Source code for ray.util.sgd.torch.torch_trainer

import time

import numpy as np
import logging
import os
import numbers
import tempfile
import torch
import torch.distributed as dist

import ray
from ray.tune import Trainable
from ray.tune.resources import Resources
from ray.tune.utils.util import merge_dicts
from ray.util import log_once
from ray.util.sgd.torch.worker_group import LocalWorkerGroup, \
    RemoteWorkerGroup, DeactivatedWorkerGroup
from ray.util.sgd.utils import NUM_SAMPLES, BATCH_SIZE
from ray.util.sgd.torch.constants import VALID_SCHEDULER_STEP, NCCL_TIMEOUT_S
from import Dataset

logger = logging.getLogger(__name__)

def _validate_scheduler_step_freq(scheduler_step_freq):
    """This validation check only happens if a scheduler is passed in."""
    if scheduler_step_freq not in VALID_SCHEDULER_STEP:
        raise ValueError("Scheduler step freq must be in {}. Got {}".format(
            VALID_SCHEDULER_STEP, scheduler_step_freq))

def _remind_gpu_usage(use_gpu):
    if not use_gpu and torch.cuda.is_available():"GPUs detected but not using them. Set `use_gpu` to "
                    "enable GPU usage. ")

[docs]class TorchTrainer: """Train a PyTorch model using distributed PyTorch. Launches a set of actors which connect via distributed PyTorch and coordinate gradient updates to train the provided model. If Ray is not initialized, TorchTrainer will automatically initialize a local Ray cluster for you. Be sure to run `ray.init(address="auto")` to leverage multi-node training. .. code-block:: python class MyTrainingOperator(TrainingOperator): def setup(self, config): model = nn.Linear(1, 1) optimizer = torch.optim.SGD( model.parameters(), lr=config.get("lr", 1e-4)) loss = torch.nn.MSELoss() batch_size = config["batch_size"] train_data, val_data = LinearDataset(2, 5), LinearDataset(2, 5) train_loader = DataLoader(train_data, batch_size=batch_size) val_loader = DataLoader(val_data, batch_size=batch_size) self.model, self.optimizer = self.register( models=model, optimizers=optimizer, criterion=loss) self.register_data( train_loader=train_loader, validation_loader=val_loader) trainer = TorchTrainer( training_operator_cls=MyTrainingOperator, config={"batch_size": 32}, use_gpu=True ) for i in range(4): trainer.train() Args: training_operator_cls (type): Custom training operator class that subclasses the TrainingOperator class. This class will be copied onto all remote workers and used to specify training components and custom training and validation operations. initialization_hook (function): A function to call on all training workers when they are first initialized. This could be useful to set environment variables for all the worker processes. config (dict): Custom configuration value to be passed to all operator constructors. num_workers (int): the number of workers used in distributed training. If 1, the worker will not be wrapped with DistributedDataParallel. TorchTrainer will scale down the number of workers if enough resources are not available, and will scale back up once they are. The total number of workers will never exceed `num_workers` amount. num_cpus_per_worker (int): Sets the cpu requirement for each worker. use_gpu (bool): Sets resource allocation for workers to 1 GPU if true, and automatically moves both the model and optimizer to the available CUDA device. backend (string): backend used by distributed PyTorch. Currently support "nccl", "gloo", and "auto". If "auto", RaySGD will automatically use "nccl" if `use_gpu` is True, and "gloo" otherwise. wrap_ddp (bool): Whether to automatically wrap DistributedDataParallel over each model. If False, you are expected to call it yourself. timeout_s (float): Seconds before the torch process group times out. Useful when machines are unreliable. add_dist_sampler (bool): Whether to automatically add a DistributedSampler to all created dataloaders. Only applicable if num_workers > 1. use_fp16 (bool): Enables mixed precision training via apex if apex is installed. This is automatically done after the model and optimizers are constructed and will