Merge branch 'master' into 200520_listwise

core/reader.py

@@ -40,6 +40,7 @@ class Reader(dg.MultiSlotDataGenerator):
 
     @abc.abstractmethod
     def init(self):
+        """init"""
         pass
 
     @abc.abstractmethod

core/trainers/ctr_coding_trainer.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.
-
-import os
-
-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
-
-from paddlerec.core.utils import envs
-from paddlerec.core.trainer import Trainer
-
-
-class CtrTrainer(Trainer):
-    """R
-    """
-
-    def __init__(self, config):
-        """R
-        """
-        Trainer.__init__(self, config)
-
-        self.global_config = config
-        self._metrics = {}
-        self.processor_register()
-
-    def processor_register(self):
-        role = MPISymetricRoleMaker()
-        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('train_pass', self.train)
-            self.regist_context_processor('terminal_pass', self.terminal)
-
-    def _get_dataset(self):
-        namespace = "train.reader"
-
-        inputs = self.model.get_inputs()
-        threads = envs.get_global_env("train.threads", None)
-        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, "TRAIN",
-                                               self._config_yaml)
-        train_data_path = envs.get_global_env("train_data_path", None,
-                                              namespace)
-
-        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)
-        file_list = [
-            os.path.join(train_data_path, x)
-            for x in os.listdir(train_data_path)
-        ]
-
-        dataset.set_filelist(file_list)
-        return dataset
-
-    def instance(self, context):
-        models = envs.get_global_env("train.model.models")
-        model_class = envs.lazy_instance_by_fliename(models, "Model")
-        self.model = model_class(None)
-        context['status'] = 'init_pass'
-
-    def init(self, context):
-        """R
-        """
-        self.model.train_net()
-        optimizer = self.model.optimizer()
-
-        optimizer = fleet.distributed_optimizer(
-            optimizer, strategy={"use_cvm": False})
-        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'] = 'train_pass'
-
-    def server(self, context):
-        fleet.run_server()
-        fleet.stop_worker()
-        context['is_exit'] = True
-
-    def train(self, context):
-        self._exe.run(fluid.default_startup_program())
-        fleet.init_worker()
-
-        dataset = self._get_dataset()
-
-        shuf = np.array([fleet.worker_index()])
-        gs = shuf * 0
-        fleet._role_maker._node_type_comm.Allreduce(shuf, gs)
-
-        print("trainer id: {}, trainers: {}, gs: {}".format(fleet.worker_index(
-        ), fleet.worker_num(), gs))
-
-        epochs = envs.get_global_env("train.epochs")
-
-        for i in range(epochs):
-            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)
-
-        context['status'] = 'terminal_pass'
-        fleet.stop_worker()
-
-    def terminal(self, context):
-        print("terminal ended.")
-        context['is_exit'] = True

models/rank/dcn/config.yaml

@@ -22,7 +22,7 @@ train:
 
   reader:
     batch_size: 2
-    train_data_path: "{workspace}/data/slot_train"
+    train_data_path: "{workspace}/data/sample_data/train"
     feat_dict_name: "{workspace}/data/vocab"
     sparse_slots: "label C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C24 C25 C26"
     dense_slots: "I1:1 I2:1 I3:1 I4:1 I5:1 I6:1 I7:1 I8:1 I9:1 I10:1 I11:1 I12:1 I13:1"
@@ -35,7 +35,7 @@ train:
       l2_reg_cross: 0.00005
       dnn_use_bn: False
       clip_by_norm: 100.0
-      cat_feat_num: "{workspace}/data/cat_feature_num.txt"
+      cat_feat_num: "{workspace}/data/sample_data/cat_feature_num.txt"
       is_sparse: False
       is_test: False
       num_field: 39

models/rank/dcn/data/sample_data/cat_feature_num.txt

+C1 139
+C2 422
+C3 1548
+C4 1965
+C5 54
+C6 10
+C7 3213
+C8 81
+C9 3
+C10 2402
+C11 2246
+C12 1583
+C13 1911
+C14 24
+C15 2011
+C16 1731
+C17 9
+C18 1197
+C19 584
+C20 3
+C21 1652
+C22 8
+C23 14
+C24 1770
+C25 40
+C26 1349

