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(self):
bert_model, _ = load_bert(
config_path=os.path.join(self.config['pretrained_model_dir'],
'bert_config.json'),
checkpoint_path=os.path.join(self.config['pretrained_model_dir'],
'bert_model.ckpt'),
)
text_mask = L.Lambda(
lambda x: K.cast(K.expand_dims(K.greater(x, 0), 2), K.floatx()))(
bert_model.input[0])
# GI
gi_in = L.Input(name="gi",
shape=(self.config["max_len"], ),
dtype="float32")
gi = gi_in
# AGN
X = bert_model.output
gi = L.Dense(self.config['max_len'], activation='tanh')(gi) # (B, L)
gi = L.Lambda(lambda x: K.expand_dims(x, 2))(gi) # (B, L, 1)
X, attn_weight = AGN(epsilon=self.config['epsilon'])([X, gi])
X = L.Lambda(lambda x: x[0] - 1e10 * (1.0 - x[1]))([X, text_mask])
output = L.Lambda(lambda x: K.max(x, 1))(X)
#output = L.Dense(128, activation='relu')(output)
output = L.Dropout(self.config.get('dropout', 0.2))(output)
output = L.Dense(self.config['output_size'],
activation='softmax')(output)
self.model = keras.Model(inputs=(*bert_model.input, gi_in),
outputs=output)
self.attn_model = keras.Model(inputs=(*bert_model.input, gi_in),
outputs=attn_weight)
optimizer = extend_with_weight_decay(Adam)
optimizer = extend_with_piecewise_linear_lr(optimizer)
optimizer_params = {
'learning_rate': self.config['learning_rate'],
'lr_schedule': {
self.config['steps_per_epoch'] * 2: 1,
self.config['steps_per_epoch'] * 3: 0.2,
self.config['steps_per_epoch'] * self.config['epochs']: 0.1
},
'weight_decay_rate': 0.01,
'exclude_from_weight_decay': ['Norm', 'bias'],
'bias_correction': False,
}
self.model.compile(
loss='sparse_categorical_crossentropy',
optimizer=optimizer(**optimizer_params),
)
self.model.summary()
if self.config.get('apply_fgm', True):
print('apply fgm')
fgm(self.model, 'Embedding-Token',
self.config.get('fgm_epsilon', 0.2))
def get_suggested_optimizer(init_lr=5e-5, total_steps=None):
lr_schedule = {1000: 1, 10000: 0.01}
if total_steps is not None:
lr_schedule = {total_steps // 10: 1, total_steps: 0.1}
optimizer = extend_with_weight_decay(Adam)
optimizer = extend_with_piecewise_linear_lr(optimizer)
optimizer_params = {
'learning_rate': init_lr,
'lr_schedule': lr_schedule,
'weight_decay_rate': 0.01,
'exclude_from_weight_decay': ['Norm', 'bias'],
'bias_correction': False,
}
optimizer = optimizer(**optimizer_params)
return optimizer
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
model = build_transformer_model(
config_path,
checkpoint_path,
model='nezha',
application='lm',
keep_tokens=keep_tokens, # 只保留keep_tokens中的字，精简原字表
compound_tokens=compound_tokens, # 要扩充的词表
)
output = CrossEntropy(1)([model.inputs[0], model.outputs[0]])
model = Model(model.inputs, output)
model.summary()
AdamW = extend_with_weight_decay(Adam, 'AdamW')
AdamWG = extend_with_gradient_accumulation(AdamW, 'AdamWG')
optimizer = AdamWG(learning_rate=2e-5,
weight_decay_rate=0.01,
exclude_from_weight_decay=['Norm', 'bias'],
grad_accum_steps=16)
model.compile(optimizer=optimizer)
class ChatBot(AutoRegressiveDecoder):
"""基于随机采样对话机器人
"""
@AutoRegressiveDecoder.wraps(default_rtype='probas')
def predict(self, inputs, output_ids, states):
token_ids, segment_ids = inputs
token_ids = np.concatenate([token_ids, output_ids], 1)
model = build_transformer_model(
config_path,
checkpoint_path,
with_mlm='linear',
keep_tokens=keep_tokens, # 只保留keep_tokens中的字，精简原字表
compound_tokens=compound_tokens, # 增加词，用字平均来初始化
)
# 训练用模型
y_in = keras.layers.Input(shape=(None, ))
outputs = CrossEntropy(1)([y_in, model.output])
train_model = keras.models.Model(model.inputs + [y_in], outputs)
AdamW = extend_with_weight_decay(Adam, name='AdamW')
AdamWG = extend_with_gradient_accumulation(AdamW, name='AdamWG')
optimizer = AdamWG(
learning_rate=5e-6,
weight_decay_rate=0.01,
exclude_from_weight_decay=['Norm', 'bias'],
grad_accum_steps=16,
)
train_model.compile(optimizer=optimizer)
train_model.summary()
class Evaluator(keras.callbacks.Callback):
"""训练回调
"""
def on_epoch_end(self, epoch, logs=None):
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
