Merge branch 'master' into add_deep_crossing

core/trainers/single_trainer.py

@@ -312,13 +312,27 @@ class SingleTrainer(TranspileTrainer):
     def load(self, is_fleet=False):
         dirname = envs.get_global_env(
             "runner." + self._runner_name + ".init_model_path", None)
+        load_vars = envs.get_global_env(
+            "runner." + self._runner_name + ".load_vars", None)
+
+        def name_has_embedding(var):
+            res = False
+            for var_name in load_vars:
+                if var_name == var.name:
+                    return True
+            return res
+
         if dirname is None or dirname == "":
             return
         print("going to load ", dirname)
         if is_fleet:
             fleet.load_persistables(self._exe, dirname)
         else:
-            fluid.io.load_persistables(self._exe, dirname)
+            if load_vars is None or len(load_vars) == 0:
+                fluid.io.load_persistables(self._exe, dirname)
+            else:
+                fluid.io.load_vars(
+                    self._exe, dirname, predicate=name_has_embedding)
 
     def save(self, epoch_id, is_fleet=False):
         def need_save(epoch_id, epoch_interval, is_last=False):

models/rank/fnn/__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/fnn/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.
+
+
+# global settings 
+debug: false
+workspace: "paddlerec.models.rank.fnn"
+
+dataset:
+  - name: train_sample
+    type: QueueDataset
+    batch_size: 5
+    data_path: "{workspace}/../dataset/Criteo_data/sample_data/train"
+    sparse_slots: "label feat_idx"
+    dense_slots: "feat_value:39"
+  - name: infer_sample
+    type: QueueDataset
+    batch_size: 5
+    data_path: "{workspace}/../dataset/Criteo_data/sample_data/train"
+    sparse_slots: "label feat_idx"
+    dense_slots: "feat_value:39"
+
+hyper_parameters:
+    # 用户自定义配置
+    optimizer:
+        class: SGD
+        learning_rate: 0.0001
+    sparse_feature_number: 1086460
+    sparse_feature_dim: 9
+    reg: 0.001
+    num_field: 39
+    fc_sizes: [512, 256, 128, 32]
+
+mode: train_FM_runner #for FM phase: train_FM_runner for dnn phase: train_DNN_runner
+# if infer, change mode to "infer_runner" and change phase to "infer_phase"
+
+runner:
+  - name: train_FM_runner
+    trainer_class: single_train
+    epochs: 1
+    device: cpu
+    init_model_path: ""   
+    save_checkpoint_interval: 1
+    save_inference_interval: 1
+    save_checkpoint_path: "increment"
+    save_inference_path: "inference"
+    print_interval: 1
+  - name: train_DNN_runner
+    trainer_class: single_train
+    epochs: 1
+    device: cpu
+    init_model_path: "increment/0"
+    load_vars: ["embedding_1.w_0", "embedding_0.w_0"]
+    save_checkpoint_interval: 1
+    save_inference_interval: 1
+    save_checkpoint_path: "increment_fnn"
+    save_inference_path: "inference_fnn"
+    print_interval: 1
+  - name: infer_runner
+    trainer_class: single_infer
+    epochs: 1
+    device: cpu
+    init_model_path: "increment/0"
+    print_interval: 1
+
+phase:
+- name: phase1
+  model: "{workspace}/fm_model.py" # for FM phase: fm_model.py for dnn phase model.py
+  dataset_name: train_sample
+  thread_num: 1
+#- name: infer_phase
+#  model: "{workspace}/model.py"
+#  dataset_name: infer_sample
+#  thread_num: 1

