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

import os
import torch
from functools import partial
from typing import List, Optional, Tuple, Union

from megatron.training import get_args
from megatron.training import print_rank_0
from megatron.training import get_timers
from megatron.training import get_tokenizer
from megatron.core import mpu
from megatron.core.enums import ModelType
from megatron.core.datasets.blended_megatron_dataset_builder import BlendedMegatronDatasetBuilder
from megatron.core.datasets.gpt_dataset import GPTDatasetConfig
from megatron.core.datasets.gpt_dataset import MockGPTDataset, GPTDataset
from megatron.core.rerun_state_machine import get_rerun_state_machine
from megatron.core.models.mamba import MambaModel
from megatron.training import pretrain
from megatron.core.utils import StragglerDetector
from megatron.core.transformer.spec_utils import import_module
from megatron.training.utils import (
    get_batch_on_this_cp_rank,
    get_batch_on_this_tp_rank,
    get_blend_and_blend_per_split,
)
from megatron.training.arguments import core_transformer_config_from_args
from megatron.core.models.gpt.gpt_layer_specs import get_gpt_layer_with_transformer_engine_spec


stimer = StragglerDetector()

def count_parameters_in_layer(model, layer_name):
    num_params = 0
    for name, param in model.named_parameters():
        if layer_name in name:
            num_params += param.numel()
            print_rank_0(f" - {name}: {param.numel()}")
    return num_params


def model_provider(pre_process=True, post_process=True) -> MambaModel:
    """Builds the model.

    Args:
        pre_process (bool, optional): Set to true if you need to compute embedings. Defaults to True.
        post_process (bool, optional): Set to true if you need to want to compute output logits/loss. Defaults to True.


    Returns:
        MambaModel: The returned model
    """
    args = get_args()

    print_rank_0('building Mamba model ...')
    config = core_transformer_config_from_args(get_args())

    assert args.use_legacy_models == False, "Mamba only supported in Mcore!"

    if args.spec is not None:
        mamba_stack_spec = import_module(args.spec)
    else:
        raise("You must provide a valid Mamba layer spec!")

    model = MambaModel(
        config=config,
        mamba_stack_spec=mamba_stack_spec,
        vocab_size=args.padded_vocab_size,
        max_sequence_length=args.max_position_embeddings,
        pre_process=pre_process,
        hybrid_attention_ratio=args.hybrid_attention_ratio,
        hybrid_mlp_ratio=args.hybrid_mlp_ratio,
        hybrid_override_pattern=args.hybrid_override_pattern,
        post_process=post_process,
        fp16_lm_cross_entropy=args.fp16_lm_cross_entropy,
        parallel_output=True,
        share_embeddings_and_output_weights=not args.untie_embeddings_and_output_weights,
        position_embedding_type=args.position_embedding_type,
        rotary_percent=args.rotary_percent,
        rotary_base=args.rotary_base
    )

    for l in range(model.decoder.num_layers_per_pipeline_rank):
        layer_params = count_parameters_in_layer(model, f'decoder.layers.{l}.')
        print_rank_0(f" == params layer {l}: {layer_params}")

    return model


def get_batch(data_iterator):
    """Generate a batch."""

    # TODO: this is pretty hacky, find a better way
    if (not mpu.is_pipeline_first_stage()) and (not mpu.is_pipeline_last_stage()):
        return None, None, None, None, None

    # get batches based on the TP rank you are on
    batch = get_batch_on_this_tp_rank(data_iterator)

    # slice batch along sequence dimension for context parallelism
    batch = get_batch_on_this_cp_rank(batch)

    return batch.values()


# define spiky loss as a loss that's 10x the max loss observed
SPIKY_LOSS_FACTOR = 10


def loss_func(loss_mask: torch.Tensor, output_tensor: torch.Tensor):
    """Loss function.

