def compute_position_ids(self, inputs):
"""T5的相对位置分桶（直接翻译自官方T5源码）
i-i: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14...
f(i-j):0 1 2 3 4 5 6 7 8 8 8 8 9 9 9 ...
"""
q, v = inputs
# 计算位置差
q_idxs = K.arange(0, K.shape(q)[1], dtype='int32')
q_idxs = K.expand_dims(q_idxs, 1)
v_idxs = K.arange(0, K.shape(v)[1], dtype='int32')
v_idxs = K.expand_dims(v_idxs, 0)
pos_ids = v_idxs - q_idxs
# 后处理操作
num_buckets, max_distance = self.input_dim, self.max_distance
ret = 0
n = -pos_ids
if self.bidirectional:
num_buckets //= 2
ret += K.cast(K.less(n, 0), 'int32') * num_buckets
n = K.abs(n)
else:
n = K.maximum(n, 0)
# now n is in the range [0, inf)
max_exact = num_buckets // 2
is_small = K.less(n, max_exact)
val_if_large = max_exact + K.cast(
K.log(K.cast(n, K.floatx()) / max_exact) /
np.log(max_distance / max_exact) * (num_buckets - max_exact),
'int32',
)
val_if_large = K.minimum(val_if_large, num_buckets - 1)
ret += K.switch(is_small, n, val_if_large)
return ret
def _resource_apply(self, grad, var, indices=None):
# 准备变量
var_dtype = var.dtype.base_dtype
lr_t = self._decayed_lr(var_dtype)
m = self.get_slot(var, 'm')
v = self.get_slot(var, 'v')
beta_1_t = self._get_hyper('beta_1', var_dtype)
beta_2_t = self._get_hyper('beta_2', var_dtype)
epsilon_t = K.cast(self.epsilon, var_dtype)
local_step = K.cast(self.iterations + 1, var_dtype)
beta_1_t_power = K.pow(beta_1_t, local_step)
beta_2_t_power = K.pow(beta_2_t, local_step)
# 更新公式
m_t = K.update(m, beta_1_t * m + (1 - beta_1_t) * grad)
v_t = K.update(v, beta_2_t * v + (1 - beta_2_t) * (grad - m_t)**2)
# 返回算子
with tf.control_dependencies([m_t, v_t]):
if self.bias_correct:
m_t = m_t / (1.0 - beta_1_t_power)
v_t = v_t / (1.0 - beta_2_t_power)
var_t = var - lr_t * m_t / (K.sqrt(v_t) + epsilon_t)
return K.update(var, var_t)
def sparse_accuracy(y_true, y_pred):
# y_true需要重新明确一下shape和dtype
y_true = K.reshape(y_true, K.shape(y_pred)[:-1])
y_true = K.cast(y_true, 'int32')
# 计算准确率
y_pred = K.cast(K.argmax(y_pred, axis=2), 'int32')
return K.mean(K.cast(K.equal(y_true, y_pred), K.floatx()))
def get_label_mask(self, y_true):
"""获取batch内相同label样本"""
label = K.cast(y_true, 'int32')
label_2 = K.reshape(label, (1, -1))
mask = K.equal(label_2, label)
mask = K.cast(mask, K.floatx())
mask = mask * (1 - K.eye(K.shape(y_true)[0])) # 排除对角线，即 i == j
return mask
def call(self, inputs, mask=None, a_mask=None, position_bias=None):
"""
多头注意力
:param inputs: [q, k, v, a_mask, position_bias]
:param mask: [q_mask, v_mask],
q_mask 对query序列进行mask，针对padding；v_mask对value序列进行mask，防止看到某些位置value，如padding
:param a_mask: Boolean，是否对attention进行mask
:param position_bias: type of position bias， 使用指定类型的位置编码对attention里的位置进行偏移
