wehub-resource-sync
159 lines
4.6 KiB
Python
159 lines
4.6 KiB
Python
# Copyright (c) 2021 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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import paddle
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from paddle import base
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from paddle.distributed import fleet
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base.disable_dygraph()
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def get_dataset(inputs):
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dataset = base.DatasetFactory().create_dataset()
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dataset.set_use_var(inputs)
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dataset.set_batch_size(1)
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dataset.set_filelist([])
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dataset.set_thread(1)
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return dataset
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def net(batch_size=4, lr=0.01):
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"""
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network definition
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Args:
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batch_size(int): the size of mini-batch for training
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lr(float): learning rate of training
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Returns:
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avg_cost: DenseTensor of cost.
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"""
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dnn_input_dim, lr_input_dim = 2, 2
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with base.device_guard("cpu"):
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dnn_data = paddle.static.data(
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name="dnn_data",
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shape=[-1, 1],
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dtype="int64",
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)
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lr_data = paddle.static.data(
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name="lr_data",
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shape=[-1, 1],
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dtype="int64",
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)
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label = paddle.static.data(
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name="click",
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shape=[-1, 1],
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dtype="float32",
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)
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data = [dnn_data, lr_data, label]
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# build dnn model
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dnn_layer_dims = [2, 1]
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dnn_embedding = paddle.static.nn.embedding(
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is_distributed=False,
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input=dnn_data,
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size=[dnn_input_dim, dnn_layer_dims[0]],
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param_attr=base.ParamAttr(
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name="deep_embedding",
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initializer=paddle.nn.initializer.Constant(value=0.01),
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),
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is_sparse=True,
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)
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dnn_pool = paddle.static.nn.sequence_lod.sequence_pool(
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input=dnn_embedding, pool_type="sum"
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)
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dnn_out = dnn_pool
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# build lr model
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lr_embedding = paddle.static.nn.embedding(
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is_distributed=False,
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input=lr_data,
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size=[lr_input_dim, 1],
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param_attr=base.ParamAttr(
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name="wide_embedding",
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initializer=paddle.nn.initializer.Constant(value=0.01),
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),
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is_sparse=True,
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)
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lr_pool = paddle.static.nn.sequence_lod.sequence_pool(
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input=lr_embedding, pool_type="sum"
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)
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with base.device_guard("gpu"):
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for i, dim in enumerate(dnn_layer_dims[1:]):
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fc = paddle.static.nn.fc(
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x=dnn_out,
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size=dim,
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activation="relu",
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weight_attr=base.ParamAttr(
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initializer=paddle.nn.initializer.Constant(value=0.01)
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),
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name=f'dnn-fc-{i}',
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)
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dnn_out = fc
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merge_layer = paddle.concat([dnn_out, lr_pool], axis=1)
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label = paddle.cast(label, dtype="int64")
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predict = paddle.static.nn.fc(
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x=merge_layer, size=2, activation='softmax'
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)
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cost = paddle.nn.functional.cross_entropy(
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input=predict, label=label, reduction='none', use_softmax=False
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)
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avg_cost = paddle.mean(x=cost)
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return data, avg_cost
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'''
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optimizer = paddle.optimizer.Adam(learning_rate=0.01)
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role = role_maker.PaddleCloudRoleMaker()
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fleet.init(role)
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strategy = paddle.distributed.fleet.DistributedStrategy()
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strategy.a_sync = True
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strategy.a_sync_configs = {"heter_worker_device_guard": 'gpu'}
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strategy.pipeline = True
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strategy.pipeline_configs = {"accumulate_steps": 1, "micro_batch_size": 2048}
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feeds, avg_cost = net()
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optimizer = fleet.distributed_optimizer(optimizer, strategy)
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optimizer.minimize(avg_cost)
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dataset = get_dataset(feeds)
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'''
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if fleet.is_server():
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pass
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# fleet.init_server()
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# fleet.run_server()
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elif fleet.is_heter_worker():
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pass
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# fleet.init_heter_worker()
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# fleet.run_heter_worker(dataset=dataset)
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fleet.stop_worker()
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elif fleet.is_worker():
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pass
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# place = base.CPUPlace()
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# exe = base.Executor(place)
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# exe.run(base.default_startup_program())
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# fleet.init_worker()
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# step = 1
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# for i in range(step):
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# exe.train_from_dataset(
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# program=base.default_main_program(), dataset=dataset, debug=False)
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# exe.close()
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# fleet.stop_worker()
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