<reponame>gkebe/EventBERT<gh_stars>1-10
# coding=utf-8
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""BERT finetuning runner."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

# ==================
import csv
import os
import time
import logging
import argparse
import random
import h5py
from tqdm import tqdm, trange
import os
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, Dataset
from torch.utils.data.distributed import DistributedSampler
import math
from apex import amp
import multiprocessing

from tokenization import BertTokenizer
from modeling import BertForPreTraining, BertConfig
from apex.optimizers import FusedLAMB
from schedulers import PolyWarmUpScheduler

from file_utils import PYTORCH_PRETRAINED_BERT_CACHE
from utils import is_main_process, format_step
from apex.parallel import DistributedDataParallel as DDP
from schedulers import LinearWarmUpScheduler
from apex.parallel.distributed import flat_dist_call
import amp_C
import apex_C
from apex.amp import _amp_state

import dllogger
from concurrent.futures import ProcessPoolExecutor

skipped_steps = 0

def create_pretraining_dataset(input_file, max_pred_length, shared_list, args):

    train_data = pretraining_dataset(input_file=input_file, max_pred_length=max_pred_length)
    train_sampler = RandomSampler(train_data)
    train_dataloader = DataLoader(train_data, sampler=train_sampler,
                                  batch_size=args.train_batch_size * args.n_gpu, num_workers=4,
                                  pin_memory=True)
    return train_dataloader, input_file

class pretraining_dataset(Dataset):

    def __init__(self, input_file, max_pred_length):
        self.input_file = input_file
        self.max_pred_length = max_pred_length
        f = h5py.File(input_file, "r")
        keys = ['input_ids', 'input_mask', 'segment_ids', 'masked_lm_positions', 'masked_lm_ids',
                'next_sentence_labels']
        self.inputs = [np.asarray(f[key][:]) for key in keys]
        f.close()

    def __len__(self):
        'Denotes the total number of samples'
        return len(self.inputs[0])

    def __getitem__(self, index):

        [input_ids, input_mask, segment_ids, masked_lm_positions, masked_lm_ids, next_sentence_labels] = [
            torch.from_numpy(input[index].astype(np.int64)) if indice < 5 else torch.from_numpy(
                np.asarray(input[index].astype(np.int64))) for indice, input in enumerate(self.inputs)]

        masked_lm_labels = torch.ones(input_ids.shape, dtype=torch.long) * -1
        index = self.max_pred_length
        # store number of  masked tokens in index
        padded_mask_indices = (masked_lm_positions == 0).nonzero()
        if len(padded_mask_indices) != 0:
            index = padded_mask_indices[0].item()
        masked_lm_labels[masked_lm_positions[:index]] = masked_lm_ids[:index]

        return [input_ids, segment_ids, input_mask,
                masked_lm_labels, next_sentence_labels]

def parse_arguments():

    parser = argparse.ArgumentParser()

    ## Required parameters
    parser.add_argument("--input_dir",
                        default=None,
                        type=str,
                        required=True,
                        help="The input data dir. Should contain .hdf5 files  for the task.")

    parser.add_argument("--config_file",
                        default=None,
                        type=str,
                        required=True,
                        help="The BERT model config")

    parser.add_argument("--bert_model", default="bert-large-uncased", type=str,
                        help="Bert pre-trained model selected in the list: bert-base-uncased, "
                             "bert-large-uncased, bert-base-cased, bert-base-multilingual, 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("--init_checkpoint",
                        default=None,
                        type=str,
                        help="The initial checkpoint to start training from.")

