Source code for torch.distributed.optim.optimizer
from typing import List, Optional
import torch.distributed.rpc as rpc
import torch.optim as optim
import torch.jit as jit
import torch.nn as nn
from torch import Tensor
from torch.distributed.rpc import RRef
from .functional_adagrad import _FunctionalAdagrad
import torch.distributed.autograd as dist_autograd
from collections import defaultdict
from threading import Lock
# XXX: we define a _ScriptModuleOptimizer here to explicitly
# compile the FunctionalOptimizer class into TorchScript
# This is because ScriptClass instance still lives in
# python unless you explictly compile it as an attribute
# in ScriptModule or pass it to a ScriptFunction
# _ScriptLocalOptimizerInterface serves as a common
# interface type for Optimizer ScriptModules.
#
# TODO (wanchaol): remove this once we added TorchScript
# class reference semantics
@jit.interface
class _ScriptLocalOptimizerInterface(object):
def step(self, autograd_ctx_id: int) -> None:
pass
class _ScriptLocalOptimizer(nn.Module):
# TorchScript does not support multithread concurrent compiling.
# request_callback might invoke concurrent compiling, so we
# serialize the compiling with a lock
compile_lock = Lock()
def __init__(self, optim_cls, local_params_rref, *args, **kwargs):
super().__init__()
self._local_params = [rref.local_value() for rref in local_params_rref]
self.optim = optim_cls(
self._local_params,
*args,
**kwargs)
@jit.export
def step(self, autograd_ctx_id: int):
all_local_grads = dist_autograd.get_gradients(autograd_ctx_id)
# apply functional optimizer step with a list of gradients
grads: List[Optional[Tensor]] = [
all_local_grads[p] if p in all_local_grads else None
for p in self._local_params
]
self.optim.step(grads)
# TODO (wanchaol): remove/merge this with ScriptLocalOptimizer once
# we have converted all to functional optimizer in distributed.optim
class _LocalOptimizer(object):
# Ideally we would only need to share a lock for instances of
# _LocalOptimizer that deal with the same parameters. We are
# making a simplifying assumption here that if there is more
# than one instance of _LocalOptimizer per worker, they will
# be optimizing the same parameters (e.g. each data parallel
# trainer will create its own instance of _LocalOptimizer but
# they will all optimize the same parameters on each worker)
global_lock = Lock()
def __init__(self, optim_cls, local_params_rref, *args, **kwargs):
self._local_params = [rref.local_value() for rref in local_params_rref]
self.optim = optim_cls(
self._local_params,
*args,
**kwargs)
def step(self, autograd_ctx_id):
all_local_grads = dist_autograd.get_gradients(autograd_ctx_id)
with _LocalOptimizer.global_lock:
for param, grad in all_local_grads.items():
param.grad = grad
self.optim.step()
def _new_local_optimizer(optim_cls, local_params_rref, *args, **kwargs):
return rpc.RRef(
_LocalOptimizer(optim_cls, local_params_rref, *args, **kwargs))
def _local_optimizer_step(local_optim_rref, autograd_ctx_id):
local_optim = local_optim_rref.local_value()
local_optim.step(autograd_ctx_id)
# new/step functions combined with _ScriptLocalOptimizer to provide GIL-free optimizer
def _new_script_local_optimizer(optim_cls, local_params_rref, *args, **kwargs):
optim = _ScriptLocalOptimizer(optim_cls, local_params_rref, *args, **kwargs)
with _ScriptLocalOptimizer.compile_lock:
script_optim = jit.script(optim)
return rpc.RRef(
script_optim, _ScriptLocalOptimizerInterface)
@jit.script
def _script_local_optimizer_step(
local_optim_rref: RRef[_ScriptLocalOptimizerInterface],
autograd_ctx_id: int
) -> None:
local_optim = local_optim_rref.local_value()
local_optim.step(autograd_ctx_id)
def _wait_for_all(rpc_futs):
# TODO: improve error propagation
exception = None
results = []
for fut in rpc_futs:
try:
results.append(fut.wait())
except Exception as e:
results.append(e)
exception = e
if exception is not None:
raise exception
return results
[docs]class DistributedOptimizer:
"""
DistributedOptimizer takes remote references to parameters scattered
across workers and applies the given optimizer locally for each parameter.
