Numpy . add numpy op logspace

python/mxnet/ndarray/numpy/_op.py

@@ -32,11 +32,11 @@
            'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2',
            'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot', 'histogram', 'eye',
-           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate', 'stack',
-           'vstack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', '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']
+           'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate',
+           'stack', 'vstack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax',
+           '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']
 
 
 @set_module('mxnet.ndarray.numpy')
@@ -916,6 +916,89 @@ def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis
         return _npi.linspace(start=start, stop=stop, num=num, endpoint=endpoint, ctx=ctx, dtype=dtype)
 
 
+@set_module('mxnet.ndarray.numpy')
+def logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None, axis=0, ctx=None):  # pylint: disable=too-many-arguments
+    r"""Return numbers spaced evenly on a log scale.
+
+    In linear space, the sequence starts at ``base ** start``
+    (`base` to the power of `start`) and ends with ``base ** stop``
+    (see `endpoint` below).
+
+        Non-scalar `start` and `stop` are now supported.
+
+    Parameters
+    ----------
+    start : int or float
+        ``base ** start`` is the starting value of the sequence.
+    stop : int or float
+        ``base ** stop`` is the final value of the sequence, unless `endpoint`
+        is False.  In that case, ``num + 1`` values are spaced over the
+        interval in log-space, of which all but the last (a sequence of
+        length `num`) are returned.
+    num : integer, optional
+        Number of samples to generate.  Default is 50.
+    endpoint : boolean, optional
+        If true, `stop` is the last sample. Otherwise, it is not included.
+        Default is True.
+    base : float, optional
+        The base of the log space. The step size between the elements in
+        ``ln(samples) / ln(base)`` (or ``log_base(samples)``) is uniform.
+        Default is 10.0.
+    dtype : dtype
+        The type of the output array.  If `dtype` is not given, infer the data
+        type from the other input arguments.
+    axis : int, optional
+        The axis in the result to store the samples.  Relevant only if start
+        or stop are array-like.  By default (0), the samples will be along a
+        new axis inserted at the beginning. Now, axis only support axis = 0.
+    ctx : Context, optional
+        An optional device context (default is the current default context).
+
+    Returns
+    -------
+    samples : ndarray
+        `num` samples, equally spaced on a log scale.
+
+    See Also
+    --------
+    arange : Similar to linspace, with the step size specified instead of the
+             number of samples. Note that, when used with a float endpoint, the
+             endpoint may or may not be included.
+    linspace : Similar to logspace, but with the samples uniformly distributed
+               in linear space, instead of log space.
+
+    Notes
+    -----
+    Logspace is equivalent to the code. Now wo only support axis = 0.
+
+    >>> y = np.linspace(start, stop, num=num, endpoint=endpoint)
+    ...
+    >>> power(base, y).astype(dtype)
+    ...
+
+    Examples
+    --------
+    >>> np.logspace(2.0, 3.0, num=4)
+    array([ 100.     ,  215.44347,  464.15887, 1000.     ])
+    >>> np.logspace(2.0, 3.0, num=4, endpoint=False)
+    array([100.     , 177.82794, 316.22775, 562.3413 ])
+    >>> np.logspace(2.0, 3.0, num=4, base=2.0)
+    array([4.       , 5.0396843, 6.349604 , 8.       ])
+    >>> np.logspace(2.0, 3.0, num=4, base=2.0, dtype=np.int32)
+    array([4, 5, 6, 8], dtype=int32)
+    >>> np.logspace(2.0, 3.0, num=4, ctx=npx.gpu(0))
+    array([ 100.     ,  215.44347,  464.15887, 1000.     ], ctx=gpu(0))
+    """
+    if isinstance(start, (list, tuple, _np.ndarray, NDArray)) or \
+       isinstance(stop, (list, tuple, _np.ndarray, NDArray)):
+        raise NotImplementedError('start and stop only support int and float')
+    if axis != 0:
+        raise NotImplementedError("the function only support axis 0")
+    if ctx is None:
+        ctx = current_context()
+    return _npi.logspace(start=start, stop=stop, num=num, endpoint=endpoint, base=base, ctx=ctx, dtype=dtype)
+
+
 @set_module('mxnet.ndarray.numpy')
 def expand_dims(a, axis):
     """Expand the shape of an array.

