!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',

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 = [