def prepare_decoder(self, targets):
"""Prepares targets for transformer decoder."""
shape = utils.shape_list(targets)
# image should be [batch, height, width, channels]
assert len(shape) == 4, "Image tensors should be 4-dimensional"
# Shift positions
targets = tf.reshape(targets,
[-1] + self.get_shape_for_decoder() + [1])
targets = utils.right_shift_blockwise_nd(targets,
self.hparams.query_shape)
# Add channel embeddings
targets = tf.reshape(
targets,
[-1, self.frame_height, self.frame_width, self.num_channels])
targets = utils.get_channel_embeddings(io_depth=self.num_channels,
targets=targets,
hidden_size=self.hidden_size)
# add positional embeddings if needed
if self.add_positional_emb:
targets = utils.add_positional_embedding_nd(
targets,
max_length=max(self.frame_height, self.frame_width,
self.num_channels),
name="pos_emb")
targets = tf.reshape(targets, [-1] + self.get_shape_for_decoder() +
[self.hidden_size])
return targets
def prepare_decoder(self, targets):
"""Prepares targets for transformer decoder."""
shape = utils.shape_list(targets)
# sequence should be [batch, seq_length]
assert len(shape) == 2, "Sequence tensors should be 2-dimensional"
assert len(self.hparams.query_shape
) == 1, "query shape should be 1-dimensional"
# Mask random positions
if self.hparams.target_dropout:
targets = tf.where(
tf.random.uniform(shape) < self.hparams.target_dropout,
tf.zeros_like(targets), targets)
# Shift positions
targets = tf.expand_dims(targets, axis=-1)
targets = utils.right_shift_blockwise_nd(targets,
self.hparams.query_shape)
targets = tf.squeeze(targets, axis=-1)
# Add token embeddings
targets = utils.get_embeddings(targets=targets,
hidden_size=self.hparams.embedding_dims,
vocab_size=self.vocab_size)
if self.hparams.dropout:
targets = tf.nn.dropout(targets, 1 - self.hparams.dropout)
targets = tf.layers.dense(targets,
self.hidden_size,
activation=None,
name="emb_dense")
if self.hparams.add_timing_signal:
targets += utils.get_timing_signal_1d(
self.hparams.max_target_length, self.hidden_size)
return targets
def multinomial_squeeze(self, logits, temperature=1.0): """multinomial sampling from logits.""" logits_shape = utils.shape_list(logits) reshaped_logits = (tf.reshape(logits, [-1, logits_shape[-1]]) / temperature) choices = tf.multinomial(reshaped_logits, 1) choices = tf.reshape(choices, logits_shape[:-1]) return tf.to_int32(choices)
def infer_step(i, recent_output, recent_logits, cache, decoding_stats):
"""Inference step."""
features_copy = features.copy()
features_copy["targets"] = recent_output
cur_sample, cur_logit = self.sample(features_copy,
decode_step=i,
cache=cache,
decoding_stats=decoding_stats)
pos = i
samples = recent_output + tf.scatter_nd(
indices=[[b, pos, 0, 0] for b in range(self.batch_size)],
updates=cur_sample,
shape=utils.shape_list(recent_output))
logits = recent_logits + tf.scatter_nd(
indices=[[b, pos] for b in range(self.batch_size)],
updates=cur_logit,
shape=utils.shape_list(recent_logits))
return i + 1, samples, logits, cache, decoding_stats
def process_partial_targets_decoding(self, targets):
"""Processes partially generated targets in decoding mode."""
targets_shape = utils.shape_list(targets)
seq_length = targets_shape[1]
blocks_per_dim = [
s // q for s, q in zip([seq_length], self.hparams.query_shape)
]
targets = tf.reshape(
targets, [targets_shape[0], -1,
np.prod(self.hparams.query_shape), 1])
targets = utils.unflatten_blocks_nd(targets, blocks_per_dim)
targets = utils.put_back_blocks_nd(targets, self.hparams.query_shape)
targets = tf.reshape(targets, [-1, seq_length])
return targets
def process_partial_targets_decoding(self, targets):
"""Processes partially generated targets in decoding mode."""
