Merge pull request #79 from WeiyueSu/erniesage

Erniesage

examples/erniesage/config/erniesage_v2_cpu.yaml

@@ -31,7 +31,7 @@ final_fc: true
 final_l2_norm: true
 loss_type: "hinge"
 margin: 0.3
-neg_type: "random_neg"
+neg_type: "batch_neg"
 
 # infer config ------
 infer_model: "./output/last"

examples/erniesage/config/erniesage_v2_gpu.yaml

@@ -31,7 +31,7 @@ final_fc: true
 final_l2_norm: true
 loss_type: "hinge"
 margin: 0.3
-neg_type: "random_neg"
+neg_type: "batch_neg"
 
 # infer config ------
 infer_model: "./output/last"

examples/erniesage/dataset/graph_reader.py

@@ -24,7 +24,7 @@ from pgl.sample import edge_hash
 class GraphGenerator(BaseDataGenerator):
     def __init__(self, graph_wrappers, data, batch_size, samples,
         num_workers, feed_name_list, use_pyreader,
-        phase, graph_data_path, shuffle=True, buf_size=1000):
+        phase, graph_data_path, shuffle=True, buf_size=1000, neg_type="batch_neg"):
 
         super(GraphGenerator, self).__init__(
             buf_size=buf_size,
@@ -40,6 +40,7 @@ class GraphGenerator(BaseDataGenerator):
         self.phase = phase
         self.load_graph(graph_data_path)
         self.num_layers = len(graph_wrappers)
+        self.neg_type= neg_type
 
     def load_graph(self, graph_data_path):
         self.graph = pgl.graph.MemmapGraph(graph_data_path)
@@ -72,7 +73,11 @@ class GraphGenerator(BaseDataGenerator):
         batch_src = np.array(batch_src, dtype="int64")
         batch_dst = np.array(batch_dst, dtype="int64")
 
-        sampled_batch_neg = alias_sample(batch_dst.shape, self.alias, self.events)
+        if self.neg_type == "batch_neg":
+            neg_shape = [1]
+        else:
+            neg_shape = batch_dst.shape
+        sampled_batch_neg = alias_sample(neg_shape, self.alias, self.events)
     
         if len(batch_neg) > 0:
             batch_neg = np.concatenate([batch_neg, sampled_batch_neg], 0)
@@ -80,7 +85,8 @@ class GraphGenerator(BaseDataGenerator):
             batch_neg = sampled_batch_neg
 
         if self.phase == "train":
-            ignore_edges = np.concatenate([np.stack([batch_src, batch_dst], 1), np.stack([batch_dst, batch_src], 1)], 0)
+            #ignore_edges = np.concatenate([np.stack([batch_src, batch_dst], 1), np.stack([batch_dst, batch_src], 1)], 0)
+            ignore_edges = set()
         else:
             ignore_edges = set()
 

examples/erniesage/models/base.py

@@ -191,12 +191,12 @@ def all_gather(X):
     for i in range(trainer_num):
         copy_X = X * 1
         copy_X =  L.collective._broadcast(copy_X, i, True)
-        copy_X.stop_gradients=True
+        copy_X.stop_gradient=True
         Xs.append(copy_X)
 
     if len(Xs) > 1:
         Xs=L.concat(Xs, 0)
-        Xs.stop_gradients=True
+        Xs.stop_gradient=True
     else:
         Xs = Xs[0]
     return Xs

examples/erniesage/models/erniesage_v2.py

@@ -27,7 +27,7 @@ class ErnieSageV2(BaseNet):
             src_position_ids = L.expand(src_position_ids, [src_batch, 1, 1]) # [B, slot_seqlen * num_b, 1]
             zero = L.fill_constant([1], dtype='int64', value=0)
             input_mask = L.cast(L.equal(src_ids, zero), "int32")  # assume pad id == 0 [B, slot_seqlen, 1]
-            src_pad_len = L.reduce_sum(input_mask, 1) # [B, 1, 1]
+            src_pad_len = L.reduce_sum(input_mask, 1, keep_dim=True) # [B, 1, 1]
 
             dst_position_ids = L.reshape(
                 L.range(

examples/erniesage/train.py

@@ -32,8 +32,9 @@ class TrainData(object):
         trainer_count = int(os.getenv("PADDLE_TRAINERS_NUM", "1"))
         log.info("trainer_id: %s, trainer_count: %s." % (trainer_id, trainer_count))
 
-        edges = np.load(os.path.join(graph_path, "edges.npy"), allow_pickle=True)
+        bidirectional_edges = np.load(os.path.join(graph_path, "edges.npy"), allow_pickle=True)
         # edges is bidirectional.
+        edges = bidirectional_edges[0::2]
         train_usr = edges[trainer_id::trainer_count, 0]
         train_ad = edges[trainer_id::trainer_count, 1]
         returns = {
@@ -73,7 +74,8 @@ def main(config):
         use_pyreader=config.use_pyreader,
         phase="train",
         graph_data_path=config.graph_path,
-        shuffle=True)
+        shuffle=True,
+        neg_type=config.neg_type)
 
     log.info("build graph reader done.")