work for multi-model training. Please see for more details. apex_args (dict|None): Dict containing keyword args for amp.initialize. See By default, the models and optimizers are passed in. Consider using "num_losses" if operating over multiple models and optimizers. scheduler_step_freq: "batch", "epoch", "manual", or None. This will determine when ``scheduler.step`` is called. If "batch", ``step`` will be called after every optimizer step. If "epoch", ``step`` will be called after one pass of the DataLoader. If "manual", the scheduler will not be incremented automatically - you are expected to call ``trainer.update_scheduler`` manually. If a scheduler is passed in, this value is expected to not be None. use_local (bool): If True, 1 worker will be a local worker running on the driver process, and all other workers will be remote. If False, all workers will be remote. Set this to True for easy debugging of worker on driver process, but could also lead to issues with Cuda devices. Defaults to False. """ # TODO: Implement autoscaling. If num_workers=-1, the trainer will use as # many resources as available. Upon each train call, TorchTrainer will # query the Ray global state for total available resources and resize # its remote workers to consume all available resources. def __init__( self, *, training_operator_cls, initialization_hook=None, config=None, num_workers=1, num_cpus_per_worker=1, use_gpu="auto", backend="auto", wrap_ddp=True, timeout_s=NCCL_TIMEOUT_S, use_fp16=False, use_tqdm=False, apex_args=None, add_dist_sampler=True, scheduler_step_freq=None, use_local=False, # Deprecated Args. num_replicas=None, batch_size=None, model_creator=None, data_creator=None, optimizer_creator=None, scheduler_creator=None, loss_creator=None, serialize_data_creation=None, data_loader_args=None, ): if (model_creator or data_creator or optimizer_creator or scheduler_creator or loss_creator): raise DeprecationWarning( "Creator functions are deprecated. You should create a " "custom TrainingOperator, override setup, and register all " "training state there. See TrainingOperator for more info. " "If you would still like to use creator functions, you can " "do CustomOperator = TrainingOperator.from_creators(" "model_creator, ...) and pass in CustomOperator into " "TorchTrainer.") if use_local and log_once("use_local"): logger.warning("use_local is set to True. This could lead to " "issues with Cuda devices. If you are seeing this " "issue, try setting use_local to False. For more " "information, see " "") if num_workers > 1 and not dist.is_available(): raise ValueError( ("Distributed PyTorch is not supported on macOS. " "To run without distributed PyTorch, set 'num_workers=1'. " "For more information, see " "")) if num_replicas is not None: raise DeprecationWarning( "num_replicas is deprecated. Use num_workers instead.") if batch_size is not None: raise DeprecationWarning( "batch_size is deprecated. Use config={'batch_size': N} " "specify a batch size for each worker or " "config={ray.util.sgd.utils.BATCH_SIZE: N} to specify a " "batch size to be used across all workers.") if serialize_data_creation is True: if log_once("serialize_data_creation"): logging.warning( "serialize_data_creation is deprecated and will be " "ignored. If you require serialized data loading you " "should implement this in TrainingOperator.setup. " "You may find FileLock useful here.") if data_loader_args: raise DeprecationWarning( "data_loader_args is deprecated. You can return a " " in data_creator. Ray will " "automatically set a DistributedSampler if a DataLoader is " "returned and num_workers > 1.") self.training_operator_cls = training_operator_cls self.initialization_hook = initialization_hook self.config = {} if config is None else config if use_gpu == "auto": use_gpu = torch.cuda.is_available() _remind_gpu_usage(use_gpu) if backend == "auto": backend = "nccl" if use_gpu else "gloo" logger.debug(f"Using {backend} as backend.") self.backend = backend self.num_cpus_per_worker = num_cpus_per_worker self.use_gpu = use_gpu self.max_replicas = num_workers