#!/usr/bin/env python
# coding=utf-8
# Copyright 2018 The THUMT Authors
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os
import numpy as np
import tensorflow as tf
import thumt.data.dataset as dataset
import thumt.data.record as record
import thumt.data.vocab as vocabulary
import thumt.models as models
import thumt.utils.hooks as hooks
import thumt.utils.utils as utils
import thumt.utils.parallel as parallel
import thumt.utils.search as search
def parse_args(args=None):
parser = argparse.ArgumentParser(
description="Training neural machine translation models",
usage="trainer.py [<args>] [-h | --help]"
)
# input files
parser.add_argument("--input", type=str, nargs=2,
help="Path of source and target corpus")
parser.add_argument("--record", type=str,
help="Path to tf.Record data")
parser.add_argument("--output", type=str, default="train",
help="Path to saved models")
parser.add_argument("--vocabulary", type=str, nargs=2,
help="Path of source and target vocabulary")
parser.add_argument("--validation", type=str,
help="Path of validation file")
parser.add_argument("--references", type=str, nargs="+",
help="Path of reference files")
# model and configuration
parser.add_argument("--model", type=str, required=True,
help="Name of the model")
parser.add_argument("--parameters", type=str, default="",
help="Additional hyper parameters")
return parser.parse_args(args)
def default_parameters():
params = tf.contrib.training.HParams(
input=["", ""],
output="",
record="",
model="transformer",
vocab=["", ""],
# Default training hyper parameters
num_threads=6,
batch_size=128,
max_length=256,
length_multiplier=1,
mantissa_bits=2,
warmup_steps=4000,
train_steps=100000,
buffer_size=10000,
constant_batch_size=True,
device_list=[0],
update_cycle=1,
initializer="uniform",
initializer_gain=0.08,
adam_beta1=0.9,
adam_beta2=0.999,
adam_epsilon=1e-8,
clip_grad_norm=5.0,
learning_rate=1.0,
learning_rate_decay="noam",
learning_rate_boundaries=[0],
learning_rate_values=[0.0],
keep_checkpoint_max=5,
keep_top_checkpoint_max=1,
# Validation
eval_steps=2000,
eval_secs=0,
eval_batch_size=32,
top_beams=1,
beam_size=4,
decode_alpha=0.6,
decode_length=50,
decode_constant=5.0,
decode_normalize=False,
validation="",
references=[""],
save_checkpoint_secs=0,
save_checkpoint_steps=1000,
)
return params
def import_params(model_dir, model_name, params):
model_dir = os.path.abspath(model_dir)
p_name = os.path.join(model_dir, "params.json")
m_name = os.path.join(model_dir, model_name + ".json")
if not tf.gfile.Exists(p_name) or not tf.gfile.Exists(m_name):
return params
with tf.gfile.Open(p_name) as fd:
tf.logging.info("Restoring hyper parameters from %s" % p_name)
json_str = fd.readline()
params.parse_json(json_str)
with tf.gfile.Open(m_name) as fd:
tf.logging.info("Restoring model parameters from %s" % m_name)
json_str = fd.readline()
params.parse_json(json_str)
return params
def export_params(output_dir, name, params):
if not tf.gfile.Exists(output_dir):
tf.gfile.MkDir(output_dir)
# Save params as params.json
filename = os.path.join(output_dir, name)
with tf.gfile.Open(filename, "w") as fd:
fd.write(params.to_json())
def collect_params(all_params, params):
collected = tf.contrib.training.HParams()
for k in params.values().iterkeys():
collected.add_hparam(k, getattr(all_params, k))
return collected
def merge_parameters(params1, params2):
params = tf.contrib.training.HParams()
for (k, v) in params1.values().iteritems():
params.add_hparam(k, v)
params_dict = params.values()
for (k, v) in params2.values().iteritems():
if k in params_dict:
# Override
setattr(params, k, v)
else:
params.add_hparam(k, v)
return params
def override_parameters(params, args):
params.model = args.model
