Source code for mindspore.common.mutable

# Copyright 2022 Huawei Technologies Co., Ltd
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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"""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