def _decode_input_tensor_to_features_dict(feature_map, hparams):
"""Convert the interactive input format (see above) to a dictionary.
Args:
feature_map: a dictionary with keys `problem_choice` and `input` containing
Tensors.
hparams: model hyperparameters
Returns:
a features dictionary, as expected by the decoder.
"""
inputs = tf.convert_to_tensor(feature_map["inputs"])
input_is_image = False
def input_fn(problem_choice, x=inputs): # pylint: disable=missing-docstring
p_hparams = hparams.problems[problem_choice]
# Add a third empty dimension dimension
x = tf.expand_dims(x, axis=[2])
x = tf.to_int32(x)
return (tf.constant(p_hparams.input_space_id), tf.constant(
p_hparams.target_space_id), x)
input_space_id, target_space_id, x = input_fn_builder.cond_on_index(
input_fn, feature_map["problem_choice"], len(hparams.problems) - 1)
features = {}
features["problem_choice"] = feature_map["problem_choice"]
features["input_space_id"] = input_space_id
features["target_space_id"] = target_space_id
features["decode_length"] = (
IMAGE_DECODE_LENGTH if input_is_image else tf.shape(x)[1] + 50)
features["inputs"] = x
return features
def _decode_input_tensor_to_features_dict(feature_map, hparams):
"""Convert the interactive input format (see above) to a dictionary.
Args:
feature_map: a dictionary with keys `problem_choice` and `input` containing
Tensors.
hparams: model hyperparameters
Returns:
a features dictionary, as expected by the decoder.
"""
inputs = tf.convert_to_tensor(feature_map["inputs"])
input_is_image = False
def input_fn(problem_choice, x=inputs): # pylint: disable=missing-docstring
p_hparams = hparams.problems[problem_choice]
# Add a third empty dimension dimension
x = tf.expand_dims(x, axis=[2])
x = tf.to_int32(x)
return (tf.constant(p_hparams.input_space_id), tf.constant(
p_hparams.target_space_id), x)
input_space_id, target_space_id, x = input_fn_builder.cond_on_index(
input_fn, feature_map["problem_choice"], len(hparams.problems) - 1)
features = {}
features["problem_choice"] = feature_map["problem_choice"]
features["input_space_id"] = input_space_id
features["target_space_id"] = target_space_id
features["decode_length"] = (
IMAGE_DECODE_LENGTH if input_is_image else tf.shape(x)[1] + 50)
features["inputs"] = x
return features
def testCondOnIndex(self):
"""Smoke tests of cond_on_index()."""
z = tf.constant(1., dtype=tf.float32)
def f(n):
return {
"a": z * n,
"b": z * n * n
}
index = tf.placeholder(shape=[], dtype=tf.int32)
out = input_fn_builder.cond_on_index(f, index, 3, 0)
with self.test_session() as sess:
# Check dispatching to the correct branch
result = sess.run(out, feed_dict={
index: 2
})
self.assertAllClose(result["a"], 2.)
self.assertAllClose(result["b"], 4.)
result = sess.run(out, feed_dict={
index: 3
})
self.assertAllClose(result["a"], 3.)
self.assertAllClose(result["b"], 9.)
def _interactive_input_tensor_to_features_dict(feature_map, hparams):
"""Convert the interactive input format (see above) to a dictionary.
Args:
feature_map: a dictionary with keys `problem_choice` and `input` containing
Tensors.
hparams: model hyperparameters
Returns:
a features dictionary, as expected by the decoder.
"""
inputs = tf.convert_to_tensor(feature_map["inputs"])
input_is_image = False if len(inputs.get_shape()) < 3 else True
def input_fn(problem_choice, x=inputs): # pylint: disable=missing-docstring
if input_is_image:
x = tf.image.resize_images(x, [299, 299])
x = tf.reshape(x, [1, 299, 299, -1])
x = tf.to_int32(x)
else:
# Remove the batch dimension.
num_samples = x[0]
length = x[2]
x = tf.slice(x, [3], tf.to_int32([length]))
x = tf.reshape(x, [1, -1, 1, 1])
# Transform into a batch of size num_samples to get that many random
# decodes.
x = tf.tile(x, tf.to_int32([num_samples, 1, 1, 1]))
p_hparams = hparams.problems[problem_choice]
return (tf.constant(p_hparams.input_space_id),
tf.constant(p_hparams.target_space_id), x)
input_space_id, target_space_id, x = input_fn_builder.cond_on_index(
input_fn, feature_map["problem_choice"],
len(hparams.problems) - 1)
features = {}
features["problem_choice"] = tf.convert_to_tensor(
feature_map["problem_choice"])
features["input_space_id"] = input_space_id
features["target_space_id"] = target_space_id
features["decode_length"] = (IMAGE_DECODE_LENGTH
if input_is_image else inputs[1])
features["inputs"] = x
return features
def _interactive_input_tensor_to_features_dict(feature_map, hparams):
"""Convert the interactive input format (see above) to a dictionary.
Args:
feature_map: a dictionary with keys `problem_choice` and `input` containing
Tensors.
hparams: model hyperparameters
Returns:
a features dictionary, as expected by the decoder.
"""
inputs = tf.convert_to_tensor(feature_map["inputs"])
input_is_image = False if len(inputs.get_shape()) < 3 else True
def input_fn(problem_choice, x=inputs): # pylint: disable=missing-docstring
if input_is_image:
x = tf.image.resize_images(x, [299, 299])
x = tf.reshape(x, [1, 299, 299, -1])
x = tf.to_int32(x)
else:
# Remove the batch dimension.
num_samples = x[0]
length = x[2]
x = tf.slice(x, [3], tf.to_int32([length]))
x = tf.reshape(x, [1, -1, 1, 1])
# Transform into a batch of size num_samples to get that many random
# decodes.
x = tf.tile(x, tf.to_int32([num_samples, 1, 1, 1]))
p_hparams = hparams.problems[problem_choice]
return (tf.constant(p_hparams.input_space_id), tf.constant(
p_hparams.target_space_id), x)
input_space_id, target_space_id, x = input_fn_builder.cond_on_index(
input_fn, feature_map["problem_choice"], len(hparams.problems) - 1)
features = {}
features["problem_choice"] = tf.convert_to_tensor(
feature_map["problem_choice"])
features["input_space_id"] = input_space_id
features["target_space_id"] = target_space_id
features["decode_length"] = (
IMAGE_DECODE_LENGTH if input_is_image else inputs[1])
features["inputs"] = x
return features
def testCondOnIndex(self):
"""Smoke tests of cond_on_index()."""
z = tf.constant(1., dtype=tf.float32)
def f(n):
return {"a": z * n, "b": z * n * n}
index = tf.placeholder(shape=[], dtype=tf.int32)
out = input_fn_builder.cond_on_index(f, index, 3, 0)
with self.test_session() as sess:
# Check dispatching to the correct branch
result = sess.run(out, feed_dict={index: 2})
self.assertAllClose(result["a"], 2.)
self.assertAllClose(result["b"], 4.)
result = sess.run(out, feed_dict={index: 3})
self.assertAllClose(result["a"], 3.)
self.assertAllClose(result["b"], 9.)
def model_fn(model,
features,
mode,
hparams,
problem_names,
train_steps=100000,
worker_id=0,
worker_replicas=1,
eval_run_autoregressive=False,
decode_hparams=None):
"""Builds the model for all modes.
* TRAIN: Constructs loss and train_op
* EVAL: Constructs the loss and eval metrics
* PREDICT: Constructs the predictions
Args:
model: str, name of model.
features: dict<feature name, Tensor>. Expected to have keys
{inputs, targets, problem_choice}.
mode: tf.estimator.ModeKeys.
hparams: model HParams.
problem_names: list of str, names of the problems.
train_steps: int, total number of training steps. Used to compute learning
rate decay.
worker_id: int, id of this worker.
worker_replicas: int, number of workers.
eval_run_autoregressive: bool, whether to run evaluation autoregressively.
decode_hparams: HParams for decode settings. Used when mode == PREDICT.
