refactored training

megatron/arguments.py

@@ -234,6 +234,8 @@ def _add_mixed_precision_args(parser):
                        'attention-softmax-in-fp32 to true')
     group.add_argument('--attention-softmax-in-fp32', action='store_true',
                        help='Run attention masking and softmax in fp32.')
+    group.add_argument('--fp32-allreduce', action='store_true',
+                       help='All-reduce in fp32')
     group.add_argument('--hysteresis', type=int, default=2,
                        help='hysteresis for dynamic loss scaling')
     group.add_argument('--loss-scale', type=float, default=None,

megatron/training.py

@@ -13,62 +13,57 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-"""Pretrain utilities"""
+"""Pretrain utilities."""
 
 from datetime import datetime
 import math
+import sys
 
 import torch
 from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP
 from apex.optimizers import FusedAdam as Adam
 
-from megatron.global_vars import get_args
-from megatron.global_vars import get_timers
-from megatron.global_vars import get_tensorboard_writer
-from megatron.global_vars import get_adlr_autoresume
-from megatron.initialize import initialize_megatron
-
+from megatron import get_args
+from megatron import get_timers
+from megatron import get_tensorboard_writer
 from megatron import mpu
+from megatron import print_rank_0
+from megatron.checkpointing import load_checkpoint
+from megatron.checkpointing import save_checkpoint
 from megatron.fp16 import FP16_Module
 from megatron.fp16 import FP16_Optimizer
+from megatron.initialize import initialize_megatron
 from megatron.learning_rates import AnnealingLR
 from megatron.model import DistributedDataParallel as LocalDDP
 from megatron.model import get_params_for_weight_decay_optimization
 from megatron.utils import check_adlr_autoresume_termination
-from megatron.checkpointing import load_checkpoint
-from megatron import print_rank_0
 from megatron.utils import report_memory
-from megatron.checkpointing import save_checkpoint
 
 
-def run(top_level_message, train_val_test_data_provider,
-        model_provider, forward_step_func, extra_args_provider=None,
-        args_defaults={}):
+def pretrain(train_val_test_data_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) get input arguments.
-        2) initialize distributed and seeds.
+        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) setup model, optimizer and lr schedule using the model_provider.
-        5) train the modle using the forward_step_func.
+        4) train the modle using the forward_step_func.
 
     Arguments:
-        top_level_message: a meesage to print at the top of the run.
-        train_val_test_data_provider: a function that takes `args` as input
-            and returns `train, val, test` dataloaders. Note that args are
-            passed and can be modified in case we need to use some parameters
-            later. For example, we can set vocab size using
-                args.vocab_size = ...
-            and later use this value in `model_provider`.
-        model_provider: a function that takes `args` and 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`, `model`,
-            `args`, and `timers` 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.
+        train_val_test_data_provider: a function that builds datasets
+            and returns `train, val, test` dataloaders.
+        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.
     """
 
     # Initalize and get arguments, timers, and Tensorboard writer.
@@ -76,36 +71,44 @@ def run(top_level_message, train_val_test_data_provider,
                         args_defaults=args_defaults)
     args = get_args()
     timers = get_timers()
-    writer = get_tensorboard_writer()
-
-    # Data stuff.
-    train_data, val_data, test_data = train_val_test_data_provider(args)
 
     # Model, optimizer, and learning rate.
-    model, optimizer, lr_scheduler = setup_model_and_optimizer(model_provider,
-                                                               args)
+    timers('model and optimizer').start()
+    model, optimizer, lr_scheduler = setup_model_and_optimizer(model_provider)
+    timers('model and optimizer').stop()
+
+    # Data stuff.
+    timers('train/valid/test dataset').start()
+    train_data, val_data, test_data = train_val_test_data_provider()
+    timers('train/valid/test dataset').stop()
 
     # Train, validation, and test data.
+    timers('train/valid/test dataloader').start()
     train_data_iterator, val_data_iterator, \
         test_data_iterator = get_train_val_test_data_iterators(train_data,
                                                                val_data,
-                                                               test_data,
-                                                               args)
+                                                               test_data)
+    timers('train/valid/test dataloader').stop()
+
+    # Print setup timing.
+    print_rank_0('done with setups ...')
+    timers.log(['model and optimizer', 'train/valid/test dataset',
+                'train/valid/test dataloader'])
+    print_rank_0('training ...')
 
