dist_fleet_simnet_bow.py

#   Copyright (c) 2018 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 numpy as np
import time

import paddle
import paddle.fluid as fluid
import os
from test_dist_fleet_base import runtime_main, FleetDistRunnerBase

paddle.enable_static()

DTYPE = "int64"
DATA_URL = 'http://paddle-dist-ce-data.bj.bcebos.com/simnet.train.1000'
DATA_MD5 = '24e49366eb0611c552667989de2f57d5'

# For Net
base_lr = 0.2
emb_lr = base_lr * 3
dict_dim = 1500
emb_dim = 128
hid_dim = 128
margin = 0.1
sample_rate = 1

# Fix seed for test
fluid.default_startup_program().random_seed = 1
fluid.default_main_program().random_seed = 1


def fake_simnet_reader():
    def reader():
        for _ in range(1000):
            q = np.random.random_integers(0, 1500 - 1, size=1).tolist()
            label = np.random.random_integers(0, 1, size=1).tolist()
            pt = np.random.random_integers(0, 1500 - 1, size=1).tolist()
            nt = np.random.random_integers(0, 1500 - 1, size=1).tolist()
            yield [q, label, pt, nt]

    return reader


def get_acc(cos_q_nt, cos_q_pt, batch_size):
    cond = fluid.layers.less_than(cos_q_nt, cos_q_pt)
    cond = fluid.layers.cast(cond, dtype='float64')
    cond_3 = fluid.layers.reduce_sum(cond)
    acc = fluid.layers.elementwise_div(
        cond_3,
        fluid.layers.fill_constant(
            shape=[1], value=batch_size * 1.0, dtype='float64'
        ),
        name="simnet_acc",
    )
    return acc


def get_loss(cos_q_pt, cos_q_nt):
    loss_op1 = fluid.layers.elementwise_sub(
        fluid.layers.fill_constant_batch_size_like(
            input=cos_q_pt, shape=[-1, 1], value=margin, dtype='float32'
        ),
        cos_q_pt,
    )
    loss_op2 = fluid.layers.elementwise_add(loss_op1, cos_q_nt)
    loss_op3 = fluid.layers.elementwise_max(
        fluid.layers.fill_constant_batch_size_like(
            input=loss_op2, shape=[-1, 1], value=0.0, dtype='float32'
        ),
        loss_op2,
    )
    avg_cost = paddle.mean(loss_op3)
    return avg_cost


def train_network(
    batch_size,
    is_distributed=False,
    is_sparse=False,
    is_self_contained_lr=False,
    is_pyreader=False,
):
    # query
    q = fluid.layers.data(
        name="query_ids", shape=[1], dtype="int64", lod_level=1
    )
    # label data
    label = fluid.layers.data(name="label", shape=[1], dtype="int64")
    # pt
    pt = fluid.layers.data(
        name="pos_title_ids", shape=[1], dtype="int64", lod_level=1
    )
    # nt
    nt = fluid.layers.data(
        name="neg_title_ids", shape=[1], dtype="int64", lod_level=1
    )

    datas = [q, label, pt, nt]

    reader = None
    if is_pyreader:
        reader = fluid.io.PyReader(
            feed_list=datas,
            capacity=64,
            iterable=False,
            use_double_buffer=False,
        )

    # embedding
    q_emb = fluid.embedding(
        input=q,
        is_distributed=is_distributed,
        size=[dict_dim, emb_dim],
        param_attr=fluid.ParamAttr(
            initializer=fluid.initializer.Constant(value=0.01), name="__emb__"
        ),
        is_sparse=is_sparse,
    )
    q_emb = fluid.layers.reshape(q_emb, [-1, emb_dim])
    # vsum
    q_sum = fluid.layers.sequence_pool(input=q_emb, pool_type='sum')
    q_ss = fluid.layers.softsign(q_sum)
    # fc layer after conv
    q_fc = fluid.layers.fc(
        input=q_ss,
        size=hid_dim,
        param_attr=fluid.ParamAttr(
            initializer=fluid.initializer.Constant(value=0.01),
            name="__q_fc__",
            learning_rate=base_lr,
        ),
    )

