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add get_seed() and set_seed()

This commit is contained in:
Yi Huaijie 2020-08-29 16:47:43 +08:00
parent c563336541
commit 4a5d115a66
18 changed files with 139 additions and 47 deletions
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4
mindspore/common/__init__.py
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@ -18,12 +18,14 @@ 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
__all__ = [
"MetaTensor", "Tensor", "RowTensor", "SparseTensor", # tensor
'ms_function', # api
'Parameter', 'ParameterTuple', # parameter
"dtype"
"dtype",
"set_seed", "get_seed" # random seed
]
__all__.extend(dtype.__all__)

11
mindspore/common/initializer.py
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@ -23,6 +23,7 @@ from mindspore import log as logger
from . import dtype as mstype
from .tensor import Tensor
from .seed import get_seed
from .._c_expression import random_normal
_INITIALIZER_ALIAS = dict()
@ -71,7 +72,7 @@ class Initializer:
Args:
slice_index (int): Slice index of a parameter's slices.
Used when initialize a slice of a parameter, it guarantee that
Used when initialize a slice of the parameter, it guarantee that
devices use the same slice can generate the same tensor.
shape (list[int]): Shape of the slice, used when initialize a slice of the parameter.
"""
@ -86,11 +87,17 @@ class Initializer:
logger.error(msg)
raise ValueError(msg)
if slice_index is not None:
global_seed = get_seed()
need_set_seed = ((slice_index is not None) and (global_seed is None))
seed_saved = np.random.get_state()[1][0]
if need_set_seed:
np.random.seed(slice_index)
self.__call__(arr)
if need_set_seed:
np.random.seed(seed_saved)
return Tensor(arr, dtype=self.dtype)
def _register(*aliases):
"""Return the alias register."""
def alias_reg(cls):

54
mindspore/common/seed.py Normal file
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@ -0,0 +1,54 @@
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# 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
# limitations under the License.
# ============================================================================
"""Provide random seed api."""
import numpy as np
# set global RNG seed
_GLOBAL_SEED = None
def set_seed(seed):
"""
Set global random seed.
Note:
The global seed is used by numpy.random, mindspore.common.Initializer, mindspore.ops.composite.random_ops and
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.
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.
Args:
seed (int): The seed to be set.
Raises:
ValueError: If seed is invalid (< 0).
TypeError: If seed isn't a int.
"""
if not isinstance(seed, int):
raise TypeError("The seed must be type of int.")
if seed < 0:
raise ValueError("The seed must be greater or equal to 0.")
np.random.seed(seed)
global _GLOBAL_SEED
_GLOBAL_SEED = seed
def get_seed():
"""
Get global random seed.
"""
return _GLOBAL_SEED

4
mindspore/nn/probability/distribution/bernoulli.py
View File

@ -27,7 +27,7 @@ class Bernoulli(Distribution):
Args:
probs (float, list, numpy.ndarray, Tensor, Parameter): probability of 1 as outcome.
seed (int): seed to use in sampling. Default: 0.
seed (int): seed to use in sampling. Global seed is used if it is None. Default: None.
dtype (mindspore.dtype): type of the distribution. Default: mstype.int32.
name (str): name of the distribution. Default: Bernoulli.
@ -91,7 +91,7 @@ class Bernoulli(Distribution):
def __init__(self,
probs=None,
seed=0,
seed=None,
dtype=mstype.int32,
name="Bernoulli"):
"""

6
mindspore/nn/probability/distribution/categorical.py
View File

@ -27,7 +27,7 @@ class Categorical(Distribution):
Args:
probs (Tensor, list, numpy.ndarray, Parameter, float): event probabilities.
logits (Tensor, list, numpy.ndarray, Parameter, float): event log-odds.
seed (int): seed to use in sampling. Default: 0.
seed (int): seed to use in sampling. Global seed is used if it is None. Default: None.
dtype (mstype.int32): type of the distribution. Default: mstype.int32.
name (str): name of the distribution. Default: Categorical.
@ -67,7 +67,7 @@ class Categorical(Distribution):
def __init__(self,
probs=None,
logits=None,
seed=0,
seed=None,
dtype=mstype.int32,
name="Categorical"):
param = dict(locals())
@ -83,7 +83,7 @@ class Categorical(Distribution):
self.reshape = P.Reshape()
self.div = P.RealDiv()
self.size = P.Size()
self.mutinomial = P.Multinomial(seed=seed)
self.mutinomial = P.Multinomial(seed=self.seed)
self.cast = P.Cast()
self.expandim = P.ExpandDims()
self.gather = P.GatherNd()

