# Copyright 2022 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""Defines sparse operators with functional form."""
from __future__ import absolute_import
from mindspore.ops.operations.sparse_ops import (
DenseToCSRSparseMatrix,
CSRSparseMatrixToSparseTensor,
SparseConcat,
SparseAdd,
SparseMatrixAdd,
SparseMatrixSoftmax,
CSRSparseMatrixToDense
)
from mindspore.ops.primitive import constexpr, Primitive
from mindspore.ops.operations.array_ops import GatherNd, Coalesce
from mindspore.ops.operations import _csr_ops
from mindspore.common import CSRTensor, COOTensor, Tensor
from mindspore.common import dtype as mstype
from mindspore.ops.composite.multitype_ops._constexpr_utils import raise_value_error, raise_type_error, make_tensor
# utility functions and values
gather_nd = GatherNd()
dense_to_csr = DenseToCSRSparseMatrix()
csr_sparse_matrix_to_sparse_tensor = CSRSparseMatrixToSparseTensor()
batch_csr_pointers_empty = Tensor([0, -1], dtype=mstype.int32)
coalesce_op = Coalesce()
csr_sparse_matrix_to_dense = CSRSparseMatrixToDense()
@constexpr
def print_info(info):
"""Print given error info"""
print(info)
@constexpr
def _make_tensor(data):
"""Make Tensor"""
return Tensor(data)
def is_scalar(tensor):
"""Determine whether tensor input is a scalar tensor."""
if tensor.size != 1:
return False
return len(tensor.shape) <= 2
def coalesce(x_indices, x_values, x_shape):
"""
Returns the coalesced sparse tensor of the input.
Args:
- **x_indices** (Tensor) - A 2-D Tensor, represents the indices of the nonzero elements of the sparse tensor.
Supported data type is int64. It's elements should be non-negative. The shape is :math:`(y, x)`.
- **x_values** (Tensor) - A 1-D Tensor, represents the values corresponding to the indices in `x_indices`.
Supported data types are float16 and float32. The shape is :math:`(x,)`.
- **x_shape** (Tensor) - A 1-D Tensor, specifies the shape of the sparse tensor.
Supported data type is int64. The shape is :math:`(y,)`.
Returns:
- **y_indices** (Tensor) - A 2-D Tensor, represents the indices of the nonzero elements of the sparse tensor.
Data type is int64. It's elements are non-negative. The shape is :math:`(y, z)`.
`z` represents the number of different indices in `x_indices`.
- **y_values** (Tensor) - A 1-D Tensor, represents the values corresponding to the indices in `y_indices`.
Data type is the same as `x_values`'s. The shape is :math:`(z,)`.
- **y_shape** (Tensor) - A 1-D Tensor, specifies the shape of the sparse tensor.
Data type is int64. The shape is :math:`(y,)`.
Raises:
TypeError: If the data type of `x_values` is neither float32 nor float16.
TypeError: If any of the data types of `x_indices` and `x_shape` is not int64.
ValueError: If any of `x_values` and `x_shape` is not a 1-D tensor.
ValueError: If `x_indices` is not a 2-D tensor.
ValueError: If sizes of second dimension of `x_indices` and first dimension of `x_values` are not the same.
ValueError: If sizes of first dimension of `x_indices` and first dimension of `x_shape` are not the same.
ValueError: If any of the values of elements of `x_indices` is negative.
ValueError: If any of the values of elements of `x_indices` exceed the limit set by `x_shape`.
Supported Platforms:
``GPU``
Examples:
>>> import mindspore
>>> import mindspore.ops as ops
>>> from mindspore import Tensor
>>> x_indices = Tensor([[0, 0, 1], [1, 1, 2]], dtype=ms.int64)
>>> x_values = Tensor([1, 5, 4], dtype=ms.float32)
>>> x_shape = Tensor([3, 3], dtype=ms.int64)
>>> y_indices, y_values, y_shape = ops.coalesce(x_indices, x_values, x_shape)
>>> print(y_indices)
[[0 1]
[1 2]]
>>> print(y_values)
[6. 4.]