:return:
"""
q, k, v = inputs[:3]
q_mask, v_mask, idx = None, None, 3
if mask is not None:
if mask[0] is not None:
q_mask = K.cast(mask[0], K.floatx())
if mask[2] is not None:
v_mask = K.cast(mask[2], K.floatx())
if a_mask is not None:
a_mask = inputs[idx]
idx += 1
# 投影变换
qw = self.q_dense(q)
kw = self.k_dense(k)
vw = self.v_dense(v)
# 形状变换
qw = K.reshape(qw, [-1, K.shape(q)[1], self.head_nums, self.key_size])
kw = K.reshape(kw, [-1, K.shape(k)[1], self.head_nums, self.key_size])
vw = K.reshape(vw, [-1, K.shape(v)[1], self.head_nums, self.head_size])
# 计算attention
att = tf.einsum('bjhd,bkhd->bhjk', qw, kw)
# 处理位置编码
if position_bias == 'relative':
position_embeddings = inputs[idx]
att = att + tf.einsum('bjhd,jkd->bhjk', qw, position_embeddings)
if self.attention_scale:
att = att / self.key_size**0.5
# value mask
att = sequence_masking(att, v_mask, 'add', -1)
# attention mask
if a_mask is not None:
att = att - (1 - a_mask) * 1e12
att = K.softmax(att)
output = tf.einsum('bhjk,bkhd->bjhd', att, vw)
# 继续处理位置编码
if position_bias == 'relative':
output = output + tf.einsum('bhjk,jkd->bjhd', att,
position_embeddings)
output = K.reshape(output, (-1, K.shape(output)[1], self.output_dim))
output = self.combine_dense(output)
# query mask
output = sequence_masking(output, q_mask, 'mul')
return output
def get_updates(self, loss, params):
grads = self.get_gradients(loss, params)
self.updates = [K.update_add(self.iterations, 1)]
lr = self.learning_rate
if self.initial_decay > 0:
lr = lr * (1. / (1. + self.decay *
K.cast(self.iterations, K.dtype(self.decay))))
t = K.cast(self.iterations, K.floatx()) + 1
lr_t = lr * (K.sqrt(1. - K.pow(self.beta_2, t)) /
(1. - K.pow(self.beta_1, t)))
ms = [
K.zeros(K.int_shape(p), dtype=K.dtype(p), name='m_' + str(i))
for (i, p) in enumerate(params)
]
vs = [
K.zeros(K.int_shape(p), dtype=K.dtype(p), name='v_' + str(i))
for (i, p) in enumerate(params)
]
if self.amsgrad:
vhats = [
K.zeros(K.int_shape(p),
dtype=K.dtype(p),
name='vhat_' + str(i)) for (i, p) in enumerate(params)
]
else:
vhats = [
K.zeros(1, name='vhat_' + str(i)) for i in range(len(params))
]
self.weights = [self.iterations] + ms + vs + vhats
for p, g, m, v, vhat in zip(params, grads, ms, vs, vhats):
m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
v_t = (self.beta_2 * v) + (1. - self.beta_2) * K.square(g - m_t)
if self.amsgrad:
vhat_t = K.maximum(vhat, v_t)
p_t = p - lr_t * m_t / (K.sqrt(vhat_t) + self.epsilon)
self.updates.append(K.update(vhat, vhat_t))
else:
p_t = p - lr_t * m_t / (K.sqrt(v_t) + self.epsilon)
self.updates.append(K.update(m, m_t))
self.updates.append(K.update(v, v_t))
