Rename np_compat to np_shape (apache#15063)

* Change np_compat to np_shape

* Fix scala

* Fix pylint

* Add examples and fix documentation

* Fix doc

* More doc

* Rename np_compat to np_shape in test_operatory.py

* Rename in ndarray.cc

include/mxnet/c_api.h

@@ -1067,14 +1067,14 @@ MXNET_DLL int MXAutogradIsTraining(bool* curr);
  * \param curr returns the current status
  * \return 0 when success, -1 when failure happens
  */
-MXNET_DLL int MXIsNumpyCompatible(bool* curr);
+MXNET_DLL int MXIsNumpyShape(bool* curr);
 /*!
  * \brief set numpy compatibility switch
- * \param is_np_comp 1 when numpy compatibility is on, 0 when off
+ * \param is_np_shape 1 when numpy shape semantics is on, 0 when off
  * \param prev returns the previous status before this set
  * \return 0 when success, -1 when failure happens
  */
-MXNET_DLL int MXSetIsNumpyCompatible(int is_np_comp, int* prev);
+MXNET_DLL int MXSetIsNumpyShape(int is_np_shape, int* prev);
 /*!
  * \brief mark NDArrays as variables to compute gradient for autograd
  * \param num_var number of variable NDArrays

include/mxnet/imperative.h

@@ -98,13 +98,13 @@ class Imperative {
       return old;
   }
   /*! brief whether numpy compatibility is on. */
-  bool is_np_comp() const {
-    return is_np_comp_;
+  bool is_np_shape() const {
+    return is_np_shape_;
   }
   /*! brief turn on or turn off numpy compatibility switch. */
-  bool set_is_np_comp(bool is_np_comp) {
-    bool old = is_np_comp_;
-    is_np_comp_ = is_np_comp;
+  bool set_is_np_shape(bool is_np_shape) {
+    bool old = is_np_shape_;
+    is_np_shape_ = is_np_shape;
     return old;
   }
   /*! \brief to record operator, return corresponding node. */
@@ -177,13 +177,13 @@ class Imperative {
   static thread_local bool is_recording_;
   // TOOD(junwu): Added numpy compatibility switch for backward compatibility.
   // Delete it in the next major release.
-  static thread_local bool is_np_comp_;
+  static thread_local bool is_np_shape_;
 #else
   static MX_THREAD_LOCAL bool is_train_;
   static MX_THREAD_LOCAL bool is_recording_;
   // TOOD(junwu): Added numpy compatibility switch for backward compatibility.
   // Delete it in the next major release.
-  static MX_THREAD_LOCAL bool is_np_comp_;
+  static MX_THREAD_LOCAL bool is_np_shape_;
 #endif
   /*! \brief node count used for naming */
   std::atomic<uint64_t> node_count_{0};

python/mxnet/__init__.py

@@ -23,7 +23,8 @@
 
 from .context import Context, current_context, cpu, gpu, cpu_pinned
 from . import engine
-from .base import MXNetError, is_np_compat, set_np_compat, np_compat, use_np_compat
+from .base import MXNetError
+from .util import is_np_shape, set_np_shape, np_shape, use_np_shape
 from . import base
 from . import contrib
 from . import ndarray

python/mxnet/base.py

@@ -20,7 +20,6 @@
 """ctypes library of mxnet and helper functions."""
 from __future__ import absolute_import
 
-from functools import wraps
 import atexit
 import ctypes
 import os
@@ -31,7 +30,7 @@
 
 from . import libinfo
 
-__all__ = ['MXNetError', 'is_np_compat', 'set_np_compat', 'np_compat', 'use_np_compat']
+__all__ = ['MXNetError']
 #----------------------------
 # library loading
 #----------------------------
@@ -735,140 +734,3 @@ def write_all_str(module_file, module_all_list):
 
