# Copyright 2019 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. from mindspore.ops import operations as P from mindspore.ops import functional as F from mindspore import Tensor, Parameter from mindspore.common import dtype as mstype import mindspore.nn as nn import numpy as np from mindspore.nn.cell import Cell from tests.dataset_mock import MindData from mindspore.nn.optim.momentum import Momentum from mindspore.train import Model, ParallelMode from tests.ut.python.ops.test_math_ops import VirtualLoss from mindspore.ops import composite as C import mindspore as ms from mindspore.common.api import _executor from mindspore import context device_num=16 device_id = 2 class StrategyModel(): onehot_strategy = ((1, device_num),(),()) twod_strategy = ((1, device_num), ) twod_strategy_m = ((device_num, 1), ) scalar_twod_strategy = ((), (1, device_num)) twod_scalar_strategy = ((1, device_num), ()) scalar_strategy = ((), ) oned_strategy = ((1, ), ) scalar_scalar_strategy = ((), ()) twod_twod_strategy = ((1, device_num), (1, device_num)) twod_twodbc_strategy = ((1, device_num), (1, 1)) twodbc_twod_strategy = ((1, 1), (device_num, 1)) class StrategyBatch(): onehot_strategy = ((device_num, 1),(),()) twod_strategy = ((1, device_num), ) twod_strategy_m = ((device_num, 1), ) scalar_twod_strategy = ((), (1, device_num)) twod_scalar_strategy = ((1, device_num), ()) scalar_strategy = ((), ) oned_strategy = ((1, ), ) scalar_scalar_strategy = ((), ()) twod_twod_strategy = ((1, device_num), (1, device_num)) twod_twodbc_strategy = ((1, device_num), (1, 1)) twodbc_twod_strategy = ((1, 1), (device_num, 1)) class Args(): a = 1 b = 2 c = 3 d = 4 e = 5 num_classes = 512 emb_size = 512 class SemiAutoOneHotNet(Cell): def __init__(self, args, strategy): super(SemiAutoOneHotNet, self).__init__() self.a = args.a self.b = args.b self.c = args.c self.d = args.d self.e = args.e self.cast = P.Cast() self.cast.set_strategy(strategy=strategy.twod_strategy) self.cast1 = P.Cast() self.cast1.set_strategy(strategy=strategy.twod_strategy) self.cast2 = P.Cast() self.cast2.set_strategy(strategy=strategy.twod_strategy) self.cast3 = P.Cast() self.cast3.set_strategy(strategy=strategy.scalar_strategy) self.cast4 = P.Cast() self.cast4.set_strategy(strategy=strategy.scalar_strategy) self.a_const = Tensor(self.a, dtype=mstype.float32) self.b_const = Tensor(self.b, dtype=mstype.float32) self.c_const = Tensor(self.c, dtype=mstype.float32) self.d_const = Tensor(self.d, dtype=mstype.float32) self.e_const = Tensor(self.e, dtype=mstype.float32) self.m_const_zero = Tensor(0, dtype=mstype.float32) self.a_const_one = Tensor(1, dtype=mstype.float32) self.onehot = P.OneHot() self.onehot.set_strategy(strategy=strategy.onehot_strategy) self.exp = P.Exp() self.exp.set_strategy(strategy=strategy.twod_strategy) self.exp2 = P.Exp() self.exp2.set_strategy(strategy=strategy.twod_strategy) self.exp3 = P.Exp() self.exp3.set_strategy(strategy=strategy.twod_strategy) self.mul_const = P.Mul() self.mul_const.set_strategy(strategy=strategy.scalar_twod_strategy) self.mul_const2 = P.TensorAdd() self.mul_const2.set_strategy(strategy=strategy.scalar_twod_strategy) self.mul_const3 = P.Sub() self.mul_const3.set_strategy(strategy=strategy.twod_scalar_strategy) self.mul_const4 = P.Sub() self.mul_const4.set_strategy(strategy=strategy.scalar_twod_strategy) self.mul_const5 = P.Mul() self.mul_const5.set_strategy(strategy=strategy.twod_scalar_strategy) self.mul = P.Mul() self.mul.set_strategy(strategy=strategy.twod_twod_strategy) self.mul2 = P.Mul() self.mul2.set_strategy(strategy=strategy.twod_twod_strategy) self.mul3 = P.TensorAdd() self.mul3.set_strategy(strategy=strategy.twod_twod_strategy) self.mul4 = P.Sub() self.mul4.set_strategy(strategy=strategy.twod_twodbc_strategy) self.mul5 = P.RealDiv() self.mul5.set_strategy(strategy=strategy.twod_twodbc_strategy) self.mul6 = P.Mul() self.mul6.set_strategy(strategy=strategy.twod_twod_strategy) self.mul7 = P.Mul() self.mul7.set_strategy(strategy=strategy.twod_scalar_strategy) self.mul8 = P.RealDiv() self.mul8.set_strategy(strategy=strategy.scalar_scalar_strategy) self.mul9 = P.TensorAdd() self.mul9.set_strategy(strategy=strategy.twod_scalar_strategy) self.reduce_max = P.ReduceMax(keep_dims=True) self.reduce_max.set_strategy(strategy=strategy.twod_strategy) self.reduce_sum = P.ReduceSum(keep_dims=False) self.reduce_sum.set_strategy(strategy=strategy.twod_strategy) self.reduce_sum_2 = P.ReduceSum(keep_dims=False) self.reduce_sum_2.set_strategy(strategy=strategy.twod_strategy) self.reduce_sum_3 = P.ReduceSum(keep_dims=False) self.reduce_sum_3.set_strategy(strategy=strategy.oned_strategy) self.reshape = P.Reshape() self.log = P.Log() self.log.set_strategy(strategy=strategy.twod_strategy) self.on_value = Tensor(1.0, mstype.float32) self.off_value = Tensor(0.0, mstype.float32) self.normalize = P.L2Normalize(axis=1) self.normalize.set_strategy(strategy=strategy.twod_strategy_m) self.normalize2 = P.L2Normalize(axis=1) self.normalize2.set_strategy(strategy=strategy.twod_strategy_m) self.fc = P.MatMul(transpose_b=True) self.fc.set_strategy(strategy=strategy.twodbc_twod_strategy) weight_shape = [args.num_classes, args.emb_size] weight_np = np.zeros(weight_shape, np.float32) self.weight = Parameter(Tensor(weight_np), name='model_parallel_weight') def construct(self, input, label): input_n = self.normalize(input) w = self.normalize2(self.weight) fc_o = self.fc(input_n, w) fc_o_shape = F.shape(fc_o) one_hot_float = self.onehot(label, fc_o_shape[1],self.on_value, self.off_value) local_label = self.cast(one_hot_float, mstype.int32) exp_o = self.exp(fc_o) mul_const_o = self.mul_const(self.a_const, exp_o) mul_const2_o = self.mul_const2(self.b_const, mul_const_o) exp2_o = self.exp2(mul_const2_o) mul_const3_o = self.mul_const3(exp2_o, self.c_const) mul_const4_o = self.mul_const4(F.scalar_to_array(1), local_label) mul6_o = self.mul6(self.mul(mul_const3_o, one_hot_float), self.mul2(fc_o, self.cast2(mul_const4_o, mstype.float32))) mul_const5_o = self.mul_const5(mul6_o, self.d_const) max_o = self.reduce_max(mul_const5_o, -1) mul4_o = self.mul4(mul_const5_o, max_o) exp3_o = self.exp3(mul4_o) sum_o = self.reduce_sum(exp3_o, -1) reshape_o = self.reshape(sum_o, (F.shape(sum_o)[0], 1)) mul5_o = self.mul5(exp3_o, reshape_o) log_o = self.log(self.mul9(mul5_o, self.e_const)) mul3_o = self.mul3(log_o, one_hot_float) mul7_o = self.mul7(mul3_o, self.cast3(F.scalar_to_array(-1), mstype.float32)) sum2_o = self.reduce_sum_2(mul7_o, -1) loss = self.mul8(self.reduce_sum_3(sum2_o, -1), self.cast4(F.scalar_to_array(F.shape(mul_const5_o)[0]), mstype.float32)) return loss class Dataset(MindData): def __init__(self, predict, label, length=3, input_num=2): super(Dataset, self).