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run_table_TR_finetuning.py
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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:https://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.
"""
Fine-tuning the library models for language modeling on a text file (GPT, GPT-2, BERT, RoBERTa).
GPT and GPT-2 are fine-tuned using a causal language modeling (CLM) loss while BERT and RoBERTa are fine-tuned
using a masked language modeling (MLM) loss.
"""
from __future__ import absolute_import, division, print_function
import argparse
import glob
import logging
import os
import pickle
import random
import re
import shutil
import numpy as np
import torch
from torch.utils.data import DataLoader, Dataset, SequentialSampler, RandomSampler
from torch.utils.data.distributed import DistributedSampler
try:
from torch.utils.tensorboard import SummaryWriter
except:
from tensorboardX import SummaryWriter
from tqdm import tqdm, trange
from data_loader.data_loaders import *
from data_loader.TR_data_loaders import *
from model.configuration import TableConfig
from model.model import HybridTableTR, BertTR
from model.transformers import BertTokenizer, WEIGHTS_NAME, AdamW, get_linear_schedule_with_warmup
from baselines.row_population.metric import *
from utils.util import *
logger = logging.getLogger(__name__)
MODEL_CLASSES = {
'TR': (TableConfig, HybridTableTR, BertTokenizer),
'TR_Bert': (TableConfig, BertTR, BertTokenizer)
}
def set_seed(args):
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
def _rotate_checkpoints(args, checkpoint_prefix, use_mtime=False):
if not args.save_total_limit:
return
if args.save_total_limit <= 0:
return
# Check if we should delete older checkpoint(s)
glob_checkpoints = glob.glob(os.path.join(args.output_dir, '{}-*'.format(checkpoint_prefix)))
if len(glob_checkpoints) <= args.save_total_limit:
return
ordering_and_checkpoint_path = []
for path in glob_checkpoints:
if use_mtime:
ordering_and_checkpoint_path.append((os.path.getmtime(path), path))
else:
regex_match = re.match('.*{}-([0-9]+)'.format(checkpoint_prefix), path)
if regex_match and regex_match.groups():
ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path))
checkpoints_sorted = sorted(ordering_and_checkpoint_path)
checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]
number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - args.save_total_limit)
checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]
for checkpoint in checkpoints_to_be_deleted:
logger.info("Deleting older checkpoint [{}] due to args.save_total_limit".format(checkpoint))
shutil.rmtree(checkpoint)
def train(args, config, train_dataset, model, eval_dataset = None):
""" Train the model """
if args.local_rank in [-1, 0]:
tb_writer = SummaryWriter(os.path.join(args.output_dir, 'logs'))
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 = TRLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size, is_train=True, neg_num=args.neg_num, for_bert=args.model_type == "TR_Bert")
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
# Prepare optimizer and schedule (linear warmup and decay)
no_decay = ['bias', 'LayerNorm.weight']
if args.model_type == "TR_Bert":
optimizer_grouped_parameters = [
{'params': [p for n, p in model.model.bert.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay, 'lr': args.learning_rate},
{'params': [p for n, p in model.model.bert.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0, 'lr': args.learning_rate},
{'params': [p for n, p in model.model.classifier.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay, 'lr': args.learning_rate*10},
{'params': [p for n, p in model.model.classifier.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0, 'lr': args.learning_rate*10}
]
else:
optimizer_grouped_parameters = [
{'params': [p for n, p in model.table.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay, 'lr': args.learning_rate},
{'params': [p for n, p in model.table.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0, 'lr': args.learning_rate},
{'params': [p for n, p in model.pooler.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay, 'lr': args.learning_rate*10},
