add ssr

models/recall/ssr/config.yaml

@@ -12,43 +12,55 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+workspace: "paddlerec.models.recall.ssr"
 
-evaluate:
-  reader:
-    batch_size: 1
-    class: "{workspace}/ssr_infer_reader.py"
-    test_data_path: "{workspace}/data/train"
-  is_return_numpy: True
+dataset:
+- name: dataset_train
+  batch_size: 5
+  type: QueueDataset
+  data_path: "{workspace}/data/train"
+  data_converter: "{workspace}/ssr_reader.py"
+- name: dataset_infer
+  batch_size: 5
+  type: QueueDataset
+  data_path: "{workspace}/data/test"
+  data_converter: "{workspace}/ssr_infer_reader.py"
 
-train:
-  trainer:
-    # for cluster training
-    strategy: "async"
+hyper_parameters:
+  vocab_size: 1000
+  emb_dim: 128
+  hidden_size: 100
+  optimizer: 
+    class: adagrad
+    learning_rate: 0.01
+    strategy: async
 
+#use infer_runner mode and modify 'phase' below if infer
+mode: train_runner
+#mode: infer_runner
+
+runner:
+- name: train_runner
+  class: single_train
+  device: cpu
   epochs: 3
-  workspace: "paddlerec.models.recall.ssr"
+  save_checkpoint_interval: 2
+  save_inference_interval: 4
+  save_checkpoint_path: "increment"
+  save_inference_path: "inference"
+  print_interval: 10
+- name: infer_runner
+  class: single_infer
+  init_model_path: "increment/0"
   device: cpu
+  epochs: 3
 
-  reader:
-    batch_size: 5
-    class: "{workspace}/ssr_reader.py"
-    train_data_path: "{workspace}/data/train"
-
-  model:
-    models: "{workspace}/model.py"
-    hyper_parameters:
-      vocab_size: 1000
-      emb_dim: 128
-      hidden_size: 100
-      learning_rate: 0.01
-      optimizer: adagrad
-
-  save:
-    increment:
-      dirname: "increment"
-      epoch_interval: 2
-      save_last: True
-    inference:
-      dirname: "inference"
-      epoch_interval: 4
-      save_last: True
+phase:
+- name: train
+  model: "{workspace}/model.py"
+  dataset_name: dataset_train
+  thread_num: 1
+    #- name: infer
+    #  model: "{workspace}/model.py"
+    #  dataset_name: dataset_infer
+    #  thread_num: 1

models/recall/ssr/model.py

@@ -20,85 +20,45 @@ from paddlerec.core.utils import envs
 from paddlerec.core.model import Model as ModelBase
 
 
-class BowEncoder(object):
-    """ bow-encoder """
-
-    def __init__(self):
-        self.param_name = ""
-
-    def forward(self, emb):
-        return fluid.layers.sequence_pool(input=emb, pool_type='sum')
-
-
-class GrnnEncoder(object):
-    """ grnn-encoder """
-
-    def __init__(self, param_name="grnn", hidden_size=128):
-        self.param_name = param_name
-        self.hidden_size = hidden_size
-
-    def forward(self, emb):
-        fc0 = fluid.layers.fc(input=emb,
-                              size=self.hidden_size * 3,
-                              param_attr=self.param_name + "_fc.w",
-                              bias_attr=False)
-
-        gru_h = fluid.layers.dynamic_gru(
-            input=fc0,
-            size=self.hidden_size,
-            is_reverse=False,
-            param_attr=self.param_name + ".param",
-            bias_attr=self.param_name + ".bias")
-        return fluid.layers.sequence_pool(input=gru_h, pool_type='max')
-
-
-class PairwiseHingeLoss(object):
-    def __init__(self, margin=0.8):
-        self.margin = margin
-
-    def forward(self, pos, neg):
-        loss_part1 = fluid.layers.elementwise_sub(
-            tensor.fill_constant_batch_size_like(
-                input=pos, shape=[-1, 1], value=self.margin, dtype='float32'),
-            pos)
-        loss_part2 = fluid.layers.elementwise_add(loss_part1, neg)
-        loss_part3 = fluid.layers.elementwise_max(
-            tensor.fill_constant_batch_size_like(
-                input=loss_part2, shape=[-1, 1], value=0.0, dtype='float32'),
-            loss_part2)
-        return loss_part3
-
-
 class Model(ModelBase):
     def __init__(self, config):
         ModelBase.__init__(self, config)
 
