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readme.md → README.md
浏览文件 @ 3e50521c
<p align="center">
<img align="center" src="doc/imgs/logo.png">
<p>
......@@ -31,12 +30,20 @@
<p>
<h2 align="center">推荐系统-流程概览</h2>
<p align="center">
<img align="center" src="doc/imgs/rec-overview.png">
<p>
<h2 align="center">便捷安装</h2>
### 环境要求
* Python 2.7/ 3.5 / 3.6 / 3.7
* PaddlePaddle >= 1.7.2
* 操作系统: Windows/Mac/Linux
> Windows下目前仅提供单机训练，建议使用Linux
### 安装命令
......@@ -67,71 +74,116 @@
### 启动内置模型的默认配置
目前框架内置了多个模型，简单的命令即可使用内置模型开始单机训练和本地1*1模拟训练，我们以`dnn`为例介绍PaddleRec的简单使用。
目前框架内置了多个模型，一行命令即可使用内置模型开始单机训练和本地模拟分布式训练。
> 本地模拟分布式(`local_cluster`)为`1个server + 1个trainer`的参数服务器模式
#### 单机训练
我们以排序模型中的`dnn`模型为例介绍PaddleRec的简单使用。训练数据来源为[Criteo数据集](https://www.kaggle.com/c/criteo-display-ad-challenge/)，我们从中截取了100条方便您快速上手体验完整的PaddleRec流程。
```bash
# 使用CPU进行单机训练
python -m paddlerec.run -m paddlerec.models.rank.dnn -d cpu -e single
python -m paddlerec.run -m paddlerec.models.rank.dnn
```
# 使用GPU进行单机训练
python -m paddlerec.run -m paddlerec.models.rank.dnn -d gpu -e single
### 启动内置模型的自定配置
若您复用内置模型，对**yaml**配置文件进行了修改，如更改超参，重新配置数据后，可以直接使用paddlerec运行该yaml文件。
我们以dnn模型为例，在paddlerec代码目录下:
```bash
cd paddlerec
```
#### 本地模拟分布式训练
修改dnn模型的[超参配置](./models/rank/dnn/config.yaml)，例如将迭代训练轮数从10轮修改为5轮:
```yaml
train:
# epochs: 10
epochs: 5
```
在Linux环境下，可以使用`vim`等文本编辑工具修改yaml文件：
```bash
# 使用CPU资源进行本地模拟分布式训练
python -m paddlerec.run -m paddlerec.models.rank.dnn -e local_cluster
vim ./models/rank/dnn/config.yaml
# 键入 i, 进入编辑模式
# 修改yaml文件配置
# 完成修改后，点击esc，退出编辑模式
# 键入 :wq 保存文件并退出
```
#### 集群分布式训练
完成dnn模型`models/rank/dnn/config.yaml`的配置修改后，运行`dnn`模型：
```bash
# 使用自定配置进行训练
python -m paddlerec.run -m ./models/rank/dnn/config.yaml
```
### 分布式训练
分布式训练需要配置`config.yaml`，加入或修改`engine`选项为`cluster`或`local_cluster`，以进行分布式训练，或本地模拟分布式训练。
#### 本地模拟分布式训练
我们以dnn模型为例，在paddlerec代码目录下，修改dnn模型的`config.yaml`文件:
```yaml
train:
#engine: single
engine: local_cluster
```
然后启动paddlerec训练：
```bash
# 配置好 mpi/k8s/paddlecloud集群环境后
python -m paddlerec.run -m paddlerec.models.rank.dnn -e cluster
# 进行本地模拟分布式训练
python -m paddlerec.run -m ./models/rank/dnn/config.yaml
```
### 启动内置模型的自定配置
#### 集群分布式训练
若您复用内置模型，对**yaml**配置文件进行了修改，如更改超参，重新配置数据后，可以直接使用paddlerec运行该yaml文件。
我们以dnn模型为例，在paddlerec代码目录下，首先修改dnn模型`config.yaml`文件:
```yaml
train:
#engine: single
engine: cluster
```
再添加分布式启动配置文件`backend.yaml`，具体配置规则在[分布式训练](doc/distributed_train.md)教程中介绍。最后启动paddlerec训练：
我们以dnn模型为例，在paddlerec代码目录下，修改了dnn模型`config.yaml`的配置后，运行`dnn`模型：
```bash
python -m paddlerec.run -m ./models/rank/dnn/config.yaml -e single
# 配置好 mpi/k8s/paddlecloud集群环境后
python -m paddlerec.run -m ./models/rank/dnn/config.yaml -b backend.yaml
```
<h2 align="center">支持模型列表</h2>
| 方向 | 模型 | 单机CPU训练 | 单机GPU训练 | 分布式CPU训练 |
| :------: | :----------------------------------------------------------------------------: | :---------: | :---------: | :-----------: |
| 方向 | 模型 | 单机CPU训练 | 单机GPU训练 | 分布式CPU训练 |
| :------: | :-----------------------------------------------------------------------: | :---------: | :---------: | :-----------: |
| 内容理解 | [Text-Classifcation](models/contentunderstanding/classification/model.py) | ✓ | x | ✓ |
| 内容理解 | [TagSpace](models/contentunderstanding/tagspace/model.py) | ✓ | x | ✓ |
| 召回 | [TDM](models/treebased/tdm/model.py) | ✓ | x | ✓ |
| 召回 | [Word2Vec](models/recall/word2vec/model.py) | ✓ | x | ✓ |
| 召回 | [SSR](models/recall/ssr/model.py) | ✓ | ✓ | ✓ |
| 召回 | [Gru4Rec](models/recall/gru4rec/model.py) | ✓ | ✓ | ✓ |
| 排序 | [Dnn](models/rank/dnn/model.py) | ✓ | x | ✓ |
| 排序 | [DeepFM](models/rank/deepfm/model.py) | ✓ | x | ✓ |
| 排序 | [xDeepFM](models/rank/xdeepfm/model.py) | ✓ | x | ✓ |
| 排序 | [DIN](models/rank/din/model.py) | ✓ | x | ✓ |
| 排序 | [Wide&Deep](models/rank/wide_deep/model.py) | ✓ | x | ✓ |
| 多任务 | [ESMM](models/multitask/esmm/model.py) | ✓ | ✓ | ✓ |
| 多任务 | [MMOE](models/multitask/mmoe/model.py) | ✓ | ✓ | ✓ |
| 多任务 | [ShareBottom](models/multitask/share-bottom/model.py) | ✓ | ✓ | ✓ |
| 匹配 | [DSSM](models/match/dssm/model.py) | ✓ | x | ✓ |
| 匹配 | [MultiView-Simnet](models/match/multiview-simnet/model.py) | ✓ | x | ✓ |
| 内容理解 | [TagSpace](models/contentunderstanding/tagspace/model.py) | ✓ | x | ✓ |
| 召回 | [DSSM](models/match/dssm/model.py) | ✓ | x | ✓ |
| 召回 | [MultiView-Simnet](models/match/multiview-simnet/model.py) | ✓ | x | ✓ |
| 召回 | [TDM](models/treebased/tdm/model.py) | ✓ | x | ✓ |
| 召回 | [Word2Vec](models/recall/word2vec/model.py) | ✓ | x | ✓ |
| 召回 | [SSR](models/recall/ssr/model.py) | ✓ | ✓ | ✓ |
| 召回 | [Gru4Rec](models/recall/gru4rec/model.py) | ✓ | ✓ | ✓ |
| 排序 | [Dnn](models/rank/dnn/model.py) | ✓ | x | ✓ |
| 排序 | [DeepFM](models/rank/deepfm/model.py) | ✓ | x | ✓ |
| 排序 | [xDeepFM](models/rank/xdeepfm/model.py) | ✓ | x | ✓ |
| 排序 | [DIN](models/rank/din/model.py) | ✓ | x | ✓ |
| 排序 | [Wide&Deep](models/rank/wide_deep/model.py) | ✓ | x | ✓ |
| 多任务 | [ESMM](models/multitask/esmm/model.py) | ✓ | ✓ | ✓ |
| 多任务 | [MMOE](models/multitask/mmoe/model.py) | ✓ | ✓ | ✓ |
| 多任务 | [ShareBottom](models/multitask/share-bottom/model.py) | ✓ | ✓ | ✓ |
<h2 align="center">文档</h2>
### 背景介绍
* [推荐系统](doc/rec_background.md)
* [分布式-参数服务器](doc/ps_background.md)
* [推荐系统介绍](doc/rec_background.md)
* [分布式深度学习介绍](doc/ps_background.md)
### 新手教程
* [环境要求](#环境要求)
......
core/engine/cluster/cloud/cluster.sh
浏览文件 @ 3e50521c
#!/bin/bash
# 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.
###################################################
# Usage: submit.sh
# Description: run mpi submit clinet implement
# Description: run mpi submit client implement
###################################################
# ---------------------------------------------------------------------------- #
......
core/engine/cluster/cluster.py
浏览文件 @ 3e50521c
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
# 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.
......@@ -15,10 +15,9 @@
from __future__ import print_function
from __future__ import unicode_literals
import subprocess
import sys
import os
import copy
import os
import subprocess
from paddlerec.core.engine.engine import Engine
from paddlerec.core.factory import TrainerFactory
......@@ -32,7 +31,7 @@ class ClusterEngine(Engine):
if backend == "PaddleCloud":
self.submit_script = os.path.join(abs_dir, "cloud/cluster.sh")
else:
raise ValueError("{} can not supported now".format(backend))
raise ValueError("{} can not be supported now".format(backend))
def start_worker_procs(self):
trainer = TrainerFactory.create(self.trainer)
......
core/engine/engine.py
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# 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 abc
......@@ -15,4 +29,3 @@ class Engine:
@abc.abstractmethod
def run(self):
pass
core/engine/local_cluster.py
浏览文件 @ 3e50521c
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
# 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.
......@@ -14,10 +14,11 @@
from __future__ import print_function
from __future__ import unicode_literals
import subprocess
import sys
import os
import copy
import os
import sys
import subprocess
from paddlerec.core.engine.engine import Engine
from paddlerec.core.utils import envs
......
core/engine/local_mpi.py
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
# 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.
......@@ -14,10 +14,11 @@
from __future__ import print_function
from __future__ import unicode_literals
import subprocess
import sys
import os
import copy
import os
import sys
import subprocess
from paddlerec.core.engine.engine import Engine
......
core/layer.py
浏览文件 @ 3e50521c
......@@ -25,23 +25,8 @@ class Layer(object):
"""
pass
def generate(self, mode, param):
"""R
"""
if mode == 'fluid':
return self.generate_fluid(param)
elif mode == 'tensorflow':
return self.generate_tensorflow(param)
print('unsupport this mode: ' + mode)
return None, None
@abc.abstractmethod
def generate_fluid(self, param):
def generate(self, param):
"""R
"""
pass
def generate_tensorflow(self, param):
""" Not implement currently
"""
pass
core/metric.py
浏览文件 @ 3e50521c
......@@ -53,7 +53,7 @@ class Metric(object):
pass
@abc.abstractmethod
def get_result_to_string(self):
def __str__(self):
"""
Return:
result(string) : calculate result with string format, for output
......
core/metrics/auc_metrics.py
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......@@ -13,8 +13,10 @@
# limitations under the License.
import math
import numpy as np
import paddle.fluid as fluid
from paddlerec.core.metric import Metric
......@@ -198,7 +200,7 @@ class AUCMetric(Metric):
""" """
return self._result
def get_result_to_string(self):
def __str__(self):
""" """
result = self.get_result()
result_str = "%s AUC=%.6f BUCKET_ERROR=%.6f MAE=%.6f RMSE=%.6f " \
......
core/model.py
浏览文件 @ 3e50521c
# 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 abc
import paddle.fluid as fluid
......@@ -62,7 +76,7 @@ class Model(object):
def get_infer_results(self):
return self._infer_results
def get_cost_op(self):
def get_avg_cost(self):
"""R
"""
return self._cost
......@@ -72,12 +86,6 @@ class Model(object):
"""
return self._metrics
def custom_preprocess(self):
"""
do something after exe.run(stratup_program) and before run()
"""
pass
def get_fetch_period(self):
return self._fetch_interval
......
core/modules/modul/build.py
浏览文件 @ 3e50521c
import yaml
# 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 copy
import paddle.fluid as fluid
from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
import yaml
from paddlerec.core.model import Model
from paddlerec.core.utils import table
......
core/modules/modul/layers.py
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......@@ -13,10 +13,11 @@
# limitations under the License.
import paddle.fluid as fluid
from paddlerec.core.layer import Layer
class EmbeddingInputLayer(Layer):
class EmbeddingFuseLayer(Layer):
"""R
"""
......@@ -31,7 +32,7 @@ class EmbeddingInputLayer(Layer):
self._emb_dim = self._mf_dim + 3 # append show ctr lr
self._emb_layers = []
def generate_fluid(self, param):
def generate(self, param):
"""R
"""
show_clk = fluid.layers.concat(
......@@ -63,7 +64,7 @@ class LabelInputLayer(Layer):
self._data_type = config.get('data_type', "int64")
self._label_idx = config['label_idx']
def generate_fluid(self, param):
def generate(self, param):
"""R
"""
label = fluid.layers.data(name=self._name, shape=[-1, self._dim], \
......@@ -85,7 +86,7 @@ class TagInputLayer(Layer):
self._dim = config.get('dim', 1)
self._data_type = config['data_type']
def generate_fluid(self, param):
def generate(self, param):
"""R
"""
output = fluid.layers.data(name=self._name, shape=[-1, self._dim], \
......@@ -107,7 +108,7 @@ class ParamLayer(Layer):
self._data_type = config.get('data_type', 'float32')
self._config = config
def generate_fluid(self, param):
def generate(self, param):
"""R
"""
return self._config, {'inference_param': {'name': 'param', 'params': [], 'table_id': self._table_id}}
......@@ -125,7 +126,7 @@ class SummaryLayer(Layer):
self._data_type = config.get('data_type', 'float32')
self._config = config
def generate_fluid(self, param):
def generate(self, param):
"""R
"""
return self._config, {'inference_param': {'name': 'summary', 'params': [], 'table_id': self._table_id}}
......@@ -143,7 +144,7 @@ class NormalizetionLayer(Layer):
self._summary = config['summary']
self._table_id = config.get('table_id', -1)
def generate_fluid(self, param):
def generate(self, param):
"""R
"""
input_layer = param['layer'][self._input[0]]
......@@ -158,7 +159,7 @@ class NormalizetionLayer(Layer):
'params': inference_param, 'table_id': summary_layer.get('table_id', -1)}}
class NeuralLayer(Layer):
class FCLayer(Layer):
"""R
"""
......@@ -171,7 +172,7 @@ class NeuralLayer(Layer):
self._bias = config.get('bias', True)
self._act_func = config.get('act_func', None)
def generate_fluid(self, param):
def generate(self, param):
"""R
"""
param_layer = param['layer'][self._param]
......@@ -199,7 +200,7 @@ class NeuralLayer(Layer):
'table_id': param_layer.get('table_id', -1)}}
class SigmoidLossLayer(Layer):
class LogLossLayer(Layer):
"""R
"""
......@@ -230,7 +231,7 @@ class SigmoidLossLayer(Layer):
}
}
def generate_fluid(self, param):
def generate(self, param):
"""R
"""
input_layer = param['layer'][self._input[0]]
......
core/trainer.py
浏览文件 @ 3e50521c
......@@ -12,14 +12,14 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
import abc
import os
import time
import sys
import yaml
from paddle import fluid
from paddlerec.core.utils import envs
......
core/trainers/__init__.py
浏览文件 @ 3e50521c
# 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.
"""
trainer implement.
↗ (single/cluster) CtrTrainer
Trainer
↗ (for single training) SingleTrainer/TDMSingleTrainer
↘ TranspilerTrainer → (for cluster training) ClusterTrainer/TDMClusterTrainer
↘ (for online learning training) OnlineLearningTrainer
"""
core/trainers/cluster_trainer.py
浏览文件 @ 3e50521c
......@@ -25,7 +25,6 @@ import paddle.fluid as fluid
from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet
from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy import StrategyFactory
from paddle.fluid.incubate.fleet.base.role_maker import PaddleCloudRoleMaker
from paddle.fluid.incubate.fleet.base.role_maker import MPISymetricRoleMaker
from paddlerec.core.utils import envs
from paddlerec.core.trainers.transpiler_trainer import TranspileTrainer
......@@ -83,7 +82,7 @@ class ClusterTrainer(TranspileTrainer):
strategy = self.build_strategy()
optimizer = fleet.distributed_optimizer(optimizer, strategy)
optimizer.minimize(self.model.get_cost_op())
optimizer.minimize(self.model.get_avg_cost())
if fleet.is_server():
context['status'] = 'server_pass'
......@@ -115,7 +114,7 @@ class ClusterTrainer(TranspileTrainer):
program = fluid.compiler.CompiledProgram(
fleet.main_program).with_data_parallel(
loss_name=self.model.get_cost_op().name,
loss_name=self.model.get_avg_cost().name,
build_strategy=self.strategy.get_build_strategy(),
exec_strategy=self.strategy.get_execute_strategy())
......
core/trainers/ctr_coding_trainer.py
浏览文件 @ 3e50521c
......@@ -11,9 +11,10 @@
# 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 numpy as np
import numpy as np
import paddle.fluid as fluid
from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
from paddle.fluid.incubate.fleet.base.role_maker import MPISymetricRoleMaker
......@@ -22,7 +23,7 @@ from paddlerec.core.utils import envs
from paddlerec.core.trainer import Trainer
class CtrPaddleTrainer(Trainer):
class CtrTrainer(Trainer):
"""R
"""
......@@ -87,7 +88,7 @@ class CtrPaddleTrainer(Trainer):
optimizer = self.model.optimizer()
optimizer = fleet.distributed_optimizer(optimizer, strategy={"use_cvm": False})
optimizer.minimize(self.model.get_cost_op())
optimizer.minimize(self.model.get_avg_cost())
if fleet.is_server():
context['status'] = 'server_pass'
......
core/trainers/ctr_modul_trainer.py
浏览文件 @ 3e50521c
......@@ -13,12 +13,12 @@
# limitations under the License.
import datetime
import json
import sys
import time
import json
import datetime
import numpy as np
import numpy as np
import paddle.fluid as fluid
from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
from paddle.fluid.incubate.fleet.base.role_maker import GeneralRoleMaker
......@@ -72,7 +72,7 @@ def worker_numric_max(value, env="mpi"):
return wroker_numric_opt(value, env, "max")
class CtrPaddleTrainer(Trainer):
class CtrTrainer(Trainer):
"""R
"""
......@@ -129,7 +129,7 @@ class CtrPaddleTrainer(Trainer):
model = self._exector_context[executor['name']]['model']
self._metrics.update(model.get_metrics())
runnnable_scope.append(scope)
runnnable_cost_op.append(model.get_cost_op())
runnnable_cost_op.append(model.get_avg_cost())
for var in model._data_var:
if var.name in data_var_name_dict:
continue
......@@ -146,7 +146,7 @@ class CtrPaddleTrainer(Trainer):
model = self._exector_context[executor['name']]['model']
program = model._build_param['model']['train_program']
if not executor['is_update_sparse']:
program._fleet_opt["program_configs"][str(id(model.get_cost_op().block.program))]["push_sparse"] = []
program._fleet_opt["program_configs"][str(id(model.get_avg_cost().block.program))]["push_sparse"] = []
if 'train_thread_num' not in executor:
executor['train_thread_num'] = self.global_config['train_thread_num']
with fluid.scope_guard(scope):
......
core/trainers/online_learning_trainer.py 0 → 100755
浏览文件 @ 3e50521c
# 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.
"""
Training use fluid with one node only.
