[Fix] Add ctx to the original ndarray and revise the usage of context to ctx (apache#16819)

* try to fix warning

try to fix warning

try to fix all warnings

use ctx

* try to fix warnings

* try fo fix warnings

Author: sxjscience

python/mxnet/gluon/block.py

@@ -117,7 +117,7 @@ def _gather_type_ctx_info(args):
         Context of the first appeared NDArray (for backward-compatibility)
     """
     if isinstance(args, NDArray):
-        return False, True, {args.context}, args.context
+        return False, True, {args.ctx}, args.ctx
     elif isinstance(args, Symbol):
         return True, False, set(), None
     elif isinstance(args, (list, tuple)):
@@ -1141,7 +1141,7 @@ def forward(self, x, *args):
                 if len(ctx_set) > 1:
                     raise ValueError('Find multiple contexts in the input, '
                                      'After hybridized, the HybridBlock only supports one input '
-                                     'context. You can print the ele.context in the '
+                                     'context. You can print the ele.ctx in the '
                                      'input arguments to inspect their contexts. '
                                      'Find all contexts = {}'.format(ctx_set))
                 with ctx:
@@ -1324,7 +1324,7 @@ def __init__(self, outputs, inputs, params=None):
 
     def forward(self, x, *args):
         if isinstance(x, NDArray):
-            with x.context:
+            with x.ctx:
                 return self._call_cached_op(x, *args)
 
         assert isinstance(x, Symbol), \

python/mxnet/gluon/parameter.py

@@ -369,10 +369,10 @@ def _init_grad(self):
             if self._grad_stype != 'default':
                 raise ValueError("mxnet.numpy.zeros does not support stype = {}"
                                  .format(self._grad_stype))
-            self._grad = [_mx_np.zeros(shape=i.shape, dtype=i.dtype, ctx=i.context)
+            self._grad = [_mx_np.zeros(shape=i.shape, dtype=i.dtype, ctx=i.ctx)
                           for i in self._data]
         else:
-            self._grad = [ndarray.zeros(shape=i.shape, dtype=i.dtype, ctx=i.context,
+            self._grad = [ndarray.zeros(shape=i.shape, dtype=i.dtype, ctx=i.ctx,
                                         stype=self._grad_stype) for i in self._data]
 
         autograd.mark_variables(self._check_and_get(self._data, list),
@@ -522,7 +522,7 @@ def row_sparse_data(self, row_id):
             raise RuntimeError("Cannot return a copy of Parameter %s via row_sparse_data() " \
                                "because its storage type is %s. Please use data() instead." \
                                %(self.name, self._stype))
-        return self._get_row_sparse(self._data, row_id.context, row_id)
+        return self._get_row_sparse(self._data, row_id.ctx, row_id)
 
     def list_row_sparse_data(self, row_id):
         """Returns copies of the 'row_sparse' parameter on all contexts, in the same order
@@ -897,7 +897,7 @@ def zero_grad(self):
                 if g.stype == 'row_sparse':
                     ndarray.zeros_like(g, out=g)
                 else:
-                    arrays[g.context].append(g)
+                    arrays[g.ctx].append(g)
 
         if len(arrays) == 0:
             return

python/mxnet/ndarray/ndarray.py

@@ -245,7 +245,7 @@ def __repr__(self):
         shape_info = 'x'.join(['%d' % x for x in self.shape])
         return '\n%s\n<%s %s @%s>' % (str(self.asnumpy()),
                                       self.__class__.__name__,
-                                      shape_info, self.context)
+                                      shape_info, self.ctx)
 
     def __reduce__(self):
         return NDArray, (None,), self.__getstate__()
@@ -729,14 +729,14 @@ def _prepare_value_nd(self, value, bcast_shape, squeeze_axes=None):
         `squeeze_axes`: a sequence of axes to squeeze in the value array.
         """
         if isinstance(value, numeric_types):
-            value_nd = full(bcast_shape, value, ctx=self.context, dtype=self.dtype)
+            value_nd = full(bcast_shape, value, ctx=self.ctx, dtype=self.dtype)
         elif type(value) == self.__class__:  # pylint: disable=unidiomatic-typecheck
-            value_nd = value.as_in_context(self.context)
+            value_nd = value.as_in_context(self.ctx)
             if value_nd.dtype != self.dtype:
                 value_nd = value_nd.astype(self.dtype)
         else:
             try:
-                value_nd = array(value, ctx=self.context, dtype=self.dtype)
+                value_nd = array(value, ctx=self.ctx, dtype=self.dtype)
             except:
                 raise TypeError('{} does not support assignment with non-array-like '
                                 'object {} of type {}'.format(self.__class__, value, type(value)))
@@ -1220,7 +1220,7 @@ def _get_index_nd(self, key):
 
         shape_nd_permut = tuple(self.shape[ax] for ax in axs_nd_permut)
         converted_idcs_short = [
-            self._advanced_index_to_array(idx, ax_len, self.context)
+            self._advanced_index_to_array(idx, ax_len, self.ctx)
             for idx, ax_len in zip(idcs_permut_short, shape_nd_permut)
         ]
         bcast_idcs_permut_short = self._broadcast_advanced_indices(
@@ -1229,7 +1229,7 @@ def _get_index_nd(self, key):
 
