def __init__(self, args):
self.eval_script = args.eval_script.split(" ")
self.eval_mertrics = args.eval_mertrics.split(
",") if args.eval_mertrics else []
self.basic_tokenizer = BasicTokenizer(do_lower_case=True)
self.roberta_tokenizer = GptBpeTokenizer(
vocab_file=args.unimo_vocab_file,
encoder_json_file=args.encoder_json_file,
vocab_bpe_file=args.vocab_bpe_file,
do_lower_case=True)
def main(args):
"""main"""
model_config = UNIMOConfig(args.unimo_config_path)
model_config.print_config()
gpu_id = 0
gpus = fluid.core.get_cuda_device_count()
if args.is_distributed and os.getenv("FLAGS_selected_gpus") is not None:
gpu_list = os.getenv("FLAGS_selected_gpus").split(",")
gpus = len(gpu_list)
gpu_id = int(gpu_list[0])
if args.use_cuda:
place = fluid.CUDAPlace(gpu_id)
dev_count = gpus
else:
place = fluid.CPUPlace()
dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
tokenizer = GptBpeTokenizer(vocab_file=args.unimo_vocab_file,
encoder_json_file=args.encoder_json_file,
vocab_bpe_file=args.vocab_bpe_file,
do_lower_case=args.do_lower_case)
data_reader = RegressionReader(tokenizer, args)
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.")
startup_prog = fluid.Program()
if args.random_seed is not None:
startup_prog.random_seed = args.random_seed
if args.do_train:
trainers_num = int(os.getenv("PADDLE_TRAINERS_NUM", "1"))
train_data_generator = data_reader.data_generator(
input_file=args.train_set,
batch_size=args.batch_size,
epoch=args.epoch,
dev_count=trainers_num,
shuffle=True,
phase="train")
num_train_examples = data_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) // trainers_num
else:
max_train_steps = args.epoch * num_train_examples // args.batch_size // trainers_num
warmup_steps = int(max_train_steps * args.warmup_proportion)
print("Device count: %d, gpu_id: %d" % (dev_count, gpu_id))
print("Num train examples: %d" % num_train_examples)
print("Max train steps: %d" % max_train_steps)
print("Num warmup steps: %d" % warmup_steps)
train_program = fluid.Program()
with fluid.program_guard(train_program, startup_prog):
with fluid.unique_name.guard():
train_pyreader, graph_vars = create_model(
args,
pyreader_name='train_reader',
config=model_config)
scheduled_lr, loss_scaling = optimization(
loss=graph_vars["loss"],
warmup_steps=warmup_steps,
num_train_steps=max_train_steps,
learning_rate=args.learning_rate,
train_program=train_program,
weight_decay=args.weight_decay,
scheduler=args.lr_scheduler,
use_fp16=args.use_fp16,
use_dynamic_loss_scaling=args.use_dynamic_loss_scaling,
init_loss_scaling=args.init_loss_scaling,
beta1=args.beta1,
beta2=args.beta2,
epsilon=args.epsilon)
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)
print("Theoretical memory usage in training: %.3f - %.3f %s" %
(lower_mem, upper_mem, unit))
if args.do_val or args.do_test or args.do_pred:
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',
config=model_config)
test_prog = test_prog.clone(for_test=True)
nccl2_num_trainers = 1
nccl2_trainer_id = 0
print("args.is_distributed:", args.is_distributed)
if args.is_distributed:
trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
worker_endpoints_env = os.getenv("PADDLE_TRAINER_ENDPOINTS")
current_endpoint = os.getenv("PADDLE_CURRENT_ENDPOINT")
worker_endpoints = worker_endpoints_env.split(",")
trainers_num = len(worker_endpoints)
print("worker_endpoints:{} trainers_num:{} current_endpoint:{} \
trainer_id:{}".format(worker_endpoints, trainers_num,
current_endpoint, trainer_id))
