def train(Dataset,
model,
loss_fn,
evaluate_fn=None,
inference_fn=None,
eval_fn=None,
write_valid=True,
valid_names=None,
infer_names=None,
infer_debug_names=None,
valid_write_fn=None,
infer_write_fn=None,
valid_suffix='.valid',
infer_suffix='.infer',
write_streaming=False,
sep=','):
if FLAGS.torch:
if torch.cuda.is_available():
model.cuda()
input_ = FLAGS.train_input
inputs = gezi.list_files(input_)
inputs.sort()
all_inputs = inputs
batch_size = FLAGS.batch_size
num_gpus = melt.num_gpus()
if num_gpus > 1:
assert False, 'Eager mode train currently not support for num gpus > 1'
#batch_size_ = batch_size if not FLAGS.batch_sizes else int(FLAGS.batch_sizes.split(',')[-1])
batch_size_ = batch_size
if FLAGS.fold is not None:
inputs = [x for x in inputs if not x.endswith('%d.record' % FLAGS.fold)]
logging.info('inputs', inputs)
dataset = Dataset('train')
num_examples = dataset.num_examples_per_epoch('train')
num_all_examples = num_examples
# if FLAGS.fold is not None:
# valid_inputs = [x for x in all_inputs if x not in inputs]
# else:
# valid_inputs = gezi.list_files(FLAGS.valid_input)
# logging.info('valid_inputs', valid_inputs)
# if valid_inputs:
# valid_dataset_ = Dataset('valid')
# valid_dataset = valid_dataset_.make_batch(batch_size_, valid_inputs)
# valid_dataset2 = valid_dataset_.make_batch(batch_size_, valid_inputs, repeat=True)
# else:
# valid_datsset = None
# valid_dataset2 = None
if num_examples:
if FLAGS.fold is not None:
num_examples = int(num_examples * (len(inputs) / (len(inputs) + 1)))
num_steps_per_epoch = -(-num_examples // batch_size)
else:
num_steps_per_epoch = None
# if FLAGS.fold is not None:
# if num_examples:
# num_valid_examples = int(num_all_examples * (1 / (len(inputs) + 1)))
# num_valid_steps_per_epoch = -(-num_valid_examples // batch_size_)
# else:
# num_valid_steps_per_epoch = None
# else:
# num_valid_examples = valid_dataset_.num_examples_per_epoch('valid')
# num_valid_steps_per_epoch = -(-num_valid_examples // batch_size_) if num_valid_examples else None
# test_inputs = gezi.list_files(FLAGS.test_input)
# logging.info('test_inputs', test_inputs)
# if test_inputs:
# test_dataset_ = Dataset('test')
# test_dataset = test_dataset_.make_batch(batch_size_, test_inputs)
# num_test_examples = test_dataset_.num_examples_per_epoch('test')
# num_test_steps_per_epoch = -(-num_test_examples // batch_size_) if num_test_examples else None
# else:
# test_dataset = None
summary = tf.contrib.summary
# writer = summary.create_file_writer(FLAGS.model_dir + '/epoch')
# writer_train = summary.create_file_writer(FLAGS.model_dir + '/train')
# writer_valid = summary.create_file_writer(FLAGS.model_dir + '/valid')
writer = summary.create_file_writer(FLAGS.model_dir)
writer_train = summary.create_file_writer(FLAGS.model_dir)
writer_valid = summary.create_file_writer(FLAGS.model_dir)
global_step = tf.train.get_or_create_global_step()
learning_rate = tfe.Variable(FLAGS.learning_rate, name="learning_rate")
tf.add_to_collection('learning_rate', learning_rate)
learning_rate_weight = tf.get_collection('learning_rate_weight')[-1]
try:
learning_rate_weights = tf.get_collection('learning_rate_weights')[-1]
except Exception:
learning_rate_weights = None
ckpt_dir = FLAGS.model_dir + '/ckpt'
#TODO FIXME now I just changed tf code so to not by default save only latest 5
# refer to https://github.com/tensorflow/tensorflow/issues/22036
# manager = tf.contrib.checkpoint.CheckpointManager(
# checkpoint, directory=ckpt_dir, max_to_keep=5)
# latest_checkpoint = manager.latest_checkpoint
latest_checkpoint = tf.train.latest_checkpoint(ckpt_dir)
logging.info('Latest checkpoint:', latest_checkpoint)
checkpoint_prefix = os.path.join(ckpt_dir, 'ckpt')
if not FLAGS.torch:
optimizer = melt.get_optimizer(FLAGS.optimizer)(learning_rate)
# TODO...
if learning_rate_weights is None:
checkpoint = tf.train.Checkpoint(
learning_rate=learning_rate,
learning_rate_weight=learning_rate_weight,
model=model,
optimizer=optimizer,
global_step=global_step)
else:
checkpoint = tf.train.Checkpoint(
learning_rate=learning_rate,
learning_rate_weight=learning_rate_weight,
learning_rate_weights=learning_rate_weights,
model=model,
optimizer=optimizer,
global_step=global_step)
if os.path.exists(FLAGS.model_dir + '.index'):
latest_checkpoint = FLAGS.model_dir
checkpoint.restore(latest_checkpoint)
start_epoch = int(latest_checkpoint.split('-')[-1]) if latest_checkpoint else 0
else:
# TODO torch with learning rate adjust
optimizer = torch.optim.Adamax(model.parameters(), lr=FLAGS.learning_rate)
if latest_checkpoint:
checkpoint = torch.load(latest_checkpoint + '.pyt')
start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
model.eval()
else:
start_epoch = 0
if learning_rate_weights is None:
checkpoint = tf.train.Checkpoint(
learning_rate=learning_rate,
learning_rate_weight=learning_rate_weight,
global_step=global_step)
else:
checkpoint = tf.train.Checkpoint(
learning_rate=learning_rate,
learning_rate_weight=learning_rate_weight,
learning_rate_weights=learning_rate_weights,
global_step=global_step)
#model.load_weights(os.path.join(ckpt_dir, 'ckpt-1'))
#model.save('./weight3.hd5')
# TODO currently not support 0.1 epoch.. like this
num_epochs = FLAGS.num_epochs
class PytObj(object):
def __init__(self, x):
self.x = x
def numpy(self):
return self.x
class PytMean(object):
def __init__(self):
self._val = 0.
self.count = 0
self.is_call = True
def clear(self):
self._val = 0
self.count = 0
def __call__(self, val):
if not self.is_call:
self.clear()
self.is_call = True
self._val += val.item()
self.count += 1
def result(self):
if self.is_call:
self.is_call = False
if not self.count:
val = 0
else:
