def set_batch_iterator_func(self):
if (self.conf is not None
and 'use_process_generator' in conf['training']
and conf['training']['use_process_generator']):
self.batch_iterator_func = ProcessGenerator(self.batch_iterator())
else:
self.batch_iterator_func = self.batch_iterator()
def set_batch_iterator_func(self):
self.batch_iterator_func = ProcessGenerator(self.batch_iterator())
class MPIModel():
def __init__(self,
model,
optimizer,
comm,
batch_iterator,
batch_size,
num_replicas=None,
warmup_steps=1000,
lr=0.01,
num_batches_minimum=100):
random.seed(task_index)
np.random.seed(task_index)
self.start_time = time.time()
self.epoch = 0
self.num_so_far = 0
self.num_so_far_accum = 0
self.num_so_far_indiv = 0
self.model = model
self.optimizer = optimizer
self.max_lr = 0.1
self.DUMMY_LR = 0.001
self.comm = comm
self.batch_size = batch_size
self.batch_iterator = batch_iterator
self.set_batch_iterator_func()
self.warmup_steps = warmup_steps
self.num_batches_minimum = num_batches_minimum
self.num_workers = comm.Get_size()
self.task_index = comm.Get_rank()
self.history = cbks.History()
if num_replicas is None or num_replicas < 1 or num_replicas > self.num_workers:
self.num_replicas = self.num_workers
else:
self.num_replicas = num_replicas
self.lr = lr / (1.0 + self.num_replicas / 100.0) if (
lr < self.max_lr) else self.max_lr / (1.0 +
self.num_replicas / 100.0)
def set_batch_iterator_func(self):
self.batch_iterator_func = ProcessGenerator(self.batch_iterator())
def close(self):
self.batch_iterator_func.__exit__()
def set_lr(self, lr):
self.lr = lr
def save_weights(self, path, overwrite=False):
self.model.save_weights(path, overwrite=overwrite)
def load_weights(self, path):
self.model.load_weights(path)
def compile(self, optimizer, clipnorm, loss='mse'):
if optimizer == 'sgd':
optimizer_class = SGD(lr=self.DUMMY_LR, clipnorm=clipnorm)
elif optimizer == 'momentum_sgd':
optimizer_class = SGD(lr=self.DUMMY_LR,
clipnorm=clipnorm,
decay=1e-6,
momentum=0.9)
elif optimizer == 'tf_momentum_sgd':
optimizer_class = TFOptimizer(
tf.train.MomentumOptimizer(learning_rate=self.DUMMY_LR,
momentum=0.9))
elif optimizer == 'adam':
optimizer_class = Adam(lr=self.DUMMY_LR, clipnorm=clipnorm)
elif optimizer == 'tf_adam':
optimizer_class = TFOptimizer(
tf.train.AdamOptimizer(learning_rate=self.DUMMY_LR))
elif optimizer == 'rmsprop':
optimizer_class = RMSprop(lr=self.DUMMY_LR, clipnorm=clipnorm)
elif optimizer == 'nadam':
optimizer_class = Nadam(lr=self.DUMMY_LR, clipnorm=clipnorm)
else:
print("Optimizer not implemented yet")
exit(1)
self.model.compile(optimizer=optimizer_class, loss=loss)
def train_on_batch_and_get_deltas(self, X_batch, Y_batch, verbose=False):
'''
The purpose of the method is to perform a single gradient update over one mini-batch for one model replica.
Given a mini-batch, it first accesses the current model weights, performs single gradient update over one mini-batch,
gets new model weights, calculates weight updates (deltas) by subtracting weight scalars, applies the learning rate.
It performs calls to: subtract_params, multiply_params
Argument list:
- X_batch: input data for one mini-batch as a Numpy array
- Y_batch: labels for one mini-batch as a Numpy array
- verbose: set verbosity level (currently unused)
Returns:
- deltas: a list of model weight updates
- loss: scalar training loss
'''
weights_before_update = self.model.get_weights()
loss = self.model.train_on_batch(X_batch, Y_batch)
weights_after_update = self.model.get_weights()
self.model.set_weights(weights_before_update)
#unscale before subtracting
weights_before_update = multiply_params(weights_before_update,
1.0 / self.DUMMY_LR)
weights_after_update = multiply_params(weights_after_update,
1.0 / self.DUMMY_LR)
deltas = subtract_params(weights_after_update, weights_before_update)
#unscale loss
if conf['model']['loss_scale_factor'] != 1.0:
deltas = multiply_params(deltas,
1.0 / conf['model']['loss_scale_factor'])
return deltas, loss
def get_new_weights(self, deltas):
return add_params(self.model.get_weights(), deltas)
def mpi_average_gradients(self, arr, num_replicas=None):
if num_replicas == None:
num_replicas = self.num_workers
if self.task_index >= num_replicas:
arr *= 0.0
arr_global = np.empty_like(arr)
if K.floatx() == 'float16':
self.comm.Allreduce(arr, arr_global, op=mpi_sum_f16)
else:
self.comm.Allreduce(arr, arr_global, op=MPI.SUM)
arr_global /= num_replicas
return arr_global
def mpi_average_scalars(self, val, num_replicas=None):
'''
The purpose of the method is to calculate a simple scalar arithmetic mean over num_replicas.
It performs calls to: MPIModel.mpi_sum_scalars
Argument list:
- val: value averaged, scalar
- num_replicas: the size of the ensemble an average is perfromed over
Returns:
- val_global: scalar arithmetic mean over num_replicas
'''
val_global = self.mpi_sum_scalars(val, num_replicas)
val_global /= num_replicas
return val_global
def mpi_sum_scalars(self, val, num_replicas=None):
'''
The purpose of the method is to calculate a simple scalar arithmetic mean over num_replicas using MPI allreduce action with fixed op=MPI.SIM
Argument list:
- val: value averaged, scalar
- num_replicas: the size of the ensemble an average is perfromed over
Returns:
- val_global: scalar arithmetic mean over num_replicas
'''
if num_replicas == None:
num_replicas = self.num_workers
if self.task_index >= num_replicas:
val *= 0.0
val_global = 0.0
val_global = self.comm.allreduce(val, op=MPI.SUM)
return val_global
def sync_deltas(self, deltas, num_replicas=None):
global_deltas = []
#default is to reduce the deltas from all workers
for delta in deltas:
global_deltas.append(
self.mpi_average_gradients(delta, num_replicas))
return global_deltas
def set_new_weights(self, deltas, num_replicas=None):
global_deltas = self.sync_deltas(deltas, num_replicas)
effective_lr = self.get_effective_lr(num_replicas)
self.optimizer.set_lr(effective_lr)
global_deltas = self.optimizer.get_deltas(global_deltas)
if self.comm.rank == 0:
new_weights = self.get_new_weights(global_deltas)
else:
new_weights = None
new_weights = self.comm.bcast(new_weights, root=0)
self.model.set_weights(new_weights)
def build_callbacks(self, conf, callbacks_list):
'''
The purpose of the method is to set up logging and history. It is based on Keras Callbacks
https://github.com/fchollet/keras/blob/fbc9a18f0abc5784607cd4a2a3886558efa3f794/keras/callbacks.py
Currently used callbacks include: BaseLogger, CSVLogger, EarlyStopping.
