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

include/mxnet/c_api.h

@@ -596,12 +596,12 @@ MXNET_DLL int MXNDArrayCreate(const uint32_t *shape,
  * \return 0 when success, -1 when failure happens
  */
 MXNET_DLL int MXNDArrayCreateEx(const uint32_t *shape,
-                              uint32_t ndim,
-                              int dev_type,
-                              int dev_id,
-                              int delay_alloc,
-                              int dtype,
-                              NDArrayHandle *out);
+                                uint32_t ndim,
+                                int dev_type,
+                                int dev_id,
+                                int delay_alloc,
+                                int dtype,
+                                NDArrayHandle *out);
 
 MXNET_DLL int MXNDArrayCreateEx64(const int64_t *shape,
                                   int ndim,

julia/deps/build.jl

@@ -54,7 +54,7 @@ if Sys.isunix()
   nvcc_path = Sys.which("nvcc")
   if nvcc_path ≢ nothing
     @info "Found nvcc: $nvcc_path"
-    push!(CUDAPATHS, replace(nvcc_path, "bin/nvcc", "lib64"))
+    push!(CUDAPATHS, replace(nvcc_path, "bin/nvcc" => "lib64"))
   end
 end
 

julia/src/metric.jl

@@ -260,7 +260,7 @@ end
 
 function get(metric::MSE)
   # Delay copy until last possible moment
-  mse_sum = mapreduce(nda->copy(nda)[1], +, 0.0, metric.mse_sum)
+  mse_sum = mapreduce(nda->copy(nda)[1], +, metric.mse_sum; init = zero(MX_float))
   [(:MSE, mse_sum / metric.n_sample)]
 end
 

python/mxnet/contrib/amp/lists/symbol.py

@@ -368,6 +368,9 @@
     'arctanh',
     '_sparse_arcsin',
     '_sparse_arctanh',
+    '_contrib_MultiBoxDetection',
+    '_contrib_MultiBoxPrior',
+    '_contrib_MultiBoxTarget',
 
     # Exponents
     'exp',
@@ -575,9 +578,6 @@
     'stack',
     '_Maximum',
     '_Minimum',
-    '_contrib_MultiBoxDetection',
-    '_contrib_MultiBoxPrior',
-    '_contrib_MultiBoxTarget',
     '_contrib_MultiProposal',
     '_contrib_PSROIPooling',
     '_contrib_Proposal',

python/mxnet/ndarray/ndarray.py

@@ -58,6 +58,7 @@
 _STORAGE_TYPE_DEFAULT = 0
 _STORAGE_TYPE_ROW_SPARSE = 1
 _STORAGE_TYPE_CSR = 2
+_SIGNED_INT32_UPPER_LIMIT = (2**31 - 1)
 
 # pylint: disable= no-member
 _DTYPE_NP_TO_MX = {
@@ -155,6 +156,15 @@ def _new_alloc_handle(shape, ctx, delay_alloc, dtype=mx_real_t):
             ctypes.c_int(int(_DTYPE_NP_TO_MX[np.dtype(dtype).type])),
             ctypes.byref(hdl)))
     else:
+        # When shape is larger than unit32 then there is an overflow error at python end itself.
+        # It needs to be caught here since the call doesn't even reach backend.
+        size = 1
+        for idx in shape:
+            size = size * idx
+        if size > _SIGNED_INT32_UPPER_LIMIT:
+            raise Exception("[_new_alloc_handle] Size of tensor you are trying to allocate is " +
+                            "larger than 2^31 elements. Please build with flag " +
+                            "USE_INT64_TENSOR_SIZE=1")
         check_call(_LIB.MXNDArrayCreateEx(
             c_array_buf(mx_uint, native_array('I', shape)),
             mx_uint(len(shape)),

