def from_local_dir():
print('From_local_dir:--------------------------')
# From local dir path
tokenizer = AutoTokenizer.from_pretrained(('saved_model/my_bert'))
print(tokenizer("Welcome to use PaddlePaddle and PaddleNLP!"))
tokenizer = AutoTokenizer.from_pretrained(('saved_model/my_bart'))
print(tokenizer("Welcome to use PaddlePaddle and PaddleNLP!"))
tokenizer = AutoTokenizer.from_pretrained(('saved_model/my_bigbird'))
print(tokenizer("Welcome to use PaddlePaddle and PaddleNLP!"))
def initialize(self, args):
"""`initialize` is called only once when the model is being loaded.
Implementing `initialize` function is optional. This function allows
the model to intialize any state associated with this model.
Parameters
----------
args : dict
Both keys and values are strings. The dictionary keys and values are:
* model_config: A JSON string containing the model configuration
* model_instance_kind: A string containing model instance kind
* model_instance_device_id: A string containing model instance device ID
* model_repository: Model repository path
* model_version: Model version
* model_name: Model name
"""
self.tokenizer = AutoTokenizer.from_pretrained("ernie-3.0-medium-zh",
use_faster=True)
# You must parse model_config. JSON string is not parsed here
self.model_config = json.loads(args['model_config'])
print("model_config:", self.model_config)
self.input_names = []
for input_config in self.model_config["input"]:
self.input_names.append(input_config["name"])
print("input:", self.input_names)
self.output_names = []
self.output_dtype = []
for output_config in self.model_config["output"]:
self.output_names.append(output_config["name"])
dtype = pb_utils.triton_string_to_numpy(output_config["data_type"])
self.output_dtype.append(dtype)
print("output:", self.output_names)
def __init__(self, args):
if not isinstance(args.device, six.string_types):
print(
">>> [InferBackend] The type of device must be string, but the type you set is: ",
type(device))
exit(0)
args.device = args.device.lower()
if args.device not in ['cpu', 'gpu']:
print(
">>> [InferBackend] The device must be cpu or gpu, but your device is set to:",
type(args.device))
exit(0)
self.task_name = args.task_name
self.tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path,
use_faster=True)
if args.task_name == 'seq_cls':
self.label_names = []
self.preprocess = self.seq_cls_preprocess
self.postprocess = self.seq_cls_postprocess
self.printer = seq_cls_print_ret
elif args.task_name == 'token_cls':
self.label_names = [
'O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC'
]
self.preprocess = self.token_cls_preprocess
self.postprocess = self.token_cls_postprocess
self.printer = token_cls_print_ret
else:
print(
"[ErniePredictor]: task_name only support seq_cls and token_cls now."
)
exit(0)
self.max_seq_length = args.max_seq_length
if args.device == 'cpu':
args.use_fp16 = False
args.set_dynamic_shape = False
args.batch_size = 32
args.shape_info_file = None
if args.device == 'gpu':
args.num_threads = cpu_count()
args.use_quantize = False
self.inference_backend = InferBackend(
args.model_path,
batch_size=args.batch_size,
device=args.device,
use_fp16=args.use_fp16,
use_quantize=args.use_quantize,
set_dynamic_shape=args.set_dynamic_shape,
shape_info_file=args.shape_info_file,
num_threads=args.num_threads)
if args.set_dynamic_shape:
# If set_dynamic_shape is turned on, all required dynamic shapes will be automatically set according to the batch_size and max_seq_length.
self.set_dynamic_shape(args.max_seq_length, args.batch_size)
exit(0)
def do_eval(args):
paddle.set_device(args.device)
if paddle.distributed.get_world_size() > 1:
paddle.distributed.init_parallel_env()
set_seed(args)
args.task_name = args.task_name.lower()
metric_class = METRIC_CLASSES[args.task_name]
dev_ds = load_dataset('clue', args.task_name, splits='dev')
tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
trans_func = partial(
convert_example,
label_list=dev_ds.label_list,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length)
dev_ds = dev_ds.map(trans_func, lazy=True)
dev_batch_sampler = paddle.io.BatchSampler(
dev_ds, batch_size=args.batch_size, shuffle=False)
batchify_fn = lambda samples, fn=Tuple(
Pad(axis=0, pad_val=tokenizer.pad_token_id), # input
Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # segment
Stack(dtype="int64" if dev_ds.label_list else "float32") # label
): fn(samples)
dev_data_loader = DataLoader(
dataset=dev_ds,
batch_sampler=dev_batch_sampler,
collate_fn=batchify_fn,
num_workers=0,
return_list=True)
num_classes = 1 if dev_ds.label_list == None else len(dev_ds.label_list)
model = AutoModelForSequenceClassification.from_pretrained(
args.model_name_or_path, num_classes=num_classes)
if paddle.distributed.get_world_size() > 1:
model = paddle.DataParallel(model)
metric = metric_class()
best_acc = 0.0
global_step = 0
tic_train = time.time()
model.eval()
metric.reset()
for batch in dev_data_loader:
input_ids, segment_ids, labels = batch
logits = model(input_ids, segment_ids)
correct = metric.compute(logits, labels)
metric.update(correct)
res = metric.accumulate()
print("acc: %s\n, " % (res), end='')
def from_community_models():
print('From_community_models:-------------------')
# From community-contributed pretrained models
tokenizer = AutoTokenizer.from_pretrained(
'yingyibiao/bert-base-uncased-sst-2-finetuned')
print(tokenizer("Welcome to use PaddlePaddle and PaddleNLP!"))
tokenizer.save_pretrained('saved_tokenizer/community_bert_auto')
tokenizer = BertTokenizer.from_pretrained(
'yingyibiao/bert-base-uncased-sst-2-finetuned')
print(tokenizer("Welcome to use PaddlePaddle and PaddleNLP!"))
tokenizer.save_pretrained('saved_tokenizer/community_bert')
# community without init_class
tokenizer = AutoTokenizer.from_pretrained(
'junnyu/ckiplab-bert-base-chinese-ner')
print(tokenizer("Welcome to use PaddlePaddle and PaddleNLP!"))
tokenizer = BertTokenizer.from_pretrained(
'junnyu/ckiplab-bert-base-chinese-ner')
print(tokenizer("Welcome to use PaddlePaddle and PaddleNLP!"))
def do_predict(args):
paddle.set_device(args.device)
args.task_name = args.task_name.lower()
train_ds, test_ds = load_dataset(
'clue', args.task_name, splits=('train', 'test'))
tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
trans_func = partial(
convert_example,
tokenizer=tokenizer,
label_list=train_ds.label_list,
max_seq_length=args.max_seq_length,
is_test=True)
batchify_fn = lambda samples, fn=Tuple(
Pad(axis=0, pad_val=tokenizer.pad_token_id), # input
Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # segment
): fn(samples)
test_ds = test_ds.map(trans_func, lazy=True)
test_batch_sampler = paddle.io.BatchSampler(
test_ds, batch_size=args.batch_size, shuffle=False)
test_data_loader = DataLoader(
dataset=test_ds,
batch_sampler=test_batch_sampler,
collate_fn=batchify_fn,
num_workers=0,
return_list=True)
num_classes = 1 if train_ds.label_list == None else len(train_ds.label_list)
model = AutoModelForSequenceClassification.from_pretrained(
args.model_name_or_path, num_classes=num_classes)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
if args.task_name == 'ocnli':
args.task_name = 'ocnli_50k'
f = open(
os.path.join(args.output_dir, args.task_name + "_predict.json"), 'w')
for step, batch in enumerate(test_data_loader):
input_ids, segment_ids = batch
with paddle.no_grad():
logits = model(input_ids, segment_ids)
preds = paddle.argmax(logits, axis=1)
for idx, pred in enumerate(preds):
j = json.dumps({"id": idx, "label": train_ds.label_list[pred]})
f.write(j + "\n")
def init_op(self):
from paddlenlp.transformers import AutoTokenizer
self.tokenizer = AutoTokenizer.from_pretrained("ernie-3.0-medium-zh",
use_faster=True)
# The label names of NER models trained by different data sets may be different
self.label_names = [
'O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC'
]
# Output nodes may differ from model to model
# You can see the output node name in the conf.prototxt file of serving_server
self.fetch_names = [
"linear_113.tmp_1",
]
def from_built_in_models():
print('From_built_in_models:------------------')
# From built-in pretrained models
tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
print(tokenizer("Welcome to use PaddlePaddle and PaddleNLP!"))
tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
print(tokenizer("Welcome to use PaddlePaddle and PaddleNLP!"))
tokenizer = AutoTokenizer.from_pretrained('ernie-ctm')
print(tokenizer("Welcome to use PaddlePaddle and PaddleNLP!"))
tokenizer = AutoTokenizer.from_pretrained('plato-mini')
print(tokenizer("Welcome to use PaddlePaddle and PaddleNLP!"))
tokenizer = UnifiedTransformerTokenizer.from_pretrained('plato-mini')
print(tokenizer("Welcome to use PaddlePaddle and PaddleNLP!"))
tokenizer = AutoTokenizer.from_pretrained('bigbird-base-uncased')
print(tokenizer("Welcome to use PaddlePaddle and PaddleNLP!"))
tokenizer = BigBirdTokenizer.from_pretrained('bigbird-base-uncased')
print(tokenizer("Welcome to use PaddlePaddle and PaddleNLP!"))
def seg(args):
tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path,
do_lower_case=True)
seg_file(
os.path.join(args.output_dir, args.data_split + ".txt.tmp"),
tokenizer,
args.max_len,
)
seg_file(
os.path.join(args.output_dir, args.data_split + "_box.txt.tmp"),
tokenizer,
args.max_len,
)
seg_file(
os.path.join(args.output_dir, args.data_split + "_image.txt.tmp"),
tokenizer,
args.max_len,
)
def __init__(self, args):
if not isinstance(args.device, six.string_types):
print(
">>> [InferBackend] The type of device must be string, but the type you set is: ",
type(device))
exit(0)
if args.device not in ['cpu', 'gpu']:
print(
">>> [InferBackend] The device must be cpu or gpu, but your device is set to:",
type(args.device))
exit(0)
self._tokenizer = AutoTokenizer.from_pretrained("ernie-3.0-base-zh",
use_faster=True)
self._position_prob = args.position_prob
self._max_seq_len = args.max_seq_len
self._schema_tree = None
self.set_schema(args.schema)
if args.device == 'cpu':
args.use_fp16 = False
self.inference_backend = InferBackend(args.model_path_prefix,
device=args.device,
use_fp16=args.use_fp16)
def __init__(self, args):
self.task_name = args.task_name
self.tokenizer = AutoTokenizer.from_pretrained(
args.model_name_or_path, use_faster=True)
if args.task_name == 'seq_cls':
self.label_names = []
self.preprocess = self.seq_cls_preprocess
self.postprocess = self.seq_cls_postprocess
self.printer = seq_cls_print_ret
elif args.task_name == 'token_cls':
self.label_names = [
'O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC'
]
self.preprocess = self.token_cls_preprocess
self.postprocess = self.token_cls_postprocess
self.printer = token_cls_print_ret
else:
print(
"[ErniePredictor]: task_name only support seq_cls and token_cls now."
