def _allreduce_cond(tensor, *args, **kwargs):
def allreduce_fn():
return allreduce(tensor, *args, **kwargs)
def id_fn():
return tensor
return tf.cond(size_op() > 1, allreduce_fn, id_fn)
def _grouped_allreduce_cond(tensors, *args, **kwargs):
def allreduce_fn():
return grouped_allreduce(tensors, *args, **kwargs)
def id_fn():
return tensors
return tf.cond((size_op() > 1) if int(os.environ.get("HOROVOD_ELASTIC", 0))
else tf.convert_to_tensor(size() > 1), allreduce_fn, id_fn)
def _grouped_allreduce_cond(tensors, *args, process_set=global_process_set, **kwargs):
def allreduce_fn():
return grouped_allreduce(tensors, *args, process_set=process_set, **kwargs)
def id_fn():
return tensors
return tf.cond(tf.logical_and(
tf.equal(process_set_included_op(process_set.process_set_id), 1),
tf.greater(size_op(process_set.process_set_id), 1))
if int(os.environ.get("HOROVOD_ELASTIC", 0)) else (
tf.convert_to_tensor(process_set.included() and process_set.size() > 1)),
allreduce_fn, id_fn)
def reducescatter(tensor,
device_dense='',
compression=Compression.none,
op=Average,
name=None,
process_set=global_process_set):
"""Perform a reducescatter on a tf.Tensor.
This function performs a bandwidth-optimal reduce and scatter on the input
tensor.
Arguments:
tensor: tf.Tensor or tf.Variable to reduce.
The shape of the input must be identical across all ranks.
device_dense: Device to be used for dense tensors. Uses GPU by default
if Horovod was built with HOROVOD_GPU_REDUCESCATTER.
compression: Compression algorithm used to reduce the amount of data
sent and received by each worker node. Defaults to not
using compression.
op: The reduction operation to combine tensors across different ranks.
Defaults to Average.
process_set: Process set object to limit this operation to a subset of
Horovod processes. Default is the global process set.
name: A name of the reduce_scatter operation
Returns:
A tensor of the same rank and type as `tensor`, summed across all processes.
The shape is identical to the input shape, except for the first dimension,
which will be divided across the different Horovod processes.
"""
# Averaging happens in framework code, so translate that to Sum for the actual call
true_op = Sum if op == Average else op
with tf.device(device_dense):
horovod_size = tf.cast(
size_op(process_set_id=process_set.process_set_id) if int(
os.environ.get("HOROVOD_ELASTIC", 0)) else process_set.size(),
dtype=tensor.dtype)
tensor_compressed, ctx = compression.compress(tensor)
reduced_tensor_compressed = _reducescatter(tensor_compressed,
op=true_op,
name=name,
process_set=process_set)
reduced_tensor = compression.decompress(reduced_tensor_compressed, ctx)
new_tensor = (reduced_tensor /
horovod_size) if op == Average else reduced_tensor
return new_tensor
def allreduce(tensor,
average=None,
device_dense='',
device_sparse='',
compression=Compression.none,
op=None,
prescale_factor=1.0,
postscale_factor=1.0,
name=None):
"""Perform an allreduce on a tf.Tensor or tf.IndexedSlices.
This function performs a bandwidth-optimal ring allreduce on the input
tensor. If the input is an tf.IndexedSlices, the function instead does an
allgather on the values and the indices, effectively doing an allreduce on
the represented tensor.
Arguments:
tensor: tf.Tensor, tf.Variable, or tf.IndexedSlices to reduce.
The shape of the input must be identical across all ranks.
average:
.. warning:: .. deprecated:: 0.19.0
Use `op` instead. Will be removed in v0.21.0.
device_dense: Device to be used for dense tensors. Uses GPU by default
if Horovod was built with HOROVOD_GPU_OPERATIONS.
device_sparse: Device to be used for sparse tensors. Uses GPU by default
if Horovod was built with HOROVOD_GPU_OPERATIONS.
compression: Compression algorithm used to reduce the amount of data
sent and received by each worker node. Defaults to not
using compression.
op: The reduction operation to combine tensors across different ranks.
Defaults to Average if None is given.
prescale_factor: Multiplicative factor to scale tensor before allreduce.
postscale_factor: Multiplicative factor to scale tensor after allreduce.
name: A name of the allreduce operation
Returns:
A tensor of the same shape and type as `tensor`, summed across all
processes.
"""
op = handle_average_backwards_compatibility(op, average)
if isinstance(tensor, tf.IndexedSlices):
# TODO: Need to fix this to actuall call Adasum
if op == Adasum:
raise NotImplementedError(
'The Adasum reduction does not currently support sparse tensors. As a '
'workaround please pass sparse_as_dense=True to DistributedOptimizer'
)
with tf.device(device_sparse):
# For IndexedSlices, do two allgathers instead of an allreduce.
horovod_size = tf.cast(size_op() if int(
os.environ.get("HOROVOD_ELASTIC", 0)) else size(),
dtype=tensor.values.dtype)
values = allgather(tensor.values)
indices = allgather(tensor.indices)