val = self._val / self.count
# TODO just for compact with tf ..
return PytObj(val)
# TODO consider multiple gpu for torch
iter = dataset.make_batch(batch_size, inputs, repeat=False, initializable=False)
batch = iter.get_next()
#x, y = melt.split_batch(batch, batch_size, num_gpus)
x_, y_ = batch
Mean = tfe.metrics.Mean if not FLAGS.torch else PytMean
epoch_loss_avg = Mean()
epoch_valid_loss_avg = Mean()
sess = melt.get_session(device_count={'GPU': 0})
global_step = 0
for epoch in range(start_epoch, num_epochs):
melt.set_global('epoch', '%.4f' % (epoch))
sess.run(iter.initializer)
model.train()
#..... still OOM... FIXME TODO
try:
for _ in tqdm(range(num_steps_per_epoch), total=num_steps_per_epoch, ascii=True):
x, y = sess.run([x_, y_])
x, y = to_torch(x, y)
optimizer.zero_grad()
loss = loss_fn(model, x, y)
loss.backward()
optimizer.step()
epoch_loss_avg(loss)
if global_step % FLAGS.interval_steps == 0:
print(global_step, epoch_loss_avg.result().numpy())
global_step += 1
except tf.errors.OutOfRangeError:
print('epoch:%d loss:%f' % (epoch, epoch_loss_avg.result().numpy()))
def train(Dataset,
model,
loss_fn,
evaluate_fn=None,
inference_fn=None,
eval_fn=None,
write_valid=True,
valid_names=None,
infer_names=None,
infer_debug_names=None,
valid_write_fn=None,
infer_write_fn=None,
valid_suffix='.valid',
infer_suffix='.infer',
write_streaming=False,
optimizer=None,
param_groups=None,
init_fn=None,
dataset=None,
valid_dataset=None,
test_dataset=None,
sep=','):
if Dataset is None:
assert dataset
if FLAGS.torch:
# https://pytorch.org/tutorials/beginner/blitz/data_parallel_tutorial.html
if torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
model.to(device)
input_ = FLAGS.train_input
inputs = gezi.list_files(input_)
inputs.sort()
all_inputs = inputs
#batch_size = FLAGS.batch_size
batch_size = melt.batch_size()
num_gpus = melt.num_gpus()
#batch_size = max(batch_size, 1)
#batch_size_ = batch_size if not FLAGS.batch_sizes else int(FLAGS.batch_sizes.split(',')[-1])
batch_size_ = batch_size
if FLAGS.fold is not None:
inputs = [
x for x in inputs if not x.endswith('%d.record' % FLAGS.fold)
and not x.endswith('%d.tfrecord' % FLAGS.fold)
]
# if FLAGS.valid_input:
# inputs += [x for x in gezi.list_files(FLAGS.valid_input) if not x.endswith('%d.record' % FLAGS.fold)]
logging.info('inputs', len(inputs), inputs[:100])
num_folds = FLAGS.num_folds or len(inputs) + 1
train_dataset_ = dataset or Dataset('train')
train_dataset = train_dataset_.make_batch(batch_size, inputs)
num_examples = train_dataset_.num_examples_per_epoch('train')
num_all_examples = num_examples
valid_inputs = None
if FLAGS.valid_input:
valid_inputs = gezi.list_files(FLAGS.valid_input)
else:
if FLAGS.fold is not None:
#valid_inputs = [x for x in all_inputs if x not in inputs]
if not FLAGS.test_aug:
valid_inputs = [
x for x in all_inputs if not 'aug' in x and x not in inputs
]
else:
valid_inputs = [
x for x in all_inputs if 'aug' in x and x not in inputs
]
logging.info('valid_inputs', valid_inputs)
if valid_inputs:
valid_dataset_ = valid_dataset or Dataset('valid')
valid_dataset = valid_dataset_.make_batch(batch_size_, valid_inputs)
valid_dataset2 = valid_dataset_.make_batch(batch_size_,
valid_inputs,
repeat=True)
else:
valid_datsset = None
valid_dataset2 = None
if num_examples:
if FLAGS.fold is not None:
num_examples = int(num_examples * (num_folds - 1) / num_folds)
num_steps_per_epoch = -(-num_examples // batch_size)
else:
num_steps_per_epoch = None
logging.info('num_train_examples:', num_examples)
num_valid_examples = None
if FLAGS.valid_input:
num_valid_examples = valid_dataset_.num_examples_per_epoch('valid')
num_valid_steps_per_epoch = -(
-num_valid_examples // batch_size_) if num_valid_examples else None
else:
if FLAGS.fold is not None:
if num_examples:
num_valid_examples = int(num_all_examples * (1 / num_folds))
num_valid_steps_per_epoch = -(-num_valid_examples //
batch_size_)
else:
num_valid_steps_per_epoch = None
logging.info('num_valid_examples:', num_valid_examples)
if FLAGS.test_input:
test_inputs = gezi.list_files(FLAGS.test_input)
#test_inputs = [x for x in test_inputs if not 'aug' in x]
logging.info('test_inputs', test_inputs)
else:
test_inputs = None
num_test_examples = None
if test_inputs:
test_dataset_ = test_dataset or Dataset('test')
test_dataset = test_dataset_.make_batch(batch_size_, test_inputs)
num_test_examples = test_dataset_.num_examples_per_epoch('test')
num_test_steps_per_epoch = -(
-num_test_examples // batch_size_) if num_test_examples else None
else:
test_dataset = None
logging.info('num_test_examples:', num_test_examples)
summary = tf.contrib.summary
# writer = summary.create_file_writer(FLAGS.log_dir + '/epoch')
# writer_train = summary.create_file_writer(FLAGS.log_dir + '/train')
# writer_valid = summary.create_file_writer(FLAGS.log_dir + '/valid')
writer = summary.create_file_writer(FLAGS.log_dir)
writer_train = summary.create_file_writer(FLAGS.log_dir)
writer_valid = summary.create_file_writer(FLAGS.log_dir)
global_step = tf.train.get_or_create_global_step()
learning_rate = tfe.Variable(FLAGS.learning_rate, name="learning_rate")
tf.add_to_collection('learning_rate', learning_rate)
learning_rate_weight = tf.get_collection('learning_rate_weight')[-1]
try:
learning_rate_weights = tf.get_collection('learning_rate_weights')[-1]
except Exception:
learning_rate_weights = None
# ckpt dir save models one per epoch
ckpt_dir = os.path.join(FLAGS.model_dir, 'ckpt')
os.system('mkdir -p %s' % ckpt_dir)
# HACK ckpt dir is actually save mini epoch like when you set save_interval_epochs=0.1, this is usefull when you training large dataset
ckpt_dir2 = os.path.join(FLAGS.model_dir, 'ckpt2')
os.system('mkdir -p %s' % ckpt_dir2)
#TODO FIXME now I just changed tf code so to not by default save only latest 5
# refer to https://github.com/tensorflow/tensorflow/issues/22036
# manager = tf.contrib.checkpoint.CheckpointManager(
# checkpoint, directory=ckpt_dir, max_to_keep=5)
# latest_checkpoint = manager.latest_checkpoint
latest_checkpoint = tf.train.latest_checkpoint(ckpt_dir)
if latest_checkpoint:
logging.info('Latest checkpoint:', latest_checkpoint)
else:
latest_checkpoint = tf.train.latest_checkpoint(ckpt_dir2)
logging.info('Latest checkpoint:', latest_checkpoint)
if os.path.exists(FLAGS.model_dir + '.index'):
latest_checkpoint = FLAGS.model_dir
if 'test' in FLAGS.work_mode or 'valid' in FLAGS.work_mode:
#assert not os.path.isdir(FLAGS.model_dir), FLAGS.model_dir
latest_checkpoint = FLAGS.model_dir
#assert os.path.exists(latest_checkpoint) and os.path.isfile(latest_checkpoint)
checkpoint_prefix = os.path.join(ckpt_dir, 'ckpt')
checkpoint_prefix2 = os.path.join(ckpt_dir2, 'ckpt')
if not FLAGS.torch:
try:
optimizer = optimizer or melt.get_optimizer(
FLAGS.optimizer)(learning_rate)
except Exception:
logging.warning(
f'Fail to using {FLAGS.optimizer} use adam instead')
optimizer = melt.get_optimizer('adam')(learning_rate)
# TODO...
if learning_rate_weights is None:
checkpoint = tf.train.Checkpoint(
learning_rate=learning_rate,
learning_rate_weight=learning_rate_weight,
model=model,
optimizer=optimizer,
global_step=global_step)
else:
checkpoint = tf.train.Checkpoint(
learning_rate=learning_rate,
learning_rate_weight=learning_rate_weight,
learning_rate_weights=learning_rate_weights,
model=model,
optimizer=optimizer,
global_step=global_step)
checkpoint.restore(latest_checkpoint)
checkpoint2 = copy.deepcopy(checkpoint)
start_epoch = int(
latest_checkpoint.split('-')
[-1]) if latest_checkpoint and 'ckpt' in latest_checkpoint else 0
else:
# TODO torch with learning rate adjust
if optimizer is None:
import lele
is_dynamic_opt = True
if FLAGS.optimizer == 'noam':
optimizer = lele.training.optimizers.NoamOpt(
128, 2, 4000, torch.optim.Adamax(model.parameters(), lr=0))
elif FLAGS.optimizer == 'bert':
num_train_steps = int(
num_steps_per_epoch *
(FLAGS.num_decay_epochs or FLAGS.num_epochs))
num_warmup_steps = FLAGS.warmup_steps or int(
num_train_steps * FLAGS.warmup_proportion)
logging.info('num_train_steps', num_train_steps,
'num_warmup_steps', num_warmup_steps,
'warmup_proportion', FLAGS.warmup_proportion)
optimizer = lele.training.optimizers.BertOpt(
FLAGS.learning_rate, FLAGS.min_learning_rate,
num_train_steps, num_warmup_steps,
torch.optim.Adamax(model.parameters(), lr=0))
else:
is_dynamic_opt = False
optimizer = torch.optim.Adamax(
param_groups if param_groups else model.parameters(),
lr=FLAGS.learning_rate)
start_epoch = 0
latest_path = latest_checkpoint + '.pyt' if latest_checkpoint else os.path.join(
FLAGS.model_dir, 'latest.pyt')
if not os.path.exists(latest_path):
latest_path = os.path.join(FLAGS.model_dir, 'latest.pyt')
if os.path.exists(latest_path):
logging.info('loading torch model from', latest_path)
checkpoint = torch.load(latest_path)
if not FLAGS.torch_finetune:
start_epoch = checkpoint['epoch']
step = checkpoint['step']
global_step.assign(step + 1)
load_torch_model(model, latest_path)
if FLAGS.torch_load_optimizer:
optimizer.load_state_dict(checkpoint['optimizer'])