self.serialize_data_creation = serialize_data_creation self.wrap_ddp = wrap_ddp self.timeout_s = timeout_s self.use_fp16 = use_fp16 self.use_tqdm = use_tqdm self.add_dist_sampler = add_dist_sampler self.use_local = use_local if apex_args and not isinstance(apex_args, dict): raise ValueError("apex_args needs to be a dict object.") self.apex_args = apex_args self.temp_dir = tempfile.mkdtemp(prefix="raysgd") self._num_failures = 0 self._last_resize = float("-inf") if scheduler_step_freq: _validate_scheduler_step_freq(scheduler_step_freq) self.scheduler_step_freq = scheduler_step_freq if not ray.is_initialized() and self.max_replicas > 1:"Automatically initializing single-node Ray. To use " "multi-node training, be sure to run `ray.init(" "address='auto')` before instantiating the Trainer.") ray.init() self._start_workers(self.max_replicas) def _configure_and_split_batch(self, num_workers): """If sgd.utils.BATCH_SIZE is provided, split among workers.""" if BATCH_SIZE not in self.config: return # Compute batch size per worker logger.debug("BATCH_SIZE parameter detected. Splitting among workers.") batch_size = self.config[BATCH_SIZE] batch_size_per_worker = batch_size // num_workers if batch_size % num_workers > 0: new_batch_size = batch_size_per_worker * num_workers logger.warning( ("Changing batch size from {old_batch_size} to " "{new_batch_size} to evenly distribute batches across " "{num_workers} workers.").format( old_batch_size=batch_size, new_batch_size=new_batch_size, num_workers=num_workers)) self.config[BATCH_SIZE] = new_batch_size return batch_size_per_worker def _start_workers(self, num_workers): worker_config = self.config.copy() batch_size_per_worker = self._configure_and_split_batch(num_workers) if batch_size_per_worker: worker_config[BATCH_SIZE] = batch_size_per_worker params = dict( training_operator_cls=self.training_operator_cls, config=worker_config, serialize_data_creation=self.serialize_data_creation, use_fp16=self.use_fp16, use_gpu=self.use_gpu, use_tqdm=self.use_tqdm, apex_args=self.apex_args, scheduler_step_freq=self.scheduler_step_freq) dist_params = dict( backend=self.backend, add_dist_sampler=self.add_dist_sampler, wrap_ddp=self.wrap_ddp) worker_args = { "max_workers": num_workers, "params": params, "dist_params": dist_params, "initialization_hook": self.initialization_hook, "num_cpus_per_worker": self.num_cpus_per_worker, "use_gpu": self.use_gpu, "timeout_s": self.timeout_s } if self.use_local: self.worker_group = LocalWorkerGroup(**worker_args) else: self.worker_group = RemoteWorkerGroup(**worker_args) # TODO(amogkam): If not enough resources are available to create # num_workers workers, this command will hang. Instead, # start_workers should take into account available resources when # determining how many workers to create. self.worker_group.start_workers(num_workers) def _resize_worker_group(self, max_retries=10): """Resizes the number of remote workers based on available resources. Total number of workers will never exceed `num_workers` amount. Args: max_retries (int): How many times to attempt to resize workers before failing. """ state_dict = self.state_dict() old_workers = self.worker_group.num_workers self.worker_group.reset() time.sleep(1) for i in range(max_retries): new_workers = self.worker_group.new_workers_size() if new_workers: self._last_resize = time.time() self._start_workers(int(new_workers)) self.load_state_dict(state_dict, blocking=True) if self.use_local and new_workers == 1 and old_workers > 1: # Major hack. If we go from LocalDistributedRunner to a # standard TorchRunner we have to manually reset the # dummy actor handle global vars. # TODO(amog): Refactor LocalDistributedTorchRunner to # not use global variables for resource reservation. ray.util.sgd.torch.distributed_torch_runner\ ._dummy_cuda_actor = None ray.util.sgd.torch.distributed_torch_runner\ ._dummy_cpu_actor = None return else: delay = 2**i logger.warning( "No new workers found. Retrying in %d sec." % delay) time.sleep(delay) raise RuntimeError("Exceeded max_retries for relaunching workers.")
[docs] def train(self, num_steps=None, profile=False, reduce_results=True, max_retries=3, info=None, dataset=None): """Runs a training epoch. Calls `operator.train_epoch()` on N parallel workers simultaneously underneath the hood. Set `max_retries` to enable fault handling in case of instance preemption. Args: num_steps (int): Number of batches to compute update steps on. This corresponds also to the number of times ``TrainingOperator.train_batch`` is called. profile (bool): Returns time stats for the training procedure. reduce_results (bool): Whether to average all metrics across all workers into one dict. If a metric is a non-numerical value (or nested dictionaries), one value will be randomly selected among the workers. If False, returns a list of dicts. max_retries (int): Must be non-negative. If set to N, TorchTrainer will detect and recover from training failure. The recovery process will kill all current workers, query the Ray global state for total available resources, and re-launch up to the available resources. Behavior is not well-defined in case of shared cluster usage. Defaults to 3. info (dict): Optional dictionary passed to the training operator for ``train_epoch`` and ``train_batch``. dataset (Dataset): Optional dataset to train with. If specified, the dataloader passed in via data_creator will be ignored. Returns: (dict | list) A dictionary of metrics for training. You can provide custom metrics by implementing a custom training loop. If ``reduce_results=False``, this will return a list of metric dictionaries whose length will be equal to ``num_workers``. """ assert max_retries >= 0, "`max_retries` must be non-negative." assert isinstance(dataset, Dataset) is not None \ or self.data_creator, \ "Must specify either a data creator or a dataset" if self.worker_group.should_scale_up():"Resize opportunity detected. Attempting to scale up.") self._resize_worker_group() success, worker_stats = self.worker_group.train( num_steps=num_steps, profile=profile, info=info, dataset=dataset) # Fault handling for i in range(max_retries): if success: break else: self._num_failures += 1 self._resize_worker_group()"Retrying training step with %d workers." % self.worker_group.num_workers) success, worker_stats = self.worker_group.train( num_steps=num_steps, profile=profile, info=info, dataset=dataset) if not success: raise RuntimeError("Training run failed.") if reduce_results: return self._process_stats(worker_stats) else: return worker_stats
def _process_stats(self, worker_stats): stats = { NUM_SAMPLES: sum( stats.pop(NUM_SAMPLES, np.nan) for stats in worker_stats) } for stat_key in worker_stats[0]: if isinstance(worker_stats[0], numbers.Number): stats[stat_key] = np.nanmean( [s.get(stat_key, np.nan) for s in worker_stats]) else: stats[stat_key] = worker_stats[0][stat_key] return stats
[docs] def apply_all_workers(self, fn): """Run a function on all operators on the workers. Args: fn (Callable): A function that takes in no arguments. Returns: A list of objects returned by ``fn`` on each worker. """ return self.worker_group.apply_all_workers(fn)
[docs] def apply_all_operators(self, fn): """Run a function on all operators on the workers. Args: fn (Callable[TrainingOperator]): A function that takes in a TrainingOperator. Returns: A list of objects returned by ``fn`` on each operator. """ return self.worker_group.apply_all_operators(fn)
[docs] def validate(self, num_steps=None, profile=False, reduce_results=True, info=None): """Evaluates the model on the validation data set. Args: num_steps (int): Number of batches to compute update steps on. This corresponds also to the number of times ``TrainingOperator.validate_batch`` is called. profile (bool): Returns time stats for the evaluation procedure. reduce_results (bool): Whether to average all metrics across all workers into one dict. If a metric is a non-numerical value (or nested dictionaries), one value will be randomly selected among the workers. If False, returns a list of dicts. info (dict): Optional dictionary passed to the training operator for `validate` and `validate_batch`. Returns: A dictionary of metrics for validation. You can provide custom metrics by passing in a custom ``training_operator_cls``. """ worker_stats = self.worker_group.validate( num_steps=num_steps, profile=profile, info=info) if reduce_results: return self._process_stats(worker_stats) else: return worker_stats
[docs] def update_scheduler(self, metric): """Calls ``scheduler.step(metric)`` on all registered schedulers. This is useful for lr_schedulers such as ``ReduceLROnPlateau``. """ self.worker_group.apply_all_operators( lambda op: [sched.step(metric) for sched in op._schedulers])
[docs] def get_model(self): """Returns the learned model(s).""" unwrapped = [] models = self.worker_group.get_model() for model in models: unwrapped += [model.module if hasattr(model, "module") else model] if len(unwrapped) == 1: return unwrapped[0] return unwrapped
[docs] def get_local_operator(self): """Returns the local TrainingOperator object. Be careful not to perturb its state, or else you can cause the system to enter an inconsistent state. Returns: TrainingOperator: The local TrainingOperator object. """ return self.worker_group.get_local_operator()
def state_dict(self): return self.worker_group.state_dict() def load_state_dict(self, state_dict, blocking=False): self.worker_group.load_state_dict(state_dict, blocking=blocking)
[docs] def save(self, checkpoint): """Saves the Trainer state to the provided checkpoint path. Args: checkpoint (str): Path to target checkpoint file. """, checkpoint) return checkpoint
[docs] def load(self, checkpoint): """Loads the Trainer and all workers from the provided checkpoint. Args: checkpoint (str): Path to target checkpoint file. """ state_dict = torch.load(checkpoint) self.load_state_dict(state_dict)
def restore(self, *args): raise DeprecationWarning("Use `TorchTrainer.load()` instead.")