# To make this operation into an average, divide allgathered values by
# the Horovod size.
new_values = (values / horovod_size) if op == Average else values
return tf.IndexedSlices(new_values,
indices,
dense_shape=tensor.dense_shape)
else:
average_in_framework = False
if rocm_built():
# For ROCm, perform averaging at framework level
average_in_framework = op == Average or op == Adasum
op = Sum if op == Average else op
with tf.device(device_dense):
horovod_size = tf.cast(size_op() if int(
os.environ.get("HOROVOD_ELASTIC", 0)) else size(),
dtype=tensor.dtype)
tensor_compressed, ctx = compression.compress(tensor)
summed_tensor_compressed = _allreduce(
tensor_compressed,
op=op,
prescale_factor=prescale_factor,
postscale_factor=postscale_factor,
name=name)
summed_tensor = compression.decompress(summed_tensor_compressed,
ctx)
if op == Adasum:
if 'CPU' not in tensor.device and gpu_available('tensorflow'):
if nccl_built():
if not is_homogeneous:
raise NotImplementedError(
'Running GPU Adasum on heterogeneous cluster is not supported yet.'
)
elif not check_num_rank_power_of_2(
int(size() / local_size())):
raise NotImplementedError(
'Running GPU Adasum with non-power of 2 nodes is not supported yet.'
)
if rocm_built():
horovod_local_size = tf.cast(
local_size_op() if int(
os.environ.get("HOROVOD_ELASTIC",
0)) else local_size(),
dtype=tensor.dtype)
new_tensor = summed_tensor / horovod_local_size
else:
new_tensor = summed_tensor
else:
warnings.warn(
'Adasum reduction does not currently support GPU reduction using MPI. Tensors '
'are copied to CPU memory instead. To use Adasum for GPU reduction, please '
'compile Horovod with HOROVOD_GPU_OPERATIONS=NCCL.'
)
new_tensor = summed_tensor
else:
if not check_num_rank_power_of_2(size()):
raise NotImplementedError(
'Running Adasum with non-power of 2 ranks is not supported yet.'
)
new_tensor = summed_tensor
else:
if rocm_built():
new_tensor = (summed_tensor / horovod_size
) if average_in_framework else summed_tensor
else:
new_tensor = summed_tensor
return new_tensor
def grouped_allreduce(tensors,
average=None,
device_dense='',
device_sparse='',
compression=Compression.none,
op=None,
prescale_factor=1.0,
postscale_factor=1.0):
if not tensors:
return tensors
op = handle_average_backwards_compatibility(op, average)
average_in_framework = False
if rocm_built():
# For ROCm, perform averaging at framework level
average_in_framework = op == Average or op == Adasum
op = Sum if op == Average else op
if any(isinstance(t, tf.IndexedSlices) for t in tensors):
# TODO: Need to fix this to actuall call Adasum
if op == Adasum:
raise NotImplementedError(
'The Adasum reduction does not currently support sparse tensors. As a '
'workaround please pass sparse_as_dense=True to DistributedOptimizer'
)
with tf.device(device_sparse):
new_values = []
for tensor in tensors:
# For IndexedSlices, do two allgathers instead of an allreduce.
horovod_size = tf.cast(size_op() if int(
os.environ.get("HOROVOD_ELASTIC", 0)) else size(),
dtype=tensor.values.dtype)
values = allgather(tensor.values)
indices = allgather(tensor.indices)
# To make this operation into an average, divide allgathered values by
# the Horovod size.
new_values += (values /
horovod_size) if op == Average else values
return [
tf.IndexedSlices(x, indices, dense_shape=t.dense_shape)
for x, t in zip(new_values, tensors)
]
else:
with tf.device(device_dense):
tensors_compressed, ctxs = zip(
*[compression.compress(tensor) for tensor in tensors])
summed_tensors_compressed = _grouped_allreduce(
tensors_compressed,
op=op,
prescale_factor=prescale_factor,
postscale_factor=postscale_factor)
summed_tensors = [
compression.decompress(t, ctx)
for t, ctx in zip(summed_tensors_compressed, ctxs)
]
if op == Adasum:
if 'CPU' not in tensor.device and gpu_available('tensorflow'):
if nccl_built():
if not is_homogeneous:
raise NotImplementedError(
'Running GPU Adasum on heterogeneous cluster is not supported yet.'
)
elif not check_num_rank_power_of_2(
int(size() / local_size())):
raise NotImplementedError(
'Running GPU Adasum with non-power of 2 nodes is not supported yet.'
)
if rocm_built():
new_tensors = []
for tensor in summed_tensors:
horovod_local_size = tf.cast(
local_size_op() if int(
os.environ.get("HOROVOD_ELASTIC",
0)) else local_size(),
dtype=tensor.dtype)
new_tensors += tensor / horovod_local_size
else:
new_tensors = summed_tensors
else:
warnings.warn(
'Adasum reduction does not currently support GPU reduction using MPI. Tensors '
'are copied to CPU memory instead. To use Adasum for GPU reduction, please '
'compile Horovod with HOROVOD_GPU_OPERATIONS=NCCL.'
)
new_tensors = summed_tensors
else:
if not check_num_rank_power_of_2(size()):
raise NotImplementedError(
'Running Adasum with non-power of 2 ranks is not supported yet.'
)
new_tensors = summed_tensors
else:
if rocm_built():
new_tensors = []
for tensor in summed_tensors:
horovod_size = tf.cast(size_op() if int(
os.environ.get("HOROVOD_ELASTIC", 0)) else size(),
dtype=tensor.dtype)
new_tensors += (
tensor /
horovod_size) if average_in_framework else tensor
else:
new_tensors = summed_tensors
return new_tensors
