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255 lines
9.6 KiB
255 lines
9.6 KiB
5 months ago
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import logging
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from collections import defaultdict
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from threading import Lock
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from typing import List, Optional
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import torch
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import torch.distributed.autograd as dist_autograd
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import torch.distributed.rpc as rpc
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import torch.jit as jit
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import torch.nn as nn
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from torch import Tensor
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from torch.distributed.rpc import RRef
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from .utils import functional_optim_map
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__all__ = ["DistributedOptimizer"]
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logger = logging.getLogger(__name__)
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# XXX: we define a _ScriptModuleOptimizer here to explicitly
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# compile the FunctionalOptimizer class into TorchScript
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# This is because ScriptClass instance still lives in
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# python unless you explicitly compile it as an attribute
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# in ScriptModule or pass it to a ScriptFunction
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# _ScriptLocalOptimizerInterface serves as a common
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# interface type for Optimizer ScriptModules.
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#
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# TODO (wanchaol): remove this once we added TorchScript
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# class reference semantics
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@jit.interface
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class _ScriptLocalOptimizerInterface:
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def step(self, autograd_ctx_id: int) -> None:
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pass
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class _ScriptLocalOptimizer(nn.Module):
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# TorchScript does not support multithread concurrent compiling.
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# request_callback might invoke concurrent compiling, so we
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# serialize the compiling with a lock
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compile_lock = Lock()
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def __init__(self, optim_cls, local_params_rref, *args, **kwargs):
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super().__init__()
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self._local_params = [rref.local_value() for rref in local_params_rref]
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self.optim = optim_cls(self._local_params, *args, **kwargs)
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@jit.export
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def step(self, autograd_ctx_id: int):
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all_local_grads = dist_autograd.get_gradients(autograd_ctx_id)
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# apply functional optimizer step with a list of gradients
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grads: List[Optional[Tensor]] = [
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all_local_grads[p] if p in all_local_grads else None
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for p in self._local_params
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]
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self.optim.step(grads)
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# TODO (wanchaol): remove/merge this with ScriptLocalOptimizer once
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# we have converted all to functional optimizer in distributed.optim
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class _LocalOptimizer:
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# Ideally we would only need to share a lock for instances of
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# _LocalOptimizer that deal with the same parameters. We are
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# making a simplifying assumption here that if there is more
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# than one instance of _LocalOptimizer per worker, they will
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# be optimizing the same parameters (e.g. each data parallel
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# trainer will create its own instance of _LocalOptimizer but
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# they will all optimize the same parameters on each worker)
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global_lock = Lock()
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def __init__(self, optim_cls, local_params_rref, *args, **kwargs):
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self._local_params = [rref.local_value() for rref in local_params_rref]
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self.optim = optim_cls(self._local_params, *args, **kwargs)
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def step(self, autograd_ctx_id):
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all_local_grads = dist_autograd.get_gradients(autograd_ctx_id)
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with _LocalOptimizer.global_lock:
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for param, grad in all_local_grads.items():
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param.grad = grad
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self.optim.step()
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def _new_local_optimizer(optim_cls, local_params_rref, *args, **kwargs):
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return rpc.RRef(_LocalOptimizer(optim_cls, local_params_rref, *args, **kwargs))
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def _local_optimizer_step(local_optim_rref, autograd_ctx_id):
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local_optim = local_optim_rref.local_value()
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local_optim.step(autograd_ctx_id)
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# new/step functions combined with _ScriptLocalOptimizer to provide GIL-free optimizer
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def _new_script_local_optimizer(optim_cls, local_params_rref, *args, **kwargs):
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optim = _ScriptLocalOptimizer(optim_cls, local_params_rref, *args, **kwargs)
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with _ScriptLocalOptimizer.compile_lock:
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script_optim = jit.script(optim)
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return rpc.RRef(script_optim, _ScriptLocalOptimizerInterface)
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@jit.script
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def _script_local_optimizer_step(
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local_optim_rref: RRef[_ScriptLocalOptimizerInterface], autograd_ctx_id: int
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) -> None:
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local_optim = local_optim_rref.local_value()
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local_optim.step(autograd_ctx_id)
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def _wait_for_all(rpc_futs):
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# TODO: improve error propagation
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exception = None
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results = []
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for fut in rpc_futs:
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try:
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results.append(fut.wait())
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except Exception as e:
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results.append(e)
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exception = e
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if exception is not None:
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raise exception
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return results
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class DistributedOptimizer:
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"""
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DistributedOptimizer takes remote references to parameters scattered
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across workers and applies the given optimizer locally for each parameter.
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This class uses :meth:`~torch.distributed.autograd.get_gradients` in order
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to retrieve the gradients for specific parameters.
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Concurrent calls to
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:meth:`~torch.distributed.optim.DistributedOptimizer.step`,
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either from the same or different clients, will
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be serialized on each worker -- as each worker's optimizer can only work
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on one set of gradients at a time. However, there is no guarantee that
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the full forward-backward-optimizer sequence will execute for one client
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at a time. This means that the gradients being applied may not correspond
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to the latest forward pass executed on a given worker. Also, there is no
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guaranteed ordering across workers.
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`DistributedOptimizer` creates the local optimizer with TorchScript enabled
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by default, so that optimizer updates are not blocked by the Python Global
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Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed
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Model Parallel). This feature is currently enabled for most optimizers. You
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can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support
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for your own custom optimizers.
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Args:
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optimizer_class (optim.Optimizer): the class of optimizer to
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instantiate on each worker.
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params_rref (list[RRef]): list of RRefs to local or remote parameters
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to optimize.
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args: arguments to pass to the optimizer constructor on each worker.
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kwargs: arguments to pass to the optimizer constructor on each worker.
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Example::
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>>> # xdoctest: +SKIP("distributed")
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>>> import torch.distributed.autograd as dist_autograd
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>>> import torch.distributed.rpc as rpc
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>>> from torch import optim
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>>> from torch.distributed.optim import DistributedOptimizer
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>>>
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>>> with dist_autograd.context() as context_id:
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>>> # Forward pass.
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>>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3))
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>>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1))
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>>> loss = rref1.to_here() + rref2.to_here()
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>>>
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>>> # Backward pass.
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>>> dist_autograd.backward(context_id, [loss.sum()])
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>>>
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>>> # Optimizer.
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>>> dist_optim = DistributedOptimizer(
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>>> optim.SGD,
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>>> [rref1, rref2],
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>>> lr=0.05,
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>>> )
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>>> dist_optim.step(context_id)
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__ https://github.com/pytorch/tutorials/pull/1465
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"""
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def __init__(self, optimizer_class, params_rref, *args, **kwargs):
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torch._C._log_api_usage_once("torch.distributed.optim.DistributedOptimizer")
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per_worker_params_rref = defaultdict(list)
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for param in params_rref:
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per_worker_params_rref[param.owner()].append(param)
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if optimizer_class in functional_optim_map and jit._state._enabled:
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optim_ctor = functional_optim_map.get(optimizer_class)
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else:
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optim_ctor = optimizer_class
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self.is_functional_optim = optim_ctor != optimizer_class
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if self.is_functional_optim:
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optimizer_new_func = _new_script_local_optimizer
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else:
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logger.warning(
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"Creating the optimizer %s without TorchScript support, "
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"this might result in slow computation time in multithreading environment"
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"(i.e. Distributed Model Parallel training on CPU) due to the Python's "
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"Global Interpreter Lock (GIL). Please file an issue if you need this "
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"optimizer in TorchScript. ",
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optimizer_class
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)
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optimizer_new_func = _new_local_optimizer
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remote_optim_futs = []
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for worker, param_rrefs in per_worker_params_rref.items():
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remote_optim_rref_fut = rpc.rpc_async(
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worker,
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optimizer_new_func,
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args=(optim_ctor, param_rrefs) + args,
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kwargs=kwargs,
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)
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remote_optim_futs.append(remote_optim_rref_fut)
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self.remote_optimizers = _wait_for_all(remote_optim_futs)
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def step(self, context_id):
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"""
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Performs a single optimization step.
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This will call :meth:`torch.optim.Optimizer.step` on each worker
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containing parameters to be optimized, and will block until all workers
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return. The provided ``context_id`` will be used to retrieve the
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corresponding :class:`~torch.distributed.autograd.context` that
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contains the gradients that should be applied to the parameters.
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Args:
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context_id: the autograd context id for which we should run the
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optimizer step.
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"""
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dist_autograd._is_valid_context(context_id)
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optimizer_step_func = (
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_script_local_optimizer_step
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if self.is_functional_optim
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else _local_optimizer_step
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)
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rpc_futs = []
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for optimizer in self.remote_optimizers:
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rpc_futs.append(
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rpc.rpc_async(
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optimizer.owner(),
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optimizer_step_func,
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args=(optimizer, context_id),
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)
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)
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_wait_for_all(rpc_futs)
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