def _attn(self, inputs, training=False):
q, k, v, attention_mask, head_mask = inputs
# q, k, v have shape [batch, heads, sequence, features]
w = tf.matmul(q, k, transpose_b=True)
if self.scale:
dk = tf.cast(shape_list(k)[-1],
tf.float32) # scale attention_scores
w = w / tf.math.sqrt(dk)
# w has shape [batch, heads, dst_sequence, src_sequence], where information flows from src to dst.
_, _, nd, ns = shape_list(w)
b = self.causal_attention_mask(nd, ns, dtype=w.dtype)
b = tf.reshape(b, [1, 1, nd, ns])
w = w * b - 1e4 * (1 - b)
if attention_mask is not None:
# Apply the attention mask
w = w + attention_mask
w = tf.nn.softmax(w, axis=-1)
w = self.attn_dropout(w, training=training)
# Mask heads if we want to
if head_mask is not None:
w = w * head_mask
outputs = [tf.matmul(w, v)]
if self.output_attentions:
outputs.append(w)
return outputs
def call(self, seq, training=False):
seq_fts = self.feature_conv(seq)
f_1 = self.f1_conv(seq_fts)
f_2 = self.f2_conv(seq_fts)
logits = f_1 + tf.transpose(f_2, [0, 2, 1])
coefs = tf.nn.softmax(tf.nn.leaky_relu(logits))
vals = tf.matmul(coefs, seq_fts)
ret = tf.nn.bias_add(vals, self.bias)
# residual connection
if self.residual:
if shape_list(seq)[-1] != shape_list(ret)[-1]:
ret = ret + self.res_conv(seq)
else:
ret = ret + seq
return self.activation(ret)
def call(self, inputs, training=False):
input_ids, position_ids, token_type_ids, inputs_embeds = inputs
triletter_max_seq_len = shape_list(
input_ids)[1] // self.triletter_max_letters_in_word
position_embeddings = self.position_embeddings(position_ids)
embeddings = self.triletter_embeddings(
input_ids) # [N, 12*[20], hidden_size]
embeddings = tf.reshape(embeddings, [
-1, triletter_max_seq_len, self.triletter_max_letters_in_word,
shape_list(embeddings)[-1]
])
embeddings = tf.reshape(
tf.reduce_sum(embeddings, axis=2),
[-1, triletter_max_seq_len,
shape_list(embeddings)[-1]])
embeddings = embeddings + position_embeddings
return embeddings
def call(self, inputs, training=False):
input_ids, position_ids, token_type_ids, inputs_embeds = inputs
input_shape = shape_list(input_ids)
if inputs_embeds is None:
inputs_embeds = tf.gather(self.word_embeddings, input_ids)
position_embeddings = self.position_embeddings(position_ids)
embeddings = inputs_embeds + position_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings, training=training)
return embeddings
def call(self, inputs, training=False):
if self.num_layers == 0:
return inputs[0]
dim0 = shape_list(inputs["bert_1"])[-1]
dims = [dim0] + self.config.hidden_dims[len(self.config.hidden_dims) - self.num_layers:]
hidden = inputs
for layer in range(self.num_layers):
aggregator = self.aggs[layer]
next_hidden = {}
for hop in range(self.num_layers - layer):
neigh_shape = [-1, self.fanouts[hop], dims[layer]]
h = aggregator((hidden["bert_" + str(hop)], tf.reshape(hidden["bert_" + str(hop+1)], neigh_shape)))
next_hidden["bert_" + str(hop)] = h
hidden = next_hidden
return hidden["bert_0"]
def hf_mlm_compute_loss(labels, logits):
"""
Adapted to our dataset with labels with non-masked-tokens marker=0.
