tvm numpy operator deg2rad && rad2deg (apache#16015)

* fix format error

* change type

* constangt must be tvm.const in tvm and add backward test, do not support float16

* add addto test

* handle 0-dim and 0-size

* add 0-dim test case

* register to npi and add wrapper with doc

* change function name, add infer type

* fix format error

* merge rad2deg to deg2rad

* fix error according to review

* change infer type

* add TVM_OP in test

contrib/tvmop/basic/ufunc.py

@@ -17,7 +17,7 @@
 
 # coding: utf-8
 import tvm
-from .. import defop, AllTypes
+from .. import defop, AllTypes, RealTypes
 from .. import assign_by_req, reduce_axes
 
 def compute_add(dtype, ndim):
@@ -98,3 +98,138 @@ def backward_vadd_gpu(dtype, ndim, reduce1st, req):
         s[t].bind(bx, block_x)
         s[t].bind(tx, thread_x)
     return s, [X, in_grad_a, in_grad]
+
+
+def compute_degandrad(dtype, ndim, n):
+    A = tvm.placeholder([tvm.var() for _ in range(ndim)], name='A', dtype=dtype)
+    import math
+    if n == 0:
+        B = tvm.compute([tvm.var() for _ in range(ndim)],
+                        lambda *index: A[index] * tvm.const(math.pi, dtype) / tvm.const(180, dtype), name='B')
+    else:
+        B = tvm.compute([tvm.var() for _ in range(ndim)],
+                        lambda *index: A[index] / tvm.const(math.pi, dtype) * tvm.const(180, dtype), name='B')
+    s = tvm.create_schedule(B.op)
+    return s, A, B
+
+
+@defop(name="deg2rad", target="cpu", auto_broadcast=False,
+       dtype=["float32", "float64"], ndim=list(range(0, 6)))
+def deg2rad(dtype, ndim):
+    s, A, B = compute_degandrad(dtype, ndim, 0)
+    axes = [axis for axis in B.op.axis]
+    fused = s[B].fuse(*axes)
+    s[B].parallel(fused)
+    return s, [A, B]
+
+
+@defop(name="rad2deg", target="cpu", auto_broadcast=False,
+       dtype=["float32", "float64"], ndim=list(range(0, 6)))
+def rad2deg(dtype, ndim):
+    s, A, B = compute_degandrad(dtype, ndim, 1)
+    axes = [axis for axis in B.op.axis]
+    fused = s[B].fuse(*axes)
+    s[B].parallel(fused)
+    return s, [A, B]
+
+
+@defop(name="cuda_deg2rad", target="cuda", auto_broadcast=False,
+       dtype=["float32", "float64"], ndim=list(range(0, 6)))
+def deg2rad_gpu(dtype, ndim):
+    s, A, B = compute_degandrad(dtype, ndim, 0)
+    s = tvm.create_schedule(B.op)
+    axes = [axis for axis in B.op.axis]
+    fused = s[B].fuse(*axes)
+    bx, tx = s[B].split(fused, factor=64)
+    s[B].bind(bx, tvm.thread_axis("blockIdx.x"))
+    s[B].bind(tx, tvm.thread_axis("threadIdx.x"))
+    return s, [A, B]
+
+
+@defop(name="cuda_rad2deg", target="cuda", auto_broadcast=False,
+       dtype=["float32", "float64"], ndim=list(range(0, 6)))
+def rad2deg_gpu(dtype, ndim):
+    s, A, B = compute_degandrad(dtype, ndim, 1)
+    s = tvm.create_schedule(B.op)
+    axes = [axis for axis in B.op.axis]
+    fused = s[B].fuse(*axes)
+    bx, tx = s[B].split(fused, factor=64)
+    s[B].bind(bx, tvm.thread_axis("blockIdx.x"))
+    s[B].bind(tx, tvm.thread_axis("threadIdx.x"))
+    return s, [A, B]
+
+
+def compute_backward_degandrad(dtype, ndim, req, n):
+    ishape = [tvm.var() for _ in range(ndim)]
+    in_grad_tmp = tvm.placeholder(ishape, name='in_grad_tmp', dtype=dtype)
+    in_grad = tvm.placeholder(ishape, name='in_grad', dtype=dtype)
+    out_grad = tvm.placeholder(ishape, name='out_grad', dtype=dtype)
+    import math
+    if n == 0:
+        ret = tvm.compute(ishape, lambda *index: out_grad[index] * tvm.const(math.pi, dtype) / tvm.const(180, dtype))
+    else:
+        ret = tvm.compute(ishape, lambda *index: out_grad[index] / tvm.const(math.pi, dtype) * tvm.const(180, dtype))
+    if (req == "kAddTo"):
+        in_grad = tvm.compute(ishape, lambda *index: in_grad_tmp[index] + ret[index])
+    else:
+        in_grad = tvm.compute(ishape, lambda *index: ret[index])
+    s = tvm.create_schedule(in_grad.op)
+    return s, out_grad, in_grad_tmp, in_grad, [ret, in_grad]
+
+
+@defop(name="backward_deg2rad", target="cpu", auto_broadcast=False,
+       dtype=["float32", "float64"], ndim=list(range(0, 6)), req=["kWriteTo", "kAddTo"],
+       attrs=["req"])
+def backward_deg2rad(dtype, ndim, req):
+    s, out_grad, in_grad_tmp, in_grad, c_list = compute_backward_degandrad(dtype, ndim, req, 0)
+    for t in c_list:
+        axes = [axis for axis in t.op.axis]
+        fused = s[t].fuse(*axes)
+        s[t].parallel(fused)
+    return s, [out_grad, in_grad, in_grad_tmp]
+
+
+@defop(name="backward_rad2deg", target="cpu", auto_broadcast=False,
+       dtype=["float32", "float64"], ndim=list(range(0, 6)), req=["kWriteTo", "kAddTo"],
+       attrs=["req"])
+def backward_rad2deg(dtype, ndim, req):
+    s, out_grad, in_grad_tmp, in_grad, c_list = compute_backward_degandrad(dtype, ndim, req, 1)
+    for t in c_list:
+        axes = [axis for axis in t.op.axis]
+        fused = s[t].fuse(*axes)
+        s[t].parallel(fused)
+    return s, [out_grad, in_grad, in_grad_tmp]
+
+
+@defop(name="cuda_backward_deg2rad", target="gpu", auto_broadcast=False,
+       dtype=["float32", "float64"], ndim=list(range(0, 6)), req=["kWriteTo", "kAddTo"],
+       attrs=["req"])
+def cuda_backward_deg2rad(dtype, ndim, req):
+    s, out_grad, in_grad_tmp, in_grad, c_list = compute_backward_degandrad(dtype, ndim, req, 0)
+    num_thread = 64
+    for t in c_list:
+        block_x = tvm.thread_axis("blockIdx.x")
+        thread_x = tvm.thread_axis("threadIdx.x")
+        axes = [axis for axis in t.op.axis]
+        fused = s[t].fuse(*axes)
+        bx, tx = s[t].split(fused, factor=num_thread)
+        s[t].bind(bx, block_x)
+        s[t].bind(tx, thread_x)
+    return s, [out_grad, in_grad, in_grad_tmp]
+
+
+@defop(name="cuda_backward_rad2deg", target="gpu", auto_broadcast=False,
+       dtype=["float32", "float64"], ndim=list(range(0, 6)), req=["kWriteTo", "kAddTo"],
+       attrs=["req"])
+def cuda_backward_rad2deg(dtype, ndim, req):
+    s, out_grad, in_grad_tmp, in_grad, c_list = compute_backward_degandrad(dtype, ndim, req, 1)
+    num_thread = 64
+    for t in c_list:
+        block_x = tvm.thread_axis("blockIdx.x")
+        thread_x = tvm.thread_axis("threadIdx.x")
+        axes = [axis for axis in t.op.axis]
+        fused = s[t].fuse(*axes)
+        bx, tx = s[t].split(fused, factor=num_thread)
+        s[t].bind(bx, block_x)
+        s[t].bind(tx, thread_x)
+    return s, [out_grad, in_grad, in_grad_tmp]

