add CN api docs & add scalar rate ST & add 1-D-tensor check for argument shape
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hangangqiang
parent
f0a99ea3bd
commit
b1623fc2e9
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@ -352,7 +352,7 @@ Tensor创建
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mindspore.ops.gamma
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mindspore.ops.laplace
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mindspore.ops.multinomial
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mindspore.ops.poisson
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mindspore.ops.random_poisson
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mindspore.ops.random_categorical
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mindspore.ops.random_gamma
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mindspore.ops.standard_laplace
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@ -1,23 +0,0 @@
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mindspore.ops.poisson
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=====================
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.. py:function:: mindspore.ops.poisson(shape, mean, seed=None)
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根据泊松随机数分布生成随机数。
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.. math::
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\text{P}(i|μ) = \frac{\exp(-μ)μ^{i}}{i!}
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参数：
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- **shape** (tuple) - Tuple: :math:`(N,*)` ，其中 :math:`*` 表示任何数量的附加维度。
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- **mean** (Tensor) - 均值μ，分布参数。支持float32数据类型，应大于0。
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- **seed** (int) - 随机种子。取值须为非负数。默认值：None，等同于0。
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返回：
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Tensor，shape应与输入 `shape` 与 `mean` 进行广播之后的shape相同。数据类型支持float32。
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异常：
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- **TypeError** - `shape` 不是Tuple。
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- **TypeError** - `mean` 不是Tensor或数据类型非float32。
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- **TypeError** - `seed` 不是int类型。
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@ -0,0 +1,29 @@
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mindspore.ops.random_poisson
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==========================
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.. py:function:: mindspore.ops.random_poisson(shape, rate, seed=None, dtype=mstype.float32)
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从各指定均值的泊松分布中，随机采样`shape`形状的随机数。
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.. math::
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\text{P}(i|μ) = \frac{\exp(-μ)μ^{i}}{i!}
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参数：
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- **shape** (Tensor) - 表示要从每个分布中采样的随机数张量的形状。必须是一个一维的张量且数据类型必须是`mindspore.dtype.int32`或者`mindspore.dtype.int64`。
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- **rate** (Tensor) - 泊松分布的 :math:`μ` 参数，表示泊松分布的均值，同时也是分布的方差。必须是一个张量，且其数据类型必须是以下类型中的一种：mindspore.dtype.int64，mindspore.dtype.int32，mindspore.dtype.float64，mindspore.dtype.float32或者mindspore.dtype.float16。
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- **seed** (int) - 随机数种子，用于在随机数引擎中产生随机数。必须是一个非负的整数。默认值是None，表示使用0作为随机数种子。
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- **dtype** (mindspore.dtype) - 表示要生成的随机数张量的数据类型。必须是mindspore.dtype类型，可以是以下值中的一种：mindspore.dtype.int64，mindspore.dtype.int32，mindspore.dtype.float64，mindspore.dtype.float32或者mindspore.dtype.float16。
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返回：
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返回一个张量，它的形状由入参`shape`和`rate`共同决定：`mindspore.concat([`shape`, mindspore.shape(`rate`)], axis=0)`，它的数据类型由入参`dtype`决定。
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异常：
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- **TypeError** - 如果`shape`不是一个张量。
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- **TypeError** - 如果`shape`张量的数据类型不是mindspore.dtype.int64或mindspore.dtype.int32。
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- **ValueError** - 如果`shape`张量的形状不是一维的。
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- **TypeError** - 如果`rate`不是一个张量。
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- **TypeError** - 如果`rate`张量的数据类型不是mindspore.dtype.int64，mindspore.dtype.int32，mindspore.dtype.float64，mindspore.dtype.float32或者mindspore.dtype.float16。
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- **TypeError** - 如果`seed`不是一个非负整型。
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- **TypeError** - 如果`dtype`不是mindspore.dtype.int64，mindspore.dtype.int32，mindspore.dtype.float64，mindspore.dtype.float32或者mindspore.dtype.float16。
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- **ValueError** - 如果`shape`张量中有非正数。
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@ -338,6 +338,7 @@ from .random_func import (
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standard_normal,
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random_gamma,
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uniform_candidate_sampler,
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random_poisson,
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)
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from .grad import (
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grad_func,
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@ -336,28 +336,31 @@ def random_poisson(shape, rate, seed=None, dtype=mstype.float32):
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\text{P}(i|μ) = \frac{\exp(-μ)μ^{i}}{i!}
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Args:
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shape (Tensor): The shape of random tensor to be generated, 1-D `Tensor` whose dtype is mindspore.dtype.int32 or
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mindspore.dtype.int64.
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rate (Tensor): The μ parameter the distribution was constructed with. The parameter defines mean number of
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occurrences of the event. It should be a `Tensor` whose dtype is mindspore.dtype.int64, mindspore.dtype.int32,
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mindspore.dtype.float64, mindspore.dtype.float32 or mindspore.dtype.float16.
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shape (Tensor): The shape of random tensor to be sampled from each poisson distribution, 1-D `Tensor` whose
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dtype is mindspore.dtype.int32 or mindspore.dtype.int64.
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rate (Tensor): The μ parameter the distribution was constructed with. It represents the mean of the distribution
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and also the variance of the distribution. It should be a `Tensor` whose dtype is mindspore.dtype.int64,
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mindspore.dtype.int32, mindspore.dtype.float64, mindspore.dtype.float32 or mindspore.dtype.float16.
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seed (int): Seed is used as entropy source for the random number engines to generate pseudo-random numbers
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and must be non-negative. Default: None, which will be treated as 0.
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dtype (mindspore.dtype): The data type of output: mindspore.dtype.int64, mindspore.dtype.int32,
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mindspore.dtype.float64, mindspore.dtype.float32 or mindspore.dtype.float16. Default: mindspore.dtype.float32.
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Returns:
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Tensor. The shape should be `mindspore.concat([shape, mindspore.shape(mean)], axis=0)`. The data type should be
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equal to argument `dtype`.
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A Tensor whose shape is `mindspore.concat([`shape`, mindspore.shape(`rate`)], axis=0)` and data type is equal to
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argument `dtype`.
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Raises:
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TypeError: If `shape` is not a Tensor[mindspore.dtype.int64] nor a Tensor[mindspore.dtype.int32].
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TypeError: If `rate` is not a Tensor or `rate` is a Tensor whose dtype is not in [mindspore.dtype.int64,
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mindspore.dtype.int32, mindspore.dtype.float64, mindspore.dtype.float32 or mindspore.dtype.float16].
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TypeError: If `seed` is not an int.
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TypeError: If `dtype` is not mindspore.dtype.int64, mindspore.dtype.int32, mindspore.dtype.float64,
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mindspore.dtype.float32 nor mindspore.dtype.float16.
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ValueError: If elements of input `shape` tensor is not positive.
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TypeError: If `shape` is not a Tensor.
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TypeError: If datatype of `shape` is not mindspore.dtype.int64 nor mindspore.dtype.int32.
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ValueError: If shape of `shape` is not 1-D.
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TypeError: If `rate` is not a Tensor nor a scalar.
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TypeError: If datatype of `rate` is not in [mindspore.dtype.int64, mindspore.dtype.int32,
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mindspore.dtype.float64, mindspore.dtype.float32 or mindspore.dtype.float16].
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TypeError: If `seed` is not a non-negtive int.
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TypeError: If `dtype` is not in [mindspore.dtype.int64, mindspore.dtype.int32, mindspore.dtype.float64,
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mindspore.dtype.float32 nor mindspore.dtype.float16].
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ValueError: If any element of input `shape` tensor is not positive.
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Supported Platforms:
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``CPU``
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@ -365,20 +368,20 @@ def random_poisson(shape, rate, seed=None, dtype=mstype.float32):
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Examples:
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>>> from mindspore import Tensor, ops
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>>> import mindspore
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>>> # case 1: It can be broadcast.
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>>> shape = Tensor(np.array([4, 1]), mindspore.int32)
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>>> rate = Tensor(np.array([5.0, 10.0]), mindspore.float32)
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>>> # case 1: 1-D shape, 2-D rate, float64 output
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>>> shape = Tensor(np.array([2, 2]), mindspore.int64)
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>>> rate = Tensor(np.array([[5.0, 10.0], [5.0, 1.0]]), mindspore.float32)
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>>> output = ops.random_poisson(shape, rate, seed=5, dtype=mindspore.float64)
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>>> print(output.shape, output.dtype)
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(4, 1, 2) Float64
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>>> # case 2: It can not be broadcast. It is recommended to use the same shape.
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>>> shape = Tensor(np.array([2, 2]), mindspore.int32)
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>>> rate = Tensor(np.array([[5.0, 10.0], [5.0, 1.0]]), mindspore.float32)
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(2, 2, 2, 2) float64
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>>> # case 2: 1-D shape, scalar rate, int64 output
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>>> shape = Tensor(np.array([2, 2]), mindspore.int64)
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>>> rate = Tensor(5.0, mindspore.float64)
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>>> output = ops.random_poisson(shape, rate, seed=5, dtype=mindspore.int64)
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>>> print(output.shape, output.dtype)
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(2, 2, 2, 2) Int64
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(2, 2) Int64
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"""
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seed1, seed2 = _get_seed(seed, "poisson")
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seed1, seed2 = _get_seed(seed, "random_poisson")
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prim_random_poisson = P.random_ops.RandomPoisson(seed1, seed2, dtype)
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value = prim_random_poisson(shape, rate)
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return value
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@ -391,5 +394,6 @@ __all__ = [
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'standard_normal',
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'random_gamma',
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'uniform_candidate_sampler',
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'random_poisson',
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]
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__all__.sort()
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@ -35,12 +35,14 @@ def test_poisson_function(dtype, shape_dtype, rate_dtype):
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Expectation: Output shape is correct.
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"""
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# rate is a scalar Tensor
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shape = Tensor(np.array([3, 5]), shape_dtype)
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rate = Tensor(np.array([0.5]), rate_dtype)
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rate = Tensor(0.5, rate_dtype)
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output = R.random_poisson(shape, rate, seed=1, dtype=dtype)
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assert output.shape == (3, 5, 1)
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assert output.shape == (3, 5)
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assert output.dtype == dtype
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# rate is a 2-D Tensor
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shape = Tensor(np.array([3, 2]), shape_dtype)
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rate = Tensor(np.array([[5.0, 10.0], [5.0, 1.0]]), rate_dtype)
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output = R.random_poisson(shape, rate, seed=5, dtype=dtype)
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@ -67,6 +69,25 @@ def test_poisson_function_shape_type_error():
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assert False
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@pytest.mark.level0
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@pytest.mark.env_onecard
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@pytest.mark.platform_x86_cpu
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def test_poisson_function_shape_dim_error():
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"""
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Feature: Poisson functional interface
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Description: Feed 2-D Tensor type `shape` into poisson functional interface.
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Expectation: Except TypeError.
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"""
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shape = Tensor(np.array([[1, 2], [3, 5]]), ms.dtype.int32)
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rate = Tensor(np.array([0.5]), ms.dtype.float32)
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try:
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R.random_poisson(shape, rate, seed=1)
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except ValueError:
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return
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assert False
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@pytest.mark.level0
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@pytest.mark.env_onecard
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@pytest.mark.platform_x86_cpu
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@ -183,36 +204,44 @@ def test_poisson_function_out_dtype_error():
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class PoissonNet(nn.Cell):
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""" Network for test dynamic shape feature of poisson functional op. """
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def __init__(self, out_dtype, axis=0):
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def __init__(self, out_dtype, is_rate_scalar=False):
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super().__init__()
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self.odtype = out_dtype
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self.unique = P.Unique()
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self.gather = P.Gather()
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self.axis = axis
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self.is_rate_scalar = is_rate_scalar
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def construct(self, x, y, indices):
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shape, _ = self.unique(x)
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rate = y
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idx, _ = self.unique(indices)
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rate = self.gather(y, idx, self.axis)
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if not self.is_rate_scalar:
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rate = self.gather(rate, idx, 0)
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return R.random_poisson(shape, rate, seed=1, dtype=self.odtype)
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class PoissonDSFactory:
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""" Factory class for test dynamic shape feature of poisson functional op. """
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def __init__(self, rate_dims, out_dtype, shape_dtype, rate_dtype):
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self.rate_dims = rate_dims
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def __init__(self, max_dims, rate_dims):
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self.rate_random_range = 8
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self.odtype = out_dtype
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self.shape_dtype = shape_dtype
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self.rate_dtype = rate_dtype
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# shape tensor is a 1-D tensor, unique from shape_map.
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self.shape_map = np.random.randint(1, 6, 30, dtype=np.int32)
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# rate_map will be gathered as rate tensor.
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rate_map_shape = np.random.randint(1, 6, self.rate_dims - 1, dtype=np.int32)
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rate_map_shape = np.append(np.array([self.rate_random_range]), rate_map_shape, axis=0)
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self.rate_map = np.random.randn(*rate_map_shape)
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self.odtype = ms.float32
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self.shape_dtype = ms.int32
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self.rate_dtype = ms.float32
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self.is_rate_scalar = rate_dims == 0
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# shape tensor is a 1-D tensor, uniqueed from shape_map.
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self.shape_map = np.random.randint(1, max_dims, 30, dtype=np.int32)
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if self.is_rate_scalar:
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self.rate_map = np.abs(np.random.randn(1))[0]
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else:
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# rate_shape: [rate_random_range, xx, ..., xx], rank of rate_shape = rate_dims
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rate_map_shape = np.random.randint(1, max_dims, rate_dims - 1, dtype=np.int32)
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rate_map_shape = np.append(np.array([self.rate_random_range]), rate_map_shape, axis=0)
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# rate tensor will be gathered from rate_map.
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self.rate_map = np.abs(np.random.randn(*rate_map_shape))
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# indices array is used to gather rate_map to rate tensor.
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self.indices = np.random.randint(1, self.rate_random_range, 4, dtype=np.int32)
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indices_shape = np.random.randint(1, self.rate_random_range, 1, dtype=np.int32)[0]
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self.indices = np.random.randint(1, self.rate_random_range, indices_shape, dtype=np.int32)
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@staticmethod
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def _np_unranked_unique(nparr):
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@ -238,6 +267,8 @@ class PoissonDSFactory:
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def _forward_numpy(self):
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""" Get result of numpy """
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shape = PoissonDSFactory._np_unranked_unique(self.shape_map)
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if self.is_rate_scalar:
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return shape
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indices = PoissonDSFactory._np_unranked_unique(self.indices)
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rate = self.rate_map[indices]
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rate_shape = rate.shape
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@ -247,23 +278,27 @@ class PoissonDSFactory:
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def _forward_mindspore(self):
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""" Get result of mindspore """
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shape_map_tensor = Tensor(self.shape_map, dtype=self.shape_dtype)
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rate_map_tensor = Tensor(self.rate_map, dtype=self.rate_dtype)
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rate_tensor = Tensor(self.rate_map, dtype=self.rate_dtype)
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indices_tensor = Tensor(self.indices, dtype=ms.dtype.int32)
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net = PoissonNet(self.odtype)
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output = net(shape_map_tensor, rate_map_tensor, indices_tensor)
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net = PoissonNet(self.odtype, self.is_rate_scalar)
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output = net(shape_map_tensor, rate_tensor, indices_tensor)
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return output.shape, output.dtype
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@pytest.mark.level0
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@pytest.mark.env_onecard
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@pytest.mark.platform_x86_cpu
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@pytest.mark.parametrize("rate_dims", [1, 2, 3, 4, 5, 6])
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def test_poisson_function_dynamic_shape(rate_dims):
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@pytest.mark.parametrize("max_dims", [2, 3, 4, 5, 6])
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@pytest.mark.parametrize("rate_dims", [0, 1, 2, 3, 4, 5, 6])
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def test_poisson_function_dynamic_shape(max_dims, rate_dims):
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"""
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Feature: Poisson functional interface
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Description: Test dynamic shape feature of the poisson functional interface with 1D-8D rate.
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Expectation: Output of mindspore poisson equal to numpy.
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Feature: Dynamic shape of functional interface RandomPoisson.
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Description:
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1. Initialize a 1-D Tensor as input `shape` whose data type fixed to int32, whose data and shape are random.
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2. Initialize a Tensor as input `rate` whose data type fixed to float32, whose data and shape are random.
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3. Compare shape of output from MindSpore and Numpy.
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Expectation: Output of MindSpore RandomPoisson equal to numpy.
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"""
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factory = PoissonDSFactory(rate_dims, ms.float32, ms.int32, ms.float32)
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factory = PoissonDSFactory(max_dims, rate_dims)
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factory.forward_compare()
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