def testNoneHparams(self):
@registry.register_hparams
def hp():
pass
with self.assertRaisesRegexp(TypeError, "is None"):
registry.hparams("hp")
def __prepare_model(self, train_mode=False):
"""Prepare utilities for decoding."""
hparams = registry.hparams(self.params.hparams_set)
hparams.problem = self.problem
hparams.problem_hparams = self.problem.get_hparams(hparams)
if self.params.hparams:
tf.logging.info("Overriding hparams in %s with %s",
self.params.hparams_set,
self.params.hparams)
hparams = hparams.parse(self.params.hparams)
trainer_run_config = g2p_trainer_utils.create_run_config(hparams,
self.params)
if train_mode:
exp_fn = g2p_trainer_utils.create_experiment_fn(self.params, self.problem)
self.exp = exp_fn(trainer_run_config, hparams)
decode_hp = decoding.decode_hparams(self.params.decode_hparams)
estimator = trainer_lib.create_estimator(
self.params.model_name,
hparams,
trainer_run_config,
decode_hparams=decode_hp,
use_tpu=False)
return estimator, decode_hp, hparams
def training_loop_hparams_from_scoped_overrides(scoped_overrides, trial_id):
"""Create HParams suitable for training loop from scoped HParams.
Args:
scoped_overrides: HParams, with keys all scoped by one of HP_SCOPES. These
parameters are overrides for the base HParams created by
create_loop_hparams.
trial_id: str, trial identifier. This is used to register unique HParams
names for the underlying model and ppo HParams.
Returns:
HParams suitable for passing to training_loop.
"""
trial_hp_overrides = scoped_overrides.values()
# Create loop, model, and ppo base HParams
loop_hp = create_loop_hparams()
model_hp_name = trial_hp_overrides.get(
"loop.generative_model_params", loop_hp.generative_model_params)
model_hp = registry.hparams(model_hp_name).parse(FLAGS.hparams)
base_algo_params_name = trial_hp_overrides.get(
"loop.base_algo_params", loop_hp.base_algo_params)
algo_hp = registry.hparams(base_algo_params_name)
# Merge them and then override with the scoped overrides
combined_hp = merge_unscoped_hparams(
zip(HP_SCOPES, [loop_hp, model_hp, algo_hp]))
combined_hp.override_from_dict(trial_hp_overrides)
# Split out the component hparams
loop_hp, model_hp, algo_hp = (
split_scoped_hparams(HP_SCOPES, combined_hp))
# Dynamic register the model hp and set the new name in loop_hp
model_hp_name = "model_hp_%s" % str(trial_id)
dynamic_register_hparams(model_hp_name, model_hp)
loop_hp.generative_model_params = model_hp_name
# Dynamic register the algo hp and set the new name in loop_hp
algo_hp_name = "algo_hp_%s" % str(trial_id)
dynamic_register_hparams(algo_hp_name, algo_hp)
loop_hp.base_algo_params = algo_hp_name
return loop_hp
def testNamedRegistration(self):
@registry.register_hparams("a")
def my_hparams_set():
return 7
@registry.register_ranged_hparams("a")
def my_hparams_range(_):
pass
self.assertEqual(registry.hparams("a"), my_hparams_set())
self.assertTrue(registry.ranged_hparams("a") is my_hparams_range)
def create_hparams(hparams_set,
hparams_overrides_str="",
data_dir=None,
problem_name=None):
hparams = registry.hparams(hparams_set)()
if hparams_overrides_str:
hparams = hparams.parse(hparams_overrides_str)
if data_dir:
hparams.add_hparam("data_dir", data_dir)
if problem_name:
add_problem_hparams(hparams, problem_name)
return hparams
def testHParamSet(self):
@registry.register_hparams
def my_hparams_set():
return 3
@registry.register_ranged_hparams
def my_hparams_range(_):
pass
self.assertEqual(registry.hparams("my_hparams_set"), my_hparams_set())
self.assertTrue(
registry.ranged_hparams("my_hparams_range") is my_hparams_range)
def testExperiment(self):
exp_fn = lib.create_experiment_fn(
"transformer",
"tiny_algo",
trainer_utils_test.TrainerUtilsTest.data_dir,
train_steps=1,
eval_steps=1,
min_eval_frequency=1,
use_tpu=False)
run_config = lib.create_run_config(num_gpus=0, use_tpu=False)
hparams = registry.hparams("transformer_tiny_tpu")()
exp = exp_fn(run_config, hparams)
exp.test()
def testExperiment(self):
exp_fn = trainer_lib.create_experiment_fn(
"transformer",
"tiny_algo",
self.data_dir,
train_steps=1,
eval_steps=1,
min_eval_frequency=1,
use_tpu=False)
run_config = trainer_lib.create_run_config(
model_dir=self.data_dir, num_gpus=0, use_tpu=False)
hparams = registry.hparams("transformer_tiny_tpu")
exp = exp_fn(run_config, hparams)
exp.test()
def __init__(self,
hparams,
mode=tf.estimator.ModeKeys.TRAIN,
problem_hparams=None,
data_parallelism=None,
decode_hparams=None):
assert hparams.distill_phase in ["train", "distill"]
if hparams.distill_phase == "train" and hparams.teacher_learning_rate:
hparams.learning_rate = hparams.teacher_learning_rate
elif hparams.distill_phase == "distill" and hparams.student_learning_rate:
hparams.learning_rate = hparams.student_learning_rate
self.teacher_hparams = registry.hparams(hparams.teacher_hparams)
self.teacher_model = registry.model(
hparams.teacher_model)(self.teacher_hparams, mode, problem_hparams,
data_parallelism, decode_hparams)
self.student_hparams = registry.hparams(hparams.student_hparams)
self.student_model = registry.model(
hparams.student_model)(self.student_hparams, mode, problem_hparams,
data_parallelism, decode_hparams)
super(Distillation, self).__init__(hparams, mode, problem_hparams,
data_parallelism, decode_hparams)
def create_hparams(hparams_set,
hparams_overrides_str="",
data_dir=None,
problem_name=None):
"""Create HParams with data_dir and problem hparams, if kwargs provided."""
