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 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 log_norm_step(self, inputs, states):
"""递归求解归一化因子"""
inputs, mask = inputs[:, :-1], inputs[:, -1:]
states = K.expand_dims(states[0], 2) # batch_size,output_dim, 1
trans = K.expand_dims(self.trans, 0) # 1, output_dim, output_dim
outputs = K.logsumexp(states + trans, 1)
outputs += inputs
outputs = mask * outputs + (1 - mask) * states[:, :, 0]
return outputs, [outputs]
def compute_position_idx(self, inputs):
q, v = inputs
q_idx = K.arange(0, K.shape(q)[1], dtype='int32')
q_idx = K.expand_dims(q_idx, 1)
v_idx = K.arange(0, K.shape(v)[1], dtype='int32')
v_idx = K.expand_dims(v_idx, 0)
# 相对位置差
position_idx = v_idx - q_idx
max_position = (self.input_dim - 1) // 2
position_idx = K.clip(position_idx, -max_position, max_position)
position_idx = position_idx + max_position
return position_idx
def call(self, inputs):
"""
conditional 时， condition 放在inputs后面，[inputs, condition]
"""
if self.conditional:
inputs, cond = inputs
if self.condition_hidden_units is not None:
cond = self.condition_hidden_dense(cond)
# 适配cond维度，与inputs保持一致
for _ in range(K.ndim(inputs) - K.ndim(cond)):
cond = K.expand_dims(cond, 1)
if self.center:
beta = self.beta_dense(cond) + self.beta
if self.scale:
gamma = self.gamma_dense(cond) + self.gamma
else:
beta = self.beta
gamma = self.gamma
output = inputs
if self.center:
mean = K.mean(inputs, axis=-1, keepdims=True)
output = output - mean
if self.scale:
var = K.mean(K.square(output), axis=-1, keepdims=True)
std = K.sqrt(var + self.epsilon)
output = output / std
output = output * gamma
if self.center:
output = output + beta
return output
def compute_mask(self, inputs, mask=None):
if self.conditional:
masks = mask if mask is not None else []
masks = [K.expand_dims(m, 0) for m in masks if m is not None]
if len(masks) == 0:
return None
else:
return K.all(K.concatenate(masks, axis=0), axis=0)
return mask
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 call(self, inputs):
input_shape = K.shape(inputs)
batch_size, seq_length = input_shape[0], input_shape[1]
pos_embedding = self.embeddings[:seq_length]
pos_embedding = K.expand_dims(pos_embedding, 0)
if self.merge_mode != 'add':
pos_embedding = K.tile(pos_embedding, [batch_size, 1, 1])
if self.merge_mode == 'add':
return inputs + pos_embedding
return K.concatenate([inputs, pos_embedding], axis=-1)
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 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 call(self, x):
seq, vec = x
vec = K.expand_dims(vec, 1)
vec = K.tile(vec, [1, K.shape(seq)[1], 1])
return K.concatenate([seq, vec], 2)
