Add include_text parameter to SFT dataloaders

nemo/collections/nlp/data/language_modeling/megatron/gpt_sft_chat_dataset.py

@@ -344,6 +344,8 @@ def _process_example(self, example):
         # store metadata in dataset, in case user may have keys required in the prediction json files
         metadata = {k: v for k, v in example.items() if k not in ['conversations']}
         result['metadata'] = metadata
+        if self.output_original_text:
+            result['metadata']['conversations'] = example['conversations']
 
         return result
 

nemo/collections/nlp/data/language_modeling/megatron/gpt_sft_dataset.py

@@ -58,6 +58,7 @@ def __init__(
         truncation_method: str = 'right',
         special_tokens: Optional[Mapping[str, str]] = None,  # special tokens, a dictory of {token_type: token}
         is_test: bool = False,
+        output_original_text: bool = False,
     ):
         """
         file_path: Path to a JSONL GPT supervised fine-tuning dataset. Data is formatted as multiple JSON lines with each line formatted as follows. {'input': 'John von Neumann\nVon Neumann made fundamental contributions .... Q: What did the math of artificial viscosity do?', 'output': 'smoothed the shock transition without sacrificing basic physics'}
@@ -81,6 +82,7 @@ def __init__(
         truncation_method: Truncation from which position. Options: ['left', 'right']
         special_tokens: special tokens for the chat prompts, a dictionary of {token_type: token}. Default: {'system_turn_start': '<extra_id_0>', 'turn_start': '<extra_id_1>', 'label_start': '<extra_id_2>', 'end_of_turn': '\n', "end_of_name": "\n"}
         is_test: Whether this dataset is the test split.
+        output_original_text (bool): if true, will keep the original text in the output alongside the tokenized ids.
         """
         self.tokenizer = tokenizer
         self.file_path = file_path
@@ -102,6 +104,7 @@ def __init__(
         self.tokens_to_generate = tokens_to_generate
         self.truncation_method = truncation_method
         self.is_test = is_test
+        self.output_original_text = output_original_text
         if special_tokens is None:
             self.special_tokens = {
                 "system_turn_start": "<extra_id_0>",
@@ -212,17 +215,17 @@ def _separate_template(self, prompt_template_values: List[str]):
         Returns:
             template_strings (List[str]): separated prompt_template with contexts/label placeholder filled with corresponding strings
             template_strings_keys (List[str]): strings point to placeholder keys or <template>
-            
+
         Examples:
             prompt_template = 'Context:  {context} Question: {question} Answer: {label}'
             prompt_template_values = ['xxx', 'yyy', 'zzz']
-            
+
             # tokenizer.space_sensitive = True
-            template_strings = ['Context:', '  xxx', ' Question:', ' yyy', ' Answer:', ' zzz'] 
-            
+            template_strings = ['Context:', '  xxx', ' Question:', ' yyy', ' Answer:', ' zzz']
+
             # tokenizer.space_sensitive = False
-            template_strings = ['Context:', ' xxx', 'Question:', 'yyy', 'Answer:', 'zzz'] 
-            
+            template_strings = ['Context:', ' xxx', 'Question:', 'yyy', 'Answer:', 'zzz']
+
             template_strings_keys = ['<template>', 'context', '<template>', 'question', '<template>', 'label']
         """
         placeholders = [f'{{{k}}}' for k in self.prompt_template_keys]
@@ -260,7 +263,7 @@ def _separate_template(self, prompt_template_values: List[str]):
     def _multiple_truncation(self, template_ids: List[List[int]], template_ids_keys: List[str]):
         """
         Calculate total tokens and truncate multiple contexts in truncation_fields.
-        
+
         Args:
             template_ids (List[List[int]]): the list of separate prompt_template ids.
             template_ids_keys (List[str]): the list of placeholder keys or <template> (used to check key in truncation_fields).
@@ -368,6 +371,9 @@ def _process_example(self, example):
 
         # store metadata in dataset, in case user may have keys required in the prediction json files
         metadata = {k: v for k, v in example.items() if k not in self.prompt_template_keys}
+        if self.output_original_text:
+            for orig_text, text_key in zip(template_strings, template_strings_keys):
+                metadata[text_key] = orig_text
 
         processed_example = {
             'input_ids': input_ids,