Fix random op signature

Fix weight shape inference using uniform/normal

Delete absolute_import

python/mxnet/initializer.py

@@ -505,7 +505,7 @@ def __init__(self, scale=0.07):
 
     def _init_weight(self, _, arr):
         uniform_fn = _mx_np.random.uniform if is_np_array() else random.uniform
-        uniform_fn(-self.scale, self.scale, out=arr)
+        uniform_fn(-self.scale, self.scale, arr.shape, out=arr)
 
 @register
 class Normal(Initializer):
@@ -539,7 +539,7 @@ def __init__(self, sigma=0.01):
 
     def _init_weight(self, _, arr):
         normal_fn = _mx_np.random.normal if is_np_array() else random.normal
-        normal_fn(0, self.sigma, out=arr)
+        normal_fn(0, self.sigma, arr.shape, out=arr)
 
 @register
 class Orthogonal(Initializer):
@@ -639,10 +639,10 @@ def _init_weight(self, name, arr):
         scale = np.sqrt(self.magnitude / factor)
         if self.rnd_type == "uniform":
             uniform_fn = _mx_np.random.uniform if is_np_array() else random.uniform
-            uniform_fn(-scale, scale, out=arr)
+            uniform_fn(-scale, scale, arr.shape, out=arr)
         elif self.rnd_type == "gaussian":
             normal_fn = _mx_np.random.normal if is_np_array() else random.normal
-            normal_fn(0, scale, out=arr)
+            normal_fn(0, scale, arr.shape, out=arr)
         else:
             raise ValueError("Unknown random type")
 

python/mxnet/ndarray/numpy/random.py

@@ -26,7 +26,7 @@
 __all__ = ['randint', 'uniform', 'normal', "choice"]
 
 
-def randint(low, high=None, size=None, dtype=None, **kwargs):
+def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
     """Return random integers from `low` (inclusive) to `high` (exclusive).
 
     Return random integers from the "discrete uniform" distribution of
@@ -75,14 +75,12 @@ def randint(low, high=None, size=None, dtype=None, **kwargs):
     array([[4, 0, 2, 1],
         [3, 2, 2, 0]])
     """
-    ctx = kwargs.pop('ctx', None)
-    out = kwargs.pop('out', None)
     if dtype is None:
         dtype = 'int'
     if ctx is None:
         ctx = current_context()
     if size is None:
-        size = 1
+        size = ()
     if high is None:
         high = low
         low = 0
@@ -114,6 +112,8 @@ def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
         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
     -------
@@ -126,8 +126,6 @@ def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
         dtype = 'float32'
     if ctx is None:
         ctx = current_context()
-    if out is not None:
-        size = out.shape
     if size == ():
         size = None
     if input_type == (True, True):
@@ -144,7 +142,7 @@ 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, dtype=None, **kwargs):
+def normal(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
     """Draw random samples from a normal (Gaussian) distribution.
 
     Samples are distributed according to a normal distribution parametrized
@@ -165,6 +163,8 @@ def normal(loc=0.0, scale=1.0, size=None, dtype=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
     -------
@@ -173,14 +173,10 @@ def normal(loc=0.0, scale=1.0, size=None, dtype=None, **kwargs):
     """
     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'
     if ctx is None:
         ctx = current_context()
-    if out is not None:
-        size = out.shape
     if size == ():
         size = None
     if input_type == (True, True):

python/mxnet/numpy/random.py

@@ -24,7 +24,7 @@
 __all__ = ["randint", "uniform", "normal", "choice"]
 
 
-def randint(low, high=None, size=None, dtype=None, **kwargs):
+def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
     """Return random integers from `low` (inclusive) to `high` (exclusive).
 
     Return random integers from the "discrete uniform" distribution of
@@ -73,7 +73,7 @@ def randint(low, high=None, size=None, dtype=None, **kwargs):
     array([[4, 0, 2, 1],
         [3, 2, 2, 0]])
     """
-    return _mx_nd_np.random.randint(low, high, size, dtype, **kwargs)
+    return _mx_nd_np.random.randint(low, high, size, dtype, ctx, out)
 
 
 def uniform(low=0.0, high=1.0, size=None, dtype=None, ctx=None, out=None):
@@ -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, dtype=None, **kwargs):
+def normal(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
     """Draw random samples from a normal (Gaussian) distribution.
 