"""
from __future__ import print_function
import datetime
import os
import time
import paddle.fluid as fluid
from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet
from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy import StrategyFactory
from paddle.fluid.incubate.fleet.base.role_maker import PaddleCloudRoleMaker
from paddlerec.core.utils import envs
from paddlerec.core.trainers.transpiler_trainer import TranspileTrainer
class OnlineLearningTrainer(TranspileTrainer):
def processor_register(self):
role = PaddleCloudRoleMaker()
fleet.init(role)
if fleet.is_server():
self.regist_context_processor('uninit', self.instance)
self.regist_context_processor('init_pass', self.init)
self.regist_context_processor('server_pass', self.server)
else:
self.regist_context_processor('uninit', self.instance)
self.regist_context_processor('init_pass', self.init)
self.regist_context_processor('startup_pass', self.startup)
if envs.get_platform() == "LINUX" and envs.get_global_env("dataset_class", None, "train.reader") != "DataLoader":
self.regist_context_processor('train_pass', self.dataset_train)
else:
self.regist_context_processor(
'train_pass', self.dataloader_train)
self.regist_context_processor('infer_pass', self.infer)
self.regist_context_processor('terminal_pass', self.terminal)
def build_strategy(self):
mode = envs.get_runtime_environ("train.trainer.strategy")
assert mode in ["async", "geo", "sync", "half_async"]
strategy = None
if mode == "async":
strategy = StrategyFactory.create_async_strategy()
elif mode == "geo":
push_num = envs.get_global_env("train.strategy.mode.push_num", 100)
strategy = StrategyFactory.create_geo_strategy(push_num)
elif mode == "sync":
strategy = StrategyFactory.create_sync_strategy()
elif mode == "half_async":
strategy = StrategyFactory.create_half_async_strategy()
assert strategy is not None
self.strategy = strategy
return strategy
def init(self, context):
self.model.train_net()
optimizer = self.model.optimizer()
strategy = self.build_strategy()
optimizer = fleet.distributed_optimizer(optimizer, strategy)
optimizer.minimize(self.model.get_avg_cost())
if fleet.is_server():
context['status'] = 'server_pass'
else:
self.fetch_vars = []
self.fetch_alias = []
self.fetch_period = self.model.get_fetch_period()
metrics = self.model.get_metrics()
if metrics:
self.fetch_vars = metrics.values()
self.fetch_alias = metrics.keys()
context['status'] = 'startup_pass'
def server(self, context):
fleet.init_server()
fleet.run_server()
context['is_exit'] = True
def startup(self, context):
self._exe.run(fleet.startup_program)
context['status'] = 'train_pass'
def dataloader_train(self, context):
print("online learning can only support LINUX only")
context['status'] = 'terminal_pass'
def _get_dataset(self, state="TRAIN", hour=None):
if state == "TRAIN":
inputs = self.model.get_inputs()
namespace = "train.reader"
train_data_path = envs.get_global_env(
"train_data_path", None, namespace)
else:
inputs = self.model.get_infer_inputs()
namespace = "evaluate.reader"
train_data_path = envs.get_global_env(
"test_data_path", None, namespace)
threads = int(envs.get_runtime_environ("train.trainer.threads"))
batch_size = envs.get_global_env("batch_size", None, namespace)
reader_class = envs.get_global_env("class", None, namespace)
abs_dir = os.path.dirname(os.path.abspath(__file__))
reader = os.path.join(abs_dir, '../utils', 'dataset_instance.py')
pipe_cmd = "python {} {} {} {}".format(
reader, reader_class, state, self._config_yaml)
if train_data_path.startswith("paddlerec::"):
package_base = envs.get_runtime_environ("PACKAGE_BASE")
assert package_base is not None
train_data_path = os.path.join(
package_base, train_data_path.split("::")[1])
dataset = fluid.DatasetFactory().create_dataset()
dataset.set_use_var(inputs)
dataset.set_pipe_command(pipe_cmd)
dataset.set_batch_size(batch_size)
dataset.set_thread(threads)
if hour is not None:
train_data_path = os.path.join(train_data_path, hour)
file_list = [
os.path.join(train_data_path, x)
for x in os.listdir(train_data_path)
]
self.files = file_list
dataset.set_filelist(self.files)
return dataset
def dataset_train(self, context):
fleet.init_worker()
days = envs.get_global_env("train.days")
begin_day = datetime.datetime.strptime("begin_day_d", '%Y%m%d')
for day in range(days):
for hour in range(24):
day = begin_day + datetime.timedelta(days=day, hours=hour)
day_s = day.strftime('%Y%m%d/%H')
i = day.strftime('%Y%m%d_%H')
dataset = self._get_dataset(hour=day_s)
ins = self._get_dataset_ins()
begin_time = time.time()
self._exe.train_from_dataset(program=fluid.default_main_program(),
dataset=dataset,
fetch_list=self.fetch_vars,
fetch_info=self.fetch_alias,
print_period=self.fetch_period)
end_time = time.time()
times = end_time-begin_time
print("epoch {} using time {}, speed {:.2f} lines/s".format(i, times, ins/times))
self.save(i, "train", is_fleet=True)
fleet.stop_worker()
context['status'] = 'infer_pass'
def terminal(self, context):
for model in self.increment_models:
print("epoch :{}, dir: {}".format(model[0], model[1]))
context['is_exit'] = True
core/trainers/single_trainer.py
浏览文件 @ 3e50521c
......@@ -17,14 +17,14 @@ Training use fluid with one node only.
"""
from __future__ import print_function
import logging
import time
import logging
import paddle.fluid as fluid
from paddlerec.core.trainers.transpiler_trainer import TranspileTrainer
from paddlerec.core.utils import envs
import numpy as np
logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger("fluid")
......@@ -36,7 +36,8 @@ class SingleTrainer(TranspileTrainer):
self.regist_context_processor('uninit', self.instance)
self.regist_context_processor('init_pass', self.init)
self.regist_context_processor('startup_pass', self.startup)
if envs.get_platform() == "LINUX" and envs.get_global_env("dataset_class", None, "train.reader") != "DataLoader":
if envs.get_platform() == "LINUX" and envs.get_global_env("dataset_class", None,
"train.reader") != "DataLoader":
self.regist_context_processor('train_pass', self.dataset_train)
else:
self.regist_context_processor('train_pass', self.dataloader_train)
......@@ -47,7 +48,7 @@ class SingleTrainer(TranspileTrainer):
def init(self, context):
self.model.train_net()
optimizer = self.model.optimizer()
optimizer.minimize((self.model.get_cost_op()))
optimizer.minimize((self.model.get_avg_cost()))
self.fetch_vars = []
self.fetch_alias = []
......@@ -74,7 +75,7 @@ class SingleTrainer(TranspileTrainer):
program = fluid.compiler.CompiledProgram(
fluid.default_main_program()).with_data_parallel(
loss_name=self.model.get_cost_op().name)
loss_name=self.model.get_avg_cost().name)
metrics_varnames = []
metrics_format = []
......@@ -122,8 +123,8 @@ class SingleTrainer(TranspileTrainer):
fetch_info=self.fetch_alias,
print_period=self.fetch_period)
end_time = time.time()
times = end_time-begin_time
print("epoch {} using time {}, speed {:.2f} lines/s".format(i, times, ins/times))
times = end_time - begin_time
print("epoch {} using time {}, speed {:.2f} lines/s".format(i, times, ins / times))
self.save(i, "train", is_fleet=False)
context['status'] = 'infer_pass'
......
core/trainers/tdm_cluster_trainer.py
浏览文件 @ 3e50521c
# -*- coding=utf-8 -*-
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
......@@ -18,17 +17,16 @@ Training use fluid with one node only.
"""
from __future__ import print_function
import logging
import numpy as np
import paddle.fluid as fluid
from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet
from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy import StrategyFactory
from paddle.fluid.incubate.fleet.base.role_maker import PaddleCloudRoleMaker
from paddlerec.core.utils import envs
from paddlerec.core.trainers.cluster_trainer import ClusterTrainer
logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger("fluid")
logger.setLevel(logging.INFO)
......@@ -65,11 +63,10 @@ class TDMClusterTrainer(ClusterTrainer):
"cluster.init_model_path", "", namespace)
if load_tree:
# 将明文树结构及数据，set到组网中的Variale中
# 不使用NumpyInitialize方法是考虑到树结构相关数据size过大，有性能风险
# covert tree to tensor, set it into Fluid's variable.
for param_name in special_param:
param_t = fluid.global_scope().find_var(param_name).get_tensor()
param_array = self.tdm_prepare(param_name)
param_array = self._tdm_prepare(param_name)
param_t.set(param_array.astype('int32'), self._place)
if save_init_model:
......@@ -80,27 +77,27 @@ class TDMClusterTrainer(ClusterTrainer):
context['status'] = 'train_pass'
def tdm_prepare(self, param_name):
def _tdm_prepare(self, param_name):
if param_name == "TDM_Tree_Travel":
travel_array = self.tdm_travel_prepare()
travel_array = self._tdm_travel_prepare()
return travel_array
elif param_name == "TDM_Tree_Layer":
layer_array, _ = self.tdm_layer_prepare()
layer_array, _ = self._tdm_layer_prepare()
return layer_array
elif param_name == "TDM_Tree_Info":
info_array = self.tdm_info_prepare()
info_array = self._tdm_info_prepare()
return info_array
else:
raise " {} is not a special tdm param name".format(param_name)
def tdm_travel_prepare(self):
def _tdm_travel_prepare(self):
"""load tdm tree param from npy/list file"""
travel_array = np.load(self.tree_travel_path)
logger.info("TDM Tree leaf node nums: {}".format(
travel_array.shape[0]))
return travel_array
def tdm_layer_prepare(self):
def _tdm_layer_prepare(self):
"""load tdm tree param from npy/list file"""
layer_list = []
layer_list_flat = []
......@@ -120,7 +117,7 @@ class TDMClusterTrainer(ClusterTrainer):
[len(i) for i in layer_list]))
return layer_array, layer_list
def tdm_info_prepare(self):
def _tdm_info_prepare(self):
"""load tdm tree param from list file"""
info_array = np.load(self.tree_info_path)
return info_array
core/trainers/tdm_single_trainer.py
浏览文件 @ 3e50521c
# -*- coding=utf-8 -*-
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
......@@ -19,12 +18,11 @@ Training use fluid with one node only.
from __future__ import print_function
import logging
import paddle.fluid as fluid
from paddlerec.core.trainers.transpiler_trainer import TranspileTrainer
import numpy as np
import paddle.fluid as fluid
from paddlerec.core.trainers.single_trainer import SingleTrainer
from paddlerec.core.utils import envs
import numpy as np
logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger("fluid")
......@@ -68,11 +66,10 @@ class TDMSingleTrainer(SingleTrainer):
persistables_model_path))
if load_tree:
# 将明文树结构及数据，set到组网中的Variale中
# 不使用NumpyInitialize方法是考虑到树结构相关数据size过大，有性能风险
# covert tree to tensor, set it into Fluid's variable.
for param_name in special_param:
param_t = fluid.global_scope().find_var(param_name).get_tensor()
param_array = self.tdm_prepare(param_name)
param_array = self._tdm_prepare(param_name)
if param_name == 'TDM_Tree_Emb':
param_t.set(param_array.astype('float32'), self._place)
else:
......@@ -86,37 +83,37 @@ class TDMSingleTrainer(SingleTrainer):
context['status'] = 'train_pass'
def tdm_prepare(self, param_name):
def _tdm_prepare(self, param_name):
if param_name == "TDM_Tree_Travel":
travel_array = self.tdm_travel_prepare()
travel_array = self._tdm_travel_prepare()
return travel_array
elif param_name == "TDM_Tree_Layer":
layer_array, _ = self.tdm_layer_prepare()
layer_array, _ = self._tdm_layer_prepare()
return layer_array
elif param_name == "TDM_Tree_Info":
info_array = self.tdm_info_prepare()
info_array = self._tdm_info_prepare()
return info_array
elif param_name == "TDM_Tree_Emb":
emb_array = self.tdm_emb_prepare()
emb_array = self._tdm_emb_prepare()
return emb_array
else:
raise " {} is not a special tdm param name".format(param_name)
def tdm_travel_prepare(self):
def _tdm_travel_prepare(self):
"""load tdm tree param from npy/list file"""
travel_array = np.load(self.tree_travel_path)
logger.info("TDM Tree leaf node nums: {}".format(
travel_array.shape[0]))
return travel_array
def tdm_emb_prepare(self):
def _tdm_emb_prepare(self):
"""load tdm tree param from npy/list file"""
emb_array = np.load(self.tree_emb_path)
logger.info("TDM Tree node nums from emb: {}".format(
emb_array.shape[0]))
return emb_array
def tdm_layer_prepare(self):
def _tdm_layer_prepare(self):
"""load tdm tree param from npy/list file"""
layer_list = []
layer_list_flat = []
......@@ -136,7 +133,7 @@ class TDMSingleTrainer(SingleTrainer):
[len(i) for i in layer_list]))
return layer_array, layer_list
def tdm_info_prepare(self):
def _tdm_info_prepare(self):
"""load tdm tree param from list file"""
info_array = np.load(self.tree_info_path)
return info_array
core/trainers/transpiler_trainer.py
浏览文件 @ 3e50521c
......@@ -29,7 +29,7 @@ from paddlerec.core.reader import SlotReader
class TranspileTrainer(Trainer):
def __init__(self, config=None):
Trainer.__init__(self, config)
device = envs.get_global_env("train.device")
device = envs.get_global_env("train.device", "cpu")
if device == 'gpu':
self._place = fluid.CUDAPlace(0)
self._exe = fluid.Executor(self._place)
......@@ -265,7 +265,7 @@ class TranspileTrainer(Trainer):
'evaluate_model_path', "", namespace='evaluate'))]
is_return_numpy = envs.get_global_env(
'is_return_numpy', True, namespace='evaluate')
'is_return_numpy', True, namespace='evaluate')
for (epoch, model_dir) in model_list:
print("Begin to infer No.{} model, model_dir: {}".format(
......
core/utils/dataloader_instance.py
浏览文件 @ 3e50521c
......@@ -14,7 +14,6 @@
from __future__ import print_function
import os
import sys
from paddlerec.core.utils.envs import lazy_instance_by_fliename
from paddlerec.core.utils.envs import get_global_env
......
core/utils/dataset.py → core/utils/dataset_holder.py
浏览文件 @ 3e50521c
......@@ -13,8 +13,8 @@
# limitations under the License.
import abc
import time
import datetime
import time
import paddle.fluid as fluid
......@@ -22,7 +22,7 @@ from paddlerec.core.utils import fs as fs
from paddlerec.core.utils import util as util
class Dataset(object):
class DatasetHolder(object):
"""
Dataset Base
"""
......@@ -62,7 +62,7 @@ class Dataset(object):
pass
class TimeSplitDataset(Dataset):
class TimeSplitDatasetHolder(DatasetHolder):
"""
Dataset with time split dir. root_path/$DAY/$HOUR
"""
......@@ -142,16 +142,6 @@ class TimeSplitDataset(Dataset):
data_time = data_time + datetime.timedelta(minutes=self._split_interval)
return data_file_list
class FluidTimeSplitDataset(TimeSplitDataset):
"""
A Dataset with time split for PaddleFluid
"""
def __init__(self, config):
""" """
TimeSplitDataset.__init__(self, config)
def _alloc_dataset(self, file_list):
""" """
dataset = fluid.DatasetFactory().create_dataset(self._config['dataset_type'])
......
core/utils/dataset_instance.py
浏览文件 @ 3e50521c
......@@ -11,6 +11,7 @@
# 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.
from __future__ import print_function
import sys
......
core/utils/envs.py
浏览文件 @ 3e50521c
......@@ -12,11 +12,11 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from contextlib import closing
import copy
import sys
import os
import socket
from contextlib import closing
import sys
global_envs = {}
......@@ -89,22 +89,38 @@ def get_global_envs():
return global_envs
def path_adapter(path):
def adapt(l_p):
if get_platform() == "WINDOWS":
adapted_p = l_p.split("paddlerec.")[1].replace(".", "\\")
else:
adapted_p = l_p.split("paddlerec.")[1].replace(".", "/")
return adapted_p
if path.startswith("paddlerec."):
package = get_runtime_environ("PACKAGE_BASE")
return os.path.join(package, adapt(path))
else:
return adapt(path)
def windows_path_converter(path):
if get_platform() == "WINDOWS":
return path.replace("/", "\\")
else:
return path.replace("\\", "/")
def update_workspace():
workspace = global_envs.get("train.workspace", None)
if not workspace:
return
# is fleet inner models
if workspace.startswith("paddlerec."):
fleet_package = get_runtime_environ("PACKAGE_BASE")
workspace_dir = workspace.split("paddlerec.")[1].replace(".", "/")
path = os.path.join(fleet_package, workspace_dir)
else:
path = workspace
workspace = path_adapter(workspace)
for name, value in global_envs.items():
if isinstance(value, str):
value = value.replace("{workspace}", path)
value = value.replace("{workspace}", workspace)
value = windows_path_converter(value)
global_envs[name] = value
......
core/utils/fs.py
浏览文件 @ 3e50521c
......@@ -13,6 +13,7 @@
# limitations under the License.
import os
from paddle.fluid.incubate.fleet.utils.hdfs import HDFSClient
......@@ -28,12 +29,12 @@ class LocalFSClient(object):
"""
Util for local disk file_system io
"""
def __init__(self):
"""R
"""
pass
def write(self, content, path, mode):
"""
write to file
......@@ -43,7 +44,7 @@ class LocalFSClient(object):
mode(string): w/a w:clear_write a:append_write
"""
temp_dir = os.path.dirname(path)
if not os.path.exists(temp_dir):
if not os.path.exists(temp_dir):
os.makedirs(temp_dir)
f = open(path, mode)
f.write(content)
......@@ -75,7 +76,7 @@ class LocalFSClient(object):
"""R
"""
os.system("rm -rf " + path)
def is_exist(self, path):
"""R
"""
......@@ -94,13 +95,14 @@ class FileHandler(object):
"""
A Smart file handler. auto judge local/afs by path
"""
def __init__(self, config):
"""R
"""
if 'fs_name' in config:
hadoop_home="$HADOOP_HOME"
hadoop_home = "$HADOOP_HOME"
hdfs_configs = {
"hadoop.job.ugi": config['fs_ugi'],
"hadoop.job.ugi": config['fs_ugi'],
"fs.default.name": config['fs_name']
}
self._hdfs_client = HDFSClient(hadoop_home, hdfs_configs)
......@@ -131,7 +133,8 @@ class FileHandler(object):
if mode.find('a') >= 0:
org_content = self._hdfs_client.cat(dest_path)
content = content + org_content
self._local_fs_client.write(content, temp_local_file, mode) #fleet hdfs_client only support upload, so write tmp file
self._local_fs_client.write(content, temp_local_file,
mode) # fleet hdfs_client only support upload, so write tmp file
self._hdfs_client.delete(dest_path + ".tmp")
self._hdfs_client.upload(dest_path + ".tmp", temp_local_file)
self._hdfs_client.delete(dest_path + ".bak")
......@@ -139,7 +142,7 @@ class FileHandler(object):
self._hdfs_client.rename(dest_path + ".tmp", dest_path)
else:
self._local_fs_client.write(content, dest_path, mode)
def cat(self, path):
"""R
"""
......@@ -148,7 +151,7 @@ class FileHandler(object):
return hdfs_cat
else:
return self._local_fs_client.cat(path)
def ls(self, path):
"""R
"""
......@@ -160,7 +163,7 @@ class FileHandler(object):
files = self._local_fs_client.ls(path)
files = [path + '/' + fi for fi in files] # absulte path
return files
def cp(self, org_path, dest_path):
"""R
"""
......@@ -170,6 +173,6 @@ class FileHandler(object):
return self._local_fs_client.cp(org_path, dest_path)
if not org_is_afs and dest_is_afs:
return self._hdfs_client.upload(dest_path, org_path)
if org_is_afs and not dest_is_afs:
if org_is_afs and not dest_is_afs:
return self._hdfs_client.download(org_path, dest_path)
print("Not Suppor hdfs cp currently")
core/utils/table.py
浏览文件 @ 3e50521c
......@@ -12,16 +12,13 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import yaml
class TableMeta(object):
"""
Simple ParamTable Meta, Contain table_id
"""
TableId = 1
@staticmethod
def alloc_new_table(table_id):
"""
......
