# Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. """ Train a network across multiple GPUs. """ import contextlib import logging import sys import time from argparse import Namespace from itertools import chain from typing import Any, Dict, List import torch from fairseq import checkpoint_utils, models, optim, utils from fairseq.dataclass.configs import FairseqConfig from fairseq.dataclass.utils import convert_namespace_to_omegaconf from fairseq.distributed import utils as distributed_utils from fairseq.file_io import PathManager from fairseq.logging import meters, metrics from fairseq.nan_detector import NanDetector from fairseq.optim import lr_scheduler from omegaconf import OmegaConf logger = logging.getLogger(__name__) class Trainer(object): """Main class for data parallel training. This class supports synchronous distributed data parallel training, where multiple workers each have a full model replica and gradients are accumulated across workers before each update. We use :class:`~torch.nn.parallel.DistributedDataParallel` to handle communication of the gradients across workers. """ def __init__(self, cfg: FairseqConfig, task, model, criterion, quantizer=None): if isinstance(cfg, Namespace): logger.warning( "argparse.Namespace configuration is deprecated! Automatically converting to OmegaConf" ) cfg = convert_namespace_to_omegaconf(cfg) self.cfg = cfg self.task = task # catalog shared parameters shared_params = _catalog_shared_params(model) self.tpu = cfg.common.tpu self.cuda = torch.cuda.is_available() and not cfg.common.cpu and not self.tpu if self.cuda: self.device = torch.device("cuda") elif self.tpu: self.device = utils.get_tpu_device() else: self.device = torch.device("cpu") if self.cfg.distributed_training.ddp_backend == "fully_sharded": if self.cfg.common.bf16: raise ValueError( "FullyShardedDataParallel is not compatible with --bf16 or " "--memory-efficient-bf16" ) if self.cfg.distributed_training.zero_sharding != "none": raise ValueError( "FullyShardedDataParallel is not compatible with --zero-sharding " "option (it's already built in)" ) else: if ( hasattr(self.cfg.distributed_training, "cpu_offload") and self.cfg.distributed_training.cpu_offload ): raise ValueError("--cpu-offload requires --ddp-backend=fully_sharded") # copy model and criterion to current device/dtype self._criterion = criterion self._model = model if cfg.distributed_training.ddp_backend != "fully_sharded": if cfg.common.fp16: assert not cfg.common.amp, "Cannot use fp16 and AMP together" self._criterion = self._criterion.half() self._model = self._model.half() elif cfg.common.bf16: self._criterion = self._criterion.to(dtype=torch.bfloat16) self._model = self._model.to(dtype=torch.bfloat16) elif cfg.common.amp: self._amp_retries = 0 if ( not cfg.distributed_training.pipeline_model_parallel # the DistributedFairseqModel wrapper will handle moving to device, # so only handle cases which don't use the wrapper and not self.use_distributed_wrapper ): self._criterion = self._criterion.to(device=self.device) self._model = self._model.to(device=self.device) self.pipeline_model_parallel = cfg.distributed_training.pipeline_model_parallel self.last_device = None if self.cuda and self.pipeline_model_parallel: self.last_device = torch.device( cfg.distributed_training.pipeline_devices[-1] ) # check that shared parameters are preserved after device transfer for shared_param in shared_params: ref = _get_module_by_path(self._model, shared_param[0]) for path in shared_param[1:]: logger.info( "detected shared parameter: {} <- {}".format(shared_param[0], path) ) _set_module_by_path(self._model, path, ref) self._dummy_batch = None # indicates we don't have a dummy batch at first self._lr_scheduler = None self._num_updates = 0 self._num_xla_compiles = 0 # for TPUs self._optim_history = None self._optimizer = None self._warn_once = set() self._wrapped_criterion = None self._wrapped_model = None # TODO(myleott): support tpu if self.cuda and self.data_parallel_world_size > 1: self._grad_norm_buf = torch.cuda.DoubleTensor(self.data_parallel_world_size) else: self._grad_norm_buf = None self.quantizer = quantizer if self.quantizer is not None: self.quantizer.set_trainer(self) # get detailed cuda environment if self.cuda: self.cuda_env = utils.CudaEnvironment() if self.data_parallel_world_size > 1: self.cuda_env_arr = distributed_utils.all_gather_list( self.cuda_env, group=distributed_utils.get_global_group() ) else: self.cuda_env_arr = [self.cuda_env] if self.data_parallel_rank == 0: utils.CudaEnvironment.pretty_print_cuda_env_list(self.cuda_env_arr) else: self.cuda_env = None self.cuda_env_arr = None metrics.log_start_time("wall", priority=790, round=0) self._start_time = time.time() self._previous_training_time = 0 self._cumulative_training_time = None def reinitialize(self): """Reinitialize the Trainer, typically after model params change.""" self._lr_scheduler = None self._optimizer = None self._wrapped_criterion = None self._wrapped_model = None @property def data_parallel_world_size(self): if self.cfg.distributed_training.distributed_world_size == 1: return 1 return distributed_utils.get_data_parallel_world_size() @property def data_parallel_process_group(self): return