forked from mindspore-Ecosystem/mindspore
668 lines
22 KiB
Python
668 lines
22 KiB
Python
# 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.
|
|
|
|
import numpy as np
|
|
|
|
import mindspore as ms
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor
|
|
from mindspore import context
|
|
from mindspore.common.api import _executor
|
|
from mindspore.common.parameter import Parameter
|
|
from mindspore.common.parameter import ParameterTuple
|
|
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
|
|
from mindspore.nn.optim.momentum import Momentum
|
|
from mindspore.ops import composite as C
|
|
from mindspore.ops import functional as F
|
|
from mindspore.ops import operations as P
|
|
from mindspore.ops.operations.comm_ops import _VirtualDataset
|
|
from mindspore.parallel import set_algo_parameters
|
|
from mindspore.train import Model, ParallelMode
|
|
from tests.dataset_mock import MindData
|
|
from tests.ut.python.ops.test_math_ops import VirtualLoss
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
context.reset_auto_parallel_context()
|
|
|
|
|
|
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)
|
|
return (self.predict,)
|
|
|
|
def reset(self):
|
|
self.index = 0
|
|
|
|
|
|
class ReshapeNet(nn.Cell):
|
|
def __init__(self, strategy0, strategy1, strategy2):
|
|
super(ReshapeNet, self).__init__()
|
|
self.relu = P.ReLU().set_strategy(strategy0)
|
|
self.reshape = P.Reshape().set_strategy(strategy1)
|
|
self.matmul = P.MatMul().set_strategy(strategy2)
|
|
self.matmul_weight = Parameter(Tensor(np.ones([25088, 256]), dtype=ms.float32), name="weight")
|
|
|
|
def construct(self, x):
|
|
x = self.relu(x)
|
|
x = self.reshape(x, (256, 25088))
|
|
x = self.matmul(x, self.matmul_weight)
|
|
return x
|
|
|
|
|
|
def reshape_net(strategy0, strategy1, strategy2):
|
|
return ReshapeNet(strategy0=strategy0, strategy1=strategy1, strategy2=strategy2)
|
|
|
|
|
|
def reshape_common(parallel_mode, strategy0, strategy1, strategy2, strategy_loss):
|
|
learning_rate = 0.1
|
|
momentum = 0.9
|
|
epoch_size = 2
|
|
|
|
context.reset_auto_parallel_context()
|
|
context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=8)
|
|
predict = Tensor(np.ones([32, 512, 7, 7]), dtype=ms.float32)
|
|
label = Tensor(np.ones([32]), dtype=ms.int32)
|
|
dataset = Dataset(predict, label, 2)
|
|
net = reshape_net(strategy0, strategy1, strategy2)
|
|
|
|
loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
|
|
loss.softmax_cross_entropy.set_strategy(strategy_loss)
|
|
loss.one_hot.set_strategy(((8, 1), (), ()))
|
|
opt = Momentum(net.trainable_params(), learning_rate, momentum)
|
|
model = Model(net, loss, opt)
|
|
model.train(epoch_size, dataset, dataset_sink_mode=False)
|
|
|
|
|
|
def test_reshape1():
|
|
strategy0 = ((8, 1, 1, 1),)
|
|
strategy1 = None
|
|
strategy2 = ((8, 1), (1, 1))
|
|