# prepare nccl2 env.
config = fluid.DistributeTranspilerConfig()
config.mode = "nccl2"
if args.nccl_comm_num > 1:
config.nccl_comm_num = args.nccl_comm_num
if args.use_hierarchical_allreduce and trainers_num > args.hierarchical_allreduce_inter_nranks:
config.use_hierarchical_allreduce = args.use_hierarchical_allreduce
config.hierarchical_allreduce_inter_nranks = args.hierarchical_allreduce_inter_nranks
assert config.hierarchical_allreduce_inter_nranks > 1
assert trainers_num % config.hierarchical_allreduce_inter_nranks == 0
config.hierarchical_allreduce_exter_nranks = \
trainers_num / config.hierarchical_allreduce_inter_nranks
t = fluid.DistributeTranspiler(config=config)
t.transpile(
trainer_id,
trainers=worker_endpoints_env,
current_endpoint=current_endpoint,
program=train_program if args.do_train else test_prog,
startup_program=startup_prog)
nccl2_num_trainers = trainers_num
nccl2_trainer_id = trainer_id
exe = fluid.Executor(place)
exe.run(startup_prog)
if args.do_train:
if args.init_checkpoint and args.init_pretraining_params:
print(
"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=train_program)
elif args.init_pretraining_params:
init_pretraining_params(
exe,
args.init_pretraining_params,
main_program=train_program)
elif args.do_val or args.do_test or args.do_pred:
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:
exec_strategy = fluid.ExecutionStrategy()
if args.use_fast_executor:
exec_strategy.use_experimental_executor = True
exec_strategy.num_threads = dev_count
exec_strategy.num_iteration_per_drop_scope = args.num_iteration_per_drop_scope
train_exe = fluid.ParallelExecutor(
use_cuda=args.use_cuda,
loss_name=graph_vars["loss"].name,
exec_strategy=exec_strategy,
main_program=train_program,
num_trainers=nccl2_num_trainers,
trainer_id=nccl2_trainer_id)
train_pyreader.decorate_tensor_provider(train_data_generator)
else:
train_exe = None
test_exe = exe
if args.do_val or args.do_test or args.do_pred:
if args.use_multi_gpu_test:
test_exe = fluid.ParallelExecutor(
use_cuda=args.use_cuda,
main_program=test_prog,
share_vars_from=train_exe)
dev_ret_history = [] # (steps, key_eval, eval)
if args.do_train:
train_pyreader.start()
steps = 0
if warmup_steps > 0:
graph_vars["learning_rate"] = scheduled_lr
time_begin = time.time()
skip_steps = args.skip_steps
while True:
try:
steps += 1
if steps % skip_steps == 0:
train_fetch_list = [
graph_vars["loss"].name,
]
if "learning_rate" in graph_vars:
train_fetch_list.append(graph_vars["learning_rate"].name)
res = train_exe.run(fetch_list=train_fetch_list)
outputs = {"loss": np.mean(res[0])}
if "learning_rate" in graph_vars:
outputs["learning_rate"] = float(res[1][0])
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)
print(verbose)
current_example, current_epoch = data_reader.get_train_progress()
time_end = time.time()
used_time = time_end - time_begin
print("%s - epoch: %d, progress: %d/%d, step: %d, ave loss: %f, speed: %f steps/s" % \
(get_time(), current_epoch, current_example, num_train_examples, steps, \
outputs["loss"], args.skip_steps / used_time))
time_begin = time.time()
else:
train_exe.run(fetch_list=[])
if nccl2_trainer_id == 0:
if steps % args.save_steps == 0 and args.save_checkpoints:
save_path = os.path.join(args.checkpoints,
"step_" + str(steps))
fluid.io.save_persistables(exe, save_path, train_program)
if steps % args.validation_steps == 0:
# evaluate dev set
if args.do_val:
test_pyreader.decorate_tensor_provider(
data_reader.data_generator(
args.dev_set,