# TODO by this way restart can not change learning rate..
if learning_rate_weights is None:
checkpoint = tf.train.Checkpoint(
learning_rate=learning_rate,
learning_rate_weight=learning_rate_weight,
global_step=global_step)
else:
checkpoint = tf.train.Checkpoint(
learning_rate=learning_rate,
learning_rate_weight=learning_rate_weight,
learning_rate_weights=learning_rate_weights,
global_step=global_step)
try:
checkpoint.restore(latest_checkpoint)
checkpoint2 = copy.deepcopy(checkpoint)
except Exception:
pass
if FLAGS.torch and is_dynamic_opt:
optimizer._step = global_step.numpy()
#model.load_weights(os.path.join(ckpt_dir, 'ckpt-1'))
#model.save('./weight3.hd5')
logging.info('optimizer:', optimizer)
if FLAGS.torch_lr:
learning_rate.assign(optimizer.rate(1))
if FLAGS.torch:
learning_rate.assign(optimizer.param_groups[0]['lr'])
logging.info('learning rate got from pytorch latest.py as',
learning_rate)
learning_rate.assign(learning_rate * FLAGS.learning_rate_start_factor)
if learning_rate_weights is not None:
learning_rate_weights.assign(learning_rate_weights *
FLAGS.learning_rate_start_factor)
# TODO currently not support 0.1 epoch.. like this
num_epochs = FLAGS.num_epochs if FLAGS.num_epochs != 0 else 1024
will_valid = valid_dataset and not FLAGS.work_mode == 'test' and not 'SHOW' in os.environ and not 'QUICK' in os.environ
if start_epoch == 0 and not 'EVFIRST' in os.environ and will_valid:
will_valid = False
if start_epoch > 0 and will_valid:
will_valid = True
if will_valid:
logging.info('----------valid')
if FLAGS.torch:
model.eval()
names = None
if evaluate_fn is not None:
vals, names = evaluate_fn(model, valid_dataset,
tf.train.latest_checkpoint(ckpt_dir),
num_valid_steps_per_epoch)
elif eval_fn:
model_path = None if not write_valid else latest_checkpoint
names = valid_names if valid_names is not None else [
infer_names[0]
] + [x + '_y' for x in infer_names[1:]
] + infer_names[1:] if infer_names else None
logging.info('model_path:', model_path, 'model_dir:',
FLAGS.model_dir)
vals, names = evaluate(model,
valid_dataset,
eval_fn,
model_path,
names,
valid_write_fn,
write_streaming,
num_valid_steps_per_epoch,
suffix=valid_suffix,
sep=sep)
if names:
logging.info2(
'epoch:%d/%d' % (start_epoch, num_epochs),
['%s:%.5f' % (name, val) for name, val in zip(names, vals)])
if FLAGS.work_mode == 'valid':
exit(0)
if 'test' in FLAGS.work_mode:
logging.info('--------test/inference')
if test_dataset:
if FLAGS.torch:
model.eval()
if inference_fn is None:
# model_path = FLAGS.model_dir + '.pyt' if not latest_checkpoint else latest_checkpoint
# logging.info('model_path', model_path)
assert latest_checkpoint
inference(model,
test_dataset,
latest_checkpoint,
infer_names,
infer_debug_names,
infer_write_fn,
write_streaming,
num_test_steps_per_epoch,
suffix=infer_suffix)
else:
inference_fn(model, test_dataset,
tf.train.latest_checkpoint(ckpt_dir),
num_test_steps_per_epoch)
exit(0)
if 'SHOW' in os.environ:
num_epochs = start_epoch + 1
class PytObj(object):
def __init__(self, x):
self.x = x
def numpy(self):
return self.x
class PytMean(object):
def __init__(self):
self._val = 0.
self.count = 0
self.is_call = True
def clear(self):
self._val = 0
self.count = 0
def __call__(self, val):
if not self.is_call:
self.clear()
self.is_call = True
self._val += val.item()
self.count += 1
def result(self):
if self.is_call:
self.is_call = False
if not self.count:
val = 0
else:
val = self._val / self.count
# TODO just for compact with tf ..
return PytObj(val)
Mean = tfe.metrics.Mean if not FLAGS.torch else PytMean
timer = gezi.Timer()
num_insts = 0
if FLAGS.learning_rate_decay_factor > 0:
#assert FLAGS.learning_rate_values is None, 'use exponential_decay or piecewise_constant?'
#NOTICE if you do finetune or other things which might change batch_size then you'd better direclty set num_steps_per_decay
#since global step / decay_steps will not be correct epoch as num_steps per epoch changed
#so if if you change batch set you have to reset global step as fixed step
assert FLAGS.num_steps_per_decay or (
FLAGS.num_epochs_per_decay and num_steps_per_epoch
), 'must set num_steps_per_epoch or num_epochs_per_decay and num_steps_per_epoch'
decay_steps = FLAGS.num_steps_per_decay or int(
num_steps_per_epoch * FLAGS.num_epochs_per_decay)
decay_start_step = FLAGS.decay_start_step or int(
num_steps_per_epoch * FLAGS.decay_start_epoch)
# decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps)
logging.info(
'learning_rate_decay_factor:{} decay_epochs:{} decay_steps:{} decay_start_epoch:{} decay_start_step:{}'
.format(FLAGS.learning_rate_decay_factor,
FLAGS.num_epochs_per_decay, decay_steps,
FLAGS.decay_start_epoch, decay_start_step))
for epoch in range(start_epoch, num_epochs):
melt.set_global('epoch', '%.4f' % (epoch))
if FLAGS.torch:
model.train()
epoch_loss_avg = Mean()
epoch_valid_loss_avg = Mean()
#for i, (x, y) in tqdm(enumerate(train_dataset), total=num_steps_per_epoch, ascii=True):
for i, (x, y) in enumerate(train_dataset):
if FLAGS.torch:
x, y = to_torch(x, y)
if is_dynamic_opt:
learning_rate.assign(optimizer.rate())
#print(x, y)
if not FLAGS.torch:
loss, grads = melt.eager.grad(model, x, y, loss_fn)
grads, _ = tf.clip_by_global_norm(grads, FLAGS.clip_gradients)
optimizer.apply_gradients(zip(grads, model.variables))
else:
optimizer.zero_grad()
if 'training' in inspect.getargspec(loss_fn).args:
loss = loss_fn(model, x, y, training=True)
else:
loss = loss_fn(model, x, y)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(),
FLAGS.clip_gradients)
optimizer.step()
global_step.assign_add(1)
epoch_loss_avg(loss) # add current batch loss
if FLAGS.torch:
del loss
batch_size_ = list(
x.values())[0].shape[FLAGS.batch_size_dim] if type(x) == type(
{}) else x.shape[FLAGS.batch_size_dim]
num_insts += int(batch_size_)
if global_step.numpy() % FLAGS.interval_steps == 0:
#checkpoint.save(checkpoint_prefix)
elapsed = timer.elapsed()
steps_per_second = FLAGS.interval_steps / elapsed
instances_per_second = num_insts / elapsed
num_insts = 0
if num_steps_per_epoch is None:
epoch_time_info = ''
else:
hours_per_epoch = num_steps_per_epoch / FLAGS.interval_steps * elapsed / 3600
epoch_time_info = '1epoch:[{:.2f}h]'.format(
hours_per_epoch)
if valid_dataset2:
try:
x, y = next(iter(valid_dataset2))
except Exception:
# TODO FIXME how.. iterate stop restart.., here hack for my iterator see projects/lm/dataset
x, y = next(iter(valid_dataset2))
if FLAGS.torch:
x, y = to_torch(x, y)
model.eval()
valid_loss = loss_fn(model, x, y)
epoch_valid_loss_avg(valid_loss)
if FLAGS.torch:
model.train()
logging.info2(
'epoch:%.3f/%d' %
((epoch + i / num_steps_per_epoch), num_epochs),