>>> print(y_shape)
[3 3]
"""
return coalesce_op(x_indices, x_values, x_shape)
coo2csr = _csr_ops.COO2CSR()
coo_tensor_get_dense_shape = Primitive('COOTensorGetDenseShape')
coo_tensor_get_indices = Primitive('COOTensorGetIndices')
coo_tensor_get_values = Primitive('COOTensorGetValues')
def csr_div(x, y):
"""
Returns x / y where x is CSRTensor and y is Tensor.
Note:
This function returns the results of dense Tensor, represents the non-zero
values of the CSRTensor. If user expects a CSRTensor as output, please directly
use `/` operator instead. Only support dense tensor broadcast to sparse tensor
at the moment.
Args:
x (CSRTensor): Sparse CSR Tensor.
y (Tensor): Dense Tensor, its shape must be able to broadcast to x.
Returns:
Dense Tensor, represents the non-zero values of the result.
Supported Platforms:
``GPU`` ``CPU``
"""
if isinstance(y, (int, float, bool)):
y = _make_tensor(y)
if is_scalar(y):
if y.ndim > x.ndim:
raise_value_error("dense tensor cannot broadcast to the sparse tensor.")
return (x.values / y).reshape(x.values.shape)
return _csr_ops.CSRDiv()(x.indptr, x.indices, x.values, x.shape, y)
csr_gather = _csr_ops.CSRGather()
def csr_mul(x, y):
"""
Returns x * y where x is CSRTensor and y is Tensor.
Note:
This function returns the results of dense Tensor, represents the non-zero
values of the CSRTensor. If user expects a CSRTensor as output, please directly
use `*` operator instead. Only support dense tensor broadcast to sparse tensor
at the moment.
Args:
x (CSRTensor): Sparse CSR Tensor.
y (Tensor): Dense Tensor, its shape must be able to broadcast to x.
Returns:
Dense Tensor, represents the non-zero values of the result.
Supported Platforms:
``GPU`` ``CPU``
"""
if isinstance(y, (int, float, bool)):
y = _make_tensor(y)
if is_scalar(y):
if y.ndim > x.ndim:
raise_value_error("dense tensor cannot broadcast to the sparse tensor.")
return (x.values * y).reshape(x.values.shape)
return _csr_ops.CSRMul()(x.indptr, x.indices, x.values, x.shape, y)
def csr_mv(csr_tensor, dense):
"""
Sparse matrix-vector multiplication.
Args:
csr_tensor (CSRTensor): Sparse CSR Tensor.
dense (Tensor): Dense Tensor.
Returns:
Dense Tensor.
Supported Platforms:
``GPU`` ``CPU``
"""
return _csr_ops.CSRMV()(csr_tensor.indptr, csr_tensor.indices, csr_tensor.values, csr_tensor.shape, dense)
def csr_reduce_sum(csr_tensor, axis):
"""
Reduces a dimension of a CSRTensor by summing all elements in the dimension.
Args:
csr_tensor (CSRTensor): Sparse CSR Tensor.
axis (int): Axis to be reduced.
Returns:
Dense Tensor, represents the non-zero values of the result.
Supported Platforms:
``GPU`` ``CPU``
"""
return _csr_ops.CSRReduceSum()(csr_tensor.indptr, csr_tensor.indices, csr_tensor.values, csr_tensor.shape, axis)
[docs]def csr_to_coo(tensor):
"""
Converts a CSRTensor to COOTensor.
Note:
Only 2-D CSRTensor is supported for now.
Args:
tensor (CSRTensor): A CSRTensor, must be 2-D.
Returns:
2D COOTensor, the input tensor stored in COO format.
Raises:
TypeError: If input is not a COOTensor.
