import torch
|
|
import torch.distributed as dist
|
|
from torch.nn.modules import Module
|
|
from torch.autograd import Variable
|
|
|
|
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)
|
|
|
|
|
|
'''
|
|
This version of DistributedDataParallel is designed to be used in conjunction with the multiproc.py
|
|
launcher included with this example. It assumes that your run is using multiprocess with 1
|
|
GPU/process, that the model is on the correct device, and that torch.set_device has been
|
|
used to set the device.
|
|
|
|
Parameters are broadcasted to the other processes on initialization of DistributedDataParallel,
|
|
and will be allreduced at the finish of the backward pass.
|
|
'''
|
|
class DistributedDataParallel(Module):
|
|
|
|
def __init__(self, module):
|
|
super(DistributedDataParallel, self).__init__()
|
|
#fallback for PyTorch 0.3
|
|
if not hasattr(dist, '_backend'):
|
|
self.warn_on_half = True
|
|
else:
|
|
self.warn_on_half = True if dist._backend == dist.dist_backend.GLOO else False
|
|
|
|
self.module = module
|
|
|
|
for p in self.module.state_dict().values():
|
|
if not torch.is_tensor(p):
|
|
continue
|
|
dist.broadcast(p, 0)
|
|
|
|
def allreduce_params():
|
|
if(self.needs_reduction):
|
|
self.needs_reduction = False
|
|
buckets = {}
|
|
for param in self.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 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. This currently requires" +
|
|
"PyTorch built from top of tree master.")
|
|
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)
|
|
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(self.module.parameters()):
|
|
def allreduce_hook(*unused):
|
|
param._execution_engine.queue_callback(allreduce_params)
|
|
if param.requires_grad:
|
|
param.register_hook(allreduce_hook)
|
|
|
|
def forward(self, *inputs, **kwargs):
|
|
self.needs_reduction = True
|
|
return self.module(*inputs, **kwargs)
|
|
|
|
'''
|
|
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)
|
|
'''
|
|
'''
|
|
Modifies existing model to do gradient allreduce, but doesn't change class
|
|
so you don't need "module"
|
|
'''
|
|
def apply_gradient_allreduce(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 = param.data.dtype
|
|
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)
|
|
|
|
def set_needs_reduction(self, input, output):
|
|
self.needs_reduction = True
|
|
|
|
module.register_forward_hook(set_needs_reduction)
|
|
return module
|