)
exit(0)
self.max_seq_length = args.max_seq_length
self.inference_backend = InferBackend(args.model_path, args.use_fp16)
def do_eval():
tokenizer = AutoTokenizer.from_pretrained(args.model_path)
model = UIE.from_pretrained(args.model_path)
test_ds = load_dataset(reader,
data_path=args.test_path,
max_seq_len=args.max_seq_len,
lazy=False)
test_ds = test_ds.map(
partial(convert_example,
tokenizer=tokenizer,
max_seq_len=args.max_seq_len))
test_batch_sampler = paddle.io.BatchSampler(dataset=test_ds,
batch_size=args.batch_size,
shuffle=False)
test_data_loader = paddle.io.DataLoader(dataset=test_ds,
batch_sampler=test_batch_sampler,
return_list=True)
metric = SpanEvaluator()
precision, recall, f1 = evaluate(model, metric, test_data_loader)
logger.info("Evaluation precision: %.5f, recall: %.5f, F1: %.5f" %
(precision, recall, f1))
def do_train():
parser = PdArgumentParser(
(ModelArguments, DataTrainingArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
paddle.set_device(training_args.device)
if paddle.distributed.get_world_size() > 1:
paddle.distributed.init_parallel_env()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, world_size: {training_args.world_size}, "
+
f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(
training_args.output_dir
) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(
training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome.")
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# set_seed(args)
data_args.dataset = data_args.dataset.strip()
if data_args.dataset not in ALL_DATASETS:
raise ValueError("Not found dataset {}".format(data_args.dataset))
# Use yaml config to rewrite all args.
config = ALL_DATASETS[data_args.dataset]
for args in (model_args, data_args, training_args):
for arg in vars(args):
if arg in config.keys():
setattr(args, arg, config[arg])
training_args.per_device_train_batch_size = config["batch_size"]
training_args.per_device_eval_batch_size = config["batch_size"]
dataset_config = data_args.dataset.split(" ")
all_ds = load_dataset(
dataset_config[0],
None if len(dataset_config) <= 1 else dataset_config[1],
)
label_list = getattr(all_ds['train'], "label_list", None)
data_args.label_list = label_list
data_args.ignore_label = -100
data_args.no_entity_id = len(data_args.label_list) - 1
num_classes = 1 if all_ds["train"].label_list == None else len(
all_ds['train'].label_list)
# Define tokenizer, model, loss function.
tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path)
model = AutoModelForTokenClassification.from_pretrained(
model_args.model_name_or_path, num_classes=num_classes)
class criterion(nn.Layer):
def __init__(self):
super(criterion, self).__init__()
self.loss_fn = paddle.nn.loss.CrossEntropyLoss(
ignore_index=data_args.ignore_label)
def forward(self, *args, **kwargs):
return paddle.mean(self.loss_fn(*args, **kwargs))
loss_fct = criterion()
# Define dataset pre-process function
trans_fn = partial(ner_trans_fn, tokenizer=tokenizer, args=data_args)
# Define data collector
batchify_fn = ner_collator(tokenizer, data_args)
# Dataset pre-process
train_dataset = all_ds["train"].map(trans_fn)
eval_dataset = all_ds["dev"].map(trans_fn)
test_dataset = all_ds["test"].map(trans_fn)
# Define the metrics of tasks.
# Metrics
metric = load_metric("seqeval")
def compute_metrics(p):
predictions, labels = p
predictions = np.argmax(predictions, axis=2)
# Remove ignored index (special tokens)
true_predictions = [[
label_list[p] for (p, l) in zip(prediction, label) if l != -100
] for prediction, label in zip(predictions, labels)]
true_labels = [[
label_list[l] for (p, l) in zip(prediction, label) if l != -100
] for prediction, label in zip(predictions, labels)]
results = metric.compute(predictions=true_predictions,
references=true_labels)
return {
"precision": results["overall_precision"],
"recall": results["overall_recall"],
"f1": results["overall_f1"],
"accuracy": results["overall_accuracy"],
}
trainer = Trainer(
model=model,
criterion=loss_fct,
args=training_args,
data_collator=batchify_fn,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
tokenizer=tokenizer,
compute_metrics=compute_metrics,
)
# Log model and data config
trainer.print_config(model_args, "Model")
trainer.print_config(data_args, "Data")
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
# Training
train_result = trainer.train(resume_from_checkpoint=checkpoint)
metrics = train_result.metrics
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# Evaluate and tests model
eval_metrics = trainer.evaluate()
trainer.log_metrics("eval", eval_metrics)
test_ret = trainer.predict(test_dataset)
trainer.log_metrics("test", test_ret.metrics)
if test_ret.label_ids is None:
paddle.save(
test_ret.predictions,
os.path.join(training_args.output_dir, "test_results.pdtensor"),
)
# export inference model
input_spec = [
paddle.static.InputSpec(shape=[None, None],
dtype="int64"), # input_ids
paddle.static.InputSpec(shape=[None, None],
dtype="int64") # segment_ids
]
trainer.export_model(input_spec=input_spec,
load_best_model=True,
output_dir=model_args.export_model_dir)
def main():
parser = PdArgumentParser(
(ModelArguments, DataArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
paddle.set_device(training_args.device)
data_args.dataset = data_args.dataset.strip()
if data_args.dataset not in ALL_DATASETS:
raise ValueError("Not found dataset {}".format(data_args.dataset))
if data_args.dataset in ALL_DATASETS:
# if you custom you hyper-parameters in yaml config, it will overwrite all args.
config = ALL_DATASETS[data_args.dataset]
for args in (model_args, data_args, training_args):
for arg in vars(args):
if arg in config.keys():
setattr(args, arg, config[arg])
training_args.per_device_train_batch_size = config["batch_size"]
training_args.per_device_eval_batch_size = config["batch_size"]
# Log model and data config
training_args.print_config(model_args, "Model")
training_args.print_config(data_args, "Data")
dataset_config = data_args.dataset.split(" ")
raw_datasets = load_dataset(
dataset_config[0],
None if len(dataset_config) <= 1 else dataset_config[1],
)
label_list = raw_datasets['train'].features['ner_tags'].feature.names
data_args.label_list = label_list
data_args.ignore_label = -100
data_args.no_entity_id = 0
num_classes = 1 if label_list == None else len(label_list)
# Define tokenizer, model, loss function.
tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path)
model = AutoModelForTokenClassification.from_pretrained(
model_args.model_name_or_path, num_classes=num_classes)
class criterion(nn.Layer):
def __init__(self):
super(criterion, self).__init__()
self.loss_fn = paddle.nn.loss.CrossEntropyLoss(
ignore_index=data_args.ignore_label)
def forward(self, *args, **kwargs):
return paddle.mean(self.loss_fn(*args, **kwargs))
loss_fct = criterion()
# Define dataset pre-process function
trans_fn = partial(ner_trans_fn, tokenizer=tokenizer, args=data_args)
# Define data collector
data_collator = DataCollatorForTokenClassification(
tokenizer, label_pad_token_id=data_args.ignore_label)
column_names = raw_datasets["train"].column_names
# Dataset pre-process
train_dataset = raw_datasets["train"].map(trans_fn,
remove_columns=column_names)
train_dataset.label_list = label_list
eval_dataset = raw_datasets["test"].map(trans_fn,
remove_columns=column_names)
trainer = Trainer(model=model,
criterion=loss_fct,
args=training_args,
data_collator=data_collator,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
tokenizer=tokenizer)
output_dir = os.path.join(model_args.model_name_or_path, "compress")
if not os.path.exists(output_dir):
os.makedirs(output_dir)
compress_config = CompressConfig(quantization_config=PTQConfig(
algo_list=['hist', 'mse'], batch_size_list=[4, 8, 16]))
trainer.compress(data_args.dataset,
output_dir,
pruning=True,
quantization=True,
compress_config=compress_config)
def run(args):
paddle.set_device(args.device)
if paddle.distributed.get_world_size() > 1:
paddle.distributed.init_parallel_env()
raw_datasets = load_dataset(args.task_name)
tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
train_ds = raw_datasets['train']
column_names = train_ds.column_names
label_list = train_ds.features['ner_tags'].feature.names
label_num = len(label_list)
batchify_fn = DataCollatorForTokenClassification(tokenizer=tokenizer)
# Define the model netword and its loss
model = AutoModelForTokenClassification.from_pretrained(
args.model_name_or_path, num_classes=label_num)
if paddle.distributed.get_world_size() > 1:
model = paddle.DataParallel(model)
def tokenize_and_align_labels(examples, no_entity_id=0):
tokenized_inputs = tokenizer(
examples['tokens'],
max_seq_len=args.max_seq_length,
# We use this argument because the texts in our dataset are lists of words (with a label for each word).
is_split_into_words=True,
return_length=True)
labels = []
for i, label in enumerate(examples['ner_tags']):
label_ids = label
if len(tokenized_inputs['input_ids'][i]) - 2 < len(label_ids):
label_ids = label_ids[:len(tokenized_inputs['input_ids'][i]) -
2]
label_ids = [no_entity_id] + label_ids + [no_entity_id]
label_ids += [no_entity_id] * (
len(tokenized_inputs['input_ids'][i]) - len(label_ids))
labels.append(label_ids)
tokenized_inputs["labels"] = labels
return tokenized_inputs
test_ds = raw_datasets['test']
test_ds = test_ds.select(range(len(test_ds) - 1))
test_ds = test_ds.map(tokenize_and_align_labels,
batched=True,
remove_columns=column_names)
test_data_loader = DataLoader(dataset=test_ds,
collate_fn=batchify_fn,
num_workers=0,
batch_size=args.batch_size,
return_list=True)
if args.do_train:
train_ds = train_ds.select(range(len(train_ds) - 1))
train_ds = train_ds.map(tokenize_and_align_labels,
batched=True,
remove_columns=column_names)
train_batch_sampler = paddle.io.DistributedBatchSampler(
train_ds, batch_size=args.batch_size, shuffle=True, drop_last=True)
train_data_loader = DataLoader(dataset=train_ds,
collate_fn=batchify_fn,
num_workers=0,
batch_sampler=train_batch_sampler,
return_list=True)
num_training_steps = args.max_steps if args.max_steps > 0 else len(
train_data_loader) * args.num_train_epochs
lr_scheduler = LinearDecayWithWarmup(args.learning_rate,
num_training_steps,
args.warmup_steps)
# Generate parameter names needed to perform weight decay.
# All bias and LayerNorm parameters are excluded.
decay_params = [
p.name for n, p in model.named_parameters()
if not any(nd in n for nd in ["bias", "norm"])
]
optimizer = paddle.optimizer.AdamW(
learning_rate=lr_scheduler,
epsilon=args.adam_epsilon,
parameters=model.parameters(),
weight_decay=args.weight_decay,
apply_decay_param_fun=lambda x: x in decay_params)
loss_fct = paddle.nn.loss.CrossEntropyLoss()
metric = ChunkEvaluator(label_list=label_list)
global_step = 0
best_f1 = 0.0
last_step = args.num_train_epochs * len(train_data_loader)
tic_train = time.time()
for epoch in range(args.num_train_epochs):
for step, batch in enumerate(train_data_loader):
global_step += 1
logits = model(batch['input_ids'], batch['token_type_ids'])
loss = loss_fct(logits, batch['labels'])
avg_loss = paddle.mean(loss)
if global_step % args.logging_steps == 0:
print(
"global step %d, epoch: %d, batch: %d, loss: %f, speed: %.2f step/s"
% (global_step, epoch, step, avg_loss,
args.logging_steps / (time.time() - tic_train)))
tic_train = time.time()
avg_loss.backward()
optimizer.step()
lr_scheduler.step()
optimizer.clear_grad()
if global_step % args.save_steps == 0 or global_step == num_training_steps:
if paddle.distributed.get_rank() == 0:
f1 = evaluate(model, loss_fct, metric,
test_data_loader, label_num, "test")
if f1 > best_f1:
best_f1 = f1
output_dir = args.output_dir
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Need better way to get inner model of DataParallel
model_to_save = model._layers if isinstance(
model, paddle.DataParallel) else model
model_to_save.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
if global_step >= num_training_steps:
print("best_f1: ", best_f1)
return
print("best_f1: ", best_f1)
if args.do_eval:
eval_data_loader = DataLoader(dataset=test_ds,
collate_fn=batchify_fn,
num_workers=0,
batch_size=args.batch_size,
return_list=True)
# Define the model netword and its loss
model = AutoModelForTokenClassification.from_pretrained(
args.model_name_or_path, num_classes=label_num)
loss_fct = paddle.nn.loss.CrossEntropyLoss()
metric = ChunkEvaluator(label_list=label_list)
model.eval()
metric.reset()
for step, batch in enumerate(eval_data_loader):
logits = model(batch["input_ids"], batch["token_type_ids"])
loss = loss_fct(logits, batch["labels"])
avg_loss = paddle.mean(loss)
preds = logits.argmax(axis=2)
num_infer_chunks, num_label_chunks, num_correct_chunks = metric.compute(
batch["length"], preds, batch["labels"])
metric.update(num_infer_chunks.numpy(), num_label_chunks.numpy(),
num_correct_chunks.numpy())
precision, recall, f1_score = metric.accumulate()
print("eval loss: %f, precision: %f, recall: %f, f1: %f" %
(avg_loss, precision, recall, f1_score))
def main():
parser = PdArgumentParser(
(ModelArguments, DataArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
paddle.set_device(training_args.device)
data_args.dataset = data_args.dataset.strip()
if data_args.dataset in ALL_DATASETS:
# if you custom you hyper-parameters in yaml config, it will overwrite all args.
config = ALL_DATASETS[data_args.dataset]
logger.info("Over-writing training config by yaml config!")
