##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Hang Zhang
## ECE Department, Rutgers University
## Email: zhang.hang@rutgers.edu
## Copyright (c) 2017
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree 
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

import threading
import torch
import torch.cuda.nccl as nccl
import torch.cuda.comm as comm
from torch.autograd import Variable, Function
from torch.nn.modules import Module
from torch.nn.parallel.scatter_gather import scatter, scatter_kwargs, \
    gather
from torch.nn.parallel.replicate import replicate
from torch.nn.parallel.parallel_apply import parallel_apply


class ModelDataParallel(Module):
    """Implements data parallelism at the module level.

    .. ModelDataParallel_

    This container parallelizes the application of the given module by
    splitting the input across the specified devices by chunking in the 
    batch dimension. 
    In the forward pass, the module is replicated on each device,
    and each replica handles a portion of the input. During the backwards
    pass, gradients from each replica are summed into the original module.
    Note that the outputs are not gathered, please use compatible 
    CriterionDataParallel_ .

    The batch size should be larger than the number of GPUs used. It should
    also be an integer multiple of the number of GPUs so that each chunk is
    the same size (so that each GPU processes the same number of samples).

    Args:
        module: module to be parallelized
        device_ids: CUDA devices (default: all devices)

    Example::

        >>> net = torch.nn.ModelDataParallel(model, device_ids=[0, 1, 2])
        >>> output = net(input_var)
    """
    def __init__(self, module, device_ids=None, dim=0):
        super(ModelDataParallel, self).__init__()
        if device_ids is None:
            device_ids = list(range(torch.cuda.device_count()))
        self.dim = dim
        self.module = module
        self.device_ids = device_ids
        self.master_mean, self.master_var = {}, {}
        if len(self.device_ids) == 1:
            self.module.cuda(device_ids[0])

    def forward(self, *inputs, **kwargs):
        inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
        if len(self.device_ids) == 1:
            return self.module(*inputs[0], **kwargs[0])
        replicas = self.replicate(self.module, \
            self.device_ids[:len(inputs)])
        outputs = self.parallel_apply(replicas, inputs, kwargs)
        return outputs 

    def replicate(self, module, device_ids):
        return replicate(module, device_ids)

    def scatter(self, inputs, kwargs, device_ids):
        return scatter_kwargs(inputs, kwargs, device_ids, dim=self.dim)

    def parallel_apply(self, replicas, inputs, kwargs):
        return parallel_apply(replicas, inputs, kwargs)

    
class CriterionDataParallel(Module):
    """
    .. CriterionDataParallel_

    Calculate loss in multiple-GPUs, which balance the memory usage for 
    Semantic Segmentation.

    The targets are splitted across the specified devices by chunking in
    the batch dimension. Please use together with ModelDataParallel_
    """
    def __init__(self, module, device_ids=None, output_device=None, dim=0):
        super(CriterionDataParallel, self).__init__()
        if device_ids is None:
            device_ids = list(range(torch.cuda.device_count()))
        if output_device is None:
            output_device = device_ids[0]
        self.dim = dim
        self.module = module
        self.device_ids = device_ids
        self.output_device = output_device
        if len(self.device_ids) == 1:
            self.module.cuda(device_ids[0])

    def forward(self, inputs, *targets, **kwargs):
        # input should be already scatterd
        # scattering the targets instead
        targets, kwargs = self.scatter(targets, kwargs, self.device_ids)
        if len(self.device_ids) == 1:
            return self.module(inputs, *targets[0], **kwargs[0])
        replicas = self.replicate(self.module, self.device_ids[:len(inputs)])
        outputs = self.parallel_apply(replicas, inputs, targets, kwargs)
        return self.gather(outputs, self.output_device)

    def replicate(self, module, device_ids):
        return replicate(module, device_ids)

    def scatter(self, inputs, kwargs, device_ids):
        return scatter_kwargs(inputs, kwargs, device_ids, dim=self.dim)

    def parallel_apply(self, replicas, inputs, targets, kwargs):
        return criterion_parallel_apply(replicas, inputs, targets, kwargs)

    def gather(self, outputs, output_device):
        return gather(outputs, output_device, dim=self.dim).mean()