    parser.add_argument("--max_seq_length",
                        default=512,
                        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("--max_predictions_per_seq",
                        default=80,
                        type=int,
                        help="The maximum total of masked tokens in input sequence")
    parser.add_argument("--train_batch_size",
                        default=32,
                        type=int,
                        help="Total batch size for training.")
    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("--max_steps",
                        default=1000,
                        type=float,
                        help="Total number of training steps to perform.")
    parser.add_argument("--warmup_proportion",
                        default=0.01,
                        type=float,
                        help="Proportion of training to perform linear learning rate warmup for. "
                             "E.g., 0.1 = 10%% of training.")
    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 accumualte before performing a backward/update pass.")
    parser.add_argument('--fp16',
                        default=False,
                        action='store_true',
                        help="Whether to use 16-bit float precision instead of 32-bit")
    parser.add_argument('--loss_scale',
                        type=float, default=0.0,
                        help='Loss scaling, positive power of 2 values can improve fp16 convergence.')
    parser.add_argument('--log_freq',
                        type=float, default=1.0,
                        help='frequency of logging loss.')
    parser.add_argument('--checkpoint_activations',
                        default=False,
                        action='store_true',
                        help="Whether to use gradient checkpointing")
    parser.add_argument("--resume_from_checkpoint",
                        default=False,
                        action='store_true',
                        help="Whether to resume training from checkpoint.")
    parser.add_argument('--resume_step',
                        type=int,
                        default=-1,
                        help="Step to resume training from.")
    parser.add_argument('--num_steps_per_checkpoint',
                        type=int,
                        default=100,
                        help="Number of update steps until a model checkpoint is saved to disk.")
    parser.add_argument('--skip_checkpoint',
                        default=False,
                        action='store_true',
                        help="Whether to save checkpoints")
    parser.add_argument('--phase2',
                        default=False,
                        action='store_true',
                        help="Whether to train with seq len 512")
    parser.add_argument('--allreduce_post_accumulation',
                        default=False,
                        action='store_true',
                        help="Whether to do allreduces during gradient accumulation steps.")
    parser.add_argument('--allreduce_post_accumulation_fp16',
                        default=False,
                        action='store_true',
                        help="Whether to do fp16 allreduce post accumulation.")
    parser.add_argument('--phase1_end_step',
                        type=int,
                        default=7038,
                        help="Number of training steps in Phase1 - seq len 128")
    parser.add_argument("--gpu",
                        type=str,
                        default='None',
                        help="choose gpu device.")
    parser.add_argument("--do_train",
                        default=False,
                        action='store_true',
                        help="Whether to run training.")
    parser.add_argument('--json-summary', type=str, default="dllogger.json",
                        help='If provided, the json summary will be written to'
                             'the specified file.')
    parser.add_argument("--use_env",
                        action='store_true',
                        help="Whether to read local rank from ENVVAR")
    args = parser.parse_args()
    
    return args

def setup_training(args):

    assert (torch.cuda.is_available())

    if args.local_rank == -1:
        device = torch.device("cuda")
        args.n_gpu = torch.cuda.device_count()
    else:
        gpus = args.gpu.split(",")
        args.local_rank = int(gpus[args.local_rank])
        torch.cuda.set_device(args.local_rank)
        device = torch.device("cuda", args.local_rank)
        # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
        torch.distributed.init_process_group(backend='nccl', init_method='env://')
        args.n_gpu = 1
        
    if is_main_process():
        dllogger.init(backends=[dllogger.JSONStreamBackend(verbosity=dllogger.Verbosity.VERBOSE,
                                                           filename=args.json_summary),
                                dllogger.StdOutBackend(verbosity=dllogger.Verbosity.VERBOSE, step_format=format_step)])
    else:
        dllogger.init(backends=[])

    print("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
        device, args.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))
    if args.train_batch_size % args.gradient_accumulation_steps != 0:
        raise ValueError("Invalid gradient_accumulation_steps parameter: {}, batch size {} should be divisible".format(
            args.gradient_accumulation_steps, args.train_batch_size))

    args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps

    if not args.do_train:
        raise ValueError(" `do_train`  must be True.")

    if not args.resume_from_checkpoint and os.path.exists(args.output_dir) and (
            os.listdir(args.output_dir) and any([i.startswith('ckpt') for i in os.listdir(args.output_dir)])):
        raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))

    if not args.resume_from_checkpoint or not os.path.exists(args.output_dir):
        os.makedirs(args.output_dir, exist_ok=True)

    return device, args

def prepare_model_and_optimizer(args, device):