hparams = registry.hparams(hparams_set)
if data_dir:
hparams.add_hparam("data_dir", data_dir)
if problem_name:
add_problem_hparams(hparams, problem_name)
if hparams_overrides_str:
tf.logging.info("Overriding hparams in %s with %s", hparams_set,
hparams_overrides_str)
hparams = hparams.parse(hparams_overrides_str)
return hparams
def create_hparams(hparams_set,
hparams_overrides_str="",
data_dir=None,
problem_name=None):
"""Create HParams with data_dir and problem hparams, if kwargs provided."""
hparams = registry.hparams(hparams_set)
if data_dir:
hparams.add_hparam("data_dir", data_dir)
if problem_name:
add_problem_hparams(hparams, problem_name)
if hparams_overrides_str:
tf.logging.info("Overriding hparams in %s with %s", hparams_set,
hparams_overrides_str)
hparams = hparams.parse(hparams_overrides_str)
return hparams
def testExperimentWithClass(self):
exp_fn = trainer_lib.create_experiment_fn(
"transformer",
algorithmic.TinyAlgo(),
algorithmic.TinyAlgo.data_dir,
train_steps=1,
eval_steps=1,
min_eval_frequency=1,
use_tpu=False)
run_config = trainer_lib.create_run_config(
model_name="transformer",
model_dir=algorithmic.TinyAlgo.data_dir,
num_gpus=0,
use_tpu=False)
hparams = registry.hparams("transformer_tiny_tpu")
exp = exp_fn(run_config, hparams)
exp.test()
def testExperimentWithClass(self):
exp_fn = trainer_lib.create_experiment_fn(
"transformer",
algorithmic.TinyAlgo(),
algorithmic.TinyAlgo.data_dir,
train_steps=1,
eval_steps=1,
min_eval_frequency=1,
use_tpu=False)
run_config = trainer_lib.create_run_config(
model_name="transformer",
model_dir=algorithmic.TinyAlgo.data_dir,
num_gpus=0,
use_tpu=False)
hparams = registry.hparams("transformer_tiny_tpu")
exp = exp_fn(run_config, hparams)
exp.test()
def _create_hparams(self, src_vocab_size, trg_vocab_size, hparams_set_name,
problem_name):
"""Creates hparams object.
This method corresponds to create_hparams() in tensor2tensor's
trainer_utils module, but replaces the feature encoders with
DummyFeatureEncoder's.
Args:
src_vocab_size (int): Source vocabulary size.
trg_vocab_size (int): Target vocabulary size.
hparams_set_name (string): T2T hparams set name.
problem_name (string): T2T problem name.
Returns:
hparams object.
Raises:
LookupError if the problem name is not in the registry or
uses the old style problem_hparams.
"""
hparams = registry.hparams(hparams_set_name)()
problem = registry.problem(problem_name)
# The following hack is necessary to prevent the problem from creating
# the default TextEncoders, which would fail due to the lack of a
# vocabulary file.
problem._encoders = {
"inputs": DummyTextEncoder(vocab_size=src_vocab_size),
"targets": DummyTextEncoder(vocab_size=trg_vocab_size)
}
try:
hparams.add_hparam("max_terminal_id", self.max_terminal_id)
except:
if hparams.max_terminal_id != self.max_terminal_id:
logging.warn("T2T max_terminal_id does not match (%d!=%d)" %
(hparams.max_terminal_id, self.max_terminal_id))
try:
hparams.add_hparam("closing_bracket_id", self.pop_id)
except:
if hparams.closing_bracket_id != self.pop_id:
logging.warn("T2T closing_bracket_id does not match (%d!=%d)" %
(hparams.closing_bracket_id, self.pop_id))
p_hparams = problem.get_hparams(hparams)
hparams.problem_instances = [problem]
hparams.problems = [p_hparams]
return hparams
def create_hparams(hparams_set,
hparams_overrides_str="",
data_dir=None,
problem_name=None):
ipdb.set_trace()
# 用命令行接收的超参数代替默认参数
"""Create HParams with data_dir and problem hparams, if kwargs provided."""
# 这一步，解析model中注册的所有hparams添加到当前hparams中
# registry._HPARAMS中存的是每个model注册hparams的函数,此步执行函数
hparams = registry.hparams(hparams_set)()
if data_dir:
hparams.add_hparam("data_dir", data_dir)
if problem_name:
add_problem_hparams(hparams, problem_name)
if hparams_overrides_str:
tf.logging.info("Overriding hparams in %s with %s", hparams_set,
hparams_overrides_str)
hparams = hparams.parse(hparams_overrides_str)
return hparams
def create_hparams(params_id, data_dir):
"""Returns hyperparameters, including any flag value overrides.
If the hparams FLAG is set, then it will use any values specified in
hparams to override any individually-set hyperparameter. This logic
allows tuners to override hyperparameter settings to find optimal values.
Args:
params_id: which set of parameters to choose (must be in _PARAMS above).
data_dir: the directory containing the training data.
Returns:
The hyperparameters as a tf.contrib.training.HParams object.
"""
hparams = registry.hparams(params_id)()
hparams.add_hparam("data_dir", data_dir)
# Command line flags override any of the preceding hyperparameter values.
if FLAGS.hparams:
hparams = hparams.parse(FLAGS.hparams)
return add_problem_hparams(hparams, FLAGS.problems)
def create_hparams(params_id, data_dir, passed_hparams=None):
"""Returns hyperparameters, including any flag value overrides.
If the hparams FLAG is set, then it will use any values specified in
hparams to override any individually-set hyperparameter. This logic
allows tuners to override hyperparameter settings to find optimal values.
Args:
params_id: which set of parameters to choose (must be in _PARAMS above).
data_dir: the directory containing the training data.
passed_hparams: command-line overrides for some hparams.
Returns:
The hyperparameters as a tf.contrib.training.HParams object.