batch_size=args.batch_size,
epoch=1,
dev_count=1,
shuffle=False))
outputs = evaluate(args, test_exe, test_prog, test_pyreader, graph_vars, "dev")
dev_ret_history.append((steps, outputs['key_eval'], outputs[outputs['key_eval']]))
# evaluate test set
if args.do_test:
test_pyreader.decorate_tensor_provider(
data_reader.data_generator(
args.test_set,
batch_size=args.batch_size,
epoch=1,
dev_count=1,
shuffle=False))
outputs = evaluate(args, test_exe, test_prog, test_pyreader, graph_vars, "test")
if args.do_pred:
test_pyreader.decorate_tensor_provider(
data_reader.data_generator(
args.test_set,
batch_size=args.batch_size,
epoch=1,
dev_count=1,
shuffle=False))
qids, preds, probs = predict(test_exe, test_prog, test_pyreader, graph_vars, dev_count=1)
save_path = args.pred_save + '.test.' + str(steps) + '.txt'
print("testing {}, save to {}".format(args.test_set, save_path))
with open(save_path, 'w') as f:
for id, s, p in zip(qids, preds, probs):
f.write('{}\t{}\t{}\n'.format(id, s, p))
except fluid.core.EOFException:
if args.save_checkpoints:
save_path = os.path.join(args.checkpoints, "step_" + str(steps))
fluid.io.save_persistables(exe, save_path, train_program)
train_pyreader.reset()
break
if nccl2_trainer_id == 0:
# final eval on dev set
if args.do_val:
test_pyreader.decorate_tensor_provider(
data_reader.data_generator(
args.dev_set,
batch_size=args.batch_size,
epoch=1,
dev_count=1,
shuffle=False))
print("Final validation result:")
outputs = evaluate(args, test_exe, test_prog, test_pyreader, graph_vars, "dev")
dev_ret_history.append((steps, outputs['key_eval'], outputs[outputs['key_eval']]))
dev_ret_history = sorted(dev_ret_history, key=lambda a: a[2], reverse=True)
print("Best validation result: step %d %s %f" \
% (dev_ret_history[0][0], dev_ret_history[0][1], dev_ret_history[0][2]))
# final eval on test set
if args.do_test:
test_pyreader.decorate_tensor_provider(
data_reader.data_generator(
args.test_set,
batch_size=args.batch_size,
epoch=1,
dev_count=1,
shuffle=False))
print("Final test result:")
outputs = evaluate(args, test_exe, test_prog, test_pyreader, graph_vars, "test")
# final eval on test set
if args.do_pred:
test_pyreader.decorate_tensor_provider(
data_reader.data_generator(
args.test_set,
batch_size=args.batch_size,
epoch=1,
dev_count=1,
shuffle=False))
qids, preds, probs = predict(test_exe, test_prog, test_pyreader, graph_vars, dev_count=1)
save_path = args.pred_save + '.' + str(steps) + '.txt'
print("testing {}, save to {}".format(args.test_set, save_path))
with open(save_path, 'w') as f:
for id, s, p in zip(qids, preds, probs):
f.write('{}\t{}\t{}\n'.format(id, s, p))
def main(args):
"""main"""
model_config = UNIMOConfig(args.unimo_config_path)
model_config.print_config()
gpu_id = 0
gpus = fluid.core.get_cuda_device_count()
if args.is_distributed and os.getenv("FLAGS_selected_gpus") is not None:
gpu_list = os.getenv("FLAGS_selected_gpus").split(",")
gpus = len(gpu_list)
gpu_id = int(gpu_list[0])
if args.use_cuda:
place = fluid.CUDAPlace(gpu_id)
dev_count = gpus
else:
place = fluid.CPUPlace()
dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
tokenizer = GptBpeTokenizer(vocab_file=args.unimo_vocab_file,
encoder_json_file=args.encoder_json_file,
vocab_bpe_file=args.vocab_bpe_file,
do_lower_case=args.do_lower_case)
if not (args.do_train or args.do_val or args.do_test or args.do_test_hard):
raise ValueError(
"For args `do_train`, `do_val`, `do_test`, `do_test_hard`, at "
"least one of them must be True.")
