import random from torch.cuda.amp import GradScaler, autocast from utils import move_to_cuda import subprocess import numpy as np import torch.optim import torch.utils.data import copy import logging import os import re import sys import torch import torch.distributed as dist import torch.multiprocessing as mp import tqdm from utils.ckpt_utils import get_last_checkpoint, get_all_ckpts from utils.ddp_utils import DDP from utils.hparams import hparams class Trainer: def __init__( self, work_dir, default_save_path=None, accumulate_grad_batches=1, max_updates=160000, print_nan_grads=False, val_check_interval=2000, num_sanity_val_steps=5, amp=False, # tb logger log_save_interval=100, tb_log_interval=10, # checkpoint monitor_key='val_loss', monitor_mode='min', num_ckpt_keep=5, save_best=True, resume_from_checkpoint=0, seed=1234, debug=False, ): os.makedirs(work_dir, exist_ok=True) self.work_dir = work_dir self.accumulate_grad_batches = accumulate_grad_batches self.max_updates = max_updates self.num_sanity_val_steps = num_sanity_val_steps self.print_nan_grads = print_nan_grads self.default_save_path = default_save_path self.resume_from_checkpoint = resume_from_checkpoint if resume_from_checkpoint > 0 else None self.seed = seed self.debug = debug # model and optm self.task = None self.optimizers = [] # trainer state self.testing = False self.global_step = 0 self.current_epoch = 0 self.total_batches = 0 # configure checkpoint self.monitor_key = monitor_key self.num_ckpt_keep = num_ckpt_keep self.save_best = save_best self.monitor_op = np.less if monitor_mode == 'min' else np.greater self.best_val_results = np.Inf if monitor_mode == 'min' else -np.Inf self.mode = 'min' # allow int, string and gpu list self.all_gpu_ids = [ int(x) for x in os.environ.get("CUDA_VISIBLE_DEVICES", "").split(",") if x != ''] self.num_gpus = len(self.all_gpu_ids) self.on_gpu = self.num_gpus > 0 self.root_gpu = 0 logging.info(f'GPU available: {torch.cuda.is_available()}, GPU used: {self.all_gpu_ids}') self.use_ddp = self.num_gpus > 1 self.proc_rank = 0 # Tensorboard logging self.log_save_interval = log_save_interval self.val_check_interval = val_check_interval self.tb_log_interval = tb_log_interval self.amp = amp self.amp_scalar = GradScaler() def test(self, task_cls): self.testing = True self.fit(task_cls) def fit(self, task_cls): if len(self.all_gpu_ids) > 1: mp.spawn(self.ddp_run, nprocs=self.num_gpus, args=(task_cls, copy.deepcopy(hparams))) else: self.task = task_cls() self.task.trainer = self self.run_single_process(self.task) return 1 def ddp_run(self, gpu_idx, task_cls, hparams_): hparams.update(hparams_) task = task_cls() self.ddp_init(gpu_idx, task) self.run_single_process(task) def run_single_process(self, task): """Sanity check a few things before starting actual training. :param task: """ # build model, optm and load checkpoint model = task.build_model() if model is not None: task.model = model checkpoint, _ = get_last_checkpoint(self.work_dir, self.resume_from_checkpoint) if checkpoint is not None: self.restore_weights(checkpoint) elif self.on_gpu: task.cuda(self.root_gpu) if not self.testing: self.optimizers = task.configure_optimizers() self.fisrt_epoch = True if checkpoint is not None: self.restore_opt_state(checkpoint) del checkpoint # clear cache after restore if self.on_gpu: torch.cuda.empty_cache() if self.use_ddp: self.task = self.configure_ddp(self.task) dist.barrier() task_ref = self.get_task_ref() task_ref.trainer = self task_ref.testing = self.testing # link up experiment object if self.proc_rank == 0: task_ref.build_tensorboard(save_dir=self.work_dir, name='lightning_logs', version='lastest') else: os.makedirs('tmp', exist_ok=True) task_ref.build_tensorboard(save_dir='tmp', name='tb_tmp', version='lastest') self.logger = task_ref.logger try: if self.testing: self.run_evaluation(test=True) else: self.train() except KeyboardInterrupt as e: task_ref.on_keyboard_interrupt() #################### # valid and test #################### def run_evaluation(self, test=False): eval_results = self.evaluate(self.task, test, tqdm_desc='Valid' if not test else 'test') if eval_results is not None and 'tb_log' in eval_results: tb_log_output = eval_results['tb_log'] self.log_metrics_to_tb(tb_log_output) if self.proc_rank == 0 and not test: self.save_checkpoint(epoch=self.current_epoch, logs=eval_results) def evaluate(self, task, test=False, tqdm_desc='Valid', max_batches=None): # enable eval mode task.zero_grad() task.eval() torch.set_grad_enabled(False) task_ref = self.get_task_ref() if test: ret = task_ref.test_start() if ret == 'EXIT': return outputs = [] dataloader = task_ref.test_dataloader() if test else task_ref.val_dataloader() pbar = tqdm.tqdm(dataloader, desc=tqdm_desc, total=max_batches, dynamic_ncols=True, unit='step', disable=self.root_gpu > 0) for batch_idx, batch in enumerate(pbar): if batch is None: # pragma: no cover continue # stop short