add dssm

models/match/dssm/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.
+evaluate:
+  reader:
+    batch_size: 1
+    class: "{workspace}/synthetic_evaluate_reader.py"
+    test_data_path: "{workspace}/data/train"
+ 
+train:
+  trainer:
+    # for cluster training
+    strategy: "async"
+
+  epochs: 4
+  workspace: "fleetrec.models.match.dssm"
+
+  reader:
+    batch_size: 4
+    class: "{workspace}/synthetic_reader.py"
+    train_data_path: "{workspace}/data/train"
+
+  model:
+    models: "{workspace}/model.py"
+    hyper_parameters:
+      TRIGRAM_D: 1000
+      NEG: 4
+      fc_sizes: [300, 300, 128]
+      fc_acts: ['tanh', 'tanh', 'tanh']
+      learning_rate: 0.01
+      optimizer: sgd
+
+  save:
+    increment:
+      dirname: "increment"
+      epoch_interval: 2
+      save_last: True
+
+    inference:
+      dirname: "inference"
+      epoch_interval: 4
+      feed_varnames: ["query", "doc_pos"]
+      fetch_varnames: ["cos_sim_0.tmp_0"]
+      save_last: True

models/match/dssm/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
+import paddle.fluid as fluid
+
+from fleetrec.core.utils import envs
+from fleetrec.core.model import Model as ModelBase
+
+
+class Model(ModelBase):
+    def __init__(self, config):
+        ModelBase.__init__(self, config)
+
+    def input(self):
+        TRIGRAM_D = envs.get_global_env("hyper_parameters.TRIGRAM_D", None, self._namespace)
+        Neg = envs.get_global_env("hyper_parameters.NEG", None, self._namespace) 
+
+        self.query = fluid.data(name="query", shape=[-1, TRIGRAM_D], dtype='float32', lod_level=0)
+        self.doc_pos = fluid.data(name="doc_pos", shape=[-1, TRIGRAM_D], dtype='float32', lod_level=0)
+        self.doc_negs = [fluid.data(name="doc_neg_" + str(i), shape=[-1, TRIGRAM_D], dtype="float32", lod_level=0) for i in range(Neg)]
+        self._data_var.append(self.query)
+        self._data_var.append(self.doc_pos)
+        for input in self.doc_negs:
+            self._data_var.append(input)
+
+        if self._platform != "LINUX":
+            self._data_loader = fluid.io.DataLoader.from_generator(
+                feed_list=self._data_var, capacity=64, use_double_buffer=False, iterable=False)
+        
+
+    def net(self, is_infer=False):
+	hidden_layers = envs.get_global_env("hyper_parameters.fc_sizes", None, self._namespace)
+        hidden_acts = envs.get_global_env("hyper_parameters.fc_acts", None, self._namespace)
+	
+        def fc(data, hidden_layers, hidden_acts, names):
+            fc_inputs = [data]
+	    for i in range(len(hidden_layers)):
+		xavier=fluid.initializer.Xavier(uniform=True, fan_in=fc_inputs[-1].shape[1], fan_out=hidden_layers[i])
+		out = fluid.layers.fc(input=fc_inputs[-1],
+				size=hidden_layers[i],
+				act=hidden_acts[i],
+				param_attr=xavier,
+				bias_attr=xavier,
+				name=names[i])
+		fc_inputs.append(out)
+	    return fc_inputs[-1]
+
+	query_fc = fc(self.query, hidden_layers, hidden_acts, ['query_l1', 'query_l2', 'query_l3'])
+	doc_pos_fc = fc(self.doc_pos, hidden_layers, hidden_acts, ['doc_pos_l1', 'doc_pos_l2', 'doc_pos_l3'])
+	self.R_Q_D_p = fluid.layers.cos_sim(query_fc, doc_pos_fc)
+
+        if is_infer:
+            return
+
+        R_Q_D_ns = []
+	for i, doc_neg in enumerate(self.doc_negs):
+	    doc_neg_fc_i = fc(doc_neg, hidden_layers, hidden_acts, ['doc_neg_l1_' + str(i), 'doc_neg_l2_' + str(i), 'doc_neg_l3_' + str(i)])
+            R_Q_D_ns.append(fluid.layers.cos_sim(query_fc, doc_neg_fc_i))
+        concat_Rs = fluid.layers.concat(input=[self.R_Q_D_p] + R_Q_D_ns, axis=-1)
+	prob = fluid.layers.softmax(concat_Rs, axis=1)
+        
+        hit_prob = fluid.layers.slice(prob, axes=[0,1], starts=[0,0], ends=[4, 1])
+        loss = -fluid.layers.reduce_sum(fluid.layers.log(hit_prob))
+        self.avg_cost = fluid.layers.mean(x=loss)
+
+    def infer_results(self):
+        self._infer_results['query_doc_sim'] = self.R_Q_D_p
+
+    def avg_loss(self):
+        self._cost = self.avg_cost
+
+    def metrics(self):
+        self._metrics["LOSS"] = self.avg_cost
+
+    def train_net(self):
+        self.input()
+        self.net(is_infer=False)
+        self.avg_loss()
+        self.metrics()
+
+    def optimizer(self):
+        learning_rate = envs.get_global_env("hyper_parameters.learning_rate", None, self._namespace)
+        optimizer = fluid.optimizer.SGD(learning_rate)
+        return optimizer
+
+    def infer_input(self):
+        TRIGRAM_D = envs.get_global_env("hyper_parameters.TRIGRAM_D", None, self._namespace)
+        self.query = fluid.data(name="query", shape=[-1, TRIGRAM_D], dtype='float32', lod_level=0)
+        self.doc_pos = fluid.data(name="doc_pos", shape=[-1, TRIGRAM_D], dtype='float32', lod_level=0)
+        self._infer_data_var = [self.query, self.doc_pos]
+
+	self._infer_data_loader = fluid.io.DataLoader.from_generator(
+            feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False)
+ 
+    def infer_net(self):
+	self.infer_input()        
+        self.net(is_infer=True)
+	self.infer_results()

models/match/dssm/synthetic_evaluate_reader.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
+
+from fleetrec.core.reader import Reader
+from fleetrec.core.utils import envs
+
+
+class EvaluateReader(Reader):
+    def init(self):
+        pass
+
+    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')
+            query = map(float, features[0].split(','))
+            pos_doc = map(float, features[1].split(','))
+            feature_names = ['query', 'doc_pos']
+
+            yield zip(feature_names, [query] + [pos_doc])
+
+        return reader

models/match/dssm/synthetic_reader.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
+
+from fleetrec.core.reader import Reader
+from fleetrec.core.utils import envs
+
+
+class TrainReader(Reader):
+    def init(self):
+        pass
+
+    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')
+            query = map(float, features[0].split(','))
+            pos_doc = map(float, features[1].split(','))
+            feature_names = ['query', 'doc_pos']
+            neg_docs = []
+            for i in range(len(features) - 2):
+                feature_names.append('doc_neg_' + str(i))
+                neg_docs.append(map(float, features[i+2].split(',')))
+
+            yield zip(feature_names, [query] + [pos_doc] + neg_docs)
+
+        return reader