__init__(size=length) self.predict = predict self.label = label self.index = 0 self.length = length self.input_num = input_num def __iter__(self): return self def __next__(self): if self.index >= self.length: raise StopIteration self.index += 1 if self.input_num == 2: return self.predict, self.label else: return self.predict, def reset(self): self.index = 0 class NetWithLoss(nn.Cell): def __init__(self, network): super(NetWithLoss, self).__init__() self.loss = VirtualLoss() self.network = network def construct(self, x, b): predict = self.network(x, b) return self.loss(predict) class GradWrap(nn.Cell): def __init__(self, network): super(GradWrap, self).__init__() self.network = network def construct(self, x, b): return C.grad_all(self.network)(x, b) def bn_with_initialize(out_channels): bn = nn.BatchNorm2d(out_channels, momentum=0.3, eps=1e-5).add_flags_recursive(fp32=True) return bn def fc_with_initialize(input_channels, out_channels): return nn.Dense(input_channels, out_channels) class BNReshapeDenseBNNet(nn.Cell): def __init__(self): super(BNReshapeDenseBNNet, self).__init__() self.batch_norm = bn_with_initialize(2) self.reshape = P.Reshape() self.batch_norm2 = nn.BatchNorm1d(512, affine=False) self.fc = fc_with_initialize(2 * 32 * 32, 512) self.loss = SemiAutoOneHotNet(args=Args(), strategy=StrategyBatch()) def construct(self, x, label): x = self.batch_norm(x) x = self.reshape(x, (16, 2*32*32)) x = self.fc(x) x = self.batch_norm2(x) loss = self.loss(x, label) return loss def test_bn_reshape_dense_bn_train_loss(): batch_size = 16 device_num = 16 context.set_auto_parallel_context(device_num=device_num, global_rank=0) input = Tensor(np.ones([batch_size, 2, 32, 32]).astype(np.float32) * 0.01) label = Tensor(np.ones([batch_size]), dtype=ms.int32) net = GradWrap(NetWithLoss(BNReshapeDenseBNNet())) context.set_auto_parallel_context(parallel_mode="semi_auto_parallel") _executor.compile(net, input, label) def test_semi_one_hot_net_batch(): batch_size = 16 context.set_auto_parallel_context(device_num=device_num, global_rank=0) input = Tensor(np.ones([batch_size * 1, 512]).astype(np.float32) * 0.01) label = Tensor(np.ones([batch_size]), dtype=ms.int32) net = SemiAutoOneHotNet(args=Args(), strategy=StrategyBatch()) net = GradWrap(NetWithLoss(net)) context.set_auto_parallel_context(parallel_mode="semi_auto_parallel") _executor.compile(net, input, label) def test_semi_one_hot_net_model(): batch_size = 16 learning_rate = 0.1 momentum = 0.9 epoch_size = 2 predict = Tensor(np.ones([batch_size, 512]), dtype=ms.float32) label = Tensor(np.ones([batch_size]), dtype=ms.int32) dataset = Dataset(predict, label, 2, input_num=2) net = SemiAutoOneHotNet(args=Args(), strategy=StrategyModel()) opt = Momentum(net.trainable_params(), learning_rate, momentum) context.reset_auto_parallel_context() context.set_auto_parallel_context(parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, device_num=16) context.set_context(mode=context.GRAPH_MODE) model = Model(net, optimizer=opt) model.train(epoch_size, dataset, dataset_sink_mode=False)