when on fast_dev_run (sets max_batch=1) if max_batches is not None and batch_idx >= max_batches: break # make dataloader_idx arg in validation_step optional if self.on_gpu: batch = move_to_cuda(batch, self.root_gpu) args = [batch, batch_idx] if self.use_ddp: output = task(*args) else: if test: output = task_ref.test_step(*args) else: output = task_ref.validation_step(*args) # track outputs for collation outputs.append(output) # give model a chance to do something with the outputs (and method defined) if test: eval_results = task_ref.test_end(outputs) else: eval_results = task_ref.validation_end(outputs) # enable train mode again task.train() torch.set_grad_enabled(True) return eval_results #################### # train #################### def train(self): task_ref = self.get_task_ref() task_ref.on_train_start() if self.num_sanity_val_steps > 0: # run tiny validation (if validation defined) to make sure program won't crash during val self.evaluate(self.task, False, 'Sanity Val', max_batches=self.num_sanity_val_steps) # clear cache before training if self.on_gpu: torch.cuda.empty_cache() dataloader = task_ref.train_dataloader() epoch = self.current_epoch # run all epochs while True: # set seed for distributed sampler (enables shuffling for each epoch) if self.use_ddp and hasattr(dataloader.sampler, 'set_epoch'): dataloader.sampler.set_epoch(epoch) # update training progress in trainer and model task_ref.current_epoch = epoch self.current_epoch = epoch # total batches includes multiple val checks self.batch_loss_value = 0 # accumulated grads # before epoch hook task_ref.on_epoch_start() # run epoch train_pbar = tqdm.tqdm(dataloader, initial=self.global_step, total=float('inf'), dynamic_ncols=True, unit='step', disable=self.root_gpu > 0) for batch_idx, batch in enumerate(train_pbar): pbar_metrics, tb_metrics = self.run_training_batch(batch_idx, batch) train_pbar.set_postfix(**pbar_metrics) should_check_val = (self.global_step % self.val_check_interval == 0 and not self.fisrt_epoch) if should_check_val: self.run_evaluation() self.fisrt_epoch = False # when metrics should be logged if (self.global_step + 1) % self.tb_log_interval == 0: # logs user requested information to logger self.log_metrics_to_tb(tb_metrics) self.global_step += 1 task_ref.global_step = self.global_step if self.global_step > self.max_updates: print("| Training end..") break # epoch end hook task_ref.on_epoch_end() epoch += 1 if self.global_step > self.max_updates: break task_ref.on_train_end() def run_training_batch(self, batch_idx, batch): if batch is None: return {} all_progress_bar_metrics = [] all_log_metrics = [] task_ref = self.get_task_ref() for opt_idx, optimizer in enumerate(self.optimizers): if optimizer is None: continue # make sure only the gradients of the current optimizer's paramaters are calculated # in the training step to prevent dangling gradients in multiple-optimizer setup. if len(self.optimizers) > 1: for param in task_ref.parameters(): param.requires_grad = False for group in optimizer.param_groups: for param in group['params']: param.requires_grad = True # forward pass with autocast(enabled=self.amp): if self.on_gpu: batch = move_to_cuda(copy.copy(batch), self.root_gpu) args = [batch, batch_idx, opt_idx] if self.use_ddp: output = self.task(*args) else: output = task_ref.training_step(*args) loss = output['loss'] if loss is None: continue progress_bar_metrics = output['progress_bar'] log_metrics = output['tb_log'] # accumulate loss loss = loss / self.accumulate_grad_batches # backward pass if loss.requires_grad: if self.amp: self.amp_scalar.scale(loss).backward() else: loss.backward() # track progress bar metrics all_log_metrics.append(log_metrics) all_progress_bar_metrics.append(progress_bar_metrics) if loss is None: continue # nan grads if self.print_nan_grads: has_nan_grad = False for name, param in task_ref.named_parameters(): if (param.grad is not None) and torch.isnan(param.grad.float()).any(): print("| NaN params: ", name, param, param.grad) has_nan_grad = True if has_nan_grad: exit(0) # gradient update with accumulated gradients if (self.global_step + 1) % self.accumulate_grad_batches == 0: task_ref.on_before_optimization(opt_idx) if self.amp: self.amp_scalar.step(optimizer) self.amp_scalar.update() else: optimizer.step() optimizer.zero_grad() task_ref.on_after_optimization(self.current_epoch, batch_idx, optimizer, opt_idx) # collapse all metrics into one dict all_progress_bar_metrics = {k: v for d in all_progress_bar_metrics for k, v in d.items()} all_log_metrics = {k: v for d in all_log_metrics for k, v in d.items()} return all_progress_bar_metrics, all_log_metrics #################### # load and save checkpoint #################### def restore_weights(self, checkpoint): # load model state task_ref = self.get_task_ref() if len([k for k in checkpoint['state_dict'].keys() if '.' in k]) > 0: task_ref.load_state_dict(checkpoint['state_dict']) else: for k, v in checkpoint['state_dict'].items(): getattr(task_ref, k).load_state_dict(v) if self.on_gpu: task_ref.cuda(self.root_gpu) # load training state (affects trainer only) self.best_val_results = checkpoint['checkpoint_callback_best'] self.global_step = checkpoint['global_step'] self.current_epoch = checkpoint['epoch'] task_ref.global_step = self.global_step # wait for all model to restore weights if self.use_ddp: # wait for all processes to catch up dist.barrier() def restore_opt_state(self, checkpoint): if self.testing: return # restore the optimizers optimizer_states = checkpoint['optimizer_states'] for optimizer, opt_state in zip(self.optimizers, optimizer_states): if optimizer is None: return try: optimizer.load_state_dict(opt_state) # move optimizer to GPU 1 weight at a time if self.on_gpu: for state in optimizer.state.values(): for k, v in state.items(): if isinstance(v, torch.Tensor): state[k] = v.cuda(self.root_gpu) except ValueError: print("| WARMING: optimizer parameters not match !!!") try: if dist.is_initialized() and dist.get_rank() > 0: return except Exception as e: print(e) return did_restore = True return did_restore def save_checkpoint(self, epoch, logs=None): monitor_op = np.less ckpt_path = f'{self.work_dir}/model_ckpt_steps_{self.global_step}.ckpt' logging.info(f'Epoch {epoch:05d}@{self.global_step}: saving model to {ckpt_path}') self._atomic_save(ckpt_path) for old_ckpt in get_all_ckpts(self.work_dir)[self.num_ckpt_keep:]: subprocess.check_call(f'rm -rf "{old_ckpt}"', shell=True) logging.info(f'Delete ckpt: {os.path.basename(old_ckpt)}') current = None if logs is not None and self.monitor_key in logs: current = logs[self.monitor_key] if current is not None and self.save_best: if monitor_op(current, self.best_val_results): best_filepath = f'{self.work_dir}/model_ckpt_best.pt' self.best_val_results = current logging.info( f'Epoch {epoch:05d}@{self.global_step}: {self.monitor_key} reached {current:0.5f}. ' f'Saving model to {best_filepath}') self._atomic_save(best_filepath) def _atomic_save(self, filepath): checkpoint = self.dump_checkpoint() tmp_path = str(filepath) + ".part" torch.save(checkpoint, tmp_path, _use_new_zipfile_serialization=False) os.replace(tmp_path, filepath) def dump_checkpoint(self): checkpoint = {'epoch': self.current_epoch, 'global_step': self.global_step, 'checkpoint_callback_best': self.best_val_results} # save optimizers optimizer_states = [] for i, optimizer in enumerate(self.optimizers): if optimizer is not None: optimizer_states.append(optimizer.state_dict()) checkpoint['optimizer_states'] = optimizer_states task_ref = self.get_task_ref() checkpoint['state_dict'] = { k: v.state_dict() for k, v in task_ref.named_children() if len(list(v.parameters())) > 0} return checkpoint #################### # DDP #################### def ddp_init(self, gpu_idx, task): # determine which process we are and world size self.proc_rank = gpu_idx task.trainer = self self.init_ddp_connection(self.proc_rank, self.num_gpus) # copy model to each gpu torch.cuda.set_device(gpu_idx) # override root GPU self.root_gpu = gpu_idx self.task = task def configure_ddp(self, task): task = DDP(task, device_ids=[self.root_gpu], find_unused_parameters=True) if dist.get_rank() != 0 and not self.debug: sys.stdout = open(os.devnull, "w") sys.stderr = open(os.devnull, "w") random.seed(self.seed) np.random.seed(self.seed) return task def init_ddp_connection(self, proc_rank, world_size): root_node = '127.0.0.1' root_node = self.resolve_root_node_address(root_node) os.environ['MASTER_ADDR'] = root_node dist.init_process_group('nccl', rank=proc_rank, world_size=world_size) def resolve_root_node_address(self, root_node): if '[' in root_node: name = root_node.split('[')[0] number = root_node.split(',')[0] if '-' in number: number = number.split('-')[0] number = re.sub('[^0-9]', '', number) root_node = name + number return root_node #################### # utils #################### def get_task_ref(self): from tasks.base_task import BaseTask task: BaseTask = self.task.module if isinstance(self.task, DDP) else self.task return task def log_metrics_to_tb(self, metrics, step=None): """Logs the metric dict passed in. :param metrics: """ # added metrics by Lightning for convenience metrics['epoch'] = self.current_epoch # turn all tensors to scalars scalar_metrics = self.metrics_to_scalars(metrics) step = step if step is not None else self.global_step # log actual metrics if self.proc_rank == 0: self.log_metrics(self.logger, scalar_metrics, step=step) @staticmethod def log_metrics(logger, metrics, step=None): for k, v in metrics.items(): if isinstance(v, torch.Tensor): v = v.item() logger.add_scalar(k, v, step) def metrics_to_scalars(self, metrics): new_metrics = {} for k, v in metrics.items(): if isinstance(v, torch.Tensor): v = v.item() if type(v) is dict: v = self.metrics_to_scalars(v) new_metrics[k] = v return new_metrics