def test_get_win_version_with_win_get(self):
"""Test version window is initialized, updated and cleared correctly with win get."""
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]
# By default, we use exponential two ring topology.
indegree = int(np.ceil(np.log2(size)))
neighbor_ranks = [(rank - 2**i) % size
for i in range(indegree)] # in-neighbor
dims = [1, 2, 3]
for dtype, dim in itertools.product(dtypes, dims):
tensor = torch.FloatTensor(*([23] * dim)).fill_(1).mul_(rank)
tensor = self.cast_and_place(tensor, dtype)
window_name = "win_version_get_{}_{}".format(dim, dtype)
bf.win_create(tensor, window_name)
original_versions = list(bf.get_win_version(window_name).values())
bf.barrier()
bf.win_get(window_name)
bf.barrier()
versions_after_win_get = list(
bf.get_win_version(window_name).values())
bf.win_update(window_name, clone=True)
versions_after_win_update = list(
bf.get_win_version(window_name).values())
neighbor_ranks_number = len(neighbor_ranks)
zero_number_in_original_versions = len(
original_versions) - np.count_nonzero(original_versions)
assert ((zero_number_in_original_versions) == neighbor_ranks_number
), ("version initialization is wrong.")
zero_number_after_win_update = len(
versions_after_win_update) - np.count_nonzero(
versions_after_win_update)
assert ((zero_number_after_win_update) == neighbor_ranks_number), (
"version clear up is wrong.")
expected_versions_after_win_get = [1] * neighbor_ranks_number
assert (versions_after_win_get == expected_versions_after_win_get
), ("version after win get is wrong.")
for dtype, dim in itertools.product(dtypes, dims):
window_name = "win_version_get_{}_{}".format(dim, dtype)
is_freed = bf.win_free(window_name)
assert is_freed, "bf.win_free do not free window object successfully."
def test_win_update_with_given_weights(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]
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_create_{}_{}".format(dim, dtype)
is_created = bf.win_create(tensor, window_name)
assert is_created, "bf.win_create do not create window object successfully."
# Test simple average rule.
weight = 1.0 / (len(bf.in_neighbor_ranks()) + 1)
sync_result = bf.win_update(
window_name,
self_weight=weight,
neighbor_weights={x: weight
for x in bf.in_neighbor_ranks()})
assert (list(sync_result.shape) == [DIM_SIZE] * dim), (
"bf.win_update (weighted) produces wrong shape tensor.")
assert (sync_result.data - rank).abs().max() < EPSILON, (
"bf.win_update (weighted) produces wrong tensor value " +
"[{0}-{1}]!={2} at rank {2}.".format(sync_result.min(),
sync_result.max(), rank))
def test_win_get(self):
"""Test that the window get operation."""
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]
# By default, we use exponential two ring topology.
indegree = int(np.ceil(np.log2(size)))
neighbor_ranks = [(rank - 2**i) % size
for i in range(indegree)] # in-neighbor
avg_value = (rank + np.sum(neighbor_ranks)) / float(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_get_{}_{}".format(dim, dtype)
bf.win_create(tensor, window_name)
bf.win_get(window_name)
bf.barrier()
recv_tensor = bf.win_update(window_name, clone=True)
assert (list(recv_tensor.shape) == [DIM_SIZE] *
dim), ("bf.win_get produce wrong shape tensor.")
assert (recv_tensor.data - avg_value).abs().max() < EPSILON, (
"bf.win_get produce wrong tensor value " +
"[{}-{}]!={} at rank {}.".format(
recv_tensor.min(), recv_tensor.max(), avg_value, 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._bluefog_delay[p] = self._num_steps_per_communication
# Update p to the average of neighbors.
p.set_(bf.win_update(name=name, require_mutex=True))
self._handles.clear()
self._synchronized = True
def test_win_put_with_varied_tensor_elements(self):
"""Test that the window put operation."""
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]
# By default, we use exponential two ring topology.
indegree = int(np.ceil(np.log2(size)))
neighbor_ranks = [(rank - 2**i) % size
for i in range(indegree)] # in-neighbor
avg_value = (rank + np.sum(neighbor_ranks)) / float(indegree + 1)
dims = [1, 2, 3]
for dtype, dim in itertools.product(dtypes, dims):
tensor = torch.FloatTensor(*([DIM_SIZE] * dim)).fill_(1).mul_(rank)
base_tensor = torch.arange(
DIM_SIZE**dim, dtype=torch.float32).view_as(tensor).div(1000)
tensor = self.cast_and_place(tensor, dtype)
base_tensor = self.cast_and_place(base_tensor, dtype)
tensor = tensor + base_tensor
window_name = "win_put_{}_{}".format(dim, dtype)
bf.win_create(tensor, window_name)
bf.win_put(tensor, window_name)
bf.barrier()
sync_result = bf.win_update(window_name)
assert (list(sync_result.shape) == [DIM_SIZE] * dim), (
"bf.win_update after win_put produces wrong shape tensor.")
assert (
(sync_result - base_tensor).data -
avg_value).abs().max() < EPSILON, (
"bf.win_update after win_put produces wrong tensor value "
+ "[{}-{}]!={} at rank {}.".format(
(sync_result - base_tensor).min(),
(sync_result - base_tensor).max(), avg_value, rank))
time.sleep(0.5)
for dtype, dim in itertools.product(dtypes, dims):
window_name = "win_put_{}_{}".format(dim, dtype)
is_freed = bf.win_free(window_name)
assert is_freed, "bf.win_free do not free window object successfully."
def test_win_put_with_given_destination(self):
"""Test that the window put operation with given destination."""
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]
# By default, we use exponential two ring topology.
indegree = int(np.ceil(np.log2(size)))
# We use given destination to form a (right-)ring.
avg_value = (rank * indegree + 1.23 *
((rank - 1) % size)) / float(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_put_given_{}_{}".format(dim, dtype)
bf.win_create(tensor, window_name)
bf.win_put(tensor,
window_name,
dst_weights={(rank + 1) % size: 1.23})
bf.barrier()
sync_result = bf.win_update(window_name)
assert (list(sync_result.shape) == [DIM_SIZE] * dim), (
"bf.win_update after win_put given destination produces wrong shape tensor."
)
assert (sync_result.data - avg_value).abs().max() < EPSILON, (
"bf.win_update after win_put given destination produces wrong tensor value "
+ "[{}-{}]!={} at rank {}.".format(
sync_result.min(), sync_result.max(), avg_value, rank))
time.sleep(0.5)
for dtype, dim in itertools.product(dtypes, dims):
window_name = "win_put_given_{}_{}".format(dim, dtype)
is_freed = bf.win_free(window_name)
assert is_freed, "bf.win_free do not free window object successfully."
def test_win_accumulate_with_given_destination(self):
"""Test that the window accumulate operation with given destination."""
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]
avg_value = rank + ((rank - 1) % size) * 1.23 / 2.0
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_accumulate_{}_{}".format(dim, dtype)
bf.win_create(tensor, window_name)
bf.win_accumulate(tensor,
window_name,
dst_weights={(rank + 1) % size: 1.23})
bf.barrier()
sync_result = bf.win_update(window_name,
self_weight=0.5,
neighbor_weights={
(rank - 1) % size: 0.5
})
assert (list(sync_result.shape) == [DIM_SIZE] * dim), (
"bf.win_update after win_accmulate given destination produces wrong shape tensor."
)
assert (sync_result.data - avg_value).abs().max() < EPSILON, (
"bf.win_update after win_accmulate given destination produces wrong tensor value "
+ "[{}-{}]!={} at rank {}.".format(
sync_result.min(), sync_result.max(), avg_value, rank))
def test_win_create_and_sync_and_free(self):
"""Test that the window create and free objects correctly."""
size = bf.size()
rank = bf.rank()
# OpenMPI implementation seems won't allow win_create on size 1.
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]
# By default, we use exponential two ring topology.
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_create_{}_{}".format(dim, dtype)
is_created = bf.win_create(tensor, window_name)
assert is_created, "bf.win_create do not create window object successfully."
sync_result = bf.win_update(window_name)
assert (list(sync_result.shape) == [DIM_SIZE] *
dim), ("bf.win_update produce wrong shape tensor.")
assert (sync_result.data.min() == rank), (
"bf.win_update produces wrong tensor value " +
"{0}!={1} at rank {1}.".format(sync_result.data.min(), rank))
assert (sync_result.data.max() == rank), (
"bf.win_update produces wrong tensor value " +
"{0}!={1} at rank {1}.".format(sync_result.data.max(), rank))
for dtype, dim in itertools.product(dtypes, dims):
window_name = "win_create_{}_{}".format(dim, dtype)
is_freed = bf.win_free(window_name)
assert is_freed, "bf.win_free do not free window object successfully."
def test_win_update_with_default_weights(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]
bf.set_topology(topology_util.StarGraph(size), is_weighted=True)
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_create_{}_{}".format(dim, dtype)
is_created = bf.win_create(tensor, window_name)
assert is_created, "bf.win_create do not create window object successfully."
# Note the buffers store the copy of original value so they will not change.
tensor.mul_(2)
if rank == 0:
expected_result = rank * 2 / size + rank * (size - 1) / size
else:
expected_result = rank / size + rank * 2 * (1 - 1 / size)
sync_result = bf.win_update(window_name)
assert (list(sync_result.shape) == [DIM_SIZE] * dim), (
"bf.win_update (weighted) produces wrong shape tensor.")
assert (
sync_result.data - expected_result).abs().max() < EPSILON, (
"bf.win_update (weighted) produces wrong tensor value " +
"[{0}-{1}]!={2} at rank {2}.".format(
sync_result.min(), sync_result.max(), rank))
assert bf.win_free()
def test_win_get_with_given_sources(self):
"""Test that the window get operation with given sources."""
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]
# We use given destination to form a (right-)ring.
avg_value = (rank + 1.23 * ((rank - 1) % size)) / float(2)
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_get_given_{}_{}".format(dim, dtype)
bf.win_create(tensor, window_name)
bf.win_get(window_name, src_weights={(rank - 1) % size: 1.23})
bf.barrier()
recv_tensor = bf.win_update(window_name,
self_weight=0.5,
neighbor_weights={
(rank - 1) % size: 0.5
},
clone=True)
assert (list(recv_tensor.shape) == [DIM_SIZE] * dim), (
"bf.win_get with given sources produces wrong shape tensor.")
assert (recv_tensor.data - avg_value).abs().max() < EPSILON, (
"bf.win_get with given sources produces wrong tensor value " +
"[{}-{}]!={} at rank {}.".format(
recv_tensor.min(), recv_tensor.max(), avg_value, rank))
