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Commit 448937c0 authored by thomwolf's avatar thomwolf
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python 2 compatibility

parent ba37ddc5
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examples/eval_transfo_xl.py
View file @ 448937c0
......@@ -17,26 +17,26 @@
Adapted from https://github.com/kimiyoung/transformer-xl.
In particular https://github.com/kimiyoung/transformer-xl/blob/master/pytorch/eval.py
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import os
import sys
import functools
import argparse
import logging
import time
import math
import sys
from io import open
import torch
from pytorch_pretrained_bert import TransfoXLModel, TransfoXLCorpus
def logging(s, log_path, print_=True, log_=True):
if print_:
print(s)
if log_:
with open(log_path, 'a+') as f_log:
f_log.write(s + '\n')
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt = '%m/%d/%Y %H:%M:%S',
level = logging.INFO)
logger = logging.getLogger(__name__)
def get_logger(log_path, **kwargs):
return functools.partial(logging, log_path=log_path, **kwargs)
parser = argparse.ArgumentParser(description='PyTorch Transformer Language Model')
# parser.add_argument('--data', type=str, default='../data/wikitext-103',
......@@ -71,8 +71,8 @@ assert args.ext_len >= 0, 'extended context length must be non-negative'
device = torch.device("cuda" if args.cuda else "cpu")
# Get logger
logging = get_logger(os.path.join(args.work_dir, 'log.txt'),
log_=not args.no_log)
# logging = get_logger(os.path.join(args.work_dir, 'log.txt'),
# log_=not args.no_log)
# Load dataset
corpus = TransfoXLCorpus.from_pretrained(args.model_name)
......@@ -90,7 +90,7 @@ te_iter = corpus.get_iterator('test', args.batch_size, args.tgt_len,
model = TransfoXLModel.from_pretrained(args.model_name)
model = model.to(device)
logging('Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'.format(
logger.info('Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}'.format(
args.batch_size, args.tgt_len, args.ext_len, args.mem_len, args.clamp_len))
model.reset_length(args.tgt_len, args.ext_len, args.mem_len)
......@@ -116,7 +116,7 @@ def evaluate(eval_iter):
total_loss += seq_len * loss.item()
total_len += seq_len
total_time = time.time() - start_time
logging('Time : {:.2f}s, {:.2f}ms/segment'.format(
logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(
total_time, 1000 * total_time / (idx+1)))
return total_loss / total_len
......@@ -146,6 +146,6 @@ if valid_loss is not None:
if test_loss is not None:
log_str += format_log(test_loss, 'test')
logging('=' * 100)
logging(log_str)
logging('=' * 100)
logger.info('=' * 100)
logger.info(log_str)
logger.info('=' * 100)
examples/run_classifier.py
View file @ 448937c0
......@@ -15,26 +15,27 @@
# limitations under the License.
"""BERT finetuning runner."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import, division, print_function
import argparse
import csv
import os
import logging
import argparse
import os
import random
from tqdm import tqdm, trange
import sys
from io import open
import numpy as np
import torch
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
TensorDataset)
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
from pytorch_pretrained_bert.tokenization import BertTokenizer
from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
from pytorch_pretrained_bert.modeling import BertForSequenceClassification
from pytorch_pretrained_bert.optimization import BertAdam
from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
from pytorch_pretrained_bert.tokenization import BertTokenizer
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt = '%m/%d/%Y %H:%M:%S',
......@@ -91,10 +92,12 @@ class DataProcessor(object):
@classmethod
def _read_tsv(cls, input_file, quotechar=None):
"""Reads a tab separated value file."""
with open(input_file, "r", encoding='utf-8') as f:
with open(input_file, "rb") 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
......@@ -429,7 +432,8 @@ def main():
if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train:
raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
os.makedirs(args.output_dir, exist_ok=True)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
task_name = args.task_name.lower()
......@@ -451,7 +455,7 @@ def main():
# Prepare model
model = BertForSequenceClassification.from_pretrained(args.bert_model,
cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank),
cache_dir=os.path.join(PYTORCH_PRETRAINED_BERT_CACHE, 'distributed_{}'.format(args.local_rank)),
num_labels = num_labels)
if args.fp16:
model.half()
......