startup_prog = fluid.Program()
if args.random_seed is not None:
startup_prog.random_seed = args.random_seed
trainers_num = int(os.getenv("PADDLE_TRAINERS_NUM", "1"))
if args.do_train:
train_data_reader = ClassifyReader(args.train_filelist,
args.max_seq_len, tokenizer)
train_data_generator = train_data_reader.data_generator(
batch_size=args.batch_size, epoch=args.epoch, phase="train")
if args.num_train_examples:
num_train_examples = args.num_train_examples
else:
num_train_examples = train_data_reader.get_num_examples()
step_num_per_epoch = num_train_examples // args.batch_size // trainers_num
max_train_steps = args.epoch * step_num_per_epoch
warmup_steps = int(max_train_steps * args.warmup_proportion)
print("Device count: %d, gpu_id: %d" % (dev_count, gpu_id))
print("Num train examples: %d" % num_train_examples)
print("Max train steps: %d" % max_train_steps)
print("Num warmup steps: %d" % warmup_steps)
train_program = fluid.Program()
with fluid.program_guard(train_program, startup_prog):
with fluid.unique_name.guard():
train_pyreader, graph_vars = create_model(
args,
config=model_config,
pyreader_name="train_reader",
is_train=True)
scheduled_lr, loss_scaling = optimization(
loss=graph_vars["loss"],
warmup_steps=warmup_steps,
num_train_steps=max_train_steps,
learning_rate=args.learning_rate,
train_program=train_program,
weight_decay=args.weight_decay,
scheduler=args.lr_scheduler,
use_fp16=args.use_fp16,
use_dynamic_loss_scaling=args.use_dynamic_loss_scaling,
init_loss_scaling=args.init_loss_scaling,
beta1=args.beta1,
beta2=args.beta2,
epsilon=args.epsilon)
if args.do_val or args.do_test or args.do_test_hard:
test_prog = fluid.Program()
with fluid.program_guard(test_prog, startup_prog):
with fluid.unique_name.guard():
test_pyreader, test_graph_vars = create_model(
args,
config=model_config,
pyreader_name="dev_reader",
is_train=False)
test_prog = test_prog.clone(for_test=True)
if args.do_val:
dev_data_reader = ClassifyReader(args.dev_filelist,
args.max_seq_len, tokenizer)
dev_data_generator = dev_data_reader.data_generator(
batch_size=args.test_batch_size, epoch=1, phase="dev")
if args.do_test:
test_data_reader = ClassifyReader(args.test_filelist,
args.max_seq_len, tokenizer)
test_data_generator = test_data_reader.data_generator(
batch_size=args.test_batch_size, epoch=1, phase="test")
if args.do_test_hard:
test_hard_data_reader = ClassifyReader(args.test_hard_filelist,
args.max_seq_len, tokenizer)
test_hard_data_generator = test_hard_data_reader.data_generator(
batch_size=args.test_batch_size, epoch=1, phase="test_hard")
nccl2_num_trainers = 1
nccl2_trainer_id = 0
print("args.is_distributed:", args.is_distributed)
if args.is_distributed:
trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
worker_endpoints_env = os.getenv("PADDLE_TRAINER_ENDPOINTS")
current_endpoint = os.getenv("PADDLE_CURRENT_ENDPOINT")
worker_endpoints = worker_endpoints_env.split(",")
trainers_num = len(worker_endpoints)
print("worker_endpoints:{} trainers_num:{} current_endpoint:{} \
trainer_id:{}".format(worker_endpoints, trainers_num,
current_endpoint, trainer_id))
