mindspore.boost.group_loss_scale_manager 源代码

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"""Group Loss Scale Manager"""
from __future__ import absolute_import
from __future__ import division

from mindspore.nn.cell import Cell
import mindspore.common.dtype as mstype
from mindspore.ops import operations as P
from mindspore.common.tensor import Tensor
from mindspore.common.parameter import Parameter, ParameterTuple


__all__ = ["GroupLossScaleManager"]


[文档]class GroupLossScaleManager(Cell): r""" Enhanced hybrid precision algorithm supports multi-layer application of different loss scales and dynamic updating of loss scales. Args: init_loss_scale (Number): The initialized loss scale value. loss_scale_groups (List): The loss scale groups, which are divided from the param list. Inputs: - **x** (Tensor) - The output of last operator. - **layer1** (Int) - Current network layer value. - **layer2** (Int) - Last network layer value. Outputs: - **out** (Tensor) - A tensor with a group of loss scale tags that marks the loss scale group number of the current tensor. Supported Platforms: ``Ascend`` Examples: >>> import mindspore as ms >>> from mindspore import boost, nn >>> >>> class Net(nn.Cell): ... def __init__(self, enhanced_amp, num_class=10, num_channel=1): ... super(Net, self).__init__() ... self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid') ... self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid') ... self.fc1 = nn.Dense(16*5*5, 120, weight_init='ones') ... self.fc2 = nn.Dense(120, 84, weight_init='ones') ... self.fc3 = nn.Dense(84, num_class, weight_init='ones') ... self.relu = nn.ReLU() ... self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2) ... self.flatten = nn.Flatten() ... self.enhanced_amp = enhanced_amp ... ... def construct(self, x): ... x = self.enhanced_amp(x, 0, 1) ... x = self.max_pool2d(self.relu(self.conv1(x))) ... x = self.max_pool2d(self.relu(self.conv2(x))) ... x = self.flatten(x) ... x = self.enhanced_amp(x, 1, 2) ... x = self.relu(self.fc1(x)) ... x = self.relu(self.fc2(x)) ... x = self.fc3(x) ... x = self.enhanced_amp(x, 2, 3) ... return x >>> >>> loss_scale_manager = boost.GroupLossScaleManager(4096, []) >>> net = Net(loss_scale_manager) >>> param_group1 = [] >>> param_group2 = [] >>> for param in net.trainable_params(): ... if 'conv' in param.name: ... param_group1.append(param) ... else: ... param_group2.append(param) >>> loss_scale_manager.loss_scale_groups = [param_group1, param_group2] >>> loss = nn.SoftmaxCrossEntropyWithLogits() >>> optim = nn.Momentum(params=net.trainable_params(), learning_rate=0.1, momentum=0.9) >>> boost_config_dict = {"boost": {"mode": "manual", "less_bn": False, "grad_freeze": False, "adasum": False, ... "grad_accumulation": False, "dim_reduce": False, "loss_scale_group": True}} >>> model = ms.train.Model(net, loss_fn=loss, optimizer=optim, metrics=None, ... loss_scale_manager=loss_scale_manager, ... boost_level="O1", boost_config_dict=boost_config_dict) >>> # Create the dataset taking MNIST as an example. Refer to >>> # https://gitee.com/mindspore/docs/blob/r2.3.q1/docs/mindspore/code/mnist.py >>> dataset = create_dataset() >>> model.train(2, dataset) """ def __init__(self, init_loss_scale, loss_scale_groups): super(GroupLossScaleManager, self).__init__() self._loss_scale = init_loss_scale self.loss_scale_groups = loss_scale_groups self.loss_scale_number = 0 self.layer_loss_scale = None self.dynamic_loss_scale = None
[文档] def set_loss_scale_status(self, loss_scale_number, init_loss_scale): """ Generate dynamic loss scale tuple and set overflow status list. Args: loss_scale_number (int): The number of loss scale. init_loss_scale (float): The initialized loss scale. """ self.loss_scale_number = loss_scale_number inner_list = [P._DynamicLossScale(layer=x) for x in range(loss_scale_number + 1)] # pylint: disable=W0212 self.layer_loss_scale = tuple(inner_list) self.dynamic_loss_scale = ParameterTuple(Parameter(Tensor(1, mstype.float32), name='layer_loss_scale_{}'.format(x), requires_grad=False) for x in range(loss_scale_number + 2)) if isinstance(init_loss_scale, list): for i, value in enumerate(init_loss_scale): self.dynamic_loss_scale[i + 1].set_data(value) else: for i in range(self.loss_scale_number): self.dynamic_loss_scale[i + 1].set_data(init_loss_scale)
[文档] def update_loss_scale_status(self, layer, update_ratio): """ Update dynamic loss scale. Args: layer (int): Current layer. update_ratio (float): The ratio of loss scale update. Outputs: float, new loss scale. """ layer = layer + 1 new_loss_scale = self.dynamic_loss_scale[layer] * update_ratio P.Assign()(self.dynamic_loss_scale[layer], new_loss_scale) return new_loss_scale
def construct(self, x, layer1, layer2): x = self.layer_loss_scale[layer1](x, self.dynamic_loss_scale[layer1] / self.dynamic_loss_scale[layer2]) return x
[文档] def get_loss_scale(self): """ Get loss scale value. Returns: bool, `loss_scale` value. """ return self._loss_scale
[文档] def get_update_cell(self): """ Returns the instance of :class:`mindspore.boost.GroupLossScaleManager`. Returns: :class:`mindspore.boost.GroupLossScaleManager`. """ return self