models/rank/fnn/fm_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 math
+from collections import OrderedDict
+
+import paddle.fluid as fluid
+
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
+
+
+class Model(ModelBase):
+    def __init__(self, config):
+        ModelBase.__init__(self, config)
+
+    def _init_hyper_parameters(self):
+        self.is_distributed = True if envs.get_trainer(
+        ) == "CtrTrainer" else False
+        self.sparse_feature_number = envs.get_global_env(
+            "hyper_parameters.sparse_feature_number", None)
+        self.sparse_feature_dim = envs.get_global_env(
+            "hyper_parameters.sparse_feature_dim", None)
+        self.reg = envs.get_global_env("hyper_parameters.reg", 1e-4)
+        self.num_field = envs.get_global_env("hyper_parameters.num_field",
+                                             None)
+
+    def net(self, inputs, is_infer=False):
+        raw_feat_idx = self._sparse_data_var[1]  # (batch_size * num_field) * 1
+        raw_feat_value = self._dense_data_var[0]  # batch_size * num_field
+        self.label = self._sparse_data_var[0]  # batch_size * 1
+
+        init_value_ = 0.1
+
+        feat_idx = raw_feat_idx
+        feat_value = fluid.layers.reshape(
+            raw_feat_value,
+            [-1, self.num_field, 1])  # batch_size * num_field * 1
+
+        # ------------------------- first order term --------------------------
+
+        first_weights_re = fluid.embedding(
+            input=feat_idx,
+            is_sparse=True,
+            is_distributed=self.is_distributed,
+            dtype='float32',
+            size=[self.sparse_feature_number + 1, 1],
+            padding_idx=0,
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.TruncatedNormalInitializer(
+                    loc=0.0, scale=init_value_),
+                regularizer=fluid.regularizer.L1DecayRegularizer(self.reg))
+        )  # (batch_size * num_field) * 1 * 1(embedding_size)
+        first_weights = fluid.layers.reshape(
+            first_weights_re,
+            shape=[-1, self.num_field, 1])  # batch_size * num_field * 1
+        y_first_order = fluid.layers.reduce_sum((first_weights * feat_value),
+                                                1)  # batch_size * 1
+        b_linear = fluid.layers.create_parameter(
+            shape=[1],
+            dtype='float32',
+            default_initializer=fluid.initializer.ConstantInitializer(
+                value=0))  # 1
+        # ------------------------- second order term --------------------------
+
+        feat_embeddings_re = fluid.embedding(
+            input=feat_idx,
+            is_sparse=True,
+            is_distributed=self.is_distributed,
+            dtype='float32',
+            size=[self.sparse_feature_number + 1, self.sparse_feature_dim],
+            padding_idx=0,
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.TruncatedNormalInitializer(
+                    loc=0.0,
+                    scale=init_value_ /
+                    math.sqrt(float(self.sparse_feature_dim))))
+        )  # (batch_size * num_field) * 1 * embedding_size
+        feat_embeddings = fluid.layers.reshape(
+            feat_embeddings_re,
+            shape=[-1, self.num_field, self.sparse_feature_dim
+                   ])  # batch_size * num_field * embedding_size
+        feat_embeddings = feat_embeddings * feat_value  # batch_size * num_field * embedding_size
+
+        # sum_square part
+        summed_features_emb = fluid.layers.reduce_sum(
+            feat_embeddings, 1)  # batch_size * embedding_size
+        summed_features_emb_square = fluid.layers.square(
+            summed_features_emb)  # batch_size * embedding_size
+
+        # square_sum part
+        squared_features_emb = fluid.layers.square(
+            feat_embeddings)  # batch_size * num_field * embedding_size
+        squared_sum_features_emb = fluid.layers.reduce_sum(
+            squared_features_emb, 1)  # batch_size * embedding_size
+
+        y_FM = 0.5 * fluid.layers.reduce_sum(
+            summed_features_emb_square - squared_sum_features_emb,
+            dim=1,
+            keep_dim=True)  # batch_size * 1
+
+        # ------------------------- Predict --------------------------
+
+        self.predict = fluid.layers.sigmoid(b_linear + y_first_order + y_FM)
+
+        cost = fluid.layers.log_loss(
+            input=self.predict, label=fluid.layers.cast(self.label,
+                                                        "float32"))  # log_loss
+        avg_cost = fluid.layers.reduce_sum(cost)
+
+        self._cost = avg_cost
+
+        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)
+        self._metrics["AUC"] = auc_var
+        self._metrics["BATCH_AUC"] = batch_auc_var
+        if is_infer:
+            self._infer_results["AUC"] = auc_var