# prepare nccl2 env.
config = fluid.DistributeTranspilerConfig()
config.mode = "nccl2"
if args.nccl_comm_num > 1:
config.nccl_comm_num = args.nccl_comm_num
if args.use_hierarchical_allreduce and trainers_num > args.hierarchical_allreduce_inter_nranks:
config.use_hierarchical_allreduce = args.use_hierarchical_allreduce
config.hierarchical_allreduce_inter_nranks = args.hierarchical_allreduce_inter_nranks
assert config.hierarchical_allreduce_inter_nranks > 1
assert trainers_num % config.hierarchical_allreduce_inter_nranks == 0
config.hierarchical_allreduce_exter_nranks = \
trainers_num / config.hierarchical_allreduce_inter_nranks
t = fluid.DistributeTranspiler(config=config)
t.transpile(trainer_id,
trainers=worker_endpoints_env,
current_endpoint=current_endpoint,
program=train_program if args.do_train else test_prog,
startup_program=startup_prog)
nccl2_num_trainers = trainers_num
nccl2_trainer_id = trainer_id
exe = fluid.Executor(place)
exe.run(startup_prog)
if args.do_train:
if args.init_checkpoint and args.init_pretraining_params:
print(
"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=train_program)
elif args.init_pretraining_params:
init_pretraining_params(exe,
args.init_pretraining_params,
main_program=train_program)
elif args.do_val or args.do_test or args.do_test_hard:
args.init_checkpoint = args.init_pretraining_params
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:
exec_strategy = fluid.ExecutionStrategy()
if args.use_fast_executor:
exec_strategy.use_experimental_executor = True
exec_strategy.num_threads = 4 if args.use_fp16 else 2
exec_strategy.num_iteration_per_drop_scope = min(
args.num_iteration_per_drop_scope, args.skip_steps)
build_strategy = fluid.BuildStrategy()
build_strategy.remove_unnecessary_lock = False
if args.use_fuse:
build_strategy.fuse_all_reduce_ops = True
train_exe = fluid.ParallelExecutor(use_cuda=args.use_cuda,
loss_name=graph_vars["loss"].name,
build_strategy=build_strategy,
exec_strategy=exec_strategy,
main_program=train_program,
num_trainers=nccl2_num_trainers,
trainer_id=nccl2_trainer_id)
train_pyreader.decorate_tensor_provider(train_data_generator)
else:
train_exe = None
if args.do_val or args.do_test or args.do_test_hard:
test_exe = fluid.ParallelExecutor(use_cuda=args.use_cuda,
main_program=test_prog,
share_vars_from=train_exe)
dev_ret_history = [] # (steps, key_eval, eval)
test_ret_history = [] # (steps, key_eval, eval)
test_hard_ret_history = [] # (steps, key_eval, eval)
steps = 0
if args.do_train:
train_pyreader.start()
time_begin = time.time()
skip_steps = args.skip_steps
while True:
try:
steps += 1
if steps % skip_steps == 0:
train_fetch_list = [
graph_vars["loss"].name, scheduled_lr.name
]
res = train_exe.run(fetch_list=train_fetch_list)
outputs = {
"loss": np.mean(res[0]),
'learning_rate': float(res[1][0])
}
if args.verbose:
verbose = "train pyreader queue size: %d, learning_rate: %.10f" % \
(train_pyreader.queue.size(), outputs['learning_rate'])
print(verbose)
current_epoch, current_example, current_file_index, total_file, current_file = \
train_data_reader.get_progress()
time_end = time.time()
used_time = time_end - time_begin
print("%s - epoch: %d, progress: %d/%d, %d/%d, step: %d, ave loss: %f, speed: %f steps/s" % \
(get_time(), current_epoch, current_example, num_train_examples, current_file_index, \
total_file, steps, outputs["loss"], args.skip_steps / used_time))
time_begin = time.time()
else:
train_exe.run(fetch_list=[])
if nccl2_trainer_id == 0:
if steps % args.save_steps == 0 and args.save_checkpoints:
save_path = os.path.join(args.checkpoints,
"step_" + str(steps))
fluid.io.save_persistables(exe, save_path,
train_program)
if steps % args.validation_steps == 0:
# evaluate dev set
if args.do_val:
