def default_exe_params(is_distributed, use_cuda, thread_num):
"""
Set the default execute parameters.
"""
gpu_id = 0
trainer_num = 1
trainer_id = 0
dist_strategy = None
places = None
if is_distributed:
if use_cuda:
role = role_maker.PaddleCloudRoleMaker(is_collective=True)
fleet.init(role)
gpu_id = int(os.getenv("FLAGS_selected_gpus"))
trainer_num = fleet.worker_num()
trainer_id = fleet.worker_index()
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.use_experimental_executor = True
exec_strategy.num_threads = 4
exec_strategy.num_iteration_per_drop_scope = 1
dist_strategy = DistributedStrategy()
dist_strategy.exec_strategy = exec_strategy
dist_strategy.nccl_comm_num = 2
dist_strategy.fuse_all_reduce_ops = True
dist_strategy.forward_recompute = True
dist_strategy.use_amp = True
dist_strategy.amp_loss_scaling = 12800.0
places = fluid.cuda_places()
else:
print('Only gpu is supported for distributed mode at present.')
exit(-1)
else:
if use_cuda:
places = fluid.cuda_places()
else:
places = fluid.cpu_places(thread_num)
os.environ['CPU_NUM'] = str(thread_num)
if use_cuda:
exe = fluid.Executor(fluid.CUDAPlace(gpu_id))
else:
exe = fluid.Executor(fluid.CPUPlace())
return {
'exe': exe,
'trainer_num': trainer_num,
'trainer_id': trainer_id,
'gpu_id': gpu_id,
'dist_strategy': dist_strategy,
'places': places
}
def _wrapper():
'''
Sampling according to the worker index to uniformly separate samples.
'''
rank = fleet.worker_index()
nranks = fleet.worker_num()
for idx, sample in enumerate(generator()):
if idx % nranks == rank:
yield sample
def train(args):
"""
Train main function.
"""
if args.is_distributed:
role = role_maker.PaddleCloudRoleMaker(is_collective=True)
fleet.init(role)
dev_count = fluid.core.get_cuda_device_count()
gpu_id = int(os.getenv("FLAGS_selected_gpus"))
trainers_num = fleet.worker_num()
trainer_id = fleet.worker_index()
else:
dev_count = 1
gpu_id = 0
trainers_num = 1
trainer_id = 0
place = fluid.CUDAPlace(gpu_id)
task = tasks.create_task(args)
model = models.create_model(args, place)
train_generator = task.reader.data_generator(input_file=args.train_file,
num_epochs=args.num_epochs,
num_part=trainers_num,
part_id=trainer_id,
phase="train")
valid_generator = task.reader.data_generator(
input_file=args.valid_file,
num_part=dev_count,
part_id=gpu_id,
phase="distributed_valid" if args.is_distributed else "valid")
# run training
model_timer = Timer()
for step, data in enumerate(train_generator(), 1):
model_timer.start()
metrics = task.train_step(model, data)
model_timer.pause()
if step % args.log_steps == 0:
time_cost = model_timer.pass_time
current_epoch, current_file_index, total_file = task.reader.get_train_progress(
)
print(
f"[train][{current_epoch}] progress: {current_file_index}/{total_file} "
f"step: {step}, time: {time_cost:.3f}, "
f"speed: {args.log_steps / time_cost:.3f} steps/s")
print("\tcurrent lr:", metrics.pop('scheduled_lr'))
print("\t" + task.show_metrics(metrics))
model_timer.reset()
if step % args.validation_steps == 0:
evaluate(task, model, valid_generator, args, dev_count, gpu_id)
if step % args.save_steps == 0:
save_path = f"{args.save_path}/step_{step}"
model.save(save_path, is_checkpoint=True)
def main(args):
"""tbd"""
model_config = json.load(open(args.model_config, 'r'))
model_config['context_pooling'] = args.context_pooling
### build model
train_prog = fluid.Program()
test_prog = fluid.Program()
startup_prog = fluid.Program()
with fluid.program_guard(train_prog, startup_prog):
with fluid.unique_name.guard():
model = PreGNNContextpredModel(model_config)
model.forward()
opt = fluid.optimizer.Adam(learning_rate=args.lr)
if args.distributed:
opt = get_distributed_optimizer(opt)
opt.minimize(model.loss)
with fluid.program_guard(test_prog, fluid.Program()):
with fluid.unique_name.guard():
model = PreGNNContextpredModel(model_config)
model.forward(is_test=True)
place = fluid.CUDAPlace(int(os.environ.get('FLAGS_selected_gpus', 0))) \
if args.use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_prog)
if not args.init_model is None and not args.init_model == "":
load_partial_params(exe, args.init_model, train_prog)
### load data
k = model_config['layer_num']
l1 = k - 1
l2 = l1 + args.context_size
featurizer = PreGNNContextPredFeaturizer(
model.substruct_graph_wrapper,
model.context_graph_wrapper,
k, l1, l2)
dataset = load_zinc_dataset(args.data_path, featurizer=featurizer)
splitter = RandomSplitter()