core/utils/util.py
浏览文件 @ 3e50521c
......@@ -12,11 +12,12 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import datetime
import os
import time
import datetime
from paddle import fluid
from paddlerec.core.utils import fs as fs
......
doc/custom_dataset_reader.md
浏览文件 @ 3e50521c
......@@ -70,39 +70,189 @@ self._dense_data_var
# PaddleRec 自定义数据集及Reader
## 数据集及reader配置简介
用户自定义数据集及配置异步Reader，需要关注以下几个步骤：
以`ctr-dnn`模型举例：
* [数据集整理](#数据集整理)
* [在模型组网中加入输入占位符](#在模型组网中加入输入占位符)
* [Reader实现](#Reader的实现)
* [在yaml文件中配置Reader](#在yaml文件中配置reader)
```yaml
reader:
batch_size: 2
class: "{workspace}/../criteo_reader.py"
train_data_path: "{workspace}/data/train"
reader_debug_mode: False
我们以CTR-DNN模型为例，给出了从数据整理，变量定义，Reader写法，调试的完整历程。
* [数据及Reader示例-DNN](#数据及Reader示例-DNN)
## 数据集整理
PaddleRec支持模型自定义数据集。
关于数据的tips：
1. 数据量：
PaddleRec面向大规模数据设计，可以轻松支持亿级的数据读取，工业级的数据读写api：`dataset`在搜索、推荐、信息流等业务得到了充分打磨。
2. 文件类型:
支持任意直接可读的文本数据，`dataset`同时支持`.gz`格式的文本压缩数据，无需额外代码，可直接读取。数据样本应以`\n`为标志，按行组织。
3. 文件存放位置：
文件通常存放在训练节点本地，但同时，`dataset`支持使用`hadoop`远程读取数据，数据无需下载到本地，为dataset配置hadoop相关账户及地址即可。
4. 数据类型
Reader处理的是以行为单位的`string`数据，喂入网络的数据需要转为`int`,`float`的数值数据，不支持`string`喂入网络，不建议明文保存及处理训练数据。
5. Tips
Dataset模式下，训练线程与数据读取线程的关系强相关，为了多线程充分利用，`强烈建议将文件合理的拆为多个小文件`，尤其是在分布式训练场景下，可以均衡各个节点的数据量，同时加快数据的下载速度。
## 在模型组网中加入输入占位符
Reader读取文件后，产出的数据喂入网络，需要有占位符进行接收。占位符在Paddle中使用`fluid.data`或`fluid.layers.data`进行定义。`data`的定义可以参考[fluid.data](https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/fluid_cn/data_cn.html#data)以及[fluid.layers.data](https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/layers_cn/data_cn.html#data)。
加入您希望输入三个数据，分别是维度32的数据A，维度变长的稀疏数据B，以及一个一维的标签数据C，并希望梯度可以经过该变量向前传递，则示例如下：
数据A的定义：
```python
var_a = fluid.data(name='A', shape= [-1, 32], dtype='float32')
```
数据B的定义，变长数据的使用可以参考[LoDTensor](https://www.paddlepaddle.org.cn/documentation/docs/zh/beginners_guide/basic_concept/lod_tensor.html#cn-user-guide-lod-tensor)：
```python
var_b = fluid.data(name='B', shape=[-1, 1], lod_level=1, dtype='int64')
```
有以上4个需要重点关注的配置选项：
- batch_size: 网络进行小批量训练的一组数据的大小
- class: 指定数据处理及读取的`reader` python文件
- train_data_path: 训练数据所在地址
- reader_debug_mode: 测试reader语法，及输出是否符合预期的debug模式的开关
数据C的定义：
```python
var_c = fluid.data(name='C', shape=[-1, 1], dtype='int32')
var_c.stop_gradient = False
```
当我们完成以上三个数据的定义后，在PaddleRec的模型定义中，还需将其加入model基类成员变量`self._data_var`
```python
self._data_var.append(var_a)
self._data_var.append(var_b)
self._data_var.append(var_c)
```
至此，我们完成了在组网中定义输入数据的工作。
## Reader的实现
### Reader的实现范式
Reader的逻辑需要一个单独的python文件进行描述。我们试写一个`test_reader.py`，实现的具体流程如下：
1. 首先我们需要引入Reader基类
```python
from paddlerec.core.reader import Reader
```
2. 创建一个子类，继承Reader的基类，训练所需Reader命名为`TrainerReader`
```python
class TrainerReader(Reader):
def init(self):
pass
def generator_sample(self, line):
pass
```
3. 在`init(self)`函数中声明一些在数据读取中会用到的变量，必要时可以在`config.yaml`文件中配置变量，利用`env.get_global_env()`拿到。
比如，我们希望从yaml文件中读取一个数据预处理变量`avg=10`，目的是将数据A的数据缩小10倍，可以这样实现：
首先更改yaml文件，在某个space下加入该变量
```yaml
...
train:
reader:
avg: 10
...
```
再更改Reader的init函数
```python
from paddlerec.core.utils import envs
class TrainerReader(Reader):
def init(self):
self.avg = envs.get_global_env("avg", None, "train.reader")
def generator_sample(self, line):
pass
```
4. 继承并实现基类中的`generate_sample(self, line)`函数，逐行读取数据。
- 该函数应返回一个可以迭代的reader方法(带有yield的函数不再是一个普通的函数，而是一个生成器generator，成为了可以迭代的对象，等价于一个数组、链表、文件、字符串etc.)
- 在这个可以迭代的函数中，如示例代码中的`def reader()`，我们定义数据读取的逻辑。以行为单位的数据进行截取，转换及预处理。
- 最后，我们需要将数据整理为特定的格式，才能够被PaddleRec的Reader正确读取，并灌入的训练的网络中。简单来说，数据的输出顺序与我们在网络中创建的`inputs`必须是严格一一对应的，并转换为类似字典的形式。
示例： 假设数据ABC在文本数据中，每行以这样的形式存储：
```shell
0.1,0.2,0.3...3.0,3.1,3.2 \t 99999,99998,99997 \t 1 \n
```
则示例代码如下：
```python
from paddlerec.core.utils import envs
class TrainerReader(Reader):
def init(self):
self.avg = envs.get_global_env("avg", None, "train.reader")
def generator_sample(self, line):
def reader(self, line):
# 先分割 '\n'， 再以 '\t'为标志分割为list
variables = (line.strip('\n')).split('\t')
# A是第一个元素，并且每个数据之间使用','分割
var_a = variables[0].split(',') # list
var_a = [float(i) / self.avg for i in var_a] # 将str数据转换为float
# B是第二个元素，同样以 ',' 分割
var_b = variables[1].split(',') # list
var_b = [int(i) for i in var_b] # 将str数据转换为int
# C是第三个元素, 只有一个元素，没有分割符
var_c = variables[2]
var_c = int(var_c) # 将str数据转换为int
var_c = [var_c] # 将单独的数据元素置入list中
# 将数据与数据名结合，组织为dict的形式
# 如下，output形式为{ A: var_a, B: var_b, C: var_c}
variable_name = ['A', 'B', 'C']
output = zip(variable_name, [var_a] + [var_b] + [var_c])
# 将数据输出，使用yield方法，将该函数变为了一个可迭代的对象
yield output
```
至此，我们完成了Reader的实现。
## 自定义数据集
### 在yaml文件中配置Reader
PaddleRec支持模型自定义数据集，在model.config.yaml文件中的reader部分，通过`train_data_path`指定数据读取路径。
在模型的yaml配置文件中，主要的修改是三个，如下
关于数据的tips
```yaml
reader:
batch_size: 2
class: "{workspace}/reader.py"
train_data_path: "{workspace}/data/train_data"
reader_debug_mode: False
```
- PaddleRec 面向的是推荐与搜索领域，数据以文本格式为主
- Dataset模式支持读取文本数据压缩后的`.gz`格式
- Dataset模式下，训练线程与数据读取线程的关系强相关，为了多线程充分利用，`强烈建议将文件拆成多个小文件`，尤其是在分布式训练场景下，可以均衡各个节点的数据量。
batch_size: 顾名思义，是小批量训练时的样本大小
class: 运行改模型所需reader的路径
train_data_path: 训练数据所在文件夹
reader_debug_mode: 测试reader语法，及输出是否符合预期的debug模式的开关
## 自定义Reader
数据集准备就绪后，需要适当修改或重写一个新的reader以适配数据集或新组网。
## 数据及Reader示例-DNN
我们以`ctr-dnn`网络举例`reader`的正确打开方式，网络文件位于`models/rank/dnn`。
Reader代码来源于[criteo_reader.py](../models/rank/criteo_reader.py), 组网代码来源于[model.py](../models/rank/dnn/model.py)
### Criteo数据集格式
......@@ -170,13 +320,8 @@ for input in self.sparse_inputs:
self._data_var.append(self.label_input)
if self._platform != "LINUX":
self._data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._data_var, capacity=64, use_double_buffer=False, iterable=False)
```
若运行于**Linux**环境下，默认使用**dataset**模式读取数据集；若运行于**windows**或**mac**下，默认使用**dataloader**模式读取数据集。以上两种方法是paddle.io中提供的不同模式，`dataset`运行速度更快，但依赖于linux的环境，因此会有该逻辑判断。
> Paddle的组网中不支持数据输入为`str`类型，`强烈不建议使用明文保存和读取数据`
### Criteo Reader写法
......@@ -235,24 +380,6 @@ class TrainReader(Reader):
return reader
```
### 如何自定义数据读取规则
在上文我们看到了由`criteo_reader.py`实现具体的数据读取规则，那么，怎样为自己的数据集写规则呢？
具体流程如下：
1. 首先我们需要引入Reader基类
```python
from paddlerec.core.reader import Reader
```
2. 创建一个子类，继承Reader的基类，训练所需Reader命名为`TrainerReader`
3. 在`init(self)`函数中声明一些在数据读取中会用到的变量，如示例代码中的`cont_min_`、`categorical_range_`等，必要时可以在`config.yaml`文件中配置变量，通过`env.get_global_env()`拿到。
4. 继承并实现基类中的`generate_sample(self, line)`函数，逐行读取数据。该函数应返回一个可以迭代的reader方法(带有yield的函数不再是一个普通的函数，而是一个生成器generator，成为了可以迭代的对象，等价于一个数组、链表、文件、字符串etc.)
5. 在这个可以迭代的函数中，如示例代码中的`def reader()`，我们定义数据读取的逻辑。以行为单位的数据进行截取，转换及预处理。
6. 最后，我们需要将数据整理为特定的格式，才能够被dataset正确读取，并灌入的训练的网络中。简单来说，数据的输出顺序与我们在网络中创建的`inputs`必须是严格一一对应的，并转换为类似字典的形式。在示例代码中，我们使用`zip`的方法将参数名与数值构成的元组组成了一个list，并将其yield输出。如果展开来看，我们输出的数据形如
`[('dense_feature',[value]),('C1',[value]),('C2',[value]),...,('C26',[value]),('label',[value])]`
### 调试Reader
......
doc/design.md
浏览文件 @ 3e50521c
# PaddleRec 设计
> 占位
\ No newline at end of file
## PaddleRec 整体设计概览
PaddleRec将推荐模型的训练与预测流程，整体抽象为了五个大模块：
* [Engine 流程执行引擎](#engine)
* [Trainer 流程具体定义](#trainer)
* [Model 模型组网定义](#model)
* [Reader 数据读取定义](#reader)
* [Metric 精度指标打印](#metric)
层级结构，以及一键启动训练时的调用关系如下图所示：
<p align="center">
<img align="center" src="imgs/design.png">
<p>
core的文件结构如下，后续分别对各个模块进行介绍。
```
.core
├── engine/ 运行引擎实现
├── metrics/ 全局指标实现
├── modules/ 自定义op实现
├── trainers/ 运行流程实现
├── utils/ 辅助工具
├── factory.py 运行流程的注册
├── layer.py 自定义op基类定义
├── metric.py Metric基类定义
├── model.py Model基类定义
├── reader.py Reader基类定义
└── trainer.py Trainer基类定义
```
## Engine
Engine是整体训练的执行引擎，与组网逻辑及数据无关，只与当前运行模式、运行环境及运行设备有关。
运行模式具体是指：
- 单机运行
- 分布式运行
- 本地模拟分布式
运行环境是指：
- Linux
- Windows
- Mac
运行设备是指：
- CPU
- GPU
- AI芯片
在用户调用`python -m paddlerec.run`时，首先会根据`yaml`文件中的配置信息选择合适的执行引擎， 以下代码位于[run.py](../run.py)：
```python
engine_registry()
which_engine = get_engine(args)
engine = which_engine(args)
engine.run()
```
我们以`single engine`为例，概览engine的行为：
```python
def single_engine(args):
trainer = get_trainer_prefix(args) + "SingleTrainer"
single_envs = {}
single_envs["train.trainer.trainer"] = trainer
single_envs["train.trainer.threads"] = "2"
single_envs["train.trainer.engine"] = "single"
single_envs["train.trainer.device"] = args.device
single_envs["train.trainer.platform"] = envs.get_platform()
print("use {} engine to run model: {}".format(trainer, args.model))
set_runtime_envs(single_envs, args.model)
trainer = TrainerFactory.create(args.model)
return trainer
```
single_engine被调用后，主要进行了以下两个工作：
1. 根据`yaml`配置文件，设置了**当前进程的环境变量**，后续的所有流程都依赖于环境变量。
2. 根据模型及环境，指定并初始化了运行流程所用的`Trainer`
进一步细化第一步工作
- 本地模拟分布式引擎会在单机环境变量的基础上，额外设置本地模拟分布式的环境变量，比如：为各个进程设置不同通信端口，分配ID。最后会启动多个`Trainer`完成本地模拟分布式的工作。
- 分布式引擎会在单机环境变量的基础上，基于运行参数`-b --backend`所指定的脚本或配置文件，完成分布式任务的文件打包，上传，提交等操作。该脚本格式与分布式任务运行的集群有关，如MPI/K8S/PaddleCloud等，用户可以自定义分布式运行逻辑。
Engine的自定义实现，可以参考[local_cluster.py](../core/engine/local_cluster.py)
## Trainer
`Trainer`是训练与预测流程的具体实现，会run模型中定义的各个流程，与model、reader、metric紧密相关。PaddleRec以有限状态机的逻辑定义了训练中的各个阶段，不同的Trainer子类会分别实现阶段中的特殊需求。有限状态机的流程在`def processor_register()`中注册。
我们以SingleTrainer为例，概览Trainer行为：
```python
class SingleTrainer(TranspileTrainer):
def processor_register(self):
self.regist_context_processor('uninit', self.instance)
self.regist_context_processor('init_pass', self.init)
self.regist_context_processor('startup_pass', self.startup)
if envs.get_platform() == "LINUX" and envs.get_global_env("dataset_class", None, "train.reader") != "DataLoader":
self.regist_context_processor('train_pass', self.dataset_train)
else:
self.regist_context_processor('train_pass', self.dataloader_train)
self.regist_context_processor('infer_pass', self.infer)
self.regist_context_processor('terminal_pass', self.terminal)
```
SingleTrainer首先注册了完成任务所需的步骤，各步骤首先按照注册顺序加入`Trainer`基类中名为`status_processor`的字典，运行的先后顺序，可以在每个执行步骤中改变`context['status']`的值，指定下一步运行哪个步骤。
SingleTrainer指定了以下6个步骤：
1. uninit：默认排在首位，通过环境变量决定model的对象
1. init_pass：调用model_的接口，生成模型的组网，初始化fetch及metric的变量
2. startup_pass：初始化模型组网中的各个参数，run(fluid.default_startup_program)
3. train_pass：会根据环境分别调用`dataset`与`dataloader`进行训练的流程。
4. infer_pass：在训练结束后，会对训练保存的模型在测试集上验证效果
5. terminal_pass：打印全局变量及预测结果等自定义的信息。
Trainer的自定义实现，可以参照[single_trainer.py](../core/trainers/single_trainer.py)
## Model
Model定义了各个模型实现的范式，模型只要继承并实现基类中的函数，并给一些成员赋值，就可以保证模型被Trainer正确调用。
我们首先看一下Model基类中的部分重要定义，对模型的实现流程有初步概念。
```python
class Model(object):
__metaclass__ = abc.ABCMeta
def __init__(self, config):
self._cost = None
self._metrics = {}
self._data_var = []
self._infer_data_var = []
self._infer_results = {}
self._data_loader = None
self._infer_data_loader = None
self._fetch_interval = 20
self._namespace = "train.model"
self._platform = envs.get_platform()
def get_inputs(self):
return self._data_var
@abc.abstractmethod
def train_net(self):
pass
@abc.abstractmethod
def infer_net(self):
pass
def get_avg_cost(self):
return self._cost
```
每个模型都一定需要继承`def train_net`与`def infer_net`，并且给`self._data_var`与`self._cost`成员赋值，指定模型入口，实现组网的整体逻辑。若有更多或更复杂的需求，可以参照下面的接口，分别继承各个函数，并实现需要的功能：
```python
def get_infer_inputs(self):
return self._infer_data_var
def get_infer_results(self):
return self._infer_results
def get_metrics(self):
return self._metrics
def get_fetch_period(self):
return self._fetch_interval
```
model的具体实现，可以参考dnn的示例[model.py](../../models/rank/dnn/../../../paddlerec/core/model.py)
## Reader
PaddleRec会根据运行环境，分别指定不同的数据IO方式。在Linux下，优先使用`Dataset`，Win及Mac优先使用`Dataloader`。
Dataset的使用介绍可以参考[DatasetFactory](https://www.paddlepaddle.org.cn/documentation/docs/zh/api_cn/dataset_cn/DatasetFactory_cn.html)
Dataloader的使用介绍可以参考[异步数据读取](https://www.paddlepaddle.org.cn/documentation/docs/zh/advanced_guide/data_preparing/use_py_reader.html)
考虑到以上两种高效的数据IO方式仍然有很高的学习门槛，PaddleRec将两种数据读取方式进行了更高层次的封装，用户需要实现的仅是每行数据的处理逻辑，剩下的工作交给PaddleRec的Reader基类完成。
首先浏览以下Reader基类的定义，有一个初步的印象：
```python
class Reader(dg.MultiSlotDataGenerator):
__metaclass__ = abc.ABCMeta
def __init__(self, config):
dg.MultiSlotDataGenerator.__init__(self)
if os.path.isfile(config):
with open(config, 'r') as rb:
_config = yaml.load(rb.read(), Loader=yaml.FullLoader)
else:
raise ValueError("reader config only support yaml")
envs.set_global_envs(_config)
envs.update_workspace()
@abc.abstractmethod
def init(self):
pass
@abc.abstractmethod
def generate_sample(self, line):
pass
```
用户需要关注并实现的是`def init(self)`与`def generate_sample(self,line)`函数，分别执行数据读取中预处理所需变量的初始化，以及每一行string的切分及处理逻辑。
当用户定义好以上两个函数，完成自己的Reader后，PaddleRec分别使用
- [dataset_instance.py](../core/utils/dataset_instance.py)
- [dataloader_instance.py](../core/utils/dataloader_instance.py)
完成reader的构建工作。
Reader数据处理的逻辑，可以参考[criteo_reader.py](../../models/rank/../../paddlerec/models/rank/criteo_reader.py)
## Metric
训练必然伴随着训练指标的打印，当单机运行时，打印相关信息比较简单。但分布式训练时，单机指标与全局指标往往有很大diff，比如`auc`以及正逆序`pn`。PaddleRec面向大规模分布式训练，将指标打印的逻辑抽象出来单独实现，以解决分布式训练时全局指标打印的问题。
Metric基类定义了基本的接口，如下：