ValueError: If input tensor is not 2-D.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, CSRTensor
>>> indptr = Tensor([0, 1, 2]).astype("int32")
>>> indices = Tensor([0, 1]).astype("int32")
>>> values = Tensor([2, 1]).astype("float32")
>>> shape = (2, 4)
>>> x = CSRTensor(indptr, indices, values, shape)
>>> output = ops.csr_to_coo(x)
>>> print(output.indices)
[[0 0]
[1 1]]
"""
if not isinstance(tensor, CSRTensor):
raise_type_error("For functional operator csr_to_coo, input argument must be a CSRTensor.")
if len(tensor.shape) != 2:
raise_value_error("Currently only support 2-D CSRTensor when converting to COOTensor.")
shape = tensor.shape
indices, values, _ = csr_sparse_matrix_to_sparse_tensor(Tensor(shape, mstype.int32), batch_csr_pointers_empty,
tensor.indptr, tensor.indices, tensor.values)
return COOTensor(indices, values, shape)
def csr_to_dense(csr_tensor):
"""
Converts a CSRTensor to its dense form.
Note:
Only 2-D CSRTensor is supported for now.
Args:
csr_tensor (CSRTensor): A CSRTensor, must be 2-D.
Returns:
Tensor.
Raises:
TypeError: If input is not a CSRTensor.
ValueError: If input CSRTensor is not 2-D.
Supported Platforms:
``GPU``
Examples:
>>> from mindspore import Tensor, CSRTensor, ops
>>> indptr = Tensor([0, 1, 2]).astype("int32")
>>> indices = Tensor([0, 1]).astype("int32")
>>> values = Tensor([2, 1]).astype("float32")
>>> shape = (2, 4)
>>> x = CSRTensor(indptr, indices, values, shape)
>>> output = ops.csr_to_dense(x)
>>> print(output)
"""
if not isinstance(csr_tensor, CSRTensor):
raise_type_error("For functional operator csr_to_dense, input argument must be a CSRTensor.")
if len(csr_tensor.shape) != 2:
raise_value_error("Currently only support 2-D CSRTensor when converting to COOTensor.")
shape = csr_tensor.shape
return csr_sparse_matrix_to_dense(Tensor(shape, dtype=mstype.int32), batch_csr_pointers_empty,
csr_tensor.indptr, csr_tensor.indices, csr_tensor.values)
# deprecated, will be removed once `csr_to_coo` supports all backends.
csr2coo = _csr_ops.CSR2COO()
csr_tensor_get_dense_shape = Primitive('CSRTensorGetDenseShape')
csr_tensor_get_indices = Primitive('CSRTensorGetIndices')
csr_tensor_get_indptr = Primitive('CSRTensorGetIndptr')
csr_tensor_get_values = Primitive('CSRTensorGetValues')
[docs]def dense_to_sparse_coo(tensor):
"""
Convert a Tensor to COOTensor.
Note:
Only 2-D tensor is supported for now.
Args:
tensor (Tensor): A dense tensor, must be 2-D.
Returns:
COOTensor, a sparse representation of the original dense tensor, containing the following parts.
- indices (Tensor): 2-D integer tensor, indicates the positions of `values` of the dense tensor.
- values (Tensor): 1-D tensor, indicates the non-zero values of the dense tensor.
- shape (tuple(int)): the shape of the COOTensor, is the same as the original dense tensor.
Raises:
TypeError: If input is not a tensor.
ValueError: If input tensor is not 2-D.
Supported Platforms:
``GPU``
Examples:
>>> from mindspore import Tensor, ops
>>> import mindspore as ms
>>> x = Tensor([[1, 0], [-5, 0]], ms.float32)
>>> output = ops.dense_to_sparse_coo(x)
>>> print(output.indices)
[[0 0]
[1 0]]
>>> print(output.values)
[ 1. -5.]
