pretrain_gpt2.py

# coding=utf-8
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# 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
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#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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"""Pretrain GPT2"""

import torch

from megatron import get_args
from megatron import print_rank_0
from megatron import get_timers
from megatron import get_tokenizer
from megatron import mpu
from megatron.data.gpt2_dataset import build_train_valid_test_datasets
from megatron.model import GPT2Model, GPT2ModelPipe
from megatron.training import pretrain
from megatron.utils import get_ltor_masks_and_position_ids
from megatron.utils import reduce_losses
from megatron.fp16 import fp32_to_fp16

# pretend this is a great DeepSpeed change too

def model_provider():
    """Build the model."""

    args = get_args()

    print_rank_0('building GPT2 model ...')
    if args.pipe_parallel_size == 0:
        model = GPT2Model(num_tokentypes=0, parallel_output=True)
    else:
        model = GPT2ModelPipe(num_tokentypes=0, parallel_output=True, topology=mpu.get_topology())
        # This is a hack to give us a reference to get_batch_pipe from within training.py
        # We need to call model.set_batch_fn after deepspeed.initialize
        model._megatron_batch_fn = get_batch_pipe

    return model


def get_batch(data_iterator):
    """Generate a batch"""
    args = get_args()
    tokenizer = get_tokenizer()

    # Items and their type.
    keys = ['text']
    datatype = torch.int64

    # Broadcast data.
    if data_iterator is not None:
        data = next(data_iterator)
    else:
        data = None
    data_b = mpu.broadcast_data(keys, data, datatype)

    # Unpack.
    tokens_ = data_b['text'].long()
    labels = tokens_[:, 1:].contiguous()
    tokens = tokens_[:, :-1].contiguous()

    # Get the masks and postition ids.
    attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids(
        tokens,
        tokenizer.eod,
        args.reset_position_ids,
        args.reset_attention_mask,
        args.eod_mask_loss)

    return tokens, labels, loss_mask, attention_mask, position_ids

def get_batch_pipe(data):
    """A modification of get_batch() to work with the latest batch instead of an iterator. """
    args = get_args()
    tokenizer = get_tokenizer()

    # Items and their type.
    keys = ['text']
    datatype = torch.int64

    # Broadcast data.
    data_b = mpu.broadcast_data(keys, data, datatype)

    # Unpack.
    tokens_ = data_b['text'].long()
    labels = tokens_[:, 1:].contiguous()
    tokens = tokens_[:, :-1].contiguous()

    # Get the masks and postition ids.
    attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids(
        tokens,
        tokenizer.eod,
        args.reset_position_ids,
        args.reset_attention_mask,
        args.eod_mask_loss)

    # unpack data
    if args.fp16:
        # cast to fp16 because pipeline parallelism skips the FP16 wrapper.
        return fp32_to_fp16((tokens, position_ids, attention_mask)), fp32_to_fp16((labels, loss_mask))
    else:
        return (tokens, position_ids, attention_mask), (labels, loss_mask)


def forward_step(data_iterator, model):
    """Forward step."""
    args = get_args()
    timers = get_timers()

    # Get the batch.
    timers('batch generator').start()
    tokens, labels, loss_mask, attention_mask, position_ids = get_batch(
        data_iterator)
    timers('batch generator').stop()
    # Forward model.
    losses = model(tokens, position_ids, attention_mask, labels=labels)
    loss_mask = loss_mask.view(-1)
    loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum()

    # Reduce loss for logging.
    reduced_loss = reduce_losses([loss])

    return loss, {'lm loss': reduced_loss[0]}


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 GPT2 ...')
    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,
        seq_length=args.seq_length,
        seed=args.seed,
        skip_warmup=(not args.mmap_warmup))
    print_rank_0("> finished creating GPT2 datasets ...")

    return train_ds, valid_ds, test_ds


if __name__ == "__main__":

    pretrain(train_valid_test_datasets_provider, model_provider, forward_step,
             args_defaults={'tokenizer_type': 'GPT2BPETokenizer'})