add ctr-dnn example

examples/ctr-dnn_train.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.# Copyright (c) 2019 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.
+
+train:
+  batch_size: 32
+  threads: 12
+  epochs: 10
+  trainer:  "SingleTraining"
+
+  reader:
+    mode: "dataset"
+    pipe_command: "python reader.py dataset"
+    train_data_path: "raw_data"
+
+  model:
+    models: "eleps.models.ctr_dnn.model.py"
+    hyper_parameters:
+      sparse_inputs_slots: 27,
+      sparse_feature_number: 1000001,
+      sparse_feature_dim: 8,
+      dense_input_dim: 13,
+      fc_sizes: [1024, 512, 32],
+      learning_rate: 0.001
+
+  save:
+    increment:
+      dirname: "models_for_increment"
+      epoch_interval: 2
+      save_last: True
+    inference:
+      dirname: "models_for_inference"
+      epoch_interval: 4
+      feed_varnames: ["C1", "C2", "C3"]
+      fetch_varnames: "predict"
+      save_last: True
+
+evaluate:
+  batch_size: 32
+  train_thread_num: 12
+  reader: "reader.py"
+
+

models/ctr_dnn/hyper_parameters.yaml

-{
-  "sparse_inputs_slots": 27,
-  "sparse_feature_number": 1000001,
-  "sparse_feature_dim": 8,
-  "dense_input_dim": 13,
-  "fc_sizes": [400, 400, 40],
-  "learning_rate": 0.001
-}
\ No newline at end of file
+# 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.# Copyright (c) 2019 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.
+
+
+sparse_inputs_slots: 27,
+sparse_feature_number: 1000001,
+sparse_feature_dim: 8,
+dense_input_dim: 13,
+fc_sizes: [400, 400, 40],
+learning_rate: 0.001

models/ctr_dnn/model.py

@@ -57,6 +57,12 @@ class Train(object):
         self.dense_input, self.dense_input_varname = dense_input()
         self.label_input, self.label_input_varname = label_input()
 
+    def input_vars(self):
+        return self.sparse_inputs + [self.dense_input] + [self.label_input]
+
+    def input_varnames(self):
+        return [input.name for input in self.input_vars()]
+
     def net(self):
         def embedding_layer(input):
             sparse_feature_number = envs.get_global_env("sparse_feature_number")
@@ -101,22 +107,28 @@ class Train(object):
 
         self.predict = predict
 
-    def loss(self, predict):
+    def avg_loss(self, predict):
         cost = fluid.layers.cross_entropy(input=predict, label=self.label_input)
         avg_cost = fluid.layers.reduce_sum(cost)
         self.loss = avg_cost
+        return avg_cost
 
-    def metric(self):
+    def metrics(self):
         auc, batch_auc, _ = fluid.layers.auc(input=self.predict,
                                              label=self.label_input,
                                              num_thresholds=2 ** 12,
                                              slide_steps=20)
+        self.metrics = (auc, batch_auc)
 
     def optimizer(self):
         learning_rate = envs.get_global_env("learning_rate")
         optimizer = fluid.optimizer.Adam(learning_rate, lazy_mode=True)
         return optimizer
 
+    def optimize(self):
+        optimizer = self.optimizer()
+        optimizer.minimize(self.loss)
+
 
 class Evaluate(object):
     def input(self):

