Merge remote-tracking branch 'origin/master' into mkldnn-v1.0

Conflicts:
	src/operator/nn/mkldnn/mkldnn_base-inl.h
	src/operator/nn/mkldnn/mkldnn_flatten-inl.h
	src/operator/nn/mkldnn/mkldnn_flatten.cc
	src/operator/nn/mkldnn/mkldnn_ops-inl.h
	src/operator/nn/mkldnn/mkldnn_reshape-inl.h
	src/operator/nn/mkldnn/mkldnn_reshape.cc
	src/operator/quantization/mkldnn/mkldnn_quantized_flatten.cc
	src/operator/tensor/matrix_op.cc

CMakeLists.txt

@@ -57,6 +57,11 @@ message(STATUS "CMAKE_HOST_SYSTEM_PROCESSOR ${CMAKE_HOST_SYSTEM_PROCESSOR}")
 message(STATUS "CMAKE_SYSTEM_PROCESSOR ${CMAKE_SYSTEM_PROCESSOR}")
 
 message(STATUS "CMAKE_SYSTEM_NAME ${CMAKE_SYSTEM_NAME}")
+
+if(USE_TVM_OP)
+  add_definitions(-DMXNET_USE_TVM_OP=1)
+endif()
+
 if(USE_CUDA AND NOT USE_OLDCMAKECUDA)
   message(STATUS "CMake version '${CMAKE_VERSION}' using generator '${CMAKE_GENERATOR}'")
   if(
@@ -739,7 +744,6 @@ if(USE_DIST_KVSTORE)
 endif()
 
 if(USE_TVM_OP)
-  add_definitions(-DMXNET_USE_TVM_OP=1)
   list(APPEND mxnet_LINKER_LIBS ${CMAKE_CURRENT_BINARY_DIR}/3rdparty/tvm/libtvm_runtime.so)
   include(cmake/BuildTVM.cmake)
   add_subdirectory("3rdparty/tvm")

ci/docker_cache.py

@@ -37,7 +37,7 @@
 DOCKERHUB_LOGIN_NUM_RETRIES = 5
 DOCKERHUB_RETRY_SECONDS = 5
 DOCKER_CACHE_NUM_RETRIES = 3
-DOCKER_CACHE_TIMEOUT_MINS = 15
+DOCKER_CACHE_TIMEOUT_MINS = 45
 PARALLEL_BUILDS = 10
 
 

docs/static_site/src/pages/api/faq/add_op_in_backend.md

@@ -1,6 +1,6 @@
 ---
 layout: page_category
-title:  Exception Handling in MXNet
+title: A Beginner's Guide to Implementing Operators in MXNet Backend
 category: faq
 faq_c: Extend and Contribute to MXNet
 question: How do I implement operators in MXNet backend?

python/mxnet/_numpy_op_doc.py

@@ -653,3 +653,54 @@ def _np_trace(a, offset=0, axis1=0, axis2=1, out=None):
     (2, 3)
     """
     pass
+
+
+def _np_squeeze(a, axis=None, out=None):
+    """
+    Remove single-dimensional entries from the shape of an array.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input data.
+    axis : None or int or tuple of ints, optional
+        Selects a subset of the single-dimensional entries in the
+        shape. If an axis is selected with shape entry greater than
+        one, an error is raised.
+    out : ndarray, optional
+        Array into which the output is placed. It must have the same size
+        and dtype as the input array.
+
+    Returns
+    -------
+    squeezed : ndarray
+        The input array, but with all or a subset of the
+        dimensions of length 1 removed. It always returns a copy of `a`.
+
+    Raises
+    ------
+    MXNetError
+        If `axis` is not `None`, and an axis being squeezed is not of length 1
+
+    See Also
+    --------
+    expand_dims : The inverse operation, adding singleton dimensions
+    reshape : Insert, remove, and combine dimensions, and resize existing ones
+
+    Examples
+    --------
+    >>> x = np.array([[[0], [1], [2]]])
+    >>> x.shape
+    (1, 3, 1)
+    >>> np.squeeze(x).shape
+    (3,)
+    >>> np.squeeze(x, axis=0).shape
+    (3, 1)
+    >>> np.squeeze(x, axis=1).shape
+    Traceback (most recent call last):
+    ...
+    mxnet.base.MXNetError: cannot select an axis to squeeze out which has size=3 not equal to one
+    >>> np.squeeze(x, axis=2).shape
+    (1, 3)
+    """
+    pass