Returns:
tf.estimator.EstimatorSpec
"""
assert len(problem_names) == len(hparams.problem_instances)
decode_hp = decode_hparams
# TODO(rsepassi): This still depends on FLAGS. Rm eventually.
dp = devices.data_parallelism()
tf.get_variable_scope().set_initializer(_get_variable_initializer(hparams))
is_training = mode == tf.estimator.ModeKeys.TRAIN
# Add input statistics for incoming features.
with tf.name_scope("input_stats"):
for (k, v) in six.iteritems(features):
if isinstance(v, tf.Tensor) and v.get_shape().ndims > 1:
tf.summary.scalar("%s_batch" % k, tf.shape(v)[0] // dp.n)
tf.summary.scalar("%s_length" % k, tf.shape(v)[1])
nonpadding = tf.to_float(tf.not_equal(v, 0))
nonpadding_tokens = tf.reduce_sum(nonpadding)
if k == "targets":
targets_nonpadding_tokens = nonpadding_tokens
tf.summary.scalar("%s_nonpadding_tokens" % k,
nonpadding_tokens)
tf.summary.scalar("%s_nonpadding_fraction" % k,
tf.reduce_mean(nonpadding))
# Get multi-problem logits and loss based on features["problem_choice"].
loss_variable_names = []
def nth_model(n):
"""Build the model for the n-th problem, plus some added variables."""
model_class = registry.model(model)(
hparams, mode, hparams.problems[n], n, dp,
devices.ps_devices(all_workers=True))
if mode == tf.estimator.ModeKeys.PREDICT:
return model_class.infer(
features,
beam_size=decode_hp.beam_size,
top_beams=(decode_hp.beam_size
if decode_hp.return_beams else 1),
last_position_only=decode_hp.use_last_position_only,
alpha=decode_hp.alpha,
decode_length=decode_hp.extra_length)
# In distributed mode, we build graph for problem=0 and problem=worker_id.
skipping_is_on = hparams.problem_choice == "distributed" and is_training
problem_worker_id = worker_id % len(hparams.problems)
skip_this_one = n != 0 and n % worker_replicas != problem_worker_id
# On worker 0 also build graph for problems <= 1.
# TODO(lukaszkaiser): why is this hack needed for variables init? Repair.
skip_this_one = skip_this_one and (worker_id != 0 or n > 1)
if eval_run_autoregressive and mode == tf.estimator.ModeKeys.EVAL:
sharded_logits, losses_dict = model_class.eval_autoregressive(
features)
else:
sharded_logits, losses_dict = model_class.model_fn(
features, skip=(skipping_is_on and skip_this_one))
with tf.variable_scope("losses_avg"):
total_loss, ops = 0.0, []
for loss_key, loss_value in six.iteritems(losses_dict):
loss_name = "problem_%d/%s_loss" % (n, loss_key)
loss_moving_avg = tf.get_variable(loss_name,
initializer=100.0,
trainable=False)
loss_variable_names.append(loss_name)
ops.append(
loss_moving_avg.assign(loss_moving_avg * 0.9 +
loss_value * 0.1))
total_loss += loss_value
try: # Total loss avg might be reused or not, we try both.
with tf.variable_scope(tf.get_variable_scope(), reuse=True):
# Total loss was already constructed on input.
loss_moving_avg = tf.get_variable("problem_%d/total_loss" %
n)
except ValueError:
loss_moving_avg = tf.get_variable("problem_%d/total_loss" % n,
initializer=100.0,
trainable=False)
ops.append(
loss_moving_avg.assign(loss_moving_avg * 0.9 +
total_loss * 0.1))
with tf.variable_scope("train_stats"): # Count steps for this problem.
problem_steps = tf.get_variable("problem_%d_steps" % n,
initializer=0,
trainable=False)
ops.append(problem_steps.assign_add(1))
with tf.control_dependencies(ops): # Make sure the ops run.
# Ensure the loss is a scalar here.
total_loss = tf.reshape(total_loss, [],
name="total_loss_control_id")
return [total_loss, tf.concat(sharded_logits, 0)]
model_output = input_fn_builder.cond_on_index(
nth_model,
index_tensor=features["problem_choice"],
max_idx=len(hparams.problems) - 1)
if mode == tf.estimator.ModeKeys.PREDICT:
# If beam searching, model_output will be a dict with keys "outputs" and
# "scores".
if isinstance(model_output, dict):
outputs = model_output["outputs"]
scores = model_output["scores"]
else:
outputs = model_output
scores = None
batched_problem_choice = (features["problem_choice"] * tf.ones(
(tf.shape(features["inputs"])[0], ), dtype=tf.int32))
predictions = {
"outputs": outputs,
"scores": scores,
"inputs": features.get("inputs", None),
"targets": features.get("infer_targets", None),
"problem_choice": batched_problem_choice,
}
_del_dict_nones(predictions)
export_out = {"outputs": predictions["outputs"]}
if "scores" in predictions:
export_out["scores"] = predictions["scores"]
return tf.estimator.EstimatorSpec(
mode,
predictions=predictions,
export_outputs={
"output": tf.estimator.export.PredictOutput(export_out)
})
total_loss, logits = model_output
if mode == tf.estimator.ModeKeys.EVAL:
eval_metrics_fns = metrics.create_evaluation_metrics(
zip(problem_names, hparams.problem_instances), hparams)
eval_metrics = {}
for metric_name, metric_fn in six.iteritems(eval_metrics_fns):
eval_metrics[metric_name] = metric_fn(logits, features)
return tf.estimator.EstimatorSpec(mode,
predictions={"predictions": logits},
eval_metric_ops=eval_metrics,
loss=total_loss)
assert mode == tf.estimator.ModeKeys.TRAIN
# Set learning rate
learning_rate = hparams.learning_rate * learning_rate_decay(
hparams,
num_worker_replicas=worker_replicas,
num_train_steps=train_steps)
learning_rate /= math.sqrt(float(worker_replicas))
# Get global step
global_step = tf.train.get_or_create_global_step()
# Some training statistics.
with tf.name_scope("training_stats"):
tf.summary.scalar("learning_rate", learning_rate)
for n in xrange(len(hparams.problems)):
names_and_vars = []
with tf.variable_scope("losses_avg", reuse=True):
total_loss_var = tf.get_variable("problem_%d/total_loss" % n)
names_and_vars.append(("total_loss", total_loss_var))
with tf.variable_scope("losses_avg", reuse=True):
for loss_name in loss_variable_names:
if loss_name.startswith("problem_%d/" % n):
loss_var = tf.get_variable(loss_name)
loss_suffix = loss_name[loss_name.index("/") + 1:]
names_and_vars.append((loss_suffix, loss_var))
for (loss_name, loss_var) in names_and_vars:
tf.summary.scalar("loss_avg_%d/%s" % (n, loss_name), loss_var)
with tf.variable_scope("train_stats", reuse=True):
nth_steps = tf.get_variable("problem_%d_steps" % n,
dtype=tf.int32)
tf.summary.scalar(
"problem_%d_frequency" % n,
tf.to_float(nth_steps) / (tf.to_float(global_step) + 1.0))
# Add weight decay and noise.
total_size, weight_decay_loss = 0, 0.0
all_weights = {v.name: v for v in tf.trainable_variables()}
for v_name in sorted(list(all_weights)):
v = all_weights[v_name]
v_size = int(np.prod(np.array(v.shape.as_list())))
total_size += v_size
if hparams.weight_decay > 0.0 and len(v.shape.as_list()) > 1:
# Add weight regularization if set and the weight is not a bias (dim>1).
with tf.device(v._ref().device): # pylint: disable=protected-access
v_loss = tf.nn.l2_loss(v) / v_size
weight_decay_loss += v_loss
is_body = len(v_name) > 5 and v_name[:5] == "body/"
if hparams.weight_noise > 0.0 and is_body:
# Add weight noise if set in hparams.
with tf.device(v._ref().device): # pylint: disable=protected-access
scale = learning_rate * 0.001
noise = tf.truncated_normal(
v.shape) * hparams.weight_noise * scale
noise_op = v.assign_add(noise)
with tf.control_dependencies([noise_op]):
total_loss = tf.identity(total_loss)
if hparams.weight_decay > 0.0:
total_loss += weight_decay_loss * hparams.weight_decay
# The new data reader occasionally emits very small batches, which
# cause the examples in those batches to be grossly overweighted.
# We decrease the loss proportionally to the ratio of the size of this
# batch to the size of the largest training batch ever.
# TODO(noam): to be more sophisticated, we could keep separate
# maxima based on problem choice.
max_nonpadding_var = tf.get_variable("max_nonpadding",
shape=[],
initializer=tf.ones_initializer(),
trainable=False)
max_nonpadding = tf.maximum(max_nonpadding_var, targets_nonpadding_tokens)
with tf.control_dependencies(
[tf.assign(max_nonpadding_var, max_nonpadding)]):
small_batch_multiplier = targets_nonpadding_tokens / max_nonpadding
tf.summary.scalar("small_batch_multiplier", small_batch_multiplier)
total_loss *= small_batch_multiplier
# Log variable sizes
_log_variable_sizes(tf.trainable_variables(), "Trainable Variables")
diet_vars = [
v for v in tf.global_variables() if v.dtype == dtypes.float16_ref
]
_log_variable_sizes(diet_vars, "Diet Variables")
# Optimize
total_loss = tf.identity(total_loss, name="total_loss")
opt = ConditionalOptimizer(hparams.optimizer, learning_rate, hparams)
opt_summaries = ["learning_rate", "loss"]
if hparams.summarize_grads:
opt_summaries.extend(["gradients", "gradient_norm"])
tf.logging.info("Computing gradients for global model_fn.")
train_op = tf.contrib.layers.optimize_loss(
name="training",
loss=total_loss,
global_step=global_step,
learning_rate=learning_rate,
clip_gradients=hparams.clip_grad_norm or None,
gradient_noise_scale=hparams.grad_noise_scale or None,
optimizer=opt,
summaries=opt_summaries,
colocate_gradients_with_ops=True)
# Remove summaries that will fail to run because they are in conditionals.
# TODO(cwhipkey): Test with this code removed, later in 2017.
summaries = tf.get_collection_ref(tf.GraphKeys.SUMMARIES)
for i in reversed(range(len(summaries))):
if summaries[i].name.startswith("cond_"):
del summaries[i]
tf.logging.info("Global model_fn finished.")
return tf.estimator.EstimatorSpec(
mode,
predictions={"problem_choice": features["problem_choice"]},
loss=total_loss,
train_op=train_op)
def model_fn(features, labels, mode, params):
"""Creates the prediction, loss, and train ops.