Other possible callbacks to add in future: RemoteMonitor, LearningRateScheduler
Argument list:
- conf: There is a "callbacks" section in conf.yaml file. Relevant parameters are:
list: Parameter specifying additional callbacks, read in the driver script and passed as an argument of type list (see next arg)
metrics: List of quantities monitored during training and validation
mode: one of {auto, min, max}. The decision to overwrite the current save file is made based on either the maximization or the minimization of the monitored quantity. For val_acc, this should be max, for val_loss this should be min, etc. In auto mode, the direction is automatically inferred from the name of the monitored quantity.
monitor: Quantity used for early stopping, has to be from the list of metrics
patience: Number of epochs used to decide on whether to apply early stopping or continue training
- callbacks_list: uses callbacks.list configuration parameter, specifies the list of additional callbacks
Returns: modified list of callbacks
'''
mode = conf['callbacks']['mode']
monitor = conf['callbacks']['monitor']
patience = conf['callbacks']['patience']
csvlog_save_path = conf['paths']['csvlog_save_path']
#CSV callback is on by default
if not os.path.exists(csvlog_save_path):
os.makedirs(csvlog_save_path)
callbacks_list = conf['callbacks']['list']
callbacks = [cbks.BaseLogger()]
callbacks += [self.history]
callbacks += [
cbks.CSVLogger("{}callbacks-{}.log".format(
csvlog_save_path,
datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")))
]
if "earlystop" in callbacks_list:
callbacks += [
cbks.EarlyStopping(patience=patience,
monitor=monitor,
mode=mode)
]
if "lr_scheduler" in callbacks_list:
pass
return cbks.CallbackList(callbacks)
def train_epoch(self):
'''
The purpose of the method is to perform distributed mini-batch SGD for one epoch.
It takes the batch iterator function and a NN model from MPIModel object, fetches mini-batches
in a while-loop until number of samples seen by the ensemble of workers (num_so_far) exceeds the
training dataset size (num_total).
During each iteration, the gradient updates (deltas) and the loss are calculated for each model replica
in the ensemble, weights are averaged over ensemble, and the new weights are set.
It performs calls to: MPIModel.get_deltas, MPIModel.set_new_weights methods
Argument list: Empty
Returns:
- step: epoch number
- ave_loss: training loss averaged over replicas
- curr_loss:
- num_so_far: the number of samples seen by ensemble of replicas to a current epoch (step)
Intermediate outputs and logging: debug printout of task_index (MPI), epoch number, number of samples seen to
a current epoch, average training loss
'''
verbose = False
first_run = True
step = 0
loss_averager = Averager()
t_start = time.time()
batch_iterator_func = self.batch_iterator_func
num_total = 1
ave_loss = -1
curr_loss = -1
t0 = 0
t1 = 0
t2 = 0
while (self.num_so_far - self.epoch *
num_total) < num_total or step < self.num_batches_minimum:
try:
batch_xs, batch_ys, batches_to_reset, num_so_far_curr, num_total, is_warmup_period = next(
batch_iterator_func)
except StopIteration:
print("Resetting batch iterator.")
self.num_so_far_accum = self.num_so_far_indiv
self.set_batch_iterator_func()
batch_iterator_func = self.batch_iterator_func
batch_xs, batch_ys, batches_to_reset, num_so_far_curr, num_total, is_warmup_period = next(
batch_iterator_func)
self.num_so_far_indiv = self.num_so_far_accum + num_so_far_curr
# if batches_to_reset:
# self.model.reset_states(batches_to_reset)
warmup_phase = (step < self.warmup_steps and self.epoch == 0)
num_replicas = 1 if warmup_phase else self.num_replicas
self.num_so_far = self.mpi_sum_scalars(self.num_so_far_indiv,
num_replicas)
#run the model once to force compilation. Don't actually use these values.
if first_run:
first_run = False
t0_comp = time.time()
_, _ = self.train_on_batch_and_get_deltas(
batch_xs, batch_ys, verbose)
self.comm.Barrier()
sys.stdout.flush()
print_unique(
'Compilation finished in {:.2f}s'.format(time.time() -
t0_comp))
t_start = time.time()
sys.stdout.flush()
if np.any(batches_to_reset):
reset_states(self.model, batches_to_reset)
t0 = time.time()
deltas, loss = self.train_on_batch_and_get_deltas(
batch_xs, batch_ys, verbose)
t1 = time.time()
if not is_warmup_period:
self.set_new_weights(deltas, num_replicas)
t2 = time.time()
write_str_0 = self.calculate_speed(t0, t1, t2, num_replicas)
curr_loss = self.mpi_average_scalars(1.0 * loss, num_replicas)
#if self.task_index == 0:
#print(self.model.get_weights()[0][0][:4])
loss_averager.add_val(curr_loss)
ave_loss = loss_averager.get_val()
eta = self.estimate_remaining_time(
t0 - t_start, self.num_so_far - self.epoch * num_total,
num_total)
write_str = '\r[{}] step: {} [ETA: {:.2f}s] [{:.2f}/{}], loss: {:.5f} [{:.5f}] | walltime: {:.4f} | '.format(
self.task_index, step, eta, 1.0 * self.num_so_far,
num_total, ave_loss, curr_loss,
time.time() - self.start_time)
print_unique(write_str + write_str_0)
step += 1
else:
print_unique('\r[{}] warmup phase, num so far: {}'.format(
self.task_index, self.num_so_far))
effective_epochs = 1.0 * self.num_so_far / num_total
epoch_previous = self.epoch
self.epoch = effective_epochs
print_unique(
'\nEpoch {:.2f} finished ({:.2f} epochs passed) in {:.2f} seconds.\n'
.format(1.0 * self.epoch, self.epoch - epoch_previous,
t2 - t_start))
return (step, ave_loss, curr_loss, self.num_so_far, effective_epochs)
def estimate_remaining_time(self, time_so_far, work_so_far, work_total):
eps = 1e-6
total_time = 1.0 * time_so_far * work_total / (work_so_far + eps)
return total_time - time_so_far
def get_effective_lr(self, num_replicas):
effective_lr = self.lr * num_replicas
if effective_lr > self.max_lr:
print_unique(
'Warning: effective learning rate set to {}, larger than maximum {}. Clipping.'
