text: # coding=utf-8
  # Copyright (c) 2020, Sber.  All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import torch
  from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
  import torch.distributed as dist
  from torch.nn.modules import Module
  from torch.autograd import Variable
  
  from src import mpu
  
  
  class DistributedDataParallel(Module):
  
      def __init__(self, module):
          super(DistributedDataParallel, self).__init__()
          self.warn_on_half = True if dist._backend == dist.dist_backend.GLOO else False
  
          self.module = module
          self.data_parallel_group = mpu.get_data_parallel_group()
          src_rank = mpu.get_model_parallel_rank()
          for p in self.module.parameters():
              if torch.is_tensor(p):
                  dist.broadcast(p, src_rank, group=self.data_parallel_group)
  
          def allreduce_params(reduce_after=True, no_scale=False, fp32_allreduce=False):
              if(self.needs_reduction):
                  self.needs_reduction = False
                  buckets = {}
                  for name, param in self.module.named_parameters():
                      if param.requires_grad and param.grad is not None:
                          tp = (param.data.type())
                          if tp not in buckets:
                              buckets[tp] = []
                          buckets[tp].append(param)
                  if self.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.")
                          self.warn_on_half = False
                  for tp in buckets:
                      bucket = buckets[tp]
                      grads = [param.grad.data for param in bucket]
                      coalesced = _flatten_dense_tensors(grads)
                      if fp32_allreduce:
                          coalesced = coalesced.float()
                      if not no_scale and not reduce_after:
                          coalesced /= dist.get_world_size(group=self.data_parallel_group)
                      dist.all_reduce(coalesced, group=self.data_parallel_group)
                      torch.cuda.synchronize()
                      if not no_scale and reduce_after:
                          coalesced /= dist.get_world_size(group=self.data_parallel_group)
                      for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)):
                          buf.copy_(synced)
          self.hook_handles = []
          self.hooks = []
          for param in list(self.module.parameters()):
              def allreduce_hook(*unused):
                  Variable._execution_engine.queue_callback(allreduce_params)
          #    handle = param.register_hook(allreduce_hook)
              #self.hooks.append(allreduce_hook)
              #self.hook_handles.append(handle)
          self.allreduce_params = allreduce_params
  
      def forward(self, *inputs, **kwargs):
          self.needs_reduction = True
          return self.module(*inputs, **kwargs)
  
      def state_dict(self, destination=None, prefix='', keep_vars=False):
          #[h.remove() for h in self.hook_handles]
          sd = self.module.state_dict(destination, prefix, keep_vars)
         # for handle, hook in zip(self.hook_handles, self.hooks):
         #     d = handle.hooks_dict_ref()
         #     d[handle.id] = hook
  
          return sd
  
      def load_state_dict(self, state_dict, strict=True):
          self.module.load_state_dict(state_dict, strict=strict)
  
      '''
      def _sync_buffers(self):
          buffers = list(self.module._all_buffers())
          if len(buffers) > 0:
              # cross-node buffer sync
              flat_buffers = _flatten_dense_tensors(buffers)
              dist.broadcast(flat_buffers, 0)
              for buf, synced in zip(buffers, _unflatten_dense_tensors(flat_buffers, buffers)):
                  buf.copy_(synced)
      def train(self, mode=True):
          # Clear NCCL communicator and CUDA event cache of the default group ID,
          # These cache will be recreated at the later call. This is currently a
          # work-around for a potential NCCL deadlock.
          if dist._backend == dist.dist_backend.NCCL:
              dist._clear_group_cache()
          super(DistributedDataParallel, self).train(mode)
          self.module.train(mode)
      '''
  
  