new_p = p_t
# Apply constraints.
if getattr(p, 'constraint', None) is not None:
new_p = p.constraint(new_p)
self.updates.append(K.update(p, new_p))
return self.updates
def sparse_accuracy(self, y_true, y_pred):
"""训练过程中显示逐帧准确率的函数，排除了mask的影响
此处y_true需要是整数形式（非one hot）
"""
# 导出mask并转换数据类型
mask = K.all(K.greater(y_pred, -1e6), axis=2)
mask = K.cast(mask, K.floatx())
# y_true需要重新明确一下shape和dtype
y_true = K.reshape(y_true, K.shape(y_pred)[:-1])
y_true = K.cast(y_true, 'int32')
# 逐标签取最大来粗略评测训练效果
y_pred = K.cast(K.argmax(y_pred, 2), 'int32')
isequal = K.cast(K.equal(y_true, y_pred), K.floatx())
return K.sum(isequal * mask) / K.sum(mask)
def get_updates(self, loss, params):
# 是否更新
cond = K.equal(self.iterations % self.grad_accum_steps, 0)
cond = K.cast(cond, K.floatx())
# 获取梯度
grads = self.get_gradients(loss, params)
self.accum_grads = [
K.zeros(shape=K.int_shape(p),
dtype=K.dtype(p),
name='accum_grad_{}'.format(i))
for i, p in enumerate(params)
]
old_update = K.update
def new_update(x, new_x):
new_x = cond * new_x + (1 - cond) * x
return old_update(x, new_x)
K.update = new_update
updates = super(NewOptimizer, self).get_updates(loss, params)
K.update = old_update
# 累计更新
with K.control_dependencies(updates):
acc_updates = [
K.update(ag, g + (1 - cond) * ag)
for ag, g in zip(self.accum_grads, grads)
]
return acc_updates
def _resource_apply(self, grad, var, indices=None):
var_dtype = var.dtype.base_dtype
lr_t = self._decayed_lr(var_dtype)
m = self.get_slot(var, 'm')
v = self.get_slot(var, 'v')
beta_1_t = self._get_hyper('beta_1', var_dtype)
beta_2_t = self._get_hyper('beta_2', var_dtype)
local_step = K.cast(self.iterations + 1, var_dtype)
beta_1_power = K.pow(beta_1_t, local_step)
beta_2_power = K.pow(beta_2_t, local_step)
# update
if indices is None:
m_t = K.update(m, beta_1_t * m + (1 - beta_1_t) * grad)
v_t = K.update(v, beta_2_t * v + (1 - beta_2_t) * grad**2)
else:
mv_ops = [K.update(m, beta_1_t * m), K.update(v, beta_2_t * v)]
with tf.control_dependencies(mv_ops):
m_t = self._resource_scatter_add(m, indices,
(1 - beta_1_t) * grad)
v_t = self._resource_scatter_add(v, indices,
(1 - beta_2_t) * grad**2)
#
with tf.control_dependencies([m_t, v_t]):
if self.bias_correct:
m_t = m_t / (1 + beta_1_power)
v_t = v_t / (1 + beta_2_power)
var_t = var - lr_t * m_t / (K.sqrt(v_t) + self.epsilon)
return K.update(var, var_t)
def get_labels_of_similarity(self, y_pred):
idxs = K.arange(0, K.shape(y_pred)[0])
idxs_1 = idxs[None, :]
idxs_2 = (idxs + 1 - idxs % 2 * 2)[:, None]
labels = K.equal(idxs_1, idxs_2)
labels = K.cast(labels, K.floatx())
return labels
def parse_func(serialized_record):
feature_description = {
'token_ids': tf.io.FixedLenFeature([seq_length], tf.int64),
'mask_ids': tf.io.FixedLenFeature([seq_length], tf.int64)
}
features = tf.io.parse_single_example(serialized_record,
feature_description)
token_ids = features['token_ids']
mask_ids = features['mask_ids']
segment_ids = K.zeros_like(token_ids, dtype='int64')
is_masked = K.not_equal(mask_ids, 0)
masked_token_ids = K.switch(mask_ids, mask_ids - 1,
token_ids) # 之前让位给unmask_id一位，现在减1回归
x = {
'Input-Token': masked_token_ids,
'Input-Segment': segment_ids,
'token_ids': token_ids,