"""
hparams = registry.hparams(params_id)()
hparams.add_hparam("data_dir", data_dir)
# Command line flags override any of the preceding hyperparameter values.
if passed_hparams:
hparams = hparams.parse(passed_hparams)
return hparams
def main(_):
# gym.logger.set_level(gym.logger.DEBUG)
hparams = registry.hparams(FLAGS.loop_hparams_set)
hparams.parse(FLAGS.loop_hparams)
# Not important for experiments past 2018
if "wm_policy_param_sharing" not in hparams.values().keys():
hparams.add_hparam("wm_policy_param_sharing", False)
directories = player_utils.infer_paths(output_dir=FLAGS.output_dir,
world_model=FLAGS.wm_dir,
policy=FLAGS.policy_dir,
data=FLAGS.episodes_data_dir)
epoch = FLAGS.epoch if FLAGS.epoch == "last" else int(FLAGS.epoch)
if FLAGS.simulated_env:
env = player_utils.load_data_and_make_simulated_env(
directories["data"],
directories["world_model"],
hparams,
which_epoch_data=epoch)
else:
env = player_utils.setup_and_load_epoch(hparams,
data_dir=directories["data"],
which_epoch_data=epoch)
env = FlatBatchEnv(env)
env = PlayerEnvWrapper(env) # pylint: disable=redefined-variable-type
env = player_utils.wrap_with_monitor(env, FLAGS.video_dir)
if FLAGS.dry_run:
for _ in range(5):
env.reset()
for i in range(50):
env.step(i % 3)
env.step(PlayerEnvWrapper.RESET_ACTION) # reset
return
play.play(env, zoom=FLAGS.zoom, fps=FLAGS.fps)
def __prepare_model(self):
"""Prepare utilities for decoding."""
hparams = registry.hparams(self.params.hparams_set)
hparams.problem = self.problem
hparams.problem_hparams = self.problem.get_hparams(hparams)
if self.params.hparams:
tf.logging.info("Overriding hparams in %s with %s",
self.params.hparams_set, self.params.hparams)
hparams = hparams.parse(self.params.hparams)
trainer_run_config = g2p_trainer_utils.create_run_config(
hparams, self.params)
exp_fn = g2p_trainer_utils.create_experiment_fn(
self.params, self.problem)
self.exp = exp_fn(trainer_run_config, hparams)
decode_hp = decoding.decode_hparams(self.params.decode_hparams)
estimator = trainer_lib.create_estimator(self.params.model_name,
hparams,
trainer_run_config,
decode_hparams=decode_hp,
use_tpu=False)
return estimator, decode_hp, hparams
def main(_):
hparams = registry.hparams(FLAGS.loop_hparams_set)
hparams.parse(FLAGS.loop_hparams)
output_dir = FLAGS.output_dir
subdirectories = ["data", "tmp", "world_model", "ppo"]
using_autoencoder = hparams.autoencoder_train_steps > 0
if using_autoencoder:
subdirectories.append("autoencoder")
directories = setup_directories(output_dir, subdirectories)
if hparams.game in gym_problems_specs.ATARI_GAMES:
game_with_mode = hparams.game + "_deterministic-v4"
else:
game_with_mode = hparams.game
if using_autoencoder:
simulated_problem_name = (
"gym_simulated_discrete_problem_with_agent_on_%s_autoencoded" %
game_with_mode)
else:
simulated_problem_name = (
"gym_simulated_discrete_problem_with_agent_on_%s" % game_with_mode)
if simulated_problem_name not in registry.list_problems():
tf.logging.info(
"Game Problem %s not found; dynamically registering",
simulated_problem_name)
gym_problems_specs.create_problems_for_game(
hparams.game, game_mode="Deterministic-v4")
epoch = hparams.epochs - 1
epoch_data_dir = os.path.join(directories["data"], str(epoch))
ppo_model_dir = directories["ppo"]
world_model_dir = directories["world_model"]
gym_problem = registry.problem(simulated_problem_name)
model_hparams = trainer_lib.create_hparams(hparams.generative_model_params)
environment_spec = copy.copy(gym_problem.environment_spec)
environment_spec.simulation_random_starts = hparams.simulation_random_starts
batch_env_hparams = trainer_lib.create_hparams(hparams.ppo_params)
batch_env_hparams.add_hparam("model_hparams", model_hparams)
batch_env_hparams.add_hparam("environment_spec", environment_spec)
batch_env_hparams.num_agents = 1
with temporary_flags({
"problem": simulated_problem_name,
"model": hparams.generative_model,
"hparams_set": hparams.generative_model_params,
"output_dir": world_model_dir,
"data_dir": epoch_data_dir,
}):
sess = tf.Session()
env = DebugBatchEnv(batch_env_hparams, sess)
sess.run(tf.global_variables_initializer())
env.initialize()
env_model_loader = tf.train.Saver(tf.global_variables("next_frame*"))
trainer_lib.restore_checkpoint(world_model_dir,
env_model_loader,
sess,
must_restore=True)
model_saver = tf.train.Saver(
tf.global_variables(".*network_parameters.*"))
trainer_lib.restore_checkpoint(ppo_model_dir, model_saver, sess)
key_mapping = gym_problem.env.env.get_keys_to_action()
# map special codes
key_mapping[()] = 100
key_mapping[(ord("r"), )] = 101
key_mapping[(ord("p"), )] = 102
play.play(env, zoom=2, fps=10, keys_to_action=key_mapping)
def testUnknownHparams(self):
with self.assertRaisesRegexp(LookupError, "never registered"):
registry.hparams("not_registered")
with self.assertRaisesRegexp(LookupError, "never registered"):
registry.ranged_hparams("not_registered")
def benchmark(self, ckpt_dir, outer_steps=100, inner_steps=1000):
"""Run repeatedly on dummy data to benchmark inference."""
