# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.

"""Pretrain utilities."""

import dataclasses
from datetime import datetime
import functools
import gc
import logging
import math
import os
import sys
from .log_handler import CustomHandler
# Make default logging level INFO, but filter out all log messages not from MCore.
logging.basicConfig(handlers=[CustomHandler()], level=logging.INFO)
from .theoretical_memory_usage import report_theoretical_memory
import time
# The earliest we can measure the start time.
_TRAIN_START_TIME = time.time()
import torch

from megatron.core import mpu, tensor_parallel
from megatron.core.utils import (
    check_param_hashes_across_dp_replicas,
    get_model_config,
    StragglerDetector,
    is_float8tensor,
)
from megatron.training.checkpointing import load_checkpoint
from megatron.training.checkpointing import save_checkpoint
from megatron.training.checkpointing import checkpoint_exists
from megatron.legacy.model import Float16Module
from megatron.core.distributed import DistributedDataParallelConfig
from megatron.core.distributed import DistributedDataParallel as DDP
try:
    from megatron.core.distributed import TorchFullyShardedDataParallel as torch_FSDP

    HAVE_FSDP2 = True
except ImportError:
    HAVE_FSDP2 = False

from megatron.core.distributed import finalize_model_grads
from megatron.core.enums import ModelType
from megatron.core.optimizer import get_megatron_optimizer, OptimizerConfig
from megatron.core.rerun_state_machine import (
    get_rerun_state_machine,
    destroy_rerun_state_machine,
    RerunDataIterator,
    RerunMode,
)
from megatron.training.initialize import initialize_megatron
from megatron.training.initialize import write_args_to_tensorboard
from megatron.training.initialize import set_jit_fusion_options
from megatron.legacy.data.data_samplers import build_pretraining_data_loader
from megatron.core.optimizer_param_scheduler import OptimizerParamScheduler
from megatron.core.transformer.moe import upcycling_utils
from megatron.core.transformer.moe.moe_utils import track_moe_metrics
from megatron.core.parallel_state import (
    destroy_global_memory_buffer,
    destroy_model_parallel,
)
from megatron.core.pipeline_parallel import get_forward_backward_func
from megatron.core.num_microbatches_calculator import (
    destroy_num_microbatches_calculator,
    get_current_global_batch_size,
    get_current_running_global_batch_size,
    get_num_microbatches,
    update_num_microbatches)

from .async_utils import maybe_finalize_async_save
from .utils import (
    calc_params_l2_norm,
    check_adlr_autoresume_termination,
    is_last_rank,
    print_rank_0,
    print_rank_last,
    report_memory,
    unwrap_model,
    append_to_progress_log,
    update_use_dist_ckpt,
)
from .global_vars import (
    destroy_global_vars,
    get_args,
    get_signal_handler,
    get_timers,
    get_tensorboard_writer,
    get_wandb_writer,
    get_one_logger)
from . import one_logger_utils

from . import ft_integration

stimer = StragglerDetector()


def destroy_global_state():
    destroy_global_vars()
    destroy_num_microbatches_calculator()
    destroy_global_memory_buffer()
    destroy_model_parallel()
    destroy_rerun_state_machine()


def print_datetime(string):
    """Note that this call will sync across all ranks."""
    torch.distributed.barrier()
    time_str = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
    print_rank_0(f'[{string}] datetime: {time_str} ')


def num_floating_point_operations(args, batch_size):
    # Attention projection size.
    query_projection_size = args.kv_channels * args.num_attention_heads
    query_projection_to_hidden_size_ratio = query_projection_size / args.hidden_size
    # Group Query Attention.
    if not args.group_query_attention:
        args.num_query_groups = args.num_attention_heads
    # MoE.
    num_experts_routed_to = 1 if args.num_experts is None else args.moe_router_topk
    gated_linear_multiplier = 3 / 2 if args.swiglu else 1
    shared_expert_ffn_hidden_size = (
        0
        if args.moe_shared_expert_intermediate_size is None
        else args.moe_shared_expert_intermediate_size
    )

    # The 12x term below comes from the following factors; for more details, see
    # "APPENDIX: FLOATING-POINT OPERATIONS" in https://arxiv.org/abs/2104.04473.
    # - 3x: Each GEMM in the model needs to be performed 3 times (forward pass,
    #       backward wgrad [weight gradient], backward dgrad [data gradient]).
    # - 2x: GEMMs of a particular size are stacked twice in the standard Transformer model
    #       architectures implemented in this codebase (e.g., h->ffn_h GEMM and ffn_h->h GEMM
    #       in MLP layer).
    # - 2x: A GEMM of a m*n tensor with a n*k tensor requires 2mnk floating-point operations.
    expansion_factor = 3 * 2 * 2

    # print(f"batch_size: {batch_size}, \
    #       query_projection_to_hidden_size_ratio: {query_projection_to_hidden_size_ratio}, \
    #       num_experts_routed_to: {num_experts_routed_to}, \
    #       gated_linear_multiplier: {gated_linear_multiplier}, \
    #       shared_expert_ffn_hidden_size: {shared_expert_ffn_hidden_size}, \
    #       gated_linear_multiplier: {gated_linear_multiplier}, \
    #       ")
    return (
        expansion_factor
        * batch_size
        * args.seq_length
        * args.num_layers
        * args.hidden_size
        * args.hidden_size
        * (
            # Attention.
            (
                (
                    1
                    + (args.num_query_groups / args.num_attention_heads)
                    + (args.seq_length / args.hidden_size)
                ) * query_projection_to_hidden_size_ratio
            )
            # MLP.
            + (
                (args.ffn_hidden_size / args.hidden_size)
                * num_experts_routed_to
                * gated_linear_multiplier
            )
            # Shared Experts.
            + ((shared_expert_ffn_hidden_size / args.hidden_size) * gated_linear_multiplier)
            # Logit.
            + (args.padded_vocab_size / (2 * args.num_layers * args.hidden_size))
        )
    )


def get_start_time_from_progress_log():
    """
    Gets start time of earliest job with same world size. Also returns the number
    of floating-point operations completed in last saved checkpoint.
    """
    args = get_args()
    assert args.save is not None
    progress_log_filename = os.path.join(args.save, "progress.txt")

    # start_time is time when job with same world size started.
    # start_num_floating_point_operations is the number of floating-point operations
    # completed when this job started.
    # latest_num_floating_point_operations is the number of floating-point operations
    # completed in most recent saved checkpoint.
    start_time = None
    start_num_floating_point_operations = None
    latest_num_floating_point_operations = 0

    def _get_field(string, type):
        return type(string.split(': ')[1])

    with open(progress_log_filename, 'r') as f:
        for line in f:
            line = line.strip()
            line_tokens = line.split('\t')
            world_size_in_line = _get_field(line_tokens[2], int)
            if line_tokens[3] == "Saved checkpoint":
                latest_num_floating_point_operations = \
                    _get_field(line_tokens[7], float)
            if world_size_in_line != args.world_size:
                # Re-start search if we see a different world size.
                start_time = None
                start_num_floating_point_operations = None
                continue
            if line_tokens[3] == "Starting job":
                if start_time is None:
                    start_time = line_tokens[0]
                    start_num_floating_point_operations = \
                        latest_num_floating_point_operations
    assert start_time is not None and start_num_floating_point_operations is not None, \
        "Should have seen at least one 'Starting job' entry with same world_size"
    return datetime.strptime(start_time, '%Y-%m-%d %H:%M:%S'), \
        start_num_floating_point_operations


def preprocess_common_state_dict(common_state_dict):
    import copy
    # Convert args key of type namespace to dictionary 
    preprocessed_common_state_dict = copy.deepcopy(common_state_dict)
    preprocessed_common_state_dict['args'] = vars(preprocessed_common_state_dict['args'])
    # Remove rank and local rank from state dict if it exists, since they are expected to be different
    preprocessed_common_state_dict['args'].pop('local_rank', None)
    preprocessed_common_state_dict['args'].pop('rank', None)
    return preprocessed_common_state_dict


def pretrain(
    train_valid_test_dataset_provider,
    model_provider,
    model_type,
    forward_step_func,
    process_non_loss_data_func=None,
    extra_args_provider=None,
    args_defaults={},
    get_embedding_ranks=None,
    get_position_embedding_ranks=None,
    non_loss_data_func=None,
):
    """Main training program.

    This function will run the followings in the order provided:
        1) initialize Megatron.
        2) setup model, optimizer and lr schedule using the model_provider.
        3) call train_val_test_data_provider to get train/val/test datasets.
        4) train the model using the forward_step_func.

