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108 lines
4.0 KiB
108 lines
4.0 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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# ============================================================================
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import datetime
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import numpy as np
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.nn.optim import Momentum
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from mindspore.nn import TrainOneStepCell, WithLossCell
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from mindspore.ops import operations as P
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from mindspore.communication.management import init, get_rank, get_group_size
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from mindspore.common import dtype as mstype
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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init('nccl')
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epoch = 5
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total = 5000
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batch_size = 32
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mini_batch = total // batch_size
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class LeNet(nn.Cell):
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def __init__(self):
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super(LeNet, self).__init__()
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self.relu = P.ReLU()
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self.batch_size = 32
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weight1 = Tensor(np.ones([6, 3, 5, 5]).astype(np.float32) * 0.01)
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weight2 = Tensor(np.ones([16, 6, 5, 5]).astype(np.float32) * 0.01)
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self.conv1 = nn.Conv2d(3, 6, (5, 5), weight_init=weight1, stride=1, padding=0, pad_mode='valid')
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self.conv2 = nn.Conv2d(6, 16, (5, 5), weight_init=weight2, pad_mode='valid', stride=1, padding=0)
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self.pool = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode="valid")
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self.reshape = P.Reshape()
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weight1 = Tensor(np.ones([120, 400]).astype(np.float32) * 0.01)
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self.fc1 = nn.Dense(400, 120, weight_init=weight1)
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weight2 = Tensor(np.ones([84, 120]).astype(np.float32) * 0.01)
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self.fc2 = nn.Dense(120, 84, weight_init=weight2)
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weight3 = Tensor(np.ones([10, 84]).astype(np.float32) * 0.01)
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self.fc3 = nn.Dense(84, 10, weight_init=weight3)
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def construct(self, input_x):
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output = self.conv1(input_x)
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output = self.relu(output)
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output = self.pool(output)
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output = self.conv2(output)
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output = self.relu(output)
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output = self.pool(output)
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output = self.reshape(output, (self.batch_size, -1))
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output = self.fc1(output)
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output = self.fc2(output)
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output = self.fc3(output)
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return output
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def multisteplr(total_steps, gap, base_lr=0.9, gamma=0.1, dtype=mstype.float32):
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lr = []
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for step in range(total_steps):
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lr_ = base_lr * gamma ** (step // gap)
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lr.append(lr_)
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return Tensor(np.array(lr), dtype)
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def test_lenet_nccl():
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net = LeNet()
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net.set_train()
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learning_rate = multisteplr(epoch, 2)
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momentum = Tensor(np.array([0.9]).astype(np.float32))
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mom_optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), learning_rate, momentum)
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criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
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net_with_criterion = WithLossCell(net, criterion)
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context.set_auto_parallel_context(parallel_mode="data_parallel", mirror_mean=True, device_num=get_group_size())
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train_network = TrainOneStepCell(net_with_criterion, mom_optimizer)
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train_network.set_train()
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losses = []
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data = Tensor(np.ones([net.batch_size, 3, 32, 32]).astype(np.float32) * 0.01)
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label = Tensor(np.ones([net.batch_size]).astype(np.int32))
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start = datetime.datetime.now()
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for i in range(epoch):
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for step in range(mini_batch):
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loss = train_network(data, label)
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losses.append(loss.asnumpy())
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end = datetime.datetime.now()
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with open("ms_time.txt", "w") as fo1:
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fo1.write("time:")
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fo1.write(str(end - start))
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with open("ms_loss.txt", "w") as fo2:
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fo2.write("loss:")
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fo2.write(str(losses[-5:]))
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assert (losses[-1] < 0.01)
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