Args:
features: A dictionary of tensors keyed by the feature name.
labels: A tensor representing the labels.
mode: The execution mode, as defined in tf.estimator.ModeKeys.
params: model HParams.
Returns:
An EstimatorSpec.
"""
hparams = params
# Deep-copy the model hparams between modes to eliminate
# side-effects caused by abuse of the linked problem_hparams
# objects which are used to share modality objects between
# problems. We do not want to share the modality objects between
# modes, since the modality objects may decide to do something
# mode-specific. A better fix would be to stop abusing the
# hparams in this way and instead use a separate dictionary to
# share the modality objects between problems. This dictionary
# could be created once per mode and passed to the constructor of
# t2t_model.
my_hp = copy.deepcopy(hparams)
def initializer():
if hparams.initializer == "orthogonal":
return tf.orthogonal_initializer(gain=hparams.initializer_gain)
elif hparams.initializer == "uniform":
max_val = 0.1 * hparams.initializer_gain
return tf.random_uniform_initializer(-max_val, max_val)
elif hparams.initializer == "normal_unit_scaling":
return init_ops.variance_scaling_initializer(
hparams.initializer_gain,
mode="fan_avg",
distribution="normal")
elif hparams.initializer == "uniform_unit_scaling":
return init_ops.variance_scaling_initializer(
hparams.initializer_gain,
mode="fan_avg",
distribution="uniform")
else:
raise ValueError("Unrecognized initializer: %s" %
hparams.initializer)
def learning_rate_decay():
"""Inverse-decay learning rate until warmup_steps, then decay."""
warmup_steps = tf.to_float(hparams.learning_rate_warmup_steps *
FLAGS.worker_replicas)
step = tf.to_float(tf.contrib.framework.get_global_step())
if hparams.learning_rate_decay_scheme == "noam":
return 5000.0 * hparams.hidden_size**-0.5 * tf.minimum(
(step + 1) * warmup_steps**-1.5, (step + 1)**-0.5)
elif hparams.learning_rate_decay_scheme == "exp100k":
return 0.94**(step // 100000)
elif hparams.learning_rate_decay_scheme == "cosine":
cycle_steps = hparams.learning_rate_cosine_cycle_steps
return 0.5 * (1 + tf.cos(np.pi *
(step % cycle_steps) / cycle_steps))
elif hparams.learning_rate_decay_scheme == "cyclelinear10x":
# Cycle the rate linearly by 10x every warmup_steps, up and down.
cycle_steps = hparams.learning_rate_warmup_steps
cycle_position = step % (2 * cycle_steps)
cycle_position = tf.to_float( # Normalize to the interval [-1, 1].
cycle_position - cycle_steps) / float(cycle_steps)
cycle_position = 1.0 - tf.abs(
cycle_position) # 0 to 1 and back to 0.
return (cycle_position +
0.1) * 3.0 # 10x difference each cycle (0.3-3).
inv_base = tf.exp(tf.log(0.01) / warmup_steps)
inv_decay = inv_base**(warmup_steps - step)
if hparams.learning_rate_decay_scheme == "sqrt":
decay = _sqrt_decay(step - warmup_steps)
elif hparams.learning_rate_decay_scheme == "exp10k":
decay = _exp_decay_after(
step - warmup_steps, 0.9995,
FLAGS.train_steps - warmup_steps - 10000)
elif hparams.learning_rate_decay_scheme == "exp50k":
decay = _exp_decay_after(
step - warmup_steps, 0.99995,
FLAGS.train_steps - warmup_steps - 50000)
elif hparams.learning_rate_decay_scheme == "exp500k":
decay = _exp_decay_after(
step - warmup_steps, 0.9999955,
FLAGS.train_steps - warmup_steps - 500000)
elif hparams.learning_rate_decay_scheme == "none":
decay = tf.constant(1.0)
else:
raise ValueError(
"Unrecognized learning rate decay scheme: %s" %
hparams.learning_rate_decay_scheme)
return tf.cond(step < warmup_steps,
lambda: inv_decay,
lambda: decay,
name="learning_rate_decay_warump_cond")
if labels is not None:
features["targets"] = labels
dp = devices.data_parallelism()
tf.get_variable_scope().set_initializer(initializer())
is_training = mode == tf.estimator.ModeKeys.TRAIN
# Add input statistics for incoming features.
with tf.name_scope("input_stats"):
for (k, v) in six.iteritems(features):
if isinstance(v, tf.Tensor) and v.get_shape().ndims > 1:
tf.summary.scalar("%s_batch" % k, tf.shape(v)[0] // dp.n)
tf.summary.scalar("%s_length" % k, tf.shape(v)[1])
nonpadding = tf.to_float(tf.not_equal(v, 0))
nonpadding_tokens = tf.reduce_sum(nonpadding)
if k == "targets":
targets_nonpadding_tokens = nonpadding_tokens
tf.summary.scalar("%s_nonpadding_tokens" % k,
nonpadding_tokens)
tf.summary.scalar("%s_nonpadding_fraction" % k,
tf.reduce_mean(nonpadding))
if is_training:
# The new data reader occasionally emits very small batches, which
# cause the examples in those batches to be grossly overweighted.
# We decrease the loss proportionally to the ratio of the size of this
# batch to the size of the largest training batch ever.
# TODO(noam): to be more sophisticated, we could keep separate
# maxima based on problem choice.
max_nonpadding_var = tf.get_variable(
"max_nonpadding",
shape=[],
initializer=tf.ones_initializer(),
trainable=False)
max_nonpadding = tf.maximum(max_nonpadding_var,
targets_nonpadding_tokens)
with tf.control_dependencies(
[tf.assign(max_nonpadding_var, max_nonpadding)]):
small_batch_multiplier = targets_nonpadding_tokens / max_nonpadding
tf.summary.scalar("small_batch_multiplier",
small_batch_multiplier)
# Get multi-problem logits and loss based on features["problem_choice"].
loss_variable_names = []
def nth_model(n):
"""Build the model for the n-th problem, plus some added variables."""
model_class = registry.model(model)(
my_hp, mode, my_hp.problems[n], n, dp,
devices.ps_devices(all_workers=True))
if mode == tf.estimator.ModeKeys.PREDICT:
return model_class.infer(
features,
beam_size=FLAGS.decode_beam_size,
top_beams=(FLAGS.decode_beam_size
if FLAGS.decode_return_beams else 1),
last_position_only=FLAGS.decode_use_last_position_only,
alpha=FLAGS.decode_alpha,
decode_length=FLAGS.decode_extra_length)
# In distributed mode, we build graph for problem=0 and problem=worker_id.
skipping_is_on = my_hp.problem_choice == "distributed" and is_training
problem_worker_id = FLAGS.worker_id % len(my_hp.problems)
skip_this_one = n != 0 and n % FLAGS.worker_replicas != problem_worker_id
# On worker 0 also build graph for problems <= 1.
# TODO(lukaszkaiser): why is this hack needed for variables init? Repair.
skip_this_one = skip_this_one and (FLAGS.worker_id != 0 or n > 1)
if (FLAGS.eval_run_autoregressive
and mode == tf.estimator.ModeKeys.EVAL):
sharded_logits, losses_dict = model_class.eval_autoregressive(
features)
else:
sharded_logits, losses_dict = model_class.model_fn(
features, skip=(skipping_is_on and skip_this_one))
with tf.variable_scope("losses_avg"):
total_loss, ops = 0.0, []
for loss_key, loss_value in six.iteritems(losses_dict):
loss_name = "problem_%d/%s_loss" % (n, loss_key)
loss_moving_avg = tf.get_variable(loss_name,
initializer=100.0,
trainable=False)
loss_variable_names.append(loss_name)
ops.append(
loss_moving_avg.assign(loss_moving_avg * 0.9 +
loss_value * 0.1))
total_loss += loss_value
try: # Total loss avg might be reused or not, we try both.
with tf.variable_scope(tf.get_variable_scope(),
reuse=True):
# Total loss was already constructed on input.
loss_moving_avg = tf.get_variable(
"problem_%d/total_loss" % n)
except ValueError:
loss_moving_avg = tf.get_variable("problem_%d/total_loss" %
n,
initializer=100.0,
trainable=False)
ops.append(
loss_moving_avg.assign(loss_moving_avg * 0.9 +
total_loss * 0.1))
with tf.variable_scope(
"train_stats"): # Count steps for this problem.
problem_steps = tf.get_variable("problem_%d_steps" % n,
initializer=0,
trainable=False)
ops.append(problem_steps.assign_add(1))
with tf.control_dependencies(ops): # Make sure the ops run.