python/mxnet/ndarray/numpy/_op.py

@@ -38,7 +38,7 @@
            'std', 'var', 'indices', 'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip',
            'around', 'hypot', 'rad2deg', 'deg2rad', 'unique', 'lcm', 'tril', 'identity', 'take',
            'ldexp', 'vdot', 'inner', 'outer', 'equal', 'not_equal', 'greater', 'less', 'greater_equal', 'less_equal',
-           'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero']
+           'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory', 'may_share_memory', 'diff']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -4909,3 +4909,125 @@ def nonzero(a):
     """
     out = _npi.nonzero(a).transpose()
     return tuple([out[i] for i in range(len(out))])
+
+
+@set_module('mxnet.ndarray.numpy')
+def shares_memory(a, b, max_work=None):
+    """
+    Determine if two arrays share memory
+
+    Parameters
+    ----------
+    a, b : ndarray
+        Input arrays
+
+    Returns
+    -------
+    out : bool
+
+    See Also
+    --------
+    may_share_memory
+
+    Examples
+    --------
+    >>> np.may_share_memory(np.array([1,2]), np.array([5,8,9]))
+    False
+
+    This function differs from the original `numpy.shares_memory
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.shares_memory.html>`_ in
+    the following way(s):
+
+    - Does not support `max_work`, it is a dummy argument
+    - Actually it is same as `may_share_memory` in MXNet DeepNumPy
+    """
+    return _npi.share_memory(a, b).item()
+
+
+@set_module('mxnet.ndarray.numpy')
+def may_share_memory(a, b, max_work=None):
+    """
+    Determine if two arrays might share memory
+
+    A return of True does not necessarily mean that the two arrays
+    share any element.  It just means that they *might*.
+
+    Only the memory bounds of a and b are checked by default.
+
+    Parameters
+    ----------
+    a, b : ndarray
+        Input arrays
+
+    Returns
+    -------
+    out : bool
+
+    See Also
+    --------
+    shares_memory
+
+    Examples
+    --------
+    >>> np.may_share_memory(np.array([1,2]), np.array([5,8,9]))
+    False
+    >>> x = np.zeros([3, 4])
+    >>> np.may_share_memory(x[:,0], x[:,1])
+    True
+
+    This function differs from the original `numpy.may_share_memory
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.may_share_memory.html>`_ in
+    the following way(s):
+
+    - Does not support `max_work`, it is a dummy argument
+    - Actually it is same as `shares_memory` in MXNet DeepNumPy
+    """
+    return _npi.share_memory(a, b).item()
+
+
+def diff(a, n=1, axis=-1, prepend=None, append=None):
+    r"""
+    numpy.diff(a, n=1, axis=-1, prepend=<no value>, append=<no value>)
+
+    Calculate the n-th discrete difference along the given axis.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input array
+    n : int, optional
+        The number of times values are differenced. If zero, the input is returned as-is.
+    axis : int, optional
+        The axis along which the difference is taken, default is the last axis.
+    prepend, append : ndarray, optional
+        Not supported yet
+
+    Returns
+    -------
+    diff : ndarray
+        The n-th differences.
+        The shape of the output is the same as a except along axis where the dimension is smaller by n.
+        The type of the output is the same as the type of the difference between any two elements of a.
+
+    Examples
+    --------
+    >>> x = np.array([1, 2, 4, 7, 0])
+    >>> np.diff(x)
+    array([ 1,  2,  3, -7])
+    >>> np.diff(x, n=2)
+    array([  1,   1, -10])
+
+    >>> x = np.array([[1, 3, 6, 10], [0, 5, 6, 8]])
+    >>> np.diff(x)
+    array([[2, 3, 4],
+        [5, 1, 2]])
+    >>> np.diff(x, axis=0)
+    array([[-1,  2,  0, -2]])
+
+    Notes
+    -----
+    Optional inputs `prepend` and `append` are not supported yet
+    """
+    if (prepend or append):
+        raise NotImplementedError('prepend and append options are not supported yet')
+    return _npi.diff(a, n=n, axis=axis)

python/mxnet/numpy/multiarray.py

@@ -55,7 +55,8 @@
            'swapaxes', 'clip', 'argmax', 'argmin', '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', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero']
+           'greater_equal', 'less_equal', 'hsplit', 'rot90', 'einsum', 'true_divide', 'nonzero', 'shares_memory',
+           'may_share_memory', 'diff']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -1330,7 +1331,7 @@ def take(self, indices, axis=None, mode='raise'):  # pylint: disable=arguments-d
         The arguments are the same as for :py:func:`take`, with
         this array as data.
         """
-        take(self, indices, axis, mode=mode)
+        return take(self, indices, axis, mode=mode)
 
