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binary.py
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binary.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.
from paddle import _C_ops, _legacy_C_ops
from paddle.fluid.framework import dygraph_only, core
__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 matmul(x, y, name=None):
"""
Note:
This API is only supported from ``CUDA 11.0`` .
Applies matrix multiplication of two Tensors.
The supported input/output Tensor layout are as follows:
Note:
x[SparseCsrTensor] @ y[SparseCsrTensor] -> out[SparseCsrTensor]
x[SparseCsrTensor] @ y[DenseTensor] -> out[DenseTensor]
x[SparseCooTensor] @ y[SparseCooTensor] -> out[SparseCooTensor]
x[SparseCooTensor] @ y[DenseTensor] -> out[DenseTensor]
It supports backward propagation.
Dimensions `x` and `y` must be >= 2D. Automatic broadcasting of Tensor is not supported.
the shape of `x` should be `[*, M, K]` , and the shape of `y` should be `[*, K, N]` , where `*`
is zero or more batch dimensions.
Args:
x (Tensor): The input tensor. It can be SparseCooTensor/SparseCsrTensor. The data type can be float32 or float64.
y (Tensor): The input tensor. It can be SparseCooTensor/SparseCsrTensor/DenseTensor. The data type can be float32 or float64.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor: Its layout is determined by that of `x` and `y` .
Examples:
.. code-block:: python
import paddle
# csr @ dense -> dense
crows = [0, 1, 2, 3]
cols = [1, 2, 0]
values = [1., 2., 3.]
csr = paddle.incubate.sparse.sparse_csr_tensor(crows, cols, values, [3, 3])
# Tensor(shape=[3, 3], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
# crows=[0, 1, 2, 3],
# cols=[1, 2, 0],
# values=[1., 2., 3.])
dense = paddle.ones([3, 2])
out = paddle.incubate.sparse.matmul(csr, dense)
# Tensor(shape=[3, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [[1., 1.],
# [2., 2.],
# [3., 3.]])
# coo @ dense -> dense
indices = [[0, 1, 2], [1, 2, 0]]
values = [1., 2., 3.]
coo = paddle.incubate.sparse.sparse_coo_tensor(indices, values, [3, 3])
# Tensor(shape=[3, 3], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
# indices=[[0, 1, 2],
# [1, 2, 0]],
# values=[1., 2., 3.])
dense = paddle.ones([3, 2])
out = paddle.incubate.sparse.matmul(coo, dense)
# Tensor(shape=[3, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [[1., 1.],
# [2., 2.],
# [3., 3.]])
"""
return _C_ops.sparse_matmul(x, y)
@dygraph_only
def masked_matmul(x, y, mask, name=None):
"""
Note:
This API is only supported from ``CUDA 11.3`` .
Applies matrix multiplication of two Dense Tensors.
The supported input/output Tensor layout are as follows:
Note:
x[DenseTensor] @ y[DenseTensor] * mask[SparseCooTensor] -> out[SparseCooTensor]
x[DenseTensor] @ y[DenseTensor] * mask[SparseCsrTensor] -> out[SparseCsrTensor]
It supports backward propagation.
Dimensions `x` and `y` must be >= 2D. Automatic broadcasting of Tensor is not supported.
the shape of `x` should be `[*, M, K]` , and the shape of `y` should be `[*, K, N]` , and the shape of `mask` should be `[*, M, N]` ,
where `*` is zero or more batch dimensions.
Args:
x (Tensor): The input tensor. It is DenseTensor. The data type can be float32 or float64.
y (Tensor): The input tensor. It is DenseTensor. The data type can be float32 or float64.
mask (Tensor): The mask tensor, which can be SparseCooTensor/SparseCsrTensor. It specify sparse coordinates. The data type can be float32 or float64.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor: SparseCoo or SparseCsr, which is determined by that of `mask` .
