forked from huawei/mindspore2022
131 lines
5.5 KiB
Python
131 lines
5.5 KiB
Python
# Copyright 2020-2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Sparse related tools."""
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from mindspore.ops import operations as P
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from ..cell import Cell
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class SparseToDense(Cell):
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"""
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Converts a sparse tensor into dense.
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In Python, for the ease of use, three tensors are collected into a SparseTensor class.
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MindSpore uses three independent dense tensors: indices, value and dense shape to represent the sparse tensor.
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Separate indexes, values and dense shape tensors can be wrapped in a Sparse Tensor object
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before being passed to the OPS below.
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Inputs:
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- **sparse_tensor** (:class:`mindspore.SparseTensor`): the sparse tensor to convert.
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Outputs:
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Tensor, converted from sparse tensor.
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Raises:
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TypeError: If `sparse_tensor.indices` is not a Tensor.
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TypeError: If 'sparse_tensor.values' is not a Tensor.
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TypeError: If 'sparse_tensor.dense_shape' is not a tuple.
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Supported Platforms:
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``CPU``
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Examples:
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>>> import mindspore as ms
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>>> from mindspore import Tensor, SparseTensor
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>>> import mindspore.nn as nn
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>>> indices = Tensor([[0, 1], [1, 2]])
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>>> values = Tensor([1, 2], dtype=ms.int32)
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>>> dense_shape = (3, 4)
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>>> sparse_tensor = SparseTensor(indices, values, dense_shape)
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>>> sparse_to_dense = nn.SparseToDense()
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>>> result = sparse_to_dense(sparse_tensor)
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>>> print(result)
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[[0 1 0 0]
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[0 0 2 0]
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[0 0 0 0]]
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"""
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def __init__(self):
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"""Initialize SparseToDense."""
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super(SparseToDense, self).__init__()
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self.sparse_to_dense = P.SparseToDense()
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def construct(self, sparse_tensor):
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return self.sparse_to_dense(sparse_tensor.indices,
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sparse_tensor.values,
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sparse_tensor.dense_shape)
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class SparseTensorDenseMatmul(Cell):
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"""
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Multiplies sparse matrix `a` and dense matrix `b`.
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The rank of sparse matrix and dense matrix must equal to `2`.
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Args:
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adjoint_st (bool): If true, sparse tensor is transposed before multiplication. Default: False.
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adjoint_dt (bool): If true, dense tensor is transposed before multiplication. Default: False.
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Inputs:
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- **indices** (Tensor) - A 2-D Tensor, represents the position of the element in the sparse tensor.
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Support int32, int64, each element value should be non-negative. The shape is :math:`(n, 2)`.
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- **values** (Tensor) - A 1-D Tensor, represents the value corresponding to the position in the `indices`.
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Support float16, float32, float64, int32, int64. The shape should be :math:`(n,)`.
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- **sparse_shape** (tuple) - A positive int tuple which specifies the shape of sparse tensor,
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should have 2 elements, represent sparse tensor shape is :math:`(N, C)`.
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- **dense** (Tensor) - A 2-D Tensor, the dtype is same as `values`.
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If `adjoint_st` is False and `adjoint_dt` is False, the shape must be :math:`(C, M)`.
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If `adjoint_st` is False and `adjoint_dt` is True, the shape must be :math:`(M, C)`.
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If `adjoint_st` is True and `adjoint_dt` is False, the shape must be :math:`(N, M)`.
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If `adjoint_st` is True and `adjoint_dt` is True, the shape must be :math:`(M, N)`.
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Outputs:
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Tensor, the dtype is the same as `values`.
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If `adjoint_st` is False, the shape is :math:`(N, M)`.
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If `adjoint_st` is True, the shape is :math:`(C, M)`.
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Raises:
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TypeError: If the type of `adjoint_st` or `adjoint_dt` is not bool, or the dtype of `indices`,
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dtype of `values` and dtype of `dense` don't meet the parameter description.
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ValueError: If `sparse_shape`, shape of `indices, shape of `values`,
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and shape of `dense` don't meet the parameter description.
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Supported Platforms:
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``CPU``
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Examples:
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>>> import mindspore as ms
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>>> from mindspore import Tensor
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>>> from mindspore import nn
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>>> indices = Tensor([[0, 1], [1, 2]], dtype=ms.int32)
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>>> values = Tensor([1, 2], dtype=ms.float32)
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>>> sparse_shape = (3, 4)
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>>> dense = Tensor([[1, 1], [2, 2], [3, 3], [4, 4]], dtype=ms.float32)
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>>> sparse_dense_matmul = nn.SparseTensorDenseMatmul()
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>>> out = sparse_dense_matmul(indices, values, sparse_shape, dense)
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>>> print(out)
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[[2 2]
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[6 6]
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[0 0]]
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"""
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def __init__(self, adjoint_st=False, adjoint_dt=False):
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"""Initialize SparseTensorDenseMatmul"""
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super(SparseTensorDenseMatmul, self).__init__()
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self.adj_st = adjoint_st
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self.adj_dt = adjoint_dt
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self.sparse_dense_matmul = P.SparseTensorDenseMatmul(adjoint_st=self.adj_st, adjoint_dt=self.adj_dt)
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def construct(self, indices, values, sparse_shape, dense):
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return self.sparse_dense_matmul(indices, values, sparse_shape, dense)
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