# Ensure the loss is a scalar here.
total_loss = tf.reshape(total_loss, [],
name="total_loss_control_id")
return [total_loss
] + sharded_logits # Need to flatten for cond later.
result_list = input_fn_builder.cond_on_index(
nth_model, features["problem_choice"], 0,
len(my_hp.problems) - 1)
if mode == tf.estimator.ModeKeys.PREDICT:
# Beam search in sequence model returns both decodes withe key "outputs"
# and scores with they key "scores". If return list is a dict, we expect
# that it will have keys "outputs", a tensor of int32 and scores, a
# tensor of floats. This is useful if we want to return scores from
# estimator.predict
if not isinstance(result_list, dict):
predictions = {"outputs": result_list}
else:
predictions = {
"outputs": result_list["outputs"],
"scores": result_list["scores"]
}
if "inputs" in features:
predictions["inputs"] = features["inputs"]
if "infer_targets" in features:
predictions["targets"] = features["infer_targets"]
predictions["problem_choice"] = (
features["problem_choice"] * tf.ones(
(tf.shape(features["inputs"])[0], ), dtype=tf.int32))
return tf.estimator.EstimatorSpec(mode, predictions=predictions)
sharded_logits, total_loss = result_list[1:], result_list[0]
if mode == tf.estimator.ModeKeys.EVAL:
# For evaluation, return the logits layer as our predictions.
logits = tf.concat(sharded_logits, 0)
eval_metrics_fns = metrics.create_evaluation_metrics(
zip(FLAGS.problems.split("-"), hparams.problem_instances),
hparams)
_check_autotune_metrics(eval_metrics_fns)
eval_metrics = {}
for metric_name, metric_fn in six.iteritems(eval_metrics_fns):
eval_metrics[metric_name] = metric_fn(
logits, labels, features["problem_choice"])
return tf.estimator.EstimatorSpec(
mode,
predictions={"predictions": logits},
eval_metric_ops=eval_metrics,
loss=total_loss)
assert mode == tf.estimator.ModeKeys.TRAIN
# Some training statistics.
with tf.name_scope("training_stats"):
learning_rate = my_hp.learning_rate * learning_rate_decay()
learning_rate /= math.sqrt(float(FLAGS.worker_replicas))
tf.summary.scalar("learning_rate", learning_rate)
global_step = tf.to_float(tf.contrib.framework.get_global_step())
for n in xrange(len(my_hp.problems)):
names_and_vars = []
with tf.variable_scope("losses_avg", reuse=True):
total_loss_var = tf.get_variable("problem_%d/total_loss" %
n)
names_and_vars.append(("total_loss", total_loss_var))
with tf.variable_scope("losses_avg", reuse=True):
for loss_name in loss_variable_names:
if loss_name.startswith("problem_%d/" % n):
loss_var = tf.get_variable(loss_name)
loss_suffix = loss_name[loss_name.index("/") + 1:]
names_and_vars.append((loss_suffix, loss_var))
for (loss_name, loss_var) in names_and_vars:
tf.summary.scalar("loss_avg_%d/%s" % (n, loss_name),
loss_var)
with tf.variable_scope("train_stats", reuse=True):
nth_steps = tf.get_variable("problem_%d_steps" % n,
dtype=tf.int32)
tf.summary.scalar("problem_%d_frequency" % n,
tf.to_float(nth_steps) / (global_step + 1.0))
# Log trainable weights and add decay.
total_size, weight_decay_loss = 0, 0.0
all_weights = {v.name: v for v in tf.trainable_variables()}
for v_name in sorted(list(all_weights)):
v = all_weights[v_name]
v_size = int(np.prod(np.array(v.shape.as_list())))
total_size += v_size
if my_hp.weight_decay > 0.0 and len(v.shape.as_list()) > 1:
# Add weight regularization if set and the weight is not a bias (dim>1).
with tf.device(v._ref().device): # pylint: disable=protected-access
v_loss = tf.nn.l2_loss(v) / v_size
weight_decay_loss += v_loss
is_body = len(v_name) > 5 and v_name[:5] == "body/"
if my_hp.weight_noise > 0.0 and is_body:
# Add weight noise if set in my_hp.
with tf.device(v._ref().device): # pylint: disable=protected-access
scale = learning_rate * 0.001
noise = tf.truncated_normal(
v.shape) * my_hp.weight_noise * scale
noise_op = v.assign_add(noise)
with tf.control_dependencies([noise_op]):
total_loss = tf.identity(total_loss)
if my_hp.weight_decay > 0.0:
total_loss += weight_decay_loss * my_hp.weight_decay
if is_training:
total_loss *= small_batch_multiplier
total_loss = tf.identity(total_loss, name="total_loss")
log_variable_sizes(tf.trainable_variables(), "Trainable Variables")
diet_vars = [
v for v in tf.global_variables() if v.dtype == dtypes.float16_ref
]
log_variable_sizes(diet_vars, "Diet Varaibles")
# Define the train_op for the TRAIN mode.
opt = _ConditionalOptimizer(my_hp.optimizer, learning_rate, my_hp)
tf.logging.info("Computing gradients for global model_fn.")
opt_summaries = ["learning_rate", "loss"]
if hparams.summarize_grads:
opt_summaries.extend(["gradients", "gradient_norm"])
train_op = tf.contrib.layers.optimize_loss(
name="training",
loss=total_loss,
global_step=tf.train.get_global_step(),
learning_rate=learning_rate,
clip_gradients=my_hp.clip_grad_norm or None,
gradient_noise_scale=hparams.grad_noise_scale or None,
optimizer=opt,
summaries=opt_summaries,
colocate_gradients_with_ops=True)
# Remove summaries that will fail to run because they are in conditionals.
# TODO(cwhipkey): Test with this code removed, later in 2017.
summaries = tf.get_collection_ref(tf.GraphKeys.SUMMARIES)
for i in range(len(summaries) - 1, -1, -1):
if summaries[i].name.startswith("cond_"):
del summaries[i]
tf.logging.info("Global model_fn finished.")
return tf.estimator.EstimatorSpec(
mode,
predictions={"problem_choice": features["problem_choice"]},
loss=total_loss,
train_op=train_op)
def model_fn(features, targets, mode):
"""Creates the prediction, loss, and train ops.
Args:
features: A dictionary of tensors keyed by the feature name.
targets: A tensor representing the labels (targets).
mode: The execution mode, as defined in tf.contrib.learn.ModeKeys.
Returns:
A tuple consisting of the prediction, loss, and train_op.
"""
# Deep-copy the model hparams between modes to eliminate
# side-effects caused by abuse of the linked problem_hparams
# objects which are used to share modality objects between
# problems. We do not want to share the modality objects between
# modes, since the modality objects may decide to do something
# mode-specific. A better fix would be to stop abusing the
# hparams in this way and instead use a separate dictionary to
# share the modality objects between problems. This dictionary
# could be created once per mode and passed to the constructor of
# t2t_model.
my_hp = copy.deepcopy(hparams)
if mode == tf.contrib.learn.ModeKeys.INFER:
if FLAGS.decode_interactive:
features = _interactive_input_tensor_to_features_dict(features, my_hp)
elif FLAGS.decode_from_file:
features = _decode_input_tensor_to_features_dict(features, my_hp)
# A dictionary containing:
# - problem_choice: A Tensor containing an integer indicating which problem
# was selected for this run.
# - predictions: A Tensor containing the model's output predictions.
run_info = dict()
run_info["problem_choice"] = features["problem_choice"]
if targets is not None:
features["targets"] = targets
dp = devices.data_parallelism()
tf.get_variable_scope().set_initializer(initializer())
is_training = mode == tf.contrib.learn.ModeKeys.TRAIN
# Add input statistics for incoming features.
with tf.name_scope("input_stats"):
for (k, v) in six.iteritems(features):
if isinstance(v, tf.Tensor) and v.get_shape().ndims > 1:
tf.summary.scalar("%s_batch" % k, tf.shape(v)[0] // dp.n)
tf.summary.scalar("%s_length" % k, tf.shape(v)[1])
nonpadding = tf.to_float(tf.not_equal(v, 0))
nonpadding_tokens = tf.reduce_sum(nonpadding)
if k == "targets":
targets_nonpadding_tokens = nonpadding_tokens
tf.summary.scalar("%s_nonpadding_tokens" % k, nonpadding_tokens)
tf.summary.scalar("%s_nonpadding_fraction" % k,
tf.reduce_mean(nonpadding))