examples/run_lm_finetuning.py
View file @ 448937c0
......@@ -15,26 +15,23 @@
# limitations under the License.
"""BERT finetuning runner."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import, division, print_function, unicode_literals
import os
import logging
import argparse
from tqdm import tqdm, trange
import logging
import os
import random
from io import open
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler
from torch.utils.data import DataLoader, Dataset, RandomSampler
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
from pytorch_pretrained_bert.tokenization import BertTokenizer
from pytorch_pretrained_bert.modeling import BertForPreTraining
from pytorch_pretrained_bert.optimization import BertAdam
from torch.utils.data import Dataset
import random
from pytorch_pretrained_bert.tokenization import BertTokenizer
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
......@@ -185,16 +182,16 @@ class BERTDataset(Dataset):
if self.line_buffer is None:
# read first non-empty line of file
while t1 == "" :
t1 = self.file.__next__().strip()
t2 = self.file.__next__().strip()
t1 = next(self.file).strip()
t2 = next(self.file).strip()
else:
# use t2 from previous iteration as new t1
t1 = self.line_buffer
t2 = self.file.__next__().strip()
t2 = next(self.file).strip()
# skip empty rows that are used for separating documents and keep track of current doc id
while t2 == "" or t1 == "":
t1 = self.file.__next__().strip()
t2 = self.file.__next__().strip()
t1 = next(self.file).strip()
t2 = next(self.file).strip()
self.current_doc = self.current_doc+1
self.line_buffer = t2
......@@ -228,15 +225,15 @@ class BERTDataset(Dataset):
def get_next_line(self):
""" Gets next line of random_file and starts over when reaching end of file"""
try:
line = self.random_file.__next__().strip()
line = next(self.random_file).strip()
#keep track of which document we are currently looking at to later avoid having the same doc as t1
if line == "":
self.current_random_doc = self.current_random_doc + 1
line = self.random_file.__next__().strip()
line = next(self.random_file).strip()
except StopIteration:
self.random_file.close()
self.random_file = open(self.corpus_path, "r", encoding=self.encoding)
line = self.random_file.__next__().strip()
line = next(self.random_file).strip()
return line
......@@ -425,6 +422,7 @@ def main():
help="The output directory where the model checkpoints will be written.")
## Other parameters
parser.add_argument("--do_lower_case", action='store_true', help="Set this flag if you are using an uncased model.")
parser.add_argument("--max_seq_length",
default=128,
type=int,
......@@ -513,7 +511,8 @@ def main():
if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
os.makedirs(args.output_dir, exist_ok=True)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
......@@ -579,7 +578,7 @@ def main():
if args.local_rank == -1:
train_sampler = RandomSampler(train_dataset)
else:
#TODO: check if this works with current data generator from disk that relies on file.__next__
#TODO: check if this works with current data generator from disk that relies on next(file)
# (it doesn't return item back by index)
train_sampler = DistributedSampler(train_dataset)
train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)
......@@ -643,4 +642,4 @@ def accuracy(out, labels):
if __name__ == "__main__":
main()
\ No newline at end of file
main()
examples/run_squad.py
View file @ 448937c0
......@@ -15,29 +15,36 @@
# limitations under the License.
"""Run BERT on SQuAD."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import, division, print_function
import argparse
import collections
import logging
import json
import logging
import math
import os
import random
import pickle
from tqdm import tqdm, trange
import sys
from io import open
import numpy as np
import torch
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
TensorDataset)
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
from pytorch_pretrained_bert.tokenization import whitespace_tokenize, BasicTokenizer, BertTokenizer
from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
from pytorch_pretrained_bert.modeling import BertForQuestionAnswering
from pytorch_pretrained_bert.optimization import BertAdam
from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
from pytorch_pretrained_bert.tokenization import (BasicTokenizer,
BertTokenizer,
whitespace_tokenize)
if sys.version_info[0] == 2:
import cPickle as pickle
else:
import pickle
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt = '%m/%d/%Y %H:%M:%S',
......@@ -784,7 +791,8 @@ def main():
if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
raise ValueError("Output directory () already exists and is not empty.")
os.makedirs(args.output_dir, exist_ok=True)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
......@@ -798,7 +806,7 @@ def main():
# Prepare model
model = BertForQuestionAnswering.from_pretrained(args.bert_model,
cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank))
cache_dir=os.path.join(PYTORCH_PRETRAINED_BERT_CACHE, 'distributed_{}'.format(args.local_rank)))
if args.fp16:
model.half()
......
