def _ranking(self, inputs, predictions):
""" Reranking generated responses. """
src_token = inputs["src_token"]
src_mask = inputs["src_mask"]
src_pos = inputs["src_pos"]
src_type = inputs["src_type"]
src_turn = inputs["src_turn"]
src_embed = self.embedder(src_token, src_pos, src_type, src_turn)
batch_size, num_latent, tgt_seq_len = predictions.shape
# shape: [batch_size, num_latent, seq_len, 1]
preds_token = F.unsqueeze(predictions, [3])
preds_mask = F.not_equal(preds_token, self.padding_idx, "int64")
preds_pos = layers.range(0, tgt_seq_len, 1, dtype="float32")
preds_pos = F.unsqueeze(preds_pos, [0, 0, 1])
preds_pos = layers.expand(preds_pos, [batch_size, num_latent, 1, 1])
preds_pos = layers.cast(preds_pos, "int64")
preds_type = layers.zeros_like(preds_token)
preds_turn = layers.zeros_like(preds_token)
scores = []
for i in range(num_latent):
pred_token = preds_token[:, i]
pred_mask = preds_mask[:, i]
pred_pos = preds_pos[:, i]
pred_type = preds_type[:, i]
pred_turn = preds_turn[:, i]
input_mask = layers.concat([src_mask, pred_mask], axis=1)
input_mask.stop_gradient = True
pred_embed = self.embedder(pred_token, pred_pos, pred_type,
pred_turn)
embed = layers.concat([src_embed, pred_embed], axis=1)
embed = self.embed_layer_norm(embed)
mask_embed = self.mask_embed
mask_embed = layers.expand(mask_embed, [batch_size, 1, 1])
mask_embed = self.embed_layer_norm(mask_embed)
out = layers.concat([mask_embed, embed], axis=1)
mask = self._create_mask(input_mask, append_head=True)
for layer in self.layers:
out = layer(out, mask, None)
mask_embed = out[:, 0]
score = self.discriminator(mask_embed)
scores.append(score[:, 0])
scores = layers.stack(scores, axis=1)
return scores
def _collect_metrics(self, inputs, outputs):
""" Calculate loss function by using inputs and outputs. """
metrics = {}
tgt_len = layers.reduce_sum(
layers.reduce_sum(inputs["tgt_mask"], dim=1) - 1)
tgt_len.stop_gradient = True
label = inputs["tgt_token"][:, 1:]
if self.label_smooth > 0:
one_hot_label = layers.one_hot(label, self.num_token_embeddings)
smooth_label = layers.label_smooth(one_hot_label,
epsilon=self.label_smooth,
dtype=self._dtype)
nll = layers.cross_entropy(outputs["dec_pred"],
smooth_label,
soft_label=True,
ignore_index=self.padding_idx)
else:
nll = layers.cross_entropy(outputs["dec_probs"],
label,
ignore_index=self.padding_idx)
nll = layers.reduce_sum(nll, dim=1)
token_nll = layers.reduce_sum(nll) / tgt_len
nll = layers.reduce_mean(nll)
metrics["nll"] = nll
metrics["token_nll"] = token_nll
loss = nll
if self.num_latent > 0 and self.with_bow:
bow_probs = F.unsqueeze(outputs["bow_probs"], [1])
bow_probs = layers.expand(bow_probs, [1, label.shape[1], 1])
if self.label_smooth > 0:
bow = layers.cross_entropy(bow_probs,
smooth_label,
soft_label=True,
ignore_index=self.padding_idx)
else:
bow = layers.cross_entropy(bow_probs,
