# 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 import mindspore.common.dtype as mstype from mindspore import context, Tensor, Parameter from mindspore.nn import Cell, Momentum, Norm from mindspore.train import Model from mindspore.ops import operations as P from mindspore.ops import composite as C from mindspore.ops import functional as F from mindspore.common.initializer import initializer from mindspore.nn.wrap.loss_scale import DynamicLossScaleUpdateCell from mindspore.context import ParallelMode from tests.dataset_mock import MindData class Dataset(MindData): def __init__(self, predict, label, length=3): super(Dataset, 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 get_square_sum = C.MultitypeFuncGraph("get_square_sum") @get_square_sum.register("Tensor") def _get_square_sum(grad): norm = P.ReduceSum(False)(F.square(grad), ()) norm = F.expand_dims(F.cast(norm, mstype.float32), 0) return norm apply_global_norm = C.MultitypeFuncGraph("apply_global_norm") @apply_global_norm.register("Tensor", "Tensor", "Tensor") def _apply_global_norm(clip_norm, global_norm, grad): grad = grad * clip_norm / global_norm return grad class GlobalNorm(Cell): """ Calculate the global norm value of given tensors """ def __init__(self): super(GlobalNorm, self).__init__() self.norm = Norm() self.hyper_map = C.HyperMap() def construct(self, grads): square_sum = self.hyper_map(get_square_sum, grads) global_norms = F.sqrt(F.addn(square_sum) / F.scalar_to_array(len(square_sum))) return global_norms class ClipByGlobalNorm(Cell): """ Clip grads by global norm """ def __init__(self, clip_norm=1.0): super(ClipByGlobalNorm, self).__init__() self.global_norm = GlobalNorm() self.clip_norm = Tensor([clip_norm], mstype.float32) self.hyper_map = C.HyperMap() def construct(self, grads): global_norm = self.global_norm(grads) cond = P.GreaterEqual()(global_norm, self.clip_norm) global_norm = F.select(cond, global_norm, self.clip_norm) grads = self.hyper_map(F.partial(apply_global_norm, self.clip_norm, global_norm), grads) return grads cast = P.Cast() update_accu_grads = C.MultitypeFuncGraph("update_accu_grads") @update_accu_grads.register("Tensor", "Tensor") def _update_accu_grads(accu_grad, grad): succ = True return F.depend(succ, F.assign_add(accu_grad, cast(grad, mstype.float32))) zeroslike = P.ZerosLike() reset_accu_grads = C.MultitypeFuncGraph("reset_accu_grads") @reset_accu_grads.register("Tensor") def _reset_accu_grads(accu_grad): succ = True return F.depend(succ, F.assign(accu_grad, zeroslike(accu_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) class TrainAccumulateStepsWithLossScaleCell(Cell): """ Encapsulation class of bert network training. Append an optimizer to the training network after that the construct function can be called to create the backward graph. To mimic higher batch size, gradients are accumulated N times before weight update. Args: network (Cell): The training network. Note that loss function should have been added. optimizer (Optimizer): Optimizer for updating the weights. scale_update_cell (Cell): Cell to do the loss scale. Default: None. accumulation_steps (int): Number of accumulation steps before gradient update. The global batch size = batch_size * accumulation_steps. Default: 1. """ def __init__(self, network, optimizer, scale_update_cell=None, accumulation_steps=4): super(TrainAccumulateStepsWithLossScaleCell, self).__init__(auto_prefix=False) self.network = network self.network.set_grad() self.weights = optimizer.parameters self.optimizer = optimizer self.accumulation_steps = accumulation_steps self.one = Tensor(np.array([1]).astype(np.int32)) self.zero = Tensor(np.array([0]).astype(np.int32)) self.local_step = Parameter(initializer(0, [1], mstype.int32), name="local_step") self.accu_grads = self.weights.clone(prefix="accu_grads", init='zeros') self.accu_overflow = Parameter(initializer(0, [1], mstype.int32)) self.accu_loss = Parameter(initializer(0, [1], mstype.float32)) self.grad = C.GradOperation(get_by_list=True, sens_param=True) self.reducer_flag = False self.parallel_mode = context.get_auto_parallel_context("parallel_mode") if self.parallel_mode in [ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL]: self.reducer_flag = True self.grad_reducer = F.identity self.degree = 1 if self.reducer_flag: self.degree = get_group_size() self.grad_reducer = DistributedGradReducer(optimizer.parameters, False, self.degree) self.is_distributed = (self.parallel_mode != ParallelMode.STAND_ALONE) self.overflow_reducer = F.identity if self.is_distributed: self.overflow_reducer = P.AllReduce() 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.base = Tensor(1, mstype.float32) self.less_equal = P.LessEqual() self.logical_or = P.LogicalOr() self.not_equal = P.NotEqual() self.select = P.Select() self.reshape = P.Reshape() 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)) @C.add_flags(has_effect=True) def construct(self, x, b, sens=None): """Defines the computation performed.""" weights = self.weights loss = self.network(x, b) if sens is None: scaling_sens = self.loss_scale else: scaling_sens = sens # update accumulation parameters is_accu_step = self.not_equal(self.local_step, self.accumulation_steps) self.local_step = self.select(is_accu_step, self.local_step + self.one, self.one) self.accu_loss = self.select(is_accu_step, self.accu_loss + loss, loss) mean_loss = self.accu_loss / self.local_step is_accu_step = self.not_equal(self.local_step, self.accumulation_steps) # 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, b, self.cast(scaling_sens, mstype.float32)) accu_succ = self.hyper_map(update_accu_grads, self.accu_grads, grads) mean_loss = F.depend(mean_loss, accu_succ) self.get_status(init) flag_sum = self.reduce_sum(init, (0,)) overflow = self.less_equal(self.base, flag_sum) overflow = self.logical_or(self.not_equal(self.accu_overflow, self.zero), overflow) accu_overflow = self.select(overflow, self.one, self.zero) self.accu_overflow = self.select(is_accu_step, accu_overflow, self.zero) is_accu_step = self.reshape(is_accu_step, (())) if is_accu_step: succ = False else: # apply grad reducer on grads grads = self.grad_reducer(self.accu_grads) scaling = scaling_sens * self.degree * self.accumulation_steps grads = self.hyper_map(F.partial(grad_scale, scaling), grads) grads = ClipByGlobalNorm()(grads) accu_overflow = self.overflow_reducer(accu_overflow) F.control_depend(grads, accu_overflow) overflow = self.less_equal(self.base, accu_overflow) accu_succ = self.hyper_map(reset_accu_grads, self.accu_grads) overflow = F.depend(overflow, accu_succ) overflow = self.reshape(overflow, (())) if sens is None: overflow = self.loss_scaling_manager(self.loss_scale, overflow) if overflow: succ = False else: succ = self.optimizer(grads) ret = (mean_loss, overflow, scaling_sens) return F.depend(ret, succ) class Net(Cell): def __init__(self, weight, strategy=None): super().__init__() self.mul = P.Mul().shard(strategy) self.weight = Parameter(weight, "w1") self.relu = P.ReLU() self.reduce_sum = P.ReduceSum(keep_dims=True) def construct(self, x, b): out = self.mul(x, self.weight) out = self.relu(out) out = self.reduce_sum(out) return out _x = Tensor(np.ones([2]), dtype=ms.float32) _b = Tensor(np.ones([16]), dtype=ms.float32) _w1 = Tensor(np.ones([16]), dtype=ms.float32) def compile_net(net, grad_accumulation_step): context.set_context(save_graphs=True) learning_rate = 0.1 momentum = 0.9 epoch_size = 2 dataset = Dataset(_x, _b) opt = Momentum(net.trainable_params(), learning_rate, momentum) update_cell = DynamicLossScaleUpdateCell(loss_scale_value=65536, scale_factor=2, scale_window=1000) net_wrap = TrainAccumulateStepsWithLossScaleCell(net, opt, scale_update_cell=update_cell, accumulation_steps=grad_accumulation_step) model = Model(net_wrap) model.train(epoch_size, dataset, dataset_sink_mode=False) context.reset_auto_parallel_context() def test_grad_accumulation(): grad_accumulation_step = 4 context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0, grad_accumulation_step=grad_accumulation_step) strategy = ((2,), (2,)) net = Net(_w1, strategy) compile_net(net, grad_accumulation_step)