[Numpy] Numpy behavior normal distribution

python/mxnet/ndarray/numpy/random.py

@@ -21,7 +21,6 @@
 from ...context import current_context
 from . import _internal as _npi
 from ..ndarray import NDArray
-from ...base import numeric_types
 
 
 __all__ = ['randint', 'uniform', 'normal', "choice"]
@@ -145,7 +144,7 @@ def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
                             ctx=ctx, dtype=dtype, out=out)
 
 
-def normal(loc=0.0, scale=1.0, size=None, **kwargs):
+def normal(loc=0.0, scale=1.0, size=None, dtype=None, **kwargs):
     """Draw random samples from a normal (Gaussian) distribution.
 
     Samples are distributed according to a normal distribution parametrized
@@ -166,31 +165,36 @@ def normal(loc=0.0, scale=1.0, size=None, **kwargs):
         Data type of output samples. Default is 'float32'
     ctx : Context, optional
         Device context of output. Default is current context.
-    out : ``ndarray``, optional
-        Store output to an existing ``ndarray``.
 
     Returns
     -------
     out : ndarray
         Drawn samples from the parameterized normal distribution.
-
-    Notes
-    -----
-    This function currently does not support ``loc`` and ``scale`` as ndarrays.
     """
-    dtype = kwargs.pop('dtype', None)
+    from ...numpy import ndarray as np_ndarray
+    input_type = (isinstance(loc, np_ndarray), isinstance(scale, np_ndarray))
+    ctx = kwargs.pop('ctx', None)
+    out = kwargs.pop('out', None)
     if dtype is None:
         dtype = 'float32'
-    ctx = kwargs.pop('ctx', None)
     if ctx is None:
         ctx = current_context()
-    out = kwargs.pop('out', None)
-    if size is None and out is None:
-        size = ()
-    if (not isinstance(loc, numeric_types)) or (not isinstance(scale, numeric_types)):
-        raise NotImplementedError('np.random.normal only supports loc and scale of '
-                                  'numeric types for now')
-    return _npi.random_normal(loc, scale, shape=size, dtype=dtype, ctx=ctx, out=out, **kwargs)
+    if out is not None:
+        size = out.shape
+    if size == ():
+        size = None
+    if input_type == (True, True):
+        return _npi.normal(loc, scale, loc=None, scale=None, size=size,
+                           ctx=ctx, dtype=dtype, out=out)
+    elif input_type == (False, True):
+        return _npi.normal(scale, loc=loc, scale=None, size=size,
+                           ctx=ctx, dtype=dtype, out=out)
+    elif input_type == (True, False):
+        return _npi.normal(loc, loc=None, scale=scale, size=size,
+                           ctx=ctx, dtype=dtype, out=out)
+    else:
+        return _npi.normal(loc=loc, scale=scale, size=size,
+                           ctx=ctx, dtype=dtype, out=out)
 
 
 def multinomial(n, pvals, size=None):

python/mxnet/numpy/random.py

@@ -110,7 +110,7 @@ def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
     return _mx_nd_np.random.uniform(low, high, size=size, ctx=ctx, dtype=dtype, out=out)
 
 
-def normal(loc=0.0, scale=1.0, size=None, **kwargs):
+def normal(loc=0.0, scale=1.0, size=None, dtype=None, **kwargs):
     """Draw random samples from a normal (Gaussian) distribution.
 
     Samples are distributed according to a normal distribution parametrized
@@ -130,20 +130,16 @@ def normal(loc=0.0, scale=1.0, size=None, **kwargs):
     dtype : {'float16', 'float32', 'float64'}, optional
         Data type of output samples. Default is 'float32'
     ctx : Context, optional
-        Device context of output. Default is current context.
+        Device context of output, default is current context.
     out : ``ndarray``, optional
         Store output to an existing ``ndarray``.
 