original_shape = self.get_shape_for_decoder()
blocks_per_dim = [
s // q for s, q in zip(original_shape, self.hparams.query_shape)
]
targets_shape = utils.shape_list(targets)
targets = tf.reshape(
targets, [targets_shape[0], -1,
np.prod(self.hparams.query_shape), 1])
targets = utils.unflatten_blocks_nd(targets, blocks_per_dim)
targets = utils.put_back_blocks_nd(targets, self.hparams.query_shape)
targets = tf.reshape(
targets, [-1, self.frame_height, self.frame_width, self.num_channels])
return targets
def infer(self, features, **kwargs):
with tf.variable_scope("sparse_transformer", reuse=tf.AUTO_REUSE):
features = self.bottom(features)
decode_length = self.hparams.max_target_length
cache = {}
decoding_stats = {}
targets_old = features.get("targets")
start_step = 0
initial_output = tf.zeros((self.batch_size, decode_length, 1, 1),
dtype=tf.int32)
initial_logits = tf.zeros(
(self.batch_size, decode_length, self.vocab_size))
# call body once to initialize cache with representations of input frames.
features["targets"] = initial_output
# Set shape of inputs
if "inputs" in features:
features["inputs"].set_shape([
self.batch_size, self.hparams.max_length, 1,
self.hparams.hidden_size
])
with tf.variable_scope("sparse_transformer/body", reuse=tf.AUTO_REUSE):
self.body(features,
decode_step=None,
cache=cache,
decoding_stats=decoding_stats)
def infer_step(i, recent_output, recent_logits, cache, decoding_stats):
"""Inference step."""
features_copy = features.copy()
features_copy["targets"] = recent_output
cur_sample, cur_logit = self.sample(features_copy,
decode_step=i,
cache=cache,
decoding_stats=decoding_stats)
pos = i
samples = recent_output + tf.scatter_nd(
indices=[[b, pos, 0, 0] for b in range(self.batch_size)],
updates=cur_sample,
shape=utils.shape_list(recent_output))
logits = recent_logits + tf.scatter_nd(
indices=[[b, pos] for b in range(self.batch_size)],
updates=cur_logit,
shape=utils.shape_list(recent_logits))
return i + 1, samples, logits, cache, decoding_stats
def while_exit_cond(i, result, logits, cache, decoding_stats): # pylint: disable=unused-argument
"""Exit the loop if it reaches decode_length."""
not_overflow = i < decode_length
return not_overflow
_, final_result, final_logits, _, decoding_stats = tf.while_loop(
while_exit_cond,
infer_step, [
start_step, initial_output, initial_logits, cache,
decoding_stats
],
back_prop=False,
parallel_iterations=1)
original_shape = [decode_length]
blocks_per_dim = [
s // q for s, q in zip(original_shape, self.hparams.query_shape)
]
final_result_shape = utils.shape_list(final_result)
final_result = tf.reshape(
final_result,
[final_result_shape[0], -1,
np.prod(self.hparams.query_shape), 1])
final_logits_shape = utils.shape_list(final_logits)
final_logits = tf.reshape(final_logits, [
final_logits_shape[0], -1,
np.prod(self.hparams.query_shape), final_logits_shape[-1]
])
final_result = utils.unflatten_blocks_nd(final_result, blocks_per_dim)
final_result = utils.put_back_blocks_nd(final_result,
self.hparams.query_shape)
final_logits = utils.unflatten_blocks_nd(final_logits, blocks_per_dim)
final_logits = utils.put_back_blocks_nd(final_logits,
self.hparams.query_shape)
for name, value in decoding_stats.items():
tf.summary.scalar("decodes/%s" % name, value / decode_length)
# Reassign targets back to the previous value.
if targets_old is not None:
features["targets"] = targets_old
return {
"outputs": final_result,
"scores": None,
"logits": final_logits,
"losses": None,
}
def body(self,
features,
decode_step=None,
cache=None,
decoding_stats=None,
add_summary=True):
encoder_output = None
extra_losses = []
padding_bias = None
if not self.hparams.fast_decode:
decode_step = None
if "inputs" in features:
inputs = features["inputs"]
# remove the last two dimensions that are always 1.
inputs = tf.reshape(
inputs,
utils.shape_list(inputs)[:2] + [self.hidden_size])
# Padding bias only used for seq2seq models.
padding_bias = utils.embedding_to_padding(inputs)
# Mask random positions
shape = utils.shape_list(inputs)
if self.hparams.input_dropout:
inputs = tf.where(
tf.random.uniform(shape) < self.hparams.input_dropout,
tf.zeros_like(inputs), inputs)
if self.hparams.add_timing_signal:
inputs += utils.get_timing_signal_1d(self.hparams.max_length,
self.hidden_size)
if cache is not None and -1 in cache:
encoder_output = cache[-1]
else:
encoder_output = utils.transformer_encoder_layers(
inputs=inputs,
num_layers=self.num_encoder_layers,
hparams=self.hparams,
losses=extra_losses,
name="encoder",
token_bias=features.get("token_bias_inputs"),
padding_bias=padding_bias)
if cache is not None and -1 not in cache:
cache[-1] = encoder_output
targets = tf.to_int32(features["targets"])
