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Commit b219029c authored by erenup's avatar erenup
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refactoring old run_swag. This script is mainly refatored from run_squad in pytorch_transformers

parent 70607664
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examples/single_model_scripts/run_swag.py
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...@@ -13,17 +13,18 @@ ...@@ -13,17 +13,18 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
"""BERT finetuning runner.""" """BERT finetuning runner.
Finetuning the library models for multiple choice on SWAG (Bert).
from __future__ import absolute_import """
from __future__ import absolute_import, division, print_function
import argparse import argparse
import csv
import logging import logging
import csv
import os import os
import random import random
import sys import sys
from io import open import glob
import numpy as np import numpy as np
import torch import torch
...@@ -32,16 +33,21 @@ from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler, ...@@ -32,16 +33,21 @@ from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
from torch.utils.data.distributed import DistributedSampler from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm, trange from tqdm import tqdm, trange
from pytorch_transformers.file_utils import PYTORCH_PRETRAINED_BERT_CACHE, WEIGHTS_NAME, CONFIG_NAME from tensorboardX import SummaryWriter
from pytorch_transformers.modeling_bert import BertForMultipleChoice, BertConfig
from pytorch_transformers.optimization import AdamW, WarmupLinearSchedule from pytorch_transformers import (WEIGHTS_NAME, BertConfig,
from pytorch_transformers.tokenization_bert import BertTokenizer BertForMultipleChoice, BertTokenizer)
from pytorch_transformers import AdamW, WarmupLinearSchedule
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__) logger = logging.getLogger(__name__)
ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) \
for conf in [BertConfig]), ())
MODEL_CLASSES = {
'bert': (BertConfig, BertForMultipleChoice, BertTokenizer),
}
class SwagExample(object): class SwagExample(object):
"""A single training/test example for the SWAG dataset.""" """A single training/test example for the SWAG dataset."""
...@@ -84,7 +90,6 @@ class SwagExample(object): ...@@ -84,7 +90,6 @@ class SwagExample(object):
return ", ".join(l) return ", ".join(l)
class InputFeatures(object): class InputFeatures(object):
def __init__(self, def __init__(self,
example_id, example_id,
...@@ -103,8 +108,7 @@ class InputFeatures(object): ...@@ -103,8 +108,7 @@ class InputFeatures(object):
] ]
self.label = label self.label = label
def read_swag_examples(input_file, is_training=True):
def read_swag_examples(input_file, is_training):
with open(input_file, 'r', encoding='utf-8') as f: with open(input_file, 'r', encoding='utf-8') as f:
reader = csv.reader(f) reader = csv.reader(f)
lines = [] lines = []
...@@ -156,7 +160,7 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length, ...@@ -156,7 +160,7 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length,
# final decision of the model, we will run a softmax over these 4 # final decision of the model, we will run a softmax over these 4
# outputs. # outputs.
features = [] features = []
for example_index, example in enumerate(examples): for example_index, example in tqdm(enumerate(examples)):
context_tokens = tokenizer.tokenize(example.context_sentence) context_tokens = tokenizer.tokenize(example.context_sentence)
start_ending_tokens = tokenizer.tokenize(example.start_ending) start_ending_tokens = tokenizer.tokenize(example.start_ending)
...@@ -242,314 +246,428 @@ def select_field(features, field): ...@@ -242,314 +246,428 @@ def select_field(features, field):
for feature in 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
def set_seed(args):
random.seed(args.seed) random.seed(args.seed)
np.random.seed(args.seed) np.random.seed(args.seed)
torch.manual_seed(args.seed) torch.manual_seed(args.seed)
if n_gpu > 0: if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed) torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_eval: def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False):
raise ValueError("At least one of `do_train` or `do_eval` must be True.") if args.local_rank not in [-1, 0]:
torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache
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)) # Load data features from cache or dataset file
if not os.path.exists(args.output_dir): input_file = args.predict_file if evaluate else args.train_file
os.makedirs(args.output_dir) cached_features_file = os.path.join(os.path.dirname(input_file), 'cached_{}_{}_{}'.format(
'dev' if evaluate else 'train',
tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case) list(filter(None, args.model_name_or_path.split('/'))).pop(),
str(args.max_seq_length)))
# Prepare model if os.path.exists(cached_features_file) and not args.overwrite_cache and not output_examples:
model = BertForMultipleChoice.from_pretrained(args.bert_model, logger.info("Loading features from cached file %s", cached_features_file)
cache_dir=os.path.join(str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed_{}'.format(args.local_rank)), features = torch.load(cached_features_file)
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: else:
train_sampler = DistributedSampler(train_data) logger.info("Creating features from dataset file at %s", input_file)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size) examples = read_swag_examples(input_file)
features = convert_examples_to_features(
num_train_optimization_steps = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs examples, tokenizer, args.max_seq_length, not evaluate)
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size() if args.local_rank in [-1, 0]:
logger.info("Saving features into cached file %s", cached_features_file)
# Prepare optimizer torch.save(features, cached_features_file)
param_optimizer = list(model.named_parameters()) if args.local_rank == 0:
torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache
# hack to remove pooler, which is not used
# thus it produce None grad that break apex # Convert to Tensors and build dataset
param_optimizer = [n for n in param_optimizer] all_input_ids = torch.tensor(select_field(features, 'input_ids'), dtype=torch.long)
all_input_mask = torch.tensor(select_field(features, 'input_mask'), dtype=torch.long)
all_segment_ids = torch.tensor(select_field(features, 'segment_ids'), dtype=torch.long)
all_label = torch.tensor([f.label for f in features], dtype=torch.long)
if evaluate:
dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
all_label)
else:
dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
all_label)
if output_examples:
return dataset, examples, features
return dataset
def train(args, train_dataset, model, tokenizer):
""" Train the model """
if args.local_rank in [-1, 0]:
tb_writer = SummaryWriter()
args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)
train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)
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
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight'] # Prepare optimizer and schedule (linear warmup and decay)
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [ 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 model.named_parameters() 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} {'params': [p for n, p in model.named_parameters() 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.fp16: if args.fp16:
try: try:
from apex.optimizers import FP16_Optimizer from apex import amp
from apex.optimizers import FusedAdam
except ImportError: except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.") raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
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 # multi-gpu training (should be after apex fp16 initialization)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Distributed training (should be after apex fp16 initialization)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
# Train!
logger.info("***** Running training *****") logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples)) logger.info(" Num examples = %d", len(train_dataset))
logger.info(" Batch size = %d", args.train_batch_size) logger.info(" Num Epochs = %d", args.num_train_epochs)
logger.info(" Num steps = %d", num_train_optimization_steps) logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size)
logger.info(" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1))
logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
global_step = 0
tr_loss, logging_loss = 0.0, 0.0
model.zero_grad()
train_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
set_seed(args) # Added here for reproductibility (even between python 2 and 3)
for _ in train_iterator:
epoch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
for step, batch in enumerate(epoch_iterator):
model.train() model.train()
for _ in trange(int(args.num_train_epochs), desc="Epoch"): batch = tuple(t.to(args.device) for t in batch)
tr_loss = 0 inputs = {'input_ids': batch[0],
nb_tr_examples, nb_tr_steps = 0, 0 'attention_mask': batch[1],
for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")): #'token_type_ids': None if args.model_type == 'xlm' else batch[2],
batch = tuple(t.to(device) for t in batch) 'token_type_ids': batch[2],
input_ids, input_mask, segment_ids, label_ids = batch 'labels': batch[3]}
loss = model(input_ids, segment_ids, input_mask, label_ids) # if args.model_type in ['xlnet', 'xlm']:
if n_gpu > 1: # inputs.update({'cls_index': batch[5],
loss = loss.mean() # mean() to average on multi-gpu. # 'p_mask': batch[6]})
if args.fp16 and args.loss_scale != 1.0: outputs = model(**inputs)
# rescale loss for fp16 training loss = outputs[0] # model outputs are always tuple in pytorch-transformers (see doc)
# see https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html
loss = loss * args.loss_scale if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu parallel (not distributed) training
if args.gradient_accumulation_steps > 1: if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps loss = loss / args.gradient_accumulation_steps
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if args.fp16: if args.fp16:
optimizer.backward(loss) with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
else: else:
loss.backward() loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
tr_loss += loss.item()
if (step + 1) % args.gradient_accumulation_steps == 0: 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.step()
optimizer.zero_grad() scheduler.step() # Update learning rate schedule
