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# Copyright 2018 Google LLC
#
# 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.
"""Train a network.
Usage:
BOARD_SIZE=19 python train.py tfrecord1 tfrecord2 tfrecord3
"""
import logging
from absl import app, flags
import numpy as np
import tensorflow as tf
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import bigtable_input
import dual_net
import preprocessing
import utils
# See www.moderndescartes.com/essays/shuffle_viz for discussion on sizing
flags.DEFINE_integer('shuffle_buffer_size', 2000,
'Size of buffer used to shuffle train examples.')
flags.DEFINE_integer('steps_to_train', None,
'Number of training steps to take. If not set, iterates '
'once over training data.')
flags.DEFINE_integer('window_size', 500000,
'Number of games to include in the window')
flags.DEFINE_float('filter_amount', 1.0,
'Fraction of positions to filter from golden chunks,'
'default, 1.0 (no filter)')
flags.DEFINE_string('export_path', None,
'Where to export the model after training.')
flags.DEFINE_bool('use_bt', False,
'Whether to use Bigtable as input. '
'(Only supported with --use_tpu, currently.)')
flags.DEFINE_bool('freeze', False,
'Whether to freeze the graph at the end of training.')
flags.register_multi_flags_validator(
['use_bt', 'use_tpu'],
lambda flags: flags['use_tpu'] if flags['use_bt'] else True,
'`use_bt` flag only valid with `use_tpu` as well')
@flags.multi_flags_validator(
['use_bt', 'cbt_project', 'cbt_instance', 'cbt_table'],
message='Cloud Bigtable configuration flags not correct')
def _bt_checker(flags_dict):
if not flags_dict['use_bt']:
return True
return (flags_dict['cbt_project']
and flags_dict['cbt_instance']
and flags_dict['cbt_table'])
# From dual_net.py
flags.declare_key_flag('work_dir')
flags.declare_key_flag('train_batch_size')
flags.declare_key_flag('num_tpu_cores')
flags.declare_key_flag('use_tpu')
FLAGS = flags.FLAGS
class EchoStepCounterHook(tf.train.StepCounterHook):
"""A hook that logs steps per second."""
def _log_and_record(self, elapsed_steps, elapsed_time, global_step):
s_per_sec = elapsed_steps / elapsed_time
logging.info("{}: {:.3f} steps per second".format(global_step, s_per_sec))
super()._log_and_record(elapsed_steps, elapsed_time, global_step)
def compute_update_ratio(weight_tensors, before_weights, after_weights):
"""Compute the ratio of gradient norm to weight norm."""
deltas = [after - before for after,
before in zip(after_weights, before_weights)]
delta_norms = [np.linalg.norm(d.ravel()) for d in deltas]
weight_norms = [np.linalg.norm(w.ravel()) for w in before_weights]
ratios = [d / w for d, w in zip(delta_norms, weight_norms)]
all_summaries = [
tf.Summary.Value(tag='update_ratios/' +
tensor.name, simple_value=ratio)
for tensor, ratio in zip(weight_tensors, ratios)]
return tf.Summary(value=all_summaries)
class UpdateRatioSessionHook(tf.train.SessionRunHook):
"""A hook that computes ||grad|| / ||weights|| (using frobenius norm)."""
def __init__(self, output_dir, every_n_steps=1000):
self.output_dir = output_dir
self.every_n_steps = every_n_steps
self.before_weights = None
self.file_writer = None
self.weight_tensors = None
self.global_step = None
def begin(self):
# These calls only works because the SessionRunHook api guarantees this
# will get called within a graph context containing our model graph.
self.file_writer = tf.summary.FileWriterCache.get(self.output_dir)
self.weight_tensors = tf.trainable_variables()
self.global_step = tf.train.get_or_create_global_step()
def before_run(self, run_context):
global_step = run_context.session.run(self.global_step)
if global_step % self.every_n_steps == 0:
self.before_weights = run_context.session.run(self.weight_tensors)
def after_run(self, run_context, run_values):
global_step = run_context.session.run(self.global_step)
if self.before_weights is not None:
after_weights = run_context.session.run(self.weight_tensors)
weight_update_summaries = compute_update_ratio(
self.weight_tensors, self.before_weights, after_weights)
self.file_writer.add_summary(
weight_update_summaries, global_step)
self.before_weights = None
def train(*tf_records: "Records to train on"):
"""Train on examples."""
tf.logging.set_verbosity(tf.logging.INFO)
estimator = dual_net.get_estimator()
effective_batch_size = FLAGS.train_batch_size
if FLAGS.use_tpu:
effective_batch_size *= FLAGS.num_tpu_cores
if FLAGS.use_tpu:
if FLAGS.use_bt:
def _input_fn(params):
games = bigtable_input.GameQueue(
FLAGS.cbt_project, FLAGS.cbt_instance, FLAGS.cbt_table)
games_nr = bigtable_input.GameQueue(
FLAGS.cbt_project, FLAGS.cbt_instance, FLAGS.cbt_table + '-nr')
return preprocessing.get_tpu_bt_input_tensors(
games,
games_nr,
params['batch_size'],
number_of_games=FLAGS.window_size,
random_rotation=True)
else:
def _input_fn(params):
return preprocessing.get_tpu_input_tensors(
params['batch_size'],
tf_records,
random_rotation=True)
# Hooks are broken with TPUestimator at the moment.
hooks = []
else:
def _input_fn():
return preprocessing.get_input_tensors(
FLAGS.train_batch_size,
tf_records,
filter_amount=FLAGS.filter_amount,
shuffle_buffer_size=FLAGS.shuffle_buffer_size,
random_rotation=True)
hooks = [UpdateRatioSessionHook(FLAGS.work_dir),
EchoStepCounterHook(output_dir=FLAGS.work_dir)]
steps = FLAGS.steps_to_train
logging.info("Training, steps = %s, batch = %s -> %s examples",
steps or '?', effective_batch_size,
(steps * effective_batch_size) if steps else '?')
if FLAGS.use_bt:
games = bigtable_input.GameQueue(
FLAGS.cbt_project, FLAGS.cbt_instance, FLAGS.cbt_table)
if not games.read_wait_cell():
games.require_fresh_games(20000)
latest_game = games.latest_game_number
index_from = max(latest_game, games.read_wait_cell())
print("== Last game before training:", latest_game, flush=True)
print("== Wait cell:", games.read_wait_cell(), flush=True)
try:
estimator.train(_input_fn, steps=steps, hooks=hooks)
if FLAGS.use_bt:
bigtable_input.set_fresh_watermark(games, index_from,
FLAGS.window_size)
except:
if FLAGS.use_bt:
games.require_fresh_games(0)
raise
def main(argv):
"""Train on examples and export the updated model weights."""
tf_records = argv[1:]
logging.info("Training on %s records: %s to %s",
len(tf_records), tf_records[0], tf_records[-1])
with utils.logged_timer("Training"):
train(*tf_records)
if FLAGS.export_path:
dual_net.export_model(FLAGS.export_path)
if FLAGS.freeze:
if FLAGS.use_tpu:
dual_net.freeze_graph_tpu(FLAGS.export_path)
else:
dual_net.freeze_graph(FLAGS.export_path)
if __name__ == "__main__":
app.run(main)