    Args:
        train_valid_test_dataset_provider: a function that takes the size of
            train/valid/test dataset and returns `train, valid, test` datasets.
        model_provider: a function that returns a vanilla version of the
            model. By vanilla we mean a simple model on cpu with no fp16 or ddp.
        model_type: an enum that specifies the type of model being trained.
        forward_step_func: a function that takes a `data iterator` and `model`,
            and returns a `loss` scalar with a dictionary with key:values being
            the info we would like to monitor during training, for example
            `lm-loss: value`. We also require that this function add
            `batch generator` to the timers class.
        process_non_loss_data_func: a function to post process outputs of the
            network. It can be used for dumping output tensors (e.g images) to
            tensorboard. It takes `collected data`(list of tensors),
            `current iteration index` and `tensorboard writer` as arguments.
        extra_args_provider: a function that takes a parser and adds arguments
            to it. It is used for programs to add their own arguments.
        args_defaults: a dictionary from argument-name to argument-value. It
            to set already parse arguments.
        get_embedding_ranks (TODO):
        get_position_embedding_ranks (TODO):
        non_loss_data_func (callable): A custom function to call during evaluation.
            It can run e.g. benchmarks.
    """

    # Initalize and get arguments, timers, and Tensorboard writer.
    initialize_megatron(
        extra_args_provider=extra_args_provider,
        args_defaults=args_defaults,
        get_embedding_ranks=get_embedding_ranks,
        get_position_embedding_ranks=get_position_embedding_ranks
    )

    args = get_args()
    timers = get_timers()

    if args.log_progress:
        append_to_progress_log("Starting job")

    # Set pytorch JIT layer fusion options and warmup JIT functions.
    set_jit_fusion_options()

    # Adjust the startup time so it reflects the largest value.
    # This will be closer to what scheduler will see (outside of
    # image ... launches.
    global _TRAIN_START_TIME
    start_time_tensor = torch.tensor([_TRAIN_START_TIME],
                                     dtype=torch.double,
                                     device='cuda')
    torch.distributed.all_reduce(start_time_tensor,
                                 op=torch.distributed.ReduceOp.MIN)
    _TRAIN_START_TIME = start_time_tensor.item()

    app_metrics = {}
    app_metrics['app_start_time'] = round(_TRAIN_START_TIME * 1000.0)
    app_metrics['app_model_init_start_time'] = round(_TRAIN_START_TIME * 1000.0)

    print_rank_0('time to initialize megatron (seconds): {:.3f}'.format(
        time.time() - _TRAIN_START_TIME))
    print_datetime('after megatron is initialized')
    app_metrics['app_model_init_finish_time'] = one_logger_utils.get_timestamp_in_ms()

    # Track E2E metrics on pretrain start
    one_logger_utils.on_pretrain_start()

    # Context used for persisting some state between checkpoint saves.
    if args.non_persistent_ckpt_type == 'local':
        raise RuntimeError('LocalCheckpointManagers are not yet integrated')
        checkpointing_context = {
            'local_checkpoint_manager': BasicLocalCheckpointManager(
                args.non_persistent_local_ckpt_dir
            )
        }
    else:
        checkpointing_context = {}

    # Model, optimizer, and learning rate.
    timers('model-and-optimizer-setup', log_level=0).start(barrier=True)
    app_metrics['app_build_optimizer_start_time'] = one_logger_utils.get_timestamp_in_ms()
    model, optimizer, opt_param_scheduler = setup_model_and_optimizer(
        model_provider, model_type, checkpointing_context=checkpointing_context)

    timers('model-and-optimizer-setup').stop()
    print_datetime('after model, optimizer, and learning rate '
                   'scheduler are built')
    app_metrics['app_build_optimizer_finish_time'] = one_logger_utils.get_timestamp_in_ms()
    config = get_model_config(model[0])

    # Data stuff.
    app_metrics['app_build_dataiters_start_time'] = one_logger_utils.get_timestamp_in_ms()
    timers('train/valid/test-data-iterators-setup', log_level=0).start(
        barrier=True)
    if args.virtual_pipeline_model_parallel_size is not None:
        train_data_iterator = []
        valid_data_iterator = []
        test_data_iterator = []
        for i in range(len(model)):
            mpu.set_virtual_pipeline_model_parallel_rank(i)
            iterators = build_train_valid_test_data_iterators(
                train_valid_test_dataset_provider)
            train_data_iterator.append(iterators[0])
            valid_data_iterator.append(iterators[1])
            test_data_iterator.append(iterators[2])
    else:
        train_data_iterator, valid_data_iterator, test_data_iterator \
            = build_train_valid_test_data_iterators(
                train_valid_test_dataset_provider)
    timers('train/valid/test-data-iterators-setup').stop()
    print_datetime('after dataloaders are built')
    app_metrics['app_build_dataiters_finish_time'] = one_logger_utils.get_timestamp_in_ms()

    # Track if training is enabled. Can only be done once args.do_train is assigned after dataloader is built.
    one_logger_utils.track_config_flags(args.train_iters, args.skip_train, args.do_train,
                                        args.do_valid, args.do_test, args.dataloader_type,
                                        args.retro_project_dir, args.retro_cyclic_train_iters)

    if args.enable_ft_package and ft_integration.get_rank_monitor_client() is not None:
        ft_integration.get_rank_monitor_client().init_workload_monitoring()
        ft_timeouts = ft_integration.get_rank_monitor_client().timeouts
        print_rank_0(f"Fault tolerance client initialized. Timeouts: {ft_timeouts}")

    # Print setup timing.
    print_rank_0('done with setup ...')
    timers.log(['model-and-optimizer-setup',
                'train/valid/test-data-iterators-setup'], barrier=True)

    one_logger = get_one_logger()
    one_logger and one_logger.log_metrics(app_metrics)

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

        if args.dataloader_type == 'cyclic' and args.retro_project_dir:
            assert args.retro_cyclic_train_iters is not None
            args.train_iters = args.retro_cyclic_train_iters
            print_rank_0("retro cyclic train iters : %d" % args.train_iters)

        iteration = 0
        if args.do_train and args.train_iters > 0:
            iteration, num_floating_point_operations_so_far = train(
                forward_step_func,
                model, optimizer, opt_param_scheduler,
                train_data_iterator, valid_data_iterator,
                process_non_loss_data_func, config, checkpointing_context,
                non_loss_data_func)

        print_datetime('after training is done')

        if args.save and iteration != 0 and iteration % args.save_interval != 0:
            save_checkpoint(iteration, model, optimizer, opt_param_scheduler,
                            num_floating_point_operations_so_far, checkpointing_context,
                            train_data_iterator=train_data_iterator,
                            ft_client=ft_integration.get_rank_monitor_client(
                            ft_integration.StateMachineActions.SAVE_CHECKPOINT), preprocess_common_state_dict_fn=preprocess_common_state_dict)

        one_logger and one_logger.log_metrics({
            'app_train_loop_finish_time': one_logger_utils.get_timestamp_in_ms()
        })

    else:
        print_rank_0('skipping training (--skip-train is on) ...')

        iteration = args.iteration

    if args.do_valid:
        prefix = f'iteration {iteration} on validation set'
        evaluate_and_print_results(prefix, forward_step_func,
                                   valid_data_iterator, model,
                                   iteration, process_non_loss_data_func, config,
                                   verbose=True, write_to_tensorboard=not args.skip_train,
                                   non_loss_data_func=non_loss_data_func)

    if args.do_test:
        prefix = f'iteration {iteration} on test set'
        evaluate_and_print_results(prefix, forward_step_func,
                                   test_data_iterator, model,
                                   iteration, process_non_loss_data_func, config,
                                   verbose=True, write_to_tensorboard=not args.skip_train,
                                   non_loss_data_func=non_loss_data_func)

    wandb_writer = get_wandb_writer()
    if wandb_writer:
        wandb_writer.finish()
    maybe_finalize_async_save(blocking=True)

    one_logger and one_logger.log_metrics({
        'app_finish_time': one_logger_utils.get_timestamp_in_ms()
    })
    one_logger_utils.finish()


def update_train_iters(args):

    # For iteration-based training, we don't need to do anything
    if args.train_iters:
        return

    # Constant batch size with sample-based training.
    if args.rampup_batch_size is None:
        args.train_iters = args.train_samples // args.global_batch_size

    else:
        # Sample based training with rampup batch size.
        iterations = 0
        consumed_samples = 0
        # Rampup phase.
        while consumed_samples <= int(args.rampup_batch_size[2]) and consumed_samples <= args.train_samples:
            update_num_microbatches(consumed_samples, consistency_check=False)
            consumed_samples += get_current_global_batch_size()
            iterations += 1
        # Reset
        update_num_microbatches(0, consistency_check=False)
        # Constant phase
        # Note that we throw away any partial last batch.
        if args.train_samples > consumed_samples:
            iterations += (args.train_samples - consumed_samples) // \
                          args.global_batch_size
        args.train_iters = iterations

    print_rank_0(f'setting training iterations to {args.train_iters}')


def get_model(model_provider_func, model_type=ModelType.encoder_or_decoder, wrap_with_ddp=True):
    """Build the model."""
    args = get_args()
    args.model_type = model_type

    # Build model.
    if mpu.get_pipeline_model_parallel_world_size() > 1 and \
       args.virtual_pipeline_model_parallel_size is not None:
        assert model_type != ModelType.encoder_and_decoder, \
            "Interleaved schedule not supported for model with both encoder and decoder"
        model = []
        for i in range(args.virtual_pipeline_model_parallel_size):
            mpu.set_virtual_pipeline_model_parallel_rank(i)
            # Set pre_process and post_process only after virtual rank is set.
            pre_process = mpu.is_pipeline_first_stage()
            post_process = mpu.is_pipeline_last_stage()
            this_model = model_provider_func(
                pre_process=pre_process,
                post_process=post_process
            )
            this_model.model_type = model_type
            model.append(this_model)
    else:
        pre_process = mpu.is_pipeline_first_stage()
        post_process = mpu.is_pipeline_last_stage()
        add_encoder = True
        add_decoder = True
        if model_type == ModelType.encoder_and_decoder:
            if mpu.get_pipeline_model_parallel_world_size() > 1:
                rank = mpu.get_pipeline_model_parallel_rank()
                first_decoder_rank = args.encoder_pipeline_model_parallel_size
                world_size = mpu.get_pipeline_model_parallel_world_size()
                pre_process = rank == 0 or rank == first_decoder_rank
                post_process = (rank == (first_decoder_rank - 1)) or (rank == (world_size - 1))
                add_encoder = mpu.is_inside_encoder(rank)
                add_decoder = mpu.is_inside_decoder(rank)
            model = model_provider_func(
                pre_process=pre_process,
                post_process=post_process,
                add_encoder=add_encoder,
                add_decoder=add_decoder)
        else:
            model = model_provider_func(
                pre_process=pre_process,
                post_process=post_process
            )
        model.model_type = model_type

    if not isinstance(model, list):
        model = [model]