models/rank/fnn/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 math
+from collections import OrderedDict
+
+import paddle.fluid as fluid
+
+from paddlerec.core.utils import envs
+from paddlerec.core.model import Model as ModelBase
+
+
+class Model(ModelBase):
+    def __init__(self, config):
+        ModelBase.__init__(self, config)
+
+    def _init_hyper_parameters(self):
+        self.is_distributed = True if envs.get_trainer(
+        ) == "CtrTrainer" else False
+        self.sparse_feature_number = envs.get_global_env(
+            "hyper_parameters.sparse_feature_number", None)
+        self.sparse_feature_dim = envs.get_global_env(
+            "hyper_parameters.sparse_feature_dim", None)
+        self.reg = envs.get_global_env("hyper_parameters.reg", 1e-4)
+        self.num_field = envs.get_global_env("hyper_parameters.num_field",
+                                             None)
+
+    def net(self, inputs, is_infer=False):
+        raw_feat_idx = self._sparse_data_var[1]  # (batch_size * num_field) * 1
+        raw_feat_value = self._dense_data_var[0]  # batch_size * num_field
+        self.label = self._sparse_data_var[0]  # batch_size * 1
+
+        init_value_ = 0.1
+
+        feat_idx = raw_feat_idx
+        feat_value = fluid.layers.reshape(
+            raw_feat_value,
+            [-1, self.num_field, 1])  # batch_size * num_field * 1
+
+        # ------------------------- first order term --------------------------
+
+        first_weights_re = fluid.embedding(
+            input=feat_idx,
+            is_sparse=True,
+            is_distributed=self.is_distributed,
+            dtype='float32',
+            size=[self.sparse_feature_number + 1, 1],
+            padding_idx=0,
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.TruncatedNormalInitializer(
+                    loc=0.0, scale=init_value_),
+                regularizer=fluid.regularizer.L1DecayRegularizer(self.reg))
+        )  # (batch_size * num_field) * 1 * 1(embedding_size)
+        first_weights = fluid.layers.reshape(
+            first_weights_re,
+            shape=[-1, self.num_field, 1])  # batch_size * num_field * 1
+
+        # ------------------------- second order term --------------------------
+
+        feat_embeddings_re = fluid.embedding(
+            input=feat_idx,
+            is_sparse=True,
+            is_distributed=self.is_distributed,
+            dtype='float32',
+            size=[self.sparse_feature_number + 1, self.sparse_feature_dim],
+            padding_idx=0,
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.TruncatedNormalInitializer(
+                    loc=0.0,
+                    scale=init_value_ /
+                    math.sqrt(float(self.sparse_feature_dim))))
+        )  # (batch_size * num_field) * 1 * embedding_size
+        feat_embeddings = fluid.layers.reshape(
+            feat_embeddings_re,
+            shape=[-1, self.num_field, self.sparse_feature_dim
+                   ])  # batch_size * num_field * embedding_size
+        feat_embeddings = feat_embeddings * feat_value  # batch_size * num_field * embedding_size
+
+        concated = fluid.layers.concat(
+            [feat_embeddings, first_weights], axis=2)
+        concated = fluid.layers.reshape(
+            concated,
+            shape=[-1, self.num_field * (self.sparse_feature_dim + 1)])
+
+        fcs = [concated]
+        hidden_layers = envs.get_global_env("hyper_parameters.fc_sizes")
+
+        for size in hidden_layers:
+            output = fluid.layers.fc(
+                input=fcs[-1],
+                size=size,
+                act='relu',
+                param_attr=fluid.ParamAttr(
+                    initializer=fluid.initializer.Normal(
+                        scale=1.0 / math.sqrt(fcs[-1].shape[1]))))
+            fcs.append(output)
+
+        predict = fluid.layers.fc(
+            input=fcs[-1],
+            size=1,
+            act="sigmoid",
+            param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(
+                scale=1 / math.sqrt(fcs[-1].shape[1]))))
+
+        self.predict = predict
+
+        cost = fluid.layers.log_loss(
+            input=self.predict, label=fluid.layers.cast(self.label,
+                                                        "float32"))  # log_loss
+        avg_cost = fluid.layers.reduce_sum(cost)
+
+        self._cost = avg_cost
+
+        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)
+        self._metrics["AUC"] = auc_var
+        self._metrics["BATCH_AUC"] = batch_auc_var
+        if is_infer:
+            self._infer_results["AUC"] = auc_var

models/rank/readme.md

 # 排序模型库
 
 ## 简介
-我们提供了常见的排序任务中使用的模型算法的PaddleRec实现, 单机训练&预测效果指标以及分布式训练&预测性能指标等。实现的排序模型包括 [多层神经网络](dnn)、[Deep Cross Network](dcn)、[DeepFM](deepfm)、 [xDeepFM](xdeepfm)、[Deep Interest Network](din)、[Wide&Deep](wide_deep)。
+我们提供了常见的排序任务中使用的模型算法的PaddleRec实现, 单机训练&预测效果指标以及分布式训练&预测性能指标等。实现的排序模型包括 [logistic regression](logistic_regression)、[多层神经网络](dnn)、[FM](fm)、[FFM](ffm)、[PNN](pnn)、[多层神经网络](dnn)、[Deep Cross Network](dcn)、[DeepFM](deepfm)、 [xDeepFM](xdeepfm)、[NFM](nfm)、[AFM](afm)、[Deep Interest Network](din)、[Wide&Deep](wide_deep)。
 
 模型算法库在持续添加中，欢迎关注。
 
@@ -25,6 +25,7 @@
 | DNN | 多层神经网络 | -- |
 | Logistic Regression | 逻辑回归 | -- |
 | FM | 因子分解机 | [Factorization Machine](https://ieeexplore.ieee.org/abstract/document/5694074)(2010) |
+| FFM | Field-Aware FM | [Field-aware Factorization Machines for CTR Prediction](https://dl.acm.org/doi/pdf/10.1145/2959100.2959134)(2016) |
 | PNN | Product Network | [Product-based Neural Networks for User Response Prediction](https://arxiv.org/pdf/1611.00144.pdf)(2016) |
 | wide&deep | Deep + wide(LR) | [Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454)(2016) |
 | DeepFM | DeepFM | [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://arxiv.org/pdf/1703.04247.pdf)(2017) |