# The new data reader occasionally emits very small batches, which
# cause the examples in those batches to be grossly overweighted.
# We decrease the loss proportionally to the ratio of the size of this
# batch to the size of the largest training batch ever.
# TODO(noam): to be more sophisticated, we could keep separate
# maxima based on problem choice.
max_nonpadding_var = tf.get_variable(
"max_nonpadding", shape=[],
initializer=tf.ones_initializer(), trainable=False)
max_nonpadding = tf.maximum(max_nonpadding_var, targets_nonpadding_tokens)
if is_training:
with tf.control_dependencies(
[tf.assign(max_nonpadding_var, max_nonpadding)]):
small_batch_multiplier = targets_nonpadding_tokens / max_nonpadding
tf.summary.scalar("small_batch_multiplier", small_batch_multiplier)
# Get multi-problem logits and loss based on features["problem_choice"].
loss_variable_names = []
def nth_model(n):
"""Build the model for the n-th problem, plus some added variables."""
model_class = registry.model(model)(
my_hp,
mode,
my_hp.problems[n],
n,
dp,
devices.ps_devices(all_workers=True))
if mode == tf.contrib.learn.ModeKeys.INFER:
return model_class.infer(
features,
beam_size=FLAGS.decode_beam_size,
top_beams=(FLAGS.decode_beam_size
if FLAGS.decode_return_beams else 1),
last_position_only=FLAGS.decode_use_last_position_only,
alpha=FLAGS.decode_alpha,
decode_length=FLAGS.decode_extra_length)
# In distributed mode, we build graph for problem=0 and problem=worker_id.
skipping_is_on = my_hp.problem_choice == "distributed" and is_training
problem_worker_id = FLAGS.worker_id % len(my_hp.problems)
skip_this_one = n != 0 and n % FLAGS.worker_replicas != problem_worker_id
# On worker 0 also build graph for problems <= 1.
# TODO(lukaszkaiser): why is this hack needed for variables init? Repair.
skip_this_one = skip_this_one and (FLAGS.worker_id != 0 or n > 1)
if (FLAGS.eval_run_autoregressive and
mode == tf.contrib.learn.ModeKeys.EVAL):
sharded_logits, losses_dict = model_class.eval_autoregressive(features)
else:
sharded_logits, losses_dict = model_class.model_fn(
features, skip=(skipping_is_on and skip_this_one))
with tf.variable_scope("losses_avg"):
total_loss, ops = 0.0, []
for loss_key, loss_value in six.iteritems(losses_dict):
loss_name = "problem_%d/%s_loss" % (n, loss_key)
loss_moving_avg = tf.get_variable(
loss_name, initializer=100.0, trainable=False)
loss_variable_names.append(loss_name)
ops.append(
loss_moving_avg.assign(loss_moving_avg * 0.9 + loss_value * 0.1))
total_loss += loss_value
try: # Total loss avg might be reused or not, we try both.
with tf.variable_scope(tf.get_variable_scope(), reuse=True):
# Total loss was already constructed on input.
loss_moving_avg = tf.get_variable("problem_%d/total_loss" % n)
except ValueError:
loss_moving_avg = tf.get_variable("problem_%d/total_loss" % n,
initializer=100.0, trainable=False)
ops.append(
loss_moving_avg.assign(loss_moving_avg * 0.9 + total_loss * 0.1))
with tf.variable_scope("train_stats"): # Count steps for this problem.
problem_steps = tf.get_variable(
"problem_%d_steps" % n, initializer=0, trainable=False)
ops.append(problem_steps.assign_add(1))
with tf.control_dependencies(ops): # Make sure the ops run.
# Ensure the loss is a scalar here.
total_loss = tf.reshape(total_loss, [], name="total_loss_control_id")
return [total_loss] + sharded_logits # Need to flatten for cond later.
result_list = input_fn_builder.cond_on_index(nth_model,
features["problem_choice"], 0,
len(my_hp.problems) - 1)
if mode == tf.contrib.learn.ModeKeys.INFER:
# Beam search in sequence model returns both decodes withe key "outputs"
# and scores with they key "scores". If return list is a dict, we expect
# that it will have keys "outputs", a tensor of int32 and scores, a
# tensor of floats. This is useful if we want to return scores from
# estimator.predict
if not isinstance(result_list, dict):
ret = {"outputs": result_list}, None, None
else:
ret = {
"outputs": result_list["outputs"],
"scores": result_list["scores"]
}, None, None
if "inputs" in features:
ret[0]["inputs"] = features["inputs"]
if "infer_targets" in features:
ret[0]["targets"] = features["infer_targets"]
return ret
sharded_logits, total_loss = result_list[1:], result_list[0]
if mode == tf.contrib.learn.ModeKeys.EVAL:
logits = tf.concat(sharded_logits, 0)
# For evaluation, return the logits layer as our predictions.
run_info["predictions"] = logits
train_op = None
return run_info, total_loss, None
assert mode == tf.contrib.learn.ModeKeys.TRAIN
# Some training statistics.
with tf.name_scope("training_stats"):
learning_rate = my_hp.learning_rate * learning_rate_decay()
learning_rate /= math.sqrt(float(FLAGS.worker_replicas))
tf.summary.scalar("learning_rate", learning_rate)
global_step = tf.to_float(tf.contrib.framework.get_global_step())
for n in xrange(len(my_hp.problems)):
names_and_vars = []
with tf.variable_scope("losses_avg", reuse=True):
total_loss_var = tf.get_variable("problem_%d/total_loss" % n)
names_and_vars.append(("total_loss", total_loss_var))
with tf.variable_scope("losses_avg", reuse=True):
for loss_name in loss_variable_names:
if loss_name.startswith("problem_%d/" % n):
loss_var = tf.get_variable(loss_name)
loss_suffix = loss_name[loss_name.index("/") + 1:]
names_and_vars.append((loss_suffix, loss_var))
for (loss_name, loss_var) in names_and_vars:
tf.summary.scalar("loss_avg_%d/%s" % (n, loss_name), loss_var)
with tf.variable_scope("train_stats", reuse=True):
nth_steps = tf.get_variable("problem_%d_steps" % n, dtype=tf.int32)
tf.summary.scalar("problem_%d_frequency" % n,
tf.to_float(nth_steps) / (global_step + 1.0))
# Log trainable weights and add decay.
total_size, weight_decay_loss = 0, 0.0
all_weights = {v.name: v for v in tf.trainable_variables()}
for v_name in sorted(list(all_weights)):
v = all_weights[v_name]
v_size = int(np.prod(np.array(v.shape.as_list())))
total_size += v_size
if my_hp.weight_decay > 0.0 and len(v.shape.as_list()) > 1:
# Add weight regularization if set and the weight is not a bias (dim>1).
with tf.device(v._ref().device): # pylint: disable=protected-access
v_loss = tf.nn.l2_loss(v) / v_size
weight_decay_loss += v_loss
is_body = len(v_name) > 5 and v_name[:5] == "body/"
if my_hp.weight_noise > 0.0 and is_body:
# Add weight noise if set in my_hp.
with tf.device(v._ref().device): # pylint: disable=protected-access
scale = learning_rate * 0.001
noise = tf.truncated_normal(v.shape) * my_hp.weight_noise * scale
noise_op = v.assign_add(noise)
with tf.control_dependencies([noise_op]):
total_loss = tf.identity(total_loss)
if my_hp.weight_decay > 0.0:
total_loss += weight_decay_loss * my_hp.weight_decay
if is_training:
total_loss *= small_batch_multiplier
total_loss = tf.identity(total_loss, name="total_loss")
log_variable_sizes(tf.trainable_variables(), "Trainable Variables")
diet_vars = [v for v in tf.global_variables() if hasattr(v, "optimizer")]
log_variable_sizes(diet_vars, "Diet Varaibles")
# Define the train_op for the TRAIN mode.
opt = _ConditionalOptimizer(my_hp.optimizer, learning_rate, my_hp)
tf.logging.info("Computing gradients for global model_fn.")
opt_summaries = ["learning_rate", "loss"]
if hparams.summarize_grads:
opt_summaries.extend(["gradients", "gradient_norm"])
train_op = tf.contrib.layers.optimize_loss(
name="training",
loss=total_loss,
global_step=tf.train.get_global_step(),
learning_rate=learning_rate,
clip_gradients=my_hp.clip_grad_norm or None,
gradient_noise_scale=hparams.grad_noise_scale or None,
optimizer=opt,
summaries=opt_summaries,
colocate_gradients_with_ops=True)
# Remove summaries that will fail to run because they are in conditionals.
# TODO(cwhipkey): Test with this code removed, later in 2017.
summaries = tf.get_collection_ref(tf.GraphKeys.SUMMARIES)
for i in range(len(summaries) - 1, -1, -1):
if summaries[i].name.startswith("cond_"):
del summaries[i]
tf.logging.info("Global model_fn finished.")
return run_info, total_loss, train_op
def model_fn(model,
features,
mode,
hparams,
problem_names,
train_steps=100000,
worker_id=0,
worker_replicas=1,
eval_run_autoregressive=False,
decode_hparams=None):
"""Builds the model for all modes.
