pretrain_gpt.py

# coding=utf-8
# Copyright (c) 2020, 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://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
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"""Pretrain GPT"""

import torch
from functools import partial
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.gpt_dataset import build_train_valid_test_datasets, build_dataset_group
from megatron.model import GPTModel, GPTModelPipe
from megatron.training import pretrain
from megatron.utils import get_ltor_masks_and_position_ids, get_prefix_indices
from megatron.utils import average_losses_across_data_parallel_group

import deepspeed
from deepspeed.runtime.utils import see_memory_usage
import os


def model_provider(pre_process=True, post_process=True):
    """Build the model."""

    print_rank_0('building GPT model ...')
    see_memory_usage(f"Before Building Model", force=True)

    args = get_args()

    with deepspeed.zero.Init(data_parallel_group=mpu.get_data_parallel_group(),
                             remote_device=None if args.remote_device == 'none' else args.remote_device,
                             config_dict_or_path=args.deepspeed_config,
                             enabled=args.zero_stage == 3,
                             mpu=mpu):
        if args.deepspeed:
            # Precompute the attention mask and store it in args. This avoids having to
            # pipeline it as an activation during training. The mask is constant, and thus
            # we can reuse it.
            attention_mask = torch.tril(torch.ones(
                (1, args.seq_length, args.seq_length), device=torch.cuda.current_device())).view(
                    1, 1, args.seq_length, args.seq_length)

            # Convert attention mask to binary:
            attention_mask = (attention_mask < 0.5)
            if args.fp16:
                attention_mask = attention_mask.half()
            elif args.bf16:
                attention_mask = attention_mask.bfloat16()

            # must be bool or the training crashes expecting bool, but getting Half
            args.attn_mask = attention_mask.to(torch.bool)

            model = GPTModelPipe(
                num_tokentypes=0,
                parallel_output=True
            )
            # 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
        else:
            model = GPTModel(
                num_tokentypes=0,
                parallel_output=True,
                pre_process=pre_process,
                post_process=post_process
            )
    see_memory_usage(f"After Building Model", force=True)
    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,
        prefix_indices=None,
        loss_on_targets_only=args.loss_on_targets_only
    )

    return tokens, labels, loss_mask, attention_mask, position_ids


def get_batch_pipe(data):
    """Modification of `get_batch` to work on `next(data_iterator)` instead of `data_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 position 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,
        prefix_indices=None,
        loss_on_targets_only=args.loss_on_targets_only
    )
    if args.curriculum_learning and args.curriculum_seqlen < tokens.size()[1]:
        # seqlen-based curriculum learning
        # tokens, position_ids, labels, loss_mask have size [batch size, seqlen]
        tokens = tokens[:, :args.curriculum_seqlen].contiguous()
        position_ids = position_ids[:, :args.curriculum_seqlen].contiguous()
        labels = labels[:, :args.curriculum_seqlen].contiguous()
        loss_mask = loss_mask[:, :args.curriculum_seqlen].contiguous()

    return (tokens, position_ids, attention_mask), (labels, loss_mask)


def loss_func(loss_mask, output_tensor):
    losses = output_tensor.float()
    loss_mask = loss_mask.view(-1).float()
    loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum()

    # Reduce loss for logging.
    averaged_loss = average_losses_across_data_parallel_group([loss])

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


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()

    output_tensor = model(tokens, position_ids, attention_mask,
                          labels=labels)
    if args.curriculum_learning and args.curriculum_seqlen < args.seq_length:
        loss_mask = loss_mask[:, :args.curriculum_seqlen].contiguous()

    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()
    train_ds, valid_ds, test_ds = None, None, None

    print_rank_0('> building train, validation, and test datasets for GPT ...')
    # Option 1 of data loading using --data-path

    if args.data_path:
        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))
    # Option 2 of data loading using --(train|valid|test)-weighted-split-paths
    elif args.train_weighted_split_paths:
        assigned_train_valid_test = []
        if args.train_weighted_split_paths is not None:
            train_ds = []
            assigned_train_valid_test.append("train")
        if args.valid_weighted_split_paths is not None:
            valid_ds = []
            assigned_train_valid_test.append("valid")
        if args.test_weighted_split_paths is not None:
            test_ds = []
            assigned_train_valid_test.append("test")

        for s in assigned_train_valid_test:
            data_groups = zip(eval(f"args.{s}_weighted_split_paths"),
                                eval(f"args.{s}_weighted_split_weights"),
                                eval(f"args.{s}_weighted_split_splits"),
                                eval(f"args.{s}_weighted_split_names"))
            for paths, weights, splits, name in data_groups:
                d = build_dataset_group(name, paths, weights, splits,
                                        args.data_impl,
                                        train_val_test_num_samples,
                                        args.seq_length, args.seed,
                                        (not args.mmap_warmup),
                                        train_valid_test=s)
                eval(f"{s}_ds").append(d)
    else:
        raise NotImplementedError("No dataloading argument passed")

    print_rank_0("> finished creating GPT 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'})