Examples:
.. code-block:: python
import paddle
paddle.seed(100)
# dense @ dense * csr_mask -> csr
crows = [0, 2, 3, 5]
cols = [1, 3, 2, 0, 1]
values = [1., 2., 3., 4., 5.]
dense_shape = [3, 4]
mask = paddle.incubate.sparse.sparse_csr_tensor(crows, cols, values, dense_shape)
# Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
# crows=[0, 2, 3, 5],
# cols=[1, 3, 2, 0, 1],
# values=[1., 2., 3., 4., 5.])
x = paddle.rand([3, 5])
y = paddle.rand([5, 4])
out = paddle.incubate.sparse.masked_matmul(x, y, mask)
# Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
# crows=[0, 2, 3, 5],
# cols=[1, 3, 2, 0, 1],
# values=[0.98986477, 0.97800624, 1.14591956, 0.68561077, 0.94714981])
"""
return _C_ops.sparse_masked_matmul(x, y, mask)
@dygraph_only
def mv(x, vec, name=None):
"""
Note:
This API is only supported from ``CUDA 11.0`` .
Applies matrix-vector product of Sparse Matrix 'x' and Dense vector 'vec' .
The supported input/output Tensor layout are as follows:
Note:
x[SparseCsrTensor] @ y[DenseTensor] -> out[SparseCsrTensor]
x[SparseCooTensor] @ y[DenseTensor] -> out[SparseCooTensor]
It supports backward propagation.
The shape of `x` should be `[M, N]` , and the shape of `y` should be `[N]` ,
and the shape of `out` will be `[M]` .
Args:
x (Tensor): The input 2D tensor. It must be SparseCooTensor/SparseCsrTensor. The data type can be float32 or float64.
y (Tensor): The input 1D tensor. It must be DenseTensor vector. The data type can be float32 or float64.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor: 1D Tensor.
Examples:
.. code-block:: python
import paddle
from paddle.fluid.framework import _test_eager_guard
paddle.seed(100)
# csr @ dense -> dense
with _test_eager_guard():
crows = [0, 2, 3, 5]
cols = [1, 3, 2, 0, 1]
values = [1., 2., 3., 4., 5.]
dense_shape = [3, 4]
csr = paddle.incubate.sparse.sparse_csr_tensor(crows, cols, values, dense_shape)
# Tensor(shape=[3, 4], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
# crows=[0, 2, 3, 5],
# cols=[1, 3, 2, 0, 1],
# values=[1., 2., 3., 4., 5.])
vec = paddle.randn([4])
out = paddle.incubate.sparse.mv(csr, vec)
# Tensor(shape=[3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [-3.85499096, -2.42975140, -1.75087738])
"""
return _C_ops.sparse_mv(x, vec)
def add(x, y, name=None):
"""
Add two sparse tensors element-wise. Input x and y's shape should be identical and have same sparse
type(SparseCooTensor or SparseCsrTensor).If input is SparseCooTensor, x and y's sparse_dim should be identical.
The equation is:
.. math::
out = x + y
Args:
x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
y (Tensor): the input tensor, it's data type should be 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:
Tensor: the result tensor.
Examples:
.. code-block:: python
import paddle
from paddle.fluid.framework import _test_eager_guard
paddle.device.set_device("cpu")
with _test_eager_guard():
x = paddle.to_tensor([[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]], 'float32')
y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32')
sparse_x = x.to_sparse_csr()
sparse_y = y.to_sparse_csr()
sparse_z = paddle.incubate.sparse.add(sparse_x, sparse_y)
print(sparse_z.to_dense())
# [[ 0., -1., 0., 0.],
# [ 0., 2., -6., 0.],
# [ 6., 8., 4., 8.]]
"""
if y.dtype != x.dtype:
y = _C_ops.sparse_cast(y, None, x.dtype)
return _C_ops.sparse_add(x, y)
@dygraph_only
def subtract(x, y, name=None):
"""
Subtract two sparse tensors element-wise. Input x and y's shape should be identical and have same sparse
type(SparseCooTensor or SparseCsrTensor).If input is SparseCooTensor, x and y's sparse_dim should be identical.
The equation is:
.. math::
out = x - y
Args:
x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
y (Tensor): the input tensor, it's data type should be 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:
Tensor: the result tensor.