train_dataset, _, test_dataset = splitter.split(
dataset, frac_train=0.9, frac_valid=0, frac_test=0.1)
if args.distributed:
indices = list(range(fleet.worker_num(), len(train_dataset), fleet.worker_index()))
train_dataset = train_dataset[indices]
print("Train/Test num: %s/%s" % (len(train_dataset), len(test_dataset)))
### start train
list_test_loss = []
for epoch_id in range(args.max_epoch):
train_loss = train(args, exe, train_prog, model, train_dataset, featurizer)
test_loss = evaluate(args, exe, test_prog, model, test_dataset, featurizer)
if not args.distributed or fleet.worker_index() == 0:
fluid.io.save_params(exe, '%s/epoch%s' % (args.model_dir, epoch_id), train_prog)
list_test_loss.append(test_loss)
print("epoch:%d train/loss:%s" % (epoch_id, train_loss))
print("epoch:%d test/loss:%s" % (epoch_id, test_loss))
if not args.distributed or fleet.worker_index() == 0:
best_epoch_id = np.argmax(list_test_loss)
fluid.io.load_params(exe, '%s/epoch%d' % (args.model_dir, best_epoch_id), train_prog)
fluid.io.save_params(exe, '%s/epoch_best' % (args.model_dir), train_prog)
return list_test_loss[best_epoch_id]
def main(args):
"""
Call the configuration function of the model, build the model and load data, then start training.
model_config:
a json file with the model configurations,such as dropout rate ,learning rate,num tasks and so on;
task_num:
It means the number of chembl filtered task;
PreGNNSupervisedModel:
It means the PretrainGNNModel for supervised strategy.
Graph-level multi-task supervised pre-training to jointly predict a diverse set of supervised labels of individual graphs.
"""
model_config = json.load(open(args.model_config, 'r'))
if not args.dropout_rate is None:
model_config['dropout_rate'] = args.dropout_rate
task_num = get_chembl_filtered_task_num()
model_config['task_num'] = task_num
### build model
train_prog = fluid.Program()
test_prog = fluid.Program()
startup_prog = fluid.Program()
with fluid.program_guard(train_prog, startup_prog):
with fluid.unique_name.guard():
model = PreGNNSupervisedModel(model_config)
model.forward()
opt = fluid.optimizer.Adam(learning_rate=args.lr)
if args.distributed:
opt = get_distributed_optimizer(opt)
opt.minimize(model.loss)
with fluid.program_guard(test_prog, fluid.Program()):
with fluid.unique_name.guard():
model = PreGNNSupervisedModel(model_config)
model.forward(is_test=True)
"""
Use CUDAPlace for GPU training, or use CPUPlace for CPU training.
"""
place = fluid.CUDAPlace(int(os.environ.get('FLAGS_selected_gpus', 0))) \
if args.use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_prog)
if not args.init_model is None and not args.init_model == "":
load_partial_params(exe, args.init_model, train_prog)
### load data
"""
PreGNNSupervisedFeaturizer:
It is used along with `PreGNNSupervised`. It inherits from the super class `Featurizer` which is used for feature extractions. The `Featurizer` has two functions: `gen_features` for converting from a single raw smiles to a single graph data, `collate_fn` for aggregating a sublist of graph data into a big batch.
splitter:
split type of the dataset:random,scaffold,random with scaffold. Here is randomsplit.
`ScaffoldSplitter` will firstly order the compounds according to Bemis-Murcko scaffold,
then take the first `frac_train` proportion as the train set, the next `frac_valid` proportion as the valid set
and the rest as the test set. `ScaffoldSplitter` can better evaluate the generalization ability of the model on
out-of-distribution samples. Note that other splitters like `RandomSplitter`, `RandomScaffoldSplitter`
and `IndexSplitter` is also available."
"""
featurizer = PreGNNSupervisedFeaturizer(model.graph_wrapper)
dataset = load_chembl_filtered_dataset(args.data_path,
featurizer=featurizer)
splitter = RandomSplitter()
train_dataset, _, test_dataset = splitter.split(dataset,
frac_train=0.9,
frac_valid=0,
frac_test=0.1)
if args.distributed:
indices = list(
range(fleet.worker_index(), len(train_dataset),
fleet.worker_num()))
train_dataset = train_dataset[indices]
print("Train/Test num: %s/%s" % (len(train_dataset), len(test_dataset)))
### start train
"""
Load the train function and calculate the train loss and test loss in each epoch.
Here we set the epoch is in range of max epoch,you can change it if you want.
Then we will calculate the train loss ,test loss and print them.
Finally we save the best epoch to the model according to the dataset.