    # Prepare model
    config = BertConfig.from_json_file(args.config_file)

    # Padding for divisibility by 8
    if config.vocab_size % 8 != 0:
        config.vocab_size += 8 - (config.vocab_size % 8)
    model = BertForPreTraining(config)

    checkpoint = None
    if not args.resume_from_checkpoint:
        global_step = 0
    else:
        if args.resume_step == -1 and not args.init_checkpoint:
            model_names = [f for f in os.listdir(args.output_dir) if f.endswith(".pt")]
            args.resume_step = max([int(x.split('.pt')[0].split('_')[1].strip()) for x in model_names])

        global_step = args.resume_step if not args.init_checkpoint else 0

        if not args.init_checkpoint:
            checkpoint = torch.load(os.path.join(args.output_dir, "ckpt_{}.pt".format(global_step)), map_location="cpu")
        else:
            checkpoint = torch.load(args.init_checkpoint, map_location="cpu")

        model.load_state_dict(checkpoint['model'], strict=False)
        
        if args.phase2 and not args.init_checkpoint:
            global_step -= args.phase1_end_step
        if is_main_process():
            print("resume step from ", args.resume_step)

    model.to(device)
    param_optimizer = list(model.named_parameters())
    no_decay = ['bias', 'gamma', 'beta', 'LayerNorm']
    
    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}]

    optimizer = FusedLAMB(optimizer_grouped_parameters, 
                          lr=args.learning_rate)
    lr_scheduler = PolyWarmUpScheduler(optimizer, 
                                       warmup=args.warmup_proportion, 
                                       total_steps=args.max_steps)
    if args.fp16:

        if args.loss_scale == 0:
            model, optimizer = amp.initialize(model, optimizer, opt_level="O2", loss_scale="dynamic")
        else:
            model, optimizer = amp.initialize(model, optimizer, opt_level="O2", loss_scale=args.loss_scale)
        amp._amp_state.loss_scalers[0]._loss_scale = 2**20

    if args.resume_from_checkpoint:
        if args.phase2 or args.init_checkpoint:
            keys = list(checkpoint['optimizer']['state'].keys())
            #Override hyperparameters from previous checkpoint
            for key in keys:
                checkpoint['optimizer']['state'][key]['step'] = global_step
            for iter, item in enumerate(checkpoint['optimizer']['param_groups']):
                checkpoint['optimizer']['param_groups'][iter]['step'] = global_step
                checkpoint['optimizer']['param_groups'][iter]['t_total'] = args.max_steps
                checkpoint['optimizer']['param_groups'][iter]['warmup'] = args.warmup_proportion
                checkpoint['optimizer']['param_groups'][iter]['lr'] = args.learning_rate
        optimizer.load_state_dict(checkpoint['optimizer'])  # , strict=False)

        # Restore AMP master parameters          
        if args.fp16:
            optimizer._lazy_init_maybe_master_weights()
            optimizer._amp_stash.lazy_init_called = True
            optimizer.load_state_dict(checkpoint['optimizer'])
            for param, saved_param in zip(amp.master_params(optimizer), checkpoint['master params']):
                param.data.copy_(saved_param.data)

    if args.local_rank != -1:
        if not args.allreduce_post_accumulation:
            model = DDP(model, message_size=250000000, gradient_predivide_factor=torch.distributed.get_world_size())
        else:
            flat_dist_call([param.data for param in model.parameters()], torch.distributed.broadcast, (0,) )
    elif args.n_gpu > 1:
        model = torch.nn.DataParallel(model)

    return model, optimizer, lr_scheduler, checkpoint, global_step

def main():

    args = parse_arguments()

    if args.use_env and 'LOCAL_RANK' in os.environ:
        args.local_rank = int(os.environ['LOCAL_RANK'])
        
    random.seed(args.seed)
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)

    device, args = setup_training(args)
    dllogger.log(step="PARAMETER", data={"Config": [str(args)]})

    # Prepare optimizer
    model, optimizer, lr_scheduler, checkpoint, global_step = prepare_model_and_optimizer(args, device)
    model_to_save = model.module if hasattr(model,
                                            'module') else model  # Only save the model it-self

    output_save_file = os.path.join(args.output_dir, "ckpt_0.pt".format(global_step))
    torch.save({'model': model_to_save.state_dict(),
                'optimizer': optimizer.state_dict(),
                'master params': list(amp.master_params(optimizer)),
                'files': []}, output_save_file)


if __name__ == "__main__":

    main()

# coding=utf-8
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""BERT finetuning runner."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