strategy_loss = ((8, 1), (8, 1))
|
|
reshape_common(ParallelMode.SEMI_AUTO_PARALLEL, strategy0, strategy1, strategy2, strategy_loss)
|
|
|
|
|
|
def test_reshape1_strategy_1():
|
|
strategy0 = ((8, 1, 1, 1),)
|
|
strategy1 = ((8, 1, 1, 1),)
|
|
strategy2 = ((8, 1), (1, 1))
|
|
strategy_loss = ((8, 1), (8, 1))
|
|
try:
|
|
reshape_common(ParallelMode.SEMI_AUTO_PARALLEL, strategy0, strategy1, strategy2, strategy_loss)
|
|
except BaseException:
|
|
pass
|
|
|
|
|
|
def test_reshape1_strategy_2():
|
|
strategy0 = ((8, 1, 1, 1),)
|
|
strategy1 = ((8, 1, 1, 1),)
|
|
strategy2 = ((8, 1), (1, 1))
|
|
strategy_loss = ((8, 1), (8, 1))
|
|
try:
|
|
reshape_common(ParallelMode.AUTO_PARALLEL, strategy0, strategy1, strategy2, strategy_loss)
|
|
except BaseException:
|
|
pass
|
|
|
|
|
|
def test_reshape2():
|
|
strategy0 = ((8, 1, 1, 1),)
|
|
strategy1 = None
|
|
strategy2 = ((8, 1), (1, 1))
|
|
strategy_loss = ((8, 1), (8, 1))
|
|
reshape_common(ParallelMode.SEMI_AUTO_PARALLEL, strategy0, strategy1, strategy2, strategy_loss)
|
|
|
|
|
|
def test_reshape3():
|
|
strategy0 = ((2, 1, 1, 1),)
|
|
strategy1 = None
|
|
strategy2 = ((8, 1), (1, 1))
|
|
strategy_loss = ((8, 1), (8, 1))
|
|
reshape_common(ParallelMode.SEMI_AUTO_PARALLEL, strategy0, strategy1, strategy2, strategy_loss)
|
|
|
|
|
|
def test_reshape4():
|
|
strategy0 = ((1, 1, 1, 1),)
|
|
strategy1 = None
|
|
strategy2 = ((8, 1), (1, 1))
|
|
strategy_loss = ((8, 1), (8, 1))
|
|
reshape_common(ParallelMode.SEMI_AUTO_PARALLEL, strategy0, strategy1, strategy2, strategy_loss)
|
|
|
|
|
|
def test_reshape5():
|
|
strategy0 = ((2, 1, 1, 1),)
|
|
strategy1 = None
|
|
strategy2 = ((1, 8), (8, 1))
|
|
strategy_loss = ((8, 1), (8, 1))
|
|
reshape_common(ParallelMode.SEMI_AUTO_PARALLEL, strategy0, strategy1, strategy2, strategy_loss)
|
|
|
|
|
|
def test_reshape_auto():
|
|
strategy0 = None
|
|
strategy1 = None
|
|
strategy2 = None
|
|
strategy_loss = None
|
|
reshape_common(ParallelMode.AUTO_PARALLEL, strategy0, strategy1, strategy2, strategy_loss)
|
|
|
|
|
|
class NetWithLoss(nn.Cell):
|
|
def __init__(self, network):
|
|
super(NetWithLoss, self).__init__()
|
|
self.loss = VirtualLoss()
|
|
self.network = network
|
|
|
|
def construct(self, x):
|
|
predict = self.network(x)
|
|
return self.loss(predict)
|
|
|
|
|
|
class GradWrap(nn.Cell):
|
|
def __init__(self, network):
|
|
super(GradWrap, self).__init__()
|
|
self.network = network
|
|
|
|
def construct(self, x):
|
|
return C.grad_all(self.network)(x)
|
|
|
|
|
|
class ReshapeNet1(nn.Cell):
|
|
def __init__(self, strategy0):
|
|
super(ReshapeNet1, self).__init__()
|
|
self.virtual_dataset = _VirtualDataset()
|
|
self.reshape = P.Reshape()
|
|
self.matmul = P.MatMul().set_strategy(strategy0)
|
|
self.matmul_weight = Parameter(Tensor(np.ones([25088, 256]), dtype=ms.float32), name="weight")
|
|
