# Original source: https://github.com/NVIDIA/waveglow/blob/master/distributed.py
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import os
import torch
import torch.distributed as dist
from torch.autograd import Variable


def reduce_tensor(tensor, num_gpus, reduce_dst=None):
    if num_gpus <= 1:  # pass-thru
        return tensor
    rt = tensor.clone()
    if reduce_dst is not None:
        dist.reduce(rt, reduce_dst, op=dist.ReduceOp.SUM)
    else:
        dist.all_reduce(rt, op=dist.ReduceOp.SUM)
    rt /= num_gpus
    return rt


def init_distributed(rank, num_gpus, dist_backend, dist_url):
    assert torch.cuda.is_available(), "Distributed mode requires CUDA."

    print("> initializing distributed for rank {} out of {}".format(rank, num_gpus))

    # Set cuda device so everything is done on the right GPU.
    torch.cuda.set_device(rank % torch.cuda.device_count())

    init_method = "tcp://"
    master_ip = os.getenv("MASTER_ADDR", "localhost")
    master_port = os.getenv("MASTER_PORT", "6000")
    init_method += master_ip + ":" + master_port
    torch.distributed.init_process_group(
        backend="nccl", world_size=num_gpus, rank=rank, init_method=init_method
    )


def _flatten_dense_tensors(tensors):
    """Flatten dense tensors into a contiguous 1D buffer. Assume tensors are of
    same dense type.
    Since inputs are dense, the resulting tensor will be a concatenated 1D
    buffer. Element-wise operation on this buffer will be equivalent to
    operating individually.
    Arguments:
        tensors (Iterable[Tensor]): dense tensors to flatten.
    Returns:
        A contiguous 1D buffer containing input tensors.
    """
    if len(tensors) == 1:
        return tensors[0].contiguous().view(-1)
    flat = torch.cat([t.contiguous().view(-1) for t in tensors], dim=0)
    return flat


def _unflatten_dense_tensors(flat, tensors):
    """View a flat buffer using the sizes of tensors. Assume that tensors are of
    same dense type, and that flat is given by _flatten_dense_tensors.
    Arguments:
        flat (Tensor): flattened dense tensors to unflatten.
        tensors (Iterable[Tensor]): dense tensors whose sizes will be used to
          unflatten flat.
    Returns:
        Unflattened dense tensors with sizes same as tensors and values from
        flat.
    """
    outputs = []
    offset = 0
    for tensor in tensors:
        numel = tensor.numel()
        outputs.append(flat.narrow(0, offset, numel).view_as(tensor))
        offset += numel
    return tuple(outputs)


def apply_gradient_allreduce(module):
    """
    Modifies existing model to do gradient allreduce, but doesn't change class
    so you don't need "module"
    """
    if not hasattr(dist, "_backend"):
        module.warn_on_half = True
    else:
        module.warn_on_half = True if dist._backend == dist.dist_backend.GLOO else False

    for p in module.state_dict().values():
        if not torch.is_tensor(p):
            continue
        dist.broadcast(p, 0)

    def allreduce_params():
        if module.needs_reduction:
            module.needs_reduction = False
            buckets = {}
            for param in module.parameters():
                if param.requires_grad and param.grad is not None:
                    tp = type(param.data)
                    if tp not in buckets:
                        buckets[tp] = []
                    buckets[tp].append(param)
            if module.warn_on_half:
                if torch.cuda.HalfTensor in buckets:
                    print(
                        "WARNING: gloo dist backend for half parameters may be extremely slow."
                        + " It is recommended to use the NCCL backend in this case. This currently requires"
                        + "PyTorch built from top of tree master."
                    )
                    module.warn_on_half = False

            for tp in buckets:
                bucket = buckets[tp]
                grads = [param.grad.data for param in bucket]
                coalesced = _flatten_dense_tensors(grads)
                dist.all_reduce(coalesced)
                coalesced /= dist.get_world_size()
                for buf, synced in zip(
                    grads, _unflatten_dense_tensors(coalesced, grads)
                ):
                    buf.copy_(synced)

    for param in list(module.parameters()):

        def allreduce_hook(*unused):
            Variable._execution_engine.queue_callback(allreduce_params)

        if param.requires_grad:
            param.register_hook(allreduce_hook)
            dir(param)

    def set_needs_reduction(self, input, output):
        self.needs_reduction = True

    module.register_forward_hook(set_needs_reduction)
    return module