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import torch
from .comm import get_world_size
import torch.distributed as dist
class ModelSynchronizer:
bm_map = {
2: 0.65,
4: 0.75,
8: 0.875,
12: 0.8875,
16: 0.9,
32: 0.9
}
def __init__(self, model, sync_rate, bm=None, blr=1.0, rescale_grad=1.0):
if bm is None:
self.bm = self.bm_map[get_world_size()]
else:
self.bm = bm
self.blr = blr
self.model = model
self.sync_rate = sync_rate
self.rescale_grad = rescale_grad
self.count = 0
self.param_align()
self.momentums = dict()
self.global_params = dict()
for k, v in self.model.named_parameters():
temp = torch.zeros_like(v, requires_grad=False)
temp.copy_(v.data)
self.global_params[k] = v
self.momentums[k] = torch.zeros_like(v, requires_grad=False)
def param_align(self):
for v in self.model.parameters():
dist.broadcast_multigpu([v.data], src=0)
for k, v in self.model.named_buffers():
if 'num_batches_tracked' in k:
continue
dist.broadcast_multigpu([v.data], src=0)
def sync_params(self):
size = float(get_world_size())
for v in self.model.parameters():
dist.all_reduce(v.data, op=dist.ReduceOp.SUM)
v.data /= size
for k, v in self.model.named_buffers():
if 'num_batches_tracked' in k:
continue
dist.all_reduce(v.data, op=dist.ReduceOp.SUM)
v.data /= size
def __call__(self, final_align=False):
self.count += 1
if (self.count % self.sync_rate == 0) or final_align:
with torch.no_grad():
if final_align:
self.param_align()
else:
self.sync_params()
for k, v in self.model.named_parameters():
global_param = self.global_params[k]
momentum = self.momentums[k]
grad = v.data * self.rescale_grad - global_param
momentum *= self.bm
global_param -= momentum
momentum += self.blr * grad
global_param += (1.0 + self.bm) * momentum
v.detach().copy_(global_param.detach())
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