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"""Module containing the SimpleShareGPTPromptTokenizingStrategy class"""
import logging
from typing import Any, Dict, Optional, Type
from fastchat.conversation import Conversation, SeparatorStyle, register_conv_template
from axolotl.prompt_tokenizers import ShareGPTPromptTokenizingStrategy
from axolotl.prompters import ShareGPTPrompterV2
from axolotl.utils.tokenization import (
chatml_to_conversation,
merge_consecutive_messages,
)
LOG = logging.getLogger("axolotl")
def register_chatml_template(system_message=None):
system_message = system_message or "You are a helpful assistant."
register_conv_template(
Conversation(
name="chatml",
system_template="<|im_start|>system\n{system_message}",
system_message=system_message,
roles=("<|im_start|>user", "<|im_start|>assistant"),
sep_style=SeparatorStyle.CHATML,
sep="<|im_end|>",
)
)
register_conv_template(
Conversation(
name="chatml_glaive",
system_template="<|im_start|>system\n{system_message}",
system_message=system_message,
roles=("<|im_start|>user", "<|im_start|>assistant", "<|im_start|>tool"),
sep_style=SeparatorStyle.CHATML,
sep="<|im_end|>",
)
)
def register_llama3_template(system_message=None):
system_message = system_message or "You are a helpful assistant."
register_conv_template(
Conversation(
name="llama3",
system_template="<|start_header_id|>system<|end_header_id|>\n\n{system_message}<|eot_id|>",
system_message=system_message,
roles=("user", "assistant"),
sep_style=SeparatorStyle.LLAMA3,
sep="",
stop_str="<|eot_id|>",
stop_token_ids=[128001, 128009],
)
)
def build_loader(
tokenization_strategy_cls: Type["ShareGPTPromptTokenizingStrategy"],
prompter_cls: Type["ShareGPTPrompterV2"],
default_conversation: Optional[str] = None,
):
def _load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
conversation = (
ds_cfg["conversation"]
if ds_cfg and "conversation" in ds_cfg
else default_conversation
)
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 = tokenization_strategy_cls(
prompter_cls(
conversation=conversation,
role_key_model=field_model,
role_key_human=field_human,
roles=roles,
),
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
if ds_cfg and "strict" in ds_cfg and hasattr(strategy, "strict"):
strategy.strict = ds_cfg["strict"]
if ds_cfg and "field_messages" in ds_cfg and hasattr(strategy, "messages"):
strategy.messages = ds_cfg["field_messages"]
return strategy
return _load
class SimpleShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
"""
basic sharegpt strategy to grab conversations from the sample row
"""
_strict = False
_messages = "conversations"
@property
def strict(self):
return self._strict
@strict.setter
def strict(self, strict):
self._strict = strict
@property
def messages(self):
return self._messages
@messages.setter
def messages(self, messages):
self._messages = messages
def get_conversation_thread(self, prompt):
conversations = prompt[self.messages]
if self.strict:
return conversations
role_key = "from"
if "role" in conversations[0].keys():
role_key = "role"
value_key = "value"
if "text" in conversations[0].keys():
value_key = "text"
elif "content" in conversations[0].keys():
value_key = "content"
# remap roles - allow for assistant turn"
role_map = {
"user": "human",
"human": "human",
"assistant": "gpt",
"gpt": "gpt",
"system": "system",
}
turns = [
{
"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
class SimpleRoleShareGPTPromptTokenizingStrategy(
SimpleShareGPTPromptTokenizingStrategy
):
"""
basic sharegpt strategy to grab conversations from the sample row, but uses role instead of from
"""
def get_conversation_thread(self, prompt):
conversations = prompt["conversations"]
# remap role: prompter/assistant, text: ... => from: human/gpt, value: ...
turns = [{"from": t["role"], "value": t["value"]} for t in conversations]
return turns
class GuanacoShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
"""
sharegpt strategy that remaps oasst data to sharegpt format
"""
def get_conversation_thread(self, prompt):
conversations = prompt["conversations"]
# remap role: prompter/assistant, text: ... => from: human/gpt, value: ...
role_map = {"prompter": "human", "assistant": "gpt"}
turns = [
{"from": role_map[t["role"]], "value": t["text"]} for t in conversations
]
return turns
class UltrachatShareGPTPromptTokenizingStrategy(SimpleShareGPTPromptTokenizingStrategy):
"""
sharegpt strategy that remaps ultrachat data to sharegpt format
"""
def get_conversation_thread(self, prompt):
conversations = prompt["messages"]
role_map = {"user": "human", "assistant": "gpt"}
turns = [
{"from": role_map[t["role"]], "value": t["content"]} for t in conversations
]
return turns
class GlaiveShareGPTPromptTokenizingStrategy(SimpleShareGPTPromptTokenizingStrategy):
"""
sharegpt strategy that remaps glaive data to sharegpt format
"""
def get_conversation_thread(self, prompt):
conversation = chatml_to_conversation(prompt)
conversation = merge_consecutive_messages(conversation)
return conversation
load = build_loader(SimpleShareGPTPromptTokenizingStrategy, ShareGPTPrompterV2)
load_role = build_loader(SimpleRoleShareGPTPromptTokenizingStrategy, ShareGPTPrompterV2)
load_ultrachat = build_loader(
UltrachatShareGPTPromptTokenizingStrategy, ShareGPTPrompterV2
)
load_guanaco = build_loader(GuanacoShareGPTPromptTokenizingStrategy, ShareGPTPrompterV2)
load_glaive = build_loader(
GlaiveShareGPTPromptTokenizingStrategy,
ShareGPTPrompterV2,
default_conversation="chatml_glaive",
)
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