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# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
"""Pretrain BERT for Inverse Cloze Task"""
from functools import partial
import math
import torch
import torch.distributed as dist
import torch.nn.functional as F
import megatron.training
from megatron import get_args
from megatron import print_rank_0
from megatron import get_timers
from megatron.core import mpu
from megatron.data.biencoder_dataset_utils import get_ict_batch
from megatron.data.dataset_utils import build_train_valid_test_datasets
from megatron.model import ModelType
import megatron.model.biencoder_model
from megatron.utils import average_losses_across_data_parallel_group
def pretrain_ict_model_provider(pre_process=True, post_process=True):
args = get_args()
ict_model_type = ModelType.encoder_or_decoder
model = megatron.model.biencoder_model.biencoder_model_provider(
only_context_model=False,
only_query_model=False,
biencoder_shared_query_context_model=args.biencoder_shared_query_context_model,
pre_process=pre_process,
post_process=post_process,
model_type=ict_model_type)
return model
def get_group_world_size_rank():
group = mpu.get_data_parallel_group()
rank = torch.distributed.get_rank(group=group)
world_size = torch.distributed.get_world_size(group=group)
return group, rank, world_size
class AllgatherFromDataParallelRegion(torch.autograd.Function):
@staticmethod
def forward(ctx, input_):
assert input_.dim() == 2
group, rank, world_size = get_group_world_size_rank()
tensor_list = [torch.empty_like(input_) for _ in range(world_size)]
tensor_list[rank] = input_
torch.distributed.all_gather(tensor_list, input_, group=group)
output = torch.cat(tensor_list, dim=0).contiguous()
return output
@staticmethod
def backward(ctx, grad_output):
group, rank, world_size = get_group_world_size_rank()
assert grad_output.shape[0] % world_size == 0
dim_size = grad_output.shape[0] // world_size
output_list = torch.split(grad_output, dim_size, dim=0)
# get chunk from this rank
output = output_list[rank].contiguous()
return output
def loss_func(output_tensor):
args = get_args()
query_logits, context_logits = output_tensor
micro_batch_size = query_logits.shape[0]
# recall we assert that tensor_model_parallel_size == 1
assert mpu.get_tensor_model_parallel_world_size() == 1, \
"Model parallel size > 1 not supported for ICT"
global_batch_size = dist.get_world_size() * micro_batch_size
all_query_logits = AllgatherFromDataParallelRegion.apply(query_logits)
all_context_logits = AllgatherFromDataParallelRegion.apply(context_logits)
# scores are inner products between query and context embeddings
retrieval_scores = torch.matmul(all_query_logits,
torch.transpose(all_context_logits, 0, 1))
# scaling the retriever scores
if args.retriever_score_scaling:
retrieval_scores = retrieval_scores / math.sqrt(args.hidden_size)
softmax_scores = F.log_softmax(retrieval_scores, dim=1)
sorted_vals, sorted_indices = torch.topk(softmax_scores,
k=softmax_scores.shape[1], sorted=True)
def topk_accuracy(k):
return torch.cuda.FloatTensor([sum([int(i in sorted_indices[i, :k]) \
for i in range(global_batch_size)]) / global_batch_size])
topk_accs = [topk_accuracy(int(k)) for k in args.retriever_report_topk_accuracies]
labels = torch.arange(global_batch_size).long().cuda()
loss = F.nll_loss(softmax_scores, labels, reduction='mean')
reduced_losses = average_losses_across_data_parallel_group([loss, *topk_accs])
# Scale the retrieval loss
loss = loss * mpu.get_data_parallel_world_size()
# create stats_dict with retrieval loss and all specified top-k accuracies
topk_acc_dict = {'top{}_acc'.format(k): v * 100 for k, v in \
zip(args.retriever_report_topk_accuracies, reduced_losses[1:])}
stats_dict = dict(loss=reduced_losses[0], **topk_acc_dict)
return loss, stats_dict
def forward_step(data_iterator, model):
"""Forward step."""
timers = get_timers()
# Get the batch.
timers('batch-generator', log_level=2).start()
query_tokens, query_mask, \
context_tokens, context_mask, context_indices = get_ict_batch(data_iterator)
timers('batch-generator').stop()
# Query and Context Types
query_types = torch.cuda.LongTensor(*query_tokens.shape).fill_(0)
context_types = torch.cuda.LongTensor(*context_tokens.shape).fill_(0)
# Forward model.
output_tensor = model(query_tokens, query_mask, query_types, context_tokens,
context_mask, context_types)
return output_tensor, partial(loss_func)
def train_valid_test_datasets_provider(train_val_test_num_samples):
"""Build train, valid and test datasets."""
args = get_args()
print_rank_0('> building train, validation, and test datasets '
'for BERT ICT...')
train_ds, valid_ds, test_ds = build_train_valid_test_datasets(
data_prefix=args.data_path,
data_impl=args.data_impl,
splits_string=args.split,
train_valid_test_num_samples=train_val_test_num_samples,
max_seq_length=args.seq_length,
masked_lm_prob=args.mask_prob,
short_seq_prob=args.short_seq_prob,
seed=args.seed,
skip_warmup=(not args.mmap_warmup),
binary_head=False,
dataset_type='ict')
print_rank_0("> finished creating BERT ICT datasets ...")
return train_ds, valid_ds, test_ds
if __name__ == "__main__":
ict_model_type = ModelType.encoder_or_decoder
args_defaults = {'tokenizer_type': 'BertWordPieceLowerCase'}
megatron.initialize.initialize_megatron(extra_args_provider=None,
args_defaults=args_defaults)
args = megatron.get_args()
megatron.training.pretrain(args,
train_valid_test_datasets_provider,
pretrain_ict_model_provider,
ict_model_type,
forward_step)