{'params': [p for n, p in model.pooler.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0, 'lr': args.learning_rate*10},
{'params': [p for n, p in model.cls.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay, 'lr': args.learning_rate*10},
{'params': [p for n, p in model.cls.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0, 'lr': args.learning_rate*10}
]
optimizer = AdamW(optimizer_grouped_parameters, eps=args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)
if args.fp16:
try:
from apex import amp
except ImportError:
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)
# 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(" Num examples = %d", len(train_dataset))
logger.info(" Num Epochs = %d", args.num_train_epochs)
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
tr_acc, logging_acc = 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 reproducibility (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):
_, input_tok, input_tok_type, input_tok_pos, input_tok_mask,\
input_ent_text, input_ent_text_length, input_ent_type, input_ent_mask, labels = batch
input_tok = input_tok.to(args.device)
input_tok_type = input_tok_type.to(args.device)
input_tok_pos = input_tok_pos.to(args.device)
input_tok_mask = input_tok_mask.to(args.device)
input_ent_text = input_ent_text.to(args.device)
input_ent_text_length = input_ent_text_length.to(args.device)
input_ent_type = input_ent_type.to(args.device)
input_ent_mask = input_ent_mask.to(args.device)
labels = labels.to(args.device)
model.train()
# pdb.set_trace()
outputs = model(input_tok, input_tok_type, input_tok_pos, input_tok_mask, \
input_ent_text, input_ent_text_length, input_ent_type, input_ent_mask, \
labels)
# model outputs are always tuple in transformers (see doc)
loss = outputs[0]
prediction_scores = outputs[1]
acc = ((prediction_scores>0)==labels).float().mean()
if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu parallel training
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
tr_loss += loss.item()
tr_acc += acc.item()
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
optimizer.step()
scheduler.step() # Update learning rate schedule
model.zero_grad()
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, config, eval_dataset, model)
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)
tb_writer.add_scalar('acc', (tr_acc - logging_acc)/(args.gradient_accumulation_steps*args.logging_steps), global_step)
logging_acc = tr_acc
logging_loss = tr_loss
if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
checkpoint_prefix = 'checkpoint'
# Save model checkpoint
output_dir = os.path.join(args.output_dir, '{}-{}'.format(checkpoint_prefix, 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)
torch.save(args, os.path.join(output_dir, 'training_args.bin'))
logger.info("Saving model checkpoint to %s", output_dir)
_rotate_checkpoints(args, checkpoint_prefix)
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.local_rank in [-1, 0]:
tb_writer.close()
return global_step, tr_loss / global_step
def evaluate(args, config, eval_dataset, model, prefix=""):
# Loop to handle MNLI double evaluation (matched, mis-matched)
eval_output_dir = args.output_dir
if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:
os.makedirs(eval_output_dir)
args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)
batch_limit = args.eval_batch_size * args.neg_num
# Note that DistributedSampler samples randomly
eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)
eval_dataloader = TRLoader(eval_dataset, sampler=eval_sampler, batch_size=args.train_batch_size, is_train=False, for_bert=args.model_type == "TR_Bert")
# multi-gpu evaluate
if args.n_gpu > 1:
model = torch.nn.DataParallel(model)
# Eval!
logger.info("***** Running evaluation {} *****".format(prefix))
logger.info(" Num examples = %d", len(eval_dataset))
logger.info(" Batch size = %d", args.eval_batch_size)
eval_loss = 0.0
eval_map = 0.0
eval_p_at_1 = 0.0
nb_eval_steps = 0
model.eval()
dev_results = [[], [], []]
for batch in tqdm(eval_dataloader, desc="Evaluating"):
q_ids, input_tok, input_tok_type, input_tok_pos, input_tok_mask,\
input_ent_text, input_ent_text_length, input_ent_type, input_ent_mask, labels = batch
