# Source code for mindspore.ops.operations.sparse_ops

```# coding: utf-8

# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#
# Unless required by applicable law or agreed to in writing, software
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and

# ============================================================================

"""Operators for sparse operators."""

from ..._checkparam import Validator as validator
from ...common import dtype as mstype
from ..primitive import PrimitiveWithInfer, prim_attr_register

[docs]class SparseToDense(PrimitiveWithInfer):
"""
Converts a sparse representation into a dense tensor.

Inputs:
- **indices** (Tensor) - A 2-D Tensor, represents the position of the element in the sparse tensor.
Support int32, int64, each element value should be a non-negative int number. The shape is :math:`(n, 2)`.
- **values** (Tensor) - A 1-D Tensor, represents the value corresponding to the position in the `indices`.
The shape should be :math:`(n,)`.
- **sparse_shape** (tuple(int)) - A positive int tuple which specifies the shape of sparse tensor,
should have 2 elements, represent sparse tensor shape is :math:`(N, C)`.

Returns:
Tensor, converted from sparse tensor. The dtype is same as `values`, and the shape is `sparse_shape`.

Raises:
TypeError: If the dtype of `indices` is neither int32 nor int64.
ValueError: If `sparse_shape`, shape of `indices` and shape of `values` don't meet the parameter description.

Supported Platforms:
``CPU``

Examples:
>>> indices = Tensor([[0, 1], [1, 2]])
>>> values = Tensor([1, 2], dtype=ms.float32)
>>> sparse_shape = (3, 4)
>>> sparse_to_dense = ops.SparseToDense()
>>> out = sparse_to_dense(indices, values, sparse_shape)
>>> print(out)
[[0. 1. 0. 0.]
[0. 0. 2. 0.]
[0. 0. 0. 0.]]
"""

@prim_attr_register
def __init__(self):
"""Initialize SparseToDense."""
self.init_prim_io_names(inputs=['indices', 'values', 'dense_shape'], outputs=['output'])

def __infer__(self, indices, values, sparse_shape):
validator.check_tensor_dtype_valid('indices', indices['dtype'], [mstype.int32, mstype.int64], self.name)
validator.check_tensor_dtype_valid('values', values['dtype'], mstype.number_type + (mstype.bool_,), self.name)
indices_shape = indices['shape']
if len(indices_shape) != 2:
raise ValueError(f"For '{self.name}', the 'indices' must be a 2-D tensor, "
f"but got 'indices' shape: {indices_shape}.")
values_shape = values['shape']
if len(values_shape) != 1 or values_shape[0] != indices_shape[0]:
raise ValueError(f"For '{self.name}', the 'values' must be a 1-D tensor and the first dimension length "
f"must be equal to the first dimension length of 'indices', "
f"but got 'indices' shape: {indices_shape}, 'values' shape: {values_shape}.")
sparse_shape_v = sparse_shape['value']
for i in sparse_shape_v:
if isinstance(i, bool) or not isinstance(i, int) or i <= 0:
raise ValueError(f"For '{self.name}', all elements in 'sparse_shape' must be "
f"positive int number, but got 'sparse_shape': {sparse_shape_v}.")
if len(sparse_shape_v) != indices_shape[1]:
raise ValueError(f"For '{self.name}', the length of 'sparse_shape' should be equal to the second dimension "
f"length of 'indices', but got the second dimension length of 'indices': "
f"{indices_shape[1]}, length of 'sparse_shape': {len(sparse_shape_v)}.")
out = {'shape': sparse_shape['value'],
'dtype': values['dtype'],
'value': None}
return out

[docs]class SparseTensorDenseMatmul(PrimitiveWithInfer):
"""
Multiplies sparse matrix `A` by dense matrix `B`.
The rank of sparse matrix and dense matrix must be equal to `2`.

Args:
adjoint_st (bool): If true, sparse tensor is transposed before multiplication. Default: False.
adjoint_dt (bool): If true, dense tensor is transposed before multiplication. Default: False.

Inputs:
- **indices** (Tensor) - A 2-D Tensor, represents the position of the element in the sparse tensor.
Support int32, int64, each element value should be a non-negative int number. The shape is :math:`(n, 2)`.
- **values** (Tensor) - A 1-D Tensor, represents the value corresponding to the position in the `indices`.
Support float16, float32, float64, int32, int64. The shape should be :math:`(n,)`.
