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173 lines
7.1 KiB
173 lines
7.1 KiB
import torch
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import torch.distributed as dist
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from torch.nn.modules import Module
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from torch.autograd import Variable
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def _flatten_dense_tensors(tensors):
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"""Flatten dense tensors into a contiguous 1D buffer. Assume tensors are of
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same dense type.
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Since inputs are dense, the resulting tensor will be a concatenated 1D
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buffer. Element-wise operation on this buffer will be equivalent to
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operating individually.
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Arguments:
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tensors (Iterable[Tensor]): dense tensors to flatten.
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Returns:
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A contiguous 1D buffer containing input tensors.
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"""
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if len(tensors) == 1:
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return tensors[0].contiguous().view(-1)
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flat = torch.cat([t.contiguous().view(-1) for t in tensors], dim=0)
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return flat
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def _unflatten_dense_tensors(flat, tensors):
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"""View a flat buffer using the sizes of tensors. Assume that tensors are of
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same dense type, and that flat is given by _flatten_dense_tensors.
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Arguments:
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flat (Tensor): flattened dense tensors to unflatten.
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tensors (Iterable[Tensor]): dense tensors whose sizes will be used to
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unflatten flat.
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Returns:
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Unflattened dense tensors with sizes same as tensors and values from
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flat.
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"""
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outputs = []
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offset = 0
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for tensor in tensors:
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numel = tensor.numel()
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outputs.append(flat.narrow(0, offset, numel).view_as(tensor))
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offset += numel
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return tuple(outputs)
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'''
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This version of DistributedDataParallel is designed to be used in conjunction with the multiproc.py
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launcher included with this example. It assumes that your run is using multiprocess with 1
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GPU/process, that the model is on the correct device, and that torch.set_device has been
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used to set the device.
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Parameters are broadcasted to the other processes on initialization of DistributedDataParallel,
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and will be allreduced at the finish of the backward pass.
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'''
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class DistributedDataParallel(Module):
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def __init__(self, module):
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super(DistributedDataParallel, self).__init__()
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#fallback for PyTorch 0.3
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if not hasattr(dist, '_backend'):
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self.warn_on_half = True
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else:
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self.warn_on_half = True if dist._backend == dist.dist_backend.GLOO else False
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self.module = module
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for p in self.module.state_dict().values():
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if not torch.is_tensor(p):
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continue
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dist.broadcast(p, 0)
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def allreduce_params():
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if(self.needs_reduction):
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self.needs_reduction = False
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buckets = {}
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for param in self.module.parameters():
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if param.requires_grad and param.grad is not None:
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tp = type(param.data)
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if tp not in buckets:
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buckets[tp] = []
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buckets[tp].append(param)
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if self.warn_on_half:
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if torch.cuda.HalfTensor in buckets:
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print("WARNING: gloo dist backend for half parameters may be extremely slow." +
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" It is recommended to use the NCCL backend in this case. This currently requires" +
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"PyTorch built from top of tree master.")
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self.warn_on_half = False
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for tp in buckets:
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bucket = buckets[tp]
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grads = [param.grad.data for param in bucket]
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coalesced = _flatten_dense_tensors(grads)
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dist.all_reduce(coalesced)
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coalesced /= dist.get_world_size()
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for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)):
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buf.copy_(synced)
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for param in list(self.module.parameters()):
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def allreduce_hook(*unused):
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param._execution_engine.queue_callback(allreduce_params)
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if param.requires_grad:
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param.register_hook(allreduce_hook)
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def forward(self, *inputs, **kwargs):
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self.needs_reduction = True
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return self.module(*inputs, **kwargs)
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'''
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def _sync_buffers(self):
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buffers = list(self.module._all_buffers())
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if len(buffers) > 0:
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# cross-node buffer sync
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flat_buffers = _flatten_dense_tensors(buffers)
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dist.broadcast(flat_buffers, 0)
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for buf, synced in zip(buffers, _unflatten_dense_tensors(flat_buffers, buffers)):
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buf.copy_(synced)
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def train(self, mode=True):
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# Clear NCCL communicator and CUDA event cache of the default group ID,
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# These cache will be recreated at the later call. This is currently a
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# work-around for a potential NCCL deadlock.
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if dist._backend == dist.dist_backend.NCCL:
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dist._clear_group_cache()
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super(DistributedDataParallel, self).train(mode)
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self.module.train(mode)
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'''
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'''
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Modifies existing model to do gradient allreduce, but doesn't change class
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so you don't need "module"
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'''
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def apply_gradient_allreduce(module):
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if not hasattr(dist, '_backend'):
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module.warn_on_half = True
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else:
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module.warn_on_half = True if dist._backend == dist.dist_backend.GLOO else False
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for p in module.state_dict().values():
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if not torch.is_tensor(p):
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continue
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dist.broadcast(p, 0)
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def allreduce_params():
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if(module.needs_reduction):
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module.needs_reduction = False
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buckets = {}
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for param in module.parameters():
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if param.requires_grad and param.grad is not None:
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tp = param.data.dtype
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if tp not in buckets:
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buckets[tp] = []
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buckets[tp].append(param)
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if module.warn_on_half:
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if torch.cuda.HalfTensor in buckets:
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print("WARNING: gloo dist backend for half parameters may be extremely slow." +
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" It is recommended to use the NCCL backend in this case. This currently requires" +
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"PyTorch built from top of tree master.")
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module.warn_on_half = False
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for tp in buckets:
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bucket = buckets[tp]
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grads = [param.grad.data for param in bucket]
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coalesced = _flatten_dense_tensors(grads)
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dist.all_reduce(coalesced)
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coalesced /= dist.get_world_size()
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for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)):
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buf.copy_(synced)
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for param in list(module.parameters()):
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def allreduce_hook(*unused):
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Variable._execution_engine.queue_callback(allreduce_params)
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if param.requires_grad:
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param.register_hook(allreduce_hook)
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def set_needs_reduction(self, input, output):
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self.needs_reduction = True
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module.register_forward_hook(set_needs_reduction)
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return module
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