```python
class Metric(object):
__metaclass__ = abc.ABCMeta
def __init__(self, config):
""" init """
pass
@abc.abstractmethod
def clear(self, scope, params):
"""
clear current value
Args:
scope: value container
params: extend varilable for clear
"""
pass
@abc.abstractmethod
def calculate(self, scope, params):
"""
calculate result
Args:
scope: value container
params: extend varilable for clear
"""
pass
@abc.abstractmethod
def get_result(self):
"""
Return:
result(dict) : calculate result
"""
pass
@abc.abstractmethod
def get_result_to_string(self):
"""
Return:
result(string) : calculate result with string format, for output
"""
pass
```
全局指标的计算及输出，需要分别继承并实现以上四个成员函数。具体实现的例子，可以参考[auc_metric.py](../core/metrics/auc_metrics.py)
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> 占位
### 本地模拟分布式
> 占位
### MPI集群运行分布式
> 占位
### PaddleCloud集群运行分布式
> 占位
### K8S集群运行分布式
> 占位
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# 参数服务器训练简介
## [分布式训练概述](https://www.paddlepaddle.org.cn/tutorials/projectdetail/459124)
如图1所示，参数服务器是分布式训练领域普遍采用的编程架构，主要包含Server和Worker两个部分，其中Server负责参数的存储和更新，而Worker负责训练。飞桨的参数服务器功能也是基于这种经典的架构进行设计和开发的，同时在这基础上进行了SGD（Stochastic Gradient Descent）算法的创新（Geometric Stochastic Gradient Descent）。当前经过大量的实验验证，最佳的方案是每台机器上启动Server和Worker两个进程，而一个Worker进程中可以包含多个用于训练的线程。
<p align="center">
<img align="center" src="imgs/ps-overview.png">
<p>
## [多机多卡训练](https://www.paddlepaddle.org.cn/tutorials/projectdetail/459127)
参数服务器是主要解决两类问题：
- 模型参数过大：单机内存空间不足，需要采用分布式存储。
- 训练数据过多：单机训练太慢，需要加大训练节点,来提高并发训练速度。 设想，当训练数据过多，一个Worker训练太慢时，可以引入多个Worker同时训练，这时Worker之间需要同步模型参数。直观想法是，引入一个Server，Server充当Worker间参数交换的媒介。但当模型参数过大以至于单机存储空间不足时或Worker过多导致一个Server是瓶颈时，就需要引入多个Server。
## [参数服务器训练](https://www.paddlepaddle.org.cn/tutorials/projectdetail/464839)
具体训练流程：
- 将训练数据均匀的分配给不同的Worker。
- 将模型参数分片，存储在不同的Server上。
- Worker端：读取一个minibatch训练数据，从Server端拉取最新的参数，计算梯度，并根据分片上传给不同的Server。
- Server端：接收Worker端上传的梯度，根据优化算法更新参数。根据Server端每次参数更新是否需要等待所有Worker端的梯度，分为同步训练和异步训练两种机制。
飞桨的参数服务器框架也是基于这种经典的参数服务器模式进行设计和开发的，同时在这基础上进行了SGD(Stochastic Gradient Descent）算法的创新（GEO-SGD）。目前飞桨支持3种模式，分别是同步训练模式、异步训练模式、GEO异步训练模式，如图2所示。
<p align="center">
<img align="center" src="imgs/fleet-ps.png">
<p>
## 同步训练
Worker在训练一个batch的数据后，会合并所有线程的梯度发给Server, Server在收到所有节点的梯度后，会统一进行梯度合并及参数更新。同步训练的优势在于Loss可以比较稳定的下降，缺点是整个训练速度较慢，这是典型的木桶原理，速度的快慢取决于最慢的那个线程的训练计算时间，因此在训练较为复杂的模型时，即模型训练过程中神经网络训练耗时远大于节点间通信耗时的场景下，推荐使用同步训练模式。
## 异步训练
在训练一个batch的数据后，Worker的每个线程会发送梯度给Server。而Server不会等待接收所有节点的梯度，而是直接基于已收到的梯度进行参数更新。异步训练去除了训练过程中的等待机制，训练速度得到了极大的提升，但是缺点也很明显，那就是Loss下降不稳定，容易发生抖动。建议在个性化推荐（召回、排序）、语义匹配等数据量大的场景使用。 尤其是推荐领域的点击率预估场景，该场景可能会出现千亿甚至万亿规模的稀疏特征，而稀疏参数也可以达到万亿数量级，且需要小时级或分钟级流式增量训练。如果使用异步训练模式，可以很好的满足该场景的online-learning需求。
## GEO异步训练
GEO(Geometric Stochastic Gradient Descent)异步训练是飞桨自研的异步训练模式，其最大的特点是将参数的更新从Server转移到Worker上。每个Worker在本地训练过程中会使用SGD优化算法更新本地模型参数，在训练若干个batch的数据后，Worker将发送参数更新信息给Server。Server在接收后会通过加和方式更新保存的参数信息。所以显而易见，在GEO异步训练模式下，Worker不用再等待Server发来新的参数即可执行训练，在训练效果和训练速度上有了极大的提升。但是此模式比较适合可以在单机内能完整保存的模型，在搜索、NLP等类型的业务上应用广泛，推荐在词向量、语义匹配等场景中使用。
> 运行策略的详细描述可以参考文档：[PaddlePaddle Fluid CPU分布式训练(Trainspiler)使用指南](https://www.paddlepaddle.org.cn/tutorials/projectdetail/454253)
## 单机代码转分布式
### 训练代码准备
参数服务器架构，有两个重要的组成部分：Server与Worker。为了启动训练，我们是否要准备两套代码分别运行呢？答案是不需要的。Paddle Fleet API将两者运行的逻辑进行了很好的统一，用户只需使用`fleet.init(role)`就可以判断当前启动的程序扮演server还是worker。使用如下的编程范式，只需10行，便可将单机代码转变为分布式代码：
``` python
role = role_maker.PaddleCloudRoleMaker()
fleet.init(role)
# Define your network, choose your optimizer(SGD/Adam/Adagrad etc.)
strategy = StrategyFactory.create_sync_strategy()
optimizer = fleet.distributed_optimizer(optimizer, strategy)
if fleet.is_server():
fleet.init_server()
fleet.run_server()
if fleet.is_worker():
fleet.init_worker()
# run training
fleet.stop_worker()
```
### 运行环境准备
- Paddle参数服务器模式的训练，目前只支持在`Liunx`环境下运行，推荐使用`ubuntu`或`CentOS`
- Paddle参数服务器模式的前端代码支持`python 2.7`及`python 3.5+`，若使用`Dataset`模式的高性能IO，需使用`python 2.7`
- 使用多台机器进行分布式训练，请确保各自之间可以通过`ip:port`的方式访问`rpc`服务，使用`http/https`代理会导致通信失败
- 各个机器之间的通信耗费应尽量少
假设我们有两台机器，想要在每台机器上分别启动一个`server`进程以及一个`worker`进程，完成2x2（2个参数服务器，2个训练节点）的参数服务器模式分布式训练，按照如下步骤操作。
### 启动server
机器A，IP地址是`10.89.176.11`，通信端口是`36000`，配置如下环境变量后，运行训练的入口程序：
```bash
export PADDLE_PSERVERS_IP_PORT_LIST="10.89.176.11:36000,10.89.176.12:36000"
export TRAINING_ROLE=PSERVER
export POD_IP=10.89.176.11 # node A：10.89.176.11
export PADDLE_PORT=36000
export PADDLE_TRAINERS_NUM=2
python -u train.py --is_cloud=1
```
应能在日志中看到如下输出：
> I0318 21:47:01.298220 188592128 grpc_server.cc:470] Server listening on 127.0.0.1:36000 selected port: 36000
查看系统进程
> 8624 | ttys000 | 0:02.31 | python -u train.py --is_cloud=1
查看系统进程及端口占用：
> python3.7 | 8624 | paddle | 8u | IPv6 | 0xe149b87d093872e5 | 0t0 | TCP | localhost:36000 (LISTEN)
也可以看到我们的`server`进程8624的确在`36000`端口开始了监听，等待`worker`的通信。
机器B，IP地址是`10.89.176.12`，通信端口是`36000`，配置如下环境变量后，运行训练的入口程序：
```bash
export PADDLE_PSERVERS_IP_PORT_LIST="10.89.176.11:36000,10.89.176.12:36000"
export TRAINING_ROLE=PSERVER
export POD_IP=10.89.176.12 # node B: 10.89.176.12
export PADDLE_PORT=36000
export PADDLE_TRAINERS_NUM=2
python -u train.py --is_cloud=1
```
也可以看到相似的日志输出与进程状况。（进行验证时，请务必确保IP与端口的正确性）
### 启动worker
接下来我们分别在机器A与B上开启训练进程。配置如下环境变量并开启训练进程：
机器A：
```bash
export PADDLE_PSERVERS_IP_PORT_LIST="10.89.176.11:36000,10.89.176.12:36000"
export TRAINING_ROLE=TRAINER
export PADDLE_TRAINERS_NUM=2
export PADDLE_TRAINER_ID=0 # node A：trainer_id = 0
python -u train.py --is_cloud=1
```
机器B：
```bash
export PADDLE_PSERVERS_IP_PORT_LIST="10.89.176.11:36000,10.89.176.12:36000"
export TRAINING_ROLE=TRAINER
export PADDLE_TRAINERS_NUM=2
export PADDLE_TRAINER_ID=1 # node B: trainer_id = 1
python -u train.py --is_cloud=1
```
运行该命令时，若pserver还未就绪，可在日志输出中看到如下信息：
> server not ready, wait 3 sec to retry...
>
> not ready endpoints:['10.89.176.11:36000', '10.89.176.12:36000']
worker进程将持续等待，直到server开始监听，或等待超时。
当pserver都准备就绪后，可以在日志输出看到如下信息：
> I0317 11:38:48.099179 16719 communicator.cc:271] Communicator start
>
> I0317 11:38:49.838711 16719 rpc_client.h:107] init rpc client with trainer_id 0
至此，分布式训练启动完毕，将开始训练。
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# 推荐系统背景知识
本文来源于[个性化推荐](https://github.com/PaddlePaddle/book/blob/develop/05.recommender_system/README.cn.md)，进行了节选。
本文代码目录在[book/recommender_system](https://github.com/PaddlePaddle/book/tree/develop/05.recommender_system),初次使用请您参考[Book文档使用说明](https://github.com/PaddlePaddle/book/blob/develop/README.cn.md#运行这本书)。
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# 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.
engine:
workspace: "/home/tangwei/fleet_rec_env/paddlerec/example/cloud"
backend: "PaddleCloud"
submit:
sk: "2907445932295c57bffab28676fc5a66"
ak: "55cb2027d7a05b9f8b213de483698cc8"
version: "paddle-fluid-v1.7.1"
priority: "high"
jobname: "paddlerec_ctr_dnn"
group: "paddle_benchmark"
cluster: "SZWG-SUNWAY"
config: "{workspace}/config.ini"
nodes: 8
submit_scrpit: "{workspace}/submit.sh"
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echo "Run before_hook.sh ..."
../python27-gcc482/bin/python ../python27-gcc482/bin/pip install ./thirdparty/paddle_rec-0.0.2-py2-none-any.whl --index-url=http://pip.baidu.com/pypi/simple --trusted-host pip.baidu.com
echo "End before_hook.sh ..."
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#type of storage cluster
storage_type="hdfs"
#attention: files for training should be put on hdfs
force_reuse_output_path="True"
fs_name="afs://yinglong.afs.baidu.com:9902"
fs_ugi="paddle,paddle"
thirdparty_path="/user/paddle/benchmark/ctr/thirdparty"
#train data path on hdfs
train_data_path="/user/paddle/benchmark/ctr/train_data_paddle/part_1*"
#test data path on hdfs
#test_data_path="/user/paddle/benchmark/ctr/test_data"
#the output directory on hdfs
output_path="/user/paddle/ly"
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train:
trainer:
# for cluster training
strategy: "async"
epochs: 10
workspace: "paddlerec.models.rank.dnn"
reader:
batch_size: 512
class: "{workspace}/../criteo_reader.py"
train_data_path: "train_data"
reader_debug_mode: False
model:
models: "{workspace}/model.py"
hyper_parameters:
sparse_inputs_slots: 27
sparse_feature_number: 1000001
sparse_feature_dim: 10
dense_input_dim: 13
fc_sizes: [400, 400, 400]
learning_rate: 0.0001
optimizer: adam
save:
increment:
dirname: "increment"
epoch_interval: 2
save_last: True
inference:
dirname: "inference"
epoch_interval: 4
save_last: True
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#!/bin/bash
###################################################
# Usage: job.sh
# Description: run mpi job clinet implement
###################################################
# ---------------------------------------------------------------------------- #
# variable define #
# ---------------------------------------------------------------------------- #
export CPU_NUM=16
export GLOG_v=0
export FLAGS_rpc_deadline=300000
# ---------------------------------------------------------------------------- #
python -m paddlerec.run -m paddle_rec_config.yaml -e cluster -r worker
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#!/bin/bash
###################################################
# Usage: submit.sh
# Description: run mpi submit clinet implement
###################################################
g_package_files=""
#-----------------------------------------------------------------------------------------------------------------
#fun : before hook submit to cluster
#param : N/A
#return : 0 -- success; not 0 -- failure
#-----------------------------------------------------------------------------------------------------------------
function before_submit_hook() {
echo "before_submit"
}
#-----------------------------------------------------------------------------------------------------------------
#fun : after hook submit to cluster
#param : N/A
#return : 0 -- success; not 0 -- failure
#-----------------------------------------------------------------------------------------------------------------
function after_submit_hook() {
echo "after_submit"
}
#-----------------------------------------------------------------------------------------------------------------
#fun : package to cluster
#param : N/A
#return : 0 -- success; not 0 -- failure
#-----------------------------------------------------------------------------------------------------------------
function package() {
echo "package"
temp=${engine_temp_path}
cp ${engine_workspace}/job.sh ${temp}
cp ${engine_workspace}/before_hook.sh ${temp}
cp ${engine_run_config} ${temp}/paddle_rec_config.yaml
g_submitfiles="job.sh before_hook.sh paddle_rec_config.yaml"
g_run_cmd="sh job.sh"
}
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# 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.
engine:
workspace: "paddlerec.models.rank.dnn"
backend: "MPI"
hdfs:
name: "hdfs://nmg01-taihang-hdfs.dmop.baidu.com:54310"
ugi: "fcr,SaK2VqfEDeXzKPor"
output: "/app/ecom/fcr/fanyabo/wadstyleimageq/tangwei12/output_1/"
package:
build_script: "{workspace}/package.sh"
python: "/home/tangwei/fleet_rec_env/cpython-2.7.11-ucs4"
paddlerec: "/home/tangwei/fleet_rec_env/PaddleRec"
submit:
hpc: "/home/tangwei/Plines/client/smart_client_khan/"
qconf: "/home/tangwei/Plines/imageq/qsub_f.conf"
nodes: 10
submit_scrpit: "{workspace}/submit.sh"
job_scrpit: "{workspace}/worker.sh"
example/mpi/job.sh 已删除 100644 → 0
浏览文件 @ c6fc563b
#!/bin/bash
###################################################
# Usage: job.sh
# Description: run job on mpi per node
###################################################
# ---------------------------------------------------------------------------- #
# variable define #
# ---------------------------------------------------------------------------- #
declare g_curPath=""
declare g_scriptName=""
declare g_workPath=""
declare g_run_stage=""
# ---------------------------------------------------------------------------- #
# const define #
# ---------------------------------------------------------------------------- #
export FLAGS_communicator_thread_pool_size=5
export FLAGS_communicator_send_queue_size=18
export FLAGS_communicator_thread_pool_size=20
export FLAGS_communicator_max_merge_var_num=18
################################################################################
#-----------------------------------------------------------------------------------------------------------------
#fun : check function return code
#param : N/A
#return : 0 -- success; not 0 -- failure
#-----------------------------------------------------------------------------------------------------------------
function check_error() {
if [ ${?} -ne 0 ]; then
echo "execute " + $g_run_stage + " raise exception! please check ..."
