Feat: Add sharegpt multirole (#1137)
* feat(prompt): support multiple roles for sharegpt * fix: add handling of empty role back * feat: rebased and allowed more dynamic roles via config * fix: variable * chore: update message * feat: add vicuna format * fix: JSON serializable error * fix: typing * fix: don't remap for unknown keys * fix: add roles to pydantic * feat: add test * chore: remove leftover print * chore: remove leftover comment * chore: remove print * fix: update test to use chatml
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@@ -1,5 +1,6 @@
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"""Module containing the SimpleShareGPTPromptTokenizingStrategy class"""
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import logging
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from typing import Any, Dict, Optional
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from fastchat.conversation import Conversation, SeparatorStyle, register_conv_template
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@@ -11,6 +12,8 @@ from axolotl.utils.tokenization import (
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merge_consecutive_messages,
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)
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LOG = logging.getLogger("axolotl")
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def register_chatml_template(system_message=None):
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system_message = system_message or "You are a helpful assistant."
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@@ -42,11 +45,13 @@ def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
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)
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field_human = ds_cfg["field_human"] if ds_cfg and "field_human" in ds_cfg else None
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field_model = ds_cfg["field_model"] if ds_cfg and "field_model" in ds_cfg else None
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roles = ds_cfg["roles"].to_dict() if ds_cfg and "roles" in ds_cfg else None
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strategy = SimpleShareGPTPromptTokenizingStrategy(
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ShareGPTPrompterV2(
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conversation=conversation,
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role_key_model=field_model,
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role_key_human=field_human,
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roles=roles,
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),
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tokenizer,
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cfg.train_on_inputs,
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@@ -142,7 +147,12 @@ class SimpleShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
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"system": "system",
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}
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turns = [
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{"from": role_map[t[role_key]], "value": t[value_key]}
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{
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"from": (
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role_map[t[role_key]] if t[role_key] in role_map else t[role_key]
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),
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"value": t[value_key],
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}
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for t in conversations
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]
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return turns
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@@ -11,7 +11,7 @@ from transformers import BatchEncoding, PreTrainedTokenizer
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from axolotl.monkeypatch.fastchat_conversation_turns import (
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add_get_turns_to_conversation,
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)
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from axolotl.prompters import IGNORE_TOKEN_ID
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from axolotl.prompters import IGNORE_TOKEN_ID, Prompter
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LOG = logging.getLogger("axolotl")
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@@ -37,7 +37,7 @@ class PromptTokenizingStrategy(abc.ABC):
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def __init__(
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self,
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prompter,
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prompter: Prompter,
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tokenizer,
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train_on_inputs: bool = False,
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sequence_len: int = 2048,
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@@ -340,6 +340,23 @@ class ShareGPTPromptTokenizingStrategy(PromptTokenizingStrategy):
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self.prompter._conversation.copy() # pylint: disable=protected-access
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)
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input_roles = {conversation.roles[0]}
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output_roles = {conversation.roles[1]}
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if len(conversation.roles) == 3:
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tool_role_label = conversation.roles[2]
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input_roles.add(tool_role_label)
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# Add roles from the config
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if self.prompter.roles:
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if "input" in self.prompter.roles and self.prompter.roles["input"]:
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for role in self.prompter.roles["input"]:
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input_roles.add(role)
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if "output" in self.prompter.roles and self.prompter.roles["output"]:
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for role in self.prompter.roles["output"]:
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output_roles.add(role)
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# support for custom roles from the dataset, only useful for vicuna style prompts/roles
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role_remap = []
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if (
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@@ -360,19 +377,18 @@ class ShareGPTPromptTokenizingStrategy(PromptTokenizingStrategy):
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LOG.warning(f"expected tuple, got {part}")
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continue
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tool_role_label = None
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if len(conversation.roles) == 3:
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(
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user_role_label,
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assistant_role_label,
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tool_role_label,
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) = conversation.roles
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else:
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user_role_label, assistant_role_label = conversation.roles
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role, content = part
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# Uses "in" because role contains extra characters
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if user_role_label in role:
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input_turn = any(r.lower() in role.lower() for r in input_roles)
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output_turn = any(r.lower() in role.lower() for r in output_roles)
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empty_role = role.strip() == ""
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if not any([input_turn, output_turn, empty_role]):
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LOG.warning(f"unhandled role: {role}")
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continue
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if input_turn:
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role = (
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role.replace(role_remap[0]["from"], role_remap[0]["to"])
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if role_remap
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@@ -392,7 +408,7 @@ class ShareGPTPromptTokenizingStrategy(PromptTokenizingStrategy):
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else:
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# everything from this is masked out from the labels
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labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
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elif assistant_role_label in role:
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elif output_turn:
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role = (
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role.replace(role_remap[1]["from"], role_remap[1]["to"])
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if role_remap
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@@ -423,7 +439,7 @@ class ShareGPTPromptTokenizingStrategy(PromptTokenizingStrategy):
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labels[:len_role] = [IGNORE_TOKEN_ID] * min(
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len_role, len(labels)
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)
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elif role == "":
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elif empty_role:
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turn = content
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# this is only ever the first part, should include the bos token and the user query
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res = self._tokenize(
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@@ -434,11 +450,6 @@ class ShareGPTPromptTokenizingStrategy(PromptTokenizingStrategy):
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else:
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# everything from this is masked out from the labels
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labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
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elif tool_role_label and tool_role_label in role:
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labels = [IGNORE_TOKEN_ID] * len(res["input_ids"])
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else:
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LOG.warning(f"unhandled role: {role}")
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continue
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# pylint: disable=duplicate-code
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result, current_len = parse_tokenized_to_result(
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@@ -259,6 +259,12 @@ SHAREGPT_ASSERTION_FAILED_ROLE = (
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"Role did not alternate between turns (gpt and human). Please check your data."
