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4a0b59e980
transformers/run_classifier_pytorch.py
thomwolf 4a0b59e980 run_classifier WIP
2018-11-01 21:05:04 +01:00

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# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""BERT finetuning runner."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import csv
import os
from modeling_pytorch import BertConfig, BertModel
from optimization_pytorch import BERTAdam
# import optimization
import tokenization_pytorch
import torch
import logging
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__)
import argparse
parser = argparse.ArgumentParser()
## Required parameters
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("--bert_config_file",
default = None,
type = str,
required = True,
help = "The config json file corresponding to the pre-trained BERT model. \n"
"This specifies the model architecture.")
parser.add_argument("--task_name",
default = None,
type = str,
required = True,
help = "The name of the task to train.")
parser.add_argument("--vocab_file",
default = None,
type = str,
required = True,
help = "The vocabulary file that the BERT model was trained on.")
parser.add_argument("--output_dir",
default = None,
type = str,
required = True,
help = "The output directory where the model checkpoints will be written.")
## Other parameters
parser.add_argument("--init_checkpoint",
default = None,
type = str,
help = "Initial checkpoint (usually from a pre-trained BERT model).")
parser.add_argument("--do_lower_case",
default = True,
type = bool,
help = "Whether to lower case the input text. Should be True for uncased models and False for cased models.")
parser.add_argument("--max_seq_length",
default = 128,
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("--do_train",
default = False,
type = bool,
help = "Whether to run training.")
parser.add_argument("--do_eval",
default = False,
type = bool,
help = "Whether to run eval on the dev set.")
parser.add_argument("--train_batch_size",
default = 32,
type = int,
help = "Total batch size for training.")
parser.add_argument("--eval_batch_size",
default = 8,
type = int,
help = "Total batch size for eval.")
parser.add_argument("--learning_rate",
default = 5e-5,
type = float,
help = "The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default = 3.0,
type = float,
help = "Total number of training epochs to perform.")
parser.add_argument("--warmup_proportion",
default = 0.1,
type = float,
help = "Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--save_checkpoints_steps",
default = 1000,
type = int,
help = "How often to save the model checkpoint.")
parser.add_argument("--iterations_per_loop",
default = 1000,
type = int,
help = "How many steps to make in each estimator call.")
parser.add_argument("--no_cuda",
default = False,
type = bool,
help = "Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help = "local_rank for distributed training on gpus")
### BEGIN - TO DELETE EVENTUALLY --> NO SENSE IN PYTORCH ###
parser.add_argument("--use_tpu",
default = False,
type = bool,
help = "Whether to use TPU or GPU/CPU.")
parser.add_argument("--tpu_name",
default = None,
type = str,
help = "The Cloud TPU to use for training. This should be either the name "
"used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
"url.")
parser.add_argument("--tpu_zone",
default = None,
type = str,
help = "[Optional] GCE zone where the Cloud TPU is located in. If not "
"specified, we will attempt to automatically detect the GCE project from "
"metadata.")
parser.add_argument("--gcp_project",
default = None,
type = str,
help = "[Optional] Project name for the Cloud TPU-enabled project. If not "
"specified, we will attempt to automatically detect the GCE project from "
"metadata.")
parser.add_argument("--master",
default = None,
type = str,
help = "[Optional] TensorFlow master URL.")
parser.add_argument("--num_tpu_cores",
default = 8,
type = int,
help = "Only used if `use_tpu` is True. Total number of TPU cores to use.")