- **sparse_shape** (tuple(int)) - A positive int tuple which specifies the shape of sparse tensor,
should have 2 elements, represent sparse tensor shape is :math:`(N, C)`.
- **dense** (Tensor) - A 2-D Tensor, the dtype is same as `values`.
If `adjoint_st` is False and `adjoint_dt` is False, the shape must be :math:`(C, M)`.
If `adjoint_st` is False and `adjoint_dt` is True, the shape must be :math:`(M, C)`.
If `adjoint_st` is True and `adjoint_dt` is False, the shape must be :math:`(N, M)`.
If `adjoint_st` is True and `adjoint_dt` is True, the shape must be :math:`(M, N)`.

Outputs:
Tensor, the dtype is the same as `values`.
If `adjoint_st` is False, the shape is :math:`(N, M)`.
If `adjoint_st` is True, the shape is :math:`(C, M)`.

Raises:
TypeError: If the type of `adjoint_st` or `adjoint_dt` is not bool, or the dtype of `indices`,
dtype of `values` and dtype of `dense` don't meet the parameter description.
ValueError: If `sparse_shape`, shape of `indices`, shape of `values`,
and shape of `dense` don't meet the parameter description.

Supported Platforms:
``CPU``

Examples:
>>> indices = Tensor([[0, 1], [1, 2]], dtype=ms.int32)
>>> values = Tensor([1, 2], dtype=ms.float32)
>>> sparse_shape = (3, 4)
>>> dense = Tensor([[1,1], [2,2], [3,3 ], [4, 4]], dtype=ms.float32)
>>> sparse_dense_matmul = ops.SparseTensorDenseMatmul()
>>> out = sparse_dense_matmul(indices, values, sparse_shape, dense)
>>> print(out)
[[2. 2.]
[6. 6.]
[0. 0.]]
"""

@prim_attr_register
"""Initialize SparseTensorDenseMatmul"""
self.init_prim_io_names(inputs=['indices', 'values', 'sparse_shape', 'dense'],
outputs=['output'])

def __infer__(self, indices, values, sparse_shape, dense):
validator.check_tensor_dtype_valid('indices', indices['dtype'], [mstype.int32, mstype.int64], self.name)
valid_types = (mstype.float16, mstype.float32, mstype.float64, mstype.int32, mstype.int64)
args = {'values': values['dtype'], 'dense': dense['dtype']}
validator.check_tensors_dtypes_same_and_valid(args, valid_types, self.name)
indices_shape = indices['shape']
if len(indices_shape) != 2 or indices_shape[1] != 2:
raise ValueError(f"For '{self.name}', the 'indices' must be a 2-D tensor and "
f"the second dimension length must be 2, but got 'indices' shape: {indices_shape}.")
values_shape = values['shape']
if len(values_shape) != 1 or values_shape[0] != indices_shape[0]:
raise ValueError(f"For '{self.name}', the 'values' must be a 1-D tensor and "
f"the first dimension length must be equal to the first dimension length of 'indices', "
f"but got 'indices' shape: {indices_shape}, 'values' shape: {values_shape}.")
a_shape = sparse_shape['value'][::-1] if self.adjoint_st else sparse_shape['value']
b_shape = dense['shape'][::-1] if self.adjoint_dt else dense['shape']
for i in a_shape:
if isinstance(i, bool) or not isinstance(i, int) or i <= 0:
raise ValueError(f"For '{self.name}', all elements in 'sparse_shape' must be "
f"positive int number, but got 'sparse_shape': {a_shape}.")
if len(a_shape) != 2 or len(b_shape) != 2:
raise ValueError(f"For '{self.name}', both the length of 'sparse_shape' and the tensor "
f"rank of 'dense' should be equal to 2, but got the length of "
f"'sparse_shape': {len(a_shape)}, "
f"the tensor rank of 'dense': {len(b_shape)}.")
if a_shape[1] != b_shape[0]:
raise ValueError(f"For '{self.name}', the second dimension length of 'sparse_shape' must be equal to the "
f"first dimension length of 'dense', but got "
f"the tensor shape of 'sparse': {a_shape} and the tensor shape of 'dense': {b_shape}. "
f"Don't meet the condition for matmul")
out_shape = [a_shape[0], b_shape[1]]
out = {'shape': tuple(out_shape),
'dtype': values['dtype'],
'value': None}
return out
```