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