# Turn off Grappler optimizations.
options = {"disable_meta_optimizer": True}
tf.config.optimizer.set_experimental_options(options)
# Create the model outside the loop body.
hparams = registry.hparams(self.hparams_set)
hparams_lib.add_problem_hparams(hparams, self.problem_name)
model_cls = registry.model(self.model_name)
model = model_cls(hparams, tf.estimator.ModeKeys.EVAL)
# Run only the model body (no data pipeline) on device.
feature_shape = [
hparams.batch_size, 3 * self.image_size * self.image_size
]
features = {"targets": tf.zeros(feature_shape, dtype=tf.int32)}
# Call the model once to initialize the variables. Note that
# this should never execute.
with tf.variable_scope(self.model_name) as vso:
transformed_features = model.bottom(features)
with tf.variable_scope("body") as vsi:
body_out = model.body(transformed_features)
logits = model.top(body_out, features)
model.loss(logits, features)
def call_model(features):
with tf.variable_scope(vso, reuse=tf.AUTO_REUSE):
transformed_features = model.bottom(features)
with tf.variable_scope(vsi, reuse=tf.AUTO_REUSE):
body_out = model.body(transformed_features)
logits = model.top(body_out, features)
return model.loss(logits, features)
# Run the function body in a loop to amortize session overhead.
loop_index = tf.zeros([], dtype=tf.int32)
initial_loss = (tf.zeros([]), tf.zeros([]))
def loop_cond(idx, _):
return tf.less(idx, tf.constant(inner_steps, dtype=tf.int32))
def loop_body(idx, _):
return idx + 1, call_model(features)
benchmark_op = tf.while_loop(loop_cond,
loop_body, [loop_index, initial_loss],
parallel_iterations=1,
back_prop=False)
session_config = tf.ConfigProto(gpu_options=tf.GPUOptions(
allow_growth=False, per_process_gpu_memory_fraction=0.95))
run_metadata = tf.RunMetadata()
with tf.Session(config=session_config) as sess:
self.restore_model(sess, ckpt_dir)
tps = []
for idx in range(outer_steps):
start_time = time.time()
sess.run(benchmark_op, run_metadata=run_metadata)
elapsed_time = time.time() - start_time
tps.append(inner_steps * hparams.batch_size * (64 * 64 * 3) /
elapsed_time)
logging.error("Iterations %d processed %f TPS.", idx, tps[-1])
# Skip the first iteration where all the setup and allocation happens.
tps = np.asarray(tps[1:])
logging.error("Mean/Std/Max/Min throughput = %f / %f / %f / %f",
np.mean(tps), np.std(tps), tps.max(), tps.min())
def main(_):
# gym.logger.set_level(gym.logger.DEBUG)
hparams = registry.hparams(FLAGS.loop_hparams_set)
hparams.parse(FLAGS.loop_hparams)
# Not important for experiments past 2018
if "wm_policy_param_sharing" not in hparams.values().keys():
hparams.add_hparam("wm_policy_param_sharing", False)
directories = player_utils.infer_paths(output_dir=FLAGS.output_dir,
world_model=FLAGS.wm_dir,
policy=FLAGS.policy_dir,
data=FLAGS.episodes_data_dir)
if FLAGS.game_from_filenames:
hparams.set_hparam(
"game",
player_utils.infer_game_name_from_filenames(directories["data"]))
action_meanings = gym.make(full_game_name(hparams.game)).\
unwrapped.get_action_meanings()
epoch = FLAGS.epoch if FLAGS.epoch == "last" else int(FLAGS.epoch)
def make_real_env():
env = player_utils.setup_and_load_epoch(hparams,
data_dir=directories["data"],
which_epoch_data=None)
env = FlatBatchEnv(env) # pylint: disable=redefined-variable-type
return env
def make_simulated_env(setable_initial_frames, which_epoch_data):
env = player_utils.load_data_and_make_simulated_env(
directories["data"],
directories["world_model"],
hparams,
which_epoch_data=which_epoch_data,
setable_initial_frames=setable_initial_frames)
return env
if FLAGS.sim_and_real:
sim_env = make_simulated_env(which_epoch_data=None,
setable_initial_frames=True)
real_env = make_real_env()
env = SimAndRealEnvPlayer(real_env, sim_env, action_meanings)
else:
if FLAGS.simulated_env:
env = make_simulated_env( # pylint: disable=redefined-variable-type
which_epoch_data=epoch,
setable_initial_frames=False)
else:
env = make_real_env()
env = SingleEnvPlayer(env, action_meanings) # pylint: disable=redefined-variable-type
env = player_utils.wrap_with_monitor(env, FLAGS.video_dir)
if FLAGS.dry_run:
env.unwrapped.get_keys_to_action()
for _ in range(5):
env.reset()
for i in range(50):
env.step(i % 3)
env.step(PlayerEnv.RETURN_DONE_ACTION) # reset
return
play.play(env, zoom=FLAGS.zoom, fps=FLAGS.fps)
def main(_):
hparams = registry.hparams(FLAGS.loop_hparams_set)
hparams.parse(FLAGS.loop_hparams)
output_dir = FLAGS.output_dir
subdirectories = ["data", "tmp", "world_model", "ppo"]
using_autoencoder = hparams.autoencoder_train_steps > 0
if using_autoencoder:
subdirectories.append("autoencoder")
directories = setup_directories(output_dir, subdirectories)
if hparams.game in gym_env.ATARI_GAMES:
game_with_mode = hparams.game + "_deterministic-v4"
else:
game_with_mode = hparams.game
if using_autoencoder:
simulated_problem_name = (
"gym_simulated_discrete_problem_with_agent_on_%s_autoencoded"
% game_with_mode)
else:
simulated_problem_name = ("gym_simulated_discrete_problem_with_agent_on_%s"
% game_with_mode)
if simulated_problem_name not in registry.list_problems():
tf.logging.info("Game Problem %s not found; dynamically registering",
simulated_problem_name)
gym_env.register_game(hparams.game, game_mode="Deterministic-v4")
epoch = hparams.epochs-1
epoch_data_dir = os.path.join(directories["data"], str(epoch))
ppo_model_dir = directories["ppo"]
