execute.py

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import math
import os
import random
import sys
import time

import numpy as np
from six.moves import xrange  # pylint: disable=redefined-builtin
import tensorflow as tf

import data_utils
import seq2seq_model

# python2 and python3 support
try:
    reload
except NameError:
    # py3k has unicode by default
    pass
else:
    reload(sys).setdefaultencoding('utf-8')

try:
    from ConfigParser import SafeConfigParser
except:
    from configparser import \
        SafeConfigParser  # In Python 3, ConfigParser has been renamed to configparser for PEP 8 compliance.

gConfig = {}


def get_config(config_file='seq2seq.ini'):
    parser = SafeConfigParser()
    parser.read(config_file)
    # get the ints, floats and strings
    _conf_ints = [(key, int(value)) for key, value in parser.items('ints')]
    _conf_floats = [(key, float(value)) for key, value in parser.items('floats')]
    _conf_strings = [(key, str(value)) for key, value in parser.items('strings')]
    return dict(_conf_ints + _conf_floats + _conf_strings)


# We use a number of buckets and pad to the closest one for efficiency.
# See seq2seq_model.Seq2SeqModel for details of how they work.
_buckets = [(5, 10), (10, 15), (20, 25), (30, 40), (50, 65)]


def read_data(source_path, target_path, max_size=None):
    """Read data from source and target files and put into buckets.

    Args:
      source_path: path to the files with token-ids for the source language.
      target_path: path to the file with token-ids for the target language;
        it must be aligned with the source file: n-th line contains the desired
        output for n-th line from the source_path.
      max_size: maximum number of lines to read, all other will be ignored;
        if 0 or None, data files will be read completely (no limit).

    Returns:
      data_set: a list of length len(_buckets); data_set[n] contains a list of
        (source, target) pairs read from the provided data files that fit
        into the n-th bucket, i.e., such that len(source) < _buckets[n][0] and
        len(target) < _buckets[n][1]; source and target are lists of token-ids.
    """
    data_set = [[] for _ in _buckets]
    with tf.gfile.GFile(source_path, mode="r") as source_file:
        with tf.gfile.GFile(target_path, mode="r") as target_file:
            source, target = source_file.readline(), target_file.readline()
            counter = 0
            while source and target and (not max_size or counter < max_size):
                counter += 1
                if counter % 100000 == 0:
                    print("  reading data line %d" % counter)
                    sys.stdout.flush()
                source_ids = [int(x) for x in source.split()]
                target_ids = [int(x) for x in target.split()]
                target_ids.append(data_utils.EOS_ID)
                for bucket_id, (source_size, target_size) in enumerate(_buckets):
                    if len(source_ids) < source_size and len(target_ids) < target_size:
                        data_set[bucket_id].append([source_ids, target_ids])
                        break
                source, target = source_file.readline(), target_file.readline()
    return data_set


def create_model(session, forward_only):
    """Create model and initialize or load parameters"""
    model = seq2seq_model.Seq2SeqModel(gConfig['enc_vocab_size'], gConfig['dec_vocab_size'], _buckets,
                                       gConfig['layer_size'], gConfig['num_layers'], gConfig['max_gradient_norm'],
                                       gConfig['batch_size'], gConfig['learning_rate'],
                                       gConfig['learning_rate_decay_factor'], forward_only=forward_only)

    if 'pretrained_model' in gConfig:
        model.saver.restore(session, gConfig['pretrained_model'])
        return model

    ckpt = tf.train.get_checkpoint_state(gConfig['working_directory'])
    # the checkpoint filename has changed in recent versions of tensorflow
    checkpoint_suffix = ""
    if tf.__version__ > "0.12":
        checkpoint_suffix = ".index"
    if ckpt and tf.gfile.Exists(ckpt.model_checkpoint_path + checkpoint_suffix):
        print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
        model.saver.restore(session, ckpt.model_checkpoint_path)
    else:
        print("Created model with fresh parameters.")
        session.run(tf.initialize_all_variables())
    return model


def train():
    # prepare dataset
    print("Preparing data in %s" % gConfig['working_directory'])
    enc_train, dec_train, enc_dev, dec_dev, _, _ = data_utils.prepare_custom_data(gConfig['working_directory'],
                                                                                  gConfig['train_enc'],
                                                                                  gConfig['train_dec'],
                                                                                  gConfig['test_enc'],
                                                                                  gConfig['test_dec'],
                                                                                  gConfig['enc_vocab_size'],
                                                                                  gConfig['dec_vocab_size'])