### END - TO DELETE EVENTUALLY --> NO SENSE IN PYTORCH ###
args = parser.parse_args()
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):
self.input_ids = input_ids
self.input_mask = input_mask
self.segment_ids = segment_ids
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 open(input_file, "r") as f:
reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
lines = []
for line in reader:
lines.append(line)
return lines
class MrpcProcessor(DataProcessor):
"""Processor for the MRPC data set (GLUE version)."""
def get_train_examples(self, data_dir):
"""See base class."""
print("LOOKING AT {}".format(os.path.join(data_dir, "train.tsv")))
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_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 = tokenization_pytorch.convert_to_unicode(line[3])
text_b = tokenization_pytorch.convert_to_unicode(line[4])
label = tokenization_pytorch.convert_to_unicode(line[0])
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
class MnliProcessor(DataProcessor):
"""Processor for the MultiNLI 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_matched.tsv")),
"dev_matched")
def get_labels(self):
"""See base class."""
return ["contradiction", "entailment", "neutral"]
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, tokenization_pytorch.convert_to_unicode(line[0]))
text_a = tokenization_pytorch.convert_to_unicode(line[8])
text_b = tokenization_pytorch.convert_to_unicode(line[9])
label = tokenization_pytorch.convert_to_unicode(line[-1])
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
class ColaProcessor(DataProcessor):
"""Processor for the CoLA 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_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):
guid = "%s-%s" % (set_type, i)
text_a = tokenization_pytorch.convert_to_unicode(line[3])
label = tokenization_pytorch.convert_to_unicode(line[1])
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
return examples
def convert_examples_to_features(examples, label_list, max_seq_length,
tokenizer):
"""Loads a data file into a list of `InputBatch`s."""
label_map = {}
for (i, label) in enumerate(label_list):
label_map[label] = i
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)
if tokens_b:
# Modifies `tokens_a` and `tokens_b` in place so that the total
# length is less than the specified length.
# Account for [CLS], [SEP], [SEP] with "- 3"
_truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
else:
# Account for [CLS] and [SEP] with "- 2"
if len(tokens_a) > max_seq_length - 2:
tokens_a = tokens_a[0:(max_seq_length - 2)]
# The convention in BERT is:
# (a) For sequence pairs:
# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
# (b) For single sequences:
# tokens: [CLS] the dog is hairy . [SEP]
# type_ids: 0 0 0 0 0 0 0
#
# Where "type_ids" are used to indicate whether this is the first
# sequence or the second sequence. The embedding vectors for `type=0` and
# `type=1` were learned during pre-training and are added to the wordpiece
# embedding vector (and position vector). This is not *strictly* necessary
# since the [SEP] token unambigiously separates the sequences, but it makes
# it easier for the model to learn the concept of sequences.
#
# For classification tasks, the first vector (corresponding to [CLS]) is
# used as as the "sentence vector". Note that this only makes sense because
# the entire model is fine-tuned.
tokens = []
segment_ids = []
tokens.append("[CLS]")
segment_ids.append(0)
for token in tokens_a:
tokens.append(token)
segment_ids.append(0)
tokens.append("[SEP]")
segment_ids.append(0)
if tokens_b:
for token in tokens_b:
tokens.append(token)
segment_ids.append(1)
tokens.append("[SEP]")
segment_ids.append(1)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
input_mask = [1] * len(input_ids)
# Zero-pad up to the sequence length.
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(0)
segment_ids.append(0)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
label_id = label_map[example.label]
if ex_index < 5:
logger.info("*** Example ***")
logger.info("guid: %s" % (example.guid))
logger.info("tokens: %s" % " ".join(
[tokenization_pytorch.printable_text(x) for x in tokens]))
logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
logger.info(
"segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
logger.info("label: %s (id = %d)" % (example.label, label_id))
features.append(
InputFeatures(
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
label_id=label_id))
return features
def _truncate_seq_pair(tokens_a, tokens_b, max_length):
"""Truncates a sequence pair 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)
if total_length <= max_length:
break
if len(tokens_a) > len(tokens_b):
tokens_a.pop()
else:
tokens_b.pop()
def input_fn_builder(features, seq_length, is_training, drop_remainder):
"""Creates an `input_fn` closure to be passed to TPUEstimator.""" ### ATTENTION - To rewrite ###
all_input_ids = []
all_input_mask = []
all_segment_ids = []
all_label_ids = []
for feature in features:
all_input_ids.append(feature.input_ids)
all_input_mask.append(feature.input_mask)
all_segment_ids.append(feature.segment_ids)
all_label_ids.append(feature.label_id)
def input_fn(params):
"""The actual input function."""