[docs] def shutdown(self, force=False): """Shuts down workers and releases resources. Args: force (bool): If True, forcefully kill all workers. If False, attempt a graceful shutdown first, and then forcefully kill if unsuccessful. """ self.worker_group.shutdown(force=force) self.worker_group = DeactivatedWorkerGroup()
[docs] @classmethod def as_trainable(cls, *args, **kwargs): """Creates a BaseTorchTrainable class compatible with Tune. Any configuration parameters will be overriden by the Tune Trial configuration. You can also subclass the provided Trainable to implement your own iterative optimization routine. .. code-block:: python TorchTrainable = TorchTrainer.as_trainable( training_operator_cls=MyTrainingOperator, num_gpus=2 ) analysis = TorchTrainable, config={"lr": tune.grid_search([0.01, 0.1])} ) """ class TorchTrainable(BaseTorchTrainable): @classmethod def default_resource_request(cls, config): num_workers = config.get("num_workers", kwargs.get("num_workers", 1)) num_cpus_per_worker = config.get( "num_cpus_per_worker", kwargs.get("num_cpus_per_worker", 1)) use_gpu = config.get("use_gpu", kwargs.get("use_gpu")) use_local = config.get("use_local", kwargs.get("use_local", False)) if use_local: remote_worker_count = num_workers - 1 local_cpus = 1 local_gpus = int(use_gpu) else: remote_worker_count = num_workers local_cpus = 0 local_gpus = 0 return Resources( cpu=int(local_cpus * num_cpus_per_worker), gpu=int(local_gpus), extra_cpu=int(remote_worker_count * num_cpus_per_worker), extra_gpu=int(int(use_gpu) * remote_worker_count)) def _create_trainer(self, tune_config): """Overrides the provided config with Tune config.""" provided_config = kwargs.get("config", {}).copy() provided_config.update(tune_config) kwargs["config"] = provided_config trainer = TorchTrainer(*args, **kwargs) return trainer return TorchTrainable
[docs]class BaseTorchTrainable(Trainable): """Base class for converting TorchTrainer to a Trainable class. This class is produced when you call ``TorchTrainer.as_trainable(...)``. You can override the produced Trainable to implement custom iterative training procedures: .. code-block:: python TorchTrainable = TorchTrainer.as_trainable( training_operator_cls=MyTrainingOperator, num_gpus=2 ) # TorchTrainable is subclass of BaseTorchTrainable. class CustomTrainable(TorchTrainable): def step(self): for i in range(5): train_stats = self.trainer.train() validation_stats = self.trainer.validate() train_stats.update(validation_stats) return train_stats analysis = CustomTrainable, config={"lr": tune.grid_search([0.01, 0.1])} ) """
[docs] def setup(self, config): """Constructs a TorchTrainer object as `self.trainer`.""" self._trainer = self._create_trainer(config)
[docs] def step(self): """Calls `self.trainer.train()` and `self.trainer.validate()` once. You may want to override this if using a custom LR scheduler. """ train_stats = self.trainer.train(max_retries=10, profile=True) validation_stats = self.trainer.validate(profile=True) stats = merge_dicts(train_stats, validation_stats) return stats
[docs] def save_checkpoint(self, checkpoint_dir): """Returns a path containing the trainer state.""" checkpoint_path = os.path.join(checkpoint_dir, "trainer.checkpoint") return checkpoint_path
[docs] def load_checkpoint(self, checkpoint_path): """Restores the trainer state. Override this if you have state external to the Trainer object. """ return self.trainer.load(checkpoint_path)
[docs] def cleanup(self): """Shuts down the trainer.""" self.trainer.shutdown()
def _create_trainer(self, config): raise NotImplementedError @property def trainer(self): """An instantiated TorchTrainer object. Use this when specifying custom training procedures for Tune. """ return self._trainer