'step:%d' % global_step.numpy(), 'elapsed:[%.3f]' %
elapsed, 'batch_size:[%d]' % batch_size_, 'gpus:[%d]' %
num_gpus, 'batches/s:[%.2f]' % steps_per_second,
'insts/s:[%d]' % instances_per_second, '%s' %
epoch_time_info, 'lr:[%.8f]' % learning_rate.numpy(),
'train_loss:[%.4f]' % epoch_loss_avg.result().numpy(),
'valid_loss:[%.4f]' %
epoch_valid_loss_avg.result().numpy())
if global_step.numpy() % FLAGS.eval_interval_steps == 0:
with writer_valid.as_default(
), summary.always_record_summaries():
#summary.scalar('step/loss', epoch_valid_loss_avg.result().numpy())
summary.scalar(
'loss/eval',
epoch_valid_loss_avg.result().numpy())
writer_valid.flush()
else:
logging.info2(
'epoch:%.3f/%d' %
((epoch + i / num_steps_per_epoch), num_epochs),
'step:%d' % global_step.numpy(), 'elapsed:[%.3f]' %
elapsed, 'batch_size:[%d]' % batch_size_,
'gpus:[%d]' % num_gpus,
'batches/s:[%.2f]' % steps_per_second,
'insts/s:[%d]' % instances_per_second,
'%s' % epoch_time_info,
'lr:[%.8f]' % learning_rate.numpy(),
'train_loss:[%.4f]' % epoch_loss_avg.result().numpy())
if global_step.numpy() % FLAGS.eval_interval_steps == 0:
with writer_train.as_default(
), summary.always_record_summaries():
#summary.scalar('step/loss', epoch_loss_avg.result().numpy())
summary.scalar('loss/train_avg',
epoch_loss_avg.result().numpy())
summary.scalar('learning_rate', learning_rate.numpy())
summary.scalar('batch_size', batch_size_)
summary.scalar('epoch', melt.epoch())
summary.scalar('steps_per_second', steps_per_second)
summary.scalar('instances_per_second',
instances_per_second)
writer_train.flush()
if FLAGS.log_dir != FLAGS.model_dir:
assert FLAGS.log_dir
command = 'rsync -l -r -t %s/* %s' % (FLAGS.log_dir,
FLAGS.model_dir)
print(command, file=sys.stderr)
os.system(command)
if valid_dataset and FLAGS.metric_eval_interval_steps and global_step.numpy(
) and global_step.numpy() % FLAGS.metric_eval_interval_steps == 0:
if FLAGS.torch:
model.eval()
vals, names = None, None
if evaluate_fn is not None:
vals, names = evaluate_fn(model, valid_dataset, None,
num_valid_steps_per_epoch)
elif eval_fn:
names = valid_names if valid_names is not None else [
infer_names[0]
] + [x + '_y' for x in infer_names[1:]
] + infer_names[1:] if infer_names else None
vals, names = evaluate(model,
valid_dataset,
eval_fn,
None,
names,
valid_write_fn,
write_streaming,
num_valid_steps_per_epoch,
sep=sep)
if vals and names:
with writer_valid.as_default(
), summary.always_record_summaries():
for name, val in zip(names, vals):
summary.scalar(f'step/valid/{name}', val)
writer_valid.flush()
if FLAGS.torch:
if not FLAGS.torch_lr:
# control learning rate by tensorflow learning rate
for param_group in optimizer.param_groups:
# important learning rate decay
param_group['lr'] = learning_rate.numpy()
model.train()
if names and vals:
logging.info2(
'epoch:%.3f/%d' %
((epoch + i / num_steps_per_epoch), num_epochs),
'valid_step:%d' % global_step.numpy(), 'valid_metrics',
[
'%s:%.5f' % (name, val)
for name, val in zip(names, vals)
])
# if i == 5:
# print(i, '---------------------save')
# print(len(model.trainable_variables))
## TODO FIXME seems save weighs value not ok... not the same as checkpoint save
# model.save_weights(os.path.join(ckpt_dir, 'weights'))
# checkpoint.save(checkpoint_prefix)
# exit(0)
if global_step.numpy() % FLAGS.save_interval_steps == 0:
if FLAGS.torch:
state = {
'epoch':
epoch,
'step':
global_step.numpy(),
'state_dict':
model.state_dict() if not hasattr(model, 'module') else
model.module.state_dict(),
'optimizer':
optimizer.state_dict(),
}
torch.save(state,
os.path.join(FLAGS.model_dir, 'latest.pyt'))
# TODO fixme why if both checpoint2 and chekpoint used... not ok..
if FLAGS.save_interval_epochs and FLAGS.save_interval_epochs < 1 and global_step.numpy(
) % int(num_steps_per_epoch * FLAGS.save_interval_epochs) == 0:
#if FLAGS.save_interval_epochs and global_step.numpy() % int(num_steps_per_epoch * FLAGS.save_interval_epochs) == 0:
checkpoint2.save(checkpoint_prefix2)
if FLAGS.torch:
state = {
'epoch':
epoch,
'step':
global_step.numpy(),
'state_dict':
model.state_dict() if not hasattr(model, 'module') else
model.module.state_dict(),
'optimizer':
optimizer.state_dict(),
}
torch.save(state,
tf.train.latest_checkpoint(ckpt_dir2) + '.pyt')
if FLAGS.learning_rate_decay_factor > 0:
if global_step.numpy(
) >= decay_start_step and global_step.numpy(
) % decay_steps == 0:
lr = max(
learning_rate.numpy() *
FLAGS.learning_rate_decay_factor,
FLAGS.min_learning_rate)
if lr < learning_rate.numpy():
learning_rate.assign(lr)
if FLAGS.torch:
for param_group in optimizer.param_groups:
param_group['lr'] = learning_rate.numpy()
if epoch == start_epoch and i == 0:
try:
if not FLAGS.torch:
logging.info(model.summary())
except Exception:
traceback.print_exc()
logging.info(
'Fail to do model.summary() may be you have layer define in init but not used in call'
)
if 'SHOW' in os.environ:
exit(0)
logging.info2(
'epoch:%d/%d' % (epoch + 1, num_epochs),
'step:%d' % global_step.numpy(), 'batch_size:[%d]' % batch_size,
'gpus:[%d]' % num_gpus, 'lr:[%.8f]' % learning_rate.numpy(),
'train_loss:[%.4f]' % epoch_loss_avg.result().numpy(),
'valid_loss::[%.4f]' % epoch_valid_loss_avg.result().numpy())
timer = gezi.Timer(
f'save model to {checkpoint_prefix}-{checkpoint.save_counter.numpy() + 1}',
False)
checkpoint.save(checkpoint_prefix)
if FLAGS.torch and FLAGS.save_interval_epochs == 1:
state = {
'epoch':
epoch + 1,
'step':
global_step.numpy(),
'state_dict':
model.state_dict()
if not hasattr(model, 'module') else model.module.state_dict(),
'optimizer':
optimizer.state_dict(),
}
torch.save(state, tf.train.latest_checkpoint(ckpt_dir) + '.pyt')
timer.print_elapsed()
if valid_dataset and (epoch + 1) % FLAGS.valid_interval_epochs == 0:
if FLAGS.torch:
model.eval()
vals, names = None, None
if evaluate_fn is not None:
vals, names = evaluate_fn(model, valid_dataset,
tf.train.latest_checkpoint(ckpt_dir),
num_valid_steps_per_epoch)
elif eval_fn:
model_path = None if not write_valid else tf.train.latest_checkpoint(
ckpt_dir)
names = valid_names if valid_names is not None else [
infer_names[0]
] + [x + '_y' for x in infer_names[1:]
] + infer_names[1:] if infer_names else None
vals, names = evaluate(model,
valid_dataset,
eval_fn,
model_path,
names,
valid_write_fn,
write_streaming,
num_valid_steps_per_epoch,
suffix=valid_suffix,
sep=sep)
if vals and names:
logging.info2('epoch:%d/%d' % (epoch + 1, num_epochs),
'step:%d' % global_step.numpy(),
'epoch_valid_metrics', [
'%s:%.5f' % (name, val)
for name, val in zip(names, vals)
])
with writer.as_default(), summary.always_record_summaries():
