Source code for mindspore.ops.composite.random_ops

# Copyright 2020 Huawei Technologies Co., Ltd
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"""Operations for random number generators."""
from mindspore.ops.primitive import constexpr
from .. import operations as P
from .. import functional as F
from .multitype_ops import _constexpr_utils as const_utils
from ...common import dtype as mstype
from ...common.seed import _get_graph_seed
from ...common.api import _function_forbid_reuse


@constexpr(reuse_result=False)
def _get_seed(op_seed, kernel_name):
    "Get the graph-level seed."
    return _get_graph_seed(op_seed, kernel_name)


[文档]@_function_forbid_reuse def normal(shape, mean, stddev, seed=None): """ Generates random numbers according to the Normal (or Gaussian) random number distribution. Args: shape (tuple): The shape of random tensor to be generated. The format is :math:`(N,*)` where :math:`*` means, any number of additional dimensions. mean (Tensor): The mean μ distribution parameter, which specifies the location of the peak, with data type in [int8, int16, int32, int64, float16, float32]. stddev (Tensor): The deviation σ distribution parameter. It should be greater than 0, with data type in [int8, int16, int32, int64, float16, float32]. seed (int): Seed is used as entropy source for the Random number engines to generate pseudo-random numbers. The value must be non-negative. Default: None, which will be treated as 0. Returns: Tensor. The shape should be equal to the broadcasted shape between the input `shape` and shapes of `mean` and `stddev`. The dtype is float32. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> from mindspore import Tensor, ops >>> import mindspore >>> shape = (3, 1, 2) >>> mean = Tensor(np.array([[3, 4], [5, 6]]), mindspore.float32) >>> stddev = Tensor(1.0, mindspore.float32) >>> output = ops.normal(shape, mean, stddev, seed=5) >>> result = output.shape >>> print(result) (3, 2, 2) >>> shape = (3, 1, 3) >>> mean = Tensor(np.array([[3, 4, 3], [3, 5, 6]]), mindspore.float32) >>> stddev = Tensor(1.0, mindspore.float32) >>> output = ops.normal(shape, mean, stddev, seed=5) >>> result = output.shape >>> print(result) (3, 2, 3) >>> shape = (3, 1, 3) >>> mean = Tensor(np.array([[1, 2, 3], [3, 4, 3], [3, 5, 6]]), mindspore.float32) >>> stddev = Tensor(1.0, mindspore.float32) >>> output = ops.normal(shape, mean, stddev, seed=5) >>> result = output.shape >>> print(result) (3, 3, 3) """ mean_dtype = F.dtype(mean) stddev_dtype = F.dtype(stddev) const_utils.check_type_valid(mean_dtype, mstype.int_type + (mstype.float16, mstype.float32), 'normal') const_utils.check_type_valid(stddev_dtype, mstype.int_type + (mstype.float16, mstype.float32), 'normal') seed1, seed2 = _get_seed(seed, "normal") stdnormal = P.StandardNormal(seed1, seed2) random_normal = stdnormal(shape) value = random_normal * stddev + mean return value
[文档]@_function_forbid_reuse def laplace(shape, mean, lambda_param, seed=None): r""" Generates random numbers according to the Laplace random number distribution. It is defined as: .. math:: \text{f}(x;μ,λ) = \frac{1}{2λ}\exp(-\frac{|x-μ|}{λ}), Args: shape (tuple): The shape of random tensor to be generated. The format is :math:`(N,*)` where :math:`*` means, any number of additional dimensions. mean (Tensor): The mean μ distribution parameter, which specifies the location of the peak. With float32 data type. lambda_param (Tensor): The parameter used for controlling the variance of this random distribution. The variance of Laplace distribution is equal to twice the square of lambda_param. With float32 data type. seed (int): Seed is used as entropy source for Random number engines generating pseudo-random numbers. Default: None, which will be treated as 0. Returns: Tensor. The shape should be the broadcasted shape of input `shape` and shapes of `mean` and `lambda_param`. The dtype is float32. Supported Platforms: ``Ascend`` Examples: >>> import mindspore >>> from mindspore import Tensor >>> from mindspore import ops as ops >>> shape = (2, 3) >>> mean = Tensor(1.0, mindspore.float32) >>> lambda_param = Tensor(1.0, mindspore.float32) >>> output = ops.laplace(shape, mean, lambda_param, seed=5) >>> print(output.shape) (2, 3) """ mean_dtype = F.dtype(mean) lambda_param_dtype = F.dtype(lambda_param) const_utils.check_tensors_dtype_same(mean_dtype, mstype.float32, "laplace") const_utils.check_tensors_dtype_same(lambda_param_dtype, mstype.float32, "laplace") seed1, seed2 = _get_seed(seed, "laplace") stdlaplace = P.StandardLaplace(seed1, seed2) rnd = stdlaplace(shape) value = rnd * lambda_param + mean return value
@_function_forbid_reuse def uniform(shape, minval, maxval, seed=None, dtype=mstype.float32): """ Generates random numbers according to the Uniform random number distribution. Note: The number in tensor minval should be strictly less than maxval at any position after broadcasting. Args: shape (tuple): The shape of random tensor to be generated. The format is :math:`(N,*)` where :math:`*` means, any number of additional dimensions and the length of :math:`(N,*)` should be less than 8 in broadcast operation. minval (Tensor): The distribution parameter `a`. It defines the minimum possible generated value, with int32 or float32 data type. If dtype is int32, only one number is allowed. maxval (Tensor): The distribution parameter `b`. It defines the maximum possible generated value, with int32 or float32 data type. If dtype is int32, only one number is allowed. seed (int): Seed is used as entropy source for the random number engines to generate pseudo-random numbers, must be non-negative. Default: None, which will be treated as 0. dtype (mindspore.dtype): Type of the Uniform distribution. If it is int32, it generates numbers from discrete uniform distribution; if it is float32, it generates numbers from continuous uniform distribution. It only supports these two data types. Default: mindspore.float32. Returns: Tensor. The shape should be equal to the broadcasted shape between the input `shape` and shapes of `minval` and `maxval`. The dtype is designated as the input `dtype`. Raises: TypeError: If `shape` is not tuple. TypeError: If 'minval' or 'maxval' is neither int32 nor float32 and dtype of 'minval' is not the same as 'maxval'. TypeError: If `seed` is not an int. TypeError: If 'dtype' is neither int32 nor float32. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> from mindspore import Tensor, ops >>> import mindspore >>> import numpy as np >>> # For discrete uniform distribution, only one number is allowed for both minval and maxval: >>> shape = (4, 2) >>> minval = Tensor(1, mindspore.int32) >>> maxval = Tensor(2, mindspore.int32) >>> output = ops.uniform(shape, minval, maxval, seed=5, dtype=mindspore.int32) >>> >>> # For continuous uniform distribution, minval and maxval can be multi-dimentional: >>> shape = (3, 1, 2) >>> minval = Tensor(np.array([[3, 4], [5, 6]]), mindspore.float32) >>> maxval = Tensor([8.0, 10.0], mindspore.float32) >>> output = ops.uniform(shape, minval, maxval, seed=5) >>> result = output.shape >>> print(result) (3, 2, 2) """ value = F.uniform(shape, minval, maxval, seed, dtype) return value
[文档]@_function_forbid_reuse def gamma(shape, alpha, beta, seed=None): """ Generates random numbers according to the Gamma random number distribution. Args: shape (tuple): The shape of random tensor to be generated. The format is :math:`(N,*)` where :math:`*` means, any number of additional dimensions. alpha (Tensor): The alpha α distribution parameter. It should be greater than 0 with float32 data type. beta (Tensor): The beta β distribution parameter. It should be greater than 0 with float32 data type. seed (int): Seed is used as entropy source for the random number engines to generate pseudo-random numbers, must be non-negative. Default: None, which will be treated as 0. Returns: Tensor. The shape should be equal to the broadcasted shape between the input `shape` and shapes of `alpha` and `beta`. The dtype is float32. Raises: TypeError: If `shape` is not a tuple. TypeError: If neither `alpha` nor `beta` is a Tensor. TypeError: If `seed` is not an int. TypeError: If dtype of `alpha` and `beta` is not float32. Supported Platforms: ``Ascend`` Examples: >>> from mindspore import Tensor, ops >>> import mindspore >>> # case 1: alpha_shape is (2, 2) >>> shape = (3, 1, 2) >>> alpha = Tensor(np.array([[3, 4], [5, 6]]), mindspore.float32) >>> beta = Tensor(np.array([1.0]), mindspore.float32) >>> output = ops.gamma(shape, alpha, beta, seed=5) >>> result = output.shape >>> print(result) (3, 2, 2) >>> # case 2: alpha_shape is (2, 3), so shape is (3, 1, 3) >>> shape = (3, 1, 3) >>> alpha = Tensor(np.array([[1, 3, 4], [2, 5, 6]]), mindspore.float32) >>> beta = Tensor(np.array([1.0]), mindspore.float32) >>> output = ops.gamma(shape, alpha, beta, seed=5) >>> result = output.shape >>> print(result) (3, 2, 3) >>> # case 3: beta_shape is (1, 2), the output is different. >>> shape = (3, 1, 2) >>> alpha = Tensor(np.array([[3, 4], [5, 6]]), mindspore.float32) >>> beta = Tensor(np.array([1.0, 2]), mindspore.float32) >>> output = ops.gamma(shape, alpha, beta, seed=5) >>> result = output.shape >>> print(output) [[[ 2.2132034 5.8855834]] [ 3.3981476 7.5805717] [[ 3.3981476 7.5805717]] [ 3.7190282 19.941492] [[ 2.9512358 2.5969937]] [ 3.786061 5.160872 ]]] >>> # case 4: beta_shape is (2, 1), the output is different. >>> shape = (3, 1, 2) >>> alpha = Tensor(np.array([[3, 4], [5, 6]]), mindspore.float32) >>> beta = Tensor(np.array([[1.0], [2.0]]), mindspore.float32) >>> output = ops.gamma(shape, alpha, beta, seed=5) >>> result = output.shape >>> print(output) [[[ 5.6085486 7.8280783]] [ 15.97684 16.116285] [[ 1.8347423 1.713663]] [ 3.2434065 15.667398] [[ 4.2922077 7.3365674]] [ 5.3876944 13.159832 ]]] """ seed1, seed2 = _get_seed(seed, "gamma") gamma_v = P.Gamma(seed1, seed2) value = gamma_v(shape, alpha, beta) return value
