# coding=utf-8

# Copyright (c) 2019, NVIDIA CORPORATION. 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

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)

'''