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fix word2vec #833

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fix word2vec #833

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33 changes: 15 additions & 18 deletions models/recall/word2vec/benchmark/static_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -74,24 +74,24 @@ def net(self, inputs, is_infer=False):
size=[self.sparse_feature_number, self.sparse_feature_dim],
param_attr=paddle.ParamAttr(
name='emb',
initializer=paddle.initializer.Uniform(-init_width,
init_width)))
initializer=paddle.nn.initializer.Uniform(-init_width,
init_width)))

true_emb_w = paddle.static.nn.embedding(
input=inputs[1],
is_sparse=True,
size=[self.sparse_feature_number, self.sparse_feature_dim],
param_attr=paddle.ParamAttr(
name='emb_w',
initializer=paddle.initializer.Constant(value=0.0)))
initializer=paddle.nn.initializer.Constant(value=0.0)))

true_emb_b = paddle.static.nn.embedding(
input=inputs[1],
is_sparse=True,
size=[self.sparse_feature_number, 1],
param_attr=paddle.ParamAttr(
name='emb_b',
initializer=paddle.initializer.Constant(value=0.0)))
initializer=paddle.nn.initializer.Constant(value=0.0)))

neg_word_reshape = paddle.reshape(inputs[2], shape=[-1, 1])
neg_word_reshape.stop_gradient = True
Expand All @@ -116,7 +116,7 @@ def net(self, inputs, is_infer=False):
neg_emb_b_vec = paddle.reshape(neg_emb_b, shape=[-1, self.neg_num])

true_logits = paddle.add(paddle.sum(
paddle.multiply(input_emb, true_emb_w), dim=1, keep_dim=True),
paddle.multiply(input_emb, true_emb_w), axis=1, keepdim=True),
true_emb_b)

input_emb_re = paddle.reshape(
Expand All @@ -131,14 +131,15 @@ def net(self, inputs, is_infer=False):
label_ones = paddle.full_like(
true_logits, fill_value=1.0, dtype='float32')
label_zeros = paddle.full_like(
true_logits, fill_value=0.0, dtype='float32')
neg_logits, fill_value=0.0, dtype='float32')

true_logits = paddle.nn.functional.sigmoid(true_logits)
true_xent = paddle.nn.functional.binary_cross_entropy(true_logits,
label_ones)
neg_logits = paddle.nn.functional.sigmoid(neg_logits)
neg_xent = paddle.nn.functional.binary_cross_entropy(neg_logits,
label_zeros)
cost = paddle.add(paddle.sum(true_xent, dim=1),
paddle.sum(neg_xent, dim=1))
cost = paddle.add(true_xent, neg_xent)
avg_cost = paddle.mean(cost)

self.inference_target_var = avg_cost
Expand All @@ -157,24 +158,20 @@ def create_optimizer(self, strategy=None):

# single
optimizer = paddle.optimizer.SGD(
learning_rate=paddle.static.exponential_decay(
learning_rate=lr,
decay_steps=decay_steps,
decay_rate=decay_rate,
staircase=True))
learning_rate=paddle.optimizer.lr.ExponentialDecay(
learning_rate=lr, gamma=decay_rate, verbose=True))

if strategy != None:
sync_mode = self.config.get("runner.sync_mode")
print("sync_mode: {}".format(sync_mode))
# geo
if sync_mode == "geo":
decay_steps = int(decay_steps / fleet.worker_num())
print("decay_steps: {}".format(decay_steps))
optimizer = paddle.optimizer.SGD(
learning_rate=paddle.static.exponential_decay(
learning_rate=lr,
decay_steps=decay_steps,
decay_rate=decay_rate,
staircase=True))
learning_rate=paddle.optimizer.lr.ExponentialDecay(
learning_rate=lr, gamma=decay_rate, verbose=True))
strategy.a_sync_configs["lr_decay_steps"] = decay_steps

# async sync heter
if sync_mode in ["async", "sync", "heter"]:
Expand Down