* TRAIN: Constructs loss and train_op
* EVAL: Constructs the loss and eval metrics
* PREDICT: Constructs the predictions
Args:
model: str, name of model.
features: dict<feature name, Tensor>. Expected to have keys
{inputs, targets, problem_choice}.
mode: tf.estimator.ModeKeys.
hparams: model HParams.
problem_names: list of str, names of the problems.
train_steps: int, total number of training steps. Used to compute learning
rate decay.
worker_id: int, id of this worker.
worker_replicas: int, number of workers.
eval_run_autoregressive: bool, whether to run evaluation autoregressively.
decode_hparams: HParams for decode settings. Used when mode == PREDICT.
Returns:
tf.estimator.EstimatorSpec
"""
assert len(problem_names) == len(hparams.problem_instances)
decode_hp = decode_hparams
# TODO(rsepassi): This still depends on FLAGS. Rm eventually.
dp = devices.data_parallelism()
tf.get_variable_scope().set_initializer(_get_variable_initializer(hparams))
# set the initializer functions
is_training = mode == tf.estimator.ModeKeys.TRAIN
# Add input statistics for incoming features.
with tf.name_scope("input_stats"):
for (k, v) in six.iteritems(features):
if isinstance(v, tf.Tensor) and v.get_shape().ndims > 1:
tf.summary.scalar("%s_batch" % k, tf.shape(v)[0] // dp.n)
tf.summary.scalar("%s_length" % k, tf.shape(v)[1])
nonpadding = tf.to_float(tf.not_equal(v, 0))
nonpadding_tokens = tf.reduce_sum(
nonpadding) # non zeros tokens
if k == "targets":
targets_nonpadding_tokens = nonpadding_tokens
tf.summary.scalar("%s_nonpadding_tokens" % k,
nonpadding_tokens)
tf.summary.scalar("%s_nonpadding_fraction" % k,
tf.reduce_mean(nonpadding))
# Get multi-problem logits and loss based on features["problem_choice"].
loss_variable_names = []
def nth_model(n):
"""Build the model for the n-th problem, plus some added variables."""
model_class = registry.model(model)(
hparams,
mode,
hparams.problems[n],
n,
dp,
devices.ps_devices(all_workers=True),
decode_hparams=decode_hparams
) # initialize transformer model class: hparams, modalities
if mode == tf.estimator.ModeKeys.PREDICT:
return model_class.infer(features,
beam_size=decode_hp.beam_size,
top_beams=(decode_hp.beam_size if
decode_hp.return_beams else 1),
alpha=decode_hp.alpha,
decode_length=decode_hp.extra_length)
# In distributed mode, we build graph for problem=0 and problem=worker_id.
skipping_is_on = hparams.problem_choice == "distributed" and is_training
problem_worker_id = worker_id % len(hparams.problems)
skip_this_one = n != 0 and n % worker_replicas != problem_worker_id
# On worker 0 also build graph for problems <= 1.
# TODO(lukaszkaiser): why is this hack needed for variables init? Repair.
skip_this_one = skip_this_one and (worker_id != 0 or n > 1)
mrt_samples = getattr(hparams, 'mrt_samples', None)
if eval_run_autoregressive and mode == tf.estimator.ModeKeys.EVAL: # evaluation mode
sharded_logits, losses_dict = model_class.eval_autoregressive(
features)
else: # training mode
if hparams.rl:
# generate sample data, it will automatically sharded, samples shape [batch, time, 1, 1]
if model_class._num_datashards == 1: # work on single GPU cards, fast sample
print("###Work on Single GPU card, Use Fast Decode.###")
train_beam = getattr(hparams, 'train_beam', None)
if mrt_samples:
samples, _ = model_class._fast_decode(
features,
decode_length=50,
beam_size=mrt_samples,
top_beams=mrt_samples)
inputs = tf.squeeze(tf.squeeze(features["inputs"],
axis=-1),
axis=-1)
targets = tf.squeeze(tf.squeeze(features["targets"],
axis=-1),
axis=-1)
batch_size = tf.shape(inputs)[0]
inputs_len = tf.shape(inputs)[1]
targets_len = tf.shape(targets)[1]
inputs_tile = tf.tile(inputs, [1, mrt_samples])
targets_tile = tf.tile(targets, [1, mrt_samples])
inputs_reshape = tf.reshape(
inputs_tile,
[batch_size * mrt_samples, inputs_len])
targets_reshape = tf.reshape(
targets_tile,
[batch_size * mrt_samples, targets_len])
inputs_feed = tf.expand_dims(tf.expand_dims(
inputs_reshape, axis=-1),
axis=-1)
targets_feed = tf.expand_dims(tf.expand_dims(
targets_reshape, axis=-1),
axis=-1)
features["inputs"] = inputs_feed
features["targets"] = targets_feed
elif train_beam and train_beam != 1: # beam search with hparams.train_beam size and return the top 1 sample
samples, _ = model_class._fast_decode(
features,
decode_length=50,
beam_size=hparams.train_beam)
else:
targets_beam = getattr(hparams, 'targets_beam', None)
if targets_beam:
targets_samples, _ = model_class._fast_decode(
features,
decode_length=50,
beam_size=4,
sampling_method='argmax')
targets_samples = tf.reshape(
targets_samples, [
tf.shape(targets_samples)[0],
tf.shape(targets_samples)[1], 1, 1
])
features["targets"] = targets_samples
samples, _ = model_class._fast_decode(features,
decode_length=50)
samples = tf.expand_dims(samples, axis=-1)
samples = tf.expand_dims(
samples, axis=-1
) # add two additional dimensions to make it compatible.
else: # work on multi GPU cards, only support slow sample
print("###Work on Multi GPU cards, Use Slow Decode.###")
samples, _, _ = model_class._slow_greedy_infer(
features,
decode_length=50) # default decode_length = 50
samples = tf.stop_gradient(samples)
# calculate bleu score use metric_fn
# train_metric_fn = "approx_bleu_train_score"
train_metric_fn = metrics.METRICS_FNS[
metrics.Metrics.APPROX_BLEU_TRAIN]
labels = features.get("targets", None)
samples.set_shape([None, None, 1, 1])
# haprams.delta_reward = True for delta reward; False for total reward
metric_value = train_metric_fn(
samples, labels, delat_reward=hparams.delta_reward)
metric_value = tf.stop_gradient(
metric_value) # to be more strict of the gradient
metric_value.set_shape([None, None, 1, 1])
"""Accodring to the metrics.py: The tf.metrics.mean function assures correct aggregation."""
# metric_value is total_reward: scalar
features["samples"] = samples
features["values"] = metric_value
# del samples
# del labels
sharded_logits, losses_dict = model_class.model_fn(
features,
skip=(skipping_is_on and skip_this_one),
mrt=mrt_samples)
# if hparams.rl:
# training_loss = losses_dict["training"] * metric_value # losses_dict["training"]: [batch, timesteps]
# training_loss_sum = tf.reduce_sum(training_loss) # sum the training_loss
# losses_dict["training"] = training_loss_sum # log_prob * r (current r is total_reward)
with tf.variable_scope("losses_avg"):
total_loss, ops = 0.0, []
for loss_key, loss_value in six.iteritems(losses_dict):
if hparams.rl:
baseline_loss_weight = getattr(hparams,
'baseline_loss_weight', 1.0)
training_loss_weight = getattr(hparams,
'training_loss_weight', 1.0)
mle_training_loss_weight = getattr(
hparams, 'mle_training_loss_weight', 0.3)
if loss_key == "training":
loss_value = loss_value * training_loss_weight
elif loss_key == "training_baseline":
loss_value = loss_value * baseline_loss_weight
elif loss_key == "mle_training":
loss_value = loss_value * mle_training_loss_weight
loss_name = "problem_%d/%s_loss" % (n, loss_key)
loss_moving_avg = tf.get_variable(loss_name,
initializer=100.0,
trainable=False)
loss_variable_names.append(loss_name)
ops.append(
loss_moving_avg.assign(loss_moving_avg * 0.9 +
loss_value * 0.1))
total_loss += loss_value
try: # Total loss avg might be reused or not, we try both.
with tf.variable_scope(tf.get_variable_scope(), reuse=True):
# Total loss was already constructed on input.
loss_moving_avg = tf.get_variable("problem_%d/total_loss" %
n)
except ValueError:
loss_moving_avg = tf.get_variable("problem_%d/total_loss" % n,
initializer=100.0,
trainable=False)
ops.append(
loss_moving_avg.assign(loss_moving_avg * 0.9 +
total_loss * 0.1))
with tf.variable_scope("train_stats"): # Count steps for this problem.
problem_steps = tf.get_variable("problem_%d_steps" % n,
initializer=0,
trainable=False)
ops.append(problem_steps.assign_add(1))
with tf.control_dependencies(ops): # Make sure the ops run.