temp = global_step.value()
global_step.assign(epoch + 1)
summary.scalar('epoch/train/loss', epoch_loss_avg.result().numpy())
if valid_dataset:
if FLAGS.torch:
model.eval()
if vals and names:
for name, val in zip(names, vals):
summary.scalar(f'epoch/valid/{name}', val)
writer.flush()
global_step.assign(temp)
if test_dataset and (epoch + 1) % FLAGS.inference_interval_epochs == 0:
if FLAGS.torch:
model.eval()
if inference_fn is None:
inference(model,
test_dataset,
tf.train.latest_checkpoint(ckpt_dir),
infer_names,
infer_debug_names,
infer_write_fn,
write_streaming,
num_test_steps_per_epoch,
suffix=infer_suffix,
sep=sep)
else:
inference_fn(model, test_dataset,
tf.train.latest_checkpoint(ckpt_dir),
num_test_steps_per_epoch)
if FLAGS.log_dir != FLAGS.model_dir:
assert FLAGS.log_dir
command = 'rsync -l -r -t %s/* %s' % (FLAGS.log_dir, FLAGS.model_dir)
print(command, file=sys.stderr)
os.system(command)
command = 'rm -rf %s/latest.pyt.*' % (FLAGS.model_dir)
print(command, file=sys.stderr)
os.system(command)
def train(model,
loss_fn,
Dataset=None,
dataset=None,
valid_dataset=None,
valid_dataset2=None,
test_dataset=None,
evaluate_fn=None,
inference_fn=None,
eval_fn=None,
write_valid=True,
valid_names=None,
infer_names=None,
infer_debug_names=None,
valid_write_fn=None,
infer_write_fn=None,
valid_suffix='.valid',
infer_suffix='.infer',
write_streaming=False,
optimizer=None,
param_groups=None,
init_fn=None,
sep=','):
use_horovod = 'OMPI_COMM_WORLD_RANK' in os.environ
if Dataset is None:
assert dataset
logging.info('Dataset', Dataset, 'dataset', dataset, 'valid_dataset', valid_dataset, 'test_dataset', test_dataset, loss_fn)
if FLAGS.torch:
torch.manual_seed(FLAGS.seed or 0)
if torch.cuda.device_count():
torch.cuda.manual_seed(FLAGS.seed or 0)
if use_horovod:
pass
# import horovod.torch as hvd
# hvd.init()
# #print('-----------------', hvd, hvd.size())
# assert hvd.mpi_threads_supported()
# assert hvd.size() == comm.Get_size()
# torch.cuda.set_device(hvd.local_rank())
# https://pytorch.org/tutorials/beginner/blitz/data_parallel_tutorial.html
else:
if torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
model.to(device)
input_ = FLAGS.train_input
inputs = gezi.list_files(input_)
inputs.sort()
all_inputs = inputs
#batch_size = FLAGS.batch_size
batch_size = melt.batch_size()
num_gpus = melt.num_gpus()
#batch_size = max(batch_size, 1)
#batch_size_ = batch_size if not FLAGS.batch_sizes else int(FLAGS.batch_sizes.split(',')[-1])
batch_size_ = FLAGS.eval_batch_size or batch_size
if dataset is None:
if FLAGS.fold is not None:
inputs = [x for x in inputs if not x.endswith('%d.record' % FLAGS.fold) and not x.endswith('%d.tfrecord' % FLAGS.fold)]
# if FLAGS.valid_input:
# inputs += [x for x in gezi.list_files(FLAGS.valid_input) if not x.endswith('%d.record' % FLAGS.fold)]
logging.info('inputs', len(inputs), inputs[:100])
num_folds = FLAGS.num_folds or len(inputs) + 1
if dataset is None:
dataset = Dataset('train')
assert len(inputs) > 0
train_dataset = dataset.make_batch(batch_size, inputs, simple_parse=FLAGS.simple_parse)
num_examples = dataset.num_examples_per_epoch('train')
else:
assert FLAGS.torch_only, 'only torch only currently support input dataset not Dataset class type, because we do not have len function there'
train_dataset = dataset
num_examples = len(train_dataset.dataset)
num_all_examples = num_examples
if valid_dataset is None:
valid_inputs = None
if FLAGS.valid_input:
valid_inputs = gezi.list_files(FLAGS.valid_input)
else:
if FLAGS.fold is not None:
#valid_inputs = [x for x in all_inputs if x not in inputs]
if not FLAGS.test_aug:
valid_inputs = [x for x in all_inputs if not 'aug' in x and x not in inputs]
else:
valid_inputs = [x for x in all_inputs if 'aug' in x and x not in inputs]
logging.info('valid_inputs', valid_inputs)
num_valid_examples = None
if valid_dataset is not None:
num_valid_examples = len(valid_dataset.dataset)
num_valid_steps_per_epoch = -(-num_valid_examples // batch_size_) if num_valid_examples else None
valid_dataset2_iter = iter(valid_dataset2)
else:
if valid_inputs:
valid_dataset = dataset.make_batch(batch_size_, valid_inputs, subset='valid', hvd_shard=FLAGS.horovod_eval )
valid_dataset2 = dataset.make_batch(batch_size, valid_inputs, subset='valid', repeat=True, initializable=False, hvd_shard=False)
valid_dataset2_iter = iter(valid_dataset2)
else:
valid_datsset = None
valid_dataset2 = None
if num_examples:
if FLAGS.fold is not None:
num_examples = int(num_examples * (num_folds - 1) / num_folds)
num_steps_per_epoch = -(-num_examples // batch_size)
else:
num_steps_per_epoch = None
logging.info('num_train_examples:', num_examples)
if use_horovod and num_examples:
num_steps_per_epoch = -(-num_examples // (batch_size * hvd.size()))
if num_valid_examples is None:
if FLAGS.valid_input:
num_valid_examples = dataset.num_examples_per_epoch('valid')
num_valid_steps_per_epoch = -(-num_valid_examples // batch_size_) if num_valid_examples else None
else:
if FLAGS.fold is not None:
if num_examples:
num_valid_examples = int(num_all_examples * (1 / num_folds))
num_valid_steps_per_epoch = -(-num_valid_examples // batch_size_)
else:
num_valid_steps_per_epoch = None
if use_horovod and FLAGS.horovod_eval and num_valid_examples:
num_valid_steps_per_epoch = -(-num_valid_examples // (batch_size_ * hvd.size()))
logging.info('num_valid_examples:', num_valid_examples)
if test_dataset is None:
if FLAGS.test_input:
test_inputs = gezi.list_files(FLAGS.test_input)
#test_inputs = [x for x in test_inputs if not 'aug' in x]
logging.info('test_inputs', test_inputs)
else:
test_inputs = None
num_test_examples = None
if test_dataset is not None:
num_test_examples = len(test_dataset.dataset)
else:
if test_inputs:
test_dataset = dataset.make_batch(batch_size_, test_inputs, subset='test')
num_test_examples = dataset.num_examples_per_epoch('test')
else:
test_dataset = None
num_test_steps_per_epoch = -(-num_test_examples // batch_size_) if num_test_examples else None
if use_horovod and FLAGS.horovod_eval and num_test_examples:
num_test_steps_per_epoch = -(-num_test_examples // (batch_size_ * hvd.size()))
logging.info('num_test_examples:', num_test_examples)
summary = tf.contrib.summary
# writer = summary.create_file_writer(FLAGS.log_dir + '/epoch')
# writer_train = summary.create_file_writer(FLAGS.log_dir + '/train')
# writer_valid = summary.create_file_writer(FLAGS.log_dir + '/valid')
writer = summary.create_file_writer(FLAGS.log_dir)
writer_train = summary.create_file_writer(FLAGS.log_dir)
writer_valid = summary.create_file_writer(FLAGS.log_dir)
global_step = tf.train.get_or_create_global_step()
