# Copyright (C) 2024 Habana Labs, Ltd. an Intel Company. # Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. """Pretrain utilities.""" from datetime import datetime import math import sys import time import json # 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 contextlib import nullcontext from megatron import get_args from megatron import get_signal_handler 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, get_num_eval_microbatches from megatron import is_last_rank from megatron import update_num_microbatches from megatron.core import mpu, tensor_parallel from megatron import print_rank_0, is_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.model import GPTModel from megatron.core.enums import ModelType from megatron.optimizer import get_megatron_optimizer from megatron.initialize import initialize_megatron from megatron.initialize import write_args_to_tensorboard from megatron.initialize import set_jit_fusion_options from megatron.optimizer_param_scheduler import OptimizerParamScheduler from megatron.model import DistributedDataParallel as LocalDDP from megatron.utils import check_adlr_autoresume_termination from megatron.utils import unwrap_model, found_kill_switch from megatron.data.data_samplers import build_pretraining_data_loader from megatron.utils import calc_params_l2_norm from megatron.core.pipeline_parallel import get_forward_backward_func from megatron.utils import report_memory, throughput_calculator, checkpoint_throughput_calculator, update_rotary_pos_emb, get_fp8_recipe from megatron.core.tensor_parallel.data import reset_cached_broadcast_sizes from megatron.utils import report_memory, throughput_calculator, checkpoint_throughput_calculator from megatron.model.vision.knn_monitor import compute_feature_bank from megatron.arguments import core_transformer_config_from_args from megatron.profiler import setup_profiler, trigger, on_step_begin, on_step_end import deepspeed from deepspeed.accelerator import get_accelerator from deepspeed.compression.compress import init_compression, redundancy_clean from deepspeed.runtime.data_pipeline.data_routing.helper import convert_to_random_ltd from megatron.model.transformer import ParallelTransformerLayer from deepspeed import comm as dist try: import wandb except (ImportError, ModuleNotFoundError): wandb = None try: from habana_frameworks.torch.hpex.experimental.transformer_engine import fp8_autocast from habana_frameworks.torch.hpex.experimental.transformer_engine import recipe except (ImportError, ModuleNotFoundError): fp8_autocast = None recipe = None 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)) ''' Since v0.9.0, deepspeed.initialize() has forbidden simultaneous setting of args.deepspeed_config (Path) and ds_config dict. So, we use ds_config dict which is the more flexible option. ''' def _create_ds_config_dict(): args = get_args() if isinstance(args.deepspeed_config, dict) : ds_config_dict = args.deepspeed_config else: with open(args.deepspeed_config, 'r', encoding='utf-8') as config_file: ds_config_dict = json.load(config_file) if args.universal_checkpoint: ds_config_dict["checkpoint"] = {"load_universal": True} # Clear config path args.deepspeed_config = None return ds_config_dict def pretrain(train_valid_test_dataset_provider, model_provider, model_type, forward_step_func, process_non_loss_data_func=None, extra_args_provider=None, args_defaults={}, data_post_process=None): """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. model_type: an enum that specifies the type of model being trained. 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. process_non_loss_data_func: a function to post process outputs of the network. It can be used for dumping output tensors (e.g images) to tensorboard. It takes `collected data`(list of tensors), `current iteration index` and `tensorboard writer` as arguments. 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. """ # Initalize and get arguments, timers, and Tensorboard writer. initialize_megatron(extra_args_provider=extra_args_provider, args_defaults=args_defaults) args = get_args() if found_kill_switch(): print_datetime(f"Detected kill switch at {args.kill_switch_path}. Exiting") torch.distributed.barrier() sys.exit() # Set pytorch JIT layer fusion options and warmup JIT functions. if get_accelerator().device_name() == 'cuda': set_jit_fusion_options() # 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 = get_accelerator().DoubleTensor([_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') timers = get_timers() if args.deepspeed: args.deepspeed_config_dict = _create_ds_config_dict() if "curriculum_learning" in args.deepspeed_config_dict and \ "enabled" in args.deepspeed_config_dict["curriculum_learning"]: args.curriculum_learning_legacy = args.deepspeed_config_dict[ \ "curriculum_learning"]["enabled"] if args.curriculum_learning_legacy and not args.no_pipeline_parallel: from deepspeed.runtime.data_pipeline.curriculum_scheduler \ import CurriculumScheduler args.curriculum_scheduler = CurriculumScheduler( \ args.deepspeed_config_dict["curriculum_learning"]) if "compression_training" in args.deepspeed_config_dict: args.compression_training = True # Model, optimizer, and learning rate. timers('model-and-optimizer-setup', log_level=0).start(barrier=True) model, optimizer, opt_param_scheduler = setup_model_and_optimizer( model_provider, model_type, teacher=False, data_post_process=data_post_process, build_train_valid_test_datasets_provider=train_valid_test_dataset_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', log_level=0).start( barrier=True) 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) if args.data_efficiency_curriculum_learning: if args.deepspeed_dataloader is not None: # We use args to pass the deepspeed_dataloader because adding # output to setup_model_and_optimizer will break the API for other # cases. We clear args.deepspeed_dataloader after updating # train_data_iterator because args will be saved in checkpoint and # attempting to save the whole deepspeed_dataloader will lead to # "AttributeError: Can't pickle local object...". train_data_iterator = iter(args.deepspeed_dataloader) args.deepspeed_dataloader = None else: train_data_iterator = None timers('train/valid/test-data-iterators-setup').stop() print_datetime('after dataloaders are built') # args.teacher_model is used as global variable to pass