# ==================
import csv
import os
import time
import logging
import argparse
import random
import h5py
from tqdm import tqdm, trange
import os
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, Dataset
from torch.utils.data.distributed import DistributedSampler
import math
from apex import amp
import multiprocessing

from tokenization import BertTokenizer
from modeling import BertForPreTraining, BertConfig
from apex.optimizers import FusedLAMB
from schedulers import PolyWarmUpScheduler

from file_utils import PYTORCH_PRETRAINED_BERT_CACHE
from utils import is_main_process, format_step
from apex.parallel import DistributedDataParallel as DDP
from schedulers import LinearWarmUpScheduler
from apex.parallel.distributed import flat_dist_call
import amp_C
import apex_C
from apex.amp import _amp_state

import dllogger
from concurrent.futures import ProcessPoolExecutor

skipped_steps = 0

def create_pretraining_dataset(input_file, max_pred_length, shared_list, args):

    train_data = pretraining_dataset(input_file=input_file, max_pred_length=max_pred_length)
    train_sampler = RandomSampler(train_data)
    train_dataloader = DataLoader(train_data, sampler=train_sampler,
                                  batch_size=args.train_batch_size * args.n_gpu, num_workers=4,
                                  pin_memory=True)
    return train_dataloader, input_file

class pretraining_dataset(Dataset):

    def __init__(self, input_file, max_pred_length):
        self.input_file = input_file
        self.max_pred_length = max_pred_length
        f = h5py.File(input_file, "r")
        keys = ['input_ids', 'input_mask', 'segment_ids', 'masked_lm_positions', 'masked_lm_ids',
                'next_sentence_labels']
        self.inputs = [np.asarray(f[key][:]) for key in keys]
        f.close()

    def __len__(self):
        'Denotes the total number of samples'
        return len(self.inputs[0])

    def __getitem__(self, index):

        [input_ids, input_mask, segment_ids, masked_lm_positions, masked_lm_ids, next_sentence_labels] = [
            torch.from_numpy(input[index].astype(np.int64)) if indice < 5 else torch.from_numpy(
                np.asarray(input[index].astype(np.int64))) for indice, input in enumerate(self.inputs)]

        masked_lm_labels = torch.ones(input_ids.shape, dtype=torch.long) * -1
        index = self.max_pred_length
        # store number of  masked tokens in index
        padded_mask_indices = (masked_lm_positions == 0).nonzero()
        if len(padded_mask_indices) != 0:
            index = padded_mask_indices[0].item()
        masked_lm_labels[masked_lm_positions[:index]] = masked_lm_ids[:index]

        return [input_ids, segment_ids, input_mask,
                masked_lm_labels, next_sentence_labels]

def parse_arguments():

    parser = argparse.ArgumentParser()

    ## Required parameters
    parser.add_argument("--input_dir",
                        default=None,
                        type=str,
                        required=True,
                        help="The input data dir. Should contain .hdf5 files  for the task.")

    parser.add_argument("--config_file",
                        default=None,
                        type=str,
                        required=True,
                        help="The BERT model config")

    parser.add_argument("--bert_model", default="bert-large-uncased", type=str,
                        help="Bert pre-trained model selected in the list: bert-base-uncased, "
                             "bert-large-uncased, bert-base-cased, bert-base-multilingual, 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("--init_checkpoint",
                        default=None,
                        type=str,
                        help="The initial checkpoint to start training from.")

