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90 lines
3.5 KiB
90 lines
3.5 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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from __future__ import print_function
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import paddle
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import paddle.fluid as fluid
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from test_dist_base import TestDistRunnerBase, runtime_main
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from dist_mnist import cnn_model
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# from paddle.fluid.incubate.fleet.collective import fleet
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import paddle.distributed.fleet as fleet
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import paddle.distributed.fleet.base.role_maker as role_maker
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from paddle.distributed.fleet.meta_optimizers.sharding.utils import sharding_save_persistables
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import os
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import six
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import sys
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import pickle
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# Fix seed for test
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fluid.default_startup_program().random_seed = 1
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fluid.default_main_program().random_seed = 1
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def runtime_main():
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import paddle.distributed.fleet as fleet
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# model definition
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train_prog = paddle.fluid.Program()
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startup_prog = paddle.fluid.Program()
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role = role_maker.PaddleCloudRoleMaker(is_collective=True)
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fleet.init(role)
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with fluid.program_guard(train_prog, startup_prog):
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with fluid.unique_name.guard():
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input_x = paddle.fluid.layers.data(
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name="x", shape=[32], dtype='float32')
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input_y = paddle.fluid.layers.data(
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name="y", shape=[1], dtype='int64')
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fc_1 = paddle.fluid.layers.fc(input=input_x,
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size=64,
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act='tanh')
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fc_2 = paddle.fluid.layers.fc(input=fc_1, size=256, act='tanh')
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prediction = paddle.fluid.layers.fc(input=[fc_2],
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size=2,
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act='softmax')
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cost = paddle.fluid.layers.cross_entropy(
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input=prediction, label=input_y)
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avg_cost = paddle.fluid.layers.mean(x=cost)
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strategy = paddle.distributed.fleet.DistributedStrategy()
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strategy.sharding = True
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strategy.sharding_configs = {"fuse_broadcast_MB": 0.2}
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optimizer = paddle.fluid.optimizer.Momentum(learning_rate=0.01, momentum=0.9)
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optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
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optimizer.minimize(avg_cost)
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# execution
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device_id = int(os.getenv("FLAGS_selected_gpus", "0"))
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place = fluid.CUDAPlace(device_id)
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exe = fluid.Executor(place)
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exe.run(startup_prog)
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dirname="./ut_sharding_save_model"
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sharding_save_persistables(exe, dirname, main_program=train_prog, filename=None)
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out_losses=[]
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if six.PY2:
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print(pickle.dumps(out_losses))
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else:
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sys.stdout.buffer.write(pickle.dumps(out_losses))
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if __name__ == "__main__":
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#NOTE(liangjianzhong): dist unittest should be imlpement using runtime_main in test_dist_base.py
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# but the runtime_main in test_dist_base.py use the fleet, DistributedStrategy from
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# paddle.fluid.incubate.fleet.collective which is not support by sharding (paddle.distributed.fleet).
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# this should be update in future.
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# runtime_main(TestDistMnist2x2)
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runtime_main()
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