    # embedding
    pt_emb = fluid.embedding(
        input=pt,
        is_distributed=is_distributed,
        size=[dict_dim, emb_dim],
        param_attr=fluid.ParamAttr(
            initializer=fluid.initializer.Constant(value=0.01),
            name="__emb__",
            learning_rate=emb_lr,
        ),
        is_sparse=is_sparse,
    )
    pt_emb = fluid.layers.reshape(pt_emb, [-1, emb_dim])
    # vsum
    pt_sum = fluid.layers.sequence_pool(input=pt_emb, pool_type='sum')
    pt_ss = fluid.layers.softsign(pt_sum)
    # fc layer
    pt_fc = fluid.layers.fc(
        input=pt_ss,
        size=hid_dim,
        param_attr=fluid.ParamAttr(
            initializer=fluid.initializer.Constant(value=0.01), name="__fc__"
        ),
        bias_attr=fluid.ParamAttr(name="__fc_b__"),
    )

    # embedding
    nt_emb = fluid.embedding(
        input=nt,
        is_distributed=is_distributed,
        size=[dict_dim, emb_dim],
        param_attr=fluid.ParamAttr(
            initializer=fluid.initializer.Constant(value=0.01), name="__emb__"
        ),
        is_sparse=is_sparse,
    )
    nt_emb = fluid.layers.reshape(nt_emb, [-1, emb_dim])
    # vsum
    nt_sum = fluid.layers.sequence_pool(input=nt_emb, pool_type='sum')
    nt_ss = fluid.layers.softsign(nt_sum)
    # fc layer
    nt_fc = fluid.layers.fc(
        input=nt_ss,
        size=hid_dim,
        param_attr=fluid.ParamAttr(
            initializer=fluid.initializer.Constant(value=0.01), name="__fc__"
        ),
        bias_attr=fluid.ParamAttr(name="__fc_b__"),
    )
    cos_q_pt = fluid.layers.cos_sim(q_fc, pt_fc)
    cos_q_nt = fluid.layers.cos_sim(q_fc, nt_fc)
    # loss
    avg_cost = get_loss(cos_q_pt, cos_q_nt)
    # acc
    acc = get_acc(cos_q_nt, cos_q_pt, batch_size)
    return avg_cost, acc, cos_q_pt, reader


class TestDistSimnetBow2x2(FleetDistRunnerBase):
    """
    For test SimnetBow model, use Fleet api
    """

    def net(self, args, batch_size=4, lr=0.01):
        avg_cost, _, predict, self.reader = train_network(
            batch_size=batch_size,
            is_distributed=False,
            is_sparse=True,
            is_self_contained_lr=False,
            is_pyreader=(args.reader == "pyreader"),
        )
        self.avg_cost = avg_cost
        self.predict = predict

        return avg_cost

    def check_model_right(self, dirname):
        model_filename = os.path.join(dirname, "__model__")

        with open(model_filename, "rb") as f:
            program_desc_str = f.read()

        program = fluid.Program.parse_from_string(program_desc_str)
        with open(os.path.join(dirname, "__model__.proto"), "w") as wn:
            wn.write(str(program))

    def do_pyreader_training(self, fleet):
        """
        do training using dataset, using fetch handler to catch variable
        Args:
            fleet(Fleet api): the fleet object of Parameter Server, define distribute training role
        """

        exe = fluid.Executor(fluid.CPUPlace())
        exe.run(fluid.default_startup_program())
        fleet.init_worker()
        batch_size = 4
        # reader
        train_reader = paddle.batch(fake_simnet_reader(), batch_size=batch_size)
        self.reader.decorate_sample_list_generator(train_reader)
        for epoch_id in range(1):
            self.reader.start()
            try:
                pass_start = time.time()
                while True:
                    loss_val = exe.run(
                        program=fluid.default_main_program(),
                        fetch_list=[self.avg_cost.name],
                    )
                    loss_val = np.mean(loss_val)
                    message = "TRAIN ---> pass: {} loss: {}\n".format(
                        epoch_id, loss_val
                    )
                    fleet.util.print_on_rank(message, 0)

                pass_time = time.time() - pass_start
            except fluid.core.EOFException:
                self.reader.reset()

    def do_dataset_training(self, fleet):
        pass


if __name__ == "__main__":
    runtime_main(TestDistSimnetBow2x2)