"""
list_test_loss = []
for epoch_id in range(args.max_epoch):
train_loss = train(args, exe, train_prog, model, train_dataset,
featurizer)
test_loss = evaluate(args, exe, test_prog, model, test_dataset,
featurizer)
if not args.distributed or fleet.worker_index() == 0:
fluid.io.save_params(exe,
'%s/epoch%s' % (args.model_dir, epoch_id),
train_prog)
list_test_loss.append(test_loss)
print("epoch:%d train/loss:%s" % (epoch_id, train_loss))
print("epoch:%d test/loss:%s" % (epoch_id, test_loss))
if not args.distributed or fleet.worker_index() == 0:
best_epoch_id = np.argmax(list_test_loss)
fluid.io.load_params(exe,
'%s/epoch%d' % (args.model_dir, best_epoch_id),
train_prog)
fluid.io.save_params(exe, '%s/epoch_best' % (args.model_dir),
train_prog)
return list_test_loss[best_epoch_id]
def main(args):
with open(args.config, 'r') as f:
config = json.load(f)
logging.info('Load data ...')
if len(args.dataset.split(',')) > 1:
# for large pretraining dataset, ZINC15 and ChEMBL
# directly load the processed npz files
train_data_list = []
for ds in args.dataset.split(','):
# use processed data.npz
train_data_list.extend(
load_data(os.path.join(args.root, ds, 'processed')))
# dataset = MoleculeDataset(
# args.root, ds,
# add_symmetry=False,
# add_self_loop=False)
# data_list = dataset.get_data_list()
# processed_dir = os.path.join(args.root, ds, 'processed')
# os.makedirs(processed_dir, exist_ok=True)
# save_data_list_to_npz(
# data_list, os.path.join(processed_dir, 'data.npz'))
# logging.info('Processed {}'.format(ds))
# train_data_list.extend(data_list)
else:
if args.dataset == 'mutag':
train_data_list, _ = load_mutag_dataset(
os.path.join(args.root, args.dataset, 'raw'))
elif args.dataset == 'ptc_mr':
train_data_list, _ = load_ptc_mr_dataset(
os.path.join(args.root, args.dataset, 'raw'))
else:
raise ValueError('Unsupported dataset')
if args.is_fleet:
train_data_list = [
x for i, x in enumerate(train_data_list)
if i % fleet.worker_num() == fleet.worker_index()
]
logging.info("Data loaded.")
logging.info("Train Examples: %s" % len(train_data_list))
sys.stdout.flush()
if args.emb_dir is not None:
os.makedirs(args.emb_dir, exist_ok=True)
train_prog = F.Program()
test_prog = F.Program()
startup_prog = F.Program()
with F.program_guard(train_prog, startup_prog):
with F.unique_name.guard():
agent = create_model(args, config)
test_prog = train_prog.clone(for_test=True)
opt = F.optimizer.Adam(learning_rate=args.lr)
if args.is_fleet:
dist_strategy = DistributedStrategy()
role = role_maker.PaddleCloudRoleMaker(is_collective=True)
fleet.init(role)
opt = fleet.distributed_optimizer(opt, strategy=dist_strategy)
opt.minimize(agent.loss)
place = F.CUDAPlace(0) if args.use_cuda else F.CPUPlace()
exe = F.Executor(place)
exe.run(startup_prog)
if (not args.dont_save_emb) and \
(not args.is_fleet or fleet.worker_index() == 0):
save_embedding(args, exe, test_prog, agent, train_data_list, -1)
for epoch_id in range(args.max_epoch):
train(args, exe, train_prog, agent, train_data_list, epoch_id)
if not args.is_fleet or fleet.worker_index() == 0:
F.io.save_params(exe, '%s/epoch%s' % (args.model_dir, epoch_id),
train_prog)
if not args.dont_save_emb:
save_embedding(args, exe, test_prog, agent, train_data_list,
epoch_id)
def main(args):
ernie_config = ErnieConfig(args.ernie_config_path)
ernie_config.print_config()
if args.use_cuda:
dev_list = fluid.cuda_places()
place = dev_list[0]
dev_count = len(dev_list)
else:
place = fluid.CPUPlace()
dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
exe = fluid.Executor(place)
reader = reader_ce.ClassifyReader(vocab_path=args.vocab_path,
label_map_config=args.label_map_config,
max_seq_len=args.max_seq_len,
total_num=args.train_data_size,
do_lower_case=args.do_lower_case,
in_tokens=args.in_tokens,
random_seed=args.random_seed,
tokenizer=args.tokenizer,
for_cn=args.for_cn,
task_id=args.task_id)
if not (args.do_train or args.do_val or args.do_test):
raise ValueError("For args `do_train`, `do_val` and `do_test`, at "
"least one of them must be True.")
