# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
# Modifications copyright (C) 2019 Uber Technologies, Inc.
# Modifications copyright Microsoft
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# pylint: disable=g-short-docstring-punctuation
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from horovod.common.util import check_extension, gpu_available
check_extension('horovod.tensorflow', 'HOROVOD_WITH_TENSORFLOW', __file__, 'mpi_lib')
from horovod.tensorflow.compression import Compression
from horovod.tensorflow.mpi_ops import allgather, broadcast, _allreduce
from horovod.tensorflow.mpi_ops import init, shutdown
from horovod.tensorflow.mpi_ops import size, local_size, rank, local_rank, is_homogeneous
from horovod.tensorflow.mpi_ops import mpi_threads_supported, mpi_enabled, mpi_built
from horovod.tensorflow.mpi_ops import gloo_enabled, gloo_built
from horovod.tensorflow.mpi_ops import nccl_built, ddl_built, ccl_built
from horovod.tensorflow.mpi_ops import Average, Sum, Adasum
from horovod.tensorflow.mpi_ops import handle_average_backwards_compatibility, check_num_rank_power_of_2
from horovod.tensorflow.util import _executing_eagerly, _make_subgraph, _cache
import tensorflow as tf
import warnings
# @DEKHTIARJonathan: Do not remove, this fixes issues:
# - https://github.com/tensorflow/tensorflow/issues/38516
# - https://github.com/tensorflow/tensorflow/issues/39894
if tf.__version__.startswith('2.2.'):
from tensorflow.python.keras.mixed_precision.experimental import device_compatibility_check
device_compatibility_check.log_device_compatibility_check = lambda policy_name, skip_local: None
[docs]def allreduce(tensor, average=None, device_dense='', device_sparse='',
compression=Compression.none, op=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_ALLREDUCE.
device_sparse: Device to be used for sparse tensors. Uses GPU by default
if Horovod was built with HOROVOD_GPU_ALLGATHER.
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.
Returns:
A tensor of the same shape and type as `tensor`, summed across all
processes.
"""
op = handle_average_backwards_compatibility(op, average)
# Averaging happens in framework code, so translate that to Sum for the actual call
true_op = Sum if op == Average else op
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(), 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:
with tf.device(device_dense):
horovod_size = tf.cast(size(), dtype=tensor.dtype)
tensor_compressed, ctx = compression.compress(tensor)
summed_tensor_compressed = _allreduce(tensor_compressed, op=true_op)
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.')
horovod_local_size = tf.cast(local_size(), dtype=tensor.dtype)
new_tensor = summed_tensor / horovod_local_size
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_ALLREDUCE=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:
new_tensor = (summed_tensor / horovod_size) if op == Average else summed_tensor
return new_tensor
@_cache
def _make_broadcast_group_fn():
if _executing_eagerly():
# Eager mode will parallelize independent control flow
def broadcast_group(variables, root_rank):
for var in variables:
var.assign(broadcast(var, root_rank))
return _make_subgraph(broadcast_group)
else:
# Graph mode requires an Op
def broadcast_group(variables, root_rank):
return tf.group(*[var.assign(broadcast(var, root_rank))
for var in variables])
return broadcast_group
[docs]def broadcast_variables(variables, root_rank):
"""Broadcasts variables from root rank to all other processes.
Arguments:
variables: variables for broadcast
root_rank: rank of the process from which global variables will be broadcasted
to all other processes.
"""
broadcast_group = _make_broadcast_group_fn()
return broadcast_group(variables, root_rank)
try:
_global_variables = tf.global_variables
except AttributeError:
try:
_global_variables = tf.compat.v1.global_variables
except AttributeError:
_global_variables = None
if _global_variables is not None:
[docs] def broadcast_global_variables(root_rank):
"""Broadcasts all global variables from root rank to all other processes.
**NOTE:** deprecated in TensorFlow 2.0.
Arguments:
root_rank: rank of the process from which global variables will be broadcasted
to all other processes.
"""
if _executing_eagerly():
raise RuntimeError(
"hvd.broadcast_global_variables() does not support eager execution. "
"Please use `hvd.broadcast_variables(<model/optimizer variables>)` instead."