self.reshape2 = P.Reshape()
|
|
|
|
def construct(self, x):
|
|
x = self.virtual_dataset(x)
|
|
x = self.reshape(x, (256, 25088))
|
|
x = self.matmul(x, self.matmul_weight)
|
|
x = self.reshape2(x, (256 * 256,))
|
|
return x
|
|
|
|
|
|
class ReshapeNet2(nn.Cell):
|
|
def __init__(self, strategy0):
|
|
super(ReshapeNet2, self).__init__()
|
|
self.virtual_dataset = _VirtualDataset()
|
|
self.reshape = P.Reshape()
|
|
self.matmul = P.MatMul().set_strategy(strategy0)
|
|
self.matmul_weight = Parameter(Tensor(np.ones([25088, 256]), dtype=ms.float32), name="weight")
|
|
self.reshape2 = P.Reshape()
|
|
self.reduce_sum = P.ReduceSum(keep_dims=True)
|
|
self.reshape3 = P.Reshape()
|
|
|
|
def construct(self, x):
|
|
x = self.virtual_dataset(x)
|
|
x = self.reshape(x, (256, 25088))
|
|
x = self.matmul(x, self.matmul_weight)
|
|
x = self.reshape2(x, (256 * 256,))
|
|
x = self.reduce_sum(x, -1)
|
|
x = self.reshape3(x, ())
|
|
return x
|
|
|
|
|
|
class ReshapeNet3(nn.Cell):
|
|
def __init__(self, strategy0):
|
|
super(ReshapeNet3, self).__init__()
|
|
self.virtual_dataset = _VirtualDataset()
|
|
self.reshape = P.Reshape()
|
|
self.matmul = P.MatMul().set_strategy(strategy0)
|
|
self.matmul_weight = Parameter(Tensor(np.ones([25088, 256]), dtype=ms.float32), name="weight")
|
|
self.reshape2 = P.Reshape()
|
|
self.reduce_sum = P.ReduceSum(keep_dims=False)
|
|
self.reshape3 = P.Reshape()
|
|
|
|
def construct(self, x):
|
|
x = self.virtual_dataset(x)
|
|
x = self.reshape(x, (256, 25088))
|
|
x = self.matmul(x, self.matmul_weight)
|
|
x = self.reshape2(x, (256 * 256,))
|
|
x = self.reduce_sum(x, -1)
|
|
x = self.reshape3(x, (1, 1))
|
|
return x
|
|
|
|
|
|
class ReshapeNet4(nn.Cell):
|
|
def __init__(self, strategy0):
|
|
super(ReshapeNet4, self).__init__()
|
|
self.virtual_dataset = _VirtualDataset()
|
|
self.reshape = P.Reshape()
|
|
self.reshape2 = P.Reshape()
|
|
self.matmul = P.MatMul().set_strategy(strategy0)
|
|
self.matmul_weight = Parameter(Tensor(np.ones([25088, 256]), dtype=ms.float32), name="weight")
|
|
|
|
def construct(self, x):
|
|
x = self.virtual_dataset(x)
|
|
x = self.reshape(x, (256, 25088))
|
|
w = self.reshape2(self.matmul_weight, (25088, 256))
|
|
x = self.matmul(x, w)
|
|
return x
|
|
|
|
|
|
class ReshapeNet5(nn.Cell):
|
|
def __init__(self, strategy0):
|
|
super(ReshapeNet5, self).__init__()
|
|
self.virtual_dataset = _VirtualDataset()
|
|
self.reshape = P.Reshape()
|
|
self.matmul1 = P.MatMul().set_strategy(strategy0)
|
|
self.matmul1_weight = Parameter(Tensor(np.ones([25088, 256]), dtype=ms.float32), name="weight")
|
|
self.matmul2 = P.MatMul().set_strategy(strategy0)
|
|
|
|
def construct(self, x):
|
|
x = self.virtual_dataset(x)
|
|
x = self.reshape(x, (256, 25088))
|
|
matmul1_o = self.matmul1(x, self.matmul1_weight)
|
|
matmul2_o = self.matmul2(matmul1_o, x)
|
|