distributed_utils.get_data_parallel_group() @property def data_parallel_rank(self): if self.cfg.distributed_training.distributed_world_size == 1: return 0 return distributed_utils.get_data_parallel_rank() @property def is_data_parallel_master(self): # NOTE: this returns true for all model parallel replicas with data # parallel rank 0 return self.data_parallel_rank == 0 @property def use_distributed_wrapper(self) -> bool: return ( self.data_parallel_world_size > 1 and not self.cfg.optimization.use_bmuf ) or ( self.cfg.distributed_training.ddp_backend == "fully_sharded" and self.cfg.distributed_training.cpu_offload ) @property def should_save_checkpoint_on_current_rank(self) -> bool: """Indicates whether to save checkpoints on the current DDP rank.""" if ( self.cfg.distributed_training.ddp_backend == "fully_sharded" and self.cfg.distributed_training.use_sharded_state ) or getattr(self.cfg.model, "base_layers", 0) > 0: return True else: return self.is_data_parallel_master @property def always_call_state_dict_during_save_checkpoint(self) -> bool: if ( self.cfg.distributed_training.ddp_backend == "fully_sharded" and not self.cfg.distributed_training.use_sharded_state ): # FSDP calls communication collective when consolidating checkpoints return True else: return False @property def checkpoint_suffix(self) -> str: """Suffix to add to the checkpoint file name.""" if ( self.cfg.distributed_training.ddp_backend == "fully_sharded" and self.cfg.distributed_training.use_sharded_state ): return self.cfg.checkpoint.checkpoint_suffix + "-shard{0}".format( self.data_parallel_rank ) else: return self.cfg.checkpoint.checkpoint_suffix or "" @property def criterion(self): if self._wrapped_criterion is None: if utils.has_parameters(self._criterion) and self.use_distributed_wrapper: self._wrapped_criterion = models.DistributedFairseqModel( self.cfg.distributed_training, self._criterion, process_group=self.data_parallel_process_group, device=self.device, ) else: self._wrapped_criterion = self._criterion return self._wrapped_criterion @property def model(self): if self._wrapped_model is None: if self.use_distributed_wrapper: self._wrapped_model = models.DistributedFairseqModel( self.cfg.distributed_training, self._model, process_group=self.data_parallel_process_group, device=self.device, ) else: self._wrapped_model = self._model return self._wrapped_model @property def optimizer(self): if self._optimizer is None: self._build_optimizer() return self._optimizer @property def lr_scheduler(self): if self._lr_scheduler is None: self._build_optimizer() # this will initialize self._lr_scheduler return self._lr_scheduler def _build_optimizer(self): params = list( filter( lambda p: p.requires_grad, chain(self.model.parameters(), self.criterion.parameters()), ) ) if ( self.cfg.distributed_training.ddp_backend == "fully_sharded" and self.cfg.common.fp16 ): # FullyShardedDataParallel always uses MemoryEfficientFP16 wrapper, # mostly for the grad scaling. But if we don't have the # --memory-efficient-fp16 flag set, then we're effectively doing # regular --fp16 and can allow the use of optimizers that would # otherwise be unsupported by MemoryEfficientFP16Optimizer. allow_unsupported = not self.cfg.common.memory_efficient_fp16 self._optimizer = optim.MemoryEfficientFP16Optimizer.build_optimizer( self.cfg, params, allow_unsupported=allow_unsupported ) elif self.cfg.common.fp16 or self.cfg.common.bf16 or self.cfg.common.amp: if self.cuda and torch.cuda.get_device_capability(0)[0] < 7: logger.info( "NOTE: your device does NOT support faster training with --fp16 or --amp, " "please switch to FP32 which is likely to be faster" ) if ( self.cfg.common.memory_efficient_fp16 or self.cfg.common.memory_efficient_bf16 ): self._optimizer = optim.MemoryEfficientFP16Optimizer.build_optimizer( self.cfg, params ) elif self.cfg.common.amp: self._optimizer = optim.AMPOptimizer.build_optimizer(self.cfg, params) else: self._optimizer = optim.FP16Optimizer.build_optimizer(self.cfg, params) else: if self.cuda and torch.cuda.get_device_capability(0)[0] >= 7: logger.info("NOTE: your device may support faster training with --fp16 or --amp") self._optimizer = optim.build_optimizer(self.cfg.optimizer, params) if self.cfg.distributed_training.ddp_backend == "fully_sharded": assert ( not self.cfg.optimization.use_bmuf ), "--ddp-backend=fully_sharded is not compatible with BMUF" assert self._optimizer.supports_flat_params, ( "--ddp-backend=fully_sharded is only compatible with pointwise " "optimizers (e.g., Adam, AdamW, Adadelta, Adamax, SGD, etc.). " "However, the sharding will result in slightly different results when " "using non-pointwise optimizers (e.g., Adagrad, Adafactor, LAMB)" ) if self.cfg.optimization.use_bmuf: self._optimizer = optim.FairseqBMUF( self.cfg.bmuf, self._optimizer, ) if self.cfg.distributed_training.zero_sharding == "os": if ( self.cfg.common.fp16 and not self.cfg.common.memory_efficient_fp16 and not self.cfg.common.memory_efficient_bf16 ) and not self.cfg.common.fp16_no_flatten_grads: raise ValueError( "ZeRO is incomptabile with fp16 and flattened grads. " "Please use --fp16-no-flatten-grads" ) else: optim.shard_(self._optimizer, self.data_parallel_process_group) # We should initialize the learning