Add FGCNN model

datasets/criteo_fgcnn/download.sh

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+wget --no-check-certificate https://paddlerec.bj.bcebos.com/datasets/fgcnn/datapro.zip
+unzip -o datapro.zip	
+echo "Complete data download."

models/rank/fgcnn/config.yaml

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+# Copyright (c) 2022 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.
+
+# global settings 
+
+runner:
+  train_data_dir: "data/trainlite"
+  train_reader_path: "reader" # importlib format
+  use_gpu: True
+  use_auc: True
+  train_batch_size: 10
+  epochs: 1
+  print_interval: 10
+  # model_init_path: "output_model_all_fgcnn/1" # init model
+  model_save_path: "output_model_sample_fgcnn"
+  test_data_dir: "data/testlite"
+  infer_reader_path: "reader" # importlib format
+  infer_batch_size: 10
+  infer_load_path: "output_model_sample_fgcnn"
+  infer_start_epoch: 0
+  infer_end_epoch: 1
+
+# hyper parameters of user-defined network
+hyper_parameters:
+  # optimizer config
+  optimizer:
+    class: Adam
+    learning_rate: 0.001
+  sparse_inputs_slots: 26
+  sparse_feature_size: 1000000
+  feature_name: ['I1','I2','I3','I4','I5','I6','I7','I8','I9','I10','I11','I12','I13','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_inputs_slots: 13
+  feature_dim: 20
+  conv_kernel_width: [ 9, 9, 9, 9]
+  conv_filters: [38, 40, 42, 44]
+  new_maps: [3, 3, 3, 3]
+  pooling_width: [2, 2, 2, 2]
+  stride: [1, 1]
+  dnn_hidden_units: [100, 100, 100]
+  dnn_dropout: 0.0

models/rank/fgcnn/config_bigdata.yaml

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+# Copyright (c) 2022 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.
+
+# global settings 
+
+runner:
+  train_data_dir: "data/train"
+  train_reader_path: "reader" # importlib format
+  use_gpu: True
+  use_auc: True
+  train_batch_size: 2000
+  epochs: 2
+  print_interval: 2000
+  # model_init_path: "output_model_all_fgcnn/1" # init model
+  model_save_path: "output_model_all_fgcnn"
+  test_data_dir: "data/test"
+  infer_reader_path: "reader" # importlib format
+  infer_batch_size: 5000
+  infer_load_path: "output_model_all_fgcnn"
+  infer_start_epoch: 0
+  infer_end_epoch: 2
+
+# hyper parameters of user-defined network
+hyper_parameters:
+  # optimizer config
+  optimizer:
+    class: Adam
+    learning_rate: 0.001
+  sparse_inputs_slots: 26
+  sparse_feature_size: 1000000
+  feature_name: ['I1','I2','I3','I4','I5','I6','I7','I8','I9','I10','I11','I12','I13','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_inputs_slots: 13
+  feature_dim: 20
+  conv_kernel_width: [ 9, 9, 9, 9]
+  conv_filters: [38, 40, 42, 44]
+  new_maps: [3, 3, 3, 3]
+  pooling_width: [2, 2, 2, 2]
+  stride: [1, 1]
+  dnn_hidden_units: [1000, 1000, 1000]
+  dnn_dropout: 0.0

models/rank/fgcnn/dygraph_model.py

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+# Copyright (c) 2022 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
+import paddle.nn as nn
+import net
+import numpy as np
+
+
+class DygraphModel():
+    # define model
+    def create_model(self, config):
+        sparse_input_slot = config.get('hyper_parameters.sparse_inputs_slots')
+        dense_input_slot = config.get('hyper_parameters.dense_inputs_slots')
+        sparse_feature_size = config.get(
+            "hyper_parameters.sparse_feature_size")
+        feature_name = config.get("hyper_parameters.feature_name")
+        feature_dim = config.get("hyper_parameters.feature_dim", 20)
+        conv_kernel_width = config.get("hyper_parameters.conv_kernel_width",
+                                       (7, 7, 7, 7))
+        conv_filters = config.get("hyper_parameters.conv_filters",
+                                  (14, 16, 18, 20))
+        new_maps = config.get("hyper_parameters.new_maps", (3, 3, 3, 3))
+        pooling_width = config.get("hyper_parameters.pooling_width",
+                                   (2, 2, 2, 2))
+        stride = config.get("hyper_parameters.stride", (1, 1))
+        dnn_hidden_units = config.get("hyper_parameters.dnn_hidden_units",
+                                      (128, ))
+        dnn_dropout = config.get("hyper_parameters.dnn_dropout", 0.0)
+        fgcnn_model = net.FGCNN(
+            sparse_input_slot, sparse_feature_size, feature_name, feature_dim,
+            dense_input_slot, conv_kernel_width, conv_filters, new_maps,
+            pooling_width, stride, dnn_hidden_units, dnn_dropout)
+
+        return fgcnn_model
+
+    # define feeds which convert numpy of batch data to paddle.tensor
+    def create_feeds(self, batch_data, config):
+        # print(len(batch_data))
+        inputs = paddle.to_tensor(np.array(batch_data[0]).astype('int64'))
+        inputs = batch_data[0]
+        label = batch_data[1]
+        return label, inputs
+
+# define loss function by predicts and label
+
+    def create_loss(self, y_pred, label):
+        loss = nn.functional.log_loss(
+            y_pred, label=paddle.cast(
+                label, dtype="float32"))
+        avg_cost = paddle.mean(x=loss)
+        return avg_cost
+
+    # define optimizer
+    def create_optimizer(self, dy_model, config):
+        lr = config.get("hyper_parameters.optimizer.learning_rate", 1e-3)
+        optimizer = paddle.optimizer.Adam(
+            parameters=dy_model.parameters(), learning_rate=lr)
+        return optimizer
+
+    def create_metrics(self):
+        metrics_list_name = ["auc"]
+        auc_metric = paddle.metric.Auc("ROC")
+        metrics_list = [auc_metric]
+        return metrics_list, metrics_list_name
+
+    # construct train forward phase
+    def train_forward(self, dy_model, metrics_list, batch_data, config):
+        # 稠密向量
+        label, inputs = self.create_feeds(batch_data, config)
+        pred = dy_model.forward(inputs)
+        loss = self.create_loss(pred, label)
+        # update metrics
+        predict_2d = paddle.concat(x=[1 - pred, pred], axis=1)
+        metrics_list[0].update(preds=predict_2d.numpy(), labels=label.numpy())
+        # print_dict format :{'loss': loss}
+        print_dict = {'loss': loss}
+        return loss, metrics_list, print_dict
+
+    def infer_forward(self, dy_model, metrics_list, batch_data, config):
+        # label, sparse_tensor = self.create_feeds(batch_data, config)
+        label, inputs = self.create_feeds(batch_data, config)
+        pred = dy_model.forward(inputs)
+        # pred = dy_model.forward(sparse_tensor)
+        loss = self.create_loss(pred, label)
+        # update metrics
+        predict_2d = paddle.concat(x=[1 - pred, pred], axis=1)
+        metrics_list[0].update(preds=predict_2d.numpy(), labels=label.numpy())
+        # print_dict format :{'loss': loss}
+        print_dict = {'loss': loss}
+        return metrics_list, print_dict