models/rank/dcn/data/sample_data/train/sample_train.txt

+label:0 I1:0.69314718056 I2:1.60943791243 I3:1.79175946923 I4:0.0 I5:7.23201033166 I6:1.60943791243 I7:2.77258872224 I8:1.09861228867 I9:5.20400668708 I10:0.69314718056 I11:1.09861228867 I12:0 I13:1.09861228867 C1:95 C2:398 C3:0 C4:0 C5:53 C6:1 C7:73 C8:71 C9:3 C10:1974 C11:832 C12:0 C13:875 C14:8 C15:1764 C16:0 C17:5 C18:390 C19:226 C20:1 C21:0 C22:0 C23:8 C24:1759 C25:1 C26:862
+label:0 I1:1.09861228867 I2:1.38629436112 I3:3.80666248977 I4:0.69314718056 I5:4.63472898823 I6:2.19722457734 I7:1.09861228867 I8:1.09861228867 I9:1.60943791243 I10:0.69314718056 I11:0.69314718056 I12:0 I13:1.60943791243 C1:95 C2:200 C3:1184 C4:1929 C5:53 C6:4 C7:1477 C8:2 C9:3 C10:1283 C11:1567 C12:1048 C13:271 C14:6 C15:1551 C16:899 C17:1 C18:162 C19:226 C20:2 C21:575 C22:0 C23:8 C24:1615 C25:1 C26:659
+label:0 I1:1.09861228867 I2:1.38629436112 I3:0.69314718056 I4:2.7080502011 I5:6.64378973315 I6:4.49980967033 I7:1.60943791243 I8:1.09861228867 I9:5.50533153593 I10:0.69314718056 I11:1.38629436112 I12:1.38629436112 I13:3.82864139649 C1:123 C2:378 C3:991 C4:197 C5:53 C6:1 C7:689 C8:2 C9:3 C10:245 C11:623 C12:1482 C13:887 C14:21 C15:106 C16:720 C17:3 C18:768 C19:0 C20:0 C21:1010 C22:1 C23:8 C24:720 C25:0 C26:0
+label:0 I1:0 I2:6.79905586206 I3:0 I4:0 I5:8.38776764398 I6:0 I7:0.0 I8:0.0 I9:0.0 I10:0 I11:0.0 I12:0 I13:0 C1:95 C2:227 C3:0 C4:219 C5:53 C6:4 C7:3174 C8:2 C9:3 C10:569 C11:1963 C12:0 C13:1150 C14:21 C15:1656 C16:0 C17:6 C18:584 C19:0 C20:0 C21:0 C22:0 C23:8 C24:954 C25:0 C26:0
+label:0 I1:1.38629436112 I2:1.09861228867 I3:0 I4:0.0 I5:1.09861228867 I6:0.0 I7:1.38629436112 I8:0.0 I9:0.0 I10:0.69314718056 I11:0.69314718056 I12:0 I13:0.0 C1:121 C2:147 C3:0 C4:1356 C5:53 C6:7 C7:2120 C8:2 C9:3 C10:703 C11:1678 C12:1210 C13:1455 C14:8 C15:538 C16:1276 C17:6 C18:346 C19:0 C20:0 C21:944 C22:0 C23:10 C24:355 C25:0 C26:0
+label:0 I1:0 I2:1.09861228867 I3:0 I4:0 I5:9.45915167004 I6:0 I7:0.0 I8:0.0 I9:1.94591014906 I10:0 I11:0.0 I12:0 I13:0 C1:14 C2:75 C3:993 C4:480 C5:50 C6:6 C7:1188 C8:2 C9:3 C10:245 C11:1037 C12:1365 C13:1421 C14:21 C15:786 C16:5 C17:2 C18:555 C19:0 C20:0 C21:1408 C22:6 C23:7 C24:753 C25:0 C26:0
+label:0 I1:0 I2:1.60943791243 I3:1.09861228867 I4:0 I5:8.06117135969 I6:0 I7:0.0 I8:0.69314718056 I9:1.09861228867 I10:0 I11:0.0 I12:0 I13:0 C1:139 C2:343 C3:553 C4:828 C5:50 C6:4 C7:0 C8:2 C9:3 C10:245 C11:2081 C12:260 C13:455 C14:21 C15:122 C16:1159 C17:2 C18:612 C19:0 C20:0 C21:1137 C22:0 C23:1 C24:1583 C25:0 C26:0
+label:1 I1:0.69314718056 I2:2.07944154168 I3:1.09861228867 I4:0.0 I5:0.0 I6:0.0 I7:0.69314718056 I8:0.0 I9:0.0 I10:0.69314718056 I11:0.69314718056 I12:0 I13:0.0 C1:95 C2:227 C3:0 C4:1567 C5:21 C6:7 C7:2496 C8:71 C9:3 C10:1913 C11:2212 C12:0 C13:673 C14:21 C15:1656 C16:0 C17:5 C18:584 C19:0 C20:0 C21:0 C22:0 C23:10 C24:954 C25:0 C26:0
+label:0 I1:0 I2:3.87120101091 I3:1.60943791243 I4:2.19722457734 I5:9.85277303799 I6:5.52146091786 I7:3.36729582999 I8:3.4657359028 I9:4.9558270576 I10:0 I11:0.69314718056 I12:0 I13:2.19722457734 C1:14 C2:14 C3:454 C4:197 C5:53 C6:1 C7:1386 C8:2 C9:3 C10:0 C11:1979 C12:205 C13:214 C14:6 C15:1837 C16:638 C17:5 C18:6 C19:0 C20:0 C21:70 C22:0 C23:10 C24:720 C25:0 C26:0
+label:0 I1:0 I2:3.66356164613 I3:0 I4:0.69314718056 I5:10.4263800775 I6:3.09104245336 I7:0.69314718056 I8:1.09861228867 I9:1.38629436112 I10:0 I11:0.69314718056 I12:0 I13:0.69314718056 C1:14 C2:179 C3:120 C4:746 C5:53 C6:0 C7:1312 C8:2 C9:3 C10:1337 C11:1963 C12:905 C13:1150 C14:21 C15:1820 C16:328 C17:9 C18:77 C19:0 C20:0 C21:311 C22:0 C23:10 C24:89 C25:0 C26:0