 ctypes.pythonapi.PyCapsule_New.restype = ctypes.py_object
 ctypes.pythonapi.PyCapsule_GetPointer.restype = ctypes.c_void_p
-
-
-def set_np_compat(active):
-    """
-    Turns on/off NumPy compatibility. NumPy-compatibility is turned off by default in backend.
-
-    Parameters
-    ----------
-    active : bool
-        Indicates whether to turn on/off NumPy compatibility.
-
-    Returns
-    -------
-        A bool value indicating the previous state of NumPy compatibility.
-    """
-    prev = ctypes.c_int()
-    check_call(_LIB.MXSetIsNumpyCompatible(ctypes.c_int(active), ctypes.byref(prev)))
-    return bool(prev.value)
-
-
-def is_np_compat():
-    """
-    Checks whether the NumPy compatibility is currently turned on.
-    NumPy-compatibility is turned off by default in backend.
-
-    Returns
-    -------
-        A bool value indicating whether the NumPy compatibility is currently on.
-    """
-    curr = ctypes.c_bool()
-    check_call(_LIB.MXIsNumpyCompatible(ctypes.byref(curr)))
-    return curr.value
-
-
-class _NumpyCompatibilityStateScope(object):
-    """Scope for managing numpy compatibility state.
-    Do not use this class directly. Use `np_compat(active)` instead.
-
-    Example::
-
-        with _NumpyCompatibilityStateScope(True):
-            y = model(x)
-            backward([y])
-
-    """
-    def __init__(self, is_np_compat):  #pylint: disable=redefined-outer-name
-        self._enter_is_np_compat = is_np_compat
-        self._prev_is_np_compat = None
-
-    def __enter__(self):
-        if self._enter_is_np_compat is not None:
-            self._prev_is_np_compat = set_np_compat(self._enter_is_np_compat)
-
-    def __exit__(self, ptype, value, trace):
-        if self._enter_is_np_compat is not None and self._prev_is_np_compat != self._enter_is_np_compat:
-            set_np_compat(self._prev_is_np_compat)
-
-
-def np_compat(active=True):
-    """Returns an activated/deactivated NumPy compatibility state scope to be used in 'with' statement
-    and captures code that needs the compatibility.
-
-    Example::
-
-        with mx.np_compat(active=True):
-            # A scalar tensor's shape is `()`, whose `ndim` is `0`.
-            scalar = mx.nd.ones(shape=())
-            assert scalar.shape == ()
-
-            # In NumPy compatible mode, 0 in a shape means that dimension contains zero elements.
-            data = mx.sym.var("data", shape=(0, 2, 3))
-            ret = mx.sym.sin(data)
-            arg_shapes, out_shapes, _ = ret.infer_shape()
-            assert arg_shapes[0] == (0, 2, 3)
-            assert out_shapes[0] == (0, 2, 3)
-
-            # -1 means unknown shape dimension size in the new NumPy-compatible shape definition
-            data = mx.sym.var("data", shape=(-1, 2, 3))
-            ret = mx.sym.sin(data)
-            arg_shapes, out_shapes, _ = ret.infer_shape_partial()
-            assert arg_shapes[0] == (-1, 2, 3)
-            assert out_shapes[0] == (-1, 2, 3)
-
-            # When a shape is completely unknown in NumPy-compatible mode, it is
-            # represented as `None` in Python.
-            data = mx.sym.var("data")
-            ret = mx.sym.sin(data)
-            arg_shapes, out_shapes, _ = ret.infer_shape_partial()
-            assert arg_shapes[0] is None
-            assert out_shapes[0] is None
-
-        with mx.np_compat(active=False):
-            # 0 means unknown shape dimension size in the legacy shape definition.
-            data = mx.sym.var("data", shape=(0, 2, 3))
-            ret = mx.sym.sin(data)
-            arg_shapes, out_shapes, _ = ret.infer_shape_partial()
-            assert arg_shapes[0] == (0, 2, 3)
-            assert out_shapes[0] == (0, 2, 3)
-
-            # When a shape is completely unknown in the legacy mode (default), its ndim is
-            # equal to 0 and it is represented as `()` in Python.
-            data = mx.sym.var("data")
-            ret = mx.sym.sin(data)
-            arg_shapes, out_shapes, _ = ret.infer_shape_partial()
-            assert arg_shapes[0] == ()
-            assert out_shapes[0] == ()
-    """
-    return _NumpyCompatibilityStateScope(active)
-
-
-def use_np_compat(func):
-    """Wraps a function with an activated NumPy-compatibility scope. This ensures
-    that the execution of the function is guaranteed with NumPy compatible semantics,
-    such as zero-dim and zero size tensors.
-
-    Example::
-        import mxnet as mx
-        @mx.use_np_compat
-        def scalar_one():
-            return mx.nd.ones(())
-        print(scalar_one())
-
-    Parameters
-    ----------
-    func : a user-provided callable function to be scoped by the NumPy compatibility state.
-
-    Returns
-    -------
-    Function
-        A function for wrapping the user functions in the NumPy compatibility scope.
-    """
-    @wraps(func)
-    def _with_np_compat(*args, **kwargs):
-        with np_compat(active=True):
-            return func(*args, **kwargs)
-
-    return _with_np_compat

python/mxnet/symbol/symbol.py

@@ -34,7 +34,7 @@
 
 from ..attribute import AttrScope
 from ..base import _LIB, numeric_types, c_array, c_array_buf, c_str, c_str_array, c_handle_array
-from ..base import mx_uint, py_str, string_types, integer_types, mx_int, is_np_compat
+from ..base import mx_uint, py_str, string_types, integer_types, mx_int
 from ..base import NDArrayHandle, ExecutorHandle, SymbolHandle
 from ..base import check_call, MXNetError, NotImplementedForSymbol
 from ..context import Context, current_context
@@ -45,6 +45,7 @@
 from . import _internal
 from . import op
 from ._internal import SymbolBase, _set_symbol_class
+from ..util import is_np_shape
 
 __all__ = ["Symbol", "var", "Variable", "Group", "load", "load_json",
            "pow", "power", "maximum", "minimum", "hypot", "eye", "zeros",
@@ -1078,7 +1079,7 @@ def infer_shape(self, *args, **kwargs):
                 arg_names = self.list_arguments()
                 unknowns = []
                 for name, shape in zip(arg_names, arg_shapes):
-                    if is_np_compat():
+                    if is_np_shape():
                         shape_is_none = not shape or -1 in shape
                     else:
                         shape_is_none = not shape or 0 in shape