if args.do_test:
assert args.test_save is not None
startup_prog = fluid.Program()
if args.random_seed is not None:
startup_prog.random_seed = args.random_seed
if args.predict_batch_size == None:
args.predict_batch_size = args.batch_size
if args.do_train:
role = role_maker.PaddleCloudRoleMaker(is_collective=True)
fleet.init(role)
dev_count = fleet.worker_num()
train_data_generator = reader.data_generator(
input_file=args.train_set,
batch_size=args.batch_size,
epoch=args.epoch,
dev_count=1,
trainer_id=fleet.worker_index(),
trainer_num=fleet.worker_num(),
shuffle=True,
phase="train")
num_train_examples = reader.get_num_examples(args.train_set)
if args.in_tokens:
max_train_steps = args.epoch * num_train_examples // (
args.batch_size // args.max_seq_len) // dev_count
else:
max_train_steps = args.epoch * num_train_examples // args.batch_size // dev_count
warmup_steps = int(max_train_steps * args.warmup_proportion)
log.info("Device count: %d" % dev_count)
log.info("Num train examples: %d" % num_train_examples)
log.info("Max train steps: %d" % max_train_steps)
log.info("Num warmup steps: %d" % warmup_steps)
train_program = fluid.Program()
# use fleet api
exec_strategy = fluid.ExecutionStrategy()
if args.use_fast_executor:
exec_strategy.use_experimental_executor = True
exec_strategy.num_threads = dev_count
if args.is_distributed:
exec_strategy.num_threads = 3
exec_strategy.num_iteration_per_drop_scope = args.num_iteration_per_drop_scope
dist_strategy = DistributedStrategy()
dist_strategy.exec_strategy = exec_strategy
dist_strategy.nccl_comm_num = 1
if args.is_distributed:
dist_strategy.nccl_comm_num = 2
dist_strategy.use_hierarchical_allreduce = True
if args.use_mix_precision:
dist_strategy.use_amp = True
with fluid.program_guard(train_program, startup_prog):
with fluid.unique_name.guard():
train_pyreader, graph_vars = create_model(
args,
pyreader_name='train_reader',
ernie_config=ernie_config)
scheduled_lr = optimization(
loss=graph_vars["loss"],
warmup_steps=warmup_steps,
num_train_steps=max_train_steps,
learning_rate=args.learning_rate,
train_program=train_program,
startup_prog=startup_prog,
weight_decay=args.weight_decay,
scheduler=args.lr_scheduler,
use_dynamic_loss_scaling=args.use_dynamic_loss_scaling,
incr_every_n_steps=args.incr_every_n_steps,
decr_every_n_nan_or_inf=args.decr_every_n_nan_or_inf,
incr_ratio=args.incr_ratio,
decr_ratio=args.decr_ratio,
dist_strategy=dist_strategy)
if args.verbose:
if args.in_tokens:
lower_mem, upper_mem, unit = fluid.contrib.memory_usage(
program=train_program,
batch_size=args.batch_size // args.max_seq_len)
else:
lower_mem, upper_mem, unit = fluid.contrib.memory_usage(
program=train_program, batch_size=args.batch_size)
log.info("Theoretical memory usage in training: %.3f - %.3f %s" %
(lower_mem, upper_mem, unit))
if args.do_val or args.do_test:
test_prog = fluid.Program()
with fluid.program_guard(test_prog, startup_prog):
with fluid.unique_name.guard():
test_pyreader, graph_vars = create_model(
args,
pyreader_name='test_reader',
ernie_config=ernie_config,
is_prediction=True)
test_prog = test_prog.clone(for_test=True)
train_program = fleet.main_program
exe = fluid.Executor(place)
exe.run(startup_prog)
if args.do_train:
if args.init_checkpoint and args.init_pretraining_params:
log.warning(
"WARNING: args 'init_checkpoint' and 'init_pretraining_params' "
"both are set! Only arg 'init_checkpoint' is made valid.")
if args.init_checkpoint:
init_checkpoint(exe,
args.init_checkpoint,
main_program=startup_prog)
elif args.init_pretraining_params:
init_pretraining_params(exe,
args.init_pretraining_params,
main_program=startup_prog)
elif args.do_val or args.do_test:
if not args.init_checkpoint:
raise ValueError("args 'init_checkpoint' should be set if"
"only doing validation or testing!")
