add fibinet

models/rank/fibinet/__init__.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.

models/rank/fibinet/config.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.
+
+# workspace
+workspace: "paddlerec.models.rank.fibinet"
+
+# list of dataset
+dataset:
+- name: dataloader_train # name of dataset to distinguish different datasets
+  batch_size: 2
+  type: DataLoader # or QueueDataset 
+  data_path: "{workspace}/data/sample_data/train"
+  sparse_slots: "click 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26"
+  dense_slots: "dense_var:13"
+- name: dataset_train # name of dataset to distinguish different datasets
+  batch_size: 2
+  type: QueueDataset # or DataLoader 
+  data_path: "{workspace}/data/sample_data/train"
+  sparse_slots: "click 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26"
+  dense_slots: "dense_var:13"
+- name: dataset_infer # name
+  batch_size: 2
+  type: DataLoader # or QueueDataset
+  data_path: "{workspace}/data/sample_data/train"
+  sparse_slots: "click 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26"
+  dense_slots: "dense_var:13"
+
+# hyper parameters of user-defined network
+hyper_parameters:
+  # optimizer config
+  optimizer:
+    class: Adam
+    learning_rate: 0.001
+    strategy: async
+  # user-defined <key, value> pairs
+  sparse_inputs_slots: 27
+  sparse_feature_number: 1000001
+  sparse_feature_dim: 9
+  dense_input_dim: 13
+  bilinear_type: 'all'
+  reduction_ratio: 3
+  dropout_rate: 0.5
+
+# select runner by name
+mode: [single_cpu_train, single_cpu_infer]
+# config of each runner.
+# runner is a kind of paddle training class, which wraps the train/infer process.
+runner:
+- name: single_cpu_train
+  class: train
+  # num of epochs
+  epochs: 4
+  # device to run training or infer
+  device: cpu
+  save_checkpoint_interval: 2 # save model interval of epochs
+  save_inference_interval: 4 # save inference
+  save_checkpoint_path: "increment_model" # save checkpoint path
+  save_inference_path: "inference" # save inference path
+  save_inference_feed_varnames: [] # feed vars of save inference
+  save_inference_fetch_varnames: [] # fetch vars of save inference
+  init_model_path: "" # load model path
+  print_interval: 10
+  phases: [phase1]
+
+- name: single_cpu_infer
+  class: infer
+  # num of epochs
+  epochs: 1
+  # device to run training or infer
+  device: cpu
+  init_model_path: "increment_model" # load model path
+  phases: [phase2]
+
+# runner will run all the phase in each epoch
+phase:
+- name: phase1
+  model: "{workspace}/model.py" # user-defined model
+  dataset_name: dataloader_train # select dataset by name
+  thread_num: 1
+
+- name: phase2
+  model: "{workspace}/model.py" # user-defined model
+  dataset_name: dataset_infer # select dataset by name
+  thread_num: 1
+  

models/rank/fibinet/data/download.sh

+wget --no-check-certificate https://fleet.bj.bcebos.com/ctr_data.tar.gz
+tar -zxvf ctr_data.tar.gz
+mv ./raw_data ./train_data_full
+mkdir train_data && cd train_data
+cp ../train_data_full/part-0 ../train_data_full/part-1 ./ && cd ..
+mv ./test_data ./test_data_full
+mkdir test_data && cd test_data
+cp ../test_data_full/part-220 ./  && cd ..
+echo "Complete data download."
+echo "Full Train data stored in ./train_data_full "
+echo "Full Test data stored in ./test_data_full "
+echo "Rapid Verification train data stored in ./train_data "
+echo "Rapid Verification test data stored in ./test_data "