return matmul2_o
|
|
|
|
|
|
class ReshapeNet6(nn.Cell):
|
|
def __init__(self, strategy0):
|
|
super(ReshapeNet6, self).__init__()
|
|
self.virtual_dataset = _VirtualDataset()
|
|
self.reshape = P.Reshape()
|
|
self.matmul1_1 = P.MatMul().set_strategy(strategy0)
|
|
self.matmul1_2 = P.MatMul().set_strategy(strategy0)
|
|
self.matmul1_weight = Parameter(Tensor(np.ones([25088, 256]), dtype=ms.float32), name="weight")
|
|
self.matmul2 = P.MatMul().set_strategy(strategy0)
|
|
self.add = P.TensorAdd()
|
|
|
|
def construct(self, x):
|
|
x = self.virtual_dataset(x)
|
|
x = self.reshape(x, (256, 25088))
|
|
matmul1_1_o = self.matmul1_1(x, self.matmul1_weight)
|
|
matmul1_2_o = self.matmul1_2(x, self.matmul1_weight)
|
|
matmul1_o = self.add(matmul1_1_o, matmul1_2_o)
|
|
matmul2_o = self.matmul2(matmul1_o, x)
|
|
return matmul2_o
|
|
|
|
|
|
def compile_net(net, input_):
|
|
net.set_auto_parallel()
|
|
_executor.compile(net, input_)
|
|
|
|
|
|
def reshape_net2(backbone):
|
|
batch_size = 16
|
|
device_num = 16
|
|
context.set_auto_parallel_context(device_num=device_num, global_rank=0)
|
|
input_ = Tensor(np.ones([batch_size * device_num, 512, 7, 7]).astype(np.float32) * 0.01)
|
|
|
|
net = GradWrap(NetWithLoss(backbone))
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
|
|
compile_net(net, input_)
|
|
|
|
|
|
def test_reshape_net1_1():
|
|
reshape_net2(ReshapeNet1(((1, 8), (8, 1))))
|
|
|
|
|
|
def test_reshape_net1_2():
|
|
reshape_net2(ReshapeNet1(((1, 8), (8, 2))))
|
|
|
|
|
|
def test_reshape_net2_1():
|
|
reshape_net2(ReshapeNet2(((1, 8), (8, 1))))
|
|
|
|
|
|
def test_reshape_net2_2():
|
|
reshape_net2(ReshapeNet2(((1, 8), (8, 2))))
|
|
|
|
|
|
def test_reshape_net3_1():
|
|
reshape_net2(ReshapeNet3(((1, 8), (8, 1))))
|
|
|
|
|
|
def test_reshape_net3_2():
|
|
reshape_net2(ReshapeNet3(((1, 8), (8, 2))))
|
|
|
|
|
|
def test_reshape_net4_1():
|
|
try:
|
|
reshape_net2(ReshapeNet4(((1, 8), (8, 1))))
|
|
except BaseException:
|
|
pass
|
|
|
|
|
|
def test_reshape_net4_2():
|
|
try:
|
|
reshape_net2(ReshapeNet4(((1, 8), (8, 2))))
|
|
except BaseException:
|
|
pass
|
|
|
|
|
|
def test_reshape_net5_1():
|
|
reshape_net2(ReshapeNet5(((1, 8), (8, 1))))
|
|
|
|
|
|
def test_reshape_net5_2():
|
|
reshape_net2(ReshapeNet5(((1, 8), (8, 2))))
|
|
|
|
|
|
def test_reshape_net6_1():
|
|
reshape_net2(ReshapeNet6(((1, 8), (8, 1))))
|
|
|
|
|
|
def test_reshape_net6_2():
|
|
reshape_net2(ReshapeNet6(((1, 8), (8, 2))))
|
|
|
|
|
|
class TrainOneStepCell(nn.Cell):
|
|
"""
|
|
Network training package class.
|
|
|
|
Append an optimizer to the training network after that the construct function
|
|
can be called to create the backward graph.
|
|
|
|
Args:
|
|
network (Cell): The training network.
|
|
optimizer (Cell): Optimizer for updating the weights.
|
|
sens (Number): The adjust parameter. Default: 1.0.