    # Only allocate 2/3 of the gpu memory to allow for running gpu-based predictions while training:
    gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.666)
    config = tf.ConfigProto(gpu_options=gpu_options)
    config.gpu_options.allocator_type = 'BFC'

    with tf.Session(config=config) as sess:
        # Create model.
        print("Creating %d layers of %d units." % (gConfig['num_layers'], gConfig['layer_size']))
        model = create_model(sess, False)

        # Read data into buckets and compute their sizes.
        print("Reading development and training data (limit: %d)."
              % gConfig['max_train_data_size'])
        dev_set = read_data(enc_dev, dec_dev)
        train_set = read_data(enc_train, dec_train, gConfig['max_train_data_size'])
        train_bucket_sizes = [len(train_set[b]) for b in xrange(len(_buckets))]
        train_total_size = float(sum(train_bucket_sizes))

        # A bucket scale is a list of increasing numbers from 0 to 1 that we'll use
        # to select a bucket. Length of [scale[i], scale[i+1]] is proportional to
        # the size if i-th training bucket, as used later.
        train_buckets_scale = [sum(train_bucket_sizes[:i + 1]) / train_total_size
                               for i in xrange(len(train_bucket_sizes))]

        # This is the training loop.
        step_time, loss = 0.0, 0.0
        current_step = 0
        previous_losses = []
        while True:
            # Choose a bucket according to data distribution. We pick a random number
            # in [0, 1] and use the corresponding interval in train_buckets_scale.
            random_number_01 = np.random.random_sample()
            bucket_id = min([i for i in xrange(len(train_buckets_scale))
                             if train_buckets_scale[i] > random_number_01])

            # Get a batch and make a step.
            start_time = time.time()
            encoder_inputs, decoder_inputs, target_weights = model.get_batch(
                train_set, bucket_id)
            _, step_loss, _ = model.step(sess, encoder_inputs, decoder_inputs,
                                         target_weights, bucket_id, False)
            step_time += (time.time() - start_time) / gConfig['steps_per_checkpoint']
            loss += step_loss / gConfig['steps_per_checkpoint']
            current_step += 1

            # Once in a while, we save checkpoint, print statistics, and run evals.
            if current_step % gConfig['steps_per_checkpoint'] == 0:
                # Print statistics for the previous epoch.
                perplexity = math.exp(loss) if loss < 300 else float('inf')
                print("global step %d learning rate %.4f step-time %.2f perplexity "
                      "%.2f" % (model.global_step.eval(), model.learning_rate.eval(),
                                step_time, perplexity))
                # Decrease learning rate if no improvement was seen over last 10 times.
                if len(previous_losses) > 10 and loss > max(previous_losses[-10:]):
                    sess.run(model.learning_rate_decay_op)
                previous_losses.append(loss)
                # Save checkpoint and zero timer and loss.
                checkpoint_path = os.path.join(gConfig['working_directory'], "seq2seq.ckpt")
                model.saver.save(sess, checkpoint_path, global_step=model.global_step)
                step_time, loss = 0.0, 0.0
                # Run evals on development set and print their perplexity.
                for bucket_id in xrange(len(_buckets)):
                    if len(dev_set[bucket_id]) == 0:
                        print("  eval: empty bucket %d" % bucket_id)
                        continue
                    encoder_inputs, decoder_inputs, target_weights = model.get_batch(
                        dev_set, bucket_id)
                    _, eval_loss, _ = model.step(sess, encoder_inputs, decoder_inputs,
                                                 target_weights, bucket_id, True)
                    eval_ppx = math.exp(eval_loss) if eval_loss < 300 else float('inf')
                    print("  eval: bucket %d perplexity %.2f" % (bucket_id, eval_ppx))
                sys.stdout.flush()


def decode():
    # Only allocate part of the gpu memory when predicting.
    gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.2)
    config = tf.ConfigProto(gpu_options=gpu_options)

    with tf.Session(config=config) as sess:
        # Create model and load parameters.
        model = create_model(sess, True)
        model.batch_size = 1  # We decode one sentence at a time.

        # Load vocabularies.
        enc_vocab_path = os.path.join(gConfig['working_directory'], "vocab%d.enc" % gConfig['enc_vocab_size'])
        dec_vocab_path = os.path.join(gConfig['working_directory'], "vocab%d.dec" % gConfig['dec_vocab_size'])

        enc_vocab, _ = data_utils.initialize_vocabulary(enc_vocab_path)
        _, rev_dec_vocab = data_utils.initialize_vocabulary(dec_vocab_path)

