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233 lines
9.8 KiB
233 lines
9.8 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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"""Test fleet."""
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from __future__ import print_function
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import os
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import unittest
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import paddle.fluid.incubate.fleet.base.role_maker as role_maker
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class TestFleet2(unittest.TestCase):
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"""Test cases for fleet ops."""
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def setUp(self):
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"""Set up, set envs."""
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os.environ["PADDLE_TRAINERS_NUM"] = "2"
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os.environ[
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"PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001,127.0.0.2:36001"
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def test_pslib_1(self):
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"""Test cases for pslib."""
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import paddle.fluid as fluid
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from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
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from paddle.fluid.incubate.fleet.parameter_server.pslib import \
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fleet_embedding, _prepare_params, _fleet_embedding, \
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_fleet_embedding_v2, FLEET_GLOBAL_DICT
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from paddle.fluid.incubate.fleet.base.role_maker import GeneralRoleMaker
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try:
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import netifaces
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except:
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print("warning: no netifaces, skip test_pslib_1")
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return
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os.environ["POD_IP"] = "127.0.0.1"
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os.environ["PADDLE_PORT"] = "36001"
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os.environ["TRAINING_ROLE"] = "TRAINER"
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os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001"
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os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36002"
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os.environ["PADDLE_TRAINER_ID"] = "0"
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role_maker = GeneralRoleMaker()
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role_maker.generate_role()
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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fleet.init(role_maker)
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train_program = fluid.Program()
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startup_program = fluid.Program()
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scope = fluid.Scope()
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global FLEET_GLOBAL_DICT
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with fluid.program_guard(train_program, startup_program):
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show = fluid.layers.data(name="show", shape=[-1, 1], \
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dtype="int64", lod_level=1, append_batch_size=False)
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click = fluid.layers.data(name="click", shape=[-1, 1], \
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dtype="int64", lod_level=1, append_batch_size=False)
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with fleet_embedding(click_name=click.name):
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emb = fluid.layers.embedding(input=show, size=[1, 1], \
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is_sparse=True, is_distributed=True, \
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param_attr=fluid.ParamAttr(name="embedding"))
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emb = fluid.layers.data_norm(
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input=emb,
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name="a",
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epsilon=1e-4,
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param_attr={
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"batch_size": 1e4,
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"batch_sum_default": 0.0,
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"batch_square": 1e4
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})
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fc = fluid.layers.fc(input=emb, size=1, act=None)
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label = fluid.layers.data(name="click", shape=[-1, 1], \
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dtype="int64", lod_level=1, append_batch_size=False)
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label_cast = fluid.layers.cast(label, dtype='float32')
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cost = fluid.layers.log_loss(fc, label_cast)
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try:
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adam = fluid.optimizer.Adam(learning_rate=0.000005)
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adam = fleet.distributed_optimizer(
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adam,
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strategy={
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"embedding": {
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"sparse_accessor_class": "DownpourSparseValueAccessor"
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}
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})
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adam.minimize([cost], [scope])
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except:
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print("do not support pslib test, skip")
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return
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FLEET_GLOBAL_DICT["cur_accessor"] = "DownpourCtrAccessor"
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try:
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_prepare_params(input=show, size=[1, 1])
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except:
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print("catch expected exception of param_attr=None")
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try:
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_prepare_params(
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input=show, size=[1, 1], param_attr=fluid.ParamAttr())
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except:
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print("catch expected exception of name=None")
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try:
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tmp = fluid.ParamAttr(name="embedding")
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_prepare_params(input=show, size=1, param_attr=tmp)
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except:
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print("catch expected exception of size not list")
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try:
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tmp = fluid.ParamAttr(name="embedding")
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_prepare_params(input=show, size=[-1, 12], param_attr=tmp)
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except:
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print("catch expected exception of size not equal")
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try:
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tmp = fluid.ParamAttr(name="embedding")
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_prepare_params(
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input=show, size=[-1, 1], param_attr=tmp, is_sparse=False)
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except:
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print("catch expected exception of is_sparse=False")
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try:
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tmp = fluid.ParamAttr(name="embedding")
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_prepare_params(input=show, size=[-1, 1], param_attr=tmp, \
