def main():
flow.config.gpu_device_num(args.gpu_num_per_node)
flow.env.log_dir(args.log_dir)
InitNodes(args)
snapshot = Snapshot(args.model_save_dir, args.model_load_dir)
summary = Summary(args.log_dir, args)
if args.do_train:
print('| Training Start')
for epoch in range(args.num_epochs):
metric = Metric(desc='finetune',
print_steps=args.loss_print_every_n_iter,
summary=summary,
batch_size=batch_size,
keys=['loss'])
for step in range(epoch_size):
BertGlueFinetuneJob().async_get(
metric.metric_cb(step, epoch=epoch))
run_eval_job(BertGlueEvalTrainJob, epoch_size, desc='train')
run_eval_job(BertGlueEvalValJob, num_eval_steps, desc='eval')
if args.save_last_snapshot:
snapshot.save("last_snapshot")
if args.do_eval:
print('| Evaluation Start')
run_eval_job(BertGlueEvalValJob, num_eval_steps, desc='eval')
def main():
InitNodes(args)
flow.env.grpc_use_no_signal()
flow.env.log_dir(args.log_dir)
summary = Summary(args.log_dir, args)
snapshot = Snapshot(args.model_save_dir, args.model_load_dir)
for epoch in range(args.num_epochs):
metric = Metric(desc='train',
calculate_batches=args.loss_print_every_n_iter,
summary=summary,
save_summary_steps=epoch_size,
batch_size=train_batch_size,
loss_key='loss')
for i in range(epoch_size):
TrainNet().async_get(metric.metric_cb(epoch, i))
if args.val_data_dir:
metric = Metric(desc='validation',
calculate_batches=num_val_steps,
summary=summary,
save_summary_steps=num_val_steps,
batch_size=val_batch_size)
for i in range(num_val_steps):
InferenceNet().async_get(metric.metric_cb(epoch, i))
snapshot.save('epoch_{}'.format(epoch))
def main():
InitNodes(args)
flow.env.log_dir(args.log_dir)
snapshot = Snapshot(args.model_save_dir, args.model_load_dir,
args.save_init)
print(" {} iter per epoch...".format(epoch_size))
for epoch in range(1, args.num_epochs + 1):
metric = Metric(
desc="train",
calculate_batches=args.loss_print_every_n_iter,
batch_size=train_batch_size,
loss_key="loss",
)
for i in range(epoch_size):
TrainNet().async_get(metric.metric_cb(epoch, i))
if args.val_data_dir:
metric = Metric(
desc="validation",
calculate_batches=num_val_steps,
batch_size=val_batch_size,
)
for i in range(num_val_steps):
InferenceNet().async_get(metric.metric_cb(epoch, i))
if epoch % args.save_epoch_interval == 0:
snapshot.save("epoch_{}".format(epoch))
if args.save_last:
snapshot.save("epoch_{}".format("last"))
def main():
flow.config.gpu_device_num(args.gpu_num_per_node)
flow.env.log_dir(args.log_dir)
InitNodes(args)
snapshot = Snapshot(args.model_save_dir, args.model_load_dir)
#if args.save_and_break:
# print("save model just after init and exit")
# snapshot.save("initial_snapshot")
# import sys
# sys.exit()
for epoch in range(args.num_epochs):
metric = Metric(desc='finetune',
print_steps=args.loss_print_every_n_iter,
batch_size=batch_size,
keys=['loss'])
for step in range(epoch_size):
BertGlueFinetuneJob().async_get(metric.metric_cb(step,
epoch=epoch))
#if 1: #step % args.loss_print_every_n_iter == 0:
run_eval_job(BertGlueEvalTrainJob, epoch_size, desc='train')
run_eval_job(BertGlueEvalValJob, num_eval_steps, desc='eval')
if args.save_last_snapshot:
snapshot.save("last_snapshot")
def main():
flow.config.gpu_device_num(args.gpu_num_per_node)
flow.env.log_dir(args.log_dir)
InitNodes(args)
if args.do_train or args.do_eval:
snapshot = Snapshot(args.model_save_dir, args.model_load_dir)
if args.do_train:
summary = Summary(args.log_dir, args)
for epoch in range(args.num_epochs):
metric = Metric(desc='train',
print_steps=args.loss_print_every_n_iter,
summary=summary,
batch_size=batch_size,
keys=['total_loss'])
for step in range(epoch_size):
SquadFinetuneJob().async_get(
metric.metric_cb(step, epoch=epoch))
if args.save_last_snapshot:
snapshot.save("last_snapshot")
if args.do_eval:
assert os.path.isdir(args.eval_data_dir)
all_results = []
for step in range(num_eval_steps):
unique_ids, start_positions, end_positions = SquadDevJob().get()
unique_ids = unique_ids.numpy()
start_positions = start_positions.numpy()
end_positions = end_positions.numpy()
for unique_id, start_position, end_position in zip(
unique_ids, start_positions, end_positions):
all_results.append(
RawResult(
unique_id=int(unique_id[0]),
start_logits=start_position.flatten().tolist(),
end_logits=end_position.flatten().tolist(),
))
if step % args.loss_print_every_n_iter == 0:
print("{}/{}, num of results:{}".format(
step, num_eval_steps, len(all_results)))
