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135 lines
4.5 KiB
135 lines
4.5 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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 paddle
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import paddle.distributed.fleet.base.role_maker as role_maker
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import paddle.distributed.fleet as fleet
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import paddle.fluid as fluid
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import unittest
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import paddle.nn.functional as F
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import numpy as np
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paddle.enable_static()
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def gen_data():
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return {
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"x": np.random.random(size=(128, 32)).astype('float32'),
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"y": np.random.randint(
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2, size=(128, 1)).astype('int64')
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}
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def mlp(input_x, input_y, hid_dim=128, label_dim=2):
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fc_1 = paddle.static.nn.fc(x=input_x, size=hid_dim, activation='tanh')
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fc_2 = paddle.static.nn.fc(x=fc_1, size=hid_dim, activation='tanh')
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prediction = paddle.static.nn.fc(x=[fc_2],
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size=label_dim,
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activation='softmax')
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cost = F.cross_entropy(input=prediction, label=input_y)
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avg_cost = paddle.mean(x=cost)
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return avg_cost
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class TestFleetAMPInit(unittest.TestCase):
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def test_fleet_amp_init(self):
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if not fluid.core.is_compiled_with_cuda():
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return
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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role = role_maker.PaddleCloudRoleMaker(is_collective=True)
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fleet.init(role)
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with paddle.static.program_guard(main_program, startup_program):
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input_x = paddle.static.data(
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name="x", shape=[None, 32], dtype='float32')
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input_y = paddle.static.data(
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name="y", shape=[None, 1], dtype='int64')
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cost = mlp(input_x, input_y)
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optimizer = paddle.optimizer.Momentum(
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learning_rate=0.001,
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momentum=0.9,
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weight_decay=fluid.regularizer.L2Decay(1e-4),
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multi_precision=True)
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optimizer = paddle.static.amp.decorate(optimizer)
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optimizer = fleet.distributed_optimizer(optimizer)
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optimizer.minimize(cost)
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place = paddle.CUDAPlace(0)
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exe = paddle.static.Executor(place)
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exe.run(startup_program)
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optimizer.amp_init(place)
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step = 1
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for i in range(step):
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cost_val = exe.run(program=main_program,
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feed=gen_data(),
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fetch_list=[cost.name])
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def test_fleet_amp_meta_optimizer_init(self):
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if not fluid.core.is_compiled_with_cuda():
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return
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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role = role_maker.PaddleCloudRoleMaker(is_collective=True)
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fleet.init(role)
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with paddle.static.program_guard(main_program, startup_program):
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input_x = paddle.static.data(
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name="x", shape=[None, 32], dtype='float32')
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input_y = paddle.static.data(
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name="y", shape=[None, 1], dtype='int64')
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cost = mlp(input_x, input_y)
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optimizer = paddle.optimizer.Momentum(
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learning_rate=0.001,
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momentum=0.9,
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weight_decay=fluid.regularizer.L2Decay(1e-4),
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multi_precision=True)
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strategy = paddle.distributed.fleet.DistributedStrategy()
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strategy.amp = True
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strategy.amp_configs = {'use_pure_fp16': True}
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strategy.gradient_merge = True
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strategy.gradient_merge_configs = {"k_steps": 2}
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optimizer = fleet.distributed_optimizer(optimizer, strategy)
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optimizer.minimize(cost)
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print(fleet._get_applied_meta_list())
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place = paddle.CUDAPlace(0)
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exe = paddle.static.Executor(place)
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exe.run(startup_program)
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optimizer.amp_init(place)
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step = 3
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for i in range(step):
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cost_val = exe.run(program=main_program,
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feed=gen_data(),
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fetch_list=[cost.name])
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print(cost_val)
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if __name__ == '__main__':
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unittest.main()
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