'is_masked': K.cast(is_masked, K.floatx())
}
y = {
'mlm_loss': K.zeros_like([1], tf.float32),
'mlm_acc': K.zeros_like([1], tf.float32)
}
return x, y
def call(self, inputs):
# PE_2i(p) = sin(p/10000^(2i/d_pos))
# PE_2i+1(p) = cos(p/10000^(2i/d_pos))
batch_size, seq_len, word_emb_dim = K.shape(inputs)[0], K.shape(
inputs)[1], K.shape(inputs)[2]
if not self.embedding_dim or self.method == 'add':
self.embedding_dim = word_emb_dim
t = 2 * K.arange(self.embedding_dim / 2, dtype='float32') / K.cast(
self.embedding_dim, dtype='float32')
embedding_wise_pos = 1. / K.pow(
10000., t) # 1/10000 ^(2i/d_pos) , shape = (p_dim/2, )
embedding_wise_pos = K.expand_dims(embedding_wise_pos,
0) # (1, p_dim/2)
word_wise_pos = K.cumsum(K.ones_like(inputs[:, :, 0]),
axis=1) # shape = [batch_size, seq_len]
word_wise_pos = K.expand_dims(word_wise_pos,
2) # (batch_size, seq_len, 1)
position_embedding = K.dot(
word_wise_pos,
embedding_wise_pos) # (batch_size, seq_len, p_dim/2)
position_embedding = K.expand_dims(position_embedding, 3)
position_embedding = K.reshape(K.concatenate(
[K.sin(position_embedding),
K.cos(position_embedding)], axis=-1),
shape=(batch_size, seq_len, -1))
if self.method == 'add':
return inputs + position_embedding
return K.concatenate([inputs, position_embedding], axis=-1)
def sparse_categorical_crossentropy(y_true, y_pred):
# y_true需要重新明确一下shape和dtype
y_true = K.reshape(y_true, K.shape(y_pred)[:-1])
y_true = K.cast(y_true, 'int32')
y_true = K.one_hot(y_true, K.shape(y_pred)[2])
# 计算交叉熵
return K.mean(K.categorical_crossentropy(y_true, y_pred))
def get_labels_of_similarity(self, inputs):
idx = K.arange(0, K.shape(inputs)[0])
idx_1 = idx[None, :]
idx_2 = (idx + 1 - idx % 2 * 2)[:, None]
labels = K.equal(idx_1, idx_2)
labels = K.cast(labels, K.floatx())
return labels
def mlm_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, floatx)
acc = keras.metrics.sparse_categorical_accuracy(y_true, y_pred)
acc = K.sum(acc * mask) / (K.sum(mask) + K.epsilon())
return acc
def nsp_acc(inputs):
"""计算准确率的函数，需要封装为一个层
"""
y_true, y_pred = inputs
y_pred, _ = y_pred
y_true = K.cast(y_true, K.floatx)
acc = keras.metrics.sparse_categorical_accuracy(y_true, y_pred)
acc = K.mean(acc)
return acc
def sparse_loss(self, y_true, y_pred):
"""y_true需要是整数形式（非one hot）
"""
# y_true需要重新明确一下shape和dtype
y_true = K.reshape(y_true, K.shape(y_pred)[:-1])
y_true = K.cast(y_true, 'int32')
# 转为one hot
y_true = K.one_hot(y_true, K.shape(self.trans)[0])
return self.dense_loss(y_true, y_pred)
def compute_loss(self, inputs, mask=None):
y_true, y_pred = inputs
y_mask = K.cast(K.not_equal(y_true, 0), K.floatx())
accuracy = keras.metrics.sparse_categorical_accuracy(y_true, y_pred)
accuracy = K.sum(accuracy * y_mask) / K.sum(y_mask)
self.add_metric(accuracy, name='accuracy')
loss = K.sparse_categorical_crossentropy(y_true, y_pred)
loss = K.sum(loss * y_mask) / K.sum(y_mask)
return loss
def call(self, x, mask=None):
x0 = x
x = self.k_dense(x0)
x = self.o_dense(x)
if mask is not None:
mask = K.cast(mask, K.floatx())
mask = K.expand_dims(mask, 2)
x = x - (1 - mask) * 1e12
x = K.softmax(x, 1)
x = K.sum(x0 * x, 1)
return x
def compute_loss(self, inputs, mask=None):
pred, ytrue = inputs
