!251 Add Laplace random operator.

Merge pull request !251 from jxlang910/master

mindspore/ops/_op_impl/aicpu/__init__.py

@@ -38,3 +38,4 @@ from .gamma import _gamma_aicpu
 from .poisson import _poisson_aicpu
 from .uniform_int import _uniform_int_aicpu
 from .uniform_real import _uniform_real_aicpu
+from .laplace import _laplace_aicpu

mindspore/ops/_op_impl/aicpu/laplace.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.
+# ============================================================================
+
+"""RandomLaplace op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+
+laplace_op_info = AiCPURegOp("Laplace") \
+    .fusion_type("OPAQUE") \
+    .input(0, "shape", "required") \
+    .input(1, "mean", "required") \
+    .input(2, "lambda_param", "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(laplace_op_info)
+def _laplace_aicpu():
+    """RandomLaplace AiCPU register"""
+    return

mindspore/ops/operations/__init__.py

@@ -55,7 +55,7 @@ from .math_ops import (Abs, ACos, Asin, Asinh, AddN, AccumulateNV2, AssignAdd, A
                        Square, Sub, TensorAdd, Sign, Round, SquareSumAll, Atan, Atanh, Cosh, Sinh, Eps)
 
 from .random_ops import (RandomChoiceWithMask, Normal, Gamma, Poisson, UniformInt, UniformReal,
-                         RandomCategorical)
+                         RandomCategorical, Laplace)
 from .nn_ops import (LSTM, SGD, Adam, SparseApplyAdam, SparseApplyLazyAdam, ApplyMomentum, BatchNorm,
                      BiasAdd, Conv2D,
                      DepthwiseConv2dNative,
@@ -177,6 +177,7 @@ __all__ = [
     'Poisson',
     'UniformInt',
     'UniformReal',
+    'Laplace',
     'RandomCategorical',
     'ResizeBilinear',
     'ScalarSummary',

mindspore/ops/operations/random_ops.py

@@ -36,9 +36,9 @@ class Normal(PrimitiveWithInfer):
 
     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.
+        - **mean** (Tensor) - The mean μ distribution parameter, which 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.
@@ -75,6 +75,60 @@ class Normal(PrimitiveWithInfer):
         return out
 
 
+class Laplace(PrimitiveWithInfer):
+    r"""
+    Generates random numbers according to the Laplace random number distribution.
+    It is defined as:
+
+    .. math::
+        \text{f}(x;μ,λ) = \frac{1}{2λ}\exp(-\frac{|x-μ|}{λ}),
+
+    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, which specifies the location of the peak.
+          With float32 data type.
+        - **lambda_param** (Tensor) - The parameter used for controling the variance of this random distribution. The
+          variance of Laplace distribution is equal to twice the square of lambda_param. 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)
+        >>> lambda_param = Tensor(1.0, mstype.float32)
+        >>> laplace = P.Laplace(seed=2)
+        >>> output = laplace(shape, mean, lambda_param)
+    """
+
+    @prim_attr_register
+    def __init__(self, seed=0):
+        """Init Laplace"""
+        self.init_prim_io_names(inputs=['shape', 'mean', 'lambda_param'], outputs=['output'])
+        validator.check_value_type('seed', seed, [int], self.name)
+
+    def __infer__(self, shape, mean, lambda_param):
+        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({"lambda_param": lambda_param["dtype"]}, [mstype.float32], self.name)
+        broadcast_shape = get_broadcast_shape(mean['shape'], lambda_param['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:
@@ -101,7 +155,7 @@ class Gamma(PrimitiveWithInfer):
         >>> alpha = Tensor(1.0, mstype.float32)
         >>> beta = Tensor(1.0, mstype.float32)
         >>> gamma = P.Gamma(seed=3)
-        >>> output = normal(shape, alpha, beta)
+        >>> output = Gamma(shape, alpha, beta)
     """
 
     @prim_attr_register

tests/st/ops/ascend/test_aicpu_ops/test_laplace.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.laplace = P.Laplace(seed=seed)
+        self.shape = shape
+
+    def construct(self, mean, lambda_param):
+        return self.laplace(self.shape, mean, lambda_param)
+
+
+def test_net_1D():
+    seed = 10
+    shape = (3, 2, 4)
+    mean = 1.0
+    lambda_param = 1.0
+    net = Net(shape, seed)
+    tmean, tlambda_param = Tensor(mean, mstype.float32), Tensor(lambda_param, mstype.float32)
+    output = net(tmean, tlambda_param)
+    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)
+    lambda_param = np.array([1.0]).astype(np.float32)
+    net = Net(shape, seed)
+    tmean, tlambda_param = Tensor(mean), Tensor(lambda_param)
+    output = net(tmean, tlambda_param)
+    print(output.asnumpy())
+    assert output.shape == (3, 2, 2)

tests/ut/python/ops/test_ops.py

@@ -410,6 +410,17 @@ class NormalNet(nn.Cell):
         return out
 
 
+class LaplaceNet(nn.Cell):
+    def __init__(self, shape=None, seed=0):
+        super(LaplaceNet, self).__init__()
+        self.laplace = P.Laplace(seed=seed)
+        self.shape = shape
+
+    def construct(self, mean, lambda_param):
+        out = self.laplace(self.shape, mean, lambda_param)
+        return out
+
+
 class GammaNet(nn.Cell):
     def __init__(self, shape=None, seed=0):
         super(GammaNet, self).__init__()
@@ -666,6 +677,10 @@ test_case_math_ops = [
         'block': NormalNet((3, 2, 4), 0),
         'desc_inputs': [Tensor(1.0, mstype.float32), Tensor(1.0, mstype.float32)],
         'skip': ['backward']}),
+    ('Laplace', {
+        'block': LaplaceNet((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)],