# Copyright 2019 Uber Technologies, Inc. All Rights Reserved.
# 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.
# ==============================================================================
import os
import warnings
from contextlib import contextmanager
import torch
from horovod.torch.compression import Compression
from horovod.torch.mpi_ops import allreduce_async_
from horovod.torch.mpi_ops import synchronize
from horovod.torch.mpi_ops import size
from horovod.torch.mpi_ops import Average, Adasum, Sum
from horovod.torch.mpi_ops import rocm_built
class _DistributedOptimizer(torch.optim.Optimizer):
def __init__(self, params, named_parameters, compression,
backward_passes_per_step=1, op=Average,
gradient_predivide_factor=1.0):
super(self.__class__, self).__init__(params)
self._compression = compression
if named_parameters is not None:
named_parameters = list(named_parameters)
else:
named_parameters = [('allreduce.noname.%s' % i, v)
for param_group in self.param_groups
for i, v in enumerate(param_group['params'])]
# make sure that named_parameters are tuples
if any([not isinstance(p, tuple) for p in named_parameters]):
raise ValueError('named_parameters should be a sequence of '
'tuples (name, parameter), usually produced by '
'model.named_parameters().')
dups = _DistributedOptimizer.find_duplicates([k for k, _ in named_parameters])
if len(dups) > 0:
raise ValueError('Parameter names in named_parameters must be unique. '
'Found duplicates: %s' % ', '.join(dups))
all_param_ids = {id(v)
for param_group in self.param_groups
for v in param_group['params']}
named_param_ids = {id(v) for k, v in named_parameters}
unnamed_param_ids = all_param_ids - named_param_ids
if len(unnamed_param_ids):
raise ValueError('named_parameters was specified, but one or more model '
'parameters were not named. Python object ids: '
'%s' % ', '.join(str(id) for id in unnamed_param_ids))
self._parameter_names = {v: k for k, v in sorted(named_parameters)}
self.backward_passes_per_step = backward_passes_per_step
self._allreduce_delay = {v: self.backward_passes_per_step
for _, v in sorted(named_parameters)}
self.op = op
self.gradient_predivide_factor = gradient_predivide_factor
self._handles = {}
self._grad_accs = []
self._requires_update = set()
self._synchronized = False
self._should_synchronize = True
if size() > 1 or os.environ.get('HOROVOD_ELASTIC') == '1':
self._register_hooks()
def load_state_dict(self, *args, **kwargs):
self._handles = {}
self._synchronized = False
self._should_synchronize = True
for p in self._allreduce_delay:
self._allreduce_delay[p] = self.backward_passes_per_step
super(self.__class__, self).load_state_dict(*args, **kwargs)
@staticmethod
def find_duplicates(lst):
seen = set()
dups = set()
for el in lst:
if el in seen:
dups.add(el)
seen.add(el)
return dups
def set_backward_passes_per_step(self, passes):
self.backward_passes_per_step = passes
for p in self._allreduce_delay:
self._allreduce_delay[p] = self.backward_passes_per_step
def _register_hooks(self):
for param_group in self.param_groups:
for p in param_group['params']:
if p.requires_grad:
p.grad = p.data.new(p.size()).zero_()
self._requires_update.add(p)
p_tmp = p.expand_as(p)
grad_acc = p_tmp.grad_fn.next_functions[0][0]
grad_acc.register_hook(self._make_hook(p))
self._grad_accs.append(grad_acc)
def _allreduce_grad_async(self, p):
name = self._parameter_names.get(p)
tensor = p.grad
tensor_compressed, ctx = self._compression.compress(tensor)
if self.op == Average:
# Split average operation across pre/postscale factors
# C++ backend will apply additional 1 / size() factor to postscale_factor for op == Average.
prescale_factor = 1.0 / self.gradient_predivide_factor
postscale_factor = self.gradient_predivide_factor
else:
prescale_factor = 1.0
postscale_factor = 1.0
handle = allreduce_async_(tensor_compressed, name=name, op=self.op,
prescale_factor=prescale_factor,
postscale_factor=postscale_factor)
return handle, ctx
def _make_hook(self, p):
def hook(*ignore):
if p in self._handles and self._handles[p][0] is not None:
if self._allreduce_delay[p] <= 0:
raise AssertionError(
"Gradients were computed more than "
"backward_passes_per_step times before call "
"to step(). Increase backward_passes_per_step to "
"accumulate gradients locally.")
assert not p.grad.requires_grad
assert self._allreduce_delay[p] > 0
handle, ctx = None, None
self._allreduce_delay[p] -= 1
if self._allreduce_delay[p] == 0:
handle, ctx = self._allreduce_grad_async(p)
self._handles[p] = (handle, ctx)
return hook
def synchronize(self):
missing_p = self._requires_update - set(self._handles.keys())
for p in missing_p:
handle, ctx = self._allreduce_grad_async(p)
self._handles[p] = (handle, ctx)
for p, (handle, ctx) in self._handles.items():
if handle is None:
handle, ctx = self._allreduce_grad_async(p)
self._handles[p] = (handle, ctx)
for p, (handle, ctx) in self._handles.items():
output = synchronize(handle)
self._allreduce_delay[p] = self.backward_passes_per_step
p.grad.set_(self._compression.decompress(output, ctx))
self._handles.clear()
self._synchronized = True
@contextmanager
def skip_synchronize(self):
"""
A context manager used to specify that optimizer.step() should
not perform synchronization.