    # Set tensor model parallel attributes if not set.
    # Only parameters that are already tensor model parallel have these
    # attributes set for them. We should make sure the default attributes
    # are set for all params so the optimizer can use them.
    for model_module in model:
        for param in model_module.parameters():
            tensor_parallel.set_defaults_if_not_set_tensor_model_parallel_attributes(param)

    # Print number of parameters.
    if mpu.get_data_parallel_rank() == 0:
        print(' > number of parameters on (tensor, pipeline) '
              'model parallel rank ({}, {}): {}'.format(
            mpu.get_tensor_model_parallel_rank(),
            mpu.get_pipeline_model_parallel_rank(),
            sum([sum([p.nelement() for p in model_module.parameters()])
                 for model_module in model])), flush=True)

    # GPU allocation.
    for model_module in model:
        model_module.cuda(torch.cuda.current_device())

    # Fp16 conversion.
    if args.fp16 or args.bf16:
        model = [Float16Module(model_module, args) for model_module in model]

    # The model_module.bfloat16()/model_module.half() above will call the inplace copy of TE's
    # Float8Tensor, which will write an unwanted value (amax calculated from the current fp8
    # param) to its amax_history. The following logic will correct the amax_history back.
    for model_module in model:
        for param in model_module.parameters():
            if is_float8tensor(param) and param._fp8_meta is not None:
                fp8_meta = param._fp8_meta['scaling_fwd']
                fp8_meta_index = param._fp8_meta_index
                if hasattr(param, 'get_high_precision_init_val'):
                    fp8_meta.amax_history[0][fp8_meta_index].copy_(
                        param.get_high_precision_init_val().abs().max()
                    )
                else:
                    fp8_meta.amax_history[0][fp8_meta_index] = 0

    if wrap_with_ddp:
        if getattr(args, "use_torch_fsdp2", False):
            assert HAVE_FSDP2, "Torch FSDP2 requires torch>=2.4.0"
            DP = torch_FSDP
        else:
            DP = DDP

        config = get_model_config(model[0])

        kwargs = {}
        for f in dataclasses.fields(DistributedDataParallelConfig):
            if hasattr(args, f.name):
                kwargs[f.name] = getattr(args, f.name)
        kwargs['grad_reduce_in_fp32'] = args.accumulate_allreduce_grads_in_fp32
        kwargs['check_for_nan_in_grad'] = args.check_for_nan_in_loss_and_grad
        kwargs['bucket_size'] = args.ddp_bucket_size
        kwargs['average_in_collective'] = args.ddp_average_in_collective
        ddp_config = DistributedDataParallelConfig(**kwargs)

        overlap_param_gather_with_optimizer_step = getattr(args, 'overlap_param_gather_with_optimizer_step', False)
        model = [DP(config=config,
                     ddp_config=ddp_config,
                     module=model_chunk,
                     # Turn off bucketing for model_chunk 2 onwards, since communication for these
                     # model chunks is overlapped with compute anyway.
                     disable_bucketing=(model_chunk_idx > 0) or overlap_param_gather_with_optimizer_step)
                 for (model_chunk_idx, model_chunk) in enumerate(model)]

        # Broadcast params from data parallel src rank to other data parallel ranks.
        if args.data_parallel_random_init:
            for model_module in model:
                model_module.broadcast_params()

    return model


def get_optimizer_param_scheduler(optimizer):
    """Build the learning rate scheduler."""
    args = get_args()

    # Iteration-based training.
    if args.train_iters:
        if args.lr_decay_iters is None:
            args.lr_decay_iters = args.train_iters
        lr_decay_steps = args.lr_decay_iters * args.global_batch_size
        wd_incr_steps = args.train_iters * args.global_batch_size
        wsd_decay_steps = None
        if args.lr_wsd_decay_iters is not None:
            wsd_decay_steps = args.lr_wsd_decay_iters * args.global_batch_size
        if args.lr_warmup_fraction is not None:
            lr_warmup_steps = args.lr_warmup_fraction * lr_decay_steps
        else:
            lr_warmup_steps = args.lr_warmup_iters * args.global_batch_size
    # Sample-based training.
    elif args.train_samples:
        # We need to set training iters for later use. Technically
        # we need to adjust the training samples too (due to last
        # batch being incomplete) but we leave it as is for now.
        update_train_iters(args)
        if args.lr_decay_samples is None:
            args.lr_decay_samples = args.train_samples
        lr_decay_steps = args.lr_decay_samples
        wd_incr_steps = args.train_samples
        wsd_decay_steps = args.lr_wsd_decay_samples
        if args.lr_warmup_fraction is not None:
            lr_warmup_steps = args.lr_warmup_fraction * lr_decay_steps
        else:
            lr_warmup_steps = args.lr_warmup_samples
    else:
        raise Exception(
            'either train-iters or train-samples should be provided.')

    opt_param_scheduler = OptimizerParamScheduler(
        optimizer,
        init_lr=args.lr_warmup_init,
        max_lr=args.lr,
        min_lr=args.min_lr,
        lr_warmup_steps=lr_warmup_steps,
        lr_decay_steps=lr_decay_steps,
        lr_decay_style=args.lr_decay_style,
        start_wd=args.start_weight_decay,
        end_wd=args.end_weight_decay,
        wd_incr_steps=wd_incr_steps,
        wd_incr_style=args.weight_decay_incr_style,
        use_checkpoint_opt_param_scheduler=args.use_checkpoint_opt_param_scheduler,
        override_opt_param_scheduler=args.override_opt_param_scheduler,
        wsd_decay_steps=wsd_decay_steps,
        lr_wsd_decay_style=args.lr_wsd_decay_style)

    return opt_param_scheduler


def setup_model_and_optimizer(model_provider_func,
                              model_type,
                              no_wd_decay_cond=None,
                              scale_lr_cond=None,
                              lr_mult=1.0,
                              checkpointing_context=None):
    """Setup model and optimizer."""
    args = get_args()
    timers = get_timers()
    one_logger = get_one_logger()

    model = get_model(model_provider_func, model_type)
    unwrapped_model = unwrap_model(model)

    kwargs = {}
    for f in dataclasses.fields(OptimizerConfig):
        if hasattr(args, f.name):
            kwargs[f.name] = getattr(args, f.name)
    config = OptimizerConfig(**kwargs)
    config.timers = timers
    optimizer = get_megatron_optimizer(config, model, no_wd_decay_cond,
                                       scale_lr_cond, lr_mult)
    opt_param_scheduler = get_optimizer_param_scheduler(optimizer)

    if args.moe_use_upcycling:
        torch.distributed.barrier()
        assert not checkpoint_exists(
            args.save
        ), ("The upcycling destination directory already exists. "
            "Please check if --moe-use-upcycling is mistakenly enabled. "
            "Upcycling should only be set for the first run when converting the dense model. "
            "All subsequent runs should remove this flag. ")
        num_experts = args.num_experts
        args.num_experts = None
        expert_model_parallel_size = args.expert_model_parallel_size
        args.expert_model_parallel_size = 1
        dense_model_for_upcycling = get_model(model_provider_func, model_type)
        args.num_experts = num_experts
        args.expert_model_parallel_size = expert_model_parallel_size
        _, args.num_floating_point_operations_so_far = upcycling_utils.load_and_upcycle_model(
            load_checkpoint,
            unwrapped_model,
            dense_model_for_upcycling,
            load_kwargs = {'model': dense_model_for_upcycling, 'optimizer': None, 'opt_param_scheduler': None}
        )
        args.iteration = 1
        save_checkpoint(args.iteration, model, None, None, args.num_floating_point_operations_so_far)
        torch.distributed.barrier()
        del dense_model_for_upcycling
        if (args.fp16 or args.bf16) and optimizer is not None:
            optimizer.reload_model_params()
        print_rank_0(f'Upcycled checkpoint saved to {args.save}')

    if (args.load is not None or args.pretrained_checkpoint is not None) and not args.moe_use_upcycling:
        one_logger and one_logger.log_metrics({
            'load_checkpoint_start_time': one_logger_utils.get_timestamp_in_ms()
        })
        timers('load-checkpoint', log_level=0).start(barrier=True)

        args.iteration, args.num_floating_point_operations_so_far = load_checkpoint(
                model, optimizer, opt_param_scheduler,
                ft_client=ft_integration.get_rank_monitor_client(), checkpointing_context=checkpointing_context,
                skip_load_to_model_and_opt=HAVE_FSDP2 and getattr(args, "use_torch_fsdp2", False))
        timers('load-checkpoint').stop(barrier=True)
        timers.log(['load-checkpoint'])
        one_logger and one_logger.log_metrics({
            'load_checkpoint_finish_time': one_logger_utils.get_timestamp_in_ms(),
            'load_checkpoint_time': timers('load-checkpoint').active_time()
        })
    else:
        args.iteration = 0
        args.num_floating_point_operations_so_far = 0

    # get model without FP16 and/or DDP wrappers
    if args.iteration == 0 and len(unwrapped_model) == 1 \
        and hasattr(unwrapped_model[0], 'init_state_dict_from_bert'):
        print_rank_0("Initializing ICT from pretrained BERT model")
        unwrapped_model[0].init_state_dict_from_bert()
        if args.fp16:
            optimizer.reload_model_params()

    # Convert checkpoint format.
    if args.ckpt_convert_format is not None:
        load_ckpt_format = args.ckpt_format
        args.ckpt_format = args.ckpt_convert_format
        args.save = os.path.join(args.ckpt_convert_save, args.ckpt_convert_format)
        update_use_dist_ckpt(args)

        save_checkpoint(args.iteration, model, optimizer, opt_param_scheduler,
                        args.num_floating_point_operations_so_far,
                        preprocess_common_state_dict_fn=preprocess_common_state_dict)

        print_rank_0("> converted checkpoint: %s -> %s." % (load_ckpt_format, args.ckpt_format))
        torch.distributed.barrier()
        exit()

    return model, optimizer, opt_param_scheduler


def train_step(forward_step_func, data_iterator,
               model, optimizer, opt_param_scheduler, config): 
    """Single training step."""
    args = get_args()
    timers = get_timers()

    rerun_state_machine = get_rerun_state_machine()
    while rerun_state_machine.should_run_forward_backward(data_iterator):
        # Set grad to zero.
        for model_chunk in model:
            model_chunk.zero_grad_buffer()
        optimizer.zero_grad()

        # Forward pass.
        forward_backward_func = get_forward_backward_func()
        losses_reduced = forward_backward_func(
            forward_step_func=forward_step_func,
            data_iterator=data_iterator,
            model=model,
            num_microbatches=get_num_microbatches(),
            seq_length=args.seq_length,
            micro_batch_size=args.micro_batch_size,
            decoder_seq_length=args.decoder_seq_length,
            forward_only=False)
    should_checkpoint, should_exit, exit_code = rerun_state_machine.should_checkpoint_and_exit()
    if should_exit:
        return {}, True, should_checkpoint, should_exit, exit_code, None, None

    # Empty unused memory.
    if args.empty_unused_memory_level >= 1:
        torch.cuda.empty_cache()

    # Vision gradients.
    if getattr(args, 'vision_pretraining', False) and args.vision_pretraining_type == "dino":
        unwrapped_model = unwrap_model(model[0])
        unwrapped_model.cancel_gradients_last_layer(args.curr_iteration)

    # Update parameters.
    timers('optimizer', log_level=1).start(barrier=args.barrier_with_L1_time)
    update_successful, grad_norm, num_zeros_in_grad = optimizer.step()
    timers('optimizer').stop()

    # Vision momentum.
    if getattr(args, 'vision_pretraining', False) and args.vision_pretraining_type == "dino":
        unwrapped_model = unwrap_model(model[0])
        unwrapped_model.update_momentum(args.curr_iteration)

    # Update learning rate.
    if update_successful:
        increment = get_num_microbatches() * \
                    args.micro_batch_size * \
                    args.data_parallel_size
        opt_param_scheduler.step(increment=increment)
        skipped_iter = 0
    else:
        skipped_iter = 1

    # Empty unused memory.
    if args.empty_unused_memory_level >= 2:
        torch.cuda.empty_cache()

    if mpu.is_pipeline_last_stage(ignore_virtual=True):
        # Average loss across microbatches.
        loss_reduced = {}
        for key in losses_reduced[0].keys():
            numerator = 0
            denominator = 0
            for x in losses_reduced:
                val = x[key]
                # there is one dict per microbatch. in new reporting, we average
                # over the total number of tokens across the global batch.
                if isinstance(val, tuple) or isinstance(val, list):
                    numerator += val[0]
                    denominator += val[1]
                else:
                    # legacy behavior. we average over the number of microbatches,
                    # and so the denominator is 1.
                    numerator += val
                    denominator += 1
            loss_reduced[key] = numerator / denominator
        
        return loss_reduced, skipped_iter, should_checkpoint, should_exit, exit_code, grad_norm, num_zeros_in_grad
    return {}, skipped_iter, should_checkpoint, should_exit, exit_code, grad_norm, num_zeros_in_grad


def training_log(loss_dict, total_loss_dict, learning_rate, decoupled_learning_rate, iteration,
                 loss_scale, report_memory_flag, skipped_iter,
                 grad_norm, params_norm, num_zeros_in_grad):
    """Log training information such as losses, timing, ...."""
    args = get_args()
    timers = get_timers()
    writer = get_tensorboard_writer()
    wandb_writer = get_wandb_writer()
    one_logger = get_one_logger()

    # Advanced, skipped, and Nan iterations.
    advanced_iters_key = 'advanced iterations'
    skipped_iters_key = 'skipped iterations'
    nan_iters_key = 'nan iterations'
    # Advanced iterations.
    if not skipped_iter:
        total_loss_dict[advanced_iters_key] = total_loss_dict.get(
            advanced_iters_key, 0) + 1
    else:
        if advanced_iters_key not in total_loss_dict:
            total_loss_dict[advanced_iters_key] = 0
    # Skipped iterations.
    total_loss_dict[skipped_iters_key] = total_loss_dict.get(
        skipped_iters_key, 0) + skipped_iter
    # Update losses and set nan iterations
    got_nan = False
    for key in loss_dict:
        if not skipped_iter:
            total_loss_dict[key] = total_loss_dict.get(
                key, torch.tensor([0.0], dtype=torch.float, device='cuda')) + loss_dict[key]
        else:
            value = loss_dict[key].float().sum().item()
            is_nan = value == float('inf') or \
                     value == -float('inf') or \
                     value != value
            got_nan = got_nan or is_nan
    total_loss_dict[nan_iters_key] = total_loss_dict.get(
        nan_iters_key, 0) + int(got_nan)

    # Logging.
    timers_to_log = [
        'forward-backward',
        'forward-compute',
        'backward-compute',
        'batch-generator',
        'forward-recv',
        'forward-send',
        'backward-recv',
        'backward-send',
        'forward-send-forward-recv',
        'forward-send-backward-recv',
        'backward-send-forward-recv',
        'backward-send-backward-recv',
        'forward-backward-send-forward-backward-recv',
        'layernorm-grads-all-reduce',
        'embedding-grads-all-reduce',
        'all-grads-sync',
        'params-all-gather',
        'optimizer-copy-to-main-grad',
        'optimizer-unscale-and-check-inf',
        'optimizer-clip-main-grad',
        'optimizer-count-zeros',
        'optimizer-inner-step',
        'optimizer-copy-main-to-model-params',
        'optimizer']

    # Calculate batch size.
    batch_size = args.micro_batch_size * args.data_parallel_size * \
        get_num_microbatches()

    # Track app tag & app tag ID
    one_logger_utils.track_app_tag(batch_size, args.world_size, args.seq_length)

    total_iterations = total_loss_dict[advanced_iters_key] + \
                       total_loss_dict[skipped_iters_key]

    # Tensorboard values.
    # Timer requires all the ranks to call.
    if args.log_timers_to_tensorboard and \
       (iteration % args.tensorboard_log_interval == 0):
        timers.write(timers_to_log, writer, iteration,
                     normalizer=total_iterations)
    if writer and (iteration % args.tensorboard_log_interval == 0):
        if args.record_memory_history and is_last_rank():
            snapshot = torch.cuda.memory._snapshot()
            from pickle import dump
            with open(args.memory_snapshot_path , 'wb') as f:
                dump(snapshot, f)

        if wandb_writer:
            wandb_writer.log({'samples vs steps': args.consumed_train_samples},
                             iteration)
        writer.add_scalar('learning-rate', learning_rate, iteration)
        if args.decoupled_lr is not None:
            writer.add_scalar('decoupled-learning-rate', decoupled_learning_rate, iteration)
        writer.add_scalar('learning-rate vs samples', learning_rate,
                          args.consumed_train_samples)
        if wandb_writer:
            wandb_writer.log({'learning-rate': learning_rate}, iteration)
        if args.skipped_train_samples > 0:
            writer.add_scalar('skipped-train-samples', args.skipped_train_samples, iteration)
            if wandb_writer:
                wandb_writer.log({'skipped-train-samples': args.skipped_train_samples}, iteration)
        writer.add_scalar('batch-size', batch_size, iteration)
        writer.add_scalar('batch-size vs samples', batch_size,
                          args.consumed_train_samples)
        if wandb_writer:
            wandb_writer.log({'batch-size': batch_size}, iteration)
        for key in loss_dict:
            writer.add_scalar(key , loss_dict[key], iteration)
            writer.add_scalar(key + ' vs samples', loss_dict[key],
                              args.consumed_train_samples)
            if wandb_writer:
                wandb_writer.log({key: loss_dict[key]}, iteration)
        if args.log_loss_scale_to_tensorboard:
            writer.add_scalar('loss-scale', loss_scale, iteration)
            writer.add_scalar('loss-scale vs samples', loss_scale,
                              args.consumed_train_samples)
            if wandb_writer:
                wandb_writer.log({'loss-scale': loss_scale}, iteration)
        if args.log_world_size_to_tensorboard:
            writer.add_scalar('world-size', args.world_size, iteration)
            writer.add_scalar('world-size vs samples', args.world_size,
                              args.consumed_train_samples)
            if wandb_writer:
                wandb_writer.log({'world-size': args.world_size}, iteration)
        if grad_norm is not None:
            writer.add_scalar('grad-norm', grad_norm, iteration)
            writer.add_scalar('grad-norm vs samples', grad_norm,
                              args.consumed_train_samples)
            if wandb_writer:
                wandb_writer.log({'grad-norm': grad_norm}, iteration)
        if num_zeros_in_grad is not None:
            writer.add_scalar('num-zeros', num_zeros_in_grad, iteration)
            writer.add_scalar('num-zeros vs samples', num_zeros_in_grad,
                              args.consumed_train_samples)
            if wandb_writer:
                wandb_writer.log({'num-zeros': num_zeros_in_grad}, iteration)
        if params_norm is not None:
            writer.add_scalar('params-norm', params_norm, iteration)
            writer.add_scalar('params-norm vs samples', params_norm,
                              args.consumed_train_samples)
            if wandb_writer:
                wandb_writer.log({'params-norm': params_norm}, iteration)
        if args.log_memory_to_tensorboard:
            mem_stats = torch.cuda.memory_stats()
            writer.add_scalar(
                "mem-reserved-bytes",
                mem_stats["reserved_bytes.all.current"],
                iteration,
            )
            writer.add_scalar(
                "mem-allocated-bytes",
                mem_stats["allocated_bytes.all.current"],
                iteration,
            )
            writer.add_scalar(
                "mem-allocated-count",
                mem_stats["allocation.all.current"],
                iteration,
            )
    if args.num_experts is not None:
        moe_loss_scale = 1 / get_num_microbatches()
        track_moe_metrics(moe_loss_scale, iteration, writer, wandb_writer, total_loss_dict, args.moe_per_layer_logging)

    if iteration % args.log_interval == 0:
        elapsed_time = timers('interval-time').elapsed(barrier=True)
        elapsed_time_per_iteration = elapsed_time / total_iterations

        throughput = num_floating_point_operations(args, batch_size) / (
            elapsed_time_per_iteration * 10**12 * args.world_size)

        one_logger_utils.track_e2e_metrics(args.log_throughput, throughput)

        if args.log_timers_to_tensorboard:
            if writer:
                writer.add_scalar('iteration-time',
                                  elapsed_time_per_iteration, iteration)
            if wandb_writer:
                wandb_writer.log({'iteration-time': elapsed_time_per_iteration},
                                 iteration)
        log_string = f" [{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}]"
        log_string += ' iteration {:8d}/{:8d} |'.format(
            iteration, args.train_iters)
        log_string += ' consumed samples: {:12d} |'.format(
            args.consumed_train_samples)
        if args.skipped_train_samples > 0:
            log_string += ' skipped samples: {:12d} |'.format(
                args.skipped_train_samples)
        log_string += ' elapsed time per iteration (ms): {:.1f} |'.format(
            elapsed_time_per_iteration * 1000.0)
        if args.log_throughput:
            log_string += f' throughput per GPU (TFLOP/s/GPU): {throughput:.1f} |'
            if args.log_timers_to_tensorboard:
                if writer:
                    writer.add_scalar('throughput', throughput, iteration)
                if wandb_writer:
                    wandb_writer.log({'throughput': throughput}, iteration)
        assert learning_rate is not None
        # Decoupled_learning_rate should be not None only on first and last pipeline stage.
        log_string += f' learning rate: {learning_rate:.6E} |'
        if args.decoupled_lr is not None and (mpu.is_pipeline_first_stage(ignore_virtual=True) or
                                              mpu.is_pipeline_last_stage(ignore_virtual=True)):
            assert decoupled_learning_rate is not None
            log_string += f' decoupled learning rate: {decoupled_learning_rate:.6E} |'
        else:
            assert decoupled_learning_rate is None
        log_string += f' global batch size: {batch_size:5d} |'
        for key in total_loss_dict:
            if key not in [advanced_iters_key, skipped_iters_key,
                           nan_iters_key]:
                avg = total_loss_dict[key].item() / \
                      float(max(1, total_loss_dict[advanced_iters_key]))
                if avg > 0.0:
                    log_string += ' {}: {:.6E} |'.format(key, avg)
                total_loss_dict[key] = torch.tensor([0.0], dtype=torch.float, device='cuda')
        log_string += f' loss scale: {loss_scale:.1f} |'
        if grad_norm is not None:
            log_string += f' grad norm: {grad_norm:.3f} |'
        if num_zeros_in_grad is not None:
            log_string += f' num zeros: {num_zeros_in_grad} |'
        if params_norm is not None:
            log_string += f' params norm: {params_norm:.3f} |'
        log_string += ' number of skipped iterations: {:3d} |'.format(
            total_loss_dict[skipped_iters_key])
        log_string += ' number of nan iterations: {:3d} |'.format(
            total_loss_dict[nan_iters_key])
        total_loss_dict[advanced_iters_key] = 0
        total_loss_dict[skipped_iters_key] = 0
        total_loss_dict[nan_iters_key] = 0
        print_rank_last(log_string)
        if report_memory_flag and learning_rate > 0.:
            # Report memory after optimizer state has been initialized.
            if torch.distributed.get_rank() == 0:
                num_microbatches = get_num_microbatches()
                report_theoretical_memory(args, num_microbatches=num_microbatches, verbose=True)
            report_memory(f'(after {iteration} iterations)')
            report_memory_flag = False
        timers.log(timers_to_log, normalizer=args.log_interval)

    return report_memory_flag


def compute_throughputs_and_append_to_progress_log(iteration,
                                                   num_floating_point_operations_so_far):
    args = get_args()
    if args.save is None:
        return

    # Compute job throughput.
    # args.num_floating_point_operations_so_far keeps track of floating-point operations
    # completed at the start of job.
    global _TRAIN_START_TIME
    job_throughput = \
        (num_floating_point_operations_so_far -
         args.num_floating_point_operations_so_far) / (
            (time.time() - _TRAIN_START_TIME) * 10**12 * args.world_size)

    # Compute cumulative throughput since jobs of this world size were launched.
    # `get_start_time_from_progress_log` returns start time and number of floating-point
    # operations of first job of this world size.
    start_time, start_num_floating_point_operations = get_start_time_from_progress_log()
    elapsed_time = (datetime.now() - start_time).total_seconds()
    cumulative_throughput = \
        (num_floating_point_operations_so_far -
         start_num_floating_point_operations) / (
            elapsed_time * 10**12 * args.world_size)

    tokens_so_far = args.consumed_train_samples * args.seq_length
    saved_ckpt_prefix = 'Saving async checkpoint' if args.async_save else 'Saved checkpoint'
    append_to_progress_log(f"{saved_ckpt_prefix}\tIteration: {iteration}\t"
                           f"Job throughput: {job_throughput:.1f} TFLOP/s/GPU\t"
                           f"Cumulative throughput: {cumulative_throughput:.1f} TFLOP/s/GPU\t"
                           f"Floating-point operations: {num_floating_point_operations_so_far:.2e}\t"
                           f"Tokens (in billions): {tokens_so_far / 10**9:.2f}")


def enable_forward_pre_hook(model_chunks):
    for model_chunk in model_chunks:
        assert isinstance(model_chunk, DDP)
        model_chunk.enable_forward_pre_hook()


def disable_forward_pre_hook(model_chunks):
    for model_chunk in model_chunks:
        assert isinstance(model_chunk, DDP)
        model_chunk.disable_forward_pre_hook()


def save_checkpoint_and_time(iteration, model, optimizer, opt_param_scheduler,
                             num_floating_point_operations_so_far, checkpointing_context,
                             non_persistent_ckpt=False, train_data_iterator=None):
    args = get_args()
    timers = get_timers()

    # Stop timer to get accurate train interval time and exclude checkpointing duration
    timers('interval-time').stop()
    # Extra barrier is added to make sure all ranks report the max time.
    timer_key = 'save-checkpoint-non-persistent' if non_persistent_ckpt else 'save-checkpoint'
    timers(timer_key, log_level=0).start(barrier=True)
    save_checkpoint_start_time = timers('save-checkpoint').active_time()

    # Log E2E metrics before save-checkpoint
    one_logger_utils.track_e2e_metrics()
    if args.use_distributed_optimizer and args.overlap_param_gather:
        disable_forward_pre_hook(model)
    save_checkpoint(iteration, model, optimizer, opt_param_scheduler,
                    num_floating_point_operations_so_far, checkpointing_context,
                    non_persistent_ckpt=non_persistent_ckpt, train_data_iterator=train_data_iterator,
                    ft_client=ft_integration.get_rank_monitor_client(
                    ft_integration.StateMachineActions.SAVE_CHECKPOINT), preprocess_common_state_dict_fn=preprocess_common_state_dict)
    if args.use_distributed_optimizer and args.overlap_param_gather:
        enable_forward_pre_hook(model)
    timers(timer_key).stop(barrier=True)
    timers.log([timer_key])
    save_checkpoint_finish_time = timers('save-checkpoint').active_time()

