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"""
A dedicated helper to manage templates and prompt building.
From https://github.com/tloen/alpaca-lora/blob/main/utils/prompter.py
"""
import json
import os.path as osp
import importlib
import itertools
from typing import Union, List
from ..config import Config
from ..globals import Global
class Prompter(object):
__slots__ = ("template_name", "template", "template_module", "_verbose")
def __init__(self, template_name: str = "", verbose: bool = False):
self._verbose = verbose
if not template_name:
template_name = "None"
if template_name == "None":
self.template_name = "None"
return
self.template_name = template_name
self.template_module = None
base_filename, ext = osp.splitext(template_name)
if ext == "":
filename = base_filename + ".json"
else:
filename = base_filename + ext
file_path = osp.join(Config.data_dir, "templates", filename)
if not osp.exists(file_path):
raise ValueError(f"Can't read {file_path}")
if ext == ".py":
template_module_spec = importlib.util.spec_from_file_location(
"template_module", file_path)
template_module = importlib.util.module_from_spec(
template_module_spec)
template_module_spec.loader.exec_module(template_module)
self.template_module = template_module
if not hasattr(template_module, "variables"):
raise ValueError(
"The template module does not have a \"variables\" attribute.")
self.template = {
'variables': template_module.variables
}
if hasattr(template_module, "response_split"):
self.template["response_split"] = template_module.response_split
return
with open(file_path) as fp:
self.template = json.load(fp)
if self._verbose:
print(
f"Using prompt template {template_name}: {self.template['description']}"
)
def generate_prompt(
self,
variables: List[Union[None, str]] = [],
# instruction: str,
# input: Union[None, str] = None,
label: Union[None, str] = None,
) -> str:
if self.template_name == "None":
if type(variables) == list:
res = get_val(variables, 0, "")
else:
res = variables.get("prompt", "")
elif "variables" in self.template:
variable_names = self.template.get("variables")
if self.template_module:
if type(variables) == list:
variables = {k: v for k, v in zip(
variable_names, variables)}
res = self.template_module.get_prompt(variables)
else:
if type(variables) == dict:
variables = [variables.get(name, None)
for name in variable_names]
if "default" not in self.template:
raise ValueError(
f"The template {self.template_name} has \"variables\" defined but does not has a default prompt defined. Please do it like: '\"default\": \"prompt_with_instruction\"' to handle cases when a matching prompt can't be found.")
default_prompt_name = self.template.get("default")
if default_prompt_name not in self.template:
raise ValueError(
f"The template {self.template_name} has \"default\" set to \"{default_prompt_name}\" but it's not defined. Please do it like: '\"{default_prompt_name}\": \"...\".")
prompt_name = get_prompt_name(variables, variable_names)
prompt_template = self.template.get(default_prompt_name)
if prompt_name in self.template:
prompt_template = self.template.get(prompt_name)
res = prompt_template.format(
**variables_to_dict(variables, variable_names))
else:
if type(variables) == dict:
instruction = variables.get("instruction", "")
input = variables.get("input")
else:
instruction = get_val(variables, 0, "")
input = get_val(variables, 1)
# returns the full prompt from instruction and optional input
# if a label (=response, =output) is provided, it's also appended.
if input:
res = self.template["prompt_input"].format(
instruction=instruction, input=input
)
else:
res = self.template["prompt_no_input"].format(
instruction=instruction
)
if label:
res = f"{res}{label}"
if self._verbose:
print(res)
return res
def get_response(self, output: str) -> str:
if self.template_name == "None":
return output
splitted_output = output.split(self.template["response_split"])
# if len(splitted_output) <= 1:
# return output.strip()
return self.template["response_split"].join(
splitted_output[1:]
).strip()
def get_variable_names(self) -> List[str]:
if self.template_name == "None":
return ["prompt"]
elif "variables" in self.template:
return self.template['variables']
else:
return ["instruction", "input"]
def get_train_data_from_dataset(self, data, only_first_n_items=None):
if self.template_module:
if hasattr(self.template_module,
"get_train_data_list_from_dataset"):
data = self.template_module.get_train_data_list_from_dataset(
data)
if only_first_n_items:
data = data[:only_first_n_items]
return list(itertools.chain(*list(
map(self.template_module.get_train_data, data)
)))
if only_first_n_items:
data = data[:only_first_n_items]
data = process_json_dataset(data)
train_data = [
{
'prompt': self.generate_prompt(d['variables']),
'completion': d['output'],
**{"_var_" + k: v for k, v in d['variables'].items()}
}
for d in data]
return train_data
def get_val(arr, index, default=None):
return arr[index] if -len(arr) <= index < len(arr) else default
def get_prompt_name(variables, variable_names):
result = [y for x, y in zip(
variables, variable_names) if x not in (None, '')]
return "prompt_with_" + '_'.join(result)
def variables_to_dict(variables, variable_names):
return {
key: (variables[i] if i < len(variables)
and variables[i] is not None else '')
for i, key in enumerate(variable_names)
}
def process_json_dataset(data):
if not isinstance(data, list):
raise ValueError("The dataset is not an array of objects.")
first_item = get_val_from_arr(data, 0, None)
if first_item is None:
raise ValueError("The dataset is empty.")
if not isinstance(first_item, dict):
raise ValueError("The dataset is not an array of objects.")
# Convert OpenAI fine-tuning dataset to LLaMA LoRA style
if "completion" in first_item and "output" not in first_item:
data = [
{"output" if k == "completion" else k: v for k, v in d.items()}
for d in data]
first_item = get_val_from_arr(data, 0, None)
# Flatten Stanford Alpaca style instances
if "instances" in first_item and isinstance(first_item["instances"], list):
data = [
{"output" if k == "completion" else k: v for k, v in d.items()}
for d in data]
flattened_data = []
for item in data:
for instance in item["instances"]:
d = {k: v for k, v in item.items() if k != "instances"}
d.update(instance)
flattened_data.append(d)
data = flattened_data
first_item = get_val_from_arr(data, 0, None)
if "output" not in first_item:
raise ValueError(
"The data does not contains an \"output\" or \"completion\".")
# Put all variables under the "variables" key if it does not exists
if "variables" not in first_item:
data = [
{
"variables":
{k: v for k, v in d.items() if k != "output"},
"output":
d["output"]
}
for d in data
]
return data
def get_val_from_arr(arr, index, default=None):
return arr[index] if -len(arr) <= index < len(arr) else default
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