## RuntimeError: tf.summary.FileWriter is not compatible with eager execution. Use tf.contrib.summary instead.
#logger = gezi.SummaryWriter(FLAGS.log_dir)
learning_rate = tfe.Variable(FLAGS.learning_rate, name="learning_rate")
tf.add_to_collection('learning_rate', learning_rate)
learning_rate_weight = tf.get_collection('learning_rate_weight')[-1]
try:
learning_rate_weights = tf.get_collection('learning_rate_weights')[-1]
except Exception:
learning_rate_weights = None
# ckpt dir save models one per epoch
ckpt_dir = os.path.join(FLAGS.model_dir, 'ckpt')
os.system('mkdir -p %s' % ckpt_dir)
# HACK ckpt dir is actually save mini epoch like when you set save_interval_epochs=0.1, this is usefull when you training large dataset
ckpt_dir2 = os.path.join(FLAGS.model_dir, 'ckpt2')
os.system('mkdir -p %s' % ckpt_dir2)
#TODO FIXME now I just changed tf code so to not by default save only latest 5
# refer to https://github.com/tensorflow/tensorflow/issues/22036
# manager = tf.contrib.checkpoint.CheckpointManager(
# checkpoint, directory=ckpt_dir, max_to_keep=5)
# latest_checkpoint = manager.latest_checkpoint
latest_checkpoint = tf.train.latest_checkpoint(ckpt_dir)
if latest_checkpoint:
logging.info('Latest checkpoint:', latest_checkpoint)
else:
latest_checkpoint = tf.train.latest_checkpoint(ckpt_dir2)
logging.info('Latest checkpoint:', latest_checkpoint)
if os.path.exists(FLAGS.model_dir + '.index'):
latest_checkpoint = FLAGS.model_dir
if 'test' in FLAGS.work_mode or 'valid' in FLAGS.work_mode:
#assert not os.path.isdir(FLAGS.model_dir), FLAGS.model_dir
latest_checkpoint = FLAGS.model_dir
#assert os.path.exists(latest_checkpoint) and os.path.isfile(latest_checkpoint)
checkpoint_prefix = os.path.join(ckpt_dir, 'ckpt')
checkpoint_prefix2 = os.path.join(ckpt_dir2, 'ckpt')
if not FLAGS.torch:
try:
optimizer = optimizer or melt.get_optimizer(FLAGS.optimizer)(learning_rate)
except Exception:
logging.warning(f'Fail to using {FLAGS.optimizer} use adam instead')
optimizer = melt.get_optimizer('adam')(learning_rate)
# TODO...
if learning_rate_weights is None:
checkpoint = tf.train.Checkpoint(
learning_rate=learning_rate,
learning_rate_weight=learning_rate_weight,
model=model,
optimizer=optimizer,
global_step=global_step)
else:
checkpoint = tf.train.Checkpoint(
learning_rate=learning_rate,
learning_rate_weight=learning_rate_weight,
learning_rate_weights=learning_rate_weights,
model=model,
optimizer=optimizer,
global_step=global_step)
checkpoint.restore(latest_checkpoint)
checkpoint2 = copy.deepcopy(checkpoint)
start_epoch = int(latest_checkpoint.split('-')[-1]) if latest_checkpoint and 'ckpt' in latest_checkpoint else 0
start_step = 0 # TODO
else:
# TODO torch with learning rate adjust
# https://github.com/horovod/horovod/blob/master/examples/pytorch_mnist.py
# TODO full support for pytorch now not work
if optimizer is None:
import lele
is_dynamic_opt = True
if FLAGS.optimizer == 'noam':
optimizer_ = torch.optim.Adamax(model.parameters(), lr=0)
if use_horovod:
optimizer_ = hvd.DistributedOptimizer(optimizer_)
optimizer = lele.training.optimizers.NoamOpt(128, 2, 4000, optimzier_)
elif FLAGS.optimizer == 'bert':
num_train_steps = int(num_steps_per_epoch * (FLAGS.num_decay_epochs or FLAGS.num_epochs))
if FLAGS.warmup_steps and use_horovod:
FLAGS.warmup_steps = max(int(FLAGS.warmup_steps / hvd.size()), 1)
num_warmup_steps = FLAGS.warmup_steps or int(num_steps_per_epoch * FLAGS.warmup_epochs) or int(num_train_steps * FLAGS.warmup_proportion)
logging.info('num_train_steps', num_train_steps, 'num_warmup_steps', num_warmup_steps, 'warmup_proportion', FLAGS.warmup_proportion)
optimizer_ = torch.optim.Adamax(model.parameters(), lr=0)
if use_horovod:
optimizer_ = hvd.DistributedOptimizer(optimizer_)
optimizer = lele.training.optimizers.BertOpt(
FLAGS.learning_rate,
FLAGS.min_learning_rate,
num_train_steps,
num_warmup_steps,
optimizer_
)
else:
is_dynamic_opt = False
optimizer = torch.optim.Adamax(param_groups if param_groups else model.parameters(), lr=FLAGS.learning_rate)
if use_horovod:
optimizer = hvd.DistributedOptimizer(optimizer)
optimizer_ = optimizer
else:
if use_horovod:
optimizer = hvd.DistributedOptimizer(optimizer)
optimizer_ = optimizer
start_epoch = 0
latest_path = latest_checkpoint + '.pyt' if latest_checkpoint else os.path.join(FLAGS.model_dir, 'latest.pyt')
if not os.path.exists(latest_path):
latest_path = os.path.join(FLAGS.model_dir, 'latest.pyt')
if os.path.exists(latest_path):
logging.info('loading torch model from', latest_path)
checkpoint = torch.load(latest_path)
if not FLAGS.torch_finetune:
start_epoch = checkpoint['epoch']
step = checkpoint['step']
global_step.assign(step + 1)
load_torch_model(model, latest_path)
if FLAGS.torch_load_optimizer:
optimizer.load_state_dict(checkpoint['optimizer'])