     Returns
     -------
     out : ndarray
         Drawn samples from the parameterized normal distribution.
-
-    Notes
-    -----
-    This function currently does not support ``loc`` and ``scale`` as ndarrays.
     """
-    return _mx_nd_np.random.normal(loc, scale, size, **kwargs)
+    return _mx_nd_np.random.normal(loc, scale, size, dtype, **kwargs)
 
 
 def multinomial(n, pvals, size=None, **kwargs):

python/mxnet/symbol/numpy/random.py

@@ -20,7 +20,6 @@
 from __future__ import absolute_import
 from ...context import current_context
 from . import _internal as _npi
-from ...base import numeric_types
 
 
 __all__ = ['randint', 'uniform', 'normal']
@@ -144,7 +143,7 @@ def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
                             ctx=ctx, dtype=dtype, out=out)
 
 
-def normal(loc=0.0, scale=1.0, size=None, **kwargs):
+def normal(loc=0.0, scale=1.0, size=None, dtype=None, **kwargs):
     """Draw random samples from a normal (Gaussian) distribution.
 
     Samples are distributed according to a normal distribution parametrized
@@ -162,34 +161,40 @@ def normal(loc=0.0, scale=1.0, size=None, **kwargs):
         samples are drawn. If size is `None` (default), a scalar tensor containing
         a single value is returned if loc and scale are both scalars.
     dtype : {'float16', 'float32', 'float64'}, optional
-        Data type of output samples. Default is 'float32'
+        Data type of output samples. Default is 'float32'.
     ctx : Context, optional
         Device context of output. Default is current context.
-    out : ``ndarray``, optional
-        Store output to an existing ``ndarray``.
 
     Returns
     -------
     out : _Symbol (symbol representing `mxnet.numpy.ndarray` in computational graphs)
         Drawn samples from the parameterized normal distribution.
-
-    Notes
-    -----
-    This function currently does not support ``loc`` and ``scale`` as `_Symbol`s.
     """
-    dtype = kwargs.pop('dtype', None)
+    from ._symbol import _Symbol as np_symbol
+    input_type = (isinstance(loc, np_symbol), isinstance(scale, np_symbol))
+    ctx = kwargs.pop('ctx', None)
+    out = kwargs.pop('out', None)
     if dtype is None:
         dtype = 'float32'
-    ctx = kwargs.pop('ctx', None)
     if ctx is None:
         ctx = current_context()
     out = kwargs.pop('out', None)
-    if size is None and out is None:
-        size = ()
-    if (not isinstance(loc, numeric_types)) or (not isinstance(scale, numeric_types)):
-        raise NotImplementedError('np.random.normal only supports loc and scale of '
-                                  'numeric types for now')
-    return _npi.random_normal(loc, scale, shape=size, dtype=dtype, ctx=ctx, out=out, **kwargs)
+    if out is not None:
+        size = out.shape
+    if size == ():
+        size = None
+    if input_type == (True, True):
+        return _npi.normal(loc, scale, loc=None, scale=None, size=size,
+                           ctx=ctx, dtype=dtype, out=out)
+    elif input_type == (False, True):
+        return _npi.normal(scale, loc=loc, scale=None, size=size,
+                           ctx=ctx, dtype=dtype, out=out)
+    elif input_type == (True, False):
+        return _npi.normal(loc, loc=None, scale=scale, size=size,
+                           ctx=ctx, dtype=dtype, out=out)
+    else:
+        return _npi.normal(loc=loc, scale=scale, size=size,
+                           ctx=ctx, dtype=dtype, out=out)
 
 
 def choice(a, size=None, replace=True, p=None, **kwargs):

src/operator/numpy/random/dist_common.cc

@@ -0,0 +1,43 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you 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.
+ */
+
+/*!
+ *  Copyright (c) 2015 by Contributors
+ * \file dist_common.cc
+ * \brief Function definition of common functions for distributions
+ * \with two parameters.
+ */
+
+#include "./dist_common.h"
+
+namespace mxnet {
+namespace op {
+
+template <>
+void _copy<cpu>(float *dst, float *src) {
+  *dst = *src;
+}
+
+template <>
+void _copy<cpu>(double *dst, double *src) {
+  *dst = *src;
+}
+
+}  // namespace op
+}  // namespace mxnet

