/
unary.py
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/
unary.py
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from paddle import _C_ops
from paddle.fluid.framework import dygraph_only, core, convert_np_dtype_to_dtype_
__all__ = []
_int_dtype_ = [
core.VarDesc.VarType.UINT8,
core.VarDesc.VarType.INT8,
core.VarDesc.VarType.INT16,
core.VarDesc.VarType.INT32,
core.VarDesc.VarType.INT64,
core.VarDesc.VarType.BOOL,
]
@dygraph_only
def sin(x, name=None):
"""
Calculate elementwise sin of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = sin(x)
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.sin(sparse_x)
"""
return _C_ops.sparse_sin(x)
@dygraph_only
def tan(x, name=None):
"""
Calculate elementwise tan of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = tan(x)
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.tan(sparse_x)
"""
return _C_ops.sparse_tan(x)
@dygraph_only
def asin(x, name=None):
"""
Calculate elementwise asin of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = asin(x)
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.asin(sparse_x)
"""
return _C_ops.sparse_asin(x)
@dygraph_only
def transpose(x, perm, name=None):
"""
Changes the perm order of ``x`` without changing its data, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = transpose(x, perm)
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
perm (list|tuple): Permute the input according to the data of perm.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A transposed Sparse Tensor with the same data type as ``x``.
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([[-2., 0.], [1., 2.]])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.transpose(sparse_x, [1, 0])
"""
return _C_ops.sparse_transpose(x, perm)
@dygraph_only
def atan(x, name=None):
"""
Calculate elementwise atan of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = atan(x)
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.atan(sparse_x)
"""
return _C_ops.sparse_atan(x)
@dygraph_only
def sinh(x, name=None):
"""
Calculate elementwise sinh of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = sinh(x)
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.sinh(sparse_x)
"""
return _C_ops.sparse_sinh(x)
@dygraph_only
def asinh(x, name=None):
"""
Calculate elementwise asinh of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = asinh(x)
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.asinh(sparse_x)
"""
return _C_ops.sparse_asinh(x)
@dygraph_only
def atanh(x, name=None):
"""
Calculate elementwise atanh of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = atanh(x)
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.atanh(sparse_x)
"""
return _C_ops.sparse_atanh(x)
@dygraph_only
def tanh(x, name=None):
"""
Calculate elementwise tanh of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = tanh(x)
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.tanh(sparse_x)
"""
return _C_ops.sparse_tanh(x)
@dygraph_only
def square(x, name=None):
"""
Calculate elementwise square of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = square(x)
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.square(sparse_x)
"""
return _C_ops.sparse_square(x)
@dygraph_only
def sqrt(x, name=None):
"""
Calculate elementwise sqrt of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = sqrt(x)
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.sqrt(sparse_x)
"""
return _C_ops.sparse_sqrt(x)
@dygraph_only
def log1p(x, name=None):
"""
Calculate the natural log of (1+x), requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = ln(1+x)
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2, 0, 1], dtype='float32')
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.log1p(sparse_x)
"""
return _C_ops.sparse_log1p(x)
@dygraph_only
def cast(x, index_dtype=None, value_dtype=None, name=None):
"""
cast non-zero-index of SparseTensor to `index_dtype`, non-zero-element of SparseTensor to
`value_dtype` , requiring x to be a SparseCooTensor or SparseCsrTensor.
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
index_dtype (np.dtype|str, optional): Data type of the index of SparseCooTensor,
or crows/cols of SparseCsrTensor. Can be uint8, int8, int16, int32, int64.
value_dtype (np.dtype|str, optional): Data type of the value of SparseCooTensor,
SparseCsrTensor. Can be bool, float16, float32, float64, int8, int32, int64, uint8.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2, 0, 1])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.cast(sparse_x, 'int32', 'float64')
"""
if index_dtype and not isinstance(index_dtype, core.VarDesc.VarType):
index_dtype = convert_np_dtype_to_dtype_(index_dtype)
if value_dtype and not isinstance(value_dtype, core.VarDesc.VarType):
value_dtype = convert_np_dtype_to_dtype_(value_dtype)
return _C_ops.sparse_cast(x, index_dtype, value_dtype)
@dygraph_only
def pow(x, factor, name=None):
"""
Calculate elementwise pow of x, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = x^{factor}
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
factor (float|int): factor of pow.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2, 0, 3], dtype='float32')
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.pow(sparse_x, 2)
"""
return _C_ops.sparse_pow(x, float(factor))
@dygraph_only
def neg(x, name=None):
"""
Calculate elementwise negative of x, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = -x
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2, 0, 3], dtype='float32')
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.neg(sparse_x)
"""
return _C_ops.sparse_scale(x, -1.0, 0.0, True)
@dygraph_only
def abs(x, name=None):
"""
Calculate elementwise absolute value of x, requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = |x|
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2, 0, 3], dtype='float32')
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.abs(sparse_x)
"""
return _C_ops.sparse_abs(x)
@dygraph_only
def coalesce(x):
r"""
the coalesced operator include sorted and merge, after coalesced, the indices of x is sorted and unique.
