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- # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
- #
- # 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 os
-
- from dist_mnist import cnn_model # noqa: F401
-
- import paddle
- from paddle import base
- from paddle.distributed.fleet.base import role_maker
- from paddle.distributed.fleet.meta_optimizers import sharding
-
- # Fix seed for test
- paddle.seed(1)
-
-
- def runtime_main():
- from test_dist_base import dump_output
-
- from paddle.distributed import fleet
-
- paddle.enable_static()
-
- # model definition
- train_prog = paddle.base.Program()
- startup_prog = paddle.base.Program()
- role = role_maker.PaddleCloudRoleMaker(is_collective=True)
- fleet.init(role)
- with base.program_guard(train_prog, startup_prog):
- with base.unique_name.guard():
- input_x = paddle.static.data(
- name="x", shape=[-1, 32], dtype='float32'
- )
- input_y = paddle.static.data(name="y", shape=[-1, 1], dtype='int64')
-
- fc_1 = paddle.static.nn.fc(x=input_x, size=64, activation='tanh')
- fc_2 = paddle.static.nn.fc(x=fc_1, size=256, activation='tanh')
- prediction = paddle.static.nn.fc(
- x=[fc_2], size=2, activation='softmax'
- )
- cost = paddle.nn.functional.cross_entropy(
- input=prediction,
- label=input_y,
- reduction='none',
- use_softmax=False,
- )
- avg_cost = paddle.mean(x=cost)
-
- strategy = paddle.distributed.fleet.DistributedStrategy()
- strategy.sharding = True
- strategy.sharding_configs = {
- "sharding_segment_strategy": "segment_broadcast_MB",
- "segment_broadcast_MB": 0.2,
- "sharding_degree": 2,
- }
-
- optimizer = paddle.optimizer.Momentum(
- learning_rate=0.01, momentum=0.9
- )
- optimizer = fleet.distributed_optimizer(
- optimizer, strategy=strategy
- )
- optimizer.minimize(avg_cost)
-
- # execution
- device_id = int(os.getenv("FLAGS_selected_gpus", "0"))
- place = base.CUDAPlace(device_id)
- exe = base.Executor(place)
- exe.run(startup_prog)
- dirname = "./ut_sharding_save_model"
- sharding.utils.save_persistables(
- exe, dirname, main_program=train_prog, filename=None
- )
-
- out_losses = []
- dump_output(out_losses)
-
-
- if __name__ == "__main__":
- # NOTE(liangjianzhong): dist unittest should be implemented using runtime_main in test_dist_base.py
- # but the runtime_main in test_dist_base.py use the fleet, DistributedStrategy from
- # paddle.incubate.distributed.fleet.collective which is not support by sharding (paddle.distributed.fleet).
- # this should be update in future.
- # runtime_main(TestDistMnist2x2)
- runtime_main()
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