batch_size = params["batch_size"]
num_examples = len(features)
device = torch.device("cuda") if args.use_gpu else torch.device("cpu")
d = torch.utils.data.TensorDataset({ ## BUG THIS IS NOT WORKING.... ###
"input_ids": torch.IntTensor(all_input_ids, device=device), #Requires_grad=False by default
"input_mask": torch.IntTensor(all_input_mask, device=device),
"segment_ids": torch.IntTensor(all_segment_ids, device=device),
"label_ids": torch.IntTensor(all_label_ids, device=device)
})
shuffle = True if is_training else False
d = torch.utils.data.DataLoader(dataset=d, batch_size=batch_size,
shuffle=shuffle,drop_last=drop_remainder)
# Cf https://pytorch.org/tutorials/beginner/data_loading_tutorial.html
return d
return input_fn
def main(_):
processors = {
"cola": ColaProcessor,
"mnli": MnliProcessor,
"mrpc": MrpcProcessor,
}
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
device = torch.device("cuda", args.local_rank)
n_gpu = 1
print("Initializing the distributed backend: NCCL")
print("device", device, "n_gpu", n_gpu)
if not args.do_train and not args.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
bert_config = BertConfig.from_json_file(args.bert_config_file)
if args.max_seq_length > bert_config.max_position_embeddings:
raise ValueError(
"Cannot use sequence length %d because the BERT model "
"was only trained up to sequence length %d" %
(args.max_seq_length, bert_config.max_position_embeddings))
if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
raise ValueError(f"Output directory ({args.output_dir}) already exists and is "
f"not empty.")
os.makedirs(args.output_dir, exist_ok=True)
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
label_list = processor.get_labels()
tokenizer = tokenization_pytorch.FullTokenizer(
vocab_file=args.vocab_file, do_lower_case=args.do_lower_case)
train_examples = None
num_train_steps = None
if args.do_train:
train_examples = processor.get_train_examples(args.data_dir)
num_train_steps = int(
len(train_examples) / args.train_batch_size * args.num_train_epochs)
model = BertModel(bert_config)
if args.init_checkpoint is not None:
model.load_state_dict(torch.load(args.init_checkpoint, map_location='cpu'))
model.to(device)
optimizer = BERTAdam([{'params': [p for n, p in model.named_parameters() if n != 'bias'], 'l2': 0.01},
{'params': [p for n, p in model.named_parameters() if n != 'bias']}
],
lr=args.learning_rate, schedule='warmup_linear',
warmup=args.warmup_proportion,
t_total=num_train_steps)
if args.do_train:
train_features = convert_examples_to_features(
train_examples, label_list, args.max_seq_length, tokenizer)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_steps)
train_input = input_fn_builder(
features=train_features,
seq_length=args.max_seq_length,
is_training=True,
drop_remainder=True)
# estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
for batch_ix, batch in train_input:
output = model_fn(batch)
loss = output["loss"]
loss.backward()
if args.do_eval:
eval_examples = processor.get_dev_examples(args.data_dir)
eval_features = convert_examples_to_features(
eval_examples, label_list, args.max_seq_length, tokenizer)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
# This tells the estimator to run through the entire set.
eval_steps = None
# However, if running eval on the TPU, you will need to specify the
# number of steps.
if args.use_tpu:
# Eval will be slightly WRONG on the TPU because it will truncate
# the last batch.
eval_steps = int(len(eval_examples) / args.eval_batch_size)
eval_drop_remainder = True if args.use_tpu else False
eval_input_fn = input_fn_builder(
features=eval_features,
seq_length=args.max_seq_length,
is_training=False,
drop_remainder=eval_drop_remainder)
result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps)
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
if __name__ == "__main__":
main()
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