examples/run_swag.py
View file @ 448937c0
......@@ -15,22 +15,25 @@
# limitations under the License.
"""BERT finetuning runner."""
import argparse
import csv
import logging
import os
import argparse
import random
from tqdm import tqdm, trange
import csv
import sys
from io import open
import numpy as np
import torch
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
TensorDataset)
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
from pytorch_pretrained_bert.tokenization import BertTokenizer
from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
from pytorch_pretrained_bert.modeling import BertForMultipleChoice
from pytorch_pretrained_bert.optimization import BertAdam
from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
from pytorch_pretrained_bert.tokenization import BertTokenizer
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt = '%m/%d/%Y %H:%M:%S',
......@@ -65,17 +68,17 @@ class SwagExample(object):
def __repr__(self):
l = [
f"swag_id: {self.swag_id}",
f"context_sentence: {self.context_sentence}",
f"start_ending: {self.start_ending}",
f"ending_0: {self.endings[0]}",
f"ending_1: {self.endings[1]}",
f"ending_2: {self.endings[2]}",
f"ending_3: {self.endings[3]}",
"swag_id: {}".format(self.swag_id),
"context_sentence: {}".format(self.context_sentence),
"start_ending: {}".format(self.start_ending),
"ending_0: {}".format(self.endings[0]),
"ending_1: {}".format(self.endings[1]),
"ending_2: {}".format(self.endings[2]),
"ending_3: {}".format(self.endings[3]),
]
if self.label is not None:
l.append(f"label: {self.label}")
l.append("label: {}".format(self.label))
return ", ".join(l)
......@@ -102,7 +105,11 @@ class InputFeatures(object):
def read_swag_examples(input_file, is_training):
with open(input_file, 'r', encoding='utf-8') as f:
reader = csv.reader(f)
lines = list(reader)
lines = []
for line in reader:
if sys.version_info[0] == 2:
line = list(unicode(cell, 'utf-8') for cell in line)
lines.append(line)
if is_training and lines[0][-1] != 'label':
raise ValueError(
......@@ -184,15 +191,15 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length,
label = example.label
if example_index < 5:
logger.info("*** Example ***")
logger.info(f"swag_id: {example.swag_id}")
logger.info("swag_id: {}".format(example.swag_id))
for choice_idx, (tokens, input_ids, input_mask, segment_ids) in enumerate(choices_features):
logger.info(f"choice: {choice_idx}")
logger.info(f"tokens: {' '.join(tokens)}")
logger.info(f"input_ids: {' '.join(map(str, input_ids))}")
logger.info(f"input_mask: {' '.join(map(str, input_mask))}")
logger.info(f"segment_ids: {' '.join(map(str, segment_ids))}")
logger.info("choice: {}".format(choice_idx))
logger.info("tokens: {}".format(' '.join(tokens)))
logger.info("input_ids: {}".format(' '.join(map(str, input_ids))))
logger.info("input_mask: {}".format(' '.join(map(str, input_mask))))
logger.info("segment_ids: {}".format(' '.join(map(str, segment_ids))))
if is_training:
logger.info(f"label: {label}")
logger.info("label: {}".format(label))
features.append(
InputFeatures(
......@@ -349,7 +356,8 @@ def main():
if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
os.makedirs(args.output_dir, exist_ok=True)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
......@@ -362,7 +370,7 @@ def main():
# Prepare model
model = BertForMultipleChoice.from_pretrained(args.bert_model,
cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank),
cache_dir=os.path.join(PYTORCH_PRETRAINED_BERT_CACHE, 'distributed_{}'.format(args.local_rank)),
num_choices=4)
if args.fp16:
model.half()
......
pytorch_pretrained_bert/__main__.py
View file @ 448937c0
......@@ -15,7 +15,7 @@ def main():
if sys.argv[1] == "convert_tf_checkpoint_to_pytorch":
try:
import tensorflow as tf
except ModuleNotFoundError:
except ImportError:
print("pytorch_pretrained_bert can only be used from the commandline to convert TensorFlow models in PyTorch, "
"In that case, it requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions.")
......@@ -43,7 +43,7 @@ def main():
else:
try:
import tensorflow as tf
except ModuleNotFoundError:
except ImportError:
print("pytorch_pretrained_bert can only be used from the commandline to convert TensorFlow models in PyTorch, "
"In that case, it requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions.")
