# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Pretrain utilities.""" from datetime import datetime import nvtx import math import sys import time # The earliest we can measure the start time. _TRAIN_START_TIME = time.time() import torch from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP from megatron import get_args from megatron import get_timers from megatron import get_tensorboard_writer from megatron import get_current_global_batch_size from megatron import get_num_microbatches from megatron import is_last_rank from megatron import update_num_microbatches from megatron import mpu from megatron import print_rank_0 from megatron import print_rank_last from megatron.checkpointing import load_checkpoint from megatron.checkpointing import save_checkpoint from megatron.model import Float16Module from megatron.optimizer import get_megatron_optimizer from megatron.initialize import initialize_megatron from megatron.initialize import write_args_to_tensorboard from megatron.learning_rates import AnnealingLR from megatron.model import DistributedDataParallel as LocalDDP from megatron.utils import check_adlr_autoresume_termination from megatron.utils import unwrap_model from megatron.data.data_samplers import build_pretraining_data_loader from megatron.utils import calc_params_l2_norm from megatron.schedules import forward_backward_no_pipelining from megatron.schedules import forward_backward_pipelining_without_interleaving from megatron.schedules import forward_backward_pipelining_with_interleaving from megatron.utils import report_memory def print_datetime(string): """Note that this call will sync across all ranks.""" torch.distributed.barrier() time_str = datetime.now().strftime('%Y-%m-%d %H:%M:%S') print_rank_0('[' + string + '] datetime: {} '.format(time_str)) def pretrain(train_valid_test_dataset_provider, model_provider, forward_step_func, extra_args_provider=None, args_defaults={}): """Main training program. This function will run the followings in the order provided: 1) initialize Megatron. 2) setup model, optimizer and lr schedule using the model_provider. 3) call train_val_test_data_provider to get train/val/test datasets. 4) train the modle using the forward_step_func. Arguments: train_valid_test_dataset_provider: a function that takes the size of train/valid/test dataset and returns `train, valid, test` datasets. model_provider: a function that returns a vanilla version of the model. By vanilla we mean a simple model on cpu with no fp16 or ddp. forward_step_func: a function that takes a `data iterator` and `model`, and returns a `loss` scalar with a dictionary with key:values being the info we would like to monitor during training, for example `lm-loss: value`. We also require that this function add `batch generator` to the timers class. extra_args_provider: a function that takes a parser and adds arguments to it. It is used for programs to add their own arguments. args_defaults: a dictionary from argument-name to argument-value. It to set already parse arguments. """ with nvtx.annotate("initialize", color="cyan"): # Initalize and get arguments, timers, and Tensorboard writer. initialize_megatron(extra_args_provider=extra_args_provider, args_defaults=args_defaults) # Adjust the startup time so it reflects the largest value. # This will be closer to what scheduler will see (outside of # image ... launches. global _TRAIN_START_TIME start_time_tensor = torch.cuda.FloatTensor([_TRAIN_START_TIME]) torch.distributed.all_reduce(start_time_tensor, op=torch.distributed.ReduceOp.MIN) _TRAIN_START_TIME = start_time_tensor.item() print_rank_0('time to initialize megatron (seconds): {:.3f}'.format( time.time() - _TRAIN_START_TIME)) print_datetime('after megatron is initialized') args = get_args() timers = get_timers() # Model, optimizer, and learning rate. timers('model-and-optimizer-setup').start() model, optimizer, lr_scheduler = setup_model_and_optimizer(model_provider) timers('model-and-optimizer-setup').stop() print_datetime('after model, optimizer, and learning rate ' 'scheduler are built') # Data stuff. timers('train/valid/test-data-iterators-setup').start() with