sxjscience commented on a change in pull request #18251:
URL: https://github.com/apache/incubator-mxnet/pull/18251#discussion_r424810814
##########
File path: python/mxnet/util.py
##########
@@ -929,3 +938,204 @@ def default_array(source_array, ctx=None, dtype=None):
return _mx_np.array(source_array, ctx=ctx, dtype=dtype)
else:
return _mx_nd.array(source_array, ctx=ctx, dtype=dtype)
+
+class _NumpyDefaultDtypeScope(object):
+ """Scope for managing NumPy default dtype semantics.
+ In NumPy default dtype semantics, default dtype is 'float64',
+ i.e. np.array([1, 2, 3]).dtype = np.float64
+ Original default dtype without this semantic is 'float32'.
+
+ Do not use this class directly. Use `np_shape(active)` instead.
+
+ Example::
+
+ with _NumpyDefaultDtypeScope(True):
+ y = model(x)
+ backward([y])
+
+ """
+ def __init__(self, is_np_default_dtype): #pylint:
disable=redefined-outer-name
+ self._enter_is_np_default_dtype = is_np_default_dtype
+ self._prev_is_np_default_dtype = None
+
+ def __enter__(self):
+ if self._enter_is_np_default_dtype is not None:
+ self._prev_is_np_default_dtype =
set_np_default_dtype(self._enter_is_np_default_dtype)
+
+ def __exit__(self, ptype, value, trace):
+ if self._enter_is_np_default_dtype is not None and\
+ self._prev_is_np_default_dtype != self._enter_is_np_default_dtype:
+ set_np_default_dtype(self._prev_is_np_default_dtype)
+
+def np_default_dtype(active=True):
+ """Returns an activated/deactivated NumPy-default_dtype scope to be used
in 'with' statement
+ and captures code that needs the NumPy default dtype semantics. i.e.
default dtype is float64.
+
+ Please note that this is designed as an infrastructure for the incoming
+ MXNet-NumPy operators. Legacy operators registered in the modules
+ `mx.nd` and `mx.sym` are not guaranteed to behave like their counterparts
+ in NumPy even within this scope.
+
+ Parameters
+ ----------
+ active : bool
+ Indicates whether to activate NumPy default dtype semantics.
+
+ Returns
+ -------
+ _NumpyDefaultDtypeScope
+ A scope object for wrapping the code w/ or w/o NumPy-default_dtype
semantics.
+
+ Example::
+
+ with mx.np_default_Dtype(active=True):
+ # Default Dtype is 'float64', consistent with offical NumPy
behavior.
+ arr = mx.nd.array([1, 2, 3])
Review comment:
Should we use `mx.np.array`?
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