     Samples are distributed according to a normal distribution parametrized
@@ -139,7 +139,7 @@ def normal(loc=0.0, scale=1.0, size=None, dtype=None, **kwargs):
     out : ndarray
         Drawn samples from the parameterized normal distribution.
     """
-    return _mx_nd_np.random.normal(loc, scale, size, dtype, **kwargs)
+    return _mx_nd_np.random.normal(loc, scale, size, dtype, ctx, out)
 
 
 def multinomial(n, pvals, size=None, **kwargs):

python/mxnet/numpy_op_signature.py

@@ -17,8 +17,7 @@
 
 """Make builtin ops' signatures compatible with NumPy."""
 
-from __future__ import absolute_import # pylint: disable=reimported
-from sys import version, version_info
+import sys
 import warnings
 from . import _numpy_op_doc
 from . import numpy as mx_np
@@ -55,11 +54,11 @@ def _get_builtin_op(op_name):
 
 
 def _register_op_signatures():
-    if version_info.major < 3 or version_info.minor < 5:
+    if sys.version_info.major < 3 or sys.version_info.minor < 5:
         warnings.warn('Some mxnet.numpy operator signatures may not be displayed consistently with '
                       'their counterparts in the official NumPy package due to too-low Python '
                       'version {}. Python >= 3.5 is required to make the signatures display correctly.'
-                      .format(str(version)))
+                      .format(str(sys.version)))
         return
 
     import inspect

python/mxnet/symbol/numpy/random.py

@@ -25,7 +25,7 @@
 __all__ = ['randint', 'uniform', 'normal']
 
 
-def randint(low, high=None, size=None, dtype=None, **kwargs):
+def randint(low, high=None, size=None, dtype=None, ctx=None, out=None):
     """Return random integers from `low` (inclusive) to `high` (exclusive).
 
     Return random integers from the "discrete uniform" distribution of
@@ -74,14 +74,12 @@ def randint(low, high=None, size=None, dtype=None, **kwargs):
     array([[4, 0, 2, 1],
         [3, 2, 2, 0]])
     """
-    ctx = kwargs.pop('ctx', None)
-    out = kwargs.pop('out', None)
     if dtype is None:
         dtype = 'int'
     if ctx is None:
         ctx = current_context()
     if size is None:
-        size = 1
+        size = ()
     if high is None:
         high = low
         low = 0
@@ -143,7 +141,7 @@ 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, dtype=None, **kwargs):
+def normal(loc=0.0, scale=1.0, size=None, dtype=None, ctx=None, out=None):
     """Draw random samples from a normal (Gaussian) distribution.
 
     Samples are distributed according to a normal distribution parametrized
@@ -172,15 +170,10 @@ def normal(loc=0.0, scale=1.0, size=None, dtype=None, **kwargs):
     """
     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'
     if ctx is None:
         ctx = current_context()
-    out = kwargs.pop('out', None)
-    if out is not None:
-        size = out.shape
     if size == ():
         size = None
     if input_type == (True, True):

src/operator/random/sample_op.cc

@@ -81,7 +81,6 @@ DMLC_REGISTER_PARAMETER(SampleGenNegBinomialLikeParam);
 MXNET_OPERATOR_REGISTER_SAMPLE(_random_uniform, SampleUniformParam)
 .add_alias("uniform")
 .add_alias("random_uniform")
-.add_alias("_npi_random_uniform")
 .describe(R"code(Draw random samples from a uniform distribution.
 
 .. note:: The existing alias ``uniform`` is deprecated.
@@ -100,7 +99,6 @@ Example::
 MXNET_OPERATOR_REGISTER_SAMPLE(_random_normal, SampleNormalParam)
 .add_alias("normal")
 .add_alias("random_normal")
-.add_alias("_npi_random_normal")
 .describe(R"code(Draw random samples from a normal (Gaussian) distribution.
 
