def main(args):
ernie_config = ErnieConfig(args.ernie_config_path)
ernie_config.print_config()
if args.use_cuda:
place = fluid.CUDAPlace(int(os.getenv('FLAGS_selected_gpus', '0')))
dev_count = fluid.core.get_cuda_device_count()
else:
place = fluid.CPUPlace()
dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
exe = fluid.Executor(place)
reader = task_reader.MRCReader(vocab_path=args.vocab_path,
label_map_config=args.label_map_config,
max_seq_len=args.max_seq_len,
do_lower_case=args.do_lower_case,
in_tokens=args.in_tokens,
random_seed=args.random_seed,
tokenizer=args.tokenizer,
is_classify=args.is_classify,
is_regression=args.is_regression,
for_cn=args.for_cn,
task_id=args.task_id,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length)
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.predict_batch_size == None:
args.predict_batch_size = args.batch_size
if args.do_train:
train_data_generator = reader.data_generator(
input_file=args.train_set,
batch_size=args.batch_size,
epoch=args.epoch,
dev_count=dev_count,
shuffle=True,
phase="train")
num_train_examples = reader.get_num_examples("train")
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)
print("Device count: %d" % dev_count)
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',
ernie_config=ernie_config,
is_training=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,
startup_prog=startup_prog,
weight_decay=args.weight_decay,
scheduler=args.lr_scheduler,
use_fp16=args.use_fp16)
"""
fluid.memory_optimize(
input_program=train_program,
skip_opt_set=[
graph_vars["loss"].name,
graph_vars["num_seqs"].name,
])
"""
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:
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,
pyreader_name='test_reader',
ernie_config=ernie_config,
is_training=False)
test_prog = test_prog.clone(for_test=True)
nccl2_num_trainers = 1
nccl2_trainer_id = 0
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=startup_prog,
use_fp16=args.use_fp16)
elif args.init_pretraining_params:
init_pretraining_params(exe,
args.init_pretraining_params,
main_program=startup_prog,
use_fp16=args.use_fp16)
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,
use_fp16=args.use_fp16)
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
if args.do_train:
train_pyreader.start()
steps = 0
if warmup_steps > 0:
graph_vars["learning_rate"] = scheduled_lr
time_begin = time.time()
while True:
try:
steps += 1
if steps % args.skip_steps != 0:
train_exe.run(fetch_list=[])
else:
outputs = evaluate(train_exe, train_program,
train_pyreader, graph_vars, "train")
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 = reader.get_train_progress(
)
time_end = time.time()
used_time = time_end - time_begin
print(
"epoch: %d, progress: %d/%d, step: %d, ave loss: %f, "
"speed: %f steps/s" %
(current_epoch, current_example, num_train_examples,
steps, outputs["loss"], args.skip_steps / 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, train_program)
if steps % args.validation_steps == 0:
if args.do_val:
test_pyreader.decorate_tensor_provider(
reader.data_generator(args.dev_set,
batch_size=args.batch_size,
epoch=1,
dev_count=1,
shuffle=False,
phase="dev"))
evaluate(exe,
test_prog,
test_pyreader,
test_graph_vars,
str(steps) + "_dev",
examples=reader.get_examples("dev"),
features=reader.get_features("dev"),
args=args)
if args.do_test:
test_pyreader.decorate_tensor_provider(
reader.data_generator(args.test_set,
batch_size=args.batch_size,
epoch=1,
dev_count=1,
shuffle=False,
phase="test"))
evaluate(exe,
test_prog,
test_pyreader,
test_graph_vars,
str(steps) + "_test",
examples=reader.get_examples("test"),
features=reader.get_features("test"),
args=args)
except fluid.core.EOFException:
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:
print("Final validation result:")
test_pyreader.decorate_tensor_provider(
reader.data_generator(args.dev_set,
batch_size=args.batch_size,
epoch=1,
dev_count=1,
shuffle=False,
phase="dev"))
evaluate(exe,
test_prog,
test_pyreader,
test_graph_vars,
"dev",
examples=reader.get_examples("dev"),
features=reader.get_features("dev"),
args=args)
# final eval on test set
if args.do_test:
print("Final test result:")
test_pyreader.decorate_tensor_provider(
reader.data_generator(args.test_set,
batch_size=args.batch_size,
epoch=1,
dev_count=1,
shuffle=False,
phase="test"))
evaluate(exe,
test_prog,
test_pyreader,
test_graph_vars,
"test",
examples=reader.get_examples("test"),
features=reader.get_features("test"),
args=args)
def abc(test_examples = [],init_check=''):
s_t = str (time.localtime()) #取名用
if init_check != '':
args.init_checkpoint = init_check
args.ernie_config_path = 'config/ernie_config1.json'
if test_examples == []: #想直接输入examples曾出现异常无法使用，废弃保留代码
while True:
break
Example = namedtuple('Example', ['qas_id', 'question_text', 'doc_tokens','orig_answer_text', 'start_position', 'end_position'])
example = Example(
qas_id = s_t + str(ii) ,
question_text=que,
doc_tokens= tokenization.tokenize_chinese_chars(para),
orig_answer_text=None,
start_position=None,
end_position=None)
test_examples.append(example)
break
reader = task_reader.MRCReader(
vocab_path=args.vocab_path,
label_map_config=args.label_map_config,
max_seq_len=args.max_seq_len,
do_lower_case=args.do_lower_case,
in_tokens=args.in_tokens,
random_seed=args.random_seed,
tokenizer=args.tokenizer,
is_classify=args.is_classify,
is_regression=args.is_regression,
for_cn=args.for_cn,
task_id=args.task_id,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length)
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=startup_prog,
use_fp16=args.use_fp16)
elif args.init_pretraining_params:
init_pretraining_params(
exe,
args.init_pretraining_params,
main_program=startup_prog,
use_fp16=args.use_fp16)
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,
use_fp16=args.use_fp16)
test_pyreader.decorate_tensor_provider(
reader.data_generator(
args.test_set,
batch_size=args.batch_size,
epoch=1,
dev_count=1,
shuffle=False,
phase="test"))
print(reader.get_examples("test"))
mrc_result = evaluate(
exe,
test_prog,
test_pyreader,
test_graph_vars,
"test",
examples=reader.get_examples("test"),
features=reader.get_features("test"),
# examples = test_examples,
# features=reader._convert_example_to_feature(examples=test_examples,max_seq_length=512,tokenizer=tokenization.FullTokenizer(
# vocab_file='config/vocab.txt', do_lower_case=True),is_training =False),
args=args)
print('abc:mrc:return',len(mrc_result[1]))
return mrc_result