# TODO by this way restart can not change learning rate..
if learning_rate_weights is None:
checkpoint = tf.train.Checkpoint(
learning_rate=learning_rate,
learning_rate_weight=learning_rate_weight,
global_step=global_step)
else:
checkpoint = tf.train.Checkpoint(
learning_rate=learning_rate,
learning_rate_weight=learning_rate_weight,
learning_rate_weights=learning_rate_weights,
global_step=global_step)
try:
checkpoint.restore(latest_checkpoint)
checkpoint2 = copy.deepcopy(checkpoint)
except Exception:
pass
if FLAGS.torch and is_dynamic_opt:
optimizer._step = global_step.numpy()
#model.load_weights(os.path.join(ckpt_dir, 'ckpt-1'))
#model.save('./weight3.hd5')
logging.info('optimizer:', optimizer)
if FLAGS.torch_lr:
learning_rate.assign(optimizer.rate(1))
if FLAGS.torch:
learning_rate.assign(optimizer.param_groups[0]['lr'])
logging.info('learning rate got from pytorch latest.py as', learning_rate.numpy())
learning_rate.assign(learning_rate * FLAGS.learning_rate_start_factor)
if learning_rate_weights is not None:
learning_rate_weights.assign(learning_rate_weights * FLAGS.learning_rate_start_factor)
# TODO currently not support 0.1 epoch.. like this
num_epochs = FLAGS.num_epochs if FLAGS.num_epochs != 0 else 1024
will_valid = valid_dataset and not FLAGS.work_mode == 'test' and not 'SHOW' in os.environ and not 'QUICK' in os.environ
if global_step.numpy() == 0 :
will_valid = False
if gezi.get_env('EVFIRST') == '1':
will_valid = True
if gezi.get_env('EVFIRST') == '0':
will_valid = False
if will_valid:
logging.info('----------valid')
if hasattr(model, 'eval'):
model.eval()
names = None
if evaluate_fn is not None:
vals, names = evaluate_fn(model, valid_dataset, tf.train.latest_checkpoint(ckpt_dir), num_valid_steps_per_epoch)
elif eval_fn:
model_path = None if not write_valid else latest_checkpoint
names = valid_names if valid_names is not None else [infer_names[0]] + [x + '_y' for x in infer_names[1:]] + infer_names[1:] if infer_names else None
logging.info('model_path:', model_path, 'model_dir:', FLAGS.model_dir)
vals, names = evaluate(model, valid_dataset, eval_fn, model_path,
names, valid_write_fn, write_streaming,
num_valid_steps_per_epoch, num_valid_examples,
suffix=valid_suffix, sep=sep)
if names:
logging.info2('epoch:%.2f/%d step:%d' % (global_step.numpy() / num_steps_per_epoch, num_epochs, global_step.numpy()),
['%s:%.4f' % (name, val) for name, val in zip(names, vals)])
if FLAGS.work_mode == 'valid' or gezi.get_env('METRIC') == '1':
exit(0)
if 'test' in FLAGS.work_mode or gezi.get_env('TEST') == '1' or gezi.get_env('INFER') == '1':
logging.info('--------test/inference')
if test_dataset:
if hasattr(model, eval):
model.eval()
if inference_fn is None:
# model_path = FLAGS.model_dir + '.pyt' if not latest_checkpoint else latest_checkpoint
# logging.info('model_path', model_path)
assert latest_checkpoint
inference(model, test_dataset, latest_checkpoint,
infer_names, infer_debug_names, infer_write_fn, write_streaming,
num_test_steps_per_epoch, num_test_examples, suffix=infer_suffix)
else:
inference_fn(model, test_dataset, tf.train.latest_checkpoint(ckpt_dir), num_test_steps_per_epoch)
exit(0)
if 'SHOW' in os.environ:
num_epochs = start_epoch + 1
class PytObj(object):
def __init__(self, x):
self.x = x
def numpy(self):
return self.x
class PytMean(object):
def __init__(self):
self._val = 0.
self.count = 0
self.is_call = True
def clear(self):
self._val = 0
self.count = 0
def __call__(self, val):
if not self.is_call:
self.clear()
self.is_call = True
self._val += val.item()
self.count += 1
def result(self):
if self.is_call:
self.is_call = False
if not self.count:
val = 0
else:
val = self._val / self.count
# TODO just for compact with tf ..
return PytObj(val)
Mean = tfe.metrics.Mean if not FLAGS.torch else PytMean
num_insts = 0
if FLAGS.learning_rate_decay_factor > 0:
#assert FLAGS.learning_rate_values is None, 'use exponential_decay or piecewise_constant?'
#NOTICE if you do finetune or other things which might change batch_size then you'd better direclty set num_steps_per_decay
#since global step / decay_steps will not be correct epoch as num_steps per epoch changed
#so if if you change batch set you have to reset global step as fixed step
assert FLAGS.num_steps_per_decay or (FLAGS.num_epochs_per_decay and num_steps_per_epoch), 'must set num_steps_per_epoch or num_epochs_per_decay and num_steps_per_epoch'
decay_steps = FLAGS.num_steps_per_decay or int(num_steps_per_epoch * FLAGS.num_epochs_per_decay)
decay_start_step = FLAGS.decay_start_step or int(num_steps_per_epoch * FLAGS.decay_start_epoch)
# decayed_learning_rate = learning_rate * decay_rate ^ (global_step / decay_steps)
logging.info('learning_rate_decay_factor:{} decay_epochs:{} decay_steps:{} decay_start_epoch:{} decay_start_step:{}'.format(
FLAGS.learning_rate_decay_factor, FLAGS.num_epochs_per_decay, decay_steps, FLAGS.decay_start_epoch, decay_start_step))
#-------------------------start training
if hasattr(model, 'train'):
model.train()
if use_horovod:
hvd.broadcast_parameters(model.state_dict(), root_rank=0)
hvd.broadcast_optimizer_state(optimizer_, root_rank=0)
timer = gezi.Timer()
loss_avg = Mean()
valid_loss_avg = Mean()
num_epochs = num_epochs if num_epochs else 0
loops = min(num_epochs, 1) if FLAGS.torch_only else 1
for _ in range(loops):
for i, (x, y) in enumerate(train_dataset):
if FLAGS.torch:
x, y = to_torch(x, y)
if is_dynamic_opt:
learning_rate.assign(optimizer.rate())
def loss_fn_(x, y):
if not FLAGS.torch and 'training' in inspect.getargspec(model.call).args:
y_ = model(x, training=True)
else:
y_ = model(x)
if not FLAGS.torch:
return loss_fn(y, y_)
else:
return loss_fn(y_, y)
if not FLAGS.torch:
loss, grads = melt.eager.grad(model, x, y, loss_fn)
grads, _ = tf.clip_by_global_norm(grads, FLAGS.clip_gradients)
#optimizer.apply_gradients(zip(grads, model.variables))
optimizer.apply_gradients(zip(grads, model.trainable_variables))
# https://github.com/horovod/horovod/blob/master/examples/tensorflow_mnist_eager.py
# Horovod: broadcast initial variable states from rank 0 to all other processes.
# This is necessary to ensure consistent initialization of all workers when
# training is started with random weights or restored from a checkpoint.
# Note: broadcast should be done after the first gradient step to ensure optimizer
# initialization.
# TODO check eager mode
if use_horovod and epoch == start_epoch and i == 0:
hvd.broadcast_variables(model.variables, root_rank=0)
hvd.broadcast_variables(optimizier.variables(), root_rank=0)
else:
optimizer.zero_grad()
loss = loss_fn_(x, y)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(),
FLAGS.clip_gradients)
optimizer.step()
global_step.assign_add(1)
loss_avg(loss)
## https://discuss.pytorch.org/t/calling-loss-backward-reduce-memory-usage/2735
# if FLAGS.torch:
# del loss
batch_size_ = list(x.values())[0].shape[FLAGS.batch_size_dim] if type(x) == type({}) else x.shape[FLAGS.batch_size_dim]
num_insts += int(batch_size_)
if global_step.numpy() % FLAGS.interval_steps == 0:
#checkpoint.save(checkpoint_prefix)
elapsed = timer.elapsed()
steps_per_second = FLAGS.interval_steps / elapsed
instances_per_second = num_insts / elapsed
num_insts = 0
if num_steps_per_epoch is None:
epoch_time_info = ''
else:
hours_per_epoch = num_steps_per_epoch / FLAGS.interval_steps * elapsed / 3600
epoch_time_info = '1epoch:[{:.2f}h]'.format(hours_per_epoch)
if valid_dataset2:
# try:
# x, y = next(iter(valid_dataset2))
# except Exception:
# # TODO FIXME how.. iterate stop restart.., here hack for my iterator see projects/lm/dataset
# x, y = next(iter(valid_dataset2))
## valid dataset2 is repeated
## NOTICE will always the first batch ... as below
#x, y = next(iter(valid_dataset2))