world_model_dir = directories["world_model"]
gym_problem = registry.problem(simulated_problem_name)
model_hparams = trainer_lib.create_hparams(hparams.generative_model_params)
environment_spec = copy.copy(gym_problem.environment_spec)
environment_spec.simulation_random_starts = hparams.simulation_random_starts
batch_env_hparams = trainer_lib.create_hparams(hparams.ppo_params)
batch_env_hparams.add_hparam("model_hparams", model_hparams)
batch_env_hparams.add_hparam("environment_spec", environment_spec)
batch_env_hparams.num_agents = 1
with temporary_flags({
"problem": simulated_problem_name,
"model": hparams.generative_model,
"hparams_set": hparams.generative_model_params,
"output_dir": world_model_dir,
"data_dir": epoch_data_dir,
}):
sess = tf.Session()
env = DebugBatchEnv(batch_env_hparams, sess)
sess.run(tf.global_variables_initializer())
env.initialize()
env_model_loader = tf.train.Saver(
tf.global_variables("next_frame*"))
trainer_lib.restore_checkpoint(world_model_dir, env_model_loader, sess,
must_restore=True)
model_saver = tf.train.Saver(
tf.global_variables(".*network_parameters.*"))
trainer_lib.restore_checkpoint(ppo_model_dir, model_saver, sess)
key_mapping = gym_problem.env.env.get_keys_to_action()
# map special codes
key_mapping[()] = 100
key_mapping[(ord("r"),)] = 101
key_mapping[(ord("p"),)] = 102
play.play(env, zoom=2, fps=10, keys_to_action=key_mapping)
def test_train_pong(self):
hparams = registry.hparams("pong_model_free")
hparams.epochs_num = 2
hparams.num_agents = 2
hparams.epoch_length = 3
rl_trainer_lib.train(hparams)
def main():
args = parser.parse_args()
enc = encoder.get_encoder(args.model_name)
hparams = model.default_hparams()
hparams.res_dropout = args.dropout
hparams.attn_dropout = args.dropout
epsilon = -1e10
if args.dtype == 'float32':
hparams.dtype = tf.float32
elif args.dtype == 'float16':
hparams.dtype = tf.float16
epsilon = -65500
elif args.dtype == 'bfloat16':
hparams.dtype = tf.bfloat16
epsilon = -65500
else:
print('Unknown dtype', args.dtype)
if args.float16:
hparams.dtype = tf.bfloat16
epsilon = -65500
with open(os.path.join('models', args.model_name, 'hparams.json')) as f:
hparams.override_from_dict(json.load(f))
if args.n_ctx >= 0:
hparams.n_ctx=args.n_ctx
if args.n_embd >= 0:
hparams.n_embd=args.n_embd
if args.n_head >= 0:
hparams.n_head=args.n_head
if args.n_layer >= 0:
hparams.n_layer=args.n_layer
if args.sample_length < 0:
args.sample_length = hparams.n_ctx - 1
if args.sample_length > hparams.n_ctx:
raise ValueError(
"Can't get samples longer than window size: %s" % hparams.n_ctx)
if args.sample_ctx < 0:
args.sample_ctx = hparams.n_ctx
if args.model_name == '345M':
args.memory_saving_gradients = True
if args.optimizer == 'adam':
args.only_train_transformer_layers = True
config = tf.ConfigProto()
if args.allow_growth:
config.gpu_options.allow_growth = True
if args.disable_layout_optimizer:
config.graph_options.rewrite_options.layout_optimizer = rewriter_config_pb2.RewriterConfig.OFF
with tflex.Session(config=config, init_tpu=args.init_tpu) as sess:
context = tf.placeholder(tf.int32, [args.batch_size, None])
context_in = randomize(context, hparams, args.noise)
output = model.model(hparams=hparams, X=context_in)
loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=context[:, 1:], logits=output['logits'][:, :-1]))
if args.val_every > 0:
val_context = tf.placeholder(tf.int32, [args.val_batch_size, None])
val_output = model.model(hparams=hparams, X=val_context)
val_loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=val_context[:, 1:], logits=val_output['logits'][:, :-1]))
val_loss_summary = tf.summary.scalar('val_loss', val_loss)
tf_sample = sample.sample_sequence(
hparams=hparams,
length=args.sample_length,
context=context,
batch_size=args.batch_size,
temperature=1.0,
top_k=args.top_k,
top_p=args.top_p,
epsilon=epsilon)
all_vars = [v for v in tf.trainable_variables() if 'model' in v.name]
train_vars = [v for v in all_vars if '/h' in v.name] if args.only_train_transformer_layers else all_vars
parameter_count = sum([np.prod(v.shape.as_list()) for v in train_vars])
print("This model is using %d parameters (%.2fM)" % (parameter_count, parameter_count/(1024.0*1024.0)))
with tf.variable_scope(tf.get_variable_scope().name, reuse=tf.AUTO_REUSE):
global_step = tflex.get_variable('global_step') or tf.get_variable('global_step', shape=(), dtype=tf.int32, trainable=False)
current_step = args.learning_rate_initial_step
global_step.load(current_step, session=sess)
if args.learning_rate_cos:
lr = tflex_sgdr.sgdr_decay_with_warmup(args.learning_rate, global_step,
warmup_steps=args.learning_rate_warmup, initial_period_steps=args.learning_rate_period, learning_rate_min=args.learning_rate_min)
else:
lr = tflex.get_variable('learn_rate') or tf.get_variable('learn_rate', shape=(), dtype=tf.float32, trainable=False)
lr.load(args.learning_rate, session=sess)
def update_lr(rate=None, step=None):
if not args.learning_rate_cos:
if step is None:
step = global_step.eval(session=sess)
if rate is None:
rate = args.learning_rate
if callable(rate):
rate = rate(step)
lr.load(rate, session=sess)
return lr.eval(session=sess)
@tflex.register_command
def set_learning_rate():
print("Current learn rate: %0.8f" % update_lr())
print("New learn rate?")
rate = input('')
if not rate:
print("Empty input; not changing anything.")