|
|
|
|
Examples:
|
|
>>> net = Net()
|
|
>>> loss_fn = nn.SoftmaxCrossEntropyWithLogits()
|
|
>>> optim = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
|
|
>>> loss_net = WithLossCell(net, loss_fn)
|
|
>>> train_net = TrainOneStepCell(loss_net, optim)
|
|
"""
|
|
|
|
def __init__(self, network, optimizer, sens=1.0):
|
|
super(TrainOneStepCell, self).__init__(auto_prefix=False)
|
|
self.network = network
|
|
self.network.add_flags(defer_inline=True)
|
|
self.weights = ParameterTuple(network.trainable_params())
|
|
self.optimizer = optimizer
|
|
self.grad = C.GradOperation('grad',
|
|
get_by_list=True,
|
|
sens_param=True)
|
|
self.sens = sens
|
|
|
|
def construct(self, data):
|
|
weights = self.weights
|
|
loss = self.network(data)
|
|
sens = P.Fill()(P.DType()(loss), P.Shape()(loss), self.sens)
|
|
grads = self.grad(self.network, weights)(data, sens)
|
|
|
|
return F.depend(loss, self.optimizer(grads))
|
|
|
|
|
|
def reshape_common2(parallel_mode, net):
|
|
batch_size = 16
|
|
learning_rate = 0.1
|
|
momentum = 0.9
|
|
epoch_size = 2
|
|
|
|
predict = Tensor(np.ones([batch_size, 512, 7, 7]), dtype=ms.float32)
|
|
label = Tensor(np.ones([batch_size]), dtype=ms.int32)
|
|
dataset = Dataset(predict, label, 2, input_num=1)
|
|
context.reset_auto_parallel_context()
|
|
context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=16)
|
|
|
|
opt = Momentum(net.trainable_params(), learning_rate, momentum)
|
|
train_net = TrainOneStepCell(net, opt).set_train()
|
|
model = Model(train_net)
|
|
model.train(epoch_size, dataset, dataset_sink_mode=False)
|
|
|
|
|
|
def test_reshape_common2_0():
|
|
reshape_common2(ParallelMode.SEMI_AUTO_PARALLEL, ReshapeNet1(((1, 8), (8, 1))))
|
|
|
|
|
|
def test_reshape_common2_1():
|
|
reshape_common2(ParallelMode.SEMI_AUTO_PARALLEL, ReshapeNet1(((1, 8), (8, 2))))
|
|
|
|
|
|
def test_reshape_common2_2():
|
|
reshape_common2(ParallelMode.SEMI_AUTO_PARALLEL, ReshapeNet2(((1, 8), (8, 1))))
|
|
|
|
|
|
def test_reshape_common2_3():
|
|
reshape_common2(ParallelMode.SEMI_AUTO_PARALLEL, ReshapeNet2(((1, 8), (8, 2))))
|
|
|
|
|
|
def test_reshape_common2_4():
|
|
reshape_common2(ParallelMode.SEMI_AUTO_PARALLEL, ReshapeNet3(((1, 8), (8, 1))))
|
|
|
|
|
|
def test_reshape_common2_5():
|
|
reshape_common2(ParallelMode.SEMI_AUTO_PARALLEL, ReshapeNet3(((1, 8), (8, 2))))
|
|
|
|
|
|
class BatchNormReshapeNet(nn.Cell):
|
|
def __init__(self):
|
|
super(BatchNormReshapeNet, self).__init__()
|
|
self.vd = P._VirtualDataset()
|
|
self.batch_norm = nn.BatchNorm1d(512, affine=False)
|
|
self.reshape = P.Reshape()
|
|
self.prelu = nn.PReLU(channel=256)
|
|
|
|
def construct(self, x):
|
|
x = self.vd(x)
|
|
x = self.batch_norm(x)
|
|
x = self.reshape(x, (512, 256))
|
|
x = self.prelu(x)
|
|
return x
|
|
|
|
|
|
def test_batchnorm_reshape_train():
|
|
batch_size = 16
|
|
device_num = 16
|
|
context.set_auto_parallel_context(device_num=device_num, global_rank=0)
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
input_ = Tensor(np.ones([batch_size * device_num, 512]).astype(np.float32) * 0.01)
|
|
|
|
net = GradWrap(NetWithLoss(BatchNormReshapeNet()))
|
|
|
|
compile_net(net, input_)
|
|
|
|
|
|