......
pytorch_pretrained_bert/convert_openai_checkpoint_to_pytorch.py
View file @ 448937c0
......@@ -14,14 +14,18 @@
# limitations under the License.
"""Convert OpenAI GPT checkpoint."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import, division, print_function
import argparse
from io import open
import torch
from pytorch_pretrained_bert.modeling_openai import load_tf_weights_in_openai_gpt, OpenAIGPTConfig, OpenAIGPTModel, CONFIG_NAME, WEIGHTS_NAME
from pytorch_pretrained_bert.modeling_openai import (CONFIG_NAME, WEIGHTS_NAME,
OpenAIGPTConfig,
OpenAIGPTModel,
load_tf_weights_in_openai_gpt)
def convert_openai_checkpoint_to_pytorch(openai_checkpoint_folder_path, openai_config_file, pytorch_dump_folder_path):
# Construct model
......
pytorch_pretrained_bert/convert_transfo_xl_checkpoint_to_pytorch.py
View file @ 448937c0
......@@ -14,25 +14,31 @@
# limitations under the License.
"""Convert Transformer XL checkpoint and datasets."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import, division, print_function
import argparse
import os
import sys
import argparse
import pickle
from io import open
import tensorflow as tf
import torch
import numpy as np
from pytorch_pretrained_bert.modeling_transfo_xl import TransfoXLConfig, TransfoXLModel, CONFIG_NAME, WEIGHTS_NAME, load_tf_weights_in_transfo_xl
from pytorch_pretrained_bert.tokenization_transfo_xl import VOCAB_NAME, CORPUS_NAME
import pytorch_pretrained_bert.tokenization_transfo_xl as data_utils
from pytorch_pretrained_bert.modeling_transfo_xl import (CONFIG_NAME,
WEIGHTS_NAME,
TransfoXLConfig,
TransfoXLModel,
load_tf_weights_in_transfo_xl)
from pytorch_pretrained_bert.tokenization_transfo_xl import (CORPUS_NAME,
VOCAB_NAME)
if sys.version_info[0] == 2:
import cPickle as pickle
else:
import pickle
# We do this to be able to load the python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
import pytorch_pretrained_bert.tokenization_transfo_xl as data_utils
data_utils.Vocab = data_utils.TransfoXLTokenizer
data_utils.Corpus = data_utils.TransfoXLCorpus
sys.modules['data_utils'] = data_utils
......
pytorch_pretrained_bert/file_utils.py
View file @ 448937c0
......@@ -3,31 +3,39 @@ Utilities for working with the local dataset cache.
This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
Copyright by the AllenNLP authors.
"""
from __future__ import (absolute_import, division, print_function, unicode_literals)
import os
import json
import logging
import os
import shutil
import tempfile
import json
from urllib.parse import urlparse
from pathlib import Path
from typing import Optional, Tuple, Union, IO, Callable, Set
from hashlib import sha256
from functools import wraps
from tqdm import tqdm
from hashlib import sha256
from io import open
import boto3
from botocore.exceptions import ClientError
import requests
from botocore.exceptions import ClientError
from tqdm import tqdm
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
try:
from urllib.parse import urlparse
except ImportError:
from urlparse import urlparse
try:
from pathlib import Path
PYTORCH_PRETRAINED_BERT_CACHE = Path(os.getenv('PYTORCH_PRETRAINED_BERT_CACHE',
Path.home() / '.pytorch_pretrained_bert'))
except ImportError:
PYTORCH_PRETRAINED_BERT_CACHE = os.getenv('PYTORCH_PRETRAINED_BERT_CACHE',
os.path.join(os.path.expanduser("~"), '.pytorch_pretrained_bert'))
PYTORCH_PRETRAINED_BERT_CACHE = Path(os.getenv('PYTORCH_PRETRAINED_BERT_CACHE',
Path.home() / '.pytorch_pretrained_bert'))
logger = logging.getLogger(__name__) # pylint: disable=invalid-name
def url_to_filename(url: str, etag: str = None) -> str:
def url_to_filename(url, etag=None):
"""
Convert `url` into a hashed filename in a repeatable way.
