Feat: Add sharegpt multirole

README.md

@@ -635,9 +635,13 @@ datasets:
     train_on_split: train # Optional[str] name of dataset split to load from
 
     # Optional[str] fastchat conversation type, only used with type: sharegpt
-    conversation:  # Options (see Conversation 'name'): https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
+    conversation: # Options (see Conversation 'name'): https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
     field_human: # Optional[str]. Human key to use for conversation.
     field_model: # Optional[str]. Assistant key to use for conversation.
+    # Add additional keys from your dataset as input or output roles
+    roles:
+      input: # Optional[List[str]]. These will be masked based on train_on_input
+      output: # Optional[List[str]].
 
   # Custom user instruction prompt
   - path: repo

src/axolotl/prompt_strategies/sharegpt.py

@@ -1,5 +1,6 @@
 """Module containing the SimpleShareGPTPromptTokenizingStrategy class"""
 
+import logging
 from typing import Any, Dict, Optional
 
 from fastchat.conversation import Conversation, SeparatorStyle, register_conv_template
@@ -11,6 +12,8 @@
     merge_consecutive_messages,
 )
 
+LOG = logging.getLogger("axolotl")
+
 
 def register_chatml_template(system_message=None):
     system_message = system_message or "You are a helpful assistant."
@@ -42,11 +45,13 @@ def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
     )
     field_human = ds_cfg["field_human"] if ds_cfg and "field_human" in ds_cfg else None
     field_model = ds_cfg["field_model"] if ds_cfg and "field_model" in ds_cfg else None
+    roles = ds_cfg["roles"].to_dict() if ds_cfg and "roles" in ds_cfg else None
     strategy = SimpleShareGPTPromptTokenizingStrategy(
         ShareGPTPrompterV2(
             conversation=conversation,
             role_key_model=field_model,
             role_key_human=field_human,
+            roles=roles,
         ),
         tokenizer,
         cfg.train_on_inputs,
@@ -142,7 +147,12 @@ def get_conversation_thread(self, prompt):
             "system": "system",
         }
         turns = [
-            {"from": role_map[t[role_key]], "value": t[value_key]}
+            {
+                "from": (
+                    role_map[t[role_key]] if t[role_key] in role_map else t[role_key]
+                ),
+                "value": t[value_key],
+            }
             for t in conversations
         ]
         return turns

src/axolotl/prompt_tokenizers.py

@@ -11,7 +11,7 @@
 from axolotl.monkeypatch.fastchat_conversation_turns import (
     add_get_turns_to_conversation,
 )
-from axolotl.prompters import IGNORE_TOKEN_ID
+from axolotl.prompters import IGNORE_TOKEN_ID, Prompter
 
 LOG = logging.getLogger("axolotl")
 
@@ -37,7 +37,7 @@ class PromptTokenizingStrategy(abc.ABC):
 
     def __init__(
         self,
-        prompter,
+        prompter: Prompter,
         tokenizer,
         train_on_inputs: bool = False,
         sequence_len: int = 2048,
@@ -340,6 +340,23 @@ def tokenize_prompt(self, prompt):
             self.prompter._conversation.copy()  # pylint: disable=protected-access
         )
 
+        input_roles = {conversation.roles[0]}
+        output_roles = {conversation.roles[1]}
+
+        if len(conversation.roles) == 3:
+            tool_role_label = conversation.roles[2]
+            input_roles.add(tool_role_label)
+
+        # Add roles from the config
+        if self.prompter.roles:
+            if "input" in self.prompter.roles and self.prompter.roles["input"]:
+                for role in self.prompter.roles["input"]:
+                    input_roles.add(role)
+
+            if "output" in self.prompter.roles and self.prompter.roles["output"]:
+                for role in self.prompter.roles["output"]:
+                    output_roles.add(role)
+
         # support for custom roles from the dataset, only useful for vicuna style prompts/roles
         role_remap = []
         if (
@@ -360,19 +377,18 @@ def tokenize_prompt(self, prompt):
                     LOG.warning(f"expected tuple, got {part}")
                     continue
 
-                tool_role_label = None
-                if len(conversation.roles) == 3:
-                    (
-                        user_role_label,
-                        assistant_role_label,
-                        tool_role_label,
-                    ) = conversation.roles
-                else:
-                    user_role_label, assistant_role_label = conversation.roles
                 role, content = part
 
                 # Uses "in" because role contains extra characters
-                if user_role_label in role:
+                input_turn = any(r.lower() in role.lower() for r in input_roles)
+                output_turn = any(r.lower() in role.lower() for r in output_roles)
+                empty_role = role.strip() == ""
+
+                if not any([input_turn, output_turn, empty_role]):
+                    LOG.warning(f"unhandled role: {role}")
+                    continue
+
+                if input_turn:
                     role = (
                         role.replace(role_remap[0]["from"], role_remap[0]["to"])
                         if role_remap
@@ -392,7 +408,7 @@ def tokenize_prompt(self, prompt):
                     else:
                         # everything from this is masked out from the labels
                         labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
-                elif assistant_role_label in role:
+                elif output_turn:
                     role = (
                         role.replace(role_remap[1]["from"], role_remap[1]["to"])
                         if role_remap
@@ -423,7 +439,7 @@ def tokenize_prompt(self, prompt):
                         labels[:len_role] = [IGNORE_TOKEN_ID] * min(
                             len_role, len(labels)
                         )
-                elif role == "":
+                elif empty_role:
                     turn = content
                     # this is only ever the first part, should include the bos token and the user query
                     res = self._tokenize(
@@ -434,11 +450,6 @@ def tokenize_prompt(self, prompt):
                     else:
                         # everything from this is masked out from the labels
                         labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
-                elif tool_role_label and tool_role_label in role:
-                    labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
-                else:
-                    LOG.warning(f"unhandled role: {role}")
-                    continue
 
                 # pylint: disable=duplicate-code
                 result, current_len = parse_tokenized_to_result(

src/axolotl/prompters.py

@@ -259,6 +259,12 @@ def __repr__(self) -> str:
     "Role did not alternate between turns (gpt and human). Please check your data."
 )
 
+CONVERSATION_ROLE_FORMAT = {
+    "chatml": "<|im_start|>{ROLE}",
+    "zephyr": "<|{ROLE}|>",
+    "vicuna_v1.1": "{ROLE}",
+}
+
 
 class ShareGPTPrompter(Prompter):  # pylint: disable=too-few-public-methods
     """
@@ -268,7 +274,9 @@ class ShareGPTPrompter(Prompter):  # pylint: disable=too-few-public-methods
     role_key_human = "human"
     role_key_model = "gpt"
     # Optional, only used for tool usage datasets.
-    role_key_tool = None
+    role_key_tool: Optional[str] = None
+    # Optional, role input/output mapping
+    roles: Optional[dict] = None
 
     def __init__(
         self,
@@ -277,6 +285,7 @@ def __init__(
         role_key_human: Optional[str] = None,
         role_key_model: Optional[str] = None,
         role_key_tool: Optional[str] = None,
+        roles: Optional[dict] = None,
     ):
         if conversation:
             if isinstance(conversation, Conversation):
@@ -291,6 +300,8 @@ def __init__(
             self.role_key_model = role_key_model
         if role_key_tool:
             self.role_key_tool = role_key_tool
+        if roles:
+            self.roles = roles
 
     def _build_result(self, source):
         if len(source) < 2:
@@ -322,11 +333,23 @@ def _build_result(self, source):
 
         conv.messages = []
         for _, sentence in enumerate(source):
-            role = roles[sentence["from"]]
-            if len(conv.messages) > 0 and (
-                (role == conv.messages[-1][0]) or (role not in conv.roles)
-            ):
+            from_role = sentence["from"]
+            if from_role in roles:
+                role = roles[from_role]
+            else:
+                if self._conversation.name not in CONVERSATION_ROLE_FORMAT:
+                    raise NotImplementedError(
+                        f"Role ({role}) not in default roles, and {self._conversation.name} does not support role remapping yet."
+                        "Please help us by creating an Issue to add support for this conversation type."
+                    )
+
+                role = CONVERSATION_ROLE_FORMAT[self._conversation.name].format(
+                    ROLE=from_role
+                )
+
+            if len(conv.messages) > 0 and ((role == conv.messages[-1][0])):
                 LOG.warning(f"{SHAREGPT_ASSERTION_FAILED_ROLE}: {sentence}")
+
             conv.append_message(role, sentence["value"])
 
         return conv.get_turns()
@@ -354,11 +377,13 @@ def __init__(
         conversation: Optional[Union[str, Conversation]] = None,
         role_key_human: Optional[str] = None,
         role_key_model: Optional[str] = None,
+        roles: Optional[dict] = None,
     ):
         super().__init__(
             conversation=conversation,
             role_key_human=role_key_human,
             role_key_model=role_key_model,
+            roles=roles,
         )
 
 

src/axolotl/utils/config/models/input/v0_4_1/__init__.py

@@ -96,6 +96,8 @@ class SFTDataset(BaseModel):
     field_human: Optional[str] = None
     field_model: Optional[str] = None
 
+    roles: Optional[Dict[str, List[str]]] = None
+
 
 class UserDefinedDPOType(BaseModel):
     """User defined typing for DPO"""

tests/prompt_strategies/test_sharegpt.py

@@ -62,6 +62,38 @@ def fixture_sharegpt_glaive_dataset():
     )
 
 
+@pytest.fixture(name="multi_role_dataset")
+def fixture_multi_role_dataset():
+    return Dataset.from_list(
+        [
+            {
+                "conversations": [
+                    {
+                        "from": "system",
+                        "value": "use get_weather(city) to get the weather for a city",
+                    },
+                    {
+                        "from": "human",
+                        "value": "hello, what's the weather in New York?",
+                    },
+                    {
+                        "from": "gpt",
+                        "value": "let me get that for you",
+                    },
+                    {
+                        "from": "tool",
+                        "value": "get_weather(New York)",
+                    },
+                    {
+                        "from": "gpt",
+                        "value": "the weather in New York is 70 degrees and sunny",
+                    },
+                ]
+            }
+        ]
+    )
+
+
 @pytest.fixture(name="tokenizer")
 def fixture_tokenizer():
     tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
@@ -196,3 +228,39 @@ def test_chatml_glaive(self, glaive_dataset, tokenizer):
             32001, 13892, 13, 28737, 28742, 28719, 7371, 28725, 562, 315, 949, 28742, 28707, 506, 272, 21368, 298, 1820, 22447, 28723, 28705, 523, 28766, 416, 1009, 772, 28766, 28767, 32000, 28705, 13  # gpt
         ]
         # fmt: on
+
+    def test_multi_role_dataset(self, multi_role_dataset, tokenizer):
+        strategy = SimpleShareGPTPromptTokenizingStrategy(
+            ShareGPTPrompterV2(conversation="chatml", roles={"input": ["tool"]}),
+            tokenizer,
+            False,  # train_on_inputs
+            2048,  # sequence_len
+        )
+
+        dataset_wrapper = TokenizedPromptDataset(
+            strategy, multi_role_dataset, process_count=1
+        )
+
+        input_ids = dataset_wrapper[0]["input_ids"]
+        # fmt: off
+        assert input_ids == [
+            1,   # bos
+            32001, 1587, 13, 1730, 625, 28730, 769, 1223, 28732, 18373, 28731, 298, 625, 272, 8086, 354, 264, 2990, 32000, 28705, 13,  # system
+            32001, 2188, 13, 21558, 28725, 767, 28742, 28713, 272, 8086, 297, 1450, 2726, 28804, 32000, 28705, 13,  # human
+            32001, 13892, 13, 895, 528, 625, 369, 354, 368, 32000, 28705, 13,  # gpt
+            32001, 3921, 13, 527, 28730, 769, 1223, 28732, 2972, 2726, 28731, 32000, 28705, 13,  # tool
+            32001, 13892, 13, 1237, 8086, 297, 1450, 2726, 349, 28705, 28787, 28734, 11182, 304, 4376, 1780, 32000, 28705, 13  # gpt
+        ]
+        # fmt: on
+
+        labels = dataset_wrapper[0]["labels"]
+        # fmt: off
+        assert labels == [
+            -100,  # bos
+            -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,  # system
+            -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,  # human
+            -100, -100, 13, 895, 528, 625, 369, 354, 368, 32000, 28705, 13,  # gpt
+            -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100,  # tool
+            -100, -100, 13, 1237, 8086, 297, 1450, 2726, 349, 28705, 28787, 28734, 11182, 304, 4376, 1780, 32000, 28705, 13  # gpt
+        ]
+        # fmt: on