mindspore/mindspore/ops/composite/random_ops.py

# Copyright 2020 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
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# http://www.apache.org/licenses/LICENSE-2.0
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# 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
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# ============================================================================
"""Operations for random number generators."""

from mindspore._checkparam import Validator
from .. import operations as P
from .. import functional as F
from ..primitive import constexpr
from .multitype_ops import _constexpr_utils as const_utils
from ...common import dtype as mstype
from ...common import get_seed as get_global_seed
from ...common import _truncate_seed, _update_seeds, _get_op_seed

@constexpr
def get_seed(op_seed, kernel_name):
    """
    Get the graph-level seed.
    Graph-level seed is used as a global variable, that can be used in different ops in case op-level seed is not set.
    If op-level seed is 0, use graph-level seed; if graph-level seed is also 0, the system would generate a
    random seed.

    Note:
        For each seed, either op-seed or graph-seed, a random sequence will be generated relating to this seed.
        So, the state of the seed regarding to this op should be recorded.
        A simple illustration should be:
          If a random op is called twice within one program, the two results should be different:
          print(C.uniform((1, 4), seed=1))  # generates 'A1'
          print(C.uniform((1, 4), seed=1))  # generates 'A2'
          If the same program runs again, it repeat the results:
          print(C.uniform((1, 4), seed=1))  # generates 'A1'
          print(C.uniform((1, 4), seed=1))  # generates 'A2'

    Returns:
        Interger. The current graph-level seed.

    Examples:
        >>> C.get_seed(seed, 'normal')
    """
    global_seed = get_global_seed()
    if global_seed is None:
        global_seed = 0
    if op_seed is None:
        temp_seed = _get_op_seed(0, kernel_name)
    else:
        Validator.check_non_negative_int(op_seed, "seed", kernel_name)
        temp_seed = _get_op_seed(op_seed, kernel_name)
    seeds = _truncate_seed(global_seed), _truncate_seed(temp_seed)
    _update_seeds(op_seed, kernel_name)
    return seeds

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.
        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.
          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.

    Examples:
        >>> shape = (2, 4)
        >>> mean = Tensor(1.0, mstype.float32)
        >>> stddev = Tensor(1.0, mstype.float32)
        >>> output = C.normal(shape, mean, stddev, seed=5)
        [[1.0996436 0.44371283 0.11127508 -0.48055804]
         [0.31989878 -1.0644426 1.5076542 1.2290289  ]]
    """
    mean_dtype = F.dtype(mean)
    stddev_dtype = F.dtype(stddev)
    const_utils.check_valid_type(mean_dtype, mstype.int_type + (mstype.float16, mstype.float32), 'normal')
    const_utils.check_valid_type(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

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.
        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 controling 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.

    Examples:
        >>> shape = (4, 16)
        >>> mean = Tensor(1.0, mstype.float32)
        >>> lambda_param = Tensor(1.0, mstype.float32)
        >>> output = C.laplace(shape, mean, lambda_param, seed=5)
    """
    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

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.
        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: mstype.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`.

    Examples:
        >>> For discrete uniform distribution, only one number is allowed for both minval and maxval:
        >>> shape = (4, 2)
        >>> minval = Tensor(1, mstype.int32)
        >>> maxval = Tensor(2, mstype.int32)
        >>> output = C.uniform(shape, minval, maxval, seed=5)
        >>>
        >>> For continuous uniform distribution, minval and maxval can be multi-dimentional:
        >>> shape = (4, 2)
        >>> minval = Tensor([1.0, 2.0], mstype.float32)
        >>> maxval = Tensor([4.0, 5.0], mstype.float32)
        >>> output = C.uniform(shape, minval, maxval, seed=5)
    """
    minval_dtype = F.dtype(minval)
    maxval_dtype = F.dtype(maxval)
    const_utils.check_valid_type(dtype, [mstype.int32, mstype.float32], 'uniform')
    const_utils.check_tensors_dtype_same(minval_dtype, dtype, "uniform")
    const_utils.check_tensors_dtype_same(maxval_dtype, dtype, "uniform")
    seed1, seed2 = get_seed(seed, "uniform")
    if const_utils.is_same_type(dtype, mstype.int32):
        random_uniform = P.UniformInt(seed1, seed2)
        value = random_uniform(shape, minval, maxval)
    else:
        uniform_real = P.UniformReal(seed1, seed2)
        random_uniform = uniform_real(shape)
        value = random_uniform * (maxval - minval) + minval
    return value

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.
        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.

    Examples:
        >>> shape = (4, 16)
        >>> alpha = Tensor(1.0, mstype.float32)
        >>> beta = Tensor(1.0, mstype.float32)
        >>> output = C.gamma(shape, alpha, beta, seed=5)
    """
    seed1, seed2 = get_seed(seed, "gamma")
    random_gamma = P.Gamma(seed1, seed2)
    value = random_gamma(shape, alpha, beta)
    return value

def poisson(shape, mean, seed=None):
    """
    Generates random numbers according to the Poisson random number distribution.

    Args:
        shape (tuple): The shape of random tensor to be generated.
        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.

    Examples:
        >>> shape = (4, 16)
        >>> mean = Tensor(1.0, mstype.float32)
        >>> output = C.poisson(shape, mean, seed=5)
    """
    seed1, seed2 = get_seed(seed, "poisson")
    random_poisson = P.Poisson(seed1, seed2)
    value = random_poisson(shape, mean)
    return value

def multinomial(inputs, num_sample, replacement=True, seed=0):
    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: 0.

    Outputs:
        Tensor, has the same rows with input. The number of sampled indices of each row is `num_samples`.
        The dtype is float32.

    Examples:
        >>> input = Tensor([0, 9, 4, 0], mstype.float32)
        >>> output = C.multinomial(input, 2, True)
    """
    shape = P.Shape()
    reshape = P.Reshape()
    if inputs.dim() != 1 and inputs.dim() != 2:
        const_utils.raise_value_error("inputs dim must be 1d or 2d")
    if not replacement:
        if shape(inputs)[-1] < num_sample:
            const_utils.raise_value_error("num_sample must be less than shape(input)[-1] without replacement")
        n_dist = 1
        if len(shape(inputs)) > 1:
            n_dist = shape(inputs)[-2]
        random_uniform = P.UniformReal(seed=seed)((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(seed=seed)(inputs, num_sample)