data/lib/extensions/syncbn/allreduce.py

##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## 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
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

"""Encoding Data Parallel"""
import torch
from torch.autograd import Variable, Function
import torch.cuda.comm as comm


torch_ver = torch.__version__[:3]


def allreduce(*inputs):
    """Cross GPU all reduce autograd operation for calculate mean and
    variance in SyncBN.
    """
    return AllReduce.apply(*inputs)


class AllReduce(Function):
    @staticmethod
    def forward(ctx, num_inputs, *inputs):
        ctx.num_inputs = num_inputs
        ctx.target_gpus = [inputs[i].get_device() for i in range(0, len(inputs), num_inputs)]
        inputs = [inputs[i:i + num_inputs]
                 for i in range(0, len(inputs), num_inputs)]
        # sort before reduce sum
        inputs = sorted(inputs, key=lambda i: i[0].get_device())
        results = comm.reduce_add_coalesced(inputs, ctx.target_gpus[0])
        outputs = comm.broadcast_coalesced(results, ctx.target_gpus)
        return tuple([t for tensors in outputs for t in tensors])

    @staticmethod
    def backward(ctx, *inputs):
        inputs = [i.data for i in inputs]
        inputs = [inputs[i:i + ctx.num_inputs]
                 for i in range(0, len(inputs), ctx.num_inputs)]
        results = comm.reduce_add_coalesced(inputs, ctx.target_gpus[0])
        outputs = comm.broadcast_coalesced(results, ctx.target_gpus)
        return (None,) + tuple([Variable(t) for tensors in outputs for t in tensors])