Examples:
.. code-block:: python
import paddle
from paddle.fluid.framework import _test_eager_guard
paddle.device.set_device("cpu")
with _test_eager_guard():
x = paddle.to_tensor([[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]], 'float32')
y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32')
sparse_x = x.to_sparse_csr()
sparse_y = y.to_sparse_csr()
sparse_z = paddle.incubate.sparse.subtract(sparse_x, sparse_y)
print(sparse_z.to_dense())
# [[ 0., -1., 0., 4.],
# [ 0., -2., 0., 0.],
# [ 2., 2., -4., -8.]]
"""
if y.dtype != x.dtype:
y = _C_ops.sparse_cast(y, None, x.dtype)
return _C_ops.sparse_subtract(x, y)
@dygraph_only
def multiply(x, y, name=None):
"""
Multiply two sparse tensors element-wise. Input x and y's shape should be identical and have same sparse
type(SparseCooTensor or SparseCsrTensor).If input is SparseCooTensor, x and y's sparse_dim should be identical.
The equation is:
.. math::
out = x * y
Args:
x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
y (Tensor): the input tensor, it's data type should be 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:
Tensor: the result tensor.
Examples:
.. code-block:: python
import paddle
from paddle.fluid.framework import _test_eager_guard
paddle.device.set_device("cpu")
with _test_eager_guard():
x = paddle.to_tensor([[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]], 'float32')
y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32')
sparse_x = x.to_sparse_csr()
sparse_y = y.to_sparse_csr()
sparse_z = paddle.incubate.sparse.multiply(sparse_x, sparse_y)
print(sparse_z.to_dense())
# [[ 0., 0., 0., -4.],
# [ 0., 0., 9., 0.],
# [ 8., 15., 0., 0.]]
"""
if isinstance(y, (int, float)):
return _C_ops.sparse_scale(x, float(y), 0.0, True)
else:
if y.dtype != x.dtype:
y = _C_ops.sparse_cast(y, None, x.dtype)
return _C_ops.sparse_multiply(x, y)
@dygraph_only
def divide(x, y, name=None):
"""
Divide two sparse tensors element-wise. Input x and y's shape should be identical and have same sparse
type(SparseCooTensor or SparseCsrTensor).If input is SparseCooTensor, x and y's sparse_dim should be identical.
The equation is:
.. math::
out = x / y
Args:
x (Tensor): the input tensor, it's data type should be float32, float64, int32, int64.
y (Tensor): the input tensor, it's data type should be 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:
Tensor: the result tensor.
Examples:
.. code-block:: python
import paddle
from paddle.fluid.framework import _test_eager_guard
paddle.device.set_device("cpu")
with _test_eager_guard():
x = paddle.to_tensor([[0, -1, 0, 2], [0, 0, -3, 0], [4, 5, 0, 0]], 'float32')
y = paddle.to_tensor([[0, 0, 0, -2], [0, 2, -3, 0], [2, 3, 4, 8]], 'float32')
sparse_x = x.to_sparse_csr()
sparse_y = y.to_sparse_csr()
sparse_z = paddle.incubate.sparse.divide(sparse_x, sparse_y)
print(sparse_z.to_dense())
# [[ nan , -inf. , nan , -1. ],
# [ nan , 0. , 1. , nan ],
# [ 2. , 1.66666663, 0. , 0. ]]
"""
if x.dtype in _int_dtype_:
x = _C_ops.sparse_cast(x, None, core.VarDesc.VarType.FP32)
if isinstance(y, (int, float)):
return _C_ops.sparse_divide_scalar(x, float(y))
else:
if y.dtype != x.dtype:
y = _C_ops.sparse_cast(y, None, x.dtype)
return _C_ops.sparse_divide(x, y)
@dygraph_only
def is_same_shape(x, y):
"""
Return the results of shape comparison between two Tensors, check whether x.shape equal to y.shape.
Any two type Tensor among DenseTensor/SparseCooTensor/SparseCsrTensor are supported.
Args:
x (Tensor): The input tensor. It can be DenseTensor/SparseCooTensor/SparseCsrTensor.
y (Tensor): The input tensor. It can be DenseTensor/SparseCooTensor/SparseCsrTensor.
Returns:
bool: True for same shape and False for different shape.
Examples:
.. code-block:: python
import paddle
x = paddle.rand([2, 3, 8])
y = paddle.rand([2, 3, 8])
y = y.to_sparse_csr()
z = paddle.rand([2, 5])
paddle.incubate.sparse.is_same_shape(x, y)
# True
paddle.incubate.sparse.is_same_shape(x, z)
# False
"""
return x.is_same_shape(y)