def predict(self, features, max_decode_len, beam_size, **beam_kwargs):
"""Predict."""
cache = self._encode(features, False)
B, _, D = cache["memory"].shape
T, V, H = max_decode_len, self._vocab_size, self._num_heads
bias_1xTxT = attention.upper_triangle_bias(T, self._dtype)
for i in range(len(self._decoder_layers)):
cache[str(i)] = {
"k": tf.zeros([B, H, T, D // H], self._dtype),
"v": tf.zeros([B, H, T, D // H], self._dtype)
}
def symbols_to_logits_fn(dec_BxT, context, i):
"""Decode loop."""
dec_Bx1 = tf.slice(dec_BxT, [0, tf.maximum(tf.cast(0, i.dtype), i - 1)],
[dec_BxT.shape[0], 1])
bias_1x1xT = tf.slice(bias_1xTxT, [0, i, 0], [1, 1, T])
dec_Bx1xD = self._embedding_layer(dec_Bx1, True)
dec_Bx1xD *= tf.cast(tf.greater(i, 0), self._dtype)
dec_Bx1xD = timing.add_time_signal(dec_Bx1xD, start_index=i)
with tf.variable_scope(self._decoder_scope_name, reuse=tf.AUTO_REUSE):
dec_Bx1xD = transformer_block.stack(self._decoder_layers, False,
dec_Bx1xD, bias_1x1xT,
context["memory"],
context["memory_bias"], context, i)
dec_Bx1xD = contrib_layers.layer_norm(dec_Bx1xD, begin_norm_axis=2)
logits_Bx1xV = self._embedding_layer(dec_Bx1xD, False)
logits_BxV = tf.squeeze(logits_Bx1xV, axis=1)
return logits_BxV
decodes_BxT = decoding.left2right_decode(symbols_to_logits_fn, cache, B, T,
V, beam_size, **beam_kwargs)
return {"outputs": decodes_BxT}
def __call__(self, features, training):
"""Create model.
Args:
features: dictionary of tensors including "inputs" [batch, input_len] and
"targets" [batch, output_len]
training: bool of whether the mode is training.
Returns:
Tuple of (loss, outputs): Loss is a scalar. Output is a dictionary of
tensors, containing model's output logits.
"""
if "inputs" not in features or "targets" not in features:
raise ValueError("Require inputs and targets keys in features.")
context = self._encode(features, training)
self._context = context
targets_BxT = features["targets"]
bias_1xTxT = attention.upper_triangle_bias(
tf.shape(targets_BxT)[1], self._dtype)
states_BxTxD = self._embedding_layer(targets_BxT, True)
states_BxTxD = tf.pad(states_BxTxD,
[[0, 0], [1, 0], [0, 0]])[:, :-1, :]
states_BxTxD = timing.add_time_signal(states_BxTxD)
states_BxTxD = self._dropout_fn(states_BxTxD, training)
with tf.variable_scope(self._decoder_scope_name, reuse=tf.AUTO_REUSE):
states_BxTxD = transformer_block.stack(self._decoder_layers,
training, states_BxTxD,
bias_1xTxT,
context["memory"],
context["memory_bias"])
states_BxTxD = contrib_layers.layer_norm(states_BxTxD,
begin_norm_axis=2)
logits_BxTxV = self._embedding_layer(states_BxTxD, False)
targets_mask_BxT = tf.cast(tf.greater(targets_BxT, 0), self._dtype)
XENT_loss = tf.losses.softmax_cross_entropy(
tf.one_hot(targets_BxT, self._vocab_size),
logits_BxTxV,
label_smoothing=self._label_smoothing,
weights=targets_mask_BxT)
# want the one hot targets for sampling
one_hot_targets = tf.one_hot(targets_BxT, self._vocab_size)
return XENT_loss, {
"logits": logits_BxTxV,
"targets": targets_BxT,
"one_hot_targets": one_hot_targets,
"hidden_states": states_BxTxD,
"context_memory": context["memory"],
"context_bias": context["memory_bias"]
}
def double_sampling(self,
features,
training,
batchsize,
seqlen,
mixed=False):
if "inputs" not in features or "targets" not in features:
raise ValueError("Require inputs and targets keys in features.")
# First "loop" - uses ground truth to supplement
context = self._encode(features, training)
self._context = context
targets_BxT = features["targets"]
bias_1xTxT = attention.upper_triangle_bias(
tf.shape(targets_BxT)[1], self._dtype)
states_BxTxD = self._embedding_layer(targets_BxT, True)
states_BxTxD = tf.pad(states_BxTxD,
[[0, 0], [1, 0], [0, 0]])[:, :-1, :]
states_BxTxD = timing.add_time_signal(states_BxTxD)
states_BxTxD = self._dropout_fn(states_BxTxD, training)
with tf.variable_scope(self._decoder_scope_name, reuse=tf.AUTO_REUSE):
states_BxTxD = transformer_block.stack(self._decoder_layers,
training, states_BxTxD,
bias_1xTxT,
context["memory"],
context["memory_bias"])
states_BxTxD = contrib_layers.layer_norm(states_BxTxD,
begin_norm_axis=2)
logits_BxTxV = self._embedding_layer(states_BxTxD, False)
targets_mask_BxT = tf.cast(tf.greater(targets_BxT, 0), self._dtype)