the teacher model # for knowledge distillation. Users do not need to set it in the command # line to use kd, but users do need to provide teacher model configurations # like args.num_layers_teacher as described in setup_teacher_model() args.teacher_model = None if args.mos or args.kd: # Set up teacher model args.teacher_model = setup_teacher_model(args, model_provider) # Print setup timing. print_rank_0('done with setup ...') timers.log(['model-and-optimizer-setup', 'train/valid/test-data-iterators-setup'], barrier=True) if not args.skip_train: print_rank_0('training ...') if args.dataloader_type == 'cyclic' and args.retro_add_retriever: args.train_iters = args.retro_cyclic_train_iters print_rank_0("retro cyclic train iters : %d" % args.train_iters) iteration = 0 if args.do_train and args.train_iters > 0: iteration = train(forward_step_func, model, optimizer, opt_param_scheduler, train_data_iterator, valid_data_iterator, process_non_loss_data_func) print_datetime('after training is done') # Clean the model if args.compression_training: model = [redundancy_clean(model[0], args.deepspeed_config_dict, mpu)] if args.save and iteration != 0: save_checkpoint(iteration, model, optimizer, opt_param_scheduler) else: print_rank_0('skipping training (--skip-train is on) ...') iteration = args.iteration if args.save and (iteration != 0 or args.universal_checkpoint): save_checkpoint(iteration, model, optimizer, opt_param_scheduler) config = core_transformer_config_from_args(args) if args.do_valid: prefix = f'iteration {iteration} on {args.eval_iters * args.global_batch_size}-sample draw from validation set' _ = evaluate_and_print_results(prefix, forward_step_func, valid_data_iterator, model, iteration, process_non_loss_data_func, config, verbose=True, write_to_tensorboard=not args.skip_train) if args.do_test: prefix = f'iteration {iteration} on {args.eval_iters * args.global_batch_size}-sample draw from test set' _ = evaluate_and_print_results(prefix, forward_step_func, test_data_iterator, model, iteration, process_non_loss_data_func, config, verbose=True, write_to_tensorboard=not args.skip_train, test=True) return model 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 setup_teacher_model(args, model_provider): print_rank_0('***>>>>> Student model checkpoint iteration:{}'.format(args.iteration)) iteration_stuent = args.iteration num_layers_student = args.num_layers num_experts_student = args.num_experts hidden_size_student = args.hidden_size num_attention_heads_student = args.num_attention_heads load_student = args.load print_rank_0('***>>>>> Setting up the teacher model') args.num_layers = args.num_layers_teacher args.num_experts = args.num_experts_teacher args.hidden_size = args.hidden_size_teacher args.num_attention_heads = args.num_attention_heads_teacher args.load = args.load_teacher teacher_model, _, _ = load_model_weights_only(model_provider) print_rank_0('***>>>>> Teacher model:{}'.format(teacher_model)) args.num_layers = num_layers_student args.num_experts = num_experts_student args.hidden_size = hidden_size_student args.num_attention_heads = num_attention_heads_student args.load = load_student args.iteration = iteration_stuent return teacher_model def get_model(model_provider_func, model_type=ModelType.encoder_or_decoder, wrap_with_ddp=True): """Build the model.""" args = get_args() args.model_type = model_type # Build model. if mpu.get_pipeline_model_parallel_world_size() > 1 and \ args.virtual_pipeline_model_parallel_size is not None: assert model_type != ModelType.encoder_and_decoder, \ "Interleaved schedule not supported for model with both encoder and decoder" 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 ) this_model.model_type = model_type model.append(this_model) else: pre_process = mpu.is_pipeline_first_stage() post_process = mpu.is_pipeline_last_stage() add_encoder = True add_decoder = True if model_type == ModelType.encoder_and_decoder: if mpu.get_pipeline_model_parallel_world_size() > 1: assert args.pipeline_model_parallel_split_rank is not None, \ "Split rank needs to be specified for model with both encoder and decoder" rank = mpu.get_pipeline_model_parallel_rank() split_rank = args.pipeline_model_parallel_split_rank world_size = mpu.get_pipeline_model_parallel_world_size() pre_process = rank == 0 or rank == split_rank post_process = (rank == (split_rank - 1)) or ( rank == (world_size - 1)) add_encoder = mpu.is_pipeline_stage_before_split() add_decoder = mpu.is_pipeline_stage_after_split() model = model_provider_func( pre_process=pre_process, post_process=post_process, add_encoder=add_encoder, add_decoder=add_decoder) else: model = model_provider_func( pre_process=pre_process, post_process=post_process ) model.model_type = model_type if not isinstance(model, list): model = [model] # Disallow training and inference with Transformer Engine # for non-GPT models args.allow_transformer_engine = all([type(m).__name__ in ['GPTModelPipe', 'GPTModel'] for m in model]) assert args.allow_transformer_engine or args.transformer_impl == 'local', \ 'Transformer Engine is only approved for GPT models' # 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(): tensor_parallel.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.ds_numel if hasattr(p,'ds_id') else p.nelement() for p in model_module.parameters()]) for model_module in model])), flush=True) if args.deepspeed: return model # GPU allocation. for model_module in model: model_module.to(get_accelerator().current_device_name()) # Fp16 conversion. if args.fp16 or args.bf16: model = [Float16Module(model_module, args) for model_module in model] if wrap_with_ddp: if args.DDP_impl == 'torch': i = get_accelerator().current_device() model = [torchDDP(model_module, device_ids=[i], output_device=i, process_group=mpu.get_data_parallel_group()) for model_module in model] elif args.DDP_impl == 'local': model = [LocalDDP(model_module, args.accumulate_allreduce_grads_in_fp32, args.use_contiguous_buffers_in_local_ddp) for model_module in model] # broad cast params from data parallel src rank to other data parallel ranks if args.data_parallel_random_init: for model_module in model: model_module.broadcast_params() else: raise NotImplementedError('Unknown DDP implementation specified: ' '{}. Exiting.'