     iteration = 0
     if args.train_iters > 0:
         if args.do_train:
-            iteration, _ = train(forward_step_func, model,
-                                 optimizer, lr_scheduler,
-                                 train_data_iterator, val_data_iterator,
-                                 timers, args, writer)
+            iteration, _ = train(forward_step_func,
+                                 model, optimizer, lr_scheduler,
+                                 train_data_iterator, val_data_iterator)
+
 
     if args.do_valid:
         prefix = 'the end of training for val data'
         evaluate_and_print_results(prefix, forward_step_func,
                                    val_data_iterator, model,
-                                   args, writer, iteration,
-                                   timers, False)
+                                   iteration, False)
 
     if args.save and iteration != 0:
         save_checkpoint(iteration, model, optimizer, lr_scheduler)
@@ -115,14 +118,15 @@ def run(top_level_message, train_val_test_data_provider,
         prefix = 'the end of training for test data'
         evaluate_and_print_results(prefix, forward_step_func,
                                    test_data_iterator, model,
-                                   args, None, 0, timers, True)
+                                   0, True)
 
 
-def get_model(model_provider_func, args):
+def get_model(model_provider_func):
     """Build the model."""
+    args = get_args()
 
     # Build model on cpu.
-    model = model_provider_func(args)
+    model = model_provider_func()
 
     # Print number of parameters.
     if mpu.get_data_parallel_rank() == 0:
@@ -140,26 +144,24 @@ def get_model(model_provider_func, args):
     # Wrap model for distributed training."""
     if args.DDP_impl == 'torch':
         i = torch.cuda.current_device()
-        args.DDP_type = torchDDP
-        model = args.DDP_type(model, device_ids=[i], output_device=i,
-                              process_group=mpu.get_data_parallel_group())
+        model = torchDDP(model, device_ids=[i], output_device=i,
+                         process_group=mpu.get_data_parallel_group())
         return model
     if args.DDP_impl == 'local':
-        args.DDP_type = LocalDDP
-        model = args.DDP_type(model)
+        model = LocalDDP(model)
         return model
 
     print_rank_0('Unknown DDP implementation specified: {}. '
                  'Exiting.'.format(args.DDP_impl))
-    exit()
-    return model
+    sys.exit()
 
 
-def get_optimizer(model, args):
+def get_optimizer(model):
     """Set up the optimizer."""
+    args = get_args()
 
     # Build parameter groups (weight decay and non-decay).
-    while isinstance(model, (args.DDP_type, FP16_Module)):
+    while isinstance(model, (torchDDP, LocalDDP, FP16_Module)):
         model = model.module
     param_groups = get_params_for_weight_decay_optimization(model)
 
@@ -170,8 +172,7 @@ def get_optimizer(model, args):
                 param.model_parallel = False
 
     # Use Adam.
-    optimizer = Adam(param_groups,
-                     lr=args.lr, weight_decay=args.weight_decay)
+    optimizer = Adam(param_groups, lr=args.lr, weight_decay=args.weight_decay)
 
     # Wrap into fp16 optimizer.
     if args.fp16:
@@ -186,8 +187,9 @@ def get_optimizer(model, args):
     return optimizer
 
 
-def get_learning_rate_scheduler(optimizer, args):
+def get_learning_rate_scheduler(optimizer):
     """Build the learning rate scheduler."""
+    args = get_args()
 
     # Add linear learning rate scheduler.
     if args.lr_decay_iters is not None:
@@ -211,12 +213,13 @@ def get_learning_rate_scheduler(optimizer, args):
     return lr_scheduler
 
 
-def setup_model_and_optimizer(model_provider_func, args):
+def setup_model_and_optimizer(model_provider_func):
     """Setup model and optimizer."""
+    args = get_args()
 