.format(effective_lr, self.max_lr))
effective_lr = self.max_lr
return effective_lr
def get_effective_batch_size(self, num_replicas):
return self.batch_size * num_replicas
def calculate_speed(self,
t0,
t_after_deltas,
t_after_update,
num_replicas,
verbose=False):
effective_batch_size = self.get_effective_batch_size(num_replicas)
t_calculate = t_after_deltas - t0
t_sync = t_after_update - t_after_deltas
t_tot = t_after_update - t0
examples_per_sec = effective_batch_size / t_tot
frac_calculate = t_calculate / t_tot
frac_sync = t_sync / t_tot
print_str = '{:.2E} Examples/sec | {:.2E} sec/batch [{:.1%} calc., {:.1%} synch.]'.format(
examples_per_sec, t_tot, frac_calculate, frac_sync)
print_str += '[batch = {} = {}*{}] [lr = {:.2E} = {:.2E}*{}]'.format(
effective_batch_size, self.batch_size, num_replicas,
self.get_effective_lr(num_replicas), self.lr, num_replicas)
if verbose:
print_unique(print_str)
return print_str
def train(conf, shot_list_train, shot_list_validate, loader):
loader.set_inference_mode(False)
np.random.seed(1)
validation_losses = []
validation_roc = []
training_losses = []
print('validate: {} shots, {} disruptive'.format(
len(shot_list_validate), shot_list_validate.num_disruptive()))
print('training: {} shots, {} disruptive'.format(
len(shot_list_train), shot_list_train.num_disruptive()))
if backend == 'tf' or backend == 'tensorflow':
first_time = "tensorflow" not in sys.modules
if first_time:
import tensorflow as tf
os.environ['KERAS_BACKEND'] = 'tensorflow'
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto(device_count={"GPU": 1})
set_session(tf.Session(config=config))
else:
os.environ['KERAS_BACKEND'] = 'theano'
os.environ['THEANO_FLAGS'] = 'device=gpu,floatX=float32'
import theano
from keras.utils.generic_utils import Progbar
from keras import backend as K
from plasma.models import builder
print('Build model...', end='')
specific_builder = builder.ModelBuilder(conf)
train_model = specific_builder.build_model(False)
print('Compile model', end='')
train_model.compile(optimizer=optimizer_class(),
loss=conf['data']['target'].loss)
print('...done')
#load the latest epoch we did. Returns -1 if none exist yet
e = specific_builder.load_model_weights(train_model)
e_start = e
batch_generator = partial(loader.training_batch_generator_partial_reset,
shot_list=shot_list_train)
batch_iterator = ProcessGenerator(batch_generator())
num_epochs = conf['training']['num_epochs']
num_at_once = conf['training']['num_shots_at_once']
lr_decay = conf['model']['lr_decay']
print('{} epochs left to go'.format(num_epochs - 1 - e))
num_so_far_accum = 0
num_so_far = 0
num_total = np.inf
if conf['callbacks']['mode'] == 'max':
best_so_far = -np.inf
cmp_fn = max
else:
best_so_far = np.inf
cmp_fn = min
while e < num_epochs - 1:
e += 1
print('\nEpoch {}/{}'.format(e + 1, num_epochs))
pbar = Progbar(len(shot_list_train))
#decay learning rate each epoch:
K.set_value(train_model.optimizer.lr, lr * lr_decay**(e))
#print('Learning rate: {}'.format(train_model.optimizer.lr.get_value()))
num_batches_minimum = 100
num_batches_current = 0
training_losses_tmp = []
while num_so_far < (
e - e_start
) * num_total or num_batches_current < num_batches_minimum:
num_so_far_old = num_so_far
try:
batch_xs, batch_ys, batches_to_reset, num_so_far_curr, num_total, is_warmup_period = next(
batch_iterator)
except StopIteration:
print("Resetting batch iterator.")
num_so_far_accum = num_so_far
batch_iterator = ProcessGenerator(batch_generator())
batch_xs, batch_ys, batches_to_reset, num_so_far_curr, num_total, is_warmup_period = next(
batch_iterator)
if np.any(batches_to_reset):
reset_states(train_model, batches_to_reset)
if not is_warmup_period:
num_so_far = num_so_far_accum + num_so_far_curr
num_batches_current += 1
loss = train_model.train_on_batch(batch_xs, batch_ys)
training_losses_tmp.append(loss)
pbar.add(num_so_far - num_so_far_old,
values=[("train loss", loss)])
loader.verbose = False #True during the first iteration
else:
_ = train_model.predict(
batch_xs, batch_size=conf['training']['batch_size'])
e = e_start + 1.0 * num_so_far / num_total
sys.stdout.flush()
ave_loss = np.mean(training_losses_tmp)
training_losses.append(ave_loss)
specific_builder.save_model_weights(train_model, int(round(e)))
if conf['training']['validation_frac'] > 0.0:
print("prediction on GPU...")
_, _, _, roc_area, loss = make_predictions_and_evaluate_gpu(
conf, shot_list_validate, loader)
validation_losses.append(loss)
validation_roc.append(roc_area)
epoch_logs = {}
epoch_logs['val_roc'] = roc_area
epoch_logs['val_loss'] = loss
epoch_logs['train_loss'] = ave_loss
best_so_far = cmp_fn(epoch_logs[conf['callbacks']['monitor']],
best_so_far)
if best_so_far != epoch_logs[
conf['callbacks']
['monitor']]: #only save model weights if quantity we are tracking is improving
print("Not saving model weights")
specific_builder.delete_model_weights(train_model,
int(round(e)))
if conf['training']['ranking_difficulty_fac'] != 1.0:
_, _, _, roc_area_train, loss_train = make_predictions_and_evaluate_gpu(
conf, shot_list_train, loader)
batch_iterator.__exit__()
batch_generator = partial(
loader.training_batch_generator_partial_reset,
shot_list=shot_list_train)
batch_iterator = ProcessGenerator(batch_generator())
num_so_far_accum = num_so_far
print('=========Summary========')
print('Training Loss Numpy: {:.3e}'.format(training_losses[-1]))
if conf['training']['validation_frac'] > 0.0:
print('Validation Loss: {:.3e}'.format(validation_losses[-1]))
print('Validation ROC: {:.4f}'.format(validation_roc[-1]))
if conf['training']['ranking_difficulty_fac'] != 1.0:
print('Train Loss: {:.3e}'.format(loss_train))
print('Train ROC: {:.4f}'.format(roc_area_train))
# plot_losses(conf,[training_losses],specific_builder,name='training')
if conf['training']['validation_frac'] > 0.0:
plot_losses(conf, [training_losses, validation_losses, validation_roc],
specific_builder,
name='training_validation_roc')
batch_iterator.__exit__()
print('...done')
class MPIModel():
def __init__(self,
model,
optimizer,
comm,
batch_iterator,
batch_size,
num_replicas=None,
warmup_steps=1000,
lr=0.01,
num_batches_minimum=100,
conf=None):
random.seed(g.task_index)
np.random.seed(g.task_index)
self.conf = conf
self.start_time = time.time()
self.epoch = 0
self.num_so_far = 0
self.num_so_far_accum = 0
self.num_so_far_indiv = 0
self.model = model
self.optimizer = optimizer
self.max_lr = 0.1
self.DUMMY_LR = 0.001
self.batch_size = batch_size
self.batch_iterator = batch_iterator
self.set_batch_iterator_func()
self.warmup_steps = warmup_steps
self.num_batches_minimum = num_batches_minimum
# TODO(KGF): duplicate/may be in conflict with global_vars.py
self.comm = comm
self.num_workers = comm.Get_size()
self.task_index = comm.Get_rank()
self.history = cbks.History()
self.model.stop_training = False
if (num_replicas is None or num_replicas < 1
or num_replicas > self.num_workers):
self.num_replicas = self.num_workers
else:
self.num_replicas = num_replicas
self.lr = (lr / (1.0 + self.num_replicas / 100.0) if
(lr < self.max_lr) else self.max_lr /
(1.0 + self.num_replicas / 100.0))
def set_batch_iterator_func(self):
if (self.conf is not None
and 'use_process_generator' in conf['training']
and conf['training']['use_process_generator']):
self.batch_iterator_func = ProcessGenerator(self.batch_iterator())
else:
self.batch_iterator_func = self.batch_iterator()
def close(self):
# TODO(KGF): extend __exit__() fn capability when this member
# = self.batch_iterator() (i.e. is not a ProcessGenerator())
if (self.conf is not None
and 'use_process_generator' in conf['training']
and conf['training']['use_process_generator']):
self.batch_iterator_func.__exit__()
def set_lr(self, lr):
self.lr = lr
# KGF: Unused. model.*() called directly in builder.py
# def save_weights(self, path, overwrite=False):
# self.model.save_weights(path, overwrite=overwrite)
# def load_weights(self, path):
# self.model.load_weights(path)
def compile(self, optimizer, clipnorm, loss='mse'):