# remove the last two dimensions that are always 1.
targets = tf.reshape(targets, utils.shape_list(targets)[:2])
# Clamp targets to max_target_length
targets = targets[:, :self.hparams.max_target_length]
if self.is_decode:
targets = self.process_partial_targets_decoding(targets)
decoder_input = self.prepare_decoder(targets)
decoder_output = utils.transformer_decoder_layers(
inputs=decoder_input,
num_layers=self.num_decoder_layers,
hparams=self.hparams,
encoder_output=encoder_output,
decode_step=decode_step,
losses=extra_losses,
cache=cache,
name="decoder",
decoding_stats=decoding_stats,
token_bias_inputs=features.get("token_bias_inputs"),
token_bias_targets=features.get("token_bias_targets"),
padding_bias=padding_bias)
logits = self.produce_output(decoder_output)
# Return logits as-is in decoding mode
if self.is_decode:
return logits
# Add cross entropy loss
one_hot_targets = tf.one_hot(tf.cast(targets, dtype=tf.int32),
self.vocab_size)
x_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(
labels=one_hot_targets, logits=logits)
weights = tf.to_float(tf.not_equal(targets, 0))
loss = tf.reduce_sum(x_entropy * weights) / tf.reduce_sum(weights)
if add_summary:
tf.summary.scalar("losses/weight", tf.reduce_sum(weights))
tf.summary.scalar("losses/x_entropy",
tf.reduce_sum(x_entropy * weights))
loss_dict = {"training": loss}
if extra_losses:
loss_dict["extra_loss"] = tf.add_n(extra_losses)
# hack for T2T metrics
logits = tf.reshape(
logits,
utils.shape_list(logits)[:2] + [1, 1] +
utils.shape_list(logits)[-1:])
return logits, loss_dict
def infer(self, features, **kwargs):
decode_length = (self.frame_height * self.frame_width *
self.num_channels)
cache = {}
decoding_stats = {}
targets_old = features.get("targets", None)
initial_output = tf.zeros((self.batch_size, decode_length),
dtype=tf.int32)
initial_logits = tf.zeros(
(self.batch_size, decode_length, self.targets_vocab_size))
# call body once to initialize cache with representations of input frames.
features["targets"] = initial_output
with tf.variable_scope("sparse_imagetransformer/body",
reuse=tf.AUTO_REUSE,
use_resource=True):
self.body(features,
decode_step=None,
cache=cache,
decoding_stats=decoding_stats)
def infer_step(i, recent_output, recent_logits, cache, decoding_stats):
"""Inference step."""
features_copy = features.copy()
features_copy["targets"] = recent_output
cur_sample, cur_logit = self.sample(features_copy,
decode_step=i,
cache=cache,
decoding_stats=decoding_stats)
pos = i
samples = recent_output + tf.scatter_nd(
indices=[[b, pos] for b in range(self.batch_size)],
updates=cur_sample,
shape=utils.shape_list(recent_output))
logits = recent_logits + tf.scatter_nd(
indices=[[b, pos] for b in range(self.batch_size)],
updates=cur_logit,
shape=utils.shape_list(recent_logits))
return i + 1, samples, logits, cache, decoding_stats
def while_exit_cond(i, result, logits, cache, decoding_stats): # pylint: disable=unused-argument
"""Exit the loop if it reaches decode_length."""
not_overflow = i < decode_length
return not_overflow
_, final_result, final_logits, _, decoding_stats = tf.while_loop(
while_exit_cond,
infer_step, [
tf.constant(0), initial_output, initial_logits, cache,
decoding_stats
],
back_prop=False,
parallel_iterations=1)
original_shape = self.get_shape_for_decoder()
blocks_per_dim = [
s // q for s, q in zip(original_shape, self.hparams.query_shape)
]
final_result_shape = utils.shape_list(final_result)
final_result = tf.reshape(
final_result,
[final_result_shape[0], -1,
np.prod(self.hparams.query_shape), 1])
final_logits_shape = utils.shape_list(final_logits)
final_logits = tf.reshape(final_logits, [
final_logits_shape[0], -1,
np.prod(self.hparams.query_shape), final_logits_shape[-1]
])
final_result = utils.unflatten_blocks_nd(final_result, blocks_per_dim)
final_result = utils.put_back_blocks_nd(final_result,
self.hparams.query_shape)
final_logits = utils.unflatten_blocks_nd(final_logits, blocks_per_dim)
final_logits = utils.put_back_blocks_nd(final_logits,
self.hparams.query_shape)
final_result = tf.reshape(
final_result,
[-1, self.frame_height, self.frame_width, self.num_channels])
final_logits = tf.reshape(final_logits, [
-1, self.frame_height, self.frame_width, self.num_channels,
self.targets_vocab_size
])
if utils.is_xla_compiled():
_IMGS["decodes"] = final_result
for name, value in decoding_stats.items():
tf.summary.scalar("decodes/%s" % name, value / decode_length)
# Reassign targets back to the previous value.
if targets_old is not None:
features["targets"] = targets_old
return {
"outputs": final_result,
"scores": None,
"logits": final_logits,
"losses": None,
}
