Merge branch 'blendable_dataset' into refactor_learning_rate

megatron/arguments.py

@@ -136,14 +136,16 @@ def parse_args(extra_args_provider=None, defaults={},
 def _print_args(args):
     """Print arguments."""
     if args.rank == 0:
-        print('-------------------- arguments --------------------', flush=True)
+        print('------------------------ arguments ------------------------',
+              flush=True)
         str_list = []
         for arg in vars(args):
-            dots = '.' * (32 - len(arg))
+            dots = '.' * (48 - len(arg))
             str_list.append('  {} {} {}'.format(arg, dots, getattr(args, arg)))
         for arg in sorted(str_list, key=lambda x: x.lower()):
             print(arg, flush=True)
-        print('---------------- end of arguments ----------------', flush=True)
+        print('-------------------- end of arguments ---------------------',
+              flush=True)
 
 
 def _check_arg_is_not_none(args, arg):
@@ -401,7 +403,10 @@ def _add_data_args(parser):
     group = parser.add_argument_group(title='data and dataloader')
 
     group.add_argument('--data-path', nargs='*', default=None,
-                       help='Path to combined dataset to split.')
+                       help='Path to the training dataset. Accepted format:'
+                       '1) a single data path, 2) multiple datasets in the'
+                       'form: dataset1-weight dataset1-path dataset2-weight '
+                       'dataset2-path ...')
     group.add_argument('--split', type=str, default='969, 30, 1',
                        help='Comma-separated list of proportions for training,'
                        ' validation, and test split. For example the split '

megatron/checkpointing.py

@@ -89,8 +89,7 @@ def get_checkpoint_tracker_filename(checkpoints_path):
     return os.path.join(checkpoints_path, 'latest_checkpointed_iteration.txt')
 
 
-def save_checkpoint(iteration, model, optimizer, lr_scheduler,
-                    consumed_train_samples=None, consumed_valid_samples=None):
+def save_checkpoint(iteration, model, optimizer, lr_scheduler):
     """Save a model checkpoint."""
     args = get_args()
 
@@ -104,10 +103,6 @@ def save_checkpoint(iteration, model, optimizer, lr_scheduler,
         state_dict['args'] = args
         state_dict['checkpoint_version'] = 2.0
         state_dict['iteration'] = iteration
-        if consumed_train_samples:
-            state_dict['consumed_train_samples'] = consumed_train_samples
-        if consumed_valid_samples:
-            state_dict['consumed_valid_samples'] = consumed_valid_samples
         state_dict['model'] = model.state_dict_for_save_checkpoint()
 
         # Optimizer stuff.
@@ -219,17 +214,14 @@ def load_checkpoint(model, optimizer, lr_scheduler, load_arg='load'):
                                  checkpoint_name))
                 sys.exit()
 
-    if 'consumed_train_samples' in state_dict:
-        assert args.consumed_train_samples == 0
-        args.consumed_train_samples = state_dict['consumed_train_samples']
-    if 'consumed_valid_samples' in state_dict:
-        assert args.consumed_valid_samples == 0
-        args.consumed_valid_samples = state_dict['consumed_valid_samples']
-
     # Check arguments.
+    assert args.consumed_train_samples == 0
+    assert args.consumed_valid_samples == 0
     if 'args' in state_dict:
         checkpoint_args = state_dict['args']
         check_checkpoint_args(checkpoint_args)
+        args.consumed_train_samples = getattr(args, 'consumed_train_samples', 0)
+        args.consumed_valid_samples = getattr(args, 'consumed_valid_samples', 0)
     else:
         print_rank_0('could not find arguments in the checkpoint ...')
 

megatron/data/helpers.cpp

@@ -60,7 +60,7 @@ void build_blending_indices(py::array_t<uint8_t>& dataset_index,
   for(int64_t sample_idx = 0; sample_idx < size; ++sample_idx) {
 
     // Determine where the max error in sampling is happening.
-    double sample_idx_double = std::max(static_cast<double>(sample_idx), 1.0);
+    auto sample_idx_double = std::max(static_cast<double>(sample_idx), 1.0);
     int64_t max_error_index = 0;
     double max_error = weights_ptr[0] * sample_idx_double -
       static_cast<double>(current_samples[0]);
@@ -86,7 +86,7 @@ void build_blending_indices(py::array_t<uint8_t>& dataset_index,
   if (verbose) {
     std::cout << " > sample ratios:" << std::endl;
     for (int64_t dataset_idx = 0; dataset_idx < num_datasets; ++dataset_idx) {
-      double ratio = static_cast<double>(current_samples[dataset_idx]) /
+      auto ratio = static_cast<double>(current_samples[dataset_idx]) /
 	static_cast<double>(size);
       std::cout << "   dataset " << dataset_idx << ", input: " <<
 	weights_ptr[dataset_idx] << ", achieved: " << ratio << std::endl; 

megatron/training.py

@@ -104,9 +104,7 @@ def pretrain(train_valid_test_dataset_provider, model_provider,
                                    iteration, False)
 
     if args.save and iteration != 0:
-        save_checkpoint(iteration, model, optimizer, lr_scheduler,
-                        consumed_train_samples=args.consumed_train_samples,
-                        consumed_valid_samples=args.consumed_valid_samples)
+        save_checkpoint(iteration, model, optimizer, lr_scheduler)
 
     if args.do_test:
         # Run on test data.
@@ -438,9 +436,7 @@ def train(forward_step_func, model, optimizer, lr_scheduler,
         # Checkpointing
         if args.save and args.save_interval and \
            iteration % args.save_interval == 0:
-            save_checkpoint(iteration, model, optimizer, lr_scheduler,
-                            consumed_train_samples=args.consumed_train_samples,
-                            consumed_valid_samples=args.consumed_valid_samples)
+            save_checkpoint(iteration, model, optimizer, lr_scheduler)
 
         # Evaluation
         if args.eval_interval and iteration % args.eval_interval == 0 and \