# TODO(KGF): check the following import taken from runner.py
# Was not in this file, originally.
from tensorflow.keras.optimizers import (SGD, Adam, RMSprop, Nadam)
if optimizer == 'sgd':
optimizer_class = SGD(lr=self.DUMMY_LR, clipnorm=clipnorm)
elif optimizer == 'momentum_sgd':
optimizer_class = SGD(lr=self.DUMMY_LR,
clipnorm=clipnorm,
decay=1e-6,
momentum=0.9)
elif optimizer == 'tf_momentum_sgd':
# TODO(KGF): removed TFOptimizer wrapper from here and below
# may not work anymore? See
# https://github.com/tensorflow/tensorflow/issues/22780
optimizer_class = tf.train.MomentumOptimizer(
learning_rate=self.DUMMY_LR, momentum=0.9)
elif optimizer == 'adam':
optimizer_class = Adam(lr=self.DUMMY_LR, clipnorm=clipnorm)
elif optimizer == 'tf_adam':
optimizer_class = tf.train.AdamOptimizer(
learning_rate=self.DUMMY_LR)
elif optimizer == 'rmsprop':
optimizer_class = RMSprop(lr=self.DUMMY_LR, clipnorm=clipnorm)
elif optimizer == 'nadam':
optimizer_class = Nadam(lr=self.DUMMY_LR, clipnorm=clipnorm)
else:
print("Optimizer not implemented yet")
exit(1)
# Timeline profiler
if (self.conf is not None and conf['training']['timeline_prof']):
self.run_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE)
self.run_metadata = tf.RunMetadata()
self.model.compile(optimizer=optimizer_class,
loss=loss,
options=self.run_options,
run_metadata=self.run_metadata)
else:
self.model.compile(optimizer=optimizer_class, loss=loss)
self.ensure_equal_weights()
def ensure_equal_weights(self):
if g.task_index == 0:
new_weights = self.model.get_weights()
else:
new_weights = None
nw = g.comm.bcast(new_weights, root=0)
self.model.set_weights(nw)
def train_on_batch_and_get_deltas(self, X_batch, Y_batch, verbose=False):
'''
The purpose of the method is to perform a single gradient update over
one mini-batch for one model replica. Given a mini-batch, it first
accesses the current model weights, performs single gradient update
over one mini-batch, gets new model weights, calculates weight updates
(deltas) by subtracting weight scalars, applies the learning rate.
It performs calls to: subtract_params, multiply_params
Argument list:
- X_batch: input data for one mini-batch as a Numpy array
- Y_batch: labels for one mini-batch as a Numpy array
- verbose: set verbosity level (currently unused)
Returns:
- deltas: a list of model weight updates
- loss: scalar training loss
'''
weights_before_update = self.model.get_weights()
return_sequences = self.conf['model']['return_sequences']
if not return_sequences:
Y_batch = Y_batch[:, -1, :]
loss = self.model.train_on_batch(X_batch, Y_batch)
weights_after_update = self.model.get_weights()
self.model.set_weights(weights_before_update)
# unscale before subtracting
weights_before_update = multiply_params(weights_before_update,
1.0 / self.DUMMY_LR)
weights_after_update = multiply_params(weights_after_update,
1.0 / self.DUMMY_LR)
deltas = subtract_params(weights_after_update, weights_before_update)
# unscale loss
if conf['model']['loss_scale_factor'] != 1.0:
deltas = multiply_params(deltas,
1.0 / conf['model']['loss_scale_factor'])
return deltas, loss
def get_new_weights(self, deltas):
return add_params(self.model.get_weights(), deltas)
def mpi_average_gradients(self, arr, num_replicas=None):
if num_replicas is None:
num_replicas = self.num_workers
if self.task_index >= num_replicas:
arr *= 0.0
arr_global = np.empty_like(arr)
if K.floatx() == 'float16':
self.comm.Allreduce(arr, arr_global, op=mpi_sum_f16)
else:
self.comm.Allreduce(arr, arr_global, op=MPI.SUM)
arr_global /= num_replicas
return arr_global
def mpi_average_scalars(self, val, num_replicas=None):
'''
The purpose of the method is to calculate a simple scalar arithmetic
mean over num_replicas.
It performs calls to: MPIModel.mpi_sum_scalars
Argument list:
- val: value averaged, scalar
- num_replicas: the size of the ensemble an average is perfromed over
Returns:
- val_global: scalar arithmetic mean over num_replicas
'''
val_global = self.mpi_sum_scalars(val, num_replicas)
val_global /= num_replicas
return val_global
def mpi_sum_scalars(self, val, num_replicas=None):
'''
The purpose of the method is to calculate a simple scalar arithmetic
mean over num_replicas using MPI allreduce action with fixed op=MPI.SIM
Argument list:
- val: value averaged, scalar
- num_replicas: the size of the ensemble an average is perfromed over
Returns:
- val_global: scalar arithmetic mean over num_replicas
'''
if num_replicas is None:
num_replicas = self.num_workers
if self.task_index >= num_replicas:
val *= 0.0
val_global = 0.0
val_global = self.comm.allreduce(val, op=MPI.SUM)
return val_global
def sync_deltas(self, deltas, num_replicas=None):
global_deltas = []
# default is to reduce the deltas from all workers
for delta in deltas:
global_deltas.append(
self.mpi_average_gradients(delta, num_replicas))
return global_deltas
def set_new_weights(self, deltas, num_replicas=None):
global_deltas = self.sync_deltas(deltas, num_replicas)
effective_lr = self.get_effective_lr(num_replicas)
self.optimizer.set_lr(effective_lr)
global_deltas = self.optimizer.get_deltas(global_deltas)
new_weights = self.get_new_weights(global_deltas)
self.model.set_weights(new_weights)
def build_callbacks(self, conf, callbacks_list):
'''
The purpose of the method is to set up logging and history. It is based
on Keras Callbacks
https://github.com/fchollet/keras/blob/fbc9a18f0abc5784607cd4a2a3886558efa3f794/keras/callbacks.py
Currently used callbacks include: BaseLogger, CSVLogger, EarlyStopping.
Other possible callbacks to add in future: RemoteMonitor,
LearningRateScheduler
Argument list:
- conf: There is a "callbacks" section in conf.yaml file.
