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- # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
-
- """Pretrain T5"""
-
- from functools import partial
-
- import torch
-
- from megatron import (
- get_args,
- get_timers,
- print_rank_0
- )
- from megatron.core import tensor_parallel
- from megatron.core.enums import ModelType
- from megatron.data.dataset_utils import build_train_valid_test_datasets
- from megatron.model import T5Model
- from megatron.training import pretrain
- from megatron.utils import average_losses_across_data_parallel_group
-
-
- """
- Pipeline parallelism for T5
- ===========================
-
- T5 is a model architecture with both encoder and decoder blocks.
- Consequently, pipeline parallelism is implemented slightly differently
- compared to architectures like GPT and BERT.
-
- In particular, when pipeline_model_parallel_world_size > 1, each stage
- either executes an encoder block or a decoder block. The
- --pipeline-model-parallel-split-rank argument controls the rank at which
- the split happens: all ranks lower than this argument execute the
- encoder block, and all ranks equal to or higher than this argument value
- execute the decoder block.
-
- In the encoder section of the model, only one tensor is sent downstream:
- the intermediate encoder_hidden_state. In the decoder section of the
- model, two tensors are sent downstream in the forward pass: the fully
- computed encoder_hidden_state, and the intermediate decoder_hidden_state.
-
- In particular, these are the shapes of the tensors sent between
- different workers:
- If rank is in decoder section:
- intermediate decoder_hidden_state (pre-transpose),
- complete encoder_hidden_state (post-transpose).
- If rank is at boundary between encoder and decoder sections:
- complete encoder_hidden_state (post-transpose).
- If rank is in encoder section:
- intermediate encoder_hidden_state (pre-transpose).
-
- Additionally, we have code in the backward_step function in schedules.py
- to accumulate the encoder_hidden_state gradient across skip connections
- (encoder_hidden_state fed in as input to each layer in the decoder).
- """
-
-
- def model_provider(pre_process=True, post_process=True,
- add_encoder=True, add_decoder=True):
- """Build the model."""
-
- print_rank_0('building T5 model ...')
- model = T5Model(num_tokentypes=0,
- parallel_output=True,
- pre_process=pre_process,
- post_process=post_process,
- add_encoder=add_encoder,
- add_decoder=add_decoder)
- return model
-
-
- def get_batch(data_iterator):
- """Build the batch."""
-
- keys = ['text_enc', 'text_dec', 'labels', 'loss_mask',
- 'enc_mask', 'dec_mask', 'enc_dec_mask']
- datatype = torch.int64
-
- # Broadcast data.
- if data_iterator is not None:
- data = next(data_iterator)
- else:
- data = None
- data_b = tensor_parallel.broadcast_data(keys, data, datatype)
-
- # Unpack.
- tokens_enc = data_b['text_enc'].long()
- tokens_dec = data_b['text_dec'].long()
- labels = data_b['labels'].long()
- loss_mask = data_b['loss_mask'].float()
-
- enc_mask = (data_b['enc_mask'] < 0.5)
- dec_mask = (data_b['dec_mask'] < 0.5)
- enc_dec_mask = (data_b['enc_dec_mask'] < 0.5)
-
- return tokens_enc, tokens_dec, loss_mask, labels, \
- enc_mask, dec_mask, enc_dec_mask
-
-
- def loss_func(loss_mask, output_tensor):
- lm_loss_ = output_tensor.float()
- lm_loss = torch.sum(
- lm_loss_.view(-1) * loss_mask.reshape(-1)) / loss_mask.sum()
-
- loss = lm_loss
- averaged_losses = average_losses_across_data_parallel_group([lm_loss])
-
- return loss, {'lm loss': averaged_losses[0]}
-
-
- def forward_step(data_iterator, model):
- """Forward step."""
- args = get_args()
- timers = get_timers()
-
- # Get the batch.
- timers('batch generator', log_level=2).start()
- tokens_enc, tokens_dec, loss_mask, lm_labels, enc_mask, dec_mask, enc_dec_mask \
- = get_batch(data_iterator)
- timers('batch generator').stop()
-
- # Forward model lm_labels
- output_tensor = model(tokens_enc,
- tokens_dec,
- enc_mask,
- dec_mask,
- enc_dec_mask,
- tokentype_ids=None,
- lm_labels=lm_labels)
-
- return output_tensor, partial(loss_func, loss_mask)
-
-
- 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 T5 ...')
- 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.encoder_seq_length,
- max_seq_length_dec=args.decoder_seq_length,
- masked_lm_prob=args.mask_prob,
- short_seq_prob=args.short_seq_prob,
- seed=args.seed,
- skip_warmup=(not args.mmap_warmup),
- dataset_type='t5')
- print_rank_0("> finished creating T5 datasets ...")
-
- return train_ds, valid_ds, test_ds
-
-
- if __name__ == "__main__":
-
- pretrain(train_valid_test_datasets_provider, model_provider, ModelType.encoder_and_decoder,
- forward_step, args_defaults={'tokenizer_type': 'BertWordPieceLowerCase'})
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