label,
ignore_index=self.padding_idx)
bow = layers.reduce_sum(bow, dim=1)
token_bow = layers.reduce_sum(bow) / tgt_len
bow = layers.reduce_mean(bow)
metrics["bow"] = bow
metrics["token_bow"] = token_bow
loss = loss + bow
if self.num_latent > 0 and self.use_discriminator:
dis = 0.0 - (layers.log(outputs["pos_probs"]) +
layers.log(1.0 - outputs["neg_probs"]))
dis = layers.reduce_mean(dis)
metrics["dis"] = dis
loss = loss + dis * self.dis_ratio
metrics["loss"] = loss
metrics["token_num"] = tgt_len
return metrics
def repeat(var, times):
if isinstance(var, list):
return [repeat(x, times) for x in var]
elif isinstance(var, dict):
return {k: repeat(v, times) for k, v in var.items()}
elif isinstance(var, Variable):
var = F.unsqueeze(var, [1])
expand_times = [1] * len(var.shape)
expand_times[1] = times
dtype = var.dtype
var = layers.cast(var, "float32")
var = layers.expand(var, expand_times)
shape = [var.shape[0] * var.shape[1]] + var.shape[2:]
var = layers.reshape(var, shape)
var = layers.cast(var, dtype)
return var
else:
return var
def _attn(self, query, key, value, mask):
# shape: [batch_size, num_head, seq_len, seq_len]
scores = layers.matmul(x=query, y=key, alpha=self.scale)
if mask is not None:
mask = F.unsqueeze(mask, [1])
mask = layers.expand(mask, [1, self.num_heads, 1, 1])
mask.stop_gradient = True
scores = (1 - mask) * scores + layers.scale(mask, scale=-1e10)
attn = layers.softmax(scores, axis=-1)
attn = F.dropout(attn, self.dropout)
if mask is not None:
attn = (1 - mask) * attn
out = layers.matmul(x=attn, y=value)
return out
def _generation_network(self, input_mask, embed, batch_size, src_len,
tgt_len, latent_embed):
""" Basic generation network implement. """
if self.num_latent > 0:
latent_embed = F.unsqueeze(latent_embed, [1])
latent_embed = self.embed_layer_norm(latent_embed)
dec_embed = layers.concat([latent_embed, embed], axis=1)
else:
dec_embed = embed
# Create generation network mask
src_mask = input_mask[:, :src_len]
tgt_mask = input_mask[:, src_len:]
enc_mask = self._create_mask(
src_mask,
auto_regressive=not self.bidirectional_context,
append_head=self.num_latent > 0)
dec_mask = self._create_mask(tgt_mask, auto_regressive=True)
mask = self._join_mask(enc_mask, dec_mask)
for layer in self.layers:
dec_embed = layer(dec_embed, mask, None)
if self.num_latent > 0:
latent_embed = dec_embed[:, 0]
else:
latent_embed = None
dec_embed = dec_embed[:, -tgt_len:]
if self.two_layer_predictor:
dec_embed = self.pre_predictor(dec_embed)
if self.weight_sharing:
token_embedding = self.embedder.token_embedding._w
dec_logits = layers.matmul(x=dec_embed,
y=token_embedding,
transpose_y=True)
else:
dec_logits = self.predictor(dec_embed)
dec_probs = layers.softmax(dec_logits, axis=-1)
return latent_embed, dec_probs
def __call__(self, step_fn, state):
"""
Running beam search.