.format(args.DDP_impl)) return model def get_optimizer_param_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 lr_decay_steps = args.lr_decay_iters * args.global_batch_size wd_incr_steps = args.train_iters * args.global_batch_size if args.lr_warmup_fraction is not None: lr_warmup_steps = args.lr_warmup_fraction * lr_decay_steps else: lr_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 lr_decay_steps = args.lr_decay_samples wd_incr_steps = args.train_samples if args.lr_warmup_fraction is not None: lr_warmup_steps = args.lr_warmup_fraction * lr_decay_steps else: lr_warmup_steps = args.lr_warmup_samples else: raise Exception( 'either train-iters or train-samples should be provided.') opt_param_scheduler = OptimizerParamScheduler( optimizer, max_lr=args.lr, min_lr=args.min_lr, lr_warmup_steps=lr_warmup_steps, lr_decay_steps=lr_decay_steps, lr_decay_style=args.lr_decay_style, start_wd=args.start_weight_decay, end_wd=args.end_weight_decay, wd_incr_steps=wd_incr_steps, wd_incr_style=args.weight_decay_incr_style, use_checkpoint_opt_param_scheduler=args.use_checkpoint_opt_param_scheduler, override_opt_param_scheduler=args.override_opt_param_scheduler) return opt_param_scheduler def load_model_weights_only(model_provider_func): """Setup model and optimizer.""" args = get_args() print_rank_0('***>>>>> Args:{}'.format(args)) model = get_model(model_provider_func) optimizer = None lr_scheduler = None if args.deepspeed: # When loading just the model weights, ZeRO can be disabled. if 'zero_optimization' in args.deepspeed_config_dict: del args.deepspeed_config_dict['zero_optimization'] model, optimizer, _, lr_scheduler = deepspeed.initialize( model=model[0], config=args.deepspeed_config_dict ) assert not isinstance(model, deepspeed.PipelineEngine), \ 'Weight loading only mode is not supported in pipeline parallelism yet.' model = [model] print_datetime('before load checkpoint') if args.load is not None: iteration = load_checkpoint(model, optimizer, lr_scheduler, strict=True, load_only_weights=True) print_datetime('after load checkpoint weights') return model, optimizer, lr_scheduler def setup_model_and_optimizer(model_provider_func, model_type, no_wd_decay_cond=None, scale_lr_cond=None, lr_mult=1.0, teacher=False, data_post_process=None, build_train_valid_test_datasets_provider=None): """Setup model and optimizer.""" args = get_args() model = get_model(model_provider_func, model_type) # initialize the compression here student_global_steps = 0 if args.kd or args.mos: model, _, _, _ = deepspeed.initialize( model=model[0], args=args, mpu=mpu if args.no_pipeline_parallel else None, config=args.deepspeed_config_dict, ) model = [model] if args.load is not None: args.iteration = load_checkpoint(model, None, None, strict=False) else: args.iteration = 0 student_global_steps = model[0].global_steps print_rank_0('***>>>>> Student model, global step:{}'.format(student_global_steps)) if args.compression_training: model, _, _, _ = deepspeed.initialize( model=model[0], args=args, mpu=mpu if args.no_pipeline_parallel else None, config=args.deepspeed_config_dict, ) model = [model] model = [init_compression(model[0].module, args.deepspeed_config_dict, mpu)] unwrapped_model = unwrap_model(model, (torchDDP, LocalDDP, Float16Module)) if args.inference: optimizer = None opt_param_scheduler = None else: if teacher: optimizer = None else: optimizer = get_megatron_optimizer(model, no_wd_decay_cond, scale_lr_cond, lr_mult) # opt_param_scheduler is the old lr_scheduler plus weight decay scheduling opt_param_scheduler = get_optimizer_param_scheduler(optimizer) if args.deepspeed: print_rank_0("DeepSpeed is enabled.") pp = mpu.get_pipeline_model_parallel_world_size() if args.data_efficiency_curriculum_learning and build_train_valid_test_datasets_provider is not None: train_ds = None # Only need to build dataset on tp rank 0 since Megatron has the # broadcast_data() function that broadcast data from tp rank 0. if mpu.get_tensor_model_parallel_rank() == 0: # Number of train/valid/test samples. if args.train_samples: train_samples = args.train_samples update_train_iters(args) else: train_samples = args.train_iters * args.global_batch_size # eval_iters and test_iters here are not actually used, only for # satisfying the input of build_train_valid_test_datasets_provider. # We only need to build the training data here. And we follow # baseline's logic to build eval/test dataset later in # build_train_valid_test_data_iterators. 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] # Build the datasets. train_ds, _, _ = build_train_valid_test_datasets_provider( train_val_test_num_samples) model, optimizer, args.deepspeed_dataloader, opt_param_scheduler = deepspeed.initialize( model=model[0], optimizer=optimizer, args=args, lr_scheduler=opt_param_scheduler, training_data=train_ds, mpu=mpu if args.no_pipeline_parallel else None, config=args.deepspeed_config_dict, ) model.set_data_post_process_func(data_post_process) else: model, optimizer, _, opt_param_scheduler = deepspeed.initialize( model=model[0], optimizer=optimizer, args=args, lr_scheduler=opt_param_scheduler, mpu=mpu if args.no_pipeline_parallel else None, config=args.deepspeed_config_dict, ) if isinstance(model, deepspeed.PipelineEngine): # hack to get batch_fn from pretrain_gpt.py model.set_batch_fn(model.module._megatron_batch_fn) assert model.grid.get_pipe_parallel_rank() == mpu.get_pipeline_model_parallel_rank() assert model.grid.get_slice_parallel_rank() == mpu.get_tensor_model_parallel_rank() assert model.grid.get_data_parallel_rank() == mpu.get_data_parallel_rank() model = [model] # Compression has its own checkpoint loading path (e.g, loading both teacher and student models). So if compression is enabled, we skip the following checkpoint loading. no_post_init_checkpoint_loading = args.kd or args.mos if not no_post_init_checkpoint_loading: if args.load is not None: timers = get_timers() timers('load-checkpoint', log_level=0).start(barrier=True) args.iteration = load_checkpoint(model, optimizer, opt_param_scheduler) timers('load-checkpoint').stop(barrier=True) timers.log(['load-checkpoint']) else: args.iteration = 0 else: model[0].global_steps = student_global_steps # 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() # random-LTD requires converting transformer layers if args.random_ltd: model[0] = convert_to_random_ltd(model[0], ParallelTransformerLayer) return model, optimizer, opt_param_scheduler def