# Copyright 2020-2021 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.
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#
# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# ============================================================================
"""Sparse related tools."""
from mindspore.ops import operations as P
from ..cell import Cell
[文档]class SparseToDense(Cell):
"""
Converts a sparse tensor(COOTensor) into dense.
In Python, for the ease of use, three tensors are collected into a SparseTensor class.
MindSpore uses three independent dense tensors: indices, value and dense shape to represent the sparse tensor.
Separate indexes, values and dense shape tensors can be wrapped in a Sparse Tensor object
before being passed to the OPS below.
Inputs:
- **coo_tensor** (:class:`mindspore.COOTensor`): the sparse COOTensor to convert.
Outputs:
Tensor, converted from sparse tensor.
Raises:
TypeError: If `sparse_tensor.indices` is not a Tensor.
TypeError: If `sparse_tensor.values` is not a Tensor.
TypeError: If `sparse_tensor.shape` is not a tuple.
Supported Platforms:
``CPU``
Examples:
>>> import mindspore as ms
>>> from mindspore import Tensor, COOTensor
>>> import mindspore.nn as nn
>>> import mindspore.context as context
>>> context.set_context(mode=context.PYNATIVE_MODE)
>>> indices = Tensor([[0, 1], [1, 2]])
>>> values = Tensor([1, 2], dtype=ms.int32)
>>> dense_shape = (3, 4)
>>> class Net(nn.Cell):
... def __init__(self, dense_shape):
... super(Net, self).__init__()
... self.dense_shape = dense_shape
... self.op = nn.SparseToDense()
...
... def construct(self, indices, values):
... x = COOTensor(indices, values, self.dense_shape)
... return self.op(x)
...
>>> print(Net(dense_shape)(indices, values))
[[0 1 0 0]
[0 0 2 0]
[0 0 0 0]]
"""
def __init__(self):
"""Initialize SparseToDense."""
super(SparseToDense, self).__init__()
self.sparse_to_dense = P.SparseToDense()
def construct(self, sparse_tensor):
return self.sparse_to_dense(sparse_tensor.indices,
sparse_tensor.values,
sparse_tensor.shape)
[文档]class SparseTensorDenseMatmul(Cell):
"""
Multiplies sparse matrix `a` and 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 non-negative. 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) - 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:
>>> import mindspore as ms
>>> from mindspore import Tensor
>>> from mindspore import nn
>>> 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 = nn.SparseTensorDenseMatmul()
>>> out = sparse_dense_matmul(indices, values, sparse_shape, dense)
>>> print(out)
[[2. 2.]
[6. 6.]
[0. 0.]]
"""
def __init__(self, adjoint_st=False, adjoint_dt=False):
"""Initialize SparseTensorDenseMatmul"""
super(SparseTensorDenseMatmul, self).__init__()
self.adj_st = adjoint_st
self.adj_dt = adjoint_dt
self.sparse_dense_matmul = P.SparseTensorDenseMatmul(adjoint_st=self.adj_st, adjoint_dt=self.adj_dt)
def construct(self, indices, values, sparse_shape, dense):
return self.sparse_dense_matmul(indices, values, sparse_shape, dense)