nvtx.annotate("data_loading", color="orange"): if args.virtual_pipeline_model_parallel_size is not None: all_data_iterators = [ build_train_valid_test_data_iterators(train_valid_test_dataset_provider) for _ in range(len(model)) ] train_data_iterator = [data_iterators[0] for data_iterators in all_data_iterators] valid_data_iterator = [data_iterators[1] for data_iterators in all_data_iterators] test_data_iterator = [data_iterators[2] for data_iterators in all_data_iterators] else: train_data_iterator, valid_data_iterator, test_data_iterator \ = build_train_valid_test_data_iterators( train_valid_test_dataset_provider) timers('train/valid/test-data-iterators-setup').stop() print_datetime('after dataloaders are built') # Print setup timing. print_rank_0('done with setup ...') timers.log(['model-and-optimizer-setup', 'train/valid/test-data-iterators-setup']) print_rank_0('training ...') iteration = 0 with nvtx.annotate("training", color="blue"): if args.do_train and args.train_iters > 0: iteration = train(forward_step_func, model, optimizer, lr_scheduler, train_data_iterator, valid_data_iterator) print_datetime('after training is done') if args.do_valid: prefix = 'the end of training for val data' evaluate_and_print_results(prefix, forward_step_func, valid_data_iterator, model, iteration, False) with nvtx.annotate("checkpointing", color="yellow"): if args.save and iteration != 0: save_checkpoint(iteration, model, optimizer, lr_scheduler) with nvtx.annotate("do_test", color="darkgreen"): if args.do_test: # Run on test data. prefix = 'the end of training for test data' evaluate_and_print_results(prefix, forward_step_func, test_data_iterator, model, 0, True) def update_train_iters(args): # For iteration-based training, we don't need to do anything if args.train_iters: return # Constant batch size with sample-based training. if args.rampup_batch_size is None: args.train_iters = args.train_samples // args.global_batch_size else: # Sample based training with rampup batch size. iterations = 0 consumed_samples = 0 # Rampup phase. while consumed_samples <= int(args.rampup_batch_size[2]): update_num_microbatches(consumed_samples, consistency_check=False) consumed_samples += get_current_global_batch_size() iterations += 1 # Reset update_num_microbatches(0, consistency_check=False) # Constant phase # Note that we throw away any partial last batch. iterations += (args.train_samples - consumed_samples) // \ args.global_batch_size args.train_iters = iterations print_rank_0('setting training iterations to {}'.format(args.train_iters)) def get_model(model_provider_func): """Build the model.""" args = get_args() # Build model. if mpu.get_pipeline_model_parallel_world_size() > 1 and \ args.virtual_pipeline_model_parallel_size is not None: model = [] for i in range(args.virtual_pipeline_model_parallel_size): mpu.set_virtual_pipeline_model_parallel_rank(i) # Set pre_process and post_process only after virtual rank is set. pre_process = mpu.is_pipeline_first_stage() post_process = mpu.is_pipeline_last_stage() this_model = model_provider_func( pre_process=pre_process, post_process=post_process ) model.append(this_model) else: pre_process = mpu.is_pipeline_first_stage() post_process = mpu.is_pipeline_last_stage() model = model_provider_func( pre_process=pre_process, post_process=post_process ) if not isinstance(model, list): model = [model] # Set tensor model parallel attributes if not set. # Only parameters that are already tensor model parallel have these # attributes set for them. We should make sure the default attributes # are set for all params so the optimizer can use them. for model_module in model: for param in model_module.parameters(): mpu.set_defaults_if_not_set_tensor_model_parallel_attributes(param) # Print number of parameters. if mpu.get_data_parallel_rank() == 0: print(' > number of parameters on (tensor, pipeline) ' 'model parallel rank ({}, {}): {}'.format( mpu.get_tensor_model_parallel_rank(), mpu.get_pipeline_model_parallel_rank(), sum([sum([p.nelement() for p in model_module.parameters()]) for model_module in model])), flush=True) # GPU