else:
try:
rate = float(rate)
except:
print("Invalid input; must be a float")
print("Setting learn rate to %0.8f" % rate)
args.learning_rate = rate
if args.optimizer == 'adam':
opt = tf.train.AdamOptimizer(learning_rate=lr)
elif args.optimizer == 'sgd':
opt = tf.train.GradientDescentOptimizer(learning_rate=lr)
elif args.optimizer == 'ada':
import tensor2tensor.utils.optimize
from tensor2tensor.utils import hparam
import tensor2tensor.models.research
from tensor2tensor.utils import registry
ada_hparams = registry.hparams('afx_mimic_adam')
ada_hparams.optimizer_adafactor_beta1 = 0.0
ada_hparams.optimizer_adafactor_factored = True
opt = tensor2tensor.utils.optimize.adafactor(learning_rate=lr, hparams=ada_hparams)
else:
exit('Bad optimizer:', args.optimizer)
#if tpu_addr:
# # https://pulsejet.github.io/blog/posts/tpu-without-estimator/
# from tensorflow.contrib.tpu.python.tpu import tpu_function
# tpu_function.get_tpu_context().set_number_of_shards(8)
# opt = tf.contrib.tpu.CrossShardOptimizer(opt)
if args.accumulate_gradients > 1:
if args.memory_saving_gradients:
exit("Memory saving gradients are not implemented for gradient accumulation yet.")
opt = AccumulatingOptimizer(
opt=opt,
var_list=train_vars)
opt_reset = opt.reset()
opt_compute = opt.compute_gradients(loss)
opt_apply = opt.apply_gradients()
summary_loss = tf.summary.scalar('loss', opt_apply)
else:
if args.memory_saving_gradients:
opt_grads = memory_saving_gradients.gradients(loss, train_vars)
else:
opt_grads = tf.gradients(loss, train_vars)
opt_grads = list(zip(opt_grads, train_vars))
opt_apply = opt.apply_gradients(opt_grads)
summary_loss = tf.summary.scalar('loss', loss)
summary_lr = tf.summary.scalar('learning_rate', lr)
summaries = tf.summary.merge([summary_lr, summary_loss])
summary_log = tf.summary.FileWriter(
os.path.join(CHECKPOINT_DIR, args.run_name))
if args.save_graph:
summary_log.add_graph(tf.get_default_graph())
saver = tflex.Saver(
var_list=all_vars,
max_to_keep=args.max_to_keep,
keep_checkpoint_every_n_hours=100000,
reshape=args.truncate_weights)
sess.run(tf.global_variables_initializer())
if args.restore_from == 'latest':
ckpt = tflex.latest_checkpoint(
os.path.join(CHECKPOINT_DIR, args.run_name))
if ckpt is None:
# Get fresh GPT weights if new run.
ckpt = tflex.latest_checkpoint(
os.path.join('models', args.model_name))
elif args.restore_from == 'fresh':
ckpt = tflex.latest_checkpoint(
os.path.join('models', args.model_name))
else:
ckpt = tflex.latest_checkpoint(args.restore_from)
print('Loading snapshot %s...' % ckpt)
t0 = time.time()
if not args.fresh_model:
saver.restore(sess, ckpt)
t1 = time.time()
print('Loaded in %f seconds' % (t1 - t0))
def make_sampler(dataset, enc, seed, combine):
if os.path.isdir(dataset) or dataset.endswith('.npz'):
chunks = load_dataset(enc, dataset, combine)
data_sampler = Sampler(chunks, seed=seed)
print('dataset has', data_sampler.total_size, 'tokens', len(chunks), 'chunks')
else:
data_sampler = TextSampler(dataset, enc, seed=seed)
return data_sampler
print('Loading dataset...')
seed = None if args.seed < 0 else args.seed
data_sampler = make_sampler(dataset=args.dataset, enc=enc, seed=seed, combine=args.combine)
if args.val_every > 0:
# Sample from validation set once with fixed seed to make
# it deterministic during training as well as across runs.
val_dataset = args.val_dataset if args.val_dataset else args.dataset
val_data_sampler = make_sampler(dataset=val_dataset, enc=enc, seed=1, combine=args.combine)
val_batches = [[val_data_sampler.sample(hparams.n_ctx) for _ in range(args.val_batch_size)]
for _ in range(args.val_batch_count)]
print('Training...')
counter = 1
counter_path = os.path.join(CHECKPOINT_DIR, args.run_name, 'counter')
if os.path.exists(counter_path):
# Load the step number if we're resuming a run
# Add 1 so we don't immediately try to save again
with open(counter_path, 'r') as fp:
counter = int(fp.read()) + 1
@tflex.register_command
def get_tarfile_name(checkpoint_folder):
"""Converts a folder path into a filename for a .tar archive"""
tarfile_name = checkpoint_folder.replace(os.path.sep, '_') + '.tar'
return tarfile_name
def copy_checkpoint_to_gdrive(run_name='run1', copy_folder=False):
"""Copies the checkpoint folder to a mounted Google Drive."""
#is_mounted()
checkpoint_folder = os.path.join('checkpoint', run_name)
if copy_folder:
shutil.copytree(checkpoint_folder, "/content/drive/My Drive/" + checkpoint_folder)
else:
file_path = get_tarfile_name(checkpoint_folder)
# Reference: https://stackoverflow.com/a/17081026
with tarfile.open(file_path, 'w') as tar:
tar.add(checkpoint_folder)
shutil.copyfile(file_path, "/content/drive/My Drive/" + file_path)
@tflex.register_command
def save():
maketree(os.path.join(CHECKPOINT_DIR, args.run_name))
print(
'Saving',
os.path.join(CHECKPOINT_DIR, args.run_name,
'model-{}').format(counter))
t0 = time.time()
saver.save(
sess,
os.path.join(CHECKPOINT_DIR, args.run_name, 'model'),
global_step=counter)
t1 = time.time()
print('Saved in %f seconds' % (t1 - t0))
with open(counter_path, 'w') as fp:
fp.write(str(counter) + '\n')
#copy_checkpoint_to_gdrive()
@tflex.register_command
def generate_samples():
print('Generating samples...')
context_tokens = data_sampler.sample(1)
all_text = []
index = 0
while index < args.sample_num:
out = sess.run(
tf_sample,
feed_dict={context: args.batch_size * [context_tokens]})
for i in range(min(args.sample_num - index, args.batch_size)):
text = enc.decode(out[i])
text = '======== SAMPLE {} ========\n{}\n'.format(
index + 1, text)
print(text)
all_text.append(text)
index += 1
maketree(os.path.join(SAMPLE_DIR, args.run_name))
with open(
os.path.join(SAMPLE_DIR, args.run_name,
'samples-{}').format(counter), 'w') as fp:
fp.write('\n'.join(all_text))
@tflex.register_command
def validation():
if args.val_every <= 0:
return
print('Calculating validation loss...')