[文档]@_function_forbid_reuse def poisson(shape, mean, seed=None): r""" Generates random numbers according to the Poisson random number distribution. .. math:: \text{P}(i|μ) = \frac{\exp(-μ)μ^{i}}{i!} Args: shape (tuple): The shape of random tensor to be generated. The format is :math:`(N,*)` where :math:`*` means, any number of additional dimensions. mean (Tensor): The mean μ distribution parameter. It should be greater than 0 with float32 data type. seed (int): Seed is used as entropy source for the random number engines to generate pseudo-random numbers and must be non-negative. Default: None, which will be treated as 0. Returns: Tensor. The shape should be equal to the broadcasted shape between the input "shape" and shapes of `mean`. The dtype is float32. Raises: TypeError: If `shape` is not a tuple. TypeError: If `mean` is not a Tensor whose dtype is not float32. TypeError: If `seed` is not an int. Supported Platforms: ``Ascend`` Examples: >>> from mindspore import Tensor, ops >>> import mindspore >>> # case 1: It can be broadcast. >>> shape = (4, 1) >>> mean = Tensor(np.array([5.0, 10.0]), mindspore.float32) >>> output = ops.poisson(shape, mean, seed=5) >>> result = output.shape >>> print(result) (4, 2) >>> # case 2: It can not be broadcast. It is recommended to use the same shape. >>> shape = (2, 2) >>> mean = Tensor(np.array([[5.0, 10.0], [5.0, 1.0]]), mindspore.float32) >>> output = ops.poisson(shape, mean, seed=5) >>> result = output.shape >>> print(result) (2, 2) """ seed1, seed2 = _get_seed(seed, "poisson") random_poisson = P.Poisson(seed1, seed2) value = random_poisson(shape, mean) return value
[文档]@_function_forbid_reuse def multinomial(inputs, num_sample, replacement=True, seed=None): r""" Returns a tensor sampled from the multinomial probability distribution located in the corresponding row of the input tensor. Note: The rows of input do not need to sum to one (in which case we use the values as weights), but must be non-negative, finite and have a non-zero sum. Args: inputs (Tensor): The input tensor containing probabilities, must be 1 or 2 dimensions, with float32 data type. num_sample (int): Number of samples to draw. replacement (bool, optional): Whether to draw with replacement or not, default True. seed (int, optional): Seed is used as entropy source for the random number engines to generate pseudo-random numbers, must be non-negative. Default: None. Outputs: Tensor, has the same rows with input. The number of sampled indices of each row is `num_samples`. The dtype is float32. Raises: TypeError: If `x` is not a Tensor whose dtype is not float32. TypeError: If `num_sample` is not an int. TypeError: If `seed` is neither an int nor an optional. Supported Platforms: ``GPU`` Examples: >>> from mindspore import Tensor, ops >>> import mindspore >>> # case 1: The output is random, and the length of the output is the same as num_sample. >>> x = Tensor([0, 9, 4, 0], mindspore.float32) >>> output = ops.multinomial(x, 2) >>> # print(output) >>> # [1 2] or [2 1] >>> # the case where the result is [2 1] in multiple times. >>> # This is because the value corresponding to the index 1 is larger than the value of the index 2. >>> print(len(output)) 2 >>> # case 2: The output is random, and the length of the output is the same as num_sample. >>> # replacement is False(Default). >>> # If the extracted value is 0, the index value of 1 will be returned. >>> x = Tensor([0, 9, 4, 0], mstype.float32) >>> output = ops.multinomial(x, 4) >>> print(output) [1 1 2 1] >>> # case 3: The output is random, num_sample == x_length = 4, and replacement is True, >>> # Can extract the same elements。 >>> x = Tensor([0, 9, 4, 0], mstype.float32) >>> output = ops.multinomial(x, 4, True) >>> print(output) [1 1 2 2] """ shape = P.Shape() reshape = P.Reshape() const_utils.check_valid_dim(len(shape(inputs)), "multinomial") seed1, seed2 = _get_seed(seed, "multinomial") if not replacement: if shape(inputs)[-1] < num_sample: const_utils.raise_value_error("For 'multinomial', the 'num_sample' must be less than " "the last dimension of input without 'replacement', " "but got 'num_sample': {} and " "'replacement': {}".format(num_sample, replacement)) n_dist = 1 if len(shape(inputs)) > 1: n_dist = shape(inputs)[-2] random_uniform = P.UniformReal(seed1, seed2)((n_dist * shape(inputs)[-1],)) if n_dist != 1: random_uniform = reshape(random_uniform, (n_dist, shape(inputs)[-1])) vals = P.RealDiv()(P.Log()(random_uniform), inputs + 1e-6) _, indices = P.TopK()(vals, num_sample) return indices return P.Multinomial(seed1, seed2)(inputs, num_sample)