    Args:
        loss_mask (torch.Tensor): Used to mask out some portions of the loss
        output_tensor (torch.Tensor): The tensor with the losses

    Returns:
        the loss scalar for this micro-batch
        the number of non-padded tokens in this microbatch
        a dict containing reporting metrics on the loss and number of tokens across
            the data parallel ranks
    """
    args = get_args()

    losses = output_tensor.float()
    loss_mask = loss_mask.view(-1).float()
    total_tokens = loss_mask.sum()
    loss = torch.cat([torch.sum(losses.view(-1) * loss_mask).view(1), total_tokens.view(1)])

    if args.context_parallel_size > 1:
        torch.distributed.all_reduce(loss, group=mpu.get_context_parallel_group())

    # Check individual rank losses are not NaN prior to DP all-reduce.
    rerun_state_machine = get_rerun_state_machine()
    if args.check_for_nan_in_loss_and_grad:
        rerun_state_machine.validate_result(
            result=loss[0],
            rejection_func=torch.isnan,
            message="found NaN in local forward loss calculation",
            tolerance=0.0,        # forward pass calculations are determinisic
            fatal=True,
        )
        rerun_state_machine.validate_result(
            result=loss[0],
            rejection_func=torch.isinf,
            message="found Inf in local forward loss calculation",
            tolerance=0.0,        # forward pass calculations are determinisic
            fatal=True,
        )
    # Check for spiky loss
    if args.check_for_spiky_loss:
        rerun_state_machine.validate_result(
            result=loss[0],
            rejection_func=partial(
                rerun_state_machine.is_unexpectedly_large,
                threshold=SPIKY_LOSS_FACTOR,
                context="loss",
            ),
            message="Spiky loss",
            tolerance=0.0,        # forward pass calculations are determinisic
            fatal=False,
        )

    # Reduce loss for logging.
    reporting_loss = loss.clone().detach()
    torch.distributed.all_reduce(reporting_loss, group=mpu.get_data_parallel_group())

    local_num_tokens = loss[1].clone().detach().to(torch.int)
    return (
        loss[0] * args.context_parallel_size,
        local_num_tokens,
        {'lm loss': (reporting_loss[0], reporting_loss[1])},
    )


def forward_step(data_iterator, model: MambaModel):
    """Forward training step.

    Args:
        data_iterator : Input data iterator
        model (MambaModel): The GPT Model
    """
    args = get_args()
    timers = get_timers()

    # Get the batch.
    timers('batch-generator', log_level=2).start()
    global stimer
    with stimer(bdata=True):
        tokens, labels, loss_mask, attention_mask, position_ids = get_batch(
            data_iterator)
    timers('batch-generator').stop()

    with stimer:
        output_tensor = model(tokens, position_ids, attention_mask,
                              labels=labels)

    return output_tensor, partial(loss_func, loss_mask)


def is_dataset_built_on_rank():
    return (
        mpu.is_pipeline_first_stage() or mpu.is_pipeline_last_stage()
    ) and mpu.get_tensor_model_parallel_rank() == 0


def core_gpt_dataset_config_from_args(args):
    tokenizer = get_tokenizer()

    # Sometimes --data-path is too long, instead we parse it from a file.
    blend: Optional[Tuple[List[str], Optional[List[float]]]]
    blend_per_split: Optional[List[Optional[Tuple[List[str], Optional[List[float]]]]]]
    blend, blend_per_split = get_blend_and_blend_per_split(args)

    return GPTDatasetConfig(
        random_seed=args.seed,
        sequence_length=args.seq_length,
        blend=blend,
        blend_per_split=blend_per_split,
        split=args.split,
        num_dataset_builder_threads=args.num_dataset_builder_threads,
        path_to_cache=args.data_cache_path,
        mmap_bin_files=args.mmap_bin_files,
        tokenizer=tokenizer,
        reset_position_ids=args.reset_position_ids,
        reset_attention_mask=args.reset_attention_mask,
        eod_mask_loss=args.eod_mask_loss,
        create_attention_mask=args.create_attention_mask_in_dataloader,
        s3_cache_path=args.s3_cache_path,
    )


def train_valid_test_datasets_provider(train_val_test_num_samples):
    """Build the train test and validation datasets.

    Args:
        train_val_test_num_samples : A list containing the number of samples in train test and validation.
    """
    args = get_args()

    config = core_gpt_dataset_config_from_args(args)

    if args.mock_data:
        dataset_type = MockGPTDataset
    else:
        dataset_type = GPTDataset

    print_rank_0("> building train, validation, and test datasets for GPT ...")

    train_ds, valid_ds, test_ds = BlendedMegatronDatasetBuilder(
        dataset_type,
        train_val_test_num_samples,
        is_dataset_built_on_rank,
        config
    ).build()

    print_rank_0("> finished creating GPT datasets ...")

    return train_ds, valid_ds, test_ds


if __name__ == "__main__":

    # Temporary for transition to core datasets
    train_valid_test_datasets_provider.is_distributed = True

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