# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.

"""General utilities."""

import os
import sys

import torch
from torch.nn.parallel import DistributedDataParallel as torchDDP

from apex.multi_tensor_apply import multi_tensor_applier
import amp_C

from megatron import (
    get_args,
    get_adlr_autoresume,
)
from megatron.core import mpu
from megatron.core.tensor_parallel import param_is_not_tensor_parallel_duplicate
from megatron.model.module import param_is_not_shared


def unwrap_model(model, module_instances=(torchDDP)):
    return_list = True
    if not isinstance(model, list):
        model = [model]
        return_list = False
    unwrapped_model = []
    for model_module in model:
        while isinstance(model_module, module_instances):
            model_module = model_module.module
        unwrapped_model.append(model_module)
    if not return_list:
        return unwrapped_model[0]
    return unwrapped_model


def calc_params_l2_norm(model):
    """Calculate l2 norm of parameters"""
    args = get_args()
    if not isinstance(model, list):
        model = [model]
    # Remove duplicate params.
    params_data = []
    for model_ in model:
        for param in model_.parameters():
            is_not_shared = param_is_not_shared(param)
            is_not_tp_duplicate = param_is_not_tensor_parallel_duplicate(param)
            if is_not_shared and is_not_tp_duplicate:
                if args.bf16:
                    params_data.append(param.data.float())
                else:
                    params_data.append(param.data)
    # Calculate norm
    dummy_overflow_buf = torch.cuda.IntTensor([0])
    norm, _ = multi_tensor_applier(
        amp_C.multi_tensor_l2norm,
        dummy_overflow_buf,
        [params_data],
        False,  # no per-parameter norm
    )
    norm_2 = norm * norm
    # Sum across all model-parallel GPUs.
    torch.distributed.all_reduce(
        norm_2, op=torch.distributed.ReduceOp.SUM, group=mpu.get_model_parallel_group()
    )
    return norm_2.item() ** 0.5


def average_losses_across_data_parallel_group(losses):
    """Reduce a tensor of losses across all GPUs."""
    averaged_losses = torch.cat([loss.clone().detach().view(1) for loss in losses])
    torch.distributed.all_reduce(averaged_losses, group=mpu.get_data_parallel_group())
    averaged_losses = averaged_losses / torch.distributed.get_world_size(
        group=mpu.get_data_parallel_group()
    )

    return averaged_losses


def report_memory(name):
    """Simple GPU memory report."""
    mega_bytes = 1024.0 * 1024.0
    string = name + " memory (MB)"
    string += " | allocated: {}".format(torch.cuda.memory_allocated() / mega_bytes)
    string += " | max allocated: {}".format(
        torch.cuda.max_memory_allocated() / mega_bytes
    )
    string += " | reserved: {}".format(torch.cuda.memory_reserved() / mega_bytes)
    string += " | max reserved: {}".format(
        torch.cuda.max_memory_reserved() / mega_bytes
    )
    if mpu.get_data_parallel_rank() == 0:
        print("[Rank {}] {}".format(torch.distributed.get_rank(), string), flush=True)


def print_params_min_max_norm(optimizer, iteration):
    """Print min, max, and norm of all parameters."""
    index = 0
    rank = torch.distributed.get_rank()
    string = "iteration, rank, index, tensor-model-parallel, min, max, norm\n"
    optimizer_ = optimizer.optimizer
    for param_group in optimizer_.param_groups:
        for param in param_group["params"]:
            index += 1
            min_ = param.data.min()
            max_ = param.data.max()
            norm = torch.linalg.norm(param.data)
            string += "{:7d}, {:4d}, {:4d}, {:2d}, ".format(
                iteration, rank, index, int(param.tensor_model_parallel)
            )
            string += "{:.6E}, {:.6E}, {:.6E}\n".format(min_, max_, norm)
    print(string, flush=True)


def check_adlr_autoresume_termination(
    iteration, model, optimizer, opt_param_scheduler, args
):
    """Check for autoresume signal and exit if it is received."""
    from megatron.checkpointing import save_checkpoint

    autoresume = get_adlr_autoresume()
    # Add barrier to ensure consistnecy.
    torch.distributed.barrier()
    if autoresume.termination_requested():
        if args.save:
            save_checkpoint(iteration, model, optimizer, opt_param_scheduler)
        print_rank_0(">>> autoresume termination request found!")
        if torch.distributed.get_rank() == 0:
            autoresume.request_resume()
        print_rank_0(">>> training terminated. Returning")
        sys.exit(0)


def get_ltor_masks_and_position_ids(
    data, eod_token, reset_position_ids, reset_attention_mask, eod_mask_loss
):
    """Build masks and position id for left to right model."""

    # Extract batch size and sequence length.
    micro_batch_size, seq_length = data.size()

    # Attention mask (lower triangular).
    if reset_attention_mask:
        att_mask_batch = micro_batch_size
    else:
        att_mask_batch = 1
    attention_mask = torch.tril(
        torch.ones((att_mask_batch, seq_length, seq_length), device=data.device)
    ).view(att_mask_batch, 1, seq_length, seq_length)

    # Loss mask.
    loss_mask = torch.ones(data.size(), dtype=torch.float, device=data.device)
    if eod_mask_loss:
        loss_mask[data == eod_token] = 0.0

    # Position ids.
    position_ids = torch.arange(seq_length, dtype=torch.long, device=data.device)
    position_ids = position_ids.unsqueeze(0).expand_as(data)
    # We need to clone as the ids will be modifed based on batch index.
    if reset_position_ids:
        position_ids = position_ids.clone()

    if reset_position_ids or reset_attention_mask:
        # Loop through the batches:
        for b in range(micro_batch_size):

            # Find indecies where EOD token is.
            eod_index = position_ids[b, data[b] == eod_token]
            # Detach indecies from positions if going to modify positions.
            if reset_position_ids:
                eod_index = eod_index.clone()

            # Loop through EOD indecies:
            prev_index = 0
            for j in range(eod_index.size()[0]):
                i = eod_index[j]
                # Mask attention loss.
                if reset_attention_mask:
                    attention_mask[b, 0, (i + 1) :, : (i + 1)] = 0
                # Reset positions.
                if reset_position_ids:
                    position_ids[b, (i + 1) :] -= i + 1 - prev_index
                    prev_index = i + 1

    # Convert attention mask to binary:
    attention_mask = attention_mask < 0.5

    return attention_mask, loss_mask, position_ids


def print_rank_0(message):
    """If distributed is initialized, print only on rank 0."""
    if torch.distributed.is_initialized():
        if torch.distributed.get_rank() == 0:
            print(message, flush=True)
    else:
        print(message, flush=True)


def is_last_rank():
    return torch.distributed.get_rank() == (torch.distributed.get_world_size() - 1)


def print_rank_last(message):
    """If distributed is initialized, print only on last rank."""
    if torch.distributed.is_initialized():
        if is_last_rank():
            print(message, flush=True)
    else:
        print(message, flush=True)


def is_last_local_rank():
    return get_args().local_rank == (int(os.environ["LOCAL_WORLD_SIZE"]) - 1)


def print_all_nodes(*args, **kwargs):
    """If distributed is initialized, print on the last rank in all nodes."""
    if torch.distributed.is_initialized():
        if is_last_local_rank():
            print(*args, **kwargs)
    else:
        print(*args, **kwargs)