train_step(forward_step_func, data_iterator, model, optimizer, opt_param_scheduler, config): """Single training step.""" args = get_args() timers = get_timers() hpu_transformer_engine = get_accelerator().device_name() == 'hpu' and get_args().transformer_impl == "transformer_engine" if args.deepspeed and args.ds_pipeline_enabled: skipped_iter = 0 num_zeros_in_grad = 0 assert isinstance(model[0], deepspeed.PipelineEngine) with fp8_autocast(enabled=True, fp8_recipe=get_fp8_recipe(args)) \ if hpu_transformer_engine else nullcontext(): loss = model[0].train_batch(data_iter=data_iterator) grad_norm = model[0].get_global_grad_norm() return {'lm loss' : loss}, skipped_iter, grad_norm, num_zeros_in_grad # Set grad to zero. if not args.deepspeed: if args.DDP_impl == 'local' and args.use_contiguous_buffers_in_local_ddp: for partition in model: partition.zero_grad_buffer() optimizer.zero_grad() # Forward pass. timers('forward-backward', log_level=1).start( barrier=args.barrier_with_L1_time) forward_backward_func = get_forward_backward_func() if args.mos or args.kd: # args.teacher_forward is used as global variable to enable kd loss # calculation in forward pass. Users do not need to set it in the # command line to use kd. args.teacher_forward = True # set timers to None if none of the timers in fwd_bwd are active, just to save the checks if args.timing_log_level < 2: config.timers = None losses_reduced = forward_backward_func( forward_step_func=forward_step_func, data_iterator=data_iterator, model=model, num_microbatches=get_num_microbatches(), seq_length=args.seq_length, micro_batch_size=args.micro_batch_size, decoder_seq_length=args.decoder_seq_length, forward_only=False) # reset timers if necessary if config.timers is None: config.timers = timers timers('forward-backward').stop() if args.mos or args.kd: args.teacher_forward = False # Empty unused memory. if args.empty_unused_memory_level >= 1: torch.cuda.empty_cache() # Reduce gradients. if not args.deepspeed: optimizer.reduce_model_grads(args, timers) # Vision gradients. if args.vision_pretraining and args.vision_pretraining_type == "dino": unwrapped_model = unwrap_model(model[0], (torchDDP, LocalDDP, Float16Module)) unwrapped_model.cancel_gradients_last_layer(args.curr_iteration) # Update parameters. timers('optimizer', log_level=1).start(barrier=args.barrier_with_L1_time) if args.deepspeed: increment = get_num_microbatches() * \ args.micro_batch_size * \ args.data_parallel_size model[0].step(lr_kwargs={'increment': increment}) update_successful = model[0].was_step_applied() else: update_successful, grad_norm, num_zeros_in_grad = optimizer.step(args, timers) timers('optimizer').stop() # Gather params. if not args.deepspeed and update_successful: optimizer.gather_model_params(args, timers) # Vision momentum. if args.vision_pretraining and args.vision_pretraining_type == "dino": unwrapped_model = unwrap_model(model[0], (torchDDP, LocalDDP, Float16Module)) unwrapped_model.update_momentum(args.curr_iteration) # Update learning rate. if args.deepspeed: skipped_iter = 0 grad_norm = None num_zeros_in_grad = None 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 else: if update_successful: increment = get_num_microbatches() * \ args.micro_batch_size * \ args.data_parallel_size opt_param_scheduler.step(increment=increment) skipped_iter = 0 else: skipped_iter = 1 # Empty unused memory. if args.empty_unused_memory_level >= 2: torch.cuda.empty_cache() 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, model=None, optimizer=None): """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, get_accelerator().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 = [ 'forward-backward', 'forward-compute', 'backward-compute', 'batch-generator', 'forward-recv', 'forward-send', 'backward-recv', 'backward-send', 'forward-send-forward-recv', 'forward-send-backward-recv', 'backward-send-forward-recv', 'backward-send-backward-recv', 'forward-backward-send-forward-backward-recv', 'layernorm-grads-all-reduce', 'embedding-grads-all-reduce', 'grads-all-reduce', 'grads-reduce-scatter', 'params-all-gather', 'optimizer-copy-to-main-grad', 'optimizer-unscale-and-check-inf', 'optimizer-clip-main-grad', 'optimizer-count-zeros', 'optimizer-inner-step', 'optimizer-copy-main-to-model-params', 'optimizer'] # 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. # Timer requires all the ranks to call. if args.log_timers_to_tensorboard and \ (iteration % args.tensorboard_log_interval == 0): timers.write(timers_to_log, writer, iteration, normalizer=total_iterations) if writer and (iteration % args.tensorboard_log_interval == 0): writer.add_scalar('steps-vs-samples/y=steps,x=samples', iteration, args.consumed_train_samples) writer.add_scalar('steps-vs-samples/y=samples,x=steps', args.consumed_train_samples, iteration) writer.add_scalar('steps-vs-tokens/y=steps,x=tokens', iteration, args.consumed_train_tokens) writer.add_scalar('steps-vs-tokens/y=tokens,x=steps', args.consumed_train_tokens, iteration) if args.log_learning_rate_to_tensorboard: writer.add_scalar('learning-rate/learning-rate', learning_rate, iteration) writer.add_scalar('learning-rate/learning-rate vs samples', learning_rate, args.consumed_train_samples) writer.add_scalar('learning-rate/learning-rate vs tokens', learning_rate, args.consumed_train_tokens) if args.log_batch_size_to_tensorboard: writer.add_scalar('batch-size/batch-size', batch_size, iteration) writer.add_scalar('batch-size/batch-size vs samples', batch_size, args.consumed_train_samples) writer.add_scalar('batch-size/batch-size vs tokens', batch_size, args.consumed_train_tokens) for key in loss_dict: writer.add_scalar(f"lm-loss-training/{key}", loss_dict[key], iteration) writer.add_scalar(f"lm-loss-training/{key}" + ' vs samples', loss_dict[key], args.consumed_train_samples) writer.add_scalar(f"lm-loss-training/{key}" + ' vs tokens', loss_dict[key], args.consumed_train_tokens) if args.fp16 and (loss_scale and args.log_loss_scale_to_tensorboard): writer.add_scalar('loss-scale/loss-scale', loss_scale, iteration) writer.add_scalar('loss-scale/loss-scale vs samples', loss_scale, args.consumed_train_samples) writer.add_scalar('loss-scale/loss-scale vs tokens', loss_scale, args.consumed_train_tokens) if args.log_world_size_to_tensorboard: writer.add_scalar('world-size/world-size', args.world_size, iteration) writer.add_scalar('world-size/world-size vs