If `etag` is specified, append its hash to the url's, delimited
......@@ -45,25 +53,23 @@ def url_to_filename(url: str, etag: str = None) -> str:
return filename
def filename_to_url(filename: str, cache_dir: Union[str, Path] = None) -> Tuple[str, str]:
def filename_to_url(filename, cache_dir=None):
"""
Return the url and etag (which may be ``None``) stored for `filename`.
Raise ``FileNotFoundError`` if `filename` or its stored metadata do not exist.
Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist.
"""
if cache_dir is None:
cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
if isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
cache_path = os.path.join(cache_dir, filename)
if not os.path.exists(cache_path):
raise FileNotFoundError("file {} not found".format(cache_path))
raise EnvironmentError("file {} not found".format(cache_path))
meta_path = cache_path + '.json'
if not os.path.exists(meta_path):
raise FileNotFoundError("file {} not found".format(meta_path))
raise EnvironmentError("file {} not found".format(meta_path))
with open(meta_path) as meta_file:
with open(meta_path, encoding="utf-8") as meta_file:
metadata = json.load(meta_file)
url = metadata['url']
etag = metadata['etag']
......@@ -71,7 +77,7 @@ def filename_to_url(filename: str, cache_dir: Union[str, Path] = None) -> Tuple[
return url, etag
def cached_path(url_or_filename: Union[str, Path], cache_dir: Union[str, Path] = None) -> str:
def cached_path(url_or_filename, cache_dir=None):
"""
Given something that might be a URL (or might be a local path),
determine which. If it's a URL, download the file and cache it, and
......@@ -80,10 +86,6 @@ def cached_path(url_or_filename: Union[str, Path], cache_dir: Union[str, Path] =
"""
if cache_dir is None:
cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
if isinstance(url_or_filename, Path):
url_or_filename = str(url_or_filename)
if isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
parsed = urlparse(url_or_filename)
......@@ -95,13 +97,13 @@ def cached_path(url_or_filename: Union[str, Path], cache_dir: Union[str, Path] =
return url_or_filename
elif parsed.scheme == '':
# File, but it doesn't exist.
raise FileNotFoundError("file {} not found".format(url_or_filename))
raise EnvironmentError("file {} not found".format(url_or_filename))
else:
# Something unknown
raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename))
def split_s3_path(url: str) -> Tuple[str, str]:
def split_s3_path(url):
"""Split a full s3 path into the bucket name and path."""
parsed = urlparse(url)
if not parsed.netloc or not parsed.path:
......@@ -114,19 +116,19 @@ def split_s3_path(url: str) -> Tuple[str, str]:
return bucket_name, s3_path
def s3_request(func: Callable):
def s3_request(func):
"""
Wrapper function for s3 requests in order to create more helpful error
messages.
"""
@wraps(func)
def wrapper(url: str, *args, **kwargs):
def wrapper(url, *args, **kwargs):
try:
return func(url, *args, **kwargs)
except ClientError as exc:
if int(exc.response["Error"]["Code"]) == 404:
raise FileNotFoundError("file {} not found".format(url))
raise EnvironmentError("file {} not found".format(url))
else:
raise
......@@ -134,7 +136,7 @@ def s3_request(func: Callable):
@s3_request
def s3_etag(url: str) -> Optional[str]:
def s3_etag(url):
"""Check ETag on S3 object."""
s3_resource = boto3.resource("s3")
bucket_name, s3_path = split_s3_path(url)
......@@ -143,14 +145,14 @@ def s3_etag(url: str) -> Optional[str]:
@s3_request
def s3_get(url: str, temp_file: IO) -> None:
def s3_get(url, temp_file):
"""Pull a file directly from S3."""
s3_resource = boto3.resource("s3")
bucket_name, s3_path = split_s3_path(url)
s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file)
def http_get(url: str, temp_file: IO) -> None:
def http_get(url, temp_file):
req = requests.get(url, stream=True)
content_length = req.headers.get('Content-Length')
total = int(content_length) if content_length is not None else None
......@@ -162,17 +164,16 @@ def http_get(url: str, temp_file: IO) -> None:
progress.close()
def get_from_cache(url: str, cache_dir: Union[str, Path] = None) -> str:
def get_from_cache(url, cache_dir=None):
"""
Given a URL, look for the corresponding dataset in the local cache.
If it's not there, download it. Then return the path to the cached file.
