.array([1.0]).astype(np.float32)
+    net = Net(shape, seed)
+    ta, tb = Tensor(a, mstype.float32), Tensor(b, mstype.float32)
+    output = net(ta, tb)
+    assert output.shape == (3, 2, 2)

tests/st/ops/ascend/test_drop_out_gen_mask.py

@@ -43,4 +43,4 @@ def test_net():
     tx, ty = Tensor(x), Tensor(y)
     output = mask(tx, ty)
     print(output.asnumpy())
-    assert ([255, 255, 255, 255] == output.asnumpy()).all()
+    assert ([255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255] == output.asnumpy()).all()

tests/st/ops/ascend/test_padding.py

@@ -0,0 +1,40 @@
+# 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
+import mindspore.common.dtype as mstype
+from mindspore import Tensor
+from mindspore.ops import operations as P
+
+context.set_context(mode=context.GRAPH_MODE,
+                    device_target="Ascend")
+
+
+class Net(nn.Cell):
+    def __init__(self, pad_dim_size):
+        super(Net, self).__init__()
+        self.padding = P.Padding(pad_dim_size)
+
+    def construct(self, x):
+        return self.padding(x)
+
+
+def test_padding():
+    x = Tensor(np.array([[8], [10]]), mstype.int32)
+    padding = Net(4)
+    out = padding(x)
+    assert(out.asnumpy() == [[8, 0, 0, 0], [10, 0, 0, 0]]).all()

tests/ut/python/ops/test_ops.py

@@ -592,44 +592,33 @@ 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)
-        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)
+        out = C.poisson(self.shape, mean, self.seed)
         return out
 
 
-class UniformRealNet(nn.Cell):
+class UniformNet(nn.Cell):
     def __init__(self, shape=None, seed=0):
-        super(UniformRealNet, self).__init__()
-        self.uniformreal = P.UniformReal(seed=seed)
+        super(UniformNet, self).__init__()
         self.shape = shape
+        self.seed = seed
 
     def construct(self, a, b):
-        out = self.uniformreal(self.shape, a, b)
+        out = C.uniform(self.shape, a, b, self.seed)
         return out
 
 
@@ -924,13 +913,9 @@ test_case_math_ops = [
         '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)],
+    ('Uniform', {
+        'block': UniformNet((3, 2, 4), 0),
+        'desc_inputs': [Tensor(0.0, mstype.float32), Tensor(1.0, mstype.float32)],
         'skip': ['backward']}),
     ('RandomChoiceWithMask', {
         'block': P.RandomChoiceWithMask(256),