)
return broadcast_variables(_global_variables(), root_rank)
try:
_get_default_graph = tf.get_default_graph
except AttributeError:
try:
_get_default_graph = tf.compat.v1.get_default_graph
except AttributeError:
_get_default_graph = None
try:
_SessionRunHook = tf.estimator.SessionRunHook
except AttributeError:
try:
_SessionRunHook = tf.train.SessionRunHook
except AttributeError:
_SessionRunHook = None
if _SessionRunHook is not None and _get_default_graph is not None:
[docs] class BroadcastGlobalVariablesHook(_SessionRunHook):
"""
SessionRunHook that will broadcast all global variables from root rank
to all other processes during initialization.
This is necessary to ensure consistent initialization of all workers when
training is started with random weights or restored from a checkpoint.
**NOTE:** deprecated in TensorFlow 2.0.
"""
def __init__(self, root_rank, device=''):
"""Construct a new BroadcastGlobalVariablesHook that will broadcast all
global variables from root rank to all other processes during initialization.
Args:
root_rank:
Rank that will send data, other ranks will receive data.
device:
Device to be used for broadcasting. Uses GPU by default
if Horovod was built with HOROVOD_GPU_BROADCAST.
"""
super(BroadcastGlobalVariablesHook, self).__init__()
self.root_rank = root_rank
self.bcast_op = None
self.device = device
def begin(self):
if not self.bcast_op or self.bcast_op.graph != _get_default_graph():
with tf.device(self.device):
self.bcast_op = broadcast_global_variables(self.root_rank)
def after_create_session(self, session, coord):
session.run(self.bcast_op)
@_cache
def _make_allreduce_grads_fn(name, device_dense, device_sparse,
compression, sparse_as_dense, op):
def allreduce_grads(grads):
with tf.name_scope(name + "_Allreduce"):
if sparse_as_dense:
grads = [tf.convert_to_tensor(grad)
if grad is not None and isinstance(grad, tf.IndexedSlices)
else grad for grad in grads]
return [allreduce(grad,
device_dense=device_dense,
device_sparse=device_sparse,
compression=compression,
op=op)
if grad is not None else grad
for grad in grads]
if _executing_eagerly():
return _make_subgraph(allreduce_grads)
else:
return allreduce_grads
try:
# TensorFlow 2.x
_LegacyOptimizer = tf.compat.v1.train.Optimizer
except AttributeError:
try:
# TensorFlow 1.x
_LegacyOptimizer = tf.train.Optimizer
except AttributeError:
# Future TensorFlow versions
_LegacyOptimizer = None
if _LegacyOptimizer is not None:
class _DistributedOptimizer(_LegacyOptimizer):
"""An optimizer that wraps another tf.Optimizer, using an allreduce to
combine gradient values before applying gradients to model weights."""
def __init__(self, optimizer, name=None, use_locking=False, device_dense='',
device_sparse='', compression=Compression.none,
sparse_as_dense=False, op=Average):
if name is None:
name = "Distributed{}".format(type(optimizer).__name__)
super(_DistributedOptimizer, self).__init__(name=name, use_locking=use_locking)
self._optimizer = optimizer
self._allreduce_grads = _make_allreduce_grads_fn(
name, device_dense, device_sparse, compression, sparse_as_dense, op)
def compute_gradients(self, *args, **kwargs):
"""Compute gradients of all trainable variables.
See Optimizer.compute_gradients() for more info.
In DistributedOptimizer, compute_gradients() is overriden to also
allreduce the gradients before returning them.
"""
gradients = self._optimizer.compute_gradients(*args, **kwargs)
if size() > 1:
grads, vars = zip(*gradients)
avg_grads = self._allreduce_grads(grads)
return list(zip(avg_grads, vars))
else:
return gradients
def apply_gradients(self, *args, **kwargs):
"""Calls this same method on the underlying optimizer."""
return self._optimizer.apply_gradients(*args, **kwargs)
def get_slot(self, *args, **kwargs):
"""Calls this same method on the underlying optimizer."""
return self._optimizer.get_slot(*args, **kwargs)
def get_slot_names(self, *args, **kwargs):
"""Calls this same method on the underlying optimizer."""
return self._optimizer.get_slot_names(*args, **kwargs)
def variables(self, *args, **kwargs):
"""Calls this same method on the underlying optimizer."""
return self._optimizer.variables(*args, **kwargs)
class _DistributedAdasumOptimizer(_LegacyOptimizer):
"""An optimizer that wraps another tf.Optimizer, using an allreduce to
combine model deltas after applying gradients to model weights."""