Relevant parameters are:
- list: Parameter specifying additional callbacks, read
in the driver script and passed as an argument of type list (see next
arg)
- metrics: List of quantities monitored during training and validation
- mode: one of {auto, min, max}. The decision to overwrite the current
save file is made based on either the maximization or the minimization
of the monitored quantity. For val_acc, this should be max, for
val_loss this should be min, etc. In auto mode, the direction is
automatically inferred from the name of the monitored quantity.
- monitor: Quantity used for early stopping, has to
be from the list of metrics
- patience: Number of epochs used to decide on whether to apply early
stopping or continue training
- callbacks_list: uses callbacks.list configuration parameter,
specifies the list of additional callbacks Returns: modified list of
callbacks
'''
mode = conf['callbacks']['mode']
monitor = conf['callbacks']['monitor']
patience = conf['callbacks']['patience']
csvlog_save_path = conf['paths']['csvlog_save_path']
# CSV callback is on by default
if not os.path.exists(csvlog_save_path):
os.makedirs(csvlog_save_path)
callbacks_list = conf['callbacks']['list']
callbacks = [cbks.BaseLogger()]
callbacks += [self.history]
callbacks += [
cbks.CSVLogger("{}callbacks-{}.log".format(
csvlog_save_path,
datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")))
]
if "earlystop" in callbacks_list:
callbacks += [
cbks.EarlyStopping(patience=patience,
monitor=monitor,
mode=mode)
]
if "lr_scheduler" in callbacks_list:
pass
return cbks.CallbackList(callbacks)
def add_noise(self, X):
if self.conf['training']['noise'] is True:
prob = 0.05
else:
prob = self.conf['training']['noise']
for i in range(0, X.shape[0]):
for j in range(0, X.shape[2]):
a = random.randint(0, 100)
if a < prob * 100:
X[i, :, j] = 0.0
return X
def train_epoch(self):
'''
Perform distributed mini-batch SGD for
one epoch. It takes the batch iterator function and a NN model from
MPIModel object, fetches mini-batches in a while-loop until number of
samples seen by the ensemble of workers (num_so_far) exceeds the
training dataset size (num_total).
NOTE: "sample" = "an entire shot" within this description
During each iteration, the gradient updates (deltas) and the loss are
calculated for each model replica in the ensemble, weights are averaged
over ensemble, and the new weights are set.
It performs calls to: MPIModel.get_deltas, MPIModel.set_new_weights
methods
Argument list: Empty
Returns:
- step: final iteration number
- ave_loss: model loss averaged over iterations within this epoch
- curr_loss: training loss averaged over replicas at final iteration
- num_so_far: the cumulative number of samples seen by the ensemble
of replicas up to the end of the final iteration (step) of this epoch
Intermediate outputs and logging: debug printout of task_index (MPI),
epoch number, number of samples seen to a current epoch, average
training loss
'''
verbose = False
first_run = True
step = 0
loss_averager = Averager()
t_start = time.time()
timeline_prof = False
if (self.conf is not None and conf['training']['timeline_prof']):
timeline_prof = True
step_limit = 0
if (self.conf is not None and conf['training']['step_limit'] > 0):
step_limit = conf['training']['step_limit']
batch_iterator_func = self.batch_iterator_func
num_total = 1
ave_loss = -1
curr_loss = -1
t0 = 0
t1 = 0
t2 = 0
while ((self.num_so_far - self.epoch * num_total) < num_total
or step < self.num_batches_minimum):
if step_limit > 0 and step > step_limit:
print('reached step limit')
break
try:
(batch_xs, batch_ys, batches_to_reset, num_so_far_curr,
num_total, is_warmup_period) = next(batch_iterator_func)
except StopIteration:
g.print_unique("Resetting batch iterator.")
self.num_so_far_accum = self.num_so_far_indiv
self.set_batch_iterator_func()
batch_iterator_func = self.batch_iterator_func
(batch_xs, batch_ys, batches_to_reset, num_so_far_curr,
num_total, is_warmup_period) = next(batch_iterator_func)
self.num_so_far_indiv = self.num_so_far_accum + num_so_far_curr
# if batches_to_reset:
# self.model.reset_states(batches_to_reset)
warmup_phase = (step < self.warmup_steps and self.epoch == 0)
num_replicas = 1 if warmup_phase else self.num_replicas
self.num_so_far = self.mpi_sum_scalars(self.num_so_far_indiv,
num_replicas)
# run the model once to force compilation. Don't actually use these
# values.
if first_run:
first_run = False
t0_comp = time.time()
# print('input_dimension:',batch_xs.shape)
# print('output_dimension:',batch_ys.shape)
_, _ = self.train_on_batch_and_get_deltas(
batch_xs, batch_ys, verbose)
self.comm.Barrier()
sys.stdout.flush()
# TODO(KGF): check line feed/carriage returns around this
g.print_unique(
'\nCompilation finished in {:.2f}s'.format(time.time() -
t0_comp))
t_start = time.time()
sys.stdout.flush()
if np.any(batches_to_reset):
reset_states(self.model, batches_to_reset)
if ('noise' in self.conf['training'].keys()
and self.conf['training']['noise'] is not False):
batch_xs = self.add_noise(batch_xs)
t0 = time.time()
deltas, loss = self.train_on_batch_and_get_deltas(
batch_xs, batch_ys, verbose)
t1 = time.time()
if not is_warmup_period:
self.set_new_weights(deltas, num_replicas)
t2 = time.time()
write_str_0 = self.calculate_speed(t0, t1, t2, num_replicas)
curr_loss = self.mpi_average_scalars(1.0 * loss, num_replicas)
# g.print_unique(self.model.get_weights()[0][0][:4])
loss_averager.add_val(curr_loss)
ave_loss = loss_averager.get_ave()
eta = self.estimate_remaining_time(
t0 - t_start, self.num_so_far - self.epoch * num_total,
num_total)
write_str = (
'\r[{}] step: {} [ETA: {:.2f}s] [{:.2f}/{}], '.format(
self.task_index, step, eta, 1.0 * self.num_so_far,
num_total) +
'loss: {:.5f} [{:.5f}] | '.format(ave_loss, curr_loss) +
'walltime: {:.4f} | '.format(time.time() -
self.start_time))
g.write_unique(write_str + write_str_0)
if timeline_prof:
# dump profile
tl = timeline.Timeline(self.run_metadata.step_stats)
ctf = tl.generate_chrome_trace_format()
# dump file per iteration
with open('timeline_%s.json' % step, 'w') as f:
f.write(ctf)
step += 1
else:
g.write_unique('\r[{}] warmup phase, num so far: {}'.format(
self.task_index, self.num_so_far))
effective_epochs = 1.0 * self.num_so_far / num_total
epoch_previous = self.epoch
self.epoch = effective_epochs
g.write_unique(
# TODO(KGF): "a total of X epochs within this session" ?
'\nFinished training epoch {:.2f} '.format(self.epoch)
