test_dist_fleet_minimize.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 os
import unittest

import paddle
import paddle.distributed.fleet as fleet
import paddle.distributed.fleet.base.role_maker as role_maker
import paddle.fluid as fluid

paddle.enable_static()

# 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
batch_size = 4


class TestPSMinimize(unittest.TestCase):
    def net(self):
        def get_acc(cos_q_nt, cos_q_pt, batch_size):
            cond = paddle.less_than(cos_q_nt, cos_q_pt)
            cond = fluid.layers.cast(cond, dtype='float64')
            cond_3 = paddle.sum(cond)
            acc = paddle.divide(
                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 = paddle.subtract(
                fluid.layers.fill_constant_batch_size_like(
                    input=cos_q_pt, shape=[-1, 1], value=margin, dtype='float32'
                ),
                cos_q_pt,
            )
            loss_op2 = paddle.add(loss_op1, cos_q_nt)
            loss_op3 = paddle.maximum(
                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

        is_distributed = False
        is_sparse = True

        # query
        q = paddle.static.data(
            name="1", shape=[-1, 1], dtype="int64", lod_level=1
        )
        # embedding
        q_emb = fluid.contrib.layers.sparse_embedding(
            input=q,
            size=[dict_dim, emb_dim],
            param_attr=fluid.ParamAttr(
                initializer=paddle.nn.initializer.Constant(value=0.01),
                name="__emb__",
                learning_rate=emb_lr,
            ),
        )
        q_emb = paddle.reshape(q_emb, [-1, emb_dim])
        # vsum
        q_sum = paddle.static.nn.sequence_lod.sequence_pool(
            input=q_emb, pool_type='sum'
        )
        q_ss = paddle.nn.functional.softsign(q_sum)
        # fc layer after conv
        q_fc = paddle.static.nn.fc(
            x=q_ss,
            size=hid_dim,
            weight_attr=fluid.ParamAttr(
                initializer=paddle.nn.initializer.Constant(value=0.01),
                name="__q_fc__",
                learning_rate=base_lr,
            ),
        )
        # label data
        label = paddle.static.data(name="label", shape=[-1, 1], dtype="int64")
        # pt
        pt = paddle.static.data(
            name="2", shape=[-1, 1], dtype="int64", lod_level=1
        )
        # embedding
        pt_emb = fluid.contrib.layers.sparse_embedding(
            input=pt,
            size=[dict_dim, emb_dim],
            param_attr=fluid.ParamAttr(
                initializer=paddle.nn.initializer.Constant(value=0.01),
                name="__emb__",
                learning_rate=emb_lr,
            ),
        )
        pt_emb = paddle.reshape(pt_emb, [-1, emb_dim])
        # vsum
        pt_sum = paddle.static.nn.sequence_lod.sequence_pool(
            input=pt_emb, pool_type='sum'
        )
        pt_ss = paddle.nn.functional.softsign(pt_sum)
        # fc layer
        pt_fc = paddle.static.nn.fc(
            x=pt_ss,
            size=hid_dim,
            weight_attr=fluid.ParamAttr(
                initializer=paddle.nn.initializer.Constant(value=0.01),
                name="__fc__",
                learning_rate=base_lr,
            ),
            bias_attr=fluid.ParamAttr(name="__fc_b__"),
        )
        # nt
        nt = paddle.static.data(
            name="3", shape=[-1, 1], dtype="int64", lod_level=1
        )
        # embedding
        nt_emb = fluid.contrib.layers.sparse_embedding(
            input=nt,
            size=[dict_dim, emb_dim],
            param_attr=fluid.ParamAttr(
                initializer=paddle.nn.initializer.Constant(value=0.01),
                name="__emb__",
                learning_rate=emb_lr,
            ),
        )
        nt_emb = paddle.reshape(nt_emb, [-1, emb_dim])
        # vsum
        nt_sum = paddle.static.nn.sequence_lod.sequence_pool(
            input=nt_emb, pool_type='sum'
        )
        nt_ss = paddle.nn.functional.softsign(nt_sum)
        # fc layer
        nt_fc = paddle.static.nn.fc(
            x=nt_ss,
            size=hid_dim,
            weight_attr=fluid.ParamAttr(
                initializer=paddle.nn.initializer.Constant(value=0.01),
                name="__fc__",
                learning_rate=base_lr,
            ),
            bias_attr=fluid.ParamAttr(name="__fc_b__"),
        )
        cos_q_pt = paddle.nn.functional.cosine_similarity(q_fc, pt_fc)
        cos_q_nt = paddle.nn.functional.cosine_similarity(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]

