dist_sharding_save.py

# 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 fluid
from paddle.distributed.fleet.base import role_maker
from paddle.distributed.fleet.meta_optimizers import sharding

# Fix seed for test
fluid.default_startup_program().random_seed = 1
fluid.default_main_program().random_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.fluid.Program()
    startup_prog = paddle.fluid.Program()
    role = role_maker.PaddleCloudRoleMaker(is_collective=True)
    fleet.init(role)
    with fluid.program_guard(train_prog, startup_prog):
        with fluid.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.fluid.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 = fluid.CUDAPlace(device_id)
    exe = fluid.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()