for args in (model_args, data_args, training_args):
for arg in vars(args):
if arg in config.keys():
setattr(args, arg, config[arg])
training_args.per_device_train_batch_size = config["batch_size"]
training_args.per_device_eval_batch_size = config["batch_size"]
# Log model and data config
training_args.print_config(model_args, "Model")
training_args.print_config(data_args, "Data")
dataset_config = data_args.dataset.split(" ")
raw_datasets = load_dataset(
dataset_config[0],
None if len(dataset_config) <= 1 else dataset_config[1],
splits=("train", "dev", "test"))
data_args.label_list = getattr(raw_datasets['train'], "label_list", None)
num_classes = 1 if raw_datasets["train"].label_list == None else len(
raw_datasets['train'].label_list)
criterion = paddle.nn.CrossEntropyLoss()
# Define tokenizer, model, loss function.
tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path)
model = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path, num_classes=num_classes)
# Define dataset pre-process function
if "clue" in data_args.dataset:
trans_fn = partial(clue_trans_fn, tokenizer=tokenizer, args=data_args)
else:
trans_fn = partial(seq_trans_fn, tokenizer=tokenizer, args=data_args)
# Define data collector
data_collator = DataCollatorWithPadding(tokenizer)
train_dataset = raw_datasets["train"].map(trans_fn)
eval_dataset = raw_datasets["dev"].map(trans_fn)
trainer = Trainer(model=model,
args=training_args,
data_collator=data_collator,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
tokenizer=tokenizer,
criterion=criterion)
output_dir = os.path.join(model_args.model_name_or_path, "compress")
if not os.path.exists(output_dir):
os.makedirs(output_dir)
compress_config = CompressConfig(quantization_config=PTQConfig(
algo_list=['hist', 'mse'], batch_size_list=[4, 8, 16]))
trainer.compress(data_args.dataset,
output_dir,
pruning=True,
quantization=True,
compress_config=compress_config)
def do_train(args):
paddle.set_device(args.device)
if paddle.distributed.get_world_size() > 1:
paddle.distributed.init_parallel_env()
set_seed(args)
args.task_name = args.task_name.lower()
metric_class = METRIC_CLASSES[args.task_name]
train_ds, dev_ds = load_dataset(
'clue', args.task_name, splits=('train', 'dev'))
tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
trans_func = partial(
convert_example,
label_list=train_ds.label_list,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length)
train_ds = train_ds.map(trans_func, lazy=True)
train_batch_sampler = paddle.io.DistributedBatchSampler(
train_ds, batch_size=args.batch_size, shuffle=True)
dev_ds = dev_ds.map(trans_func, lazy=True)
dev_batch_sampler = paddle.io.BatchSampler(
dev_ds, batch_size=args.batch_size, shuffle=False)
batchify_fn = lambda samples, fn=Tuple(
Pad(axis=0, pad_val=tokenizer.pad_token_id), # input
Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # segment
Stack(dtype="int64" if train_ds.label_list else "float32") # label
): fn(samples)
train_data_loader = DataLoader(
dataset=train_ds,
batch_sampler=train_batch_sampler,
collate_fn=batchify_fn,
num_workers=0,
return_list=True)
dev_data_loader = DataLoader(
dataset=dev_ds,
batch_sampler=dev_batch_sampler,
collate_fn=batchify_fn,
num_workers=0,
return_list=True)
num_classes = 1 if train_ds.label_list == None else len(train_ds.label_list)
model = AutoModelForSequenceClassification.from_pretrained(
args.model_name_or_path, num_classes=num_classes)
if paddle.distributed.get_world_size() > 1:
model = paddle.DataParallel(model)
if args.max_steps > 0:
num_training_steps = args.max_steps
num_train_epochs = math.ceil(num_training_steps /
len(train_data_loader))
else:
num_training_steps = len(train_data_loader) * args.num_train_epochs
num_train_epochs = args.num_train_epochs
warmup = args.warmup_steps if args.warmup_steps > 0 else args.warmup_proportion
lr_scheduler = LinearDecayWithWarmup(args.learning_rate, num_training_steps,
warmup)
# Generate parameter names needed to perform weight decay.
# All bias and LayerNorm parameters are excluded.
decay_params = [
p.name for n, p in model.named_parameters()
if not any(nd in n for nd in ["bias", "norm"])
]
optimizer = paddle.optimizer.AdamW(
learning_rate=lr_scheduler,
beta1=0.9,
beta2=0.999,
epsilon=args.adam_epsilon,
parameters=model.parameters(),
weight_decay=args.weight_decay,
apply_decay_param_fun=lambda x: x in decay_params,
grad_clip=nn.ClipGradByGlobalNorm(args.max_grad_norm))
loss_fct = paddle.nn.loss.CrossEntropyLoss(
) if train_ds.label_list else paddle.nn.loss.MSELoss()
metric = metric_class()
best_acc = 0.0
global_step = 0
tic_train = time.time()
for epoch in range(num_train_epochs):
for step, batch in enumerate(train_data_loader):
global_step += 1
input_ids, segment_ids, labels = batch
logits = model(input_ids, segment_ids)
loss = loss_fct(logits, labels)
loss.backward()
optimizer.step()
lr_scheduler.step()
optimizer.clear_grad()
if global_step % args.logging_steps == 0:
print(
"global step %d/%d, epoch: %d, batch: %d, rank_id: %s, loss: %f, lr: %.10f, speed: %.4f step/s"
% (global_step, num_training_steps, epoch, step,
paddle.distributed.get_rank(), loss, optimizer.get_lr(),
args.logging_steps / (time.time() - tic_train)))
tic_train = time.time()
if global_step % args.save_steps == 0 or global_step == num_training_steps:
tic_eval = time.time()
acc = evaluate(model, loss_fct, metric, dev_data_loader)
print("eval done total : %s s" % (time.time() - tic_eval))
if acc > best_acc:
best_acc = acc
output_dir = args.output_dir
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Need better way to get inner model of DataParallel
model_to_save = model._layers if isinstance(
model, paddle.DataParallel) else model
model_to_save.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
if global_step >= num_training_steps:
print("best_acc: ", best_acc)
return
print("best_acc: ", best_acc)
def do_train():
paddle.set_device(args.device)
rank = paddle.distributed.get_rank()
if paddle.distributed.get_world_size() > 1:
paddle.distributed.init_parallel_env()
set_seed(args.seed)
train_ds = load_dataset(
read_text_pair, data_path=args.train_set_file,is_test=False, lazy=False)
model_name_or_path='rocketqa-zh-dureader-query-encoder'
pretrained_model = AutoModel.from_pretrained(
model_name_or_path,
hidden_dropout_prob=args.dropout,
attention_probs_dropout_prob=args.dropout)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
trans_func = partial(
convert_example,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length)
batchify_fn = lambda samples, fn=Tuple(
Pad(axis=0, pad_val=tokenizer.pad_token_id, dtype="int64"), # query_input
Pad(axis=0, pad_val=tokenizer.pad_token_type_id, dtype="int64"), # query_segment
Pad(axis=0, pad_val=tokenizer.pad_token_id, dtype="int64"), # title_input
Pad(axis=0, pad_val=tokenizer.pad_token_type_id, dtype="int64"), # tilte_segment
): [data for data in fn(samples)]
train_data_loader = create_dataloader(
train_ds,
mode='train',
batch_size=args.batch_size,
batchify_fn=batchify_fn,
trans_fn=trans_func)
model = SimCSE(
pretrained_model,
margin=args.margin,
scale=args.scale,
output_emb_size=args.output_emb_size)
if args.init_from_ckpt and os.path.isfile(args.init_from_ckpt):
state_dict = paddle.load(args.init_from_ckpt)
model.set_dict(state_dict)
print("warmup from:{}".format(args.init_from_ckpt))
model = paddle.DataParallel(model)
num_training_steps = args.max_steps if args.max_steps > 0 else len(
train_data_loader) * args.epochs
lr_scheduler = LinearDecayWithWarmup(args.learning_rate, num_training_steps,
args.warmup_proportion)
# Generate parameter names needed to perform weight decay.
# All bias and LayerNorm parameters are excluded.
decay_params = [
p.name for n, p in model.named_parameters()
if not any(nd in n for nd in ["bias", "norm"])
]
optimizer = paddle.optimizer.AdamW(
learning_rate=lr_scheduler,
parameters=model.parameters(),
weight_decay=args.weight_decay,
apply_decay_param_fun=lambda x: x in decay_params)
global_step = 0
tic_train = time.time()
for epoch in range(1, args.epochs + 1):
for step, batch in enumerate(train_data_loader, start=1):
query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids = batch
if(random.random()<0.2):
title_input_ids,title_token_type_ids=query_input_ids,query_token_type_ids
query_input_ids,query_token_type_ids=word_repetition(query_input_ids,query_token_type_ids,args.dup_rate)
title_input_ids,title_token_type_ids=word_repetition(title_input_ids,title_token_type_ids,args.dup_rate)
loss, kl_loss = model(
query_input_ids=query_input_ids,
title_input_ids=title_input_ids,
query_token_type_ids=query_token_type_ids,
title_token_type_ids=title_token_type_ids)
loss = loss + kl_loss * args.rdrop_coef
global_step += 1
if global_step % 10 == 0 and rank == 0:
print(
"global step %d, epoch: %d, batch: %d, loss: %.5f, speed: %.2f step/s"
% (global_step, epoch, step, loss,
10 / (time.time() - tic_train)))
tic_train = time.time()
loss.backward()
optimizer.step()
lr_scheduler.step()
optimizer.clear_grad()
if global_step % args.save_steps == 0 and rank == 0:
save_dir = os.path.join(args.save_dir, "model_%d" % global_step)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
save_param_path = os.path.join(save_dir, 'model_state.pdparams')
paddle.save(model.state_dict(), save_param_path)
tokenizer.save_pretrained(save_dir)
if args.max_steps > 0 and global_step >= args.max_steps:
return
save_dir = os.path.join(args.save_dir, "model_%d" % global_step)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
save_param_path = os.path.join(save_dir, 'model_state.pdparams')
paddle.save(model.state_dict(), save_param_path)
tokenizer.save_pretrained(save_dir)
# yapf: disable
parser = argparse.ArgumentParser()
parser.add_argument("--params_path", type=str, required=True,
default='./checkpoint/model_50/model_state.pdparams', help="The path to model parameters to be loaded.")
parser.add_argument("--output_path", type=str, default='./output',
help="The path of model parameter in static graph to be saved.")
args = parser.parse_args()
# yapf: enable
if __name__ == "__main__":
# If you want to use ernie1.0 model, plesace uncomment the following code
output_emb_size = 256
pretrained_model = AutoModel.from_pretrained("ernie-1.0")
tokenizer = AutoTokenizer.from_pretrained('ernie-1.0')
model = SimCSE(pretrained_model, output_emb_size=output_emb_size)
if args.params_path and os.path.isfile(args.params_path):
state_dict = paddle.load(args.params_path)
model.set_dict(state_dict)
print("Loaded parameters from %s" % args.params_path)
model.eval()
# Convert to static graph with specific input description
model = paddle.jit.to_static(
model,
input_spec=[
paddle.static.InputSpec(shape=[None, None],
dtype="int64"), # input_ids
paddle.static.InputSpec(shape=[None, None],
def run(args):
max_seq_length = args.max_seq_length
max_num_choices = 4
def preprocess_function(examples, do_predict=False):
def _truncate_seq_tuple(tokens_a, tokens_b, tokens_c, max_length):
"""Truncates a sequence tuple in place to the maximum length."""
