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clean up examples - updated to new keyword inputs - #1246

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thomwolf
2019-09-18 10:01:27 +02:00
parent 62760baf46
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examples/contrib/README.md Normal file
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# Community contributed examples
This folder contains examples which are not actively maintained (mostly contributed by the community).
Using these examples together with a recent version of the library usually requires to make small (sometimes big) adaptations to get the scripts working.

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examples/contrib/run_openai_gpt.py Normal file
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# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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.
""" OpenAI GPT model fine-tuning script.
Adapted from https://github.com/huggingface/pytorch-openai-transformer-lm/blob/master/train.py
It self adapted from https://github.com/openai/finetune-transformer-lm/blob/master/train.py
This script with default values fine-tunes and evaluate a pretrained OpenAI GPT on the RocStories dataset:
python run_openai_gpt.py \
--model_name openai-gpt \
--do_train \
--do_eval \
--train_dataset $ROC_STORIES_DIR/cloze_test_val__spring2016\ -\ cloze_test_ALL_val.csv \
--eval_dataset $ROC_STORIES_DIR/cloze_test_test__spring2016\ -\ cloze_test_ALL_test.csv \
--output_dir ../log \
--train_batch_size 16 \
"""
import argparse
import os
import csv
import random
import logging
from tqdm import tqdm, trange
import numpy as np
import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
TensorDataset)
from pytorch_transformers import (OpenAIGPTDoubleHeadsModel, OpenAIGPTTokenizer,
AdamW, cached_path, WEIGHTS_NAME, CONFIG_NAME,
WarmupLinearSchedule)
ROCSTORIES_URL = "https://s3.amazonaws.com/datasets.huggingface.co/ROCStories.tar.gz"
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 accuracy(out, labels):
outputs = np.argmax(out, axis=1)
return np.sum(outputs == labels)
def load_rocstories_dataset(dataset_path):
""" Output a list of tuples(story, 1st continuation, 2nd continuation, label) """
with open(dataset_path, encoding='utf_8') as f:
f = csv.reader(f)
output = []
next(f) # skip the first line
for line in tqdm(f):
output.append((' '.join(line[1:5]), line[5], line[6], int(line[-1])-1))
return output
def pre_process_datasets(encoded_datasets, input_len, cap_length, start_token, delimiter_token, clf_token):
""" Pre-process datasets containing lists of tuples(story, 1st continuation, 2nd continuation, label)
To Transformer inputs of shape (n_batch, n_alternative, length) comprising for each batch, continuation:
input_ids[batch, alternative, :] = [start_token] + story[:cap_length] + [delimiter_token] + cont1[:cap_length] + [clf_token]
"""
tensor_datasets = []
for dataset in encoded_datasets:
n_batch = len(dataset)
input_ids = np.zeros((n_batch, 2, input_len), dtype=np.int64)
mc_token_ids = np.zeros((n_batch, 2), dtype=np.int64)
lm_labels = np.full((n_batch, 2, input_len), fill_value=-1, dtype=np.int64)
mc_labels = np.zeros((n_batch,), dtype=np.int64)
for i, (story, cont1, cont2, mc_label), in enumerate(dataset):
with_cont1 = [start_token] + story[:cap_length] + [delimiter_token] + cont1[:cap_length] + [clf_token]
with_cont2 = [start_token] + story[:cap_length] + [delimiter_token] + cont2[:cap_length] + [clf_token]
input_ids[i, 0, :len(with_cont1)] = with_cont1
input_ids[i, 1, :len(with_cont2)] = with_cont2
mc_token_ids[i, 0] = len(with_cont1) - 1
mc_token_ids[i, 1] = len(with_cont2) - 1
lm_labels[i, 0, :len(with_cont1)] = with_cont1
lm_labels[i, 1, :len(with_cont2)] = with_cont2
mc_labels[i] = mc_label
all_inputs = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(t) for t in all_inputs))
return tensor_datasets
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model_name', type=str, default='openai-gpt',
help='pretrained model name')
parser.add_argument("--do_train", action='store_true', help="Whether to run training.")
parser.add_argument("--do_eval", action='store_true', help="Whether to run eval on the dev set.")
parser.add_argument("--output_dir", default=None, type=str, required=True,
help="The output directory where the model predictions and checkpoints will be written.")