params.input = args.input or params.input
params.output = args.output or params.output
params.record = args.record or params.record
params.vocab = args.vocabulary or params.vocab
params.validation = args.validation or params.validation
params.references = args.references or params.references
params.parse(args.parameters)
params.vocabulary = {
"source": vocabulary.load_vocabulary(params.vocab[0]),
"target": vocabulary.load_vocabulary(params.vocab[1])
}
params.vocabulary["source"] = vocabulary.process_vocabulary(
params.vocabulary["source"], params
)
params.vocabulary["target"] = vocabulary.process_vocabulary(
params.vocabulary["target"], params
)
control_symbols = [params.pad, params.bos, params.eos, params.unk]
params.mapping = {
"source": vocabulary.get_control_mapping(
params.vocabulary["source"],
control_symbols
),
"target": vocabulary.get_control_mapping(
params.vocabulary["target"],
control_symbols
)
}
return params
def get_initializer(params):
if params.initializer == "uniform":
max_val = params.initializer_gain
return tf.random_uniform_initializer(-max_val, max_val)
elif params.initializer == "normal":
return tf.random_normal_initializer(0.0, params.initializer_gain)
elif params.initializer == "normal_unit_scaling":
return tf.variance_scaling_initializer(params.initializer_gain,
mode="fan_avg",
distribution="normal")
elif params.initializer == "uniform_unit_scaling":
return tf.variance_scaling_initializer(params.initializer_gain,
mode="fan_avg",
distribution="uniform")
else:
raise ValueError("Unrecognized initializer: %s" % params.initializer)
def get_learning_rate_decay(learning_rate, global_step, params):
if params.learning_rate_decay == "noam":
step = tf.to_float(global_step)
warmup_steps = tf.to_float(params.warmup_steps)
multiplier = params.hidden_size ** -0.5
decay = multiplier * tf.minimum((step + 1) * (warmup_steps ** -1.5),
(step + 1) ** -0.5)
return learning_rate * decay
elif params.learning_rate_decay == "piecewise_constant":
return tf.train.piecewise_constant(tf.to_int32(global_step),
params.learning_rate_boundaries,
params.learning_rate_values)
elif params.learning_rate_decay == "none":
return learning_rate
else:
raise ValueError("Unknown learning_rate_decay")
def session_config(params):
optimizer_options = tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L1,
do_function_inlining=True)
graph_options = tf.GraphOptions(optimizer_options=optimizer_options)
config = tf.ConfigProto(allow_soft_placement=True,
graph_options=graph_options)
if params.device_list:
device_str = ",".join([str(i) for i in params.device_list])
config.gpu_options.visible_device_list = device_str
return config
def decode_target_ids(inputs, params):
decoded = []
vocab = params.vocabulary["target"]
for item in inputs:
syms = []
for idx in item:
sym = vocab[idx]
if sym == params.eos:
break
if sym == params.pad:
break
syms.append(sym)
decoded.append(syms)
return decoded
def main(args):
tf.logging.set_verbosity(tf.logging.INFO)
model_cls = models.get_model(args.model)
params = default_parameters()
# Import and override parameters
# Priorities (low -> high):
# default -> saved -> command
params = merge_parameters(params, model_cls.get_parameters())
params = import_params(args.output, args.model, params)
override_parameters(params, args)
# Export all parameters and model specific parameters
export_params(params.output, "params.json", params)
export_params(
params.output,
"%s.json" % args.model,
collect_params(params, model_cls.get_parameters())
)
# Build Graph
with tf.Graph().as_default():
if not params.record:
# Build input queue
features = dataset.get_training_input(params.input, params)
else:
features = record.get_input_features(
os.path.join(params.record, "*train*"), "train", params