init_checkpoint(exe, args.init_checkpoint, main_program=startup_prog)
if args.do_train:
train_exe = exe
train_pyreader.decorate_tensor_provider(train_data_generator)
else:
train_exe = None
test_exe = exe
# if args.do_val or args.do_test:
# if args.use_multi_gpu_test:
# test_exe = fluid.ParallelExecutor(
# use_cuda=args.use_cuda,
# main_program=test_prog,
# share_vars_from=train_exe)
current_epoch = 0
steps = 0
if args.do_train:
train_pyreader.start()
if warmup_steps > 0:
graph_vars["learning_rate"] = scheduled_lr
ce_info = []
time_begin = time.time()
last_epoch = 0
while True:
try:
steps += 1
# log.info("step: %d" % steps)
if fleet.worker_index() != 0:
train_exe.run(fetch_list=[], program=train_program)
continue
if steps % args.skip_steps != 0:
train_exe.run(fetch_list=[], program=train_program)
else:
outputs = evaluate(train_exe,
train_program,
train_pyreader,
graph_vars,
"train",
metric=args.metric)
if args.verbose:
verbose = "train pyreader queue size: %d, " % train_pyreader.queue.size(
)
verbose += "learning rate: %f" % (
outputs["learning_rate"]
if warmup_steps > 0 else args.learning_rate)
log.info(verbose)
current_example, current_epoch = reader.get_train_progress(
)
time_end = time.time()
used_time = time_end - time_begin
log.info(
"epoch: %d, progress: %d/%d, step: %d, ave loss: %f, "
"ave acc: %f, speed: %f steps/s" %
(current_epoch, current_example * dev_count,
num_train_examples, steps, outputs["loss"],
outputs["accuracy"], args.skip_steps / used_time))
ce_info.append(
[outputs["loss"], outputs["accuracy"], used_time])
time_begin = time.time()
if steps % args.save_steps == 0:
save_path = os.path.join(args.checkpoints,
"step_" + str(steps))
fluid.io.save_persistables(exe, save_path,
fleet._origin_program)
# if steps % args.validation_steps == 0 or last_epoch != current_epoch:
if steps % args.validation_steps == 0:
# evaluate dev set
if args.do_val:
evaluate_wrapper(args, reader, exe, test_prog,
test_pyreader, graph_vars,
current_epoch, steps)
if args.do_test:
predict_wrapper(args, reader, exe, test_prog,
test_pyreader, graph_vars,
current_epoch, steps)
if last_epoch != current_epoch:
last_epoch = current_epoch
except fluid.core.EOFException:
save_path = os.path.join(args.checkpoints,
"step_" + str(steps))
fluid.io.save_persistables(exe, save_path,
fleet._origin_program)
train_pyreader.reset()
break
# final eval on dev set
if args.do_val:
evaluate_wrapper(args, reader, exe, test_prog, test_pyreader,
graph_vars, current_epoch, steps)
# final eval on test set
if args.do_test:
predict_wrapper(args, reader, exe, test_prog, test_pyreader,
graph_vars, current_epoch, steps)
# final eval on dianostic, hack for glue-ax
if args.diagnostic:
test_pyreader.decorate_tensor_provider(
reader.data_generator(args.diagnostic,
batch_size=args.batch_size,
epoch=1,
dev_count=1,
shuffle=False))
log.info("Final diagnostic")
qids, preds, probs = predict(test_exe, test_prog, test_pyreader,
graph_vars)
assert len(qids) == len(preds), '{} v.s. {}'.format(
len(qids), len(preds))
with open(args.diagnostic_save, 'w') as f:
for id, s, p in zip(qids, preds, probs):
f.write('{}\t{}\t{}\n'.format(id, s, p))
log.info("Done final diagnostic, saving to {}".format(
args.diagnostic_save))
def train(args):
"""
Train main function.
"""
if args.is_distributed:
role = role_maker.PaddleCloudRoleMaker(is_collective=True)
fleet.init(role)
dev_count = fluid.core.get_cuda_device_count()
gpu_id = int(os.getenv("FLAGS_selected_gpus"))
trainers_num = fleet.worker_num()
trainer_id = fleet.worker_index()
else:
dev_count = 1
gpu_id = 0
trainers_num = 1
trainer_id = 0
place = fluid.CUDAPlace(gpu_id)
task = tasks.create_task(args)
model = models.create_model(args, place)
train_generator = task.get_data_loader(model,
input_file=args.train_file,
num_epochs=args.num_epochs,
num_part=trainers_num,
part_id=trainer_id,
phase="train")
valid_generator = task.get_data_loader(
model,
input_file=args.valid_file,
num_part=dev_count,
part_id=gpu_id,
phase="distributed_valid" if args.is_distributed else "valid")
# run training
timer = Timer()
timer.start()
if args.Model.model == 'NSPModel':
best_metrics = 0.0
else:
best_metrics = 10000
shuffledatafile()
for step, data in enumerate(train_generator(), args.start_step + 1):
outputs = task.train_step(model, data)
timer.pause()
if step % args.log_steps == 0:
time_cost = timer.pass_time
current_epoch, current_file_index, total_file = task.reader.get_train_progress(
)
print(
f"[train][{current_epoch}] progress: {current_file_index}/{total_file} "
f"step: {step}, time: {time_cost:.3f}, "
f"speed: {args.log_steps / time_cost:.3f} steps/s")
print(f"\tcurrent lr: {outputs.pop('scheduled_lr'):.7f}")
metrics = task.get_metrics(outputs)
print("\t" + ", ".join(f"{k}: {v:.4f}"
for k, v in metrics.items()))
timer.reset()
if step % args.validation_steps == 0:
# shuffledatafile()
metrics = evaluate(task, model, valid_generator, args, dev_count,
gpu_id, step)
if args.Model.model == 'NSPModel' and metrics[
'nsp_acc'] > best_metrics:
best_metrics = metrics['nsp_acc']
save_path = f"{args.save_path}/step_{step}_{best_metrics}"
model.save(save_path, is_checkpoint=True)
elif args.Model.model == 'Plato' and metrics['loss'] < best_metrics:
best_metrics = metrics['loss']
save_path = f"{args.save_path}/step_{step}_{best_metrics}"
model.save(save_path, is_checkpoint=True)
# if step % args.save_steps == 0 and trainer_id == 0:
# save_path = f"{args.save_path}/step_{step}"
# model.save(save_path, is_checkpoint=True)
# with open(save_path + ".finish", "w") as f:
# pass
timer.start()
def do_training(self, fleet, args):
"""
begin training.
