Add Neva template

nemo/collections/multimodal/data/neva/conversation.py

@@ -263,6 +263,17 @@ def dict(self):
     sep2=f"{DEFAULT_SYSTEM_TOKEN}System\n",
 )
 
+conv_nv_dpo = Conversation(
+    system="\n",
+    roles=("User", "Assistant"),
+    version="nv_dpo",
+    messages=(),
+    offset=0,
+    sep_style=SeparatorStyle.NVGPT,
+    sep=DEFAULT_SEPARATOR_TOKEN,
+    sep2=f"{DEFAULT_SYSTEM_TOKEN}System\n",
+)
+
 conv_vicuna_v0 = Conversation(
     system="A chat between a curious human and an artificial intelligence assistant. "
     "The assistant gives helpful, detailed, and polite answers to the human's questions.",
@@ -400,6 +411,8 @@ def dict(self):
     "v1_mmtag": conv_llava_v1_mmtag,
     "llava_llama_2": conv_llava_llama_2,
     "nvgpt": conv_nvgpt,
+    "nv_steerlm": conv_nvgpt,
+    "nv_dpo": conv_nv_dpo,
 }
 
 

nemo/collections/multimodal/data/neva/neva_dataset.py

@@ -381,6 +381,8 @@ def preprocess_nvgpt(sources: dict, tokenizer, cfg,) -> Dict:
     - The function asserts that each message in a conversation alternates between the defined roles and skips messages not starting with the 'human' role.
     """
 
+    """<extra_id_0>System\nA chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n\n<extra_id_1>User\n{user input}\n<extra_id_1>Assistant\n<extra_id_2>quality:4,toxicity:0,humor:0,creativity:0,helpfulness:4,correctness:4,coherence:4,complexity:4,verbosity:4\n"""
+
     conv = conversation_lib.conv_nvgpt.copy()
 
     # Apply prompt templates
@@ -462,6 +464,105 @@ def preprocess_nvgpt(sources: dict, tokenizer, cfg,) -> Dict:
     return dict(tokens=tokens, labels=labels,)
 
 
+def preprocess_nv_dpo(sources: dict, tokenizer, cfg,) -> Dict:
+    """
+    Preprocess a given set of conversational sources using nvgpt conversation template
+
+    This function processes conversations by first ensuring the conversation starts with a 'human' role, then tokenizes the conversations, applies specific token replacements, and finally masks labels for training purposes.
+
+    Parameters:
+    - sources: A dictionary containing conversational data. Expected format is a dict of conversations, where each conversation is a list of messages, and each message is a dict with 'from' (role) and 'value' (message text).
+    - tokenizer: A tokenizer from the Hugging Face Transformers library used for tokenizing the conversations.
+    - cfg: Configuration settings which include 'add_extra_token' (bool) to determine if an extra token should be added to the tokenized output, and 'context_length' for specifying the tokenization context length.
+
+    Returns:
+    - Dict: A dictionary containing two keys:
+        - 'tokens': A tensor of tokenized conversation data.
+        - 'labels': A tensor of labels for the conversation data, used for training models. Labels are masked based on the conversation structure.
+
+    Note:
+    - The function includes specific token replacements (e.g., DEFAULT_IMAGE_PATCH_TOKEN, <s>, </s>) and masking techniques for labels.
+    - It is designed to work with conversational data where messages alternate between a 'human' and a 'gpt' role.
+    - The function asserts that each message in a conversation alternates between the defined roles and skips messages not starting with the 'human' role.
+    """
+
+    """<extra_id_0>System\n\n<extra_id_1>User\n{user input}\n<extra_id_1>Assistant\n"""
+
+    conv = conversation_lib.conv_nv_dpo.copy()
+
+    # Apply prompt templates
+    conversations = []
+    for source in sources:
+        conv.messages = []
+        conv.system = source.get('system', conv.system)
+
+        strip_end_for_inference = False
+        for i, turn in enumerate(source['conversations']):
+
+            if i % 2 == 1:
+                turn['from'] = conv.roles[1]
+                conv.append_message(turn['from'], turn['value'])
+                if not turn["value"]:
+                    strip_end_for_inference = (
+                        True  # in inference, current turn is empty, thus end tokens need to striped.
+                    )
+            else:
+                turn['from'] = conv.roles[0]
+                conv.append_message(turn['from'], turn['value'])
+        context = conv.get_prompt()
+        if strip_end_for_inference:
+            if context.endswith("\n<extra_id_1>"):
+                context = context[: -len("\n<extra_id_1>")] + "\n"
+        conversations.append(context)
+
+    add_extra_token = cfg.get("add_extra_token")
+    # Tokenize conversations
+    tokens = tokenize(
+        texts=conversations,
+        tokenizer=tokenizer,
+        context_length=cfg.get("context_length"),
+        add_extra_token=add_extra_token,
+    )
+
+    labels = tokens.clone().detach()
+
+    # Mask targets
+    sep = conv.sep + conv.roles[1] + "\n"
+    for conversation, target in zip(conversations, labels):
+        rounds = conversation.split(conv.sep)
+        re_rounds = [conv.sep.join(rounds[:3])]  # system + user + gpt
+
+        for conv_idx in range(3, len(rounds), 2):
+            re_rounds.append(conv.sep.join(rounds[conv_idx : conv_idx + 2]))  # user + gpt
+
+        cur_len = 0
+        for i, rou in enumerate(re_rounds):
+            if rou == "":
+                break
+            parts = rou.split(sep)
+            if len(parts) != 2:
+                break
+
+            instruction_len = len(tokenizer.text_to_ids(parts[0] + sep))
+            round_len = len(tokenizer.text_to_ids(rou + conv.sep))
+            target[cur_len : cur_len + instruction_len] = IGNORE_INDEX
+
+            cur_len += round_len
+        target[cur_len:] = IGNORE_INDEX
+
+    # Check if masking working correctly
+    # print([x for x in zip(tokens[0].numpy().tolist(), labels[0].numpy().tolist())])
+
+    if add_extra_token:
+        tokens = tokens[:, :-1].contiguous()
+        labels = labels[:, 1:].contiguous()
+    else:
+        labels = torch.roll(labels, shifts=-1, dims=-1)
+        labels[:, -1] = IGNORE_INDEX
+
+    return dict(tokens=tokens, labels=labels,)
+
+
 def preprocess_plain(sources, tokenizer, cfg,) -> Dict:
     """
     Preprocesses plain text sources (no template) for tokenization and label generation.
@@ -604,8 +705,10 @@ def expand2square(pil_img, background_color):
             images_tensors = torch.tensor([])
             sources = copy.deepcopy(sources)
 
-        if self.conv_template == "nvgpt":
+        if self.conv_template in ["nvgpt", "nv_steerlm"]:
             data_dict = preprocess_nvgpt(sources, self.tokenizer, self.multimodal_cfg,)
+        elif self.conv_template == "nv_dpo":
+            data_dict = preprocess_nv_dpo(sources, self.tokenizer, self.multimodal_cfg,)
         elif self.conv_template == "v1":
             data_dict = preprocess_v1(sources, self.tokenizer, self.multimodal_cfg,)
         elif self.conv_template == "llama_2":

nemo/collections/nlp/modules/common/text_generation_strategy.py

@@ -329,14 +329,17 @@ def neva_process_prompts(prompt, tokenizer, multimodal_cfg, num_media_latents, c
         DEFAULT_IMAGE_TOKEN,
         preprocess_llama_2,
         preprocess_multimodal,
+        preprocess_nv_dpo,
         preprocess_nvgpt,
         preprocess_v1,
     )
 
     list_data_dict = []
-    if multimodal_cfg["conv_template"] == "nvgpt":
+    if multimodal_cfg["conv_template"] in ["nvgpt", "nv_steerlm", "nv_dpo"]:
         record = {
-            'system': 'A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user\'s questions.\n\n',
+            'system': '\n'
+            if multimodal_cfg["conv_template"] == 'nv_dpo'
+            else 'A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user\'s questions.\n\n',
             'conversations': [{'from': 'User', 'value': prompt}, {'from': 'Assistant', 'value': '',},],
         }
 
@@ -348,7 +351,10 @@ def neva_process_prompts(prompt, tokenizer, multimodal_cfg, num_media_latents, c
         sources = preprocess_multimodal(
             copy.deepcopy(list_data_dict), multimodal_cfg, num_media_latents
         )  # HARDCODED FOR NOW
-        data_dict = preprocess_nvgpt(sources, tokenizer, multimodal_cfg)
+        if multimodal_cfg["conv_template"] in ["nvgpt", "nv_steerlm"]:
+            data_dict = preprocess_nvgpt(sources, tokenizer, multimodal_cfg)
+        else:
+            data_dict = preprocess_nv_dpo(sources, tokenizer, multimodal_cfg)
 
     elif multimodal_cfg["conv_template"] == "llama_2":
         record = {

nemo/collections/nlp/modules/common/text_generation_utils.py

@@ -181,14 +181,16 @@ def megatron_neva_generate(model, prompt_dict_list, length_params, sampling_para
 
         clean_response = clean_text
 
-        if conv_template == "nvgpt":
+        if conv_template in ["nvgpt", "nv_steerlm"]:
             labels_str_regexp = re.compile(f"<extra_id_2>quality:.*\n")
             last_match_end_position = None
             for match in re.finditer(labels_str_regexp, clean_response):
                 last_match_end_position = match.end()
             if last_match_end_position is not None:
                 clean_response = clean_response[last_match_end_position:]
             clean_response = clean_response.strip("<extra_id_1>")
+        elif conv_template == 'nv_dpo':
+            clean_response = clean_response.split("<extra_id_1>")[-2][10:]  # [10:] for removing "Assistant\n"
         elif conv_template == "llama_2":
             clean_response = clean_response.rsplit("[/INST] ", 1)[-1]
         elif conv_template == "v1":