-    model = get_model(model_provider_func, args)
-    optimizer = get_optimizer(model, args)
-    lr_scheduler = get_learning_rate_scheduler(optimizer, args)
+    model = get_model(model_provider_func)
+    optimizer = get_optimizer(model)
+    lr_scheduler = get_learning_rate_scheduler(optimizer)
 
     if args.load is not None:
         args.iteration = load_checkpoint(model, optimizer, lr_scheduler)
@@ -226,8 +229,10 @@ def setup_model_and_optimizer(model_provider_func, args):
     return model, optimizer, lr_scheduler
 
 
-def backward_step(optimizer, model, loss, args, timers):
+def backward_step(optimizer, model, loss):
     """Backward step."""
+    args = get_args()
+    timers = get_timers()
 
     # Backward pass.
     optimizer.zero_grad()
@@ -255,18 +260,20 @@ def backward_step(optimizer, model, loss, args, timers):
             optimizer.clip_master_grads(args.clip_grad)
 
 
-def train_step(forward_step_func, data_iterator, model, optimizer, lr_scheduler,
-               args, timers):
+def train_step(forward_step_func, data_iterator,
+               model, optimizer, lr_scheduler):
     """Single training step."""
+    args = get_args()
+    timers = get_timers()
 
     # Forward model for one step.
     timers('forward').start()
-    loss, loss_reduced = forward_step_func(data_iterator, model, args, timers)
+    loss, loss_reduced = forward_step_func(data_iterator, model)
     timers('forward').stop()
 
     # Calculate gradients, reduce across processes, and clip.
     timers('backward').start()
-    backward_step(optimizer, model, loss, args, timers)
+    backward_step(optimizer, model, loss)
     timers('backward').stop()
 
     # Update parameters.
@@ -285,7 +292,11 @@ def train_step(forward_step_func, data_iterator, model, optimizer, lr_scheduler,
 
 
 def training_log(loss_dict, total_loss_dict, learning_rate, iteration,
-                 loss_scale, report_memory_flag, writer, args, timers):
+                 loss_scale, report_memory_flag):
+    """Log training information such as losses, timing, ...."""
+    args = get_args()
+    timers = get_timers()
+    writer = get_tensorboard_writer()
 
     # Update losses.
     for key in loss_dict:
@@ -341,8 +352,10 @@ def training_log(loss_dict, total_loss_dict, learning_rate, iteration,
 
 
 def train(forward_step_func, model, optimizer, lr_scheduler,
-          train_data_iterator, val_data_iterator, timers, args, writer):
+          train_data_iterator, val_data_iterator):
     """Train the model function."""
+    args = get_args()
+    timers = get_timers()
 
     # Turn on training mode which enables dropout.
     model.train()
@@ -361,8 +374,7 @@ def train(forward_step_func, model, optimizer, lr_scheduler,
                                              train_data_iterator,
                                              model,
                                              optimizer,
-                                             lr_scheduler,
-                                             args, timers)
+                                             lr_scheduler)
         skipped_iters += skipped_iter
         iteration += 1
 
@@ -370,8 +382,7 @@ def train(forward_step_func, model, optimizer, lr_scheduler,
         report_memory_flag = training_log(loss_dict, total_loss_dict,
                                           optimizer.param_groups[0]['lr'],
                                           iteration, optimizer.loss_scale,
-                                          report_memory_flag, writer, args,
-                                          timers)
+                                          report_memory_flag)
 
         # Autoresume
         if (iteration % args.adlr_autoresume_interval == 0) and \
@@ -389,23 +400,23 @@ def train(forward_step_func, model, optimizer, lr_scheduler,
            args.do_valid:
             prefix = 'iteration {}'.format(iteration)
             evaluate_and_print_results(prefix, forward_step_func,
-                                       val_data_iterator, model, args,
-                                       writer, iteration, timers, False)
+                                       val_data_iterator, model,
+                                       iteration, False)
 
         if args.exit_interval and iteration % args.exit_interval == 0:
             torch.distributed.barrier()
             time_str = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
             rank = torch.distributed.get_rank()
-            print('rank: {} | time: {} | exiting the program at iteration {}'.
-                  format(rank, time_str, iteration), flush=True)
-            exit()
+            print_rank_0('rank: {} | time: {} | exiting the program at '
+                         'iteration {}'.format(rank, time_str, iteration))
+            sys.exit()
 
     return iteration, skipped_iters
 
 
-def evaluate(forward_step_func, data_iterator, model,
-             args, timers, verbose=False):
+def evaluate(forward_step_func, data_iterator, model, verbose=False):
     """Evaluation."""
+    args = get_args()
 