losses = []
for batch in tqdm.tqdm(val_batches):
losses.append(sess.run(val_loss, feed_dict={val_context: batch}))
v_val_loss = np.mean(losses)
v_summary = sess.run(val_loss_summary, feed_dict={val_loss: v_val_loss})
summary_log.add_summary(v_summary, counter)
summary_log.flush()
print(
'{stamp} [{counter} | {time:2.4f}] validation loss = {loss:2.4f}'
.format(
stamp=timestamp(),
counter=counter,
time=time.time() - start_time,
loss=v_val_loss))
start_time = time.time()
def elapsed():
return time.time() - start_time
def say(msg):
print('{stamp} [{counter} | {time:2.4f}] {msg}'.format(counter=counter, time=elapsed(), msg=msg, stamp=timestamp()))
def sample_batch():
#return [data_sampler.sample(args.sample_ctx) for _ in range(args.batch_size)]
#say('Sampling batch...')
r = []
times = []
for _ in range(args.batch_size):
start = time.time()
sample = data_sampler.sample(args.sample_ctx)
end = time.time()
elapsed = (end - start)
r += [sample]
times += [elapsed]
total = sum(times)
avg = total / len(times)
#say('Sampled %d batches in %.4f seconds (avg per batch: %.4f)' % (args.batch_size, total, avg))
return r
prev_time = time.time()
avg_loss = (0.0, 0.0)
if args.debug_before_training:
import pdb
pdb.set_trace()
last_saved_time = elapsed()
while True:
try:
now = elapsed()
if args.save_time > 0 and (((now - last_saved_time) / 60.0) >= args.save_time):
save()
last_saved_time = now
elif args.save_every > 0 and (counter % args.save_every == 0):
save()
if counter % args.sample_every == 0:
generate_samples()
if args.val_every > 0 and (counter % args.val_every == 0 or counter == 1):
validation()
v_rate = update_lr()
if args.accumulate_gradients > 1:
#say('Running opt_reset...')
sess.run(opt_reset)
for _ in range(args.accumulate_gradients):
batch = sample_batch()
say('Running opt_compute...')
sess.run(opt_compute, feed_dict={context: batch})
say('Running opt_apply...')
(v_loss, v_summary) = sess.run((opt_apply, summaries))
else:
batch = sample_batch()
say('Running opt_apply...')
(_, v_loss, v_summary) = sess.run(
(opt_apply, loss, summaries),
feed_dict={context: batch})
if args.float16:
v_loss = tf.to_float(v_loss).eval()
summary_log.add_summary(v_summary, counter)
summary_log.flush()
avg_loss = (avg_loss[0] * 0.99 + v_loss,
avg_loss[1] * 0.99 + 1.0)
now = time.time()
print('{stamp} [{counter} | {time:2.4f} | {delta:2.2f}s | {ops:2.6f}tokens/s] loss={loss:2.4f} avg={avg:2.4f} rate={rate:0.7f} step={step}'
.format(
stamp=timestamp(),
counter=counter,
time=now - start_time,
delta=now - prev_time,
ops=args.sample_ctx * args.batch_size / (now - prev_time),
rate=v_rate,
loss=v_loss,
avg=avg_loss[0] / avg_loss[1],
step=current_step,
))
counter += 1
current_step += 1
global_step.load(current_step, session=sess)
tflex.check_commands_with_args(
session=sess,
stamp=timestamp(),
counter=counter,
time=now - start_time,
delta=now - prev_time,
ops=args.batch_size / (now - prev_time),
rate=v_rate,
loss=v_loss,
avg=avg_loss[0] / avg_loss[1],
avg_loss=avg_loss,
step=current_step,
train_vars=train_vars,
all_vars=all_vars,
args=args,
data_sampler=data_sampler,
ckpt=ckpt,
saver=saver,
)
if tflex.should_quit():
break
prev_time = now
if args.debug_print_all_vars:
print('all variables:')
print('name/shape/parameter_count')
param_count = 0
for x in tf.all_variables():
shape = x.shape.as_list()
count = np.prod(shape)
print(x.name, shape, count)
param_count += count
print('Total parameters:', param_count)
args.debug_print_all_vars = False
if args.debug_print_trainable_vars:
print('trainable variables:')
print('name/shape/parameter_count')
param_count = 0
for x in tf.trainable_variables():
shape = x.shape.as_list()
count = np.prod(shape)
print(x.name, shape, count)
param_count += count
print('Total parameters:', param_count)
args.debug_print_trainable_vars = False
except KeyboardInterrupt:
print('interrupted')
if args.save_on_ctrlc:
save()
if args.debug_on_ctrlc:
import pdb
pdb.set_trace()
else:
break
def compute_metrics_v2(problem_name,
model_name,
hparams_name,
ckpt_dir,
data_dir="/tmp",
eval_batch_size=32,
eval_steps=100,
extra_hparams=[],
mode=Modes.EVAL,
num_threshold_bins=100):
registered_model = registry.model(model_name)
hparams = registry.hparams(hparams_name)
hparams.mode = mode
for extra_hparam in extra_hparams:
assert len(extra_hparam) == 2
if extra_hparam[0] == "mode":
continue
hparams.setattr(extra_hparam[0], extra_hparam[1])
problem_instance = registry.problem(problem_name)
problem_hparams = problem_instance.get_hparams(hparams)
# Build the eval dataset and get the examples
eval_dataset = problem_instance.dataset(mode=Modes.EVAL, data_dir=data_dir)
eval_dataset = eval_dataset.repeat(None).batch(eval_batch_size)
eval_dataset_iterator = tfe.Iterator(eval_dataset)
metrics = {}
def _merge(metrics, metrics_partial):
for key, value in metrics_partial.items():
if key not in metrics:
metrics[key] = value
else:
metrics[key] += value
return metrics
with tfe.restore_variables_on_create(ckpt_dir):
model_instance = registered_model(hparams, mode, problem_hparams)
for i in range(eval_steps):
try:
eval_examples = eval_dataset_iterator.next()
metrics_partial = model_instance.eager_eval(eval_examples)
metrics = _merge(metrics, metrics_partial)
if i % 10 == 0:
msg = "Finished collecting predictions for eval step {}.".format(i)
tf.logging.info(msg)
except:
# Seeing rare CBT deadline exceeded errors and don't know how to modfiy
# the deadline... More likely to run into error with more iterations,
# wasn't seeing it with 10 and almost always seeing it with 100.
# Could conceivably have to do with running out of examples in the eval
# set... but there should be over 20k and this would only go through
# 3200.
msg = "HACK: Squashing inference error."