trainer/cluster_training_local.py

-# 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
-import os
-import time
-import numpy as np
-import logging
-import paddle.fluid as fluid
-from network import CTR
-from argument import params_args
-
-logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s")
-logger = logging.getLogger("fluid")
-logger.setLevel(logging.INFO)
-
-
-def get_dataset(inputs, params):
-    dataset = fluid.DatasetFactory().create_dataset()
-    dataset.set_use_var(inputs)
-    dataset.set_pipe_command("python dataset_generator.py")
-    dataset.set_batch_size(params.batch_size)
-    dataset.set_thread(int(params.cpu_num))
-    file_list = [
-        str(params.train_files_path) + "/%s" % x
-        for x in os.listdir(params.train_files_path)
-    ]
-    dataset.set_filelist(file_list)
-    logger.info("file list: {}".format(file_list))
-    return dataset
-
-
-def train(params):
-    ctr_model = CTR()
-    inputs = ctr_model.input_data(params)
-    avg_cost, auc_var, batch_auc_var = ctr_model.net(inputs, params)
-    optimizer = fluid.optimizer.Adam(params.learning_rate)
-    optimizer.minimize(avg_cost)
-    fluid.default_main_program()
-    exe = fluid.Executor(fluid.CPUPlace())
-    exe.run(fluid.default_startup_program())
-    dataset = get_dataset(inputs, params)
-
-    logger.info("Training Begin")
-    for epoch in range(params.epochs):
-        start_time = time.time()
-        exe.train_from_dataset(program=fluid.default_main_program(),
-                               dataset=dataset,
-                               fetch_list=[auc_var],
-                               fetch_info=["Epoch {} auc ".format(epoch)],
-                               print_period=100,
-                               debug=False)
-        end_time = time.time()
-        logger.info("epoch %d finished, use time=%d\n" %
-                    ((epoch), end_time - start_time))
-
-        if params.test:
-            model_path = (str(params.model_path) + "/" + "epoch_" + str(epoch))
-            fluid.io.save_persistables(executor=exe, dirname=model_path)
-
-    logger.info("Train Success!")
-
-
-if __name__ == "__main__":
-    params = params_args()
-    train(params)
\ No newline at end of file

trainer/single_train.py

+# 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 os
+import time
+import numpy as np
+import logging
+import paddle.fluid as fluid
+
+from .trainer import Trainer
+from ..utils import envs
+
+logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s")
+logger = logging.getLogger("fluid")
+logger.setLevel(logging.INFO)
+
+
+def need_save(epoch_id, epoch_interval, is_last=False):
+    if is_last:
+        return True
+
+    return epoch_id % epoch_interval == 0
+
+
+class SingleTrainer(Trainer):
+
+    def __init__(self, config=None, yaml_file=None):
+        Trainer.__init__(self, config, yaml_file)
+
+        self.exe = fluid.Executor(fluid.CPUPlace())
+
+        self.regist_context_processor('uninit', self.instance)
+        self.regist_context_processor('init_pass', self.init)
+        self.regist_context_processor('train_pass', self.train)
+        self.regist_context_processor('infer_pass', self.infer)
+        self.regist_context_processor('terminal_pass', self.terminal)
+
+    def instance(self, context):
+        model_package = __import__(envs.get_global_env("train.model.models"))
+        train_model = getattr(model_package, 'Train')
+
+        self.model = train_model()
+
+        context['status'] = 'init_pass'
+
+    def init(self, context):
+        self.model.input()
+        self.model.net()
+        self.model.loss()
+        self.metrics = self.model.metrics()
+        self.model.optimize()
+
+        # run startup program at once
+        self.exe.run(fluid.default_startup_program())
+
+        context['status'] = 'train_pass'
+
+    def train(self, context):
+        print("Need to be implement")
+        context['is_exit'] = True
+
+    def infer(self, context):
+        print("Need to be implement")
+        context['is_exit'] = True
+
+    def terminal(self, context):
+        context['is_exit'] = True
+
+
+class SingleTrainerWithDataloader(SingleTrainer):
+    pass
+
+
+class SingleTrainerWithDataset(SingleTrainer):
+    def _get_dataset(self, inputs, threads, batch_size, pipe_command, train_files_path):
+        dataset = fluid.DatasetFactory().create_dataset()
+        dataset.set_use_var(inputs)
+        dataset.set_pipe_command(pipe_command)
+        dataset.set_batch_size(batch_size)
+        dataset.set_thread(threads)
+        file_list = [
+            os.path.join(train_files_path, x)
+            for x in os.listdir(train_files_path)
+        ]
+
+        dataset.set_filelist(file_list)
+        return dataset
+
+    def save(self, epoch_id):
+        def save_inference_model():
+            is_save_inference = envs.get_global_env("save.inference", False)
+            if not is_save_inference:
+                return
+
+            save_interval = envs.get_global_env("save.inference.epoch_interval", 1)
+            if not need_save(epoch_id, save_interval, False):
+                return
+
+            feed_varnames = envs.get_global_env("save.inference.feed_varnames", None)
+            fetch_varnames = envs.get_global_env("save.inference.fetch_varnames", None)
+            fetch_vars = [fluid.global_scope().vars[varname] for varname in fetch_varnames]
+            dirname = envs.get_global_env("save.inference.dirname", None)
+
+            assert dirname is not None
+            dirname = os.path.join(dirname, str(epoch_id))
+            fluid.io.save_inference_model(dirname, feed_varnames, fetch_vars, self.exe)
+
+        def save_persistables():
+            is_save_increment = envs.get_global_env("save.increment", False)
+            if not is_save_increment:
+                return
+
+            save_interval = envs.get_global_env("save.increment.epoch_interval", 1)
+            if not need_save(epoch_id, save_interval, False):
+                return
+
+            dirname = envs.get_global_env("save.inference.dirname", None)
+
+            assert dirname is not None
+            dirname = os.path.join(dirname, str(epoch_id))
+            fluid.io.save_persistables(self.exe, dirname)
+
+        is_save = envs.get_global_env("save", False)
+
+        if not is_save:
+            return
+
+        save_persistables()
+        save_inference_model()
+
+    def train(self, context):
+        inputs = self.model.input_vars()
+        threads = envs.get_global_env("threads")
+        batch_size = envs.get_global_env("batch_size")
+        pipe_command = envs.get_global_env("pipe_command")
+        train_data_path = envs.get_global_env("train_data_path")
+
+        dataset = self._get_dataset(inputs, threads, batch_size, pipe_command, train_data_path)
+
+        epochs = envs.get_global_env("epochs")
+
+        for i in range(epochs):
+            self.exe.train_from_dataset(program=fluid.default_main_program(),
+                                        dataset=dataset,
+                                        fetch_list=[self.metrics],
+                                        fetch_info=["epoch {} auc ".format(i)],
+                                        print_period=100)
+        context['status'] = 'infer_pass'
+
+
+def infer(self, context):
+    context['status'] = 'terminal_pass'

trainer/trainer.py

@@ -14,6 +14,9 @@
 
 import abc
 import time
+import yaml
+
+from .. utils import envs
 
 
 class Trainer(object):
@@ -21,9 +24,20 @@ class Trainer(object):
     """   
     __metaclass__ = abc.ABCMeta
 
-    def __init__(self, config):
-        """R
-        """
+    def __init__(self, config=None, yaml_file=None):
+
+        if not config and not yaml_file:
+            raise ValueError("config and yaml file have at least one not empty")
+
+        if config and yaml_file:
+            print("config and yaml file are all assigned, will use yaml file: {}".format(yaml_file))
+
+        if yaml_file:
+            with open(yaml_file, "r") as rb:
+                config = yaml.load(rb.read())
+
+        envs.set_global_envs(config)
+
         self._status_processor = {}
         self._context = {'status': 'uninit', 'is_exit': False}
        

utils/envs.py

@@ -24,17 +24,17 @@ def decode_value(v):
     return v
 
 
-def set_global_envs(yaml, envs):
+def set_global_envs(yaml):
     for k, v in yaml.items():
-        envs[k] = encode_value(v)
+        os.environ[k] = encode_value(v)
 
 
-def get_global_env(env_name):
+def get_global_env(env_name, default_value=None):
     """
     get os environment value
     """
     if env_name not in os.environ:
-        raise ValueError("can not find config of {}".format(env_name))
+        return default_value
 
     v = os.environ[env_name]
     return decode_value(v)