total_sample = len(q_ids)
i = 0
tmp_loss = []
while i+batch_limit < total_sample:
tmp_input_tok = input_tok[i:i+batch_limit].to(args.device)
tmp_input_tok_type = input_tok_type[i:i+batch_limit].to(args.device)
tmp_input_tok_pos = input_tok_pos[i:i+batch_limit].to(args.device)
tmp_input_tok_mask = input_tok_mask[i:i+batch_limit].to(args.device)
tmp_input_ent_text = input_ent_text[i:i+batch_limit].to(args.device)
tmp_input_ent_text_length = input_ent_text_length[i:i+batch_limit].to(args.device)
tmp_input_ent_type = input_ent_type[i:i+batch_limit].to(args.device)
tmp_input_ent_mask = input_ent_mask[i:i+batch_limit].to(args.device)
tmp_labels = labels[i:i+batch_limit].to(args.device)
# pdb.set_trace()
with torch.no_grad():
outputs = model(tmp_input_tok, tmp_input_tok_type, tmp_input_tok_pos, tmp_input_tok_mask, \
tmp_input_ent_text, tmp_input_ent_text_length, tmp_input_ent_type, tmp_input_ent_mask, \
tmp_labels)
loss = outputs[0]
prediction_scores = outputs[1].view(-1)
tmp_loss.append(loss.mean().item())
dev_results[1] += prediction_scores.tolist()
i += batch_limit
if i <= total_sample:
tmp_input_tok = input_tok[i:].to(args.device)
tmp_input_tok_type = input_tok_type[i:].to(args.device)
tmp_input_tok_pos = input_tok_pos[i:].to(args.device)
tmp_input_tok_mask = input_tok_mask[i:].to(args.device)
tmp_input_ent_text = input_ent_text[i:].to(args.device)
tmp_input_ent_text_length = input_ent_text_length[i:].to(args.device)
tmp_input_ent_type = input_ent_type[i:].to(args.device)
tmp_input_ent_mask = input_ent_mask[i:].to(args.device)
tmp_labels = labels[i:].to(args.device)
# pdb.set_trace()
with torch.no_grad():
outputs = model(tmp_input_tok, tmp_input_tok_type, tmp_input_tok_pos, tmp_input_tok_mask, \
tmp_input_ent_text, tmp_input_ent_text_length, tmp_input_ent_type, tmp_input_ent_mask, \
tmp_labels)
loss = outputs[0]
prediction_scores = outputs[1].view(-1)
tmp_loss.append(loss.mean().item())
dev_results[1] += prediction_scores.tolist()
dev_results[0] += q_ids
dev_results[2] += labels.tolist()
eval_loss += np.mean(tmp_loss)
nb_eval_steps += 1
# pdb.set_trace()
dev_scores = {}
for i, q_id in enumerate(dev_results[0]):
if q_id not in dev_scores:
dev_scores[q_id] = []
dev_scores[q_id].append([dev_results[1][i], dev_results[2][i]])
dev_sorted_labels = [[z[1] for z in sorted(value, key=lambda x:x[0], reverse=True)] for _,value in dev_scores.items()]
eval_p_at_1 = np.mean([precision_at_k(x, 1) for x in dev_sorted_labels])
eval_map = mean_average_precision(dev_sorted_labels)
eval_loss = eval_loss / nb_eval_steps
result = {
"eval_loss": eval_loss,
"eval_p_1": eval_p_at_1,
"eval_map": eval_map
}
output_eval_file = os.path.join(eval_output_dir, prefix, "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results {} *****".format(prefix))
for key in sorted(result.keys()):
logger.info(" %s = %s", 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("--data_dir", default=None, type=str, required=True,
help="The input data directory.")
parser.add_argument("--output_dir", default=None, type=str, required=True,
help="The output directory where the model predictions and checkpoints will be written.")
## Other parameters
parser.add_argument("--model_type", default="bert", type=str,
help="The model architecture to be fine-tuned.")
parser.add_argument("--model_name_or_path", default="bert-base-cased", type=str,
help="The model checkpoint for weights initialization.")
parser.add_argument("--neg_num", default=5, type=int)
parser.add_argument("--config_name", default="", type=str,
help="Optional pretrained config name or path if not the same as model_name_or_path")
parser.add_argument("--tokenizer_name", default="", type=str,
help="Optional pretrained tokenizer name or path if not the same as model_name_or_path")
parser.add_argument("--cache_dir", default="", type=str,
help="Optional directory to store the pre-trained models downloaded from s3 (instread of the default one)")
parser.add_argument("--block_size", default=-1, type=int,
help="Optional input sequence length after tokenization."
"The training dataset will be truncated in block of this size for training."
"Default to the model max input length for single sentence inputs (take into account special tokens).")