tf.logging.info(msg)
for key, value in metrics.items():
metrics[key] = value / eval_steps
return metrics
def compute_metrics(problem_name,
model_name,
hparams_name,
ckpt_dir,
data_dir="/tmp",
eval_batch_size=32,
eval_steps=100,
extra_hparams=[],
mode=Modes.EVAL,
num_threshold_bins=100):
if not isinstance(num_threshold_bins, int) and num_threshold_bins > 0:
msg = "Num threshold bins should be int > 0, saw {}".format(
num_threshold_bins)
raise ValueError(msg)
registered_model = registry.model(model_name)
hparams = registry.hparams(hparams_name)
hparams.mode = mode
for extra_hparam in extra_hparams:
assert len(extra_hparam) == 2
if extra_hparam[0] == "mode":
continue
hparams.setattr(extra_hparam[0], extra_hparam[1])
problem_instance = registry.problem(problem_name)
problem_hparams = problem_instance.get_hparams(hparams)
# Build the eval dataset and get the examples
eval_dataset = problem_instance.dataset(mode=Modes.EVAL, data_dir=data_dir)
eval_dataset = eval_dataset.repeat(None).batch(eval_batch_size)
eval_dataset_iterator = tfe.Iterator(eval_dataset)
with tfe.restore_variables_on_create(ckpt_dir):
model_instance = registered_model(hparams, mode, problem_hparams)
predictions = np.array([], dtype=np.float32)
targets = np.array([], dtype=np.float32)
for i in range(eval_steps):
try:
eval_examples = eval_dataset_iterator.next()
prediction = model_instance.infer(eval_examples)
# We've concatenated the two embedding vectors followed
# by the label so we can obtain the label just by looking
# at the last value
prediction = np.array([thing[-1] for thing in prediction],
dtype=np.float32)
target = tf.squeeze(eval_examples["targets"]).numpy().astype(np.float32)
predictions = np.concatenate([predictions, prediction])
targets = np.concatenate([targets, target])
if i % 10 == 0:
msg = "Finished collecting predictions for eval step {}.".format(i)
tf.logging.info(msg)
except Exception as e:
# Seeing rare CBT deadline exceeded errors and don't know how to modfiy
# the deadline... More likely to run into error with more iterations,
# wasn't seeing it with 10 and almost always seeing it with 100.
# Could conceivably have to do with running out of examples in the eval
# set... but there should be over 20k and this would only go through
# 3200.
msg = "HACK: Squashing inference error."
tf.logging.info(msg)
metrics_set = []
for i in range(num_threshold_bins):
threshold = i / num_threshold_bins
metrics = _metrics_given_threshold(predictions,
targets,
threshold=threshold)
metrics["at_threshold"] = threshold
metrics["num_threshold_bins"] = num_threshold_bins
metrics_set.append(metrics)
midpoint_metrics = _metrics_given_threshold(predictions,
targets,
threshold=0.5)
midpoint_metrics["auc"] = _auc_for_metrics_set(metrics_set)
return midpoint_metrics, metrics_set, predictions, targets
def _test_hparams_set(self, hparams_set):
hparams = registry.hparams(hparams_set)
FLAGS.output_dir = tf.test.get_temp_dir()
trainer_model_free.train(hparams, FLAGS.output_dir,
env_problem_name=None)
def create_hparams():
hparams = registry.hparams(FLAGS.hparams_set)()
if FLAGS.hparams:
hparams = hparams.parse(FLAGS.hparams)
return hparams
def create_rl_hparams():
hparams = registry.hparams(FLAGS.rl_hparams_set)()
hparams.parse(FLAGS.rl_hparams)
return hparams
def testUnknownHparams(self):
with self.assertRaisesRegexp(LookupError, "never registered"):
registry.hparams("not_registered")
with self.assertRaisesRegexp(LookupError, "never registered"):
registry.ranged_hparams("not_registered")
# -*- coding: utf-8 -*-
"""
@author: 代码医生工作室
@公众号：xiangyuejiqiren （内有更多优秀文章及学习资料）
@来源: <深度学习之TensorFlow工程化项目实战>配套代码 （700+页）
@配套代码技术支持：bbs.aianaconda.com (有问必答)
"""
#6-19
import tensorflow as tf
from tensor2tensor import models
from tensor2tensor.utils import t2t_model
from tensor2tensor.utils import registry
print(len(registry.list_models()), registry.list_models())
print(registry.model('transformer'))
print(len(registry.list_hparams()), registry.list_hparams('transformer'))
print(registry.hparams('transformer_base_v1'))
def autoencoder_factor(self): """By how much to divide sizes when using autoencoders.""" hparams = registry.hparams(self.ae_hparams_set) return 2**hparams.num_hidden_layers
def create_loop_hparams(): hparams = registry.hparams(FLAGS.loop_hparams_set) hparams.parse(FLAGS.loop_hparams) return hparams
def create_loop_hparams():
hparams = registry.hparams(FLAGS.loop_hparams_set)
hparams.parse(FLAGS.loop_hparams)
return hparams
def _lookup_hparams(self):
self.hparams = registry.hparams(self.hparams_set)
self.hparams.data_dir = self.data_dir
self.p_hparams = self.problem.get_hparams(self.hparams)