"""
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(
from_logits=True, reduction=tf.keras.losses.Reduction.NONE)
masked_lm_active_loss = tf.not_equal(
tf.reshape(tensor=labels["mlm_long_labels"], shape=(-1, )), 0)
masked_lm_reduced_logits = tf.boolean_mask(
tensor=tf.reshape(tensor=logits[0],
shape=(-1, shape_list(logits[0])[2])),
mask=masked_lm_active_loss,
)
masked_lm_labels = tf.boolean_mask(tensor=tf.reshape(
tensor=labels["mlm_long_labels"], shape=(-1, )),
mask=masked_lm_active_loss)
masked_lm_loss = loss_fn(y_true=masked_lm_labels,
y_pred=masked_lm_reduced_logits)
masked_lm_loss = tf.reduce_mean(input_tensor=masked_lm_loss)
return masked_lm_loss
def call(self, inputs, training=False):
if self.num_layers == 0:
return inputs["bert_0"]
dim0 = shape_list(inputs["bert_1"])[-1]
node_feats = tf.expand_dims(inputs["bert_0"], 1)
neighbor_feats = tf.reshape(inputs["bert_1"], [-1, self.neighbor_num, dim0])
seq = tf.concat([node_feats, neighbor_feats], 1)
for layer, head_num in enumerate(self.config.head_nums):
hidden = []
for i in range(head_num):
hidden_val = self.attention_heads[layer][i](seq)
hidden.append(hidden_val)
seq = tf.concat(hidden, -1)
out = hidden
out = tf.add_n(out) / self.config.head_nums[-1]
out = tf.slice(out, [0, 0, 0], [-1, 1, self.config.hidden_dims[-1]])
return tf.reshape(out, [-1, self.config.hidden_dims[-1]])
def call(self, inputs, training=False):
self_vecs, neigh_vecs = inputs
mask = tf.cast(tf.sign(tf.reduce_max(tf.abs(x), axis=2)), dtype=tf.bool)
batch_size = shape_list(mask)[0]
mask = tf.concat([tf.constant(np.ones([batch_size, 1]), dtype=tf.bool), mask[:, 1:]], axis=1)
rnn_outputs = self.lstm(inputs=neigh_vecs, mask=mask)
from_neighs = self.neigh_weights(rnn_outputs)
from_self = self.self_weights(self_vecs)
if not self.concat:
output = tf.add_n([from_self, from_neighs])
else:
output = tf.concat([from_self, from_neighs], axis=1)
if self.add_bias:
output += self.bias
if self.identity_act: return output
return self.act(output)
def call(self,
inputs,
past=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
mc_token_ids=None,
training=False):
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
past = inputs[1] if len(inputs) > 1 else past
attention_mask = inputs[2] if len(inputs) > 2 else attention_mask
token_type_ids = inputs[3] if len(inputs) > 3 else token_type_ids
position_ids = inputs[4] if len(inputs) > 4 else position_ids
head_mask = inputs[5] if len(inputs) > 5 else head_mask
inputs_embeds = inputs[6] if len(inputs) > 6 else inputs_embeds
mc_token_ids = inputs[7] if len(inputs) > 7 else mc_token_ids
assert len(inputs) <= 8, "Too many inputs."
elif isinstance(inputs, dict):
input_ids = inputs.get('input_ids')
past = inputs.get('past', past)
attention_mask = inputs.get('attention_mask', attention_mask)
token_type_ids = inputs.get('token_type_ids', token_type_ids)
position_ids = inputs.get('position_ids', position_ids)
head_mask = inputs.get('head_mask', head_mask)
inputs_embeds = inputs.get('inputs_embeds', inputs_embeds)
mc_token_ids = inputs.get('mc_token_ids', mc_token_ids)
assert len(inputs) <= 8, "Too many inputs."
else:
input_ids = inputs
if input_ids is not None:
input_shapes = shape_list(input_ids)
else:
input_shapes = shape_list(inputs_embeds)[:-1]
seq_length = input_shapes[-1]
flat_input_ids = tf.reshape(
input_ids, (-1, seq_length)) if input_ids is not None else None
flat_attention_mask = tf.reshape(
attention_mask,
(-1, seq_length)) if attention_mask is not None else None
flat_token_type_ids = tf.reshape(
token_type_ids,
(-1, seq_length)) if token_type_ids is not None else None
flat_position_ids = tf.reshape(
position_ids,
(-1, seq_length)) if position_ids is not None else None
flat_inputs = [
flat_input_ids, past, flat_attention_mask, flat_token_type_ids,
flat_position_ids, head_mask, inputs_embeds
]
transformer_outputs = self.transformer(flat_inputs, training=training)
hidden_states = transformer_outputs[0]
hidden_states = tf.reshape(
hidden_states, input_shapes + shape_list(hidden_states)[-1:])
lm_logits = self.transformer.wte(hidden_states, mode="linear")
mc_logits = self.multiple_choice_head([hidden_states, mc_token_ids],
training=training)
mc_logits = tf.squeeze(mc_logits, axis=-1)
outputs = (lm_logits, mc_logits) + transformer_outputs[1:]
return outputs # lm logits, mc logits, presents, (all hidden_states), (attentions)
def call(self,
inputs,
past=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
training=False):
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
past = inputs[1] if len(inputs) > 1 else past
attention_mask = inputs[2] if len(inputs) > 2 else attention_mask
token_type_ids = inputs[3] if len(inputs) > 3 else token_type_ids
position_ids = inputs[4] if len(inputs) > 4 else position_ids
head_mask = inputs[5] if len(inputs) > 5 else head_mask
inputs_embeds = inputs[6] if len(inputs) > 6 else inputs_embeds
assert len(inputs) <= 7, "Too many inputs."