model.zero_grad()
global_step += 1 global_step += 1
if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
# Log metrics
if args.local_rank == -1 and args.evaluate_during_training: # Only evaluate when single GPU otherwise metrics may not average well
results = evaluate(args, model, tokenizer)
for key, value in results.items():
tb_writer.add_scalar('eval_{}'.format(key), value, global_step)
tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step)
tb_writer.add_scalar('loss', (tr_loss - logging_loss)/args.logging_steps, global_step)
logging_loss = tr_loss
if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
# Save model checkpoint
output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
model_to_save = model.module if hasattr(model, 'module') else model # Take care of distributed/parallel training
model_to_save.save_pretrained(output_dir)
tokenizer.save_vocabulary(output_dir)
torch.save(args, os.path.join(output_dir, 'training_args.bin'))
logger.info("Saving model checkpoint to %s", output_dir)
if args.max_steps > 0 and global_step > args.max_steps:
epoch_iterator.close()
break
if args.max_steps > 0 and global_step > args.max_steps:
train_iterator.close()
break
if args.do_train: if args.local_rank in [-1, 0]:
# Save a trained model, configuration and tokenizer tb_writer.close()
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` return global_step, tr_loss / global_step
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) def evaluate(args, model, tokenizer, prefix=""):
model_to_save.config.to_json_file(output_config_file) dataset, examples, features = load_and_cache_examples(args, tokenizer, evaluate=True, output_examples=True)
tokenizer.save_vocabulary(args.output_dir)
# Load a trained model and vocabulary that you have fine-tuned if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
model = BertForMultipleChoice.from_pretrained(args.output_dir, num_choices=4) os.makedirs(args.output_dir)
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)
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
# Note that DistributedSampler samples randomly
eval_sampler = SequentialSampler(dataset) if args.local_rank == -1 else DistributedSampler(dataset)
eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)
if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0): # Eval!
eval_examples = read_swag_examples(os.path.join(args.data_dir, 'val.csv'), is_training = True) logger.info("***** Running evaluation {} *****".format(prefix))
eval_features = convert_examples_to_features( logger.info(" Num examples = %d", len(dataset))
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) 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 eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 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)
for batch in tqdm(eval_dataloader, desc="Evaluating"):
model.eval()
batch = tuple(t.to(args.device) for t in batch)
with torch.no_grad(): with torch.no_grad():
tmp_eval_loss = model(input_ids, segment_ids, input_mask, label_ids) inputs = {'input_ids': batch[0],
logits = model(input_ids, segment_ids, input_mask) 'attention_mask': batch[1],
# 'token_type_ids': None if args.model_type == 'xlm' else batch[2] # XLM don't use segment_ids
'token_type_ids': batch[2],
'labels': batch[3]}
# if args.model_type in ['xlnet', 'xlm']:
# inputs.update({'cls_index': batch[4],
# 'p_mask': batch[5]})
outputs = model(**inputs)
tmp_eval_loss, logits = outputs[:2]
eval_loss += tmp_eval_loss.mean().item()
logits = logits.detach().cpu().numpy() logits = logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy() label_ids = inputs['labels'].to('cpu').numpy()
tmp_eval_accuracy = accuracy(logits, label_ids) tmp_eval_accuracy = accuracy(logits, label_ids)
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1 nb_eval_steps += 1
nb_eval_examples += inputs['input_ids'].size(0)
eval_loss = eval_loss / nb_eval_steps eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples eval_accuracy = eval_accuracy / nb_eval_examples
result = {'eval_loss': eval_loss, result = {'eval_loss': eval_loss,
'eval_accuracy': eval_accuracy, '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") output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer: with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****") logger.info("***** Eval results *****")
for key in sorted(result.keys()): for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key])) logger.info("%s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key]))) writer.write("%s = %s\n" % (key, str(result[key])))
return result
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--train_file", default=None, type=str, required=True,
help="SWAG csv for training. E.g., train.csv")
parser.add_argument("--predict_file", default=None, type=str, required=True,
help="SWAG csv for predictions. E.g., val.csv or test.csv")
parser.add_argument("--model_type", default=None, type=str, required=True,
help="Model type selected in the list: " + ", ".join(MODEL_CLASSES.keys()))
parser.add_argument("--model_name_or_path", default=None, type=str, required=True,
help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS))
parser.add_argument("--output_dir", default=None, type=str, required=True,
help="The output directory where the model checkpoints and predictions will be written.")