exit 1
fi
}
#-----------------------------------------------------------------------------------------------------------------
#fun : check function return code
#param : N/A
#return : 0 -- success; not 0 -- failure
#-----------------------------------------------------------------------------------------------------------------
function env_prepare() {
g_run_stage="env_prepare"
WORKDIR=$(pwd)
mpirun -npernode 1 mv package/* ./
echo "current:"$WORKDIR
mpirun -npernode 1 tar -zxvf python.tar.gz > /dev/null
export PYTHONPATH=$WORKDIR/python/
export PYTHONROOT=$WORKDIR/python/
export LIBRARY_PATH=$PYTHONPATH/lib:$LIBRARY_PATH
export LD_LIBRARY_PATH=$PYTHONPATH/lib:$LD_LIBRARY_PATH
export PATH=$PYTHONPATH/bin:$PATH
export LIBRARY_PATH=$PYTHONROOT/lib:$LIBRARY_PATH
mpirun -npernode 1 python/bin/python -m pip uninstall -y paddle-rec
mpirun -npernode 1 python/bin/python -m pip install whl/fleet_rec-0.0.2-py2-none-any.whl --index-url=http://pip.baidu.com/pypi/simple --trusted-host pip.baidu.com
check_error
}
function run() {
echo "run"
g_run_stage="run"
mpirun -npernode 2 -timestamp-output -tag-output -machinefile ${PBS_NODEFILE} python/bin/python -u -m paddlerec.run -m paddlerec.models.rank.dnn --engine cluster --role worker
}
function main() {
env_prepare
run
}
main
example/mpi/submit.sh 已删除 100644 → 0
浏览文件 @ c6fc563b
#!/bin/bash
###################################################
# Usage: submit.sh
# Description: run mpi submit clinet implement
###################################################
#-----------------------------------------------------------------------------------------------------------------
#fun : get argument from env, set it into variables
#param : N/A
#return : 0 -- success; not 0 -- failure
#-----------------------------------------------------------------------------------------------------------------
function vars_get_from_env() {
echo "xx"
}
#-----------------------------------------------------------------------------------------------------------------
#fun : package
#param : N/A
#return : 0 -- success; not 0 -- failure
#-----------------------------------------------------------------------------------------------------------------
function package() {
g_run_stage="package"
temp=${engine_temp_path}
echo "package temp dir: " ${temp}
cp ${engine_job_scrpit} ${temp}
cp ${engine_submit_qconf} ${temp}
echo "copy job.sh from " ${engine_worker} " to " ${temp}
mkdir -p ${temp}/package
cp -r ${engine_package_python} ${temp}/package/
echo "copy python from " ${engine_package_python} " to " ${temp}
mkdir ${temp}/package/whl
cp ${engine_package_paddlerec} ${temp}/package/whl/
echo "copy " ${engine_package_paddlerec} " to " ${temp}"/whl/"
}
#-----------------------------------------------------------------------------------------------------------------
#fun : before hook submit to cluster
#param : N/A
#return : 0 -- success; not 0 -- failure
#-----------------------------------------------------------------------------------------------------------------
function before_submit() {
echo "before_submit"
}
#-----------------------------------------------------------------------------------------------------------------
#fun : after hook submit to cluster
#param : N/A
#return : 0 -- success; not 0 -- failure
#-----------------------------------------------------------------------------------------------------------------
function after_submit() {
echo "after_submit"
}
#-----------------------------------------------------------------------------------------------------------------
#fun : submit to cluster
#param : N/A
#return : 0 -- success; not 0 -- failure
#-----------------------------------------------------------------------------------------------------------------
function submit() {
g_run_stage="submit"
g_job_name="paddle_rec_mpi"
g_hdfs_path=$g_hdfs_path
g_job_entry="job.sh"
engine_hdfs_output=${engine_hdfs_output}/$(date +%Y%m%d%H%M%S)
cd ${engine_temp_path}
${engine_submit_hpc}/bin/qsub_f \
-N ${g_job_name} \
--conf ${engine_submit_qconf} \
--hdfs ${engine_hdfs_name} \
--ugi ${engine_hdfs_ugi} \
--hout ${engine_hdfs_output} \
--files ./package \
-l nodes=${engine_submit_nodes},walltime=1000:00:00,resource=full ${g_job_entry}
}
function main() {
package
before_submit
submit
after_submit
}
models/contentunderstanding/classification/model.py
浏览文件 @ 3e50521c
# 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 paddle.fluid as fluid
import math
from paddlerec.core.utils import envs
from paddlerec.core.model import Model as ModelBase
import paddle.fluid as fluid
import paddle.fluid.layers.nn as nn
import paddle.fluid.layers.tensor as tensor
import paddle.fluid.layers.control_flow as cf
class Model(ModelBase):
def __init__(self, config):
......@@ -22,7 +30,7 @@ class Model(ModelBase):
def train_net(self):
""" network definition """
data = fluid.data(name="input", shape=[None, self.max_len], dtype='int64')
label = fluid.data(name="label", shape=[None, 1], dtype='int64')
seq_len = fluid.data(name="seq_len", shape=[None], dtype='int64')
......@@ -46,12 +54,12 @@ class Model(ModelBase):
prediction = fluid.layers.fc(input=[fc_1], size=self.class_dim, act="softmax")
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(x=cost)
acc = fluid.layers.accuracy(input=prediction, label=label)
acc = fluid.layers.accuracy(input=prediction, label=label)
self.cost = avg_cost
self._metrics["acc"] = acc
def get_cost_op(self):
def get_avg_cost(self):
return self.cost
def get_metrics(self):
......
models/contentunderstanding/classification/reader.py
浏览文件 @ 3e50521c
import re
# 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 sys
import collections
import os
import six
import time
import numpy as np
import paddle.fluid as fluid
import paddle
import csv
import io
from paddlerec.core.reader import Reader
from paddlerec.core.utils import envs
class TrainReader(Reader):
def init(self):
pass
def _process_line(self, l):
def _process_line(self, l):
l = l.strip().split(" ")
data = l[0:10]
seq_len = l[10:11]
label = l[11:]
return data, label, seq_len
return data, label, seq_len
def generate_sample(self, line):
def data_iter():
......@@ -33,6 +38,7 @@ class TrainReader(Reader):
data = [int(i) for i in data]
label = [int(i) for i in label]
seq_len = [int(i) for i in seq_len]
print >>sys.stderr, str([('data', data), ('label', label), ('seq_len', seq_len)])
print >> sys.stderr, str([('data', data), ('label', label), ('seq_len', seq_len)])
yield [('data', data), ('label', label), ('seq_len', seq_len)]
return data_iter
models/contentunderstanding/classification/train_data/part-0
浏览文件 @ 3e50521c
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8 83 9 91 67 79 31 20 31 88 2 0
64 95 46 95 78 63 4 60 66 63 7 1
10 39 78 45 36 4 89 94 68 75 7 0
8 83 9 91 67 79 31 20 31 88 2 0
64 95 46 95 78 63 4 60 66 63 7 1
10 39 78 45 36 4 89 94 68 75 7 0
81 52 70 11 48 15 40 63 29 14 8 1
94 49 30 14 53 12 53 42 77 82 8 1
40 88 46 20 54 84 76 15 2 73 2 1
40 88 46 20 54 84 76 15 2 73 2 1
71 50 79 54 17 58 30 16 17 99 1 1
74 79 74 61 61 36 28 39 89 36 6 0
53 45 45 23 51 32 93 26 10 8 3 0
1 97 6 67 88 20 41 63 49 6 8 0
3 64 41 19 41 80 75 71 69 90 8 0
31 90 38 93 52 0 38 86 41 68 9 1
50 94 53 9 73 59 94 7 24 57 3 0
87 11 4 62 96 7 0 59 46 11 6 1
53 45 45 23 51 32 93 26 10 8 3 0
1 97 6 67 88 20 41 63 49 6 8 0
3 64 41 19 41 80 75 71 69 90 8 0
31 90 38 93 52 0 38 86 41 68 9 1
50 94 53 9 73 59 94 7 24 57 3 0
87 11 4 62 96 7 0 59 46 11 6 1
77 67 56 88 45 62 10 51 86 27 6 1
62 62 59 99 83 84 79 97 56 37 5 0
19 55 0 37 44 44 2 7 54 50 5 1
23 60 11 83 6 48 20 77 54 31 6 0
27 53 52 30 3 70 57 38 47 96 5 0
75 14 5 83 72 46 47 64 14 12 7 0
19 55 0 37 44 44 2 7 54 50 5 1
23 60 11 83 6 48 20 77 54 31 6 0
27 53 52 30 3 70 57 38 47 96 5 0
75 14 5 83 72 46 47 64 14 12 7 0
29 95 36 63 59 49 38 44 13 15 2 1
38 3 70 89 2 94 89 74 33 6 8 1
28 56 49 43 83 34 7 63 36 13 7 0
38 3 70 89 2 94 89 74 33 6 8 1
28 56 49 43 83 34 7 63 36 13 7 0
25 90 23 85 50 65 36 10 64 38 5 0
35 94 48 38 99 71 42 39 61 75 8 1
28 73 34 22 51 8 52 98 74 19 8 1
12 40 65 12 7 96 73 65 12 90 5 0
28 73 34 22 51 8 52 98 74 19 8 1
12 40 65 12 7 96 73 65 12 90 5 0
42 42 48 16 80 14 48 29 29 45 5 0
58 20 4 0 69 99 15 4 16 4 1 1
93 30 90 5 23 63 25 30 99 32 7 1
58 20 4 0 69 99 15 4 16 4 1 1
93 30 90 5 23 63 25 30 99 32 7 1
91 23 20 26 84 78 58 76 58 90 5 1
33 2 36 59 55 9 79 34 92 57 9 0
33 2 36 59 55 9 79 34 92 57 9 0
80 63 84 73 22 40 70 94 59 34 5 0
49 95 50 32 90 22 18 66 46 32 2 0
47 72 3 94 33 78 87 43 11 67 5 0
47 72 3 94 33 78 87 43 11 67 5 0
76 44 86 81 95 48 79 46 11 65 8 1
59 51 97 75 17 5 40 59 32 62 6 0
41 13 58 7 54 84 8 84 27 55 1 0
59 51 97 75 17 5 40 59 32 62 6 0
41 13 58 7 54 84 8 84 27 55 1 0
24 80 44 26 86 99 68 80 81 22 9 0
12 45 16 44 66 76 33 53 3 20 9 0
22 3 79 6 32 38 75 66 15 25 9 1
51 48 26 53 33 26 18 74 9 39 5 1
12 45 16 44 66 76 33 53 3 20 9 0
22 3 79 6 32 38 75 66 15 25 9 1
51 48 26 53 33 26 18 74 9 39 5 1
35 67 89 91 29 81 23 52 19 11 6 0
64 50 43 1 43 49 19 20 84 19 8 0
34 4 9 77 24 61 55 82 42 76 9 0
37 84 94 33 67 60 3 95 78 8 9 0
82 10 54 12 47 23 78 97 6 51 5 0
70 40 38 47 5 38 83 70 37 90 2 0
64 50 43 1 43 49 19 20 84 19 8 0
34 4 9 77 24 61 55 82 42 76 9 0
37 84 94 33 67 60 3 95 78 8 9 0
82 10 54 12 47 23 78 97 6 51 5 0
70 40 38 47 5 38 83 70 37 90 2 0
42 21 62 27 43 47 82 80 88 49 4 0
68 68 67 12 38 13 32 30 93 27 3 1
5 44 98 28 5 81 20 56 10 34 9 1
5 44 98 28 5 81 20 56 10 34 9 1
40 46 11 33 73 62 68 70 66 85 4 0
9 46 11 84 6 31 18 89 66 32 1 1
6 78 44 98 77 29 69 39 62 78 1 0
47 90 18 0 3 8 12 20 51 75 4 1
9 46 11 84 6 31 18 89 66 32 1 1
6 78 44 98 77 29 69 39 62 78 1 0
47 90 18 0 3 8 12 20 51 75 4 1
21 29 74 19 12 29 41 22 63 47 8 1
22 59 64 62 18 89 19 92 87 8 8 0
6 21 24 58 14 53 18 93 62 15 8 0
20 33 88 25 37 52 1 72 74 11 2 0
90 49 28 53 28 80 22 81 0 46 9 0
87 31 51 27 15 31 68 93 5 4 7 1
21 72 60 2 24 79 22 24 77 61 9 0
20 4 6 40 28 14 16 78 58 99 7 1
80 35 98 20 91 35 47 29 3 19 2 1
57 21 24 61 60 39 83 34 53 2 2 0
22 59 64 62 18 89 19 92 87 8 8 0
6 21 24 58 14 53 18 93 62 15 8 0
20 33 88 25 37 52 1 72 74 11 2 0
90 49 28 53 28 80 22 81 0 46 9 0
87 31 51 27 15 31 68 93 5 4 7 1
21 72 60 2 24 79 22 24 77 61 9 0
20 4 6 40 28 14 16 78 58 99 7 1
80 35 98 20 91 35 47 29 3 19 2 1
57 21 24 61 60 39 83 34 53 2 2 0
74 86 78 78 18 44 20 94 85 71 4 1
27 48 44 92 10 18 74 54 25 85 2 0
74 77 28 75 74 91 69 36 95 68 7 0
32 84 17 18 55 79 59 57 21 69 2 1
69 77 40 98 83 40 4 66 39 83 1 1
63 24 32 39 75 92 81 49 2 51 5 1
35 40 84 71 3 16 82 91 44 52 8 0
21 78 66 4 57 27 21 89 4 34 7 1
69 77 40 98 83 40 4 66 39 83 1 1
63 24 32 39 75 92 81 49 2 51 5 1
35 40 84 71 3 16 82 91 44 52 8 0
21 78 66 4 57 27 21 89 4 34 7 1
94 18 57 49 88 26 29 76 56 67 6 0
14 91 71 30 5 36 28 74 16 73 3 1
14 91 71 30 5 36 28 74 16 73 3 1
93 36 43 46 77 44 59 19 56 84 3 0
11 16 2 67 11 96 20 91 20 59 2 1
11 16 2 67 11 96 20 91 20 59 2 1
72 79 26 99 90 71 56 46 35 99 3 0
29 87 20 40 13 14 14 40 61 27 6 0
41 64 28 51 56 52 87 67 37 91 6 1
33 14 5 30 99 54 27 80 54 55 4 1
33 14 5 30 99 54 27 80 54 55 4 1
60 44 73 91 71 53 54 95 59 81 6 0
69 33 11 83 4 53 34 39 43 84 1 0
73 31 19 4 50 20 66 73 94 88 4 0
30 49 41 76 5 21 88 69 76 3 2 0
69 33 11 83 4 53 34 39 43 84 1 0
73 31 19 4 50 20 66 73 94 88 4 0
30 49 41 76 5 21 88 69 76 3 2 0
18 50 27 76 67 38 87 16 52 87 5 1
33 36 80 8 43 82 89 76 37 3 5 0
98 21 61 24 58 13 9 85 56 74 1 1
84 27 50 96 9 56 30 31 85 65 1 1
65 74 40 2 8 40 18 57 30 38 1 1
76 44 64 6 10 32 84 70 74 24 1 1
14 29 59 34 27 8 0 37 27 68 3 0
6 47 5 77 15 41 93 49 59 83 4 1
39 88 43 89 32 98 82 0 5 12 9 0
78 79 30 26 58 6 9 58 37 65 8 1
33 36 80 8 43 82 89 76 37 3 5 0
98 21 61 24 58 13 9 85 56 74 1 1
84 27 50 96 9 56 30 31 85 65 1 1
65 74 40 2 8 40 18 57 30 38 1 1
76 44 64 6 10 32 84 70 74 24 1 1
14 29 59 34 27 8 0 37 27 68 3 0
6 47 5 77 15 41 93 49 59 83 4 1
39 88 43 89 32 98 82 0 5 12 9 0
78 79 30 26 58 6 9 58 37 65 8 1
25 28 66 41 70 87 76 62 29 39 7 1
models/contentunderstanding/tagspace/model.py
浏览文件 @ 3e50521c
import paddle.fluid as fluid
import math
from paddlerec.core.utils import envs
from paddlerec.core.model import Model as ModelBase
# 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 paddle.fluid as fluid
import paddle.fluid.layers.nn as nn
import paddle.fluid.layers.tensor as tensor
import paddle.fluid.layers.control_flow as cf
from paddlerec.core.model import Model as ModelBase
from paddlerec.core.utils import envs
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
self.cost = None
self.metrics = {}
self.vocab_text_size = 11447#envs.get_global_env("vocab_text_size", None, self._namespace)
self.vocab_tag_size = 4#envs.get_global_env("vocab_tag_size", None, self._namespace)
self.emb_dim = 10#envs.get_global_env("emb_dim", None, self._namespace)
self.hid_dim = 1000#envs.get_global_env("hid_dim", None, self._namespace)
self.win_size = 5#envs.get_global_env("win_size", None, self._namespace)
self.margin = 0.1#envs.get_global_env("margin", None, self._namespace)
self.neg_size = 3#envs.get_global_env("neg_size", None, self._namespace)
print self.emb_dim
self.vocab_text_size = envs.get_global_env("vocab_text_size", None, self._namespace)
self.vocab_tag_size = envs.get_global_env("vocab_tag_size", None, self._namespace)
self.emb_dim = envs.get_global_env("emb_dim", None, self._namespace)
self.hid_dim = envs.get_global_env("hid_dim", None, self._namespace)
self.win_size = envs.get_global_env("win_size", None, self._namespace)
self.margin = envs.get_global_env("margin", None, self._namespace)
self.neg_size = envs.get_global_env("neg_size", None, self._namespace)
def train_net(self):
""" network definition """
......@@ -78,18 +89,16 @@ class Model(ModelBase):
self.metrics["correct"] = correct
self.metrics["cos_pos"] = cos_pos
def get_cost_op(self):
def get_avg_cost(self):
return self.cost
def get_metrics(self):
return self.metrics
def optimizer(self):
learning_rate = 0.01#envs.get_global_env("hyper_parameters.base_lr", None, self._namespace)
learning_rate = envs.get_global_env("hyper_parameters.base_lr", None, self._namespace)
sgd_optimizer = fluid.optimizer.Adagrad(learning_rate=learning_rate)
#sgd_optimizer.minimize(avg_cost)
return sgd_optimizer
def infer_net(self, parameter_list):
self.train_net()
models/contentunderstanding/tagspace/reader.py
浏览文件 @ 3e50521c
import re
# 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 sys
import collections
import os
import six
import time
import numpy as np
import paddle.fluid as fluid
import paddle
import csv
import io
from paddlerec.core.reader import Reader
from paddlerec.core.utils import envs
class TrainReader(Reader):
def init(self):
pass
def _process_line(self, l):
def _process_line(self, l):
tag_size = 4
neg_size = 3
l = l.strip().split(",")
......@@ -40,10 +47,7 @@ class TrainReader(Reader):
neg_index = rand_i
neg_tag.append(neg_index)
sum_n += 1
# if n > 0 and len(text) > n:
# #yield None
# return None, None, None
return text, pos_tag, neg_tag
return text, pos_tag, neg_tag
def generate_sample(self, line):
def data_iter():
......@@ -52,4 +56,5 @@ class TrainReader(Reader):
yield None
return
yield [('text', text), ('pos_tag', pos_tag), ('neg_tag', neg_tag)]
return data_iter
models/match/dssm/model.py
浏览文件 @ 3e50521c
......@@ -12,7 +12,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import paddle.fluid as fluid
from paddlerec.core.utils import envs
......@@ -25,11 +24,12 @@ class Model(ModelBase):
def input(self):
TRIGRAM_D = envs.get_global_env("hyper_parameters.TRIGRAM_D", None, self._namespace)
Neg = envs.get_global_env("hyper_parameters.NEG", None, self._namespace)
Neg = envs.get_global_env("hyper_parameters.NEG", None, self._namespace)
self.query = fluid.data(name="query", shape=[-1, TRIGRAM_D], dtype='float32', lod_level=0)
self.doc_pos = fluid.data(name="doc_pos", shape=[-1, TRIGRAM_D], dtype='float32', lod_level=0)
self.doc_negs = [fluid.data(name="doc_neg_" + str(i), shape=[-1, TRIGRAM_D], dtype="float32", lod_level=0) for i in range(Neg)]
self.doc_negs = [fluid.data(name="doc_neg_" + str(i), shape=[-1, TRIGRAM_D], dtype="float32", lod_level=0) for i
in range(Neg)]
self._data_var.append(self.query)
self._data_var.append(self.doc_pos)
for input in self.doc_negs:
......@@ -38,40 +38,40 @@ class Model(ModelBase):
if self._platform != "LINUX":
self._data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._data_var, capacity=64, use_double_buffer=False, iterable=False)
def net(self, is_infer=False):
hidden_layers = envs.get_global_env("hyper_parameters.fc_sizes", None, self._namespace)
hidden_layers = envs.get_global_env("hyper_parameters.fc_sizes", None, self._namespace)
hidden_acts = envs.get_global_env("hyper_parameters.fc_acts", None, self._namespace)
def fc(data, hidden_layers, hidden_acts, names):
fc_inputs = [data]
for i in range(len(hidden_layers)):
xavier=fluid.initializer.Xavier(uniform=True, fan_in=fc_inputs[-1].shape[1], fan_out=hidden_layers[i])
out = fluid.layers.fc(input=fc_inputs[-1],
size=hidden_layers[i],
act=hidden_acts[i],
param_attr=xavier,
bias_attr=xavier,
name=names[i])
fc_inputs.append(out)
return fc_inputs[-1]
query_fc = fc(self.query, hidden_layers, hidden_acts, ['query_l1', 'query_l2', 'query_l3'])
doc_pos_fc = fc(self.doc_pos, hidden_layers, hidden_acts, ['doc_pos_l1', 'doc_pos_l2', 'doc_pos_l3'])
self.R_Q_D_p = fluid.layers.cos_sim(query_fc, doc_pos_fc)
for i in range(len(hidden_layers)):
xavier = fluid.initializer.Xavier(uniform=True, fan_in=fc_inputs[-1].shape[1], fan_out=hidden_layers[i])
out = fluid.layers.fc(input=fc_inputs[-1],
size=hidden_layers[i],
act=hidden_acts[i],
param_attr=xavier,
bias_attr=xavier,
name=names[i])
fc_inputs.append(out)
return fc_inputs[-1]
query_fc = fc(self.query, hidden_layers, hidden_acts, ['query_l1', 'query_l2', 'query_l3'])
doc_pos_fc = fc(self.doc_pos, hidden_layers, hidden_acts, ['doc_pos_l1', 'doc_pos_l2', 'doc_pos_l3'])
self.R_Q_D_p = fluid.layers.cos_sim(query_fc, doc_pos_fc)
if is_infer:
return
R_Q_D_ns = []
for i, doc_neg in enumerate(self.doc_negs):
doc_neg_fc_i = fc(doc_neg, hidden_layers, hidden_acts, ['doc_neg_l1_' + str(i), 'doc_neg_l2_' + str(i), 'doc_neg_l3_' + str(i)])
for i, doc_neg in enumerate(self.doc_negs):
doc_neg_fc_i = fc(doc_neg, hidden_layers, hidden_acts,
['doc_neg_l1_' + str(i), 'doc_neg_l2_' + str(i), 'doc_neg_l3_' + str(i)])
R_Q_D_ns.append(fluid.layers.cos_sim(query_fc, doc_neg_fc_i))
concat_Rs = fluid.layers.concat(input=[self.R_Q_D_p] + R_Q_D_ns, axis=-1)
prob = fluid.layers.softmax(concat_Rs, axis=1)
hit_prob = fluid.layers.slice(prob, axes=[0,1], starts=[0,0], ends=[4, 1])
prob = fluid.layers.softmax(concat_Rs, axis=1)
hit_prob = fluid.layers.slice(prob, axes=[0, 1], starts=[0, 0], ends=[4, 1])
loss = -fluid.layers.reduce_sum(fluid.layers.log(hit_prob))
self.avg_cost = fluid.layers.mean(x=loss)
......@@ -101,10 +101,10 @@ class Model(ModelBase):
self.doc_pos = fluid.data(name="doc_pos", shape=[-1, TRIGRAM_D], dtype='float32', lod_level=0)
self._infer_data_var = [self.query, self.doc_pos]
self._infer_data_loader = fluid.io.DataLoader.from_generator(
self._infer_data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False)
def infer_net(self):
self.infer_input()
self.infer_input()
self.net(is_infer=True)
self.infer_results()
self.infer_results()
models/match/dssm/synthetic_evaluate_reader.py
浏览文件 @ 3e50521c
......@@ -14,7 +14,6 @@
from __future__ import print_function
from paddlerec.core.reader import Reader
from paddlerec.core.utils import envs
class EvaluateReader(Reader):
......