parser.add_argument('--train_dataset', type=str, default='')
parser.add_argument('--eval_dataset', type=str, default='')
parser.add_argument('--seed', type=int, default=42)
parser.add_argument('--num_train_epochs', type=int, default=3)
parser.add_argument('--train_batch_size', type=int, default=8)
parser.add_argument('--eval_batch_size', type=int, default=16)
parser.add_argument("--adam_epsilon", default=1e-8, type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument('--max_grad_norm', type=int, default=1)
parser.add_argument("--max_steps", default=-1, type=int,
help="If > 0: set total number of training \
steps to perform. Override num_train_epochs.")
parser.add_argument('--gradient_accumulation_steps', type=int, default=1,
help="Number of updates steps to accumulate before\
performing a backward/update pass.")
parser.add_argument('--learning_rate', type=float, default=6.25e-5)
parser.add_argument("--warmup_steps", default=0, type=int,
help="Linear warmup over warmup_steps.")
parser.add_argument('--lr_schedule', type=str, default='warmup_linear')
parser.add_argument('--weight_decay', type=float, default=0.01)
parser.add_argument('--lm_coef', type=float, default=0.9)
parser.add_argument('--n_valid', type=int, default=374)
parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.")
parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.")
args = parser.parse_args()
print(args)
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
ptvsd.wait_for_attach()
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = torch.cuda.device_count()
logger.info("device: {}, n_gpu {}".format(device, 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.")
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
special_tokens = ['_start_', '_delimiter_', '_classify_']
tokenizer = OpenAIGPTTokenizer.from_pretrained(args.model_name, special_tokens=special_tokens)
special_tokens_ids = list(tokenizer.convert_tokens_to_ids(token) for token in special_tokens)
model = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name, num_special_tokens=len(special_tokens))
model.to(device)
# Load and encode the datasets
if not args.train_dataset and not args.eval_dataset:
roc_stories = cached_path(ROCSTORIES_URL)
def tokenize_and_encode(obj):
""" Tokenize and encode a nested object """
if isinstance(obj, str):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(obj))
elif isinstance(obj, int):
return obj
return list(tokenize_and_encode(o) for o in obj)
logger.info("Encoding dataset...")
train_dataset = load_rocstories_dataset(args.train_dataset)
eval_dataset = load_rocstories_dataset(args.eval_dataset)
datasets = (train_dataset, eval_dataset)
encoded_datasets = tokenize_and_encode(datasets)
# Compute the max input length for the Transformer
max_length = model.config.n_positions // 2 - 2
input_length = max(len(story[:max_length]) + max(len(cont1[:max_length]), len(cont2[:max_length])) + 3 \
for dataset in encoded_datasets for story, cont1, cont2, _ in dataset)
input_length = min(input_length, model.config.n_positions) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
tensor_datasets = pre_process_datasets(encoded_datasets, input_length, max_length, *special_tokens_ids)
train_tensor_dataset, eval_tensor_dataset = tensor_datasets[0], tensor_datasets[1]
train_data = TensorDataset(*train_tensor_dataset)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
eval_data = TensorDataset(*eval_tensor_dataset)
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
t_total = args.max_steps
args.num_train_epochs = args.max_steps //\
(len(train_dataloader) // args.gradient_accumulation_steps) + 1
else:
t_total = len(train_dataloader)\
// args.gradient_accumulation_steps * args.num_train_epochs
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=t_total)
if args.do_train:
nb_tr_steps, tr_loss, exp_average_loss = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs), desc="Epoch"):
tr_loss = 0
nb_tr_steps = 0
tqdm_bar = tqdm(train_dataloader, desc="Training")
for step, batch in enumerate(tqdm_bar):
batch = tuple(t.to(device) for t in batch)
input_ids, mc_token_ids, lm_labels, mc_labels = batch
losses = model(input_ids, mc_token_ids=mc_token_ids, lm_labels=lm_labels, mc_labels=mc_labels)
loss = args.lm_coef * losses[0] + losses[1]
loss.backward()
scheduler.step()
optimizer.step()
optimizer.zero_grad()
tr_loss += loss.item()
exp_average_loss = loss.item() if exp_average_loss is None else 0.7*exp_average_loss+0.3*loss.item()
nb_tr_steps += 1
tqdm_bar.desc = "Training loss: {:.2e} lr: {:.2e}".format(exp_average_loss, scheduler.get_lr()[0])
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self
# If we save using the predefined names, we can load using `from_pretrained`
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
torch.save(model_to_save.state_dict(), output_model_file)
model_to_save.config.to_json_file(output_config_file)
tokenizer.save_vocabulary(args.output_dir)
# Load a trained model and vocabulary that you have fine-tuned
model = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir)