python/mxnet/ndarray/numpy/_op.py

@@ -34,7 +34,7 @@
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
            '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']
+           'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'hypot', 'rad2deg', 'deg2rad']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -1245,6 +1245,42 @@ def degrees(x, out=None, **kwargs):
     return _unary_func_helper(x, _npi.degrees, _np.degrees, out=out, **kwargs)
 
 
+@set_module('mxnet.ndarray.numpy')
+def rad2deg(x, out=None):
+    r"""
+    rad2deg(x, out=None)
+
+    Convert angles from radians to degrees.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Angles in degrees.
+    out : ndarray or None, optional
+        A location into which the result is stored. If not provided or `None`,
+        a freshly-allocated array is returned.
+
+    Returns
+    -------
+    y : ndarray or scalar
+        The corresponding angle in radians.
+        This is a scalar if `x` is a scalar.
+
+    Notes
+    -----
+    "rad2deg(x)" is "x *180 / pi".
+
+    This function differs from the original numpy.arange in the following aspects:
+        - Only support float32 and float64.
+        - `out` must be in the same size of input.
+
+    Examples
+    --------
+    >>> np.rad2deg(np.pi/2)
+    90.0
+    """
+    return _unary_func_helper(x, _npi.rad2deg, _np.rad2deg, out=out)
+
+
 @set_module('mxnet.ndarray.numpy')
 def rint(x, out=None, **kwargs):
     """
@@ -1388,6 +1424,42 @@ def radians(x, out=None, **kwargs):
     return _unary_func_helper(x, _npi.radians, _np.radians, out=out, **kwargs)
 
