"""Run TinyBERT on SST-2."""
from __future__ import absolute_import, division, print_function
import argparse
import os
import sys
import csv
import io
from transformer.tokenization import BertTokenizer
import torch
from torch.utils.data import (DataLoader, SequentialSampler, TensorDataset)
class InputExample(object):
"""A single training/test example for simple sequence classification."""
def __init__(self, guid, text_a, text_b=None, label=None):
"""Constructs a InputExample.
Args:
guid: Unique id for the example.
text_a: string. The untokenized text of the first sequence. For single
sequence tasks, only this sequence must be specified.
text_b: (Optional) string. The untokenized text of the second sequence.
Only must be specified for sequence pair tasks.
label: (Optional) string. The label of the example. This should be
specified for train and dev examples, but not for test examples.
"""
self.guid = guid
self.text_a = text_a
self.text_b = text_b
self.label = label
class InputFeatures(object):
"""A single set of features of data."""
def __init__(self, input_ids, input_mask, segment_ids, label_id, seq_length=None):
self.input_ids = input_ids
self.input_mask = input_mask
self.segment_ids = segment_ids
self.seq_length = seq_length
self.label_id = label_id
class DataProcessor(object):
"""Base class for data converters for sequence classification data sets."""
def get_train_examples(self, data_dir):
"""Gets a collection of `InputExample`s for the train set."""
raise NotImplementedError()
def get_dev_examples(self, data_dir):
"""Gets a collection of `InputExample`s for the dev set."""
raise NotImplementedError()
def get_labels(self):
"""Gets the list of labels for this data set."""
raise NotImplementedError()
@classmethod
def _read_tsv(cls, input_file, quotechar=None):
"""Reads a tab separated value file."""
with io.open(input_file, "r", encoding="utf-8") as f:
reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
lines = []
for line in reader:
if sys.version_info[0] == 2:
line = list(unicode(cell, 'utf-8') for cell in line)
lines.append(line)
return lines
class Sst2Processor(DataProcessor):
"""Processor for the SST-2 data set (GLUE version)."""
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
def get_aug_examples(self, data_dir):
"""get the augmented data"""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, "train_aug.tsv")), "aug")
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % (set_type, i)
text_a = line[0]
label = line[1]
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
return examples
def _truncate_seq_pair(tokens_a, tokens_b, max_length):
"""Truncates a sequence pair in place to the maximum length."""
while True:
total_length = len(tokens_a) + len(tokens_b)
if total_length <= max_length:
break
if len(tokens_a) > len(tokens_b):
tokens_a.pop()
else:
tokens_b.pop()
def convert_examples_to_features(examples, label_list, max_seq_length,
tokenizer, output_mode):
"""Loads a data file into a list of `InputBatch`s."""
label_map = {label: i for i, label in enumerate(label_list)}
features = []
for (ex_index, example) in enumerate(examples):
tokens_a = tokenizer.tokenize(example.text_a)
tokens_b = None
if example.text_b:
tokens_b = tokenizer.tokenize(example.text_b)
_truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
else:
if len(tokens_a) > max_seq_length - 2:
tokens_a = tokens_a[:(max_seq_length - 2)]
tokens = ["[CLS]"] + tokens_a + ["[SEP]"]
segment_ids = [0] * len(tokens)
if tokens_b:
tokens += tokens_b + ["[SEP]"]
segment_ids += [1] * (len(tokens_b) + 1)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
input_mask = [1] * len(input_ids)
seq_length = len(input_ids)
padding = [0] * (max_seq_length - len(input_ids))
input_ids += padding
input_mask += padding
segment_ids += padding
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
if output_mode == "classification":
label_id = label_map[example.label]
elif output_mode == "regression":
label_id = float(example.label)
else:
raise KeyError(output_mode)
features.append(
InputFeatures(input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
label_id=label_id,
seq_length=seq_length))
return features
def get_tensor_data(output_mode, features):
"""get the data"""
if output_mode == "classification":
all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long)
elif output_mode == "regression":
all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.float)
all_seq_lengths = torch.tensor([f.seq_length for f in features], dtype=torch.long)
all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
tensor_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
all_label_ids, all_seq_lengths)
return tensor_data, all_label_ids
def data_main():
"""preprocess data"""
parser = argparse.ArgumentParser()
parser.add_argument("--model",
default=None,
type=str,
required=True,
help="The student model dir.")
parser.add_argument("--data_dir",
default=None,
type=str,
required=True,
help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
parser.add_argument("--max_seq_length",
default=64,
type=int,
help="The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--eval_batch_size",
default=1,
type=int,
help="Total batch size for eval.")
parser.add_argument("--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--inference_tool", type = str,
help = "inference tool:benchmark or msame or ais_infer")
parser.add_argument("--bin_path",
default="./bert_bin",
type=str,
help="The path to bin file.")
parser.add_argument("--output_mode",
default='classification',
type=str,
help="The mode of output.")
args = parser.parse_args()
processor = Sst2Processor()
tokenizer = BertTokenizer.from_pretrained(args.model, do_lower_case=args.do_lower_case)
eval_examples = processor.get_dev_examples(args.data_dir)
label_list = ["0", "1"]
eval_features = convert_examples_to_features(eval_examples, label_list, args.max_seq_length,
tokenizer, output_mode = "classification")
eval_data, eval_labels = get_tensor_data(args.output_mode, eval_features)
print("eval_labels")
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, drop_last = True,
batch_size = args.eval_batch_size, shuffle = False)
if not os.path.exists(args.bin_path):
os.makedirs(args.bin_path)
i = -1
if args.inference_tool == "msame" or "ais_infer":
path1 = args.bin_path + "/input_ids"
path2 = args.bin_path + "/segment_ids"
path3 = args.bin_path + "/input_mask"
if not os.path.exists(path1):
os.mkdir(path1)
if not os.path.exists(path2):
os.mkdir(path2)
if not os.path.exists(path3):
os.mkdir(path3)
for input_ids, input_mask, segment_ids, label_ids, seq_lengths in eval_dataloader:
i = i + 1
print("[info] file", "===", i)
input_ids_np = input_ids.numpy()
input_mask_np = input_mask.numpy()
segment_ids_np = segment_ids.numpy()
if args.inference_tool == "msame":
input_ids_np.tofile(os.path.join(path1, "input" + str(i) + '.bin'))
segment_ids_np.tofile(os.path.join(path2, "input" + str(i) + '.bin'))
input_mask_np.tofile(os.path.join(path3, "input" + str(i) + '.bin'))
elif args.inference_tool == "benchmark":
input_ids_np.tofile(os.path.join(args.bin_path, "input_ids_" + str(i) + '.bin'))
segment_ids_np.tofile(os.path.join(args.bin_path, "segment_ids_" + str(i) + '.bin'))
input_mask_np.tofile(os.path.join(args.bin_path, "input_mask_" + str(i) + '.bin'))
elif args.inference_tool == "ais_infer":
input_ids_np.tofile(os.path.join(path1, "input" + str(i) + '.bin'))
segment_ids_np.tofile(os.path.join(path2, "input" + str(i) + '.bin'))
input_mask_np.tofile(os.path.join(path3, "input" + str(i) + '.bin'))
if __name__ == "__main__":
data_main()