Args:
fleet (Collective): Collective inherited base class Fleet
args (ArgumentParser): run args to config dist fleet.
Returns:
tuple: the value is train losses
"""
args = parse_args()
logging.info(args)
gpu_id = int(os.environ.get('FLAGS_selected_gpus', 4))
place = fluid.CUDAPlace(gpu_id)
dev_count = 1
exe = fluid.Executor(place)
train_program = fluid.Program()
startup_program = fluid.Program()
args.num_trainers = fleet.worker_num()
args.trainer_id = fleet.worker_index()
args.run_params = json.loads(args.run_params)
dist_strategy = DistributedStrategy()
dist_strategy.enable_inplace = args.run_params['enable_inplace']
dist_strategy.fuse_all_reduce_ops = args.run_params[
'fuse_all_reduce_ops']
dist_strategy.nccl_comm_num = args.run_params['nccl_comm_num']
dist_strategy.use_local_sgd = args.run_params['use_local_sgd']
dist_strategy.mode = args.run_params["mode"]
dist_strategy.collective_mode = args.run_params["collective"]
with fluid.program_guard(train_program, startup_program):
with fluid.unique_name.guard():
sum_cost, avg_cost, predict, token_num, pyreader = transformer(
ModelHyperParams.src_vocab_size,
ModelHyperParams.trg_vocab_size,
ModelHyperParams.max_length + 1,
ModelHyperParams.n_layer,
ModelHyperParams.n_head,
ModelHyperParams.d_key,
ModelHyperParams.d_value,
ModelHyperParams.d_model,
ModelHyperParams.d_inner_hid,
ModelHyperParams.prepostprocess_dropout,
ModelHyperParams.attention_dropout,
ModelHyperParams.relu_dropout,
ModelHyperParams.preprocess_cmd,
ModelHyperParams.postprocess_cmd,
ModelHyperParams.weight_sharing,
TrainTaskConfig.label_smooth_eps,
ModelHyperParams.bos_idx,
use_py_reader=args.use_py_reader,
is_test=False)
optimizer = fluid.optimizer.SGD(0.003)
if args.run_params["fp16"]:
optimizer = decorate(optimizer, init_loss_scaling=64.0)
optimizer = fleet.distributed_optimizer(optimizer,
strategy=dist_strategy)
optimizer.minimize(avg_cost, startup_program)
train_program = fleet.main_program
exe.run(startup_program)
train_data = prepare_data_generator(
args,
is_test=False,
count=dev_count,
pyreader=pyreader,
py_reader_provider_wrapper=py_reader_provider_wrapper)
loss_normalizer = -(
(1. - TrainTaskConfig.label_smooth_eps) * np.log(
(1. - TrainTaskConfig.label_smooth_eps)) +
TrainTaskConfig.label_smooth_eps *
np.log(TrainTaskConfig.label_smooth_eps /
(ModelHyperParams.trg_vocab_size - 1) + 1e-20))
step_idx = 0
init_flag = True
result_loss = []
result_ppl = []
train_info = []
for pass_id in six.moves.xrange(args.num_epochs):
pass_start_time = time.time()
if args.use_py_reader:
pyreader.start()
data_generator = None
else:
data_generator = train_data()
batch_id = 0
while True:
try:
feed_dict_list = prepare_feed_dict_list(
data_generator, init_flag, dev_count)
t1 = time.time()
outs = exe.run(program=train_program,
fetch_list=[sum_cost.name, token_num.name]
if step_idx % args.fetch_steps == 0 else [],
feed=feed_dict_list)
if step_idx % args.fetch_steps == 0:
sum_cost_val, token_num_val = np.array(
outs[0]), np.array(outs[1])
total_sum_cost = sum_cost_val.sum()
total_token_num = token_num_val.sum()
total_avg_cost = total_sum_cost / total_token_num
result_loss.append(total_avg_cost - loss_normalizer)
result_ppl.append(
np.exp([min(total_avg_cost, 100)]).item(0))
train_info.append(result_loss)
init_flag = False
batch_id += 1
step_idx += 1
if batch_id >= 5:
break
except (StopIteration, fluid.core.EOFException):
if args.use_py_reader:
pyreader.reset()
break
train_info = [round(i, 6) for i in train_info[0]]
return train_info
exe.run(start_prog)
train_prog = fleet.main_program
x_data = np.ones(shape=[1, 2], dtype=np.float32)
label_data = np.ones(shape=[1, 1], dtype=np.float32)
out = exe.run(train_prog,
feed={
'x': x_data,
'label': label_data