# argmax the logits to get teacher-forcing sequence
# ensure this does not have any EOS apart from the end token, before passing into next loop.
new_input = tf.reshape(tf.math.argmax(logits_BxTxV, axis=2),
[batchsize, seqlen])
# nucleus or top-k processing
# new_input = iid_process_logits(logits_BxTxV, seqlen, batchsize, logits_BxTxV.get_shape().as_list()[-1],
# top_k=0, top_p=0.9, temperature=1.0)
# def tensor_loop(i, max_decode_len, logits, new_input, unused_targets_BxT):
# def f2(logits_BxTxV, new_input):
# topk_probs, topk_indices = tf.math.top_k(logits_BxTxV[0, i], k=2)
# topk_inds2 = tf.slice(topk_indices, [1], [1, ])
# return tf.tensor_scatter_nd_update(new_input, [[0, i]], tf.cast(topk_inds2, tf.int64))
# def f3(i, new_input):
# new_input2 = new_input[0].numpy().tolist()
# return True if new_input2[0][i] == 1 else False
# new_input = tf.cond(tf.py_function(f3, (i, [new_input]), tf.bool), lambda: f2(logits_BxTxV, new_input),
# lambda: new_input)
# return i + 1, max_decode_len, logits, new_input, unused_targets_BxT
# def finish_cond_ref(i, max_decode_len, unused_logits, unused_new_input, targets_BxT):
# add here condition to return reference summary length
# def f4(i, targets, max_len):
# targets2 = targets[0].numpy().tolist()
# if targets2[i] == 0: # padded token
# return i
# else: # if not 1, still needs a number to refer to
# return max_len
# ref_len = tf.py_function(f4, (i, targets_BxT, max_decode_len), tf.int32)
# return i < ref_len # T/F -> will change depending on padded token presence
# def finish_cond_max(i, max_decode_len, unused_logits, unused_new_input, unused_targets_BxT):
# return i < max_decode_len
# _, _, _, new_input, _ = tf.while_loop(finish_cond_max, tensor_loop,
# [0, seqlen, logits_BxTxV, new_input, targets_BxT])
# find target length -> py.func() as it has to be outside graph
def f5(targets_BxT):
try:
exist = targets_BxT[0].numpy().tolist().index(1)
# do they all have an EOS?
except ValueError:
exist = targets_BxT.get_shape().as_list()[-1]
return tf.Variable(
exist, shape=()).read_value() # token prior is the last token
cut_off = tf.py_function(f5, [targets_BxT], tf.int32)
# implement cut_off for new_input
new_input2 = tf.slice(new_input, [0, 0], [1, cut_off])
new_input = tf.reshape(
tf.pad(
new_input2,
[[0, 0], [0, new_input.get_shape().as_list()[-1] - cut_off]],
"CONSTANT"), [batchsize, seqlen])
# Second "loop" - uses predicted sequence as input
context_2 = self._encode(features, training)
# targets_BxT = features["targets"]
bias_1xTxT_2 = attention.upper_triangle_bias(
tf.shape(new_input)[1], self._dtype)
states_BxTxD_2 = self._embedding_layer(new_input, True)
states_BxTxD_2 = tf.pad(states_BxTxD_2,
[[0, 0], [1, 0], [0, 0]])[:, :-1, :]
states_BxTxD_2 = timing.add_time_signal(states_BxTxD_2)
states_BxTxD_2 = self._dropout_fn(states_BxTxD_2, training)
with tf.variable_scope(self._decoder_scope_name, reuse=tf.AUTO_REUSE):
states_BxTxD_2 = transformer_block.stack(self._decoder_layers,
training, states_BxTxD_2,
bias_1xTxT_2,
context_2["memory"],
context_2["memory_bias"])
states_BxTxD_2 = contrib_layers.layer_norm(states_BxTxD_2,
begin_norm_axis=2)
logits_BxTxV_2 = self._embedding_layer(states_BxTxD_2, False)
targets_mask_BxT_2 = tf.cast(tf.greater(new_input, 0), self._dtype)
# mixed parallel scheduled sampling
if mixed:
bool_mask = np.random.choice(
[True, False],
[batchsize, seqlen,
logits_BxTxV.get_shape().as_list()[2]],
p=[0.75, 0.25])
mixed_logits = tf.where(bool_mask, logits_BxTxV, logits_BxTxV_2)
else:
mixed_logits = tf.zeros(logits_BxTxV.get_shape().as_list()
) # empty - satisfies exit criteria
XENT_loss = tf.losses.softmax_cross_entropy(
tf.one_hot(new_input, self._vocab_size),
mixed_logits,
label_smoothing=self._label_smoothing,
weights=targets_mask_BxT_2)
# want the one hot targets for sampling
one_hot_targets = tf.one_hot(new_input, self._vocab_size)
return XENT_loss, {
"sampled_BxT": new_input,
"logits1": logits_BxTxV,
"logits2": logits_BxTxV_2,
"targets": targets_BxT,
"mixed_logits": mixed_logits
}