# This is a simple heuristic which will always truncate the longer
# sequence one token at a time. This makes more sense than
# truncating an equal percent of tokens from each, since if one
# sequence is very short then each token that's truncated likely
# contains more information than a longer sequence.
while True:
total_length = len(tokens_a) + len(tokens_b) + len(tokens_c)
if total_length <= max_length:
break
if len(tokens_a) >= len(tokens_b) and len(tokens_a) >= len(
tokens_c):
tokens_a.pop()
elif len(tokens_b) >= len(tokens_a) and len(tokens_b) >= len(
tokens_c):
tokens_b.pop()
else:
tokens_c.pop()
num_examples = len(examples.data["question"])
if do_predict:
result = {"input_ids": [], "token_type_ids": []}
else:
result = {"input_ids": [], "token_type_ids": [], "labels": []}
for idx in range(num_examples):
text = '\n'.join(examples.data["context"][idx]).lower()
question = examples.data["question"][idx].lower()
choice_list = examples.data["choice"][idx]
choice_list = [choice.lower() for choice in choice_list]
if not do_predict:
answer = examples.data["answer"][idx].lower()
label = choice_list.index(answer)
tokens_t = tokenizer.tokenize(text)
tokens_q = tokenizer.tokenize(question)
tokens_t_list = []
tokens_c_list = []
# Pad each new example for axis=1, [batch_size, num_choices, seq_len]
while len(choice_list) < max_num_choices:
choice_list.append('无效答案')
for choice in choice_list:
tokens_c = tokenizer.tokenize(choice.lower())
_truncate_seq_tuple(tokens_t, tokens_q, tokens_c,
max_seq_length - 4)
tokens_c = tokens_q + ["[SEP]"] + tokens_c
tokens_t_list.append(tokens_t)
tokens_c_list.append(tokens_c)
new_data = tokenizer(tokens_t_list,
text_pair=tokens_c_list,
is_split_into_words=True)
# Pad each new example for axis=2 of [batch_size, num_choices, seq_len],
# because length of each choice could be different.
input_ids = Pad(axis=0, pad_val=tokenizer.pad_token_id)(
new_data["input_ids"])
token_type_ids = Pad(axis=0, pad_val=tokenizer.pad_token_id)(
new_data["token_type_ids"])
# Final shape of input_ids: [batch_size, num_choices, seq_len]
result["input_ids"].append(input_ids)
result["token_type_ids"].append(token_type_ids)
if not do_predict:
result["labels"].append([label])
if (idx + 1) % 1000 == 0:
print(idx + 1, "samples have been processed.")
return result
paddle.set_device(args.device)
set_seed(args)
if paddle.distributed.get_world_size() > 1:
paddle.distributed.init_parallel_env()
tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
model = AutoModelForMultipleChoice.from_pretrained(
args.model_name_or_path, num_choices=max_num_choices)
if paddle.distributed.get_world_size() > 1:
model = paddle.DataParallel(model)
train_ds, dev_ds, test_ds = load_dataset(
"clue", "c3", split=["train", "validation", "test"])
if args.do_train:
args.batch_size = int(args.batch_size /
args.gradient_accumulation_steps)
column_names = train_ds.column_names
train_ds = train_ds.map(preprocess_function,
batched=True,
batch_size=len(train_ds),
num_proc=1,
remove_columns=column_names)
batchify_fn = lambda samples, fn=Dict({
'input_ids':
Pad(axis=1, pad_val=tokenizer.pad_token_id), # input
'token_type_ids':
Pad(axis=1, pad_val=tokenizer.pad_token_type_id), # segment
'labels':
Stack(dtype="int64") # label
}): fn(samples)
train_batch_sampler = paddle.io.DistributedBatchSampler(
train_ds, batch_size=args.batch_size, shuffle=True)
train_data_loader = paddle.io.DataLoader(
dataset=train_ds,
batch_sampler=train_batch_sampler,
collate_fn=batchify_fn,
num_workers=0,
return_list=True)
dev_ds = dev_ds.map(preprocess_function,
batched=True,
batch_size=len(dev_ds),
remove_columns=column_names,
num_proc=1)
dev_batch_sampler = paddle.io.BatchSampler(
dev_ds, batch_size=args.eval_batch_size, shuffle=False)
dev_data_loader = paddle.io.DataLoader(dataset=dev_ds,
batch_sampler=dev_batch_sampler,
collate_fn=batchify_fn,
return_list=True)
num_training_steps = int(
len(train_data_loader) * args.num_train_epochs /
args.gradient_accumulation_steps)
lr_scheduler = LinearDecayWithWarmup(args.learning_rate,
num_training_steps, 0)
# Generate parameter names needed to perform weight decay.
# All bias and LayerNorm parameters are excluded.
decay_params = [
p.name for n, p in model.named_parameters()
if not any(nd in n for nd in ["bias", "norm"])
]
grad_clip = paddle.nn.ClipGradByGlobalNorm(args.max_grad_norm)
optimizer = paddle.optimizer.AdamW(
learning_rate=lr_scheduler,
parameters=model.parameters(),
weight_decay=args.weight_decay,
apply_decay_param_fun=lambda x: x in decay_params,
grad_clip=grad_clip)
loss_fct = paddle.nn.loss.CrossEntropyLoss()
metric = paddle.metric.Accuracy()
model.train()
global_step = 0
best_acc = 0.0
tic_train = time.time()
for epoch in range(args.num_train_epochs):
for step, batch in enumerate(train_data_loader):
input_ids, segment_ids, label = batch
logits = model(input_ids=input_ids, token_type_ids=segment_ids)
loss = loss_fct(logits, label)
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
loss.backward()
if (step + 1) % args.gradient_accumulation_steps == 0:
global_step += 1
optimizer.step()
lr_scheduler.step()
optimizer.clear_grad()
if global_step % args.logging_steps == 0:
print(
"global step %d/%d, epoch: %d, batch: %d, rank_id: %s, loss: %f, lr: %.10f, speed: %.4f step/s"
% (global_step, num_training_steps, epoch,
step + 1, paddle.distributed.get_rank(), loss,
optimizer.get_lr(), args.logging_steps /
(time.time() - tic_train)))
tic_train = time.time()
tic_eval = time.time()
acc = evaluate(model, loss_fct, dev_data_loader, metric)
print("eval acc: %.5f, eval done total : %s s" %
(acc, time.time() - tic_eval))
if paddle.distributed.get_rank() == 0 and acc > best_acc:
best_acc = acc
model_to_save = model._layers if isinstance(
model, paddle.DataParallel) else model
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
model_to_save.save_pretrained(args.output_dir)
tokenizer.save_pretrained(args.output_dir)
print("best_acc: ", best_acc)
if args.do_predict:
column_names = test_ds.column_names
test_ds = test_ds.map(partial(preprocess_function, do_predict=True),
batched=True,
batch_size=len(test_ds),
remove_columns=column_names,
num_proc=1)
# Serveral samples have more than four choices.
test_batch_sampler = paddle.io.BatchSampler(test_ds,
batch_size=1,
shuffle=False)
batchify_fn = lambda samples, fn=Dict({
'input_ids':
Pad(axis=1, pad_val=tokenizer.pad_token_id), # input
'token_type_ids':
Pad(axis=1, pad_val=tokenizer.pad_token_type_id), # segment
}): fn(samples)
test_data_loader = paddle.io.DataLoader(
dataset=test_ds,
batch_sampler=test_batch_sampler,
collate_fn=batchify_fn,
return_list=True)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
f = open(os.path.join(args.output_dir, "c311_predict.json"), 'w')
result = {}
idx = 0
for step, batch in enumerate(test_data_loader):
input_ids, segment_ids = batch
with paddle.no_grad():
logits = model(input_ids, segment_ids)
preds = paddle.argmax(logits, axis=1).numpy().tolist()
for pred in preds:
result[str(idx)] = pred
idx += 1
j = json.dumps({"id": idx, "label": pred})
f.write(j + "\n")
def do_train():
paddle.set_device(args.device)
rank = paddle.distributed.get_rank()
if paddle.distributed.get_world_size() > 1:
paddle.distributed.init_parallel_env()
set_seed(args.seed)
resource_file_urls = MODEL_MAP[args.model]['resource_file_urls']
logger.info("Downloading resource files...")
for key, val in resource_file_urls.items():
file_path = os.path.join(args.model, key)
if not os.path.exists(file_path):
get_path_from_url(val, args.model)
tokenizer = AutoTokenizer.from_pretrained(args.model)
model = UIE.from_pretrained(args.model)
train_ds = load_dataset(reader,
data_path=args.train_path,
max_seq_len=args.max_seq_len,
lazy=False)
dev_ds = load_dataset(reader,
data_path=args.dev_path,
max_seq_len=args.max_seq_len,
lazy=False)
train_ds = train_ds.map(
partial(convert_example,
tokenizer=tokenizer,
max_seq_len=args.max_seq_len))
dev_ds = dev_ds.map(
partial(convert_example,
tokenizer=tokenizer,
max_seq_len=args.max_seq_len))
train_batch_sampler = paddle.io.BatchSampler(dataset=train_ds,
batch_size=args.batch_size,
shuffle=True)
train_data_loader = paddle.io.DataLoader(dataset=train_ds,
batch_sampler=train_batch_sampler,
return_list=True)
dev_batch_sampler = paddle.io.BatchSampler(dataset=dev_ds,
batch_size=args.batch_size,
shuffle=False)
dev_data_loader = paddle.io.DataLoader(dataset=dev_ds,
batch_sampler=dev_batch_sampler,
return_list=True)
if args.init_from_ckpt and os.path.isfile(args.init_from_ckpt):
state_dict = paddle.load(args.init_from_ckpt)
model.set_dict(state_dict)
if paddle.distributed.get_world_size() > 1:
model = paddle.DataParallel(model)
optimizer = paddle.optimizer.AdamW(learning_rate=args.learning_rate,
parameters=model.parameters())
criterion = paddle.nn.BCELoss()
metric = SpanEvaluator()
loss_list = []
global_step = 0
best_step = 0
best_f1 = 0
tic_train = time.time()
for epoch in range(1, args.num_epochs + 1):
for batch in train_data_loader:
input_ids, token_type_ids, att_mask, pos_ids, start_ids, end_ids = batch
start_prob, end_prob = model(input_ids, token_type_ids, att_mask,
pos_ids)
start_ids = paddle.cast(start_ids, 'float32')
end_ids = paddle.cast(end_ids, 'float32')
loss_start = criterion(start_prob, start_ids)
loss_end = criterion(end_prob, end_ids)
loss = (loss_start + loss_end) / 2.0
loss.backward()
optimizer.step()
optimizer.clear_grad()
loss_list.append(float(loss))
global_step += 1
if global_step % args.logging_steps == 0 and rank == 0:
time_diff = time.time() - tic_train
loss_avg = sum(loss_list) / len(loss_list)
logger.info(
"global step %d, epoch: %d, loss: %.5f, speed: %.2f step/s"
% (global_step, epoch, loss_avg,
args.logging_steps / time_diff))
tic_train = time.time()
if global_step % args.valid_steps == 0 and rank == 0:
save_dir = os.path.join(args.save_dir,
"model_%d" % global_step)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
model_to_save = model._layers if isinstance(
model, paddle.DataParallel) else model
model_to_save.save_pretrained(save_dir)
tokenizer.save_pretrained(save_dir)
precision, recall, f1 = evaluate(model, metric,
dev_data_loader)
logger.info(
"Evaluation precision: %.5f, recall: %.5f, F1: %.5f" %
(precision, recall, f1))
if f1 > best_f1:
logger.info(
f"best F1 performence has been updated: {best_f1:.5f} --> {f1:.5f}"
)
best_f1 = f1
save_dir = os.path.join(args.save_dir, "model_best")
model_to_save = model._layers if isinstance(
model, paddle.DataParallel) else model
model_to_save.save_pretrained(save_dir)
tokenizer.save_pretrained(save_dir)
tic_train = time.time()
def do_train():
parser = PdArgumentParser(
(ModelArguments, DataTrainingArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
paddle.set_device(training_args.device)
if paddle.distributed.get_world_size() > 1:
paddle.distributed.init_parallel_env()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, world_size: {training_args.world_size}, "
+
f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(
training_args.output_dir
) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(
training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome.")
