# 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.
# ============================================================================
"""Configuration of parameters for strategy-searching algorithm in auto_parallel"""
import threading
from mindspore._c_expression import CostModelContext
from mindspore._checkparam import args_type_check
__all__ = ["get_algo_parameters", "reset_algo_parameters", "set_algo_parameters"]
class _AlgoParameterConfig:
"""
_AlgoParameterConfig is the configuration of setting parameters used in th algorithm.
Note:
Creating a config through instantiating _AlgoParameterConfig object is not recommended.
Use algo_parameter_config() to get the configuration since _AlgoParameterConfig is singleton.
"""
_instance = None
_instance_lock = threading.Lock()
def __init__(self):
self._config_handle = CostModelContext.get_instance()
def check_config_handle(self):
"""
Check config handle.
Raises:
ValueError: If the config handle is none.
"""
if self._config_handle is None:
raise ValueError("Config handle is none!!!")
def set_fully_use_devices(self, not_fully):
"""
Set the flag of whether only generating strategies that fully use all available devices.
Default: True
Args:
not_fully (bool): The flag.
"""
self.check_config_handle()
self._config_handle.set_fully_use_devices(not_fully)
def get_fully_use_devices(self):
"""
Get the flag of whether only generating strategies that fully use all available devices.
Return:
The flag.
"""
self.check_config_handle()
return self._config_handle.get_fully_use_devices()
def set_elementwise_op_strategy_follow(self, element_strategy_follow):
"""
Set the flag of whether the elementwise operator has the same strategies as its subsequent operators.
Default: False
Args:
element_strategy_follow (bool): The flag.
"""
self.check_config_handle()
self._config_handle.set_elementwise_op_strategy_follow(element_strategy_follow)
def get_elementwise_op_strategy_follow(self):
"""
Get the flag of whether the elementwise operator has the same strategies as its subsequent operators.
Returns:
The flag.
"""
self.check_config_handle()
return self._config_handle.get_elementwise_op_strategy_follow()
def set_tensor_slice_align_enable(self, align_enable):
"""
Set the flag of whether to check the shape of tensor slice of MatMul.
Default: False
Args:
align_enable (bool): The flag.
"""
self.check_config_handle()
self._config_handle.set_tensor_slice_align_enable(align_enable)
def get_tensor_slice_align_enable(self):
"""
Get the flag of whether to check the shape of tensor slice of MatMul.
Returns:
The flag.
"""
self.check_config_handle()
return self._config_handle.get_tensor_slice_align_enable()
def set_tensor_slice_align_size(self, align_size):
"""
Set tensor slice align size.
Args:
align_size (int): The minimum tensor slice shape.
Raises:
ValueError: If align_size is not in [1, 1024].
"""
self.check_config_handle()
if align_size < 1 or align_size > 1024:
raise ValueError('Align_size must be in [1, 1024], but got {}'.format(align_size))
self._config_handle.set_tensor_slice_align_size(align_size)
def get_tensor_slice_align_size(self):
"""
Get the tensor slice align size.
Returns:
The size.
"""
self.check_config_handle()
return self._config_handle.get_tensor_slice_align_size()
def set_dp_algo_enable_approxi(self, enable_flag):
"""
Set the flag of whether to enable the approximation in the DP algorithms.
Default: False.
Args:
enable_flag (bool): The flag.
"""
self.check_config_handle()
self._config_handle.set_dp_algo_enable_approxi(enable_flag)
def get_dp_algo_enable_approxi(self):
"""
Get the flag of whether to enable the approximation in the DP algorithms.
Returns:
The flag.
"""
self.check_config_handle()
return self._config_handle.get_dp_algo_enable_approxi()
def set_dp_algo_approxi_epsilon(self, epsilon):
"""
Set the epsilon value used in the approximation DP algorithm.
Default: 0.1.
Args:
epsilon (float): The epsilon value, should in the range dp_(0, 1].
"""
self.check_config_handle()
self._config_handle.set_dp_algo_approxi_epsilon(epsilon)
def get_dp_algo_approxi_epsilon(self):
"""
Get the epsilon value used in the approximation DP algorithm.
Returns:
The epsilon value.
"""
self.check_config_handle()
return self._config_handle.get_dp_algo_approxi_epsilon()
def reset_algo_parameters(self):
"""
Reset algorithm parameter attributes.
"""
self.check_config_handle()
self._config_handle.reset_algo_parameters()
_g_algo_parameter_config = None
def _algo_parameter_config():
"""
Get the global _g_algo_parameter_config. If it is not created, create a new one.
Returns:
The global _g_algo_parameter_config.