allocation. for model_module in model: model_module.cuda(torch.cuda.current_device()) # Fp16 conversion. if args.fp16 or args.bf16: model = [Float16Module(model_module, args) for model_module in model] if args.DDP_impl == 'torch': i = torch.cuda.current_device() model = [torchDDP(model_module, device_ids=[i], output_device=i, process_group=mpu.get_data_parallel_group()) for model_module in model] return model if args.DDP_impl == 'local': model = [LocalDDP(model_module, args.accumulate_allreduce_grads_in_fp32, args.use_contiguous_buffers_in_ddp) for model_module in model] return model raise NotImplementedError('Unknown DDP implementation specified: {}. ' 'Exiting.'.format(args.DDP_impl)) def get_learning_rate_scheduler(optimizer): """Build the learning rate scheduler.""" args = get_args() # Iteration-based training. if args.train_iters: if args.lr_decay_iters is None: args.lr_decay_iters = args.train_iters decay_steps = args.lr_decay_iters * args.global_batch_size if args.lr_warmup_fraction is not None: warmup_steps = args.lr_warmup_fraction * decay_steps else: warmup_steps = args.lr_warmup_iters * args.global_batch_size # Sample-based training. elif args.train_samples: # We need to set training iters for later use. Technically # we need to adjust the training samples too (due to last # batch being incomplete) but we leave it as is for now. update_train_iters(args) if args.lr_decay_samples is None: args.lr_decay_samples = args.train_samples decay_steps = args.lr_decay_samples if args.lr_warmup_fraction is not None: warmup_steps = args.lr_warmup_fraction * decay_steps else: warmup_steps = args.lr_warmup_samples else: raise Exception( 'either train-iters or train-samples should be provided.') lr_scheduler = AnnealingLR( optimizer, max_lr=args.lr, min_lr=args.min_lr, warmup_steps=warmup_steps, decay_steps=decay_steps, decay_style=args.lr_decay_style, use_checkpoint_lr_scheduler=args.use_checkpoint_lr_scheduler, override_lr_scheduler=args.override_lr_scheduler) return lr_scheduler def setup_model_and_optimizer(model_provider_func): """Setup model and optimizer.""" args = get_args() model = get_model(model_provider_func) unwrapped_model = unwrap_model(model, (torchDDP, LocalDDP, Float16Module)) optimizer = get_megatron_optimizer(unwrapped_model) lr_scheduler = get_learning_rate_scheduler(optimizer) if args.load is not None: timers = get_timers() # Extra barrier is added to make sure all ranks report the # max time. torch.distributed.barrier() timers('load-checkpoint').start() args.iteration = load_checkpoint(model, optimizer, lr_scheduler) torch.distributed.barrier() timers('load-checkpoint').stop() timers.log(['load-checkpoint']) else: args.iteration = 0 # We only support local DDP with multiple micro-batches. if len(model) > 1 or mpu.get_pipeline_model_parallel_world_size() > 1: assert args.DDP_impl == 'local' # get model without FP16 and/or TorchDDP wrappers if args.iteration == 0 and len(unwrapped_model) == 1 \ and hasattr(unwrapped_model[0], 'init_state_dict_from_bert'): print_rank_0("Initializing ICT from pretrained BERT model") unwrapped_model[0].init_state_dict_from_bert() if args.fp16: optimizer.reload_model_params() return model, optimizer, lr_scheduler def train_step(forward_step_func, data_iterator, model, optimizer, lr_scheduler): """Single training step.""" args = get_args() timers = get_timers() # Set grad to zero. if args.DDP_impl == 'local' and args.use_contiguous_buffers_in_ddp: for partition in model: partition.zero_grad_buffer() else: optimizer.zero_grad() if mpu.get_pipeline_model_parallel_world_size() > 1: if args.virtual_pipeline_model_parallel_size is not None: forward_backward_func = forward_backward_pipelining_with_interleaving assert get_num_microbatches() % args.pipeline_model_parallel_size == 0, \ 'number of microbatches is not divisible by pipeline-parallel ' \ 'size when using interleaved schedule' else: forward_backward_func = forward_backward_pipelining_without_interleaving else: forward_backward_func = forward_backward_no_pipelining losses_reduced = forward_backward_func( forward_step_func, data_iterator, model, optimizer, timers, forward_only=False) # All-reduce