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# set_seed(args)
data_args.dataset = data_args.dataset.strip()
if data_args.dataset not in ALL_DATASETS:
raise ValueError("Not found dataset {}".format(data_args.dataset))
# Use yaml config to rewrite all args.
config = ALL_DATASETS[data_args.dataset]
for args in (model_args, data_args, training_args):
for arg in vars(args):
if arg in config.keys():
setattr(args, arg, config[arg])
training_args.per_device_train_batch_size = config["batch_size"]
training_args.per_device_eval_batch_size = config["batch_size"]
dataset_config = data_args.dataset.split(" ")
raw_datasets = load_dataset(
dataset_config[0],
None if len(dataset_config) <= 1 else dataset_config[1],
cache_dir=model_args.cache_dir)
label_list = getattr(raw_datasets['train'], "label_list", None)
data_args.label_list = label_list
# Define tokenizer, model, loss function.
tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path)
model = AutoModelForQuestionAnswering.from_pretrained(
model_args.model_name_or_path)
loss_fct = CrossEntropyLossForSQuAD()
train_dataset = raw_datasets["train"]
eval_examples = raw_datasets["validation"]
predict_examples = raw_datasets["test"]
column_names = raw_datasets["train"].column_names
# Dataset pre-process
train_dataset = train_dataset.map(
partial(prepare_train_features, tokenizer=tokenizer, args=data_args),
batched=True,
num_proc=4,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on train dataset",
)
eval_dataset = eval_examples.map(
partial(prepare_validation_features,
tokenizer=tokenizer,
args=data_args),
batched=True,
num_proc=4,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on validation dataset",
)
predict_dataset = predict_examples.map(
partial(prepare_validation_features,
tokenizer=tokenizer,
args=data_args),
batched=True,
num_proc=4,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on prediction dataset",
)
# Define data collector
data_collator = qa_collator(tokenizer, data_args)
# Post-processing:
def post_processing_function(examples,
features,
predictions,
stage="eval"):
# Post-processing: we match the start logits and end logits to answers in the original context.
predictions, all_nbest_json, scores_diff_json = compute_prediction(
examples=examples,
features=features,
predictions=predictions,
n_best_size=data_args.n_best_size,
max_answer_length=data_args.max_answer_length,
null_score_diff_threshold=data_args.null_score_diff_threshold,
)
# Format the result to the format the metric expects.
formatted_predictions = [{
"id": k,
"prediction_text": v
} for k, v in predictions.items()]
references = [{
"id": ex["id"],
"answers": ex["answers"]
} for ex in examples]
return EvalPrediction(predictions=formatted_predictions,
label_ids=references)
# Define the metrics of tasks.
# Metrics
metric = load_metric("squad")
def compute_metrics(p: EvalPrediction):
return metric.compute(predictions=p.predictions,
references=p.label_ids)
trainer = QuestionAnsweringTrainer(
model=model,
criterion=loss_fct,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
eval_examples=eval_examples,
data_collator=data_collator,
post_process_function=post_processing_function,
tokenizer=tokenizer,
compute_metrics=compute_metrics,
)
# Log model and data config
trainer.print_config(model_args, "Model")
trainer.print_config(data_args, "Data")
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
# Training
train_result = trainer.train(resume_from_checkpoint=checkpoint)
metrics = train_result.metrics
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# Evaluate and tests model
eval_metrics = trainer.evaluate()
trainer.log_metrics("eval", eval_metrics)
test_ret = trainer.predict(predict_dataset, predict_examples)
trainer.log_metrics("predict", test_ret.metrics)
if test_ret.label_ids is None:
paddle.save(
test_ret.predictions,
os.path.join(training_args.output_dir, "test_results.pdtensor"),
)
# export inference model
input_spec = [
paddle.static.InputSpec(shape=[None, None],
dtype="int64"), # input_ids
paddle.static.InputSpec(shape=[None, None],
dtype="int64") # segment_ids
]
trainer.export_model(input_spec=input_spec,
load_best_model=True,
output_dir=model_args.export_model_dir)
def do_train():
parser = PdArgumentParser(
(ModelArguments, DataTrainingArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
paddle.set_device(training_args.device)
if paddle.distributed.get_world_size() > 1:
paddle.distributed.init_parallel_env()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, world_size: {training_args.world_size}, "
+
f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(
training_args.output_dir
) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(
training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome.")
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# set_seed(args)
data_args.dataset = data_args.dataset.strip()
if data_args.dataset not in ALL_DATASETS:
raise ValueError("Not found dataset {}".format(data_args.dataset))
# Use yaml config to rewrite all args.
config = ALL_DATASETS[data_args.dataset]
for args in (model_args, data_args, training_args):
for arg in vars(args):
if arg in config.keys():
setattr(args, arg, config[arg])
training_args.per_device_train_batch_size = config["batch_size"]
training_args.per_device_eval_batch_size = config["batch_size"]
dataset_config = data_args.dataset.split(" ")
raw_datasets = load_dataset(
dataset_config[0],
None if len(dataset_config) <= 1 else dataset_config[1],
)
data_args.label_list = getattr(raw_datasets['train'], "label_list", None)
num_classes = 1 if raw_datasets["train"].label_list == None else len(
raw_datasets['train'].label_list)
# Define tokenizer, model, loss function.
tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path)
model = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path, num_classes=num_classes)
loss_fct = nn.loss.CrossEntropyLoss(
) if data_args.label_list else nn.loss.MSELoss()
# Define dataset pre-process function
if "clue" in data_args.dataset:
trans_fn = partial(clue_trans_fn, tokenizer=tokenizer, args=data_args)
else:
trans_fn = partial(seq_trans_fn, tokenizer=tokenizer, args=data_args)
# Define data collector
batchify_fn = defaut_collator(tokenizer, data_args)
# Dataset pre-process
train_dataset = raw_datasets["train"].map(trans_fn)
eval_dataset = raw_datasets["dev"].map(trans_fn)
test_dataset = raw_datasets["test"].map(trans_fn)
# Define the metrics of tasks.
def compute_metrics(p):
preds = p.predictions[0] if isinstance(p.predictions,
tuple) else p.predictions
preds = paddle.to_tensor(preds)
label = paddle.to_tensor(p.label_ids)
probs = F.softmax(preds, axis=1)
metric = Accuracy()
metric.reset()
result = metric.compute(preds, label)
metric.update(result)
accu = metric.accumulate()
metric.reset()
return {"accuracy": accu}
trainer = Trainer(
model=model,
criterion=loss_fct,
args=training_args,
data_collator=batchify_fn,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
tokenizer=tokenizer,
compute_metrics=compute_metrics,
)
# Log model and data config
trainer.print_config(model_args, "Model")
trainer.print_config(data_args, "Data")
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
# Training
train_result = trainer.train(resume_from_checkpoint=checkpoint)
metrics = train_result.metrics
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# Evaluate and tests model
eval_metrics = trainer.evaluate()
trainer.log_metrics("eval", eval_metrics)
test_ret = trainer.predict(test_dataset)
trainer.log_metrics("test", test_ret.metrics)
if test_ret.label_ids is None:
paddle.save(
test_ret.predictions,
os.path.join(training_args.output_dir, "test_results.pdtensor"),
)
# export inference model
input_spec = [
paddle.static.InputSpec(shape=[None, None],
dtype="int64"), # input_ids
paddle.static.InputSpec(shape=[None, None],
dtype="int64") # segment_ids
]
trainer.export_model(input_spec=input_spec,
load_best_model=True,
output_dir=model_args.export_model_dir)
parser.add_argument("--hnsw_m", default=100, type=int, help="Recall number for each query from Ann index.")
parser.add_argument("--hnsw_ef", default=100, type=int, help="Recall number for each query from Ann index.")
parser.add_argument("--hnsw_max_elements", default=1000000, type=int, help="Recall number for each query from Ann index.")
parser.add_argument('--device', choices=['cpu', 'gpu'], default="gpu", help="Select which device to train model, defaults to gpu.")
args = parser.parse_args()
# yapf: enable
if __name__ == "__main__":
paddle.set_device(args.device)
rank = paddle.distributed.get_rank()
if paddle.distributed.get_world_size() > 1:
paddle.distributed.init_parallel_env()
model_name_or_path = 'rocketqa-zh-dureader-query-encoder'
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
trans_func = partial(convert_example_test,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length)
batchify_fn = lambda samples, fn=Tuple(
Pad(axis=0, pad_val=tokenizer.pad_token_id), # text_input
Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # text_segment
): [data for data in fn(samples)]
pretrained_model = AutoModel.from_pretrained(model_name_or_path)
model = SimCSE(pretrained_model, output_emb_size=args.output_emb_size)
model = paddle.DataParallel(model)
def main():
parser = PdArgumentParser(
(ModelArguments, DataArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
# Log model and data config
training_args.print_config(model_args, "Model")
training_args.print_config(data_args, "Data")
paddle.set_device(training_args.device)
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, world_size: {training_args.world_size}, "
+
f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(
training_args.output_dir
) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(
training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome.")
