pretrain_gpt_modified.py

# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.

"""Pretrain GPT"""

import os
from rich import print
import torch
import math

# The earliest we can measure the start time.
import time
from datetime import datetime

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.core import mpu, tensor_parallel
from megatron.core.enums import ModelType
from megatron.data.gpt_dataset import build_train_valid_test_datasets
from megatron.model import GPTModel, GPTModelPipe
from megatron.training import pretrain
from megatron.utils import get_ltor_masks_and_position_ids
from megatron.utils import average_losses_across_data_parallel_group, update_rotary_pos_emb
from megatron.arguments import core_transformer_config_from_args
from megatron.utils import (
    report_memory,
    throughput_calculator,
    checkpoint_throughput_calculator
)
from pathlib import Path

import deepspeed
from deepspeed.runtime.utils import see_memory_usage
from deepspeed.accelerator.real_accelerator import get_accelerator
import subprocess
import wandb

import time
from torch import nn
import torch.nn.functional as F

# from ezpz import get_logger
from ezpz.dist import get_world_size, setup_wandb, get_rank

# More imports
from megatron.initialize import initialize_megatron
from megatron.initialize import set_jit_fusion_options
from megatron.training import print_datetime, _create_ds_config_dict
from megatron.training import setup_model_and_optimizer
from megatron.training import load_model_weights_only, get_model
from megatron.training import load_model_weights_only_modified
from megatron.training import get_optimizer_param_scheduler, cyclic_iter
from megatron.training import train, train_step
from megatron.training import train_step_dpo
from megatron.optimizer import get_megatron_optimizer
from megatron.checkpointing import load_checkpoint
from megatron.data.data_samplers import build_pretraining_data_loader
from megatron.core.pipeline_parallel import get_forward_backward_func
from megatron.arguments import core_transformer_config_from_args
from megatron import update_num_microbatches
from megatron import get_num_microbatches

# RANK = setup_torch(
#     backend='deepspeed',
#     port='5432',
# )
RANK = get_rank()
WORLD_SIZE = get_world_size()
LEVEL = "DEBUG" if RANK == 0 else "CRITICAL"

WANDB_MODE = os.environ.get('WANDB_MODE', None)
DISABLE_WANDB = (
    WANDB_MODE is not None and str(WANDB_MODE).lower() == 'disabled'
)

if RANK == 0 and not DISABLE_WANDB:
    project_name = (
        os.environ.get(
            'WB_PROJECT',
            os.environ.get(
                'WANDB_PROJECT',
                'AuroraGPT'
            ),
        )
    )
    print('--------------------------------------------------')
    print(f"Setting up W&B from: {RANK} with {project_name}")
    print('--------------------------------------------------')
    setup_wandb(project_name=project_name)


def model_provider(pre_process=True, post_process=True):
    """Build the model."""
    print_rank_0('building GPT model ...')
    see_memory_usage("Before Building Model", force=True)
    args = get_args()
    config = core_transformer_config_from_args(args)
    if wandb.run is not None:
        print(f"Updating WandB run: [{wandb.run.name}]({wandb.run.url})")
        wandb.run.config.update({"args": vars(args)}, allow_val_change=True)
    if RANK == 0:
        git_ds_info()
    if hasattr(mpu, 'get_sequence_parallel_group'):
        dpg = mpu.get_sequence_parallel_group()
    elif hasattr(mpu, 'get_data_parallel_group'):
        dpg = mpu.get_data_parallel_group()
    else:
        dpg = None
    if wandb is not None and wandb.run is not None:
        assert wandb is not None and wandb.run is not None
        print(f'Updating {wandb.run.name=} at {wandb.run.url=}')
        wandb.run.config.update({'args': vars(args)}, allow_val_change=True)
    with deepspeed.zero.Init(
            data_parallel_group=dpg,
            remote_device=(
                None if args.remote_device == 'none' else args.remote_device
            ),
            config_dict_or_path=args.deepspeed_config_dict,
            enabled=args.zero_stage == 3,
            mpu=mpu
    ):
        if args.deepspeed and not args.no_pipeline_parallel:
            model = GPTModelPipe(
                config=config,
                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)

            # For prertaining, since sequence length is fixed,
            # cache rotary embedding in args, to avoid communicating around
            if args.use_rotary_position_embeddings:
                update_rotary_pos_emb(args.seq_length)

        else:
            print(f'Building model check..')
            model = GPTModel(
                config=config,
                num_tokentypes=0,
                parallel_output=True,
                pre_process=pre_process,
                post_process=post_process
            )
    num_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
    # print_rank_0('\n ------------------------ ')
    # print_rank_0(f'num of parameters {num_params}')
    # print_rank_0('------------------------\n ')
    print_rank_0(80 * '-')
    print_rank_0(f"Number of parameters in model: {num_params}")
    print_rank_0(80 * '-')
    see_memory_usage("After Building Model", force=True)
    if wandb.run is not None:
        wandb.run.config.update({'num_params': num_params}, allow_val_change=True)
    #     wandb.run.watch(
    #         model,
    #         log='all',
    #         log_graph=True,
    #     )
    #     wandb.run.config.update({'num_params': num_params})
    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

    data = next(data_iterator) if data_iterator is not None else None
    # # 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_ = data_b['text'].long()
    labels = tokens_[:, 1:].contiguous()
    tokens = tokens_[:, :-1].contiguous()

    # Get the masks and postition ids.
    skip_mask = args.use_flash_attn or args.use_flash_attn_triton
    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,
        skip_mask)

    # For DS's sequence parallel
    seq_parallel_world_size = mpu.get_sequence_parallel_world_size()
    seq_parallel_world_rank = mpu.get_sequence_parallel_rank()

    # For Megatron's sequence parallel
    if args.sequence_parallel:
        seq_parallel_world_size = mpu.get_tensor_model_parallel_world_size()
        seq_parallel_world_rank = mpu.get_tensor_model_parallel_rank()
    seq_length = tokens.size(1)

    assert seq_length % seq_parallel_world_size == 0
    sub_seq_length = seq_length // seq_parallel_world_size
    sub_seq_start = seq_parallel_world_rank * sub_seq_length
    sub_seq_end = (seq_parallel_world_rank + 1) * sub_seq_length

    tokens = tokens[:, sub_seq_start:sub_seq_end]
    position_ids = position_ids[:, sub_seq_start:sub_seq_end]
    # For DS's sequence parallel
    if mpu.get_sequence_parallel_world_size() > 1:
        labels = labels[:, sub_seq_start:sub_seq_end]

    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 = tensor_parallel.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)
    if (
                args.curriculum_learning_legacy
                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()
        if labels is not None:
            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, moe_loss, mos_loss, 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:
        # assert max(args.num_experts) >= 1
        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
            }
        print_rank_0(
            f'>>> total loss: {loss}, '
            f'lm loss {averaged_loss[0]}, '
            f'kd loss {mos_loss}'
        )
    else:
        if max(args.num_experts) <= 1:
            return loss, {'lm loss': averaged_loss[0]}
        loss = loss + moe_loss
        return loss, {'lm loss': averaged_loss[0], 'moe loss': moe_loss}

def dpo_loss_func(loss_mask, dpo_loss, 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:
        # assert max(args.num_experts) >= 1
        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
            }
        print_rank_0(
            f'>>> total loss: {loss}, '
            f'lm loss {averaged_loss[0]}, '
            f'kd loss {mos_loss}'
        )
    # else:
    #     if max(args.num_experts) <= 1:
    #         return loss, {'lm loss': averaged_loss[0]}
    #     loss = loss + moe_loss
    #     return loss, {'lm loss': averaged_loss[0], 'moe loss': moe_loss}
    else:
        # if max(args.num_experts) <= 1:
            # return loss, {'lm loss': averaged_loss[0]}
        loss = dpo_loss
        return loss, {'lm loss': averaged_loss[0], 'dpo loss': dpo_loss}

def batch_seq_logprobs(logits, labels):
    """ Function to compute a batch of sequence log probabilities """

    logits = logits[:-1, :, :] # skip last logit
    logits_logsoftmax = logits.log_softmax(-1) # compute log softmax of logits

    labels = labels[1:, :].clone() # clone labels

    # # Loss mask to avoid padded tokens while computing loss
    # loss_mask = labels != tokenizer.pad_token_id

    # print(f'Labels shape: {labels.shape}')
    # print(f'loss_mask shape: {loss_mask.shape}')
    # print(f'loss_mask dtype: {loss_mask.dtype}')

    # Gather logps and squeeze last dimension
    logprobs = torch.gather(logits_logsoftmax, dim=2, index=labels.unsqueeze(2)).squeeze(2)
    # print(f'seq_logprobs shape: {logprobs.shape}')

    # Weighted sum over logprobs using loss mask
    # seq_logprobs = (logprobs * loss_mask).sum(-1)
    seq_logprobs = logprobs.sum(-1)

    return seq_logprobs


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()
                csl = curriculum_seqlen
                attention_mask = (
                        attention_mask[:, :, :csl, :csl].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. '
                    f'Student: {stu_output.size()}, '
                    f'Teacher: {tea_output.size()}, '
                    f'CL seq length {args.curriculum_seqlen}'
            )

        student_logits = F.log_softmax(stu_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.
        tea_logits = F.softmax(tea_output / kd_temp, dim=2)

        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 calculate_dpo_loss(
        args,
        stu_output,
        teacher_model,
        logprobs_p,
        logprobs_u,
        ref_logprobs_p,
        ref_logprobs_u,
        tokens,
        position_ids,
        attention_mask
):
    mos_loss = 0
    alpha = args.kd_alpha_ce
    beta = args.kd_beta_ce
    kd_temp = args.kd_temp
    kd_temp = 1.0
    beta = 0.1 # add to cmdline args

    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()
                csl = curriculum_seqlen
                attention_mask = (
                        attention_mask[:, :, :csl, :csl].contiguous()
                )
                # No need to truncate labels
                # as we do not need it for the teacher logits
            ref_output, ref_other_losses = teacher_model(
                tokens,
                position_ids,
                attention_mask
            )
            assert stu_output.size() == ref_output.size(), (
                    'ref and student output should match in size. '
                    f'Student: {stu_output.size()}, '
                    f'Reference: {ref_output.size()}, '
                    f'CL seq length {args.curriculum_seqlen}'
            )

        student_logits = F.log_softmax(stu_output / kd_temp, dim=2)
        # Labels ?
        logprobs = torch.gather(student_logits, dim=2, index=labels.unsqueeze(2)).squeeze(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.

    # Get ratios of preferred log probabilities from model and ref model
    logprob_ratio_p = logprobs_p - ref_logprobs_p

    # Get ratios of unpreferred log probabilities from model and ref model
    logprob_ratio_u = logprobs_u - ref_logprobs_u

    # Difference of logprobs ratios scaled by beta
    scaled_diff_logprob_ratios = beta * (logprob_ratio_p - logprob_ratio_u)

    # Losses computed as negative logsigmoid of scaled difference
    losses = -F.logsigmoid(scaled_diff_logprob_ratios)

    # preferred dpo rewards
    pref_dpo_rewards = (beta * logprob_ratio_p).detach()

    # unpreferred dpo rewards
    unpref_dpo_rewards = (beta * logprob_ratio_u).detach()

    # Implicit DPO rewards
    implicit_dpo_rewards = (pref_dpo_rewards > unpref_dpo_rewards).float()
    rewards = implicit_dpo_rewards.cpu().mean()

    # Compute mean loss
    dpo_loss = losses.mean()
    # print(f'Loss dtype: {loss.dtype}')

    return dpo_loss, rewards

def compute_dp_loss(logprobs_p, ref_logprobs_p,
                    logprobs_u, ref_logprobs_u,
                    beta=0.1):

    # Get ratios of preferred log probabilities from model and ref model
    logprob_ratio_p = logprobs_p - ref_logprobs_p

    # Get ratios of unpreferred log probabilities from model and ref model
    logprob_ratio_u = logprobs_u - ref_logprobs_u

    # Difference of logprobs ratios scaled by beta
    scaled_diff_logprob_ratios = beta * (logprob_ratio_p - logprob_ratio_u)

    # Losses computed as negative logsigmoid of scaled difference
    losses = -F.logsigmoid(scaled_diff_logprob_ratios)

    # Compute mean loss
    dp_loss = losses.mean()

    return dp_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()

    if args.data_efficiency_curriculum_learning:
        args.curriculum_seqlen = tokens.size()[1]
        if (
                hasattr(
                    args,
                    'data_efficiency_curriculum_learning_seqlen_type')
                and (
                    args.data_efficiency_curriculum_learning_seqlen_type
                    == 'seqlen_reshape'
                )
        ):
            args.data_efficiency_curriculum_learning_numel = (
                    torch.numel(tokens)
            )

    if args.mos or args.kd:
        # The forward func can return either the loss or the logits,
        # depending on whether passing in the labels or not.
        stu_output, other_losses = model(tokens, position_ids, attention_mask)
        if (
                    args.curriculum_learning_legacy
                    and args.curriculum_seqlen < args.seq_length
        ):
            assert args.curriculum_seqlen is not None
            labels = labels[:, :args.curriculum_seqlen].contiguous()
        output_tensor = tensor_parallel.vocab_parallel_cross_entropy(
            stu_output.contiguous().float(),
            labels
        )
    else:
        output_tensor, other_losses = model(
            tokens,
            position_ids,
            attention_mask,
            labels=labels
        )
    if (
                args.curriculum_learning_legacy and
                args.curriculum_seqlen < args.seq_length
    ):
        loss_mask = loss_mask[:, :args.curriculum_seqlen].contiguous()

    moe_losses = []
    for moe_loss in other_losses:
        if moe_loss is not None:
            moe_losses.append(moe_loss)
    moe_loss = sum(moe_losses) * args.moe_loss_coeff

    mos_loss = 0
    if args.mos or args.kd:
        assert model.training
        if args.teacher_forward and args.teacher_model is not None:
            mos_loss = calculate_mos_loss(
                args,
                stu_output,
                args.teacher_model[0],
                tokens,
                position_ids,
                attention_mask
            )

    # Output_tensor stores the standard loss,
    # loss_func calculates the total loss.
    return output_tensor, partial(loss_func, loss_mask, moe_loss, mos_loss)


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 GPT ...')
    files = []
    if args.data_file_list is not None:
        with open(args.data_file_list, 'r') as flist:
            for f in flist.readlines():
                w, fname = f.split()
                files.append(float(w))
                files.append(fname)
    elif len(args.data_path) == 1 and os.path.isdir(args.data_path[0]):
        path = args.data_path[0] + "/"
        for f in os.listdir(path):
            if (os.path.isfile(path + f) and f.find(".bin") != -1):
                files.append(1)
                files.append(path + f.split(".bin")[0])
    else:
        files = args.data_path
    print_rank_0(f"file list {files}")
    train_ds, valid_ds, test_ds = build_train_valid_test_datasets(
        data_prefix=files,
        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=True,
        # skip_warmup=(not args.mmap_warmup),
        train_data_prefix=args.train_data_path,
        valid_data_prefix=args.valid_data_path,
        test_data_prefix=args.test_data_path,
        data_cache_path=args.data_cache_path)
    print_rank_0("> finished creating GPT datasets ...")

    return train_ds, valid_ds, test_ds


def command_exists(cmd):
    result = subprocess.Popen(
        f'type {cmd}',
        stdout=subprocess.PIPE,
        shell=True
    )
    return result.wait() == 0


def git_ds_info():
    if RANK != 0:
        return
    from deepspeed.env_report import main as ds_report
    ds_report()

    # Write out version/git info
    git_hash_cmd = "git rev-parse --short HEAD"
    git_branch_cmd = "git rev-parse --abbrev-ref HEAD"
    if command_exists('git'):
        try:
            result = subprocess.check_output(git_hash_cmd, shell=True)
            git_hash = result.decode('utf-8').strip()
            result = subprocess.check_output(git_branch_cmd, shell=True)
            git_branch = result.decode('utf-8').strip()
        except subprocess.CalledProcessError:
            git_hash = "unknown"
            git_branch = "unknown"
    else:
        git_hash = "unknown"
        git_branch = "unknown"
    print(
        f'**** Git info for Megatron: '
        f'git_hash={git_hash} git_branch={git_branch} ****'
    )


def main():
    # if RANK == 0:
    #     setup_wandb()
    if os.getenv('TORCH_PROFILER_ENABLED') == '1':
        from torch.profiler import profile, record_function, ProfilerActivity
        with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA]) as prof:
            # Initalize and get arguments, timers, and Tensorboard writer.
            initialize_megatron(
                                # extra_args_provider=extra_args_provider,
                                args_defaults={'tokenizer_type': 'GPT2BPETokenizer'}, 
                                # external_args=external_args
                                )
            # Set pytorch JIT layer fusion options and warmup JIT functions.
            if get_accelerator().device_name() == 'cuda':
                set_jit_fusion_options()

            args = get_args()
            timers = get_timers()

            # model = model_provider()
            model, optimizer, opt_param_scheduler = setup_model_and_optimizer(model_provider, ModelType.encoder_or_decoder)

        prof.export_chrome_trace(f"{args.tensorboard_dir}/torch-trace-{RANK}-of-{WORLD_SIZE}.json")
    else:
        # Initalize and get arguments, timers, and Tensorboard writer.
        initialize_megatron(
                            # extra_args_provider=extra_args_provider,
                            args_defaults={'tokenizer_type': 'GPT2BPETokenizer'}, 
                            # external_args=external_args
                            )
        # Set pytorch JIT layer fusion options and warmup JIT functions.
        if get_accelerator().device_name() == 'cuda':
            set_jit_fusion_options()

        args = get_args()
        timers = get_timers()

        if args.deepspeed:
            args.deepspeed_config_dict = _create_ds_config_dict()
            if "curriculum_learning" in args.deepspeed_config_dict and \
                "enabled" in args.deepspeed_config_dict["curriculum_learning"]:
                args.curriculum_learning_legacy = args.deepspeed_config_dict[ \
                    "curriculum_learning"]["enabled"]
            if args.curriculum_learning_legacy and not args.no_pipeline_parallel:
                from deepspeed.runtime.data_pipeline.curriculum_scheduler \
                    import CurriculumScheduler
                args.curriculum_scheduler = CurriculumScheduler( \
                    args.deepspeed_config_dict["curriculum_learning"])
            if "compression_training" in args.deepspeed_config_dict:
                args.compression_training = True

        # model = model_provider()
        model, optimizer, opt_param_scheduler = setup_model_and_optimizer(model_provider, ModelType.encoder_or_decoder)

        # ---------- Reference model -------------
        # model_ref, _, _ = setup_model_and_optimizer(model_provider, ModelType.encoder_or_decoder) # throwing assertion error
        model_ref = get_model(model_provider, ModelType.encoder_or_decoder) # works but does it load from a checkpoint or randomly initializes?
        # TRY deepspeed init and load_checkpoint directly here from model_ref = get_model(model_provider)
        optimizer_2 = get_megatron_optimizer(model_ref, None, None, 1.0)
        opt_param_scheduler_2 = get_optimizer_param_scheduler(optimizer_2)
        model_ref, optimizer_2, _, opt_param_scheduler_2 = deepspeed.initialize(
                                                                model=model_ref[0],
                                                                optimizer=optimizer_2,
                                                                args=args,
                                                                lr_scheduler=opt_param_scheduler_2,
                                                                mpu=mpu if args.no_pipeline_parallel else None,
                                                                config=args.deepspeed_config_dict,
                                                            )
        if isinstance(model_ref, deepspeed.PipelineEngine):
            print(f'Doing assertion checks on model_ref..')
            # hack to get batch_fn from pretrain_gpt.py
            model_ref.set_batch_fn(model_ref.module._megatron_batch_fn)

            assert model_ref.grid.get_pipe_parallel_rank() == mpu.get_pipeline_model_parallel_rank()
            assert model_ref.grid.get_slice_parallel_rank() == mpu.get_tensor_model_parallel_rank()
            assert model_ref.grid.get_data_parallel_rank() == mpu.get_data_parallel_rank()

        model_ref = [model_ref]
        iteration2 = load_checkpoint(model_ref, optimizer_2, opt_param_scheduler_2) # THIS WORKED!! After commenting out assert args.consumed_train_samples == 0 in load_checkpoint()

        # THINGS THAT DID NOT WORK FOR LOADING FROM CHECKPOINT
        # model_ref, optimizer_ref, lr_scheduler_ref = load_model_weights_only(model_provider) # DID NOT WORK - train_batch_size is not equal to micro_batch_per_gpu * gradient_acc_step * world_size 32 != 8 * 1 * 8
        # model_ref, optimizer_ref, lr_scheduler_ref = load_model_weights_only_modified(model_provider) # DID NOT WORK -     optimizer = FusedAdam(TypeError: FusedAdam.__init__() got an unexpected keyword argument 'beta1'
        # ----------------------------------------

        if args.data_file_list_u is not None:
            print(f'data files list unpreferred: {args.data_file_list_u}')

            # Number of train/valid/test samples.
            if args.train_samples:
                print(f'args.train_samples: {args.train_samples}')
                train_samples = args.train_samples
            else:
                print(f'args.train_iters: {args.train_iters}')
                print(f'args.global_batch_size: {args.global_batch_size}')
                train_samples = args.train_iters * args.global_batch_size

            print(f'args.eval_interval: {args.eval_interval}')
            print(f'args.eval_iters: {args.eval_iters}')
            eval_iters = (args.train_iters // args.eval_interval + 1) * \
                        args.eval_iters
            test_iters = args.eval_iters
            train_val_test_num_samples = [train_samples,
                                        eval_iters * args.global_batch_size,
                                        test_iters * args.global_batch_size]
            print(f'train_val_test_num_samples: {train_val_test_num_samples}')
            # print(f'args.data_impl: {args.data_impl}')
            # print(f'args.split: {args.split}')
            # print(f'args.seq_length: {args.seq_length}')
            # print(f'args.seed: {args.seed}')
            # print(f'args.train_data_path: {args.train_data_path}')
            # print(f'args.valid_data_path: {args.valid_data_path}')
            # print(f'args.test_data_path: {args.test_data_path}')
            # print(f'args.data_cache_path: {args.data_cache_path}')

            files_u = []
            with open(args.data_file_list_u, 'r') as flist:
                for f in flist.readlines():
                    w, fname = f.split()
                    files_u.append(float(w))
                    files_u.append(fname)
            train_ds_u, valid_ds_u, test_ds_u = build_train_valid_test_datasets(
            data_prefix=files_u,
            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=True,
            # skip_warmup=(not args.mmap_warmup),
            train_data_prefix=args.train_data_path,
            valid_data_prefix=args.valid_data_path,
            test_data_prefix=args.test_data_path,
            data_cache_path=args.data_cache_path)
            print_rank_0("> finished creating unpreferred GPT datasets ...")

        if args.data_file_list_p is not None:
            print(f'data files list preferred: {args.data_file_list_p}')

            files_p = []
            with open(args.data_file_list_p, 'r') as flist:
                for f in flist.readlines():
                    w, fname = f.split()
                    files_p.append(float(w))
                    files_p.append(fname)
            train_ds_p, valid_ds_p, test_ds_p = build_train_valid_test_datasets(
            data_prefix=files_p,
            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=True,
            # skip_warmup=(not args.mmap_warmup),
            train_data_prefix=args.train_data_path,
            valid_data_prefix=args.valid_data_path,
            test_data_prefix=args.test_data_path,
            data_cache_path=args.data_cache_path)
            print_rank_0("> finished creating preferred GPT datasets ...")

        # Data loaders
        print(f'args.consumed_train_samples: {args.consumed_train_samples}')
        print(f'args.dataloader_type: {args.dataloader_type}')
        train_dataloader_u = build_pretraining_data_loader(
                                    train_ds_u, args.consumed_train_samples)
        train_dataloader_p = build_pretraining_data_loader(
                                    train_ds_p, args.consumed_train_samples)

        # Build train iterators
        dl_type = args.dataloader_type
        assert dl_type in ['single', 'cyclic']

        if train_dataloader_u is not None:
            print(f'unpreferred train_dataloader is not None..')
            train_data_iterator_u = iter(train_dataloader_u) if dl_type == 'single' \
                                else iter(cyclic_iter(train_dataloader_u))
        print_rank_0("> finished creating unpreferred train_data_iterator...")
        if train_dataloader_p is not None:
            print(f'preferred train_dataloader is not None..')
            train_data_iterator_p = iter(train_dataloader_p) if dl_type == 'single' \
                                else iter(cyclic_iter(train_dataloader_p))
        print_rank_0("> finished creating preferred train_data_iterator...")

        # Get batch
        timers = get_timers()
        timers('batch-generator-unpreferred', log_level=2).start()
        tokens_u, labels_u, loss_mask_u, attention_mask_u, position_ids_u = get_batch(
                                                                                train_data_iterator_u)
        timers('batch-generator-unpreferred').stop()
        # print(f'tokens shape: {tokens_u.shape}')
        print_rank_0("> finished extracting batch of tokens, labels, attn mask etc. for unpref train_data_iterator ...")

        timers('batch-generator-preferred', log_level=2).start()
        tokens_p, labels_p, loss_mask_p, attention_mask_p, position_ids_p = get_batch(
                                                                                train_data_iterator_p)
        timers('batch-generator-preferred').stop()
        # print(f'tokens shape: {tokens_u.shape}')
        print_rank_0("> finished extracting batch of tokens, labels, attn mask etc. for pref train_data_iterator ...")

        # Model forward
        # output_tensor, other_losses = model[0](
        #                                 tokens_u,
        #                                 position_ids_u,
        #                                 attention_mask_u,
        #                                 labels=labels_u
        #                             ) # OUT OF MEMORY ERROR even with 4 nodes

        # Computing logits and logps for preferred and unpreferred data batches
        output_u, other_losses_u = model[0](tokens_u, position_ids_u, attention_mask_u) # THIS WORKED with 4 nodes for 7B model
        print_rank_0("> finished a forward pass to get unpref logits ...")

        output_tensor_u = tensor_parallel.vocab_parallel_cross_entropy(
                                output_u.contiguous().float(),
                                labels_u
                            ) # BUT THIS DID NOT WORK WITH 4 NODES - OOM ERROR for 7B model (but worked for 1B model on 2 nodes)
        logprobs_u = torch.exp(output_tensor_u)
        # logprobs_u = batch_seq_logprobs(output_u, labels_u)
        # print(f'Computed unpreferred output_tensor: {output_tensor_u}')
        print(f'Computed unpreferred logprobs: {logprobs_u}')

        output_p, other_losses_p = model[0](tokens_p, position_ids_p, attention_mask_p) # THIS WORKED with 4 nodes for 7B model
        print_rank_0("> finished a forward pass to get pref logits ...")

        output_tensor_p = tensor_parallel.vocab_parallel_cross_entropy(
                        output_p.contiguous().float(),
                        labels_p
                    ) # BUT THIS DID NOT WORK WITH 4 NODES - OOM ERROR for 7B model (but worked for 1B model on 2 nodes)
        logprobs_p = torch.exp(output_tensor_p)
        # logprobs_p = batch_seq_logprobs(output_p, labels_p)
        print(f'Computed preferred output_tensor: {output_tensor_p}')
        print(f'Computed preferred logprobs: {logprobs_p}')

        # Reference model in inference mode
        # Computing logits and logps for preferred and unpreferred data batches from ref model
        with torch.no_grad():
            ref_output_u, _ = model_ref[0](tokens_u, position_ids_u, attention_mask_u) # THIS WORKED with 4 nodes for 7B model
            print_rank_0("> finished a forward pass to get unpref logits ...")

            ref_output_tensor_u = tensor_parallel.vocab_parallel_cross_entropy(
                                    ref_output_u.contiguous().float(),
                                    labels_u
                                ) # BUT THIS DID NOT WORK WITH 4 NODES - OOM ERROR for 7B model (but worked for 1B model on 2 nodes)
            ref_logprobs_u = torch.exp(ref_output_tensor_u)
            # ref_logprobs_u = batch_seq_logprobs(ref_output_u, labels_u)
            # print(f'Computed unpreferred output_tensor: {ref_output_tensor_u}')
            print(f'Computed unpreferred logprobs: {ref_logprobs_u}')

            ref_output_p, _ = model_ref[0](tokens_p, position_ids_p, attention_mask_p) # THIS WORKED with 4 nodes for 7B model
            print_rank_0("> finished a forward pass to get pref logits ...")

            ref_output_tensor_p = tensor_parallel.vocab_parallel_cross_entropy(
                            ref_output_p.contiguous().float(),
                            labels_p
                        ) # BUT THIS DID NOT WORK WITH 4 NODES - OOM ERROR for 7B model (but worked for 1B model on 2 nodes)
            ref_logprobs_p = torch.exp(ref_output_tensor_p)
            # ref_logprobs_p = batch_seq_logprobs(ref_output_p, labels_p)
            # print(f'Computed preferred output_tensor: {ref_output_tensor_p}')
            print(f'Computed preferred logprobs: {ref_logprobs_p}')

        # Compute loss
        loss = compute_dp_loss(logprobs_p, ref_logprobs_p,
                    logprobs_u, ref_logprobs_u,
                    beta=0.1)
        print(f'Computed loss: {loss}')

        print(f'args.ds_pipeline_enabled: {args.ds_pipeline_enabled}')
        if args.deepspeed and args.ds_pipeline_enabled:
            print(f'In train step if args.deepspeed and args.ds_pipeline_enabled..')
            skipped_iter = 0
            num_zeros_in_grad = 0
            assert isinstance(model[0], deepspeed.PipelineEngine)
            loss = model[0].train_batch(data_iter=train_data_iterator_p)
            grad_norm = model[0].get_global_grad_norm()
        # return {'lm loss' : loss}, skipped_iter, grad_norm, num_zeros_in_grad

        if not args.skip_train:
            print_rank_0('training ...')

            if args.dataloader_type == 'cyclic' and args.retro_add_retriever:
                args.train_iters = args.retro_cyclic_train_iters
                print_rank_0("retro cyclic train iters : %d" % args.train_iters)

            iteration = 0
            if args.train_iters > 0:
                print(f'In train step if args.train_iters: {args.train_iters} ..')

                # Turn on training mode which enables dropout.
                for model_module in model:
                    model_module.train()

                # Tracking loss.
                total_loss_dict = {}

                # Iterations.
                iteration = args.iteration

                 # Translate args to core configuration
                config = core_transformer_config_from_args(args)

                config.timers = timers

                timers('interval-time', log_level=0).start(barrier=True)
                print_datetime('before the start of training step')
                report_memory_flag = True

                while iteration < args.train_iters and (args.train_tokens is None or \
                    args.consumed_train_tokens < args.train_tokens):
                    print(f'args.train_tokens: {args.train_tokens}, args.consumed_train_tokens: {args.consumed_train_tokens}')
                    print(f'args.consumed_train_samples: {args.consumed_train_samples}')
                    update_num_microbatches(args.consumed_train_samples)
                    
                    if args.deepspeed:
                        # inform deepspeed of any batch size changes
                        global_batch_size = mpu.get_data_parallel_world_size() * \
                                            args.micro_batch_size * \
                                            get_num_microbatches()
                        print(f'global batch size: {global_batch_size}')
                        model[0].set_train_batch_size(global_batch_size)

                    if args.curriculum_learning_legacy and not args.no_pipeline_parallel:
                        print(f'args.curriculum_learning_legacy and not args.no_pipeline_parallel is {args.curriculum_learning_legacy} and {args.no_pipeline_parallel} ..')
                        curriculum_seqlen = args.curriculum_scheduler.update_difficulty( \
                                args.iteration + 1)
                        if iteration == 0 or curriculum_seqlen != args.curriculum_seqlen:
                            if args.use_rotary_position_embeddings:
                                update_rotary_pos_emb(curriculum_seqlen)
                        args.curriculum_seqlen = curriculum_seqlen

                    args.curr_iteration = iteration
                    print(f'iteration: {iteration}')

                    # model[0].backward(loss)
                    results = train_step_dpo(train_data_iterator_p,
                                model,
                                optimizer,
                                opt_param_scheduler,
                                config,
                                loss,
                                forward_only=False) # FAILS HERE with the model backward step: TypeError: _VocabParallelCrossEntropy.backward() takes 2 positional arguments but 3 were given

                    # Update parameters
                    if args.deepspeed:
                        increment = get_num_microbatches() * \
                                    args.micro_batch_size * \
                                    args.data_parallel_size
                        model[0].step(lr_kwargs={'increment': increment})
                        update_successful = model[0].was_step_applied()


    return model

# def main():
#     # if RANK == 0:
#     #     setup_wandb()
#     if os.getenv('TORCH_PROFILER_ENABLED') == '1':
#         from torch.profiler import profile, record_function, ProfilerActivity
#         with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA]) as prof:
#             model = pretrain(
#                 train_valid_test_datasets_provider,
#                 model_provider,
#                 ModelType.encoder_or_decoder,
#                 forward_step,
#                 args_defaults={'tokenizer_type': 'GPT2BPETokenizer'},
#                 data_post_process=data_post_process
#             )

#         prof.export_chrome_trace(f"{args.tensorboard_dir}/torch-trace-{RANK}-of-{WORLD_SIZE}.json")
#     else:
#         model = pretrain(
#             train_valid_test_datasets_provider,
#             model_provider,
#             ModelType.encoder_or_decoder,
#             forward_step,
#             args_defaults={'tokenizer_type': 'GPT2BPETokenizer'},
#             data_post_process=data_post_process
#         )
#     return model


if __name__ == "__main__":
    # git_ds_info()
    # pretrain(train_valid_test_datasets_provider,
    #          model_provider,
    #          ModelType.encoder_or_decoder,
    #          forward_step,
    #          args_defaults={'tokenizer_type': 'GPT2BPETokenizer'},
    #          data_post_process=data_post_process)
    import sys
    import deepspeed.comm as dist
    model = main()
    dist.log_summary()
    if wandb.run is not None:
        print(f"wandb.run.name: {wandb.run.name}")
        print(f"wandb.run.url: {wandb.run.url}")
        wandb.finish()
    sys.exit()