},
fetch_list=[loss.name])
# EXE testing
test_prog = fluid.Program()
with fluid.program_guard(test_prog):
y = fluid.data(name='y', shape=[-1, 1], dtype='float32')
v1 = fluid.layers.collective._c_allgather(y,
fleet.worker_num(),
use_calc_stream=True)
v2 = fluid.layers.collective._c_allreduce(y, use_calc_stream=True)
y_data = np.ones(shape=[1, 1], dtype=np.float32)
v1, v2 = exe.run(test_prog, feed={'y': y_data}, fetch_list=[v1.name, v2.name])
if role.worker_index() == 1:
time.sleep(1)
print ""
print 'rank:', role.worker_index()
print 'allgather:', v1
print 'allreduce:', v2
import os import paddle.fluid as fluid import numpy as np from paddle.fluid.incubate.fleet.collective import fleet, DistributedStrategy # new line 1 from paddle.fluid.incubate.fleet.base import role_maker # new line 2 role = role_maker.PaddleCloudRoleMaker(is_collective=True) # new line 3 fleet.init(role) # new line 4 x = fluid.data(name='x', shape=[-1, 2], dtype='float32') label = fluid.data(name='label', shape=[-1, 1], dtype='float32') y = fluid.layers.fc(x, size=1, param_attr=fluid.initializer.Constant(1.0)) fluid.layers.Print(y) # testing code v1 = fluid.layers.collective._c_allgather(y, fleet.worker_num(), use_calc_stream=True) v2 = fluid.layers.collective._c_allreduce(y, use_calc_stream=True) fluid.layers.Print(v1) fluid.layers.Print(v2) # end of testing code cost = fluid.layers.square_error_cost(y, label) loss = fluid.layers.reduce_sum(cost) optimizer = fluid.optimizer.SGD(learning_rate=0.0) strategy = DistributedStrategy() strategy.mode = "collective" strategy.collective_mode = "grad_allreduce" optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy) # new line 5 optimizer.minimize(loss, fluid.default_startup_program())
def main(args):
"""
Call the configuration function of the model, build the model and load data, then start training.
model_config:
a json file with the model configurations,such as dropout rate ,learning rate,num tasks and so on;
context_pooling:
it means the pooling type of context prediction;
PreGNNContextpredModel:
It is an unsupervised pretraining model which use subgraphs to predict their surrounding graph structures. Our goal is to pre-train a GNN so that it maps nodes appearing in similar structural contexts to nearby embeddings.
"""
model_config = json.load(open(args.model_config, 'r'))
if not args.dropout_rate is None:
model_config['dropout_rate'] = args.dropout_rate
model_config['context_pooling'] = args.context_pooling
### build model
train_prog = fluid.Program()
test_prog = fluid.Program()
startup_prog = fluid.Program()
with fluid.program_guard(train_prog, startup_prog):
with fluid.unique_name.guard():
model = PreGNNContextpredModel(model_config)
model.forward()
opt = fluid.optimizer.Adam(learning_rate=args.lr)
if args.distributed:
opt = get_distributed_optimizer(opt)
opt.minimize(model.loss)
with fluid.program_guard(test_prog, fluid.Program()):
with fluid.unique_name.guard():
model = PreGNNContextpredModel(model_config)
model.forward(is_test=True)
# Use CUDAPlace for GPU training, or use CPUPlace for CPU training.
place = fluid.CUDAPlace(int(os.environ.get('FLAGS_selected_gpus', 0))) \
if args.use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_prog)
if not args.init_model is None and not args.init_model == "":
load_partial_params(exe, args.init_model, train_prog)
### load data
# PreGNNContextPredFeaturizer:
# It is used along with `PreGNNContextPredModel`. It inherits from the super class `Featurizer` which is used for feature extractions. The `Featurizer` has two functions: `gen_features` for converting from a single raw smiles to a single graph data, `collate_fn` for aggregating a sublist of graph data into a big batch.