 
+@set_module('mxnet.ndarray.numpy')
+def deg2rad(x, out=None):
+    r"""
+    deg2rad(x, out=None)
+
+    Convert angles from degrees to radians.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Angles in degrees.
+    out : ndarray or None, optional
+        A location into which the result is stored. If not provided or `None`,
+        a freshly-allocated array is returned.
+
+    Returns
+    -------
+    y : ndarray or scalar
+        The corresponding angle in radians.
+        This is a scalar if `x` is a scalar.
+
+    Notes
+    -----
+    "deg2rad(x)" is "x * pi / 180".
+
+    This function differs from the original numpy.arange in the following aspects:
+        - Only support float32 and float64.
+        - `out` must be in the same size of input.
+
+    Examples
+    --------
+    >>> np.deg2rad(180)
+    3.1415927
+    """
+    return _unary_func_helper(x, _npi.deg2rad, _np.deg2rad, out=out)
+
+
 @set_module('mxnet.ndarray.numpy')
 def reciprocal(x, out=None, **kwargs):
     r"""

python/mxnet/numpy/multiarray.py

@@ -53,7 +53,8 @@
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
            'tensordot', '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']
+           'copysign', 'ravel', 'hanning', 'hamming', 'blackman', 'flip', 'around', 'arctan2', 'hypot',
+           'rad2deg', 'deg2rad']
 
 # Return code for dispatching indexing function call
 _NDARRAY_UNSUPPORTED_INDEXING = -1
@@ -2725,6 +2726,42 @@ def degrees(x, out=None, **kwargs):
     return _mx_nd_np.degrees(x, out=out, **kwargs)
 
 
+@set_module('mxnet.numpy')
+def rad2deg(x, out=None):
+    r"""
+    rad2deg(x, out=None)
+
+    Convert angles from radians to degrees.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Angles in degrees.
+    out : ndarray or None, optional
+        A location into which the result is stored. If not provided or `None`,
+        a freshly-allocated array is returned.
+
+    Returns
+    -------
+    y : ndarray or scalar
+        The corresponding angle in radians.
+        This is a scalar if `x` is a scalar.
+
+    Notes
+    -----
+    "rad2deg(x)" is "x * 180 / pi".
+
+    This function differs from the original numpy.arange in the following aspects:
+        - Only support float32 and float64.
+        - `out` must be in the same size of input.
+
+    Examples
+    --------
+    >>> np.rad2deg(np.pi/2)
+    90.0
+    """
+    return _mx_nd_np.rad2deg(x, out=out)
+
+
 @set_module('mxnet.numpy')
 def radians(x, out=None, **kwargs):
     """
@@ -2760,6 +2797,42 @@ def radians(x, out=None, **kwargs):
     return _mx_nd_np.radians(x, out=out, **kwargs)
 
 
+@set_module('mxnet.numpy')
+def deg2rad(x, out=None):
+    r"""
+    deg2rad(x, out=None)
+
+    Convert angles from degrees to radians.
+    Parameters
+    ----------
+    x : ndarray or scalar
+        Angles in degrees.
+    out : ndarray or None, optional
+        A location into which the result is stored. If not provided or `None`,
+        a freshly-allocated array is returned.
+
+    Returns
+    -------
+    y : ndarray or scalar
+        The corresponding angle in radians.
+        This is a scalar if `x` is a scalar.
+
+    Notes
+    -----
+    "deg2rad(x)" is "x * pi / 180".
+
+    This function differs from the original numpy.arange in the following aspects:
+        - Only support float32 and float64.
+        - `out` must be in the same size of input.
+
+    Examples
+    --------
+    >>> np.deg2rad(180)
+    3.1415927
+    """
+    return _mx_nd_np.deg2rad(x, out=out)
+
+
 @set_module('mxnet.numpy')
 def reciprocal(x, out=None, **kwargs):
     r"""
@@ -4598,7 +4671,7 @@ def hypot(x1, x2, out=None):
     Notes
     -----
     This function differs from the original numpy.arange in the following aspects:
-	    - Only support float16, float32 and float64.
+        - Only support float16, float32 and float64.
 
     Examples
     --------