if needed. if args.DDP_impl == 'local': timers('backward-params-all-reduce').start() for model_module in model: model_module.allreduce_gradients() timers('backward-params-all-reduce').stop() # All-reduce word_embeddings' grad across first and last stages to ensure # that word_embeddings parameters stay in sync. # This should only run for models that support pipelined model parallelism # (BERT and GPT-2). timers('backward-embedding-all-reduce').start() if (mpu.is_pipeline_first_stage(ignore_virtual=True) or mpu.is_pipeline_last_stage(ignore_virtual=True)) and \ mpu.get_pipeline_model_parallel_world_size() > 1: if mpu.is_pipeline_first_stage(ignore_virtual=True): unwrapped_model = model[0] elif mpu.is_pipeline_last_stage(ignore_virtual=True): unwrapped_model = model[-1] unwrapped_model = unwrap_model( unwrapped_model, (torchDDP, LocalDDP, Float16Module)) if unwrapped_model.share_word_embeddings: word_embeddings_weight = unwrapped_model.word_embeddings_weight() if args.DDP_impl == 'local': grad = word_embeddings_weight.main_grad else: grad = word_embeddings_weight.grad torch.distributed.all_reduce(grad, group=mpu.get_embedding_group()) timers('backward-embedding-all-reduce').stop() # Update parameters. timers('optimizer').start() update_successful, grad_norm, num_zeros_in_grad = optimizer.step() timers('optimizer').stop() # Update learning rate. if update_successful: increment = get_num_microbatches() * \ args.micro_batch_size * \ args.data_parallel_size lr_scheduler.step(increment=increment) skipped_iter = 0 else: skipped_iter = 1 if mpu.is_pipeline_last_stage(ignore_virtual=True): # Average loss across microbatches. loss_reduced = {} for key in losses_reduced[0]: losses_reduced_for_key = [x[key] for x in losses_reduced] loss_reduced[key] = sum(losses_reduced_for_key) / len(losses_reduced_for_key) return loss_reduced, skipped_iter, grad_norm, num_zeros_in_grad return {}, skipped_iter, grad_norm, num_zeros_in_grad def training_log(loss_dict, total_loss_dict, learning_rate, iteration, loss_scale, report_memory_flag, skipped_iter, grad_norm, params_norm, num_zeros_in_grad): """Log training information such as losses, timing, ....""" args = get_args() timers = get_timers() writer = get_tensorboard_writer() # Advanced, skipped, and Nan iterations. advanced_iters_key = 'advanced iterations' skipped_iters_key = 'skipped iterations' nan_iters_key = 'nan iterations' # Advanced iterations. if not skipped_iter: total_loss_dict[advanced_iters_key] = total_loss_dict.get( advanced_iters_key, 0) + 1 else: if advanced_iters_key not in total_loss_dict: total_loss_dict[advanced_iters_key] = 0 # Skipped iterations. total_loss_dict[skipped_iters_key] = total_loss_dict.get( skipped_iters_key, 0) + skipped_iter # Update losses and set nan iterations got_nan = False for key in loss_dict: if not skipped_iter: total_loss_dict[key] = total_loss_dict.get( key, torch.cuda.FloatTensor([0.0])) + loss_dict[key] else: value = loss_dict[key].float().sum().item() is_nan = value == float('inf') or \ value == -float('inf') or \ value != value got_nan = got_nan or is_nan total_loss_dict[nan_iters_key] = total_loss_dict.get( nan_iters_key, 0) + int(got_nan) # Logging. timers_to_log = [] def add_to_logging(name): if name in timers.timers: timers_to_log.append(name) add_to_logging('forward-compute') add_to_logging('forward-recv') add_to_logging('forward-send') add_to_logging('forward-backward-send-forward-backward-recv') add_to_logging('backward-compute') add_to_logging('backward-recv') add_to_logging('backward-send') add_to_logging('backward-send-forward-recv') add_to_logging('backward-send-backward-recv') add_to_logging('backward-params-all-reduce') add_to_logging('backward-embedding-all-reduce') add_to_logging('optimizer-copy-to-main-grad') add_to_logging('optimizer-unscale-and-check-inf') add_to_logging('optimizer-clip-main-grad') add_to_logging('optimizer-copy-main-to-model-params') add_to_logging('optimizer') add_to_logging('batch-generator') # Calculate batch size. batch_size = args.micro_batch_size * args.data_parallel_size * \ get_num_microbatches() total_iterations = total_loss_dict[advanced_iters_key] + \ total_loss_dict[skipped_iters_key] # Tensorboard values. if writer and (iteration % args.tensorboard_log_interval == 0 ) and \ is_last_rank(): if args.log_learning_rate_to_tensorboard: writer.add_scalar('learning-rate', learning_rate, iteration) writer.add_scalar('learning-rate vs samples', learning_rate, args.consumed_train_samples) if args.log_batch_size_to_tensorboard: writer.add_scalar('batch-size', batch_size, iteration) writer.add_scalar('batch-size vs samples', batch_size, args.consumed_train_samples) for key in loss_dict: writer.add_scalar(key , loss_dict[key], iteration) writer.add_scalar(key + ' vs samples', loss_dict[key], args.consumed_train_samples) if args.log_loss_scale_to_tensorboard: writer.add_scalar('loss-scale', loss_scale, iteration) writer.add_scalar('loss-scale vs samples', loss_scale, args.consumed_train_samples) if grad_norm is not None: writer.add_scalar('grad-norm', grad_norm, iteration) writer.add_scalar('grad-norm vs samples', grad_norm, args.consumed_train_samples) if num_zeros_in_grad is not None: writer.add_scalar('num-zeros', num_zeros_in_grad, iteration) writer.add_scalar('num-zeros vs samples', num_zeros_in_grad, args.consumed_train_samples) if params_norm is not None: writer.add_scalar('params-norm', params_norm, iteration) writer.add_scalar('params-norm vs samples', params_norm, args.consumed_train_samples) if args.log_timers_to_tensorboard: timers.write(timers_to_log, writer, iteration, normalizer=total_iterations) if iteration % args.log_interval == 0: elapsed_time = timers('interval-time').elapsed() elapsed_time_per_iteration = elapsed_time / total_iterations if writer and torch.distributed.get_rank() == 0: if args.log_timers_to_tensorboard: writer.add_scalar('iteration-time', elapsed_time_per_iteration, iteration) log_string = ' iteration {:8d}/{:8d} |'.format( iteration, args.train_iters) log_string += ' consumed samples: {:12d} |'.format( args.consumed_train_samples) log_string += ' elapsed time per iteration (ms): {:.1f} |'.format( elapsed_time_per_iteration * 1000.0) log_string += ' learning rate: {:.3E} |'.format(learning_rate) log_string += ' global batch size: {:5d} |'.format(batch_size) for key in total_loss_dict: if key not in [advanced_iters_key, skipped_iters_key, nan_iters_key]: avg = total_loss_dict[key].item() / \ float(max(1, total_loss_dict[advanced_iters_key])) if avg > 0.0: log_string += ' {}: {:.6E} |'.format(key, avg) total_loss_dict[key] = torch.cuda.FloatTensor([0.0]) log_string += ' loss scale: {:.1f} |'.format(loss_scale) if grad_norm is not None: log_string += ' grad norm: {:.3f} |'.format(grad_norm) if num_zeros_in_grad is not None: log_string += ' num zeros: {:.1f} |'.format(num_zeros_in_grad) if params_norm is not None: log_string += ' params norm: {:.3f} |'.format(params_norm) log_string += ' number of skipped iterations: {:3d} |'.format( total_loss_dict[skipped_iters_key]) log_string += ' number of nan iterations: {:3d} |'.format( total_loss_dict[nan_iters_key]) total_loss_dict[advanced_iters_key] = 0 total_loss_dict[skipped_iters_key] = 0 total_loss_dict[nan_iters_key] = 0 print_rank_last(log_string) if report_memory_flag and learning_rate > 0.: # Report memory after optimizer state has been initialized. report_memory('(after {} iterations)'.format(iteration)) report_memory_flag = False timers.log(timers_to_log, normalizer=args.log_interval) return report_memory_flag def save_checkpoint_and_time(iteration, model, optimizer, lr_scheduler): timers = get_timers() # Extra barrier is added to make sure # all ranks report the max time. torch.distributed.barrier() timers('save-checkpoint').start() save_checkpoint(iteration, model, optimizer, lr_scheduler) torch.distributed.barrier() timers('save-checkpoint').stop() timers.log(['save-checkpoint']) def train(forward_step_func, model, optimizer, lr_scheduler, train_data_iterator, valid_data_iterator): """Train the model function.""" args = get_args() timers = get_timers() # Write args to tensorboard write_args_to_tensorboard() # Turn on training mode which enables dropout. for model_module in model: model_module.train() # Tracking loss. total_loss_dict = {} # Iterations. iteration = args.iteration timers('interval-time').start() print_datetime('before the start of training step') report_memory_flag = True while iteration < args.train_iters: update_num_microbatches(args.consumed_train_samples) loss_dict, skipped_iter, grad_norm, num_zeros_in_grad = \ train_step(forward_step_func, train_data_iterator, model, optimizer, lr_scheduler) iteration += 1 args.consumed_train_samples += mpu.get_data_parallel_world_size() * \ args.micro_batch_size * \ get_num_microbatches() # Logging. loss_scale = optimizer.get_loss_scale().item() params_norm = None if args.log_params_norm: params_norm = calc_params_l2_norm(model) report_memory_flag = training_log(loss_dict, total_loss_dict, optimizer.param_groups[0]['lr'], iteration, loss_scale, report_memory_flag, skipped_iter, grad_norm, params_norm, num_zeros_in_grad) # Autoresume if args.adlr_autoresume and \ (iteration % args.adlr_autoresume_interval == 0): check_adlr_autoresume_termination(iteration, model, optimizer, lr_scheduler) # Evaluation if args.eval_interval and iteration % args.eval_interval == 0 and \ args.do_valid: prefix = 'iteration {}'.format(iteration) evaluate_and_print_results(prefix, forward_step_func, valid_data_iterator, model, iteration, False) # Checkpointing saved_checkpoint = False if args.save and args.save_interval and \ iteration % args.save_interval == 0: save_checkpoint_and_time(iteration, model, optimizer, lr_scheduler) saved_checkpoint = True # Exiting based on duration if args.exit_duration_in_mins: train_time = (time.time() - _TRAIN_START_TIME) / 60.0 done_cuda = torch.cuda.IntTensor( [train_time > args.exit_duration_in_mins]) torch.distributed.all_reduce( done_cuda, op=torch.distributed.ReduceOp.MAX) done = done_cuda.item() if done: if not saved_checkpoint: save_checkpoint_and_time(iteration, model, optimizer, lr_scheduler) print_datetime('exiting program after {} minutes'.format(train_time)) sys.exit() # Exiting based on iterations if args.exit_interval and iteration % args.exit_interval == 0: if not saved_checkpoint: save_checkpoint_and_time(iteration, model, optimizer, lr_scheduler) torch.distributed.barrier() print_datetime('exiting program at iteration {}'.format(iteration)) sys.exit() return iteration def evaluate(forward_step_func, data_iterator, model, verbose=False): """Evaluation.""" args = get_args() # Turn on evaluation mode which disables dropout. for model_module in model: model_module.eval() total_loss_dict = {} with torch.no_grad(): iteration = 0 while iteration < args.eval_iters: iteration += 1 if verbose and iteration % args.log_interval == 0: print_rank_0('Evaluating iter {}/{}'.format(iteration, args.eval_iters)) if mpu.get_pipeline_model_parallel_world_size() > 1: if args.virtual_pipeline_model_parallel_size is not None: forward_backward_func = forward_backward_pipelining_with_interleaving else: forward_backward_func = forward_backward_pipelining_without_interleaving else: forward_backward_func = forward_backward_no_pipelining loss_dicts = forward_backward_func( forward_step_func, data_iterator, model, optimizer=None, timers=None, forward_only=True) if mpu.is_pipeline_last_stage(ignore_virtual=True): # Reduce across processes. for loss_dict in loss_dicts: for key in loss_dict: total_loss_dict[key] = total_loss_dict.get( key, torch.cuda.FloatTensor([0.0])) + loss_dict[key] args.consumed_valid_samples += mpu.get_data_parallel_world_size() \ * args.micro_batch_size \ * get_num_microbatches() # Move model back to the train mode. for model_module in model: model_module.train() for key in total_loss_dict: total_loss_dict[key] /= args.eval_iters * get_num_microbatches() return total_loss_dict def evaluate_and_print_results(prefix, forward_step_func, data_iterator, model, iteration, verbose=False): """Helper function to evaluate and dump results on screen.""" args = get_args() writer = get_tensorboard_writer() total_loss_dict = evaluate(forward_step_func, data_iterator, model, verbose) string = ' validation loss at {} | '.format(prefix) for key in total_loss_dict: string += '{} value: {:.6E} | '.format(key, total_loss_dict[key].item()) ppl = math.exp(min(20, total_loss_dict[key].item())) string += '{} PPL: {:.6E} | '.format(key, ppl) if writer and is_last_rank(): writer.add_scalar('{} validation'.format(key), total_loss_dict[key].item(), iteration) writer.add_scalar('{} validation vs samples'.format(key), total_loss_dict[key].item(), args.consumed_train_samples) if args.log_validation_ppl_to_tensorboard: writer.add_scalar('{} validation ppl'.format(key), ppl, iteration) writer.add_scalar('{} validation ppl vs samples'.format(key), ppl, args.consumed_train_samples) length = len(string) + 1 print_rank_last('-' * length) print_rank_last(string) print_rank_last('-' * length) def cyclic_iter(iter): while True: for x in iter: yield x def build_train_valid_test_data_iterators( build_train_valid_test_datasets_provider): """XXX""" args = get_args() (train_dataloader, valid_dataloader, test_dataloader) = (None, None, None) print_rank_0('> building train, validation, and test datasets ...') # Backward compatibility, assume fixed batch size. if args.iteration > 0 and args.consumed_train_samples == 0: assert args.train_samples is None, \ 'only backward compatiblity support for iteration-based training' args.consumed_train_samples = args.iteration * args.global_batch_size if args.iteration > 0 and args.consumed_valid_samples == 0: assert args.train_samples is None, \ 'only backward compatiblity support for iteration-based training' args.consumed_valid_samples = (args.iteration // args.eval_interval) * \ args.eval_iters * args.global_batch_size # Data loader only on rank 0 of each model parallel group. if mpu.get_tensor_model_parallel_rank() == 0: # Number of train/valid/test samples. if args.train_samples: train_samples = args.train_samples else: train_samples = args.train_iters * args.global_batch_size eval_iters = (args.train_iters // args.eval_interval + 1) * \ args.eval_iters test_iters = args.eval_iters train_val_test_num_samples = [train_samples, eval_iters * args.global_batch_size, test_iters * args.global_batch_size] print_rank_0(' > datasets target sizes (minimum size):') print_rank_0(' train: {}'.format(train_val_test_num_samples[0])) print_rank_0(' validation: {}'.format(train_val_test_num_samples[1])) print_rank_0(' test: {}'.format(train_val_test_num_samples[2])) # Build the datasets. train_ds, valid_ds, test_ds = build_train_valid_test_datasets_provider( train_val_test_num_samples) # Build dataloders. train_dataloader = build_pretraining_data_loader( train_ds, args.consumed_train_samples) valid_dataloader = build_pretraining_data_loader( valid_ds, args.consumed_valid_samples) test_dataloader = build_pretraining_data_loader(test_ds, 0) # Flags to know if we need to do training/validation/testing. do_train = train_dataloader is not None and args.train_iters > 0 do_valid = valid_dataloader is not None and args.eval_iters > 0 do_test = test_dataloader is not None and args.eval_iters > 0 # Need to broadcast num_tokens and num_type_tokens. flags = torch.cuda.LongTensor( [int(do_train), int(do_valid), int(do_test)]) else: flags = torch.cuda.LongTensor([0, 0, 0]) # Broadcast num tokens. torch.distributed.broadcast(flags, mpu.get_tensor_model_parallel_src_rank(), group=mpu.get_tensor_model_parallel_group()) args.do_train = flags[0].item() args.do_valid = flags[1].item() args.do_test = flags[2].item() # Build iterators. dl_type = args.dataloader_type assert dl_type in ['single', 'cyclic'] if train_dataloader is not None: train_data_iterator = iter(train_dataloader) if dl_type == 'single' \ else iter(cyclic_iter(train_dataloader)) else: train_data_iterator = None if valid_dataloader is not None: valid_data_iterator = iter(valid_dataloader) if dl_type == 'single' \ else iter(cyclic_iter(valid_dataloader)) else: valid_data_iterator = None if test_dataloader is not None: test_data_iterator = iter(test_dataloader) if dl_type == 'single' \ else iter(cyclic_iter(test_dataloader)) else: test_data_iterator = None return train_data_iterator, valid_data_iterator, test_data_iterator