"""
global _g_algo_parameter_config
if _g_algo_parameter_config is None:
_g_algo_parameter_config = _AlgoParameterConfig()
return _g_algo_parameter_config
set_algo_parameters_config_func_map = {
"fully_use_devices": _algo_parameter_config().set_fully_use_devices,
"elementwise_op_strategy_follow": _algo_parameter_config().set_elementwise_op_strategy_follow,
"tensor_slice_align_enable": _algo_parameter_config().set_tensor_slice_align_enable,
"tensor_slice_align_size": _algo_parameter_config().set_tensor_slice_align_size,
"enable_algo_approxi": _algo_parameter_config().set_dp_algo_enable_approxi,
"algo_approxi_epsilon": _algo_parameter_config().set_dp_algo_approxi_epsilon}
get_algo_parameters_config_func_map = {
"fully_use_devices": _algo_parameter_config().get_fully_use_devices,
"elementwise_op_strategy_follow": _algo_parameter_config().get_elementwise_op_strategy_follow,
"tensor_slice_align_enable": _algo_parameter_config().get_tensor_slice_align_enable,
"tensor_slice_align_size": _algo_parameter_config().get_tensor_slice_align_size,
"enable_algo_approxi": _algo_parameter_config().get_dp_algo_enable_approxi,
"algo_approxi_epsilon": _algo_parameter_config().get_dp_algo_approxi_epsilon}
[docs]@args_type_check(tensor_slice_align_enable=bool, tensor_slice_align_size=int,
fully_use_devices=bool, elementwise_op_strategy_follow=bool,
enable_algo_approxi=bool, algo_approxi_epsilon=float)
def set_algo_parameters(**kwargs):
"""
Set parameters in the algorithm for parallel strategy searching. See a typical use in
`test_auto_parallel_resnet.py
<https://gitee.com/mindspore/mindspore/blob/master/tests/ut/python/parallel/test_auto_parallel_resnet.py>`_.
Note:
The attribute name is required. This interface works ONLY in AUTO_PARALLEL mode.
Args:
fully_use_devices (bool): Whether ONLY searching strategies that fully use all available devices.
Default: True. For example with 8 devices available, if set true, strategy (4, 1) will not be included
in ReLU's candidate strategies, because strategy (4, 1) only utilizes 4 devices.
elementwise_op_strategy_follow (bool): Whether the elementwise operator has the consistent strategies as its
subsequent operators. Default: False. For the example of ReLU followed by Add, where ReLU is elementwise
operator, if this flag is set true, then the searched strategy by the algorithm guarantees that strategies
of these two operators are consistent, e.g., ReLU's strategy (8, 1) and Add's strategy ((8, 1), (8, 1)).
enable_algo_approxi (bool): Whether to enable the approximation in the algorithms. Default: False. Due to large
solution space in searching parallel strategy for large DNN model, the algorithm takes fairly long time in
this case. To mitigate it, if this flag is set true, an approximation is made to discard some candidate
strategies, so that the solution space is shrunken.
algo_approxi_epsilon (float): The epsilon value used in the approximation algorithm. Default: 0.1. This value
describes the extent of approximation. For example, the number of candidate strategies of an operator is S,
if 'enable_algo_approxi' is true, then the remaining strategies is of size: min{S, 1/epsilon}.
tensor_slice_align_enable (bool): Whether to check the shape of tensor slice of MatMul. Default: False. Due to
properties of some hardware, MatMul kernel only with large shapes can show advantages. If this flag is true,
then the slice shape of MatMul is checked to prevent irregular shapes.
tensor_slice_align_size (int): The minimum tensor slice shape of MatMul, the value must be in [1, 1024].
Default: 16. If 'tensor_slice_align_enable' is set true, then the slice size of last dimension of MatMul
tensors should be multiple of this value.
Raises:
ValueError: If context keyword is not recognized.
"""
for key, value in kwargs.items():
if key not in set_algo_parameters_config_func_map:
raise ValueError("Set context keyword %s is not recognized!" % key)
set_func = set_algo_parameters_config_func_map[key]
set_func(value)
[docs]def get_algo_parameters(attr_key):
"""
Get the algorithm parameter config attributes.
Note:
The attribute name is required. This interface works ONLY in AUTO_PARALLEL mode.
Args:
attr_key (str): The key of the attribute. The keys include: "fully_use_devices",
"elementwise_op_strategy_follow", "enable_algo_approxi", "algo_approxi_epsilon",
"tensor_slice_align_enable","tensor_slice_align_size".
Returns:
Return attribute value according to the key.
Raises:
ValueError: If context keyword is not recognized.
"""
if attr_key not in get_algo_parameters_config_func_map:
raise ValueError("Get context keyword %s is not recognized!" % attr_key)
get_func = get_algo_parameters_config_func_map[attr_key]
return get_func()
[docs]def reset_algo_parameters():
"""Reset the algorithm parameter attributes.
Note:
This interface works ONLY in AUTO_PARALLEL mode.
After reset, the values of the attributes are:
- fully_use_devices: True.
- elementwise_op_strategy_follow: False.
- enable_algo_approxi: False.
- algo_approxi_epsilon: 0.1.
- tensor_slice_align_enable: False.
- tensor_slice_align_size: 16.
"""
_algo_parameter_config().reset_algo_parameters()