samples', args.world_size, args.consumed_train_samples) writer.add_scalar('world-size/world-size vs tokens', args.world_size, args.consumed_train_tokens) if grad_norm is not None: writer.add_scalar('grad-norm/grad-norm', grad_norm, iteration) writer.add_scalar('grad-norm/grad-norm vs samples', grad_norm, args.consumed_train_samples) writer.add_scalar('grad-norm/grad-norm vs tokens', grad_norm, args.consumed_train_tokens) if num_zeros_in_grad is not None: writer.add_scalar('num-zeros/num-zeros', num_zeros_in_grad, iteration) writer.add_scalar('num-zeros/num-zeros vs samples', num_zeros_in_grad, args.consumed_train_samples) writer.add_scalar('num-zeros/num-zeros vs tokens', num_zeros_in_grad, args.consumed_train_tokens) if params_norm is not None: writer.add_scalar('params-norm/params-norm', params_norm, iteration) writer.add_scalar('params-norm/params-norm vs samples', params_norm, args.consumed_train_samples) writer.add_scalar('params-norm/params-norm vs tokens', params_norm, args.consumed_train_tokens) if hasattr(args, 'actual_seq_length'): writer.add_scalar('seqlen/actual_seq_length', args.actual_seq_length, iteration) writer.add_scalar('seqlen/actual_seq_length vs samples', args.actual_seq_length, args.consumed_train_samples) writer.add_scalar('seqlen/actual_seq_length vs tokens', args.actual_seq_length, args.consumed_train_tokens) if args.curriculum_learning_legacy or args.data_efficiency_curriculum_learning: writer.add_scalar('seqlen/curriculum_seqlen', args.curriculum_seqlen, iteration) writer.add_scalar('seqlen/curriculum_seqlen vs samples', args.curriculum_seqlen, args.consumed_train_samples) writer.add_scalar('seqlen/curriculum_seqlen vs tokens', args.curriculum_seqlen, args.consumed_train_tokens) if args.random_ltd: writer.add_scalar('seqlen/random_ltd_reserved_length', args.random_ltd_reserved_length, iteration) writer.add_scalar('seqlen/random_ltd_reserved_length vs samples', args.random_ltd_reserved_length, args.consumed_train_samples) writer.add_scalar('seqlen/random_ltd_reserved_length vs tokens', args.random_ltd_reserved_length, args.consumed_train_tokens) if args.log_memory_to_tensorboard: mem_stats = torch.cuda.memory_stats() writer.add_scalar( "mem-reserved-bytes", mem_stats["reserved_bytes.all.current"], iteration, ) writer.add_scalar( "mem-allocated-bytes", mem_stats["allocated_bytes.all.current"], iteration, ) writer.add_scalar( "mem-allocated-count", mem_stats["allocation.all.current"], iteration, ) if iteration % args.tensorboard_log_interval == 0: # This logging write various optimizer states to tensorboard. This # feature may consume extra GPU memory thus is set at false by default. if args.log_optimizer_states_to_tensorboard and optimizer is not None: opt_stats = [0.0] * 8 opt_stats_2 = [0.0] * 4 for _, group in enumerate(optimizer.param_groups): for _, param in enumerate(group['params']): opt_stats[0] += (torch.norm(optimizer.state[param]['exp_avg_sq']).item())**2 opt_stats[1] += (torch.norm(optimizer.state[param]['exp_avg_sq'].sqrt()).item())**2 opt_stats[2] += (torch.norm(optimizer.state[param]['exp_avg']).item())**2 opt_stats[3] += (torch.norm(param).item())**2 opt_stats[4] += torch.norm(optimizer.state[param]['exp_avg_sq'],p=1).item() opt_stats[5] += torch.norm(optimizer.state[param]['exp_avg_sq'].sqrt(),p=1).item() opt_stats[6] += torch.norm(optimizer.state[param]['exp_avg'],p=1).item() opt_stats[7] += torch.norm(param,p=1).item() opt_stats_2[0] = max(opt_stats_2[0], abs(optimizer.state[param]['exp_avg_sq'].max().item()), abs(optimizer.state[param]['exp_avg_sq'].min().item())) opt_stats_2[1] = max(opt_stats_2[1], optimizer.state[param]['exp_avg_sq'].sqrt().abs_().max().item()) opt_stats_2[2] = max(opt_stats_2[2], abs(optimizer.state[param]['exp_avg'].max().item()), abs(optimizer.state[param]['exp_avg'].min().item())) opt_stats_2[3] = max(opt_stats_2[3], abs(param.max().item()), abs(param.min().item())) # print('step {} rank {} before sync opt_stats {}, {}'.format(iteration, torch.distributed.get_rank(), opt_stats_2, opt_stats)) if args.zero_stage > 0: # ZeRO partiions optimizer states opt_stats = get_accelerator().FloatTensor(opt_stats) torch.distributed.all_reduce(opt_stats, group=mpu.get_sequence_data_parallel_group()) opt_stats_2 = get_accelerator().FloatTensor(opt_stats_2) torch.distributed.all_reduce(opt_stats_2, op=torch.distributed.ReduceOp.MAX, group=mpu.get_sequence_data_parallel_group()) if args.tensor_model_parallel_size > 1: opt_stats = get_accelerator().FloatTensor(opt_stats) torch.distributed.all_reduce(opt_stats, group=mpu.get_tensor_model_parallel_group()) opt_stats_2 = get_accelerator().FloatTensor(opt_stats_2) torch.distributed.all_reduce(opt_stats_2, op=torch.distributed.ReduceOp.MAX, group=mpu.get_tensor_model_parallel_group()) if args.pipeline_model_parallel_size > 1: opt_stats = get_accelerator().FloatTensor(opt_stats) torch.distributed.all_reduce(opt_stats, group=mpu.get_pipeline_model_parallel_group()) opt_stats_2 = get_accelerator().FloatTensor(opt_stats_2) torch.distributed.all_reduce(opt_stats_2, op=torch.distributed.ReduceOp.MAX, group=mpu.get_pipeline_model_parallel_group()) # print('step {} rank {} after sync opt_stats {}, {}'.format(iteration, torch.distributed.get_rank(), opt_stats_2, opt_stats)) if writer and is_last_rank(): writer.add_scalar('optimizer/variance_l2 vs tokens', opt_stats[0]**0.5, args.consumed_train_tokens) writer.add_scalar('optimizer/variance_sqrt_l2 vs tokens', opt_stats[1]**0.5, args.consumed_train_tokens) writer.add_scalar('optimizer/momentum_l2 vs tokens', opt_stats[2]**0.5, args.consumed_train_tokens) writer.add_scalar('optimizer/weight_l2 vs tokens', opt_stats[3]**0.5, args.consumed_train_tokens) writer.add_scalar('optimizer/variance_l1 vs tokens', opt_stats[4], args.consumed_train_tokens) writer.add_scalar('optimizer/variance_sqrt_l1 vs tokens', opt_stats[5], args.consumed_train_tokens) writer.add_scalar('optimizer/momentum_l1 vs tokens', opt_stats[6], args.consumed_train_tokens) writer.add_scalar('optimizer/weight_l1 vs tokens', opt_stats[7], args.consumed_train_tokens) writer.add_scalar('optimizer/variance_abs_max vs tokens', opt_stats_2[0], args.consumed_train_tokens) writer.add_scalar('optimizer/variance_sqrt_abs_max vs tokens', opt_stats_2[1], args.consumed_train_tokens) writer.add_scalar('optimizer/momentum_abs_max vs tokens', opt_stats_2[2], args.consumed_train_tokens) writer.add_scalar('optimizer/weight_abs_max