7
mindspore/nn/probability/distribution/distribution.py
View File

@ -17,6 +17,7 @@ from mindspore import context
from mindspore.nn.cell import Cell
from mindspore._checkparam import Validator as validator
from mindspore._checkparam import Rel
from mindspore.common import get_seed
from ._utils.utils import calc_broadcast_shape_from_param, check_scalar_from_param, cast_type_for_device
from ._utils.utils import CheckTuple, CheckTensor
@ -26,7 +27,7 @@ class Distribution(Cell):
Base class for all mathematical distributions.
Args:
seed (int): random seed used in sampling.
seed (int): random seed used in sampling. Global seed is used if it is None. Default: None.
dtype (mindspore.dtype): the type of the event samples. Default: subclass dtype.
name (str): Python str name prefixed to Ops created by this class. Default: subclass name.
param (dict): parameters used to initialize the distribution.
@ -56,6 +57,10 @@ class Distribution(Cell):
Constructor of distribution class.
"""
super(Distribution, self).__init__()
if seed is None:
seed = get_seed()
if seed is None:
seed = 0
validator.check_value_type('name', name, [str], type(self).__name__)
validator.check_integer('seed', seed, 0, Rel.GE, name)

4
mindspore/nn/probability/distribution/exponential.py
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@ -28,7 +28,7 @@ class Exponential(Distribution):
Args:
rate (float, list, numpy.ndarray, Tensor, Parameter): inverse scale.
seed (int): seed to use in sampling. Default: 0.
seed (int): seed to use in sampling. Global seed is used if it is None. Default: None.
dtype (mindspore.dtype): type of the distribution. Default: mstype.float32.
name (str): name of the distribution. Default: Exponential.
@ -92,7 +92,7 @@ class Exponential(Distribution):
def __init__(self,
rate=None,
seed=0,
seed=None,
dtype=mstype.float32,
name="Exponential"):
"""

4
mindspore/nn/probability/distribution/geometric.py
View File

@ -30,7 +30,7 @@ class Geometric(Distribution):
Args:
probs (float, list, numpy.ndarray, Tensor, Parameter): probability of success.
seed (int): seed to use in sampling. Default: 0.
seed (int): seed to use in sampling. Global seed is used if it is None. Default: None.
dtype (mindspore.dtype): type of the distribution. Default: mstype.int32.
name (str): name of the distribution. Default: Geometric.
@ -94,7 +94,7 @@ class Geometric(Distribution):
def __init__(self,
probs=None,
seed=0,
seed=None,
dtype=mstype.int32,
name="Geometric"):
"""

4
mindspore/nn/probability/distribution/normal.py
View File

@ -29,7 +29,7 @@ class Normal(Distribution):
Args:
mean (int, float, list, numpy.ndarray, Tensor, Parameter): mean of the Normal distribution.
sd (int, float, list, numpy.ndarray, Tensor, Parameter): stddev of the Normal distribution.
seed (int): seed to use in sampling. Default: 0.
seed (int): seed to use in sampling. Global seed is used if it is None. Default: None.
dtype (mindspore.dtype): type of the distribution. Default: mstype.float32.
name (str): name of the distribution. Default: Normal.
@ -94,7 +94,7 @@ class Normal(Distribution):
def __init__(self,
mean=None,
sd=None,
seed=0,
seed=None,
dtype=mstype.float32,
name="Normal"):
"""

2
mindspore/nn/probability/distribution/transformed_distribution.py
View File

@ -62,7 +62,7 @@ class TransformedDistribution(Distribution):
bijector,
distribution,
dtype,
seed=0,
seed=None,
name="transformed_distribution"):
"""
Constructor of transformed_distribution class.

4
mindspore/nn/probability/distribution/uniform.py
View File

@ -28,7 +28,7 @@ class Uniform(Distribution):
Args:
low (int, float, list, numpy.ndarray, Tensor, Parameter): lower bound of the distribution.
high (int, float, list, numpy.ndarray, Tensor, Parameter): upper bound of the distribution.
seed (int): seed to use in sampling. Default: 0.
seed (int): seed to use in sampling. Global seed is used if it is None. Default: None.
dtype (mindspore.dtype): type of the distribution. Default: mstype.float32.
name (str): name of the distribution. Default: Uniform.
@ -93,7 +93,7 @@ class Uniform(Distribution):
def __init__(self,
low=None,
high=None,
seed=0,
seed=None,
dtype=mstype.float32,
name="Uniform"):
"""

3
mindspore/ops/composite/__init__.py
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@ -26,7 +26,7 @@ from .clip_ops import clip_by_value
from .multitype_ops.add_impl import hyper_add
from .multitype_ops.ones_like_impl import ones_like
from .multitype_ops.zeros_like_impl import zeros_like
from .random_ops import set_seed, normal, uniform, gamma, poisson, multinomial
from .random_ops import normal, uniform, gamma, poisson, multinomial
__all__ = [
@ -41,7 +41,6 @@ __all__ = [
'zeros_like',
'ones_like',
'zip_operation',
'set_seed',
'normal',
'uniform',
'gamma',