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).add_flags_recursive(fp16=True)
|
|
|
|
|
|
class BNReshapeDenseBNNet(nn.Cell):
|
|
def __init__(self):
|
|
super(BNReshapeDenseBNNet, self).__init__()
|
|
self.batch_norm = bn_with_initialize(2)
|
|
self.reshape = P.Reshape()
|
|
self.cast = P.Cast()
|
|
self.batch_norm2 = nn.BatchNorm1d(512, affine=False)
|
|
self.fc = fc_with_initialize(2 * 32 * 32, 512)
|
|
|
|
def construct(self, x):
|
|
x = self.batch_norm(x)
|
|
x = self.reshape(x, (16, 2 * 32 * 32))
|
|
x = self.fc(x)
|
|
x = self.batch_norm2(x)
|
|
return x
|
|
|
|
|
|
def test_bn_reshape_dense_bn_train():
|
|
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)
|
|
|
|
net = GradWrap(NetWithLoss(BNReshapeDenseBNNet()))
|
|
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
|
|
|
|
compile_net(net, input_)
|
|
|
|
|
|
class ParallelReduceMeanNet(nn.Cell):
|
|
def __init__(self, conv_in_channel, conv_out_channel,
|
|
reducemean_keep_dims=False, reducemean_axis=-1, strategy=None):
|
|
super().__init__()
|
|
self.conv = nn.Conv2d(in_channels=conv_in_channel, out_channels=conv_out_channel,
|
|
kernel_size=1, stride=1, pad_mode='valid', has_bias=True,
|
|
weight_init='ones', bias_init='ones')
|
|
self.reduce_mean = P.ReduceMean(keep_dims=reducemean_keep_dims)
|
|
self.flat = nn.Flatten()
|
|
self.reducemean_axis = reducemean_axis
|
|
if strategy is not None:
|
|
self.reduce_mean.set_strategy(strategy)
|
|
|
|
def construct(self, inputs):
|
|
x = self.conv(inputs)
|
|
x = self.reduce_mean(x, self.reducemean_axis)
|
|
x = self.flat(x)
|
|
return x
|
|
|
|
|
|
class CrossEntropyLoss(nn.Cell):
|
|
def __init__(self, reduction='mean'):
|
|
super(CrossEntropyLoss, self).__init__()
|
|
|
|
self.reduce_mean = P.ReduceMean()
|
|
self.cross_entropy = SoftmaxCrossEntropyWithLogits()
|
|
self.reduction = reduction
|
|
|
|
def construct(self, logits, label):
|
|
loss = self.cross_entropy(logits, label)
|
|
if self.reduction == 'mean':
|
|
loss = self.reduce_mean(loss, (-1,))
|
|
return loss
|
|
|
|
|
|
def test_flatten_reshape(parallel_mode="auto_parallel"):
|
|
batch_size = 16
|
|
learning_rate = 0.1
|
|
momentum = 0.9
|
|
epoch_size = 2
|
|
context.reset_auto_parallel_context()
|
|
context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=8)
|
|
net = ParallelReduceMeanNet(conv_in_channel=3, conv_out_channel=64, reducemean_axis=(2, 3),
|
|
strategy=((4, 2, 1, 1),))
|
|
loss = CrossEntropyLoss()
|
|
predict = Tensor(np.ones([batch_size, 3, 32, 32]), dtype=ms.float32)
|
|
label = Tensor(np.ones([batch_size, 64]), dtype=ms.float32)
|
|
dataset = Dataset(predict, label, 2, input_num=2)
|
|
|
|
opt = Momentum(net.trainable_params(), learning_rate, momentum)
|
|
model = Model(net, loss_fn=loss, optimizer=opt)
|
|
model.train(epoch_size, dataset, dataset_sink_mode=False)
|
|
|
|
|
|
def test_flatten_reshape2(parallel_mode="auto_parallel"):
|
|
batch_size = 16
|
|
learning_rate = 0.1
|
|
momentum = 0.9
|
|
epoch_size = 2
|
|
context.reset_auto_parallel_context()
|
|
context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=8)
|
|
set_algo_parameters(fully_use_devices=False)
|
|