     # Turn on evaluation mode which disables dropout.
     model.eval()
@@ -420,8 +431,7 @@ def evaluate(forward_step_func, data_iterator, model,
                 print_rank_0('Evaluating iter {}/{}'.format(iteration,
                                                             args.eval_iters))
             # Forward evaluation.
-            _, loss_dict = forward_step_func(data_iterator, model,
-                                             args, timers)
+            _, loss_dict = forward_step_func(data_iterator, model)
             # Reduce across processes.
             for key in loss_dict:
                 total_loss_dict[key] = total_loss_dict.get(key, 0.) + \
@@ -437,11 +447,11 @@ def evaluate(forward_step_func, data_iterator, model,
 
 def evaluate_and_print_results(prefix, forward_step_func,
                                data_iterator, model,
-                               args, writer, iteration,
-                               timers, verbose=False):
+                               iteration, verbose=False):
     """Helper function to evaluate and dump results on screen."""
-    total_loss_dict = evaluate(forward_step_func, data_iterator, model,
-                               args, timers, verbose)
+    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())
@@ -459,8 +469,9 @@ def evaluate_and_print_results(prefix, forward_step_func,
     print_rank_0('-' * length)
 
 
-def get_train_val_test_data_iterators(train_data, val_data, test_data, args):
+def get_train_val_test_data_iterators(train_data, val_data, test_data):
     """Build train/validation/test iterators"""
+    args = get_args()
 
     # Shift the start iterations.
     if train_data is not None:

pretrain_bert.py

@@ -18,24 +18,28 @@
 import torch
 import torch.nn.functional as F
 
+from megatron import get_args
+from megatron import get_timers
+
 from megatron import mpu
 from megatron.model import BertModel
 from megatron import print_rank_0
 from megatron.utils import reduce_losses
 from megatron.utils import vocab_size_with_padding
-from megatron.training import run
+from megatron.training import pretrain
 from megatron.data.bert_dataset import build_train_valid_test_datasets
 from megatron.data_utils.samplers import DistributedBatchSampler
 
 
-def model_provider(args):
+def model_provider():
     """Build the model."""
+    args = get_args()
 
     print_rank_0('building BERT model ...')
 
     model = BertModel(
         num_layers=args.num_layers,
-        vocab_size=args.vocab_size,
+        vocab_size=args.padded_vocab_size,
         hidden_size=args.hidden_size,
         num_attention_heads=args.num_attention_heads,
         embedding_dropout_prob=args.hidden_dropout,
@@ -46,7 +50,7 @@ def model_provider(args):
         checkpoint_num_layers=args.checkpoint_num_layers,
         add_binary_head=True,
         layernorm_epsilon=args.layernorm_epsilon,
-        num_tokentypes=args.tokentype_size,
+        num_tokentypes=2,
         parallel_output=True,
         apply_query_key_layer_scaling=args.apply_query_key_layer_scaling,
         attention_softmax_in_fp32=args.attention_softmax_in_fp32)
@@ -54,19 +58,17 @@ def model_provider(args):
     return model
 
 
-def get_batch(data_iterator, timers):
+def get_batch(data_iterator):
 
     # Items and their type.
     keys = ['text', 'types', 'labels', 'is_random', 'loss_mask', 'padding_mask']
     datatype = torch.int64
 
     # Broadcast data.
-    timers('data loader').start()
     if data_iterator is not None:
         data = next(data_iterator)
     else:
         data = None
-    timers('data loader').stop()
     data_b = mpu.broadcast_data(keys, data, datatype)
 
     # Unpack.
@@ -80,13 +82,14 @@ def get_batch(data_iterator, timers):
     return tokens, types, sentence_order, loss_mask, lm_labels, padding_mask
 