29
mindspore/ops/composite/random_ops.py
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@ -20,34 +20,14 @@ from .. import functional as F
from ..primitive import constexpr
from .multitype_ops import _constexpr_utils as const_utils
from ...common import dtype as mstype
# set graph-level RNG seed
_GRAPH_SEED = 0
@constexpr
def set_seed(seed):
"""
Set 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 op-level seed is also 0, the system would generate a
random seed.
Args:
seed(Int): the graph-level seed value that to be set. Must be non-negative.
Examples:
>>> C.set_seed(10)
"""
const_utils.check_non_negative("seed", seed, "set_seed")
global _GRAPH_SEED
_GRAPH_SEED = seed
from ...common import get_seed as get_global_seed
@constexpr
def get_seed():
"""
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 op-level seed is also 0, the system would generate a
If op-level seed is 0, use graph-level seed; if graph-level seed is also 0, the system would generate a
random seed.
Returns:
@ -56,7 +36,10 @@ def get_seed():
Examples:
>>> C.get_seed()
"""
return _GRAPH_SEED
global_seed = get_global_seed()
if global_seed is None:
return 0
return global_seed
def normal(shape, mean, stddev, seed=0):
"""

3
tests/st/ops/ascend/test_compoite_random_ops/test_gamma.py
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@ -19,6 +19,7 @@ import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common import dtype as mstype
from mindspore.ops import composite as C
from mindspore.common import set_seed
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
@ -30,7 +31,7 @@ class Net(nn.Cell):
self.seed = seed
def construct(self, alpha, beta):
C.set_seed(20)
set_seed(20)
return C.gamma(self.shape, alpha, beta, self.seed)

3
tests/st/ops/ascend/test_compoite_random_ops/test_normal.py
View File

@ -19,6 +19,7 @@ import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common import dtype as mstype
from mindspore.ops import composite as C
from mindspore.common import set_seed
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
@ -30,7 +31,7 @@ class Net(nn.Cell):
self.seed = seed
def construct(self, mean, stddev):
C.set_seed(20)
set_seed(20)
return C.normal(self.shape, mean, stddev, self.seed)

3
tests/st/ops/ascend/test_compoite_random_ops/test_poisson.py
View File

@ -19,6 +19,7 @@ import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common import dtype as mstype
from mindspore.ops import composite as C
from mindspore.common import set_seed
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
@ -30,7 +31,7 @@ class Net(nn.Cell):
self.seed = seed
def construct(self, mean):
C.set_seed(20)
set_seed(20)
return C.poisson(self.shape, mean, self.seed)

3
tests/st/ops/ascend/test_compoite_random_ops/test_uniform.py
View File

@ -19,6 +19,7 @@ import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common import dtype as mstype
from mindspore.ops import composite as C
from mindspore.common import set_seed
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
@ -30,7 +31,7 @@ class Net(nn.Cell):
self.seed = seed
def construct(self, minval, maxval):
C.set_seed(20)
set_seed(20)
return C.uniform(self.shape, minval, maxval, self.seed)

38
tests/ut/python/parallel/test_initializer_weight_slice.py
View File

@ -21,6 +21,7 @@ from mindspore import Tensor, Parameter
import mindspore as ms
import mindspore.common.api as me
from mindspore.common.initializer import initializer
from mindspore.common import set_seed
from hccl_test.manage.api import Hccl
class Net(nn.Cell):
@ -112,5 +113,42 @@ def test_wrong_order_set_parallel_mode_without_initializer():
net.set_auto_parallel()
exe.compile(net, x, auto_parallel_mode=True, phase='train')
def test_check_initializer_weight_slice_seed(init_name="Uniform"):
def get_slice(rank):
set_seed(1)
hccl = Hccl()
rank_save = hccl.rank_id
hccl.rank_id = rank
context.reset_auto_parallel_context()
context.set_auto_parallel_context(device_num=8, global_rank=0)
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
strategy1 = ((2, 1), (4, 1))
strategy2 = ((2, 4),)
context.set_context(mode=context.GRAPH_MODE)
exe = me._executor
x = Tensor(np.ones([32, 32]), dtype=ms.float32)
weight = initializer(init_name, [64, 32], ms.float32)
net = Net(strategy1, strategy2, weight)
net.set_auto_parallel()
exe.compile(net, x, auto_parallel_mode=True, phase='train')
hccl.rank_id = rank_save
return net.parameters_dict()['w1'].data.asnumpy()
slice0 = get_slice(0)
slice1 = get_slice(1)
slice4 = get_slice(4)
slice_shape = slice0.shape
slice0 = slice0.flatten()
slice1 = slice1.flatten()
slice4 = slice4.flatten()
expect_slice_shape = (16, 32)
assert expect_slice_shape == slice_shape
assert all(slice0 == slice4)
assert all(slice0 == slice1)
if __name__ == '__main__':
test_initializer_weight_slice()