def test_timeline_neighbor_allreduce(self):
x = torch.FloatTensor(10, 10).fill_(1).mul_(bf.rank())
x = bf.neighbor_allreduce(x, name='test_neighbor_allreduce')
time.sleep(0.1)
file_name = f"{self.temp_file}{bf.rank()}.json"
with open(file_name, 'r') as tf:
timeline_text = tf.read()
assert 'MPI_NEIGHBOR_ALLREDUCE' in timeline_text, timeline_text
assert 'ENQUEUE_NEIGHBOR_ALLREDUCE' in timeline_text, timeline_text
def diffusion(X, y, w_opt, loss, maxite=2000, alpha=1e-1, **kwargs):
if loss == 'logistic_regression':
rho = kwargs.get('rho', 1e-1)
elif loss == 'linear_regression':
rho = 0
else:
raise NotImplementedError(
'Task not supported. This example only supports' +
' linear_regression and logistic_regression')
topology = bf.load_topology()
self_weight, neighbor_weights = topology_util.GetRecvWeights(
topology, bf.rank())
w = torch.zeros(n, 1, dtype=torch.double, requires_grad=True)
phi = w.clone()
mse = []
for i in range(maxite):
# calculate loccal gradient via pytorch autograd
loss_step(X,
y,
w,
tensor_name='neighbor.allreduce.local_variable',
loss=loss,
rho=rho)
# diffusion
with torch.no_grad():
phi = w - alpha * w.grad.data
w.data = bf.neighbor_allreduce(phi,
self_weight=self_weight,
src_weights=neighbor_weights,
name='local variable')
w.grad.data.zero_()
# record convergence
if bf.rank() == 0:
mse.append(torch.norm(w.data - w_opt.data, p=2))
return w, mse
dynamic_neighbor_allreduce_gen = topology_util.GetDynamicOnePeerSendRecvRanks(
bf.load_topology(), bf.rank())
for ite in range(args.max_iters):
if args.enable_dynamic_topology:
send_neighbors, recv_neighbors = next(
dynamic_neighbor_allreduce_gen)
neighbor_weights = {
r: 1 / (len(recv_neighbors) + 1)
for r in recv_neighbors
}
self_weight = 1 / (len(recv_neighbors) + 1)
x = bf.neighbor_allreduce(x,
name='x',
self_weight=self_weight,
neighbor_weights=neighbor_weights,
send_neighbors=send_neighbors,
enable_topo_check=False)
mse.append(torch.norm(x - x_bar, p=2) / torch.norm(x_bar, p=2))
else:
outdegree = len(bf.out_neighbor_ranks())
indegree = len(bf.in_neighbor_ranks())
if not bf.nccl_built(): # NCCL do not support associated P yet.
bf.turn_on_win_ops_with_associated_p()
bf.win_create(x, name="x", zero_init=True)
for i in range(args.max_iters):
if args.enable_dynamic_topology:
num_out_neighbors = len(bf.out_neighbor_ranks())
sent_neighbor = bf.out_neighbor_ranks()[i % num_out_neighbors]
dst_weights = {sent_neighbor: 0.5}
def exact_diffusion(X,
y,
w_opt,
loss,
maxite=2000,
alpha=1e-1,
use_Abar=True,
**kwargs):
if loss == 'logistic_regression':
rho = kwargs.get('rho', 1e-1)
elif loss == 'linear_regression':
rho = 0
else:
raise NotImplementedError(
'Task not supported. This example only supports' +
' linear_regression and logistic_regression')
topology = bf.load_topology()
self_weight, neighbor_weights = topology_util.GetRecvWeights(
topology, bf.rank())
if bf.rank() == 0:
print('self weights with A: {}\n'.format(self_weight))
print('neighbor weights with A:\n')
for k, v in neighbor_weights.items():
print(k, v)
w = torch.zeros(n, 1, dtype=torch.double, requires_grad=True)
phi, psi, psi_prev = w.clone(), w.clone(), w.clone()
mse = []
# construct A_bar
if use_Abar:
self_weight = (self_weight + 1) / 2
for k, v in neighbor_weights.items():
neighbor_weights[k] = v / 2
for i in range(maxite):
# calculate loccal gradient via pytorch autograd
loss_step(X,
y,
w,
tensor_name='neighbor.allreduce.local_variable',
loss=loss,
rho=rho)
# exact diffusion
psi = w - alpha * w.grad.data
phi = psi + w.data - psi_prev
w.data = bf.neighbor_allreduce(phi,
self_weight,
neighbor_weights,
name='local variable')
psi_prev = psi.clone()
w.grad.data.zero_()
# record convergence
if bf.rank() == 0:
mse.append(torch.norm(w.data - w_opt.data, p=2))
return w, mse
def benchmark_step():
global args, data
for _ in range(args.internal_num_iters):
bf.neighbor_allreduce(data)
