def _reshape_mask(mask, head_num):
if mask is None:
return mask
seq_len = K.shape(mask)[1]
mask = K.expand_dims(mask, axis=1)
mask = K.tile(mask, K.stack([1, head_num, 1]))
return K.reshape(mask, (-1, seq_len))
def call(self, inputs, **kwargs):
inputs, tasks = inputs
if K.dtype(tasks) != 'int32':
tasks = K.cast(tasks, 'int32')
task_embed = K.gather(self.embeddings, tasks)
if self.mask_zero:
task_embed = task_embed * K.expand_dims(
K.cast(K.not_equal(tasks, 0), K.floatx()), axis=-1)
return inputs + task_embed
def call(self, inputs, **kwargs):
inputs, tasks = inputs
if K.dtype(tasks) != 'int32':
tasks = K.cast(tasks, 'int32')
task_embed = K.gather(self.embeddings, tasks)
if self.mask_zero:
task_embed = task_embed * K.expand_dims(
K.cast(K.not_equal(tasks, 0), K.floatx()), axis=-1)
if K.backend() == 'theano':
task_embed = K.tile(task_embed, (1, K.shape(inputs)[1], 1))
return inputs + task_embed
def call(self, inputs, mask=None, **kwargs):
if isinstance(inputs, list):
query, key, value = inputs
else:
query = key = value = inputs
if isinstance(mask, list):
mask = mask[1]
feature_dim = K.shape(query)[-1]
e = K.batch_dot(query, key, axes=2) / K.sqrt(
K.cast(feature_dim, dtype=K.floatx()))
if self.history_only:
query_len, key_len = K.shape(query)[1], K.shape(key)[1]
ones = tf.ones((query_len, key_len))
e -= (ones - tf.matrix_band_part(ones, -1, 0)) * 1e9
if mask is not None:
e -= (1.0 - K.cast(K.expand_dims(mask, axis=-2), K.floatx())) * 1e9
a = keras.activations.softmax(e)
v = K.batch_dot(a, value)
if self.return_attention:
return [v, a]
return v
def call(self, inputs, **kwargs):
if self.mode == self.MODE_EXPAND:
if K.dtype(inputs) != 'int32':
inputs = K.cast(inputs, 'int32')
return K.gather(
self.embeddings,
K.minimum(K.maximum(inputs, -self.input_dim), self.input_dim) +
self.input_dim,
)
input_shape = K.shape(inputs)
if self.mode == self.MODE_ADD:
batch_size, seq_len, output_dim = input_shape[0], input_shape[
1], input_shape[2]
else:
batch_size, seq_len, output_dim = input_shape[0], input_shape[
1], self.output_dim
pos_embeddings = K.tile(
K.expand_dims(self.embeddings[:seq_len, :self.output_dim], axis=0),
K.stack([batch_size, 1, 1]),
)
if self.mode == self.MODE_ADD:
return inputs + pos_embeddings
return K.concatenate([inputs, pos_embeddings], axis=-1)
def call(self, inputs, mask=None):
if mask is not None:
mask = K.cast(mask, K.floatx())
inputs -= K.expand_dims((1.0 - mask) * 1e6, axis=-1)
return K.max(inputs, axis=-2)
def call(self, inputs, mask=None):
if mask is not None:
mask = K.cast(mask, K.floatx())
inputs *= K.expand_dims(mask, axis=-1)
return super(MaskedConv1D, self).call(inputs)