# TODO(KGF): "precisely/exactly X epochs just passed"?
+
'during this session ({:.2f} epochs passed)'.format(self.epoch -
epoch_previous)
# '\nEpoch {:.2f} finished training ({:.2f} epochs passed)'.format(
# 1.0 * self.epoch, self.epoch - epoch_previous)
+ ' in {:.2f} seconds\n'.format(t2 - t_start))
return (step, ave_loss, curr_loss, self.num_so_far, effective_epochs)
def estimate_remaining_time(self, time_so_far, work_so_far, work_total):
eps = 1e-6
total_time = 1.0 * time_so_far * work_total / (work_so_far + eps)
return total_time - time_so_far
def get_effective_lr(self, num_replicas):
effective_lr = self.lr * num_replicas
if effective_lr > self.max_lr:
g.write_unique(
'Warning: effective learning rate set to {}, '.format(
effective_lr) +
'larger than maximum {}. Clipping.'.format(self.max_lr))
effective_lr = self.max_lr
return effective_lr
def get_effective_batch_size(self, num_replicas):
return self.batch_size * num_replicas
def calculate_speed(self,
t0,
t_after_deltas,
t_after_update,
num_replicas,
verbose=False):
effective_batch_size = self.get_effective_batch_size(num_replicas)
t_calculate = t_after_deltas - t0
t_sync = t_after_update - t_after_deltas
t_tot = t_after_update - t0
examples_per_sec = effective_batch_size / t_tot
frac_calculate = t_calculate / t_tot
frac_sync = t_sync / t_tot
print_str = (
'{:.2E} Examples/sec | {:.2E} sec/batch '.format(
examples_per_sec, t_tot) +
'[{:.1%} calc., {:.1%} sync.]'.format(frac_calculate, frac_sync))
print_str += '[batch = {} = {}*{}] [lr = {:.2E} = {:.2E}*{}]'.format(
effective_batch_size, self.batch_size, num_replicas,
self.get_effective_lr(num_replicas), self.lr, num_replicas)
if verbose:
g.write_unique(print_str)
return print_str
def train(conf,shot_list_train,shot_list_validate,loader):
loader.set_inference_mode(False)
np.random.seed(1)
validation_losses = []
validation_roc = []
training_losses = []
print('validate: {} shots, {} disruptive'.format(len(shot_list_validate),shot_list_validate.num_disruptive()))
print('training: {} shots, {} disruptive'.format(len(shot_list_train),shot_list_train.num_disruptive()))
if backend == 'tf' or backend == 'tensorflow':
first_time = "tensorflow" not in sys.modules
if first_time:
import tensorflow as tf
os.environ['KERAS_BACKEND'] = 'tensorflow'
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto(device_count={"GPU":1})
set_session(tf.Session(config=config))
else:
os.environ['KERAS_BACKEND'] = 'theano'
os.environ['THEANO_FLAGS'] = 'device=gpu,floatX=float32'
import theano
from keras.utils.generic_utils import Progbar
from keras import backend as K
from plasma.models import builder
print('Build model...',end='')
specific_builder = builder.ModelBuilder(conf)
train_model = specific_builder.build_model(False)
print('Compile model',end='')
train_model.compile(optimizer=optimizer_class(),loss=conf['data']['target'].loss)
print('...done')
#load the latest epoch we did. Returns -1 if none exist yet
e = specific_builder.load_model_weights(train_model)
e_start = e
batch_generator = partial(loader.training_batch_generator_partial_reset,shot_list=shot_list_train)
batch_iterator = ProcessGenerator(batch_generator())
num_epochs = conf['training']['num_epochs']
num_at_once = conf['training']['num_shots_at_once']
lr_decay = conf['model']['lr_decay']
print('{} epochs left to go'.format(num_epochs - 1 - e))
num_so_far_accum = 0
num_so_far = 0
num_total = np.inf
if conf['callbacks']['mode'] == 'max':
best_so_far = -np.inf
cmp_fn = max
else:
best_so_far = np.inf
cmp_fn = min
while e < num_epochs-1:
e += 1
print('\nEpoch {}/{}'.format(e+1,num_epochs))
pbar = Progbar(len(shot_list_train))
#decay learning rate each epoch:
K.set_value(train_model.optimizer.lr, lr*lr_decay**(e))
#print('Learning rate: {}'.format(train_model.optimizer.lr.get_value()))
num_batches_minimum = 100
num_batches_current = 0
training_losses_tmp = []
while num_so_far < (e - e_start)*num_total or num_batches_current < num_batches_minimum:
num_so_far_old = num_so_far
try:
batch_xs,batch_ys,batches_to_reset,num_so_far_curr,num_total,is_warmup_period = next(batch_iterator)
except StopIteration:
print("Resetting batch iterator.")
num_so_far_accum = num_so_far
batch_iterator = ProcessGenerator(batch_generator())
batch_xs,batch_ys,batches_to_reset,num_so_far_curr,num_total,is_warmup_period = next(batch_iterator)
if np.any(batches_to_reset):
reset_states(train_model,batches_to_reset)
if not is_warmup_period:
num_so_far = num_so_far_accum+num_so_far_curr
num_batches_current +=1
loss = train_model.train_on_batch(batch_xs,batch_ys)
training_losses_tmp.append(loss)
pbar.add(num_so_far - num_so_far_old, values=[("train loss", loss)])
loader.verbose=False#True during the first iteration
else:
_ = train_model.predict(batch_xs,batch_size=conf['training']['batch_size'])
e = e_start+1.0*num_so_far/num_total
sys.stdout.flush()
ave_loss = np.mean(training_losses_tmp)
training_losses.append(ave_loss)
specific_builder.save_model_weights(train_model,int(round(e)))
if conf['training']['validation_frac'] > 0.0:
print("prediction on GPU...")