elif isinstance(inputs, dict):
input_ids = inputs.get('input_ids')
past = inputs.get('past', past)
attention_mask = inputs.get('attention_mask', attention_mask)
token_type_ids = inputs.get('token_type_ids', token_type_ids)
position_ids = inputs.get('position_ids', position_ids)
head_mask = inputs.get('head_mask', head_mask)
inputs_embeds = inputs.get('inputs_embeds', inputs_embeds)
assert len(inputs) <= 7, "Too many inputs."
else:
input_ids = inputs
if input_ids is not None and inputs_embeds is not None:
raise ValueError(
"You cannot specify both input_ids and inputs_embeds at the same time"
)
elif input_ids is not None:
input_shape = shape_list(input_ids)
input_ids = tf.reshape(input_ids, [-1, input_shape[-1]])
elif inputs_embeds is not None:
input_shape = shape_list(inputs_embeds)[:-1]
else:
raise ValueError(
"You have to specify either input_ids or inputs_embeds")
if past is None:
past_length = 0
past = [None] * len(self.h)
else:
past_length = shape_list(past[0][0])[-2]
if position_ids is None:
position_ids = tf.range(past_length,
input_shape[-1] + past_length,
dtype=tf.int32)[tf.newaxis, :]
if attention_mask is not None:
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
attention_mask = attention_mask[:, tf.newaxis, tf.newaxis, :]
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
attention_mask = tf.cast(attention_mask, tf.float32)
attention_mask = (1.0 - attention_mask) * -10000.0
else:
attention_mask = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
if not head_mask is None:
raise NotImplementedError
else:
head_mask = [None] * self.num_hidden_layers
# head_mask = tf.constant([0] * self.num_hidden_layers)
position_ids = tf.reshape(position_ids,
[-1, shape_list(position_ids)[-1]])
if inputs_embeds is None:
inputs_embeds = self.wte(input_ids, mode='embedding')
position_embeds = self.wpe(position_ids)
if token_type_ids is not None:
token_type_ids = tf.reshape(
token_type_ids, [-1, shape_list(token_type_ids)[-1]])
token_type_embeds = self.wte(token_type_ids, mode='embedding')
else:
token_type_embeds = 0
hidden_states = inputs_embeds + position_embeds + token_type_embeds
hidden_states = self.drop(hidden_states, training=training)
output_shape = input_shape + [shape_list(hidden_states)[-1]]
presents = ()
all_attentions = []
all_hidden_states = ()
for i, (block, layer_past) in enumerate(zip(self.h, past)):
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (tf.reshape(
hidden_states, output_shape), )
outputs = block(
[hidden_states, layer_past, attention_mask, head_mask[i]],
training=training)
hidden_states, present = outputs[:2]
presents = presents + (present, )
if self.output_attentions:
all_attentions.append(outputs[2])
hidden_states = self.ln_f(hidden_states)
hidden_states = tf.reshape(hidden_states, output_shape)
# Add last hidden state
if self.output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states, )
outputs = (hidden_states, presents)
if self.output_hidden_states:
outputs = outputs + (all_hidden_states, )
if self.output_attentions:
# let the number of heads free (-1) so we can extract attention even after head pruning
attention_output_shape = input_shape[:-1] + [-1] + shape_list(
all_attentions[0])[-2:]
all_attentions = tuple(
tf.reshape(t, attention_output_shape) for t in all_attentions)
outputs = outputs + (all_attentions, )
return outputs # last hidden state, presents, (all hidden_states), (attentions)
def split_heads(self, x):
x_shape = shape_list(x)
new_x_shape = x_shape[:-1] + [self.n_head, x_shape[-1] // self.n_head]
x = tf.reshape(x, new_x_shape)
return tf.transpose(
x, (0, 2, 1, 3)) # (batch, head, seq_length, head_features)
def merge_heads(self, x):
x = tf.transpose(x, [0, 2, 1, 3])
x_shape = shape_list(x)
new_x_shape = x_shape[:-2] + [x_shape[-2] * x_shape[-1]]
return tf.reshape(x, new_x_shape)
def call(
self,
inputs,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
training=False,
):
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
position_ids = inputs[3] if len(inputs) > 3 else position_ids
head_mask = inputs[4] if len(inputs) > 4 else head_mask
inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
output_attentions = inputs[6] if len(
inputs) > 6 else output_attentions
output_hidden_states = inputs[7] if len(
inputs) > 7 else output_hidden_states
assert len(inputs) <= 8, "Too many inputs."
