mindspore.common.symbol 源代码

# Copyright 2024 Huawei Technologies Co., Ltd
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"""Symbol implementation."""

__all__ = ['Symbol']


[文档]class Symbol: r""" Symbol is a data structure to indicate the symbolic info of shape. For dynamic shape networks, compared with only setting the unknown dimensions ( ``None`` ) in `Tensor` , providing more symbolic shape info can help the framework better optimize the computation graph, to improve the performce of network execution. Args: max (int): The maximum length of this dimension, which is valid when it's greater than `min`. Default: ``0`` . min (int): The minimum length of this dimension. Default: ``1`` . divisor (int): The divisor( :math:`d` ). When `remainder` is 0, it means this dimension can be divided by :math:`d` . Default: ``1`` . remainder (int): The remainder( :math:`r` ) when symbol is represented by :math:`d * N + r, N \ge 1` . Default: ``0`` . unique (bool): When the symbol object is used multiple times, if `unique` is ``True`` , the shape items of this symbol are considered to be same length, otherwise only symbol info is shared by multiple dimensions. Default: ``False`` . Outputs: Symbol. Raises: TypeError: If `max`, `min`, `divisor`, `remainder` is not an int. TypeError: If `unique` is not a bool. ValueError: If `min` is not positive value. ValueError: If `divisor` is not positive value. ValueError: If `remainder` is not in the range :math:`[0, d)` . Examples: >>> import numpy as np >>> import mindspore as ms >>> from mindspore import nn, Tensor, Symbol >>> >>> class Net(nn.Cell): ... def __init__(self): ... super(Net, self).__init__() ... self.abs = ms.ops.Abs() ... def construct(self, x): ... return self.abs(x) ... >>> net = Net() >>> s1 = Symbol(divisor=8, remainder=1) >>> s2 = Symbol(max=32, unique=True) >>> dyn_t = Tensor(shape=(None, s1, s1, s2, s2), dtype=ms.float32) >>> net.set_inputs(dyn_t) >>> # the shape values of last two dimensions must be equal, because "s2" is set to "unique" >>> net(Tensor(np.random.randn(1, 9, 17, 32, 32), dtype=ms.float32)).shape (1, 9, 17, 32, 32) >>> net(Tensor(np.random.randn(8, 25, 9, 30, 30), dtype=ms.float32)).shape (8, 25, 9, 30, 30) """ def __init__(self, max=0, min=1, divisor=1, remainder=0, unique=False, **kawgs): Symbol._check_args_type(max, min, divisor, remainder, unique) if min <= 0: raise ValueError("For 'Symbol', the 'min' value should be positive, but got {}".format(min)) if divisor <= 0: raise ValueError("For 'Symbol', the 'divisor' value should be positive, but got {}".format(divisor)) if remainder < 0 or remainder >= divisor: raise ValueError( "For 'Symbol', the 'remainder' value should be in the range '[0, {})', but got {}".format( divisor, remainder)) self.max = max self.min = min self.divisor = divisor self.remainder = remainder self.unique = unique self.id = id(self) def __str__(self): return str(self.to_dict()) @staticmethod def _check_args_type(maxv, minv, divisor, remainder, unique): """Check the type of arguments.""" if not isinstance(maxv, int): raise TypeError(f"For 'Symbol', the argument 'max' must be int, but got {type(maxv)}") if not isinstance(minv, int): raise TypeError(f"For 'Symbol', the argument 'min' must be int, but got {type(minv)}") if not isinstance(divisor, int): raise TypeError(f"For 'Symbol', the argument 'divisor' must be int, but got {type(divisor)}") if not isinstance(remainder, int): raise TypeError(f"For 'Symbol', the argument 'remainder' must be int, but got {type(remainder)}") if not isinstance(unique, bool): raise TypeError(f"For 'Symbol', the argument 'unique' must be bool, but got {type(unique)}") # pylint: disable=missing-docstring def to_dict(self): # Convert the symbolic info to dictionary. # This method is not necessary to show in public api document, use comment instead of docstring. res = {} if self.max > self.min: res["max"] = self.max if self.min > self.divisor + self.remainder: # the symbol is "d * N + r" and N >= 1 res["min"] = self.min if self.divisor != 1: res["divisor"] = self.divisor if self.remainder != 0: res["remainder"] = self.remainder if self.unique: res["id"] = self.id return res