python/mxnet/numpy/multiarray.py

@@ -50,12 +50,12 @@
            'sqrt', 'cbrt', 'abs', 'absolute', 'exp', 'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log',
            'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
            'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
-           'tensordot', 'histogram', 'eye', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit',
-           'concatenate', 'stack', 'vstack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip',
-           'argmax', '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']
+           'tensordot', 'histogram', 'eye', 'linspace', 'logspace', 'expand_dims', 'tile', 'arange',
+           'split', 'vsplit', 'concatenate', 'stack', 'vstack', 'dstack', 'mean', 'maximum', 'minimum',
+           'swapaxes', 'clip', 'argmax', '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']
 
 
 # Return code for dispatching indexing function call
@@ -3751,6 +3751,82 @@ def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis
     return _mx_nd_np.linspace(start, stop, num, endpoint, retstep, dtype, axis, ctx)
 
 
+@set_module('mxnet.numpy')
+def logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None, axis=0, ctx=None):
+    r"""Return numbers spaced evenly on a log scale.
+
+    In linear space, the sequence starts at ``base ** start``
+    (`base` to the power of `start`) and ends with ``base ** stop``
+    (see `endpoint` below).
+
+        Non-scalar `start` and `stop` are now supported.
+
+    Parameters
+    ----------
+    start : int or float
+        ``base ** start`` is the starting value of the sequence.
+    stop : int or float
+        ``base ** stop`` is the final value of the sequence, unless `endpoint`
+        is False.  In that case, ``num + 1`` values are spaced over the
+        interval in log-space, of which all but the last (a sequence of
+        length `num`) are returned.
+    num : integer, optional
+        Number of samples to generate.  Default is 50.
+    endpoint : boolean, optional
+        If true, `stop` is the last sample. Otherwise, it is not included.
+        Default is True.
+    base : float, optional
+        The base of the log space. The step size between the elements in
+        ``ln(samples) / ln(base)`` (or ``log_base(samples)``) is uniform.
+        Default is 10.0.
+    dtype : dtype
+        The type of the output array.  If `dtype` is not given, infer the data
+        type from the other input arguments.
+    axis : int, optional
+        The axis in the result to store the samples.  Relevant only if start
+        or stop are array-like.  By default (0), the samples will be along a
+        new axis inserted at the beginning. Now, axis only support axis = 0.
+    ctx : Context, optional
+        An optional device context (default is the current default context).
+
+    Returns
+    -------
+    samples : ndarray
+        `num` samples, equally spaced on a log scale.
+
+    See Also
+    --------
+    arange : Similar to linspace, with the step size specified instead of the
+             number of samples. Note that, when used with a float endpoint, the
+             endpoint may or may not be included.
+    linspace : Similar to logspace, but with the samples uniformly distributed
+               in linear space, instead of log space.
+
+    Notes
+    -----
+    Logspace is equivalent to the code
+
+    >>> y = np.linspace(start, stop, num=num, endpoint=endpoint)
+    ...
+    >>> power(base, y).astype(dtype)
+    ...
+
+    Examples
+    --------
+    >>> np.logspace(2.0, 3.0, num=4)
+    array([ 100.     ,  215.44347,  464.15887, 1000.     ])
+    >>> np.logspace(2.0, 3.0, num=4, endpoint=False)
+    array([100.     , 177.82794, 316.22775, 562.3413 ])
+    >>> np.logspace(2.0, 3.0, num=4, base=2.0)
+    array([4.       , 5.0396843, 6.349604 , 8.       ])
+    >>> np.logspace(2.0, 3.0, num=4, base=2.0, dtype=np.int32)
+    array([4, 5, 6, 8], dtype=int32)
+    >>> np.logspace(2.0, 3.0, num=4, ctx=npx.gpu(0))
+    array([ 100.     ,  215.44347,  464.15887, 1000.     ], ctx=gpu(0))
+    """
+    return _mx_nd_np.logspace(start, stop, num, endpoint, base, dtype, axis, ctx=ctx)
+
+
 @set_module('mxnet.numpy')
 def expand_dims(a, axis):
     """Expand the shape of an array.