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("--get_table_repr", action='store_true',
help="get representation of training tables")
parser.add_argument("--evaluate_during_training", action='store_true',
help="Run 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=4, type=int,
help="Batch size per GPU/CPU for training.")
parser.add_argument("--per_gpu_eval_batch_size", default=4, type=int,
help="Batch size per GPU/CPU for evaluation.")
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", default=5e-5, type=float,
help="The initial learning rate for Adam.")
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=1.0, 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('--save_total_limit', type=int, default=None,
help='Limit the total amount of checkpoints, delete the older checkpoints in the output_dir, does not delete by default')
parser.add_argument("--eval_all_checkpoints", action='store_true',
help="Evaluate all checkpoints starting with the same prefix as model_name_or_path ending and ending with step number")
parser.add_argument("--no_cuda", action='store_true',
help="Avoid using 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=1,
help="random seed for initialization")
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("--local_rank", type=int, default=-1,
help="For distributed training: local_rank")
parser.add_argument('--server_ip', type=str, default='', help="For distant debugging.")
parser.add_argument('--server_port', type=str, default='', help="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() # Barrier to make sure only the first process in distributed training download model & vocab
config_class, model_class, _ = MODEL_CLASSES[args.model_type]
config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path,
cache_dir=args.cache_dir if args.cache_dir else None)
model = model_class(config, is_simple=True)
if args.do_train:
lm_checkpoints = list(os.path.dirname(c) for c in sorted(glob.glob(args.model_name_or_path + '/**/' + WEIGHTS_NAME, recursive=True)))
logger.info("load pre-trained model from %s", lm_checkpoints[-1])
lm_checkpoint = torch.load(os.path.join(lm_checkpoints[-1],"pytorch_model.bin"))
model.load_pretrained(lm_checkpoint)
if args.model_type != "TR_Bert":
model.resize_type_embedding()
model.to(args.device)
if args.local_rank == 0:
torch.distributed.barrier() # End of barrier to make sure only the first process in distributed training download model & vocab
logger.info("Training/evaluation parameters %s", args)
# Training
if args.do_train:
if args.local_rank not in [-1, 0]:
torch.distributed.barrier() # Barrier to make sure only the first process in distributed training process the dataset, and the others will use the cache
train_dataset = WebQueryTableDataset(args.data_dir, max_input_tok=500, src="train", max_length = [100, 50, 10, 10], force_new=False, for_bert=args.model_type == "TR_Bert")
eval_dataset = WebQueryTableDataset(args.data_dir, max_input_tok=500, src="test", max_length = [100, 50, 10, 10], force_new=False, for_bert=args.model_type == "TR_Bert")
assert config.vocab_size == len(train_dataset.tokenizer), \
"vocab size mismatch, vocab_size=%d"%(len(train_dataset.tokenizer))
if args.local_rank == 0:
torch.distributed.barrier()
global_step, tr_loss = train(args, config, train_dataset, model, eval_dataset=eval_dataset)
logger.info(" global_step = %s, average loss = %s", global_step, tr_loss)
# Saving best-practices: if you use save_pretrained for the model and tokenizer, you can reload them using from_pretrained()
if args.do_train and (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)
# 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)
model.to(args.device)
# Evaluation
results = {}
if args.do_eval and args.local_rank in [-1, 0]:
checkpoints = [args.output_dir]
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("transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for checkpoint in checkpoints:
global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else ""
prefix = checkpoint.split('/')[-1] if checkpoint.find('checkpoint') != -1 else ""
model = model_class.from_pretrained(checkpoint)
model.to(args.device)
result = evaluate(args, config, eval_dataset, model, prefix=prefix)
result = dict((k + '_{}'.format(global_step), v) for k, v in result.items())
results.update(result)
# if args.get_table_repr:
# train_dataset = WikiEntityTableDataset(args.data_dir,entity_vocab,max_cell=100, max_input_tok=350, max_input_ent=150, src="train", max_length = [50, 10, 10], force_new=False, tokenizer = None, mode=1)
# checkpoint = torch.load(args.model_name_or_path)
# model.load_state_dict(checkpoint)
# model.to(args.device)
# get_table_repr(args, config, train_dataset, model)
return results
if __name__ == "__main__":
main()