update group text function.

pretraining.py

@@ -425,9 +425,11 @@ def tokenize_function(examples):
 
         return tokenized_inputs
 
+    def tokenize_wo_pad_function(examples):
+        return tokenizer(examples["text"])
+
     # Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
-    def tokenize_and_group_text_function(examples):
-        examples = tokenizer(examples["text"])
+    def group_text_function(examples):
         # Concatenate all texts.
         concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
         total_length = len(concatenated_examples[list(examples.keys())[0]])
@@ -535,8 +537,16 @@ def tokenize_and_group_text_function(examples):
     with training_args.main_process_first(desc="Dataset tokenization and grouping"):
         if not data_args.streaming:
             if training_args.group_by_length:
-                lm_datasets = raw_datasets.map(
-                    tokenize_and_group_text_function,
+                tokenized_datasets = raw_datasets.map(
+                    tokenize_wo_pad_function,
+                    batched=True,
+                    num_proc=data_args.preprocessing_num_workers,
+                    remove_columns=column_names,
+                    load_from_cache_file=not data_args.overwrite_cache,
+                    desc="Running tokenizer on dataset",
+                )
+                lm_datasets = tokenized_datasets.map(
+                    group_text_function,
                     batched=True,
                     num_proc=data_args.preprocessing_num_workers,
                     load_from_cache_file=not data_args.overwrite_cache,
@@ -553,8 +563,13 @@ def tokenize_and_group_text_function(examples):
                 )
         else:
             if training_args.group_by_length:
-                lm_datasets = raw_datasets.map(
-                    tokenize_and_group_text_function,
+                tokenized_datasets = raw_datasets.map(
+                    tokenize_wo_pad_function,
+                    batched=True,
+                    remove_columns=column_names,
+                )
+                lm_datasets = tokenized_datasets.map(
+                    group_text_function,
                     batched=True,
                 )
             else: