def allreduce_grad(self, model):
self._init_comms()
stream = chainer.cuda.Stream.null
params = _memory_utility.extract_params(model)
itemsize = 4
n_elems_total = sum(param.grad.size for param in params)
n_elems_per_node = int(math.ceil(n_elems_total / self.inter_size))
n_bytes_per_node = n_elems_per_node * itemsize
n_bytes_buffer = n_bytes_per_node * self.inter_size
self.gpu_buffer_a.assign(n_bytes_buffer)
self.gpu_buffer_b.assign(n_bytes_buffer)
_memory_utility.pack_params(params, itemsize, 'grad',
self.gpu_buffer_a)
# Intra-node reduce
self.intra_nccl_comm.reduce(self.gpu_buffer_a.ptr(),
self.gpu_buffer_b.ptr(), n_elems_total,
nccl.NCCL_FLOAT, nccl.NCCL_SUM, 0,
stream.ptr)
# Inter-node allreduce
if self.intra_rank == 0:
_communication_utility.inter_allreduce_gpu(
self.inter_mpi_comm, self.size, self.gpu_buffer_a,
self.gpu_buffer_b, n_bytes_buffer, n_elems_per_node,
n_bytes_per_node, stream)
# Intra-node bcast
self.intra_nccl_comm.bcast(self.gpu_buffer_b.ptr(), n_elems_total,
nccl.NCCL_FLOAT, 0, stream.ptr)
_memory_utility.unpack_params(params, itemsize, 'grad',
self.gpu_buffer_b)
def _allreduce_grad_async(self, model, stream):
self._init_comms()
params = _memory_utility.extract_params(model)
grad_dtype = _get_param_grad_dtype(params[0])
if self.allreduce_grad_dtype is None:
allreduce_grad_dtype = grad_dtype
else:
allreduce_grad_dtype = self.allreduce_grad_dtype
n_elems = sum(param.grad.size for param in params)
needs_sync = self._assign(grad_dtype, allreduce_grad_dtype, n_elems)
if stream != chainer.cuda.Stream.null and needs_sync:
chainer.cuda.Stream.null.synchronize()
self._pack_params_to_buffer(params, grad_dtype, allreduce_grad_dtype,
n_elems, stream)
self.nccl_comm.allReduce(self.gpu_allreduce_buffer_a.ptr(),
self.gpu_allreduce_buffer_b.ptr(), n_elems,
_get_nccl_type_id(allreduce_grad_dtype),
nccl.NCCL_SUM,
stream.ptr)
if self.div_by_size is None:
self.div_by_size = chainer.cuda.cupy.ElementwiseKernel(
'{} x'.format(allreduce_grad_dtype.name),
'{} y'.format(allreduce_grad_dtype.name),
'y = x*(1.0/{})'.format(self.size), 'div_by_size')
self.div_by_size(
self.gpu_allreduce_buffer_b.array(n_elems,
dtype=allreduce_grad_dtype),
self.gpu_allreduce_buffer_a.array(n_elems,
dtype=allreduce_grad_dtype),
stream=stream)
self._unpack_params_from_buffer(params, grad_dtype,
allreduce_grad_dtype, n_elems, stream)
def allreduce_grad(self, model, stream=None):
self._init_comms()
if stream is None:
stream = chainer.cuda.Stream.null
params = _memory_utility.extract_params(model)
itemsize = 4
n_elems = sum(param.grad.size for param in params)
n_bytes = itemsize * n_elems
self.gpu_buffer_a.assign(n_bytes)
self.gpu_buffer_b.assign(n_bytes)
_memory_utility.pack_params(params, itemsize, 'grad',
self.gpu_buffer_a)
if stream != chainer.cuda.Stream.null:
chainer.cuda.Stream.null.synchronize()
self.nccl_comm.allReduce(self.gpu_buffer_a.ptr(),
self.gpu_buffer_b.ptr(), n_elems,
nccl.NCCL_FLOAT, nccl.NCCL_SUM, stream.ptr)
if stream != chainer.cuda.Stream.null:
stream.synchronize()
ret = self.gpu_buffer_b.array(n_elems) * (1.0 / self.size)
self.gpu_buffer_b.from_device(ret, n_bytes)
_memory_utility.unpack_params(params, itemsize, 'grad',
self.gpu_buffer_b)
def allreduce_grad(self, model):
self._init_comms()
stream = chainer.cuda.Stream.null
params = _memory_utility.extract_params(model)
itemsize = 4
n_elems_total = sum(param.grad.size for param in params)
n_elems_per_node = int(math.ceil(n_elems_total / self.inter_size))
n_elems_buffer = n_elems_per_node * self.inter_size
n_bytes_per_node = n_elems_per_node * itemsize
n_bytes_buffer = n_bytes_per_node * self.inter_size
self.gpu_buffer_a.assign(n_bytes_buffer)
self.gpu_buffer_b.assign(n_bytes_buffer)
_memory_utility.pack_params(params, itemsize, 'grad',
self.gpu_buffer_a)
# Intra-node reduce
self.intra_nccl_comm.reduce(self.gpu_buffer_a.ptr(),
self.gpu_buffer_b.ptr(), n_elems_total,
nccl.NCCL_FLOAT, nccl.NCCL_SUM, 0,
stream.ptr)
# Inter-node allreduce
if self.intra_rank == 0:
self.cpu_buffer_a.assign(n_bytes_buffer)
self.cpu_buffer_b.assign(n_bytes_buffer)
arr_b = self.gpu_buffer_b.array(n_elems_buffer)
arr_b.data.copy_to_host(self.cpu_buffer_b.ptr(), n_bytes_buffer)