        # Decode from standard input.
        sys.stdout.write("> ")
        sys.stdout.flush()
        sentence = sys.stdin.readline()
        while sentence:
            # Get token-ids for the input sentence.
            token_ids = data_utils.sentence_to_token_ids(sentence, enc_vocab)
            # Which bucket does it belong to?
            bucket_id = min([b for b in xrange(len(_buckets))
                             if _buckets[b][0] > len(token_ids)])
            # Get a 1-element batch to feed the sentence to the model.
            encoder_inputs, decoder_inputs, target_weights = model.get_batch(
                {bucket_id: [(token_ids, [])]}, bucket_id)
            # Get output logits for the sentence.
            _, _, output_logits = model.step(sess, encoder_inputs, decoder_inputs,
                                             target_weights, bucket_id, True)
            # This is a greedy decoder - outputs are just argmaxes of output_logits.
            outputs = [int(np.argmax(logit, axis=1)) for logit in output_logits]
            # If there is an EOS symbol in outputs, cut them at that point.
            if data_utils.EOS_ID in outputs:
                outputs = outputs[:outputs.index(data_utils.EOS_ID)]
            # Print out French sentence corresponding to outputs.
            print(" ".join([tf.compat.as_str(rev_dec_vocab[output]) for output in outputs]))
            print("> ", end="")
            sys.stdout.flush()
            sentence = sys.stdin.readline()


def self_test():
    """Test the translation model."""
    with tf.Session() as sess:
        print("Self-test for neural translation model.")
        # Create model with vocabularies of 10, 2 small buckets, 2 layers of 32.
        model = seq2seq_model.Seq2SeqModel(10, 10, [(3, 3), (6, 6)], 32, 2,
                                           5.0, 32, 0.3, 0.99, num_samples=8)
        sess.run(tf.initialize_all_variables())

        # Fake data set for both the (3, 3) and (6, 6) bucket.
        data_set = ([([1, 1], [2, 2]), ([3, 3], [4]), ([5], [6])],
                    [([1, 1, 1, 1, 1], [2, 2, 2, 2, 2]), ([3, 3, 3], [5, 6])])
        for _ in xrange(5):  # Train the fake model for 5 steps.
            bucket_id = random.choice([0, 1])
            encoder_inputs, decoder_inputs, target_weights = model.get_batch(
                data_set, bucket_id)
            model.step(sess, encoder_inputs, decoder_inputs, target_weights,
                       bucket_id, False)


def init_session(sess, conf='seq2seq.ini'):
    global gConfig
    gConfig = get_config(conf)

    # Create model and load parameters.
    model = create_model(sess, True)
    model.batch_size = 1  # We decode one sentence at a time.

    # Load vocabularies.
    enc_vocab_path = os.path.join(gConfig['working_directory'], "vocab%d.enc" % gConfig['enc_vocab_size'])
    dec_vocab_path = os.path.join(gConfig['working_directory'], "vocab%d.dec" % gConfig['dec_vocab_size'])

    enc_vocab, _ = data_utils.initialize_vocabulary(enc_vocab_path)
    _, rev_dec_vocab = data_utils.initialize_vocabulary(dec_vocab_path)

    return sess, model, enc_vocab, rev_dec_vocab


def decode_line(sess, model, enc_vocab, rev_dec_vocab, sentence):
    # Get token-ids for the input sentence.
    token_ids = data_utils.sentence_to_token_ids(sentence, enc_vocab)

    # Which bucket does it belong to?
    bucket_id = min([b for b in xrange(len(_buckets)) if _buckets[b][0] > len(token_ids)])

    # Get a 1-element batch to feed the sentence to the model.
    encoder_inputs, decoder_inputs, target_weights = model.get_batch({bucket_id: [(token_ids, [])]}, bucket_id)

    # Get output logits for the sentence.
    _, _, output_logits = model.step(sess, encoder_inputs, decoder_inputs, target_weights, bucket_id, True)

    # This is a greedy decoder - outputs are just argmaxes of output_logits.
    outputs = [int(np.argmax(logit, axis=1)) for logit in output_logits]

    # If there is an EOS symbol in outputs, cut them at that point.
    if data_utils.EOS_ID in outputs:
        outputs = outputs[:outputs.index(data_utils.EOS_ID)]

    return " ".join([tf.compat.as_str(rev_dec_vocab[output]) for output in outputs])


if __name__ == '__main__':
    if len(sys.argv) - 1:
        gConfig = get_config(sys.argv[1])
    else:
        # get configuration from seq2seq.ini
        gConfig = get_config()

    print('\n>> Mode : %s\n' % (gConfig['mode']))

    if gConfig['mode'] == 'train':
        # start training
        train()
    elif gConfig['mode'] == 'test':
        # interactive decode
        decode()
    else:
        # wrong way to execute "serve"
        #   Use : >> python ui/app.py
        #           uses seq2seq_serve.ini as conf file
        print('Serve Usage : >> python ui/app.py')
        print('# uses seq2seq_serve.ini as conf file')