rate scheduler immediately after # building the optimizer, so that the initial learning rate is set. self._lr_scheduler = lr_scheduler.build_lr_scheduler( self.cfg.lr_scheduler, self.optimizer, ) self._lr_scheduler.step_update(0) def consolidate_optimizer(self): """For OSS, we need to consolidate the state dict.""" if self.cfg.checkpoint.no_save_optimizer_state: return self._gathered_optim_state = None if hasattr(self.optimizer.optimizer, "consolidate_state_dict"): self.optimizer.optimizer.consolidate_state_dict() elif ( self.cfg.distributed_training.ddp_backend == "fully_sharded" and not self.model.use_sharded_state ): st = self.model.gather_full_optim_state_dict( self.optimizer ) # only returns on rank 0 self._gathered_optim_state = st def state_dict(self): state_dict = { "args": None, # legacy "cfg": ( OmegaConf.to_container(self.cfg, resolve=True, enum_to_str=True) if OmegaConf.is_config(self.cfg) else self.cfg ), "model": self.model.state_dict(), "criterion": ( self.criterion.state_dict() if utils.has_parameters(self.criterion) else None ), "optimizer_history": (self._optim_history or []) + [ { "criterion_name": self.get_criterion().__class__.__name__, "optimizer_name": self.optimizer.__class__.__name__, "lr_scheduler_state": self.lr_scheduler.state_dict(), "num_updates": self.get_num_updates(), } ], "task_state": self.task.state_dict() if self.task is not None else {}, "extra_state": { "metrics": metrics.state_dict(), "previous_training_time": self.cumulative_training_time(), }, } if not self.cfg.checkpoint.no_save_optimizer_state: if self._gathered_optim_state is not None: state_dict["last_optimizer_state"] = self._gathered_optim_state self._gathered_optim_state = None else: state_dict["last_optimizer_state"] = self.optimizer.state_dict() if self.cfg.distributed_training.ddp_backend == "fully_sharded": # save meta data for recombining checkpoint upon loading state_dict["fsdp_metadata"] = self.model.local_metadata_dict() return state_dict def save_checkpoint(self, filename, extra_state): """Save all training state in a checkpoint file.""" logger.info(f"Saving checkpoint to {filename}") # call state_dict on all ranks in case it needs internal communication state_dict = utils.move_to_cpu(self.state_dict()) state_dict["extra_state"].update(extra_state) if self.should_save_checkpoint_on_current_rank: checkpoint_utils.torch_persistent_save( state_dict, filename, async_write=self.cfg.checkpoint.write_checkpoints_asynchronously, ) logger.info(f"Finished saving checkpoint to {filename}") def load_checkpoint( self, filename, reset_optimizer=False, reset_lr_scheduler=False, optimizer_overrides=None, reset_meters=False, ): """ Load all training state from a checkpoint file. rank = 0 will load the checkpoint, and then broadcast it to all other ranks. """ extra_state, self._optim_history, last_optim_state = None, [], None logger.info(f"Preparing to load checkpoint {filename}") is_distributed = self.data_parallel_world_size > 1 bexists = PathManager.isfile(filename) if bexists: load_on_all_ranks = ( self.cfg.checkpoint.load_checkpoint_on_all_dp_ranks # TPUs don't support broadcast yet, so load checkpoints # on every worker for now or self.tpu # FSDP requires loading checkpoint shards on all ranks or ( self.cfg.distributed_training.ddp_backend == "fully_sharded" and self.cfg.distributed_training.use_sharded_state ) or getattr(self.cfg.model, "base_layers", 0) > 0 ) if load_on_all_ranks or self.data_parallel_rank == 0: state = checkpoint_utils.load_checkpoint_to_cpu( filename, load_on_all_ranks=load_on_all_ranks ) last_optim_state = state.get("last_optimizer_state", None) # If doing zero_sharding, do not broadcast global optimizer # state. Later we will broadcast sharded states to each rank # to avoid memory from exploding. if ( not load_on_all_ranks and self.cfg.distributed_training.zero_sharding == "os" and "last_optimizer_state" in state and is_distributed ): state["last_optimizer_state"] = "SHARDED" else: last_optim_state = None state = None if is_distributed and not load_on_all_ranks: state = distributed_utils.broadcast_object( state, src_rank=0, group=self.data_parallel_process_group, dist_device=self.device, ) if self.data_parallel_rank > 0: last_optim_state = state.get("last_optimizer_state", None) # load model parameters try: self.model.load_state_dict( state["model"], strict=True, model_cfg=self.cfg.model ) # save memory for later steps del state["model"] if utils.has_parameters(self.get_criterion()): self.get_criterion().load_state_dict( state["criterion"], strict=True ) del state["criterion"] except Exception: raise Exception( "Cannot load model parameters from checkpoint {}; " "please ensure that the architectures match.".format(filename) ) extra_state = state["extra_state"] self._optim_history = state["optimizer_history"] if last_optim_state is not None and not reset_optimizer: # rebuild optimizer after loading model, since params may have changed self._build_optimizer() # only reload optimizer and lr_scheduler if they match last_optim = self._optim_history[-1] assert ( last_optim["criterion_name"] == self.get_criterion().__class__.__name__ ), f"Criterion does not match; please reset the optimizer (--reset-optimizer). {last_optim['criterion_name']} vs {self.get_criterion().