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add numpy op logspace
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@gyshi
gyshi committed Sep 16, 2019
1 parent 5c136c9 commit cbd203c
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86 changes: 85 additions & 1 deletion python/mxnet/ndarray/numpy/_op.py
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Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,8 @@
'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'mean',
'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign']
'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
'logspace']


@set_module('mxnet.ndarray.numpy')
Expand Down Expand Up @@ -2483,3 +2484,86 @@ def copysign(x1, x2, out=None):
array([-1., 0., 1.])
"""
return _ufunc_helper(x1, x2, _npi.copysign, _np.copysign, _npi.copysign_scalar, _npi.rcopysign_scalar, out)


@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)
79 changes: 78 additions & 1 deletion python/mxnet/numpy/multiarray.py
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Original file line number Diff line number Diff line change
Expand Up @@ -52,7 +52,8 @@
'degrees', 'log2', 'log1p', 'rint', 'radians', 'reciprocal', 'square', 'negative',
'fix', 'ceil', 'floor', 'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh',
'tensordot', 'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate',
'stack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign']
'stack', 'mean', 'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
'logspace']

# Return code for dispatching indexing function call
_NDARRAY_UNSUPPORTED_INDEXING = -1
Expand Down Expand Up @@ -3986,3 +3987,79 @@ def copysign(x1, x2, out=None):
array([-1., 0., 1.])
"""
return _mx_nd_np.copysign(x1, x2, out=out)


@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)
86 changes: 85 additions & 1 deletion python/mxnet/symbol/numpy/_symbol.py
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Original file line number Diff line number Diff line change
Expand Up @@ -35,7 +35,8 @@
'rint', 'radians', 'reciprocal', 'square', 'negative', 'fix', 'ceil', 'floor',
'trunc', 'logical_not', 'arcsinh', 'arccosh', 'arctanh', 'tensordot',
'linspace', 'expand_dims', 'tile', 'arange', 'split', 'concatenate', 'stack', 'mean',
'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign']
'maximum', 'minimum', 'swapaxes', 'clip', 'argmax', 'std', 'var', 'indices', 'copysign',
'logspace']


def _num_outputs(sym):
Expand Down Expand Up @@ -2778,4 +2779,87 @@ def copysign(x1, x2, out=None):
return _ufunc_helper(x1, x2, _npi.copysign, _np.copysign, _npi.copysign_scalar, _npi.rcopysign_scalar, out)


@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_np_symbol_class(_Symbol)
23 changes: 23 additions & 0 deletions src/operator/numpy/np_init_op.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -30,6 +30,7 @@ namespace mxnet {
namespace op {

DMLC_REGISTER_PARAMETER(IndicesOpParam);
DMLC_REGISTER_PARAMETER(LogspaceParam);

inline bool NumpyIndicesShape(const nnvm::NodeAttrs& attrs,
mxnet::ShapeVector* in_shapes,
Expand All @@ -51,6 +52,18 @@ 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 @@ -143,5 +156,15 @@ 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 @@ -47,5 +47,8 @@ NNVM_REGISTER_OP(_npi_arange)
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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