     def one_hot(self, *args, **kwargs):
         """Convenience fluent method for :py:func:`one_hot`.
@@ -6900,3 +6901,126 @@ def nonzero(a):
     (array([1, 1, 1, 2, 2, 2], dtype=int64), array([0, 1, 2, 0, 1, 2], dtype=int64))
     """
     return _mx_nd_np.nonzero(a)
+
+
+@set_module('mxnet.numpy')
+def shares_memory(a, b, max_work=None):
+    """
+    Determine if two arrays share memory
+
+    Parameters
+    ----------
+    a, b : ndarray
+        Input arrays
+
+    Returns
+    -------
+    out : bool
+
+    See Also
+    --------
+    may_share_memory
+
+    Examples
+    --------
+    >>> np.may_share_memory(np.array([1,2]), np.array([5,8,9]))
+    False
+
+    This function differs from the original `numpy.shares_memory
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.shares_memory.html>`_ in
+    the following way(s):
+
+    - Does not support `max_work`, it is a dummy argument
+    - Actually it is same as `may_share_memory` in MXNet DeepNumPy
+    """
+    return _mx_nd_np.shares_memory(a, b, max_work)
+
+
+@set_module('mxnet.numpy')
+def may_share_memory(a, b, max_work=None):
+    """
+    Determine if two arrays might share memory
+
+    A return of True does not necessarily mean that the two arrays
+    share any element.  It just means that they *might*.
+
+    Only the memory bounds of a and b are checked by default.
+
+    Parameters
+    ----------
+    a, b : ndarray
+        Input arrays
+
+    Returns
+    -------
+    out : bool
+
+    See Also
+    --------
+    shares_memory
+
+    Examples
+    --------
+    >>> np.may_share_memory(np.array([1,2]), np.array([5,8,9]))
+    False
+    >>> x = np.zeros([3, 4])
+    >>> np.may_share_memory(x[:,0], x[:,1])
+    True
+
+    This function differs from the original `numpy.may_share_memory
+    <https://docs.scipy.org/doc/numpy/reference/generated/numpy.may_share_memory.html>`_ in
+    the following way(s):
+
+    - Does not support `max_work`, it is a dummy argument
+    - Actually it is same as `shares_memory` in MXNet DeepNumPy
+    """
+    return _mx_nd_np.may_share_memory(a, b, max_work)
+
+
+def diff(a, n=1, axis=-1, prepend=None, append=None):
+    r"""
+    numpy.diff(a, n=1, axis=-1, prepend=<no value>, append=<no value>)
+
+    Calculate the n-th discrete difference along the given axis.
+
+    Parameters
+    ----------
+    a : ndarray
+        Input array
+    n : int, optional
+        The number of times values are differenced. If zero, the input is returned as-is.
+    axis : int, optional
+        The axis along which the difference is taken, default is the last axis.
+    prepend, append : ndarray, optional
+        Not supported yet
+
+    Returns
+    -------
+    diff : ndarray
+        The n-th differences.
+        The shape of the output is the same as a except along axis where the dimension is smaller by n.
+        The type of the output is the same as the type of the difference between any two elements of a.
+        This is the same as the type of a in most cases.
+
+    Examples
+    --------
+    >>> x = np.array([1, 2, 4, 7, 0])
+    >>> np.diff(x)
+    array([ 1,  2,  3, -7])
+    >>> np.diff(x, n=2)
+    array([  1,   1, -10])
+
+    >>> x = np.array([[1, 3, 6, 10], [0, 5, 6, 8]])
+    >>> np.diff(x)
+    array([[2, 3, 4],
+        [5, 1, 2]])
+    >>> np.diff(x, axis=0)
+    array([[-1,  2,  0, -2]])
+
+    Notes
+    -----
+    Optional inputs `prepend` and `append` are not supported yet
+    """
+    if (prepend or append):
+        raise NotImplementedError('prepend and append options are not supported yet')
+    return _mx_nd_np.diff(a, n=n, axis=axis)

python/mxnet/numpy_dispatch_protocol.py

@@ -127,7 +127,10 @@ def _run_with_array_ufunc_proto(*args, **kwargs):
     'tril',
     'meshgrid',
     'outer',
-    'einsum'
+    'einsum',
+    'shares_memory',
+    'may_share_memory',
+    'diff',
 ]