forked from huawei/mindspore2022
239 lines
9.8 KiB
Python
239 lines
9.8 KiB
Python
# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""base process"""
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import copy
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import mindspore.nn as nn
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from mindspore.nn.optim import LARS
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from mindspore import log as logger
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from mindspore.common import Parameter
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from .less_batch_normalization import CommonHeadLastFN
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__all__ = ["OptimizerProcess", "ParameterProcess"]
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class OptimizerProcess:
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"""
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Process optimizer for Boost. Currently, this class supports adding GC(grad centralization) tags
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and creating new optimizers.
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Args:
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opt (Cell): Optimizer used.
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Examples:
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>>> from mindspore import Tensor, Parameter, nn
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>>> from mindspore.ops import operations as P
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>>> from mindspore.boost import OptimizerProcess
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>>>
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>>> class Net(nn.Cell):
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... def __init__(self, in_features, out_features):
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... super(Net, self).__init__()
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... self.weight = Parameter(Tensor(np.ones([in_features, out_features]).astype(np.float32)),
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... name='weight')
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... self.matmul = ops.MatMul()
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...
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... def construct(self, x):
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... output = self.matmul(x, self.weight)
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... return output
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...
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>>> size, in_features, out_features = 16, 16, 10
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>>> network = Net(in_features, out_features)
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>>> optimizer = nn.Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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>>> optimizer_process = OptimizerProcess(optimizer)
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>>> optimizer_process.add_grad_centralization(network)
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>>> optimizer = optimizer_process.generate_new_optimizer()
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"""
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def __init__(self, opt):
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if isinstance(opt, LARS):
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self.is_lars = True
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self.opt_class = type(opt.opt)
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self.opt_init_args = opt.opt.init_args
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self.lars_init_args = opt.init_args
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else:
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self.is_lars = False
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self.opt_class = type(opt)
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self.opt_init_args = opt.init_args
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self.origin_params = opt.init_params["params"]
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def build_params_dict(self, network):
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"""Build the params dict of the network"""
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cells = network.cells_and_names()
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params_dict = {}
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for _, cell in cells:
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for par in cell.get_parameters(expand=False):
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params_dict[id(par)] = cell
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return params_dict
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def build_gc_params_group(self, params_dict, parameters):
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"""Build the params group that needs gc"""
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group_params = []
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for group_param in parameters:
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if 'order_params' in group_param.keys():
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group_params.append(group_param)
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continue
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params_gc_value = []
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params_value = []
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for param in group_param['params']:
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if 'beta' not in param.name and 'gamma' not in param.name and 'bias' not in param.name:
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param_cell = params_dict[id(param)]
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if (isinstance(param_cell, nn.Conv2d) and param_cell.group > 1) or \
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isinstance(param_cell, CommonHeadLastFN):
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params_value.append(param)
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else:
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params_gc_value.append(param)
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else:
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params_value.append(param)
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if params_gc_value:
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new_group_param = copy.deepcopy(group_param)
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new_group_param['params'] = params_gc_value
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new_group_param['grad_centralization'] = True
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group_params.append(new_group_param)
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if params_value:
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new_group_param = copy.deepcopy(group_param)
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new_group_param['params'] = params_value
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group_params.append(new_group_param)
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return group_params
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def add_grad_centralization(self, network):
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"""Add gradient centralization."""
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params_dict = self.build_params_dict(network)
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parameters = self.origin_params
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if parameters is not None and not isinstance(parameters, list):
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parameters = list(parameters)
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if not parameters:
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raise ValueError("Optimizer got an empty parameter list.")
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if not isinstance(parameters[0], (dict, Parameter)):
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raise TypeError("Only a list of Parameter or dict can be supported.")
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if isinstance(parameters[0], Parameter):
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logger.warning("Only group parameters support gradient centralization.")
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return
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self.origin_params = self.build_gc_params_group(params_dict, parameters)
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def generate_new_optimizer(self):
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"""Generate new optimizer."""
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if not self.is_lars:
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opt = self.opt_class(params=self.origin_params, **self.opt_init_args)
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else:
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opt = LARS(self.opt_class(params=self.origin_params, **self.opt_init_args), **self.lars_init_args)
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return opt
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class ParameterProcess:
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"""
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Process parameter for Boost. Currently, this class supports creating group parameters
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and automatically setting gradient segmentation point.
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Examples:
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>>> from mindspore import Tensor, Parameter, nn
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>>> from mindspore.ops import operations as P
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>>> from mindspore.boost import OptimizerProcess
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>>>
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>>> class Net(nn.Cell):
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... def __init__(self, in_features, out_features):
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... super(Net, self).__init__()
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... self.weight = Parameter(Tensor(np.ones([in_features, out_features]).astype(np.float32)),
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... name='weight')
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... self.weight2 = Parameter(Tensor(np.ones([in_features, out_features]).astype(np.float32)),
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... name='weight2')
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... self.matmul = ops.MatMul()
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... self.matmul2 = ops.MatMul()
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...
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... def construct(self, x):
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... output = self.matmul(x, self.weight)
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... output2 = self.matmul2(x, self.weight2)
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... return output + output2
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...
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>>> size, in_features, out_features = 16, 16, 10
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>>> network = Net(in_features, out_features)
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>>> new_parameter = net.trainable_params()[:1]
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>>> parameter_process = ParameterProcess()
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>>> group_params = parameter_process.generate_group_params(new_parameter, net.trainable_params())
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"""
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def __init__(self):
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self._parameter_indices = 1
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def assign_parameter_group(self, parameters, split_point=None):
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"""Assign parameter group."""
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if not isinstance(parameters, (list, tuple)) or not parameters:
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return parameters
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parameter_len = len(parameters)
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if split_point:
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split_parameter_index = split_point
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else:
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split_parameter_index = [parameter_len // 2]
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for i in range(parameter_len):
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if i in split_parameter_index:
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self._parameter_indices += 1
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parameters[i].comm_fusion = self._parameter_indices
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return parameters
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def generate_group_params(self, parameters, origin_params):
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"""Generate group parameters."""
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origin_params_copy = origin_params
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if origin_params_copy is not None:
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if not isinstance(origin_params_copy, list):
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origin_params_copy = list(origin_params_copy)
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if not origin_params_copy:
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raise ValueError("Optimizer got an empty parameter list.")
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if not isinstance(origin_params_copy[0], (dict, Parameter)):
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raise TypeError("Only a list of Parameter or dict can be supported.")
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if isinstance(origin_params_copy[0], Parameter):
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group_params = [{"params": parameters}]
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return group_params
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group_params = []
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params_name = [param.name for param in parameters]
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new_params_count = copy.deepcopy(params_name)
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new_params_clone = {}
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max_key_number = 0
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for group_param in origin_params_copy:
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if 'order_params' in group_param.keys():
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new_group_param = copy.deepcopy(group_param)
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new_group_param['order_params'] = parameters
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group_params.append(new_group_param)
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continue
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params_value = []
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for param in group_param['params']:
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if param.name in params_name:
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index = params_name.index(param.name)
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params_value.append(parameters[index])
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new_params_count.remove(param.name)
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new_group_param = copy.deepcopy(group_param)
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new_group_param['params'] = params_value
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group_params.append(new_group_param)
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if len(group_param.keys()) > max_key_number:
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max_key_number = len(group_param.keys())
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new_params_clone = copy.deepcopy(group_param)
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if new_params_count:
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params_value = []
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for param in new_params_count:
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index = params_name.index(param)
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params_value.append(parameters[index])
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if new_params_clone:
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new_params_clone['params'] = params_value
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group_params.append(new_params_clone)
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else:
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group_params.append({"params": params_value})
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return group_params
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