"""Run TinyBERT on SST-2."""
from __future__ import absolute_import, division, print_function
import argparse
import os
import sys
import csv
import numpy as np
import io
from transformer.tokenization import BertTokenizer
import torch
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 examples"""
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_label_ids(features):
"""get the label id"""
return torch.tensor([f.label_id for f in features], dtype=torch.long)
def simple_accuracy(preds, labels):
"""calculate the accuracy"""
return (preds == labels).mean()
def bin2predlabel(test_num, args):
"""(adapt to benchmark inference)change the bin files into logits"""
logit1 = []
logit2 = []
for i in range(test_num):
n1, n2 = np.fromfile('{}/Bert_{}_1.bin'.format(args.result_dir, i), dtype='float32')
logit1.append(n1)
logit2.append(n2)
logit = np.concatenate((np.array(logit1).reshape(1, -1), np.array(logit2).reshape(1, -1)), axis = 0)
pred_label = np.argmax(logit, axis = 0)
return pred_label
def txt2predlabel(test_num, args):
"""(adapt to msame inference):change the txt files into logits"""
logit1 = []
logit2 = []
for i in range(test_num):
txtname = "input" + str(i) + "_output_0.txt"
dir = os.path.join(args.result_dir, txtname)
with open(dir, "r") as f:
line = f.readline()
n1, n2 = [float(i) for i in line.split()]
logit1.append(n1)
logit2.append(n2)
logit = np.concatenate((np.array(logit1).reshape(1, -1), np.array(logit2).reshape(1, -1)), axis = 0)
pred_label = np.argmax(logit, axis = 0)
return pred_label
def txt2predlabel_ais_infer(test_num, args):
"""(adapt to msame inference):change the txt files into logits"""
logit1 = []
logit2 = []
for i in range(test_num):
txtname = "input" + str(i) + "_0.txt"
dir = os.path.join(args.result_dir, txtname)
with open(dir, "r") as f:
line = f.readline()
n1, n2 = [float(i) for i in line.split()]
logit1.append(n1)
logit2.append(n2)
logit = np.concatenate((np.array(logit1).reshape(1, -1), np.array(logit2).reshape(1, -1)), axis = 0)
pred_label = np.argmax(logit, axis = 0)
return pred_label
def main():
"""postprocess the data and calculate the accuracy"""
parser = argparse.ArgumentParser()
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("--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("--result_dir",
default=None,
type=str,
required=True,
help="NPU benchmark infer result path")
parser.add_argument("--model",
default=None,
type=str,
required=True,
help="The student model dir.")
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")
args = parser.parse_args()
test_num = 872
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_labels = get_label_ids(eval_features).numpy()
if args.inference_tool == "benchmark":
pred_labels = bin2predlabel(test_num, args)
elif args.inference_tool == "ais_infer":
pred_labels = txt2predlabel_ais_infer(test_num, args)
elif args.inference_tool == "msame":
pred_labels = txt2predlabel(test_num, args)
result = simple_accuracy(pred_labels, eval_labels)
print("acc:{}".format(result))
if __name__ == '__main__':
main()