>>> print(output.shape)
(2, 2)
"""
if not isinstance(tensor, Tensor):
raise_type_error("For functional operator dense_to_sparse_coo, input argument must be a Tensor.")
if len(tensor.shape) != 2:
raise_value_error("Currently only support 2-D Tensor when converting to COOTensor.")
indices = tensor.nonzero().astype("int32")
values = gather_nd(tensor, indices)
return COOTensor(indices, values, tensor.shape)
[docs]def dense_to_sparse_csr(tensor):
"""
Convert a Tensor to CSRTensor.
Note:
Only 2-D tensor is supported for now.
Args:
tensor (Tensor): A dense tensor, must be 2-D.
Returns:
CSRTensor, a sparse representation of the original dense tensor, containing the following parts.
- indptr (Tensor): 1-D integer tensor, indicates the start and end point for `values` in each row.
- indices (Tensor): 1-D integer tensor, indicates the column positions of all non-zero values of the input.
- values (Tensor): 1-D tensor, indicates the non-zero values of the dense tensor.
- shape (tuple(int)): the shape of the CSRTensor, is the same as the original dense tensor.
Raises:
TypeError: If input is not a tensor.
ValueError: If input tensor is not 2-D.
Supported Platforms:
``GPU``
Examples:
>>> from mindspore import Tensor, ops
>>> import mindspore as ms
>>> x = Tensor([[1, 0], [-5, 0]], ms.float32)
>>> output = ops.dense_to_sparse_csr(x)
>>> print(output.indptr)
[0 1 2]
>>> print(output.indices)
[0 0]
>>> print(output.shape)
(2, 2)
"""
if not isinstance(tensor, Tensor):
raise_type_error("For functional operator dense_to_sparse_csr, input argument must be a Tensor.")
if len(tensor.shape) != 2:
raise_value_error("Currently only support 2-D Tensor when converting to CSRTensor.")
indices = tensor.nonzero().astype("int32")
_, _, indptr, indices, values = dense_to_csr(tensor, indices)
return CSRTensor(indptr, indices, values, tensor.shape)
def make_sparse_tensor(indices, values, dense_shape):
"""Call make_coo_tensor in this function."""
print_info("WARNING: 'SparseTensor' is deprecated from version 1.7 and will be removed in a future version. " +
"Please use 'COOTensor' instead.")
return make_coo_tensor(indices, values, dense_shape)
make_coo_tensor = Primitive('MakeCOOTensor')
make_csr_tensor = Primitive('MakeCSRTensor')
make_row_tensor = Primitive('MakeRowTensor')
row_tensor_get_values = Primitive('RowTensorGetValues')
row_tensor_get_indices = Primitive('RowTensorGetIndices')
row_tensor_get_dense_shape = Primitive('RowTensorGetDenseShape')
row_tensor_add = Primitive('RowTensorAdd')
@constexpr
def _calc_out_shape(sp_input, concat_dim):
"calculating the COOTensor output shape in sparse_concat"
if isinstance(sp_input[0], tuple):
out_shape_list = list(sp_input[0][2])
else:
out_shape_list = list(sp_input[0].shape)
for i in range(1, len(sp_input)):
if isinstance(sp_input[i], tuple):
out_shape_list[concat_dim] += sp_input[i][2][concat_dim]
else:
out_shape_list[concat_dim] += sp_input[i].shape[concat_dim]
return tuple(out_shape_list)
@constexpr
def _set_sparse_concat_input(sp_input):
"split COOTensor to normal tensor"
if len(sp_input) < 2:
raise_value_error("For sparse_concat, not support COOTensor input number < 2.")
in_indices = []
in_values = []
in_shapes = []
for element in sp_input:
if isinstance(element, tuple):
in_indices.append(element[0])
in_values.append(element[1])
in_shapes.append(Tensor(element[2], dtype=mstype.int64))
else:
in_indices.append(element.indices)
in_values.append(element.values)
in_shapes.append(Tensor(element.shape, dtype=mstype.int64))
return in_indices, in_values, in_shapes
def sparse_concat(sp_input, concat_dim=0):
"""
concatenates the input SparseTensor(COO format) along the specified dimension.