         # Get the ndim of advanced indexing subspace
         converted_advanced_idcs = [
-            self._advanced_index_to_array(idx, ax_len, self.context)
+            self._advanced_index_to_array(idx, ax_len, self.ctx)
             for idx, ax_len in zip(adv_idcs_nd, [self.shape[ax] for ax in adv_axs_nd])
         ]
         bcast_advanced_shape = _broadcast_shapes(converted_advanced_idcs)
@@ -2433,6 +2433,23 @@ def context(self):
             self.handle, ctypes.byref(dev_typeid), ctypes.byref(dev_id)))
         return Context(Context.devtype2str[dev_typeid.value], dev_id.value)
 
+    @property
+    def ctx(self):
+        """Device context of the array. Has the same meaning as context.
+
+        Examples
+        --------
+        >>> x = mx.nd.array([1, 2, 3, 4])
+        >>> x.ctx
+        cpu(0)
+        >>> type(x.ctx)
+        <class 'mxnet.context.Context'>
+        >>> y = mx.nd.zeros((2,3), mx.gpu(0))
+        >>> y.ctx
+        gpu(0)
+        """
+        return self.context
+
     @property
     def dtype(self):
         """Data-type of the array's elements.
@@ -2580,7 +2597,7 @@ def astype(self, dtype, copy=True):
         if not copy and np.dtype(dtype) == self.dtype:
             return self
 
-        res = empty(self.shape, ctx=self.context, dtype=dtype)
+        res = empty(self.shape, ctx=self.ctx, dtype=dtype)
         self.copyto(res)
         return res
 
@@ -2646,7 +2663,7 @@ def copy(self):
         array([[ 1.,  1.,  1.],
                [ 1.,  1.,  1.]], dtype=float32)
         """
-        return self.copyto(self.context)
+        return self.copyto(self.ctx)
 
     def slice_assign_scalar(self, value, begin, end, step):
         """
@@ -2904,7 +2921,7 @@ def _full(self, value):
         """
         This is added as an NDArray class method in order to support polymorphism in NDArray and numpy.ndarray indexing
         """
-        return _internal._full(self.shape, value=value, ctx=self.context, dtype=self.dtype, out=self)
+        return _internal._full(self.shape, value=value, ctx=self.ctx, dtype=self.dtype, out=self)
 
     def _scatter_set_nd(self, value_nd, indices):
         """
@@ -4542,7 +4559,7 @@ def concatenate(arrays, axis=0, always_copy=True):
         assert shape_rest2 == arr.shape[axis+1:]
         assert dtype == arr.dtype
     ret_shape = shape_rest1 + (shape_axis,) + shape_rest2
-    ret = empty(ret_shape, ctx=arrays[0].context, dtype=dtype)
+    ret = empty(ret_shape, ctx=arrays[0].ctx, dtype=dtype)
 
     idx = 0
     begin = [0 for _ in ret_shape]

python/mxnet/numpy/multiarray.py

@@ -921,15 +921,15 @@ def __repr__(self):
         elif dtype not in (_np.float32, _np.bool_):
             array_str = array_str[:-1] + ', dtype={})'.format(dtype)
 
-        context = self.context
+        context = self.ctx
         if context.device_type == 'cpu':
             return array_str
         return array_str[:-1] + ', ctx={})'.format(str(context))
 
     def __str__(self):
         """Returns a string representation of the array."""
         array_str = self.asnumpy().__str__()
-        context = self.context
+        context = self.ctx
         if context.device_type == 'cpu' or self.ndim == 0:
             return array_str
         return '{array} @{ctx}'.format(array=array_str, ctx=context)
@@ -994,7 +994,7 @@ def astype(self, dtype, **kwargs):  # pylint: disable=arguments-differ,unused-ar
         if not copy and _np.dtype(dtype) == self.dtype:
             return self
 
-        res = empty(self.shape, dtype=dtype, ctx=self.context)
+        res = empty(self.shape, dtype=dtype, ctx=self.ctx)
         self.copyto(res)
         return res
 
@@ -1051,7 +1051,8 @@ def argmax(self, axis=None, out=None):  # pylint: disable=arguments-differ
 
     def as_in_context(self, context):
         """This function has been deprecated. Please refer to ``ndarray.as_in_ctx``."""
-        warnings.warn('ndarray.context has been renamed to ndarray.ctx', DeprecationWarning)
+        warnings.warn('ndarray.as_in_context has been renamed to'
+                      ' ndarray.as_in_ctx', DeprecationWarning)
         return self.as_nd_ndarray().as_in_context(context).as_np_ndarray()
 
     def as_in_ctx(self, ctx):
@@ -1864,7 +1865,7 @@ def _full(self, value):
         Currently for internal use only. Implemented for __setitem__.
         Assign to self an array of self's same shape and type, filled with value.
         """
-        return _mx_nd_np.full(self.shape, value, ctx=self.context, dtype=self.dtype, out=self)
+        return _mx_nd_np.full(self.shape, value, ctx=self.ctx, dtype=self.dtype, out=self)
 
     # pylint: disable=redefined-outer-name
     def _scatter_set_nd(self, value_nd, indices):