ModelLink2/pretrain_bloom.py

# coding=utf-8
# Copyright Information:
# Huawei Technologies Co., Ltd. All Rights Reserved © 2020
# 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 BLOOM"""
from functools import partial

import torch
import torch_npu
import torch_trans
from torch import nn
import torch.nn.functional as F
import deepspeed
import deepspeed_npu
from deepspeed.runtime.utils import see_memory_usage
from deepspeed.accelerator import get_accelerator

from ascendspeed import get_args
from ascendspeed import print_rank_0
from ascendspeed import get_timers
from ascendspeed import get_tokenizer
from ascendspeed import mpu
from ascendspeed.data.gpt_dataset import build_train_valid_test_datasets, build_dataset_group
from ascendspeed.model import GPTModel, GPTModelPipe, ModelType
from ascendspeed.enums import AttnMaskType
from ascendspeed.training import pretrain
from ascendspeed.utils import get_ltor_masks_and_position_ids
from ascendspeed.utils import average_losses_across_data_parallel_group


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

    print_rank_0('building BLOOM 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:
            args.pretrain_causal_attention = True
            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

            # Predompute 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=get_accelerator().current_device_name())).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()

            # Attention mask must be bool.
            args.attn_mask = attention_mask.to(torch.bool)
        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'].int()
    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 data_post_process(data, data_sampler_state_dict):
    args = get_args()
    if args.data_efficiency_curriculum_learning:
        if 'seqlen_truncate' in data_sampler_state_dict['current_difficulties']:
            args.data_efficiency_curriculum_learning_seqlen_type = 'seqlen_truncate'
            current_seqlen = data_sampler_state_dict['current_difficulties']['seqlen_truncate']
            if current_seqlen < args.seq_length:
                data['text'] = data['text'][:, :(current_seqlen+1)].contiguous()
        elif 'seqlen_reshape' in data_sampler_state_dict['current_difficulties']:
            args.data_efficiency_curriculum_learning_seqlen_type = 'seqlen_reshape'
            current_seqlen = data_sampler_state_dict['current_difficulties']['seqlen_reshape']
            if current_seqlen < args.seq_length:
                orig_num_token = torch.numel(data['text'])
                reshape_len = (data['text'].size()[1] // (current_seqlen+1)) * (current_seqlen+1)
                data['text'] = torch.cat((data['text'][:, :reshape_len].contiguous().view(-1, current_seqlen+1),
                    data['text'][:, -(current_seqlen+1):]), 0).contiguous()
                num_row = math.ceil(orig_num_token / (current_seqlen+1))
                num_row = min(num_row, data['text'].size()[0])
                if num_row > 1 and num_row % 2 != 0:
                    num_row -= 1
                data['text'] = data['text'][:num_row, :].contiguous()
        else:
            args.data_efficiency_curriculum_learning_seqlen_type = None
    return data

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
    )

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


def loss_func(loss_mask, output_tensor):
    args = get_args()
    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])
    if args.mos or args.kd:
        loss = loss + moe_loss + mos_loss
        if args.mos:
            return loss, {'total loss': loss, 'lm loss': averaged_loss[0], 'moe loss': moe_loss, 'mos loss': mos_loss}
        elif args.kd:
            return loss, {'total loss': loss, 'lm loss': averaged_loss[0], 'moe loss': moe_loss, 'kd loss': mos_loss}
        else:
            print_rank_0('>>> total loss: {}, lm loss {}, kd loss {}'.format(loss, averaged_loss[0], mos_loss))
            return None
    else:
        if max(args.num_experts) <= 1:
            return loss, {'lm loss': averaged_loss[0]}
        else:
            loss = loss + moe_loss
            return loss, {'lm loss': averaged_loss[0], 'moe loss': moe_loss}

def calculate_mos_loss(args, stu_output, teacher_model, tokens, position_ids, attention_mask):
    mos_loss = 0
    alpha = args.kd_alpha_ce
    beta = args.kd_beta_ce
    kd_temp = args.kd_temp
    
    if teacher_model:
        with torch.no_grad():
            if args.curriculum_learning_legacy and args.curriculum_seqlen < args.seq_length:
                assert args.curriculum_seqlen is not None
                curriculum_seqlen = args.curriculum_seqlen
                tokens = tokens[:, :curriculum_seqlen].contiguous()
                position_ids = position_ids[:, :curriculum_seqlen].contiguous()
                attention_mask = attention_mask[:, :, :curriculum_seqlen, :curriculum_seqlen].contiguous()
                # No need to truncate labels as we do not need it for the teacher logits
            tea_output, *tea_other_losses = teacher_model(tokens, position_ids, attention_mask)
            assert stu_output.size() == tea_output.size(), \
                'teacher and student output should match in size. Student: {},' \
                ' Teacher: {}, CL seq length {}'.format(stu_output.size(), tea_output.size(), args.curriculum_seqlen)

        student_logits = F.log_softmax(stu_output / kd_temp, dim=2)
        tea_logits = F.softmax(tea_output / kd_temp, dim=2)
        # The target logits is expected to be probabilities.
        # If we use log_softmax, then we need to set target_log to true when initializing the KLDivLoss.

        mos_loss = kd_temp * kd_temp * nn.KLDivLoss(reduction='batchmean')(student_logits, tea_logits)

        mos_loss = mos_loss.div(args.seq_length) * beta
    return mos_loss

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, 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)

    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 = [], [], []

    print_rank_0('> building train, validation, and test datasets for BLOOM ...')
    # 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 BLOOM datasets ...")
    return train_ds, valid_ds, test_ds


if __name__ == "__main__":
    torch_npu.npu.set_compile_mode(jit_compile=True)

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