.. note:
demo API now, and only supported CPU
Args:
sp_input (Union[list(COOTensor), tuple(COOTensor)) - the list of SparseTensor which need to concatenates.
for COOTensor input.
concat_dim (scalar): decide the dimension to concatenation along.
The value must be in range [-rank, rank), where rank is the number of dimensions in each input
SparseTensor. Default is 0.
Outputs:
- **output** (COOtensor) - the result of concatenates the input SparseTensor along the
specified dimension. OutShape: OutShape[non concat_dim] is equal to InShape[non concat_dim] and
OutShape[concat_dim] is all input concat_dim axis shape accumulate.
Raises:
ValueError: If only one sparse tensor input.
ValueError: If Input COOTensor shape dim > 3. COOtensor shape dim size must be 2 now
Supported Platforms:
``CPU``
Examples:
>>> indices0 = Tensor([[0, 1], [1, 2]], dtype=mstype.int32)
>>> values0 = Tensor([1, 2], dtype=mstype.int32)
>>> shape0 = (3, 4)
>>> input0 = COOTensor(indices0, values0, shape0)
>>> indices1 = Tensor([[0, 0], [1, 1]], dtype=mstype.int32)
>>> values1 = Tensor([3, 4], dtype=mstype.int32)
>>> shape1 = (3, 4)
>>> input1 = COOTensor(indices1, values1, shape1)
>>> concat_dim = 1
>>> out = F.sparse_concat((input0, input1), concat_dim)
>>> print(out)
COOTensor(shape=[3, 8], dtype=Int32, indices=Tensor(shape=[4, 2], dtype=Int32, value=
[[0 1]
[0 4]
[1 2]
[1 5]]), values=Tensor(shape=[4], dtype=Int32, value=[1 3 2 4]))
"""
sparse_concat_op = SparseConcat(concat_dim)
in_indices, in_values, in_shapes = _set_sparse_concat_input(sp_input)
indices, values, _ = sparse_concat_op(in_indices, in_values, in_shapes)
out_shape = _calc_out_shape(sp_input, concat_dim)
return COOTensor(indices, values, out_shape)
def sparse_add(x1, x2, thresh):
"""
Computes the sum of x1(COOTensor) and x2(COOTensor).
Args:
x1 (COOTensor): the first COOTensor to sum.
x2 (COOTensor): the second COOTensor to sum.
thresh (Tensor): A 0-D Tensor, represents the magnitude threshold that determines
if an output value/index pair pair take space.
Returns:
A COOTensor, the result of sum.
Raises:
ValueError: If any input(x1/x2)'s indices's dim is not equal to 2.
ValueError: If any input(x1/x2)'s values's dim is not equal to 1.
ValueError: If any input(x1/x2)'s shape's dim is not equal to 1.
ValueError: If thresh's dim is not equal to 0.
TypeError: If any input(x1/x2)'s indices's type is not equal to int64.
TypeError: If any input(x1/x2)'s shape's type is not equal to int64.
ValueError: If any input(x1/x2)'s indices's length is not equal to
its values's length.
TypeError: If any input(x1/x2)'s values's type is not equal to anf of
(int8/int16/int32/int64/float32/float64/complex64/complex128).
TypeError: If thresh's type is not equal to anf of
(int8/int16/int32/int64/float32/float64).
TypeError: If x1's indices's type is not equal to x2's indices's type.
TypeError: If x1's values's type is not equal to x2's values's type.
TypeError: If x1's shape's type is not equal to x2's shape's type.
TypeError: If (x1/x2)'s value's type is not matched with thresh's type.