x, y = next(valid_dataset2_iter)
#print(x['id'][0])
if FLAGS.torch:
x, y = to_torch(x, y)
if hasattr(model, 'eval'):
model.eval()
valid_loss = loss_fn_(x, y)
valid_loss = valid_loss.numpy() if not FLAGS.torch else valid_loss.item()
if hasattr(model, 'train'):
model.train()
if not use_horovod or hvd.rank() == 0:
# 'train_loss:[%.4f]' % loss_avg.result().numpy(),
# 'valid_loss:[%.4f]' % valid_loss_avg.result().numpy()
logging.info2('epoch:%.2f/%d' % ((global_step.numpy() / num_steps_per_epoch), num_epochs),
'step:%d' % global_step.numpy(),
'elapsed:[%.2f]' % elapsed,
'batch_size:[%d]' % batch_size_,
'gpus:[%d]' % num_gpus,
'batches/s:[%.2f]' % steps_per_second,
'insts/s:[%d]' % instances_per_second,
'%s' % epoch_time_info,
'lr:[%.6f]' % learning_rate.numpy(),
'train_loss:[%.4f]' % loss_avg.result().numpy(),
'valid_loss:[%.4f]' % valid_loss
)
if global_step.numpy() % FLAGS.valid_interval_steps == 0:
with writer_valid.as_default(), summary.always_record_summaries():
summary.scalar('loss/valid', valid_loss)
writer_valid.flush()
else:
if not use_horovod or hvd.rank() == 0:
#'train_loss:[%.4f]' % loss_avg.result().numpy()
logging.info2('epoch:%.2f/%d' % ((epoch + i / num_steps_per_epoch), num_epochs),
'step:%d' % global_step.numpy(),
'elapsed:[%.2f]' % elapsed,
'batch_size:[%d]' % batch_size_,
'gpus:[%d]' % num_gpus,
'batches/s:[%.2f]' % steps_per_second,
'insts/s:[%d]' % instances_per_second,
'%s' % epoch_time_info,
'lr:[%.6f]' % learning_rate.numpy(),
'train_loss:[%.4f]' % loss_avg.result().numpy()
)
if not use_horovod or hvd.rank() == 0:
if global_step.numpy() % FLAGS.valid_interval_steps == 0:
with writer_train.as_default(), summary.always_record_summaries():
summary.scalar('loss/train_avg', loss_avg.result().numpy())
summary.scalar('learning_rate', learning_rate.numpy())
summary.scalar('other/batch_size', batch_size_)
summary.scalar('other/epoch', melt.epoch())
summary.scalar('perf/steps_per_second', steps_per_second)
summary.scalar('perf/instances_per_second', instances_per_second)
writer_train.flush()
if valid_dataset and FLAGS.metric_eval_interval_steps and global_step.numpy() and global_step.numpy() % FLAGS.metric_eval_interval_steps == 0:
if hasattr(model, eval):
model.eval()
vals, names = None, None
if evaluate_fn is not None:
vals, names = evaluate_fn(model, valid_dataset, None, num_valid_steps_per_epoch)
elif eval_fn:
names = valid_names if valid_names is not None else [infer_names[0]] + [x + '_y' for x in infer_names[1:]] + infer_names[1:] if infer_names else None
vals, names = evaluate(model, valid_dataset, eval_fn, None,
names, valid_write_fn, write_streaming,
num_valid_steps_per_epoch, num_valid_examples, sep=sep)
if not use_horovod or hvd.rank() == 0:
if vals and names:
with writer_valid.as_default(), summary.always_record_summaries():
for name, val in zip(names, vals):
summary.scalar(f'step_eval/{name}', val)
writer_valid.flush()
if FLAGS.torch:
if not FLAGS.torch_lr:
# control learning rate by tensorflow learning rate
for param_group in optimizer.param_groups:
# important learning rate decay
param_group['lr'] = learning_rate.numpy()
if hasattr(model, 'train'):
model.train()
if not use_horovod or hvd.rank() == 0:
if names and vals:
logging.info2('epoch:%.2f/%d' % ((global_step.numpy() / num_steps_per_epoch), num_epochs),
'valid_step:%d' % global_step.numpy(),
'valid_metrics',
['%s:%.5f' % (name, val) for name, val in zip(names, vals)])
if not use_horovod or hvd.rank() == 0:
# TODO save ok ?
if global_step.numpy() % FLAGS.save_interval_steps == 0:
if FLAGS.torch:
state = {
'epoch': int(global_step.numpy() / num_steps_per_epoch),
'step': global_step.numpy(),
'state_dict': model.state_dict() if not hasattr(model, 'module') else model.module.state_dict(),
'optimizer' : optimizer.state_dict(),
}
torch.save(state, os.path.join(FLAGS.model_dir, 'latest.pyt'))
# TODO fixme why if both checpoint2 and chekpoint used... not ok..
if FLAGS.save_interval_epochs and global_step.numpy() % int(num_steps_per_epoch * FLAGS.save_interval_epochs) == 0:
checkpoint2.save(checkpoint_prefix2)
if FLAGS.torch:
state = {
'epoch': int(global_step.numpy() / num_steps_per_epoch),
'step': global_step.numpy(),
'state_dict': model.state_dict() if not hasattr(model, 'module') else model.module.state_dict(),
'optimizer' : optimizer.state_dict(),
}
torch.save(state, tf.train.latest_checkpoint(ckpt_dir2) + '.pyt')
if FLAGS.learning_rate_decay_factor > 0:
if global_step.numpy() >= decay_start_step and global_step.numpy() % decay_steps == 0:
lr = max(learning_rate.numpy() * FLAGS.learning_rate_decay_factor, FLAGS.min_learning_rate)
if lr < learning_rate.numpy():
learning_rate.assign(lr)
if FLAGS.torch:
for param_group in optimizer.param_groups:
param_group['lr'] = learning_rate.numpy()
if i == 0:
try:
if not FLAGS.torch:
logging.info(model.summary())
# #tf.keras.utils.plot_model(model, to_file='/home/gezi/model.png', show_shapes=False, show_layer_names=True, rankdir='TB')
# import keras
# keras.utils.plot_model(model, to_file='/home/gezi/model.png', show_shapes=False, show_layer_names=True, rankdir='LR', expand_nested=True, dpi=96)
else:
logging.info(model)
except Exception:
traceback.print_exc()
logging.info('Fail to do model.summary() may be you have layer define in init but not used in call')
if 'SHOW' in os.environ:
exit(0)
if valid_dataset and global_step.numpy() % int(num_steps_per_epoch * FLAGS.valid_interval_epochs) == 0:
if hasattr(model, 'eval'):
model.eval()
vals, names = None, None
if evaluate_fn is not None:
vals, names = evaluate_fn(model, valid_dataset, tf.train.latest_checkpoint(ckpt_dir), num_valid_steps_per_epoch)
elif eval_fn:
model_path = None if not write_valid else tf.train.latest_checkpoint(ckpt_dir)
print('---------metric evaluate step', global_step.numpy(), 'model_path:', model_path)
names = valid_names if valid_names is not None else [infer_names[0]] + [x + '_y' for x in infer_names[1:]] + infer_names[1:] if infer_names else None
vals, names = evaluate(model, valid_dataset, eval_fn, model_path,
names, valid_write_fn, write_streaming,
num_valid_steps_per_epoch, num_valid_examples, suffix=valid_suffix, sep=sep)
if not use_horovod or hvd.rank() == 0:
if vals and names:
logging.info2('epoch:%.2f/%d' % (global_step.numpy() / num_steps_per_epoch, num_epochs),
'step:%d' % global_step.numpy(),
'valid_metrics',
['%s:%.5f' % (name, val) for name, val in zip(names, vals)])
if not use_horovod or hvd.rank() == 0:
with writer.as_default(), summary.always_record_summaries():
temp = global_step.value()
global_step.assign(int(global_step.numpy() / int(num_steps_per_epoch * FLAGS.valid_interval_epochs)))
if valid_dataset:
if hasattr(model, 'eval'):
model.eval()
if vals and names:
for name, val in zip(names, vals):
summary.scalar(f'eval/{name}', val)
writer.flush()
global_step.assign(temp)
if test_dataset and global_step.numpy() % int(num_steps_per_epoch * FLAGS.inference_interval_epochs) == 0:
if hasattr(model, 'eval'):
model.eval()
if inference_fn is None:
inference(model, test_dataset, tf.train.latest_checkpoint(ckpt_dir),
infer_names, infer_debug_names, infer_write_fn, write_streaming,
num_test_steps_per_epoch, num_test_examples, suffix=infer_suffix, sep=sep)
else:
inference_fn(model, test_dataset, tf.train.latest_checkpoint(ckpt_dir), num_test_steps_per_epoch)
if num_epochs and (global_step.numpy() % num_steps_per_epoch) == 0 and int(global_step.numpy() / num_steps_per_epoch) == num_epochs:
logging.info(f'Finshed training of {num_epochs} epochs')
exit(0)