_,_,_,roc_area,loss = make_predictions_and_evaluate_gpu(conf,shot_list_validate,loader)
validation_losses.append(loss)
validation_roc.append(roc_area)
epoch_logs = {}
epoch_logs['val_roc'] = roc_area
epoch_logs['val_loss'] = loss
epoch_logs['train_loss'] = ave_loss
best_so_far = cmp_fn(epoch_logs[conf['callbacks']['monitor']],best_so_far)
if best_so_far != epoch_logs[conf['callbacks']['monitor']]: #only save model weights if quantity we are tracking is improving
print("Not saving model weights")
specific_builder.delete_model_weights(train_model,int(round(e)))
if conf['training']['ranking_difficulty_fac'] != 1.0:
_,_,_,roc_area_train,loss_train = make_predictions_and_evaluate_gpu(conf,shot_list_train,loader)
batch_iterator.__exit__()
batch_generator = partial(loader.training_batch_generator_partial_reset,shot_list=shot_list_train)
batch_iterator = ProcessGenerator(batch_generator())
num_so_far_accum = num_so_far
print('=========Summary========')
print('Training Loss Numpy: {:.3e}'.format(training_losses[-1]))
if conf['training']['validation_frac'] > 0.0:
print('Validation Loss: {:.3e}'.format(validation_losses[-1]))
print('Validation ROC: {:.4f}'.format(validation_roc[-1]))
if conf['training']['ranking_difficulty_fac'] != 1.0:
print('Train Loss: {:.3e}'.format(loss_train))
print('Train ROC: {:.4f}'.format(roc_area_train))
# plot_losses(conf,[training_losses],specific_builder,name='training')
if conf['training']['validation_frac'] > 0.0:
plot_losses(conf,[training_losses,validation_losses,validation_roc],specific_builder,name='training_validation_roc')
batch_iterator.__exit__()
print('...done')
def set_batch_iterator_func(self): self.batch_iterator_func = ProcessGenerator(self.batch_iterator())
class MPIModel():
def __init__(self,model,optimizer,comm,batch_iterator,batch_size,num_replicas=None,warmup_steps=1000,lr=0.01,num_batches_minimum=100):
random.seed(task_index)
np.random.seed(task_index)
self.start_time = time.time()
self.epoch = 0
self.num_so_far = 0
self.num_so_far_accum = 0
self.num_so_far_indiv = 0
self.model = model
self.optimizer = optimizer
self.max_lr = 0.1
self.DUMMY_LR = 0.001
self.comm = comm
self.batch_size = batch_size
self.batch_iterator = batch_iterator
self.set_batch_iterator_func()
self.warmup_steps=warmup_steps
self.num_batches_minimum=num_batches_minimum
self.num_workers = comm.Get_size()
self.task_index = comm.Get_rank()
self.history = cbks.History()
self.model.stop_training = False
if num_replicas is None or num_replicas < 1 or num_replicas > self.num_workers:
self.num_replicas = self.num_workers
else:
self.num_replicas = num_replicas
self.lr = lr/(1.0+self.num_replicas/100.0) if (lr < self.max_lr) else self.max_lr/(1.0+self.num_replicas/100.0)
def set_batch_iterator_func(self):
self.batch_iterator_func = ProcessGenerator(self.batch_iterator())
def close(self):
self.batch_iterator_func.__exit__()
def set_lr(self,lr):
self.lr = lr
def save_weights(self,path,overwrite=False):
self.model.save_weights(path,overwrite=overwrite)
def load_weights(self,path):
self.model.load_weights(path)
def compile(self,optimizer,clipnorm,loss='mse'):
if optimizer == 'sgd':
optimizer_class = SGD(lr=self.DUMMY_LR,clipnorm=clipnorm)
elif optimizer == 'momentum_sgd':
optimizer_class = SGD(lr=self.DUMMY_LR, clipnorm=clipnorm, decay=1e-6, momentum=0.9)
elif optimizer == 'tf_momentum_sgd':
optimizer_class = TFOptimizer(tf.train.MomentumOptimizer(learning_rate=self.DUMMY_LR,momentum=0.9))
elif optimizer == 'adam':
optimizer_class = Adam(lr=self.DUMMY_LR,clipnorm=clipnorm)
elif optimizer == 'tf_adam':
optimizer_class = TFOptimizer(tf.train.AdamOptimizer(learning_rate=self.DUMMY_LR))
elif optimizer == 'rmsprop':
optimizer_class = RMSprop(lr=self.DUMMY_LR,clipnorm=clipnorm)
elif optimizer == 'nadam':
optimizer_class = Nadam(lr=self.DUMMY_LR,clipnorm=clipnorm)
else:
print("Optimizer not implemented yet")
exit(1)
self.model.compile(optimizer=optimizer_class,loss=loss)
self.ensure_equal_weights()
def ensure_equal_weights(self):
if task_index == 0:
new_weights = self.model.get_weights()
else:
new_weights = None
nw = comm.bcast(new_weights,root=0)
self.model.set_weights(nw)
def train_on_batch_and_get_deltas(self,X_batch,Y_batch,verbose=False):
'''
The purpose of the method is to perform a single gradient update over one mini-batch for one model replica.
Given a mini-batch, it first accesses the current model weights, performs single gradient update over one mini-batch,
gets new model weights, calculates weight updates (deltas) by subtracting weight scalars, applies the learning rate.
It performs calls to: subtract_params, multiply_params
Argument list:
- X_batch: input data for one mini-batch as a Numpy array
- Y_batch: labels for one mini-batch as a Numpy array
- verbose: set verbosity level (currently unused)
Returns:
- deltas: a list of model weight updates
- loss: scalar training loss
'''
weights_before_update = self.model.get_weights()
loss = self.model.train_on_batch(X_batch,Y_batch)
weights_after_update = self.model.get_weights()
self.model.set_weights(weights_before_update)
#unscale before subtracting
weights_before_update = multiply_params(weights_before_update,1.0/self.DUMMY_LR)
weights_after_update = multiply_params(weights_after_update,1.0/self.DUMMY_LR)
deltas = subtract_params(weights_after_update,weights_before_update)
#unscale loss
if conf['model']['loss_scale_factor'] != 1.0:
deltas = multiply_params(deltas,1.0/conf['model']['loss_scale_factor'])
return deltas,loss
def get_new_weights(self,deltas):
return add_params(self.model.get_weights(),deltas)
def mpi_average_gradients(self,arr,num_replicas=None):
if num_replicas == None:
num_replicas = self.num_workers
if self.task_index >= num_replicas:
arr *= 0.0
arr_global = np.empty_like(arr)
if K.floatx() == 'float16':
self.comm.Allreduce(arr,arr_global,op=mpi_sum_f16)
else:
self.comm.Allreduce(arr,arr_global,op=MPI.SUM)
arr_global /= num_replicas
return arr_global
def mpi_average_scalars(self,val,num_replicas=None):
'''
The purpose of the method is to calculate a simple scalar arithmetic mean over num_replicas.
It performs calls to: MPIModel.mpi_sum_scalars
Argument list:
- val: value averaged, scalar
- num_replicas: the size of the ensemble an average is perfromed over
Returns:
- val_global: scalar arithmetic mean over num_replicas
'''
val_global = self.mpi_sum_scalars(val,num_replicas)
val_global /= num_replicas
return val_global
def mpi_sum_scalars(self,val,num_replicas=None):
'''
The purpose of the method is to calculate a simple scalar arithmetic mean over num_replicas using MPI allreduce action with fixed op=MPI.SIM
Argument list:
- val: value averaged, scalar
- num_replicas: the size of the ensemble an average is perfromed over
Returns:
- val_global: scalar arithmetic mean over num_replicas
'''
if num_replicas == None:
num_replicas = self.num_workers
if self.task_index >= num_replicas:
val *= 0.0
val_global = 0.0
val_global = self.comm.allreduce(val,op=MPI.SUM)
return val_global
def sync_deltas(self,deltas,num_replicas=None):
global_deltas = []
#default is to reduce the deltas from all workers
for delta in deltas:
global_deltas.append(self.mpi_average_gradients(delta,num_replicas))
return global_deltas
def set_new_weights(self,deltas,num_replicas=None):
global_deltas = self.sync_deltas(deltas,num_replicas)
effective_lr = self.get_effective_lr(num_replicas)
self.optimizer.set_lr(effective_lr)
global_deltas = self.optimizer.get_deltas(global_deltas)
new_weights = self.get_new_weights(global_deltas)
self.model.set_weights(new_weights)
def build_callbacks(self,conf,callbacks_list):
'''
The purpose of the method is to set up logging and history. It is based on Keras Callbacks
https://github.com/fchollet/keras/blob/fbc9a18f0abc5784607cd4a2a3886558efa3f794/keras/callbacks.py
Currently used callbacks include: BaseLogger, CSVLogger, EarlyStopping.