__class__.__name__}" assert ( last_optim["optimizer_name"] == self.optimizer.__class__.__name__ ), f"Optimizer does not match; please reset the optimizer (--reset-optimizer). {last_optim['optimizer_name']} vs {self.optimizer.__class__.__name__}" if not reset_lr_scheduler: self.lr_scheduler.load_state_dict(last_optim["lr_scheduler_state"]) if ( self.cfg.distributed_training.ddp_backend == "fully_sharded" and not self.model.use_sharded_state ): # if use_sharded_state, the last_optim_state is already sharded, skip this last_optim_state = self.model.get_shard_from_optim_state_dict( last_optim_state ) elif not load_on_all_ranks and is_distributed: last_optim_state = self.optimizer.broadcast_global_state_dict( last_optim_state ) self.optimizer.load_state_dict(last_optim_state, optimizer_overrides) self.set_num_updates(last_optim["num_updates"]) if extra_state is not None: itr_state = extra_state["train_iterator"] epoch = itr_state["epoch"] if "previous_training_time" in extra_state: self._previous_training_time = extra_state["previous_training_time"] self._start_time = time.time() self.lr_step(epoch) if ( itr_state.get("version", 1) >= 2 and itr_state["iterations_in_epoch"] == 0 ): # reset meters at start of epoch reset_meters = True if "metrics" in extra_state and not reset_meters: metrics.load_state_dict(extra_state["metrics"]) # reset TimeMeters, since their start times don't make sense anymore for meter in metrics.get_meters("default"): if isinstance(meter, meters.TimeMeter): meter.reset() logger.info( "Loaded checkpoint {} (epoch {} @ {} updates)".format( filename, epoch, self.get_num_updates() ) ) else: logger.info("No existing checkpoint found {}".format(filename)) return extra_state def get_train_iterator( self, epoch, combine=True, load_dataset=True, data_selector=None, shard_batch_itr=True, disable_iterator_cache=False, ): """Return an EpochBatchIterator over the training set for a given epoch.""" if load_dataset: logger.info("loading train data for epoch {}".format(epoch)) self.task.load_dataset( self.cfg.dataset.train_subset, epoch=epoch, combine=combine, data_selector=data_selector, tpu=self.tpu, ) batch_iterator = self.task.get_batch_iterator( dataset=self.task.dataset(self.cfg.dataset.train_subset), max_tokens=self.cfg.dataset.max_tokens, max_sentences=self.cfg.dataset.batch_size, max_positions=utils.resolve_max_positions( self.task.max_positions(), self.model.max_positions(), self.cfg.dataset.max_tokens, ), ignore_invalid_inputs=True, required_batch_size_multiple=self.cfg.dataset.required_batch_size_multiple, seed=self.cfg.common.seed, num_shards=self.data_parallel_world_size if shard_batch_itr else 1, shard_id=self.data_parallel_rank if shard_batch_itr else 0, num_workers=self.cfg.dataset.num_workers, epoch=epoch, data_buffer_size=self.cfg.dataset.data_buffer_size, disable_iterator_cache=disable_iterator_cache, ) self.reset_dummy_batch(batch_iterator.first_batch) return batch_iterator def get_valid_iterator( self, subset, disable_iterator_cache=False, ): """Return an EpochBatchIterator over given validation subset for a given epoch.""" batch_iterator = self.task.get_batch_iterator( dataset=self.task.dataset(subset), max_tokens=self.cfg.dataset.max_tokens_valid, max_sentences=self.cfg.dataset.batch_size_valid, max_positions=utils.resolve_max_positions( self.task.max_positions(), self.model.max_positions(), ), ignore_invalid_inputs=self.cfg.dataset.skip_invalid_size_inputs_valid_test, required_batch_size_multiple=self.cfg.dataset.required_batch_size_multiple, seed=self.cfg.common.seed, num_shards=self.data_parallel_world_size, shard_id=self.data_parallel_rank, num_workers=self.cfg.dataset.num_workers, # always pass a fixed "epoch" to keep validation data consistent # across training epochs epoch=1, data_buffer_size=self.cfg.dataset.data_buffer_size, disable_iterator_cache=disable_iterator_cache, ) self.reset_dummy_batch(batch_iterator.first_batch) return batch_iterator def begin_epoch(self, epoch): """Called at the beginning of each epoch.""" logger.info("begin training epoch {}".format(epoch)) self.lr_step_begin_epoch(epoch) if self.quantizer is not None: self.quantizer.begin_epoch(epoch) # task specific setup per epoch self.task.begin_epoch(epoch, self.get_model()) if self.tpu: import torch_xla.core.xla_model as xm xm.rendezvous("begin_epoch") # wait for all workers xm.mark_step() def begin_valid_epoch(self, epoch): """Called at the beginning of each validation epoch.""" # task specific setup per validation epoch self.task.begin_valid_epoch(epoch, self.get_model()) def reset_dummy_batch(self, batch): self._dummy_batch = batch @metrics.aggregate("train") def train_step(self, samples, raise_oom=False): """Do forward, backward and parameter update.""" self._set_seed() self.model.train() self.criterion.train() self.zero_grad() metrics.log_start_time("train_wall", priority=800, round=0) # forward and backward pass logging_outputs, sample_size, ooms = [], 0, 0 for i, sample in enumerate(samples): # delayed update loop sample, is_dummy_batch = self._prepare_sample(sample) def maybe_no_sync(): """ Whenever *samples* contains more than one mini-batch, we want to accumulate gradients locally and only call all-reduce