# Ensure the loss is a scalar here.
total_loss = tf.reshape(total_loss, [],
name="total_loss_control_id")
return [total_loss, tf.concat(sharded_logits, 0)]
model_output = input_fn_builder.cond_on_index(
nth_model,
index_tensor=features["problem_choice"],
max_idx=len(hparams.problems) - 1) # total_loss and shared_logits
if mode == tf.estimator.ModeKeys.PREDICT:
# If beam searching, model_output will be a dict with keys "outputs" and
# "scores".
if isinstance(model_output, dict): # beam search
outputs = model_output["outputs"]
scores = model_output["scores"]
else:
outputs = model_output
scores = None
batched_problem_choice = (features["problem_choice"] * tf.ones(
(tf.shape(features["inputs"])[0], ), dtype=tf.int32))
predictions = {
"outputs": outputs,
"scores": scores,
"inputs": features.get("inputs", None),
"targets": features.get("infer_targets", None),
"problem_choice": batched_problem_choice,
}
_del_dict_nones(predictions) # delete the empty ones in predictions
export_out = {"outputs": predictions["outputs"]}
if "scores" in predictions:
export_out["scores"] = predictions["scores"]
return tf.estimator.EstimatorSpec(
mode,
predictions=predictions,
export_outputs={
"output": tf.estimator.export.PredictOutput(export_out)
})
total_loss, logits = model_output
if mode == tf.estimator.ModeKeys.EVAL:
eval_metrics_fns = metrics.create_evaluation_metrics(
hparams.problem_instances, hparams)
eval_metrics = {}
for metric_name, metric_fn in six.iteritems(eval_metrics_fns):
eval_metrics[metric_name] = metric_fn(logits, features)
return tf.estimator.EstimatorSpec(mode,
predictions={"predictions": logits},
eval_metric_ops=eval_metrics,
loss=total_loss)
assert mode == tf.estimator.ModeKeys.TRAIN
# Set learning rate
learning_rate = hparams.learning_rate * optimize.learning_rate_decay(
hparams,
num_worker_replicas=worker_replicas,
num_train_steps=train_steps)
learning_rate /= math.sqrt(float(worker_replicas))
# Get global step
global_step = tf.train.get_or_create_global_step()
# Some training statistics.
with tf.name_scope("training_stats"):
tf.summary.scalar("learning_rate", learning_rate)
for n in xrange(len(hparams.problems)):
names_and_vars = []
with tf.variable_scope("losses_avg", reuse=True):
total_loss_var = tf.get_variable("problem_%d/total_loss" % n)
names_and_vars.append(("total_loss", total_loss_var))
with tf.variable_scope("losses_avg", reuse=True):
for loss_name in loss_variable_names:
if loss_name.startswith("problem_%d/" % n):
loss_var = tf.get_variable(loss_name)
loss_suffix = loss_name[loss_name.index("/") + 1:]
names_and_vars.append((loss_suffix, loss_var))
for (loss_name, loss_var) in names_and_vars:
tf.summary.scalar("loss_avg_%d/%s" % (n, loss_name), loss_var)
with tf.variable_scope("train_stats", reuse=True):
nth_steps = tf.get_variable("problem_%d_steps" % n,
dtype=tf.int32)
tf.summary.scalar(
"problem_%d_frequency" % n,
tf.to_float(nth_steps) / (tf.to_float(global_step) + 1.0))
# Add weight decay and noise.
total_size, weight_decay_loss = 0, 0.0
all_weights = {v.name: v for v in tf.trainable_variables()}
for v_name in sorted(list(all_weights)):
v = all_weights[v_name]
v_size = int(np.prod(np.array(v.shape.as_list())))
total_size += v_size
if hparams.weight_decay > 0.0 and len(v.shape.as_list()) > 1:
# Add weight regularization if set and the weight is not a bias (dim>1).
with tf.device(v._ref().device): # pylint: disable=protected-access
v_loss = tf.nn.l2_loss(v) / v_size
weight_decay_loss += v_loss
is_body = len(v_name) > 5 and v_name[:5] == "body/"
if hparams.weight_noise > 0.0 and is_body:
# Add weight noise if set in hparams.
with tf.device(v._ref().device): # pylint: disable=protected-access
scale = learning_rate * 0.001
noise = tf.truncated_normal(
v.shape) * hparams.weight_noise * scale
noise_op = v.assign_add(noise)
with tf.control_dependencies([noise_op]):
total_loss = tf.identity(total_loss)
if hparams.weight_decay > 0.0:
total_loss += weight_decay_loss * hparams.weight_decay
# The new data reader occasionally emits very small batches, which
# cause the examples in those batches to be grossly overweighted.
# We decrease the loss proportionally to the ratio of the size of this
# batch to the size of the largest training batch ever.
# TODO(noam): to be more sophisticated, we could keep separate
# maxima based on problem choice.
max_nonpadding_var = tf.get_variable("max_nonpadding",
shape=[],
initializer=tf.ones_initializer(),
trainable=False)
max_nonpadding = tf.maximum(max_nonpadding_var, targets_nonpadding_tokens)
with tf.control_dependencies(
[tf.assign(max_nonpadding_var, max_nonpadding)]):
small_batch_multiplier = targets_nonpadding_tokens / max_nonpadding
tf.summary.scalar("small_batch_multiplier", small_batch_multiplier)
total_loss *= small_batch_multiplier
# Log variable sizes
_log_variable_sizes(tf.trainable_variables(), "Trainable Variables")
diet_vars = [
v for v in tf.global_variables() if v.dtype == dtypes.float16_ref
]
_log_variable_sizes(diet_vars, "Diet Variables")
# Optimize
train_op = optimize.optimize(total_loss, learning_rate, hparams)
# Remove summaries that will fail to run because they are in conditionals.
# TODO(cwhipkey): Test with this code removed, later in 2017.
summaries = tf.get_collection_ref(tf.GraphKeys.SUMMARIES)
for i in reversed(range(len(summaries))):
if summaries[i].name.startswith("cond_"):
del summaries[i]
tf.logging.info("Global model_fn finished.")
return tf.estimator.EstimatorSpec(
mode,
predictions={"problem_choice": features["problem_choice"]},
loss=total_loss,
train_op=train_op)
def model_fn(model,
features,
mode,
hparams,
problem_names,
train_steps=100000,
worker_id=0,
worker_replicas=1,
eval_run_autoregressive=False,
decode_hparams=None):
"""Builds the model for all modes.
* TRAIN: Constructs loss and train_op
* EVAL: Constructs the loss and eval metrics
* PREDICT: Constructs the predictions
Args:
model: str, name of model.
features: dict<feature name, Tensor>. Expected to have keys
{inputs, targets, problem_choice}.
mode: tf.estimator.ModeKeys.
hparams: model HParams.
problem_names: list of str, names of the problems.
train_steps: int, total number of training steps. Used to compute learning
rate decay.
worker_id: int, id of this worker.
worker_replicas: int, number of workers.
eval_run_autoregressive: bool, whether to run evaluation autoregressively.
decode_hparams: HParams for decode settings. Used when mode == PREDICT.
