# 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. import numpy as np import mindspore as ms from mindspore import Tensor from mindspore import context from mindspore.common.parameter import Parameter from mindspore.common import dtype as mstype from mindspore.ops import composite as C from mindspore.ops import operations as P from mindspore.ops import functional as F from mindspore.nn.optim.momentum import Momentum from mindspore.nn.wrap.loss_scale import DynamicLossScaleUpdateCell import mindspore.nn as nn from mindspore.train import Model from mindspore.context import ParallelMode from tests.dataset_mock import MindData GRADIENT_CLIP_TYPE = 1 GRADIENT_CLIP_VALUE = 1.0 clip_grad = C.MultitypeFuncGraph("clip_grad") grad_scale = C.MultitypeFuncGraph("grad_scale") reciprocal = P.Reciprocal() @grad_scale.register("Tensor", "Tensor") def tensor_grad_scale(scale, grad): return grad * reciprocal(scale) update_cell = DynamicLossScaleUpdateCell(loss_scale_value=65536, scale_factor=2, scale_window=1000) @clip_grad.register("Number", "Number", "Tensor") def _clip_grad(clip_type, clip_value, grad): dt = F.dtype(grad) if clip_type == 0: new_grad = C.clip_by_value(grad, F.cast(F.tuple_to_array((-clip_value,)), dt), F.cast(F.tuple_to_array((clip_value,)), dt)) else: new_grad = nn.ClipByNorm()(grad, F.cast(F.tuple_to_array((clip_value,)), dt)) return new_grad class TrainOneStepWithLossScaleCell(nn.Cell): def __init__(self, network, optimizer, scale_update_cell=None): super(TrainOneStepWithLossScaleCell, self).__init__(auto_prefix=False) self.network = network self.weights = optimizer.parameters self.optimizer = optimizer self.grad = C.GradOperation(get_by_list=True, sens_param=True) self.reducer_flag = False self.grad_reducer = F.identity self.cast = P.Cast() self.alloc_status = P.NPUAllocFloatStatus() self.get_status = P.NPUGetFloatStatus() self.clear_before_grad = P.NPUClearFloatStatus() self.reduce_sum = P.ReduceSum(keep_dims=False) self.depend_parameter_use = P.ControlDepend(depend_mode=1) self.base = Tensor(1, mstype.float32) self.less_equal = P.LessEqual() self.hyper_map = C.HyperMap() self.loss_scale = None self.loss_scaling_manager = scale_update_cell if scale_update_cell: self.loss_scale = Parameter(Tensor(scale_update_cell.get_loss_scale(), dtype=mstype.float32), name="loss_scale") @C.add_flags(has_effect=True) def construct(self, x, sens=None): """Defines the computation performed.""" weights = self.weights loss = self.network(x) if sens is None: scaling_sens = self.loss_scale else: scaling_sens = sens # alloc status and clear should be right before gradoperation init = self.alloc_status() self.clear_before_grad(init) grads = self.grad(self.network, weights)(x, self.cast(scaling_sens, mstype.float32)) # apply grad reducer on grads grads = self.grad_reducer(grads) grads = self.hyper_map(F.partial(clip_grad, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE), grads) self.get_status(init) flag_sum = self.reduce_sum(init, (0,)) cond = self.less_equal(self.base, flag_sum) overflow = cond if sens is None: overflow = self.loss_scaling_manager(self.loss_scale, cond) if overflow: succ = False else: succ = self.optimizer(grads) ret = (loss, cond, scaling_sens) return F.depend(ret, succ) class DatasetLenet(MindData): def __init__(self, predict, label, length=3): super(DatasetLenet, self).__init__(size=length) self.predict = predict self.label = label self.index = 0 self.length = length def __iter__(self): return self def __next__(self): if self.index >= self.length: raise StopIteration self.index += 1 return self.predict, self.label def reset(self): self.index = 0 class LoopLayer(nn.Cell): def __init__(self): super(LoopLayer, self).__init__() self.matmul = P.MatMul() self.relu = P.ReLU() self.matmul_weight = Parameter(Tensor(np.ones([64, 64]), dtype=ms.float32), name="weight") def construct(self, x): out = self.matmul(x, self.matmul_weight) out = self.relu(out) return out class Net(nn.Cell): def __init__(self): super(Net, self).__init__() self.exp = P.Exp() self.mean = P.ReduceMean() layers = [] for _ in range(3): layer = LoopLayer() layers.append(layer) self.layers = nn.CellList(layers) def construct(self, x): out = self.exp(x) for layer in self.layers: layer_out = layer(out) out = layer_out out = self.mean(out, -1) return out class Net2(nn.Cell): def __init__(self): super(Net2, self).__init__() self.matmul = P.MatMul() self.relu = P.ReLU() self.matmul_weight = Parameter(Tensor(np.ones([64, 64]), dtype=ms.float32), name="weight") def construct(self, x, b): out = self.matmul(x, self.matmul_weight) out = self.relu(out) return out def test_loss_scale(): context.set_context(mode=context.GRAPH_MODE) context.set_auto_parallel_context(parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, device_num=8) predict = Tensor(np.ones([64, 64]), dtype=ms.float32) label = Tensor(np.ones([64,]), dtype=ms.int32) dataset = DatasetLenet(predict, label) net = Net() opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 0.9) net = TrainOneStepWithLossScaleCell(net, opt, update_cell) model = Model(network=net) model.train(2, dataset, dataset_sink_mode=False) def test_loss_scale2(): context.set_context(mode=context.GRAPH_MODE, save_graphs=True) context.set_auto_parallel_context(parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, device_num=8) predict = Tensor(np.ones([64, 64]), dtype=ms.float32) label = Tensor(np.ones([64,]), dtype=ms.int32) dataset = DatasetLenet(predict, label) net = Net2() opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 0.9) net = nn.TrainOneStepWithLossScaleCell(net, opt, update_cell) model = Model(network=net) model.train(2, dataset, dataset_sink_mode=False)