提交 434be194 编写于 作者: S suweiyue

1. fix mp_reader queue, 2. fix erniesagev2 biasattr

上级 5782fb81
...@@ -48,6 +48,9 @@ class ErnieSageV2(BaseNet): ...@@ -48,6 +48,9 @@ class ErnieSageV2(BaseNet):
cls = L.fill_constant_batch_size_like(src_feat["term_ids"], [-1, 1, 1], "int64", 1) cls = L.fill_constant_batch_size_like(src_feat["term_ids"], [-1, 1, 1], "int64", 1)
src_ids = L.concat([cls, src_feat["term_ids"]], 1) src_ids = L.concat([cls, src_feat["term_ids"]], 1)
dst_ids = dst_feat["term_ids"] dst_ids = dst_feat["term_ids"]
# cls = L.fill_constant_batch_size_like(dst_feat["term_ids"], [-1, 1, 1], "int64", 1)
# src_ids = L.concat([cls, dst_feat["term_ids"]], 1)
# dst_ids = src_feat["term_ids"]
# sent_ids # sent_ids
sent_ids = L.concat([L.zeros_like(src_ids), L.ones_like(dst_ids)], 1) sent_ids = L.concat([L.zeros_like(src_ids), L.ones_like(dst_ids)], 1)
...@@ -81,14 +84,16 @@ class ErnieSageV2(BaseNet): ...@@ -81,14 +84,16 @@ class ErnieSageV2(BaseNet):
self_feature = L.fc(self_feature, self_feature = L.fc(self_feature,
hidden_size, hidden_size,
act=act, act=act,
param_attr=F.ParamAttr(name=name + "_l", param_attr=F.ParamAttr(name=name + "_l.w_0",
learning_rate=learning_rate), learning_rate=learning_rate),
bias_attr=name+"_l.b_0"
) )
neigh_feature = L.fc(neigh_feature, neigh_feature = L.fc(neigh_feature,
hidden_size, hidden_size,
act=act, act=act,
param_attr=F.ParamAttr(name=name + "_r", param_attr=F.ParamAttr(name=name + "_r.w_0",
learning_rate=learning_rate), learning_rate=learning_rate),
bias_attr=name+"_r.b_0"
) )
output = L.concat([self_feature, neigh_feature], axis=1) output = L.concat([self_feature, neigh_feature], axis=1)
output = L.l2_normalize(output, axis=1) output = L.l2_normalize(output, axis=1)
......
...@@ -57,14 +57,16 @@ def graphsage_sum(gw, feature, hidden_size, act, initializer, learning_rate, nam ...@@ -57,14 +57,16 @@ def graphsage_sum(gw, feature, hidden_size, act, initializer, learning_rate, nam
self_feature = fluid.layers.fc(self_feature, self_feature = fluid.layers.fc(self_feature,
hidden_size, hidden_size,
act=act, act=act,
param_attr=fluid.ParamAttr(name=name + "_l", initializer=initializer, param_attr=fluid.ParamAttr(name=name + "_l.w_0", initializer=initializer,
learning_rate=learning_rate), learning_rate=learning_rate),
bias_attr=name+"_l.b_0"
) )
neigh_feature = fluid.layers.fc(neigh_feature, neigh_feature = fluid.layers.fc(neigh_feature,
hidden_size, hidden_size,
act=act, act=act,
param_attr=fluid.ParamAttr(name=name + "_r", initializer=initializer, param_attr=fluid.ParamAttr(name=name + "_r.w_0", initializer=initializer,
learning_rate=learning_rate), learning_rate=learning_rate),
bias_attr=name+"_r.b_0"
) )
output = fluid.layers.concat([self_feature, neigh_feature], axis=1) output = fluid.layers.concat([self_feature, neigh_feature], axis=1)
output = fluid.layers.l2_normalize(output, axis=1) output = fluid.layers.l2_normalize(output, axis=1)
...@@ -79,14 +81,16 @@ def graphsage_mean(gw, feature, hidden_size, act, initializer, learning_rate, na ...@@ -79,14 +81,16 @@ def graphsage_mean(gw, feature, hidden_size, act, initializer, learning_rate, na
self_feature = fluid.layers.fc(self_feature, self_feature = fluid.layers.fc(self_feature,
hidden_size, hidden_size,
act=act, act=act,
param_attr=fluid.ParamAttr(name=name + "_l", initializer=initializer, param_attr=fluid.ParamAttr(name=name + "_l.w_0", initializer=initializer,