It's typically used in a following pattern:
.. code-block:: python
optimizer.synchronize()
with optimizer.skip_synchronize():
optimizer.step()
"""
self._should_synchronize = False
try:
yield
finally:
self._should_synchronize = True
def step(self, closure=None):
if self._should_synchronize:
if self._synchronized:
warnings.warn("optimizer.step() called without "
"optimizer.skip_synchronize() context after "
"optimizer.synchronize(). This can cause training "
"slowdown. You may want to consider using "
"optimizer.skip_synchronize() context if you use "
"optimizer.synchronize() in your code.")
self.synchronize()
self._synchronized = False
return super(self.__class__, self).step(closure)
def zero_grad(self):
if self._handles:
raise AssertionError("optimizer.zero_grad() was called after loss.backward() "
"but before optimizer.step() or optimizer.synchronize(). "
"This is prohibited as it can cause a race condition.")
return super(self.__class__, self).zero_grad()
class _DistributedAdasumOptimizer(torch.optim.Optimizer):
def __init__(self, params, named_parameters, compression,
backward_passes_per_step=1):
super(self.__class__, self).__init__(params)
self._compression = compression
if named_parameters is not None:
named_parameters = list(named_parameters)
else:
named_parameters = [('allreduce.noname.%s' % i, v)
for param_group in self.param_groups
for i, v in enumerate(param_group['params'])]
# make sure that named_parameters are tuples
if any([not isinstance(p, tuple) for p in named_parameters]):
raise ValueError('named_parameters should be a sequence of '
'tuples (name, parameter), usually produced by '
'model.named_parameters().')
dups = _DistributedOptimizer.find_duplicates([k for k, _ in named_parameters])
if len(dups) > 0:
raise ValueError('Parameter names in named_parameters must be unique. '
'Found duplicates: %s' % ', '.join(dups))
all_param_ids = {id(v)
for param_group in self.param_groups
for v in param_group['params']}
named_param_ids = {id(v) for k, v in named_parameters}
unnamed_param_ids = all_param_ids - named_param_ids
if len(unnamed_param_ids):
raise ValueError('named_parameters was specified, but one or more model '
'parameters were not named. Python object ids: '
'%s' % ', '.join(str(id) for id in unnamed_param_ids))
self._parameter_names = {v: k for k, v in sorted(named_parameters)}
self.backward_passes_per_step = backward_passes_per_step
self._allreduce_delay = {v: self.backward_passes_per_step
for _, v in sorted(named_parameters)}
self._handles = {}
self._grad_accs = []
self._requires_update = set()
self._synchronized = False
self._should_synchronize = True
self._starting_models = {
p : torch.zeros_like(p, requires_grad=False)
for _, p in named_parameters
}
self._register_hooks()
def set_backward_passes_per_step(self, passes):
self.backward_passes_per_step = passes
for p in self._allreduce_delay:
self._allreduce_delay[p] = self.backward_passes_per_step
def _register_hooks(self):
for param_group in self.param_groups:
for p in param_group['params']:
if p.requires_grad:
p.grad = p.data.new(p.size()).zero_()
self._requires_update.add(p)
p_tmp = p.expand_as(p)
grad_acc = p_tmp.grad_fn.next_functions[0][0]
grad_acc.register_hook(self._make_hook(p))
self._grad_accs.append(grad_acc)
def _allreduce_grad_async(self, p):
# Delta optimizer implements this logic:
# start = current.copy()
# step() -> computes 'current - \alpha.f(g)' where f is
# optimizer logic and g is the gradient
# delta = current-start
# allreduce_(delta)
# start += delta
# current = start
# In order to suppport this logic using function hook to improve performance,
# we do:
# delta = (start - \alpha.f(g)) - start
# = -\alpha.f(g)
# set start to zero and step computes -\alpha.f(g)
# where f is the underlying optimizer logic
name = self._parameter_names.get(p)
start = self._starting_models[p]
stashed_params = []
for group in self.param_groups:
stashed_params.append(group['params'])
# only want to step on p
if any([p is v for v in group['params']]):
group['params'] = [p]
else:
group['params'] = []
start.data.copy_(p)
super(self.__class__, self).step()
# compute delta = curr - start
p.data.sub_(start)
# allreduce as before
tensor_compressed, ctx = self._compression.compress(p)
handle = allreduce_async_(tensor_compressed.data, name=name, op=Adasum)
# reset stashed parameters
for stashed, group in zip(stashed_params, self.param_groups):
group['params'] = stashed
return handle, ctx
def _make_hook(self, p):
def hook(*ignore):
if p in self._handles and self._handles[p][0] is not None:
if self._allreduce_delay[p] <= 0:
raise AssertionError(
"Gradients were computed more than "
"backward_passes_per_step times before call "
"to step(). Increase backward_passes_per_step to "
"accumulate gradients locally.")