python/mxnet/ndarray/numpy/_op.py

@@ -31,7 +31,7 @@
            'arctan2', 'sin', 'cos', 'tan', 'sinh', 'cosh', 'tanh', 'log10', 'sqrt', 'cbrt', 'abs',
            'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2',
            'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
-           'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
+           'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot', 'histogram',
            'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'vstack', 'mean',
            'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
            'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot', 'rad2deg', 'deg2rad',
@@ -741,6 +741,53 @@ def tensordot(a, b, axes=2):
     return _npi.tensordot(a, b, a_axes_summed, b_axes_summed)
 
 
+@set_module('mxnet.ndarray.numpy')
+def histogram(a, bins=10, range=None, normed=None, weights=None, density=None):  # pylint: disable=too-many-arguments
+    """
+    Compute the histogram of a set of data.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input data. The histogram is computed over the flattened array.
+    bins : int or NDArray
+        If `bins` is an int, it defines the number of equal-width
+        bins in the given range (10, by default). If `bins` is a
+        sequence, it defines a monotonically increasing array of bin edges,
+        including the rightmost edge, allowing for non-uniform bin widths.
+        .. versionadded:: 1.11.0
+        If `bins` is a string, it defines the method used to calculate the
+        optimal bin width, as defined by `histogram_bin_edges`.
+    range : (float, float)
+        The lower and upper range of the bins. Required when `bins` is an integer.
+        Values outside the range are ignored. The first element of the range must
+        be less than or equal to the second.
+    normed : bool, optional
+        Not supported yet, coming soon.
+    weights : array_like, optional
+        Not supported yet, coming soon.
+    density : bool, optional
+        Not supported yet, coming soon.
+    """
+    if normed is True:
+        raise NotImplementedError("normed is not supported yet...")
+    if weights is not None:
+        raise NotImplementedError("weights is not supported yet...")
+    if density is True:
+        raise NotImplementedError("density is not supported yet...")
+    if isinstance(bins, numeric_types):
+        if range is None:
+            raise NotImplementedError("automatic range is not supported yet...")
+        return _npi.histogram(a, bin_cnt=bins, range=range)
+    if isinstance(bins, (list, tuple)):
+        raise NotImplementedError("array_like bins is not supported yet...")
+    if isinstance(bins, str):
+        raise NotImplementedError("string bins is not supported yet...")
+    if isinstance(bins, NDArray):
+        return _npi.histogram(a, bins=bins)
+    raise ValueError("np.histogram fails with", locals())
+
+
 @set_module('mxnet.ndarray.numpy')
 def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0, ctx=None):  # pylint: disable=too-many-arguments
     r"""
@@ -2063,11 +2110,11 @@ def logical_not(x, out=None, **kwargs):
     --------
     >>> x= np.array([True, False, 0, 1])
     >>> np.logical_not(x)
-    array([0., 1., 1., 0.])
+    array([False,  True,  True, False])
 
     >>> x = np.arange(5)
     >>> np.logical_not(x<3)
-    array([0., 0., 0., 1., 1.])
+    array([False, False, False,  True,  True])
     """
     return _unary_func_helper(x, _npi.logical_not, _np.logical_not, out=out, **kwargs)
 

python/mxnet/numpy/multiarray.py

@@ -50,12 +50,13 @@
            'sqrt', 'cbrt', 'abs', 'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log',
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
-           'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
+           'tensordot', 'histogram', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
            'stack', 'vstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices',
            'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'arctan2', 'hypot',
            'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take', 'ldexp', 'vdot', 'inner', 'outer',
            'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal']
 