in the last backwards pass. """ if ( self.data_parallel_world_size > 1 and hasattr(self.model, "no_sync") and i < len(samples) - 1 ): return self.model.no_sync() else: return contextlib.ExitStack() # dummy contextmanager try: with maybe_no_sync(): # forward and backward loss, sample_size_i, logging_output = self.task.train_step( sample=sample, model=self.model, criterion=self.criterion, optimizer=self.optimizer, update_num=self.get_num_updates(), ignore_grad=is_dummy_batch, ) del loss logging_outputs.append(logging_output) sample_size += sample_size_i # emptying the CUDA cache after the first step can # reduce the chance of OOM if self.cuda and self.get_num_updates() == 0: torch.cuda.empty_cache() except RuntimeError as e: if "out of memory" in str(e): self._log_oom(e) if raise_oom: raise e logger.warning( "attempting to recover from OOM in forward/backward pass" ) ooms += 1 self.zero_grad() if self.cuda: torch.cuda.empty_cache() if self.cfg.distributed_training.distributed_world_size == 1: return None else: raise e if self.tpu and i < len(samples) - 1: # tpu-comment: every XLA operation before marking step is # appended to the IR graph, and processing too many batches # before marking step can lead to OOM errors. # To handle gradient accumulation use case, we explicitly # mark step here for every forward pass without a backward pass self._xla_markstep_and_send_to_cpu() if is_dummy_batch: if torch.is_tensor(sample_size): sample_size.zero_() else: sample_size *= 0.0 if torch.is_tensor(sample_size): sample_size = sample_size.float() else: sample_size = float(sample_size) # gather logging outputs from all replicas if self._sync_stats(): train_time = self._local_cumulative_training_time() logging_outputs, ( sample_size, ooms, total_train_time, ) = self._aggregate_logging_outputs( logging_outputs, sample_size, ooms, train_time, ignore=is_dummy_batch ) self._cumulative_training_time = ( total_train_time / self.data_parallel_world_size ) overflow = False try: with torch.autograd.profiler.record_function("reduce-grads"): # reduce gradients across workers self.optimizer.all_reduce_grads(self.model) if utils.has_parameters(self.criterion): self.optimizer.all_reduce_grads(self.criterion) with torch.autograd.profiler.record_function("multiply-grads"): # multiply gradients by (data_parallel_size / sample_size) since # DDP normalizes by the number of data parallel workers for # improved fp16 precision. # Thus we get (sum_of_gradients / sample_size) at the end. # In case of fp16, this step also undoes loss scaling. # (Debugging note: Some optimizers perform this scaling on the # fly, so inspecting model.parameters() or optimizer.params may # still show the original, unscaled gradients.) numer = ( self.data_parallel_world_size if not self.cfg.optimization.use_bmuf or self._sync_stats() else 1 ) self.optimizer.multiply_grads(numer / (sample_size or 1.0)) # Note: (sample_size or 1.0) handles the case of a zero gradient, in a # way that avoids CPU/device transfers in case sample_size is a GPU or # TPU object. The assumption is that the gradient itself is also 0. with torch.autograd.profiler.record_function("clip-grads"): # clip grads grad_norm = self.clip_grad_norm(self.cfg.optimization.clip_norm) # check that grad norms are consistent across workers # on tpu check tensor is slow if not self.tpu: if ( not self.cfg.optimization.use_bmuf and self.cfg.distributed_training.ddp_backend != "slow_mo" ): self._check_grad_norms(grad_norm) if not torch.isfinite(grad_norm).all(): # in case of AMP, if gradients are Nan/Inf then # optimizer step is still required if self.cfg.common.amp: overflow = True else: # check local gradnorm single GPU case, trigger NanDetector raise FloatingPointError("gradients are Nan/Inf") with torch.autograd.profiler.record_function("optimizer"): # take an optimization step self.task.optimizer_step( self.optimizer, model=self.model, update_num=self.get_num_updates() ) if self.cfg.common.amp and overflow: if self._amp_retries == self.cfg.common.amp_batch_retries: logger.info("AMP: skipping this batch.") self._amp_retries = 0 else: self._amp_retries += 1 return self.train_step(samples, raise_oom) # recursion to feed in same batch except FloatingPointError: # re-run the forward and backward pass with hooks attached to print # out where it fails self.zero_grad() with NanDetector(self.get_model()): for _, sample in enumerate(samples): sample, _ = self._prepare_sample(sample) self.task.train_step( sample, self.model, self.criterion, self.optimizer, self.get_num_updates(), ignore_grad=False, ) raise except OverflowError as e: overflow = True logger.info( f"NOTE: gradient overflow detected, ignoring gradient, {str(e)}" ) grad_norm = torch.tensor(0.0).cuda() self.zero_grad() except RuntimeError as e: if "out of memory" in str(e): self._log_oom(e) logger.error("OOM during optimization, irrecoverable") raise e # Some distributed wrappers (e.g., SlowMo) need access to the optimizer # after the step if hasattr(self.model, "perform_additional_optimizer_actions"): if hasattr(self.optimizer, "fp32_params"): self.model.perform_additional_optimizer_actions( self.optimizer.optimizer, self.optimizer.fp32_params ) else: self.model.perform_additional_optimizer_actions( self.optimizer.optimizer ) logging_output = None if not overflow or self.cfg.distributed_training.ddp_backend == "slow_mo": self.set_num_updates(self.get_num_updates() + 1) if self.tpu: import torch_xla.core.xla_model as xm # mark step on TPUs self._xla_markstep_and_send_to_cpu() # only log stats every log_interval steps # this causes wps to be misreported when log_interval > 1 logging_output = {} if self.get_num_updates() % self.cfg.common.log_interval == 0: # log memory usage mem_info = xm.get_memory_info(self.device) gb_free = mem_info["kb_free"] / 1024 / 1024 gb_total = mem_info["kb_total"] / 1024 / 1024 metrics.log_scalar( "gb_free", gb_free, priority=1500, round=1, weight=0 ) metrics.log_scalar( "gb_total", gb_total, priority=1600, round=1, weight=0 ) logging_outputs = self._xla_markstep_and_send_to_cpu( logging_outputs ) logging_output = self._reduce_and_log_stats( logging_outputs, sample_size, grad_norm ) # log whenever there's an XLA compilation, since these # slow down training and may indicate opportunities for # optimization self._check_xla_compilation() else: if self.cuda and self.cuda_env is not None: # log minimum free memory over the iteration gb_used = torch.cuda.max_memory_allocated() / 1024 / 1024 / 1024 torch.cuda.reset_peak_memory_stats() gb_free = self.cuda_env.total_memory_in_GB - gb_used metrics.log_scalar( "gb_free", gb_free, priority=1500, round=1, weight=0 ) # log stats logging_output = self._reduce_and_log_stats( logging_outputs, sample_size, grad_norm ) # clear CUDA cache to reduce memory fragmentation if ( self.cuda and self.cfg.common.empty_cache_freq > 0 and ( (self.get_num_updates() + self.cfg.common.empty_cache_freq - 1) % self.cfg.common.empty_cache_freq ) == 0 ): torch.cuda.empty_cache() if self.cfg.common.fp16 or self.cfg.common.amp: metrics.log_scalar( "loss_scale", ( self.optimizer.scaler.loss_scale if self.cfg.common.fp16 else self.optimizer.scaler.get_scale() ), priority=700, round=4, weight=0, ) metrics.log_stop_time("train_wall") return logging_output @metrics.aggregate("valid") def valid_step(self, sample, raise_oom=False): """Do forward pass in evaluation mode.""" if self.tpu: import torch_xla.core.xla_model as xm xm.rendezvous("valid_step") # wait for all workers with torch.no_grad(): self.model.eval() self.criterion.eval() sample, is_dummy_batch = self._prepare_sample(sample) try: _loss, sample_size, logging_output = self.task.valid_step( sample, self.model, self.criterion ) except RuntimeError as e: if "out of memory" in str(e): self._log_oom(e) if not raise_oom: logger.warning( "ran out of memory in validation step, retrying batch" ) for p in self.model.parameters(): if p.grad is not None: p.grad = None # free some memory if self.cuda: torch.cuda.empty_cache() return self.valid_step(sample, raise_oom=True) raise e logging_outputs = [logging_output] if is_dummy_batch: if torch.is_tensor(sample_size): sample_size.zero_() else: sample_size *= 0.0 # gather logging outputs from all replicas if self.data_parallel_world_size > 1: logging_outputs, (sample_size,) = self._aggregate_logging_outputs( logging_outputs, sample_size, ignore=is_dummy_batch, ) # log validation stats if self.tpu: logging_outputs = self._xla_markstep_and_send_to_cpu(logging_outputs) logging_output = self._reduce_and_log_stats(logging_outputs, sample_size) return logging_output def zero_grad(self): self.optimizer.zero_grad() def lr_step_begin_epoch(self, epoch): """Adjust the learning rate at the beginning of the epoch.""" self.lr_scheduler.step_begin_epoch(epoch) # prefer updating the LR based on the number of steps return self.lr_step_update() def lr_step(self, epoch, val_loss=None): """Adjust the learning rate at the end of the epoch.""" self.lr_scheduler.step(epoch, val_loss) # prefer updating the LR based on the number of steps return self.lr_step_update() def lr_step_update(self): """Update the learning rate after each update.""" new_lr = self.lr_scheduler.step_update(self.get_num_updates()) if isinstance(new_lr, dict): for k, v in new_lr.items(): metrics.log_scalar(f"lr_{k}", v, weight=0, priority=300) new_lr = new_lr.get("default", next(iter(new_lr.values()))) else: metrics.log_scalar("lr", new_lr, weight=0, priority=300) return new_lr def get_lr(self): """Get the current learning rate.""" return self.optimizer.get_lr() def get_model(self): """Get the (non-wrapped) model instance.""" return self._model def get_criterion(self): """Get the (non-wrapped) criterion instance.""" return self._criterion def get_meter(self, name): """[deprecated] Get a specific meter by name.""" from fairseq import meters if "get_meter" not in self._warn_once: self._warn_once.add("get_meter") utils.deprecation_warning( "Trainer.get_meter is deprecated. Please use fairseq.metrics instead." ) train_meters = metrics.get_meters("train") if train_meters is None: train_meters = {} if name == "train_loss" and "loss" in train_meters: return train_meters["loss"] elif name == "train_nll_loss": # support for legacy train.py, which assumed this meter is # always initialized m = train_meters.get("nll_loss", None) return m or meters.AverageMeter() elif name == "wall": # support for legacy train.py, which assumed this meter is # always initialized m = metrics.get_meter("default", "wall") return m or meters.TimeMeter() elif name == "wps": m = metrics.get_meter("train", "wps") return m or meters.TimeMeter() elif name in {"valid_loss", "valid_nll_loss"}: # support for legacy train.py, which assumed these meters # are always initialized k = name[len("valid_") :] m = metrics.get_meter("valid", k) return m or meters.AverageMeter() elif name == "oom": return meters.AverageMeter() elif name in train_meters: return train_meters[name] return None def get_num_updates(self): """Get the number of parameters updates.""" return self._num_updates def set_num_updates(self, num_updates): """Set the number of parameters updates.""" self._num_updates = num_updates self.lr_step_update() if self.quantizer: self.quantizer.step_update(self._num_updates) metrics.log_scalar("num_updates", self._num_updates, weight=0, priority=200) def clip_grad_norm(self, clip_norm): def agg_norm_fn(total_norm): total_norm = total_norm.cuda().float() ** 2 total_norm = distributed_utils.all_reduce( total_norm, group=self.data_parallel_process_group ) return total_norm ** 0.5 should_agg_norm = ( self.cfg.distributed_training.ddp_backend == "fully_sharded" and ( self.data_parallel_process_group is not None or torch.distributed.is_initialized() ) ) return self.optimizer.clip_grad_norm( clip_norm, aggregate_norm_fn=agg_norm_fn if should_agg_norm else None ) def cumulative_training_time(self): if self._cumulative_training_time is None: # single GPU return self._local_cumulative_training_time() else: return self._cumulative_training_time def _local_cumulative_training_time(self): """Aggregate training time in seconds.""" return time.time() - self._start_time + self._previous_training_time def _fp_convert_sample(self, sample): def apply_half(t): if t.dtype is torch.float32: return t.to(dtype=torch.half) return t def apply_bfloat16(t): if t.dtype is torch.float32: return t.to(dtype=torch.bfloat16) return t if self.cfg.common.fp16: sample = utils.apply_to_sample(apply_half, sample) if self.cfg.common.bf16: sample = utils.apply_to_sample(apply_bfloat16, sample) return sample def _prepare_sample(self, sample, is_dummy=False): if sample == "DUMMY": raise Exception( "Trying to use an uninitialized 'dummy' batch. This usually indicates " "that the total number of batches is smaller than the number of " "participating GPUs. Try reducing the batch size or using fewer GPUs." ) if sample is None or len(sample) == 0: assert ( self._dummy_batch is not None and len(self._dummy_batch) > 0 ), "Invalid dummy batch: {}".format(self._dummy_batch) sample, _ = self._prepare_sample(self._dummy_batch, is_dummy=True) return sample, True # Given that PCIe/NVLink bandwidth is significantly smaller than DRAM bandwidth # it makes sense to do the format conversion on the CPU and then transfer # a smaller buffer to the device. This also saves GPU memory capacity. if self.cfg.common.on_cpu_convert_precision: sample = self._fp_convert_sample(sample) if self.cuda: if self.pipeline_model_parallel: if 'target' in sample: sample['target'] = utils.move_to_cuda(sample['target'], device=self.last_device) else: sample = utils.move_to_cuda(sample) elif self.tpu and is_dummy: # the dummy batch may not be on the appropriate device sample = utils.move_to_cuda(sample, device=self.device) if not self.cfg.common.on_cpu_convert_precision: sample = self._fp_convert_sample(sample) if self._dummy_batch == "DUMMY": self._dummy_batch = sample return sample, False def _set_seed(self): # Set seed based on args.seed and the update number so that we get # reproducible results when resuming from checkpoints seed = self.cfg.common.seed + self.get_num_updates() utils.set_torch_seed(seed) def _sync_stats(self): # Return True if it's using multiple GPUs and DDP or multiple GPUs with # BMUF and it's a bmuf sync with warmup iterations completed before. if self.data_parallel_world_size == 1: return False elif self.cfg.optimization.use_bmuf: return ( self.get_num_updates() + 1 ) % self.cfg.bmuf.global_sync_iter == 0 and ( self.get_num_updates() + 1 ) > self.cfg.bmuf.warmup_iterations else: return True def _log_oom(self, exc): msg = "OOM: Ran out of memory with exception: {}".format(exc) logger.warning(msg) if torch.cuda.is_available() and hasattr(torch.cuda, "memory_summary"): for device_idx in range(torch.cuda.device_count()): logger.warning(torch.cuda.memory_summary(device=device_idx)) sys.stderr.flush() def _aggregate_logging_outputs( self, logging_outputs: List[Dict[str, Any]], *extra_stats_to_sum, ignore=False, ): if self.task.