# k is the number of layer,l1 and l2 are the different size of context,usually l1 < l2.
# splitter:
# split type of the dataset:random,scaffold,random with scaffold. Here is randomsplit.
# `ScaffoldSplitter` will firstly order the compounds according to Bemis-Murcko scaffold,
# then take the first `frac_train` proportion as the train set, the next `frac_valid` proportion as the valid set
# and the rest as the test set. `ScaffoldSplitter` can better evaluate the generalization ability of the model on
# out-of-distribution samples. Note that other splitters like `RandomSplitter`, `RandomScaffoldSplitter`
# and `IndexSplitter` is also available."
k = model_config['layer_num']
l1 = k - 1
l2 = l1 + args.context_size
featurizer = PreGNNContextPredFeaturizer(
model.substruct_graph_wrapper,
model.context_graph_wrapper,
k, l1, l2)
dataset = load_zinc_dataset(args.data_path, featurizer=featurizer)
splitter = RandomSplitter()
train_dataset, _, test_dataset = splitter.split(
dataset, frac_train=0.9, frac_valid=0, frac_test=0.1)
if args.distributed:
indices = list(range(fleet.worker_index(), len(train_dataset), fleet.worker_num()))
train_dataset = train_dataset[indices]
print("Train/Test num: %s/%s" % (len(train_dataset), len(test_dataset)))
### start train
# Load the train function and calculate the train loss and test loss in each epoch.
# Here we set the epoch is in range of max epoch,you can change it if you want.
# Then we will calculate the train loss ,test loss and print them.
# Finally we save the best epoch to the model according to the dataset.
list_test_loss = []
for epoch_id in range(args.max_epoch):
train_loss = train(args, exe, train_prog, model, train_dataset, featurizer)
test_loss = evaluate(args, exe, test_prog, model, test_dataset, featurizer)
if not args.distributed or fleet.worker_index() == 0:
fluid.io.save_params(exe, '%s/epoch%s' % (args.model_dir, epoch_id), train_prog)
list_test_loss.append(test_loss)
print("epoch:%d train/loss:%s" % (epoch_id, train_loss))
print("epoch:%d test/loss:%s" % (epoch_id, test_loss))
if not args.distributed or fleet.worker_index() == 0:
best_epoch_id = np.argmin(list_test_loss)
fluid.io.load_params(exe, '%s/epoch%d' % (args.model_dir, best_epoch_id), train_prog)
fluid.io.save_params(exe, '%s/epoch_best' % (args.model_dir), train_prog)
return list_test_loss[best_epoch_id]
def train(args):
"""train start"""
logging.info(args)
gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
place = fluid.CUDAPlace(gpu_id)
dev_count = 1
exe = fluid.Executor(place)
train_program = fluid.Program()
startup_program = fluid.Program()
# For Distributed Training.
role = role_maker.PaddleCloudRoleMaker(is_collective=True)
fleet.init(role)
args.num_trainers = fleet.worker_num()
args.trainer_id = fleet.worker_index()
dist_strategy = DistributedStrategy()
with fluid.program_guard(train_program, startup_program):
with fluid.unique_name.guard():
sum_cost, avg_cost, predict, token_num, pyreader = transformer(
ModelHyperParams.src_vocab_size,
ModelHyperParams.trg_vocab_size,
ModelHyperParams.max_length + 1,
ModelHyperParams.n_layer,
ModelHyperParams.n_head,
ModelHyperParams.d_key,
ModelHyperParams.d_value,
ModelHyperParams.d_model,
ModelHyperParams.d_inner_hid,
ModelHyperParams.prepostprocess_dropout,
ModelHyperParams.attention_dropout,
ModelHyperParams.relu_dropout,
ModelHyperParams.preprocess_cmd,
ModelHyperParams.postprocess_cmd,
ModelHyperParams.weight_sharing,
TrainTaskConfig.label_smooth_eps,
ModelHyperParams.bos_idx,
use_py_reader=args.use_py_reader,
is_test=False)
optimizer = None
if args.sync:
lr_decay = fluid.layers.learning_rate_scheduler.noam_decay(
ModelHyperParams.d_model, TrainTaskConfig.warmup_steps)
with fluid.default_main_program()._lr_schedule_guard():
learning_rate = lr_decay * TrainTaskConfig.learning_rate
optimizer = fluid.optimizer.Adam(learning_rate=learning_rate,
beta1=TrainTaskConfig.beta1,
beta2=TrainTaskConfig.beta2,
epsilon=TrainTaskConfig.eps)
else:
optimizer = fluid.optimizer.SGD(0.003)
if args.use_fp16:
optimizer = decorate(optimizer,
init_loss_scaling=args.loss_scaling)
optimizer = fleet.distributed_optimizer(optimizer,
strategy=dist_strategy)
optimizer.minimize(avg_cost, startup_program)
train_program = fleet.main_program
orig_train_program = fleet._origin_program
train_loop(args, exe, train_program, orig_train_program, startup_program,
dev_count, sum_cost, avg_cost, token_num, predict, pyreader)