models/match/dssm/synthetic_reader.py
浏览文件 @ 3e50521c
......@@ -11,10 +11,10 @@
# 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.
from __future__ import print_function
from paddlerec.core.reader import Reader
from paddlerec.core.utils import envs
class TrainReader(Reader):
......@@ -37,7 +37,7 @@ class TrainReader(Reader):
neg_docs = []
for i in range(len(features) - 2):
feature_names.append('doc_neg_' + str(i))
neg_docs.append(map(float, features[i+2].split(',')))
neg_docs.append(map(float, features[i + 2].split(',')))
yield zip(feature_names, [query] + [pos_doc] + neg_docs)
......
models/match/multiview-simnet/config.yaml
浏览文件 @ 3e50521c
......@@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
evaluate:
workspace: "paddlerec.models.recall.multiview-simnet"
workspace: "paddlerec.models.match.multiview-simnet"
reader:
batch_size: 2
class: "{workspace}/evaluate_reader.py"
......@@ -24,7 +24,7 @@ train:
strategy: "async"
epochs: 2
workspace: "paddlerec.models.recall.multiview-simnet"
workspace: "paddlerec.models.match.multiview-simnet"
reader:
batch_size: 2
......
models/match/multiview-simnet/data_process.sh
浏览文件 @ 3e50521c
#! /bin/bash
# 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.
set -e
echo "begin to prepare data"
......
models/match/multiview-simnet/evaluate_reader.py
浏览文件 @ 3e50521c
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
# 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.
......@@ -11,18 +11,15 @@
# 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 io
import copy
import random
from paddlerec.core.reader import Reader
from paddlerec.core.utils import envs
class EvaluateReader(Reader):
def init(self):
self.query_slots = envs.get_global_env("hyper_parameters.query_slots", None, "train.model")
self.title_slots = envs.get_global_env("hyper_parameters.title_slots", None, "train.model")
self.query_slots = envs.get_global_env("hyper_parameters.query_slots", None, "train.model")
self.title_slots = envs.get_global_env("hyper_parameters.title_slots", None, "train.model")
self.all_slots = []
for i in range(self.query_slots):
......@@ -52,6 +49,7 @@ class EvaluateReader(Reader):
if visit:
self._all_slots_dict[slot][0] = False
else:
output[index][1].append(padding)
output[index][1].append(padding)
yield output
return data_iter
models/match/multiview-simnet/generate_synthetic_data.py
浏览文件 @ 3e50521c
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved
# 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.
......@@ -14,10 +14,12 @@
import random
class Dataset:
def __init__(self):
pass
class SyntheticDataset(Dataset):
def __init__(self, sparse_feature_dim, query_slot_num, title_slot_num, dataset_size=10000):
# ids are randomly generated
......@@ -39,7 +41,7 @@ class SyntheticDataset(Dataset):
for i in range(self.query_slot_num):
qslot = generate_ids(self.ids_per_slot,
self.sparse_feature_dim)
qslot = [str(fea) + ':' + str(i) for fea in qslot]
qslot = [str(fea) + ':' + str(i) for fea in qslot]
query_slots += qslot
for i in range(self.title_slot_num):
pt_slot = generate_ids(self.ids_per_slot,
......@@ -50,7 +52,8 @@ class SyntheticDataset(Dataset):
for i in range(self.title_slot_num):
nt_slot = generate_ids(self.ids_per_slot,
self.sparse_feature_dim)
nt_slot = [str(fea) + ':' + str(i + self.query_slot_num + self.title_slot_num) for fea in nt_slot]
nt_slot = [str(fea) + ':' + str(i + self.query_slot_num + self.title_slot_num) for fea in
nt_slot]
neg_title_slots += nt_slot
yield query_slots + pos_title_slots + neg_title_slots
else:
......@@ -67,6 +70,7 @@ class SyntheticDataset(Dataset):
def test(self):
return self._reader_creator(False)
if __name__ == '__main__':
sparse_feature_dim = 1000001
query_slots = 1
......@@ -75,7 +79,7 @@ if __name__ == '__main__':
dataset = SyntheticDataset(sparse_feature_dim, query_slots, title_slots, dataset_size)
train_reader = dataset.train()
test_reader = dataset.test()
with open("data/train/train.txt", 'w') as fout:
for data in train_reader():
fout.write(' '.join(data))
......
models/match/multiview-simnet/model.py
浏览文件 @ 3e50521c
......@@ -12,16 +12,14 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import math
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import paddle.fluid.layers.tensor as tensor
import paddle.fluid.layers.control_flow as cf
from paddlerec.core.utils import envs
from paddlerec.core.model import Model as ModelBase
class BowEncoder(object):
""" bow-encoder """
......@@ -97,13 +95,14 @@ class SimpleEncoderFactory(object):
rnn_encode = GrnnEncoder(hidden_size=enc_hid_size)
return rnn_encode
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
self.init_config()
def init_config(self):
self._fetch_interval = 1
self._fetch_interval = 1
query_encoder = envs.get_global_env("hyper_parameters.query_encoder", None, self._namespace)
title_encoder = envs.get_global_env("hyper_parameters.title_encoder", None, self._namespace)
query_encode_dim = envs.get_global_env("hyper_parameters.query_encode_dim", None, self._namespace)
......@@ -115,19 +114,19 @@ class Model(ModelBase):
factory.create(query_encoder, query_encode_dim)
for i in range(query_slots)
]
self.title_encoders = [
self.title_encoders = [
factory.create(title_encoder, title_encode_dim)
for i in range(title_slots)
]
self.emb_size = envs.get_global_env("hyper_parameters.sparse_feature_dim", None, self._namespace)
self.emb_dim = envs.get_global_env("hyper_parameters.embedding_dim", None, self._namespace)
self.emb_shape = [self.emb_size, self.emb_dim]
self.hidden_size = envs.get_global_env("hyper_parameters.hidden_size", None, self._namespace)
self.margin = 0.1
self.emb_size = envs.get_global_env("hyper_parameters.sparse_feature_dim", None, self._namespace)
self.emb_dim = envs.get_global_env("hyper_parameters.embedding_dim", None, self._namespace)
self.emb_shape = [self.emb_size, self.emb_dim]
self.hidden_size = envs.get_global_env("hyper_parameters.hidden_size", None, self._namespace)
self.margin = 0.1
def input(self, is_train=True):
self.q_slots = [
self.q_slots = [
fluid.data(
name="%d" % i, shape=[None, 1], lod_level=1, dtype='int64')
for i in range(len(self.query_encoders))
......@@ -138,22 +137,23 @@ class Model(ModelBase):
for i in range(len(self.title_encoders))
]
if is_train == False:
return self.q_slots + self.pt_slots
if is_train == False:
return self.q_slots + self.pt_slots
self.nt_slots = [
fluid.data(
name="%d" % (i + len(self.query_encoders) + len(self.title_encoders)), shape=[None, 1], lod_level=1, dtype='int64')
name="%d" % (i + len(self.query_encoders) + len(self.title_encoders)), shape=[None, 1], lod_level=1,
dtype='int64')
for i in range(len(self.title_encoders))
]
return self.q_slots + self.pt_slots + self.nt_slots
def train_input(self):
res = self.input()
self._data_var = res
use_dataloader = envs.get_global_env("hyper_parameters.use_DataLoader", False, self._namespace)
use_dataloader = envs.get_global_env("hyper_parameters.use_DataLoader", False, self._namespace)
if self._platform != "LINUX" or use_dataloader:
self._data_loader = fluid.io.DataLoader.from_generator(
......@@ -161,15 +161,15 @@ class Model(ModelBase):
def get_acc(self, x, y):
less = tensor.cast(cf.less_than(x, y), dtype='float32')
label_ones = fluid.layers.fill_constant_batch_size_like(
label_ones = fluid.layers.fill_constant_batch_size_like(
input=x, dtype='float32', shape=[-1, 1], value=1.0)
correct = fluid.layers.reduce_sum(less)
total = fluid.layers.reduce_sum(label_ones)
total = fluid.layers.reduce_sum(label_ones)
acc = fluid.layers.elementwise_div(correct, total)
return acc
return acc
def net(self):
q_embs = [
q_embs = [
fluid.embedding(
input=query, size=self.emb_shape, param_attr="emb")
for query in self.q_slots
......@@ -184,8 +184,8 @@ class Model(ModelBase):
input=title, size=self.emb_shape, param_attr="emb")
for title in self.nt_slots
]
# encode each embedding field with encoder
# encode each embedding field with encoder
q_encodes = [
self.query_encoders[i].forward(emb) for i, emb in enumerate(q_embs)
]
......@@ -201,7 +201,7 @@ class Model(ModelBase):
pt_concat = fluid.layers.concat(pt_encodes)
nt_concat = fluid.layers.concat(nt_encodes)
# projection of hidden layer
# projection of hidden layer
q_hid = fluid.layers.fc(q_concat,
size=self.hidden_size,
param_attr='q_fc.w',
......@@ -219,7 +219,7 @@ class Model(ModelBase):
cos_pos = fluid.layers.cos_sim(q_hid, pt_hid)
cos_neg = fluid.layers.cos_sim(q_hid, nt_hid)
# pairwise hinge_loss
# pairwise hinge_loss
loss_part1 = fluid.layers.elementwise_sub(
tensor.fill_constant_batch_size_like(
input=cos_pos,
......@@ -236,7 +236,7 @@ class Model(ModelBase):
loss_part2)
self.avg_cost = fluid.layers.mean(loss_part3)
self.acc = self.get_acc(cos_neg, cos_pos)
self.acc = self.get_acc(cos_neg, cos_pos)
def avg_loss(self):
self._cost = self.avg_cost
......@@ -253,19 +253,19 @@ class Model(ModelBase):
def optimizer(self):
learning_rate = envs.get_global_env("hyper_parameters.learning_rate", None, self._namespace)
optimizer = fluid.optimizer.Adam(learning_rate=learning_rate)
return optimizer
optimizer = fluid.optimizer.Adam(learning_rate=learning_rate)
return optimizer
def infer_input(self):
res = self.input(is_train=False)
self._infer_data_var = res
self._infer_data_var = res
self._infer_data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False)
def infer_net(self):
self.infer_input()
# lookup embedding for each slot
self.infer_input()
# lookup embedding for each slot
q_embs = [
fluid.embedding(
input=query, size=self.emb_shape, param_attr="emb")
......@@ -276,14 +276,14 @@ class Model(ModelBase):
input=title, size=self.emb_shape, param_attr="emb")
for title in self.pt_slots
]
# encode each embedding field with encoder
# encode each embedding field with encoder
q_encodes = [
self.query_encoders[i].forward(emb) for i, emb in enumerate(q_embs)
]
pt_encodes = [
self.title_encoders[i].forward(emb) for i, emb in enumerate(pt_embs)
]
# concat multi view for query, pos_title, neg_title
# concat multi view for query, pos_title, neg_title
q_concat = fluid.layers.concat(q_encodes)
pt_concat = fluid.layers.concat(pt_encodes)
# projection of hidden layer
......
models/match/multiview-simnet/reader.py
浏览文件 @ 3e50521c
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
# 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.
......@@ -11,18 +11,15 @@
# 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 io
import copy
import random
from paddlerec.core.reader import Reader
from paddlerec.core.utils import envs
class TrainReader(Reader):
def init(self):
self.query_slots = envs.get_global_env("hyper_parameters.query_slots", None, "train.model")
self.title_slots = envs.get_global_env("hyper_parameters.title_slots", None, "train.model")
self.query_slots = envs.get_global_env("hyper_parameters.query_slots", None, "train.model")
self.title_slots = envs.get_global_env("hyper_parameters.title_slots", None, "train.model")
self.all_slots = []
for i in range(self.query_slots):
......@@ -55,6 +52,7 @@ class TrainReader(Reader):
if visit:
self._all_slots_dict[slot][0] = False
else:
output[index][1].append(padding)
output[index][1].append(padding)
yield output
return data_iter
models/match/readme.md
浏览文件 @ 3e50521c
......@@ -9,13 +9,7 @@
* [整体介绍](#整体介绍)
* [匹配模型列表](#匹配模型列表)
* [使用教程](#使用教程)
* [数据处理](#数据处理)
* [训练](#训练)
* [预测](#预测)
* [效果对比](#效果对比)
* [模型效果列表](#模型效果列表)
* [分布式](#分布式)
* [模型性能列表](#模型性能列表)
* [训练&预测](#训练&预测)
## 整体介绍
### 匹配模型列表
......@@ -25,22 +19,22 @@
| DSSM | Deep Structured Semantic Models | [Learning Deep Structured Semantic Models for Web Search using Clickthrough Data](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cikm2013_DSSM_fullversion.pdf)(2013) |
| MultiView-Simnet | Multi-view Simnet for Personalized recommendation | [A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/frp1159-songA.pdf)(2015) |
下面是每个模型的简介（注：图片引用自链接中的论文）
[DSSM](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/cikm2013_DSSM_fullversion.pdf):
<p align="center">
<img align="center" src="../../doc/imgs/dssm.png">
<p>
[MultiView-Simnet](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/frp1159-songA.pdf):
<p align="center">
<img align="center" src="../../doc/imgs/multiview-simnet.png">
<p>
## 使用教程
### 数据处理
### 训练
### 预测
## 效果对比
### 模型效果列表
| 数据集 | 模型 | loss | auc |
| :------------------: | :--------------------: | :---------: |:---------: |
| - | DSSM | -- | -- |
| - | MultiView-Simnet | -- | -- |
## 分布式
### 模型性能列表
| 数据集 | 模型 | 单机 | 多机（同步） | 多机（异步） | GPU |
| :------------------: | :--------------------: | :---------: |:---------: |:---------: |:---------: |
| - | DSSM | -- | -- | -- | -- |
| - | MultiView-Simnet | -- | -- | -- | -- |
### 训练&预测
```shell
python -m paddlerec.run -m paddlerec.models.match.dssm # dssm
python -m paddlerec.run -m paddlerec.models.match.multiview-simnet # multiview-simnet
```
models/multitask/esmm/esmm_infer_reader.py
浏览文件 @ 3e50521c
......@@ -13,19 +13,19 @@
# limitations under the License.
from __future__ import print_function
from paddlerec.core.reader import Reader
from paddlerec.core.utils import envs
from collections import defaultdict
import numpy as np
from paddlerec.core.reader import Reader
class EvaluateReader(Reader):
def init(self):
all_field_id = ['101', '109_14', '110_14', '127_14', '150_14', '121', '122', '124', '125', '126', '127', '128', '129',
all_field_id = ['101', '109_14', '110_14', '127_14', '150_14', '121', '122', '124', '125', '126', '127', '128',
'129',
'205', '206', '207', '210', '216', '508', '509', '702', '853', '301']
self.all_field_id_dict = defaultdict(int)
for i,field_id in enumerate(all_field_id):
self.all_field_id_dict[field_id] = [False,i]
for i, field_id in enumerate(all_field_id):
self.all_field_id_dict[field_id] = [False, i]
def generate_sample(self, line):
"""
......@@ -39,25 +39,26 @@ class EvaluateReader(Reader):
features = line.strip().split(',')
ctr = int(features[1])
cvr = int(features[2])
padding = 0
output = [(field_id,[]) for field_id in self.all_field_id_dict]
output = [(field_id, []) for field_id in self.all_field_id_dict]
for elem in features[4:]:
field_id,feat_id = elem.strip().split(':')
field_id, feat_id = elem.strip().split(':')
if field_id not in self.all_field_id_dict:
continue
self.all_field_id_dict[field_id][0] = True
index = self.all_field_id_dict[field_id][1]
output[index][1].append(int(feat_id))
output[index][1].append(int(feat_id))
for field_id in self.all_field_id_dict:
visited,index = self.all_field_id_dict[field_id]
visited, index = self.all_field_id_dict[field_id]
if visited:
self.all_field_id_dict[field_id][0] = False
else:
output[index][1].append(padding)
output[index][1].append(padding)
output.append(('ctr', [ctr]))
output.append(('cvr', [cvr]))
