forked from huawei/mindspore2022
102 lines
4.4 KiB
Python
102 lines
4.4 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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Convert a sparse tensor into dense.
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Not yet supported by any backend at the moment.
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Args:
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sparse_tensor (SparseTensor): the sparse tensor to convert.
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Returns:
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Tensor, the tensor converted.
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Examples:
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>>> class SparseToDenseCell(nn.Cell):
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... def __init__(self, dense_shape):
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... super(SparseToDenseCell, self).__init__()
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... self.dense_shape = dense_shape
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... self.sparse_to_dense = nn.SparseToDense()
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... def construct(self, indices, values):
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... sparse = SparseTensor(indices, values, self.dense_shape)
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... return self.sparse_to_dense(sparse)
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...
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>>> indices = Tensor([[0, 1], [1, 2]])
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>>> values = Tensor([1, 2], dtype=ms.float32)
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>>> dense_shape = (3, 4)
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>>> SparseToDenseCell(dense_shape)(indices, values)
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"""
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def __init__(self):
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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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Multiply SparseTensor(of rank 2) "A" by dense tensor.
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The shape of sparse tensor is :math:`(N, C)`, and the shape of dense tensor is :math:`(C, M)`, then the shape of
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output tensor is :math:`(N, M)`.The output data type is the same as "values".
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Args:
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- *adjoint_st** (Bool) - If true, SparseTensor is transposed before multiplication. Default: False.
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- *adjoint_dt** (Bool) - If true, DenseTensor is transposed before multiplication. Default: False.
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Inputs:
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- **indices** (Tensor) - The indices of sparse representation, support int32/int64.
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- **values** (Tensor) - Values corresponding to each row of indices.
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- **dense_shape** (tuple) - An int tuple which specifies the shape of dense tensor. The dense_shape is :
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math:`(N, C)`. If `adjoint_st` is True, its shape must be :math:`(N, C)` after transpose.
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- **dense** (Tensor) - Dense Matrix. The shape of the tensor is :math:`(C, M)`. If
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`adjoint_dt` is True, its shape must be :math:`(C, M)` after transpose.
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Returns:
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Tensor, the shape of tensor is :math:`(N, M)`.The output data type is the same as "values".
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Examples:
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>>> class NetSparseDenseMatmul(nn.Cell):
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... def __init__(self):
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... super(NetSparseDenseMatmul, self).__init__()
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... self.matmul = nn.SparseTensorDenseMatmul()
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...
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... def construct(self, indices, values, dens_shape, dt):
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... return self.matmul(indices, values, dens_shape, dt)
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...
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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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>>> dense_shape = (3, 4)
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>>> dsMatrix = Tensor([[1, 1], [2, 2], [3, 3], [4, 4]], dtype=ms.float32)
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>>> test_SparseDenseMatmul = NetSparseDenseMatmul()
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>>> out = test_SparseDenseMatmul(indices, values, dens_shape, dsMatrix)
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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.adjst = adjoint_st
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self.adjdt = adjoint_dt
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self.matmul = P.SparseTensorDenseMatmul(adjoint_st=self.adjst, adjoint_dt=self.adjdt)
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def construct(self, indices, values, dense_shape, dense):
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return self.matmul(indices, values, dense_shape, dense)
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