python/mxnet/symbol/numpy/_symbol.py

@@ -34,11 +34,12 @@
            'expm1', 'arcsin', 'arccos', 'arctan', 'sign', 'log', 'degrees', 'log2', 'log1p',
            'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
            'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot', 'histogram', 'eye',
-           'linspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate', 'stack', 'vstack',
-           'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', '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']
+           'linspace', 'logspace', 'expand_dims', 'tile', 'arange', 'split', 'vsplit', 'concatenate',
+           'stack', 'vstack', 'dstack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax',
+           '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']
 
 
 def _num_outputs(sym):
@@ -1381,6 +1382,89 @@ def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis
         return _npi.linspace(start=start, stop=stop, num=num, endpoint=endpoint, ctx=ctx, dtype=dtype)
 
 
+@set_module('mxnet.symbol.numpy')
+def logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None, axis=0, ctx=None): # pylint: disable=too-many-arguments
+    r"""Return numbers spaced evenly on a log scale.
+
+    In linear space, the sequence starts at ``base ** start``
+    (`base` to the power of `start`) and ends with ``base ** stop``
+    (see `endpoint` below).
+
+        Non-scalar `start` and `stop` are now supported.
+
+    Parameters
+    ----------
+    start : scalar
+        ``base ** start`` is the starting value of the sequence.
+    stop : scalar
+        ``base ** stop`` is the final value of the sequence, unless `endpoint`
+        is False.  In that case, ``num + 1`` values are spaced over the
+        interval in log-space, of which all but the last (a sequence of
+        length `num`) are returned.
+    num : scalar, optional
+        Number of samples to generate.  Default is 50.
+    endpoint : boolean, optional
+        If true, `stop` is the last sample. Otherwise, it is not included.
+        Default is True.
+    base : scalar, optional
+        The base of the log space. The step size between the elements in
+        ``ln(samples) / ln(base)`` (or ``log_base(samples)``) is uniform.
+        Default is 10.0.
+    dtype : dtype
+        The type of the output array.  If `dtype` is not given, infer the data
+        type from the other input arguments.
+    axis : scalar, optional
+        The axis in the result to store the samples.  Relevant only if start
+        or stop are array-like.  By default (0), the samples will be along a
+        new axis inserted at the beginning. Now, axis only support axis = 0.
+    ctx : Context, optional
+        An optional device context (default is the current default context).
+
+    Returns
+    -------
+    samples : _Symbol
+        `num` samples, equally spaced on a log scale.
+
+    See Also
+    --------
+    arange : Similar to linspace, with the step size specified instead of the
+             number of samples. Note that, when used with a float endpoint, the
+             endpoint may or may not be included.
+    linspace : Similar to logspace, but with the samples uniformly distributed
+               in linear space, instead of log space.
+
+    Notes
+    -----
+    Logspace is equivalent to the code
+
+    >>> y = np.linspace(start, stop, num=num, endpoint=endpoint)
+    ...
+    >>> power(base, y).astype(dtype)
+    ...
+
+    Examples
+    --------
+    >>> np.logspace(2.0, 3.0, num=4)
+    array([ 100.     ,  215.44347,  464.15887, 1000.     ])
+    >>> np.logspace(2.0, 3.0, num=4, endpoint=False)
+    array([100.     , 177.82794, 316.22775, 562.3413 ])
+    >>> np.logspace(2.0, 3.0, num=4, base=2.0)
+    array([4.       , 5.0396843, 6.349604 , 8.       ])
+    >>> np.logspace(2.0, 3.0, num=4, base=2.0, dtype=np.int32)
+    array([4, 5, 6, 8], dtype=int32)
+    >>> np.logspace(2.0, 3.0, num=4, ctx=npx.gpu(0))
+    array([ 100.     ,  215.44347,  464.15887, 1000.     ], ctx=gpu(0))
+    """
+    if isinstance(start, (list, _np.ndarray)) or \
+       isinstance(stop, (list, _np.ndarray)):
+        raise NotImplementedError('start and stop only support int')
+    if axis != 0:
+        raise NotImplementedError("the function only support axis 0")
+    if ctx is None:
+        ctx = current_context()
+    return _npi.logspace(start=start, stop=stop, num=num, endpoint=endpoint, base=base, ctx=ctx, dtype=dtype)
+
+
 @set_module('mxnet.symbol.numpy')
 def expand_dims(a, axis):
     """Expand the shape of an array.