 .. note:: The existing alias ``normal`` is deprecated.

tests/python/unittest/test_exc_handling.py

@@ -21,9 +21,10 @@
 from common import setup_module, with_seed, teardown
 from mxnet.gluon import nn
 from mxnet.base import MXNetError
-from mxnet.test_utils import assert_exception, default_context, set_default_context
+from mxnet.test_utils import assert_exception, default_context, set_default_context, use_np
 from nose.tools import assert_raises
 
+
 @with_seed()
 def test_exc_imperative():
     def imperative(exec_numpy=True):
@@ -181,6 +182,7 @@ def run_training_iteration(data):
     mx.nd.waitall()
     mx.profiler.set_state("stop")
 
+
 @with_seed()
 def test_opencv_exception():
     def check_resize():
@@ -199,6 +201,17 @@ def test_np_reshape_exception():
     assert_raises(MXNetError, lambda: mx.np.reshape(a, (-1, 3)))
 
 
+@with_seed()
+@use_np
+def test_np_random_incorrect_named_arguments():
+    random_ops = ['uniform', 'normal', 'randint']
+    for op_name in random_ops:
+        op = getattr(mx.np.random, op_name, None)
+        assert op is not None
+        assert_raises(TypeError, op, shape=())
+        assert_raises(TypeError, op, shape=None)
+
+
 if __name__ == '__main__':
     import nose
     nose.runmodule()

tests/python/unittest/test_numpy_gluon.py

@@ -26,12 +26,13 @@
 from common import with_seed
 
 
+@with_seed()
 def test_create_np_param():
     M, K, N = 10, 9, 20
 
-    def check_block_params(x, TestBlock, hybridize, expected_type):
+    def check_block_params(x, TestBlock, hybridize, expected_type, initializer):
         net = TestBlock()
-        net.initialize()
+        net.initialize(initializer())
         if hybridize:
             net.hybridize()
         net(x)
@@ -59,12 +60,14 @@ def hybrid_forward(self, F, x, w):
             return F.np.dot(x, w)
 
     x = mx.nd.random.uniform(shape=(M, K))
-    check_block_params(x, TestBlock1, False, mx.nd.NDArray)
-    check_block_params(x, TestBlock1, True, mx.nd.NDArray)
-    check_block_params(x.as_np_ndarray(), TestBlock2, False, np.ndarray)
-    check_block_params(x.as_np_ndarray(), TestBlock2, True, np.ndarray)
+    for initializer in [mx.initializer.Uniform, mx.initializer.Normal]:
+        check_block_params(x, TestBlock1, False, mx.nd.NDArray, initializer)
+        check_block_params(x, TestBlock1, True, mx.nd.NDArray, initializer)
+        check_block_params(x.as_np_ndarray(), TestBlock2, False, np.ndarray, initializer)
+        check_block_params(x.as_np_ndarray(), TestBlock2, True, np.ndarray, initializer)
 
 
+@with_seed()
 @use_np
 def test_optimizer_with_np_ndarrays():
     class LinearRegression(gluon.HybridBlock):

tests/python/unittest/test_numpy_op.py

@@ -695,11 +695,9 @@ def _test_np_exception(func, shape, dim):
                         if hybridize:
                             test_gluon.hybridize()
                         if is_int(itype):
-                            x = mx.nd.arange(120).reshape((2, 3, 4, 5))
-                            x = mx.nd.array(x)
+                            x = np.arange(120).reshape((2, 3, 4, 5))
                         else:
-                            x = mx.nd.random.uniform(-1.0, 1.0, shape=shape, dtype=itype)
-                        x = x.as_np_ndarray()
+                            x = np.random.uniform(-1.0, 1.0, size=shape, dtype=itype)
                         x.attach_grad()
                         if func == 'max':
                             expected_ret = _np.amax(x.asnumpy(), axis=axis, keepdims=keepdims)
@@ -1812,6 +1810,8 @@ def test_np_randint():
         (5, None)
     ]
     shapes = [
+        None,
+        (),
         (3, 3),
         (3, 4),
         (0, 0),
@@ -1825,7 +1825,7 @@ def test_np_randint():
     for shape in shapes:
         for (low, high) in params:
             data_mx = np.random.randint(low, high, size=shape)
-            assert data_mx.shape == shape
+            assert data_mx.shape == (shape if shape is not None else ())
 
     # test generator
     for dtype in ['int32', 'int64']: