def test_timeline_push_sum(self):
# Use win_accumulate to simulate the push-sum algorithm (sync).
outdegree = len(bf.out_neighbor_ranks())
indegree = len(bf.in_neighbor_ranks())
# we append the p at the last of data.
x = torch.Tensor(
[bf.rank() / (indegree + 1), 1.0 / bf.size() / (indegree + 1)])
# Remember we do not create buffer with 0.
bf.win_create(x, name="x_buff")
x = bf.win_update_then_collect(name="x_buff")
for _ in range(10):
bf.win_accumulate(x,
name="x_buff",
dst_weights={
rank: 1.0 / (outdegree + 1)
for rank in bf.out_neighbor_ranks()
},
require_mutex=True)
x.div_(1 + outdegree)
x = bf.win_update_then_collect(name="x_buff")
bf.barrier()
# Do not forget to sync at last!
x = bf.win_update_then_collect(name="x_buff")
file_name = f"{self.temp_file}{bf.rank()}.json"
with open(file_name, 'r') as tf:
timeline_text = tf.read()
assert 'MPI_WIN_ACCUMULATE' in timeline_text, timeline_text
assert 'ENQUEUE_WIN_ACCUMULATE' in timeline_text, timeline_text
bf.win_free()
def test_asscoicated_with_p(self):
size = bf.size()
rank = bf.rank()
if size <= 3:
fname = inspect.currentframe().f_code.co_name
warnings.warn(
"Skip {} because it only supports test over at least 3 nodes".
format(fname))
return
dtypes = [torch.FloatTensor, torch.DoubleTensor]
if TEST_ON_GPU and not bf.nccl_built():
dtypes += [torch.cuda.FloatTensor, torch.cuda.DoubleTensor]
bf.set_topology(topology_util.RingGraph(size))
bf.turn_on_win_ops_with_associated_p()
for dtype, send_rank in itertools.product(dtypes, range(size)):
tensor = torch.FloatTensor([23]).fill_(1).mul_(rank)
tensor = self.cast_and_place(tensor, dtype)
window_name = "win_asscoicate_with_p_{}_{}".format(
dtype, send_rank)
bf.win_create(tensor, window_name)
left_neighbor_rank = (send_rank - 1) % size
right_neighbor_rank = (send_rank + 1) % size
if rank == send_rank:
bf.win_accumulate(tensor,
name=window_name,
self_weight=0.5,
dst_weights={
left_neighbor_rank: 0.5,
right_neighbor_rank: 0.5
})
bf.barrier()
bf.win_update_then_collect(name=window_name)
associated_p = bf.win_associated_p(name=window_name)
if rank == send_rank:
assert associated_p == 0.5, (
"associated_p for sender {} is wrong. Get {}".format(
rank, associated_p))
elif (rank == left_neighbor_rank) or (rank == right_neighbor_rank):
assert (associated_p - 1.5) < EPSILON, (
"associated_p for received neighbor {} is wrong. Get {}".
format(rank, associated_p))
else:
assert associated_p == 1.0, (
"associated_p for untouched node {} is wrong. Get {}".
format(rank, associated_p))
bf.turn_off_win_ops_with_associated_p()
def test_win_update_then_collect(self):
size = bf.size()
rank = bf.rank()
if size <= 1:
fname = inspect.currentframe().f_code.co_name
warnings.warn("Skip {} due to size 1".format(fname))
return
dtypes = [torch.FloatTensor, torch.DoubleTensor]
if TEST_ON_GPU:
dtypes += [torch.cuda.FloatTensor, torch.cuda.DoubleTensor]
indegree = int(np.ceil(np.log2(size)))
expected_result = rank * (indegree + 1)
dims = [1, 2, 3]
for dtype, dim in itertools.product(dtypes, dims):
tensor = torch.FloatTensor(*([DIM_SIZE] * dim)).fill_(1).mul_(rank)
tensor = self.cast_and_place(tensor, dtype)
window_name = "win_update_collect_{}_{}".format(dim, dtype)
bf.win_create(tensor, window_name)
# After the collect ops, the neighbro tensor will become zero.
# So second win_update_then_collect should produce the same value.
for _ in range(2):
collect_tensor = bf.win_update_then_collect(window_name)
assert (list(collect_tensor.shape) == [DIM_SIZE] * dim), (
"bf.win_update_then_collect produces wrong shape tensor.")