src/operator/numpy/random/dist_common.cu

@@ -0,0 +1,43 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you 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.
+ */
+
+/*!
+ *  Copyright (c) 2015 by Contributors
+ * \file dist_common.cuh
+ * \brief Function definition of common functions for distributions
+ * \with two parameters.
+ */
+
+#include "./dist_common.h"
+
+namespace mxnet {
+namespace op {
+
+template <>
+void _copy<gpu>(float *dst, float *src) {
+CUDA_CALL(cudaMemcpy(dst, src, sizeof(float), cudaMemcpyDeviceToHost));
+}
+
+template <>
+void _copy<gpu>(double *dst, double *src) {
+CUDA_CALL(cudaMemcpy(dst, src, sizeof(double), cudaMemcpyDeviceToHost));
+}
+
+}  // namespace op
+}  // namespace mxnet

src/operator/numpy/random/dist_common.h

@@ -41,6 +41,13 @@
 namespace mxnet {
 namespace op {
 
+template <typename xpu>
+void _copy(float *dst, float*src);
+
+template <typename xpu>
+void _copy(double *dst, double*src);
+
+
 inline int FillShape(const mxnet::TShape &lshape, const mxnet::TShape &rshape,
                      const mxnet::TShape &oshape, mxnet::TShape *new_lshape,
                      mxnet::TShape *new_rshape, mxnet::TShape *new_oshape) {

src/operator/numpy/random/np_normal_op.cc

@@ -0,0 +1,69 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you 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.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_random_op.cc
+ * \brief Operator for numpy sampling from normal distributions.
+ */
+#include "./np_normal_op.h"
+
+namespace mxnet {
+namespace op {
+
+DMLC_REGISTER_PARAMETER(NumpyNormalParam);
+
+NNVM_REGISTER_OP(_npi_normal)
+.describe("Numpy behavior normal")
+.set_num_inputs(
+  [](const nnvm::NodeAttrs& attrs) {
+    const NumpyNormalParam& param = nnvm::get<NumpyNormalParam>(attrs.parsed);
+    int num_inputs = 2;
+    if (param.loc.has_value()) num_inputs -= 1;
+    if (param.scale.has_value()) num_inputs -= 1;
+    return num_inputs;
+  }
+)
+.set_num_outputs(1)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+  [](const NodeAttrs& attrs) {
+    const NumpyNormalParam& param = nnvm::get<NumpyNormalParam>(attrs.parsed);
+    int num_inputs = 2;
+    if (param.loc.has_value()) num_inputs -= 1;
+    if (param.scale.has_value()) num_inputs -= 1;
+    if (num_inputs == 0) return std::vector<std::string>();
+    if (num_inputs == 1) return std::vector<std::string>{"input1"};
+    return std::vector<std::string>{"input1", "input2"};
+  })
+.set_attr_parser(ParamParser<NumpyNormalParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", TwoparamsDistOpShape<NumpyNormalParam>)
+.set_attr<nnvm::FInferType>("FInferType", NumpyNormalOpType)
+.set_attr<FResourceRequest>("FResourceRequest",
+  [](const nnvm::NodeAttrs& attrs) {
+      return std::vector<ResourceRequest>{
+        ResourceRequest::kRandom, ResourceRequest::kTempSpace};
+  })
+.set_attr<FCompute>("FCompute<cpu>", NumpyNormalForward<cpu>)
+.set_attr<nnvm::FGradient>("FGradient", MakeZeroGradNodes)
+.add_argument("input1", "NDArray-or-Symbol", "Source input")
+.add_argument("input2", "NDArray-or-Symbol", "Source input")
+.add_arguments(NumpyNormalParam::__FIELDS__());
+
+}  // namespace op
+}  // namespace mxnet

src/operator/numpy/random/np_normal_op.cu

@@ -0,0 +1,35 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you 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.
+ */
+
+/*!
+ * Copyright (c) 2019 by Contributors
+ * \file np_normal_op.cu
+ * \brief Operator for numpy sampling from normal distributions
+ */
+
+#include "./np_normal_op.h"
+
+namespace mxnet {
+namespace op {
+
+NNVM_REGISTER_OP(_npi_normal)
+    .set_attr<FCompute>("FCompute<gpu>", NumpyNormalForward<gpu>);
+
+}  // namespace op
+}  // namespace mxnet