yield output
return reader
models/multitask/esmm/esmm_reader.py
浏览文件 @ 3e50521c
......@@ -11,21 +11,22 @@
# 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.
from __future__ import print_function
from paddlerec.core.reader import Reader
from paddlerec.core.utils import envs
from collections import defaultdict
import numpy as np
from paddlerec.core.reader import Reader
class TrainReader(Reader):
def init(self):
all_field_id = ['101', '109_14', '110_14', '127_14', '150_14', '121', '122', '124', '125', '126', '127', '128', '129',
all_field_id = ['101', '109_14', '110_14', '127_14', '150_14', '121', '122', '124', '125', '126', '127', '128',
'129',
'205', '206', '207', '210', '216', '508', '509', '702', '853', '301']
self.all_field_id_dict = defaultdict(int)
for i,field_id in enumerate(all_field_id):
self.all_field_id_dict[field_id] = [False,i]
for i, field_id in enumerate(all_field_id):
self.all_field_id_dict[field_id] = [False, i]
def generate_sample(self, line):
"""
......@@ -37,30 +38,31 @@ class TrainReader(Reader):
This function needs to be implemented by the user, based on data format
"""
features = line.strip().split(',')
#ctr = list(map(int, features[1]))
#cvr = list(map(int, features[2]))
# ctr = list(map(int, features[1]))
# cvr = list(map(int, features[2]))
ctr = int(features[1])
cvr = int(features[2])
padding = 0
output = [(field_id,[]) for field_id in self.all_field_id_dict]
output = [(field_id, []) for field_id in self.all_field_id_dict]
for elem in features[4:]:
field_id,feat_id = elem.strip().split(':')
field_id, feat_id = elem.strip().split(':')
if field_id not in self.all_field_id_dict:
continue
self.all_field_id_dict[field_id][0] = True
index = self.all_field_id_dict[field_id][1]
#feat_id = list(map(int, feat_id))
output[index][1].append(int(feat_id))
# feat_id = list(map(int, feat_id))
output[index][1].append(int(feat_id))
for field_id in self.all_field_id_dict:
visited,index = self.all_field_id_dict[field_id]
visited, index = self.all_field_id_dict[field_id]
if visited:
self.all_field_id_dict[field_id][0] = False
else:
output[index][1].append(padding)
output[index][1].append(padding)
output.append(('ctr', [ctr]))
output.append(('cvr', [cvr]))
yield output
return reader
models/multitask/esmm/model.py
浏览文件 @ 3e50521c
......@@ -12,83 +12,84 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import numpy as np
import paddle.fluid as fluid
from paddlerec.core.utils import envs
from paddlerec.core.model import Model as ModelBase
import numpy as np
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
def fc(self,tag, data, out_dim, active='prelu'):
def fc(self, tag, data, out_dim, active='prelu'):
init_stddev = 1.0
scales = 1.0 / np.sqrt(data.shape[1])
scales = 1.0 / np.sqrt(data.shape[1])
p_attr = fluid.param_attr.ParamAttr(name='%s_weight' % tag,
initializer=fluid.initializer.NormalInitializer(loc=0.0, scale=init_stddev * scales))
initializer=fluid.initializer.NormalInitializer(loc=0.0,
scale=init_stddev * scales))
b_attr = fluid.ParamAttr(name='%s_bias' % tag, initializer=fluid.initializer.Constant(0.1))
out = fluid.layers.fc(input=data,
size=out_dim,
act=active,
param_attr=p_attr,
bias_attr =b_attr,
name=tag)
size=out_dim,
act=active,
param_attr=p_attr,
bias_attr=b_attr,
name=tag)
return out
def input_data(self):
sparse_input_ids = [
fluid.data(name="field_" + str(i), shape=[-1, 1], dtype="int64", lod_level=1) for i in range(0,23)
fluid.data(name="field_" + str(i), shape=[-1, 1], dtype="int64", lod_level=1) for i in range(0, 23)
]
label_ctr = fluid.data(name="ctr", shape=[-1, 1], dtype="int64")
label_cvr = fluid.data(name="cvr", shape=[-1, 1], dtype="int64")
inputs = sparse_input_ids + [label_ctr] + [label_cvr]
self._data_var.extend(inputs)
return inputs
def net(self, inputs, is_infer=False):
vocab_size = envs.get_global_env("hyper_parameters.vocab_size", None, self._namespace)
embed_size = envs.get_global_env("hyper_parameters.embed_size", None, self._namespace)
emb = []
for data in inputs[0:-2]:
feat_emb = fluid.embedding(input=data,
size=[vocab_size, embed_size],
param_attr=fluid.ParamAttr(name='dis_emb',
learning_rate=5,
initializer=fluid.initializer.Xavier(fan_in=embed_size,fan_out=embed_size)
),
is_sparse=True)
field_emb = fluid.layers.sequence_pool(input=feat_emb,pool_type='sum')
size=[vocab_size, embed_size],
param_attr=fluid.ParamAttr(name='dis_emb',
learning_rate=5,
initializer=fluid.initializer.Xavier(
fan_in=embed_size, fan_out=embed_size)
),
is_sparse=True)
field_emb = fluid.layers.sequence_pool(input=feat_emb, pool_type='sum')
emb.append(field_emb)
concat_emb = fluid.layers.concat(emb, axis=1)
# ctr
active = 'relu'
ctr_fc1 = self.fc('ctr_fc1', concat_emb, 200, active)
ctr_fc2 = self.fc('ctr_fc2', ctr_fc1, 80, active)
ctr_out = self.fc('ctr_out', ctr_fc2, 2, 'softmax')
# cvr
cvr_fc1 = self.fc('cvr_fc1', concat_emb, 200, active)
cvr_fc2 = self.fc('cvr_fc2', cvr_fc1, 80, active)
cvr_out = self.fc('cvr_out', cvr_fc2, 2,'softmax')
cvr_out = self.fc('cvr_out', cvr_fc2, 2, 'softmax')
ctr_clk = inputs[-2]
ctcvr_buy = inputs[-1]
ctr_prop_one = fluid.layers.slice(ctr_out, axes=[1], starts=[1], ends=[2])
cvr_prop_one = fluid.layers.slice(cvr_out, axes=[1], starts=[1], ends=[2])
ctcvr_prop_one = fluid.layers.elementwise_mul(ctr_prop_one, cvr_prop_one)
ctcvr_prop = fluid.layers.concat(input=[1-ctcvr_prop_one,ctcvr_prop_one], axis = 1)
ctcvr_prop = fluid.layers.concat(input=[1 - ctcvr_prop_one, ctcvr_prop_one], axis=1)
auc_ctr, batch_auc_ctr, auc_states_ctr = fluid.layers.auc(input=ctr_out, label=ctr_clk)
auc_ctcvr, batch_auc_ctcvr, auc_states_ctcvr = fluid.layers.auc(input=ctcvr_prop, label=ctcvr_buy)
......@@ -98,27 +99,23 @@ class Model(ModelBase):
self._infer_results["AUC_ctcvr"] = auc_ctcvr
return
loss_ctr = fluid.layers.cross_entropy(input=ctr_out, label=ctr_clk)
loss_ctcvr = fluid.layers.cross_entropy(input=ctcvr_prop, label=ctcvr_buy)
cost = loss_ctr + loss_ctcvr
avg_cost = fluid.layers.mean(cost)
self._cost = avg_cost
self._metrics["AUC_ctr"] = auc_ctr
self._metrics["BATCH_AUC_ctr"] = batch_auc_ctr
self._metrics["AUC_ctcvr"] = auc_ctcvr
self._metrics["BATCH_AUC_ctcvr"] = batch_auc_ctcvr
def train_net(self):
input_data = self.input_data()
self.net(input_data)
def infer_net(self):
self._infer_data_var = self.input_data()
self._infer_data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False)
feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False)
self.net(self._infer_data_var, is_infer=True)
models/multitask/mmoe/census_infer_reader.py
浏览文件 @ 3e50521c
......@@ -11,11 +11,10 @@
# 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.
from __future__ import print_function
from paddlerec.core.reader import Reader
from paddlerec.core.utils import envs
import numpy as np
class EvaluateReader(Reader):
......
models/multitask/mmoe/census_reader.py
浏览文件 @ 3e50521c
......@@ -11,11 +11,10 @@
# 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.
from __future__ import print_function
from paddlerec.core.reader import Reader
from paddlerec.core.utils import envs
import numpy as np
class TrainReader(Reader):
......@@ -44,8 +43,8 @@ class TrainReader(Reader):
label_marital = [1, 0]
elif int(l[0]) == 1:
label_marital = [0, 1]
#label_income = np.array(label_income)
#label_marital = np.array(label_marital)
# label_income = np.array(label_income)
# label_marital = np.array(label_marital)
feature_name = ["input", "label_income", "label_marital"]
yield zip(feature_name, [data] + [label_income] + [label_marital])
......
models/multitask/mmoe/model.py
浏览文件 @ 3e50521c
......@@ -12,7 +12,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import paddle.fluid as fluid
from paddlerec.core.utils import envs
......@@ -37,22 +36,21 @@ class Model(ModelBase):
if is_infer:
self._infer_data_var = [input_data, label_income, label_marital]
self._infer_data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False)
feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False)
self._data_var.extend([input_data, label_income, label_marital])
# f_{i}(x) = activation(W_{i} * x + b), where activation is ReLU according to the paper
expert_outputs = []
for i in range(0, expert_num):
expert_output = fluid.layers.fc(input=input_data,
size=expert_size,
act='relu',
bias_attr=fluid.ParamAttr(learning_rate=1.0),
name='expert_' + str(i))
size=expert_size,
act='relu',
bias_attr=fluid.ParamAttr(learning_rate=1.0),
name='expert_' + str(i))
expert_outputs.append(expert_output)
expert_concat = fluid.layers.concat(expert_outputs, axis=1)
expert_concat = fluid.layers.reshape(expert_concat,[-1, expert_num, expert_size])
expert_concat = fluid.layers.reshape(expert_concat, [-1, expert_num, expert_size])
# g^{k}(x) = activation(W_{gk} * x + b), where activation is softmax according to the paper
output_layers = []
for i in range(0, gate_num):
......@@ -62,52 +60,53 @@ class Model(ModelBase):
bias_attr=fluid.ParamAttr(learning_rate=1.0),
name='gate_' + str(i))
# f^{k}(x) = sum_{i=1}^{n}(g^{k}(x)_{i} * f_{i}(x))
cur_gate_expert = fluid.layers.elementwise_mul(expert_concat, cur_gate, axis=0)
cur_gate_expert = fluid.layers.elementwise_mul(expert_concat, cur_gate, axis=0)
cur_gate_expert = fluid.layers.reduce_sum(cur_gate_expert, dim=1)
# Build tower layer
cur_tower = fluid.layers.fc(input=cur_gate_expert,
size=tower_size,
act='relu',
name='task_layer_' + str(i))
out = fluid.layers.fc(input=cur_tower,
size=2,
act='softmax',
name='out_' + str(i))
cur_tower = fluid.layers.fc(input=cur_gate_expert,
size=tower_size,
act='relu',
name='task_layer_' + str(i))
out = fluid.layers.fc(input=cur_tower,
size=2,
act='softmax',
name='out_' + str(i))
output_layers.append(out)
pred_income = fluid.layers.clip(output_layers[0], min=1e-15, max=1.0 - 1e-15)
pred_marital = fluid.layers.clip(output_layers[1], min=1e-15, max=1.0 - 1e-15)
label_income_1 = fluid.layers.slice(label_income, axes=[1], starts=[1], ends=[2])
label_marital_1 = fluid.layers.slice(label_marital, axes=[1], starts=[1], ends=[2])
auc_income, batch_auc_1, auc_states_1 = fluid.layers.auc(input=pred_income, label=fluid.layers.cast(x=label_income_1, dtype='int64'))
auc_marital, batch_auc_2, auc_states_2 = fluid.layers.auc(input=pred_marital, label=fluid.layers.cast(x=label_marital_1, dtype='int64'))
auc_income, batch_auc_1, auc_states_1 = fluid.layers.auc(input=pred_income,
label=fluid.layers.cast(x=label_income_1,
dtype='int64'))
auc_marital, batch_auc_2, auc_states_2 = fluid.layers.auc(input=pred_marital,
label=fluid.layers.cast(x=label_marital_1,
dtype='int64'))
if is_infer:
self._infer_results["AUC_income"] = auc_income
self._infer_results["AUC_marital"] = auc_marital
return
cost_income = fluid.layers.cross_entropy(input=pred_income, label=label_income,soft_label = True)
cost_marital = fluid.layers.cross_entropy(input=pred_marital, label=label_marital,soft_label = True)
cost_income = fluid.layers.cross_entropy(input=pred_income, label=label_income, soft_label=True)
cost_marital = fluid.layers.cross_entropy(input=pred_marital, label=label_marital, soft_label=True)
avg_cost_income = fluid.layers.mean(x=cost_income)
avg_cost_marital = fluid.layers.mean(x=cost_marital)
cost = avg_cost_income + avg_cost_marital
cost = avg_cost_income + avg_cost_marital
self._cost = cost
self._metrics["AUC_income"] = auc_income
self._metrics["BATCH_AUC_income"] = batch_auc_1
self._metrics["AUC_marital"] = auc_marital
self._metrics["BATCH_AUC_marital"] = batch_auc_2
def train_net(self):
self.MMOE()
def infer_net(self):
self.MMOE(is_infer=True)
models/multitask/readme.md
浏览文件 @ 3e50521c
......@@ -9,8 +9,7 @@
* [整体介绍](#整体介绍)
* [多任务模型列表](#多任务模型列表)
* [使用教程](#使用教程)
* [训练](#训练)
* [预测](#预测)
* [训练&预测](#训练&预测)
* [效果对比](#效果对比)
* [模型效果列表](#模型效果列表)
......@@ -42,16 +41,17 @@
<p>
## 使用教程
### 训练
### 训练&预测
```shell
python -m paddlerec.run -m config.yaml -d cpu -e single
python -m paddlerec.run -m paddlerec.models.multitask.mmoe # mmoe
python -m paddlerec.run -m paddlerec.models.multitask.share-bottom # share-bottom
python -m paddlerec.run -m paddlerec.models.multitask.esmm # esmm
```
### 预测
## 效果对比
### 模型效果列表
| 数据集 | 模型 | loss | 评价指标 |
| 数据集 | 模型 | loss | auc |
| :------------------: | :--------------------: | :---------: |:---------: |
| Census-income Data | Share-Bottom | -- | 0.93120/0.99256 |
| Census-income Data | MMoE | -- | 0.94465/0.99324 |
......
models/multitask/share-bottom/census_infer_reader.py
浏览文件 @ 3e50521c
......@@ -11,11 +11,10 @@
# 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.
from __future__ import print_function
from paddlerec.core.reader import Reader
from paddlerec.core.utils import envs
import numpy as np
class EvaluateReader(Reader):
......
models/multitask/share-bottom/census_reader.py
浏览文件 @ 3e50521c
......@@ -11,11 +11,10 @@
# 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.
from __future__ import print_function
from paddlerec.core.reader import Reader
from paddlerec.core.utils import envs
import numpy as np
class TrainReader(Reader):
......@@ -44,8 +43,8 @@ class TrainReader(Reader):
label_marital = [1, 0]
elif int(l[0]) == 1:
label_marital = [0, 1]
#label_income = np.array(label_income)
#label_marital = np.array(label_marital)
# label_income = np.array(label_income)
# label_marital = np.array(label_marital)
feature_name = ["input", "label_income", "label_marital"]
yield zip(feature_name, [data] + [label_income] + [label_marital])
......
models/multitask/share-bottom/model.py
浏览文件 @ 3e50521c
......@@ -12,7 +12,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import paddle.fluid as fluid
from paddlerec.core.utils import envs
......@@ -33,65 +32,65 @@ class Model(ModelBase):
input_data = fluid.data(name="input", shape=[-1, feature_size], dtype="float32")
label_income = fluid.data(name="label_income", shape=[-1, 2], dtype="float32", lod_level=0)
label_marital = fluid.data(name="label_marital", shape=[-1, 2], dtype="float32", lod_level=0)
if is_infer:
self._infer_data_var = [input_data, label_income, label_marital]
self._infer_data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False)
feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False)
self._data_var.extend([input_data, label_income, label_marital])
bottom_output = fluid.layers.fc(input=input_data,
size=bottom_size,
act='relu',
bias_attr=fluid.ParamAttr(learning_rate=1.0),
name='bottom_output')
size=bottom_size,
act='relu',
bias_attr=fluid.ParamAttr(learning_rate=1.0),
name='bottom_output')
# Build tower layer from bottom layer
output_layers = []
for index in range(tower_nums):
for index in range(tower_nums):
tower_layer = fluid.layers.fc(input=bottom_output,
size=tower_size,
act='relu',
name='task_layer_' + str(index))
size=tower_size,
act='relu',
name='task_layer_' + str(index))
output_layer = fluid.layers.fc(input=tower_layer,
size=2,
act='softmax',
name='output_layer_' + str(index))
size=2,
act='softmax',
name='output_layer_' + str(index))
output_layers.append(output_layer)
pred_income = fluid.layers.clip(output_layers[0], min=1e-15, max=1.0 - 1e-15)
pred_marital = fluid.layers.clip(output_layers[1], min=1e-15, max=1.0 - 1e-15)
label_income_1 = fluid.layers.slice(label_income, axes=[1], starts=[1], ends=[2])
label_marital_1 = fluid.layers.slice(label_marital, axes=[1], starts=[1], ends=[2])
auc_income, batch_auc_1, auc_states_1 = fluid.layers.auc(input=pred_income, label=fluid.layers.cast(x=label_income_1, dtype='int64'))
auc_marital, batch_auc_2, auc_states_2 = fluid.layers.auc(input=pred_marital, label=fluid.layers.cast(x=label_marital_1, dtype='int64'))
auc_income, batch_auc_1, auc_states_1 = fluid.layers.auc(input=pred_income,
label=fluid.layers.cast(x=label_income_1,
dtype='int64'))
auc_marital, batch_auc_2, auc_states_2 = fluid.layers.auc(input=pred_marital,
label=fluid.layers.cast(x=label_marital_1,
dtype='int64'))
if is_infer:
self._infer_results["AUC_income"] = auc_income
self._infer_results["AUC_marital"] = auc_marital
return
cost_income = fluid.layers.cross_entropy(input=pred_income, label=label_income,soft_label = True)
cost_marital = fluid.layers.cross_entropy(input=pred_marital, label=label_marital,soft_label = True)
cost_income = fluid.layers.cross_entropy(input=pred_income, label=label_income, soft_label=True)
cost_marital = fluid.layers.cross_entropy(input=pred_marital, label=label_marital, soft_label=True)
cost = fluid.layers.elementwise_add(cost_income, cost_marital, axis=1)
avg_cost = fluid.layers.mean(x=cost)
avg_cost = fluid.layers.mean(x=cost)
self._cost = avg_cost
self._metrics["AUC_income"] = auc_income
self._metrics["BATCH_AUC_income"] = batch_auc_1
self._metrics["AUC_marital"] = auc_marital
self._metrics["BATCH_AUC_marital"] = batch_auc_2
def train_net(self):
self.model()
def infer_net(self):
self.model(is_infer=True)