-    def get_correct(self, x, y):
-        less = tensor.cast(cf.less_than(x, y), dtype='float32')
-        correct = fluid.layers.reduce_sum(less)
-        return correct
-
-    def train(self):
-        vocab_size = envs.get_global_env("hyper_parameters.vocab_size", None,
-                                         self._namespace)
-        emb_dim = envs.get_global_env("hyper_parameters.emb_dim", None,
-                                      self._namespace)
-        hidden_size = envs.get_global_env("hyper_parameters.hidden_size", None,
-                                          self._namespace)
-        emb_shape = [vocab_size, emb_dim]
-
+    def _init_hyper_parameters(self):
+        self.vocab_size = envs.get_global_env("hyper_parameters.vocab_size")
+        self.emb_dim = envs.get_global_env("hyper_parameters.emb_dim")
+        self.hidden_size = envs.get_global_env("hyper_parameters.hidden_size")
+
+    def input_data(self, is_infer=False, **kwargs):
+        if is_infer:
+            user_data = fluid.data(
+                name="user", shape=[None, 1], dtype="int64", lod_level=1)
+            all_item_data = fluid.data(
+                name="all_item", shape=[None, self.vocab_size], dtype="int64")
+            pos_label = fluid.data(
+                name="pos_label", shape=[None, 1], dtype="int64")
+            return [user_data, all_item_data, pos_label]
+        else:
+            user_data = fluid.data(
+                name="user", shape=[None, 1], dtype="int64", lod_level=1)
+            pos_item_data = fluid.data(
+                name="p_item", shape=[None, 1], dtype="int64", lod_level=1)
+            neg_item_data = fluid.data(
+                name="n_item", shape=[None, 1], dtype="int64", lod_level=1)
+            return [user_data, pos_item_data, neg_item_data]
+
+    def net(self, inputs, is_infer=False):
+        if is_infer:
+            self._infer_net(inputs)
+            return
+        user_data = inputs[0]
+        pos_item_data = inputs[1]
+        neg_item_data = inputs[2]
+        emb_shape = [self.vocab_size, self.emb_dim]
         self.user_encoder = GrnnEncoder()
         self.item_encoder = BowEncoder()
         self.pairwise_hinge_loss = PairwiseHingeLoss()
 
-        user_data = fluid.data(
-            name="user", shape=[None, 1], dtype="int64", lod_level=1)
-        pos_item_data = fluid.data(
-            name="p_item", shape=[None, 1], dtype="int64", lod_level=1)
-        neg_item_data = fluid.data(
-            name="n_item", shape=[None, 1], dtype="int64", lod_level=1)
-        self._data_var.extend([user_data, pos_item_data, neg_item_data])
-
         user_emb = fluid.embedding(
             input=user_data, size=emb_shape, param_attr="emb.item")
         pos_item_emb = fluid.embedding(
@@ -109,79 +69,115 @@ class Model(ModelBase):
         pos_item_enc = self.item_encoder.forward(pos_item_emb)
         neg_item_enc = self.item_encoder.forward(neg_item_emb)
         user_hid = fluid.layers.fc(input=user_enc,
-                                   size=hidden_size,
+                                   size=self.hidden_size,
                                    param_attr='user.w',
                                    bias_attr="user.b")
         pos_item_hid = fluid.layers.fc(input=pos_item_enc,
-                                       size=hidden_size,
+                                       size=self.hidden_size,
                                        param_attr='item.w',
                                        bias_attr="item.b")
         neg_item_hid = fluid.layers.fc(input=neg_item_enc,
-                                       size=hidden_size,
+                                       size=self.hidden_size,
                                        param_attr='item.w',
                                        bias_attr="item.b")
         cos_pos = fluid.layers.cos_sim(user_hid, pos_item_hid)
         cos_neg = fluid.layers.cos_sim(user_hid, neg_item_hid)
         hinge_loss = self.pairwise_hinge_loss.forward(cos_pos, cos_neg)
         avg_cost = fluid.layers.mean(hinge_loss)
-        correct = self.get_correct(cos_neg, cos_pos)
+        correct = self._get_correct(cos_neg, cos_pos)
 
         self._cost = avg_cost
         self._metrics["correct"] = correct
         self._metrics["hinge_loss"] = hinge_loss
 