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
data_args.dataset = data_args.dataset.strip()
dataset_config = data_args.dataset.split(" ")
print(dataset_config)
raw_datasets = load_dataset(
dataset_config[0],
name=None if len(dataset_config) <= 1 else dataset_config[1],
splits=('train', 'dev'))
data_args.label_list = getattr(raw_datasets['train'], "label_list", None)
num_classes = 1 if raw_datasets["train"].label_list == None else len(
raw_datasets['train'].label_list)
# Define tokenizer, model, loss function.
tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path)
model = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path, num_classes=num_classes)
criterion = nn.loss.CrossEntropyLoss(
) if data_args.label_list else nn.loss.MSELoss()
# Define dataset pre-process function
trans_fn = partial(clue_trans_fn, tokenizer=tokenizer, args=data_args)
# Define data collector
data_collator = DataCollatorWithPadding(tokenizer)
# Dataset pre-process
if training_args.do_train:
train_dataset = raw_datasets["train"].map(trans_fn)
if training_args.do_eval:
eval_dataset = raw_datasets["dev"].map(trans_fn)
if training_args.do_predict:
test_dataset = raw_datasets["test"].map(trans_fn)
# Define the metrics of tasks.
def compute_metrics(p):
preds = p.predictions[0] if isinstance(p.predictions,
tuple) else p.predictions
preds = paddle.to_tensor(preds)
label = paddle.to_tensor(p.label_ids)
probs = F.softmax(preds, axis=1)
metric = Accuracy()
metric.reset()
result = metric.compute(preds, label)
metric.update(result)
accu = metric.accumulate()
metric.reset()
return {"accuracy": accu}
trainer = Trainer(
model=model,
criterion=criterion,
args=training_args,
data_collator=data_collator,
train_dataset=train_dataset if training_args.do_train else None,
eval_dataset=eval_dataset if training_args.do_eval else None,
tokenizer=tokenizer,
compute_metrics=compute_metrics,
)
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
# Training
if training_args.do_train:
train_result = trainer.train(resume_from_checkpoint=checkpoint)
metrics = train_result.metrics
trainer.save_model()
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# Evaluate and tests model
if training_args.do_eval:
eval_metrics = trainer.evaluate()
trainer.log_metrics("eval", eval_metrics)
if training_args.do_predict:
test_ret = trainer.predict(test_dataset)
trainer.log_metrics("test", test_ret.metrics)
if test_ret.label_ids is None:
paddle.save(
test_ret.predictions,
os.path.join(training_args.output_dir,
"test_results.pdtensor"),
)
# export inference model
if training_args.do_export:
# You can also load from certain checkpoint
# trainer.load_state_dict_from_checkpoint("/path/to/checkpoint/")
input_spec = [
paddle.static.InputSpec(shape=[None, None],
dtype="int64"), # input_ids
paddle.static.InputSpec(shape=[None, None],
dtype="int64") # segment_ids
]
if model_args.export_model_dir is None:
model_args.export_model_dir = os.path.join(
training_args.output_dir, "export")
paddlenlp.transformers.export_model(model=trainer.model,
input_spec=input_spec,
path=model_args.export_model_dir)
def do_train(args):
paddle.set_device(args.device)
if paddle.distributed.get_world_size() > 1:
paddle.distributed.init_parallel_env()
set_seed(args)
tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
trans_func = partial(convert_example,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length)
if args.task_type == "cross-lingual-transfer":
train_ds = load_dataset("xnli", "en", splits="train")
train_ds = train_ds.map(trans_func, lazy=True)
elif args.task_type == "translate-train-all":
all_train_ds = []
for language in all_languages:
train_ds = load_dataset("xnli", language, splits="train")
all_train_ds.append(train_ds.map(trans_func, lazy=True))
train_ds = XnliDataset(all_train_ds)
train_batch_sampler = DistributedBatchSampler(train_ds,
batch_size=args.batch_size,
shuffle=True)
batchify_fn = lambda samples, fn=Tuple(
Pad(axis=0, pad_val=tokenizer.pad_token_id, dtype="int64"
), # input_ids
Pad(axis=0, pad_val=tokenizer.pad_token_id, dtype="int64"
), # position_ids
Pad(axis=0, pad_val=0, dtype="int64"), # attention_mask
Stack(dtype="int64") # labels
): fn(samples)
train_data_loader = DataLoader(dataset=train_ds,
batch_sampler=train_batch_sampler,
collate_fn=batchify_fn,
num_workers=0,
return_list=True)
num_classes = 3
model = AutoModelForSequenceClassification.from_pretrained(
args.model_name_or_path, num_classes=num_classes, dropout=args.dropout)
n_layers = model.ernie_m.config['num_hidden_layers']
if paddle.distributed.get_world_size() > 1:
model = paddle.DataParallel(model)
if args.max_steps > 0:
num_training_steps = args.max_steps
num_train_epochs = math.ceil(num_training_steps /
len(train_data_loader))
else:
num_training_steps = len(train_data_loader) * args.num_train_epochs
num_train_epochs = args.num_train_epochs
warmup = args.warmup_steps if args.warmup_steps > 0 else args.warmup_proportion
lr_scheduler = LinearDecayWithWarmup(args.learning_rate,
num_training_steps, warmup)
# Generate parameter names needed to perform weight decay.
# All bias and LayerNorm parameters are excluded.
decay_params = [
p.name for n, p in model.named_parameters()
if not any(nd in n for nd in ["bias", "norm"])
]
# Construct dict
name_dict = dict()
for n, p in model.named_parameters():
name_dict[p.name] = n
optimizer = AdamWDL(learning_rate=lr_scheduler,
beta1=0.9,
beta2=0.999,
epsilon=args.adam_epsilon,
parameters=model.parameters(),
weight_decay=args.weight_decay,
n_layers=n_layers,
layerwise_decay=args.layerwise_decay,
apply_decay_param_fun=lambda x: x in decay_params,
name_dict=name_dict)
loss_fct = nn.CrossEntropyLoss()
if args.use_amp:
scaler = paddle.amp.GradScaler(init_loss_scaling=args.scale_loss)
metric = Accuracy()
global_step = 0
tic_train = time.time()
for epoch in range(num_train_epochs):
for step, batch in enumerate(train_data_loader):
global_step += 1
input_ids, position_ids, attention_mask, labels = batch
with paddle.amp.auto_cast(
args.use_amp,
custom_white_list=["layer_norm", "softmax", "gelu"]):
logits = model(input_ids, position_ids, attention_mask)
loss = loss_fct(logits, labels)
if args.use_amp:
scaled_loss = scaler.scale(loss)
scaled_loss.backward()
scaler.minimize(optimizer, scaled_loss)
else:
loss.backward()
optimizer.step()
lr_scheduler.step()
optimizer.clear_grad()
if global_step % args.logging_steps == 0:
print(
"global step %d/%d, epoch: %d, batch: %d, rank_id: %s, loss: %f, lr: %.10f, speed: %.4f step/s"
% (global_step, num_training_steps, epoch, step,
paddle.distributed.get_rank(), loss, optimizer.get_lr(),
args.logging_steps / (time.time() - tic_train)))
tic_train = time.time()
if global_step % args.save_steps == 0 or global_step == num_training_steps:
for language in all_languages:
tic_eval = time.time()
test_data_loader = get_test_dataloader(
args, language, batchify_fn, trans_func)
evaluate(model, loss_fct, metric, test_data_loader,
language)
print("eval done total : %s s" % (time.time() - tic_eval))
if paddle.distributed.get_rank() == 0:
output_dir = os.path.join(
args.output_dir,
"ernie_m_ft_model_%d.pdparams" % (global_step))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Need better way to get inner model of DataParallel
model_to_save = model._layers if isinstance(
model, paddle.DataParallel) else model
model_to_save.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
if global_step >= num_training_steps:
break
if global_step >= num_training_steps:
break
if paddle.distributed.get_rank() == 0:
output_dir = os.path.join(
args.output_dir, "ernie_m_final_model_%d.pdparams" % global_step)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Need better way to get inner model of DataParallel
model_to_save = model._layers if isinstance(
model, paddle.DataParallel) else model
model_to_save.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
def main():
parser = PdArgumentParser(
(ModelArguments, DataArguments, TrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
paddle.set_device(training_args.device)
data_args.dataset = data_args.dataset.strip()
if data_args.dataset in ALL_DATASETS:
# if you custom you hyper-parameters in yaml config, it will overwrite all args.
config = ALL_DATASETS[data_args.dataset]
for args in (model_args, data_args, training_args):
for arg in vars(args):
if arg in config.keys():
setattr(args, arg, config[arg])
training_args.per_device_train_batch_size = config["batch_size"]
training_args.per_device_eval_batch_size = config["batch_size"]
# Log model and data config
training_args.print_config(model_args, "Model")
training_args.print_config(data_args, "Data")
dataset_config = data_args.dataset.split(" ")
raw_datasets = load_dataset(
dataset_config[0],
None if len(dataset_config) <= 1 else dataset_config[1],
cache_dir=model_args.cache_dir)
label_list = getattr(raw_datasets['train'], "label_list", None)
data_args.label_list = label_list
# Define tokenizer, model, loss function.
tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path)
model = AutoModelForQuestionAnswering.from_pretrained(
model_args.model_name_or_path)
loss_fct = CrossEntropyLossForSQuAD()
# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.
column_names = raw_datasets["train"].column_names
column_names = raw_datasets["validation"].column_names
train_dataset = raw_datasets["train"]
# Create train feature from dataset
with training_args.main_process_first(
desc="train dataset map pre-processing"):
# Dataset pre-process
train_dataset = train_dataset.map(
partial(
prepare_train_features, tokenizer=tokenizer, args=data_args),
batched=True,
num_proc=4,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on train dataset", )
eval_examples = raw_datasets["validation"]
with training_args.main_process_first(
desc="evaluate dataset map pre-processing"):
eval_dataset = eval_examples.map(
partial(
prepare_validation_features,
tokenizer=tokenizer,
args=data_args),
batched=True,
num_proc=4,
remove_columns=column_names,
load_from_cache_file=not data_args.overwrite_cache,
desc="Running tokenizer on validation dataset", )
# Define data collector
data_collator = DataCollatorWithPadding(tokenizer)
# Post-processing:
def post_processing_function(examples, features, predictions, stage="eval"):
# Post-processing: we match the start logits and end logits to answers in the original context.
predictions, all_nbest_json, scores_diff_json = compute_prediction(
examples=examples,
features=features,
predictions=predictions,
n_best_size=data_args.n_best_size,
max_answer_length=data_args.max_answer_length,
null_score_diff_threshold=data_args.null_score_diff_threshold, )
references = [{
"id": ex["id"],
"answers": ex["answers"]
} for ex in examples]
return EvalPrediction(predictions=predictions, label_ids=references)
trainer = QuestionAnsweringTrainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
eval_examples=eval_examples,
data_collator=data_collator,
post_process_function=post_processing_function,
tokenizer=tokenizer)
output_dir = os.path.join(model_args.model_name_or_path, "compress")
if not os.path.exists(output_dir):
os.makedirs(output_dir)
prune = True
compress_config = CompressConfig(quantization_config=PTQConfig(
algo_list=['hist', 'mse'], batch_size_list=[4, 8, 16]))
trainer.compress(
data_args.dataset,
output_dir,
pruning=prune,
quantization=True,
compress_config=compress_config)
def run(args):
if args.do_train:
assert args.batch_size % args.gradient_accumulation_steps == 0, \
"Please make sure argmument `batch_size` must be divisible by `gradient_accumulation_steps`."
paddle.set_device(args.device)
set_seed(args)
max_seq_length = args.max_seq_length
max_num_choices = 10
def preprocess_function(examples, do_predict=False):
SPIECE_UNDERLINE = '▁'
def _is_chinese_char(cp):
if ((cp >= 0x4E00 and cp <= 0x9FFF) or #
(cp >= 0x3400 and cp <= 0x4DBF) or #
(cp >= 0x20000 and cp <= 0x2A6DF) or #
(cp >= 0x2A700 and cp <= 0x2B73F) or #
(cp >= 0x2B740 and cp <= 0x2B81F) or #
(cp >= 0x2B820 and cp <= 0x2CEAF) or
(cp >= 0xF900 and cp <= 0xFAFF) or #
(cp >= 0x2F800 and cp <= 0x2FA1F)): #
return True
return False
def is_fuhao(c):
if c == '。' or c == '，' or c == '！' or c == '？' or c == '；' or c == '、' or c == '：' or c == '（' or c == '）' \
or c == '－' or c == '~' or c == '「' or c == '《' or c == '》' or c == ',' or c == '」' or c == '"' or c == '“' or c == '”' \
or c == '$' or c == '『' or c == '』' or c == '—' or c == ';' or c == '。' or c == '(' or c == ')' or c == '-' or c == '～' or c == '。' \
or c == '‘' or c == '’':
return True
return False
def _tokenize_chinese_chars(text):
"""Adds whitespace around any CJK character."""