    parser.add_argument("--max_seq_length",
                        default=512,
                        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("--max_predictions_per_seq",
                        default=80,
                        type=int,
                        help="The maximum total of masked tokens in input sequence")
    parser.add_argument("--train_batch_size",
                        default=32,
                        type=int,
                        help="Total batch size for training.")
    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("--max_steps",
                        default=1000,
                        type=float,
                        help="Total number of training steps to perform.")
    parser.add_argument("--warmup_proportion",
                        default=0.01,
                        type=float,
                        help="Proportion of training to perform linear learning rate warmup for. "
                             "E.g., 0.1 = 10%% of training.")
    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 accumualte before performing a backward/update pass.")
    parser.add_argument('--fp16',
                        default=False,
                        action='store_true',
                        help="Whether to use 16-bit float precision instead of 32-bit")
    parser.add_argument('--loss_scale',
                        type=float, default=0.0,
                        help='Loss scaling, positive power of 2 values can improve fp16 convergence.')
    parser.add_argument('--log_freq',
                        type=float, default=1.0,
                        help='frequency of logging loss.')
    parser.add_argument('--checkpoint_activations',
                        default=False,
                        action='store_true',
                        help="Whether to use gradient checkpointing")
    parser.add_argument("--resume_from_checkpoint",
                        default=False,
                        action='store_true',
                        help="Whether to resume training from checkpoint.")
    parser.add_argument('--resume_step',
                        type=int,
                        default=-1,
                        help="Step to resume training from.")
    parser.add_argument('--num_steps_per_checkpoint',
                        type=int,
                        default=100,
                        help="Number of update steps until a model checkpoint is saved to disk.")
    parser.add_argument('--skip_checkpoint',
                        default=False,
                        action='store_true',
                        help="Whether to save checkpoints")
    parser.add_argument('--phase2',
                        default=False,
                        action='store_true',
                        help="Whether to train with seq len 512")
    parser.add_argument('--allreduce_post_accumulation',
                        default=False,
                        action='store_true',
                        help="Whether to do allreduces during gradient accumulation steps.")
    parser.add_argument('--allreduce_post_accumulation_fp16',
                        default=False,
                        action='store_true',
                        help="Whether to do fp16 allreduce post accumulation.")
    parser.add_argument('--phase1_end_step',
                        type=int,
                        default=7038,
                        help="Number of training steps in Phase1 - seq len 128")
    parser.add_argument("--gpu",
                        type=str,
                        default='None',
                        help="choose gpu device.")
    parser.add_argument("--do_train",
                        default=False,
                        action='store_true',
                        help="Whether to run training.")
    parser.add_argument('--json-summary', type=str, default="dllogger.json",
                        help='If provided, the json summary will be written to'
                             'the specified file.')
    parser.add_argument("--use_env",
                        action='store_true',
                        help="Whether to read local rank from ENVVAR")
    args = parser.parse_args()
    
    return args

def setup_training(args):

    assert (torch.cuda.is_available())

    if args.local_rank == -1:
        device = torch.device("cuda")
        args.n_gpu = torch.cuda.device_count()
    else:
        gpus = args.gpu.split(",")
        args.local_rank = int(gpus[args.local_rank])
        torch.cuda.set_device(args.local_rank)
        device = torch.device("cuda", args.local_rank)
        # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
        torch.distributed.init_process_group(backend='nccl', init_method='env://')
        args.n_gpu = 1
        
    if is_main_process():
        dllogger.init(backends=[dllogger.JSONStreamBackend(verbosity=dllogger.Verbosity.VERBOSE,
                                                           filename=args.json_summary),
                                dllogger.StdOutBackend(verbosity=dllogger.Verbosity.VERBOSE, step_format=format_step)])
    else:
        dllogger.init(backends=[])

    print("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
        device, args.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))
    if args.train_batch_size % args.gradient_accumulation_steps != 0:
        raise ValueError("Invalid gradient_accumulation_steps parameter: {}, batch size {} should be divisible".format(
            args.gradient_accumulation_steps, args.train_batch_size))

    args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps

    if not args.do_train:
        raise ValueError(" `do_train`  must be True.")

    if not args.resume_from_checkpoint and os.path.exists(args.output_dir) and (
            os.listdir(args.output_dir) and any([i.startswith('ckpt') for i in os.listdir(args.output_dir)])):
        raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))

    if not args.resume_from_checkpoint or not os.path.exists(args.output_dir):
        os.makedirs(args.output_dir, exist_ok=True)

    return device, args

def prepare_model_and_optimizer(args, device):