acc = keras.metrics.sparse_categorical_accuracy(ytrue, pred)
self.add_metric(acc, name='clf_acc')
ytrue = K.cast(ytrue, 'int32')
ytrue = K.one_hot(ytrue, num_classes=num_classes)
ytrue = K.reshape(ytrue, (-1, num_classes))
loss = ytrue * K.log(pred + K.epsilon()) + (1 - ytrue) * K.log(1 - pred + K.epsilon())
loss = -K.mean(loss)
loss = loss * self.alpha
self.add_metric(loss, name='clf_loss')
return loss
def call(self, x, mask=None):
x0 = x
if mask is not None:
mask = K.cast(mask, K.floatx())
mask = K.expand_dims(mask, 2)
# x = x0 * mask if mask is not None else x0
x0 = Lambda(lambda x_: x_, output_shape=lambda s: s)(x0) # drop mask so do not put mask to conv1d
x = self.conv1d(x0)
x, g = x[:, :, :self.o_dim], x[:, :, self.o_dim:]
if self.dropout_rate is not None:
g = K.in_train_phase(K.dropout(g, self.dropout_rate), g)
g = K.sigmoid(g)
# mask is none
mask = mask if mask is not None else K.ones_like(x)
if self.skip_connection:
if K.int_shape(x0)[-1] != self.o_dim:
x0 = self.conv1d_1x1(x0)
return (x0 * (1 - g) + x * g) * mask
return x * g * mask
def dense_loss(self, y_true, y_pred):
"""y_true需要是one hot形式
"""
# 导出mask并转换数据类型
mask = K.all(K.greater(y_pred, -1e6), axis=2, keepdims=True)
mask = K.cast(mask, K.floatx())
# 计算目标分数
y_true, y_pred = y_true * mask, y_pred * mask
target_score = self.path_score(y_pred, y_true)
# 递归计算log Z
init_states = [y_pred[:, 0]]
y_pred = K.concatenate([y_pred, mask], axis=2)
input_length = K.int_shape(y_pred[:, 1:])[1]
log_norm, _, _ = K.rnn(self.log_norm_step,
y_pred[:, 1:],
init_states,
input_length=input_length) # 最后一步的log Z向量
log_norm = K.logsumexp(log_norm, 1) # logsumexp得标量
# 计算损失 -log p
return log_norm - target_score
def call(self, inputs, **kwargs):
logits, token_seq = inputs[:2]
seq_shape = K.shape(token_seq)
batch_size, seq_length = seq_shape[0], seq_shape[1]
if self.pad_token_id is None:
sequence_lengths = [seq_length - 1] * batch_size
else:
sequence_lengths = (
K.sum(
K.cast(
K.not_equal(token_seq, self.pad_token_id),
dtype='int32',
),
-1,
keepdims=False,
)
- 1
)
# only tf2
# return tf.gather(logits, sequence_lengths, batch_dims=1, axis=1)
indices = K.expand_dims(sequence_lengths, -1)
return tf.gather_nd(logits, indices, batch_dims=1)
def call(self, inputs, mask=None):
if mask is not None:
mask = K.cast(mask, K.floatx())
mask = K.expand_dims(mask, 2)
inputs = inputs - (1.0 - mask) * 1e12
return K.softmax(inputs, 1)
def normal_shannon_entropy(p, labels_num=num_classes):
# normalized entropy
p = K.cast(p, K.floatx())
norm = K.log(1. / labels_num)
s = K.sum(p * K.log(p), axis=-1, keepdims=True)
return s / norm
def compute_classification_acc(self, inputs, mask=None):
_, _, y_pred, _, y_true = inputs
equal = K.equal(K.cast(K.argmax(y_pred, axis=-1), 'int32'),
K.cast(y_true, 'int32'))
return K.cast(equal, K.floatx()) / K.cast(
K.shape(y_true)[0], K.floatx())
def _decayed_lr(self, var_dtypes):
"""重写获取decayed learning rate 方法"""
lr_t = super(NewOptimzer, self)._decayed_lr(var_dtypes)
lr_rate = piecewise_linear(self.iterations, self.lr_schedule)
return lr_t * K.cast(lr_rate, var_dtypes)
def call(self, inputs, mask=None):
# 只是计算loss，并不改变输入
if mask is not None:
mask = K.cast(mask, K.floatx())
return sequence_masking(inputs, mask, 1, 1)