test_pyreader.decorate_tensor_provider(
dev_data_generator)
outputs = evaluate(args, test_exe, test_pyreader, test_graph_vars, \
"dev", trainers_num, nccl2_trainer_id)
if nccl2_trainer_id == 0:
dev_ret_history.append(
(steps, outputs['key_eval'],
outputs[outputs['key_eval']]))
# evaluate test set
if args.do_test:
test_pyreader.decorate_tensor_provider(
test_data_generator)
outputs = evaluate(args, test_exe, test_pyreader, test_graph_vars, \
"test", trainers_num, nccl2_trainer_id)
if nccl2_trainer_id == 0:
test_ret_history.append(
(steps, outputs['key_eval'],
outputs[outputs['key_eval']]))
# evaluate test set
if args.do_test_hard:
test_pyreader.decorate_tensor_provider(
test_hard_data_generator)
outputs = evaluate(args, test_exe, test_pyreader, test_graph_vars, \
"test_hard", trainers_num, nccl2_trainer_id)
if nccl2_trainer_id == 0:
test_hard_ret_history.append(
(steps, outputs['key_eval'],
outputs[outputs['key_eval']]))
except fluid.core.EOFException:
if args.save_checkpoints:
save_path = os.path.join(args.checkpoints,
"step_" + str(steps))
fluid.io.save_persistables(exe, save_path, train_program)
train_pyreader.reset()
break
# final eval on dev set
if args.do_val:
test_pyreader.decorate_tensor_provider(dev_data_generator)
outputs = evaluate(args, test_exe, test_pyreader, test_graph_vars,
"dev", trainers_num, nccl2_trainer_id)
if nccl2_trainer_id == 0:
dev_ret_history.append(
(steps, outputs['key_eval'], outputs[outputs['key_eval']]))
# final eval on test set
if args.do_test:
test_pyreader.decorate_tensor_provider(test_data_generator)
outputs = evaluate(args, test_exe, test_pyreader, test_graph_vars,
"test", trainers_num, nccl2_trainer_id)
if nccl2_trainer_id == 0:
test_ret_history.append(
(steps, outputs['key_eval'], outputs[outputs['key_eval']]))
# final eval on test_hard set
if args.do_test_hard:
test_pyreader.decorate_tensor_provider(test_hard_data_generator)
outputs = evaluate(args, test_exe, test_pyreader, test_graph_vars,
"test_hard", trainers_num, nccl2_trainer_id)
if nccl2_trainer_id == 0:
test_hard_ret_history.append(
(steps, outputs['key_eval'], outputs[outputs['key_eval']]))
if nccl2_trainer_id == 0:
if args.do_val:
dev_ret_history = sorted(dev_ret_history,
key=lambda a: a[2],
reverse=True)
print("Best validation result: step %d %s %f" % \
(dev_ret_history[0][0], dev_ret_history[0][1], dev_ret_history[0][2]))
def main(args):
"""main func"""
unimo_config = UNIMOConfig(args.unimo_config_path)
if args.task_type == "dialog":
unimo_config["role_type_size"] = args.role_type_size
unimo_config["turn_type_size"] = args.turn_type_size
if args.hidden_dropout_prob >= 0:
unimo_config["hidden_dropout_prob"] = args.hidden_dropout_prob
if args.attention_probs_dropout_prob >= 0:
unimo_config[
"attention_probs_dropout_prob"] = args.attention_probs_dropout_prob
unimo_config.print_config()
if args.pred_batch_size <= 0:
args.pred_batch_size = args.batch_size
gpu_id = 0
gpus = fluid.core.get_cuda_device_count()
if args.is_distributed and os.getenv("FLAGS_selected_gpus") is not None:
gpu_list = os.getenv("FLAGS_selected_gpus").split(",")
gpus = len(gpu_list)
gpu_id = int(gpu_list[0])
if args.use_cuda:
place = fluid.CUDAPlace(gpu_id)
dev_count = gpus
else:
place = fluid.CPUPlace()
dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
"""load vocabulary"""
tokenizer = GptBpeTokenizer(vocab_file=args.unimo_vocab_file,
encoder_json_file=args.encoder_json_file,
vocab_bpe_file=args.vocab_bpe_file,
do_lower_case=True)
reader = Seq2SeqReader(tokenizer, args)
unimo_seq2seq = Seq2Seq(args, unimo_config, tokenizer)
if not (args.do_train or args.do_val or args.do_test or args.do_pred):
raise ValueError("For args `do_train`, `do_val` and `do_test`, at "
"least one of them must be True.")
