def build_transformer_model_for_pretraining():
"""构建训练模型，通用于TPU/GPU
注意全程要用keras标准的层写法，一些比较灵活的“移花接木”式的
写法可能会在TPU上训练失败。此外，要注意的是TPU并非支持所有
tensorflow算子，尤其不支持动态（变长）算子，因此编写相应运算
时要格外留意。
"""
bert, train_model, loss = build_transformer_model_with_mlm()
# 优化器
optimizer = extend_with_weight_decay(Adam)
if which_optimizer == 'lamb':
optimizer = extend_with_layer_adaptation(optimizer)
optimizer = extend_with_piecewise_linear_lr(optimizer)
optimizer_params = {
'learning_rate': learning_rate,
'lr_schedule': lr_schedule,
'weight_decay_rate': weight_decay_rate,
'exclude_from_weight_decay': exclude_from_weight_decay,
'bias_correction': False,
}
if grad_accum_steps > 1:
optimizer = extend_with_gradient_accumulation(optimizer)
optimizer_params['grad_accum_steps'] = grad_accum_steps
optimizer = optimizer(**optimizer_params)
# 模型定型
train_model.compile(loss=loss, optimizer=optimizer)
# 如果传入权重，则加载。注：须在此处加载，才保证不报错。
if checkpoint_path is not None:
bert.load_weights_from_checkpoint(checkpoint_path)
return train_model
def build_train_bert_model():
"""构建训练模型，通用于TPU/GPU
注意全程要用keras标准的层写法，一些比较灵活的“移花接木”式的
写法可能会在TPU上训练失败。此外，要注意的是TPU并非支持所有
tensorflow算子，尤其不支持动态（变长）算子，因此编写相应运算
时要格外留意。
"""
bert = build_bert_model(config_path,
with_mlm='linear',
application='lm',
return_keras_model=False)
token_ids = bert.model.input[0]
proba = bert.model.output
def lm_loss(inputs):
"""计算loss的函数，需要封装为一个层
"""
y_true, y_pred, mask = inputs
y_true = y_true[:, 1:]
y_pred = y_pred[:, :-1]
mask = mask[:, 1:]
loss = K.sparse_categorical_crossentropy(y_true,
y_pred,
from_logits=True)
loss = K.sum(loss * mask) / (K.sum(mask) + K.epsilon())
return loss
def lm_acc(inputs):
"""计算准确率的函数，需要封装为一个层
"""
y_true, y_pred, mask = inputs
y_true = K.cast(y_true, K.floatx())
y_true = y_true[:, 1:]
y_pred = y_pred[:, :-1]
mask = mask[:, 1:]
acc = keras.metrics.sparse_categorical_accuracy(y_true, y_pred)
acc = K.sum(acc * mask) / (K.sum(mask) + K.epsilon())
return acc
mask = bert.model.get_layer('Sequence-Mask').output
loss = Lambda(lm_loss, name='lm_loss')([token_ids, proba, mask])
acc = Lambda(lm_acc, name='lm_acc')([token_ids, proba, mask])
train_model = Model(bert.model.inputs, [loss, acc])
# 优化器
optimizer = extend_with_weight_decay(Adam)
if which_optimizer == 'lamb':
optimizer = extend_with_layer_adaptation(optimizer)
optimizer = extend_with_piecewise_linear_lr(optimizer)
optimizer_params = {
'learning_rate': learning_rate,
'lr_schedule': lr_schedule,
'weight_decay_rate': weight_decay_rate,
'exclude_from_weight_decay': exclude_from_weight_decay,
'bias_correction': False,
}
if grad_accum_steps > 1:
optimizer = extend_with_gradient_accumulation(optimizer)
optimizer_params['grad_accum_steps'] = grad_accum_steps
optimizer = optimizer(**optimizer_params)
# 模型定型
train_model.compile(
loss={
'lm_loss': lambda y_true, y_pred: y_pred,
'lm_acc': lambda y_true, y_pred: K.stop_gradient(y_pred),
},
optimizer=optimizer,
)
# 如果传入权重，则加载。注：须在此处加载，才保证不报错。
if checkpoint_path is not None:
bert.load_weights_from_checkpoint(checkpoint_path)
return train_model
def build_train_bert_model():
"""构建训练模型，通用于TPU/GPU
注意全程要用keras标准的层写法，一些比较灵活的“移花接木”式的
写法可能会在TPU上训练失败。此外，要注意的是TPU并非支持所有
tensorflow算子，尤其不支持动态（变长）算子，因此编写相应运算
时要格外留意。
"""
bert = build_bert_model(config_path, with_mlm='linear', return_keras_model=False)
bert_model = bert.model
proba = bert_model.output
# 辅助输入
token_ids = Input(shape=(None, ), dtype='int64', name='token_ids') # 目标id
is_masked = Input(shape=(None, ), dtype='bool', name='is_masked') # mask标记
def mlm_loss(inputs):
"""计算loss的函数，需要封装为一个层
"""
y_true, y_pred, is_masked = inputs
is_masked = K.cast(is_masked, K.floatx())
loss = K.sparse_categorical_crossentropy(y_true, y_pred, from_logits=True)
loss = K.sum(loss * is_masked) / (K.sum(is_masked) + K.epsilon())
return loss
def mlm_acc(inputs):
"""计算准确率的函数，需要封装为一个层
"""
y_true, y_pred, is_masked = inputs
is_masked = K.cast(is_masked, K.floatx())
y_true = K.cast(y_true, K.floatx())
acc = keras.metrics.sparse_categorical_accuracy(y_true, y_pred)
acc = K.sum(acc * is_masked) / (K.sum(is_masked) + K.epsilon())
return acc
loss = Lambda(mlm_loss, name='mlm_loss')([token_ids, proba, is_masked])
acc = Lambda(mlm_acc, name='mlm_acc')([token_ids, proba, is_masked])
train_model = Model(bert_model.inputs + [token_ids, is_masked], [loss, acc])
# 优化器
optimizer = extend_with_weight_decay(Adam)
if which_optimizer == 'lamb':
optimizer = extend_with_layer_adaptation(optimizer)
optimizer = extend_with_piecewise_linear_lr(optimizer)
optimizer_params = {
'learning_rate': learning_rate,
'lr_schedule': lr_schedule,
'weight_decay_rate': weight_decay_rate,
'exclude_from_weight_decay': exclude_from_weight_decay,
'bias_correction': False,
}
if grad_accum_steps > 1:
optimizer = extend_with_gradient_accumulation(optimizer)
optimizer_params['grad_accum_steps'] = grad_accum_steps
optimizer = optimizer(**optimizer_params)
# 模型定型
train_model.compile(
loss={
'mlm_loss': lambda y_true, y_pred: y_pred,
'mlm_acc': lambda y_true, y_pred: K.stop_gradient(y_pred),
},
optimizer=optimizer,
)
# 如果传入权重，则加载。注：须在此处加载，才保证不报错。
if checkpoint_path is not None:
bert.load_weights_from_checkpoint(checkpoint_path)
return train_model
