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add numpy op logspace (#15825)
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fix test files and other errors
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@gyshi @haojin2
gyshi authored and haojin2 committed Oct 13, 2019
1 parent 38ee854 commit c95de90
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93 changes: 88 additions & 5 deletions python/mxnet/ndarray/numpy/_op.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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')
Expand Down Expand Up @@ -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.
Expand Down
88 changes: 82 additions & 6 deletions python/mxnet/numpy/multiarray.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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.
Expand Down
94 changes: 89 additions & 5 deletions python/mxnet/symbol/numpy/_symbol.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -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):
Expand Down Expand Up @@ -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.
Expand Down
23 changes: 23 additions & 0 deletions src/operator/numpy/np_init_op.cc
View file
Original file line number Diff line number Diff line change
Expand Up @@ -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,
Expand All @@ -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)
Expand Down Expand Up @@ -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
3 changes: 3 additions & 0 deletions src/operator/numpy/np_init_op.cu
View file
Original file line number Diff line number Diff line change
Expand Up @@ -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
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