-    def train_net(self):
-        self.train()
-
-    def infer(self):
-        vocab_size = envs.get_global_env("hyper_parameters.vocab_size", None,
-                                         self._namespace)
-        emb_dim = envs.get_global_env("hyper_parameters.emb_dim", None,
-                                      self._namespace)
-        hidden_size = envs.get_global_env("hyper_parameters.hidden_size", None,
-                                          self._namespace)
-
-        user_data = fluid.data(
-            name="user", shape=[None, 1], dtype="int64", lod_level=1)
-        all_item_data = fluid.data(
-            name="all_item", shape=[None, vocab_size], dtype="int64")
-        pos_label = fluid.data(
-            name="pos_label", shape=[None, 1], dtype="int64")
-        self._infer_data_var = [user_data, all_item_data, pos_label]
-        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, inputs):
+        user_data = inputs[0]
+        all_item_data = inputs[1]
+        pos_label = inputs[2]
 
         user_emb = fluid.embedding(
-            input=user_data, size=[vocab_size, emb_dim], param_attr="emb.item")
+            input=user_data,
+            size=[self.vocab_size, self.emb_dim],
+            param_attr="emb.item")
         all_item_emb = fluid.embedding(
             input=all_item_data,
-            size=[vocab_size, emb_dim],
+            size=[self.vocab_size, self.emb_dim],
             param_attr="emb.item")
         all_item_emb_re = fluid.layers.reshape(
-            x=all_item_emb, shape=[-1, emb_dim])
+            x=all_item_emb, shape=[-1, self.emb_dim])
 
         user_encoder = GrnnEncoder()
         user_enc = user_encoder.forward(user_emb)
         user_hid = fluid.layers.fc(input=user_enc,
-                                   size=hidden_size,
+                                   size=self.hidden_size,
                                    param_attr='user.w',
                                    bias_attr="user.b")
         user_exp = fluid.layers.expand(
-            x=user_hid, expand_times=[1, vocab_size])
-        user_re = fluid.layers.reshape(x=user_exp, shape=[-1, hidden_size])
+            x=user_hid, expand_times=[1, self.vocab_size])
+        user_re = fluid.layers.reshape(
+            x=user_exp, shape=[-1, self.hidden_size])
 
         all_item_hid = fluid.layers.fc(input=all_item_emb_re,
-                                       size=hidden_size,
+                                       size=self.hidden_size,
                                        param_attr='item.w',
                                        bias_attr="item.b")
         cos_item = fluid.layers.cos_sim(X=all_item_hid, Y=user_re)
-        all_pre_ = fluid.layers.reshape(x=cos_item, shape=[-1, vocab_size])
+        all_pre_ = fluid.layers.reshape(
+            x=cos_item, shape=[-1, self.vocab_size])
         acc = fluid.layers.accuracy(input=all_pre_, label=pos_label, k=20)
 
         self._infer_results['recall20'] = acc
 
-    def infer_net(self):
-        self.infer()
+    def _get_correct(self, x, y):
+        less = tensor.cast(cf.less_than(x, y), dtype='float32')
+        correct = fluid.layers.reduce_sum(less)
+        return correct
+
+
+class BowEncoder(object):
+    """ bow-encoder """
+
+    def __init__(self):
+        self.param_name = ""
+
+    def forward(self, emb):
+        return fluid.layers.sequence_pool(input=emb, pool_type='sum')
+
+
+class GrnnEncoder(object):
+    """ grnn-encoder """
+
+    def __init__(self, param_name="grnn", hidden_size=128):
+        self.param_name = param_name
+        self.hidden_size = hidden_size
+
+    def forward(self, emb):
+        fc0 = fluid.layers.fc(input=emb,
+                              size=self.hidden_size * 3,
+                              param_attr=self.param_name + "_fc.w",
+                              bias_attr=False)
+
+        gru_h = fluid.layers.dynamic_gru(
+            input=fc0,
+            size=self.hidden_size,
+            is_reverse=False,
+            param_attr=self.param_name + ".param",
+            bias_attr=self.param_name + ".bias")
+        return fluid.layers.sequence_pool(input=gru_h, pool_type='max')
+
+
+class PairwiseHingeLoss(object):
+    def __init__(self, margin=0.8):
+        self.margin = margin
+
+    def forward(self, pos, neg):
+        loss_part1 = fluid.layers.elementwise_sub(
+            tensor.fill_constant_batch_size_like(
+                input=pos, shape=[-1, 1], value=self.margin, dtype='float32'),
+            pos)
+        loss_part2 = fluid.layers.elementwise_add(loss_part1, neg)
+        loss_part3 = fluid.layers.elementwise_max(
+            tensor.fill_constant_batch_size_like(
+                input=loss_part2, shape=[-1, 1], value=0.0, dtype='float32'),
+            loss_part2)
+        return loss_part3