elif isinstance(inputs, (dict, BatchEncoding)):
input_ids = inputs.get("input_ids")
attention_mask = inputs.get("attention_mask", attention_mask)
token_type_ids = inputs.get("token_type_ids", token_type_ids)
position_ids = inputs.get("position_ids", position_ids)
head_mask = inputs.get("head_mask", head_mask)
inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
output_attentions = inputs.get("output_attentions",
output_attentions)
output_hidden_states = inputs.get("output_hidden_states",
output_hidden_states)
assert len(inputs) <= 8, "Too many inputs."
else:
input_ids = inputs
output_attentions = output_attentions if output_attentions is not None else self.output_attentions
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.output_hidden_states
if input_ids is not None and inputs_embeds is not None:
raise ValueError(
"You cannot specify both input_ids and inputs_embeds at the same time"
)
elif input_ids is not None:
input_shape = shape_list(input_ids)
elif inputs_embeds is not None:
input_shape = shape_list(inputs_embeds)[:-1]
else:
raise ValueError(
"You have to specify either input_ids or inputs_embeds")
if attention_mask is None:
if type(self.embeddings) == TriletterEmbeddings:
attention_mask = tf.ones([
input_shape[0], input_shape[1] //
self.embeddings.triletter_max_letters_in_word
])
else:
attention_mask = tf.fill(input_shape, 1)
if token_type_ids is None:
if type(self.embeddings) == TriletterEmbeddings:
token_type_ids = tf.zeros([
input_shape[0], input_shape[1] //
self.embeddings.triletter_max_letters_in_word
])
else:
token_type_ids = tf.fill(input_shape, 0)
if position_ids is None:
if type(self.embeddings) == TriletterEmbeddings:
position_ids = (
tf.range(input_shape[1] //
self.embeddings.triletter_max_letters_in_word,
dtype=tf.int32) + 1)[tf.newaxis, :]
else:
position_ids = tf.range(int(input_shape[1]),
dtype=tf.int32)[tf.newaxis, :]
position_ids = tf.where(attention_mask == 0,
tf.zeros_like(position_ids), position_ids)
extended_attention_mask = attention_mask[:, tf.newaxis, tf.newaxis, :]
extended_attention_mask = tf.cast(extended_attention_mask, tf.float32)
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
if head_mask is not None:
raise NotImplementedError
else:
head_mask = [None] * self.num_hidden_layers
embedding_output = self.embeddings(
[input_ids, position_ids, token_type_ids, inputs_embeds],
training=training)
encoder_outputs = self.encoder(
[
embedding_output, extended_attention_mask, head_mask,
output_attentions, output_hidden_states
],
training=training,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output)
outputs = (
sequence_output,
pooled_output,
) + encoder_outputs[
1:] # add hidden_states and attentions if they are here
return outputs # sequence_output, pooled_output, (hidden_states), (attentions)
def call(self, inputs, training=False):
if self.num_layers == 0:
return inputs[0]
hidden = inputs
for layer in range(self.num_layers):
next_hidden = []
for hop in range(self.num_layers - l):
neighbor = tf.reshape(hidden["bert_" + str(hop+1)], [-1, self.fanouts[hop] * shape_list(hidden["bert_" + str(hop+1)])[-1]])
seq = tf.concat([hidden["bert_" + str(hop)], neighbor], axis=-1)
h = self.dense_layers[layer](seq, training=training)
next_hidden.append(h)
hidden = next_hidden
return hidden[0]