assert not p.grad.requires_grad
assert self._allreduce_delay[p] > 0
handle, ctx = None, None
self._allreduce_delay[p] -= 1
if self._allreduce_delay[p] == 0:
handle, ctx = self._allreduce_grad_async(p)
self._handles[p] = (handle, ctx)
return hook
def synchronize(self):
pass
@contextmanager
def skip_synchronize(self):
raise AssertionError("Skipping synchronization is not supported when using Adasum optimizer.")
def step(self, closure=None):
loss = None
if closure is not None:
loss = closure()
missing_p = self._requires_update - set(self._handles.keys())
for p in missing_p:
handle, ctx = self._allreduce_grad_async(p)
self._handles[p] = (handle, ctx)
for p, (handle, ctx) in self._handles.items():
# This means step() is called before backward_passes_per_steps finished.
# We do a synchoronous allreduce here.
if not handle:
handle, ctx = self._allreduce_grad_async(p)
self._handles[p] = (handle, ctx)
delta = synchronize(handle)
delta = self._compression.decompress(delta, ctx)
start = self._starting_models[p]
start.data.add_(delta.data)
p.data.copy_(start)
self._allreduce_delay[p] = self.backward_passes_per_step
self._handles.clear()
return loss
def zero_grad(self):
if self._handles:
raise AssertionError("optimizer.zero_grad() was called after loss.backward() "
"but before optimizer.step() or optimizer.synchronize(). "
"This is prohibited as it can cause a race condition.")
return super(self.__class__, self).zero_grad()
[docs]def DistributedOptimizer(optimizer, named_parameters=None,
compression=Compression.none,
backward_passes_per_step=1,
op=Average,
gradient_predivide_factor=1.0):
"""
An optimizer that wraps another torch.optim.Optimizer, using an allreduce to
combine gradient values before applying gradients to model weights.
Allreduce operations are executed after each gradient is computed by ``loss.backward()``
in parallel with each other. The ``step()`` method ensures that all allreduce operations are
finished before applying gradients to the model.
DistributedOptimizer exposes the ``synchronize()`` method, which forces allreduce operations
to finish before continuing the execution. It's useful in conjunction with gradient
clipping, or other operations that modify gradients in place before ``step()`` is executed.
Make sure to use ``optimizer.skip_synchronize()`` if you're calling ``synchronize()``
in your code.
Example of gradient clipping:
.. code-block:: python
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.synchronize()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.clip)
with optimizer.skip_synchronize():
optimizer.step()
Arguments:
optimizer: Optimizer to use for computing gradients and applying updates.
named_parameters: A mapping between parameter names and values. Used for naming of
allreduce operations. Typically just ``model.named_parameters()``.
compression: Compression algorithm used during allreduce to reduce the amount
of data sent during the each parameter update step. Defaults to
not using compression.
backward_passes_per_step: Number of expected backward passes to perform
before calling step()/synchronize(). This
allows accumulating gradients over multiple
mini-batches before reducing and applying them.
op: The reduction operation to use when combining gradients across different ranks.
gradient_predivide_factor: If op == Average, gradient_predivide_factor splits the averaging
before and after the sum. Gradients are scaled by
1.0 / gradient_predivide_factor before the sum and
gradient_predivide_factor / size after the sum.
"""
# We dynamically create a new class that inherits from the optimizer that was passed in.
# The goal is to override the `step()` method with an allreduce implementation.
if gradient_predivide_factor != 1.0:
if rocm_built():
raise ValueError('gradient_predivide_factor not supported yet with ROCm')
if op != Average:
raise ValueError('gradient_predivide_factor not supported with op != Average')
if op != Adasum or size() == 1:
cls = type(optimizer.__class__.__name__, (optimizer.__class__,),
dict(_DistributedOptimizer.__dict__))
return cls(optimizer.param_groups, named_parameters, compression, backward_passes_per_step, op,
gradient_predivide_factor)
else:
cls = type(optimizer.__class__.__name__, (optimizer.__class__,),
dict(_DistributedAdasumOptimizer.__dict__))
return cls(optimizer.param_groups, named_parameters, compression, backward_passes_per_step)