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is_sparse=True, is_distributed=False)
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except:
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print("catch expected exception of is_distributed=False")
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try:
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_prepare_params(input=show, size=[-1, 1], \
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param_attr=fluid.ParamAttr(name="embedding"), \
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is_sparse=True, is_distributed=True, dtype="abc")
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except:
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print("catch expected exception of unknown dtype")
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try:
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FLEET_GLOBAL_DICT["emb_to_accessor"]["embedding"] = "unknown"
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tmp = fluid.ParamAttr(name="embedding")
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_prepare_params(input=show, size=[-1, 1], param_attr=tmp)
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except:
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print("catch expected exception of unknown accessor")
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FLEET_GLOBAL_DICT["cur_accessor"] = "DownpourCtrAccessor"
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try:
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_fleet_embedding(input=show, size=[-1, 1], is_sparse=True, \
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is_distributed=True, dtype="float32", \
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param_attr=fluid.ParamAttr(name="embedding"))
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except:
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print("catch expected exception of unknown accessor")
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try:
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_fleet_embedding_v2(input=show, size=[-1, 1], is_sparse=True, \
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is_distributed=True, dtype="float32", \
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param_attr=fluid.ParamAttr(name="embedding"))
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except:
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print("catch expected exception of unknown accessor")
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adam1 = fluid.optimizer.Adam(learning_rate=0.000005)
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adam1 = fleet.distributed_optimizer(
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adam1,
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strategy={
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"embedding": {
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"sparse_accessor_class": "DownpourSparseValueAccessor"
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}
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})
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try:
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pre = FLEET_GLOBAL_DICT["emb_to_table"]
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FLEET_GLOBAL_DICT["emb_to_table"] = {}
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adam1.minimize([cost], [scope])
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except:
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FLEET_GLOBAL_DICT["emb_to_table"] = pre
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print("catch expected exception of empty emb_to_table")
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try:
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pre = FLEET_GLOBAL_DICT["emb_to_table"]
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FLEET_GLOBAL_DICT["emb_to_table"] = {}
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FLEET_GLOBAL_DICT["emb_to_table"]["emb1"] = 0
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adam1.minimize([cost], [scope])
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except:
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FLEET_GLOBAL_DICT["emb_to_table"] = pre
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print("catch expected exception of error emb_to_table")
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try:
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adam2 = fluid.optimizer.Adam(learning_rate=0.000005)
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adam2 = fleet.distributed_optimizer(adam2)
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adam2.supported_embedding_types = []
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adam2.minimize([cost], [scope])
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except:
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print("catch expected exception of embedding_types")
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try:
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adam3 = fluid.optimizer.Adam(learning_rate=0.000005)
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adam3 = fleet.distributed_optimizer(
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adam3,
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strategy={
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"embedding": {
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"sparse_accessor_class": "DownpourSparseValueAccessor",
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"sparse_embedx_dim": 999
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}
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})
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adam3.minimize([cost], [scope])
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except:
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print("catch expected exception of embedx_dim error")
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try:
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adam4 = fluid.optimizer.Adam(learning_rate=0.000005)
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adam4 = fleet.distributed_optimizer(
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adam4,
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strategy={
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"embedding": {
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"sparse_accessor_class": "DownpourCtrAccessor",
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"sparse_embedx_dim": 999
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}
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})
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adam4.minimize([cost], [scope])
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except:
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print("catch expected exception of embedx_dim error")
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train_program1 = fluid.Program()
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startup_program1 = fluid.Program()
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FLEET_GLOBAL_DICT["emb_to_accessor"] = {}
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with fluid.program_guard(train_program1, startup_program1):
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show = fluid.layers.data(name="show", shape=[-1, 1], \
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dtype="int64", lod_level=1, append_batch_size=False)
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with fleet_embedding(click_name=click.name):
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emb = fluid.layers.embedding(input=show, size=[1, 1], \
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is_sparse=True, is_distributed=True, \
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param_attr=fluid.ParamAttr(name="embedding"))
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with fleet_embedding(click_name=click.name):
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emb1 = fluid.embedding(input=show, size=[1, 1], \
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is_sparse=True, is_distributed=True, \
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param_attr=fluid.ParamAttr(name="embedding"))
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fleet.save_model("./tmodel_000")
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fleet.save_one_table(0, "./tmodel_001")
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fleet.save_one_table(0, "./tmodel_002", prefix="thahaha")
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fleet.load_model("./tmodel_0003")
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fleet.load_one_table(0, "./tmodel_004")
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if __name__ == "__main__":
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unittest.main()
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