@param : step_fn : decoding one step
@type : function
@param : state : initial state
@type : dict
"""
batch_size = state["batch_size"]
beam_size = self.beam_size
# shape: [batch_size, 1]
pos_index = layers.range(0, batch_size, 1, dtype="int64")
pos_index = layers.scale(pos_index, beam_size)
pos_index = F.unsqueeze(pos_index, [1])
# shape: [batch_size, beam_size, 1]
predictions = layers.fill_constant(shape=[batch_size, beam_size, 1],
dtype="int64",
value=self.bos_id)
# initial input
state["pred_token"] = predictions[:, :1]
# shape: [batch_size, vocab_size]
scores, state = step_fn(state)
unk_penalty = np.zeros(self.vocab_size, dtype="float32")
unk_penalty[self.unk_id] = -1e10
unk_penalty = layers.assign(unk_penalty)
eos_penalty = np.zeros(self.vocab_size, dtype="float32")
eos_penalty[self.eos_id] = -1e10
eos_penalty = layers.assign(eos_penalty)
scores_after_end = np.full(self.vocab_size, -1e10, dtype="float32")
scores_after_end[self.pad_id] = 0
scores_after_end = layers.assign(scores_after_end)
if self.ignore_unk:
scores = scores + unk_penalty
scores = scores + eos_penalty
# shape: [batch_size, beam_size]
sequence_scores, preds = layers.topk(scores, self.beam_size)
predictions = layers.concat(
[predictions, F.unsqueeze(preds, [2])], axis=2)
state = repeat(state, beam_size)
parent_idx_list = []
pred_list = []
for step in range(2, self.max_gen_len + 1):
pre_ids = predictions[:, :, -1:]
state["pred_token"] = layers.reshape(
pre_ids, shape=[batch_size * beam_size, 1, 1])
state["pred_mask"] = 1 - F.equal(state["pred_token"], self.pad_id)
state["pred_pos"] = state["pred_pos"] + 1
scores, state = step_fn(state)
# Generate next
# scores shape: [batch_size, beam_size, vocab_size]
if self.ignore_unk:
scores = scores + unk_penalty
if step <= self.min_gen_len:
scores = scores + eos_penalty
scores = layers.reshape(
scores, shape=[batch_size, beam_size, self.vocab_size])
# previous token is [PAD] or [EOS]
pre_eos_mask = F.equal(pre_ids, self.eos_id) + F.equal(
pre_ids, self.pad_id)
scores = scores * (1 - pre_eos_mask) + \
layers.expand(pre_eos_mask, [1, 1, self.vocab_size]) * scores_after_end
if self.length_average:
scaled_value = pre_eos_mask + (1 - pre_eos_mask) * (1 -
1 / step)
sequence_scores = F.unsqueeze(sequence_scores,
[2]) * scaled_value
scaled_value = pre_eos_mask + (1 - pre_eos_mask) * (1 / step)
scores = scores * scaled_value
elif self.length_penalty >= 0.0:
scaled_value = pre_eos_mask + (1 - pre_eos_mask) * \
(math.pow((4 + step) / (5 + step), self.length_penalty))
sequence_scores = layers.elementwise_mul(scaled_value,
sequence_scores,
axis=0)
scaled_value = pre_eos_mask + (1 - pre_eos_mask) * \
(math.pow(1 / (5 + step), self.length_penalty))
scores = scores * scaled_value
scores = layers.elementwise_add(scores, sequence_scores, axis=0)
scores = layers.reshape(
scores, shape=[batch_size, beam_size * self.vocab_size])
topk_scores, topk_indices = layers.topk(scores, beam_size)
vocab_size = layers.fill_constant(shape=[1],
dtype="int64",
value=self.vocab_size)
parent_idx = layers.elementwise_floordiv(topk_indices, vocab_size)
preds = layers.elementwise_mod(topk_indices, vocab_size)
# Gather state / sequence_scores
parent_idx = layers.elementwise_add(parent_idx, pos_index, axis=0)
parent_idx = layers.reshape(parent_idx, [batch_size * beam_size])
state = gather(state, parent_idx)
sequence_scores = topk_scores
predictions = layers.reshape(predictions,
shape=[batch_size * beam_size, step])
predictions = gather(predictions, parent_idx)
predictions = layers.reshape(predictions,
shape=[batch_size, beam_size, step])
predictions = layers.concat(
[predictions, F.unsqueeze(preds, [2])], axis=2)
pre_ids = predictions[:, :, -1]
pre_eos_mask = F.equal(pre_ids, self.eos_id) + F.equal(
pre_ids, self.pad_id)
sequence_scores = sequence_scores * pre_eos_mask + layers.scale(
1 - pre_eos_mask, -1e10)
_, indices = layers.argsort(sequence_scores, axis=1)
indices = indices + pos_index
indices = layers.reshape(indices, [-1])
sequence_scores = layers.reshape(sequence_scores,
[batch_size * beam_size])
predictions = layers.reshape(predictions, [batch_size * beam_size, -1])
sequence_scores = gather(sequence_scores, indices)
predictions = layers.gather(predictions, indices)
sequence_scores = layers.reshape(sequence_scores,
[batch_size, beam_size])
predictions = layers.reshape(predictions, [batch_size, beam_size, -1])
results = {
"preds": predictions[:, -1],
"scores": sequence_scores[:, -1]
}
return results
def __call__(self, step_fn, state):
"""
Running generation.