+
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
 _NDARRAY_BASIC_INDEXING = 0
@@ -301,7 +302,7 @@ def __getitem__(self, key):
             except Exception as err:
                 raise TypeError('{}'.format(str(err)))
         if isinstance(key, _np.ndarray) and key.dtype == _np.bool_:
-            key = array(key, dtype='bool')
+            key = array(key, dtype='bool', ctx=self.ctx)
         if isinstance(key, ndarray) and key.dtype == _np.bool_:  # boolean indexing
             key_shape = key.shape
             key_ndim = len(key_shape)
@@ -363,6 +364,8 @@ def __setitem__(self, key, value):
         """
         if isinstance(value, NDArray) and not isinstance(value, ndarray):
             raise TypeError('Cannot assign mx.nd.NDArray to mxnet.numpy.ndarray')
+
+        # handle basic and advanced indexing
         if self.ndim == 0:
             if not isinstance(key, tuple) or len(key) != 0:
                 raise IndexError('scalar tensor can only accept `()` as index')
@@ -752,7 +755,7 @@ def detach(self):
         check_call(_LIB.MXNDArrayDetach(self.handle, ctypes.byref(hdl)))
         return _np_ndarray_cls(hdl)
 
-    def astype(self, dtype, *args, **kwargs):  # pylint: disable=arguments-differ,unused-argument
+    def astype(self, dtype, **kwargs):  # pylint: disable=arguments-differ,unused-argument
         """
         Copy of the array, cast to a specified type.
 
@@ -1236,7 +1239,14 @@ def tile(self, *args, **kwargs):
 
     def transpose(self, *axes):  # pylint: disable=arguments-differ
         """Permute the dimensions of an array."""
-        return _mx_np_op.transpose(self, axes=axes if len(axes) != 0 else None)
+        if len(axes) == 0:
+            axes = None
+        elif len(axes) == 1:
+            if isinstance(axes[0], (tuple, list)):
+                axes = axes[0]
+            elif axes[0] is None:
+                axes = None
+        return _mx_np_op.transpose(self, axes=axes)
 
     def flip(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`flip`.
@@ -3400,11 +3410,11 @@ def logical_not(x, out=None, **kwargs):
     --------
     >>> x= np.array([True, False, 0, 1])
     >>> np.logical_not(x)
-    array([0., 1., 1., 0.])
+    array([False,  True,  True, False])
 
     >>> x = np.arange(5)
     >>> np.logical_not(x<3)
-    array([0., 0., 0., 1., 1.])
+    array([False, False, False,  True,  True])
     """
     return _mx_nd_np.logical_not(x, out=out, **kwargs)
 
@@ -3604,6 +3614,37 @@ def tensordot(a, b, axes=2):
     return _mx_nd_np.tensordot(a, b, axes)
 
 
+@set_module('mxnet.numpy')
+def histogram(a, bins=10, range=None, normed=None, weights=None, density=None):  # pylint-disable=too-many-arguments
+    """
+    Compute the histogram of a set of data.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input data. The histogram is computed over the flattened array.
+    bins : int or NDArray
+        If `bins` is an int, it defines the number of equal-width
+        bins in the given range (10, by default). If `bins` is a
+        sequence, it defines a monotonically increasing array of bin edges,
+        including the rightmost edge, allowing for non-uniform bin widths.
+        .. versionadded:: 1.11.0
+        If `bins` is a string, it defines the method used to calculate the
+        optimal bin width, as defined by `histogram_bin_edges`.
+    range : (float, float)
+        The lower and upper range of the bins. Required when `bins` is an integer.
+        Values outside the range are ignored. The first element of the range must
+        be less than or equal to the second.
+    normed : bool, optional
+        Not supported yet, coming soon.
+    weights : array_like, optional
+        Not supported yet, coming soon.
+    density : bool, optional
+        Not supported yet, coming soon.
+    """
+    return _mx_nd_np.histogram(a, bins=bins, range=range, normed=normed, weights=weights, density=density)
+
+
 @set_module('mxnet.numpy')
 def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0, ctx=None):  # pylint: disable=too-many-arguments
     r"""

python/mxnet/numpy_extension/__init__.py

@@ -25,7 +25,7 @@
 from . import _register
 from ._op import *  # pylint: disable=wildcard-import
 from ..context import *  # pylint: disable=wildcard-import
-from ..util import is_np_shape, is_np_array, set_np, reset_np
+from ..util import is_np_shape, is_np_array, set_np, reset_np, get_cuda_compute_capability
 from ..ndarray import waitall
 from .utils import *  # pylint: disable=wildcard-import
 from . import random  # pylint: disable=wildcard-import