def after_run(self, run_context, run_values):
if self.step % self.log_freq == 0:
current_time = time.time()
duration = current_time - self.start_time
self.start_time = current_time
loss_value = np.asarray([run_values.results[0]], dtype='float32')
acc_value = np.asarray([run_values.results[1]], dtype='float32')
#lr = np.asarray([run_values.results[2]],dtype='float32')
#epoch = np.asarray([run_values.results[3]],dtype='float32')
examples_per_sec = self.log_freq * self.batch_size / duration
sec_per_batch = float(duration / self.log_freq)
mc.average(loss_value)
mc.average(acc_value)
format_str = (
'%s: step %d, loss = %.3f, acc = %.3f, (%.1f examples/sec; %.3f '
'sec/batch)')
if (mc.get_rank() == 0):
print("available values = ", run_values)
print(format_str %
(datetime.now(), self.step, loss_value, acc_value,
examples_per_sec, sec_per_batch))
self.samps = self.samps + examples_per_sec
self.perf = self.perf + sec_per_batch
self.sums = self.sums + 1
def on_train_begin(self, logs=None):
sess = K.get_session()
# Split variables based on type -> float32 vs all else
test_v = tf.Variable([0], dtype=tf.float32)
all_vars = tf.trainable_variables()
float_vars = [v for v in all_vars if v.dtype == test_v.dtype]
other_vars = [v for v in all_vars if v.dtype != test_v.dtype]
# Initialize variables and broadcast from head node
sess.run(tf.variables_initializer(all_vars))
new_vars = mc.broadcast(float_vars, 0)
bcast = tf.group(
*[tf.assign(v, new_vars[k]) for k, v in enumerate(float_vars)])
sess.run(bcast)
# Validate Broadcast
if self.validate:
py_all_vars = [sess.run(v) for v in float_vars]
var_types = [
np.array([v]) if type(v) == np.float32 else v
for v in py_all_vars
]
if mc.get_rank() is 0:
if (mc.check_buffers_match(var_types, 1) != 0):
tf.logging.error(
"Not all processes have the same initial model!")
else:
tf.logging.info("Initial model is consistent on all ranks")
def end(self, session):
self.loss = self.loss / self.step
self.acc = self.acc / self.step
mc.average(self.loss)
mc.average(self.acc)
format_str = (
'EVAL Session ENDED at %s: step %d, loss = %.3f, accuracy = %.3f (%.1f examples/sec; %.3f '
'sec/batch)')
if (mc.get_rank() == 0):
print(format_str % (datetime.now(), self.step, self.loss, self.acc,
self.samps / self.step, self.perf / self.step))
def end(self, session):
lr = session.run(self.lr)
#epoch = session.run(self.epoch)
format_str = (
'TRAIN Session ENDED at %s: step %d (%.1f examples/sec; %.3f '
'sec/batch), learning rate: %.5f')
self.epoch_true = tf.train.global_step(session,
self.epoch) / (self.step + 1)
if (mc.get_rank() == 0):
print('Epoch: ', self.epoch_true)
print('global_step: %s' %
tf.train.global_step(session, self.epoch))
print(format_str % (datetime.now(), self.step, self.samps /
self.sums, self.perf / self.sums, lr))
def after_run(self, run_context, run_values):
current_time = time.time()
duration = current_time - self.start_time
self.start_time = current_time
loss_value = np.asarray([run_values.results[0]], dtype='float32')
acc_value = np.asarray([run_values.results[1]], dtype='float32')
examples_per_sec = self.batch_size / duration
sec_per_batch = duration
self.samps = self.samps + examples_per_sec
self.perf = self.perf + sec_per_batch
self.loss = self.loss + loss_value
self.acc = self.acc + acc_value
if (mc.get_rank() == 0):
print("Eval step {:9d}".format(self.step))
def learning_rate_fn(global_step):
global_step = tf.cast(global_step, tf.float32)
#cstep = global_step*num_images/eff_batch_size
epoch = (d_steps + w_steps) / (global_step + 1)
total_epochs = decay_epochs + warmup_epochs
tf.Print(epoch, [epoch], "Epoch: ")
#current_step = tf.Print(cstep, [cstep], "Current train steps so far: ")
if (mlcomm == 1):
current_lr = learning_rate_0 * math.pow(
1.0 - (decay_steps - warmup_steps) / decay_steps, 2)
if (mc.get_rank() == 0):
print("Using Cray learning_rate_warmup_poly_decay(): ")
print(" -> effective batch size: ", eff_batch_size)
print(" -> batches per epoch: ", batches_per_epoch)
print(" -> initial learning rate: ", learning_rate_0)
print(" -> learning rate base: ", learning_rate_base)
print(" -> starting global learning rate at first epoch: ",
current_lr)
print(" -> decay after ", decay_epochs, " epochs")
print(" -> decay steps: ", decay_steps)
print(" -> warmup with ", warmup_epochs, " epochs")
print(" -> warmup steps: ", warmup_steps)
print(" -> number workers: ", mc.get_nranks())
#print(" -> Finished Epoch: ", tf.get_session_tensor(epoch), "/", total_epochs)
#global_step = tf.cast(global_step, tf.float32)
def lr_warmup():
return (lr_0 + global_step * (lr_base - lr_0) / w_steps)
def lr_poly():
return (lr_base * math_ops.pow(
(1 - (global_step - w_steps) / d_steps), 2))
return tf.cond(tf.less(global_step, warmup_steps), lambda: lr_warmup(),
lambda: lr_poly())
summary_op=train_summary))