vs tokens', opt_stats_2[3], args.consumed_train_tokens) writer.add_scalar('optimizer/variance_l2', opt_stats[0]**0.5, iteration) writer.add_scalar('optimizer/variance_sqrt_l2', opt_stats[1]**0.5, iteration) writer.add_scalar('optimizer/momentum_l2', opt_stats[2]**0.5, iteration) writer.add_scalar('optimizer/weight_l2', opt_stats[3]**0.5, iteration) writer.add_scalar('optimizer/variance_l1', opt_stats[4], iteration) writer.add_scalar('optimizer/variance_sqrt_l1', opt_stats[5], iteration) writer.add_scalar('optimizer/momentum_l1', opt_stats[6], iteration) writer.add_scalar('optimizer/weight_l1', opt_stats[7], iteration) writer.add_scalar('optimizer/variance_abs_max', opt_stats_2[0], iteration) writer.add_scalar('optimizer/variance_sqrt_abs_max', opt_stats_2[1], iteration) writer.add_scalar('optimizer/momentum_abs_max', opt_stats_2[2], iteration) writer.add_scalar('optimizer/weight_abs_max', opt_stats_2[3], iteration) if iteration % args.log_interval == 0: elapsed_time = timers('interval-time').elapsed(barrier=True) elapsed_time_per_iteration = elapsed_time / total_iterations seq_len = args.seq_length if hasattr(args, 'actual_seq_length'): seq_len = args.actual_seq_length samples_per_sec, tflops, approx_parameters_in_billions = throughput_calculator( model, args, elapsed_time, total_iterations ) samples_per_sec_per_replica = samples_per_sec / args.data_parallel_size tokens_per_sec = samples_per_sec * seq_len tokens_per_sec_per_replica = tokens_per_sec / args.data_parallel_size tokens_per_gpu_per_second = tokens_per_sec / args.world_size tokens_per_gpu_per_second_per_replica = tokens_per_gpu_per_second / args.data_parallel_size if wandb is not None and getattr(wandb, 'run', None) is not None: tput = { 'throughput/iteration-time': elapsed_time_per_iteration, # 1000 ms / s 'throughput/samples_per_sec': samples_per_sec, 'throughput/samples_per_sec_per_replica': samples_per_sec_per_replica, 'throughput/tokens_per_sec': tokens_per_sec, 'throughput/tokens_per_sec_per_replica': tokens_per_sec_per_replica, 'throughput/tokens_per_gpu_per_sec': tokens_per_gpu_per_second, 'throughput/tokens_per_gpu_per_sec_per_replica': tokens_per_gpu_per_second_per_replica, 'throughput/tflops': tflops, 'throughput/approx_params_in_billions': approx_parameters_in_billions, 'throughput/elapsed_ms_per_iteration': elapsed_time_per_iteration, } wandb.run.log(tput) if writer: if args.log_timers_to_tensorboard: writer.add_scalar('iteration-time/iteration-time', elapsed_time_per_iteration, iteration) writer.add_scalar('iteration-time/iteration-time vs samples', elapsed_time_per_iteration, args.consumed_train_samples) writer.add_scalar('iteration-time/iteration-time vs tokens', elapsed_time_per_iteration, args.consumed_train_tokens) log_string = ' iteration {:8d}/{:8d} |'.format( iteration, args.train_iters) log_string += ' consumed samples: {:12d} |'.format( args.consumed_train_samples) log_string += ' consumed tokens: {:12d} |'.format( args.consumed_train_tokens) 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] = get_accelerator().FloatTensor([0.0]) if loss_scale is not None: 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) if args.curriculum_learning_legacy or args.data_efficiency_curriculum_learning: log_string += ' curriculum seqlen: {:5d} |'.format(args.curriculum_seqlen) if args.random_ltd: log_string += ' random ltd reserved length: {:5d} |'.format(args.random_ltd_reserved_length) log_string += ' actual seqlen: {:5d} |'.format(seq_len) 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]) log_string += ' samples per second: {:.3f} |'.format(samples_per_sec) log_string += ' tokens per gpu per second (tgs): {:.3f} |'.format(tokens_per_gpu_per_second) log_string += ' TFLOPs: {:.2f} |'.format(tflops) 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, opt_param_scheduler): timers = get_timers() # Extra barrier is added to make sure # all ranks report the max time. timers('save-checkpoint', log_level=0).start(barrier=True) save_checkpoint(iteration, model, optimizer, opt_param_scheduler) timers('save-checkpoint').stop(barrier=True) checkpoint_throughput_calculator(model, timers('save-checkpoint').elapsed(reset=False)) timers.log(['save-checkpoint']) def train(forward_step_func, model, optimizer, opt_param_scheduler, train_data_iterator, valid_data_iterator, process_non_loss_data_func): """Train the model function.""" args = get_args() timers = get_timers() # Write args to tensorboard write_args_to_tensorboard() setup_profiler(args, get_accelerator().device_name()) if args.random_ltd: # random-ltd requires different randomness on each rank import random random.seed(args.seed + torch.distributed.get_rank()) # Turn on training mode which enables dropout. for model_module in model: model_module.train() # Tracking loss. total_loss_dict = {} # Iterations. iteration = args.iteration # Translate args to core configuration config = core_transformer_config_from_args(args) if not args.deepspeed: config.grad_scale_func = optimizer.scale_loss config.timers = timers timers('interval-time', log_level=0).start(barrier=True) print_datetime('before the start of training step') report_memory_flag = True if args.random_ltd: assert model[0].random_ltd_enabled() args.random_ltd_layer_num = model[0].random_ltd_scheduler.get_random_ltd_layer_num() while iteration < args.train_iters and (args.train_tokens is None or \ args.consumed_train_tokens < args.train_tokens): trigger(on_step_begin) update_num_microbatches(args.consumed_train_samples) if args.deepspeed: # inform deepspeed of any batch size changes global_batch_size = mpu.get_data_parallel_world_size() * \ args.micro_batch_size * \ get_num_microbatches() model[0].set_train_batch_size(global_batch_size) if args.curriculum_learning_legacy and not args.no_pipeline_parallel: curriculum_seqlen = args.curriculum_scheduler.update_difficulty( \ args.iteration + 1) if iteration == 0 or curriculum_seqlen != args.curriculum_seqlen: if args.use_rotary_position_embeddings: update_rotary_pos_emb(curriculum_seqlen) args.curriculum_seqlen = curriculum_seqlen args.curr_iteration = iteration loss_dict, skipped_iter, grad_norm, num_zeros_in_grad = \ train_step(forward_step_func, train_data_iterator, model, optimizer, opt_param_scheduler, config) iteration += 1 args.iteration = iteration new_samples = mpu.get_data_parallel_world_size() * \ args.micro_batch_size * \ get_num_microbatches() args.consumed_train_samples += new_samples # This actual_seq_length is used for actual consumed tokens calculation, flops calculation, and logging. args.actual_seq_length = args.seq_length if args.curriculum_learning_legacy or args.data_efficiency_curriculum_learning: args.actual_seq_length = args.curriculum_seqlen if args.random_ltd: args.random_ltd_reserved_length = model[0].random_ltd_scheduler.get_current_seq() if args.random_ltd_reserved_length < args.actual_seq_length: args.actual_seq_length = (args.actual_seq_length * (args.num_layers - args.random_ltd_layer_num) + args.random_ltd_reserved_length * args.random_ltd_layer_num) // args.num_layers if args.curriculum_learning_legacy or args.data_efficiency_curriculum_learning: if hasattr(args, 'data_efficiency_curriculum_learning_numel'): act_mbsz = args.data_efficiency_curriculum_learning_numel / args.curriculum_seqlen act_token = act_mbsz * args.actual_seq_length args.consumed_train_tokens += mpu.get_data_parallel_world_size() * \ get_num_microbatches() * act_token else: args.consumed_train_tokens += new_samples * args.actual_seq_length else: args.consumed_train_tokens += new_samples * args.actual_seq_length # Logging. if args.deepspeed: if hasattr(model[0].optimizer, 'cur_scale'): loss_scale = model[0].optimizer.cur_scale else: loss_scale = None else: 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, model, optimizer) # Autoresume if args.adlr_autoresume and \ (iteration % args.adlr_autoresume_interval == 0): check_adlr_autoresume_termination(iteration, model, optimizer, opt_param_scheduler) # Evaluation if args.eval_interval and iteration % args.eval_interval == 0 and \ args.do_valid: prefix = 'iteration {}'.format(iteration) eval_loss = evaluate_and_print_results(prefix, forward_step_func, valid_data_iterator, model, iteration, process_non_loss_data_func, config, False) # Exiting based on eval loss if args.eval_loss_exit_value is not None and eval_loss <= args.eval_loss_exit_value: if args.save: save_checkpoint_and_time(iteration, model, optimizer, opt_param_scheduler) torch.distributed.barrier() print_datetime(f"Reached target loss value: {args.eval_loss_exit_value}. " f"Stopping the training at iteration: {iteration} with loss: {eval_loss}") sys.exit() # Checkpointing saved_checkpoint = False if args.exit_signal_handler: signal_handler = get_signal_handler() if any(signal_handler.signals_received()): save_checkpoint_and_time(iteration, model, optimizer, opt_param_scheduler) print_datetime('exiting program after receiving SIGTERM.') sys.exit() if args.save and args.save_interval and \ iteration % args.save_interval == 0: save_checkpoint_and_time(iteration, model, optimizer, opt_param_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 = get_accelerator().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, opt_param_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 args.save and not saved_checkpoint: save_checkpoint_and_time(iteration, model, optimizer, opt_param_scheduler) torch.distributed.barrier() print_datetime('exiting program at iteration {}'.format(iteration)) sys.exit() trigger(on_step_end) # Exiting based on kill-switch if found_kill_switch(): if not saved_checkpoint: save_checkpoint_and_time(iteration, model, optimizer, opt_param_scheduler) print_datetime(f"Detected kill switch at {args.kill_switch_path}, " f"iteration={iteration}. Exiting") torch.distributed.barrier() sys.exit() return iteration def evaluate(forward_step_func, data_iterator, model, process_non_loss_data_func, config, verbose=False): """Evaluation.""" args = get_args() if args.vision_pretraining and args.vision_pretraining_type == "dino": compute_feature_bank(model) # Turn on evaluation mode which disables dropout. for model_module in model: model_module.eval() if args.curriculum_learning_legacy and not args.no_pipeline_parallel: # When curriculum learning is used with pipeline parallelism, we need # this logic to ensure that the eval data is not truncated. If there # is a seqlen change due to that, we need to call # reset_activation_shape() to reset some buffers in deepspeed pipeline # engine. if args.curriculum_seqlen < args.seq_length: args.curriculum_seqlen = args.seq_length if args.use_rotary_position_embeddings: update_rotary_pos_emb(args.curriculum_seqlen) model[0].reset_activation_shape() if args.eval_micro_batch_size != args.micro_batch_size: reset_cached_broadcast_sizes() model[0].reset_activation_shape() total_loss_dict = {} with torch.no_grad(): iteration = 0 total_iterations = args.eval_iters if args.eval_iters == -1: print_rank_0(F"Evaluation on the entire set as eval-iters is set to {args.eval_iters}") samples_per_iteration = mpu.get_data_parallel_world_size() \ * args.eval_micro_batch_size \ * get_num_eval_microbatches() total_iterations = math.ceil(args.eval_total_samples / samples_per_iteration) print_rank_0(F"Evaluation Iterations: {total_iterations}, Total Eval Samples: {args.eval_total_samples}, samples per iteration: {samples_per_iteration}") args.consumed_valid_samples = 0 num_eval_microbatches = get_num_eval_microbatches() while iteration < total_iterations: iteration += 1 if iteration == total_iterations and args.eval_iters == -1: num_eval_microbatches = math.ceil((args.eval_total_samples - args.consumed_valid_samples) / \ (mpu.get_data_parallel_world_size() * args.eval_micro_batch_size)) if verbose and iteration % args.log_interval == 0: print_rank_0('Evaluating iter {}/{}'.format(iteration, args.eval_iters)) forward_backward_func = get_forward_backward_func() # Don't care about timing during evaluation config.timers = None if args.deepspeed and args.ds_pipeline_enabled: # DeepSpeed uses eval_batch() and already aggregates losses. assert isinstance(model, list) and len(model) == 1 loss = model[0].eval_batch(data_iterator, eval_micro_batches=num_eval_microbatches) loss_dicts = [{'lm loss' : loss}] * num_eval_microbatches else: assert args.micro_batch_size == args.eval_micro_batch_size, \ "evaluate (training) - Megatron's forward_backward_func options - " \ "Unsupported for split micro batch size" loss_dicts = forward_backward_func( forward_step_func=forward_step_func, data_iterator=data_iterator, model=model, num_microbatches=get_num_microbatches(), seq_length=args.seq_length, micro_batch_size=args.micro_batch_size, decoder_seq_length=args.decoder_seq_length, forward_only=True) config.timers = get_timers() # Empty unused memory if args.empty_unused_memory_level >= 1: torch.cuda.empty_cache() if mpu.is_pipeline_last_stage(ignore_virtual=True): # Reduce across processes. for loss_dict in loss_dicts: for key in loss_dict: if 'moe' not in key: total_loss_dict[key] = total_loss_dict.get( key, get_accelerator().FloatTensor([0.0])) + loss_dict[key] args.consumed_valid_samples += mpu.get_data_parallel_world_size() \ * args.eval_micro_batch_size \ * num_eval_microbatches collected_non_loss_data = None if process_non_loss_data_func is not None and is_last_rank(): collected_non_loss_data = forward_backward_func( forward_step_func=forward_step_func, data_iterator=data_iterator, model=model, num_microbatches=get_num_microbatches(), seq_length=args.seq_length, micro_batch_size=args.micro_batch_size, decoder_seq_length=args.decoder_seq_length, forward_only=True, collect_non_loss_data=True) # 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] /= (((total_iterations-1) * get_num_eval_microbatches()) + num_eval_microbatches) if args.curriculum_learning_legacy and not args.no_pipeline_parallel: # roll back to actual curriculum seqlen at the end of eval. args.curriculum_seqlen = args.curriculum_scheduler.update_difficulty( \ args.iteration + 1) if args.curriculum_seqlen < args.seq_length: if args.use_rotary_position_embeddings: update_rotary_pos_emb(args.curriculum_seqlen) model[0].reset_activation_shape() if args.eval_micro_batch_size != args.micro_batch_size: reset_cached_broadcast_sizes() model[0].reset_activation_shape() return total_loss_dict, collected_non_loss_data def evaluate_and_print_results(prefix, forward_step_func, data_iterator, model, iteration, process_non_loss_data_func, config, verbose=False, write_to_tensorboard=True, test=False): """Helper function to evaluate and dump results on screen.""" args = get_args() if write_to_tensorboard: writer = get_tensorboard_writer() else: writer = None total_loss_dict, collected_non_loss_data = evaluate( forward_step_func, data_iterator, model, process_non_loss_data_func, config, verbose) string = ' validation loss at {} | '.format(prefix) eval_loss = 0 for key in total_loss_dict: eval_loss = total_loss_dict[key].item() string += '{} value: {:.6E} | '.format(key, eval_loss) ppl = math.exp(min(20, eval_loss)) string += '{} PPL: {:.6E} | '.format(key, ppl) if writer and is_last_rank(): data_type = 'test' if test else 'validation' writer.add_scalar(f'lm-loss-validation/{key} {data_type}', eval_loss, iteration) writer.add_scalar(f'lm-loss-validation/{key} {data_type} vs samples', eval_loss, args.consumed_train_samples) writer.add_scalar(f'lm-loss-validation/{key} {data_type} vs tokens', eval_loss, args.consumed_train_tokens) if args.log_validation_ppl_to_tensorboard: writer.add_scalar(f'lm-loss-validation/{key} {data_type} ppl', ppl, iteration) writer.add_scalar(f'lm-loss-validation/{key} {data_type} ppl vs samples', ppl, args.consumed_train_samples) writer.add_scalar(f'lm-loss-validation/{key} {data_type} ppl vs tokens', ppl, args.consumed_train_tokens) if process_non_loss_data_func is not None and writer and is_last_rank(): process_non_loss_data_func(collected_non_loss_data, iteration, writer) length = len(string) + 1 print_rank_last('-' * length) print_rank_last(string) print_rank_last('-' * length) if args.eval_loss_exit_value is not None: eval_loss_tensor = get_accelerator().FloatTensor([eval_loss]) torch.distributed.all_reduce(eval_loss_tensor, op=torch.distributed.ReduceOp.MAX) eval_loss = eval_loss_tensor.item() return eval_loss def cyclic_iter(iter): while True: for x in iter: yield x def build_train_valid_test_datasets(build_train_valid_test_datasets_provider): """Build pretraining datasets.""" args = get_args() # 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 if args.eval_iters == -1: print_rank_0("Evaluation iterations are set to -1") train_val_test_num_samples = [train_samples, -1, -1] else: 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. return build_train_valid_test_datasets_provider(train_val_test_num_samples) def build_train_valid_test_data_loaders( build_train_valid_test_datasets_provider): """Build pretraining data loaders.""" 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: if args.train_samples is None: 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. ds_sequence_parallel = mpu.get_sequence_parallel_world_size() > 1 or args.force_ds_sequence_parallel rank_in_parallel_group = mpu.get_sequence_parallel_rank() if ds_sequence_parallel else mpu.get_tensor_model_parallel_rank() if rank_in_parallel_group == 0: # Build datasets. train_ds, valid_ds, test_ds = build_train_valid_test_datasets( build_train_valid_test_datasets_provider) if args.eval_iters == -1: eval_total_samples = len(valid_ds) consumed_valid_samples = 0 use_all_eval_samples = True else: eval_total_samples = 0 consumed_valid_samples = args.consumed_valid_samples use_all_eval_samples = False # Build dataloders. train_dataloader = build_pretraining_data_loader( train_ds, args.consumed_train_samples, True) valid_dataloader = build_pretraining_data_loader( valid_ds, consumed_valid_samples, False, use_all_eval_samples) test_dataloader = build_pretraining_data_loader(test_ds, 0, False) # 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 or args.eval_iters == -1) do_test = test_dataloader is not None and args.eval_iters > 0 # Need to broadcast num_tokens and num_type_tokens. flags = get_accelerator().LongTensor( [int(do_train), int(do_valid), int(do_test), int(eval_total_samples)]) else: flags = get_accelerator().LongTensor([0, 0, 0, 0]) # Broadcast num tokens. if ds_sequence_parallel: torch.distributed.broadcast(flags, mpu.get_sequence_parallel_src_rank(), group=mpu.get_sequence_parallel_group()) else: 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() args.eval_total_samples = flags[3].item() return train_dataloader, valid_dataloader, test_dataloader def build_train_valid_test_data_iterators( build_train_valid_test_datasets_provider): """Build pretraining data iterators.""" args = get_args() # Build loaders. train_dataloader, valid_dataloader, test_dataloader = \ build_train_valid_test_data_loaders( build_train_valid_test_datasets_provider) # 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' and args.eval_iters != -1) \ 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