 
-def forward_step(data_iterator, model, args, timers):
+def forward_step(data_iterator, model):
     """Forward step."""
+    timers = get_timers()
 
     # Get the batch.
     timers('batch generator').start()
     tokens, types, sentence_order, loss_mask, lm_labels, padding_mask \
-        = get_batch(data_iterator, timers)
+        = get_batch(data_iterator)
     timers('batch generator').stop()
 
     # Forward model.
@@ -108,9 +111,10 @@ def forward_step(data_iterator, model, args, timers):
     return loss, {'lm loss': reduced_losses[0], 'sop loss': reduced_losses[1]}
 
 
-def get_train_val_test_data(args):
+def get_train_val_test_data():
     """Load the data on rank zero and boradcast number of tokens to all GPUS."""
-
+    args = get_args()
+    
     (train_data, valid_data, test_data) = (None, None, None)
 
     # Data loader only on rank 0 of each model parallel group.
@@ -202,6 +206,6 @@ if __name__ == "__main__":
         'tokenizer_type': 'BertWordPieceLowerCase'})
     exit()
     '''
-    run('Pretrain BERT model', get_train_val_test_data,
-        model_provider, forward_step,
-        args_defaults={'tokenizer_type': 'BertWordPieceLowerCase'})
+    pretrain(get_train_val_test_data,
+             model_provider, forward_step,
+             args_defaults={'tokenizer_type': 'BertWordPieceLowerCase'})

pretrain_gpt2.py

@@ -17,6 +17,10 @@
 
 import torch
 
+
+from megatron import get_args
+from megatron import get_timers
+
 from configure_data import configure_data
 from gpt2_data_loader import make_gpt2_dataloaders
 from megatron import mpu
@@ -25,15 +29,16 @@ from megatron.utils import get_ltor_masks_and_position_ids
 from megatron import print_rank_0
 from megatron.utils import reduce_losses
 from megatron.utils import vocab_size_with_padding
-from megatron.training import run
+from megatron.training import pretrain
 
 
-def model_provider(args):
+def model_provider():
     """Build the model."""
+    args = get_args()
 
     print_rank_0('building GPT2 model ...')
     model = GPT2Model(num_layers=args.num_layers,
-                      vocab_size=args.vocab_size,
+                      vocab_size=args.padded_vocab_size,
                       hidden_size=args.hidden_size,
                       num_attention_heads=args.num_attention_heads,
                       embedding_dropout_prob=args.hidden_dropout,
@@ -50,20 +55,19 @@ def model_provider(args):
     return model
 
 
-def get_batch(data_iterator, args, timers):
+def get_batch(data_iterator):
     """Generate a batch"""
+    args = get_args()
 
     # Items and their type.
     keys = ['text']
     datatype = torch.int64
 
     # Broadcast data.
-    timers('data loader').start()
     if data_iterator is not None:
         data = next(data_iterator)
     else:
         data = None
-    timers('data loader').stop()
     data_b = mpu.broadcast_data(keys, data, datatype)
 
     # Unpack.
@@ -85,8 +89,10 @@ def get_batch(data_iterator, args, timers):
     return tokens, labels, loss_mask, attention_mask, position_ids
 
 
-def forward_step(data_iterator, model, args, timers):
+def forward_step(data_iterator, model):
     """Forward step."""
+    args = get_args()
+    timers = get_timers()
 
     # Get the batch.
     timers('batch generator').start()
@@ -107,9 +113,10 @@ def forward_step(data_iterator, model, args, timers):
     return loss, {'lm loss': reduced_loss[0]}
 
 
-def get_train_val_test_data(args):
+def get_train_val_test_data():
     """Load the data on rank zero and boradcast number of tokens to all GPUS."""
-
+    args = get_args()
+    
     (train_data, val_data, test_data) = (None, None, None)
 
     # Data loader only on rank 0 of each model parallel group.
@@ -162,5 +169,5 @@ def get_train_val_test_data(args):
 
 if __name__ == "__main__":
 
-    run('Pretrain GPT-2 model', get_train_val_test_data,
-        model_provider, forward_step)
+    pretrain(get_train_val_test_data,
+             model_provider, forward_step)