# Add an op to initialize the variables.
init_global_op = tf.global_variables_initializer()
init_local_op = tf.local_variables_initializer()
#saver class:
model_saver = tf.train.Saver()
print(
"Rank", args["task_index"], ": starting training using " +
args['optimizer'] + " optimizer")
with tf.train.MonitoredTrainingSession(
config=sess_config,
checkpoint_dir=(args['modelpath']
if +mc.get_rank() == 0 else None),
save_checkpoint_secs=300,
hooks=hooks) as sess:
#initialize variables
sess.run([init_global_op, init_local_op])
#do the training loop
total_time = time.time()
train_loop(sess, bcast_hook, train_step, global_step, optlist,
args, trainset, validationset)
total_time -= time.time()
print("FINISHED Training. Total time %g" % (total_time))
#clean up comm buffers
mc.finalize()
def resnet_main(flags,
model_function,
input_function,
num_train_samps,
num_eval_samps,
shape=None):
"""Shared main loop for ResNet Models.
Args:
flags: FLAGS object that contains the params for running. See
ResnetArgParser for created flags.
model_function: the function that instantiates the Model and builds the
ops for train/eval. This will be passed directly into the estimator.
input_function: the function that processes the dataset and returns a
dataset that the estimator can train on. This will be wrapped with
all the relevant flags for running and passed to estimator.
shape: list of ints representing the shape of the images used for training.
This is only used if flags.export_dir is passed.
"""
# Using the Winograd non-fused algorithms provides a small performance boost.
os.environ['TF_ENABLE_WINOGRAD_NONFUSED'] = '1'
if flags.multi_gpu:
validate_batch_size_for_multi_gpu(flags.batch_size)
# There are two steps required if using multi-GPU: (1) wrap the model_fn,
# and (2) wrap the optimizer. The first happens here, and (2) happens
# in the model_fn itself when the optimizer is defined.
model_function = tf.contrib.estimator.replicate_model_fn(
loss_reduction=tf.losses.Reduction.MEAN)
# Create session config based on values of inter_op_parallelism_threads and
# intra_op_parallelism_threads. Note that we default to having
# allow_soft_placement = True, which is required for multi-GPU and not
# harmful for other modes.
myrank = 0
numworkers = 1
if (flags.enable_ml_comm == 1):
# initialize the Cray PE ML Plugin
# config the thread team (correcting the number of epochs for the effectice batch size))
#totsize = sum([reduce(lambda x, y: x*y, v.get_shape().as_list()) for v in tf.trainable_variables()])
totsize = 25551401 #Specific size for resnet50-v2
mc.init(2, 1, totsize, "tensorflow")
myrank = mc.get_rank()
numworkers = mc.get_nranks()
if (myrank == 0):
print("ResNet with {:9d} parameters".format(totsize))
max_steps_train = int(
math.ceil(flags.train_epochs * (num_train_samps + num_eval_samps) /
(mc.get_nranks() * flags.batch_size)))
#(0,0,num_steps_before_going_nonblock, max_steps_train, verbose=1, how_often_to_print=100)
mc.config_team(0, 0, max_steps_train, max_steps_train, 1, 100)
flags.model_dir = flags.model_dir if mc.get_rank() == 0 else None
flags.benchmark_log_dir = flags.benchmark_log_dir if mc.get_rank(
) == 0 else None
flags.export_dir = flags.export_dir if mc.get_rank() == 0 else None
else:
rank_id = myrank
session_config = tf.ConfigProto(
log_device_placement=False,
inter_op_parallelism_threads=flags.inter_op_parallelism_threads,
intra_op_parallelism_threads=flags.intra_op_parallelism_threads,
allow_soft_placement=True)