    # Prepare model
    config = BertConfig.from_json_file(args.config_file)

    # Padding for divisibility by 8
    if config.vocab_size % 8 != 0:
        config.vocab_size += 8 - (config.vocab_size % 8)
    model = BertForPreTraining(config)

    checkpoint = None
    if not args.resume_from_checkpoint:
        global_step = 0
    else:
        if args.resume_step == -1 and not args.init_checkpoint:
            model_names = [f for f in os.listdir(args.output_dir) if f.endswith(".pt")]
            args.resume_step = max([int(x.split('.pt')[0].split('_')[1].strip()) for x in model_names])

        global_step = args.resume_step if not args.init_checkpoint else 0

        if not args.init_checkpoint:
            checkpoint = torch.load(os.path.join(args.output_dir, "ckpt_{}.pt".format(global_step)), map_location="cpu")
        else:
            checkpoint = torch.load(args.init_checkpoint, map_location="cpu")

        model.load_state_dict(checkpoint['model'], strict=False)
        
        if args.phase2 and not args.init_checkpoint:
            global_step -= args.phase1_end_step
        if is_main_process():
            print("resume step from ", args.resume_step)

    model.to(device)
    param_optimizer = list(model.named_parameters())
    no_decay = ['bias', 'gamma', 'beta', 'LayerNorm']
    
    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}]

    optimizer = FusedLAMB(optimizer_grouped_parameters, 
                          lr=args.learning_rate)
    lr_scheduler = PolyWarmUpScheduler(optimizer, 
                                       warmup=args.warmup_proportion, 
                                       total_steps=args.max_steps)
    if args.fp16:

        if args.loss_scale == 0:
            model, optimizer = amp.initialize(model, optimizer, opt_level="O2", loss_scale="dynamic")
        else:
            model, optimizer = amp.initialize(model, optimizer, opt_level="O2", loss_scale=args.loss_scale)
        amp._amp_state.loss_scalers[0]._loss_scale = 2**20

    if args.resume_from_checkpoint:
        if args.phase2 or args.init_checkpoint:
            keys = list(checkpoint['optimizer']['state'].keys())
            #Override hyperparameters from previous checkpoint
            for key in keys:
                checkpoint['optimizer']['state'][key]['step'] = global_step
            for iter, item in enumerate(checkpoint['optimizer']['param_groups']):
                checkpoint['optimizer']['param_groups'][iter]['step'] = global_step
                checkpoint['optimizer']['param_groups'][iter]['t_total'] = args.max_steps
                checkpoint['optimizer']['param_groups'][iter]['warmup'] = args.warmup_proportion
                checkpoint['optimizer']['param_groups'][iter]['lr'] = args.learning_rate
        optimizer.load_state_dict(checkpoint['optimizer'])  # , strict=False)

        # Restore AMP master parameters          
        if args.fp16:
            optimizer._lazy_init_maybe_master_weights()
            optimizer._amp_stash.lazy_init_called = True
            optimizer.load_state_dict(checkpoint['optimizer'])
            for param, saved_param in zip(amp.master_params(optimizer), checkpoint['master params']):
                param.data.copy_(saved_param.data)

    if args.local_rank != -1:
        if not args.allreduce_post_accumulation:
            model = DDP(model, message_size=250000000, gradient_predivide_factor=torch.distributed.get_world_size())
        else:
            flat_dist_call([param.data for param in model.parameters()], torch.distributed.broadcast, (0,) )
    elif args.n_gpu > 1:
        model = torch.nn.DataParallel(model)

    return model, optimizer, lr_scheduler, checkpoint, global_step

def main():

    args = parse_arguments()

    if args.use_env and 'LOCAL_RANK' in os.environ:
        args.local_rank = int(os.environ['LOCAL_RANK'])
        
    random.seed(args.seed)
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)

    device, args = setup_training(args)
    dllogger.log(step="PARAMETER", data={"Config": [str(args)]})

    # Prepare optimizer
    model, optimizer, lr_scheduler, checkpoint, global_step = prepare_model_and_optimizer(args, device)
    model_to_save = model.module if hasattr(model,
                                            'module') else model  # Only save the model it-self

    output_save_file = os.path.join(args.output_dir, "ckpt_0.pt".format(global_step))
    torch.save({'model': model_to_save.state_dict(),
                'optimizer': optimizer.state_dict(),
                'master params': list(amp.master_params(optimizer)),
                'files': []}, output_save_file)


if __name__ == "__main__":

    main()
# coding=utf-8 # Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved. # Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team. # 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. # ================== # store number of  masked tokens in index ## Required parameters ## Other parameters # Initializes the distributed backend which will take care of sychronizing nodes/GPUs # Prepare model # Padding for divisibility by 8 #Override hyperparameters from previous checkpoint # , strict=False) # Restore AMP master parameters # Prepare optimizer # Only save the model it-self