Supported Platforms:
``CPU`` ``GPU``
Examples:
>>> from mindspore import Tensor, COOTensor
>>> from mindspore import dtype as mstype
>>> from mindspore import context
>>> from mindspore import ops
>>> indics0 = Tensor([[0, 1], [1, 2]], dtype=mstype.int64)
>>> values0 = Tensor([1, 2], dtype=mstype.int32)
>>> shape0 = (3, 4)
>>> input0 = COOTensor(indics0, values0, shape0)
>>> indics1 = Tensor([[0, 0], [1, 1]], dtype=mstype.int64)
>>> values1 = Tensor([3, 4], dtype=mstype.int32)
>>> shape1 = (3, 4)
>>> input1 = COOTensor(indics1, values1, shape1)
>>> thres = Tensor(0, dtype=mstype.int32)
>>> out = ops.sparse_add(input0, input1, thres)
>>> print(out)
COOTensor(shape = [3, 4], dtype = Int32, indices=Tensor(shape=[4, 2],
dtype = Int64, value=[[0 0], [0 1], [1 1], [1 2]]), values=Tensor(shape[4],
dtype=Int32, value=[3 1 4 2]))
"""
x1_indices = x1.indices
x1_values = x1.values
x2_indices = x2.indices
x2_values = x2.values
den_shp = make_tensor(x1.shape, x1_indices.dtype)
add_op = SparseAdd()
indices, values, _ = add_op(x1_indices, x1_values, den_shp, x2_indices, x2_values, den_shp, thresh)
return COOTensor(indices, values, x1.shape)
def csr_softmax(logits, dtype):
"""
Calculates the softmax of a CSRTensorMatrix.
Args:
logits (CSRTensor): Sparse CSR Tensor.
dtype (dtype): Data type.
Returns:
CSRTensor, a csr_tensor containing
- **indptr** - indicates the start and end point for `values` in each row.
- **indices** - the column positions of all non-zero values of the input.
- **values** - the non-zero values of the dense tensor.
- **shape** - the shape of the csr_tensor.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> import mindspore as ms
>>> import mindspore.common.dtype as mstype
>>> from mindspore import Tensor, CSRTensor
>>> from mindspore.ops.function import csr_softmax
>>> logits_indptr = Tensor([0, 4, 6], dtype=mstype.int32)
>>> logits_indices = Tensor([0, 2, 3, 4, 3, 4], dtype=mstype.int32)
>>> logits_values = Tensor([1, 2, 3, 4, 1, 2], dtype=mstype.float32)
>>> shape = (2, 6)
>>> logits = CSRTensor(logits_indptr, logits_indices, logits_values, shape)
>>> out = csr_softmax(logits, dtype=mstype.float32)
>>> print(out)
CSRTensor(shape=[2, 6], dtype=Float32, indptr=Tensor(shape=[3], dtype=Int32, value=[0 4 6]),
indices=Tensor(shape=[6], dtype=Int32, value=[0 2 3 4 3 4]),
values=Tensor(shape=[6], dtype=Float32, value=[ 3.20586003e-02 8.71443152e-02 2.36882806e-01
6.43914223e-01 2.68941432e-01 7.31058598e-01]))
"""
if not isinstance(logits, CSRTensor):
raise_type_error("For functional operator sparse_matrix_softmax, logits must be type of CSRTensor.")
sparse_matrix_softmax_op = SparseMatrixSoftmax(dtype)
logits_batch_pointers = make_tensor([0, logits.values.shape[0]], mstype.int32)
logits_shape = make_tensor(logits.shape, mstype.int32)
shape, _, indptr, indices, values = sparse_matrix_softmax_op(logits_shape, logits_batch_pointers, logits.indptr,
logits.indices, logits.values)
output_shape = tuple(shape.asnumpy().tolist())
return CSRTensor(indptr=indptr, indices=indices, values=values, shape=output_shape)
def csr_add(a, b, alpha, beta):
"""
Returns alpha * csr_a + beta * csr_b where both csr_a and csr_b are CSRTensor, alpha and beta are both Tensor.