tokenizer = OpenAIGPTTokenizer.from_pretrained(args.output_dir)
model.to(device)
if args.do_eval:
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
for batch in tqdm(eval_dataloader, desc="Evaluating"):
batch = tuple(t.to(device) for t in batch)
input_ids, mc_token_ids, lm_labels, mc_labels = batch
with torch.no_grad():
_, mc_loss, _, mc_logits = model(input_ids, mc_token_ids=mc_token_ids, lm_labels=lm_labels, mc_labels=mc_labels)
mc_logits = mc_logits.detach().cpu().numpy()
mc_labels = mc_labels.to('cpu').numpy()
tmp_eval_accuracy = accuracy(mc_logits, mc_labels)
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
train_loss = tr_loss/nb_tr_steps if args.do_train else None
result = {'eval_loss': eval_loss,
'eval_accuracy': eval_accuracy,
'train_loss': train_loss}
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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# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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
import argparse
import csv
import logging
import os
import random
import sys
from io import open
import numpy as np
import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
TensorDataset)
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange
from pytorch_transformers.file_utils import PYTORCH_PRETRAINED_BERT_CACHE, WEIGHTS_NAME, CONFIG_NAME
from pytorch_transformers.modeling_bert import BertForMultipleChoice, BertConfig
from pytorch_transformers.optimization import AdamW, WarmupLinearSchedule
from pytorch_transformers.tokenization_bert import BertTokenizer
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__)
class SwagExample(object):
"""A single training/test example for the SWAG dataset."""
def __init__(self,
swag_id,
context_sentence,
start_ending,
ending_0,
ending_1,
ending_2,
ending_3,
label = None):
self.swag_id = swag_id
self.context_sentence = context_sentence
self.start_ending = start_ending
self.endings = [
ending_0,
ending_1,
ending_2,
ending_3,
]
self.label = label
def __str__(self):
return self.__repr__()
def __repr__(self):
l = [
"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("label: {}".format(self.label))
return ", ".join(l)
class InputFeatures(object):
def __init__(self,
example_id,
choices_features,
label
):
self.example_id = example_id
self.choices_features = [
{
'input_ids': input_ids,
'input_mask': input_mask,
'segment_ids': segment_ids
}
for _, input_ids, input_mask, segment_ids in choices_features
]
self.label = label
def read_swag_examples(input_file, is_training):
with open(input_file, 'r', encoding='utf-8') as f:
reader = csv.reader(f)
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(
"For training, the input file must contain a label column."
)
examples = [
SwagExample(
swag_id = line[2],
context_sentence = line[4],
start_ending = line[5], # in the swag dataset, the
# common beginning of each
# choice is stored in "sent2".
ending_0 = line[7],
ending_1 = line[8],
ending_2 = line[9],
ending_3 = line[10],
label = int(line[11]) if is_training else None
) for line in lines[1:] # we skip the line with the column names
]
return examples
def convert_examples_to_features(examples, tokenizer, max_seq_length,
is_training):
"""Loads a data file into a list of `InputBatch`s."""
# Swag is a multiple choice task. To perform this task using Bert,
# we will use the formatting proposed in "Improving Language
# Understanding by Generative Pre-Training" and suggested by
# @jacobdevlin-google in this issue
# https://github.com/google-research/bert/issues/38.
#
# Each choice will correspond to a sample on which we run the
# inference. For a given Swag example, we will create the 4
# following inputs:
# - [CLS] context [SEP] choice_1 [SEP]
# - [CLS] context [SEP] choice_2 [SEP]
# - [CLS] context [SEP] choice_3 [SEP]
# - [CLS] context [SEP] choice_4 [SEP]
# The model will output a single value for each input. To get the
# final decision of the model, we will run a softmax over these 4
# outputs.
features = []
for example_index, example in enumerate(examples):
context_tokens = tokenizer.tokenize(example.context_sentence)
start_ending_tokens = tokenizer.tokenize(example.start_ending)
choices_features = []
for ending_index, ending in enumerate(example.endings):
# We create a copy of the context tokens in order to be
# able to shrink it according to ending_tokens
context_tokens_choice = context_tokens[:]
ending_tokens = start_ending_tokens + tokenizer.tokenize(ending)
# Modifies `context_tokens_choice` and `ending_tokens` in
# place so that the total length is less than the
# specified length. Account for [CLS], [SEP], [SEP] with
# "- 3"
_truncate_seq_pair(context_tokens_choice, ending_tokens, max_seq_length - 3)
tokens = ["[CLS]"] + context_tokens_choice + ["[SEP]"] + ending_tokens + ["[SEP]"]
segment_ids = [0] * (len(context_tokens_choice) + 2) + [1] * (len(ending_tokens) + 1)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
input_mask = [1] * len(input_ids)
# Zero-pad up to the sequence length.
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
choices_features.append((tokens, input_ids, input_mask, segment_ids))
label = example.label
if example_index < 5:
logger.info("*** Example ***")
logger.info("swag_id: {}".format(example.swag_id))
for choice_idx, (tokens, input_ids, input_mask, segment_ids) in enumerate(choices_features):
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("label: {}".format(label))
features.append(
InputFeatures(
example_id = example.swag_id,
choices_features = choices_features,
label = label
)
)
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 accuracy(out, labels):
outputs = np.argmax(out, axis=1)
return np.sum(outputs == labels)
def select_field(features, field):
return [
[
choice[field]
for choice in feature.choices_features
]
for feature in features
]
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--data_dir",
default=None,
type=str,
required=True,
help="The input data dir. Should contain the .csv files (or other data files) for the task.")
parser.add_argument("--bert_model", default=None, type=str, required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
"bert-base-multilingual-cased, bert-base-chinese.")
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("--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",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
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("--no_cuda",
action='store_true',
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")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument('--gradient_accumulation_steps',
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.")
parser.add_argument('--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument('--loss_scale',
type=float, default=0,
help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
args = parser.parse_args()
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:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
args.gradient_accumulation_steps))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
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))
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)
# Prepare model
model = BertForMultipleChoice.from_pretrained(args.bert_model,
cache_dir=os.path.join(str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed_{}'.format(args.local_rank)),
num_choices=4)
if args.fp16:
model.half()
model.to(device)
if args.local_rank != -1:
try:
from apex.parallel import DistributedDataParallel as DDP
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
model = DDP(model)
elif n_gpu > 1:
model = torch.nn.DataParallel(model)
if args.do_train:
# Prepare data loader
train_examples = read_swag_examples(os.path.join(args.data_dir, 'train.csv'), is_training = True)
train_features = convert_examples_to_features(
train_examples, tokenizer, args.max_seq_length, True)
all_input_ids = torch.tensor(select_field(train_features, 'input_ids'), dtype=torch.long)
all_input_mask = torch.tensor(select_field(train_features, 'input_mask'), dtype=torch.long)
all_segment_ids = torch.tensor(select_field(train_features, 'segment_ids'), dtype=torch.long)
all_label = torch.tensor([f.label for f in train_features], dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
if args.local_rank == -1:
train_sampler = RandomSampler(train_data)
else:
train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
num_train_optimization_steps = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size()
# Prepare optimizer
param_optimizer = list(model.named_parameters())
# hack to remove pooler, which is not used
# thus it produce None grad that break apex
param_optimizer = [n for n in param_optimizer]
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
if args.fp16:
try:
from apex.optimizers import FP16_Optimizer
from apex.optimizers import FusedAdam
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
bias_correction=False,
max_grad_norm=1.0)
if args.loss_scale == 0:
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale)
warmup_linear = WarmupLinearSchedule(warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
else:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
global_step = 0
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_optimization_steps)
model.train()
for _ in trange(int(args.num_train_epochs), desc="Epoch"):
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
loss = model(input_ids, token_types_ids=segment_ids, attention_mask=input_mask, labels=label_ids)
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.fp16 and args.loss_scale != 1.0:
# rescale loss for fp16 training
# see https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html
loss = loss * args.loss_scale
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
# modify learning rate with special warm up BERT uses
# if args.fp16 is False, BertAdam is used that handles this automatically
lr_this_step = args.learning_rate * warmup_linear.get_lr(global_step, args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()
optimizer.zero_grad()
global_step += 1
if args.do_train:
# Save a trained model, configuration and tokenizer
model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self
# If we save using the predefined names, we can load using `from_pretrained`
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
torch.save(model_to_save.state_dict(), output_model_file)
model_to_save.config.to_json_file(output_config_file)
tokenizer.save_vocabulary(args.output_dir)
# Load a trained model and vocabulary that you have fine-tuned
model = BertForMultipleChoice.from_pretrained(args.output_dir, num_choices=4)
tokenizer = BertTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
else:
model = BertForMultipleChoice.from_pretrained(args.bert_model, num_choices=4)
model.to(device)
if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
eval_examples = read_swag_examples(os.path.join(args.data_dir, 'val.csv'), is_training = True)
eval_features = convert_examples_to_features(
eval_examples, tokenizer, args.max_seq_length, True)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor(select_field(eval_features, 'input_ids'), dtype=torch.long)
all_input_mask = torch.tensor(select_field(eval_features, 'input_mask'), dtype=torch.long)
all_segment_ids = torch.tensor(select_field(eval_features, 'segment_ids'), dtype=torch.long)
all_label = torch.tensor([f.label for f in eval_features], dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
for input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
tmp_eval_loss = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=label_ids)
logits = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask)
logits = logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
tmp_eval_accuracy = accuracy(logits, label_ids)
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
result = {'eval_loss': eval_loss,
'eval_accuracy': eval_accuracy,
'global_step': global_step,
'loss': tr_loss/global_step}
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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examples/contrib/run_transfo_xl.py Normal file
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# coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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.