startup_prog = fluid.Program()
if args.random_seed is not None:
startup_prog.random_seed = args.random_seed
if args.do_train:
trainers_num = int(os.getenv("PADDLE_TRAINERS_NUM", 1))
train_data_generator = reader.data_generator(
input_file=args.train_set,
batch_size=args.batch_size,
epoch=args.epoch,
dev_count=trainers_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) // trainers_num
else:
max_train_steps = args.epoch * num_train_examples // args.batch_size // trainers_num
warmup_steps = int(max_train_steps * args.warmup_proportion)
print("Device count: %d, gpu_id: %d" % (dev_count, gpu_id))
print("Num train examples: %d" % num_train_examples)
print("Max train steps: %d" % max_train_steps)
print("Num warmup steps: %d" % warmup_steps)
train_program = fluid.Program()
with fluid.program_guard(train_program, startup_prog):
with fluid.unique_name.guard():
train_pyreader, graph_vars = unimo_seq2seq.create_model()
scheduled_lr, loss_scaling = optimization(
loss=graph_vars["loss"],
warmup_steps=warmup_steps,
num_train_steps=max_train_steps,
learning_rate=args.learning_rate,
train_program=train_program,
weight_decay=args.weight_decay,
scheduler=args.lr_scheduler,
use_fp16=args.use_fp16,
use_dynamic_loss_scaling=args.use_dynamic_loss_scaling,
init_loss_scaling=args.init_loss_scaling,
beta1=args.beta1,
beta2=args.beta2,
epsilon=args.epsilon)
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)
print("Theoretical memory usage in training: %.3f - %.3f %s" %
(lower_mem, upper_mem, unit))
if args.do_val or args.do_test or args.do_pred:
test_prog = fluid.Program()
with fluid.program_guard(test_prog, startup_prog):
with fluid.unique_name.guard():
test_pyreader, test_graph_vars = unimo_seq2seq.create_model(
decoding=args.do_decode)
test_prog = test_prog.clone(for_test=True)
nccl2_num_trainers = 1
nccl2_trainer_id = 0
print("args.is_distributed:", args.is_distributed)
if args.is_distributed:
trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
worker_endpoints_env = os.getenv("PADDLE_TRAINER_ENDPOINTS")
current_endpoint = os.getenv("PADDLE_CURRENT_ENDPOINT")
worker_endpoints = worker_endpoints_env.split(",")
trainers_num = len(worker_endpoints)
print("worker_endpoints:{} trainers_num:{} current_endpoint:{} \
trainer_id:{}".format(worker_endpoints, trainers_num,
current_endpoint, trainer_id))
# prepare nccl2 env.
config = fluid.DistributeTranspilerConfig()
config.mode = "nccl2"
if args.nccl_comm_num > 1:
config.nccl_comm_num = args.nccl_comm_num
if args.use_hierarchical_allreduce and trainers_num > args.hierarchical_allreduce_inter_nranks:
config.use_hierarchical_allreduce = args.use_hierarchical_allreduce
config.hierarchical_allreduce_inter_nranks = args.hierarchical_allreduce_inter_nranks
assert config.hierarchical_allreduce_inter_nranks > 1
assert trainers_num % config.hierarchical_allreduce_inter_nranks == 0
config.hierarchical_allreduce_exter_nranks = \
trainers_num / config.hierarchical_allreduce_inter_nranks
t = fluid.DistributeTranspiler(config=config)
t.transpile(trainer_id,
trainers=worker_endpoints_env,
current_endpoint=current_endpoint,
program=train_program if args.do_train else test_prog,
startup_program=startup_prog)
nccl2_num_trainers = trainers_num
nccl2_trainer_id = trainer_id
exe = fluid.Executor(place)
exe.run(startup_prog)
init_model(args, exe, train_program if args.do_train else test_prog)
if args.do_train:
exec_strategy = fluid.ExecutionStrategy()
if args.use_fast_executor:
exec_strategy.use_experimental_executor = True
exec_strategy.num_threads = 4 if args.use_fp16 else 2 # 2 for fp32 4 for fp16
exec_strategy.num_iteration_per_drop_scope = min(
args.num_iteration_per_drop_scope, args.skip_steps)