Parameters:
x (Tensor): the input SparseCooTensor.
Returns:
Tensor: return the SparseCooTensor after coalesced.
Examples:
.. code-block:: python
import paddle
from paddle.incubate import sparse
indices = [[0, 0, 1], [1, 1, 2]]
values = [1.0, 2.0, 3.0]
sp_x = sparse.sparse_coo_tensor(indices, values)
sp_x = sparse.coalesce(sp_x)
print(sp_x.indices())
#[[0, 1], [1, 2]]
print(sp_x.values())
#[3.0, 3.0]
"""
return _C_ops.sparse_coalesce(x)
@dygraph_only
def rad2deg(x, name=None):
"""
Convert each of the elements of input x from angles in radians to degrees,
requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
rad2deg(x) = 180/ \pi * x
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64, int32, int64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([3.142, 0., -3.142])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.rad2deg(sparse_x)
"""
if x.dtype in _int_dtype_:
x = _C_ops.sparse_cast(x, None, core.VarDesc.VarType.FP32)
return _C_ops.sparse_scale(x, 180.0 / np.pi, 0.0, True)
@dygraph_only
def deg2rad(x, name=None):
"""
Convert each of the elements of input x from degrees to angles in radians,
requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
deg2rad(x) = \pi * x / 180
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64, int32, int64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-180, 0, 180])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.deg2rad(sparse_x)
"""
if x.dtype in _int_dtype_:
x = _C_ops.sparse_cast(x, None, core.VarDesc.VarType.FP32)
return _C_ops.sparse_scale(x, np.pi / 180.0, 0.0, True)
@dygraph_only
def expm1(x, name=None):
"""
Calculate elementwise `exp(x)-1` , requiring x to be a SparseCooTensor or SparseCsrTensor.
.. math::
out = exp(x) - 1
Parameters:
x (Tensor): The input Sparse Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Sparse Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
dense_x = paddle.to_tensor([-2., 0., 1.])
sparse_x = dense_x.to_sparse_coo(1)
out = paddle.incubate.sparse.expm1(sparse_x)
"""
return _C_ops.sparse_expm1(x)
@dygraph_only
def reshape(x, shape, name=None):
"""
Changes the shape of ``x`` without changing its value, requiring x to be a SparseCooTensor or SparseCsrTensor.
Currently this function can only reshape the sparse dims of ``x`` , but ``shape`` argument must be specified
as the shape of the reshaped tensor.
Note that if x is a SparseCsrTensor, then len(shape) must be 2 or 3.
There are some tricks when specifying the target shape.
- 1. -1 means the value of this dimension is inferred from the total element number of x and remaining dimensions. Thus one and only one dimension can be set -1.
- 2. 0 means the actual dimension value is going to be copied from the corresponding dimension of x. The indices of 0 in the target shape can not exceed the rank of x.
Here are some examples to explain it.
- 1. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape is [6, 8], the reshape operator will transform x into a 2-D tensor with shape [6, 8] and leaving x's data unchanged.
- 2. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape is [2, 3, -1, 2], the reshape operator will transform x into a 4-D tensor with shape [2, 3, 4, 2] and leaving x's data unchanged. In this case, one dimension of the target shape is set to -1, the value of this dimension is inferred from the total element number of x and remaining dimensions.
- 3. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape is [-1, 0, 3, 2], the reshape operator will transform x into a 4-D tensor with shape [2, 4, 3, 2] and leaving x's data unchanged. In this case, besides -1, 0 means the actual dimension value is going to be copied from the corresponding dimension of x.
Args:
x (Tensor): The input sparse tensor with data type ``float32``, ``float64``, ``int32``, ``int64`` or ``bool``.
shape (list|tuple): Define the target shape. At most one dimension of the target shape can be -1.
The data type is ``int32``.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor: A reshaped Tensor with the same data type as ``x``.
Examples:
.. code-block:: python
import paddle
x_shape = [6, 2, 3]
new_shape = [1, 0, 2, -1, 3]
format = "coo"
dense_x = paddle.randint(-100, 100, x_shape) * paddle.randint(0, 2, x_shape)
if format == "coo":
sp_x = dense_x.to_sparse_coo(len(x_shape))
else:
sp_x = dense_x.to_sparse_csr()
sp_out = paddle.incubate.sparse.reshape(sp_x, new_shape)
print(sp_out)
# the shape of sp_out is [1, 2, 2, 3, 3]
"""
return _C_ops.sparse_reshape(x, shape)