# 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.
# ============================================================================
"""mutable function for setting constants mutable."""
from __future__ import absolute_import
from mindspore.common.tensor import Tensor
from mindspore._c_expression import Tensor as Tensor_
class _Tuple(tuple):
pass
class _List(list):
pass
class _Dict(dict):
pass
def _check_all_tensor(value):
"""Check if all the elements are Tensor."""
if isinstance(value, (tuple, list)):
for element in value:
if not _check_all_tensor(element):
return False
return True
if isinstance(value, dict):
for element in value.values():
if not _check_all_tensor(element):
return False
return True
return isinstance(value, Tensor_)
[文档]def mutable(input_data):
"""
Make a constant value mutable.
Currently, all the inputs of Cell except Tensor such as scalar, tuple, list and dict, are regarded as constant
values. The constant values are non-differentiable and used to do constant folding in the optimization process.
Besides, currently when the network input is tuple[Tensor], list[Tensor] or dict[Tensor], even without changing
the shape and dtype of the Tensors, the network will be re-compiled when calling this network repeatedly because
the these inputs are regarded as constant values.
To solve the above problems, we provide api `mutable` to make the constant inputs of Cell 'mutable'. A 'mutable'
input means that it is changed to be a variable input just like Tensor and the most important thing is that it
will be differentiable.
Args:
input_data (Union[Tensor, tuple[Tensor], list[Tensor], dict[Tensor]]): The input data to be made mutable.
.. warning::
- This is an experimental prototype that is subject to change or deletion.
- The runtime has not yet supported to handle the scalar data flow. So we only support tuple[Tensor],
list[Tensor] or dict[Tensor] for network input to avoid the re-compiled problem now.
- Currently we only support to use this api outside the network temporarily.
- Currently this api only works in GRAPH mode.
Returns:
The origin input data which has been set mutable.
Raises:
TypeError: If `input_data` is not one of Tensor, tuple[Tensor], list[Tensor], dict[Tensor] or their nested
structure.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.nn as nn
>>> import mindspore.ops as ops
>>> from mindspore.ops.composite import GradOperation
>>> from mindspore.common import mutable
>>> from mindspore.common import dtype as mstype
>>> from mindspore import Tensor
>>> class Net(nn.Cell):
... def __init__(self):
... super(Net, self).__init__()
... self.matmul = ops.MatMul()
...
... def construct(self, z):
... x = z[0]
... y = z[1]
... out = self.matmul(x, y)
... return out
...
>>> class GradNetWrtX(nn.Cell):
... def __init__(self, net):
... super(GradNetWrtX, self).__init__()
... self.net = net
... self.grad_op = GradOperation()
...
... def construct(self, z):
... gradient_function = self.grad_op(self.net)
... return gradient_function(z)
...
>>> z = mutable((Tensor([[0.5, 0.6, 0.4], [1.2, 1.3, 1.1]], dtype=mstype.float32),
... Tensor([[0.01, 0.3, 1.1], [0.1, 0.2, 1.3], [2.1, 1.2, 3.3]], dtype=mstype.float32)))
>>> output = GradNetWrtX(Net())(z)
>>> print(output)
(Tensor(shape=[2, 3], dtype=Float32, value=
[[ 1.41000009e+00, 1.60000002e+00, 6.59999943e+00],
[ 1.41000009e+00, 1.60000002e+00, 6.59999943e+00]]), Tensor(shape=[3, 3], dtype=Float32, value=
[[ 1.70000005e+00, 1.70000005e+00, 1.70000005e+00],
[ 1.89999998e+00, 1.89999998e+00, 1.89999998e+00],
[ 1.50000000e+00, 1.50000000e+00, 1.50000000e+00]]))
"""
if not _check_all_tensor(input_data):
raise TypeError(
f"For 'mutable', the 'input_data' should be one of (Tensor, tuple[Tensor], list[Tensor], dict[Tensor]) "
f"or their nested structures, but got {input_data}.")
ret = input_data
if isinstance(input_data, list):
ret = _List(input_data)
elif isinstance(input_data, tuple):
ret = _Tuple(input_data)
elif isinstance(input_data, dict):
ret = _Dict(input_data)
elif isinstance(input_data, Tensor):
ret.set_const_arg(False)
elif isinstance(input_data, Tensor_):
ret = Tensor(input_data, internal=True)
ret.set_const_arg(False)
setattr(ret, "__ms_mutable__", True)
return ret