"""
if cache_dir is None:
cache_dir = PYTORCH_PRETRAINED_BERT_CACHE
if isinstance(cache_dir, Path):
cache_dir = str(cache_dir)
os.makedirs(cache_dir, exist_ok=True)
if not os.path.exists(cache_dir):
os.makedirs(cache_dir)
# Get eTag to add to filename, if it exists.
if url.startswith("s3://"):
......@@ -213,7 +214,7 @@ def get_from_cache(url: str, cache_dir: Union[str, Path] = None) -> str:
logger.info("creating metadata file for %s", cache_path)
meta = {'url': url, 'etag': etag}
meta_path = cache_path + '.json'
with open(meta_path, 'w') as meta_file:
with open(meta_path, 'w', encoding="utf-8") as meta_file:
json.dump(meta, meta_file)
logger.info("removing temp file %s", temp_file.name)
......@@ -221,7 +222,7 @@ def get_from_cache(url: str, cache_dir: Union[str, Path] = None) -> str:
return cache_path
def read_set_from_file(filename: str) -> Set[str]:
def read_set_from_file(filename):
'''
Extract a de-duped collection (set) of text from a file.
Expected file format is one item per line.
......@@ -233,7 +234,7 @@ def read_set_from_file(filename: str) -> Set[str]:
return collection
def get_file_extension(path: str, dot=True, lower: bool = True):
def get_file_extension(path, dot=True, lower=True):
ext = os.path.splitext(path)[1]
ext = ext if dot else ext[1:]
return ext.lower() if lower else ext
pytorch_pretrained_bert/modeling.py
View file @ 448937c0
......@@ -15,18 +15,18 @@
# limitations under the License.
"""PyTorch BERT model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import, division, print_function, unicode_literals
import os
import copy
import json
import math
import logging
import math
import os
import shutil
import tarfile
import tempfile
import shutil
import sys
from io import open
import torch
from torch import nn
......@@ -56,7 +56,7 @@ def load_tf_weights_in_bert(model, tf_checkpoint_path):
import re
import numpy as np
import tensorflow as tf
except ModuleNotFoundError:
except ImportError:
print("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions.")
raise
......@@ -164,7 +164,8 @@ class BertConfig(object):
initializer_range: The sttdev of the truncated_normal_initializer for
initializing all weight matrices.
"""
if isinstance(vocab_size_or_config_json_file, str):
if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
and isinstance(vocab_size_or_config_json_file, unicode)):
with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
json_config = json.loads(reader.read())
for key, value in json_config.items():
......@@ -343,8 +344,10 @@ class BertIntermediate(nn.Module):
def __init__(self, config):
super(BertIntermediate, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
self.intermediate_act_fn = ACT2FN[config.hidden_act] \
if isinstance(config.hidden_act, str) else config.hidden_act
if isinstance(config.hidden_act, str) or (sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
......@@ -416,8 +419,10 @@ class BertPredictionHeadTransform(nn.Module):
def __init__(self, config):
super(BertPredictionHeadTransform, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.transform_act_fn = ACT2FN[config.hidden_act] \
if isinstance(config.hidden_act, str) else config.hidden_act
if isinstance(config.hidden_act, str) or (sys.version_info[0] == 2 and isinstance(config.hidden_act, unicode)):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.transform_act_fn = config.hidden_act
self.LayerNorm = BertLayerNorm(config.hidden_size, eps=1e-12)
def forward(self, hidden_states):
......@@ -542,7 +547,7 @@ class BertPreTrainedModel(nn.Module):
# redirect to the cache, if necessary
try:
resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir)
except FileNotFoundError:
except EnvironmentError:
logger.error(
"Model name '{}' was not found in model name list ({}). "
"We assumed '{}' was a path or url but couldn't find any file "
......
pytorch_pretrained_bert/modeling_openai.py
View file @ 448937c0
......@@ -24,6 +24,8 @@ import os
import shutil
import tarfile
import tempfile
import sys
from io import open
import torch
import torch.nn as nn
......@@ -160,7 +162,8 @@ class OpenAIGPTConfig(object):
initializer_range: The sttdev of the truncated_normal_initializer for
initializing all weight matrices.
