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216 lines
7.1 KiB
216 lines
7.1 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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import numpy as np
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import mindspore as ms
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import mindspore.nn as nn
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from mindspore import Tensor, Parameter
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from mindspore import context
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from mindspore.common.api import _executor
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from mindspore.nn import TrainOneStepCell
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from mindspore.nn.optim import Momentum, LARS
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from mindspore.ops import operations as P
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class NetWithLoss(nn.Cell):
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def __init__(self, network, strategy3):
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super(NetWithLoss, self).__init__()
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self.loss = P.SoftmaxCrossEntropyWithLogits().shard(strategy3)
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self.network = network
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def construct(self, x, b):
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predict = self.network(x)
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return self.loss(predict, b)[0]
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def compile_net(net, x, b):
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net.set_auto_parallel()
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net.set_train()
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_executor.compile(net, x, b)
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def test_momentum():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, weight):
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super().__init__()
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self.weight = Parameter(weight, "w1")
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self.matmul = P.MatMul(transpose_a=False, transpose_b=True).shard(strategy1)
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self.relu = P.ReLU().shard(strategy2)
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def construct(self, x):
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out = self.matmul(x, self.weight)
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out = self.relu(out)
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return out
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context.set_auto_parallel_context(device_num=4, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 1), (2, 1))
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strategy2 = ((4, 1),)
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strategy3 = ((4, 1), (4, 1))
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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weight = Tensor(np.ones([64, 32]), dtype=ms.float32)
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b = Tensor(np.ones([64, 64]), dtype=ms.float32)
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net = Net(strategy1, strategy2, weight)
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optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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net_with_loss = NetWithLoss(net, strategy3)
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train_net = TrainOneStepCell(net_with_loss, optimizer)
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compile_net(train_net, x, b)
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def test_momentum_with_loss_scale():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, weight):
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super().__init__()
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self.weight = Parameter(weight, "w1")
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self.matmul = P.MatMul(transpose_a=False, transpose_b=True).shard(strategy1)
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self.relu = P.ReLU().shard(strategy2)
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def construct(self, x):
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out = self.matmul(x, self.weight)
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out = self.relu(out)
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return out
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context.set_auto_parallel_context(device_num=4, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 1), (2, 1))
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strategy2 = ((4, 1),)
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strategy3 = ((4, 1), (4, 1))
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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weight = Tensor(np.ones([64, 32]), dtype=ms.float32)
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b = Tensor(np.ones([64, 64]), dtype=ms.float32)
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net = Net(strategy1, strategy2, weight)
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optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9, loss_scale=0.5)
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net_with_loss = NetWithLoss(net, strategy3)
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train_net = TrainOneStepCell(net_with_loss, optimizer)
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compile_net(train_net, x, b)
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def test_momentum_with_dynamic_lr():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, weight):
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super().__init__()
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self.weight = Parameter(weight, "w1")
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self.matmul = P.MatMul(transpose_a=False, transpose_b=True).shard(strategy1)
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self.relu = P.ReLU().shard(strategy2)
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def construct(self, x):
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out = self.matmul(x, self.weight)
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out = self.relu(out)
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return out
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context.set_auto_parallel_context(device_num=4, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 1), (2, 1))
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strategy2 = ((4, 1),)
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strategy3 = ((4, 1), (4, 1))
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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weight = Tensor(np.ones([64, 32]), dtype=ms.float32)
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b = Tensor(np.ones([64, 64]), dtype=ms.float32)
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net = Net(strategy1, strategy2, weight)
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lr = Tensor(np.ones([6]), dtype=ms.float32)
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optimizer = Momentum(net.trainable_params(), learning_rate=lr, momentum=0.9)
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net_with_loss = NetWithLoss(net, strategy3)
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train_net = TrainOneStepCell(net_with_loss, optimizer)
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compile_net(train_net, x, b)
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def test_momentum_with_loss_scale_and_dynamic_lr():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, weight):
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super().__init__()
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self.weight = Parameter(weight, "w1")
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self.matmul = P.MatMul(transpose_a=False, transpose_b=True).shard(strategy1)
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self.relu = P.ReLU().shard(strategy2)
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def construct(self, x):
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out = self.matmul(x, self.weight)
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out = self.relu(out)
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return out
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context.set_auto_parallel_context(device_num=4, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 1), (2, 1))
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strategy2 = ((4, 1),)
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strategy3 = ((4, 1), (4, 1))
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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weight = Tensor(np.ones([64, 32]), dtype=ms.float32)
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b = Tensor(np.ones([64, 64]), dtype=ms.float32)
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net = Net(strategy1, strategy2, weight)
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lr = Tensor(np.ones([6]), dtype=ms.float32)
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optimizer = Momentum(net.trainable_params(), learning_rate=lr, momentum=0.9, loss_scale=0.5)
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net_with_loss = NetWithLoss(net, strategy3)
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train_net = TrainOneStepCell(net_with_loss, optimizer)
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compile_net(train_net, x, b)
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def test_lars():
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class Net(nn.Cell):
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def __init__(self, strategy1, strategy2, weight):
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super().__init__()
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self.weight = Parameter(weight, "w1")
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self.matmul = P.MatMul(transpose_a=False, transpose_b=True).shard(strategy1)
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self.relu = P.ReLU().shard(strategy2)
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def construct(self, x):
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out = self.matmul(x, self.weight)
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out = self.relu(out)
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return out
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context.set_auto_parallel_context(device_num=4, global_rank=0)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy1 = ((2, 1), (2, 1))
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strategy2 = ((4, 1),)
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strategy3 = ((4, 1), (4, 1))
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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weight = Tensor(np.ones([64, 32]), dtype=ms.float32)
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b = Tensor(np.ones([64, 64]), dtype=ms.float32)
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net = Net(strategy1, strategy2, weight)
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lr = Tensor(np.ones([6]), dtype=ms.float32)
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sgd = Momentum(net.trainable_params(), lr, 0.9)
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optimizer = LARS(sgd, epsilon=1e-08, coefficient=0.02,
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lars_filter=lambda x: 'bn' not in x.name)
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net_with_loss = NetWithLoss(net, strategy3)
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train_net = TrainOneStepCell(net_with_loss, optimizer)
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compile_net(train_net, x, b)
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