pretrain_glm.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
# limitations under the License.

"""Pretrain GLM"""

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_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

from glm import build_train_valid_test_datasets, build_mask_matrix

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

try:
    from torch.distributed.elastic.multiprocessing.errors import record
except ImportError:
    # noop
    def record(fn):
        return fn

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 and args.pipeline_model_parallel_size > 1:
            # GLM has dynamic attn_mask, so don't set args.attn_mask

            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 process_data(data):
    args = get_args()
    # Items and their type.
    keys = ['text', 'loss_mask', 'target', 'attention_mask', 'position_id']
    if not args.finetune and args.deepspeed:
        keys.append('task_type')
    datatype = torch.int64

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

    # Unpack.
    tokens = data_b['text'].long()
    labels = data_b['target'].long()
    attention_mask = data_b['attention_mask'].long()
    loss_mask = data_b['loss_mask'].float()
    position_ids = data_b['position_id'].long()
    task_type = data_b['task_type'].long() if 'task_type' in data_b else None

    attention_mask = build_mask_matrix(attention_mask, tokens.size(0),
                                       tokens.size(1))
    attention_mask = attention_mask.to(torch.bool)

    if task_type is None:
        return tokens, labels, loss_mask, attention_mask, position_ids
    return tokens, labels, loss_mask, attention_mask, position_ids, task_type


def get_batch(data_iterator):
    """Generate a batch"""
    # Broadcast data.
    if data_iterator is not None:
        data = next(data_iterator)
    else:
        data = None

    return process_data(data)


def get_batch_pipe(data):
    """Modification of `get_batch` to work on `next(data_iterator)` instead of `data_iterator`"""
    args = get_args()
    tokens, labels, loss_mask, attention_mask, position_ids, task_type = process_data(data)

    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, task_type)


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, task_type = 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 or args.multitask_data_path:
        train_ds, valid_ds, test_ds = build_train_valid_test_datasets(
            data_prefix=args.data_path,
            splits_string=args.split,
            train_valid_test_num_samples=train_val_test_num_samples,
            seq_length=args.seq_length,
            length_per_sample=args.length_per_sample,
            aggregated_samples_per_sequence=args.aggregated_samples_per_sequence,
            args=args)
    # Option 2 of data loading using --(train|valid|test)-weighted-split-paths
    elif args.train_weighted_split_paths:
        raise NotImplementedError()
    else:
        raise NotImplementedError("No dataloading argument passed")

    print_rank_0("> finished creating GPT datasets ...")
    return train_ds, valid_ds, test_ds

@record
def main():
    pretrain(train_valid_test_datasets_provider, model_provider, forward_step,
             args_defaults={'tokenizer_type': 'IceTokenizer'})

if __name__ == "__main__":
    main()