"""
if isinstance(vocab_size_or_config_json_file, str):
if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
and isinstance(vocab_size_or_config_json_file, unicode)):
with open(vocab_size_or_config_json_file, "r", encoding="utf-8") as reader:
json_config = json.loads(reader.read())
for key, value in json_config.items():
......@@ -442,7 +445,7 @@ class OpenAIGPTPreTrainedModel(nn.Module):
# redirect to the cache, if necessary
try:
resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir)
except FileNotFoundError:
except EnvironmentError:
logger.error(
"Model name '{}' was not found in model name list ({}). "
"We assumed '{}' was a path or url but couldn't find any file "
......@@ -641,7 +644,8 @@ class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
hidden_states = inputs_embeds + position_embeds + token_type_embeds
for block in self.h:
hidden_states = block(hidden_states)
return hidden_states.view(*input_shape, hidden_states.size(-1))
output_shape = input_shape + (hidden_states.size(-1),)
return hidden_states.view(*output_shape)
class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel):
......
pytorch_pretrained_bert/modeling_transfo_xl.py
View file @ 448937c0
......@@ -27,6 +27,8 @@ import tarfile
import tempfile
import shutil
import collections
import sys
from io import open
import torch
import torch.nn as nn
......@@ -124,7 +126,7 @@ def load_tf_weights_in_transfo_xl(model, config, tf_path):
try:
import numpy as np
import tensorflow as tf
except ModuleNotFoundError:
except ImportError:
print("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions.")
raise
......@@ -239,7 +241,8 @@ class TransfoXLConfig(object):
proj_init_std: parameters initialized by N(0, init_std)
init_std: parameters initialized by N(0, init_std)
"""
if isinstance(vocab_size_or_config_json_file, str):
if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
and isinstance(vocab_size_or_config_json_file, unicode)):
with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
json_config = json.loads(reader.read())
for key, value in json_config.items():
......@@ -503,11 +506,12 @@ class RelMultiHeadAttn(nn.Module):
return x
def _rel_shift(self, x, zero_triu=False):
zero_pad = torch.zeros((x.size(0), 1, *x.size()[2:]),
device=x.device, dtype=x.dtype)
zero_pad_shape = (x.size(0), 1) + x.size()[2:]
zero_pad = torch.zeros(zero_pad_shape, device=x.device, dtype=x.dtype)
x_padded = torch.cat([zero_pad, x], dim=1)
x_padded = x_padded.view(x.size(1) + 1, x.size(0), *x.size()[2:])
x_padded_shape = (x.size(1) + 1, x.size(0)) + x.size()[2:]
x_padded = x_padded.view(*x_padded_shape)
x = x_padded[1:].view_as(x)
......@@ -797,7 +801,8 @@ class AdaptiveEmbedding(nn.Module):
emb_flat.index_copy_(0, indices_i, emb_i)
embed = emb_flat.view(*inp.size(), self.d_proj)
embed_shape = inp.size() + (self.d_proj,)
embed = emb_flat.view(embed_shape)
embed.mul_(self.emb_scale)
......@@ -905,7 +910,7 @@ class TransfoXLPreTrainedModel(nn.Module):
try:
resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir)
resolved_config_file = cached_path(config_file, cache_dir=cache_dir)
except FileNotFoundError:
except EnvironmentError:
logger.error(
"Model name '{}' was not found in model name list ({}). "
"We assumed '{}' was a path or url but couldn't find files {} and {} "
......
pytorch_pretrained_bert/tokenization.py
View file @ 448937c0
......@@ -14,14 +14,13 @@
# limitations under the License.
"""Tokenization classes."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import, division, print_function, unicode_literals
import collections
import unicodedata
import os
import logging
import os
import unicodedata
from io import open
from .file_utils import cached_path
......@@ -129,7 +128,7 @@ class BertTokenizer(object):
# redirect to the cache, if necessary
try:
resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
except FileNotFoundError:
except EnvironmentError:
logger.error(
"Model name '{}' was not found in model name list ({}). "
"We assumed '{}' was a path or url but couldn't find any file "
......