assert (collect_tensor.data - expected_result).abs().max(
) < EPSILON, (
"bf.win_update_then_collect produces wrong tensor value " +
"[{0}-{1}]!={2} at rank {2}.".format(
collect_tensor.min(), collect_tensor.max(), rank))
def test_asscoicated_with_p_random_test(self):
size = bf.size()
rank = bf.rank()
dtypes = [torch.FloatTensor, torch.DoubleTensor]
# Current, nccl version hasn't supported the associated with p yet.
if TEST_ON_GPU and not bf.nccl_built():
dtypes += [torch.cuda.FloatTensor, torch.cuda.DoubleTensor]
dims = [1]
bf.turn_on_win_ops_with_associated_p()
for dtype, dim in itertools.product(dtypes, dims):
tensor = torch.FloatTensor(*([23] * dim)).fill_(1)
tensor = self.cast_and_place(tensor, dtype)
window_name = "win_asscoicate_with_p_random_{}_{}".format(
dim, dtype)
bf.win_create(tensor, window_name, zero_init=True)
for _ in range(10):
random_weights = np.random.rand(
len(bf.out_neighbor_ranks()) + 1)
random_weights /= random_weights.sum()
self_weight = random_weights[-1]
dst_weights = {
r: random_weights[i]
for i, r in enumerate(bf.out_neighbor_ranks())
}
bf.win_put(tensor,
self_weight=self_weight,
dst_weights=dst_weights,
name=window_name,
require_mutex=True)
bf.win_update(name=window_name, require_mutex=True)
bf.win_accumulate(tensor,
name=window_name,
require_mutex=True,
self_weight=self_weight,
dst_weights=dst_weights)
bf.win_update_then_collect(name=window_name)
bf.barrier()
bf.win_update_then_collect(name=window_name)
associated_p = bf.win_associated_p(name=window_name)
# Because the associated p should operate the same as tensor always
# the following assert should be true no matter what order is excuted.
assert abs(associated_p - tensor.data[0]) < EPSILON
bf.turn_off_win_ops_with_associated_p()
def synchronize(self):
# Here synchronize just to make sure win_put ops is finished
# in one iteration.
with torch.no_grad():
for p, handle in self._handles.items():
_ = bf.win_wait(handle)
name = self._parameter_names.get(p)
self._pushsum_delay[p] = self._num_steps_per_communication
extended_parameter = self._named_extension_parameters[name]
extended_parameter.mul_(self.self_weight)
# Last dimension is the push_sum weights and we want parameter / weight
extended_parameter = bf.win_update_then_collect(name=name)
corrected_parameter = (extended_parameter[:-1] /
extended_parameter[-1]).reshape(p.shape)
# Update p to the average of neighbors.
p.set_(corrected_parameter)
self._handles.clear()
self._synchronized = True
sent_neighbor = bf.out_neighbor_ranks()[i % num_out_neighbors]
dst_weights = {sent_neighbor: 0.5}
self_weight = 0.5
else:
dst_weights = {
rank: 1.0 / (outdegree + 1)
for rank in bf.out_neighbor_ranks()
}
self_weight = 1 / (1 + outdegree)
bf.win_accumulate(x,
name="x",
self_weight=self_weight,
dst_weights=dst_weights,
require_mutex=True)
bf.win_update_then_collect(name="x")
associated_p = bf.win_associated_p(name="x")
mse.append(
torch.norm(x / associated_p - x_bar, p=2) /
torch.norm(x_bar, p=2))
# Do not forget to sync at last!
bf.barrier()
bf.win_update_then_collect(name="x")
associated_p = bf.win_associated_p(name="x")
print(f"associated p at {bf.rank()} is {associated_p}")
bf.turn_off_win_ops_with_associated_p()
mse.append(
torch.norm(x / associated_p - x_bar, p=2) / torch.norm(x_bar, p=2))
bf.win_free(name="x")
else:
def push_diging(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')
outdegree = len(bf.out_neighbor_ranks())
indegree = len(bf.in_neighbor_ranks())
# We let w = col{u, y, v}, i.e., u, y, v = w[:n], w[n:2*n], w[2n]
# Insteady of three directed_neighbor_allreduce operations for u, y,
# and v respectively, we exploit one directed_neighbor_allreduce for
# the combo vector w. This guarantees u, y, and v to be transmitted
# simultanesly and avoids the mismatch between them. Experiments
# show directed_neighbor_allreduce(w) is crutial for convergence of
# push_diging.
w = torch.zeros(2 * n + 1, 1).to(torch.double)
x = torch.zeros(n, 1, dtype=torch.double, requires_grad=True)
loss_step(X, y, x, tensor_name='w_buff', loss=loss, rho=rho)
grad = x.grad.data.clone()
w[n:2 * n] = grad
x.grad.data.zero_()
w[-1] = 1.0
grad_prev = w[n:2 * n].clone()
bf.win_create(w, name="w_buff", zero_init=True)
mse = []
for _ in range(maxite):
bf.barrier()
w[:n] = w[:n] - alpha * w[n:2 * n]
bf.win_accumulate(w,
name="w_buff",
dst_weights={
rank: 1.0 / (outdegree * 2)
for rank in bf.out_neighbor_ranks()
},
require_mutex=True)
w.div_(2)
bf.barrier()
w = bf.win_update_then_collect(name="w_buff")
x.data = w[:n] / w[-1]
loss_step(X, y, x, tensor_name='w_buff', loss=loss, rho=rho)
grad = x.grad.data.clone()
x.grad.data.zero_()
w[n:2 * n] += grad - grad_prev
grad_prev = grad
if bf.rank() == 0:
mse.append(torch.norm(x.data - w_opt, p=2))
bf.barrier()
w = bf.win_update_then_collect(name="w_buff")
x.data = w[:n] / w[-1]
return x, mse