@param : step_fn : decoding one step
@type : function
@param : state : initial state
@type : dict
"""
batch_size = state["batch_size"]
vocab_size = self.vocab_size
pos_index = layers.range(0, batch_size, 1, dtype="int64")
pos_index = layers.scale(pos_index, vocab_size)
# shape: [batch_size, beam_size, 1]
predictions = layers.fill_constant(shape=[batch_size, 1],
dtype="int64",
value=self.bos_id)
sequence_scores = layers.fill_constant(shape=[batch_size],
dtype="float32",
value=0.0)
unk_penalty = np.zeros(vocab_size, dtype="float32")
unk_penalty[self.unk_id] = -1e10
unk_penalty = layers.assign(unk_penalty)
eos_penalty = np.zeros(vocab_size, dtype="float32")
eos_penalty[self.eos_id] = -1e10
eos_penalty = layers.assign(eos_penalty)
scores_after_end = np.full(vocab_size, -1e10, dtype="float32")
scores_after_end[self.pad_id] = 0
scores_after_end = layers.assign(scores_after_end)
# initial input
for step in range(1, self.max_gen_len + 1):
pre_ids = predictions[:, -1:]
state["pred_token"] = F.unsqueeze(pre_ids, [2])
if step > 1:
state["pred_mask"] = 1 - F.equal(state["pred_token"],
self.pad_id)
state["pred_pos"] = state["pred_pos"] + 1
scores, state = step_fn(state)
# Generate next
# scores shape: [batch_size, vocab_size]
if self.ignore_unk:
scores = scores + unk_penalty
if step <= self.min_gen_len:
scores = scores + eos_penalty
# previous token is [PAD] or [EOS]
# shape: [batch_size, 1]
pre_eos_mask = F.equal(pre_ids, self.eos_id) + F.equal(
pre_ids, self.pad_id)
scores = scores * (1 - pre_eos_mask) + \
layers.expand(pre_eos_mask, [1, vocab_size]) * scores_after_end
scores = scores / self.temperature
preds = self._sampling(scores)
predictions = layers.concat(
[predictions, F.unsqueeze(preds, [1])], axis=1)
scores = layers.reshape(scores, [batch_size * vocab_size])
preds = preds + pos_index
scores = gather(scores, preds)
sequence_scores = sequence_scores + scores
results = {"preds": predictions, "scores": sequence_scores}
return results
def _init_state(self, inputs):
""" Initialize decode state. """
state = {}
src_token = inputs["src_token"]
src_mask = inputs["src_mask"]
src_pos = inputs["src_pos"]
src_type = inputs["src_type"]
src_turn = inputs["src_turn"]
batch_size = src_token.shape[0]
seq_len = src_token.shape[1]
src_embed = self.embedder(src_token, src_pos, src_type, src_turn)
src_embed = self.embed_layer_norm(src_embed)
mask = self._create_mask(src_mask, append_head=self.num_latent > 0)
if self.num_latent > 0:
src_embed = F.unsqueeze(src_embed, [1])
src_embed = layers.expand(src_embed, [1, self.num_latent, 1, 1])
src_embed = layers.reshape(src_embed, [-1, seq_len, self.hidden_dim])
latent_embed = self.latent_embeddings
latent_embed = F.unsqueeze(latent_embed, [1])
latent_embed = layers.expand(latent_embed, [batch_size, 1, 1])
latent_embed = self.embed_layer_norm(latent_embed)
enc_out = layers.concat([latent_embed, src_embed], axis=1)
mask = F.unsqueeze(mask, [1])
mask = layers.expand(mask, [1, self.num_latent, 1, 1])
mask = layers.reshape(mask, [-1, seq_len + 1, seq_len + 1])
else:
enc_out = src_embed
cache = {}
for l, layer in enumerate(self.layers):
cache[f"layer_{l}"] = {}
enc_out = layer(enc_out, mask, cache[f"layer_{l}"])
state["cache"] = cache