def __init__(self, optimizer, name=None, use_locking=False, device_dense='',
device_sparse='', compression=Compression.none, backward_passes_per_step=1):
if name is None:
name = "DistributedDelta{}".format(type(optimizer).__name__)
super(_DistributedAdasumOptimizer, self).__init__(name=name, use_locking=use_locking)
self._optimizer = optimizer
self._name = name
self._device_dense = device_dense
self._device_sparse = device_sparse
self._compression = compression
self._backward_passes_per_step = backward_passes_per_step
def _prepare(self):
self._step_count = tf.get_variable(
name="step_count", shape=[], dtype=tf.int64, trainable=False,
initializer=tf.zeros_initializer)
self._is_first_step = tf.cast(tf.math.equal(self._step_count, 0), dtype=tf.bool)
self._is_comm_step = tf.cast(tf.math.equal(self._step_count % self._backward_passes_per_step, self._backward_passes_per_step - 1), dtype=tf.bool)
def _apply_shared(self, var, get_update_op):
start_slot = self._get_or_make_slot(var, "delta_start")
# initialize start on the first step
assign_op = tf.cond(self._is_first_step,
lambda: start_slot.assign(var, use_locking=self.use_locking).op,
tf.no_op)
with tf.control_dependencies([assign_op]):
update_op = get_update_op()
with tf.control_dependencies([update_op]):
def update():
# delta = var - start
local_delta = var.assign_sub(start_slot, use_locking=self.use_locking) # reuse var's memory
# delta = allreduce (delta)
global_delta = allreduce(local_delta,
device_dense=self._device_dense,
device_sparse=self._device_sparse,
compression=self._compression,
op=Adasum)
# start = start + delta
new_start = start_slot.assign_add(global_delta, use_locking=self.use_locking)
# var = start
return var.assign(new_start, use_locking=self.use_locking).op
# if its a communication step, then apply logic above
# if its not a communication step then just have the underlying
# optimizer update the model parameters according to its logic
return tf.cond(self._is_comm_step, update, tf.no_op)
def _apply_dense(self, grad, var):
return self._apply_shared(var, lambda: self._optimizer._apply_dense(grad, var))
def _resource_apply_dense(self, grad, handle):
return self._apply_shared(handle, lambda: self._optimizer._resource_apply_dense(grad, handle))
def _apply_sparse(self, grad, var):
return self._apply_shared(var, lambda: self._optimizer._apply_sparse(grad, var))
def _resource_apply_sparse(self, grad, handle, indices):
return self._apply_shared(handle, lambda: self._optimizer._resource_apply_sparse(grad, handle, indices))
def _finish(self, update_ops, name_scope):
with tf.control_dependencies(update_ops):
return tf.assign_add(self._step_count, 1)
def compute_gradients(self, *args, **kwargs):
"""Compute gradients of all trainable variables.
See Optimizer.compute_gradients() for more info.
"""
return self._optimizer.compute_gradients(*args, **kwargs)
def apply_gradients(self, *args, **kwargs):
"""Calls this same method on the underlying optimizer."""
return self._optimizer.apply_gradients(*args, **kwargs)
def get_slot(self, var, name):
"""Calls this same method on the underlying optimizer."""
tmp = super(_DistributedAdasumOptimizer, self).get_slot(var, name)
if tmp is not None:
return tmp
return self._optimizer.get_slot(var, name)
def get_slot_names(self):
"""Appends local slot names to those of the underlying optimizer."""
return super(_DistributedAdasumOptimizer, self).get_slot_names() +\
self._optimizer.get_slot_names()
def variables(self, *args, **kwargs):
"""Calls this same method on the underlying optimizer."""
return self._optimizer.variables(*args, **kwargs)
[docs]def DistributedOptimizer(optimizer, name=None, use_locking=False, device_dense='',
device_sparse='', compression=Compression.none,
sparse_as_dense=False, backward_passes_per_step=1,
op=Average):
"""Construct a new DistributedOptimizer, which uses another optimizer
under the hood for computing single-process gradient values and
applying gradient updates after the gradient values have been combined
across all the Horovod ranks.
Args:
optimizer:
Optimizer to use for computing gradients and applying updates.
name:
Optional name prefix for the operations created when applying
gradients. Defaults to "Distributed" followed by the provided
optimizer type.
use_locking:
Whether to use locking when updating variables.