Args:
a (CSRTensor): Sparse CSR Tensor.
b (CSRTensor): Sparse CSR Tensor.
alpha(Tensor): Dense Tensor, its shape must be able to broadcast to a.
beta(Tensor): Dense Tensor, its shape must be able to broadcast to b.
Returns:
CSRTensor, a csr_tensor containing the following parts.
- **indptr** - indicates the start and end point for `values` in each row.
- **indices** - the column positions of all non-zero values of the input.
- **values** - the non-zero values of the dense tensor.
- **shape** - the shape of the csr_tensor.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> import mindspore.common.dtype as mstype
>>> from mindspore import Tensor, CSRTensor
>>> from mindspore.ops.functional import csr_add
>>> a_indptr = Tensor([0, 1, 2], dtype=mstype.int32)
>>> a_indices = Tensor([0, 1], dtype=mstype.int32)
>>> a_values = Tensor([1, 2], dtype=mstype.float32)
>>> shape = (2, 6)
>>> b_indptr = Tensor([0, 1, 2], dtype=mstype.int32)
>>> b_indices = Tensor([0, 1], dtype=mstype.int32)
>>> b_values = Tensor([1, 2], dtype=mstype.float32)
>>> alpha = Tensor(1, mstype.float32)
>>> beta = Tensor(1, mstype.float32)
>>> csra = CSRTensor(a_indptr, a_indices, a_values, shape)
>>> csrb = CSRTensor(b_indptr, b_indices, b_values, shape)
>>> out = csr_add(csra, csrb, alpha, beta)
>>> print(out)
CSRTensor(shape=[2,6], dtype=Float32,
indptr=Tensor(shape=[3], dtype=Int32, value = [0, 1, 2]),
indices=Tensor(shape=[2], dtype=Int32, value = [0, 1]),
values=Tensor(shape=[2], dtype=Float32, value = [2.0, 4.0]))
"""
if not isinstance(a, CSRTensor) or not isinstance(b, CSRTensor):
raise_type_error("For functional operator csr_add, both inputs a and b must be type of CSRTensor.")
if not isinstance(alpha, Tensor) or not isinstance(beta, Tensor):
raise_type_error("For functional operator csr_add, both inputs alpha and beta must be Tensor.")
csr_add_op = SparseMatrixAdd()
a_batch_pointers = make_tensor([0, a.values.shape[0]], mstype.int32)
b_batch_pointers = make_tensor([0, b.values.shape[0]], mstype.int32)
a_shape = make_tensor(a.shape, mstype.int32)
b_shape = make_tensor(b.shape, mstype.int32)
_, _, indptr, indices, values = csr_add_op(a_shape, a_batch_pointers, a.indptr, a.indices, a.values,
b_shape, b_batch_pointers, b.indptr, b.indices, b.values,
alpha, beta)
return CSRTensor(indptr, indices, values, a.shape)
__all__ = [
'coalesce',
'coo2csr',
'coo_tensor_get_dense_shape',
'coo_tensor_get_indices',
'coo_tensor_get_values',
'csr_div',
'csr_gather',
'csr_mul',
'csr_mv',
'csr_reduce_sum',
'csr_to_coo',
'csr2coo',
'csr_tensor_get_dense_shape',
'csr_tensor_get_indices',
'csr_tensor_get_indptr',
'csr_tensor_get_values',
'dense_to_sparse_coo',
'dense_to_sparse_csr',
'make_sparse_tensor',
'make_coo_tensor',
'make_csr_tensor',
'make_row_tensor',
'row_tensor_get_values',
'row_tensor_get_indices',
'row_tensor_get_dense_shape',
'row_tensor_add',
'sparse_add',
'sparse_concat',
'csr_add',
'csr_softmax',
'csr_to_dense'
]
__all__.sort()