models/rank/fibinet/data/get_slot_data.py

+#   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.
+
+import paddle.fluid.incubate.data_generator as dg
+
+cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+cont_max_ = [20, 600, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
+cont_diff_ = [20, 603, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
+hash_dim_ = 1000001
+continuous_range_ = range(1, 14)
+categorical_range_ = range(14, 40)
+
+
+class CriteoDataset(dg.MultiSlotDataGenerator):
+    """
+    DacDataset: inheritance MultiSlotDataGeneratior, Implement data reading
+    Help document: http://wiki.baidu.com/pages/viewpage.action?pageId=728820675
+    """
+
+    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 continuous_range_:
+                if features[idx] == "":
+                    dense_feature.append(0.0)
+                else:
+                    dense_feature.append(
+                        (float(features[idx]) - cont_min_[idx - 1]) /
+                        cont_diff_[idx - 1])
+            for idx in categorical_range_:
+                sparse_feature.append(
+                    [hash(str(idx) + features[idx]) % hash_dim_])
+            label = [int(features[0])]
+            process_line = dense_feature, sparse_feature, label
+            feature_name = ["dense_feature"]
+            for idx in categorical_range_:
+                feature_name.append("C" + str(idx - 13))
+            feature_name.append("label")
+            s = "click:" + str(label[0])
+            for i in dense_feature:
+                s += " dense_feature:" + str(i)
+            for i in range(1, 1 + len(categorical_range_)):
+                s += " " + str(i) + ":" + str(sparse_feature[i - 1][0])
+            print(s.strip())
+            yield None
+
+        return reader
+
+
+d = CriteoDataset()
+d.run_from_stdin()

models/rank/fibinet/data/run.sh

+sh download.sh
+
+mkdir slot_train_data_full
+for i in `ls ./train_data_full`
+do
+    cat train_data_full/$i | python get_slot_data.py > slot_train_data_full/$i
+done
+
+mkdir slot_test_data_full
+for i in `ls ./test_data_full`
+do
+    cat test_data_full/$i | python get_slot_data.py > slot_test_data_full/$i
+done
+
+mkdir slot_train_data
+for i in `ls ./train_data`
+do
+    cat train_data/$i | python get_slot_data.py > slot_train_data/$i
+done
+
+mkdir slot_test_data
+for i in `ls ./test_data`
+do
+    cat test_data/$i | python get_slot_data.py > slot_test_data/$i
+done