net = ParallelReduceMeanNet(conv_in_channel=3, conv_out_channel=64, reducemean_axis=(2, 3),
|
|
strategy=((4, 1, 1, 1),))
|
|
loss = CrossEntropyLoss()
|
|
predict = Tensor(np.ones([batch_size, 3, 32, 32]), dtype=ms.float32)
|
|
label = Tensor(np.ones([batch_size, 64]), dtype=ms.float32)
|
|
dataset = Dataset(predict, label, 2, input_num=2)
|
|
|
|
opt = Momentum(net.trainable_params(), learning_rate, momentum)
|
|
model = Model(net, loss_fn=loss, optimizer=opt)
|
|
model.train(epoch_size, dataset, dataset_sink_mode=False)
|
|
|
|
|
|
class ParallelReshapeNet(nn.Cell):
|
|
def __init__(self, dense_in_channel, dense_out_channel, shape, strategy=None):
|
|
super().__init__()
|
|
self.flat = nn.Flatten()
|
|
self.dense = nn.Dense(in_channels=dense_in_channel,
|
|
out_channels=dense_out_channel,
|
|
weight_init='ones',
|
|
bias_init='ones',
|
|
has_bias=True)
|
|
self.reshape = P.Reshape()
|
|
self.shape = shape
|
|
self.reshape.set_strategy(strategy)
|
|
|
|
def construct(self, inputs):
|
|
x = self.flat(inputs)
|
|
x = self.dense(x)
|
|
x = self.reshape(x, self.shape)
|
|
return x
|
|
|
|
|
|
# the shape of input and output of reshape is the same
|
|
# reshape is optimized before step_parallel
|
|
def test_flatten_reshape3(parallel_mode="auto_parallel"):
|
|
batch_size = 16
|
|
learning_rate = 0.1
|
|
momentum = 0.9
|
|
epoch_size = 2
|
|
context.reset_auto_parallel_context()
|
|
context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=8)
|
|
set_algo_parameters(fully_use_devices=False)
|
|
net = ParallelReshapeNet(dense_in_channel=2048, dense_out_channel=1000, shape=(128, 1000), strategy=((16, 1),))
|
|
loss = CrossEntropyLoss()
|
|
predict = Tensor(np.ones([batch_size, 1, 2, 1024]), dtype=ms.float32)
|
|
label = Tensor(np.ones([batch_size, 1000]), dtype=ms.float32)
|
|
dataset = Dataset(predict, label, 2, input_num=2)
|
|
|
|
opt = Momentum(net.trainable_params(), learning_rate, momentum)
|
|
model = Model(net, loss_fn=loss, optimizer=opt)
|
|
model.train(epoch_size, dataset, dataset_sink_mode=False)
|
|
|
|
|
|
class CrossEntropyLoss2(nn.Cell):
|
|
def __init__(self, reduction='mean'):
|
|
super(CrossEntropyLoss2, self).__init__()
|
|
self.cross_entropy = SoftmaxCrossEntropyWithLogits(reduction=reduction)
|
|
|
|
def construct(self, logits, label):
|
|
loss = self.cross_entropy(logits, label)
|
|
return loss
|
|
|
|
|
|
def test_flatten_reshape4(parallel_mode="semi_auto_parallel"):
|
|
batch_size = 16
|
|
learning_rate = 0.1
|
|
momentum = 0.9
|
|
epoch_size = 2
|
|
context.reset_auto_parallel_context()
|
|
context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=8)
|
|
set_algo_parameters(fully_use_devices=False)
|
|
net = ParallelReduceMeanNet(conv_in_channel=3, conv_out_channel=64, reducemean_keep_dims=True,
|
|
strategy=((4, 1, 1, 1),))
|
|
loss = CrossEntropyLoss2()
|
|
predict = Tensor(np.ones([batch_size, 3, 32, 32]), dtype=ms.float32)
|
|
label = Tensor(np.ones([batch_size, 2048]), dtype=ms.float32)
|
|
dataset = Dataset(predict, label, 2, input_num=2)
|
|
|
|
opt = Momentum(net.trainable_params(), learning_rate, momentum)
|
|
model = Model(net, loss_fn=loss, optimizer=opt)
|
|
model.train(epoch_size, dataset, dataset_sink_mode=False)
|