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)
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CONVERSATION_ROLE_FORMAT = {
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"chatml": "<|im_start|>{ROLE}",
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"zephyr": "<|{ROLE}|>",
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"vicuna_v1.1": "{ROLE}",
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}
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class ShareGPTPrompter(Prompter): # pylint: disable=too-few-public-methods
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"""
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@@ -268,7 +274,9 @@ class ShareGPTPrompter(Prompter): # pylint: disable=too-few-public-methods
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role_key_human = "human"
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role_key_model = "gpt"
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# Optional, only used for tool usage datasets.
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role_key_tool = None
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role_key_tool: Optional[str] = None
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# Optional, role input/output mapping
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roles: Optional[dict] = None
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def __init__(
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self,
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@@ -277,6 +285,7 @@ class ShareGPTPrompter(Prompter): # pylint: disable=too-few-public-methods
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role_key_human: Optional[str] = None,
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role_key_model: Optional[str] = None,
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role_key_tool: Optional[str] = None,
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roles: Optional[dict] = None,
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):
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if conversation:
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if isinstance(conversation, Conversation):
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@@ -291,6 +300,8 @@ class ShareGPTPrompter(Prompter): # pylint: disable=too-few-public-methods
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self.role_key_model = role_key_model
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if role_key_tool:
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self.role_key_tool = role_key_tool
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if roles:
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self.roles = roles
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def _build_result(self, source):
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if len(source) < 2:
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@@ -322,11 +333,23 @@ class ShareGPTPrompter(Prompter): # pylint: disable=too-few-public-methods
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conv.messages = []
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for _, sentence in enumerate(source):
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role = roles[sentence["from"]]
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if len(conv.messages) > 0 and (
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(role == conv.messages[-1][0]) or (role not in conv.roles)
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):
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from_role = sentence["from"]
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if from_role in roles:
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role = roles[from_role]
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else:
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if self._conversation.name not in CONVERSATION_ROLE_FORMAT:
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raise NotImplementedError(
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f"Role ({role}) not in default roles, and {self._conversation.name} does not support role remapping yet."
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"Please help us by creating an Issue to add support for this conversation type."
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)
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role = CONVERSATION_ROLE_FORMAT[self._conversation.name].format(
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ROLE=from_role
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)
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if len(conv.messages) > 0 and ((role == conv.messages[-1][0])):
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LOG.warning(f"{SHAREGPT_ASSERTION_FAILED_ROLE}: {sentence}")
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conv.append_message(role, sentence["value"])
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return conv.get_turns()
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@@ -354,11 +377,13 @@ class ShareGPTPrompterV2(ShareGPTPrompter):
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conversation: Optional[Union[str, Conversation]] = None,
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role_key_human: Optional[str] = None,
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role_key_model: Optional[str] = None,
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roles: Optional[dict] = None,
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):
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super().__init__(
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conversation=conversation,
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role_key_human=role_key_human,
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role_key_model=role_key_model,
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roles=roles,
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)
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@@ -96,6 +96,8 @@ class SFTDataset(BaseModel):
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field_human: Optional[str] = None
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field_model: Optional[str] = None
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roles: Optional[Dict[str, List[str]]] = None
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class UserDefinedDPOType(BaseModel):
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"""User defined typing for DPO"""
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