Other possible callbacks to add in future: RemoteMonitor, LearningRateScheduler
Argument list:
- conf: There is a "callbacks" section in conf.yaml file. Relevant parameters are:
list: Parameter specifying additional callbacks, read in the driver script and passed as an argument of type list (see next arg)
metrics: List of quantities monitored during training and validation
mode: one of {auto, min, max}. The decision to overwrite the current save file is made based on either the maximization or the minimization of the monitored quantity. For val_acc, this should be max, for val_loss this should be min, etc. In auto mode, the direction is automatically inferred from the name of the monitored quantity.
monitor: Quantity used for early stopping, has to be from the list of metrics
patience: Number of epochs used to decide on whether to apply early stopping or continue training
- callbacks_list: uses callbacks.list configuration parameter, specifies the list of additional callbacks
Returns: modified list of callbacks
'''
mode = conf['callbacks']['mode']
monitor = conf['callbacks']['monitor']
patience = conf['callbacks']['patience']
csvlog_save_path = conf['paths']['csvlog_save_path']
#CSV callback is on by default
if not os.path.exists(csvlog_save_path):
os.makedirs(csvlog_save_path)
callbacks_list = conf['callbacks']['list']
callbacks = [cbks.BaseLogger()]
callbacks += [self.history]
callbacks += [cbks.CSVLogger("{}callbacks-{}.log".format(csvlog_save_path,datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")))]
if "earlystop" in callbacks_list:
callbacks += [cbks.EarlyStopping(patience=patience, monitor=monitor, mode=mode)]
if "lr_scheduler" in callbacks_list:
pass
return cbks.CallbackList(callbacks)
def train_epoch(self):
'''
The purpose of the method is to perform distributed mini-batch SGD for one epoch.
It takes the batch iterator function and a NN model from MPIModel object, fetches mini-batches
in a while-loop until number of samples seen by the ensemble of workers (num_so_far) exceeds the
training dataset size (num_total).
During each iteration, the gradient updates (deltas) and the loss are calculated for each model replica
in the ensemble, weights are averaged over ensemble, and the new weights are set.
It performs calls to: MPIModel.get_deltas, MPIModel.set_new_weights methods
Argument list: Empty
Returns:
- step: epoch number
- ave_loss: training loss averaged over replicas
- curr_loss:
- num_so_far: the number of samples seen by ensemble of replicas to a current epoch (step)
Intermediate outputs and logging: debug printout of task_index (MPI), epoch number, number of samples seen to
a current epoch, average training loss
'''
verbose = False
first_run = True
step = 0
loss_averager = Averager()
t_start = time.time()
batch_iterator_func = self.batch_iterator_func
num_total = 1
ave_loss = -1
curr_loss = -1
t0 = 0
t1 = 0
t2 = 0
while (self.num_so_far-self.epoch*num_total) < num_total or step < self.num_batches_minimum:
try:
batch_xs,batch_ys,batches_to_reset,num_so_far_curr,num_total,is_warmup_period = next(batch_iterator_func)
except StopIteration:
print("Resetting batch iterator.")
self.num_so_far_accum = self.num_so_far_indiv
self.set_batch_iterator_func()
batch_iterator_func = self.batch_iterator_func
batch_xs,batch_ys,batches_to_reset,num_so_far_curr,num_total,is_warmup_period = next(batch_iterator_func)
self.num_so_far_indiv = self.num_so_far_accum+num_so_far_curr
# if batches_to_reset:
# self.model.reset_states(batches_to_reset)
warmup_phase = (step < self.warmup_steps and self.epoch == 0)
num_replicas = 1 if warmup_phase else self.num_replicas
self.num_so_far = self.mpi_sum_scalars(self.num_so_far_indiv,num_replicas)
#run the model once to force compilation. Don't actually use these values.
if first_run:
first_run = False
t0_comp = time.time()
_,_ = self.train_on_batch_and_get_deltas(batch_xs,batch_ys,verbose)
self.comm.Barrier()
sys.stdout.flush()
print_unique('Compilation finished in {:.2f}s'.format(time.time()-t0_comp))
t_start = time.time()
sys.stdout.flush()
if np.any(batches_to_reset):
reset_states(self.model,batches_to_reset)
t0 = time.time()
deltas,loss = self.train_on_batch_and_get_deltas(batch_xs,batch_ys,verbose)
t1 = time.time()
if not is_warmup_period:
self.set_new_weights(deltas,num_replicas)
t2 = time.time()
write_str_0 = self.calculate_speed(t0,t1,t2,num_replicas)
curr_loss = self.mpi_average_scalars(1.0*loss,num_replicas)
#if self.task_index == 0:
#print(self.model.get_weights()[0][0][:4])
loss_averager.add_val(curr_loss)
ave_loss = loss_averager.get_val()
eta = self.estimate_remaining_time(t0 - t_start,self.num_so_far-self.epoch*num_total,num_total)
write_str = '\r[{}] step: {} [ETA: {:.2f}s] [{:.2f}/{}], loss: {:.5f} [{:.5f}] | walltime: {:.4f} | '.format(self.task_index,step,eta,1.0*self.num_so_far,num_total,ave_loss,curr_loss,time.time()-self.start_time)
print_unique(write_str + write_str_0)
step += 1
else:
print_unique('\r[{}] warmup phase, num so far: {}'.format(self.task_index,self.num_so_far))
effective_epochs = 1.0*self.num_so_far/num_total
epoch_previous = self.epoch
self.epoch = effective_epochs
print_unique('\nEpoch {:.2f} finished ({:.2f} epochs passed) in {:.2f} seconds.\n'.format(1.0*self.epoch,self.epoch-epoch_previous,t2 - t_start))
return (step,ave_loss,curr_loss,self.num_so_far,effective_epochs)
def estimate_remaining_time(self,time_so_far,work_so_far,work_total):
eps = 1e-6
total_time = 1.0*time_so_far*work_total/(work_so_far + eps)
return total_time - time_so_far
def get_effective_lr(self,num_replicas):
effective_lr = self.lr * num_replicas
if effective_lr > self.max_lr:
print_unique('Warning: effective learning rate set to {}, larger than maximum {}. Clipping.'.format(effective_lr,self.max_lr))
effective_lr = self.max_lr
return effective_lr
def get_effective_batch_size(self,num_replicas):
return self.batch_size*num_replicas
def calculate_speed(self,t0,t_after_deltas,t_after_update,num_replicas,verbose=False):
effective_batch_size = self.get_effective_batch_size(num_replicas)
t_calculate = t_after_deltas - t0
t_sync = t_after_update - t_after_deltas
t_tot = t_after_update - t0
examples_per_sec = effective_batch_size/t_tot
frac_calculate = t_calculate/t_tot
frac_sync = t_sync/t_tot
print_str = '{:.2E} Examples/sec | {:.2E} sec/batch [{:.1%} calc., {:.1%} synch.]'.format(examples_per_sec,t_tot,frac_calculate,frac_sync)
print_str += '[batch = {} = {}*{}] [lr = {:.2E} = {:.2E}*{}]'.format(effective_batch_size,self.batch_size,num_replicas,self.get_effective_lr(num_replicas),self.lr,num_replicas)
if verbose:
print_unique(print_str)
return print_str