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# coding=utf-8 | |
import json | |
import os | |
from dataclasses import asdict, dataclass | |
from pathlib import Path | |
from typing import Any, Dict, List, Optional, Type, TypeVar, Union | |
from huggingface_hub import ModelHubMixin, hf_hub_download | |
# Generic variable that is either ModelHubMixin or a subclass thereof | |
T = TypeVar("T", bound="ModelHubMixin") | |
TEMPLATE_FILENAME = "dialogue_template.json" | |
IGNORE_INDEX = -100 | |
class DialogueTemplate(ModelHubMixin): | |
"""Converts all turns of a dialogue between a user and assistant to a standardized format.""" | |
system: str | |
messages: List[Dict[str, str]] = None | |
system_token: str = "<|system|>" | |
user_token: str = "<|user|>" | |
assistant_token: str = "<|assistant|>" | |
end_token: str = "<|end|>" | |
def __post_init__(self): | |
"""Ensure that messages is never None.""" | |
if self.messages is None: | |
self.messages = [] | |
def get_training_prompt(self) -> str: | |
if len(self.messages) == 0: | |
raise ValueError("Dialogue template must have at least one message.") | |
prompt = self.system_token + "\n" + self.system + self.end_token + "\n" | |
for message in self.messages: | |
if message["role"] == "user": | |
prompt += self.user_token + "\n" + message["content"] + self.end_token + "\n" | |
else: | |
prompt += self.assistant_token + "\n" + message["content"] + self.end_token + "\n" | |
return prompt | |
def get_inference_prompt(self) -> str: | |
if len(self.messages) == 0: | |
raise ValueError("Dialogue template must have at least one message.") | |
prompt = self.system_token + "\n" + self.system + self.end_token + "\n" | |
for message in self.messages: | |
if message["role"] == "user": | |
prompt += self.user_token + "\n" + message["content"] + self.end_token + "\n" | |
else: | |
prompt += self.assistant_token + "\n" + message["content"] + self.end_token + "\n" | |
prompt += self.assistant_token + "\n" | |
return prompt | |
def get_dialogue(self): | |
if len(self.messages) == 0: | |
raise ValueError("Dialogue template must have at least one message.") | |
prompt = "" | |
for message in self.messages: | |
if message["role"] == "user": | |
prompt += "\n\nHuman: " + message["content"] | |
else: | |
prompt += "\n\nAssistant: " + message["content"] | |
return prompt | |
def get_special_tokens(self) -> List[str]: | |
return [self.system_token, self.user_token, self.assistant_token, self.end_token] | |
def copy(self): | |
return DialogueTemplate( | |
system=self.system, | |
messages=self.messages, | |
system_token=self.system_token, | |
user_token=self.user_token, | |
assistant_token=self.assistant_token, | |
end_token=self.end_token, | |
) | |
def to_dict(self) -> Dict[str, Any]: | |
return {k: v for k, v in asdict(self).items()} | |
def from_dict(cls, data): | |
return DialogueTemplate( | |
system=data.get("system", ""), | |
messages=data.get("messages", None), | |
system_token=data.get("system_token", "<|system|>"), | |
user_token=data.get("user_token", "<|user|>"), | |
assistant_token=data.get("assistant_token", "<|assistant|>"), | |
end_token=data.get("end_token", "<|end|>"), | |
) | |
def _save_pretrained(self, save_directory: Union[str, Path]) -> None: | |
save_directory = Path(save_directory) | |
save_directory.mkdir(exist_ok=True) | |
with open(save_directory / "dialogue_template.json", "w") as f: | |
json.dump(self.to_dict(), f, indent=2) | |
def _from_pretrained( | |
cls: Type[T], | |
*, | |
model_id: str, | |
revision: Optional[str], | |
cache_dir: Optional[Union[str, Path]], | |
force_download: bool, | |
proxies: Optional[Dict], | |
resume_download: bool, | |
local_files_only: bool, | |
token: Optional[Union[str, bool]], | |
**model_kwargs, | |
) -> T: | |
"""Loads the dialogue template from a local directory or the Huggingface Hub.""" | |
if os.path.isdir(model_id): | |
print("Loading dialogue template from local directory") | |
template_file = os.path.join(model_id, TEMPLATE_FILENAME) | |
else: | |
template_file = hf_hub_download( | |
repo_id=model_id, | |
filename=TEMPLATE_FILENAME, | |
revision=revision or "main", | |
cache_dir=cache_dir, | |
force_download=force_download, | |
proxies=proxies, | |
resume_download=resume_download, | |
token=token, | |
local_files_only=local_files_only, | |
) | |
with open(template_file, "r") as f: | |
data = json.load(f) | |
return cls.from_dict(data=data) | |
# Default template | |
default_template = DialogueTemplate( | |
system="Below is a dialogue between a human user and an AI assistant. The assistant is happy to help with almost anything, and will do its best to understand exactly what is needed.", | |
) | |
# Supporting other templates | |
no_system_template = DialogueTemplate(system="") | |
alpaca_template = DialogueTemplate( | |
system="Below is an instruction that describes a task. Write a response that appropriately completes the request.", | |
user_token="### Instruction:", | |
assistant_token="### Response:", | |
) | |
SUPPORTED_DIALOGUE_TEMPLATES = { | |
"default": default_template, | |
"no_system": no_system_template, | |
"alpaca": alpaca_template, | |
} | |
def get_dialogue_template(template: str) -> DialogueTemplate: | |
if template not in SUPPORTED_DIALOGUE_TEMPLATES: | |
raise ValueError(f"Template {template} is not supported!") | |
return SUPPORTED_DIALOGUE_TEMPLATES[template].copy() | |
def prepare_dialogue(example, dialogue_template, is_train=True): | |
if "messages" in example and example["messages"] is not None: | |
dialogue_template.messages = example["messages"] | |
elif "prompt" in example and "completion" in example: | |
dialogue_template.messages = [ | |
{"role": "user", "content": example["prompt"]}, | |
{"role": "assistant", "content": example["completion"]}, | |
] | |
elif "prompt" in example: | |
dialogue_template.messages = [{"role": "user", "content": example["prompt"]}] | |
else: | |
raise ValueError( | |
f"Could not format example as dialogue! Require either `messages` or `[prompt, completion]` or `[prompt]` keys but found {list(example.keys())}" | |
) | |
if is_train: | |
example["text"] = dialogue_template.get_training_prompt() | |
else: | |
example["text"] = dialogue_template.get_inference_prompt() | |
return example | |