# Set up a RunConfig to save checkpoint and set session config.
run_config = tf.estimator.RunConfig().replace(
save_checkpoints_steps=500, session_config=session_config)
classifier = tf.estimator.Estimator(model_fn=model_function,
model_dir=flags.model_dir,
config=run_config,
params={
'resnet_size': flags.resnet_size,
'data_format': flags.data_format,
'batch_size': flags.batch_size,
'multi_gpu': flags.multi_gpu,
'train_epochs': flags.train_epochs,
'version': flags.version,
'loss_scale': flags.loss_scale,
'dtype': flags.dtype,
'mlcomm': flags.enable_ml_comm,
'log_freq':
flags.global_perf_log_freq,
'weight_decay': flags.weight_decay,
'init_lr': flags.init_lr,
'base_lr': flags.base_lr,
'warmup_epochs':
flags.warmup_epochs,
'log_freq':
flags.global_perf_log_freq,
})
benchmark_logger = logger.config_benchmark_logger(flags.benchmark_log_dir)
benchmark_logger.log_run_info('resnet')
for _ in range(flags.train_epochs // flags.epochs_between_evals):
train_hooks = hooks_helper.get_train_hooks(
flags.hooks,
batch_size=flags.batch_size,
benchmark_log_dir=flags.benchmark_log_dir)
if (myrank == 0):
print('Starting a training cycle.')
def input_fn_train():
return input_function(True, flags.data_dir, flags.batch_size,
flags.epochs_between_evals,
flags.num_parallel_calls, flags.multi_gpu,
numworkers, myrank)
tsteps = math.ceil(
float(flags.epochs_between_evals * num_train_samps) /
(numworkers * flags.batch_size))
classifier.train(input_fn=input_fn_train,
steps=tsteps,
max_steps=flags.max_train_steps)
if (myrank == 0):
print('Starting to evaluate.')
# Evaluate the model and print results
def input_fn_eval():
return input_function(False, flags.data_dir, flags.batch_size, 3,
flags.num_parallel_calls, flags.multi_gpu,
numworkers, myrank)
# flags.max_train_steps is generally associated with testing and profiling.
# As a result it is frequently called with synthetic data, which will
# iterate forever. Passing steps=flags.max_train_steps allows the eval
# (which is generally unimportant in those circumstances) to terminate.
# Note that eval will run for max_train_steps each loop, regardless of the
# global_step count.
esteps = math.ceil(
float(num_eval_samps) / (numworkers * flags.batch_size))
eval_results = classifier.evaluate(input_fn=input_fn_eval,
steps=esteps)
benchmark_logger.log_evaluation_result(eval_results)
if model_helpers.past_stop_threshold(flags.stop_threshold,
eval_results['accuracy']):
break
if flags.export_dir is not None:
warn_on_multi_gpu_export(flags.multi_gpu)
# Exports a saved model for the given classifier.
input_receiver_fn = export.build_tensor_serving_input_receiver_fn(
shape, batch_size=flags.batch_size)
classifier.export_savedmodel(flags.export_dir, input_receiver_fn)
if (flags.enable_ml_comm == 1):
mc.finalize()
def rank():
return mc.get_rank()
def build_model(config,args,print_summary=True):
# feature_size = (3,3,3)
# Pooling = kl.MaxPooling3D
image_shape = config['data_handling']['image_shape']
input_image = kl.Input(shape=tuple([1] + image_shape))
logger.debug('input image = %s',input_image)
layer_num = 0
outputs = []
for i in range(0,image_shape[0],4):
logger.debug('i = %s',i)
subimg = kl.Lambda(lambda x: x[:,:,i:i+4,:,:])(input_image)
num_filters = 64
x = subimg
x = conv_layer(subimg,num_filters=num_filters)
# Instantiate the stack of residual units
for stack in range(2):
for res_block in range(2):
strides = (1,1,1)
if stack > 0 and res_block == 0: # first layer but not first stack
strides = (2,2,2) # downsample
y = conv_layer(inputs=x,
num_filters=num_filters,
strides=strides)
y = conv_layer(inputs=y,
num_filters=num_filters,
activation=None)
if stack > 0 and res_block == 0: # first layer but not first stack
# linear projection residual shortcut connection to match
# changed dims
x = conv_layer(x,
num_filters=num_filters,
kernel_size=1,
strides=strides,
activation=None,
batch_normalization=False)
x = kl.add([x, y])
x = kl.Activation('relu')(x)
num_filters *= 2
outputs.append(x)
num_filters = int(num_filters/2)
logger.debug('filters = %s',num_filters)
# logger.debug('outputs = %s',outputs)
x = kl.Concatenate(axis=2)(outputs)