build_strategy = fluid.BuildStrategy()
build_strategy.remove_unnecessary_lock = False
if args.use_fuse:
build_strategy.fuse_all_reduce_ops = True
train_exe = fluid.ParallelExecutor(use_cuda=args.use_cuda,
loss_name=graph_vars["loss"].name,
build_strategy=build_strategy,
exec_strategy=exec_strategy,
main_program=train_program,
num_trainers=nccl2_num_trainers,
trainer_id=nccl2_trainer_id)
train_pyreader.set_batch_generator(train_data_generator)
train_resource = {
"exe": train_exe,
"program": train_program,
"pyreader": train_pyreader
}
save_model = partial(save_checkpoint, program=train_program, exe=exe)
test_dev_count = 1
if args.do_val or args.do_test or args.do_pred:
test_exe = exe
if args.use_multi_gpu_test:
test_dev_count = nccl2_num_trainers
test_resource = {
"exe": test_exe,
"program": test_prog,
"pyreader": test_pyreader
}
eval_data_generator = partial(reader.data_generator,
batch_size=args.pred_batch_size,
epoch=1,
dev_count=test_dev_count,
shuffle=False,
do_decode=args.do_decode,
place=place)
eval_func = partial(unimo_seq2seq.evaluate,
resource=test_resource,
graph_vars=test_graph_vars,
dev_count=test_dev_count,
output_path=args.checkpoints,
gpu_id=nccl2_trainer_id)
evaluate = partial(evaluate_datasets,
pyreader=test_pyreader,
reader=reader,
eval_func=eval_func,
data_generator=eval_data_generator)
if args.do_train:
train_pyreader.start()
steps = 0
last_epoch = 0
if warmup_steps > 0:
graph_vars["learning_rate"] = scheduled_lr
time_begin = time.time()
skip_steps = args.skip_steps
while True:
try:
steps += 1
if args.save_and_valid_by_epoch:
suffix = "epoch_" + str(last_epoch)
else:
suffix = "step_" + str(steps)
if steps % skip_steps == 0:
outputs = unimo_seq2seq.evaluate(train_resource, "train",
graph_vars)
if args.verbose:
verbose = "train pyreader queue size: %d, " % train_pyreader.queue.size(
)
verbose += "learning rate: %.8f" % (
outputs["learning_rate"]
if warmup_steps > 0 else args.learning_rate)
print(verbose)
if args.in_tokens:
current_example, current_epoch = reader.get_train_progress(
)
else:
current_epoch = steps * args.batch_size * trainers_num // num_train_examples
current_example = steps * args.batch_size * trainers_num % num_train_examples
time_end = time.time()
used_time = time_end - time_begin
print("epoch: %d, progress: %d/%d, step: %d, loss: %f, "
"ppl: %f, speed: %f steps/s" %
(current_epoch, current_example, num_train_examples,
steps, outputs["loss"], outputs["ppl"],
args.skip_steps / used_time))
time_begin = time.time()
if args.visualdl_log and nccl2_trainer_id == 0:
visuallog_dict = OrderedDict()
visuallog_dict["ppl"] = outputs["ppl"]
visualdl_log(visuallog_dict,
outputs["ppl"],
steps,
phase='train')
else:
train_exe.run(fetch_list=[])
if nccl2_trainer_id >= test_dev_count:
continue
do_save = False
do_eval = False
if not args.save_and_valid_by_epoch:
if steps % args.save_steps == 0 and nccl2_trainer_id == 0:
do_save = True
if steps % args.validation_steps == 0:
do_eval = True
else:
if args.in_tokens:
current_example, current_epoch = reader.get_train_progress(
)
else:
current_epoch = steps * args.batch_size * trainers_num // num_train_examples
if current_epoch != last_epoch:
if nccl2_trainer_id == 0:
do_save = True
do_eval = True
if do_save:
save_model(suffix=suffix)
if do_eval:
evaluate(suffix=suffix)
if args.save_and_valid_by_epoch:
last_epoch = current_epoch
except fluid.core.EOFException:
save_model(suffix=suffix)
train_pyreader.reset()
break
if nccl2_trainer_id >= test_dev_count:
return
if args.do_val or args.do_test or args.do_pred:
suffix = "output"
if args.do_train:
if not args.save_and_valid_by_epoch:
suffix = "step_" + str(steps)
else:
suffix = "epoch_" + str(last_epoch)
evaluate(suffix=suffix, do_pred=True)