state["mask"] = mask[:, :1]
if self.num_latent > 0:
state["batch_size"] = batch_size * self.num_latent
shape = [batch_size * self.num_latent, 1, 1]
else:
state["batch_size"] = batch_size
shape = [batch_size, 1, 1]
state["pred_mask"] = layers.ones(shape, self._dtype)
state["pred_pos"] = layers.zeros(shape, "int64")
state["pred_type"] = layers.zeros(shape, "int64")
state["pred_turn"] = layers.zeros(shape, "int64")
if "tgt_token" in inputs and self.num_latent > 0:
tgt_token = inputs["tgt_token"][:, :-1]
tgt_mask = inputs["tgt_mask"][:, :-1]
tgt_pos = inputs["tgt_pos"][:, :-1]
tgt_type = inputs["tgt_type"][:, :-1]
tgt_turn = inputs["tgt_turn"][:, :-1]
input_mask = layers.concat([src_mask, tgt_mask], axis=1)
input_mask.stop_gradient = True
src_embed = self.embedder(src_token, src_pos, src_type, src_turn)
tgt_embed = self.embedder(tgt_token, tgt_pos, tgt_type, tgt_turn)
embed = layers.concat([src_embed, tgt_embed], axis=1)
embed = self.embed_layer_norm(embed)
batch_size = src_token.shape[0]
src_len = src_token.shape[1]
tgt_len = tgt_token.shape[1]
post_embed, post_probs, post_logits = self._posteriori_network(
input_mask, embed, batch_size, src_len, tgt_len)
state["post_probs"] = post_probs
return state
def _forward(self, inputs, is_training):
""" Real forward process of model in different mode(train/test). """
outputs = {}
src_token = inputs["src_token"]
src_mask = inputs["src_mask"]
src_pos = inputs["src_pos"]
src_type = inputs["src_type"]
src_turn = inputs["src_turn"]
tgt_token = inputs["tgt_token"][:, :-1]
tgt_mask = inputs["tgt_mask"][:, :-1]
tgt_pos = inputs["tgt_pos"][:, :-1]
tgt_type = inputs["tgt_type"][:, :-1]
tgt_turn = inputs["tgt_turn"][:, :-1]
input_mask = layers.concat([src_mask, tgt_mask], axis=1)
input_mask.stop_gradient = True
src_embed = self.embedder(src_token, src_pos, src_type, src_turn)
tgt_embed = self.embedder(tgt_token, tgt_pos, tgt_type, tgt_turn)
embed = layers.concat([src_embed, tgt_embed], axis=1)
embed = self.embed_layer_norm(embed)
batch_size = src_token.shape[0]
src_len = src_token.shape[1]
tgt_len = tgt_token.shape[1]
if self.num_latent > 0:
post_embed, post_probs, post_logits = self._posteriori_network(
input_mask, embed, batch_size, src_len, tgt_len)
outputs["post_logits"] = post_logits
if self.use_discriminator:
pos_probs, neg_probs = self._discriminator_network(
input_mask, embed, batch_size, src_len, tgt_len, post_embed)
outputs["pos_probs"] = pos_probs
outputs["neg_probs"] = neg_probs
if is_training:
z = F.gumbel_softmax(post_logits, self.tau)
else:
indices = layers.argmax(post_logits, axis=1)
z = layers.one_hot(F.unsqueeze(indices, [1]), self.num_latent)
latent_embeddings = self.latent_embeddings
latent_embed = layers.matmul(z, latent_embeddings)
outputs["latent_embed"] = latent_embed
else:
latent_embed = None
latent_embed, dec_probs = self._generation_network(
input_mask, embed, batch_size, src_len, tgt_len, latent_embed)
outputs["dec_probs"] = dec_probs
if self.num_latent > 0 and self.with_bow:
if self.two_layer_predictor:
latent_embed = self.pre_bow_predictor(latent_embed)
bow_logits = self.bow_predictor(latent_embed)
bow_probs = layers.softmax(bow_logits)
outputs["bow_probs"] = bow_probs
return outputs
