!3537 add sparse operators
Merge pull request !3537 from riemann_penn/add_sparse_operatorpull/3537/MERGE
mindspore-ci-bot
5 years ago
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Gitee
commit
f1e717554c
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# Copyright 2020 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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"""
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Sparse related transformation.
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"""
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from .sparse import SparseToDense
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__all__ = [
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"SparseToDense",
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]
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# Copyright 2020 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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# Copyright 2020 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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"""bprop primitives"""
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from .. import functional as F
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from .. import operations as P
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from ..composite.multitype_ops.zeros_like_impl import zeros_like
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from .grad_base import bprops, bprop_getters
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# Unused parameters are placeholders.
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@bprops.register("MakeSparseTensor")
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def bprop_make_sparse_tensor(indices, values, dense_shape, out, dout):
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"""Backpropagator for primitive `MakeSparseTensor`."""
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return zeros_like(indices), F.sparse_tensor_get_values(dout), ()
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@bprops.register("SparseTensorGetIndices")
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def bprop_sparse_tensor_get_indices(sparse_tensor, out, dout):
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"""Backpropagator for primitive `SparseTensorGetIndices`."""
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return (zeros_like(sparse_tensor),)
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@bprops.register("SparseTensorGetValues")
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def bprop_sparse_tensor_get_values(sparse_tensor, out, dout):
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"""Backpropagator for primitive `SparseTensorGetValues`."""
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return F.make_sparse_tensor(F.sparse_tensor_get_indices(sparse_tensor),
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dout,
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F.sparse_tensor_get_dense_shape(sparse_tensor))
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@bprops.register("SparseTensorGetDenseShape")
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def bprop_sparse_tensor_get_dense_shape(sparse_tensor, out, dout):
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"""Backpropagator for primitive `SparseTensorGetDenseShape`."""
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return (zeros_like(sparse_tensor),)
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@bprop_getters.register(P.SparseToDense)
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def get_bprop_sparse_to_dense(self):
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"""Generate bprop for SparseToDense"""
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def bprop(indices, values, dense_shape, out, dout):
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return zeros_like(indices), dout, zeros_like(dense_shape)
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return bprop
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# coding: utf-8
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# Copyright 2020 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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"""Operators for sparse operators."""
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from ..._checkparam import Validator as validator
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from ...common import dtype as mstype
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from ..primitive import PrimitiveWithInfer, prim_attr_register
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class SparseToDense(PrimitiveWithInfer):
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"""
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Convert a sparse representation into a dense tensor.
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Inputs:
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- **indices** (Tensor) - The indices of sparse representation.
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- **values** (Tensor) - Values corresponding to each row of indices.
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- **dense_shape** (tuple) - A int tuple which specifies the shape of dense tensor.
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Returns:
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Tensor, the shape of tensor is dense_shape.
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Examples:
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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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>>> out = P.SparseToDense()(indices, values, dense_shape)
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"""
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@prim_attr_register
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def __init__(self):
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"""init index_select"""
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self.init_prim_io_names(inputs=['indices', 'values', 'dense_shape'], outputs=['output'])
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def __infer__(self, indices, values, dense_shape):
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validator.check_subclass("indices", indices['dtype'], mstype.tensor, self.name)
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validator.check_subclass("values", values['dtype'], mstype.tensor, self.name)
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out = {'shape': dense_shape['value'],
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'dtype': values['dtype'],
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'value': None}
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return out
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