models/rank/criteo_reader.py 0 → 100755
浏览文件 @ 3e50521c
# 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.
from __future__ import print_function
from paddlerec.core.reader import Reader
from paddlerec.core.utils import envs
class TrainReader(Reader):
def init(self):
self.cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
self.cont_max_ = [20, 600, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
self.cont_diff_ = [20, 603, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
self.hash_dim_ = envs.get_global_env("hyper_parameters.sparse_feature_number", None, "train.model")
self.continuous_range_ = range(1, 14)
self.categorical_range_ = range(14, 40)
def generate_sample(self, line):
"""
Read the data line by line and process it as a dictionary
"""
def reader():
"""
This function needs to be implemented by the user, based on data format
"""
features = line.rstrip('\n').split('\t')
dense_feature = []
sparse_feature = []
for idx in self.continuous_range_:
if features[idx] == "":
dense_feature.append(0.0)
else:
dense_feature.append(
(float(features[idx]) - self.cont_min_[idx - 1]) /
self.cont_diff_[idx - 1])
for idx in self.categorical_range_:
sparse_feature.append(
[hash(str(idx) + features[idx]) % self.hash_dim_])
label = [int(features[0])]
feature_name = ["D"]
for idx in self.categorical_range_:
feature_name.append("S" + str(idx - 13))
feature_name.append("label")
yield zip(feature_name, [dense_feature] + sparse_feature + [label])
return reader
models/rank/dcn/data/get_slot_data.py
浏览文件 @ 3e50521c
......@@ -16,6 +16,8 @@ import sys
import yaml
from paddlerec.core.reader import Reader
from paddlerec.core.utils import envs
import math
import os
try:
import cPickle as pickle
except ImportError:
......@@ -55,7 +57,7 @@ class TrainReader(dg.MultiSlotDataGenerator):
self.label_feat_names = target + dense_feat_names + sparse_feat_names
self.cat_feat_idx_dict_list = [{} for _ in range(26)]
# TODO: set vocabulary dictionary
vocab_dir = "./vocab/"
for i in range(26):
......@@ -63,7 +65,7 @@ class TrainReader(dg.MultiSlotDataGenerator):
for line in open(
os.path.join(vocab_dir, 'C' + str(i + 1) + '.txt')):
self.cat_feat_idx_dict_list[i][line.strip()] = lookup_idx
lookup_idx += 1
lookup_idx += 1
def _process_line(self, line):
features = line.rstrip('\n').split('\t')
......@@ -81,18 +83,19 @@ class TrainReader(dg.MultiSlotDataGenerator):
if idx == 2 else math.log(1 + float(features[idx])))
for idx in self.cat_idx_:
if features[idx] == '' or features[
idx] not in self.cat_feat_idx_dict_list[idx - 14]:
idx] not in self.cat_feat_idx_dict_list[idx - 14]:
label_feat_list[idx].append(0)
else:
label_feat_list[idx].append(self.cat_feat_idx_dict_list[
idx - 14][features[idx]])
idx - 14][features[idx]])
label_feat_list[0].append(int(features[0]))
return label_feat_list
def generate_sample(self, line):
"""
Read the data line by line and process it as a dictionary
"""
def data_iter():
label_feat_list = self._process_line(line)
s = ""
......
models/rank/dcn/model.py
浏览文件 @ 3e50521c
# 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.
from collections import OrderedDict
import paddle.fluid as fluid
import math
from paddlerec.core.utils import envs
from paddlerec.core.model import Model as ModelBase
from collections import OrderedDict
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
def init_network(self):
self.cross_num = envs.get_global_env("hyper_parameters.cross_num", None, self._namespace)
self.dnn_hidden_units = envs.get_global_env("hyper_parameters.dnn_hidden_units", None, self._namespace)
......@@ -64,7 +79,7 @@ class Model(ModelBase):
net_input = fluid.layers.concat([dense_input, sparse_input], axis=-1)
return net_input
def _deep_net(self, input, hidden_units, use_bn=False, is_test=False):
for units in hidden_units:
input = fluid.layers.fc(input=input, size=units)
......@@ -81,7 +96,7 @@ class Model(ModelBase):
[input_dim], dtype='float32', name=prefix + "_b")
xw = fluid.layers.reduce_sum(x * w, dim=1, keep_dim=True) # (N, 1)
return x0 * xw + b + x, w
def _cross_net(self, input, num_corss_layers):
x = x0 = input
l2_reg_cross_list = []
......@@ -92,10 +107,10 @@ class Model(ModelBase):
fluid.layers.concat(
l2_reg_cross_list, axis=-1))
return x, l2_reg_cross_loss
def _l2_loss(self, w):
return fluid.layers.reduce_sum(fluid.layers.square(w))
def train_net(self):
self.init_network()
......@@ -104,8 +119,8 @@ class Model(ModelBase):
deep_out = self._deep_net(self.net_input, self.dnn_hidden_units, self.dnn_use_bn, False)
cross_out, l2_reg_cross_loss = self._cross_net(self.net_input,
self.cross_num)
self.cross_num)
last_out = fluid.layers.concat([deep_out, cross_out], axis=-1)
logit = fluid.layers.fc(last_out, 1)
......
models/rank/deepfm/data/get_slot_data.py
浏览文件 @ 3e50521c
......@@ -19,7 +19,6 @@ try:
import cPickle as pickle
except ImportError:
import pickle
class TrainReader(dg.MultiSlotDataGenerator):
def __init__(self, config):
......@@ -69,11 +68,12 @@ class TrainReader(dg.MultiSlotDataGenerator):
feat_value.append(1.0)
label = [int(features[0])]
return feat_idx, feat_value, label
def generate_sample(self, line):
"""
Read the data line by line and process it as a dictionary
"""
def data_iter():
feat_idx, feat_value, label = self._process_line(line)
s = ""
......
models/rank/deepfm/model.py
浏览文件 @ 3e50521c
import paddle.fluid as fluid
# 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 math
import paddle.fluid as fluid
from paddlerec.core.utils import envs
from paddlerec.core.model import Model as ModelBase
......@@ -14,9 +29,9 @@ class Model(ModelBase):
is_distributed = True if envs.get_trainer() == "CtrTrainer" else False
sparse_feature_number = envs.get_global_env("hyper_parameters.sparse_feature_number", None, self._namespace)
sparse_feature_dim = envs.get_global_env("hyper_parameters.sparse_feature_dim", None, self._namespace)
# ------------------------- network input --------------------------
num_field = envs.get_global_env("hyper_parameters.num_field", None, self._namespace)
raw_feat_idx = self._sparse_data_var[1]
......@@ -26,8 +41,6 @@ class Model(ModelBase):
feat_idx = raw_feat_idx
feat_value = fluid.layers.reshape(raw_feat_value, [-1, num_field, 1]) # None * num_field * 1
#------------------------- first order term --------------------------
reg = envs.get_global_env("hyper_parameters.reg", 1e-4, self._namespace)
first_weights_re = fluid.embedding(
input=feat_idx,
......@@ -44,7 +57,7 @@ class Model(ModelBase):
first_weights_re, shape=[-1, num_field, 1]) # None * num_field * 1
y_first_order = fluid.layers.reduce_sum((first_weights * feat_value), 1)
#------------------------- second order term --------------------------
# ------------------------- second order term --------------------------
feat_embeddings_re = fluid.embedding(
input=feat_idx,
......@@ -59,12 +72,12 @@ class Model(ModelBase):
feat_embeddings = fluid.layers.reshape(
feat_embeddings_re,
shape=[-1, num_field,
sparse_feature_dim]) # None * num_field * embedding_size
sparse_feature_dim]) # None * num_field * embedding_size
feat_embeddings = feat_embeddings * feat_value # None * num_field * embedding_size
# sum_square part
summed_features_emb = fluid.layers.reduce_sum(feat_embeddings,
1) # None * embedding_size
1) # None * embedding_size
summed_features_emb_square = fluid.layers.square(
summed_features_emb) # None * embedding_size
......@@ -78,13 +91,12 @@ class Model(ModelBase):
summed_features_emb_square - squared_sum_features_emb, 1,
keep_dim=True) # None * 1
#------------------------- DNN --------------------------
# ------------------------- DNN --------------------------
layer_sizes = envs.get_global_env("hyper_parameters.fc_sizes", None, self._namespace)
act = envs.get_global_env("hyper_parameters.act", None, self._namespace)
y_dnn = fluid.layers.reshape(feat_embeddings,
[-1, num_field * sparse_feature_dim])
[-1, num_field * sparse_feature_dim])
for s in layer_sizes:
y_dnn = fluid.layers.fc(
input=y_dnn,
......@@ -106,28 +118,28 @@ class Model(ModelBase):
bias_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer(
loc=0.0, scale=init_value_)))
#------------------------- DeepFM --------------------------
# ------------------------- DeepFM --------------------------
self.predict = fluid.layers.sigmoid(y_first_order + y_second_order + y_dnn)
def train_net(self):
self.deepfm_net()
#------------------------- Cost(logloss) --------------------------
# ------------------------- Cost(logloss) --------------------------
cost = fluid.layers.log_loss(input=self.predict, label=fluid.layers.cast(self.label, "float32"))
avg_cost = fluid.layers.reduce_sum(cost)
self._cost = avg_cost
#------------------------- Metric(Auc) --------------------------
# ------------------------- Metric(Auc) --------------------------
predict_2d = fluid.layers.concat([1 - self.predict, self.predict], 1)
label_int = fluid.layers.cast(self.label, 'int64')
auc_var, batch_auc_var, _ = fluid.layers.auc(input=predict_2d,
label=label_int,
slide_steps=0)
label=label_int,
slide_steps=0)
self._metrics["AUC"] = auc_var
self._metrics["BATCH_AUC"] = batch_auc_var
......
models/rank/din/model.py
浏览文件 @ 3e50521c
# 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 paddle.fluid as fluid
import math
from paddlerec.core.utils import envs
from paddlerec.core.model import Model as ModelBase
......
models/rank/din/reader.py
浏览文件 @ 3e50521c
......@@ -13,24 +13,28 @@
# limitations under the License.
from __future__ import print_function
from paddlerec.core.reader import Reader
from paddlerec.core.utils import envs
import numpy as np
import os
import random
try:
import cPickle as pickle
except ImportError:
import pickle
import numpy as np
from paddlerec.core.reader import Reader
from paddlerec.core.utils import envs
class TrainReader(Reader):
def init(self):
self.train_data_path = envs.get_global_env("train_data_path", None, "train.reader")
self.res = []
self.max_len = 0
data_file_list = os.listdir(self.train_data_path)
for i in range(0, len(data_file_list)):
for i in range(0, len(data_file_list)):
train_data_file = os.path.join(self.train_data_path, data_file_list[i])
with open(train_data_file, "r") as fin:
for line in fin:
......@@ -43,9 +47,6 @@ class TrainReader(Reader):
self.batch_size = envs.get_global_env("batch_size", 32, "train.reader")
self.group_size = self.batch_size * 20
def _process_line(self, line):
line = line.strip().split(';')
hist = line[0].split()
......@@ -54,22 +55,22 @@ class TrainReader(Reader):
cate = [int(i) for i in cate]
return [hist, cate, [int(line[2])], [int(line[3])], [float(line[4])]]
def generate_sample(self, line):
"""
Read the data line by line and process it as a dictionary
"""
def data_iter():
#feat_idx, feat_value, label = self._process_line(line)
# feat_idx, feat_value, label = self._process_line(line)
yield self._process_line(line)
return data_iter
def pad_batch_data(self, input, max_len):
res = np.array([x + [0] * (max_len - len(x)) for x in input])
res = res.astype("int64").reshape([-1, max_len])
return res
def make_data(self, b):
max_len = max(len(x[0]) for x in b)
item = self.pad_batch_data([x[0] for x in b], max_len)
......@@ -77,7 +78,7 @@ class TrainReader(Reader):
len_array = [len(x[0]) for x in b]
mask = np.array(
[[0] * x + [-1e9] * (max_len - x) for x in len_array]).reshape(
[-1, max_len, 1])
[-1, max_len, 1])
target_item_seq = np.array(
[[x[2]] * max_len for x in b]).astype("int64").reshape([-1, max_len])
target_cat_seq = np.array(
......@@ -89,7 +90,7 @@ class TrainReader(Reader):
target_item_seq[i], target_cat_seq[i]
])
return res
def batch_reader(self, reader, batch_size, group_size):
def batch_reader():
bg = []
......@@ -111,7 +112,7 @@ class TrainReader(Reader):
yield self.make_data(b)
return batch_reader
def base_read(self, file_dir):
res = []
for train_file in file_dir:
......@@ -122,10 +123,8 @@ class TrainReader(Reader):
cate = line[1].split()
res.append([hist, cate, line[2], line[3], float(line[4])])
return res
def generate_batch_from_trainfiles(self, files):
data_set = self.base_read(files)
random.shuffle(data_set)
return self.batch_reader(data_set, self.batch_size, self.batch_size * 20)
\ No newline at end of file
models/rank/dnn/config.yaml
浏览文件 @ 3e50521c
......@@ -13,13 +13,14 @@
# limitations under the License.
train:
epochs: 10
engine: single
workspace: "paddlerec.models.rank.dnn"
trainer:
# for cluster training
strategy: "async"
epochs: 10
workspace: "paddlerec.models.rank.dnn"
reader:
batch_size: 2
train_data_path: "{workspace}/data/slot_train_data"
......
models/rank/dnn/model.py
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......@@ -13,6 +13,7 @@
# limitations under the License.
import math
import paddle.fluid as fluid
from paddlerec.core.utils import envs
......
models/rank/readme.md
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......@@ -58,8 +58,15 @@
## 使用教程
### 数据处理
参考每个模型目录数据下载&预处理脚本
### 训练
```
python -m paddlerec.run -m paddlerec.models.rank.dnn # 以DNN为例
```
### 预测
```
python -m paddlerec.run -m paddlerec.models.rank.dnn # 以DNN为例
```
## 效果对比
### 模型效果 (测试)
......@@ -69,7 +76,7 @@
| Criteo | DNN | -- | 0.79395 | -- | -- |
| Criteo | DeepFM | 0.44797 | 0.80460 | -- | -- |
| Criteo | DCN | 0.44704 | 0.80654 | -- | -- |
| Criteo | xDeepFM | -- | -- | 0.48657 | -- |
| Criteo | xDeepFM | 0.48657 | -- | -- | -- |
| Census-income Data | Wide&Deep | 0.76195 | 0.90577 | -- | -- |
| Amazon Product | DIN | 0.47005 | 0.86379 | -- | -- |
......
models/rank/wide_deep/data/get_slot_data.py
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......@@ -37,14 +37,15 @@ class TrainReader(dg.MultiSlotDataGenerator):
line = line.strip().split(',')
features = list(map(float, line))
wide_feat = features[0:8]
deep_feat = features[8:58+8]
label = int(features[-1])
deep_feat = features[8:58 + 8]
label = features[-1]
return wide_feat, deep_feat, [label]
def generate_sample(self, line):
"""
Read the data line by line and process it as a dictionary
"""
def data_iter():
wide_feat, deep_deat, label = self._process_line(line)
......
models/rank/wide_deep/model.py
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import paddle.fluid as fluid
# 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 math
import paddle.fluid as fluid
from paddlerec.core.utils import envs
from paddlerec.core.model import Model as ModelBase
......@@ -8,32 +23,39 @@ from paddlerec.core.model import Model as ModelBase
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
def wide_part(self, data):
out = fluid.layers.fc(input=data,
size=1,
param_attr=fluid.ParamAttr(initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=1.0 / math.sqrt(data.shape[1])),
regularizer=fluid.regularizer.L2DecayRegularizer(regularization_coeff=1e-4)),
act=None,
name='wide')
size=1,
param_attr=fluid.ParamAttr(initializer=fluid.initializer.TruncatedNormal(loc=0.0,
scale=1.0 / math.sqrt(
data.shape[
1])),
regularizer=fluid.regularizer.L2DecayRegularizer(
regularization_coeff=1e-4)),
act=None,
name='wide')
return out
def fc(self, data, hidden_units, active, tag):
output = fluid.layers.fc(input=data,
size=hidden_units,
param_attr=fluid.ParamAttr(initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=1.0 / math.sqrt(data.shape[1]))),
act=active,
name=tag)
size=hidden_units,
param_attr=fluid.ParamAttr(initializer=fluid.initializer.TruncatedNormal(loc=0.0,
scale=1.0 / math.sqrt(
data.shape[
1]))),
act=active,
name=tag)
return output
def deep_part(self, data, hidden1_units, hidden2_units, hidden3_units):
l1 = self.fc(data, hidden1_units, 'relu', 'l1')
l2 = self.fc(l1, hidden2_units, 'relu', 'l2')
l3 = self.fc(l2, hidden3_units, 'relu', 'l3')
return l3
def train_net(self):
wide_input = self._dense_data_var[0]
deep_input = self._dense_data_var[1]
......@@ -44,26 +66,28 @@ class Model(ModelBase):
hidden3_units = envs.get_global_env("hyper_parameters.hidden3_units", 25, self._namespace)
wide_output = self.wide_part(wide_input)
deep_output = self.deep_part(deep_input, hidden1_units, hidden2_units, hidden3_units)
wide_model = fluid.layers.fc(input=wide_output,
size=1,
param_attr=fluid.ParamAttr(initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=1.0)),
act=None,
name='w_wide')
size=1,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=1.0)),
act=None,
name='w_wide')
deep_model = fluid.layers.fc(input=deep_output,
size=1,
param_attr=fluid.ParamAttr(initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=1.0)),
act=None,
name='w_deep')
size=1,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=1.0)),
act=None,
name='w_deep')
prediction = fluid.layers.elementwise_add(wide_model, deep_model)
pred = fluid.layers.sigmoid(fluid.layers.clip(prediction, min=-15.0, max=15.0), name="prediction")
num_seqs = fluid.layers.create_tensor(dtype='int64')
acc = fluid.layers.accuracy(input=pred, label=fluid.layers.cast(x=label, dtype='int64'), total=num_seqs)
auc_var, batch_auc, auc_states = fluid.layers.auc(input=pred, label=fluid.layers.cast(x=label, dtype='int64'))
self._metrics["AUC"] = auc_var
self._metrics["BATCH_AUC"] = batch_auc
self._metrics["ACC"] = acc
......
models/rank/xdeepfm/model.py
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models/recall/gnn/data_process.sh
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#! /bin/bash
# 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.
set -e
echo "begin to download data"
......
models/recall/gnn/model.py
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models/recall/gnn/reader.py
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models/recall/gru4rec/model.py
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models/recall/readme.md
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models/recall/ssr/model.py
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models/recall/ssr/ssr_reader.py
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models/recall/word2vec/model.py
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models/treebased/tdm/model.py
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models/treebased/tdm/tdm_reader.py
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run.py
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setup.py
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tools/tools.py
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