Work batch-size name changes into task code

megatron/arguments.py

@@ -134,9 +134,12 @@ def parse_args(extra_args_provider=None, defaults={},
         assert args.lr_decay_samples is None, \
             'expected iteration-based learning rate decay'
         assert args.lr_warmup_samples == 0, \
-            'expected iteration-based learnig rate warmup'
+            'expected iteration-based learning rate warmup'
         assert args.rampup_batch_size is None, \
             'expected no batch-size rampup for iteration-based training'
+        if args.lr_warmup_percent is not None:
+            assert args.lr_warmup_iters == 0, \
+                'can only specify one of lr-warmup-percent and lr-warmup-iters'
 
     # Sample-based training.
     if args.train_samples:
@@ -148,6 +151,9 @@ def parse_args(extra_args_provider=None, defaults={},
             'expected sample-based learning rate decay'
         assert args.lr_warmup_iters == 0, \
             'expected sample-based learnig rate warmup'
+        if args.lr_warmup_percent is not None:
+            assert args.lr_warmup_samples == 0, \
+                'can only specify one of lr-warmup-percent and lr-warmup-samples'
 
     # Check required arguments.
     required_args = ['num_layers', 'hidden_size', 'num_attention_heads',
@@ -353,6 +359,9 @@ def _add_learning_rate_args(parser):
     group.add_argument('--lr-decay-samples', type=int, default=None,
                        help='number of samples to decay learning rate over,'
                        ' If None defaults to `--train-samples`')
+    group.add_argument('--lr-warmup-percent', type=float, default=None,
+                       help='percentage of lr-warmup-(iters/samples) to use '
+                       'for warmup')
     group.add_argument('--lr-warmup-iters', type=int, default=0,
                        help='number of iterations to linearly warmup '
                        'learning rate over.')
@@ -568,4 +577,3 @@ def _add_realm_args(parser):
     group.add_argument('--indexer-log-interval', type=int, default=1000,
                        help='After how many batches should the indexer report progress')
     return parser
-

megatron/text_generation_utils.py

@@ -36,7 +36,7 @@ def get_batch(context_tokens):
     tokenizer = get_tokenizer()
 
     # Move to GPU.
-    tokens = context_tokens.view(args.batch_size, -1).contiguous().cuda()
+    tokens = context_tokens.view(args.micro_batch_size, -1).contiguous().cuda()
     # Get the attention mask and postition ids.
     attention_mask, _, position_ids = get_ltor_masks_and_position_ids(
         tokens,
@@ -294,7 +294,7 @@ def generate_samples_unconditional(model):
 
     num_samples = args.num_samples
     context_tokens = [[tokenizer.eod]
-                      for _ in range(args.batch_size)]
+                      for _ in range(args.micro_batch_size)]
     ctr = 0
     while True:
         start_time = time.time()
@@ -310,7 +310,7 @@ def generate_samples_unconditional(model):
             length = len(token_stream)
             token_batch = token_stream[0].cpu().numpy().tolist()
             length_batch = token_stream[1].cpu().numpy().tolist()
-            assert len(length_batch) == args.batch_size
+            assert len(length_batch) == args.micro_batch_size
             for tokens, length in zip(token_batch, length_batch):
                 tokens = tokens[1:length - 1]
                 text = tokenizer.detokenize(tokens)
@@ -321,7 +321,7 @@ def generate_samples_unconditional(model):
                 if ctr >= num_samples:
                     break
         else:
-            for _ in range(args.batch_size):
+            for _ in range(args.micro_batch_size):
                 yield None
                 ctr += 1
                 if ctr >= num_samples:

megatron/training.py

@@ -223,8 +223,11 @@ def get_learning_rate_scheduler(optimizer):
     if args.train_iters:
         if args.lr_decay_iters is None:
             args.lr_decay_iters = args.train_iters
-        warmup_steps = args.lr_warmup_iters * args.global_batch_size
         decay_steps = args.lr_decay_iters * args.global_batch_size
+        if args.lr_warmup_percent is not None:
+            warmup_steps = args.lr_warmup_percent * 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
@@ -233,8 +236,11 @@ def get_learning_rate_scheduler(optimizer):
         update_train_iters(args)
         if args.lr_decay_samples is None:
             args.lr_decay_samples = args.train_samples
-        warmup_steps = args.lr_warmup_samples
         decay_steps = args.lr_decay_samples
+        if args.lr_warmup_percent is not None:
+            warmup_steps = args.lr_warmup_percent * decay_steps
+        else:
+            warmup_steps = args.lr_warmup_samples
     else:
         raise Exception(
             'either train-iters or train-samples should be provided.')

tasks/eval_utils.py

@@ -80,7 +80,7 @@ def calculate_correct_answers(name, model, dataloader,
     args = get_args()
     start_time = time.time()
     model.eval()
-    saved_batch_size = args.batch_size
+    saved_batch_size = args.micro_batch_size
     with torch.no_grad():
         # For all the batches in the dataset.
         total = 0
@@ -103,7 +103,7 @@ def calculate_correct_answers(name, model, dataloader,
             ds = dataloader.dataset
             if hasattr(ds, 'sample_multiplier'):
                 actual_batch_size *= ds.sample_multiplier
-            args.batch_size = actual_batch_size
+            args.micro_batch_size = actual_batch_size
 
             if not mpu.is_pipeline_first_stage():
                 input_tensor, _ = communicate(
@@ -145,7 +145,7 @@ def calculate_correct_answers(name, model, dataloader,
                     recv_backward=False)
 
     model.train()
-    args.batch_size = saved_batch_size
+    args.micro_batch_size = saved_batch_size
 
     # Reduce.
     if mpu.is_pipeline_last_stage():

tasks/finetune_utils.py

@@ -129,10 +129,10 @@ def _build_train_valid_dataloaders(train_dataset, valid_dataset):
                                           args.num_workers, not args.keep_last)
     valid_dataloader = _build_infinite_size_dataloader(valid_dataloader_)
 
-    # Now that we've built the data loaders, set args.batch_size to
+    # Now that we've built the data loaders, set args.micro_batch_size to
     # the actual batch size the model will see for this dataset
     if hasattr(train_dataset, 'sample_multiplier'):
-        args.batch_size *= train_dataset.sample_multiplier
+        args.micro_batch_size *= train_dataset.sample_multiplier
 
     return train_dataloader, valid_dataloader