p83571920
/
mindspore
forked from mindspore-Ecosystem/mindspore
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

!44765 Move SparseTensors to sparse_tensor.py 

Merge pull request !44765 from 杨林枫/move_sparse_tensor
This commit is contained in:
i-robot 2022-11-01 01:10:43 +00:00 committed by Gitee
parent 73562a1890 968d05afee
commit ccc94b1f4b
No known key found for this signature in database
GPG Key ID: 173E9B9CA92EEF8F
21 changed files with 923 additions and 886 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

3
mindspore/python/mindspore/_extends/parse/resources.py
View File

@ -20,7 +20,8 @@ from __future__ import absolute_import
import ast
import math
from mindspore import RowTensorInner, RowTensor, SparseTensor, COOTensor, CSRTensor, MapTensor
from mindspore import RowTensor, SparseTensor, COOTensor, CSRTensor, MapTensor
from mindspore.common.sparse_tensor import RowTensorInner
from mindspore.ops import functional as F, composite as C
from mindspore.ops import Primitive
from mindspore.ops.composite import multitype_ops

3
mindspore/python/mindspore/_extends/parse/standard_method.py
View File

@ -17,11 +17,12 @@
"""standard_method"""
from __future__ import absolute_import
from mindspore import Tensor, CSRTensor, COOTensor, RowTensorInner
from mindspore import Tensor, CSRTensor, COOTensor
from mindspore import dtype as mstype
from mindspore._c_expression import Tensor as Tensor_
from mindspore.ops.function.sparse_func import sparse_add
import mindspore.common._monad as monad
from mindspore.common.sparse_tensor import RowTensorInner
from mindspore.ops.composite.base import _append, _insert, _pop, _list_clear, _reverse, \
_count, _extend, _dict_clear, _haskey, _update, _fromkeys

3
mindspore/python/mindspore/boost/boost_cell_wrapper.py
View File

@ -23,7 +23,8 @@ from mindspore.parallel._utils import _get_global_rank, _get_device_num, _get_gr
from mindspore.communication.management import get_group_size, create_group
from mindspore.nn.cell import Cell
from mindspore.nn import SequentialCell
from mindspore.common import Tensor, RowTensorInner
from mindspore.common import Tensor
from mindspore.common.sparse_tensor import RowTensorInner
from mindspore.common.parameter import Parameter, ParameterTuple
from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
from mindspore.ops import functional as F

5
mindspore/python/mindspore/common/__init__.py
View File

@ -25,7 +25,8 @@ from mindspore.common.dtype import Type, int8, byte, int16, short, int32, intc,
from mindspore.common.dump import set_dump
from mindspore.common.parameter import Parameter, ParameterTuple
from mindspore.common.seed import set_seed, get_seed
from mindspore.common.tensor import Tensor, RowTensor, RowTensorInner, SparseTensor, COOTensor, CSRTensor, MapTensor
from mindspore.common.tensor import Tensor, MapTensor
from mindspore.common.sparse_tensor import RowTensor, RowTensorInner, SparseTensor, COOTensor, CSRTensor
from mindspore.common.mutable import mutable
from mindspore.common.jit_config import JitConfig
@ -57,7 +58,7 @@ __all__ = [
]
__all__.extend([
"Tensor", "RowTensor", "RowTensorInner", "SparseTensor", "COOTensor", "CSRTensor", "MapTensor", # tensor
"Tensor", "RowTensor", "SparseTensor", "COOTensor", "CSRTensor", "MapTensor", # tensor
"no_recursive", "ms_function", "ms_class", 'jit', 'jit_class', # api
"Parameter", "ParameterTuple", # parameter
"dtype",

6
mindspore/python/mindspore/common/api.py
View File

@ -31,9 +31,9 @@ from mindspore import context
from mindspore import log as logger
from mindspore._extends.remote import kernel_build_server
from mindspore.common.tensor import Tensor as PythonTensor
from mindspore.common.tensor import CSRTensor as PythonCSRTensor
from mindspore.common.tensor import COOTensor as PythonCOOTensor
from mindspore.common.tensor import RowTensor as PythonRowTensor
from mindspore.common.sparse_tensor import CSRTensor as PythonCSRTensor
from mindspore.common.sparse_tensor import COOTensor as PythonCOOTensor
from mindspore.common.sparse_tensor import RowTensor as PythonRowTensor
from mindspore._c_expression import GraphExecutor_, Tensor, MetaTensor, CSRTensor, RowTensor, COOTensor, \
PyNativeExecutor_, verify_inputs_signature, init_exec_dataset, _set_dataset_mode_config, init_pipeline, \
_ms_memory_recycle

888
mindspore/python/mindspore/common/sparse_tensor.py Normal file
View File

@ -0,0 +1,888 @@
# Copyright 2022 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.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""SparseTensor implementation."""
from __future__ import absolute_import, annotations
__all__ = ['RowTensorInner', 'RowTensor', 'SparseTensor', 'COOTensor', 'CSRTensor']
from typing import Tuple
from mindspore import log as logger
from mindspore.common import dtype as mstype
from mindspore.common._register_for_tensor import tensor_operator_registry
from mindspore.common.tensor import Tensor
from mindspore._c_expression import COOTensor as COOTensor_
from mindspore._c_expression import CSRTensor as CSRTensor_
from mindspore._c_expression import RowTensor as RowTensor_
from mindspore._c_expression import Tensor as Tensor_
from mindspore._checkparam import Validator as validator
class RowTensorInner(RowTensor_):
"""
Implementation for RowTensor, for MindSpore developers only.
"""
def __init__(self, indices=None, values=None, shape=None, row_tensor=None):
"""Init RowTensor"""
self.init_finished = False
# Directly init a RowTensor from another RowTensor
if row_tensor is not None:
if not isinstance(row_tensor, (RowTensor, RowTensor_)):
raise TypeError(f"Expect input `row_tensor` to be a RowTensor, but got {type(row_tensor)}")
if not (indices is None and values is None and shape is None):
raise TypeError("If input `row_tensor` is provided, `indices`, `values`, `shapes` should all be `None`")
RowTensor_.__init__(self, row_tensor)
# Init a RowTensor from indices, values and shape
else:
RowTensor_.__init__(self, indices, values, shape)
self.init_finished = True
def __repr__(self):
"""Avoid PyTest Segfault when RowTensor is not initialized."""
if self.init_finished:
return RowTensor_.__repr__(self)
return ''
@property
def indices(self):
"""Return RowTensor's indices."""
return Tensor(self._indices)
@property
def values(self):
"""Return RowTensor's non-zero values."""
return Tensor(self._values)
@property
def dense_shape(self):
"""Return RowTensor's shape."""
return self._shape
class RowTensor(RowTensorInner):
"""
A sparse representation of a set of tensor slices at given indices.
An RowTensor is typically used to represent a subset of a larger
tensor dense of shape [L0, D1, .. , DN] where L0 >> D0.
The values in indices are the indices in the first dimension of the slices
that have been extracted from the larger tensor.
The dense tensor dense represented by an RowTensor slices has
`dense[slices.indices[i], :, :, :, ...] = slices.values[i, :, :, :, ...]`.
For example, if indices is [0], values is [[1, 2]], shape is
(3, 2), then the dense representation of the row tensor will be:
.. code-block::
[[1, 2],
[0, 0],
[0, 0]]
Note:
RowTensor is deprecated from version 2.0, and will be removed in future version.
Args:
indices (Tensor): A 1-D integer Tensor of shape [D0]. Default: None.
values (Tensor): A Tensor of any dtype of shape [D0, D1, ..., Dn]. Default: None.
shape (tuple(int)): An integer tuple which contains the shape
of the corresponding dense tensor. Default: None.
row_tensor (RowTensor): A RowTensor object. Default: None.
Returns:
RowTensor, composed of `indices`, `values`, and `shape`.
Examples:
>>> import mindspore as ms
>>> from mindspore import Tensor, RowTensor
>>> indices = Tensor([0])
>>> values = Tensor([[1, 2]], dtype=ms.float32)
>>> shape = (3, 2)
>>> x = RowTensor(indices, values, shape)
>>> print(x.values)
[[1. 2.]]
>>> print(x.indices)
[0]
>>> print(x.dense_shape)
(3, 2)
"""
def __init__(self, indices=None, values=None, shape=None, row_tensor=None):
"""Init RowTensor"""
logger.warning("'RowTensor' is deprecated from version 1.7 and will be removed in a future version.")
super().__init__(indices, values, shape, row_tensor)
class SparseTensor(COOTensor_):
"""
A sparse representation of a set of nonzero elements from a tensor at given indices.
SparseTensor can only be used in the `Cell`'s construct method.
For a tensor dense, its SparseTensor(indices, values, dense_shape) has
`dense[indices[i]] = values[i]`.
For example, if indices is [[0, 1], [1, 2]], values is [1, 2], dense_shape is
(3, 4), then the dense representation of the sparse tensor will be:
.. code-block::
[[0, 1, 0, 0],
[0, 0, 2, 0],
[0, 0, 0, 0]]
Note:
The interface is deprecated from version 1.7 and will be removed in a future version.
Please use 'COOTensor' instead.
Args:
indices (Tensor): A 2-D integer Tensor of shape `[N, ndims]`,
where N and ndims are the number of `values` and number of dimensions in
the SparseTensor, respectively.
values (Tensor): A 1-D tensor of any type and shape `[N]`, which
supplies the values for each element in `indices`.
shape (tuple(int)): A integer tuple of size `ndims`,
which specifies the shape of the sparse tensor.
Returns:
SparseTensor, composed of `indices`, `values`, and `shape`.
Examples:
>>> import mindspore as ms
>>> import mindspore.nn as nn
>>> from mindspore import Tensor, SparseTensor
>>> indices = Tensor([[0, 1], [1, 2]])
>>> values = Tensor([1, 2], dtype=ms.float32)
>>> shape = (3, 4)
>>> x = SparseTensor(indices, values, shape)
>>> print(x.values)
[1. 2.]
>>> print(x.indices)
[[0 1]
[1 2]]
>>> print(x.shape)
(3, 4)
"""
def __init__(self, indices, values, shape):
"""Init COOTensor."""
logger.warning("'SparseTensor' is deprecated from version 1.7 and will be removed in a future version. " +
"Please use 'COOTensor' instead.")
if not (isinstance(indices, Tensor) and isinstance(values, Tensor) and isinstance(shape, tuple)):
raise TypeError("Inputs must follow: COOTensor(indices, values, shape).")
COOTensor_.__init__(self, indices, values, shape)
@property
def indices(self):
return Tensor(self._indices)
@property
def values(self):
return Tensor(self._values)
@property
def shape(self):
return self._shape
class COOTensor(COOTensor_):
"""
A sparse representation of a set of nonzero elements from a tensor at given indices.
For a tensor dense, its COOTensor(indices, values, shape) has
`dense[indices[i]] = values[i]`.
For example, if indices is [[0, 1], [1, 2]], values is [1, 2], shape is
(3, 4), then the dense representation of the sparse tensor will be:
.. code-block::
[[0, 1, 0, 0],
[0, 0, 2, 0],
[0, 0, 0, 0]]
Common arithmetic operations include: addition (+), subtraction (-), multiplication (*),
and division (/). For details about operations supported by `COOTensor`, see
[operators](https://www.mindspore.cn/docs/en/master/note/static_graph_syntax_support.html#operators).
Note:
This is an experimental feature and is subjected to change.
Currently, duplicate coordinates in the indices will not be coalesced.
If the indices contain out-of-bound values, the result will be undefined.
Args:
indices (Tensor): A 2-D integer Tensor of shape `[N, ndims]`,
where N and ndims are the number of `values` and number of dimensions in
the COOTensor, respectively. Currently, `ndims` must be 2.
Please make sure that the indices are in range of the given shape.
values (Tensor): A 1-D tensor of any type and shape `[N]`, which
supplies the values for each element in `indices`.
shape (tuple(int)): A integer tuple of size `ndims`,
which specifies the dense_shape of the sparse tensor.
coo_tensor (COOTensor): A COOTensor object.
Returns:
COOTensor, composed of `indices`, `values`, and `shape`.
Examples:
>>> import mindspore as ms
>>> import mindspore.nn as nn
>>> from mindspore import Tensor, COOTensor
>>> indices = Tensor([[0, 1], [1, 2]], dtype=ms.int32)
>>> values = Tensor([1, 2], dtype=ms.float32)
>>> shape = (3, 4)
>>> x = COOTensor(indices, values, shape)
>>> print(x.values)
[1. 2.]
>>> print(x.indices)
[[0 1]
[1 2]]
>>> print(x.shape)
(3, 4)
"""
def __init__(self, indices=None, values=None, shape=None, coo_tensor=None):
"""Init COOTensor"""
self.init_finished = False
# Directly init a COOTensor from another COOTensor
if coo_tensor is not None:
if not isinstance(coo_tensor, (COOTensor, COOTensor_)):
raise TypeError(f"Expect input `coo_tensor` to be a COOTensor, but got {type(coo_tensor)}")
if not (indices is None and values is None and shape is None):
raise TypeError("If input `coo_tensor` is provided, `indices`, `values`, `shapes` should all be `None`")
COOTensor_.__init__(self, coo_tensor)
# Init a COOTensor from indices, values and shape
else:
validator.check_coo_tensor_input(indices, values, shape)
validator.check_coo_tensor_shape(indices.shape, values.shape, shape)
validator.check_coo_tensor_dtype(indices.dtype)
indices = tensor_operator_registry.get('stop_gradient')(indices)
COOTensor_.__init__(self, indices, values, shape)
self.init_finished = True
def __repr__(self):
"""Avoid PyTest Segfault when COOTensor is not initialized."""
if self.init_finished:
return COOTensor_.__repr__(self)
return ''
def __neg__(self):
return COOTensor(self.indices, -self.values, self.shape)
def __add__(self, other):
if not self.shape == other.shape:
raise ValueError("Input tensors should have the same shape.")
if isinstance(other, Tensor):
return tensor_operator_registry.get("tensor_scatter_add")(other, self.indices, self.values)
if isinstance(other, COOTensor):
return tensor_operator_registry.get('coo_add')(self, other, Tensor(0, self.values.dtype))
raise TypeError("COOTensor add with %s is not supported." % type(other))
def __sub__(self, other):
if not self.shape == other.shape:
raise ValueError("Input tensors should have the same shape.")
if isinstance(other, Tensor):
return tensor_operator_registry.get("tensor_scatter_add")(-other, self.indices, self.values)
if isinstance(other, COOTensor):
return tensor_operator_registry.get('coo_add')(
self, -other, Tensor(0, self.values.dtype))
raise TypeError("COOTensor subtract with %s is not supported." % type(other))
def __mul__(self, other):
if not self.shape == other.shape:
raise ValueError("Input tensors should have the same shape.")
if isinstance(other, Tensor):
other_values = tensor_operator_registry.get("gather_nd")(other, self.indices)
return COOTensor(self.indices, self.values * other_values, self.shape)
raise TypeError("COOTensor multiply with %s is not supported." % type(other))
def __div__(self, other):
if not self.shape == other.shape:
raise ValueError("Input tensors should have the same shape.")
if isinstance(other, Tensor):
logger.warning("For sparse divide, zero values in the dense tensor are ignored.")
other_values = tensor_operator_registry.get("gather_nd")(other, self.indices)
return COOTensor(self.indices, self.values / other_values, self.shape)
raise TypeError("COOTensor divide with %s is not supported." % type(other))
def __truediv__(self, other):
return self.__div__(other)
@property
def indices(self) -> Tensor:
"""Return COOTensor's indices."""
return Tensor(self._indices)
@property
def values(self) -> Tensor:
"""Return COOTensor's non-zero values."""
return Tensor(self._values)
@property
def shape(self) -> Tuple[int, ...]:
"""Return COOTensor's shape."""
return self._shape
@property
def dtype(self) -> mstype:
"""Return the dtype of the values of COOTensor (:class:`mindspore.dtype`)."""
return self._dtype
@property
def size(self) -> int:
"""Return the number of non-zero values."""
return self.values.size
@property
def itemsize(self) -> int:
"""Return the length of one tensor element in bytes."""
return self.values.itemsize
@property
def ndim(self) -> int:
"""Return the number of tensor dimensions."""
return len(self.shape)
def coalesce(self) -> COOTensor:
"""
Return the coalesced sparse tensor of the input.
Returns:
COOTensor.
Supported Platforms:
``GPU``
"""
shape = Tensor(self.shape)
res_indices, res_values, _ = tensor_operator_registry.get("coalesce")(self.indices.transpose(),
self.values, shape)
return COOTensor(res_indices.transpose(), res_values, self.shape)
def to_csr(self) -> CSRTensor:
"""
Converts COOTensor to CSRTensor.
Note:
Currently only supports CPU backend with LLVM 12.0.1 installed.
Returns:
CSRTensor.
Supported Platforms:
``GPU`` ``CPU``
"""
row_indices = self.indices[:, 0]
col_indices = self.indices[:, 1]
idx_dtype = self.indices.dtype
row_indices, sort_idx = tensor_operator_registry.get("sort")(
row_indices.astype(mstype.float32))
row_indices = row_indices.astype(idx_dtype)
col_indices = col_indices[sort_idx]
values = self.values[sort_idx]
indptr = tensor_operator_registry.get("coo2csr")(row_indices, self.shape[0])
return CSRTensor(indptr, col_indices, values, self.shape)
def to_dense(self) -> Tensor:
"""
Converts COOTensor to Dense Tensor.
Returns:
Tensor.
Supported Platforms:
``GPU``
"""
zeros_tensor = tensor_operator_registry.get("zeros")(self.shape, self.values.dtype)
return tensor_operator_registry.get("tensor_scatter_add")(
zeros_tensor, self.indices, self.values)
def astype(self, dtype: mstype) -> COOTensor:
"""
Return a copy of the COOTensor, cast its values to a specified type.
Args:
dtype (Union[:class:`mindspore.dtype`, numpy.dtype, str]): Designated tensor dtype.
Returns:
COOTensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore as ms
>>> from mindspore import Tensor, COOTensor
>>> indices = Tensor([[0, 1], [1, 2]], dtype=ms.int32)
>>> values = Tensor([1, 2], dtype=ms.float32)
>>> shape = (3, 4)
>>> coo_tensor = COOTensor(indices, values, shape)
>>> print(coo_tensor.astype(ms.float64).dtype)
Float64
"""
data = self.values.astype(dtype)
return COOTensor(self.indices, data, self.shape)
def to_tuple(self) -> Tuple[Tensor, Tensor, Tuple[int, ...]]:
"""
Return indices, values and shape as a tuple.
Returns:
Tuple.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
"""
return self.indices, self.values, self.shape
def abs(self) -> COOTensor:
"""
Return absolute value element-wisely.
Returns:
COOTensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
"""
data = self.values.abs()
return COOTensor(self.indices, data, self.shape)
def add(self, other: COOTensor, thresh: Tensor) -> COOTensor:
"""
Return the sum with another COOTensor.
Args:
other(COOTensor): the second SparseTensor to sum.
thresh(Tensor): the magnitude threshold that determines
if an output value/index pair pair take space.
Returns:
COOTensor, representing the sum.
Raises:
ValueError: If any input(self/other)'s indices's dim is not equal to 2.
ValueError: If any input(self/other)'s values's dim is not equal to 1.
ValueError: If any input(self/other)'s shape's dim is not equal to 1.
ValueError: If thresh's dim is not equal to 0.
TypeError: If any input(self/other)'s indices's type is not equal to int64.
TypeError: If any input(self/other)'s shape's type is not equal to int64.
ValueError: If any input(self/other)'s indices's length is not equal to
its values's length.
TypeError: If any input(self/other)'s values's type is not equal to anf of
(int8/int16/int32/int64/float32/float64/complex64/complex128)
TypeError: If thresh's type is not equal to anf of
(int8/int16/int32/int64/float32/float64)
TypeError: If self's indices's type is not equal to other's indices's type
TypeError: If self's values's type is not equal to other's values's type
TypeError: If self's shape's type is not equal to other's shape's type
TypeError: If (self/other)'s value's type is not matched with thresh's type
Supported Platforms:
``CPU`` ``GPU``
Examples:
>>> from mindspore import Tensor, COOTensor
>>> from mindspore import dtype as mstype
>>> indics0 = Tensor([[0, 1], [1, 2]], dtype=mstype.int64)
>>> values0 = Tensor([1, 2], dtype=mstype.int32)
>>> shape0 = (3, 4)
>>> input0 = COOTensor(indics0, values0, shape0)
>>> indics1 = Tensor([[0, 0], [1, 1]], dtype=mstype.int64)
>>> values1 = Tensor([3, 4], dtype=mstype.int32)
>>> shape1 = (3, 4)
>>> input1 = COOTensor(indics1, values1, shape1)
>>> thres = Tensor(0, dtype=mstype.int32)
>>> out = input0.add(input1, thres)
>>> print(out)
COOTensor(shape = [3, 4], dtype = Int32, indices=Tensor(shape=[4, 2],
dtype = Int64, value=[[0 0], [0 1], [1 1], [1 2]]), values=Tensor(shape[4],
dtype=Int32, value=[3 1 4 2]))
"""
return tensor_operator_registry.get('coo_add')(self, other, thresh)
class CSRTensor(CSRTensor_):
"""
Constructs a sparse tensor in CSR (Compressed Sparse Row) format, with specified
values indicated by `values` and row and column positions indicated by `indptr`
and `indices`.
For example, if indptr is [0, 1, 2, 2], indices is [1, 2], values is [1., 2.], shape is
(3, 4), then the dense representation of the sparse tensor will be:
.. code-block::
[[0., 1., 0., 0.],
[0., 0., 2., 0.],
[0., 0., 0., 0.]]
Common arithmetic operations include: addition (+), subtraction (-), multiplication (*),
and division (/). For details about operations supported by `CSRTensor`, see
[operators](https://www.mindspore.cn/docs/en/master/note/static_graph_syntax_support.html#operators).
Note:
This is an experimental feature and is subjected to change.
If the values given by `indptr` or `indices` are invalid, the results may be undefined. Invalid values include
when the length of `values` or `indices` exceeds the range indicated by indptr, and when the columns indicated by
`indices` are repeated on the same row.
Args:
indptr (Tensor): 1-D Tensor of shape `[M]`, which equals to `shape[0] + 1`, which indicates the
start and end point for `values` in each row. Default: None. If provided,
must be :class:`mindspore.int16`, :class:`mindspore.int32` or :class:`mindspore.int64`.
indices (Tensor): 1-D Tensor of shape `[N]`, which has the same length as `values`. `indices`
indicates the which column `values` should be placed. Default: None. If provided,
must be :class:`mindspore.int16`, :class:`mindspore.int32` or :class:`mindspore.int64`.
values (Tensor): Tensor, which has the same length as `indices` (values.shape[0] == indices.shape[0]).
`values` stores the data for CSRTensor. Default: None.
shape (tuple(int)): A tuple indicates the shape of the CSRTensor, and `shape[0]` must equal to `M - 1`,
which all equal to number of rows of the CSRTensor. Default: None.
csr_tensor (CSRTensor): A CSRTensor object. Values' feature dimension should match with
CSRTensor's feature dimension (values.shape[1:] == csr_tensor.shape[2:]). Default: None.
Outputs:
CSRTensor, with shape defined by `shape`, and dtype inferred from `value`.
Examples:
>>> import mindspore as ms
>>> from mindspore import Tensor, CSRTensor
>>> # initialize a csr_tensor with indptr, indices, values and shape
>>> indptr = Tensor([0, 1, 2], dtype=ms.int32)
>>> indices = Tensor([0, 1], dtype=ms.int32)
>>> values = Tensor([1, 2], dtype=ms.float32)
>>> shape = (2, 4)
>>> csr_tensor = CSRTensor(indptr, indices, values, shape)
>>> # access a data member of CSRTensor
>>> print(indptr == csr_tensor.indptr)
[ True True True]
"""
def __init__(self, indptr=None, indices=None, values=None, shape=None, csr_tensor=None):
"Init CSRTensor"
self.init_finished = False
# Directly init a CSRTensor from another CSRTensor
if csr_tensor is not None:
if not isinstance(csr_tensor, (CSRTensor, CSRTensor_)):
raise TypeError(f"Expect input `csr_tensor` to be a CSRTensor, but got {type(csr_tensor)}")
if not (indptr is None and indices is None and values is None and shape is None):
raise TypeError(
"If input `csr_tensor` is provided, `indptr`, `indices`, `values`, `shapes` should all be `None`")
CSRTensor_.__init__(self, csr_tensor)
# Init a CSRTensor from indptr, indices, values and shape
else:
validator.check_csr_tensor_input(indptr, indices, values, shape)
validator.check_csr_tensor_shape(indptr.shape, indices.shape, values.shape, shape)
validator.check_csr_tensor_dtype(indptr.dtype, indices.dtype)
indptr = tensor_operator_registry.get('stop_gradient')(indptr)
indices = tensor_operator_registry.get('stop_gradient')(indices)
CSRTensor_.__init__(self, indptr, indices, values, shape)
self.init_finished = True
def __repr__(self):
"""Avoid PyTest Segfault when CSRTensor is not initialized."""
if self.init_finished:
return CSRTensor_.__repr__(self)
return ''
def __mul__(self, other):
res = tensor_operator_registry.get('csr_mul')(self, other)
return CSRTensor(self.indptr, self.indices, res, self.shape)
def __div__(self, other):
logger.warning("For CSR divide, zero values in the dense tensor are ignored.")
res = tensor_operator_registry.get('csr_div')(self, other)
return CSRTensor(self.indptr, self.indices, res, self.shape)
def __truediv__(self, other):
return self.__div__(other)
def __neg__(self):
return CSRTensor(self.indptr, self.indices, -self.values, self.shape)
def __add__(self, other):
if not self.shape == other.shape:
raise ValueError("Input tensors should have the same shape.")
if isinstance(other, CSRTensor):
return tensor_operator_registry.get('csr_add')(
self, other, Tensor(1, self.values.dtype), Tensor(1, self.values.dtype))
raise TypeError("CSRTensor add with %s is not supported." % type(other))
def __sub__(self, other):
if not self.shape == other.shape:
raise ValueError("Input tensors should have the same shape.")
if isinstance(other, CSRTensor):
return tensor_operator_registry.get('csr_add')(
self, other, Tensor(1, self.values.dtype), Tensor(-1, self.values.dtype))
raise TypeError("CSRTensor subtract with %s is not supported." % type(other))
@property
def indptr(self) -> Tensor:
"""Return CSRTensor's row indices pointers."""
return Tensor(self._indptr)
@property
def indices(self) -> Tensor:
"""Return CSRTensor's column indices."""
return Tensor(self._indices)
@property
def values(self) -> Tensor:
"""Return CSRTensor's non-zero values."""
return Tensor(self._values)
@property
def shape(self) -> Tuple[int, ...]:
"""Return CSRTensor's shape."""
return self._shape
@property
def dtype(self) -> mstype:
"""Return the dtype of the values of CSRTensor (:class:`mindspore.dtype`)."""
return self._dtype
@property
def size(self) -> int:
"""Return the number of non-zero values."""
return self.values.size
@property
def itemsize(self) -> int:
"""Return the length of one tensor element in bytes."""
return self.values.itemsize
@property
def ndim(self) -> int:
"""Return the number of tensor dimensions."""
return len(self.shape)
def to_tuple(self) -> Tuple[Tensor, Tensor, Tensor, Tuple[int, ...]]:
"""
Return indptr, indices, values and shape as a tuple.
Returns:
Tuple.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
"""
return self.indptr, self.indices, self.values, self.shape
def to_coo(self) -> COOTensor:
"""
Converts CSRTensor to COOTensor.
Note:
Currently only supports CPU backend with LLVM 12.0.1 installed.
Returns:
COOTensor.
Supported Platforms:
``GPU`` ``CPU``
"""
if self.ndim != 2:
raise ValueError("Currently only support 2-D CSRTensor when converting to COOTensor.")
row_indices = tensor_operator_registry.get("csr2coo")(self.indptr, self.values.shape[0])
coo_indices = tensor_operator_registry.get("stack")((row_indices, self.indices), 1)
return COOTensor(coo_indices, self.values, self.shape)
def to_dense(self) -> Tensor:
"""
Converts CSRTensor to Dense Tensor.
Returns:
Tensor.
Supported Platforms:
``GPU``
"""
return tensor_operator_registry.get("csr_to_dense")(self)
def astype(self, dtype: mstype) -> CSRTensor:
"""
Return a copy of the CSRTensor, cast its values to a specified type.
Args:
dtype (Union[:class:`mindspore.dtype`, numpy.dtype, str]): Designated tensor dtype.
Returns:
CSRTensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore as ms
>>> from mindspore import Tensor, CSRTensor
>>> indptr = Tensor([0, 1, 2], dtype=ms.int32)
>>> indices = Tensor([0, 1], dtype=ms.int32)
>>> values = Tensor([1, 2], dtype=ms.float32)
>>> shape = (2, 4)
>>> csr_tensor = CSRTensor(indptr, indices, values, shape)
>>> print(csr_tensor.astype(ms.float64).dtype)
Float64
"""
data = self.values.astype(dtype)
return CSRTensor(self.indptr, self.indices, data, self.shape)
def mv(self, dense_vector: Tensor) -> Tensor:
"""
Return the matrix multiplication result of the right-multiply dense matrix of the CSRTensor.
The CSRTensor with shape `[M, N]` needs to adapt the dense vector with shape `[N, 1]`
to get the dense vector with result `[M, 1]`.
Note:
Currently only supports CPU backend with LLVM 12.0.1 installed.
Args:
dense_vector (Tensor): A dense Tensor, its shape must be (csr_tensor.shape[1], 1)
Returns:
Tensor.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, CSRTensor
>>> from mindspore import dtype as mstype
>>> indptr = Tensor([0, 1, 2], dtype=mstype.int32)
>>> indices = Tensor([0, 1], dtype=mstype.int32)
>>> values = Tensor([2, 1], dtype=mstype.float32)
>>> dense_shape = (2, 4)
>>> csr_tensor = CSRTensor(indptr, indices, values, dense_shape)
>>> dense = Tensor([[1], [1], [1], [1]], dtype=mstype.float32)
>>> print(csr_tensor.mv(dense))
[[2.]
[1.]]
"""
validator.check_value_type('dense_vector', dense_vector, (Tensor_,), 'CSRTensor.mv')
return tensor_operator_registry.get("csr_mv")(self, dense_vector)
def mm(self, dense_matrix: Tensor) -> Tensor:
"""
Return the matrix multiplication result of the right-multiply dense matrix of the CSRTensor.
The CSRTensor with shape `[M, N]` needs to adapt the dense matrix with shape `[N, K]`
to get the dense matrix with result `[M, K]`.
Note:
Currently only supports CPU backend with LLVM 12.0.1 installed.
Args:
dense_matrix (Tensor): A dense Tensor, its shape[0] should be equal to csr_tensor.shape[1]
Returns:
Tensor.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, CSRTensor
>>> from mindspore import dtype as mstype
>>> indptr = Tensor([0, 1, 2], dtype=mstype.int32)
>>> indices = Tensor([0, 1], dtype=mstype.int32)
>>> values = Tensor([2, 1], dtype=mstype.float32)
>>> dense_shape = (2, 4)
>>> csr_tensor = CSRTensor(indptr, indices, values, dense_shape)
>>> dense_matrix = Tensor([[1., 2.], [1, 2.], [1, 2.], [1., 2.]], dtype=mstype.float32)
>>> print(csr_tensor.mm(dense_matrix))
[[2. 4.]
[1. 2.]]
"""
validator.check_value_type('dense_matrix', dense_matrix, (Tensor_,), 'CSRTensor.mm')
return tensor_operator_registry.get("csr_mm")()(self.indptr, self.indices, self.values,
self.shape, dense_matrix)
def sum(self, axis: int) -> Tensor:
"""
Reduces a dimension of a CSRTensor by summing all elements in the dimension.
Note:
Currently only supports CPU backend with LLVM 12.0.1 installed.
Args:
axis (int): The dimensions to reduce.
Returns:
Tensor, the dtype is the same as `CSRTensor.values`.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, CSRTensor
>>> from mindspore import dtype as mstype
>>> indptr = Tensor([0, 1, 2], dtype=mstype.int32)
>>> indices = Tensor([0, 1], dtype=mstype.int32)
>>> values = Tensor([2, 1], dtype=mstype.float32)
>>> dense_shape = (2, 4)
>>> csr_tensor = CSRTensor(indptr, indices, values, dense_shape)
>>> print(csr_tensor.sum(1))
[[2.]
[1.]]
"""
return tensor_operator_registry.get("csr_reduce_sum")(self, axis)
def abs(self) -> CSRTensor:
"""
Return absolute value element-wisely.
Returns:
CSRTensor, with all values being non-negative.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
"""
data = self.values.abs()
return CSRTensor(self.indptr, self.indices, data, self.shape)
def add(self, b: CSRTensor, alpha: Tensor, beta: Tensor) -> CSRTensor:
"""
Addition of two CSR Tensors : C = alpha * A + beta * B
Args:
b (CSRTensor): Sparse CSR Tensor.
alpha(Tensor): Dense Tensor, its shape must be able to broadcast to self.
beta(Tensor): Dense Tensor, its shape must be able to broadcast to b.
Returns:
CSRTensor.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, CSRTensor
>>> import mindspore.common.dtype as mstype
>>> indptr = Tensor([0, 1, 2], dtype=mstype.int32)
>>> indices = Tensor([0, 1], dtype=mstype.int32)
>>> values_a = Tensor([2, 1], dtype=mstype.float32)
>>> values_b = Tensor([1, 2], dtype=mstype.float32)
>>> dense_shape = (2, 4)
>>> alpha = Tensor(1, mstype.float32)
>>> beta = Tensor(1, mstype.float32)
>>> a = CSRTensor(indptr, indices, values_a, dense_shape)
>>> b = CSRTensor(indptr, indices, values_b, dense_shape)
>>> print(a.add(b, alpha, beta))
CSRTensor(shape=[2,4], dtype=Float32,
indptr=Tensor(shape=[3], dtype=Int32, value = [0, 1, 2]),
indices=Tensor(shape=[2], dtype=Int32, value = [0, 1]),
values=Tensor(shape=[2], dtype=Float32, value = [3.0, 3.0]))
"""
return tensor_operator_registry.get('csr_add')(self, b, alpha, beta)

864
mindspore/python/mindspore/common/tensor.py
View File

@ -13,13 +13,11 @@
# limitations under the License.
# ============================================================================
"""Tensor implementation."""
from __future__ import absolute_import, annotations
__all__ = ['Tensor', 'RowTensorInner', 'RowTensor', 'SparseTensor', 'COOTensor', 'CSRTensor', 'MapTensor']
__all__ = ['Tensor', 'MapTensor']
import math
import numbers
from typing import Tuple
import numpy as np
from mindspore.communication.management import get_rank, get_group_size
@ -30,9 +28,6 @@ from mindspore import log as logger
from mindspore.common import dtype as mstype
from mindspore.common._utils import split_to_slice_if_need
from mindspore.common._register_for_tensor import tensor_operator_registry
from mindspore._c_expression import COOTensor as COOTensor_
from mindspore._c_expression import CSRTensor as CSRTensor_
from mindspore._c_expression import RowTensor as RowTensor_
from mindspore._c_expression import Tensor as Tensor_
from mindspore._c_expression import MapTensor_
from mindspore._checkparam import Rel, check_is_number
@ -4164,863 +4159,6 @@ class Tensor(Tensor_):
return tensor_operator_registry.get('ne')(self, other)
class RowTensorInner(RowTensor_):
"""
Implementation for RowTensor, for MindSpore developers only.
"""
def __init__(self, indices=None, values=None, shape=None, row_tensor=None):
"""Init RowTensor"""
self.init_finished = False
# Directly init a RowTensor from another RowTensor
if row_tensor is not None:
if not isinstance(row_tensor, (RowTensor, RowTensor_)):
raise TypeError(f"Expect input `row_tensor` to be a RowTensor, but got {type(row_tensor)}")
if not (indices is None and values is None and shape is None):
raise TypeError("If input `row_tensor` is provided, `indices`, `values`, `shapes` should all be `None`")
RowTensor_.__init__(self, row_tensor)
# Init a RowTensor from indices, values and shape
else:
RowTensor_.__init__(self, indices, values, shape)
self.init_finished = True
def __repr__(self):
"""Avoid PyTest Segfault when RowTensor is not initialized."""
if self.init_finished:
return RowTensor_.__repr__(self)
return ''
@property
def indices(self):
"""Return RowTensor's indices."""
return Tensor(self._indices)
@property
def values(self):
"""Return RowTensor's non-zero values."""
return Tensor(self._values)
@property
def dense_shape(self):
"""Return RowTensor's shape."""
return self._shape
class RowTensor(RowTensorInner):
"""
A sparse representation of a set of tensor slices at given indices.
An RowTensor is typically used to represent a subset of a larger
tensor dense of shape [L0, D1, .. , DN] where L0 >> D0.
The values in indices are the indices in the first dimension of the slices
that have been extracted from the larger tensor.
The dense tensor dense represented by an RowTensor slices has
`dense[slices.indices[i], :, :, :, ...] = slices.values[i, :, :, :, ...]`.
For example, if indices is [0], values is [[1, 2]], shape is
(3, 2), then the dense representation of the row tensor will be:
.. code-block::
[[1, 2],
[0, 0],
[0, 0]]
Note:
RowTensor is deprecated from version 2.0, and will be removed in future version.
Args:
indices (Tensor): A 1-D integer Tensor of shape [D0]. Default: None.
values (Tensor): A Tensor of any dtype of shape [D0, D1, ..., Dn]. Default: None.
shape (tuple(int)): An integer tuple which contains the shape
of the corresponding dense tensor. Default: None.
row_tensor (RowTensor): A RowTensor object. Default: None.
Returns:
RowTensor, composed of `indices`, `values`, and `shape`.
Examples:
>>> import mindspore as ms
>>> from mindspore import Tensor, RowTensor
>>> indices = Tensor([0])
>>> values = Tensor([[1, 2]], dtype=ms.float32)
>>> shape = (3, 2)
>>> x = RowTensor(indices, values, shape)
>>> print(x.values)
[[1. 2.]]
>>> print(x.indices)
[0]
>>> print(x.dense_shape)
(3, 2)
"""
def __init__(self, indices=None, values=None, shape=None, row_tensor=None):
"""Init RowTensor"""
logger.warning("'RowTensor' is deprecated from version 1.7 and will be removed in a future version.")
super().__init__(indices, values, shape, row_tensor)
class SparseTensor(COOTensor_):
"""
A sparse representation of a set of nonzero elements from a tensor at given indices.
SparseTensor can only be used in the `Cell`'s construct method.
For a tensor dense, its SparseTensor(indices, values, dense_shape) has
`dense[indices[i]] = values[i]`.
For example, if indices is [[0, 1], [1, 2]], values is [1, 2], dense_shape is
(3, 4), then the dense representation of the sparse tensor will be:
.. code-block::
[[0, 1, 0, 0],
[0, 0, 2, 0],
[0, 0, 0, 0]]
Note:
The interface is deprecated from version 1.7 and will be removed in a future version.
Please use 'COOTensor' instead.
Args:
indices (Tensor): A 2-D integer Tensor of shape `[N, ndims]`,
where N and ndims are the number of `values` and number of dimensions in
the SparseTensor, respectively.
values (Tensor): A 1-D tensor of any type and shape `[N]`, which
supplies the values for each element in `indices`.
shape (tuple(int)): A integer tuple of size `ndims`,
which specifies the shape of the sparse tensor.
Returns:
SparseTensor, composed of `indices`, `values`, and `shape`.
Examples:
>>> import mindspore as ms
>>> import mindspore.nn as nn
>>> from mindspore import Tensor, SparseTensor
>>> indices = Tensor([[0, 1], [1, 2]])
>>> values = Tensor([1, 2], dtype=ms.float32)
>>> shape = (3, 4)
>>> x = SparseTensor(indices, values, shape)
>>> print(x.values)
[1. 2.]
>>> print(x.indices)
[[0 1]
[1 2]]
>>> print(x.shape)
(3, 4)
"""
def __init__(self, indices, values, shape):
"""Init COOTensor."""
logger.warning("'SparseTensor' is deprecated from version 1.7 and will be removed in a future version. " +
"Please use 'COOTensor' instead.")
if not (isinstance(indices, Tensor) and isinstance(values, Tensor) and isinstance(shape, tuple)):
raise TypeError("Inputs must follow: COOTensor(indices, values, shape).")
COOTensor_.__init__(self, indices, values, shape)
@property
def indices(self):
return Tensor(self._indices)
@property
def values(self):
return Tensor(self._values)
@property
def shape(self):
return self._shape
class COOTensor(COOTensor_):
"""
A sparse representation of a set of nonzero elements from a tensor at given indices.
For a tensor dense, its COOTensor(indices, values, shape) has
`dense[indices[i]] = values[i]`.
For example, if indices is [[0, 1], [1, 2]], values is [1, 2], shape is
(3, 4), then the dense representation of the sparse tensor will be:
.. code-block::
[[0, 1, 0, 0],
[0, 0, 2, 0],
[0, 0, 0, 0]]
Common arithmetic operations include: addition (+), subtraction (-), multiplication (*),
and division (/). For details about operations supported by `COOTensor`, see
[operators](https://www.mindspore.cn/docs/en/master/note/static_graph_syntax_support.html#operators).
Note:
This is an experimental feature and is subjected to change.
Currently, duplicate coordinates in the indices will not be coalesced.
If the indices contain out-of-bound values, the result will be undefined.
Args:
indices (Tensor): A 2-D integer Tensor of shape `[N, ndims]`,
where N and ndims are the number of `values` and number of dimensions in
the COOTensor, respectively. Currently, `ndims` must be 2.
Please make sure that the indices are in range of the given shape.
values (Tensor): A 1-D tensor of any type and shape `[N]`, which
supplies the values for each element in `indices`.
shape (tuple(int)): A integer tuple of size `ndims`,
which specifies the dense_shape of the sparse tensor.
coo_tensor (COOTensor): A COOTensor object.
Returns:
COOTensor, composed of `indices`, `values`, and `shape`.
Examples:
>>> import mindspore as ms
>>> import mindspore.nn as nn
>>> from mindspore import Tensor, COOTensor
>>> indices = Tensor([[0, 1], [1, 2]], dtype=ms.int32)
>>> values = Tensor([1, 2], dtype=ms.float32)
>>> shape = (3, 4)
>>> x = COOTensor(indices, values, shape)
>>> print(x.values)
[1. 2.]
>>> print(x.indices)
[[0 1]
[1 2]]
>>> print(x.shape)
(3, 4)
"""
def __init__(self, indices=None, values=None, shape=None, coo_tensor=None):
"""Init COOTensor"""
self.init_finished = False
# Directly init a COOTensor from another COOTensor
if coo_tensor is not None:
if not isinstance(coo_tensor, (COOTensor, COOTensor_)):
raise TypeError(f"Expect input `coo_tensor` to be a COOTensor, but got {type(coo_tensor)}")
if not (indices is None and values is None and shape is None):
raise TypeError("If input `coo_tensor` is provided, `indices`, `values`, `shapes` should all be `None`")
COOTensor_.__init__(self, coo_tensor)
# Init a COOTensor from indices, values and shape
else:
validator.check_coo_tensor_input(indices, values, shape)
validator.check_coo_tensor_shape(indices.shape, values.shape, shape)
validator.check_coo_tensor_dtype(indices.dtype)
indices = tensor_operator_registry.get('stop_gradient')(indices)
COOTensor_.__init__(self, indices, values, shape)
self.init_finished = True
def __repr__(self):
"""Avoid PyTest Segfault when COOTensor is not initialized."""
if self.init_finished:
return COOTensor_.__repr__(self)
return ''
def __neg__(self):
return COOTensor(self.indices, -self.values, self.shape)
def __add__(self, other):
if not self.shape == other.shape:
raise ValueError("Input tensors should have the same shape.")
if isinstance(other, Tensor):
return tensor_operator_registry.get("tensor_scatter_add")(other, self.indices, self.values)
if isinstance(other, COOTensor):
return tensor_operator_registry.get('coo_add')(self, other, Tensor(0, self.values.dtype))
raise TypeError("COOTensor add with %s is not supported." % type(other))
def __sub__(self, other):
if not self.shape == other.shape:
raise ValueError("Input tensors should have the same shape.")
if isinstance(other, Tensor):
return tensor_operator_registry.get("tensor_scatter_add")(-other, self.indices, self.values)
if isinstance(other, COOTensor):
return tensor_operator_registry.get('coo_add')(
self, -other, Tensor(0, self.values.dtype))
raise TypeError("COOTensor subtract with %s is not supported." % type(other))
def __mul__(self, other):
if not self.shape == other.shape:
raise ValueError("Input tensors should have the same shape.")
if isinstance(other, Tensor):
other_values = tensor_operator_registry.get("gather_nd")(other, self.indices)
return COOTensor(self.indices, self.values * other_values, self.shape)
raise TypeError("COOTensor multiply with %s is not supported." % type(other))
def __div__(self, other):
if not self.shape == other.shape:
raise ValueError("Input tensors should have the same shape.")
if isinstance(other, Tensor):
logger.warning("For sparse divide, zero values in the dense tensor are ignored.")
other_values = tensor_operator_registry.get("gather_nd")(other, self.indices)
return COOTensor(self.indices, self.values / other_values, self.shape)
raise TypeError("COOTensor divide with %s is not supported." % type(other))
def __truediv__(self, other):
return self.__div__(other)
@property
def indices(self) -> Tensor:
"""Return COOTensor's indices."""
return Tensor(self._indices)
@property
def values(self) -> Tensor:
"""Return COOTensor's non-zero values."""
return Tensor(self._values)
@property
def shape(self) -> Tuple[int, ...]:
"""Return COOTensor's shape."""
return self._shape
@property
def dtype(self) -> mstype:
"""Return the dtype of the values of COOTensor (:class:`mindspore.dtype`)."""
return self._dtype
@property
def size(self) -> int:
"""Return the number of non-zero values."""
return self.values.size
@property
def itemsize(self) -> int:
"""Return the length of one tensor element in bytes."""
return self.values.itemsize
@property
def ndim(self) -> int:
"""Return the number of tensor dimensions."""
return len(self.shape)
def coalesce(self) -> COOTensor:
"""
Return the coalesced sparse tensor of the input.
Returns:
COOTensor.
Supported Platforms:
``GPU``
"""
shape = Tensor(self.shape)
res_indices, res_values, _ = tensor_operator_registry.get("coalesce")(self.indices.transpose(),
self.values, shape)
return COOTensor(res_indices.transpose(), res_values, self.shape)
def to_csr(self) -> CSRTensor:
"""
Converts COOTensor to CSRTensor.
Note:
Currently only supports CPU backend with LLVM 12.0.1 installed.
Returns:
CSRTensor.
Supported Platforms:
``GPU`` ``CPU``
"""
row_indices = self.indices[:, 0]
col_indices = self.indices[:, 1]
idx_dtype = self.indices.dtype
row_indices, sort_idx = tensor_operator_registry.get("sort")(
row_indices.astype(mstype.float32))
row_indices = row_indices.astype(idx_dtype)
col_indices = col_indices[sort_idx]
values = self.values[sort_idx]
indptr = tensor_operator_registry.get("coo2csr")(row_indices, self.shape[0])
return CSRTensor(indptr, col_indices, values, self.shape)
def to_dense(self) -> Tensor:
"""
Converts COOTensor to Dense Tensor.
Returns:
Tensor.
Supported Platforms:
``GPU``
"""
zeros_tensor = tensor_operator_registry.get("zeros")(self.shape, self.values.dtype)
return tensor_operator_registry.get("tensor_scatter_add")(
zeros_tensor, self.indices, self.values)
def astype(self, dtype: mstype) -> COOTensor:
"""
Return a copy of the COOTensor, cast its values to a specified type.
Args:
dtype (Union[:class:`mindspore.dtype`, numpy.dtype, str]): Designated tensor dtype.
Returns:
COOTensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore as ms
>>> from mindspore import Tensor, COOTensor
>>> indices = Tensor([[0, 1], [1, 2]], dtype=ms.int32)
>>> values = Tensor([1, 2], dtype=ms.float32)
>>> shape = (3, 4)
>>> coo_tensor = COOTensor(indices, values, shape)
>>> print(coo_tensor.astype(ms.float64).dtype)
Float64
"""
data = self.values.astype(dtype)
return COOTensor(self.indices, data, self.shape)
def to_tuple(self) -> Tuple[Tensor, Tensor, Tuple[int, ...]]:
"""
Return indices, values and shape as a tuple.
Returns:
Tuple.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
"""
return self.indices, self.values, self.shape
def abs(self) -> COOTensor:
"""
Return absolute value element-wisely.
Returns:
COOTensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
"""
data = self.values.abs()
return COOTensor(self.indices, data, self.shape)
def add(self, other: COOTensor, thresh: Tensor) -> COOTensor:
"""
Return the sum with another COOTensor.
Args:
other(COOTensor): the second SparseTensor to sum.
thresh(Tensor): the magnitude threshold that determines
if an output value/index pair pair take space.
Returns:
COOTensor, representing the sum.
Raises:
ValueError: If any input(self/other)'s indices's dim is not equal to 2.
ValueError: If any input(self/other)'s values's dim is not equal to 1.
ValueError: If any input(self/other)'s shape's dim is not equal to 1.
ValueError: If thresh's dim is not equal to 0.
TypeError: If any input(self/other)'s indices's type is not equal to int64.
TypeError: If any input(self/other)'s shape's type is not equal to int64.
ValueError: If any input(self/other)'s indices's length is not equal to
its values's length.
TypeError: If any input(self/other)'s values's type is not equal to anf of
(int8/int16/int32/int64/float32/float64/complex64/complex128)
TypeError: If thresh's type is not equal to anf of
(int8/int16/int32/int64/float32/float64)
TypeError: If self's indices's type is not equal to other's indices's type
TypeError: If self's values's type is not equal to other's values's type
TypeError: If self's shape's type is not equal to other's shape's type
TypeError: If (self/other)'s value's type is not matched with thresh's type
Supported Platforms:
``CPU`` ``GPU``
Examples:
>>> from mindspore import Tensor, COOTensor
>>> from mindspore import dtype as mstype
>>> indics0 = Tensor([[0, 1], [1, 2]], dtype=mstype.int64)
>>> values0 = Tensor([1, 2], dtype=mstype.int32)
>>> shape0 = (3, 4)
>>> input0 = COOTensor(indics0, values0, shape0)
>>> indics1 = Tensor([[0, 0], [1, 1]], dtype=mstype.int64)
>>> values1 = Tensor([3, 4], dtype=mstype.int32)
>>> shape1 = (3, 4)
>>> input1 = COOTensor(indics1, values1, shape1)
>>> thres = Tensor(0, dtype=mstype.int32)
>>> out = input0.add(input1, thres)
>>> print(out)
COOTensor(shape = [3, 4], dtype = Int32, indices=Tensor(shape=[4, 2],
dtype = Int64, value=[[0 0], [0 1], [1 1], [1 2]]), values=Tensor(shape[4],
dtype=Int32, value=[3 1 4 2]))
"""
return tensor_operator_registry.get('coo_add')(self, other, thresh)
class CSRTensor(CSRTensor_):
"""
Constructs a sparse tensor in CSR (Compressed Sparse Row) format, with specified
values indicated by `values` and row and column positions indicated by `indptr`
and `indices`.
For example, if indptr is [0, 1, 2, 2], indices is [1, 2], values is [1., 2.], shape is
(3, 4), then the dense representation of the sparse tensor will be:
.. code-block::
[[0., 1., 0., 0.],
[0., 0., 2., 0.],
[0., 0., 0., 0.]]
Common arithmetic operations include: addition (+), subtraction (-), multiplication (*),
and division (/). For details about operations supported by `CSRTensor`, see
[operators](https://www.mindspore.cn/docs/en/master/note/static_graph_syntax_support.html#operators).
Note:
This is an experimental feature and is subjected to change.
If the values given by `indptr` or `indices` are invalid, the results may be undefined. Invalid values include
when the length of `values` or `indices` exceeds the range indicated by indptr, and when the columns indicated by
`indices` are repeated on the same row.
Args:
indptr (Tensor): 1-D Tensor of shape `[M]`, which equals to `shape[0] + 1`, which indicates the
start and end point for `values` in each row. Default: None. If provided,
must be :class:`mindspore.int16`, :class:`mindspore.int32` or :class:`mindspore.int64`.
indices (Tensor): 1-D Tensor of shape `[N]`, which has the same length as `values`. `indices`
indicates the which column `values` should be placed. Default: None. If provided,
must be :class:`mindspore.int16`, :class:`mindspore.int32` or :class:`mindspore.int64`.
values (Tensor): Tensor, which has the same length as `indices` (values.shape[0] == indices.shape[0]).
`values` stores the data for CSRTensor. Default: None.
shape (tuple(int)): A tuple indicates the shape of the CSRTensor, and `shape[0]` must equal to `M - 1`,
which all equal to number of rows of the CSRTensor. Default: None.
csr_tensor (CSRTensor): A CSRTensor object. Values' feature dimension should match with
CSRTensor's feature dimension (values.shape[1:] == csr_tensor.shape[2:]). Default: None.
Outputs:
CSRTensor, with shape defined by `shape`, and dtype inferred from `value`.
Examples:
>>> import mindspore as ms
>>> from mindspore import Tensor, CSRTensor
>>> # initialize a csr_tensor with indptr, indices, values and shape
>>> indptr = Tensor([0, 1, 2], dtype=ms.int32)
>>> indices = Tensor([0, 1], dtype=ms.int32)
>>> values = Tensor([1, 2], dtype=ms.float32)
>>> shape = (2, 4)
>>> csr_tensor = CSRTensor(indptr, indices, values, shape)
>>> # access a data member of CSRTensor
>>> print(indptr == csr_tensor.indptr)
[ True True True]
"""
def __init__(self, indptr=None, indices=None, values=None, shape=None, csr_tensor=None):
"Init CSRTensor"
self.init_finished = False
# Directly init a CSRTensor from another CSRTensor
if csr_tensor is not None:
if not isinstance(csr_tensor, (CSRTensor, CSRTensor_)):
raise TypeError(f"Expect input `csr_tensor` to be a CSRTensor, but got {type(csr_tensor)}")
if not (indptr is None and indices is None and values is None and shape is None):
raise TypeError(
"If input `csr_tensor` is provided, `indptr`, `indices`, `values`, `shapes` should all be `None`")
CSRTensor_.__init__(self, csr_tensor)
# Init a CSRTensor from indptr, indices, values and shape
else:
validator.check_csr_tensor_input(indptr, indices, values, shape)
validator.check_csr_tensor_shape(indptr.shape, indices.shape, values.shape, shape)
validator.check_csr_tensor_dtype(indptr.dtype, indices.dtype)
indptr = tensor_operator_registry.get('stop_gradient')(indptr)
indices = tensor_operator_registry.get('stop_gradient')(indices)
CSRTensor_.__init__(self, indptr, indices, values, shape)
self.init_finished = True
def __repr__(self):
"""Avoid PyTest Segfault when CSRTensor is not initialized."""
if self.init_finished:
return CSRTensor_.__repr__(self)
return ''
def __mul__(self, other):
res = tensor_operator_registry.get('csr_mul')(self, other)
return CSRTensor(self.indptr, self.indices, res, self.shape)
def __div__(self, other):
logger.warning("For CSR divide, zero values in the dense tensor are ignored.")
res = tensor_operator_registry.get('csr_div')(self, other)
return CSRTensor(self.indptr, self.indices, res, self.shape)
def __truediv__(self, other):
return self.__div__(other)
def __neg__(self):
return CSRTensor(self.indptr, self.indices, -self.values, self.shape)
def __add__(self, other):
if not self.shape == other.shape:
raise ValueError("Input tensors should have the same shape.")
if isinstance(other, CSRTensor):
return tensor_operator_registry.get('csr_add')(
self, other, Tensor(1, self.values.dtype), Tensor(1, self.values.dtype))
raise TypeError("CSRTensor add with %s is not supported." % type(other))
def __sub__(self, other):
if not self.shape == other.shape:
raise ValueError("Input tensors should have the same shape.")
if isinstance(other, CSRTensor):
return tensor_operator_registry.get('csr_add')(
self, other, Tensor(1, self.values.dtype), Tensor(-1, self.values.dtype))
raise TypeError("CSRTensor subtract with %s is not supported." % type(other))
@property
def indptr(self) -> Tensor:
"""Return CSRTensor's row indices pointers."""
return Tensor(self._indptr)
@property
def indices(self) -> Tensor:
"""Return CSRTensor's column indices."""
return Tensor(self._indices)
@property
def values(self) -> Tensor:
"""Return CSRTensor's non-zero values."""
return Tensor(self._values)
@property
def shape(self) -> Tuple[int, ...]:
"""Return CSRTensor's shape."""
return self._shape
@property
def dtype(self) -> mstype:
"""Return the dtype of the values of CSRTensor (:class:`mindspore.dtype`)."""
return self._dtype
@property
def size(self) -> int:
"""Return the number of non-zero values."""
return self.values.size
@property
def itemsize(self) -> int:
"""Return the length of one tensor element in bytes."""
return self.values.itemsize
@property
def ndim(self) -> int:
"""Return the number of tensor dimensions."""
return len(self.shape)
def to_tuple(self) -> Tuple[Tensor, Tensor, Tensor, Tuple[int, ...]]:
"""
Return indptr, indices, values and shape as a tuple.
Returns:
Tuple.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
"""
return self.indptr, self.indices, self.values, self.shape
def to_coo(self) -> COOTensor:
"""
Converts CSRTensor to COOTensor.
Note:
Currently only supports CPU backend with LLVM 12.0.1 installed.
Returns:
COOTensor.
Supported Platforms:
``GPU`` ``CPU``
"""
if self.ndim != 2:
raise ValueError("Currently only support 2-D CSRTensor when converting to COOTensor.")
row_indices = tensor_operator_registry.get("csr2coo")(self.indptr, self.values.shape[0])
coo_indices = tensor_operator_registry.get("stack")((row_indices, self.indices), 1)
return COOTensor(coo_indices, self.values, self.shape)
def to_dense(self) -> Tensor:
"""
Converts CSRTensor to Dense Tensor.
Returns:
Tensor.
Supported Platforms:
``GPU``
"""
return tensor_operator_registry.get("csr_to_dense")(self)
def astype(self, dtype: mstype) -> CSRTensor:
"""
Return a copy of the CSRTensor, cast its values to a specified type.
Args:
dtype (Union[:class:`mindspore.dtype`, numpy.dtype, str]): Designated tensor dtype.
Returns:
CSRTensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore as ms
>>> from mindspore import Tensor, CSRTensor
>>> indptr = Tensor([0, 1, 2], dtype=ms.int32)
>>> indices = Tensor([0, 1], dtype=ms.int32)
>>> values = Tensor([1, 2], dtype=ms.float32)
>>> shape = (2, 4)
>>> csr_tensor = CSRTensor(indptr, indices, values, shape)
>>> print(csr_tensor.astype(ms.float64).dtype)
Float64
"""
data = self.values.astype(dtype)
return CSRTensor(self.indptr, self.indices, data, self.shape)
def mv(self, dense_vector: Tensor) -> Tensor:
"""
Return the matrix multiplication result of the right-multiply dense matrix of the CSRTensor.
The CSRTensor with shape `[M, N]` needs to adapt the dense vector with shape `[N, 1]`
to get the dense vector with result `[M, 1]`.
Note:
Currently only supports CPU backend with LLVM 12.0.1 installed.
Args:
dense_vector (Tensor): A dense Tensor, its shape must be (csr_tensor.shape[1], 1)
Returns:
Tensor.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, CSRTensor
>>> from mindspore import dtype as mstype
>>> indptr = Tensor([0, 1, 2], dtype=mstype.int32)
>>> indices = Tensor([0, 1], dtype=mstype.int32)
>>> values = Tensor([2, 1], dtype=mstype.float32)
>>> dense_shape = (2, 4)
>>> csr_tensor = CSRTensor(indptr, indices, values, dense_shape)
>>> dense = Tensor([[1], [1], [1], [1]], dtype=mstype.float32)
>>> print(csr_tensor.mv(dense))
[[2.]
[1.]]
"""
validator.check_value_type('dense_vector', dense_vector, (Tensor_,), 'CSRTensor.mv')
return tensor_operator_registry.get("csr_mv")(self, dense_vector)
def mm(self, dense_matrix: Tensor) -> Tensor:
"""
Return the matrix multiplication result of the right-multiply dense matrix of the CSRTensor.
The CSRTensor with shape `[M, N]` needs to adapt the dense matrix with shape `[N, K]`
to get the dense matrix with result `[M, K]`.
Note:
Currently only supports CPU backend with LLVM 12.0.1 installed.
Args:
dense_matrix (Tensor): A dense Tensor, its shape[0] should be equal to csr_tensor.shape[1]
Returns:
Tensor.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, CSRTensor
>>> from mindspore import dtype as mstype
>>> indptr = Tensor([0, 1, 2], dtype=mstype.int32)
>>> indices = Tensor([0, 1], dtype=mstype.int32)
>>> values = Tensor([2, 1], dtype=mstype.float32)
>>> dense_shape = (2, 4)
>>> csr_tensor = CSRTensor(indptr, indices, values, dense_shape)
>>> dense_matrix = Tensor([[1., 2.], [1, 2.], [1, 2.], [1., 2.]], dtype=mstype.float32)
>>> print(csr_tensor.mm(dense_matrix))
[[2. 4.]
[1. 2.]]
"""
validator.check_value_type('dense_matrix', dense_matrix, (Tensor_,), 'CSRTensor.mm')
return tensor_operator_registry.get("csr_mm")()(self.indptr, self.indices, self.values,
self.shape, dense_matrix)
def sum(self, axis: int) -> Tensor:
"""
Reduces a dimension of a CSRTensor by summing all elements in the dimension.
Note:
Currently only supports CPU backend with LLVM 12.0.1 installed.
Args:
axis (int): The dimensions to reduce.
Returns:
Tensor, the dtype is the same as `CSRTensor.values`.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, CSRTensor
>>> from mindspore import dtype as mstype
>>> indptr = Tensor([0, 1, 2], dtype=mstype.int32)
>>> indices = Tensor([0, 1], dtype=mstype.int32)
>>> values = Tensor([2, 1], dtype=mstype.float32)
>>> dense_shape = (2, 4)
>>> csr_tensor = CSRTensor(indptr, indices, values, dense_shape)
>>> print(csr_tensor.sum(1))
[[2.]
[1.]]
"""
return tensor_operator_registry.get("csr_reduce_sum")(self, axis)
def abs(self) -> CSRTensor:
"""
Return absolute value element-wisely.
Returns:
CSRTensor, with all values being non-negative.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
"""
data = self.values.abs()
return CSRTensor(self.indptr, self.indices, data, self.shape)
def add(self, b: CSRTensor, alpha: Tensor, beta: Tensor) -> CSRTensor:
"""
Addition of two CSR Tensors : C = alpha * A + beta * B
Args:
b (CSRTensor): Sparse CSR Tensor.
alpha(Tensor): Dense Tensor, its shape must be able to broadcast to self.
beta(Tensor): Dense Tensor, its shape must be able to broadcast to b.
Returns:
CSRTensor.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, CSRTensor
>>> import mindspore.common.dtype as mstype
>>> indptr = Tensor([0, 1, 2], dtype=mstype.int32)
>>> indices = Tensor([0, 1], dtype=mstype.int32)
>>> values_a = Tensor([2, 1], dtype=mstype.float32)
>>> values_b = Tensor([1, 2], dtype=mstype.float32)
>>> dense_shape = (2, 4)
>>> alpha = Tensor(1, mstype.float32)
>>> beta = Tensor(1, mstype.float32)
>>> a = CSRTensor(indptr, indices, values_a, dense_shape)
>>> b = CSRTensor(indptr, indices, values_b, dense_shape)
>>> print(a.add(b, alpha, beta))
CSRTensor(shape=[2,4], dtype=Float32,
indptr=Tensor(shape=[3], dtype=Int32, value = [0, 1, 2]),
indices=Tensor(shape=[2], dtype=Int32, value = [0, 1]),
values=Tensor(shape=[2], dtype=Float32, value = [3.0, 3.0]))
"""
return tensor_operator_registry.get('csr_add')(self, b, alpha, beta)
class MapTensor(MapTensor_):
"""
MapTensor is a tensor that stores a map like data structure.

3
mindspore/python/mindspore/nn/optim/optimizer.py
View File

@ -26,7 +26,8 @@ from mindspore.nn.cell import Cell
from mindspore.nn.layer.container import CellList
from mindspore.common.parameter import Parameter, ParameterTuple
from mindspore.common.initializer import initializer
from mindspore.common.tensor import Tensor, RowTensorInner
from mindspore.common import Tensor
from mindspore.common.sparse_tensor import RowTensorInner
import mindspore.common.dtype as mstype
from mindspore._checkparam import Validator as validator
from mindspore import log as logger

4
mindspore/python/mindspore/nn/wrap/grad_reducer.py
View File

@ -19,12 +19,12 @@ from mindspore import context
from mindspore import log as logger
from mindspore.nn.cell import Cell
from mindspore.communication.management import GlobalComm, get_group_size
from mindspore.common.tensor import RowTensorInner
from mindspore.common.sparse_tensor import RowTensorInner
from mindspore.ops import functional as F, composite as C
from mindspore.ops.operations.comm_ops import AllReduce, AllGather
from mindspore.parallel._auto_parallel_context import auto_parallel_context
import mindspore.common.dtype as mstype
from mindspore.common.tensor import Tensor
from mindspore.common.sparse_tensor import Tensor
from mindspore.common.api import jit

3
mindspore/python/mindspore/nn/wrap/loss_scale.py
View File

@ -20,7 +20,8 @@ from mindspore.context import ParallelMode
from mindspore.parallel._utils import _get_enable_parallel_optimizer
from mindspore.nn.wrap.cell_wrapper import TrainOneStepCell
from mindspore.nn.cell import Cell
from mindspore.common import Tensor, RowTensorInner
from mindspore.common import Tensor
from mindspore.common.sparse_tensor import RowTensorInner
from mindspore.common.parameter import Parameter
from mindspore.ops import functional as F
from mindspore.ops import composite as C

2
mindspore/python/mindspore/ops/_grad/grad_array_ops.py
View File

@ -28,7 +28,7 @@ from mindspore.ops import functional as F
from mindspore.ops._grad.grad_base import bprop_getters, create_tensor_by_element
from mindspore.ops.primitive import constexpr
from mindspore.common import dtype as mstype
from mindspore.common.tensor import RowTensorInner
from mindspore.common.sparse_tensor import RowTensorInner
from mindspore.ops._utils.utils import range_op, get_1d_shape, generate_shape_index, is_shape_unknown
from .._grad.grad_base import dyn_rank, convert_to_tensor, dyn_invert_permutation, dyn_size, dyn_ones, dyn_fill
from .._grad.grad_base import sum_grad_reduce_axis

2
mindspore/python/mindspore/ops/_grad/grad_comm_ops.py
View File

@ -23,7 +23,7 @@ from mindspore.communication import get_rank, get_group_size
from mindspore.parallel._utils import _get_enable_parallel_optimizer, _get_grad_accumulation_shard
from mindspore.ops import operations as P
from mindspore.ops.operations._inner_ops import Send, Receive
from mindspore.common.tensor import RowTensorInner
from mindspore.common.sparse_tensor import RowTensorInner
from mindspore.ops.composite.multitype_ops.zeros_like_impl import zeros_like
from mindspore.ops.operations.comm_ops import (AllGather, _MiniStepAllGather, _HostAllGather, AllReduce,
NeighborExchange, AlltoAll, NeighborExchangeV2, Broadcast,

2
mindspore/python/mindspore/ops/composite/multitype_ops/_compile_utils.py
View File

@ -24,7 +24,7 @@ from mindspore.ops._primitive_cache import _get_cache_prim
from mindspore.ops.operations._inner_ops import TensorCopySlices, SliceGetItem, DynamicBroadcastTo, TopTypeof
from mindspore.common import dtype as mstype
from mindspore.common._register_for_tensor import tensor_operator_registry
from mindspore.common.tensor import Tensor, CSRTensor, COOTensor
from mindspore.common import Tensor, CSRTensor, COOTensor
from mindspore.common._utils import is_shape_unknown
slice_get_item = SliceGetItem()

2
mindspore/python/mindspore/ops/operations/array_ops.py
View File

@ -34,7 +34,7 @@ from mindspore._checkparam import _check_3d_int_or_tuple
from mindspore.common import dtype as mstype
from mindspore.common._decorator import deprecated
from mindspore.common.parameter import Parameter
from mindspore.common.tensor import Tensor, CSRTensor, COOTensor
from mindspore.common import Tensor, CSRTensor, COOTensor
from mindspore._c_expression import Tensor as Tensor_
from mindspore._c_expression import CSRTensor as CSRTensor_
from mindspore._c_expression import COOTensor as COOTensor_

3
tests/st/dynamic_shape/test_dynamic_wenet_ascend.py
View File

@ -28,7 +28,8 @@ from mindspore.ops import functional as F
from mindspore.ops import composite as C
import mindspore.common.dtype as mstype
from mindspore.common.tensor import Tensor, RowTensorInner
from mindspore.common.tensor import Tensor
from mindspore.common.sparse_tensor import RowTensorInner
from mindspore.common.parameter import Parameter
from mindspore.common.initializer import initializer, _calculate_correct_fan, One

2
tests/st/pynative/test_graph_param_transform.py
View File

@ -14,7 +14,7 @@
# ============================================================================
import pytest
import numpy as np
from mindspore import RowTensorInner
from mindspore.common.sparse_tensor import RowTensorInner
from mindspore import context, nn, Tensor, ParameterTuple
from mindspore.common import dtype as mstype
from mindspore.common import jit

3
tests/st/sparse/test_row.py
View File

@ -17,7 +17,8 @@
import pytest
import numpy as np
from mindspore import Tensor, RowTensorInner, nn, context
from mindspore import Tensor, nn, context
from mindspore.common.sparse_tensor import RowTensorInner
from mindspore.common import dtype as mstype

3
tests/ut/python/graph_syntax/python_builtin_functions/test_isinstance2.py
View File

@ -18,11 +18,12 @@ import pytest
import numpy as np
import mindspore.nn as nn
from mindspore import Tensor, CSRTensor, COOTensor, RowTensorInner, jit, jit_class, context
from mindspore import Tensor, CSRTensor, COOTensor, jit, jit_class, context
from mindspore.ops import Primitive
from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.common.mutable import mutable
from mindspore.common.sparse_tensor import RowTensorInner
context.set_context(mode=context.GRAPH_MODE)

3
tests/ut/python/ir/test_row_tensor.py
View File

@ -30,8 +30,9 @@ from mindspore.ops.composite.multitype_ops.zeros_like_impl import zeros_like
from mindspore.ops.primitive import constexpr, PrimitiveWithInfer, prim_attr_register
from mindspore.ops._grad.grad_base import bprop_getters
from mindspore.ops._utils.utils import generate_shape_index
from mindspore import Tensor, RowTensorInner, context
from mindspore import Tensor, context
from mindspore.common.parameter import Parameter, ParameterTuple
from mindspore.common.sparse_tensor import RowTensorInner
from mindspore.common import dtype as mstype
from mindspore._checkparam import Validator as validator
from mindspore._checkparam import Rel

2
tests/ut/python/pynative_mode/test_graph_param_cases.py
View File

@ -14,8 +14,8 @@
# ============================================================================
import pytest
import numpy as np
from mindspore import RowTensorInner
from mindspore import context, nn, Tensor, ParameterTuple
from mindspore.common.sparse_tensor import RowTensorInner
from mindspore.common import dtype as mstype
from mindspore.common import jit
from mindspore.ops import composite as C

3
tests/ut/python/pynative_mode/test_sparse_pynative.py
View File

@ -21,7 +21,8 @@
import pytest
import mindspore as ms
import mindspore.nn as nn
from mindspore import context, Tensor, RowTensorInner, COOTensor
from mindspore import context, Tensor, COOTensor
from mindspore.common.sparse_tensor import RowTensorInner
from mindspore.ops import composite as C
@pytest.fixture(scope="module", autouse=True)