src/operator/numpy/np_init_op.cc

@@ -33,6 +33,7 @@ namespace op {
 
 DMLC_REGISTER_PARAMETER(NumpyEyeParam);
 DMLC_REGISTER_PARAMETER(IndicesOpParam);
+DMLC_REGISTER_PARAMETER(LogspaceParam);
 
 inline bool NumpyIndicesShape(const nnvm::NodeAttrs& attrs,
                               mxnet::ShapeVector* in_shapes,
@@ -54,6 +55,18 @@ inline bool NumpyIndicesShape(const nnvm::NodeAttrs& attrs,
   return shape_is_known(out_shapes->at(0));
 }
 
+inline bool LogspaceShape(const nnvm::NodeAttrs& attrs,
+                          mxnet::ShapeVector *in_attrs,
+                          mxnet::ShapeVector *out_attrs) {
+  const LogspaceParam& param = nnvm::get<LogspaceParam>(attrs.parsed);
+  CHECK_EQ(in_attrs->size(), 0U);
+  CHECK_EQ(out_attrs->size(), 1U);
+  CHECK_GE(param.num, 0)
+    << "Number of sequence should be non-negative, received " << param.num;
+  SHAPE_ASSIGN_CHECK(*out_attrs, 0, mxnet::TShape({static_cast<nnvm::dim_t>(param.num)}));
+  return true;
+}
+
 NNVM_REGISTER_OP(_npi_zeros)
 .set_num_inputs(0)
 .set_num_outputs(1)
@@ -149,5 +162,15 @@ NNVM_REGISTER_OP(_npi_indices)
 .set_attr<FCompute>("FCompute<cpu>", IndicesCompute<cpu>)
 .add_arguments(IndicesOpParam::__FIELDS__());
 
+NNVM_REGISTER_OP(_npi_logspace)
+.describe("Return numbers spaced evenly on a log scale.")
+.set_num_inputs(0)
+.set_num_outputs(1)
+.set_attr_parser(ParamParser<LogspaceParam>)
+.set_attr<mxnet::FInferShape>("FInferShape", LogspaceShape)
+.set_attr<nnvm::FInferType>("FInferType", InitType<LogspaceParam>)
+.set_attr<FCompute>("FCompute<cpu>", LogspaceCompute<cpu>)
+.add_arguments(LogspaceParam::__FIELDS__());
+
 }  // namespace op
 }  // namespace mxnet

src/operator/numpy/np_init_op.cu

@@ -53,5 +53,8 @@ NNVM_REGISTER_OP(_npi_eye)
 NNVM_REGISTER_OP(_npi_indices)
 .set_attr<FCompute>("FCompute<gpu>", IndicesCompute<gpu>);
 
+NNVM_REGISTER_OP(_npi_logspace)
+.set_attr<FCompute>("FCompute<gpu>", LogspaceCompute<gpu>);
+
 }  // namespace op
 }  // namespace mxnet