## Other parameters
parser.add_argument("--config_name", default="", type=str,
help="Pretrained config name or path if not the same as model_name")
parser.add_argument("--tokenizer_name", default="", type=str,
help="Pretrained tokenizer name or path if not the same as model_name")
parser.add_argument("--max_seq_length", default=384, type=int,
help="The maximum total input sequence length after tokenization. Sequences "
"longer than this will be truncated, and sequences shorter 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("--evaluate_during_training", action='store_true',
help="Rul evaluation during training at each logging step.")
parser.add_argument("--do_lower_case", action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--per_gpu_train_batch_size", default=8, type=int,
help="Batch size per GPU/CPU for training.")
parser.add_argument("--per_gpu_eval_batch_size", default=8, type=int,
help="Batch size per GPU/CPU for evaluation.")
parser.add_argument("--learning_rate", default=5e-5, type=float,
help="The initial learning rate for Adam.")
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("--weight_decay", default=0.0, type=float,
help="Weight deay if we apply some.")
parser.add_argument("--adam_epsilon", default=1e-8, type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm", default=1.0, type=float,
help="Max gradient norm.")
parser.add_argument("--num_train_epochs", default=3.0, type=float,
help="Total number of training epochs to perform.")
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("--warmup_steps", default=0, type=int,
help="Linear warmup over warmup_steps.")
parser.add_argument('--logging_steps', type=int, default=50,
help="Log every X updates steps.")
parser.add_argument('--save_steps', type=int, default=50,
help="Save checkpoint every X updates steps.")
parser.add_argument("--eval_all_checkpoints", action='store_true',
help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number")
parser.add_argument("--no_cuda", action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument('--overwrite_output_dir', action='store_true',
help="Overwrite the content of the output directory")
parser.add_argument('--overwrite_cache', action='store_true',
help="Overwrite the cached training and evaluation sets")
parser.add_argument('--seed', type=int, default=42,
help="random seed for initialization")
parser.add_argument("--local_rank", type=int, default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--fp16', action='store_true',
help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit")
parser.add_argument('--fp16_opt_level', type=str, default='O1',
help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html")
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()
if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir:
raise ValueError("Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format(args.output_dir))
# Setup distant debugging if needed
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()
# Setup CUDA, GPU & distributed training
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")
args.n_gpu = torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend='nccl')
args.n_gpu = 1
args.device = device
# Setup logging
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt = '%m/%d/%Y %H:%M:%S',
level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
logger.warning("Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16)
# Set seed
set_seed(args)
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
args.model_type = args.model_type.lower()
config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path)
tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case)
model = model_class.from_pretrained(args.model_name_or_path, from_tf=bool('.ckpt' in args.model_name_or_path), config=config)
if args.local_rank == 0:
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
model.to(args.device)
logger.info("Training/evaluation parameters %s", args)
# Training
if args.do_train:
train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=False)
global_step, tr_loss = train(args, train_dataset, model, tokenizer)
logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
# Save the trained model and the tokenizer
if args.local_rank == -1 or torch.distributed.get_rank() == 0:
# Create output directory if needed
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
logger.info("Saving model checkpoint to %s", args.output_dir)
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
model_to_save = model.module if hasattr(model, 'module') else model # Take care of distributed/parallel training
model_to_save.save_pretrained(args.output_dir)
tokenizer.save_pretrained(args.output_dir)
# Good practice: save your training arguments together with the trained model
torch.save(args, os.path.join(args.output_dir, 'training_args.bin'))
# Load a trained model and vocabulary that you have fine-tuned
model = model_class.from_pretrained(args.output_dir)
tokenizer = tokenizer_class.from_pretrained(args.output_dir)
model.to(args.device)
# Evaluation - we can ask to evaluate all the checkpoints (sub-directories) in a directory
results = {}
if args.do_eval and args.local_rank in [-1, 0]:
if args.do_train:
checkpoints = [args.output_dir]
else:
# if do_train is False and do_eval is true, load model directly from pretrained.
checkpoints = [args.model_name_or_path]
if args.eval_all_checkpoints:
checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))
logging.getLogger("pytorch_transformers.modeling_utils").setLevel(logging.WARN) # Reduce model loading logs
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
# Reload the model
global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
model = model_class.from_pretrained(checkpoint)
tokenizer = tokenizer_class.from_pretrained(checkpoint)
model.to(args.device)
# Evaluate
result = evaluate(args, model, tokenizer, prefix=global_step)
result = dict((k + ('_{}'.format(global_step) if global_step else ''), v) for k, v in result.items())
results.update(result)
logger.info("Results: {}".format(results))
return results
if __name__ == "__main__": if __name__ == "__main__":
main() main()
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