    def gen_sparse_config(self):
        """
        gen sparse config
        """
        sparse_config = dict()
        # sparse_config['sparse_table_class'] = "DownpourSparseSSDTable"
        sparse_config['sparse_table_class'] = "DownpourSparseTable"
        sparse_config['sparse_compress_in_save'] = True
        sparse_config['sparse_shard_num'] = 67
        # sparse_config['sparse_accessor_class'] = "DownpourCtrAccessor"
        sparse_config[
            'sparse_accessor_class'
        ] = "DownpourCtrDymfAccessor"  # for variable embedding
        sparse_config['sparse_learning_rate'] = 0.05  # sparse_lr
        sparse_config['sparse_initial_g2sum'] = 3
        sparse_config['sparse_initial_range'] = 0.02  # init_range
        sparse_config['sparse_weight_bounds'] = [-10.0, 10.0]
        sparse_config['sparse_embedx_dim'] = 8  # emb_size
        sparse_config['sparse_embedx_threshold'] = 10
        sparse_config['sparse_nonclk_coeff'] = 0.1
        sparse_config['sparse_click_coeff'] = 1.0
        sparse_config['sparse_base_threshold'] = 0
        sparse_config['sparse_delta_threshold'] = 0.25
        sparse_config['sparse_delta_keep_days'] = 16.0
        sparse_config['sparse_show_click_decay_rate'] = 0.98
        sparse_config['sparse_delete_threshold'] = 0.8
        sparse_config['sparse_delete_after_unseen_days'] = 30

        sparse_config['embed_sparse_optimizer'] = "adagrad"  # op_type
        sparse_config['embed_sparse_learning_rate'] = 0.05  # sparse_lr
        sparse_config['embed_sparse_initial_range'] = 0
        sparse_config[
            'embed_sparse_beta1_decay_rate'
        ] = 0.9  # args.beta1_decay_rate
        sparse_config[
            'embed_sparse_beta2_decay_rate'
        ] = 0.999  # args.beta2_decay_rate
        sparse_config['embed_sparse_weight_bounds'] = [-10.0, 10.0]

        sparse_config['embedx_sparse_optimizer'] = "adagrad"  # op_type
        sparse_config['embedx_sparse_learning_rate'] = 0.05  # sparse_lr
        sparse_config['embedx_sparse_initial_range'] = 0.02  # init_range
        sparse_config[
            'embedx_sparse_beta1_decay_rate'
        ] = 0.9  # args.beta1_decay_rate
        sparse_config[
            'embedx_sparse_beta2_decay_rate'
        ] = 0.999  # args.beta2_decay_rate
        sparse_config['embedx_sparse_weight_bounds'] = [-10.0, 10.0]
        # sparse_config['nodeid_slot'] = nodeid_slot
        # sparse_config['feature_learning_rate'] = feature_lr
        return sparse_config

    def test(self):
        os.environ["PADDLE_PSERVER_NUMS"] = "2"
        os.environ["PADDLE_TRAINERS_NUM"] = "2"
        os.environ["POD_IP"] = "127.0.0.1"
        os.environ["PADDLE_PORT"] = "36001"
        os.environ["PADDLE_TRAINER_ID"] = "0"
        os.environ["PADDLE_TRAINERS_NUM"] = "2"
        os.environ[
            "PADDLE_TRAINER_ENDPOINTS"
        ] = "127.0.0.1:36001,127.0.0.2:36001"
        os.environ[
            "PADDLE_PSERVERS_IP_PORT_LIST"
        ] = "127.0.0.1:36002,127.0.0.2:36002"
        os.environ["TRAINING_ROLE"] = "TRAINER"
        os.environ["FLAGS_selected_gpus"] = "0"

        role = role_maker.PaddleCloudRoleMaker()
        fleet.init(is_collective=True)
        loss, acc, _ = self.net()

        strategy = paddle.distributed.fleet.DistributedStrategy()
        configs = {"use_ps_gpu": 0, "launch_barrier": False}
        strategy.a_sync_configs = configs
        strategy.a_sync = True

        sparse_config = dict()
        sparse_config['embedding'] = self.gen_sparse_config()
        strategy.fleet_desc_configs = sparse_config

        optimizer = paddle.fluid.optimizer.Adam(learning_rate=0.01)
        optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy)
        optimizer.minimize(loss)


if __name__ == '__main__':
    unittest.main()