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!6600 Update the convention that random seed works.

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Merge pull request !6600 from peixu_ren/custom_bijector
pull/6600/MERGE
mindspore-ci-bot 4 years ago committed by Gitee
parent fe219b5680 d3f06fcb5e
commit 467ed2ccd0
4 changed files with 103 additions and 35 deletions
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4
mindspore/common/__init__.py
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@ -18,7 +18,7 @@ from .api import ms_function
from .dtype import *
from .parameter import Parameter, ParameterTuple
from .tensor import MetaTensor, Tensor, RowTensor, SparseTensor
from .seed import set_seed, get_seed
from .seed import set_seed, get_seed, _truncate_seed, _update_seeds, _get_op_seed
__all__ = dtype.__all__
@ -27,5 +27,5 @@ __all__.extend([
'ms_function', # api
'Parameter', 'ParameterTuple', # parameter
"dtype",
"set_seed", "get_seed" # random seed
"set_seed", "get_seed", '_truncate_seed', '_update_seeds', '_get_op_seed' # random seed
])

62
mindspore/common/seed.py
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@ -16,8 +16,13 @@
import numpy as np
import mindspore.dataset as de
# constants
_MAXINT32 = 2**31 - 1
keyConstant = [3528531795, 2654435769, 3449720151, 3144134277]
# set global RNG seed
_GLOBAL_SEED = None
_KERNEL_SEED = {}
def set_seed(seed):
"""
@ -25,12 +30,12 @@ def set_seed(seed):
Note:
The global seed is used by numpy.random, mindspore.common.Initializer, mindspore.ops.composite.random_ops and
mindspore.nn.probability.distribution.
mindspore.nn.probability.distribution.
If global seed is not set, these packages will use their own default seed independently, numpy.random and
mindspore.common.Initializer will choose a random seed, mindspore.ops.composite.random_ops and
mindspore.nn.probability.distribution will use zero.
mindspore.common.Initializer will choose a random seed, mindspore.ops.composite.random_ops and
mindspore.nn.probability.distribution will use zero.
Seed set by numpy.random.seed() only used by numpy.random, while seed set by this API will also used by
numpy.random, so just set all seed by this API is recommended.
numpy.random, so just set all seed by this API is recommended.
Args:
seed (int): The seed to be set.
@ -45,6 +50,7 @@ def set_seed(seed):
raise ValueError("The seed must be greater or equal to 0.")
np.random.seed(seed)
de.config.set_seed(seed)
_reset_op_seed()
global _GLOBAL_SEED
_GLOBAL_SEED = seed
@ -54,3 +60,51 @@ def get_seed():
Get global random seed.
"""
return _GLOBAL_SEED
def _truncate_seed(seed):
"""
Truncate the seed with MAXINT32.
Args:
seed (int): The seed to be truncated.
"""
return seed % _MAXINT32 # Truncate to fit into 32-bit integer
def _update_seeds(op_seed, kernel_name):
"""
Update the seed every time when a random op is called.
Args:
seed (int): The op-seed to be updated.
kernel_name (string): The random op kernel.
"""
global _GLOBAL_SEED
global _KERNEL_SEED
if _GLOBAL_SEED is not None:
_GLOBAL_SEED += keyConstant[1] + keyConstant[3] * (2**8)
if op_seed is not None:
_KERNEL_SEED[(kernel_name, op_seed)] = _KERNEL_SEED[(kernel_name, op_seed)] + (keyConstant[0] ^ keyConstant[2])
def _get_op_seed(op_seed, kernel_name):
"""
Get op seed which is relating to the specific kernel.
If the seed does not exist, add it into the kernel's dictionary.
Args:
seed (int): The op-seed to be updated.
kernel_name (string): The random op kernel.
"""
if ((kernel_name, op_seed) not in _KERNEL_SEED) or (_KERNEL_SEED[(kernel_name, op_seed)] == -1):
_KERNEL_SEED[(kernel_name, op_seed)] = op_seed
_KERNEL_SEED[(kernel_name, op_seed)] = 0
return _KERNEL_SEED[(kernel_name, op_seed)]
def _reset_op_seed():
"""
Reset op seeds in the kernel's dictionary.
"""
for key in _KERNEL_SEED:
_KERNEL_SEED[key] = -1

2
mindspore/ops/composite/multitype_ops/_constexpr_utils.py
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@ -71,7 +71,7 @@ def check_int_positive(arg_name, arg_value, op_name):
@constexpr
def check_non_negative(arg_name, arg_value, op_name):
def check_int_non_negative(arg_name, arg_value, op_name):
"""Int type judgment."""
if isinstance(arg_value, int):
if arg_value >= 0:

70
mindspore/ops/composite/random_ops.py
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@ -21,27 +21,45 @@ 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():
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()
>>> C.get_seed(seed, 'normal')
"""
global_seed = get_global_seed()
if global_seed is None:
return 0
return global_seed
global_seed = 0
if op_seed is None:
temp_seed = _get_op_seed(0, kernel_name)
else:
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=0):
def normal(shape, mean, stddev, seed=None):
"""
Generates random numbers according to the Normal (or Gaussian) random number distribution.
@ -52,7 +70,7 @@ def normal(shape, mean, stddev, seed=0):
stddev (Tensor): The deviation σ 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: 0.
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
@ -69,15 +87,14 @@ def normal(shape, mean, stddev, seed=0):
stddev_dtype = F.dtype(stddev)
const_utils.check_tensors_dtype_same(mean_dtype, mstype.float32, "normal")
const_utils.check_tensors_dtype_same(stddev_dtype, mstype.float32, "normal")
const_utils.check_non_negative("seed", seed, "normal")
seed1 = get_seed()
seed2 = seed
seed1, seed2 = get_seed(seed, "normal")
const_utils.check_int_non_negative("seed", seed2, "normal")
stdnormal = P.StandardNormal(seed1, seed2)
random_normal = stdnormal(shape)
value = random_normal * stddev + mean
return value
def laplace(shape, mean, lambda_param, seed=0):
def laplace(shape, mean, lambda_param, seed=None):
r"""
Generates random numbers according to the Laplace random number distribution.
It is defined as:
@ -92,7 +109,7 @@ def laplace(shape, mean, lambda_param, seed=0):
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: 0.
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.
@ -108,14 +125,14 @@ def laplace(shape, mean, lambda_param, seed=0):
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 = get_seed()
seed2 = seed
seed1, seed2 = get_seed(seed, "laplace")
const_utils.check_int_non_negative("seed", seed2, "laplace")
stdlaplace = P.StandardLaplace(seed1, seed2)
rnd = stdlaplace(shape)
value = rnd * lambda_param + mean
return value
def uniform(shape, minval, maxval, seed=0, dtype=mstype.float32):
def uniform(shape, minval, maxval, seed=None, dtype=mstype.float32):
"""
Generates random numbers according to the Uniform random number distribution.
@ -131,7 +148,7 @@ def uniform(shape, minval, maxval, seed=0, dtype=mstype.float32):
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: 0.
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.
@ -159,9 +176,8 @@ def uniform(shape, minval, maxval, seed=0, dtype=mstype.float32):
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")
const_utils.check_non_negative("seed", seed, "uniform")
seed1 = get_seed()
seed2 = seed
seed1, seed2 = get_seed(seed, "uniform")
const_utils.check_int_non_negative("seed", seed2, "uniform")
if const_utils.is_same_type(dtype, mstype.int32):
random_uniform = P.UniformInt(seed1, seed2)
value = random_uniform(shape, minval, maxval)
@ -171,7 +187,7 @@ def uniform(shape, minval, maxval, seed=0, dtype=mstype.float32):
value = random_uniform * (maxval - minval) + minval
return value
def gamma(shape, alpha, beta, seed=0):
def gamma(shape, alpha, beta, seed=None):
"""
Generates random numbers according to the Gamma random number distribution.
@ -180,7 +196,7 @@ def gamma(shape, alpha, beta, seed=0):
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: 0.
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
@ -193,14 +209,13 @@ def gamma(shape, alpha, beta, seed=0):
>>> beta = Tensor(1.0, mstype.float32)
>>> output = C.gamma(shape, alpha, beta, seed=5)
"""
const_utils.check_non_negative("seed", seed, "gamma")
seed1 = get_seed()
seed2 = seed
seed1, seed2 = get_seed(seed, "gamma")
const_utils.check_int_non_negative("seed", seed2, "gamma")
random_gamma = P.Gamma(seed1, seed2)
value = random_gamma(shape, alpha, beta)
return value
def poisson(shape, mean, seed=0):
def poisson(shape, mean, seed=None):
"""
Generates random numbers according to the Poisson random number distribution.
@ -208,7 +223,7 @@ def poisson(shape, mean, seed=0):
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: 0.
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`.
@ -219,9 +234,8 @@ def poisson(shape, mean, seed=0):
>>> mean = Tensor(1.0, mstype.float32)
>>> output = C.poisson(shape, mean, seed=5)
"""
const_utils.check_non_negative("seed", seed, "poisson")
seed1 = get_seed()
seed2 = seed
seed1, seed2 = get_seed(seed, "poisson")
const_utils.check_int_non_negative("seed", seed2, "poisson")
random_poisson = P.Poisson(seed1, seed2)
value = random_poisson(shape, mean)
return value

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