This class uses :meth:`~torch.distributed.autograd.get_gradients` in order
to retrieve the gradients for specific parameters.
Concurrent calls to
:meth:`~torch.distributed.optim.DistributedOptimizer.step`,
either from the same or different clients, will
be serialized on each worker -- as each worker's optimizer can only work
on one set of gradients at a time. However, there is no guarantee that
the full forward-backward-optimizer sequence will execute for one client
at a time. This means that the gradients being applied may not correspond
to the latest forward pass executed on a given worker. Also, there is no
guaranteed ordering across workers.
Args:
optimizer_class (optim.Optimizer): the class of optimizer to
instantiate on each worker.
params_rref (list[RRef]): list of RRefs to local or remote parameters
to optimize.
args: arguments to pass to the optimizer constructor on each worker.
kwargs: arguments to pass to the optimizer constructor on each worker.
Example::
>>> import torch.distributed.autograd as dist_autograd
>>> import torch.distributed.rpc as rpc
>>> from torch import optim
>>> from torch.distributed.optim import DistributedOptimizer
>>>
>>> with dist_autograd.context() as context_id:
>>> # Forward pass.
>>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3))
>>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1))
>>> loss = rref1.to_here() + rref2.to_here()
>>>
>>> # Backward pass.
>>> dist_autograd.backward(context_id, [loss.sum()])
>>>
>>> # Optimizer.
>>> dist_optim = DistributedOptimizer(
>>> optim.SGD,
>>> [rref1, rref2],
>>> lr=0.05,
>>> )
>>> dist_optim.step(context_id)
"""
# dict to map a user passed in optimizer_class to a functional
# optimizer class if we have already defined inside the
# distributed.optim package, this is so that we hide the
# functional optimizer to user and still provide the same API.
functional_optim_map = {
optim.Adagrad: _FunctionalAdagrad,
}
def __init__(self, optimizer_class, params_rref, *args, **kwargs):
per_worker_params_rref = defaultdict(list)
for param in params_rref:
per_worker_params_rref[param.owner()].append(param)
optim_ctor = DistributedOptimizer.functional_optim_map.get(optimizer_class, optimizer_class)
self.is_functional_optim = (optim_ctor != optimizer_class)
if self.is_functional_optim:
optimizer_new_func = _new_script_local_optimizer
else:
optimizer_new_func = _new_local_optimizer
remote_optim_futs = []
for worker, param_rrefs in per_worker_params_rref.items():
remote_optim_rref_fut = rpc.rpc_async(
worker,
optimizer_new_func,
args=(optim_ctor, param_rrefs) + args,
kwargs=kwargs,
)
remote_optim_futs.append(remote_optim_rref_fut)
self.remote_optimizers = _wait_for_all(remote_optim_futs)
[docs] def step(self, context_id):
"""
Performs a single optimization step.
This will call :meth:`torch.optim.Optimizer.step` on each worker
containing parameters to be optimized, and will block until all workers
return. The provided ``context_id`` will be used to retrieve the
corresponding :class:`~torch.distributed.autograd.context` that
contains the gradients that should be applied to the parameters.
Args:
context_id: the autograd context id for which we should run the
optimizer step.
"""
dist_autograd._is_valid_context(context_id)
if self.is_functional_optim:
optimizer_step_func = _script_local_optimizer_step
else:
optimizer_step_func = _local_optimizer_step
rpc_futs = []
for optimizer in self.remote_optimizers:
rpc_futs.append(rpc.rpc_async(
optimizer.owner(),
optimizer_step_func,
args=(optimizer, context_id),
))
_wait_for_all(rpc_futs)