__class__.logging_outputs_can_be_summed(self.get_criterion()): return self._fast_stat_sync_sum( logging_outputs, *extra_stats_to_sum, ignore=ignore ) else: return self._all_gather_list_sync( logging_outputs, *extra_stats_to_sum, ignore=ignore ) def _all_gather_list_sync( self, logging_outputs: List[Dict[str, Any]], *extra_stats_to_sum, ignore=False, ): """ Sync logging outputs across workers. all_gather_list_sync is suitable when logging outputs are complex types. """ if self.tpu: raise NotImplementedError if ignore: logging_outputs = [] results = list( zip( *distributed_utils.all_gather_list( [logging_outputs] + list(extra_stats_to_sum), max_size=getattr(self.cfg.common, "all_gather_list_size", 16384), group=self.data_parallel_process_group, ) ) ) logging_outputs, extra_stats_to_sum = results[0], results[1:] logging_outputs = list(chain.from_iterable(logging_outputs)) extra_stats_to_sum = [sum(s) for s in extra_stats_to_sum] return logging_outputs, extra_stats_to_sum def _fast_stat_sync_sum( self, logging_outputs: List[Dict[str, Any]], *extra_stats_to_sum, ignore=False, ): """ Sync logging outputs across workers. fast_stat_sync_sum is faster than all_gather_list_sync, but is only suitable when logging outputs are scalars and can be summed. Note that *logging_outputs* cannot contain any nested dicts/lists. """ data = {} for i, stat in enumerate(extra_stats_to_sum): data["extra_stats_" + str(i)] = stat if len(logging_outputs) > 0: log_keys = list(logging_outputs[0].keys()) for k in log_keys: if not ignore: v = sum(log[k] for log in logging_outputs if k in log) else: v = logging_outputs[0][k] v = torch.zeros_like(v) if torch.is_tensor(v) else 0 data["logging_outputs_" + k] = v else: log_keys = None data = distributed_utils.all_reduce_dict( data, device=self.device, group=self.data_parallel_process_group ) extra_stats_to_sum = [ data["extra_stats_" + str(i)] for i in range(len(extra_stats_to_sum)) ] if log_keys is not None: logging_outputs = [{k: data["logging_outputs_" + k] for k in log_keys}] else: logging_outputs = [] return logging_outputs, extra_stats_to_sum def _check_grad_norms(self, grad_norm): """Check that grad norms are consistent across workers.""" if self._grad_norm_buf is not None: self._grad_norm_buf.zero_() self._grad_norm_buf[self.data_parallel_rank] = grad_norm distributed_utils.all_reduce( self._grad_norm_buf, group=self.data_parallel_process_group ) def is_consistent(tensor): max_abs_diff = torch.max(torch.abs(tensor - tensor[0])) return ( (torch.isfinite(tensor).all() and (max_abs_diff / (tensor[0] + 1e-6) < 1e-6).all()) or (self.cfg.common.amp and not torch.isfinite(tensor).all()) # in case of amp non-finite grads are fine ) if not is_consistent(self._grad_norm_buf): pretty_detail = "\n".join( "rank {:3d} = {:.8f}".format(r, n) for r, n in enumerate(self._grad_norm_buf.tolist()) ) error_detail = "grad_norm across the workers:\n{}\n".format( pretty_detail ) # use FloatingPointError to trigger NanDetector raise FloatingPointError( "Fatal error: gradients are inconsistent between workers. " "Try --ddp-backend=legacy_ddp. " "Or are you mixing up different generation of GPUs in training?" + "\n" + "-" * 80 + "\n{}\n".format(error_detail) + "-" * 80 ) def _reduce_and_log_stats(self, logging_outputs, sample_size, grad_norm=None): if grad_norm is not None and ( not torch.is_tensor(grad_norm) or torch.isfinite(grad_norm) ): metrics.log_speed("ups", 1.0, priority=100, round=2) metrics.log_scalar("gnorm", grad_norm, priority=400, round=3) if self.cfg.optimization.clip_norm > 0: metrics.log_scalar( "clip", torch.where( grad_norm > self.cfg.optimization.clip_norm, grad_norm.new_tensor(100), grad_norm.new_tensor(0), ), priority=500, round=1, ) with metrics.aggregate() as agg: if logging_outputs is not None: self.task.reduce_metrics(logging_outputs, self.get_criterion()) del logging_outputs # extra warning for criterions that don't properly log a loss value if "loss" not in agg: if "loss" not in self._warn_once: self._warn_once.add("loss") logger.warning( "Criterion.reduce_metrics did not log a 'loss' value, " "which may break some functionality" ) metrics.log_scalar("loss", -1) # support legacy interface if self.tpu: logging_output = {} else: logging_output = agg.get_smoothed_values() logging_output["sample_size"] = sample_size for key_to_delete in ["ppl", "wps", "wpb", "bsz"]: if key_to_delete in logging_output: del logging_output[key_to_delete] return logging_output def _check_xla_compilation(self): import torch_xla.debug.metrics as met compile_stats = met.metric_data("CompileTime") if compile_stats is None: return num_xla_compiles = compile_stats[0] if num_xla_compiles > self._num_xla_compiles: logger.warning( "XLA compilation detected on device #{}; too many of these can lead " "to slow training, but we expect a few in the beginning".format( self.cfg.distributed_training.distributed_rank ) ) self._num_xla_compiles = num_xla_compiles def _xla_markstep_and_send_to_cpu(self, data=None): import torch_xla.core.xla_model as xm xm.mark_step() if data is not None: from fairseq.utils import xla_device_to_cpu return xla_device_to_cpu(data) def _catalog_shared_params(module, memo=None, prefix=""): if memo is None: first_call = True memo = {} else: first_call = False for name, param in module._parameters.items(): param_prefix = prefix + ("." if prefix else "") + name if param not in memo: memo[param] = [] memo[param].append(param_prefix) for name, m in module._modules.items(): if m is None: continue submodule_prefix = prefix + ("." if prefix else "") + name _catalog_shared_params(m, memo, submodule_prefix) if first_call: return [x for x in memo.values() if len(x) > 1] def _get_module_by_path(module, path): path = path.split(".") for name in path: module = getattr(module, name) return module def _set_module_by_path(module, path, value): path = path.split(".") for name in path[:-1]: module = getattr(module, name) setattr(module, path[-1], value)