output = []
is_blank = False
for index, char in enumerate(text):
cp = ord(char)
if is_blank:
output.append(char)
if context[index - 12:index + 1].startswith("#idiom"):
is_blank = False
output.append(SPIECE_UNDERLINE)
else:
if text[index:index + 6] == "#idiom":
is_blank = True
if len(output) > 0 and output[-1] != SPIECE_UNDERLINE:
output.append(SPIECE_UNDERLINE)
output.append(char)
elif _is_chinese_char(cp) or is_fuhao(char):
if len(output) > 0 and output[-1] != SPIECE_UNDERLINE:
output.append(SPIECE_UNDERLINE)
output.append(char)
output.append(SPIECE_UNDERLINE)
else:
output.append(char)
return "".join(output)
def is_whitespace(c):
if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(
c) == 0x202F or c == SPIECE_UNDERLINE:
return True
return False
def add_tokens_for_around(tokens, pos, num_tokens):
num_l = num_tokens // 2
num_r = num_tokens - num_l
if pos >= num_l and (len(tokens) - 1 - pos) >= num_r:
tokens_l = tokens[pos - num_l:pos]
tokens_r = tokens[pos + 1:pos + 1 + num_r]
elif pos <= num_l:
tokens_l = tokens[:pos]
right_len = num_tokens - len(tokens_l)
tokens_r = tokens[pos + 1:pos + 1 + right_len]
elif (len(tokens) - 1 - pos) <= num_r:
tokens_r = tokens[pos + 1:]
left_len = num_tokens - len(tokens_r)
tokens_l = tokens[pos - left_len:pos]
else:
raise ValueError('impossible')
return tokens_l, tokens_r
max_tokens_for_doc = max_seq_length - 3
num_tokens = max_tokens_for_doc - 5
num_examples = len(examples.data["candidates"])
if do_predict:
result = {"input_ids": [], "token_type_ids": [], "example_ids": []}
else:
result = {
"input_ids": [],
"token_type_ids": [],
"labels": [],
"example_ids": []
}
for idx in range(num_examples):
candidate = 0
options = examples.data['candidates'][idx]
# Each content may have several sentences.
for context in examples.data['content'][idx]:
context = context.replace("“", "\"").replace("”", "\"").replace("——", "--"). \
replace("—", "-").replace("―", "-").replace("…", "...").replace("‘", "\'").replace("’", "\'")
context = _tokenize_chinese_chars(context)
paragraph_text = context.strip()
doc_tokens = []
prev_is_whitespace = True
for c in paragraph_text:
if is_whitespace(c):
prev_is_whitespace = True
else:
if prev_is_whitespace:
doc_tokens.append(c)
else:
doc_tokens[-1] += c
prev_is_whitespace = False
all_doc_tokens = []
for (i, token) in enumerate(doc_tokens):
if '#idiom' in token:
sub_tokens = [str(token)]
else:
sub_tokens = tokenizer.tokenize(token)
for sub_token in sub_tokens:
all_doc_tokens.append(sub_token)
tags = [blank for blank in doc_tokens if '#idiom' in blank]
# Each sentence may have several tags
for tag_index, tag in enumerate(tags):
pos = all_doc_tokens.index(tag)
tmp_l, tmp_r = add_tokens_for_around(
all_doc_tokens, pos, num_tokens)
num_l = len(tmp_l)
num_r = len(tmp_r)
tokens_l = []
for token in tmp_l:
if '#idiom' in token and token != tag:
# Mask tag which is not considered in this new sample.
# Each idiom has four words, so 4 mask tokens are used.
tokens_l.extend(['[MASK]'] * 4)
else:
tokens_l.append(token)
tokens_l = tokens_l[-num_l:]
del tmp_l
tokens_r = []
for token in tmp_r:
if '#idiom' in token and token != tag:
tokens_r.extend(['[MASK]'] * 4)
else:
tokens_r.append(token)
tokens_r = tokens_r[:num_r]
del tmp_r
tokens_list = []
# Each tag has ten choices, and the shape of each new
# example is [num_choices, seq_len]
for i, elem in enumerate(options):
option = tokenizer.tokenize(elem)
tokens = option + ['[SEP]'] + tokens_l + ['[unused1]'
] + tokens_r
tokens_list.append(tokens)
new_data = tokenizer(tokens_list, is_split_into_words=True)
# Final shape of input_ids: [batch_size, num_choices, seq_len]
result["input_ids"].append(new_data["input_ids"])
result["token_type_ids"].append(new_data["token_type_ids"])
result["example_ids"].append(idx)
if not do_predict:
label = examples.data["answers"][idx]["candidate_id"][
candidate]
result["labels"].append(label)
candidate += 1
if (idx + 1) % 10000 == 0:
logger.info("%d samples have been processed." % (idx + 1))
return result
if paddle.distributed.get_world_size() > 1:
paddle.distributed.init_parallel_env()
model = AutoModelForMultipleChoice.from_pretrained(
args.model_name_or_path, num_choices=max_num_choices)
tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
if paddle.distributed.get_world_size() > 1:
model = paddle.DataParallel(model)
train_ds, dev_ds, test_ds = load_dataset(
"clue", "chid", split=["train", "validation", "test"])
if args.do_train:
args.batch_size = int(args.batch_size /
args.gradient_accumulation_steps)
column_names = train_ds.column_names
with main_process_first(desc="train dataset map pre-processing"):
train_ds = train_ds.map(
partial(preprocess_function),
batched=True,
batch_size=len(train_ds),
num_proc=args.num_proc,
remove_columns=column_names,
load_from_cache_file=not args.overwrite_cache,
desc="Running tokenizer on train dataset")
batchify_fn = lambda samples, fn=Dict(
{
'input_ids': Pad(axis=1, pad_val=tokenizer.pad_token_id
), # input
'token_type_ids': Pad(
axis=1, pad_val=tokenizer.pad_token_type_id), # segment
'labels': Stack(dtype="int64"), # label
'example_ids': Stack(dtype="int64"), # example id
}): fn(samples)
train_batch_sampler = paddle.io.DistributedBatchSampler(
train_ds, batch_size=args.batch_size, shuffle=True)
train_data_loader = paddle.io.DataLoader(
dataset=train_ds,
batch_sampler=train_batch_sampler,
collate_fn=batchify_fn,
num_workers=0,
return_list=True)
with main_process_first(desc="evaluate dataset map pre-processing"):
dev_ds = dev_ds.map(partial(preprocess_function),
batched=True,
batch_size=len(dev_ds),
remove_columns=column_names,
num_proc=args.num_proc,
load_from_cache_file=args.overwrite_cache,
desc="Running tokenizer on validation dataset")
dev_batch_sampler = paddle.io.BatchSampler(
dev_ds, batch_size=args.eval_batch_size, shuffle=False)
dev_data_loader = paddle.io.DataLoader(dataset=dev_ds,
batch_sampler=dev_batch_sampler,
collate_fn=batchify_fn,
return_list=True)
num_training_steps = int(
args.max_steps /
args.gradient_accumulation_steps) if args.max_steps >= 0 else int(
len(train_data_loader) * args.num_train_epochs /
args.gradient_accumulation_steps)
warmup = args.warmup_steps if args.warmup_steps > 0 else args.warmup_proportion
lr_scheduler = LinearDecayWithWarmup(args.learning_rate,
num_training_steps, warmup)
# Generate parameter names needed to perform weight decay.
# All bias and LayerNorm parameters are excluded.
decay_params = [
p.name for n, p in model.named_parameters()
if not any(nd in n for nd in ["bias", "norm"])
]
grad_clip = paddle.nn.ClipGradByGlobalNorm(args.max_grad_norm)
optimizer = paddle.optimizer.AdamW(
learning_rate=lr_scheduler,
parameters=model.parameters(),
weight_decay=args.weight_decay,
apply_decay_param_fun=lambda x: x in decay_params,
grad_clip=grad_clip)
loss_fct = nn.CrossEntropyLoss()
model.train()
global_step = 0
best_acc = 0.0
tic_train = time.time()
for epoch in range(args.num_train_epochs):
for step, batch in enumerate(train_data_loader):
input_ids, segment_ids, labels, example_ids = batch
logits = model(input_ids=input_ids, token_type_ids=segment_ids)
loss = loss_fct(logits, labels)
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
loss.backward()
if (step + 1) % args.gradient_accumulation_steps == 0:
global_step += 1
optimizer.step()
lr_scheduler.step()
optimizer.clear_grad()
if global_step % args.logging_steps == 0:
logger.info(
"global step %d/%d, epoch: %d, batch: %d, loss: %.5f, speed: %.2f step/s"
% (global_step, num_training_steps, epoch,
step + 1, loss, args.logging_steps /
(time.time() - tic_train)))
tic_train = time.time()
if global_step >= num_training_steps:
logger.info("best_result: %.2f" % (best_acc * 100))
return
tic_eval = time.time()
acc = evaluate(model, dev_data_loader)
logger.info("eval acc: %.5f, eval done total : %s s" %
(acc, time.time() - tic_eval))
if paddle.distributed.get_rank() == 0 and acc > best_acc:
best_acc = acc
if args.save_best_model:
model_to_save = model._layers if isinstance(
model, paddle.DataParallel) else model
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
model_to_save.save_pretrained(args.output_dir)
tokenizer.save_pretrained(args.output_dir)
logger.info("best_result: %.2f" % (best_acc * 100))
if args.do_predict:
column_names = test_ds.column_names
test_ds = test_ds.map(partial(preprocess_function, do_predict=True),
batched=True,
batch_size=len(test_ds),
remove_columns=column_names,
num_proc=args.num_proc)
test_batch_sampler = paddle.io.BatchSampler(
test_ds, batch_size=args.eval_batch_size, shuffle=False)
batchify_fn = lambda samples, fn=Dict({
'input_ids':
Pad(axis=1, pad_val=tokenizer.pad_token_id), # input
'token_type_ids':
Pad(axis=1, pad_val=tokenizer.pad_token_type_id), # segment
'example_ids':
Stack(dtype="int64"), # example id
}): fn(samples)
test_data_loader = paddle.io.DataLoader(
dataset=test_ds,
batch_sampler=test_batch_sampler,
collate_fn=batchify_fn,
return_list=True)
result = {}
idx = 623377
preds = evaluate(model, test_data_loader, do_predict=True)
for pred in preds:
result["#idiom" + str(idx) + "#"] = pred
idx += 1
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
with open(os.path.join(args.output_dir, 'chid11_predict.json'),
"w") as writer:
json.dump(result, writer, indent=2)
def run(args):
paddle.set_device(args.device)
if paddle.distributed.get_world_size() > 1:
paddle.distributed.init_parallel_env()
rank = paddle.distributed.get_rank()
tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
set_seed(args)
train_examples, dev_examples, test_examples = load_dataset(
'cmrc2018', split=["train", "validation", "test"])
column_names = train_examples.column_names
if rank == 0:
if os.path.exists(args.model_name_or_path):
print("init checkpoint from %s" % args.model_name_or_path)
model = AutoModelForQuestionAnswering.from_pretrained(
args.model_name_or_path)
if paddle.distributed.get_world_size() > 1:
model = paddle.DataParallel(model)
def prepare_train_features(examples):
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
# NOTE: Almost the same functionality as HuggingFace's prepare_train_features function. The main difference is
# that HugggingFace uses ArrowTable as basic data structure, while we use list of dictionary instead.
contexts = examples['context']
questions = examples['question']
tokenized_examples = tokenizer(questions,
contexts,
stride=args.doc_stride,
max_seq_len=args.max_seq_length)
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
sample_mapping = tokenized_examples.pop("overflow_to_sample")
# The offset mappings will give us a map from token to character position in the original context. This will
# help us compute the start_positions and end_positions.
offset_mapping = tokenized_examples.pop("offset_mapping")
# Let's label those examples!
tokenized_examples["start_positions"] = []
tokenized_examples["end_positions"] = []
for i, offsets in enumerate(offset_mapping):
# We will label impossible answers with the index of the CLS token.
input_ids = tokenized_examples["input_ids"][i]
cls_index = input_ids.index(tokenizer.cls_token_id)
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
sequence_ids = tokenized_examples['token_type_ids'][i]
# One example can give several spans, this is the index of the example containing this span of text.
sample_index = sample_mapping[i]
answers = examples['answers'][sample_index]
# If no answers are given, set the cls_index as answer.
if len(answers["answer_start"]) == 0:
tokenized_examples["start_positions"].append(cls_index)
tokenized_examples["end_positions"].append(cls_index)
else:
# Start/end character index of the answer in the text.
start_char = answers["answer_start"][0]
end_char = start_char + len(answers["text"][0])
# Start token index of the current span in the text.
token_start_index = 0
while sequence_ids[token_start_index] != 1:
token_start_index += 1
# End token index of the current span in the text.
token_end_index = len(input_ids) - 1
while sequence_ids[token_end_index] != 1:
token_end_index -= 1
token_end_index -= 1
# Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).