)
# Build model
initializer = get_initializer(params)
model = model_cls(params)
# Multi-GPU setting
sharded_losses = parallel.parallel_model(
model.get_training_func(initializer),
features,
params.device_list
)
loss = tf.add_n(sharded_losses) / len(sharded_losses)
# Create global step
global_step = tf.train.get_or_create_global_step()
# Print parameters
all_weights = {v.name: v for v in tf.trainable_variables()}
total_size = 0
for v_name in sorted(list(all_weights)):
v = all_weights[v_name]
tf.logging.info("%s\tshape %s", v.name[:-2].ljust(80),
str(v.shape).ljust(20))
v_size = np.prod(np.array(v.shape.as_list())).tolist()
total_size += v_size
tf.logging.info("Total trainable variables size: %d", total_size)
learning_rate = get_learning_rate_decay(params.learning_rate,
global_step, params)
learning_rate = tf.convert_to_tensor(learning_rate, dtype=tf.float32)
tf.summary.scalar("learning_rate", learning_rate)
# Create optimizer
opt = tf.train.AdamOptimizer(learning_rate,
beta1=params.adam_beta1,
beta2=params.adam_beta2,
epsilon=params.adam_epsilon)
if params.update_cycle == 1:
train_op = tf.contrib.layers.optimize_loss(
name="training",
loss=loss,
global_step=global_step,
learning_rate=learning_rate,
clip_gradients=params.clip_grad_norm or None,
optimizer=opt,
colocate_gradients_with_ops=True
)
zero_op = tf.no_op("zero_op")
collect_op = tf.no_op("collect_op")
else:
grads_and_vars = opt.compute_gradients(
loss, colocate_gradients_with_ops=True)
gradients = [item[0] for item in grads_and_vars]
variables = [item[1] for item in grads_and_vars]
variables = utils.replicate_variables(variables)
zero_op = utils.zero_variables(variables)
collect_op = utils.collect_gradients(gradients, variables)
scale = 1.0 / params.update_cycle
gradients = utils.scale_gradients(variables, scale)
# Gradient clipping
if isinstance(params.clip_grad_norm or None, float):
gradients, _ = tf.clip_by_global_norm(gradients,
params.clip_grad_norm)
# Update variables
grads_and_vars = list(zip(gradients, tf.trainable_variables()))
with tf.control_dependencies([collect_op]):
train_op = opt.apply_gradients(grads_and_vars, global_step)
# Validation
if params.validation and params.references[0]:
files = [params.validation] + list(params.references)
eval_inputs = dataset.sort_and_zip_files(files)
eval_input_fn = dataset.get_evaluation_input
else:
eval_input_fn = None
# Add hooks
train_hooks = [
tf.train.StopAtStepHook(last_step=params.train_steps),
tf.train.NanTensorHook(loss),
tf.train.LoggingTensorHook(
{
"step": global_step,
"loss": loss,
"source": tf.shape(features["source"]),
"target": tf.shape(features["target"])
},
every_n_iter=1
),
tf.train.CheckpointSaverHook(
checkpoint_dir=params.output,
save_secs=params.save_checkpoint_secs or None,
save_steps=params.save_checkpoint_steps or None,
saver=tf.train.Saver(
max_to_keep=params.keep_checkpoint_max,
sharded=False
)
)
]
config = session_config(params)
if eval_input_fn is not None:
train_hooks.append(
hooks.EvaluationHook(
lambda f: search.create_inference_graph(
model.get_evaluation_func(), f, params
),
lambda: eval_input_fn(eval_inputs, params),
lambda x: decode_target_ids(x, params),
params.output,
config,
params.keep_top_checkpoint_max,
eval_secs=params.eval_secs,
eval_steps=params.eval_steps
)
)
# Create session, do not use default CheckpointSaverHook
with tf.train.MonitoredTrainingSession(
checkpoint_dir=params.output, hooks=train_hooks,
save_checkpoint_secs=None, config=config) as sess:
while not sess.should_stop():
# Bypass hook calls
utils.session_run(sess, zero_op)
for i in range(1, params.update_cycle):
utils.session_run(sess, collect_op)
sess.run(train_op)
if __name__ == "__main__":
main(parse_args())