    # Log E2E metrics after save-checkpoint
    one_logger_utils.track_e2e_metrics()
    save_checkpoint_duration = save_checkpoint_finish_time - save_checkpoint_start_time
    one_logger_utils.on_save_checkpoint_end(save_checkpoint_duration, iteration, args.async_save)

    if args.log_progress and not non_persistent_ckpt:
        compute_throughputs_and_append_to_progress_log(iteration,
                                                       num_floating_point_operations_so_far)

    # Recover timing
    timers('interval-time', log_level=0).start(barrier=True)


def post_training_step_callbacks(model, optimizer, opt_param_scheduler, iteration, prof,
                                 num_floating_point_operations_since_last_log_event):
    """Run all post-training-step functions (e.g., FT heartbeats, GC)."""
    args = get_args()

    # Send heartbeat to FT package and update timeouts.
    if args.enable_ft_package:
        ft_client = ft_integration.get_rank_monitor_client(
            ft_integration.StateMachineActions.TRAIN_HEARTBEAT)
        if ft_client is not None:
            ft_client.send_heartbeat()
            # TODO: We are always calculating timeouts in the current implementation.
            # If we want to rely on manually setting these, then we need to add additional
            # arguments to training and pass it here.
            if ft_integration.can_update_timeouts():
                ft_integration.get_rank_monitor_client(
                    ft_integration.StateMachineActions.UPDATE_TIMEOUT).calculate_and_set_timeouts()
                print_rank_0(f'Updated FT timeouts. New values: \
                    {ft_integration.get_rank_monitor_client().timeouts}')

    # Bring CPU and GPU back in sync if on right iteration.
    if args.train_sync_interval and iteration % args.train_sync_interval == 0:
        torch.cuda.synchronize()

    # Straggler detector.
    if iteration % args.log_interval == 0 and args.log_straggler:
        stimer.report(num_floating_point_operations_since_last_log_event, args.log_interval)
        num_floating_point_operations_since_last_log_event = 0.0

    # Check weight hash across DP replicas.
    if args.check_weight_hash_across_dp_replicas_interval is not None and \
            iteration % args.check_weight_hash_across_dp_replicas_interval == 0:
        if args.use_distributed_optimizer and args.overlap_param_gather:
            disable_forward_pre_hook(model)
        assert check_param_hashes_across_dp_replicas(model, cross_check=True), \
            "Parameter hashes not matching across DP replicas"
        torch.distributed.barrier()
        print_rank_0(f">>> Weight hashes match after {iteration} iterations...")
        if args.use_distributed_optimizer and args.overlap_param_gather:
            enable_forward_pre_hook(model)

    # Autoresume.
    if args.adlr_autoresume and \
        (iteration % args.adlr_autoresume_interval == 0):
        check_adlr_autoresume_termination(iteration, model, optimizer,
                                          opt_param_scheduler)

    # Profiling.
    if args.profile and \
        iteration == args.profile_step_end and \
        torch.distributed.get_rank() in args.profile_ranks:
        if args.use_pytorch_profiler:
            assert prof is not None
            prof.stop()
        else:
            torch.cuda.cudart().cudaProfilerStop()
        

    # Manual garbage collection.
    if args.manual_gc:
        if args.manual_gc_interval != 0 and iteration % args.manual_gc_interval == 0:
            gc.collect()


def checkpoint_and_decide_exit(model, optimizer, opt_param_scheduler, iteration,
                               num_floating_point_operations_so_far, checkpointing_context,
                               train_data_iterator):
    """Save checkpoint and decide whether to exit based on arguments (e.g., if
    --exit-duration-in-mins is set). Actual exit happens in main training loop
    based on the return value of this function."""
    args = get_args()
    timers = get_timers()

    # Exit based on signal handler.
    saved_checkpoint = False
    if args.exit_signal_handler:
        signal_handler = get_signal_handler()
        if any(signal_handler.signals_received()):
            if args.save:
                save_checkpoint_and_time(iteration, model, optimizer,
                                         opt_param_scheduler,
                                         num_floating_point_operations_so_far,
                                         checkpointing_context, train_data_iterator=train_data_iterator)
            print_datetime('exiting program after receiving SIGTERM.')

            return True

    # Regular save (persistent and non-persistent).
    if args.save and args.save_interval and \
        iteration % args.save_interval == 0:
        save_checkpoint_and_time(iteration, model, optimizer,
                                 opt_param_scheduler,
                                 num_floating_point_operations_so_far,
                                 checkpointing_context, train_data_iterator=train_data_iterator)
        saved_checkpoint = True

    elif args.save and args.non_persistent_save_interval and \
        iteration % args.non_persistent_save_interval == 0:
        timers('interval-time').stop()
        save_checkpoint_and_time(iteration, model, optimizer,
                                 opt_param_scheduler,
                                 num_floating_point_operations_so_far,
                                 checkpointing_context,
                                 non_persistent_ckpt=True, train_data_iterator=train_data_iterator)
        saved_checkpoint = True
        timers('interval-time', log_level=0).start(barrier=True)

    # Exit based on duration.
    if args.exit_duration_in_mins:
        train_time = (time.time() - _TRAIN_START_TIME) / 60.0
        done_cuda = torch.tensor(
            [train_time > args.exit_duration_in_mins],
            dtype=torch.int, device='cuda')
        torch.distributed.all_reduce(
            done_cuda, op=torch.distributed.ReduceOp.MAX)
        done = done_cuda.item()
        if done:
            if args.save and not saved_checkpoint:
                save_checkpoint_and_time(iteration, model, optimizer,
                                         opt_param_scheduler,
                                         num_floating_point_operations_so_far,
                                         checkpointing_context, train_data_iterator=train_data_iterator)
            print_datetime(f'exiting program after {train_time} minutes')

            return True

    # Exit based on iterations.
    if args.exit_interval and iteration % args.exit_interval == 0:
        if args.save and not saved_checkpoint:
            save_checkpoint_and_time(iteration, model, optimizer,
                                     opt_param_scheduler,
                                     num_floating_point_operations_so_far,
                                     checkpointing_context, train_data_iterator=train_data_iterator)
        torch.distributed.barrier()
        print_datetime(f'exiting program at iteration {iteration}')

        return True

    return False


def train(forward_step_func, model, optimizer, opt_param_scheduler,
          train_data_iterator, valid_data_iterator,
          process_non_loss_data_func, config, checkpointing_context, non_loss_data_func):
    """Training function: run train_step desired number of times, run validation, checkpoint."""
    args = get_args()
    timers = get_timers()
    one_logger = get_one_logger()

    # Write args to tensorboard
    write_args_to_tensorboard()

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

    # Tracking loss.
    total_loss_dict = {}

    # Iterations.
    iteration = args.iteration

    # Track E2E metrics at the start of training.
    one_logger_utils.on_train_start(iteration=iteration, consumed_train_samples=args.consumed_train_samples,
                                    train_samples=args.train_samples, seq_length=args.seq_length,
                                    train_iters=args.train_iters, save=args.save, async_save=args.async_save,
                                    log_throughput=args.log_throughput,
                                    num_floating_point_operations_so_far=args.num_floating_point_operations_so_far)

    num_floating_point_operations_so_far = args.num_floating_point_operations_so_far

    # Setup some training config params.
    config.grad_scale_func = optimizer.scale_loss
    config.timers = timers
    if isinstance(model[0], DDP) and args.overlap_grad_reduce:
        assert config.no_sync_func is None, \
            ('When overlap_grad_reduce is True, config.no_sync_func must be None; '
             'a custom no_sync_func is not supported when overlapping grad-reduce')
        config.no_sync_func = [model_chunk.no_sync for model_chunk in model]
        if len(model) == 1:
            config.no_sync_func = config.no_sync_func[0]
        if args.align_grad_reduce:
            config.grad_sync_func = [model_chunk.start_grad_sync for model_chunk in model]
            if len(model) == 1:
                config.grad_sync_func = config.grad_sync_func[0]
    if args.overlap_param_gather and args.align_param_gather:
        config.param_sync_func = [model_chunk.start_param_sync for model_chunk in model]
        if len(model) == 1:
            config.param_sync_func = config.param_sync_func[0]
    config.finalize_model_grads_func = finalize_model_grads

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

    if args.manual_gc:
        # Disable the default garbage collector and perform the collection manually.
        # This is to align the timing of garbage collection across ranks.
        assert args.manual_gc_interval >= 0, \
            'Manual garbage collection interval should be larger than or equal to 0'
        gc.disable()
        gc.collect()

    # Singleton initialization of straggler detector.
    if args.log_straggler:
        global stimer
        world = torch.distributed.get_world_size()
        rank = torch.distributed.get_rank()
        mmcnt = args.straggler_minmax_count
        stimer.configure(world, rank,
                mmcnt = mmcnt,
                enabled = not args.disable_straggler_on_startup,
                port = args.straggler_ctrlr_port)
    num_floating_point_operations_since_last_log_event = 0.0

    num_microbatches = get_num_microbatches()
    eval_duration = 0.0
    eval_iterations = 0

    def get_e2e_base_metrics():
        """Get base metrics values for one-logger to calculate E2E tracking metrics.
        """
        return {
            'iteration': iteration,
            'train_duration': timers('interval-time').active_time(),
            'eval_duration': eval_duration,
            'eval_iterations': eval_iterations,
            'total_flops': num_floating_point_operations_since_last_log_event,
            'num_floating_point_operations_so_far': num_floating_point_operations_so_far,
            'consumed_train_samples': args.consumed_train_samples,
            'world_size': args.world_size,
            'seq_length': args.seq_length
        }
    # Cache into one-logger for callback.
    if one_logger:
        with one_logger.get_context_manager():
            one_logger.store_set('get_e2e_base_metrics', get_e2e_base_metrics)

    prof = None
    if args.profile and torch.distributed.get_rank() in args.profile_ranks and args.use_pytorch_profiler:
        
        def trace_handler(p):
            from pathlib import Path
            Path(f"{args.profile_dir}").mkdir(parents=True, exist_ok=True)
            if args.rank in [0]:
                print(p.key_averages().table(sort_by="self_cuda_time_total", row_limit=-1))
            p.export_chrome_trace("{path}/trace_rank{rank}_step{step}.json".format(
                path=args.profile_dir, rank=torch.distributed.get_rank(), step=p.step_num))

        prof = torch.profiler.profile(
        activities=[
           torch.profiler.ProfilerActivity.CPU,
           torch.profiler.ProfilerActivity.CUDA,
        ],
        schedule=torch.profiler.schedule(
            wait=max(args.profile_step_start-1, 0),
            warmup=1 if args.profile_step_start > 0 else 0,
            active=args.profile_step_end-args.profile_step_start,
            repeat=1),
        on_trace_ready=trace_handler)
        prof.start()
    elif args.profile and torch.distributed.get_rank() in args.profile_ranks and args.use_hip_profiler:
        import ctypes
        roctracer = ctypes.cdll.LoadLibrary("/opt/dtk/roctracer/lib/libroctracer64.so")
    
    # Run training iterations till done.
    while iteration < args.train_iters:
        if args.profile and torch.distributed.get_rank() in args.profile_ranks:
            if args.use_pytorch_profiler:
                prof.step()
            elif args.use_hip_profiler:
                if iteration == args.profile_step_start: roctracer.roctracer_start()
                if iteration == args.profile_step_end: roctracer.roctracer_stop()
            elif iteration == args.profile_step_start:
                torch.cuda.cudart().cudaProfilerStart()
                torch.autograd.profiler.emit_nvtx(record_shapes=True).__enter__()

        maybe_finalize_async_save(blocking=False)

        # Update number of microbatches first without consistency check to decide if a
        # checkpoint should be saved. If the number of microbatches is different
        # from the previous iteration, save a checkpoint. Then run consistency check
        # to make sure training configuration is still valid.
        update_num_microbatches(args.consumed_train_samples, consistency_check=False, verbose=True)
        if get_num_microbatches() != num_microbatches and iteration != 0:
            assert get_num_microbatches() > num_microbatches, \
                (f"Number of microbatches should be increasing due to batch size rampup; "
                f"instead going from {num_microbatches} to {get_num_microbatches()}")
            if args.save is not None:
                save_checkpoint_and_time(iteration, model, optimizer,
                                        opt_param_scheduler,
                                        num_floating_point_operations_so_far,
                                        checkpointing_context, train_data_iterator=train_data_iterator)
        num_microbatches = get_num_microbatches()
        update_num_microbatches(args.consumed_train_samples, consistency_check=True, verbose=True)

        # Run training step.
        args.curr_iteration = iteration
        loss_dict, skipped_iter, should_checkpoint, should_exit, exit_code, grad_norm, num_zeros_in_grad = \
            train_step(forward_step_func,
                    train_data_iterator,
                    model,
                    optimizer,
                    opt_param_scheduler,
                    config)
        if should_checkpoint:
            save_checkpoint_and_time(iteration, model, optimizer,
                                    opt_param_scheduler,
                                    num_floating_point_operations_so_far,
                                    checkpointing_context, train_data_iterator=train_data_iterator)
        if should_exit:
            break
        # why is skipped_iter ignored?
        iteration += 1
        batch_size = mpu.get_data_parallel_world_size() * \
                    args.micro_batch_size * \
                    get_num_microbatches()
        args.consumed_train_samples += batch_size
        num_skipped_samples_in_batch = (get_current_global_batch_size() -
                                        get_current_running_global_batch_size())
        if args.decrease_batch_size_if_needed:
            assert num_skipped_samples_in_batch >= 0
        else:
            assert num_skipped_samples_in_batch == 0
        args.skipped_train_samples += num_skipped_samples_in_batch
        num_floating_point_operations_in_batch = num_floating_point_operations(args, batch_size)
        num_floating_point_operations_so_far += num_floating_point_operations_in_batch
        num_floating_point_operations_since_last_log_event += num_floating_point_operations_in_batch

        # Logging.
        loss_scale = optimizer.get_loss_scale().item()
        params_norm = None
        if args.log_params_norm:
            params_norm = calc_params_l2_norm(model)
        learning_rate = None
        decoupled_learning_rate = None
        for param_group in optimizer.param_groups:
            if param_group['is_decoupled_lr']:
                decoupled_learning_rate = param_group['lr']
            else:
                learning_rate = param_group['lr']
        report_memory_flag = training_log(loss_dict, total_loss_dict,
                                        learning_rate,
                                        decoupled_learning_rate,
                                        iteration, loss_scale,
                                        report_memory_flag, skipped_iter,
                                        grad_norm, params_norm, num_zeros_in_grad)

        # Evaluation.
        if args.eval_interval and iteration % args.eval_interval == 0 and \
            args.do_valid:
            timers('interval-time').stop()
            if args.use_distributed_optimizer and args.overlap_param_gather:
                disable_forward_pre_hook(model)
            if args.manual_gc and args.manual_gc_eval:
                # Collect all objects.
                gc.collect()
            prefix = f'iteration {iteration}'
            timers('eval-time', log_level=0).start(barrier=True)
            evaluate_and_print_results(prefix, forward_step_func,
                                    valid_data_iterator, model,
                                    iteration, process_non_loss_data_func,
                                    config, verbose=False, write_to_tensorboard=True,
                                    non_loss_data_func=non_loss_data_func)
            eval_duration += timers('eval-time').elapsed()
            eval_iterations += args.eval_iters
            timers('eval-time').stop()
            one_logger_utils.track_e2e_metrics()

            if args.manual_gc and args.manual_gc_eval:
                # Collect only the objects created and used in evaluation.
                gc.collect(generation=0)
            if args.use_distributed_optimizer and args.overlap_param_gather:
                enable_forward_pre_hook(model)
            timers('interval-time', log_level=0).start(barrier=True)

            if args.enable_ft_package and ft_integration.get_rank_monitor_client() is not None:
                ft_integration.get_rank_monitor_client(
                    ft_integration.StateMachineActions.EVAL_HEARTBEAT).send_heartbeat()

        # Miscellaneous post-training-step functions (e.g., FT heartbeats, GC).
        # Some of these only happen at specific iterations.
        post_training_step_callbacks(model, optimizer, opt_param_scheduler, iteration, prof,
                                    num_floating_point_operations_since_last_log_event)

        # Checkpoint and decide whether to exit.
        should_exit = checkpoint_and_decide_exit(model, optimizer, opt_param_scheduler, iteration,
                                                num_floating_point_operations_so_far,
                                                checkpointing_context, train_data_iterator)
        if should_exit:
            break

    one_logger_utils.track_e2e_metrics()

    # Flush TensorBoard, WandB writers and one-logger.
    writer = get_tensorboard_writer()
    if writer:
        writer.flush()

    # Close out pre-hooks if using distributed optimizer and overlapped param gather.
    if args.use_distributed_optimizer and args.overlap_param_gather:
        disable_forward_pre_hook(model)

    if args.enable_ft_package and ft_integration.get_rank_monitor_client() is not None:
        ft_integration.get_rank_monitor_client().shutdown_workload_monitoring()

    maybe_finalize_async_save(blocking=True)

    # If any exit conditions (signal handler, duration, iterations) have been reached, exit.
    if should_exit:
        wandb_writer = get_wandb_writer()
        if wandb_writer:
            wandb_writer.finish()
        sys.exit(exit_code)

    return iteration, num_floating_point_operations_so_far


def evaluate(forward_step_func,
             data_iterator,
             model,
             process_non_loss_data_func,
             config,
             verbose=False,
             non_loss_data_func=None):
    """Evaluation."""
    args = get_args()
    timers = get_timers()

    timers('evaluate', log_level=0).start(barrier=True)

    if args.vision_pretraining and args.vision_pretraining_type == "dino":
        from megatron.legacy.model.vision.knn_monitor import compute_feature_bank
        compute_feature_bank(model)

    # Turn on evaluation mode which disables dropout.
    for model_module in model:
        model_module.eval()

    # Disable result validation during evaluation
    rerun_state_machine = get_rerun_state_machine()
    rerun_mode = rerun_state_machine.get_mode()
    rerun_state_machine.set_mode(RerunMode.DISABLED)

    total_loss_dict = {}

    # make validation batch size independent from training batch size
    eval_batch_size = args.global_batch_size
    eval_num_microbatches = eval_batch_size // \
        (args.micro_batch_size * args.data_parallel_size)

    with torch.no_grad():
        iteration = 0
        if verbose:
            print_rank_0(f'Evaluating on {args.eval_iters * eval_batch_size} samples')
        while iteration < args.eval_iters:
            iteration += 1
            if verbose:
                print_rank_0(f'Evaluating iter {iteration}/{args.eval_iters}')

            forward_backward_func = get_forward_backward_func()
            # Don't care about timing during evaluation
            config.timers = None
            loss_dicts = forward_backward_func(
                forward_step_func=forward_step_func,
                data_iterator=data_iterator,
                model=model,
                num_microbatches=eval_num_microbatches,
                seq_length=args.seq_length,
                micro_batch_size=args.micro_batch_size,
                decoder_seq_length=args.decoder_seq_length,
                forward_only=True)
            config.timers = get_timers()

            # Empty unused memory
            if args.empty_unused_memory_level >= 1:
                torch.cuda.empty_cache()

            if mpu.is_pipeline_last_stage(ignore_virtual=True):
                # Reduce across processes.
                for loss_dict in loss_dicts:
                    for key in loss_dict:
                        if key not in total_loss_dict:
                            total_loss_dict[key] = torch.tensor([0.0, 0.0], dtype=torch.float).cuda()
                        val = loss_dict[key]
                        if isinstance(val, tuple) or isinstance(val, list):
                            total_loss_dict[key][0] += val[0]
                            total_loss_dict[key][1] += val[1]
                        else:
                            total_loss_dict[key][0] += val
                            total_loss_dict[key][1] += 1

            args.consumed_valid_samples += eval_batch_size

            if args.exit_duration_in_mins:
                train_time = (time.time() - _TRAIN_START_TIME) / 60.0
                done_cuda = torch.tensor(
                    [train_time > args.exit_duration_in_mins],
                    dtype=torch.int, device='cuda')
                torch.distributed.all_reduce(
                    done_cuda, op=torch.distributed.ReduceOp.MAX)
                done = done_cuda.item()
                if done:
                    rerun_state_machine.set_mode(rerun_mode)
                    print_rank_0('Exiting during evaluation, timelimit reached')
                    return None, None, True

        collected_non_loss_data = None
        if non_loss_data_func is not None:
            collected_non_loss_data = non_loss_data_func(model)
        elif process_non_loss_data_func is not None and is_last_rank():
            collected_non_loss_data = forward_backward_func(
                forward_step_func=forward_step_func,
                data_iterator=data_iterator,
                model=model,
                num_microbatches=get_num_microbatches(),
                seq_length=args.seq_length,
                micro_batch_size=args.micro_batch_size,
                decoder_seq_length=args.decoder_seq_length,
                forward_only=True,
                collect_non_loss_data=True)

    # Move model back to the train mode.
    for model_module in model:
        model_module.train()

    for key in total_loss_dict:
        numerator, denominator = total_loss_dict[key]
        total_loss_dict[key] = numerator / denominator

    timers('evaluate').stop()
    timers.log(['evaluate'])
    
    rerun_state_machine.set_mode(rerun_mode)

    return total_loss_dict, collected_non_loss_data, False

def evaluate_and_print_results(prefix, forward_step_func,
                               data_iterator, model,
                               iteration, process_non_loss_data_func, config,
                               verbose=False, write_to_tensorboard=True, non_loss_data_func=None):
    """Helper function to evaluate and dump results on screen."""
    args = get_args()
    if write_to_tensorboard:
        writer = get_tensorboard_writer()
    else:
        writer = None

    wandb_writer = get_wandb_writer()

    total_loss_dict, collected_non_loss_data, timelimit = evaluate(
        forward_step_func, data_iterator, model,
        process_non_loss_data_func, config, verbose, non_loss_data_func)
    # Timelimit hit during evaluation
    if timelimit:
        return
    string = f' validation loss at {prefix} | '
    for key in total_loss_dict:
        string += '{} value: {:.6E} | '.format(key, total_loss_dict[key].item())
        ppl = math.exp(min(20, total_loss_dict[key].item()))
        string += '{} PPL: {:.6E} | '.format(key, ppl)
        if writer:
            writer.add_scalar('{} validation'.format(key),
                              total_loss_dict[key].item(),
                              iteration)
            writer.add_scalar('{} validation vs samples'.format(key),
                              total_loss_dict[key].item(),
                              args.consumed_train_samples)
            if args.log_validation_ppl_to_tensorboard:
                writer.add_scalar('{} validation ppl'.format(key), ppl,
                                  iteration)
                writer.add_scalar('{} validation ppl vs samples'.format(key),
                                  ppl, args.consumed_train_samples)
            if wandb_writer and is_last_rank():
                wandb_writer.log({
                    '{} validation'.format(key): total_loss_dict[key].item()},
                    iteration)

    if process_non_loss_data_func is not None and writer and is_last_rank():
        process_non_loss_data_func(collected_non_loss_data, iteration, writer)

    length = len(string) + 1
    print_rank_last('-' * length)
    print_rank_last(string)
    print_rank_last('-' * length)


def cyclic_iter(iter):
    while True:
        for x in iter:
            yield x


def get_train_valid_test_num_samples():
    """Train/valid/test num samples."""

    args = get_args()

    # Number of train/valid/test samples.
    if args.train_samples:
        train_samples = args.train_samples
    else:
        train_samples = args.train_iters * args.global_batch_size
    eval_iters = (args.train_iters // args.eval_interval + 1) * \
                 args.eval_iters
    test_iters = args.eval_iters

    return (
        train_samples,
        eval_iters * args.global_batch_size,
        test_iters * args.global_batch_size,
    )


def build_train_valid_test_datasets(build_train_valid_test_datasets_provider):
    """Build pretraining datasets."""
    train_valid_test_num_samples = get_train_valid_test_num_samples()
    print_rank_0(' > datasets target sizes (minimum size):')
    print_rank_0('    train:      {}'.format(train_valid_test_num_samples[0]))
    print_rank_0('    validation: {}'.format(train_valid_test_num_samples[1]))
    print_rank_0('    test:       {}'.format(train_valid_test_num_samples[2]))
    return build_train_valid_test_datasets_provider(train_valid_test_num_samples)


def build_train_valid_test_data_loaders(
        build_train_valid_test_datasets_provider):
    """Build pretraining data loaders."""

    args = get_args()

    (train_dataloader, valid_dataloader, test_dataloader) = (None, None, None)

    print_rank_0('> building train, validation, and test datasets ...')

    # Backward compatibility, assume fixed batch size.
    if args.iteration > 0 and args.consumed_train_samples == 0:
        assert args.train_samples is None, \
            'Only backward compatiblity support for iteration-based training'
        args.consumed_train_samples = args.iteration * args.global_batch_size
    if args.iteration > 0 and args.consumed_valid_samples == 0:
        if args.train_samples is None:
            args.consumed_valid_samples = (args.iteration // args.eval_interval) * \
                args.eval_iters * args.global_batch_size

    # Rely on distributed-aware core datasets, temporary
    is_distributed = getattr(build_train_valid_test_datasets_provider, "is_distributed", False)

    # Construct the data pipeline
    if is_distributed or mpu.get_tensor_model_parallel_rank() == 0:

        # Build datasets.
        train_ds, valid_ds, test_ds = build_train_valid_test_datasets(
            build_train_valid_test_datasets_provider)
        # Build dataloders.
        train_dataloader = build_pretraining_data_loader(
            train_ds, args.consumed_train_samples)
        if args.skip_train:
            valid_dataloader = build_pretraining_data_loader(valid_ds, 0)
        else:
            valid_dataloader = build_pretraining_data_loader(
                valid_ds, args.consumed_valid_samples)
        test_dataloader = build_pretraining_data_loader(test_ds, 0)

        # Flags to know if we need to do training/validation/testing.
        do_train = train_dataloader is not None and args.train_iters > 0
        do_valid = valid_dataloader is not None and args.eval_iters > 0
        do_test = test_dataloader is not None and args.eval_iters > 0
        flags = torch.tensor(
            [int(do_train), int(do_valid), int(do_test)],
            dtype=torch.long, device='cuda')
    else:
        flags = torch.tensor([0, 0, 0], dtype=torch.long, device='cuda')

    torch.distributed.broadcast(flags, 0)

    args.do_train = getattr(args, "do_train", False) or flags[0].item()
    args.do_valid = getattr(args, "do_valid", False) or flags[1].item()
    args.do_test = getattr(args, "do_test", False) or flags[2].item()

    return train_dataloader, valid_dataloader, test_dataloader


def build_train_valid_test_data_iterators(
        build_train_valid_test_datasets_provider):
    """Build pretraining data iterators."""

    args = get_args()

    # Build loaders.
    train_dataloader, valid_dataloader, test_dataloader = \
        build_train_valid_test_data_loaders(
            build_train_valid_test_datasets_provider)

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

    def _get_iterator(dataloader_type, dataloader):
        """Return dataset iterator."""
        if dataloader_type == "single":
            return RerunDataIterator(dataloader)
        elif dataloader_type == "cyclic":
            return RerunDataIterator(cyclic_iter(dataloader))
        elif dataloader_type == "external":
            # External dataloader is passed through. User is expected to define how to iterate.
            return RerunDataIterator(dataloader, make_iterable=False)
        else:
            raise RuntimeError("unexpected dataloader type")

    if train_dataloader is not None:
        train_data_iterator = _get_iterator(dl_type, train_dataloader)
    else:
        train_data_iterator = None

    if valid_dataloader is not None:
        valid_data_iterator = _get_iterator(dl_type, valid_dataloader)
    else:
        valid_data_iterator = None

    if test_dataloader is not None:
        test_data_iterator = _get_iterator(dl_type, test_dataloader)
    else:
        test_data_iterator = None

    return train_data_iterator, valid_data_iterator, test_data_iterator