if not (offsets[token_start_index][0] <= start_char
and offsets[token_end_index][1] >= end_char):
tokenized_examples["start_positions"].append(cls_index)
tokenized_examples["end_positions"].append(cls_index)
else:
# Otherwise move the token_start_index and token_end_index to the two ends of the answer.
# Note: we could go after the last offset if the answer is the last word (edge case).
while token_start_index < len(offsets) and offsets[
token_start_index][0] <= start_char:
token_start_index += 1
tokenized_examples["start_positions"].append(
token_start_index - 1)
while offsets[token_end_index][1] >= end_char:
token_end_index -= 1
tokenized_examples["end_positions"].append(
token_end_index + 1)
return tokenized_examples
def prepare_validation_features(examples):
# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
# in one example possible giving several features when a context is long, each of those features having a
# context that overlaps a bit the context of the previous feature.
#NOTE: Almost the same functionality as HuggingFace's prepare_train_features function. The main difference is
# that HuggingFace uses ArrowTable as basic data structure, while we use list of dictionary instead.
contexts = examples['context']
questions = examples['question']
tokenized_examples = tokenizer(questions,
contexts,
stride=args.doc_stride,
max_seq_len=args.max_seq_length,
return_attention_mask=True)
# Since one example might give us several features if it has a long context, we need a map from a feature to
# its corresponding example. This key gives us just that.
sample_mapping = tokenized_examples.pop("overflow_to_sample")
# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
# corresponding example_id and we will store the offset mappings.
tokenized_examples["example_id"] = []
for i in range(len(tokenized_examples["input_ids"])):
# Grab the sequence corresponding to that example (to know what is the context and what is the question).
sequence_ids = tokenized_examples['token_type_ids'][i]
context_index = 1
# One example can give several spans, this is the index of the example containing this span of text.
sample_index = sample_mapping[i]
tokenized_examples["example_id"].append(
examples["id"][sample_index])
# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
# position is part of the context or not.
tokenized_examples["offset_mapping"][i] = [
(o if sequence_ids[k] == context_index else None)
for k, o in enumerate(tokenized_examples["offset_mapping"][i])
]
return tokenized_examples
if args.do_train:
args.batch_size = int(args.batch_size /
args.gradient_accumulation_steps)
train_ds = train_examples.map(prepare_train_features,
batched=True,
remove_columns=column_names,
num_proc=1)
train_batch_sampler = paddle.io.DistributedBatchSampler(
train_ds, batch_size=args.batch_size, shuffle=True)
train_batchify_fn = lambda samples, fn=Dict(
{
"input_ids": Pad(axis=0, pad_val=tokenizer.pad_token_id),
"token_type_ids": Pad(axis=0,
pad_val=tokenizer.pad_token_type_id),
"start_positions": Stack(dtype="int64"),
"end_positions": Stack(dtype="int64")
}): fn(samples)
train_data_loader = DataLoader(dataset=train_ds,
batch_sampler=train_batch_sampler,
collate_fn=train_batchify_fn,
return_list=True)
dev_ds = dev_examples.map(prepare_validation_features,
batched=True,
remove_columns=column_names,
num_proc=1)
dev_batch_sampler = paddle.io.BatchSampler(
dev_ds, batch_size=args.eval_batch_size, shuffle=False)
dev_batchify_fn = lambda samples, fn=Dict({
"input_ids":
Pad(axis=0, pad_val=tokenizer.pad_token_id),
"token_type_ids":
Pad(axis=0, pad_val=tokenizer.pad_token_type_id)
}): fn(samples)
dev_data_loader = DataLoader(dataset=dev_ds,
batch_sampler=dev_batch_sampler,
collate_fn=dev_batchify_fn,
return_list=True)
num_training_steps = int(
args.max_steps /
args.gradient_accumulation_steps) if args.max_steps > 0 else int(
len(train_data_loader) * args.num_train_epochs /
args.gradient_accumulation_steps)
lr_scheduler = LinearDecayWithWarmup(args.learning_rate,
num_training_steps,
args.warmup_proportion)
# Generate parameter names needed to perform weight decay.
# All bias and LayerNorm parameters are excluded.
decay_params = [
p.name for n, p in model.named_parameters()
if not any(nd in n for nd in ["bias", "norm"])
]
optimizer = paddle.optimizer.AdamW(
learning_rate=lr_scheduler,
epsilon=args.adam_epsilon,
parameters=model.parameters(),
weight_decay=args.weight_decay,
apply_decay_param_fun=lambda x: x in decay_params)
criterion = CrossEntropyLossForSQuAD()
global_step = 0
tic_train = time.time()
for epoch in range(args.num_train_epochs):
for step, batch in enumerate(train_data_loader):
input_ids, token_type_ids, start_positions, end_positions = batch
logits = model(input_ids=input_ids,
token_type_ids=token_type_ids)
loss = criterion(logits, (start_positions, end_positions))
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
loss.backward()
if (step + 1) % args.gradient_accumulation_steps == 0:
global_step += 1
optimizer.step()
lr_scheduler.step()
optimizer.clear_grad()
if global_step % args.logging_steps == 0:
print(
"global step %d/%d, epoch: %d, batch: %d, loss: %f, speed: %.2f step/s"
% (global_step, num_training_steps, epoch,
step + 1, loss, args.logging_steps /
(time.time() - tic_train)))
tic_train = time.time()
if global_step % args.save_steps == 0 or global_step == num_training_steps:
if rank == 0:
output_dir = os.path.join(args.output_dir,
"model_%d" % global_step)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# need better way to get inner model of DataParallel
model_to_save = model._layers if isinstance(
model, paddle.DataParallel) else model
model_to_save.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
print('Saving checkpoint to:', output_dir)
if global_step == num_training_steps:
break
evaluate(model, dev_examples, dev_data_loader, args)
if args.do_predict and rank == 0:
test_ds = test_examples.map(prepare_validation_features,
batched=True,
remove_columns=column_names,
num_proc=1)
test_batch_sampler = paddle.io.BatchSampler(
test_ds, batch_size=args.eval_batch_size, shuffle=False)
test_batchify_fn = lambda samples, fn=Dict({
"input_ids":
Pad(axis=0, pad_val=tokenizer.pad_token_id),
"token_type_ids":
Pad(axis=0, pad_val=tokenizer.pad_token_type_id)
}): fn(samples)
test_data_loader = DataLoader(dataset=test_ds,
batch_sampler=test_batch_sampler,
collate_fn=test_batchify_fn,
return_list=True)
evaluate(model, test_examples, test_data_loader, args, do_eval=False)
def do_train():
paddle.set_device(args.device)
rank = paddle.distributed.get_rank()
if paddle.distributed.get_world_size() > 1:
paddle.distributed.init_parallel_env()
set_seed(args.seed)
train_ds, dev_ds, test_ds = load_dataset(args.dataset,
splits=["train", "dev", "test"])
model = AutoModelForSequenceClassification.from_pretrained(
'ernie-1.0', num_classes=len(train_ds.label_list))
tokenizer = AutoTokenizer.from_pretrained('ernie-1.0')
trans_func = partial(convert_example,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
is_pair=args.dataset == "xnli_cn")
batchify_fn = lambda samples, fn=Tuple(
Pad(axis=0, pad_val=tokenizer.pad_token_id), # input
Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # segment
Stack(dtype="int64") # label
): [data for data in fn(samples)]
train_data_loader = create_dataloader(train_ds,
mode='train',
batch_size=args.batch_size,
batchify_fn=batchify_fn,
trans_fn=trans_func)
dev_data_loader = create_dataloader(dev_ds,
mode='dev',
batch_size=args.batch_size,
batchify_fn=batchify_fn,
trans_fn=trans_func)
if args.init_from_ckpt and os.path.isfile(args.init_from_ckpt):
state_dict = paddle.load(args.init_from_ckpt)
model.set_dict(state_dict)
model = paddle.DataParallel(model)
num_training_steps = len(train_data_loader) * args.epochs
lr_scheduler = LinearDecayWithWarmup(args.learning_rate,
num_training_steps,
args.warmup_proportion)
# Generate parameter names needed to perform weight decay.
# All bias and LayerNorm parameters are excluded.
decay_params = [
p.name for n, p in model.named_parameters()
if not any(nd in n for nd in ["bias", "norm"])
]
optimizer = paddle.optimizer.AdamW(
learning_rate=lr_scheduler,
parameters=model.parameters(),
weight_decay=args.weight_decay,
apply_decay_param_fun=lambda x: x in decay_params)
criterion = paddle.nn.loss.CrossEntropyLoss()
metric = paddle.metric.Accuracy()
if args.use_amp:
scaler = paddle.amp.GradScaler(init_loss_scaling=args.scale_loss)
global_step = 0
tic_train = time.time()
for epoch in range(1, args.epochs + 1):
for step, batch in enumerate(train_data_loader, start=1):
input_ids, token_type_ids, labels = batch
with paddle.amp.auto_cast(
args.use_amp,
custom_white_list=["layer_norm", "softmax", "gelu"],
):
logits = model(input_ids, token_type_ids)
loss = criterion(logits, labels)
probs = F.softmax(logits, axis=1)
correct = metric.compute(probs, labels)
metric.update(correct)
acc = metric.accumulate()
if args.use_amp:
scaler.scale(loss).backward()
scaler.minimize(optimizer, loss)
else:
loss.backward()
optimizer.step()
lr_scheduler.step()
optimizer.clear_grad()
global_step += 1
if global_step % args.logging_steps == 0 and rank == 0:
time_diff = time.time() - tic_train
print(
"global step %d, epoch: %d, batch: %d, loss: %.5f, accuracy: %.5f, speed: %.2f step/s"
% (global_step, epoch, step, loss, acc,
args.logging_steps / time_diff))
tic_train = time.time()
if global_step % args.valid_steps == 0 and rank == 0:
evaluate(model, criterion, metric, dev_data_loader)
tic_train = time.time()
if global_step % args.save_steps == 0 and rank == 0:
save_dir = os.path.join(args.save_dir,
"model_%d" % global_step)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
model._layers.save_pretrained(save_dir)
tokenizer.save_pretrained(save_dir)
tic_train = time.time()