See Optimizer.__init__ for more info.
device_dense:
Device to be used for dense tensors. Uses GPU by default
if Horovod was built with HOROVOD_GPU_ALLREDUCE.
device_sparse:
Device to be used for sparse tensors. Uses GPU by default
if Horovod was built with HOROVOD_GPU_ALLGATHER.
compression:
Compression algorithm used during allreduce to reduce the amount
of data sent during each parameter update step. Defaults to
not using compression.
sparse_as_dense:
Treat all sparse gradients as dense tensors. This can help improve
performance and memory utilization if the original sparse gradient
has high density. Defaults to false.
backward_passes_per_step:
Number of backward passes to perform before calling hvd.allreduce.
This allows accumulating updates over multiple mini-batches before
reducing and applying them.
op:
The reduction operation to use when combining gradients across
different ranks.
"""
if isinstance(optimizer, _LegacyOptimizer):
if op == Adasum:
return _DistributedAdasumOptimizer(optimizer, name, use_locking, device_dense,
device_sparse, compression, backward_passes_per_step)
else:
if backward_passes_per_step > 1:
raise ValueError('backward_passes_per_step>1 is not supported yet with '
'op != Adasum')
return _DistributedOptimizer(optimizer, name, use_locking, device_dense,
device_sparse, compression, sparse_as_dense, op)
elif isinstance(optimizer, tf.keras.optimizers.Optimizer):
if op == Adasum:
raise ValueError('op == Adasum is not supported yet with Keras')
if backward_passes_per_step > 1:
raise ValueError('backward_passes_per_step > 1 is not supported yet with Keras')
import horovod.tensorflow.keras as hvd_k
return hvd_k.DistributedOptimizer(optimizer, name, device_dense, device_sparse,
compression, sparse_as_dense)
else:
raise ValueError('Provided optimizer doesn\'t inherit from either legacy '
'TensorFlow or Keras optimizer: %s' % optimizer)
if hasattr(tf, 'GradientTape'):
class _DistributedGradientTape(tf.GradientTape):
def __init__(self, tape, device_dense, device_sparse, compression, sparse_as_dense, op,
persistent=False, watch_accessed_variables=True):
if hasattr(tape, '_watch_accessed_variables'):
super(self.__class__, self).__init__(persistent, watch_accessed_variables)
else:
super(self.__class__, self).__init__(persistent)
self._tape = tape
self._allreduce_grads = _make_allreduce_grads_fn(
'DistributedGradientTape', device_dense, device_sparse, compression,
sparse_as_dense, op)
def gradient(self, target, sources, output_gradients=None):
gradients = super(self.__class__, self).gradient(target, sources, output_gradients)
if size() > 1:
return self._allreduce_grads(gradients)
else:
return gradients
[docs] def DistributedGradientTape(gradtape, device_dense='', device_sparse='',
compression=Compression.none, sparse_as_dense=False,
op=Average):
"""A tape that wraps another tf.GradientTape, using an allreduce to
combine gradient values before applying gradients to model weights.
Args:
gradtape:
GradientTape to use for computing gradients and applying updates.
device_dense:
Device to be used for dense tensors. Uses GPU by default
if Horovod was built with HOROVOD_GPU_ALLREDUCE.
device_sparse:
Device to be used for sparse tensors. Uses GPU by default
if Horovod was built with HOROVOD_GPU_ALLGATHER.
compression:
Compression algorithm used during allreduce to reduce the amount
of data sent during each parameter update step. Defaults to
not using compression.
sparse_as_dense:
Treat all sparse gradients as dense tensors. This can help improve
performance and memory utilization if the original sparse gradient
has high density. Defaults to false.
op:
The reduction operation to use when combining gradients across
different ranks.
"""
cls = type(gradtape.__class__.__name__, (gradtape.__class__,),
dict(_DistributedGradientTape.__dict__))
if hasattr(gradtape, '_watch_accessed_variables'):
return cls(gradtape._tape, device_dense, device_sparse, compression,
sparse_as_dense, op, gradtape._persistent,
gradtape._watch_accessed_variables)
else:
return cls(gradtape._tape, device_dense, device_sparse, compression,
sparse_as_dense, op, gradtape._persistent)