Returns:
tf.estimator.EstimatorSpec
"""
assert len(problem_names) == len(hparams.problem_instances)
decode_hp = decode_hparams
# TODO(rsepassi): This still depends on FLAGS. Rm eventually.
dp = devices.data_parallelism(hparams)
tf.get_variable_scope().set_initializer(_get_variable_initializer(hparams))
is_training = mode == tf.estimator.ModeKeys.TRAIN
# Add input statistics for incoming features.
with tf.name_scope("input_stats"):
for (k, v) in six.iteritems(features):
if isinstance(v, tf.Tensor) and v.get_shape().ndims > 1:
tf.summary.scalar("%s_batch" % k, tf.shape(v)[0] // dp.n)
tf.summary.scalar("%s_length" % k, tf.shape(v)[1])
nonpadding = tf.to_float(tf.not_equal(v, 0))
nonpadding_tokens = tf.reduce_sum(nonpadding)
if k == "targets":
targets_nonpadding_tokens = nonpadding_tokens
tf.summary.scalar("%s_nonpadding_tokens" % k, nonpadding_tokens)
tf.summary.scalar("%s_nonpadding_fraction" % k,
tf.reduce_mean(nonpadding))
# Get multi-problem logits and loss based on features["problem_choice"].
loss_variable_names = []
def nth_model(n):
"""Build the model for the n-th problem, plus some added variables."""
model_class = registry.model(model)(
hparams,
mode,
hparams.problems[n],
n,
dp,
devices.ps_devices(all_workers=True),
decode_hparams=decode_hparams)
if mode == tf.estimator.ModeKeys.PREDICT:
return model_class.infer(
features,
beam_size=decode_hp.beam_size,
top_beams=(decode_hp.beam_size if decode_hp.return_beams else 1),
alpha=decode_hp.alpha,
decode_length=decode_hp.extra_length)
# In distributed mode, we build graph for problem=0 and problem=worker_id.
skipping_is_on = hparams.problem_choice == "distributed" and is_training
problem_worker_id = worker_id % len(hparams.problems)
skip_this_one = n != 0 and n % worker_replicas != problem_worker_id
# On worker 0 also build graph for problems <= 1.
# TODO(lukaszkaiser): why is this hack needed for variables init? Repair.
skip_this_one = skip_this_one and (worker_id != 0 or n > 1)
if eval_run_autoregressive and mode == tf.estimator.ModeKeys.EVAL:
logits, losses_dict = model_class.eval_autoregressive(features)
else:
logits, losses_dict = model_class(
features, skip=(skipping_is_on and skip_this_one))
with tf.variable_scope("losses_avg"):
total_loss, ops = 0.0, []
for loss_key, loss_value in six.iteritems(losses_dict):
loss_name = "problem_%d/%s_loss" % (n, loss_key)
loss_moving_avg = tf.get_variable(
loss_name, initializer=100.0, trainable=False)
loss_variable_names.append(loss_name)
ops.append(
loss_moving_avg.assign(loss_moving_avg * 0.9 + loss_value * 0.1))
total_loss += loss_value
try: # Total loss avg might be reused or not, we try both.
with tf.variable_scope(tf.get_variable_scope(), reuse=True):
# Total loss was already constructed on input.
loss_moving_avg = tf.get_variable("problem_%d/total_loss" % n)
except ValueError:
loss_moving_avg = tf.get_variable(
"problem_%d/total_loss" % n, initializer=100.0, trainable=False)
ops.append(
loss_moving_avg.assign(loss_moving_avg * 0.9 + total_loss * 0.1))
with tf.variable_scope("train_stats"): # Count steps for this problem.
problem_steps = tf.get_variable(
"problem_%d_steps" % n, initializer=0, trainable=False)
ops.append(problem_steps.assign_add(1))
with tf.control_dependencies(ops): # Make sure the ops run.
# Ensure the loss is a scalar here.
total_loss = tf.reshape(total_loss, [], name="total_loss_control_id")
return [total_loss, logits]
model_output = input_fn_builder.cond_on_index(
nth_model,
index_tensor=features["problem_choice"],
max_idx=len(hparams.problems) - 1)
if mode == tf.estimator.ModeKeys.PREDICT:
# If beam searching, model_output will be a dict with keys "outputs" and
# "scores".
if isinstance(model_output, dict):
outputs = model_output["outputs"]
scores = model_output["scores"]
else:
outputs = model_output
scores = None
batched_problem_choice = (
features["problem_choice"] * tf.ones(
(tf.shape(features["inputs"])[0],), dtype=tf.int32))
predictions = {
"outputs": outputs,
"scores": scores,
"inputs": features.get("inputs", None),
"targets": features.get("infer_targets", None),
"problem_choice": batched_problem_choice,
}
_del_dict_nones(predictions)
export_out = {"outputs": predictions["outputs"]}
if "scores" in predictions:
export_out["scores"] = predictions["scores"]
return tf.estimator.EstimatorSpec(
mode,
predictions=predictions,
export_outputs={
"output": tf.estimator.export.PredictOutput(export_out)
})
total_loss, logits = model_output
if mode == tf.estimator.ModeKeys.EVAL:
eval_metrics_fns = metrics.create_evaluation_metrics(
hparams.problem_instances, hparams)
eval_metrics = {}
for metric_name, metric_fn in six.iteritems(eval_metrics_fns):
eval_metrics[metric_name] = metric_fn(logits, features)
return tf.estimator.EstimatorSpec(
mode,
predictions={"predictions": logits},
eval_metric_ops=eval_metrics,
loss=total_loss)
assert mode == tf.estimator.ModeKeys.TRAIN
# Set learning rate
learning_rate = hparams.learning_rate * optimize.learning_rate_decay(
hparams, num_worker_replicas=worker_replicas, num_train_steps=train_steps)
learning_rate /= math.sqrt(float(worker_replicas))
# Get global step
global_step = tf.train.get_or_create_global_step()
# Some training statistics.
with tf.name_scope("training_stats"):
tf.summary.scalar("learning_rate", learning_rate)
for n in xrange(len(hparams.problems)):
names_and_vars = []
with tf.variable_scope("losses_avg", reuse=True):
total_loss_var = tf.get_variable("problem_%d/total_loss" % n)
names_and_vars.append(("total_loss", total_loss_var))
with tf.variable_scope("losses_avg", reuse=True):
for loss_name in loss_variable_names:
if loss_name.startswith("problem_%d/" % n):
loss_var = tf.get_variable(loss_name)
loss_suffix = loss_name[loss_name.index("/") + 1:]
names_and_vars.append((loss_suffix, loss_var))
for (loss_name, loss_var) in names_and_vars:
tf.summary.scalar("loss_avg_%d/%s" % (n, loss_name), loss_var)
with tf.variable_scope("train_stats", reuse=True):
nth_steps = tf.get_variable("problem_%d_steps" % n, dtype=tf.int32)
tf.summary.scalar("problem_%d_frequency" % n,
tf.to_float(nth_steps) /
(tf.to_float(global_step) + 1.0))
# Add weight decay and noise.
total_size, weight_decay_loss = 0, 0.0
all_weights = {v.name: v for v in tf.trainable_variables()}
for v_name in sorted(list(all_weights)):
v = all_weights[v_name]
v_size = int(np.prod(np.array(v.shape.as_list())))
total_size += v_size
if hparams.weight_decay > 0.0 and len(v.shape.as_list()) > 1:
# Add weight regularization if set and the weight is not a bias (dim>1).
with tf.device(v._ref().device): # pylint: disable=protected-access
v_loss = tf.nn.l2_loss(v) / v_size
weight_decay_loss += v_loss
is_body = len(v_name) > 5 and v_name[:5] == "body/"
if hparams.weight_noise > 0.0 and is_body:
# Add weight noise if set in hparams.
with tf.device(v._ref().device): # pylint: disable=protected-access
scale = learning_rate * 0.001
noise = tf.truncated_normal(v.shape) * hparams.weight_noise * scale
noise_op = v.assign_add(noise)
with tf.control_dependencies([noise_op]):
total_loss = tf.identity(total_loss)
if hparams.weight_decay > 0.0:
total_loss += weight_decay_loss * hparams.weight_decay
# The new data reader occasionally emits very small batches, which
# cause the examples in those batches to be grossly overweighted.
# We decrease the loss proportionally to the ratio of the size of this
# batch to the size of the largest training batch ever.
# TODO(noam): to be more sophisticated, we could keep separate
# maxima based on problem choice.
max_nonpadding_var = tf.get_variable(
"max_nonpadding",
shape=[],
initializer=tf.ones_initializer(),
trainable=False)
max_nonpadding = tf.maximum(max_nonpadding_var, targets_nonpadding_tokens)
with tf.control_dependencies([tf.assign(max_nonpadding_var, max_nonpadding)]):
small_batch_multiplier = targets_nonpadding_tokens / max_nonpadding
tf.summary.scalar("small_batch_multiplier", small_batch_multiplier)
total_loss *= small_batch_multiplier
# Log variable sizes
_log_variable_sizes(tf.trainable_variables(), "Trainable Variables")
diet_vars = [
v for v in tf.global_variables() if v.dtype == dtypes.float16_ref
]
_log_variable_sizes(diet_vars, "Diet Variables")
# Optimize
train_op = optimize.optimize(total_loss, learning_rate, hparams)
# Remove summaries that will fail to run because they are in conditionals.
# TODO(cwhipkey): Test with this code removed, later in 2017.
summaries = tf.get_collection_ref(tf.GraphKeys.SUMMARIES)
for i in reversed(range(len(summaries))):
if summaries[i].name.startswith("cond_"):
del summaries[i]
tf.logging.info("Global model_fn finished.")
return tf.estimator.EstimatorSpec(
mode,
predictions={"problem_choice": features["problem_choice"]},
loss=total_loss,
train_op=train_op)