self.inter_mpi_comm.Alltoall(
[self.cpu_buffer_b.buffer(n_bytes_buffer), mpi4py.MPI.FLOAT],
[self.cpu_buffer_a.buffer(n_bytes_buffer), mpi4py.MPI.FLOAT])
# Reduction in GPU
arr_a = self.gpu_buffer_a.array(n_elems_buffer)
arr_a.data.copy_from_host(self.cpu_buffer_a.ptr(), n_bytes_buffer)
arr_a = arr_a.reshape(self.inter_size, n_elems_per_node)
arr_a = arr_a.sum(axis=0)
arr_a *= 1.0 / self.size
arr_a.data.copy_to_host(self.cpu_buffer_a.ptr(), n_bytes_per_node)
self.inter_mpi_comm.Allgather(
[self.cpu_buffer_a.buffer(n_bytes_per_node), mpi4py.MPI.FLOAT],
[self.cpu_buffer_b.buffer(n_bytes_buffer), mpi4py.MPI.FLOAT])
arr_b.data.copy_from_host(self.cpu_buffer_b.ptr(), n_bytes_buffer)
# Intra-node bcast
self.intra_nccl_comm.bcast(self.gpu_buffer_b.ptr(), n_elems_total,
nccl.NCCL_FLOAT, 0, stream.ptr)
_memory_utility.unpack_params(params, itemsize, 'grad',
self.gpu_buffer_b)
def allreduce_grad(self, model):
params = _memory_utility.extract_params(model)
itemsize = 4
n_elems_total = sum(param.grad.size for param in params)
n_bytes_total = n_elems_total * itemsize
self.gpu_buffer_a.assign(n_bytes_total)
_memory_utility.pack_params(params, itemsize, 'grad',
self.gpu_buffer_a)
_memory_utility.unpack_params(params, itemsize, 'grad',
self.gpu_buffer_a)
def allreduce_grad(self, model):
self._init_comms()
params = _memory_utility.extract_params(model)
itemsize = 4
n_elems_total = sum(param.grad.size for param in params)
n_bytes_total = n_elems_total * itemsize
self.gpu_buffer_a.assign(n_bytes_total)
self.gpu_buffer_b.assign(n_bytes_total)
_memory_utility.pack_params(params, itemsize, 'grad',
self.gpu_buffer_a)
self.mpi_comm.Allreduce(
[self.gpu_buffer_a.buffer(n_bytes_total), mpi4py.MPI.FLOAT],
[self.gpu_buffer_b.buffer(n_bytes_total), mpi4py.MPI.FLOAT])
arr = self.gpu_buffer_b.array(n_elems_total)
arr *= (1.0 / self.size)
_memory_utility.unpack_params(params, itemsize, 'grad',
self.gpu_buffer_b)
def allreduce_grad(self, model):
self._init_comms()
stream = chainer.cuda.Stream.null
params = _memory_utility.extract_params(model)
itemsize = 4
n_elems_total = sum(param.grad.size for param in params)
n_elems_per_node_2d = int(math.ceil(n_elems_total / self.size))
n_elems_per_node_1d = n_elems_per_node_2d * self.inter_size
n_bytes_per_node_1d = n_elems_per_node_1d * itemsize
n_bytes_per_node_2d = n_elems_per_node_2d * itemsize
n_bytes_buffer = n_bytes_per_node_2d * self.size
self.gpu_buffer_a.assign(n_bytes_buffer)
self.gpu_buffer_b.assign(n_bytes_buffer)
_memory_utility.pack_params(params, itemsize, 'grad',
self.gpu_buffer_a)
# Intra-node reduce-scatter (1st dimension)
self.intra_nccl_comm.reduceScatter(self.gpu_buffer_a.ptr(),
self.gpu_buffer_b.ptr(),
n_elems_per_node_1d,
nccl.NCCL_FLOAT, nccl.NCCL_SUM,
stream.ptr)
# Inter-node allreduce (2nd dimension)
_communication_utility.inter_allreduce_gpu(
self.inter_mpi_comm, self.size, self.gpu_buffer_a,
self.gpu_buffer_b, n_bytes_per_node_1d, n_elems_per_node_2d,
n_bytes_per_node_2d, stream)
# Intra-node allgather (1st dimension)
self.intra_nccl_comm.allGather(self.gpu_buffer_b.ptr(),
self.gpu_buffer_a.ptr(),
n_elems_per_node_1d, nccl.NCCL_FLOAT,
stream.ptr)
_memory_utility.unpack_params(params, itemsize, 'grad',
self.gpu_buffer_a)
def allreduce_grad(self, model):
self._init_comms()
stream = chainer.cuda.Stream.null
params = _memory_utility.extract_params(model)
itemsize = 4
n_elems_total = sum(param.grad.size for param in params)
n_bytes_total = n_elems_total * itemsize
self.gpu_buffer_a.assign(n_bytes_total)
self.gpu_buffer_b.assign(n_bytes_total)
_memory_utility.pack_params(params, itemsize, 'grad',
self.gpu_buffer_a)
self.intra_nccl_comm.allReduce(self.gpu_buffer_a.ptr(),
self.gpu_buffer_b.ptr(), n_elems_total,
nccl.NCCL_FLOAT, nccl.NCCL_SUM,
stream.ptr)
arr = self.gpu_buffer_b.array(n_elems_total)
arr *= (1.0 / self.size)
_memory_utility.unpack_params(params, itemsize, 'grad',
self.gpu_buffer_b)
def allreduce_grad(self, model):
for param in _memory_utility.extract_params(model):
buf = _memory_utility.array_to_buffer_object(param.grad)
self.mpi_comm.Allreduce(mpi4py.MPI.IN_PLACE, buf)
param.grad /= self.size