logger.debug('concat: %s',x)
# Instantiate the stack of residual units
for stack in range(2):
logger.debug('stack: %s',stack)
for res_block in range(3):
logger.debug('res_block: %s',res_block)
strides = (1,1,1)
if stack > 0 and res_block == 0: # first layer but not first stack
strides = (2,2,2) # downsample
logger.debug('x: %s',x)
y = conv_layer(x,
num_filters=num_filters,
strides=strides)
logger.debug('y: %s',y)
y = conv_layer(y,
num_filters=num_filters,
activation=None)
logger.debug('y: %s',y)
if stack > 0 and res_block == 0: # first layer but not first stack
# linear projection residual shortcut connection to match
# changed dims
x = conv_layer(x,
num_filters=num_filters,
kernel_size=1,
strides=strides,
activation=None,
batch_normalization=False)
x = kl.add([x, y])
x = kl.Activation('relu')(x)
num_filters *= 2
logger.debug('out = %s',x)
x = kl.AveragePooling3D(pool_size=(1,1,2))(x)
y = kl.Flatten()(x)
# x = kl.Dense(2048,activation='relu',kernel_initializer='normal',name='dense_{0}'.format(layer_num))(output)
# output = kl.Activation('relu',name='relu_{0}'.format(layer_num))(output)
# output = kl.Dropout(0.1,name='dropout_{0}'.format(layer_num))(output)
# layer_num += 1
outputs = kl.Dense(len(config['data_handling']['classes']),activation='softmax',kernel_initializer='he_normal')(y)
# output = kl.Activation('softmax',name='softmax_{0}'.format(layer_num))(output)
model = Model(input_image,outputs)
line_length = 150
positions = [.2, .45, .77, 1.]
if print_summary:
if args.horovod:
import horovod.keras as hvd
if hvd.rank() == 0:
model.summary(line_length=line_length,positions=positions)
elif args.ml_comm:
import ml_comm as mc
if mc.get_rank() == 0:
model.summary(line_length=line_length,positions=positions)
else:
model.summary(line_length=line_length,positions=positions)
return model
def train(self):
train_step, loss, lossL1Train,train_true,train_predict = self.optimize()
lossL1Val,val_true,val_predict = self.validation_loss()
lossL1Test,test_true,test_predict = self.test_loss()
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.4
### taking config from the MKL benchmarks.
config.allow_soft_placement = True
config.intra_op_parallelism_threads = 1 ## default
config.inter_op_parallelism_threads = 2 ## Default
#used to save the model
saver = tf.train.Saver()
global best_validation_accuracy
global last_improvement
global total_iterations
best_validation_accuracy = 1.0 #Best validation accuracy seen so far
last_improvement = 0 #Iteration-number for last improvement to validation accuracy.
require_improvement = hp.RUNPARAM['require_improvement'] #Stop optimization if no improvement found in this many iterations.
total_iterations = 0 #Counter for total number of iterations performed so far.
#initialize the CPE ML Plugin with one team (single thread for now) and the model size
totsize = sum([reduce(lambda x, y: x*y, v.get_shape().as_list()) for v in tf.trainable_variables()])
mc.init(1, 1, totsize, "tensorflow")
hp.RUNPARAM['batch_per_epoch'] = hp.RUNPARAM['batch_per_epoch'] / mc.get_nranks()
hp.RUNPARAM['batch_per_epoch_val'] = hp.RUNPARAM['batch_per_epoch_val'] / mc.get_nranks()
totsteps = hp.RUNPARAM['num_epoch'] * hp.RUNPARAM['batch_per_epoch']
mc.config_team(0, 0, totsteps, totsteps, 2, 50)
if (mc.get_rank() == 0):
print("+------------------------------+")
print("| CosmoFlow |")
print("| # Ranks = {:5d} |".format(mc.get_nranks()))
print("| Global Batch = {:6d} |".format(mc.get_nranks() * hp.Input['BATCH_SIZE']))
print("| # Parameters = {:9d} |".format(totsize))
print("+------------------------------+")
#use the CPE ML Plugin to broadcast initial model parameter values
new_vars = mc.broadcast(tf.trainable_variables(),0)
bcast = tf.group(*[tf.assign(v,new_vars[k]) for k,v in enumerate(tf.trainable_variables())])
if(self.is_train):
with tf.Session(config=config) as sess:
losses_train = []
losses_val = []
losses = []
val_accuracys = []
data_accuracys = []
#do all parameter initializations
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
sess.run(bcast)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
elapsed_time = 0.