models/rank/dcn/data/sample_data/vocab/C1.txt

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models/rank/dcn/data/sample_data/vocab/C12.txt

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models/rank/dcn/data/sample_data/vocab/C14.txt

+26ac7cf4
+cf1fc48d
+ad1cc976
+e8dce07a
+5aebfb83
+b28479f6
+64c94865
+1adce6ef
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+91233270
+ec19f520
+8ceecbc8
+07d13a8f
+0601d3b5
+cfef1c29
+0bc7c8c2

models/rank/dcn/data/sample_data/vocab/C17.txt

+07c540c4
+776ce399
+8efede7f
+3486227d
+e5ba7672
+1e88c74f
+2005abd1
+27c07bd6
+d4bb7bd8

models/rank/dcn/data/sample_data/vocab/C20.txt

+b1252a9d
+5840adea
+a458ea53

models/rank/dcn/data/sample_data/vocab/C22.txt

+ad3062eb
+49e825c5
+ccfd4002
+c9d4222a
+78e2e389
+8ec974f4
+8651fddb
+c0061c6d

models/rank/dcn/data/sample_data/vocab/C23.txt

+93bad2c0
+c3dc6cef
+423fab69
+25e3c76b
+b264a060
+85d5a995
+be7c41b4
+3a171ecb
+c7dc6720
+32c7478e
+72592995
+dbb486d7
+bcdee96c
+55dd3565

models/rank/dcn/data/sample_data/vocab/C25.txt

+e8b83407
+7a402766
+fd2fe0bd
+ea9a246c
+445bbe3b
+8b8de563
+9d93af03
+c0812fc5
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+1575c75f
+33d94071
+f5b6afe5
+c9f3bea7
+f0f449dd
+60c2b362
+cb079c2d

models/rank/dcn/data/sample_data/vocab/C6.txt

+7e0ccccf
+f1f2de2d
+c05778d5
+fe6b92e5
+c76aecf6
+6f6d9be8
+13718bbd
+e3520422
+3bf701e7
+fbad5c96

models/rank/dcn/data/sample_data/vocab/C9.txt

+7cc72ec2
+a18233ea
+a73ee510

models/rank/dcn/model.py

@@ -122,7 +122,7 @@ class Model(ModelBase):
         return fluid.layers.reduce_sum(fluid.layers.square(w))
 
     def train_net(self):
-        self.model._init_slots()
+        self._init_slots()
         self.init_network()
 
         self.net_input = self._create_embedding_input()
@@ -163,6 +163,5 @@ class Model(ModelBase):
         optimizer = fluid.optimizer.Adam(learning_rate, lazy_mode=True)
         return optimizer
 
-    def infer_net(self, parameter_list):
-        self.model._init_slots()
-        self.deepfm_net()
+    def infer_net(self):
+        self.train_net()

models/rank/deepfm/config.yaml

@@ -22,8 +22,8 @@ train:
 
   reader:
     batch_size: 2
-    train_data_path: "{workspace}/data/slot_train_data"
-    feat_dict_name: "{workspace}/data/feat_dict_10.pkl2"
+    train_data_path: "{workspace}/data/sample_data/train"
+    feat_dict_name: "{workspace}/data/sample_data/feat_dict_10.pkl2"
     sparse_slots: "label feat_idx"
     dense_slots: "feat_value:39"