models/rank/fibinet/model.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 paddle.fluid as fluid
+import itertools
+from paddlerec.core.utils import envs
+from paddlerec.core.model import ModelBase
+
+
+class Model(ModelBase):
+    def __init__(self, config):
+        ModelBase.__init__(self, config)
+
+    def _init_hyper_parameters(self):
+        self.is_distributed = True if envs.get_fleet_mode().upper(
+        ) == "PSLIB" else False
+        self.sparse_feature_number = envs.get_global_env(
+            "hyper_parameters.sparse_feature_number")
+        self.sparse_feature_dim = envs.get_global_env(
+            "hyper_parameters.sparse_feature_dim")
+        self.learning_rate = envs.get_global_env(
+            "hyper_parameters.optimizer.learning_rate")
+
+    def _SENETLayer(self, inputs, filed_size, reduction_ratio=3):
+        reduction_size = max(1, filed_size // reduction_ratio)
+        Z = fluid.layers.reduce_mean(inputs, dim=-1)
+
+        A_1 = fluid.layers.fc(
+            input=Z,
+            size=reduction_size,
+            param_attr=fluid.initializer.Xavier(uniform=False),
+            act='relu',
+            name='W_1')
+
+        A_2 = fluid.layers.fc(
+            input=A_1,
+            size=filed_size,
+            param_attr=fluid.initializer.Xavier(uniform=False),
+            act='relu',
+            name='W_2')
+
+        V = fluid.layers.elementwise_mul(
+            inputs, y=fluid.layers.unsqueeze(
+                input=A_2, axes=[2]))
+
+        return fluid.layers.split(V, num_or_sections=filed_size, dim=1)
+
+    def _BilinearInteraction(self,
+                             inputs,
+                             filed_size,
+                             embedding_size,
+                             bilinear_type="interaction"):
+        if bilinear_type == "all":
+            p = [
+                fluid.layers.elementwise_mul(
+                    fluid.layers.fc(
+                        input=v_i,
+                        size=embedding_size,
+                        param_attr=fluid.initializer.Xavier(uniform=False),
+                        act=None,
+                        name=None),
+                    fluid.layers.squeeze(
+                        input=v_j, axes=[1]))
+                for v_i, v_j in itertools.combinations(inputs, 2)
+            ]
+        else:
+            raise NotImplementedError
+
+        return fluid.layers.concat(input=p, axis=1)
+
+    def _DNNLayer(self, inputs, dropout_rate=0.5):
+        deep_input = inputs
+        for i, hidden_unit in enumerate([400, 400, 400]):
+            fc_out = fluid.layers.fc(
+                input=deep_input,
+                size=hidden_unit,
+                param_attr=fluid.initializer.Xavier(uniform=False),
+                act='relu',
+                name='d_' + str(i))
+            fc_out = fluid.layers.dropout(fc_out, dropout_prob=dropout_rate)
+            deep_input = fc_out
+
+        return deep_input
+
+    def net(self, input, is_infer=False):
+        self.sparse_inputs = self._sparse_data_var[1:]
+        self.dense_input = self._dense_data_var[0]
+        self.label_input = self._sparse_data_var[0]
+
+        emb = []
+        for data in self.sparse_inputs:
+            feat_emb = fluid.embedding(
+                input=data,
+                size=[self.sparse_feature_number, self.sparse_feature_dim],
+                param_attr=fluid.ParamAttr(
+                    name='dis_emb',
+                    learning_rate=5,
+                    initializer=fluid.initializer.Xavier(
+                        fan_in=self.sparse_feature_dim,
+                        fan_out=self.sparse_feature_dim)),
+                is_sparse=True)
+            emb.append(feat_emb)
+        concat_emb = fluid.layers.concat(emb, axis=1)
+
+        filed_size = len(self.sparse_inputs)
+        bilinear_type = envs.get_global_env("hyper_parameters.bilinear_type")
+        reduction_ratio = envs.get_global_env(
+            "hyper_parameters.reduction_ratio")
+        dropout_rate = envs.get_global_env("hyper_parameters.dropout_rate")
+
+        senet_output = self._SENETLayer(concat_emb, filed_size,
+                                        reduction_ratio)
+        senet_bilinear_out = self._BilinearInteraction(
+            senet_output, filed_size, self.sparse_feature_dim, bilinear_type)
+
+        concat_emb = fluid.layers.split(
+            concat_emb, num_or_sections=filed_size, dim=1)
+        bilinear_out = self._BilinearInteraction(
+            concat_emb, filed_size, self.sparse_feature_dim, bilinear_type)
+        dnn_input = fluid.layers.concat(
+            input=[senet_bilinear_out, bilinear_out, self.dense_input], axis=1)
+        dnn_output = self._DNNLayer(dnn_input, dropout_rate)
+
+        y_pred = fluid.layers.fc(
+            input=dnn_output,
+            size=1,
+            param_attr=fluid.initializer.Xavier(uniform=False),
+            act='sigmoid',
+            name='logit')
+        self.predict = y_pred
+        auc, batch_auc, _ = fluid.layers.auc(input=self.predict,
+                                             label=self.label_input,
+                                             num_thresholds=2**12,
+                                             slide_steps=20)
+
+        if is_infer:
+            self._infer_results["AUC"] = auc
+            self._infer_results["BATCH_AUC"] = batch_auc
+            return
+
+        self._metrics["AUC"] = auc
+        self._metrics["BATCH_AUC"] = batch_auc
+        cost = fluid.layers.log_loss(
+            input=self.predict,
+            label=fluid.layers.cast(
+                x=self.label_input, dtype='float32'))
+        avg_cost = fluid.layers.reduce_mean(cost)
+        self._cost = avg_cost