def _init_ranks(self):
my_ranks = _communication_utility.init_ranks(self.mpi_comm)
assert my_ranks[0] == self.mpi_comm.rank
self._intra_rank = my_ranks[1]
self._intra_size = my_ranks[2]
self._inter_rank = my_ranks[3]
self._inter_size = my_ranks[4]
def _init_ranks(self):
my_ranks = _communication_utility.init_ranks(self.mpi_comm)
assert my_ranks[0] == self.mpi_comm.rank
self._intra_rank = my_ranks[1]
self._intra_size = my_ranks[2]
self._inter_rank = my_ranks[3]
self._inter_size = my_ranks[4]
def create_communicator(param, use_gpu):
if not param.multi_node:
ranks = _communication_utility.init_ranks(mpi_comm)
inter_size = ranks[4]
if inter_size > 1:
pytest.skip('This test is for single node only')
if use_gpu and not param.nccl1 and nccl.get_build_version() < 2000:
pytest.skip('This test requires NCCL version >= 2.0')
communicator = param.communicator_class(mpi_comm)
communicator.set_config('batched_copy', param.batched_copy)
value = communicator.get_config('batched_copy')
assert param.batched_copy == value
with pytest.raises(ValueError):
communicator.set_config('blah blah blah')
if param.communicator_class is PureNcclCommunicator:
communicator.set_config('allreduce_grad_dtype',
param.allreduce_grad_dtype)
value = communicator.get_config('allreduce_grad_dtype')
assert param.allreduce_grad_dtype == value
if use_gpu:
chainer.cuda.get_device_from_id(communicator.intra_rank).use()
return communicator
def test_deprecation_single():
ranks = _communication_utility.init_ranks(mpi_comm)
inter_size = ranks[4]
if inter_size > 1:
pytest.skip('This test is for single node only')
with chainer.testing.assert_warns(DeprecationWarning):
chainermn.create_communicator('single_node')
def test_deprecation_single():
ranks = _communication_utility.init_ranks(mpi_comm)
inter_size = ranks[4]
if inter_size > 1:
pytest.skip('This test is for single node only')
with chainer.testing.assert_warns(DeprecationWarning):
chainermn.create_communicator('single_node')
def setUp(self):
self.mpi_comm = mpi4py.MPI.COMM_WORLD
if not self.multi_node:
ranks = _communication_utility.init_ranks(self.mpi_comm)
inter_size = ranks[4]
if inter_size > 1:
raise nose.plugins.skip.SkipTest()
self.communicator = self.communicator_class(self.mpi_comm)
if hasattr(self.communicator, 'intra_rank'):
chainer.cuda.get_device(self.communicator.intra_rank).use()
def create_communicator(param, use_gpu):
if not param.multi_node:
ranks = _communication_utility.init_ranks(mpi_comm)
inter_size = ranks[4]
if inter_size > 1:
pytest.skip('This test is for single node only')
if use_gpu and not param.nccl1 and nccl.get_version() < 2000:
pytest.skip('This test requires NCCL version >= 2.0')
communicator = param.communicator_class(mpi_comm)
if use_gpu:
chainer.cuda.get_device(communicator.intra_rank).use()
return communicator
def create_communicator(param, use_gpu):
if not param.multi_node:
ranks = _communication_utility.init_ranks(mpi_comm)
inter_size = ranks[4]
if inter_size > 1:
pytest.skip('This test is for single node only')
if use_gpu and not param.nccl1 and nccl.get_build_version() < 2000:
pytest.skip('This test requires NCCL version >= 2.0')
if param.communicator_class is PureNcclCommunicator:
communicator = param.communicator_class(
mpi_comm, allreduce_grad_dtype=param.allreduce_grad_dtype,
batched_copy=param.batched_copy)
else:
communicator = param.communicator_class(mpi_comm)
if use_gpu:
chainer.cuda.get_device_from_id(communicator.intra_rank).use()
return communicator
def create_communicator(param, use_gpu):
if not param.multi_node:
ranks = _communication_utility.init_ranks(mpi_comm)
inter_size = ranks[4]
if inter_size > 1:
pytest.skip('This test is for single node only')
if use_gpu and not param.nccl1 and nccl.get_build_version() < 2000:
pytest.skip('This test requires NCCL version >= 2.0')
if param.communicator_class is PureNcclCommunicator:
communicator = param.communicator_class(
mpi_comm, allreduce_grad_dtype=param.allreduce_grad_dtype,
batched_copy=param.batched_copy)
else:
communicator = param.communicator_class(mpi_comm)
if use_gpu:
chainer.cuda.get_device_from_id(communicator.intra_rank).use()
return communicator
def check_allreduce_grad_mixed_dtype(param, model, use_gpu):
# Checks the actual allreduce communication is performed
# in the correct data type (FP16 or FP32)
comm_class = param.communicator_class
if not param.multi_node:
ranks = _communication_utility.init_ranks(mpi_comm)
inter_size = ranks[4]
if inter_size > 1:
pytest.skip('This test is for single node only')
if comm_class is PureNcclCommunicator:
communicator = comm_class(
mpi_comm, allreduce_grad_dtype=param.allreduce_grad_dtype,
batched_copy=param.batched_copy)
else:
communicator = comm_class(mpi_comm)
mpi_comm.barrier()
# answer type: see the document of `create_communicator`
global_dtype = param.global_dtype
allreduce_dtype = param.allreduce_grad_dtype
# assert test configuration.
assert chainer.get_dtype() == global_dtype
answer_dtype = None
if allreduce_dtype == np.float16:
answer_dtype = np.float16
elif allreduce_dtype == np.float32:
answer_dtype = np.float32
else:
if global_dtype == np.float32:
answer_dtype = np.float32
else:
answer_dtype = np.float16
if use_gpu:
model.to_gpu()
model.a.W.grad[:] = communicator.rank
model.b.W.grad[:] = communicator.rank + 1
model.c.b.grad[:] = communicator.rank + 2
if isinstance(communicator, PureNcclCommunicator):
communicator._init_comms()
with mock.patch.object(communicator, 'nccl_comm',
wraps=communicator.nccl_comm) as mc:
answer_dtype = _communication_utility._get_nccl_type_id(
answer_dtype)
communicator.allreduce_grad(model)
# dtype that was used in the actual communication,
# which is nccl_comm.allReduce
call_args = mc.allReduce.call_args[0]
actual_dtype = call_args[3]
assert answer_dtype == actual_dtype
else:
# For other MPI-based communicators,
# all communication should happen in FP32 as of now, so
# here we just check the results are correct for
# 16-32 mixed models.
communicator.allreduce_grad(model)
base = (communicator.size - 1.0) / 2
chainer.testing.assert_allclose(model.a.W.grad,
(base + 0) * np.ones((3, 2)))
chainer.testing.assert_allclose(model.b.W.grad,
(base + 1) * np.ones((4, 3)))
mpi_comm.barrier()
destroy_communicator(communicator)
def check_allreduce_grad_mixed_dtype(param, model, use_gpu):
# Checks the actual allreduce communication is performed
# in the correct data type (FP16 or FP32)
comm_class = param.communicator_class
if not param.multi_node:
ranks = _communication_utility.init_ranks(mpi_comm)
inter_size = ranks[4]
if inter_size > 1:
pytest.skip('This test is for single node only')
if comm_class is PureNcclCommunicator:
communicator = comm_class(
mpi_comm, allreduce_grad_dtype=param.allreduce_grad_dtype,
batched_copy=param.batched_copy)
else:
communicator = comm_class(mpi_comm)
mpi_comm.barrier()
# answer type: see the document of `create_communicator`
global_dtype = param.global_dtype
allreduce_dtype = param.allreduce_grad_dtype
# assert test configuration.
assert chainer.get_dtype() == global_dtype
answer_dtype = None
if allreduce_dtype == np.float16:
answer_dtype = np.float16
elif allreduce_dtype == np.float32:
answer_dtype = np.float32
else:
if global_dtype == np.float32:
answer_dtype = np.float32
else:
answer_dtype = np.float16
if use_gpu:
model.to_gpu()
model.a.W.grad[:] = communicator.rank
model.b.W.grad[:] = communicator.rank + 1
model.c.b.grad[:] = communicator.rank + 2
if isinstance(communicator, PureNcclCommunicator):
communicator._init_comms()
with mock.patch.object(communicator, 'nccl_comm',
wraps=communicator.nccl_comm) as mc:
answer_dtype = _communication_utility._get_nccl_type_id(
answer_dtype)
communicator.allreduce_grad(model)
# dtype that was used in the actual communication,
# which is nccl_comm.allReduce
call_args = mc.allReduce.call_args[0]
actual_dtype = call_args[3]
assert answer_dtype == actual_dtype
else:
# For other MPI-based communicators,
# all communication should happen in FP32 as of now, so
# here we just check the results are correct for
# 16-32 mixed models.
communicator.allreduce_grad(model)
base = (communicator.size - 1.0) / 2
chainer.testing.assert_allclose(model.a.W.grad,
(base + 0) * np.ones((3, 2)))
chainer.testing.assert_allclose(model.b.W.grad,
(base + 1) * np.ones((4, 3)))
mpi_comm.barrier()
destroy_communicator(communicator)