pytorch_pretrained_bert/tokenization_openai.py
View file @ 448937c0
......@@ -13,11 +13,17 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes for OpenAI GPT."""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import json
import logging
import os
import re
import json
import sys
from io import open
from tqdm import tqdm
import logging
from .file_utils import cached_path
......@@ -82,7 +88,7 @@ class OpenAIGPTTokenizer(object):
try:
resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
resolved_merges_file = cached_path(merges_file, cache_dir=cache_dir)
except FileNotFoundError:
except EnvironmentError:
logger.error(
"Model name '{}' was not found in model name list ({}). "
"We assumed '{}' was a path or url but couldn't find files {} and {} "
......@@ -119,7 +125,7 @@ class OpenAIGPTTokenizer(object):
self.max_len = max_len if max_len is not None else int(1e12)
self.nlp = spacy.load('en', disable=['parser', 'tagger', 'ner', 'textcat'])
self.fix_text = ftfy.fix_text
self.encoder = json.load(open(vocab_file))
self.encoder = json.load(open(vocab_file, encoding="utf-8"))
self.decoder = {v:k for k,v in self.encoder.items()}
merges = open(merges_file, encoding='utf-8').read().split('\n')[1:-1]
merges = [tuple(merge.split()) for merge in merges]
......@@ -196,7 +202,7 @@ class OpenAIGPTTokenizer(object):
def convert_tokens_to_ids(self, tokens):
"""Converts a sequence of tokens into ids using the vocab."""
ids = []
if isinstance(tokens, str):
if isinstance(tokens, str) or (sys.version_info[0] == 2 and isinstance(tokens, unicode)):
if tokens in self.special_tokens:
return self.special_tokens[tokens]
else:
......
pytorch_pretrained_bert/tokenization_transfo_xl.py
View file @ 448937c0
......@@ -16,16 +16,27 @@
""" Tokenization classes for Transformer XL model.
Adapted from https://github.com/kimiyoung/transformer-xl.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import os
import glob
import logging
import pickle
import torch
import os
import sys
from collections import Counter, OrderedDict
from io import open
import torch
import numpy as np
from .file_utils import cached_path
if sys.version_info[0] == 2:
import cPickle as pickle
else:
import pickle
logger = logging.getLogger(__name__)
PRETRAINED_VOCAB_ARCHIVE_MAP = {
......@@ -55,7 +66,7 @@ class TransfoXLTokenizer(object):
# redirect to the cache, if necessary
try:
resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir)
except FileNotFoundError:
except EnvironmentError:
logger.error(
"Model name '{}' was not found in model name list ({}). "
"We assumed '{}' was a path or url but couldn't find files {} "
......@@ -422,7 +433,7 @@ class TransfoXLCorpus(object):
# redirect to the cache, if necessary
try:
resolved_corpus_file = cached_path(corpus_file, cache_dir=cache_dir)
except FileNotFoundError:
except EnvironmentError:
logger.error(
"Model name '{}' was not found in model name list ({}). "
"We assumed '{}' was a path or url but couldn't find files {} "
......
setup.py
View file @ 448937c0
......@@ -33,6 +33,7 @@ To create the package for pypi.
7. Copy the release notes from RELEASE.md to the tag in github once everything is looking hunky-dory.
"""
from io import open
from setuptools import find_packages, setup
setup(
......@@ -58,7 +59,7 @@ setup(
"pytorch_pretrained_bert=pytorch_pretrained_bert.__main__:main",
]
},
python_requires='>=3.5.0',
# python_requires='>=3.5.0',
tests_require=['pytest'],
classifiers=[
'Intended Audience :: Science/Research',
......
tests/tokenization_test.py
View file @ 448937c0
......@@ -18,6 +18,7 @@ from __future__ import print_function
import os
import unittest
from io import open
from pytorch_pretrained_bert.tokenization import (BertTokenizer, BasicTokenizer, WordpieceTokenizer,
_is_whitespace, _is_control, _is_punctuation)
......@@ -30,7 +31,7 @@ class TokenizationTest(unittest.TestCase):
"[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn",
"##ing", ","
]
with open("/tmp/bert_tokenizer_test.txt", "w") as vocab_writer:
with open("/tmp/bert_tokenizer_test.txt", "w", encoding='utf-8') as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
vocab_file = vocab_writer.name
......@@ -49,7 +50,7 @@ class TokenizationTest(unittest.TestCase):
"[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn",
"##ing", ","
]
with open("/tmp/bert_tokenizer_test.txt", "w") as vocab_writer:
with open("/tmp/bert_tokenizer_test.txt", "w", encoding='utf-8') as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
vocab_file = vocab_writer.name
......
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