learning_rate=learning_rate), learning_rate=learning_rate),
bias_attr=name+"_l.b_0"
) )
neigh_feature = fluid.layers.fc(neigh_feature, neigh_feature = fluid.layers.fc(neigh_feature,
hidden_size, hidden_size,
act=act, act=act,
param_attr=fluid.ParamAttr(name=name + "_r", initializer=initializer, param_attr=fluid.ParamAttr(name=name + "_r.w_0", initializer=initializer,
learning_rate=learning_rate), learning_rate=learning_rate),
bias_attr=name+"_r.b_0"
) )
output = fluid.layers.concat([self_feature, neigh_feature], axis=1) output = fluid.layers.concat([self_feature, neigh_feature], axis=1)
output = fluid.layers.l2_normalize(output, axis=1) output = fluid.layers.l2_normalize(output, axis=1)
...@@ -101,14 +105,16 @@ def pinsage_mean(gw, feature, hidden_size, act, initializer, learning_rate, name ...@@ -101,14 +105,16 @@ def pinsage_mean(gw, feature, hidden_size, act, initializer, learning_rate, name
self_feature = fluid.layers.fc(self_feature, self_feature = fluid.layers.fc(self_feature,
hidden_size, hidden_size,
act=act, act=act,
param_attr=fluid.ParamAttr(name=name + "_l", initializer=initializer, param_attr=fluid.ParamAttr(name=name + "_l.w_0", initializer=initializer,
learning_rate=learning_rate), learning_rate=learning_rate),
bias_attr=name+"_l.b_0"
) )
neigh_feature = fluid.layers.fc(neigh_feature, neigh_feature = fluid.layers.fc(neigh_feature,
hidden_size, hidden_size,
act=act, act=act,
param_attr=fluid.ParamAttr(name=name + "_r", initializer=initializer, param_attr=fluid.ParamAttr(name=name + "_r.w_0", initializer=initializer,
learning_rate=learning_rate), learning_rate=learning_rate),
bias_attr=name+"_r.b_0"
) )
output = fluid.layers.concat([self_feature, neigh_feature], axis=1) output = fluid.layers.concat([self_feature, neigh_feature], axis=1)
output = fluid.layers.l2_normalize(output, axis=1) output = fluid.layers.l2_normalize(output, axis=1)
...@@ -123,14 +129,16 @@ def pinsage_sum(gw, feature, hidden_size, act, initializer, learning_rate, name) ...@@ -123,14 +129,16 @@ def pinsage_sum(gw, feature, hidden_size, act, initializer, learning_rate, name)
self_feature = fluid.layers.fc(self_feature, self_feature = fluid.layers.fc(self_feature,
hidden_size, hidden_size,
act=act, act=act,
param_attr=fluid.ParamAttr(name=name + "_l", initializer=initializer, param_attr=fluid.ParamAttr(name=name + "_l.w_0", initializer=initializer,
learning_rate=learning_rate), learning_rate=learning_rate),
bias_attr=name+"_l.b_0"
) )
neigh_feature = fluid.layers.fc(neigh_feature, neigh_feature = fluid.layers.fc(neigh_feature,
hidden_size, hidden_size,
act=act, act=act,
param_attr=fluid.ParamAttr(name=name + "_r", initializer=initializer, param_attr=fluid.ParamAttr(name=name + "_r.w_0", initializer=initializer,
learning_rate=learning_rate), learning_rate=learning_rate),
bias_attr=name+"_r.b_0"
) )
output = fluid.layers.concat([self_feature, neigh_feature], axis=1) output = fluid.layers.concat([self_feature, neigh_feature], axis=1)
output = fluid.layers.l2_normalize(output, axis=1) output = fluid.layers.l2_normalize(output, axis=1)
......
...@@ -25,7 +25,7 @@ except: ...@@ -25,7 +25,7 @@ except:
import numpy as np import numpy as np
import time import time
import paddle.fluid as fluid import paddle.fluid as fluid
from queue import Queue from multiprocessing import Queue
import threading import threading
......
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