for epoch in range(hp.RUNPARAM['num_epoch']):
save_path = os.path.join(hp.Path['Model_path'], 'best_validation')
total_iterations += 1
start_time = time.time()
loss_per_epoch_val = 0
loss_per_epoch_train = 0
for i in range(hp.RUNPARAM['batch_per_epoch']):
step_start_time = time.time()
_,lossTrain,lossL1Train_,train_true_,train_predict_ = sess.run([train_step,loss,lossL1Train,train_true,train_predict])
step_finish_time = time.time()
elapsed_time += (step_finish_time-step_start_time)
samps_per_sec = mc.get_nranks() * (epoch * hp.RUNPARAM['batch_per_epoch'] * hp.Input['BATCH_SIZE'] + (i+1) * hp.Input['BATCH_SIZE']) / elapsed_time
if (mc.get_rank() == 0):
print("Train Step: " + str(i) + ", Samples/Sec = " + str(samps_per_sec) + ", Loss = " + str(lossTrain))
loss_per_epoch_train +=lossL1Train_
global_loss = np.array([loss_per_epoch_train],dtype=np.float32)
mc.average(global_loss)
loss_per_epoch_train = global_loss / hp.RUNPARAM['batch_per_epoch']
losses.append(loss_per_epoch_train)
losses_train.append(loss_per_epoch_train)
for i in range(hp.RUNPARAM['batch_per_epoch_val']):
if (mc.get_rank() == 0):
print("Val Step = " + str(i))
loss_,val_true_,val_predict_ = sess.run([lossL1Val,val_true,val_predict])
loss_per_epoch_val += loss_
global_loss = np.array([loss_per_epoch_val],dtype=np.float32)
mc.average(global_loss)
loss_per_epoch_val = global_loss / hp.RUNPARAM['batch_per_epoch_val']
losses_val.append(loss_per_epoch_val)
if(loss_per_epoch_val < best_validation_accuracy):
best_validation_accuracy = loss_per_epoch_val
last_improvement = total_iterations
if (mc.get_rank() == 0):
saver.save(sess=sess, save_path=save_path)
if (mc.get_rank() == 0):
print("Epoch {} took {:.3f}s".format(epoch, time.time() - start_time))
print " training loss: %.3f" %(loss_per_epoch_train)
print " validation loss: %.3f" %(loss_per_epoch_val)
print " best loss: %.3f"%best_validation_accuracy
np.savetxt(os.path.join(hp.Path['train_result'],'loss_train.txt'),losses_train)
np.savetxt(os.path.join(hp.Path['val_result'],'loss_val.txt'),losses_val)
np.savetxt(os.path.join(hp.Path['train_result'],'losses.txt'),losses)
#np.savetxt(os.path.join(hp.Path['train_result'],'train_pred'+str(epoch)+'.txt'),np.c_[train_true_,train_predict_])
#np.savetxt(os.path.join(hp.Path['val_result'],'val_pred'+str(epoch)+'.txt'),np.c_[val_true_,val_predict_])
if(total_iterations - last_improvement > require_improvement):
if (mc.get_rank() == 0):
print ("No improvement found in a while, stopping optimization.")
break
coord.request_stop();
coord.join(threads);
if(self.is_test and mc.get_rank() == 0):
save_path = os.path.join(hp.Path['Model_path'], 'best_validation')
if self.save_path != None:
save_path = self.save_path
with tf.Session() as sess:
saver.restore(sess=sess,save_path=save_path)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
loss_test = []
for i in range(0,hp.RUNPARAM['iter_test']):
start_time = time.time()
lossL1Test_,test_true_,test_predict_ = sess.run([lossL1Test,test_true,test_predict])
loss_test.append(lossL1Test_)
print("Box {} took {:.3f}s".format(i, time.time() - start_time))
print " test loss: %.3f"%lossL1Test_
np.savetxt(os.path.join(hp.Path['test_result'],'test_batch_'+str(i)+'.txt'),np.c_[test_true_,test_predict_])
np.savetxt(os.path.join(hp.Path['test_result'],'loss_test.txt'),loss_test)
coord.request_stop()
coord.join(threads)
#cleanup the CPE ML Plugin
mc.finalize()