print("last uid:", unique_id[0])
gen_eval_predict_json(args, all_results)
def main():
flow.config.gpu_device_num(args.gpu_num_per_node)
flow.env.log_dir(args.log_dir)
InitNodes(args)
snapshot = Snapshot(args.model_save_dir, args.model_load_dir)
metric = Metric(desc='train',
print_steps=args.loss_print_every_n_iter,
batch_size=batch_size,
keys=['total_loss', 'mlm_loss', 'nsp_loss'])
for step in range(args.iter_num):
PretrainJob().async_get(metric.metric_cb(step))
#PretrainJob().async_get(metric.metric_cb(step, epoch=3))
if (step + 1) % args.model_save_every_n_iter == 0:
snapshot.save("snapshot_%d" % (step + 1))
if args.save_last_snapshot:
snapshot.save("last_snapshot")
def main():
flow.config.gpu_device_num(args.gpu_num_per_node)
flow.env.log_dir(args.log_dir)
InitNodes(args)
snapshot = Snapshot(args.model_save_dir, args.model_load_dir, args.model_save_init)
print("num_accumulation_steps:", args.num_accumulation_steps)
metric = Metric(
desc="train",
print_steps=args.loss_print_every_n_iter,
batch_size=batch_size * args.num_accumulation_steps,
keys=["total_loss", "mlm_loss", "nsp_loss"],
)
for step in range(args.iter_num):
PretrainJob().async_get(metric.metric_cb(step))
# PretrainJob().async_get(metric.metric_cb(step, epoch=3))
if (step + 1) % args.model_save_every_n_iter == 0:
snapshot.save("snapshot_%d" % (step + 1))
if args.save_last_snapshot:
snapshot.save("last_snapshot")
def main():
flow.config.gpu_device_num(args.gpu_num_per_node)
flow.env.log_dir(args.log_dir)
InitNodes(args)
if args.do_train:
snapshot = Snapshot(args.model_save_dir, args.model_load_dir)
summary = Summary(args.log_dir, args)
best_dev_acc = 0.0
best_result = {}
for epoch in range(args.num_epochs):
metric = Metric(desc='finetune',
print_steps=args.loss_print_every_n_iter,
summary=summary,
batch_size=batch_size,
keys=['loss'])
for step in range(epoch_size):
BertGlueFinetuneJob().async_get(
metric.metric_cb(step, epoch=epoch))
#if 1: #step % args.loss_print_every_n_iter == 0:
run_eval_job(BertGlueEvalTrainJob, epoch_size, desc='train')
result = run_eval_job(BertGlueEvalValJob,
num_eval_steps,
desc='eval')
save_model = False
if task_name in acc_tasks and result['accuracy'] > best_dev_acc:
best_dev_acc = result['accuracy']
best_result = result
save_model = True
print('Best result:', result)
# if task_name in corr_tasks and result['corr'] > best_dev_acc:
# best_dev_acc = result['corr']
# best_result = result
# save_model = True
#print('Best result:', result)
if task_name in mcc_tasks and result[
'matthews_corrcoef'] > best_dev_acc:
best_dev_acc = result['matthews_corrcoef']
best_result = result
save_model = True
print('Best result:', result)
if save_model:
if not os.path.exists(args.model_save_dir):
os.makedirs(args.model_save_dir)
# snapshot_save_path = os.path.join(args.model_save_dir)
# print("Saving best model to {}".format(snapshot_save_path))
snapshot.save('best')
flow.sync_default_session()
print('Best result:', best_result)
print("Saving best model to " +
os.path.join(args.model_save_dir, 'snapshot_best'))
if args.serve_for_online:
print('Deleting the optimizer parmas from model_save_dir...')
remove_optimizer_params(
os.path.join(args.model_save_dir, 'snapshot_best'))
# if args.save_last_snapshot:
# snapshot.save("last_snapshot")
if args.do_eval:
print('Loading model...')
print(args.model_save_dir)
if not args.do_train:
check_point = flow.train.CheckPoint()
check_point.load(args.model_save_dir)
print('Evaluation...')
run_eval_job(BertGlueEvalValJob, num_eval_steps, desc='eval')
# flow.tensorrt.write_int8_calibration("./int8_calibration") # mkdir int8_calibration
=======
# flow.tensorrt.write_int8_calibration("./int8_calibration") # mkdir int8_calibration
>>>>>>> tianshu
if args.val_data_dir:
metric = Metric(desc='validation', calculate_batches=num_val_steps, summary=summary,
save_summary_steps=num_val_steps, batch_size=val_batch_size)
for i in range(val_batch_size):
<<<<<<< HEAD
# if i<=10:
# InferenceNet().get()
# if i ==10:
# flow.tensorrt.cache_int8_calibration()
# else:
# InferenceNet().async_get(metric.metric_cb(epoch, i))
=======
# if i<=10:
# InferenceNet().get()
# if i ==10:
# flow.tensorrt.cache_int8_calibration()
# else:
# InferenceNet().async_get(metric.metric_cb(epoch, i))
>>>>>>> tianshu
InferenceNet().async_get(metric.metric_cb(epoch, i))
snapshot.save('epoch_{}'.format(epoch))
if __name__ == "__main__":
main()