""" PyTorch Transformer XL model evaluation script.
Adapted from https://github.com/kimiyoung/transformer-xl.
In particular https://github.com/kimiyoung/transformer-xl/blob/master/pytorch/eval.py
This script with default values evaluates a pretrained Transformer-XL on WikiText 103
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import argparse
import logging
import time
import math
import torch
from pytorch_transformers import TransfoXLLMHeadModel, TransfoXLCorpus, TransfoXLTokenizer
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 main():
parser = argparse.ArgumentParser(description='PyTorch Transformer Language Model')
parser.add_argument('--model_name', type=str, default='transfo-xl-wt103',
help='pretrained model name')
parser.add_argument('--split', type=str, default='test',
choices=['all', 'valid', 'test'],
help='which split to evaluate')
parser.add_argument('--batch_size', type=int, default=10,
help='batch size')
parser.add_argument('--tgt_len', type=int, default=128,
help='number of tokens to predict')
parser.add_argument('--ext_len', type=int, default=0,
help='length of the extended context')
parser.add_argument('--mem_len', type=int, default=1600,
help='length of the retained previous heads')
parser.add_argument('--clamp_len', type=int, default=1000,
help='max positional embedding index')
parser.add_argument('--no_cuda', action='store_true',
help='Do not use CUDA even though CUA is available')
parser.add_argument('--work_dir', type=str, required=True,
help='path to the work_dir')
parser.add_argument('--no_log', action='store_true',
help='do not log the eval result')
parser.add_argument('--same_length', action='store_true',
help='set same length attention with masking')
parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.")
parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.")
args = parser.parse_args()
assert args.ext_len >= 0, 'extended context length must be non-negative'
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
ptvsd.wait_for_attach()
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
logger.info("device: {}".format(device))
# Load a pre-processed dataset
# You can also build the corpus yourself using TransfoXLCorpus methods
# The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
# and tokenizing the dataset
# The pre-processed corpus is a convertion (using the conversion script )
tokenizer = TransfoXLTokenizer.from_pretrained(args.model_name)
corpus = TransfoXLCorpus.from_pretrained(args.model_name)
ntokens = len(corpus.vocab)
va_iter = corpus.get_iterator('valid', args.batch_size, args.tgt_len,
device=device, ext_len=args.ext_len)
te_iter = corpus.get_iterator('test', args.batch_size, args.tgt_len,
device=device, ext_len=args.ext_len)
# Load a pre-trained model
model = TransfoXLLMHeadModel.from_pretrained(args.model_name)
model = model.to(device)
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)
if args.clamp_len > 0:
model.clamp_len = args.clamp_len
if args.same_length:
model.same_length = True
###############################################################################
# Evaluation code
###############################################################################
def evaluate(eval_iter):
# Turn on evaluation mode which disables dropout.
model.eval()
total_len, total_loss = 0, 0.
start_time = time.time()
with torch.no_grad():
mems = None
for idx, (data, target, seq_len) in enumerate(eval_iter):
ret = model(data, lm_labels=target, mems=mems)
loss, _, mems = ret
loss = loss.mean()
total_loss += seq_len * loss.item()
total_len += seq_len
total_time = time.time() - start_time
logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(
total_time, 1000 * total_time / (idx+1)))
return total_loss / total_len
# Run on test data.
if args.split == 'all':
test_loss = evaluate(te_iter)
valid_loss = evaluate(va_iter)
elif args.split == 'valid':
valid_loss = evaluate(va_iter)
test_loss = None
elif args.split == 'test':
test_loss = evaluate(te_iter)
valid_loss = None
def format_log(loss, split):
log_str = '| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(
split, loss, math.exp(loss))
return log_str
log_str = ''
if valid_loss is not None:
log_str += format_log(valid_loss, 'valid')
if test_loss is not None:
log_str += format_log(test_loss, 'test')
logger.info('=' * 100)
logger.info(log_str)
logger.info('=' * 100)
if __name__ == '__main__':
main()